From 59c39dc5d14cc0a69687e9641debda72abfc1a18 Mon Sep 17 00:00:00 2001
From: github-actions <41898282+github-actions[bot]@users.noreply.github.com>
Date: Mon, 23 Jan 2023 19:14:37 +0000
Subject: [PATCH] Built site for AMR: 1.8.2.9099@eef0006

---
 404.html                                      |  29 +-
 LICENSE-text.html                             |  29 +-
 articles/AMR.html                             | 557 +++++++++---------
 .../AMR_files/figure-html/disk_plots-1.png    | Bin 39107 -> 38754 bytes
 .../figure-html/disk_plots_mo_ab-1.png        | Bin 55128 -> 56475 bytes
 .../AMR_files/figure-html/mic_plots-1.png     | Bin 28168 -> 28324 bytes
 .../AMR_files/figure-html/mic_plots-2.png     | Bin 39232 -> 37611 bytes
 .../figure-html/mic_plots_mo_ab-1.png         | Bin 36941 -> 38328 bytes
 .../figure-html/mic_plots_mo_ab-2.png         | Bin 50186 -> 51436 bytes
 articles/AMR_files/figure-html/plot 1-1.png   | Bin 41953 -> 41824 bytes
 articles/AMR_files/figure-html/plot 3-1.png   | Bin 28407 -> 28505 bytes
 articles/AMR_files/figure-html/plot 4-1.png   | Bin 78232 -> 78282 bytes
 articles/AMR_files/figure-html/plot 5-1.png   | Bin 49896 -> 49897 bytes
 articles/EUCAST.html                          |  29 +-
 articles/MDR.html                             |  87 +--
 articles/PCA.html                             |  29 +-
 articles/SPSS.html                            |  29 +-
 articles/WHONET.html                          |  29 +-
 articles/datasets.html                        |  29 +-
 articles/index.html                           |  29 +-
 articles/resistance_predict.html              |  29 +-
 articles/welcome_to_AMR.html                  |  29 +-
 authors.html                                  |  29 +-
 index.html                                    |  29 +-
 news/index.html                               |  51 +-
 pkgdown.yml                                   |   2 +-
 reference/AMR-deprecated.html                 |  29 +-
 reference/AMR-options.html                    |  71 ++-
 reference/AMR.html                            |  29 +-
 reference/Rplot005.png                        | Bin 12930 -> 13003 bytes
 reference/Rplot006.png                        | Bin 12462 -> 12528 bytes
 reference/Rplot007.png                        | Bin 12686 -> 12678 bytes
 reference/Rplot008.png                        | Bin 18894 -> 19061 bytes
 reference/Rplot009.png                        | Bin 8531 -> 8444 bytes
 reference/WHOCC.html                          |  29 +-
 reference/WHONET.html                         |  29 +-
 reference/ab_from_text.html                   |  29 +-
 reference/ab_property.html                    |  31 +-
 reference/add_custom_antimicrobials.html      |  40 +-
 reference/add_custom_microorganisms.html      |  31 +-
 reference/age.html                            |  49 +-
 reference/age_groups.html                     |  29 +-
 reference/antibiotic_class_selectors.html     |  29 +-
 reference/antibiotics.html                    |  29 +-
 reference/as.ab.html                          |  29 +-
 reference/as.av.html                          |  29 +-
 reference/as.disk.html                        |  29 +-
 reference/as.mic.html                         |  29 +-
 reference/as.mo.html                          |  33 +-
 reference/as.sir.html                         |  60 +-
 reference/atc_online.html                     |  29 +-
 reference/av_from_text.html                   |  29 +-
 reference/av_property.html                    |  31 +-
 reference/availability.html                   |  29 +-
 reference/bug_drug_combinations.html          |  31 +-
 reference/clinical_breakpoints.html           |  29 +-
 reference/count.html                          |  31 +-
 reference/custom_eucast_rules.html            |  29 +-
 reference/dosage.html                         |  29 +-
 reference/eucast_rules.html                   |  33 +-
 reference/example_isolates.html               |  29 +-
 reference/example_isolates_unclean.html       |  29 +-
 reference/first_isolate.html                  |  29 +-
 reference/g.test.html                         |  29 +-
 reference/get_episode.html                    | 180 +++---
 reference/ggplot_pca.html                     |  29 +-
 reference/ggplot_sir.html                     |  31 +-
 reference/guess_ab_col.html                   |  29 +-
 reference/index.html                          |  45 +-
 reference/intrinsic_resistant.html            |  29 +-
 reference/italicise_taxonomy.html             |  29 +-
 reference/join.html                           |  29 +-
 reference/key_antimicrobials.html             |  29 +-
 reference/kurtosis.html                       |  33 +-
 reference/like.html                           |  29 +-
 reference/mdro.html                           |  29 +-
 reference/mean_amr_distance.html              | 118 ++--
 reference/microorganisms.codes.html           |  29 +-
 reference/microorganisms.html                 |  29 +-
 reference/mo_matching_score.html              |  29 +-
 reference/mo_property.html                    |  31 +-
 reference/mo_source.html                      |  31 +-
 reference/pca.html                            |  29 +-
 reference/plot-1.png                          | Bin 25832 -> 26292 bytes
 reference/plot-2.png                          | Bin 26417 -> 26402 bytes
 reference/plot-3.png                          | Bin 28235 -> 27461 bytes
 reference/plot-4.png                          | Bin 37733 -> 38130 bytes
 reference/plot-5.png                          | Bin 38005 -> 38019 bytes
 reference/plot-6.png                          | Bin 36805 -> 36797 bytes
 reference/plot-7.png                          | Bin 36934 -> 36814 bytes
 reference/plot-8.png                          | Bin 56841 -> 56739 bytes
 reference/plot-9.png                          | Bin 26275 -> 26089 bytes
 reference/plot.html                           |  31 +-
 reference/proportion.html                     |  31 +-
 reference/random.html                         |  60 +-
 reference/resistance_predict.html             |  29 +-
 reference/skewness.html                       |  31 +-
 reference/translate.html                      |  39 +-
 search.json                                   |   2 +-
 99 files changed, 1788 insertions(+), 1376 deletions(-)

diff --git a/404.html b/404.html
index eb7347f00..4c02e0c3e 100644
--- a/404.html
+++ b/404.html
@@ -36,7 +36,7 @@
     
     <a class="navbar-brand me-2" href="https://msberends.github.io/AMR/index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -59,60 +59,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="https://msberends.github.io/AMR/reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="https://msberends.github.io/AMR/articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="https://msberends.github.io/AMR/articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="https://msberends.github.io/AMR/articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="https://msberends.github.io/AMR/articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="https://msberends.github.io/AMR/articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="https://msberends.github.io/AMR/articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="https://msberends.github.io/AMR/articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="https://msberends.github.io/AMR/articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="https://msberends.github.io/AMR/reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="https://msberends.github.io/AMR/reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="https://msberends.github.io/AMR/reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/LICENSE-text.html b/LICENSE-text.html
index a15ea075d..605f43713 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/articles/AMR.html b/articles/AMR.html
index f647f63f1..2f5de92d8 100644
--- a/articles/AMR.html
+++ b/articles/AMR.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -61,60 +61,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -381,12 +386,12 @@ data set:</p>
 <col width="13%">
 <col width="13%">
 <col width="13%">
-<col width="28%">
+<col width="26%">
 <col width="5%">
 <col width="5%">
 <col width="5%">
 <col width="5%">
-<col width="8%">
+<col width="9%">
 </colgroup>
 <thead><tr class="header">
 <th align="center">date</th>
@@ -401,9 +406,9 @@ data set:</p>
 </tr></thead>
 <tbody>
 <tr class="odd">
-<td align="center">2016-03-25</td>
-<td align="center">M2</td>
-<td align="center">Hospital A</td>
+<td align="center">2010-08-18</td>
+<td align="center">D8</td>
+<td align="center">Hospital B</td>
 <td align="center">Escherichia coli</td>
 <td align="center">R</td>
 <td align="center">S</td>
@@ -412,58 +417,58 @@ data set:</p>
 <td align="center">M</td>
 </tr>
 <tr class="even">
-<td align="center">2013-03-28</td>
-<td align="center">F10</td>
-<td align="center">Hospital A</td>
+<td align="center">2011-03-28</td>
+<td align="center">H7</td>
+<td align="center">Hospital D</td>
 <td align="center">Escherichia coli</td>
-<td align="center">R</td>
 <td align="center">I</td>
 <td align="center">S</td>
 <td align="center">I</td>
+<td align="center">S</td>
 <td align="center">M</td>
 </tr>
 <tr class="odd">
-<td align="center">2010-05-31</td>
-<td align="center">N4</td>
-<td align="center">Hospital B</td>
-<td align="center">Escherichia coli</td>
+<td align="center">2016-12-20</td>
+<td align="center">M9</td>
+<td align="center">Hospital D</td>
+<td align="center">Klebsiella pneumoniae</td>
+<td align="center">S</td>
+<td align="center">I</td>
+<td align="center">I</td>
 <td align="center">I</td>
-<td align="center">R</td>
-<td align="center">R</td>
-<td align="center">R</td>
 <td align="center">M</td>
 </tr>
 <tr class="even">
-<td align="center">2014-08-03</td>
-<td align="center">V7</td>
+<td align="center">2012-11-13</td>
+<td align="center">Y5</td>
+<td align="center">Hospital A</td>
+<td align="center">Escherichia coli</td>
+<td align="center">I</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">I</td>
+<td align="center">F</td>
+</tr>
+<tr class="odd">
+<td align="center">2013-06-06</td>
+<td align="center">Q7</td>
 <td align="center">Hospital C</td>
-<td align="center">Escherichia coli</td>
-<td align="center">S</td>
-<td align="center">I</td>
-<td align="center">R</td>
-<td align="center">S</td>
-<td align="center">F</td>
-</tr>
-<tr class="odd">
-<td align="center">2014-11-23</td>
-<td align="center">Z5</td>
-<td align="center">Hospital A</td>
-<td align="center">Streptococcus pneumoniae</td>
-<td align="center">S</td>
-<td align="center">I</td>
-<td align="center">R</td>
-<td align="center">I</td>
-<td align="center">F</td>
-</tr>
-<tr class="even">
-<td align="center">2015-01-13</td>
-<td align="center">X7</td>
-<td align="center">Hospital B</td>
 <td align="center">Klebsiella pneumoniae</td>
 <td align="center">R</td>
 <td align="center">R</td>
 <td align="center">S</td>
+<td align="center">R</td>
+<td align="center">F</td>
+</tr>
+<tr class="even">
+<td align="center">2017-05-04</td>
+<td align="center">U8</td>
+<td align="center">Hospital A</td>
+<td align="center">Escherichia coli</td>
 <td align="center">S</td>
+<td align="center">I</td>
+<td align="center">I</td>
+<td align="center">R</td>
 <td align="center">F</td>
 </tr>
 </tbody>
@@ -500,16 +505,16 @@ Longest: 1</p>
 <tr class="odd">
 <td align="left">1</td>
 <td align="left">M</td>
-<td align="right">10,396</td>
-<td align="right">51.98%</td>
-<td align="right">10,396</td>
-<td align="right">51.98%</td>
+<td align="right">10,375</td>
+<td align="right">51.88%</td>
+<td align="right">10,375</td>
+<td align="right">51.88%</td>
 </tr>
 <tr class="even">
 <td align="left">2</td>
 <td align="left">F</td>
-<td align="right">9,604</td>
-<td align="right">48.02%</td>
+<td align="right">9,625</td>
+<td align="right">48.13%</td>
 <td align="right">20,000</td>
 <td align="right">100.00%</td>
 </tr>
@@ -622,9 +627,9 @@ takes into account the antimicrobial susceptibility test results using
 <span><span class="co"># Basing inclusion on all antimicrobial results, using a points threshold of</span></span>
 <span><span class="co"># 2</span></span>
 <span><span class="co"># Including isolates from ICU.</span></span>
-<span><span class="co"># =&gt; Found 12,236 'phenotype-based' first isolates (61.2% of total where a</span></span>
+<span><span class="co"># =&gt; Found 12,316 'phenotype-based' first isolates (61.6% of total where a</span></span>
 <span><span class="co">#    microbial ID was available)</span></span></code></pre></div>
-<p>So only 61.2% is suitable for resistance analysis! We can now filter
+<p>So only 61.6% is suitable for resistance analysis! We can now filter
 on it with the <code><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter()</a></code> function, also from the
 <code>dplyr</code> package:</p>
 <div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
@@ -635,7 +640,7 @@ on it with the <code><a href="https://dplyr.tidyverse.org/reference/filter.html"
 <code class="sourceCode R"><span><span class="va">data_1st</span> <span class="op">&lt;-</span> <span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="../reference/first_isolate.html">filter_first_isolate</a></span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="co"># Including isolates from ICU.</span></span></code></pre></div>
-<p>So we end up with 12,236 isolates for analysis. Now our data looks
+<p>So we end up with 12,316 isolates for analysis. Now our data looks
 like:</p>
 <div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">data_1st</span><span class="op">)</span></span></code></pre></div>
@@ -675,14 +680,14 @@ like:</p>
 <tbody>
 <tr class="odd">
 <td align="left">2</td>
-<td align="center">2013-03-28</td>
-<td align="center">F10</td>
-<td align="center">Hospital A</td>
+<td align="center">2011-03-28</td>
+<td align="center">H7</td>
+<td align="center">Hospital D</td>
 <td align="center">B_ESCHR_COLI</td>
-<td align="center">R</td>
 <td align="center">I</td>
 <td align="center">S</td>
 <td align="center">I</td>
+<td align="center">S</td>
 <td align="center">M</td>
 <td align="center">Gram-negative</td>
 <td align="center">Escherichia</td>
@@ -690,31 +695,31 @@ like:</p>
 <td align="center">TRUE</td>
 </tr>
 <tr class="even">
-<td align="left">3</td>
-<td align="center">2010-05-31</td>
-<td align="center">N4</td>
-<td align="center">Hospital B</td>
+<td align="left">6</td>
+<td align="center">2017-05-04</td>
+<td align="center">U8</td>
+<td align="center">Hospital A</td>
 <td align="center">B_ESCHR_COLI</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">I</td>
 <td align="center">R</td>
-<td align="center">R</td>
-<td align="center">R</td>
-<td align="center">R</td>
-<td align="center">M</td>
+<td align="center">F</td>
 <td align="center">Gram-negative</td>
 <td align="center">Escherichia</td>
 <td align="center">coli</td>
 <td align="center">TRUE</td>
 </tr>
 <tr class="odd">
-<td align="left">4</td>
-<td align="center">2014-08-03</td>
-<td align="center">V7</td>
-<td align="center">Hospital C</td>
+<td align="left">7</td>
+<td align="center">2015-09-03</td>
+<td align="center">S1</td>
+<td align="center">Hospital A</td>
 <td align="center">B_ESCHR_COLI</td>
+<td align="center">I</td>
 <td align="center">S</td>
 <td align="center">S</td>
 <td align="center">R</td>
-<td align="center">S</td>
 <td align="center">F</td>
 <td align="center">Gram-negative</td>
 <td align="center">Escherichia</td>
@@ -722,53 +727,53 @@ like:</p>
 <td align="center">TRUE</td>
 </tr>
 <tr class="even">
-<td align="left">5</td>
-<td align="center">2014-11-23</td>
-<td align="center">Z5</td>
+<td align="left">8</td>
+<td align="center">2010-01-29</td>
+<td align="center">D7</td>
 <td align="center">Hospital A</td>
 <td align="center">B_STRPT_PNMN</td>
-<td align="center">S</td>
-<td align="center">S</td>
 <td align="center">R</td>
 <td align="center">R</td>
-<td align="center">F</td>
+<td align="center">S</td>
+<td align="center">R</td>
+<td align="center">M</td>
 <td align="center">Gram-positive</td>
 <td align="center">Streptococcus</td>
 <td align="center">pneumoniae</td>
 <td align="center">TRUE</td>
 </tr>
 <tr class="odd">
-<td align="left">7</td>
-<td align="center">2010-07-28</td>
-<td align="center">Q2</td>
-<td align="center">Hospital B</td>
-<td align="center">B_STPHY_AURS</td>
-<td align="center">R</td>
-<td align="center">S</td>
-<td align="center">R</td>
-<td align="center">I</td>
-<td align="center">F</td>
-<td align="center">Gram-positive</td>
-<td align="center">Staphylococcus</td>
-<td align="center">aureus</td>
-<td align="center">TRUE</td>
-</tr>
-<tr class="even">
-<td align="left">8</td>
-<td align="center">2012-06-28</td>
-<td align="center">Z1</td>
-<td align="center">Hospital B</td>
+<td align="left">9</td>
+<td align="center">2016-12-13</td>
+<td align="center">D1</td>
+<td align="center">Hospital C</td>
 <td align="center">B_ESCHR_COLI</td>
-<td align="center">R</td>
-<td align="center">R</td>
+<td align="center">S</td>
+<td align="center">S</td>
 <td align="center">S</td>
 <td align="center">I</td>
-<td align="center">F</td>
+<td align="center">M</td>
 <td align="center">Gram-negative</td>
 <td align="center">Escherichia</td>
 <td align="center">coli</td>
 <td align="center">TRUE</td>
 </tr>
+<tr class="even">
+<td align="left">10</td>
+<td align="center">2014-01-28</td>
+<td align="center">E1</td>
+<td align="center">Hospital B</td>
+<td align="center">B_STPHY_AURS</td>
+<td align="center">R</td>
+<td align="center">R</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">M</td>
+<td align="center">Gram-positive</td>
+<td align="center">Staphylococcus</td>
+<td align="center">aureus</td>
+<td align="center">TRUE</td>
+</tr>
 </tbody>
 </table>
 <p>Time for the analysis!</p>
@@ -802,8 +807,8 @@ readable:</p>
 <code class="sourceCode R"><span><span class="va">data_1st</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">genus</span>, <span class="va">species</span><span class="op">)</span></span></code></pre></div>
 <p><strong>Frequency table</strong></p>
 <p>Class: character<br>
-Length: 12,236<br>
-Available: 12,236 (100%, NA: 0 = 0%)<br>
+Length: 12,316<br>
+Available: 12,316 (100%, NA: 0 = 0%)<br>
 Unique: 4</p>
 <p>Shortest: 16<br>
 Longest: 24</p>
@@ -828,33 +833,33 @@ Longest: 24</p>
 <tr class="odd">
 <td align="left">1</td>
 <td align="left">Escherichia coli</td>
-<td align="right">5,810</td>
-<td align="right">47.48%</td>
-<td align="right">5,810</td>
-<td align="right">47.48%</td>
+<td align="right">5,959</td>
+<td align="right">48.38%</td>
+<td align="right">5,959</td>
+<td align="right">48.38%</td>
 </tr>
 <tr class="even">
 <td align="left">2</td>
 <td align="left">Staphylococcus aureus</td>
-<td align="right">3,302</td>
-<td align="right">26.99%</td>
-<td align="right">9,112</td>
-<td align="right">74.47%</td>
+<td align="right">3,259</td>
+<td align="right">26.46%</td>
+<td align="right">9,218</td>
+<td align="right">74.85%</td>
 </tr>
 <tr class="odd">
 <td align="left">3</td>
 <td align="left">Streptococcus pneumoniae</td>
-<td align="right">1,840</td>
-<td align="right">15.04%</td>
-<td align="right">10,952</td>
-<td align="right">89.51%</td>
+<td align="right">1,827</td>
+<td align="right">14.83%</td>
+<td align="right">11,045</td>
+<td align="right">89.68%</td>
 </tr>
 <tr class="even">
 <td align="left">4</td>
 <td align="left">Klebsiella pneumoniae</td>
-<td align="right">1,284</td>
-<td align="right">10.49%</td>
-<td align="right">12,236</td>
+<td align="right">1,271</td>
+<td align="right">10.32%</td>
+<td align="right">12,316</td>
 <td align="right">100.00%</td>
 </tr>
 </tbody>
@@ -875,14 +880,14 @@ antibiotic class they are in:</p>
 <col width="9%">
 <col width="9%">
 <col width="9%">
-<col width="11%">
+<col width="10%">
 <col width="3%">
 <col width="3%">
 <col width="3%">
 <col width="3%">
 <col width="6%">
 <col width="11%">
-<col width="11%">
+<col width="12%">
 <col width="9%">
 <col width="5%">
 </colgroup>
@@ -903,13 +908,58 @@ antibiotic class they are in:</p>
 </tr></thead>
 <tbody>
 <tr class="odd">
-<td align="center">2010-05-31</td>
-<td align="center">N4</td>
-<td align="center">Hospital B</td>
+<td align="center">2017-05-04</td>
+<td align="center">U8</td>
+<td align="center">Hospital A</td>
 <td align="center">B_ESCHR_COLI</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">I</td>
+<td align="center">R</td>
+<td align="center">F</td>
+<td align="center">Gram-negative</td>
+<td align="center">Escherichia</td>
+<td align="center">coli</td>
+<td align="center">TRUE</td>
+</tr>
+<tr class="even">
+<td align="center">2015-09-03</td>
+<td align="center">S1</td>
+<td align="center">Hospital A</td>
+<td align="center">B_ESCHR_COLI</td>
+<td align="center">I</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">R</td>
+<td align="center">F</td>
+<td align="center">Gram-negative</td>
+<td align="center">Escherichia</td>
+<td align="center">coli</td>
+<td align="center">TRUE</td>
+</tr>
+<tr class="odd">
+<td align="center">2010-01-29</td>
+<td align="center">D7</td>
+<td align="center">Hospital A</td>
+<td align="center">B_STRPT_PNMN</td>
 <td align="center">R</td>
 <td align="center">R</td>
+<td align="center">S</td>
 <td align="center">R</td>
+<td align="center">M</td>
+<td align="center">Gram-positive</td>
+<td align="center">Streptococcus</td>
+<td align="center">pneumoniae</td>
+<td align="center">TRUE</td>
+</tr>
+<tr class="even">
+<td align="center">2012-05-22</td>
+<td align="center">G6</td>
+<td align="center">Hospital A</td>
+<td align="center">B_ESCHR_COLI</td>
+<td align="center">I</td>
+<td align="center">I</td>
+<td align="center">S</td>
 <td align="center">R</td>
 <td align="center">M</td>
 <td align="center">Gram-negative</td>
@@ -917,76 +967,31 @@ antibiotic class they are in:</p>
 <td align="center">coli</td>
 <td align="center">TRUE</td>
 </tr>
-<tr class="even">
-<td align="center">2014-11-23</td>
-<td align="center">Z5</td>
+<tr class="odd">
+<td align="center">2010-01-09</td>
+<td align="center">L8</td>
 <td align="center">Hospital A</td>
-<td align="center">B_STRPT_PNMN</td>
+<td align="center">B_STPHY_AURS</td>
 <td align="center">S</td>
 <td align="center">S</td>
+<td align="center">S</td>
+<td align="center">R</td>
+<td align="center">M</td>
+<td align="center">Gram-positive</td>
+<td align="center">Staphylococcus</td>
+<td align="center">aureus</td>
+<td align="center">TRUE</td>
+</tr>
+<tr class="even">
+<td align="center">2015-03-09</td>
+<td align="center">S9</td>
+<td align="center">Hospital C</td>
+<td align="center">B_ESCHR_COLI</td>
+<td align="center">R</td>
+<td align="center">I</td>
 <td align="center">R</td>
 <td align="center">R</td>
 <td align="center">F</td>
-<td align="center">Gram-positive</td>
-<td align="center">Streptococcus</td>
-<td align="center">pneumoniae</td>
-<td align="center">TRUE</td>
-</tr>
-<tr class="odd">
-<td align="center">2015-03-21</td>
-<td align="center">K1</td>
-<td align="center">Hospital B</td>
-<td align="center">B_ESCHR_COLI</td>
-<td align="center">R</td>
-<td align="center">R</td>
-<td align="center">R</td>
-<td align="center">R</td>
-<td align="center">M</td>
-<td align="center">Gram-negative</td>
-<td align="center">Escherichia</td>
-<td align="center">coli</td>
-<td align="center">TRUE</td>
-</tr>
-<tr class="even">
-<td align="center">2015-02-07</td>
-<td align="center">G7</td>
-<td align="center">Hospital C</td>
-<td align="center">B_ESCHR_COLI</td>
-<td align="center">S</td>
-<td align="center">S</td>
-<td align="center">S</td>
-<td align="center">R</td>
-<td align="center">M</td>
-<td align="center">Gram-negative</td>
-<td align="center">Escherichia</td>
-<td align="center">coli</td>
-<td align="center">TRUE</td>
-</tr>
-<tr class="odd">
-<td align="center">2012-10-03</td>
-<td align="center">G8</td>
-<td align="center">Hospital A</td>
-<td align="center">B_STRPT_PNMN</td>
-<td align="center">R</td>
-<td align="center">R</td>
-<td align="center">R</td>
-<td align="center">R</td>
-<td align="center">M</td>
-<td align="center">Gram-positive</td>
-<td align="center">Streptococcus</td>
-<td align="center">pneumoniae</td>
-<td align="center">TRUE</td>
-</tr>
-<tr class="even">
-<td align="center">2012-11-05</td>
-<td align="center">F10</td>
-<td align="center">Hospital C</td>
-<td align="center">B_ESCHR_COLI</td>
-<td align="center">S</td>
-<td align="center">S</td>
-<td align="center">S</td>
-<td align="center">R</td>
-<td align="center">M</td>
 <td align="center">Gram-negative</td>
 <td align="center">Escherichia</td>
 <td align="center">coli</td>
@@ -1014,50 +1019,50 @@ different bug/drug combinations, you can use the
 <tr class="odd">
 <td align="center">E. coli</td>
 <td align="center">AMX</td>
-<td align="center">1492</td>
-<td align="center">1218</td>
-<td align="center">3100</td>
-<td align="center">5810</td>
+<td align="center">1510</td>
+<td align="center">1283</td>
+<td align="center">3166</td>
+<td align="center">5959</td>
 </tr>
 <tr class="even">
 <td align="center">E. coli</td>
 <td align="center">AMC</td>
-<td align="center">2743</td>
-<td align="center">1143</td>
-<td align="center">1924</td>
-<td align="center">5810</td>
+<td align="center">2778</td>
+<td align="center">1154</td>
+<td align="center">2027</td>
+<td align="center">5959</td>
 </tr>
 <tr class="odd">
 <td align="center">E. coli</td>
 <td align="center">CIP</td>
-<td align="center">2024</td>
-<td align="center">1859</td>
-<td align="center">1927</td>
-<td align="center">5810</td>
+<td align="center">2112</td>
+<td align="center">1820</td>
+<td align="center">2027</td>
+<td align="center">5959</td>
 </tr>
 <tr class="even">
 <td align="center">E. coli</td>
 <td align="center">GEN</td>
-<td align="center">2037</td>
-<td align="center">1738</td>
-<td align="center">2035</td>
-<td align="center">5810</td>
+<td align="center">2063</td>
+<td align="center">1825</td>
+<td align="center">2071</td>
+<td align="center">5959</td>
 </tr>
 <tr class="odd">
 <td align="center">K. pneumoniae</td>
 <td align="center">AMX</td>
 <td align="center">0</td>
 <td align="center">0</td>
-<td align="center">1284</td>
-<td align="center">1284</td>
+<td align="center">1271</td>
+<td align="center">1271</td>
 </tr>
 <tr class="even">
 <td align="center">K. pneumoniae</td>
 <td align="center">AMC</td>
-<td align="center">556</td>
-<td align="center">263</td>
-<td align="center">465</td>
-<td align="center">1284</td>
+<td align="center">579</td>
+<td align="center">266</td>
+<td align="center">426</td>
+<td align="center">1271</td>
 </tr>
 </tbody>
 </table>
@@ -1079,34 +1084,34 @@ different bug/drug combinations, you can use the
 <tr class="odd">
 <td align="center">E. coli</td>
 <td align="center">GEN</td>
-<td align="center">2037</td>
-<td align="center">1738</td>
-<td align="center">2035</td>
-<td align="center">5810</td>
+<td align="center">2063</td>
+<td align="center">1825</td>
+<td align="center">2071</td>
+<td align="center">5959</td>
 </tr>
 <tr class="even">
 <td align="center">K. pneumoniae</td>
 <td align="center">GEN</td>
-<td align="center">429</td>
-<td align="center">422</td>
-<td align="center">433</td>
-<td align="center">1284</td>
+<td align="center">402</td>
+<td align="center">411</td>
+<td align="center">458</td>
+<td align="center">1271</td>
 </tr>
 <tr class="odd">
 <td align="center">S. aureus</td>
 <td align="center">GEN</td>
-<td align="center">1099</td>
-<td align="center">1066</td>
-<td align="center">1137</td>
-<td align="center">3302</td>
+<td align="center">1171</td>
+<td align="center">1032</td>
+<td align="center">1056</td>
+<td align="center">3259</td>
 </tr>
 <tr class="even">
 <td align="center">S. pneumoniae</td>
 <td align="center">GEN</td>
 <td align="center">0</td>
 <td align="center">0</td>
-<td align="center">1840</td>
-<td align="center">1840</td>
+<td align="center">1827</td>
+<td align="center">1827</td>
 </tr>
 </tbody>
 </table>
@@ -1138,7 +1143,7 @@ I (<code><a href="../reference/proportion.html">proportion_SI()</a></code>, equa
 own:</p>
 <div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">data_1st</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="../reference/proportion.html">resistance</a></span><span class="op">(</span><span class="va">AMX</span><span class="op">)</span></span>
-<span><span class="co"># [1] 0.5813992</span></span></code></pre></div>
+<span><span class="co"># [1] 0.5802209</span></span></code></pre></div>
 <p>Or can be used in conjunction with <code><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by()</a></code> and
 <code><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise()</a></code>, both from the <code>dplyr</code> package:</p>
 <div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
@@ -1153,19 +1158,19 @@ own:</p>
 <tbody>
 <tr class="odd">
 <td align="center">Hospital A</td>
-<td align="center">0.5883615</td>
+<td align="center">0.5650667</td>
 </tr>
 <tr class="even">
 <td align="center">Hospital B</td>
-<td align="center">0.5790089</td>
+<td align="center">0.5838028</td>
 </tr>
 <tr class="odd">
 <td align="center">Hospital C</td>
-<td align="center">0.5716704</td>
+<td align="center">0.5864369</td>
 </tr>
 <tr class="even">
 <td align="center">Hospital D</td>
-<td align="center">0.5820106</td>
+<td align="center">0.5923414</td>
 </tr>
 </tbody>
 </table>
@@ -1190,23 +1195,23 @@ all isolates available for every group (i.e. values S, I or R):</p>
 <tbody>
 <tr class="odd">
 <td align="center">Hospital A</td>
-<td align="center">0.5883615</td>
-<td align="center">3729</td>
+<td align="center">0.5650667</td>
+<td align="center">3750</td>
 </tr>
 <tr class="even">
 <td align="center">Hospital B</td>
-<td align="center">0.5790089</td>
-<td align="center">4278</td>
+<td align="center">0.5838028</td>
+<td align="center">4260</td>
 </tr>
 <tr class="odd">
 <td align="center">Hospital C</td>
-<td align="center">0.5716704</td>
-<td align="center">1772</td>
+<td align="center">0.5864369</td>
+<td align="center">1799</td>
 </tr>
 <tr class="even">
 <td align="center">Hospital D</td>
-<td align="center">0.5820106</td>
-<td align="center">2457</td>
+<td align="center">0.5923414</td>
+<td align="center">2507</td>
 </tr>
 </tbody>
 </table>
@@ -1231,27 +1236,27 @@ therapies very easily:</p>
 <tbody>
 <tr class="odd">
 <td align="center">Escherichia</td>
-<td align="center">0.6688468</td>
-<td align="center">0.6497418</td>
-<td align="center">0.8898451</td>
+<td align="center">0.6598423</td>
+<td align="center">0.6524585</td>
+<td align="center">0.8848800</td>
 </tr>
 <tr class="even">
 <td align="center">Klebsiella</td>
-<td align="center">0.6378505</td>
-<td align="center">0.6627726</td>
-<td align="center">0.8777259</td>
+<td align="center">0.6648308</td>
+<td align="center">0.6396538</td>
+<td align="center">0.8686074</td>
 </tr>
 <tr class="odd">
 <td align="center">Staphylococcus</td>
-<td align="center">0.6741369</td>
-<td align="center">0.6556632</td>
-<td align="center">0.8885524</td>
+<td align="center">0.6738263</td>
+<td align="center">0.6759742</td>
+<td align="center">0.8922983</td>
 </tr>
 <tr class="even">
 <td align="center">Streptococcus</td>
-<td align="center">0.4766304</td>
+<td align="center">0.4564860</td>
 <td align="center">0.0000000</td>
-<td align="center">0.4766304</td>
+<td align="center">0.4564860</td>
 </tr>
 </tbody>
 </table>
@@ -1279,23 +1284,23 @@ classes, use a antibiotic class selector such as
 <tbody>
 <tr class="odd">
 <td align="left">Hospital A</td>
-<td align="right">58.8%</td>
-<td align="right">36.3%</td>
+<td align="right">56.5%</td>
+<td align="right">34.9%</td>
 </tr>
 <tr class="even">
 <td align="left">Hospital B</td>
-<td align="right">57.9%</td>
-<td align="right">35.7%</td>
+<td align="right">58.4%</td>
+<td align="right">37.2%</td>
 </tr>
 <tr class="odd">
 <td align="left">Hospital C</td>
-<td align="right">57.2%</td>
-<td align="right">36.6%</td>
+<td align="right">58.6%</td>
+<td align="right">37.4%</td>
 </tr>
 <tr class="even">
 <td align="left">Hospital D</td>
-<td align="right">58.2%</td>
-<td align="right">36.6%</td>
+<td align="right">59.2%</td>
+<td align="right">37.7%</td>
 </tr>
 </tbody>
 </table>
@@ -1411,16 +1416,18 @@ classes) <code>&lt;mic&gt;</code> and <code>&lt;disk&gt;</code>:</p>
 <code class="sourceCode R"><span><span class="va">mic_values</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/random.html">random_mic</a></span><span class="op">(</span>size <span class="op">=</span> <span class="fl">100</span><span class="op">)</span></span>
 <span><span class="va">mic_values</span></span>
 <span><span class="co"># Class 'mic'</span></span>
-<span><span class="co">#   [1] 0.001  0.0625 32     0.0625 32     64     &gt;=256  32     4      0.125 </span></span>
-<span><span class="co">#  [11] 4      32     128    32     0.125  1      0.0625 &gt;=256  16     64    </span></span>
-<span><span class="co">#  [21] 0.025  64     16     16     &gt;=256  128    0.025  0.005  0.25   1     </span></span>
-<span><span class="co">#  [31] 0.5    32     0.0625 16     128    0.025  0.25   1      0.005  0.0625</span></span>
-<span><span class="co">#  [41] 0.025  0.025  0.25   0.002  0.001  2      8      32     0.0625 0.002 </span></span>
-<span><span class="co">#  [51] 0.001  0.5    0.25   16     0.125  0.005  64     1      0.0625 0.001 </span></span>
-<span><span class="co">#  [61] 0.025  32     16     &gt;=256  0.005  0.002  16     &gt;=256  1      0.125 </span></span>
-<span><span class="co">#  [71] 2      0.125  16     16     64     64     0.0625 0.5    1      0.005 </span></span>
-<span><span class="co">#  [81] 64     0.125  0.025  0.01   0.0625 0.5    0.002  0.25   0.25   128   </span></span>
-<span><span class="co">#  [91] 4      16     0.002  0.25   32     32     128    128    0.125  0.25</span></span></code></pre></div>
+<span><span class="co">#   [1] &lt;=0.001 0.025   128     0.125   0.0625  8       0.025   32      &gt;=256  </span></span>
+<span><span class="co">#  [10] 4       32      0.005   4       2       32      0.5     0.0625  128    </span></span>
+<span><span class="co">#  [19] 0.01    16      1       0.01    64      0.0625  8       &gt;=256   0.005  </span></span>
+<span><span class="co">#  [28] 128     &lt;=0.001 8       1       0.025   1       64      128     0.0625 </span></span>
+<span><span class="co">#  [37] 2       0.125   4       4       4       16      0.0625  16      0.0625 </span></span>
+<span><span class="co">#  [46] 0.5     0.5     &lt;=0.001 64      8       128     1       64      2      </span></span>
+<span><span class="co">#  [55] 1       &gt;=256   &lt;=0.001 0.002   0.5     4       4       32      &lt;=0.001</span></span>
+<span><span class="co">#  [64] 16      0.002   128     128     4       0.002   8       8       0.0625 </span></span>
+<span><span class="co">#  [73] 0.002   0.0625  8       16      0.125   0.005   8       &gt;=256   8      </span></span>
+<span><span class="co">#  [82] 0.01    0.025   0.005   64      1       0.025   0.002   16      32     </span></span>
+<span><span class="co">#  [91] 0.005   32      0.002   &lt;=0.001 0.25    4       &lt;=0.001 0.25    16     </span></span>
+<span><span class="co"># [100] 0.025</span></span></code></pre></div>
 <div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># base R:</span></span>
 <span><span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">mic_values</span><span class="op">)</span></span></code></pre></div>
@@ -1454,10 +1461,10 @@ plotting:</p>
 <code class="sourceCode R"><span><span class="va">disk_values</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/random.html">random_disk</a></span><span class="op">(</span>size <span class="op">=</span> <span class="fl">100</span>, mo <span class="op">=</span> <span class="st">"E. coli"</span>, ab <span class="op">=</span> <span class="st">"cipro"</span><span class="op">)</span></span>
 <span><span class="va">disk_values</span></span>
 <span><span class="co"># Class 'disk'</span></span>
-<span><span class="co">#   [1] 18 29 23 17 30 20 29 20 17 19 22 25 29 30 24 22 29 29 24 17 30 19 25 23 26</span></span>
-<span><span class="co">#  [26] 20 25 17 20 23 27 19 26 19 26 19 21 19 24 20 18 19 30 17 27 29 21 22 29 21</span></span>
-<span><span class="co">#  [51] 22 30 20 23 27 30 27 19 24 24 21 24 24 17 21 20 31 27 31 20 22 26 25 29 31</span></span>
-<span><span class="co">#  [76] 31 18 20 18 17 26 25 24 31 20 18 25 26 19 18 23 31 27 18 18 28 30 26 30 27</span></span></code></pre></div>
+<span><span class="co">#   [1] 29 26 20 20 26 31 29 17 28 21 27 17 30 23 21 19 23 23 20 23 19 27 23 20 23</span></span>
+<span><span class="co">#  [26] 25 22 25 28 31 19 31 20 31 19 19 28 31 20 29 23 25 28 27 18 24 26 21 30 23</span></span>
+<span><span class="co">#  [51] 22 31 27 26 27 26 23 22 17 19 18 24 31 25 29 21 29 19 27 20 31 23 23 25 18</span></span>
+<span><span class="co">#  [76] 31 18 27 31 20 21 23 25 22 31 26 28 21 30 17 31 29 19 25 24 17 19 19 21 21</span></span></code></pre></div>
 <div class="sourceCode" id="cb42"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># base R:</span></span>
 <span><span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">disk_values</span>, mo <span class="op">=</span> <span class="st">"E. coli"</span>, ab <span class="op">=</span> <span class="st">"cipro"</span><span class="op">)</span></span></code></pre></div>
diff --git a/articles/AMR_files/figure-html/disk_plots-1.png b/articles/AMR_files/figure-html/disk_plots-1.png
index 84bd96656e28297bf0610a755d95f2974643f575..395ea3bba8f7953f553250efe08fcd111628deee 100644
GIT binary patch
literal 38754
zcmeFZ2UL^I_b!TEETCeiD!qS#bV3mjB2{`X(xgiXB}n;HL{M62(ovdp={2BILPvTD
z0RaI+2uMi+gmNbM{r=^gd)GPZu5-`5>)!J&C1mnu-kDvV{p>w6glTCgUp#mF92FJS
zMW~9RHWk&WOe(4qU(TKeEiJF6KY^FiPgIo^!7I3S6dHoS3-vP<BTp(S#!$-tV^It1
zwp3IsR8U2EUEkCdj9(m`-Xv<>!l&Nh_U-6^$8wjn9-m~Gk~@7;_Y&!pVtc?%My2oT
zdZz9-rk=H4d+%;=HBMfs?bPGTii(#QPRU~*Ysp=X%Q3k<=yzDFmwPt<5qrK@{VZ96
zlk-EtDpFIA=h|*(u|NCKfd2Q7|1E+4RszvQQf%>c(v51K+?)FCcZ-eJS2V<l;J${k
zM&~o&HTV%f8@FtTho8WLc0TXfc>Z(@HHwLf%2F&L1^;jTHE>+{{?D7>cPz&){_}gs
z+5gaNdF26Uws|mf)GrkaSw-#y71c_h2OBK*zKXUiRGqyzVXGvYldU)YYNxT6w#wiK
z#IA~@skT(oO$CJLBV%4HJ=8*Vw=|o5v{}rKb)1J!SsRix-;j}gqIe0(CK6K_nvF+{
zb~7xsA*K_y%CZrBttJD+xM7PytI@2G9*OO&>?pQg#C^TRZML_b41Y%1kj)`PUHk?$
zuK<M=;g$WJmu!=>%f+K=uEf!E#eUxm@5c`eWlduRY^OK5bhsRvx&z~&_55F(*b4KN
zO+v{<^S2W>a5jh(jT>B5^9(T)qz4)I*!@>LO^F}W279R|;R88S&KYHow9i1#n>~pn
zz0dx&)E1?dHvi}Oa{72)hqrs|_4-|GBiib6d5GW0X?y2uF~I_HK36u3qpAn*p>vRV
z&*OSyp!ZR{LDs4GA*h@(TQOqzyRvCMTPE_#jJdftHz_Pi+>;&hW1xD>`fZQdsW6)j
z2B5-EaqnMU0tzE(nG8xpMV`AhC-z)A5g~kiAd(pC<$tOwUgd+@-P+jh-4BR|wx94)
zrU{2i+Juv~QYr;&<*Vy)tR6LaJrd=-d+IT)4j;3vxD!h62=k$=n9#bp6d`7QTRC@V
zbdrin?g^=r?L+K<{Gi|0&?{rl5*O&$st~&Bi-#qCLeSAjgg`tthar1e2s+Xyfa{Oh
zdiKRd63HuUqgZ4LMP7-6%-HU5A_9MN)oA3}uYN$>=7tw32rczwEKI-jwMZ&!<$WZs
zKsZnuq)LRXsY=ReH}%|eVD*e_-K?Gp`kBwSB>qRDmp(xj0!$9O5k&p_*l`Vx%gIm0
zsRTP|8b0&%Rm*m5si|k@XBV<D#@I=Zum>!cn<);4$vmO{h7`d2M`WaNIuxqb&6{;G
z2x4#U;NlRxSjNQNL{2XHpO+X|=_=#9E-_Tua+tg`ScSF8%J?C!*T&V&<fN%({F_2e
z?~q<izYWiz>eicMt4y>(M7mO=a%4#TQHtwVJEV3}Kat#{Cw*IIMJLRB67k(O@X^yB
zkjkd!3RNeQf}3RI-vO;2E)JxR-5d+8jiYv2n$_l+`=iX8FM!urj`_r(>aD^v)C3Z@
zNIkDA=%gOscCvsbLV3ELkxwCH;)dD#>OG*L-ToV`nE|Y%)}Pb@-I(dZGuIBsbLUK=
zU@j*dSz|UX3`=~o-7!G~61i%=*!q=ZXFW-<B~u>Mar?-18>*#~*bJmCRE%+tz3Z|q
zjz@7dO$c<O9!;{v?md5)D@M8})DnJjJ!m2qArpP;P+UK$$MCy3(q4E$KXJhTWu(tV
za(~Z!o`V58C>jMTKJJWC{Z9SsIJDSo6q%gqY&b{~z}2@IB^3Y59XsAX^k6o(EQ;EQ
zg>?Bty~jf;q$l0|o#XGD4}O&)w(v96pzQQaOQ(GuYg@0yK-DDEew1UbWVkFmT4uvu
zf%Zd_c{f`W^VP0Q2i8W!GQ@bLDeueEe0A4*5dif*8=1i55>hpPfby@658%`=Ns>Vw
zqoU$r4}1(JJznL9nQR_Z_LD`Qv_f3bw7XH5oP7r&6AQH)z3^9hi_RNP6?R1WGb1sU
z#hDhfYEPV!OLzRO^o1MH^Xw45ST=~-V(8mt#0Fzjw6a+U^<+WORQqs3&w8ik?`L$S
z1~6Nxqo<vuehMtr7dkv7!G*Sc{q-4rE=x=n8}j2e9Z&9)%0{}9YhQ~)cjLoXmzWUn
z24_;ZGNulZ+jU`}O!?7(t@1}wD=)WcjAN^j{zRWU0O@n`$8S@tqI$yGOqh8g<;=A=
zw9StswFmDYI8=5aof}AY*jcWG1BAJj_hECR${?+K!Wwse8evy4u{^4K>!Y`4{uJl^
zqMibF!)8qcVL!o<g}XrQUk(*rEnOES*wCT8Q^mA^WRqHTla@f-?pFKd{dYFmwq1mH
zTX@Wc?@5hsQ`}a*azH;<14Kj6J<KVtDzL13H<Agq*!6C0rMjQhru|{xP$ZdYgUd5T
z*<U{LSU|I~)DF|aRC}<%Z!XVpWfOVoBhaEaa`}_GsaT_n(p^<VE`!U|PsJ{>2jH|1
zv#-wO-*L$A-hHE{IpWOz0axEzX_R}zs}WZClhMsH(c`k8=7zygmT5Hg)6Rew&0>%)
z2K?m=ljFvSgC*@nt0<wQgf0mhDk?4RdnbX?zm&M8qU!xk6P8rqf1M~hIcs01`Pl#x
zvirz}WBRSwR0!L?-r$KYjVth%*<*8jrQ8n5s!8ad`zPcBqF`&jMYzQ>fu|Pre6kPM
zQ3fRfn+ozgtKLR3ik+If20DXy?)KTMTa?E|i?C$^Q}YfGgcnI^4h{*;%P$#5B?!?C
zGbXNge0<%xWl4|<Go?>l=+<^V6oUpKZe@w3ciV7q^qj6?rk<?lBf*>5*WAB0)bo|H
z3o}5KlIF{$4LG1pd4#p->^_7{qJ*2ydSmv6c=u*8ERZ9M58)NjLwh>0);Sjc@9vfN
z6O#Inhn9kezduzqspX=)Wug2w@VH4xl&8&Wj@k~^T<f8Cc{z<J<tB-*f%XqnIOJEC
zAW3ssO^%3f0t?-2jDq>!C#&VOnW2SrmGoPe7?gLWEShOc?})QuA4HX_+FU_ZU4`7(
zw0$jv<&Ays<Jvr(6Y=l(IJ$CYXj3F98>5X+HSP8~E}4dMtRv~GbEKbOb0IyoS9}rB
z{|YBP=Pok^{|i+=V;|6mVr0U-(2xsd+kbEKe25LXP%iNJxTG?NU&gA$FV16W`A30B
z`^i}#=wM7sDS{>u8-PV=3Rxf2N4zzii0fptXgM}a$jM%1!YOxO=zJWLzEReD8tNG^
z>Qmff6B4Md+eSQ~QN8`XmFT7_sWjd4);CN=QejX5guAoy=@e|g_WI;~U@xol%?M-F
zUCFQBZ*Aen4iW2bUaZ&me81IpY;>or5%q<MSJ+uWX-A%eLon-V5Z}CSVB)a+V5m$r
z(qeF;V7qmRF9~`kNb@u1By^wMMYRI?xrg@r8I|P32Rm&-3ih=Ty=jqZss9Ql4}YpG
za|_epA0^Fl=s$v+YP;dbp;nbVc4s4_vynFK^h}Oz#K@jkPV_eLpx5mCe8Tb=U+4)o
z5n@FPcdIq|t*Qk1UWYwdUR*$)PhGo+q$vblY!h1gb{)FOj$zA>w{9?vs(#7zq9tEt
z@RX2(b5Wwijf9TW6#SFwfQg)9MTKXrLn<8W2XFKOvw9?Il@?K{+(O82QSrJn$^n1i
zlj5!aW0!Vf>=A1W=Ha_yX|^{*;b0A^Q-3HSrHgTqm|#jqMY(ML;N+h-{}+B*CMNRc
zr(~BQu0NXOb3kTe5wD?+KeSyNvxT4#Vdf`49w7P>?;F?!b<au5^cs6j=!{GcnL=~I
z4-eX8stwWhEhqFZpzQMZ6<1@!N-g9FITg+FDD7JlsHOMfIPHl;YN3pmlJ28sf<YNk
zz8Pfp(TxL{Cx$jI{s@$4e&ATE<I<a*#~HS4*uR{}IYKC>G(bNi5EG>1sOmdAvaV%O
z>oW8Cyq=9r@s);y50mEC6S6(6=iGJ72DP9WhU7}q!Kcs&p>c0X+dO0RsLCZ~iV!Ss
zlxJkSu`6AXc16V9&>_x@)~8>)>J*)>H5Xw1zUo(bUI8*MB7Qrdl3BRoYL*S+!DT3a
z#m$$AJ3{MtI;{Zgire=x-G{_=2PS*7a&k~`)xJt1fpy_fcyNflZ$6-PJ*NuaDS#W=
zDG7tsSd%|r;dCtQWUHgA+K<Un>p&4*qk=f!C?n1V3|E+I(??0(h8izui}K|=ou1SX
zTOA!0_3>M|@n@ui3Inn?o8!DvvmrL{{DqFKT&V|x-J_xb)?EfwHaRY|N+dEBeu~%I
zA|`k>X+eCiyni;Mg7Ho0ai#v6JwDVG&61DndbF8MUPQ59hCHlG>(p&Us<wwS71gCn
z<<lB+WPCjCs_t<~%-Zcd>7?|g%TU?fcWy7LFxR0cY~jBbjiZtgldAQ)gY(qOWdV*O
zV&hB1^{ZWMZD%^!Yzw;?-adz(>E4p5iLG0QpAWqn5M-z7Xcb?|KlE1Su0umll$U&U
zn(LJIRw|~&7V>9Kk2M#gbfoTGPB|Mk@_3QBU?y+xM|$H@h!HzxHe$-XX^u?A%Sl(p
zhPgXbpU7uvKp${)%gAJ$)JnvVcetZmR!({eDd>&AP?0;cZ{``NH+DD4isddsW@~(U
z=cRSxNf$C9Om8MLc=>H!g4#FfyVz85oTk?O0h62d`!-JdEw`RZVah{IaLPHC=&7ih
zY@DXRz&>Y@uj1ulWj*+M^XuI2UqNjxNqnXmBV3nW$(r;t+l!oYJ7d%@Tz*@rZ306S
zGa|10aR%3y)TA??%`8!)-fdVshug_OOjnuvBo2SKC3}y08V=fry(m@`<(7<7?ia@?
z8}`?9Y@Lm=rS0@6>$${65zK_2(ELM-Vl$G)87jF1isQ17&#|Z3Bb(H;{lW>p>V{&y
z#y-(K_z|&gHGN%22yG&{Hm$F`irtJiHhTQ7gGNtvXve#yy|k=VG@D46&RUs1k|>^$
z=ZFf+nrv9b_8}Zd7YYxqc^q1!WfBD2`Di<*k^>X7Plam`w<6Wngk%*M?#Fwj^^|#P
z?23qMNC(ag-9{W}+BF*c&lRF=;LUp3)1#XouXfkOCVlQszvS{#t$+K2hvrjguE9bM
zVmdCV!?~iz<#*!KrLZ6MM*Ym)Qw7sHgPuZbE&W0e9UR9SPfe&;S7WFNTXFnWWi}0y
z?W&%aP{T4p0I~HqK*VcrJsakwmkU1at6x=BE$np=Y7vZkWXuu!b83h+P!=(vY~r8P
zznG}cH`A4>X}%P>x!t<4X+D0=7XIUP{O(SMgM7f)_;SbAUVQeLP2a*;Q4p;cx3vYU
z!lNm2efdrsJd%-4%3@&imwIBks%>9{sZ#83`ASB`36CT;Sjoq+XB4aaBCvQ?$qlMu
z&jBx8e}SjYD2awHx21gg6%;P(nqRT9`(9|X*u`T35@TsN$bFAMB#cip(Hd_sy<m;^
zW{>52r;>ct9>X0TX@A?Fm+<@Ky+h6E`KRUynS0~!@%xQ=i5&rRg)k4z(Nn#CPW#i`
zam}jJD7z%px;x=C+?UoalUZLt?#sJDOKandinozhJ=rlaEPc)OtqwJ0v7}d36+Jlj
zY9}LV;uvqjOURSY%;wa7f}N|@Dub*{Xt%^$%bZN50cu<3;;@?Pv;@lrXPU2<hkYXO
zriCS%jNQ9N-TK_SS>ZZ#$`=gkonwo_Z@y3vnvo}pjOEwuF^)-$ik>*`EcK~e*#s?}
z_+XzRkzk%5$CG6DM6->eGHi0vI;2zvn4X^DN-o;gmEt^^YAYL$wY`asT{d<KNK&Jn
ze^8kITFBaba)A^@hmIMo=N2F@#0P6sVH|~!tgMJWA3nE|r*lp-;`oaRP?qY-<<Fe{
zlNN&}`gI1TMC(k}bYN=_&~$DivA1Li;qQfj9VQ7OmmlZ{kh`{O->THlu%~D4cV3P%
zaf6EyxAPBUp+B#6GN7Y-{QYvX3+Lh*MtExUa%-Gfm7*L@B$;FF#jE>$T5GjA)}5He
z8hpt5PW$)VC+XdaO<07dR4(%N-G$nB?)|tt&<9yd!#}j)*q^jm_t&08x8jNoW5_97
z*U8T@ngji2DvPKy{u}kW@Z+2fQFoMN{b#yNHCMvMmsM}){UV5ncCJ=Mg%M>(Ws6)&
zH9wn)(tW2G<cd>|1w;3_YY<mgpE5O1=L-GB_j^W*`;U~A*fJY<ZhJbXrZM6guPpa-
zC;QSC;4{0b%d3qNZT^w{Hw~5N$xmBO?y>Qz+P)rmqnG_lQ7N<`&a7}dtnIGS;TQbu
z74&>K$0O?r<i-?7iKnVu9@_Q165VUO%@vLAqM!!C6T%e)G_#$o(471+tf{#}R(1`0
zrA;%s%3upZ`!Uh#ZqP_1M59}p$sZM$#!bp@PBGU{>J^e@*dCa?JpVF0$aJ-O)PkX6
zPtj?|tB#iOSz1-E5))o#VX(xr!B@6gck2M&hENevk~Il@ol}-m9>sb!LU{V8TV43{
zo$tbSX~?Mab8)KPhn&V}RKxtY&ZcR!Y^opRNq>dck&INN(oM>I`o`&2>-M+xxUhQ4
zeoOQANzA_*U#o}3V^4ayD6JjT@%8O8g&#bfvn{H^;Ez=^>Q3AaZ#uIsqfAAW9dZvO
z1RXL`+;OYEsex0h!qd)0z2OIsSG@gy7l$97sfG|2Jd*CY%-0RTSGi-wX&%($0|kdF
z^o1bW83|DDL*)Tz3gWW<7IERklTlbav}-@}hnsto5yvdOQ$E4e?CX?khKdgg7p*oo
zfL~Won@AY-JOMIg-rbi<K}pldz;U|1;utKFKCGg*bhKjAQ(JwoLP&Nn^)4w4ZD-P7
z7Ut#86@F2uMYYvwtPX>|*UbwLLOxlyPWg!HSc_uLU$KtqiRSW7@{Mhd?`BiXNkdH&
zl7s)6*k7}O9RBk3?<5N&)aIs3S(=?zq?Bbw`{+6%E7AoY@9lHN@5tK+C6P+U%S!&v
zrnl*v7&`Gr*zA<h3@yyIy(W+=qJE`6W2RqG(uLDxbZ27SpYb%+7cojv!X_i_os-Ei
zmo^-=Gx<Awb-7%|Tu`1n`BT#JV-l?pWH)<@lk<Ezrc=mTWsN)bojJd7&ufzxT0#)v
z(nP3e$yg-hda0*FH&c+e_KSL*vMF1?x{pvfS%tHWIors;G&gRc21Yj}gs88VqN?AQ
zdwcw3O$wWl?36k-reoKGmGC%Rzr#eHV#?Wu`a>m$;oeMRSvDMAMckfm;rWDdN63mb
zA-!sU@x3ITS=Ci%>-js>w1Brqe|o|AJ|VnKUtv*L`0--j-`Yx1Euu*zx7bF~SbiR2
z1~qRJxbmKBt<^1zu;<_7LEi2e?Yn{x;`9I3uZXmCp^Mx0oFgiPW}LKZY~FmaTSCn(
zX{xnSOF-OLUEI}CU-ijTSyV0ud4SGX^zdUlzHJwwb<Kofj$Xr|7*65*9~o>&LG1mu
z=0Ladbi7s4)HtNgnvRIAWYjfU+Ev@zK1Ma(`zMGe81}>BcT6mh?FW@(A|6H-bLCS^
zu20loR2!J|S4r1w2!8TTqq{7Vy^Z8@S?b<OZ8GEV(=%B2=WFM9j%TthywaL{uBP>4
zoT;XAjrsdPgjtA?wXy1;J+#uH0c1f!YYXv0EleOQ%cO~@2w=wTSU{^iiwJv7-Mb7*
zbl5&9t-pl%STt78+>B#ZSAD=5a6WjLx6Rm&-HYuNeA62v^8qsM+FKiV=>bpdHbjTx
zgOS<5yd^E;y8e~2-0~>zTQ#%(&O}D)nRdRKsQ#e&xb?|H-xap(ne~1QqT77MrYU2M
znjA^HM7Z;5ExE-u@LkMY#*hg+(h^COns(36ZbOvB++ylHh;cLwzn9C)Hsra@1>Lma
z=#AK)?IBjo!(dU8CMNx1a1!T`x0Y?%m|wHYF{&T*l-f@DnkNe9T4Hj_nl=gsD_Y7h
z9c>_Fmiv2(Xne0m%2fM2EqL*?rpa5)9CL>>{kOVJdt5@#&!_&KZ1f)2@MXTSsuLDP
zOPTN>`v-z;Ya!T?mZ|ERc|!Qr$;gfi?gMJMp*#6P*8CX)!cDltHs_#h0)wFYFAmLt
zJ2#r#Cvwx(UiiJto|&uk3BE!y24&+ONpw}H{WBVoOM)`~4dW;El-5cO_(D6?DwBAH
z*UhDpn0+3EuQo%bR~n$LDP1y5&XAy`GWGQAz)_r_bpjg2>CKEJ&h$RP)g|@gb*xC*
zZrHkbKR05zd8s!a?Q%nV($xc{^|e&-ZsQq)=^3f@qL?H_{f$-mG;Z%TT$9k6lcpcZ
z*#hT_7xngw%vLBGQ#GrG=76{%85C`K_W_mM56W^tbbtvP#0?p!m9!stP=S(^I<^Hx
zz$U4)=pfY&AD{Mr9$ydAiFa+dlf;xP!e+*gz2LOtA3pQ8muY_cF*M4MFhO^Ik|vVh
zri@ca%Iuo(9ZRJOI={9_zxOX~6m8&PhsqodPn&DRP|o(G)vaZho81~60fMCIJ$&)f
zxOp1(or&w=uip`kvGPQ33&$m<H`kEO=4DgXkPMa~&w#p~C@F6@a&%8?u(n4m280Zv
zeScw(gAj6fM&GN^i5vcW4xcr4(2(Q=sUc1AK!{p*!Z;+S(Cd>sS<07YM3g4o)2V9K
zr_(f4KQNL*zlX0zr0@!B9C9O?66%K9a3wc8*{1Moz1F7H4u!wmcO<`5zvSXo3i`Cq
z-swR_wfz88`GUJ<J(5oL1tJdeTW1Eyypt(Bw^*+tVUzi<KhhD^BP*u6l}9%NJ(y+T
z*#v3j0h5s-Ah0Z(_Ks9;;nm-&pzO59E_}y|uOalk$(6F4DU@nDYpjaHhhfCMc+Bjm
z=f^u72-_$eJ-TINwG%69WhZ^cxZlm!+4DVtK}x?KGd%&Vw68$%XnHv}av%$TQ84Y`
z&SIfB&Vvn8{M2L2<@tuM_5{jrbhAtVXQBchga`QbNRqc(={eZqsc$Zs^oRPn#-|?|
z_A7ThKT}&)y0BTb>Dz)0980x?xW(3Sz%kU8GnYNoUx+?gfYyJ3@28KwKGq$R6iif0
zYTf@J1of9)El+pT-m@}vtxo6UI`C1Ebji~ht&SovxINJLLx{6W)uNi-s6|bfPr6Ud
z-A#Ih6+X&457EZc!Wx(%MwlR?&ShHTN@aYK<#+QNXoWEW|L7@<ql*qte)C5Na=ByA
z%C&~EG?KLl<~m<nt+)05<cO=A(+(V3#n=rp;@-PL^AIUUOMcq!EssJw{!)P<6Q7q^
zNZ+t7PX=5m;?_Zn%xIPEbieA8u5CX}<aDBtQ;H-L`s8f0zSN0WzA?SQn@QPb{7O;E
z>PhPXLDkC;;apQ%rKklhC6b`>qW7USyBWQXU0l^ZOO)?)c{TQGMPp$W62~s$)tbz?
zX--VIZ!Q%Sxca$^{ZY{*x{dtz$B!hyumy`@f;^Iy4^(oO)>4Er^51wf?0${XC6>iz
z^{j^n9=x^TpsQNby2+FW0a`&Tm+t%MJdK|y`E_%A{at0X|NXG~G!Ekhu`afkPKMo}
zCV1$pJ77I{$NbczwFbMGsudaLGEvV;YfX-E^F_}=gWNnn5ft&1&{zd6!-Dbl(<M4~
znK8P~$6MpYx*IL8e|GL^U60w3M={Ygzy7$SqqrVw^(YVTHD8UfADM+IU#2L`@`ihK
z4|7b+$*6(J9Z`GP&aTTsVQ+n52cNd-F6~0SUfXbNZiep&3fO|Rue?E5g``(7&{5CR
zJFzyA9c;^KD?c?GQK8`+$EV<3<|!<rW%tkqAt$}kKdy&f3z9ZmYLT!b!8T-=E+_p|
zmyY7#x*_E^kvF;#V7A=-c+0nq-lJvb<!dzSwC{v>L)j7n?d@tEZSWIt#K9rf*FoKT
z&#ap@^vlUy?12`2;W`h)SFP!zV6JN+^yJ;|6V~)n#~2^a^>@7yUpQ}bW#63KXciWV
zRat1rt+V!X+Og)0#rLnv%S=|1z82g27qMLV?)Yt#@UOi9>eiDaxW&}#1P2abrb@I7
zp+^oa-<nqVi}sU<X*DPsgLTp;4}_4%P@P+%e6hEz36P}p3U$-29d-TD#9JOul6shT
zREy!~=i}b;r>KY}xpNtGv#}<2u)QH}aK&mImz?lc(1%>cOP8e$-5M1wteki6jK@Yu
znJflOaCtsIZ3076+m(K}ZQQlhWXk8cC)S}h>ixGj6H^WkTc$~u#RKlUo73WMJA%>J
z015p?U*{NXRlUj}{!!{rNmIMpI6ZxgY!}<HsF6sdmO|vsISi9vQoo*}lm^#z97|H?
zK!Oq8rSeIDBB(VxV*)25FeS+-?`vhvflnk97x&OS!d4TW;gNUTX5xPkU+UZqBn@Cy
zpgJ}j+EYB6JB|9Qba8Ne)(q(`6Imb(1*vxD$fScj@qaGG$P@e7g2yvew42R8v|CL8
zt6V-KLZp58_W39nfRf^3g^Q-{g!W=&be_#>`nWT`<xvu~fZ+E?NI6}n;_*q`EWu1y
z!Oo*EL}-k(X0y8SbontyTJ_5}WGyFThWVcb?%GEjVHc=Wdi{mQChI3?PX5N4C3#i{
zR-ZUhjIIkP>$7aq#MO<OXW-?in2Fbs8AqQSy8{-&ugy~@&E61#>~Kbx7}&m+3u8Q>
zrsy*``=2_eZtYZS3Z>=$+gHPjR8Zu#BwY$qoK!7H!2emQRT*%TkdSDbpgI1@aig3O
zl<proK|{b>iZFt&?sJ8mydRzY-!Bi#J&gW8sv7^lxLHL$Lnc7uV#otJYvxm@PHEnE
zncPY^*e5eLKOOk^(HX@JWt8%6<hpuJy>TJIgr>urkE$SnyxLIevecIrI-IYwl;P*+
z*MIHm)x8FXq1+l6ZWLc+;##*541Qa%mufeQLBsslkfbIK&`0WDRu8&aaJ>m#*J-_F
zP3Oy7a1|MSut=wKRJ!JV6JXIq`<~Ni5oDLN7Gwg2LNQ)A$B}{&`|d=+X4xd6g@uJW
z@c+a3f~~Es$XJn4#UZ_0W7%PfTazD=zh=r~GeT^-mJM@rX!g1j&t#2T(8QQU5C*pO
zYk;-1ez{;le)v{g7Xy8C(KGSXp=uX&Ve{bunFc$mS7Fx+V-DCFATUaLjnCEeyuL`k
zA?dv${$!ymp3!=)%L3fc1V%RD)0Zm2DN7u;wkS16?zVebPnm5aHR{Z2y1Tm-u%`Vl
z_himKuzh>xTA@XAASV(?Do^=tOsdy%XLmPwzaCdYFYNKNqjWD}_IBbetoeKqjN4+V
zHkV3nLFxEyx%m6A<zf94Q5Q!BMYEabo4vF736NizFYWk@E1#zBuSL*{y*7Wo-iCbm
z?&dwOme&{g-I{h5>H8t1sZEp0r)45<3@i8<XqFNZ6GI30t?$wCcsk3Ki#m@-8<?8z
z<*_VJ*1B6)aO>o*U_K~>3hpOh2eMEhyapvJH_wGsrw1MEMA6WTIuA6FS559h!F=;Y
zJr{dk>*Q%bEO#~*ieJ8Q_xTDt_s00MrF<eDU2`MbL{prhMAW+d9Sz8#>e_8jkBL(S
z(}VQ5pfc<B+6Vi3e1`VKhKeHpfv{LH(bLnjJMFN<tijv2Bl^w^3_G|m`jS>=dj@FL
zkA(5D2tC8(YZ+*KF=zIzV3V)zVAg~qv)uB=rbpFo>@n^{S@#bA<cc48*<H^GBL*VP
z+jDQH5%=wS-lv3Kq$d`82E4t_U!Cs1T36TyM54!i=Co!*H_YE>Lep33_o9=o|4cP^
zyxGo1q4CZjFEk$LAmBOim?cSpBKzxIosX@!s%BTZS&yBW|6b?0q+aDTcHlnW9?{#h
zyIi=4C{GAJtp!{7sUlL4cQ8|>#kHgHbSO{L&2h4iyLoF+)gXIHoN7DTgx{j>)QC1u
zN^IiZ)AOca1?`is+Jm!Hw-QoP=*33%XG57s@gs#}*o*X{3~7mpB(;d!BJFDpYfUX~
zNWv)56&BJmn|hh`p_end8h=g1Y4kbY)pHOFUEQr`4c_>Yn=;RR6M-qLZY91)zvAm0
zCUmeyRh|ProXBr7s*WhBTTDi$#tMo$jjnL`qX!2Er$FBgW@9C0XU0k`4omj-_VUi1
zJD2`+x^X9BDb-UW%J<%6DqVk(aaBPk#cmBzOLdb~PEq@t8Hdup(QeKBUI|bRQK~Q(
zOuYyjG)9H3rn2%r_K;V<)I8t;*VSb4po2Z*^-<sx^~w7?7eZU4Y&&%7`k;%+@T~#m
zjKNy>dGzMyW(GGGm+wcVx7WuqfURM`farbhmg$zX^x=jFb5xzSRwg#z!3oQl6d)B-
zB%H7cS#PC_dYe|uH-U@G2&>$yPJ5>tsVuptp}0sQvrHdsLpW3M{3g{!zu#L&O5{Aw
zdg!ybumBmNJn-fCj7`*<(hV~&MKDCeLjB^<sXi<6&1$Ey)pIrbovn4+@j3nxv4ahL
z?h%`^MwZ`~T$L<<x*l%@pANoYSo_SX7>HRkHk%0o=Zi3twfqt+6U(W2km70N;NbAJ
z+_t+O<29s?Nrx}zYaWni4yP>)4YyI3m}j4f=K&)(C^0OX)3UDv<GXL#^xUsyp*xYe
z%%&3&=GMGV3`)h#t9Kxr&4BTHj9v~?^tc=IW^*R!dBz0>@y}~crATk{6`4|?lNRBA
z=^?AGIPMdRy(vC0G7;~Se5p6x*KT(UOEwGKSr|)*iyN*ObD6Bn?*yo*;WX2<e-HRV
zy)$?YFH{t0j?wRDj1zwB#2Fgt)e!@WwCm?Wp3ewh2~*s-qJDg#T4n?{)ybJLwH9w+
zE+Ly+|015(!29_|C#RHvd1Gy_eMSKBm8UJwn3WW9V`JkxZ)u~)kDNqv_%1+E$4MN3
znD28{W9CwB0P0s|{fR4=LEM;fj)yKw#y`DVY%@vZ9hr}zk)?}a-wgN>w<0n7+#-~Q
z5e_#pGEzs?S2inaOEdvbBm0gP83h@a+jM3`NpEy~D5K-BfMat)DWmYQvYLGj(Bg;H
zQoU~95JstfOq`p5V;HNIom3G&QlDsvJ5?|jT<(Ni@FP`K&kUd#BoLD4OSX|BBTvj{
zs}}F~r=D;Bo;BmpOy8>kj=-O2<~;#XP2iJo19CkPM8?<1)5`Asep3&_1vWov3vZzW
zqwtC;jL!_lwJrQw=;G2+Vk8q}w;hNm9T`9-{tW>8HZXJMixP)>n<!Do;it$9@z`Rg
zU*A8p#i-cGp{reF6SAdNYpreAuH3SeO%-khei$@#%Rcp9;AEvkVu?lb0iXX&df+aW
zmpJKEs$^2_?BKh%j<Q(3HWSCKBT%H1*S8!-$4Mi|%`M@H8!bMO0Z^{nX{==58c49X
zqF;vmPWQ=)XhYH}Cdg%?eBn40J|7|0S7KDLxNVMvrv;*2YVgg^X1~=U_g^5oV6Fgw
z&G4;i;C_pPA1`SICc%Wi6r0r0?Cov#3)Zfbcdt53R$^&RSmnVPz2W<WvHoI8$XlOB
zS?FnNd)Pstok^O~>#gzrD`ltxijA)f<@FQ1`%#&tI-Qkx<x4159mI~z=Nu1>ar2;k
zf+rX*auHbB9x7Ko<+Re>m}9a)U?SKR1xb_-ab+Uv+O=!N`->@VkvFA%e?KN})J($*
z53-_Uc4?uzW6cLJ)uCMV?kOT?ho`N<7m<<;i+O~LY)selh!eq>Q^;>rEEg%bGVnp`
zQ{tAgTa;|zxD$Z5kJzES!-`LzxSR#iApZS|QD(X&04+rlCl49H*qg_3j$<W^O+OK;
z;Zd>&+c=XN*ZpnsTC=PrezK}m)OE@?g)IOmqnI6=9jgKNZhdpv0=*C(9{vqe4<iGS
zxiarWNR8BcczATNa|;UgSNU|%X-ePY^)><>v`~D9HZLP^_Y+_hGKQw6Q?|3;LaHQv
zRvWp^89~6`v6?8it&xht7}=*0=7B@4Le?5L+xbjuf4_ewzE@d+LS&3nZEvoln4_?x
z#pr8oZEZn7T~299!uB-pB|LK<Ml$5Tc=19#lnMR|O`7&!OQHPM9*kCBo~*QLdEJO&
z4toBHEzPq}f>wqwtRrO_1lUcj-^zGdhGb%nC!;rr9_YpXj39LoU>@OZm(qNuy8!B$
zj)CD&1A9Z;_q@O7QUXYnY~ZU4z^NahU0+BtB_UQw@;pngbmZl~x&bG;HIXU*QjT~J
zGY_PK!N+o_*XINs9?(+s93(sYiY|f4XUbhR&At5=D{G9(>X*MypweKYMzZx)%s=bO
zd1r4M)RnsDS2JtcHn$)9vigGQJ5@9XSzpP1qt3X>(WE_+kz6`%#Q|pbp&DJ|Cirx$
z1mnCE6r2VKQW*@O9hvJ&*iW@|X+J@KKk)d#_-xzmgencOMHbwnhgMcret@^iBC-Kj
z#V=H2C>}VG<ox;K?fbvZK+lD?>;TtSa1_3NOyv`}#g<pUs1`S2pMjhMAvRSTL}2B$
zdZ6*qWDu1dLYV`%Ujd@B{hI}?PMo~&y)qsEc;Kev@Tal^pe+)C)l_oKD$f;Dk6c!J
zHx6b%@CPw6S7lID5IH=esn&U}9G@AfoXbWIi0+b@9WJ$KPJHo5M)fPhOyK@*F};vY
zhb%kx+j+r8Y_tN+t$Hw~T=4jG(v)XEa%m(`w124Uos;Q8Q~Rlj!2ck!*Dq-#pSFWY
z+YYreZ4Nnq<N8HrnY~vMo{Oo^Cmr%IheWg+EgsWAVy@BdOA#xLXXCeMYIHI;H@{&d
zd-CDk%jE0d7`P=}e{3vt<5YPPvguuGt-i);%GBAe&$S_OfW72uX2`e^)@FjRVVvo;
zxnPdy*f6P%`Z+&ban16KXFmke1o&BB^T9689H^?)wX}j%Tw7b4Zn6`}C{+?!(+K1d
zlbV{kp~8kR(bk@m@Ma0#y2vEG9s2d<nPwJj$Vj1q0l-${A<m5F8y$d<F<F?KGu;+*
zostEhF1)feq-jB?=-+9cBD23Wgk;BlIYGNOzIL+&YZ{scC$8*R)QCn(E`KVW_FH1S
zcH>6PCY?_C3$Q{vAH&@l$eit=A<pRDdin2F6D^d_RINL6e>$XLDA6L&Z<rF>uU)_H
zy8SCFs-0sJ<(!_UcWkV-%P-=5`F>a65#T^FkFi{=tE&r|dIIS4U9)<xiTfb(Es<t|
z4vi>`)*hJlwZ3&Mh|q};w#|&`XX2)pXhJtzW}gLutSOyvR)W*kJ;7z*W80{v?EY4{
z0xiUAvb6bN^7+Qk&LAlY3Jez-tnDQ5o0$7h0DMhsl?4|r;`{qY)-DlG{`KybaNiy_
zFN4U>%UkHMX3*1A#}R%0+&NP>OiyPgRPqBAOMrTcC_lxJd@Ahw16TJ+Gl%}JuC9iN
zhY`0&1V4!)kfQs*8midy<zJq5_pfS;?SqZyLt{Cl)UzEaDbIB%4?lnLyjY3N$q8>Q
z3a0PNs4S#DD*!QRRJW^+dSjd7)XeD81{h8h<1%HRN0HXWv06r3@)UK!e-zgVN*tgD
zJO#Tuq$!K{M=|=W$Wjs_w){_@o`Qm4%+Bu?S_`%6X<#C*2R~yq)2AE#Ha!9EJKYCa
zhW;AJ*sNv($@p1T<;WFEx`YMrIx&CEEYSrp2Y+b_&?IK-XhH}xiKZT%c@!F=OTi-V
z{xnJ)lmQ9NvCdVg;I1rx(rPo)^WR^GmcR;MGqB8nMwhPc?)ihu%`Rxse%aO7I?<65
zGyhmN;BtM0$Z$ifd8iA}la6`~48*eHJJ67jkmelYAf+nC;q^~^+7g_p9-c4p8Fa(z
zW)${#&$`{z;gAfd1LJ*%!R)#dr!TxgD@-NyZdUSadPc-*Q<!JrIMJudiGfO2?}&Cj
z1qtsO1is~KgkbXh@wX>_0T>o{LU!BR8+jtRmX9Al-tH0ZH-%VzdnG~1iw{8pRm;W6
z$ysjs^+nU>Qop9-X_D@;$5eIgOAvazgP_a9`9o;I=DiIQq(8}g1|YOcANt`Tsrj(#
z5l53vUE66ELy)awxS$`jo`$ab0iv$Tj$O(K5_n_a`t`*r|Ly#l{;JNVT#6lslJQm7
zhhf{`sn3u)Hj6z;WT<v`0)KN@jSL2-E9rtdx9qEXJwKK1+GR~i6eUj}0cu_+!$5Om
z3y&sEycMW_`i?CP#A#N*|5MiVL_sw7nfH!}eNhLb(QQiBXqXhCp(@}3S!DDbTIt_k
zQV^8@4fO#J_yy8EK;LqJ*P$s?4Fd8$Zxt;U*xK~|*tc)r2#77hn8jG1t{K1%!T4;D
zz_sJ!wT+F9)e(TV&T0JE+QpTM@d*gfI5)?JU6gw!>Nd@gEakKM7%>b;31d6fdHB=g
zGBzYg=g4im{zK|wX>pHI)=ShWJh6OgYU(#|#@nV8PP&uN`3d(M1OeYc;PTBn8xA7{
zdZ*Sv^6Y@{>f+9OzG8Y~7{(J{jj>tw=QaV>A;kS=9$jcqS}|H|GFER?VP~O{B8sm<
zG5I^M9y`PES4DPq_5q+L62pbg(Mw&ct+?Bqwu_P|5rKW;{KN<#w_19jUZ<54fGkrS
z3o=g~oPVv9lxC5w5K3br30vNZWW)F^rM*1zWwb!!?{8<=v0u+HHx>Y}&g%o{7Ktg$
z0V?qUUR71vxY5-|Ua9Cml)&L;nh)1Oa<BpcF4YbKJ~<0Dbd-|e#@1R92*fTZkSzr4
zZtXy+vyCaJ+!ULguaid%BPq*+kfyIPNHMl4kOQFIL1DrVQR$7J+$$`pFWBVHOZBLk
znNKWIEIXz*KzycXFe4(nfU22sdsN?>mkxI<1<r2n1u}Y{<a^c+A!JWJC>TL^2BlgA
zdWo2-bkL!L-j?wA>1DqIEFpje3i$4d1?t_cq1>V(K4l?=vkczjR!e*!ONcT-gMe`R
z{?pGczkhs<f>LtNg8>j&>23&^8g@733Q)4e<-*ccFAxbCoyOt;-f;u#^a5ByykYM9
z!9kp%k2<tP0?-G?$uet{NsZf#xgo%iHBfe0uc}bf8q3$6^p0I!H>`a6_%X;W0sM0`
zZFKQQXk+p+L0H*#2i)8HGigXBZ@)dZZymYbPA}DyCPium<t&#Z5QZ^CMkP>FO4b5d
zsR*UgbNS^NTA8lmecirzU6P2NtcJfTFH?{}4(j!(x3R8*7^l>@T~H`X4U#uo0>q0d
zc<Zmz_k(%zLB`9Ds4TZBWHLFhHiqee{@9{3=WYT7iE9cVGa<nfBS8TNtcG=RmNLo)
z?x8@1hYQ$m;%h+Z=6h}l+oj$rErRSsxb!BAT)e?&_}y(V8(M+~)rpn#_6T|uJ^)ms
z4!2RViyEK?_ib)&P5`j|=NtfY5V5g3(?GyEfH%+cAF)O-pkDK8XQP^dEM*H>_|`AR
zW#_@{P6UT~a?vhGEuu88T9;pGyYA&L$Ds!-M_N$O4M)VI&ojZ{4wD2C$Kei`P@W;E
z@O=P~M>22pbzcLy4hq&lp$fpD9Ce!m_eUiXXFyqTlT$r;3lB0EVjU3w8Zc<yG~2uO
z>3)mWg24LQ)|b^5Cbs}j2>J|!4F>_WFO!lD*aH3#6vprQ`|Fuz?4C~nAZeg{uvkao
z&ch%MLjHRCJ(F>i!T@}MDqVeOWZ6#nq&>=U5yUdl`X4Xt5<02Pv!r;JlDli@CcM*)
zS_5&)G3vI+@@;56zyMblfNuH75(+P}03|yUGHLDb_h!HBA&E{y+zOjypI$fQ`#nFE
z7rWvlbng6lRW=7qs-MG{ZPX@C`Cpl>_~DDjzEkefR8&|{#m{_HKNrrM5U|-Rb_l8?
zFi;*{9w|&LHGh&X4oZ?i0Dng8Dak?oYCWz`eCKB@Ga|b)ma|zHlxOP1FkU)Qr_Ntz
zC<kL2rSKCFy>P&jch$jAmn;CfXGr1TpbVD*Y@%mRY}$7|`Wml31JG|f2XOxwfG~T{
ze*AqV(7WZLSff8kdCAG_kdb<d{L!spbfGhFTyb6?=-~{dAQ7c__k#-C28z^q!{Yfp
zB0^Ol=@2Bh1hX1fvlM`&<p6L6aDaq{Qd0euSPe<;9P}F9Ivu^1-qmZ@LMTNNw0&>#
z2nVJ<S<J0z)h&pO_6e+M{_)~`lN(?rIe>aUA@6K1G1r2k(P)a8o9k7Y&_bZK5;s8S
z)~!MriCBEijEoQ92;33;pgLoC0NzhOTdi9n?trW}@&JU3lpB;UfJDOH2N1<!jWkIY
zFR&2|?g9r9=5P}Pj36&y+Du4Lxp@ir%NP=bl1KC+j+Gw)g<!_O#>_mQbZ2`odP@Qt
zD$5L9&mYWqy%Quv*mA$k#r7)45#u2ci%3jCd+S&VA$!YDcN7}vpakEmH*So@WovO&
z;@_}F8fG{*Bq6#aVxXN&GnCRDz&i|uG=k~`x##-_1)q8sN^KeU`ogV^eo8X#J{xjl
zdN@lt%BpNFfTJ%yQ+oTy%fsZqtiIiGrQ6xj14#?{)8i~VGDhY<ph!joFk~t@IFFLx
zDYiC=^n^8w-M^jRD&9q@^7MFv{qWgYlsa9hX<hmJ_WFG3Z6TXCYpn+*%OLaE-vs0h
zvh|8i!*>JJk^)%K>tB9ID;0?Q1iuOdymNg&>ju@!dzrkW2Z#fHtUdHH^evkiZ|9iv
z?}Up8Q4?CQs>0#~sKXGun3xY4*w=I~SXcmc(o_{WgVaK{0Cwzg=*1%2GthdSK~2P5
zq{1oisqRAn3KAeC`!xs<Ic$QVi?Y5IPe1=e|D8Nym1vkD2+jpsgL5+4eo7}P-^ebY
zq!`b{pX*ms&N0X>Jf!{A!yA47>-riq8zb+~dAKHYNu``KS=?gq{9M4%av9jfeBScP
zw;n0^!NO5o23u83CAVG5186Keq2>Sr%^mY<O#f~x1j%KemesvOsq2F4(;PV5sB#SA
zBD|)H!o98X+%Q5svb{254YtunZ@u%SoWZ7Cx>juB!fTgMIFfWV*o~<@2pMheM{EfW
zbl>`OgWNTx<C2@k66S7OW&3BLv__@<iCb?}9-R2|@h7ld+umHf6VHlx-Fe~bog^!%
z_BU+cGzjGi=6QY6Yq8k9vCjwNsC*f^#4YJJyMS1|@E6CQ8>l+OuY6A;#m~di*t+6X
zCDjMXqs^r;Ua`LY|J<N^nN3)DO&En+geG7F@=LQrMw{IduAXE0&pqVN!}6|nxy28g
z4Q^O|`$ufS;qqYU*CouEQ=TxIurc0s(A%B;r(e0Yj&5(NhQCD+edXqfIgk6@5R$69
z&6TW2D{Nrs=c2)Z3@!HivBMT$x84gzS0-kgu)EM?%IIE>QTgc$7ULqpwwzRu1B!y8
zheoI~D&`_cq0pKV3vqtEDPl7Sydh9<&TnfCjlLDCWd|0lB6R22u-=Fw8<I6&9Q}9Z
z-1if=!S1U2fBlejQn`KmSsE}x%Xw5(v&ZIQFMkE2a&-|XUoQYxNk4J`%S53^gBBHx
zir0H%hWXL*nRC&>ubHjciW9G%%Pe7KgGE<L2G@g!zIH)Z7poxZnWnT(ooeV0VjSDk
z{m4FZkPGraBp132OwUXM0-c=CJfVw(W;vs@t*YIoKf*j0q1yY-s2AZ?<h^Tud-YT>
z$=6zN95cJ36#!1N9={;nRJFYJm>+GW``#$yARF|Ufd7lK$-1bs3_0@SjY2u&!w~NM
zS!JnX5ts|qzY3O~DWO&m#8dvJg2IK-KrIL{_qo=42rtwO>m+N<!|Im&zYfXpkX*GL
zL%^VC!1(rc${B$>e!6-WtiJ8P6zKlfUVu;Gz!f3PJd!GqSe1`m9i5%j_EM**<Zj<x
zKy+0wDusk8c3q<h1^-x|hf*vczq{A_A8mvA(j6Twq`d#ni=WoOe20Q@^UD7iG?m_Q
z>R&x%jREMNWo)nmqekA%_@9pC9)3Sj|AaqYH{FgA|MDz2+k12gMF2bJk01UD5BQut
zcpt}0rvU%wqff_gzF^&)_GxrYFJ}3tE%Reca9^V)Ln*1>`077xT3>-9P2Y$dGb_JN
zK?=&H`xnJKWKlR-d)_0j25q07sy%q`N6&uov$-2M|FL{*_#bPhx}|)){)rmR=aLO7
z+`p{&ofYNa*U|N#r~Q8-jsHJ4E1FlIkc<DWJ=l3}@yy$o#rA!WKa#V&p?u|1;1s`u
z+!q+r&1b+DjxL^CSHAzYj?0cmJUBKyqM&ULRXy?P{GZR{*dHFh9r)Z8^5R*u+VNdd
z@bW+Ar3Jl5ednJJSXhrv`X9X$dO<ltcXa(vY2K6d_hAym8jbE84q^Kqu$(`6_7Y2Z
z>}RfXmu=2W1ie_{I`Q&u$JygA-Zji|y?xWs(eb7)xZ`u!UDlU;FFu~VaF@lz@ZzO}
zlTTRq?l|QI1?dvJ2E2SU@-oP}X+vuy9LyYMFtTsDv@E0kpb17I{Pl|JHf8j|cTR$x
z$l$tg=g-Ci%d^M+OibZy=8>h|d_Z;dXAr(zI!-z4OGWki64>AfuKyWbv{u`S$(a<e
z7O)l;j}Vv9BReCJ?8GB~dM0=BJI+lN*1c>#a-uM0&3Y}ga7^E=XxhX~s8NbCfZ(vR
znPX#7x`IU|b?y6NDM9-WVGjwd%YvRlM{^se>IpVyHYrHd4Z=*fEV=1hB+0IJg36<A
zX;=TzoW`k6CgJ9jMzXG_XBa`M>K$MqcCw*ah4V{Mp*<GOP{L2UybK?>3SnxN@o3Hr
zEXTHdIdz+BfA3*?HRNSWcb~#sXCT_vs6B#C;i2TrVfXp9LsQt?d+#IR<=zQqS{pcz
zJ2c<7s|t{HGS!9@?(T<-F?k06AqyAS!41>raWgVsjQb~5&5q_3&k7oI&*Ow?KaU#u
z=O5YVXOENL=gn$APs4+&t_PVfF<bH<-TMJF@6UhR<v3%A%MKw((R{v6Y5$^i{MY&c
z+-iSr`E$47()S&Lf5E@^gZyYVFXZ|dnODCaI9h8(C|Qo%*?e1&s6NPe^ko)w0_KbS
zk3Asz8Bo)=Ae1c&k8~FXxq5qk^GrU~jV`b*EEGAP8dOw&;3^W7y-WPnel%a2S5)wT
zMr6HaMcFMWH$~sHWdl-0ANz<xp_u0A5kvXH^6*(vMst3N_P#!BHqhMvF=1tSds&cU
z^=NpOXE}jR#|kXmu0NRhTyF-8U51ZT6|g2ptZY9a<S);U$o4Xm#43-DRvPjg+ww5g
z4e&asx@_7E*BVNFKjX*R*23}W6#WE#$>L0wXQ8YU@dM>>=I1c#Ory8<y0l5Pi=A|1
zFC!KSBlo&ppM(imKs+bZDbu#5+;ONKP;MrsC4EWO6jQJ}ezMAvRjPiuQ><yRoNQ>Y
z1|g>)H;YQg_Qoh2)Y<j;)6u!Cky3SCoJa5RU*|ar&d^VmPHnZ)Oq8z(jP`HJ9)zSF
zIn=XDm*m`}Yf8M^_14VoSSin=BHs<3_B|+*^lFlM)2DtE1L%A2bh{pX7~Bi}uO08N
z6Y$1DP1F9+quGCX%>vBC6JPJZLixfM`4)#iVej>`<M4oh#`>RBPN~vLe;$=Zorxm*
z!YX!l$%UIVpQl4^VUHB%O!e^DI}$O3P?a!p6dk|7>ExnF=^+HD>Yu1&nMV2Q?{B*a
z|EXc++b&JF<I$Kck#*FwzB8Ygge)wi<&mr$pN_izE0tmkcHfK>)eQgW_9p9}-BSOz
zLdyR$zy=(KivAmPq$PRr#9QWMStc3zBmcAvp%_)30mbZ~bYn*#qW_L6xoUf4`Q^wc
zwIimmULCf4vWlX@;A<SgcKkAW5YLas29DR|EW{rlg)~d0x_^Su{}NFCUn;>T1ysA&
zM!4xlk3?hnGKJ@_KrKT0^1EgGLSA*<qw}}H7af=Bx($jCG9??}X0ViFGe<WEU$iWE
zwtafUO>f5B7aus%P*AF?SrD1>%m2v!J|9!NCKpt)OIUutTry&EV_K>N9!OXlIm~wz
zn<X8^7)vE}#*-GN`)05jyLs{SK|bGUtjvhiVkw9PU9B@m$CQJw-P>T>FdA7iKpEue
zmYVvmg^zfqL@fF0z)+63FXj}*Q5o9f?wy-QEF(vv43rI`3N%c751B^Lg{At<T4pYc
z@IQX?nULPL*SnlELP$9^@HVm}-7M|#z78zZu^TQ5{(uAUVXslCV3|rY&h$esX0ofv
zz5-!(S+b?U*i2d1K;;qza;Ti;aM$E4Y9_FYZXzi=&qG5<(?%GiWMe&)Fc(5{)eb1A
z-`%a0m~tQ4FU%-pGB)$0^{>{C%5Tz28(SIPZVMo-f#bH|S&!&%$yt;bb?nA0&m{Ny
z$T%NN*A&hal^9`4cG1h<mWP)IM#4ry7yWDcNUmO*`3Fe23!!mYuh(^WyEOz7s<&2f
zP%CLQOHfEoV>k?5?l(S+#Zh{uBD*ZJc&frSmo<~qyaV+rbb6$ah4uC@HNw{NTmO7d
zIBX_liIh>>Paq19tl<}shm$21IA;Ak(?`Y1l@iDOD4kfwrtk#IRPSQ#zOJ7s%rO2G
z5J*6eRHgKK2YOb-TuEHq3L&IF)N{US@X7r1?>*064alK00lVWQ=pvf?GjFkf<}oI7
z0=rf`LMW)=AKo6Zn+H`eaOsa$hb=$PnYeiR1~P8~imB1RVfH#%il#FEntQW%$U1Y0
z>xA=^%E;T~kX~6sq^niG*=}F-L8Tq5__WUcm%S>ozTmA<5lUBIjwQoSqC!T5Q+l5@
z!3gaFCfkie`!||I3&{LM8Q)8O*V#3T$x2zwI3H}c5}!YKPX0EH`2(v(sredm%{4~9
z_Y!FMo`F_@fbOn_wr{&9{Cy8w8X!SyGLHP_L2r?($<rkL!w(KJpKDw8*56P&9y=52
zuP58M!jbS3J#xNKc(nG=wRg4Z*<X^SyIITUyIlc}2b<-d%XE`^oYwm*`8>`IKfAx_
z<9~n;7&e~r|7N@T^l;yU5FO?;?K=nmvPfty;N@=!W1k#!CLS8^8NsUlMi$k#6z1_a
zP`3DbC-DT^tqC}eIn3|}keqS+{=Q8$J7aJCYnXkVoQUT~8d5Nq*5xpz^#O!|qv5d@
z2IB2Fy?@<lIArzQgpV1`{8G2rb?gn=ck;3s5uDkYWGsU%bDI?6AcqIN)4$s)oDKs^
z!J`b%=$!g)lF}<yBAExr-Y)g`g)Qvt(xNw4<|9^P@%8RM+((QWkU>5FrMx!}OEUfc
z#c7<;=~L#+G?Q&EGnr<KmTRswHB*tPQ!1{AX=?7}nkZ=Fw0$Zq+*c|!6L3Y`5U8vi
zcT>?6kxCRrkraUdf!_no=kuL&&h@>1fBeol-|L*;^9R?(^W68%ec$ix^<JKv=s{6a
zsA~!s^{%F7A{57Vbzdw3?62`DB1q#}7vtSd4Uebom51!q=4HZyMR<9+R_O=x&U^1~
zofvq#LkVB&1D{At#k`npzd2HQyKSz7Wx`vRHH<jHw2qVu$U29Pv~flmGf9uQOsJQ$
zd#6;j{m*O*_NU7qTo>#T&9qya;zd7nL|;s6tC#a=&fChE^@x>J*(rV(_|hQ1Gz$Z#
zjzm<2f1~7+fIq6gn72EG2b1;2ks%kz-Z2+HWzn?Ri+gZc?o}Qbtm8Qy#*zi!4ZDe*
z;u2w%B!=?qs;qWpTp8^cu%Y>mSEeY3N$<Uc-7#eC<AovVjhD96J0?H&hxYHKk#Sm0
z2R>2<UP$S<n^sctRd>NZIrW?g=~Hoi-)}0g0G)C-dYA?Cf;Z}obG^KHD050b>jVu#
zlL|jdy8B(MJ;{v{F><u6eq#5$H7`h|YMz}b;C#8}IhnwJ-ihOnUu8T3c<JnDFII9f
zp*Q4$EEikn8OpoB<ayL#b<Z(s+fxCc$fYex2i#_q^93D<0JJ-Umcd@!<*w&HlW?be
zpPcs#z+7>2aOvtRp@Z@aC}4YLoh^T#iP0W&rDrXcwb!G1e<3UHgX+Hk*jN#JL3S&|
znnAKis@5faT+iJqP^r^r|4>pRT;RKDkolVf&Nf;rrOw@fjG(`Wpn8_toHJDoflsdv
zIW~SUopLInXI*SMKplAM=mk5Ej7fMAJx;EZRBn{2+68=dHsOnQ&SQ>{b|)!C#-TYB
z-<y5wkcxsN(8Vh242t~8O?}8i8=6(?QVW=(B(0QzQfkt9K{oBHts>PRz?5RY?0TH#
z;aZdiqu3(k66?vheJWSunW6dqbpQD1<Dtry=@|o~e}SQg-zoVKaHnql%OgA4pH4v}
zWN?aT-%77A?sPS&o<Bnr+on^T1dlnA{1wD!C+(Txk=d^^t2|;B8*dIO2T{(kG@Wum
zP;#9OuSSt1`J7@}=530KoS<DV-8ZbwXpix#mw$sDJr||wVfC>3<mIWL3$pBupUeHX
zAOImWGQopw&Z%HA$f_RTUeiPP7FYMJ$}Z`ekpNliw8uOPo_-GN?RpCg+^ap7^xxc|
zx~JK~6<M4=Z*x=xtN+>ghhv^HcflkloF>H;JA0`NQMzUb{g(uPhq?r@n7_+I4@-f{
z;u4m3F4|vyj8aX}s}Y!h)br-@d%*#ys9;_t450dJXKg<74@iyyl&PNOIhFSUvbqg4
z2cY=pZ8N-}JRAwQ{*9divES|{*|i}qCv<T_XtQ&5@Cz3PZ__UD1ywkm41@|i1Z;e=
z<zW`i{~QCUnG)kmPNh&{1d`jIkww7mC%d0tp@kR~YIm~Zgbj*89D%*4?e1&1q=FUc
zdjZomJ-qZKzk$z=sjWQ+YiPe@I_~Eu8%sq7CiMKByJHx&Ah8+^i8UX)%-E$pCc->b
zVIC0lnY;Nzy|#MPaU$~GI1+UMNxF@=fM~(%(k%46rV`~>+mOp&fC9`(Cj>)V29Bg^
zd!+dx<0tM#9tV3lJPteM9Q{2Vr)_QbjXLN+FhDSuO^Xc0^_C!&>X1{EgMpb#;BTJ9
zm)~C80II)YPi=qISnTx}2L(4T8CpGJRxh^#R%YIorf69*1O+4ke8D8tJ1We^jt2hj
zxP7LY#`N?xdMbFx8K9j4oG#F_*YUs5h`OoNEu;?(`OQfqM@59Hq&Z(XWj*TIxy+}w
z?@^-VTQhZyOa5d1$?&7)rm}wD9?Ss{bQEBqmdPLVW1<o>o)eFzUXZmZCkV&;Tb!%e
zWwGhuxFqyTP`Wc`e+$X^l>g!%!7UBZ1E@i1qWaM0_*wcCwmkZ(Om$N{bSg=G2vm=L
zQglzSd|hybL{92<tR+pqkih<~I{epL1=E0l1#KIPDfo{~->`<$9vj5f)_+WmF?#4)
zTh>v3>a!zOe;WXl7!QXaieob(En(PS7>(0~A}_rsR=G1v{x(89ZG>*wC03_Z)ADh9
zw1pi+TPh-$Q2YawOqRrK6{<K4&=qFsl-71P!SC0{Yuo8%q~-%xYY`U`MB&mukSdM^
z)ZgZZI1_A{7pw&lA|N^}i4zI$#1)(seuS#Q+l$SbSZN{0a}IvWqIeVXG{I&t#6iXP
zyT3g7)zF=Rx&gnNyw8I0k3z&>lHmSVBuBk4{L8LGSRj*>toTv|Xh2>Y03!3Mx0F5{
z`2I&L)sGx__E${uhSD1B@$oG{{sLgTH?(v7aa4=s*)M-h_9sAB!^s$_A`U=_8)>fY
zRJT%xe{133d+%DGA_4_@oE{q38n#^(=6v=Kuer_hIy5)-;v!VbC-Ae%J{vh<{p0$}
z7n0|Hd7SCphwRcRmoCUU|9-<U{sK~5necftS$R6gm_WGW+JD^GaV*VMz}_?nCsjDA
z7P376&@;N8QLs+RAzCih&NIp9s_X^l*S|A`JcANf#+SeKXAmWvf6Hl}&F?cM92?pS
z4ScBKvo2LvZbo<i>j?a->$Guf4Ajn0B3Jzkz5q}^uCDGXt&{E?SooKsfBW)34Yi$3
z*!sfP)7KD7wC3sheRexG0?JYmlraAbQ5X{c?6VE|<L*1vPXyco^c}c$V&g2`U6K4=
zkmO!h=TevGK3=;FV<cY~<j+YzxpcXASxb=)1c@Y88OjHaqyhu9Rgl`Df>1u2hu8H%
zUeQXy_`=ADWP-lr3Jn0@&ZE62n=28=1SOny5?OScV@zY>liXB4n*Yju&(SS)j=$-6
zAX~lbJE}e=SD9l;9z0woUQTRZ2kd%LC(gQ`>R+FQ^u<xJ^2fX_7+kbF`4J-QWR+@+
zUC-Qqo;!h?nOp2~*RFYA#O0GMH`OXSm3`^%qJlK!MLlVm2P39s+@;2`ogR^eM_p)K
zR`pc}a47283ke(mUs|U4w0I@>A#0B*>hm3p++BUI%3p*Sy@)x_XqqryVVia+`VznT
zOpya&fco3AX>k+K^N#jd3+8FQM_ku+!9vOezy7*l5!dCZ^dLJpZ0z{K4&g&cyGmlV
zU&MkCD<L<e=Dp%u*9DKZ3L3+sTk9QTOo4KOdU+*sRFZpL5aMt|#mkZ3(E$iw)PbA0
zJA)-b6)E?|50z~}bt#wjDgfz34AM%8Zm5@!I);}v*HdP%Sr9SNZZh|g)PO5J>sJWV
z@!x?!PvWaWMAs%7S&n({KX9^&7#+HKvrw9=r*A&v&+q)slba-6!PZ|yMgo+qyW|Oy
zRRP{joqvyIm%AzasvLV8;ccmaGEymywYCvjDcf9s=Pjyd(g9O#|BNE+$}VDDiaj%q
zY1IXg!pXr})a421gKs=mTfpA6%%{r)rM5pw<K{8GtZx9=kBXHrI9OT(tqP~`B3JXa
zxVLyD;pBLj^{~`OKs5x=@_H2Mi}{o$Hk?hu>6O)K;Y{8-)w~)R4Lv)|n6$5!6h9Ha
z6m=EJ*1=}VfXLgWEp_d}6Fla7&$iXZ6mM%bO2|hpC_1X=s3@<;oj8|P9@%bn(&!*5
zKOs(@Kgi+NCBwu_{lnLkHXTQYR#|O=)OpDg2-Mn@GS1filde-w+Y^{JJr`04t|J3V
z((*l(3WZtc?}?0PaWIYY1R=9IvXttC^6LWMC*hYhDN{>o8OBo`-XOAU*b2ZV=AO2*
z8tpF<T>KJuEzU*o6z>K=JE1Rrh`B<@luZ|#knSb$Yg{qS=IQE31Kb#Y)Q41+#r%eh
zZ;`ZA>w3t35u6|)2VFg^qqq6RKzUdfOkJHHxyClU#i6d2au}{arIhgiv08q?A3ytc
zkCL08v6QOa(USpSVM5zbitTJWS;7yg3Wym!*gD;p=niiDxSkU-V_r5I=?Ce5oio*F
zOK3kqU}9#pYunBck|*rgk!g<v4xi5`u4e*pC1P7Eg^ajLxU&0{$M{p1r<r9`;Si^W
zPu9rLvLwX`T=MJ6n2|z37nW_YDL;+my<PS&<ic7qt&Qec)bA24mq!)fvsS4JFK@U0
z^yy-q{y80<BmYytT4RV&K79P(E#|^&ew~9&U<(`mba}G&0l-MWbs*y9#!JFw9u5a}
ztsa=pT6Bp>+%K`E8ELGk0MX&Lb4pXH>&IxAI6L3l52wC(QMMJ=Ik)JV^zrUY>%)nA
z>KHhzPD0^Sa3uXGjL9XdJ(8vn>#+4>0$OFf^00EyWsnk*MI`c{lj<(;qKX+_bsP$9
z(P%lC#=W$yBSu(!SOqgRs*O)w_9J>1v~PFB5VB-x)3rywH+|ZC_N^)k339UX0Ff&v
zHqHgH2bq4b^7g=lH~zJ4t&$qC<RvHP_Fn+-E5O(@+hH44HLy~P;-J*9>DGFR;@>6#
zFn0evI8*H$;70)c!T?Len5H*mEe`m4r7{2BiyC?XDG$b9dHLnv>?W01Ki#f3u0yGp
zPyM|ylU(l@-6ArX=t>y(^`b^R_E+x2XDc1!?Zr$1*QWHnkZAthgrNj4B+0ZNww3X?
zfQ6!EYYYF%<XAlbWQc$b?Hlic^6FeBd#9slomj0y9l{(=@!v<NMIcCXN0|?8iIUg^
z){C+hDBH6xa=iZ^On+I+fA~v4_{fh@jd=bd!jlm-2&4aC?@6w2Paz^-IR0yvN`J}2
z6<_NMn4w8sVJgY{W9oUqC-G;n5otzOr^(I=4)E*t6!l+ste7YXC6uYQamyEghC2oI
zom-4cO5v?@`u-aq#)kHYFUUhisUp_z-W{fGzGVIF=P!23pkLwz?qBJ-?5~Dm{JiuW
z{_e_v;a<$-$3?0+Mg}NtVhci+Dg(#<LzOmq_t4q{1QR^t)X=T~2o8pg+B&AP<pFAi
z`}I+Rj<LObwvl@buGv#hDe9*EYDS#T#$Jj0DQOAwy8cWbMxb29`_F#9*z>J20|5ek
zQ~*Av<mqjW>RUYom@546znzxe?~Z{xVC8oHRu?|wpva%{_okyDr0AoqEg+xMDhP1-
zeldXAmdCz3uQE~fw{>C{d{x5*C^r6Q3A^nHTaR1i+d;t$rRNYRzdHIQajbU+M>hqc
zKH`&tVw!e714vjOF0_MY)ij%s9_($ckj!wnPRnZV9{vc#q;)+VB0m(3m$pvtJLL|@
z(m|k!<f33ISg9_~&W&S9CcsebK3`l)PuQbu4=0a2lmkb7eEyGs<}gQ{w2KLKoX)MI
zK3cxGLpOCAE5Y`&H+q+YVPxSN0Wp(L@kw~wsPwN46TE0dy;y}wQh$WX3`}cNM;Gek
zxK-v+&?4uU+WexmbnyU*-hRU&C3bRyuyZ@S#^3y0jb7xoV|9YTmuKF#2RmuCj^*Yv
zUbjo;ZP?43ft^Wro-ZiIOdT;|UY&@-7ssS7W!tG;0*$@7rapuzx-{sb!7otlX}WXp
zKZ{kBL7-<zZ3m4Wg0JqVQ$2a7AM5=)%h!2YMOaXA=3jLEugRVMZw=G`4@uy+%}eTU
zs}x>QrFHO*zLP2z6y}2vL>VMuKJ`$#D(YnQ{l79kFE-AvWP;Npw88qVh=lHFUYZyb
zuG&;p29(d;S$;*I(sXmY`Rs3>&)od_Z(Rj(=Mr9{{NuRS1=^D3zjpnA6UbqDf<`O4
z_x=-ubG^plG^HLkV^?d5{OlafeEqPC6fH=hrD6Slb7+>l;WPzX5i!e92TIt<=BbPF
zt7~5py&PO?mm8I_R9{-1JH|V4&$A-?M#K={2c!A8P<K7Q#65tuo-+bcZ5^CYthQ$z
zE*dKH6)e&S*gN^-lSK~LcIHb<YUl^VL1uG^Ui0og!mMizDh0?XMyxJzILaL3K(z2Z
zkor<4Wd(dwS^`#aUiM*q#i-~jlAeDcJYuDl^Mf)O<j~$8gHuF=D59#j*j#iXFWq$U
zNi<#Ln7V>-<0v^ZV*JnD0fH!;Bs*08TbC{p5EyUk7$kcKc|I4jPtgI`zp>ju)(z$^
zJ1=Mqsd@x}l&1)PK`-sA-iRm+#hZNLu}@MNc)8%I?t!`c&GCZ~eqn$#;IvBPmK%LR
zCgmiid?#%7%&CI_<cS-bV4IQvUVo<_cUa*e4z?xQR!>0Yu~qJvnrH(4hKlG<4AcF~
zxHO){uHKb88V#G|VOniMeMwP0H2g8$pE3vq4@C~F?(L8MB&y^ri^Zx(y9(0dECEFO
zv^_*Hz?tXtJI9THl~2+uzd2TyXO5+NYQ-dW9#`GcjG#B}dPiD%>VJ-0Z5!N8Rt@17
z^2Iz&KnwX%Rk85w#>M|W((^xX?f=Bmh5uUd`~R?z&#6iz*2t<~W1DpeSl13f=II`*
zA35g$UWJ(8%e={P6Zb&%K)khV?0KOj_o=*9atClbaPtA{H@}ebF3=c8waGQgsL?@s
zUCUypFyqbm)?;eB`^wxIKr;Q{o7}C(I(3zq&n~EGb+gNFi&toB*sQa^Z2|@4M%EzS
zm`9IT)*@I+_T^pW<);kjrByr;EB*<J^z+5JtT$*Rj%wCXl~E#bD5}zxI=^?8nNjbl
z%(i!Mc!t;no_HqnHfI9}pQ9&{;}Xdcdc-BGvHo!i>OH{60oVQHwnm9dqUm_0u8+&I
zK~5Zc))Ti?#ZCWHD_H)IWrP2;ny2{?X@ihHt3AdxsZk;3bf@=Uu1RdGB^a7FEfYvv
zn|gFDpSoA)!7NHsfi?WW4QrBQpM-EA6rRhjV{`z)s$9+mCyuH+P*Jod`P9I%kzQ2{
zadK&Q_~xo!RvRy{uio@b100<@G>4+n-yI(zE+z=q$(X19xfoT~`i)=Pc9Bv@(Yrt8
zhQvK&4j+t{1B#8~L)$f%qC2%@(S%W<qWVW0ZMsr`*uWK<wWyxm!8wIx_hY~n-iNtt
zm63_``drZh-Ly!hcTs?d6OP*-?e%Gf_7$z1%{w##<Vquoj;-7ByAHSjTnb!X7k~<y
zS|I!LdZ$ovJfV%Vu|MQE{5RRzUd=~oP3^LmmaYX#KU$ef0%~xgn1eDqtP!-!J?oNH
z^UONO_>k!zsI!7oO1&<NBz(I^ukfL&G^hRr(7e=J#}cmR18cnzk?5Wk4ivkzAYuvM
zNb=R!TDECJd1X(>)Qmnyu<=K&+jj9ruM3U<!OFe;FD6?CF^WLJE^(o18+ZM2IIcr<
z2wz9UjQjgB2$E;(;*sKz%_;-@>@u*3X;QtM(tBC<Rev<-Q+3#+s+*S{@Wc^tX}SPk
zBsu}d*`zW3IVLB$y!FM(03MY}@$c3AS~RyvJA}o6-(@P73L3ixLUicR4j5qty#DBu
zsw&6&)w}Oh5C8Ypj{P4id7l+<Cl(V^q;=4x0IX}R42=M3FjcJx$pd`&O8<;et;(gq
zZ{J`MvGSIqg{|2-iU=3X(5knNs@z;?0Z>Kq)*pHKu@AiPsfu*<AEuQ5kOP>||Jtg)
z{};-9RJ0_q-#5Y%N<UKBE*pRRL;u9BudnI<_|4qO`^LY2-LL<p@9#ei?cWzjJ&ybH
zz=`to%^x!K9|l_Q*RT6iziFsX|7*>r)7v$_*|>4zhR$!k({D3qYZH~pgJ{WvwnkOh
zL=P@(hRwGZl)%_{^4KKRhZsS#ji}QEl^zDE)WtEQ&7X5_LmSmTS3kzT0DZ1{{9o1N
zwgaIS<n0uD5nYa6a*5s7t%de`a1nXqb1Jjo)`s~7U#D2Mf!5ho0wmg6pG!4Nc0woZ
z75{?nEFD)-_u*dkhgFL|Lge<{Sr%v0=+iTzY`+2)1Twwbyj&^M>0bqC&M$a5tzr#g
zoD|R5PT8qAQJhhiQL-C)ylYXV5DtRW3P)Df4XEx8ij!y~qc&%BxtssZAoT8WX&X)?
zbwjU*^enM8vrex*9*EeiWTwMEstqwn=g?KqK}@dMCdn&^Bj#dSEv4c$f+vEXULE$A
zRC%T;Y7C6x-C)WN>51FZm#1g2Tu+1KMD&S0b61XbZ$fueNEmf4UMkl%-DL;RA_L21
zyA+IXlCV??_i&Q1ul<Vy@2qG-Ys0=pI&zgendY)e3S;YSv!0iApf)8t_s`zk!tP?q
zs=YmfuqN%R*ao3pD}@r<AQ>oVID;mU*>2tu4JQ2M4{91+)1pxkGNe)-f$09Sp`9lM
z7c&lw)*IfYxjJ$jCJ4u$x3i=guct*DsB4rZ2En#LvRpZXq3cO-iUMScC;P5ICY$Nv
zY#LO|;JFj@z^FO;qh3?J6ehv{laV)_RQGDKucgz|?}(5RDqFz>k5hj5CoR`Alg!JP
zvrHQoZ|(8H_oo&cKtmM^2MCLQ?(qe_+NB1pJ9g$PK6p$3X}uP&VsSHy_Nl>EROesJ
zhl@mq#zH7Hdb_EE&jQUs46+)ec7i@#+F0_sl@nt&Bl`1XlE|PX+GbF%sRZs>dpg!1
zU0jA-_ypg+ZOU77MCa`4tO2<FMT6wSt*{tba6@>JFq3`DW27m`L1fktULcgPQw77d
z95Wu}@bO7gL-B(+Q?CiHdy}=%^4vPum<Mc*@wB0x_5*WWIP`~8_^21-{Xb)1qsse#
zVlq5YzWAL_$m6-%$SPcWAL(3>z|9lI{-7N2<ULx3mR~9a?PQA`@Y`hx{q_L#73XYF
zVQJdwSf3baCdASFXa6b#8$ye0z(A`^zwX@9nbmS*wfQJ7SN_la$(P-sq9<AThT_Su
z7wR6co-o{lmtlFV-@hH!D`bV}(1NGAuB$ZQ+XL3iOyCDm_gU-T4)dI1`O0r5uHu^)
z+)~^e>CiVnKqzL%B@U42H|KzP=NdOmqB`eM!ipxG$5N2q>DcI^uJEKYB%fDauv}J&
zMHa0#$E#+bN#eX~>+YA~ttTJ@1D6`Y`F7Jk+2jn@7P)Nkbqe#uZO3&QbHj7IWS5|E
zjWdWd_@Zfk#Gy(i#WsIK)J{w2F|{n`<hU9mv3t+EXGevG;;Xsib;R>_N|^v{8+%|(
zXtYw#1edZ{-|~+dcN<~tw^a1?sA35PzPPL1^n+7&-5M>j<pU78RX4R_s^lZdIDdu<
ziH$_>GHi1~pPJ+3e+|~}PHD%Ps0O{m`q?<5pvLv%(Hz$Mk=oDIm3*Epq%1r3MDaQG
zGA)DTpV5oX)E-synT9XBBA1-K$Hiq37f2g_;hx*r9m-GL<8I4eBcSN4HI!}v#90Lo
zRPFyS``9))i9Y~3@UFnX+BuePkOYb48MI*KD1rez=Kf&TUh#ZE!qpJ8c4iyTg7^b&
zveux=P+VvrIvsl+O@>hNSR;_>JXQ_FM29+Q{^nPFqLKKvLDtz=k%5un1ZH@7x{Q@T
zX5R@s)O`gKjrp!zai29tW&-5%N1LNNY`Rmj{f-36Pd$0bX)+KkIVe)mNrvJ@1MRa2
zklUySzWq`aVJs2f9$iHkebvBqw#j1d?Peg7F$Ke8l;Tz0RCrIaY*UR6MD&EQCG+%Z
zuE97Cm2@1v$*3=BXZtOhsbTW$*9o64p`~~4qo7aT)Q+8>zN6(7OEqXY9qWNM-7Ef0
zX;8e-2fDy_t1sC>d#7S}?XW{`O!A`FAz$Eg@KZ2{l=K5Gc!{Kj|KsLfZ@tw|4{jvO
zyfQi@4yd-=@&#yd!{p^NXHErrQEsB`kKEzAwwVMbNDbIp-TVObO;>+ewT(kRbB{FJ
zKKzdkSLayqeS3Jpt1vg%OYEIQTdm1Vk9=<YSo_iDtgQR&uJaU@>F;4J8Ts7%yp^qU
zHiuv@ISqSQZDp)IH9F2M?a`i96`LPw_Da>VbzZwS>EMEwTaqSf^J;k0!T7ue-t=tf
zdZU0rx<zXgGB!Y8bZkDRNAGzPkK<}}6j#N&oVq>2YredDWGE58JgiTZtiUIa-ZI`^
zIYM2zipWUifsdo=`UB%HDhEgmq-T0~a^rR5uSAxozV~sBg`gLkubzb!hS;^9$nClo
zp0Y#ZVe#u8(AqqgqkZzvxC!-##V^APrCb<Mxn;_Y<C2mDJE(V&B9lNO9+{M$`|-Yk
zp3CY~juX=5Ozb@ajB|_83JEHvwq>l7fLU?0W_#U2vZwTC8!1GCQ&oNl4>q-oCCS!1
z!=AiLI|re7az#V~tuwJTIY+t6vx1Judm|9;?lV;`POGnTu3fjQ>}ZxYPN-)>7B0aG
zYPu48((SYQ$2JrX3RkD;y`oQk$dLD)lMZ;7_-}FErHXbXhuxBO%zt=0ofGTW4o4SP
zG*#bQ&ph3>uCSr6`u;KaJdBt@o+vpYf@hb&W{LaMh5+K}rc;7Ef52R;oupOMhv7M_
zBjk1KRmc6^hqCP*yASx-KdJ3o_3OAK6V?V2Kd-+J*agR#d?Ii9S9~6kx21mAu{7HC
zLz?GoTFaJI%#$mD31^#8Z>OV=O_Cu4)x(Z~OGN|IM;oxz__RaamKqf`iUab@6`V~{
z=D?WKcDT7d*Blp*aawrCW$o~2Pjg-P#w6ohAkB50O-+7t65rkzQwGqE9%=`ikz-N>
z2_mipuknpDzQ1!6#2DSzfCX>T5bVgK?Q59KH_MLR!`)%52FE?V!>5@E=n>^F!#$+S
zyIwoMYr>hxoWV~XJW|UWJ)c~P-LvzYRRa`vKkrWCC%v+nmz+PsF^1OJSC8k)je|z7
z_}|@%OY)T3Uwmnn7gqnlh>+};zXl-~`HpAqvak#G?8*QAUQ<$3%@e}70W<@D-Y9X{
zE1{HWzwqHL=jTSfXyd`;!_jybtdL%?v87LK2*UTOk$~cd?V+ytVOZHP&(AWdtS+yH
zNr8=c0h1$-SwJPrzh)0!MT!E4<wN@gjD?{_<g*RkgC2C#)3H~fKNLRVdC;9R)5w!@
zQQo2%52(-Cp*gHLL6mJhG#;Gi=n*CIO<CDyoYd=ru6xp_zr?<b24l;>SJ$!qrG^cU
zmC9ka)FhJ|Kty^wB@bj=2_)Ke`=-zbT~;fh5f;=uvBOBSVS|s;Y6^|7$g4rtdb|lC
z`3O-dBVG$6>#ST#${UusKa=b=CjVyBYmmjkd@g*mza8$MeQ%SpQyW{5*#<2;?<cyV
znb<OF9qvqjHDk|{I_mcpg$WJ&GV#sSA)|f=EO*@UPd=F@9L)497t;)&-$!+Y7X~qf
z4U8k*0t8)wW=RjRY)oI@ro{m7m(Onc#P_Ltv+gq|I!iBl$$2u73g6p}BVqeWB5<&p
z<5Y4OevMJwb%se6hjvh;v7(z^ht(fmXq!6HeGeF|faPnB@5RgfdP<wJW8Jbxvi*#T
zlPn$0x=Y=nn=bcG^H4@OZGx*Oa2U~j`6HUJ)Tg%hpzI=1cEd3&8Wxq-@pBoR(aig4
z>Ky6zzjx6pGb*WP_Enba>L8g3zzw$1({XzMEm%WVA5ROzjE{K#Fw=xHs_5HB-PbM8
z*%4QFW_85aI>-m=9Oxn1N-1I4F+4(Ri3ukD*HO}a4L$-2u*pg$EdiL6OR77Q_I0{?
zyU(eG7+flk9!McrK=3Z7#X~Sad;6m?Vb#DL7krr?H!iw)fbw=3HL<fLLv7duy(c@>
zuDEOM1ag>cSzfcLC9h`1HvB`;fd7b>X5;?FKZ)PsO!&n{GrIKh^UINNCWC@XHRv6G
z4FJ8`9H*x7?2gErR8#)Wq4Jkle>KEVP#XGF=oO8<lCYh)s$lbNrpvPEn4Xjs2jl!i
zc#hc~K~kelYx~(rdu`JoD%r{f53S)*E&#|2AjmynEeoV1#=ivagMmNO0g=`-8`c{z
z>;>D?-Amx#TtT4C-NrP0)d3l7Hzqh+3GM8<$po9;h(q0)XnV_IopD=K3cXs=hp)un
zYq?^drF&m^cR%BhMg<NAtX~Ew6YZ0?Y5KUK+ffIguvu$YL!0KV3Pqc2kZ=^fyLhJ&
zlnNZ%!d<dI-GEmcDu0b#0Ij{AnJpbfSONVG;Nr8K#eR0+j3B{>gRYpS{<xJ!A>I1y
zcQ_9Y$*8<5+(WV)mHN64SkNLMm}`s;c~EVm6?n~Rs*6&LAc2Co2?TrQ=PasA>@Rj6
zWrbIQ1oIYm-n`Zxkcc05EnT0m&!cv~^NtFIhPg%p<uKOVU-hPT>;?43aiiuMuY_Et
zeIe_uIufu?g&Wu2S{<+pDu2ysjofm63g=oBtdp*539T|pjz*IzHZLzNa0y1;nr<Qa
zfN9_^KC+u@x(X~~5!>W?woUAtP3ffHWRdnM2@*j?cD6S75Gg5U{&?sk-t_Cxb?h30
zw5(NgoT(EKMnrxG?;X?XEpEEc?$~Y(b;Sxv_Lb#n89BWV#?gM}-PI6B-%7hZcO2A$
zho4Ihn78s*=W~j^MybB6tFjALSu=-rpxp9ZJr;@w9=AT73_TZeZt3DEz-UXQZ>@r8
zzoPgR{XxD?h3`{_n^S@RC%Ja0{ag?+kCid8duTosu)HM1;F8m7?S9@S!Y9$l4oheq
zb~wh5BYTjOkeqcoEJ=EP%%vtYzSb!D7by*cZ65UOos4mPokiO(WEcVHeKbi=WRChl
zx(r*pnMVf9(deY(ZRQbQyTIOyC0;skZk&GiJ&g)RDND^mVssZaGEv+F(<vQ3hfJ}u
zgKJmB`@76nwjn)U2X7rqf{vj(42ru}o>(k?q}EKbO*DI}?U3WUs~XL%qVek9V=j^O
z2F_1He&EioL{cRAm*jKfk9Dk}S@V?&R8Jcj6TCCM>?Fx{zpqAW(Uwbn#Q-1t9x3xn
zaZ=ZML2Wvd;=e6!Tf8<m+*``c2F`o|*oue)1SDpBO|=F(5im_-fN45&NopXU+)oW|
zGl=i3upUc6WUN`^hT%s~>abr~*lJ|e^?V9A6&xSt**x$LaiVB2Ams{?8KyZ5-{Wi7
za7Xwm1r5;PZt2_RAezS#`?*CH*T%z!$~d<Jw$mEg6%u!U?b?Z9;H~a83%Un+%j!?l
zUq+$5XuT&b!p!MljVzsV3SrcP<ntOOt!VJ6q)3~<GumX{v}-mX(2FFId05(|9nxjc
zf%5+BPeg3TmXIrBg7EMXFLLE1J6dztl%tc?QhbvAuG3@3;QZ6OwM*S&%`4VBYC7R*
zD=7brwHEv^#`3&#Xv{D$&q&oggO@_1Cc6?_uz2cXeTfMmeHg6JuE4Cgo*6`TorH%i
z9DQ<OlFLcLHtLRFuz?j=Kt;7ehoQ?DNlDt`w>?Ef0L7RI>GWGPfW<>sc2NIBiheEv
z<n}xhc^@J@(L0u;;bk0E_EOJtR)F5fFFFM%90!^scv@d3bXhu5?ipti&#-B|()@Y>
zs+033cTb}rw%;>k&0Dj|U5Xjkm&Ld*VC(|D38N<gW_Y4v23sM);!Y4L)MI*=oe9o4
z`!#^6bVVzlwq*wG40%&4Pw|-UR6dA$$$1;Dc%n8W*onVm24V!0e40@yz70OtDbj-j
zo_D5_^qzF9g!5)$6S(QD5@n8zK~fKOi#_~}oc)Py<}P@DD0hSvvdQRT1UvFv=&nSD
z&D9gRiV5$MYYpY-)SrqI(vn}}q|=cwDWA0=EGc*~g})q>W4$TrRrvkjg_q4bxUiQf
zXh=e&DYhqd^822M6T2FGayQI-H{tfUMQ1i&Qo}~Q=HT~ZW^PnCnEBFaKOKDj=JQO8
z8+?QmDc(^-8$f=)DA{11^gZX;MZg6fhWzrgyY&%(CA=j7OH|inUq!mz4sJjD6fg#;
zCzI@+&Hm+=0ZwiI^z5x=DY@2GBf=Q2&dHQlADUxU1~m$LSM`D$2Sj@A$;kbcL8R;|
z_XSF!SP_`hq=!T0J=#i7#6S5#>RkZTtmPNi8i}>s_Qjni+@jfd_laf>1`=h%;yZ-x
z*6LAq&?=8)@|Dy6Y3`K{DZiUlRW2qH&&q_&K|}zzdY9B~YvWD7+=p4|ol1&%d`~`P
z1}JkV)YoWb++A#sy^iQF2QU%x#;_>c-mp8NapJw5SvM>@)!rAJ!yAFV3JsV_6u3dk
z3Rhpv&qv;SQ`$((27rCSAzDksc>h@8=Cq;XW#F8~keQQZnA5Svkz0=Yfqki$>x{(W
zZ9QBc?FLTh8@mPo?-Vs)nwzt@YdO0XruG14ZmD-p9DZmvTF^2$w)eZS?2-p!y&;T;
zo!86aZveWDpw}<-NM3hU`WaH+_$OADi1hMUQ;|`gSta#TVe^k+Tu56Ea?hY>Z(Ed=
zMgri&zR5{Axp=i-+An;vqU)E3O3`irR7;8{HyjXL81l7g5ED5md-N+$=@b(0CXQh4
zbvf)}A=jEyYbRnERW)2MMjD;stb1q3#%VH();Vl{lIJ2`ZTp(DLrWGo%)3&vK|DsF
z-f);M5$k<A(|+3{pH1}+w}ZlnBgsR;1_wtw>fjcEScX8yRHcebk_h1%O3a>!*Uf%I
zr@@nAFLD>LFowOaX@s8e6FpH2w-rX!$ggHjd#Jp?&!2z!g8x+df`waqW4$-@5y*D-
z;iRNbDcM@&5ggakXaXGeEBjb+10v?kDIxksFc>`RH2-)W3lqd0?at1&KU_J}a4Yej
z5Nelxj5FXldul>Nt2v?t$tS>8HwRjB6Kw$*!Q$5EAf8=gbztda&b7unUDZo>55?JC
zqFc~uasz5k?4Gt?N1^xuNa1WMD#J|SyA78{e8f#8-jxY80TG*!E_cTda(jo8TqnhM
z;nyiC!vGk1#2dJK@I~5-beyl#s+7jhAHe<;w~RE-ADOo9PCL^U$*aiG>H!$!EV`vK
z{)|b{IUV+MI(q6zWd=f#<J6o95xa^t+)QSq(u3pql@Ky`Hsnl}OB?>vXil##7{k(m
z$j$nfn>8&&`4G8rutTR?_LRx&CK}N#<XuTA3!dF!KD(}{DI7)r%5Q<lZGf}b$&C}W
znGo;q-ti%hg{(9~@#UUAIC>Mhq@eNpXA2PB`(}6KK#Z!DYFWvv?~&m&CER`h{}TWY
zuogU%U(KrptID$eFN-AouUXEsKsttX3_X~bqoDZ9HP9WE<z|M0cadW&Wys;X-&M_^
z?ajNvMq>Y5kx}C56uN;aWVH=@R}E4|YZVgSB#^w_qgwVt$9>RR!AAx(MiL{@2iyYV
zq&vpH0?qZ5d3yp$&7*qGt3gP4;;-mC=#oMf-T-rU^}T_1rq}D~>GEDm&-C<QGFPi6
zn2;HP9JaVOLe5UDA7nQ3q<0Mlx!H-L`>`QWJGmj>5-0Ln+le{Xx+>OBuyxJxPUE@-
ztWg8cy~%IxE}F$qidkPlP~h&x>=HEV<JpKWL2-fOK=2K8u*6|vH-1vy3Pl(1O+JmT
zsz}|a5E;OoR{z{SRdk&UnSOJa+drK4amRej59%M2jKsKU;ihaqtIqa5T)qHRR>Jz3
zfd3lQw-w0TJhPbE*S&~d_wCU)8i|t{oDwuETzeuK?1IPUe9(5#u(b9`)UW8piZyNP
zVMoGZ&lhov7a-AZ>2iWM^kOEgWYs(0AZ;hJ1qgWt9Mp4doKBgFr8CFQhk*R8g9G?G
zAQV_7+_lYJ_A<<)ISV;~AEbx)<)cux#R(nbUyAKJdf4(4!}8=@;f)i+jYlWLot7dG
z6|cxX-CZ2cg&BJi`uH|+{!@m<-1i&IYd3-VzEV8+3S`)TMD_{{G_q5Tx~sD7O}keh
zDDoMCX)tUaIr3Crp_rsXV!Q7*opPs3?VfI`FIavMYvPJ#4>ta^>0O^esHj;HkoN;H
z=k#Aa2Gc38zLqkqeM65cz-q(GuCe;vp*LB{G6U)Z7Q>jpuRXitqAVUgZjc;rrQsZ2
z-k0v^*exbYcAs7)oEWxlFzpUPcO2*zr6iyt&~TU7i>A)m-Z<HZ_-rgCp-;<$%3L^C
z<<QEFN!VeHLn&UWcS#8z5|nlQ)ZyibB8wPnNFfoyHZbK@GBL4g{YjO(M-q0i8cd73
zfLA?4NoQ8kIX^$`%}zBAj(W@W+}!kNvDuwo%-UkiV^Ez|Ul}kLxhWxNMOkb{yJ5DU
z<kfPSvH8lN(``l}q%4nBptJhvy#Y0!<z~G2E&M(!tpZQA`{&}5-6Ou)sRYkuZ}LoA
zSNcp_NS$1RARrS)QWiCJa;T5s2YE%Ttn5@uKKq(~Y1c|~q%OTap*B=JIp9P>1X|HM
zH||+dCRi`7Z<w!0z0G_pDmKnYx0~3$S+<_uQHHL`aLmvhpKgvB*)X4dUR_|N9>a6c
zk8(3IY`c66X6mw|{*u|Hx27&eoBvFkIt2qF5IjXhMvx=<4EPcVfgZOA8;eG>5D>?)
zhj*E06jnukWF79AHVFNpJ;tgcy1d||FE(oP@hvH-Cc)pt+~+pXvswLWt5cX7wNF&5
ziY>0&&!Z5*j=1M5B@bAwIznnl2GUwLX8GZ|rroPmJqaseEj?9IoyE7*93EUHmflRv
zjdVCAPwZ3{6{nX$)E~x_)WGMYHD5VI9>(hqMXqiS1xcbON+zx*qv>1h8$+T?%*vCz
zkfu(nzjMU<Hy?AA6dCrbpNqL~=F}QyQ@8uRCnIwjW7bQAl!<oT4|N*}cOn9)bkX?8
z^n&GNy+dM5)P43VV?w*$Nd1-;z2D?zIhE-}x`U$gA?$I(9qG~K>zWA%!S~H}QQ(f_
zJHN?BEyK8JfO|qnjS6MdS}pa=rEu<O4lC<3a)J3~H<rA5E!SGg4tX2!_H}>#n41aC
z(YV|4d<d?#a<d^I=_k+~N6NL1VW2V@-Pp<JZ1|zz<P5~Ih0ycre~g_UgHw8&EuuE>
zbj0?syKauIcz8_yoN*KOd1ak54S6-@7%ju(RJ2~toTi2A08PV3$~YYfua)~C37%a+
z&9mP5DgK!Cq9PApL>StrYARsO*4QZ)U%9zir<nCK5Tootyh`s#{3jzBvYgF&8cCVc
zI=kZ+=4rx_KhySg18fqUw0EL3))70ier?07;!5r^+91oRZDp>^e`5S}GBx$_8IjyE
znTs7x4Lnu|%cZ&<G<6BvIzI5ud);|d@vJrZU%L~|s}=4@T2Q`j=>^H&a*WzMsYsGk
z?6BCLc#)_eb?pQ1um{QDSZZl@NW=!~D|P#sPUrKkz3nbD(CZo7kONWI)bpM-kCe?K
z#J7Gn-%Hm`kDjq~zR@+a>|vuZLY39-O}>&Ve6iHAg(rMoOBhLe7#7oPl7FTA`AxGS
z{N(wq(#C{$(x)31I0&SpZ_MxBb_u?d0)whGNXo_LEeDs^5GB9KWF<iVk2heD>2sCT
z{w-l+SZZ48F@IoT4td%kAHnxe3KsVB_S~#sR6C9URQDDpQavlFCo<MR^-V=n`d@+`
zI>VnfOAkFi^5>&)>4B|dmCbpqWQZIuTEA=1;<Q?tx2nhumo^w%chI|^8-SVT3-3I}
zjmMKDmY@1_Hq4abjl^lnEcf~iNHk4fG2%9s!|3*al))VI*cj_wb8UxoPK%8OZrE8*
z@z`#8-%oTaTw=on>*59QZ-8Y0*Oc+%1uuY9^~m|LWFGtSnY)V!7wqC{+L2&r>aMXy
zo2!zHoKKnIMRi>JT$m~O4QqV^$f*}77C0Ow05L3Izg1uj6r*(1@gB`Fp^I15z2Yth
zvS^(FS$-hTxIOb!BXjzQ0rxt@@onG)zxK{}yipduSrKx2GQ(Ky{H|ql1DVJzM80LU
zJ6!tu#PAj~Uep3UT$+b;i;|$w<{Ilrhi<C-;7xL3s5{+`(ZL%>fT`vcQ<}`aIekU7
z=CIaCu=wW4@*-?-O^1y+F@lO>S1uWdPZE(M>I)kJ!nlPAUxCtd-Lx+GIfm#eY`XK$
zo7|DZjWbMmr&p(QEZr2A!Oi|#yF@~U7h6O#Hve8L%e$r2&wO_HW5wpc-IwYI>E@|J
zafMCdqGf>PP4qqrHkT!|zwe|8xi>0g7kkpj^z%LFGwb}lg-Bl?r`xYGQj*I`VuQL6
zrSB1Fr)<lN;+M6Q@7^<SR@8(lqTyyAZskY}gOf||rI~Rjh2|58v%3`EgvF}0|J-h(
z;2SH3!kmy4`AMd`%$ZLtt!iwk0#klQfV_OvA8tqwBP9@#%LU1P_gOcy0XP&>v3bD0
zLiG0Ibk1HaM=OIm#O-um#Z*ph|8us1P5Y$$d?#`srfmzqwjbDgLD3j|sW==L<3aBX
zIS996@wz`guQv>t$cxTl!64CgFC1lK>9v-(VX`v&qXyiEM`Pf-+90y<8Tm|r&n*0K
zAgXhesomv`3Q}<d*?|h7Yl041I9&#TMxaa}HnOV>hGw<6h0HS?gWr8}o+$Yip)deI
z*Nx4~{E`R#vwF3~ECD3Z^|!hUMO&g<9KNP}J&AC>8Mc#-cotR@gPkZ0@wGJ!-X__X
zG049(Ru?ortGBc4qJcm^JlmTZiPhkBkta1kdB1PC1PUmME@Az_5mSlx*&Yzbd{!<*
z_kmgYN0SLZI{#Y;3i(W~{bX~bJcuVwJF`01WcM{}@TFb#dq?Z2hIJFm1y|i8fo0j0
zCwA5F3}2Bmk*0~|HizbAMnIz$lCu4(T97F8&8n$e-1!_)Pi@X~8Zjr0)rYf1DeQ_9
zq9t$hyZhSL&(Rj&F)#N7o3<mGrPs}IUxE5IsIZv>d6KekhA|pmaKbo3ooQ4Y@yIda
zPe%YE2K0QV1&Q@RD++~q2k3Cx>_Avz23cdXtC_iACjmUFFVitR7*hhsxon~YBHJ_3
zZg2`L!Z(Ah1CwEe9=?*?>A?f&DZ^Qeh4vaJ?x=6*9k>xcA`R`7LKmHuT9bAQWq=UV
zp>-q_p^z4Mgr+8B_hY{U9oAA7{*1(iqmA4LF{O!PFS%?D$iPw*+_W=vi+^rx5HU$q
z1~%yS9huf&D@K5Ud}-E+VcmSQ11bKn^HB=c{ic-c^>C-zOi59+ND?k(-%v&jt7-hS
zoeg*sXVM|=z+B|Vtc#x}+o^{yB*RiXc<kDoiM4F(V36~eB#N|dqT{k!IGu>x<9frc
zZPGC!ga}Hv2>{;xpPyVdpcVj!GVmy6thBp@%zJ2iD_~l>rL06Uw{r(7qk3)F-k{qK
zF{qcr;)aEOBgv%+aoM}+MvP^Hw2|Y<ut`j8=WBzu6m+o&(vru@IzOhRThTk-B+RD?
zfjr}J5r4^_j5hO4LkoK7R@~<Jqs0-Mn0qteRd$BSE}McA7hG}N@rsG|F_N1+>`EYp
znno7!S(_D^v|q(Agey_jX}&M*9D_XRnDXy=U!vFM5KUuBS>48quPE&jsRso0Q*rN)
zcf(!L)H6FkNFTIsWnUlta!t-I9h80X;!(wkBIYeFzy-TwfD=HnAk(?srn!pW(57q6
zb;YcyoM5n3$k}B`+sbs88Iyp?Hk;v_V%d5?aG3IKIT<KjSRF8+PUur>;Uk)Q-E+3X
zr=D#7hYWbdW#(<J*)!1%gTbdl>Fkjn&Ttcb7-6L_jysDMRjuy{m=OS_G!!WTor=A_
z+Mbw*1}{YMT)h2fgN@mE1bH>WpzT+*5%5Y(&t?gVunhK@6)o<t-`aj>m9#b;E_DGG
z|833QwwM^{(q?KnV4OiuQ|*I~g*ya8S=TrXj;t&GlEJ*1C>C)9KeBaajvIJO#pX<!
z&?7C)H?bMWpkd>AgRG1pqbHj$YvK5_5Px?EswZY${Z&}!9$YFa^0Z8jJ!2PnkF{7a
z5rH~~2Jb$9)5?FO!rMO6byfCl4<f23VA!RoseH2AL(*k^8vG})LhjBS;>y*NL|LPw
z_})$u6*sKqJNKaq+aw#KPG_%{=~gmoLKhqV0lfQ=jhdPIbYn}eo@HLSZF`Tx<1>0p
zAWdE#+0S*`IjRQAE7}2gRtAZcn|q?|CE}Clu8O`nyU-VA)FLw+pSiO_cCzO#dDH8C
z?BCEM+3QH280i-Bg^StO$mkx;DT=v%zdXljRT1`k?34HC&6sW!swaKI=qAguX50c|
zayIN{sKsgNXwGcVSl2<E(@3-v#%Z-xeL=T~HR!XiE<(d?aw>Z`GEwT8ZGTWkpSN=h
zDT!&h5<Nw^>q?(ZT4>Cfaq9U&jigagdG$A<><OhB6!7bI;B}M9_+Qa=l~aH6_Cj=X
z8wZwGAaZKXthFJrjD<ArwU{yvPUKS8RvquN;BI62l(f1f^Q3_%4^9{_)>0Zo>(dbV
zk!+ZH8nkEfMV3?HhU8y+j=fJgZ^8hZbmtrWnN}osJ%hv)nLSL|7o51_dtMzI{lH8O
zQ4mIX0$J_PA>K{1?>=zeKl=p4#5vg+Fn}Xu?GQ@d^xNpksD(PW4~mYQ!r@&<Qh<!7
zORUU5ae!|VK+Wznj<^Q5n?=fjIFjsy@s)M2o}F_^PO*fXVzkh%xzm`?t5;<9?keg=
zOQ}^aZ*o%#7zo_)?{ex-m%WvW3v&*nDY+0dALQ9XQ(H7vd%u>1i!`CGyi+>;z!mVL
zy?wD7uB*S*gui5Ie575!_Nb~5@Y2O3tI(gpp|2APJ(Xd8`6H!+3S7^QCtvP)$GK)O
zUUCIC_r~1qLmadZrRuZ2+oz}h+y37v?LltQ`fX*g<{ru(|9`y#3T5!!x`+(F(>qiL
P#6W&^`K9)F!0-PLL`%0S

literal 39107
zcmeFZ2UJtt*FG2(<+Z`9AgGieMU<+5ARQ}&F1-eoCLlHR5P}seNDz>Y7(sd`v=BQT
z1PlQ}h)4+}1cX3<guvYJ{@!o>XZ_cjvevAbH8bB?kel4xd(S!h?EUO#?{iL`TACXk
zK5+5?2n0F|F}i960`1KMf%bg-Yaejt%fs;xz+vBQ6T_>(5hxp1nvuX^e~^(Q3<MI3
z=l<_XoL=$-fh0hXtCwsdv*t-rEoQ4*ZA)vK>_dFKCsb^&KDN4yxiocDZXi-#4x_d?
z#=7>CZh!KEVRAsz;f9_E#*K0*&upwvPXdkv2i!Pw>Wil4d<_yAL-185I{8`n<s$w>
z>xr|gFm<hiPH_5SxE{N?)ouSygZ}&Fza{WLOF)`t4w)9`*SFSLZb`06U2vk0BnK_*
z0)dz}qecR_*B95mLT?H#Pe)Ceyf2losbkJVF0KLR;gw@|HU52IC0r78_&*=o|JwcI
zKX2n+Nc<0{g--E+KnAB*dc*f{zxM#|x&s2m=~^V9l~BD&KMk$iAtlmg$t!5|2ujk?
zFIx~5ibg|GS`c5w!SAooFHu8AEb{>jYD>x|X?5}C$enp51w$*>bOt;ReWjFwkhy@W
zO+(XBLmgR^N2u|Xl_K<jLM^76E>%>4ephN}o$mKX76LZOIO|j0y#Wy-T>Ph*;U+IV
zb~_!(Z8JpqI?owypc$&ytQxx}voCJ2x_DMLM=7hb3)TxZi8_nysk10G!sNwH8Im}~
z0fiqfEH~eu8eC?$Rj2aFtnm5k(Wr!WQTUDf{>IEPN~GwOMzr(qpQ|XtR>%GIwy04^
z#$vx0rmgE)Ec9qnk)P;44QMY`N7bLHG9VmC&vPo?HEXlGRYQ8XzVj~w;ST-3qOvVq
z=iW@;AP?@J>PgzK);x{tkU?nentg&!K{3tqT#I<}IJ?QQ(AD-e$KCYql$AUSQf#84
zO2o0R$sOD}@{DgExy%*y6-5Y4+ScM6r&Y!JkXpCH^YG9-w6<JPdE_&X-JisCnEXj<
zDp$hDb5)!QRPnbCqPdlBBEkYaCK<?XcaduTLKf6Soi;j|aeIrZG8iH{om?R2Itg?l
z#VcaHPZD_ZyV>pZU)e9hQA&Tco-KbIbr-B$)QH}lzTG_2!Q)6YIjNv(tZ9VzxL+)$
z%gj}tn~0-@W}H^_yjto4(fvJz{o&5`M#ZjlVC=j+7LrgW&qSABQ6Y5aPV3%pc-igp
zJ4GhzZOFD<QEpo+zqFXbS+|+2+SiTO$6DkU!BNGX7x?~>D333AgFLidjOH9WtW}@7
z|KayczK<5wgG!~L5O#8R`_IUeCQVKQ{XX@D8R$OUfosy)C<EvFLAi#UScW$RGD=Na
zlup!<g=j&a9yabsS;;ZnFqxb>9C9GxNHEpd^FV@=%5%l0X}I+QMb=_KCsBt#@@Mkg
ze+}p}`Dw8qaLB^0N|$f^m)Vfhv$My7T(W*G$w)%s2d2R>2^y@6Mv92hiMnA|`^1%L
z6)C?5KZXijW3B6WPQ<mIG8eJlZ?N4{sE5-mPyEv^Fw{Hv-sU^EdzLci_ws^39~Tyn
z<|3|4i<_3eTEV6J^qS8b_>`)nl+tXnCRhh$5Ylr&X^3Metq$bU*F1#m;>W2DaUyoS
zBz`J3z`Cm2+pQ#y`lL{Dk0;;S@KyraEoQlgF`OemH~hwbr1d+10M(B{YTSAhEl#&j
z&&WgkVwROsR{VY`hG=xTvyVeEJ?`iBb-tL<gL*k7No*+-9R}i67~nR!wDg0cfh%_l
z#%8KGC)50|E$75<RNc&wf{f18R7AzCuJ6|Zxt=L{)YeJ>L~6qN<nAV%=0DQJtrRG6
zFMK2x=k|V=eud1ph}G{fZw@NA(1j+Jp}s@36z)y_@l7PS*T@ChRAf@c>Cf7EPG_4l
zjWB0EHHFB8y$Vf7Q)GHlH*V#dp8hLPzhwrJVAM2TxG7Qit3j)`N?Lobp=xf^=h3Bg
zk~lPq3@ef!<l=xq%2#D7P<gWk&Y5j5{YpPc?NX%Ul5ov)TM3_6Xl0N4=@)k2wI~!E
z6+l}-ETZRW!i>dR;&Uz6ZxBN~3%oaM2Vx-nT|AIP)W$@920ErqYRcN~Mpjd?T!m(x
z3@mneQm3WiWpTOhPHpf1!A1S0>sq{?&E0Cpp@V{7VWnuoRBPUCx$7PJzno6AT>s$q
zel~bPrl`=ci4fD-o1*6E^(+Bv<?9;$6>hP4|8q)FQ&)X)_)7pzZ4T^!ZZoDwkDUQX
z4wrWl6?YhwkQDk5y@ZOjP(fNi=(~MQ+#t$w&D(BJi8;eGI@`$CAFKCGYEeMt-3RQU
z)V2GMZ5=OVad@R2#{-MhrLWn}odi^kTLw~P7dJeGfhWJ@j!7?@cG}SDO8Q=s5^5!`
zyXAW<Y$m$r+B`m|LI$y5Jzmqf@MN${XY|6rDDH<l5}vC9^PGubYRycjz@#WXZSMRV
zQ;np;RtZX6?8m~KnA{jB%s`kmrAaC*&*O>W_J~^?t8B(Um9q3UoYtpGUVv)Turi8_
zGuzXGqt>niw-Y%>(vylu4qPTI-5K;szwsl0ZP$szvZJP+w<D8qC-pY&A2Ki*Ke&md
zCsQuwGNbn9$;{!zqr{mV$v#CcT->rfY8?a|=C{7H=9>xb@C0<c%8yiuP07W*%;NW!
z(4y|o@PI*Aqk(OsXv>lPmn+g%9(OTMq6#9X8eO;a|NhAsS<C5bRnlYMXV$ebC#Usr
zAs5oY2dsh<+nAS(WBYL@Q42;@V&<3JxYapc0i6VaZmzAN`dSZ4>q@DDLlai-oUSn-
zJv|ZV_|r1|Na<50Ny30T8zo5FHXF$Gn3)ZOHFke~IPz;%>XLjSJ$4Z@rLP?6ok+jn
zSs+!UkQ;foFJ(V!(Ve3$UGxsE>{(8<X^|-U><MqJQ%?GJ=DCckJmbM9PjR1W#J^HK
z!KmFV95kAqkR9<67O!Kbb-fr~kSZ$beqsfm3Qp+$$&<ETJNHnHIB2@XkRl(YG^n1E
z7Lc%*(oKJD5VaTeS$v~QCR@6w-Prj|Q5xDdn0h$+K!Uq~&iGYutaXslz&E`p;SQbz
zKe=ednJ!b$0q4`_7EN)z?-ERRqyPxQU}#=Ob@@Vi9_cn+)TJvFq?O$!7nNvdR-nsk
zGP+>h_vd0;&VB;Bwr%6j$xouRdpun*y@iz#Vc<&Xxk728#<c-$arW=Mc`%RrYje;C
zPkHiG!*&b-V)_ZdkxK7jiK680*{-Xb3aXR}LQA`?S)=Kn3!(;&ZYES}4f1vOYtfXs
zIi7*iG&Su{%jL<ArRQ!4*fhSweL=BgleSzTg<Z(#Bl<@m-Vm1evkmzxul}*zQ(Q(<
zNm)-s3!z$HCI`IjKJ&{>bv*xO%URX1>yS9lSh;P@%C>e>qNQW(YoFxYq>$XM;hsgn
z`LoX4tK~P+vPc21XPvE?{k0f8@G|YE{Xo4M?O@ktWl$wr^X!z{8P%}Igt4)?_OJhX
zGmonY=mizVkMdXP0k<EaWu;etCOi<lUKCRB>o3Tty;tkRDLdkLvSW}xwtKd(67A+8
z^g%?nC>Q<ssc6svdKCvRmCLy(`-$}+=yq1UnsL>beg{vwaHq>bXX&1l&>HkiLTIC2
z7_aW$c<U<kbB}^y2U(b+0;*BnxeblLzblWl4r=g`5Qs#6?Zn-fIXNcDaPO5)zcC?e
zRGelImA>OHfKmNFSgg{L>D-_R)Ldy4=edLg;v>*}ct{QD-}^r<JmtEcogyLim%)F~
z;NP$R&mEhwE)ZAgmq}`F16~lCv{{OpjTIs>W>PxEgqpK)dY_W?++|3OX98wydMRqi
zDNVO|6rLepWV*2EHt+sCMNdS$b~Na6Dm9T3o6cO_|Dh!`L1VVw<{EQDbKKKSu1z00
z1G6N0k`EitGbWggZXW)NnFb<44n7vEkYs9vS?N%Mx_-t@afnu@G0tHorYvQp3Z10V
z2Awq$vHDF>WS&?0#Qd40>6U2hbjIgh_eP}Qz{fYt$fDCePtbj(?oM((uhIKXlxk<U
zB$w6VCu{k+{jU_!v>Z@@Bwb9*l$riEg%}$pU{Rwd(Xz+3-%z~E!bWOJu!V<@Z6j*I
zs?|TQckn!tL}hqZ9gdyptX+(Y>olQ6J_s<e^~lZTxG-hlo4f$=jhqL+{i-iT(jASm
z+D%^{HHK%jZH2b_-6l;Mf$cU8{O_5*(uR`2+RG1*8ZE9y>X^f&i_Y8&8G`uESzq4+
zk=I>*EECpiK$@#>ev~p3kUP1X+tm5%O=nrWlLVCc>)N#LGj*cr{h@{?(RkxQ?bF-k
zLZ!8Iz{`P4vkkx1AK5j#;22<-Kkv7mc30!8;oE$Ib8IT{_i6inrtQJJe?{NR5O_9{
z*13cWd|yGUBJ&x@YRM7KegK6wkuR7>sYoHDP_f39h{>{0XX(|n6Q45ubbXC{<$WsA
zvcF%uv&ZBD4erm81Uq=X^(F1^v`f|$-azpspFjlg!bsN-#tR~lUJzg3=?WRuXL^!8
z&E1Kimxi(fn%~b}XijU3<@V`gKz?~QxG?;tr25}SOR(MauI2{`%itqKV|V~4+8&nD
z4PW)G7y13J&QwI$yZKa#hlTdI#|VW|1L~|c)Yqpce+OEvYWqm>2`Kaj`9hnMr{b&h
zeUg%MQn@R=U{U@=>xg{4noe!myydZ<ulK((l+IqP-uAe*2YXG4E$hSvbHzME_n?E*
zFB3r^Bcu8m5@06UO>X^1Y|=W%%4y#B?(ctNvzOWaIn2?2@BKgCw2i^i`p@LILyVQu
z`uyd2pSDZ|_GXK)fun{)AOAi}y8<4LEA0L1CO>4bl;A2urzf@6t%|0uTx<DmcPdjb
zyYpRh*Ve=kqfABWv-^m3wTqnUUdXGk!pw+-?v{SSoi*&>F3>Aap1fV4_Et%VutSA%
znoZJe&DeuOW>rivIT(CZQRVru6vw_VQ@VkLs)33SLI;m@y%9_LH2sJQLle1ph8+%h
z>C&nDNsf?1VJMFVPkgA)F>I>wpz3zXAsbt^hwJ;MP0kaqs9*}niat5{&vmh~s!a+>
zXZZj^51kp)6y}`#HIQ?aJV-@tS$MTN!6!<Hp4V}<1vx{E`+^Kis$*pr^ltzt+t*Jm
z^WA}X*-YPr1a>KS)H}|ouXoilE(f`mQY<_JY!{X0qSDtUgwvCX!hYAnzz~g&mUw6B
zvZR$4=y1KL1?$lB&Auq5q?NZR$ai=l?<N66XL0G|OG2ubUADDHf!mqn+N-DA8xt+q
z0!iKHM@Ktj)N%Er)uuDi%gy0g6j?jT#YbgEUOWK~MQ+>aLO&y3meqH+yFsF_%s9n<
zVMQPlb;7|XtTy`zwmlU?u=%Xvjb~89K=_oa0?dC(?&L%M`~`_+yLO%;*fIK-9rE0q
z{_Yr7Ml(BQmNRp(UO1-7>huRny=6agS}(9jb+PG<4y;VAaoNr2oWk77n-Dk~jiEe9
z-7NfN^d8#5b5IHe4IhbU%4ej1A2-%jFwfkU-;PpKPE1CcnzF8r4|Yvq|KOdXcxm@%
zTIyoA1((T++pwCiQe=s1g@QrwuUk)UTA2N?9`F;7vg<&+zFm&io~&gcbw+&%Y>o7|
zI@P{RrMV2-dy}hvUAOf7E5Wh5qVzk&-HisM^yV;KrmK_R7mrV3h0R^dYOQ~xyJiGM
zFChrS`<y%x3&TTgqxDw(VB4&dnxjD}h8Z5V-vvcc8+_GlmqO0#;V8~m<3Z(*O%FQZ
zSt&Ccf}K0eb$+)JYU4{FB4b+mXld=9sCYT8-SnH+9vsW{qr}CjE>cw99dZYf2;Ow%
zv=g^nJW^^f-C1ejTMt}D!(Y?|qS}Ht_U2iaVGO)NJ+2NsN}#jPo?Q2xv?7DjLAO;)
z4#BGmA4p@}r{O)57g^e)u;y-~JMf9JCKW~@^hC03|61td?hj3`Y@s6QM<R|u_#<m&
z?;t~J+x*n9!tkx1+nqei&pdnmjb^Yq&I5R*jeweE|CAGoM1y{qlbe?f%Q0bPBSF3V
z)qWrg(IZZ3#g8IeOc0Z$2!=<2NKrstjl)2rihktPT1GqE8+#=E8jcUeHZs<JNxbu|
zQ)fKVzp8?;=NM<~kw<JyrErCbr*u$t+i|Ov8jNXyewFa;5?P<pe2(AUS0q62V#rdQ
zvrpiYWTnhqvw5w4cf-L9`?h+?YkipnR|RWPpP}}wWc{udx1tX(K%w5(h@!;agakzF
zu2ssh!!5kF5tdPz&OSq@9rd^vc~kfnue4rb+PQ?Q_ftGo2Jd{8vkQHjZ!R81XxFWD
zD4hj2kaR|w5A*L0I50oxR6L@!?EWz~M6~ITyXBW2H+YmrhQ4#)JoMQ8ApkimeCFgV
zyX0tEJG{wgVOMqivsTKb7b-6($lA##gCV(XTj!&^+qGvG-^ZtT*L3sqKD{$>XYxvj
zzVey_TlwWk?6W#VaZ4dz!?-c*sRh;o{*z`(VY-`mf-Bsdf1kBnsYz+CRjap22yV!y
zhdOU6+V^YoG_}pwBnIc0w0a~W@28B@6AsEY<1omJ4ER^5_uRg`2dwg>@Rs`9i&<5|
zV+__(iO~QpVY9bQSw;WHhFRuO`N3VYPS3_Uu*R;ffM;8GetW-2Sx5lU_~Zh^u95d(
z`i_7=KDYRQELe3P-+}kqU~kEVl=MLLA+KxOXP8a8>*|f$_x$@1jW%B9&KeqyeI(D_
zZI_HN<6w{LL&jcLsj4cU7Lf8ycdS)DKO1k23F>a`Rk_p%KDMdYi)rVHaaXL*DLMOw
zNS~aeMv9^K>qP6uzUUx!N0;Z`blrHib+g~zD>P7YelNwY>&5uX2K2~HU2T2Yy{Mez
zjii~-DCIHruIPlrXrlFv(zJBw=Q5~wQ-piHJ#&dwCv$`-sB8FiYdxv+yik|v)7z9_
z=gqRqrzPa*I&0<R-48p&k-W5xLY_gBt=uG!-D^(|EH{5|g-u!0TjJ&&Xd5bxV8iro
zel6aP7Nkr9VpWE82G)~v!a#e__6&n#Y3LP8Ll)df<!qR&s3w_{I%dLR!;jMAcY%zK
zbMuCP#J<-RKWrMizMH65oudt(j}VbLb8XLsWJKrI4X>-YueW}CTpH58;l($H@VMV!
z;!!a2=fK~_ULG@tRX%+W8x8*YdsRGagwfY3S~zc`usRHVd*|<SNoRS+lh|jvtp)D3
z1rIScRCWpB+Uno5ga=Kzr#~WVwd;cju3o)!7E_$b2O{iI@w01rSZOgIUF|+vmC8Y_
zp;wL9u-(yj>%^Ij(PU$sW`QZY4MjYL6(<eP-4<S(FYnZa;_8QsyJEafw~uLB7R*Ks
zpJr_Nqqdi{!t9sZBJ;Zw#RR6n>xy+b$i$I=++4>|$Ck-NPv!L!fr6#}Ex(cGEwQsk
z(s6Ul2&g6b!l`S82cKnQpeV1-75%--t=iXA^iiZUsu@j9Xr>D6Q(&^s_Em`D;9Wnh
zWe=fao5^T?>FiT$Tkz!7yB1wj-^9;~i&R<^F61q1t$F`$;|F~dPvEle5lnk@`MYj1
zi>k3!h^@HSwq=z_XNC%tO#Z}nMwicJ$^~D-(_EhX$qfNt^J%v9npzQ4`Bb760<JnP
z@~6)5b=ix-A)Cm(Lhq-pYem2OY$`o$E3H~sCyhwDnqAK;oh{3NLS<Cxml{n$RHL*N
zY$*jP<Jh-<h`}q)h6x@<YYXenPei)9df7x>NRP9=mrv>3!mUcu+m#sfi}ad7#IYJ|
zbt0ic5p@FFbm{aTc)h7XO-4TDQ;yc}Z0XUA&hw*DEd`5JBN5UH&$)u!ZkSrqj@1bN
zTcv-a4|~5WcK&9pBw2td2JYQD^<go?Tt)x8(E?$`4x;Q-A&|Y;U^;LaLg+M2^w>7K
zp!Q9@KwerkWZeM*^APHJ$(iV8KA#M`kgMYC(6@AbP1YJfziduSei*=k&T*459^|wH
zf~$`8UU%hOL|qvrSC<KYnOAKHyYRvqgVhfYy;FSu112Z@5R`OwvH#vnkD1r0-M{a!
zJ9$Jxj)_%8))<bvl~MApBfT&4Hzs~w5xEsWBrior9SAe@C}_ANsrq?SSp}w;dsY=k
z+x~{u{~jHFP&B&3I<)iAqRzn2l>O&`Y(=ICf>(a)170-!V`&W(9=(rZvnK!$-nV@p
z<k*p%(U8~Omopnx4ldTEZUoDsXP1bK9&?j?Y?0{Xts%ymp3k!MY84bIRzuR$uWFl(
z*n|o>^_d+t%{>5r9j!{XnZb?_qOqM-x*rx#dRwmBH<C4j%j<BMO<crSX{zH@yJhgD
z?6b^S+PKQ1U&Kb@UAM_d`h|$71hiBs;koVr+KMYSeWS|1Pk|(Y{-KVFC}<^AAKe|R
zzdhoeD+XdIbg0nd`p@e5oII<gq7c4Cwt4RyXyo<KD-1#~(&`%BY8^AI8i9%G%gr4r
z(ShH(M7&j%uE4zP<y{;i#8Y}VT%zKtwgv3fiznS@mXKXMhv{p~)Hk8bS#w<f<Er_%
zG5%%o9`aChf*3x%w`H1{$VcwbKkiY;cG6BXD4plB73>hBLq&>^*16f>o><S{p>XJo
z3})C>uI&zzxw)KmO4XR-dX3VYnont?ALk^HyYA8&%L0p6hOHlcb)F4Ew)s&K#V^GW
z1pV5=cDKyHBebL*MbCpT%fr<I%DZ(qg(fV!8FOJ=^KEH?APi0;am{GZxSz4?9~Z`(
zWdCf-lI@4u0C@giT<j};Ebp=VBCjzk{x{MPjGnzy>~Y9`Wyq(H`b=q9qu&#kn^;EK
zGp&nU`HQw8a4pc*!oF!W;dQzc>~VL?mx}goUTj78{fAZaM{Yweef3(-QQi<pH!!w-
zFZ7~_s^!?XRPe?qRktE={x5sDbRpq>dH`Ql4tW^@d%Y^EZQtZdj)MI3uA#^!Mg(jM
zVN19Idd|7ht)avYktV&n6GZTf4bcEJNR+n0lgKfX(e=c(N_yoYV#f;hLp*nVZyaLW
zXe}CG1kyI{q%VS$!`<}kwfi)(79x5&h8XJUdi{&$Aa=qlzsa9R<ShoS4bj#(NB1ch
zdhHsm@!Y*;*vUf|2*#|&)QlD0YWx;0uDrJRDwJw2TR{iWM*J_C{}Z91G2KsEpL9PN
z5g!d6|9v|>Zmz~IWBmLE7*ih+aTZ?uZho%{riXaxzMaa!L!<25hO?tl{3#e>shTH>
zC8sq|Qr``p2`2@3iu%(T8Ethb`4s67(Qqds0a83Y43!X2d*0r?HXmA9r>mgaS@v-h
zYUWkD5N(JExYx-LV^^f6Z++1t)On2eb=_W;Ud~}*m8b18M)iE|{{ffeY(Zvd3O==_
zKgtX8DeoS4Z?@tbrgOxSD>CybAy4w#(xhO`+xt=F&pT8HjFAZz@bRjF&?h6~BR9y0
zQOxk#3l=lj`LjM*;ai(yKGV;I9qI-fIR-aI+GvE-iZX=qS*^OuMqX2kM3vm4C*fJF
zQ%8zK-FXdjk_9=N!d(ideaUanb-&kRZ#Ia%BQL?vRxjVR59Pb7Aypchc=|^DAdmd^
z_4J!TVSez0?iFyNT(tRU<FUMzRzD#J;(|q9nVIP7@bmrkDH~ZUsV97(zl_|@4CW$c
z+X-s-qV=pd{d99gul11*$mshU<#h9V9!Up>&TB4}$Vcm5o6<XMtrb>C3?OpsPQ*qC
z?nEx(0`Z-jM@~Ft9Dko32s4*<&~!N+G+{Sm5#kniF3G^#$m_g5PJJ6M6$s3H*-%N&
zr*DcSv0tAs^Ss}EB;5d;EVda8k3rv&_~?h84+|dEI(?c`{&!rjbuH&sV;?Uq<|1Xj
z6}m-{k`!nOX57h&sd}D<2=EV_ya7$fr*!#u9;Wc0gz{rHs_D`06*3i}P}nQ`8Gz`#
zZ?+H-PurOI9-Ce2k$B_Tc54<5W%x|o-6R(QU3?_mjGUe2M)QV(+chd<GRU{4dE~`6
z82K$Vv7#8lJY{(N^3$pZ9Zbsl4<2f5;wL{3c&WthA1{O7FV6hC7Qmgl26t;INcE9a
z1=kYJhxHkdF4bz=_k%4<;OHIk%zIr{dAF;r^M$%XTDtoxB0Z_*rOvX<$<k6;)#<NC
z)Ag5you`Ewr=ItSSO}*_ow4uWkpgnCPnfG6$>t!fk?1RSnn&m@mDz)ib{Q$7OD$8K
zFTQ8XFk0`v*3{oqT1^MlkZlh4cwBc=dab!l^Az%HHqy38td{4=%RE~ZXLWT38$7-Y
z<R)1mFS<8|B~^XH?xD2>_1p0fucp^2b|XMWahJu9gm;!#64V<=;>ziLwpX>4&Ir*g
z)4%nnI;{HFj<Nt_qz_jM6ltit-m_@xyf}V!G~{9TD#q69Q#IvS_LcEg`A;<7!pNh#
z*9+aH%}|a;!6n@khE|laB{ky3(^lbt`DW>yKaj~o25s=K3G3lnR;O<QIW-;s)cs7p
zWJed3BOS4XVg2KZMwKxgF-npux&H9Con9&t4|Nn_YhS%+$L-n`f})bbMU%uVPVfE^
z%a2>kr>Z=M^`^~{acPZR@2V_vLaBWDBts{*zzO`$Y+_uY$NhFt+|L%}LBdc+_?0*w
zv*boEnrHJVF3!ZciU5$@-g3o*H7Zg~`deZb`+0xg&Np#?$L)*jTtauR988C93K$V+
zD%N>l&9{MBa3BLd@SnV<oNK_QH22K^r_09$30dM{AT9sos-(ZnF^^`ko$jJ%F<Dtz
zsJj}whw)RM&AUL%<BHP9Jez^Q2q@@TSqM+wJ?UvL;G2I+97%xd0e6bQbwI%XdGEpV
z|FY9@f-6)!P5G_moy+s@Dl7kVe19oI9>oNblZ6x<EVJn0##U@5U3veb%K`z|)u9J9
zBq309Dti%!vu#Td<HeV|vcXYNQGLQfLVDv3!IQ-1(7BL>GR%1NpV2x$Mh)pZDajs(
z-QHrmi5z%z`UP0O)$!G|mx-+(KT~HVcw}O2#G)|woyD%WVtHj?Wa4bPvov89tqArw
z)XB+7mQ<ag5xDhj;Jpdh=(NSPYu5sR|1;rmpw5^05_|B<kaM8e3X>R^8$knV4H-1k
zP&CH8ocrVJCpTK}58uF$V~J5eKirTiZ)H*6UwVDwb@7<GgyTfe#X{Q>JHqClFZ}cY
z#ggkUPx|R^txt*ot;Wdo>(B@leSYLt&YxrQcD0E<_FGhZ39%H4ZR(NDjR_fbPt;}7
zi)~8+fV$w%uCmWo#5U*WOTIQoichUw`n%W(ZrR9MnS)VleFl49T$R+Ed!3w?w#Hs1
zxamiKeR6~>oe{66q&h$cQYdHFU@K=qmSIQY_Enl>=tPAu%rez49Lg_UY-N{Q7f12y
zuKbugD5P-QZyP@O{pH&<XtWP`;!8ZgBQ6$E;~da9U2ty?FLW5Fs_atj1>)z4pdBHR
z^o3O4%3%&04Y5{f3}a*CEy1gfA{qgAReT1}9Acf{T3CKmQy49L>De*)(fG@6%Y>nk
z6Ys9X^SS;gFw3-pIANp1KD{`h%FNM;TK;5`rsRL*s>qt)inYPdmM8`vxfKDw)inL^
z%f$V;!9tA5X^<H>?Ziqj2&4=U==p&J|Aw?2dWfZQl)U7T3!Jt2T54ZUPg&4bvt60f
z#H9!O5Xkk3cyjpgt$1DNjT=c3+nhBF{Ld#|N`dW-lfY<|7|Q5ol(BZ9mGqVEOY~@I
zAzRk_uoV_FoGhE1z}Ag|@Ahs8yfx~2qBx@K$J;Aunrj;VqoK5q_x9NHiCzry>^%IS
zLias++&V#Ei?4Co3GX{7_r66Qig1;OMky9?^{>qv3}Wi+wD+>6rrd$|>e+~E(!ai6
z44oeiwk~t3iydtUc25Rs^U{Yn9yHi995y{=gfDr&MXs|vvvKK;b)mWcU21R9ah=FN
z%dh2m<9McorsOe|67O3yqdNNP4Zyr0gK2kQ;Y)kv*q95!a?KEouqiZ56!%`hu8qH2
zUS7^AgJM|?>i(mLn(6h#(dd+8eDScy{NV9cKtt_^KHQkBvOKeLIXHOfOPMpKHDtK*
zR(#-CLvSJPYm1Nvq@dJLJPI9kMlhq&qXVUc3Z>T(#ur>-svyayHHbc4ot+_zjg*|B
zZ0!hf%L*vAnZaP>Dk&($gy1;L=DAHq-{Q>B^-S1AdWyWg5OZ#j;z8TgaVxPaJCEFA
z<g%5W>r30yWz*|-Jqe@@m7Rh-yDHh}>FEjCcB^u#^PRl%_Q%6U7-cAwi1Wo4?`AVj
z@Wn?wdsR5l2Pv@7U1*pz4;-$X5Zl<;=o11^Vg%4r5v7(tpUeG*N`qaE6jEY`M=TVf
zEe+9Y3#7hcXUqbEiztLL=e1UAbIs|m!;iX9^unW#M)Lhl$w{3XMuzzZlVn(kU*S4W
zp#r_ER0T(?(9NZ`-0{3i51P3G_2*W2Ni?vUvW7BI($o?>VD`bWW=0=dev2(yA3$Om
zS4?(%t*e$d!6YUR&?vK$N8J?8&H7=g*%v_uf>L?AB=*eo<+{NDdoz<Ra>fKacVAlr
zFoE#(slfKkm>p0WxWd*T$H3AGb7NUG+nW@9e!*kMIOo3^UKH*1AvLYqcBLun52Ocn
zr7FUeU7JD|-<za~WAF6m?*}Y<0hi4t0i#!6`s~>O%`U(nX(kzc-B{FbwP39+lG!gc
z#v3G<Z|uumMizkZ(_aHHT3~fUcVmG+v0hv8iI=jDY8dtBo{EOw&uGRbCbUsDpg}+H
z%yg%F*ZLEg>w6Chxy1gu$D4YZs18~ZgLR+xn<sGCYx+w72<09<@aSl(xAf7cQ{S9<
zI{lEvoH3um>>)}W0or`7py4wY_K?(~!ck?HhRSJSx0od~%v+9ANn$>G_UwmW1vb3>
z*QW=4WA*;dhv8%ZU@}{mzKaOY<F>aD-aQ!?S`chzW|<CT)p|*2z_?)H>icNDfu#1!
z9vv~@i!yMti)6y7E041rp-||TDrNd&l1=a8aFx4$wkB*QS4h!mh2ox|>18y3zvBTf
zzh211=SRBFpFJDmA_l+q+WU~~g|h&7qJOeASq=KsC(RBwe-HxHS^tin0id6a7W$l(
zR&4ZY(+!mG&kt4Mvp?Qym+scPJ6!oi);wz+v$?scnC2F{fqgEd*mM(ca~vd_j)95C
zu1`z=qTRT)u{>?KN++&`dbh$C8VgsKr@Ion`}&kcF8Ka5lea6q7|6Yei#fe*V5H9M
z=Yz_kgMbc(J*`z8seXLugw@ffS6Owwc&as|iP5WVcvUhfmieXeatzQa_3W@A$H@ZI
zbd^X_Qy68mjoGc*=h6tMhSG4gXV<uFcId!O9MddAZSFVVlqxdL-TGWPFfdSq2_TJb
zZ%}e^`2ddUr=+D3bQW6}y~gkTLBDCEb+pG`JsCa8Xut*2T25Z@eL?Gg*Om^rLG~pE
zr7uE^F8tbt)d;|ESp6PV*XD4<>KA_f77xI(IXh48QpUB+E(5Qa;K}dWrVq1y_2*Y+
zG6VTa?aD^K1Ls^_ot&b>eSGFdEc7;}Y~cXhbSgU6&yz~*D@5j#5-+V;cl^wLBGGGD
zAk9GBG?~t*#BR)F?(xGH*RhHUb69M^yYxQ2D#?l6m`?K>)O;M;yIAU2BQCF`1Z9vS
z$YQ!TKgPM!=|Hc{u>e}GETUE_kQ~|&z)jNNVivCmoeOb#<<zy{jv=sx?ZPDJrp2cZ
z=RihZfYFR{IU`6t#W_DZnFwYAya2ZsKxmF(aB%SOcs<biA5w`rDyFFM$jO)6+7k~2
zV(ZrM#kTS>Bc5p~S)l_EjoLf?zn}8!t>=+5F8D#Y(4`JxtqfW?8x3$9KlW$h3$BiF
zOjtM`pgPOWX-cko=C!1v)Rg38>r)T_xpLlJ$%$F+NUtISaCl4)=tZm!qg&OG3-|mf
zuV0JmA-6U&#I(c5rtGfMN9&L-)gB#Uy|9RdM#>goy7g?OhF|ylfx*((6XnyN6s>w~
zL<C9C3|i=mkpKvhHvwFEZ5VB6RMo3{R>S@Kp_j+cUw-uHQD%b9a(l#L^I|I|5qS4<
z7a`MzTpP<_waDf~-hLvTjojMc*X67XYOm<ii5RkuSNEH~Y~sLNeD>wHKk5MthY|rk
zpaLiE6?Uo22T+fI@x}XY0@}xaTsJzjtE8fpl?XodfLdr(L^QFaNLiyedPdV3L2?QT
zoBsXz*QBU~3UK@R0(dO#XHgAjI?b)ME|A>HkNfla;5K}@LsnUD>8k)zk5*c99k7M5
zEe<<TcWn%YTrSpc_w$Un47RUhH`vf3tnmO44_lwxeG)X_K`x#>q$eyWc=rU*VchM)
z^mPi5lJf;@?-Ae+bEY@>E3F3$%rN4SzwUtzC6ltA3dk<_1qS~7(7zAjRP6zI9!eDT
zaO^lECs#Z>Ts7H-QuFM5jld3YEl?o)(>|gmC5!P+`q7t(O)zrveBH6<ps#p<%mVhF
zM&^vc`dp*B&$pS%DJgBahmwZMoc(&UG#M|<v$bC0Rt8M8_c{hvBL?4`cOSK}v6-^1
zfFei&7CK2k5`*SezL*2ZA4NKGyy`;W_~x3pZnP;1Z4Zrl50msawtE%E8>XisGqU)=
zrhI>)9)i_!)aH-lftgLf4SUl8DA_s%;ev0Ep=j{eS6p5g(4YS;i;|1nLf}|6QQ3B$
zE5WwSi3h8<ipSe_eayLk<Ok^0<175Sp{)CR4}LfHg5ow96k&c5_4z5Txz?)oUYxua
zK}JLv_2uYpH>89BUFnhWnHzYY%~)RMOIy8iQ~GFC;HmDUe8xT{*QVMW^ifC?fbA6B
z)t}bRLP{>fH36h1MffUp)f}LJ!nq-xXG^%lt(zXs>zJAuF&<So4yZbj4yX&Y;ntVO
z2q2t*_KbUjogom2YsDs@dv4S9*t|Rm6ksz)o?Z<xF)=|<_;_Bm?%K0&^CDpV!ZO$f
zz<TbVwJuPL1>8_Vx-OgvId<&W+bZ|AEv$V7^hzIKRQO9j)9#X205DushQy18v==_@
zoyaiPpZ|%4uWd)-gXgQ-Vj1J`#UeBs9riy9{9U4+)(`iC)XfAjZRVh3jaS$@i&l~7
z3g}E)et!NcpbblJBHP~d&_eAE!1fo|4HC2pDnb-AfA;vU!yupbo*vGm&=<=yf|@`e
zp#k_k+M@@2T6esFAY~ljT=4g>*d{k5Crr6xvrt2nPxvvQVnG+P+yJ`kt$VrmnqyW}
zrn-OADx*&yx7jY0m{kqPi*uUmoenBB+zbUggWH{<(vj62{C--m-ClkB@bPoMhR3mR
zeJuoQr3SERiu&~Q^o9A!XPWALppaX)-crgkH8nLMyn-Nf1ps-|5B42q#SWD^_8|dw
zy`7nvDUym#k}_oOFHZ1a7XY52EL9p`;YJR#$;=F8-6{lvA$skr;xd3hI!hOomX?O+
z(~YnHuXPmwg+Mcz+%$>4#iz14>qVR<AV9Fj&LF}&XTq9F-X;=n)K_HZQU3>Vo%jQ|
zk%YjJYEQ2IHuhu#s`BH>5vkTB^zYxl6;X~?XMh2TK06XZ#026tYWnoxUW}QZK8tjf
z_n%hg1~a+MHw0Ww=^${ImMIXHP`So&)icdA6W|R{Wq?&~8xOc`EvL09H_uo1ai3EJ
zm?@E-q^%phauN3Z#R(2dHymVZNGNyJ5CvinYf5i65d)}4nR5of#0dJoG5*9Eb{j$k
z!KMG|kO82lx{Dv4qy*^SWdoY#7fE-n@iGg8$jQmE6!FXQB@21IhPFwa7bae3ip;NP
z<@GLZZZP~3#C2J6Y%%~$+7ti?b4V3#`h+9D>+eDfo2##~E}pH6P0%Y3p^-@dYZ<mZ
z4?vwtUKC>n7eO1M+d`s&zVeO|`Smyd9ECVF1l@9i9S{PXPM;oo-gdGraoYnhS^gM+
z52Z~_O<kSo$t1uR#u_1IzjBNn6No|)e*oMPlZJ?Cg+{L7fk3IyyDx_Z;4`i#ia3kh
zW|L<+YLy+K{(Jr|)e58DeT}v}Q~PDJ1)%Aks1?Bd3pPz>Y6Ol$pT5pDB73YAan-o+
zs$tn)JU^F(<wQK|esg}(0OAX%XG8#dkX|@Yq-|$Oc~=a0gby#6Xjm*a^hgAvY7rnL
zy|p-w&264SnOrv+cq9zHcpEU5jo7FMEp|*bC?ZSHRNoMs*2u~Z$LR5a!fk<wOyY`o
z%%4vf8GuTM^#ClOpCjV~gg(z<Fc@YV@a*hEVp=Ot@~BiU=K-uW47EbXkcT>L9JvlX
z8GXTTs3F1lth~JZD|AbX4q<QBcnqTl3wY1n+6x||`y?&`{p0J&(PcY8d`GIM`52+<
zcYfquMM0ycQ}&RSxmOn!SQR>xLjj(m+|ko>7|`AKTLAA(G)8CTu_hArShGLDvFo=A
ze+_n~$R8{;%M5=kogK@X%hg)ji??`hg-J6-D0`a75w4!z8ypK?0oG9i>g25EGcZL1
zu&rfj*gdgFZ~fN;@7JlRL)tTM+#jz`A6|AENnT$Kw(?>D>~@6~QPaE{45};v_O{$i
zW75A{k^Xo>j1xtKJ;D)5;q+1exgT%8z)_(D*NDTf&;S<?Y+nEU9E?K8i$~8=q1b4S
zCt&<_^c#v?Rt&@tMPYbb`!JnfaeKD<9H9V+TTsb3WM4<JtcFW-I2|mNBcu#<!CK_%
z6>^)^)zMKji!UMcJ(I#$JJsLaz)G9t=rpDg!FvIta!`T*Hu&u+P`fL8l}4aI1_0u;
z3{VAX0yi?!eVT*w_~s{4DL*QnFq_d#DcE#ieknlHVpwy9YyLovMTX!4Gm8gu2!uNQ
zHdHOpqs^tsHVr=@gnGhtc_{P|sq3a;GmC?TeetLjK(9G{v;FU!Q`6EYMqUT*y-U!d
zZaz9F%v>(k9%(HC2oG`Jq2qtvNWTP7eGvdjP0y{(jSF<Wunv^WwVleiUXZ7`lnZu2
z7hQ(mUWwO+0-g8{q=jTf`6w-$%Uv%4@IVrobmDXB&msngOZ&oR_7~?GzYkEJ%Si;n
zD?uP<vLDEteFnmzAql+u0b^jCB?Fq~3c{JeqlD0WI)t)09y(}7_v*_*7+<>dDHUKa
zWA5Y|YQP{Y0s85}`7gSQrCLloK~^2CR|j85)NVedSVx!UV$RL6{wvJ+x)<NKHw;+Q
zGRIx3(Tv`A_~dKjfoB6{(Rb%8d{S&JEiF$Py>od1aDFSm*b<!^Lp<5sG1Z~h4i3E#
z(O4J+hDR8;+$sN{z#=!62|#LJc$c4Yth+N1kBkFQOJe}UA3mfJI{O1IqJDQ)i*@ly
z_14xJt}h2r*>5s3GVvOIL-Qj*ved}j-2B>?$A^Wt0d9<e27THqe?37|(+?1_E8suE
zzI?dxF8oZ*jd$0d0}ZO>1{69<P?6+g75<k9ydG9SR3rz4>h%HSR_veLJ_YmU7NA3s
zN;85bfU(vFO?=KdD6BLewTNAdF5qfl12-YWrA1{hHe!vWu?B!Dv>s5FJ>^Z*;%4AQ
z&O4_a2$ea*&y`yvi=5MoR?N60As&cJ%Gm%3$mRyjIKUmOR#ZjtY0p<;vbW9az~Ndz
z-Y*vkWS_P}dNnCJk-*D*WyOaNcR0?0*sypQ>(tqXSh<uY4jQgEzqh5hQ_!f$MPLcw
zdhLytjw)8VIIc}y2$^d4lbPEBa-(GcIrn6?a$yxY_dYFvM#L<B*>O(Xz~DI-!n0={
zm;>WYaY()aJVy@Vr=Ol2?#R@^Jv9PS5>|`cl!UN{zVKIIwiO)@Qj2pPNU?`DP66be
z-3BBcsc2`rL~-4tCuLHf37B7;AOH>kdBe(TxsX<Y+bSJ2*QV#9)PjG1U7P96)@B2a
zcR@XHyhUoL+?5Dmr6jj|u0VDJM;mKojLDZb{5J7+1bh`dd}X#DMvkVHNu?^g#kK<>
zm&i2WUwjGWoXJ!1qRZca#Kc8<!(@WVos<=uF6zMhu0Vm)rpLpH{us_Q(0P4yZEY=7
z`sn^^l1V@^gPX@YpXM4qGNv&e`EHGiels&OsTu)ekr04^>PJr{8G2<$Q;W`$V!;-Y
z{(HiYef#!pLL0~%C8Vo**PCKWIRMzx$y-d!qJSB)|C%u82O5(6Ng2ChMFe6N@oIo5
z3IR|a@Hb!lK_ELZbODI)r#`$(U~ML7FT6o~+{OF)+l(?m{vGP?j=bE%%QwF|5f2u}
zaH$se`Z^#X^t^eY$uA_HPfR@;SZGJj90ybki37q`Fgc9i+NS7KJ6m}eAkmp=Y4$?^
zFP{b?XU_UU^Wv7Yx&@FI8BF)i*_Z}&ew-T-SU3Yw<OH`(4mDmqcp&o@yA80UH$X0N
za(P(i(yDdQPce&P$~%B}JeGcR`K>=U%x&_Z^}Mzze`;#X+)PbLndYYAz5{HL+#Ip!
z9e?OV^`O5qfM$-`fR{pY?YR?B^z|8ljURrASX2!z<C%UNFU}&YFHI)?acWn-0SFS8
zD5@D^mp?c7Z?fd;UIe`W&P9=;qwfTPEhmym?GV(nY7#KXG|<n@&DoVY^v43}MSK2}
zYG)=sZY}u!`gji+JCpAHI15l;3+xt)7U<cmd#=GeNv>Q>qC7VwB;@E;|G~IVqB}b-
zt$;u;S2QOxvl%fzTs2q?Oc?+>gUq<ADnKCVGQhmOfy_!gknMwCRxb<zP8#69x4Z$4
zAe5b@<=^|`zfin(W@bK&a%uKIrTs5p<EG_VhlhW2KUY2nQmPVrU;N)c^TM4U8kSK@
z`f2}Z^X82+K5h^UWK`533KR|l!qeeEz}4^qtsep4xU$X-7YjS7b_ZA#MBG_~ZXoxU
zfz*+AGOPRz-Bi?L9+%T`q#u)s`mydn;voAjM#_1p!fpGvnFdMiERnv`afxF*c}Y+D
zQA3aedo73hf&Cj!#|#`nOc3ColvB~=hkfNWWMzpxMQT7c(|4pmB?WNV-QmtVHya(>
zbw~Pk!s=h2_o(ce9f<4PQyIVR$9UY?M}qDCvA9_a?NU$iY(BAbG5QgJGo8!f^&)Yu
zgYp$EM^j&KCmP9KyA1CgZG4_qI)Hv8)7jJGsueP2RPc*?OSLiz*h0}P39K`cy9PLf
zi$wpAO6B>NM`8O6<gJd_T@(fW68>!LOq<OYGqc`$?9>FzR~ldA?m^lqua5kq8r%;j
zF8%jut{na!kxAdnQoDcG0{mYxB;38*Td!o#8d^O<2c`aF0zl_{q(IqXhh;d915Z&~
z(sKrojRcMeumwoXYyc;tm?Bs-rM_b=05CZHWnf>PidskuHO>l!L#Up**C8ysfo~`;
z&)P7qpn&qX#7tbd4l?;fhp6)Ue8Ed}qKvLo?7usADxXV$Ts6&;)rOW9D`ZHg=E5Ov
zPQ=eD@3KTXO}oxJ40xZP&XS$GklvX8pV{3!W1xLecp2>P^$2}PyipTm;4g@n=gfUR
zNVJO;KZGHzD_N9`=E#_ZOzGbkm`)G)w=VQ^Q0Ppyynst8x>?376vC)Si<A-sr0JC5
zH7r_HnlwAij5EC{@=v=##R@Y5y)DV26<{W0tpTlFMo5D+DS2(XqMEuCOy9^0ypC0W
zbNajOnK`4qj0fLZhg1iFmF@NxGXYO@`-LEGM$bI{nf(Vz)cj@OJ$^ocpud4;k1VlV
z=%R564#a53?S-<=g9eFd=}0XjUS1dR%94%CRyB7uB%kl=GQF^~1OOE74(wX1csv>Q
z$U=)^^szwPXKON&BsDE0vuK;1)b%!Uzd%qN8X?U&Zs7Q_ytGJK%oUd!!pvjuvP%uX
z^E=lYk8}RnLO%QwSnAR;jT&{T@G}~nPb$xtT#Zgt!)Zeov%Q*8OBq}sLY2Q-JHH$L
zn$V1~UJw_I+iLp_aI2EV8q%R1xmZFR9-d=(X+y!2>BiKlNKTRAN$)ql?wyMNQHnTY
z1CSQu^hIc5iVaD|>@7Mt0g0QvkqXmFtQfc}{2$?Z2=1EgW*ZFf577!xttr61{wt8f
z(qgiz7?`}gS>skNR-2zdE2VL^&69f>25)x@8Sip_EOkIP=s3@fXL%B-j?;Pf^#yqp
zemzY4op{MpFVFVj14$LFQ%?@vkKf~P{jA^_*!ynFdzYQH(2!W9js?>3p>&V=&}d)s
zJb~6UREP5`gOk`qY=yj;9(#PP7&-RIMfNaN<0a_~w}~I$%<t00mK5if>z&ibL06Tv
zPi!BgN9NAvp8Od&%oy6~MuAiuRE%PSsTg+<YfTQuIbdUC*T`-pBouik>pz#Aii7HU
z+soSUmy27H%0~#>9K2tNu)Q$!i)j5nd)b&LchBO(d{#gI1H_dUjH8!#9)J4OKmPOP
z(cS;7X87+DJ1aT<H{}5h;I)mnr3sP#+8IXU{dx9;Dm<dttwP*V43mAHRuXUT_Ccvc
zsl2>++=1j1Ho9YG!!Qvc(#Mk_UaO^a7o_QGOBBLjHGx{||9##*TTdVAq+2_x14-K%
z7pbb<(P5juME=~8E&H&FC{7_FaJ{80w_@uiy7ixtlZwk3zlCS;QiN?1%VWz}!0$tt
z6ekzQ_w<o(kQzq9MQJ4>wt-^ZOU*s?jGbE?oOaL7k($fgDE#&sK0-#@kTBoX9O?(^
zg$L*LM-{Thb@Y1!>p9<%hi0($J9n6vf^@NPeyX9ZJzuYey7PY5u;&|Xh8pm>U-jyT
zzXxb5b1(ZiuypkE9Ld)*w<enn9-rAnXs7$FZ<d8$^gT)N%>p}!s(cnzs941ZKM%Nl
zNvWc>ZuT_D;X@dHiThdIj_eRB020tFAHjtUW%S^Yw$65cx~s~1N_|YiZppKqK4nx1
zZCg6{6GVVyFJ)_*n1%F~g&l5#Mqsp^8oh136LcsYQhjZ|%Sy)GIKT2m9ABB?PvG?x
zn~s2lW<2o^gh*d^>%NVi$<G8GAF(bWJBA7z47FQrcNJyB@nF;8u4?#=)j!uXc6Av6
z{e6kQ#E5n!vj;1~h??Yni|)tn8uTc?=6O|ong~U<HU5J0Uc}sAuqqdIHU8X0?V#<w
znCd1atgm&N|7c4q>n+a@kl%=ptsywdKxC*QJA0wLA;g;8>fw&}7a|?}$YWkI8q|KO
zjEJOPJ>#tIPc1f4D%(JAD=pXUuJ@yFKCQf%TN)b)1rz7(RURjd&BVan>ou5@@bOV4
zZNJzT6PkGU%)O)Y57bMY39<`y^;<ah3PX<!My|k~2keQHSz_96UDaT1eGw*oFWzX@
zaQSgXd^_P9X4knQaOCHTu%F20jeMfRg|ryfrrx>;4(HrAK2?=C`^LF~S^oApY|dMs
zpy{m_-0%O<%30e)RfyvHfF)ZG+b$<&k^N+0B$y=*5d97qJM0B?io?m<s-b#4U}V}T
z=Ma#3+v_d$I2@yn(&t78fA`(t>o*So7h<uj-z|D*mQh&jY7tccf27AwHP_Kq6AX)8
z&Fo?JH=C_CP;2mbTvybM)l}O#ajtsZ)EL<l9i3SMx1rQt_=B7HwN*gfh4;i?DM_>E
zwL#V<Yl4U8ryp}K1(|dmn8gPNgc+}biH)(oHTWLeiVxzr<f!ty_53TD*B5p+t=<f^
zGpNY1u)~xm6ekq5+9D9zlR387YSspo5C0b5R*bQ&@T<4lp{F<J0P`kaW4L>_4)<by
z?bPBBUH{LU|4o?q-v^TapI?zPo{sOcHB<o16ew|cX#ml{w#sF#w%Gh8v(^Rf*^ehB
z{&IEYFCBMA)J2<yjol{haI`lGpy=}nB{|Nn4%n(DSY2$=;hpm@%s|+t05=XvonT(E
zWwF{rT`Q)Qhg)+W1+xCzprX8{gl%hRpIF+e(hw23%8M5+j<8=V<#V9#+j+ou$vEhX
z=fyR}izVhITj81PZ7hpIMN!l79VMeXYXv|4rLNF)??QmdYAbaMf1?L#wa&(e&8)3e
zh->KgmgKl#>sh^WYa4I8h!0*Y?Roxy0>%YCY_$8*L<MKi2eCqk=8<B~niA0KxC>NF
ziL4qMKIa6pUDd`Pj0oPGoS7v2zFcC~v(*zmlrOR?&VPSiTH7^zuD$Yy*oG0e9Px_{
zNZ-|GQJj402EMW6CviXiyi{M*p9}ns2jXq?v9qUzW<OBdjsm;Y6*@VY{u3BGj%l;F
z0l8vnn^di#DYZ)tWOAnz#G1y#O4jT%4KrY6qXYA_^*rhk{GMXj3kUC2Fe^8zoJu5O
zOITmNJ3{{aq}mtvy*NiG*1W@sMXbmM*37Eg;Z=LtG03eVWyK~SLN9jK*?1}z{d<**
zIgPVdz;ly*tYlI&a|X92A{M?3k&38oX<hA4T&vQLUC1Yb;>1qM$F&C5d|dIvTNkI<
zl8^f=)Xh8N-PG7mx66v#G}v0o#cAhV&JXC6__~Ju+~r3T-pA*J6GFm{fNpA*=}-Mq
z7Y(1~;uwuz@6KB8YTfR*_bTC^Lt|E3s5HD^G12Ug=1{&Op0}9S-p~%`f(U*qwll^q
z52`*-h-knsjsKaBTr2MlsF!1%$5%-q%Ig-0J$6?svdOret^Q17_q{)a)!>s0NqE;1
z%%KG_;$7tIcJWqY)0Zc?5sJj>*&P!Gg?r6QtQS+s#o)ktv9|P{`;-z=O?2Dl=wz|q
z%gujCm%~x(@~f?t(dJ=vYAyBH1Gu4m-`#rMHjxr=(U8S(mn5l(1!nBjd$Y&kW!X7(
z`$a$IZmd*syd8yqAs31*V_jd(#E03vEyS-ZP+kA)S~~WPayis0draYpxK=*v9kmOu
zomnOd#bosC^!Hp2o-c1LkoJs%8|`Qy+HYf6L1AAyns)&FW@yPVgWR8Y!x%C>%$U}}
zU*kCYh!Vci06T1C(4rT<uvoyZ&}_$kzN>hG5J9VPR4Kf727NK+<K}oovHpj0d(vAL
zf!axJp{5lJl)xzNgub?&XIBcJ(2j%Jld7X{)!=(8?$$rs@b$Kjs7NTyu|Wj$GGfY`
z2){P<q}FzR8Sdl%(%zegCAGGF!&bZ8?K1D`-kF(tSD3kT$XRosVV9Aqr6JB#rsja=
zfTk$8w+*}Pw8J5X6wA~C1c$&BkwytktrRB|YCB+vQYw%j@-1=i=e?ir`QGFE=l$z>
zpY;dF!CLFO)^(lNIsVRbUF(%imQ~NWE^h~AbR)Fu_+3~JaruF42}p*IR5(ZHwye^I
zo&DnxOzqI74Y#<xQPWvX=XH{TyG>BT|5S}J)6!cljdHNGHW7W4NB=cBX((m2nVGe>
znxah*&MxK9+1(CvC0qHc^Zya(kB5GktZF{!B5e9=O$TkWDQi>G#E+8G&WW*ssk32(
zu}Lw`8<yRtG9qf5Y=9n*v*Y<<5$R)dB&?@oKtx_ndx%!`<WpBq{rK(iQN_(49i`Pr
z)5v#HREu$8V;1nd<g=KR^*suHg=FvXmD|(P-uSFpa8nmo9lSQ%a5PPDimvLV?z{FM
zDsp%AmBPM^6dp|*|M$}jn@5@f&*SBqf|%UCO{&{nF932480&VRmzx=~(S2go9G`95
zc`$u>;Hcs{FqWrB6_fuy7A0W37X^B>FK=_b8IE?C*Z=7m=bVzSF3SX(gy1GiNs$1;
z_E#S$ho*%*3{pMNXRXf3d($lTXLk7fJg-C_PI5kvAT9eNM-k=nTtSnIP(}T8gG2p4
zOyRTHx&*D5c*S+WY!`K6&?a@cQy`fSQ_*GcR+jb=SfH=M6L3mf1s`UzniJ5Xe1R6>
z;K`MJ-~bvK9Gcv*`?x+q&l0?)Hm3-#Oyp=*tu|(!`SFNmV97fbj&-3+8?{MQGOzXs
z@Wob+Ge58H|Mm=2J-kkKUC_=J<1@U!jAYh?t6tAeXWJyIDJt2U@Oeq|$$*9gj%Uu7
z-kR8zpQTeqHF1BBk+N2{pWUm@hZD*@>5P`A!xkU-s%sa_%mH?Ib5v0-aAz0n>r$bX
z&w{QHP3+wD`xr%E{u-Lj!F1=?vzWMt_N!fHuCR0X*_d77-IUokp{Ph+LXe7PLMX8G
zaEC{lI>41Hfyk5MV!;EzXJYcjOajW7WW6%R^aAaJ>FA+~w%$|mvj?{jxpv@kMqcbR
z0Yh9S9S(Pp#`w|OQXpe#yp_Av_FH=YvA8TCPl_|~$4{0d!LQOn&B7h&vN!JSii=1Q
zf>Z`eODK)he2k8NqO!kg`z<C<58r|+&j!}WjvzC)^0}oNO<ms@4m;yV1sgNw8}Y{p
zrBK_L9aG?>tIBXdjY?Eu(V-TNeO>eT#eiGgJu!Y|{%MUVG04xR-SO?G6vlb_+I=|A
z^CI;vf&pmm$mDVen{2xYFS2DM@&xOHtyX)uQNMOdeqJ8Km%Z<zmiu%@TRbuY8qqWb
zJT5E1BPvo&(YsNFDW@Auh9vgtY?z-;JT&Q@A@g!5o?bedHa$A4>gmFdS-`q|xidMW
zl~&y{(dtZXx#|$SWmlh0lEnZZn+tjp%C@lcVK&{G#_snpj-l6^)zk(ZoSw)g5?hZ1
z!>RQtr^S;eY}Y<}vE(C0AuzVTq>UB|Zm{8i75DA|B=^UCU5#e40qTZ#H84zLnYk!J
za?69gX6D<p{Zhu~&dP+QNHkui57_A!Z|2vi)k&|VHflwap}D8jH0lZ!V79In5Toc8
zsHM083mVE?u1^_hMZ1ej1Xx6S>%-bL3tf0k;RRCZ76E%{)AUP1BVN4kt+)tqgk4;U
zHmRl4d(lu^g&6+1Vk{S)U`Ckq*H`%96s54cwf|Vt-=e^~FAd3&eO(@bD;MX;frEzS
z@!pHdk0mQ{<Pe*W7m;%a++pLWwrhtbp=^Ecfyf=6yyhV?Xmc&0+}=*f2_GIMWwTK(
zCDY6My4I;Z3z~hWb8m3Z00S`RKGv|Hh1wHMYR|UWf0MgUeJL$(1$>0An=iNcCTuc(
za~>RPN0BK6ypoyZR0xkIkiHVM&j-V~a)fK6i7_eBuW`mwuU81$60ze8w?%duStDSx
zDe64|gcRRK6Gwd8AMhcSSQL&7u34yOmzE&NUmglfySx>8hru_a;c0Q`rSUgu#-;Iu
zTbsKzYs&$Sllh1jLO;!UmE3qQMeH~U_YU++t4(>gG?d-dmRJg_1S-`55gN4y)Cv=Z
zyE2XE;xnoC`YA|pYv_IPJOML{YAPGN+xldvV`AXE%Vv`D+0tNn2Gw493^@?$pR&?i
z&6rXTL=%Ldf&RKhH?|b&V(VW|bAmxV$_ET>I3<_hQcTHuahNw8{yd&<>;Gfg%g}?X
zJIobweGXQ{aQf92?+MD;)q&S$HM*z|9<_Lg%kRj1Lt$2^_aEbdARg>f=2;e?>&Q?}
zgx;U8DM@o;)c@hGQXi6M0P|hNjJZ%EKgF~8>SEoKf{>W{DgX^o4Q`O1MOn`*Qy1HF
zMmR%l-FGzv`nw;mB4mXtZ8bKCCusIes@aH&x);m<qn5_12x9!o-?F<;E9?JdSOyWZ
z2ret$c2{56f3i7U88SV<=6uSKiT~Z^pxG7ZMC9PdRF{Jl9<_Q7JNK=^O@Olh@d(_<
z2^pjhO{+K@q8@B}UX$3Z8{p8`a_v7%3*dY~JtC%^yKPC`gf>52i=Fd@f;qvDh&10%
z96VUNe$@^aJ}X+Y0A-gE_?jeLMpqGyYJH7#n9$-+34i6RqJq!b;0^DfIs_M(A=HD=
z)?8-J3*X_0^K^<^UPT<`MH<_?<T&<rKJ#do<OY{K<lG}kQTcX0>#BdKxCMX~4L{&k
zO@)DlWXCGnd3II%s1?2O@xMBP_p^QBsK4{#s^w8p7ll{26oqB|LxuOI8(jFyD(yE1
ztRXID;Ds;)@9)hxuj|Z^WC`xwk*P*|0Rj+*5piP-Q<;(|ek9PsgGp`9o+_wC&G?rS
zi0Ae7DGB09!BYV}TkEE3-Ri#E(X75uD2SbxJruNU60TqADgBO#%-}k1XP?yHXLZT5
zqTjDsXg{h5-ZZUp4AS4598TwA&g!?+nW~<>bM(QwDb5x_{EmKA7iyLMm(|iT1ni7?
zjL%tp)jek}ygW7U_`r{`X{;a0JfHM<*6}GiMEvW&I~8EEh})BFU;|C6Z8QI0LyvwQ
zdrrI{7-l<Y4X$qJw|!^i`h~|`)PcbtVctBVDT$i3@Rz@A0jxhxFI<HIs7bbFj{K^c
zYI<7HYKABs^1Vv=s})`U`$U#_+Y65O^vRM)7q0c!RT0QqiFIb!fX<@q^HLvCO(E;e
zUn_X#67k1((n#vD#YA?@W)h<^B~aWP;VM?Jo$$2H?T#p*76~|3-7_$BfV`Z7UKRmi
zl38?^(2}qnf7g1`1O0P~aOCnSx^Si+Y6z=2?*h|;>XV(Jh@cB_)#DCY077*E>5jZ6
z{uKIruDvWv87CfNqwxA9=RxEAwuFahiMRC8@`IMy#90MQ{04Z}Xjl~#Ka1WKsX3*7
zp4mNpt7-nrVnpd=aO92v<;W?zaj7j-X#-B29AZy%(BA@(0b;!1M)ID0E03(xTA^#t
zW`QQ)nrdnaaHOC^?hdXKrtIZ`Lz73@WK96h<2)GO&UVA&YPS^eP(EUbplXb&^2863
z0F}lIKMQaEl~3B$m$duwU3%A1cIF0tOgL3KZ+0GYUU3O|UOfD~2pmbBS@qsO0P&n2
z>7jGN`W{`YN96E=FKq%6d(XTqlI<a(qb|a|%gzNJ0)wAWbOu!miMkDV$ab{DX+b=S
ztOjtfT{egvR@7H~dj&cb`FcxxAk>K@qb)9`mDCmnX*5Mr#mt@HIy=O2tO59u+J%98
z?}h5ck?%v;jrbnnpWjfnoaIQ9mev$^F)MI4rbp1$OKI#Q5dl*W^*$SL=(rIpVJ||X
zLWyrVC(e$%Hu)x0z8gMMuut63v?$7um8_j;0yLx^qd+(1Orw@t&gbzv{B;+oCweI>
z7-C>hS#xHv@?i`2I^CGo#om_`D^x~El4z6&2DQx&kaC~<fX@Q+?s=3GLxq2>At-(6
z^+TJ)RK-1TkOGMmE)Clf7}_i31QhR$fkTqg3|Ze-|DpTL^GeTJ{fi`ZYxy$fGT?uF
zi&pn`Qg|C8zuYume#3QWRS>kqO*mSew*-BC9ijXy@S4n*m;K$Q7euK%n7kBon2|UZ
zDSpCKo@MT)BnBP*IvZ~)Brh-bPSAmY)wuxPB$*fuFB^z5<5!l_tzzN<>~}l6P*BZA
z$Y*PJ1G0LB8WC>NLaap{iD|i6U&?2qYiD=tfFmU9GR#LBeAUw2l(lNYYI)hUR2Ly5
zaqUzmX*dC?M!zIw@HJZc&D8_vpQXK;bn_=|selRFz_A&mq_X3r<d4pm6zFN-<Qib_
z-$UMNfsN1}TT&2wGzyJigv!-<(ds>^_!j2U5Jeyq)J%$Hp(rQOn-=O#DX+Z2XoaEE
zovcIE0+C0NnW1J{sn;6QGMWN^r976&0WZwIv^Fc~VDylbw4_+7aDGrGDsx)W{iNJY
zSZ!*QrF&$+_$->U^sPD6|HN)?lz8?v+n~1gUGmiNfvd`K0o|b)v+%>DN+<d}Qjn70
z=nZ`^zPcPIt_oeV;HQC0dhHSPQlY=IPgK;fidH#Uzy9G$8;4AtKB{~TbTXSEE29ml
zw8b+)(NF2lloog+y&~ouV7JkO7vA1xubG$i&d?54!ma)c5RuX0LF5T7Th${`ztpdr
z3I^cJB+iZcCUTGsSp=m=j|WLk0BNni5C(P;>WDrs3Oaizwe5=G7(6g7EeOmQa{Xa7
zjBo*L27p=qy7*{uu@nHWa!59_ABzci!=CBwgx-^aRfAvv5I>DW-SCEgUP^3h-PA+B
zc$OFT?*>;>yDt}SGxKLWPQNc{=f8Uje>o8id3;lyKCs<?D0P70bv6q+lj3tSPEM_A
zXagV=`X49aKRFSA^l3is@0xaPYEDWP7u<1P^<4lRxOOTNui%SY$~072q6>(fk(yV`
zwyOSoqyfu{Nj&SnS_Jd#%9;h84dSi)x>i+gP!yog*cmd@|8!!MJq^Tn4L_{H*Ihus
zNjhAa;);wy28k=#H@EUtmiL00h4+yqAU-SQ^9O3Veh7Sbt+Oqe%+lT`R;{9O$G-rk
zS^Y-9vtL(0lA2+R!@vNnsbtj;Anpl{UfJRlIFt;)VCXx4a8&&h?px6{JO9IS)3zT`
zMgi*T+UMW?LNzpp%G6|L9NCNKy6fK^i&$xuZXFGT%2dk1J*vP~O<o6xp(=V)Bi{Sl
zzW*14|6fi-`6R3p_9=<|J=0ode~D_raIczdJYqhPMCRQv=fp4TNr3&IFH)4PI{=Ur
zI8X&n8Wmvc4@HDp&gjXNGipAj)Q?46iuB{qC%sX_1^Pi2qeVSlb@!Qwq_tC;Gn4U=
zY(A$YyS@C|heVRExk~hnYezR?V=Plv-7R4}@p6!p4#AB4rC6lj2fs1^pHxk$?kbQB
zNu#&8HzMrH&y}%B>MR%)DbJbC<pDXK$A>FL+`N>e(Uj${>04K|Qelx=N{S)02am2w
zL5n-t9x%RH!{Z-iF9pj$P$^gmDuKiGB#^)A#KUWW;@#?&698f$Em=>4nUE-flJQU{
zGGJiz5!4@KWBcB!6oRq&ngu~J2KiE;tVKsIf7QaHVETa9Li=+cTa;xr5MQ$X-Q8R(
z)q2-GY#;2s8`xAYn-Ku8V9ELqe87Vkd$wMp>gv$pH4B&S!OA{F86>ca*<l2Z34YJY
zn9}PvR%nHJiJ4vAM=^CR?#M*(UH1J=LKR|l;h$@#UdyAzFWLL>T|h?144Tv_Rq-dV
zsfXR&my?3^08)b?=RD$`uZ3S;#qm+c)aU_i0i*S@p-?m9)!b?%0$BLeToZOD!PdLR
z<f&=_(nt5F2L<u2>^P=cIAKB5D!)Dh{S6Bp`x_SepA#!<b>@Fh4^`4*DqT<{z%JH1
z8J?HcMtjwgoqZJ3OZ!%uJ_|akMo-Ckyi9CSejFZRXAm}R7|??|1($~DJ1W0%1q*@)
zw^LM$wKLnq&YMZWdq_rspjBA}XUr}w?Ew<pjtPF^+k!9kI;*#pssTChV5IQl(w|h)
zCFhEiWaKIFgXcvx&?Mrlr&G*T#WzH^ith`tfOOQHZ_>ZVYdqX=xMk?~$!vBR9*mdZ
zau@FlZnGT-P0-xp+~(oqrM39gxK5P}N1X*zWOpMvmzL7l<gRG~*tzm>n8SQ)r6_2R
zV(f1y>#*6Ld)!<N0fXDE81Tk8+bMYfz(f>WoR^h%QD=QLT+V|(*n#VwzF!(^`FDY}
z)d66xZUq?;kAChxDI2e7TZ~S65{L12x;AbfV?LLvq8xVX5f4$((X`izvoXUf*<O;Q
z>}Xov=<Jn=<%Yvg{lG5*Y{~9;XNMO7s{6NX2kz&vV}UHSzZ9c4JlL=Gk7yA(165jz
zxDorzM`QNarnkA~jx{M!jbxRW)oIQH8Tz!uv-(eO#n(noONIU@JC%q$dq@QEA9aF%
z&-56+CnoIeOAckmM55n^8+zHc@-(&m2EYw8psrxLl&VmZ8PMzo?qtYDoWfVWK%;gc
zVnt7))Wv1et0WywLDK9e)gaaK8rG(y;I2?LT-aT}C7Ol+!+Qz9YQUlDv0dQ3uS@(@
za1QW_{xV;OHm+IVP84KDtM0l08(OC_VgDa--hWa1|8ru6X8$V?{+k}JZl17x_Pg%k
zM`nS4K>!2KfDAd1A7>CY5lq;(@H|qE-}Bu-<XyaK)m>cyF)lw_1_L6zk)>f1LJbs7
z(0f|(7c&60M!K5#^0qv=97cW=bWf!6MCx+?k?1aZCb*F?Z{}TN3)Dx93ihxE_)_%!
zl#n(Ra`DXh$UA#FjUs;i3p{cq7hig#8;%e13uL4yQr{{{YHv#DDp{69sLEu%SZ2t!
zvR`ww<9)?mY)gVAK@NjK!Ei)*%k#iG(&6UVV_z#>id2eyaMSk{HA$sM6)_IlO;srY
zl*dDxHt7cho>P2S;_fYD8$0UvYGygEnz75@z3ZiYs#unoW+}S_9|h;SB-h&>$k;n{
zjxlJmI}wWS;{yppKTXAC9a2e1-7BC0=gEt&{&*Ldiw)=3v90WhF6yMu`R^6jMf02d
z^SKDBzRe2#0Wo_(`<xbXR>a8ciF&N(M^8jA4+_HfNmL5$ko}s4g>Icb7a%6@0t!?(
zlY9nLfo=<1Y5OebSdl$lxwCHo{;3fLO(J&}<G_z&B^OoNs^}h|;6#Am{Zyw1eh#pF
zeReY|t<;EHfIgkA9(nM(>`eicoOZEiHAC*ByGq6a!Si`phf@*XpABSHMX@fbW`rYv
zqhP{5c(P=gQ9{pJt<d;9^cTYNUxAgs`RPB9mj5}iLdXA|z>QM<Y{@*zn^j*j@uV7E
znv4Sc&7h{nUsUwZy!<y5_+JX}E^1DFE;}@rioT+{Q~2WwzgE<mt%e6amc;51m}C9$
zd4%?-Yr@oS5u7do<Ywo$G6BAQ(U!|ijKZUndKlN$gg+ew3e%W`E|eYfaj;dxvFS<X
zU_20T*2!jrYT-Z)R0F(Gxm*L(U!+rx0sN2)YbqK5pAV$onhzWj#C-mmFL(^-XkjR!
z!fa>w3Y41o(xiOpRgR;$Ix{+$ohlE;2}gJZ4(o_E02gxm`_*JJwxQNQhu+X?W_;)S
zmWa_^vWY&*0`G^qpI7P~rJDs_e}Rk7ep)ePFEeEQf5DL#3N}{MNQaK5)ts<a>4Li9
zl_oJTDQ*CkeTa8--&@t`6+sew<80**JO25XwW(kE77cLZ{bSXo2W-h|P455CiD$|I
zZrQEQL^kq;vfRPo`agc%yl3xszg|C5q;s_T`@L2f|A^Z7`w>FLZ;Bsk_mVefueJD1
z^Vjs>M<1oP?#+02%rfI&R`2#+v^oL;fxg>({8ailc6^eHlI24APSf}9;fJA=q-<Qz
zd|!NGS)*h)QW*hd<9WUmKwXsmv5JLPY~Qw;qf9skya5Uv>F%di=kRXh##J6Tyk|B3
zJ7Nf0MLegjf44fj|0bQhD)+TbCmPsWu+Bje4ej&C)gh!)4bCqURm*+m=d~%ae(Z=n
zYhB(_6qciLKu(5mvEzXd=R4z9`L!w=aOL)Q3kgmj7S3?-&drG!4o!i^Zpw2sVwDDm
zs%TZGMqJ~h8wm+27)N}@rM6kIno__?aj95agXA<vmCx~P?H_OM<?sy`s|3roSVwyn
z0=v6x&Te<!_)~6AAf1Q9j@x>Lf@7}zjMYMNFR2!5BjiYu=p>}vZxrL*VLYW}xR{Dn
zG|RBNrs+J)9+oR=0vDuEG-2<(bBkBaW_Zt<Q92XKA)lHPf^MB8j$OrSfs!F&8oaD7
zaHUJNGHm)RHv>PzclR0CBwNkE97>YBZCP{hqYYbPyPr7CNfXQrW8CLx;e7oawo{5$
z^d_WR*Tk(Yd?ruv*w;1~Z`v}4ua`JHZEK6GmvtA_pT^SYd@tX~dXxFtKi8=<xzavq
zT4>dBWD4uM`WBHKTsnHNuYUh+wuiI8X{hno^OkYBdOKIThOy8l9Bn*YFPp6zV^F~h
zFr~R%Lh3%)FO}x%6U-cyxsBAYn9SPZm|LcdRAE|A$|oG~?TNM>{+7Hg@YvUb@^Uc~
zQy3`sKHv=tvX`{MWqFJ@q<uM8>aFJySf4UQ4f!o;Uv3oecjn74UzwZX%bBncsdW(V
zQsg8@2E6Mm7-6I!x-p}?rooa$D*Eb@`MONEwJj)hY(QfZ-66z2JC=>R+^&Bodv+EQ
zJsMXAzS7)A3b-@g3bJ*QfA|VMWc;*oSD{1Ecqas%a3fg!dTS?F)_IBjM5x=dl~5fb
zN|_D0#I6&Hdlm&%fs_z#+QsV)@H2D!_(63I^}8tW(WQfM8WGrulXfl`NjTWHICFpf
zU0_R@mX|;BS{|*Ozr#}}FHwE0jZx%-W-@meqByCtT~I?>>VCb_VfHS9y8hE|SkT>s
zzI)9!5yZXT#NXM)?G_HoKgOSMCAxX3p})4cL*hbX<Yf>&$D@Ijg9YO*UfZS+J|6QA
zCmrp6BB0(gUGq{A*XIUK#q~y)SV&D><DP#%k$WbtF=yC9Br#kR0RK+KU2*{)4n7sP
z{QZQ~pgDQv-`$#}mvxWnMj`}Z>UR)a`~FfnsA=jEzzZGuJ_yEz1Te}|#{Ma%UK3<*
z+Z9Z4sDjsCB9czWxtA~_V5f=RVSbA0?2TJ4S&!TYg@rxwp)6|0+}VBlGtABKR&CV+
z0h-1Bl#jMPO+;G}?1y5V47}aQi<CkO0B|O+LF}^jKC&*?Ejg~%aPj=<xC^^cd8w`V
z=>flRRY{%Nrs+5NzP>uQ$2<(}lV2TnKRtIFpR>-WF-|2hll?!TClEVk%f>S}XlVfm
zQ#ua!G<E+rnok~^fy6~&b@n&AVqGQ$@U7@0SZTt*s!iCz%JA$z<d6H9c!2e?X7s8`
zwkCN<tfS$?uQrvSa!pY3QS7uEgQdDMR{tF{O6cZ0uviXphcwT9&i3lhY}?Bk@FIPm
z;*HH+K<gDjTvY>7-T&Y0XUFg`QAh3I$0CreTO0?J1c?)Yn()i$Mi4yaY)`Ji;#3jh
zS}0a4r&(l0`H?V80@dtaECxwW#d%`skha3{UI@2vycS}r%NVkJe}b53xOf``u%;Ab
zxPJ)uLBuT|N7Ch)mrPi(kZ9btN`USA={#Whg0Pm@R(lpIFTnhg!ln9^;0Z`7995=b
zllCu8gS5`1to7=H5?iKf8v7H8EweR^eXpBm+>&(}u2`B_;A~z+PP3h`|JIg2AU1oF
zxPP=X5p1`Y1;YlAWuqG*ruXW&;Zu9P8Aam-`<{HNgv?qVTl6hH$?$VcHgz^;DX=4Z
zSx`t@{`heSw_rRNLfWmgs(vuzV>17gK8>cF36_$ADN(>LZ|rWkkP9Ui#tOI8ZPP2f
zoc3nMWmmsASVJI4l23SdSNkK{BVHOAt-F0utap0um2o$CqUAUCOx`{4!|n`-o^$ei
z&E$d=CP(No_h_rY*_t(RT#$NkM~w$^E5TIu+3|{lcOb9{$@HHi&d$fm^j#PKV0>Ra
zsu@H*PG@@f@Sh}XB`|Z&@>8(6PZDiL1ugJ0EcS?ww^V(<L-u2!ai?Rnr~z)m8Kigm
zn2Z6x(r+O;w4|+s5MA0j;`L<65q(a_Py%7bwzafov%AafUJ>=GUhlg^;_QctHkkxI
zY<JseXUIE7EGi`<MFc*UM((<_`7-S{cEgrSoE#`jk)W3l8(o?D(a$3ef}N&ba}O*I
z1wTCwW&RR@(%$~gE!bd<Qwl?NW2+#5`dvbviiiX>B?}<mB?=Mv<&NEr6>?7y3y~@!
zV#i-X%1h^<xIApYK2})oOy;*hf{ZtER1pZwM_f8LVblnT{$q~PMgh6hq>Aw*j1x<2
z2rlTRXcH>2spMf%Bt$QN+|MX>c$6@mJhZDOZ`i)Ty{vFN+vxTUhbl*^ykST^2cmr?
zu&9=w*pYrXx2ykpR~QN@nqi|-NzRR5F3yyD8v^S@3{tkNaM*8()~P>7xcPG_2@H@e
z<g=HKSohAHSjmqf#BAlN*RVAuq4Dh_7`M1Y=)eO|1?@LzS$bPAOau1|NydK6>44{t
zo6}{s0_UGtCV7s|tb=}zHKez5lxy$XX98bGXftO6UquuHzpYHO=|Os?kID3)?uDwf
z!=r7#DFdO((Zi20CGEG_=0iXljJ763y5GL$C;4yn{)bUxukg$76co_F?z7Zo@;8U}
z7~o4Yhy58ZzLl&A!TA=*13J`V40|4RC{P{gSGBXY5zsAYMX46z6~0q83DM$H)2Lny
zzTiQzmA8)mNiYuk(C5{630{@2@MyE$R-r!Lm9HY;9cNmy8^Ruf+PqI_kzt)y(d6uH
z1gI`8W~Wpf0cFlswlc}GL3;MrclfN)HHDsc&Hfx93EB>1DktsTiSCai;c@1-4bM;D
zEKch1U)$BEBvE>fpLA@uFgS>|DCHv|I15w*a^~sLMz6%G)B}SDPy_vbhf!{qtl#{k
zamg?nIw0=KGwrT*$11M6BD*tUgOQ#TUi+D+A?`;E0pldtIQ1}@Sng9ma|{`2ju=j*
z`%z=%iEhCZe6ZB7*GHSC<;ES$9?gU>j}xpCYmRqHHmHNZFa+aYtWUnymv6Sg5VrP8
zafF=bAKlqXP=|$C9Z+hrB_2;k3VmhgDWfqnul*kem^eV%N-Q$!a4#Xtj{sEjYpG3A
z$*bR2lta9bX4jO#mV}tF?KOe5ngzwoAixf+yMBoGOMa95Gn|rF5hg4VRI2o+<}`bs
zC{;uZF=yO@LxfyM@AlafdD3ga(o4R~<HkETUhy&IfOobgdTSKdi}o*Gj*;(&L|az~
z%Rvn2C<YG8F7cwd7{f6Ku2@W8I=9;wfw}Kdj5^ODN1o-Q2s1d{<`eS-2=Tyh4%>p+
zHWN<^#!ic=N&}YE|51EU)^-B#Ok7-t`WHTh`(Tmswx^hCeVgspxY&9c3rk`b*$;R#
z{BFdeRtRUCTTqXhUl|+lG2Z$Mpo)vlBj&8^6ppfJ+}|>)lc)&jD0q?=cP_6tFTk)A
zX6<ajD)TaZa<!8yLK_ma8a-gZTghja@3SRke$k!AipvzmP3Ms4Km?}sXc>HzD%u-x
zj&^&0{eu;KwOek}jY#Sk$kfM;<4_FRPplq~ccf&i2x2sL`~-cb*Yi@tPwk&!9;b>;
z(C4rUBg;v^d#~{_ZGS>6d4<<AOtohvwM2FYL{3w#{n$POFfW@qyUGW#+%XtZ*!I)d
z9<^Ol5xP~3S^+&KQ{4RlRDXESgj*7zy-^T!b(N(11Hhqil%D3H!}3csc}V2QmMsTp
zr*h8B%|w;=YKo1d{aIAg5kG4C^rPE=47vqdzrhpt4V)yl5M#=-P|>-kn;X*7kaC&i
zjxk(c$J(3b+4g{5J|5z*lvX^Du4mva-t{gcTAt^4II}c=swSLRUfAW+`cz7z8}+=F
zK1`*^ALMI4X5@8^3oOFtDc7`_GnCj%l!#!d1~4ON`z!(wxQ;>k^xp!fW%j3-T`8?e
zbwX&L*+qy1B_}7MErUGbLOWy^IxpPw3A7ENvf9yMhRsPfVac@y%}22{q46L6m@zR9
zZn$DfKIN-TH~;;*j`DK_w2AaMN+uzV1V&r(UmU34b@|5)^@!Jaf^3on;!BDaFbL(m
zako0N8sKyCLI>Kx!O*DCdW};Av1EURZM2!_A-mE1G%?7x02S3CA70enP0$KUZhweq
z+hI2SH0>p*-mj`2WsySk2AIDcwFD>)nx1Cu3qa|GBfsulCP2%2!XI}hAA%6krvBtq
z$0^AGB<D&E%hEEMs8<$hKK$*OFAu&@L_bu@pR?6=B+R_U3vX*olgZWeDOTCJ6}_?f
z66bd)4A2{pZp1rvk~qKkNG_tW-V*S%b2>w@e+H#>7)P#6>GLToA8&@tVzDDby__%)
ztWNb?Q`#Ya`M~icV)$1;VGck;a7A%noAw9aYSMh1bneNbUn_A;qA}JU!ET>rU90!I
zQIFCQeI-~Vic%b({1BX!Ud5JVe)FTY`EnoLILv?jv_1t;R})6-6gm%6b(2k4)_M0z
z#v7r{mJNfJXZO4H!;7D1+;yo5c=cd;bT4ySvoTpOhV>=0B^Cb@FJZ^)6Kv;9i_A-g
zmF*FKgy_GbY7<NarG|)dj$g-SULKWAE4SDL)U)g&P(b=`LyuMBLe3mX65kuTJ01uf
z)e8MeuCa_D20;sW-|$iw&;ALgy;Zy!S6>H8`TOSb@vSqq82m7r?$;lSvOp8PV};HT
z2CzW#r@)--5=ECBp9ojx)DO%_w-Na->KS0!zp|axu_M=x42?pZ&lX@_44RbDA#(P6
zqjl`2u=p>?qa7b7t_Kx!V9cjcFC^U^e%QO0=N-BEq3f2Lpz@KbPqBidz{lmng<07;
zgrkMXe{fa}ZBcvksHs2WLF=Q)_c=g)-a-ckJVBx3LQWs?sD$el)NaI8Z_G{x_#o(y
zT2W*S>mz8%mC(QsF%LVM^qyRZ9C3yay%9n#pur020TaL*gT)Hl0(HjjulhVx_$#<S
zyU7^7arDsV8{%?q#Zni3PAlhqd$M`<^XFrP;MBUx;Dm4(wfker@sgeks8|XwT%!ZN
z&tJVhQ}P^z_0ed^${3^uv!O!%bE}>idv+|Ew$fz8N4K;K^m*N_{J_HR7Odn@`j)x9
zLEixpExYri)n`i%Pd!(+lHS+{W%#wn${(&sf&2+?Oe;;u^~jTcgX++vmY=U(0sH~A
zgOy!cUn8MN-M4=dr`dggLUN__jPA;Z_Z`%Wu>}TtixBKs2|P8#gDS~pu&%6ImSsri
zcjYrepv%NNfbq;*VHTZNR9%8;FgUW;xrqj4%4l11dv8E40r)vu-79pvgjOpxk{>iF
zV_Kb|!4>XEA`^M-o++R~P%ydPm8Twx63q109P7wHF(G7kQ`1%gRGZfGrxW_lg{-$!
z0AsX-{CxC0b2h^&ITsF0=3jX^w+az}Bj-7sx)l=u9dO-GjMU5CPLQ6LFI~%+<EYm&
zv$$`Y--$ndYzJI3f8^CH((XI8@PX8jlQ{O((-dX0Mu+kwbh&w-US#3$Z|r|{TK$Fq
zykI7}Ff=w=O3abA=j5`hA;D0?3MSpD?Z$l3P2UBAGDuhSNmrlXhIl0yj8Il@oJ!H?
zKLG$SL*zol2-lnFeNpI~IY2m}N7L*bcR~Xy^yQhudVbaQ4$nHmr%$Asuoj_#hHl&^
z&6e)OAn%#p@k^TwFHe7rIv1vs=wx^8I8-_0d+*iYIRIhmqB0!EC}}ZNk|KF7y@A#s
zZcdh(=h$vSyaq_Fy+ZQvS7`50WV9JRXYq;AZn6df(AwMHHBP}?ssnZ--fzPr+66>>
z`Pj{TCkuZ$d#~|x;M*vG&w?tg_mkAiiClFpq>%gb3RCQ)*_s1P;oA-{#p7D7ud)Z*
zIv1?(58KttiX(6bEw5$22pl>*{pY&irUXgLuHD3{)SZviWi?bE%-tMTzh);UpS6L9
zIm;G5&i`e`z1*`v9y+PaIQIcfl=<E~8pWwE@fL<%^yYhGMiZP^n4%ZvhSr%rWqGet
zoxAsXa~Ov)u{*|IR3MmwCwP+WmdDe%_=Yf#t8c?Ux4=z$pVC(%qrtQtWLTzSV$7v{
zAjHt4*}iWF_;_W=1YpPm=f#fr8;M<&fDKE#`9YfJxSxe=u>a^_!3xYvb@b=ui+a6Z
zhFuszi@hM_#dFW6rlPXnmo*^&U;r_q38g7=psT-lGv?i~GH`xF=-A0J+^M+IsQr6L
zxLW<E19?lSo0v0xTJ?gk_v-ZsW<*Inj!NyBS;*h?d1N0;11s#DoM!uVumv%=T2~O!
z(0<=M?i~)OqJEY64`8u+u}c>_WbMqVR5x@bLwGuQ6yt-9Z=}GT;$DooAJ<Yucyz#q
zYz3)sEGN|Raiaq(x0-0_$Bg8fQEjvN9~-hbD-lu7C`|zC1Qe#g2@QyFub!rf46tYh
z#E>VM%TXh<Llv`?P3-utLXI;6Am%bhf{63K{@fQ>2))8d^OU5HTRkVv_FS9N%0o9r
zY5*T#QdkQmlW#G99{jxDJFsoJ%ac<tz~j0zfVq7gT1-xS)FT?mOLdrd$!{%Pnay_z
zocfeACq*#KD65!4CZLh?Q3^kK9fwCn`ISw{?4O<)lK^qoYV-yCngSs#peun87Afq$
z=Tk`j@tGrs_Oh0Wy2Y?f?scFKqmfueo=w7*C)xyQ_2$cF;n#%xn$+(D{|RUW^cKV9
zIIMd0X2%`x-OWEI=>Wn0?A>wvo{6CHImbYK2FtS7n^-;S5q7`U2MOglein?#vcuq|
z*aNg{_u9J$Eu<M#w?_Dk$DFH?7S{h~#B0I5eBW-qt=%N1^HMh1=E*3zFm|L3Xl^1O
zbSj>Wlv`wt<|P7-+(lvelkEgRI4t*Ne!(63DCoy7p^?g}#)vofQ1Fg{a8c<vxGEJP
zdYEt9K{i}GgKetXeA=|+oDTmv2RmXOlAfZ>Cr9b#&Ee)Wt5dT@qQ(II!u{gq{7fUK
z!xNFOYpPRczRp9qcgD?ng%O-1@tERqoM*qKqcv;B)A?+mL&%2iA;_{d%fTy1$*|7z
zZn&~+!vfd5{n!Gtk+aQ>T-T;Lvkdc=dO-T_#fz?SN3q+z3fI~>vDSHpzQ@>39Gs1D
z({>=veT&G5Ma8$zb{PW!`ZORtyJ$L^|7}5~|Kk{0-JrzK{*UoST{eRZQ~m;q`8>5V
zFXgHY=UR_*$jRHiv4BDu$K|Dx@$&5s`E&&KE@&|TnY9j5&VDLxe2--Ko<~Rxya)WS
znjz*vn$<IdAXxRQNr`3jCbhQ(PK`hspzf-3$v9KCH1j)1%fQ}}<qL5x24D))-CuX2
zP)(-HpHL_;AL;m{u*-%dHtMx<m%7hAcgAqqM3z{`CXb=f<2ja*y;HWCkGk4kv1_Y9
zdU<o2WvI7xCV{96Sg*W*g7FiUK-jq$4dDKZ3J}>N?lktLC1R`E+uMPw1CU&{^ra{g
zYeBZIbANz5AdCN47Og;RckYao+@M3a@9TtJAHa*77n*+9xHJk`#y2TmfbMqHnam49
z=r}{e<afq8Yt`m|2J%ToH+2k>1F;xaNSuXJlSkb77`gJH;|`ivD1v0r91NAz5N2SY
zX7`S3v6VtgTD0+@CcVP(oAeoQX~^-(!PiD2BxJ7UYx2q)4DEAYLDO`ONs|I$60+eV
z(ra63&#Ac?{8CB>jO}sHJ6Jw=w@cEBXh(eJ*E9~bqukE+yd<Q|f*%TTu#gzLhsK$e
zwQaAu`x~@*xjj!!0*4Cb5QWL;u6tTdtWeCgXIa1oLGjNqm)E32qTswZC)RTi4>I=|
z)Z+@IY2f4zp{uV}3eRfS8zn;$gAK5FDBQ}HEN>ZJYg~<b*ji}FnhzWLWBt`oY$J+;
zIn;R*81u6S_^>0qOV<^%rL~s#l{f%SQ8CbQK`R=|J2f|(Ka>os2Kj=^#;qZGWnB*;
zh<xpLOhbFdK>0cdI`P!veu+Wy8ElflBAVP4<2Kg`$q^QWTSQXe_o6*1O*ue$k<?*S
z(4gL=sC91d!t=oO&li`Ypl!VM#<p4VqWxWRH_y;ouKh|XNLmwcWIoV{pD?-s37Dy5
zdX-F3hHV8IY(yqw&8mnRG=<JO#8itJTe83Tct^ZcgiSSPNmJwiS2Jn?Do8BQU35Jy
ztmDkmyyvB)P;8BXedw*jXgik3VDWrM$}51Q^o^!PIe)eo@fZ3*JfW_X$Xy%>ezq9U
zIIx^CBpVs}+$_GiC5LezZY(Mp&&^A1gYka}DrJ79MwN5wkfgB1!ERSZ$|W25%Z>Yl
zD@oPMOW!4UI=_k$%DeJGAKh)R_i_4X2fP}rKU+>E&LHGAZbxwW*gnoFE*EuVsM`HZ
z+^2ME&^>J36|9v|t7iL#KJY9+n%iuVGBP9Hnf?>_z#1o3aEaBlEhLT((vQ5F#XGHZ
zE(xIP&zx>P#i=-qvw4W9EIJ9tBS3p@jXK}OzS6mj5=ubH^Xb61AtTo+2W~$3wDEWj
zj%$u%e@yK@6*sa)4sU$krqg7bbahF?;q{!ptPi=6o+5kPGjFZY(KA<Hb_-n)m6p9U
zb5p*em7MZG6*2c}^(W=E>z$0Xy$!8ON7^ArcqXNmI%XbmoL|EF-)4J-E^LVug+PMN
zj>2zzx9~7G{DblBl#G1e7jC)p9cz$m#gxLRF`%?BnXv|GV_n;JKC&b|$m8}`_uTh4
zXX;Ur8Hdf>i`j~zn~0~1^f3{{_lHNH7gNnb6440kcL_R7-dW}xkmmR#XUEsBG5^fx
zvX^UY4}d$FiT#R*m!v4?nzq3TM_{;Ti$QD3@+XXKB$x|0i<j7oI$AINJQn@L5D4jx
zg+J27p!jMfNE0&Go>iXiTq~0|c@;h>>htTE+kiawIm~n0u}Gt7GhT>)u?_ZSE?%(s
zbq^vH3QasaSC^@pn1!QW<j~>y#S2)b;o|+Mpd<6+0Z#9SMaM45<HSrsDcwe|v9??G
zlrMfve(Wo*y1PF)u4Da4a$W<7>36yT(4(3_vIt3pZGIG4Ggx+a+!>P7F&Vu4EFUC0
zS)2`0-UoS{hIGuHZg7|oKgyX`->_SmONhG*N)G6_VJlt8Nz2aXcN*g3L?W*N68^2>
zhTV;XG0{IeGreT5D3;fDV780FhoXV_%~+q~D<urm2x;wsvX#0T?+*_*P7Qd=mF_r;
z4D3Z0Z#e^%HD;Kx{hhK(#<35l?zp;LiWks9A=<x{2_bX$queUaV69RwLwoAPcc&-z
zZRpC6a#%Ck9$d0HmpMup+kHm4Oe-9J4ISFlIDfd6lNT6PgLx1hLp8k{Tlu`k;vG@u
zxn161{WYyGJq}58#?@*)CziF;3RAFNvK1G9d!~bvp5b{6*wBHzuJs}en{<96rtx?&
zMIh4GKMR8~d3v3jEWhcWs+1)_rFV{Z%=VRsJ5w7SC5a4J0pobaoLb$Jl7N4Gr&i$x
ze?*m=JU4%HKSF+RJGF{hIGzkyCMGGfKuxZ5WrYG|L4>^CNUN0tC`&N!gSplTSnlUU
zaqyQ?qm5%_M8ic)UMe9;c@q-Nu2}B%>dzkq(jDc2qxyWDt<Ge#neM4Y!|t07wj(0G
zSK&X0SW5S}aHMkaGwuLmFAL`_O_E7ojNt(&u0L7Czj``rI?)|JJ*K?WLHuSz46#g>
zW#oU$i73gPanz*+rBt$})zyqmfVzbX!-DTdt=k8cc#yJ>gFe==zF*=#Se>28bfPCI
zM<KI!mRYZ2`)*ezZ8kz(#A;Hn36lq22e;(8cyln84~5V3ulL=QZ#GgY@{1W6sSU$z
zU;k*xc#DD`CJeWQ@H|V~aN}>+VBj8eW^Bd9+)-_|#9d1yr*L~jo9$<B#)Mk3FcBGZ
zFB6iNUWQA*lEP#;^F8;&!PNOUA!zX=C9QYE#F`7?GsVbtz+2Q_hE}HfjXBDD<E}>V
z%w@O{<ZcouIi{oJv9iR%pL}}^C66Y~%LVz`zu#g;aokD8bL=k<LFQYm2Kh7E<qHA(
z5zf^P_CJ+&s;4?-gq1~qyGlu;?Fj)FW8du>Z-_wM!?b}KMXr%X_1CVFyYswEg+64<
z0FSuvhR~~dgKXZ<-=up&riU1uyIFR7tLPBlps+FA85&h_hFad+4*t0$AU*mIQm%3_
zA2ao&3Qb==$@5gW&DWRJ1jm?RQdJxS6o;gT(~y+3*&<}XALF<394%Qf4?$siA83ec
zF&$WSeH~92{pV*%o~EY<Lg(xuIV38h?<#KIKl){B<h=PPgkCMyk{1pO{MnK^?asRz
zizo|6?fzHB@(<bA)`KjB%U}&}*E`DodIBy_{Z_v2Klw9}`)tGEh!M>0Gt=L+vV3@l
z#QfK>g}kE4U<VH-H;DI8u|aJ|iGiJ3dN{%CR~y#)9#hC1$H;Oa8i%z*d)b6t=Ym--
zd98agn2uqW;}>sD?@vy{j&6SvSyauMq2_(etf*xs6jj&PZg-DNbM5N3l7BEaYJBGc
zaqg+~1?L3nE8l(DyYF${BezgR!((@Dt{kDS=JNwkRP)Sg)Q+{0t-wUy?<QbVO<4!?
z9QTHt{1n*GOy%#xF3HjrW@jK#%fX^$%;~wwC#Ba(VSknZ1SzFnTQarQ_RurGRF(&<
z>tp>4f2j$Dt%0ARh85;SdZ&F($_uDzN<(9B)dc)DGnLQpAmxt>{5rsKlTclTvaHL-
zmOj&YIW%qQ9DDjB?`j7#C_x-0-4xdUgIdu#)!3Zq!)*t~0{bG0jvGa8;2D-i-gl0C
z;|w!MzR<Bv6B6f#RTfJM4|3p?iSF>kU+C(a%`7a%yBfiL6-&DN9l3UZn9CdO^JFd!
zNJtKP7Dx+3zZye^0E2_2fXP6}oR9es@Gi3FQ&gD=y6i1k0WqCH@b=BXg>i#?0x6eA
zO&JAtT_XON`Ph9BXG=J#_RMjmCaHR&59IM^7VI!Xyv^y+#NHd?v<2Pl+1>in#ES^g
z>?r0GwyBy9S||m|XABl|k9X+ZwK#|h3iL!M$Ny+R<t;_HGIhn~qqAbRq({Su=D_kI
z$dy)q*0)&Av^{YsZF~;Keu|u4esP^SJxl;Psd`(DJFht0f4P=N&E-|SggEzdNM_@#
ztA;R67%)DyL+UH6O`36^%Y-<eSIkaBmYGH3(F3et%hbt_?;+6x9OrwVw2DrX-yX-d
zRwgNrLb&CL*mq<i6<#(z=sW==qf2+>3{)wMw4%;nX@<?oxVT8j@_`V=$D=K&XCg7a
z2!><G{seH(Eajk4qu9`w+=}R(b-mTtO#KpQ@h!F`7|VZh^{uHQoT4bP@*e=gcd&`O
za9&E5qRa1n!qvCfq3?3N5Xvu`a~O|igAh2UMsKxeyI1HrOg$Jf)9DXH&mAfCWYRJk
zip9N|jQhTFIB%Tnxvj#(HI9&9oUB7Ch`awXH!NP4k2y8Jsb2St@2_JBx?E|_qFF4$
zCcydQo9Hvs`|WK*hH|sD^H83qt<rkn!>{~@Z@(>N3YM9kp-*F8U5UYZVOs&gw|PXK
zW+uFJX=u(_xMKvl4+oyPo94MKHRjY@Rs;jCH|7&qk_h+fnFNRAK<1Y7G2cZwa%o=u
zNf>Xe2jyxjsjO_=BZWirDugBbc0_v9gBGn<vgLW$)O{fv(ipx{{aS%syt#bi=<Bbv
z1#X1g-O_G(ub*oik1H9{UanmUYb)ly0*XAPgZO408v*28S{HC8{v5zZ@U1blvU5*!
z_yfC}Fhf{grV68yuubPDxHwkcJHu+w(_56qIglBCmPhbN%lPxjr#nz5u+76X475O(
zdU6i=L1@l6z|!9meq3k8Ez<+smgVo6DTvb+;f8Lqb2H=kr7@j+{ma8?pA$85D6y!M
z6h(uxIC~e3LFmx*pZv21-=OGcaP#Kw=~eM+!=@X4px|b`&|{RZ8oGLzKA#-H6n+_I
z{U6?#8==7?{M{BcpNLSPJTGss0GmJhR6L&z%7qknC3RnCE=6<@yD^bxB|1iy64RYy
zcDJzi%Ev`0si@Rq`&BGxq1j05s=PbhLhjXuReEgS9k$Dq^`#g<SU_)`?H`iDovI;m
z7f*v7D~}G<R2%CzI!~%ZW}z9B8+ms2K4A85j3H+(oW`L%OS+<!5!hQ*82z-cj;#3^
zn;@Mgv$0;uS6YH^$D+Z6&N`ZUe$}<J6h(DgmD+_9D;0%^7ND5L({x;l1?8Uh4U>KY
zDd8P+I(dE`b8Yz{yJqt(XcBk|QvcK(*=X98$4C-dCXuS|95<RKwbdsnxsYY^yflqv
zVm{@D{#}c8bHC^R{0ZgR)`AJZnt7a6S}Ab(5@&0J`=|pUiC_Iaw?Ugu6|Zr6Yo*@{
zS`6**YX_H)|7euUx(~Wr?YQ5=y?Khe$rA1w6J<NowlHAy6%*8P=5uxt-H;Vy*o?+1
z3l`4|ipH;Y_<bC3PcCQJ7}R49806@?`o3I>hC~;}0gxs?(Jmx$@wnHv9Ddj}UikdC
zK-5JMzYUaBeokixYdZ=I&YSC6Eo+wz7R=53BF_KWPB98t=@`dr?Fbd@;`6;`Pv|A_
zQ|+dZa{7LQlUlEAb5s|04V88IzUwKD^`B(d51sh{PWZdU0Zn%ZN$*r#7tlt}$<n|%
z?6pu;!<~MISQW7Qz3~*m!DeDDcG^hGOZ9FPAP4_f9)&~7_31-fEtYoq8m$lja>D&*
J(y<G_{y+18b{_x$

diff --git a/articles/AMR_files/figure-html/disk_plots_mo_ab-1.png b/articles/AMR_files/figure-html/disk_plots_mo_ab-1.png
index 89c398aafe9dc0f4d883ed87b8ce02346d2f322c..eb26cd1fa4469f5217eaf643400696c702fac48f 100644
GIT binary patch
literal 56475
zcmeFZcT`l_w=E3jY}+OTfmSeqf@DF05hy_v$yr6R<Rn?!fPo;TfFe0lWF$uw1trHq
zK|n=f5hN5jym{1q_xJv|W86FLci;Q|xQx-=*q%DI&pvyvHRoJ&?FSbXWHzp2UdO<|
zu#qHt<`M(L%5VmT<*(MP!v86~(-DvVthy>Ea|Zv#&&=rp5Bz7Xm8^y>0|Tok@eiYK
z_ox8_!(R-fGbfdA1P``5-FVu(u(Z@t_4tGIUwhu~Js8;>`Nztb+%sDqFN=H=sJSiO
z^yC@Ei753)xyo8uxnGwxFWYP1a&^1nnNM5(sMx<O^6<L*hxyihdLR0He2$~!a|p>$
zj$dSJ>A1Cyt<F@H+pulPh=7qHc`8`6{E8RfvY#Wv04jphH~s#dq3)R9P2v%VpO1g9
z5d8HU!>b*f;f%k3&;E;X<L_V7R<1Sv{p-ymvL}}R{{8yNWz4^Sy}bU<y5GMtXg&T<
z-a0-rQ`sV?Eq|B(-vLFml!7FiM>~15C9)S6=Cpd-3Y}gK^_pGxnH*pF>l>y`|D-Y0
z9eo-YD5G4{HL7lKdLJb!rm#@_+sBKI_3p*L$D!x-?+rtd`^kX|0esSBO!T2P5$f09
zA25FPA9*y3@0-}f<7L>K&XmklTRsgAP6{}pW%}dG>mV_E%GlVLsZOVkWBb0Gwj()h
z$?qQRTz#aM?wob?-MV(Ssdllc&$j*n0kPB5H?ppOy40R;-<Z{+n`qJ)Yce-E#9!9b
z`{$AE+qW}Iy5?^0BpnEeUcP$MnV4a&7G0NQm8DAF6759&xru&#*5gJs!QtJ7x~bGu
zz0}q0t`{X-CY8r~s{Jt@9-VyS@9+JBjytBDJ9lot<<Jg&ukC?tL#o!+)*toPtzW<3
z^PtIx_4vel4&Qi>rA5>F$h&`T-D=-z64xL5!lL2ZyGJUDid(F8();v(O-($j=N^H^
zgS+%6rn`J;#dE{!HOu<#OeY6F4YZ_&;j_u*MY^fGH_5!xAI|OCbpAR2S#qY;*EcuU
zZ*iY$cN}Pp^PL=SXHiUgX;|G*7jb&|Fb)4JfJcXm)S7AXq(*$a>Si|vMsMp}R83S%
z4d4uM>Upl2e)VpUkl7B!4St$grvCf_0{cj;#~qqdbPD#0@f<&{m8_K$p!{k0?%liR
zWMzMFca7c<G@~AFx2=_EE%ESpKTCe{gih76G^q<exu^2%nCmY6?{<qL1@V!QdrKDo
zkiBvxB1qVLKuzZ0>P@@rMm&~WXftbLbqXAUL@agddh^Z+&N2OZ{=w2UebS~)@rrw%
z)Lt@(bi&(q{(5(3q(jGP&rSVR0<~Vp_?R|+`t+&d{rfcX3%s<Ze0y4CsB1tp2Gc@u
zoEmO#P1lz>Q(iP)bDTQKzBFCV{K1IiytFVS<U0K$?SoMQKco1KAD0(sdP@(Fj(ya?
z11lW+dIx_)!o!r*h|vxo(a7L5ey%>j_iIp18`t(p8{-`WiQ2~6mzcGrXy)0(Jbd`@
zJKb4DTRTa}j2bWF%SMitTIj!^czo*ZgRLsc%ABNgBvSng%i;vLxygj2BzfJ`7O|yU
z%U5UK7`@u=)Ej~&Lm8jRHgAhnx*(xa;5ZV%+3rkBp6!eBH5EE?#ynS>hgiwfg<S%M
zcO=S~q8@BLpgC3U!^)B|zso2%LZsuyX_v`CMH1-&mM0e%m)-P_E7f6AdYIU(3Lj>n
zbDo=6DW(3qS+U|9&+gRWOl{#Nos*Mmta>hB|KrO^4DvmVPDx9<SQ*GW#X|p_swc%N
z>3ZyPh)jX+uiz9u{Os>2zeBvdYHiu($<g1xyuMBEa2ff~ZPR>^Mt8O^-6+!MEz%J`
zdP67f%dwg7Pm-_w=&63;@8_qON?R+mvK&ji8Mo!mo0(|5Ft^WhD8q3?*S@drB>B5T
zf&KUQTi(5UxAI7d)3{-XJynAot(vTPh?|@H@*Iyw#u=VKIqbF#t&5kcdxtDN=3cP5
z{&;=UKwosFC!h3A9XmhCrFmOY!^)l7;b$N1See{LqYn%urcyl?$Jt2@@BKKI*SusM
zlKdTh=Z-GR_tU^q_jgs0m95jVEZTl_=CDf5S#kX`uXqI&#fuj&n}<0MBq%24<Ok|H
zlkncM$hEdTRia#29Acwyw<e#tp`r0yHA#I-#(YzPQu2_?NRf!KP2Pnt$^7YWk52Co
zi>c@x3R{|wvtN3*Q_o9qBFYq#uR12m!=n)9I!1c`HoLLE$t<<V@cadS-1+9CBW%gP
z2Y7RqgR%Ynbd8W}t;E!w5a+)nC3QDT&096qbjDp^H=Dz4jXu8XH?3rf8tp1Ko9L@2
zb3GYk5x%~rfJrZNrY2Rwq#-)I8!uSMZPt#q_~UD7l+9x7`4<D<pIivwG%zqo$jlVv
zqDwboQJR={V#924tM_~M%vd6aoN3)v#%wS+{5uZaXZy3x+iS%5Gj~ev-bnq~sS)<9
z@i~Q+d={UNh1gUa3OZ&TNd(0C`FZDNx4ybPI`+*f&SM`A>{C%NO4l#n6yi4diB8Gk
zacy(xD0Hg$@L{dGMSbMiu^Zy#=&CCDEQ@xr>+LZyF+oxu#bw?saiJ0}6nh%=p|3CE
z=VZZ9YgVP${*SI7(uv(-=JMs$tu5@19ZtO(ZCHqj>76zIOj_Sw#`gP6`tyG}47KI>
z_T_f@N=cgX+4VP^9q+AGv$4tGG(k+rA1R)jc+oj8UHw8tO;a<-qIkyeh*q{dQa~2P
z<<(6_>inw^G5ZDtYr8jV51ctN*(!VPT!Vd!j#0bENYT`?0GDqM4kQh&-MaryQX^9o
z@<@WlOGD<Pmv4r;O=={or=LPl;^O93a&pS+tX9*|(2z(eV?(IO%0?QA`u_dez5@ri
z`S=tcJa~YI%&{nbK$n{Tx|wR->9OSI?L0L2&&=~+=4I&#Yu+5;RNE&hDM_)OptW4U
zR&FR5DNd~9DX6l%Rq*+<_W3YLog$YhRcGgX$>}e*@6+hs0^HnkPEJmW-UZnfB?}H_
zb3~~1Ucktx_w52Jw_7ou(Az9I9d41=9kA-Te9Dp37Blbp0Isdy&z?Pt0CEYT1W`#@
zRn@TFbt_F}+*xkjvDKN^^w@s^krQ@)H!aRkcy-mZwRhKBJ8+73b#>9_I+t{9duvqi
z<dZ{falTTE>KxKuX*<qV)z-E=-g8>epyKKMfq8^ALBw5>KdDTh=Gf`uj>BHggMMPY
z!M0BW0`@1^Y#YYp&ri<Pl)H^ri+IXUjC{^h@|Bo;W?y1d8;VR3ip-YCA>;GDYZPIt
zf=R!fpPxU{^{n4|krA8RyW65(`}`k^;@Iv@3$raH*~24kIlPfw029V)vSiM<#sr6<
zRuil!BAh>b^oWkgq%}8^+vRg#S|Bbarhj3oGbh&g;Nio{fjl~y29-P7u5NyZEIdCw
z`ZE}-x0NxS4?b3(FJ)Nu?6G~(>_8%Q61xpRgI~wFKSu29ovnZ?hvOPGI1-4ZJF9!@
zgx}$To!76~Z1*gUs0lf)HI-x8IUFeNJ28nwFSE@ybM|)zjC{1hj^oMm=O*SJ*iKtm
zUs`=VRq$r*K?D%TPPd#I(cay(bnKL~c1gb?No;+Tj2vZmQ9F|KeP3VUPN(oNk3}al
ziykcSgIEz{<niMr*RKDCD4&ykK>pbu4lArJEK)YHiYd3GEnu4VadN6ptzWn94ckk^
zyflMZ03Y)?-Ol=pVgGwZ_aHWHU=glrb;m|{<D-0pm$&8gW{GQ#>Wl=0pgQ2^ZMo>~
zP3J?zREUvse&`w{(&Fsk%i2gb{bS|q9)->meR~r4cj*fCjuYXN-;eXDn@n$h`*2!s
zh`5uD&YO)DQ+d;4mAs_^Q>^W*Rrj}W{M>WEt(g|Dn@{WfgA*ceW0q`3-|bAqE+j_-
zXL#=xZLm}n(JNzWcW5(5z<E%SCnqOIf3<w`8A1M>++6jSMzz_)VK=@#$Px=I{aV*}
zK`Pmk-Tk9;l3J?n7T;N)SZr?fwkP|~UotUCh`kVIA<;7zbK+?6gG-$W;>$F0t*$hc
z2<8rER3;6%4Cj?wGQJ)#tO-uQdOvnO1UXlwB}Hc+Nor~S<*JR$+a}lhaS?eJQM;Gf
zm%aL*iToMlbK5P>zYwm@Uz%*Qr2V~~r*4)`OMbViva&MEq;WrepjnQ$XyTG`?b1hm
z-vooofcybUV&>)7%NDR$DDm-+m7o8p!RBg+k!w!Y66x;gIY<8U&p(?G4#Yx6B0Sl~
zUeFwg1ukT!y1*N~W7P(xzv5nw#Ryt<h!YF<+!t(Awf_G8E3{nPIcX>^)cIUjnRg@7
z&1E;Ii@Lfg=55*iWh|D;CGHE6?i))zH|J#MT>lhV>^4UvG^#x~0gaYgLKSM!j$*Ma
z<ckzz-Q+1b|KoI``HsWt;XpM5cX#U6A*|2uC^^i{-3W?7|LVz30SqSg$HXixb#>B|
z@zg&UE2Q*H#y2K+MK3-AlOxiH+^>)V7IOY_Yjx6`ZmLM%(=CpltvzGqpYKhu;k4*1
zc1w0c0&%xpD`PtRInSNL%8lONC?glh<Iic{n)$&pIxV%oF^<;r!ZLff_8A|?w=*V!
zpY0mYGs^JPhDk|9-bpX-Qp~Yv->ZAky6PF9X0dBtwnaOD@=GIMF}uEtQQGmniTQX)
zN#ll`{0~nK(0q;$rww~LvuG4JB=Z#hIA!KOD2o>sTej@E!#`m0qvw`7@5fkQNV5Hb
z%A0zIayqknE~TV`iMY|5)n?mA+;=L@G&Lz7QBV7e>%nVmUm9`EurCl>t<;ih5zV{F
zJf~f?ix0e}qsT?ZuCTJZ!6?iv*2~K)&47RrML*uGN90Ph4~{6DAFI3)RuCP{nXK_r
z8sX6^w}VZ>*?4Jjfm!^9)!T9}+!lE#dGyBNe|C>)Dk*9CO^INII5wy6TiRX6s{zl0
zdTK%v5kt84>^Zqp+v*l_ekzfn#6UD6^N(rw)`SqyufI3UgIee`KJno|m@!uBEMDr<
zXV2mlLd5##$7@@F5StU#c!f;<EaEwK{o~P?=;%Id=s->a@`~Afzdu~Ev=|@4@$6-d
zm>k}dqN-{D{o}`UeKUVf1z^)aJ@*8FxqiK+*+2lTh})|-a~(Ny{`vFg1I>wn_cpS`
zVr{iruf^jx0)53H6q=NI?b6J+_P}AJBiXPzD8X^0BOkK@M51;`R8#}3C5tkL@ZJde
z7LNN7bc%>d6P-(oYI9Q~$Y#L`QVVwWBON*mvveh_`6Q>R<qr)0Nqa*_SFZj|U0>~x
zfBg?{|BsP%<0fKNd6p`Uv|&du(d2%A+}--$A;F$}WpBmS{QkZZU8QUOI~9T7U-KUm
z8>H1;riT67h637#s6hGptUl&o9z9jhqfXvlm4G)v6<VE7q{H-0Q#-$^_a84gqo!l;
z|JfpEc5;Y?c5z^^f;;soK5P)EV<@*!f1RU1e<6lq+Qqnh)r!x0oenR+>~Xb4!BF<>
zowaweY`V|owObe%8dflErCDLDH&?|FOLqbEf1+N7-K4)EhG48f2HZ%j#2O5?t*MU*
z)TMMTPUf6bP}p>%kWae1yIV$dAPT@4^viT{erAjR)D3ZAnWkfcWyQ8&O~fXqmJ<jW
zOHcTWvIq!1f#hnAj})qVUOG_5L}MnBVx?$gv$PBcRgXe(=>PQ6$lHx>o&zlA&3asw
z%~!@_uB~&gSZr}v;L4RN&t15HMUPzMJo(AUjMdTKvUo;%X{Oc#eEk#=3AlK8R837O
z3ZmWH2((;LRn>$o61Vk&xZ)<y(rk5cHs9ifNv(yclBH&Bx)k2x=~D#X1Dgh6T@ivL
z#4KhP&x1?>5_MKNu7%bd7v@eOcp+pRsvn2UQ$N3m*SA*_F>uqI?yl-*kt!`%gz?1Q
zOH@s6_?%~J|8Be5nWi0>+gE(A!|RD1ng?;hD>_Xiu+o6-#ur8<b#?V91oBDsZejD*
zZMV78JHhHWOyekR&`EaF9(!zI*6-9Pa<)&NJ=a`KqnC-<$tfuKY;PI-^irQyA>^8Y
zfksQ3X)`6hg}quh<2Y51t5aAv(^%1a0n-OA!$LBIjB=$?nl{caCDqC0I5(8jBGy9@
zL*n{l)TNT3YMG(4WmmoUhyZuc2hsDT>&#dh9dz|$uyxt4Jv8Uu5Jw+P`TC^90m}yf
zc)NVdw`Lz9mW=*Nj&PO)=ox(?B{SY*<KrA$BcnGafJZ5Xh1y8pxt+zpLV~klPl~5H
zT&R;3{@l$`GQP|lAr%!B+t}GXGEdK)HS=;af{K(S8H-@bD08r^0x&Ll2q;40o?nv=
zjhbver1g76uWKozo8-*TyGqcTl*rL|LeIl2)wPwg1VQj+lgLDNji<T@e(&j_0NZLJ
zow$#`=v;AF32K6u*M_0a5~*uCrS~>I!CnD^zV3S{$eut2Ky*z+BJGb0Gev%<iygoN
z)~O#|ZO1VI;zQBOv2b6K?*wg`Ma=aN3*!alPi;y3_U-D>r<XE>C^XX>#$I|~e+22t
zj}tqKOF%&2OYQX#1zn!U(ANyjmk`DI%kC7Jf6nD|U|jtJJXXE!K(N)R4IC!9#vF4q
z+15I-S!)664~;n&4b(?@DjXlm4DV)c?d>K&-FI7r(@>XTQQ{l1rEn2gAJ@Tybvd2x
ziBp{(dU%J*r%wHCJ}P^uGT;aX>u3UwNNw7NEu@>OhP^WjoU;&Gvgh<YApKQIG(egR
zaxG@9nMi{T4VRpXbXkvCorse5&ZyYLA}o&>pIyGKI-f{sZWD^624vKlofj}WC;XYb
z^6z;gPGQ5w(Vx^KNIVD1J!?0J$F1T{Exc-bXQPs#VXWUZ_o;%;l}D6xb!)cmK6>cT
zA!#xRjL80=y+gH73lnL-goFe&LQp-^Z-LS}4cK<yv<RW4Y{-Fy)YI<Rxlyym2QOK{
zr9R^HYT7iYX3OlfaoHsnQBl!Y;8f}i1ib=g1L$wwjs1dTc}&8Gnwm}OEk|-bUu_lu
zYuD1xh)M}_pZ&m<<GwH}7Pb{nU@Xtn@acV$ZjmMcd>|(hsxj!XDbVJbatH8Sk<V7A
zy~{yzxqnHa#P@zIvF?&K^Jsn_i%B#2oNF!CAN_=oP@#%lfF`?1Ea;H34m2k!$WAI&
zGN>uvmkU`)oymmA*&FJT0#T1pP_f=<SOdzsy1K7y-4^IA)VyD^%iKtbrG%<|V&&hr
z?H?FnIKRj{wOLJ%HDeKEz5L-wcc9)T_5HlO7Z6YzbFI7VUa#C44?ufPUcSjBE+iJv
zQ4wn(vAHh^YqSW{liw3$X4gPEa0u!LK^chfw$R1Cv}XFrK>kF?!rT;Xac-oA`0(4e
zX9&^Ac`&{F>5CUE)=3T@s;gB_p1j3r0!B|y;RX3oij}Xw$++BZezcq$cLcss9qA~d
zLy$FYj6Hu+rj<nIlcu$#wh;3`h!9TWJxCj>z=adNQcD_U)TFgH<`Gu8NCWK!IV!aX
z4)$H%A{0CXA?M!P#LE0bbbV}O<SFP4Eq(P-_OtzQ1kr~a(E|RmHz@l%um`b|^?fDw
zlK^I#q5lxmN~qB`1?{aF26EnG7sN-;ymw|f#d7yiGgB1k#7_idk^6NOL9Za{C_&30
z#L4r|j-D)V7+U8N+8S?GFwJ%Na8sLQNix)Y1!BIS5jV%k?UeZOYWbBo^~Z}XWr5l@
zq*n`mlI6|Gnpx+tBgxT#@vDv)mG>IBDJv`Y;Y9{=65waou3d`BoeBvF3C?{Ho=I~m
zEnm%$VO7J3DYpA;UA{7j2H3YD*IY*4p{W_;rschmbaM`=U5j-p$i*k`Ub>3l3ep|(
zn7jw)LcZ=Gx20XVo#lZ^H$(<KxpUNnn)G}ID&60qiOxEy#j~c~Vqy-1#zaQ$LD*QE
zXLV6qTl<xwm7W`2<;?ocP$`-8_p&hRgx&Ds>%oG`t|*#pH9HyROHsTQsN<mV>fI2g
zha*3mP=LR8cdvAlq(I>;=iImNZt@6JK%uMNnIrQ*N;eyVC!C}yoIA&_=dob_QGF=W
zVMvoZ6mnsheM))P=(B?1eD26zV>~k>4Up8czctJ3V;7Jw%`tc+{ypi*{^wYxjo|L#
z<+U$Fcn9gONbZo>g!gnYiP~JNJDX=?YnyBXswv!-V<`fPB{cB<!-oV#_t9_fPIj5D
zG2cc?!oqtL&nq7)F8Wz%i(h{Bqg0QjxlZcAuFE-)dRItv<ajU7(u=OO{!4|!__$r)
z9~r@{VS)5r_JgB0lJpU;^_k^#s$PiHigf}Hn+~!ccS!1-b32I1c&?Hi9{wu(*&xO2
z+uH}yWb1<V!_ownWRgB1!iC^!Sws(ru5enzTQWZu#a8-=w>TaIWOaL3SXin>dwxIQ
z`xP1rET*rv7#2s3Srm>+n?)pCuBfaO0wp3g=#^RxJ-WBi+xuInk#a419z}rNI$a;J
zq$+51E(r;pk-~9>q8&L}FAcq&=>ywsI49Qg7KQ_cB*w?b*YrMko0wY6oKd@D`}PDd
zZ_wE^A)kw`y-7phIw7>Yq3pH^fOBc($)`#R38Nq1++F{(U;?bM!NoOBA!L4d&L`Tu
zG1i->c>3xQ&CG*+qiT9?_V4u=<zEP&->K)Co{^Dps*KOJM_vL|AjT<(=#JD$MEfRe
z3|29__o?zoQAF(_%9Tz_Y`EBZkgP5OXd^65%J1?yK2UT_$E`J{;`V0Ac#O4`*<g27
zykb-)M8%tPu5GyxyM!iX8Z2JVL;4Dce!Tvb(4E@K0py;1haoP~ED|UYNSS1mkE6tr
zoxNNFS!?n+Csgx(B(}{OEdZ#d<}GooZ{A%~(;>)Kbj8_yQ-hzBQpbrRqW7sJfiL&j
zI-OQW@i|k@nM<di<L3#iB&gng5s{V~MY>9wnx@cSEunxV@gpW~zL7Fuzi;2Z=XfFQ
z-@kuvV(Kq&rkj>z_)5;kBgY7{`IdvSzW@q)hBu)#*KU#Xp|*DOjgIv^xvymTPv6_H
zwsq52=@T;Cp>N)tG7~gGo#8xmkSt5+%+yg95sMAXX83d7w};$Sz;)GxQ~(a<-9i&W
zVPo62ZO>2!dG?4W|2@U*_1`NQX`iyp0y~8^?&5!Y9c-ME8X5U!H|GAZM#fv{NJ+XJ
z;V~6k#+DYRN|K9_axIGI$K-4W65=!sdTPViC-R4c!5+DNWTkypXLu~RC;9-RJ-Z+|
z^Y)sN(Tj%<*U^GrzI<7IAk6jZs!h9%fz;-GI`i!l^<L;Wd@=wH6xt6ciK#-dN*-z#
zA^h(GTt39ZGkM${oW;G@F;Bbaxlt$|#M&}!e%$jV)R#t-j}kvaAX%<kiC~}1k7O%g
zSvY!c223}}46`NzjUlTi%U&!*=o??zGn5KGj0EBBJ3+8Zes;=#b7sk&EX7=PZ$3lp
zk{;BcJU^lMhUAphnwpeUHP}(q=EWjXL+K*oow^L?KpRgPlYl|RHrjM<?lA$Uv1>2!
z#7c^aSHJ;2R8;VHmHXyYWLkDgen}O?0xT>nR2bTEf8>4t#ga>DwDFAMC?6?kFC~+#
zQtpMH5O5D2J}f?v-c$w-AdkG^cWoHZ<E8;cx+)B4`3g-d?eeYdE@^tV&T#B67#Ld^
zTrad)6f4gv=`YCX*E<S7&3g7i?zpd6ly!dZDy?HEU{wVp5vk@uWV)9O46Jjq>t{wB
zF5N7(LA{Jd4^oVh*67x~@%8S_?#T2tR|LC8L)fkX*q1SNiAcNyK4lZEk#!Phvt69&
z^(-7tOv$eh>lLUKSEAWA#g0G|d*biEJuQ^oW4@ltk(Qi874tDB$;rL^W|gPCpNEHM
zFxlB=2)D@|Sd+a*?I=hOfo`aysG}s%g}j*f-iS#`NVE?EJzvgyFkCnIj~75CQB|W`
zHAP!P-vj6%>!4crnY>g80h;#6h?KHL<j$JbFf#d1hx9bc7`T?Zx7)@9?>Fx>S#Gb|
z$WNB94V9qTxeB&+zq+-YI*Jdin`YDPhJ=+<u`0+Sg5q6ZPuuZhIzPJ&Q6_2pUQm8%
zN^PYvryCIqOKM+Jv{1FErnl%>>Uu2D!`=8>)6cxYk^{!m&^zP*^l8&U;%&eCb8x+}
z9?(cmMG1gfHkP^$lzG+p&Eh{!gwzry0YT^w_4lEeYqpt~nD9Ngf)#dhrvi53xYkii
zY`i@*W)X{EWK<?AJw-)7dgCzmq^z2@_1RZ8n><gWv=kg%Q;{MG=_Re@;?8=XIE1qx
zai@&viiGvx%q1F1wiAd%h@2OS^c}=P@)suC_AOZQ9n~YsH9}?qoC$dQ`4}I~t>z+|
z$GkOB%=uD`${is91xDYG-IR73^K4^dlQaiN4~V?S5-&;x((3$xNo!_8)&gi#GuJ)H
zjuk=0J^pIC_2DOPo4T<U*U~c#D*s;Gwy0Jz>z~ES&aS7wgmNmFOe%O$^>YCkl-5ty
zeu_QMlBdl>T9^B*V}52ldFbx?okW#ulha0(?&U{xTOPL_)K1E(r>Jm~GCGc%ax)K&
z1(wXeYt=1s&LXP4;RZ;As%9=w5rrnzvPMx{Y`a<BlGUE^@@4YS#YS;KMD)u<X~+Cy
z+DV8)<1X_^jm|?^Eq3kpEeXJ91E3S&=WR@9H9jy!wOJILU%%t<ML>Eiv|T^4z8tq#
zy>#i)M`uLZEFHu*Ie7=FKZ<Qds?Yx>$|;X!Pl)U-UfPD~dB=H=kQu)(zO0pOYn=?a
zAo3WzgukyC^=<XGoX<9L@123hlkX6<)rG|^K0A(o+l8d>fAa=#8jyJk>PwiM1K&8C
zE=VmzCn&`cJ_#|@WATj#bK1<?7k_w4$x`IQ)xv6<r-Fv_X(`UtLaYx4J_HP)fWDu!
zrJCKsGoRC$(!)siNA;nay*1C)sQpHlH<UHf$7^<H&))sXtS$k`EwAxqQ^4YCb}yN0
zA3;_P3^#D=wX;A@?kpY~voT8<2hjcrrRC)_Ek;L2iIUf>1{Oe)C^>Oz3Mq!$<i;n1
z!2Zv+wHuu_7I!BWKaFqr`PQ1dbhEENUz~`O>VLDIR~hVJK{w7KM@zu4>M>^s%ClL;
zvBsHlAZFW3fze9cNsmKAEzH$CO2*8)PNe$DwjKGSllK8|@a+JzHDQ4f!4Ox7GUK{+
z>oQTHAvig+pz&j3LixE>lj;)ULYaT8e;f6hMkkyHgu4V{ItrrFAs`}*m9y^dXEH)>
zS~iF_1!~bBd>GEhF4#}H(pyf5f<7xb!&$a;u8>WVeh`Y{!k($)Aqr+q;UjyMZe^Oa
z@bpP`Uen=R`<(JUhI9MQot{VZv>e`cSsj~zCNnEB`Fb_`K_MZb$Y+ESn`PR34z=id
zlqKTZ3mg-pqu*NINwMTln?3lFh>EQ#Bj3*Z#m^c^JiBve1!-O0U%G!O#VI8FRgD*&
zoxOW!WaoY$&wzyBHwV8DfdQu|`a(GdJe_Ee<4picB`RJ#dEfsezKGx_SZ^#x_hj4l
zD$wNq#<%9GnqPe9$Qe4arul@h#b;x4wF@c0%#8!QfR03s@X(=j3or=i7%yj^kJvhZ
z6U;Ps7u5BH;f1BOe9<bN{brBQ8C{pII`ifB=BJ^dDS&lm4beYg5eoki%u)S`O$MWt
z#+i>sPCdZmuGQ)g+44EtT&u*b*nD^2P65A6EjI#D$qJgcDie5)SPUjL!J>qSKoki>
zZvzZ|PA&4ihn&3;Wh?=s8s$ru$b>vKW*Z5ZPL1z5!SOBexa)LUmX6S@R4X5Aaz5S`
z!{eJ4%C0iODA-1p-KR$FAuDG1`9vqresC9Tq8rg19ti%V1MXAEr=|COT%gk}xbx`k
zEMqcj8?LW)2<7;!yvvWM6=bnmJheE6g$WU&0hr3)-#-TGp$YI3z(@ng?$e+k3Pi#=
z)~*+W6AR0tgk86q8->`bzRyrms<6X8DihpGCw)|5KiQ`hIKs>M_vjJ51v$#SCh4zN
z{iBUSoumQF6y4q3-;Or#V`TMq_>^JrD2w&FDW@LfrkB}?7m~Z$cy7gxu4%D|m{y3}
zGEiA|?}wAdPQ-bHR*J6c=vg!6g7U8PSUE9f6n5=C><jDz(N#Nl?qN2nIgJX(j|^Iu
zEYef=k&vLq=Umrb1MZ_bq$NC$-0~#EUZp5yxtl2A2HS1Mtg5<+{>@H|kkv_)dCw+C
z-R39*JjI?Vmm=LE6l=+E7VjK?iVUdPcB|)Ua*asDmI0$r8xMs-8P*_ur(FB+^pH}f
zaed@tdwjyoq8E#}Xsm*pD7avAejmL7LrwCQ{E?C+_c~|SrG7+bmyc$q@q`}yIO-|K
z0)O~`(J-LhBhyn6xM}`(vK-ckAPQiFjz;E+^EikVS}~)*_-#&LzEvGIo#+Ztb+@fn
zx2i&ptC0mSzq-jhN4aX9rwvVNi}$M3d=>Vyx$umU{8J;WqQ1CQ8eO~ABs(h{Exc|g
z4SIkbxPu(U)$IbPMW^eyx6tgTDSJ)-zJLMYl`;SX;LbVOrPa60yy~%;xD#wDHh0dr
z#w=x7gQ+Z2Nd1G-{$`Z*2#aZcDDi6aJm$ODlZPn6Sz7My#rcjSDczNU+(JSsQ0}~^
zU(O@qJyrhHQLH4s^4py2oglNqV!lr*YSz~2bXrip0wci=1otE2Jcr3bvklVEZKz4U
z12c&ib}6<aJ0uU~`@DTJFY`{NT8>#BK@v!pBY{8SO1W{Laru+5Fw5!(u4TK7jPc^A
z@olhooPxDro#qlMPf36Oy^PcVNhX5DBKIVS(nT=UZac9BY35X&0^vwAX8^ZSA9b@T
z*WPI2#hJ4tw`ZCtEznavvWDjrZht$jd7Lb&ygcDt<YuO$e|;DF-rGw9oC;9Ri#N0{
zjyg+g+oAFx79u>A;u`*r#(W8CN7g`51BemK4UJGwniG@`WEt0=Iv*sMUNeN{a8^bu
z%QWWc)2EtxN`kHLL=<;hFh@a5(;$tUXrw4-S*JO5p3c%bhH!=#;by&q1__QJmD#k}
zLTVZ0-FV1bX4o*MpE6>K7iVc{AFk*X>opMy#ahi_=mXSLHwDR=?~mPziOCKex%%v@
zJBg^$>|T!?&A(IkMnqP;5)^Ho{Jy>1q(5s2OCRiR&9r%xY00QZ?Xc2Aew6lRHfZ0R
z)2Dq9vLRKro`?q6vL`Ej*rhsxZaW|{*h}`-KP+d8LVCZHmzQUgr>&W56+tGGCq~QJ
zxlwrohhsiwb-6C@k=v8=D&zZCj{#mABYd8mDlacLL;NI1(`hYcaPyt46m}?%YUMcp
zR5b^5fhZ{Rnka|xNrT&G*q7kjsSeGb`^giiVl>Up1HDl{=YRdvrT9U?iAO(F7M^x=
zjT(O-@4HgF0P)hKnW7@Z#*6JEO`RNQ=Dsdb3?!xS3rRr*^HUa;vVn@XJhZfHv=7OP
zikkI46`lt5opke^31g4axYqCF*`R5G`w$h}=nCF+itnZ=^RB9@D&}%evmC2SN=nI2
zV{RosHgHm*XV6jH)@XzO!zZ~HI!mCK{XjH?Spr+l0$Q6@4Y4YONxCHsL3F6K3^me&
zc3hIBL+55JxSA2g^I>V{a$K5hD}gt4BI-(AAAo-+SOmuhS$>7MMsX-$J%Clh1cJ`X
z$l$mA@``aBMWg3(Jb7<r2~L%&*oiHkl#jfjtiJ>;C24`|)ixwdh$NyYgn--;q!8w`
z#v&KT#HxLP+J7E(=*)MY{_6R-B@fl9l5uFE<NFUBC}-050u_dtr)U?RK1+&+r-sm?
zlDGu~8fEzjob-2SYGL7L3XPDkj)Y=cba0JB%A6b0!V<_L%J%D+j6ktfpfk50)+_$(
z+ySrW3<UdXuU!K2$ZvXuDz$;BmL&_+)GGK*szLV6k!zA>Q4XsDSFOfkHfvIRQM`3{
zh7Byh@0x=6=-bELgFm3RN$)l~3gRk6mPh5MYNEff8j_7XmMxwV)Q6zH0@vREK@$hT
z149PEbFX~|oNi7SXTU@GV#n+Q{QV8EQ?}Yd4_B{M^v9;tD!2HUw&z+BgjPPXS7mp`
zz!V)RX0E6NUQ+@07SQjih|```W8Ib2Fr<2OzJ$!gOrI{>7VmL~s{}8cd5(pM19V@H
z`6#{MnVFfgXB92V*ly1SjC^LAQ{4@Kw+#J>R9u>^_!m~2z855oNf+NGAPjkw!|dl_
zI58qDPbK5mQMR~NaJ?-%nL4!N!3&FgtOlqa8?=O5!vb-0RUMQeT$1%iP`Ge{-0*W?
zh)D8?v7^pK`0ipqL`iCjju}tEAb&}AN4~vTlYT-5<|ok`zJSY{*d?`l^M}FgxMLv`
zyg<481wgO9ya9UwT(Aq09ErL+9m-IRpolL{cTt6ui+G8+UCs|@R}(c^>pU2Fr&PS(
z=YbbDi@`nLgqC*OJjb-J@+uHsOVansg9g60I2(mL4r7UW*%LAxp{V?*&&hZ(ZTYdA
za6zaL0taK|XPe3+S87Kb3Kl|BPpmc8_j)d9a_MbLlls;=ppr3K3E0vVOEBHZ#L}rp
zcdc@eaUm@%GFvO}vn_3J&9T($`iQw}Y1O-U@sBa*)f<_waX$a44hvj;3-X@>v85$m
zpPHmWU8GzyJXqW<ckbM>0F#5}+jyv;8iLooAQ~H@08d?jmdkaWWu;P>L&7DA*SiNM
z5z<yCmF9Rqdi2Pw-NH{DC3+Q^=D?c~XU4}HMEbc8MNU10-a(sgE&J<(*KSe%FZO4-
z8QZhWS~T?<5h4FfOF^mV8FuFxI~}b0NyP`KgQ=tneC$1j`Ls&9vd%|(z)PU!B0K~s
zC5yB7D}cQhP-3Mn-z<JHb-V+$CFkM1-oJ-0z>16gJs_TX)jqXkF+b~z?oqf)QId^K
zftnm2+?a9$hF$_7$&x{y<3XWTiMu22Qft4$B0v}&nGHgndUhFHySBo64<fam`%Fyd
zg~;2xu+ci)=Z0=Ry35K<=}GeEiaUFdmv^&E;^Iph^p}BJk44*$whIGlB@NiH$q+D#
z%Zl-v4%YrgEtzJ!qs5fiSH@VeaA7b{sOx*dLGxEahQ~?)U>mb7ITwimfn*J!j<B9t
z3F1G};kN7)${4Y*^Z(F2Z4zabJ_(m#$<o5e?a(g5QG`7+-!qw9`zD!g637MS)v#Sn
zd&}4~XgbOc=TF8_5=;XjsjUd!dyw3FEEpPXo#u3%BV+mH!TCb3??rDMTcca^Q4ocj
zbV7_>Z>OL3g=RseX80IsP({}jJnJFq7*|T~vHCxY)M@!z>Y04^o+Ye>_fv`FX<UWm
zc@<fy8CO$a-!=gh$w&URhfKx|I~$-g)($h@)jL?sF{q+Tz+t=R36G!EvE8#JOLJ1F
zKq`VCC<2;x+vM57`qqcSCl!^GsQkYOX7l~SlPqwTcKARBsL3z1c><A1n+hB)B@mQR
zh6zg&B@L;zpl&c*%D#B{h&I6IUhZ3$bTA!+oeB~HpyL2c2}$WEE?>PG3C<Ib@Sugg
z2=Y#b6~TFOFo8hgQj613@Q6&@Pc{3Pc12Z+V33;G*AE3&#sKhAEIW&X1Wm|<0|n}<
zX101MOeINp!OgR|1LMmeE-RzJa|gu%A-q%iNT(jkix6#;P+Jf(tYX6UmPf%r4d{D0
zbr(B(Dg+HvY;k4*L$8`+T^le`%zGlF8fFRSOu}J>;7C1|&2@#@g6%8(ei{G{<Y<U8
zyvBLhQwvb0sR%Xpoo-VJFl_+*lPkL~NlV`=^I=V_m7J6Bbe}V?n+Zw0`p`~yh%y$@
zV`M>WYE{@MfUZnnk;(6id<2s=Q3I4(9QD<NWC&>^0aZ8iPZ?(~8JrSE&G4FbL{pp&
z<?Xw7{QxRWP;cyz8{+ZWfo%pprbi((MCIA`ngYv<AtBtRZsD<J|6sdY0TTRh!H6WO
z0fvVy^?d2mg&Uw1a?HZBns`Vl)_D5ISK_4tGysD7KYknz7)J{-OQ}arPYDj*4I`T(
zPayYz^~XafIuO_#HrV9^8$|tzm|CKQ*HW^m0{d$|1dpq%h9N~ui*w{?!eIe=?Q2am
z<D4k{a<o}M?Zgo`xCyDtWWoDxBQCOc-ABaEK(C7#-<y!*j<ZMJuKD;5Zg~h<6*V=~
zU{PB^<GOHl&lR|b;F_NuFy7~YkCG2rhPsY@V3N75{|-6NKO`g>0qoAgK7vJA9o|E<
z`Y?xJ#i}9x5bzP{WH-&qYC|rz+CCCY9^ZH^Ot<0UR|-Un(21xg0Re_b9%^;2bhu?j
zya1xz0Izh`V`*gRo!D+%8GiE0CgUa++kg6n*4)|hZ#_as|D|<E`kyNp{=e!d!nYGY
z|1tm2f4vW?o|DRW<_VuPuru_jIIQqEh`R~#@v=mRkKW=~;N$Y1o*q*~xK_yE#WOuD
z-QT~*qo|99k&E!e5!Kn2L$TYrRr?_Quhr;5l4?oySfGhDZ~pZt3>#IV&YeirE7A28
zACvV)zm4VlF1SYQQTlHASqg3-?~}{C_Tf)@U;XK=`{f^3c2iX3%b1+RL(|ZH_VXt^
z-9&k<F@giU?u1cCY+<4id5s5)mT%A+{G9N!p`J{be_~=|EBfsy=nx_5(1a}&R}O?Y
zcGRO|Nh|Inv4gNuG8M|D-_Ypf_rGTGO0$C%D?^hE#){(7C$YJ~m&tReWNcwY5R8Q7
zq~(S<3f}l0wCvQuO{a!%A_q@NVk1jdqsfR}n~cwHMRYSH_r??6sfh_Y(j}llged*p
zih~GK%4^rIB}Zci_y-1-AB+5GBNjqbyg3u#nt{ul&*$JI1Wx#4coS?Mh9i42$!yaC
zZ&QS+i4&~`$}n;e7opIWf{;XNK!zw?Y>i6?9#Fzg*+)Xmg5OBii{oJt96drNmuBKR
zfE9gacquTJ`;*CgNrWq1QBje&1kDSykygYy(X#rtx)55CF1bjke=7s!5EtMP;6Em;
zA!uTuetoNr5RM;C5(a{@ySMe3^cK{tNo4pw6%-V_q8=0xzWn|B&r#-M=)7M&>==$Q
ztKT`g479>uSyW{e>7ONWf5Y0oHPb#^iOhcg81vR7E1rLSB@p3&L`J-@TnLWMM&T+I
z6{<6E$SPqH#2kl{fTbuv-8RQ|*KgSQdvP$Y__OZHQ-A+{WGM9fSeLWfr}UzRpOuY@
z77;Lz>$x~MPH&c&c>LMI@qL#Bo3BNFe{A^NPtlt4_+C}FjmCH`TcvC73d`2aem*JQ
zINr`YcVB~I+v>RJ=-uOPZ>JuK<+Pn=_w!&!!CeF19i~1Gp_xfP%1k;gbK?Yr8SK<8
zU)EV7bEA4XN9O9)y(O+qOS&QoaZe=oE=iJ~K2`30P4eF27EQKfCn+do_iV5<_MtBw
zVc6})8GibQbI~l43=xTStlw=PAgn($qGtk{Y%|yrv8umXq2enG>fMM0vYE&l1P-~g
zcIz8z7Pe&g9JC8y24;TG($Pov$BnPjuXs%O1@&<(VyyLSi>Rp$Jz839sFRtmmobg>
zosRNtI>SC%-f6TrJ(m=F@3Ranl*d}9@2q*l6a|llrk?BQU39MrLB|zq24|aM_9fCL
z%FMIi5i#(Qso)Q)fQ#VIN}XxHt(f7j|6*jl$tPVaKJG<CSomlAP?}3O2<-vDPeicQ
zEO9R)cs@Vlkeyi8wGZU*Ic_vufsV(QGi}6vMjiUG@-8EoWQdhWEGte<Su(<gimonO
z*Zr%N{dR<nl}w2Bz>?T1E<ga(+S2i{F=g}s;*vx{x7@Z2JpG?d0bGP~r2_fslP6s{
zmxt5Q1W*(7FgiN=4E#>Nx0Z#sG*r$Xd{V6+h8(!}?K?|W2J(oEj3mASB_is<&U>?t
z-|L?ugJM`D{}P_VhYmd;_Cn;@^}qHDLgZVq;Ae6W>tmIVf%w8tK?H%AxHvgVFh9SD
zhzPb^=*W^2v6HyDA6cKdHM8iui9u%h+CEbXxt1tz6Jiz|^dD!(3zQT@&l0f=8~${F
z?Xlf^dZ*C~VwuKn(acI)Vo)>k676Y=o~5(XhW5<lFuL85{BzT<f4yZNQcRdNs!sQr
z@~bo6>`{mp?(#!KoMA=jTSH0LC$&VF2fehMD?Cqh%)NcX`_HCgmp-wy<i$t8umE1Y
z6vE=@F;l5`{eq;Cfq@B%xMbzHm7D%~<9qa1WX?aS1psvhnK$!y)df4bw_#VLC!6#8
zkx@jw>R?eN*eyq2uqR={v23VI3!O_Oa*gENbyko0(fYjK9UBbBe=#?&=>vz>0WYR5
zP*y%i5?@bLzMWlRy&`Z_(i})c3-G``5^OF0?(lo8mM8~)wMVznv_ucxxqI}6>ES;O
zz3K^*GMaS$GIk>xlCW9!=8FA(#%Tj0t0dUa+pT;(HP^gi+M4NORjwKcof%GdGdKhZ
zD<J_wF&KDSrFHV{6e0B4L-2e5)#{8kVHYHR<}V3SR@fng!m(ef$=-q}CB~{20~9*|
z6#B{y#ld>`4(FvW|7!eUc$I!r^#4J>@~_eU_o9oSx5V_;<>pqDJc;^NrzmN()k*$p
zcgUEH(4j-3l-|J7gok&AWu}5rO^)yWv|8m#p$t73H7!`T$kDI^!JxFed7OP`SiD!$
z4*V6vA8Uyg?Y}5N+uTz2$FTU$I#t>%=CFD^tu=4DLv(;|e4jazP3b#$;HLi2=Lsrc
zo}lc1)>i&|YX0**;Qu$~^xtu|{}(#f|4X9|e7lB%7xMF50Nm*RLGS(9C+e$j_E{CQ
zW6`8<;y`Oy0?Kk_VqVVxhF)fG-MV!<J3D)+@zvU|R_bj$fsZ8jCXk*i6-gY}x9<ZS
z)klLq+KMerxtSn=_Hj^Lla-N?1NipetDhFQdp^2BUB(*N$7fXwubZC8hYuh4yrQrC
zaI1zIS~4lBI+K0!G_554OPkEMuC9e)_C(5}wAuX;>+~;kQyPc@ot+cq`S+$h5aF51
z*DVGvtGkFsKh9u<Ufyr)hdfKaPI<PaQ=H$>1AlyGJu=~UuGsBw$!|+R)?y(OTbf<j
zO=QIt;(Xqv$}?-0Obl^sJd&;c#f?+2wRGuapyW5-l3Fg65svb!e+z7zl^q=ISQ#Gi
zEhNDYr5fN*jik~wh)oB<;mD`}5k^kr!c=G!z^#utSw(HmphAvhmkl(anQ6R-1aTi7
zN<Z@~WDv7!uJ9TFCSKjGcwNY(;SAUxIT~{B_HEnV0=1fT6y^zyaY;#)ob)F9@^T;V
zS$uW0Ofa`L2auB%RtGQ5Dxq_dJ1{c@;7k}BftT}tC(oVg8<UsHPGOFOY|_v}2G0qR
zcN-cTyRD9)TGh`%nOD&S&g3LGNK8!m$6B{##wav!3cZ)KIqW_R>?|`OKK(OUmZnQY
z|8p+HWtRV|n_=Dqr(rzPHV6?JzCaHxIxX`T;|BhlL236_oY~@c_G#yR*)w+9hE|Ny
z+g7c(_TnUyvejv~U1x4>xwZa}ZGWtAv%jY3d1ejQvMUd!_j>)2JsAC@gW~RS?NThs
zQJ9j?)jwC9QhcmQwAZOvw6|YV(ro}<B9%SAPK5~PTJejFD&J-Jd5FbL${)XI=*8{J
zezmqxPBHvE0ON$pnqNsKejVelgEIc-Cja;6#F%2w=&#J>pMi5F=KC*rR3vG<<cAJt
z=ip$N)9z5IVHA?3V18IT_b3EveRuS{7{Yh-2@>eIk7FSRsx_9(EG#0q*``_RiLiY!
zRXBlR7XGu!yx!1))~<I~twSa~(7t1fb1>*_?vR?EOSm{#6w#@{aE+Uoo9m3^Q<`~*
z&z_KIi;n$beEoVATF*ji-6vn>!OLI^5}!I=EEjP211MDJ)%0i3p2Q)kx}mSTN89GZ
zzE9}88Hd-h7gDJDNYFobD{6~g)+bUt>MaTr$T-YS5?a^O&r193&a0X0fR^4*qg6`I
z4L<RhFZYnw($ZRY_f$4C_F#|2854{FuHtc6oKn@!H=F(>bKxE4ml!ARarvK{8TIuD
z<`n&QpF>qk%I5p~-8iX$MDCpL5fQTddFf^Szi!q%WLe>0Z@-U}-Qi4Q%yDXb(eacM
zlj369@$+e4m7QP+xrSZ{5*bK3Iz8R`mHB>!;AfmOgx1qss6$N2P4AOR$?M}+qs2#I
zEa&KrztC(gOSW@#j7v?mtUHmXIW$S9cNdMgjf4*otuft+2{a8QB_-ju>n}9}6>aaH
ziYOF!82tG3Tnw|SlAf2}c3ny7=@$V3JM=jh%4J<#TsCrR=+tHeY%4I;c~{4^ZJoUc
z4-506L&)pt(JnJ}lAAi+kuWxzn3*MB5u#sd=DTFsm9s;Yl>=)1;gSpNHL7e+?%i8O
z-0nR}x&mNh#5ZA4&@8-?oQv;0f<$94wpr3++3E8PA<=Ph2S^RAtzvz`iK|M9)v>Ja
zTn_rfKc8%|cXXVZ`$Mr-I6%zvT*SrrL=l-HUx{ZOqMMuZz4o*?e~CEcr9Q<HxM^RB
zz(RA|h`dC64j;dE?S-NcPnBj{^Q<pU*X+w=J<8q>c=;`Q`8Dt2$WlAFjoL51Cj3^!
ze!LNGo+nRx&Ddo1^z@vZboA7@+b!heD#K~a%*+F`XRkGj7}2$RFDCSM)!QyfXgHKk
zndy{0Hgg|eG%-#L;|>{c@@jgh-w@WN=rJ%P&cu;uD?e*1u9`FU>=+w2{wN?lD<D1K
zJI>I=!DvUuAq)FRaFu;JnWK??U4y(mVk}>KLA&39g@r``TkK>DuWn(kc&}a2p>6B>
z`}$<b6O)sMU*FsnN{OG8_GQyq92i0GRi#>G%k5J}M)$h1cEfZgn_*GXcKQl>Si3DR
z9EKsfsHg}Z$PNk$s%VtAv$r=um6x~!4q6Dr)N^u|S*EKeS)cW2Y;6`A?6{1jknh53
zVQI;`#OI3kxYEVtc)4Z}o}O^_4-U#385wzVQ;Hxe1$KH++oqH6W@l%6u}O%1c*vJA
z<~D0Zt*`ex74aAwab~=?B|kWb1!j0@AMH`^Jh)>`ymvBh+zF*jHsjkH%Jz{sTDg^L
zJ8gJaH?DVFMJj6fhLxRuq>2lLyy&Sd;U=rm;R?ehfu}Ag=Vu5><NM=;(Yx)ZxZ`qe
zz_PpQa}UD7dGa6xvj1ayrH1XcdB2B5W{2G^F<B2&Ojt)aKWKH%Qvc;vOQ}=EQ^XYK
zq55V6PhV_7!Ew^qw@17^IATWL($W&(EC5Y8Hd$`Oxd6KzK3<uDUqG+k;U|9-Hhj?}
z@i$mzsK>OY$HAqg3Ksqa7@2FYc(SUx&%X>?xQ@1fF!wnh77-r3={GxVFzB9Ev(#z~
zxXp3_yl*_O*3IIT`=fVPXJ=<u6_P%3oBR$F*`$?qq*X<qIw`)J9Yj7u2NXq5tP`C0
zWmUnV0_ZZSMb~)P?M-3_SMRnuF~jqfmZ&-O^X#dnm*=@|T9}ztV(4QJTl@O@-Yd$m
z`bYVQnTAY>q8@IGW@^=c!pQUAjP8GuEyMo+kbePX=d*ZjRv4R`pGN{ru2fGfD=YJ2
zJ)TW9Xwp_{>~yEY=Hh=fUA@$FmS$mo-ZA-6{4GBHvgnI4`s3aD(aK!SX+f>r+tziX
zv}Rb#p)u;3-|&o2f56mh+wm7l0~Gu1rxPz8Y--PSonhKF%#6g+eip%|r1#TT(`Nl&
zsYHU!+}zv*u6-iEbki&)(7g19QkzJmI$XOkF@bmE#*OwO(yDGt$Fv27gqqu(7-Y47
z9iw)_KX*cZYkhOG|M2i^p+(lkgV9k@9HfSpmOz{m){-09$|B)xH}jAVnKd>pF0O3*
z!ug0q?$Qy>TM+j4k(3s+-|^#Qk1-V?D7WVk4G{d2V`F18b8-%loSmIjgHx|qK-lR{
zqz4?<xTMyJhP?h=4wgsH#awDHD$dHu2}~W+fo)btXP@5s<mBXT?$-tR`3;{wX>>g_
zwZt(Z-P~d{^Lgqi%rK&Q2&YD|s}stIs`%?vj1?)GC#=i#3hH6I5J<__;3DcDU^dj7
z+Vu*85SjDc)%4-A_e2mK8q$dyPa80@yBYCi-T$EJ|6Ya>nLu61(3I5?y3L}<;eEl7
zg$YSdU!NS^)a-vM{aExupze6<wHA?$g=4-`(LdhmpVqo?`m~sq))P)QH@8D}j+;%E
zNSB<aq&Ux4pY9-p%j;F(MrM}|$NqT4;#t@hy0oB?aP;lM&W^>YL!16Qv6HPx>aO0x
zby?b>eHz848&9{+Em40gxrMJPLcD)fR=tg={xLB8|C(7LoP@JYOiX;g&*7sUXC8MU
zEY#P1B8v8T(h^P2+E%Yuntkp8s;)KDn`^bVAUa+d2(Y}Uhe@MlwxbIrS9d7jTufHB
z4wpYQ@jP+CP48O_VkBEzTjD6AlPx%OWqjuQ0jcKzAw<jl?b+$3I7xpraNRg9M^tDT
zKK4CZ?#%ibh7c0jv52MvW&BgPFf;RRQ{rzg-|%~wayCJ^0%w^$<4n2yv&5#pd0Ai9
z4Rk(Y<39KC@j=609<`geoZ-4)6=Ts3kxo}z@^|zVUWmB(@r#2y+6a4%q~<ghaq3WP
zQIQ0!+10mJvpoxOXzkzr4;NZdXI6*%!R`pek)2aCv^|Rsq7oR0!q$v9o+A%M{FnHr
zcN|B@X!ZsSgOdR5Nc7F7x^{D?=9Wd&H8nj2KWNJL%I8NM(hRJt&G6%RnM6I?^vdvs
zdB8oN=5oUXGp8L*;h~xcmzD2Nx~m0dXT_M^rzNarWo12-nGN{NXrBGaFpDQ5Y`8nQ
zpT+QYG@>${rX?dIlPnj{4J=<p*vWLHbrO0<wbsK=RDmWqQyuAcV`Jl^(z=L{d}vpc
zPp*t-@%HvcFuvDySNF(~BQ!g<b1@BnC@=pZvaC;N8V9$%;QWLGO@ybb-{Fqq&ud-x
zqdqGKrU8B>cKo;i?1dm)^D@NoZmu|(uNJJXn40bE;<8$&cjeFb7f;+P7iD}hXJg!j
z&M(41XxXj$#HiYE)WdbSgG47k>I<M3NF1fmPatzGAF0u#C#!T?8XCyO#l;&_UbcdQ
zJV{zp0o3K4m{4@@-n}us@A!kx0%Js|>8^9cF5UR!xL}=r>B$6T<_S@QW4?Z;SiL<}
z!ej=9W<2mG=S#Kv(K|41W@gpTi*M)rwyPFP$$9xjFEJ(LOc`Ze^_np9N~&Q>;5BpJ
zykwIYdi<v8))Sk*;fZQ++Cn47g5{9F<#ru(_qnjSCOi<u;yCBVY(qFmfGwcghJ2z~
zR3j~1^1!--$fK!?Aan-*Mgl^zv$yMY>mk1&7ny3;cWw(fQu$*pLo)-`>gWh3`u296
z(lLu<9UUDF;rF)ZA`;Eief(f3*?%NCizf;OIcpOOT|UE|M>Lhyu4tM$PNlirR^;*@
z7`D&Fsy%<6yXGyLE<N2Yf|O}4O1Ht(@vpgyA+n>)@<?V@mH`Z{BvmgzgUWkd(bpF2
z?d-6GqSX&GbM#BO)#7lX#%7_ao)^roD*D-gatAEDHk7v%g@%TL`z5+duVz|T%BQAr
zKnjOqJmK`*Z6m9)#6RumG37`igGQ(&|E#{=)coVb=J3>1-kc6+GxGQF+nXq+dPK7{
zdAr)7pJ@F0xfONGr_mz*tfQmjdRDXQ^JCUmn`fN>!EJGL+kTSnRDO#}GCQ$xf8OoO
zJr)aD@^0(5TWFaJtd5`kN;e%pOq|m+CsB+kbRBW&tR$=i?&G(Q=oO2jxuB|KzOsaq
z)G=Kqk}|sLHgP8gmZ+}|zTsJrBo&Eu%lSdqWhhHwZrh`G6Lfk^PEJl|SInhDgmY#b
zW4lr3riWp4<E*jHAAkQG_rOzzSp#=0_XDJkS!xbJsZ-;VCnqNf0pq!-t>JLdR0sT<
zTM2YP$Pj~>jS8ROgX7293)|M+J@@pdn0QfbMIEp0`#qlDAD`Z&k?OVy#@F39zT8@#
z(uPf7`|*lrcUtMD#4}F6M!oU3OXT0gx5#gdP!AkqUi{o>mpPP28JyqTSt|;+&Nzmv
zmT&U&lfrwqZw?Ox=iznhm-~c+Dqq4((+h)*{lj-xV+wvgQh1Gu_)SS1HPC={&iONv
z;ov>PM@O>X<NV2|fM06)$A8{!K>6MBKEkd<h&UJA$DRyA2O*Ks?ZFo|Q)NWNHP=CX
z_DTpl+Wi(kZyqmz0X&J0iP<-TgBsB6J@p-}I~661v=ZX@5E2>Ap}FU1U5-@<2_lT2
z=hy%IoUo$G+Hke3YT>r;;FZ;pXCI9rv=UL`0{J_7I&0vC6~Ole<1B&a#~m%Qex1;B
zd5wz1O<kzThgP%NT_oO9FR_Vt7XP>lJMMm*#ZZeOzd+HpJ7mPwXmPG&={N$&X%*ru
z7093b*BB=#`_BhGMTa91mWbwu3g4w~J4@aZ5);UDZ{c{2ZR088>YLslAFr;%dO^E^
zp5#QtW&^ZuFIdRt!m1S=5wVAuC7k)`M|>)yl80sB&tFq=bI<zlojlsli2gOg`-u?f
ztBrL?A|qkVhb@hJWL$pz_dF6(4J;&2ZEHjikS-nGVcVoA(;bK7N-z2oo%;+Y?kWr2
z<k|6Ph6vUuB^m*v&Y<Zg1dYqWB%4=^tG}ZmPX(<pGlc!r#M1Ht8U2=fiEb7`a<e(U
zv^cV)Th0G#RDZ{s3W`~Fz$Vh|KZ6H;hGM@zyCpIaURaR6kLdob2NbmRSy1Y<a&m$_
zq50s?w11O}a)ZGQEp0skS{D6NA7Q{H<2{QI*9E<J(UK6NlEb~@(e~sm#FWjdarr}t
zSEBJ#wIVL6_iorAY>yS<G5IpgKTwLEaH0FoR)zNjd^l;oFfO%Z8&g5B<jWjNhj%nK
zH>(hzJAL{#1M{L9)*_POe&TU9!2pIMvj(<j*~1W~7SBb@kFctTlZmxO*ql@rkA5=q
z`n$XH7M^MbGTO&{V<Zk<2|X2YvEPt*c&6CUVb?<<;xji0>J)o#6{BFG{JZ7GUj-N#
z%o>_zI!z8sN=3T~+X+V-naGi&>)KvWJ396Zl|0Ly@LF8Dms#um$#-e?jP9VOBrX#c
z4k+PjZ)|*m1h!|$!&T0#Bd6Nobn5|zR~O@NFobZTJ&v2#mx(X(YTGh~v{AEx=OJY&
zGM<H<DjdC(!nL}$FO?o+oN&&aduKF0O+Jg^%bsTwl7`-Xoy^SD{snfU5SUbn4J11D
zo}(pyA-$YUt78$zJS5o>9l;C?O|qCC92b*U=vu{Bo@76F!S&nL?r!vnq&NJ6Ll-$P
z@0L(n#>QAWe%#BlD8Vt6PXj$>8^cude?e5!Xv`EL%Z!boGm4tO07_U<2}i$2Oy$o-
z6E4lgc7j(u?Te+iNK(<vsfeRrew{CU{WJaoM%|w7?v#|2EQ?OOa$RUussB1Wcz6>F
z#c@<~mc<hKI`RtV19A#u{&jf3ow~Y`=xW6y1{P1mjE%u?ui0<&^AY3y9<Ht<GEi%4
zE16Sjw%&J)eQA{akpKT;?@ge&Z2NX$-HPf?rBTL0lSE`LLnTs)%o#JKj78?L+g&Lk
zB$W&$3L#`3D?<n+^H}B~gv`@-oPRy<d%yep)?V-4-`acawb$Ev)_R_YzyEbz=XIXH
z;rJcD<9C|c(z!n>xF<F);dFq2uyF6f$bY<JKhT!zwQb~Qmi(~Mh!M+#{?&MP`vMOk
z<JtgRx|EnZS%$s?owi|!@>GEzb3lh;xy>IS!m;@(FH8g%$?283$KVg0xn9>1XFcei
zR3AkHXDZLN$Yf9hAxya&Ca)!;IcGd{<Se8Oq1V<tG71Acfnc^2X00x}{aq6?&y(0M
zeE^~!lJHs-QhNcM6r(cc@3$^%K|OCQ$YuHiTY}7f^5k#6IAWKl0D|thvp1}7Y8q0+
zLFgUhBG7(7m%l7)=^)&v=7Wqc{X;|Y3JO(>=@7NpaW%xY(cZP0nTN}5)vwb_W4ucN
zMErQ6=Wn-6*pFUpqVoMOpDM-kldD{eR6;p{xC>n5Bid1(lJJ)#*GU>@6%-Um&v*;!
z2Q$+pl3}-Q#TGd$(yM0v;S_})MhPNsL!dVBd&Izhl}D5g>+l%oHTtMJrAqYXJ=mCI
z=mm(z8WC@eIJImL=qJDa`@etWFD*8@FD;m9bVv8Y7`(Dw6Y_M|%w{>{-iyN{i#%4Y
zgIZ|anlrl1Vz*I(@Er=MNW<Z7ISPjNeLQR5HgBvjI?>A`nq7L2wBvuBAy%)pZURU<
zT4~F(+a?ms5l!z9G*yB6K(NcOO?Wwn-%<X{A!x{~1jjJEK6lu52cLm7K{Z`)Fe*V&
zKh!4yj+dgZ_+*1QDl6pIcBqL!!c6qBr2AsT+-tGzBn#+ewyil&HVDYOA)ti`K*&kc
zA{;zjkcD<*b65o!#l@#_u|+<7_^<`}Pq#(AabAz@kiDz<$D{_03>ixBGeFSzKu(ZL
zF5WHba2m-^6TLk*I|~~cs1)<IsgqQ0B&OeO=)nnc1jqP(Wo6}2NXyQEy8Z-|+}GQ@
zws{vZgZt75`5kNMa`%w;3|7l{;bw&{#Nl>!XpXM6B$nA48a~F*<f?>r(9^wP4(!BX
zLO8+(t7-!0mWjZC=Cy0L_9sZhsiuLBtc{f;{uh`^fR<(EZqO!8^3a>O!Qrg#;)vc%
z%D^>^<|M6lxB8wSW^bS0)x?31#N=?6j#o*BSGn{YHp^j54?sw;cmRD3_E3uqKwqm!
zYisNNZQdl1`k^$;{Vy&6D;jy8qQ@6V@WSLwnR;FTSn!9C!epYkEd`+<f;ognFe{>>
zLvO#Tg}j_agWf0TP2b0I7=h1DP<(_x+YV|zKvxkbF4Kt1j-%a{PIC~%zTl~xUUA4K
zn84|NMcN0u$Sr3L0(>tze|W93OIV$Om>GkOCKmvj`JO1HnECNx`+Vn6zb~`DvU#|!
zaSwzpgBk;*Gy~Pi_nf6<NfCD#LnEOQh9%T2A#)FSheoQiI-)*>_Icx>IaoIZq{m29
zG_|zG;r}I&UDxxw%}<X_dT%>ehEOJqGZR~9{EMHPfMJQR{4>Q<_yZu_w@DSwu{KUu
zwXGLIC1p6Xj}T3?;K3Tf_^+<3dx$g}{f9lTB7Muqy<necgZkuIhZe3HBC$y#a9o<$
zGvX{}D(&+EIYkl5syCY$Z(KZ$XlvKP`e>z+=WVJ7JxGR&lO6_MHXqr8q^tM`Vpg*S
z;u$=?4<8PnIZJ|J=BouO<N}BxAXNnkh*O$0TclTjdN>X$V%~GtbD-DC)rnY*$GU;~
zwMT9dQHcaZeFnvbY_J!jHMo0flocu<%Y+gFv9bYOuq8X*h9VNRD%d##S*WuC1SG(F
zZ4|40E5X%h73F&}*DO{hl7w(1)!a4bL~I(BxhV;@=AkR_2_>1spo)EZB6vV@rc_}J
z*#kdO?zRK_9?!x;f_ARzcEp)BfD?Mi;}G0}oN7u9Fg~rxwBWO?F;yNvr_S~RiOCY!
zZKP(fKBJ<{F_8`1^jPx+QTfvH)U>oqkohTv@wiW7vqZ-Vv%1VQ+w-Mv2W+_qbl|Dc
z_t))X<zfiYe@!=YnEooHm|=Bwb*JqqB9<X<$*Zfda<geeb+Q-OLlxva$8KzJ*-1L1
z+MvkNi=H$0lV4B2e%p8{q?_1mvR$Cp!IeK<#2df~aF#e0YbXI<f>txH)e?>cr(9Zt
zfw%2&Cn_KE$?O_B&2-yfU1)TKffO982Qhd*k+D$ikxwwn4-(rpk${K*S%J#>@O0S3
z7O3=)to=YMg$l6l3~-9ff!H{Hh?+y|h!>{6LQTsewQ??T|ECtyxOnUCAoZSrGd!h{
zeiBarM0DZMRp7?M6Wu1_&>eA(SXyhA()PdlY?G|l_Y;K$k|ESP<_8m>UftFv@hvOp
z-7vg`P{zWuzI4iVbXnhrqEJH4d?ee#sp)m(O>a#b@L2#(QC?1Jr!CFRN5193h<?*-
zCLt{`Kg`ZQl4j6ia-Y}_Bmpz;x92Ig^^n&P(1l(}-QFymo+-}K`1H1{%DHt$ZyaN@
zv$Jh5m0+)&s&(n<Nv#;e^CJj!svaMHnS;FGh<X+!VPlvi$_)>K)!U99x<D_b5Gp2E
zS};z-iz**9J&K&B(yhb#Ra5fqb2?e%0+99W2W$PKSgOtEW3;>F5^~1M=FtzOJ0hkw
zTkpP5zv5+l2E?-hInjV%kGglGRcqOSV7<~BW~S<qi(J3A5V^~omWBq6{vL7yF9r%>
z^SdCfuE4;JAkq)p9`uP6qU42JA5gCs*(qmTNH1#yIACEC0~aL2IA{7~C7+NuqV%rg
zEjDDMq*O-&hd1rKZlm6m7zvR}YKuvd#wQYU;3bNcwjb{`IvFuHf@DHYt>w@t^7MUr
zKCJ-mA@jqJAjL9&zgSaqGZB#4D@S~dHsn+;010tiAYKT^w~HW&$j@lRW!w?wjD6qW
zp3!25^p>=FF*|l4O|)|9WQrR~8Ke1&tYAdRMGK)9?eA0uG-|R*6v*c00P9ChfdF6Y
zsH>}kE?{K@e5o8SG7RNYq~7??_3PJ-5VnzvlVh&!Hq#w9v=pP@%7xtK6FHxV<X!>~
zNlM~_1u`R5lg;Nu^b6eBuvuKXIM_;2kyjp0!S5l+SBZO0hMGt(R7{4`tI(V9m}JEl
zvaVnGosn?eJibd_UY;-;Z0atU#M>49?3I|xB>szhO5*2@4Msm2lDgg7VcrNt@YHR#
ze<6CCV`if3klD<;4Q}@<gxineTTqdh`FvaA0jDUMZ<FDIUZ+(_E__AAQ;7f*Qrj<(
z^b#)!DXuz~<GT9!W=eOKw<o#x?rsv21X;3@vmtbaSZr4ENQjI|Nh<9))`du!#H;8D
zEtOmvm+Vc*9xj9ZKJxAQ29$6<B3!cuCAABjY+FEbDLAOzDAB4xPWv$&fm?waZ)R6O
z3owS6h7yT~^*KF)pkJJY|0CcA$tpn@nKTt{FuQ#DBV5!I+ygJ$Tti7R`Y&H%L5T?%
z2du!4K&fn-miZT}aJVe84#Y5^>Egi;oH0V_KpMgiw(C>qiV$x|FpUjP_atD&dypTL
z-9|2sH%$Q@oa(d3+Q9)99ekZa(_#Ys%g20%GHv5wQ$|RaK^-}RP!`B%)49?E1NQR7
zb?ZEPD<SK^(&{o^;RsYI1mF%hBrn@v^r@7<X?$&M)fwccc{`S9Q+l^_1c|%`DG=ct
zq5OU(*<d(|ZlOt5aEqI@{94KEygK7`nH}4C&lD?YxSq6EXFNAu@Oit6nbp$V7C#Zw
z0@a&?c_Hr;-GmBP)#{Fh)bIWW+-G?%x#NHXb;U8Y1M%JoXO-*O>hiwtdGR3ny5(Z6
zX7CpUtStiO08aE}XeA2Q*4Cx<WncN>^cL5QQTmW^{hUa4o$;^3Bz}0{h$bZfsQn`k
z=1h7<cYB+H5%J`x>Dj!Q;#}axk_2JivKH|cf>^is@|a8{m=8~c?LPDHMO<85@z&b5
zzt<dSlbiyo3F44oLUjIys(E8E+RT}z=mB{at**EGv*T^E!3?lk2i%ar@7dw*d-Pl`
zs>PRIKvI3HC3$JrmCKhk4!(-wClL$jvn2xUAULQGT5HhC2U%W)Zb>7+gYhC>H$H?`
zmvqsA*&C;~V7gx9n+N2#652yWXp;wC^pkB&Wu61$s{n+*z4j@IRN=MLE9dIJ)-LD3
z<{5XZmaB_Ya$6HTKxWE5(8@{qOQvVA8rc7`zX7z5L<&VSxWI}3w`lagbJ(~fR+UJh
z&i9bOTo4nL3&07p+?PdK!>A%|#cq=dJ{{F^^#h$Kz$p?@)YirSinnq-K=Pgdm+yb-
zol^dK->8FfU6l)})ooCZ=mjKNMsmGW%Vw+;FG%ll0fY|)j6F_9j`G=cNwJ?+EWiZ3
zVU`%d0V7@2LAP$*$QJwNL0wjE-)gHaShk#o(~yjg0BkMTv~mF`P*}`#^SVC<SS}zW
zga|4jZ2@VNhA!z`CXuXq7G)iT3=E%W@u%iS))~K6HGHNxxG7>-<*3JE+f<&qZr-=Q
z{-ntrbBu!4U~n)S)Fn>`Q!*_X%iz(?W3^YT;%#>n&;ZeJEn%^L5De`MO<kUKW7{KC
zt;D7gT&OI=2to#^G^FzA=h8j&e0LQ<<t!fejALeMs%`G%(7-_KRlyIkq^Ro4d;#!I
zEKI@B&=5fH9<_x_1;~Dsxq?hJ<lS{FEOz+j-yvb|2P(sqn$)9bOcfR4Q(^in?zh((
zA3WH<qr++79iR%O%NB9AJB;J<)<9;_oKU0ML5;fIxLFQ#r0n{1)sm0+l&MDPfK#wg
zl)$IrQzuTGX!Y?|ZTG9uvS2d&{PT2eC&PsuVKs&psc!~GNKu9KRue|lz%?M^KNHc+
zHe7ik3b1i)*54CpX**V26p3-5YHe!^BQaj--MwBx>LME^z3X8wbi*(xA*y_b5K=e~
z8}oYapw0*tE`m1~*VgDFf``3NOiZZM5wGQcfYYPIe9`>jt$qicuOp`73-65fO-xQk
zf7X!fe1?DX?_wz*K6B-fgkMebU~{bDi#F4^m!o1T1Eb=fV|Q8Q)(SC~oKkY{edj#A
zNC-vYX5K{|GCRCfn_}B93(1%!Vn>WSYD~0PaIej+ihA?ohGuZm`nCIgMc>q&l+uZr
zK3s{pD?KDDkW)Jw!*{KtP|z}eU@H-Cw<ph$m7P6zb7q2+4&p6}R6UCf_{i`BLO3G3
zMe-MVW4Xn}Cv3#8Jj*^psnSEAQ?8s!J?Xl=vB+X;7WiX2p7ROYoSbfrF!aZSZ&2$j
z_Br6bS{H6nXJPae3i4xChX%BP98`-ryP8>9jR?yO?Eh?ryyI$iUMgz|$$VxOPUYrG
z#=Y!)`%KAiBoMnudEXY`76S=snnc@E^sN?Df2@A)v0V^N4AY&uby`oaT?{lcm?@R4
zaJdifL%^47+RC`<^H2V*RjQaEp{q*-!T>ngL;qoHj8<~`-SWPtlJddXkU)}C<}P;x
z#)~AITUqE1ttvqrU=5L6PPt+~ws6E&5&s0SWD!KL&3~hn3t08%HY2EHSD~r<i`Cs5
zB>n|aX;8Skc!S$d&xyP%yC?^LB92R0fEDL$21#WIL0*aN_C<*3HmE02KTez{m_)A_
zqbMI&<WS8{uOlfXiQU*{KFZRx64}-npb$?<JW|U=X$bLPj2Vz0NJn;{4LP8hs-=0f
zPJKG;2wvt_L~94r8rX>oCi~pfj#9>vqEi(FnvS3V&fX#oMcQY56cEfqOfhvoY3B!a
z+V(G|@W~=flR{ApUzYxEZmHLO(PUNJvVBs6okkSj4qsbEGs3&fF>Vl0aarq&;8f7c
z;R6%R0w5S20{*zooqfz7l|zYCZe>GYqLV;n5GdC9Vh3+Of>@^ukX_v6?2s&ynv<Xh
z*`k`(6f)Ms_<sbCD4K14P^ny$=n;m_TQQ3%M3}u_!xyikh|PxU*p;fP>jXtd-z65!
zb|^8Fw>vstg0E`<9Jy%4eVlRtuOzw_@(s)5$Fq_;Z|CR<(Wy)D?e%ub0@@2&$(TtU
zYGUdF6J56+2^gsS26z><z~V#1EC;6E>Fn3x21z1)GkyL2G1GjlxSfhk?IfBboY(bY
zeG%9bk{=OK*Z>lE*uIjQs^IFIi0lLQN2jjGmor#rzqMZ-(RlH&?$eOohrF<7CrryS
zM!~)oI=wagFGE+vvo3>L?WY6g0en@E7p?h_@}l49V)}zG_<?7iiX_!V)kHm^`7D<V
zjns$11m3Jq*hpSn!)glN09cFwAq2;O60yr3g|MfPgfu3h^U^~8l6|Tx8rslK{;&mS
zUoHSyFIwUbc88N4k?KlzDaoT6LD}QS#v#=%5}@+CFV5S>4grw<L;w|dOdj%@aIn;y
zdx-h>743OMs;N6*pM6C-yom5i_au5T!`<i7-OJ&u9^p2+F$jhbc<Y_+3B8!af@@#@
za+0!7Ku?8m>_Jcj)2nM!d#;4n3D920_M(7DCCoNwXW9=TrgedGQx%yfSDVHbJ1+3R
zRHA<@7V<BsZ@kjqO_o7%Nv(2&3r3=KL0f)Un0J9>OGHvwZ?-zv8fRmw*{W9bCX(XW
zAs}S42QkW{G!w3Ic1V)I2VvqzD4#^mZURKev(65ZCzP$0Z+l=)E3*Q?M6iPEfbv3*
z>LL=GM+8rQj2SPw6S3;X=)fd-fa3yzQqWB*P5@b(pYLeVxOS_q4WR)tpaTT_n@MV4
z7`C6mAQIAnE--?~U476jO0Z&uD?qz$xZ5974J0Xp%Gy!FJtSigo&uSSOHEfwt^BGO
zJG1q3T-#5hT53u+?7HF2;nUK-60?0rfv*#s4X9W`M#o)FO3ezD=ZER75Wj{oEz|Ep
zISg>4%AzC0)^*w@{|5lIW>T>tIUG5aK;1yMB75Jhnc3zOpD3{M!>Bm{vg6jx5kXdC
z9?(S~9;Ca80O3YT$%VmC1z1G@F1Y~0LZNQkc=d#t<iM*)f*)TZ78UsF0~C^h`}N|v
zKf$yuz1}vK5J$2QVjRXGPW;fyAOjYA9=UEM-~;k*2}syIbbIzF-OVSopW`a$C>4lX
zL1beT6nC;MerFp)b?pcWDUN`~P-qzQTblM;PQ)@1PpUV+ghz%v{_y`G=w=5}XODCH
zoGz&m<0P+0CzYIW1Z|=r7HJo7F+$`e5h6n7kL0~l9tCBJ!*Is<15(L*8EU6V4lF8l
z1n_xv3V>N9lpM4JLH|S9RGbu6k*j@eX?dESzc7S}oNg&So0Dh&?HZ6%XP#8F?TV=>
zSG_f<-w_&20MyxdE?DG06H_?x3^nE?4edb%u;~cePq5w@*es2<sYiysG0UZ#H*#W~
z_YTvz%zr+yK|X-Q^n<NE;J-e`Tf=P&B6VcA8ZrfHyBq<CjX&F=7xTw4&)y)C(bck=
zSz1;iG7sS-rE6MqL*pgno=eL9O;BO2<SczDF%d&RdgT7F^(w}U-pF<j%h5bdakk*H
z4ZC$5;2CT-qTGbhmzkNO<W2dC4ZIqQ8Y8g^@#o9sqKkn70Hs#2u}QkQ1Z*8=OiTn4
zzgv!sL2wh!*P0;kwj6<-9X{KU>?CzEH90ic8Hj3-QPHoh)P&ew?QYtxt~6H`!b;}h
zc-CbN)NCIdB-Lmbx~IgGVFCqDJwJ>SCqcNf+AP!t$lhxI`wi;BOOsYh53!+bKA)k)
z?H-GGH?%G;7V4~trKo0vbXlb^zb=gxE^*F;!A+eM)Yz7h*1wl#Zpz~F5TF}{fE9^u
zx>t<%<>f(F<tfp~9Mmq)imh>ET#I5JBCzVz#YlW9N%_oC+H*t!JRIo9>0pW<e*H2?
zT>&cU6OQo3`R|5u0yeswz*irbg0%2E6sa%{aZsm<WcmR7U&hBD#AH>AN%-7*Y?2a)
z;*lZ3yIpu0;U+JbUp+3c1%7?$tK@?6?=<RG0f8+!Gm+5`N;J4+(#5+1!95o+k77g_
zc6-~3^cgg#WVKB!le!41L!FjARyIhN#232PMwfE%4t+ro&FKiZk4D@5KR{xv$yr?b
zC(POb%?5B{mgM<upx!slV%#hzj98UZeaaZa2BEnRq`r1+3NANr8HT@gM>yv@om>DJ
zjEEF2E*?9&iolMXjWEwP5Q#7WiL`ryB$2291)we=3F%UXC(rA@+^Wl~RnzJwj4}Lc
z8+(}ErQklR3_AvE2A!fXm=*~pl`8a}Kjy@iR<;H}7~E?b2B~tgyIdyX^jNO9%l98$
zL+O*c_(((4mHZvLHy(loOR7PrBWXE?RADx;D0mpj&@6ZPM$PsSRYwxe?%P6KD}tik
zK+&CmX_`$`G1T0u19}x~f&$n_RA^G*-lMU{@RA7LpG+`dpu~D3n&kE63|Ux}|F!DR
zWO(hMUbnDA`p5I;yLo$`d-E{<hg*BZZ+sfauRp1P?7&GOnLMS^q|%14s0(aAO%zZ9
z(JQ_i?!|^|HGG_SV#J=N1nwuLJHh$jb>=6`*udB&EE_@>bR06%3xQQS3oMk7>&HqY
z7x}=oYmLx^1lT0;>l9<f$e2u48=xQsjzjQ!>9ebF)VRvNcum_?E{NU;w|95Q*#N?r
z*lJ~?oa`{#0lQxhZroN1!pA9x3N0q^y=PQcamIl>hpZsL2y-l{-GZL0t(DB&b2D5=
zZ;=0%_NaI8?#{ih^PXoPpxf(mvG32=)bu6SyRyuJ-(ug}w#$E4mt^Yr%QSJiVld!g
z9r|z}HIy-S)Bs13*F7kX^({{pzr2q7D}-jT1eKAPuJ1K{woJzw78ZwNQ<*;4exUmQ
z1<N=W*lyr?8l1xVRPgPp+8dj7v(HSnx>S1%p38K<#HyEk*$d?5(#Ybj5UUmf;e7p9
ztH$(Q%K#O+rK5xDj+QgYn!$W8qVF+<^A3+i0}rbD|3qlw?#tJt2-}+be~tc;RyU-!
za4(Oq54aclL)M16pX$sxW2RGy4Crh2#7*LBD11S>nw!z5g&hFas_s6!Sqfq0lBDw#
z>t1!8cE2U@!_MWydm1@aIAjw1wY(yv6(iT2lAcz2pz%g`|4GkHTg`&b6yBAHxc|qU
zG<J*gQlop%Z)9|l+PeR{+G?50aU0j2qB}|VecP#1ll=AVZ2G}}|1cNQuX-xiGvjh}
za`e#;QJbF7syvgPPlHcqKt!<P<^akQE+G!5h;+LMN&=EU=U~;yW;Nn}tdZw3bID|o
z6-^}6CZ2>XLiNUm0WbqPF8n~j5P?tPFzBphO+<8a<EJS5bETL<!o7I}Sdm1vV4ZIj
zZoMA3k<#gSaW6moUX03JQj)udvs|+4HuEN8UJB+t7x*8aB?YZ(n154*tgi!5kT)sr
zV&>9~8#CM#Gux14EbHWnD1V`#hKWRN>8rzqgUY)*j<h$5+?WT!^>UaAxlNDIk6yH&
z$-FjNONGb$#&LrqsIgP0CqhfkpNCTT@py1=8DCk~!aDGy*DX`q^4lb7FZrEe-VdPP
z5Fs4;!Lc>VR!hfyVRnK#T}x9_<~M^w3D5EigaFa2b0~Q?56q$gjrCU^xj5C21{tKY
zC|c2264Hk+VFM*(4dZFHynJe1R*)|V*5|$7gpC(Qh17k(VT<R04S9L(3pzatvc_%+
z5EKg0xUvbF%wEI{lZHSM*f0Y`w&(Y4VQ23^SnW$vX^v(71l9h=S=B$%&+iGm11yT?
zQdJlWxiBj|Jt~}cQ3q4atwQgj0oauOrAEJ>!|v*1b1ju8d!u!V)r{GL?bayT<KBH6
z9QfXm>Ir21Dn)?4bwVz@J7N$zRtHJVRRj^$%_{P!>tb8d+zSg6qjhNBuxwfr!66j3
z#uXA0C8%F*hv<bL^oWWOt!$;IC&f7fyj>sQ7<(svQWk1uawC2CF`;Wm3@YwFDEy;O
z-qy~p7*Wh-LPtBuN>XBvQ0YwVbJ;7uslJk1?u%_6%6$2JzFF_3PEnn4ioa{g>$h<p
zSxSLa1o|NDWT1)C(<i~h)^_NVebcb!0o#(!gM?X`CS!A*1bj{6eGMv|7?lnPYx)r!
zJPG5o4nCZi1MRlN0Vo~^dk6Abzvw-CjVX!Wk9C{mfC)QyqQ-j{JIOh25LJ{C$@veG
z($a-%_CDQ6D7?>?Ay89p|1;KZeJ9u$SBLu{7R*5XDo%HS)MJ=42%^V%o|5ZR?I&jm
zlxQz9GLoGg_MRRzn<aW!elKkv;l5d{LREpqa`SINr6D#7bO5ctO@faa7dF$9n37`i
zZIT?4f9}~}?hE`^oq@+wryqX&DPqvd>B~EU_`X<X7VX@PLl6D7PHbl_t@Qpp6zDj#
zNtMy%?%bV!aRI2PuIw14%8H9K`(_uT;xuz3_H2=VRWGe2cnZV+w&C!RSw!)31w-G@
z#w7Lc_zE&I+iw!lsZyLkw&?;|8q0PRX|!~f^p+;{Xf55z!u<0ypoI7$(Dd>Wfhv?C
zlu4M_F5llTLR_8(Kx}Dgpwm1Aqxt8-ZT<3vo)D983=nPjp}15sg%libo_dg2o>9_J
zma<=%pb{42C;ued;G)U(vi&cmL0q{ciJ}k48qS5X_>v4H(Xd=EbS~u}n)X5PK(>J1
zL>gjTRwUP>D3bd~8FR#inZ?;-@nofQknFQP(j>KApFjl(#}q0jz(Us$SFN9)JJLRL
zj?}W{9#Aaik*5Yp3^$MKr-Zvmuq{owFKs6TDN-CKDI$)0OkJa1^Qpci$@)mAg!s1I
zn{WYAfB`>1p|C_^GAVYD#4KJ!Z^egz5pqEkV|e5>np&OD$Ee`maYTHf0FB{w+Vu4F
zq%7S58Naic#~C!01DM=DFtu$2+O`4HN)o)ti6o#ru{2Hfc|v$O=?q}%1Z<!{Q%By)
zC&oQ2H8Mcm<r>zjkW8vTtf@;P2|na?H3M3x@|pTo`05u{dAOl#cMCbKIj9RVPwN2n
zKvAES4=RZ$CdvH3VR=t6$qna7xfurwzx9S1o^39)W%hx!#e{G}9UMb0Qq7tMH-i-f
z_#M|5lw!6Jx^O2kb0B(xh%cMdFK>015J*XzwPn(xqK(uBd#!w^89hg=hf=~em>H;-
z35U$hqCCl}k*>I}5anCO@Y$M|29V{s1Lj5pOj9R@>D++|Sp5m#Mo>@K@RpM>B~K&O
zRF%{TlaPRt>k(4)D8N1!?>X}`7V9kWyo+RDDX=Jx+{QvOk3I?E(5`w?+98kHoVtK|
z@Gi`$x|Kl~Jv^1uVN*GKQOVl@|B{`y2HTLzGe(7LG>X^tig%9L%vd+EJsE&KIuPgH
zfzSgT_kp0(7}xVD10`Oe>?(LQ27*&)yNni%!dVuf@0j+XY6!KHR(fi`5K3&9Y_zPL
zl2F+h^&Uli#4N=^0-NU=l*x+##%;wN>647c(^qckXeGhO$o2{mF9zA043U*3VI&`7
zs)}LZmL`T4ukaPQFrIWRZT^c?L8px)Zp1E_k1UxK-Q9W(2crbQnjyq>zSP%mM_BJi
zQfV|+n@9ryUcF>Rp0MFD8I?d!e+{0%N=RNdpRmfvN}<+7Z2-Lb%h#`)yRAhbE6YR>
zm9FubM__or*2{PMCPA3(B*nf4o^2s2H_S6G5@p@iX#FiF|4(neV>?G+>AJW)i^e+A
z$@)r#i&}p=RyB^MCpQ5r3wX4by15nK(1}p^8Yabwt-blaVCf1R-xrt-)bzY%*<7!G
zY?2tgqfkiZ9Vm!Ts5~g|2A+6_Y*KpqQ34l`%>0IJdXx~2aEFN$CJu~_B3F@>F2slk
z1Jsr|7B=1ygO~3>&b}jQC>J>%S;c+9<<}r%R7mh`^bPtD?3f2HnDovVpl?Bo`2sn=
z9_j<b!@iIf-$n2wFRx>Vqo8D6LI~B5kVUk(FD-<~-#h}RK%yyNjw0kzP<*^Yl7Dug
zbHcbyUT_VQNRhVXWooM6B=nXu05jau>76u>lRU-7tvE7ulh;MxyWIyIZNg<KA)*1~
zV3G>`iMm<fW+LDfC*N>SPfab7+Z(!t%-10FrE8#97UHaCSETY~(6c2G<`Q+feW9qL
zOx7JYgf%iGl0)y%HK1fC;YtuNJ_6C{6NU-ncysGy354M&(YtDs9bxCP7vm!eTs}a$
zQ%p>(gY*CZZZ1TUxLuaTwr1NDfiNSrc}k$CpgJLD?moKRot~a4CC)9;xpfd`9@8o-
z;u4%W(*?C*I4(;UM%aj24Uo=+i2U~Jx)w6$*p;t>!GK7ESSie&*ef%;%Z?%F^ls#`
z%=Gl6Qjo3zEKA~Q+5{xNb-p%{J8R(kRh5#{UqqvTp@1Z!rPaw_`q7Eh(>dkO9*fzp
zY#XPlj5gAb5=szg*PW!Fnc^tI=ezcf-j<x#|LD!Eb~vEfDT;9GK&Is_I!4Q~B{VdY
zdg&z$j#q;S%djHhXB=~vM4sc-;P6nC!vx-(@HmJUm<DZ%8EiRXcgO;41&`UAM6aV{
zipxyOVMnkFX?pC#fV4ymlKyMFdpvsbC0nwU-WX0Jb`(C;el<zs9=sYii6lsP+zHAz
z!>=dCkn8gG^0Ll6x=}ZO?B&vNrx0C5(s-F<-CZ^f)Q*+nG2n0Rk#@0*mfaRJG)t!}
z&6J6WG(;$o1ls&GFs{u^Lgox(O9GEbcrtSPNWNh6rNUm1HW<S6jInO;m$m>r`0{2r
zeDda>MErKTvC3^5QTdOIi#q`N;U-WJ?t%_-H6-LGGIeh+FU+TkGBuoHvzP^$Sh6b(
z>JhJXveO2}u!+)bKId4s45U_SHeElP+-UCVBj=gda4DtH>)qn|iC^h4NwKamvIFOI
za&Jj@4!^B!Zr4|CG#8q1<&K>?B|dd707=D%#r8O~Y`scvVjRtlZVNv=p{>O+YTqDg
zF>7DZZJmU|M+r2E#~TnebYjN$Jpy!j|M`6)ycCY9z8LUdhP>Yt;S46^%?@N>>X;$1
zgy^LyCW*`=n0|_BhMy`%(~Ii3#Yt(>!Zezi=z0*qrU4RU;tm;vEw#I>Gd~*fBYcOy
zG7eeH=d{8uU}=rE$vKMPXB7P<CBUm$k-q!De%XgSaX|J#KWhE)=%YnPhp`zK*n!w%
zw($1A*i>69fjk%hHu#VLF+F*CK7dni%U>NJsd@I_6~FE=ZWX_>-TyBT#XqjE4hhO@
zbC9^{1jb_BwjtH9I3`W4$i(7_8c*mjoQzIAIK)p=i#B1qH6f+;>C!R6LtLf@0!wQq
z=Cqb*?>l`-&l4@9n>-#+aVc){W1mtU3aqz*9>*G1*VH($DdTmu=J~Fx&A)P`@P2%l
z<e6H-C83UEcM(qMFR(B_XcHwq9u<)-1PXi#tUn3!@G9CQH;8uK_wvfXl-)E@kKuaU
z9BFVMiXnUuy~u#TNvBQtoHkJ%xdxJ3baFhX<s9tngow0GY~drMozr4?ObrXCeh~1-
zzs*P<t;r?8TpJ;ltqH-0lSqk%hli(8=HE)Fl7z)ecJ#4CZcX{6$KMbPs@69OT*Xj1
zRq8%}rRkBVFZR8kHz$qUYm#)*G+Ht@Xfg(FEu@v#)0i>r?jB;dLvZSg@VPs}cD3rS
z)+j2F14r%}==eNEb3y`<%LeYj{6pW#{7*fQEex??+-m*oK@F2GEKa!}*rc{pDo9eK
z`n<_htrja}nDq2tT3U|w*=^_6_!(;!cgZaEd@G`8;yq=Z0tNX%%{k~+4<AOBkW5S@
z;S-4CKDBY`C53lB{K-G(cXXnPTKtiG?~BH!^Yf&bIgpIEbTC?C$JxaVezJ>g&1qR}
zmC-X9e$r`X$D54TeeAZTb{|0tySi+IY=;5Wk>?0lA0G=l5u|@K(N1Pz{BKpQIxj#3
zM#-hDoGsh1(Wp%(9av&Ed~jq-h>~}v)X3f9g8<x3j3{z%Gt-uLMR2KFBJF2xIWfDv
zGfi?)&*#ADcK^iYgOhy>^UZ%by^#1tE446Q9gy&B^1EJ)rbCJE?!eMQ6cD$KIFuae
zz)A4i;M#YB+PxXOmz*&Y!CV7oS%mpmJ5Y!ERFM7#8JS^sS<p31AO)y@xPxWHK~`FN
z9)YqeB?v_jMO+fFz>_#KB$~Vije8mCLTB)l^lQ__o5ipdI=AOfpL79m>z6;5o!|Ui
z9W3<ENJoIacc6*vj&N?jynn@|PGYYxAyNc`7CVNRE8?3qEmZ0q+AW&bLx$`wA~yn{
zupwV(u%i-(q>Pt?W5?9HU%$+s{)mW)x$-e3+$K|-cqrB2L<~6jDEfTP*vUC^G|%hp
zXrGEPL#Ofq_>3=gX`^xT3xVDB-ZDe}2<lIE(L6TT+b%q&Zq~X2ky2m$*wfOkai3<6
zgA<2M3IJu&+Brb_S6%&?mD#h;SC24gWviWF%*x85S$_)`N=SJBB+Y<tp!e!Ii*>ae
zu;8z*2~Io%;d0=S`Oy1gM<PiyeiXz5^5Pg`We56Q6V(90w&vFVKz9{A-}R;FsIayv
zQ{xQr03-(Q|DF{+=B?g~%+Hw!jEtg|SV-#ZBEV>A9+CmAqYw@$gmLO10ZN=@C&q)Y
zWGo;7E+icnv4iKZcZs8<i%T17tkYYl)hi;tgQOXx8b!VL=R$M-f)*)&{wtFpxb|p|
zaK59r1&RKB3A_D6td*8UyilyOYUXK#>j)@RLbqpXaIc^L(KnkP`{tlc>970<Nz^}7
z?VpDu==JilcmkbZ{9|V)VUL&BbV&Y8$j4s2yfkT83Bp>(T(2+W-4me2zr35|LFC|x
zfahia<$+PcNB<^4!dAlK7$rQ?Bt+^mkf|eE6$MCb%rhrZJrqY6)O4Ct;t45~<g*R{
z>bHZ9!8Bd0vUDu!?4Hd8pLQXS0|vdcP>2t~*C|0ZiK<E0_Jrx}l-T%M8Qa8xl~HH|
z>Utak4i9y?{V&O4%(&n%Xy*A<1DFjN4DxeBj2jufC7h%0ZM&QeSvfa1%S0=ZC$@7S
zb3w_yY3Yohro_T;@7&4M&Op$9lG|yusv(LaOWu54W(1<nGUi(JOjQGUqs2^dJXWTr
zcOhI}jILc;9f=x^c`O6dSds~3l)Dzv)tEfZ3auVmrN9tvSXqR^Vf&kr;8LRwl*{4c
zfk#<-ccVUV4g6@BKcvW&^sk7jH&uk&>Ie=4Wc|dp7n5lKjjO21xki>S3a+jMJ3FZi
z6oNCERkQ$XV-vv(h;#HdCS`R*ow&vk;$*4yZaSd4(uszVJ1}8DWSAy%s>wyDj@*^W
zmiZik*n(6;5f5tHh9chN#fTu`zGUl6<|w5rpEDCmd|8;tyhGA*l7xz;1{Cryl@qSA
z2=<U(t2^b(BG#{oFznQ>AX|Whxn!jes3+V*Xi2OM={7~)C|kIKmU-jjvzM~dB+&<u
z0rGYS7`$GP*(ip~G8pSBy@tNSB4CKf4I_1EDYT5sEC1*V2D^Zdz;}4kww}34Km}yJ
zMVA;fgS-IY7NU%lmM%^1#e?PUZ`qntfBzPHkiOTjg_#rr*h5;6R`G0EdxD4U6ON)x
z>@qr~Bw(=O7YU}-Q**+X|K?)v#kI?b)TYtcN)I<s?PX$@e_+R-$mPL$#O}wbS_<gk
zStcMTd)-$q`(AvRfDig~-hBP}n%qf5g7JDW?k7v5C0Bx1nA0v}R<`>COQnUfJ9XZh
z%f8+bz)qp3MDQ4=G~#HS%3k+P^{NPkS?nSGTk7uLALGA%K$i7Cu^!UOO{=7ul9!zH
zRm!5CNL<oWR^F1#<hS&|yXfPCazWR5ht3=4AKSWEux++edR%tvvQGM|9zU&NOJA2g
zo9XG}Zytxb-4hA7q=}02=0{#c`vIgV@Rg;aD_=UmUvylaE%{fqLjGBKHu2}xb-JCh
ze}`&_vSsFVr@A-f1zWuwwT)K#`eF;~R<av?esh=p6Sw_u`}Kd~MGHjfP;nSyI^mbb
z#?0D@*{0^mmoyVKArRh?ww#|n7M!ctsX1^ASk!fu9e!=+BcG>}IF}L!^Y1|%tPyft
z{E8Wp^Yf}sFS1hw#{?5zvJ#w;Ki)!XK2PbD^kU|gwWk|VvN5<G#Jd02TZ~98UKPcS
z7-gEP)BmCQWcT<wA|Ph4TdU0K^4-&(6Q_hi;+r@O%Wg8NE3=`N80Raetla5(7IThO
zOCnS3Pe5d0148ozu|1jE3B|Q9C``vTvspUC%%w*BSArF7CR#Y849#-HvqM#o${p7q
zWYfL-LBqu4K~*ou*JiJzfo}-1eyQ#7L%7yyI&cDzON6*mzuBj}9kH_b6}be!DgT&t
z$q^0PeWpe)b()<_I_+mv)=lmzR@|f-;lI$v<Mmn==Xr!%#-qVKSvZ1d!a;-gx#B(*
zmOyLjJl_Z9#^PgZXI%0I9;tA7=I$S9xi#Sb7E!gVv};|o?5(<`bm~{DR{T1LKUrs1
zBA~oapoKQi%ZDxJKx?Zfk6EGPW!%hl^&Nf)VL&eOdRNTZUVmR4ZC@OE@v?V|iiiBG
zAKo|>U9;IIV@?<8kYbdqrgzb;=667IRQ1#$3{lO$QEKWJd>id(nzbn7P)pG>rx$I<
zLo`7xj}zPD!yvVxmcxfpYS6!eE_Flr!^Mp$tnIW+U#>cH29`q3?ulukHib#-!T4KX
z^Zx4JGMab(>j%WP{Qp{y%crwRZRSGSFY~uK4i*(`=^1(ZPd`2@E&X=eT0!^u<IP7R
zYbK+gNb*Q=WVu%y9{nmQt~*|0VQC2@;&jv|Xg2luNt%o-bkYrl3c71krTM@a?UW13
z``GQSK6kr9Rz)`}G;StR_JQV0nf*6QR$Z1h8_OO2_GeX;j!cE^%Z*OdZ-#!$L`$*%
zrb=@DOFH%cm>)c;=84cn$?VV3LA@n0Qp_Y0Fie;)=mHrrA&4_D5*XhQ<<RzoAp)fQ
zw}`SUc~>4zyFGlp2*eMJ?rqUXID%|UkdnjvaL)BeR~~^A0dT>h$u3Ii4%%01ZOLCz
z*$Ms8=LN)p1ktEOaTp*8^(AKTQVOEQPC(y&B;+L#45_Acpv$YzPP`cXtlLP6wOXAL
z1t{i!TERjO)8z{p`2&>80ij&RGQKo5F{0|@N7Q%#_yRGpoG|Y(O}=n~(zqtDEvD}f
z$>r&juEpLXME`Dz#1NI;x<C5#0hLm!P|B}YlDoZFqQAaRUn9xjq_p%|G{~VzC{?F9
zqx+#&2VqdnLsax039(U~CiN3iFTk^D<+|gU3EZU`Y>#9g^_{x(zzP`--LXT#<tGK`
z+J?K2xo6vy8}a+c@w571M4-I9=hUgpnm6LrDnp`&@72E!J)g>2YQ#7mFA!3pHst?3
zCvoUrtgozbGtb4Gl+PSmu|llPPX2?TCwYRuawIBR9A&f>lN3+Rd^TCdm?{e*c&x7+
z2V<-(RdrX;l>@+Oh-e9x157xpDLc{+bfX#b!LAdO=Nh8p5;SRc0_1Krb-<XWP7b!2
zQ?=dDuJl47*fQ0Zcr52>;}HSKp|NBpBpeHlkGXbmA_7GYvABD(3R+sfYBIu{4gq%f
zxSrzBmVq6-iy>7fqZ4LkziP?vk%WGHn`SXOclz0uT!kn_#WLVg&ofW^b=3plNndto
z(&|B@`=%0n@=Q#%6<J6nNI-Q*V4kf!o&8l_%5qd5_Y~LHMTelS?O8^?2UQe@X80kC
z;^@T0$g|TVU{T@b7H-nGMBedQ#)Z6S_X9AE^DT+y3X(b6z4}xjmD6g#{uDGEO>3Pb
zJ3tt26_O5O5^MOv4--Q$Jqc^z@!35{NIQwlhPG3G)F|kP#RTkv8tj^RN}Y<Tn!_W3
z5Dv%~LR1!)wvuf~>SN8Rp-9FpMzevvip>A~V2DtgKuH?lgOX|hG-NewkKhW3s#R@g
z(68`3(Q2@l6W!e3x8?<w_Ky3r6i*A>MOvJy_uHwW2Ep%nPcG`%T(k;TzrQ+o9^Ply
zS?`hO)0;CGkOCBL9mE>*fN7|u&IewWYo_>zKXQ;LE<+{D0#+1!RGo>SRK}B803>me
zR*XM?92HMSLKh(`#8)HX66sdS6f4X4MNxe0bb{5YngA-NjRd*o(Pmhh318YlAa5dH
zd&kAq1Y_7pLYe{GpCFMZXg&k;1i@F&b_SJ+4zwV*3>aQQuc^0!ofA;y^D+DqK2zPf
zK1WPvxH`YQ5>LjUb+4X-FV($V^gc|U(o1FCzlAYfc1(+g>T&h#Wrb&9Z_&zs^4V!W
z*mAmbPt+J=^EQ}xw<fu&-)~5wtL3nWQW9ud`R9p=Q}*^8=k9MqJx3XPWAvg4LK`vG
z+*}_~`Eo_^YA@R4(@_DejyLI;gbKYgdyWMQ0ulsyJ4btGAiWf|V69hX+5OoMy(obk
zhKAKP6lcy{cS|DOaqR4Mudft7_M#L^LLXB(Z4S9rMbcuD;K|MZv5(|jUJP|NDP>kF
zJ5HZTy2e<N;%U6u31(`7NMD3!2WfHY_&z>R8)t~*ou0ng_QnZfYdoBK*7Y?UbqNEJ
z(*+c&=IEb}V5;$fc`O1-ityY5hH!SGh<cA?nJIs+AoVARICYY8ptN*O_d%;W;3e7`
zEkeo!|7_py_)I?vO74DgX)iA_io65x`>U*B(o9%H`eG~GFy$&zh0Eev_jkBJFh+cd
z22d-Rm$w~H?!2{@!ZxVcyW-aY1m(LDvvLorSEG~XF5w=dg_)5Q27(T#%YdpyvP5D6
ziCypTpkD8FcF*BWQOjSjb;Wgx1toPF%c%uq>01H3GhT1R^ms!kOq0hPYLg&0Uf}zY
zd0CPCpU(5;okOGl&IQ27{`pJtXaDts-y-e*$3z<cOF|%oFCPBHI75!Palk2;h2@Km
zr_+Eu8iBh&TK;ToW~zNgoRm2$VOqYnv5HeNDUe!|H>WWNp4PKE8ctFzGc)f=F7f1N
z@dsy3wH3rkh0{p0Ic?fC>i(U!UZs8ejDc!>(T<WG%Z)#8`eqOEp`<g!FIjr!(YU05
zBkUDD>ggw^XK(X>>9s6sLA9(qwD#A*pdNDi)%TAgMC~X;YrU8APkP*O%s*4D0f*}e
zR)ykqKH=kbF=M)crADUO^0S(nnz54~;h;{_#jbF;&^gpN^aB1Hr`^&vS4q#)waHly
z{nAEti3-{Gvd(jFGjzpmb&sP@Q^FjhUistU7V`MQ52G30)Njk5l<coAQIGBM`<=Va
zUppbtCi~52<cO=P6Q>rpOs8v?cWl+;kM_bweqh|4w1VjkVoZ?5<!f2{`>zz9$frmC
zrdskso2NeG$uDr*VmtYsF3g9XS5WX{ZIp&37Grg#n-#Ts#fmzM?W-!s#2Pd+Tot6^
z-7Gc23D~ylHa+ttMu9CKd&kemBzLxXjekbfXdjYBBx7LhEhl4d#FbvvanLa^pUkP@
z@O1GleK7kxce49Mf(ADNpgN_P?{<gCU3u)&`c(3Yv0ixtQouCp%<so%o;ewb%w0Va
z36&=j4`?&T%C4nz`e{$@=xUNS&mhG;nl(@hcp@tm3Mw^P`B_R{&>(?gsHN!sCldbu
z`V(Su{}-bw|9cmiz*aRO|76Sj8<UGvX9ynz8Aet!4|#H_+)Ywe2Bho?fLAtCn{~Ps
z3I~Q@&s>9GRME}tx(q1d*`_Ry1aP{v);E|??*B>0fZ_rZk#Qo#2MYBIEjZgSqx*Hp
zZ?*6vD}J5%&+{6x8h+o{X3({ef54Fg*e0BhgAdVtTEbVt3~&w5vHd_Z1%_y#Uk%iF
z)gN=Sv+V2!h~YdFh#KKZ=|a+@T=QGmo#QI~doKsnG4UBR)7>F;cCp_K2oT&{xtb4x
zUIIW`RVNRW^CD0=(lnJtgT2Sp>YHXDG=L(=x~$h41pY!fK|5&lat$lG{9>v(f^H4@
zM{|iQm0gNd>U0`7hK%O{4cp659TYCk2C<f2Vj7vY+Y{K>zySm&0VN{+SICL@1rzqT
zBF@7c#K}|2%3n8DF^MO~L`N6TH$=+{1y@{}_;>)9Rc8s55m51?dJ)6d9;K_lT(xF6
zN?Og3%t?g%0cR3aJZGAXpq31djcva0=Hf9ZxZA`DPNb!toFcamX#wek(^C1-#i=!(
zpdt6Y=4Ta;kN=`fZWpX-BibGR?oqB>xkK?NQM)M~g#ncK-yTKLt8L4GoEChJfO^0G
zd{)ms!Eb-crBc7^Zu7n6QVFTflgdGS{m#!~zOT2t#Q!)}yt)k@!-Q$dGtQ^<>SU_D
zP@{R;fWtkfQHnK(#0IcD4%Q!-Gl&$&qT=D)KA(ul$jE_50*GeOv#1~XNG(R7qn}gi
z$;!|4gq>^9dAr|KsC5lW${(8c<GB-M2EQ-yYDUW|{NfupZ#Qoi%QHAzyrll&^ni5p
zs{wD{g^O=l-Okc^$82a!SmU`U@wIxBPW$XNpEx<aODeLp#zTAE*ku-GZlht_^_9~e
z6J_<c^16xu0i_ah2cGxbVe{^(f2#4pU_dKvVuG%TBUE*J>JPEK&$VCa^V#lH7Uw>4
zah_*1GO|q%pZSn+Z2iDQ_O~~W{dt4&jk8ZuY9(ozC!2Jbl4bX-ORAdn8*x#1z~y7R
zFV5JDRn)qg_1ed9`PUF2|6bJ;JL0E3F>Dywa`D*UtEXPHuM~7cJGv7OOjOk$eWcXi
z&GPL#s|8!>5Y~1YaxL1nGiJKuPx|_I6!%|h)GmB9s-<W3_e6L<cHV7fzrrovOzt#+
z?;Ws>tm@dht=Ar@s-4|kuRYF(?MKR?CoY?c2UMx+Jv{D*65l%J`?1-qS1ennl}|L9
z+kLyJi|lJ&(LM3Sul2RTk!ET#`CvHQnDRkArYXEs>8@97d+=q){+D(_L&(WQu5QeT
zzU9JpE?xFks^6jNTR;5Mg56B_2A9*h92A8=R$Dr9ud2>1Jtsci;@i4+Do!sH?}fep
zHfIv*f9KWcBNUVA&fZ4r$a4*gcD`dL7FeqL{1;uP5zy!6rY_|v?AG%NW0=nr6AEM*
zD>%0a7hSY^Q>TgJCW9liKRj{v+}EJelQq+vee#mMQ8<|2^TVc=hNw)BQL)gY3E#)d
z3uQEQotH}|b5J*KFxl_dWtY-u?lS7F_d3f3XDbO~ruA!8%}`xRp~})7d>w^^`vyxO
zp7^@N%6flup(7h1p0Hbd6K}=Wg=(|o=&#{o`)d$@^w&$@pR+cK>{<A3fUri4cK-m5
zb_y~yoVN<67hm!%n=yN2qf{>n&BK<TuJ8(__P;w<_1NdeeDrq%u4gWx{gS7U5dJ!4
z{&dred|A@RB6{U{<@D3^Oyt`Y61`E@)rE)5XLqnfY-6<Qczx{bqc@{*Q|O33Tg&l)
zFzuwJiTq8>??EuKI{|C~)wyxl0K>Qi`@~3!<K;z^7XJAWA3aG?G~a><M(Fkij(8#>
z$p9V{=}n$kw&6ckL;O{gbR__%C+tQr-x(jp^%I^h5&J}RL^^8roNE~?dk+!OJB=wO
zB`E73dH&l*`)98Heg?8~|D7rzZLSr#1WGXieJ9`RRxD4zurmU6oiyMiWDUfE`^iK&
zj!A$Q9zmUeaDlHuJ8-@B?i}5;<rzR#D?cY5LPs{C_Mr>cq+yCQt^(9DEBu!uI83`d
z^XdNvu-fY^l-yqEtW0bJe*$FaU7#9Glx+hdw_jCBPl#o^p}|qz((y|+#fE3PtsO5i
z-@9FZ{~bf%!3jS(_*U~eM}~z&j^a+^FyWBHtRX3q$)cS}Q7l)^5>)`ZHB!{{Qh^y@
z_z+^Fv^1rS1iiv9wa?E27GWbIWY{<108%;$J+^FX)F9dl@1bnWgX;2RT6z&_{I@>Y
z9|9DW=<EQwL$hZg5vIjYH33Kx^<C^r%nqS&IUj({f=c)@q(BKlkXS1x?9(Be7iDr#
zOu!psAPTCu+K3_BoW^U7BzAW8VbJFZ#76D`qzE50m?8q$i(8x~0<*XpY3AD=rxt$~
z2a+IGP2DesNt*)E(qd;{M5m(==?!^-ha<FN&^f%Ieok6N0_;R%(D8Sm6Qm8?D<MK(
z0}Uox`+OEqtYy|9>53@A=?lS?=8A>$=$CtB@&|QBPG_=v%zjg$G{;bF+C{0=QthPw
zJ(TRf^Ml9ucDd};c9wwl(A|%Q6X=lNC3;V|N1W-cV3KO%(Bq(?UiTPRF6$j11sOzI
zS{h!R{CvOqgFK>&NC?67^bmmZMc@#H8;7Phg)r{r1(~*D^uj;HpKE1W8-pthYQQ9T
zoR=X+nfd&BJbs{-puK~ho|MT+%V_uA>M3Z_orI|Xo{T2H<i=u&k&ECO5NbX8Hi%bp
zAnkKx*>OVI!>eRL0wGeAL)J>^IKR_h!xy)JQw|C$3CD1qU*bR%ArJl>qELiy`?g%J
z6{@i;%Z81h)}$(kFN_5h`SXb-cV~OnR+ZF|74sqM`#0XIOY`_^+cUMbTe$hv*XD9L
zeA%+`cKOz#14mCj=#8>EcBA9^`8}&vbmy&A=owAo{$uNRU1Kh#%g5GwZP~x&#zUpG
zp_f<72aYfHl{xO0uU^;7DL=dLIw-aJsm<g}XnUiao&98EuR4{KloZwT$jDY^|2YhA
zrb8nnmB+`Ar{0>@mce4u3)21Y*n`dZ``-nDk!OeSE`I;xWU`Y&==%GgKX=0l`G5UN
zS~~e6t-!y2MNeP)zN&S_eUnWqS70cii2LGEYisMP(?9hu3osTI6@3SD{Y^&bxtAZ%
zS8w!H0(!}gIq0)iy2*V+*2W;q_1wAjiy(b=LibkI)>e=-`2%cY{&0Ik392C@e?{K+
zRhenXbrv2Q8@qUj;WOqaufuqF%(qtDw}nS!?G=m@?8NjS5x04f$}n+nj5&O3*~*xd
zl*Axp{z%?Fn3tDV*2ziCms#{L{7(Ah$?s6^EB^M*^ZVf7Ao=>kGgs5x1XU}$r0>qw
z)`-}gb;@NmsR}!3XD4(nRP5&S=j_unGtbcbed^Sy0{q=G6)qhIf$ICSM}vcc^ueIr
zxN-$%9Txz1HM*<KTG_>M`0!>pon*~Ls0jW9CG<2)+Upmw9GLChkZQIa>!YZo^p}^{
z>cWzePTY)4h>(Sm#M*w5CmXCRQ^e*L2IYrT3udP7rKetqnqK@BJ<0V=`&>?H#c63H
z-kUpD|CQ~zvnnt!&=a0Z*dZ6AoTqUSZp)Wn`+<X84w>|S^5ibyT~Ws8<)TkENAU1s
zuJXsKs`QLXSSUW1>8mCmuJ14zJFl$l2@2^hUZ{|ct)QmaxqJ6235hTpdRb%R-Ee?r
zwxO7UYXDZ=Hl`koH-9AIDz=X1gr_m4k3TwM&-2G0e{kMWx2){>@OZXXMKo~p{sDF6
z&L4cP(9dm1w+b(^Q03YR>C%q#ELW7mO^$6mWYcpN(LN2;XUtiK=>GWt4V`z5F3d+A
zHe%F6X6+C-j~+ZaSt46s9*{dPyYg58gqgfC`dyQoLtLE2%}w5&+57O!&pM&F6w%SN
z$=H;U9aiRK#rm;g946KsJ{D^1?6Lw24><(o2+ZN<C!6oKuyONdPXMh3(7*Dr>>U~L
zV>x1{p{<LT0IMnWOmQbBN|)U??!A1D>LM6JSSs18R}aCm-g)$hfxP_W<V_fh+>mVi
zCu-84<J5WIn$@4{;qbq8cxD;`#{R*<xA8iW$!x|q|9$+Kl)nBhsKt7`e$5Sh=v|}?
z6MDPxwmJCt=tGa#%bYs3+Qr315$lgo?>qXTWwF_WtlI9W>pDign^aX*g%_h)M?;PF
zgH<_Tzph@rx&WO4Ahk1h_4XQq=!6g4V)CtY`}fs18E@Po>U<U_6AUDFr^z38a8J{7
zb0-q(I{|%955y(@oEynEM3>Su8e@)Ly8fi5R}^~{O!;?^U>b!T&HT3d#covzfYBC2
zB+*X4Ys{a0FNlgovlD~gdwXx^qwA^f>&p#)P*b1R((=aPFfuaAn103FdsYHFbl~i{
zoTS`=AI`;0&95t6+?}+XYd=1I>R=cW<n?jaAO0(?ZERMhY94e!Q^>iX6yx%3$Kuug
zgm#`})S6E|KT1o1adW?_HT@1@s!PFWOVv?~t9|dwa)i^j+Sa^6_QHk75o&Sd8(K21
zMZOyw7%<(nf4>|)bllc9_3W3dHn@nD_%-M5-D^@)Q|TTi>gDT_??B;*kvlFSVH<?7
z_LD2%ff81$+xc{N>MFU?2pUiJNXJ@)aGChZ#nrBbuENF|)6}coWm_M!Ul(__R#H{n
z4es)3)LZXdof$JGw+(Rd+W8j=Uo<4vGmGrRcj`|Kw_18tiZhm%`Y;I>j(jt#PsrSL
zyxzLUrZA-Gb@c1*7ou;~M~n}$RU}n3HB6b<m?f_LYr*1f&*w|K+4)Os5>~M%?p^%{
zn!;BUn8~m=9^beUW|T^*w=%RM!7%6bIA*W&c$e$&=;o}Z>Z^&_{QkoSChiUE*B2Mu
zpk6^$;>Tux#oXEn4xg%~_I7M+tW){QV;g1nJ_+&T7ZK5LsWQ5Bsf;<sOru=ZrW>z{
zFT9dj8Gb_SdLI?ltAvDyM}_Q@_U^@5xs{(U+2%4X@3&)xC9l=%sY+9BvJF-#3cpNx
zdGDg_tw`I#kmlD9t*f=0<LZz<V3Ok8qPZ^&mvi$LX=x=xS^>cwxNlFH$YUBB4D1{n
ztt`If=*{~z+{#3ysHk|+j>RxC6@%o-1C^DdF%gv8`$e`*=~f%4MC^V%I%8nH>Z{|}
zaOn2LoE&ERk+xM-NjIE%J0)FZ{aAzcAFoe-=+{$5HkU-G=$_DXx8J`%PE`PZv+s)b
zu#wq0U_9H6OiT<x;at6L-Sv}~Ex-Frv|XsPW?%gWno^(_51&0$ozYVCMXaO3LhX#`
zm4u<%;wBTRant|iVLF*V*BF_%=dn<sh|lXVbea1MqE5fg62DFavjsR+erV5K35)aj
zvH-R=+tslyIk=i<ShC^us&>%pT1lmy>r~qzcJky&Di1(T8Jv6654Gh)pSZn#_1d+u
z=|4b2r1JRr^QyP74}Y6&n+Rjc*9-slntYi9cdpKGTM$ERwLVwZ5ACxYc%hYtbjlt+
zy_~9vh8s4xL<5t6^yE}uC>GE{v!6OaH}~%S`x~zh*3ls}*@AKO!?r{8W}+C(D<v;a
zM-_p<U>aUF$#zif=~2f`TtRCgBKfywe45B??Pk&0#y%{IndlX~RkNAP=PcB;@+3oW
zqMQHi4X<JJp^;pgKTNfA*RFRJUov#(CWk=jH+`+0xe1muE*(Mtcge+0$wTluep#Mr
zkr0^Hr;>7WlPD`I+sMeMX*C0bNa6*mrS?}-*98Ryr_pu0f{Ii;8!tM-6L~CIy$vy(
zfB7mpn#L)&&EP}(W&yl;idm5u5{(A;T_5c!8&@7<F0FfZg4Shzw4@`dwymJ<DMTud
z#nrNliZ-e~%e*OW%PA?T=R)j0hQa@6PTzniVH)jR@aV=qwW^HecdiMTI5>!q-Qj}d
zp@TnO%{$k5$^Zl79Z;(tMGT(Jc}aA6W$KEpt6Q4le7Zk0H@}{Aa~HZjX=-ytc_V%4
zyCa&kU89IN{-E;m_TDVJSwI+0TJQ*CL{=8#%*>3gV0f6|_wV2P8dLUCU4%Wx;5Yu2
z$2@kf-p9iu1I3t?$1r1{t}g4aiX6i_8YG9b#>U35Vtpt+dfHwWW1$)hrVSDLCeOQl
zLLjXxGV|J=y={*aWPRll>+7BM9z1xklb2V)#mZ4k%O(KMi97?)#>=f)sos&-S+vIK
znVIham)wq~pkn;@?V;v$L%gq3=g!?iH63#GAfO!IF3Y<?%i194`XZR+#C;sH?m7j3
zwxT)Rs)V$acc4{%1r>&sb|S9e@zFD_bP;kg3#qMpDiAywdvNCRdJ8pYY?(8^w~Y1o
z)t5~-K}Y(Jh~a4AiZ5Z}&Tq^c`QKjoN(0^lv2t)Rv_3MP3v*MQ5GR&VQ`?3O?~6Tb
zfBQf-ZmSbT;SUH1enJVQPHWqiEq^1$AT(iCN&A?ctI5_Gg$vm!A;F3a;81p0xL&Jl
zYLZO>bFMD0#8i#HY+6sG=)YDg2HuxY=RLBf<j$X`N1AdI-R<8$KRbb>E2hM!fZy2c
z99COm;g_<sjEq6w=Vv_dLqB3TdZKp%|6t%ZT8+urhptp}8W|fu%gAVmwEAdbcJAH?
zwdXqzL}1@BJZod{DjFJthBx}KuiIn`h^z?D9W0fbkdxuRmP(vrOqcgiY<l{BFR!jH
z$MD%M+qHlGd2Ogk5S<i>Sy%4Fztublnhp&WdLbmTuJ`}p?(PnY3c4H_-7*a+ZW?S@
zl!miJHR}~)RHm_CN6}fhQ%o#Nmq+cj#;&$2Ee`#Wdl9T7Kz;x0#O=@NR(b#}YCDbw
z6#L)BEbndQ**3lFJ+g}P_X@cTmCD+Dr}zma=0FS_Vs-g>EC-YWFJIqcT;^)u=UtE4
zABKyIHfCJoz`$`5;Q1x~`S8?*kt5gpcG@X2^?FQ5Pj<aPzj4R-ix-?JgPeQz{6PhD
zW`iH|P_H@5Xt7XSyyz94AywzWQ#O}V{z!8r2Pt;Hl^HonJ{%FHL#S0#Q(^bBwr{*v
zh!)Hh5th2SZ{8eA86=~C(6767J?l{~kmnsNdT;E<)Y^4SLhC-<qBDELEiAm9>T{&*
z2KC#!*K|q>)2?Th!dV7IbX{De)V+xg<Z0V_%d=rvsUpyDHMa5~2jt(GMFKZleR^3_
z0=mXeFm&`0Z^0um&}?Ak)_<FzZ*}gT@*bplG4J?0qYBfl+Ka&vrJ~Yv8C@l2_hX;y
zH-7)fHjqtrfJ^=%UMrpRZguL9iaY&pxuIhg-e`-m{&Wtk+2Z12^(yYe`v<%b=?|64
z*<4N()0EB0tZ?ErYYzw2Cbf{M3C=X(TjP>{K*`s^iH{#YQc)%5aW*h^u4Xs$x!%dI
z5PCrVG%}WK`w<$db<854E50;T{R$7s$PjrE9W6AgV1DY9m&0c6Gr~!wm6bG9zed{i
znmmkqPD$j19u^%fb}6$x8#eEV!0nfltLN=&zDi%a!=w|$loZi0z$zN#!6CY4^=c{C
z0>^d?cp8_JKBfX2zhZugZVfu5E)T?y?0@l!qpj{zs_<UiaZsmC;a9{38e;ivExH!>
z_SQMFv#TQ*$gIUlB_Sh17_D5xWZ}b=2OK8`RK1se?ifj9-La$pTBO{INi+={kNmQe
zi>oYBrgw$yQVX|s#zykms=~xa-_f!){HpRf{>(!&o`%XC*6iIFh9ZB1|D{zFp8cpy
zGN>NPezj(S!|Y)84YS*k8!37r$AJ$#DE&$OoY%SllM)@vdgHWhUtoOwkA0(WL+r%@
zH+SX~7yH&Z8(+Hg2Ne!=;f!vGSj>QzaOwq?(mTTG>fN)<qg_IAyX*2Kb5nDh7!)kA
zK{GcUi71VSq@@;B0TSG<o>MJ*j~>;|S$7b*6vT@+%7$M_)tsMeUn<6EJnD#<uK%aF
zE03mnZQDOZBt)SyHystpkRfB{oaC5}B1$PqY{SkFLP#=3r&5SgQpS{-qDZGQC7Fjd
zC2S%ok$SJkdEY<Yf4=qo^Q~{M)~Xeo-+qSszOL)OpX(V$GzU2n2RN%KKTjAW3RwFd
z2Ws$hvYDHktA9>S3T0ouPDx2n{PdT2yI<YWB0E-ao?SHe?Nc3^W55{}Q=7JK)kl_a
zK5?SC+{Z1h)a>$u?-K?MgEhia!>{Bn;b-C;S&8TS$e9ByBL)+8Wha-42TZU4HAYlb
z?cA_o!*ef=go~r<!dkmEh7|<dWyA#p1ooABZUpzT{1k3&lOt$tZS9LbL5iyMn|=Z@
zL}K?3Pxqo&E;04hgzKVpF&ca%YN~MLq$<gzIPrvb8s$wcFKz`i`tD9@ZBF(*;osV!
z%5ZhHI`&wrGN+Q$*DWa|;(057$gtNHJ*C&qg{u`Dqi2e%?RmGoQ*?c}QBu;ODNWn)
zWoBk(RaV|3&b0AZhPJl0yn(dVF;DXY2V${;(r?~;a`-z&$iUOHpl-W;%HM`i5O7e!
zwaE6;?`I*q`E~C~Kw68W=i#32EIKDX6F)gRAKC^w5%cDa<%2H7at6-!P9>ISt0gVp
ztQmSu=$Pj^{ds5S<Q*Accm`_EEw>1O!1eQ$$|hi(Ub;5lWP?4?CZp`!eq&9b)k#?B
zS#Ny|?2)M9Uu3vPajm}S<}F)vK<+Dtm5gIV)!sU&uD<Qp=xdSG+VL%>xNvVu4%sG`
z7;`O2oms(C*n3J+eDUkm2Re5+CrH=x9iJ^L>{CW4<47A{A^ivKd6MUL$+c_Ol8KR5
zP`K~brwQ(-o8Fv;Boz@8QwtM>(2pOTIvAIu(!cux^-KbJ9tH=QnQlAi@on6$rl!Uo
zBf87j_)BI3MQ7E{SFO8u``aED7ip5~zI-`}wDk_jJt8vl39=qJY3#?0*lRfTj(N<~
z;)9b(a!=9Sf^UhS8S$4dhvNc72}wzIHa4~r<OnE76A<u7u0_87mbjVq)#S4&CYS#d
z--;C>uwh;=CAF-hqhr&iO~F=K`sLnZM7%-#zX@6-)^gr4Hr;Cw&L>qkwm$(BIsguX
zMa!w9t2+sEEeBlwCN`ZOv-w!BLT|exn}~m(uLIa*{KAsjj9r*hxkcXu(Gs$VRP29=
zS?)Ye{}Hy<txI52O9K`D_iLBnf9qcPi?99XOA!%As{ha4c+>G6s@0}jF5!=g*T`Rk
z?^9Jgbfh2W=bL{1EGp;HE{h~zfrG`4SGumP1bhG_TfcepQrE5`f#OpDqoX-@TAl9$
zE|H{1kX%V`IJfWy4Vhzq+pSVRW9b!I@rR9#&|klA>^(Ik>T`~vbdE<f1hyan0qU&U
z?mj9+z!PhPSjWr9cNO7BXWzcWj-x19;aBKm)$l<jsj1OJf)Gc}qCK?L=1prEzQxn#
z@_o<kr#g39wC}XsC@Ol(^xRJ<RQPi7Sg?id`y6e?@vMBJqA{3P#eI$Tvh8b1D+Y6y
zUEdj5)|?D@&jXxI1*0`{06w~2biLP`wr#6}Q^qnd8FDUsoTtw9x-Vnt7iP!GbIZjI
z3=Ecle3$EzIsEJ-LbS&{vG?iBUZN0^C<c|q9+$F{i_dwn)&MMQD}0-K4*B0cJ@@tM
zSiqw;2=n#cnk!GOcVMqHxpP8ZOU39A`bL{Zr(sH=Ed7(W-Ij1BY66|7%BAAXWDTfu
z8$$Gquj?wMOL~uYpGUJUKWun{b#6H2n;bcEYp)l-QT65I0QBxMrXWzJgOg5t`{tJ4
zi<xDiFbH)(R1-K>S6z4K_|Jy=vL=-encwebXV<{AdI`15$jF1feO&p-bKbCX=hE}@
zd6BfAO}u;BBf0{rO?;j6wO?!SFP6mZ$5(*2i!TXf0UtuNuZ3N_tZV1?nc3OM(K{I#
z#6p4=-9DGB;V($>ii^iVy-OSmcsn|cZ)T%@Ku|RV_DFIWQsBG#?fsR5PwZvW-1;5?
zw|V=h(1r#ELUB0`D+LqRBR?f{u`mW%fBNjgFke1CBu@cTLN{73EzN~I^7O~|DKi9L
zm_Kb}r~)MEYipm$(w(-!qfx;{u3a{^bq^grpr&zA<F|DQR<5o&DnnD~ZSE1-(;^2w
zlBz;q1VQB`JA}xLHrA3&_SuQ#5#TSmlOIhd6$dgEEpU?}*hd%$$X$y&(w8A;=DT(&
z;Y{z66jd)B$P_lkv>4!;ititi+A&A>va&>DeL!0di-3urJCNs-DWc8mKGCmD-h&av
zE>TQKNXUoGl=Sl9Ul(zs(w=~M9PV2}NJ~^+&m4pS{$f#B@l_r@fBrn904=H!dCGzg
z*fV4!`3`VLjjH(Ny5uY)l0`5?YH<un=<m06oSZgc`-Q_aAQky4zijOMO39jeenG)S
z6hJ#sU=f;G+1PGCN}j<<W(*;%P}={wwX*S_mX`Kyxpp91M7H4*6N^ntlh89XbbyxJ
z{x~QoJ>8)G{U$9fanjLz78>*o7<Uoz84`i;tEc<T%+A2FdJ)?B&)`9>$k<I_7cGWp
zs3e+hkY0_)A~G;B4}r2kzD@|r;NW0l&j*NJh(90S<|NHvFnfe5B+V@>HumHd1_<!;
zODHM|ps=iKYRY@~@L?C#KnrX}!5|l$nZ3h|)o!4FXi!}tCMMJC@>-=HL4=QQ$4-fL
z>zsAANJ+6#i>O2Bu~u^1Yilb_R@i!{!cM=J+w96V$Y`DVe5*q~GBPp@{yOJJt^FW{
zDz|@yhYjO18h8`82Ei$op^A$oL#plbzt0%=RQm5lh3v1|83u#Fnqwt}tiefzVdi>W
zc3k%S3~s1&ZSj8bj8dC&@0=MwthrVPz>qv@r!PWfy^PNX+Ya+pU;HX%y+$mvj?T@^
zEkj5v^yT{Y_Iz|SSJY89cJ^g(Moel=Zr2$vJ5(mVzaa=paad#|XA=Ek;JjbdQLF{y
zY-PzmuJc3etj}E~DEJ${fPi&<OGAp<+5Ud}tovE5&f4kCZ@L=3(4Tce-q(LUf9+)(
zGT&L)VsCH%iF91#$AspsVn#^-bSRbOW;I?dABkv2cw(avocZyHu1hy!SKRbdS%gef
zq0?358h_yeMaiz_Dsg`oV%N1SBpCKFoZw~&b&j&DCr~mx1iuF=SU}3EAI?*9{txu_
zlTN4hBG;ta_mU>bJI&2kJhaKteXs20d0i?U#z|4iM;_?iX?cgf@EYWM;jL~sOiEge
zq-ENiwi6x;FgX_6YH5)r40Wyc!dhLIYg#^lrmNMwvuod@Rk%6`##IAoh+ZvgA6)BD
za%$=~1nF8&)Z3O8+Hl7hI4E96tY@IA>LPpZt(o~J<*i$}anSk1MA`aDT1NpJ#eCF!
zJ*(kr`B!#oF*^^dsj_G@PQto$hLh+!0+PVTcPfT&y+K6d)YQ~iXHiMXYRr)^B>^;y
zaHjj`p90Xt@drYFD3N`zS|JpltAGFY&6}N|zE|8nLHYvo&zDlNxO(;KKjc2?2#Q}f
zva^jVv>j+Pnc1&nQ7DTZeE#Bvr;|8-#7egxPRxKcsK{0d>s-?u@2!-gm7~$j?<J3@
z>fpqEn{{oa@~WPL99DS;w*H*Q2TZ$lixr$pHHGK#ub=*vWlA6y2HS>H6;BA-6gr=U
z+?NK)65$))fvXIBQCFlWQvNCT$?KUED!oZdG|y_qd<o~;mWF`BM10S?ckc;QJvpZ)
z3Cb&_7v^jZ>F?{WYY0Op0_&?ncB!S8La3X}xQuenuHSzT!Cr?69E>}-Co7UwJQhJe
z<)!+utDe7nc_Kd!awv>t#VC>?u^!VhGdWaKOuGB99BS`66ciMU%vUuPx$fr(3C0b9
z5qT*)=o!`i<a9;CcpJ(zA0$*%R*$~TS=snNg2US<!OGGyEjycwVg^1gVI-H*gDBoL
zYc^{RsW%a1!Q(j=d;;ruR3s%tmzVV&IN7RVJDg6cJQ}lSv-{5<r#!yH_L>>n)y*#g
zit+@lcfXn3w?AIS?$0H(eOLD%-rhwWURZ`_k<21Scc|8fNO0%`1XQ#_8sj~wnzF9}
z6w1Lv<8DU3`&U~<@9t;An<)+)OP1Xq4i52~aiT<Su}eq6lF`JsOL1>3|Eg6<TSpP`
z7=G|(D46q&wzX3hN^cvjJ#F0?cRV&EH1zE2*X9pKF<sfc{G8}WyVx;}GGq4ji!jKt
zqa?z@!#N?mSviQSp3FpO`O<7CEDc}s?2=R*%eIj=9GSoNVOg*{Or;FI^&|q|C|}#V
zTaJv5U(mPLPTLu?=(u-2d77j>=ZN)yq0djSva?4ue(ElDXe$COkq9__nuP*NHmUQ3
z&~tLEQveIbsV!F&QD&&g$<_i2D^@ZUj4nwQgnYU3>kwjo!r-dz?rFuN=BZNTJ3z@7
zQy9kQjH9z4ixBVy4ebKqyleF`McGZPv&i*?w>8R)vi4OJwUb86ed?iHSoRJ$0Gw(u
z<^8F*=i@9+ALuS%!i@C|pxhC}E#0v#DmK;ttq4?F@1~<^v=@IteOpAKD?%YGENoxQ
zJoI*oN>=k}xGNm@qOfmp@Q6B!<2-doEzQl>X$HZFo7rhKPX91I&$83h^umnh5Wfgn
zFR_OfwmWxA>7St?A<RxC2?+{|Z81f1H$G=I%m>`Rd^GE>IMtF{fV^=QeqH>KQT35J
zJ-#xnPkwBw&Xs=!(zPP>7mm|CD5Du0b0Jix^d7U=@V>O{%JEplLMJafv>SSxrmA|q
z80jvD++Jv7Wos)ETqZ6f@-DZGY#+$Phrl*SK_w(41b2tjp7VHg06OHgYeHE^HDIGI
zc;%|M>LZ|Y!8UjXf9$PP3!W9t+G$6N8TtsxA{=>`d^nWpP<9VqUG8CWim>!Tp>jpf
zF(0@`@YEf7p)$Y3{?|}O_t2}JIR$y|eJ^)>QYW}id5Z2sVMxs7QW{(0<IkY3RE^yc
zjtebFqXMaglt=@N;qIvFu%#=dV~;4JKc%>42%12#Dx+CUnbysl>cebr9x21*YxsS+
z5D?~Wv9@(#3}WaC`&X`7MI_$EnZjOq=67G^9%EJ&)U>b7ZX0#wE)?^56N5@}RJxbz
zjh112`mHi<9R;^*k3Qqeq<F=|XkJ<@EG(2zZ*=2B`oRSnV7;}@(jjH&?eoGu$T?~v
z7cB=hkB#?=6HH(f`p-H?T`-~%p^a&}MovzSsC3TG&ICk#di+Za-5udpS+%etZOXM%
z!(Y#w*8s?^Ox41<n<nQ%o?6ds+PqAggSdjR;Gj?bZHHZv^!L&&CfiO7YOc!76^1?F
z{j8}D!J=n_PRv;EakbsK^FnuScvv|(WzKZ>-i=y#pj0!|4;IQC372<cgx}aM(7g&B
zBct;wK6!C*aZg2?3{bZD2q!l}XpoL>qSy<Uz%^7qShOLqZmlaYfb_G3l$4k=i`m~M
ziPf0lV=|@?qMweGg-G<{hA)#(7Qbht=)`!*0w#3Y<|+)09LQKN3IFW&kFyzcr7pG8
zXa2IaR_0m~s!~k1x0gUFi9rUtaqk`{5(zRzh{R4_R-jU~3h}#z$6E{n0{g32<P7$U
z6-9@;InQ}*&@{XgJn0Bkkxwg6sJtwg<jhz9<V<yK-jRC$IjFr+SN83$t*c}I`|rOQ
zedrKQX`MdDwI&30=$V<BeyH>xyNb8b`W}s0a%CuUDD=L0l>oct-LHqYZfuFa3`AH^
z(hEC`lo5^x-)yhSr@T$OpEL~x%VRbMci$x$>b|VL<dy#LAwSS26$}N)0d?4j=x9C2
z$^8QZ7N5?AXKZJS9#&@}*d%0kKMORDKk?XHE(QUQPK&;ETs}pz42YN(9frq$PQOps
zxB4|?GT5J<K{@Jt-tCNxPP>L}kAH+fRoI{MqUvEw!{sEiY0%OgJ9bRKq8Pn$qy|4}
zPBVn`&9zz<?q{c_98tk~&Gf@iBLBPlYI(}RU>;z~v5k+F1wM?qt)7D_dkw0qs~LOx
z1Aj~!GW#C)G0Koj8DBVYnQ}(`+=Wo7(ePoBXK!hazNq{{dThFT+Gw+^EHBL8S5Okb
zGYG{zR|G^vq&3}`2Hxi5dwHPHFx01UJ(DMW`<}RA0k$}euZhCz*COP9wq4K~;ttEM
zHU((!?zSbhDUM~!o}wOK`yMSN`gk8xrWi{O^n=vZ<>lo~ZEUu9yZ2W5ufi9f!02yd
zuG{FYk^_}~YawTYuMah*0;6e|T3I>z1Q?_S&fcA=fVQ;{P*|nF2GxGl{{ZQBT^t=;
z6bu&P(c1|>9d<Of2@cie=H%!fI3V^DC3gn|e5;r7qqb}6uPPV@Tu)3S#w0g^Tz1;A
ze+P`6#L+i<6jvw;g@y{;8sR;r%aeOgIHTn>d=hvt7L4v{Yyj{Q(oxWKd-tkxTFDS6
ziaETgk{dSIqb#T5GcH09)uY_DQ9>h=lSM$h`&=abkZNWEz~Ze8zw9nOSV|Bq72H$q
zQ|^*_eI2Buojr5i@kw2T8uJkaCv)i>dM!|^t33w=7)ek^M~4@IElzVbvzPlPEcDjj
zx^+vXun}ZXH^&O@U^>Yr5|})w6g7ZWEEE!b4H_L>WgejHta5sGJ0JiqA`T+>5)hH6
zmQZtYYY#PIa5EO45N|*?M{sJYc$xQD<f~W4&`#4}P{}i<D-#$PI0KoA<<0yBoWWfU
zs;7O!w5qR99MP`*!Lj9}bpwGvz4<ei-2zER*&!n6F}&tDxVdpsC^#-|Im+VqJq8Rs
zzC#0Ry7#A4`Pa4Vdt137QG-q{%1n~kxOOcImh9m`NN{q8p0J`Sco?IaAd+N>_m0ln
z7y3>fhNC^(nKNh55Op{g>RB_X>7;6kqSVa&0%a;_g>0s6$Atw2m!R!3(5lB@Al!29
zQq=uNtN8gNw2Dkc2bLd3DeMyS8EMDUtLI)AZljjrlZ>b24&EiG%sqwO*gm(P)&#6^
z;4s0oOpfv$2$3ot488Qx9w}8-p~pXGrEH2^&I5Rol5<<$!D2O&vpQ$6`FL=fbat&5
z8AZ%+muR{adT{q7GoB<vF*%ILZ*|A}!kjG&X`-qT9<yfPXf60xKYYIKmj~=9DWFN)
zJlw?$R+Pki0w9r|yumktgR-=4l^>Xe=?@!xY9a-sgen|=WXqe>ud54uKY<269Lil3
z?7R5Sdr4WnzUgKIaWdos9?>G*ZSi%43OC(YOaeb}3ts)C&<WPYD=Io>*@3*^i#+Q-
z^<j@oZ^arkG!oX2#+|0S7Lq@v^MY!T{5(*@I62<kapN?>Ju37MT$lkfl$dEfWjFHk
zo5nrSCY4VgGGX?VuYzo@P6z^2crH;@esEts5E5Vxkw`NdszIbMfaVV(i6F88DH0Ks
z&;(p}%Z^4Q>#a`(hF-vP6{(~4`_vGQ^q)kNT{Zp(iv&7v4M9ynH00^LG6PCW?$NL?
z-y!g~KRmv2#mbc^{BmJ+g@v(VKshi)hQ`LxtcDxnU_-eA6q7Wl!N5=v%?sy;l;!1D
zVB(}nD-)CHf;(p$UDgmu2tZ!;=nH;=R`Jv_9eE-@=I3EoYfO1rM4F~RSf;9UmurL}
zh?W0a^tsKO-BXP&DZDBU=<A>LRd|BTgQ6k25lgRB+gCvWjzd|VbXgb~8IAK0$@aN}
z2kSzsp_xKknb!%d8Sb6)Nxv!WWnV6)XJoV#Z1k9yjqn1z79rvtd6C8fh-lCj5%Cz%
zrRYApXf4oHL^S;Palr-=7RsM4#OHssMXU(y$XZz1vQWV5h~wjQR5r=Cqj3HV5u-<x
z2R}HzZh+&zbmjm2M)F(#=N`!r(FKx37N4HphGqZz=F0!?H%9$hoLQ_EbX{R%2|0sh
Mq;H~^t79MXFQf|aGXMYp

literal 55128
zcmeFZ1yq*pmoAPi1{NqPq0%5JNQXhZba$wLlz?=Hi6S8{(%l`B(jwB`(gM;Y-Ej78
z{AT8?Gi&BIXU<yxwf@8UzGc1fJoj_odtcYJuf5+&OA4PpapeRK4$f(e$bDHHoFkDq
zIEOwTKMH?S@p3Q`emVL`O!z+h3O}3oid^6q+@~Tc7C1P>Zs`95-eZfpI5^jEF!%4s
z+lI~#*xAbI?(Od@T@5yQ66Yr<{Z;0eAU?iKkIsd&_(us23(1_7!QVaZHNw^8u~m8c
z5+T7^>3(VXv-dv`AGmY-<F%S-*VNpD^z;KE{bl@HGeIvYC3?r-tA=~e)LDhC^shS&
zPVgmQwwuDXy_l*95B$0=9GtU{e1`G<{2oV|&Bqnp0Q&j%{4ne9-{E||NFMq3^?BD0
zoc{CInMZK-|NPdKUPR!~pWi>ZbMVTa-#$2bzU9wvan#=blOIjZ$S5V5&~8ttz2i^J
zX81wDq!vq&SKU?OU^8PI!fu+kYpc+hRI2&=3tSTn;XeHC-MMo|dQ6=mKp`*ZzEvVV
zKK^0itD;<;-6wy)?C00x|K2t0vN-S9Ue?y}km*j>s388cD6XO&|A}>&q@DjcTwy;R
zq-R-4phV5br-{K}rrQ&|DJ4S_N2|R93QA;RgleZO>!vIX;WnJ>3kFQm)C*+K@UFa1
zFJ2O*ASb8h;!=fo8TMqw?d`3X8MVb?dOvErc`y@j3W_~^7<KgY6`e|(CeDe;mZ*Ez
z4T`=E?eE#7>?X-2_$Qf6G=-%VT8uX=sx{478&$kMZ9koq&=Ga-SZA_=*y7@1;l{{2
zHp@xr#t?R?f`S49UV?YbRqpjumb6q<R1d;ZQ*UN)mG>4{B!t_IU$$Bp)UcZFka_YX
zmEqI(g9BFG8md`3Z%>>&`ND*bXr1u)Qr1y^c^{O7^<cK&+niY481>$!zi|0-Qct#C
zidw#jyQgQa^SpHt!MSt8*-eMe@I>NXpcKJgq*wi6as7*y%dSEgw@p{6)An<BtMP`Q
z>F$h(W+(JlS-SP)nA+Of!XH>MNB{b|y0+YI%@j7HE)gt4!H@H3(cllKXkR}_(=1Un
z?#uHdXESKKUu3g@m#k@Mm^|H?LY0&_l%17@f8oNYLe-eL0ersw#;}{!&+p~3eeb-z
zyM;-eVh2kc0#q3SXLLPebbjpfy{l_#S}P=VtgWk)U7hYM>7{Ory60xO+{muhT^;fI
z^i{*XolWNqi;vQS+Q08V^9)&JO5($c*M7l{{_f`+owin5ubLL0%Nix&wU-Z2H^;QR
zEteLNkWg42Z%q5~xH+6R*LipAJyit7R2^MLPVYdrp11XU|7H0SZ~nb+y``gG+@j<W
z6b4<XDpy$br9c13mj8e=51VMvZX)a&*_nvW6s4Ke?4}cej{dPPj-8oq6JzXbixb=R
z$F1i5eQaHqt{>1i(iqOG)*dgB9iFe>dN)NeP1f2vuiqr!+}u3JdQLUhWIz?W;_b3S
zrc+JAZJnBxolSvZGwf(_BXv$ZbPSK0pI^%)zmIDV?mtJT_B`*#_ZRN(-qEcu=9vzq
zFE{gRIjqh4kPkR4lx!{4GuE@x-&f%v!sA_Ox!1K!BNu--RokQW+ehuR2{K`?o4GrJ
zmo#F}@NEb<tj{Z$n54snz(%r~{l-kMoGC!Bnv+?FclEZour~>Pbx`P)V|YZ;+iP=U
z)<qN;TwL7X+s46oq<pDgzTAZqqpPcHQ0Y#f853S)H50F0?X~%2S$^ox^PuLB|DO2h
z#EBCfk#`Q!(exxpkvhT7H0L0qs4E;W8F1d7+xq_EjOb2rvE0*8hxr0U7F&2ShJ(d+
zubL8aXgK_DszjR%6spuV2533=&~ZKqaN1r=G8?VJk|(q$U7(VrrlqYj-Gn1F11s-G
z-kGMRL_<Tvw{0`k76)H5-4gX;XfHriGk<?)f*&0~NB`3k_(WBNxu!#!Lr&{q<Vg}C
zw^fU6KMv&$nB<!jj9qkE>(Rr;!b)$2>T!4cJzBFB$0unYkJb4pE^$&)3XwXk_%y?A
zV7;{Xfky5wmxAEq$ML!7=G;;Td`ouM`lfqxQ~VsmZ|ODBVEDw^dh{ZUDZVT(FXx(%
z)ydXNg>tm^I<K?}?@|VE+svmLr5D6^z~@;G4BF4o&uoU6mR^1`nb6@y%$@qfLSFtu
z>E70i?1erbA0PXLlJ(RnOuR(MxW&gxIV<nqc_4B`L_mo-+~PF<&d-Vi2_$d4yt>Nv
zRx{ocmsw0Sb*-tHmJmEOE6if4(r*Z4zQX7DDK?sXyUO$GA%?0;hT+Rb-dEY1Obd#O
zc_hMldhGf1TO!{RmD#UObFKGVP+(wH>+0)IVv3?xhTTY2`_Az1Je4N3{909|+O3Ga
zZQLs#?J@9ovF^GZwj>zkyt4lKs>$(W$@Texd)AK4N!NtDNz%%8#u%@_9>4wMTcn4F
z$BA60l}VY^g&{4*(v9fY=&74<{*vk$ic;^+`Mc>2HAV1i85kJw)zfPhD>DR$VLS4S
z)!@`IKIpTvvs2A{@C<z@9x+#<W{JJKmzRJ0)QbJ=U`b!{sA=hsrzl9~!l`bKoYAoA
zROnh;o9h#s&0i+@of-x7eO%M;dN6C^772M?Iij%8aI5*0%Bf3Qjvp`HRF2%(u*I~6
z^E#|RD4lVqOP5Yl&HeEHePq!>sakyiqte5N4}*H=TVveV_@Bb*&b643472J|x&E%?
z=E%s1WuI}rN;Cidjzz?nrtR<%qwSB?By_nF+8&e=!Lh+?M#*r(>{mOLU`vR`#mCda
zDL-=Lh~^oo&CN{$j;ju-YWYpQ^Zf<cBcr1Pq@)?&zA0B$Rnc;B_1fD~cA1u}Dy9sY
zmhCD=Yj7_x|Biq!FXB$p{`iq}{2)EK(oHTdu2?h6nJx+VqRxZ}=VVwTJNl;q;o<4E
zzEml436i4X;vI0e=PZlD&EjKYCpQ*HuX`4y&X?^w8*T6JmhCG&a)AiWc4J`p>4rK^
zOS_yDZyX%d1X)Vo`nox5HL|j}=)-mp%^+s^g@mN^_p6UhOsJh{{%XHiJcxB{IzZoJ
z^Jj}*%YiL=or+O2z}NSJJTLe8@YdGW!p-pzHq${K0(|_*&(98RO{ZkA>eQSylKJZ4
zacXt0Pw4`s`0?aM#<Cq*2<JZXA9RW*qu=Eib*I0tCP}SlEK`S=gmo7hhJ|W3Dw}ZZ
zw;6J>NqW1dQDi+;FcF>|ZZp|(XL+Kz8#0^a$XDC~bq0uAm$g%cA=KP+6e>T-x?mpp
z3jbcYm&2*ws~9DCu<*p&H*Z?B_P3N~HlH0j_B`2U<LASoyI68TA)#c*Fw;HR32+4C
zLqmy5c9!b9_KuO*UQ29kKmn4?$e&H{o}!{ZL+QpN<DRU_yl-MC4>N05DK{sG2R$;r
zs?BOWt30#$<{X)*vC-DbRM%SMofkQdo8}re16Ev+Qt;t<*VNRU#|+$a!&6wncRcY}
zhTnzE_nqMV`-v*qkGqzMxvgy}nqN5a?C&h!sU%AN*B9?@(3h8%ySv)0bxc98HCoX6
z)u<c<5fb~!>l)6RV_nT%8Cs<B12r`wCF_F@zk@+*AH7=MsY2&OyQV|CTQeDI7Pm}$
zvIODaV`CxN+EZvdseF9&DtGsrw)YXDJE5?NVuZc@*`F|;J9p0g?c0yxdXFC;P0fZJ
z;Ht}IGylZcA$%xF+UFnFb?;!I`(>sFuDQEkPBI=QN>Y`Uj)YCwJD=%@kVWcTM@L8G
z4BVgo$k(^_lTmKu7&!20YWZFHL(W&12oG^CH{9whv@*g7*8UvfjG&SX)%U}tjGE=-
z>YlPZ$}<m}xBf!9R(SVFHL1%f<v|O}eExf{PkqcIb(m!y8yoXu{B(kP;cs4UxpqLK
zQr)7FEm<Wyo}7l8TMbrvRkfqhmsx0iz$T)tt?jP)EW|_J%^$vZJR2HoYg=b~vePE?
zBAl#)yZ}W-K~hrL-|8+)jX=qBQf4S>1r|r8Z?wu&PE#|SQY!q(Zo~4u11>xl;=<+Z
zHnQ|vqu;yew?OzZ+J-Q?rYi7L2LeaCl9m%eW+Nc6M8S6#v9XY)6Kxg-5AdI*vHt$~
z0Pj)_8HKYm?cYN#PXvd&3ScYX(XQpow;m@#4(rW)_#*e)gFAPgVSRl&KWYo3By^p3
z&!U+(AvTsAMc9*<7&;cp_R}GAV+3&*;C#Uo7l;4l?d=Q6X&|jZe537N40ql#$S80I
zOpz3HOHXm<eqAI45O<H8TMEJaE~Qmo#6}cPi>&98zkdDdYP_;AG}P-n6&Jv2zoI`k
zR5rBrGcqz#pfBIFJIrP<3DWng9U_F3jnQ_3Q`w1<+A=jYbxWRcAM4Za0s{c1?&MyE
z1ud|cP<6_FI((N+zeT{bc=7ex{J>1^hOV=7DIN(=BK&hQU>0;fR`#|Rwx6v33)Cn(
zexbo#dqa-73NqK7Er{4JX!mEco2P53B67lu229H2gm`{_J`~LK@Of;s&nLK$+%?z~
zc8wH7sdmfbcAlSCI|?lH(2`(3goe(t$$o49QR{03dqEAI+VhsttoZYVGYk=A0hewn
zpOydeZOlALx8DB_V<N|+ugBArKi&nD?MFU0P^9E}m7PH*J!i%$tNg{WZHcY$Cp#O9
z_TQf!OPz=-@qoZCIoF$;JzkBcxVyC~cJsZ#U$}^~%>=Q>9byk)zezv&HN7g2%U$Yq
z1O&vyX%Ws_81_M1ZRs%xe`&8yUV2T=%;C84v@wvm!k+KRH>MIm2?QqW%5cqEZzDLv
zB!M%~ByQok-5YY=P77u?CCQw9$5@&OA97DdMn=~wn-!A4Y1=tSR|trnCgx(Dw1&G#
z12CH`6~T9>`||B4fn5NrMka=ftAB?_Y3t4}#5l9(Af)Mq+X+Fu%|{sx7SUj)D-A+{
zOvr8H4j_dC_A_Z*qaL^J7ILU(KW<#voQRmI@gYN-2=I(ino73N_WFVnkQxA$$tfA7
zsu1F3s=bI6(o`o+$`2emnh0sR5N?VB19&x3GL$2VD%|#lPW8_Tz<Ua?c79mwqunPz
zYJ7A>&r^|;Pr}LX7^!@>+Qhw}4`_|caU=8m`SS=gz!8->bo7+;i=(G!IgSVl3QhrV
z?%rCRVbiU{r`ITyhy5<o7|hn0BuAy;vb#DxR8|Iw*U!&SAwI8}WAv2MV&x_Dc|aS|
zP}&`G*)Ku6GBJ@_!*=+UVP~>mAz)yHSC<2~&o})Ie7Y6*M86jf2S<(R$gw}a|IbnQ
z7}35$tCSlzZqV-N0}1{lb>#QkH`=fNpIQG64`#~#JAc0~nMCNnT?P2_+5f!2aIiPu
zG$K?o0sgyfI!HAC^vS4o5u08U(dztw8Zf-<rk-wXNQT3B>r%s(1vYygTk8vq0QA!}
zie7VnFfW>l5BXuqh0vc`?-^av9^#n@1vAq1$<~UAJ@>V~uWv7mS#ntctXCixzZR*G
zq6qXp8Tcqm)k|*l2qI;ZF3#gTC<FRG1IVERh;5Gjs^MH;z7*sz!`_@EhO(VsDY$U0
z*9aijLUt*$5MZ(dj%m4i!We*phUL$X-0`z1P`@oK&n92#FafkGS*y%BXeKT;Hc>K+
z>-K$lK0q5v5Qx(Yr<0$$$ud+C&Jdx5SkHX7Q#j#97k|o#^AsosZ=DF{@>f<YJ&8DF
zA=B+|HoH7emTO(FZ)gywprjPV&KE84l>j2r^(y)XKr*$NZ(ID`Mfx@={@qvr9-ZkL
zYA&1gT9!S!0ZDM1@O3Gcz+UKX-1w5G42lb+`b$pxyX#x<K`Rw!`1KbCOX`5WtLTNr
z(Pb3N!ue?jB_RQ}aAdD#(ZJSti0MlCF;acdL%7UFUP8b~=X2Vcgty~us9vrMyxXY=
zao-Yxy&*g=_~&qwPs6Vxw+_PMa?lgXt3Xa>0t6G_ga32*DMy2^6rVv>R+eZ^w#|ZO
ziaO9;!ltjzV*WRAlOx<oQRuO$aXF!|I0%SFB52-k#I0`*&t}p;Cx?|T9b^kP?oe|O
zU_LfIgs;19%KyPh$0pT9G2E%xc5&qVHeIi&c9jP+wgDFNkpJn<ZCxRu*9!BH8W=4a
zDvehrTgfK7{k{3NYD~EaJSz#cU*i#9ktfPE>b^~<n0ox%;79H11|Y-ohH9iPJBu{$
zXg(Md?9;bqKD;+-UT|;?owcWh4}i=dXs=;bNl;f)b3D1YeylCKnJ?w}^MkwqzBIS5
zu$!oll}T<vd{gT~>>k3Pn1^LubF-4A<w_FXoKza^Aoomn2BSq7B>4@ZC_)BU$E-ZY
z;-6obgk+u3=3gB=@n=g%@}-NE>FVfA#sz3%$vrRMitosZax6<t+}UwNQURpaGX_No
zcOh$YSx!b;&2&uxIr*tVNkt_LRJa>bj+n90XV(MfTw&9F`5AM}ud>o0>C)2BV2+(3
z>0lpnL|a*LG7#=hK%tzdMWF@Z0)Kin``uf-h8@(qkUrs7-}lY=_suCvN_s&aP5~@1
z4d@(C`|I#JgibMRZ=P`qr0}hsU}GKfGKTm5j9Mnfq@3pWs{&oU0g}98d_d~FF?`Hu
z8%ZSE<bIc&boiV37@Bi##|1$e2HimwQq4(*PoGT->eie&%|`CiZ4wa>#C2;rNFZ?n
z@;<x>b~4MYTdQzl^^p+6WtsTs(W58tgt73$3Rl}0!9Nrn{AJ9%cFvma)-5?Lr&Yi1
z)fu>Rcyn_ef-`iR{UAX&ZO`Qci}mfB>jHky3JEi1*7d-l4}N~S4&qTy=vY`};fSS@
zPr(kFctM3ChBBd$MuiZFFP4)n<y&?7fYus+@qr@U`iskOFnWm|j-m8IR@-ko>gik$
z`X<D9anCDk9^`T{LY@!+OhCe6#frm4f4Y~HGfB2hux<t!@@}sQ1PL>)Fd^*bO1spq
zfA;8ZR&={2t4aU2<TA*nEZt{xZ|eH^`#(19N;M!}=+<(fm!YJitG_QKbOl36Nm&&e
zAHVfvCfs5EV{Z_TT~-=%Piq(K<cEHJu!Re}ap4Z+DNaqLK7w_NA)r$f!5VfXM!|+j
z0t$1dvjikaD_E0&jZGj&5LzL8=E4`B-)1-N)tO@?>B({0w)Q09Rh<a6@PcFr6k5pu
zkc_kNEx)cV#EidQkV13>w8hm;g*je_waKJ}@HRgs%_JbtAfYD0W3&g!&T41FETS5A
zOo&zLLg}_7mer{1=8RsY_tH%c<#1c;g~7DRNqs;p1LpPgae4cT)ud{R5@B4OdO;Aa
zjX>$U!fT(?n`00IU=~>L^z)MpEeKzZ)dvI^SfV}3x4R;DnutRhU2Akqj1@?~(}gqX
z?JoP<S`k`F49z7LKfmAMUHp2X@cZ+V_*bu{h%uJPH}kHCwzapv@aklknwqL>X!t<B
zI@ucYWe(!G{+E}y(?IW=c$UQlg@sdqK2LWf`65A<f{JRJ*nkRCMQHpj^NtkH;!AWC
zRdWq#KxqaH*+J&b6Agi8D|Nq-eMkh$Wi_pnn{C)hi@>g^sHkihmsLlG7Jo!{Lj>S)
zB)nC)o#}pcNiz{>K4^a;z+7+*d$G5gIPOU%$f3(5rvd#P)SvJ|n}u``RY5B@toNs<
zs4M&V<z!r*+T|^3>C`I1xxo^(h2mu|ld@fF4f9$GQ4tYoN5^7@k`-CrwQt(J0L(s*
zeh{?Vu9O3g`nVx5s?VfI0+3w%BW-Of068-NnNM+4K3^&0n{SJI*SuS<IqHj~UC<@4
zv5<_n==G|%bp`P+UYrJM=1ZPy*eMEu=ZwL&b+Sg$uLCz@w`gapn)f8gN{SZ*B!mJT
z^HxXRF>dwekx_E@!V+Yq4*<TdcM?bEOcYmp>FRAlR{i&EOXCn+PhvuzUBvc%GK<M|
z87g&py!4~i`O;z%P_@megoquInIC!aQW2Wbo`5`+$21rM>_AQy)8IgY-~{z2K*}_U
zuiQY^^kgpgbWIoGsUo~(H7%dg4;tyjx;?)4jhW7rfac1%rKP3kT=|d<+lcs``xjQJ
zZv!B?aOu*e%o5NSKfGc4=AgH~xBD10N#{PVckgcX<r$wOObG)_tXH|*rDp0^L>y4B
z3pkaagALtq^KDSD3{-RU&t>iv+btJQ+j+SYv73Z~fX)o0V^z%sHuHhzi9XXZw$+(#
zMm8^`HEMjI<K-QoWp`M60t_0@H!x5++A}97=loc}O_dS$^tu+|@~>P5ZLyK_uNFtE
zj}_wIp)oD9-TL7xl`2!UM<N$5zNj{o?d`OxmD&9*VB}m5q9D1U#0vogm#t>a3sMUK
zA)Ge4ImYa==Qv|~mEGj#M;E}&ni=bHQ?L{Yw{P8Q&kLM2G@AYPQ3wlR>88+ecrt`S
z0!lO{`D#e<jE%NCo>8{9B}Y;L0%g*0h1>Q(gpW@YB#vn)wjAgS<}izHKKD30W@)~7
zS<)zq#s!JLu+5#erV<W#tH=;|>ealz-W4qd`~t-f(GK82DG;X5U%0ReXI@F)tO!m`
zGKdIqUN4<TJ=r@zG}hJA3)to;59D*QPg;KhViTA3Y$BW*wNYx%iO^icT5Y%yXV-LE
z?ToD38)I~#S<D71J%yqKNcn`B&Jegu!X3O~)3nM;(+kG0fS&lxbXU!!Ag;sofOs$~
zrd!>@Q&v`1)KeRxjQw10x8wXa2q<ZDaGED!<94;i2$$5|7ZglVOjD((i*R(Vq!lAQ
za={2t&1XW2sb|MX5`T_Z5=;d=lHy(cfTU!j4<GrV@Y4=B({X-zcJy&$u-`XQ@v_Zj
zIAJ2PNhd!myk4*OMUv7l-j4z!DD5XL?&|_wx?<GV&9=SPXG(zq0te^wags5Thq|l<
zx2{WzTjzGhOI*bWz7w4vD9XQEjlfFs%cpvu&vAt><Gp^gBL6YV<B^{Z5uRHRmsK)G
zN{SuQ*pIQXpwm}x2aY#}sOdr+nO|0>$|tyVNpH|)-^thCU-41w0ti^eb$r{i*_3Q-
zouAYh<fNq^pgj%HTzk%jD~ytitYX5fFULTeJP7i})|{IFS>!zeDTpR)!8sUTN=in?
z=3aInrYU=%k?m9k2L$K=P&|4I?QWNSXJ21mZFuHpMFW(u=~6X{x^hg11`Umdzr5n>
z>nU-_@8$&MubZ5TN^Oqd;>BRlnS$fQ0)l}M%IhD_k!QWT2gvf{d>A0G<xy|`V1Oe-
zwAl~J50*_B=fkU%%?{r(?+<`YrfQ7RsUt{X=w={dK`ILlHbnHZJzG!8HzHy|!3E%&
zrc)QtifrJ(zCJz=(Zv<0Q^JW%RWE2;ndxr+@#BYWX9Sc!x)+J@-0a5ul*C0v-Qh*?
znVBtcp&`HGkoD}h;bnE$AG&@9-!h{%p}5hox4WHYQCm~P1fe>6JlE61<K{VU=Bi?l
zXEZ19FJCs$bX*jqi*Kvj?$rwV9l>vb0J;pdM6?1SKoC;)mzI~IgyhfwnJMiyiSx$8
z2;;*s(Yud4m_Z)RoE8=o41%Z-0yi8C=K{bhx;jvXJQU)g%E?z2H{4SVJZ5Z~U5iIp
zGGLAVNYNl89oMuisHU2`+>aXwv&}|@eEs~a>a=OzLEsC7^i5XF0~I8`dVKG|GSFNP
z=H-JjLuc(WbP!h%w%^$1pi(c-57>5}r=9~w&azv6JVP3egixJ1f3~-iro*v-SS@NP
z0+h!<cfXXGHc(`<?TK_}#RAZBwr&FdYs`FDk!p^#o@VnX<(Et!7HuWZus~f@+hZ{}
zd;}o%`c=J5fPe)+gv59kU^&&Q9bx_J96hr7xuF~#8ymavWMX~DCBF_;z;wOH<bm?1
zLIKR}t-Je)4GzwNwN52172^%Hm-P|Ao7w>irtF1sTGTn>A$@R~ijp!ImTv22KCr=*
z{$zy|(b;B_VCOe~y#NgjITy#4{HWJ0K?P1p0|pxK%>~K(+eRIU=bnDd99aSh&+4s{
zUy}F1O4W-c4(qqE;%<1vbqf6WyXKQE*HfGa3ay+<UthY)W1Fea)oppQ(y2d7S1e++
zzRy%G@5x6mwcZY5>k_TkH>S2>+t`<!xyTPS#|KBxkUT+Wvus@_Q5*Ml{TK^;P&VJ2
zy2eI&8MOJ_D~FD?ghM&yf!{vBcH_6TO-PJS+lX*nsx>FQ3eP8zHt>1OiQ4-5?7R!-
z&wn{z@&5X~9fnYR0)nyJ2ZLW;odmi7WC`@dx6MvHJw07YMZhsKu9|!**DHAQ=6DIP
zVUYg{JE<gs@qZH4k##Hrd=p1iba_^*a+kejQX@kzD8fvFsG0t>0jgAm`+M6IyQa)c
zQc$J|=Cp_>Pl6C<|FirUonu2A(yWt}GQyjk0E>3Q!=cCAksyBH2E;cmE>5Kx@;M|9
zQ72ZA0ij&JY?uIrsIE1DwleXAn)Xu^Mq9xkJ1N0BGc4QLYR&)(+}@?#0c@pbEG#S>
z@Qokzb)Zm*fM$bl>3Ky*NAHkiL#-Ezg4iDx%q2%oUR3TwTpCIQYSWzf0qeQ88oJXl
z0w4VaVSy=X?dPadq*v<s_cx`C96%OAqT`M~lFQTP0MCE;rqy2!G$pNhYiusRqeI@P
zD93<=wqiSuKcjRj`L;=aA_zftyF18)0GJIGgAnLO6-l7w0Zf7_UkPKj=ZmC3m6aD4
z_kf@=wro>wF`fG9(<j$Ct}scXqR7b4{(-Yk=x*Qc+S1THf&32wHx6jLvfY!j1Qee6
zmC5$!ciD9<>mJ>%ya~s`(tgW!3FmcUF3XzU(ur^fQDZ7TzCqFBrh~;jGo-`PAIZXf
z$b?mK-+=6{3gN&V3h!_Xo=e`{h1`phH-z{YP>hv6e*6gJv1=6J8~G0c{C0dw%@$YK
z4CP;q2jf-M*82PAKIxIgJ`d*a#~Z_};5Q$dMhZmNP82LvJHq7<uaAlgT>^**rB6R{
z5F%?Qb|#<c=;^8L+CW~PhLpOd)@30%0TP_U933@v<~Y|7XkDPA#Q_AKIR}C#P=Xmg
zAk@KZ!b`R54v=8{SZ4vOfIp*7wz)jpanbF3C9NFk5%<*}p8^=Q(p`2&Ne#Qx!+;qU
zJ4=O0Cd7*e-N10ccB^-kjG7F9@)X@`Sc9WG@2@d4xAyXFuGRr)+VwizXIg6OECQ@x
zCSP6Gw(Mr3Zh%HS>=J4qlu+yI2Cje%3UsibwAoOw3v=1q<l`6MA%I)~<&pTe7pQ1K
zK?h#f#yJr>nw>haEzC57YfBj>w*55a26NAVA}kFB&G(&+UlG$4BIZaC4sJJfE5O~N
zj$Jr3U5R&^1>+r2>KJkguPuCVo~5!@3{zKIi>q9kI{Sv|#*MqcsH3`@OE*SNg8ajs
z7>LLSDtnU&d9?HwSmf$8lZ=gz+k3XGiXRQ<shdS{F^{g$R*uPiUu7L4oe)-3<7{&P
zT(7^#xE<f~U4bvlhkQY7smGUiC_fE?%4LdPGY%qANy#q2sA=$ci~tc$0~46;N=pHt
zI|T=JHRx<#u2I11_3|<(GPA7SVQ%)Z4JVU#>d7(i&2=(2w*s}YYj?!D$S*7`4VHbz
zCn6#P(!mVha`9umP_x%cYT+l1_eM$gkK5vfk4b43r?ag3$lIq@qI(1RD;d%R2rHqb
z+w<JS!2Q1JZ#^1Q*<r!o9Vw6=fy&uI|8?=h?D{RAjUx0&x3{XqnRXRr{DyZ~2q8Xq
zvvsS-pxuB@Kd1*tz{xOr3OK-Fbv;R1Sve7cfdSkX_$cH6!6hwRfIyQi#I}9qoJ$tK
zPnEvPnEiXi?Ma5ed`eJpskHdoQ|Ct~=Aq9^@cc5x)mf3=k&IclyLM%HCIJsd?Pk*Q
zi^H44xY-l!+Y_YLw+bEY2(?p<i-h_6>^ubIbPpSWQKHK_Ql{`3N0=mlFmbmtq>7NI
zFEVPT0+yzupb!M4qX>wY4S-SxS9q;t^BJbPFSlB)L2>g}&9fB^f-vD1C=`1Gx5!uc
ztbN|t=4o6Pp8ANL>k7YumuMR>nP>oIv}9ytlN)uuu6TlFPoM<ZyT;CjO#r^3)<-3k
zHrm5j8ozkF!!&L&R5B*#%vHLIZC$(j_wP^cEH`%pwm0xJ1dX%H5|eNj3?hY=Q_(>3
zli=C+X6e2I2hwxb)jcSDiDJQkGQYYvQr{HDoo4JJck9&+O71?|y^^)nVSL!<PYaXO
zRXF8EbaY~omYXz5D%)(QAms-(4!^=7@I}Ec!Q%g<>yN_#A<T1Xq;X(reko+2Ri7@t
zn5tDaD0<@(l^CUXU}WXHO98!keRFx^)?RPlwn7O$1)@JKoKBEV0oY!N;s!;bY|eJC
zy$qEgC*)NIAVtJ^$>Ob5xQ=8Lbf>D^ip6~ei6luZfFYRG01LFg6Z}{h!FK*1<KwDh
zJPA09J6D`lQdM(BWm}0-!*MyZ04GynKm!nd>s0v>AVq<CdZf^cOZ<`~f}bD?U<AwV
zJrgQ-@P`m<_cqq_*Fyk2u7q4<vC%^dfQF|gB3$x>_#O7bFH#l8ElHH0b9dgH%O6Ue
z6R`MxMSBG_HBs8&r3wo^ttl6${u~3jh<3UUU8c`$z5MBo3&ZPvI;+Uo^qaZmYV;>q
z*VoiYLqyDZRCxmFzfh|=y;V{S#1iYi)o0;OrNxsWY+&K3#7bGz3xVTfsfOGjjQ~6f
z*W{NFa&DRb5HUieLP=4vi-vcvR!%7N2^7M+tnGwHFoQ88?h_QNRP5|Z;9P@ZdrPJ^
zt7YOSz}L+2`=zMxZaTzg)G>FOy+0E6>Xz|7)N_7`CQgvr3jx!1elpWm$k5b)aHdJ-
z*`20#se==hr49(ED(Hq=v<*Uv%Jb|>*DNXA+g)%`gYx8^MWQs~2BPFre5zIlaDz;9
z>VWtOAB=Us*vS(#q2KDhCTJF&q}Lp-OS@pSSXkE~{Ol3EbDAXw(H2lNHeecT;6OnJ
zIglb!wV~Q)1kM7hnPk|px_k-JEARB8Ia*kSz>SlG9W0GkpqbZpSuPELMFs(}rWbFe
zT;<acBV97!sa}Q!EBbd(+|fX(3~0XJhYzuk+m49iIV|l&B5`6_Z60b3UHMK;pl!%5
zY`RzJKs0;z@aPuhFUgn-m6pMx(4^WyLr*UO^@V&a`Lo&n0y$vOGOxV$Vg2DD%<-)Q
zyD|Kh`LJtQ;9Zj+B?Cj#6`DfZ#Ygm7rTwNo$S+{g0$!`){%RhDMM7;@I(8afUJamw
z09|C&)k90Srs*obex-&72HM*Sx6MbrtR&k(9j&#6p$zazZ4Qt^8ii1Y!6L&A?8Z%c
zwN5)P!!x&K?6Uy#q8hR$CdT)&HY>;@Gj<?LrOu5Mv{Ot0uVPqUTwYpAEwDO7vBq=3
zru5=4-5zZTsNZ05J4CV#dSoZaP+~hg?CjlFRQf<ocb&8KU(e<Ejif*+Qj~4osexy*
zWOpi{J`VY2uHJs~UOq=e+fF`5D>nMf7kBWR)tDAQ$TotyE!LgyE0>++Cq2@9Sn~V3
z0=pMQAReaKE%EaIT%qU(HDk5kqFJR6O78_})u3hNqtYP&GGzM$c~R|cLCK*h@XX{Q
z4cnn{d3Ne8Rd{CJyE2LgO5)Zz6m~A!k$^<UOi&#zk>yl!D6GPjgmwe+%NR-)mNp)!
z_CE%VhmsrOSU^OCDZpBt(w!x-_BgRiWVIfk#@}}UQ8Tv}5+uV%7pV1jIb)z2AB^e@
zp&Vvk2(`t*8`@KBmu&zgL4|mTHhkLaPmDlCiVL3ra#8HH4gQJ{C~|0fFhl6tf$i`Z
zZoX>|N{JRoC^gPC?&}g^fii8(&?q=#l#P)TnrEx&vTK#nkgAehyoQ#ocO7)&ZI4?(
zw`oB!(pOAXeyCxS-)HPa#Q6x2&m$=77LU-EYKEgqPzDQ>K3U+_Zh?-c=uquV$_{~q
z1zZpGtwJjEVFKN7_*#XqoWT6W==I#D+7pfuC-4Jpy@eGAOxy@+9n!wh-7!}MLQeM$
z4Slk;HfKbf0J&Oo6)X#LR`i<Ao4OW2lYoSJFbkIHfq`UvCZp^&Jfoa+nU7#wQMO_?
z=~p&P1?tL#^{5!mg!=%bVOLTLu#Qf10^ZpG5x3EufKI&3dDnv+0r&HBwpBl7_Ndc#
zppqdvn<4ImPu;~m|EYqm35*Qd;;jqvs;a?IqwwMc@lzbmZkjoy;WOWaOJS!$Q55L^
z4BH-5znN52R1|l?@7me#vcIcY4Twc9X*UZ}63e<86RPl&)v_A2kw55>F4KCfqoZs*
z0I$>aDgmxC4kZ~Ds2wo@w`1Yt<gA*rzN+m$52XH7w#fijJ7@?QrGwy6snMqKht*k?
zf&I}42cvGF>%|#9{mPC+88W2pAWg-Crm%XxWE~W^nvtQC+}bB})hfeX_UvYe+_f%&
ztIxIlGQbW)P$wt>b<U7>JRl*E5b(ajM*E!^$ZZc)Q=324W8bq@1;S%F>SRLd`19G2
z^<5uG`aFnJuARBe@^yll#P<7vl!o41BPV}gI}Qg)hNdj-H5j#U^|d`NL+PqzLSErQ
z3kX%qg)TAC(Fgf}1&nsSo;Y^$83{8p^Z57j7ZfAZlH*71Ay_8fAAs^>M{h(>kjnHX
z)P?#)SboWxk3rbewKv8{NYJ-dH3Z#C+pVl^XtVWiZfsmtiO*V*iBv8Fua_$`)PDQL
zgIFr<_u8fSkIa&}(6%vFN=h;XtZhIlbj$;(0#;UcKL5c@jlvGYn2md%+g>j1w%wYd
zqe<*`hclAmtfKv12a0*q%f)y7Tus2?6x2Kg>3+4R3vjJC)HPplWq|w<iVeKm(^YG{
z<v@y#yCl1Kxs0HOWviel2Ga_+^@5fZObo&#cK4<DHg#TkaYHo;kzMEY?nIe;NF!Y@
zcBgWH5^CC!^&J4kTR$t#tge^s^O9HR(1p*=KCzl=6U27JVlGI4qf>7eLavII8<bg#
zSXfv@KOmi`qu9=(j2Wyl$eXaTC7QzziL(phh$F*A>q6@AY(?7Oi&2r0*S?Sb$j~@L
z%q8#5vn29a8%UHLs+U0Vq2c^Ck^rpcdf6Uat!yv1r42tAbLYiT@beW86;=__tACP~
zZQUJfj>Y(<3igY(!}TB^3xe50fmd!nNkp8|m>|Fu82-?!pGdcK&jsj!%y+vaF~|a2
zWS!RCUrM0zDfh;%?34aaW`)DAuZpH=<(p`rT1QxN{3k7d<Fgj-kq^?%t|9x%MZOjF
z&#k1~LyRx%(td{CPD&+#^al3Pg8tQdvcP4U@W<0S;B*J=H{u{e{N|v;f&o+|6UgJG
z^O0f7C~6q2gjAEze*w}Z%lQ@sWhjKQIc-@2-j_$dD0oCx^92*Mw6vm713|S-P%c<4
z#>L|j5@w)QLd`aH-U9@R-J$(m5a)Vh&yfiw!omQ{F9cWKDwv^X;4`jkyssX9st=SI
z4nY9ANW@+ti<+64p@I$ytB3)2g~nRbnL?=m8CSZ#2)?_-n0KC<3K7tY4C>Tw6;U9@
z4L}yAxOm*d(-ZX95LWG~Q-C`#@JXoJ3hZ9fZj4|T3W$YJlzG9Ifz-%#8Q2j3Bgo+s
zpnL_-Q31k2I|zDU^V6y#N!bAgV)^~^GnQ^{JI(KqQ_;;yLwy(dY5-0m*5&T*E(?)^
z6|6=857fD>XQh29B^>iELxP8dW&ls*3iG3v426?7IEnBotE*E0vLkCBTzaBGd%Rzw
zS}4<LR_~uce?NqT&|#2WV8{QRyL%f|gkv=L?52?b4>yZ{=@J!ODL`K%C=hHxc0#q+
zjj*8tj`#-?oeT_Gy*mwlaiDZv0nZkMT*I~59>by15-@gv@@830Y@-iSE)>K3prVuu
zg;KDa^?dvGO?H%4LU1ci8LWuoO<^e@+8Du2gMFeD!Wkg)Xn1`30HnE|es@Kk1!cyE
zMf;Cgcpd^BuMOc5jQ#MdOV5)pc&&?RAu|wEtu-NwK<<12W)3j~hyiyZ2NGH-u$Gm8
z3?(Wi)($R>v8Nn&N!-Fvp#kLL8At@ukX+=8tc}47gnV3EaIVup$r-TjGakmv?gzD=
z0>cH#5Qt$4lQ41&%j2CJ|It7oEd(a(7r8iXAVzj3%ET}UAI9MbBZm5ss>f4cG@2)w
z4_bq|ZP*g|Ojb_rkHv+%|2nd2`yC{M)*S&q*%U>3qq-1q-!eC}ez*JxfF4Z1$&l&}
zlwr^dLnToMk1Op&t1ziw#c&HE<G^fQaTA3eMSB?221IQ$A><0eoS7F;=a2LlIXSuV
zyAegpXByYug8>HZ3S>;$TgxwlH^K`eWgp_{%M)7swFUirb?#ppf|&kQpV0q@j6-nQ
z=;uG@nELJP#=-gbnx5oO+Ta{No!>{VE&i$E_XqohRtz7vIgTSqMjn~+2DKIN^YiyV
z+`xzJ{q^g8j4U7?Ad3}5G8>56Ku*NQg7}#NLA&AHD<vSe5Jku^v|woj6&}3|cEvp~
zH)CTVU;xH{%^CeO8KMYCQ}h^cAjmy_{5X*8L>a0VLu|uZLNqKGm}e5k*92Q0Y&U;$
z8i4fRra5kLe^?3jC_KxRgh4<>Qrl;eV`5?yVuVg(P!|bEz32;})UJ&C28^RGIfH+`
zg98AOlmY`t2Z=}U83BQTi4Z8k`3wN;X8-U@4PXxd56Lk>K|#pbk6r^23S(ZN4hQC>
z1?%vo-<ETXtdLMu#E6R}4hKQt^S`V6f7M6!d)0CNc?a2_H~#l*gnzdC?LWBcchK#2
z?SluisFW0hfTA4dU3*kB2RZ#${}`CNj&=$`NQbZzhvF7=HE;n^wS-FJbg)snPsK2`
z%@ga~KV5M+ehPk~0;b?CXa@7a+lZop`N(P0p;8TC8Hq}p5Umj|L=G&l<EBhbPO?<%
zDi!^?MCl-fBpOuzKn@+)Gc%wzbZL9@UbGwlg&mmy%!>w4?q`Ek+8A_8*q2JG3slP(
zE$2<a4Cs9e(#}fJ`14_jv&r%Yx3cOP+5?%jJHS{BR80!f-JNde4NHf@47mC-z;Z+f
z(jvI%P&7v%7IF~scP1qz>AKgZTcdymKap7}vbd8=ELD>bf8Qfla@p6tPoFqYT@xGY
zN2m0<<nQ-x+`4ukE&9koV4&m}u+uE5j6;vs2l6zHqT91`$Z-ZgpCb<7>SMs2>_<*P
zK~W1$3O7-82h#>gvtp3p_lt0LE~7*5cL&^IvHKJ+oZfnR`jJDMLj9QerUZ3gXF5<?
zR1(Rv0Hl`!so;S233kYUfEL`iJu=^U><sDgqoNNprAMng1LT8I5}#ttlYoZJdWXp1
zuDkNur+$$5tn;E24UR6(r`{ia*m-v(E6{oP$m4~ti-5!*Dyc^plSIU`Pcm#%Rm2au
z5lp?rRr<7UBSRtJ#h*WCH$(<2+qG~0orGrG&iiY1b%23JvfPyA{?NhCNx}zJWx+8-
z?zbfzKf!NilFyxZ*fOzi06^jpD%(JWS<-5H^X3h|U}%CY(|X%fY})`?`rWZKRm<r!
z4t3s>jb@Lv6}b1KnS_Vc;);Z64{TMaLG<e8cl}Pnd3jv~(@!?|Nmz7*WU*@K=itV6
zYHXdOH=L8F$%}YV`@75vJX+a|4Bh)}cgpve4Pn1Y63Zzkrm}~hv>{AT7xxaKHU5)9
zcI@7}cI}t?2V=1#`QqTtiH(jm&y3J{orSs%z$^V_VGq<`qo%1kN>H@{LivzGpqvH|
z|3fe89n|r1;R0EbezN~a>rz4ryaytj2<_Imz~Ty<-(BbM8wUfD&CM+={N&zh%0a|@
zToOM%HZ}#i40=BprjmdtU}S+4_M6y1tj&Nw8qs2bwL;6<D~QJ_5M@^%ZU8rv+-{Ww
zcnPMP_wp<2O4ioaC^o-*dDsZ&zV0sfk~{(w;GiL=p`#OnR!#H;5b$Gu$5SX+z2Xkd
zd<Wf?@$wvCVM{vXDS7Gb+qYQvql$o2-5?+@@9;B%QVG2Kp;rxi^M31r@A9y__e%`+
z0CPD{Jlxw|AELkjIWtl2^?dV&+G-E^8(=|>60329&F|wxG!R<!uGiL1XeDh`1h%sr
zpjSwrkxp9h5q0f8H}50xNw~O`RFG%eb?>_s`Pr?Cs~Kbzt)vuMN>dkbsK11sp;fhv
zv%MY+WPkG}Vyc)n1tkrFwZG@`d$a4|9Cx8hLr*JCV`G8f<$T?PN-5YWl(4bZbG@(r
z&9U6w-|idQi--G1Nc7g@hRkt8F7qF6!fi)z;hN!TC?Zen+d_~&6u`Z+3NnRT&c7V&
z6j=*_iScnm0DoP}BS(UeNe~tMw>F`^-C647kWmPz<(J12?A~|b3tE6L6|W)L0Lams
z=27S#gg%}K3nr!5fBmtz`bqk$0|NZ(`DX<{)coZF2kV<^_G3>_4dR!ZxTQeIkPBeY
zaAdeac&exuK_~cetxFZ9h}3@ch}CAk|3t12B+H`tLNN$RN-b7}(CL62$i@HiP1<<>
zUd!_D&%^nj^oSPX--BTP&6)TArltMQ^ZE~<NY((w+rM4_^p1bJbN)Z~HUF<a8f8Ho
z+91;jx<_Nr<s2H`94EHnAO{bl;3xHjwGC{;{;pcG%=QP;OTVr+l9uei>Ca;Kg$i%r
z?ONA)&jgu!af}HEfB;D7h2|^(;mBqFCmS060?6Nc<NLn$LepqW^1`lnS+n?K7GGYW
zo^x@o!I_EIDx+_R$&k|eznSdpjV7Rul%F}elp=N~iXlgHS~fFUS}=_+S~KmY==BrI
z1cwP0N2+dQ={{r$stw)TEpH5bAM^w?sE3-()i2ztIqTPb7dD%)HcgIa&{cmvfA}Aq
zN9^ohCWLeI4$eOYDI7lVk5LQ%+=~sYFS%|oodVBh`#eiD%c+<q^Q^8Qk=*UC!%xYT
zA~=Xn^l7{}4NMt-j)x<RriZk4P2GyadB?#ZtJD7!K!mVvKY4ivaV8<WWL`>m-Irl`
zN49O5xo)3WhlSkQC~fl2Ma_1mEwWnv3aSHJF{=PD_WO@tTRa}=eO5C=$-to21>6G(
zU>TZYAWMRRd!NQ#Vm}gZ+Bt?VM<IQg-@hC(Ok1|rBker^tj5nLwpjt_5Xi<hmoXF|
z<<FnTiULrDe?uPtj<Ft%yRwV6JWIAb)&V~98;*)ffTTd+nq?p}0GL@-a~&$IenCM7
z@BuG|mZxDvKvvPqK-pdIHk|W}n-|1bF$xNPj(iaVy7zSng~)YIzq*S!ej`Co^XTIJ
zgRBwF3DeVtA#__lDhj$bxOvc!@LpZ!%Nqg1HQ{S#rL9zg;GG;{O*M>m<qxF&{E{~G
zu!V2|H(YE};iU$_x0Q4<z%41FjDP&_kM(Wi^crX2H9mA1h-~czNCebVWOUO_eV>R=
z#&;@9p{JvxGw(E2IbSl~S2<@byvH=WzD;oa@Dq3+04bJJ_lBP;LvxsQfOb)@%lFjO
zRCy-CoPrzMV?Ta~V8z>cHte`6DV7)P+>$wMaNZw3+#>u&n)*WyDb~oPgo+4{$~f$;
z{PuL?1ExTEQcX{3k)(}RsRGxo30@6(CLk*5ZyFq*>^oVYx_y---e8IAhTwD7sqY&S
zt2l+$pXvxX6W2Q7b=SrYP4-3zV>d12Zgi4dSP>qe;UFZlTD-g$34fiOk}~D1N=SAL
zDv~wkEZ*nhzvH~uPYO&y8yx+dcg6X~&=1^$|HxMq$Nw=n#Q&P>-_at<&;RGe|9@JI
z8;$b^ar&IrKH}q1<<!3KPKoTJ$DAPLxTc^)mE%Olt~HFOalV%lCzh5>okI9$%FFz#
zYr3_TTojllfuJwA&KbADjPb3<sd6N6l1AIJoRUwxI^P?g&=#}eZi$iA)r~v&OyF)`
zv$#c)gSAPAl|%B*R+o}bi#<6__bE|0K=R-U`_i`%9+uL;s`RDr2oCF+1FW*)qr;bF
zt(c;W6?ccqL{8%xYn+I%_P6X@xX@3F6H7^!qZYpFO<6oJy(`-o!QOshun@n7L9xXl
z{xtm|kt1iEOErhK=^$oBov>K8vRQ}_(pWDqC0(hxboGL|&+g;Z%aIq%mu49b;j|ow
z)AHx%f3`l(zYYXwXJGFz5q1C+56Lk$Gy90lR+OX;&`lZ4>tOw&uOXOCj4Jf$UEg2*
zC1@4X4jfq7$9;8IqBk9=cq5ky2a(Cjx8bLsRH_gEUS-!VR~%I=&}`_<)J`7)6&`u8
zFTakGljMvOG!_qJx-$`GqbVl#_w|ZyoRJ|9z%YZ2MSe7s{35~XryP@pe;!)WEBTyr
z5Tnu12y(L{xBmP!>!AadWPC7jVbhTh%BG3HKdb}(B964976R-e(3;{y4swF%>xVfT
z93Y^^5895Nb^`Y}N)<9PKFG`k$~;Mz*&of~n#wr=_RkZ`P|a1q#%4Eh&18CksO?{~
zgk&D0ZU#hxO6*tLh;8~?C;Qu?q(z_wMY3TPHNr{q@2*rNGoOQQa?1`G;T{bk;_W$x
z&qu*gAH>}BVra&XzkHSa1+<xSMz3H#)n&+Do&Kl>*|!+8rk*xzUF17&^%XC7-!=$5
z)Y=raQPwW82?_Zd;inaucE?W4=N7qt1xv{LTl{w6DY?~ex@Y{VM?ZcH{vo|hLQ$OF
zMND@2^5rG_lG}>DM21@kgA5RYx8FM8{Zobjx^cUjn^NWMS1?d;!?4{pD)R!kKu3hv
zr+gZR{LI10qs8EN#Kyuy5`?r!&m{zTKfPp46?{a<*9m!&FG31DJe_Dz0&0Xg!}HTM
zUNWqA^5fcqH?g0hl2U%~RpLB<E^nRJ55Mx9NwOC#tr&jOOErv7oN?OoT+!v%K3Suz
z{yMx$)|f(OZ1jX%w@(*^O0%B^A=xUk5cW{S@QnNFWxmVg%QMHzIb83Pxte?i3kDsA
zmVv=!kr~`+N(&!B)e3{TE#OZJLF1jU*z<E>wgBzh=%+H=8gn;iSW~R?g{0l8kOr&x
z^G)|OpgYkpBG#OrqOZAWMnq#p*r~Rcs#ad?My0(*u3VPaxuO=*)y@^%U9+>=<eOi)
z;L4q`b0zV)JbGV-pSHao#qkn9+`{~%FHZt0G0-Td2*ZTv4Pu}Wjs_=zZ6pD;l9l81
zLX%EUmM(L5fS;cMyhibZ-M3Q}(xBtSzECn`x1`7?#x5_6btNY!WBIFWnH~A!MudH!
z7>#jXQfLxT5cN7&HpHlPz9c?2E>18$C^0!%*3PZ~pc5k~#Nd5uD_=AUj)y;knuSDT
z__Laf%tH%7eslQ%T7t5gh2>5BQ&i0}BKG$7Kdu_X6XT0$9(SJwa|3Bm)io7<VM=WE
zs0^4ZV_#^;W@WX(zbQ#eU#no!365Wzmq@1}xf}I<fP!3|iO^$Hofpa~&r>jXpnLRQ
zkPIKv1nfuFzWyN~fbQ(^Z|X9Zxh}%TT<?ois>fjl9hW6AZBWb7pW1oEzga6qPBX>V
z^5<NA9=_q~bp!u|0d$*UW5EEY*4qW^Mqze{=Ff2!COJ4s&9}Fqt)Qfo0;LaBW73^N
zJ{qI0R3Gv%+mW~axqpwh^)*+o5%{Y2<j}BIi0Na50x0O=9dIgTCKlV;WO?^CM$vc?
zc?AUmyZ(jsl-St&W!nP_<d}qT`($X}I-4;4_sDZTz3}DU2Y-J<sET7_p@zq5K-$hq
zaN?D`hK9z(;!}=PH2VRDl9)F*LG#yruxI3s9r<%90@xd_d|$!rLWe<pPd98bjFaGi
zH->R5fk=tOh&+=4+OnYx5LS?>=?YlFp;Jkhu-2mc&+IguH<u1h-cyIRa#ThP$!!Lg
zJoGDOCeFZ~PJjxYAynoTseiE<SS;1k<NpKY{?EV`<+Jw>NK2@+$3k$8+n7%I6{%xm
zpK{zJBNW%)#>Vb%rF{|3mvBVdx#u_gNcEF>&f!0;!GV6N;ywA1gU?>(^Jld$PKPO-
zwHhd9WH)e#V~kn_11Iz<Vq~fD;XhIRZDU@|-8?m!yM2(qL=GRE#MhqPy;GGy3Sgzz
zC)?*|SKL#-q1TGAy{;&oJ2e^OR0J?(xXGmb`gmdESX{5HX7Fdj*=~wkee>txLT;A-
z%gBKLZi4#%j{5%3C;yjKga7K%#rA06j8wo7!!dKVLXB@zAA(%%u@WG9<Hxw%#Z9NU
z4D_7y(TZ@g*SUat0w<4`jUR}~nkb9o$=%zhJZ^Fmc~O=O5>K6~cyqHzogwHkEuV$U
z2miHE;b5JsWJ2!F{i1ox(%V+CNf9CE<JLALC{BmD^=6nQkq1@j8oz!>u3?<SA#{c7
zOs>|bEJjsTRn}9&ZM52G2c^n9dB;7ROeI6-UfzSCgF|O_cI(^@iMJzeB2|x$u5N8m
z$W^O>!Nc9yQ&2_9f~(ZEFQmPoF2z#Q)Z|Yl8u2n^r&sSDueC+dK;0W=q1kcsf)63F
zxjhtM%>#|X{a1H-UXjP~_A)JnqS^Eti^ZB5ZP=B9V3<t=h1a6`H#MhXN4PUS`1qV#
zUgaUEU`=Qb%2_b|b>X=$O68%q`r<N=?Yi9sbqVvDx;oLqk18=VWf6T8fPpj)y5ir%
zaeDc4d-ufA7oH}U*5V%R+=O``m~B~RArFSL$Mv;R_kM0)P!q>e)6@GA$)rmkm!~KV
zB$rlDh)X87Z-OX+1OqM(=QBhc4=e=NKp1gXSCE6}v!o;d3l60S4E(DTX2n_WaQz1!
z8NHDWN4{4cKkSIXKH$Uj8M0Isi{0I!r6uzsvCK-ac|CF_!F?sWuq_-VO}#d8D+J`3
zTBcJ$@peD6WzS<O*Gs0SrvsreZ*cuQBpvl_j~|;a{C=TAA94kGdDbZ#OK!r~QIL;{
z>T%xRMWRu>!R@IRh3D|-2+E#l2giS5-lAGf598RlWO_@;g)lw{t4)r70@J!hj{nGm
z{8_Kog;Q=7G?&F$;cw%9FplJQmY!$msd_^u*esC++oRbh-q}R-gS^w;ZA%iE+*qu0
zCBRKIuUFR0gLS1aI5~q1E<LI0@tj%ElLa;;AiCZct5p9Z#pArk`L7;^|5(X0I}>^6
znLzOw)EJL`^k0pT2!JQI6%93nQ3e+m?)`-msy?5NwK$h)LG!XVG;haVzxR3)9A<t7
z!HXt;Rg?EW;O#@u_7CM$Y%H>~fD^?KMsJ}(Td+|oi#UIO4~_uV-2sOes;zzj0W;7(
zp#Wx33Jgrh>_Ve7p(ur>(?K_aTye<Xbs!I7rNf)HF0v{=a%mFvu|lhL3UtjOv(3_`
zqy5NRs$bprnVHh4Kmv_&qy>+V;v>xGRv`5-o3`AuP#1`F{^E1z%bk9>#)C{mEMdmK
zSbf%lBLPdr@XNfQb9gkowm)~%qNDGYKVXWQa|kowsv4Eh55Z}{_dnc{Yc=1m0yfHS
zKv7Y^c?_UCM76*?4tcFcMjqMmkPJ93H=>zJPz5%%Lbsb4RJ6m%nZhS#Tg2v*f1QS`
z+}Mq{V2RjC@44C$*AA<h>V;Y*4bU2KSKFWG1<TsH59s0u@R~(DCqr{TPLuMfLmzB9
zJdJp8@KM8DE_e!X2})qOv_!L3p)oT5Zj^L%-JcpTjxzbf1D3|n_0*DM(4GTbh){zP
zB5{F!3`>{+fX3>9@B2smt3S`8#hrr370;~#W>18nc`q;uvb5<mE~>Lb(HcFVygm~-
z7(GGaF#GX-!0|tCd-LMukro(N)D44FQc<s4PP=4#wBS2(40KX$E$r`^qB%0?(!s7~
z*{xxN0iB}*j7O_r_N7WjUGC^dAXNE^KuCNN5G$$5%1W3K(haV?9B5C$w80pYUnMK>
z4-|qA6(BVg2Dlrt)vrSB`@Yw5HUC~~9m^k-igQsUdn6LZ7PUS<a?%nQtP$`FpK4wb
zZxfge=t;`230GwLlMpd$9Xnt{iXZ-N{7mWoLzC|q3Pr%;mcFbE_B3R>LzYAEe@Jji
z&drIpdl-5CzGM++VA|ymTIN}T8<{*P^&WioVVFO#f+nk~gu|mxFWa+6GZO@>x8_V;
zu4ZKYep^djV0=<?GHp@4{%yAk(AIY%;7Fl?vS3F@hqhFjTep@oy;f_;yuT*V{(eu6
zqL#u3gDEf5IffV+$%>XhlULRO2gv>4jbLc?c}Td(ck}lvWjk(bz0%yUe>b4ZO4~RL
z;vL@boAm>@5q}p4dqhb<45TPsot~N1Dr2?ZZ^IFoIk!$13GVWE@a#f|Aex5hX~J(d
z^0j6SJi%7Q^?yI$8#&x0w9}kdA>ns+jOp7SXD@quvkYctEHeBZ;`G25t}Fu>u@2bA
zrA#chp+!vCA3kT*_8+=KCI;3T=4CQvg@%OmO|Aix_89{k2le3CpKHQ-a(*4h9tx%W
zB>k-$!>23W6f*w#r%0=J4mbl~G^{aKP*8~JJNBZy##~cV^MAWcm|}9H9wsou6#yw=
z$=b<|*uD90Kfe%c7Fj$nU`r^!3<}LR>Mv<n%KUF1gDjQa)nu4o^Mkv~_7s37Pv=1;
zwH=y7DbP4RG(`8`zR40a0T{6|RkN=Jf(M$K6>dLGgW5s1nqBr{kTnrZpJ+smPSNJ_
z1RYE_{>O!-$y!<EqJB>3Ku>|%G;DU%XNKk`g4t6F)-iX0B99jJTf)p1Xk{a{2(f75
z6bAJKSa;&`28Jq(Jijju)Ro8iS7!o~ZZx{T0Q0--udztb>?MXpOk8VkDS$b@433-?
z%z{>y`CYrhX_<yWp=!I^>)r688J;~D)G-N`x>m5JP-C{BW^DyAwpZp+ikyZ<+5@I<
z-)}>RoW3p5lV4CRPGDAcu&`>ud7T^01+yA|$tMq58Pc9mL{#+qvx{<Q;p{+@aVvZU
zI7k>_R)HuC)P<4-V2Ukhm(8)9lGT7ZyE(_rck8a7GJl+yyaF}q_LGdo*MK5I7lZ)}
zL_6VDf!Z2jiWE^0-5**1^Bs8LT_9Keur5-Nm5pv_Xs9WK2uck3><Wv{o7bl<KhpLS
zj)$<_qw(b&)peH}P{i4)m2!?oLq*WQ4;TO-x$$S{<$bsfEfFlT8*>d?$OXpJ{3;3&
zTZF)DW_eg~WDF{?0(A=s8_@ZpPHfd71HC`;5IwBw>GLPS&h2T}0COh{VOj?l%vl**
zweQR|Qj&n_3NZZ0yPz2vO~Ig@43ZIg)G$L6+3jIq9{V_7?!==#@awWc<E|A<>;&1S
z5cL}YN4DRF5dn&D1;1E7at^nl9q{Ye^~E=I_jg!_#85c3sy_AP`+aQQ--oGF<QUXz
zVi4RoSoV?A?OxRT=jD;0-IP=xJAEY<Cd`T>@elgasi67<-FTDGXeb90T}uQySS7&3
ztO5-+9nc+%rq<OjtQ&#d3XQDpOpuDOXWu>PR#CWEbrn5QG@h{+8drCm%TU`d_d>B8
z#NBRaKZ@?syme_Ajt(?vvj3XB0@K4mX3T{aA2g5=)G~n%KF*bij7_B?(knAX<9U67
z6;toJ{!xt)xdBU-MQ(sH0{GIe7GGfxhki>Jq(#!+yx9SP0V-ag)1WVhoD4Bp=Jmsl
zo(b?;cf!TP*2smi2!8(lb*vIc4jwQbELMS0AxZ2~tMIR^&}NAI8X$xKT49lp15GCw
zOkD?`xx&E4Sa2K%=4!&BM!sTb^kZme9b4W+BQ7ymlfo%V*k{5%#!}pKtgk8_B1sU7
z#s$5q7_KRUnNE@ai@o;%$|~!&MKOmqXC<}r34(%xNRptff@DQR$toxrBuh4DKt2Q{
zqa?{nl&p;sB<BoD&PdMi#sd34=iaJQ@11+<o_clbT3y{Od|~gk*P3C>F~`iatMIY#
zKEPn-()UklewRY^@CfBM?Kn?muxi^%o--Pb%03ylj^eoWqD>?*sL@M@Lg=l&W@Uto
z2#gWBJObT8u4W)-9R@Ko)=oz!3?L($IDt}#R6By~++!O+2q7}*@bwj(Idt1=LlM*P
zHr#%IPV#s#UN-JIc8bAS?D6$OkjVm=R0)Ni19<f4g%-FK1rD$^nk*|oKH2+te@t~m
z^K1vNHE!^rI6mV{x}EO*Us=h<8j^Kc%B;=*Z~+_-s^RXA{1JfN<<6lRj*fY-&o|0B
zGYCQda6)w9w-pE*sMCw7gsuiRgbl>Y5ZiD=@|FmS2@HH{m6w+fBKp+E264dss1-lb
z3KNKWCp8E}Wp!9=oibsxAbaR|rHoZ6<4^?}n&PlZQqed{H^PS-ez}k0y!W^$m2?GO
zR#4yx3+Z^Ol{(b(Let~cx6`~vO_6|1PhMC52QPBs4)wM0YO<d(z;o9D7oE`mA)a!X
zd@aIIS9J9d9-I#J*^PB#K7G0hQfvhYRndk?1Y)MUWgWsnsS(|%s<-@?*Q<C|-RM|9
zuA9%XA$OU3ds)RmL2Abc--+_qbZqEY!(vncswG{x^iW!pj*en&sWnWSK2ZBj=q?94
zdnHt(NEg>x1|r)<zI}9b^bYTDH%nJa`0;2X=*2FnMjI08a#qFjQc5ukBSdk;HoYIf
za5o0TPBbXJAb3J(?@3M8lGL`LJ7#bNy%3|mfcESI^$Td?pa$U~@FY7T9FY*%6Uk%o
z;M;{mJ4{tU{wgphp(L)B-IJH!uRlBDC{-06{0W2gp>4vJ;O>JWF9-Rymd4+f^A(kB
zF=%6u{Y=6!EDVT}In0C27v9u@F-g>&Ai`Y<<=7Bd3_VcXiAXS!_ag@q?K6%Q`{(wY
z<vmwOS4Nbca8Fd`LCg1$uRs<0oa&YBeP9eHPEJ14<|R9>MR?69o+z!NJru|P33=}F
zMc8SG_!sY1<k`R!tD>q(Z{7^M#%*ouoD&GW6h-n3iK0Bt-yy)R2<2T2WQ4}##Yxj}
zSz8J^5)X?*F<abq;u&+5VCB72BsX%QC{iV};~MXbor^^*k`6_MT$32J^cK52OUCxk
zc01Tbmv%4jH4<x9biI3bK)kebBYCL!(%Qap-44%a-?=$#a1ToQXqhDOtlnlm0DQ57
zp|XvDCAQYQV$xw@aN(S9;th23P=k=^qJ=rID8=ir$7U^EC--<-$=};$pd##&W^K1w
z_RO!P3;4V>ia@ywbAt)k|8sb{=Fyr>sg^M$vVf$iJe5e(A`+~uT>$>;PEkc)4$(TK
z(EDiJL9@_4s1o1*a;?Gw(YvA0Sj8nGn0UazD<ljqLKMftbyTD42>k}Lg~=Qd34uFV
zUw5nB*>7^f;?RyVG(kq8Q?A<c9TU{K!2YCC#=6VJ$Ww%+XX>4^&m_WR=tq-`xDw-3
zD@Gdlqd&Dg&;7XK-d(5x2BFRKJarz+Q(Yh0vHCvgL@E&0mXfSnvMxw5Ny}HPN@~{c
zn2<X<JX`{z8~!Km{JNH)Q!a|Q?D2J2KvMF;S;dwivVR@L{^~{xnDOu)Rz)e(J{@h`
z2eOV41x=!<bn8|R5)<dT0kkJalL)B?@&Q58klW-?P0<E@oK#a_=0gN#q`Y~gi9FIK
zI=+CVxcw5$C^}ow>EQ#Jv2p7w8=9fpMm(Z91dGYh1n>OWtS$H`j~+e3an$t<JGaRk
z7QEL}%a7>8YVLUqf{8h#FeIrY2=KM2(-|9|f^TCN_Y$^Cfvf_eI{2?Kn9dWne{G6u
zeEc<V=_n$E5Or@xn01GdVQEeH-fi5+ecs<;KAE@<5>2A?2k(KzoVZCNhZ$v}&x-)!
znLy)%>fTB;x0pkcqZ`i5t-QN}HPq+b8b;d-z~^z>$`uxv-Vt$7(!DD@BDxF|O`<{y
z$OOkp>W^JVN+6rx+jy-QJw-UmB#(sDCmU%40*y3>!6uRvH^Ea@K=c+B=X`w+QA2^S
z-YpUuqPxcfv>x#GD<O<UI*o`ML$J0j&Y8T=Za9yHG%sC72?n7pseUeC_z2PI6Kf1b
zwvTO81^I*5&{Mc@DCpX;_qX{PYFPOrEoiqnXPmdt84|vRt6%JW+MPN12-g}4Taj86
z)b)G++&I4BjF9!{1$ZfL``O7|^i1HjqE|j?03ncdFX=f#qj057L+j9@9smNM=Du-a
zl+nSKrZ{5;FN;_@2EB_-w~|=IiSNW{1+Ro+G<r4tklyaWuDok(2({S*iz!c}g$8|G
zVpjhP&0R=y5Y3V#0pDeazB6gET*MHPa|&{j`M}q`(*5G)|0H2iS73UvAuBw;)(=kf
zLeEccc)l>XjsEw&yh?*D{D-PVLvp?tsDU*-`CUh*iT?|j4?>jqJB$QaCx3bxHf)Jn
zaq0Hb-vGA%JKERThR`2RLt<m)0L>^I+w+?AYMt0x_z%*cuc=4_`KK`mn0G;8xPt71
zsE8rx_2}ZZs6hm<U}xX&$@lxSKi_Z<N(X#Fr2QZrLEBi>IRei)?Ml%>BPb~p?mYO<
zoo*|j@&_DE?TbA>DTs5O4zj8V-#cE6h$+b(Mk{ROp?@Wv4?d|cn}bek*J3f7j2y7W
z?m&YR&h|~OTj~91<lU;M2DNr_e(!iJEyi7iBlYbH7uD_|WB#tQ$HIe#;*0DR@@RK7
ztpF-<#@I%?xVi0AoJT6#qWXK<j#6^dlvr<uy;hCt>btw1!T~@|BG&|MDw?%Vhjs_l
z@zT#Cjrq6NbE22pa$VuB!)sQ^UBttK91dG7)Z&{W?IOa%TU35i?3Cgz9MZ(=s%l=+
zoML-sL*t9caM2r2F288{$TX5g?awVwk6@6;!GF@l0Jr1enI0FDn*c<7e&s1tFmT{*
z5He@tP>4PYrTfS0eFXBjJyUl@3Xtouf4wu|4}A}f1xbr5rF>c9G#lv-3^cDf#KGZo
z<fv*&d};pz+M~|7lg0m^(soqZzPApYr$Wj-66WZ^#&e$cHDDXTgh-^Y0$D=R>S*~7
z%17h<Rp@R}K|#gwVnHTxqIQfVAOsP-91_P^a`MeFxu??8q!mU`XI%P^f1Z8b;Q`ZA
zl4WErOXgg^(LNFTG@^(J&rqT0(4|sLGH?h9MIHJF9K5z&a=ErRkw?Kc6eJyM(}0Gy
zntKD3BeEA{`pD&*+(fU0sr=qoE=Xhe-u`<XyTCkJ)`$#a56<ojmA!Bx3o3|MspZ}J
zrJTPn=TA??U4TwtL=q6x%t5808fg+*(s>^JwIBacAZHd(yIUczBp;o^p&hzr-tLpo
zTIYt6wr9v*D}A`kI!HH?@hxZ%<eK}9q1|P23SMT0-m54K){%kpP&wdr@(5|*gK*RC
zq*h>WdLY&#5rJ@O84x8m0Qsa>hJdh%`nB(%QbS%~thfF50i&L7F#4;ej-PI*pNi1*
z@Q(G}7~xS7V?TBn*d+KRNFT}c9`t5H{k9Z@jh4E9x<YCkY3lwW91_GI6dkm_k5y@D
zYMzZwR7*Cw`Ip{5Kybu2yY)`!>pdahVRD9yfy()~&jvd8>cC^DK~Q`?*Vqv%G8aZ}
z76^qp8v2Y6<p1+-NyNAIfDv|VSgo9v*|=;J07Dp&UvI|n8E8+E*dNLTqznwMsA5hX
zCJ<4&y8@jt$mC^;G(xh2;tMmG3-v!fB6p<}IAwDe-m3P{?eb5#F(wv1?s|QXT)e-~
zxtTK((I0PXv**bxa~<2j)ILM;%sJ0hN!1RUjT6hVF;Y#lsPrh9b16s&-litj(_$W!
z5i+{QZuOs~h%Y7(uvAu7a#jPE#QD*~z!sMrRHQNTr&U$r1rGed*fbDb5N1n~xhIJJ
zNRuabL{V%km$=B|g3>$bYT|NWY98A0e6Kt{1pot6hcRh9hhKhQ>NV@>Iea3e(uD;v
zQqleU?|a9YKe=nZ!sCtHGk39jES!t5V9*fqn+0RXQ=!3&u=V90jjx8t|Ik#Gl=?D=
z3OxqPb>iPzhjLqoYE(rZcE?<(WJrxTpGK6!=y%SU2ld>Qyw#T(!RUhar}a0|qC4KO
zXWj`sK`Jk`EIK;tlCN0HfdIoflV!NkQ9bqRbb9xB6qauIJt-KLpN$fQuG;0(FDJa@
zHz+n5TDtm000LW9`6okk#<cDn)QU=Gr$z{h^tJyJ@|6md<HrDJ5|we|lLIaT81Yh$
z9u#tLNEBpV<qNz@DZ@4yii5qYO+R<=QHw4$h}0rFNEELp3yLP*GURzNKn{rjVxoOX
zL2hmd$+gf5N)%ra+JSt8mrnPpFN71M0#cJD2!g$B3%m}0p<OSb>u{S(`uIVS^UJhV
zmmhAH|K+-X#rgZDnyG7>Nu`Y-UHFAxGv|2ZHIHhIhnd`(a7>A|ZsziIQ)m8k0O{_f
z=<zG{s-&pl^e46ffGIBNa%$^4D`3D7i28D^VP^EafNbKy41>D(QUoNSh}{?CI7LNI
zZ2KGa>Z~*WFFc$wPQB%0k3)*y3#lh&mHraiFU$Ay;hWwxHKPYt{HmW$yim~Fy8h7)
z!@m}Sp5Nx9GffNX*%KBWP0D~lJ)bzGk9X_|o7PXipRX=?``ozcLQl&3!HYkCEPFJb
zsdwhw`1+G0JIBYLrEti96u&h!vo1RK)?>+R&dld2Y_@wQvb78MS<{PG9*(&&mh-bg
zB>2Q1kN$e;qO@<?_LfK8?zIP%4PLsW$c-JhJrlze_MRXAnv~v7GyZXGXU?$=WZD)W
z{W72v(NOuj45h6PaY64%Q3y{+2VmVaY@Zh$X=#z5lJQ}t-rv7JaHR)f=fs_{IE0s&
znzPf?4yy%OmS$~f(VM|?gD=h#9uJCbp+9RDa)IK=?+g^#kSTI7=R986_LA$HOaR->
zoTnmHd)BR#GkFO%iiDv<dXD1-PcHrUIfe);zU<MU2yOW=vEuPY7>9z1>tg1~Y3rg{
z_R1?Lu)XiHYYB^12)FCZ=PrDs5rn+#OC@Cu^20b;InLKc+AnK@F{cBy(3=Kfdp?1x
zl_R)WSe;EK{?4z7v^mjH%h&R=Oy`d6(O2TQwnv;dYEwxaL<I^^dG+c~mO$`$AfM<S
z+dblLVG)^nM$s8;3Mr1dJaT^?6_T&BTjtQyBuPd0jP;wHO99lMJADYKgP0u2AzaD{
zt6@zdpa7~i;lRvyI?RVQ2?HE-1KNuWnIev6RUp4kvo`$+)6Hgc2u!%qVMQ9~ky#Lc
z4vmAA*Mt>P6QN71grbWtzK;zK2J&-N^&J=~QHG8(GSUGUkv42R@*ummJJ4^bWSsxC
z233nQ<jXPR0)oW*`UBl77&fdPD><_{3}Pk(+eC}_D8f4)zSMhWw>N3ylW|FBg*r0l
z$Bv@HB0PND3^#ynkpP}jt93m~kKjRS|L#MT^L05Gxph<H;6^gQA?N{boL)FG2sGdl
z7!Hk`h;^cqqzkVh`8;L2e#x;Tlf7mC1IguSc+d#bpRI`d2&U7BLpL^XQ^7{&=Y>P^
zTA6jtbZY@XVjcn@ygq>$qlQlxp;l1=Iif(o6X$%(5<?QfYLb9)36F~oN%u1Fvl~~G
zesk<?5wG$*dQp4)7iL4NqE#a_ocKi%z=Mu|N^=gnJ_*31iCz=wP3=i&E~~Q>_FuhW
zPXO%BSq7p^xi}yIX^#L%jy(yN1K;uoK)q8GZKR<^8c50<k$1)b=-0!HKQFGE66vj#
z9~Kwo%Bh(-5qn>L#mob7Ktmu(4Zp}^C6aZ_3|;*a0+Vh0@<`I#Vs=5dLX?heX-Q6n
zPZk>42f_h&&kSY)0}GFgtN^gUDxT7xSl=U+Z&Ifd%1XCM?C)H-E^rsc=2dThS><BT
zS>PFncp(BA6S6wI>$Q#-*>}it!Ssi+WWegsZQhUp0}c|1hnmYR4Hy&U$Fw3IG<R1^
z^EL)yhvCNrBg04{Y7hzoS204Q1lbosIz2;Jfgd8kk)RTe?E6bx3Gw5=eF$lcB+tTA
zZQH?I11(N_YPg-~DD*x`lQy3n%ISqBVkIQgn0Psl;F6%O5D5nc64_-y`cC@(T_dm8
zCZezepQAaYB{Jj5egemz=rdFk3)D%qMri;n$GHK^Ojn98vM(r4oJpTU;uV6Bwa4k?
z!26^DlKpMSDj|DFyYv9I-C|oTl(M)9pAHRr(tcNd*AiZV2NknWMoODJ26ccE5IxQH
zT*)kJ_fY2$N1OWPbUQ4q?*c>ke*#rCz(SNmw;>u)*tzt#sbrRcsKa!*KcT{cwE$h`
z-lGmvFW{;(raEW|GDs51e*<ALyO3~r5fsU4X5Y_>vO|_-4IB_U^<$9Oh<N}0JTh*;
zA3)X;6{1yYe?8OwOti_+^c(?~j866!6(*(Uc6U4SHoc;NftS=||4cyk*_H{KI_^Tz
z1uR4_Ah8lOM~9#!a3ppR=Au<q#`R&=bu!n>3#$4AV07HW`4>@GL`+J0rh&2cK>0p$
zBH_`#5S{F_8AGV#K}mhTICQ^VAl4BKC5tX#O;wP_u7Ge9QMGrUw_S=_`Gy-1uwP^(
zqPi4u)HQX+IU!yNc-<T|>+ije_9y$vR-?va%Eg<UWMqOGtBX$<VvVewfpQb5gUStG
z{66Qs&H`j193}e+K1{*+J}pLXE}w2^JuzUA$Gr=+Esvo0rWU-+YMrcGktxULv_9kS
z6hE_i6Wx1s|KIT_rx1Nsm?GG}c<2%tG^SjJXF;@a3B_arFrh%eGad9S7*QV?+(Xog
zA!R~FiI6@1cr#-(^vl0@OC`*FUJ81D+&(uGl0o{bB?b6mj7<p;LM5pI(tY+Go)eeU
z_3PqUJB(3$YSLE5p>>{~1Ypn9{bO`zDaI$94?md@Q!!kpXK2l*nfnSQ2%KktEUE=#
z8;QCkgTt)x973ToP%!s;?tdHNP6|@BIWMcNBp~DYSJ!R6(I+Cary(Ho!b;rg04Ulr
zga`^jIq&qr-$csQNvu$7Ky@DVrW$wm-wDduuPN_82JgNov1afQ^BiCP=`*^`bb}Yb
zJ}*tYar2&|w8+-pPCcD|_z$<B_g6%!->>`T-5Z@cP=?ZZZJUx8+u}MioGSL_jVWcs
zXj#HOASjuPKsN=QI(IyN=U4%ry3v02(6%vyJTi#rPqyDrl9$wrsRbFqjxz}&*p_j<
zL_>@SspBw0?(00_O?l7~03Lxr48JUQq4`4L6C1y6`%$QlahU7<7shdF`6wpmQ86o&
z8nl^bb7fhb%~;PtaceLEMqjAU9_ZnLyR?q$B?H|k-6+$VR<u^BST$~o>K3f!?fji(
z-hBuJ)0MD>&Z>$)V^6#cJcChK1m?)-JK8$^5KeQakRhXNhYv?25jCeunCRv{Yu#zS
zs=m#nx>aE395izFpSHT7rv!$kj>3(z?b%SJ?2nWmA~oVvB0Gzkv<#3-moaQV$oGd4
zodkg8EFoRYx;p(Z&_GNAkw%T7v@NA>JM`%9pC3T5z&FMq-HtmqhCyWNR5Bj6H_%!5
zc|m0wiUiW0T<6FztpE8syt++M$K!xIN5K?6nTS5Q7!b|&M$mZ{^=B|HVX3aPR1ZQs
zHnGp3PDe8`g}7G<Lq?yJo8Wfk5eu~~f-fhoeWoE*QJ4*rIK91mw*$~Ea0FXiHq^&N
zLUW)KSEfV^AQo-&w}uHs)&G#4PCY*apb`+l2?+e)>>!n8-LruWMmLdj!x<!&nb6IO
zME0nNRs*E#^3eW3jUp65#$~{orO+?XpA~<H%?Z&DG7OH4soE~XcNzu~{@sgH<1t}U
z@7%dF;k`Seqn@`<8066I0NYE{^Z_+!6C@l=hsCwhe5iMYe$5p%t6(&mwOXAQF9X%3
z#lC<Enj)TP?g^~lo3G|JG~2xW!bOvHvNjifI;p;kHkHaa8y>xI`7x8?WPIF?jGl>$
z)q7)v*;^eux1j^4AeGT|*f~1$z=eZ3V;;;If2R7{WfYWZ1V(fD&3V7HY&w7Y^!V|G
z;m9_bn2AqY>q1`&ImEv`V6%#=_~NmVhW8lqy8BR6Pu5Cw`+X>D)7w7RYwFZvFg%bh
z@&$bOSE`W#ywg{93I#j3#`VN4zKZEeM&RJf6vM1}p%lBUy05!luZ$LXo3uKi8+6`6
zMmKFqE1mm?W3PA==+CuR+_UhwC<baisJ}X1Pt08@hi5JNPfj<fqqA|{<u~3jsWuDT
zJt;{R_7P$kHE(`A@8gXqrty8{y4$>QL2Ixnt3ABeW+<~W_GsRwEl(S^ZjpPXy!BZ?
zX3a{<k0`dQZCB4ZiG5=WeR9cp_uA<@R`I7;zqXz>kbI#aAN}qAW`hLzv;6lr-ieg>
z^9I{%HjlO9YsJUSZ`nJ1)!!bc#X^Z2m*R@49_eFoRn)7p?KtW0sW_shS7qHRv_Lgq
zrMca=@2jFs=if}u^)0r+a;&IDc-?=l6sQn!6FCZN)Xa4@*#+~|{MuN#gBoj8JWY~)
z?%lg*ab*Sl+q#}rP3ZjJ&fuYGvu>$B(BcUD<^VuI-jK1`uz7PQpkdm~jL=XP;$S3#
z<&gKq<~5}N5w@M4nNe0<;#3$^4AV?Q8^Gf6lP9Kd)GUe)i8+YEtp#mmsG@-`z}Sxw
zl>>q_7x?fWPinOA-m(~jTw^I4rb$tVXvgpdD*(b7bchC~nkF(lbj{0CS=}V~QDz@$
z9JJ>J7?z)uF&5<mKx^vDW_zFrehNz%=PBCP%ITkRNUUZ{XdOm)9pV%x(>_WBQ#0p#
z$JV@B0QaGVuXxc5pLB6wQ=*O-C+8?5POP#IsR}Y?+Cq*cF=c69TU}85vQZdzetWCf
zbbsFW@@%ahnIpb~@&Eq!**Ui{7G`W$r#jYqvc0LPNnwfkbILnj@UC$C>r2^3n;{v7
zFK|~j^!$~$`0af)N(`qmC(;M$GiUuK(pW@he{S|JvO#^MlYa0{&g8<v49a0qOFW~y
zJUUi1LKqv8hSI+*wL&)`ZSvdEBITxGciR!~<^n|RBjx_{+#m?rfFS2XXIj7sRWNC4
zTF>Pe2r3Re|JNnyPaU~ed@)S^JEGgy1f*G*bY6HvNk#?61kmJ(^g@_;#41Ta@@xr9
zw`p-Z*u-I<&`f;lOLn&4vVm;@t++9nDu1b8;K}7%W{mifK82}jhq>x?o!-Z_WJvYW
z$K*Tf-&*8endJvX4%BuA_H(SLS^a2bN#3;3TUpfeZ5L*mi;?6FqXg5Ac^FdEK7byz
zn5+k_nbaNT6XeZ(E5En9GoDVoeey=(h1Y*C?{dVfL#8M67FRc&?E4V*=_GlP#kMi)
z^u~iU_17hq`dn*n$%=SRKM>Ki+^MX<5jwlTu5G!q<YEZrE(0B10g`byY-&p0{wH)?
zJ1vRe&S#l``P^c;nU>Ic)ol06;mfrr*&nScaTI03tSnRE`5|X5G^uz?*Z(-~KP80#
zWQdX%Dm)hUNoN2s`JXi6XHUz^M_7DcB!<@P-9a7Sv~goQ2!i>@PZPdbK?c`DWF`(X
zvwW;NJdrWTY+b_rk9}Luf3VXVWP_n!gh5tBmNP_aL2zl`02V#>dNHaxMT<qBC%YX6
z|AaUf^oD2iGiN1H$MTQ}=K48{HL01rxzx4(xhN1jcX!6GM??7#iq%c&99a#H=$=el
zY_Oz%zk5OJ<g}l9ALz6%Q-#fq*~g*Pb<;fJe%(bXCk;)!c>anm=p$;*ny9Nco2K;t
z`gQw7#9|B+po%SA3>L|(NtD7&A^i#d9XXg-JK6qVlfe7RcJg3oN|<3p)W>5z9Mzk=
z&gkl=<%}ZzQvO|AFu;kQb~LhWooqL<>s7`HiJ9+-$v&DnRcK`R?=OIC)VaAiaVl^!
z5%4q%rcZkP*g^-K+Y$xT*OXec@0osQ4gDVu=I76+Kunyb<N*W6;E_JtZb|j;5K5X9
zB=4KgS(s$>rZZ_p4b-8Cg`Jd0?)sLVY#MSJ9UnIwuCA{3fM6#dxv-HaBfG<nqJfP!
zJKSPyZ(7^fj6hq~Xd)IOcHTJfL^78BePn__a-vN2FpYqC)33+8FBz3uL2CF8V>m9o
zybHwL4g@ZJIN3<F1VFz~+8Ruj=<_Bmef=X)^zxpCifEh2e5;5FY|t&t8MLa5P;*m?
zs;Uy;t*Ba0%V5gV(^e;-x__A&H4S}BMF=LmNM)~E5Nk*~0t)J&Rds69W%<42gFj?V
z*JsYvbMJG+KNY=Wk!tAzNFL3pZT<^W{v(f$+P*E^`FC6RIgVN20rnY~%iW2Lr3k5z
zJ1GZ;RqEDaoQ5femfiv-<Xn>w`P9A=9^n`z7!Z!sKB{F}E4@{l3$*!V?YTTuEfm2D
zYe=lLM8F&0pHa&hZ@amxd9Ja!@cI*a_vR_z=DnHGNlCnL0=LQ1fl1H~qq|#}?C{S#
z)>*X3(G(N)6??|4%cn4ddfo?wRQ|SCXSk5;(1C6=^_4}~1#`@#_#C1X@{#Dhx{}^=
zxfYkqb?n%y#jkoIJo;7;h@E$#NG)eTl7%8Fl}31Eamb}8c}N;F^FGEb{A8TJZrcPa
z(102AQ-LDWfpiu%*Z1z(lY4p%{cCbxQ>D)K+}&^JjlXW~?(Z)|i0O*5#}O3E?KcT@
z1#H-`p^cnZ-i#r~Lc<XiU5y*;%`2vnt1CW!{8%adDp{;+hfnNtwEeMyF&DuTPqIC1
zh$83}pf|SI4%#&5<M3pMR_Z1c_=|`%&ooW&Bp1rs54+B`v54f6ht!FBY7tn-oWfDK
z7ysT^9yQS@h5ELdDq4U2A=G6Y2<{;5K81Qg2jco08So1oR7s9J5IOmNzR~A%LO9&)
zV~I95?}i+>GEcD6j*|)n*fF2-`V$j*n5%|`0ybljhw}Z%HPdLPFIpIrGI16IlQ&Kq
z$yXdvia$iG0P+>T4VyNFsKs=mf|UmeAvfqPlFLU3x;L{BNVwmi-Js2)ul~GZGjzNu
zW7a}v&YZErHX26pL?l>FC!)uOBi}ktQVkISC&ObA=#jPNq#JVYn<6@%L+u$qRvpbH
z50}h(#mQ)O?uP5k!-2+CzX1<NyR5Ehs>ref(SW5Nrkr4R<ElC&)u-jCZSP)ie1`&2
zcBdUj0_R6Kc@Vs&C)<%qBv}uU8!Smch2JB_w*BxF7TS~I-5=#m!S(0i31pzT`7lWs
z)TYZkLNdwavO)<+k8JGOZ|bw>$XoSd(k*5!$iAYs9bFhcYWj7n7@Bff@LD@NJN}lZ
zOeUZ;XyId;!v&IVD$?E`eYSjTBR+ki9g`5hgPZ58n;<a%hS-TV|M_lzD&-0q0D=<c
z$i5WDAW$uk8zsWzB^(?ah(}LrXEze37LM07)z!VZnlIMpJ80WEc{Aq}vBY-`Eb*F`
zj7Fd2!Zfd4`_ss#zP_x!-W%i%R@j-kTS-$WZ~9uN`w>=Nw^hyjkInOciVuToVK<us
z)5$A7jKap9)Lz5(*u<)JEIzL&+Zpn$G(Zq8eOPT|t74!O`s&PW6|hvrsX!umL0;C5
z7+l13Q?eho9x<|w{em)wmMha%ef_Ma8~~U<nOO?zCymaWn#DGF**pL<uYvy4;T(tA
z9IN)j>YkZ1shRl>fP7`Fsady_k|UeP@yfOpjFYFFK$Qe5TVs}U<Ax0saS-bSy89=9
zbF7PL{vbAvnrdRIs6BCUz?S@o1xN;x5{r2&bY>-|lQG;c7KeQnXtEY@)#3}}sbgTl
zxY2^e?42wP4Gm&si090l^3Ke|PUio~Ujy-~XB6>tHO9>`4l_ns&qjHK-!|d!xd0#H
zBWpTbyf9mARqoyNdc|hJ+p{&_fw?sIq$npxI@+~t_s_j3M(=hw`qKdsgbNrXRVU6^
zewNMcJ65oND9XwZ-jWfQR|_p^!rpUMx5U`icA!WFDCfKZ@6M=9gk0#gbVp}?l`we|
zOhE6TZJrbz$3AKRO=LU6a?32gG!ze8vAEt{gTz<xB=^In`Rhd|Ge3CnVCv^)hd0G`
z1Ey9#|4@H}k~lYb0qXXs@*0`<N2w@K$v~$mkeK}D=jUf}+Uzj1Q#eEGPGBiqcdn@t
z!=<c?ab#0nTSIKF3do=0)FjlT_tMhVYfb}z^rs{PcTJoUM$|v@Zg=`2(p_LbnZ-!d
z`J`Z#NnDi3-ix}l6x9}~ZV`-_AC8ad?AeFnu{XXw?^L1v7LjOuZ@cfoMM{RYFL4Ct
zj#^)w?{A)G2Z*oQG@h8`C|iH~#^2<|80WggOtBh<6LY>e?c!B~k5npRZx#7?Qh**v
zkK;TQ8YUszp<u{a@x%P|mgk2PQxk6`%F`Amzy*1vwH>S<TVzj2Ma#3bqvmZ(ZNsFC
zViFF-BOL3%G-+un9r`_zL17v}Imbzsao@i0hyWH>5fQ`0<e0;BsDl?u%MnxGj@ol(
zTrp2qD<(dem{)Ns!b<aqo_hD2>0RfJN>E9-QP(!4k0gbKA}&1nWphU}?k`(bdlGbo
zPB2Ich>174Y*2qgGNIJx%pxD<^QNwwM{N|HMCK<kg@TRvVP4Pix0~$_4PuNX*;TV1
z3&ZNMz??$l*@ETSZo)MaYm1@je`Bn;;Vh5HT(_(#rT}*muZ|-p$L1!7-BfaVO*_)6
zB8;#fxwqN&T7)hxb*_40THTf6506t0i=ms~g>Yu5RfSA3A!!yuC({PwoM5>y<OA(i
z#Wrg5eZ})f{AU_E6SeqQ5`ycr4O5qQA&o@P6q1x=`FA%?SE~cr$EWlRYW|p(oo|!n
z;a{hYhylQnA0bgP&i+_~3?(NB<_bYW9=xCldYm+eTE1C*JFLcO?;R1H!iLOHdA=Se
z(dx?|YOj#O;W3e*D7nK+FBZ5K9I*an)%w^-(0I8>M5neeT}^DREcPJg2JLgqp46DG
zO3H8}=YhZHeIRBm$AO2{i5?XjdrXGube`{WPPF9nu9Gw1PEyUe%Ro=>YvwTmHlMTX
zgeJL%&sF5>ObSm_>l<ePOu1gx&~ItqX=y@^F4C0xmRNEO>Z&t%V(j|$l2VGGNFR7J
zU!rQT(hgWIkFQ#kf@zagXM5U6k@xLsO^qQ0f%;`LY9FqB2}){~Gmt9SzeE-~^l(Xm
z=Pp|E7US#x4?j4YaQP2w1P&f1R^@^=W@ct(-P@C~`IRm&ah(h*VrixctukG;ocDKk
zTpu0Xl8Ecwzx1maN;0P_W0Wp0F^f)m_OB1H1S?LVa`6hbvt72K?oCC9B|F<?Ws7*_
z8l5|r$+DD`O62&x@hfgXOVC||CEc6=CO3)T*ZC)#SPLw^dc8fgl!*z*C94#{aiiBT
z@j)Rcc{9z9zn1X8Q~Q5oU;mjOg!gmYt$MEz!RnEeCiGSzDuid%m>Jo%ucv2Ne0aFN
zC79jEX3=#W>Z)Ss?(OydB=$Wxy5FUVS%^y^EJ*m<nA!b|jEvXQ%zIovW`rFLSs@=$
zDepCU-b(uB^9Y?xxv<ys%&jgiO=0XIXQb+wiY=Ojx44d-H!`vuKQAjQTh3YQHCE?a
z7&ORaQ1vVU_iW*DtR3yQ<(+RsL$#hqbZ<ZmKws_M&#mZ86Yol6%~DEEmU+Hx=LL23
zD(TZgzPA8(hQY!8jNf@lMUn+~<5{XHxZ1|%I3Kw@oTk?-dt9?`CmdeU1#CmgsEkR^
z#+plz{1OZ^sXeE7MV}n6cx+u4g=ktx+FDcdvZZ0J?s$e0qgRt#j3;hT-uq?Vujd2x
zuP(E>d5!1Be>rhUMOv$CaL`DAxpB`akNozK)>1>)F{4y74QF{aw~*p9>gtw;TcDz@
zRL<F*?YP6$uDJecVCkFtx-|A@XwqG;0)ZD56H5|otxM?6aQ>-@7KFT)p)5XCj;)-6
zf<8Y(y<g^B<IOH6374zXhw6^p+gHYOOG+-R_8vX|_;HGf6y{U7j2h0jN6RaF>KMEa
zZCt%(O<an&^XR6{mW}I0;e^VivjYXkrBaO5gD?j4RwU=pn^8w6nNg?IOA*z*(l(ml
zUXdHQ3cFv<38&=UqFWcE5OIRu*@C12#Ms4DJETWf@arZF)K~Be2-L-kq0V{oTcz1!
zw7f9Oq84(zKl_zAV$+BwF(6q8Kb=|Muf3LTIAj0mQoYya8p92aJlZ_MjRMTlGr^@@
z-#K--_vM|{C<|_Nxl=fsm}ebCi@-j5_6D!#t=7`Q)`~)By93smmzu<Qs`FY)^AgOr
z4>EZV3&--eE;D;KFw?kn954M>Hva#@S^2+j`7fKB|C5#wrZeCYi7G^<OPz-wFwXwD
zS$Ogqs-~Xc53=XbnM$cmeZSK2l*y?kq`k?!T2&Ec<Dd~0(3tpVe`O`<Of8ykUkMst
zatx7{nDV5~U&)-lj2;#zgbDQgKOb(7MIZuu(iz_exK}AE@tf7+yDWH?cTtcaq3~Y_
zu+QhrW+84gu1LW>5anawy(*YdqnLKbn1D8EV^KzpAv1T7BzxlvD%qnK5!nD!{?%qw
zRg2n#yH#$VmH~r^q-$;x2bVH0&y$vh!ox1k6mi3}p=|bn!Um~i{r8J0(!hb8jq8tE
zm%gAzipXaJG^y{<UAnFf%e$g3#8l@TofsQS__%mb@6tcfIA!z*MG|K5O?*G@g8pvc
z?LGt=!tsSs*@f5Yvn{(l`Au&*3G}=_a`nTX=#Mj#2rk=Y)U3^oP|TUzR~S?D<Rg^r
z1!$Fx)lUWjY3Js@r^)NLJQhJ)L*GNMc9Sbk;=yvBpTyYQqUoIF!YW3CA{wWK%Ob@j
ztPEK~8x>!}LJP&7@Yg#T7}9!iu&95|HQm`P0`?~AiSCGSY_g1fp_!6FV-1sQdW=Qy
z%=uus?oUnnp;2>v{n^!<OVpNkvG3WtH;+VJ6n+dqOtRv~C?DF)G~21+BkCD~z@?H%
z7}lo2ICB*M2}`;i;#u#e(U6ex81yF~t9N)|zCqgG&s<r3Tt)+Nx%AD6Mi!sM8_)H+
zSEi?@FM6$+nI*49_E7t}>>y6T*KWkLPft&CrHea9_V?!h+EHD;j)d!S>I=83c}08o
zDm6X6Ul)n=deM0q80nt5q;kO<&a|&dQ?Tbu#PQjR9lI-*VZyhct}EwW`$U#84jU#`
zk9>FlkB=O*FV$)H?9nEk>=69hzj?ABN>K{l!kf$eyZ_$o6)EC>DGsjeRRSDygj$57
zDDS|H6~rY2FBT#o2-*A9=>z5@TblIo;6$s{>=5IEF_<fH!fA)xNJ>cthEMfriFlJB
znFLp4gD+=T43JLEtPSwi=H8bKrohO;eDgv+(9Mx(%XB?&cWiE-!f0G(E_zg7gNV|J
z5HKHkkSjqoFjYdfy#2LAFd;-qhYkWshfY`R4WR@jgNUA{+LumFA&EO%%h?GOX5Zbx
zz$$%=*^#5)8*pH}7>_WAJuhA_9>)EpNkJp<lDgEI_m#TR>WqyIOxtx{S{ON3jOBiU
zLyZ^9&dv_`<W+PGGel?2%OBZvN>?%U$Ns$H1#ml%eq1d?jHfbckO?pg{X~jRXkJFA
zL1d{mU*^tZ!7EF)<K|!mF3t}w%w>|(Jq!f;!z=$my4#<Z5g3~sTukXPE2BwK;c#YL
zuXzC72}P)4Cf)H5e*4y}d9w=@jy$AP(>7%=$0k$lq8=AR@Xw#oeJ-GE@Px^H*v7-v
zkRZg>5Gf`qnvx{;m{<Mn(MrF<QJ#u&!>p{Vy#{4Wc7cJaN%u>&xxMT+dE`{xsxSKU
zqXsR~Gi{v~&hD7Xl!2~p*Rfopj708~kFUlI5yS&<Qk%oarou3)HWDi>K9i)HVtl3w
zvqj50uWP^0cd{GP1J|suN22&)d5SU9H%+0~k8)v@yQ@Rwc{|u#>$V6xbe?P``3ZUE
ze>oquKoZI<t`OOk&9rq9l|OlW*{CSbCVyvy{kF(~Ic8TLLZ|m_>KIGXB0N&#X;yz!
z(_0sN1Y2jAl&^py>I=gH?_v-ZL60mkc~t%Kb(<D<lz^vDK%CB`EjgFKWF}45`wzmn
z`WR>LovO(ZPRtu4G6ewkj{<gO$ouu{*W?(`H8R#|SY)AdL5jLq8Um<U?X_m{5=-{?
zE%R;j$eg{I0nC_Ld=XYf5kk6ij=Z4_S>il%b!G1u%1VVrf(_c-r2Nwcz)dG5jg$}+
zKi&RmA-~u;SmL>|Br~{FTgtkszte=dkz~$jkoit_HQ3jk=mRK1yocn|pP;q;z{O2*
zoyb%Z-kA||3%#$qGA5swt!V>)W16Sg9toWyQlW?C<UQmr`5~B<Xtm^NP9Dg)yI4^P
z*z#Mybp8JS`vWZF|6LF5Md$tBv>h%QST2hfMe-M3$u8onAtbcd`@Xe+G_Y9-=#uam
zXcK>kC?p-pi?WR-5Si`c=L6-6ZqxSYBmq;=b!FHarEtl+#aiGC6iVcJdYf(kzRH{N
zB66XjTc?aEIHA(4{*O?AIo7U2xw$Lt%2zS>x(L?W&76{|VwyX%Xx2(lkusC|ep9GS
zT;OA(VV?^kj!~<w{(j#UU4b*WmYlI$Ct=%Z`YCst#7R=i=Eh#c+YtQohLMpG+uZgw
zG>v=c;VnmbX`OWw4%-C9#2h6K`(jTD9eZAzkX(#>3fJ9%!9o9HcMV^T-wfI~7O$M3
zB%L7c{L(!?Ox9S)H=tXOIeo8hi;}=;k_s0$xtQ=fvPOqCB8!^#dMTE)T1-etNLrn@
zaSw<4Vur~9xPP`;CMwDscY@z7CZYZP1_I31D+5f|23T$bY6-_@vQkbdiKB6X(}$F)
z9CsdO_NlT{H=?4tuz`24bJ>q;NF{;lQ_EHwJ!8sJtz{y<LB9(B!T%q=5-nvt!;_jJ
zuk2>nP^@|T_OU=|>mVb|pk9Sz`y53TxMzI!D*NhOQjuj@Ok-)cJBu&WTun@?we3W{
z=slhbmqnb%2cU={8eBY8cHUWf2}<Qd+8%%yfwM{1W%kQOm^XL)LeVIsIIAw9aZ2(z
ziV9liTocAb1M7LrWty015sZ4C(5u=Zzw(0`e8}Q1m0I>&OZ?Gq2<F1t|CS#-)lDU}
z?SN#K$5ri54*Pix&(PoL=!PpqK@f@x7T*UKCWXce)aJ?|AF%9h)wWac7-k=yR111X
z%+G-f!`Uue=8y(l%3}GZFG~^6{|aXFe^L_lAK!~%R1V`mT!8-q!1W)0!T(P_^j+P+
za)Cme#o>e)JN9PureYlW_X0nmlOUYyg8c0d%VeCt$RX^K2V(>AAvY>Q*L%jMC(R|@
zkDv-FX#$k79feA9b#?WeBa7n(0$ll5=EMjgGDgIK7h4l0k_NExk|BCvT6y_OJILon
zu{ZN$wM*YQDK2(#oeJH4YM~B7H6(hOZ5o_YBh|9ei3ws%{q4r{Fs{=Ja3C2oh-%H?
z)Aq%v%uE0!bfl-shRFxTO{x^N40sUwcUz4`sQXfqfvwkNsF0(3m;N4&K(Fi;KEPWP
z{vUT&F#B|(U?WAH?aiFmtM?$si{=B<?QMTkFfzrA+C~s^Qt}z;LtHFQ1;@VAK+zCs
z6-LX=g_MI4`MD1*tjdvLYGFYc-;ynaVH3rT1|OCqNOzaHz~OO8hl`=HjtSWn3|MJ`
zkR0OQge5Dxox9gX&Y;D(t`=7OH6Y962D|zs$>6eS2G83{#XGK)?}git3*-J)8Q-ia
zgzjozA-O087OTcC7styHjZ+EI&idgh9K9~h_+2?-<Wfzc<s~wWQ><gk3NdQFZM@fi
zorQpdJB8#HQ3`td4((28z&e3;k=boRn${PS!V??ECbSxNb=FM*gbymC1gLWo*cDJ^
zfi@Sz13SCDKE%~wWQ&~NHK~1BXB2CHW0mFJqq%iWO&f?axd!ZCJS@f`u9r42aIfwe
zq5djxF8qMg#-^|&aM&{GB<}wt%v}OJ6UC}UJTqLk+Mw@<n*_4J>D#=~zESF%4SATA
z<D%2!NN$*;Q5P4nnC#$bhG>*I(w>ApE+6Vr|2QQe!MkTEW&GswPAST;(n*KpgEGsJ
z4+`X=UO*jF5Am+ObMTW|j8Ub8@x}Z`20NB?=5*7JUDk}g@6&SBq(cj5N3Vo@pl&J^
ziJA(k_H)Z{RTFyj&Ft}Tf#P;G^`8F1g2P5{2q4LA5YQdpZDg$#GB@uX`oXKH#7wrR
zr(m==;|3678n<p~ma#rl3+r*~@*TU%YrosgSn4<zXbXf{W}3QAcj0HtBnq=)nI`V2
zMT_)rymVDctH$oWqcyFHZOiP-3cX8LrDw)odpm6AGaj5vi50sg9a}W*>EZ9C6qw3$
zb4;d;DU|N^ZKq~af7Zy5<9)TwwsvhVV(u99sP2lFiFkVFZc$q!Yvxokbc|2P27$e^
z{#TWufl<4OWU$=)ugwb|x<|o?J^bK-^2b!gWNKeZj(exx$Bj1D)xBflIf2JCG<p}9
z%N`GPb$8bWiS&%|L<;iudPo$%b`~c^>xJo~Zw-3kyF1G3O2}Srr415@7PFqNT9AHg
zk}R0P3CQ-QW^8A~p);;y?HrdBvR4B&Z}q1|E^{f9MUq{-yjgN|e%h!<zk!KVlxXjT
zRs{KH_4m6(DT!$(h}#4$R6AS>l?!{ha3tA4h^uPWswPlyETSDWp;nu<iNRu`Ib3`K
zL({b{UsG8bTv2Yf=X(v;{qlse<&hH2;xi|$HM0b40XdhHR4g=wa&mcnk(yO%jFR^=
z%aYm3GvKl@^O~=2p>>eYK%Yxp?)h?AFVA2Ju5Tw#8_f#EunU#dOizq#ET;b~zvUE5
ztwB;Fl|qDo%dL3Qo?VHkjB1ihcFSk?$hQK`t=ViE@dc^gMTP5y1LgoBNG8gZ4O})2
z+i#lM+USbAfAV~?3d80Ay#z0Yycp;5x!#F~U%q@vv|Tqqsnj)hy?xO+lF@bJ&lZ%w
z>*8GAJ%B(;*WyWz6L&V7DK?myp3Yv;g+-54uJ(PBUQ?4zdoul2)tX`2Coc@vwVy0_
z?6|{G^o+CS-2T%%qq!h**;sgW9Yemad+pja*Fh_TTAdq*^=Iknud$gm--}~DrdKuX
z-?Tv3U@4CvN&to)ok{L~1NBAI``PIfa6lj>tu|i;0ika%T4LCMgi)4wbkU+;6Z`XS
z(X_vR+JJeCB((t~NN;II5b#81vqUK3YN%}{*(DPbN^^2K2<*NP5d+VYv^$uBi|EK0
z(M<?cpz{C3u+^JIKK#fL3yY7~f?m}2@_12hj_~+LcaGm*)j@ff2^1x@{gVH*yfEmL
zoiMcdKrxf@M!`rs#5i5@ggMP1T@Cd??_ZSb^>M#VyeEw{69GRLxifm|GfITy5_$FG
zfjF(=BArlQcc`D=oIUc>H+Ja_GR$}9*}?Vvj+c3Z4PhB!6nE{*eE@+0?Mq&-#)W_N
zk|e=@bHw+*C9Zrb7g(wtpSm`&8L%l~10ybc+iVD=q#{H@AV0hHhSbX(qmh)_*F!Zb
zpieNaY5ggrc_3TUlr}r4R69r|kum%6#Mag!S~Xs8E(Y&d;aJwzkID2pQl#(#6#}!Z
z%eWt*bgfDl85B~?HbucTKKb2vD%4^|EFC5wrI<PSA~RP~ZJ5e)r9zEJ7N||6R38*4
zG#$dL)Av5CoB8OE-5sD&$Wm3%F38{aA*)X1q76Hu4?s;8)HXuLgv41x$MZV$wiV@L
z6oD&+l$i<kKt2{)h(%yrdQcWg9)v7YxHF*{zw}*%g29oE1{55HA665h6TXgFylE`j
zvR!D<N|syz2}G>=KIXu^x+3HVZETNkB>XE$j%14XaOQmV!goU2#dq0{bQTu_usRNG
zig0-KE%^;5K*`%rLIxD%C%>``qpF;U@4A9|Hb&ULp%5aRNvk7oB35Q*G*rZwP!U=^
z90n)sV+waJGh3Z1uy~mMGwH=4)UiZUUtH@{h^dZ4Rh^QD1eFVHNpg+OLgQEca40Ex
zf2jSskAh_&xplzx_Mmkzi=*GkWp~<-RR+=T=b*Y$ig4|?I2A~z7BXP`Fr8HQ&^u1a
zGfeGw2WRF3Sx&T=`5;50h;PCSU9bko-9P|EngK)LD0vudAKPp{lYk$hFG>-g;7TEc
z^Y85gO@luO=Zp|8D7Bzq#e#s9M3w@A8eTAa$ZH7b)Xx#xPwI?>Iw4La?51LyrV;Ff
z8KGaFSAf(QrVv3WT6L;2^%&eBNaS{?ZFg5}$_19$guM|_s@(=kjVyI^Y^?o5)+RmE
zbNv(Gdv?MYNl*#Hs1{bGUS;bkt$*^lrvptp7lEp~fB%QY`?Cp+t3GG|2vkHfo&9*6
ze><VAfZF4WvGVP}`0~Y2mkCLtrKc}c)bdTZJTa8bo54wlM@pAXekPhr@np<Fw80xZ
zD51?lqZd%r^d?T4F!0qzys9;cqQHHzQh}&_&V&!Dm|~`bDB4MQx+|^YL`)v)Gad*I
zS2@2+P>51;Bcz%|_B^5FBY`d;4++hn%!C-1)YCT%m8<X78RH3yJni2cS|7CMd5SpL
zBgWk!_`IstZ>XkezDK_PICE52j7*%6GZwcU!XyPaTO+_P`N$RuuhiXyE};T%-lN#{
zvPMROJA}Qew8uu$kT;}(PcH>8j9_sX%>U$o?=L<IwHl+?a1%OEt{e+s<`e=~Z0d>q
zqcWU9F~ins9uPezVc&tfM2JPlNlgh4#|x_w@dFD`FTikeM4)%vettlzHzIF5^Oi-T
zd6j~#{nB3@^NLc6*8fs*!`2UMJolkB86P$R3ad$6FDdPG5;9zQy`z&;$7WFF5_^Of
z-3bE!hN^9$4T(<TvGyt)pMKmwqMR7nEjWmvf7q)!?Uqfx4_Ezh1|}w^sa!E(Dr|-f
z-yC7{kk^74QRK`=qHI`En=&g>ygH)t=|lJPDbV*#(Etllr4f$-bXdTimn;L?L$!X9
znBMwPXs$9WQA<jxRNeQM4P!Z^Cc14ge$mjn9z+4#j^qBdZ|}z|h!g|QCyJiv%b-#`
zIW<Hvr=!ljLSSd2r656mJD{!TKRQlWx@wbu?>4;Rdns(EP#NG}P((X1fL15vCE>7y
zDV%f=aqr*j>}&38Zj+;_rL5lAR9#T?sVF7g-~VL!bkAx$?vt$ohd(EF&7KewkPDW}
zL)pU=&Ag;`m<v34M8zcUpmLfjK%xw^Eb#hfpyrg;t2ziu<9F=C>hIJ)lA<)I6vFjF
zhon0wH=TsHxmAqL1%Me~l}^%IKscb8pt%=-+u8zBmhdVOYE_Nj(5K<=hvU$9-3J#;
zsDCC98%4&~PEc;}l4Bd-mZuQ6q@3wD01U>3p^iQvj`4&1cHFe10Hw8;pjx8^C7)Es
z2yzV;c(HQxbXB3@0>0-KbQr>_d--9|OpzT111TeBg79|n%#VOl{~e-^^}o9OQv2zh
z`@3Ug5`;8j(_3qwB~Ebm<D#%7np{Cg;DHy=A{?xU%3;!X2Lia(2wK0|3H=K)(CJxy
zzEwS)Q|KW4PO@Tj%yg12k^5pLN;t+KjHZ*MROpf0?$DhYCgsw+piWLn;6#B|HBUKe
zl_y!&-0c7H<41&G3(ZZwPfnNzJJ2kwrW`T8cVDvizH->}_wfUV+<F2uXe;V&Y8xc<
zm>GLI%`$kfS|kiqhDXtdUz#wteSX>4Tc`2E0mFc*aPu!4&z@c8vSnS~+}sz}lc%Y#
z`k5)VBE_zq>ylYIaYZb7Kik?G=z@ZR_@>_LYsm@=CKT(+$_ReU)E(5z-aZpKZfkF?
z<Z!vz&*#n!f48T(%*$O?Xx_TD9w;H<zV(M|fm?Q8E^G&$z{#AI9e-7|wT)G)?}i0O
zFApSa;e%G9q^sYY(fH+}fOGKFp+kqZ;I{$tY>$XAQ>~hu+<~v(zOBP29%5y6Ii%Ei
z(zxXz`87mtp>3Y_eH>Pm{Q3>Ak(!$U8o>WXgY+tVD7I?O-k%cDDYk+|7Y9$D@J!%0
zt}xYOvvESk`v;U-x8jR#k90^sJ76ZPXCkMfa%2Nkzpkd;`FOiEd*kUa`Sn9XLo&}R
z_`~yaF!q>yCOCCDt4fna&H0mUCZZ?z+Rls{>^|G2GM?sqN6x?URkBaT<N2P}g7!e3
zam9$4*+_%A%cnnN$X=wKjy!ud`%|&ez1-Gy_wTl~Y0~_9<9XhF%gSYJZtH1ELZoQ}
zZ;p!e+D`s@Ti#;ghT__C8nnVXzIyeFt$xMw<t3?>Ml_%ETv>egdB29f+wy>SLOXWv
zzEa1$_CDXq(i-d6%a$!`(PKVw;&RJ*-z*hj-NQB}EzhRnJKxya*_B2r9;Z1aF8(Y_
z!*r*f34oOYlarG_;FQe0_x`9F{%T<q(_;e2Wf#p(y?v?q8x(A6>O=*Ti*?yp5)v|X
zqa7=<g(H#?$<(N?5=@dkeYQ=T?rzAwR+#?_Ve)G?Z4yOp6Om=S&r|9#w0D-%K$i8T
zd#R-o)O)+)KdA(N_^^eh<lVVHTyJ{u7_6tk_}qt=ZkVjjG9PA8-yMVBqP224&Bq5b
zd~+gp(1F=@u1lq3_@Z*HD|oY^LOBR9U-|lOq=61w@|jkXgOa8uJr%5UCoMK$r1fKe
zfB)lunXZ%7pBU%z*nWD;FErZI(99!t;y=N0<zs^59TWdbc^O?jhlG^*y%$oaBRhme
z^fVlH{%R0jwbdoic%ybWCv+lL)7)uyyCP`8<Ial0{ueCw5cX>;*vHvudff<o9lc@D
z5~f8@sc3zo!Bz%_fu0Ko(J?JV^BJSMrQGxK@|Mx4B%6F=<>Y@b-*N(SfDbY=9fA!Y
zwX=fe;lqb-(zIXChmTCx@(mRI+f!<Pv-0$32t|C*ujSHCP};yKOo!(w_qbc$z3pt2
zg2q5Y^3#_ucdT(0bPG1nL`!x}x(zRi){j8Bd-&vu285TpYCZ<$jENPr2fcs47V-)-
zccJh00}=FR7~b2^7KId~88k}(+O_pvvDECf31pd2ENl<r8NX794LhdDKDD@EWd|>9
z*~-6fO+c6^p6w(SU>Oav0S>3^DLlL9ke#@Wab0hq_z-Nc{?T%@Bdc?weLN}Uej-GV
zbyv}RvYq0BQ0A5$JLGk2baYsu)4cqLgRiya6$ORIkUCgT^9xMlsPE%zHZ#yns)si2
znG-pOD=yJZU_LoN5RhV9Av)gnb^C};At%u5jnKBZ|L58ksYGD*KLJtgc2MeHccf5U
zPHtO;?ul*Nw)yPzlDrCaMD>g#O~v{7XVlafpuggbvBkD9w6F5pso6b8D%;PBcps-w
zR%Xr)|B*2_u564Ndv=E1%m<@p;WQNX_ngpued&ffHEPh&uR$w{gC!v<Q%O}=Fuho}
zzf;Sg3qAg1v+NKIv^-@v2!Q~ay9kkuc}^Rc_Exb6#w|l<{q3pYo6btI#XhfIZ6S+I
za||L34WEox-2d~`mX&QC9S1l$x8DDgh6WASs(ZuFbeRc&+42X3<u9<gX+!+ijccFd
z>bq0^YiKQgY;@r0F+7qdW}pG1%jus;=c*79HJXx}pPz-4<ZHEiY8*GqZVr{txQSzi
z4SbNg_?)+BM%S)g`}yIz?H_b2*=AvgYZ47;p;-JBldf~yO?-rGc0t?lw#jJO2MWuy
ze^GO$10!N__Q#v(pF`bO0~>o1IHu!JYt9N9s;etv?w=Kt^o4#CqO$#QN?xvYW$FnO
z#)P-Ux`kMn?ig!p|NBqRv-vEOKJhnlyJhk!qai$Xsg_5<=zk88gzHcd(Jl|!2t}_I
zZW52s-u?saIbupm`>-`1V+&t-ayVJ1Jme?QlR*<T=k>oIO4QHJ&2d5l2JNR``>Q#2
zu3V;WH?|WpT#qo|`z8I!D(z$ebGD}Nu?H)%ab$)vQi}v*xfv6l3GokowXHLef`-jk
zXwNK@Pm_VF><5!}3H^poTc2!Z%!UVUFkJ1vU()nlsrE6*JUo2-_yBklWbsZIe686=
z!M;Y5^ck!KCl(g^KKn5)HR@=tmX6G5%nDzbCk#T?*Pt@f!ULV+4Rm~4;CNa#)Idcv
zn|0`<S30&(jaC9Th|`(6(zHWu!VwAn{t0h0d%Eq`A2`68+?*<G<<r-v-;iNPYm(d$
z$f0zDH9^S{q2zKJbWFVfg7hUPXAPI<23!fzR|K_~9?TEG6@Py#6?pR3+t26}cg7Q9
zI&vgZjsp=ghnkMcLb;eFW$kiw&pd?^VIb5C-aywP&zv%Le5a|YsdUv9C8hNN??wil
z=+*ULU27JtE0a@ReLXF7*mIC#y!`OtSVNlCcC3^8p&Q+buC5S-34lo<3bFM`t1i*i
z4tSr?W8QS3xB9NRSXCR*MrxUZI}W^g<Hijdn(7#J%lumV@x-mNegABhM8}H9!iTTb
zE*_(O!|~)CRq(CwQZa-nqoDz#X`FtmvPz~s{i$y$)<DBV^GCbO*C1Q@3eAaQI%O1R
zdck9D{r!$)FbbxLUrvOq>2exKGqv@7p!;i~xDXz?6cXg1doLV2`N!F2j)A6hJ(Kjb
zla>P*o0Vk9E?u%MmWMIn-s1KUn0}+lwU@}oA~xT`BIENJc4hFhwr^IQPnU<(9*Eex
z8BF_cgBrpXXq&8{v4ya(c5Q5M>k5yjJNh%AZ~+&#a-cbLFyN4Hex1)Aj{IRH4tv-G
zFC6+h17r4UXvkRQ^ET77v_tX25hwV=HqKpEu73)X%cwOlQeHk25pw4*9Gl#|4KpHg
zCN@e@yI5ISX?~Q7tm6?Ad628ZqMBK#S}kEpiBfn0c^VoTNPYCTXh&*I7E7bsYTNeh
z5pyg&$Btb{Tk9V7qK3FaniA-z<g7lzsXY9%yDtxM`lWCUlg^1tHYV>Xbt4)^^<M8Y
zv}BP>aEXjOLUTJmc%^>cGsL{aCI4{PO|kWn2o9HTQssN+ct^><vd-s@wZrA-6I(3@
z<GgP4%vzf`BnS-DbUb_N<G7-vO^!H0D=U7yTX+0<o}W}^bQ|%3^I4&pb=HUn>I<2T
z;~5=hw<5%WMvHkwt<|Y-BD}CYpUKloHq}C`&a!LHl*u&@A>A?>AOT^q>GAP9@X%IX
z;GD@-t?}9{du!49$<e#Q2kdhi7(Z^UY)T>+5;b;7FT`0xtWdavV=xByg#jzZ0bd(A
znsj7kMtk@5u2Ew2@bX$uqYZu5PfdE#o$q;C#O)N6O<MBiE{8RVH6X-!o?I*|+aIHl
z<2$qC?AfBP%d}0x$W_z)f*@@&-?r*?>(*^`c+6woBM--Z&c`l9Az}#Su-(8lR@|*~
z4Mkdd9b;Y~Gr22<S?$7wyAagAj!yhnd75vaPpv#}YT8w@|LobbKaknP_4s>ZPyfa2
z`t$R>6*ZrXnZAGjPD3*_HFaJ}>MWKMx>h|ryT8ZsGWz9`z*@MUKji-X<>>j?1by!v
z>W5E#+mHb|(@#ijd_e;Bv$OLF${JytzWfXlD4w2>v$U37)t4`J@#0p%Blqyi%PH5~
zm7+vpZiQ!mouMI96<-j|DC%VJH6vOpC`(@iC^w7L2sLxxPihgOfnT5=`O$XDgyy~3
zy}!K*Tw?ahcE{CuZO1y*)r$&773&UmhK7z^SytRnf(37>W3!D1T^_+5x3so?%QP48
zo!qXLC8PY;U&|pUs~7JCQL;3Y_h{In4}p1b^!nGbI2INt2EXAmJ^=Z7sVWc`i<$QC
z|6(BUVZ5ys;yi%NAD3G1T)ztX7X22-#S!fsj812B%`1pz{iK<<4Z`Ya-&-N4c<#oH
zccq;$;Wy@IrwmQq%xy+6ASA#m+s}vZon0mOI2~rZ*e9&egegTnV){a*OWfRVYz!TK
zyD>Q6_W8)7YqG*nK^y|6=pB`bkb``;GW&_2PkG?}9w2Iy$T!$S#PRDl924j{(TXi(
zqt5GBrJj(qcv80d`}<X>uj(s)#xkz>?5X}whD_hUeR&o8Tpl}n&)ByjC81%nzu#U&
zZ*?ZIgL3THmct5>r*w7MNSK0p<(Xi)x6oUD`t<4NfYT4Q?Apbw*dX`u1S7<y>4>N_
z@{To7U#5lEc?g#l2<@1s&z>1ae4)`;dHPstc6eB|^x3m3L34bD)B^76C!QbnJo^m|
zjnYg9ha}%2T|MDGyUpQw`i0NZi=)CKg+dUbP!1mai9m+WewGgb`voPX=a@3lf<7)$
zdHEgI<9*#cl56+0KI^PysOn(CRfAW02HOa-=Z|4Q;E%X<%2pDcG%2YKn5+1M=Ye)M
z5(hFDBvK`L8;<c;oOEL(Su0nq`iXow3+g2eJ(9F%#?x*Iek@*JGoCW^bw1pW-Qa8a
zbfnYd>}dRaI1^5%ox*_wlE$mKxBlyo_<g-o+yC`97YZK@Zuy@-)E_ke!%J6`{PUZ$
zXDRD8{cn5YA1i0kKeX!u(FLen&uiFlSB2w=3P%nIb98DB7?^U^0rD+by-DA2*!O$n
z58;r}HX7>4m<iA$-*^nS$!*wh)wYPJyM*~5AO%QN3e|4Ja;J_Y+0R)&Vem)~GW=uU
z^F@OPg0cvC+f4Ygof!vvE~s=sDHg%))923-9!GC_g6)&rUukSkgL{Ac{Kbo{baeV=
z-YOho(3mTQI(@uRGXp0lXIE_;%RX-H3kK;3N&`#z1XKdYs$Vbr!%=jFqv!(+APcN#
z&wKXg+C74Ij*SmAD&O?Jp1zq;`0UlIdki6G7YtdVI4%<%@y1;Z_n7KL4J?LQ&fyzr
zXo~!VUZC?L``cE2(d*auGfTapf$u`}@^|IWPg4HEb|o<Rbo?eO;ph<VMsIpXt~hG(
zDOd7MfBBeWgfQxp^%xswP&lEH{syLOt=+KUtcnU<wA%mE+?D@RnZNHx$eN*1wu9p+
z6@y5Ga1d(7NU~KT6yhj{v1U0#i~Y3OO4b%@qe9k1B*TOWB`QQFrgY3$BKcmA*Yx=t
zzUS4eAC9BW^E~hOeP7po-|y>!-&vW#pIFyPB8EmMP(Cx~%Z}T5A(G{s-`$h0o=I^<
z;|LT&(jct7O?{P50{($jgdt9dOV4eT#=^N!J1p!)qxqi#Cw+(|Oaea=F0d?>ifxp%
zw0iPa$5R?FW`02pcw`AeQaOm7NaKYzC*bQ!u+T6c!5zm?0&iWN_q(}4hET72vUfIE
z%n?K$f>pTxR?QCS=WsxgH!(2*T}?vXJ_2Wj|E|=xw_{lt*GweJ=!is9V|9Qdp{qJN
zKK>S)5QIvwuD-ryIxa5GvFymNLW;9H$2+t6h+Sr8xnP*M+=_#$)BpU#Mc{<sn{Kdm
zY5jT2U5_JB*Xw%!%U`eS^_!ZSoMe;Rq4YHj2FycPX70G)@<bFD-~a2f>{_vXNym0n
z2@`;ohK7bDYX|Gz8vM~oeEs?nb?CD>dce>H_IUn51#-nDB>)svO!rI2g!w#hXjY)j
zoknX_R#%sJ9-akQnUG(o3so=?f_!4Y<zpjWc%HF?5_~yvc&xBOA;T-3!6=zekL<gW
z3jrm%TAHa^+S`tdZAz2RytH1_1gaokbk_v>%r7kD!BGPwm-JIjKupQGe!cl!2)5Mb
z#~P5d0K~sKA;!aXPDIuuJbWjV9AjZ!LRC`}fs$TjmDPUT&yb;PX@w(`13~xqzdQfp
ztq#CBry5MYuv}G1$$YR9`-#Jua7)CF!6wB`|2g%}jMIUa>j|ogiV%{%M%cf9ah);w
zH7AEkqIy)?Ws#ja40RM0O$YY|1!)sheSH}`A$}nt@OmTo^dw@h=arRpF%I{gzw0Xr
z0nsK@Kv1_~LFiQxd=o<r`1B*>6h9zWZ_gy@_HJ0naiW&zqP$+!Y=^p90ung1(h7gq
ztAm8oJc2|wjATIAA!#N82`pahW|%UAJCXcs{?l*>fYb2Q6dOFj74#ATysCQ~6)!&j
zYRd5N@Q6G9vUC0(UsUAPZ`I+zqY5CNc=OZToCUOoRrMlGEs`l>4Ghu}p;u#f=u@YD
zbFA9tgr6tUq-f+zh9A3n`xA(N@er#BR|q4nHzKWw!?V)T(sJyJReT$QK3u6<0#Y}h
zeA3n?c17zrEp=O9LVSGDG^XG8#t$GxA20uk!MeC(z^lC1tUNg!Dcs@<TNy95UgDUU
zn+tkZ97HM;Blt;m=I$`du~E|gPMa_FwQxWMbm}-9M5KGIsCNW&u|D-3Iw|$)L}&Hg
z>#A4~u2`)eld~L-7&N2ZrX}T0evi<iG%3i>FB-YL6nszj&gmZCM}9`BnafC>{J6L4
z$8pO8l_sSj+4bUtRkoq({O8Y7suC2+52K@_>3=*!U^vZU5%=z3qQv-yja^vQ@M2>1
z091HoYh2tv*D&9nEeo5OQ0_e0Ta?o8uJaD7PJ-ai&CTsi4V={tu#@S&3e9kWj>sHs
zUtIA+E}4mJ1w4_1E?9UCR%ztr=W`Ky*F+p{JKdT198Eof;?0vk{s;_V;LWM}IXN_P
z6p4q^W9=MNt)=T7^I$$RW5;+^(>po~FEi9bjUf(U8};+1vFbl%ws|Cm7;Ep>l3ug&
zDR7jR3ur$ir8$SSjRs-KfS;jKIym9DrnkKELRMBX-Z)Sm`4ItRD<sG+4WKlYK)Os+
zMJ#IL(vD3_etcEShk?$!S&-nM7uuTO085f5BpLrgsUYP*MWz40k(VxB-0DC2M43IX
zc8cWu4=02^=uT!<dq)u~waRtvVgOQq<c*WjXWuajONmBfYaD$d%K3`LUm6A>MK$sn
z5p)0-tIQq0LeL!55d47v<_J2bj<rFm;CDxv^;o?~7bBhb$uA1o&4cT%=Jxd10{??9
zn%%v4@uD>>6r6K3wr%_VRc&x9u0<XgsK1<z*#}M&d!aE6%G-7|)a%0^pcgq301Zax
z;{(t(_BFKMe_hWZ3}*H%7<6lHKyBzJ7;PFC_nPE)lf|=wo<8$L$$cO8E`aSMLx;^B
z5E24zr>$<qnCBaNcqpN9dYqt>C8rMqo`H2?PrYQ$UjA_-oS)+nnexiZZ9AfvObxV(
zyYHWLA=bsbHiCCp#SCUbxD^x>cJJBK2(I1|P>t#<CodJg;%3Nqw#w^ZnZiO5Jbrwm
zq9QkB0SO{=XXskgdkXHdQ93{)Z8ae(DH#h6ptxxhADUc)Z&0ypx#120d*z5mGIsMv
zv%pT`*rW#S?>=tGkcbBw7-}$~(+M;dRkA#dMN8y1s46R)7#njDj!R+8$GQPyUkR#~
zmTg^UAVPU;EMs&~;5k<DyZ#!Ub5#B&2%5Hxt2x(I{aHDYDqi)=`-@deXdK$N`Z5HP
zkq-HBP(&=vacjk3%yz(km5<}lUo>xr&7lw!wAQxw*K`1}nAqB0?$_fn7R8jRV<%2i
z9J!mBpE^`F=VTW=xG=Vb;D^G$&cSn5x4wUzEUS`w2z|~J&9lBHYH30+kT9RD2^ddy
zJc00MFVD`pH((xEez3GkegC3H$38lpbsiGp<%57zDyq3;Jy=pTc({r8-uO|-3$7v*
zHgDdIX2VvQQ^-#QJp}%dMGT{;uXR<~=v`hdY3*Lz-XTE@W1Oe=W33)&Myd91H{JYj
zb7sHFVIHY@8YAJqRqiAasa=Z{@aCw-s#U9+7h$bsZ*U(<2cKqVR}-M+9UmQmo08xp
zNyP$%M_MBqQNCA_d~lLWVf*%V#3u+U<X|qsygl^Pskb;Uk#=~osErbFTeuL_<~I%U
zL>E@hC8K9)ZATp;JGx_X8s#&cLsK}gLO$gD0qO1Dry)+pMZlunH@Ws^#Ts>WbtHc=
zq72{MyS+1G8y{I!xi9r}$JDD@9msa@=8pj_SVJY5DA4*zs=!h67;4o73xd8t`dy5_
zv%{wah#?gCgARw-7A+}l*pP$LuXgJVME^~%qafc}5_Y?1yrXxKo4`~$C-CXU9c2Dz
zJmLb%nwC84Q%vf`%{XE56GO=B2~l}@Hb|T+j{oC;bt{~rN=SdW!{oj9Lj@K77hC{5
z7FeAnMUT=mGbx;}P_c2ZWFTRI<$BH;`p_)0id&`L%_HdQ9J0@~j^*yb@hgqm*%Nq-
z{WUa2MK0TLrmK)cMH03SZOaw`G<u9-6oq8@9TZhx|2sKTWUG8RB_*S@IBNN;(tRlr
zy$4W1R%&lMs`<;`FU06$zuM){?k@z8^2hXC>xXaNxYT`KSZI3w+#&IB!_A!p@Y>a+
zSCqn8eMn_qb=SP^63JcmHDdonz`Kd1_wV^iZa4*J;YK{U^dA;acB{&|rMiv#Euao0
zV$mP}4$~lwFt=&;#ZyVwI=&VQ+{a(37?T3^n_e6F4YB&A*nL&#F@-dsnBdX=JHFgR
zPuuXe(>{V0*T`Go*r0t+7nNDEw#$k?FeiAX$xC;MNZqo)<%)^U;{_>IdA=CsFwHFW
z5dgHWb~@pI3cJtp#IsI1FAL=F-XdpahSz$=E&OcEBC5_1QeYUIEt1)(V^OpOap0bJ
z=@he-iz3(f1$RG^jfEy&zAVm(#6d|$Q9kU39IO2|qJVKeCFKlaj2l3Lb#ZUHVT2yQ
zKr|xL)l&E0FK-!HLN*fcu6TLVQEn4|wB6V-%?#7oNx@WMVcij@{&v5vc1mIAnR^3|
zvENFi!z)kU(CC-0EscRons>v(F|WVjc(md5{h;@>!p@B1xm$BXjobtsjRDF*b$q#f
zzKtu3jvvT(<Ym3Hv$OVh^hxX{NHnC9JO<73=>Oq#Fl_}DmG;p9)crO;_J_^jzesq0
zPE#wrUzFvBEF;Q$v<pVFFc<c+ey@clMZD+)OBY_$BViwO9oXA4bwhD1*1KKic0c38
zG;i`~M`iA__std8mcD!;OYTnSS-|lXTm-Nj9lmHPRadwF-O`SOaJy(;#P<+)FCX=2
zW6WQvbU8F~FHk^oul>P;$`4%Z<|bE>&`UF!B(S5iJjQ71et;ZmbGD;?d|H|WnjOsz
zQ48-k-5Y}t|2b%yhJ=PHAU}|K4{78>eB-B8t_ulQGW_AEa!xo*|KhM^JIrja{s0kP
zgWA<iB?b;>h10edd~}Nd^m2++q|us2`nzoWJO-Y0c3L1)5l4WKBz65an3$T9g(3V8
zb_;pAN~&D$RU(UU!jZK<8R&9zcZG|EM&c9#gj(Wt4^5{{2**#=5>JyWKY~eI0TaMP
zb`wDM9XLvG?ld>=DxTC0oDhe()RCYq+>ea)e^N8=Ma7b06iI=w!wz^}lH%nYj9$QB
z;;-If3E~K}eRo7Gly}3|Lj+!a6`@8p8cu1au%pe}k=$T<m0oHQA8<oLRFvw>Lt0!t
zMv2%m6>ft?Rdr3^5<VQm>q8xtu2pC6R3}KG6Y)I?kYzqD$=Ju|o;#mfL!7^S`84pv
z)fL=0<q)i(Z}HJV5JMLudNYVFOs3X$_OQAjx~?NlyK#B{qc{L7;_KEaAgX6aebtSQ
ziMfNP&s;>chNLvblM$bWxKI=C0+vS;oeG6u%1}5Qdys}>xVTrxj;US~n8O$X4gUgw
zdz#u|lUr8T0A`K^hKJ!?srXdzWZxRZ3NcT698$?NijqTDqL>-uM36P2+lTZczl4A{
z>~WH>t*fJutssn*dE%WmhJnpBm3k6+&$+v~m7!h+j<>2nlG?z0yZUwbKR=_b!b1$b
zeH#O~Jx~;}yYT>kay1*G;xzP-o%9_=c?N5Pk(EDj1B`k0^3G+FZJd!MP!-E7S^^U4
z&dvE2?B-UGQq<0vU*?$|*YuzNMGf=wD+`H2!_vd^Uw!`uo6wnF2QC7WX3Up{ugp+A
zV#PnNR+YHFkzD9xWoF-+S<KFX8YbvuSguBom1p&8nx^JjjP)P5A@n5zQ+s3?_3+}(
z(CH08R-k^H)iw>`>5;<f1_rW(HTti@vU)uudZ6~P*tRq(mFz=U09t}D`^NtU4RlE@
zFHu%}MgfTZF2q6Vr8etZR<dN$DJ(3EO-?4cFyrMpTQsR+I*pbo^cD#L3;=*IowHPm
zVY9!3%?}$%YXn|Q#&}FcrF-K8cnSC;#j@EF%U>aICnhe=gG)f}C99F>8;s1Rs5s;S
zZ`*$`6@2QARu2U(H-@bEF&nS|F|65lo4^pUu+32UMZb3RynDdquCA^hT^xb<_@T}n
zlbtPpR5K&J7?c!#T(Z~@rdC!~DPiJ*ytsjct?)|>&zUM+ikq@yFAF}}x0n+Dpx*P?
zu{f7XYu+UZH>jEWy*MLJ;y7=amHVA^v)ilr>K3>GH35lmQ0}uo7}Y0@Xl`a>V{;?B
zPv%*{K1Il8*dF@Ggi-1|h9JpXeALrZapz77hHUQWS)tJnxitY)ck<da;iLF4%ivT=
za<|k?%M6+t)727MhiEx^1s&%r>|93x0NB@cWo^MWcWj6>AQxcqWLosLfybDyY2k8o
z|MKO_`HYM>93N@Cy|mkP^vQ*JxRI%p?m3YFy5JCMx$Jzy<%(JS(vP`&vkk$m2&ywZ
zMY*r&xr>+W@(m#}MIB(TL``i}$RIa*7L`X7l)1nNtD=?-gQi|5p|w>zF*`f^7-{w#
znq#iGnUdNJ$>`|lp|P<uC<tTf^7TU$=14q(;grWWBg(yd1)+{>YHckF7avhMxnyAP
z;z^{=7Er}+z2gFx218tesEkYkC=-eE!1}dYHgA`gmnRV%#sXrKlFpdq(t*2S0T+!%
zi5uul!Twvx{d$?q+o=59!6tR8`9*+y!f5c|9V`%5db;Ef&UKQA-YxKab=|*Y!?YhD
zS-iRVYO<$`pY!nWVD~8M=}8Mos5K)EcRh$W#YefIwJ<luMn~lDmlZ-#Th)G9>8q0U
z2IqF`U~P?g89B0!3y(<2Qfy?oD*rJ-tiaR2sSjIQTemExL}SsfaM5)?#ao{tXIAK#
zi0VR@O~S$ybYS$s(dcTGd6M9XU1(KNR$i-a9)h$}?mN)eB37Mhb?Xs&6mc-aU<_2Q
zVKSND>R|uXb@!=0&oSn<%+Tbek0mJe*^{Lm@F*Y$x1a+uWOyi&BWW}F!aPA&nX4&C
zKN*!sJCb5k{|u}-8Ug_BE*@ig@goKNj=vB3&?M*H#V;t>1U*KA08$TTYTXW)|Ktk!
zzI=dANxfe%uCns+Q{O(m@;euY$Qt3d*F_$(mi6tmd+iE~{r1B>eq@sS_S&$5K)(I#
e|MxF)mOs`dFQjN2yeH>~_n2Ajx@~eK`u_lu<o6{2

diff --git a/articles/AMR_files/figure-html/mic_plots-1.png b/articles/AMR_files/figure-html/mic_plots-1.png
index d3cc9358a229d0c18a7d3a5698bdcc6ff3cfa668..643a3794662e0bd0e1c1401f4d5e52ac6d68138e 100644
GIT binary patch
literal 28324
zcmeFYcT|(x+AkVxSQcVgih_V8z*-hS5fG4K#Yj`>B@_WcLQyavgeI;9x<Jq%(u;+b
zkc1jcC_zy$Qi5U#Jt`#-N+b|s5|W%ZVeS3xeZKq8x#ONOzH!HX$AE-6=R4;!pYnUk
zoIJc@Z7#Q2aWe=6l7n12e+>j$UjzcJZP~a1Xleapv<&#zaNWZEJn#uTymJk4z{jRr
zmz={uAmt><k4)Ocf-eZP9|Spn#_n$3>`3HmUq>iw#>QUlz~gQE@BMLU=lUJ{PhLLs
z;9mUxNAYLYy)t0lKiD6?zv#u=g%`C^6Y)j;U(Y-_vQcH%I_$bD(+B>#b>^}9HqA?_
ze9ai<%5g{QV}CXEjWz^yH-+0bu@*Z^+j7EEs$yYtP|g<-wyOQ8l(hf+`oGPAuVzah
znQwJdbsU|4rMJ;Gf#O5L`cYdzpm_K`2j%8AUR`&UVY4*|6rG!o)|65ce?J|hDESr-
z*;pha`I7a=KN?<dIsgI<jf^-zf#%APbv+<Z;JZ}j64MWDWN&WI!n)fv74i8A$Lnz_
zE<!VS2J;Zr)LvW@MH#n`Lm#m>b&83DFEZPxpQxFrdPiKjyL(Jj@=VTPDzl&Yl(|6;
zC)hyMMWY=Zv6|Rsr>6REGwd8@5LMst3d@0b5uV2Es~H=lVwtwoT(rKu7VK__9?TJr
zPGk{Su)AWLynJy0XMovCl~o$$aw#ft@Intc$rb|7iLXIlx80tc<zKBaX;9e5u&*D2
zb))TBZYOR;aP+AYdROR2XyqZsG?aUt^|8fvx;>rnhQNLQ5F?Dz4BU8F|2u7Wia7dQ
zivg>2=qHRWxO)xGlj~bzt`A#TOb{9EZcN;Q*1#^hX*p}pA5*H}s8btue`)Aj_?#%O
zt;ZC}J?AtxG9volElTKV!E+~f%O;uYRoof9bZfb(hKKE|MBP2)J=)Lbstp8IMrJEh
zLjo&Po6@$>vJr7jzHb(rJ=c0ioLtuhdn9JTmgt3`s0hx(MBH@LvRb_R?r5#W`Ymnw
zb=|M?sVuF?QQvlbRBvB4iMb#+MFQk^>NkQ!c**+)wk?g^xi1t~d7+bP6!Gx>2SfIL
zfz7pYyfNnQJG+`lyQ!QpIhNpretD|vtvQ~+?bCIdfB#?K&BOy<=A7@$CxTO=&2p-J
z!%>hzB|DqZcAgAq>t$Jf+(8*oR(BApA>@Vlwj82AmZo0F;OY;=9h$c_4orw_XA}F}
zeVb~CV}{;w0dIIUC>zbaTwWrTP>)K*rDhq^vCOPQ<7NfS{^nRt3!JIgtG2U~H%3i6
z_IjDmyh!~f$7<&&p5@jxmK;cN?|8X8FV4M{p4>6I*s3UMj*j5`NSrg1aQACbt^bzP
zj#Pa|TF}OBP1?3<O#P#l_tdmbJ}YqJU~}~=@g@uA&}`pUb4P^=TlyF}U2#pja*d%4
zHn!Lc{bzao*D`+7`nqF7L;T9!Bs@ZMPZ))%85Foc?q=3)nKVV@S9u4&S;(C^O}kP5
z^;F3>L@4GsE?|y3S-G3#E$UoPS$wF=j}2IMp@m&<<<f)ndK4stZQawcW~xT%>|j$<
z2svwnI!D_RCRP*LcwD33xZmZAP>Cx(aMyiuGqtyGS|@R)?sfMC{l<oo1d)PX1y;GW
z+=8jNct62cW@(I8e!E6ysrA`uTD1^Eig=S{!C%H)+uc+kU$N){EEItM3Icg%n>#3A
zZi{wVv^AomS``^I!-#Co*>7DxIcl`nT7JLb7>Urm&zpBExs|NyeoMqq$cwWc!wiZ(
zAYmG5OZ#RBcgA@t^<P)AsRWC*rm#1>e5(8`e7;(K&2qSqsi5RAxfZl9RDuIX7u(h|
zOo=|7VG4QTx5(p4O*g+qrTRNLH$J1(-qoE)tt=-jl89}Dj{3W*lcp-Ac>#0s`!s6?
zgiTy(mdw%wYUb$cW%-$urdRx*39wSk?fGL{_$Cc}mvgVhZ<(LuhF-WGGEJ8O$r4(n
z@H5nxsknU3y`x$?@{=6(*@#nc#kTo(YBcx3%FA%0NM6FC{>_aVjF7`W?{DG97IC<K
z*l)(oH#tJNZ_{w4H51;7!HxR#kSDO9uaRxqH$X7}PYP^}VfhQNpfk4c8QpH7Grjpl
z)F}K$dm1Ibp)%@BYtqJK2RC5HmC=J+K`jS6&UX0@86O?|?0H1QBKWh$QQVL>iy?jb
zXj6|{bM4DfD`o42yD=}CJG+^FN+u`oI&zYz_Z8QC8B<z5VG=#xU$=acTDe(ZRx^vZ
z?nmbj(hPSy!|XX0L;XoIi>YM*h9X)w3t#FPsDM0OcbcsQResRVbaTa@<s__U2<c%#
z6hLTIa{73*S2EJ**i^m$F1Q?v*P-Y@ORei7J2>r5ps_vgqTRe6g(*1J2P`=3o8jF{
z#4%DihHF6kep}fFK+(aWpoYI5{rrf8(dgBO^%x^DGY(3bgp2Tx6)|lIRg@5(?0Wf-
z1v$C;nnQhr*b%GFI)L2G9@t>lm=I1QbHyr^WZSwMaOtw^WljRW(<w&@;Uqlc<L42o
zt%4ik$1KIe{Z`B;=Cq;+CvLF7id{JQa_EN<dX4%<gDTvI1YqqX$I{^ISYP^1)4>8-
z(}4Ft?3g{POr@{#8lNfmeCeFxa-OKg(3?f*hJ)hQCIW~Sf1kXbYOn<heQ6l81vZeP
zpmj=|EQc<lxoj7ZnZ_2AjTc)*-7p@N8G%aE4OBW#8jSgrRW*mzbh$OR!J=)%E1eQ{
zrgg@x<Ast@>dgqwKw^+Vfrl}X=+!D3N^W)}`E6mY0rl@#;|~~=o{dN797`!-z;`Q7
zMc>^(iCxUx1<Nx<MNK3ew+Z0s{oa0zuso<nvvv&)3S_z75@}+Z+SyO!hFk*sUSxcg
z4_ee08lj@tCSNM+!(5}+k}-UM`<|hzvul-WI^UW*EQj_|wT&@Hh)koM&S?v6sqIQ{
zM=;BATBqXJ35#0Je$6;7!m@Bnw?|dfH^ar$Y0Q>x0Mf`>f=W+EcnwpGwjNAkL|%o_
zPc8l!sN)1jIl=F=;?+D*v0i5i%^f0w$MhkMqX~<ugJ17T5UGE8bhr{3&KfJsOd&85
zjyIrp?oKhdPR=qv7KwFaor{roV!J6BXu6%?+J);5Ylj@-2uC(d8ofC1z+;I&?+lIY
z<A%x&U5$>)XFzv%vN~wfvU)$G^rQ0X#tACbKEnc~gs~%dzrr7_XqbHjY~ut&5O@1c
z*veJJDBZL?wu~6wikj}29Jva`@CVd5!*KG9gs%9tAK&aTW0kpAj`Q%|ASg#ksUqal
z=E)}Gu=|T$sy5D{L9?JJM^3p_ANM1(b3N_Z`W_?21i|rydCr<Cp@GRrn}ZKvsoMhI
z$beej_(!ycE28@^d&f0;u8nE7Yl^wQD2(Dfv^p(LMr#DW5Q~Vfh+=g<se~BOQLo_a
z69FeN<(Iu?9SD`*TK~M@`T+#mcT0kE`zkasu{A_%j!o{Kh#T-R$YB?{2D%>8%eaYY
zDqL{s#syz%y1h20&{=riRW-J`{9DO=2CkPCNi{|#FOLy4%VVpES2;L=w#Zn{)$izF
zCL*p8<rO!RX05FcYWcVi59l1o^SWwKKmBKaV$q@i?RxT$PILW=-%QX^(AM2@s;*Yq
z;`Vf<w;wL(A<|p49P2~wcA2`5lpEF&i@aE4j&^Ya1z*E<CEK|VRcf)*>`e!M=o%9F
zmpE)D?-9bgSzHkeRi;<c*+Gh<g-Uv}wCLDY!NT*z9$paD2&Gk_!#bph*CxD~eGO@q
z-LIkB*!)rvPy2Zt9?ls}WHsp96)MlXmqvyXN&P=QfrF{`5|1YvKXRyhLi`or+lg>2
zadqrgYl+SSkNSalNzU@0N6LZt`xLCbLW}seQ!CBeybkfcPTG7c-talmgTtUAP+OGF
zGCESV>~R<s$NbORF0YjgXgNzmyN}i<b}1Q{T*=2LL`fxc3Us=nOO}FAjT@-6mz7Rr
z$2k-Od%QBrgiu2qSvmO)uldu=pUg?BmR$&I=-iZ}uf<mBK-7_we{^K7|G;&9Ns;e@
z#<jT9tWhF-h}?5k1N$oJkbS3OON`$v^kyyz9uZ<`E@S*L2G-;Awm>OKfv)(_tkDh>
zqHB<b6~WGscA_YqgEqL>7~G5zie_he3bB$--O_#}tejOI`(n`Gt(&<$-P!ond)9g&
zuZW}t^nYZ1uva+oLd$`IYR12!q%s*OTl}jRn5u>-2)qxqMFxZ?QR@9B*jP1Tcqd2D
zpI}m-H?!z?sPA<_=F%MEDvPyH>gr?)DBk9RPd~*W^sVjpX^|ruA@>zaPmMNr&uoZa
z`_>Ds`E}c^FK&X*z$}zHTvHJR!5EAfX8fhcazxkHo;IVGsg_&gx>dY@<j6Dah+Tdx
ze|qF+o4z~zm|#vW<nB2Zf-VRU&dK7gz=v<Q!^Hx69Gh6O%%j9LXErYt`wm?IZQZeE
zYC7LG-w>v19}{zi#PdiOd`>W701M&^;X!TfD8ICmv^u3VQ_1Ez@<6^7wwOzA)agFu
zP4%6vw~79~Gjn~9QfToJXt`jN+q~1ER=3-Lk=FrO(W&23s!Fvwy2OKDf&f`G6PAZ|
zFJ;qf1ZaWVc8R1~c5gGYVZ9HVGrc+I@sj99Cu*=gw6b8aMrq=H%Bn%CtHl?L&Y;sM
zfV51Ugje8a;n8qLVpG0N0f$h@7pxD1`e9=x`~L)h2?R<ZtMv3Xy#CgPVC5M%-{6Qf
zr^U9%Mm6|Jj6v~haj{hWEX_epx})~_4oyVjcVw(7w8@{$iLf)(gOLz<pC!|PN)E)2
z8iru3D^%=8NJOhvfjE_^iefl2usTUW`jQb6Hy;Iyc^Nf2EBO<2s!W=~0N>KLnMoB*
zdy^#K_)o+CAbx~|m^+v|8aY5WO61aVV^dLm-)(9W+T4+g!xAqRLaFD_Hg*hq540xM
z9h%>)>nqi&SrQP{w;UKhPSxo<B`#wQ)GTkO=AvBZsD6HM+Q7nF<{N5py4@zpq}n@<
z6xFwB)rS9c;|QpGYnD})=e{kD#=hWVo=BW0U_mLMs3>zsN;YDpiur`9<)B)Sk2nL*
zXZqbpqub>VS1Pw-nMA5-SJ%QHiF2nVuYmB!WR{lZsRdk~DauxH&9rHmHKrSdb!!U5
zQk0e6PFwi38^{fXtO$l1{nkldsC=}ks5AmcSU!Z>!cg(}Qn`c3RIf2r9k|u|rU`n2
z7Qu>?+5%o#|3Kbo*W@4zfXTR7&QJQYPF+*cjV*{rA?B@m`@N$*Z)a?u1bOZ=cc8h$
zw;vKX!QY^gffYRW1BR)~81(QM<MR;4qDdrSQsC}gcx14V;b5xI&ePu$X#&#oGv~}F
zVxDhRXO%Z2s26}2wIxRXB5~IZ-IYob0s!5<yNwZrb$s_~adA+;GgVnJ_`z!uP;y{!
z6wI@>#K%N9)%@V)&>ANVq4lJZh6pa%CbpV+5sd}#INo?&Pp{w#JlR?BnmOEr^|*;^
zhjE<IEZJpJt(+iv;CP~+1IaEX_!SS-FW(S8$J7$DwH%>su?^!$3*urvtC4vEAgVt(
zcbhdUC~Qs@;6wg$RCF7XFn^pTqQzjPy5hNIV^Jx$9)K#QYn<-Sr%WCoS&Aq+9L8cr
zzYSdR6$MwUk#e*|!WSj9P4}Q<*{Caktk9TB!xAsuMQ_v=znJcF6~`FX?A<_)*wHbs
zYjJ`JcfDo|5CCd1sg_+@(!|WWH=&L1i6gq8q*uf^xI5=7^8$4{wKubq7oV7`Bvqk6
zR|m6@M_fDo3Q^3X#ZtYBU~JTFPKU)Lq0&orO2-@v05u??JHV)XdHebbtediHOfmnh
zqOjnq5YE|24MM*}aUF3u_fM7Ar1dn<p`B(SO<I&xUy}j$u~U-QL3m@=ThcH8L9+k(
zKyJf2PEB)cuOI?>8(<a~FrbCKXaxt4a(B0wkkJT9>(_1jH;%Z-mx=25ft)fX3Vlo(
ze0dtKnWDZ9;8ZY&nN+GW3W`3us`G(srZ8|94r$pig2wbfY1xyUq2B1^RTN7A++R~2
z(F3TXexv$LVxd)h409)S2#v5a4}{;7tOxSmZiZ{iL^B-dS4U9%+$JoY80E|1FuACi
zpqp^%kVB^esq6QB4|fzh2%X>)iMhXuJg8JZnmkU$$VgNZb&VrW)vtfujc`Z{%#%NA
zoh^)<*giRUSlmPuqLTLvnA%YQSOUx^mf+YLLQ}C!?l5r?M#C3STfAHeH#tw4Y_z7&
zc@L}#R7gW@xmONgewvnJoSP5=PX@9InJ}6S#{D!*?99ny8oSyJ3DHJrrx|q@3qlTO
z;$i{Fvi#b&38*!HHRGbGTpaPirqeh+&0gURp8YlP{cYJ?{Yk&Dtukw-=KLZ!smyy+
zitZmIn+j_n;M1w`Zr@E^s$}x0*zJ>i9@8AnP1UA3^gF|UeoNwhDK&vp>t^e>Xf;;0
zn~D~6yJ<T>ASVpeKJD(iLx$6@(jA~3v54-lf?i2CdvP;8*pa%|FYnV_a72SWt(xvG
zVrOzE^+|+iT-bwcW+5RS<)Yeez4Tmr((pFRkh(=w+}@j5M5k!j_;a-Dbw+po$oJj#
zPVl{gxr8m6PH@0uwR{M^bzkWYv%C~bOc91JeKN-p*Wn2;=!}c(d&*=;oM6Zix+bvv
z{>{R&`vddLeCmDF5#5SQqVdZ|q>A8_1md?WQWJN5lKrUiQ6kfp1D8QDUk!`!l~M_B
zHU39N^-cYcUZbYLD&|3H2R;XIguy|Qvm($wq$C#xo))kqkp&*hvmj}8{67u<!}#&N
z5=aEkI-;!0!tDyno2v&yrSj5i-#j(vYYs02j#V<z@(!w5b*5KFV&JPWcVH?Y>rAw}
zu~9+IE1;InLW}L8evf8oICuM|{T|Yg`YUMvhrCgsM&F&v)tu(mGTjmR0tzz*_)TWs
zez}?{YHtT3p~?ELyR8W(w6a*=`GqjRmnqrnW3g9^Ve@=j^w`A;X?Edx2gqzjVzI)7
z^)V{iwAc%rAI#h^MmHKjg05kLAoZHEgz%R0Km=jri;WpgFp0>8iTOx^iYZO><e!dT
zQuNp=lSHYDuxp@Ah1C$22P`HHfuy5D_<KM))@|hZZIjB^qq8=uv4L@!xOqF}D(2aX
zcY-;SeImq%=P<w45VV^w_0N>4MrI*;3lMx(UNb`XqP-oVA0Bjv{N&E9*ek55_>#&D
zr`k)}H2r^agFe00jbHl=V47-qFp!(kw#CoQeIP7fu$UzJV4E_|ki*cjsizr_%nQ;M
zjLV~DQOQ{?P!tVI+=$N**l9uG+jstCQPUlv>ng8tE8Lrz2i1R!C&rD%pQAz1ac+|C
z5$0{Q<{D1`<)@qQ#xhG#wAk;FNeDXjR-eg>_ec6B9c|;Ho-*|&<GKqFw2fgSue^3G
znVV-N--I?XpW0ea{~Sy6kffUTRaZP21&H7-cZE(u6!oD-K|dUa7?B{938&l!4kUz6
zS#Zx~R?8-hydJVfVI$LJdC1RXG23M2n|x**J$`_+wp}$Yx->8JV^mCi6D4-JCO>9*
zV1lmyb-yG5q}TiHn$|&Q+I!H^lr4ihfz&sOkwCzj(qg-u)(5wkcBLLaUBfSC!VYz{
zznc#$6x?AL(IX`(YN_$u&8FqL-6l2tn2?wm72XGP3DPZBXEs&%nF<Ebnrr(n21j<d
zF88;q{3Lx@LOC{}{7#SUUF`_3$>qFmSN^#rk#c&mGnNq%>qqqkS+<!p_99@M1!n<D
z?#%oyE^`sd+*z}%qqJNzIF!odPO6Bz9_UH|GQ}EX+%!U!rx-Wew=VBZ3wTZNkQ12D
ztNE6#kFLAB<ZJ6s22ee81HWB8=hT#OWc6HA&q|V{+6u>q$6liR>^)GU^jk0QHRugr
znRvl$mh)PQr0Sz6bqw`*1^ZI_%-{>(q*f^SsM6!g_*3pkriX*FPK0PfjrTG0dCt$B
z)cnDkVyU}t!TYXX>f>hu<(1=91-;|*d5QL)==L%G39A|RML(dsq3GMi`xF$`HO2<s
z%8a5;_|Z&9wiy;eo6!94mk$GxHxM(+198IuH0Ex&bm@I}{x3kd<=r#7AkgYv0FdSN
z3K0JS6>~998sEmR9d^365Q7HJ>KsW<%0{0XSLrBfbFYey=FBp;P&-gVmOu8bHiOzW
zC)r>N>|(;^hnrCK4uxnHdu9F4VN$nq3$*7*yvT`@OU*zT+2e8>!rg#)XpwpZJ(T)$
zb>2W*Z`n5rj$*eFf|pTh`^j~bOoBP5WPs4_;Hbm{&KZHHU%kZbVRSI3llceJftt*~
zLW}uLiE{?-(2?drU9V$ON&>4e{hP-=GwCQ<`*u5dY<G4;ERD&bDxll#9q9=<(m`4M
z4y^Ypb<Fitj$*=8WV@XW&IH;-Fa+FZ#%g_2y8yGbNIrz}7lY*VnN`b_qsH0MbtCPi
zucri~*G$cvtOyoGwH8oHDNuw3Jd+tpeS~6Apbb~y;qdQ$Po)WKi&M!jMOq#sq^J<i
zlSE;DS{77rgX0REbPqY;Fq#!W40Z{AjZ@%Eht@Pp=iX=5wzVk2P9IZ1s~qIvlpKzG
zOk$`bs3Z0^6($7Ff;30JPUB|o2e?%IwX0e$<}g0Wq4NuSDp8T6JSxjR&P7O5pIrIr
zCO`@7r6>$?4F+TzCa1qAFT$a<Z!Zq5IhbS+!3w!5U9zIvNoL7cGtg;nMI&Zr^JI+g
zj5IiiU;72<AlU;UTyHZb+<n~lP`6Y!TXriRi8l#)6)AoF)TLQBxKu!(eR*vzGEL~H
zcNM}3=6a=lTsjKfJs9T%J-wQSfhzie8N1MaU4u6L?_Tdj`E?Gm%0^H8SFh`>l^<>b
zRsG}horLARhp5tBw&-a)fMSve%Sk}{%djB*6Vf#N)Ps~=W*%`^;UlXQqY!Ki^U`YF
zv{Fve9*K>aR&SdtfO*_}<sv1te?!~mB2#@@^RRlGvI$M}Z{tZp*ci5YTMW%jF!!&^
z3I_?xxWMbf-i$%q-tyhjiSYNT)=ZJ|VdC>}u3QSin^Q?>*HZb{PAvzU9K;HWd6Rk@
zwq%Tty3Vl)V-#GCx$#d@wkqAmYS>3$f^`uqu$r-6Y6IE=&B9`<Xs_dda_Kw6e~|1V
z1BG9OH!yjKeA6nTYb$;qB8d{`p0B3_T;>2g>=~a<5Rv(od4O6@twD!Wd}1io!1OC(
ziA2wF#eYmPoZvkwK$~}^zC?N7V2}m}pRXoevf1TprXuN=rhbu(y!Bx(V#{hJxa=Q+
z;`x6BiY0?<`R|zH%pB^I+&$ctn;)ej_e4KZHltVZ)0t6%Q|_x{Z#~|l6EDhOMwsWZ
zuGGCe&_eP8@|X^)EUE@=;pike9RRhI05U54T%SlOL>IU<z2v*pAzm>I`bV1hy-Y=z
zn;;qpD2iQOR!f;CfWE~73S9wQIC=YI(xfNfS=j_ezYQUYuNp^@IcNv1@_9Goqt8tw
zb60jf+GR$EV}-q&3K>E(#G0uKD*eDp3ifgKF_m6$UK~0Vu)}QTBcKv}Y^UTEPd6j;
z9L6g^!!G75Ub*eV@e51ypjC1gZR~AAHZI}ZgspCcrYOyN`}I<doJ|Haz^1ECYwykO
zbMNy}M>+=!(eutR-Z&G6J-6IM4-st#Z#Y&iWu~VpKo14iZ5;K=Kz<iD8zv5gze$Ky
za7@cbM6<|;r8Dk%<Ag9111G>W%hOc!#T@RHTQh{^nZ9WOpRjzaP0RPWM_ED7DOD%8
z+sllHhdS5WzP{c{f1lnra~DLYjk?T9LTdt6(XSQv<xTzmxk9uohPctw(0hFkt4#DF
z>Q-xP8x7NJ@OfCaft3d<Y_O(lb=BD@lb-&G9r}51;p!oZtT%6->44U}zDEACM3Az?
zLgv>7jW27_NhbqMH}CrcupV@5iD3!<6OgC~QQ|$fMfa(Za|3ImnuIl4`N~E^gCH(&
zCVhTb_pKWVYFx|zu`%q84>nFX!+lzM+E)@V$VY!&Yy54K4Ui@QO7Osa>4gIP-FEW|
zNzl+DYf_o4N%RLofSpQ!z{!Ewj<cU;99cJ=ssy+gffZ*rfx?Gw1!~8!cl$=Em9)i!
zMuRnSZH}c!1LS5f-J@lXVzpW>E64u)ri_@aLTh@Hl*2lYqcs6l-7`?tu7bF-uE!{R
z7Rb$7b^|V{IRgGuk%7B50)NFgwP(0C#A9N`BW%tarKP#oRICzrr{aY~IY1@ale_m-
zutsLwNbaqFEnL7>KO{&y90CAb{p(OySXqN#$J!~S&N}+Z+pB&^;0tsmJef4uBRq>(
z4sxf^fD`n3!g4MbBXyba7_Tj<)53uVVNEZiK2!+5ZD&Zabx%H!@v-awzLMqnvSf3S
z4QAePq?rhT^9Fb?8T>pu<>Rop*Mr7aS68ZN>6cl$O-)8M1QxMwQa!o`{p@gi2&+)#
z;Qvy;edxiMlEVo~5^xy*SzJt2xG~4DGrCwIT`ztY!0ORYl?l9$HSK;tEdg3g^na5D
zoQ-x3YS`B+{wu1nU^ek9Sf&cKR9|~ul`+WuH_R){gPFb{`rIEA!LTtyPXbo)K}o{r
zRyP3@0Uc`c8IBB_WBnV9N|ce57Pime?5e8@+zF_xM<I+g`0jN_SDmTewbfk)t_9jd
za$&0Vv^tB-l!kdX0p{OuI)|`40=S3gVRMd;?4-~L8fpQ&jm6}k_@!ag{KAV}J*5t$
zlQi?cQEJ;>z=_pI0e2lH0DYEISTe4NGUF5w!qUo{|7}%!n?Q<5w}zwGIR;OB>sCm!
zXE<sFQ@S>LcnIG-Sgo`GK4-6)2av8rbq^E9xXiH9<#iN$3TFNq^u+bQced7hLyx_J
zeOd+9*0_%USuv)0VB2YWWz^3=Df)tBvs=}3xG$+=e(g`>1ZP=+{fq&3id4~(;w3oE
za4Z~1VEoP3{|?nY6~|5mJIw9zDu_tcUOoAfa=@AT?NH|;X`o^8)HI53y;2yv`|h~c
z&TVHd{Q2OoJ@wnwu-jfj_HTc*eQfK&P3dI|rOqcsZE9fTZ=bfCD`$MQ5q?W}a`l1Y
zOYc4P4^HiWQSI+zyZgej?A^Ws(_qe0OwK$OHIHCAjKq>Wa8aCclr1ARq|lWy%J0L3
z<zBkJdS0`^a<v3uw%saUM)>`gXPxeu<J%#r7S$H4s(KAV(eu^1=$@TcYDK07K0?wZ
zb}ATmDKrpCvSD{5USHkzFSWYs+b6-e%WNEE`7(R%WaclcWwu{x4P=+@AZg1VWx+h*
zZg1)EBCjBiKYo06;Sn$wqzz_cgVtst*o?TBb-zg&>+d$SX{xS{&F@<^^`hHB8b$J$
z3@!g-($Wb?k#=JZ{~n~fY{aytc$LK8Jgv^n<vsS{fH)<{*(c*az)4gU`qOct+<PgM
zx9sshEE97XUr34E<C=c@82QnEb%bqB%}sASMs7-vZhHUsbVcPlMXJvNq8^<Iyc}wa
z970;^*G5aJYqPZ7G^MKlWz{Duf13PtHQc_FwQkjaU%0e-9Cqc8e>9wgZ-09(-BR~U
z-&s-9ErE2JlV#=WmK@V7g32&|KDE9j6-dv1+gsAkE9Y(f%uMyEdq0w%jcWy@#T_FL
zK(Loj<d2IWsh2|UV!Dl@IMR_*k4tv+6k1h#Ak&uS;|`G*kt=}(L=BAree!hqj!zok
zNE>9~$zNA(&%3Dn_fz4Pp=Xd#q}$WORi{1Jk!^L5f`ailje+0E5$P2nzpX-4n;Ec4
zhT1@lqlpQ5QpUZe&QZU72Td#BU;F9k43g2i8BJ71A@#~pDLE<k0CH@~GxX&ah{x*K
z=}LdC0^0GOFBdk>%vIQg3gU6Djz@4ozdh58yosbXv2O-#kV;6qqNF=sbOW<ed%&Ih
z#f1lxd;eKY)smgoq$HWT<^0XVGQuhsp4g(g9fTV=MxL${6#D-cJ+`-^50Zz{XY+ly
zU{h<|n0F-bz-h{f!h-QbkmXt9a9gC9^xUbft0{9~kgu>#pgy3-;LT)y(v!??b`{WY
zeYMEzNY#>@4qgGI`*Mc1u|ByNQj&feOnyFKQbp7yrz3}fjgCz+PwJ33x{IgvsAdh4
zw?P+9X2e^gY6|2n*#xjGSH(4BoEBBjTltN=y@@8>UC#k4^`i9gk6=;`^Ls+OMx;BE
z8paH?Vo%#C^a~2hyHngZ>1hzcKhTCr4x*d$ChJ#I*Zu%QnS8rV6Jb&H3Tn=UKACpC
z7lxmWFIVKf3PY}(Ae)qze7lQ4a&P=N&o1e-rdN3^K9`$)14*^kQNTn;vshc@YS)9b
zAKXu0aVP4=45ZZv|7?+o4~_~(-XGlh;!JOO$y`(AoY|iI9h;_z({`H}LtC``@{}Ks
zt0GqzV3svI2~v<Tt`W3B_tj2YwW8dm%EY0yAg2$A^wY(2`#1lzMs^HtT?6{<1_|s}
zt9)LDVi|gA=!~{u@y;G_ggS2wT&l*iME!nQbcn2!p7+$p0s+uxRj#RR5Mh{6eD@-%
zrbsKe=ol#e%q$XYt`h09-0z)+_jblpV&=mL3vEj2qre@C{JJpYEO7>Fz$|+?nCd$a
zOqh6<2k&!!=$*Bz)NT#mt33ALk1uwcXow{hI>CV<lbxUVt$kWlyN?d3jzf~H*~C%0
zcly4J@fW+t6yK>qMn7M1ZFqIrEF`eZ7!-f+^kO!p^Pa3qfB2q`WuR&TQ<sqgRe%~;
z&4^Uv<$ipt6CRBv2B^z)ZSH-S*9=Ff^8i%=M}{F=(`PdRRHis#Oq1hOn+;kPY$Lh$
z<5)e7$QV@Q#<-`44+xv=Z12qWUw4zL$|KkbnWhMoPIlo{#O&k+W+8A$8UxYQ56#rv
z+)I(Swc0NxjtXgD!a*{wZr3)+J7&E(m(6U{42;P0O#%<g&_XcR65hv(#=j!IhF-I3
zUwEB(J^NSJ^pzNZnWS8wLNMKu{d`y$cw6#z{KH;=K|P)7kF_p^`XFJlcSjTgCj9h&
z?RW*O5qCNCEHVg^3JJAB>5y{^4odCS(<kTht>#w_u@_J^2-(|k%*rcZiSt9qMpLp7
z8GVS1E$=?)F>>RIWC2jeCt!g}7hZw^#Y&4!G=fc->;BpSZqOsAr#EVEv4O7I_x8S%
zU%J=ACK{ggw-GmdpGuept6H$NoFf-k-I4NVV47usPRCMW%h&zk6&bj|-=1M#AGhBj
z&6zw8>H>a-MEi*7jtm|;h!bSE@N)kP#Q}7{)v>pqYn=b-!VCMicCrgk84`{i@tFE$
zDoHI8j!9ibX0s%~ZbcW6q`8a;o6uO~{oQ2^>SR?g&wZ{6kO1bnlXQQ3)&k&=KHim`
z-_C0Rn_7m(AoBy@&3WTku&NEb>MhtngB%CbUUdj<X6c)zjzP9su;&N85tQ=9+D`R?
z^zkN0!4n_YeW^r1+8c5#%{0goml<k?tXC)8EHI3Cb@{aF({Y<9+^U!K?)x=>%La!`
zjr0}gC`8$#A`7}Wzewn*Tmw)&`a9VU*XY$97NJJKKA(`%seJ&@6MCLc5bkuH(*<dh
zLt@EfeE9Y=(m(JLMfl5QVDD;u{p7QF(OFC*>|D^QeBJ<W7hXN0I(j8nI^41nz;gQK
z&~RLIK;C*F`2d#wt_uJik^$z0B8}5mw8=OP=U&fMhh_<QqIZR}b#<5saWh2Zhr~dd
zGsjWxghIy2U!~<M&@mVwq**YG5g83fnsbAt%T(;QvR(~CKw?M0?Xf;zF_L!NIS=@t
zM{;#i*Jx&bR82<sw$plK;nRk{1Br9bkny4`H?&T@zOu#zxvC?7!U8s@5;j1ttvXaM
zG~ZFm&-ibU|J^J9H#hy?8Y-M8XH=j144qEHsT6fb$5waVl~T56XNQ%Uxa_&1PRTPx
z>B%8z?ET+iz^_$Lb&9myzRyTUI-PljykzofVKuOs+V>Ct{?E|hzpeTIWy8B1{931b
z;g_n-kwb`j-fZqTH+>({2aaT^^RBV9Bowxse;lupZ;WizB7drDZ_XZH55ZO#FZ)48
zO&E1rQ$k@ll5QCq_I!_&i;CWxQ$M|SM0QliuniyuJ&2X&CV>`tB%K0Jvk1M3gyw9O
zB39AO9X&4>Op&wB4RNkFDAz|1klT^7PshKw<kzc5h613OcMV9h4Y<--+mP$-M9Kor
zcTV&K5^fPnvGY3_V$FUEj>`mex!W7rcz{ex&r2V-LBMb9OJ`ODNGOl5Qj>v>Uae!(
z)p^m#=~3Y#va#B+fG1c6D|GgD12^&aezpnGz9=2rM%d^TNxFI@&ZmGuxy+`+xZ>MS
z@T0lo6HV+V<t6ibGK@cWD(vR^7s(Z+l-sd5<@mKXdoU2aI^}iVH|sk^?6NKMQzP*o
zQFek_O~UWwKKy+ke|;t)oc7VNYjK6b7$${2!E2&3RGY^iBy#cKxY1j=@&T`nJ$WWs
z<FE1e{rfo(mAu2-<#a^94aM<>)<+cQU}qZfYwsOjcmz(uE1z#|&JV~h5!kN-?a|oL
zGeM;^_Mf6GhEc9(;kr}!*;exIq32s04DDY;!`B93Y^pW8KPTM2<BzQHAeo>Y^%ufJ
zXhHQO&!(rR5gL(Z$mGG)q$>7nuycJdG`!y{N#a$F=3gIj#RocPjz4)4pSU+kovhg~
zi!3iEi@hkA`q3OaZ<-yrNG5*MA%M9U$TBlS!O(L!Z=g^{E8NT=-^UeI_-iVA=VAk*
zUcFI=j81p!y@D`nwKkY|I!h9Zf|9BN+m$2SG)XILe5+?l)AwDt58KW6eFFSyIGe1&
zSS%Dr4|%mX@jLxYdLOP^>MdBVwY2FlS_?`iU)b2u@WNsAc6N}JFP;+Ye8W{DJJ1D#
zwZul2dSoE3(GfL$bkfk|zE#LopadLuR7X=P-521Z4gnXe#mZ1c@^<#9s#Ib|0w!3;
zG^A!1|Jm}*)zNy`MC-GGzI%0STIPT>$Thp#9k0pyY}b1WOm58_=+g<92sv6K&1~Z(
zGY;jAf0C<%Ly(S3A5DYHtfWgF1Q%&^XaN6jAbC8mOvX#E%oc6j<S%Xc>+ydY07m@J
z<;MfvAla6lKH5hmm(^<>Me=@TX?<rPqT^&Oq@@Nyn>>>~YjR#mUyaZdA$1~qTmdj!
zWI$rG#(e>UFkXHD4zXe{g5!Fwt%h_!!k5;znBExlgh5m1-9`4ku#qT3eCp%bqap2J
z)rt|ZGk~J--Agg)jbO4Cf})%|zVnk5f6Zhs-Fe1M>2yE!gHb+tPeikWFkr~&ljK;4
z2@SnCZ0C1wMoBW8=L^hDqxfB4xd9V9k;8g6`9|q0!AKy$QYU|c;K7l%kwL|4_qBX0
zzaDb%chQGYI#khr3uw<U3ap`KP&8+zSbL0I4r%@pz8?Vn6+pd>UO9+99^{myH|ml5
z>=cehpFuu?q|O-AO2%x}-sgTPmM4L<H>qTgYXNyeyzn3p5QDre)({(ounAY$GI1tk
z{>k{R^2t9&v&PeUgLm+4kSn%Gs$4DSGuT|6P<9UwihqN#LJEc%CI+IPV?#jL4xDlp
zPJFSov5J}j;>;9{O;c?AR_8cFG8#Gi3~=1Q&N2c5Qt`+heKmuc=lFsifzejmWKhdr
zH65A_<K;Fn0YhF#hl(@Z4bvrt)|RzpBqIWfIX$)4uvZeqI<Q3O^v1e3SrV3k{z?Z7
zJgJTye6BIr6^L34N+ZN@$mp*zQlqupNbY2dA~22g$G8#Xcs<gsr$Ib<Qb%4#@~+u!
z0D<!zkC+B*!qKzF$&lDDmaDaamj?i7{IiZ7qFMDw4zd-(?wv@IDvGuuY;ddR`q!G|
zEm0Fz9sZ1G;52KtUq`AmMDSb(Fg@3ry^uIaJV1TFan<z|{JuKrZ)ev?1DJRKV5B|&
zY4{(;kG@)E&^e=%K-j&)`nzONxo6W9d65c+AtuV-uO1oHlK&A09*`Iephw$xtcKga
zdTpIzF9*Fg_K<E2wDSyLOGk7NpIRVwn&h!wOE!Wu>Y;E&a*P(AnjU{+o9ZR@_r#&-
zyRCCljqtv>CaP8wQXP~Xbh_f9S>jSTm<y>U-ovXr9gl^?4w^~bWy>Ck(j&p7!6T;X
z<ou4n5ZjBU#q9%4t~*Dw$ElFu03hK79#2a`5j2wV<)h%(>OI~fm;@B+9ZbspF2h~4
z1PDwHm}tlEmL`QQ4<Kfl5f?BkPw@$o89;k<6%HI_^?}W~A6*swL!fJeq|Lu-$QUaY
z22^5#<nYoLucZ7($t9fmKQ`%VZN$Vs?*javzyAMX#eaQ8qVU<5t2N2i5C@Hcobgm}
zgj#m+@zs#a_es1;Q#m-ziai7Y(%bFHUz&a+XG5AFk2ivc{xX*quX?s^n)<{a0kf<^
zNis9y0M>E|n5G_SkF;se{v=^Y%Q|h@C4Wmb-W6m`^({6J4>n~9^C`^`lMKK=hO7>G
z{9)gtdUb=6a4`{#vts`M8<jGXpy|U6+z5Nt!u|9{L+SuUC)GMMbm4*2PM$#ky%nfO
zLgz%ew)yLYsbJGf)dwO9uXz)>KjqUK(T-wV07YyHQMsZnb>Q(gE&+<|hFNI_hd`>~
zAN25Q2EUVyU}?&v&eswmKz|_tn|k2F3jrcX$d+1ZHB~ztU{K5EA3t1pL|;57dHvwA
zIW>srL|3#|RRoLGr05Ux(F-2V@=i4<nbUq2G=vX2N19x#`qRwXRJgR}Q%awl{;;;4
z5O8c2J7Z#L&<HtUj(+1exdE>v<;~}HTxf1#T~|1yl?g4z$|6PU*RHAIs2R*TDxC<2
zY2Z22(}WslPQ>8N(uowE?%%d``3u>Zq<tunO)NXzdWqNeNGU0{ONsuoZT9t8>iQRZ
zF)&;cKCU<?tlL0C0ea`^bN`DB{LpTq1NUvJ!DN=DmDR2XH)<1=$0T=@7@azlqrj5C
z7&SxTRQvorbiJU#lS?q`(+lscQ$lD^{bBP-T+AGd@bi1}mJ2$D_4HUl4=$3|;Zb0Z
zRu|{J^{VIg+L#*q6~=^1Rw~I2Rz!((X>Pp_7lIZiyMmI;EF?kpQ=b?dAi_I}7WVXp
zUkdGAh(OKSsWoWy(=9~taK`vwVEIPi;;zfq;;Z-f6+`pvWJRqiD`{0h5X~7|1{<uT
zA2e7yws~&xev<JI)v$UE-pxY8OfdDH_}W%cd%wKkG)aw|mVVm*&)^wJIWcVPk^b(j
zem*7XZiA)W8Ibwm#>hou2meA-K7R(MrQGq`$dgwd=I%K@QLTw`wn}<|d(K=s)Eg3Q
zfif|jVxn=Hzz}FogNM-#Y;yYHs^fv7-YIe?nxXkVt<R3R_ThRK<`$Hd=r>I6ADgTO
z56uJ2w?9%#QRVa&E*rhe|1DEvn%l*G4+&lD(q~NOhG?pOnsWQh;K7i?MBk2QP{*%$
z>m*CjG-!;a14(6OsOFD1bokGF1=Cp>riJ4ld~0=6>>(;o58oU8V|q!|r=4s)J$gYk
z4k<GFSq>IxWUtu%eq$ud=kd!$W8`qdt)nl2$S>`)cuOS}vemMFS&pW@cYGtq1QLE^
zAF$bMk?JsVTm_=ybQ1Zg;q?6&2*Ql0+dT60P3%oL1uXbA)%WQJWx7MLB3)f<SLIAA
zW^!Yft*Q~<oGwmd?O2PkeZ_{U)5s&&9<hY8g#p5b?~`_d#n^D0x~7QtcTfu$I_JIv
z`zPK8C3JBNwA5|p2wvQ5w`)HR4fWk%1ev>6p6b!?{h8gS$=GlUPGs>BV72%IjYp}u
zFhepA@{HfGTnFjK)N#xoonNhqzjMiCTK<{+Zecw{#n4Ad-a6DB^T?{a`=iv`>~V$i
zvpJ`Xj*>}Tk+W=YsRl0`Rk&v)&nFBONr<rFguQ52i;P80YcXSwkQF-qlv;BO*x(>_
zePK!F?V+4;uDSARR5r#cfQSsK@7E_nPKR6$y@qVMs4DsA2~f-Dtb~`!Fx#$~l~-U{
z4PFLVP@JR1$AV2Q;1_x<kkL)t2-_o1q=hxf`2XKMoiHfUJ3U&REC&g;2@OW-s||#_
zeKus5+n^qK9;u)n>4@Bt9*7#5Z+Q39sQ^NEXlRDkQB8}V8E1@df{Z>JPjJuG=M2WU
zN($(&Kft&Ub}mF@p@r>04kLZe0kw7BEu`P_b=5BxHA1~!)^f<XTH~!3333@zFR@(8
zt7(3g5rF8FS3}3ix&zH(ZjkPa6Ao$yN2$xAd@xW_)gh-L^PY@ffP~*X^7=qVv{=(m
z-0lB#_GQKil{*8kR9g2U;&;vcUcS|`RDmFjLA?nb&IHJ(FninXAO&*U6}qyPg6d|5
z>|KSn0Ryrcgcmlsy^!YWmYG7M7MNeK(zTctm3JM9{t<>`nrvXizLY4X!=KEd^M#OJ
z&hdx?#cc(Uh|BZMBB;eF<-Bp`5{rw0xM!abjFmhEh=~weM-~hG)ly`k`VZT0wW)Ys
zKwR9Q>S>xYxz*0%Xgyiak(tzYyYJ7Sx{kkT(Ax8&OMOR=PCZF+KOU=oYN$W)^3qjD
zf!_{zWGB?Jyvh%0e7I(YSne=??d`PVHMM6+(_B)Z+UGY<qr`euaitZXV`_J_-Oup+
zocb-rxaBWA0#BcI&l*1t=_}#$Mw<zO`c{f!?}XmSBv2p>K$0M#c1U37=C~2fIDchF
z=zv25aaD@Krv{u5AUp+gnRNykKBZuJ2yjp|T5o1#Ud$&fc0?T?IYQoArx!l=K6EZ`
zLU~xDng3?trQl2cuR%fQ)c9q|LY;D=;pDPyz-+c1EZr?TKv{f}YWFMD@>G8A0EehU
zwY!M$!_y<I*{=dgLYvY-leVi5xgoZKh>yRIm>=;y?X*dKdQW%%+LYn@$h@ra9{k#A
zo>sQ<I#TAJ48^E(m{HyG4dmOd^JRS#2}gxRnZ+}o@rH#EI(Cp*xT9nA0QGdB4xE(E
z4)dMQ8<0QOTRw?)wG@8+x!w1iQnp_G^E1EnnnPy`vDr(<>wj-eHBM*ZY%z3sWQDc<
zPV0sng7LQN-jqSG_$nH4XJIO*?h7|WNf42!^OuRsrm;8C?#O__{FuS5uhs{tN_6eX
zfDu{MPe6#>3Oin~7gjAiotqr9^Yvk}4GgYodZR;mg7~w?FbX>8pm2ixx5RN3R6vh|
zJ5p|6A-TKGTg5QLkG&a~)H|7SMo<P5_FhOoJ*gLzRpqzC_*Bgd_v@%Pz&_*sH8XdB
zV{n@?s(3Nsc19&Yn5!USgCjA`;p1KHLeq}fsDeypa%sNnBmdgL(}_CEmStD-C)RY-
znUKSf(K=)fWHg=Ga;ps*v`)AEUUrk>tbKX&diM`_J4c`V+v8cs>M?Mylf+s~_|;r-
zX-Otv(vRu{;t<r4n`v)(b?o^=E|agbo^l5-&mSV9JBBz+aN%4A9XVdT+xvG+Gj34N
zOgQbr%SrU!YaTRD-)lK-;sOC!HeZpUnme8j3APBmfo!!Eo_NtQI7kC{cd!o}8Gwp>
zP{JVO7Y2Oi-R8B;Ok_WrdnLm(t#yd28aGpt@b45XU<sf~(+63P#`8<xRdaF1rujY;
z-!}b}m=iz6#W25qJV8#*yT^p-BEItpoJO!xx-px55@%KC?g&WqvLog0iuDcJOwk1<
z1`-Ov%#Ti-gMGO+O8N(xHvux2t=K3#5;7|n-T##7qLg;2Mq#t6LXa{bW%VqbqP_aI
z5S4;)Wr!YdE<~j|`MG5SWs(hz?O>i0KW>bZV`mg!5}b6bGB1hxk+J;n_ww$`Xi|8&
zQOS;uAQ*JdBh4T*^vk5UeA?Hv+Cg+1>7jWXTl|yb!b=Cvw*Ij5TN-{GV7hc4ta_Pk
zRIb@~a+CT*6aJ|Q^Q^ch$U+&>HX=;V8$P0%;S-izmGW^B890|gvEpQp7i23Rq7@r1
zE}+Xb?_}7Sc8GZ!y#p<B0o|~zPILGU=myMIEt^#suo+noCe(3<2kmSxxMc-c-i6{u
zucaI=Jf4D=B1iRz7FA}51K>rHehD$_vy4SxgNdo_&idBfPThVm_=fCrVFA_+Msf|;
z*Jm1igx~J_G6lcs3jF{MaSY<vJoF_sFAp0<rD3etZ^6KUCIWT8N2^#7&1y*LpXpQM
zT?iq#B4<C9#tLUs-Ullo8-E)U`(DZ&>M2T`eu?GSC|I$xhF*EK7eJ~G*Z8e1JV9B6
znv5;TK$<f|W0Yf%f{|+}9d%Ft)_YW%1N*M_10ZI~5BS()MG!bG+y`rQ-RgCaY^h8y
zO#+5M{b~F?aA4ISZ>E&qsMEFL<7>AzT<8}i?_AXFrr+lthI`(JnhiB4Xqwcfx@Y34
ze_FD`sSe_&PRp_|N>c?lT8J8092tGWWOQ2SW{m69ExVs=fo(88RX}J$>|J<t7NCEB
z;@m&!e_EVkoPPa=OQDBPcr1^I)ySKF65!_S-^q(b5x5#uiPp{1_ga^mxJ-||(e5kx
zs2qJ_QA{E|9e)5eTE6L*x1(njTAnw77v;CATCv|*PJ$MMo!IswKbW|7Ij4BO$l_?k
z)0yw*X8xW3aTUwMM{J>4Hy_`p#C3lrMXv~4j8ykl<}=iJx!^Pl-OS;f(V4ziIqc~_
z%VTdir!8b#q=fuBIGniQc%UjA6G!w%whxya^_|UL$F<g#dw&P1p>?ISV^b-knAqRH
zUx4lXZ2IJ6>NB<|$Xncf>+;g4%c}Cvyi4uc)y2;m#FY9HLs-;~+L&MbW@QrfxUP-3
zS2?yp$o#?q&fcYz8l?c7Gjp$>+A~0T7M{cZ-KtIP5W$mv4?Jvi{U%e(x2}u0q|3S`
z4E$>vY_4@cL_#--I_(_&Lq2YWG+%*<2UA#eCjr9wzf;ZAznDT#lD&g>UQRU*%J~>r
zJ$@+p%S^!AY5Qy_&M19fA#=@r^fh5fpGM?ml!3b+Ge#pl;$RmbiI<}!oyHP@e{q_u
zRg^#SNVQPBQn`C<@0U~iB;fwMYvoR9Y=aKD;hNP^(}${xl#jE-)4}olFvhT(=|u@R
zRV>M4f~G#>iH{+pt1igoX7{4sjfDjvw2tWL^FwD>$u+B?lZ%5k#g=RQOYLmW%jFI$
z*8!gB{j;mYH1*<Drcdml_V+$}X^Wv@AmQ$>+!c0O3vLL&?Xk&Up!rOnoNT_vuW49k
zi%4;5fVlg!F5oL8Py4^^ytB`5d_MB=G&retw{zUs9SP^3`B3a0PVub+CV|^A!0neS
zuupO<<?Kh|G8rcQ&=79%nr!P1De+$b;+g+Od~Elk(Bwg9P{`vgwG|(i{mw<7@Fl&g
zpcw0874ibYlyWG)3H*2POOw@s-~@Fw&G;oAaU)%ZG*TK2kc{h^H55`o94?MZ!h5=@
z9i0#w{UtQ&==D*>EG>AJI}{u0aAUH?34hQp!7EPum29d-eJw>;r_8O$tkWdR&;V3&
z_`b*Ek08PG#`OrKe(B&nX#TiO&C-#qW_4;j{mWeC`e$LcF{eN5_2c3)w5)XZYhHLb
zfv?j{xd@oAX27EO%Tjfm-H{M$vHV1YIxl!m6vO}Gkv?9C+}GP6{sgAjRAVSJ&&EEx
zL{_0=>u=_b!d}k5giNWbdnkER%8d1>l&tje*`u+6Zl+%2%)v1&Rr|{Ir>h)9^)gJ4
zt4CPr3)a8sK3@{$Prv|x<=@o?juveLSAtN(ZIR`%yQ=Wqm5C++qEyjOWR5iA*BYRr
z+uX+P0;0+G+|iSg+>xG_#jKbTKOHBP=A@XGH$N2P$~7M^2_^8Qdf2g8@kcK4*m!Oo
zdyDLYndv&Vsoa&gr~kVbiHj&`=kGnqBX}d@x@Pz8C@mQ@eQFI-b~-}8cGuZLSCdtz
z#|K1}MDFYB?=7M3UFm=A9)UE4^_3oU`Is4+t|z}#d3m0G^CAF6569l@pD$z@I?`Sl
zZvToy&VEMB_W@;-j6Bo*WI_7M^#^$K3`&_2&HI$M{FQfG%T0b2hs}$^GN!>EfCQFv
z(V_F}<uegRf%hgVx|SDr8{c`7<)B8WcHnM`>GL`+HF@WUrb9ul|AMhD%W8Fo-1(Xn
zcpWiWXc=5;=l8Vp3nJp8PU#TQ&hJ|1QLfh^r^#!z1i3E%m;baCuUg&Z+Hd;RE8df)
z`y#(|8K~9FED(X9VgC?L09G4%#ups};$mkjjs%`|hvMvP&wde8bm}qXB~MR*J5=Q_
zW>B(@&r9O-qB3ZD<NhHJUt+9MCPZM<8EDDy`xdfC+~~mKAB`)uyH0rNl9|DN4j;aA
z(>0&+>Tg%4YLAY%xZ5;7#uYQ4w!5qbhBy5Y?OLdpTdhw0)B46Ugm;_Zx_40UxuB&_
zuQ)a|OCYg%fCrL?AF9*tVpF4<ncCyY6dmGWvI2}lx^|j);}}Oc1nAUc9guD8lb>8n
z$~M(n(1OGsq3(T`?!QCop+5}kQ%_%_-S`o*aM3pn?0Q0p770Wp12RkQ1F_rktl6^3
zwGn4;Fan||8#7%|UA&4v<S}Dq7DLQv^_YeLbll~m2rCrfxF0k2cQWpV-T|T^)n<pO
zNoki~cW+<8fGBWer=X%^f9hp+MEUX|t~KYL*Q8mvJxbhOgIw{kj;4fo4ya|^#mQAG
z0}uqATaBX&cX-=m-dT9z9Y$$u&?P6Q4+H-(+(QJX0VyP2HEydayt1Pfh*#5rC?Aj`
z@svtuX{-aAYx_t(JvMyql@x5B<X-0VrlbQG+0G6+h%aL4bG^<hN)4vv)EGLjnHOE<
zopIa$h*EN$j1)oEz9WCW<K;Rh_0=bvX2HCSKvT8zq@n38wPD}$q82%GXX~MpH?_)5
zv^y1A`!0Mp!ch3B$t8l@kLSv!O_-r#%NLqygyi3kFW-SU?zE<S`072?Ip>WzZ<w`%
zMK4)qJ=I~FFqwC?X}8N`FW^0f?w#~UN`wG1COv6T?dfXDM~Y10Dtb4+d<nIvHK3M0
z`ZL%@<)A~N{`61)O&Y3_eJTA~Lxq^io;kVXS_5v&-K?R?O;fz!`cfjO11y|vCday6
zUK2%#`N9!p|F8D0`yH;fTaS{Ehy;;{=)FXHqmE7z3_+AcCkUeV8iTw>5Q2<eM;#?W
z^ft;XL6jIJ>R|LiFo{uznK_eu-?`5B51e1lb$;B}wfFO^a_{F_>t1WGEn~yw&tmMb
zyA72P$}1??!7N7~9-*BP<wTJqqNfANz@lWgL?W4-S-Sp4Zd5_O@i+P-WMxEii^!DW
z0#R^~ipIlH1XLoJLAF*pz9^9C@j-JtQ}gerxzcH-S~pN@jwaPQe`n+^8#J6}>hSuj
zqfl@>|5%l&*`li4lm!bK<Y!XVAnj#%G`VvE*VTNy6CR;|<j8hqD}78Y^@TuU8}5CS
zt@X@J12RXljel)AQ_?0qMf@?u57Q46e9=vqQ4l^O+Uyha545JVZMbf<docZu!~W0l
zB>!6q^&EKU)Qb$paM%g1xx|{H{(<wm$j$CMpv>5Ysh<_3D+-cnb-T)G!neIQ6QB;E
zxvU-1`PU3bibCh7DDn)pl&<%4)N^otc|i-6FF#-kn4?@Mdy@{KsS}k`M&0qyQ!JIZ
zgIbE#Y8+ugX?Dw@%r{M)NiFD?K5h5<yg;@o>nb1#9X~y&QmXa)^#;&V%6iH*`DgdT
zQcqRQ-A3*Os-2Jv0~7aP3(nl)mg#PZ^J(jn?#c3X$(_JKP-Szq;lnO+(bT-yKlL%P
zO~pQLCep27M$P>_J~u2d;#1o5%!+qT6Lw&fk`MJTs;C*!aMvTaZtuf8C&8yo?#^u(
zxz>dmlr%2cm2S~X=U|``pZ!ZZ9vfZOk6=3SC%2{Kx^)_Q05jxNcpFe78chSa^8@=v
z?wH_=@#_HkgLm8jo1f)W(<p&KZ00rU=MM|=*YVfn;d|LTV%ui7;%UuC8lbzA_qxl!
zsawiPJ1=IePf+fW>%fkk2aySRG(F~0cc`QWiIpcOTl`8lo{+Me4$3cE_g}9Z{izrd
z*P1~VwBBzPE`-LJd>glc8Lu_+cV~zWA_S*=iStVYZK1OSQy05l1#?%&-YS0Vemc^?
z&nBU+(N%OXazEWrzrK3R&tQiDR)@MUTawGwWA;a|m#US|h|v_b=h3!{$TD&Oog$*}
zT+0l;e5b`pFco;DZgo6-Jn3dV`^tmi1C%}EpME_pZzeQN>K%W!g1kA>WRdv51e_p!
z9vQNbhsjwQu$^}Qfw(+RW+|U#;c3!d37qLVr%EM+KHm#89A3V%MPIpDWnDqwxc_QA
zPue}1Tu&r79SBeL^+@RX=|W=<<Nbld;l|Jtc>B-Ud{7r`{RkA4U;gf#KhzP5OI;{f
z-{B|MFvnEu_Ux>V@156x4)QDs5Bfc9-b`UX`%a>IH3yeBJgf#+Q8VGgdAjM1;gQgL
zuWGxw2Xmpjog+>)Sd}gTl+aU*t#E(|)KFsV*c3WIrWR`nWXTkXnpK_wNQzaf3M?mV
zoy!;;8t8&OI|32Ss)?LbjpQ!b51r0qcF#vJY}o`g8|aJ0`|~-5gCvY!%OdN^V?S%C
zv~ed|9-fo5{-JVn8f<;JV|kU#_wrB0+^xm-1%RqN3Gv$`_i^|%c`{nGeG96+3B);N
zMM@TGQ<#cbkt@CHjfU+wgwhdiWSlSUwZJ}D?aNt&y<-1jkxd|p1+Db?&I#>jt(Sp_
z0)y2cFWn<mpj?CF==&yfn9TDI=>c0SW6$V7eW5}-3wJC9eIuFP{>XiFLzwJ{>Ul!n
zC}p^R?P$L#SJ-8J^^=Y#6AO^VDt=Pdo>~gBvom5`mOUby+$$3He#7(YF;@kt!Z2J&
zj0r{26c#<wNm#HX#E>(@>fuk9LF)2t;%T$Z&=m>IR=KB?4s}jlA*{pJ=<+@M-XyNE
z!wl+u{pUPsa%qu9-}pNXJ4yA#hbEZ4R?D{dfcKKdlj5`@hFi(h;(U@_X_5jWiT%_%
zy!>$@B5@*=8n3@R9ES+srZxS$9}*Y;Rm3k%Gx2g7_d`k^3PuA44Pesewf%yNacKiJ
ze`zI^4(X0pH_(Pt-zU#Kyn~U-oefALy!_l$li`EGQ?-k5eT(7j$fdJR2qBh%VaN_w
z|79j^&&q9>;z5Q<dF|TVQ}p6npB28cZbWTSUIWumE9-3FwR7O^d7)?bJ8+SpU1-NU
zhOynMZ*Alc4m!3{!AAq8zv|jQ@?d4c1&K^NJ$7dOC6K87@o}te_7e%_5~yTtNX-vG
zzy)dOm6EU_lpi!9tpfyfLL8{4S!HXp1rnFD`4X>2M^KK<FlvRBh4VxP#s=EvKS7s3
zjPwSCnz5PgJNwcAm3^@52Yo<$Lf4o!`tL));gJjt47X?VU>c~);Lg%pf%raSr(6i$
zAP^^_{F?cwW2T*xxnB54-R=c$#fy>nu617@29^Z{RsPY^M$?k}K6;08%!*M<w>$%9
z(z_@J-gjZ4*h-6F)1y~MGUEHLb2I@nnWFZq437gP^qY7^apLr$LWJyA`P^i;+W7+R
zk%l}qgDdLTyBlitt7kP-V+q3exf8|z(URWY9(JK;{`5KK)0XU+cm<fc5Vyy(e5bd%
zP8t$5KpeSIuz>Q-KcUnH4A(2RP(<YOZpmu@DtpD}bCsu}K8}GM)$+Zoefb%TV#^n+
zXXQybW^)*$ioab-Q;75A@Ub?<F!dG(k!*+LUzWZ!f!Q*rr{1^2T*IA&Qu$EYD<+<{
zrbT<yR*TfSb#q->(xF8>y_AJaI_PRG>bzHsD3`<9R-;P%awsi46W2nL=7S|g(@%nX
zG<qgJA|6@HN%B}HUhL)gsj{d4x??YzyjXuc@?wKScPrMH+lT#p?ieifxceKJS>c@z
zB8YckFNJG><Mj;1$-@oX??$=)`WT4B`3pPDgsE>~5bO;i_2O|}k@-D7tlj{V781z-
z-v|-Gq1nzdn4*%X)`vw4u#WjLr@U^VckhD8IH`JiD=Nb1u%n9Gh1j>N`c`rse5nu%
zC+dwlJP%Ae0dh=*(yS!dr+=QiNLYO>8`@$|WSOrbY$*X$M#)(51cZJ6gOwnPtw5@w
z909SdYz*N`N}%R>%*bkjE?dQoXCr_})(so|qeoQ-twyih$%AjXfBz#OqShJ52y;Td
zJFuHik>||LxgYD7t}rIA-Gcjm3|7|O>o`vzQi8k2y6v*2``(zsVK#67)g9UWiPv!E
z-&XN-ykbAPp`r2Fo`uIlQ7B+e=v*-qjyEv1oy^-K8^W4Vg~Znz6EE7(Gi2N;Lb1ix
zfS8$9Mr#8U3-A@&nnnC%6A-MQ0>a+-uwAg{B5PZfhaw%wLk}s(^zD2M-X7F!RgALG
zS9rg1z`qABFT6R<XW0O2^x?oXg#&Gpo=x=X$@47uEvqpJ0Ac@)VsO%dS7C&2H5sLu
z56{q(DZ%%8Uk+$c<QznF3l`Bop*G#2RAitW4K0Y&7i$^1UM9D%plRU*=0X~sdnvn}
zN!}g0%g@<ld+UirLix4RhPRlGC8QD6SS6wPU8eR1m+h}`JBqD;`J4IIFN@riNGL4c
z>>-kC>dAD2-k!%*b#Io9>W)_>$>hU8?JG)2)C4L6vB$tt`CW5H7`x0V5Yaw#ffAe|
zC&?-;YrefN>omOLC!%lbdzfJMs0v_IUAI(fEKh}k{L?sP$~3ud&vymPu?UX0a4)Dx
zeJ$yEY7v$pWgN)9s;+6GHVX)s3JZVufT-V7^(82eqs-1O71JsVw$(3S-dHB$8HYrF
z;6j4NgV(lYM!ki}lR|mdlUc)S22~zrHLz7h*VchLwf#Nuo@~Kd+Y;J~^4lK_lg_cs
zc3JrDj9QKpmsP6<SZ<W0eA*G;PC2~tph}o9b-R{r>PE?-t>Z^CsHp?M659AATu^tw
zU%eokfJkBp*qRiFNJ65O8Ot)KH+PyxL%O*<zMfACF9v+@nJI8RpMXm~F7_rAnmXr8
zKH64Ms%RO!|F#=L1>x3Dt$nl|efMCALOFjn<Hu{EhC>?B0$7nb`;ibN)I7GS=rClE
z)~OG#T2OMlSjKGPq^wCBPVeYWJ{=ge%4)wg2hJTgytLx-Jj<8UPslb{Rk`av>FqBK
zAt+LvUiDGS#gZNv5#eVE9TP4v@(cr!>*}OWa%N4w5rSTNz6NdCja>oyng!NfPxh)I
zbeU^RSls@9sHuEyA+5|18L<!Yy|*vi;#sbHWm*=Ty<0M>Z`98IMf68I*i(7(xJ>_?
z?5}-~QOcf^l0${AWp9?U?#$Di*ph(Bxc+miMkgvCjf^GOH*YqztWP~05Y(FdUkKT<
zfezD6w_5BP&<*_#<Ju)#^igr6?R};ZwPFhoHTuBQw{atf@_lMZa{v6H6|FxTNoXaj
zfQ}uMHG@Eq8phqLHly|*z`%YBk>#hOG3AzBL3yzF;gUm}D@VN-{v>WID(|5Q^2onq
z0aZa8yTkYVP0xROFJ--@g-eqbLU2Alb;Y;IT3CKHjxO_k;s}>BQ^UVp>(B5!HBD2k
zJG53d3=56GH_K6wZmDp}$nOdVmS(M^!_BR9m13vhXs;XLnGyIWGtGyptTgHaU?Bsv
zF-b1=;kH}}yo13GU@pJeE&yVr8iSU`tlwj{i;Lh=5Z5D#FOFtbac_{~s$Y4>;OiqD
zx>Sk-P*x+cjLBD(xqr$2db!ff_b69c-S?a{$lOvVNt1+}xW^i?6E}+~IRtT}(M$#_
z53g>s<iOAG@Gl{2Z~mR;*<1rx&c2ML2^yL>q3{+&Bxl~#QkE>9#k%Mhf+v@RP;o4r
z3)ONn>wN>tffPL|ISqh5<x{n<&AxB3;pQs;A*c&;2_v$!HSqjqs_W?A%t*R6e@!CG
z$rQtp*Nsi2oh3%rsC|#u>S+#9%|_2xW4mpn+opT;Uh$#lXPK&hfOALzNaZ!a5`zk<
zC}}62g;fdabsL=5zs72%%?MeFS)nR+7lHp33dEJqWNCpT42M|)!>@+${4|6V?pgLe
zS_qN(Wl;K~>f3LjL+<NhRqCF}yn?Rc2xhb4<CCJbo};~DKS#v~@_V_m^$J^&Sn8cj
zey3~ddeGZB2P}CsS&^uAEu488aq0~s;Wc`@PJ9QQd$$1nUMh@9b9rZ<=SMu1tq3ll
zKOw2Bng=V^hxiGQ=Wnij$0n?$xJd^if{t;%%l%0^ZFHy44o^kfS+kWq2O7i*HrG4(
z()?YI{TY8wGY)O*p-%phau(5|L+jv_KM%+%qs?;5I}PFU5B~}dMYOIqX7gFcMg1fy
z6!VZqOD`4PD4oSba@M%=#beh&HZ$tb?^J(~u_?DHsTIJPYLn<yWI=bKpvQH2*Cf^Z
z%_jxau`JSk*oX0x`#1lDC_P~!43T%nw~h+qK5tX|Mk?v27))l=0!^Qp^#1J*NCpI7
zn`-|@4uEml{!EKQVp{4V<BoB<Gue(2Qv2$?s~;*8)J6fZ#?~@l^-;gX60i=y=(Cc2
z)M-dsS7IYD#pTMt#o2@Dxo5d##pH|*2Oiu}X1WmQnY-e2Jih6QP4)bkIMJ;@c1J?;
znbY@VWh9ayvIHfBv!7ylP_l177ax9N(GdV~NCCt_mM=GHDH_6wL&-NrYce)z{oOD`
zdWAgRxVkI`=8ns!pMS~etIXbRr3vBd3t(@6gj$$6bt4JC;sO_$(@7oIIQ2vM`gE2(
zX<x^z2~!Ft&{BLx8Yk*p4q@4*b7{Io<E_e3CRt{6LfL&wB|_vUw-1kx)Jm~o7cJ`X
z@hM<g1=77CAJwVrWU%%taq&|4{V)VmYZk-L5$z}>gH{$NAnz)oq&TnRb@GdCi{4Aa
zbPY3Ka8r^3*6Io|QnhC;chKx(o=Y^Jv#7eV%35e@_(rPpVV_-IV<=oG&<0`+$#RCT
zU><A3M)>9Urs6r#<;eQOeGB{)GhbE<@8dZ?s-JksfwS;t6^VI`>zWkRg=I!p?&dg=
z8~{NN{n7m4@!3=n_7FcJw<w05>Yy5V@cmtdB~v;s1`>9Xd;pVzE7m3+$X~jglz1?`
zXxm}(y<Y>x$ILf|p~dMMpt>3vPdcj}<+Sh9fe*#+EOf6bno-P5UIZ8LiQ$1dCa}v2
z?eN{Bx#Kxw;^LK(9pia<8fy*4ERIIB$UysOm2v*eyC=_5TW)IA!}lW*owdaX5GOLN
ztAqM9nel{ClVPv@_Qtx^RB;}7*M$s+PlV&b9L1_;>5{`3jz(anpTX9@!)Ikt>;yRm
zBtBk9Zo~~KE`J^;&u&jUy4X6aLXzrFrGxk$kzD6Tt3<qOD-deB6?V|?eA?u9wX~U&
z%Jxy6j%oTit=+0Ot6swQSXl^n-wfM9KJ7&y&5_N6J4SZR3aFt3hiXA<+E!rE?&Ot8
zhmm&^J=B;qP8iD3#*B$Fz|LdnPH3J~tlv~V&t!<Q<BuyXlXg-LKsH*FkUY(KojL4H
z3F9Ppd{i?z7hSlFP~t(?m3FqAyWtv722Bkak{BUI3k^3t^in%$6KF^;)C*)G+wQB~
zXYFsp2VSVjQ=(nxk{&S=%3VJw71Sn7z}zjm`70eS7YcwTq`EY+cGd$leV+62v<BW<
zp*!w^FpkaFSJOOW==vI&BgOHLR&Q?s7yn2HelTaIx#>!X0MFJDu=i0S9I)~L66$e7
zV8jgUGTkK$F{&wNylt(dR`gNg%RhM8o|&*3tbOm+qzeU>|FNDg%}@Rn1!aJ>UKZcU
zCo!}gh2d11Jo!}GbzLU(ctJ!WYIvxJ<QOFq7pX6RMO@$jbzm+n?kZM23w?HVIr7SR
zdj^Af2Oxy?le~}Y8lA^=oa&u^0Z3>%Qs6NA0*iq9ykDK`=}u`zgPTL=&tFu9C!GpF
zT>as5!B_mB!T{mvKY^bzd?bMJ4q-_uB{Rw+n+Hmc!~&lcmj#2-h$Y>ucAekY=66kK
z;lh<mr0~;Hbw9f~N^fS^DsB^BUT^U%%xh+=jc;H<B8uhV5tr<1yuj~t02=1Y_-Jem
zc+)GPbo;>hE$(y8O|RjKppx0ls!&97xvGN7^-QNgbaF6Q@AvJcmsUfWDKfncTE(Go
zq*$NuR)f$&hLM=yW63la)}ZFAzvJ;jlcR^3m-gFc4yFIzMN#$vt!iX0?^c}ylpZS6
zl*F6IH%)~iuktj5lBE6;vkw7&;K{=TagP8ax%%3?R8m=2l)uu@dUo8@R{Ph^;<Mw-
z8d)J7sSF?Am)5iP@@7sdUJVnO!Zfw-Wev(SOeaAeiDIIng%)oA(X8~n>7_z>!1?2P
zBdE;Z-vNh~f(@;Ozl}srmbKWUBB)#MuyCd|ZlLH->6iVMgET*d^4V*2-+SG{NICV#
ont#^$^ThvO{;yf!XoW-}Kp$Y0n4}*2n|KC2GI&^{`TXzy0;YB|x&QzG

literal 28168
zcmeFZc~n!`)-M|D&_cwf6a|5@2%v?4ih%T5s2C7X>3fvE7h*t~5K1j9OBxN*w~B%!
z^j!!c1VzC}69htNgBDF7KtdN{0!i*pIQ4z!ygR->-h21namT%9k0FG$*V=2Yx#s-M
z-<)g5CpRojC4Z3l0R#d`LatrD1p@6X1cA1<?%oBov^}D}1%B<iV{UpG_zhef6C48g
zwdd|NM<@s+_gMH(EM<Dx8wC0Z1i5_4=6>$NcvPdey(@Fx>W#{tmwO&P>V2fV<H1jF
z<FD<uxN+vEKJiPjkA8A{b7+6g!^NG44S)IJhrU0=u?H{N{CMHu#XrpAPriS7bN8jj
zOENou>)q8y=6F_QUh%H1XIErHBcmp0Syt`Y(5~vZK>JImmcS_;36Wg?{_#Is14GGf
z_O5mB!)APrd`$Ze+Xnh-wr^-*8wliaSlmXu;%?j<XplU?5(IJ~#^tzfNu!?%k`aE2
zhwLsC6Mo2i^moJk-6udG=W)0;4rumZ3hz5SbtMzYDRw0`k8PRdZa48|+|LhU7<RmC
zuoSuznP7`>=G|ts5Yc=e%3Fpl+@2Lo#@IZzcYjND#8-3C^nO_v%0PrIr6T0!c+EHH
zL`~Br*gaS`9Z6p$i_65hQXTL`+!5sg-1FpehV!_8#Ms*r0@sGsKvzIf&Tj?{1`6oO
zk_1{y?Kc{tYhg13B`7nxl{dNs6?|P_Wvp}ig==U%vNlM6bvRkmmm-zZJ&ga_Sk*Ky
zALxdOW+P)F>=AZy%?VsLj2Rq_z0Km<24orYU+IM;1u~O7oj9v51N&_%K9Wv13J5aW
zdZJSN7YpE|$t*H?(4k-UNZE4w&ece@?|Y?f33Z=i@Zs;^zLnL3qc%*(8%(nfv`kB@
z1G-=4*LgfAYXUCE+%6QznL6rp{J4ByRezM)yJ0z>c~{JB`(-BvXTuJmPK<uo-MNV3
zcgV~Stv33l)w9XN5>&Q#U}L1cNpEu>&zSIj6TYV~(!Rj&yOwJ=bw_XK1Ex1W_8pv7
z7CsS2W~xU`cz4*Nab-1eWX<((xY&76m12}|`<@=}+rKs*k+do(eo1cAl?;{}{Hub@
zqqvV`Z(d@l9xBII9wD<HN*c4hL#*YnUq{T@O|L#C2m4*A9v3jG(0WDDeQs2QUt5M&
zq|-TCI=L2jJSbkyA163*5EMT*hSh_~t)>Lc-i}FywUC$<P`;BvWAxaL`Q+*D^5)KH
z0zDr+wlTYtMk+PJPPGUEhVo)M=x^zT!yAk4a$kBUW4`SB>kvx8;Z4O^SG{-H#tw!-
z@9Jw&&i3Nkn+74&{Y{lqNs_nM%<`MRZa$UeM}~1ooAC((d~6`=dqTudLhV*5ss+3Y
z<19DOeI(xvzmq&M8W6lgy;S~84#C}|cm1Q%Y9;1%3#%dGLNmAh7nWgBQ<%xu#y~sT
zGPz&YN$vXhgCe?)^gu%yh9#!P4`MstCGk*9|KJ>}bu_ikgSTpjjqEydoKW>SrW~`g
z<A5YV#lDHgD&MTtE&K@YDyaPmH>!R=X4P#pACT<-ofk>d>e>m&6(8*B2!gwrT*H+h
z9K%TrdWh;6bOgy!(I^eYzniJa?{E*=ou8pw$E~-WN&GgWRssF8oylnO;9rr%1$Ny1
z3)$4Z`O3o)#7ndda0vzR8kLy!WdxSokuj*heMnl(>}srE8fhD-s>uJH7^wA}6-wRh
z+h$<K*lIqVX?KCphwUss@<U|@NgcahNZ+(&2u6MhOq^C!+owVZeaBI7zRQnF!US$i
zq`T<Hy=FUfdn;D5`z3k`9N`f}IMeNJ9Dyz$lZ)GY8gH0)!?60cuMw!#HW>hjcyE5(
zJ5Gukmd26jL4*(u1L1z(0`>(AIW{8&e#;?a-75n13j%A^T%6O>jWdFG7p84g5#R1&
zS?g@7v*)f}aeE!tyNi|fSau(MQletq`**0XM&`yCy;JfVzY|?@zV3>yHbP}PD57z@
z2!I;OG|sP?xDjj4M19##PB(N2DcjG;RHC?%tIT1l9Q00FX#al89%Gv&H;I|Jx8w~?
z%93VFFw0f)+g!KIwrNEf`DPvC(C4xh-{9?Md_eFuWFQG0=bN@+A8^SUR&uxp|5}xq
zm>=0cy!;SKQ%AWCE-Af#B}V~)M$<z_0qpZ~e2Xm`Z+n%#OLp0pbOoP^Hfj`O65QO;
ze7|?~j~AD@M2Q)-kt=X452X<Nz3;0bnQ5QmxBEs)z^r%d*Rg8B=T4h8^@T;BVdfJa
zcZSr`SN+KhhjJ&6wCv4{yunf=8|y6e8VBXEw}XQGxA}oUM>r$Mz|M%bah~s@5bG|(
zQ0ZKRQ`^JE0Kc2zpC3d$jkpOc5@FHC7^xn~4SM8#;=Y<xg3jlcfZAU-c|LUxE1Qv*
zB;@1la(08NT)&$GM)@?TKH_lT@<%0#<xtfxgETxGicad3a~{8=-6D7p<36&Y@q_%t
zU*a+$-s0uqJfC@oKtSA6E;sUQ#$QH<u~;xF!pubC2mk_?K$4(V%M}Il3d6c`Z0)^B
z7Fwc$x3o}TjQz@eHQuka=_t@icM)XK8xzJqY-nu;MbvBMAA*)EQTCNtx^y^o##tG>
zRW)Kqr+D+)(NtT*irXb?UnF0y%}E-T7}tHqvz@2)K&=fY;&*{OR5({zUv^TG%Ppy`
zPJ@$)J81<&RT9x}<f;qGs2HzqPg$;h<x~mEy|O3NPxdTf6#m7~bJW$f`Rj;U##kg9
zfF~UpBbwO}!=bS2IJ6+huXDMNeu`W!!wuEo;-hhrZzVt;Cc-c`<e!h98gbX&QQLXk
zfwy*lx{N4|qEy`FSJ7b-j3}SZ(bvbZQzJR)F1Su)pyx)$P8y*kE#|aBU+>@@{E@{S
zg6D&RUP2qnckzm33^(Yh!cW+n?m!^EZYfgP>Suc2PD%y2N_M@J%)<ReY7;-dqd%p>
zh`OH<QYXNE-CUOFA%v%~T56_7BGoP+SoCRhnSL=w;{t$a*Wx%CJ2&hxm3%AMB>sMZ
zecj4KO{rX0=f#(gZ6E8@sd0W~O-jrdlyVt^{AV9>G%g6fX;GzwND0ItnTS3{RI@kH
zPzZAjdb9*X*N<pu<+kD$I`ot)-!g?C40oyXS}a|dSZZyqA7D!dIxq_pev11-!@0xi
zhsPeuj@*omPGh?s>0)+Q_8Sg<kD^59)=uFq-NPruWgkqUBd-)JZAZ)6vHf>X(`bme
zcQMU{s&OqEWi6Pdn{5?yos@A~R}A-y5^F4mNE6BxAMCBNs~3mLP>UjzAH8VLOVJv{
z_=&IP=hwEgW>VZ8!-E$<(E_^ld>Io)&XTB5lPHYT+QIhRF^=BZyAdASRKNIX7ij26
z-B*CBsEmyFT6$_a`7b=pD8Jn!jf$)Y!xxkrRVTBJi@0xKT@&^hb|vMWd|9-3Lo@!<
z&p7mBI|4?U9rEI-OpA-dz^{P0jUKqT9aLrQYRCH()=v+mgiU0TG1qT>p&L2kJ=1gv
zgP(bc_A4y9HhH4*HUo+>tzZ5|Gd^!{Z)qOg6%z)Vp#KHGUulk3vBOmG!!#HOca!-<
z02|mJ=ViIhh@c1cVj%5qF%b{DzThdzOR5~-e=p9SwJ3RF^_!&rpQKXiSnsQEo86~Y
z35Mtsi7sAvsXfhwP0nq;{CD&h^bd4dNqmaCGcU}3M?p?CWZFU!{#?P%@a;Fs>=Jzf
zML@ZYG)m2EQ0SKAo07bA=Gf9I*4BhDQ>n9QU^KR0k5CkC&%)Xc!hbc9NOkT>9z7o`
z7CxCkigYlENg~<PY!&>z!`YE!L^A7}aFhP)YcQDNBMsahZDkywtoYsqfhx|ej|!P5
za1CYc=oNjq{wrYo|GxfTYM|^6pxf4lyEytya-3or%mL;Ci)Jwu%fvyTyS4zoR+ObN
z1Drp0plG(Zj4s`}Xfs%zWT~HLY*F;>Hkx=mI@ex%vlG!Zc}14H2beq}A9J2RL2h`z
zT#k-qJP!U%rsRTK)o0qSp>$!kFmxmirH30qJgS$!k>`@9^WF&<WAFXg&T#gU6n(ZR
zg}UIhK8$wf(T!HKNCOK{wJ<;O9+lkQQzg0QqFk*vdLJ7NI8#^Bop{jgLFU{12LIZm
zV-?F`MF}7-^7$aZha1gsG(+a_6P5hNVef_7+8(|66;QNAvOc3xe&yNY>kePqFRXTH
zYDg7|MZC@%P!VRnpm?>*TjtXW{(sohzZ?G5zy4S2<FEK`^>{(V#d>s8S<}7LKv5bw
z<hyOQI}A^sq7TqV(av__J+6d80B>gK8t7HjxU7q!a8p~24qOZ^%dscejw8~dC}j*S
z{e0Iv=RCJOLw}gC14yxNd-SA1#ch?<fN!g2mT#$cys(z@qFW7n#MavHUu3<<QUtdQ
z(a}CEgbz(UcxXBnpU`kTrLt$(Gtbl(LGD!*Ww;*Zhb~$%d+B6orGg#YoV7~Uu)&1R
z?!+TlkI9DUNPALcORZi7_bGP6syzP!?2BlU4Kt7--t_#VPFsQ-Z}<l&zc{)*DE2C>
z4I4<am#T;hVT?g#9s~>f#;chFeK)$35C~$kajvZ3`wtXEee`Njv%ASj5%N`eM-`5*
zS!T;30*K9|t|Wz<yajnmB&laSaGh&J?qp+|>Z4rR_AQ=mscM{AC@@W_Is)35c|(p2
zjX0nf6Re7O4%AXwmG=V+slqOMo6juf%TD;c%a!U%Vqz(W%O}d6UHYj_+)wjjRYu}=
zfUo|nywoW27v}^}bhJXbWp0ubx~0a0dICDBaXNFAg<XcRt1DRFpCs_f8<pB&l5y2~
zc)id^^`IfsjYW)m<^7MU+PFk`#N3;V^-rco;kBK>T0Nw;&CcO#RkzKiqI6DYvh$#m
zk*70j14B07z{>&+BQVYIPtmbauVJ90?<!nzdi1wNe?KH7LWn|TttPumBHPEl|4x3X
z+NIkV{bg+4*tKdfRzHQ!^xP^=q#gv;Sw^3%T~0zl(aiD+dpYvbQi+sJ2WiH|hBy77
zK15H)2)gjA$l}^x@9bCuvc4Pmn04+hvcT<};ENjL5DyNWBn-3rggXQ(R*a_}WtKAf
zdIcQ1`HW$)(>nNVq^(p=(uBUHzo<+9{)?fx82b*Jl(&{>1`sn5_ACn798IdINobH2
zb`kPXs)KvEn}NP)M8tWi6FY>TJPryuJ8&rr2`g~@yWxL|K4!}opV5~BfVe1P7*%fD
zW|LrEtX6=``;%dInY9QYl~O@Tpf&6>Y(n2!QZvtqEM$Egpj)lBpRCanPTw|D(X^g+
zaa+NS@vs!#4O@1;oJM6_V^a|F7g6V{cf{A)zc1VED?rC#ngl_c8?L&s)czuryL_p-
zaC75{y9-Mq@3We*6q>yiD&GO_6~XW(TcA67+yo}DSQGtZ9y-Ssp^s@NY1OR?hk%A^
zcJ%gd@GHLS5(rIKOn&0Uke`rs$y#ViS2xzz<N|zn;k%#7MiL_5navl85p)}HG`L{w
z`yTQYXr)HT&>mM<&aiR8z*6r!LsvbWND@rcui?1ViCpR;t)1!kjl03V&$BWg%q9rd
zC47^Zt#qIO^!vvzie+M;g}ixtow0?VuikTHQCm0?ae$Rd902@m7j&|+>@IIUrkOv2
zte>ID<Aiwf6r~^^)$N?pEDx$0W69=UsqW`*vQhN9ZgoKS4#Sok5})kug~2`rHKRxH
z9V}PXW-7GD45~5C?i*ZSk@rYST^_U72^iYRn>#SL4K&EuMHUlRh`p<WC0c0X(%})`
zewU8mb7+*MpL_vPo(f&Qjf^3+o7xqOPja=^@8kP&(`MrQCd{2{?McaJ4NACA=&Oq5
z;-Ip&unsxOYJp(0TivrDFNT5T4W;mgd@=xql)VSwg`JFDW>?(hy)bl}KoFhSd9s2@
zH+zw*R*4BWVimhA!JVOYVBWgv$qL>r6p(_a`8|}JaRiod6n#;+WAUeeU1qb%D!!8@
zXek?o@*Z?gd9vS7_vS!?N~3}vj^9s~^XceE2?$NxpgakbPUtn3a3ClKA~iebYM1AP
zFHzkl;HU^(MwX`5CC~~LZa3v@3}cAdQla-7PYAoVrh5a%cHbR_rWe>>K-_6#%%C(;
z1uiu`S0v$uq~lm4VXycv!(#X_BNw<SP%s@KOQT8UXP6k2ak;#0o{gLxyh;xp9o)7B
zWASQMApEj=rd(%i&9vGD;c!rc!9PCyXNmW}tNH%BL8z(!(F!VI575QYirTpS!l%b4
zf#dV3wdf3c_p+Y1jAsR+Fmm_sPrJt*q$>H<ToRo~hG}Dti#pHkF4XCOp(aX=obfN4
zV(JAxM?@BUMjK#1P3A1RVb3@&I*|1{T^i-7({xfa2rycm!BHTqrJ&^9c^zZ*XnTSS
z{8h9skd_uEsf)z$I4vF&A!dJ5+J;o=b^~t8T1)WYhDV}+GXxhy4EG^jQPyR$dDJc?
zC|Bb&LvMq2La}V;nFO@GRnYhFyCscP-zMY=Mizc;cq?n0g5F16`1R@<mL5g6*8!I4
zaf@BSs2{$PxUfLi%5mLsMow&PX2g|e&8nhjOG=&E{79c2W^G58bSERq^Bs9GR=s4!
z>akjxj(eTYDcgsfrO<KSwNEo!-;u2V?&Cbrk5&naIB@6!LC>@HDG*^{SM>uYFOySc
zw10Q08~z<64BK+j=NIf^ZUY(g%hfhoMZwTvL}@IfO}&I2d?VP{I<NnNl-XuWf@`nf
z9FWy`UZ|rFVp?D-dH4E!C^aObrQf9Ptq=R!%QXzw2i<;bH@+AWIiHRh<4-)!hYdOk
z0cM(V98~eczH&X@TEG6+_(g@e=I<Xh7&V>h`ftNlc6mG<;R{0RR~IU%w!DihflY}m
z!A4F~hgMzdN!dkStEOX2o#wfcVrw4(06X*NWQuW1zomX~ZRe0L0E$b~V&~C~ZE}p>
z(vtipVQPdBun`0UfJ{{UEYIzI9MOAeR8V~rv1W=GxFS9LeXoAr<NYDQ?qzN^a-&tM
zBM3Wy9)dtYvfE}CZ%48i^aErq8Ts5bqDA>`;SG<&zzv!?`6*g9BtnEdpw8Wen9A{g
zoCdX?`<uXowfackJ$yI3K2WG_;2QE15<Y0HXadmCUZ=aF<}-VMvj*Xny8oi+q~rgR
zhNG(SaaUj+2PmRNd*sNfo{75#vt-TDpOEWKorJ{@pr{qFAlGSXODZ?A#}_xP?qG@{
zb*uIny9=?gtS)*Wl-5Fsj$vg1{{CDMFB$~YP#nb8I6!Vfd23lpHHcjNM2|!X0I$qD
z|JTMA-?@?`x5NDw{tcIr5OLfpcz3==<OE&8hn8>W8ac%V>H@+*ni;OL;)|x)P|ysA
zyt9#uPUKMSI@)~`7$!ilXDdhZco=+HHL(+gK*`^fyUDxD`huNjSM;Ea+<Et4Yr6(S
zIMez;yGeRA1D#>36T15qP41>4j(ZO{kDCghTs<P1tM$INUc6D2YJ`EHQfZZOi|5MX
z)0I>0g-0Nu)>-Xe;#>TIh*o)iE!HTCF)~bM+lbp^D%|uU7YE5v<g>EY%EF!?&#ZIt
zElw~e)^GF)8Lqy<dE9zEJp(OkqhcG1Qb+OY_3%w$td`+1o-b6S$)T(5`wAm%G39P!
zRihj$*@(05j72gW6=*}Sfrr1-Q>bJZv$?-1Qm}F^MNL;=?}PRV=cy`BJbcl;mir~)
z>vufalQzhYW|h&eO5*cKW$KP?*$M@+?!K?}Fio!Hhq4ig?l<zB;eOiu8!-Aznuyi|
zsa0ZKkFSjUnG4L^gu!E>73$QdT9M4ao6aBJk=?u7X|`4_XF>&Z5}N6Lm-mRyA}iRZ
zRH(~VUlq<D|0M~SpYe*WgO*<J*3V=QTKeI`Xmo1rvb%Z~I}qTRd<sx-ZAl_ZX)Pr1
z>#Khd?L_8#2ek|Q(k6G<eO6-oo&vO+W@M24YOp3h<Xx^Ox`c#}i-S3sEN!ZhUy_+!
zt^zXs(}6(I(4*skQMt;Dm@HVJleRCEl>YrLKprCmI(g;YW*e=&vRvLB9?Tf~ootOV
z&e3N-yNMDRcjhMtvGY&I?}WsDd!WnCk`*i<>+{d*#I^nhm1?Jp0ZH_u;qMRV-hs``
z=VX}A<V2<VvC{l<FaEbgoD=8UpO^yK0*qIAN>38O)BR789cPXJLSbN6QvND%?toL!
zae>-#_OSJ9tRN+U-FSqyQ4M$D2k5>mS&cx?@VT+?P8$xCXoH5^zUxQzXDSUhXsoLa
z5}^2xA$E9_Du1*7P%J(=oYmf~z9U`|hN15yleKZr4!GVhe8v3=qzcxus`305?D9m}
zWQ!*5=lB+czGi%!wf&A>b_Jhsc&53ii4qihgO#*1u$x)TUpq-I?_NaZBsb4!U3HTJ
z#fL_iwOxIlH}i5&p;$$Lyv=xkFh0Q#UW6V#V+Vq>vCF1|VG|uQ?UBbyUZ~MDfY{bf
zDs{7ld#C7X%>N1E4w7-wWw7^S_Z0=bybp988K?3EbOBo48h#(hwm}Xb4H;o6@*6`P
zArZ$m7Rp26ff`Xnb8XySkoK1%dDdG7v|yvmDC}trr_-G(OI=FnR^P7O1?VvL43`WW
z(-GWgIb@3RMn}c4mH~6-RR|hR1o2nKa_#L1cH@O7Ubn>9GY;d?+-@udp>2Ux9T{Ib
z*1q)S`JfocLvo+tT5q!MOs0@ayH3jRx9i2Dn-r5T+%etMU#x1|H@a}p!zAfTKZc$S
zo$Lfw8vCiqT5uVQ3k|s$-jT3=%!=`6!RSXxsl%Ho^r-|l_^KhY(wPS_kw`&VO&RNp
z?69G+dfO~e_>gkz0PZJ6uJjDCZH>>fxm5{|wc}pCq>w^^QVS_ycacKh{;TdoRYjYc
zWT`F&zVW3iG5P_JtN`vKsbeRR9C_Yg?`!&ZIu9^7WuW+wY}0AST1m+3tSn8=)JJp<
zS0hc<@AIA{JSHZR_idr{InTxTxc;&4bMuW%f9NFoSMw(rJ5yV$awAOghS5lsDpxt`
zzJhR$DvQMW?OYOCy1P9E#joQ|pvRyjdKk2lbhI?RoFOYIC9|1F_aQH$oNaX~E3|R^
zxrAO$Gu@C((|H5DpaVz=2aJNpp=XgvBsW4!bObBMHL8SJk5;iIT!>^~WTNHW(-ta>
z&i$i?kP9eAX4`CxePk}4pEbNZN5@M_b#DyPhYT4TG`g>8nP?61Y93|sy|&W!k>zep
z9UM>8hD}Mt^b#iph3jI70M|qOm}Y!M(_5g3Qym?`A|IZKg+(mdkv)MKMUSXzOB{aS
zNfk${kdoU_ias>Lz~`4+L0rGfMr^IQQu&?;<{CdA!|fCyJjmv6UhwnZg$-*~yiu`U
z3-J2JCp|{Y-1%wYqCr((07Paw;;WiCZd8q}zC$P4gWfH#(L+Z!mW~NK#mjk>$@OA@
zLrn(D7v*cqM76jq%Fn@t_dr9+?LQPM80#37=wX}uUIT|D!p4Ml;DDcrUG_X^BwB;Z
zGytI!_d_<nJs%VqmWQm+y9dU;_x(%5Ja+k;V!5#11ZvgR1~x<mI+<$fbV%3`9~@K$
z;<}Or*-7rV<n;QQgpHseYtNm%wkhSn3n5L>k+7Tt6p=1Ga&$dj!`yV8=c|V2524t$
z?r#C2PM4uXE&U_it0D>ocaUp*&k3|;%g-5#9tVV|AmC_TcnN}mRog50%ZdJq_ygGR
z(8RiZqQCSX9TBm8(BEeytzclq)g^}j3Ll9Mv33oP;4fiybt?8{CmG*b%zt}CG)(*5
zkl5ODvMP${*fxf<L7bDFAVe>I0>A{gs-6H-huO4Q&Ty7E50qBjZQ&u>ac=d2AA}Ws
zK&f89N^vp~XWXId+%7toT#tGxJqCrax`w~EljTfNB6AWN9Ehur`}ty=6_7mi2R1T8
zXWFHXmu^YW5DzRvAzRk?Wz)SRHg99>`|IZcqV;-6034|WXfTX%8O2YvNYL6bvj044
za;DvVqEl<%v6ovqXb@_ovMY)0-T@aUC6N^VxpQP!Qp(umh_^WdH$3)zQIRZ)AgxD)
zPS%cHE(JW=bf&%XU&h$?Bq4Bmu?*HLxn31J<Fh)ZTL%bL4}tiPXyB#|f6pf4(9UX^
zF&9}&bU2ij?*xx}{SWPI1GW<#SE|arpy196WEGHKLZMU_I9{(vRo?Et9V<o=fKA{6
zWB^_!fWX-FC@3v?#Ixs=>j49MLjpIS-gjVL*aIlA92L4`WArWHB+(wNQFewFDEV%+
z9MwT$Z0YK;uE#5wv7Mo3#PPmf>1aZN5h>bm%hGpVKYWq25zSJNT{6=DSV5nW)c;Uv
z>v{wJR<E)t=+L}K5e^wiy9*t0)@W(lj*7dB1R1CF<^U5_(Y+yaPj>vw=bkd^IsJ6b
zBAr!!+{oUwB-)$xSAy$SS=4#NLBB9`;=;>WoT-B+8fyys`U^C!F#7Y@{8O}XX-|2`
zO~V>Jzlg<Z@`SyH0_M7KRq<hf4;J*(Ps}^H9FAO<zlHZ=uyWAiHtu=)?C*3d^acQY
zg)a|x=xD#XC>nr&xwB(N|Bc=~ZT@u_p+z^gqNytSE{wmxx=nV~d9AnA>HOfnS=0K4
zGaJw74dgj8*_X!mW#vDo4<*YR=<tb0m;o)Xqn%jv;dycGg>>p}aT|<3kfA2a+?LdC
zub?|Oz2bFRF1fodU+Fp5VM8fsOiu!8UHk-r0-$S;jm8jIQMdY?%Q&E#BOM%ZB1nk5
z_P~2fR$2!4n0v@YX!lAZom*<7-FY+Z=$)T^yORj;&09yJZ?iI=Qw+kUSVt!`dXn>I
zUeg6Nk+j=2X{@|f0$f^s+t1R^u3Qnjvh!8Zv9K%R;#%rA&+K}2a+~_Gj>~rvvl_dO
z9x4L2e^Jp3aQQ3!-A=_2&&1CCE}ECO|NI&C>Btdp?CFe7*BJf<vQ<Gy6AsnX<6MH`
zu14jrt~ryLQ6%O-480uXo)=AGr+*iSLDa3Flap`1Uq+^m>;v{WKEK>5{E81k*Z5Qm
zGGG_T?vI9pr=3%Cv+3Jntjt#`A@azys0iwB^(&Q41Nb*~@;=+x^QArUZsQl7+2*h#
zYm$<6R~)6xkZ|_64vyIpGg`fOfjeJGTEtA`gT;O4cC2kjmN{ZVTp0+#vH%&?Uu=P$
z$qqzXB7oV?Xs?^sbdJ;qLcjhd-XPc6+7L91RIO}2x6HZs(SLO>Va~?d%;niHC)P$w
z)$1^QL7x|N97k~j<6j)@-eikMI~b+%N#X8YIApV)Dm#%~y^73e23x`D)}#A8JxcN-
zoLZKXLf2db&rJ3aaT7eog9DNsR&gt-xzY5};K#tmww_xg2>6^<aiiXdquX(G6gqr`
zsDhE(rRKNs^P`M`&mHwguHahSRU`G=?*CE?k8sY~%3!m<tRF9N4xBEjJMwYKz#!7N
zlS6lkVuN)ws`(>M9BGT4A{@N&>*$`@=wD%()@F6no9Y6@4HTzJy0#^yL1?ugC-VA_
z$5YDFNP%j9e)+QaL!nf88o~P9R=wv^eblbC-yl72$Bk;=Tri9Aj<#BxpiT>{D$@wK
z<}EQ2ib{8^srK=<MMGc*@P?@dy2P9H8yN60L1&cuLYrg*s|~EN_yRHLffG%b6!?o6
zkI|J!!YYiW9?yHD(PBCc4c>~@y=ZM(JoVYsB4j;j4lL@fY-#O#Q6W7bI>{{v5^j#-
zB$vpJh^mSp#p2Q(b(}v$K$Kb3eQK*%nCWq_S2Xg+6JnyXtBVKzZul~AWw%0(7wToh
zkyVY~M@4fMUb0XsB;<Jon*^&B-+Le{92&nX>%kUj`8iu}JGars_oT5p5rG~T<bmBS
z!)=jsV7Apt7uc$T9OYXgFPiPpYd{UssmGs99m*S>FkjJ!@ZrBTF|XAikp0$5uiT$c
z;UVgwC%1yn)r;2FcgxJf-H<WL*q?0gM7^9^O`Jy>Xb}lY!%D;y<if~})1t>F#up##
zC8T;)T5_f#4eM1s>z3oM&0Gj?Upk2H9~A?{N1*NaHx=exZ>TDa{_{91R2pWjn%dyR
zc_R5y1gWjN{eBU1C1l<<hUzk1R4ar3Q=mPwOv-0db~P1&N+b0D^J5Ci(u~u+Nb-YN
zdt`qg#(4Z+Zd$H%T1KUSjm=jUP8*cBXM;(z1H)+Ogc^h&X;Li`^31ludzF2uYLNVA
zQ}1mcG!~@gg_kK3igZDG=@Ctk2HMa=6M!*fO;se^*@`PW&Zos&qBOImAS-F$$B_)@
zw5hN>?-LnHQ9iy~XKc#X03CKUo+Gv)4Rp!Py`$1^YYmCVAo<z!L_eahaN_tq15?L(
zHH%Yo#~s~Xcu~OY>*vy@*1?hY=v#8VGXodS7h5akGEX*dv74*c`>nU^`XVx1Xn0eN
zV8JPJZZCUwjNn|}vmLoGa7u8xc#Ab=p0rRYWSha3-<7a#u=hYLnJ)@br#dA&M9RMS
zG&}c!XMv6HIZ7%?HvKK)Dl$iJn*F-z+6mG0hh70DV}sJn1fI=zLT-Q^3%urko)0o#
zNw?&PLFE5M&newO+c?c&l3BQ`)I7Z2E-vIhl$&jKk<e(z=YdCKT$pqx&ZmD?q7ll@
zD3mHF&Tt5|F5mp)Uv5>|wFG7bFN|H>qHu|S0Jau6@EN_~5b9bU>;2CY2+YfyDb0dK
zhdX%j!Os)_WXOAFD=D8z3!eB4%4c`~f3_#mz-Q+58t29JJ0eAr>-GNOh$3I9L^ju3
zQx%C=$UfKC48UxxRwhT_m5}h5f1X!`n4_tmvU&odem&d-=^6lQ0gMD(@Ok0m4<ySK
zf=uF;pC0J-+7|7MEJ8MyL`)d1(vRm2e}E#JHHiJlpwKg0H0|oe)IGD{;q0jau((Q;
zGt&9UTc|JU!|F>&TxIQ6mqR`lFC!4or;_r#X*R3BR+?#%YO(nui?kCuA`YBIf1%T}
zG2sfS2=&uf?S^FK)l4Y{?-K2IZza%07Fo9Vq9h%4&E4q5aEI+S8R1qRRki3ATm1Qf
zXxqEK3>@pV2S@pp7^HbMy^K(K*Rs9LS@6I^0M?)32@=FcM8y3h3a}1)&763uA+cPt
zUVl$T&%$|`hm8@xZCsBO&6ikKT>SMykzHK4NwBElOuG=S1WKH6q^}C+F0vLmHh5z+
z^-dsip6f#6*RN<{H-6mOYejVFj(bQ04WhAflo?VYwfO=OlNuCwS9Wr?7`*W&J7Kai
zwOIqNg2Z@5aFe%ZOO^XRR?$7toqJaBsAmEuS*LZH1X*cpc<48zle#(4@DLP#BI+`7
zQq!?NgLj%ZQ7;E%H~0E&wsAG{8hj3-*%oPIBl%MukP7+>T$G#1aw76!>Lv*+&?3el
zH$#w~sV-EX8}MjrlvJ>VUfs8KGsldnT1Z5I{<8%9d7@FUxERP}B_C|sAa_}8!Xg|p
zdI<=fw*b>O){$`c3xy#~Bhu3XtdaK0oNGu8aFheGpu~)xpT2y-WK$BtSF=UzqdS)N
zJZP!<ts&vI1v1aB(k;*0QuocG!>$I{{9x?nq=FT4d?a}%D8Y{c)?FX+tYuSUFaRYm
z5^zYg;+zn(=KSrTGrIt%e)xuWhIk3WHz3aIA*>+b*O3xViD+qKtCjVHkr5T!ZPDf?
zi;^F+wF1N6?@_6fYj4#Kr5dEh?}|T6J4U*Hb)W9kvxc^#Lzos;dDadfg-vW$o>{X1
zqQNlsH}Ae+ixsX7^cVN^Urz8QZLIGl_UC>QnU1SgT0HP>UCk|*%z%AU6j%sBnRqy>
zSz=sdqN+pT2YWTHhu`tN&1q2Kply6F2~HB@AT0$`MRQ%&qC)zao^(y|HB-%)ckc-9
zuX`jkbH?1t_NA#JH#@)@Rx2%F6%|f7xZ@30b3pXWgbJGnt3?o_FOIVzrbsC;)*Z=I
z8a5<SkYyp7TSqpRF0@6|xnxW|Ded`bVkCSkZf$q$`SJJ*t-PsJ2>%qZ$Qe*8aCr=a
z3r#8ArO2%a?MeKHHU9UZ`afd#L$^mummm?R?+WPh^wU!-Vv{1dJTC9ej&CV@si#?-
zE-P9YsOaP0Ppi(=EF`(lM2mD*_ERSWtnlUjkD|Y>AO8ou-*_a{J8)gOc2R^O|F<`M
z$Zpu~@LPmgjW1GPuk+V+tCdkm^0gX0fQ`f!t%^$g9;dHelp^Irs0CBsN8l4y;lCnP
z1KD_OqC3f%4Zs4zVnr6B{o|hpgnBJJTm1ECa*PW0JTw)6itjx<_TpGv7$ZNnBIX{_
z9644y2NG>%wz8Pv@dB$F`^ELeEW@W!CE$YVHRebu3cXp4cu2@6ae-{6CR1DZ)Yfar
zcy(qk*y`<goE<Vo$+3W*I8zSEe=((zTDCaAVSz)huZ)8M_<LxXcTA;34=n;<4=>2Y
zjL7Tdn}c9MrdJbmLmc8pK1JH(&4X!by*-iV*n8(nw2vwZr?@x?F6@jkE8l#wlP`D^
zg%N~2X`tGx8V3#^5XrmuolsL^`zq5aoxTbPxse;6l}4KC?D+NGrUL|PWV3pA?Ct7E
z8q-L!4zT*JY;0UtPq27jVNPo9vng7z_%_h#lpXh?Vg^6wNPicsM`5H<nv*?FoOhkc
zZxN<H4pF%YquW98X^h6b?8|Alqnwaz3;?H91_yhm$J2*v<4Vry3!vkJU2gGrUkXar
z338=9m#L+tpqQl?2=%!aY<n>N1o_I38vcc9fX@xuAan2aL3FvZMWe~y9tuF6Hhj?0
zS1FIT4b+|r2x9N0ErwSo7K@E!e_l9$6tl0_l28ob@eOBLnE-$!JG63H^H9RZc;(`m
zT@9llxO%y5pkC0P*_F5#Q#bQgsE`V`!k*6l(czDQf!<KyHFV%owAj|vjDW1vSaMD{
zUJ&Cg%a1nMmv2s9dLRl_CxdO-zwDiL51cOA6*T|MiG$nTu$8X7&@Al4fyw8O6TjNO
zj&myC#EXq?4KV|rSPqdNzTqsb!r@tRd?o8e1v0>M*Y0!f0pD~GUTdF^|5yC~KS1U0
zgy7!|LXG`rg7p7KAAuwL;#(G-R6~Q*wqAQx&lNLtd92LAfeB{k@}@Syl~y&T3c+K@
zspOt5x>5r!d`wG8b;+Dkf~+&f*h}D0^Oa68DfE=OP_Fo&f%}s?A=H)+0*W%n2AOiX
zK-dmC^lMFRGYn#6gX1wFaf)QeUbe-`Utk>=Khg|oWlGrUbn#VfbBXu*``-xHwWQW4
z)=GJm7AtJXzH2LKmU)x6@+8Ycg&pIo8;Tb}cJULaG$2V^-$@TLTUh`zudQT*J5+|#
zr#^u7&BTWuk|F!dH6hC}I=uipJMB#YO9klvkpNJgc7XeUW-|akG9CErt9CO~kEozD
zoKJu7y9#(Xcj`@^pnfhiwYlt@wp4H<P+QFm3qguw<Sw_x<xf4U6AS}LsR1~)oQ>+l
zpb5_y5}pDk6a-k#c0=5<A}0Lu)257z^$tbfU_-P7tV$U_;2itN%2%-N_pc8}lZy^y
zxW`2y4bBj4z=dM*XBEX<sq04bRw}w$MEos_cguf*Bj03a7nSSe6wpsw+_y*Ct6-~N
zt^=@eNdY*QKc}Td2C5GF05)hEsxkM15>=#|RGD0-sNj_O>nggs#0))*d)`#AWV@(Z
zeIplGr}W@wHr*312g%QvTIgA%C~^8{HHE75xPHwiogG@gA_lL(sS*2Ab17Kf%y~^I
z(cOz-z%qG-c5xrh4Nl}vJ%OkPDsR0h^6eRb>6=a*o%G@McCaZJ>$9|#gC%%cjEa?k
zC@Q)yt*PfO>SL6nypWWA$3+3Ab^o=C89I4W){^xwPt?-`BAZGy_;-94ZpHs<HdE%W
z58|#5fvxja2zAFqgsk-zxUhqS0m8B{9}+e7D*xBJqg&WHO8B-O>n{`^l(-WJFBM%2
zSGISMUqbQ=e2^0<%uQFyMdypP^3SI*=NzRk;K4*ei2iO+VI#m28W(?(IiEKLh4h5#
z?*|=yvEw}cJ`_1A8?xF9MAKbt3`D*^V*k!q4Wfpy?09nKH-OXv4F*cJDodc&A!NLK
z#naS<qg6?5*}Gp`2@J_POth@u{4t1p#?<>=Bipj;osq|57OEWyad2UY5qNzsRy%0u
z*}ZiMP^JS;i6{(`3)LOMB4S7jXC`*}sr&Wj$6_A80%jT+hI|bBOvv}@k6#QXE|sh`
zo+ggU1O$?7_ROkD){CEC7$(I<?9-bq6s85kz6FLrvH*Y*Yo0>;aL6i1Kk+x$IL@@@
z9zRg#ZgoF<>M-DB!8=6}?@SQjS9#W|IqaiC%{iO}=nD>TGn>Gh!oNX#vxG5z;ImbL
zs%R79Z$l5fUkNb}k1c5~|7|@Yq)=2Qi~mcAwzZNC^+oTtN`rr%{l_8N|Be*d<p2ZV
z?_2*bfM}WjI|}*#SumkHolEngfpK8RJg*p}e(D%dEBM$bdf={~#SCMuA@XTcK(#Al
z>YMoh{wHD<q$PVw91`(kil~%Pc;aAh8w-eI=HUeSdE>I$O@HJ(GB<k)8s;O)d(Q0Y
z0%*52#3*~p+|hs&8-sL@;H!HHzy%g7bF(_ai9GgR^V-)jip+Jv!Qn__vz0(dggIFX
zr!tILjkKll+ftkL$ipPfWUH0rzYd6!sJ+(!j<%s3<+`|@W1Y8?*8?`Ts5u*1aLcm^
zD)=43FSZs))(Zr;k>W4*h*FBeV}SAE>+IqXNMrLFJV`qOtgA-E1+ek4ty`=8Q%Lx;
z>Q~(N4h{N0trNY*btZhUPOkNbrNH_X*7gP#`pSsf;ztM{EF|7mG$vlwFVGLjxN}pj
zjIct^>e-RpLg9QBH_q(0GR9v#-v*mZ(pk3^^qFhq{b59yufTELXi1^5o)IJdCELF2
zrPNd0M(uf_ajQAH>@lds6-@)oc!aY%3llnFbfAN~wzfH3*0ce9sQ{_jbD>E1z<vuI
zmaSl=Q={MyqlxAy=)`>UoJHv8CfNKBmo;4|u92>6GcDf&c!YZ6*9X6T*$W&`Xhf7V
zKIX-)5VNwgf7#!Cm!;nh2dWUHEZ=;`liA<qB4dT80m^q`M{B&zf#*%Dg=YP+yvUv3
z*!Z-W$Bb2xcD;A=rC$OK`2NvsOkM_l!P~JuV3TxefqmZ?c+YQjS_6T-alQ@OsfJmz
z6^sN{=KZl8JU>_-{P?0d01~4}FN$Rp3^jK?9nGirA?*A)@&!{>38T%mK>Y=)N<0kN
z*9=JDwC~MLe6u<3WGVGKdt(OiXz^OOe}*36T1`}Ey4t**Ux5OZDJwV-X^ISN8o*uv
zx@pk51-o%oodS#!#}O%B-s@?oGZ70F;^O;nd%J=B59oM~SX}Omp?dzl&QT0){FcyH
zy|+IP(gXw8V6jff*oFGA$@UYE&YvG|$GoPmnpC!M>B25b*o}J689<tYvOHy-s}35I
zqhyG7$Y?__<*;6hq0jqvOcu$*9DAOatj}zQl7CoSHFbenU@(oI)6$S8cx~)WWYAF6
z30du+lL}v!T*jsWc8LuJ#^WkmJ@fXQ<CkntY|#Mg7F)-8Ar&Nh2fsn$`c4Tvk-n0>
zcE)x}Q8!RKq7N1K!=K41;k;JVx%Z=*2(4C1x+jU>3(U^W&w%mG`V*SOmft;(70W@?
z&Dk2Qb}G8yRMyB{SEL*DB~^up|DogD^_)K5LucfeBJsB?ipN^qUt=`<rFy)QLNTu~
z?oQ=aHSXUW5=tq{!M3R7;7zN;IWRaXj_D%+$NZS=oyjm`88CflHcU^T22|S44%uf`
zFG#&(f}*U3tY3pQTm3m_GwF<(7cZY$4kp-C2BzMDqL#}?r7h-f;Zw4}_QjagfqmCm
zeAunA>|frEb>nZr-LihfCx>2Ca6{6GK<#D>+{ts@7y;7;Mz|VDmqq6#twSTa<g6bw
zpG>YI4eChR^0`yu_1X=u^;EY?1YQ<>q%zatn-G2!6;8Mo9*sm;unrYTLK=#*Gh$L~
zj8&rVA*Eku^ItLMAT4(4E+R;4Cu&-|F8N2td&eLr<RiX{8WUSsK#7g~aVHmI5DE!*
zLgF7QoD<$@UCDe<Bo}DiJs<0ZY)b{$uUP5K(-u`Cp`_{O*^iKxQ4a1++O@5GQ1qz(
zvwp;%t$`w~k+CO;Nf5@ha7(1IMR;pwFs^hx!fIs+tZ{us2hw9z^QK`e@>q2evVq(D
zAtJ|b^>x?cL`bKxhUTDl612cdX;_083g#%}HK<#J2O^UoaoJOoko^8(H;uKZ)od9Z
z^R>vDbKGYCHiLvU()Uv=7rDrJq)QIAW1e4r)utT<Hhm+*L}X4ifOC*8=~Ibd4YP1k
z!-IKiBAW+<rvl&@ug6>~nTiZJ4Ua(rwHO~iE25FpsmXU>SqkYyyS&Gg!6|ai$2p~R
zf|*u)61O2IfH|Zdd|d_Wo^m$n)t1}=t4gjTnS@wO%2CsIjciVs>&ROTS!;Ow!qozu
zQJO_^juKZtYttxnq$=eEPczQU(7t0F2W9gL>K^rfgH%|u<0kT_Y)`G~zB;8e!B)th
z`cr-cDCT_8eA@@FJJ|jZqLW-;l4#iaDat8YVU2Ip-4YcPJ!;XQL)<gFB|;Dsc}O9(
zm&R*e&T*vnDjC74K?Rt!ct$-TmB?5vt`FNgSFmhN$LjUSKOcsV-%_=@^?vTR(u1A6
zwT*(hgZ`mrc&72o#}U3qAcYYx7)5Dk<jZzUuIqjn+Mbwec#i%1(k2)nhry_VfyDwG
z!ZT*!j}YA4dVb#2D1`qr@hk-JGWom+@hHWShAojoYWd1vpTijwCv#pH87q@XQSN>I
zG%6&5UZ<5tFRATN#)i1%Jl6P9Y!uo+$~i9F%yU>cwXPxgZzYjO01O!EFutaB_#;?3
zw}-e87i^|wK{Co(WRD|{XJID=_fV6bKc?NE5WMahXUnGMX421QM&(98R-S<gIa9oc
zj2&>*aZ^=q1BX5BbI?Dk#g9S+inZJOQ)<6&8i$sVaoXfot$<Pa!Is>NX~F@yox!ZA
zCyUzSK0J%owu~W0W7caU=EsmMc&%|>fKkK8+0~hj_1|9{*V&k3dWCN^`v=RJaDBsB
z8!mG8i2MLST(%cXS@T<6%#5;5dUQpF#mcJF_~cVlO4)Qv+~SLXd~y~Hk-3~D5%cEg
zRfY0L%N{2*EVUMU;(+qog9hWrM`Plj*C|73Fc<nL))rDxN``m+5ge1bajxeddu?8)
z`{YRqguWWLNpp(9oW5yQpd%R4c)iv*9Eea!9i~|Xt{&|(PAxB>(Jw^b^<gvL<!vaR
z{k9h0#pov+4j`_S^A)5M(T@Ou@TdlAF_;}-bbgmjQ;OZ^`&G{<J`u)<XY{8f<4#F_
zjU$`K?<^d%e$%t<!OgI1K9#-Ewy01a=~_jy?JZw}zz+@MGcAd!4e})iN69q2P*)w;
z`W1P}IvvZAxP{Rw<Oc3h+Woy}kEd?BxaLhg&08r@!q$CsFvHc`QNNh*=A1Yq37*HU
zSVO2lsboTveYObxX2VY@NnB#sM%DNiqAzkRe+oFqwq$kw0T~<&%&YcIgk02|%fF$D
zsCmqFuS_e@xicC9b#I3_r&vE(q_0x1-zs2x(wsOzN+hciY`h-qK;*f;H#0~$nhc4n
zXaSCMt>ONwgXx~9f=!T!`4`NBsa_>|1!U!M{piHW!vni@&k(8gqY8m4d(-cFd|v-z
zCL?uZg$f4B8%&l~{YF6!{XwLxwX5l0-~1WxX+^KgDNWlw7mhg}?7iO97BCHsev2SC
z!^ESi{h~*M8J!pW=fi%f9`D;&`P0T%eJqo1(Pxz!d!86t+;p<LF;l2BL`9l$|D+x_
zHnyW}8gK($zzuHd0+=@C$biBI#*K;kdeffBpy0FVw>f#>QT=Hs@_2?9+^0aGU!u3V
z47~YqFU`idQ!u^XGpG~sHVu(JRot-c_Dv-~m&xK1San+@hhJL!6Q>r8f$=fgWSaxD
z5}8A8lv2#`N(k)BRB?Jy`Y}^i=PjJ!-r?rdhN0{bdgL@~Q}5BXv5c_%?M>xI8r9<m
zgIm1Z@;;^~LEK&}UmLIhv>iYL6X<odYQ%i=0Wz)IX#MFa-H`SE38L3}RR4$&o2Te(
zte(2vo2F0w_fTG)v8e{M`R&L}!aYO_HJjem5muK=siaT_qkL&C^$@qrsX!oXq<L|#
zqF!&pWfL=~FX%;S2J&lnY>}qLdM4543?sYE^F+v8=*XYk*nW%CrfLW_vTj3!S7lk!
z9dljzS-sue^)QCvucPvSkwUlqE&e)J*~2HwDrGddAIyr<C`QJ<9PD}8G(e%H6}PW{
zd?BN=vB>l~C-iN3z_-t-(E!oi(gO<O(y+$3{Ihd=N@89|%d4*+=Rr_W(;eh+H-eFk
zGfk<q{ex=ln!|dJO@G>%<8QcrZ&Jj04mrOJZ)Ba`ypK>MYNnRW_w}4qDjeklZY6eg
z(CVE2(($oLJ(lXY)XJ;jwT8!7jmg~j5H!g=2y;27Ly7YgoMKsHkCL7&?U~@+g2%+V
z&b0U$mvjVUyph~yX@qUy<e|B^;&`LV^I8oW#RHAQq5lj5{<<V`SXC&>|73BhLD+M>
z)ZDw=JR51*d8M$G=l86@Nw21x<KQ(7J?LAm_p38HW(8j1`Lab7mlev_mOb|4UPr45
zXk)yy6Z!PAxv<`K+I~-|Gc`Tk3x)mj$1HUQ2aiqdEh)wrcEOL)cHDD@yWPPBFLqi&
zTATuDO^NGT+m%a6I8NsX*SEN7ThyU7&R4U<oHtzkV+0@?o|HF%sp1js7DQS=PVu>%
z)0__nS;|`9?gjX;1Fex6D#I#dXxJ~r3gGZ0AeoXCZQS)e&mRL=O3L71R+i6KT<OdQ
zw+#C7F!1^VhgGXol6ri?W8rkgoRQPq<fT;k?5RJy;9V&JXY<{X-fxNC@?uzw_Qi0Z
zz_??wLa}b~HsGHsg`@P<gQLN#-^Z1RGfvKUWxtU#5O~W#6BAXwF?_z@YcK7gfB4w(
zs|b}zGR;Evqo-d>+L-a=9S0J*;dns+A;i396cGLFw>rd(vhF^IR}4DXmrN(Tx___R
zsb`+zpQW#ypY?_S-pFwpSN-eyhueMt+^*d3eUvqKq36?BT$W?9pIQy$>Q;zyWOgmY
zL$6D*n9s4>M}FFF)Bi6fterC9#W?O<6ni@l2m`;DeI5(lFis8k{gu8t9T+fn+y@Y|
ze@o00s`ZHWcU1-Gwgyw5R58-*t;mCiED<k;;n5~iEGZP3P~qXCbehJRp9VyXm}hf5
z{fwnOJfpG~iqeeaPg77fzB4Vx67J8IVoKdc&0UV;P8A7Rfo8k%oW;S@Q?Hz`8Jf8a
zcU5;}z%!Y&U<_cuugg11KSYc&R?P#Z-&BN*H2_j_2Em-iV3o)a0URj}9{$qS)cKM$
zs<ptkY%d|@XsdVhnk^RGq_jg}L-J{K-!90?v<)%!W4btb5Z4qT=u9{qBxdzCO@MvO
z1qwwQ-gSq}EE>z~sM!n%+X512gM$~eE8b~Q-VixmrkU2qcIVWfYby_J2!A?^as(QB
z7y!R}VvUG-t|PG)>2X~fuF3Q;_p8eQDcA}rzzb}Is>fWb@-)Hie-3QHIMTfRJwp9&
z>t;ZJt8~ton$I%a{<RHaX*Rs$u~Dd%EH5t+2*SZG$uHAJjEl<30dux1G0Q*QaM~_1
z*En#nWZ1Ds+Hz7`>K-9(0RMSBk)A<FMFC%U{jgjp4ZNo{Vk7+#cap53!WiaE1Vi|z
zZNvFr!3x(_&_*L}X>Sx(<1*Y5>01Uo=)-XNRm$}h0|mApq#MP3Xb?HysS6{2+psRQ
zk{dS^znFF&0ypx^u<^a#HGr`CESPdHkn(vJR+|Lq91ZCF&yL<$RV?DFtuu+NZJ-n>
zz}_cxGWQwMXPWhEH<p{^>Bc`n+3;Lsa3`GC)Yw36aiueIu_-A9@1#tv;dOl-4?m9S
zlV9v-bwv3L=LeWQQ9Sqk{?f?K5d|+@ROk9to``Qbh7ti|4mlziWTphNLau3s9?zk!
zSFL;J9_od}h!A6;GuuY`UZy{DCXs(74S<YD(wL52s1-*C&W*j>DW@|!%k(k;0J$h)
zFL|!uOaCAgc6`%dlGhS;g4DfW6x`Fdnpk1BQt!m8{d0?I$yqxy6HoJE@5hugyv+xS
z9D~=sfPqNJ89$vn#w!6^S;2NKUyyk$BKcm6SzgXQ;`s_-PJP$D0CvyVZ~l73Ra_nC
z&#s=AYt$1VxDPF-y~YmpS{Pdfvm*P0<x{6Ltl{(cP?XM9O^qS6-l(27ae8$m=28Zb
z1H4}2xMjr@Pdlf-9hHDnV+%BVbSHiKd3o0(3UtboJg*57OAeCAkaJI@<#G_()L@9^
z^%|pE(r{j?fih7yYa<rEQuR=-H++I!3DIJ+x|$}gtyFczWnuKlFSZwa9_yg#TI{f}
z8ksGE9G5zB%UG48QriAF%orEpVkDxFc7pV>(G~1HK{}Hiz?v|g3f38>+8k*)a|xdm
zEU_v0q<%K}fwDr0_6ClqZRO&Xw`g9+elGu)>|r2@2+gubyrfOs=`@t~1MGT2n88@E
zBhPUFR&rYb2#E%1_bV)s5Q__jYWNf>%}y2PCv`eXRJQRMXo)7hA(y&j^kuwi!|ztd
zLylWEUHX=Io!qsD|KQ*LR>biMuWom09yQKs)G8b`Cz35g`-pS?kY{diAU!MqN5~0)
zR_lmKs71bR>iy8jYra3W5zCh)#HGhkzt64W=2gVTfmnABp`uy@m~z*Ds+72=q@<Xu
zdtU?28%$ic(S1W@D?G#cDyla$pPkgwSqC5UQLtFKe`c<IQDmO}xYya0bAs30Q{(A2
z#`+si!Q$&*ED6L?Wz4u&Olp3-Wp-5V)t7`EKd3DVEm44^k4`HnW0lC5-pJS7J#!nk
z;6uPy6*`g2K$<lH$TqVPJ8-C1^$j3anNTd1J+p5tqTl-mqjX)3Gp9Cftf4{XENhkq
zaAp?H{BP}D`9IX{*B>NPN=e9`3?fUCof&kCA(FMSOl~53maz}n(oKv;c4G}uvK!kV
z#bn>Nv5c~eb?nO+%rkX=e|Y|Y=lSJ%J->W@I<M<>o%cD{xz0JC_jO&PxvN&RV)T;{
zTIMtdWL_8$<n~~D#AJvq_S>B7^6L@p1Tg=D3Xk7u`2X2v!T)&c<7}l&M-qtm@KByh
zjmC**sR1cfe)UOKZo-d?lx=?=ma!4<z!<Gl(kCxIzp?(#&Z`)A0IA|-2FQKF(|XfK
zyV!1#A2pylrH&QwXh>`09tNaj|Id-LN>|##nT7|hE(xR++)ViEs-7!2O^p2H_@aru
zz~qKp?R4TVYp8(^tymxm*U5DhfXqSUzflSzN;gho6E6@H!IzSFc61Tw!22jsc<06o
z*?5i;UAg<N&~4FbO1fX{bo_jk32wAdh(I&8#E=rSfGCOFus!LMDmyLL8zu}E?F{|5
z83^7RpE0OjM-=@>IeR1Fa{HP<gYZeTci{7%GOpzd?V!>JBYkf(J%acp6c!T?l`+a4
z@S=T+q$F;zAj)AsMXAas#JIFPa1>AOp~KT<Yc#Q*d3NPk1Gz^^oyX{%rN0g8)Azi0
z#51(73s2fD(z-#P<mrMsq0vNZxOBSg+doexSQ=%Yq$dPb7@xx4?EtQwJ8KJQpMZFu
z@@Ls$XuC+E*{KV1a$IbL%R1_Sbu61^g<Da!0ms<uBfQglVsc>AyhjnL+t~=)2b-)r
z`2nIzj!Y!qK5vFkDHHDqR9=u4t!rv~jK7;W-|W*VU{I0Q?_;ofj8I1vvj1ix-u}7*
z8Bnb-3l6@tdRk?CaI2^{jr9CHONxw`@yoY(`Gl>EfNV)7J5m)l(U-K^_PZ^F*+Tu~
z!4bbIwoknlwcqy$aOo<qTF&kUvmi4MnhRjI((6@w;k2J>0DxJ_5!3rXQKegz(3E`M
z#}*q)yP2<MQk=eYw&4k4m7G0Fl|Ix`QGat(myy+lwL#E^!FIfq2TMhd5RITX5{Z)+
zCPPtUq7#Tvc~Zi>KD1ZgQycXuA?S9;y@u~ThOU#PEA^rsJ5GDZYvqV#bIc{2z5sKK
za5$4v4Jsd58d;;1DDFrO1)2MdLZmygL?#?sTuJ!45Q{|-QzGo9w;rlbVsPICr7cWO
zH=yts_rQ-wOmIuqYTq%B32Gu8)CYSDBL%%wU;XWprzdKGFCb8oxkcd^?3u)TU@9U*
z%AjHg;41lHeokqrT*VfFN)}NP8rea36))>Fp`F~OwF@!KQl!NB10$=CTdxfsa26_$
zX2p}5Ib59}$rRP4gW3{{^m$hF3DpD@V`O#B&9-=JpP@)92{Uecd&vtkxoSC`jk8;x
zE9ZoDuFX{rM{;T}$az?y5>Us+s7cfmYQ^7E*B+PQF!Hxr1<uD|CeT$6eqmG&{VLeB
z1j}@{pT=M>3mb-Huc7Cct0(o)jE9Q`=$<0*?rz#El26VD^0@|6=&6(!B)$)3-8|2~
z-`pby1!KPN%iEKfR;Yu*P397;lV7U?u-#aMWy$H^v}VeFJ`4-WPFLm7<+Xr@a0*(c
zi_?`4%Rl6Omfqmw*w7T@7V2|guzkWmdh*Q#`ngTQn>X0zU5!uIlm{_UZ7sw|IKqC^
z-x9gn?fawgF^h%0N{rZ^pFDi+3v{ndTN>Bp+)%Z%$d5m?K_Q~bvbqrbyMa@q2*-B;
zJIMltx-U+t`y}vqTm^Xc@L)!d{u~{>8PL@K-MjmS#c!X#FuCCB{kie*PSacOvZof=
z#yO_=ccJ)l!>eUAiFl(nx=_af{NSJ&i2Gb#jv2EqtJC>>Mw!2Ehh35S`qImt>gO{}
zbo1#WWmX3EcJv&`R(fS6>`XaiO;a+wJcB7~8;+m)it1x7*T{%8a3kSu#Fez@`nk>2
zL$3%x$P*qUD}BAFHg&&~dtFoh78xH##Iva5s4#yR^Ewm-g5qQ68Tw;7?4O}w+bYHp
zT&i9XPi*@20RenA1?Fh!`BFEJEQQf=f^EbTy~43ILSOP)Ll<DT44GdfLboK}uE}Z3
zyuy_2o}+x2;kL2jl@)%boCVZPBX(2F_m^O&waxu`XarCrXL)|>+b&DHrr)K2J4MZr
zr(-sCH^=(q;c)OIcSB4O=bHzZ3cxOZMCu@Xk}UJnEnNy(84K`6d+>bXd6g;jHs<8%
zidV4#puR+AVkFSf>QkOP!RV3iaMnLjl{=sJbJwAonG3rfXuz(<qZ2vCJ`CThOc0L)
z!7)eex1J5}f&kpDt>ES4vfP&5;w9ff3&RGWdf}u{i&%t(ttgNVnb>_PQ?-8^EC4V#
zlO#f!Vm@TA$R6XJNG9>MC`Dv<f&>-!!z`(tVHQ?rv!f6a`f}&gF)|$W*DMSn0JPiW
ztD#(kl!%1W!A4yG);`)ScDdstTA8dYniA42+rky$eQnFXgK5IrV$gcP19G<U-DSU9
z=WoNVjUb>}A?Dg}f(61apA?5LI&UW@^L9sRX&ifFY=!{90rXJ&A_+Ax-P&tp9^v(%
zyH0Z+n&L=yXov`EKTh=RH^s|}Z|7W&l$RW)3czPG^b}zfHj<A{-<;<xyyfIkgL&03
z(PSi0Gu^i0)4^c<FZFd+cDOb<RZ*kH?7ni6fpB+b*7Xkyeq1{+o@Fyp03XwjWDZqA
zn=<p3)^HFv_iq5jO|QNR_SkjT(&*idnFzOo{b)F^PS7SNXK1x7(vDp}#S_&cq+e?p
zJR?d+YbOs+$B<z!V&d{v0lT+p&Qd_Pq=EbGENP6{n7tRy(Ph~VPY>7-B(^kZI}Vsh
zT8`>t8M<Rf6JOaj&Z2D`HWlp<w5X;~90J#x_Oo|F^LA?eQYY}PFBYvI1+ggmAiJx5
zbw}tD6=`S$NAC~|L{cG5FcZVV_5UCtU!D^0RcGE`M$iI&nqkB9CRkyUNB2rTVv94@
z^Nt@_@~Kf7S|k6tVcdeB3SK>vtpI|%OZhj&0K7AfKCWoyfM{x;m^=amguUcB=2an@
zTXKv*7_)K=Xip48{C*=q_UPaXeZMK}WpDJ#7dOW&X;OLfxr$vOua-9UlLk;Rr8SJ$
zz@mO>=W&`7_bYyH32&L2>?eSuX?BY0^<tHK<X=x+nX*fxSDFiRN&FELaaj7h<hfw|
zSg<;Wp&0X$I#96?ep+qcptaw%=RpRzF2E6BCccKP&kK;E7oU>-MgtzHD@hx0ZQ^MD
zj>rf<o4p!0DPqlE9isVl$BHp~@?V`<v$B@C_h|O^v)Vm|vb(COjP(@*cEPmU<r@_0
zfwXYI<R7T#s*5`6cRy8vqS`xnf5x!4r~omBg}+igLkJ#_%<J78s|#@-r<@1Ai%=RH
za64Wt3b{BfX6ko0WGB{0+}kFHPcPlp=Bh1o>ofya_3Tvq`$C|sX_O!f$RswLYxds2
z9NVpxKKfPX{khhU$44mTLahZo-Id{Ymq_jc*(HrqeGWXf3I>H9j#?Jj51fJ_IvWRR
z!$J`m(}ui3v9T+1X-;?C(#NkQPG-Pi`JZHz)|!ojY*wh;r=&6n8zJpDgR$xH!04r$
z5A}`bO&^iFE(W_B5|Euy7d@I$A9xx1Q6D0I$v#r*rSsR=_Tm#<>d#jJ!hnJPcf_T|
zN=8WFxw+ulToN+RbEKrzb#gi`zvJ<>w(%qDFFki)Z#tuD$_V0m9jBS;K#eqU*gu^(
zlVL2AUx7W$jV@ESQ{bjwaF~n7umJ0J*}k*Lf!`LuwXGhU6&^P&Y*%!r<}y9(WIUnl
zBVWXm<)Mxm$%)`Au9JC^*r5U^nW0CG_igptU7f;SS`ryx^!$Gl=R!cmu*BwCNvpUH
z8!Gx2VD~`JnVo0R;n$4ah8V`!U1HiK&p_Pj<Gw*_WS7l9Nb;;8$sG0G7Vs2RWvF_7
zD(QWnUNFC|&n?n*jO&=G=wtJ}=M^!C-unwdmv5D>pURat@aZL-#{TW%`uPiei*Mfc
z?WWlcHrjc-9bf*~PE%QK0jK_CuGxwYjVQD`4<xCqg)d@q&S9A_IyJ|@>?}97N9Ofj
zYxhsv_&i{i8ss*gZx`B&1%AbNTU>Q5DpsGrX`NRtGRL9mHb(_z48?@XMKmsw=dz-M
z3vV)cbJp`Fd^=TX^OaJa-t>(kr`Ae{6D(|-Z&R_&$!ehr!V48`5)D_f5^AK6bWq&4
z0g}+CI)jswNTD;W!IJ*R7pAAuBqZ(N;rSkhwz*Yi3iV5Kajy}FCMICn1Ir9iwXCeB
zq9?P>xj3xnH<k*d@_n+x#@Ls^zF;{n427OhkS;S^V}*FoPS<Zk_-ClPJ|ulE*E8YC
zkn0<1+SNeYO~O#tKOQr$X9s%|%%i9vMwMG%)H5|CQdt9iW|-m6pZ5er{k2elNjVV8
zWcM@aA^hXOtbi7K`kZZIo$8P<-QILTQBABgpE}H19unQ@P~Fe}mO$=~8Im^1rz{DB
zdrPl`z1Od)G0J=!e!xHxU0@1TV6(<`0cA5qJK`u$gq8@k$PSTTkxb_AN4c?w8Dmy}
z&G;2@^&}>0V2AvJE%c@Is780F(RV-<$Wf4dZo=g|^AES_)9)|pE3dct3tsy8h$?Ia
z{|l`WK_=^UHHwHUSlNBj8KFKb?2x*tmQ2R1Ci`odG=0i4Fs;2wu1ksU1k7F>*b#7Z
zglyfgb$;P6fb&qzDtEQhSYlX)TvyglYM)=|epo6KSAU$DB(-vQck{-q08nGnu~loz
zPk!rkc+4ydhj<{rMKeQ?9rok?)d?a+x@;%6P|MoFDpG6~4JDVR`8q%I=%<FVuE9|6
z{}}egHteOm47CP+adGS8u^u~|ldgHf1~!aUFjZ~NxE**!b9SW(e89%kxH7-|mXi-*
zp*1a5(YOh~jIJ58VNgBZ4>-Xm-{y&IEw7XIy`(o|m$oie$;$dp^*UH*EY2ess=VOa
zURV590qfrR%d6P}#&(sF3WjKh!>VXv!^#c~=X(1N&tzQ8t+;fh)<__*6iEwK0_s@6
z?tmTc7<mGBy}pH3g^$U_gWxT{@y@A>A;+*<wZ7oYvv^SuDj>38+QKTlCjyMAT({V6
z^tosbU`kX~IA^J~Pwj9{h1_c@((mX?G@%N}vFMyK+9cVfLT*UZo-JdnEi9eclV`9@
zdvDIV-B*Yy4@p_{&+qD%fn4*wYqK>{9|JJ};1aCv>cmdc+-Ku&r7UtKDgDsTr0I2l
zQhXthcuYo+DvLx)!-Km~vADS<HMZmYmPk4LT5jJQSS0;1PhyA<%^Dn8JI*(={4<*l
z#<r4vgB5ZxQL6Ddy7R>PdLpR$U$s(&&U6nRCkkv>SzYX5^>ivcy{H%}@uvLE<*N<n
z4yS+m=<|*9)C%|sJyDJm)3hGlqY)uapS2*SIjX4^yP5iRp)Mku))6hOj&^hjP|cRj
zN;_E;-Hg^rWMx`(xI$K3r}@wqrdKc|fAa;uYqh+JNiPRPts-1+_y=9x<ZmTxJGa=>
z$>X+y+DVLHE;VarODNE<K(<8`DEk0OKf~OZmyM>pf;{Xrj$lqo7HwuDeu8P%zIw?b
zSW0%dX3etn2L`&Vhn*DqRbXB9(7~qItbYqrsQai(WegZeNpblV^N2kDZZ8)fFdKr}
z{t6|J*wM~KUjN`y$L@!S-52@cIT8~6^WoVPK(LY+L3__7<ahpaR8-2=^u3!7BCD>_
z%vQbgnt!i`R{GnUNXFsGL~e=|*9gQ`htdcxhOu9J%r+v~fc2q%L}F_s?PU9jK!0J}
z`s1`WIO~BM`mV@GsdkQCe%Kaxi~(bK;+rWXaWtVfl5$qD?!)?e$Nq%nN%*<Sm6ct0
zmfARp+moxcs~M|zR=p^e3hEZ7GX^QNdm0bt)(_6(c^1f@Wqmjqs4}xMn!wx==YE5F
zcFE3(HV=7!zFieJq}YV^fRBbrQ)48!aHUbSN=~F9K)S^F$?(hwEd@MD^#kqAnAgw=
z#PT=iW|t7P`#2G&#I~$*l^^X7NA|y>_E}=^loM`BG6?>Is~-g_D#C@DzmvRTI<L{1
ze5mqzPzY{wOHgNPBs#!>K4hZx)a^$1$3BbqE@Z88c{ZstQ`ULwJ3vR7#k0%LE^;pL
z!=`>VeGxsg1csT)Y@ZQ~@*F@5&iR3DC(mJqVDisYWFxNf6Aj%9)_l)#ibW-YvwtsF
zU%HJik2XwJur>c#teKz;Az)=~;r;Ps@q;TImrfrcl`YOKJO64X*MtQWf*`&Cg3~w>
zTCrtZorvxm(^}}0n`#CXJwKZ*3^jr@-PeJ8@(U`Kg<qbk58Y&>Fh+3o<Even%-EWK
zgrvpCJ!7)5>AS?b{Ux|!-Zwps(HaU2K*YUd%@bEY1PUV8w<3Z@coI%sg}Xh~lFMfC
z`Eq!td%T|s=Y#U+PSkW$5JwWT=!mMT2Y`pHz28J;?KR`3Orcl^L#C9YJ1r{B$hJ^I
z*(0t(6j<q>S4J?~V(0_}-{v9rUN|JhoBKgDgNduJsKUIfcLmFQxHe;2ezwG3kS?3(
zqk%z-65_B2*JPIo{*pg$v{i<N<ji>ZP7pl=65#ntzc|?j7vEO8vw50k(FLV$D&5xi
zlU9n6P{ZX6Y^Zkj4yxFmCcXUWFy3nrJOkCS`kmxLQB_?kC~^@q=v?df(w>Ne*F4%&
z$u-}LT#^<uRc;2j^~6&cH(Os&MPr{)e~W{l=-M!erL7Fs1Fb<nf7uDjHw__DSGHL^
zpWY+>jUlm12A96cbu<aIHqbUwn)o37mTJYTt}*Q?PUxINv<t^>Bu)N02kRhn{)Vpu
z=kP?ls_)KaO5#(4QH-&6*`F1M`e3GiIjoNWVh;Gs|2zs5Tap0WV4V!b(k6F9?^UzV
z?#?sKUMJv&7lqZc7oxLojX@7|za%X%L^Qd6pXmEDQ%6ppJ5Mf)%*bB}lS92%(0DxW
zY3ZOLopXpNaZ!1GcUq!4qfBRk_vP9f%8frz+>Or#>|S{J{icdAl;I<-^t8Vl!>s#f
ztaW)>9>y`NYw2e=_{E)Z^HWQ8%XGxE9hTOx;4ubwp)<vQl>H<7XRLq0{r~U(0s<?o
aR0hoFcY!CCJodla&QL9bzrJWb3;QpWGx!w%

diff --git a/articles/AMR_files/figure-html/mic_plots-2.png b/articles/AMR_files/figure-html/mic_plots-2.png
index bc95205fc235ba4dbaf6a58084dca38ef292abb0..b1759b173e940763cdf63d476ed265b18dbc4720 100644
GIT binary patch
literal 37611
zcmeFa2UwI_vM$`1*ha(vNL1V=L=Yusm0$o70YRdIN)ix|OapEM85Bfv5CqAH<kW2-
zY(SEdQ%lY?xuKhezgDyN%$#%X+-K(Ad&2$i@nN<C-CwV7eYL9Uee10n?;F<@ckf``
zfx%#QU%4!I6NA|rhrw+A^vgE*$=7GS$?(@U!)uCi@HhNfxm4l~e{Fws`L-<v!{Lek
zM;ACWdmn>2jJYCrLESNGvd8Ihw_TacTHC#FrJ(7!T-hO$qAv$)xDVdoy_#_*x$zpq
zt%}_jWVdL_W$a*=Kh>7@Mc%s4uqX8Nfm0WY<b`$eco%orT=2O}r=vZe__!e^oWMo3
zyKEA%-J&=AilbzGwODMm>oC=+&gG5f+_w|N>H|BTU!<+>6DEzvfsXdiT`HSQX#WV>
zE4vx}<K;69EBf2x*Da0cZ_KYpJkUL24sWLuM1Nzh{1Qid@8ye_-ROJSegD8ElHRGf
z_V0gH_uyz`UFB2(L4Vrj#WCAm@b*uS)YYapCA;eB=`}SqF)}hfzxeiB%Py5+7Wkz{
z-|vV1?J|z(ISqxVo;(nL<7D~5*pFJhJ!l<g<^CrN(@YLOZ>%63>Hkoe&3&cTfSWr$
zC+9p9pDv4Wl(oWS0l`hxz<?|GxU!FJgOmuT+j5_NmD56#;=q@k{C=m@lFGdpj~X^e
zk^I~#1@69)mc^&$R$TFBE9EDoRnv!-x~zz87Pm5W4;fH8+YDRNZ)cNwT@>M#CW}U5
zteaI8E8uUooy>fG#<AFZ_u$!0Wt1KwUn_-5bYI`vW8ax;TG7R)TlnZbWoUjy3+L*j
z>)7|)dNRAJa<nNSAv;@OkM#0SQR>2vfuu-FpVrpap0d+Y@IVbMIvxsZYinQaeK66Q
zA!Yf)AW)iYqH9xqOu%;d8;gjgZgX2()oFv38qxW0=LcY+f%WzEZvMdyBSvH26Rp=)
zMyga5ISv|C1)Py)-XpU*<47tYa1bcHB*O}ygRseDJubAnmo6n8UQD_CBT3m;f>?T<
zo14?|=M!}O(xtpCe~q+TQwzee)Jfxk@7MVKm5%S{;NY-sy=`2<;J*AddfFXrfki<N
z(@_P#(VRDdZVL&sbyjY088=E7VQJ;Uvoy`D0Ee;(z3j(h-=(X%GZPYynwgoA^Q_#d
zvw`k@Mgd~Sxw-Y%=0d9m0%dx$x`c#;D)e2Z4l*)oXlP{HO|<?#WFxgSHa_lSKpoe#
z{=AjBe7q%%!kz_t=e9N<TXp3`wEA2-HGJUW+r1hZ4b-~iq9U>Ji3tk31KK%G!cBhC
z`&c*e>taK%wela{j-^cHG)QNgG{uV!zPw~#+(1}gBZUcDXiF|Ot6>B3da~RW(;Q*-
zy;)dv9#~kT=%lu+E*0SYH}4URYnTz^?*CZJ=L-**($vy&(8{H&i@YoLDEBm&g5!h^
zLHv$guU(s7HW5czl5o8~l$;RYqne-~U!H$Hn+v<p<kmACieJ5NP09Unz3Yf;fLGxf
zd19bhP36oXX~l;--Hq!a4$3TlW+2~oa-!{#_x=-a-~;lhZ~GcAG5AYN7bdh#v}Ncp
zd$QYH#(y<NkMYF`x`n+nDJoc=h{(u+Frxr(7h;(R{QGEZgQB^)`RW=8ABZLO6ciFY
zWmfw{C(?8JTXn4bD=RCPn*!YqG)T@XJ+`xx*VNQp9pGB`BH>7771U7P+xtk}whfkT
zg*HPqqSJ)~&aT8Cv1^Yd=YBuh^!@w36<6uv;$p+vH)m!G@YFMI<R7t>k-ZB^J<^s|
zscC5gtMd)~`ncf;^Q*l{aHvb2=6ymNz|N#e?k#Eh2W6;ZDg)jpV!Xice;=7|>gYJs
zAWeD5t6wU*M`H3rKaOklC%sWmv0cd{m2C~F8fk{(%_$Z4Z&wCzu+5J&1PYnA${8CQ
z56-W&7u%Wh-v0Qm>UP8V2j52RPK7wl56j!y+RDlKv5TE^Tgr*8fcM9jl}W(?hc}p*
zntCI%u*6fBmY1t!ICt;f{nXZ&_QVC>{7k;l6Z+GuoykidT+*xRd-m<5bt-<HnIAiL
z&BfK~%d_pX@GL6YvyB|u@o;Bw6D%SGSNOOnr{Qqx#THFs?wgynm*6NnE~Xi9kO=PX
zau0X%hw>Y6W;s@zCOcWt_o}H#9i&VZ7|6-p%jszUc>h{+YwPsmb^PiOzxC)_PX)R1
zYX;=+S|#=tS+>|J4hK0qJ3Drl$?P(Hyq<JyAPK)torR|%$jyygNaU}0&a7YfgIZg(
zMe*$ouN1ZSm0W8xo?c##JrnxO1wHm%*Q@GB=4S&ON{?oiOc#53A-Ch8WllMm<tU%7
zTXc>)ziw)<I+&8a9}LQ)uli8qrBBPV0|Rrl`~=gVD?y>9zh0M^%H<@$yC<A?gUO!@
zS8@fX%cqY66Lv_$(Q`V+u^=Nhtp!__E1kU0e>x_mtG8F*=JrdX8>ysXssSE_MD&E|
zkE9B=-kBHDUq`$w%rCo-NDxlxufZYfA$+Ir@i}<fr_a{H!lK)z_6&Ag7r261u*$+h
zx$4!Gd57gs6?Ks!%mwR125ZMIv>^N6wRO|zk)LX@Wc6x>@d;d1MVNq5e_j^WB9`L7
zuoWGa=;-JYT|%Jy>OlX}xA5~WyfUiSV=3JuK3xXu<Ti(5105Zm!1JF}q!yd$#MU4z
z_*_`+%rj5O$gq&D<S>z;bTs&MG3!3MU`?IGA?JJBI9j80bwp;psA>`1=V?pYO|0}m
zQUL2wLF@VbXdrKq3h9_=()DQVxm`UYv3;FQP1)uZ@(Wr#gOfQ8y+hX9I>C0dy7hBS
zRMQRcxHhU~$yEL*_&YBz<Q$he&9j1x3i9$Q`YU_~f@4?jAq&WE(|3`FFa&nPX%Hy0
z<~S={mJy=69a(e#l1}g38#luAXhI;Lr3{=@smuvn_wOfUR;t4UO>cgS6ybjZW*8}K
z(dlnSGx)1p8ZXJZ*I%B(3D3m1OoxAr;)I+ca7bj2=ueLe685Lya7al%vi|(r*0O05
zaea+gF;!$cvLyBZERBdIx(JvxCyTBv(`+R$@mi>YnwnZ}L4hLt3Cx=A?CNqD=zV8P
zEF&=$yjPB7^7r>|YHw#*TwFxa8LYY@Yr2o@(mNm1KNaITSIud}={oZ{+i|wv<l#d<
zFAJ`lD@6@hKK=QKwsMG@Mw9K?)=M46ReBCRc{;`fl;GUxK=+W66Yk)!-ZQ(Jcu*mL
zUCg9vIxx_6@My)<HU6^4o)eJ~5k3=_z{ioueeOMa5Q=BtGGLPpzYa3<#5OQDtOc|)
z1&N4=D1zh2&Cgc=#|d}Aj4$?_SaHQUmW*W4aWD!PRr+ZaSnBDzOy$kC7U1%#xUL94
zY~QPCfVE?H=(f(T;&dtBEz<pRDPR*?7}F*b``Y=h)6-nin%e_J`@Q--1v25daY8KM
zY+IiT#q(OmVwXBpH8l%<WTP`sp^2Ld=^ru*loDn-qkRBOQvG4a$7A4Wxxt&iy>Ze_
zIY!dSgV?lF&1`C@**!fuEzMW$y4W$x;<2RB(NQl5+ZA;p1a_|FPxSG{#p2xD@T?D<
zh;_AbosT3aY41cP`cwr|XIXvkoRswAnH4&#A~<9f#?tfQ{X^!NMM;HvCl46*mO53`
zjzrmBk2Ru>D$4L!#yXFuShwjpRBS&HwRiuC>wXYFl2cPVk2GS*WA7bEe3R<#((PFV
z=YvaX@13)5yg+}DKT9h+UeTkMkX6f9l78<D3hUtH%aM_W$+)}a=H`~S;g<6H7rEdw
zj0P(Gk5wU~yfeC2Hm559qFw(I9Qs<VE5zcg&D7ZSmDq~Ba4xV9<QV1T;1o$q{np-#
zynmsT!*q9$F@sh6ha;^SIt6L>iHC!$<@iER>rXdy44=N0ezK?`*Yt-^R}az1EH5vQ
zmM1YPva7E4LU0r@siKEZJ}gBiSTY4xZD36i!LKMwuFibDpZ-7*BB}$s)8H${YzzN^
zDRXIe$f(Xt|A>_>qI48>Jj@@o&MKX&(K}V3f&#O33B^-pT8VVN)M0ot*Rrg&%XD{9
zQ)_F!dBs%xG$|z|#q>rXO0;FQ!9cp+dUAFim2>?O*?Ry|CO#v1i3kY?OKZx6!MbxU
zoc<O~1HIK=3BpQ!kkMm&U{C1WXq}ZirK@nc-`C;ocU;+GhSeEMgGZmYN=eJktPD5A
za-n!sEx>>R={X)0;G-UYzj7>ey4|uRRbzHc#l5(S)Y;kDztdp(BEEjj5+WjUg%!2@
zE*-UGJ6%{C=EXMHi!;I1<-cv+=@Z(PsmF$X*&lR`54@IReBmIZHIL@1PsI97WR~z<
z7aw~!IyuSyniYJ~a&l_t(qjYgYv6IRSbDF=N=q%Z8wCa#f%|=5Nab3e<(jQlaXYsL
ziJ%<BGcT`zfB?tASBm|9=j*)=oqzCaA#+!2Oz-q(dM>}QAMdMBSRf@r28r@`$F`E*
zgDA<zSAGLi8}Jul8W}+;&G!Ar%QZCh4ys7b%AwOsvaqrWY)R9s@)zl4Id5F=2l-T`
zirZW;N9AgbCT^e0bdfQv=!9k2V(T)v^V_cJJD*>C*qM83x^%u?6du0O*B84P8DG^7
z8P>c$RU#~aHb(tjgbnkjnGfr0l&baBhV}B<DlRTYMo5VGibvxMN8^2KbUTELbos8!
zP#5~cYYkTWnSI;z;Q3Hl2hLbIT~msY6hmIl%d2p8u9g;L$JWqU14I6m(|Po5Pjcyd
zDv2P|3pqQ}B@}c|?~$DO1WET$h-Rgt47n+2;Chs(z?(BVOj+*Ki3Hdf<cCJ09rU9d
zdYwu%h<R=LY!E3d@JnSVWT#srd;8Qlvdd)KH@M{iSaaZ42AR_C#D#H4^LqMRz>KRr
zPc9{b(|~YIsDOOn+BBsl%S|Y=csw;ZBLn-{?#&#a8<__UF+C&F5E!fhK9!HXkL@K|
z2SQ$`RpxB>EkY=bP_XioUWNtml2stFrKP1}ok~~_Nw+Qm|5=_<(A#O`#E3TX1--HC
z`8XN^h+SKFzYGS?iYK>Z8CC}!%XVFvLz$f)s}Rp|<%k2GTyBn~D|J=@BDh(9Rkibu
zDxWVgNC{R3a&eirWu9>(!UJ=;aAX$JvVk=UF|f<B<c!<QQ?BywiPwzkB7_9&C)-7b
z!;Jd<tz3;t=c9>!U@Y$ncQP`n>gcfTWa9OrOqcLy77a%f4g^RT4%LLv0vqJL{T5a(
zI><^`+1QK#SMZxmhL#Q<v;MkUq&z~%yuS^<X16-+fcGsS;v{Zm>2rW{nax@!X9?V|
zc@4RU!nZJiqYD14Efb>j9FK4CYJE5y1Bs#*oDP)ZWP^W)By^1tLZbE0C-jx(064=S
zy^WJ$K%2xO8GaG`z5{K?FuP>l{6s(fpJR8PFR(WU!j&Er;Pv5Hhf*?W?^&1m*La^>
z?c{+SLwqP$LE2aKKLF;mH~ug21Am8Nh)?R?IukWKJPgt5q{E?OEzQl~rG34;mX?-G
ztgQHL<wH(H)*)Q)g|Gqm6khnz`^-!}GjsE!-0_KtC>qx2st%=OWd)9pk9&C`JArKb
zoTj=HD=TY!YAO$;Fb{i*^SX!BqX4)<&cVot0Na@}XYOYv&#7x@$X~j2Nlxyuy*&!_
z;ipq2$!@0b+#nD$Gm_WpM=6P8v@d@a0dDREYIGCn+K)fK*nF^f91a&|S9W95=#K9#
zEkeI;8cj`2?e~3m<K!;y2znhzU#$b?R!YUjQKn`cATA*xG^H;nD7X-o;3{n}?8Bo1
zna?b<YE}-%{d0$iZO59Mn-?6T!xK2*W9{5I$0%hJy(G_?hSI0X1b$@#F%oAGfiuu1
z;kX!wlbL&B74^-_U_r1HcM6xj{qtj@v<LN15jcP60sS)^(EnYRNIpfQ$kcI=4xMA-
z)w+>C(AF$TnlJ|-*)c9U_Ac0Pok}7u76X_$jPmIVnWJJ|Bgr>T%0c4TG48ldA#gzo
zuGkU4AbXz?v1>POg8fL&mhT_pOPB=A?cm%4PWhWE3O}JGTQEFc|3#mS&=8-;$e>LT
zi-4hAQLW<zba23^yYH*#G9FdfRa9kn_TIVh{w2t{A^#d0msUi4lF_jca|-+YbKwxo
zrD}AQ@291t2q+$FZfTJhjy&UV=-`I6>XBlE0T!IQb+hQjMFOea;=Ik^@6k)mkTPD#
zOgiU(=zLPY`*b7eS{fW-T|Uj}b3EEcMhR_L{sroHc!xU<7%e?q2B!EobVLD46xE(g
zfxraE9caFU1f$E%lI`FR67utf0lPL$mtOWv10GOFsH^ukNNgZk4lRnW39cQAPc`e3
zNkthwI8o$Ca5!neuHY_!1~}y)b};4b+auiEzzo1^-V{aDs6m#xS^*5kX}At@Gk-by
zY&)>{va+)7W9rYqS4FRN_et4A=I7`8tad1&L;A;;#mNQgNY`4}^mhyT4}0v@jEzTL
zns~r@mtwsM-~u@t2%Qx#0Pzd|%Gvh$7JQ)VyqSeXORfvVBAthq!A(Wj^V<~Z&hl6F
z`r5y)zURE9ao9u=5o88(5)u-1s%WEt=~|@jD<)B46P_sWqn-BrW&x|VC+nYlM@ULc
zOys6Kr+E#l0Oz(cVnIQHi`69jr1QoorJ+D#eaLwCe+X6lf0vv1_odDMGxx%*Mi2N2
zW1uey11j!hwT^(;+97Uk1T9@$T)+hajyGyfR=qDF4dG-6$^;(Ifcf4JY!k6oPoaYY
zoFA$TcrYKi_J|g6{+((I|CyWgj~7Y6hu(>M{P=Nsl$b3!k2u+pGf+mvTH&koiyxc1
z4zLkMZDRrX@**n5EpR*9u(RE}BQfIK@o8yk29&b7kSx~=157)f2N^N%c+M<%9KGbz
zBaw%jM&TG2&Q`KHIAeiNafL+I4-y$jf|vO?2BK{1z4u=iy{ma`3wnd2s#8-s9Tu>;
zyc`XSbUd^V1#Irzq{K|SprwoIuc^;39?8V7k-dH~K1-_#?FwR+$CN=uz{Jur0MOY{
zC?w!*LTSI7i9h?W)nmH$%k!N_x%*$MUH4Wxo>M!3$XUyxp*LE2W*UBn&L7_+J{lJ$
zjdf-REK?%HNBgWtf|Hv^A-h}t%C)wakr4=yNlX7l2p0znfP#rnNufcjFi9sHUDw$P
zs<w_ZDy;z7ub(BczB+F-{4KmhW8lsxXMS5-n<Bt>BcYHK3OmZfApfq_QoawNC_%xW
z2SQOArFb3~nNxp>N~m}do;DWUwl18Y7?{~!Wm57`SZ7w~&C@^9Coxtrn|p^&r|4dK
zOQeKbM7}zzIGt<YNVQ5JGU)XXvTTcVA(n+?N7OW91K{%#-oAb7*P?zLi_d-ANQ%u$
zOEV9(l7Krzxjr{HDz50ceHRfFEO#1-W>!m5@&<?_cRbh|cAyZd0R1`y@-4~bpHF8O
zv)rYI>!WpS(*4;)Dj`R3F3T({!~TwKyn8&@ddXq?aB}Y3rBZ%+TXpTk`d4rV5Ec$n
zSS&`U#e%l#5n?<U-w8W;F2L_bmV_a-e;@cttW$x&#raLu6yAH)oj-ZYrTYO}8nMfW
zrStL%lX6+&3q&A<1J?$Wn-y535#%?4sD6V`2O?4YxMXk**+7^T{Wx(8(pHFzdYQIo
z?cASo@CY&FPubpca~=^xA8&E;n`&aKP&=g774;Q2963WG#duI=I&#DG(%k@_yv)z7
znkwfNyGALO{`wpKd@<Hmx&^4)AP@${!hxSp8Lr#-A<895DSTgTD1V?Lr_;N87-}S8
z^}+i#i{4T(;BQ14kL@XhJiotVu&%DIsk>VjCC!ku&k`QxKkV2COGTNyAB#ZzvQ;;m
zh@~H73<v-y1acw(#>klMMkPaGf%Ymk5kAPzp|+?#Ng1hsqvxF6@!{?zdA^<-aJW`O
z3m}NMmpaixGvELKVioQsZ6p^6nO49Bh>MG(JmAm{tUHjsLws+!7ZzP2arn9yyV*8!
zrb$@>e})Ue%G7i=YFeIz!-GAWSXi8ad_$4IPfG~cAkWGkW;Qd48YuH+;@4+qWCRw)
zP+e2=ghc(jE5Y2{{T;@!%J!&!it%{+pnY2-f(}2FMc`1wVGH6k2TaN+@R(^gBC0Sf
zyS@s0xB*^7MNc3^W5dqgz4&n;U9UuVx{TBd6#vYp&A}YB*WO7!lux$+G!8enVSSVs
zwR0V~HLqY5X)!+Baz7`5P-t!REmlS*#<Aa<b7CbgKfm(h{n~ybE{c|%TqywZ0Jx%y
zN9x80lV1#Z?zQYL6xg6JM9HHG6uU@QX)ai=gzH{y;Dy;N3y)Y9_PvPiUHY_{!4LT2
zY3I*q)tIl1A9lr|+7crpA}$;#!46e^hxQ`^l~*U97cE_@$mTxeVwI@=0LBskaGXzn
zF;!Fkc<>>w?}#77yzsw!NH!K$Yu)dC0vFe%1pHExN^I|h-9S}fAiA;Z(kp{cPSODM
zPx-s;nRFX<ZKv)33x)1~Fc_V6lJEjtPSXPXJD1-7!4>`eRj5LJzFi0&fPoQ}ym(*C
zURhZgz<`$*An-s)Za+37B&^78Eq6NrcJ9+oQu%(B@*gTPdT)$0{}p<^4G0_=K|*Sc
z*pqiT;8=iDfJ=foK#Hp%CwJ}IHK;F!BnFf%b)E(;NBxb_k6?_mm-fedS0oo2F9CE)
zuk>d-;Q_bBa&a2JzJ@568kBDU<+{lvs2D0C2{}1A-S=O&Oz4xu=^j6yv7{N}KO|K6
z-`=hNBXWuVUd2D2Sp94DiJWrL&Yxlce^fPE@c8Y>X=WzPL4cIUVzEWFqZ-O}w)`0n
zg}aDVixuxE?7%t7Lxeb#mZH=XD*{X`1TFMUxDpgMQi$|Gbs`i8$n#a8Dyi)@fHV14
zElWrM0L=rqDkh!9;vE`@lpg+^I>1Wsd*t?^<Q9!%CWqTc;>b9N&_%kL+_C&UT(J=m
ztXMpA4_5(XZJhYv8@G8@#3T=p*XF0{ZF?l#w*uoo=5U@#*2<+Ha)s9=enB5Q(lM#b
zJ%mC)d9-}wi0+w!DO>ewB7MlRue=x>MjxzSKEUfHgKNOO9%O$VM<ivssO&dfvOKwf
z4eSwyMM+GV>aTY#w2-I|bCn@DRx|*(1D>b@bQU~I4lijrOHPh$sMSRDyWbE927HNM
z0#;l_S9xdxDvc1RLQ4Gfg#-l;5C$Qzen&fa52P^_Kb@c+Q?jTatn1GyRlIy@jJ!Aj
ziUI_y&x7}e(vJ`DlG#LhodU}<4%-jj1*ZJqN>x%sL<GzE2TF2ssB|%07g>v#Iw(>>
zss+9P0I;Bl2)c+Gl>ZPh_m)Uhf`|!KVMGW*#54x{tQ;vEQu55_161+>XTK#{<-p~m
zxA6cnehRFEGX%WT=ennQNjlovlJ8xbAW-)d3u)`<)ao)y{OIh|&c>hE^BlMC3gVRZ
zA(^O^by*ZQFn`}8{->OAWY-?*F>d5Bbnms<kbZ7>VMxi57j8kt-jJd8Qc)<C$w98t
z5rBuZW@GR7Le|5p!@=)VeJvi+)Q45{qk%wdeSBDDX=369WC$apPX0r0$csKU3jqIu
zl4J1ZjRxTBLBMhIjgfIqM}I>eN~O{gl9G<2EaD?O8;j#6lNT8)^CO``ENNTl*&oS9
zL7ilX@9Eu1RO~^OCq#DDOU{2oX?#&FX7tBFO<hLt_sEk6hz*B1R!Q`#PM^b&b0776
zah&-~58&}Ous+9;SRtfj*BuY7-)KPN|F9(OzkTdK?_*hJFvS2w;yh_s^3E@;GZ<&2
zifeFRo6mM$9Dl;OAs#$(@6+Z{Amj@dzF#vUU`hT+#}Rbw8juGJ3wLR!EBVo8PT5%Z
zwG|`^<nFJRAo_`v_U%SST!G4RAUj|k{YuN=ZoJ(l=@BDhrT-A}eK)8~m%mcvJjBRI
zt5re;OTc+y6e`%#dl>*?$#OBF>wD}t|C8>qoE($}3d*J|XvHU_qcK?i!VD(>SUfjC
zos}2pIS!yg%PP4Izx4JwZDn88gD}M<sQPy-RCgbO3l+Z1Rm~}Cu*^PBu7Hjl<0@b}
z2jYGCe4zC5j&CdN124Rm^ytlmqw(Nd7~{1!yxypm2nwzt7J!i`M@3gdwc&43V&~m}
zRZ-N`Y`YUed$TO}Qzlu50!W;zpt88)h?IvKS<V`>l!9JobERl;A)1tFxNiQBHC}3u
zqi+P1jszO-W1P*D!p^>}qThENUJ2mEaDKRM7AYanyV`VZ>+Xd9g4Xy6!;*_}2H!h7
zW#ulh)aycYtTWf&k6*6fhipGkYRT(#R_VFcECVjI{xdrH<;?IqFE4;b*2Ce(mGSbv
z+tQS1o3!oncZ_&!LsgFhfg{JbAt2kRsuF1&p}q#S7?H9u<=GMfl@A<z|52`$%ZLHs
z<^~dywrkIKGBZ`Jd=JIF{<v|qeaTk@MvLtx`k}UE?K*VYz#C%CK|Byn{pVv>l%hcP
zAaM7~GwCYPzGwS=x=1|^20rI#wsF}6EbVsAg9@nHwhy?ViaJt1T7TJoLJpi2zH$s_
zARWuq$hz@rD|oezWl>@wwq9(c96oK<5k9Txnh|#N5*KUoN7}uy+ym28fs!dI(TWbe
zR*TQhwl1h7*E`_O%WBXrbhaH#(hc;P+`-km7CzA4wo5=`vsB#7A@q%h*A`=0MLF=q
z2MQ)8Cg>j;&|dN)fKFyJoRgNO#>Ty<CfaHGKBRjc=#NL%>qvJIS4+EmoDy7Kzwpsr
zYYONV_=?sK8AqvhBlQ%0F+1%_HRyyzTH%TcvF&#~;TkV5?Kx%1ubpf1x0e9n<GOA)
zr<C*KYN}!Y`|WNV)S>^yB^D>zWJDY_#`?~OD`geXjPZ^M+YE-B$d5I(F3HHq7+eD_
zXT=n76}ujb(LVe2l{#4lWTRE+L@_cV$!VZajLSSI&B_X?#cgGl1p#Fs<Xo;4!8MIY
zhw9niW`FdFu0(JRP;ftaA}bJ4BFN4tM7;Y(r54V<s-9liH6vPmahK+1w`~hRlKFOp
z>e^hXrt+69eD7aMznx=0(GGSGXrH{wb6`h_;L_1R$$jsm?X|lfechdHKh<esZtm;J
z>3lM?u-~Wf=Wjd7cKIS`*`Iu?J!0vw00I@DgovCI)VgV$3kq3Mt<JsE__YaqKj@#X
z$jJd3sq7-GEzza){mG!oR{D4$<o_*NLIg?xQ8qQF1dQvWkf;XSU##_5VrV5)I+zG>
z+lC<G*lbTTZjZN?pBg++n2lXu9jU10TKQeE9~57HNG?13UWR&VjpV*A;x_ARP~N1-
z#bF$6cgq@&6pg5;OqcRj28g_i-HrqPs4A)U{<=R2h(k~rfa#SZ+>2DEQ0qXVRY4)4
z-J{kt6MFHHjv?+SH^R2C5vP4Zk))95)Zl`|3D)I|isdQqV^~48sWMAOQr20hN=wL}
zD#-wqBB+vpjspR4M<4mcix>SJt=B^NIGQ_YPoL*@5Rj5Iz`qJ^WFq_c!hBtfl*Fxc
zEk>k!OFd`b1&`ThLV^fSxop}Nw7>Fl3%|6`mZd+r&2{(a^b)~+UF*Z03&=12!S{gH
z*l=}uR=~Qy9JIVw0!l#ERXFrU#}HH~0^n0cN8_Fvk{8CTAw?h+Guasc^TW)@h-4X%
z<CBUTo^9U`#0Y2=2xVV3w&2w}x&?d4=#eG>wB4+JUn}4N-Rib9GxJGGN-CVMmoS<e
zthS!5U{(~4oFZvK(DCsaxj5Vl!aYSfIpDbgj7|wAgZLJ~O8C<@ffT3hR(L8|L(`zj
zF&pwLo_eg6{wNpQi-eoD>eq#Y8p^1s$n__vXv=9BvVps+(NZSDbE?t1DF6GK`(zLH
zrJkJ={mjU;i~ZBvUYosl|GN2*{%@amoW?!l4ijui*>oZ4Yn+Dpb{^d0aix?~JdNBM
zrw?Ux9(w1o%V@v+*^}*LrrsUdd*d>0oQe~D9V%;hu~)dng)}|IB~irpfz+#P<K{Bm
zOLbinpA;iSc^cG9HAoSpC?y;m8U%C7ciV{DP^i45A6SWNfMl5MV`Zs-nLd<#*b=P$
zrtZR_4|nI<-eC$Tr`=YI5G!uBh>bN>Y#FB*QEDEywzOz0x?1C9oMi~fZ4=q+mQzzx
zA3e7v5sfEg>lc%X=Nudys;Z=yvPUN-e0zI)0p~Xn2wX_u>N;g^;ERMV)<~fOa@*sj
z4P|$&GZ=J?BESCr`{lX^GzJvTve0yKgX{2lq{80bl6b^FW?4!~N~5i0g-uGu%wIJ{
zJ)+z!l}o0<BlE^dC2!lrk`f8xzRHks{@Y0rnVFfXL?OC`WKak-mvRdI29+<73OlH^
z?$jIyrNDvjLLY$3dx2+?AEo$b-Xf6JxHQs;6Gs!AouPy#)LcTadhOCtDkCGVY4(GZ
zqcQcQ)7vDqBpoWBnycyW-=9D#S1oYTNUNU>n4>+{R1LHr#uq8wE=M{f7#18W^_P<_
zzTE0k1~rkuSFc``*8bjt*s`Zwt9N?u!*M!$A?;R%4-yy?&)MX`Z7wgomx*>bboPky
zLD{x*R=uTY`w$D95GXC)hd~55(3Ox*rFMaLJi)E-b=(<`B@dpEIrR1G*MUq4kWQsZ
zb5LzsmIR{x{QR2Pv&J~n75vt0?!}KZKi1%7Vzc*y)1+FIG}&8La%BBiW8$J!F&hg@
zvNLB3dF$}w?*szjB{A>Kn>RK4>EW6JXV1R0mG^%Sp<u3gpt14BuHCzbbX5lch9s}%
zef{*wqrr5y5<%uHdF>=<9kj9QvN3Vy$4l&|QpsC#rT3}wh4v8J`rPo+SQ#DqIcDDK
zO`|tc4uAquvD<kfV-{$;TR4voF;!gdk{VZN%@RQHAo0w8w>r4TqueN+(HL=XgJuOt
z!N7H<m*CIWizbX0M@u+dU#@Jcu+Kfx!kU#bGpuZJ@19)PO~n*-vvz^;$;otG+impp
zY;#zCFIK><>Wh*#!9r$_9(_1MFo*LQf+>ELMel@OxTWzTdG_Fr=T|X$yi8kf>`s40
z7$L7Uza_S5rV)2)D(>F1=T@{rJR@N}xQ^<EohD6yP1qg&QTOZiI8^5soyz^u4;j$4
zYzlR0FjxiftCbb;nZb{)E;cwz0sxwkW}O`*X<6;VILb7rSWueOaR4<HVH&rbV~8uT
zB5)$27d@G|oZ#!?r;t)k7yt4uaLFNY%TO7Ko|yoCqI5!!=!3dYEK6rAm*L^B1TDHO
zO?o3C6E`<%VVK-O!#835a9RA?VwUEY5eSs$Q1ij|h62~a`S4L9J&)B&r!1^|fpfj}
zW%|jJCypQ|@2@pjvjKfNFs8E+ZKcBpvkdTVSXWS|`$HY+2w%zM$Ix^M)EF=vX@zu`
zN1;@~hE&@~S%&y_O<imGT?`D01_p$8tM0UTu5>V*z7fJ{!RcB8b=u*?nb&7RYqKqT
zijhLL#RAoopitCOIH91R5U<YTFTvFUvA!Q9<$=EZI8jtrG;d9JswM-}tdeKedwge|
zh6yps$LBF*kkY2{@<YLCqDQj@_L~-gA<eZ0=urU-@1f0`H=|JP2#$vh4x2NtcJ9d?
z&%d}$xzF}jSoheq(PXe}!VkSb4O$76ETfgVA=HXujgQ)k9?u=|+6@ST<Qt5P!JIYq
zAo4;X55%JFBW--ZM?;<FmTe?L2Q?w5jevls=y0R=e#&hMTHMnhHWF1cS062r(<_C3
zicymrrdt3lzSiwE=&X3XI8BRLVchV*5RR!~rqc%%U^cKC=?t`AeLC=Rg}o8rmTnYi
zpK19$qU1p-e6*iyt&htP8hQj^mx>KGiGsd0{TglO57WcWqb3N$Y^@a{JY&?wAl=s%
zT08XDSEziZeFBDYdyEHeB*x`q@3D<F-heeC{e8Lvts?p9z`Y%`W~;xFBXYxPkNk`q
zVx+R~*S<N)<jcrt4bsf(m3L`Z{9W}UrdXG+N?D}mK&Za6DQXDG(kf^}%4J6|MhMvG
z8RzF;sV=9z;WF=)PKmQ;_o7-4qz@(2`BW-(_Bp>RAT~(Zqre?rQ{6{fTrh7ndm}8?
z@|deM>Wo4V^39uBhCFy~HFF#e-LK*|a%G#BP8cfG7Rt{QbVDrx;iXk@v;L61A{wZw
zXALCqnY=qU=>7;rxMxsA>fF``iDJu%SW<+T?SuOu*ypPMBl)%oa>A4Ww^B0)*@nSw
zz5_c3EM=Ci(TiC1M%iuT$b-LvT_RQR{UYGhV)Z3NMZ>IxXzLz4HjgQWIQvnTC&bMm
zjz`TJJgUZ<k%rjVzkM_GN7>$Y;#M>B!x}n+$no|@xmET$7zlWh$(lLTBT?LCVArA%
zGKAXJCTwWe|NRQw0BB&LZhV@XTYA=8rF?0sOOZja{s225OsjPLGEjP>hB7TEJ+}_g
z-t;o~+vd@jn3xT*^uw<FGpISP&A=Udz_+4C7`@LGI)2=%Un7XuGs1s{MghcG=Owm}
zg&W5l($urla0L+MgJ2`Q^vsjcboUkA23oPk04FJbYUAF2oj(yrDu%SYAJR`B)IOw9
zAO;|3Kod{ahypqwrH1yx!zZr9c=S%pd}Rnk1tO%ibOg?606xkaqK7Aky%sxUu}Eb9
z+LK1_A6C4wd9)1bZ-&r*kbUR#Z`Gn3kAL^!jsqM2QnLD!EdJefN5D?L!oyb+zq{er
zBFr<ZKhT&9%p5#gDr!yhspxmZ>u<C;HnPK>8<2|IDrROLwJaVzdUO_G87j>3$moDH
z6UzGc^k{AzBUU5nfk4)Y+@78MrF)T9eE@*4Wz?fD+mQ-XPDf|>&mHf^J)1$DT13|o
zi9c(ss4WO0<~$GWo;>`9m;gpXp>-KakQo`FCyQZ#US|11%pVtdtGjvB%L|f&;*YCn
z+4zeaHlRLDx6tks3vDc1BU-exi@9AR<-rMYo;Pwc?Ox-Y203K|qe&}BW*iW*VFro$
z<jBcLNL995<XCj&qXfMF5*PW#(1=ke?SO`L!d{dV7sqSyPR~!gf$AKzG}`~gin`zb
zL@iU-_=<i-qBs-N>avZ!KNqyJzY$r(>#K<oT`2Ddxi&k}(xRFjw0`99ZLoY%_c>*e
zhNQtcl(r<L3&W)wCh_+>P{ZA~_r<P_#a6=eX)W;wR^}c~h4!ZJ)sh~lSAwI$P++2G
z$Hoy)3Ik(5cI}N^wwB-L&!5|&q&YMK`K$HnY!zyXf+F*;fO~i*XhL;J*^3X*E@t!E
z34?`Btg4?s??l<PLMCPnn)WK8HeI#t(B@P#WU~rw8h#<CH7lVu$p-o_>xqnl9dBtH
z7jb!*?s1B`4{E>=w(Mp(5&ifkh^eKTT9>|mg(pnY_(DUV^?1trN_4bER|bOzp3rk5
zvFl%4^_knm8E64bRVtfp06lruFR@ji2J=tL!oitF7&2^xMnJ@Q-{X{Zhc?4hUM~05
zS-S!m&@=PYuFvtW2mP&=fdHP&+vZGm$^t}OGZ*d98~!)HcAjLfm$08qE}>@XmGIO;
z9`m<eD3ni6`!)`QCli)<2<6rWw0@CK&2Qicy$W=F_YsWMT@-25Q@f-=A3yt<!R^Bw
zv?-4&=J0LUcNDA=4=oA)kk1|E=DwPE6XhM&y-p()s7aJYgt@Pi+y@XUFNGE&U*O^t
z;jwvnK_lVRY<@x6nZLL_s@3*xMIZ-IQlyn_D8D-6DFZJAYe3rTRNRA(kM~i|0Z`<x
zx8c4?+;E|GixsH)3#m(814#1SGDRknX+*#(1j_(m#SbLX3;?`jtVeGeKxEVR(M-V9
zH5fs!1I-!CH^_jlABYE&(qW-2@27jjPqy@J#K7AhG0y3L3n~%3SA^cqd+f9z>ap$R
zpW!s@HMqsej{S&)H_T>XWCRl&yvM#lWwIaJ8CN@G)BX5|GL2+_L~hQ<&IOBjGKhbC
z+}wQl)j1RM$k$4=v*uAA^sWq29wdt|1f}|fWfveX#C$@yMQjEjm;=XXYdc#V^o<S+
zf%w*!5vbTop;iVH(?jbw0Ptt<60EB@PSwy}NJ}FaBxhui3?1wq87Zj3euzRhY(ie1
zUH25QK%+GuH@e5;oZz_{K;u)$5U`+{*g2;xxc^&kfXV1f-=Me-pMbn%qAe?nW}hKL
z7vG+pUNXWzegw(>U!M*`oICKn>Tf{DNE*v~`t<1`O`4aG)$bg)`e^0s;$j4;G3fGn
zLU>d}H6wkDOiYqLO1iJDqK~=5S~#AX)(@6q!B+)QI;5c(d^fmP+Y}=R63EPkY#w4f
zQtnj+aLhs*;F*Gfg4|s72fimmt`@b>tP8qSMUKf_hmZ=Ili}rF5{{WMLCp+Epn~Bg
zqu4NgC`=>ocXl`G{mqcvBA^H2mD5DXQIekBOc9)tkx-DEgbu-*0l_N=*paimzhW#y
zx!p6B1+5Y_b4B;*Jfr{A0*u5&{_XLD7HmZNFfzt2m!B}CEKMcQvl{ORKs~#td4Q3T
zp3C(SuU@g>U(Nvd7!?&2r0n44&bR1dLOtgbS$Jum3d8%6=zY@AMuWds<DX~VHa#E8
zM9UEy3(v)F22SVO`3AqV%wittg|`e95Z(HrA?ee#R=jKG2y!q$Hpj&O;R%s9cR_6`
zv(S-T;Q|fgsa=SsolP^K^3=<$ud3hw<HY=`B1^Pe?(?idtP+sX%90T7XW^sdz!`qs
zBf*P$-B9a8%~#r^eVG8x86k~r+qT6+>InVboQ#a%dQeP9oKdSM?R9Y%AW%U2&;SCc
zi<UGi4KsurAtQvyi}G6snx^B^fmh(Rp&4D{jl*R7tG`XO!Go@sest}!D7atdLR)W=
z*RN`ifH4;WlnuSij$r2>rD>@SMoo6u;Nmj4><UB-E=S^5A`!D@xVE}t-6YRk0bzbR
z44ScW^ID__yNhgd_-Jw0gB{L711M%7qqyd_RKf2ifCIw)`sgCAkcKVd#O_INrMW|l
z+^>$C$k16?{g>DWhs4V9E8AZZ@|UOn5byVU#CS}PjitEu{cLP(Jl)E`z_30oYY{H<
zu;($-Ld8AZ6VaFt+vc5S>I&F>c3cx*JWL(~Az9flivSrHxx?u3??*fb^}ZB7sEY`3
z*uJ|Dl^j~;O+Z!`tgCD!4pKC>wX#;|wuYuyr_iRx;*ydCM6sN!Oe9WF_P-@^g+cLk
zNblmMM$$xD3&|L|SE^eClK=yOMwC)G(M?nY3cQezb%~Ibb&J4%U<bPJ_}EzZj8m@2
z=3XxP*E^VfD<bHL)6<#h{Gqk<Hp!-U9UUFJ54%ey-aKV3<evp5Fx^v<mdSPKP<Xi+
zt6GYBs+fYGeif^oAKWJoS2~$OVVe^d2P=SkxO?YL+_;lq(}Z(QSB|mDC0rX!N8l0p
zf`|-A!T%sK_I-m-eIfOJ5sP)_e&Oi}oX7$-CE*sKO;G^|F8Y0U1cQ4@95i>w^$<%4
zvA@uG4Llz29+YOP1hw=1(t&#h`wRn*iM+%X&9=N$V5^Uja!Jj!DrcAq&gg~h)i-Mv
z7zc)=+iTCRU4nz2ydt*~Af-r+QS@hRRn7+-_QEv_v}YQdMip?JgQk<geHDh;cdITF
z+DIo19UTkNKWUs>-*~b%)_v08s){lAbd9Pn)HM?W2fP7vc^G8ih@q(oOGES2C#xZW
zVjJ1bKhf9G^rj_X4N`)0+S>XN#^^~*RM`S4_dsI<Vu{u8dce#DysPX4X&-7~z+eRF
z)&5ZPmeWbml_7XKPCGeq)$BJgQh-1I@_pEU*KPl+X$W+Bw_*fS^-Am!;Stp}+tt;T
zka&NA^bCxe78et;viPBy_;qb|I48<d*XL!{gKM*)(-A<9oLo83vj7NnLB{3S;L^Jv
z97_=gS5gTa7KHI%cg?aT5@Vr%6s6K55L@HKKt-3z!piCcB<8T5!ZgTWAJGY3fhIp{
zEJ@pMTcfNSFm!sp-2EgsA_+>;vPnuCY{dW5dac|cJ=r(<pn6_L-GMM`g}xt613|zX
zy!Am}X8r&fa)OKxfG11*k_%KstDufzZaKfw;4Y1171T}{bS!1hn?+!pQF;SVn|M%$
z00k_VA0Um2Z>ZpyuARpN90<vjFk~YJ>35F>?JzG#CvA^|hmM*|QD>^?cydB~L4gQ3
zQ$rJzlgKh)<V0vc;xQqUs6;*aRPjnh>A6=(&+*E71D@8Um=1eLL*b?$-NgXVqNsX~
z28{sV0S?{|Kv+4Hz{b41mZ7?;Mlu~X)7Fl7P)J)uP^7T;IMOF8mXE3*l5m**R<t26
z7#6YEGzu8C#s-?P{y?ru49e%}A4{QHq?{a@T>vC^wgH7UwFEwUomEAK`gmb!sS+p~
zE*M1Oi<-%yD)W(-rhC6r6Ujuk0PMEw*fj%~ser~6QOaUb^YX`47y?v|&^(L@azVOw
zzM{b(5aA@31P;}PRsl4s)GIJ+Np*zj6k>0+kK3d_XzT1`g|c{Nkz293l~tCQf*cf*
zG&mBn-Ngi=?Hn95ITAF>#x-bR7PcofKvhMh;aANcVUW?L1vIU#85VX-Qup{OwD$D$
zd|21_m$nbSun|K;Ytg&DBmNnENx*a(7*~Srwwop?;-A)1-7Ls%*gUdyg4rT9l#P?K
zmpCAZ#YmkyN8@FY0rf6sI#d8>TGBunot_Q|#7-JDwY4!rVgUOQkH^a(S><wP8;%Jn
z1Nv)DXZgXTfR>)N4f0<jqb0bRQt8-Y>^Gh!tWasG{`SUM1v{1mvY9_)LQ)Nj@^jYr
z=hvkdi}c%)-lJDV@<^RgsZ5q0P>U5vZCEVMuP$a1vcu@{g$}^vlAB2Lo_()6NE9^v
zBy$;%>18e7hAcM9zSA@tL_vi!Uv{G2YE<?JJ8yiw4qFH0t39z-YPuMzJlkcg$F}>c
z#@xur(B)%G5I6IGmCdGM?x*Xka43NcgtDJ1hd<eOG@I*c?a)W}ET~bny@xN_(qISA
zD=ZfAwQc-R<o1Z_k8O=`7Epc-rnk<CPRVME0aYU?vKXSByn%tiLnLU1u?)p@3y|2S
zWp)+W8bb@I-NmXiOuiI#<MTWrSB0w5)OnN*h5St+o%sT#7a>tmEjvHp-K9~Aw6?aE
zYLp9Ya#u2w1X_ySG%1M#k8`k;rBrtt;6Klz*8mqSJQy2nZ*Nc2sX(n0D1LZj)M?gO
zTozG9E(5pT@5_JPM#%pPAxnjjWd#VKsi#M3S{&=%4B3BthQ;dTO2`*PN8RwkSg63z
z>N7(R>Z5MkjmJ4_FHLCdwzOJyg<fz4xG$PB(Ed)%;<Rg4Wn}DKrX52gK|PF=u&Wyg
zfVr3rT&?Hcd!ykSUM$r3t+W>?2(=twhb_y5u?B_E-`{VS<)Y^bqjZ!xrI99n8i-sk
zFDm)BOX%eu4_X9OWCM4{GJ~)kIN>qr=M8`Z0}r0QRSpuFxu94p0!}Iu^uNnMXzd5g
z7ZS^`Ygajv4)Cw_gHoj*^u>NqnGot&ZJ;ec@JW)>CNiQ1QTIE{rUIBL3O(|vmXyHt
z<v?pdANWCI1pRnIAh%jRCIhMFU#bsO)TxvW=XY~5C%RnRil8z^>J|t#SNxNQY@EJO
z#;2x$78qbdMLt@m2GX`c3Wto5HvbAiXEcHX$OqpI)p>-%k1dS=Woh%DC{!|#;}uZ(
ztJD*LIs_so(&0aN5)Gi7?GdbFZ``;Orhnaa{r}$%+kewK*Et7mK^~oJ6#Tjb)RBT}
zv%5MwQOg21H^5g^cDxCiDb3QMGgj3@?NC@*+YdRZU?I{~%mPz9qNk9#M{IB}+CrrZ
zNk3^j`oA}~?!QHX|GY@>-#pj<h)cwuA#PfZhI&4<(nL4r@9)VB&jT4Oh!9#9pI^MY
z4{+OJrB@S4FHc~cNQ@l>APMS@76A}sM4)dVk$`VTN5kNoAD~u)hztl8MPvZIX-f`R
z_uQ-rO5PY{5%3}S;c)aOMMY1X0ek@tBeWiW;l;v{U>5si0fEW|DjDK0_z0SRAE)rQ
zegC-LAW$+V@)C&F?IHu4R}`(7VAkQV=N%(K@okm|%%C8hA+7*W8^w1s$Qr~IEI%sP
zJ$|gtaTI|-qrv0mpmfn}$!`3yK=lU54Z#aiDflmf-$DwTMnOTPr_sPHrA))Df4X}p
za;$L~;|;D1L#7QOhhu+dz83HC%%s_ntBGhHLfztz9$?Pv1Pm9jnil^uMhP~V-^xJ&
zu0^1;%!Pz@1l5jVdK!yE^m_&c3mBbXhRGl*g4h7W@1wL0;)l8<dL`@ve8KCdQ@)Br
zK)NO5*G5|WbM3$w7+3<?K%x5H!k^oyR{njC|IgCRe~NkiKjek~BTnmoZ|DDDjH-nb
z^UE)wcAyOiT!o2oflz>}05QS<!}<)vK!z389Li?G-EK6hGeY&p)<%SOT*uX7eGnLG
zfRe#0+FT?w-Uz==!J}E9FFN>99F)y5OFboOKiz!@369|9C_n0lIZ@XUp+5`h%~2R~
zu@?<Pg8Fdr-6dKrr}0|^kH;F!gyaG;ZtW+-+F{_oNoe{b;5U^0xPtI85FA=Nv`Cgh
zn!Y=mCUo_9&?-pxxJWkuWW3Ol7`s{>TWW{}XIwhK0o|rvNd8Hp(oVc!2_Q`a0J<#f
z>`<(M4BNk?4k<VQ5~Ad-o3yc#$%x|~YoIrTP6Z@aq}3cOkn)6)5$RiO34h$Cd2NYD
zIt+e0%I(IyEzr2q4}{`Ke3N)H#Vjt$29HYGkV^QMOe<SCtFc3(YiI7`wY{Zx60mK2
zNlD2#8A=h}kbXNG$w9SN2g1U?Przsu6ey<)2jaV>L6(fe$uQDz+uEm3pOEWB=*`@;
zV|^qna=6BTm+7eC!dF=#BmRC!WA_&)8<!E#B&`jN91QRggf>y#f+R05sNs(SzuK5z
zm-_zw`{BrY;9xbXoQuOi#84*y)T=i<{443nv(4gx5Zc~TDZF1;lp#<S2&8^xY0*6G
z(MmFpy~J2~GJefC0j>t6kYFv<ffOMH6!w-zZajmf=4C2o$|v}#Vga6NY&J0x%#IQ6
zsWRavQGjTrz1;2$D+@~)yj+gj&~7wUcbTO4R+lf7L}}SQh>Du1*B!OCqS=a-Jr73e
zqi17Cr6*uUCGZr=Cq3$&@krI%a$|v}0Ee=;ojJ{5!gwnTt?}#5d8RQQF+%3I<m6xm
zSIW4s%X)ey3}O7@{yk{X4NVNyr0#A;QXaVU{?yYdAot=Ax4%aW6`Yd*T0G?aF#Wb|
zdqxZ7>R^6HnoS>i%1bKu{Ea7nyae%@&+!%P-6TLthajo18Jf#o_w}4bRuB6qC-?g5
zu^%;l2{1KdZt&~O%m<mzT#yIE{>@icJ5B~LcZmbBz9_yjVFPl~{ws2jvi3yje2X9*
z5;*@UZ@c?fdD}M&JP;IV#g?#gwdsCoNlD2evzT2*4Z()lCVrJoFelUWG7xq@)>NKW
z{s1ASSSV1-Nz})u2F9Qw@NIn<yL8paN)FJj0Bcpd)5&XzK?13lpaclrl|u^Z1TI25
z4os>iJrH<#z>QNwbh!e~$FS}MmV{aVDT*|kKSGl{OVrIP(=pUosp8MToZJdpM%Als
z3mTqdp?<`d{>|Bh^4e^sLm8kiRDRV=Cjk52ZQo@9wZfWH?+0;z_=Ps`3v%T!6Z0T!
zD-6sE(8_tB4CfKN1S&WIoDJyf>qDYPG27wN{=C0IhCyQ+o-ZjaDSR^&ZFg?whq7lO
z47Nk<;@Lopg{H^Cq(I%rP4WXc`+N6(g~X?DzLE3yzr1e;D58<k!T1BcZrAxbnragg
zAYR?Vv#6B<$(%{ow#;|`=`)K#>TI|)*@0?7&}%&O%mDSQBB}K-g$91ZG9%3PY|KSr
z_561ko&8?{f~cN^a$2eHoo3*=5dTL!cl;jFRE;k*A+drepg%oim3Hy%OSNPE=94Uk
zVrRty$89-VhS^gaSd{%p+7|T*WN;O|#*4tm_^DcC%5VGwTbYbQfvZp{WEKn;b)Qk4
zGcXa@C756O>#YoJD145@y1ThTrw%$qwo`d+l`_;HGGHwCL-m;4SF7BkprSK^6!1te
z&nO_Se<JG9#rlInF;I_}Sp)L=JhuYD0hLM74-s7lvhf5E@1ja~X|1U!$jeYM8;0mY
zv71?>`_U!zLKsEC3wp9rFBYj-)JPC@+Dwu*g&2*Fm6M~**Z@ZG?1&F*186pw4PbH_
zXj9SfYX{tXw3ueI<Jywdx-breEQ)gFpb*)g<<c`@lbJk=M(83<1jIKOXvmH_R?sIR
zIRlc=Aw~|d7(^S^`cI!^QI`Q6p>G0J<psFK473g-${NxTLu-dCdLwjjAVuT>Mn?A~
zV|ON`uCRrV(RHUT&Z2>QAOVA!w3X07@(S%0ac!E||3=5s+sg~pbkWGS5m56MAxRb*
zpy=gA8*vVDtQIlN*KZUxY1zg<QB(TUR?IqZSQj)b8<r2U(#pG@9B<G9K=SAXPgR<m
z8yY^D|9mn?0*&g`pnTx4Q-AE>fEKj^gD^P2g9dnQh=e*j(FW2qHsB-x>G{k1G7afn
z|9a@qA#Wtw;pRroS5Wi*2Id0%D;iP&Aq7ojM=cM7aLuMaiBq;N5_CMZFRw36OO|?@
zt#d6)m`3uQy1}=Vv6<Wa_6{b-@{4pGxc>Y5<qwo^&D!+aReY%Y-in>Sb4UK-bNH~2
zM?dqym+~FVY0qoyUunqN*R(SIl4xq<=DrXrU=b`>{q5fIDaxWGHs;f}sFiQkBT?Z|
zO7+j4KR0E6wKh{q44-$B;pW~1qSx=D+|)iNykq?c)#vt}Q1d^v0HELlufwYNP6mDh
zD*1k{^<~dW#<9S8Cp`2JosFzRze}7dZJ;xOxjcXTxw-ZI&%=Ly!_BSn2onJb*B^h>
zKmW^@(#L@r{PE}V!MED<vgF|KaBraEenOs90j8x2?l~9mUWRtw#mkqyj^C%`MDN`c
z9v&`gKgj~=q&G;gznJBHxpOuSlr<4t!T7ER4;~m2aM;T#DphYUc|M|&?OvbO+_QcA
z_EE^n##=MkW(IELJTxfi!C@tmlp}|;zj^OJ5f80v9|Nv<YqjvUiLE<Kbv`$6TiMFw
zyVcv<+Y#u=)c|>atri~5>pHK36`d`v$*%1$X-mKT6iQh~jvn0%E0()?b6;nHRp8YS
zo}%tp8M~7O96WlSHl0szIsNMSeUaJE?yx7Tg(%C!1-^{>OP}R3%!`Ajp-*|VEsN{f
zUYIFb8EW8`Vs|-W|HV5X>RDbHiM3uBRqZae+Zn>6@#QIlv=^+g4}xE-xSYnR7Q(V~
z^Z_Mko!!GB{wz0FaFT%M0u84kxJT&Qc``V7FW0PP$MN9mq5Q<`tef1-zh!5AACKtB
zCVp=Z9P1O8xPgtm@XL_{r~QBH?fdE}U|9Z3`_vM5tyycjH;kJ+3{gMsKBc4wT<%%>
zNv%KuQ#++K%gE?xe`ze)(Juwaa7R#=xsqR|SCM)M76levFW~>Fz|qeXT3WmU14(@+
zecs>V;E7&G?E@gNa1Hp%=A62@$fANE`Q2P+-^HJZ-=3*pHYhyf4oZ_>c2g-Xreby{
z-*cp@damWuXUtvRlQ_Z8G9kMsuk+D|mZ$r2MH#m4&&wRUDt=@O%z^QKO3$zf10&Xe
zz2E1b@cm7TS;b7>SL@Yf>w<{|B7rMf+-}Dm^(V&)@UtW0r`lok21lfbm3>L~L*W4!
zD9U@TUA>dclW229i^7wU8Gg-NCTY>mK8}L>Id$QRwTNZ+CmpM@ttCAHK*H~X5jAp-
zj-q^e#m}INgsUo$s|sfH&H&iin*LBs;Q6y>aj+-tWiBF<_7V{yRsqe;>Vi<HqEbk0
zlVO7o&CjyDeEs?vj5v;i37cNt-cOoL<SZ@uppf7X8+Z83nca|fT_X0DX~^u6YJK(9
zgKqN-NbXnIb7Y!RFBPrC!n+~McngLuYTdu(xKdbL^O4i6X{<v$Ew@~&kCx>h!)W1L
z#UR7*gHlQ9%5WqS)I%%PlhRVjC>eK7G_v=j|314>a|!jdJyZ(@?ZH><X3Yj4TAr|*
z<=^$ZlT4qug)DjCVRK>9>D|Da1i%i*L(}{+j493AqsaV*PtOO&mK^2b*>O}cKn`jS
zj8IygoO}qNjn%-NDg@d7Q)r(^0MDWAnYN>d>MzrF<#Q5@ZW<-vWmtvGo`Eu$o0oT&
zaO7_Aj}@S@e*{+U$0D!^MB<*G@4U=EZGjEBS+qAD8*;1dTVfv953;yKBbIB#<9Ia)
z+wbOdG{Ok_g{dxRzG6JfvMU+l+41X9UN2wneV3HP4e;d7{aWu7>w(Hb(81m<=xTy!
zwTbY?)Je%E|BPDFt``hk0dVf)FFxP#%l7S0KHrgrry3L#Gy_AWHe+xr!))yjg<AqI
z1=<bX5!{yPqL#L2Xm}m$stGc+Ziu#CK0Y_c9~}(T@V5yuJB=HBrQh`(G?yP7XALo*
zppC>2&Yqlt0{EoEaHH1N){c-k__*d|$Z?Q6_q3#5a_@S|1Wti<k@+Dwg$RrGBP`<=
zPCb!k*vcjZ#$60HY6!s5q%D&JJW7_GXfb4$tQgs=S9h>NkHCiyaNFCEQv*Uw0cN7m
z4H7!EZdQ+3t(INiA&ez2TzG#y`UVWX3R5}`uYC66MZ9*N8H<dYq@Ti3nCbnS?MVH>
z_DsP=jjKZ!&yqB>_0EU3x+&F8JUm0jeYZWx{*ISvCq%;+pz~I0q5g8@oz<}O(&X)X
zPU`Bf7yN5L&b|3&es|b{qmo(GA}HH0QwpsI6tX8mE5&a{XlH-iv(R$OC!sBW%b<JC
zZVZ6>XYmfZwy1HW%8!Y^yp%@%p~mq$qu+|+F`X->I#(kEjJAFG@?{1JeFq9J<p}P-
z_{lIEg8?trXA=zuPxm|5frR+x;_Ew(L)a|@$sq<)VA(Tg+7;BSc`9V*WSY<1>l05#
zt57_EK~{S-Gj(UpT{4n8aJi!tQB20ATWJR&!Glo`yC85qhr{+01_q~2pFe+oKXfY4
zVa5j4!V;R+!2sw^=~yZ`OCHY~u!+g}4zr=I8F8Fa&d|C|Pc}PU(S+RA9hfzNiCgAi
z`K<0~rdqsburYOQL7m=ai_w~D^{tzoZ*ID+QytzVCf*%wge=ReQJlR89+=7ZciS<A
z)Y7n+m^~Pi#<&Zg`gbsy>7UrTKdptQXT=q!&XaR3u8gI<uGzYzk?B+uEW2O?ivDuI
zkh753-TnFL38Fb}xZaJwMfsje+&N8n_4=@V35Q}JX9d&&A$wyIjoICita|h@@GSnU
zLN-M`P<4K$BE3>RLKT9U%kUX73q<e&n=pn(Mm^G!pJtnzU_SbCF28&28@6E1z$;UQ
zd$^=`9bW%xd+**oc%p;i+ITmq)=WKL$Y;2ZAKwN+SrPCl+`wjey!WlNJuR+QubKSp
zep=|v3I{H9zWMm^<IBj%orm4W-@;%sL+E5_FR<eJ_8{kRfVD%xlM}toZ~3RrhS~I9
z7?TY8(-@M;KU}0<x-B~^YX%Js4&M3x?#g8)rPtd|jaq?$UvhR9hnYzMO$mw#ke8!l
zTVePa-7}Oko6~Qrk+(}wmFp(n-8ua16U_BL>oBbghj?MM>FdZ?(ABF3iE?}&vTU*n
zB~?qf%%Qvo>C6kb5hUIOKz$q&2i+8--xGK4+rPhT?Tc)RfT&fnU37*9p^DCA_x2-?
z$P1=WbY%o}(W`xNV3MPdMnu5>Nx|fH*<bqFbgwx%Ic;47>U{T4B7<h8=ssx?5oY1Y
zx{3GQWSu__N@Yl7<Y_6o#l<!QQvb=T_8ej|7epX+fZP$fde86Mw{Hew`<Hv?ob1>u
zRR7r4hju@&{jsg*&Y!0nWJtAuW#l}l3(BvquKo;e?1Sl#<ESYJdH|VAeX6p#ti~Hd
z?OBw>h+ee)^(Ft%fy1`3ztYRLu)k2frWX?vyL{zJllTF>R8U|@udmv|tY-`qxle!i
z2Eh^V1hcr^xO(bv9O;dA+ZAW~@#a&z4xQU_@t3jb8t8eYr<sziyH&_L+*NPgI|f4$
zB2Mx4G7kh<Ro`FdfN2Hvo9XwILjv{zT-RiG(SCXc&MlZ<j~#mpEyT&1Sus`Sl8Kc+
zeG-Q4@^4|e=;MT5zIycp7{@;7$qtpCLDF>EE(@9ef4zNqG?r`o_HAw>G)S3BWtP%L
zhLns^nW7{inv;2mGKDCaQm7P#G9)w@vNIGtq{NewOl3?m51D<(Wxv07t>2&DAK%w%
z?X~yXhWokh>pIWlJkH}huFFU!UPlg~aZs0Qr`srZDC+=q&#}+bA;_bsrH|WcK6cYK
zF*V^B3+KjukAD#K3w-BPR6GZ+O-@d>b8DoU-;g+5lV@yfyjuPg58m3P`1sWbi{>(`
zpo`_eV!jSwV%vgG`_K57!&XAN&d9b3Z33w*`-RRo9k}>`Bv#rDMu4(6NLRHmd?RHA
zL2Lk@IqrQ2-rrLbz#Nu3tp1|n<IU~2V~g)3TihyZ`}GaA<Bw>Fdi~QJvEJ+Yr!MR}
zXd<-Z;%dzLzCN4%s{7N)6nZy^7+Jr&&V64t3GVb2#Hl|vSD5tH-DHcz_Vck=mp38Y
z$=IS+e#<QA(^7?nrN!?QtVcE)&6xx4Ve3RiMft;b?sro@H%|9kjCtj{_80^Kl4yo-
zox+AkFg5Pvvc-yTH=a>BoLL;nRLhWe^bKpT$HLdI=3{*g+<JO?q2b}5<z;Cxn}n|X
zygGX2U7X{+*-ifjM1^)lK8$Wp!s|U6Km=g5(RIi`-&Uw_x-h2EUIzn{1gvukD)GQV
zsNnqYgY_}}nPwqpinr-=#HtLXW=-BE$T@;xD*zSF(2vwOtLw4aT3Siy2^F~a#k$eT
zpib|UTjkhSo9MAxMmhV0aMiNP=}%{cD(`#DvFuTc%ImcJbW;oA93Vv3(o#^x?Pn6w
z;|t$8Z}JidP)20OHAYL3^j_QM+5k~aj(swW#*G@7GDQ5+>2q?|?nR_9%Cb;EExcTP
z-m4!MH(-!fgrFNCRSz40m<r!Ai{&#?%KuqutxR9I_y54{2gI$MIjrK+*q9qe3b-+^
zw21^qGBOJF>DB&^0s#dzUor+e2Avq+do6c<)_85}@pnIMpZe0a=|OVfR#em9l+V!V
zw|lhf$=d&ky1fNKJh<G6AI9$iIMlYt96LUU9OC~~*{JrXd4oGWV(LbxiS&WRZK)|#
zc}}{<vJ1Gw1S18h4M$$OITxP8hN37`-EnSwY0~<i?-zNj=2Xu1z!Ku{yJ88C&-bPa
z?^e~%%eqcn>nq>AdtDj5VUN#|f|!_?KnSClC`_scO-`OinOJ7Cr4)exY9$(a0-*^B
zf(Q`m9jC^1tFU-0moL8rH8&puyiWhO7tY=9*MReW)1fH(BKvyGVn!A<9HU1D<1N@L
z@>R0<J^U86!Kk6pV%+B+CY4fBD6uZ(w}C<dbC<lRni(k647!Ad#X#<~{BViMill>S
zMhukG)X4GtQGVEz{zVyKA}(>F%dyeLPU@dm(g8cf3Y%11-3)+GwJ~5%u=vzP-B{r^
z&lepT#>uP%T4P*n>iJ#OGZh&pge3M2x+iHJmVE3a-1}ma^1|MJQqxwCd^quJOa&qP
z=+UFvUy#y|ywH5rb8+3s?g;gIIQ6M>WsMqc9#{Wnlzi?cGiYcA3fnxVaw~108qTWL
zpMe$z>eC}bT~yPIs}hB05#y<m9z*vryI>4eGlCE(2b|h+tdfT-O4;}qN|803tg~QH
z%27e&T=?UFQte%rw`k$Ni^o2qVEFtu4YeKQ<=l@C=W{jXJF-Q!2}AT_49WN`_ZbAE
zV*)RPHC9*l*=J1`U#~mNyezQHU&<^a$#bw3gLRT5UxG&BMA&=-mct*@gh!DOckkJw
zX<%TG+odd~{%1UUi20|SoE-h4AxcNj!fe!$R4FQ>-=E}W{*$=$E4rGMMa9Hy5kY)g
z!p*MtRo;yeH`Jdox@3`?afnSHdrRW;0e0yGa=HP~(vVR6OV%nH$5+@&=J*~xQ-43x
zBdsTa+f=xU9Aj+X`|k6-slpON^=A%?UdjBpRU>qL^_~s1Bd71N+6h)C_+8mEb)&#Q
zO7d!T<n`8rlF`^uv(+)`f9wd_i4ZI!VK`Zr8}}`+V`btVHcbM$4Pzd7{f-^2T~v;V
zol|f%XXpQ9mSbC18zCfcuHHhm`Oa*@Jq(d4DH}<!1eDXn>?q7#n18nvHS}*9#Q85r
znc*`pu+#Tqv^mlUCn>7DqUV~6eFUKQ-{3#%n0Qgm!0+9^fawZ3x+?Bcc@8aCQP=&z
z2-9$pt5mvsXANeBQW=<2g>T$H@R)=_1Tu-n0||xi7E3k44N(-rwa202j;48x2<eZ0
zS4lGt8TfYJ%5_H+DumnPd@`sIVy7NG@=hu>$vk&+B^uvOGl$*|yPkX2C^KiRSDaic
zL`%4bTFPBDGzUSSQb$}I^!3<P<8GoS5){=P>`<E&P~DY!KYL^w-`9g(quUG_>mmVa
z+ROifP>~yTZF%i|&0&a$?gJiA!bK$<MBq_`vs29l&bgk`QVOg+f_GNo^p5!m`hU5C
zQfuSJjW&}*KS;Sr3UT7Mrl7!!i!BV)#dZt=9dWWUK|^y1U~D}O7Uk6Sj+eYH03`t7
zE7c(#=$iaRWV`6aWu9tuv6cE~4v{Zqp<x8Ia^KGcQhx*mF+&TX<I$6{NHIKn`ZUL;
zj4?sQJ)<-*#l^)X3SoU)>v<-s79(_~=_VB)`S%YfeSTb`zF#`>`6=o7ZW1HWapVya
z3P-ZG#ke(ndmi!!O02iI=8Z}!eIwpke?P$^?S#;@W*7iz_%ufz(skA>M;_MPsN?n%
zd1p@b8xstlkta`EB!MgG__%Z7#z4+{ATa#fHOz!FQK*m74<&ea4<cz70)LPq9Jjda
z?onK~u-fphN-&I~*u)JI)U~vjugAo2*dM-)b#=M_e9X*cWFybqSAx`W70wDgtp+f2
zT?vdTU<^XC0>0koHtRt>3#*f7>;9|>8g*pc?FnDI0PN<a<U9aJJe;2A5>)a>>MOKd
z#m5)Iy<?I>fvg>S?rRPM9z;t+<5H8O$Lyx2wtlSK5C&iXU56j>I~Grg5iy~^Ft{^J
z5gfRE_?^v`^9d`+97X{KZ(C&J1UQmPRNlRRuLWN1$df^ywQD0`xFj{dLd7p)|A%SU
z&XT|uhd5ux?u#aS^z?Qi>8h<wh^v2>te(%S?3L1x=Qcm<m6DROa#Hf<GtfsX3zJ6k
zxw`T+6@JyWO(dBiFu2XJ*dZ|J%(K|3N$d(6HQHj_cl*h6r+RBQH+hVCK5$pMjVCDo
zUKSXA^JbUFUDN?0qN1tgT$if)9cK$s9HQ)#^x&zoS0tt?+S9k`Uk*y%<$%-gY3C1|
zX>FvPy_8SZQXMPo<~$l3_@$vSU|>eS+i|yk6moPDxIC2HN7N_cfTrLP1=50d_r84<
z5EvT)*|`0zWx$WPIezX@2^fZ|JIb-0vkB@3_AY(j<o-lG>IzWeQmCGAZvwo;corW}
z5Q6QSsu=NTq*uh0<XwTRr#?Mg3Ze9)cNO^-?rA#nstYC8+jjlR<)Ye}t|ky(EW;#~
z9kb7&7@J&%yjFq1H6z3yB2xWHDxS5gE~aZ1_8VoX+-MSJyrt`sb}O*V1cNKaer4v|
z`hSgiNtjD|?bA&)+`E?(J#~M;4LjsnCEr;kl=a9EfQ8KY+;pkaonzZvvSY?%6%|*(
zY^N-{Wd17F;0F*?fzOPh)PcvLpbbPMCI5mUfKzT_*d9%5GrDV;KtS;c*7`vjds}0+
zu7%}EHkCFnHhFBe|NKe6<f;yng8%%9mQtejY5n(4#&_y}eJNAzfB%<O3Om>Tvu})!
zNZ8@SFBbC`{?7av+$=ZqpcE52kz@1sVB&G$P(_#ix<A@l)20^R24InHv-Q>xuCzNX
zq+k0j*NzQ-=3t1{1)-K-zn-01d`z!ifIfQ2Sy~%x<a$5n)vSFl<cIwu{q!CWBSOr9
zOtao0g%-Xp*P8nxnwQgFZtnSWaDZ*L-9<ENM#ZHw)HDQVHyVIRN?Mu&ih3RRB^tqx
zBL2e(EnVGYy%@3cxS(+ufV%@tBa4tj87!K@GyHa^l^z`f$Bha+$0BPI-gn9FN*R51
z$>b?<gP+t~;_{wc$U&133?{qctaH4_&!oN<UB6zagRFftx8T|*LVk%p6SodFZL%!L
zsmwD~QEv6S^WmYp#4f$6vVrT}_<iRwhr_dCMIh)FE{sW+BJLf{HFrXZlJe-09sH$!
zw!Tuut`0#_{sxU<A&>NxoL+*Qa;)FVudDz~Hw4n+K1eo;kmL-A=WTGB$n%n`t88xJ
z3Gw+DC|1tZR8fnp^nvy3=_N*4OlTdj>zsJD2K>%zc-i14B=exlFL$PXvsdgL5COwe
zUQxmFJJ1*Jsw~_q-H&h|K;%K7AhW;ZH`>EEQt<HsX%K>GE^em$H^%Y^V@-k~wdLZZ
zQ+TqmwS@&gm9+QP)-<!s*!C{`BL3XmTsT$;;zNgkVwWuD#=~&8GGNzsh8(o=V!`vE
z6M#YFp@IP%5~;akH(hm>N7D%vTwY$z82C~1GG|%qs|(R!0E$|;P~QNSbf5^H1SYA)
z$ivbNV(dFGdAp2I!!UWmK|U)K@e3_yR*EzLuIY4zECo4-9mR%Ql+HU{nIE|Xt739;
zI^zgsDk{oCZ8`T%KC-VSUXF<xdMn_VgjPcaO-Cy=gohDH{Ki8%AT$j)UJhR;f2Mv3
z?9@ST`h(#_(rIsRAHuBU4-{@?sCUUm3y<{Qy)ZH|lA@d#!X=d9EdT3SQ~x9`LuH`w
z^Gwg}RP!FAs01ZvMhNnY&dk6Aqu-hIb1Dtthqj!ufi+g;`}b~z1|A-Y#d8yfsnY7r
zC6@NLYPhAFk-wt5fR&?T6IDIAD5rsDWdZ;YOm)y2Crt2g2dyYvuckO+?al~t>y|c*
z=`d&+4*C@S+K($|!w(%g6u-kq1?@@^9Ex%ptphX9+{k=C<Q`@hPwRg>>Fl~kOjNY&
z*jBy4z%RMd+EsrFw!CnuxQT*!1C|UA7}#k47Krv5;^?ZGAyv<rmu|hwaZ-LYWN)6c
z2Ducrz1bDmR<>HrYh)uV*~3wrmf0&NV|tl%jCRaV+8sN7-0ND^=L;?)oqFU$5Dp9<
z0?i~25oTcVr7=}Oz}oLKz4_I=<?+MOlW^{-5z5YUjs-+P)?lbyNWX}kmvJEL*XO*Y
z{PLw}5D$73(%m0w2_vftz;J~2_s8vl(1D9=-!5pDVOj>Su*<L()SGdk+2(#nw^Soi
zSj`EeHX9)97#<#8t>m;00-V%oxv3ahYj@(a$B$)zj9DcmC5a4+M?fG9<5K9E3*b|}
z!Cq@_b^hgecr-P!*3Q8Tz9%K?2sEpGazr)JmZQE<cS!)uGrr@FG#z_;8FI9-!M|gA
zH@4;xU~y2@vDd(e{kf09DU{jfmY$M-fHQ2>-?kP9X!q{j6os^R@WmX@vS_X)XaSDs
zdwAqg>!?t)ORSfOXyGYXC^^6Ri*Zdbz}ZdlU^bgiV+nEgOTPvjyz0;bjtqSSQwY3=
zp!|{Dw9~cw8ZK&kF*f+x5ByC#uIqEUu)TQTEO4B|pU+@}qT}MkgLp{455Mqrmp8AJ
zDGw=S1U_L@KqRYA;^SSQN_L7^BYo2T-Re|xzbufg$Wk}`k9nr0vd{E$jLbf1^0^7~
zoLL~>aW899q#u4b+6Km-;|6SJ2{=}BSg~rA7$h14iVt7YdQLUe_~Ap9@I0{LI(h6^
z!KVF7L|j~5Q!m28C!Xfs;svXa)H!0+;Cs_Jp6)KTZGjjsu`T1}HM8ex{>ST6*91_A
zl*bI18ji4v2j9pmEbdI?(ZJv?Ez?K-?3caRbdt-Hc`E*_(D=D6|LP|kC<b+eKQBwe
zOjkn#gNXB(acdcnmXm`uqt9Vfk~$O-&;`*(5JtqQP#Dc*RBm;wu`7n({v~SNUa^>*
zM*FALFgoyQ7b1CgD=r;gPHpj>Js(#sG%E12NP2$+TH3r`TVMSRjspR4!{*I~6UETE
zg#oq|94Q9Tg4ql40;eC0q{RhVAg=GoD{o$a>sX=4&6kutQd>LJyWf#E2tsE?{|F-U
zMlrE=u-H%kbcKV&(&OfGrJZ>}3|L-|bXA9Ka)i&n>>$5K^M|h}>P{Ee;}N`M&Vr0I
zDaJ`j`|4Bltq1O{ROiP>zk1asg2|zohv%OF(8|q+{KKDdT|h199ou|skO|-N&K>R?
zsDI7a_%MMt&aZe9_r2G`+|$42RVJ-@<SjbOOQ{`d5}S>BghiNNLTUx5RoAbAShWlM
zL(4#N4zxcMH9gVa_j%fdSO!sqTtnHRKR$>2sq<Wz0bPCeRZj-|i=8_zkNF{wOXkc2
z5~mzTYg%K;@j-vqkwLLJSWRl5IPoy2sUJP+tLWBT1)$RF!vI`s*YM=aY*ZAmtv%yB
z+Px2rstO>r3am(Q-KiS`@@G*dQR_%Pm$fdIu~)o<<Y|jqmCU@wyaVwSRUKbnz@%c7
zd*MxkYEsufG>dM1w8(K;Y4P)HB-~I`S7E?<jGhZq&$7|T)34?h-u^TFr8XS50}99<
z*}8RW${%uV)&sC4beEW_jZdFF3sWmjOOqJxs^q(j!Sm&Z^|W@G##vv55{jd!#p>7`
z&JPFm0MSu+@aF}@8D<I{wmp%wA2pD-q{ag$%}OP2dGTwn!Ten%=LhPC@Em-Y3|awu
zu~i;LM>@N@uHdv^#pw1GkP^IwOw*u0>3QHGZ0jCqw}XtsYU$7I^7ek|<|#qJnr5q6
zWL4bu355l~jtwZuWv~XSgmg;3_uHcgy{F6MC2OJXNF9MewRoc(yYa=Q&xmpx{{DNv
zR1ZA3h6|po>bZyCsn-W~i}&XRxO-P1529^v5>T+cg`vOM%CbcydrYXd#;c>Z#uG2t
zLC_zru_M<r8BU1={rsF6M_ru{@<rLp=#OSW*qEO_m-?N(V$Q?u+HtucsPrrx(WH@W
z7(Q-Iq7@>jZ1pX*3myU7d9d+yD<pmPRCnA++3zBxz?#IwTeR(>kbmr`@Y$QvRQdee
z%#qj_Gzrk-lgRQ!%OUZPJBUDAwqt?`Y%5XS^tuQED_QwY9giGz{L!K6+#W=m6W-}-
zY2gW$N4CLPxU=#3=@{Bbb7P5LiRoK}?tz1Ag42ypx{PLnA)u?E-!VEBZ*~3$3m8Zd
zqK(M!YqVFayRXT{ppM|H``K<pG}7n8PDiv)>Hu6K1EY(}CA5am9gHmO3J0aX@<N0Y
z@8P5xwTM4;M_zG6FZha!i(gQn)Ery4Gjtl(wO^$()vgNDmKwwxi9G`Agx(d4ijeEQ
zu*gM<NKoECeaq3xN*I<bnxv<BRcZ$NWIfzaW#g(zc>-My6E!i=l37_SWt#Tg<q$x1
zY6DD1mJt3cuJ1E48VCv&N^tvmBagkU>=R#ao{wa@B@NmCgE6CY^b%ihL7M4p!{lhp
z@3}&|TywaypP<ySL0aa6T5Pu1puI|ez9!LkI-TaS-^9cTcg<X|d<6amT_<J=m!39u
zsYG~7z&Q%Vw_0pZ#Hc0*QuN@#H8u4ae=ARUBD+Jtw~g}0{d*K@=!c$cqYLOvkeZ$H
zCx8?C*`Yy!Kb*)%C<-BBc(U}pdY<IT*3QIjz<R9Q!l0J&9q#@fxtff?bU;Vv^4LpY
zIx9!MP}h%`z5~6%PMwGIaV`3Oab2s!b->qr%q$S<^SZ6Crw8pCeddFtQfR2Y>|#Yy
zRSyS90Md2h;%rTM=a?w_wqhZ4dLY$-wPUc<On>eDKw4J>!xqu*ZvZ7(O~-52=3o&{
z=l6k-ls|>(=Q8fGGaTJtZ+epjt@yC!8<>^e@xhV>*$aLH&=6R8w;&Nk_TBz);<UQ`
z!u*_Y&!_&n<dB0iYgIjDC>!|B6?pVp1RMv`LoXeJ?t^Ff@^-UZ!NDORd$Pgc%u2nv
zW#KL+zK~$YzxCJ>PS!x$Q}!aKAD31(LGWOscxTy~HIj4q>J5NFi}hN1X&eJ~zj0T5
z=aAw8^&KG++HWorxlu|344J4HAas2&w9hq1=P~nCLU6BK{y*;6-M#L|j-@m-v9vdL
zSZ2ezE`fF)04o^rEwT^!RRo#~sunOJvQMf?uF@B<!oNQzG?X!Hu`J!XCR~S8NM$8O
znnP6S4axmfCwKg$YJsUBjZF~|Vm(DE-q|-%jJ`;R$E}VUQ3@TDaq|ef()WpMC#Iz1
z?GFA@<2v@F*|W_@I8Gp32ffM_)DYxn?^+X8;fNb+)qqyy*Kr{xGs<aZ;#_{8S(e4;
z%sJ1WwY&I#PyNcwW|wJt>7ME4nU%@*&MOn0F#1{1f#+tKHJzPpAlFhF00b-En#PZo
zCVxH?Tw%Klz+f!y&Ydu1Ec2`b=2>4D`<p%`*{^H5@welsBUt)0)AZ#iyZj+pXh*4E
zSydGs@oXs9BI|&5hs@r{!qhyN=qHqHyb=~RY929alcyT7xei{sJF;D2tz9Xw7!*zX
zJ8F{QY_v{IO$c34hNzuF0Tvfut=Pgd!+vaUD;IO0oQI<%Z%nI;cBFg%_H~_C@~tI;
zgM*Lax(3Q0;tbx(2H<}t3XZyg_*7G?d)pEQ9{|V{{qoFR)qU=6l~+kH&(n9tXTKX~
z3QmYEprGBjPjvZX#^}SXF85mBezG}#G(hQ(Qa-on#5lxKOq7++xS5~k$Q~3U{+FyX
z;L20=dQor2(u`gOUFa}66--op=0R=%&xWm!lR9R96cjLYLk*EG#5MI}toM^}TvNY?
zpek-QGCjAp#*_CR64=-AyMyZ{x=v2qo9(k+V2oP$G=+@DRJm}a&`!1)BbilFZGggv
zuha;eMMJjr=FIJC^@onujLrxvyOj6zOPghEeAW8WU0Q2i^XseW(QbnyuX3CE>CH8u
zNJzQmjOHqsRwig-kRMfwp(bONkyY!C%};RM-79I<>GGtZecFne9Q0f0^El+-@Z`n{
zNN-}mu{pFAZ@N2}X_mPvXKwj2W1GeDY_|x+Im{GDf>o2-ygp7DJbOLbty1JYo-x46
zC}I7hSB$+031aNq)X9nYp)t|m(T}v9Gr#|iMCZNpJ1%acDDb=Ief}JGmOA)E=0ozY
z_*j~A$Wh3tns+>|;|pY6dff^hJEOvD6AlWh)!%v)Pl9>k;N(AG3sM>&V>XkK6_r%Z
z`iM>D)bVyHRoY$=HjIzDK~|QluC8tie>h8Vr|bK1fzZ|C@pz#%B7Vip#xbLs%W&aR
zyK@VE`X{@E?~~Wjjh<0x2`@?L$#X!JRywgqODmva@wEoK{i;tdExDWMV%S=Uwr^?!
z{5J1ay9e>Gu(s^BMj8o08CYZ)8y6RXs`t>er!zN?YsHj^#XaFAYj}A@)r=efGQU1O
z9@gX@!o5~@-)EvhnG}o>M_-GTlCdrp1YcMRV`pl1E=r>i?&blIc`~cdzoA>?+4m0r
z@T!RrR<p3F)!*#iErWPvs5ZXRf0p4X$lUrz{Q%l2Mddw^L;=}J3n%aV=YyTK^2e^<
z@e5yX5_0&XjFWEF;sRW~a%C6zI_%q|-d$ik(WYjkCQ+lI0q0j>`OzVtZ)S*wtjAE9
zs3H>;I8PdVW7C*qSn}2PN~lE6B2|zQm6n#qxU&rxlz&l4N!pJ~(@sZx!j%S#$}$53
zF8|rGYOF~Irtf2RNW|<=084<kb#ox^S);v@(|3x1dbBnvb>xpQpHs2YHKW3Yx8<uk
z?v5Y_6py_Gf`Ve=;TQ*t2s5rq4+rZ(?!iO5>)b$7e)dN>FFIgDulOtun<h>oieVJo
z4|Y$n1~MRlhEyG6qyYEWJX_rPNU@Dtk~jX@7t{l&5K!wWfEo#b^kC&0qfeT5dAnCX
zXG2<GC0#8gU|#vJQbFqYu|(>FT7tK*+7)Cky$_rLktYx`9HmIg0;nJ`6Xis_c$%JK
z%XYGh;2L8LKKG8J@{Ys{*`o}x2qFQgNI@;|&3xaLYF7`;SUoISiF!dkB1NLdd*9x@
z<)}SU6m9aGh#r%$z^L+^4qt9@prP)ds8`<OCfmMinC#!r&{@(}W392`zk!DyPIP$S
z(5aD~JUd6<{~dbmS`g?MbMw{U4#GeqbYPTQ3!6|OBGz&Z(jlryV&}GAMTH*=hO1Ie
zea*Q)wqg)17a7Zf+HBIQly|Sd;~Cx8V1bGO5-<D(DWPw%4NNWWq_{RVNO~dw<B-$&
zLt|)ZX&IpEkDVL}CEPS`oT^UpGRUN4sokF9^D}J=I<Wh(ok;`lj7bBgXN{ceO8Tz}
zY~unQqiJfo5*gZ57kmUMj&dRMiaXwWzlJ>%<YEBB4;W}90mN^)wb#TFsb4c})uxnP
z@+TpBqt}a5TTt3#X>krTGVWk0ZeYd%8>k|5*#;+c1pY<!!Bxku54d3!r2w^31rZvM
zL@U5fT!U&6FLh&*+%Kf_uFx4|D|%9))$_9rT<5+$N6v|Kg<3MTc-zK}%<jMH%TUrp
zCnQ`Wk}-+tC>ny`?!Ye*su{0_;e4kzWz?LARCZ%#thZxa(+Y=X@?UYX+i~cs=!P$j
z^w!>If(GE%a8FmtT~M|JB|5~#N$zKbdW^I}=2~fJ8eN@DM5_IK=BDq<!6ERg`rOA|
z#qkK%KI6RJ@IUw5dk?APQE3SqmtflD4q%iJu)E$qw6wZY<vB;+5TPO8p@$(V(lVf^
zaQw8*&Eu_`NI)e5uK=&^7u|b=XKcScKP}wD7a1B_ibFOGk6702I!FrJ5QapSK2Q-}
zpTUPcg6uo2lxhre1&#tLr@6*oGtERO=R>v*e>J;^ixLALZ=$-><4%xqxXSb2s%HI>
zzc@JFAV%ieR<M&z4U!W@3t9})q_~2E+bZlnix52rY%)yL2z-5ZlAFK=^y>sfL`29<
zIk>NJWdhjqiNJ^GUS7rrSOFq*7hdIdE`2obh>(Klv~{kn4k$0zLM^%u!cn}sGUS+0
zM1v08;&YQXMjUZX8vzIPgWC65bwyEaU0pkrf(6(Uq=QlHGxGqQ$t4^dfmO#eG&C4t
zOruTEH#y?CkI>S9Hzh>HHT7V&6+PFzm(Bt{3L3FpV6jLvN#Fm703G_>@4+u1@dv*{
z{1|KV9j%FUOyx;Ifzoy~+Hpb9Ryv)&#kpPUb0iJrrZy-(oQOcctOwY&kbPdluXl0T
z3P$NNUK<hO-Y!pTXqGa-3mmig0XFQk;67tyb-;Qd3{;(T8*CLK9}fJhipTFz6ex^v
z$j5&M$#)q*)6Q<c^JI-6HDz7TOb5$LQDFBH4Lz+8LWbm)Sg#fRw{v1uT~$T00u%v?
zj;+_g%bXQjzHCZDT6&N7>L1`8NPjAP!^j6xU~U$McStzWh9Cv=5*D;YS*R$e>VdLY
zHVT8ACT|d`Gq_T$U3rHuF*J1Sis!$5m-JJBW8jGJQ8iF2z2FK7oi%!5smv*$$f;h&
zc9=B~N0Q80(o0~ac(nILN7+@BIyR-Oi3~P)ft8h&>WBiVWlrnYOBMd7?~`%~-vnM#
zXS#PD2Y)hZ(}Q*0r{16?^#)|{b1vPdkRjm$83y=-vY|s2#KWWU9UBvQnS-d}aMddV
zMZi8guhi})Ur=k5Ka5-zlH?+x8m5MjA)i@qTrb6<#KJ~;PSKaF{N~&*;(ieA`JGWa
z9mUtXzrU@(0RrSLQB>p0&kAu--yt*8g>WP6$YvZ}l1<Ny)?PS^I{H-OQ&FN4gBAi?
ze_sb!@A2Wkb}Y83q-NkDq%^tLl#!EPehF|8U4=6dvGFhTlfPWV-77eo46SUsTBa|Z
z*tr(RAb3%>fzIP3eO;7GWKH$cK&%qIH_=dI9hR+F!IWGV3ci4(gL2sXgh5S`jJRqM
zqOYZux^l6AUi@kJhuIL<|C+b>-$N9O=={H5`hPKf@&ErD7r1Gv2~Q|7V_Zc^QF_`2
KT2C}=F8v=A*yTI`

literal 39232
zcmeEv2UL{Xl5S(d2%;z%6jT%hL_t78qavt)WI>_=iV_7Rhvt}Qk)R@A02)Pd5>Rqb
zNg_FeB*_^hG@+rpU+wmI=ghqIX3f2G-<o-|UaxzX(EMTl``^2&zOTNj^GN-Y(vGd{
zTQL~Sjtl1%G%=WsQ5eknPn$QvHzoIb;^Ci7rpihR@GpE;&gSFbpDnk~Uvb1>xO~z7
z>4Ju4uVXO#Fc%a~YrBR|cDp?{y#5mVGtS*4ga6_>1;x|X%}=nOreAlUbc4Er<aU8T
z|0ult9cG1qt^RlKzhsiSH&DJ$_};yeLkgjE9osMNGrysIV3)$(^ZUv-oO#xhpXiMn
zRO(R9YrH_3GU)b{8(EObPO>dr8jG{F>CGee7^dqTMWe-Fwx=g1Z=?Nm_0BH&_2{Q3
z_c83~U!MaT>e0WL1N(hw2k+ZLCr10?!saMC^uzfxm>uX}`CaR7qJJ^ZcK!!0alvaY
znBbq6mlvOu^f-T}NP5CS@t`caLzHHVxe+GXK#mZUnwpBoAKv?zP4~p9F;R5wkZ(-?
z`*1E8d9Ms<9NinOceK1K*D+I=9v#Z6`0tD-x_)hCJ~}m3czk@kzpa?!q@Zxe#igP~
zqL-70RPSpVB)f<&FE4L*tdku`&aCy@BY4@`DZGLQCsQjuQ}9B{@rFO!i9@j}FL%v&
zFXaz}8rVKM^X6E*zT8TsTBv32>U2K6(`%ii%VoMnb0Lz8?Pg|eMlJy)66wY9>lZoi
z)StF*y<$;@ZdZq;uKjqG9??UkEQO7eYh8*mjj|(7SX>&*lIZoE_nFwU^?_cIr(3v7
zkDLAI8{hKf*@5WXT&cxwmtIL?vli{%i;Ig9WUY&SWu^(KFsC=ItU8Bn^!4@KyKi{N
zP?iVm`yQ~A!xNh|zPr%S-p-ol(LK@sq3IHB=JWFN>BTR-QOB>&6k}_pM#3dWqwbmZ
zmp+Q|x>oMbHcMG5#ukyM>?*#62*MQG>IR_c-5zA9;c#NS`sE4!>B6NRJbt+)&3jMq
zAyvQZ!YP~C>JxR_U0>IEFJ{`$ma!-ahdqXux%^d0ZddPW&(bDCYScXjzx?SuLAZlf
zR%j4zZD96enwJnSuQ#cQ5Gu>mH{Oyo;5Fl6(uJdvuUB|p(ACv#Zf@Rg+5Or5#~<|l
zKkhLG;_<r0q?XyiU^RwuXREf<03+v)gS@<niHUDxVh*yiv%9AR*NqH(w@M3u7b$T$
z&DhAfV0d_VxsJM4=h~Lv^RdRpE~%o-R5US>GH0Y^Wo0FH@?<DiW=u>>o0s&9xJMQQ
zualP~`|j)I-MQ64bndjt&Y8;UG^LW4cy34Z!YdQCs10`|jm2f>cH{9@X@w^#i*{6J
z^@(*bMaMij>14l=7viu1u*8VhOGfZ`W#QZdMe`9e3b4Fg;0M~5zTEDIdu9V&wqJ=@
z>UJ@iELxqu$3W1MLhqp~$DS#D==>zCJx}%Q)rKH%ZGFlQdThIhXCOXjwwz<&UGSyv
zH<mfm4`SqCp$+8slBf3x*0ru@l-b!7r`C)9H1zo4dIrgT)43y&DaOU#WKug!Dm|AR
z+Un}L8|tIG;^c@4H*IVJ?lH<qh6)+%O-V`7IL!REuu#^UGEb<O&Yv;XUxJtFALKd6
zXFn3*qUh-8=t*7e4RV@jJqHitDaQlvkY`{$&$9e2@<HX$(zA@RADYBd^*;*SsEh2L
z$WX(Pnr7S0g3cKj7%*>r@O5N9-EDq&xqHT&Mc2g3iPOH6NxHB*B_TC6(6Lr3ZecY<
z*EqMPllgI(-iMnSV@n&i?R{=U-C^f?*3gUCGTW@BcFx1Y1FkeWJsmhgz{ySL3<SCk
zaP;cwFO~36ZYP@*d(%^%_07YuOOvl;UTn%x36~U2%^Od2g_(GppD(@j!I=*qK9oK?
zsO-)~l(Fk6NVyb~akJqqnh88U-K?UV5w~38I{eb&ZD!^PI2!H-o9i)J7Sr&SmP`5N
zm{e6&OXe%k3x98<@>z5%G5ju{sq#Mem7y?L_Y>LE1nSy|>yV&H$3(`hrr5IRAUXEk
z{Ff%Pz0>vl%>tzk*%lI8@p#Fp4^91kf~l<ULqDr9&c;d<9`RnOGEpoI{pqkWH#GaI
zt>DHJ+*G{>tTr=bpYV9t>g@$?j?JyD3^|lE9C<?WhtGOm-u{p@&+G8OH<AjAMj}1l
z=H;DYkX@)xF{VB{^JXWGx`g9F6Rx1(Ii_slH(x7l_lcMpeixZfIIEwfc=?=y!dgx5
znrfF*o6-6}lf%sYvNLXKhBz{jiOGIEA#U-t)v9=!n`$z=T;8k&=VJ1rpn)LF04Ai0
z306~cg4RGrl`zM#pPprc#!NhGKj=M-oXNyCJ8@XGUxys*8_uzWco)O+bxABTA&=|E
z{FE|PKL7gqGAGk)qHba0op-ht79%I)Hm<a07wPCzCM~eiSJOtSU+(tha@OmBqpLU@
zd)I~~l#6lj%2}<)N^FO|g~a^K&B{7y3**V?bJo$(fg58Ft7<s3=;#uq`}$*5Utg?B
z^O~~St&M!vr`t^OK@J2`b6l3&+oH&(D=IIJ?eAI4$__Cqwm6&v`vjI&Panzq=RKb1
zp(AyKUMte0qh8aF1VJGa_pzAuscj7n4HKJx246aL>wFgWM)eELoy9k5UX6~9vOcr-
z{f<^o;~eZdgIC!6aBwKd0&0@TRLTt1-3SY|qj656c%|la1cnL5<MZu+5BCs*%+F6Y
zsZEYs1K}0g%x~^4Qp$05a+=b!XDW={Dn(vsa&ZlR<6JFT@jTQne=5rrjC)pIkC99F
zo)u3yUS0;d<sa?W!;G?|oRyT6y6h{s>=!#MD&F{Vv*%A{C^xsYDH$0Vr4jfEo*8#u
zr__WPZb56gajrp$M|QNckeFg8B!mT1QkXg+)%~@@g23f5oojJa|HI)>5fg3>3EQqm
zmdN$9+O8Ybusx>qZZVw4_1p6!N2<nR^o}mR@*W_<VtjAoVesnJEBA=ovky6J+sECh
z3u;ttedC<l*E&QWmzjF5cJ>C^50sZZRpMp~GjhIy41K!u)c4WJNq=w_##mV=XXois
zli=En>s1%49EYnPf$hF&Ya0j?dDGh3AKYo9THS5(3}t%eV)iQR`Fg8rucdAYWC+l4
zcp?6TwXP947CW~p1JkwTSyB7`Qc0M=0eIfdgy<Rc6zA12vfpebZVpEn;Vc#+^sh~}
zr<Y_+eO_*_kh`p3wA}xA)u<ST)wovQ($Ye{L}_bt8;M}yA&)8vo>b{EesJc^oduWD
z&?f~SKZ@%c7(|23{NC9aotc@r&~(C3gn4UBCoy9EDPGf_0=INnQ#sRl6a8E>u1aYl
z7fxEY@6<K4KUW<oD}6{Ul2fAlj@EZ9Gg=E=hkOp5@G6;(<R>I;u=AdOt;}y`_|Z}a
z`SZe5D$Nnd!Lvjc6?uP3hbW}CEUb8KsBs3o$;~csRkI6CY8AsZulWddO5oj1)AdLl
zcE#j}%OdOrpR4o3NmYI@gW%kD_oMg)rW_f%{#FCK0If?>2NRQ$_;^hR$^-0S36-@K
zEq;9W5ypjiZ?>V{{7_gG@zmJWULOf7*k!XX>pWv=_WFvx@ZQJiU_l#aEW+&-JJO4&
z<e9KgwNqp7a%PHF`{Sbn{l=Q&l)!mt>*x@--641_c0`bOXIUQWn`;fcI4u}fRVKGO
zsYUXF-CVeC;WNe1q4Hp7*JZfPWLBrG{X&CMg^sS#9vd5*S+H|4cgPi3%B(8{8QD?{
zXnZBg7}Z$tI7a$K<B3Vflg%gGYuA(0sHC)*+?PCVgHQD`Z>TCLpo<mWZBMz9VSeG3
zTi=%loigsOB@cI?H^e2BA7&`bvSY`Nm|l9vcPkM1(2FmIOF3SwlBJe6TUuESgge$g
zLOVC6irag+?^>rEy7fIipF>)(tsHKx@MBjlB<rQ~mROQH!H5ogTF>Z@7jon*78{w*
zE+VEhp1QZ?xd(TIfv#?L&6F_&u+o~-K8w9LYNJElZY;yQk%sP}aEEV4Vul`UV^LI6
zs#s*n-tA5?f-r}x)do54_crTp9(VdgN58O@DM+#(uUF{q^l%R``*z=##<`d<V|Fes
zF8i`Qy3d6@W;}ZHYed~C0kUy(?XL)wg*f}q8(AjhW7d0ANcU*y%Pn;|dPK;)PfJS8
z6F~0hbiiu4L{Yj}z_&c7iGHIQ_qM3m89bi0K$-}YJ84y?PMzxCX0rSxDv)9e9+8K-
zToKc2LQK@%VVRHSVml9=*voHV=}UG<Joh$%IWHL3N%Pw#QgAHNeLSJ@ISZ4)mrCqf
zAEQ0^g^K(hVk#J0?ojq1nYpj=iQnhcb5W$#@b+8`#$~!ISJZ88&?eBn{}F#^D0Rq$
za<CSb$?^aXmG!}y{k**33j5P5xF0imjh+<+N4}rRaS0Y(;~Za*%r3`~+JF#2-B3y(
zEONbc({lwgUNf{q?{Q1Og6l7PtW3@YM`-r_!>w5`Ub1M2V26&Tbar&GCF!OGmOW;v
zKmjyqVQiL%I?F>YUz-an=G2N+d9+(iMs&JhzIGWVMmsMJOq+FAjwlL>Ux=D1TDQN4
z;Xy2FI$Zs-vzHak&9lVHJWM-8i~Ysa)#8D#d_n$rJh)~EV!Xp~lodAJtaE42o(+dV
z#PEd`FPDjgrb@#rq8Bd*8_l&km2Co(#0S2(<9b*bnEI1HNNBqqy^?W&+iRgAB7RL|
z5u)DZPxNx?TRmq0U$uM86w(_~=c+`4P^^8A-2<K^{mxjU<lvLDZ<CS)ktN!;_b48s
ztV!(}Kr2RuF5OP|^p{8zX*1$=;%M9<pPA~l@2|{{R0ONZv7NY4{Wb4-%BhLuOp?)M
zUEQ{c1Aar=y1Er8>Td^2la)J-rOsn3Q1Ba{lF~hWs%i!B&2ALRb~u4==m($Vr==$A
zlwU6Q^V51W;&_YwWO@Yy=_C*3r>|K@#`Qwsq_ngk<mWq+=MU~aq=E<dmD$G3hlZog
zpd_y)Y7Egs1;q2zQajvgXAV5KS>>~X>7FZdH*ei~gd9wy$21x&KD(GaWxw3#tE5`<
zbE=4*^R~Q7#3}LC<SSZv)}QZg?A)_U4dO!-bd@7#3URc{0A+P12x2JVLzlu?>3KYF
zbHZ+)?O7_4fKVc|Uk(5L`}dr_haCNVzC3|~X$8koh^+@!+Mh#07>p3tjOU2-K<REZ
zf8@Gu-RhX0HJSC(Emf81X0DT6GF)4(zy=b?lNPgpXUYNcL__#OA*E4I-KY&`n;))u
z3?Xt^j{QK^DYV{qOLW~nn{1FRYzuxrhrHNnHr5z33kFb>orA+{rf?|`fiKz|u6m;>
z`m|WN2Nq=OIrIzW80p1b8oK{?Q9au%T0;<|Ns}GF?)Nn($DwmS#q<7K0$>pErsxDO
z`YVRji<9mjpaE#-{vAw$WrncNn}){5UEST?SHjXE^j6i-(b)sz0JNJS9M?ap<|abq
z=QV|?W7E}ggK3F|@Rv7Bh97W*zhL4~l9n-St>$KHH-q}`-`T-<4EPB=raad*G2!|6
z@gvh4tU*&I7QD;Tx{(v9@fmPICnqNjU0oI?rm3kZzJ6Ki?MA{J_yBOc5N&}SMBC@j
zi)d->2CwnwMIs_18ag^&+Mf%Ffib{P_|D+6Vl8lBtKLRO^9}fArKG2)FTBbx1Ox_v
zkcUZR6NDHsK28UkrMm#dUZi^orW!jWE+Mfr9^~^eJ2o~}69;&Gqq*0@_R)O!LEZ}n
zh#VDVaNdnhorg#y7Lo?Ule6I_nz*eG?ot*SGO>#U3JiD(dmRw;s2Ur(31!*;yGdQ0
z37~Dt^RTL+i{wW?*}SdXy}fz1kln_%<#S!zO7o}xF+S(-eUblnSf#&r6tJAG9)=Cl
z{{Gt8wP}~)$G$w?lH9I8{y+c_Y)0wI2mu=}ifp4qkE@x?@{fD7U$%*q8hcFbLDpvA
zeF?*3ppdvyg<5iGHxIGY-dJB1CK3dW?tSmjgEQCEvvFY4`ZEb+>og({>?~IZVQ8$)
zxyO6Yd3lkY+d?c2(aAm+PbS9xhzI<OP&m!MEdg57%embbWtR`o5q`P`2$UAGAyVVD
zU6E93PQTxBQZXsX6<lL~5@0Fkw&%aECy}^rRHAT96$NOA7_e(U5iWR}kZ`L$+o-_R
zesF%}g}Bw_aL>8QhWh%QxrV~Amm=813>_{YGu{}ZY&P}%W2cT*qCJi26X=fL8s(x5
zsP(mN-Vt8jOER;i-fa@%;$<32Tzi<9mU?lx;Q<7MG5#CYjkY`7{S+7oBuzWS<k@+(
zr|G$vnV1-57u5fDQ1tkL`_h(V62MB)>))QkqxX!TYKm2TjK@P5bv)P}Y}eXib}`pa
z(uLqd;1f%ttg0>whY1R`HMf&VjfBszdP&n**#9M#0%~;>7`Kv6$jnub@lHZyaT9em
z23tt%#*L<o%&R&%eH<B)BP~`~dD?mLz`}n`jw^^HCnP2|66D;AZj7nnWLF1fTL=}p
zZrmnA#7Q;bFb*bi$ICkT5Ng|wdjT90ilruBex%$2?19JE*46@ragTIc{Q1CMcquD`
z!StLXjmjbcB@EKmuf=uzAcO>u^)=7mFBUsArsg(K5mc9VAN>NC5}w#4@V!0VEK~T+
zTlkw<xql`6t+lHw+-b`mRopi#OUv)6^O(Xe*xvyG0WozW3vKoF$9NgstNeahE7<B+
z!ZgwqFCv5DyqvXebXj?QRd<K%y*|W6VouSP<S}-cwVx%k!GyH~6UUD`q43$Dm(-eK
zyi{dEIc{4pTYe3!TmkTVK>#<e8*O^Odqe$f7;Xr6t7tf-a%4Vc=rg5rQ9bZ=)lfe~
zDx9=e5z>7r2AEqxt_<w1aV}euRwU&)7^;@m*3d``X=!QlHcy_vec9anoZ<NB__*Jh
zH&4JAp2K}>BFwp8+Ex!nd}4kNUo>{2EiH&Y(v=rR_|djIkFK}4_$TbMRZ78ZfL-#}
zIthsWH4F?mm<%!^rl+TW+3}w9lR-x$og0;@euaR!>Dq2V9E$I;!}vNP<C!!1_VK0y
zZvZgquyszigv6jF1Vp51x+E`*87P>eQ}i_RCvok|{YHBKn%5HBd`PTVZ_gw9It$z~
z8Gm{qHxB!omlp`d7+!4DjSIMM9MaC#W7r~qdP=1nqkUq4`Wq*7t*j<4kyra~Hg|Oy
zlCqbwva%q4tj>A+?Jd}Eaq*BI>W7aXb<EF)!~lBL#36(TKykp2lvMiap#jAAJ_I}C
zZ`+M)zY+PB;nl`q(!oc-O^_36ZfOZ2H9;sUBqYS9t9=EGxpM9`U_`BZ3Z%e~*jU6q
zbKDe*e<9=M0MyM&9gh3w`XLnOL(E=dWgNWn%R2G>fjO1MDZbpOp;`yw#O1atS=Lt+
zN`2;!?-=>Z5!KfmfL%~fC<WxwHba7E$sx8H@bUIPIm=M#`O8*4mB)~&g{$2&fw0UG
zYia0MBOa66(wbtxf(Wl@px00gqZM~)%B2$s1pYwj>71M&ceg#f^XC<;wIlAohe?2N
zYAM7UH?~0wqG2jW1(%EA*j#3voHV@EmWeFbHbIYt^A5{VSL!On+YEQN89J(_O)LSG
zxD4L0uw@FiA6=bZ?ukEbk9o9!PavL7ZLw{B&NMxz#c8qAu6P;DI|nl2M~(8tA|c2|
z@i&6xKszcuuQe_z)C{2IK-4Q0buisv4j;3K^dyKPR2~=MtRL?D{novr8pto00U9|g
zCydP8r}H-${*I&vItBB0L_7aIoaFx>#Xph^6TiZO!C2F<?F^2(s*(kq8y9$3Uf#JC
zPl!cT#+OHxu<Xbun0Lam*e(lsHElew>16-|GKKlHfXtiN1%F{Z6Z-@HflT2)C-p|V
z*59!TX)$+j)li1;wS4~%?!IU1BEcuotUJVw0vSpndRlu03K>K4sM=e}M}X#^=wu|b
zy&%WQkjK(yMjl_BR6E%E{J|fjC~Qdn%%a#XOHNz9z7>9Dk?bWbP>2wa9I>=itnGeT
zHDo$BSP5ROVF6A)Vh8D}D`Dyl0KQ&0Z86X>FnH~><>hNhd($#(5x>fdC*Ue=fYw9D
zlm~Ex{qmg1KDQp!{vc{rUJ3|2Lpa(L0%$}zwO(J)dL+Ih8?YE%FSR``dyp%HLV2H_
zj^I-!I!;VX0nkC~b?xM*jRYVIxQeE}UO0Lr3D|$I*qOp+uV1}>%@<a64uYW2(9q<n
z1W6hM`FQ<CIJgfBL)D=M@NJ86kbeSnRK7<i6wEye>G0pC)Z_@@|EoV{WnCT|$ba+Z
z&B8f5NI5}7_a`v%^70zn77GY4h$y!ZR{(AuWk>ONNbdnRQv1u9fR9y#10i8~=gy?I
z>ywK>^0po{PmPW?`?Q4R>vQzB1mLk}h4LQI0yDd_v$LXwg#{W(BzbInd`@o$5K-Pj
zOxgLdx?0X-2Yt8?0-{hV4K~u!(i-dCTJQwyCWjUo!l7?gH*X%^SP!sg58L}22}MOk
z%<?XG?r59KAG*`w(5u})uo`n4avcHFNj=CV(cQ7Ih{MDO0km)AScCw{Z@W*1HZ1&c
zHJ}IZu4>ZZ#_m&oJ8xU7f<qsZa)0OP!*eOuFcmKA<fJobl{``^L>QsZvs;LBSn39L
zQuCAkw47%yaFI=f<LDM_N(d)GQ1+4~{Qgd~l>c2UON(3ou?+R!yG{OcW|Gf7)91Gq
z;D5|R{l^~U?IDycZFd8I8;F>mm)7}-i2@F}h6)M*P*f}-H8%8I%M_@&mo_{Y%%~dZ
z4UTO^92-zK!A@*1$6;^eZ2;u}qt<QOTl+`s^}iz)pk4Eytbd?g^Y1XizgL$=uoGQK
z6hz(#e4S45Sevb=pqKmk$LSz1NJ>|re0atLqReMWZN=X8W`9FtR1rcQqh(=R;j~ky
zxNW*#*Km88seOJq%p16vEgR~YP=F2Zfcq!uqg7kEFHp0<0f7<q=lWjGW)aS$#O2f+
z7`ho9f2{w-diR}>6fT07156|+DQtbq;}aV#(O-WIofF900hkP-3;brKV>Hgmnx1aq
zWU^uIafoTnz{&$#>tSw0qZmHnuQ80;<g5(8h8Xky?n`(>;p~zDTILG4&WaOtGP@D=
z2dM)7;o1leP*==El?yR%O}FmIpi3jjdAWlc<A(19hp;TQ6&Shgf(a@#|J0U^y>$D#
zK^+Bn4{dXtO52B_qJ+{4m);89gFU(-dQG>Jn#pk^URqiMQ!Xa5t6E^Io-SPr1xOjP
zObR);s^$h<Nnxm9I#$*S%ur$?`_H1;JkjHe-4|0tmEi<i9$fDDN;~%vFiD2ZC$6wT
z;(Ye8$r>-@;UMQ4!9MYbKk~3pno<7A)>I>or5+Ev$?RTlM88`*+K!o~vjXYL`jezA
zQYgkCJ^4N&(Oz=MAs2xV@Iqq9oqmIin;~*&7pH<4S((f#0#lYNTw=WYq<8gvh&>7L
z!><<yZs|`X3H}KAL+~HJ(qz?n{@w!Vv3;%W?UFwx!YB%2Vq)5B6CU70AQfm~q*w-Y
z7GNCb<#5N6grp=ZU%QE5TQ)|y<*Nz`)h|T($^*Ib&zgw=%)h&F8)C6V0WQJPXaM?*
z5xaJKt+SYHfcR+pv6!&(FAui)X52x`z^8gkRMj#{yuc!Xq?L=S|H~5=i}}f*UZHUj
zhyX{Df*9qn@>k+krsK^C>jNRsK*{DhpSI$;kTie59$cx8IWT4g8gyd?XLKb@%iefg
zO~D_(>X-xA8KEWI*%kr?{BB`kAusiT6rs+-=*%#<Y(WQV3hWj#$2D-1G*3HTN6AF1
zfE)+cUf^MXu*E#V0(VMUda4SMX|i!W4YW(aY2X+EeicVZy|w)S0G9nH5h4M{7fA^+
zL!{ssn6yTXBml9CL&#mj>S)yH<BP#cpSb=E<BmaT14JFeVWz!aOR5<GAlZ>cm<aJh
zu=<m&F-AaM!ZFi6ZFXSKfWy>$F7<AFVnUg&|9rel4sp9AARO|Mw2^`ju&`)e7`(bJ
zPCyhh_r|R%j6Y;~8Nw7;_a9JH;idEF%@?$$E#Sogf-EfbQt<r5da0(SrjW#EJ@$)N
z5ZIUusUgT*KSk6eP>^uF*?s$#IzpegMYV~LkMt6bwzRc{t!o7r4op}L1Rjl!`Y1!_
zSHPI5ZD7zT=)=7QR(!(dh?E-`mo9rH1qDvWYS9a7e(&LN6ctM6>#ziC4zvs~S9Z$V
zA%%#6Z7zs&AR!rc#~|r)iXR@oW%r>2-M=ir?cMTrWoh2TBnWf@yPZ3Q5Wj9MZPl9m
z=z3M?Ifc?5kC_N->AzjDY==bGP9`QG4f>;6i{YOAkn?@KO*kj2ZAheO+0@XfMF&Yl
zh_A8}0Ahv;8?jS=GE(6bJNKs7rjXy@dOQA$)w&nFb8)u+u}C>e6~W)VCJaEl1?iX-
z&!XN+9E5RQcbZh~n^YxsxOPHTM|!WYVa~utno-j}Om~MzIrQiwJpL?$)By-1T`ZH~
zu}b&o8Zs$|2?xO7%Mx{xkdOv(Yhb$UP_DP<=R*lt&>Vh^zsBH)$qPbKkU**77gKq^
zQKmtmE$j(e{uQ$L63j!igZ){D|1A^$pOWIy=JX$MMgLqu;J+RPEhcbXSq5r=MNO8d
zeW)`vF%c1JOiaMo!z(%D6b_t11JGraSxR|Gcy&2h?`dV0;C1Si_g7D=ygYs|7_y|=
zuRyt1`mxRUb!C=guA#P$jZ|u;-ciLj>R;al0#A1oax@W@S=Zi!f8G8XXt{E<a3QAn
zCXmCq<D|{^g^XTAxMvE}VWLd$I6D_j_VR$;xM8%;I{1hrZP5QOgqJYT{~2-efAT>T
zR9iP56NA`NTPGlSX1Ah#Vafims*q43f}3d&cEZvEl-T45ZXJ31+te1qT$%+*%bWVf
zVYSJzm+R3?hh`Jub5gUHjPfF3PVo$`peU@^PGDA!!?uSfRALu*XNbO8sQco;P(2Ub
z!eJ!01eaZt=YP5i!l_%0?~<$HZ_=U@2~eXXE4L@UVi%D{@eSlEZHRdgzg$a=odYS-
zwNDi;Mn>JoAdgiZ#Dl$x1IOT-`|8^X+pZixoEPipY~5bIF|zeu&`<{d$r^*|1<-AB
z)o9!<D|TCm1=bgNkEyLtT)XTl?+K8}Y5;G+nVN&!PfSbWVVW7rr;*3cd^Au_3T~bO
zAQvvU8@LLX3`8(aI0#$<^gL#eruzEi^<NMdARf|{@$#Ou>oI()BtJ60lC1o-3#>c6
zj3#h@9>_@oTe-eSs)Zm#W*c3xv*_6wh;)T}R!H^r_3EU%2_HYUI9@|3s~C|NaA%p;
z)p}Yea*+o-cc(1NqUbJCQ%_GMfc^<kRQ)6><Gw9f92ne}0Lq}tgR2A>1rh?`g~Xlj
z+%Y%QxQ@hw;2`nsb8_AXWLjN@efGgSfK(pHaZXGe&q~P6&1Eidog15p;U{b+cd(O>
z%*3!P;f2TvrI<YR!{!h*d@Edmh=|Pey#tpOqlxVTd0ptbQRMsp%{I>$n*n@QEcN)_
z_YON$xG>~V8cNabca#r>^>UF8RKzal7AwL8hGsa@*oIcks0m#a&Y-mRQmh%7-*W|4
z24!giLGQJS>CwomwD8-f`oF}0iw+{dKBCUJ1=+Dro3&3gufGI_Q*JRmsO+UhBpW1|
zJJlH(kw6?6R9Tc5Q^|^`8sY^4L_q;45YzM*7r2n%rC{-6S`F}6PriUwW?(!it=7CV
zWRBzw`QuWMO`PaBi9>e-y{4h(Y#EXSS4?(ff_h_?X8=g$M~MQNULYYq2oV1<i;1Tw
zBmzR}Mv&Egs4Yjpbrct6XJ=a&t$TWPD-wtD(>VElhzU@#<)|~rAeI5|AZ;4wcn!@<
zs=s+C<6{iMQS0v?j(CIEyz~!xZdN9yiPn_Ipm8V#8TG((txJ!<KWr!7pq&zb?80yv
zVqB0gceeg4V_B0LmJ2j4EQq1v<t<t&oI&EG-K&$CYAAE>+;|4foR5aUN@+D{ZBf$i
zm@zyc#3ItBfKHj1(oM_!Q1EL8G?hJNWuyXx$YFU%;Dy0FY<M5R1}x1X*flIDh-gXD
ztvJVG#0>T#063o<$bryw<4AE)hod<1V`$@K-S|+*XZjXWl*704v&#cP6&w&zG!B7A
z+@kuGxFAw1Jl-qh@aOoLtlQU*0@OfpCrA~KKy1}IasPhijT%2#wCx{mX?cRC@$sYG
z{N<pzmFhZ3LygBaz-@XUGwKNfOFSqd#CzsKvr7>J&%_kWuNGJ(QY>Q(>O(z{P$4@}
z1VR->1qINWHC>VxLK1~Of!*jqefB>S^ATp=N)tKER`BpZ?!`Z|_LLw@5nM}&(_Uc>
z1fGu(?yP{-2d3|gf&xgf%OU^D12TBWTj3&JW6DRWXxnAqAFr72rJ5AWAzfxE620ja
zdwY$|J$(3Zx!OW*cO1dnUFG%3lu1^AtlGC$K$~$%;}9AMX3GvYj2EP1%R%zvVU$Is
ztX4c=F{T}dRJ4}ikw|a9KLx2VF+K~hgo;3Ap<IR3HJ<&u33!B`rNvs6u%w1J0<?UA
z&$W|-bPEe(O#^H5bwRr?N$v+M22miA?48x#gD!+AvH7}w6eYMEa=X+SX{UYKtR>#O
zek+0YM)BuwZ@8nN0K({7x#{WlUt2e#v#t)Z&hbU*rWtdA9dBrEW=1;N>|6?L%`5s;
zq%BnsqeY=VsqOh~`*O#R<9?{sk)J(z^IlBv^B|8V-4+cNQ-x^5lcItm>btIl5x?js
zeR4nTVlv8}ng2tW_w9v*A=^C}QG1h4=dz03+|O>i?evCjp$lvqAAB4PRXAhoIPr3f
z`uK(OGZn(i1LO(ELo1%f5!F&&wZU;Xue{aHIGpOGac=ou@71Z6A%L)Vx~6&|nWGbi
zbgAyY1}9HD0^-+}p|*ly@c~;Z()Z@Ak!NtlJwxa@F;9Z?=<BhN-bt#0<S&QIYJyhW
z<uK|9juQ9RBRaEeR-0;Hm-l)lXtU2}(N0ydbz+P%AvK0w4x`_+sl2<$P*^w2!`*%S
z7qftoO7OsV0eztz2J&c=pnNiVE#q@&yZm*0A|^!zd24pmMY{o89EgQ=!g@gl+>*~p
zyNQG55{3i{ugA1>cFs_!K|dr<9kc~PM6(N#nzNk44l=^6t*y&d#cNf6y)f{aWk@GD
zUMvFQTa>`3?ItB2FELO_5y}Af_WPfxMRfu1^12ayBRTxS>~EvLbs@b8K?s(jpAVKH
zIkpP<yuqhR{UDlmshLX49e#z%7((9uGAJ-k!Kft?$}0q6Da&$>XJ+h>bR1Lx0?4Sx
z2>eq2gdfOV=bk3zI9!^N-NQ~MlW9^>$hdKYTs#=7M&RZTzkS9j^;c#qQ0Bt=(LyBm
zLoy~1yQMSv(k>H)d8YcAwtPP|sQ%j;pz|l`7k>V{-38_@9WuJh9d+K9$#h?4k$N@Z
zBF(-;{qbx=JxIgS1|~D>0+FP?Y?^kt7aL$r+4UC5UXq^r&|c`_LbO9$I^3Hy*3i>q
zJX1nDN?je^JeAx_Eo7Ig14xCu(+i-23K?mmlmFa8R@z()VrE#4xkG#VPM}x$Nh5KZ
zZUIT<$LGK=O%!_2UhttuumC|K7B4IzA<^csMEg=@7u-MuGM3|WwGq<)>QJ;1ld6!l
zEp!3*2-iKJ0ZRl?S75^c{vHf7++~nuov^f@cBvN|^v4TJ&?5qNWJP&dGmy7}5UhY#
zZYj482@sL=P(vW(xE&5Cy|(-VwDTO)moV<YxM&E6B~hnmW`e4O^R2&Rr)diig=tRP
zhqKx1Pnizbkc|sa^r1du^vis2X_58up0YAA_}dpR|E>B<W9f)wd1G4}i$S(+fI}`P
z6H}%|oo(qg+5~^q$iU=jCeYRh(kKJtI_u-);RW=TKYA2i-qeD*Zy;(re~5<R<xMX%
z_E0DkR4aqD+yk}{#Bm^T8?btJw+SO@;k(1XhP_GoIQ;@xUcDWJI;c3x0bVRR;nw6?
zA>)FaJWo)@y*o@R;)3Uchd_cE$l(Uz@jwNh@Lq`Kp@k>FYM9w#K^!`s@vB(v$5-?g
zKy4l`oMv2@Hyl#uq5AT8Ox_x#ivPNo-9d5EG6c#xoiOE?#uuGIl!nhj23gI2-R=o-
zk%__(bnrCWfMFRoq>0-wZeUKjo0_zYxVgD0O@CObl!;18>KR2sL0DlGZPxbjBZEXG
zwdscOACD;?6^vyO($}7GpuNwp7rNJv4poKehM`&+uttDP4UCGw&rkftUKIkZ2^9oT
z;DL&5(!Ev}W$D??wg!U!@EFuEaWOFgd1QSQrs}WO5qK!XQ2<qvWz%U|8!kl?vVsEF
z=`}i07|f4XY*)@$>}v*+(l**y52uDQOM7*8_Uvsl_OLO8iiUK^Ke{5da2<&};$8%{
z%@h=dM=t*PXcyMOs89)0t?W|Q@*qE!jY>43iVvSekwqO8Mu>VWPHJCQX(uoBnnW<X
z3r$KZI)!w&XTMV)ixjbDcRL%%EYzQE5iCJWf5jDxh&}KYeLtw7>G9!J-Yc)95Ptj1
z7W;t!iE?6_2~VBOyowB{+(l(%I7K`<s`~e&xt;?Y9xJnUIzd|izLuFj>1zsZWA>4n
zC$FNSVj`|H$KH&V9zarrm1+wWENw$@+CSS=?*SUWGN^Jv(OH0G*R>Up$nFIC$hLbi
z8)U*r9PID!U%HXSaOaF`g*4)AkXRrGq~xHlio~N34XOdhomGB~IDR!~=Ot%VRiaI$
zOYv$qD>ru_5?rDBmUF;3KED`)Y!WJ10<MCS<D@kn(u8fqUt$Zl-SYg}3EHNhvSMO_
z`mcfkA0jZ&ElDXe#Z=0}R$9U36aDVEA31~1l*=QOrK(^~Oe!qrh={NH^hut^(3(}-
z7%%0ul_9A%(w!qQkDa+2ZN*&xBS87yR<w7>VdaS(!3E<5wOi$k5IHS_1Ll#Hl|^c4
z6u}|2F&H2QOA`nzc%X{tporTAF$syF+!_?JF)@L9*6zfT`0?YrKp3oBL*$RRqsa-}
zAH9wVx8hPgqX_qTT*jDM7J1b%9vl8jvoKETl|R5b%Rm!y<+NJLNMrP)*<hh0tQ)(m
zGlV+Z+)Ug&r*iEMl`KoOFC9?IBd?Ye0R_5vMYx}p$7rBIV4_GgHFMQ$jTJj^%hpzS
zFxPHjURT4atGBOUQ^G1A5h@Oyw(r={Vu*!7p_O9Yo%fNHjj9*mzT-yU_PfDLORRb=
zVHXI~UIF|o*ZfFb5CpDkQR7|dW=aZcN6fjEAwR01u$_US96VMH3l*H;<y6Bfj#ywR
zUom6na&!~4{U9V6Hd1^urgoBCc@ng01<I_!K4x`ZT2^cBbX*PH-6>eM4AQ`k^2o6o
zKMJZ9331tCCh*N_RtCW8UhlO@ubEHnNG%F5d*=Aoq<HcXQ`LZ0aA@;u(bx{My>Zt(
zXDasNbz<CMNRjZDS*?`h{>Yi08AKJ4mu}{xC-9Y9cteXnbkf{)x?BeWa*NU@L0N@@
z!St)2?~aa*J<?2I1{Qo4lyPE3YeOOxBHpu)kt7+)5lp|Vc6n5od-;i;@@HdXW2$BT
z1bEG?DpbfV?CEPrXfFp*mxEqSq=BdqY5m#tG-eGwraq?1!4C4dU=7<W-JLjIQ1!J1
zl?5Sjb1)BocIi@#h}SJzK@Z~ck+@mfc@k+EhW3sh)qDTLr`QRqYtbpFiU+N}^lK##
zQ5Bvk$k2CxAL)_znvtnDW0N&;Zfz4)j?qLZEb%rdDV%NS?38|!H`fg6rx?v60wEU{
zh{PeI+80ogvnStq>RY~l7jWM<0%N-KU3gl^kMN^YQ$psL?uG^p*&sM}kHgl$r%#_2
zD1S9FF)^uOG-Mo+U=C!4^oMxeY0~b7-d>YT?3G@5X?=4dzZwXCTPnP8INbia2jz8<
zZIkCXvC(OM?5wP}g}drTDX>b|t5#obC$n*KR_EXE+QGokDnlKui+sFlN#qx_lhPkm
z%SxueeYuD$<<s5y{A;9<+rpf+#QbC!K7vT)1#a{GtG!fjPcXEPQ89>Hs8}pgn?io2
zAF81q0nMT+=@WU<Mqfce26X@8*&Q5TvOat;Hzs$1n2t?;rae9SiO7p3C_mHKG3}>s
z$JEPwQZq)gvcS%d)$*ji#`;kpDJ?PGot*|>SHHa;mK`DMk9d%o-Ulkl6H?;o?r3Q>
z$*~;btZie_Ld)=SGX_{r4+2J?^<x@P9bS+v=+FgnK#uMXOh)Y<uCFil?n5WnELWa{
z1X3pUoDBa-{mZyT7fCIzVam3{U_DcWaS(4*r9H6^2Q?~~G|=#=ET8niG2ezICf>^|
zM_ro-sV6vzl4M%)K1$DlF-pm#hz!MB$0{yn$byQJiK(Hf=|o@W!oq?!u0EzUJQrn|
z6cobk%h`Od>_=uzK7Ei|KB%ax5O2Z~)PCYYc~8_bQ~^i+oVpmJi7KU`G-AF+g6Dlv
zTAB!|27|B}6^8o$a%6sd5GHu^t{OY7pmI`Sacc-RZ9&KHifS8Q^28MvW0B?;)p$Yd
zf(oS(%B1gt+8`oII_9j+RWa(h0G?L@(}2Wn;7+t62seRUNOu_e#>&p_2hom-tMM(G
zo2Yy{jXfjzl;<`F8`~qB&aASj>|X9LWA|IcndmT%1PHRef3!kkTk&kEmBG`I!Jyaw
z)fXVxtjuqZSt>fOudh!O$9W6lMEju--Ey>XYhH<`gGkP+wJu!hgdAlyh^Gg&8Gux{
z4}{Pl)!=MQyD9RkaL;(g;n2IdIDRNdPb+=6<5$T7QrVJLS5a9Ds_QI=SZsG(xWh&8
zTi~)GLLLC@V2P^9Xd4zP4N+u)>PGZHKMdZXZ3Rce<WGtrf~HRbo`kYOph|)LWK&gL
zso8woi?pBzBEzEwS(6}QHm`h!e9iDlB=Y-B1@Txuq76FuB1A_MsrkYWeSJ@4gT)Vo
zI@go%e0$P7hXkX+bN5`Q9rz>@Zibtez-j$0v>lQdp<!l>lGy*TKY<gK>B~~Vh^#2A
z-<fWdavU$B1s<422<ylxc3y%IE)}{6`p_Kj3v-x5G<opl_U+qCAw8r?_5i_7^=k<c
zSZ652*hEjCG;s+_UVwNl-sK2wbc68#F_5cSzkWTUe3TSQAt^clbu?)Y>zJ6pyU<EZ
z%2-S|QIZvwc`11A`@i}cC_RBkD=8_Nh4L#=Zm3qHl`8^ug%1t&V$>1{=|`j$q>)5w
zW#+4wXM=E@+gODC5uOC6F=2!RV;GFo&BP@JJE-FtZ|0!F`*4$AmeB_a7nStDwuJoj
z7nwTRk+l2uE|*IrRzb-GKyToO67tq+?7TDo8eD=AQgf1;7#}GFawDk<rLq4S?<&>x
z6#!{vpWXY>O4lhDNZ!Am45ETmVd!O0GhmAfDMjsm5g>z?VHcuuai<kj!P!58rMlJF
z@OVt7FGEJTbG#V4@P_fPVLJ(RVnFs1ObCR7NR?RtrS$$#5_AGmHOv5>)B02Mr~Qyx
zbhwVdGvpw}%S$7z2rvt6zr*|El(Y@p@4!tWW(gMNQB<S|Zwcj~VMmGEWuj%8?`#9R
z;9s!3_r^P=kvzK%vu%yKK+ry$*H)N1?2JmLP#Xhph?x|D-t>b`An<`eKh38nNn4-{
zIX$F#pZxw&Fw$-Kr7Nrmuqnu47JzpSg192~lnm`8DF}aqP;Bu++QqgyLOT0jfVRpn
zppDs)yDkq*T9$1$JMG~hebtRGp@p|kqRr{<#ArS;uqzZtMFH(lHAFYDNP>E9RuKDI
zD<~Mc4TV7M%o7zFtKn1n>|G&Xf3%r%5E0g$m7Ogix7tw$_!Y&!@Mz~Xeyyh+q2>&u
zsG@;gjsWzlJyaSgYBja{>Vpw*T7mra1q~Vm#7M=%MjDL{Dn|y6iOC|;?Zz6=5q=1_
z!OE%V@&@s2%k^Jd^wZO5*eQ_6RW~w*+Qk|{!qO{fkKwce(vTz<DcYP2zJo(Z#EB%d
zJd=dOF2;yv^N^$OA#nZIL!gRG$H>;s4jbReuzh=TF0FY9W)6s(ytufyJb(#naqC-N
z_?g|l{XS0yqL6=ivE1^Agh&%sGrt(npsNBqKK`$(7obg=FjO~Uxm{Kv&zP0x30AQ6
z_gaD*xI>*9B-5Fgn0QvcDw(b>PI)khl{3>ecd~II<VXT#7u%7&{Ogv1;+=um*S3%a
zNB#h6OZz8GsP50JhQ?1Kt6&J6_Nq!QLUddUL3?R$Slv+Qh%+fGyHF^#aPfQ2LZh-7
z_#0G>=0YH@))76so!ZKvYD4Qe?QkI8yf-cwqjC;fZ41ID;@MZAmk2<w;zvuxRC2;M
z+9dDezmV$FGlA#}4!Z);)M5&WR_QqwM+gM3<JwVWyf@9YOb|6e80w>8!LJ%%t91*<
zbs&D?0h7iJks2P4dLuv;0RmX#Uca)VTM^+h5QOE1rAF)#qTMf8rF+CDQr5%SDOWS0
z`LA>f>V`5t7sw?&{#RxO;-uBYDkL-b8{P8Md~4uDS}_!02}6lCY9gUKbC!1TV_TYm
z_(65Wu2lv-y9jH8`~!(eseYh6DW{bRL)|$yDnW#Jif0bE^E8(Wzm{4Qj1>BrL>mgv
z%x7Dr<{nA~er-#44<ie0J~`RO<u=k-35>#loOL8H%JEB(p@7ee``{{yAqSH3w=e$O
zvGTiooi?JR5Ro2z81=AXVheHWKQ@z1t!Q`MI{99+4-%3)qAD6&T6By|60{jc)M<ZG
zIUf6i_QO9S{M8@-7>!%@#o=RPz2+w!a>H}Ij{=@a)cx~e<8A2{5UW_CUmW(EJ-Pb3
z;!84FFe=-+Lo@9Z?f#NqI4@&lZN7hSZ#li*Yg~)q7a{F3>@caBply_@Es)y`XlD=T
ze5kLg(&>CWyBw;7Q2J!ZVUo9i@(90N?<>blouQJ8y8;<8pBfTu9pz8B!BS^MF|>ad
zgb^daS=C>K+@un5f`G@<;&l*`9X9_jmDj&LII}K32=a;0OrSR3{{ml<Zj5HaA0f<Y
z3EIa)%K_0FeziZI7YiBJcx?fx%O|pU7gHKCYTHQBG{o9U*j|p%fS7o$a7=t!oKjHt
z+twHS03b=}M$HsSs(CSe6HlGVKm);=+oXfx(E3y1ZB6S&`4oUU013nOGo9R1jPJoy
zA;PDIsN{j;<%P)Gv*G(q^iAJA|H#NlK%!d8kK-cXN5mbM8@2)X{bA{60g)m=96&TX
zQe#~~S;9)Le9}UI#L^~REg7iGXd!He`sGT`9oidqdllk7fQ~W{s%r|W4krTy0KF4n
ztM$z3v5FJp<B4t*(O5nZNfG(a!r*Esu03C;>!8mEhoS21hwN;%WujWHq1@oW`E`Li
zA@E7}Seo8qV^EsJOABuor#h`tdDz%)T@^2T-a-JQvgxv>N@up6h&e_UJl;6{20)C5
z2hF@daXhojA<U_FRIL<fSF244zVmlS&Zh&scr|j9{HPrwj2Z;PPUgJo?*MWbe%ZcV
z41TE`SBnIEoyfCuVR6wtc?Ps#vhX|a8C?1CJS^b72Qn;+2s@sc24%5jByNHKZE<n&
z4V=4&2YL#)O!H)&g5z7hmlSgjPR{V36NJJ#e%~_~ulvC3ME-o7!uwD&AQXyp3$Y;=
z%eF1q!x}!bsXn^G7Lw(Idcpvt5H^|@9bSKo%oap3Zz5Qef~T$;t4TDDG7e=-mqfRW
z+A;1S%2hXw+7b4A#0uZ;aAZhz>dleg?=i`aC7s|7O0&K~?&$6FG^;hF5!wIj&Ret{
z{ohRP^AEKU`)~aqLT*t0f%!BD6;v)|TrOQtUD$Hn>pXgB$w`1Q^82?$f$NYo2j2;#
z<(x2|F8nmJ3lsJJZ=U1-5lQmjuLDz1?%Bv(h6rI=!B>zR=_cx?;Ey7q54W03DQhdU
zEVx;g*>BK3?a87CQZ_?T%!%w=(s&Z;cXjF=nYK5WLola!ohRFwSd8v8(BXPBt}8>=
zC%M)%6CQ+$Ah)mJ=s16RDjyK`&!0bYP@xS5!+8s-odrTra|wHBcvcF;E-NshD8+j3
z-o06<ax4Wfvf>K80%$RsOW}l(?;U?e&mZgUfwOAOrLp-w{m1F=D4-Ss6le({iZVM0
z)q*!eqyU&%)V6A^2}{<3PD?20Sh5_}fA#7EEi{$PJAaP<2kN1uF!@rzY@{|qN|*LX
z$D}q#@n1DJf0x$<$``YPv=ImUO2L=Fz5((9K{iwt%DnUHf;Kd>_juO!c|#j6B{elQ
zgiAw@UfC5F>23+o#oi9lqS;*z$~8okKP#BLF7jGkXtz|)Qz#aR9?k<Z7NdCqR}R6e
z##cH2$niM%`U&>7<*B!_Jm#(L{#=uc-Q15B_+L}Z0roZ#wqGINx;2{>1&waz#np`!
zgSvrbIwKdxjxFQDM$f#41SqFqLQ1@oxTdnQa?LEznn5CDQSb8L76KfJ+zzn)yS*u=
z!Z}L?AJ`+x6-_iWkAuEQ4qsRQk94hP*J%ih`d?Y4CgggzB<i3(AeZ7pc{f=EKo-^%
znh%M$vCN+HiU$7qh*m5!#lE?_I|2yUT^L}PEoIBW)+Z$;-N0?&B%O#%rns)(3#Iu9
zh_cmUKDS+xui%fMcE?_3uQl*xEOQ)%=aLf|K!ju!NO&tZ&5@9jqKZ3iP8?Aec`Ne_
z=*Drihsl)5G(%yYIr{>w%40Cmx<*bq)}2{Gs6ikq24-~U0WN>Gj}a%i_o&`ZSYNQ@
z-U7rzbZ$A~lYk5KW<p9>Sf>Y;p|k-$gGv~qvw-4g!VahzKe|^)pBWX9G2X%gAd~!I
zhw?nApA&)%@b<5w1|xvLa#5b4h79kW*0{)#43c=$rRnb7i3xa<D!X?VW1e*&$Hnxt
zgV5mkmM6t|rpM^3U=$SVCh5j6EiUeLILgS#I0D`g{GpZcSTfdwq&2|_VzFijCgRf4
z)Nsdv+k^z)E3#C$pnf3p6>_B(@c6{7#6SM{<4oYt=g*&kW$lKv#Qq93STOL1w}Axs
z8~XOgkE>`w!0bb?n~4Q9fTEHGeghhX*qg&FAM^%xDeC$MY3^b(=*Tmh9^`fOLc18O
zcs<Z`B8fRF<*ml#A)0ACjgmM_;*xe&Ffr-9A81gjp4gD#?(~5^84PJA7RcLKEzooU
z70xcd1T^qm(W&n|CQKg2gU#;!d*x@`c56&<zQcNy2T__s#l8Jl^Tb~L_WNUsmQa4p
z?;~v5ZnehjvZk<h%kMdi`|JBM`l}x9>hR^PXzxu@uqrMCnlH3W1~DF6Ebx6XI^Wi(
zB7XgN8+mdE0YGvO8aHYKG!~xiF=FEl$nhB$ZJY07gu*vP-#Xl)1#YjPZmJ69p1PXc
zin)RU@IeP;e<f6-U)~)E0>&G)SwqDZuEZo0F6x{J^_h&PHyEOR)O7?e`Hn}D#=#vv
zt}9<r)Hjc4J7^>)8UNGmFMQ?F3Lw+Y0mzQxk%1t%T}Q+pbUd1cR#IY6%@4_TSLl6W
zOrUN@D^dP*&bm{)k&tvpotq#Z1gM-QupZ7rdJ9yi(LQ3w$Hxa;jOV8yJJ^SaGf6he
z&l;v&l3N*sbVJ!SZCZL<U1i-U84jp6c?!Af-95Tst$@-qd9;J$WN8T_mJ>bt!qQR&
zlF@)_I{<0CE=dhNKOAzAc$f<fCrTZRHZp#wkk{jDwh7RP(C@~)+6f7-kVXQ!gfX~L
z(Xp7|r&7qf{-e!F(V72k>vVrb7V0Q)fTbZ^!41?f;HCAU94XoY=MuEP4YfnuaR_mk
zU*CiDA_o{%6kZ?@-H$S6h(ry3^8dxl{|Ku7U554d$2|Yce!BW|!ak&%mX?0tZ~<fq
zgzVDQ^1M=b>ia4X#D`=;11|#u`+$=_+R768n1JOs6E_ny?PX7aCI-W1$qAJsUudR_
zmdu2f6JU?QFC%;GPq4l+z^@ivF>M|{dozUST@-W9dSERa?3|nDbt~=2_$DPhsGzoB
zZwD@70Q91G%`$aq-KpcN16!=saDEiYQvgEegs-VY{#fdg_&Ci&e)Qs$lVy#gI#aKh
z1en;@!12{kpc{fXfVQ#e>C}$jn3NO@wL0LW0kyFMATx(dC?HNG-JLnkpkDflJq4QT
z4KFtkWJg};qheOe+u~&)5H`-lf>-?0pFnlWeM0fKp}GA$71G^&3Xr~n9I@)nPS_-+
zEzAbsTtQ))K1}-48UBeyQPIf$^`l66hTYd*<mItH%_nlyZTx%|=r3N4yex4$gVluA
zfYCReBx0GQU8a-W0=YRktziTZ<^Zj&lZge~iw2QFYU=InMF<@HFDdv9`22bCJH`-B
zB+=clv$KQLBA`*XuqXLYQTu8j4pb(eTbR{gpf!}qp2B?i1+XIrD{FiQ!6@GuO;aUQ
zEisI&7o}TZGBr0>c<;C~!(FGeKJ}?Uh+n!`Ak-4maOgDXs`pnoFj-lCfX8|jBnpZ5
zAjZv0GFbtH@9=I(!9o+n4;09!Be1N9i#KRq@)y2cS!B0Rv0@=pj*{EX$Jpxo3VM&V
z(X8Nqkr`}w=0b;2dIpjtFmqvMjF5hT+&nEwKYUpUVhf0K3<Gyy)WslI($aP_pcP_w
zAt8^RMMj7zA=VIfLUA~xJcKJY?Zd6R>2nHvRuMDwKBa~M6eFRokWfnI7Dk6r-;CN<
zKq|YpGT+~j41yM?0w9Fz^0#9gOn)<C|B;^7|E=A||5z~j&or$6aq|vdSX~U+3|gm6
zq!s_Ix&m%psur5!0cR-O<xPu1{4PR^Q4n2&fOuH}<VD)lEU5K>1dSQ$<t8`(vQ7a~
zN7oi<c`<eK*dNQ$K(LkrE`ZH`Ko}&5%cuzw)Qx)3q9#PKgAkJmDh!3I|GdQ|3|R?v
ztTS_UmF!uFl_*0(awzjdI2UDii7mQlJ0P@xN;dFh8mG-10RS3Bm{6YzsWQ+YalS8t
z!bha<!sAg|$gx)ht?N(b`+Ndru{$M?9fKM=kV>*TzD8PT$R?uJLQB6cr?LqIVvq@=
zz%e3jmCA2(bKR<K0zr!k5O2y3_j>i$Xb;^BE!cMWE(+dD315DsgP;qNPOxpaGcvYq
zf37v<0?&=o5U*CHpuM1_@%6%D%>=PGQ-S7ysl?mF#ksl(RbT_E!IpWHtEI?sP7Uyz
z*14rX7ldl3-BK>xW8gWg<Ph!yP<sQXuU}v->Yc%c?Leq*F%FW+zsxL+q62rCas&4r
z3R)4HnU&Q#sRl3PUeXcn0shys!N_mkz*!dB$-XitTHt(xU0e@xGob`r2G5J=-sS**
zf0a>HW7#DGz^LD?(o%8>xsD^K7kx#_5nj}28l>rmK1@ADEtBRyw{&-N0W8^(9IuxV
zS-`uv5C_SiKlbcVyNpSpfv!gdNl6p&GisN8;)7bQ5q@g{upJ{e0Z$`habkMfnNs=0
z$@`?YKVcS#WOxi<kio<w!g&^21-UgZ@msA;+S%2*9ljWHF~;F*w%i#R(rEN-f|{2A
z%8nu#YDL!#pdMyZT?z%Ez5$Y{Du;biN4$M~eUs+T1zkr-8yeLI7|>+=pFSTBtMXk-
z;ntN`Dh0m}m;kyRCgrMvu0~-pSIq0JiG9k5H&X9Frcu1D4k{6xr@N#;y(0!3FU0a1
zAa-j@HF68nk_3m{k|w6|a5b|5Kp?a-BFbbyZ_}SE{YCIQD6*XV?t-GH;_KJ*wR`qF
zhDU}Bdl4%C4FJ~`E-0{<T#3X}<gHGC|1`*|h3SA!;WI9AX?1m#abi4~?<w#WHa02V
zj&@hC?t@glV`{t}TwGbXO0Fpp(u9MC8t5dT@5e(b;B^8Za!kGH>3d=*0Ja6{R|L(=
zfK#e*dqDsZARhk$a#q5tHV}Db7YqrBw}B4FOjG6syuKtY7L|wY95xGW+zTV}q+pww
zV2{#B%4WT#79>Ps1h=$x=h6xHy!gUm<xd2-YlDa)B!o7auik<oZmJ)0xPtjWXqa~l
ztGZ5C;l04$Il81b<j`SNN`}&F9YP5#p#=+UbX-dlA$@8`6cJb%&Unbc^hPr<GEUTa
zE1F!8xk7T3EWY)lEra(hWIajcW2lP9@ippd$IDwQIpE*5M6)r+*fvL*)x|}E7SLEh
zp$k&;^vN?56OZAE`M?Z-9ALP}1Z{htN@(+>g!+=gX3GJcBubFCq-EK3=ZPbY2V`uJ
z><qXE5?!_tRAHcWx^VH@T;4R|KQUL2pP*mh0X>rt(hBS4I-tJfhd?ZoSx5SvkQ23&
z2Kog2xLK`>tK=$8#_^&WS|U~xu-GEV>6hDNcke~gNfu+*{h)^4jS6d_A#2bI-(9y~
z_lf$tp?*~gls^3+j0^;3(e+C<>g)>$@!j<AOIH6=I#g1N3#A$D-hN-jP*lIiDhn_l
z*_^|CrAjj$zwfzDmTtjDrqfKN8%5UL)t|o3B<!~?V>5ma^J)2=bPrgVpQxG(;+daJ
z@jgC(W!v)<p*wGGIrLyjH&i+&E6op$EzQe#MSiIcU#Sr(mU<bfQVVAE*XQX`NenbC
z{{6}Lff4)tCnl!KyX)(J|3yLJz%~YXD60AM^FQXqGko`VUZg)AneVlFa{H!xjn^d2
zuh_B&D1JWQTxov%baaFKX1bJ60%M4FM!xBeJr!&17IMXy;d1uHx<yg!VmdWFXyy0+
z$eCW%L0&S&lV;wWCr0qNlCW}8dL7TIMWJyJM9|q;Lm06Y?YK)3x95jd6%-XwPXD5B
z^eG6&&$+vwx(ggJ)WaR&3LIY;Z`lT2-03ir-T5r1PoLhZ^iCK!ml6;z(qY<j9r>XT
z1ashdqfOgceW1597v{a&{EaT?#fuIEfzDoUGEo=v36v-5Df;3M_%y@%N_@?r&;{cI
z5@2p|%Vu>+UMDtb=cAxU!(c!}AUfWbmUdfV{J4F;0;mhvF~?k{4aID`^!o74*TK<)
z*oqCaPfChSNdE&m_ob`{DmvJV0T3n-={a`+-6XAuv=uZEbQn*dxJMgf9)Dp<sDl3r
z!ZF8T70jp6hUi%+$q?Xtng{(lc0sAvCd?YB6VA-^6z+kCffjGR5Wmo2dOYV<3xJaU
zw&~q*5M+Fg*NDbo$g4{gQK#>rr`FWeeDeJH2Fz$vT+o8_o{nrgg7ygz_lIjM|KZ0|
z_qWx}^iNx*^_%zKZ#=EuVZXL)&%Adp@8QFni4$$Qr#M@r+~y8!-MTfZ;buLQw8fNY
zL_h|#4+NSR9}vTxiEcOyj~g!M#qQ?jhVj|7gIzvp$ihDq)Nc2}!#OeX*49VQoH>J@
zs<E;0_DC%kM!q}G=}prZ`U(=2zLS&I7<r`R-n%yz-j74ymyPB(Zmh#tLJI>telwbO
zuv>jl>xc1S7q^s8YZHV!f+7z(6mnpE5b+5#t_HlaT!*2psYdymF!)E0PQx$g4#$rl
zzfBz1!9X8z8K13t1kh*=^PP9-rW(>=P_+drp+GOkwx{3`+=mX62*mQ}=uLEAsP5>4
z5)+IM=*AeK5%y)cN#(b1U!bm#4g<fJgPti4#{4WIA^`HNbQp-4G(a(o&IP2qPe=%m
z7AWJjlP;_F;Rnm;Zo;zu^Yd(@T@d(;-=8bDF|VL-;m?ox=YM@DeH2W^pP%!4KVPJm
z|MsoAx)d5D{DckF2Ma4+Cu#2~&<=y1Yv19+=bt)vcU<25M|HKz+R6x_J==~4ZJ5E~
zVO}{oZXj4Ac`Nqo=1*I{e);lIi7(^8r$5$9dap?W?e$=8sESWmm;nN5#X@(Yr#Gj=
z$BEjkBS(%vPq2sG&irbT{vf-{^HS;Y(@Aw6I^|oy6=yi*?hxpziM9OpM~uJ!cE}}h
z!`45msJKZ$C-MIM`y1{|wl|D7)rEW|aOOHL$*3oK%k)jHt(Y-%9aNfHF<_91DfI}%
zKAp%DdIGCs8|($&o$G*jSRQ_D_nhH^{h7^^o!N{aayfA+;?x6JSlZx3;XmfVns;aH
zE=+>}*IVm?mWmvA*+CJ1MZQ2DY#?;5;goXNFg7-}nSntO+;2mklNCf?n@&a4j%Qs@
z!Obb|ST>^$R!R-t3V8mpNO?zFebM-Y*qP0JA6vhsg-hD6i&hM{TWK!vG5K<e{tK~N
zyUv_F>-9Y~zjGSK0uAUyh*Z@10Q6#pa+}mlpJp1lWYyHxD)tt6O_eu8b!Y*s8)0%E
z=iwDPADB<niphE471$F~GqVVptrg9oSgG3mQvJ`PjnwLxTqcxMrH<|XU(J1aG?(l9
z^&=TlrqU#{3MJ`886&flv5+BA4iPeCo>GxA7Y&3E5+buqO%fpynKMh7=c(U*I_LXc
z>-W#Q*8A7{p4C~a)#{_qb3gZeUHjU5U)L?yL*6u!?K)}p=vS6O9$jUnkB@hT!dm~w
zSjizj?b*H!c0=`?jy*y}R`qcXJ@jSX^^<%K#syS=aj#bj<p_qKkb>ah<%K^@`$nR6
zROR`K58=G_OEZ@qjc#HSV=^vwnlwFK)^~}suWmL>28xTu3!C;Mk7{P!rOA5{v?(>r
z(|o3M=@+bj_mBN&aYn2yP3E1~zFml@vh6MlcIo!ozBh6MViFsC?KcqojY{3OVoEQw
zdfYK`0&owEoU><39$((Y!a{BU5$ms;FsA$sz>Dyu#@&N7zDkM5<>c1EzZ-xk&a-bH
zok7MnDk`ehavvF#d^hp>1Wn4=V1pHKT+9}+Y~Yf@txZDjelmy-*0&FZ8QFjQoLNxE
ztoK9NMqJo1+l&el-Zhj$=r@#fMK2iQ$<5cV<q#+?U`r+EPdZ}>*?YR&;uTh1<w91l
z>pJ#W{CbckP1Ds|GtcJ3TjgvRucoS6iTi6NFkhz!@J~~M?F^Y2$frUuy2ByOil>mj
zaz!}j<x4c^NqB+-xqsM#f%T~E9qe^}_RJQywyHu$>3$7z>-$mCJkd&2N<E#n8By@O
z2&?^MuOd2)HzG;CM%WkjS@X0MVZBt6(<I}TPQ8-n<1o)Qm8<m8pgVa+G`D_P;<$Mu
z5{!bG*%OitOfwf37w-i+WxjRmRxe`TG3%B`hu9^YRCe#(8-zi^-N_3Bf$t1|ecbwG
zZFN}@9~*AVOXn99yt^vaZ`BfM<MOkWd-Ikp?+`qVgIbKOYE4uhKb9{G`m+6(goJWO
z8mH)B4_*q-p+j2akA#b_B-g~_Rjf>rgGKPsI!f^$Ra?qx;~um*^azPN4jmK^yB#ww
z?)N@+G+*}W@|?|QSW6~hfD1yy$nCdp6+V}^*d@Qw?!bJIgXF*4RqBx;#3^cZSLNhM
z7L2Kxw7>ZAN^)rUsQU+#{-L4U2tl{-F?*ze=zRLSYt@FLOe(991zjoZ&9uvzz5TCl
z>ORTplc%lW;=VqE*$U@MPaHspTi7vaRaMnV)e?ty{UJF~ad7sYZmas$UcjDd@^z}u
z#;P{%;9}ooiF=6Hldo*hmevsyNY?JNA?xf0>rH-iL==oxZ8<TZaZ3D3w9Hoys;9DI
zajz}`Hq&m{Q0YrS8Pu6}HfjTR*rP`qiPW<%pEf&V-%=BWjzb~!y;IvAj2Rbi;~d};
z8cM<!5$ZqRzG5v+#6y(FqC3xH-j+ljEm<Zmq8Amb-P8p>x7ZWKo5)a(l8r*v-BF<0
z(`>x6q=gSJFjDfPfw$mLw$tBTA9JEO?Q|}zc~*0Vb~ih|WfmQjrS&uMUZf%#(2bKa
zKeFln@DKyo7s^&v`))FFhP-$oj2uoMtmMD<)I)+mPJW*0*i%74$U@Qb+7>MnHjNjt
zV-vMevC{@(0@L-2b8Wb0G`gNq%xQf6md-4`oQc$S9x|irue=sv*NOA+^}joKY<Ku*
zjP|pvKDGw)21G@IfI5DJli|E~&M?lrXJe+(p`yg+127G2A%fQ8QG2-*dUFj~K%B=E
zaY~_cuL4t3`Ekw(>}K-Il+LB)u}%-Et+}q$J?5?Z8p`SzcJJPeYuDBhviJ6#x$>h)
zfP9MqjGQ;mWho<6huyz#Ic>Y%<JAKhpEO6cu1ro1{(CZ_0~N+)A7dXn%Kqs2fdl@r
zzP{?NfDRKU=Rtb?hVG^L@lZ+{h8|qZP>G^c3rgI2Dk|=yN4KMJssO0eO4mJrZzVgT
zc}B5rlsgac8OIJzV<F@j_nBcogoWrf+Usl8yF@G2s*fm(^5}Ip<E5NN$Kj(RHm{MK
z!((DL5ta?{6vbD61uZPNMjzwH{Zofwz}y&ZM))doxUJ{(obdZHTH{QgaoVJcV)!q9
zNy%Lm-9G$hF9?m{k#__ur93tp{dafYjl&;}q@@7}N4=RHxT8qFnC;nSi*yUPLk9D=
z@7{e!D`9|oilZxANa`V<vVOa&@sHxYmse8{(L8wYfC!>)!QCm_5sK8>_R7d`0_wcO
zHzo0FFJ>9o@3{S-V0!4Fv+Z7GM9W(%X$NS=rp}Bt#ZYQLmTa-{vjV{TfTet4`sOZ!
z;=QqQwB_JF66m%RN_Tc>U4C#bs|pSU4Va{<3=Wht*{Eh>@gzLv1S29MxUPqd<$B^a
zx`|2}P8E|?7u|U5uvBt~iR<t>%o-yVpb1J5ar+Q97@LeY8zx;>0d(5`Y&}HCB5#m+
zANFR+S2wrFRalA?vrrPitrDe~F)a-zW}Vr5h5R>BQxeMs6NZnk?2W8WlX)U_NiXw$
zhnDWtg})w5;64x%HX#TpT)zDL&4sZ2+cer|MV{hqZrb_R`cn@@j-NiwBEm}V;OLlb
z*L{qP*~+0D?;x~JLQ<BHVo+|nFJm&5(&sYAW}oNXbf0p%Y28t~F7n1SGJC{Z)ISbH
zM<fn5@(BZmSM7pjK@R&ATW?&LLwt%{-L3*QBy^J@jbKEbj-u%Rwy>OzQrnFReCjb3
z<of{rd4elBP(>MRKX@*8DfkXT3kkQ;zH7R6BCO9{<<|*@C*~I~_TXe;kNJJ!bf?z-
z-G7Pxg-=f1J=XA&aXR21BO|un-d;^jO<%x0G9jWr{3xsp!SV52PCuG9kdz`YQbyNt
zux}PlV;$dq?O+REW;IAF3!!W<*SvD&bB(Q#gtcIIV9cshb>!87plWt=ZW%dROpKsM
z*QMm<&6_9CtI^KbCC4>b)uxv?<1XEnZ*$uq&&qV7r?T~>1!JaBQSapX)jy%QX0pT~
zq|iXds@BgfzV_l@2_R?5h>~P(YHB*%QFPeB!68X<GscD-F3J9Ik37=i2+&A91|;@z
z9AXsb8gO;!+-8aQjJX|w_wVn(gQ$*NIc<%Y0><gcKE2F4(50TB6NZF<;Fa_3x<k;z
zxEYO9m3U6(ty`0w3ZH%dg{_$m$2s^F%J^YvL1}4r*r47*7=K~iBD9;2Ptuvsl51Au
zaTBr3H)@o|Y%*^w5zByC0b=lP+XGhj0zfaI-4oqD)S!wf{>6n-U7`Cu?f;zV@s7L4
z2Q_YE3CtX0U%iqd6n(CUd(edb6Y1AUV<j<rjuM@Da`^g(JQuD+58m4}vvPL^rHcxC
zi5tUSjXwvOv1)uo+^pJwJjrN=kMtf#f^W8I&$Ff>#9YQ=oQ|f7It<ox{xBL_n!KJh
zl`_ays8-#kqsE}Jvr8&&T_v3E;RvLk6P2S!q*^26;zGZ@w4fw#Dd)A;R(*gqS&#x*
zEqzGh@6m@Gw6fxfUPRgNmd)=TwaBmSMQsSDJr~9s-j;Cw(SsfxcuS)@ZPsP`OeImb
z%G?_~5zOD|LPbL(pVhKGox<hmw~6annF;rPjJnb|?L3Wwh^Roj|K&O@SF$vd4Q3Vn
zCqM9pO<x#mfG^Dt$%#ae&Wl1BvUQ;cM6JKR1)Q-5tq@&sKs9)AdQTo_`O&)Yh!bR9
zIKL>G4{o5vYT3Q?+)`<LJi*P)o%}Ui(g#0!U3{0syyB|s>+P%8+pu+#m-R*aeLXco
zrmMTzrCgZ^<ggy?np(%!t|CVo99J3w_d4*Uq7lBnSI3{M>-Zn4HFvn&YXYSyXjI6A
z{HLI;y_u$FEv_IlVC8{QVkNH9VkBSR^8C0mg8uW&?WwDgd`JjsVtUpJx5{cPMS8Ja
zDuBAeBO=zt53{-A-<Tw?`AOAD32FY-?@F6+FpFHHmGS_tXhlUu)oJnMloUf_K2g!F
z!~|wYEF>8hnK>xO4_4*K-wq9}LOs?|EWclK`htdm!NXimjWophq`|)Vuj1O;)k|6z
z7*Ma+!vjdUu$;iMXJBw}n_>BGlYWg)&SUA=8Xb!WH-%~=Z94J{9}4;<;?~{xY$-U)
zd!0Y;mEd39X@A?DfB&jhieE|bmAy0zEnBl9*Ze<GdaOAxYe^`w`Z_2MvLPJA2MtDF
z`QN?Ea;ccSF{(G#rRzB2>L-fx)AF>&tJ$+mawaD7`<wsZokmL$#NG7-()W|5DV&8B
z+_**r9W$L${Da<PYeSjV!JSfteujorE#6ls(QREU{qA#m;P~*+?{n?*V<m$~+U!E9
zTZn#+u8h^EN{r#yNaz>X#x~D=a-nK^c1G<XV@s{xqK<X!a*nqYx5e7Z9-1-joVlh5
z$|?4ux)fQGwL!Fg|08rUy`9`S<+5cZ4cn%r;>b`d{^A7y8zFo0<fd_Np~}e71X<**
z!5g-%FA|yu{JLa{3@Lxc1r-|dI?Nf%(ZW_njaD0Z;89^5Ud;8`2^OlN{P(IIS;v5^
zkjMYA#b3yF`6DLQ9x@o_lgs*OGxF68nXeMj<+KyE0D3Cf(e){R{5S>CgA}*p@ZX#0
zHg5c!q`D=YvG`lD^ZsI&@emT~r~h)#$ta-G_O4}b1CzqMeLG1nxpgGqk8*NyUYxI7
z7bESF-})wbYI-``qMqXwX7Uie*n^1(%C>*kN@lm!#Pa=Gdf4`(D$?<7=aQBo0}&6_
znM_>Bw%He}H(xqt=wImzhp8!M{u7!%xwoF^*U0QBQoDsM#I`tHfGw2SHl4y2w$fi{
zyMKSJKeM@)tf+*9!9zOB{tw{`=r&0zIWRpvJ&6+F0xYV8FS?clwxt*pd7xS+DF@to
zN`{ff<sF(%p2(Z;aF_!QThwt03U11P(wA>@b|kJp)i0+}#!Z`CZZxL9hh5MgQRIo-
z4Z0m-SGcwvJVy?*jR$T3QF0|s+D>bV)!<f{I<H{)Mp^TZ2zQ5|zNOcgnVC%>Qu7K5
zK5xpw^a_&mv$wa`m8rBZFxJTbR9~+QzAoA4(aF6d)<RKy6M#4k&(7>m|K+iNTi(DB
z2%$!))2zYo$E4({tE*jsVQhrvMYhR$EmqgoDwKP#KDWKFZP%{blYLblv(~tY@dv-l
zSpLQjjtt)tpDs=IvrQ&>H{GRtIWyz5Y1fhGt$LY)v#3*=sNyy+M(#%hr#aCa%7==}
zr`E5eKULbN#_#CDl5|zv2j@=RZTB6w!?=WY@qO$ePyRZ5_>j}E=0$V!Z$>G2Z##}$
z)|X`n{I>!<h*?i#S7cSQS}tH5X7W-rS!9+_uVnmNWcL3kfy{IZC0-&v8`OQrT=kza
z-)}ya*}(vi0R(6uZeupZoR~tl>AhCX&u)N!Nr1D%*5~1fD$09nYmPlNzg-5VqhxCs
zC+3Y5=c1N-$x1WUCh^G=l4HfZGMcosG!->9d2jDy&rYXZ18?{QlyIs^d0ETBy5{>b
zc5(6!?*uGLG9U9kZbCdKIbAZ)qCa#CnGKMP<MEnZF@UNe!|-T;kIKS6ABO>kiJwvv
z=76VlD~oC*lQgz+;NMz);M3@leTGk-JRuK3Qc}`+P{U$gOCmsXS`br!kH2{p>c&qb
zg-*`6t9$})hYN|>Rxm7u<jPPLg+)gPoVd3y0R|Nn6_pC8#@{FQ&*RinInayMe3`%G
zVx^E-mfqjFVwLn(G&gHB#^ryj2Q>fx2@Q+ksLAD1z8p{a@*R|rcR8naU8O%wa?1iB
zfo(xws_v^iPK!B3g&(JN>pC&!NEUw)cWs}RU@6B*6B>4s*u8tVENz057)FfmVe{RK
zOBt@A8AZ*<=#kIb(&)jI{JfPn9V?E=)9Ish+~<XN;Mc32WoF9><!8TYYHLUQTz=^p
zEWOCvt(D<wEmQY%w7)u1n?d8T)X3+ub!T*Sw~;IkQhg#~M8oJT0+9ml#-6~SD3Xqm
zRaXybG>y8nrd^f!_UyJU9r6HN{J?~Im1N{k1oUl@cAxQ8Pk95Jp8R`yrt~=+j+RZS
z%q0B@(?H#$@0*#!ul*5)`u7aVWPXw{@tVD-(|*9H@I_Pp=_3zJd=fvWI`Ml~Kl1PN
z+snsCLrJ|!7Nx*B_mMq-K?H!gERZD7=@pdC=r-LB$e@gAQHQSn)CUsW$A0xe|3T-U
z2kn<W;@M0^SSO)uCt#f2i2&Y`5|?o^h{M7=HlZ<c4P{cwxH)=>D2Pq0BD5$|`t)xF
zs<@E^uYv-LB@i_Jov@ggJBTH`Vy$!S8J{ihj<Nk^XQ6TO<Q<K*hWTdi55E_4boHah
z#GQYzkZAOxg@t5Z*=NhQ*Eejz>I=I~U3`$3n8Vdj-wCSS6U-Rl8_uh|k1g0P>FLvT
z1pKfZO_|2+={5y6?b}eb7dnk>K4RMuC}dphn&*XAEZs!`4XxiCE8&Y$8&8i@VeZn2
z@nxr$iuVoO+0A2D;y$~tx3BLu{V~2hd%ia$$bYD-yIFk^^r_sra~yS#rEinE3`TM2
z37hf!o<?_XB``l+$t3(~{J!uOv@->%fduG|cA8RU=-$ROV2t`>U}tUMJjcX*Z|gtX
zEeD6v6odici(ubs>;is<@P)x+7gjY=`UuZH$?HtR&FRr1_mrSKkgUSa!w;L@pu2d-
z3sr^Vr~i%z9Y+eX5H#?M2_zv3hnb(>w!k&9KZHX%2z;i1=X!-7diwhOpBAN%%Wou3
z(gW;dm9Mc94e5FZ&(0y4pdk;u#suzuugPJ=BC-Eb5374HnTV2rEo2+2Y~7aXx;k>;
z00n^$7wh~oIQC)!CoegQQ_5A-z<Z=|4^e?oE{w#4mM|I!^yFSeHDh^L$YtIUob4a|
z2q=~bK_B(t1#GnBLAzRr5Ewyl4egk0?xh?UMZGZbhS47MTS?U#T8K|9JBegnM49X)
z5j!K1%I$48@paVtHd~(ACk!hTzT7HIvJ?2EhScQxh{#BG_2U<;O)xR<hI)#;KQ(>Q
zZ|jEm<7ojNAv+Jh|GWLtrAs>?rICCWls>2^@xU+(7~6+B!u+U^b(Wfe0V@drA^&ip
z!yFW;KhrwrA5DHZiRdsMIQdK%rvvc=qF^t8g}Tj!eBhCv9YtGYKezPb0u2(UO6J*+
zwrbq9*=}6bE<T+bH9a-;!t5R0%vk3x3=mRAj~lrDo3LTiVFqKw+yxEnMGP7=oEiR1
zi9DqU4-cqyk~4r@pz-yBLqSMvhK$<Tvu82sP#SsXCfWkH;F82jW8*Q9><?0J`>WBW
zSTW-fz9wD0kQEfM{QUd`;WXS(Ik4b4*~NRk0uJy=JTEUkiZN8m@M27&JW(A_0j+-%
z4+&^9{-P9`056y$uovJD><h~zkyn7qXb8|nnS#q)JUpD6AvpJE;~-MV5=`LB!<pm)
zv1+sg*}S7$a5mR6oZzx*O69)JM2?Kt#$?Wp7sa1Yp(Q&ra2_+VS_Gw>G_YOBhzUK+
zk!!|NkV@rns5#Gls|Qhhc`OC*=E;i}nzY<+L0>8A>)&sl-KYDUzwyf#n?cvf-gk|~
zgL9WL4kL}=B7<6b&Aq^G<{DO338z#Y`;z4)_m>CstOj6)H$%_)k$Ig=Bk{@Z-)k*@
z*XGYT4}9GE$i`sp4x4z+=|@V5TAMhJb&Op_xrG3?RegXQ4%b*@vp9|IxRE=k#a>D2
z=BFb!n4ZMP%RVb$=2dDnZg~hBsa9JHj)ieo*EYMM+Ajv%<N~4mYGG}}p8KJ^YQ0Pj
z^<ZcxIbuwn&0&iyasGK?bptm^r^L2t;O#5u>guNTIq|70uU=>R@qE7pv<*#y>CmA=
z3@vAROZjAEOvw3HN9MPH>6@CGQb1+Bo*?-x38@go7Fyy7kY;j}`1HBK=eXf)10k34
zWkcfUVJ$-ig(Sg0A#Zmi4mu@4o=4{*H*a|l7jySNH|st=X(xae4OFaXYMP+D`~Y;|
z(%dg=$3;jN6PQrZgK%bv(VhU_8JRA9@9Dv%yR+PuToLuQgL(DYJv3~yA)dY{k^<2;
zIXPMAxtOE8Y_IrxZ8bR{;G3E7j)s>O8mN$7n|eTT&UPPqv1t3AL)u+sI$6jd_k*n=
zgyZlZHXp*c8Rm^0d>~y?X^O->D{W~x(^dB%W%j7Y+-0ZHucsbby=6xOj4YIZH{?|c
zx3QI@izV}Ro5bqu5q~D5M=J1mklAl5C06PCEg)&S5Is}=Q-3zTY=Ld(1g5!u>!=4(
z2nWOZ(#snAl9Cv1$%^~ZUYLl>7ztm{k*G9uzj`q<hu7ZapunD_TmN;;x6Erff5r!r
zp<w<RoInBxYHDj)<XnF<SjG>BG@?C19a1k{Wc6-16U3Z`!&1=%kz0o~l<LxOa>^yE
zIg(!&esGfK;6ZXV<Sg?HM$}(|9>9r#YTpZ~;<cp+oqr`41BVnphx;&hS_Y1xq4d=v
zTnl|>C8W@uCYLWO>u9?}p6<14*T|{5$*Ucu9(q5!%T0g-x0X!Tvnnq`JH4N~0tR@l
z@c@n<0Pj1qN5ZDpK79BPcCH<g_bn7Esx{6Mu}ExJUGKmq(u->(nXx{b_eQ2QEy)Kk
zY$X(K&~5$M)!gz#aU+Fnu2u8L@7Z^qezl1TJca}oT~MHy8B=8F1LK3hS9N%t-;oFO
z+_}eiKJvtN6xz#U64#kmm}{py?px+M`6Sot^y$0khYMHH(AZAxfBXj{1DhCECpVoe
zib|vW3l4c9#u?>66+zHUFk;Gz)J+8R_C~69lzAEA)#~9}oUpP(WvV+rdLkyI&N#36
z-b=uy<f|n895A}PRBC}p(l`|s7G~yUcl@}YnEOmBFfaeM$VAEb1NGajT|RhBGg#IX
zXS)nZGBc-f{!yb}RkRVgOmgIRUB|yVy9h`@M?8TOt?{>;2Vs!%<rcbg^lgSfmN&Zl
z(=g6i%8+Y@f>w9_F!(n!pVl=*dfgUD``+uhb*K_DGI?8w+^k-gx6Krm8s|*x@&|qp
zGx)jB$5IeJ$Tp@%_o4bY7dMltP|gC$9Xb&MzP(6F&oja3zD1pBJ3o8C?GBbVyQCZZ
zf!gR;TVBl%i70<5(A&<gf_vD-9TeKuFuc>j&BL(&!vntA7Uc6wi>MqsB%bP>b5kS~
zIcYnJoofc&5!^^VkzhhadGFVKql^oBqxC!Sry_P;9b>*p`|TwpT$*+D>LjpUZ{}$X
zCl`B5t5#UsK9(>kX5m8_u^hgSkJ>#Zf}UDtzkfv8fJmloh-s}pQdYQ}>h3!MF;uD-
zXHaxd5=g?yS&Q4YZR^Epqasikp3L_GEZ&LZ7}snLPDKgtkb($cV4rf8*}7jhw-|8N
zws9PGl?sR#mz+yM#>E?<#nKlFup3Kg`hR1Mo{85P_tev>h|>3Ee9C6V0mD3U4=R(?
zHpBu#7R2;r?;FO6pU>lHH|Fkyu$0_+H2~=nn7egM0`!}Lr>7Kg9%ZC%%b_8%=C;Jh
zxX!uf*`DW3vFVMvx>$=G25nb6OFg#3up>?MfMI`!8oh6&azzFV0(@tb#Id!73kr&g
z-;DIqn|smbMM=a+xiyZ-ANF!qzt?|qRa`#3*=)EWf$#-^>fZ5K8WBnF;>C+9<$CG5
zpOS3`yvI>;S7HPPL6myTlQ&P8<<SEyVkV_Hdx4v=#}@%NHCNZd8FxoI1|FW@@BjEH
zc8X%kjh2=cDOi9gp&;O9!0!V9X2G<Wx4S@%{V8;m(br9)+ZM(J8=;pgeM>M-6cjy%
zObt*%!SdU`Yx{pzbgT084IpIwC1@-@^Ohzd5Si7=8QH`d#)`osv9d@y^apk>I(f_t
ze{WjDW@RMwU;a%Gi^<u`XOS>Ogr#je#Go^YH$P2=W;inc{J9{)gVLYy5H-1b&mI`F
zT5YNxqBd8{rblf6tOm@|r~wEBRuA@>_%9<tUOTR=E;(m$MyAAM$Pk5Bf3f2I`6Eyz
zC<U|9GBQSo=%jp6o!&AuIEVyXk#29GZD?H73mn-vj-MuEvwz+r)cl+YP(VpGYJgoh
zp;ucdf|eqnz7w)=xUy%C(+^V2;V3U$^npi?@gJ0{g4eV#@tyD;`H}+W_|zlnc*}~n
zv3`3j9{jd^xd*fj0j*oQ4gP?R3yu#K>-G@ne#vhSajU3}4KR0mZPGVCI69hc;5M8P
z@KdP8Dv$K95IsQ2Q&>(IbWSamqR3!qIm)O@A&U_CX_l0jQs%nTboee+48iqsApbH5
z!p6?LuN?eCr1q{-?lCH4_-7Xym%5)>l0I_etEtpieTHMKo2XHl@c<Gm3GnmFf?0!R
zu!&ked%C?V{&`kiT0~4nIZCWVas-)(B@-x3|J+;=$kA$5XQ%Y(sr~i(a+C6lr?wV#
zdkD&KL3__`zd0bAFFnZE$cdbjVokaO{+J8Hf7ZHiWWWGJ!@yR7Xh*?vN|}q#WsLf+
zqfA*4%@rFa{oqN-H;*P$({!7nEDqODaC^k8Y6|S$y&l@oIs*3>B<O!qj}c=g8Vc-8
z?jAJhdZ%(*zw7vehzOoh_UHLxj}E(-IC5XFviTsokb0V3QpX1)%Ojp{o>X>dxwl_1
zr6Bw04%^_4ap|a`fP%ScOunQfx?KC{UF00pu3kOj+spY3Y5Mnf(h3<~%i%e3d?X};
zfgK}DV37-j#fpDtRCo4DU+ZUL8pTOJLqNxswH3FLnp1nXtgTFI5Miq;FJ7fjVB`x!
zOTnw#t<70&?NeyH1(w$vnji0Gj32fh99+Djd}qh^tL;F;@DZ)k6!}nm>C0Zm#52lu
zf@kZ-J*N6q3))hRigIR_!SiwjaWsxKg&uYpeHL7OF=Fs4!^VwoT1<R`F<7@3kVTj^
z7+JD$GQ%M6Bj2I4j7%fbvoc+e)!*qvxj8q@udDF+y=+oavyqN2=^;6TM`~l8DjaEz
z%U&o(ymB;ncp_;2<SozhC>C(XQf;l-@4Z83yDsTE)ETJ(WovfI5?cktn-x@*L)gN-
z3sxSMO0s#Wm@dvv!uI!Ztax#8k(78S2(X3f-F_n~P@$9DPJhywzCSLEV4V4h=7@wf
z09oTWG^U)+fI)G2dhwTA>(NC1V~RbFfHQzmrTm~_e(ipHx5$E&nIi913AkzK3Cwoe
zaL#+t{O#MfT=4W7WaPKfcnubL4}|F0I1kX(EZNV?GZpC{W3k6n*{dz9$aQRUjDw8T
zGw!ABI$eby9nOv7$S+=u`|lZDl#LcL4q9!W+A!ZAgD|eYM6QHx3$q-{E+<~+2S@EV
zu*poF5s$df`K&sRbcb+o{T)0YpUWn_=`Yk2?(rQ_2Mk|$Enj^!8p0;7{qUJ-g)fCr
zm3ci-5(Tke_jv_MAzaL{oA8lIYS;oQRRJ53V38KgKrMEaGsa>BMQt*5MpMM$mxyv?
zkuCeVr#0Hdd&FTERKCX8UO?O9Cr$+EU2qE6@BDLLO;OAzM=nE0fS8dYs(jb|>Od>|
zLE|xP-AWa|Kx1h|*1><A3uTLgyU4Ofx8?AIlMI||v;<-$ji!j#{VeLW`dCT6)iuo1
zTKZorg8Cvm%T`%QX&-w80!d1UNcjjr+$}r@FEns`xdD{N6KJGW_1PUM0sfvA`5|&F
z|1fJXP!az_%tM7)fFRlm?8YY&&VJRe^N=$C$Wr3+k+K4YlijObnZ}aP3)Q-{ndEu^
zHR0f7L~5sC`PAN#Wg-*Zmy?22)!4Ei=``$%Di9?x@L_Xbz9=g_F5;nxRfmY@I>7~r
z`PdzH$!*9T2ai}}z{7>_NcxRXlu`g&<VnOoy%}@%FUD5N3YbP4QZzWBk{hvFZk6Lg
zK><Lhgh~R_<iHA@9+^kP=M@mx03c2lP|3fKvIc*E=>+gx7+*iY|K;N=0Fqj1+IvRk
z@p^V5KWekna$iB=0y8-$_yq<|so_szfIFGOKD0UZeM&cY1wQ?ig~|nEtJ9nq?w{Nn
zBjHRJE8+YayXI#;5E)6m5*3(!#${5mhh%|eKabihawF<2a{fsp;U<t}-($!!5kDOI
z9mXJDa0jfx)W9jJdRZK>GF}H!!w@Pp75MsdgedGisLLy0QV&FP1C)~WG&Fq}`x#!=
z#SW9%jg8xjo6F8+ML*3zhY&AHUfN(KN-SusdI0*LSqx>kxhVh|qSl~-*d+qOG+2r>
zSmnViDsXlC1oQ_jRJsLbFBO4Jkl1O65r%h&D&4g7^s^_H!7sf*36#)+h#pH`J0lCf
zGV&!wuoCY;D)bc8s&8@b&0b22_Sb4dOTSG|ebBg=1wLB%Fe1(NnUqNx<X;w`kH5#V
zWee{quXO|@4!92lU%!5xw1S5rzmp|y-M~q{0de44NOiNsRWLG<V^>S<`=|{HY!5X1
z>82|>If;-bmh|Zg2vl3xKO>`Ih~$9-r&vYwZX=S%gIy<r2Ct04Kio7=O+}ReOt3Hz
zZPEY>B<aNSoehwBEaj%nT6m=U%&$8bb9eF?jk3(w`!bsFHfDWcbj6Kh&b@!k@{7SJ
z+f_`O^Pi#$f*0;VXI50C16&-WeD<hj9P}ZZ*cAejaD4mn{iAm0(taP_u?Y-kqu%@h
zFH93{l>c^l10CIKuynW4?@-7dEe!$Tz}z^N(<2Y7INAp;K)w6Wvu3M9W^ZX%Fxn!!
z*a)Fh1Vj}YBQ{|}*cOny5IJ%8-QYvLh;-+lK_mefYa#`(0Z{xJ7<l9hxk2l!zU3r~
z_*!}UrW#<Az>C^7c2@})iWFay#hWtde?A)O97Dfo4QI%M(aO&ns<3U`<q#XGW#LBY
zZ~KR6<SA#~y!jd_d*Q_oH>GU>7Yna0DL;@NE|`O4F%hrPG=d%*)~<Z@2+GgE<NnkM
z&`4<Uu$aJk6YS=9FzXyLmd{{rAcZivy07DTCE4z7qN9^@aQKVNr*~LI>2QwlDO}#M
zo-{;i$gHw}VtRwEKCERlcXz)Y16V2qw^iQ(`ERfGAVnb3z6M4n&)Cdp3nN06tcnV)
zlLR|l9uv4nrWeGd^51{&g{p7n_>4=m|9KL+K#oh~!ywHA77LzxATlMaoUqrDQ&MuW
zs>?OQFJ)dHs353dwo!(Tr3vfPNFk}0L7ychB$ZRA-htCLm*&tLZQTk4NroyM_nUxf
z+&nz%;E8}-2O{t=>A7x7*Q+10f0msYgQet=)_29U5jtfZ1AYA+B)tqiU?+l+GK|_J
z3kzI+0pt`|04DN02)I~b1)c>%g&_Es;@|7Rf=flX0xlXAkHRV4q{`djX7rE7;-&~;
z3658A?PNWZYtybf5*GM@3bbG28(NIc)gaa2?}0{+y>B;+$Oj;PeuudsD$2Obb0=Oe
zB>@(KblK!oA#7>Fz>Sil2i)q+$C!oxl0o=QJjN}oS4zaGXSQs9cCYa}Q~2$VnBa~B
zLh!F?s`+T@>j#om<kCC@BdyCti{FD_QogT29Y=8<Xc}Y!X<ZAVzqthf?4RSs9KH_f
zZnpQT`^e}r5MZx=e&EX8#g$kjXcP=-PGS+rtmko~Ge+*oknTh7_1BreiZGC7mcl|Y
zlD{FyeR&%ENb4NrjIBgZ@X@Y`v$cN!r6&ew{@ijaaNYWp9x#xgsuK#j+&5y--`TX^
z^UW>ow^TU5r@aPee+VvH@~@u=pd=uta5lip888Y{?su4Fa%zu{2O+Q|Ah}yMCT-Bx
z(MkAh$r2n882HLxV+8M?hex{=++Jm+1&_>8s4_58DU1jk=|XVBfR|H~l@^5Mx12q$
zp=``rJ>OZ_{`B6^Ap~1~eZG^Vra*1(V`pHdiYGTIY!8Xbp=8wyn+_=m0OI$cZ|)6(
zg+1_SQ<jOzh~}+8eqVdC)cenW5me)(F7==P{%?Hs{~Y{(Kl%UsgOfg2ho)ZKj(T^5
QYzR|TR9DE9JAdte01F0kO#lD@

diff --git a/articles/AMR_files/figure-html/mic_plots_mo_ab-1.png b/articles/AMR_files/figure-html/mic_plots_mo_ab-1.png
index 7809a0af74a51137b59993c71c10e30a5a9e4c92..897b254c847a4ea1e918f58f8a09eb57e0787d1f 100644
GIT binary patch
literal 38328
zcmeFZ2T+q;)GivapeUjuHk#5qpH%4}Dugc5t5T(R5JIzoh_p~*kftELNUs4ErG;kb
zEhs&N5GhHdpB?=EJO7z~?%Z?co|${*&e=00VZU$oyZ72_J?mL(@0}<;9W{EoYjh9@
zgdVDXM;`(?o&|v%{e1ESsA+wH%?EEMY&6yGfOk-~R2##<+bK_VQy&O~HIn*wByN7q
z9s;=zf!<Luc#^g<1xrhw-9K2Pq)JBS=IZ0rKN`F;kb0kEz}Jc7$RUg@Xuft!wC(LG
zK$X`Tz|?eZtmwUf8^rS4Kai!D6&GN2kf?Up+pk-lFe_Qzv05}0AHZ)tDQ-9OnI6F@
z7Q=eBCr27YP91jV|GfMk3H(<H7~C7+N|u$!qV2LjF~l;08^{UiII2QJ?%Qd0d6}$M
zok6yG(Lf-d6)VK1{-Xiqp*xUk|Nawy=~&j0e?L2^{HJQI+b1EA@YkgQxy;nd*KZ!)
zckXm%P9nD@cR^mxHTWgXASi|lDrd3on<#My!w+9pH{*6n$vIFrgOiPjB{ex#a9K^+
zdzkxhIxe?_O=J#*8^!OWDpM;s6%1e2aOT8$A_<BXm@n{aP+4UR8}uFbp6)_jlPK;1
z^!wGv9)Ysz`qt2P^Ee0iE__%smDi~>r$B6n$0`(>Zr5ASXh#mSb=i`FzT+xM+C+7S
zT08HdgnyO%J05|oF}MaR8_^W+V&e2nB<=##lC!H=)akuiMph0#eBI{pbQK>VI=3JP
zcYplC{u5QqjhiXyuiCt^8Tv?Ndm(qPPHQg4pJynJ-<t^uaXVb|?W4I@MeKu@;Mm0q
z`j7C{Sp5+}k*$UH(P4l1rTVH7+*)Bb{TmY!;*=S0MPFn4eTKi1)%(?x2>badRZDl#
zgvPPmbbbhdGi1@sh+_(|f<HO>PAW5J!p2o?E?YultTUCUPt3WpzHLX-qq9ZyRnOFd
z$nrUrbl;5ONq|%t|C~VMMYa^<;G&wCSvFFTg1^S{g^$6|28A!{r<9LE!UdwBCxHZh
z=B8?85tptEv<Qk1wl0db%6|J$lv6q5VWyRlvZ<WfrITXbD3Uxzv>JyNE>^hPd=>T%
zi0{_Bn7r5cTo+>pZN1QKZMopkv&QCh7U3KAZ2l+&VgOCv%Tx!0%IS-O_rBr`Q9#~w
zHUDPXH|v!f_k9b#50CJEV4s@?Gj)V6pXVk%M9gPunk^=jspkf`^Fryl=FLc3+(YoI
zvlAt>YmeX}v6xZ=x}5jjn%tIi6|0<3HI1%>G4>L>XRA6`#Ho97S63^7pLOqi#V(GC
z5fNl~Y4X*x$Nma6A3hoJ^Ar097pnl{Ep+0kio4AB9>(SP6mOb}J*}CR$4GBJwr*0D
zyQn_S^f9DX(^-VkbJLXIDvaYnzQ2S~I2OsJu#Ax^D=(>>Z+3HRn3H*cae7^{0Zqwa
zu;{Wr&+Q=)`SZ%vkEKr{f97-h=*Z3^MHWL6g(+U$2dnyhW|39+61SM#^6sqwn=?*-
zbuL_^RS<%@&7;nYGmn#3fljfkZ&K95D%5sn=v{+i0(akO{T$QugJyrofxa~I_Q;!f
z5nySFL3C}FYQL;uBWTRW(3C+o3HQgZctEyA+57@yJVMemGs4NN0^i+WDR;*%0izU%
zxp51~;HUlDoT;<uX>v!UI)){3UI*>^<Js4>x*^-CKzQgIc*`w~s|6MMt6w@pMbHO*
z_FIV>mSqG(HoRG0e(d8eeGBYK#5@tY{hf3AHe%ist`lYAFn#is@7o-_UC;uU?UjI&
zK%pR?(d{DW(+cbI^A)?saq{IlW>>UJwfby$ndh<0(hpYsOhYSMD}w9eT6cY&Q#m`g
zBAyFl*HDbfT@c@loD>l}_BCAkI*u3NXUUS%69C<LkwXysvbV@RbzNMjOJzpxDg=^0
zQz3qcPX;FDc5SNuGfZ0k@AP_HZ0Ze;|GEY%h8gA}obRsZqSyV!zI6VE9*5QESmrLx
zifcNsvkMngYW?2SLxy_`Bcmu^2kM=!sVWr2gU55m`RQtGt0nE~sP?O4*3P2$OG04x
zB3+ymMCi?p1f1aA+Q(|yMu0GrF9l3ct)@*dA168ad?NnD{8~%i#AqkHs)}Vf&bU0s
z(PG&0;u^&h;p>jdcQb{T!t0$rsbgBWEIAmJ-L8wLc<GF`5-r|v4V-SQSd-<gsG`)!
zDNNoX6^0s?Pn|i55OjK#Q)U;mH1bMZ2?9}mdG8Qy-CrvV2a@!r<}1<;6w(!bRqWnF
zMiIEg??81ApTu3+mq%5Pp?`SizX+Zb38alcYx+gn!?^t5J9;cX(l~tNLw;NU7;kVX
zm`WDH-@TTyxGNO8VG>|cU(KNyT}{zru6fQ?U2c^*4-fdz<Z;nLkaCJ%uws%A&k&O>
zROZ9MTB9Kg#dyJa)VCFScNOd-9s*h9UpSopEP=75^3~gh-5DQMpbQPgjbDTI-=TwQ
zu><fe>w`yeq*`^^JGs(cDc#L$1vcG`587=lV{>Y}lIHz%((?NK+jo9n^RR$lQs0i&
z(#72qQ^5(^x20KK;wB>R4^uJa+M#FBOJ0FjjoVMDz;#60Yrff5px1#Ks>qRES@JqX
zsUc}Q-w;>k9H0!)?fVX+uYO}!-2?T7`J8Ie4Xq06esPr<mSPjQZ*Dg226P(ZT;f9Q
zcc9YhvbP;}-D-WRRb{G9Lm*ie=fZ(^R5lbJx19>Jr>V5^=893b^s%Y9i<vl!c2Q4V
zfG;WrG9>Ev*-p{2;TxV5YgT($reyLg-b^X#JmYd+v^L|X>cxYcFiEbdOl4+{Jr2q#
zE;OD8$X$8-{NKhmUgw0P8)U^$j}8P1qHZ5BIHHf`(rRu)dFsruY+k0=ar-U7`&|>i
z%1bNJ>#6XJIDL14keLd3O&)dkX`$bm%X~N&Xb)rlBDaig8q&5TS_=1`i>c#`Iq0UO
zh*Jbq*05X`KH6o!wSHv-NRV2Xu=2=^s)RTOh4nNXoR_cQy=I0*oP4Xoa%zI|&WR_3
zOz7;v>=oZNwAS0pnmgE6@0%=;%1smGbbkJ6jd#4_EZ-yeNI}OJ7-e+wzSW`16%{G@
zc#XJVO?#SlNv3PO@@o1IpeXxi>%VZ)je|Fh-tWoj=(9t6?1Ho>=C`?q5WdN3FdfS1
z*}whC(l2;@<Z)&Wl#OKg4N}E5b;)*TGUy+*ZP=Unmr88EO%ThFDr?O4tqEyu3`_*y
zMANT`{2U@cGxdX}zf)d{uZC?5w3JMv>IvSm1n9cX<RwJCmw?~okB~6u3b|w(SgT>i
zthSfPJ;kcFZFiiD6vw%@1Mf|y{N`|f1{X=%>^rueg3w34JMFI80opK-{i|n&$<S;3
z1s`)vz2;!LnEOsn_kHnPA=kKj_+;W7SLOns1g;Pk)$tb12CaGa-84aQ+)v_tjCTB>
zF=mx(nNGby2a^DAR=<950r_5hvTa_c%@?^=a(X~d3g|$aonr&l6)2}Nb-nShFy`qW
zGsR@uJ?lO$rtn$`A@RWBRRp{EK)2GQDB~MnMk3c!b8f%Hu?0<TQqrIiMg&<NH=VMX
zcKd0cmaqPnvV+B3G*>#W&l@_f68L6rPLu}WTkteg)=K{<axFzSX-rA2<ITu!WB0y^
z2&3=50fq+Ja*sbCzxy0dq<Ngbn6z>#+SQsK`Abqt)9eN=Ol)zA1^Q0E+6#2?f4q2Y
z*#8|A_c^Ucrn?Maio^Hr8T}=O)MoNi50T=qMa(mZ#Q%8H|MgD2d3)$G2ksO!nu`!$
zWr{fiZRKKJf_pUO=tHLlroSa^XkHV-i6aTZeoumt<-_(m_HwzVaE-a(T@H4+l1=3A
zx}k^xaf2|4xX!9F-Pc{34zLGPDX%-QN_jHHnsz(99Zd|*a>b37<tU{zEJd?qKE(EM
ztBoLGjLqr7UUAeQPjQc}Z8s<2oj642N*M=qgDVC;MsE6475cq-nuC4T)>W4kxhH9a
zxd5H8Be%ogC+=3YYfC~^xkKFD2I!#K+>+|HQ=N0^&L2zIK88@o7%nj92`%C}o-Mx$
zPhw4=C=EBP&JPY71l{{l8+gCX3?Y+Dcq?VFIr~`2!7)Duev0lmw`AW#<|d(dr|I|n
z0-{KQwx78G2kbz?&*U<Z%<lA&9aj;WV-ptV8{o8vyjeACE=Z6O!6$ANM{RnVY=?iK
zb@s7;Hu+;Y<8n$nyJ}Evd)S96HTb$MpJQc?5u3e4eAO_whqC^n#9+E>qq&(A+xn;o
zwX4cZ8146>Z>BY5RWV1zP=mo@D4b5xW<zprz@7cBOT|^JS^fU(1tGBSY&<mmQ{t1c
zCS!}gMUn2WBIRH9)~21Q{SHmJO(;rvwhkY|8q|K|pqp0Of8`hDdJt)}-5`P|Kzxf)
z-mM=6An7ZP+I6nTjAj)m-qv;cnBbHSbk9EMw}WZiHTbwjgAwK%-~4EERTuLW+aTU8
zF_pDw`)3tHcI3OUIkt8|nG;_u@~~e+U&i(YLwkyMtL=h*&~g(J>Y<3PhD5j8i?WUk
zo~DN!JfCB^D_ExbC}YCYcy)q(XtM>AZ&E<q=<s;h(f;I~7_s(cl9u#0Rc54=2-;V*
zt8>2wwN^ptTjb|MukOira>lTR$2#WInG4YL7wi|yd>3#j@BZhU3X~m|!_e~UiEC1W
zEveoR`<-8)52}p}57fiVpT+O+UtH=5Fj4w$c2GtYGnTQKlzrhb2&is$4%WdSkcS>|
z{DaSzospUNAk*T(V_E_<jgG$--qg?(cvg^ho$i_cB0R<i8r?I$A4;wz#2`>%is~w+
zwdQ?sd0EB2P?BR0{qTkoS5K8yK(fI!cL+0H<~pwEaZHJFji5x5T0kU;vv?6^7xY4{
zBGRtj%;6;5U>Nj6)UGY2+0>ANI133s!wI2>C@;)wHhc{VNzbV`hF(kk<epl+`WTsM
zJS{A4dOwOJZ?QV-AF)`%jWa@qf75|x-pNtWagbkRZ|!Q<)#r@#)~)TTZr_sEh3T>k
z4f|<Qm~>H>&F0Heb`Wkb>*fpPlcE~~n~_|?29GQ980>-&-^ISE*g|VNR~mmqTTaMU
z@ebRXSz@N`+KQ@qn;Ud<DdvI8P70zr|Aa9kPc^Qb`cc!d`G#G95}slgWN0ObvqVO9
z`*-Y~*}q1RJ8y9rzx4o_Sr~xW{Lt<7=Wec}b2=YEULBJz*6x{?qYckU9PH)n%cj$I
zFxJKx!WT@oYE9iv=>|lona)?EevD%#3w=_%f1l=tr=X9?Fswfg6Pg*jXKs$9EYupA
z8+E4XS}p4Q*^j!2F8?%--uz6@x3-})rB5=oEfOBsFot7OWDwIe`>RBFd-J_*CKSc*
z?nV)bMTW;e_V(hBMv*{7XBx99-SJ-!?gt30r*s}GsJz$^ap<`V-!d|7L%qesf52ic
zyXyY&eXga~=k(zN`<%SH(-UmZ`M^siP8D+s2@jPq_2=iwWOa`?TH!#3FA3@>_^C@L
zxKYcWRxMt(iO1~nlYaBL?W<sCA6s4KK43hLvz5sm=?IxA;;5&^>kIGZks`U`4{ih!
z-_46`Mes@Fgb#48^E^|KP|<~YG|Y`%U$lB{3TJzT={;D)=rm$mI*t{$ZfFIlk2KM^
zmKmGJu04Lf_nWQ*78Ss}2hUhnZ&7o|XXmw=xI!>_fp`9xeu>+XC-SHIkYkKnyx}n7
z5qjGMJuKk+fZHPblhouAoX2YZ=CMdS#pf$7?sd7Z-C9CzKGyTS^ve?<78s>oX5?e1
z;*Z{`UDJi==@c$>6t{^J?Ru<HKJ9n{-EqO@!R8}Re>YioZHm2Ff%+9yHyh+edNmvB
zgdlH<5_Z*YRS~#crkF)0=9$8Es<0MFY=R2(d25QaA>VJmx2hpW?P*^1l!&c#X2iyC
zW+W4}5rif7;fx)-pxYCS9z)4jl}Hh<<jdIx#>7y|yGDVLott&1HMScyVfqB!M+7gt
zHnELOiA0N3wtCv#OhU7ukHFo%I|9m*JrG(RV<+ec14r$HK3ZQ%QRFR-aj~OmA%|AN
zYKaxYnG1T;Ph6&MBY)j1G$#nf`$l!s$7PWTf$UZudJdQ}&bi4%jLz(!dAWS8w3+$J
z2Fdd!HXs!Fj)6Sq7qxjT76_aUMKS8M&|QfdU4~xE>nBNyb*xs${HRgFSeI1QszDK*
z5Z^*Y7gw_60_Kzb79Byl*1BBlv}PsLAu~<Lr|H6dwj)cubk#By>8!28fyW1}4fuX%
z6gE6au6>!D%h$Vp7{MmLV2%GHX7-iO=9E!N$C>Q&1<LQEUh8;vHbQ%!ey4fa{L|mZ
zY2waHnvV%9R)Ow}BlX9#5=&~4w~w?+i5<zCAD(tcnV|mI)8y+|Z~8FNZCVzNe)rZw
zbjc_W8@;#9cx@H&V^@hVHCr_}>!p4NJLb)>-H3ZMKX$`s_WC8^*6}|%X%uwp@OBYr
zN~XPV<_*gGqwbX$i~0LRE<{l4fXtLDKaoIk)|9BHL)F?`O=(N%kc!pIPuEFovFG1B
z{dDy_e#OmVDP9yy$aMV}u>Jk+t(_o`>028?cQPD1Ph>qHnQ{_B-0$vtw`F+t5f+S_
zb85e?M=FIT)T=B?x@zS3(q_5vZ{joYEW-g0mU5#Iyv4qhyicnJ^ChMIH$8*ck3ixt
zwgIMpePbm{uVU7#ZRR|3&4H7kx1?D<(f6E~ym8qTwt{97vmKYM9|&3%VZ5dD(hk1|
z&2=oX3(8UCWn9@WyPYWYoinSy4-xmqWW<z*uU4nvAZlYu?8vk_x_Hr9qo<FMjk`u3
z7iE0n3oDN9n3xPw{HLwK`(neRAI7->9zrXp7Nxp2vGlSzW6lbZExO*^sOvt73l{ad
zlTy<`v1f87RPVbcT($aIfc@=DR1*?Cl$wygU{#zAYAc~b>Qcv<D4RvE)ZG#-p6dbx
zIdbX9u@`IZVYK+bkT*DPB1K*P>5j?A^~$#W`HsBt0Gctc;SEV|Z0rRjv#eSNOUu_!
zezY15stakT>&FRNMj73Us;qzrA0Y3Tl6ORhDR;#D+TTg<q8RahS0LeUslgEt*+UDw
zcRsp+_Y=VuUtft1%CmZ=&!;Col)d67rm&qe$IGi}k9uI&7O$wza|Na1@Rq(7^4>1U
z0V~4jI$4Z@31JuX5eoY&MfbaOzR@A(?dgsmvAufxr-BUStGryBf10y`wFu+GwV;*Q
z4i{Rrg-)&AVHNC=x>Ye-oiGl{I|iK<A6Qb_b25CeGwAGeSzd?Ml8Nd9?=ttWErRrk
zG6XmZ>v{WeY0cFKOToT2dC_T;m5p5iFx0qBk;p~{QfLGH^ukMWt2=VG))G6xT-rD@
z-6n45?yr>+@UwO_XkWpm9u>CWVhv0auJFsT%IZ_isn`38UX4G_YeEKaFe_lCyL9xI
zFUq*1xO$T7_vgCyM)=|NQ2$6RkL6|fIQq@;7W3S_2VB(}6(yY=DM*9gx^P9fZ*yAN
z^hAjx%!EhUUw5q|JVXp2r^lad-^pE&t68K{@c2N@l0{jK16^xW+kVxEq^GI%O2s6b
zyNzv9gS1JXq{4&mB8H?0-8+ADWhfu2=u>bqAuU?J3uDt@LibJt6WHusva2M|yFb0l
z-<%Qaj{3*;l>a>Xjgv6c`uqJn0ue5fD3c=Hbq1y<z!<(f7vGE^?GkU{Wu}7mJM2-q
z@0El8<U}fbwK4|zO)G7lpGigi%@qf=R)wfB`)1~kA^r^>94QD2IjH)s7|OV|>sTml
z$1#Z_{wnsB_ng@1?2;(g?DTfGKWy*zqF)%EM-%oolp^J=CWzn8-folKuQ&G`i)QVP
zoA>#;|6T15>4ssZByY#$n}oDJoF&|<#;RQw>lKy54R=qt%GTKol@$6S#e)_d@cDBQ
zYTcog${aY`P1YR6JG0r>RApq7R^#%3UDLPpwwqlBoa`?V3SlKTM7tCY-ui57>4x8O
zM3CGS@>j4H^x1=MWpOL4srd$j=DIN2GPZaa@$)?~TMbPI@2cna&*)9*NG45aZerQ6
zPhn74o+<;-$<5_Q?s8l!q~&AFXS>dL{kW4GxfLj8D{OsTycs{u0b)Z1x#MT2>;xs|
zzG@&O{OoDE&AIk~zesqEbs6Gid`n+8!j^F(IR8i<c?`#K)L<TkvT~ELa`@0A1d?6j
z9g~x5f>nFuCzax}k3Bdhp1dLLW~-&TLPLs2rtb;WQgr8}g4|l(aS!_>UK!y1y8012
zDV{7NMQdIr{X$rG$Wi-=-XCL`1Mis&E+HO)VXL>s_{5`%=cb;^TCQM6Q1-Dd0f7|P
z>5JrXw-xWqoY~?Lv;~GsKcH|uVmkgI$8Csm*I(22*9hti+RMT0Yz0@e%gRAE1Jmrs
zk7QTMV<hzR9Zlx`v_xstShZ*|^en^OQ+(f7@2bjBY%1Gtu?gb%i{xQ8Dib#okV51;
z4wv(Td5#Hr_+Ky6+d1oVE-p(;bnT$)7(Kxj7H~(lF(A*_qgCBsd6l=Gvf$yOphjNS
za+vhq$!*mLq<y~PJMBEp_Uceff!~5wc?Wz#UUv7eHrAr79c+~On>$x8jNRKoEhaAp
zBdwN+f)d#3i{nZ<6HC>5?uwNe6G6Eeu?hz2RZ(XdPQh412RNBBC6AB8G9a~U*w*w+
zWd(9f|N3goJ8p5EgL-%~V(Vk{NSO^6qi3y>w5|_y!0i3rdI%j}=dKmMZ#H@4*2WRY
zIYyd(kbpQdf<ESX7O}~2_u!4<?e|!NwJY!Yb@N&cjURt)=0A%^bysFEAa?%z;4E3x
zy|`TvQ~@suNb0_Vth8@q4b^s#UwUrO!}Z3Fa?a;8hZJJxNoQuqG5u8j@`qY?1D=ss
z++IaJ8Jpd{NA|3Sc{gak#m({Iw^-vO+RghCY(GwrEH9~rNWqHNAMdoeU>eu4R%JD#
z(l*ULCX0!ipKgD#tX=C&=c<UYHtEZ2EcW2Rnc_(#;7<2uCA8Ez82=IZlo3bKN=`d4
z>*MVTK2cX*Hh;Q`_@uQnba=BeBLHo|hzdRR@PK13h0pI!SIVX%o)l?y>OnWJ_z?mG
z<aT(GSs_Af6&XbM#y>&<=6#HFyI0{_AWp7kbykqm?l@fO%)cFk-dsA}wfUCt9@Q=^
z5`XHgqQ^%ZYr=uj@cQam`@WNcu=4J{1;TX|7v!lE1iu6YUqX`_dJ1KfdI1{3w2U(}
zUx==^tbK5m*9<?|=H*t>uLw0nDU{5=;~t_j&k$skINkMz!m_IF5~!SuFfXlcO4ah1
z5`ILIIW8c|U>=s({hOWKT*~buUl!HDh`vdZ75QkCI?m>#|0~Q$VQ;p3#1wM{nPsW{
zlB`CLTnh;+?_xcdQ1<Tjj(7g;iN(aN`U4!?J*GpvOCzjY4Ti16_`Wj1$`m(Ga1SxK
zxYTr)ukbrHkwXsTAI?W#GN}ffCX0{iozb<!4aOI*nSBu**q~S<DF%8lhP`vqcWTO2
zO)|upf(|ZsXz@57+<sK28baRp8oyUY2uSppAIt8)GOBaW4A<6n-@mTYXx~lj%g^Ou
z?@TZbTc&YdLK>Z(ccx@?iGAGn{$ZX+q|wAvHOYaQn<+S_4CWL?-Gv}2ULotsE6ZCJ
zKl(_IgXsv3lCQ%?<6V)=_A0>Hli&UsVv@~#=FJwtkEF)cgquZY%B(LQ62`(;+4jwM
zPGrm9bD7SRX}?g+a)YtXQ@{t6#(Vt@cRvn+?CvY-K{U*1^FY$oQ6zhsVHFt$<PR;L
z4wj@-gOQcbm2~3bnCOnxk&;l78s?d=)UB`5^pDY1D_vh)d)UhU%hrFIhq!k>=@j<X
zRc3DMxjB1lGc<z_e_vkzzV|h6&fSQJiB_j3*G6oIPp5GD#Fe@EdH|8Q)gKWD9g}tC
z*6o5=x1<m32u+rC_4jVB^X0t+6QH~dtO}UXBTHEyT8!`An(IqEwFz_EV7tqn%bC!Z
zn)?WCE+FOq&sZosJF99sS*niY8kh7M;ye3l8x<Qp(J!{0LjUzY_5x_zwLSNr$X#{H
zmt!LI>Mxye_Cp*d!NLXFL<g8X)3iKV^^rG?l#*~V6CK>v#b3ID#8h>J5L@t;ZUO1(
z8`gH%#|<5CRO?lEuGAawDEod=%dn&AUmM2PcAavbjv{=ueEiY02u0CM(ejww5^J}A
z*S&xBak3ljTU3PeW08R!@2KwmX%2DIiOg!^Z@%OkkSUAu<4MoN+Ie<)4+6zdqM7ky
z>~i)&B_b)lEgxv>b>hKFW{Dv=2zmV{ZoKhwp;9ruj#3<=dq*H2#d|T@x2k*YF}h^C
z#?&oDBe&h(ZmfKe87VTtX&LD1A><~bv#>kCE&gaMnl9#s$AQ2XLc{}!a(COhL@v|~
z@q`%>BxSM0J2Pw0h1pi&lFo`XRBKb6ku5xy{Ay*5wKP%db11Sqbo>V63af~Cy`9j^
zUU9j{72(+O;Law|XJSM**oUqKv6hoat_^$?>#$UD0`lj2-*}^%zXa~2GEAXKF}e_~
z)myK=u)Uw%T_%4GHV4gJHK=M#tX531B2eQ<NZo7RS7#vV-H~D=<FZKiljpLLVvDXQ
zE$PDrM7?_8w!$~%G#1QYuv})EZ$Hod*a0h15J)@+NEBxU%X5nF-frMMI0h%Bbf4o(
zXjM8?_;b=!EC(4qZv>OV|Nl2<=rR4FZXDQ&eE83(k-mE_b&(9@2%m*+Yz4nWx*yXU
zJIr{KnT5IM9UIP5FG9E;qqu1LmDB!bw;UYi=MP)>?{*^mZ&iK$RGb3Y1Ru*PcecQ?
zk*rivS!vykM(^m(w#5+fn2%^~%A`+U!ri-fWsvT+h6BwZJ4L&p2XnC1`o)e8zepDE
z4&l(2u=N+G7*q2qEFOk^mlP47ELH2Ym~HCnNqRlZwxSe7TK3DBCT6m1b)eC(T(8+S
z$|hlg()~U9@@2b*^b15ri1JW8b)$ze$3ky9OnJIFgeWK?Qkv*$Dr{Eu$NBqfrm#5P
zz7%2eiiuheC7Yg9NkS`vmgZ?%46`&_4QQ}caceHoCnIzlN0fFQ^>nOoo3yTSn;<t3
zTMn8xmVRWbVSNX*1=BD|$;tM~$;n%m!&_6scV`Vn^sqEf!^`~e!_o(v1G)sbaM8V(
zYj-ymdKyudjs9}BJ*|==AOvcIu8w5em3mptJ80Z>Vdyf>R1E3OknveK{^emmLQ7_B
zY^-J4f5Of7UM$O&WpUtSFA9UfTw|7Ym!C-wz{<=_1x%w>GJx22dgX`~saiYm<Sq)d
zcqB`!V2N-P!W9dV2-k309xM>9@tDT`!(?A#QNRA;$%Zd}n9wEZGF(O3*=sz(z$WiM
z`sq<LgWSp|8|ZkQm+k7xSS4OJU6$0rBxqEnb(fiP`Kj{tqT=H2%oW7_tcr;WdrkjG
zkA7w9-yg1Vn@DY1Yh#YAav52jFxl{o(PSv~QttTr@^su(rK7G@#r!dvr<10Y4%N}T
z5I4E)rEEuGtEMMudlc~1-Lj_jt}mST-dt(0N2e?VOovd?pP!(=Hd$<1Y1vs*$&tS_
zlJy#^2vME^v0MJ&*`<CRH#Lh)J?z6zTA5a2T&uz0E0!@H&0D#7TrpS07gis&MwHN5
zPg5HJEt$zZ?$4?|C3z;Jd28WaIlRrGfmJ!Mqq7$i(I={L>%df1agg_PSgat!NL}}Y
zb*aw|+W+fBSR-q}?DCKXAMx!+7YPTa8N`R;;^K<mEiOZaOjpG0I^x>dU@NG07N730
zuCA^McGya}f1J|(pJy)wp@+I>s0_KL{BZ%@rEg^AZi9UO<wZr$+nd?tTwB4-6)t5c
ze?Ob#qsqRDnFc7cnS;`;y|n<2*=w{}^7Hog{L`7EgLmppaY#(QZF}qlcxZL#M2(wv
z^Ui8xE?n#qNE2iUJiKrl@@k;evT+xF@yv-z4GoQ%=2)X5Be!S3ESesEd6ue!2YMUt
zq8q_@@)-Fw+i{vB)345Rj@P<RZAV;_be1PjcB}>X`2HBblX7#vq8R+A96ZE_mGk#(
zoKmUJazS3k#HUBCJ3ijt8GTeE19Mmll%)=ztNo6J_rlJf**N^>(vR6^+a!EkZ*OmC
zGqB28sv7;GU|}BSwLxEbJI!ibO-ZX$jn*dMxoVXF3X08ZSF;_{{f4h5Fd`I4gAatM
z#y^in1SI5`yVuV*|AszIm2mP#q)QEVKCvm0V~bj0$g1d^lho1Jl{Gdj_0o48sW0@5
ziT2YUt9BV#AmIk`GKWep138htb@FCTh7A=OZvCj?9rod9Y6cB2X8MmiElNG^|M_KQ
zWo40B%JoT@K#eqZ4>n|IoW4Za;T<V9-N<xb{DfkdsB-doK0(fzwHV8^v$wA&o2btz
zFcKwu@gWynOxLWQ13_1?h5q>^l`a>+W>RK_iuCsO=9!<LR~V>r8jurh*<E*cANh4>
zx^}IG0iw6nfAkt;#uo$Sb*r$Z5T_5`)MA5NQm2V=^>TjyT<MOLLcST;R=)nzWgxdF
zrpUN#^<s1IVsARhxY*==7(gjr5z7YO^&J2@(;ZEVX+Aoc($m2{p5JDgs1_VX9&?-!
zHLb`QGPVvOY5nPn7HP;IN#cL7f1$ZJCx^>pYkl5wwE<r$lE|aIy&HD0Gd;bru|e71
zDpY_Dzw>Ag9IPI(f_(uq<T8q{aUH{~5NfBJXW~`Q;^e~ich3_hYNtW&(dFIqFIl2j
z&O{ufvl@jCc0QRYkTZ%}rOwOX-+4(c^)d?T!&?~Ew|y5YSb1|*{A|nqHr_)RfUZl~
zL~{r%yWVFpV;Wffz$H<u;Hh7c>{cvDC9Ds}Zrxc{+0AEEaC?M@@AoEk?6XR{Pv-e7
z_U;W}K5FVJ;&Kur?&-V@CRC63n^o9B`*wGCg?$#fw~YTZ-06MrLddKte}M``>iR&<
zQe-|t=ic&o$N0c9zdbN&8p+z<+o6Q+5NgX_75fmzs|gul`&<4V0JrX12d{naahs@_
za`B${@8@g~=vGP3J85e^D6wgy*Iv?j@XB<yTHNW?Mq)GPC1JC<(OFTZLsg(oTT)`;
z1F@jZB{`r=K4TdQux*d&=DnG~jRpFv@=ty<(qEGpWkGs$#NS9Ki2ee8AS9$WO~x4X
zrsx1z@pjlAX(X%}Ock5cWF646Z)e#RcSrdj<nD6w@;Wa1jaX7TfGo;J0jM5KgTc&%
z?cuFC0lIGtEDsgM5hrSi_3ngp#gOeR=b=J3W~I<w?eE{eAAKDiO)J0AEiTRG`T2-T
z!0M!Fi*cD%?F5QF@+_;_l`|(qou?W|dGO88M;YFnIr04RSR6gG)a2E^=GS?vCSds#
zl6*H87@f_COsSXQ&NWIKvxkLgqd$Q@kyn?^%ECha;Yg|F9#xQdDpWOBRaGt6BP{(N
z*=5}6JvFh38=wUS=`vf{r?@i$^vRk9Jac;<VeJleV1|#iY%KPLHDqNy9Oz0G)MdHl
z`IDc#4gjPKJ6djgAOJnsr?e1(OuPz!S*}|YEK@CzM?2Nm6ByrWd=7xJi=e}<ckLSP
z#5sL9K+8ns6+bCcAuSJ0g#LPYIW|+uZM^!Ov`6zdY|SEYml4ky6rZg1rpw_o0~e4Z
z4b{R}id$h>0@%tKDHjEa57$-N^VLF#Si18G3GGrL*10c3VdukGrK8JSM?x1GJ%KHF
zkVjz;+58b8=eJxX{L-%`DTg||m*+X}1$&5!^6{PY#bM@af$erjcUJ=6wi3n2dp0=-
z4e+58n#j{bZw(m94y&&G+Bd{6G*I1zFo3ubnwSZXmc7~m&;?_dWr<le&4`7M_AjJk
zMj#BGBFr1Q`9tlp+jteOB!ZrKHMV$yNFiE%diX^c)bW=iEWN%nxit9U{mt>iCA=4z
zm{?^Uy5AZ}Px%h8TdxEwS|hpa9UXaxT165XrgEIY)IxcH-J-lK7wg&#_gpN1I*tfi
zH}CF0d-g0GwF1`^F>QfSR@KVtW?tWnz9cLweD0*YBL$;7mv$0?Ku{)tN~*Cv`T3N8
ztj&(%kuK{;ZrWZhIwWJ(qNq*G0SQ0iF?Q84ZEfS_Wvg3aSI#()h|^({+z5*bY{|Ys
zhM?h(5$mv!!f7J$#@-J+Iqs~P_B$y{dE2%(VJ|Xa`rhpl2?z6&T>3_U*Z?s&xX)FX
zRXZOSX<VuByKLe4B_FsSB~ufVCo3QBMmzhjjC#TB#-IFW_`T3y=QWENDl%5u{CZk+
z(i4C&>b{=dZ#CUQV249#asjJbylI|aUlf%!Z@(}Y8q2?*T>`HP-day7UkIFu;yo)U
zD2Oh#LTxXAS!~<<xR80U>xfr?k>{$N1{^~FAPPMJf1L39^DDCN^oi%qz-n1ml5#Z>
zA|5ZpepOdL{xL&gQwVCh0WCw3%@$2+x!DSe<qUU@=4*+0#U#$O^m@f;Hm|-8jcNS-
z^>p5y6A*6POth#KFBI@D$EgOtu<m<l9Wv)jbu$&qJ_`I1`si>#243$mK$C;`96`XM
zN@ru`+?U9N3FcbYvGwT7z#LV}w#5T8j4qKso&WV^adL7povLjuoRM$i;c~fAyP}Ic
z$@bqlm6TeF0nOOpavHC4iXC|lL<b2kjAQGE#J_lYl(v)^SR@*5h5htMbrpEHYf3vS
zmAoOq?>MFa(%T-xOc}941<^PDe0Exj$9#k}V6y!8m}BNty<I0f1CU@hAow!MHkr`4
zQe4?PZjz^yuAmdxyt|H8<-+A>$8Jju-?V7?GZTFYUS;$BbuXa3?OhF~SI?XvWG-#z
z^j|3zG%C*LE%>J*4S1=8zLMAnIx_Q(42pl0U%q_lX5;jrN#8V}kW8z0HF;4|=k<YX
zyQIiggW$GgX~+(lIW1*zVc|rGqO~dTwgMs|`?kfScY32dc{85aXw(WD?gRj6$?VNk
zYH6*V4uy5C&!ZdX78gB`(jHjGA2l}<7oF@qfz|byH9c9o)%m8N>G6*b&wjN=Qg-GN
zb$ewFb~g_8_jia4gt5v+D^(jVA1%f$>t1aEwD&p|MjV38C8#xoZ1hO80?!om8#so-
zF|ht7C?r&jPm;z`gdthW*|ZXn9CZ*LX-1y8x<0$RwPC%xlpVJ(3iR$eH2Yr6a^mat
zcaknOcz_>Gr4RCS25Q`=#(lez_`~XFUJJGjmY7pMF7&*c_6KyB^*nMB45f%0O3U^n
z%a(zam+FX*z_&P<P99sQd8({){agn>(C8Yr{oheGlWnoCgZV!cQ4y}%sS;!SP;PGS
zYFjsB`G2K4_HpML@At6u*=va4DW<(^!0Z>d9|ULi)$0D1Oz`2LVnzTryiZ$zLZsSx
z!CnJUe6Z~+<Se3dqe`r!t8069cYSB?i*1~O{|<F<vNNwZ7l8KITYGnMOW$Ejw2O7(
zI6&7!?M)L-oMm~ULByikds8J{KKCgd1e-WHInl!!Icj=tzG5NnT!QG4egIO1$#NMf
zHII{7czgBqC7~sA6O++ler;1z<Se3lsxhE3bjuYu;8@VYZau4<f72}hktZr0dYspO
zsIjxQ03XQ+0;{n{z=Mi61I&u@-v+qihRFHwfuo&GK!=Ww&fjd}_jWsb*sezL7RTQ1
zMxW&5FleQqfopzo@gU{#*1X}N;w9(cfrtYBipY>~4FPObL{PAL&qu#H?6R<#flv3_
zo6_T9eQ7cqGgP8h320hJd0v4qRlB4@wl_}!s9y#TP+}(oH_UR=d#;1H;@8PG@Z<m?
zSXSW*sF1?Xj|i)6C8#W5Bl|u*k|V)mz=II{mIuddDRsmj+3m+G9s8R3ux_-Fm6oL=
z^bjX~U~6vhIm!;@+EGzn-n={T(5-odd|vSB%OdjE&Bl%&KmPQ{(G#w3|9SM47g*V!
zoirJr()F$2FE3ADCIi2sqAfR7-p*#u2c<HOQ{`l{$_*jY3a)~3%f)Ww?D&QQd1Wk8
zQy;{(#<C?0J6B!mM_Bo(z~+nzj7hX<1i%*Eva0@9CimG0P=|w*q$Im^K*(!Bu-$}3
z!CE{43y(?2`SP4LZnDbhL98(rc!ixrz_50)CFZpw+_<%NcJ_<SB8Ehy$pJ!B&D3fn
z>$268Unil<{qI|M%7A*q02ogO|9ncjdX{~4K9W83@p;{yndBA83lP0Eu%k6x0+5Nw
z1K^|O?!v(|SC9OO28@g^DF?7$f@fY+z?9#}*xK+2mGo_GZN1~h0m@xwFWLeaS&E~*
z{c-=*$-tjft{g06PtUFxY-(gQoI#aJ7Kk`mFMF7~0zFw9Q`*}cP&|@PjZ1pKYzISS
zL9#=M-r5*U@C7J9*hH7O65k;L4^>DSUBI+ttby<SZdLoP&_70VE=@hpU}YLO3k!V%
zgTV?w6{dCGNf8KnnW5X2oN@Br{$5^Qs6-uEr~JQidPmhyOXzr0pigwUZ9AKdsO|`?
zjSyCEkivwP%m*=mF&t!ISb1mD6hngczJC2$Wg)2V7jq0^AP>?;2F~AIhMh>?E;GX9
zH*9YE>7+{FegZdQ?FS$$;H{*K(Uh=x&F;;#`=InvQ!=%{<;_tY4HVOn!0Gs6kvNdo
z#{vxkRBM0w?M+*=<dl?cu8oax5Jv8=b8Dt-dKcCLF6>AqPPZ%$7Cd0S#Kg4sW7cV`
z!hUFH6X#<DMx>D0XFLr2L?*!xfY#4d;CK{;p#TwgT&P(Qv#Q7UwHu8`0XT1fSbyQ{
zVrJMr?pI(bNFVTW<3P~YQ~*frVh@1vn)Ey{XneG2bH3kfjI_V}X20seLQN(BJ}ba@
zSHpk_Fw@c;vA%xpHwxH&49Ne45dki*;{lhR1tD@XL*GE5VG%nwHFttaYyPKQ;9t5F
zHOhE$c_H!=*RBa^fLw>~2Eor?hqEzKN6kI`+1u}==B_|wHFo`nn?^ElH71jRi|K?F
zLRBBmWFoP9AGOgdH*x4d=NA@M(}3o-`il;N;7BLmv~r;>S<pBGxZKYVCYN>RETF#T
zjFRr(PfYFmJ-Qu-WUkFAu>^oSG4@_Hl7S2Ng_fP=v8e9CTb}inw0C#9Ue8|}1To4G
zd=mtO>0bDf+L~IWqksS>`3z`F`^f`kWRJB!;N~zKQHLmhZ@tnF5%>raE*CX1rPi;j
zLdV}c?o<HXy40Qsc>G`s)rV`Q2;sImIOFPaK|<4>$|dtbka6*)Xc(1@Qt4jN>({T(
zqCggiotg@2-8e>j=F0C);H!fD>;gf)s<_4T*H=YAt_oe!TO;UUIlyFKB$Ggp@LO;N
z>kq56?7^OEAY>FtziHd{F<~nfpn5>-0UL;6^2}>o`JaHmFQS|35I|n%#H}Gm-P;Ru
zRRcP(wN?ftc_QFSp}XJ(>tY!ix}Oq&4P4y+o#~^2u|C)y>BBymjS)P3`gEKdwrOia
zF;jGDW2@_$lxv;q-ZqI+=l+ClnGAoucjQ|(00w(NKN<n3C4!BODcqJ;ActhZ%MqPa
z;F6X!>KE%`u~6nk&aq>fcif0dWBdVffBeDg)law@!l)F+4EXAXyKs;kDu;ng!ULPK
zq6c}s8OBzD^H>1Z`*<pZw}kF(3p4-o@U!Bq3a@u5S7pgsM_-?4N{;l%rMsHfk`+XJ
z3S-z5wqpP|W;XZ}#>v3pyFKZZS#*V8P|A(k7hU8^sIaL&ame;TYEd$b8s*=MV++{<
zX-SGe<^Ak?n#_{URb+fwi=w0%^K5dV3l}77<_07^P!T%BWr&zVT#Ppm!UaIP&z-5O
z`xU{QQU-E^>>!NF2N_a@pv_kWh_{5ku>B2PKnm)~$;MVrjUcI%E#*4e@7*5DN)OD*
z9SC~?#37g10P<O5!w&W`S%a3|b0=(p6a(6*#LTZfOXYb`4^n&)5R_V}p1!{6Nsv4$
zV+;QMrGQn|cXZ)m8WJQtHvri;{SA_sEVUqolntVx;f}?P<G{b8R;dheIaY4<Z6Jt`
z_Lm12DAQpFEdzPFnUtT;88R9`oMj8hP>`p;zdsbrmQes<+z+eDps)$G%K$_?7(_?U
z`lu-ocdFMb&Ck!bH#If&r}n)qhPiBIvVP+kkkh_P?Vj(K<ClZM;FlPHt}gqeh**L>
zGOTjfhCxVg3z9NQX}7#)-hgi*?k(UzBHM!+0f!P%lne?0J2pcL3k#4V*?#`zp+%-~
z)F7*N#m>=JYAPx!KLJ5(30s}4F9FG5r-9tNVOwbH&^MAH)WCX%8dxJ6+n6q08jj~`
znQqzn#;7~V;(@}!J7f}c)>fzCaCk8=xykb<k9h}CR`+)M->aVHM6UoT{D{Ayx_>it
z_YX4_3mZY~CndB#nm20!P3FxEUY};<(Yn>^_7!8obn)V@qVr(>R)Mo|9W}lj0S-3U
z(8T0V-h_K2;j0hOuS{~o9jtiguIOccpkSyIRNAU*0l3C#EO2yKkg+eRsHo5Y7Twh3
z*q6bs1hRJ<AnaXv5uyK@=P#r@xDmv^;us8%O&(OkY{6s6>N_zx(p)G(oF>%X$%8eX
z=gY(2TF@<|J1wS+no<NWxqE-sW<9Qn*{OG{h(hwu)U^@Z`m<Dcl>l6+?vU`56>|?1
zNhf#W=<|(>5R3z=Z&4GiEt(}Tt7EgZ%!}iP(zd^CPAAoAVord)@F<a>dtm2i7R?{P
z&|`bLy2i!bC+m<%cW}N1BEZ9QLx^1w+NU0*x%y?101vInNS8>FxQjv0)Pa?o>#Z7^
znjydyDb@rKv5Dd`Nz_fK0#ZTBh&Wnq%kyN)J!qVIkS>Nn%vLdy;!N&*b85;0Qvko$
zaV(1)#{tE2?MbH7Q%{&S&4I|{=+n>KV61=Jz~oM=z}-*n?6@PX`fa&F1fBB8toodf
zg%5Nvvqsw!Ux%l!Go~G$P6f+nbkWe010Fd!u;LM0Zp*|wrnYv^`rw=4IdX8m(|pE<
z$4|k@g1NUZ&Z$EbvIdB6K8;X#O^5jq-iJ$2Km=cb$%m@FL}B0BnYPp=Q3XIPOlOZQ
zZJy3NKbdJ~`l}sJ;X;GMQPdJ%#R0y+9*I&E>#T@92~&{ntoVBca=qlFa^AH0qcUEN
zF6~cgEP^c$>i@l@_v>7^P7C7VHIeM>oIY+=Hpi=OTp=6<i6n|Uv@b8m@8A34dB%|L
zpF@8JKts5Cz-Hj<P6au)p`seww;jB{mWVx_cZJfbJA-{9%_jOquf%qs0y+V${|@E)
z)n+Z}hf3ZN-Jh;@YTYs6DQL+V(Z_IPu^fN?MVz{_#(MY^u(H;-_%0K750Xr;>Y0=o
zKs|SpnS;5tv6>=brmuQF_+RnrLHm!&ihJ9&`6)udah0>lg9iGN$QyFQP_r9O$eh4o
zpC%QTtlRHFYz9ge90Y0ckhbvNH&gKDYPMt*MrcTKD%(aN{A`MESq>{dA9WW!)T}u;
zkU(9>&;_A8^4CoG3jloq8z3K6(@&((icej77`C>JFJoV$B1Rg{-#|)f_jjuiRL#DD
zHQ7QBLo4Q!{YVKMGcLcvDNgnkFhod5e76VFbNvX2(owD;8^8<3IvOSk^4Y`Uy|-))
z!HVMwgHfr4DIUOdz4@+yqC0-1tk0LR@U<?NU#BmpUg$|>&P%pjZbsgWeB(MvgF2Xq
zi%G}9bYJ8X78V9Mv`C2uI)qfD`&lxloY7vEf}FoI;Ph!vlx6q@1PVnuje9ab&N37y
z;}#4A%t?)A)vif62Lm&6P0CVF^<B7u=zyEb76&B8B>0hn{x71~k)@f7$w2ADFBS}}
zJ!WDV4@22n{l{Jaj)9)fp>{nU9?pHS=}7kzpR5a$XGgM9=*6erXnb6Ow2&YB<i5M_
zR5EI1Lz`8ec>`j(HFy%%6Xu++h3)YL_84BdHrsA}{>+IX@~4Hu#J+#}`IM%6QVWEm
z5Ar}>q_1TYqdATCg@x~_0B>vNHdb+0n640q#n_5XrFFebSbqBAT)2p}n)>#;oJuZM
zKBuT01b0kwm1LV(je1wPmADn70DWfP<qltP5FxxMi|cq-m%*jE3CS+qtYWRF%E}<I
zYQNiZGU;777g$#*C>9!UJMxLWqs#XwnNu3Nt%J$52d3&<l|#$DC*-6YF%E=%SirJv
zL$HMq4jc-Uf%9ml^2PqUKprme?4jfKJT&zjnIa3#LvKD)RL9K2L#v1BQ?A0F&;Dzm
zSs2cKWN040gxAkLv03F7QAS@YQCuKYn_IgUW5GAToN9ZxX&z{-K4%9qbnzvs*$m?!
zDZvfjhF-JX3rWy=m^%9(w_rFvBSYI?qW1eaE%h)laJKUO<~x`c?k&vV;XxPBWpzQC
ze%uJh3aRD_CN6$c7f*T!)s4FT?*XQ8jaLxg?c+0A>`4+@4kyoco|(9L@7Vi)ZPq~S
zSk{1t?I=F&%EmEn%NsahJQ2^W@b;_Jzc)}11nRU9zgmsNa#|yEHn>CV_Clg_8o%BA
zw}*PrbKP1kWnZnNW5kJUP!zXt#eTCnT<N&m{{>i_0I;khKqgpu-xA)SVV01(@UN_u
zx2}V0(XCdQ84s_@akKI|xvls7`{eL->dyeI&`N1Ed<(Kny(7~C1!oF9ptayg|6%Dm
zLWM|;)U*#3ukCm^|2<J1P8WB0u=wzn|20eS)P7{?U*W?!d|ZgQ3%2}*(slhiIK8Lm
z!|w?-KxJjW+q1o$S^u|d2LBw(Iv-ucy{8HUVLv5=(}!w4&;!SwsU@854Mf7`J`b+(
zVzOb<2k*bN5ChMD-39nx{q*1c8PvP0u7jg3f6M<}b@V|W78R-KpW=r-@Ne+p;Nm*=
z^yK-IDq8=%;rMpF<G%8*3rVWq7&+)Z{HBTU`u1hy%YBvkb0;Lu&YTwO8aw*>&9kNJ
zZ_@54Kf8JDv*(!?r(PU8$%R{`#1(2?)()s_+%au*ChiB97L_)P6jrY`boMIkE_YTQ
zJKRD7(YpP&f1kPjcd4imhC!rWa2AO(Z#@6rvq&^Qv3WRMMsFdh)lVLgvY*4S?w;WI
zGB~^R{bZJDw32&`cOX98s<zbKC?r@}^zh2dQ`9T(TJGJd4bmr#sq9f}!<pu9yr6#g
ze>>~}xxDR!<vM$Y!=8Q0IMzR~Lt17i>@ySvD^+2u)JJ^2p?t2Pnav7SxotrDwtNrG
zN+l_fhq5$F^L6)0lgk4`)`pb)8WErC)cWur$NKSjks)@1|7z&aL7}4gp1xltiH-Ve
zF_4FJG)pAX@baUfQ$w?bx+1~;t|atOdtt3HLw>9Y@yMak-8-)=+CD!NI5cAHXjFzJ
zY<QIIf7lKt6)z9^rGo>Lpc%-KO>aW+{(ITRvBIUyRYBvs>{bf(4f)%P4h%P&u(ExJ
z*6FEytiNb>eYr*2oGB!4nW-@(G>Ejme0u0~(L@>Em)a6!2MR$2`5O@nLqEwjZx<}X
zr%JavmotY04{<Ggk0ad5Ewd=Jw!xI-)?=i62sf@LX_jIhIg(bFBMX@g;tCX-2cr%f
zYd&#gv(4vjr9zgj75VyM)ec8Ew$5J!98d!#Jmmb}7XFEHL$m!n=UMBFt^mJ>1B!_U
z%@Ph3GIsQgXWw#pjOon-_61`5*`-5&Q2fsk3755{<&`fAx>qL;PvM!|q<K6%<2A%?
zXimGiUA}x|XzOUuq4)%~AU$TmHQR|QsAk*!W?Q|#Dt7l&mUKqtI7zRyR@mnp^^w%x
zc*uK{p3NLFC;Q!?R(%#@ty%ete4}aq#gJBxD4-HgP&Tp;@A!1%WL6&Km%oC<VQW9*
zA4v*R+khl~q6rGIG$GvTV9SfUatMeIox$iVP10|c5%nzn;`d5>Q`o4tem?Q=bSw_#
z^j<O0RFzr*DV9F6xrr^;%dDy5Kdcx=LV|)8MF#^c#xEXL;I$wMsOZbfjlwR6vm8!Z
zxbb43@q;YP%33aK`u@?N!`rlUASgvEg-;`~Pf>>zJhrC~l?TFe18j@{<>3XI$A7Q<
zKUb8yjUD~;DA>|A^iv%+oDJ`0?l)5r`7@q`b8e^3;AqZS7xIDM!>b}irIzG#kOz#)
zm6!TTd#_Gz41U?m^)?}4m;KRfFI0z_i*|%yCn4e5%c6xuQ}Q{M3X$UG`tjx!6-X{u
zxP!2wiKV_9Yd?Ow$4DjYk#ZTy(|6<d)^BE(+PV9;a#kkCB#kXh{T3?JjzC7g^%l#n
zz2KB{MW@*JeZ4U^kh!HlByHGkgb)q57Vboaj*W$~^Svb*gF^k-f6mISziQt5-hx`W
zdFzG6&UaBVnSHCkKJEy_Q~e3ig?%)Nm!+P1ZE~~N*#CR-h!E^dxZkh;HBfUX>Hp7B
z`Y$LC6h=xf&_)yAwy?`&f#V7g$fvKz`is906jstw<lX)=C|eir7mUkhjZh2%N=2^p
z9|lGykEpiyVHx*4XzG(GB!rX|N^Rrw>-BTDsUQBQG;AV80kX4cJ2V`)_rd<`UmR`$
zxJ7t5^lQWHuTyj7tND4@@WTs6%DO}zS})=Z!!ce%Ij~Iffc6LAo<N)Rfr*7wSx!7=
zd851XYcl0X$f23?Jo*o{e{_6vyFRzUPKme`@p9WXgmj7Juu(xxU{L77cTy7(v&f;q
z5(V|<2Fr<nH2Kg&@)nL8eP$b!XV4;EmT8R&2>pw|K*4p1h0BhGa)#_>z4?kJdo89<
z4==ge0F2)#T{apj>x(;ZA$j!BI8mPyuB`?^HJ28IzHhMfyI-li9Wqo|#EH0dDF4r%
zRBw_cTN2c@`zLLAX1N!A?a;a(N*vi-cP$Ie9urui7)h64%}{l+4pBabV>@#r+^RXc
z$fJnOoRH6E8uFpRV_%W_tj{NSep=AW6<#)wF57RGEe#5(_Cu;3euw4Ekxl3NobBG_
z$ae~#4x#H|>o`EYKLT<^2*WVL^!hndY8#NVZy`Z`A-Sd}QFIw5d-}FU-Rufs2ir$b
z;kY!tJxjm!3ff=szHX$!<p9L*DM!Py!dT0%0j<eHt?v}xf**yFIu20VOW&{ci$dJ~
zI0B;S8@c?(p5e<4!WUuQ@cq)=-(S~RrdL!3sW2oJbN|RDx^U5m=uB2tkV51c^gZsT
zTe2<+ZP@-9ZuR+FtlVBr`hHTTCHV@=ZIQ5PrGQ_R87(U}8^(^d<R!9iXW7SnxiR<O
z%6k*AB-8g_oEEdj%9_6Am?_PKn&ncKnwi=%W>{*un`?`jxsaL~xL{d1GnE#amK&D2
zfr`0rfL4=<YbueTQX`6jNQppzz<I%$`Ofb;=Q`(H*E#3^JJ<PpT_(M}@B2K@{e15G
z{@e>Mmylwrji3zYBlrOX+Ba4X5<erWd*pZ!&*SSyw|##<-lHMNpBSM{6vm{A6X9pU
zf!aG7W5a0G$lltnPn&A_GsW4~$KBJkqi!RG-k!kUH?{V_-a+qsy>Fd)OXgvEFci1=
z!cSHkV_}g3bdcEjfI_S(kInb&AUN{(k~K8?t;@YnAwuiYJ{BCBX;X^wBj}{cAh<fY
zqV-rTm-RpmUGOm+IU{$K+Qm|C(v?cp@!NLo)d4=zh*)$mEh>N5@CKoO6{e|oz`8};
z+DTVBy^i00;BR_mExH~X$){sr*yP61)bFrP<%MQQJOOc%PsDPz^w$~cZ<r6^D<10u
zs`>GZ=Fxx<d%ikZ-QCvRx_LBCyR6riNMDMz<1eyw$@-KF)%=CetS>?8N_5mrd`K|k
zJnmaL?TWRZ7mP-cah^E`eKsTko*J=HRdLUbL+qC;5$`{f@fi7-Vc<kwn;X(o8pFqv
ziO#mv*5;bg-@c?qiGMk!I2lyh=)YpofP5*JvnuILBwCup@9A5~bj`EQ|8kBu5b5dX
zRxlPGw0QFRh1j?4gcGskV}iEBKFcmlt0T+j+73VIQNE0oN*-w1n<%?jHz=AW`(cTZ
z+baFfN`H>M$4cUbHTC1h%O~p3qT4HfRF3+AHUmPOj+L<PkeOJ|gFe3}7BqR#{l<$B
z=lNgpi=tEfjyscF@DU~g(RDnDI#jB>$}6O}@zWu~=v8Jwcq0b>J_i%xPV`B-Zi`61
z{EYDam}8T#!Vy=>!o5?K7gUx3pe;$_6W(yIG{(Nw!iH@uV)mzMU$ygXKg(rz-cz2H
zN1937<UbzXz*7^Gw)U!C-37x(73{^-G5HOv#M)*>%VK2!5Rsp?#v@LsHd5g*{{uA?
zQ#>LNr@bmcxix2k-9Sk>$C8xy{0Q0TBT&8qg*z2{9EDZs*y2oZ{<Uu<$<3|r+hv@$
zbC1?5PU!gVhpk8H&k*zNvfri6Ts<-{Y#W9MKBz<#=(PD&wREmuxjfo-K0$h1+HtH-
zdE1(mz;uLO<+e3op;U65bL_72aNavWr5e@v8iGjzb{lJ0wNGiWm4kqZXSp9UByq+g
z7C#(Z&Y<lL967Ppq$<(|mr+Tme=FG-P>uuMn;b81bq1)!w^EU$9IWJLDO=6<q$K<B
zYv4(FtR=IFc<dc9CpGv&JJVAgJjT1<`6ux|uqQ>vj#C-5^|ZeDKxhqrmi)TN`v-12
zEB7i8I>cXT{7#(u7ZD7114+Bc+<rE=%Kw~n02eiY$8;I)ASMnP-WO(z!fwapsSKjQ
zBg=&HT(RJ@^bCKmS{Dvmc&?y1Eez&mT_w)D6Eje4V|iscZ1m_T-#TcfR>buzu(VAk
zEyY6l&g68Kt(ygnLqJw0U--1_Qz}!=?JWvxwEq(CVM66BLCNu+LN4|-Q^Pa<>7V+p
zXKI>K-P$Vtm9@x%lQjg_`ZT7|1s>KI@xmwNhX+7jD|R(~@>(<@Z(-stVJ4@UxGMwC
zls*ZR*Xy=o@wo})x{}EqV%(p&rKIMdJu8+^*zz~it`MZyRJ1zA@>u1-{@V@TSDx5`
z(9%9i$0Cl!vQ|u(q$qFm5ppuJ^J@&5e*M$u1AlGz)<L?Lbo$lT5;6{m5~?fXHj0(h
z6=ePk0RG8sta!J0Y_-ma(g~+*xH7VBhtQ`S{P>G<4WAxb>`5W~eq0JmMJ)UFn-9J#
zvVD0L1|Cs7WDu0HL9t6rH9lXJzf6@^X>C19y#9<pD=JjZAv~~n<xIRg(H4iqwN4Zf
zmJgl<4r0)ivKOtPKpb-aCSIu{9|IdVuy`am?$UHuQ`LlLQX?XSf5%J!e^&%oUguQ{
zOj_uf#{Imsjx1f1q*OhS*1LB`N_z?VB;Ve!QvC5)l9!`;MSp0o`nIMgMEbSAf>B`Z
z#3SYE*x3Xu_lGgwi+`XpI40qGtbar7QJlVq?O7==#a}UC$9D=MOmXAx7-7#X>u$<c
z3wKZwf?va$Cu@@5^LUhi=IL$Z%{=ica{sTv%5?;;JE=OT?_q+SZ@U}Db-`cvx2-{N
zJ}vuYujjAV2wJ1+9{1k+%$t%l%iPI9Jh6zJee+M%y$s04sLx{`*w!{ViO(mm>l3I?
zWQJh_o>H31`*-}Un}9v>Xj{n;x~Z2Nd%sRXwwy)Ei~byA1#l00!;uVJBkn69!+0cK
z`=6I(+!0&1B{jx?g?<fZkv)H3L|emenK~{lSv40ml47}0C8U9U^8w6fdZ$wO5>r5V
z6M+${`gHa+Z?iWZ0qY#>2^LPXzQFN80UAnx7!H>k9opvkM`1H9#7YoFuhL*OZOm1M
zfhk+}0XE|G@gz9_<o9ZP_L_fKA7b>gWT#l^ok@IEiVYqy^vY~Gx=C@oJfbs*u1>B(
zT#ZcxuyTw{oSU)&pO=1q)e?i*tJ+t4)oJ?}Y$QXe1E9?9TaS8LSG9)lwaF0}HOy%&
z+sK#qmZj6D<94IK!kuV#E!~~>kFallv>B(N@6{C3B){!zBxS4(w;nx!o%e|VA6iD&
z-ay88Gng=pz8llS)?0c#_EjV$@ugTAe2jl5m53z!eVh=!@y8C^uU86SU&H$qi?K5N
zX!4+QRr=LNlf?^t0{BE_f;G4A4q1~Y-bi*=9p$pZXO&P12eH2%Z8LF4SK7RPreQ+~
z&tS9j|Lq46s|V9s;|)j3j3Sg7h?H2pl)?#-8ecodTq)F!!&(ihi@>u<)-1w>DLUBc
zKkkSld$ud55{L%?Pk;YJb@IX5m!5rF$)h|mb%iv2`=HW>1V$IHydvO`S0!+M_^34a
zfu;a#z!4D=DA;|R0GPIqqLvU3uI542;g22#@1Iz|h8<aUcQq^Se^(UHf~LsJSeE2T
zEEu4;t>Fs8e2aB1HKEX^S@{Hynv2;DDqUveEf`qkybr#vHR6)sRB*e+L7x?PK-Kar
zd{m?rE0h6l%GSnS>!NrM=jwi(Q-#iX{Ka2+HSxG`xn~QvuZ5gh`-ipyh35Fp31Xf5
zNWXG05MrBW*I}#%0+jIF8}?LTv$uA|62d}V!wr`+1KE~J9^OGPOi)?;tj6DeI!`H^
zl+W4PN6`&ui6Nn`%FFkSnXa1g@xYxwB+JxYPM>zHKFL`j{dkqVy_)7SUeg*9n{kKd
z>1p!A!5)ld=rv*x?z|ErUYUSZ;)zMpd|{V>H9-zp1Ax+I?54bK*{aoKx8w>ISQgkk
zH+jOZG0Vf<1U!s0tP%hAysuwUV{0wdaz<kDn;KSIb#!uZ^D;p>69E|4o5zyOi`B3P
zruo_!?8aI;d2~p*eIG$b1|t!$^X1O|ACz$bi0ueOLjQfRD8ioqh!Diu&)($hV|c-I
z#1<i%^xp!>jD=^KI;+9LRdvi2!SC?5S|d=(@X7r@&F+&ofLm1$YdkLMCnL`vl4r9T
z$o7~5vW1%vH{WutFJ_veltj(10BYowhg-y8-Xsy1TqORcQ=Q}y>%t!jKDKxXORu&$
z_-{{bbSS-Y!d2U+fNtnD;rcDH;50_!Cin8gAz-n+f7>htV#}TKDH+w(-mk4`Ov(qt
zX4#{E(jayJa(WwBSU^0Br2q4>Hlq<wZdVe;c!d5BegHyzy>M4jm%}@!#{Zo9r#)8*
zmr!#myM3^dK6Zj9vhl5Co^X~J1mHcM$<kM3NT2fJ)e96JFvT$v*tA-J;}8SJTgAPU
zX6735X^r%N{a3cVO}JR6>X?3=^(k2af{x!b0bltX5ga?o`o>yC&H%zUzq&MTyEESw
zvxUh7a*v-C?C4jrys~lQ3_rC`CgXzt3gUr<W3g>+8V6}TqT8$itTx#SgRF}4XsYUG
z?QM$w=s(^d9#UNH+LyfVhtGn(l{1`Fv2q{wUUdWc)wvHeRrq_3M<&nL1WQ!ua1)`g
z+c+}WM2XM5cfX{OjKtSsPsR=eOOA6xAF~B4_dbu#3G-!g(NTIZT4gad&I6W9Onz~=
z^l&59#m_q_Ie9tHp7-%z0K+K&f`tL+C2Qp^E1$ggxu$$|)NK5_nPF_2dpxXBfAK%~
z(9!H=+vKVVsOQJI&#6Mc*A4L%{)cg3FMQk=L*?~`?*WG&ITo(VyZ9|pWK)fFj~#7K
zPOeM)mlBsXPyl942YosdmzKWqfjq3f8)a^)@c#=B3T%px@kqeo4juaqPn0W3*}5Na
zn@h*>v9|M8(a&UG{t3Y*qY-@|OiNE1i)Y^vSN#n)VE*))vvedt>KBhoTmOk>oNqbW
z)ergk1`MztxWf8t50)R*9JmDl6S7s@08di5$$GqRTNUC4pKvFJ>E`)Q4_u8n-zJD6
zIL031SFjLjl=hcm7yd$=b>$aFGHgTspOfBannGx7wdXJ0iLsdA<R&!jy-|G_*9vP$
z`kNv@T5+AMFzv3GGEemQYqNmxZAZ`^){)7}p-EnE<F8^_0)IrrE(Z_<{t%81Nw8Ml
zeupADp<o+(*(P&>(HxBPe;}n(K;{V1LomMl`f%%hpT-@s1`{P}%~Jt0D_DImZxm-v
za|(LlvteFoek-2<$A2eV^s$Y|{l}8LH=rXL<Aonq;M1=NxCLp>3QTn9SzJ7Q2SfBN
z&(IplB!8U?Mp7=VX-^KD;9X){*eFm{vmV$yPoojxj(<{H0jq&07S?{Jd`h;G4p$>i
z@!zuEux_#ZAJ8r-kEd)1c00NR@K$Z!WS%?HH3A24*USD$BX!Nc<1_#SGS(OCeERt$
z_R_NJ{_!&7&m7hl<b(4olIDt(69I*P56r|wou!mjhhEgq6^rUkVeE9<9}X?YWqI#z
zt{yarq^wdEZab=0p_I<XC?NX%IDY>4Ri%oUzkE#)=qg{<$p5QU_<!dBo|=xS4v6sn
zs{jy2J2rv5+Hgso*;M^G*IG1qtiO)sR5|Z4UMv2YZ$Xxia&oK4#TdEEczAN8J)c4z
z##-RIij+gEvIAGm+*{)pQ_$BwB+O@xCLEH#VZnLgUsy)ur)_>QUa>?GTODKHC^)Mm
z41osWoiA;YORdue+~rk>Pe3G0IGi{!p5Z|}a%H)u(Y@o{cQeSt9g>ai6kH+k+S2FX
z3yb~1ShRDMze|!2Ka&?WF%+(q&TfRX1o7L*cxD&T%etvG{8h`cph!1YcgJtH`F&bY
z6jyc7me_hJmcR5(rSH`=4Ch~Wtq+e0(?BGaj|#N7SGKuP!pV#G^z)YYf9EKWOH@B!
z4yIRsX<_BE<WDIfy>#@T=iLFyDB&7semQCBkzRGMRM?gO?&4p)099)I1i=@Ps7+h`
z+6G}XA+JinUu}2Aq#XnjxmNx3jDP25XJ*F}pUIYVY~(|cT>)eExZlHIaUEMY#1OX-
z^?A7XFW<2laOJtIayY(Z^tGx+#OUj<^OU(+Do53pnk#kScPavE;Z-1@e)Si@Qcy=?
z1H1F4z6TnpOKHu;wU>nnu5%D;nz=X`ugITiu2vKuAW~ZJa8sS*hwy-bqSvJjaPhRS
ztAy{_E7bT_>NkE;>}^WyPT+Q6lM$lpc;x4AT3C%fw%i7Z^iP<ei}ZLB98bS^jWQ&B
zatkPdz^ZL*a5zf1)jf504R`r@ZA!Qq&yq~$h1s}C6JyIn>~8Y!<d-~~98Z{U&N4UN
z_|Uqjtf{ya$uEpkku{zFmYBd_MRq6e0ur6}ZbU*xp*l=(?SyhRkAr~p@Qr}j^Zbm2
zLr6p&xeurT$|h~ZzT@HG8H#$L(kKNU+FL{!AiBp_rsE>LlB!$7)hX6(2s?f;kd(TF
zK~_w-;TuOCmJ_~@^;7(jIRgQbg|7m<iqh0{km{CSfHF7zSqfYCXT<)Wb>JhA`~A2{
z$JM%Fzpd8}TO@JzpP_3LmT=+Pz$I0o(w2DKSJZA@_*!JZI?7J-w~+1rO13DfZgZ73
z`^zbhQ^iW%-P?ac@$`5Q=*jJ650e62TlAlDARNe*&5<`?^4xqd&K_u=`Q>0=()Ha8
zxwZr_$DOPNRsdFw$-hIE=2$3IwL_-Rk9REGG>*xgY^@@rJ&<k%wP^z|AAB_eDvMOC
ztYgj2cli?q0ILh&(z{{@%5~~#EovC#1ZQj)1`Re>MjRbK17&5OG4n~TAiA#)gZ4RO
zgF9T>>c{UR^D#{0MA;(i&CLS}Cj(lq52(br1}kH3DmTq!1XrcwnH2ST%$+>of%88k
z9Q<)uDZev613Vq(TaKgln9=cP<*#fI*0IlIgjI#hH3MzMg40r7YXC;F-`bZ47!Nzi
z4FB03ak}b(k>tbGqYm?%ZN0`2GD6T_sf~3&F|(5;;ECU`h7+2n%kPMDlNPCjv*>FN
zV>lV~isUQT1`e$G8wLmja=aQOP9}rrKj${N)6H-h<%ISe|9jX96HU``YJ2nHRz=;&
z!n4dxL;deE?&6}X9<k7F(}Zy3i^i+r(6*H`G<WXAX&lXX1ong3r|s=gvhAP8SWxoT
zzqlsp#M(092i7j0_#~O`)-@m3w-=ky$Bv=#Wbb@1Ax1lhh)?$w(gVE)7SU8Ep13o4
zATI*_^$qKrs3k1d+U-jwc^u>EZsFa`WMgv7X|IZdFYpo7Da%<epz_TBN?>hP>*{hF
zr~)|iX?`9M&j&<oJ84_*{X>#F{|;-2rOFigyN;JoU+SbTQ;^*WM{#J|D~-`2Gjfy~
zlNc(aW){a;$-H8@aS_Y*r>3?t#XmrfZH<a`iXBZJ6Fv*ZK8LwWX{-ON--G}*M-pA9
zy1}NYHI{Eg4kcFr8KD7_&~1kU-0)@MUKc&3r#}Q#+^0splLBQ*n0&k*-l+t_|83nK
zs&39#fSeiq5YjgJ-@x*|m*y$d9^*0oN|)^d>byFx_+|w_E@4$Y{~ePk3X2#POqJg%
zUrJ*BuhbR)=WE;l<LdX{XZ+t>tgJVqxfD>JY~|^%pHYG%90XKE3tX(*#Ir0B`4WbU
zwW;rYCK!g30du}o`tQvsoB#}rn+e9>wcuP*jktuRNp9%Tis+m0AIB@Uh++n0S)Trz
z1o+!>F_G?43d?{e1mkqv3W#r7KK!A}qpW3mIAe^p|4OLu5-bLB{#o$sYn(Y0{a5W0
zaE|F}nBm;2+tr85TAN#WwQH2%{=pH1KSIOO6{&nY*%901VN#v${fDno!%wU&%HjUO
zqG7r`tT6T-=<@kIqS7(f++26a^L08)h1`cF;?lAH0TF)FXO-8eo&e#`8&?qO#GixJ
z4V8@IV~wH^P9XKOf`I0o5M#J9yPvX3FU8*v9lGtfQp3Yf{atnbKLV!z`I{=cFMb?*
zCVu&LDe!9nPgtz_k6x;8B1K~i%<8X<nWB=9tdvzNwk{Q$;PxN2tC$Q=MHCDfrV!%(
z>WqP}{ZT2X|11XbUojyiC4qGrjs$r9+4K^Sx)tcEs%OcuHhw_kQ-$~r>zj1u>nMxI
zTdtpzz6Y!^8f#px<34^Rd0qen5vQ?fCZWreA0Qr-E_DxLOU8gY&fg!u8>uWl1ZHlE
za{_$)9knhnZk`gpb+Hd)@0QbNEBVUWDq1ex=A{(HAcPT=)Kw-Cjp~b9xp(@}<+7r~
zEr{`@+%z+v4IJ1qTCN=LpS8a6EIB-ON~gL7Bc5Pc@+Rx@REGa7$=}ue1W<)-EaG@q
ztQA(@b08lqKg&-bXa71)1v^(YTI78V3~<gBF5Ts8DMJ}Apqpwjvf*sN%e!nOL0kow
zPLkn9wznrtJJ<Kf=t>};Q8WW_ByedzYh7RCr}EK8?Sasin;e{nPo7i~U4vuxCnVpH
zzEzUX>|?;m&c-8YxUYo;01385gKaAi;*O^q-~7U{h^v;2C}7Yr1oSn<cOFkll{Uld
z`444s!Tgc%#^UNN--LLrA8&73dvmAN+LO1=-2m-3+_K~6%l0ZNc5Ww+?r;8_^6A#j
zl~EUtZjM^<=$!el9rq1?`&o0av*t8p(_qbAUA>2l=<dG0BxVMyu)1luXLf3FmLTN8
z3wiC#q_$R!6Vbg)vefG2zMzh$qq7;5Kafff9lo}+SAfS+iK6=h)>#Z0)Mij3eQMx(
zL5bQ*>lHJC0v`^Nwqt;3+Kor~*5tF6-?<CAF0parAcgtxIp=5!1nt*;Sose2Wf}Xs
z9V&!}jQ4nHsv3hwhQvE8WZel<3bkhC%0UjzX#t}<6&ppmcw2c%*ucM*OMCUl|75cs
ze|}TJ;CCy41_t2g{Ey4+Do6MJ`JAaMcmDhWgDT~hg#f+ATmS4R{oiwwi_Vm-fxE^G
zyHo4(O$-SQLRXpsiz&PEckXmtazfb~TMk$mTiPln7udJ<=<pkFDB;Ov;r9!z_N3i2
z8Ad-Ub9I3a)&Xxx5H!>Fow}f;K&VLRKf*{qEie6l&t(6X#Fo%n4H|WbK7+cHIp-7|
zi^A!O5Gc+@wvrvyY+5lxra46CK<Jqa2`b<GM-EZX(yH4Bm7mAB#%@sRr`PwuNr-DG
z+f&hXcV~$uP15tId#EFp-Rw)3l_k@f6}>C)+kK$^g$zeca3bMIcMihVz6FY+=)z-{
z=n5I8Y}om*WjpaH!yZc~K4fU1#`bqd8OYNnGsl{RF`is*nVx7g>-|7d#+e0L6$NNP
z0pxxR08?8istl-L9tir^B3IASq+8}2<M4npL6;kKt0l~${jFs&(8y7zSf$*vPk>sJ
zGO&UG6oZoKYkf&4E%S&)w;F~heRGSibdgsc1Hjff%aOmm1-FhrLr{zZ3JU?yaOk_S
zmsI(yTks_K8A62Q5yZJKno!exj?09eeis(BF#x`gYsIVZ^Gq%7{`}gHvJ)CT+GX>M
zQ|FRKl<}*v4<RixRY>$bxoE5!+j)+G=`m)veg{Q4FMaN*lTdN<t=o6lRkwfpLvgC*
z<a=0O;kb|DZttc%eaW#~7oJKzr{`fwNDBGW?&vRHzO1j`VYIr47Qe4M79nYKEggR1
zsSWa<+A5xf&et%G5E~gw)CE37L$8H=b0CQ@iMf^~zG6R>Y})Ofrmi!F<qSWnrpoLy
z8G~Cr6~EWrRMMmU8l|c_?TjHvP8{&xi)pCD=f$<Qxr{>e(&Ci%)`4^Lb<oyt9bfN2
z0xawCNv~9Lz%xZRX6ASswZCqKNhTgLp5didx}r}j;RU&jU3jU5J{)-6chM6CJF~9H
z<aHd1my<==vqS5i%}{}JY2qXDoh=8N!&Z1yfPa~}`pcU>ly6gUt!1m<BgO&k$N0w!
z@Bz@))-O{KzYTXL@`eP@)X+|~#Nyfv=jh>d()3JY0pnIL!Djou&Wy6_)f@=dxfa%+
zDA0pDKc;NScCk`!p!tXW(2bWj9g-bIxd!)63ZVT}3x}SV?om3pz=)@}E}8Y8itZnz
z%XR_ZCMU{SoDOh^b|*z-=tC?myT?&S_E6e&=VZ`{5B|$Q`Up`!^>jEo;U<R#p%YoZ
zDfOU6R*hzOL#unkZ!%M%g?Ws-Se&V1{Eoyko$nvu4S0v?2f8buk$H@11D+kyx|`|Z
zvD(_UyA5Hh9_O-L>r&45|FM5k`n59|U8wdSyMKpPpIRt{5VIN6VgB<TsLag7cvQgb
z6#pwEz_Hf08xHk<#P|`vkb5H>m3knYd9XK!JcF@rau;}@hBNaYGgje`m~f^NJWvuy
z%ZJ-c<2EEj{H~P!JYxr%Q4g`rV%&k+WEDg(N;mML`a`yQjM~?Sn!m@;fj?J*z0pme
zz}fi%#!(b!9gXK|{P4reG5a&!#S?<Ec1>sA<N-!Y0mE;@!ez78s{;{_ldhuF$`psR
zFBQ^H%1<pma{OQihJVajXv`d|yx`w2s*fNi{hSv(eEqA<bH~@$dzRW&K&NAg2jx43
zBtvd0!?dSh>QxodI>|wr_-_1+Ujws|l)SheZoCULUay=w3zKnuYf4;yh=<?e=7?O!
zA+qEb^9{qmmju>iJDqCTP$myvd<Kbks<r5@hFCxsp3fz|Mtt0!Eg6L197TZ$CPZI%
zl+qKncgp-lnr_RL-2<-r3~%H+{P0}bDb?j=aGxz|=)=!ANamY3mogJwQC<4*?k9=V
z8St+*wZFE`?NWDI=!Xy=4e7zq3@ZL`FX9*@-`S^?wTpT%?|F}KbHw&ipJ}EM$SXIX
z`_qG&*P6Cza|bZj(axfjmS3lIau}}eg}5gV8)Xj@sJEtSxBE3<%}EnErd=_%oP<o@
z_;vjJ%h|F=GrRK2du&YmRfi^29>v(yo$s=787`eYpQDiB(_~2ZQv)6syn(8a;ucPK
ze+y0T!Sm6_j5>EJjNTC~A=Ht$L+fS%NgDb!0wdVeImGPj=5MRK<mxD~f1VMZTRniE
ztWQ$R-jz+YzZ*Jyy@U<yOx+ph?jq}gJjNq~fCcS})kT&+RkE~@fi00aoenItllO(B
z{MXQaOfvG*nrxa-c2#~`m(hiosr55zo2!Bl&r4-JP(te|m5?Ijj+Ry%VVqrFX@%n~
zARoWcESr^%JIV!I6vGT^>>M43lDs&xcU7h4iYp(_2+IZX+GAp3m4FCa)x)tWrOKQB
zeP<utmJ56dy3fC)Wfp8-4NTteeQD@bSMe|Aj-KWv8ArF6I4x{|ChG;&C(~cMZWBzP
zP0^xI@A>Cb9V=HCT{tlQjEZzlT}0~uJ!r5uevfIw0<tV@yAAVK=){xSwPUiZ&0rJf
zg`zAw?HT_Z&ehQ%WLSgzSBTs)1V6psS&8UDeoNEde$l}O17H_Q_p(O-RmuMRJZmS<
z$YmQ_!iVJWb!d5}x)XpeFz6me%uavh8QhCE4zs${j{qIjpLjAl9Yn(n!SpTTUXvV2
z<<Crn3zk`oHCu*0@OmAo0)5fv@W^Y^r``v(osSzOx>*a_@I;Q|!U^a^HsiM}wQAz&
zr^NYEwGq7eY2}QDgTPN;^jWSe;644lRq!a++%czh9uPIQW^Z?a%gIyVUNag64%NvU
zEj@~1=J~&kd%L46XycXiwI`3h4#uQ<1cW|JwzAS_Yw5rHxa~26ta&ElOEJ{==>Z(|
zCDi}v0>^TE7US1&P7r~SW<V?rf*_0XGtoqU@s`Xni*8uSnEAIhgL|v`uvH4ovY+?Z
z45=^^gB#`*5(;Ba*o*5!w?!S&^^&~EGEYE5fMg=^1ZnX`PU-qJ@S~`(s>a_yHQqL;
z&ZeTKr>I7pf1g-S%VVC0Jt8|YY;Skur*0Ue#mAur=FGKBK;`F~Zs$LrTQV2_81v!5
z@3G`+>TZb%wyXclf@m&0WU^6SAhMVAQf<Fax;%V+)Lt(%?B{fa_E2nw${dFqssV%H
zn3L`p>da*1qC0U;nr8U^`zdRR4sZuPInLw(aQK5|sULB+WdK`<j6;pbF!qxO5Lm?m
zVfXxZvksn|mIt)~{?;Fee6F9t-s=H-r!IyCkk;O}xXe`jWZ!>I-Y5o?t1{DA@(vQ=
zyUCJyAS0Hu>sRwWgcnmWg}U#`AT9f>Y*W$iFC^sxe7xuYj0B7vxnW3sVA7l$aUE+I
zzZalcOoDOGhHBj@+LyftB&yNbSgc^|<miAlVrlLS;RfQuW5KI~U={pte@Wf6+tlQj
ze4h84!verG#@41MUH(@@uKAqfI#Z%KWycD90i(KrtkAUdwS;rG`xbw_O6)d@>RK<A
zHJF*id!ll5MWYj3oa4eOgQinP!xWduJDj%H&A00@%8}(Zi;_&2>0&KrC|PEd>5Dt2
z?Lk(E*$)u2N2ev)uF41ezSP@-{lWe%@HgAfAbuwS5){=F<5GHSR#FC`=PgW;TK3|!
zcid6vV^Td8X8PObZDd)O3&Glt5-o&?uvsbyRPCnjVpGdsntsV}mt=apD6<AUfjNm#
zL?!N-61<0Qgopz95{olXB1^gUA^>sb%*5;q1%N5&JAA&(#`J-CUwflV>Bk#Rq>Fh>
zJ@`e^+8Kx_V}fhp5S;-*7R&}zFC~)d5k1s?uf?4gT-O1rATGNqPuu=NUVT-L`_McV
zrYoXB)VWfbKA<~ssbNi60g-yq@;=2qA+mT*J^dT)prZ+^Rp}Kr0$w2^x<ADGLrKP!
z;Yw`m>@LSE!@aC+&_Y!0H=%vz5$o>QgbY6*l*~-LL|U0|;<zxSxiIng`wssPYd~5-
zu(<w=IMVI<HdG?UAwis*oe)lH8j&s%bEbKp``J&jHkV6H$EPU|s;MzPLFj;zmakb0
z85Kj3`j!{Yv>J=;iOJ$qLSPn|hNk+@Vr+WqH!8eDLDdOx=yAaKW}=pUj!T}Ohk5FY
zCbL=!G_<davNnrMY76$;Yxl0$aI~zjpi}F;<bX_z98fuwwErG)UDLVwucM;RpDXK;
zz_U4nZZ;=B&3RE^H+H2|2?zD6A-s7eZNb}}s`mE9>~n>GZk?^oxPO4Fa90sCwSXwX
zZ^b5pD?*14^1t2RI8QL^mb%WLv_t^`>tKZz#4@Wh?|ln&{z<25LuT!P=7s`>sy;*b
zX8m+2;_G)k*88pOy^kaur|RkCGT;VuWBQ?~Rj1@XUGv1r)K7aGR##6JZ!^sK-LTD1
zcf4WN8fsiQ1bn0v*8S-?ES?mRg4<5w{kUu4e4Nrwl7qmSA%$u`F%zjn?f_0l|K;ar
zb!FJl$9j)w7gQ(nYy4)NbwIp4<7+(>O%;gG=se)P*uy#P;Wb~EHUxIy+3l#d`ia<e
zzoLu0h4ro0psVMqVV!bhNJ~WA1nY2w<lcUfp^{_iu`wCpLD#kNKA3YYe=-5a9I;c3
z3Gwq)j7ghxImx28vwt%jHMNCxQeUq9F}%^D)bRUga@KWX4r8+c>^$3le!jl$m)GUI
zaaoo-3v6-8zrI*0dGs00silfFLGozQ@7`M?_!&QgM_2m^9)(7PyV1f;Rc-?NKN19<
z9WM0_VGk<I?=-&*n!L9=q_z5#8dcn`Qa~GyRY|#nx1P!_20OLfkxv+?-N?^jz+5~6
ziC@<4iO(gTT0puI))72W!!9R_b<nlt0Nb3%Vc;^`4+LfHVHfwrHO$r8X4X;#Nv_6!
z%%d54Py#BN#@2H6$R^$}Xn;9^N5k2OdCqTw-Wo?P;E;fiY5^ipMwb7NG4}s6%Sqwa
z+je(Cz=vP;T$awA4pmjn%*Ve;)NjBUlUAqO*t0D!?CrKK-61*9Rz=!8dmG|e+D@QT
zqx?YSbF9{tpnb*hA)9tuJz=0AZ6P;g)^`h$`?^o)zo64-f?n5A%h+>z!{i9AmZw{=
zR>Xz#g??Erk2}L8^Sapy_09e$dwqBmN*yA)idqFVKC|!^GH@!I(i<nFw%GyCqu34o
z`IuYvWxn}KaIX)un4dJCfEsISlGFS=UYuxi=|88Md89!sF`c+XL@`<nBG}~3TtH0I
zi-i!{3EJ#|Bl6f?h4Pwpv+9DOd<*9w=7`<XGoDR&T2)bdk~<uB{vsih>ZuW=r~3=p
zL}-25P<Sf-;z{)I+XxMPQAL*g06S!hq_aKNW;>l3r{A)Bsu?{V1w457{F;dAJS@?K
zZ7o>iZlivFCm59d&f*qm$GYUw3$1KDk<5JW%9@|lofm%ZjdQZvAC$G2=~J<JU-xTN
z{um7zm9ur$OP(JKM`biTl<PuewM}x#W**2(&FL*Dyq3t4CSFEOZhwJ1)gW)Z8#H%X
z^K&d~J^#F$Mo{$PV5tSY*kyXAx@_czH;Q{|ohcV8$|p7nB?YJ@Vb=ScnvLm=F|Vp2
zL<&tF03{4w@N2Dq%+ODeAW@8Kr>BlI33=JTGsazjj`fbsl>|FpAz7wweq$bvuSaH@
zUTy9E*N(yX7iT(OMh5BSS@Ms1Miv$fUrj6*HtEIbTJGxW&#9cy6Qnv;#%Rnx?qpvV
zrUOrJ_X+uOcr0S(We~9|xjl!{a{3+6dUJ?0qOn_F7jD1i*b``^yET_s3I`#OB;UuA
z3-p}M=awO!NMa011JbfHXn#$HKKvXCmg_QLM`{lN5?9_g=8mE=AfggTSH`<ZE!5Ig
zlu*Crr|uBc(m9lC=?Le8`)G)hHHsm@EXMoQU3yBczc(4wbSJ9wlO5H=v?b}mo|wJ5
zS42Y)G|O7P3Yu)x{a~9R?=YSSCiNGwz5vhg$ZQWjGvNcBM=|c;mxN`#u}j%m=7@`_
z;s+9XQUZz&6)muia=)@6qOLvC4ci%Lhy}KAcyZ(Xa}x!?%9Bx-w!I6>G7lKAqeasB
znMaJf!(7FZ#|FcX0Q(g=-c~Yt>z=+I+=X=fLBJe&b}6edIpYk`=el>7CCv6o!}te*
zo~Uu+D{)V^z3aBSG;}l}DU@^(KIxxoCz-Dowoso()N%wL-{hIzNYXQEZ#L%sN^w~D
z2!T09d!tUfin}v{uJywM-6;v?y-o5__E`t5N+T;!Qt)HCQO-jkt$yua6((mRpD<)(
zZsy4qHg!4Eq^x^|r7LDa)pR$g&c*49hUeOMf+vzg|JdB`KV1mH0S{4^&9xULzseF@
zM;#xJbS()p6$*A?7AIE@`f2x6=B_Tv8Qn&`;?o^|Z_~Wvx}#_I@b;@^!3zxF6dK!}
zk?<NKANkZ`oAdpf?ICVfiLalur}y;p`kl=V$zH6!Eglf1-yP75YkSr@cA-D<Z77@X
z>p>gOT=?BSQ?QU_AUaZaOo!_jJ-KxUvt1haA<idZ_{sIxv^3Y1xjmSefML6}Iq&so
zS2G;RIP%`o7k+8NH!z2FUov_oQCTVTsTkro_86f%&}{*oaU*<m#f+Fo8Zh}0$O(Vi
zZMq|6BByh0WmyKie?DvT5t>2qD^%RdL721VESYg@sV&=UT1zV{9!e@N={i_RZ%Q|4
z>jN!fGJ-tnfK|U&tQyJ#En1mce;cDGhxeOmth+eAJIKv{m?{5yTf6}*^wDF)jAs=M
z$|ktz0Y+fBOaC#?#i4+F5#<70aMn7fqKXpZ9T6XptMZeC)(l!K%z8BB$P-;9jhjd4
zFilquI#MI5LO}?t5<g!UG<H7h`xJkVyl}QJbwty>FyjfTku7}V!^|zF+DCZ4=&c)*
z<Dnq&TX(S}{YE0NT0hPDx(wsLm+M4&Wh1>;bM9urFqXLpI~D<?IOv2Yj8225w~-~Q
zk2rPrf9k$-+L-qWB7{bV<9aWl5}nr^!*SLdx8j@@7+FQ6>a0&6Id@qW#?9nl5Q5xg
ziuGPO*mFfKqQ2?{9MnqGKD97|=}j`YN0ri_UC%uEn^V|u8zwQgm(udMpeu2A_oQp-
z>+*n{&pT<-E`|lG*#x@qC98v04&v2e>gnbsgb=q-WTg1>1OB5u=+K*$-j#pwed{@d
z%e#rhQ_<&9VZsF(xkut;5O7k~v|qI3^th|Uz!jc9h8^%QA-0v(zdpRF`M1gZ25|3i
zq{ROTL&L=%$1WS=3?j9iPc5WC(3J~G-6+<W1t-6z*L?ItOMS)Rvk5eV(6&V9F;ZaF
z{bV1+DbkFnDl61-9#xQ3Of*|}l(J&slKqj1w};jEIpwd12LZ2g-)s*^YDA`=RI!w3
z1fxlxZnal|7F9HnkLoPC?cAw(#udFh7vuHzE%DXxXE#_+>bov8RXF97SFAvI5@4Px
zSIpSaj%!8ieQE@h9gh3Axo%sWD@&Tq57MhMx$J?gxW0Qbb*m3Fxyd2n+({(VEOAU@
zDtDJn+IXdde8C`;{iwom`m9dL#>I$`7T!ps6k$w_gV|=$Q_Hjme`u;)_;&f^0Gigc
z*NP_8wtwL*i0Y@RPlq<ueatasF1#rE+<tG9la;<<onvFHaRh6Xd-CvfICsJN>wVT&
zld{D9`$_W<nAV;q08xJUdYCrvhSrK11gkhSK+z|>ge+b;=wr~-31jB+_Ud?+AdmBf
zjVlL-jjt7Yl~`Yb1||30UT8FZl6={!JEnAm3kNbpI$v+lR?N8T^i&3KIBG&&wi_#E
zx*QF7Yuu?PG>Oe0YF7=KV29+3S@j7nC#{^m8(a^31aJGukD9C9-l|+jeSdAfOhn(n
z)1E?{h&^)QX8YX+<+|DDdWhD#I4J8JyLct(HOpXC@})PEJvRMzs|Fou9K-o@Q%LUS
zjHazA{=rHG`dKQTgFPhl2;X>mWY}JF@Yml!jyVq!x4KiK;$iihQ>txKk6eYqSME!L
zGtsM3VrvVcFFc0WqN3Ge1FT)<ap$YMf((j(*MkQ%*oMA;<4sz6ce|@8bLli+Qho<{
zm0KmycdQt^yNlzMJ-V$GFcmEcfvD4{{Bat+z|<vnE9>a1`nYSmyI~$gdWeG<$Zdss
zWrVm()U`9G-;-s$w=Y639%@EjQ!G)0FfQhN7!EyxbnwqyB;H;cIAcRuOxhpRKY#i9
zmxd4SU`}4ic>f<uSCLPP=gUH|j@4qh8*)D%e8482%>6ZEPI@Gt0V`whr|zS3gR|r=
z9JzG;fX|2qM(R^l;|sbA1V`6i+=y)m7+4{?KQYi$wpyFINXSxq1+6X@;H=Hb(LI$J
z5}xZ&asw3|hy7Bk9noca+Gpl@L{i-Dfv5==4n2C^lv7Oj8u`FL$?DKjct``1npMx#
zZNXgdn|Q<s!%G7+zkc&rbFVmAH%R9LTzk7XiM|p}rY2i!M6^-*12OmSxvmW0HG7br
z$3Zn86NpXPGZCI1G@uoe9(o{R|FE(6N<?-Xt?6;Jue0+)Jk%yLOh>MPT^yb^@y|A=
zF)(uHSddullIw#;lJE`t@--(oEVrS=!s660C7@i8Y7pRC?U2-K;eDY6v>y1<qEDy3
zmO!}k9NQ{@0%?x@*7h!f=4iI=gJav1@Z#RrWZAV%VIOPR7WaxdUcnUs8WHTJSttsE
zt`>Mk-9(OGl^1V7^GdEFslU7(=slZr0@}~wpiyBH=E=+~m3dTj!qVqn?!q%V?4T)m
zZh4vfBYoiThm=FFV|Ib_jur8qWPS2M(tIM4hF3oFTYOJ7<d_@8Xd!n`nI$=)B&EG!
zHi=7ZSmyoQRjnH$8oS-)XqJ@*!DceXQ&f1vw?Y1O&6J+ifHc#0Z?{k&=rRf(%@&(^
z3vz5gy});ir0jyq1%kGc)vt7D5-FxWCd_n4ckY_g?4q|HKDu$P6sLO*c%|7^2+;c$
zy{w(ZUzQf@4(BuI7`lGnhPG3hj@L!DXiDiwjDKA{Z8CEyj2e2Pr*Sr-c4JDl*eSo!
zOiCZCqN0OF7EMP2Ks&smMnP^sFnb8K<hMrSXJ>~6HN4ceH=236<jR8OALpwmw;J<@
z_Kh21q~5UoXnX5DUp8()yJl=FjD0b45Z&`hAU|-R`GK?<k}=C68u6U9&n28_kiXGo
z<W$nxmX(Fc7pT|4nIl~yq|2?Wy6~Rdh0EGU%)0pr612mD)Ij>JIcxJko9UtXEmr09
z)~0Un5jFL~Bj)Zx7Nvc5WlGbNLP&R@&eDLO(H+B0Y`*e)(5kaa97G4LDc+2J)LBI{
z;glY>e}V9Sr*-g@JR-vV_J{GGte7U+%w)2x@?Q7@Qx0!dww`dd)jMvY+P2>sD^(wG
ztuQDYm$aA!?Ma2ez4+f^a@?Ho?>;8g<=cTNU-^~V?{=<DD((eu8b@~?)DynzpEdh%
zE{_r_wbLdRGSZ>`=<n;t(F?kf*16xrp^@IP{z7xhdpLJV(|qN&;UuF}p|EW__g-Oo
zw0ss?Yt@}05Xb`sSB)w+iUU3#6M+_$%BxA}5c$SUH1=OK22$BE4pcle`T6gyg9L8D
z!nbt>#rm1wH@61%N3&8qGvSBXcJ|0X2kN+HuNoR6AFOez`<Ax;<Y9q^jg&kw(1U6Z
zgH9zL=_a}n(FIfYd;OXyF<#bv-2)~M2YM20<8|STm7@Bf56Sy7#x?J6OZF@sIIuKh
zkkRDMjD=-7YWD}uFCe0RkF6_Mu>09X+?7=Nf_N-frO3JO(-S#@fH8VO$Sj*}T?49D
z!n7}~E@uC8W(n>0ne>B_%%u>Rd=b*5of)||D5`O?>qvj_4Jb-010D|K3xmGE7Y6EL
zZZB2ZP$Y1NQsl|1RVZSPsY7&rfV1~CBdD=c^n`Ci-rQLfQz&qDeu^X@wIneIB<(|!
z;UOXW=0~!A_slbvH9uy|YbJ&cHbvr?HDcrJvJhsu#Cs{ZLH^spyjAdcQdn}HWluiY
zP#UO{cHl}zd=KN%C!kNFat*xkoMM%AH+&l88w$u5Tg7HbySEufxM)e<ji0&9AgGOb
z2!2}>qQRD{@d9Lc6X=7m&*fyLcq3&bIyygJGoq}wVqEA9oi8}H=@QK0>|7r-Ij+II
zScL*z=!U2d1RhQwH{jJ44oec~CMI4V+OSJV(@c9;!rs(J9bGS0Z__C_)^XX>q2r60
z=KWJnDDY|Cow{ImRhX=jiiBsgUqNjBrO7!EwDZDqpfDvGDkH9?d2oeR5YcRwe9Bh8
zg?zS<{SEQKrjU_=q0_S&8Lle}rJFzb80<R3eaK*<mfro6XnbnHR#CPl)FhtcTJjV5
z6erQj&)o*RT;gGD-YP^ku=^Yca+qdo(@k@o{xFYz9k1U4M}_FYk*|-?%6SSET#9Hw
zGW)OYAGd_syr&VNFnOEl51sA$L8NTtsfEEcqKKe<o>KH>e(H)w)DmSvrt;K=3WO@3
z<O$9syWTjImhr=Z0XNZ+@Z+ti_=A%t-8k3<xNB;(bH$KtvRx1X<6vvF?;3p%r<oRp
z3m``g#YL^zHQ3}j{n)qZ(o?%}!AYZ``nkrzlQajtCR}T8461$CuzY}Ka4*@(w8~Mx
zJM}Fisip}Zyj$w%a*}LTL61Xu25)*&aDB|tpB>e&xrfJ5W0>_7yPT{wx0WFiNWUz#
zCR*p!k0(L<{l5H=`TfrclJ%m+rgQhA8>8~;7E|FgPUt1l4-MAEROI{bSz{DNeof^v
z)11UC`P=A5P`JM~IoI(>Hz7n<Y4X5k_;@KIaYQ1o5nBsb>!}0voTrx-+T0)0P0V+L
zQ?_XWk7@5^4MF6(sdmp#irh$cOA-&4UDRuEhlhX2&p?H@AtwIE;V>u7bGG3yn3n&)
z*w3>}R!-1%a?482+<S>XrJ{91FvuMUeOc*QvU)}qeEt<F$RK8SM(csg+5rR9&MeyO
z!1;aX69%p~j#&1cBqbl7I!$tS1n&#8ebNPVU{oJ#O?%-@kHq$~C-m}Kf0{CLAnl$X
z*ZvTcn^9({v-EgfsIl`?La7W5{^8MlCms!?d^_&*KR!MtdD{3O!5Vy)LyUa8u)@m}
z=oSjpk>{I|yN6wFt@12e-PG^Y(n0Vet-WGS>x6{7@O>xuCS70cS!OsV$}`|q%icd>
z_Oq^`5=vI%{c{#{-`0aZYq(Co#3zsv$xI<D9!}a#2{`3_jU(EyaQ^OLFC-il-{?D$
z<8l&R_=I8aT3T|*q?_X6pEjTi4<qepDH9IicXm^{7)s5@q5u!lZQ#1pZmD}V?$Fe|
zm+|@l_m5kAwNA9d>8a;8gaOYA=&T}^?O@BYI;cPo(XQ^Q%=Z0QwebF7_35v*S@6f`
z%%NbhoY_>82#s<<$sY%IjRzM{@Q&tW^seLpQoSX6F2HpflbnbmI>W&q(b2~6i))5s
z#%dA$Inj<%t4lJ|se5R5t2!Ys)xcFlk**AHr-d*_R6tP8Z?We{EuSA~Q-Zu%72)aH
zh`G1qOG3XWJc^NrT>vXQ0Ps_F1G?r|MqZq+Mj$>BxrUjh74Ms4SCHTN@_ufYWWOdj
z-~W#1B1v{;jIa2B%8nl+3y*b7S!+{NylmSJ{GcMOOlQ~rf;Sje;}67#q@UtdPh5ie
zUBHW--W5fv&lV*D7p0U?9oE?pZBvW7H2pl*dikiIv9#ZDCb}S<ZAxm$Y&S0{w17UX
zJ8ZygVVCq;$}86Q$EptZtz|-!bxR_dT{9uTO#)M`cc<<=gD*gTU%BFk#>chqrdrc)
z5#mmwDQ_dx=Sy=h6+EmxH5G@liuYACf27>G|J_lrLp05JuS!h{kgJf+21gxJyit5o
zJB4tl8{G9OwPV`KvRhqO#DjpL>&ycRYK!Pt)Y4IhBITzUfRq3K^F#e22hD!9TI<tN
R;H6kK)XwSW>f?d8{tLvAs9FF3

literal 36941
zcmeFZ2T)U8_$M5(H_#V5RcX>SA{~N?K<FLmDumv93t%rOC6pMt66w7~I##5H-a=6+
zp#_AHAPIr*1mFMee|BeQzTKIf`SzPVqq)f~=brPF-}8H(dk-)4b=8j_xo`vmfgFcx
zs2V~b`!gYsz0HRXfIXidPvnBlf%{tOs$dJ+=B>I2usQ6dVeSioa6M!G-xD*lihw|P
zA#l|j#zCn|<AGtP{I^?HHyn%(?TfWBE`^A^jNE@(^@RS7(zE(qpEhqP-7ZbN|Mls^
z<9hW*aSEq7a)P);M9!%06MN1ZbmSq*NM9vR*XOYtb)zNln{^UqL^?(1)8b~(l3hH;
zGi79CMKEJ?c|&y1?uh>H$N!bUe<y*+1*c>z^jV9-h})@vkBj>tkVxxkhdQRksBqSx
z);Oyb+;35T%Yz+v&&rK>{6`0w4woRu|NZg(5KH&JUn5`f{-@n%E`t74Oon6#yQjup
z*ga1}Ri)P(B@y3mX~+eS;%~U@jKUU41czu`5Sb5xALmC(IzPomXjr7KnywT!VwF*~
zn)2Gc7f~<y-DB7Duz>;%g3kT|IMF^9)CEl!p?MX|l(K%W4Juh{^p;@_mV(;mBiGi8
z&a=Y>%$+<Dqeg}iaCP$-Q`AFLc#@QQuN&M5(WO)V5LNr#%2&NTb?3|=wG%a{Nz?7c
zL5av06#8sJ4AZGW%YEpeDdHPcZ$~Pzdw;l2QB%M_M!4byc0YIUCsekEh(;!|?^mT{
zdV@g2`5f_{7&8G<{N0f}r;EfX;rPSGg1^+zTZ{HAsXg1dUt*M~-Z<>1eFUlT_pjKj
zdJkGfzt!&L@?HpO6_^~`M;N`)dlOBSSF%Z}c@UTT0D5E5xGNaXzwod3=GV*luyGEe
z&d;Z6{8-W`s27B0y<~4C$NehNLb=`TI}_{Y`;hb;nebIu!P?h)AuT~=B2qhZM0m%Y
zb-wSg$}@o?RIttzTmV8k{fD|Hi4M#7+KT3zKN%C!z)j%yk1}fHA4lEa+?TlrqVoF7
zrN?0UUPfAi-GNRI=O>de3qP-6oVot#vF^=ll@Y(Fr%@|(3z5@aWE~MJw_+o&;WAym
zl9ymP?V*RsyPQwoVci3d7dWnWHNqW}a$P+bE2rN(cdGp$kZD{#ohJ_T{yo)>KP^?2
zrUvyLUQV%Bis8p7pLB%RO6{GY%^>As%DdwFMRg}GN)TZ2>+$N1{?IJnIt>>Q;mLf-
z%X#S?oRc1W&)H)AY*GU@g%ch?cX;Qm&`2Il`8$T`9h**k?0$i3#oAX!TMwa0?1+i8
zR5^TNaFbtx-}??fUT}ZVR9;U@?|;U4!{;1<X*PeL9V<Q|#WI7^#q|=u?;Dj4#(K{7
zG}`9`Tt-zY>C!#o`Yi;1oGTq2G3}*5jl}Ikj<CW*y5GCo_*xR%7{doXOrPhSR~3-{
z!>`^ODlsD&6F-C(mR0+jCpsc5yU=PCuNyp(K(1*`q$|#{)%91jT_oWL%f9v9oTVuU
zKB)Y6OjSQmN7_iRV_#FM)YD6`+^oR|La?x|EM)5e>4sUw34Zn<s-b1?8(HVAZnb5S
zpXKl3IxQL$(^D1hw9+F1^E?%_KhyI?-#7cc-RO}1nd?oIIEmn!qyG7)a8u?Nh<Ct!
zT%@vpfK4R&Cn`YJE#fmD{wBPF>FgC>j~)@45{yAQYSJ$1Zlh!oe`XYF4hrdck8BH}
zNil6r287AbCD%nGu2thdRudX%(uxLcfNzooO_D=J$BZ~k9(wK{i7l{Ko81e6ECn1=
z0h{J_bqPY0(wgFIIYTB!@r8uNIlab?@ArjVr<E_rST6gFgg^RReX|DtdFwH(Gj2NU
z^T(v6t%<y~YY=N*XBZk;($dO-TF+{4^k5~-)?&FNoujn9cI4ng-?gL4bN*x;gFyPd
zYX0I=e&KN_`J%$=5y3j~+}WzqRNEUL&T!LhlFq-kNUWbNZ+N8b1r0Iys4U@(7TMnF
zK*_Z{(_9c?R~)w$%(T*T_}MV$=RwuY<c9q@AL+4NwqgK}co9G8eB=ULzU>}ivTE`B
zoD<zV2BzvR8ZQN(6?{l|?h0h-5>-b6GWD7|-M^l^{Lx+WQQIFvpZl^s+`{lMMW{fv
zw@-rJ^?DQeSW6yf&|l37N4Jhi-MKvPiRwZh&Xi8Xw985^ob;1WL!EzOJsxDuE;qV4
zoW|{us%Fwu_{HOZYCBrWxc4lSZ+(#;!vle2YVz%(t&9LSy<RQXBXFc^GQI0tom-Qx
z!##yoZG&EdKpNq)OKU1Q51Qr5NS?()gZ6(ESZVe`<dI5?tPIcA=zTqjUxk)9^4i=4
zQ&@Ny!2+i6BRgNmEbh4uH=Q-N(v;sZE+F0Ib9D|jU=W{EJB`$wkU3JEK!5nu#5_Ee
zK^HPe8oC~Lm_vX|bY26_F2Im%-D*H&-vz*xdvaI(nS$n1Wh<N}opJB)!8dA`>V`wM
zE84?xJ$<N6%boBThK%N@x`UK=bVtqMT$e)jzGgeybSxE~FypV2n)lsXi2gg-(+V}K
zRcq{k_4CDKt_whYbtPZN2dbnpz<37^>~R8{etv>Lk=F<=2RkKFCf@pHshlB#G3eIi
zyB~q79M0-cLd*sR_&mbW`5C;|+*Q||0&$ArN3}+OmanM9gek=rI@Wb~%X?c6_vqb&
zyCSk@f;4-tqgYXx2m;B1WPF$eWRrV}36K!|zE_mt4NpvSAzBg6$8goXDXkIm`Ccd&
zyw7TFOnk-J@IvrXDBjLiCI%T!A4Sk#_@vv_Bz07tY4zbatZdvWNi$OifH-<$(X2+2
z*W6r&3G>ish!ohkB%}GCSW&OXo<AO1giU(6rIael{)YR~ygKHG2O_i?5o(DhNq!af
zENE#gmLM!@hDhkvgwuu*q0!p0V~yq~68g=1(I|@?eSDOUjqEl2=vmL6TW}E+rrQ1y
zR@AEZ7~1dQKeqg3uiL4J!?OUBnGFOOD4(+(4^Pna`5!1Du{?}cdRUAipW;Gt`c3+Q
z;Qqw#SshtR+K1c3-k>$;HaeO0VHlNq!s_u@W2g7xv6$%l{;44iEabd;-4>ys`WMQ|
z<lM!7V#2iSSz7#0V}um-dM8k{wtX{MeI<c;Q{K0`R`$Ue{8gy)*WJ+^vHhyO2yT*G
z>y+%@@kF}37d();7bO_nZ+3cbH;NhFd^tXHF8<ut9u+<|Rfb=I`04lJ;nkkW*gz|4
z@czub&<CL~mhn}V?s-L1(Xx{xpr-*ABQLPY6g<+dofG#uPpbE-@B=9heq{39U>UeY
zqADX^U_4Hh=J$mExb`JR9jYsKWBroHCsbG5`p~I0(<CS+<*0|zchHBi!e8ARnP^-P
zaE8IJp}ucKV{buw-~9<kt;18L?4}hn%Hnt63%v7L`+Akd=2=X_fBRN<ynD1074Pv5
zyNwFK8klMHnxI&dq$HngMIo}gyi(8#T%K(mp4O=k%7GD-3;s)&poMoF!l#}>A9NL?
zSkZ@}XIo!g-q!A=Lye@iV$&S9Ju0=Y^y-vH?ej?Jzcu@2hc^$0rJ}0aJCIvXv5uz?
zhdJ2AwuM5;moh}>wJ}4v8oe7RoA~vFE$cTc9B_5Gi^#RfwX0Afkr%U>yd&hRoYcB6
z4HhS5+w1Aw#wB>|N$Z2btVbQkV+WxvX6g<huPLY`Em};6zl>I|MH4IQUoHK=`{Z?g
z;3mU7HTS?L<)p8ZKfu>v_i6iGqRHGJnfzkc^qJpz`F25r`SpLTUAKwGdZ4~(SzLB5
z0DLf8>w-jHnO38aKq7nrvnfcExa|BKtA~ijb^Kfn9<)F!4Un#P#u<wcha{<GothcP
zWA3=i3TshX$g$dV+NQCMe9K~>W&S1lR_a`-Wa3oDWwLVaO6kfO0qV9_$fRUh^@d*N
z@u<SeoM0@RQXyzsmn#iRzsn86g=UlSY*<k|pq=M1c9PFEIgEc1#i^_I4@u6vzKz`I
z$~(Od81iZ~HtPQ9RCW^f9!j|)+PMZ>hDvrQUG`hkt?C_&?o(#ULFKqlCU!;O$&{i8
zI)Nj8gJ-ZYJ+a>urd0H&rT&U*QG5CVHCftdYp5r@qr;Fxfc?}FEtj;f(m@!>zD5R8
zL^5rPQYUT_hYY6%45OD;dEDOf%#=qY{1Xwg?&nO5+&8*fl}PnNGv>nvztqA+kQ%Fd
zt2UuGmV^B9FWT6(c^e2*Jl>;x@F#~8Dr^inVxY;I7BeUr{yf(M3%a;*y1DZ*Sytlj
zP<wC1MVxC&@+-$G*{kL4w^!@q;+wZMO>VrpSzuY(Tm9@(3$iqT>vU-3A(f2|ABBGq
zwx63E>Mn90IaM>URz7w{NbY52NfO)4d*AwS+<TFi%CL`bka?Ut2Tf^DCFmrG-@7cI
ztj*Jrx*HXO7R4PJlcm@UcDG4Y(Q#?`UWD53**h`vs8Ow2!(JorwlbtgqKxdvb=@O~
zhrfue`w*_JsZomXXRWW4agS<rvz|P9S+DFXP*uJ*6o39UPm-~OZ+sWNw$B}Rs>Wf%
zYlMwCsoaYf+O~XM4hr7id$CNuEeu&BI48Dpvdq1Teju|TWzpWIAOZnqFxpz_?Oe@a
z2oJzb=_Db;mC0|#Lf6UsF;W2#c}$*&Tll_A#&)6HgotZght!FCaGqdnOykcW)zn&c
z3dUq2SUHi1E!J*U6q~>KjFEHspE+^AVc6Hv9o(`0G>?g_B8Huz69*_Qv<}vXg0#$$
zO&I!c!3R^a7-G&J!6In#K&jjdX==F##_6aOff_IkQs@s!j~g&GP9G-)#UnB@Ho`71
zo>|;H$<x+$stM*=T$a9bX&$*qTt+2-z<N3=J>y57EGaYje&%-e+T46;1eOw()A9WG
z9cqFYx?TImFURru*)*YRp6kc+YbO055c~7Y@LOJ@-}`kCZwAlTDB~>IznE=KXHDD;
zp>RQ?PA6L6`KFaU9sMRkP#&nm8gB9H-rA#lh*{t5i}_1+0@njCZ}1mbg{I@LRgpg#
zkgQ|;%v$?HqfNGt)a+EaXSAd5S06+)CWmKW_rDaQuBUg*m8)jUI$fN1!1$hRm>atL
zjLJ_-9!y$y#B92do|G&nooAS0LM-u7h8>f-4j&~#Z&V(K@=ZN!Rmv2=7$ll$hFCgW
ztSExoK5DvIC$Vp+Gh|zJwqQX?pn;bxza?@FT6H5eU|D!x8Z+dTf~`UUSNq6^fAmt!
z!Az?cfpRe4l(BSwp&MAPZl360zdG7Gn(v$3v3~SD3c#q`#=29)C6t_+WhiE2cF5GS
z5nYpBUz|2aW*3bc`eb_f8jB3Jzhp<7yjbzg)Yf6TgumgUuXxivhH|$+%((i2(-G_b
zCgc<fr-D#G`lX>Bf<!R7&5A?r<-VVcTtZC!Y3Pjw&f7F*U<0_|`|6S{T-Q+`q-66U
zX@5p>+~EZ|apQGcIlSeCv_PbG`le|zXRsvyIh3E8b`4Ugmq5OuHDTn1H7v1z_~ag3
zQejEqR@|nxU6ZELCEb)A(UoASl9%xjVcIlRn?BL$#@_?-H3t{B&M*dONW<?D8TI&1
zC_3!cX^dg>{K-)EkLk&oIP8$sT456T@>fpy#usPePEBQB@rEHL(%&y#hO4)#n5AWL
zAnCK>=v`Pn%bE>J>02hYMX?vy6J_el;<>4YGd;DI&N*M<=uTnTEm~ojwS6KbxW77l
zu-fG1nDL82#r)$#HCVVQLi^)I44$_R|GFCYk3tELrE?dh%%f>7{qFnVi%-^=Gzv1%
z%!AloCB!<&z1Sy8Qki)B@|B9how&PSiKEwfPo!=5Vho;^@#T4xQ_-!ju1Hk2IbXU)
ze4n<%`>dMFl@vNK^P!%lJ8v*W@|rwU%vqzK+5o~S*AM;4yBNrQ*MeiAWjT-d8|-F6
zJ7;7Ms8ta?Fmc_~PnO3Kvw97Jf_m>tK)^Ndzfn-X*IsSxiGb2lFLx6c_W(DpXWe6I
z(`ZMO{ktei4tE1H<P(cs<XRg?%n&55Sybm;B371l<gWX^>m}Z<@_w)VV@TPPI!7f9
zZnJYOg!Yc&R2*vKMokr>_|qhPKl2q@t52v8aU_|qFY?O8U@QA%Uz-+oOuZgDLl05X
zkK3@;pPhZw5M(XF_aGh!{L)Ip3WCNSda+s!C3v~F>Dz&khUlvkt1Y#InxR^$ej~?J
z=oE9i3i=ynYGXWIUxSQ7SQsZ)L5b6{m?6>Ai6<kM^EFcJ2qSVX8D=Vs{kK_G!UsrZ
z6UZW|MU)swNdPm}8Q${(KPB*rFYYcPuS4oW*~?SrxpPkFde<4wJ7R{@JY(Dv?xC7-
zD$Zh|#_5Dl{vLbg{1)BcYc!XpX*WA#D0PI(rjj=>ko-OvZS&<m+IHp_fwg3bIN!*^
zhD#+iJXy!4Hen>iUzHtGWqR5Z7#}uT-8^DuM8tQB_ayyx)vAG92AW>PK)1NT21b>w
zmyHgNxxKIH$eMslP}FjKYex)6n8p~ib~@No04n}kJkgFbPeUQ?OhmiEGzW{49d)GT
zL-R%3@)p>L=F0l(D5ro3&q-Nw$QK*O`2(2@T47v3WA*(eIXlS@!~k-Ee($2eW)@FV
z+gK(!Sz5p}dv7=8)Fu^g!b%Ul?9=z9JxFxiiD!{2>gj&lW^c~MNeIOG9xD?v@vKMQ
zEATj8;*Q05N)MXe-WCp^hy1qo**-`}r~A}&q*m*CRHTQt?Tco_dNjb=UKJ*GJnwfz
zJRILKweQKce|&0_w)XKVy%w<QaMX^icjGO=9~`cE6f3RmuyN(|Wdqnj1WSxZOve4S
z>gse3p*v>jmt@3f?XQ+~9yk0LyMXDbyZ20rqlh@S0dK?-5gxs%7dM^|W-ah#5Bx$!
zULJ?`X~_<7q%DuXAtfT~ZQS8sSZ{Y3ypw?$%6+?p5jI#-)QxH~`Myu}y)|)yVi9>|
zWNO7;X(#n=NGn|lrTs(MAUu3<c#?BdwN8Rp;bTbS&8`*$5#L*Ybq<xty4c#macx!=
zj`Z#>zti8tl%--Zle7}&1Vq!$KS({SQDAv+)}+D<>SqXq{}q!VjyN8i8a2IXt;_eo
zoyQz+ZB12p{ZGQB2g!Ndh#mfG@ANbyyrHngv`g2Xt*1jj&J95O+&=6-BybH%K7+YC
z6+%+Hzv_UlUD|ZCjW!ApNvd-z(dR$(=KCp@mG8l)W}OdLPwiXWyorHC4cVz{+bGCF
z)g6A~bk|s$DG3-~nqAFgnAd$<g55M{B^oO+=(`@QWaDt7MILHvAX&vtD0+r0Rxa)J
zd@bD9Xzqi%G?c}zV7>G!UCE>dnA{mlcg#kIJ;swYLBxkU;Cp?z<utOsCq!h^PSJeU
zp~XJ<V7OKxap$WW4v)7Yi2&CuKfCox=E$J5!Rn-thm8C`hwsWWGKfQtI{I<*5APF;
z(AW_uL$7hHDeV#_#U9u8D!W)bFRR1qw}SmB?6zTw6d^g<3Uhlu8+!dMwqklL!m})$
z<ct{`=KE^5mpb1MoOh0a`LMYMGTeDG@&JUFo^%I)JYln>F{G2>yyCi0-t+}gr!@X0
zS=PU#I(;!DTQk*`q9&>O$4oBfUFGq(^KwcSf<Xf+U3_Qk_BK+Vmz#KxCLh5yze^a?
zP{$d(d9knV2x7J}2`h=p?W2}rkJ{U`FWP4-U5-Dj;_t07*%GoHilU_2D@Dwe!!k8^
zl|p(x+Ze!xlFpmhXbJvEDEVGfzEf@UE^P$S$d!W(s4cl)ZAM)Usk#l#GLvcB7k;pH
zpOmYx5BE|Y-vs<!Q66VjT8lf4JB9K3@rqqFfk=e02G>VVzTkSS?YHfV`%!*Y;Z4SO
z;$z0^n7QqbCHUklgvU3HuA%h+_)cZGu3zJnAv{W8{PHy=6f)i==%u^1nZCpH?^V=n
zt5`Be=u`*QwI5%l*fxd_iI%B_VQLv|9OHN)i?e`|G3zlZROESzugs9~_V8yrTjb*A
zSv13gFL$ZOs`FRB`$`NKSF#9f!5y3S$ZX>~((kS>M(({ph3u#e^y5>`+iGGH%cu>R
z#gUSpNldp^SJssVwKoLn3tI5)BmU12*&m#QuYKCm#?&O?lQ5GM{MGrGs`NCLXC6MM
zF+M*P&{D?{EFB#D?moFIcfayfU5Gv0p*G~Ihd-e-5NSkT@+;eu-==I+Qq3t)(d`i3
z@hOyK@3Fk^v|M;-<H$z5lcz{a&>PCuBlTn}IHNG5DSE^sBV*WZT|QhUs)(3$I9u<D
zl4e7dqX>C)3BjnBcd4_@xZZQ)=j8NO=L>QX13AN}nX-S~aj6`&acDuiOx<=PG1xk^
zE!3Vqnwv2`E5W8A-%AV5(@Hj80C>25{P3Ae`yhXI>mDOGo!+QHe2hp%2EHYDsba56
zhJblC*A@8zJ{{lNYEQp*?WE-UEgo-s)m<n)e*jLrLx9DSFH46;PqhdrIV3ttia9BW
zNymS+`M%_4()BPhv6FTBwCj*FE9zv@d4X+DOy*n0=)}qRF7EO9>!VkaxMJxB%UPQQ
zOMF?Dd>;lk);Rw=t^UysU}IH_hraB}N%5{y#-(dM$eNB$uFy)PzQa8g;hL|@hH`53
z24W&n3}djcA_ASPIwc3wZJG1m@=&fyCxm8e#weL+)IVX}ctMk+Wk2J|xPE&yI1Pf^
zoXAQ`ys1Qe-QC9hI)(3=-p+YcW8!9Z)DZ4IKf4zWCZp#E?=y`fuNB4k)8ouuj^#4M
zTf+eQJ!Ybx$>#y+p47bpjN7ZQEH*=XDHEug$lAcXQ`KqnQqPy4!zFzSiF;?1Bi1i3
zZra^$%ug@&Tjb)qrar5U34Uf9hhYnm#Oyz8H^TR2?BVmG8D-nG7U+WnQ%&)6Mgv^#
zN-Zh(FPT&88_W2tNO7qiM;+wlzJ!Fg^K}{w`Yu_`2O!V#8)S5*7bs5DtXixWm&Xhh
zh*Mv`@#LiL=N~W8@EURt_YunDoKjre<VQ1#_?7G!Vg4P;^etH(c2pb7D{>JrOVQ{(
zadMD+6`HZfW2rH$F(A!Tb{wB<R7m84BHs<k>{;@BJa#T8`c4gM+ljID!0cwme)}_-
zT>R`GRxYtu>U}{Dl1V=4PiI&cgWsjXZuRCVq}5k5iX3zH%k8qi>l(J0lgN9RlNAk>
z<od6-p?ms>g;GenwVltS6Mb)&<_!>%sLj!OE`BG`issA)$$-bjX&d@Ab?ur)B6&@h
z_!<`4x9mKINXFl;4l3h<tq3AMx?A};Totkb8)85pBOW^3!)&H!ak;s(wZ8Ic$5juZ
z&KK2P%va7)nR#MBFF|9rKPwMW>}~L(_t*H>l0zCK5T?mAZs)DvJO$|w;IoS&&Z*kn
z#l@G1S<xL^jZGFatS?)KZpHb^X~#_+{T?>H!w;AFI(^>mJq(|vv~tH;{;S){1(Qi}
zRI+N-b+g&3CGmMUiMCzS(sov|)CF*w8wR_arlbfdDGaT;2@q?A0A0x|4M?Y5JekL7
zmy@fw_f+uC*;X+@mxx>U9^XPzH@)T6N~QvhoMwd0>{;D}vI_lTgbV^ITTQn+p&x&Y
z4pJjK)_Gz?eRSnzPiL+xyPh#8aBF53iDxa8on?2EBrDI0Uh7o^m4XM_5z?X}wgvkZ
zHnmqvC_yYQQ|l5n!$)JSFdT?`7!G-A>Yu99HR+##+Jw`Xc{S-`!Lbt0Q@i?dd43DA
z{o+_l@T-AhTVGxNJ937CUO$az@jf<@SK?G0^&PIlN~Cu_3V6!znKNE+zr<e3+8*5Y
z$`pEIwp2S)IhkwAJ0uLN!Q1SPUDEL$l~)!-`X5puiu7*BrV<~$4p+sGAS>$e>URW#
zrlwbAH}9X7q>qh;M`{J{x!r~COW^2{ks9OHB_jxwZ#G&U^JBBTB^^@~7cIRQ>8)F{
z-(E9(%TY>d!rO~JjkmakeO|dMQfnH{cr0zdtx&(90O2y-+Gx{wwzVJnAiTs*!feY~
z-f**UM8Sm~uK9{fyLz(anV^0S@|Xu7!EW<pq{m64rlCcvoXNwW*0uXCz%S`4Z7s}Z
z=6H0T<ADWg*0|iuF_j_`YWFFm{^xxNwY|Er=iToJUTax62<3B3shif4x-+8T(DniW
z-I<oGY7_VSel|rW-2+P2FHSnFCJ}e|1ZgHtC=KFQPB~EI^J&!oUY!&1j<cyVk&NQI
zb|J91W3k+PcI#MmTlL-!8>8!$N#~I@cV@RdW6cXmF{Az=2@5A8J&L}tXx_sN3DQJ&
z_CvEITiR1&+@V>fXW<u${@f_eLS$>1BN7=E+9!VVlN66!KR0k7r=&$|7I}KG>*&j`
z^2QF{l?YR0N>R14;I;b-)k~@iuXWHXamCPRnYe0BVPA$j+PrjW|LxgWTvOtYfX~9|
zsxenb%TP9n<{I909dxo_jimm9|Em{`XLOf5;fgbHg7Ofqm6Hm;6fDkSA{mAla;1sj
zu)<9T?^acQy1Cut6^n$so2X#h$A}36^=4ubqVXgYK1*P?EV8>JT+uwkQ$>Eh6a+mP
zNeu}?o*76oPxXDPy|ewO1E**aG!4)3fQ^oS9nJWj8^BCvAXWnGss|w0*}a&?>KoM@
zEc|mwjBB+4_{Kb%I{_+{DzjXd`i3S@q^FR>!|JcXca`>s3D?HaIf!iA@9V!ZRBCO|
z7#4v5nh^6c2ucldfw|S6_xUBX>jTVR|G#Z64^^r;Aa^%Tus$Pbwc!I4c4w;5ldZz5
zpGs1L4Cr_;kK@WRu%Fyyb|o8miXjFn!m-%Yzjb5g75dkh1odyT-?#h3hxtQA?Zp3K
zyEQ)FVpHm`_9b@RS{y3bL641%ojr2o$ac@W+fj9O{)++IX@L{Lzia&$*UHDgC1=~U
zM5FgV`q&C~5<iyFsUwaV9FIQ!n0~=7R86h0x*K?#2hHWdFpx*4n$?UL4van;SI<yJ
zb{#wLDBg}||Ni}YIH#UpUE2&g+nq`$1A}UU@T&42NdK|j2d3Ej)TNQo^_9t;WfTf^
zU_Pm8rI8+97Ph(eEWWkk-sh)d(Oe4ARy96|mO0W}{mY)6$=44Snpez5#=y4!tP7<F
z{UQt$m`x^ma0H$u5pYcl1kWate8AGM!H{hP!zV}T^y$-b;QtLY@&*pM8kBg{xLW`6
zDJMcT={V@Y?wMbEzEMtBt(@=dSnu0g&)i?kzKv;rrAm`+ZGdU!#E?Z~Eb}cw^=oL9
z#lTVeN>fCDEGH}0NY}$-dB>x1bJjac?KMrXN6}-*#@yUI?YiqA=lY<fC)YZa;A!hc
z1j6(txZ}j(Imr1nG7{y)lOS%=fP_T$1AEdJ*GZH2pA@&OmLg{rf|)PR*^}4jdZti`
zqGka=f`n(s&aeIX{qyr#F0bZ20;o!d&g3o3HAiWTy#K<-cC_RqK1O<3;^td@-P^Zs
zUm1^3d$8c~`RVbRuV24DqZ<s)cBWW1(iRHneskSF7gfpKEr<h1&J$&KKR)DZYi~c=
zD=u{E$PE6?)vR04Jx+wT&$#97fFZCiP0DyqaE8slM^i7z`AnA{=a8;!oQhWPsGEBi
z^|aDuz$LVnRCpvmgvWS*a1f%w1sq~vlsip6m+`&8sEvcwY%ZSsOfYRF#j`PwJYZ%^
zNy2(heH*_T_UHFX)&>rAK)-#~x^m%Nl!Qg1;pZRo1t90M>YW<#IH-Sq&BwQrR^e;Z
z6|vCGw!Zq%7S(o(2aT-Lg5l~oh3Xk~Eg$8}4H^>GqAkk}r;@M}r)Q?8ja7GSN^Bas
zyWif5kp1<XZ&`k+^z*iXfk7O-`xcvj&~%*oY>pK?IVb}><75FZ1Laq#=tlb-Su<+q
zWX(x%r>)P6^9!FiWNZ~YlPGFfzzO+vbRTxK%5cY5{G4|9iStO#$Ts8H_^eNNU9{L|
zi1rTftgbW#&pfk|34Dr-r^jZe%;b`|uqzWATv)*CN0I#QlZ{H3;Xl1(b1EyP=EEyZ
zi!9igzOzXt4*8EZg-{m4La(OkU#>J-zkKS*ezj=MkurBe^-zDF5%I;{D}U}A81zhz
zR=EatK0nD9JzO7LvB>#=2f1$Wh+S9q=PO_gue;jYLyI#rf6Rlv$D#J$W0zfriv6uL
zma<}$UAha*N-9#MoT1V(G8;9ZBUo_ro`bJGEEd;|)(x;XXbkqg*K*xh8X>1XJ<g&0
zCMAV_6Ic(ABBkF*FWUOUQdtW(LFU{sU7Kn*a~XK;>Nk#+Xq!{Hi1s8L!I|Cql~7M@
zd|cZmBtONC>TS5du+o{I!hWIEBzLz6)aEYbn-m;j`pwIM;<F0_1v1+orlL9iFg7O7
zep5(SEqO6VOA@y<4f^#$D7~lBxi8e$*Ehp;uyDi)7A-SE4jn<Dx!8{#5E?47+$ixF
zDn+sqfGl1UR_%XTjHFFZPk)HMqcNit@5V!<D8nlY?dh4ly}hZxNt>*PimeaIxb)98
z*4Nj=nJBc<urYzN4*K~-X^i&McoNrI%?8nDTw%EYLHU7s7FR`Z%X<B2Eos`ORo=bw
z=C{{^muxDB=hgE$41hb`#nyF;riFKU1VZT5?u2A9+s69VwVC!9^K$#v>A*?k#$;Kr
z9*nUbUeZ7vjeXO(U(~F4=}*Tsyq{Hlkavn?<DXyib7{o%P>&2@qim}S4Mmlem3K$V
z5#t_B45~-Gpbl*bwzG-<{N%|K1tfC7hJUeY$VOqhO|(QYv?ZF$e+kJU?dC-J#^GsI
zk5{IzXD9?Ne+S^`QMI<@*8yw{1h}jS{A&@|@_ohD{<K_U{6YP3`+}Hw!xf^7A&=wx
zjU}RsCJ*}iyYjvqJ=pIB;Bt9bEcR`KXM2)(rz1WcX-cP(^sFkKdQ1*-2i=9cZQBDK
z4fzk3-g87JCJww9MN@aQ^HL`dvkQhnL#a!j(PHL4W57rh!~#A}i<Z=vxBvPY6?*o}
z8ILyr2q)idy!T1FbpPk4C;k9W#tcy*>vLuO!a5=)&LYTE0-YaIjv_rez{)wi@mgl$
zgKTw|Nr9<<&~gn?z8;_!u`5Mp<tdd!SgHKrp1(q{2T+q6es0fk2z+6pf&Oi<$Wk&E
zi<<~pd<fuBafE5@Ob~r^M#5@N_7|RQV=^M5?zE_>Uu%22D3?OOk~`2!eP(@q*biWM
zT`AWbySgvldiu(9v~r#{9Cqs&E6HVteWD?BBLc*k#c~i(*v1->^k&JXt+g04YyW}0
z;ue*|&Zyy&d}^Fm5p4-t<h8YGu-}AD%)EU1*V@|JgA{3Z`4!IyI^p|);}^C*-2eJA
zi?*>6@zgSOGK}0A<uZ_8wb?3^QQwHg+UvvNt(iF);j~&M>ag7a)3Vm*`-o>k*QZMF
zeSZ1*=kFST2Mkpd5J|ehPp>~#^fG`N1e6?jLX~z1)^$+ddRN7Z#JMS#0G<t?R2##l
zaK=EqkRG>zp&`@>sC2I_F#CeVfr1TR5TeG9N__Z{I;@yy#a=A~hV%|}SQI>qaV#D$
zN3;inn~Z_TN%`g%eKCy;jBHHk<;$0Y;Tz+DEyuVO{{)576;|r|=2lyHnoLpK+Z#=j
zBY-+UaZ{^gi@bSFEiGR`o#aY0zyUlGu|O|{9LiyT)`tD^8ng4Z#fg7)e&M=H<xHw?
zSFq1$rL$SFZPRubeK;LS9dV_Ox|ZA~RJ*%E?>0w@j*liS(Fa$Q|AFXJyo63d41WXB
zy;>YBB3x&NIM2z(hV2f_HGG<!Z?@X&3-2F3d?;>R>leK7@gPrFofcXm1zG3|gUgdD
z`_g$pB*Rgu@7}%B$9arP`!5c<>$;_b&^E0M@a{AJ{HLRmVSc?>ctzGm;L@=7;-A$i
zSj<FqFnRWeU5t|SP1Hot)N5FVUoX6AgbwiRZ0)a?qAw2~JLlCNPA6^jKjR4xS_UX<
z>j^L{dbk!qeKdgj$zcE<hM|P7XKm}t)nmA2aPEYFUw~jyd1O4SQWgQ^Y(y-vJWzSU
zKQlX<ef^U}V(s`Jf91#pehy@+iKiih6KU1CGPMBcS|3o6+S${mpDei(Ga|O}z@~$K
z|B902#(Dkx#D42-cLnt_!?^f`6M(#wBr$VnJ_x&woOZQD+>J*EW|(%I#-MNP5TXDg
z<rwM8A?ATVx7y{1sn<j}S&7FuWxYOa5;JxxEE(Z8qSt%Sw&B4yubf*FU??w8H}+VB
z0vvre-<Pu^KpTzk4b?H)wCvImK?sZ<RAQ|6RoLMG8<X_=xc}^yAE1gc&b`>yETAcM
zF{=-y9>lIR+?Q+}Dl)~54jz=T@n!De-48tKvSr!*uSh&>XX|<kcj!_9;4)1y*wIF0
z==ey5V}}2FkGfv{)>?ZEwV(@78qWkQh(IK?Q~K&u3y(es@s@P~B#NFA#rWn<{}!br
z!rjuw#)gU6H9b4Vw_OMF2dI156uoo-b<@T`5t2Dl<F)>wN;DoD0>BG;lTAZNg9yB&
zeob>oHAdik_#kNr1hsg#b#KEpru1q4W)$URV6T{eI3NjFa+ku25d6rKo529_`&J50
z=J@ES0yaG<8oob6Afn3?c;Mi1F8OsylwYd=dMq4}4^~1sLMw^^SmDG`$@<?9%oro}
zx&=o=N1xCA@C^^&t_E}2Q3`l@Sh!(GDzRy1?2oxA^vKbpqVj>uit4Cros`*O9)5b`
zen@}VtH=Y9QcP#e`S|GIj!`enti&b_<~~|^2Cqo&C<Ayh9|@fH$K%87#iYI*{O=09
za;Q$N|9Hq^(b06^>+Vr!C6~tm#7HA)>%ln@JKI`7di~drUy!;S*hU5hu)+s_#_982
z=tb90dC!S@q=570+Ke>_&@r+86;MuAf^E-njW{_gw508XhM~8coKh%tE5?$mg(qT5
z2G~gY%cv-VKySAFra8`*xHmUiq#`CBwE|$Ox1=tA!yBNn6{%7Q27_@P+9&eqr<X2(
z!WG}I*+7yCvv2Iel%$)wR=@<5F$#emIn1#92PoZNR7`B_iep#m>T2}Wlp$w-uerC+
zx<F#lv#+%yuO2wTI{?k8YXF@x!UxSAmA01aR#+Fu>o#<a?qkWx*=x8|4p_U-8qlCr
z$d3mHL&7KnW@$1WV;{_dgM-CGGmvh;T#u)%3H}#RLoz`3@7(6P-@-c56gHl)!M1+u
z$;ruGgkQMkpbdkyc{1&bX}$Rp*0tJ%58hJ~M%mgm>rB5K0zy7(iSs~`#{o;wcT3kv
zksgT0etXTK5Z&#)#>`eOVj{4B!QZ%is_iH+DUhtx4Dy|8(0@f3i|b60nMCx*7J;b~
z`11f|2!BV?e461;XTpO-A2Kx|45rS54j(sZh!MSf*^40(&U<25;7@Yf^^-;63>v|x
zmG})$4qa;%inzZsR%D5r_aG~)x6k@pZ~+3f6^;bC-S3m?oMnjRu^LHkzwR3qq9P(b
z%-Oi!w~hC+=`-F=@||)tFFU}-9Tqm(h~&w*YWFSEHhgm?vb?-pp6dOTGsW(N+{lJF
zBy-kopA^JiWn+0<hC5*RULO&kG1>It!-oc=<d-jxv_wDwuKuLq9RZ=ig#&!RdyLmo
zo;deqUrz&_Hu8l3`Drj2vBT&AN!5as|3d$~ZP@BpqW~I+QSNbA0NHZU`rd}d#+9A&
zPa}{37bFFeB?kel8Me4HnX;>v;k|o5vyIzJ3TqkiJSsY<JY`+Y;*6{qt)}JV!It;H
zyxLn=1H+<iiGfUB((;-k5FjEmv72ZI{mg?vyC~b+pVYZnDIZGilo6^1!&?MLe43Ru
z#(cYT4YC@`Hr~^d1?e~|L?R*u7(O?X-evI51G=}>a@x#lHJCX;DW~qtF>dckCM^LL
z8|+cu!U`EiUImmBiUnw#A|QP3*s|0?097`5{0!P+$$s6<Gh%m2)$7ZnUcS8f=)iFc
zCGTHfVt|ir{{~5~#L;~$KldL!vz`Wv2)9-k2Wm4AgEND5&x>e_3bL3@y=+m9wPR=U
zjQpCzYzO9`rXdKGO}?W%l<R|<B??JQPjrTkw7_3beAm5Ohqwx|Dc*nxB=)5Hb$L2F
zJ9hyp9;9{ol*k+bw?al+2aL$VFMzZnx5{;BHh6|Y2E==*Zl!T&u_vpgiGMy$oxA38
zNQUhSMs+8tUDezM*-gg7B4YWOx*LyREZVNDFOP>Jr@p-oRMp#9o7~wBoU|WB7y8jP
zASwg5Kq(4(7mV>ElRF2&UHY;+b;**_0Lt#5Y(OHmH*IYK#MT@EOIxN<ojG%+7>)I8
zVpOL$F!BqEEt2Bn*+;A0EvEqaT+3B$yKwdXm)_LW)HUE{Bsq|6^2NJHkw7jaPXfp^
zCIG1d{Ocj$g*#$!`KedelwVAk6_^$o!Y%3oiWZ+2X%CRTm5}%J^CynCz`K~x|I5EF
z&lzbYGNdZF=^Y+QS@7RxhHlr9@|mH&Y@N_4AhAE|KOV8An;RPT&JEkeaLpzT;aw4d
z<nMej_2O`5T-X43G}$tdp11LeFzFMXl&HaDY+;cs39qTCiH`CHWrP$R0GQW$&HfCi
zvq18>ck@$JBFy=#ktCKHM0=93kSgWeYXe}je(5QP$DEYiw^vU=M(_tIp9S1vMkiIa
z%pXKSHTRM7_ci>l0l^CK+Xe{GiNZ3O!VW;3q3ps!F<wl`5VwE7!C48L`VTQi4S)^s
zT=DwRJYkfh>s}4~aZ+6oP^iiP5W{rEg@v6@0el9@F*&NC1aJuj00Uc(`E}l0`8f)D
zPpbuxTT9I?1psw=!t3+#v@ZF@!uWS&E1y<@7|mWF`4A+t9Fz)B9I2qkG_m?%@`82X
zxPKZ`4Y0bj9R172-FZeiJv&=OnC0V}Z&g4t%nbM9ppFy7gynm{6~fOk>03`Q;EUu(
z{Ge`971Ianc%k{;Lnb_pjra7-+ft7Yp5bjVD-mNTzqFBsDeN>|j`m?`!#nDXxX2Mu
zj$)?XLpGsB&iO6VXh4!w#{vlMTuW1xCG)C;_^!<cQZ+$7j5eQ>GfO0Trht+VgL=6i
zFc7^$OWblYGqrdF0xr*rX@tm-W`0X@{^54B#U#lnqmcS0hg;u92bcvUP|*p|LoI`J
z^COk|FMh^rn!i}T(GWthxCmz=TC2>spOHHK+FjCURWK-2A&J+zWT&=OyKF_a7Wno+
zOxS!?_U0xfOS@ZKf)gUFb24&tV$fqo;o;$M=zM!y+e2mmZwOlbHNmF%JJPil6f?aU
zl%be;U~IK>BsS$gKehmM+oS{AAVrM(4~>U{kK4q<Y!{^FUPNq@GtezbI8Z<kJbFfK
zZey)ovTl&+E(epZyH@u-Pjt*c1`LAl7aBJvLWw#V$_zG;H16DTYy8Zrutb`h%m`a+
z)suD`)?Wu1>M<sIXP&?2aLgwkZ}YgLmFW|o00Z}$4JM{-d<P%6zQ#|VJ{|XLpbXB<
zmH2A*g6x*QTlIl^d#|1Zfhk<##G%J~AE?}}aOh0h-Xh}zYq>wL`G>Afg+W?#L1K6t
zl-#Hw!+O}HTqi|Z1HxITJz-wxM)5XuVR|Z+E*ol4p1)SL33MAIA|i6C;g1E>P^pPL
z+gTvv(D8;tU>;PsY74CE0^U;_L3WnVwBD-~NCDMH&s_x9m77U;4{U7|p*&|?oah&Q
z{X9s=1yJjw`Tnb@ZwP$*60XdyrEwJJImr$RE(6{R{dt|b0J^FS7fPJ-BiiNQ?+aCF
znSfmf&Ahp4H!ERTT}T)%v-3O;n7S@P+H)cd4u)ff+?+uReQ~ub|5x42`dRPwA*%`|
zxYFA}a>0SMGzImne=Z0((5HBI{@8&ey@Xkj8^ve9c47g|Ucmw7W`p8HQ)#Ie5=iZ3
zjQm0_9%LAd`EB}&?S&L@p!65B96|wgwuNh;w8Ab8m`)%Fk+cBPDAF4cHC<AFUY|qG
zj}CC&&KTe>vrUviOZDPAWxrwp`;cJ+F$dIecKp6yd=}=tScGeg_;grkYqo!Ox9Z^u
zuq&0B6g(EtO5&^r2+`o<>#GHl;M#Yg8_T|cel^C}kpb(TX#lxUL<pUP+qhGD_hTG0
zi7R>c?h=kUdf=jsi=$PGTc9XG)54LN$wP!3DC4q8fRv?=S!ehH(*2ZyBFjJ>P}_k8
zE#&27gUa$dF=;(!Hm8%W82p8aDzku|d1=7m@GLJ`&STF$!;v=c4FeVD)7f~>gS_Hx
zPnLR2x>9IBieLLE826BM$ozX4f3mVA;8Gz>F4dr13>Zr^KOjb2Eu6j`H2cNQ1Yl9x
zn$)eNq@>yP#i5l!{mThMzzB$-Y-Y-7>F{6pzMk^t^mQ}<Kax_&`Z;d;sN*k-)pme_
zv<*-&=bo>=>--Kd^KGU%)`VAyj>3h~)^~G|+rSNeGV^gcW<9``gv_gV?A^4KFx^eA
zkbtF5m3;X1a?`VZRc#U|zN)pgRoJ!X-PdwZuZUO&ai>!WWVwdPn!?;oG-hx$5>OL{
z<ZgcY`5OX%0t&0p65vF*LaUncsdzo*U<Rm^va^QlQSn#d!Bs>_)Xsfidi}6)PU>0*
zGbuH#ChUE*cnSiYC93eo#--S(=zQbcU2JK{<kviL@$p8=_dY$|s2D#39)%d|>@HA%
z@E3t7Qwqu+d^9RaK`zBe4jjyMv6B!0TL`=zk7s*!^DVhzocR*Ju{+3RnEOGq-xqa)
zCHIur%|pyzxwCg3pU~I?Qbgu2(9YlZ``ikAjXnAIR{wi;GapRkKS@H-+A}!L>_<^i
zerRlwa)R0U=vcp?Y7T?(*W60Aw*Yd3IYcka{{GBn+Y5}!1c(?s5YmwXg)t>SpUG6P
zBR~(FC(3>0ZgU{>u>FNSPW<;%u@98d=9axzGTJ*HhrxL?^W$d%j6v!_#ejsbV*Hg0
zkx51015Y0K*#I6%>65k?D-lg^0sp^UO?WWg2SR9Dw8HX-i<E@{LL8|Yq9S-QQti?=
zxP_$f+%5AKjRjukDmH}4Q$@Hxi=3RxV2*k<!LaE1Tv)El=_?R<gP&_h+-V^%n9oUM
z2<||{xq0AuKotM{8$n892K)B_-%<_?xn1yyh{&3Z`)Gn5<bsNhI^@EOHz1|@U8)VK
z{gSgLa+_Du%w1x~t~$k_sLH7^=g31pxfe&=CyRA#C0hfo0rdnNePkhHFs=yuMu5D)
z;5;fcZ~b(W*$WZbj|(yw;ZE|a!nzh^6x7P2s%KA47c5Rk*w={<Sm;JB3!a}55TGr6
zg28)d0q8#+@c=KvtLP-8q|})jW(nkQ=Be!u9`Rre;B&X3Yo`Q0?g!PgZ(2!WBX3Al
z-g`emM2urT-aG#J<R0UfFRuh{#2cJs6+LwXjsm5Y%k7=aNsow#bnMOLf2*$n{()-I
z@zo9G$YcqjziWn>=GCwLzthzIztFc-Yv=ZVybHjb@&C}d{`y5WSVaZ=f>cDzWJBzM
z{J^om_wo;P2HYyO9KGb6j3teDF#Y$(S(a{%FG29VND1<ZjP|{dX0}`WO6erEySRWv
zELd#;-a~~%+8$EjJ2@rb9uLb(ib*)XpON;r&u=I{uR5jv%}k*7qXB$TNgmy1lOC~!
zt|!1%xx&n=cr4jqiel`M6SGFa5Xc{o=GD-7>d&<D@ncJB%bSGz3cITl`!D|sC*ZpO
z;ZpJc!3y&K+1;4HA}mZJ9}S+x|7|LWOOUi+I{_sj=Xh)@D*6<W>+gYPk4P1#nG5{R
z>EPvlH4T^4@!9{lWcEa4SM)d3_N93<c%J|z;Z%piqkoSzd+y5=HIsptd~s8N_iDpA
z_+O(c9T76jzY4)@&<7B|8QPs*In)IWu=FH37wIoBF6^9$)f1qg6KqG0g9Y)st%-S$
z*C_7zenZ2vaHIQF9s!`ZA{2K*f3~6Jzbo<}%Dj*?>wEAFEqOBs<MoV6a_*cWd<bqK
zG4G1GeazL&hvQ#l<`vkNSpfP=k3D}HiN!{ITk@22F2}l~1U2O=|7(BnOAu?yV}VYz
zUpiKa-x{o=*G}Gvugpae_WwIB_(h0yl{;!3i&YZflDG!)<i9O{KTG$(tvkIeaH3Ye
z<h&sqhN!yz@3G%IOXH!H2uE`w-P6G21WJXa?7^Xvhh*4~9X#QD?yjoSi`RSIBrj_|
zl5*jRzNF@4>FmjApnBpV?`ZDvcRnz8LhrFXJ!(!W_4{vJ{YT<3?lCXF;N!=}`3qZR
zrfNDbHOmSX(n5=@DVwjZwuH}G`6|=lFF$I8FLo+49ow@$=!=L_%G9%@@a_)hAYbG}
zEwwmg_ebQTlg!n}5XhA%t^)2K=ez={+(E+2Y;Mwf*0;9r59~eWwYV@T<?wY&DG658
z2qYgl@IjBh&ES;on^?dXXqhP;N>PV&s?~o8>G+ZEczl_uoUq^=;|PrQ^MT;dD6YM-
zYJXrH(!ZKaL-R>{L&kGzLn&|fG=S40Z}}Fuw5%#r2X^J*EGS}}w~9xr>Tp&e!2|B5
zN=H+)K)+j?Yv+E}pV_lcJV0TNm9@t`hA`p@YnI@2OFvptn~keqT{DUKc;rwfF8yHT
z%KN7)7BjryL<odA*N_QQlGPvqU@&t}v&gvp!0wD8UVi`lJNKh|z?*}hRg@lF-_qq8
z!=rb@eRC3TWcuV+Qm8X8^Jw$c&n_0sFA#@&``2e{Ge><ND|K;R-DLvJWBa<vb(!+6
z=99SE*+X<|w73%U)JXB>Go0kmMXa*B$$ygTgi;><&d4d|8ITlhh*Hyr&H}y2ZP!a8
zbp*j}Z^zS?OpfnbZ|O+=W3O;C)5)sB5}xjr4|{fdhBydCPH>iR47=i+ZFW1VDBJ(n
zFDg#_w-ftc*fCtTuxI!m-F)ueH<Dt@wOjM;Y<D6y3Un<gCphDaOC~I=t9~(Alk80v
zC+S}@`77tg$gT%A_I|5%q?i?OKeQWdFAypWVM-_Z6(srfAI|}u0-*gglkxYo?~yjm
zVIF2x<Rsi&lH(Y1pu+1|&GO5L`TPjlUGW_ma8>i5V59@zwqNioFB^Z4?Ftt#m=9CV
zX{+qS;*ev}7tt9z6Wc#Acx%czaEoL2dHxE);bP>j^6s8taoUy2|H(Vb><0Fh3Z&{Q
zeYk3SXUuuimk_x-%jOI0DitRs^SBj05X~Racdf)#jp-}l{ytSmm68&VvLm!#yDkQa
zyv`Kl#0Nz+*%Emi4Sj>bluP9G83E>Buz)~1ZvOp?B+X*`c8T-<)Q*6{5D4o6J)hK0
zdtbo03*JBU4`&V$JWUkPRe#Cwu%X+>-61xM6#~n4kNGh&e|BGvX(*MxWu)E>R_cL8
zdZsLMo5(^W!HM&Q9`}#Y>OIKaOC0VU{kx8K*XKq|tbLy2!v?V1LAJGjjlFsF9~Jz{
zwE>ca+vnA>g&{&vY?w8@dPUZK>#zRFW&2yUbW6hR%$ssU@N2tx-E5!(e9kqWC%>_n
zrs{7>dAs0P&cekUi0sS5nKF1=v%uo?2F0Dtg}ve|&dmLhS<#RXUs8U$&3Hv^>1|%d
z#6s{ur>QJ>sU6s=7t7p0P=vo4CBB`UV|vzv&1yh}c>t1iIMd8AkkT%0dZQ_))|%3_
zfEYL|ntGIZ?CzmG>kHP#NOua>MUV1$A%5U9nYfEhZueNaD5W_eaQ|luTm#|-GA0Bw
zxm~nqjys`Jg4W6}Dd8skdh&$X4a9OV+JvncJ#5)f9%ARea~kjSXevKq9JsBn<@T6#
z=I#(nzwPT@T&xhkIp_a7Wnk*S##Zvje+_{Tn1_uS5@AQhHo8VN6vLGN>m_bq7SSz-
zh$ci+G~2>1x)ch7kV1~6k}|f6O;~r|Mf1ACGFL=&ALv4mZih2R)I3_-n*SGze$4Uz
zw+?xaZEiD7{zHc3?jOC06u$v<5k=VORbgHlY4>!XlKBPF@#)B1r~i#A;-Sz~x%*5~
z1&ORVoJkD4;Xhd3=;_~il3FKW`8SSPMvBYMZVbYaqxpY{&9ED=r-o3&jIwfJ=9Lg>
z3?w9cC8dAE@;}3FlptlK;KJ@C?p6dJ{<&3L*yw6*3(K7J-ut)b4qZrtoQ+KBQ2K+0
z-HsgW{=*{wJEXZP5*d#0-+8brSIF=$wf)~F%^T`!v9!2_!AG>++mzmZZBrJOvhZZU
zF&|dQE&F+8x7WKz&z)wSEDCX;Y_?W;7EPF2n=+jI+cGD2c}Me^D<YANA7~>B*oAuo
zmVy~+h7FlC;zEQ88~2~kT}QcNl=bMxVgan6G4E>tr=I&s#mOI*`8dfEr6QWyqX^jU
zU0m<CylQ}1r<pNaC{+V7CX*FgOQr)QVlgr%O~#B2myK6suRk6Z<2V2}sq&e-8Z6!4
zab9Ket{%l~V8e7LkU|o#UX(>{ZpyV!wJn$rv=?Z}+>?-AmMK`_7$c|u2N*eAW0}h<
zu-MnpWU16hOKWw+6#{>nFkqn15d4?M$4<&c!t$35-Fdnfb%Af#LjS|$q!l73%#|u{
ze+Z-ASPs>v5C>q!F@?&|9VgB&JYq2?y9&CBlofJ!SJIVct2I6T{VjFkK7vflm!W?B
zNmj^zW+vlWCjMDVC<8d^75ag{4khP%pj)P-v5rPxxHX_x8HBMOAQtT4YHv|~Ea>Gg
z=5Lry+K!intgKCt+W!i4_@>&yZbE*jNz=gnDsln+7rL{df;I~@aDG6@1B+KLP}k{d
za{ryWE7=aWe(r7|+#S+ZpSbCDJ!?ybCM9K{h4n2_U5H}F+2gl+mRqgCo^^z{%@tY7
z@)=9V`uC&$=ae?M=k<4w7Ix)AXa|O>Kqsiip#9{Z;LT*_f#x&UMIyHgfUr{(>Ib@H
zX4wu>|C%iO>m!*wQn|aUuOaF$QUDn5wrJffQx(sW^!mb*T${#^L~cvu*Ya?0e?}%q
zLqNMz2F8IbI4ic8q5PQWVX5S4zT;1czCqqST6#aL`bW?-c6;hp(6UZE&ES#4u8G|}
z{tDa<ng<Iygh$#NhQ?iFp5AO=Zwx`kz>$=Tih-S2W#4YG-ChbGfP1vYhu|+N;sOmb
zq@0-b7YJmq8BDSqj;y#9<UzzX`uyj!>jWv|tUqfM^cI@VN$o-;#PzN*gdr49Yhn9Y
zlcTdB!<M}35Run`Lu{|a)4T`xq<?qZD%g>gix|i>-@V11y;&@CcvF08YSUZV`-OzP
zW1)*cX&Ji3N+q>^|K)~Udqb5f<o}=Y-ZZSKtXmfaQP5JMEK3nV&@w`yB1$U=3D`kY
zNJ-y=A|P#~hd=_hXn~*!tw2I4jU@D~v_UP!fHWc@K!BhGB0-2G1WX{wSqW9&SNlHq
z-gECh&wlQH&dm>gBy-I*=a{2;$2-Palr)RjYZ_UdhsZShIJ}6^EI{=Mthh@zftS(&
zpDNk&<%t!e_DCdmKv02mL53#>8!ao~JRMB0^VT<N@%XWH1UZtt;^^m~T~-0^Fm=?1
zoO*`1%DwwiOfN_#33+*fn?_DMmbMe`daTlO<mMSkRFO*63E$Lj>!=YSIq8!#K|mBA
zYHTwmoN#Rz^EH#4tk>2bfoM-0xk_Ak+^aGs`OZmKjJkTrZ7yYZz&4F=Al%!^ASA~W
z7*rv}A7qA*_u;1iX=e56%3FNV@AkgK!G{z2w*BSBOFu|2ogrk|MVr27Y+8@|77uo4
zXSBRbb4(chl+-6#Iq<kud18AUH;=^gA#2+uxVziW?^^;Mss}#9zntIH4>2jFx#zpP
z8$LIbeCgW%T<c_S&LHg4ci@dfz`K6lEAKL+h1w)|{RWc^Xs80zLPLYQU~Vc|?eHsL
zmIS?0n9ATz@qk3FAmGG*ha#5?u#yXct^lV=tU&oWi=rae3fkjK<?IyPR3*O!26lFM
zKz<vq;V(1c1Pe!&@-XghBY-@(4gU&Fz}1waarspyL<{Z&lg-p75DbSIr(}C1j-lgs
zz=Vk9NJ3aih<(29muShnRd-WHWh6*Js{P&bUHy%F1{v2Qn3Je>J<0D4cYsYO%5shN
zx{uv#TyIc6iFdY==xoPPKxV$)$DS{%TuIZl*d{U6<Bd%lzUTr=r2Pf};P_a<pCr@h
zvXJI7h|_$&f2Ms)!5i9b{Hg;*<qC1Grsyxvn<8HTEA?<Tjh9&gKSuy2Y{%ultSw7!
zY|u^wm-9QA=htOua&RTF-6}cb{l{;--*1nJMwz<Z{Bz`no3&+_Xc$x1!C*a%r~`)q
z53Ih$qfhg4@s(vE_Vdk;{VFQ4O=T>n@r3@Pb2CT`A9W;m-MwJM?xnw7inqpe#a}e|
zHl_w+!j<*9o13oN;M(vK^OrK9J~u5;gOuXj<|ih@)p7?FRK+7|_!T}#K_aSi`xnU?
zkGB9KJEP9!ts)y%_V>B#ma<H*XGShH-)vjn_EoaH<GH|)e06Sp(#Y$s32r2YsrnG8
zV5W=nojrlBkt=TntI{gJcjIA|Kw9CY`b;Rz37_BksIhUeTs_2~R9jVV>y+R;S50ok
z#QO#i@#JaYgpzW^=2T}2V4I2+h##fAT7i4C;4O~L+2UbV;8VkyJEC(2St`JlZEXmz
zWNpRN!Fjx^qsT-Yki6p~$@6JMXN`qDi)ryyC)8Hg6!~l1mgu_g(Px09QROb6yzmfD
zDZzcFk8=EioCa4}w&zt0kthM%hN1`i04x-{fP}H~Y~gLp6DRMP@aIMSICsPwd?%1d
z>RpA=@RUnThNFgcFFa_|QjXTGcghPp%;4{3T*}U)Ch>9->7C35mR&;dbQ{ueOrSnw
zEw(&ET;fiU>Ps9Pw?CP_zS6LpnlW4>!QW1SfXd_j>a>4ubL9$m$C;}^niyK-dNN_O
z5%<lN;Hi9a9hwDtU6TU*+9T#UOkm>(3!B7!qg}JgWqu+r{M-A<_S?7MOYBM<`|!sg
z9i}+=4insE!8f*&#1K+-PWdudA2UlbJ43aW@3upek=V5QHY{x47<_3xi<YX*K9Y!S
zZfK6!iRx=^elgm_{7Z3i{|0(jbsfxV`!<oW2fv2S+&7oZJYxVL9$0;_8IHKG-+SEc
zxBU7tmaBXC9YIBRro{Rspa>Q1efq<W1Ix{^fvX$7e8el1g-CF6Vvjd~GXDbr903eD
zEztYQawxeC&jK=}*5xtgnt6EHGjZ~5FABH6ExB`0F(B(e`9-0~&6J1;<Er63E5N#d
zu7-ao1jqfI#Ww(h{us44EdscVZ-7&Yv!hgA#~QgiBPlhii<IcG#B~={>uL`WMvVxU
zWUa&2mF=|%?hlNdk8`riqq<cRVBQhrAo%H3!jr)?kH3)VwnnBDf>J0n{L2m#KRN6(
zW91EMl&up4QukaVr38skq_C@4vSP0n_Okuac#l&?qT9Q~oG}oUw?%DZatZ@hWf!Op
z<IViH%mbJ?ioLedKjZ4F_817`Omz#wi92FmE19ZF)}d)r=Pq(Mp6Vd-ySp9)kKew0
zq@XXTy6x?GH%E65Neoy|I-PU@T~m-}zDA4=eJ*-@(BVB!?{C-$fCZywfeZJ{?YIe~
z*c^e3r)P7)$bDkzv9bn*FOe6L?fV_KeUaFH*$HVq<;8*N6R%>)O=O?je90~X1OCLe
zOV;)65a3i%X32`=o>_O@6^R0S6#!>I#pmBfjAu8=gh<B8*8I(Z?5W?t|DfgYE2N6w
zhna&{dUzu1MqQEi-1|&rjTMuGSP6ubvUm(kA}X^cprT`ismvAvOl5Myv(&oW<HIla
zQ8wJq%-8>hKcly%e91hr7Jj9EbEd-2VA>>_nTscurdCea<{$qj!f7GcW^xrAS^#je
zFx^(axk4oPCpv-S23baT7!!u2qVa!zU#r25O!_v)#qj6kdL&M`WAC0_W-w3Rm|x?J
z)C^0b<^KVIP>e#<@Bx#R?HK<2zZdyKfu~VvLP;}M7Hd?Bctc+!|A*h)-5bha6<{aR
z8W}jD`We5mJI>nKqotKE_?HSq#q(SZ=*>)X@~Z2d-bTFe`KWDM3%?`)9+}3JMuvWC
zYeC$Iod4&-UCOSvyK?U_vE6}&rK&GX<ms=NJG%WOr+D)B6hLsYijIEvwBf6sU&iHL
z04eb(GuNf;{%)`&3<Hj|BC-AD1hc$*isq<c*Z91_-q9ZCI<X$4F9!k1cK|%OgBPwV
zTDPmA_g}Qu--A~01P=I3@-qVbdbP$<PZkB1Hx}N>Ebivt9?6tkKlxn8I1ya*rdv*}
z{brn+UE5V>2p%Dr{0lDr@!0joSn-<+GydP>-<U5X1aotI1W$KsYp&B7zXEC9#L(4E
z98x#O(B9K|96uU)u+PoYG~Ah1-7Na26y)l(&A>!{CKJ8gWTg|sr^^285bNcZrZl)1
z!Z;8nyfOTjw|%d)29xLaa_+u);3<?~<H_p)s71Xi1Sb&tmR0FZf$H2gIUt%a>><W4
z7~=r9CRXvG_W*#l#_&g9e+?Z=G|KLTG&q&F1Dp>AzY|Z8U6N?c&@eD)JvPR?oIF1k
zWaDXaefQfql9)<mjxh)RCV}*shwiYR43>Od8NuE*iOzxwdjF*c;@E~AOgnjpfx2Ns
zyz(#LEOw9TTn=HU2E(vn1ONSQ*Artm($=}2$zOE<U>?Cu?&i}0NTwS?pV&wC#;*X_
zTVK0KvVoI$z%vlAAKH<5EQdBC-!5{Ej%~D~I8+8K*1W<uIuH$RtaCm?9q*+0eC7di
zvN|QCmS(CD==sf!J4b#^7?)v4%pFKcwiOVf>|v$Wl~{XsRAL;M_Dc--8UNGtI`mfY
ziF!Y{a*zzuN}F=|x)q7py=p?8lLW8HJ%G5u?V}B4l@qMmK*B{i!MbWS+y{2J3t%(a
z=DpS*w!0Cur(UiuYqLbQ-_8MoQM3v;vR?Jwv)fFxSlB-OPq<34X2Smio>e5zWpUwt
zyCm?j;{@CeC^PIMyW(wu#8aU>YTH){sPEXVb9g_hr-p)^=FJlz<x!5Ta&U7QF^{!J
zPclI22oM#84FL@43V7v0t%*biC-0?<-MYP)O0X_#C<h=8@JT$QAFydj3jnC2`8N6P
z<E8v-$-x(dvBbCrM_3uE;*EoP{qQIqhWE3FQ&Y?(R+>5psLa;ee3c%oiz!Qrl2C{E
zRMYOyU1kbb$HOtqyy?X_^3Kslnt}KZ9WKUsQvB+9YV*{JY;Ty}>L2ndV1QPLyv(&A
zC$A2Ci%m!)9IHV24&D$CmkK|bXIkLtAXs3(IFS~-BOSbO<DPL>o>SZPFjOcvooRtr
zI9A!#YTU-ZInARITIz>Wz2hRqWWm^EiE9u;vc#%}^T98xiCJQRU)_Fz?@Co%B33qg
zMvk-B4r#rbNpKf@qGOXBC@@6)i#N;4tcv!i*!jy^!gVK8R`l9gy5|qx5L7^4zT0Qt
z0_08=#(_MJCNlJU{Oxf}+zLyA;H#o$T%>!zD>;J<wMov(e6-L}ZdM;D1REkKCLv;C
zEd=~`l-mptQb|xjlT18n9>u^xEGuW*8X;dF$d8BP)zro~6@N1#=K+?>ogCVYaIKFf
z_BHOCj2FepN3a$Sss+CWJGc*hzI)$-d1rS5M1FUj#7-=vfdUM;zg<xdYlB1(`(UH2
zHM13!FB4l+<LkD^efZ0iF@ML(u8W%)D-}=yT;Ut<_428NL30GMyHMEu8Y|}DrMz|m
zOI@L9aN%|<d3m%p#B7bQvKy!UwPW=ZEsD3cUFbEKC>{u$)qvK?1($iF4(`123x*ju
z{tl<?S))Df`i{eDo&qHI@$R^jv~Rz(6UzWq361SbBH+q}K|J0FS-TFdJ$nS-81(9^
zWU8cjOPMcQ<;KHr3q0RN>kZqQJ~bvHkMy!kc=6HPak4&nC*IM)upEnggiZ5b3_Byf
zDzV;YYiiHp1T%#Rm%kx+&wM(~Bj805kHD1rVL86l?RAoL&%zA{Gtu}JaCF(KStOB3
zXAY5xb~vLBWFNu9-LwJ-QKx~N+m%3RbkdYijaAF?8kWN5Pqr$q6;7&hoCoslf0M|y
zf5V3VM5W}v^b&DhE>Uah``h>{)#3kv4{#D>y)<Q+yjZ8gd^h$7{pm+IA}+Cv*S$IX
z#r<zO;JqsF3kMH`2RRjOkL9)RGY=)sr0mwZ%T9FOyza@e@BTOW;*FTkerR0U99&=C
zT*uEMr%DXs#`Us94{fSk7Bc(qUI0AnIoQtqvcNX-Wlehl;!o!J^$?>6u)pJFh$(i=
z2lEB0hain9B3MH@7%R^werq#HkYL4*-TU9=(PRkWnVmE>db=mSJ(3v6suW%!2BuY_
zt`4tLcDg&v`rysMZ*jDDrj3=T_%Pudc?;Q;PziuOkK?>GXR@Y@p9CoEu?8$0o;|46
z_Bc*ex75>9a6?Sy0#ycrhhful{+VLQ+Oi)2M07N6kTUkplx9+jLp}0<=mRkD3QRIf
z)fS+nlIY#h3vDYOjZ2JM&@I(Av8+^pF<1AIeoMMyk<3d=*9J0TpnYM_0ic|cgpbn_
zC$nmk*eP{x#qFh0(^7cko$!6X;|SOjFBtiM<AxOSbIvLTitK#|cqJ(E<L#v<6~*oJ
zDh=ZZPbXE1!pY^Man`RTyPo_N1k5bT9%R^Ss7_+DV9YqxLW-$6H#hCt^D|3dcN6NO
z<xhIN_{yv9QI_bR(<Z=KO*kVXoD<G*OUPQwEew5fXBIbhUrgn6FuC$luczUS^AhX+
z1XN0?uBKsODA8`J-`e#^HhZ*UxAv~qI^z~Za3qzaE5akrtu}o79+TU$USFSl1QcaD
z;|QIwUf6Sw-`qU}-rT(1pV-=Uo1YPD&+S24MMg$Cb4|PbqU{v|XdYeS8%YKFcjDzv
z-dpoX&gf2X8V?H-5VAUNsx;DgorI)%_(h2o0L}&|=JILeV0?)K4#4E#v8(n=`2>ac
z$Jf&{z{$Ydwd-O&Om4-u*sWCbK3{MdkLU3YX`N<lC2$CR71w}&bpd#-gZmRb^4rLA
zS(4<W<8tS274i4~ugYovi3;5R!X;M$`FLH7L2x+_d9SWg*$l_+|H#IwYey&Azs1~n
zEU9}W>R;O`Z}6GndZ+)CtO~LDYwY5`{K&5Upuq)m<gz?dqTyvme?g=FUJk_^=~<6-
zCPrl3mK=-9Vd*8rYIy*oX1AGS*{;9;l~82g-38S0=XaQ#65N^`4Cq7Q5}2D2k}>Zu
z05+ah4_AJ8aFHbAMl~qV7Eji3^dA+@36mE5Mft^?$|r+rtc#74lDj#%itC%j&F;y;
zC$X*qgt7v<GLW{sujHYcBs6(}8z!@9vt;<uIYoPxvj_D8*1_H0-E=aBUBlRC00&-h
z1cdNi;wFP){vXUaJj~9|UJZ{bF0b7na3DV*FUq7U3!hlDW{Z24q@27reCgp($oT|6
z?Y@L|zFD#vVq6fB{O$Xn(w)g!rjVN0DanQi`x7VJ0D{MHrR-SH4ohMq!XvVFS8x3e
zik|P*RQFs{Nsd*u_y2?XW^NW&g8E#?{tx9%2@1b4{8v3wQsqt5BK^0uHgSo(;ZBJi
zgbD!1R8Qer*FWp>A)fg<FBm;?e<gR4PKgK1ti81x_=*G#d$qm&5=aUh=ye0;@8-yg
zeS;GJ;uP+&-h=v|D%<}rIF0{#w}9BKOg_p#URwD#qvd&TSAXtY3X^D$t5|kOa5QZ*
z=KY9p^FW5ginUd$OiOxchPTKRkarR)ojh6`xn;~6=7}RuBzp&1IllBPnv%oi*}Pt+
zJ^3QXPoe@UhwkKaTAaDA7!J&iQRlP?xyoE5`(@$;NlY>?d_Ak#aI2SR+KSYcZ!LQR
z2DsWC#tAk2Hf9}jdv~1b_*MNn$a??5#hqcjEGm$p;|cON`t->M2@LH%BH`Te`96uf
zhilg3GR2AsIrR=1M<}sxac!;o7Dv#wqY>AFGK4s0$=;p!X<<Ch;J*(TOUu{bW+=Z9
zY2b4mVIcrPp@>%ts6VhY?)ufybl|~1vxD)Z7|{e%lME$Kxdc%|gSmqail;?R+^+$v
zi5`8=QB}=1%p~r{>!G*|?jM=#(^lIYBGdP^9NYiP9h6;UNb-;TS>5-ImvMUs8BBAe
zShk7!j6>0!I8h;Z_D3w$7HPp9n&!bAAXyMehU}I6qi-(@uFmyO`u>$mHgp8Gjr))t
zd|Z63xW@=f^R!~dslDZRK5qxdsjs`r6ad2{7?$=Ky;pT~^mIh7z3Y^SdsxNe^hx4H
z0?U0!Iad19LtYjWOinQV{6I3B!A;<@ID+GLCbv(^<Gds-sapP*>-;ZO%Ky3A{$Gz+
zw_vsB*a>m!*R~c*{r3U;9$awG2ftuI|9z4`8;&J6Pkd+)chhQq$3J^^;s|N-%*g2i
zuvj1+PdGoFmPqFRd6Uw25w(SL!#TA%WRe!1_2}V`n-7>r{g|@l{N&yH2fTheyS#DJ
z&*|>c=N?&^$7Hx?Do$@Y=ds&-(%#@fqBg<uXY+IWkN&jffu+;ypLSY?g=4yVOj&CB
zQ-k@Z=5l6dbHc$m#w=sW$kx_=yadtAppfnLPXCn^od4^k^ZlE}Zw~=Jdk?HP4w)VN
zt3~*_<Q06tFAx8E;ZVVjzxq@EZzidJHvhw;dXSukksfIo<ansbgtQAf<XX0lzJShV
zMYt9z7&B%{(gqoYo*hlN7<L}La)@^pjeJOv@I+qLz@WJas8El@)wm?`&Y#YK9<yYB
z2eIyBKZqCF@xyW1rK@F!Vv*=*znE{l0T69=O@-bJoxswy784{Y?T~0Tb9K!eon%i;
zI6}f;#&k%ZG(|}V(JCK#P34@GooJH-MJwSH8FXvm;@)*IU@6rcOS#CdW3KoWE!`46
z1Nmu;WXL29I<eLSG)!w5MmS5JgIx-H=8Km&vg2wS`g`K8)eE~oeb!;*^w_T#*|$L2
z<-+Aa-66?{g3G{&7K?ges}cwC@=?6Rs4{y~8;(?M8mn;;4W+~)qb=L5;w;f6zF@<n
z6(4!AODb%aY^37?sYG>Uuz~Q>p$6_r)we{uvEPXLmz|#@9o;@e?LvbpQQb|k3_0Rx
zl>YGP_PE82V8;VcHXVl4>>CS3QORCgdHm__56cM)uW{X}_4UA1yTfJ`ijl_=!uE!_
zt}&s&uikxo!z0sGzD0o#0eJh_1QwJOIn-FPt)%*Lq*TmfT0hJ>Y%Pz|8sE#)pZ*=h
z&}dp*fXCNP<Ezdg|0oJ0rqktCftM+j&VNKVj0zRe^v^67=?#tc(Pr21=h2|ml~$nj
z8g^t^otKu`B|MF`ZI2%b8<X<s<{{ACF2An0Lfadok%D)eW8>qe7yC!K@>-$Ejj^mt
zVJ?1xAt+e)jE4q@7w;!%gDx1I@%T8LNl&uf@P$)_rRNz<*KbaGTbNeIxTtAZGkU`g
z0n_UEB~oTbaOC3`C^Vtb&cK!G18pC&841*+#2G*DzsFK<sw%39F0Mmff9;6*8c_Q6
zAlj|6sAgnz2$PpJ%dn6+A9J?rWj&T&29gUtj?0IFJ*K%|ng%6$nV1oI13f;wDHW1W
zf1$~FaSjc$i~S0n&5#^*Ro%9i)q6_RjcQqNPiwX!RH&c7n1x>3KV98<5TlYyF9a#P
zznOMtT#F?4gm6r($5I5ks;0L~MPHR$1|5}LvCe;Yvz|b)7<$(w#h3{UeG{~z?fYSg
zeg>r+I5}Fa>*L0-Mdv-D`$9t=>3A&dg0?@_Irzm03LfP(m1I1Tgi^`u|GWLETbKkX
zz}USYMfIkmq)v>9E@}JTUs?A-8tS}hkoHCP2q+Fejj*NHXVQm3kSzL&O~a=#5Divy
zgfs%3cti(lQjH!YEtCDm!e)Nv`siyh_k6%Q+b5f(UH@3t9P1(XvL^8JsBCsG86B^?
ziVLiu20WA+4qHgS7LDfa=uWmdrpoxVbi$`xqe=KPZ#?1FT61?rx^~=@$<^OYD46(&
z-}yC&-LWax`^ui4!&%h%JB+ke-u4>N4-J;N#F#ev7PB+aw<svEWkbiMg(q}1o%Y6;
zEKscXjH1c)?mwVHIJ%_KeTTza9kqj_y4_wmX*;AGk^_~qiLQrY-TVY{!44Y}%V*!E
zTfm#6%^<mS187w7m|Js*e5);juhP{PjZOo_dZ0g|$9^Fl$Y&c4a2RzU_{G^B(YYWC
z4Ws9^+XFSzny^^<t*=Bajg)t_3+~Z#n#@--18+|}HLblcYAhB+O|0kNf_UEx@`M@{
zqmGbbKgjG&+_c)ZWDUk$R8kCh<YzbAo->1u{~qx~@HsLDp+V!OSI^uly{)Wa@SCE;
z>zaKB_2+YZ`iD+DwW?LpNABC$9r6U&%bPPch>dF0sB^m~jWjzyjzAlLCwvk&Q6CFB
z=TykAy<E|ij{XTx|1j42oJ@f^`nZ|=l0PZi9h0*35$kW8+j-xh#}_2E9pg#uIEqDk
zoLW7LmMa{S8VL${JP|eVlw&A;-68Wt!4>^+leR@;;*&6oh-@U(!wqNyDn48<<!3)<
z(crMv;pJf!K*qr?cfB_zl0T2$J2&iJPBS36V%(n4Q$W$yN)1=Z!7z43FTWO(N4@Iq
zb!?+}&Ocm4-CG6{s$4{OjVL~kna~<xTfIxw4-TIIP4fQ;Oe$5jn__VE)e-dCZ(Z5%
zJS&AL8c5B$=7^7c^3K|3j0WTYDO59mBbFBS*PQ!2%M;>nEqTtzvGCaY3C%7Eb9Y#=
zm%7HMw%Q&$-)qh0gH|r&=-uz}-<7&!KF2_-8<40>kg9T1!_9{;_8)o||2qAXCN`gr
z?hQO;`}V#t2o30zi^lkQ6HT92GqGAF*1yec$FvsKqFm5wK~)P<$7`c~nyXJz@Jn-0
z`QQR;t2AWH4Kyo?P0OXb92^*$N=|i&I>gH;3o*lf%3R!2>fcva-s62X*H>3ZR?l@@
zk7Ce&Cm0%%KX3*ClW(m!d5UyKcl_INZp=rHlXbqAGI#{K%9gHFmU)s<B&SzaJZC>g
z%iWI@o!y_*(`fwA^&&eHL|Ir9R)G|I4$#hES~YLO7RnlYz~}4yGG7AP?>?&65pbP3
zDqCEwJ?%T`ZChcxkVg+D&vk0>IO%gaM60kgkfUqRu1>^RwCFMW+AyomO;PF{<6{da
z_Tv^BhpQLYSVzJ_wDD||_uG|wEeUAGAtO!FWoT5yMD0ORH56quU0u!(SwhaZ`Hj}6
zbXsODzt_3W+4G@zBxNbl_ek#YsO;|9{`9%~y5U}Fbb<}SO8d6A4YL+YPdzj6MS6S3
z>HLg|2Mi1K_`bX3xYQ6pj>tvCl?;}WrtXHm*XGa($7!Uw$*!85qf;l)*ZYp9`Iw$>
z27JOpDLq$vTd@qM(0RDodvBcX)GuwI!&M$z1D0?X5slEmV$>~ZkSM>MY1VzK$rKlK
zmP2g2DTi2Ksz=Jm0dIcIc5U!J8|OQ6lzKu3u!zGu5`78If7Wk*|GGDQKZmLl<2FOB
zcc{h66%J0LK}l(r8dAW51($)TKdM_MSS~|I28&K(Cd8QFi){X3Vq<X=7G7D})dWA|
zQx(%40l2WE&wPx}pZ`$oylX>$(4pj3K-+-m+W_^*dAai^)Y0kYr?U?_HA1f;498et
z^>Qh_Bi+yq4j&cqTqvSq9I%k9U0VX3=PlWenwTt*&j!3)@ukBb?r1yA`OZz4WKN4+
zuS0iOO3E`UnWI_f0DCSRvbi@LgX-GYjk9EDr>piu+0(r&3N$PUl#ownpNIu(9!fN=
zoF0_+88h(tQVrteo?wYSF_E4Gp^)I+AR}c!zq7OukA;}?X=MYD%0G;Io-z6WXa1s#
zq3_?GHBP{@7brKJkM`<_t-m`+DfKZF@OVF%7L5Eb`l8`xXh=3))9A*ADJ~E-*kvF?
zs@DWe=@H{-cd1O*i*6M*a!|HWLF)Wyj-d06NyypJU}(Edk!cffBmSGY{`8i?aTDRm
z9?yQ&ylc_+GmG1^IzQJT#lmc9NR!;^C+X`3Ed~|AUfItuYcA;UFt6jw`1IU^jon{i
zf~{sURnw-1Z999NXH8uOS&f7UnsxXY)_eAwx%*!e7QcPgOE7Oa=u^B9lB4+MPt9B_
z*UtOP@9|Cz980TQU-D+N+MIeI*1R@3eEW743l`Rxj^*c)vp&1Yblh@3LYi2zHp5wl
zbuYCSsO5)+I^oh2{LiA&pzv*_jzI-rWx$c!Ewh|g#<r)spVA_2wOu%-h6$4%XfvD0
ztf5)M*tn4dn#ql~Vb0)Su>c8$55-=g_NPSO+9xZ3_Td*Y)_5QV>zsG+BgeNKkSaf^
zg@I&pQd1U6zYQOrKVnhn+C8~t2eW2bK1Ps!fM*(gYen9*0=%Cnr!XQU+*?DMlIG3_
zX%m7!NH*rYB#s`;8A<e*pcot{SQMM&N8WBYGOz*g^FsiRmU>TjhoTHfak^_CW(!k3
zEZ<4rNj6D2d_=R2!B@+`E}dvIXXPiEr=_qiY{_n|zR7ly58elO7~SrQdGErDxxN{C
zu|RODC<TJkAg<nB+6%Fvzlsk|XTSSU32K6&PZeDni-<z6fMSEu94g*ztMC^Rv2aPR
z*&Dsn*4GsLi;oVW788G%<f*~4>;kz3g%IM!NB-)Uv>n0gO^5%xA+i6O{Q%D(T&U3?
z`5DIM9L3$X8GVTb`Mp0uyF5>LsB^R2kAPAyJH$&$4^D7su^&lKy%grtB@`@X&TTOB
zai#8(O4LLiZIc?of)o#pfZwdL`d5UeP;2GKC_lfE>c?FnzbX6Zc#JQjV$6LEKJ0!w
zJ#s!LaCV-W)>A!J{&_D{HJ!c-3eKmmLZdW{Afy(w$wAV*&bZ&9<i_7SU!I?9weJg$
zhJ}^%@-L2-Std6W#h;&tvIm(g&dE-cHfgKP=#c9#6F`C&)Zd9IThY>hEeabIVnnkC
z2UxR(MsWu-#tRI(uC+GLkH<?-@3YHTSumP8g-6z3_O-bMR5Kvp&M?E4XFA_?gNMVe
zplOyoNNv2s;>qFb`Hi1rL*md|Z5mdav?xVZ8pRY}R2_e{wYQprXE#x{@uZGhENtFT
zrL;D5XSM=MM_D;+^fF(_rBibjbmyO^dkmz(3(t4_TB2dpP%Hf1;K)GDg5OXPw0+o2
zsCbCi-OI<FeJ|ss`Te-8SBAiz!H_|#fX(@<c5`rCRmyD}L`?qib8Wul^EpBLMwggz
zUR`^}xsUc%USwl5&1d#$Qh29M5oyLGR^d|n9iJ*PcxS^Q-c&E22I4s+NBVzsL3^^w
zhz6wgdtI^S48}wBSp6S50Fz}^hC0hh4-jOj$FN57>sZiPeN)npD*g4&_a>bhbTDKU
zM1hv!JK^hq_y$fRH0WYjfck3dwM9TGPH&7GKc&vjmrxtsvJA#SZGsy%v_KBH6H6t{
z?F@ehq-4WancBZvXz*?z%(E|txV5qTbYi6tj$KX}ui21a+o0jS&xNR5khWErN7pqk
zhI(u)T20Be&$-Lw5D8a&$)w>FiGf)QK_8T*FP?#-AS7q#v5V|_P^ER)Sa$Px;o;R#
z??w>s?qR`vW{_*K{zCU+EA>^C*SDj=N0i3fWiy&Gf2xbOM^{Enh-N{3m%{Rux=qSB
zD{Ui?=cACzU$7wfKJ<zUnyMb_tRZsO^xj9*E_$e{Z!WkeV4;2?sTGg4j*iC)sz9RO
ziikrSc^V`xVdWBk<J<je7vYe641H3Gy|<7gFbbs3H~XS3)MH({7EwN3H$ZAoCD)b0
zO9r1lUxupY>Rg^`JQZoIQB+PlFooNlPl*ayI+@)tJ`|<iWbe01%cF1A-1&O`0<RTm
z-*qka_43QI&V?5ox8Nazk%+jwKU+RhA<xu3+%m&IlhG!6q*aU?km#pR(Lc>1XU1yr
zbDBG6sK6UYa|dJtIr3(ZiZ0gq@OMBpZum5C0u<}8M&`=pHYpq#8t&7Tg@wxWPpMyE
zDhvbA!*4{iHCDuKIoNoEuM-W5?Yz&9@)2ndrm5NzJ5!!htq2jAls2U3u%JpR!j-C1
z4&Bht3EFB^SAMM-x;eUd<Nos*yh)`llb}lzTz0B7&*KycV)bo8qj+QWJ*7c;nh~h-
zb*+fe3%^t)fNt3wN#kI4wwKU)R+(XIvK*!2Qh`xrL(9SGx1FH*X-#i-?9j&8cpcJ-
zt;4+!RpmZ5P)A#m3g<k*SwHpXIp(*h=ij-Z8eCJle6keb*>>g9sLx3aq2-0d1SeI+
zqKy$Od%=9_PI~SV;t(;kLo5sm<p}J_85F=-?H*7|1pnHm>I73`W9KzZV}@)IcMtV!
zEP$jBd<;Zu(;=rib!htLv5G}YE{Jb;xT}i?d<>NmJ(e0S1wN5lG8sL+Gx$i3iIm_|
ziUK$~D+ylp-uadj1>heHXIDLSpP~d*LHiD#o9Urn>j<1xLtW!Nx#@ic-Nv)p@82@6
z#D;j9GD;=;jr&b~>)EKRE7D)~X_^*k^e|R6VN|`Qe9mSw*g!}iMz(-rJNia{#*R@T
zmUjTg3Ylrm96a2&ng*pV8!f6ERXMH}OxL7`X0{MvKP7^TFC=71SN5HfYSU_qTE1&U
z2PpQS0|MzUg_pj56#QiHYgKp{7rSuqxz)ykmhMK(*s19c!}aNjouMO;m@r{pw+V!l
zk5L)bEZtxSJTt4%20d$T$C(60!?eHDV<)l}2xt@Vq31PPhfYRu+WFhJhUovF2xi<p
zN+b3+`x$dh%&6d^XY*y#bDj0G!S-NG3Z*Gi&=?CxTNG_ey3EQF?x&95qZ?jt(Y_Ey
zOYfi-NO8jPb&Y#%X|{wqm<loW46?n;&uxF;-sE9iZNs?Z^7M)#)cR7Gr3NuLzZISz
zrptCrqH~h$RTWFNO@{`RfCKZ_LU04}b7qsjG=z^%c^#Z@nDc>R^J<6{IqbSK(Gb-f
zx+=8YZhE!Lh};x7=J(2kdd(JBgu%<y8!BTOdcr@uAi{kjr~^nFL|<jmw(d~s*My*m
zEZ_tp6W98sD-BQdoks4gPuK%sio`!;Hx(q5@u>y-{Z^7k90yiUPuNNETepeOwYsED
z_M>w>rSEHP+xN(am8g6_w|M7(ze2Cj^SWHH#YgEc=R&&cinp8fjqZMO-IWv}{bl?h
zdlaklho9j*!NAE^M{w4cYgN>yq*~^5k!@i=x>e`9y1gjM^N~zk{BjE_L&)_W7&5ih
zP&yO)$hf}|%S*MTE(!DM=O><O6XVjT1qVXxrWcF0sX`h9U6<jsMwLgt6I7%5(5lB#
zrykz6dF!USOK<E8=*|>)=gf$DF8^r$U<qOE?Zc6Y8KcEPCYMz_mc8d%99m&1;}lei
z{|tx?YHY+#=`V;c&q57<0D`O#II|^}F7I!1MmOT$ye2HARR{qIAjTB)I_>;n`L;O_
zgQu<aOZU{AVeGXN+_)<l%NBC26A@B~$ABC0m+7x<xT`+upyMvZf9&1W>W~oyzXz+(
zXGty1;g^U;>hr1T6RFQO=n1Gu$`<CMr45H)Z!m^#IT`(NjktxgkG>ZfX%cp?m9O6h
z@e6K=tennDAqA#4Q}_jmVMN5cM8@_QB(Uc|DZ~J<=Tmk(nt#Wf)usZ*`hL&0B}7?0
zY6ng|7f)dssaHN*Wf~omLUIq!SCtq(*$g<2{R(-uSgD|n^PM=EM4uWg`^{mfDw@4H
z(sA5&p>l6U=ON9uH|KeF_27VkKmEJb*6vd%n6*IeYQckq09@IS#rD$vJ3S3rfWHi`
zD=#!LJ>GQ=+WVyp4x24)-W<9AircB-T)G0D#xhYCw1fPtozfU~b0yoMoPq$s-7^E1
zo<d#_{lr^rf-pWr&3)4I&{MCx+-!J2bmSQ%u(>^7$2VH%<ad|AxjpkwipQ;|@&b_9
zwuMfAywx$j$c~oHXQ#8=my8yr^Kq-__#2lzXg%Nl@R#M41_)TiF|@LF`*K>$&FKyO
zVhX6|vlba)DofWjhCtGPinu;dV$=R(YM669>RrDbN2_*X0iFt76$Bv1p1tBUkQ;oS
zH1waR@)4fc*R7Q<#1AT6`xyO8LLAL<%Rma}ae&~@6aIbH?Au%B*q=aI(L)LFj=8{D
zt5kkldZ?3O0G!5>>v`l@J}VBNrl7VzbdFux5+D30*+#wo;)Tj3_k5pdzor_OWxI<j
z(A-c=PD5zQP_cibd!Qzjy97$er3V__SS7j_sTracTlGfN6$J@+nSR>dCjnLGusanl
zvUI00Ib~XH(YN&6#~%bhFkw?+mzlxs3mdvO6{G?GEF<?#G&;VlR#?~Dr(Cr1>%!5r
zU|0a!Yy5>a#wxw3(uJjPm75zaD6SUom0$m%p;rk9g0jg0$Q_>FDm}^*uDn`_FL6<$
zJX;L39rS^t6{CmJx^JdEq^bMdYAG~PC2A{2`_otyrR)HN6|2vg8e(eA@l$^nm@TY+
ztYD?xAA94%xAK;+WtkPBH{Thgei+r8H_-G}PB8GnfgvP!o45KbnsA;RC;L!({Ej-6
z{!|3xVzxS7;!TKmSUJ>}Uodt})F-@Lkx6HqntQEY8+4%!HiP^qmk7KrMW!kzLJbbl
zJoMvoe=iGSshg($+SkCps!l$jY`YaXMV%Q#PLGTUt`~l3r!YeYRy#{@L+v1uhX5Q9
zH0<1!eKuFbdFYaX2pQCyU%n3#W%`cxx0WMv%VY|YQ9qUVV}x*P{7?x18Wp(P6M<Ju
zD<}RgmMe)?ymI$TH55DMEP~{)VoHC;xLLTBFS_)nPpMyuejR`uwRms%bYWM_+8tJD
z+*TOXHX;vRxbj^zH4v8~_y$r~C`;I{tY-Xlxi>&?BK)1UbGu-qQWbS&?1tL2K`Jjz
z)4a<+Gj@Ay%{J^4`jdPvkSBU}g~rERNMi)g2CrP!8ibEf>3Ylua%uaX;E`M~E@DH6
zaw-g6XeckFoa+9C3<JAWP1Z<1mx#$N#{>SSU*XD^Wx{A_Z?Rq4*4Xkb12lZ;@BJ^7
zeMO5GlbQnS!Sf*3+bJ#J@r;S>MRR_lPHvJ>k&9^T{9*mK;in)97nHj8L#-=45?<zY
zm1-!pT%6IJozhw88(mns)lVV4m3i7Sp!}9DKavph&}WdoQnyf;>IiD`K}Q#E42~`u
z`?EZ5r>8C^*D*h<&B}U;9UxFICtq#t{s=((V{+CA<9Fk4HGqnq!d#hXc8UDNdEXL*
z2>koeFF<Y3t9Dj&i}UcSXxO;6>4l4E_Jn`5I<H=K?lcF5dOv4Tyo9}UVUpRpJ=0dM
zRb8p178|jo*jYG^op|vL%3j$y<i9ro=K<?n__Ksa<HbVJX}zU+;Sy)2lmqJ<^P%Dd
z%q!Mdy))oS*4kJmU0Rp^G2K7y_QPSU2mDi-@7l@N3VS&S+enXP1EW&p>_c}CSjCH=
zA={9%py}@~IN3{-u4~d?b`DTyh!aj^UmFNXceYhlO3zH}d1k^#5_Fy_7LSv@805M`
zg@jsE7&>=5v@ui1h`Qs9ho62c013}{P&4U~v+l6=6RRtaa6^X1khJdafrG~Td7YYV
zprqKSt~-HHAa$!Xv}^gWtR8#WPe4y!WVEDJ6Zd~iJ7W4Cv8?bm|B()lS%`sD9BM)$
z47WFD1}7{Na93jy%dAMqLrCcm?Gfp%*#x!*=u&*CQ=;D!TD4<!ATUkCCj$1(RU@`{
zW30hth7TO`@w-{Q@QnC~5IEw2*Kycr$f`!DsJHJTIzIF^_sJU)nSUPsXv1+ipk^Cn
zFsCV9yYM|DLC4t0)&-_y+cJkkk7>5;f7_d0FdT<5xl(QaOgiZh&j!6(Y^c(?oF3}j
z_jRppE}_?Hs1v`IVEQc17NysQ?T{<kxLY;dqvM+Eg^q9Kvs_DSbdn$YL2a?U99-Dy
zl7O6<TLYy%M-hY3aB)sWEgV~#wSLtw#1ni65p&qYH3rCVp*vh@#czHLR9S%%nV~6#
z8%<hFJ191YY*ifn0aR6IKaPW5WA*Zv)0H!|S*<0imBL5q%A3<*N|ZG%=dnHgA64X(
ztFd&`eC|PX{I5Pi^9z38mqCV>0Bh%GmF>4vq6rTb9OfGR%Xd%Z=g=X>bCmfqQ4qp3
zJGyY&2zv!;ox7h{zw{QL5WJhF<fTRGix}$b{yrfD*bUMAl&0s^A&gtxy?7L%i?Ot0
zb~$2T+uY^I-!l4IG=5!qaaizdx7|!GVQFfEOmyjhd|d1D6Z?G@T8hOK8KkL?8_9>7
z=|30@?+00NLL>=G#45fUC|M=x3Pr70oms}OQ=<{+u|Ug-IOA^D)d5e#BFAwM*A>Pt
zUfNLmtRW7KG<~0g^g&&}*ZMZr>jG>D+DP=k>g*>PtS~^qD+M52cBnGur?ad5Kl388
z%rRjG)%jwn1UG8PYkK#yQvw9j;m^Y<Al#O}A^f+)M9MmZ-<|U{+$S0hDMLYg%sdRs
zJ=7wEEJvJo<%LQ?iYrzhL{xU<36?ib%k=LzEs#$1ch94j4CI0W9#s~`KM)O`4mi8>
znAH}WwW;igG;8+nM(HaP>8e~oR8*!AT7As2X~#Fx7B<SIt7^|&t=)|cee<gV-u6uH
zOuE!~+}lHCZfKo*>;jE5nV|_o%Y`P^qR&d4kzAcSom0Bbu#w8*u|l`OZ_*!C<&+nj
zKxujV$B{ZC)#G*&-^8p*N2aHHj8R?7x=%8SFR8Az?eITlrPN+5#IaN#EllL&91ro9
z(z(N#9;-@?+o-se=R-8G4^QfS4iF!zc8KzH21y@nZpk<nZEWnd<lRDY0JgcT0icVl
zc)y4kny+`bW$08h)CyBtfX7u>`9N5TzO)6=W;(6@_J!1^M%%a5ZOE<|G9!W+?SDa=
zqET|`#q!fF>?cXaP6__#G(ES8Xd47hHG_Ud^R-PLKwhT{zC8r7U`hv9=+@0y>P=TI
zQQZxYRl$CeEl?ZQ`Li$1c7xBVO-KszC`oj|-$GmYabJ6S=-tZ4I_;cX^qOB+!@`9v
zWo;)(F!d%VI@%}nX3s<5uos)tLo;x=0pDg3L!of2@T4(`cCBVUq^7rfLS2Kr@1U?C
z{YrS|2(c)|Uho{0kQJsnoWXWxXD{7jytvH8vgl^x8s0*k>9>7PO)EGYUClF!;wFW%
zR@ZByKekGt2NvgUrT5*7u6N6dElsL1s$<oA<0%U@lZZp4G>2(g?fQS9-n-bfbW4>I
z*mSHTtjQCBQ|PR7%W~eAJJGOhZ|CYuCDlBSJBATmLKhRRxIZ%JCqql^Nw%20JBpj1
z<=#SBcSq*D=}rH1w;30mru=B;Sw3Zg*nx=Lm*X+M@bNTHOgV<vXZ{oqKGIguSsLuH
z*~w+)5-(*KL$Au5(|LCQI+1D9H&>kEoS)v;6j+#IB+6I<&n*|`tM)!y^SChH`(UCE
zn4{x|7Ds>(LKtZra+2wXYx$tNedaeIEoBobw!1EjTk9to>-eC%xS&S=CP|TASLx@(
ziCnsGu)_~tvOsDaU6fDSBo<Laim?>(3YzlgpSL@hrwmBi8nNE!zLJf(l^1gQSv^qH
mi9^3hdO^iY|NqU$>bppJyYdIfcRfi{5%iq(*^1L%xBdr^VKIaN

diff --git a/articles/AMR_files/figure-html/mic_plots_mo_ab-2.png b/articles/AMR_files/figure-html/mic_plots_mo_ab-2.png
index 406ce91976f5710cfc400fc67a9523ca32b59ad9..b75c53820c3505251c430d6ae28c2dbc3df8e180 100644
GIT binary patch
literal 51436
zcmeFZcT`kq^d*V`ZF3tD1Zfp8pdcV1*{nqnBuZ8hkc{LEtzrhG1j!UAL9$5BMvz!Y
zC^Cp-iy%3}?5o}HH-Egh*36qXGrzaiu<TZ#>ejvAcfvmV?DIWRxO{QbdY1JJ3=Eq{
zm(D3NFfc|iFsyp_#~S?P<GuFR_-oA#nTzM}H@;@i=DFjqb(WXZtr-~D9uWVn@aY-V
zXJGh?fpqS)vP0-#n{%k#<WkA#bVZ<qaKjhQvMWb8JG(8r6?xWf&Anoy$a9#}=g|4M
zs)6E^GhZ3cA3pD=`TCjjIgWEYIUU8_{02{%1=6Ocr%S}AM4dxiL(1QlOgKsIogcj_
zK+h|rrxiMnG`URKyB2OO-hJTLP2qp*)o*66`uCe1@*EK>{`n^JuN9mA`R5zPb+`Wc
zr^m5Nr~ZAp>FJd$|NQvchHcgV{F6cRnFp~1#8=~;)hB-ci{ag_|K3ZpoV1K!-TVN5
z+-5o-SUylX%2)jRVl&)FwhlA=>2^6*NnKr?%%LY6`08-@ZoS{XUD(OD>i^-s?iW90
zKA`vXDW7p$u5I?me;!pq_P^zh*)Ev1Wcq*XeD<-ky0<cflhoAI6f9uq?KC}{^rJq3
zn}_GJnzpU~UiXFDcwwcXl$iZ+-3H$Lh&7v7Uf(Pw$16nM8P4w7Tp1!P&lA9Xi*?o7
z&FSCqWb9|W-ACFBoajkebvf3C+ICI)j~_j{;<<ij-Rmm{B$j5nh>y7(F3xP_?s$87
zaaxm4KJ+A?d`YHhQ;n!yRj7ztOO6&#l5UX>Z~o6a!NL~F{Q6~w_U$_@+-iB(Y4S(3
z<9P4k0|#D31l-^&zIt1Pb#ruV>{<z~W5+7#feJ&dIZ7${V~?9N%@SmIa?ahpeLJ@|
z*kt18$1U^+>~0Enc6MJ}iR)|SD9TGr`mqQaM_Kihsi%0(P15O0^CJy2qg@$#C7YS`
z&I~jC-U6ngKh9n_WU7&A8W$}SAWarBYmOqW?X^dk7J2r;!d`)!U;Zpunxj8{_)tMr
z^~K!OP=dVV>?e!)3tlWnqu)OtQGXlNA+&1E#_GmYJx<b-UB}kv{@L02@j-o(rZDb=
z|C(4<V&YN%Ls#}kWkzZgIAsK%FxpRwzw+w-)L_&0n%g82DPB2_%yF2XU(v|O=&_gA
zq;Z#xsG#ed&8EHLS08L)-`uEs=FFYKneQ8Hf7BhmmZwnMxck+YFUzh6ZKej*KY#wr
zI4Df4P|l_)J4vhVhr>C2dy~}DxUS{Jt0Wk|zr&b2TD)gfW=nx{7V(_e&DExTLb$%|
zKn-V9=I@2bp5a@S`|xV|E#KsVsb-H-LlO($jeA^|Ll4E)#<f@!Nn9IKC)1L(6E?Gn
z?$8J=e{oDH!?^y;WyzN4E3Ymb(auee3Jq$6C2btYHmZ$LZ`mR-p^)62#!(q6qSTgW
zza`k^h1IrqVPf{Q5_Wgi8{M60PLsykZnHh7pHmg90yf&xY4v1InT1S|+u!e*=Z_XY
zdrtkwiRk-QXrB{boTwFbo=U{>y~Jw=2Ak5Xa<t06e5uA$Dko_OCJgEMNW>Jn&Kq}r
z@RYuEsk*!Lx$VNtZIQ6c_`@ddps+>!r$@cp=f^uW^6bq1)@#kPPti!f)j64G818tX
zCCft1$thQhCm{54EKK#4cAlM4ncrdK`goPUsdO1-WxsHzABX9EK9X~X`uU1?Z(^5h
zoy&1wURu$j>%KS{%&-4=YN#dl>C=s{L489*IzE36R;QApBavW6k#x2B{^^m6R;lOh
z{_1^#CbWtprHaW~B67heNEdzfs@U0O;<IaNYSM3fUZ0{)HET{E)x4lT^Rtt9YK}PN
zIK{bzajGretZ!gY)?-!LV|7?ULi?gO>(?z(3lRq?E{db0qf@#^f3Mo(jnbC)d=1xa
zK74SWs6oZ<^XJZW8_)Mq+~aS2eymfJpcwr))4VMn4$@<6@$^cGH=CgQk_)Nk;})sJ
zb=$dSo*t+h`_p}SzGIS}QY2kQx|R&LRnu|Qf2gK8vzhf|1VV`n88MGotN#95MMJ_C
z?TNXzbZPPt)x_7IKAofJ72GV{cK-bNIJmXsbRL#PV&>ajVxc$&dIEH5h&kWV%udQ!
z?$)bIR2NvB>GB~*g;*4-KVjmXYRM|^sVSIlPe>`8lP1?CsPR&4GvWTyLv49#idBuY
zIHiNv692W3>uYSbUdgf2_u4DbZ1G{dMuxZk>=udc$vnKf%n+u#fB*i89|<WMQq)k9
zv{R#4-BM{u84*&mB^QNr;4ydd8w0w5fx((%8~DvzPntM?dA853Bv-%uMQ+av&6Qk~
z=unm#c9)-jPma%z6tNrkGu3&si6wSh+t{QRE>5+0JR17)<&x{%K&+}Gne?1%R?C!6
zP_VwaaPCLK1`*@mx4C02N5qqdu@pDa{xh!OJx9x497`zn+OyVlBxr{?tp06U+BjPW
zes64+W!6GVSU7i5V!G9uHuc5#z+xtRE3{$b&*8Q_rm|U?7e|dazi;2Z-FCP&=gdIX
zao45UKHAi`_ji+;oBsav`E#5_M<I>6N7%fgJ_b)gn_A26@~6#;e{%f7<TDSR{VYPJ
zyVJc4*LfRM1~Z05Ixih<NK8yzU_9vPzuvLi_u^Qa{B?R`>Z^=`YwuTviQ1MK=6!y`
zR6bxMO(wih+W!dS(0pr-HDTp~x2pdVv}o7zKcb>2EBh37(V#~Sm!R=2{os613j@7o
zmSxc)+1<4?OLf<1fwxgJI?`IdM4Y~BQ?H_C1UuI#aw||&R2*YI_KyW`-&TG8+@Ul@
z3HmCv*9z~eEc18k<(FA#IS*wp4Yt1jR8r!6EPH0ML3iGC+I@N2oi<e+d3M#5(bKO8
z^eVYFMj0jzmtS4<p_Tb_`}_GtRaD68>+3s8h{J()eKQ_RHE$C&$;0X(S|}jIe*gY`
z2NP3lVBl_gXVBH<>7r#bwk$Zf)?&Nm$4$gj`agU2*I`57vc7QV%gZw*Qpz#1%>LYJ
zmwm*?9=<rHsT$EV{rldt9elCCcPb5dZq460S(JVzPBEING3V^)r|t4rTT+UaoRSw2
z`Bh!}U%x&?*}&lB)al7I(f`V4SLsbF9e9dKZ<gQlzog*$RpAl|2rBwFZypj5sC9DS
zqG|I4*oLOCRFmss<o1M}ynRN%phDrnX14r74$|ezw0rBe*S$Qy!Jk)GC(V$WIdbMV
zdQjl-^|+wWcQ8+aiNd|!Ja>T0gs-^3dD^(IDokfSRxZTAOls`Q0c7;imWE{QyoqL$
z6m=;$wbq7RN2~u{wbrJ$f*Fw%H^;1(coFune)jPhro`bf&zYC|xwtOCRDFOqVv+9H
zL~SmO_fgeg0Xui@<RnRxNMFP3+v8I8N}Bg>`Dc4KZ>#>}$o~BwOuoJ!-=5#Oi<vpj
zkm5?4n|<XY`8Lywk44fYd(yhdZE@w4pySw0lRW3?VI;61^SmJ;B5@GAcJt=V<fvia
z0~gmjt&{Y8`<hY|RBFC;>(+Sfyv)gQxM)7PFH-Wt)7@JYD|~%@<25qko3kuZ;Gsn9
zv?!Q-k>UQ%V}&TP;U(AK>-6pW#g#1kjK03~%#bjui8_Aadx#-hzkR!7Nql9#e)%p=
zE-q<uylT=9|EH7sNG89m?rzmzm$CGZwjUhZlNs>v;UAxii#4Pk@YlK^u%2#72LNiG
z$}iloPgGQcz$V1<)9s~}7seUA{sDKLXYsKEUtc}QSgaK8xYd$rcKs<=Ryj|e?4~_J
zett@QjoRamt=YF#Llm}^g{N7wZN6AN?#(Q~CDl1++vO#!(GtEvYOYHpuZ0pW;jGbe
z_QB@*9P7R@Cao0?I%<lFHH=bHzgPal-PI?~C}x>!r*PazP))Ld?;XG3*8=BB8p2Zk
zfJ+A-zkX*jZ+<wB_ZEA2L%(|2$AI%sb`3-a=#*WIT0#<f-@I4c;Z4KJJAEN1jL5xI
z3VS*g2u__-*_&M|`Er;j<FU3*J0vTK@d|+)H}~OI5j@7xsHTpAfq`H_lNXcYJJqzd
z*5v$tddtxRE4-eaJ9n-h*(THNvaD<!ejSN?bAEnmC{0@Dl1ORk;`}rn=|C~|a+qIO
znCs~qrVYtq`wv*_)DRZ>v-4xpYI8a*Nu|(bHp10rsJXt`ZM;&Lh)hKG=LwJ@`}zAv
z54B_^*pIXu*T%?2>Ykk*Zc7?ErkVK~n4&pV?}05FfI1Ln2r^krdx7(VI>U44?)6rM
znRr!x@Z6vhExi*-$r(mO;TI4{$_xq$5_Oq*gBK)Znzj5GE9Y&15%;EbH0U_X?L4Ba
z09VSe>^#FJW>?{q?8QfTLQS-cN|Bo@=fQ(A<QsZ=^@xG_TQ>gQ8y;!S4{QuPdjSyY
z>^odz?S`63WB>ylm(lYDGvA+F_da^j)HD%GtzW6NG~&K&GPFnB!Sa%8x<!W$@Znd4
zAr2A|^!mTPTFe}2NTH}EUX4uB%-Z$W*m9hDTFhRDZ!5S5@=Tlffo#ex=K1uBZ@~pl
z&lfC=m1h`KvIv?rD`OF)FJD$&URta#a$jCqBCc1wu`-bFKvbr#>!iP0n91+E-C<GT
zaUne2tVPv%YVgY9+~h~L$MQZ>OAg5$0PiX1cdUB!_;JlxPq_;AEW)hAp=%SddXK%m
ztE@x+mJL05#lFo}?VS*S3ouy{o@%F65$vRn0MIbrXFdb9u|BjU%>eo|A0OY`P*%sC
z;sMo6Q}tt7+0_Z3{`e=9NEw_uKsBy=9f|k$Pj|RCObpa!07ns}20%3dPm^<h9H~#y
z)m@%zD6&BX6Myc}hH3r;&8*}jYRQR!D|CC<7gdRzt(9Xnhoq9>FnYsvVYEae$12);
zuLOaV;t)4-Cuy3t9j?=HNPl)(cV51FHHjC~0jy{j*<GVJWnSDujYNDRuw^8Wvo?UF
z3bG5J-B{f;JSe^?&5(}WA-v!PuWmBMZH@$(uL>xUoHr~Adp!ksawSB_Oc^<>$hAIM
zTN4N~gK#Xy4ZHer&3e4o=I6K50RM5SNgBqj*{P!T!!GZ2*Zy9RLoEBB@^Q7E{{0;T
z!((=7%YXm<-&PFsJcP+0I=_796C)RLe;%yCudjO#|L+UshsTQpE!X}N$`}s01^#cY
z$^W@sqTK)d3CXM5@+AJG1BqHb(AyJG9xY<s`&udXGEsZkRu=Akeo%h1eKwP4QJG2K
zjrt4|HP~+k@Ih;2cT1an$FT<=BT@M@*0^8y_H9emdD`05lB_L8ncpC4&{gd99Hn-b
zkCe`{<9`<=swAkEc3QrMt?(D$6Jy1l{KW($^980Xs9&~Ya=1;iLEAoT+qP}WZ*P)_
z?Iz%Z0W~}+J^ciwghh{tQUGEbSSLO8o?b!xvn^q>?*}lxqNw=mW-rGIwC>a0@wm&R
z4@Jg`qR^kyCy^nu&r<bK?v<65)krPRQY{~ld5d?`S&%c0%0<?jJmoo;$aG*j@PtLX
zcthbGZ*?oHw{z1YDahA^>r1vo#l-Z|7hMJ$`BL0^D}wZf<_BUaXC9BhnP_|{8#m78
zt{-z8rgkhZ*>k@@rPVudHLJsYS(~WJiE93zsyrUGMX-=r{EAg;`pbEX>XsJg3&?@E
zY27aOr7qZbnNlJc5iz?#qDHIFvd|?Kfe4mbTA|4~y(|(x->nK3vo{Zy%0uYeb@XZk
zP%kPLX`-ydqsgQ85|5&Ke0ZF2JS7F?W<1<jHAN>0+v_R53oHJPQSVPX!>Uj|AkR0x
zJ0`a7+^K(m(;fpjVg6hrf_J?E;{WyBZO)6J5B7Qa&hy>={>k!z%audkDPah{-2)ll
z-LkckEguZ`)Ws>Onx+|4uD3rqOAZXYrTr1L&xV5K53C3B^a^%6dXIen{KR^Qw?9x~
zelYC<d7wTaphM{J;lr~|QU|%XoF)ZKXo=z_!J(n1Y~uictIc$GQ~C-~!Isb)Q8rkE
z33!(LJ}{II2~GQp-t=cC-K!-AhK7A63siR->_@kQ7hjB8iq5`CO~k%5RB+Gc#2v??
za==Wini591*sx9Qx-MnD;}Wb-5KA33N_YT|-?lSX3EqSNtA1qiHbrNY^J==TDhTjI
zfj>EqM`4<8;VD|0Gcj>SMlf!C6N#W!(0tRt;0PC2@0ka7SGchz6!#?&Y6<EvdB7{7
zbI;xL?1tWErsNIh9=||#2kd`4IjIt;chf$R>D9F^&1ZZv$aJvT!z8DvCc|;2Yg-ks
z;-QI{PlhM@1qB_37tW6N(ZijG*Exj)oC}&ZDxfgfaya>k9Bh-YD5BD5U!Hl;VF%a{
zuN3ROnO&0gO%Z^J{N@K|0m}LqA0@n*%Rx|9i9Ex|Ch2k&u{n%L&m-0=u>zi6+gPs?
zONiLtL6=H^8P@@R$NTy10FcEWFia*Lg|5MpuDLA2=B&jJKhlvv0yqSXYR;iHFZFkM
z5%uMgPR%nURs%D3DL1X+zz84kL-%>FNdbj2nam^UZ_(p8&!l&gmpTg0$0~A4!fx;?
zE>1jAgO;`2B`}?hdix3}UVI2Rijl>89)?l+z{{~nIHhwCuu2i(o`aN9Fr{&+#AlCi
zm9*r=ix=t0cQp7};<?P)0cB(3cu;Ix2tuX6X;Kj=z}7`sS^3@Vy#fYL>+KLH+*&Hb
z#Dt?9+Kwo{jzG{O$RA=UVD;TLqCHkQe!;=<SaRnAu95t40?8LG7EIL0sE*|zn`sof
z<Wz-;9mKXAxaj>_o+7E*f`z79Jo1C1y-DE(LxplWo@3zA!SHY81Yf%2nBC=+`|dme
z<p?URxu4%68TA53?qfq)wQIceAf=J@`q$bh()I(Tt+ox?ZY?+U_0{#H*LfEu!=0H)
zF<6bx&C#cTwhTCe02CgG`R#wg_cp-U#iDSw_sozmH+T6-*3Ab>D}X+(X;QbbM*6+~
zS(3bs)y_TK`2|JP;b(+8SgXl#tka$FSAY<z^I$F?>`>Fm$K9(gqi#Mrv9wCW_J`kL
zWM$hWuu=1-0WV${>_2e8i(~tqJtkx_*(K9?;_JnvLN3|BYtCApZB*F1QHC~PT4B{X
zJZa<K123<?1v*N)ntFz;9It#B`SCKGZPP&G9`o$eTO?;98b-j2Tst<A<~VLZDKTr!
z{%YJ6&Y12xZ?}o%#3lFnmX1VE2N1j4EpKz^E)Ignph_+08#g2;WoMr(%K9*ZpAf_-
zL0sOux0)y{LD1HNa7d|*mG3hyGHVf{Ye_B5yy7rIe9qY=TsCs&uA|pQG6Kjel&#2F
zZO$VFE9NOeX2CmO&F?ankCms;?l5jR0TA5gGG3{(;ZGv}fW9lQJsLAHVFOTi78G-B
z$I_JW=TD#d-<I>#nq{@c0DCs{C6&y&4za?gi2Ec0Gl4hE+j5<TG8+6sLs_lz?JfZ&
z!Z11FfO0%*3~$_cS?tZ00Mt5wD=C2qIHD3Cwd<HhzrUK!S0IU%Rk8xbw}2F#Kv>l0
zJ6ii6R=Tim-MV<)qQY>;Ugjk2JX+r<;lmtps5n%mP@pIRP!Zm(ZB@$oqbB-EM_*;Y
z;Gn5180H@>St$f)B5q70!}tfF)um_|o`MNQtcA%`4>05el2|;}kzi^;OMr_eCoC>6
zb}TavW*vHZ<;_(uzUDMTAIYT|J%XMh$Wj&Xa-19-ukolXqBgGyqIN>u(YNFFZ5bJv
zTBL)?cdND#OQnQ_WuD!+hA3@r9bP%$-qjMX!cCb!>*_|$0>25OL7J?QWgZr*_aykk
zlDHJb+0+81AM8dhtJUg>;MrNghRqJ9VE?8@I(SLO)e(RHsEgaKsKxZ;t$z6rM25F+
zcDnT07d+a=1@b!1LxS2)1JRN#1-MPgtReX2d>r`tNmlv5<NI{`R>M?Hw-$F#>uL~{
zjrtM5b(O#8H|X!n!989zBMcNK{>}bt2&#~f4!8MZQEpuF;i2>ySe*kKnCD86NUM0#
zx3jT@nQ*mQ6fN$m*)M@mRUBAtc&bl$Zx)JNjUma!38f<HBj;ij5D!+zEbYNVJxfjk
z7u*ZD;Fj{)47?zhq@?66cckIcorXF(Vr#eTJu%)}Io{&#;*#H#X{K52F!UA}i~9xP
zJ*QkjE(lnDd-p6n7F_(?pdN^c7@Y#A(>@eoi|~fq;c1c}JB?~;YvoYoE$%J4_Wn*p
zLz>I1l~RsXkH_taANA!UD^<hOP8(JR>)Q=BUM~yar6l+O<@FdZgV&t#O2CF6N(GRX
zjB5B0$ftKlV&}fnk?d^O*`5H}Q9XAbkh@<-W+h4*8X7(47UsT(8-cQ)j4*pLx3M4l
zH*Et#nc|wqWFdR$(i_QTm)oLYv{9sq!*z|VA4FC)@74oaxfbUB@%|>smWh#$4!Xe6
zX8?{@&6Jds%)H9I-R^H;H|Jn}@bUePd)TW({DCXj)}S=ibs6=H3JrG6!j>@lbS_SJ
zs39vY4$G5BL|tV&(f`$B@hK8R-(b?XVK<z(MZ>g=0_+AB|2znwKN^#d?>G@ol>Qrb
zO!j|03$~=v))Xs+xUXF3;wTp)bgW}TD;!KUtKG@uT~PH%!E`vvhD5MLy>wPbKF6_c
zj}&+i^#@JUf*_=aQfI!ar)8eLyN2-xeYAu<%Q$6?HQ2hIxwe<*pV(9iHuN{>x^HrF
zNz%y3cbf_&X@Y`nvnoHPk?)Y&(S_@`d)Tjh$}Xit82h&E+gBCy8P#wUfH0Mu9%?UW
zX>Nb3|AcTLuvi}w<WK>Daetn$INQ*ts$6`??<%0pjuRJ1EXS{%`kTpx`%Ctv8lqE$
zAlDGAKZq3Nnr<_oNSPNe$wJJ@nR>u)u;hZxB-?!D^)s06n}xF`nNd@%8n`dRb<rwV
z*vk&032A~vc3T*|$cEmXLKpXXTB5q1*}O9wk8Yus;$;asc$cHs+_T#3LNc_Ujjre9
z5u3}I?G54VIVzb0YSo28G>{I-azuRm1`JEHLlLlZ`*!R1db@$DUcP*J;`aA*DDcaB
ztOh`*5Y(paya5_WPE&)-ZzxzbZPdJTgK1P7^mLGjnf3CQYR7Bkr3RkMOQyQHP1dV{
zvg80-!xj?=4xN+CDbVrDnbL>qlC@hj-hEl5#EXsO3p9McaOKLCeY(!4zu$iH@L{#z
zVWmXIJBWro{`~_TMMVT?Ak_e(s-WDUqyh|G0hq0Me|N2ac)0m7Wq*JIFs1S8X$E8~
z>1#7Gi3G)tVmQmKaJ)(^!>E=kp$PGoU>d<Ye|4R1V;Va-D`47q+@D8VL)UFKda}6`
zMF)Yaz|Uxq{Xn*&5Z*DPHQSlPaQ*d$kXftlFIxecE|ZlDoNQg^hq9!lr4?afww0hk
z=cZaL>XE;-Hh@aVtkgIvOBS{1kHAf7Y#<{lU?EL<%KQ=gWs)_gB(@JwC8k4#OdBH*
zZVvPDT|qH{0M(ps^c)<(K9aPY+@$m$PIIu>Nl>c4*t+<`N-_P2Y_yE1sHj^5HPP`p
z1-TqXKfk?43+YR}kD)AY;mk|4e%irNaC7NBCD<Ka%{#}gsrkVWKTjd-Ob+Lb5VR4P
zk<4OV0Rbhr7(w17W<KYV8*93FXuT-UNa1{P#%^_3G7|l(j}NwRi-~FC{vJJgMBw10
zDG8^EpR<eseStI5%{}&ie?VrZ!$`?ddo1$#y1O3l7n7oT@i{F{HgNX13@|e>84e|Y
zwLw=-gH(e?z^bWn;7w;XVKgQe@h_(C4KjJr(a}ce!IGn5!=0pISduM6jj3nJHptU!
zv=bZ0tq&YNTuRFx>@Qig*BW#4A&~L)oiRZT?nWWP7MbR;tZ=Z%iJ%7l_2lKx5Ap1k
zUc&9SGQ=#uoqFd+<iF#YNa9z`5R&PQP5KZ2)dHk>mG|(w&P}8?6TD7N^A6hnd!9Re
z_}V$oz{T{R^6B&(2K(A6Oj9@+cDXkDc9%y(8>(x%%c~1sW#b}abIrF;vU2h9eKpL}
zEAdtz+Uq=+Dqf<v8<f4_=RZ-kp%%F-`S$Hwt4RU$rxMuRH>l1&hus24-(ZtMZV}hL
z2%0&mt2}_$0@cA_S{q85Bs<`SvJ5HoU^G6(yUtH>fjb`&@!2cE^5)$8Ra?Zb4e_eP
zD_1k7whp055^AD*h2_<?)ZX*psh)Dw<~xqNu<2Ic7C}4J))XYK_2S}G9auFF{dKKf
znm~n=l0N$i0!zf!u!VD;<nWvbA8yVtNp>4N^!|EDUrq}eJF8yk%uLkECoLY758v3>
z0LLCkTP=f9Nf!4}LC}QLi`!+hr7el20hNEC6n)HkP}(!50byZgpBum^Ml7Q5)^03i
z)_kJn``ZKxqO->67QhYNK0>>&evbem5F!lC4mt3{wzF7@MV<>g*w}_LuC;@(T$~aB
z^fR%G<Ta#*XPA$e(-MP3?FRePv_WgOyUm-K7}TI!a#!c9+YUQ%cpMFBk)x-=frz3>
zHLQTr4RD(cJ1lPAj0$)7zK*o4xv`=axatHrbCM#m<3#s!`Ca`kkwoMJ+UvH~#e&sm
z-_|3okFz;-T}DnW!$4K~twF|GC!qcFoYH<@>I3=`C&7LT$luOKR8^e~MpOM9(PA3U
zaFFZ(JRNs#ai|OyOfu6=;{`dEjC!p;PU)g`Z^ae(R)@7}vQ|@W$h(FcP9Pu(G74^e
z-LLJ~<G&*1eOr6OO1tw$Pw#vVX4=WE>K7G}c69M@e|A3DQ`!_koZ-pl&E^~ReAHV2
zUdl2$Bnk;u&}A>BgKs=kg(Z>o4)6Q+T9n94iL0NXP=jUq`=!0nLL#;vD(dnUOEjDA
z^N=D~R*(w$1~i$Pudgmn0%-pDd+lBYRM7;or4NU_tl0eu?QX3NOgze{*H9hxOcxBP
zZ#(M8{Au%pkIhwOWp9u1<jp;L@+56(N#PD5SUJzQuK)PY1^G|li#~2U3sYG+OvHV2
zv$eUN;|M{U8>wLF6V2?`F}Q<7O1$?IT^H+mU!IM2pK6QT-RV-3yaf|v_>AAPXA$7J
z?)lxD8>dPMqOpl~p*l$QXtcUegeR61EiYP-L`uiU={Zh1)OUZc8rL=y<*kyx7D^2}
zv`$s3b@MDKG+QPwlxUf#7F5E-3G!S6nWXxr4dS=TQUqYSq)F6Tuo&tl3&Xi|o;GyL
z+*+JyHj+gf&miZi&|LsSjwr=E#m6RIL{V_##*M@VjYr!!6In|8irQ;yY7#$P$6g?Y
zz4B=I*3`ZjeNXe3M~1Uc0|!sQjS>i+2T-@cxxUj9VO^5Uk-#dav%^M<pPxT*e0roq
zs-J0fPkBIACUse}N2IW@{XnF55O28dHpdtK*}I!29m5ef{BJY!-+V|d8j6<oR|MU{
z+2cp%UqM^<%u2GMXnDywIwr=r>8-vKItMVggD3;8n!(uOkl330n8MF{miH(EcAUUs
zqQ_&5yg%2{vDBMY(AVgxX!l8AfVF=^-koB7Ln(mR7f}&{%OFKPHh8YK+3mn}>I0vQ
zGC4W>`(uy!0|yQysoz~Pj*%`?ah`AqFU?ih#vh)Rk3@4flgcgj{Sk6GXyxI_JYcc}
z)26ql=iPh!)s$0joS&pO$XoYS)uTb-@9Wzs_?#-R)nUmrkqDtJjOyKocw3iKCl3k<
zsghUnJhU&o-BsKp-R&rOW|9BHcsUuFz9)X?FugP9OF%3dkl>F<>~1rW1QU*Zjzf78
ze>L?b&{7QSdS3SAP;*B4+{<qS_4Y;YTd6fIxbLWnu8?`_Ei(#53jsY~GWd3m7NJxi
zHshj!K@5>U8-n|+?Z9q0P4?te_X6CoO|Xc!P>MXXudp0AY?)||Xr?+l)>Dsmr5CG+
zB2n624n65>(wn5|w(H%|{%i*xa$|~aQN)nM_V<X_qs@KT*WnlI^eYB`!ht;}3c-M-
zShLZC9&WU~pWf&EL2X-OGIbo}L2+~8^3wcb<{luts8HrX%`EdxaU+nJ{CJ^h{uMWq
z1lKquD5wI0V8W@g)op%AC&lOGe&lQIT$`^^q~|->*;9a;85ak+ue`h`n9$T9yFBhl
zo3!irdhMc?-4x-iI|Km%-&xa|kg%*PjSK!f6LffwbV&ZuC8oy<?V08Q3SG0`?rzC<
z3kRQBt%p);&^FJ-&7sO6@6R;Kkf{6{MdnD2)Ek}|Y^L|k51~Mvn;3Y7$Z`a@2092;
zQ~~~hfv<tV<6r_r&!B-Xy!5heR^@{a0=_C)=4~EkZJhcnCvH7U@Vj(++l@YI_V{4=
z_~v!)SnB2TD!<%Fx(1$Q(-&rcgp-rAduHsp2Ty-CuJ^flvocHR5X>wylggXWCBju$
z=CFEC+9`1vmF~$W#dlnK9bVLnxGgwbDshnP(=YQY&FDy`T2IS5kkA7S?%?eM(`%gJ
zj9Pr$HW`giZP&?qCw!MQE*e%9p)bLcj^;I_Lec_;RS5sl>FBpVV+ywAZOlemju7~a
z%F)Pdo?BSu>g}elJ`+U^W;(mpGGb{5mDAt4x^G3>mS&cAIkq+-`G=>~w|9Sg%%cGg
zw9iR8ZPB$k>Wt@lT4qY{+rDvG<Hp-2nQWsA(8;LoZwh<5Tg)zT7q8A>mTo|~e!ZQy
zmsdSxGB5GDhq3QKWgxDCONFg_Ws4RamiiosUTsS3YbSJlR#h2noP5GCZPo=h=9yS_
zP>WY4q0AwYrGIE>B0{cA+2Q49zP{Hp#*eQ0?r7qylc<p)t$4XWk-9p77j^D)4y03_
z21)y)fag}CVf!jsZKbLxwpms^(j<|Q$ZKieBw88}W7KN04taryn1+0a0C0*`^Ue!1
zqjM+%lbWdEBP#YUHOq9@dEfCa2bG>QbZznmy5nFDqe89N!)-01m%S`-Yc4PL{c*zJ
zy+hTJf@jfAa9#s5XRi5Gqb`?QNwV^GF5x2`MIH3=d#7}(9jmJMO1b4Xbi7<}=#PAW
zbY>E9=n_iJyK6QP`Xhg68Q?FKwMBBRkDOv_2UK#)n5ccn6*f(NcnHeD*Q&7Yc88!J
zW!qoaC=a$vryh{dD@+=K%0vMTH>Qb&zJRAEJFrSgLkm?3e))#QIVR8Np8NVn!s{-T
zk&GMp_T;|l0{Blg-cRVKL@mF02##kWf{8*3LoM_{x2vvBnN)*vW&$1*4?IawI3Vo`
z446|ivv{KDjxgvuRf9*W#7%12QI=#@{lSZuEFoKJNIAbaGa8!8CS7nvxAoBk?1eGk
z8z{nr%MjI%)zEaPmwNT6<w1RdAdQ_W-1UZAT5rcu)#;8hL_`#)nlKX;EUQf^XTa-o
zkG9crV0Ib(w*YPsiQ>d{l}?v@7P!J{YA_1MBdI$+g?waqbW&|xqB@YVE9j5HXS$(|
zFT$aeQM?rGO3+ItEgc^UJQp4J4yKi|2b8OPYpx8BCXLOvA2o&LDIv&a*wR&1UMrkN
z^M6C|Zb?dCfIQJ}?I2nisl(TD$I7@@75Z#Ako;k=q|(}#dd(j4Tm}ZF^?(dU&FOI&
zZ))R|Vj-qVduFk&J;yp0s=+5K!wB-D4&pS|;qs~~Rb;P3%H3n4OqW2z2rAh?#Yo61
zIx?w}^NE5X2`F!8CHp)dm+XF(H&UosT3Y%TO<WZGL_7q0%#F0d%*<@uQCL9bcwqRl
zR9m1a;O^Qj#@%0h-KI-EeR>U#Ax9xcnZSzOT6Woo3pAgVBhOjQ{Jic3?&X@-RlTQ>
z7T#KP!P;JJ8EzgRpFj>0rz``>s=BsaE0v&jhozjKX%!F(yBz!CsQR`EsP#r@K=#&)
zk_Qfa4Ayo=4|mN_Nm@Y)<B!KRyBVL@V*SWg#vJoU-v8C}bmV}K_)FY^OqqHVpA-r+
zl-$2zV$l%MDMCX@rfhZ_eIckbysHJlUaMs7p0sO_WSP`|nO=BXYYurSt*;4<=z{i-
zg{JNKO>>D}5NB%AbB{|=)6&uejq47PMC|w6v+6sep%FaEIAD@eu&c&#aa4RMCdB-R
zg0AOvpmF%&3lKn2_}duUn)Ac|!OLHQENiMnpGsd-Es&XoS>A}t8-e5xKT4kWh5b4C
zqn=wq7yEiDlJAcY)R21~e7w%VOyc6_ui3aqsKOLAa;Ste%{U+^$jF;j<OZ1XJ>WLD
z50r|d0j1*#i39bAAxa8KYS#6SYZAOTx-3stFzLEn2PvnA&ajwR3-Lr?h2I$yq=?5N
ztGL7Qe%oY?j52yYdIR1BT@H~W;wE@Wi(LB=^C2H^Zz0ghRrT^Hcb{@tqlq~yp$2?c
zPyO_0yO|f9u|2i9B2=W-qyyHH=n537n5;()6Ehs*)h*0dN0yrmMMuvXB-;4bi|j!&
zGY%<N+o=ArNc<lY<iJ=~qU4bUdZ_OJ`mMyB$)@NOSWWsiG+chu=TaSU`T{xU`(y5v
zncSx6*jCljYbc1%ETkB;#0);%TSqh>h^O1FCwt+-{U$I1IiN6-tTS$X{Xn0QaGr9I
zU?)^RB2E*p!QB-HhEA)4vz`S3A%|3&*jH)>SEQxx5i<Q_H?(hhXb_+Xog6Vjc++v$
zh3dD*RszW=6oggc)zl9*12vy7@uQTnPIeR58=GbsZE(XCa;BJ~`PT2EpM?l<r=hg;
zV?M4vS3<svEXb;qW+118)_y`8sH3~yzPyZV0Z~yLDMcf|LqZ6jv{V?oki7Wly#^70
ztlgHfnHC-4eS8}}!;wunf<V=-HVYw>Z-kAY74xNGwv}i^rO@36Q%fpf*>czve4vCc
znw3P2x1E{UC}SQ$bmf#LnsCEKQsfHYfis0X(++cU^9^sVpB?WFu?R-F6o}SOAo>Db
zRyj}kL{hpCG1EZN)<&LPo08g;Yin|;&uzNRK1t7RxRu$e;=L(^lq|LI%Mk3xuGdA#
zuQiX;Kt4fJC`cO*_w&1nlAbSx>N@S*mYltQey$aKz;*4r^A-}Y@ITD~p9qqYYu~>2
zbSc+J@%6zTdrw+ktF}IO{(KOjRUSN8cTN|3bEYs89L~((t2f)KJ-1TPj!Rs`hE6zo
zqtATR4BF%nlVy|zEEBCLt>`&p{vJZbfvA-3yGVQ%T+tRQ&z{m?ZewFpeGx2%^<>xV
z=QD2zEscoFjAgQ9Yia8>ABcT>MD4)b(3a}z>O2}+k)02erkl4lTZDjF?H#a?fkH7^
zGDe<aK4gFJ#HvwK3h;ND+!bc92gJgh)=NZ}au)STBE=mvSA<@-{ORJ)yPtEvXYJ<$
zgF$h1@V0Pratbdwx&v%l>YSzfihdu%l^TkdMuwoH6+m~MvN(xqMt<ZG6!}*lm7QAO
zHJ*?D3T?!PO>EbggE`h*bhutkcd|L4!GR+P9J!~9dET~a1dj6Y@g=m~W`;&8f&^lq
zS=pz(40_4T2MxTD1-He;7NSM)$6APko1j}sf$#-n@qwr*z$~ZUAj1=;jc-<l*#l-d
z;qDiQ!IJoaX;X+0y%#PyKPX`GRl*(g=EQ6tg^<+q`dK&cq@q4S&C5+v^kNYt3L{F1
z535K`E@vUwJX-vOIk=uNO(o^BoSu1v;mKUvMhft(5gHwmOdIACekQuI-1^ExeUQ@f
z+!<2<kYjW8&l=Mb<M4i>k5ry9l17zOf|=Z%>f~A5RjR%66&jYmwD)>md>Yx7kx`)y
z0a9HhK3Tg+8=~N)8o4%cL>s7x4>?=~l-TKctE?2L?mIsJd|ZZfkpX0%3mxY|co$5c
zhQhYsN6)UCowPeopZJa2#{!6~@IkZhB}M|evy{D@W#iRShSSa|9HLqr$9pq&Nls|x
z^X-Vt&q{myc7gVZ9bH(J-u;2F6GAh-?^ycPYU((=Lc_g;keO#()jN&|kV;$a0JUEJ
zu7ec``_A6Kg`hPn+0GLw-uHL17)BwA7N<0;8P7<ezXIKfE7MWrdP3zv`(BFX#H}w4
z*gl3vo{Dvr4krPWf}p-5M1whi1_XZ&HZ2C~yBM%N2B;+5EY?+y9HoJI4R%|c3j#e8
z2y|mzD<|yOX0X>-DiY5fKorDr=)Xa$#gCPWK|$Y(Sia6_-spOjN0o&*U~<C32LzT?
zQ|^%T5fQQSolVEo;0w@H_&8%c-}UcZU0xqMkApB0#(uvob3?mPm^Qmm>0t*c03UJ&
zKL{Rv$UKe%561wu$Dq4!0MKB7{$7sv;mraO$d@50>TYOf{A@9V1b`VJ)u<&i=`gJD
z5z-F;wGc=c0fHb>uj+I$X-F0%z9*79arbB>#sKNX0Kynx3P94(2VtS*62VZeYs@mt
z@kCE`jh8+QsW+!DTmWs^<9J}MBS*^MBPB$?=BRF=__s+d0MZL&N6ft34KGP^MGba0
zbJO&0&us~yI{_d05!?h2W&F6+s>CO%&k`R-a~9?0<UHd`xjhQGCx0#GKhT%bCl(8=
z^mWXj7y=FlB-oV9wsz4iWXHx9){Ie>7fLSi!)?|#4lgiQM@k}<hj=W8BfH239)E8$
z+}flJrt92=3&Eg4f<Yh!;aS@t9XZ(1@Pa}e2kT0G?23<fl<Wie{x&`d=?*i|NC<-1
zHW+&rf=>=1-VCJ)(d<UQ*^Fqn!<*A0(!RRpC<sKHb-Xv0L&0W3g=Cg%GjM~zL113x
zu(*{N0??({do=D0U~8Dh6p#j`DAMHuA@yRCzQ^Yl^G9SO7&YC*^PZdciob^Kdy4F5
zc&A&B44(A)5Bs6!o{T>Zc;tg0AcR5awVyx^BywTvKH?&~2x*$fhGf|we$|{1;dX}u
z6<-;cvXF}o9X?FLhN*%W`oOjsFa738h|m6b0>3$Q<cKsuSrF4k!<HUTXwUYY<sgj|
zEf*p>ePAU9!oGR^el;-zwBh?Mo7>voQV7o^l!hpmBgBTY543jTg$y|gtLAv`vHUw2
z#=~LoAERUMH~z;&*?%HY8*$sj*Z(IoY*+TTu2`r)8CGTatDFGtL?21vCdYUy14H|z
zQ@W?npae0#-hQ_`bAXoBBh+B}PrsrMjBR-w2TJ7#!8F;-O&CF6kKNyK@$1*G1mDMj
zX|A)VUDjEjJCuxWYzhbhG`qsY9r;^3BLcstZD(3Z3G3_>8D?O}9<As2{Y?KaM)3Z5
z<Nu85zW*GThAQ~fbu^x6&`_iU(=Q_ViUOmfU-}Avzec9a1^0U9haQw67(<~DeZ~4j
zb){c=*YaaoaF`z}wFq8?_}a1MGuMhRGWq3R38(J4MTlME2<2)inUm0e-QUdiL)bT;
z5Uw!upF{MYYl8-tC{Br)Kolfu;Bd`;Wg_BYpGm2tQy0&jBP5T;(9IK4TY^CVq$sPX
zs6+gLJj=oeG81q`!NXu#wMt?^v-Vd<VuXuG!gXi~5$Y2FZld!w3C{c$xL}AcZtYGc
zJfHaDk^CI9#s(D91lp$Y*FWArf}#T6zGLgwNQhqu!4}%?&pG5MlG;$fHH?BG^@nZ>
z@fxk0EnsV*XFY>}LVLLNz>b|eqapJ{h#5Ge`|l8@mfvH!m7;;an!``|h&E!g;nS}O
zwwxruFLcvUFcZ@Uq#Ce&X5Kq~-xtH<2Su_go70lVXKh2DKYtF6U4*0v%FAVM2j(J4
zHOR4MkN^Dl#Q}J+6U16%t8EpiF?Hs_c<v(+%ytLk`UK^;{Ukz;;&p}S(c$aV=oQml
z`!;f*-AEKJ97fP;=S;;$MO~WHB{wnI*NEabC`c?`I)SzZ+d!teA`P;L*_nplxVUuw
zLa$5FHmndbz<m*x9csf3K^*=^)fI~ak6*a^otVF9wmZqXAa~cSH6;TnLdWpUCQ4ZL
zpEH9Sw@}4=Rxns{Pp!KJO&IcyHiCd6svGdbNm^!TFmUwnRCbmMbPihOEO15O*)t$f
zqRb<EBy8h+y!ecX^i+&zwYxi$$A=xv%me=Ur{5nH&sMuk*8St<xpNpKnC<w$b|LkX
zp<(CkuuF<Gi#_<(m&`WI@c8xXvU=t3&7zqTVPTc8S20*|)6`MBwmZ>}5p7T7ny5#>
zH(hCAVj=^{tA?J`4y0$!`e4H0^GD61WEIh6^$UNFa=s*;diBM6c>b)>NWre*<Ca?=
z8r7F(5_zF;N6YGTz6*dVH7GOdPy%7xvXL%yR30R!F9)VP=7xANBcgcut!TBZ7fSfo
zKz)+a2ww^9CkE9Z5jub2LfP(DKzR4XSa&F4(J{Q~@26B%;(C!$ArR@4m_Z^bq7a1u
z?*r?V(f3Kqe|YlUcmKF^zogjER^MQj;Xf^X(SKIBFXvt>zFfJI!9z1u_Eg?;D!Q3!
zAmdKh^jCYNy})<8B%+72NVAu;8w}(aDC17q=rD|W;rzvm^R0c5_WAm5{h=T6r|-3Q
zp4XmsW~|W+UC#Y(yGF|)e`N5zq2WIM3x%;Q3^`4cg<Xpq8Gc4L){}qAN;W_e4|SJI
zb+Z*%31VuPgH6QhlVJ$38Hr2`Ywa?B_sb9&7!+i8CJ<|e60wfZ^kagA7)jW7%#<)N
z4BJSP4JrbCP24d@iHQ)Xnb4AlIO?Cbe^y4Az@u@iCMp04P5}EHlut13xLv}A>Dp=-
z=n2~&%D*I2gw!3Jv<|xZgdz%k3DXA7e=c(>RngS=6gyODxX?2WXh|%7;rL880C1k7
z1*?<}LQ_U46~o<^9Oc49RiFTxY}SSFfEY&cWIy@Om3Am02o9C*J`h{WY+1xhOiv*2
zBnAl>8Ux>nLSIYNAi)2xVepQ@SN?qvcU!zq8zZMsTV}mm^uwSPAv4~K>F+ZSi7|WP
zYbp1!+@^n_!>O-l{-;i$|NcmZ|Mlrl>+qJAf3*PrLo@XM+n(V6+%>WE|J^Y1+j3Lh
z^vQ%DP8Q!N5&@eI*pEhhWBmU5u)|up13o1(I-=3%Rb%Q?w{a`^7^oZj_{2mXtQ<?N
zd1ZHnJ>#f=MOaDAgz}lSmn8lQeC->$<HH{N`^*N^@dppq-SoKL_+-<JTanJV)3GTX
z`t<xTdr^}~uYk!iA?#;R_+tg3JchFS&)*>Zf6fmc-q&FIX`rO5t4rV3enL80u_W_$
z>>-O#Lx1V$`}J?4;}am~eIgT0o<8w<MTdQT+E`9na&lFps*JXFvx)Yq5KqN8zuY^8
zBdSY5j0~!Op0c=!LjqpguCox0RxHg<yE1{&jK=wss~WvNi2GjA^iBCxH-ES$w{Y}I
zsHE#$UGBYO7feRl)vwa`Hy*_+4iv0+lGlWgZ{N_$H;&tJK}@|77i2+oONvvLc6N5&
z#WT@$@^e4<7z~J$ah3z8TYt=-n>KA~GEPV$a~wN%%*fn4`Sy8D?<u>64R6hdhKAhk
zC%p8hhN0DXSyNNfi@TXWomX9vzO7L<Jv}`tDPlT3H&>KNpWzM5iir!fni~A7{X}z1
zi&(O$!0-<J&%D(DIL*N{8jVHu;|t@SXX%P@4)S+de!8kja8twrcy%E;;Ee7m?-F3M
zIUlV^(mXw41R~uteZ#xn%9V1z_HxG=hF`(;)V|ffi?ILmgZ~`s@iLOes34)#R}h>z
zC7o2>cyq$c<X>AmdR2jLCB$qStvK~V>J3ew;a$iDquJ0pmEMNjr*b7VG0M$lByz6)
z6Qv>-N3RXdHtB+c&*~=QZJ8MuR5(4_?<~-jd<;j&Xc__UxstWi0@!^--u}K!7g;wp
z)lrf7z;vq2i$$v4*@ZK2-}bsuq&0oojJ4M=oKjK$rIumvIP=HvlVA?rWB7&R|9}01
z6Lq;B#>N&FR4CBKDg>xEYobUX!CcS+Bcs77%%tPcso}FYWZ@o?keq9rG_M8b-;u7_
zp;1XD$P|XmtD4FT5>WDLK`fsjp%bS}b2LR{Xl`z%<@}z!)=oRmSUov8$t++X+dh?<
zr4QPgN?DqS)xv=;^!j2$$?+go>k2+bl|P6p{_IV~>p=$V0%;X_yHkOW3R+$#q#YF^
zetk;tlIE{Z{*U!Hy&_|&{1GC%XT(%KRd;DD0MwckU}MGBi>$s9Q;i3r72{|<zusml
zw7S~jCn`ESQ*?@iQL|N|$q|Z!4+NIy-IquF%6l^QE<e#-*pEYNRAQ;<>G4X<mdeL#
zjFheJwmBcCT!!LOo6euPo7=r(a;QZZK}|66{;~S9@kAV~K^JHGYFia%^Z9<bm}bD_
zrRbc;jH#b7H(()MkSpBqVtU~Dd6nb`$CMzndaH7}XiIuV#?BfZh7jQu6U&VVn^vf%
z4H17KrFBOhAa(+EZVyC1cA!3kEnK?xh@fLeYlgsB1`J<TabH{aGr7MVuRc|b<EVIP
z*~?s4d_Ppom0F{M$8NE%6n~W<bTd3jCR*`uls1Eq#mfF=KBtNP`n~k}1hu76_GLcE
zdaW;u4W)wv+y}{9sa=a%R<RvoI?kVI8L&pczirDAvU>cDlj@Q_HCvIxdXsVaG~1rD
z*y*gkV3D%9<GKuufvc;v-+bIGb`y6WB<^UngdwnGK@Ycvymo*40=jmXq*|OZc;l2E
zDlzQ)`^i&NQ)l`*L*zim)}j-|QHsG4#CDCY$Bo+rg@j^si`+=$-rnA$8r^4U_1{}b
zYNq`;X|1iH#Ybo#UzlLn$Vih`kfW-ReLVT^81CSZkhJuOj<vrt;7^eSp1Jv=<#~=$
zLL=NIO&AY@(3X|`Ji#K?2IMIB<t4#nqB+5EZTv5f;XBX?+{B3~ByyWwOHy$?@e2mi
zy{oH9C@m-$e4d{jr!0q}Xt5P<$35W8l5X9l9$hjAVXF5xGAkzV|1vNkZp}KmKSDx6
z#yhvTXLoh@OLLOOpdIRQ9dXtz>7o$T)6e&7?HBS23M7cR$#GoQ#mj6Yv&Ed)*!8b}
zg_UdN>2L#54ULvrRnp;5G7l+6AGAkQ9Jj!a*+KL@1u82mo4^meE!pebU43>-C3ptK
zBRMI*@X9GQvnwlXKyah)J8eU69Lj9r8FB8ISItrU9kzzk*8HykHvenj)Bp7;w_e+>
zc-Pz0&J<47ODRyM^9wNpj>IK6FIqUFs+EUF+&&-Tz5T&3S$l4RPD`cogi<sq?lEOM
z%?f|~_)$~+bMA1SV%$y0!~~OX)uxVkm{|=@eI#-K1B1z59&2djiJTk8Kbi10<ZcvN
zHL{mKy`_Xlqun_CE2n0Fb6qZBQnU}-h&f#FUjGMZ5X<nN4`&fT1A+u0AzJS0#t6r&
zYJDTBHgc}6u33E=_`iR`br}^kwZ7J}3!SZ{#nsi-&vMPHGN$I{l;gFwXvXB`=9*K}
zY)1uH8N#*#8l!K)oj9Zzr+l@=R75(uW=-WsP>r9B8_DRACbU>5sf~X>qW1AcycUl|
zwR5obLG9GYzN#0Wd(w}^-8gNU3JTCax4kXIB&onj|GGc@dbu)BKy5-qhYSF%&ZRsC
z)ArTXdzzs5P`%oMgyWe}`QlYUf$QDgPVgA<S2t<CMHkK1Ir*4II*6<{(Z)tbO%c;4
z`Yz|*I7<!lI(F=~++W(Mx4yni&#<#j7O}56kDiu{%;};4p%W*fFxcXmzUf`B3`2o$
zMn*>cuQfT87@2UyUm6P%{r&7Go67qY0$17h@Kb+q?_6<08-a;7l%pE2{GGtr$OA_h
z!^Dn>tUhHJ%0_HC!++%G{{g^su6|Yjix)49g^8I+hIa<9f2^1gYB7DjfhYSfj4qzZ
z2>ErQ-ZcTmNrv2<91-35=7zUOLEXqh<rZ$^A<9SmjqQJ(%lD2e8E+Qp3w>^t6UssK
z+N{6qdvFt(uF}46JnZPzHxbQkzasdXNAXS!u1)#@`sj1zpyOrzZpB&-k`ACxsN3AY
z05~kkrBNS4@PYNizluZ;Ia%B&2PTXir}H|(mKUhY6%a*@_3*ljXqsiHkh>p9EnL6g
z%^C!KKraRet!mE@59D!7dEbhOGP~B>B?Nv7l5%&$aT$NJy8L`kBBP&WIM|p<d>l{y
z&kq&h5_$c<k40#FxPD87+O1o+zW83;8`QDTE9{8le5{~+g-}2eC07+^fEF2DtM1pY
zyvs)1rt<(Y%o5)cAIWg&%Y_XQYC<=^Jkzp#&ltp~cL4&4ucI@(?z82@2Qy<dgC}p0
zU-;Os<k<MsY=R*dsfy6QK>ZPjIi3`&vmfqmkvwT@Ypb4ZdA$m9l#RQO|Ak&!S2534
z;zK-+G~QY<L4eA(5jWwp_cxZJe?S4AlOXY9<ZgkoJnlY*1Alsii)Q68>EZW;x9}th
z{)5x7vKPKH6*XSf|3yeEaC)w(Mhb;+d_({<cEsmU76%QXYYF8jMRzcKdB$h?3v^u8
z#mt68QYVpPVq&x$Iz4kRjw{wYTc%d1cr%E&DTW;pkq<BELbl(EUh(V?wW44`o8C1_
zNG*W)Zb(kX$?Ic8$Hc5FP|tgyF&tXDt^eFdMEu%n9E+fZ)9%7m_O`$iv0(w-cpXVk
zPcNWZ-LVL0t)2xI<RP3T9ZiNZ9CgX0s+wBB{rhK<53W9RuD&C634@>odh-I5S=lO4
zdg5R%J-N-8UQ1cB$tWWpxWAf4djXJ9kGImQtC4SnxK-Rsnn7AxV~=}rW|=%BN<y!b
za0i*vDulr%nvZc?kL#usBHhu96sR%zh9#XDJgcr1s7QFYVG2m;h<|(=aZcN*dWqTQ
z@|^KmLlf;{(V=z=lNBf4sK+PR-Th#QUo$X}S0;O0CPU(6XDSTIsu`gQ18QPdDOx$_
z$)xi|RnyErDYN+qWH)1?(^5m}wW3q*n5<3E{@pbZgdfgfW?;B+sV-JBqiT_W&UU8D
zgN9NGb|SwnR(A=mf*Io?J!gvRlzQJ1j@MEvnc{UDePSH&%TrHzDdWB&iWSS8pQLA1
zEzU#mn7W9Q43zVJr&Z%9xH1-CDyx`IYjcTH{xNa&_nlg(Y>8OAiKQ;J0BU2~;SSUx
zscl8>%m4MIC2`z?Sf*h`U~1JO#d$c#dnl`Daj2f>*I_0eEOAZ@7^iRa9>VbwH^b)L
zhfH+ojgmN_GXB4QNQn_gc?{PDam)_Ly9Zlf+S<s%V#KBc!`b^VeJDeYN=@ZspLw_T
zzkZifLQ-ZK#+_-SBLY<?53FUiJY`-mNm~F?{G#V0@q%!WwR;_|U6z=v^ImlOd6zv3
z(t_W2<{^9)WDI(4$xqqDgy2#njCi7R7L0Sl7IQl9?-_#(xgqycWicjd6nO&18;X`}
zljhlM>#iU!1wp!W#0S%2!^AgI?n_S71u;U`m1-2uy&_=YSdxV+w_%vAD*5JEcj@ex
zi;{1P7mJoAQ~IKwUq1432eG=UV`zl*d92#}1zI09M;FX61__#|x=p1~I7v9HFm4bK
zHsjFkw6`v^W9gZh$<{?k5|^jxKSt@bT&B|TZj2M_m&bUQ$xz%F_zOmM+(LYFM7=Za
z$?FpNnxFWP8Fr*Mk-;|~`B8`r!r2@#C!gjp`W=xR*X$*Zgo4V=Pu}JG_P7M@qe3g&
z&#li{>EsCoi3kp8Fhm(J{$T^}O-zwtN2n-e0@&<ZQ}vR5k>ihr^@^X$hKUOHScz=s
zlD!095)C&&Bqg#6635X8Y<5>SCA9J{VnQd`cAdiZJw5X3SiFi3%y9RyjW}G~EIIRS
z5GtWxF5}@f7-SeuA`_7_;T~1{N}Mv*9zySiY$^^Q2tclZsMR^64~96_#lCzJhxXWH
zqN{}IVQ#|mt5A80Pu3}!m|CNIA%ccMT8nvJbX?qF^gXGDQu9p^D(HKah|hLE7lFU=
zyUhGNAt<QtBQf;_=XQkRa1C3plEnO7;{f^r7`)KOQGx>~H@%@ZkVSvafAOGPupjON
z{c|o-Cgy#;M_~gw7*<egD&nRK+DCUx)`z8f7>}tF%>)nOM;P-sNFs_&>FBrZB`GGA
zotc@LSo|0uj*OBmBHhnIEkCSt{Py=+B8KaO_6riVH73SPo?<8~;}j^lZjcETP~!QT
zB<FC+OD)=9TLiG9F369S?U<)+_F4w3XI&-IcM}ClMMi^82nzie<Z$Rn%g-}u`ytU7
z!WP45g*T4%YMSS85>+a3QYSK@7$%%5uyW5=Z9e%|$4s%X>K-5A!l|9Q7OZz|z1rp`
zT6XT?-=!e8lGt=Sk?T~`K@vZt|3m^eDPYM8cAHGl?SUwiD4Gbp4#7Su#zhgIA1hBK
zV1i;?dKn*aev>r@kE67B`k>pYT9_SIic>~^jSM=YTSj-T#+a_Gi&y0Qh)>|Z@#!H^
zLPL)%2i6s@wMKV2HaeQ~0jt#|q`CBB4eH6Rr>qz{bR7N6ge$!TY1#^b7Ku!CUmAD(
z{^%eI%8?>>tc?I_bdFLa#y}Lx-YND)^KEp&3)(|j3GsZp6gK_)XhdBYY?6#qyI@X`
zR$a1dy#62*-k5`ukBA9OH6xGpIRH9x2jTukrlztWZwdAR`LPexc!kG`RiaZAV2>)S
zFh?oGwc}rX+)hA`E{9hlyMd<(Ajq28yrTI$Z#^PkLmwvwwPXd9V%D$&LjQHxUz)Hx
z3?c>~Gz&p&Sc*`WS*(F6`XGEdH#FOqaY2MFNo0jyL^2t2D0U`cuxS74j_MGhWQ!sb
zr6gG5p`#w=CMM;CpA#QMte#z)Z3E$T^aUbKMx4a4l}^xf2ZFp2Cyl}3yYKHY|3W$#
z=7&zbK2%<g5U0jB_hFdu`OPG{P|D!@wd}Iwg<7dar70L758F6S;)#W&5}7|ZI9Rl;
zZ*j!ko!pJ{Vbt}+0<`VaW3P`v+X%3T!ztfFdCOCuE*alskx{%dJ`zX*8N}Cpp~SuQ
z7JC=&i6MeMh2cPP9O|I{=GtipCteW54L++9U@4G*bI=&fPNukr;la|6EP5f1$A;Ny
zT!#3r72eA%Y_9c-AOoW?a>7P?t4GQoq`VuSRcJ7?0IT<mOg6dH$F?aDMHd#^sUB4t
zyMXE5FF2$D4ak_A&{{i|Yqr<)H0QnyUQ{(X8o?63_Y{>PB}Fh4m`iCLZG;M!AL=lo
z0q`5_R?A_VD^IUw^SirQ^bir3b%2jt`p>fein3c=zO4$vz|tDtW4;`k76rBjRiQyp
zIP(%I&TY0_mkY=33<jlDiE?AU)DHU5y3}PE-U2_4CIsHR){;;WYjXbH#AN!KCuU}5
zCN9YUs3tDvh*5}Y();LKYH1zDUX03T0SZQ3`o!2e$8oUUIP|Orc|(~AcEZHMDmP(L
z&>Er=fYyDuJvDwD|7Hs1qPC=)+4WxVgcO|oBLc;ME5SJ#U<`mrh*pq^_2H=1FF5|2
z2%Fxb<RZqe+Nn1-4G>x^B01-}wC;gh9>Vnb$VAT0_vXk4xou0u{aG4%E9w;{1tZ)S
zy4dYamnUMSc1jZH#Pot{VQL37NTQh_)Jmq%m==?;gS3=xS_CI(!ZKJj8^!iu7@R8D
zp!FP)Qwt|(u+0zRs09)k$sn)X)`vy#2+0bkj@ZryRwWvR{XmT)j<Y&RikI3|D%}zU
z;okr<FtD$w1<<Rg<~b7UuzmvY&)zg8+ws~MKniBuIl@x*h`TQ>wA$d1infl&wlQi|
ze(keW_FN<aDG-T8+qmz`0Uud1fq5N)UTJyWI|~<jc<pmdKjXZga1Mf(&1^PK@Geo)
z%E*r_w@Uoi4PQGk6d)(sm^L&tsQtc$Tq8n2`d`?4@2IM?ZcFr_R++^dK%i7iC?Fyr
zAi;zlP!U9OP!W-=M9Gv|T1NCBpc2(WRFX)}MuI3&a#G10f)XY7T*s>KeXslV=)SK<
z_a8lO4euD=RXAb)_FikQx#pU?wpHGLAHTC86!#@UT3fT=I-A0dPP0W(wVo;e;}!Q~
z3~ffNoV=pywwl>X!33fh<=zpQH%A^H$ivL)T>=K9;{?I6t0Yi*7|D!8Y6uaYAn+sT
zIXlRwVs~APTk|HSVb<}l$nI5VB^=Q*LX&-%LpE<NE(Wk8LYm3KG`Or&?b&@xMf5Ab
zyV=><5geg^0+~I=lhZ~|1F_*#u_H)RYwVmrx86J^iz-A!pRX;yOV!zz@)&t){rh((
zH$JTPj#vWOPojXzn*$ogPR*0+FPg)%Z3CK02zmIk0{U^d&;<N3d)X;E;jg$oo>zYl
z8UA3p_1w45NZ)JWjY**t!~%sLO8o@2!pGriQc75hMF{0l;mF9y96p(CkQEmq6bDj9
zh*)WdbKi95$e}zk{lX^-hc6f2)C`#A`SCZlC<>w--+cp63P3VlkG@*LW%>RG{g}yU
z9FPD|c>Cu9q7^lDbenEOr}w@eYul&r9xejvLcS&F1qPn`3eJ<tFQlDGq0z}cg-JVB
z6H7&enWBGa_wL<bfk{H;FnQThF^jE!1K}C_!X10u!BPQ8y3Cp%%j0D&({yWhFXm=j
zvG4LBtP+Qm1pLZleGyZ9gkc<@AlfpE)*tG$Pq~a5jU1LBy6|wsOMR-SFvW5B2$6XZ
znAMY*?dXngfH$W+ngyI{yH(i6S*3%`#>DaD%Cm15qzrGnbM4VTT7Z@SaSZg7SwGi8
zxkV71s?{8YiSGcC^Pa}U6oTEx8cMH)EciW8YTA1-u;FAcO$riGw=c7+5P;Es#jw%{
z@z=wsLGJ=Fwcz*l8kYyzzTfDKWMGE`eF|cA04V8^wnu$w4dWuzBQYQKUM{wp9$ah=
ze*gvq{)&ncj*5z+n&7=BGilVo-~2kF+*5b=EZ)@5sE`T(y6dS;0r9c6$@dYdIMj>)
za|zD}6}EnPu-UMF+}q__%i-vVeD9o<Rb7o)eII<B&lDIv(_WzWOw@a&ubQ4y&-mn(
z3d4eTs)wR&_gY?=SK~Mu7;7=qBmMo$_blZ;-R@8Gm?atww`%G=_9S_WiY5!+_~^rV
zfxlZ@sScKA9Zh$lGwkbRdmmTGx8%l$*jJ}IbzaKO&hBSd#N0}}R)lsdSopDTRqZK_
zg@vC|10AX}8p?;WC#Hu#c(Cdq=IupHuf@Z`qzTdQBZ_{>h?F@2sh7;9wj)ljJoA!^
zQ)?4vgC4^X!ig)PT>-JtAmntQ>%;qNHJrNd`7n4FIc%GwOZ9C)VyrL?EN*U`o%|aw
z1wSvICnJALW^XM%^GiQQFLb-saR{5rIVg1NJtxN_>Nt|l#U1xdCC3BQUbp1_H$3dK
z%xU{f;cj%_-^fE6Pl(CF$d%1?Yt9QjdGLBl8_-rQCjC;{9$wo@n<`lE3!J<QAj|U+
z^fz=d<KdB1UdJhS6<^I_x`OGq13qY>8d7G+0F&wy14fe)Ok12)>f`La!{Je0Drx5O
z6s-?DvuocEH_Sk`C`>desB2X?YvC?|b2)>UA5-7+cZIB27UMPXMquI=vV|<OmwWOX
zw=-G>N3gdMB2WmG4K|>-kWCZ-GI5*e3yE=L(h>#ne6owU-bu~T$kONa?iL196q8U(
zPfQGDHN?8ZNhj+2`_?76n4vCq94-o)oO0Sr!lh!~o8D-?OJ%8Zyo5E8i3#=c)u4ga
zSt;VH9JK6t$-(JH$YMXM=%T0i6uSP;>&x%NFf5+|WT1=T*<IOP{3yqT4^FQygtX{^
z7NsEh(a%jQdfD>0gUcQ>O&s1PChK-g@@waVDrbSZ?Jc?aWa6uv=?g@vnN^8zOc+bS
z7tnLh(nypEEkDYLOak{ZozMO-r8L19(Whbvs|np%y-P|_%su_dD_Cfl2M4GA!u=`r
zx`VlB*4hP%Qv)6(G^~2gi_lyXJC6QHl-;D~Xp$$2`7ak2{d-wHLaiB2F;Ddud_Tr2
zWIImIy!pK`r}bGxv@(lWCwfc?`J8PMs)X-6gea~sQtZtVBo{qLSOZcLHnXzosx$=v
zk#4*$uBl_&xDP+$(h$#)V|pFbDysm$0!Igt$w8Y*KUMdRBhEl6b_gvoy<@x;E}4W|
zu$`8w{S<adsPAV431Uju{HAov-9Y5N;jGD_NypSegjl<GOSDeHP5Eeh90)p$0OyMr
z?(hNVG{FbZ!A=~+NC#122Ij`~>CVGdg&uwllM_RHT4%EQf_grzy1bHwNBQg%hh6=4
z>aaH63(&~eI;Gjq4YWW$RUe3NtAO1I(8ZM=l0|9y^x62q;2qMCX6KoEYYF*^@J%At
ziV;fHtnx$|dr#6z7cnlAiUz-#oHc8ExNB<L5+oBiSoi^=FR4~VI>hLcPf|vgRTY}e
zec=k@8wqy%-!vdr5yc3$`by8S@57&`E8HdJaE#};Mdn~aOfv<Vqgn>OfTA%)KZU{1
z!zwp$^=H38fop;U&;Bt4?3wD;NmU~DAr<Jul{xa|-+vG#0H4qu>x1moVNP}n<|3ys
z>UT7X6K9U8XlSvgNd5vEVi?;xSdiX5xw?;dTzUqAc|`Wy(B=H_%a&&yT8be68)g<3
z?7{Dd=|j)74_15#kP;<=okjU4sJkyQDMOet*#xqyI5iWcPH_+&BOnZWTLA=6DF}kD
zMvDcCS?G{5SyqWv>(Da~>K4ad7tEt}c|u|tnt|CBMxYpkd?iEGpWK{#?{do)gQG&e
zAC}EB4UUc7?({;;GS~IKbN0`MEVe0`V(Gx4nkOViLx_q_vh#7;A&F8It!{Jw5);UJ
z_V*^;N)bmSXR?4qK^8}eJ;@~E8<eu+h(6A3AEx=Nk0n^NT%M8oP(#QBIj~MXGbfV&
zC|ch-$eO8g6(b@>n$6~w>KO1czEEEpDBFH@uAEciX7=sGu%ny*6ee$hBNb_`c2Nnc
zA)6y@<t*>fp)=(D7Mv()kw2xfpQd5(wl=i?#t^g$QAqk>bjt*Bl=3YF+Eb8mCy3a^
zZ9(P+zp;_5^@31nI^N)LT}15nq3Vrc?d(FiONh4@*E58nnanO5x?dgXdoYS$lF&71
zkxyacaMBJa1ck++@o*K%CaP?Bc+Blo-!9SGJk%E3a?GmvWs3UKNHNasJ(YLJyjJ?N
z*Hj{?j`D{9v10bnmq=+Q3kOvQb&H;`a&08OOu9-07kCrLR3j~KqYz08lOqwA5AL>F
z*dPe4?R$W}+X(wxDT=&+xm+#guOw0yn(h6Tae}X{ATmAL!JzJbPaQc@qz^>dboHlz
znHjydCEy|+JcMCaeV?zjpna!bbIWz+w-)f{fqGRN0%fa~V>hXv?f&c7-eB2`1}lD|
zT0je!K&($d#}37`3D{Sn5b={+ZR*nrroyL<t=q@#75z+y5tKf0HXEID8In;DIa>?T
zMjEc7QZ9U(<m<P9f0yWvvu|gI1X!Lvt<5egyv*-%ny~SqkO=HF7>Y@SmLyo%*oa12
zC9cG2x+i60nB;9^X7==igk>Jpqg^S9Aoe(Yv9K_c|7Xki>FJhezjLXyrP9nQ3T?5l
zch`*7R;4}!m69*{S<cFe2a`pq>FW%{|6_FCAFkr*O65i$C+{SnUI8)v?Xp57bMB_{
zephVu@^j5EDfJ2Fp$3)GHK3GK)(<92kV&N9(buifPc(#p{Vc|JzxhAG?xmN{o{CK|
zgp>LKbv%eNeWF*;*&$w78l1GIo}(i)J4EVkKYN08lBk2_Az=H6MDiW?mg#sxzl;vY
z4GH_7L|<F9L`Jr6&LG&s58y8E;oc$g`jy69S>NPD!=#>R4O^UBv%3c~mxaJ!w*ipC
zL4Q5C%>V4Yg+a(!x}OLPr`_8H`|Mp7eApC|&cd)?7{o6mk*IBFZ#ZFXxmf}&D#M{+
z|Ew{SgXV1zv*ZM{BAryW%WtYbFmK1Vmp3$*VSN=P;4LW2+zK_cwQ#_TXbF#+>T0M!
zQgw4tBl_*0CoM>ziP~c6gVh$4)rgtg6rH?!jnZ{(;bd$!4r@vx2=HKe7)1*LTqS)(
z!+5t-9O8+S{+8rjXi<V}3?xc91usNo<TfcOQ)DX+(xXZ(ct=WUwN~UiH=-I)f)v1%
z>@LKOrql;S8E%U{U=uIsM)St+*%g^z**A=zN5|@gc%+@!o&7MaJfUC!b?Ut!$Lfvb
z!b@QRQ!a>E{CQ$ID}8{FBTFUIu(th9a1%IH`TY3sU{Y2P@t&|^xZoevQILF-S4sqU
zZ}Jw7(}o6A@|v#h_-dB$dAvmg$qG!zn#Vk<wlg-4aAwri*4D-vM0mgQQw~dSmFr=$
z#+ueE=yVNJ3ummX1AKLkQWR<XLUqvy3K7hsP8k^Ym;v)z^O3R`4_^?{+pv<k=&`^T
ziG?Zzt9KwoUamFJuE2cgmr)Sm!NlT%F_&s^vm-eSeh7Jj3OG*A*9#w^m8XQfV2#pA
zFa$c7L_qJDewK<j2th#_?|y;)vqZTI8g)$&#C#-hfmfm>%I38>z#=4DxJY%!ZNVyF
z`O^COV7LLTcOHM3{r^FLmJQ~qdTb?NG=UHJ7>W&;?4`6n4qypVQ5l{e>|n|t`3;aq
z63r@G3>pUEz3@USfth*xc~ZrrQ2|oJ0Bu|!>c*fDYdhDC$E%d>EfDhPri@Ss7E`t>
z?(emBM=Es$>(pfvqr=w&$cVNYE->+K0{_&b-8zVw#DZUz>EA-*k8nRJbIdi@VCi38
zqU{eq+xvof#B<~Lo<y4=_i1?WeUzfYG!L<AJX%}mGnLN3O~EL>?kI=hMb-dBxl+{*
z1R82Q2{(+^^u7X>-jVPU${I9yN?d@Y<p;EES=eBkJ%^k;E6#p^A%ZO)xGC~9NwMg>
z^><c@<0}*22VXo}c=eqEb5AUBxi%Y4e`K&g-A{T!bMyD>(Yn*ebqP!sC|FnDajlCX
zye_5L16@v5&m@+X4WehYoemiM-a_fa;?)I4D|2j7A+KZdyZ09#9qcLCp<P81JVHFQ
z9-5<5_c>YSCJ&;rTr=IhRkq<CSQQhnXVSRMr4%>zpcBq#5gB{m?$J_!7hp>V4`F};
zvUVWqpZX4bIUXHHLaIYENn@2clo8X&2K{@0MV8kQJ0?gdmtc&>p+9)YZA@E4#8%K|
zoWh)%M$T9k;wA#o`Qz0Ppb!)lgsuR^&v^=C-vsmE_<4&s1j2`eR;g!?vOdB*gcxjA
zLUc{~3(UT5VfLy{_N!JQFQ(alAQ2%p7-dSVUWa`MwdI&Byou?JF2cb<m%t^v*|Ip{
z0M3$W8l@voPGyzxTr-!&y)3fsW66y-RaoSx5wW8_$oct;Ya*#ngkJT{;+2Ue2D|_h
zv>)zEY@$_`iOwdsDOxd!LA?lR9bo_(6x;Uz=My|2o+Cg@d~%M<Ze++j6bn+xlYqfD
zV|@MlwM!c})c^+cT@V=it;f00^~FcYn!lo>MZnTq5F70Vg{LyKV^YcTSRXIl8k|Q$
zfTt$LhDjb8jeDkug!w5_0Ov)hE26vsZ&O-Xc^4sAZXch6hQ~;0NK4k_XjVn?yn(9>
zx4m~OmTZcCF;U{58UOwxLu*2J!A0^^z0nn^{kZ7{`W?3kfibrNT!>VZ9WCnw4Xb8_
zUT1s%_a$0cFxxrln?nIK5nJ7!oh<P*JK1BLZB(e&Fj7ui2ch&3MR^C2Va}?|W!Hyk
zL#8lnG|BdCK0jNe^0T)PwyNEQNptf4g~P1~EY)g49q~OGl5h(BpCQ9rC73TTVrU99
zMbsJ$Xp@>K&#@}v0{{C|_*I7WyY=5HPFYDkWS52)@`UiBS@Zb7J@wqwyuZIGA%6MH
z#}RkvJ8Fvfr#aN#*D%)S<>9gYAePgFG0Q_BQOnrse?M`X<S&;T4%OnJQ-z9YM7H)2
zb+NswaYy7aLeX<pY!#$i%qaeW?aj>3i4k5@4ev2KOv`ihgs{8Mfn}~d?nK;;ai(7V
zv+tIWDg?&C^=kWilNc{HY3lEFh4!Ios=yCLe{a`EkQ#+SX%Qr!wA!l^8q%bihUL_7
zFprY$J7!X*@3H1-K{Cb+#qd;;WG38S&s<MQ0jNnes^w$?F%qqw6Rkg0Juj7B^v<o*
zG+cPRd5aBHcV?%x<uvbO##E-J2f3HB7Sy`()cfp(4q4VT@T?M?7E0V;QmD%MH&$Jz
z6x|J$Aj+(UU;l_EqIsTd=kw}DB%hysHAsC7{{5qd?#Ic(4T|s41`KK%Xx=N5+V+qd
zF~b#};}tgpgdHDi+P%<ymibdC84!bMt#o78cP@!}Y_5JpOdtEq%r5Hk!QUXt$&&yu
z>m*8AKJN^a*Pz^i5uH1phNpfixXPPKe(3*i0zUmU5%5g|qj#+m$~5`BW0JQz?DkjG
zZt$phjE!$mcW?UM>RV^6(8iv*KJXT<_z?zc<xEw5EpEjmr4DIt#^~enct^F=KKX?+
ztnxD8EmWetDTVGLxawoSA($H~MjBJVFG|o2I6vdubcG+UFc#wK+o<sUimD(}9vS5n
zn)*ybBW@=Q`bvY{rLxp4%Lq)p9Uw~!L_#Y2LZHV1pZ}inSCh?g<6f61qrNe2YxCGr
z2aug)WuvpHdx!DC5Ry>%hONN0A0bq6GhYCWwW2ef3#(AsU?TaBsLJ`$KY=?nYFtj2
zdP=wd<s(yVZDy87s{<EPX=d+78xK3ApxWQMVRfY2$nLpHc}Vi)=3w*>Z}N6neHeHk
zTN>nCv;;){5wHw50elCd!_yi^z@u%!4GW(0WZFW(^BV1E;XBLXZbn5`#hWg|N!2z^
zElN&C#tVK9gV-!Y<(>~=(#Mr9Z4Xv#JAD|%268=$ZqhE1`hvR8*lfLspT`m!75eTK
zo<!oLjur_rFo}Z`*@?m%H*;u_%;(T3UDelj+uMypuj~sUIXs4q322aR0i%hnTGB0u
zPq`bz16jEBj#3s0eTb!p_z;9iGm@Ns*Rvw*PbomNLnxpEx!v1#Q_nSrZGnu9PPQ#*
zsHhk5xx8dfI*i849aPU@8bSolrO{yW5riw#S!z=r9W;vF_Lg*FC>AbVx`ZmB7Ezu(
z!CeK#c37HmG4K}<_}p2g5l?Zf4X_bX$9dAkqtTTJ>7&jLsUq+t)84xjQnf)%B~#e4
z^Znk->tJeanQIX#`OkJFrim^h1jI0)RteP{(8mB$67&S|)4l^xic)a6K)VgT3tD!z
z&=r}YKgwyF5RnC^zk6Gb?;@)0&?prlb|{USA*wi6-|Lt3S7xR>-Ka!(gSvr`5$JWT
zGe_{JUf@=YD7c}OSBRK!i=Iw==^^RnJ4d$S$~8;sG_J%Gwnc046wdGP>t&g@Xd(cQ
z32j?dC2#&9O6(knfS4k2vzQ(e()rf)!RJo=I&bA&EFuVL8T9CTI4zmPNzxT~jQyZq
zKONM4-zw_s>wPC#)<bC`b$r*BXjS2sCmFm0yUr#7Saq?Pdk(ah4!&V~G;6ibnk+N0
zS3{i=Lun1|a*r3)ePzMCyn37#Liv|R0^@B$0u4|6mRQ`kECALv#PYnZp3J>?`NcWs
zuE}Sw9!LE><5H93*?Mn%-{-(Ajo8(R8b<umLXQ$B(j0m!udFw8m0qSGo$`K3>{=Pi
znl8KSXjjM4AKc!PNAFvV#qN@HKR7TqrK@Y7{pgS0^ED-vJDy%$5*udTJ@hnwpfXA?
z$M5l>L+#Zn5&8@C9uA7_dp!PE=`6D}JX%u3{?)abR3p$-IP~=#syjd3+@L@v!M}l(
zXq9yfdk#95veZD~62h+Nn@WPgtch%~O>l?gDxaV<Ziz^8&XKKGW82n$O1vYTGSa0i
zRpgDH>@Jb6`h^?`1f%i}s22qQf=(9h7W<UCIB`C}P!5<yY~Rg#iJN`aPj+=A08?LI
zDZLd47&`-9K$RRj3}X|@i7?=Btwkh>%@>&Xc9{f-`=4q&bC9-N1r!QU_(li!;M9ZM
zTgRNY{j(QwqwE<WJ{8NvJrUnYv`8tGwjYSh^%qH$>cs<}oE(=JgO8J@J@t+8k?gVW
zS=KEtUxtQietodTM9%Bn9xKnq!Nr5I{NhQEYIJpVEKGfC_f_pavdP4$uPEBrPv>#u
z<0zBh!*;iKjr*-IH#a}^=aWCZR{nabyFnm(+SZp_>^zh2eHnJ!(ekC)-Jqh?qi{#P
zeuaBR%l*Q4)t`2FStD@(4iD17CYUOYB72);^q3?9IKKOPl6=zVrp_rP6%+|m0bZXu
zaJmVMPp`amxzB+rayXRnJ*dac)c2e7Ua09gMXR7QniwDY=o&digB=LlPc<XlM~>U{
zi;jn_w?In(%u1&|NZWqhqz1F_AQ^wp{zn33sXqmL9e=fO^&2?Vm8GOVP7Cpe0IN+O
zY+ahg-x50q^*)BnJL$ky%(s+xE!(VJl!8iEpKXh`Loe3nJ{kf7Zf9Dd5Avx<9iJ;z
zThyG%`L}*65D#elc{_=Cb{Y$CM8IX%qp)a{r?c8R>G^e0$P<UZJ1xwQQ`=yBsesPp
zN!DK=(Mgh3Ka@FyTUf30aH8)MH%m2=ErA$Ryj0n;njjr1glIzJUmqwzVeJ_{5V7D8
zq;po<9q>SqT+bw&OkpdcuJzXLd3pGlw;&Sz`AeEMFK=lCT&hUBnqX1;uzm@ne?LA<
zQ^Mt$_2?szc$E~bO{C)UuOX=j2nj>bP{+QA<BLA8cd)cQ^WuuBI@cX_L9auGX^Tck
znHK|H6Qs<)q0jPx60o3iX+Zs*sHA$C0LvH5r%8kkwI6X!HJ2ARJL?{M>R6-ZW-r5H
zxY@iwSK<^YTm@|Chn{Ql@$n6a91j|JcmMYrH7+wbQ*V-c%EARy2dct546=2PO0MvG
z-#uEMHpj~$(Qrx|U|}<nN+@o81R-S{A33LoSMrkRIQMz7<a?h5n|{f$a_x;Nr$f1g
zmMUsRlu?B#&5n%QiYn!3x$scP<ZuXqBvkRpaXh?rcQ}KIMFNYNuFH_|XzYdrI_yNs
z!4+r*2<9O``onz4Zv&@|;WZGM`{+nRU+|Fqr9u)@c4K3e$%hS~e(BcF1nsAMhtqCU
z<iip1fOyFk{#fgnl#5A!2_T}1wzq#x59+a?{GTz#YvK_$EGqo^lpV+zW%a}9wOBK3
zYM{`Hf8W%w50&vpwyjIif2xsM>+$aLyv%Q}XD9rvE|q!e%s|s$)k%bV<@Pf{gVZm)
z5%IIDWN6N>2&?r-yOfNKaT}ZX`%kwc>EhL{FGm2xO8&j`Y09l3r8a1zgl4+2OKtMm
ze}8dz$etWU$mGDHZh~qrS$|rKPyhZIiHrh&BbY}B4o&dp1gVY_vu+|nNTENj%(6_Z
zIwcJm)4!j_)Pw{HI#xb{qVPN*DLWPQbQ)SEiL#$S)6*erqm&1dY)s$o+4PmPrRc~(
z`KL8P^$7Ld)1l)oB;wmVLAPfsanfN(4@7Ln<lsImeNu0djy3c}b=6_^dEIBuXh~5m
z+4_kGHzu5bw1GQnv7$HXHbOEu&X|iXxOlrgJ?<YZz|2L;cSs}lauz)M`~dKx`_VY@
z1IAG0AR?j^shI~(oUPm1TWx-Pcqsh==iP)BDz48X$rprB>qsA@nf8$y`t-#~r=k9g
zb8Tkow~QWl(K2%h{J|+9VmlLwe;t#=yrepDh@RiZ&7(4JqD3l88Yr8aBZH?iNyCZp
zNlB(C89(R&wm?Yoq>0o1e$KN=J|+S@md`=SJQCUyULsLd=HDvZXL{}^YM$1(PE9z{
zCmZU7a;6holq1S3sKCO-VRFp`+kq-{h$_}|0>#T;+b{&6%p!SFds~}!mm_$I9=rD&
z!-*>{gb?Hfjzr5QqhV!~v!%_0VCunCS&){;6g8qM8VqQdB?aOYNe8D{<#EtX(a9+i
z0ZJ4DX!qpZU05drtP*w1y4lnO391n>NZp2hj2@?+Ktwr!m%xm{ZBTn+xOwNZx#zsJ
z&KD~5s3AW*NmVp-O5*59k-!&M9dY^%M+bHMQ$u4ShWU!Tal<Fzu0H!X?W#(KMnoBh
zD};@zrVI5Dsa_=^EeU&Jq}QbntQAag9ZnnuW+6nS(*5w6-*4D7k$@OV&iWK<2Rp?W
zw)Uo*K)RVFwdI@SEx+4^jOtSr=$q{Zb3$A6SDQ#c204H~C_-aj7r(+rH)O<Sbk7aV
z#z@sj^%Up5VbK{jo~pO3-$5d4D!Cc<=ns060aB<<3T{vG5ro|PrE;nLW~bu4%S>p{
zR>E9^RDx7aKYFx=%5;F37ihnkz|gu7(~vSllzNC8zYfJ1Oh3*bAe0mr-=+!y!w&_m
zFo;2u)VeH5#JCGpGLmr6@gI<9%eH1jYeo#`K%ZD}mdbEE(&#+Qq>zrWpr;*T$^CdN
zq@W~rIl;mG#~XnH+WKHm<*})8$(dRvcFKBY=8t!xr-18&YNAyPiYm%>c*&y`C+m;g
zti}F9Ph`U}BFG_tCCYJqo{&@LI-2F|y;tbAPR}xSFX@NUyR2*obpfeLLTeRhU&0(N
zPs;R#XsQK0ay5?4rxB|uYl58xnP=KxP(TBwkj9jd9f?P1lO=U@=4t0&=#QQ<(2Tj^
z!X~2jkgM+j-r;M}ybEqK4irp*R#Y=>l0-HnzvE=VpR6@Ux{SZ}7mTQlt*fIg=IrE&
zZ{MviX++89<)!wEiw?B~+9gBX0)+^t`%IiTQadE$+9o0BOp9ZQq!GxqM%`j)ww1}E
z7Hv=r74l3L@uC^gX@15I6-Yh-f(8Yx>9xzUe!LSgGCF(6s&e^~d*>UW9kOOML0<nJ
zUHc>KB)l~v^jE*axb&}YepTK6<)ZJzYxlkRJ@WD-C2%=if7K{|>Q_XrWV<X33rM0}
zI}Eb_9;wzzwdJD#8HR;h?w5{FwviRC)CWcXTAmnWjHi|i%OxZx{$ys$y3;^kISr5W
zro}FUVvao$DvCfm4(_9wkPUs(&HBk(s5S%V6pR|E6=F0yB;FJW4|?K<2_t0`NMH6x
zh((a+NS|noY9+9wfqV@vpwwZqZsOkEZzBUn`2vrVzV057Y;fx%MIvWGN%lvRNqv4v
z>0x%(&7gXm(z<nTCOjm~-wHVwdm4Hg>vqb@M;9_RyQh7ChleNCEhEiNZ@1@Bz(c6Y
zs!j!1*#a#%W)4DDD~B^@M5W+KP!)!a)LmjUM{VypgLF=!XoB^e2^L&^6Py3^sDHyp
zz`cs-{-X5gOgue>sQ4Zl13*J-E73xv4kV>v6^_3h6{C3B0`L)4UDX!SnjF5FpbLq?
zsZ)Rv3&+-=FUSc{OR%@`f0H`3osiUT%&nlv2?jD*RIsCzs8VJlXZ8r@9h9p3)Hrk+
zTnB08NuUPW!NwK=A`D=ZAW&U4ngVGo!O4l9$zUomX#iYSV9#AaG_Z79tGz0=mAkx*
z4sIjxqu8(oB_|L7|1f7{AMIO#vCq?GD}+6_e8G))_e9;>RjQ6(=5rbMAZWy@=-wqF
zQ4Sp;OAs=ETpXm7_Z}n&V4*g1knYCJPbIrL^+jT8sKOIOVvy6SF&v=%gqlD<8Fo}a
zr_g)}ViQ>o(5b-WNXt>XegrutTOn$gzMXg|;;N{N92DaT@%=|`96DmVgPe{!+1odv
z70F4xg4BpiJ8GnwU5#DN5+uQj)`*8+4H}_7f<`}HFY853li2tBC29&3Ku(VZX=u~5
zVw+nz?DRY~izCg_>>UD3;uJkl{bLOjw9V>JhWHh_ciT~Ox{t%%18GBqPjhE@L=<Bo
zPy9gK)X*^LuSovv+!xRg2)QD{?6z&&PGZ-k;Ms{od`mf~N7G7f1)ET-k02#$BsF5d
z5iSaxqjS)!B@GdEhH!T7q2$G@q<@)flS{~z_EyI5+351>j`I_tT3q%B$n^WDbec!d
z7jfVmwIZj@S+hZ;PxLEue?hY|<@5R3oO$xuMn86ueg>~<`66%K3OKM4cTcT55=%j%
z3oQ*Uet$fcpEagZj~wtA0SzQ^KCv;h$<gtmB#_k6k*7`dAjq5m5+qQd1v)}^zXYik
zVU-v=qKeRuGkxjBMaQo90k``GqB}_58=s_5AQBZN(#9=RW@v8@{cUT=bbE;p)#Wyp
zGBPq(59v<+ZsycfW-_6FLBfK6%WoCe1Bx2QMV-fnQryeqBqINu=x9*t3CfW2H1-}G
z@5&c#*_4%)mA#}`fG@sTa=3yor~lj}mR)aYQc}h9)gzB~&b={QJn6LGd&nku=+QRe
z%$Q3{ij9Q%M~dUQYg6`(YVxNl_}WB`zDcY6p^<ZN|G;?o;uUGgDUjj{Z*`oB5si-!
z)RubGEouKToG>6_8YLv#$;uWzL<0}dv1|0$GEzS-{(Fpjl^18uH1wZ2^S%GfwEi>S
zz2Ik^z_|L4OVA$Us9#4un9BQbP1P)j^9Lr%9xzBjC17!MAq6IegGL-<ghYTY2_Div
zJ%$t~|K7cft8Ha`MKs_V)xr7U(2(|{9BHI(8HL~H6B0>+yCtKXmRLw7C;Y4(TR0!F
z&4TCVIUD-FjC@905{J=zsf00ylP|q8hfYUkYKuQVyNKgt#2bX1xlQuwUWRg?1=W<e
zxBH(@|BtuH>VN!k{>KY&V?gAXF*?ZVJqs&yQ3)Fk97o)p8}UE^++@eu7-*5pl6giV
zE?xSyRkQqBeZNlgQH&>t1OAus59%hQNUK=rbB`avFY(L{lHKI1eETd{Tg|j0b`>hE
zt2eq%syYok=Kke^eFZ?PORdEP!yv6j^R^hwZn!ucu<QOy7T6+YB+|<8NVSDF_c;C7
z*3iotiSc2383&UP(83am9NIB2ef9cm#fEiWhK0n8pD;!ldcwHW6I<PI;0xN(=Gt?|
zs}Q;R)Q=RpSk>>~+Ey2bF&#rbk~H0{50(w^-~9bSfHmR%YXAPo3ZcH%3i@h5Wu|8z
zuuHJ6&~eA-Rx<hx<Y&j2ir81p?|;lau%+&3l;*(fckSZk%UTbWU&bpwEBg^JFE#({
zOs|2w-81OfGB*5o_tgK7r}AHK%l~|d|D{d-f9-PaG0vD>d6=4CaQH6aNs>lOn$(_A
z;a6!-GDal1`i+i8l%aNwao|WF8L9gi<1*Ae>??4}jH@@{{9M0ti<d_b*VM87od4H+
zXEldgRs2_c+%mnNU5kz>-EP^3#vZ9)2<^>-K!m2DpK1tM)OkX!>I^|vsy-ZJwxU_D
z>pzDb`QX8WoKeTt1}?#vgJz#f4aNBu)|CawUvMZ6kheSVC=K|K6+Bq&#q~YcKJ^ho
zp5g<$@J2p{0YsGrC_al*cA!O0(}N`YpwvkOD8}Ut8z-n;h`w=Yb1Jo3py-G(%h<GV
zP6;1s5K4#MLjqthD4J2%*4x!;f6YT4t=*nwNY)X?Fm$LZuCX(%dDs#!8hLzYzaEXe
zd##dVCxk)l;c<igz>^pdUDvY*^n8%om#kRkm3fdct_4Lw0Vyr0tAx~yk)k}n4^Cs_
z>ow5=nt+2GVGCeMq|;N*lDeN~jKrXalJ?#L#R*4<2rLqa0R>XslI93{tr*8Z8q=l$
zV}JIq-28#0#P*#dR~{hW+1B>t_}YF`klpOFF)6N>x;xt1&RCmdoasL4t__;I<c*<)
zQ}<s1%E7V`W#&A*@8&QfKiqr3n+SS=J|;EONZ%px>%Ux@L@FHY?P&ll?EQfR^Kz;3
zXx*q&c!qNIMn$`wg!Pl3=VT<Fd60E9oQ$FVj@B-&-ImrCDo5tBA56Lck(Fjfnr3L~
zByU-_<e@Bn?8RTpa^fYxM{hL|U_^Yo=w)3E<jVT%v=@hjDGHepn$`{mOS!lD!Zc3g
z)RyJ4mm-M-dNybcqZjEA{WEXd%vW4bgJYbLFqAX}G{9wKlz#3k72qVp#tadp2Q=cA
z@tz~tZ#%N%;+7aW?G~CV1atem+KM_)2T<nAX;N9wC`y_c+E<-kKsbwDhRP&-p7+2N
z;utrh-ApZ1aSYsP2nBEYNn{#KY0Kh~cRNQ?VsgD25aGO5sL__`Kkaz_$I#+GWB7l@
zaMXJL3aS4KpzWW0b~@6Di#Grh*A;2gByAxr)^e|`Y<s?QXwJ&*IJ=T(zP#i_yUNPS
z2xDECC>@vRH_aHClT?)A(~TQsm=1>vU80OXzl<+S<{t?sV7Vk!#KG{F&`t7lV@x5z
zc$e+?B0SBd^fDSq5|I}tB2!XQax%)%)FBLq(0w&nZ<TYGxTejBrP=P?GUh;VS|}uh
z<T?{fK7O2XIyI~*1!E2wxyg)vt0`>*mIycRvO#ja%YHohL^C3z;|-LApPuuK#6c!!
zdWf(a<Vq6JmnO`ibIXu<4A&dJ3^^xBQ61J}p7Pz35|BN|i?-{;9BnD|u2E~diQhj-
z_zl(Gzwp~nSk3qkApie7p8t6~|92eE`<PQ{AqP0>@ug!3VTH8qNfO+5f?mZq=o6=(
zBPH&a7%soi-7Un8;Qk@|Oi47?NUTfR7#gdgLw$1z$6u$P@A2m!5p-bSeTV))E;Z9=
zd>>{$#>7Tjg2^ZO6bU6<{5c)ABGVb6lN}Ikn1LkdyXlm^h7{Vmy1K-pAo0<Z5{;+E
zo0Za_wu}GI_WJr6p+2awYH?dDlhx?0;Nd$!=tmYJXTdy>_i=_6oW5LJ#Iz#ku2&ae
zAi!jiHp9R=4nsX_nWXfKNlN9QppmWIs}9g$$699nw@Xrg0UPF9(G;^_9vHfm>{uET
z9{cX?*=cJ)^dB4y3^itD08W+wu{Dls4cSaHjERknMGUHJ>)$g3Zl}NF?yq&twRs~&
zpFZ8WL^WxwSeHcf<q7h@m+Ow}zCM=`(>hBb4<nAOG2xTlG)Bu31lxx(Pou6s-r@_}
zLVakTVhvE60;h0Sovsd)!l*8@EJ4J`)hA(i>XL6!2)y5u5mwI?pXY>CT>kVEM8ZDA
zrOXtU@lCs6pAYK-@nicD#@eARGHP^b3n6x>`kHa|eaIRj{|<zL<Tk;=BR!K)6S;gl
zs5Hx4PA~L5iWL5Oq#Nb5!t%3FyFsOG?-RM+-}&}cJVAUE*8O$hTti*`x5s1WUaP^p
za>D$KagWEw!Hf$NVr3X2Iz?eJqg~F-<xR1~vr7|^CAhnHRI)o&>ym-}L7?CU^A-9%
zvlx}RGeY~46b20Cx$U*8YbG5V-rt{yJWY)yV%|k665``e7C+o87irsd=_pT0KKqde
z?haoIPz{<Cv&zv<(!6)-`;V)z{J7@v<%1)LvY&%(_{456?@jZXlFjc+_<OQa<k{Pf
zz4R(DVu|2;_bm$-Y=1J?aW}EbYi`f7_XAC}DJ~hKb1M2PL>IOeNSNEGmRvKevG2R@
zTT&tI6utKqTm8sWu5T^{bC@L062y>sxp|U~M{#jwWpZ{}2&a>V=A=P18wUVWV=ihN
z*(ac!4)W9;$LMEy3Tk_Zw(UJ~4b#uXUgN!b+T2{XeRZ|i4R!2#mSCCr(Ga!kcI(*}
z>`dK>I6Y)ElacSdOOYj<Scw66D7PZ*!~3q%o)nC!`@+GmFRKYXzTxs$sB%LK%)nY4
zE+m3^HE@6!;7mp^6phw-6<DZBDt!HO4NDLc&4wo9&~^95g^X`z_F{6tJ)@ihZ;P~M
z;y}VP;SkCPs49GEZU@_v$wH4*j7%M<6N(o3DGUobM03o1LT$dyf1O1)UXWfk(7EMa
z3a0q&fWu?rJ!obeddm(Cj+L8Y!x}HcS8x^L^us6u(aDBJm-IB~h~&aqgCt}w{u~2W
z3J*r6*Mt)}ZX7;`WS-WCan5YN+5a`kR1J-`GM8nsv2EBf7^#4=LFo~Cc-YuXnaSB}
zBhM`_v$zwQqiy6DLRHkSbIycJF-f23eRKAPy5G=!emzf!3ga-Y0QCeza?6y{BT+Q>
z4opK9E;Q)s;=fyAAR)$G7!Upsi<($fV8Ge>3&xAl*$nY^?=)tzsP#muw4hB7v!HLh
zK$Bd!vT-xKCR{V}AB4z2R!<%@>zNeGJ28ELOLHgMNK_)v(R|*-RfyEub%>t9kTkTm
z;?z<90kE?tOgBoCZ7}W5H%vmD#El>n_#ut#Fqk*~34O@EpCKCL^C@_D(3_uzZ!3vn
zS%OgMIYVg>AfvU%bAZfPGBWB?d>*1ho<$46WI_Kaj~bWrTYz%p6y+mG4Mj3g8d*Ax
z!7KKYIA!d;`?otY2ur<}GxYz_0&w=-;{S%c+*Y2(kmM)tU))I-Lh4qU!X-|hB%hq=
zlcJ*MhB-mFM42Chqa=)?<~JE&nPUEsEHg-4OolWO?pbJyt%q<z*ZAN+eg^!Ws*zQf
zKB;WFkERf5fm+cA%*P0MU$#`H7!>FEn5McfmC=6CATvb1GtIp2G6EufASA|MYORx|
z0&W&}Nrp&6Hs!k446A?K*}k-6ZTZnF@}E>JXSmoI{>so?=XEOM96Yi({&(1eeF^w<
zq(Y`)7}A5%tR~x}*14~x8GTJ{lSmrqCB6evl^1XXJB7?|V7UEnawj(0JR!B9VRl{I
zs(}()d?=D^(gcdHp(nb#<b}i;v!aE|&d%bq9XQ^g`H&ml5fdCQpY+VHuJ(IHtpb(%
zLk&+D_{ywjua%r8Cnq|U5wTG5NG*6(aoE=amNY@UkPl;ykl&|HZ=qQ;^01&@B6&2x
zgs37v8OMp%iVQ>e94k4!3z!wbdeRSqQ-x*vXLiEyFqk+fXXtRUs)z8u6oRP_DK~Pj
zNSs$!R9K3<sU6CW(u@r0Oi4?Qf+Y_$Fb#0h%yvZQBks{?b@xgLZOK3j)59D!A?Z6I
zx~kV5DplbPQI~t-ue~g6<jh`|D=r=F&MO$&zS5x^yZ@(g(7@L2bpe(v8jipNwn?l!
zaSfmYrc)>JfM`}@qN`y-m`Q^<(cimaA~Dvd(;yjdsLYAt<VMYE!fE=58=-0DhWb%I
z?LCIyu9uH3sVlr9-<hvG@P+(<=uL?HNd(n42UtUIv_0&G>QnwtDv{UJ-IG^m?2#Mj
zPY1E{K3wMNGtCH+$g%{XZyyt4Pv_>ZbvDh%#yc`}*5@RQ#@gJxfe>R#Gw;Mb7%YM0
z&PR|$12iGfHDti+o_t)$xpU)Q(E-tc&Yc-=ZQZ|X=U$@g7D=~CpFWl{z%MgHH=097
zKqGuC^4;vR4FE<GW%ja-CR4i`*KI0fm-{%+-e|nLGt4~UK6&&oZJ{!B9F<GkEnt0D
zncr2pSY=#A&h@ZptiI8Xr7w=_!f1*7B*?{t<h(TPGy2OiM*r>>ug6c|*07P;dr2z&
zc!KJ#2F{fPs`A4p>v9enx`rO=t^TD908TTlPB^KSMUvq{`B6*iVWQ2&(d)r8i!9I%
z_2gAs{h84Zu?pFGFbU(*un`SbQFMT6<rJn4ILc#e(+4#Jo5Q)K4y^T-3u_CT2n(i-
zW9>fhz_4SI;5s2{OlIh9#l=5vXf7ULU55~u6o`Z1U&#@{HJ!TSq}nJ?xe3-y*a$9`
z<<1I>P}gZii>YiC^h}%4{=WBGC3roeWCwSha=%`Kb3r-@rY*v2^w}DmrH6LT@WG?b
zbT`;Ah^p6-00|w&^zxxcqmpk8?<2yoDnwqA*iGN<`@5jg&FI$t9UW}ZNp9IKFjAyz
zp>s&UI$3$(_(qqflTWp{JJxE(XO0{e9q`006S-urLPL+?fws#0h?8SutqO&>;gE)N
z1@ka$#<8$~+~t$*=o8(j=(9IR%1BV_-7%e5<b!GiVPCW;lo@=#aB|<ieMusRm9N)8
z=0aL`lAGP!{`F6y^9-z|FsJF(>l5;U9A<ELC{6(>6==3z1eZ_K<%rZ|a~=5Pq?xd4
zu%uT(As913^lvM{S#s=IY5tBT-$-If9-f3~XzC6--Y#mO?af@hr}CS3iVmm_v|%^1
zeY<yO#Gf@s`~U>nim7u5beaKqKA?Gj2iISp!bYd8hmi}~t*0>B*VZfYgJc<GCdl_}
zI-`6KzT}M?F$tBuq0yT*)rS?;g$Gr*)MA&@1Doub9M7UrBms!bG}F59-L#yeAh@F=
zOj0`%20*o(5B-k$$l=r*b(bnt@<kuZ%E*x2K{K2&JM}9dL!kGJ$s93<HlwE*xvCI8
zdhnd<|8gltL!kV=VMkWE6P{hFdMfw8LUIpB%lZX&{Ykpe@oyz$3)$R}hKBf1vMI{Q
zpT<Z<b7~`9pvGkB#GQ(A41i{7t#j9j$7xPEpKR%SkkuJ!6ey##P;1(|bxAFRW3r<6
z{R(}VR+fKe#3La&ijZfCLANX3Z5@+D7Y_Ae<xAWvrS5dR(JfIKuNk2b(($QJ`-#7#
zJUo`*sA14<r=|M1ioxi};VW94@NTId+<0MYQC2WH1hL?Hj;rB-e0T3PHq<V%P=I|z
z{&00qswA~Pr3ys)_Ok7fmWV9P%RVz-p!wh=?SbBRt1T?0&_UF!QW6;C3mbW^*{?pC
zl<9&9vu;3sX-C*Z41^e@yn_DUh@_`X7WUjq`=XuFkcG!VH0ziWa(VugmzS7$#zf(R
zv;uog$t4|H4nvaw@WShvWb77IaaJomxgNXP4(hV<rg=e_Uwsy*WNwPjg)E4+dSV_O
z&<{bfQZ<%wAB9Fm^pnqobs4D&3<~4JE$uJKKJHG5)vCjN^Cy+@BRDPmo@3u>rK#X7
z#i0=-n`rV7!B!PBm{{$(mw(p`2JbqqZF^CRa=ep(FF6AdaPBlP`>$(?8+uWi9Isg#
zX>-Jgg<HFd(X+gF1K52sqRE;=x7N9w+T9gK8YCK0Ac+AYn2YZ~eor-V60L#v^4s5J
zhHD56`WaTAyVJchV~-Q(fsXiNr;tYiDb8s;4Mz#v$gY$ou&6%46ipp}AsV5_-kh9w
z`l>91X3%HY0)sT85bGX0HjJ)F3wcXY{)S7dh`?%b#8G0>hk+aQ<7kEX0ncdc#@a}q
z+%!_amGnV#8`J+eh+4E5`5uNlt#4C;tOOy+?s2cS#JN%atdk_WL$jF#cZ3;&><mfE
zNZq1LjQy_9Q|8`qp#Qj$<}+?rWl<|P<pF{?cRDCOq(D*C2P;?^NNy(sFi*LeGYv}<
zB|q=f7?b&<5ZbZ`^(f7V0g>|)JwSl(A$qB5PQ}nPjg>2*nAFs9LJDgopD}7s5!EBH
zVu<WcXyOE)_+0t6aB@xl=sOXK>snK!;bo(l=H6Rfx1IDS{-wf2uDt5NJ=lM4oadW*
zV~P9csRFrxT3IC)BhPI%6<;QsFsjfCCVNo8^d9NIy6Q7|jixv9^|Xz7;`;~{UvRnN
zaP%SfKuLBp?F15k5z}Na@#0~yiwi+dlUei1uVLnbqmd*)Xjb~z1jwB>0U4{n_Aoie
zv%aS0Y=xw1E!$IpA~wy!Qm2ZL8*K&pD6m_IBNZxXKT$k@M{?;Of9}Y8LevV1TDpOq
zQ}m+#ahgDGF^&5unGgL`5Qem$<)?^g{lz^R3|NyYul;>MV!U2k)M|(Fz-L>RE8QE4
zFT0pE(jbnD|8u1bf}t^I|LL^co%-`Cg#Uobfe3T2$*c@n?yDWjHH-=SHCmx+&jpq8
z_I%o~T50a0M>}tqoIbu{`ih)n8>W5j!?VY-!?qPH6cf3kBqnk;$9>a{{4ICSUXzhw
z&0qZJeD>PqZ|9n*B|aW?^ZYgt^s7slR{!u(hof)gh5}Ilq^6B@*%hu@jsGqmn{xlp
zfBm=e{LItzw?F^0ov|+T%D?_Cchz05b*qzne0_gmvVLPOEGaDw2}sNxhD@!A9e_J`
zj~KL!Totcby?UtsVY(xnA3(=_!vRc@xueTQ$#zR{CT^enWB%*1FY^~HSRlQ4QPG~0
zzOP^J8qQDX20os9ZLn<eM6~M;kFjK3=~dG{nM-^!Psn)VWTvNq`%G}}rA)l=INO4~
z%+1dI-90PZu=l=oi@12{<HTUC;o;%RREJI2+ZS-ronn~!tBQ(ce6o&jfoi`a>wJa|
zFnV6!ADX-)u?b?CGeR~xJ3HUS{jGQxxaA$uy4-jMki$!msSPS-Ou^Od=H5MfLZRw`
z*VyKH0`itIXJ{>I7t*JesPng|GsZ=rOS_Wr$<0Sgt}&g)C3tQ`?0)Lod9Tx>9v$I1
zC^0T%Who%hUYW})`P-T`Q}^!Odv36Eh5C^rv8O)rz@<&V!^7jQxaqvBSFh^x4bFZl
z+22?_|52Q<_Hzc$jbCvw@G2fj%eIa)E1$*3FUDn?7j&btN!_Si)A?OG@ywT9jF-qr
z56aA~uKMEB<p!0Vbztn#a7DM9J^}7>yesP@((y@b!tY`6>89e{spHpY&YERgz0BP-
zac=_eXxM0)V;2vCmGI)li?uZj9~J*5FVAnDzyq<~nKSnNtIyRNrTy~DFF%kExfl?3
zojJShray}{h4DlyhF>?q++=7Z&8g?6mlxxtsp%SN=@rP+(RDSyVGG$#U&(cFkAx&{
zQb(f_H<f<DE?Acj|D8!+8=dCB+j~yh4D~eZ)7I7wYqGPn+>E5;b^iR6(+Rrxvv}FE
zM%)rpnc*t+`pp}I<zi}Gac;x5_!nFWJM-16SJCrwzBqQ*&SWeXKQ-r$@~Ed3h8q9$
z_MXmw=S*|T#P{=95kt!Zii^~4V6Io?&uiDFGB)eHczd!6;{7QM*nM1m`BL$6j?x~{
zd8?*Dn<c0kwqW7H^G2TIOF$CuygZNZaEzhchQ+h+=bW?istp@vKsWprTj^|j$y$(A
z+3)r#Hch<G&u^9v2|pOn^7ZseRfv87sTf`ukd%&uVgXE=b$$93lt9JDyJ+L5aJG;S
za{qkB8C*l&)ZDyc^O3`cy)s;frZCWfw@5rR{%%%jX=#L&s)~wFlBucbRK|<AI5F2j
z%W2c6zp1I=<&&|03AXrp_DK~zs2{~CdeQjww}FAZCr=7uS#+!>vd6~U+`vDagT6FW
z{poysH8>_O{^O57lp3oNtpaL;Q=Hf0@y}wink+tdRCeVBS0oGV8(6Y?>(;IFmo2-D
zrvJ_`^)H}Hly>bpU-ho%dV<5`g!_Kh#YC%`_mdViiB<{k<ls(f^}Jp$$@(}#bX#qT
zxQ5UY+)M2RPa)&x_rAG>g;Fbg9$472%Y~tl;KdEUe<USIAz4mcHSPR`3#*t5;gn%q
z?0g^RK#+MpCPq|QMMb!JUjzCQXK?#3V@c9`+!Om5I@8$G4KQQngT16=YF&Nn2mbNo
z<mBb(1-vSHu)8=?NBmQi-m>3+|Glj=oEsxjn^J_0ak8G{GFQy8GY;P0_B-Br*REY#
z%t{2^kd)^sJigy=tTGysiKP-;yLOd`$UK8o`<;r4jP#6*1uIuxM;7{w!c=KrV0nMS
zsgI)<We0_i6a>p%d+}l`LlJ|tkyk(Jgvjlh5xB#7A=(Z~_|>tov7#6wh0f{(m5Bpg
zb!k$9%-7)D50*O&bR5Mo*!fiBA+Y4q1fxY<TwJFsqWKuda8c(xi_3I`!=^2`y=2y`
zS<@I?if&*=cFkB-ooKa*@h5JK;;mgTCZ@)i5C5Y1yJp1Nx2vbzMBBl<8GaqYHd*Js
zpGfFJC!lu_*1CVf=R4n1%l*fXAAfGrie4_qZ&6d7>JU+!2%kP-?Abr&@6t~{r-qHa
zVsi!VjXgWmZE)ecWqrDiNO(m4-9te#GZ`mfc>HUUtgNF-`_KcJ0c$UIJs1-kF`}%g
z>8Blc>PB&5M@5V%TrSo})TBD~1Q*K;2p~v2(Fjp`v;fCIJjFXaJiHPY0T<tW#vjt{
z5H)$aK0RgrLt6!pZ=ZiJmic4;!^p@3-8N8GNa>W{g-9l^3rm)y<e*{FV|U78vQ+xa
zXRmMHzl+sO50-Z~KQoDte6LPHMn)GaDcyP3yu>@I-E-FrHC<hEHC_;FOOXm3uc*!F
z>shob*`{HuDI5Z(c+HqKYpYJd(Ga<{Yu7%+YAMysnmv2byLay%Vy0?}7i9Vq$6jRq
z_;HR)k-Avbk}C2!kz$?`Zd{7{S%TYmZhRcT(Or<jdlN}808eW+V~b7wjteyoeY`xp
zzGm0nzI|)Hsg%7*NN5*q5Y-F~t?l%nR)2+Y-Rss0woNl|=lvgnfh#XwzWnH~zuq3h
z+@b!Sefw%wdP|twbq=Ox7j$mIV|t6K_PVv`h7F3^+RJTiZ7r?y7oys|y=<PiQj{hd
z;lI;jR*h)J7+RhA^xS$|BZ#Y}Vf96e7GZz&x2dIM!9uUfvgT5CZKk`d*VSQ-g9gSH
zm7{@Gk0ztEW2cCTiS=r%DX)Hhd@64C{n+h^2n6Gbxazh~&jN?<mzYLR>NJ8(Ypc&l
zJJMS3kdtY01!<eh>*(xeX?EPc8NcI>>M{5+ZPQ&REIbz%q$|}tF~@B*3@^1uN;(?#
zOL2g>7<YVqzaV<gv+<@q-L4<Rwf<rCyP_Fwl<z(7-Mi=e9?oe7^?v^T>n}d<nQ=Pw
z#J2WmgS0({hQUSV3A>^+uVE1HPb^xV$<3A0C%&DFwA%FBZ;C5%U+xa~5e=jPHGTcf
zAR*SkBxXlRBo5RRFUsBLetdgSbb1@TLVWzT;&`(oDbW#HF`5>IYHLD+7Nmtm@^M+^
zOE{VsegF1tJ`7|0YlMV_mtYw80*<0VSh1&s85{1)y@R;dDL|&~D`<G%uUEajmvQs&
zxE0ftU{SFL*M_GXumsf(A3iqt!_Cd@E&3pD!KYu;jEK_ofW^TSFEDAp=Phu@Vk}_n
zNwTgxz<*hcTR{GfaR;u^zR~gIy;|!BzX>OdCM!D(^fVaJQAEm{9i_PmXLB#Ex7-dK
zv>c>;!h)i#iIIr(7$}$bA2_fW7RXC5<>sNV|KLI2eV3=q$6>c`6Voa6j_UG67i=}o
zMrT8o5kB`mc~#Kpi<d6F+Sgim&AKi%Z@?X8fMm{wi^CPs2xTfrK6wL?x(V-3>S(zQ
zu2k^Q7iny7*J;m4!m-p(wP)IQRVS$HKUPv?XBp19aVu$P#_4enWAi3ioCY<kchj~l
z(0O-witdb6m<3tw)sa+GcvoyUV?M^TeI`u{r^O30zakO*YMkLYnW=A$lI7QY?NUE)
zZ|`$(WqIYI8L6$-eys~N+8n?9+}vCFk=n5js~z{P{rRX4D%RNEFeuYv{Mp}ce_;T-
z>I!yMelzauj<jau3Zzquw#tmme22)(v-we(`8!Y?2MrsZ<zQhC`gBbIwb)i+K5qHu
zM<GqpSkSzY>r7QufA$Z|_gHA`J9=}wY2siOa&?{wZp1q=AeI69n<-Cz?EIYPvtB9R
zY#YylB}>*a{=~iUvi)}>+jGhX6;w{+Z~6JHx2`ZdczxGh&#qMCh8J&vDL!~8E$5_z
zRQBo4^x0}>GuEtGBdn|#W00oFSaKwO`H7xs_G)Uk^&3C|pRjY^ga5iO9XT`qwgD#0
zmN0JJy7gN_i=Lt3O-T8;7+=4BU0KqNfv0bvTzG5CaS1!3$5aMj(lP%;0K?N~^sp1h
z(m`{b1ED;}Z}z--N?3U=25xsslwPM&Hdxtp_C`>Z7N+B-I;%Z>wnI)X)??gdxCc|T
z<t>BPHwp>8h8r=10W2%-GCOV8gO`}HZ8j6noEWk28OVP3?g9Ayb@^nGHe>c|;3wVq
z++!>1oUy&RR<Bueu-x?e#KS-4V%kuRZ)BxIfn_^wK>}@z&u}?=iSdH<`SV&PpO%>;
zv#elz10nyWp`qclPMG=?x(9^e_2I(-pV_Bm`Xs^%Sreu+@%KnHxH87a$11vTrBkP#
zp9@mO*p4k*ioT8qey{3-BUGTQ(>%r&`?kaJHVyU1<dnk}T2w?QY_K+vK5}GZl}}Pq
zl3RxKFVnF{<-DnENo)J`bkW%OxSG29dCcx{F%E^u-(ay80+-?$Z2zn7$&(E}H4Zbk
zxPC~ei8EDwRMVez4ClS_wDcdlekaVK*eu$AQ!~IgsY1f>l#!T}X&dZ1^@ed(`^0HG
z*X-I1S1k|u<FBXrn1BokoUfv)DkUA#jo`f5+!N(A!wXyVD~2|F6Mg;t^RKkiwB)yM
z-=04yc-B!KwMn6{urTIdOE@BiYG>Dj!*?Sio*`#QC*m3HLQPXsqvt^o2bY@vA;`QX
z__V~E3pXy<=89FlE}59lK!ACRL$P4dq6@f8;U#`t{t&?3@Re&<uO8OaJXNth!WIGj
z>3Y@R+(KxcgzF1egvgDwIv~xjYcwl+wgkcKfL~r=;XY;MS&UGGnsxcrJ|cM@J@s<o
z2~4b2(`Eqb+Xfqal@A0+&Sg9Y=3u-870(sA`nY;&6H7W()G`~Gf{Q^((+Wl<g>8~`
zxoa6SX3W^4<1KPr)905s^JkJd)-e{HA2Lf2pM7f3kI_xEoXJ4)yY#VZ1+4ma4?9O_
zMt$(BSo@3-nw(sDItBB@?;Z|5dib#EWFg?EkQHdwo~Gi2OH1#>X`s|<ZXH~1#(rXV
zN@X$)v1V+Cpx|4}14rA}^P|}W6nI!&eYL{-_wTU|zb5F;P0%%HAFvYI$o^1qM_y&L
z0nxG;2MjXI1!z>fjnYe^Ul1@YGe!f;@kShm5A|-gTrcDw7hs-0fBr!EqmU1hN<(X0
zfO3SpE$Aqp<e}P7D)sh0CX<QayjNX)q1V;+87tpa-}Wjvov$ON<1@`MsWR$#%-!0k
znB>Nka@864PPB6?2L}h=m9|^QK<$;O9ug#F!_PQ|Uir7azPPO=lf?piok#l3E_<Jd
zdS7C9Dt}<<u1XE>M+?29JKp&9XT5#9$64rh_4^g>`RXI3HFuRSX*JX5thg*!4V1lz
zaSX1#5_6v_0aP+HCe&^qBeyxfLgcRch=#t9cO|yWGRAM)wk-y(JY%AraCKstvE9s;
z&Q8mHmv>7b=$hE3K@SH2%JuR=4EMO&A5u2;+w=@Qr^QMnRR#3Iq<4YhyQn_o(5YTm
z^*%V*eZl#vdpv^Yin3~2=;~sZrrZSq9d?)PG<_$=GLio^dm6CZ!^7ZSrmMxyNZ8A*
zqSTSfi}Y96)!E$q$H4$e0uUQEZ@#GX=y%NsUvHo`_e0n6`9}T1`bTeNglok5PB=>p
z)z4;NI?1Ql%EZLPHslBH+OTmwvs6e##806wK*FLW-jV9|z(2rjyodnF%*E*K?G+k7
z=vORYqUlhe?+(GBwg0rx$)_@PdyclZxA)qO*K}IE{=~gxB*{BQFR9=pfyV7;K4Xj$
zNNA~d=C*JY{k=P$rJ6b0ubG3&$I3od$pUknIv(;a{il6qx87@<*ZM)@0JP9?E^k{g
z@W(qme3MVMbE&^vm7Q(~Fz18k&*w9qJFqoDaq{fkxf7M!A25UM-yLEdZDjhk?It}<
z_b2NV&TnT;Jp2=8JW@J?FzH;^AYiY+f{6&vL8l`}M(p&Q+KOJ8buC1wlb%icPYVUO
zby(JbR?7Om`%*R{3h)TbdbM@epUN|(Y!whD_bMq}dS%tz+`L@YF>)*^<(Ic}ijDT<
ze>#6lUS7WUye&T5J0!mwenDc!mv$U|gF_j4Gw%-WF=a4-w=N(c!8BCh<>b<h@kjUU
znU-qbJ|*--Y&_t_J^dZ34FE_&YoltL1KT6?l0;x)Gb42M&Oy+4dI?$n{{B0Z-GCOi
zyN|@z8Ar}k8I4WoZ?!G|=xLb?@ZKab`+BVD_3p}~&CNYM+@7AE3|~)0yXl#knQfJ^
zYZ%9{qRF<+S6|ucMwNGd^#=5rc38~euDH(xEXJ;ljUnfp(M_Y%Sn!&5xUaXjYFJGY
zETIkf)T0)zT)DZ>W(1_v@z#Q%4I4Ld9lo9XAvaeMW^qlTc}<xz_HABZjdCA#ZR42>
zj`rfWZ)g5`{CEljhQel!#hZ8cx5^nNpLsC)Akc)tn6ZbsXQxJ9ac_wG;JvYQEZqM6
z`)~Cep1<q2O`B$;{@S-|*A%a-&H?*w@R&Fpc#Q0wpY#m|-?PD}fDS{dj#C+*^~V%c
zu)hm38D0RaB)}s^DB3VX*Q`J{LH7pM8>n}AhReV%c6n69+E2A#0*{8NR8J-9N6hWV
z8T|~GqlXe_tvU=20ftWs;6n~1s0Cxxlohg1NolpD<Wd~C4Z^~{lQG4oZ~6OQMpvC5
z@OoQ7V1lp2cqI=e9$8~<@P(wuQak<P{M?Tp-;E5g!G@f0{Ib(EhqE111$H7S8?0nH
zjI>H@-aH3;(%)#RZq!o1=G__^i{U$D9zPGb8*CSY0X+D%(ko)SQ};@*tL&)B-P^E5
zi~BeXqm+91AwC#7Ofn9CH#vv)&$q$BOS>L;g@><4#JT`$;Ln&D+zrCZV5RViMZCOJ
zZbN@E2m9dghQ&{ggwLx^vJq|g+V$nj?>Bg*%Aa^3)TkQV4h+1GMk_L>B1;f71(=$A
z6EGZC;S2TNQxzBsNW}|pr2cddRx515OI|7KJ$MWji~Yp_BFOZCp~xTf1H|+%NU{lf
zykHadtEgO14x4iL`^Xh<?|r9FZ$Od^J-ngGZt?Qvf3~%qUMcJN;g~`9nSy67=&=i}
zy$JWjn?*&3Q8?x_x3;3HpCNtaF>1Q~!6VZcxFg`|ty}YvBJ)~7LCFx3f-BucSPXvc
zu1lLIx6WcDRzi@;+LwxH&(&MEs&`MtM*~xU^ST+e!2(`h_NV1_u{Ju&%Z8Arn*K^j
zYeNKw%^_Fl3I0}$Y2^20@bmMZu1l3b(7BYGyC01H3dMoF{Nm!`&3`3W?@3teCu$@z
zG~JHd*j_;hcTBwgy4R56r1>J^d}}|zwY9k>e(sg4S6|~L4%F)&ie7@p0FcX5X9!XZ
zhvF+P)rdOFi-;06sKCjn!VRf0aVGgooz*0bckI}~;avpe)3e3y(G6D0o~rk^<#k(K
zymX05f~DTeft2pbyUWPq3NrH=+SN-yI^p=eEG>;w8U?W7!I6sER*@b-#ZBzVyoEk}
z*Ib>Q(N&yHfH#o;5!Em_?72*hy44T6@!)^kxG}HkjuKd=y|%V5c3gHTdz)!p&^0kW
zsyE(vH(KGY>+;?o-;JGLG3}0ABs#846M$wx$_iCgRg7Rv!JebN3=RuVc|L#?%$FKr
z-n<Om*v5`6dDDOg(VSh4+84t!d!Q-#j6Dbg+eJoh-+uv1XJvT$83Z?on!lbfHeLYB
z@2wl#@go;6PSelw;6pL82c<oh=W}D@1+=%0LEMCzbrtsQsUrs-e~K{@fB_n!+JM)@
zdCS2jHXGc+o~D+T6KDc1moQ(f8-JP$9~Rr&v8(zEdLb{dZEoXK!}5S9P$-wL;2t{B
zb8dS_p~430^16I^`jjbCiXyZ`(Su)(T9mc#yjB@dZf%+qH=qjVMH4MI3TPcZJRdi6
zUjvc2YVF!zhywocT^mdPrK{@(ViRuFn+oG4FK=&dLFO*-j3}AT<A$S0NY)}(iVa5n
zq=965U%y6du|K=53>5n+Fv1wL&cc;Cp7hlW4Gr7Ch)LSiU&jx<%+FT|whccTq3Jtl
zl;I-bbydJT&<uH=;f28$U5Q@mO+1lQn_=Ovz8;M73w0)QrklI_gW}U?zP_Dl-&0>#
zwras`-`>8y=65q-XMPifc<T1m?2{)?<|^!<unFhQUl>hIO}}l~GM|CiH(fzNA^a^$
z#s9L4OFV^bDn2l<^T}+uz2^t0%6QkN44Uvb@o?XtPoPd7aOi9Suy$X^*WQtJ;QV=h
z;2;%yKxN|TTMPE=kpY&W`~G|fp6sJ*e_eOY58&*!mF`a-hUI>94*{F{7j_gTTT1!?
z4gF%11fF7%Sq9Vu4DC;Ae*@FN6QyXEtux-4?kRg41)T7>arNN|VDZ@nTn_X2)KqN^
zEv+U2CMOQyNQVfpt?mLW!V3QVsbp~Q@!<jX`!wF~&bw>HZ~!>BF<0s{a8R9*0XTH@
z<hK&2zjNIJ-0!)07r5%C2Y6<1`0xG3`T6Qf$A184It@}!iGZ58K;<Q+77Pvf`+l+k
z6S#Twxx<H<K{e>%XA8Gq0Tx9U)<&C`ytyH$si_GpaP@k@Lz5GCbcur68An%zuAWh&
zR>cQw@<;-QJ<3WY!m_Wd5Ck60`S8;dV+NqF8y+`+nwhQ#H7$VjiNq;hs#fLidVqs>
ziNNtVP>9Y829~?dz-|U`l(`jH&HniP+Zi}83T#<b01rgoQ2stHB{fyi#N^7=g`mdG
z-U5iffJ_juWN`=PS{Rt<Yyg~TfCD~I;}#AqS%A$VICvre%I+{Q@ESkXKbO@{b*YvI
P@6z&g^>bP0l+XkK*jjBY

literal 50186
zcmeFabyQXDw>FIV*nx#esDM&}fPjL83gQMqKuS_6rIl{%1W_aeBt*KqOA(MR=~5AC
zkd7_zUQ6_h@B8D7_vH7z?-+;S^Aln3z1F(#dCz&xYhH8yp2*5bY~4h?iGqS+E9rum
zJO#zNU<!&gg&Wr6Cq?(WBJtOHZ7B&c{Ee^qb2%>fYvaueS1c$fXde;(t#a=j*Q1~~
zNI?=iqhR}HxYNc_L2+q$+=g4XzW<=O?B1NitJ&C|?GAIivu@o1*36LIa=V>2@D1ni
zscVS7U(L+?bgP2csy#b)>{xqd%Le(-U5{T<Qpz9Y>6?{swy=44Q7X%qBB8H`%e_>3
zG^@{f)Yo^5<ByJ-CtM{|tA5`U1%+(2_B+}?-&1_~F1DKb&p)nirmX$@bF~+@iH9P-
zTJEkrvGN^7;a<kzzaNu+aMjj7KTce?@y4HTZy&oLy5`UK#%KPWAN_VG!}(7GmgNJM
zgE?dQ&#X@#Wh$Xs`LKu(*1ByJb$!-3z2;erq>|Un5*Fs#t}8cllT~~Dzjljm^dg!i
zRQq*4rx`Whc=YGizA*k<Zq0JH)mWE?OvGjU8EQ`OVcfHKui?yaEA?sHM86XTAs;`A
z1vt&LX7>d;q#87Y-(EEC%vQW4JRho>VHy{$7!z&UnVtQ^&*;SUY8t!Kfr+<VsklBo
z?I9lR<Whj3&0xXuV#31Iz`KS^A{yi4<I!pv@olLF?;J+6BvP)|G<_|4#4wt`Xqwf#
zm+c43<#5mbmg)dOwbJT9A;BAh?xpW|jhgz)js_`|KBrZDcYZ6=(W67(o^dt>pSed|
zvbVR_Yg(SYgo=$Lq9LlIqa(3Y_xg1*?XIXewT!{m>p_aMqn(O5)+S6xjtu5bRyWYv
zbi5tQC(Ar|@L=a`@3?KMaVxjb&%!l^4dHHvjnR5}dsWj6!_-W(#9tkeuJ=BsrtA77
zQ)^{^-ppxl+5a|jpd%|~FQ+CuNs>g88)-{xOgB**Y0u#7=X!VEt$s6SZt&~ZR9WgA
zTWJLpa4FN;8I!iux(^RFE2bGX2AhoLj0sD>K7Pg{_3Hb(iJNzZhlfXN6*#M=8(;Zk
zt;yjdr5LU7B+?{QDOTmJ^++4Z!;jA-npV(S%u?OnK6|h^fs^#q%}vqTI-Qb|@>g4C
zr)kw|u5;Ukre4TjzI<-R=gphg?IOQq^BiWkwQ_iH@s%+1&A4=L?EU_onTv}{x1hDP
z)w#wbGh=dTk!)48mH$R>x!0j~Uc=89CVx~lF!`ucCU?$^v^Qp1TpwvoIjSp|u%Aai
z(yTjwXWF{o>rnW_eSfXj$Ght`TQ1G_J-R3?RsW1LKN6ewX~cRcIwpRTucLRUVvJIh
za)M^>-aZc|sn40WdLF;3v4#b?MJp$8MBO+t(-f!1ATZkFE|Q*ZR<JmuU3Q(c@N2TB
zvA|_HvFv&j^Qw-BOZ0h@)q*?E(+ROD#n$e>{$?lNJo_G}vf2>w&Bs(znQ!=?d$eu9
zLyF_c$@wk6@9p;G?Hl_gzrFv5UO`6Yd0U!ML}QFH+tH&EbnX&%c6O@yj#<?Frp9S`
z<k1{n%YNCaAQ7#yvN9~O-!{6_%o**+GTP&?bc0Q5W@&Al8rcdlN^z_fu7WnB3K22^
zzno`(7Hv1eLX}rld6o9OY#(V#(kV?fY3Gv;64ubjwq%*KTXX9qjs2H9|HKA$JCjEe
zOR02D*iYRU>Vv62GIpsG-)DgHfLrsKn3(uJZmXc85?S<U`%t@CfntUCvAFbe54Y64
zJ@-iR7SkS`?tG`g+PC)<J;IdYRQ&|3qPNkU4F2%ptleokz3bPF2I|9{Qv!3WhW4hq
zmiO1a^Lq7)ffTJ2=O;Y(Wesfd(z<o)CTvgR5AL@S8$R7>(><_69#fW=_gLJwd1cKc
z?@`joeOJ4Cj#cvUbGQ<iNpTytHV9i4*gZzgXB_EvfFB>LTHJO($8x$+*=V#Q%Og~9
zH1pi6qnEZ(Nrd(uT3To>81&I{eyQMDsGg$lRxL0TyCEU{7i>sE+V8|m#!~?@u;Poi
zZrwW5o}g8ri4A<gh~2qlU|_JS)V?l6-25cXU`vut;i4vXE2GoeKm1Y<2g&}2@Aawg
zuP&`OK2SwTQ$l6cUwcO6*Ux*50}~aT%{W%~W=6(sEgPiy2jFA87`IZNV7?kKl2R=o
zg<bPhIFVlE&%b>?+sYZ15vsf$&q#gZM)0kkl6BIyf!1vX*^4vni_WtH5kc!=%<>m6
zx&}Ioo*L?_5n0Z?I35-nszTT!=|I)@6vhC%AHGe-op<lvP1HMcMCQ#66L)G})_xV6
z(T*$1>bJx{eNRzPIJZ#6-<|60+~dZ7cx&ESlh)*7N#WAs;(?)rf^EJn&)wV_9mevm
zdmZ%^ure$x-kXw~96MrBBb4#x-AX_zEW5qdl4<<Qz3mS*B5N|<h*$aX!p*e57;$=a
z_t7?5`D@p%MfKn_W@AT2J9EfhL65^r=^QHC=y>!m)>_Zc&pY!Jl4r-XIQkM))1ti|
zUy_o#?Ck8EHKi6vn1&Jg?IXHm!|RVcUcZint*MG6z%Q%#6Rx-2EPr0VW!;WHVQ28r
zDh-mlb*tXB_Brn_{k3n;wd~I>tueRCQV-iKGr_jUyf47UJ3DB?8g`-9PWX_Q3=*r;
z)iQ6s?J&(?jMB)lCj47V{^aE3^hidx;rCB3vCZ$`$r9$~Y+)7!@a=-JI(HkTo`=I>
zR=16|rd<D6y0|#zl9A4D)}?M-V3zd~pE@|4Qqu%8Pt&iW*3;Mb=2l(V)HfF|h$>UQ
ztdyLN+peBrsv%f3uH!n+R&WbOtlW}SM3R)8P`f#vQX{;%WmIH&-aT!x|DF58;RdRA
zq*q6x?576@>)t&oaXS#9SK-YfB&303W1gk%yj=_5=w(tn#A@cXwZZ07JYK!>f`dg=
zL_lE`7Vfg2JJPf~U*n=$I?|Pwqf)R>%P}c`aYhYsN{UrAbRwyguAzHrhQBdV?i}2I
zeSl3@6qA&<r@@TSAA?k93}$<BGsU-}EuZ~4XPj)DYT7128^+PRSxqDIgMFIJ=^{cx
zozdGzZk}AtLD4^iVE9?Xrc;sSRT&ckSgdciyxiT>aB-(johq-WxU8uexM|a-*hgmC
zojv0!rR$<s9V2ySTQy*tPgwl8I6c@{F(&2V>bgZb@br}c0V{d@H&wh(tHtK+${j^|
zUJzx7?RnLi|ExxFy^p4SR2o$eEF;ZoNU30v-sM+PniKXMPcM8!t3BPMVX{WVY_TIr
zM*;`!lEch!9bDpffS7SxUDqT!{E)Mc?WuZZcEz4?adAh-T+__2>#KZuBoOP!Gb0Th
zSr)uEzlffq*+!Dr*AI`+n^8r4d_QBmayzniFK(Y#flqRNwv8_6i(7h2i;B?1Cx)gP
zgpKqx6W2OeL|o9Ltq1ldCm_IFX<sDIHY4avxx4&2cm2(+huE||2M(yQN9DLIFC}Z9
z6@AG*xX)NWSs5qYkJm_lF;}l*Z^3uFP<QMeSIBD}9UW{J!}M_L<;xK=il(OVOXTil
zRq_}jH?5HUiIDu2JG=RfVU^os%fZiOZGkw*yBRiCW^9{^Nys;B)bC2XcCWs^o{{86
z%{w7gvOd{zK;DR4Cqche@ZL!V2E;8|R+AQIZEbDO7cZ_Z(v)4CJf@a@-=qOy*QkAV
zf%UAq`WrgiFRL`{2f~`hoEHWnHo5(QrFG|4Q}5h={Oawr#YSZ<JhHT$Yj`FBuzWPE
zeM3V8O1u1Ob{&;TZ4>j5oe2mSvJ@x^wD~nR9=4G`Qi>z%W7~|z92Zr0Yv=ndG$(7@
zoTus}yn=$XvcHrU&Suv)G_d{pwep0Iw^7zUzVK}Cv0omK5!~bq4e^w$qj-$8^itRT
z?|%JqgcY?ebi&`!q;KywnmX3I^AnK4)KtSIpTqTEaJ2MHM5b!bId9%TXEyjb%1_Y7
zv_)7g-PpiO_LS|<VEfro+H}iD&G}B`DTLO2WG83m{6O1pPaf;L^!@l@*c5xEspfj1
zoxWJ#AJ_QAeeH?El)HAls}UwwE}S}cOd7uq4|JNj0xVX+YIkT4(@~qTuDDAgiwcP3
zN^q4AKDNA**j`++H!rlW%0Hs+orK!Pj(ClnXqf<kYvj!Fk^^xg2whhgB%Xin%5#9>
zGLm4N{`_W5U*6wq1e7!{c!=n!)_#D`ShhJ{BMk;KB%sdW!)MYK>b$sM@bk<2ozweq
zQ=A3A>Z$$bC;MWq6+N_HoNfjr=h(A*w~EQ;O`AOZ{G#y!`OX}hW}un3v)wKtR||+R
zGuRXtbHFTDZnCeY87J(TP;&_tJe=Y9w_>1DFK%vbM$*QO8x3)g%l`S3v>Xj3(uJeX
zEvipRbURME*-!N!A|ce<4@N~yeZG82{8aK`ya%!F1+T??_wrIk+8Esa(@6I~A%|$Y
z$scX7QAUG~%v*p&>a{aP54Sedhe@R&Fmj76iS)g(cy5|Et;9BvqgzI)ar6DUGwtp`
z2eX|vS}o2sCwsQUsb|RpWcXC{%AY^~@Zom)ct9MVQ$Kupt{eWS^xc`Z2rF0|R9{I*
zzI-p)a>Mfi@OYX|={ER6*0#56Eo+2kcasAe2By@+xOP7(dOKFItVzV{_L(pr&3eSk
z_{_{q%F_Mio-CIwJFS`@7{BuLB*3LXZSa|$vkt9#J}kVvicSktPv%}9I`{Af{9Caz
zJw1Jbmdhfq@f%I#51)U{PcBNju9)<f@1?j66&#v0>k(B%eltHVorm5W8qs(qv!I|F
zAW1V4hd98Uf#s#KWy@YK<!DK-rw+bpH|ov;UC64c`c3B$kIGC^k5M|d2ppn{XxC-e
zM~gFj@t<upgcJW9uOX;m*Xxx%|HHpA@!B~-jx)eM3kYFY6_Ei%!ln@MXQ>u_GKvYB
z8gRn(ipm;cgb@IcmRDDE$aO~qSo`JAecMcX@}_8tepNl}B>~nU7Znxt^x3lp1cykV
z=oqyOQ`nC`&>CH8f_|0X00LdZ`+NWJnsuFaf(fc-nTO4ccE%uT5!-0ko*oATdiiU@
zpG;9<8#3ozBL`O&h=SrMz2wcm-~Ze4Th@;Vo_lufy8J@|9{rsY%b&L&{#O$3k5oDr
zQ}6w~#v8}i|K(}+A6H57@6O<lJ`r-5SxL|h7Z;}Mww3Y$jUrW58tckaEO2%j@>?Sm
zF73Z}<mROf5#>gGVpf28Nav%Cn&a!Xg?)}MSh5GGA>yp}QiX-<@bhW0@3h@?LiTGr
zw3@j5Pu>c7BNGAQA~5;>#y82{oLWTcicGHzdqAxFZEc{L!xHXvjZ*^+ytY5DYS?~#
zASolG)EKSUm~Ca$Y1bF9fE?H}Ab|F#ijFJXkUEF*Ith(iu3UqyDG~8mKd7Am#w0IY
z%7{NzTQp)VA^3gQhqAf0Q;u^tKXD$tE3}PXm}aE*nJ>RtAl|*SG+Q7c=qb-tqS9Q8
z_@h=Wr>Uc`Gb!+Ku1-B-xi-06K!5-K{nWyanRt01An<LI<m6<@Y3C*6Z@(6Ng&fk;
z#!Bg3T$%IVNw}+)f&@7HZHTp^&+fR(;*eIH%EN86sv$ag$cOla$ipcF{xfWjkAp?>
z8n?*8i8MRSc1D0#5X-R{n~~Da?i`hOTOM~GiqDA`OUmvv1%?*KnQZ|6B-Q{vS^3G6
zCjw)+la@nq8G{v^1?=P3eM)=ka_uHR7r3}ICTJ0f-2wu4Jp8@H)P7mbTV8$BX=dF-
z=281}4;%P{H;+qKW5pwnua~nKZsD>hrM00VU8CRjsOa?g$6ZGR1=|zcfnTqf$ji%z
zSTfBlAbCGb%`m0|cT~4+{{Btwd~2n6^(!IGPiMc5+lJ6)2O);2Bmu6owSD!Jgr~6D
zirkfX_FpVPFn{wt4)*kdSe=<q4DM7UefY66?Ycm-gNtvxy?-><#9m2}q%nd=wW`dU
z?K+U|Wak+8PcnRF0f32)w2X|5So773byPF`bqwE<$cUP8I-56dmf~G16^YMZ7<}Br
zTk`tMz4hXs&2S%8aOn(#lRv)}{Rnx+v8lBi+oXQzme=gG-Ll(fz?>fI90uy`ScTva
zg`FXWLrrlD?cGb^m6er`+j2Thg=RY}fQ9YHi}%HK^BdHE4=os(fP+a%_u<rvui(fb
zGCF4yK+fIt^jdXq18QYTnyZQ6`%MR&MijV^10gJVChh50+V^SNUfa#UlD9BD)V{F5
zTEmqr*tAP7bNpDvYXrN&ULP%QMgrP}ZBC?QNvN3=4ZrAnm}D*xz{<@00(%RjNH7-n
zg)W^rbB7~mnA@Z)_f$xx%n2ALVNyy7nsEh-BW7pXdw={OBAleO^w!pHfD?8cF{6aM
z^t0bs6P)97CnO02f*W1<;cxaChg&{q7gm7T%=63R5Rs+1bMW62<eI_9I~k^6TFg8=
zm*E8PCo3z9$lUcSU%y2<cAo27{{kF2BMH#sa*p-LQ>Gv$7CvvE6QRKPD((Mlr}N}G
zGOUBBUAOt<;~<x%);8fDSDp|mQg1O;YlV&DffF}m<DD*n4#I~M=&f3CEcfyAEYK4S
z_96##6bhUd&E5ES3yRcWi+N2uPLk>oJ>tN+kvsxjmgXNn4}wGSa(s?>z?P63LRjk)
zV~`lJvFwXT^a)CZ_eSkOCPqe;QbC&MFM3Wp|FVr4`BCM6FP)6Y#qhK_2A@XFrF|bi
zi@^I?5Cly#)Nn&ru3Q0Li|;m?XfsLx-LbK)8y7cW3IYs@BqB8uO#qIz0+)h^TlQ<G
z<ssDF^S;26kXcS%(ALqJ?y#sKc@UXQ{zb$Z(O&JoDZ{mW<F*%(@x`gJb7p>7odRa*
z@$*hV5j!9Yljn&O2F>vSFLmQ{Mze!W;BI)sJ-?i^Ld37Szzq-h%$ixRK0!-_q3m<i
z<)dwH6;xec=H>P{%=h~E7!`2%RGbWuVJNG7bBae-t(%hOrRVz%CGnIr)-Hi$@1V9E
zn}jQgSMN+u0{wjmB(gsx^7K%%#BOC$?o6AJ(lTP{vDu|_r_8@Uj?&D_wpxO3`p4KO
zAixCA<Hq<3U>yK?O?nPIQ*8c`OM%7?>b;#c*jj@$jTC+9sOpU{q?S7aA#%y)y^_bk
zAbBXnsq)5Y=BXkTc-r3ru1tj>H@e3_v+5k1N&$YnT?&j@ZP*jAF4b;Q-;L&!yvuxr
zR_p`Y5_fuf^IM^!i^CmRm)%8{GLf%@fBg6{IOD?@enJqj1e^N(yDpYSiolfs5O3jw
z_Zk%#G#nXHUtvm+p>d{X${FmVVK+GKkqTc@Tv}QI3Od~1bW`gxT!L|Nf8K+(X1GcM
zH4t|IK%CXRTI}9G+nbO~NuGGd!q2bFbG@1{$tj<LU*ceM1%U9Ti8Lz#Nn4?syA-*N
zBrV;Dpb!t@?h-t-VO_`r*r}bKuC5qO0xf{7r<WHK3Hi1})|9WeclgF#M<NmHh)>!B
z)CSiOgQUxTruDj)hX<v(z8}|lqo&w8;v>NlM4xw~CPE=tIU-#!!c(tuU&bM3BmqGs
zFI)k^w|+ZaY_`=<G(zfNdxlwU-?0~9%$&$08Po!nksP@{&mwn^2Fj&AdGo4Rn3Rm*
z$O!~&qZvJ<9$gM&LIfiYd-G}-`229|fv0K9H7-lLlDo@~pZ}`OTXF-oYmfZbvfU(u
z;2!1RD?Gso>^-L1kHn}EcljYOf>BbCLlac%0*Hp+5elGBSz9}VBY#d?2(jgw)Xodb
z2f8^lve$Od4@^4b?qO4k3=S6cAUJ$VTD4Zs*}nPlTpfpjFdyPxv%fuMi``$2cqj9T
z@b+mAv!*0@IO|3PyvN@J<UYq<xeTwHI-N4B>Yiacu07;5fwMo9+8_hc17Lbnrin64
z^%9&>T%Ya4_p&g7qcGL0&zO%CTrxn;oS`4(h%{4gl+)wE_ojFaqcqSk?!Kq(%xlHX
z04`0Pfh8X=`0|;kCiVF9nGD*vwvX_Ec$WS2!1~*_Z%?c4lc$@t7*s<e5)*5wcFk|;
zuP^E0)hpi}(+R3--_)t`>dZmF>!fv~6O)q++3CohUc*+`=j~0vrTjxfLlYOl5yq;#
zVX_+;?#yYf^yN8vW0^!E=_B@E>q)D?+v8Na94D)|r+ck)s^Fgw+XsS-r%80j3GxG9
z|4_)e`pqfl()sEI8XkSuvZ^Xx__VF|zpzdpG;08%&5#l8;C_w>xlW_7+=j0o0k;zG
zya)?=I(-ZDl=esg9B}Y0#fMx^8JplF#HUoLM4TNnBMu)9qurN)WB1Wdk7jrr*~33V
z1!HsTsdFv_b&KZmNc*2KpiDQ>3tFYo71WmxPMIpvZ0wsGph#1n)<M&Zc7c4{PA|2;
z@`>xB2XrcNSFw``8aaGd{gF|SogO}@k5T4~F;P<Tj=R-bR|9Utjj_j4;EL-GY?jx0
zp5S+dJ>&gF+eJJT6jH{(wnzV@>ay+GifY4&{-w&wi^!}bk%DY!t@-$5*P7}s0q65w
z$YSG)!|&e8Qpbr%(64^8KUu?$+356|f3~IWWu^v|U=99qxCLIZyk|TTY4O5bkGoV}
zVJAvFL<02i&Bn!rp@f)ALBi!_Wh{-SB(>pp4r!%;bjqG6XH!&Bx#7TmXRayM1oq{-
zK-7z3+hV@HLG7s6Ipga4)gZp?77*b-(7C){w{LE7SnyRkwt%qf)SF*F1ebP$!aX#$
z_Voz^kNC`Qo5I4v9=Pg&F|fzwSC6*mJLRP1T`O9z;W%C_VA=md%Re*oBxss<D&(Go
znwVr)ImV>$(9rvCavlI_`P`gyO5V3#h8_)al;`iXe8pRF@^^ZOgnHHN{Btv;Q%^_S
zbt~`AgIZ=BxUa=rHCi+2F1+xmZHAW3EbrU!u<<di{L}s8Nl8hDt;u1G1CQvPYmqsZ
z^X8XdMa`tiI<fgH%!xB9GH@E)F<o+j%dZ~#ChIFDY#y1z{e1|oJ_@OG-?6BYPMX-g
z;L|T}KfepEU#%T*NYT;J@j>ZN0QvH-H*Ww?mVf#LkRt~t1kyvNGk?H5>%`43g)bI!
ze}333DJfY`9xF%yy!_NN{)wt*HYkut1;LlbPfvn7j+mOV%<42n-3dED<iklb%acLN
zsglemjG8J{3qO&Aq6q}l^6mV#M-1PLIHgV#M8BV~Q+ArDtPh9AFG?ZYS~J2r2Z=~$
z!S_=i_XeA#N~E5kMZEm|jgBdJd3l8qwH+{69#18&u9b=%K_J3AGo14gLE*Hur<GCC
z!0!#f=@Z#616{;bP&yllJU7U3;>mOy43V$}OJqTk9*C#NtV6hiVoo^jJLqb8Ek$%@
ztUKY!uA`D3L<usXU`cggV1SjKJvIIm$XO)zY4$V2SB&qZOZ)R(dG>%LpP-qS3PQo?
z3^$Mus7)dbcOuUmv#!WDJG@A6ut06R*25~Ws<IL5n8ZX`78Vx6w$vy_7B;pD&O`k}
z#O{JtWg;1Vj(my2Lp-=6)n*T0->Bke`(oe&JVGDQ*<U~rsd!=BZ6GQl&;!LEDb76I
zVqI4`;L&M4smyi5tPND+$t{}T3^g4_cu_lGW?(3yF1UMlHL*<Cz0@|GF5av3FPVoj
z3CPi;tqTB6E#jPu@*R#5YEW^hsV4{+tVCY5X8qb<cPnfzu4WpM%n|*HsA={6(@s)7
zQKlQe<@GWksIP=75BA~lG=J*i#fzyk0ic5fmVZq$k#tI2H@4RB)ST|I>A*Re0tit4
zw9nhRqPn^XWfmsV_R~Ladw6)PPH#`vd&W3`Xt6y<C26ehJnD>u^KqXY%ly7fM^ybn
zSS5K9E*YRgoM9S~3!^I!tMTxbdvTZZyn?LkRgSFhlrfz+9A{@zbV{GUNU{PcYDmTw
zuYb~VDD1Bmz_@4Jw(`ZHbFtRgQ~M;o-n0$Z?22_<U7oyvE7-5&s%7_-pkQyrzPy>!
z8ID78D1lsZz$~?z-+H)(GU#1s0&PTt0eIEb>0}^+SZkYZCz~faEZgDqlC^K1q&dRD
z!7*IaYrbdbI_P>Ok@tIJHyz>UZ$4q%QY+vLz{QmR{LY$xR7;zqE|-Rehp#uz&@SE-
z(>c*w@oc1~ygYet5|Ki)Ff(tkuZDNL&n|Zp`K2m3Mloiu^%-_EGGPDSc4glZX7yO6
zx*QubhBC|HmL!EzA1ifOtr@KvY*E{#z-cSh@q@ZoR*%i4!!W;$htZ~NJ;3J~Y5B-(
z<2KUHya==0@e=LD7P-4c&uc6atcIE%u;={*`nW>gj^sQ!0f$}W>PBw>5I<PKYd9K_
ze)7>bH@v<OO9PmC-opTvT2@i9xg{R)_GyB{?C3QzFsNH2k(H+(<H%V+*h%{4^6kk{
zNh?#r0p)V75!i&-8#jU~w>Z;7EX8^aS7r{5YP0vbAT9qHxsLGHH2gu-+}u32Jx9>8
zKNucB;A#%ixIFmzrpizpg$H>L>zT)F2`oC^Q!2&kH$GR%Q=<}gO2B$J5~n2c;&@3(
z$snG_YpS5Q(6P6#&#1(8zegyd>>!d7&W8EJ<8XY@fWE6{i5eib+AH+2<UgJn$Xkr`
z!x)W}93myaNGgU&dTBe&SF0|=e5H1mA{#UIN+42&Mlc1GTF=HN^K{ERf-~HLVE9ee
z!L2?(6iWK~;ePyP@jw-9u!#Os$g{3?O7FPAm4~81Q=2Ao0HQ$oqWOyn{f$*Q<tG5`
zjy(ZeO_Fc9;{D#!N<Gkikj2OWeqI0x+ud}cU>r9BpR%WYjJA83T(G>^*3qD}(niT3
z(|p67`wo|K)Z;g?t<vr5o4>zt7vr8|?9GUs+T;YVF-Gm=Q#&eA15X9zSl~PDVfm$S
z6>@ur?CB;oqSRnD+)|hA5W&q;(vQ+(9r#MG7cYW)D}4TOG9!DheWtrUsbL^BU4z5N
z<W_6rCE&>E*+-N#*wRS$3!)Nt##|0RrxT*~qFTR&YKQr<*7Aa~k>KZJZ<@7SoIW`d
zc4^`uMX~nPn>WTx_nSeD#6GvQOJoQH<WO6Vasldt;)DRPSTG6C`wLiW-do?qrUx1%
zt_N6ogKh7%6o@ND>Fg3oylk>(C~HBp2H;Cl<9asZ^GjE0YW$fuDvNY%3nRU;mCB(3
zps`lUs{$8EFypGJH{@D$Ew5dBtGE;laKFkZ^wFzVuXNnHt(r9=kPn@_q1NqJUFvh@
zW|q2A+$2$KCU8F786A?0r|#|<nnqAXBpEW&FWc8tSEo}x%Cn!A2L?T>dC$yY%!g9b
z%fR(RvAtu5ls9|bz3sx0P%sc_t@D^7GXIQjXEl#l^Ak*RO}B7hQmRE9=YA$??LY=u
z+9teY<^=?2?=B{we`n5_^j%Hqo0x8MpB;R-Vu>Col2yy;$}vjP{9U&1LPO-`k9-tZ
zC12OKfKQEqwH~{YECy0P8I_DU<Y6&Y$$Aw;U|av|LrU^b+tbwCx{pyPj=X<=Eg-54
zk=Wf$pQX*YQ2u=GNWeL#YsyWT!^07<J&)b#KSZ#nvU0?pnyqG}P7z}oz{%Q@CBnzo
z<i|@Ds~;VzohISl=O6ow@lcOo1sgv<zcJY(nIRAd)amE@f1C@*I9Lh@*@=rHVA_Zh
zK72?xQFb6i45$uvUli^Z`!b%Av^3YV-6)6s8I9NdS&{VjjEsyI<Ug$&f6jmF+ehsu
zr@JLWwa(8uU*SgnrJeR+jgaHM`H9|}Cp|qpLNdDSr9g$(Vg(z+r1lX<7`xU46JLGg
z4P2zYlibe)mqoU3Tt9mSjjC+C4G$t$NlACV6mQy|US(fpdzy|=q;PW9*zexG8<|g`
zecZh8b%OEM(#qd#f-3dBYYFc&7RNWYD28)+oGl%kMv;<$roe=+;7#7qJI|dV%4mF(
zO))1nU-h<9cicy!;WJignaO|h1rNP2GD+JlYEvP`t#6(An^DnnL0(z>d|F!Uyui7(
zS)rZG8;zU$=9RUa^h~<VD~_FMccbDuD_^%|22XSTvklTqtKvP%{m_$q9&Sw`un+cd
zAx}R(dyuuDSwc?^NC%}WL);S(nmqn%smW(mj8;kUTT?Y95@@DjHb%SE`lH@;Vq-=1
z&aD>M<aj**_$0O^w(04PvWu-3+B|i~3odxhq+8{PQP=pJ<qynGBKh7w^BSeS`A!Zk
z>U^FqR_&P-jlPY_tPJu}J{)Q3MOC$K_mQ9?ni9?gxW?e?hX+K0i|Ba5{QHGQ<b$w+
z3BeNn*8TnI^B3lhB;z3NOpeP4y@<Vd=0(XOb#{1ezfHc=eAl6!u4d$(g2?ELI=eZP
zNYqHeh&n7-x$`ty29&jWv_1%5D(!D28SIFvPSeb|o|R)MqwIe7n>u@+nngYJaIRDF
zuA%8p769wz+E3Npmy0u8JkvgHHyW^!lY|A`w$Q7N@<5z5V&D0wYSatf0k|sTUk|M`
zGumm>V<p5ZP{9Tmor3x#1~CdHs&gMVSK03~?#Q*XXz~4qz?YVO&&!`R0T5eap?fHu
z@!36s!?li9Z_U3fW^5d5)gIeEG7}S{Du;N#%e(-@AuN)SW!R+S?dLeP=C*ALmuGDA
zNm~U?tI`<)gPa%2BpDrE?c8<bV(o7^$q=gQZPh59*(1yl^*cR1J)p)4l+$_Ug@LVt
z7w4AxY$4XUmJpFlq&jD-^<Af)QH+`N;3RO)F541576sEfL`%<?FW-U_kYr_De&OSD
zJ#F&Xnr{w94kUK1e06!bJo)am$_FieRK8@KMy}T)Y{tIr(K*u$!4ThfU8UnGqX?Nt
zqWXn)o9@juBZHyaC$*py&b4XZ5YmdN!Zq73u-dy^@;gC-gvy_n+&Y^#C~YYf#`Yer
zt~JjgBaJp*GnVUoPl?+r1%=U~jM2EMg7LgBcQ>#0KD2>X|La^eYuqP!A?!|iy3?!0
zw{yZryBNbc1%+!+LmipbI~&Q_?|A(Nu242+CTGwA2EQqNmREK^80%=D7S(LaC=_X<
z@B!Za-AB5_^04-78haj(Sx`kkw72`H*tK{;Tx-yFjG9$?Bay=vZet97r#tuaYtxKL
zMrZi&@bD&~<#8s4=NZn6_C`6WJiD2eeQ$NOg}(pZ+iQR=^myt)ZC&}0W6YMDhbPYO
z(>@K$mjrcIm7BLP7y~)b+uKg`r)_1yR(gaEG(-%gx0_yudQTzEP_ekAWTA>%Gtb^i
zta)-UCgEW~s}=WT9LWJxM!Nt*E{dxQvjxj0i^FQ0ifu})8K=*$89!3*Fx9_vLV&oO
z?;BbHOXl=$8$SIjrA7Xp1Bj4M+Wt{3jOTWf%T&p8VWZvlDBY|(9`cI8yPG*=!2|=d
z>>28yb1zl3OvR-M;nb6asMwxw{><C>Q*7b8l+1Z$NqZ1=V&>@#D?f}&uT}oO?|Ob%
zM7T@<wVXi^a^=#tZRt17>|6pLk&QQ%^o)CES*Ms8c+}_fSF|JXdrQPt>?)zB1dXzr
zkIV6NH)oJA4rlNP%uT=Zy=Bvt>kl01eI(R-Tit!e4hMZ5-A0tfST}9#XEx~0&r3@r
zq&lB*=a0=cc4b?|2><$7#2v64m(f)ZOItI_#?DSSnAo7AV*>jmAsOSnYzZ%;nygEz
zO?N*%@tD`&2+JQPd|ezUz7FJ10+KHXIgY6$ih{Kv$YY2UJ$-y4kQ=POXg+tuFPHb#
znpV)TScP4dE>MFk%#5&+kYl<Xk@nl2I#O0`bKW78R4<Bjh}WP#^aZcB8<!uq-qUFd
zENz^L*@z|SH#y<B1oc5ozG>$7K(h&s+QW`r`~5*%L#(jLVHB*bQSs9~#m+%Ea#isJ
zbS;|cX&0_tZS@-|WCN6w#Cbi|cwK$*v}6{n`)8n8ptgDyYWTWm9NC`;ka(un8r2)w
z<)uZYJx))oepTM=9ydY#KdGn4?aE0Mj8hH2lbu@)GEm4Gnq=qYZ5$J#`2-F#uBPq&
z{rkab@fDMkxyUw=6nQCFW}IQP;eb9>P3r~WMs^19Q+4-n=1tXePb<<P!+E=zBU2O}
z@-l>^gxXenx}2eo5QLDC!cN9a4iAl&(r2XS*$B7nSGJq(shE{{4FBLA#ffbV?w}Hr
zHZ+XBB@l4l<E1X>>Z)CrPG5s@=n`Pw8d4^GJjf5=d2YBSE{{qE_tW2+@iaNH&Zuv#
z4m868D;*$}3|RU>L0U^AXkCdqEnwS~0_Qw~&g`_taKxTNWNe>V%aA&#)^0m3r)fnC
zq@+5oTrl|IuDtfCvF`3R`hEMZ1Cgr627!g$Px_HP6u;RX<h<TA5BNZ}9y`=0<M_au
zXLaYV+q_qW90-MrcIl74J~i^t=O`W2;|+!S@QF01ZEJH;r9Z;LQpTw?X3;x@Ola1q
z0VTc^5ZsrMd`{a;^~<F=&YSbxs14S3@|;TBHF}EvlzHe&-Rjeml`lrh+p(CEQ`uYe
z!Ty~0fGjl-tY&g+hFK5`JNt<es#h=)tUzU2v!+o1K|ec}j<qBejm^)Exhxw&a(NX>
zHm>k~CvR_W{rsf`qXrZphU_NVkT+WmgIYLOB5>`)T86TdG}~faw1^TWnamBkp<w7%
zPUjEQEcKz(3`eEoyvIDKqNme62QDWzUr3yR5QZha;I}J5Vq#rzl81-KjZ1ti228<g
zf)hnjQ>u`l*=fiR5pkl<?h98+nIH!n8zUo8zLuPl5~q`4)@@4ESPCp+)iOA~zc%j5
z&HAXb_X*^gMHje1k!)?}<Kbx#=tjn&7CmXVh(zDH*#)u9AIIbXHpP|2{!(aIn6I*?
zUGc|~64eocVSTaa-Q1ToJLb;F1Jit*P$3w2XK6_TO28k;>-k^_yigeOqap_HM5Xgo
zv((quS082XaL9h0by7fDKdxRx;pj>!G~>MAUKhb5B$cEhfBb=Jq~72dGQo^*#vPeS
zS0GTB&W(tO@CS3(K4#HP*34jwpU4+Zz(dw$uOI8Shk+k#11xTZ&_LSUJh|It;jo=2
z?<yhfkB<}kkooJ^1PUD@KRHo{&|C00!2}A2Rb=LbwU%}tQ$QOUEf?NdvnH@7pFEn<
z77I;e%MuDqY{OXrG7;Stdze?5xN?Coc`|1&)3kO9PVbjy4tSYs^kC9hn%u|sZT?CJ
zL~w+cH%*1UOV>100?zjttsoS2MlMQ9N}r6N!_0W8n{7Qpomz!!Nlj&7qw-YfdGsq2
zXB>9<y`4+1A(HiQP?eb~1>nUJx%kYwItc4-swsd~wQy|;XYbK=W%0hcZTdBYQiZ~f
zr2zOD#BPc|+k3^ywt!+w3ID2NEFrVj(1$^2BU88p;f-d<tQas|oR}VS@3>tT#7;xA
zF2BYlKKA&6kE2Biw`P8X4W*w-=;jX#XJ~BfC;Y~o9bpCcW{BE%1Ag;3e)*ldaIz4V
zKEH>&b;gV+iXNn9e!o+X2)YEayjpnsaa$>p`>&7ao8VvM!Y7{tGAE#<TDu6iOb~4q
z3Xf`=!C#mi%iiZ&^tJXMddFZW>ciBl4U|uxI(2HWKI|D|6#Sdt$y?fmJrI)VbW(Qb
zIoKE}+9yD7us)83bx-HSB69t?P63+APcIp!QCV51&OWU0RgHY&bBIR1yJ*JI?bIhO
zz){_7nIuRr@X@biL>8yC$z3kXPF5pryvFoh=Ew7MnT`_*W+db5g_@||f#r~5wRc*_
zV^Gg%^fFZz((h<6e81}?)P&>p^z_`F&S^%5#$&z4mW1EC1%-9^0n^Mw{iiqzewm}-
zQ=Xt{dS$Qav5L$K;x0Bs)%`_j6OtgXif5!6ZA%H3WR=uf@rhp#8}gkTjJ}@hE<V7p
zv&^Br*L+>v6A_{YTPc1L4AW;&PYkLzl467U9&M*jUwTeZXJ$zyH^E<+#%=srFU2{3
zOh!eX=q>nO_DaerSL3i<^*Ie290H;sleqbQKEWiwp4iyqCx-iQwwk_`*>qYde{9C~
zQM7QBZ@f8o5|p_w^y7qV@YGOj%0-wDB1Jfg<oaI<@8}~OpE28bY=pfDpw1WCHD4r9
z)Q^g;apX<O7qnM0j2xAR){_Uf!HwtVhD3sqiOCn~m@l#?-r<pm-cxI`Q^%3l6-;bK
z*=>94G7=+ff1>_Xdoza_(6=Y(ou*z6AbW!zms=*H0mNCIE3IG?D|s9`#w&fB`PQ_t
z!Be3S(>OKS*-n6Uc+|^K^fsg!DdQkfrC9V;YnQ&+!V_#^ZZ64NVFsPq8FJ;PPsaiH
zxKaP)hYj-J;D%pG(bsQDyry5~!L+L46pa~vPW+n?LHxe?qeuM#p?m>;{0QRfjZj<c
z6&{ANE&I6{b-VL>_u_o)<?{xt^&^DUsivK6c;5Y35Mp0&<V^FIm&h-gEuiN|e0wf9
zy1{|2dj_mGyltD#Zc;3QSEV0sg=K&3dz7RvLnvl}5+orFR2v)c@br8vW>8X0=u|JF
zpnCD%`mI>2<M7tJV7+*d5U?m^_t<ugZ{<6}WaoTtzPVsYj}f)Ew)@65zqa&WpeG{l
zC+F?70=h&M8tzU9b*-xi`_fQ-NF#@;L@UZwxNf;YBetr(d1&cOi-ld5eo+QZLJ_fL
z&R)cN`1L#xMHL9Ei+~2wsH$Eh-i#!Qbm78re9Hp~6c0)Q&MmF|cN9M3x^yocNB8aB
z>&KZt_hD+Jy%}}KhL6R?z5uztF3aTZRbv!MY#f5|V?dF~!Y&N+u0(dmD(T8~CVa!!
z&`D5BYR@)}bmk~yZJ(QqQQW@V2wFR?cteIs`;Uwb47WXCM20PiZ&4TEb(k@p9_<ut
zJ4682y@V|7_9kVt0mXDwfqLKFd*k-5b_iU&0t4gMZ>5ez<f+^C4_^8c5?Klgv10Jv
z-*29rLN~_%&INvV@*1k}k{(lFl|OyGDRzqJ3oE22TDx}aAxPxCACC=v%Px#E1l583
z(~D?xGK$YS()JZEq$rkMv#|T@*1wHld!FpCC4NkNsU2TKd=~L_b+i6|yg%$eEL>Z;
zHg4p<v8zqti8Vz8HB$8vB9nt)#s(#%r*3+I2Z%-N--LD+OAyF69^ERB)W}JPG3vPz
zy(}TL+hpWXHBvl#_AcW9@?0p2<eqOh`fjC$cg}-I5rC1!PaS>}3ohjy*etZT&<Z=A
zZ2S6!vH1E?*404)bzcQXDJX2m8yWvR>wj~r-v7~C@!z=1|H?-zhR{-!*CdN<|I|A!
zCg>{p&TvMq_~XY<RPTtL6MQ3Cv~$-xw!r|pk>eB;C-puWFH8TZSyQ*|T|M)~3$o7Y
zZy3wI|B!s}K<;@YWmaOC!aM8KcZ3ZoGX`He{K~mIboi`tYLO^YpQ{@a^q`{R3)k*w
zyQuFTVP<u^yS)7Kn|I~%_!bvGqEqwaz|XNi4HOjrsdmi&+2j9`*4Jl*6rJLBPjR+&
zg`Kf=YEk3~9DXmCkB3OybncWLqs$Ttc9{YD%Dg&w+phgSzITb<+DJ?7i-9!p0k9JJ
z@i}9`kiFu)J-6QM{o`K_$=>~=QHE3f|G9DZe`hJtVuH6XyA^$XY`uXZp~!squ3b7N
zf;8LE6w7?xV&bt|FIi6P2p^w!fdSv+Cr_@aS6nuZy_l*~xbgkhn-_9t%O8w&A72Vr
zq*>bf55<Q2Yc1EI<mheUL_YH2_Eofr7(3AhmJ>DkksUJBqdJO;H&3?7eG&U&DWK?8
zV9@)#=IMmjh$X8?+nF!-WeMhMyo=3drF!CSO%-wf!-o$y(48H5AL8Ofl*rneGPo+^
z&e)bvscAim@B@JX4HQY<+L~{<pW`T(UF*|l);mrf0y)p@gU=5~ed6hpCoj3qBg?DX
z)))Gfg&9wJ@(kz<wug8@h@=-Qlv&<W%OY&%7Vl{4qVQbDS=La<Zb4x9VV)I2ySi{F
zk!HR+97(sXamACOkt1lI0buO1;X7V(JieE?q-W=B)FDdr=2DL3nU`Ki<+)D$&XGUf
z{{Q%M9!WW}a;titK5Tk4uGYJ~9!ANFkLolsk^+A@vK&2HzEkn=6U8v<v=G{L6km3q
zUFjW{xyjYe$NHnHFNF4d*w>eqpZl(xE-unH#QRqsvyl+oLQ%+azh>po;Eeren$Q1t
zHvFIHYyY==wEKzb;J;dcm6iIpwHN#`fd6#3zr9@A6(Av@*u6~e(s@ze<IthCN$l75
ztYW(MN_sbo-p=XfO}ruEvd=a}8t=KaYFu{r?!mihk+Q+8H+PHgJd>!k`+hVdb%=`6
z#v6=Y;xmbiuPSHA_w@Z<m~dzDpZhR*OQt%@mwctmBFpzsLdU#1Zd+={zaT;OGZg<a
zOJMD)e`)2XT>CE&^55{{mtQ$3M5mJn`VWl=TXA>jsUIJ<&N<9bljr%Px3{cs@<CR}
z=F;h{{*eZ8EjN8u_T<B33Tn{@jyB&Sn`jqKb?60C_fu)PQ~XA&x1Z5S-Tbq>6#pf6
z`0sk0{~KS7EhxO`T%dLIQs8H>+Js=2kolkiQ(42x3k1HZem9b%vrv_Dq*Os$Xo(8q
z>nq%UR{66EFY9fYOP89_-0@6kbaK9YWN4P$J8fTK@;B6Nka61-Y{KUcky8|*zk)cT
z5!&BK&^<~wT*0Qu5{fqQJw#%KufpPY=hxL@)`C)Bb-<c#dei_v=!l6Wvh%I{p5pbL
z)IEQ`y}EJr-{j>sli_Dd8hw=<>$oK}unzVGxhR2-hal)P$W@fRWFB7gY!_i;lOi<H
zw}LuNn+bl@x=i!|)x)=uk&!RR?Z$k&V$MkGXSAOaZr)PR`Q1?Va;tH=WM24NT`#8u
zQ8`V2>E3S2121T{J#yXQGV{wi=k$^0dwym5DP{U~MyI7zS5ruyrEm&_)@pq^bT6^c
zkr^TXHAHn{=~tgf#n+pjt;^_)Z*<+yBiXSJ0%3xlP&K>ue(m?$jE!iq`krqs`2OHs
zNF;F&xeqVT|Gt1}Mupsy<`bTk$6Y1!l!GHvtaBzlXsnH17tz4t@L0sbG~#lEbo@WO
zuR2YYUBwf|>9)J9Uq!K7f?`n%SztZnQH@|62$A&hEvQr9KwC4RLnIpg^X9%i^Mfch
zQ)*=#0>y2^I}{5AApTT`laRM8{@^c=Lnci${fC8zqkaDzvRSliGafl&SoxZZY1HB*
zjex^UtO0LO&Dg9OAD^Lj`I|@Tjkm96smIsW@cKOS-+ho7@_W<G-D;eCxF3&eR1@Sy
z2roC<oZ?+S7O=q^EqBh36AXlXDu^{ls#M|3$-Cv;%a<RwP}~l&y}scr_z*~mmBS(;
zhCsz}^4vDX>;-USm%j-I)kO4^x~nN3*|EEzI{`y8UXUlHPTWNcDE&m1enpLPYT0zK
zJ~1>P;iD1tQ!r=Vq2&e2!OTTHJ2!_NqJjoa9&32sS-5Zi-EZTz-_!ef1A7-JD44#k
zPC2OIJm2dfc6((W0|mvI!?(8)ZM~ru{FH0|SfQsog}?@)jUD|lj05Ft30o|9|NKf;
zXosj2y5E|J)<2YH2RgEimI-kRS|>#o2QGPpq7#|z(4j->dVkDcG)?}U>DhksEK79n
ztj(XDck~Dam#&o2VJ`J$9I`$YXhk+Tj?I#%!C4MMIm>P}j8+MGj4ntIHvDtlNMAz*
zien_rgp7{TMQ4|nPJm8+A};x;LU}=@4qYvjmA`wH(CHB*?Bx0SHFMFI{Q5s{-w{i_
zM^r*Wf)J!1nl=G@fr%#Ghm|G&p6Rot)c+no|9_)n{GWUN4~O;tS-SskmJYG|i?&Wm
z>j#b1vQd+@$G7ZaJ0rb!=gyrIS$lWB@0Qs4ru@xy=74KdG|D{UVxQ<osb!C~81u1w
z4ic*7SzC5|i@3@Zb<OfuxVl5P<Fyfs(u|Goo>^OjFca`dPEPKK(mg+RK0dzh7F7;M
zl;%sNIX3Wm6n&MAeJ~ccFQz=72u0f-{SHMBFvp<v+^<%P(vrd74)^KPr}DpJk$b7f
ztyL6_2Uc0`5wsfg)YBm^s!!hjqVVIiWlrZ2rMrIYM~@!G6>j$aT>l^#1AwyF9nJDu
z&~U8w@Y<)b*`X<e5cZTg+cB{hsQl{QpY!`sQ^u7={aJzN*HWG8+P`z>TU;Gu2`Ch_
zemQFuLlRn~v6{o&xu8J4?``uCZxZ!qT{?H-B0t87i`eFAcBvhcZJph;ThBJ{n}y+q
zhwUTYXMeTwoTkId6!%S*8Pyr(cA94Bsso^wtd{=c82`%=@c$f@|L0K&|ECsvs=u9R
zIwQU!A70!Z3`K#wv@f?fQJ!`93-?5Mwe2ZJ%(JG@fF6zN6wwF*5rW0Mqi{;Z-{ejG
z3*LTI<_+_sqC+HNJOCkT1hL+a`b=Zg<%r5auspxNZr<S*x*d4RB3vd&I1X(S7f^fg
zVZ?L{1(1P?=%gV!`)>e{>R(!!GDJa<S@@bxR0^k|cFcKJkZ4RVFE6iubxAl1L_5g?
zHD_riCMHzSNYGq-34ieCO0ws!P>Swr0&*cnETN@J6^IpV1QV$(%OV1^JrpBkXr%)L
z6rnpy&EEazU7~xEPv7R)v44LQ)YJVaIy7T`1FufW7GOp-%ywvqxTFj+nixgmgx0|c
z`#eR(y?@?Ycvx|-=ss^Ka52*g`u%h0W4lu-GXH%Ss@qaH^o{6C$}%|g=j94Iigcpn
zMQ1=QLJfod0lAk4C};3eI2Hk?g;b*Vf#^3y<8SOB<=?eF5}8b~fQp{|NLn*yfCNLW
zY5<jhM<||cz|4j0FZ1T)Tg-+~N1Jlp<x4_Lq=|V)YRDRdq1%jRB}%iz*HucXH8+6h
z67m-$A@?TqK!i9Fg(#8-geVb2|FwYQTp}^u2IF=}9uN&ewV7t#TV8mQXc+VQ3#2I6
zcNi20qr0u)Tk*4L$WHqKgYT5Sd-rY{%|JuM)F@Q{%4vFtra@z~uH4qA+<zZtFRXzM
zgC<Y~Qv9B@jaJ|W3R2kv;r_ZA?f0l7ZVCp~TsL(4eZybA{g|9bq2PnY`3K@+V$9t2
z9zz>f6;g&5XfL8+Ws9*0q@VWvZy$X|1@=V=F-fbEb;X7b77k6ba&mHZ=qae^>N2i6
zaoezXN;wv<BLtYl$T|>Zth`O=6PAu9CKy5$lR2gHXnL$i0+-7gln}JCXz>2Zv4(=R
za*Dzp9sS%}iiWSPJjKO~#gB5wx-<AY^gLrs0;DzsuTqfOslWZg++QsFf<1G!s)xCS
z#hP}MFH_ThUjdZNV9^v?@GN>(V8~He0yN6oW{se$k@iM`S+{UX#ouMR|71W}G(kO!
zJJD%*an`4O337x6C_Y(83z+qE0veJODZpChmq|Zp_iJ9ttjN5YxkNTN0hqqQhpn<v
z{p7Fo3-d05j2(K!wHrMKR!u>-w7MM!jy1yxka{&C4&x%|g-Gs_?CoKv?>4Q@>gCU3
z-d`v4JM}v#7S8!T33HALt?{OE6D_nY7tWxM;=1*zzt3}=OX#*RM8M1I-I-l%i;hbU
z|2@NzU2qWS0-<lIuBc$kfY@)FacOC(zs6s`aPT5tK*;A>sy}^V&R9f!en$(@7*xeU
z)a8h;9Y^4`V5)aUAlAbgVA#f1W7P6L4V$mLfP&&0e3cWblxzY5svc9Yydf`T&6nL{
zoJk`XE|ZFRBqR^yqtFlP|G)1#9=S>vG>V0@kY`-RtPu$8%?7Z-b%a_wOv)!3yF$%t
zs1V#qG|Hm<^mlU#kwm!@Gxwn3Mzb^~KoAY&gg`$H-D?oaaCR^$9l&e|PdG3_O|72;
zMZg(|HQ{&ICu~t1C$!N40UBSkqz>%Ld?BXF)qgCbBcxzyI)N}}MiQEsve6bRWwH!C
zh5>RyCC`-{=Jr`=rJ^AqLG?T3QxAlUgl9yee~?6Uo1<U7{HCW$9H`Q)&QbGr{10XO
zy7q#@I;;0lHYD8U_!%LxNW$PHmgFN-^YDm7bEptHPnEn@4Da?b(nrGKL($j>IRd0a
zr0X?-UNwEM(8B}`U@D}SM33gv$6yUs-`tt>eN+Exl=<r+PC)XN>ay%i3xy?E52%0@
z(2KdoNPH!;DYQrLeOtPTKT>?;O3?J~YS{;`i|VWbx+N4bz^+I|uWAaL{2*uTLH6kH
zm#);+)n)&hsIbB~7h*&phT8Cc<@vq!qI=rWa>_{jRQyBV)j3CDhzU>8JeO~;#Aqq3
zHJfrgdwR{#^dRQgHM;S45#x-+P=h3f<k2|{b3wrt^O*~JmbYk38A4|1*)X60p)N19
z1L%uO`z#D;LNf}GjmdgaAiN2h<C4&{Co%TUFzAC3*D|__2`3BL;_iL>BG8s(fZSia
z4ess=u8&!D9-(lh|2%hx8Q|zWP2H}a_v7l1xJcRO>#iOVmRecyn=c=`xp^^yxsetQ
zO8Br?p=QxCG+ho6@!0G>hg@iDiM9YV*~VZ_6Tuz9e26((3Fz(Cz7tu~*DyEUL(m(<
zoGj?ch=d<WG|^LrUXi30x8+~X1w>Z?`W%R1eC3suo)x|I5PqI8X*)p-bA+)mHEAa9
zorJ>RbF?DcG4)JYSn6k-IWP*WM2|CAD($46TT2$4a{|j$o;Jg*+-ApFE!Zh|cK%^4
z)fok>1{q1e<HvJawI7X{`U_Bhr$Kp#9%@5`_wt$=AD9uYjl7)b8QK3(F0_W|M?7Wm
z0~PigklZ1i_pa%~{JDIzf1iM+Q+r6E2G2zdRY5BYkz)J%vmu6|=aLx62t+doF%{Z)
zFB`_;95+BtWd)HtpbEjoK!NHJDnVx_*6lPKz!(K~im+~MJmUafi0|(tbT5wD>=8Ks
zzWM+=54L6fAJXggGchn+kc=?=m8O0UCG8>v0mLXVEammG+;tTf2wNi>k~&R&u2B)F
z9Gf%!VKZkWLiC*(AP~?$$&4(RJO!1F4?3T9sUCucC8PqN=mwPw78HZ=eAfwoPIN|K
zD~S1WA@APlQ9Z=V33+~1fS@v@Q1+07NX7B4U>;e7S9}yP9R`!eFa_z72R`Ed;gQW}
zf0lQpCWTCT7su+Xdi_3`qHSoJm`>w&{MuatqCplC30%_aykdQY0t5&HxdH%iltheY
z*vqaG3{g)MRAfXaDV(9v;2S)+M<{o*2?z9fRvw`kQ<s4$8alF0e8w#Yu^q&u5SfUS
zk9_F%h+#AUwGsB5P^M58eMjSvln0+_M<m#zV8iGQJbk=FDir#UQ?+ZM046A~sATtB
zEAITDEk3>h9r>7)#xl~9bdH#-B|+~LiaR!d2@!HIVe&8!x{XRb3HQYd=FE*&kZAI+
zhxW?~TG1G&37H<ouUln%<^r|r-I&;;(te@DxY(BPtDQsOKD`BG-g7hcJ;QDx3Z`OI
z9dz3lFj3=5ZQ8n`O1mn7y|?!xG^M1Z5R**<gdF&Osz4<38Qm-5;^MwSf2IYWB~CpA
zALpCZE!;4DApt8nu-#?uUQ>crLc3{>G(=kQQ}vV{mSs#n#9R=tNW^@EDU9X2;DIfk
zu06Ni;KTim#LUCsMfg@m5*lST*gq!_nZUkSZ<~AK3C(JQ`GjZB<~(%h4AIh{+o}6!
z36BficE$s*`Dy{TMI1DcPPM}ZD@%Db8Q{<Wk|xOu7iyuDv;6tx3^5d>e@X9wFZ47`
zKyJJ)OO6D(z;xO=1WR=PvI{|MjP7DteBv6b3(K2B?#U%SoFe*^FvDP7=a?fPrX1Rl
zpO81*?R@?E^^4Mkr+-#VYs~^&?N;LgsGaWDtlUN8g|7$5IEic1w-#+Mb~qhKNk&so
zT*o+|BC>>|ZV>)L*jo4Il*RXHdw_0NQDU;Blm|xT?6>TM`1mCw=3xl*3p25p1PnsP
zjp08w9=fD1y&t*yw3`dJ03aZ&+!?`<^cOn4a#i-!-=O7v)tWGiRhAH=hk7(`tp-zw
zDO#+jwcZOfJm3ifYpCgwik40U0=CFoXoovUQq8qZa-RG8Xj8M?7wi)It=6Xi0+JpC
zy$-;(j*c_aFe|yjT*0y{`Lz|iAJP;Ix?xN-NolR5qg%Q9A&in~1a}yVRUU*<ipT2;
zJO9cg00WvBNgk$9@1ogY?|=)2a!PuPW)BfvBP4;0>Yo_V)d)o<5w-iAh+(D#BmvvL
zt#V*;9@8+w<ldbpEok+#2W++i<7q(ZGku-uI6qO?*rJ)B3^{lMQ7T}s+X?m?sy31b
z(PfFX7{HJ3EFzZyf~!ZJ0exBWgIt6r8PdJIRDvQRS`g_rLifZ(B2vPaGwb-btD_e$
zsz!urPe2Cy-=)Tgk8Q^Lgp*dB#|};KyBIp?^BMDZi7`snIU1l9j&C7c8DJZc(PUc=
zgb?j;D$7JQ0)C{Pct~{U?0<O3zbYJIi1o;k^TcR~CzGRHd1~lqh=9<N`gMbTN)$1d
z2a}VD5gwR;w0nS1gcEI7KqQSAQ~(IUP;`7ZFa#!Q+yJgGj;C@SPAWZt;DTeT<T6)8
zk3+i8))^zKk(m)({L3)$>REW`eoPsVgI|Q74G9T3fdRjydZbQaaNK(gmP<vJ<UCF|
zEm#6nh@U@CwB1AE*1LR9LAz_>^{thicc&8)NX!G7pYmu0u!=q=JS@^%qpn;#V)h8|
zfL=yB0roIF^&dK0(F6RZ3aYBAPb!2U^uwT&{wGHRWzl=3o6>68sHi*uxwI05`LH#`
zZ@I*TKTNT(LVDbvT*2|GO8+v^1Tj0702Qhcvae#(#C_mrpo(Wkvpb}_?r4HPj8!E%
zK0ZF)*_LaU0;Qj9sQWc4`>zl75&cE*rV4<lKKL*gK2f?5LXs$np|8MSQbOk$&b!v)
zED;lriCqMcA$BkS*Mt-j=QI^Es{g2A-GC63$RBex6QFnh<?M2NnekAK8r|;@TBKDM
z^zdLTM*d-<_ZTs%#nsjI@Uv#EN=M-!z$eqzIh*kK<|~)_>8A^e-{T2v9JX!9T8?o@
zH|l&FBpthfxA7H8<8!Y^t>&`He9j&x^T4t_Z;zNu*eE-9&>os~e|=o6+`?C7r(x)g
z2*(tkhaH_NzxelSIq7SkiHvg8S!QR_+#|20qn7QO#Zzwf_*q~<AI1D5H;#g98xVbA
zC^}^=N@$KIrd3&t$s8jaAc100ZWr+BFE-7CL9s+M>>o8Z427*4A@vimo6t=Y@VT>D
zBeZo59pEkYxEby8?+{laanS5BJr?#aLJ%LHXfI32+um|maRy+x^z11*F}vgN=J*_S
z4)_F){-wR@Fa8#IH>_!BzWrxRBZX-4mreZ;cKQnG)&jN31fGsU8bOqlh+}{;)o3P+
z4&G-ZVmA>=4Cf_gw+Y<(b|3cg+n=F0=3S+qGPnQl#jCF2;&E#HDUY_OqE#Qm3N(jr
z1=Z|RSSDsNq42|6?+im~OVW8nOvC~bVP$14PqqI8O|P}2^nEDU&m>FhODFn>FhEG>
ze+OBBo(1SZc@g7-7XpQyR1w^O+!}BytpHTmSXm_teoZ|4J!=yJPT;pD5E#dt{}Ly%
zJf9;evLVqOL<atajEJ`mecI8AU{_%BKBLHX3ww}3Kr@<>w0H#rURVA<0??g*oG6vS
zRUvHVM_K);nutm(F!3L%#OP|5rAhilun@%U#b}pr8YeF<0I4&PfMgt!7KxgjG=C=}
ziI|HQo%*-%85z&MIe5{zfDnWeNFOhg$GNlw;WLEk9Gvx|IGRn+Y6FxqBL9miXK5&V
zb=1qp=P1drK9uyt1j=qhQa#$JHT0}jmXAWzO23Sf25xuF`Yjs<4Zps5L$lb+>GG0s
zE8S@o^gXAAgmM#uu01T_L(BD3yjsHH;5`&Z_jHIX|4JbSt$fS>^yyP#SLn_cj77&;
zt$3>HozLH%Y#o_-QNvqg#!oP{eExCr2?nOSjLuXk`A$|ErLbhGY4>QO(?R#XZT$R*
z^@BchIt6|{f9Z6|+;A_Z!y%Hwy3kYUvu_N3v}Hf#h9EMA?n7fAg^?{2^G}zXa{x&0
zjKEYMGX1ai-ZLu7ZQB+tb3#Fv5fl>`Kt!@+OeiWrBuEBP$)H5Zn82k3K?#BcMRF35
zoK#eDlnjC>xd4?Yv0k6(I-$Mx?tACmd)mD}HqEwo!&O#&HNW|d*+(CJ^x+}~TKOgI
zUO6iy;65*L0NduZ%b&=Eg$TUl!{3wQ7tpK*Ya9o-a6b=^zzDSQ?4C?(UA27uc05HR
zSv`EuN(Hi*>(1kJ@Wd4aAEBF)jz>XNXREK>cM+ou_Nw=Gau!Ijnoo!{w#h`v1gX;-
z?#ua{7_e}?>Y;|qDe6r?WaxxoB2J<{V^l4gD!Ldho;-Qim|yMhMV4srUfhc4T8B=h
z%hpoe<~dawG=&CVj(bb=LaT}t!xo;1I?8ys(ZPfHnwez>lpi)_UiYU^c7wqn>i8wd
zC#w!m?mY?@ms>*!kwF?Va?nfcaJ_96cJAxA-ICU;qEYOLna9y^bLfVag2EF}=Evhx
z9Gt`(ukSwUD@dOeyXY-^mDkLvmzDh$+AnbgJb}pO{EWr5Sv1|B?SIFjt8>52{ocSN
z_l~PB@9<x*7Wgx|uKzWcEi~mUt=dQ9Ggv>&O$qzRJr~?bJgANLv$8HrKc~k0;ztna
zz$-tz{{U3~-YkHu(RKXjsPU<+S8J%R!t<zz+~ZtZ@92~z{Cm$TtX5zyI2j(Ru+JsE
zyZ2yAmz^9B_HDQFS3Zv0;_Z9R_}YHw6=9x_b`675<H7e96<@qER{FrXZHj(JcXI2{
zt#0<XhXcR*ulaQ>mHP~!Lqj@uYv@6Hd_+9^qXdPd_AIZK?Z|)Y#)F-wy0=Kl12e|`
zd7!jC-i2Bx9wxk)01z5B$u0K3^EA!AtnRJ#ODL|Qb(j#>1ygImV}E;j`J?QCl#dwJ
z6T3{K$9{hM7JR=H{eK^<hc0&wd3)0?;<kk*csitIyXCBg72~HReFT4l1Ab&V{NO8^
zh<%&$e{A}Mn8};-EDYKM&^an){+x{<ETT}~%`Bd8Yg129NtN_jaEJ~6J(@&&QDNG}
z^-&G3Iqv3DJN%_kIEa0o@||Jp?XIq_Hxo#W%1mhJ{S<vAdZm~lwQN-jn!W6|xjx4u
zVhAGk7JDkNKr9Xe816*F+>=!Cn+*Tduw4e;gYz@P*osr9PlF>d!0jPD7$BpAH~4=!
z1MrQ8aaJkhI4}*JW8=#}ij3hC>wmTPj!O9ui}25AIKNedE_NNz%5i5pkZS9Lx+T4i
z#Eupib#rqEFqP;;ldf+pacCqzhxbKlE!u(BX<(|;K9*nKuR>Hii>^o5(X4xo7ONUG
z;XgQ(^%Fgf3cn)nvHrBnyT$4JT)5SL;-(IsfCYy*vV_Fhd(WOdpjnwnwnd2IO*?jI
z^rMi!@bl%e&h(~gA6~cz5-aw`xyg<s2nP+(x_fFOw4h;>WEbcMI$OoGV@Co}cN@Bs
z^aKKMFeq0dsDA>c%fqK&J-2M-GkgNmP)k^+`L*m56jUMBbdv5{brk<O*j$b*@J=`?
z>q$mMeC1$dRSn`E@|gOtV@uF*c^<@TY5$*hrs!NTplH15ydI6b7EW`g6wm)dGG<aP
zr7!*GmoHx>|6O}^xBuLAcclE9NdjH-v|mNy=i~o5sTmGqTf_c7vdz?{cy3kLpN|Q<
zcEoX&CqQj1S_31T$N&PYoq@L)sSC{gUaVJsXfubYC^6B1(*xojeDu^w;!IL+Fk$y{
z-W!Gk;$p}Fu|6s_wt<r+2$DV=uBVvIkUxPzLq8w8h>d}+YuP;|`ona%(|JJ4VJ#5C
zUqTvH10_ciA=i-sK5*yFBL*P&*=UXiNJ8j9nt6&DE;jOT4<krIsus<i3-(>%?+rhE
z`?)lY(WQ0haI(g$Id<-And<uEU*0jK2QeVG)xvXr5j`N|*fwaRJO&!2lkNBVg?$|T
z|H>Y&R5M;0wtN+pwa8VckHB7}X((6dI>KR`xa}P!>^6R)lfeWLF%6J74t^vH`(AN!
zbIX%0iGJWXbPl;Dmo69?ud!L>m$NlwFWcck=Tb~*k=C9e-78wV7v6}%*9Hw1ixeat
zY#YQ@6rv8<c^#|zq~#jWI@Ve>%lvAxBl0L>lw}Iv0S)sKRtQ-yfF&4f()EJbq(+2i
z790^hKmnowG6M7q@hwF+pgs<xh>2!6k4btlC!isSxcuUTCkERj9Lvva)O<KajH8ec
z62lhI$;kDin^ICiarhzr5q(PxsEb?QzFj~18UTLJ&>fYXf&sSdY)^F8$7(lZiCzEV
zLTCo`TeBl&LbqWJZLWjnD{OtL^3J<aJg>5Z$;RD5!a-=-iy=m*JgIPq!S4`i(KaZ%
z97P`wX_|<RK0v$~SjFhsD?MU2Oi;3aWLLp(OKL0o@0}do3{f_#6poa~RlRZ7X(J84
z#_jUVb3T7K&Y#p62ahkK;zzqleT~$f0Bi_<N$kmC0P}E}jaaBGbVJHWH_7N7%sU{3
zH0(K2!Kx!Z!2<KxDQN0@(UM`7lx5M&27UxD5w;$zMh7l2n?;Sx|C0S%)pGc>kPHyk
zZ^sF<93>mt0G&}CngEHu>Vs#|R8`&=>a4C@dvaWH?%U|{<durHRCP|6CLm|~1Zsg~
z&g2r?GA|H098B$0z6Lyhwqn_69!PuQJE>{gy$KE$gmtBkD-8&`nya7o+#UmWoJLi2
zG;0EAU#>Dk(u2tR1ivW~8?vU+BQtH$hC1|N8i$p&gZTagf%s1#Hl%97d%;Gmi96{d
z->0Zhr&z?QgIrt}h_edLI%GmHECs@gfq7Z!5%)cgWRM7<aFKC6+`_4Zp*?sFs_ni=
z+E9euq!widnln(;J{?Q!3yl*_GSHl4w#)qN6v$O&Bq~;5Eai=M2$()wzHWOodIpPo
zM<FIYje6pf1;1QkOUxn#mhExJcV%p7Rw=~IM;+hk9l(PY#8q>xwr<ycdgD;JH7HZK
zMqMBgs0-vnP*5BqC!Azap$rD8c_~z`^3GxsX#mBoKrZ20rskaBWfdl0wfo68!>z9H
zt4;)UWdz%JP)aKY?O_ZI)3+C-7y=eY>u-xdM^lBgS1YQ~?RP<Y1`WV#by@#BtZM)7
z^y|50|L4)K@2dWte$`1QV{hPh8c0SlfvCXQhpuHcBs<{y;V5@z25b0^B=jV0K(=#e
zVuh!iLQiG`cF?0cn$C0MfRmqKNkZb!!NZ4T@Nl!IN%B;ATnG6#G3C(h1WdO>(EKEB
zF!5hl>_|HB{wY*R?Ec?E?gk8j)j06;Cn_RRK5&y)>-tH)C!pQ6vp>|sQi{@0)F6B`
zIpeVrRwRDZgsB7Q?z6!H>b);j0QJESUQKN-olR*SB6*&q=k{%v%%X89f>z<r`E!~*
zivl5v>?0V5A=y}DFaz94{aJ{C9`RHGyLCQ@6959K2bQ9Ktw38COaZjW7?!gdZrQsi
zoGV9fU%2i&j?C^s8{MlP%EuV;k(J699lR9T<p7xuDHpNyLz#K3anqp%;x&L{_wMKK
zi`FDMRl(nK<mQ3iV>+<<Sr?;aP<}KKWX&^TN=bYLad6baS(8++d<D%iE2UEJ0v+7H
zzj6+r-++r_o4fEw8M>4cclRG6nOI_@__$Vmn<H4)dzRtveAICkp>jjUxkM1}b7BjO
zno=ZGq+~b?sOH19^Eg|4UP6PhRP^*Q3KD_e-{|eyzS8XV8en_QeO6`&Q%yEac{&<s
zb*TJWYV%o#+}CkWP6*PkhSGHRIJ?KTB0e0hh>{n-%-~#mBD`?fCj&*l0K%1u-hkGa
z44?qtwzOgH_fa8nlMqEDN<$?9=o1N&koeFZo8)Bw%Md;LFkOvDFDnt3DxW?3fS??3
zCjId3QHFInPS9=9C#JP1+d$Uq`*Wy8f;;EvbLvIIy!+R0-_EruGe~@V|NgFslVZ71
zL-NWDvYE=&olbD~W=2Lt^us*L=*tWSIvha9efb)TLMxfT4`Y;RfWq+R6TeYJ*n^Gv
z&(%6v*ROW7g<RI#`-5%-soMxUSz5kEOn-^)WP`E38lv3g1xrrmq``PFn<WOD!RMtf
zyYlj2KCAxPQ$DYOThHE0#KuzueeyswBs4S$fI-Hm?v+mAKWupC=2Bbjh*IW8m>~C|
zfAoo^sjW9MY*HARF4LOdklB1$>K(509v|`Jzz@T#-;}jodTka(=-@#q`4VdDHN<v6
z)lDHu$jp6Sf<=3yi%5~jVgSPuL{??FZyVdHO;9(RhF+CT{q!w(n5MjsIE|!$t-sB&
z9G*3sZsvX|tt&lzORmPY?DFsBTf(l`lR1rGy^Z@MBs|qbc~00z^x?|mXc&M0muw$~
zfY`)1R)`3^yY*ZDPx-RP8>bFY$&x%9$taW=i)X;+C2nF@TH4@(M5UDs&v{9Mdjz`_
zd^~c9fpdSGT_#yAWJs9wbMD#XQ@xNHHM6`3Sb7K^i9)px5EQ8!YrnHjxaHwGM_A0U
z2IuH}ML~IqvaWreCP6k3;c%w#p1`31XarMeShxdaMdGhlOn_D+n;Ucuan*_`(npj;
zAO9uoLC_T(u+@fC+@A&BDd$eLY-!tNiY1K@qiGWoU!ms`%!384pU8_<-<iz<Oz`&7
zYkhCvYsh_ES2zAOaD#{o$P_6JiwabmYEH9Cfgm8j=cPrbwW+D;-=nVPrgx{eCrJEB
z9Iks8BWR<l{Zh<vIvSKeVi-5w30IwHIQ$@dHK49aoY3+xOy<Khk+2XS(R9<#u|ReZ
zkvALjqi}lxrD~!DIQz^7+5{p7s0T25^9(c&%6;THBBUQ`n=j9VBJtD*-Ak0>#L*ft
z0l8^$@fm=W<H$oetQGroy+k8{GzO4qP8aT+2mZ`_Gio6e4ClN$5OWU?ahT`BbN?7z
zqdoteR+~0Opda_F53x5yZkljr=61wxGG`65SMcV*<4B|<XPqCNEmsSX^STER(I)r(
zI76we9CK15(})FKcji1mW;Q$rkS!AFS9Gym6{+wMB^PeWipAZd_smTddISSK{%w;H
zr%VDEL3&<<aRFF@!#SVHXM%Yq;}=^pZ-m2M;_U310u<~KKnNls=^j>wv{h(2O<hHz
z0Eno>S}BYQ7x7ak+&Gy6fVG8EKhBvpPf9(c<9%?SA+%SjaCt-%IicF40k%?_jp0=F
z6e;I(EOs0YvQyJw6UYLg*hF>#(MI6Sh(ru9f5FBaR;=ULs&%@&m*1~6>B!f>Ij@Lp
z16}2onEOEnPY^lP!!+6R=M`}O2tq@tbNOxYf{vZ#;!?$<e*5cmaTpQXARK;dB1z51
z1?l4zYQ7Cv=R7>bDt}$~135(_NnY2Sa@7v?qX$C*1cSO3ybxd8ory>8frCTgp=Cya
z3VOicMP55IjAw-}Hd5pF&pOw?xmWQ&AltQkJ6<hKpZ)wmOt7l!f!tqN;W_N$!=8_3
zPM_bpr#tIBXnFb97xSB+)3oaTpXv7Vi7WZMf79*hSByd8A-Z1TPTzzw0RZow6lJ!r
zbuHKZFCH%V#%;?a5=(Yca3%g?Z<^_6RTz`jgI0~J&oWNe3w2bw7<s-1#xBqC#SXW1
ztJfKp3aZRnNBE8b#;Ted*hqt6T8N)6Ynq972>ALqfP~J&%lPj0FR}z%4exJ|w6HuL
zdxP;68el!}EF#SS;pC*Pg|aSCnThp3Q)q6J?sBB{l8{Cv(-p3?5scmqG$LT!6qcJS
zGKc9W{wySYCS<_k&QwcNu2I~6!ebDE9?=Oby7bCF-2PoD1qFqBjfu-kb)lymw<9=!
zjEp151h;YHHWf?8!Xu<KM6`Pa5dWkhYa?$@W;CHt(aie$%TXxrcD);1gee-#m+W*Q
zYWVQ3_nk3+5KdCae$q{ts3N~<@Lh-CcDA>mLo2~-!Zk1=^4Vj;QeZNZw(!A&F>MoM
z?4UO2`Hp{sP!x#(P<v)=nOv_lKqD>#o2os7w8y`>dtqJiKkwd;Aw6WQ1YO0JF^3Yt
z;#OFC{AUH$XE^D+nE=bCY&ruS*Q@t`=AJ?DR)M1!ric*hk%t#rnivz<5GCTZ0zc*g
zxOB_^v)T(Kj0R{kSkYghjADHQkQbT0M9S1HFsXxU*~QPO0h6{LGBdCrr>g=gSN~Bn
z*4@GS>NZ_rH6i`Tcvf;RS%_c~4s-@Fjs$Px8s%Jd0{&>xB>MMU?#>XPv)R=BFn-JM
zYRWDatXpkX(5ghl4&;CZg!LyfRMvDV)FVhBV-VNZi!H3W3tMm8XW7^a(HP1bHov9#
zckIudM}s>0(2!M&<sznwWbl*EOAv|ZM>&T2Lb{>d?`_L{>SRDZcT9JzZUyDAso%2d
zdngT)T#@h>|8P`thf%T_-oO?w)Ij&;s}k#<mcTdnifNyir<^P4Y_@fC8y1!IHHkv)
zCvi_viV}=`4GbI-+H*cYSSp)7{RTo$N+v7(M;vnxLAh9#nVYZ&)Rn>sd@clAgeb)s
zgP9MWmuFVzLU$#Ev1=>_U>YB|cfBzFvmv5a9g?z#zbq=|VbBG=XiJAWg`k=}QbbpY
z?k2-iQg|&bG|%44Cys4&`D|SN{$L5pj`elBPVQROc}+s2hEjS;BmDM(@bDK)Za;8e
z$97y>jC0B5jrR}z;4feKAbc0w?K^k2NJ!q*m$-1m<^G#p+7zkdiNW~_j?UUGONFGO
za;(`t+lHjsT~G*4r4DsfG&?#u8t%lW@+a@fkZ~+iYp>lYw$rlu7^a!cby&4Wj4LMD
zDbl_n_k4cxw|bFR;`5R|t+@f^tby1#9~MA0;_4NII~JLhW&36bik-&!qThNTmzwb5
zMPsM7Xw=beA`7SHj@XjnXXE1YpT9dPfB&sU)PIz{qMG>V(!QY%I3b>>z_!G+phtQI
zHsDV?|AoyWai<RD1_)KBcV=W{nAPQj*@*qq(!_ff7la`fW8%Y9>P#zBg6=`j;o;%=
zA_%|YZT?*J8N9|nZ}94z*NW7a9|M)oSD_5k{`+CA-epA}*7573z=U`{WCi@0a|N&;
zo)mY&a>)P3i#)yk#z`R|As?2Vzuv4Efvvq5%nT2`y#c5LeTSfwk4bv-z9hT)xPd=U
z$XfYHN_J{04^heZTmgW3%4;B>KarLMmL|$M2pq8>ZMKS*+u*~5mo!Uuyk@|?aPhDx
z=PG)ckYJLKG%+zThyfF}t*z~TB$}tGCe&Q*c~!XXoSK>%Y2c%`kH_?2yqE((L4OBZ
zTxdnw;piwVsnm$8K<;yA1CN5)Zcf|Aoc1&&iYS-|hxyUa?=%|imlv%V)9O@TEGW7g
zs#{xwLPA2)^0RYtMxAMs&Qs7BJ%!Wn(IltwxKE9ZjnzWRjY{-!1-%s&J*r#kE_zdh
zq&zXpM+8{RcBZu>ztW@~tJs4KLbn~fh(8;-cP@(@#^bc46O_*3Pnv24i02XCBnODC
zzwM+Qh3nOYJM2p(@A&!L5l4Pm1s9%ihi-PQ5>%vofSQjwIy%}`T7*2_lKW*DzUldr
z3__Y}?`X)?QjU->fr^i;R@bc+%*B}a=UwGAkt#Tx`HfMj18#{OuO^odL=7uTe8)7f
zW|$$5zSxo%P0LD8PbbUzluK6<QR(St>sILn4f3mPc7;ssk65w*zAh#xHD+O-PUbwj
z{%%z>W`e;VDF8YN=Bcjz;!p3b*|ILvQ43zW;|XTn^+`@v>3ADUYwK%3L*=K%r)%2q
zc~wnLjz$vn*71m_tR^NV7zn26a|L~L>PAD<owHw7%(YI8jp6;Kq}oSACa*MV*huT?
z=|OlHpzA5PkIY(Xoa#MdJJR(!Ev*~k8saJFL+|iq$x~Q+2?+~ZR3BT>?Iu3k$urR$
zkgfePZe~+85pdMj*DHQs&|Npt)fuKabnV?)0#2{N&Iz>FQ*mb};?CWMi4af49PMaE
zSY^R4E=$ZY<ToL$3S|^;kT83JC9HuH|2EDUN}o$gFk0K^vBRi>YkGRRLABMcB8bBY
zpH#VQi~6gE0D7@s+L*oaRUP9arpBVggCPqU-!>kvSu~Z?F=t9PO4`SQPFE&#(DGgJ
zsg<S3pBh@sURk|8qddtThH6Xie9?R|ZZ5_B5Fn|dPZ!)*)s3Ed)*sINa+^#l0j?=Q
z;X4)5dWE=p5loiIf;0c8&!2;rtUJ03`3tq9mn@|zgz@K}a<k$+^{}0BCqLEK`(p4D
z&(8TDZWm(|mMeCAbWg+d=m@2_akox5ZJwcJH>Rm_kd5CVKD<Wj*E<KQT1P3;cL3Z^
zG*L2Wuzzr{92ael8yugSh}?i50zL<3aPPPp2sgO3W?CA3SYWZNisZemVCZArvb`FJ
zJdicz(tcv+5B%0zEsMXDyoWZfMf2sUJi1pZ<i-b6pF?WthxP|s%EMbVr_GL<VB3FA
zE6~%^Q-RIbIIUiZ?CFsRPRD~N+Z78GOjXR!kp@VX5)2CJJ|7|?n2?YVqrhfhW>$s7
z1a57;SAtB^w<!dl*k)w)=(IZJ^*>qwF}bk(7ODNgPr^Ng*sAbsAuXt-iRN{#H7n+8
zRDaAu*0eE;rq(gt;PCUIR(Z$V=Z*;Zn3v(<dm)3dod3C##zn4qLYsQqJ8}54Bc6!~
z@pMgs-`@1}V;;}2mPKY(mSAvjux*iWyEmRT2gHX;M4M@MN2lpep^>TyJx?ymsPxX|
zd5Vk_vy`ArI5*!LGSk+(PYOz}2^~B-$?z1Mo0Rog)M%kOgr}A1#}r><oh!!$Z`Pyv
zh#@ReFa^sb(P!LgK|VjAJuXyc)NcF(m!L`gYuHHDar`IdNMtuik2cJz?qKM^Lp(Q=
z_tV4dypfTS3`Wpxw{Fszs<g>}Oj=*5c_p3Mbr$haImjDFy}cY*?1$2nAx3VbiCS4(
z?~@|>3ajCq!ga!Qzh(#Gl9l5xaScvuv&7m{=6c0xf+SK}v=~HP1<^pBPZ`1PCm>rn
zo$-}N@B-UBEwa6zOd?#QIahSHx3{+eZao#WxHdm3PUA7N`xLZ1H=_}|&WEMmFikwy
zX}WPBs6WB>VjiUU{2<|++kbl6bU|o!qkXnVpm!PP&11BdQZJM+!>Eb9myD)KY{EI(
z0|k5qAZrLb?vp0%?Xr7%dKQK59BgSgqFRr}B3zwy+l``URt+k1RgM#>p%v#eM|==t
zIg8f`558(&AXrM(EIxPQ9}YrCHPuP)9MCH^nMKb1<?5=?v*dy~oKh92VCtiiIi3wI
zHVACxn4IAQ=BEc6xok`Rk9&hyG|Vrw#!9=jp$+0rVqS<YYl-M&+hbXWuYdLT^%YO%
zPv(iB2s@|j+Gv<&X=7tnck@lhrA|Uvy0DZ0?wUjDu}`XfJkcg1Bf}=4QET?=mi<!1
z7I1d~x`IznU=#ug(^*65t>zei^D-%klMJ;j{Fsf9<HHh)on;P*VsCSmlQqp_<>M>J
z2beUNemwEBqd!``LzJWpTep$J05ps+o-Z@<kv%)U-#kq-iZeTZ6Kd2h-JLU+V9)8V
zQ_yj2#f~RkF>#O1dGJ9&%u)ia=g6~Ok=|`HpfC?f)wZ+<eaVDz_3$errefED+cjb2
z6oQzhaGF5Mup4Z?XY+r3;M^xA_Tv`)OW*-o8bHhyWFk^Nc@r({SX)~VQY|z)Wo2c+
zlEm^5QDK1&2MY|HnXZc(@i24POZ0y<#rk?fJ`#ONh-E3+?Y5medfJt{0V@*BGS$+U
zpWZN;+JY93P!>T%OhD|}NSdpwtjN!kGNR*kWPs3}SQA~7=)+sr9(qgG;f<>1M2dg+
z2TrFOh-1&d6jF`yCThH#cuDw#5$wopq4?~H^cKT7L?`@#cfdXpn-LsSE`NA1@8|@d
zy51o(XZsM^Y;ROr8lUf3){2S>@=TcKPxYJ+<UP1^s`&JB#a6kGcD=$KPgw*p1fsT~
z0sgOkIO)cdEC^H~L*`I;FNJvdF}5K-Nrb9WRjMa8`qZIwjn>x6oxMT}*X_DV!p7P<
z5-12~?2`QU*49=Up@`oUCr`8WIZydJt5Ekj;36<{SB6XmedaJyLgs7*1_kjr&Hg$c
zDkd8B%=|2<B!6YZmbn>qj0DYs`}L?SZA{h{9~OWMI3I9iH}d7{n8xB$9x5)50Cy+W
z=zuPI0a?kd(L~athp;OJSYT&oCm&ZoT@XcJ8;+AL+)$wK^6_0I&qxk-GI9F0t+H?D
zX%nwpwD2w1(sV2>f$L~ROO`WUJCS5&5L<6pbBfv_Hkp0s`}mEu4M_<x2Rj09^gWuS
zz1!*JZ?r{3q3FTZgn6rhhofIjLPcLX2IV-1dVbJKv^eE1DY|1O%04b*oh>c$al23a
zK&<X5_p>{6<%2EyAK4_Sn>(=;WafJ=<5E?WoxRC@HFWooTGqD)h5Xk)1-@S{yBKss
z-_T6>d2thtmS&tt58XA6%gV}TXJ^+=etvjyF0_nEqjI^T_0wm5pL*bK%ZfW@U@1ts
zb8Xe{W~B#zyjkS#{_#TD^QX6T<8GG)ML^ifKRMZ{KZL}A7ZI9Ef-P&M3=CC;tA_jg
zRlZY~2!e*9DsI5-q)^r>WKgDaM=O~csBwSf1Cnmp%c|)rwG$V+)w+Ii^F15k>3bwl
zk<EAaWT2StE7wnYuee<6^*jye&<AmmyH4|y9-VxIyi`PWTfg2>%5v^_3K6{}Deq8o
zN07zz^L_Tq!{uY>;^?uBsF&7;Hv?q4wHY~<eT=fd5WMEHf+0@XUpm`AZB}H9sZVB>
zj5-_PW8zw)SJv8^8tXZJ#15ryv4j(YX(2zqrM|8lKO&Ttkr9<zZHwdC%=^Zot<Bi5
zAQ&3PV|5=UW4g*^-9|&1=i8ojj&%ka<rGc>A6zcZQ|edk+$%(fF|)GSt~bu}*l=%6
zQ#H9{p;zBlvMT`)!gu$Cs>z23Ldu2u^#DQn$9p5BrKPuF*tB=@LqqDBk0$kfZ2ZQ?
zdg1Jpts8nLgD%<F*o2?cUZWgW?^^E$&}S9zS`&|mTAbI=)@pS7vK+9TtJV3`n=9_{
zJrj!?8~@N-x78ha!Ik^CoS}L>y0dAj(pt|)@vJuN`R3O!o1GCkWo2e!xs4L;H%jE2
z+7Ef;zq4{}Fj}5&ypRTJKm1f%>*KdMd8LGblgYx3mstO&VdVeE7CHW#F#gwDppEfg
zYeD(9(D6U97O#1;T`E4H+bsY+Rq-}K@CDaHsj&6rrUfR^-FR_d0YY&<mD>8{xAc|H
zlQM144&2hsmt-p)UbYaNw*HJ?G{-9~J%485*MVa#3oWw$zEfW<ANw@hcUruZ;B1U>
zI)I57#UQsWFjqzo8177jeDjxil8ulvYLS+afk6o%gvxgxop2m}I2hO3(r^w5DHZBB
z)Yfu$X~ro?%hJhMSd6~%G|01xzgJ^&)KSUAR3^<c?TB4kdU_=Hd(F7C*H`8;6x^b4
z`b5}nEqXnEBa!k(;#sG71+Y#l?H5woGGsG;q4Q%=<WLlw|LJA+qe~Y8AHmUbo)r!z
zXt&8iiz!w0d*JfF9-Z(Z8Oe$EjI^{g5^EuDvqk`E<mLZtmFQLzgY!PP@Mt7Qtb%-;
zuBf|=@7b%DEP6*88scN|t0sstX)1$#g5F@$p#%&3cbwUUfx?YZ8I4KSi*D{)G^T}j
z<li51^D^otAwWeh<zub@QX}6{8$k_N%%<(e=}G`d*#E}WL@ZEr?kjPRFl%p`CxnVL
z4Rk;NDiYmy|B@*t8Kd^w+r~&MsM~u_aoSyI2jpM(OBJ}0%$Cjw3{6zBSi9v8&oePg
z#U*zTN=3&ssn&BmcR+h6Tbkah$06eAxLH#BI(m<N+Yxn11RkH5*c>dZrV2-(_=@r)
zD{+>OazkH4vNtkcxRFUh7L({uO4{)w%`hzxjDa89U(1nlUWNDViFPeYg|?NI)!MAa
zz?_`g3%zz*g8>?9Mcs8(A8WO9uPlTQkEm#}9OIoDWUq9|jcghk)9%OmhlZr9j(8eS
zKee<3qS(A~8<UH7TujX}_l-G&Dd&7vK3dqc%cUryXGp@}6J^S>l5EH~uB*aU=)AqX
z>^BJk34z)5->yJ=?@)wFKxaM0ScU@_S|@?8Sx<8S@a(A!4x)GZaYxDr?6Ck6gA?JU
z3j)op7uxO~k^OwB2Q8rG=pD|gG$Gm-+!3R{loqP<M^aXWjn-vXl9|-xZJ@L31`3tM
zyQU-aCPa#0U)UY)tEEpA*oN5`*#9G!a2Hzuj4a--KizeIucyA{?;1n+?LRu9e}z2C
z|B{gR58(Qrh?@U;i+{(j<OJc6zWBI2^dpnZP9-*9A*FZvN-+wqZAR1XZy9Am-+tVx
z6e<|Wbp`;fM9yA1l<N%A5#Qq$QK6lbt@kN*f3;e-nmqI{NzpsP=3Dw|`c+<Do@YIR
z>rQbe1lJ5>hwLr&S-T`7Knc6={K#c;A;L#>2UW*1R)MYg+E&N(+&s~^flKt^jX<lP
zgx+gXw~33LqpH(~U$nQk7g`hSkAJv%+ZGpZX-eMT92%;_MekHA4)T(yH`w1Fqs!`k
z4b9)%&65<fl_IkUXdNFLyHbxx(<8GyTq$m?(eNN3=o!4|d?Uhly7|@MIGxu6{rv~6
zHCyWHp7?F%T0$}6;W54(r<65+?;}pk!hyp4{QQOE>eDBlE={B1{`C?(2LL?#nLlz>
zeBD9UaJ{GUtd^F8jK|x);b9rqCL`YvX2*|VW8>oy834@SX%+zUGn<8|_FqUP{<X9J
z6(~To{NGZ{|IU4J@?7|urnW<xxkpLO*a__EKUJS~f?bccUw^a60NNKz+XZ~M*Og+o
zmdsZrGz=Kz`QDzY&`?OedND_<5{?u7uQM_PUL7YdcJVIUu-!S$vcDE5EISAvLKT{$
z&8imzs!Q)}(F_Q-ZaIjm1)10IsqeRKj3gbPlq0AZS@02>KOZP}ff$tm>H+sej$pzl
z5mp9+P+;wlEa|@hSC8yj8TJ?@19dRoQa&Jw2eu`70golwRv7uPpsn%SzJ!ItTKnY&
ziQX6{`b5<<HmQ-^UQx3*Cg2eLS)`mWg3&Z3d*NG3atrJ}KLTwOEPBV|^&&`-(BwDK
z3pGOixaT-7iqwq0xg_xk2<TyMdSUcN*CY_msDKsEBvCt7ifEiWSN6z0L7q>P>nN_c
zP$+V|A$hZ8M(3DHT9#n_TC?Q_CMMEXk)s+JNNEKlDjFIZo*hN(Vq#>Xgj8;G0{QO8
zGNo25@(7TLMG92`V7rV;22*%D{AZBsrZSWb_TaKEPxgN=%(e7EUd<w9Z$BfHp~7=L
zQ7JB{TQO`)-bh+Z!!yZLCttenTQ(KRHVd0|SQ!S#>hQKE>Xtv6G-1wB6WSN7R%Ey}
zEl|icZKgrbnPdI2)MJG~!bQ15j@vA{gsBQ^!d0%mvj#@L>gCAOdE2Z$nNc#qGua~%
z{>JVUAz8=sB2qM=>LvXsnXYJQm`r|w&R#(?N2=NihXgSF<S4l8%f$b@R-|n-{6Yl&
z3g;CnBpMnT9*6Z(xp8Wosjca%PF6KSUQqPNu-+|RInHYG%2hj8u6Mt(>$z+B`1r<Y
zKG?(=v@KCA{pV_V+_S9WOa_!n^|go9y*g*<8XJDrZILPurYa_^O=?VUNq0(f$APf+
zz)j8U=?(!({IzRmQXn?pF9p%Yt4b5471kUa9MrsNTT)~r+V3w$co-n#-7Xnn?2Z#|
z{;CuN)a_pkw<atQ_b05CmHi(xJ<vBX(V()laCyiJ35BedGD-^+3*Tz7Kj?N=DwnF7
z+KhkFQkVL+-MdPxTSdn&WRSv60()@d7&hlV+kw@33wxRWJS8~FbY13jWe<|HVi#hX
z!YMUU>z{A=xc)cj^&y})xI$|(yOC8YALTpoZ{vgwUm^bMm`@kl7^nWj(?hF=05JsQ
z5gC0$I$p3XAG44Frsk0IMd9g(Qm<;F)vV*o*@fdza=F0n**FLeg$TJ#hViJQkvt0;
zy+%97ukZh!LUP#>Ht_qwV_r1I2*HdWng2?{5dq=IL9U_iQgE#L#AvK?9vdDekHhQ9
z>$TrANQhSegZO{*6h2HUl;OXvR<_o}!$%%J|DMPG*Ll7F&Y=5WU>Cv28hRYQeC|Sm
zQtHj0UnTvMKSV3Vp>8HsOVc`_Q&AYL8NO8XV`PfmaD2U?fuVWu;;SNaV}&<7<dr4^
znOiN<g`QHcq{f#`N8#sLtS^~xf?kvt+mFm1CWTGj?Cp{<RSrEoQxh2lpx?i4DF{GY
zSHas&T#TPdRqwlfM-Yo3W{>k==<9Oj_xj_d%K6-o0heG#j5+!|_{lq@Rtm9`TjtY+
zp8IkXP+Gq{*y=&TY7ZL4oPD~Y!#zt$jd{89QVN<xej8=|7r@br-z%~BED^ueqV!ML
z?Wf!kHQh(flF8_{7-{Xp!U>k0$FwbzT<2@!=9D&P+8k64s6?UgrhA9|@@t~7L?G20
zei==Q^;n7XK7-(AQ7SdQ4hkCn2o+1{MINy<o8}Cqpm4^UzKhay`ww+{;N8NAWi(j*
z4+!C!Wv{v~<=(sjdH4fVeF}<UN5Kt`o9*VsyGhNuwyiC<<{PSI<k%&pZ*=`-boB(8
zRiaxgrAlmulx>=p8%?1e5wsv&!B<W}BZ@HySVDJ@dKW6Fw!t$v+dLp-skAE!B?T*}
z$UWvm4kWcZM*DAlU^n?(^K!H+kN-REwR(wnD@t%3=E!0rk<X)7V+wkw_C6nq_DKeb
zRH-(k%Yx%|7g2hi2@OKIogD3C&^O^&KDD>!U(jvGb|6)cke{=G-?<*)d?LrQ)5lRb
zQQu6&xH5G#GBjhw$xc~xCrmZL4_JCU|8#;<tHe0(@w^#1POgoto^E!3KW7|JUwwzK
zEo%e+7KG@~xk0^8xTh>kK+P@%>m@F>VT2EP8=#EberdDP3#WJZB3O#R!ivnQGKVA*
zr7yv!5QIv4w6?W9gVLglmD5%VlaHQ(%HSlIEs&`N!3AnP=Fl8mg<yqj*;)X9sBH-6
zA&BA%H*B2LQ6eLPmGsIR|1e|~lJ)1mJ?T4<_x>ZBm591DXL7Qu5EioDDDkdj4ivpS
z7@M>m9wJ$oBgq8yjQfEGm&i=?i_U;_D`N+E*^V+hYK5T4N)Ql3LD5B_#0;Hz1*og^
z3*QKsK)LmzIaESDf>pLMA0eubo<r+b=tUVpR;cKmNok!|Kp+axA}>!AYL4Q9d1}Ev
zDU2K&8P{yCuImb|IwSvretZK{T;0#>e7M?$g;h+-+Q4W4>8K!dzUJWSdLNxwM=Bjh
zz8<|85v`ig;C$~5mUP^`5v~BO&S~wS5;LWY)ci>;vE*VJr$+YcxAk)L9s^i`K*_H^
z8Nzriy8VXW^H)l0pAhIc8(1EsT0sL50rrfXKEP<<Ws6+apQcD(Kxb1Wl({}EgkoCW
z)<f=$PZz2D?~5~XY*3#x*KO3fI$9SkR(CGmfQ6ZU(ODj4(`HD$7lMD$c8l{eM?3GT
zqnsV|+2j<x&p%oK&OJ_OO4TDoA4-Ft;2YLwjvbt`ocEOI&-Z^x`RIvKRj9ei<rXNQ
zvXo#`XOs-6hGMDlwHDQ&Fx!yz*~@7xTpcoRTj;L2Q4Ga&F+NfyLqV5bS<?6#V?DMg
zpUDGU+?z8zO<6uQ{aC*e8R4NPJLmdNXU$JjE#L&PUkcXlfkgiUD>Fxawz0XNUe3=r
z9sAEQX%yk8e=6%V^0?||eq0=T=v28Ld9ts-1CdGeFy+U_MZBjlCY+_D<GAmwc(G=Z
zN&@qq`0mn<x(L(|`=v&1(K~48wW2DTH|MnzaF-OEr}|}`1JPm74K32mmX?+W4W(cB
z$Oe$jI5;iGxoB_6$8ukH8RYEEh<Yj5KMRZ^+DUwAQ=zmHSRy<DVBaqVWsr9xlwD6j
zXD#pjpqjw{GVML`?iCL6kocLSW;XJi-cdiiXeni05_eQoR8*>g=3UC?{3sr&^_nmo
zOhl1$NcHuBB>T_Q6g+hFnV(@6Wr0WxPq6-{M|6&3Prwhyn`ntbQCPLWDcwct{HVL~
z|Fn6NZ9ITArh9p1=K!nr%WZ?|C4S*3%Rv%Z27zKNR)RQCk*cNzbUxkKeP=B!EY^vR
zRg+kXy0*sh*(+5#Zae%CsN)ZhlORU!&L{-D;^;lF()hU4g~OK;_(D}R#4x#@8nW(6
zMcl@~K*rQVwh2mQ2mp^s?;nv>5dm0|Me8jwr^e>yr#J%0{J7Fp`=32R+u}8lkj-HE
z&iPgBGASjS_FT!r?nvRXU{#AG>MT^so8cKo%E_}Z{3=yc*6<}`!qDs^JpsccC<j6h
zOw3~)r|P1_d?C{BfiD!tgc#*j#Pz?9KTvf{djlCoLsmtpFZ*MWO_jV~2YL}gLGX7G
zCB{UDGmR91PiV4_qYA6(%<$Sy`UIod#c@P=M@B0VY}THaA-Eh@l%$EQD=r~@z}fUb
znX$3EnbT;H?MTUfqV?#Pr9>}Lmbh3)pc9Mj?9ZactdM?8c?@JBnlS+bladaZ+w+so
zv;{DSTjlDwX#%QxAFL=ruJIRr=^7(RY;@%vxf$*6QgQ5WJR>nsFcAF`ky!OWu6y1G
z6@XmGUdrQehz&vzuw+;{DTtvRpDX3_7oP8Jtx9qA3rYlYu%|3i&}vj&g&3)&Z*rLn
z$35J(edhxnVR0d_==DC$Tmm!DSv_MOglv|Qb6V52(yFb2`;TF!y(~eN+3GiLLVvU0
zcEDb5R4yA2#hsesZ_hOsGbYrjm-GQbjY4;}TYRcSmf)6&6?xdRW(y^7xZoon)A+u1
zJbzWE>qj<6R(^{6=EeFCz*X6PrfT{VRUJV{bMC@th+C)luebDo8$iASDjGP!Ae=CT
zkT3Kq+5k(7#3qunpDo46<1ldvbDrRe(`2*IDTU+cTc!V6-hy?n7Iq(CRw${p0~L9Z
zz0N|!mg~?1Pm24fIqVp_Ii9_$6Za&evJuCbcsGGkL_!Cl$GVw3iuSoLJZ2khpc26e
z(dEiyS{&i8*+RS1tu;U5{|rv~VBRZW3AG}Zs{z`=(7m2V_w0nEovKuYWg}17ezBi_
z9}8V7N`z^BFCLCSzZP$`H#speu{>B<fXMwGi&#Z`1mKX%2;7#x0l1>+(utwM@f~Z%
zH@p&Ls>HfZ5?Lf|gp=k&G&Pm{t;UbZ5dp=)Ic}&-A&IaEf7dI0bv&@T!F9RI+c>1^
zs^Jf{$8vz%N!mdFs|DS278A{SQQ_g=wk2sG>O*Ea%7flXhk=Wss?;=GP%c!9Lo^!7
z67@s5J%3lYiH$wa{kkcsk48gbD%x2z<7`Aqd)f+U>}S)&_7wmvlM|Raqm+u<-0RL$
zc?OBAU54(bI2(i;*UJYSGxK%v>>OP<*nyC)jLkr7<407T1g0&|U8!yHIy<V!gUO#f
zKOPXUOD;Wk{gu@DAbt*XD=Yv!vQ7j}qp{E&$4L+5$Muhr?y59q8ySfe<aZ<+>j!5>
zeHX?tLlhc>x*(bjobKXCOi4^h#WcyTusiEYpB%iXzCQUWTfN)gnwNhJe8wTv$v6z0
zl$LD48r}gtA(O^p%BxkGzh?xw?D=m>bAp0#7#=~53tVOy6j@@1?GL5D1B*~;GmhA7
zZhQ@{c2_UvZ+T_gB0oPwK)wt_AnmmINrCJq>!~DK;b`K9x&pUa5`t*CCltn}1Ax{;
z;#rz=<3tQ5H<M_4Ie*#+s-~(YFbNPC??-GLKV0zq`-h7SgU=t!2*vc-CrnKWxBWG{
zcDyvwAql-|lXK3l1#p{k8`li4Cj}tdbP}x;m6;&4lSDzejMn%vY<WmibN8cn|0>C?
z`?xdo_dfR$NdL&8BclRjPK0#m6zS@iAL?M#khy{Osw=o+a}+Tr#Ff2l2LW$vQ9ev+
z!_;GP{7@ASQ{G&YqO3K3UBACEu7HT&EaYZ1ARVo0^5^(8P(TCJmGtM{Q_nNVY=RFB
z`Q{}qMyjR4(7&l$)|?^*wp1JrhqUrV3a0Cm%wg2kgMv{pyx)RpleBp)oRl?vDH&&Q
zsi%*71ht@t3z>>Pk<|j&YlN2_M4z8x*4GE@P<TErfaF_?nr2t7OTdK>3-YuX`2;05
zMRsT!<0ER8QT8fBm`g4NE~4Uk&u&Pnsx~;D0X(G^*y)cuQMuvgD{cLv;9$o?Z)q*l
zc~Zh9$CygWm6EXZ?|`vr;d=_o2eezxbt~glk~0OL3F;qAt&;J1W?p)5ZDJ%GuX<Re
z2x#4s*LTWlCk-K_JQc*5$n1~81qmKO8CK5p6qkxy>|wWd3&V!&6HZt(6Owan$}VFL
zx~&?Tx1y2c;_(S&6xTu>Csf!S#QI-89)b32A9778XgDCn#>qH}_wPcNMO7IjU(1!~
z_Etg7`v_41(NrAsn~ktc!%+X7^Vtr_WMCLeGk}QZA{qP5QX)<p755s@(!@wi{zbe;
zt`P0@C}7S3DH5s1P3-|^S*64f+poFpus)#4^r}0$-cWELI(*u{wh7>36i4<|<Q>gM
zNqO9mVc~?sA4^NiE8sG0p;UWDB=V%%i#(+5%XbRDb6#HFoRJtj7fQ%#;X;Y#a)G9d
zZJOC`+vP3ggPp-i+JYYD(NiS$t_r)QBiIw5>%&59AA;clN1wSMLJ$7#091(fefvAQ
ziMFZlFa&3ATT%y()43#^8ii5|@^g3)*jb~cfk@nl8dPjNMm8ZevNN|4*ggopm4yq+
za3sk@tVUG)JPl^Jy8WYBRgQ<zG^B+SW+7yfry~Ufu2sV*w0q&SIsVs)U3gofq@meV
z_q9-CLY#|am1C8co1-?{dt&FboHYOjutyJszXp|v#B1AUO*Os~#*}E?P+1;wHXW#=
zb+qTLIAt5`OrZ_$S##)a*%=QX$rnE#yuJI;`}P}sZSD9ab&TKmeBAlh$0Z+K%gt#2
zG+?XAYr_{C{@$m*aqYGQfBEx2H%L>@9RIoD)awUtB^gdG;>fr<IXnL)BWlf{Z{Dkx
z?|Rqt+Pg*VD@XI~t!-{XtIiYK`ZWZ-F9ND&r%o-y!*&ZjX}_kY_Z&E|676D5xwbrv
zjEv<UuMCRDL`T0tTPl}f-GN`fercoai{ZF7ynf_j6awI7q1a0rbMecM(a{q|MsbQs
zT$a;g2G2xnH%JsEjC}a;0TT56@peP7j=O_)!N)T@94oiicm)Lod54C2AyxeXk?(DP
z|1HOlA3p*ULkC3F6po95fq}1#8V`fkSOzCC7cVb*bSB<{?cW#dsvFZ><1crrwbJPL
zczMIZ!YFtwj#*mrqJgIK+qXNga@7Gdd=xHmCF`R^=i-}3L_0kl%btO!-l=b7w1mRK
z!qT)cD9*|LE(2b*6hxcrmoM5&moD7}?<q9o`4+@r8OT@UHwkRchTn8`UER_M`M<$G
zU#fSKUAkh$UJ;S)lvi+4jY>Ol^5n_7xTWj8*D^9OEu$=w$i*%`zH&F|@TJ^G&-FG(
zX;afg$}V)O9=gkL85WEwp2#XpyU~QUdoonmCb4zi$7e$p4aebEcuS1fHoB=MY(}RZ
zx2;HeI?1V?8ndHA8g%TM-FJ`L3_trZF~P5zXx5Sc75#sb-<siOcPJ{U><NrUR#U#h
zz=A)!qdr-!>=iwS>g(dzCi`jTNhPhA`1p-zOy+C$D<~-Vg{<4yG%h>4<w~%t7YvoG
zV`by&A3zKFY0yYSRP+=)J4ynwnM3C-Onz;JeLceq3-QF57%r74hU1X4l+61*eQJ;%
ze-Nb0lOabqU5Lhpv!4#-v7ZTTzqMq=UDbs2rWLRb)E*mmTel<S*EGz-YwGH{Uh57>
ziKj=<R_#7Xc8tkMNy!JUuFKutAGq87ebw%^3^NsOG_fv{xa;Z~$kC`)APV02GMLYO
ztq<<sX8`Sa8Jd>GE>Pe)>7?g_p<PY642ze~6Dp&^*B+t^b`|A#g#4-mB_kV0+?$ib
zu6x7&E;dDv!4eAiEn&$?YOeq<AKNEpxQTsMiWI1;tGf$J?C`w2&%^0xOMU=`BB;%|
zt9U(#vt^2kiuWHpIDYQj!y0pFKF-L<qzf~viIgV`4~SY{)oRAmLae7B&oD7Dp|SQ>
zY%E(&YsyDRSGwQnMC$W0BB|a4+vd&GvdT(+A;U}Jfj59~%=7EuXOe2QY15{X`E*#l
zJTzf4vOwoEMX2W@-z0PEyPDqe^>bzbrzkx8Ui4@(gxx9m^k38Hv)wHwmfzY}m#9AV
zl>15yt@$fJ(N=M%+;hF{Rz>+&J<wIS*hP9xch*40>6N$Y>J$i3!KD)U`t?Tml<sTo
zV-pr;q8x!&O<ZZ~ULheyN_9hnEBf4z=83}iYL7p4i0<fmQFV6@mwabh{;V2|PoqZQ
zJ?=X*K&9Se@Z7Ozt)=_E&p9?p(Md^Lgo0)8O>ayLs&K(aQ>6#S43b_&4e59$Kfo8}
z0~`|W%0&Hzo3{tns|S3~3f3B9-q&ImGGZ+{Az_nhf)d`~f~qPTx`Yo<+1S~s4I!zL
z5;=RUjhclPw}eb)3d}rkb8~BgpSp2eTAHE*BJ>^+k=##x1tG7kettcbMT5g{k4(l<
zxR^#|LLPJwm833v`|i?B=;LRbgyOlTsY(CmFJQ4IjK|2d`5sc;Z~3{DQ}y*33d6Bu
z$F7|ogJanm${v3HhQS*F@@i`Ax{Q!ydJXiIs`QqJ%-i7Mm9ZlJ6VHTgWI#x*36|O4
z(%O1TLnD|t4F#YhCu4mz&{;vZ(UK)gPT|4Nw4(Y|>+_DxguQ%u(sSLiUC@bttJ6)1
z*xA}bL;d0udcK=rL!D_m8Yv=+g={Nu)<UWv`Tpa_(rMyDnxnh%-H2Y}ju}5bto>C{
z_y=>Tr3*OOgC^TocGJ$zdV1Kz$mp*vht(N?`luWIv({I;hHlZ_+qFY<am%ZY0H!G!
zJP2d2E-wzeZ+!XCB96NZC{JvWm{jXSm|>&bfAVDGl)&kIVW^%(CnoYUQqk9aH46cE
zD~IkmHKZxn&PDp7FdZBi^BTfTau2^8hpCi9sbFACXM%lQtWOO#r0^{=sj_cVb6n<f
z)OkL)#tumGJ^bXaW|fwn4Qj<bO$~Td!*l)sqC&O)HY^<?p5DaVkex$gl9oX0Fphps
zl?nJ1^Q(S5QerXK#FaY!)O>!{R;36N32MTHvdupVre@HHvq4HZTFH&YA^~krQFd%u
ziU|RFDbJMRHo<sovCCF5hs=v!C4NrEMzmB{{4I=4u08fnCqbqvQFwyGNImoV+plJK
zlbh7XTf?1qN(bBQ7Ha<vm#Vl8)oAZr+!yfT!`9RL!-4_>uQ)>R>-TvaPxtyPCYiU#
zPOeM7`SUX64a!vACiz1{LpTwaP_*GZCSx!Oa8U@zYibEk$vK8io0d_I!lvLv3z;!c
z;}{bYV=j!xjWhSh?HY58^Se_}AWr#$ooiimCBaS9`gCqlB1W*~Psge&#Ii#_Y)pLe
zX#jddrd3ZOBlm3=ciK*2U}7>F*=&99_^okjW0?Vu*mU3C)bYbADNFAxU(2o5(;pQo
zdGh2^iZ%?PB+L4$PRPkIz;33zLGMB5i+kAWqOpaKQY)W|&LyR#Z6d}LlrI3ig~VEf
zx~_+2z{?0L{Uwwi$WYebx^-(`83?Rdr2We%Xc!ODIP%Fi^x%nxF>xb2%V54%QsgdO
z(zkK+%hc^iQIeMSXj`{xRr0h|;;$j7{^svsw=JQ7X}g83!<2A3Mq$tX{Y9~v<K5-b
z5nX4*#KcDCdNyt+d-MR=quVdz(^t~bojiAL4Lo62QI1*-Y(xR)3zhm1W)D%Xg8~Bd
ztqa5!_1w?0{`T#g^f1rt3VU!D`^pY(Tn&WFLlL5#_-;&ZG3aUMrO~W>sAAjn<IvfY
zrf9*Sz0MK62-joQjNYnHv-J3+q@?rrgT<ZO2OYA}3XvQ;2k6g4xekk`HZ)*=%Ac69
zei|?j%7Gp~aRY;}C^}~<z{@)pz3xP%klw_YcwqoM+rs1ylsDAlxbSu75Eb2lH0a0A
zpWgJC$#T=y_E>~G{#a#iz1~~7BWn9&(fA=}ikzC-LsVN{Mn+!#@#QUfbx{oXi`n;J
zErC=l^q*;DVLclV%)8RPyu9j@EIycdb=s1tJQW{#5NQF*Q?CN7$*HT;QO=w_%dOVn
zJtj3VFktAPCWYnfVmmMKm-h+Wq+A#<Qe4o9y_nJt%U8;dRgXjV`5&sG6<&LQY9&P?
zuD<1S0|@zR=Alkq8z=xdK?$crk5Ef)tB8&tDH@KRZ1Q!y3P-SOlkC1{>9%n%rCc9s
z;mc{|iAYaZGNs=kRCn3d@x^Ki8Z)H*><R8O6o>!UK7071)*EyvYdNx>Ze&!Gt5hYH
zMXjD&4XE)J*JNoqjx*_P%v_i6ehA3sJ}+X(%LujW^N5I3Vu&9&m6m%*F7c53)IVx+
z_#m~k>d9`SnGPU+4>Y9j#yVqg!%4tc?2fJ*)}l%tlHaP2=9m}dcN=5#apj)bz{qHr
z&RlpU{owS&J{W|(&Q2l^vbQZ+nZmx)%$qiDL}*^RY}qo3P~_h0o7GM)&VIq6{Nc*E
z4NadfXWR?FY@U(-)w8$d$nGniX<MEE9C`ctE~8w6eO{E<3`_wjlxtNX9xg5vroD5V
zg%_T{IKX`RKtp1@sNO_w$MKap12>euCTC2);+x|X5Kxw39eHJbsw2g(bBw>or&A;A
zYevzJ(a!tnq}QMF2Jx@`(<8f=$;s;_dLJ6%E}rySy$=vZCj@?Vf|t=zzlg%Hd9&-=
zJ1ZuyU83Jq=<LqH8sPk^bA_JU2~h<V6$9%6nW#dT6fJra6G~rr;U2<k#-;Y_OOrV=
znuA7(9}nR9D0CzoE}m-T;})IQu3c+UERc_R4;PoEVS8)r0Df`?EZi&_d^>0JySuyj
z3-S@q%x6#tyWMx`ZcD5i-(IWF9x_pnErdaqfBPgW{sHEo*J<;P`e~|tmO{1;fGZX%
z>cdSL@mHJNwu?qQ$fK)@i}<cdA<*;uFyvv69ls*+&6<0w5|fpsGT$SPvfE)J|NHmv
z7b^z1?Azl;tdb~Uc#=L@q<D57NKLfpVT_H7v&dng%qcha@5>g#In&0UTamxNTqF8O
zuH!5sFTa{B!UnumDU_A;^ci+z5v*jxjYbbdb=P<u*^Z;3<O8EgGB8(Gu%F*G>rRn#
zZJp1aJ~Ti$=J)zFi{izhjM0&i?VJ7g3Yx0|p$~t7(I&+ObqhL7Ia#@O?LEu`eRB3{
z)EdX>EGLci;p9ZwHt{?}ed&#Tg{RiT?#m>75D#0qlXxGe&(!qvgno8V2=}wrX`p#4
zl^k+Y6W=Z6T`yQiQg4~6waQuh%iv_il7U%CX0P2DUR-?V(IzV+v+l4~zOkj2y}d9F
z%l)BhsuJZ}3s1c+=U5q|6xV&*90M8_Qk@i#O1>BQazWPc7hNF*R0;*CGdec*9qLb%
zSsU-;_8;EA--WYBqUf>#EPbwRsp7HhF7=g>Vz&|0bIr}oJz_PWfWVM~e)QtVM%>o2
z3tutP1Ht!6q3+-+qnYFcx?b~!Dre(zxWse^E{i<MEon2T*036(T3dUUOXZg8wp&;l
z7$_{E0AuYxtdV`?HC!W8Gk<*x5w)jSp5M_^`jQ55GiOF9G$8(I=dcogqEt-%fJ}zj
z%+%Dl^%;6!<Z&fa($45W*MX~vzJbA_YVXWkhbeA!SRbHC?VB72`?80VQ`R&6lVMus
z_0GkxwU?8ZUrf30<&}}C@5kjj%ABN`*D|O-1*w*@scgWJMF6-@Y!0m4EvKULH!Qhy
z0GEKbO+t9|JS(`K{<Ucnt68V)nKKf)x_cGaHp$D&Z<jAP{#?SzNesVI!hoJp;yp@I
zW>~q&STrD5DJUv-B5ONy=FA)XUk+;>_L5bEY|9b}J3AqGH{DA~;eKko>3A5PXJ;t7
zxNL^oOv6PQ<5l)%eEa(Kk+=7)krDIP>FK2fMro>bjg8-s<GzLKEyq5+p{wV_(?6g}
zx0mcxSFi>pV!Q52XlU$!MV{;Wy^@#IZ&ufEW#f?Wo8NOrM@NVAbZu~_t;hZQZvcq8
z^(3M`b%HB8bN>9v3m3dgge--2q?>iDqX4L8TPzv=3WAU#)NWR|riSdug)no!?F?qP
z{y>3XkwlSrA!@B8|K7NI8R8xnrnA5Sc_~~l_Q3b^b6JhgLHGmVtSUtQ(b?O(0*C}k
zer-65jchi<aY^_T(bx`%@ntkFT)K20?xn|{%SkMphC0yFrwl@VaRjsHrX1kvwQ}|9
z+sB`~$j6-2*Vm6){|02ys@b_Y`;Gnw&qi!0nECvjBH<}BTVdw31jnjd+WHL}Zr9ez
zgS%MV_iHv@J!=D&KrXIc?!d;8?f359-6bfv4Zd=Gs{X>yQWHZ^2#{ITiJWEcp+j^4
zG#@uHFt`9uJUz*?k&%%tN^Fq};FK<$rI29z^`eDA3|I6Hr91@udq|xH#{-P}i_$?2
zn7=f_T_E#qcFM8&u3We7Bly4S_I6K<c=7+(2fjgdIuoYwS8Cfdz&B_kWyMCj5yEHW
zH#<E3;|JL*H(*Ws{=<je+}s-^CQmV!m6ctsRTY2H!no7n$E`tnKjz3jPB!fQ)`3%+
ze<LU|@57O`KI<t4?%D>Wa{=hWcZ<i~NN_w6pBk@K(B3r<3Y`v<0&im3?b{?}WMnc5
zfaQ={*;4b{F=(Xwg>mG~n>V%LxD>!=q~xW`t%rOhIE3@kt2dn{ckkVM4n|;LeK?jI
z?xi=W2^rZ#<QO2X!!Yw+AuC(OoF~S?u@-SBxF_(tN&1+sTD)3D<>2gB^h5*d9@4zo
zxy-+aECC}dy}195)jC<HabV->jT<)x1lQ`rV)MYELr*NCZKMaOy=_o$q*QDv8k>7t
zTNr?8PNmECb4ag0j@|e>T`8_Wg7X!^=%x&_oiGCJq*B+Usd^xz9qcIBNsO6mV369r
z?r}M8I>aVinC##k65@%`hHnZBm!JqDLhue)L3(RW-c8%K{SCY7-Fx>g!A`mM*oT>!
zxheC;UfA9|h>t&nPbL4zFpUv}^&9vV5`+d>Nz10kg7fq92rMhAxyD^OZyS4d_4c|U
zfv>Bt*TF#-z@xVg{0<%Pdb9%DT{pLHm<ss;gG@Z9r|seqg!&YY4PUQxhh;;yNIJ4D
zz^g)h(%ZLhN#KCB9TJq?r*GR}c!U?w8`er2?CMqw!V*cWu9idF?J9^sj@`Jyg)+Ek
z)K0Hop~V~&%96`cCyyy`%r$KWhCaZ-aY|DYyCi^;rn=Vm>^)>NxHaqGKJ*a@xjn0-
z&5)WOmb3KUJ!JBDdM$g0hCJTAJ3b46O(Dz&I=_FXN1r1LE2|EwZsca>=H+!i5eDHf
zG&T*WicHH;-2nDO6c=zjExaRv*q41eFBsAfN01crO1Pij=3i4&$KvYAJp&`*VCHu6
zB)_4){-cNMVF$PAgezTt_kClFsgbL;og{Du@EUBOgvG|L#^7p-3ziE+TqX;1u#6jS
zVOV73a`?4gZ_53JcmGZ003Gy`A|O8Fc!JVie*W}~!rd>JSFT(saqZeZm&!G2VsyyK
zf1<smxR;m%bf-J%kbBX7wM36{)OLggyg)VPxId-a52nLzBiABnytfRrJ)XC3FSeyR
zDJgB<(!9s+e!qAjQo&42j5BlF3!+8`RfkN={tcifcinYiSWM3>@&@H=24-eYuwWDl
zx>Rd2%(SLja7b*W{FtUD<?!}kTcHk75j2%|2A|@^OY+B)C)Z7)FpRl|Vto@f%I)nk
zCo6wKP<R<eGiPLFi|}%L;q8<+q{<6@Q}bm(5=v3hl?ifdSJiT!<AYf-7v{EM*vpCM
za$|-fc*~QO?-k<-c4AF8ZhqIrQ?pkaHKj|L*LBR?bW(wqJ8?(yLL8QpkavAM13>Ux
z(wBv-tBV)UySKOZQOiCC$elV`I~i*BvVjc+gL?@W3@0nY)~zc+fdU78`>;jpJ*s}U
zkkun#j9W*O52sliUABS%>)$_u%T6O!{qy5}@yNm&lNaJg<}HxVzx@a<`>Xo*-;@9C
k+DL-jKR^Hb|KU$()?a)Qscn85L`W%hO6FwZaqZjx7jj6pb^rhX

diff --git a/articles/AMR_files/figure-html/plot 1-1.png b/articles/AMR_files/figure-html/plot 1-1.png
index bec55939b6dcae0651eff1984bdee47de91aac95..22ab1193de3ebb8b265dac517a759d3528e744f9 100644
GIT binary patch
literal 41824
zcmeFZcT`nbmpyvbQmRZyiHgLMAW8<wSw)bXb5f8XIV1661SDL^N{*62as~rIa*%up
z0wOtQE*F05fYsl4-S3V5zW3|?qsM55HL&Dx&c1uEz1Ey_&3$|o<Rs3WCOwTpq0UL(
z7gIu^PR659CqDju3jXEGi;*<=cFI6nLJYpc*UsGvG<^HR=Dwyq3Ps_K{C5m9xnhJu
z{e_YgyRGshdVUxks$=9KvfSP>-?nJX(->SE{J@M!?Ccw_Pvk2EV$?)t7@`%?^FDRC
zt9SDl4BVI=o6VG@T#%OzR7&`h(KP#~U$+mRf+sQiWiGD-Q8wlpTH@L_qIdGIRYb+a
zyj!Aj<G60jBeM1G-F&6q)Q{+>GUwPS`?!I3Woxkl?X98BlXizg{m9nbTYmUAl=$^y
zoQHpt{5}2{@|XK}Q0I{EqJJJYMZTkgFNmH%zQ1{aB1OJ?(f-zfd`Hn<^Fn3<_17Q&
z=`Bn%9FLvF+)21lXOP!xq(E<1rXwZ>!?_VA#g%hPyjte^#ew;c&gHSr3KRnt+#K&@
z;Kx!aHzJz3a()-Drf7IeG*&=xsZ56@GAhbk_Y}%e1|@W7eSKX_Utd4FIsW{kf&mg9
zD;=@3?(8dr=D9tk)FeAvT3QC{)15>_ox{Tv!^6Xew+ajlG_|x0@bO{RE*48rNVC1i
zw(98QB(D7Jqr}&o92Wh(3q&UxZo_BatR*H#KHk6bIsXf$>VNz#WQ$l}(!^f}VC_14
zu)I@G$c4*El2%i+B95U%DK0`VePr7iD9B#<W`Z@-74OkmF3x!?oSxeY8%3dkbZY(V
z;jCv!{ikslX{L+nASvE58OPR;D9>?Z7`wZ%xAE^6A&-KaDIYhDXLz0BlgiE6SMHy|
zXJGNZmkzmkcl1r(!-xC}!QQDdcbtSWJ3G6xw^wbBBkh)~tSr0Je7{x%5fPCr)?D|t
z<D^|#`Bk^BW@9wY!^+&;oRf#A?*pmn*)pA+{GpK%O8EJ!o;@{bX{#l&fWH<N7TBG#
z3_Ha0*4C_A-m-ILy>L}x&8<OovmRUjU6CcCYULsk(L0&<`8q5SNh%CIA0kIHLO3!q
z^y*`0`&SYwV`H7!f*1Dc<b{NmnkMUapFT`i$%x8JEf-uweXRad6mtPvVvVV;uGWgU
zP4U&ZfZb}S)W_$>WxqVfxn5Fr&iD$}i$u&tEJ<dwkLM+GQ&Ljwde=4=ut^ma74?@s
zeeLV)RNNU)t*8(<{qjx#BWXupA0<(My||6Myu8KmEJ>lF!`~aHPtUFO4>qydkG2ZZ
z(i7xUCq5jI&JiEx5mbiwzh6i;0#1~_0$ItVL2J2tc5_-}pYvve;>B4*$_KD2ryvnM
zc%ju7=7;SoJY2V(uaroJ5shI#NpQGq|E;;|fBXjgFU;HjI~V_@W$?daIw1*)o{r+N
z4m4^GuRB2~cp<lX*GwjcpKAm+vQ=L(ieLOXX@~1kOHlCHS({q=R!p_q;XRHy*v2@Q
zE$ri#Fm--cmR3CdE;GO8so818sa`?0m}nK5?XmTX@`6O_I=1(q6gOF#_h$KWnfKN%
zl#EjL>DbE_78cH{N7WhF#uW?fPA556Wb3djZR}6lxzx-aA_-n%|1^~ATyJ*XgHO+(
z(%Wthm^AF|tR<zS(Dml(Q>p8FI0~+{+{$~hGOqFA!6)I8fuSMt<_K0QJN&D2WaEi`
zEUy6=+%jn+x2uq5t>PTns@_`Y+doVJ<W1t;<Q;G^#vqR*HQG)}$L7<?&cQ*_cQV(n
z%SDqQpGSw~3Zr_C|2Z=5v)XZtYFR!$J|cTxMEu=_MSeI9%=?g<A-D3XB4tlEEz=3a
zT2(Mp8+7nRNd%Cp@}1a$$)PG1j$qclOf)uce0b?7yTS44)+n9^NRu?w2CF|BpIg~?
zNz&gFt9?!=%**5<yz?Ujfg2MI7|c!O_h+S}4o6E<=kxA_>qLD35fS^t&o5v_o?G2|
z+?DL(^8_ZvJEfg!f7zRe2$>;?>mdUZ#m=K1&O<I!F)MTW2mJg30u!YZfqrH7Q}ydJ
z-?m)!4+nF%)8~))it=*m@sew64+aMZsi_llb6H>4jla3QQ9Zr57#N42H-RU*ZO&I&
z9kkqPR|)5_=)XY5ZQ(<8u-(9JSnprD+QhiUi*^tb%hT}3c+4B#%{gc>5iV%(`>X41
zWMpJwK|w*Uk(ij++NPGef-Y1ttNPe=r+w<Yq9VuK+?k%Pksb|wo8rPkF%uJeKw?r(
z)z#t>5)uIJNY&P~nVD&R61>7CS5*n|3rO^opUrqQ)h=(?5Saatt~*32U1jc1RGK}_
zbuSj;(?smcWA--$sMx}!Ql6iVr+V`JsgKWFK6_(-N+H*;3*rH!m*V!<Qw@8wG&=_d
zuEIi>S5!<~soB2gzLBXP&_IP2%&FQOL_$dqx3_e#zjUq7(b-u-`J0am@73#2Nv-x-
zB3~Ef#Fs<COV6?3*5<hqE96sBc2|izD&YP5T@ts9xd(wWue4(X@tM~nnqQVw)c@f1
zrr5>d%yo~W9Ia}}x#oFMgY1Os4_?=eUVlJ9c7DjNnVE=aC4tbx6oYMz<T5Y84q~wr
z-OBO->B_0~slprS8fA7ymFr#7?6%{z&oll&7B4>5|1HY3LH?tj>rBeEl9`zqU!SKR
zb|18bx~$Y)F&r*;3~HY;++Cj`7;5{`7=nD}JhPsRC*!{Kn<Szp1q0iS@}3+&cA>gA
z3HEaq!#;uN^7DCGKvZlopfvINl*5)0wSf*&UHA9*`%CkLVck}D+$a0kgoK13M0MdB
zi{VI&PXapSR@eIGW_^7C;IUJxU4T>o<`A*7UBU+Hf9cV(wssRM>;(WdQ0{1MG1RXP
z=qI?Xt*vP)O*V?Vr?s1>8!(NugM&kN`B~I#KSX5%hKh=ah_z;K_05_<u6~U$HFcoi
z>euJ9bBh&Afj&MiYd<O0<%Kc)vuPSdW~vs0g^w{<BaU~b*49*Z9(fu=lV`0-``YX~
z!<#3|cOEFv8>y&P`3v=^>bF<yrX^_|uAi&x{9H<)S$b+7eN_yVIYvleTLAUITD*nH
zF(xJ^%ug8qlc(mYLzFkA>kVq^k^RjQDjShH19qz#{HBi&FMj<SVQCCSZ;mb*(U<{%
zPZa8-&n?*BFqdmTTm5`|w5_WgOEzG-Y#S(?#l&hG>Eql#;srPR4Le6isdCZyIm51G
zNrEA{+K&#`1n#Un{`Q_iMH{&hYQwnpw+<pAs=e<7G$D<(uP-qg%ygx6_VKV$4KwCe
z{(0sKi$AQ(TS%z!_ROQO-EDg{P3pqb^~xHAs}Jx?5@NNGDz~)S!<n_m-$z-!o{Dv|
zitwY^goSDfW1PruW`Pkxf@Tlo9ry7Vkw>2Db@vnG8GeKBcV4LLI;t}1l(L1h=#olD
zunZgFLvJdNpV8m`9KXdJCZ))9IjN=w{dKd%V&G4V$o><tc`PRYR6eVUh2qkMV(hCS
zV^|a<b5|9gMDA^qc2)71gIKNq)m!5~qmIfA48-u;ds%kJ*=}SyCknsG38eX*+Mh!2
zn~0s283U+X+_xYgAYj-SOqXXj+4N*O!P{VAu&5J@CA&y3=Rfl;wIQ0%e!SlhGd!7S
zi#!KKbMO6pa1qTNb#-;4CL&U8*qXKz_4mYpts$hXyEO6Gu*bXYIe~^>pdRhM*xg#h
z@|@xE<Hx?8NfHubV$ROa4$jVfaA}QB1P1M5DuZl<5X`(UVM{-P-h=<<flu|*FP(k~
zX!ya-!~#%~EziWy_iaC6%MEsE8{z{X|J{m=Jk|Dau+-LY`-Ws~PQz^!+gl<ofUwBd
z#@&ApZvQ)K{J&1P|92Gs0&w$vuLYpzV?uUnq0s*5dn(nU_V8@x&Y>Z}4z#>Hv0q)r
z2EP1f+mnNSXSHk*6pA<W570_@Z(yaWR$gA&PwzESY4u%@I0OIqfC~B1d4)CQgNp+Z
zy*-dMZ;~H`-*61fbi4}BxF!biV&i4kE=<vi5zzw9`X1dxC5C7(Uik0SFAjt1UldkQ
zXE8&5tYZ6l-sV83*X2Fzi35e-BXk;ta{J5c6v;J?T&vgbxiXO-6E`@BA1XcZIq#kw
z>N@|>a~_^PAN-XVM}r&Bg-bV}rmrkZ7UdjAZZ!KE70Fp_>8Dt-C!L*{7CHWdM{LXG
zAob`mqPqV)H4{_LKI0X%SiW&^+1bkFLxu-8*Ezu=kfxA{-o%&W*032D$!D6yA&>2*
zKgLPB={~xvQMBNz6`{zbo3f}Cf&4@?hV8$Q4RZE?9!6r-XRUELcilQ&3k^~eR7){2
z;?pmumzHw1Mu~{f>@%1oz6oXuNVK~X-xyFdZO+{L5`x3HIb>vL;h8<t97iG;8{6;7
zHpm$NuQap&u!Rx1<4aO{`px_S{$1gRZr0h1X&DyWWKgW1=fM1_=DaTkd5nO<Pl;Rk
zxtW=XJo;k#qsXYtLH%qlF0Ok{KJ0CeNavQa0e@>1Fjox6w)6C2-IbM<d*Yk}Hc8{!
zr;-G}l9UjW&UuK0G@jil>Fw^;Xs>V=rpnHCbT})eB!C_<eLR5o@UB-;kGp#;O*+8w
zh2XQvwVm?akIIXzV*1&BX^DWkvneQ^=a^f4Q23CY&CGx5s$a_{DY5e10Sl)N+xzML
zgQR%Xg}h>zU(R#v^#Pl=(~^>C22cGFpBY`(t7C6(@8F=a#Z^*M#`hYz4nej8U)ORf
zauwiH(ML3&+aVo&V2wl2zN%+MlAH>T*L$~Tbo9PNJc`_BSCWbE2FQJiH@e%sYh?Pu
zO>-TIR8?*eCz?;x-|pMk<nGmcsH>}+$L>Vsw^%JB=AY?#o2v^dIvI~ux>=lTZq?<0
zP@YW?7e_C&R~Y04QGfCQpmWdsdJT3#yW}U0fy2g`jG&Q`k^WL!ZB?AbaCKF}`S<1J
z<%zss*A{#BXRAkg20xnbDJ<a@h|aB>Rf66E2rNiWz|;akCi<QLcy2aL+66EsQvO)m
zArUIK4dg-fqXX98J+95nJn_*rBSmV~Y|q@{L!N2vSo-FuSQ7pmV4+vosRTqeZFtaL
zQBNADwI@q5F*-70`f6}Woa{_)U4YS4!=ND{YCf}mH-O_X1XXGNX_fXrMBM+@o&Our
z`+wAQe!TW`XDSW@dws`USS?qt3cD*o>5`nkUw1`crdK|@+g$hK;!!+sZT<+6S3BMV
zXxJ_n8v)@+d|L4a@n(6o4hxlyuG^eR>DjIQ6B|kCy@$w=vz+4TY~ZB$w`m+9&AY=U
z!uJcejG96-ek<ykH9_|vx0d;Is2TkGs(sF+Ih=U~;c_cG?P%eVoi^wC7k^w#guD_c
zygOaG{P~Pw-RnzkG2>%F5|rsWyh|`%e;=PK`tIzPS#(Gws5~4Bi;C+0ppU+Q-f~7?
z0i}epFkoY@PeM2jGZ83|V}}KFum0G%q2}e8D~onDQ*r3(e5;Mw9$aC&yx@wM`~F%S
zhNpTZpc8nJH5!MVD4)ymv&0skbBV*xm;jhvsblqIm*byF2KIvaa$bO`jd#MobnF$G
z0gRmsdk`BNTe{J!JyE&V=686buC6~N$Zf?cmpqpIay>UP!y3-t|3Z#5RYdR3%+2`$
zx4PXY1b}*F{hNG)EYC18eT;+BkbS!lyK&3g+a>tnGgHGb>XrSi8gzsutQJM4dTwB5
zZZ*GJw$`Nva{Csmg?Up$Z>4nN6s5-zSVR)=Sds;e10gN8jdT(7b~q3$NC<_uAGojh
z>HEhW>`hg?_=x)^pRV$nF{hMze}Cz~GXe`z@I<WJqOaRx8NKjUS$myx4Q`khH(%gf
zfo{U<SG&7JJCC>kovdnJ0HwzdGF_dn^YEp(ofebvqCP!8;44+d1oI*$hfSoj<DUqq
z{FUW}9OGD=wz91~%uAMCOdwg?&Zeh|qtOR8;id9cFn7bBr(zr(9qU@&-9pGPLHGX4
zD|)5c+j|wDMMvAW@!8I0mryED5sdH7<zjBN3)+_~Url2<Y(#a~{A`W7BC`F7a2$Tg
z2d2MCvqZbZDu95>olTB!GSr9Ut?|T&+tRSP8+s=KeHC;NUu~;0I&SL`@)V_;@B~2+
zv{v5pRNbVe<_5)NYuI^|WlLah`Ad=xO9OCxhLUW}g6mC_t1JhXnYA1~;>t}s6G7Ek
z39xogQo@2dWZ09TiU=$|B)|KJZ02DWoUw$0%cAaUQKMOV2N{_;uR)eVL>Fj^$qQ|$
zM7#Wa7q&sQ^HstC<}vIbb?~6bysti01V7Vs(9S=X^<+M;fdS))Mx!l;O4)%%SMzQS
z*>AbkJoTVI%<!aA5Ydy(5e)?J(ccQX*cBERi(e1%1v*PXL9tR?QpK#6r9rgxq(Ok1
zdfZ$eHz-`R12RP&@Jz$`{=A^ZM~MI~<aozkY|qG}wFVZwMfS$xx7v*hp0f}}KsKNi
z-e19}n>A0Zt@M^ub5VM3kMZtrPtZ`0Bdq?4-jjzlgLuE1ZNH|~)yZbVvCl78%KNHV
z@&_z9xwz`0M%LfMns{EgqU)dt>=3=WV2ySwaeC!;kXt(bx@mR1&d+cl-^g}vr9qu?
z4qybLXz<zptnEsXmaRGV2ZF3oC{GC>gzfG>Z-HFar|Z1nh`Z-^xo*sxN_e__6jwEx
z`C<SAnPK@-8Acf9UA4_&U$?cj^>AfY4<L?B;;;*#jUfsc+@k4bYHg*<+Nog{py?4c
zdy`Th+$REf7VW7B3~-wZD^*J#tRNn&hL_Y%!3IdiA9K5YG~HdztTp5?i+$sutohSm
z1UV3(64$Q2@9~3%3gn#lqTF0-jAe}H?u?s`i2ctNAD<JkCOZ!hrP56Y4N{I;_`zJw
zzIhxL-)iltthxV#UZkwS5{tz$9)kpw>0I&kyUk!(4m?N@TB7;YURCDMNJYwe&t?f4
zSF)mx-cZL?dH^#<G|gQIxcQZRM7$vaf)CquZ1ojS<wML|!GHxK3CBW^ZTIAQ7PT)_
z@2xh6O;W7sEH%O&V^n?v%~aZ`O#}SyM%W|>8FVuz`kmX3K50`)^+q=SbllcGp*W)o
z)*{uUePVlU!uL8X>fv)cwok>68o{LE(0^)K7y?)}y7!Z3qHHSGFI&4r9D*DY5oim5
zc$l<{v+8@!V<5|VV&<e%lmsEo!7}^^l328_xOiPAjY$Nui`%g0)J`sTaB#CM4qvpU
zGbAs(^C;ib%i{>lkk%s_3bLhre9-tlbO6S4J&7GE%JlTKj|F!{^VI@Ep>+jWGxEiq
zpqjOqrFmOC%qL^Bz|V}4D@YDxY(Cg)Kd5Z5baQiK{ALa9H$#lcU8?#0C7nL?>eVpa
zHlI@&@`v1>6Sk;=<_J6M+5^2OV=qfL2hB4TmBhsSNx3reD<6J&_0SRrDy^&FO6|qx
zIde-(7(bTswAF(uT^U=GB=LaP0|h3a=2UHM2QDk4P)A;X5i-!DN$jv92Wuj^Eocu&
z$!7<9&l6M}Ff%jTw((7Y+?1i3IbKg@Ett0H_5AP8J*T%--n1$qvfEjDu#ZbhO{J$k
z_4E7)h!gG;WTQT?a0`E`rE+`jZ8xn(RIjJ>7e4;>p&0%74|-%58DfhqiP&w}YZ0w<
zWF^qK_6wm1IT2B6jFXnkI}W3o!K&R^m?nr--?^L$79=4OotW6!TWfCsbU+3BO;=0|
z+L{d~ujqWx7Wh(ifOG<kcDkfl1J>F_L=fdCb@eSE)#=&U*NFUVJNEL5TK!29&%>@%
z{6m1d+YshgiHHDg`@_^Uq$ws*hct@8uKwfV_1b$^bcykJyzS0pgnD)kR1a7gaWSzW
z$39(l_*7pXaey#N6W8AG`Q;hH(gkE|?C$Q4f1%p90Rd7B^4<IQ?~$y8Bw!+<pyo+L
ztr}S>9^J@st^-9~edoX^Lie&ag_DMnV5-c!KcPe7%s6LVz4MdgmN67gz%Epd-HH{_
zpZ15BJb40zHv7L$t%VKN2U3tBkY~u@n=GyZ`_c0V-<>mB8LtcbV9y5IZe=<NQ|~si
z7R#W<#im-n#Bd0V7`~~K`yd0Y-kW44Eb|0U!pia&!UhD@P$}R>L6~H`;zTaR65+Sx
zJ|$qgzcWQx)@rkKFeP%Zqfzeg7$$sa%j4<TuON`p*473wl_t+&rt9r36FCS(2H;XH
zu|fiuS@p>0`f@Rbfqd_09b+82Bv&%^@eC{=#PWh{>CMdB`iR4Jz$l^)^3|UadajRU
zA~|n@8>4a~+d2Tf?TtP}PDl+nVI>mLTnE#BMP3B&pkrHii5<cMY8<;+cN+3o_en;5
z-w1tAKH<GF!lk?zVjOz0YzkClvI#`5By|6N;#aQ&`Sw=8*-FQ4p(xL3VGwYV$aJ;W
zC!;2oll;^Uj*j;ZT^wm&9V~qV$!e4kRfKovT&Ks#!BvOhxGEbD_qni&3~hyT!yFq2
z^={&ErPzgE8X%z^CTPYd55H2T%%fh^{$5}BvZeDrs0zJZo6aqP)gkf}4#NTmQ(aHW
zxB7aR(_ZO4Y2HfW3K?D4)X!;ds1Yp0wUbF?97%&|i$fmS&divf4OH1?aYfxi2bd(@
zNkXRzeAqs-ZOa5z`t5r~wg$LP+pW&$`k`h=k|8!jM{(hoi@`JSwq)j^W*A9r@L=#b
z6`e0hX+L(lGwIN^(sK1*w$A*gECNxl-h2kggA~t|k0%Ig0SJ;Rr^*FDyR2$u(YZ#z
zu58G&#Q6Zf)c`eitF$%op5HiB9Qz)#Lv1I`nTEy76Y9}8KtCU7%9L3`BO}kaHL?`H
ziJgcv&+!cp567Do=&(qDHb2MRlXcX@7zy~78hpo!&@(9!_0X5BF8|dA(F;i9i;rN{
zrvzOdfo7%OpPfl8D&m52Clk%f4&aIylo+)fovYBb;*H-d7{F8IbaNfn!0t?<i=se3
z5V@L~+F*Zgry`q*%S>f*eiFA-Q}fXL=);A>Rn{Ywe8twIlte@!^zxJrQ6X~t0PQH<
zmp{GAKKdwH;w|JINJ{eYfixt>5<%t=B^h!Z8hZup!fv7-Lmpehwo@_A^+@%Yo}c#z
z?!fZ1Er#q{sys&KdPw1WsBxnQ`&-_JmAxJSNp)^T@W|!}^vzVk)1;-XO`yxuZ78C|
z#lu6$e>r7-u{|ir;y-FMl=ECJwB{gX_s%a~DQKONXu<9YKQ=SV*j>x%)SFn_aStW9
zIuU-yuxiPK<K4A`-pEMOIhP0!Z;BjiqrZLDn-`=@@JlQBlCZ6(!4hGv``OdHa6<dR
zqpR!^UqSaETEU7svsqeNf-$PE?+P?<YesLx0WxS>g|4f?msjUjCa4a0foTw_>$w;~
z!hXaR);zi06;aLkNC2E3mgJBgah0s;VYoA$>T}c?VAHy1F3}J_7fHrMY+-Kh7kIm#
zx}fS*UAF>v6omu15(6Mnw5nTHHqcAiyZ$RrTL#cK=Ac#=-#$LgV3Nnx2aR6}iXM;F
z1#7E71LVr$;^MF8S1ach8N1^n2OB5t?pD|H^kY2{P#ZS7n0B#8xGlkRV~a{lBL2wc
za(C&QnQ+`z9ih!vQubpG32e)sOypU=3V2rl2Z}o}t(YWUhrns*xu_w=6(|*pi;LSp
z-iY+^v+5Ho4OAHO+$!$NOd1MBk$|zhP81d~a$x5UnBLh5Q?VOcWH*E83%$yb75jz-
zw<jlVUPikGcI`(~@IY^T*vcH}A5a^v_i{0$al75A29r%;D^MBhYJD$;bcZ%R8gJj9
zZ-1pD;<Eaqzt|E>xs{YrULny&)h7(5oKBwFZ7MU9-^jB-Sh{_1xdO*Q#GbKxvgbZ9
zGE1y$#-NEYZqqb5_XF?~3#xG!b%wAE9a&3YqRdhQ!}{i~ZuUf-ys5XZoPOC~oLz3h
zIk}5V3i7-FaFT5DlitLcVXn0IPN(!H2+;VO#H)wb5>_tpbS<Gsp`vI{-jde!*o=Rl
zKQMod53lg;^?}d4;eqNL$7TlSLjSY~ZVzl#HVWsfoPDqPQwS^vd`EinKe&a2PCQgl
zR{r)O=CL4rGGuzQw2ByFp;xJUe0nVFW>jlEKtX{YyE!>IQra7TeZ!IktS40}rE~t+
z=Fvh8_x15iYpioVK<O?_JyO=!FH|jR7=+RZGeSc3dTp1&A7;d-Wii1`UtgH?rq?3q
zv9K44YHFS))<0$K@q6xXYmGK+Jytcfg}2~TLJ2Kbqo?tqF(<xtbt(N34-k)R5g-2P
z-R0w6Rae<V9tBc~Ktoc`b7xYwjbUV9cViAH%vTx=d2h9IOI0O+sPgoqtk5eigBl(f
z&@B7?yS3+9)a?=%XkY<*0_KAIuvrDQOzPZ*(nSQ3i)IJBMezfP05$<v^lDoVTY7*~
zY6yrr0Nm9^dP40Q)~a<SI<Z)8OD)~*-;m!@AcQAS5Ul_&VuPHQrIAOCbnHAlPONoS
zL2o|KX6!TQ&lt@D;{^`+we!G4^`jkoHEk!u)EDh=|Ig;&AOG*o!G}jF?z;lF0m~f{
zcAL(c%$#xtL>+;~U_gp=#A3c@Q|Qf=m#pp&J8<D(yS(gIq05#7RM)a(_;Sgx6ZQ>W
zE{dBOb_U!0QSK#g%J@@sYkj>I0pc(3T%+#Nv$3xCziPYI%F~c<)QrS_9!%t0en+!t
zAV9W*k5lilL|}6_!OZYabOQrqt}7pILKN!!$;RW;c4g#9a-GOM*v&PB;#8519b?qr
zk;>98VWFnZDWA<SHRIn7u7|!eNI$kZ?kjaT_c+8bF;r#`c^5kGymMIv<5>l58D_G`
z%%i;XX^%NrTU#Fv4{GC*BNoXAkmiVqh)m)<oLVBe81G3QUaO)R>~{flkg~94m$qoW
zm5{hS+Wdh}75oc@-x!(!%HLN!e|RAm+x)S01XKfcCIpAb=KO%|Qq@-d{6K+s^wc)s
z!yG20PXu4$fe#7et;=WLA2MHW!8I3eXi7G%vZ8mI$5;@fTl^U0-6v2o4R%&15!I#^
zq%UBY4LF#Kj9$by1IQHcFp|4+%|?qH>aUuJ3CHE?5Ln(oL<Pv*rHkcr8C_H=Up}-S
z^BDEmQVqulES5IeT@Sfh=@rAmzrEVbO6bgo4Cvfapui&1nqP2o1DT=64Dsx=a`hpN
zSKwK@sp!{aR;3AM_&y~t&dzVeLUUWh2lNZ{x&&G!Y(Jj#75?lr^r^w^*wvM<zcpO#
zfn!rgnoOb~>LaSn3TzyKSdYyCjbaPU9`h&boe~q!^Y$&NTCfaiHAVs>KYr8}2zdQN
zZ;Hzj6pkv{(oY}Wnm^`XVq#)cropsdoOJE|`1IUdCX+lcmeOWqE=`}9MB9qHFS(8S
zVcdVvtPr4=3D{lNr!)F&@Ed(VzDD<2M{65I_E#XZk@Zlk3e+lHs>VUHq9Iu_1e)C-
zG9l2kbbH*-J6aNT{@xc#(V7X0r8K6c=1@|BICgxFbCvng<KpXGU0uhcM)!=tcFeoc
ztvnGd&Eo6hW1drHjM)<S`PL9wBxG+Q6j<JU{31IfMqn2*EpZ|uAlK;*I6>R|P&Y(W
zOhh+CLKWKSTm7Dh6Un+pXavH&q11N50HhgPSl8MbJhtXuy2_v>j!rrpRvGyf@iLte
zOFTesM8N5UNtgav#A?qm3Q{5D9B>m6mV3f(Ss7^e<bE$3bs4HYLbdDT_8Q~nIZnAL
z-yj|r*`E*@zZkbIW)%G%=n$fdOyo6ClC28^Tl9m94Jy{aL@4vth?_MMP$0Q(l~Iy@
z`9RQtSjiZgfsA4{%kcp`BO02*ph<aG9Qvls=5s0*Ujr(l!cR*ep0APJ-jaGi!V@MU
zIL$2m8qcq|jjb&Sh%Q8Y3q-si`Hv(kVFeI_>Y-%%;-Z|7YNlEpFr|ZfvXSLirL!5C
z<1?vofyh?v#CQ@TjA^1xV2QkN0lY9z+TBJD%^MKoG<qHzJqQg&v`b*r(Y!OsA&C_g
zZV@;nJs_?q<PIu)Np6TxO11rwATcwOF{fsK_X%)m4b2jmNmk8#TA)WQU{WY?62T3$
zBL*yhM=Mkl;08eolSYM;<&e#nbGE>-=WWkAZ>GXdvE7|XZ3w25W|&w<xN|GdD7m#u
zv$jEJ;=PLDR4_xef$^=s+|hh*Z%TwJe*xL^AfYqpvwS=cffolE9_elo5g`=X5M<;N
z6z4cjHngMn*EA}h*oujfJM}+Aa_YImt$PqxB|vDMFEc9adHwNSsViEq@0`3Rc6(a%
zwp#Y-r2DGkq64ZIf{adGBGAnAPi(n=TU6q=ut|Eqcw!PUF`|14-iaqejrw1|j=!TR
z^>`+h+vQ+7s(;YoIa3kdGmyr6MKf1Ww#)8ko8t+T`+Q1-UNCI{DVLPeLpe_4781!W
zx1}oe#eI-Ey0%v)!h(aTqgvmuuR8=#3TXv*r^-_~E)Ho5xovhghR_=o41|V-O_J*h
zJz0Jbk@SuVy=MyuM)n*jXGfntnmMy4V{gxWVW2=lHj=Y5N4H}A`*Xs}*oFserl7Q+
zK7YP9L|!;OIhl5<9JB+*Yu9eGv$Ka$o}B;Pncy5Lt%yjC!JKhhbUN&fCJSzJU_?^V
z(jl$J%6fXS!NI|tun(2ANB8wYuFDph^)S%U(>I$HH5nJ&WM-CKsyV1We&WO>GBWLX
zc0NST309$YaK0}u-v>MF5;=L+c?y1&*}SA(dp8sX3CXnk#C=)Wo{c{JTqvTKaiR1u
z97L$f3nzmdqnAR@d|2aAqCJ~)xiw~eIl8CMo>c)=F{au3>{V*X*M5HX18s?E33t8C
zpmt_xm*kv1_59iM=Nq$`xlN{=vj7~;x{`fCwoUNy`TgqIQzw7>%@j<BW<#ZUN=ix&
zu;~pN1Uz;NfhW;Q4wl)c@;lDLB#3}FZYs?zq%j#99dkg2Sc|P=_#F!$emN!C1@e5#
z(2&l;XpKHta8&N!_Zgp<U>yFk0}@533Q72tu<-Er{rwt}N(KgrrwA#t-n_X0kKV!I
z=3bFmbwK<()?HFpnHB^WpX$LrUbV*4V`pcl@d4WnT3WFx*A2TsV*L*fZu{aBlah?`
zG>ECFa$8zj{+tupTfQsV1qfMnjE$VjTOyVSxwyEO*LgAe)p-gnCNHRey)aXYdk4^q
z$#C0RjJ<K#K|-N~*I|#}XCSw=;CL6^Y+X8bip#9~eq&?f<R*J8+rvA)M&PEJ?Mm^Z
zof$BRbG*8{IrB{-cpWsUE82^uWCAwdMd26HYCR#dI0km?Lm`&RT7v6A)yafuF#Y=G
zTAL0uhyzal@yE@zywZG&0S-yJDG7U-uKpwm!h84bsqU7Bb><mLNWNT9@@$7MzVV@+
zT@XMtgVzB(KKXEGPtj$b#y5yY0$DiM?J;lZ;h#?lC_4y6_TtCBc)MjBooqmD`?MN=
z10NM_*jf5>r=H)^h!sJ5zH5LAVpBH6zFGpjlcmqw+gm!=&d%;Nv-58wkX7Z=r{;UJ
zqi7#*Ee^BvwMX+QgAY~R-Mun6B!tUqh<Dg^F55`(bYGTQCYS}2VCyijv3=+-x30mf
zby?po7a$jWq5!s3brqG+XV0EJ;*K)nj*6KQcHjQcj3>9eNB*$j3vAFBOKHe}t|dLb
zvtN=7#}P9zvwpQsSy>ss)BM|gKpwMxtkoUxal*pFQlLdK3+=GAM~843g<1|8u5f1L
z;!=hEtN<2VnB%_OINHaM_vr47CrX*meb>+%-0jkOn4Fw^KNvJw^;tgXcQB?eWAQoL
zJ3E)oe1Md&qJ&WmhdVVJ7nG65yKK&DN-BXc%{tRxv-SB5>kSSLr2>=oUVs;&@7~Qs
z|CJXXFB%+ufUkmm09L;{qwZ?~0MjOPs$q{yf=4f8NQowcDworUJ*u@F1l&s$6xmQ@
zb&)9)74_ngJ36X>qOvV~AS)*aU9_U;Buk1CP*Sv%#_rF+b#B=#5~cIMC+e2jj6F*p
z6<0jagZ%+DMs@0dkes(;@{#wX?Qf;d!;b2{j)C?9&!(G$LPL|m1KMPeXA1Cw-~Ok+
zJ@uW6;hf6KN{%g7egh9E&B3%4!;9oZ3vG&nX?oMAk5DA4q`9fdZj(Fw2l->|bv1`V
zOC6o)J~Q*4jIiWUSEh<p5M!l-2a7GW4RpoDUoBSStR!Dra=32Jr`OhsnN$S5hMtR7
z<&(Tf9_t)H==rs~QC8)W!G)gqT`7vbFvz%)!Fn!}mo^rMwZY*162>LTkKT7LYarpZ
ziBJB#NfYca-Ej?ah7`bm?RQCyiUmYR$G}h@7r3p4Zb+JqRF>I?W@knjCa(li2sq4k
z-^}fH0)<41A$o5KvIJdkYDx;91kV<f{C<A;*iTOoFhFS7+S;07i?flW_TdAY5E#G*
zE1a!g&hfggr?Kc&f*cH}W{KodmDMmm1;0bu=LSbQ8k)OGV?P3NK;x_!G6Xwvnr?*?
zzvo_QAf-?Sz@hsH@<(eHCAj`_JD^0<fk!bE3}n51`&Md!$79z)vI`uLQVVrVl3-U<
z9If`ClLU8o<@z1QYY;N9M?cV}LrNPgv5vb%D`NM<hu?XT3F^_?w<O@w%<8X}4rl6w
zLf{A#M?hQYx05GlpcVzuIxdaq4h#%j%9y}!jYiNytIz{>@2gj{(1{MEb)4?FZQhro
zs-_m6{8_IRjIn(n$uo2(Cc(3UlR;}TZI2adbGml(=FLcG+W9t~l%9CfQefQL1w@jz
z%!=wb?{*o^i$)@lsYui19+O^We?Sic*P)eg>z4AvhY#BhoFDPTxv%=R7#G+BQ)uRT
z@X^ZvcbS-&SKC;n8(g@F0CnYpyDOZR>`Kr8O^S?LN$cwBW&lXB-1^`Y0oisIR%sWM
za^g#uR1G{HJxVU<%liV#Z!$DE8X<r|7OJ~0GT)K#96(|s;E*^{Set?pd(9EoIp&uy
zU#0`wOR-v;FK9=ypH`u1(5<wCDoBRmZY&K$7W$q$Db2;yV64sT5PKa+G<N1P(;M1z
zl!9-|Mij1>5Ol!orgxKdE&Uy4rTULuWIHD#IDBIn!R8-9yRkH?fBW|BNvqLxW%`#e
zzrgHvq45}9g%N@g-hko@(sy5!b#?jzwj>i$LX3cOuMZIo=Yx+Zq#$mpWfo~jpH&aJ
zC>O)8O69)%6bfHYiM2iy$=vR>I0*KJK#FfL`sTAk0pdx(kke}RH*#SU#R}usm66>5
zO<NU6z)(wW^nkB<sKK0#q|m7OGPA}9(Y2}eJ}BkD9uq1S%cWuR3bladB%t_j+_>+7
z+jM-jmL!2;ai;3Fup*34N=yumi_@QV1+WC7B=Tw6jKJT@wE39qXnBX|+1M07dk)^D
z;I?=R+MHUthk>CXf=|X4V0=o-%F;|CuIr}na8<a)3RlnuSj)~Qb*4eV*WVgNqszF!
zxE`U6X?$ciaAJ@>;h|y_w`FiL$KBb)!6Nh216<8PW=C;O8@;5brzfe9t3!H%?Y%e}
z@Dk*7IC=eeigKN5i@7wo2n|A4Xv!i{Q)D%)g{($g9QKfT!ALWrp#j_Yh$9_5Qq?Rc
zmv6+$-gyG@1cR`!9;Cf*fDTjjs$6wf>5u%qsE<^Q&>#<si);6iQE?MK6QA;V<QA}<
z8ahcTkM$sExPNaxI;RSExBBr7r4<Y<_n(zDj~f3Ry>uw*!Vhd1;|MzL)fj(pSu>_m
z`?r6khkxbc<7>mMAK9Kj#+&^zb<Cl&y1it`&X17skbA@Lmxf?d2MPx)E)8CXq=ZBc
zWS;0|zY&CadySsiZg=C*ky&%Rsu#lb<i(@)ozesb%etHgF|BMn<!5tu=CU8>Z?7yo
zgpcEmJz?rsN1fTDHG^QD4bb3Ak2p||5Bp@6{}m+<-BTHF3a(y{k+H{#SY~FO_TM><
zikDj1k(d!rQfwDFs4TV`W}u0iKXhaQUoINk#Q0u;0EAM_QNsRC=Jc5}-@s3i2^H?^
z=;-M1p}P}CigIsRIJOSmobkoGe^0``mzi7=6Y@h8N^-}!_N$G#x_*QrOGV8<A06tW
z(cd|eOZvf^`KPz5!QpB4n=$_A*<8;)Jy>@91Enybv-<n=a{|I9tCEU>%aTfv6R?=0
zI{*DEJICq#F!S~`Tdv7lWP6t^G8X1IP~Y=)>`=q+Wyv(N)tq~&B`N<XI+|_r>wBE?
zjBQq%j#nqkbFB*1INe72j9?@Qzbu?6)JGZ?m*3VQU65q|LP@9_J#+*eKm%+|z;Su_
z<z@Ej^3j=G_+JcITYvrgliz-wKt8-r8G(Zlj9_wVDxyLcs{56Uzt{XpWW#Y&s6^0}
zHUoD8c)F9I;%JQFf87U#cBlL!o)wCa)o}SucJ|JT-bV)xQK-FRrBwul!&d4tGXA&R
ztwyV>fZtzFS}9QmtRMxA>o<*$f@$&iC3Ii8<6I%sF6W3}i-}q$g8YUgKsYXXP4YE>
zT%bDHpPmy4d@+`s9j&QB7z>r>_U+4l_n;5?9+GFmNR?Z~Q4GP@yfCFgUV6nA@7DIS
zGz(szY_I8qjxU^bF>h*YL^gG3Z0zFVzwH^}@lDh&Ko`3ajK9A_nQW);Dd0Aql1g%N
zfg_$c%fCmaFcBl{p#?>-`}?Qoj>C?9H~;!eG=Pk!7s4qKa;RI%tE?=hx`u|i1aC^a
z-V2b}2q>S3`dwjF)6$AmpG`}fo;H2gY7BQ7>`PJ6f~kQLge2FLz?YGY5SI|y^OaP@
z#6K#n*+`<d{7!X@jg7TIembbG_?17>I@@9dQcs;D1!a}8OJJ!IDU3Ji=_TmpqB}rI
zbO57IhDm#DQ+xZC#yJ?QiiXD9Q)fxy!x+{1&F{;}^}>8}x^LScB>^e9J!Tsk6k$P-
zTaC=pp@7d84-UI9b<04`J#pfME(-==_DDP6s}WXG88qlai-%$x$Zg`cNY2h;OLF0G
zS}rWtJr@@jz)?*tEh+%B_we{4;7?T`Pf+q50Hj>+xJ!7Gl~w-ktFxW4LT-RWLFs<~
z?AdWBJ96MnP{mLRA?*K_C{dd6-jK&fU^aSC<%5%feQAtKj6nfEd@q0|6YMSk!H#>|
zwvt_NNG~QS365RG;79|k+P@sdu36#ovQ4&LRe9|yr0z@*Pfnjc-Bq<Uk_qi7eOG9_
z{+ODoE}aGr*A3~KPBPA8^W7pi&J5;&VSN7brKiYT-Em=16-bpT^pdb``pO>AXjWa-
zNP9OFyFCJ@nA&iGe`y6HU~&r2TD|?*<p3bE<H%BfUee!-C3+Pm`?@5|N?m?*vr!3_
z3uwG#7cc@1YIi3(1_mj>Wz3Fv$mSwI{|5)|?#iQgL%=t2GDZYl-R%LPeGNcaVIgOX
zpo<bHONv0bE|HL=L94qR4xA|GEjTX@-Goa3$Vw^!>OfdZ2?XV3?1)*>>^FIl9>~+(
zS(>*_o;sCbJ5f&xwQ&<*Y(hE6%=CyZ0skohtKHpN%7v=SS0aSouLR=~nin&gt<AFl
zAE(<eqOWA<<I@wQw~YO*Dp$mTS5E_v`8I(O(VLIH#HVJI%WQ_Al6#{vpbAb_jsi@W
z!Xs#47X%4FaMuAkW6RuYeTe=JAC%FD0gE;qP$DO+ZOy^HTMV!5onTN%-Zt}x4_m>G
zo#@U`Wvp7MCu^hZA~;Xp(Veb*332LpGc<KJyMVq?{xi~?(|J(``AtntO#t{&u(Fi0
zal%#o!uQ-LuK=XO{iU7VYhWKG*@fJ&%FP*p9ugQz1YycW^CpyxdTLt^7AorK=)7}8
zauae!>)FXO>kxjk(Ein`axF?sOe{3-W1$f7)B*oSz&=<j`7t#BV}>CNcDE-mx|9`#
z)ver7vamv(aOjS{HAX;PB9L4WG6;bE_YkCv3=A2y$$mx~Ue%%Ievu6e$H0G4xV8^5
zvrvhK`acUwqIdFV$_`-pT|!$!N%m81lEKt8GyqRgf2SZX4q95;`@x56KtNEjP#S1m
z1xJ#U0HT*=86r;w(}5xgU`2)v=Z#EEfDs~64S+`lP;|;2W)l7V{gp5)aDeFXkFP=e
zpoLg)N97k36gkW&Np^uTFbR%Y-4Et>nEneAKj04OV7XYqn?SlM@sq6Rn@&U5Wg|_&
zD<UN)7v5v$*t-G8J{bXc<>lo`Nl7sj<p3Xfytlm~B`FCCwX&;gxj2TBS#tJUDh3Eb
z7VP|4IG~5*2}mig%AsJgY#zJg@}G!M(q=*0oiJ^XBppmm(}3rPhJ+-6)Okr8VYsGH
zo#?JJkV9<$dGR6~j2qJZ#SEo_Vq&u4MBDw~b@ag&+Dd2>XccYa)gO8p{{CkJC<N3X
z!$OLPK-dd_lINe|LB9zgVZUQtx$-JMKfe%;VJTs5nRfiu8YiL;9o`hsoR}{Ay*hg}
z5e$+F!G*AC46p*v`d5z+<QX<(prO~GUsJuew-@aQuC!*pJ1(D|olIF2ahO(Uean6q
z6qT?~4-{-{J|JZHYJfNry@+}Thu#1SUxN{Nd7)%u1vOz8t4T?{h5%Aan0x!rsZ3Aq
zscsG?8n1lKLKm6!*p0mAAUR7a>|WaS?Hk0%O(cW??jc-PyJq|-S9;}NA|{@OW$Xj;
zjVP4E?rW`9Li=#gnNAW$%j_7HSQU0i?#uhRAybEe90LiR_`-$ntGph&>p`?0%by5R
zV&~qO#M+iss^{tjpVod=8f92rkneg;{w-TVEeAA69A~=j1+N3)X?nmG+F!r&77YpY
zbk8aXP?EvWeMm@4OFO-wtXN%IygjjeAEmn}86eJO+98T~rhMNVzDxy$iUmm&5jkgp
zOd_mwqlq!M6i*IJCs@T8_wsP0F!u)7`JpmpM8JF$@w8~bLQPEU1?>#fZ5SrFN$tu6
znV4jh9ya$&J1&2}9Y7_br>Lk1HNviUa{9Q<_VSq0mSgw6TVT*v1W}a<3kvE4L&zy7
zkWw%!l7A`^a9JH3$F#Q(6STCp(y1IFtF%PeOo#-yz5Qfken1&i%@Mi{JhMD(NIFTN
zlPoZm7k4Q@SqcgYqAQk!J*1-H?1B$GbNaNDuDaHox_Xiy;{@1nk!06A^ggRxs$z4p
z2$gOz3^hHB79mkeIMo-aIm{UZm~}5oc7qqyU^@A;;MozH{pd_Swg!z6M|idwq)5uB
zkU6jy1=E6lIXV!S#FTO}J5aH3Wo4!A`UwX|0nh|+l%gkT>F6HMcBkiSFlm)o#UNTx
zv`>ds2_q|Od*()H9lOCGk=Th5z)9u66G5m_s&HCxK$6yEgnn;s&Hn7-w<Am#|L5yh
z?I1<<L6;a1<tA%`0H^^nzym{Rt9E`yMYu;pfshOq263~bdr}U1CLy#)=XQ3uq9mtu
z{PdLAi(rTSJr^o9_2`KcPl0RVO8@NU1T;b~8~GzzVHt>DB(H5EDVJF?q$hLGgx1}U
z0(qGO0EIV|rv?JiVw^|PL8RzN8Y@ug_k0MaNrRJpFVdR<WsPu%UL3BF4hBT}9$@4r
z%Z)I4PEM8J(4Zj2S?)kbkVZi<j1|J-U+N|z)HE2ur=YETZRm-=ZtI2;vK-`0Nll#t
z%%}v4ZcMZD(nvOFMK>W20=!W)Ffb_da66h18y8p*a35!a!Z-`{l7)-S(FjHZV}0%G
zo4#JQRJkU1DE&J$Gnbfx*$r~<8#{aZ80?RqKW94<?nbg3zG^mn@#+-=3yWMZ2o5RR
z+uH`rLXZRz(Fuf!o7b)-ba914$BH}nG*u0x<uY0LoOF_avpXQ;Mr*1=9=R?bn+5Xg
zfaQixPDh6Vg3N-GL83=G1#M3gPo6x9C`51*aliAWF*t#i%nvKu*(D?3t5lnTO6Uq9
z70s$8sa5S>2D*7NAS}RA2VF3$f{I5QKr|o%G(1pr)PvU{GC)yPFfJIFT!4K?x5;|r
z#+x29X08{^?ND2DKtySJ#4!VHFa_8iAbdY!BSHS;X>{isNi%Eai}8%Q7gWP2|7>^*
z3l36JYD<gEITAL}YuB!|;ZGbtj$Bw*UthivwS^H}^-2ExDcra~6UaGCE<#r9qX2LZ
zd*T+PWMx$$(x5QF6I62bgpfoG+KRol(Bd*=Bs19X2yTUZ0^sV#^G|<4Q$QE&cL-7>
zVLM&22I)veLE#Moq!Dol41#{+<Kxk??W{Vb@$lw?G=NrS!{tS5trw2geGv|wM?cKZ
zj2gCb#_<8Dd_@ROtZtR6wKjoS5P1QI-2m+YiVXrYp@}F`=)9yyM@yRq-USv6yLm4Y
z9c;?aFaCgY>|x-Tf<8x2wTBy|BS~Lh-*WI%4XHW9%!R#q6Ax?5z{J#Ywf0Jt_^Ic&
zwTmq}K@B6mdi5qe0+bN5>5c?MsDpO`OcuKieKO@d<a1E=C_UB{k=DTV5J?2uAcF)v
zo&mvJ&2q8F|I?zcbGTjqg44oqg*s9Z!7-Ty9NS<{!37TCtC|_B-gS+9HnLli+q@k4
zOuX-gd51#8T>XKdymY(es?}}b!r9#`4id|<bm1nGS2;H(Y^YRzkg4}H_KjBPEzk1d
z_e<1|QT0VP(%M;9>$;RUIhHMk-$j4xjMlHzenW^KGR|f?W$!zAAYjy+UwZ-h<r2M!
z?AUCMDp$GY_k=%c&=~dh8EH*CL1w&d$e+#ald0)-``#W3LDoaW{W5W!_QUNf&+$)p
zmoUfla=tpha**kZqprm9n126U#-VqAW0ucjX1w%faOcFtD+XFl&h7zLZaTV@rakOJ
zk7WY?yPpHX#j1_nZb~Xx9j=@zTEITipFVx6W=Z-CyEH5!BDDt3CVj}(`Tl0>dJu+C
zepem&P&Sh8s}cTC@PT%&gUjVH#8?{~4L^z!RFAj(aj?f(Wb4=Hb#`{X_V@2b(y8Ck
zVKlgi@<$PB(750+NK3BJ{#S+eDM0Cp{+8tDr_R~U$)x<c5o&bxNm3RxERZH%<Smmx
z7M&X-ghh}NUIzpq+=>`Fa5GAOU#<5e2*ocXqzgihI*8@xDFrnwbb%?tyFk*A6bhtZ
zwRYz3QL1`jPfQDY>`nvikoTR00|RM*>ar1$yq<x>@7AxoezYH=6t(XE@X4hH{FETi
z=0he#V8WvE-*?Jj1C{_uy{|vd5Ni8ha8A7xxBTn(y+nS8p8}D6^XY-j*2BMl2hC<>
zT`#l!x6biK@R!FEe(Nun_+OHUs492D*mKuHPePDxa@dqV<VvHwnt#1Z31t>K$D}^P
zuR95{OT75sN(k2}d-Y>p&c7s7FVZhoCAjk6P-%+4-t8tDkWby2SR1$Jp(|dd#naMf
z*f~(mastKT*V*^0(FS@R{jV1Yf$kvjkC6#JJ9_;a4DvhCuY-Z`_x~>y=GPP93d-9@
zN95tJ%fJ8gx9*AVG3x;@PXZ*ObZA%rmQeyZu?Hv|GiWxTxa*}GLScv0h-Akabd7qO
z=B3)&+OHPea{xAstRQvM0+B@CO~p8Mw+DJY%<#Y9*Tj{F{@X+<w1U6eaCWdL^JmMu
zB<ND#08u?XJ$*3W$PY;#V3m>*!LNr%m;<|u6!s{G{u=xlc`|Vh6PwFmEBJF2JV<hC
znX2HA$W?Fi&R~Ql^x)pvqthxdon>GP5r^FT4aBX`m>6cL=}7w(@(&2gT~IX~u$Co_
z#sw}}ui)|100(A)qWXjW%%l+yS7Kt8Fw;=BlpvZTxh-eslu#urYmUckFaYxzX=!Ww
zjCcp|GEHlaM0!8K8t1w(Tc%^eXTD;`RekBtKNDTY=yLBFpTk9cZg0=+o_di={2Kw|
zXr{?}HuC$)q2|z1t<(vye(CMa_)#OXcJ+-674gK~W5<qVsIz{|oN6i2Q2E|-YgY}1
z`^aIXykN2?t4~LBY3106$LU=iVA_>?b8;@%L*_?>QskkdC(of)WqTKBNHxo+#@tu<
zOl|<1PwCJLI2lh*+1)eim<2d)X5woENd=U+UN{Sq17!mbP9$JlT~I)$gGj;8OfTsQ
z??ORTXJ)O!`-q?cpn5E5O_eSVz3)<njsAyVZ^TAYsi4~9_~R=Pq$#P~s?Es#-M*UL
za&n#bq??)LyG_l_%D`Q7o3~|+-$VF6zXBZL1`o}b$@1A%N8i^?ZEb3={LF*GOx_Sr
z<Da?L0?(wOY0jMD{PCkZjc22>QgAYKwS4VIT0q7P=tiBa4_@Np<MRwQYx8~pi+}a%
z2)?lSQBiMNZU{nC7snkV9%pODIS~^Rm+tA(ORAYOr=P-KLRg;v*`-(Vp4?GpT_TOQ
zI5;w7P#~IEUOhlFr&OJC|74t*9l1U^0?=R}TXo8iB&3nHCUWzpD3=OcHT+~EfZyi3
z7mH5m_aOU6CMM=!nc^S~KmvHSH#-xg_{Z?p7^71iFlUF2XK0inT{j4O#KjO68=C{*
zO<VcBj3P)*bdpo;aedGsOhhmdsFFQkRmp-*&J47IST?quDs;gU-Hr5AUcC4XrYsFe
zJSkvw$|v8CP2pm{Ai_fJMfvGXtaV>%oTnD`4LM24PB=A?8XZke!DlB1yDvNWb8HLH
zte%yL2GBdk^24E>DjmGLxiDvk2ljecMPP%pO&~`c0SAY!k%l&y6+xHKQmeo?0HpB@
zeKcMb$mcf%1*gWkN&&f{+iV1c1DgWKYa3A6S@V6cYJWpCF_2cx;Z(+@j8sne1h70O
zPcBj{20>{92&N3;K?e}q?!<d%gh0fD#z$c|tN!BNlGm{lI*SK22Y7j;l>@*LX=-v=
z4yr)!`5WRp0r4xmzj}I&2XX|oF|}$utJb1W;3esTuAi<&93Tw_e*VFC^jsGGANtp9
zAYZlhwWRC`dORKdvq#L+vwC$kqwTHXT`Q||yQ%6A`&9=CP>Y@gS+kH*n$3fhPn<Bd
zM&I~h0&&E%0Y;d~FuQ<-&5Sh+hxOOi*04_stL#&g?^HvMPs0zvD8Y2{B-f4A8+*^I
zKGt!ywdQK-+vi~yQiS_f*JLv!k2#F}_50HPIa1S-zBCj+J$-7^ZylHY>UIdf4-E}5
ztl@f%3(~HiMEWHhgBkTj5y@Ib867qjI{A=x0IW$eoNW<&2u?LnHx6rcrW!NITXKLF
z2CF?P<l=<0ffZE@wRaLY1B%$ij_#{jd}xHOev#{j*#huYq_+%;PuRO1EWreV;(($+
z(WjaLFXc_!dDvBTwY8?O&UN8n(bt5-xoTjK*#H?g4Oms1BaOJf0%))BN~1*ZCC>QL
z$J*q;S+?1Mf>aOyifqR2m~1ntX3?)f96sWB*A1>}gJw(asD0=rnJ*5N)rC_DILRQZ
z35OiRqN8<YxtPE_V@nrkKVJJs6%b8V+(HQ#bcaEoDC^(-_)!$1Od8s~ig)fjgBJxk
zMKpMB^r#yZX>r;8_-Z$|{1Q}_BI{8-5W{R)uvKt=5vUR<3yjyVi>qb!OnO}hnF#{j
z97+>Bo!3SbQTo6sos2XkL0YNn6^N~Yj&RS{AR0d-G5GA?VASw-DAt?Y+{OK#_u?3V
zxzuek?&7wTJY%aB>^-HA8VMhvNX)>=EtNQ7k4G?|$6d5N>B<c>Q}7C@2wF+-Uj@^G
z{1R~WmU!?w9B&P)H~^`X({t}hN^&y1|M4ckBWOT|({6w_20~R0D52x(4@o$jU}R*3
zq>bQY*f|wvFa7^=WzC#jq<aj0SE>8=6`&J9={BDyr5gv$0tV>J0=8pd|NI1MY@~p5
z4s;0+t_mtJJbQ9-5>#<X=z+4ZD^+kJ$`LTM(CFy-v)W+bUjK(4S4SE0oW|6-P;sXU
z>}8firHT-V2%}7)tSC84#w~*^O5mpRi!X2@Kpp6$uT5DRIKj+dEzAb;k@m~jd{Qh3
zPbbe@z5&v`Zn?wF4YE5Jq0P7Af#m6sBN2U3KtLOe(P6ZRHKDHB1@2EUu;66!)&rTV
z1*a**t><TtmBzcclKtvCiJr@|9u<aG2x5eSRw!i9T{r=<4jw_?6WY!=@QA_2VFAhd
zGTAl$M_(^UDuF&*Hkv^KO>|I*6CksIigbgQR|6b38BqKYDHl;D;c)wFA0NPv3ed?y
z7%wDoMmoBCK!+KfDq+Ot;H?39iV|{Fql@e>Wf$7r2ml{NA9&<>02)dHz2bf_Sb<q4
zfBrNBijOD`ckbK)Z<hWHWgF6)O8)$zG7VN_YPuh!cWBIO04oKtZFOs_?;OuiJiMxD
z4vq(<b+{mwseL>y6FAlC+FA$<4KM(pjud#0lm@tE=12Vk1H<bK6y4q3^Avxc2T3A<
zb_UXRh7HN0EWLbeH)M!OKq^2Cw4medP<)|$GF%(c9hJ6PzkcliD1C{N67&V9bSO8V
z;k23a7}WlbDCZ!uc7YnCOKI2<e>)Od3OSXlQs7R59?H3^9JM>gyffDXatt;5)|Ak(
zBWfG5$m>`T<oR}Vs}~P`hfrXbLptq+J+5F?p}iEy%fkO~8~J=dC2#WaX+rv8Tq6OU
z;YMD|=%_wKND!?MoJeK4I|_W^ey}HKd<M-RQBzBjf>j2pm5M<PtRBCFDH<{xU9U5!
zg3!V00TqE078-Gf1Q*&)h`)UCB6LSXM-#eu$)Dp((%OY_GQr(oEyHZBAw7xY&rUaM
zrr?X*cF_zEeH<o9SFq{|YK)~|%N;Y5KTpMHD(*w&WGzzP?#<Q)*^m=5z{yaKiNXn&
zO*mIys)M}auT*CwQ=L_kVda{)K<LKWI`=Qg?M0dh+Gktt>eS>Uq8lQFdmW@^#7Q%`
zc{7IzaOu@s#`hVn%8{NuW%5A~y}!qSQ68-I2n?$Ehh`|%GWz}ZO5KfDPSfB<Oa3hU
zDF@84|AZIq!}gHms3$HvdGxrPGw+k-cyR3GrlsA0WY=iH-E6@v(C)Fl{4fH?H~5<O
zv#~ei1Ac6{4m9Y(GK?U`+t#jB-4hp=Vz9uEnR&>_$fUFtprNU0=lNi)X^H-XLqFIb
zX&-|-lEoZcUTsrTkjr!_C;m+w4F=<>Y97J(=ahZHD&JpZCjJz}w>k)bh&R%4zF(1m
zkP->cVb{QaIN5CYK0U1Q#M53n6=WoPLsyIJ+3Fy1Xzytn*oVQOzEml-YnE8)N_Ii6
z(aG6bTVuTZKm?AUrch=wfk+w-^78W77wIekx6LeCRnupmUc))!E1+higNM}|_%lc~
z4svg%NiOL-IJheUa#H|x7Y?M7#qinJm$^f24F-Msx@tLPRO?Scbf<P_>~K!6`sVXs
zC~eZa%*&3Gpc*@r3G(rkwP-QgExZogW6j9V*797k^Nbxq-Z_PQDLlUvbHN50ZqPLV
z#fl}Jtu_;IpKnkSyo$3Z`Lm5RpfD>T=l|2*cSl8ex7%V&G_l6S5)1Mfu?JKT5D?H9
zf`cNc6cvytO}c=9bfTY{*w6t)L7GyeD`k+O84coq^d<~NrFUd#Lp}Q$^*i6W-?`_m
zb?>?NkGrx~vX(K!F!R3e?<xD)&)&yYy-iyCn`7?w?e`295B05EzrM3l$G~A~8WS`9
zgN{d6t9ZF$71-}++s?GLzh$ptFc<-H4l$_1@LWbguXE?~6M4UlCM_0zoQ>+8!31P<
z<F{<zL$$<WlKvyO$??t88H_S)TeaFvqWnJ*&D(jt?Y__C<Owr0(l$u>1;?*6U{B{n
zG+R8c<)3fZCElMKIT%Zu+8A<%_Ep_)yABqu>--3_Pvp3FU&<n>eqhEW8$ZW9ESBIT
zO!I53!wJwP$Or93#;RXk%hhBUOpJRCqMO)BR$OUD8P&p=y;G<=b5L)?q_pajA7;*8
z;0w(4CnmtfZ~y@vEUxty`yI~4T1KB`=bxF#EC{gd$7IqWJrjqC=@EGPam<;#;&R<j
zO|bbk+_zO6U2X5We7kFr)<m7tj0AN~v-2Z$E-fhM2MB3Ig+Jp=qps3F<&FtEF97K2
ze+zf+WY-cMlj30g-tFIi|9wS*zC6uMFf^<LM_rTwkR>Osc2_*A5@xvP^mt=YmqrDG
z`9tKQ{Fdw+U#*7-ssLFwl$tt$ag}I#Tw41;0pHnW?xzGO<%n8Y?XH9}4v?m!Bg@{|
z+M32~QJ~LZb<zpU=Zerh7gW1*u(iBDVAM)bZUAWXt?6QcGC*^8Q8R1}`N`!)Amf!_
ziRK3eb;5X)ws{=yRkHU%6aZU$Gb$~B<Xc#M`0x~2X}nW(dod;^V-TjnDZQ<n2wEC)
zAEOMvy3NiVANhpadu7Jq%L{~49D9zp{oUc;(P8bdrNYAUj=rL5ced@pQh+UME*|Jo
z4E6RO5I?#i&oEfZ;x@IT9Eb6nG2s{oB0l&J3=Et_>DYyO%(vd!A4i-9udU6u!otFe
zO8Mwdu6TP_qc9BWvj1mX`wuIrG_&%b55Mgy)+)Aen%tH3>yMqYLqBl=K82+fTQ+Vi
z!MJ%VLS76?VI*SwgH(9s%9S!KCq)AA#<|?QbLZ=pDZil69suE`z^OMdFn4fD=K``Q
zXU^fCdEGNSC#1~|N{||aHXtbhZ(oO&5;*Ego6JL(7m8^A=VZU*KLL#2gFdnJuB7P>
z{CQ4gKj6Mi7&8Hb!we0dLi_pUg*qlCk!a;&Q92V#oX_td{e3iYI$;gvKmge&vi$-A
zVo>A)9tTVOA;@4f0+2i)?rr$fC@OaZ<_HAqEr0u4=#S%LWneH88Knb#>Jc;_#yeI3
z=cb1iDYP9Gm2We<Cl4$=Vm%w1n3zBHngP<bZT5}N{(+#&DXltl8Hy!8F?V3J@v$yL
zipe?lCLttB<`b%=X-Hgw!-ttPt%dxr8kIaNt`of;jZGBw#2~U#>BucgcjMbJXgY!}
zShIH+SMx9)D?E~I^AZ#aKkyI1Veq7pJnB4<ejnmI9e;f4BC!+V9KjBYTX!jz^HtQr
zpcur-EuhDNX(!H`z+aT1AKh{4&)+0eep`9{XEEF@%%jR4cq_)BEI~opfgS8p<d9VR
zwuAqHcZhmG9iun;(61kbfkhvKD$1{RR`Uoz?R#sh)|nxMEU7K72HH-8K^&a@_!)nW
z(cxI_c<MUL&;?M1+3Zrrz^E2rJyqi<fHd#|^i+$n_ds(gu&7lJ9H<a>s(Z_FK`Tdq
z0L=u6fV%MfDv^>WU2PrnXJw=*YM)oZFi9u6Mr?kE*&LUy#%%HD7IRkUKZx72S3hCp
zG;f!fLK-LI3w}vVmqCf}A2NPNgZ)fPD!lcJSLq4sYi%&_>^nZz%?^Gul9M%ObaY@t
z<miK!S$xNq`r67Cw(f#SH5Gf0_rT%@?E_PjzI(Oq_H>``v4U^*x)Dh3qR(<+<qvks
ziUlg(oSsrRp%Aszct|Af{0lYJAC&w<BA1Sf4DV6SwmtHTq<h&b2lcFl!?Qbcl(Z)o
z?e3KbH)KyOep{cjXXdgZzxO}J5Ej2IPwiISjv3`GbLP(nZ80SLS>lMcpQmazTTF99
z`GlMPrRoD&8<bNEwVG=89|q?_@5Y_@n2%uklHZrEn`&m;m}`}>PiC*aT67!3TfD|i
ze?eV0w6v=poqV2x+lr>Lp}M8woKv0cWkNiVDt%99oG&;cHO2f09Gl<#z_gTgoqXm*
z%x9oy*mCi&S@4DbrBCQ=e-au^V33Xco^Q@op}-gdsv>9`0Y8L4`|5KUtJ-h`T*l^m
zJIjqE_(`wKe>eT$h9Kl+UI=6!G#ucJ0R=;ZXa>7<ZrZA&CAsahkMru?soGfhAovoi
zF5%E>nuVSd*kKn9+<}=4qVJIytJ(YAjE|i9&{_-3kxsbwNCC)h`p5hHpP<M}1_F_C
zAkk|2)-R7}K5!$9{Ohar|B>FP`e0k&ii<UF@C&K<@*`v&`ZIDdYQ7PN<$fmHzH@`D
zv!2Aq5?N#Ick^aF(-Z2KSik1O=8llif1nnA%IE*vp!WYoy!@}X7Sy=^Pi7N;UF(0n
z*8Lbs1Y=wMi%H1VloG)e!(n~2Z{XEsR7C7w2L1K@at)}hQ(Z@_K|a`rDND?es2j&q
z%_C4Grb0oj!aZlKb4%e}$*5ONdAYgK!2J9Y<_dln1}RDA%XW(>x*bJlh?dG;97|nZ
z3s&t61uH@WK!^^4>Sni0AVWX`!v7oq@ZE|1uWA}EpvvAXEnQ{&#43%erJ<qGD%<Vo
zda>(cT=i1AV!mXzLFTzbAbkPrq*|2NgZN~W{|R0j#BK*cdYC=N_ixAan|R*%qQ_7R
zX`v1PVuOm1NOhE9G0!G)0Wn1VT<+ye_Smqo7G3v#+s9w{W?|UzA+IU0elUX%rNRR&
zi(9#;ZUIXHQnq;6vJKeXLs8leF6R<O6L7151%=bMz$gWyZOp_m=Qs?)9kwyBNa*do
z)jmAA^k}@e)W@sk8(SNGAK*7pZAGKO;aXRP{X?$oXM7?CbDg;<W=*@MQJ>a3>V5o?
z(Fd{8GX^n`@(m2!!oarNtpY3BCuGoGNS$ND{jA6><+>l!fKW_^dHGDaj~%drzt0R+
zXkvkqgo5}i*Lk6c;t$c=1{Zw!<wl1LB#NH^DL#ha!ZNUQH)DMZ$ap#N3YX8jWLIpM
z#>LoKhgKo9!<x?R*NVy2HGgL)em&OW(6@=+X(IM&882pC2z#N>TITC0W?nn=KG*)d
z#RPXCATlLSuRP(4zT9BbZQq4&w!40HDvqB%e(-HqQMDF2Ufas`UI*3H{Reelc3)Zh
zw<3&z&?iLiaZm>AXof{OW;jICeA9cdFVUCj+4^cZTJE2Ty-Vd0*t-BG{L^zl`~$l&
zx(l48cyWEU=6#pqq`+&N6~q%gzzf!r-&NNlSFrr5f9(5sOtLO!JXv~MkA`TVZipvd
z*s$dDFE$A#ZXm*a;cs_+>&WD!cO*FGbGF@{SbFfxrG;1Cueq9gzyC~Mc3W$8OhlQf
z*Fz8Bb2M+;@-kN1ASU)^QbUuNR9AI2D!G;x1E+k+yGA3uo80M%h&j*WRK)96SFhX2
z*9!Ek)INagyX5G_Y`4G=1D~nSz7W#8V$O@LHBVLDoH76B(%sKYlJ&KBp9J%aaA%B%
z(4mD83DcijnV_!=-OIm&UXh3<12sK?!!)f2RXP=jOJT5rF3yR+-lv19sf>#}JU%Vr
zDnGV6<FH0Q_q%mX#@D`o#O=Y}#%<8Kbd^<9EI>(#u;yZC@0$W_*%BX_q61A|fXjw8
zHydGm9{P$W4e-6f0n<mVP9Ia(B{8`xaa5zLb54i!2ZV-(K5a?esHbVwV3)5odau9t
z$v)jZYAY@u8?D{k<6xRr9sbz%;#%*4>+6&>#Wrr+7bE-NkGUb=Hn<nZ|K9gr+Q0Ap
zM1#|#nrksXm&f_W_K##cHQHOeb9K{NKEn!&rSUQ`WnB%{GK?5mg-yUV{R}R8WL2j$
zUKH}mkCir~7D|+XrPBy&9}IG%x5Z^wlosQVfumZAzS)2PBvj5%nx{;uu>h7xsIM^S
zim!xfjU=eOjYxndNHq?bc=Q7GZQ)T{UREogJA~3KNi}`Sc&InwhJ>V~!yiWnjvKWs
z7j@>#vlS5|-V9ZT8))BF?fc1Kvbni=W1_ExUis7-UqIP<dpX(%@7^_GtZCT#QUyh)
z!`mMtb&Ab6<DNbBuIKcPgSPhf9jqPnnEB`V+n=%i_B`T9+!ed!VMq7Gm;Ab@#9qU8
z*OlMi7;6L%c=2{_oIPvLg4I`VZ1XxA`lYh?XWAQ=MQi^3$CmFmE`1bnbWT=@ML;)C
zMg7!%pB*)YwO?`Cc)Q$#&n6a(>&kcOTlCmec~p-L#+zT&?*ihjzIX1Vb?&7l`WkB0
zW?kRqSl5S!FB86wIPGU#5aSvz`S9`MWy%vPMOY0a6!ZMDOyg*>8-Ku>m*;iq(%X(b
zB35bU*k_AQ>{{LDp7dV#rS8hZ-*`2|yXtYyo{zL0z5T<nq?(|jKo^L``G}%jbXB(6
zaj}?$3f3REbCXm-1AD^{GMzG;gc(e-n+dEEj8aPK2jRYf2eQ!9K|kg6p{u-CLGm&T
zyQec|pFKAjc&QkONHI7B@-1t0u$gbbw2Tv>AQ`%$1Ij|tn)v_+d_<uZ_xjWr9&tMi
z@YgXG$hC(qh|kayZv(3O3S-DXFc(@t#_IL$cg8fbFzlSg!tH{(@?3H!-iNu$eXw+s
zpXyx&SI0>8kcM5uy+EIsAg!8!_kI^!%~uV@AlXq5oeNbGFep4>Gbf}E-?JP4@*%-l
z+1u}o3zQBm$7DD)6t2GU1Xuxb#s*UKV;Vu#q!@eCdL1T$uYGpELMLNB-n+naBG1zZ
z{K&hC?k@P%*)@akO=!C&S#;XA=EaK_)6<Ll3PV~mk<RM?DlnL&W+Drogs#;zq%JrZ
zD+Np&i2F<8!3X-ATxm?gysbc=>pEG29G6)$h>0U(7;{mQj!Or04_xqGCgcSHG9N%T
z^92@RU0$*rrf8M3S4pciR-nJ-JrJ4Ph&Sbc1;K+F=Q^~|g&34!ushz2Y+6YEJ>i<0
zwe&t{?l@I8ebz2o14bM#z1UqmWW27eHtQ8SkYK<^Zda>GTd$2AVscZjt&)`hn!&*m
z=;jlGt)8cWhrCzJ6y!6n-cLRbYd-b~i$B>Q{`D7v^5C!QA!uiJ|MfWhPacQ+>N?AI
zw>^xF-H%ZTn|&?E!m_<{PD~7oC5__N%=S3u9I<+8)ho9BvioYEav@uMW8~b_RQ1Is
zPrW)>)wuxN{5|3`^Q5G*y82?VMp5JyAXYqM=Ix<`kuVr@?!Bbx9OzoJ-Q&&EUl~X&
zR#`j`A2z$e!8z-Fra_BE$qC16A`(XX(rQ|v!`m7H9J=nJpQUv<+vNhlMN<u#v34DC
z{|xZq*y#-p7waxmR#cpgeHx*6#OFnV`28!Y5r@r>r}x7g2Sy4O_@61GYeN0q#rGYa
zT)6jo#9^267Ej&DgtX}Rcr~lS+kMJvYJGB3zN+gc6Mlic!lu;z)zv%BhFIJ*H?uP6
z3Tm6|w6^aXv(KAutY()wc=$9d9dq1}nwnbQS<8O>SjK%|X6QGcwXY^SoMS*#bo@)>
zQcfweq1=<peROiK_Ez1hcPYJtuF;oJiy9xpZ_Mo+iV)@M<b;Zsm6Y5xj_>|fxg*)d
z!KGZGgyQ6wc0*Ic@SfAi2*-3{xbKFvVmq1h;yw208=)M;;89D9+l}8uugB=sWySG!
zr!CTP?dQ$e2X*sC9lB<%%W6NJ33~1iy4v*?L3^%W&oW>22Ylrm0tMN!qujJf<s~Gh
zodV?zR<5`dCF7o=4+-$jE_R-4h~<R#asB$+)oX-G!jYi`k4Z_>N62L3C-c}J|N0BM
zGJjo<4|(^m$3ajo{TGkJU`OX?R~}i`B-MfX$A^AeASiWW*vsRP&ih2U(&Q0y3yTb+
zEagf;ZCRo}^h5|64<SWl=eR>!S~_PK483M>r(S|Z8qnu4lF?C}XrNWCtEnb9r1i(V
zSDZIQZz2oAOZn^9cMQE1qygZ#TVn)0(XOJD4<WArC}*nkq1>udIkZ&kWA#=2;Inpv
ziFeD)PM>}W0>VAABbyu_frmgoiZ=TO=e7uhf2!xG5Q~XVdl(@2jqHro0_9fJj*^T4
ztY*4h?ZA-r6)YU?il#%@IyR}{{s(Q^bCT`bXAEKwI0@~tC<hd?;iYZPP<jg#B`wC7
z|KWuYx%Ls2?|!rw*F^W8Dav3~<}Rq`XdSaT^g+2Wb2(j6xdLpbF((vCtT<r(F-1We
zSKqsLZ*15Z)A%$=3v)}$SE&DW^^0K~aTCjn$=AiAlU`g(NC=I=J4+h9yty=L2ta#&
z<=OZdGiRlYo~VfHI)93-p*#N5$*Jl0;c4x-#$%}itE(#p@8l%13P8D#42^7Dnz-<2
zRYt>QxW&v{ez?V|*Enw!9LV)zgBhtqMdNCnoi49h95O6-_1QR2Y|`8+|6(D~EUqOl
zGDHiTSd`Kd6hgXOn1Y<<mmw@<d3Ck@MLh$95F^9Cl?JG7J+NZsO0Gj#l2}*Is{lx@
z!(hT7l^wkH#?JeNjxq7O&io?X&34s+V^Ks<!)?X*%xTwotAl1<?$c?x!Y<OCb?vmR
zfn^-yMRw}kIi26z2y4f<gjlXp{C<Ac!^TR>oW16{&fbkugF!W6*5~}Tbjr;fo4+q7
zK<q-vq6@A%IV)u|r6<R<Wd45owXM+#*F!m7l1BSf@$L3V&2+6y?-iNVQ`NbBD*idW
zcKZ{X827J^PY>%e!=A@*PcQ4_CK{xD6)IC1r0Ck6<X)DuY*OLOQ!kH>>gy~gG%osp
z>+AKrzX}~6ta-9Y%}_ioJnU_sQ(MYN?-MJ!#aYXJXDwtj`l$DHO0X|_a)*5QY5Gbv
zmBwe&XSn-FD%DnM4O&XM_WA`VjC9gVEQTjX62!xFUmJ#d=%m!{Rl0+JU(@RsxnFMn
z)ybd=f1WE+=h~!nrF_IEq@(kjb`wR{eL6ZqLMah>pO-wpWR*Gh6#BdPB8avGh_1Zw
zcGyGBLbr;0R|-W$Gl|^=I*3E}zOrL*b;RLl=SE8JCxCs3Cw%FW4M4;MTqXt+P=N_*
z0e!tigKjft%{!evlZPhSY4&Q(KW?ts8=#at+P%jIua<6f`$9;pW4r0^m>wxIl8iV^
z?YzUl)&xCp!&G9vb%Bd~GW3XjL2Z9f*#UXVm#D6|db6{hM>>`H7Z{7Y44c!i^B&V<
zcJ3Dcf<h&k9Xr~cG`_Iub_$#dX^2g`{>S{?c|&)$i+}+ij3-r;1I6g$_e{a4WH4tg
z*crOHODtwR9c3?>3v`rKBMt=bl4VFBEQv)X!Wx5AgP6^s7Z$|iyIlRC_57wV6qk!(
z2bNTn=4N9s1*!Yw-CUdq3KukU1He%;!g|nhS-a+?1r7hy_a4;mWBnjNoBB<(H;+8}
zHKVmSBm{jjYo`^)o9WPuF3a(p7K<cWfWMxdpzXW|*}ouGgOxrj>)H%8pMZb>OMEB>
z6Ke547-VN&-Euw{yN4djQ8F@(K>9qE<>IhR=W~Td9Mr(X5HNy+@lwGpB4|R%(i%%{
zFTB=sTC4X6l`vQHzJ9hIsw+0w@w-By8B_9uSb(jBuGe}ng7Yz;-r}+Q87uA|K`(i=
z)9kZ0EelQuJej#7rdYT{!@bw*Qm6gw9b&ey6Vxq#@WP9G=chZLxB!9`w9xU7!)lE$
zKT<6&{(t=!8zFgWr?c!2mM%<FJG;6v*z?%G$~bl$j;>Jl+uaw|xTpGcN?(VQ#;!K`
z<A-&0{1FG8V6Au7@UzqHC&4Pcjn01;Ljqps6Yv@6cy}eVEd%$-9uk_O407oqzngCQ
zYx(I3o;^U#4Gi(D!Ln6np3V@QJpkBlu$fg_#~^+1ipoj{TV<iAfxoW#FeLpiQuV)-
zw!R3z>9JRZ<chJgHUO`cbqoky_yQEkqUX;BmAW&SU`$aGhm-)pSn#-E$V&^A3?`KN
zq|iGFzJn{oJd*+Lh;qQ}4GLH>A~{1y_k;0^Q0UiFM*pksl-e^P<tNR|{dUC)BbE-?
zU>vjp-%k8dIdJ^lvYb0G1K321$+H%$IyyAWwFBVP3|ZM?tUU%VTKlslZ)|gYRZ+r)
z-iT=LV8>cB{?pltCGU1&_;RKQ2~w1UwdWuZpJmjyoSJ@UlJmp;sj8~ztq$>*E?pvY
z4da+1aHN8$nR)%_B%wgV;HO@L7Bj>;o0b*?LFLpL-_joHf=0mR^fjEjV%!%bFFf*h
z)iD#NUjLr=CW#_K=Xl@jE?+CPV@G&c*vVL)l(e*VSb$h+T6O{k>6Z@1$Cp|4bj!7$
z2pGN!+52~c5F1firgvr!vsu28e-Q5<QW&}Hv8QIT!O}yX7F`wo>>`u`1F4DbVo=H&
zFi9lIPl!=*Ao<A@ogSH<YRtp{r_nMPz?k*8YwWP>E5!q*u_-o=Z5}nNZHC8Yc*$&?
zu|uI2HCHg~pW<Q*gyi0!WXgw9Cc;ht`MoTcceK>;|6Eubpc(eoGlAGGhu{)P-gkpd
z7LB&q(9jUP06$QZjSw4w3+n5O53U$nFSL8;tI&>8w=?ZeBG)WrA9sqr&`EsR0a9tl
zwmJuv8NPt;oE#VzA9;?rfy|hXxfe*{W6kI1ithtuB`~V8s>%dAlQmESOacqj;iO;5
zcssnQD>ryuk~xiPhvcm%urgBw)YJ!okKnRCm+A}&C?}KCNp1_7pOBcfGXi)rlFqg7
zwtr8ru={h7iV^65R;gKlSfym@&g@<7(jG=q)}Vl^+>mX((+xQct17Y)X~UvOG2~^K
zRK$Yf@Cy2fcgVek;_%f1OF8Q{FcX8Zh_{KJENImExpnC_=0oeER;tQGij^8Kw_?Q#
z36N7^F_Xgqtv?xrvU$RCJHTr!04|%1aat<Bp;UpU3w#(E1%RvE_U`J|B0@snvp08$
z1VS#rmRzPLV2)6LEvRIyz_!9++v2@-x9W}Mx?QH+<DRY^`*V*09A+BP+WQZ0Y6HM{
zk|v>HUUf57!#2Zts1c(~kh2)9o{_W;{x4u__cSy%KFrD+!cJftlr+=<d+?G&CVxQZ
zFP6}U3YU|Hfgss#z)WO7o2xF}P&x)XkT$@b#=+1qLCJ1{Bl-%A&jGA4<P<9X>@?`c
z60_1OJK@^4)xUrbla)464t&8XPI2K_v{itblI0E$^|@2Bncs==%;j2TGV*0xeTMaI
zq&nK)XEU?8rDL(_AvziA4n}vaI&`w4)o1K>{WA3Ax7V*<r^=s)_+&LaMvo3griwBR
z=L{3lTLTIezlP)B1onB0&_1whBydXXuo*F22h=qka=gdgUKhYi><)Gu&L%&8h=rmM
z$b+<@=XeDf;f!1fIR9V|L>U~b^qQ`%nf*V~lLW_|wfHq2&^=AgO;T%eEP{Or*k0UI
zTpw0mZCophrIn}dG2kO+PO8ADto%StN*%+EMTXFGsMO+vY!!^?-v&)e4>D-d%uG-n
zSMedaUw?ha8k0oGhsc0HUdY%G<O?npn?Hn`%jg<+GOcJ_G<MiF;CwtzTezOMQ~OHU
zz}}TMZrXoDHbvSL^d)w<mE4+~ye?;_mlzvcfLv}5x_B$TkHwI89Dx6QXcoxPTAN#6
zf!I#b(g+3=fmx(1+KLSqEMyxFkbewzhq#t5>)Mi4xiR5cs-4{0*V$@K)7iG&ld9f^
zHrjr}g~P)Ik=PW@(&~7hU6##l5ZtHacvb)CaDyS1;uk_aYrHP0y#n={M7_ZX^P|&R
z12Y?VcluD-gz}S5zD~Kb{m98J9d>h=h=<&B>5LZiN@!8SA3xqsw+>gbvh#A_e(`is
z5af&$-~X;|gvbz`m{>e>;jWvI^4Uq=(e&l!t=Mro+1NU3sEQgj?3d(|K^DcU4lGz;
zc<MvJm5kQxTJM^+cE%=J4;2Mt_rdtxpTEmOKUdOiwBONlG-jB^Y9+HbGRDI|M3D}=
zxw&}~J_{*glXDnJxRW7X0k>h8<rwdskc@s$(0oFJ{x%X&GmbFcetfw7+7vn1v?%mB
zt5f`(C!)A(XlN)MV;`R9RPi*2G8RaOTWKJ~%)hZ*x%$K19=-+W$`tG-S#YztsW#ri
z^3pl*e;B}(lkyxZ53z#)1dFX_Ix!`el#_Ev#k&hsgCeY)-9@%a`EGX(1h<@FlY(b0
zGot=^-vRI-LBKAC#-IcRb;yOotU(ZbNtT+A3AZoVOi?Q-qnp6w9gDyrZeZQBk&j9C
zHmuiQ=bpTF((Z_rzKvDa`|P#uX={gc?3EHtbp6DS{N7L%B^0V)&Snd3ETa$)u58L!
zA6Qn6mQxgoI4=@qZov8nfd$Ee?g%{@<ZJ@))H>J`H7y-arYK$B1=w_(w`%7$*xTXt
z^yt{*i<lr0i&Z(jTywb@%-JF&(8HVksHh?r!|6fwX<t#@I$6?1pQ!en)PY0{6W&%E
zal%YHJ3H}Oi8F7Wy0z~W$p<a-9@Yz5P@Gr|xi6Qo)+4hcQ^+IrWD6zW{<l1Qa3g=u
zY++w2rl)V9%D6-JG?;23%YylAno+G%Z8j&w>GjFWdSF~|7b`kxPrrs$6J6TYsYCcw
z3qATT@TpcYYSNEVlXH3X@aIY4u&ql>(LeDaq#a_^q?4jF6s_JO8Nxq+7mGCG^Ar;H
zAnl(RU32dDs;cd5Ne@rq8pP-ac|+jx8bdXR?kV(slW-{zctshI!GGcy6iCG+)H1ek
zUhvAW&2$T}az#aj5xR&1`sny`GbGFM5%(%G^*$YND}_c!P1DtR4f9wLHmoQE>zLH8
z_@bQ+I%Q62+MZG)Jad*-hlF3JmHQl_+Py+*-qnLzxwogX-f!_yds_`MY;9^UXfNv+
zm|$v@9*v$J*g9SIu1n4EW!C%i<0#0~)AfqB=dA+t&0gy74zhNL00=p5H9{Khm-{jw
z4zsk)vC_0J$#&Htc1~pC3~VUKVcC-#>ENI}y%XnBw_607#|M&Ze<MAT4IQJ~ABY8M
zd775`v+7k82X5&hKo>S;f$6Y=ZjE?m|Fa1)s-xpJfv_tc-ZMP)k3)iIYQw!VKQePJ
z7pkG)rNk=A!L6<^DO95^Fi{R+L&8ujzuJLvmLnLRQg-Kc`Q0ntY!%BKW(~P<4+S}|
za!r1y-Duh45-WLQgj?pMd!%qOGr&rVeIUZcFyu3@`0bz1{`Weoo|i)bW1;8hb3Mlz
z<Gb=s)-+c{F)9fWD)+0)_-Y~t0~AHiRycC4s&NgSlry*Av|vAYhh>RZ*l4JKm!uXp
zhKTptJi~8SU3@K<Ed}ATQ-qipH)J{=Lz#dU#Am{Nd>W;Zw&sS)AsRE1#&<7D8Vav`
zICqqEW+zDnRVp1{X&q|Qm|cVQR~pA=Qn*0zQv25MXQ|G<jqcG$u^3cH=|2v4gfbyn
zAy9FQ+jSHS^<47Q;=nW@5Z!Ou{UMvKzQncT<14l7^gJ8K8~o|;A^E&SeyOihrytIo
z8talBeRDw)2T|I>x@>W8W|w}SxkE6`p2_&^hXZlVZrWzLekp13ZQL83ubN&5GD_Vg
z%_je|i)?|5%=!}5rku+y8Gu}ZV^5)(Hgl=)^RaY0SC$83Me+a&beFP0#lt9jw|AuW
z0UUewI>@*i!>=i=D{Nyt7RH+Nb85M@xA7-a(EY87%BzOU-6;$i;CcpzEcJxzr}nPJ
zDyBo;1ft1VW%RHLa0Zg7(GgZM(R?s<qr^YP6eAz!Hu=W8PT9PmyAa8RAJkpcDN1zN
zP}OBlpk3?ffB)#y(PlQepz5`U0=Fr$6s(O*lfb`P&){8nUF*{Rc4fR?x&%W4oObOt
zGnbb-<+7ILA@X*Lu_^JYwcIM6m5M{FZFk|r-?D4MKb&5h{f$;?|9*ITaCnb@kIMNq
zO`iP{-PT3jvs0yw^Hu_0(YN`*DPs+lP+-K~#^V2av&m;qHmWVOAA2E_eG7^9(Yqnd
zfju#}JOLnsnBelD2Pmk{nHuVb%;)l5=guHOBEr(72a*4*+4ZvrkXy(EA_AEln5tR!
zx9kTZMRKv8(1eVvoPA3A;w%&!79R0y^RAg!G@ckcT)hH`HhZixvGqvEm?m8{s?h0h
zWcc^RH5#alj1--zIHY2Xt8`b^-UKeUHiU&Zzxj2x1Xh@VWkl9@nX${+n_il8u$9T2
zm|Vr`C>E9t(W=#6*R#YS+p5cZQINx`uCG|BHlJeMvQj6iG^v6y^R626Lj@BkIcrDC
zeT?xaDU)HGpdu%OJCwVv@Nl&WZWHc1r^4*KmKPAtzrZJ$IwmD8+T6y%VygeLk|;wF
z?hCD6GZ(x>nhFr<Wu{E-xzrmQ8M(f3^o7ho@JQwc0=mfbaDnH(?zpMa+B?@_8uk)T
zuD_^QLs)&V;m^7;^*KWk327Zcp2EVH9wvKu`t|m4PoZ4oAn4zr8k%q*k8y6}dIq_j
zI<+qhM-H%H=6J8;G64<j;<P?rqWMqHiiJ~!B7g*4eMenyLxo({U?D1$Cig||X#wNT
zQrwucch$6N69+d5z1}A@zo%lytT%8j3^W+e0|!HtnoK!|165Vj1~cry!ZJlEcQNh5
z_ofOfd?+zh+WmT<Ob9wjGKnQ#2!T`3(NEpG<}{8Dcyv*^yFzy`AcmYR$^p~5G9e@X
zxSUB!MD3(56o%E-97<|Am#^j|%^F@ZRx~#0P&L)C2w|I`4ts&67qfY$$ms6NRpo<1
z>3Ek6{*7Wa$W6*&U*4X1ec@`0!yJBG8EdmyB6AFbQiNMvQ}5-OSo@@|(Yl%~U0K?6
zRK4_gW8Lu_%fx@lc*=vZ4Yg51yM^WlK0(Zq#G*|mRI&=4<j`G!$0JH+gv=a+r|Va4
zZC2uQ)Ymhzhf@<<kUA{0k3`~;hxKx!9ri+uftmyzZp1RtK(xC1X2t+y{A9^n*Z#{}
zgyR<@px>e6N4|woDd}e1%DSp5xaY2BFv-~%DPE6vIT7-qt&|5KH*xgz_~KX$DzMsB
z2nP!u7Fb$R$`2Nx6KzN^mXGzM2?D<urCh7KGf?Psw2O9-rr5xk8Zwwpb#Az^S~BL4
zpkZ{u<b_A>KvWlEkwqrM)f$r(0tbs=;w#9DzGz5oSQFM6J-0>qLy4aC?}D6yhQ_;I
zPBq*m5|Jne`jSa7_c?H9+sUhmH8zDPFa!2>CA#-m9D}g}axDIVI`tj$qNCXhioy`G
z&?zj?`uJqV4!XG_{B2;MQ0X&<Mg&88wKoJsO7sl&%kg#&n>-S8x3`!~%93k3XTWHz
zKR#JmR~%cT9Z?|aHx&D!^aO0`?O3Y3tOb-GGy7m_ETW@pM$_&fV70<Y%`BGJi_4^Y
zEt-!J&Mv}CDDpuMDlxZ3aWHJ5!=r(!NuJWQJw30u1cW!)IAq)4P6kz(_~2;oM~Y1B
zMte;qm>sU2&GyN;2BSYBYHFXJFSscvbsb?pYtw=oSU?a9buJhd)paHXl`0XSrw8nt
zhDK?3hJROg`yM&gYRYq6-ZhqHvS-)++!%B=P_?LYn{8l$ZqujypuE-9YKZdO!2;BF
zvNQ!&!)|>Xggu;qufAP>ww98vt0{zeJr`h<M9zN<COK-}A9Cy1Z;%fxd;nCZ<^qGM
zfu-e6I11$IT=#BBwaC<urNwads~CB^&~v9u{n;b9<Tr~whLZ!;Rs!%ML*f?Dk#d)I
zb#yl!bxQki6V|eZ=q6aH|KlcTmL<CAA{P&Ty}P{0p?QJdk%O}9pw4)PAW3SO=wf4V
zC4t>>vVx%!AZ?Uwj|@QpaFy+!i_*~oNkUYib+#b5kl}cgqHTue=9X}ugZYb8R^6i7
z7Gf8DJakr(fWn=*+=km!H$cSL^YZHEid(GM^!WJ7ezLns$8EL8!dn}(8D62D@+S(}
zD-<F@D5AeAbn!xUEI?`q2BiL<4$uhRKK@b(?h_SepinDO^+NCuH1v#sArt^l@+TeR
zWalliCX4kwRcg!puL#&)FrPk2Fh3mJcaYpX!92dI5#4xS{0etFC^VV^n6b94Yr-dJ
z!J}1-HSU`LRg(CDLTXNOPE{Z7<2WXm0f=excxd9D#G`H|Ku$18C6^#-sWCB2MuA|0
z9VfbyfzvNDqyKR;tLP3J<H;eO9!v!S2$n_p{EF<c2@n&js;bF*+-t2n)><`YtYV2)
z4roj8zjpD5HtX2sehw_TiV|NYxVP!}>fE88^WWEbPEXPt!w%=56=J46%oT46BjpG6
zg}iP^%-SI&q@^Y9Zx1srQ3gGe;t3cIY-ILLf#NJRfByV*;DJdX&}X(O^l_-#tz3t>
z@)zI0vO(hsMLL9}W|Ktsw{s2J@0(+6V@?`T8dAss*lkx=<lQYcf+KEwCp4tJ&O3j(
zMlhzm*#Gs9f<MgpVDJo}`QxU{f7@To!PlZ(unKEWHL(1ms>7|@P^17gv_FtzQO;#V
z5`ge6fakcKV=&JOf}YZPc-$WV2pu)=yKR4?UYiFS$L+AD@2k}t#zUzFpG$P8Ngyza
za*%HcbEOQh9ONSiDEOl$kb%^kg}pvO-aK{y>wy%ct$)GK*p)Fhr0&eh&Q_%JOHR1m
z+2g%5F$d0b8vu8k=VaovA6Zs`B5s}x;N7eDA))I+gL>$)3aB{)25FUCZG+oV2SwfG
zN*->4SweMn6y6s_$!UA{v<4RoYBbRbxFlDTUS6xW=Y*lh=yMShEU~9UtU&@k4q~@~
z_yL@;nc@R?DIn+P!d7Rf@$&#Kq9*1aBpY_B8O5r~UcY{wnGj!t@nS4@1k7$@B7j2a
zP{!3AmEm0(n-kb$^(Tif+5V?9dC;j(iB+J_|4g_<X!jaq6>{Mcd)B6=FxLKnD(xz$
zFjNldBf{dp2A00%tt5%yyE|=9(UkdKA2Mj&_ihceGSB;Uq1!L9VWTYAfyz$wKTrEI
z1ka$$9E`0re#0JG8!Gh!%)uV645fGKw+ZQ_&!yz)v0zvKQ}WxRy4GGF?C4jRo7RYO
zzzMYnDq{ix09*uhVB}O3HBL{B$YQ&~N0hh)s8Vb7+K!n9`N4gm73(Gz39-1$*kvz(
zl7x?_pm0EGWz(fw8iS38Hh>ien8cxVMv6>^9xkyYY^eFRgVu{F;?kBl$nARF|L*)u
zp^bk6y$SThMZPRER|AypK(t*O@OB1-sSrKZDgn-dMXCZooQew!O+lzdk~gKxkK)`8
zIu5Ng5CGay-g>9Z!NGxgk^wMyQ~9aD>5U*sLp67RK@KEECC$phhi%Tywv<cYfy<jr
zQ%315tZ>l805Ww!)9;n0RMya@$6^lMxG3FJb1j~JA$8%gPJLAXCg62v8`wpuPfJJN
zPn1Fn>&uYFC)tRt@|+j8VO*CpjX;6Yj*VtUW2bXF7!~78QCu^N<ZP?$SpKM}s#?#0
z!L9C5dHf=MWoQVTWn(lYH9es;-8OA$-+&2{2|Ba*gXHQPkY}&RZ}x$%a461XC?EM`
zvJ3-O9Nh?%{g+qS?c*iUq&K#!zE-~w1Zx}XoQ=5q1!%;Bj0`oV$*7oaEDt<intxqP
z#mHAf3=t=^+-k)H_8r-U&Tx#U5sr40%?hFF>S|LO+(H(MmjfKb6z@hSW%ajg4(8pu
z`hAdY;l{<{$}yOTCDLtNZMsCkZUY=Qz<ouOh%k~;n7!)EZY0sm-H)CxkVUD-gA&=Y
z<Dg9MJ_I)K?kLR2g#&UlH>b+QKNNZrFD=iF3_B+%U5MLE<h`i@|D0wzpHMjoz#zXB
z4}^dMgJ)Me`Z%7YA~Ic&t4HuInc&Ucn)#@bKaI8qJ#U_{ljRg2K$Tj$p7lr$uBtWn
zQp(A4Ay<nTJj5<;Y7ug4H=b)ge&8OJKh)^Jf&Hc^AB}GijR5?XK~1pa3&F?b;=TrY
zPQml9sx^~RC)e%h8zRoMh5!@J?#Xpv0#DsBJpsU$CD#bb%$C(EY&242gBv)lgfn%8
zc=9D$g11|VUX<wcqUj)S^MDhwb{gOuX7^v*nv$MC!xapo&M}PutWp&wpZ0nV44JJ9
zl^S7`E>?6z+@BY*)jhh>fjrA8hX(GwjiATox8W@6Xl%k|uBL2sakR)OyL^I26*vYa
zar}cY6Mq4pJjuH|%nk!8POWqZg2Zy6l8D{QHT-;?Y$gmHy-9h4*M%Gig5xnXjg2jy
z(|yxHbcw%BGuclgC@^+>M5#khK&cU@pCQdi!l-Z;R^(;zk?ea?t6A(DOSWhW80H!9
zgRg^^7A8+R0nghB)TA*eIOPqyrZI+^Q};dH&g!UGJf1F*G;M|)_i5;=2E)@(5qWll
z?!CU7dlzt^3NwIbIU+K$CFeQ>E9Isq8>fTRfiyFi1hYfj&^EBgM((WgBDDWLo{;k{
zrYMGQeecY(uf(D!<Y4sk*`JJ`_GlJ4A$=ic1aly{+tA7O0PtsuG-1Uen1&V!xSCL{
z+?|>ZLi@&_PzQn>5)93dRJmdr7$ea<;3qU9K(gfeq7;Xs5o|4zANyz2E;qe*YwrTt
z@X8KiEnwZP98`*>QLxAP<1Tz^0o1-Wk;ji4LRqymz6i|ul6nlfy`ByBH7X%%s|e6C
z_{T1nJ9`>ib0G`e+xHUiEP>(^ugT%}zQHriO3w!2-vr$)6{Z6486zNYX$v7LA_-wX
z_;!kL;A=&ut*tOF*y1)0S%E;B<`9lT1te6Yj1h2TJ0Cu<gK|d;P0Ypy%_irX0@D@%
z!0_Q~MMqHxuv`M`^aR%;QWtN)l%ozR5q!w|2GBOhMVp1VSf*S2xGtUZg?9fzb2rMN
z45qG4mXrwpr3(8AZnKcwRe5|>@nk$Mt+qiv&43@;*{AS7+Rsv4+6Kl?z7xuhT?l;y
z9DHcah}#3FDtQXrGzt~v98wYX!wt#4x&?JxsN{0WE3XjSC5`ia<BN23bpvVSX5-4`
z#%L_T+gH9@fVbb@gQACW{l#MZox)A}EYAopL0#>RaoSzFI8?vFOY0T|@!7&>HEuw8
z#%=I{h?;<gt%)C;^HCl&^y^D+@Speq|LtYmfBmNKr-dvxt!N*L<J_WO&rm-2Q^JqG
Goc}k7;V*&!

literal 41953
zcmeEu1yt2*yXQ8sJ*WsGaU?`qkWPbe14?%(Eh*h`M3F{8K%~1tq#Kox?k)l8hRxnI
z^Zdbc=bKsg&Ue?EZ)VnAYgnGMar@u@c;DxJo?krg_L6!kdhs0bITQ+Y5&c+528B8m
zgF>CCI&&J{`Tlw&1zt{TiHi!sEBtIdDs_dIvo9YjTcS{;Z;}6w1x&8$qELUL&_WO7
z>>?J19p9<xMhfmrO15H}sDjR;l}>y){m2*p%H5Awq&<ZwGD7fQ-g?8iOGj8EkB?Jj
z>d){I41J?in(#qhVVoG#Xeg#kneeQbLCZ$^1kv>~Nf%zdKE*=y#}A1gL)+LLZH^~e
zdbbPhOK3W?6}#mkXV)?7PAy@xOP$LCdl3@FYD{>CcX}bmLxu40x2hjhZphQ1{yck(
z?eHD+nV4h9FOMIhE+Vh@uN*f(UQvO6+&_W5zI%-#Mqb^hPPQShD5_ik^b~&iOm4C7
zTc_id7<x)Bcz79e+{DM9wBQy?l<`ad>pgQ&z0jjaqxvOewo;@QFJ63KuR}o*Cr2k-
ztd?aie0Ob;uh#d>GJnbsz1;S$F1dMD6jANTHcBByMMVr2i=6Q<4-b!zcb`8L!Z_bu
zZJ^KYDxx6T`V=0n-Sm+KAHTi7|3+kFBzx8wk&1$X0)rPXygWUzSnQAK^~3j8C+bAC
zwMSKd`1@ZX-~9vMx$Xfx^l9yLmkuBBf9J{n1%vlr>=a(L5L;buwP?q~Lc<N!MY?|x
zH?9ttzflQA;z~;PxIqk^!@01{>F9uT>l^Zxzlt6@?dO@~Go5gwn{UyNEBu%WZ+Cv8
z7nOZf+yjf}uJ2h0$!DHJ<<I4IcT;;*LZ#UW%knAp$l6sQ6d)Fwj{R?kYFtjwDHQ63
z@tlD#-a&F=kFH!ZOVAq2x@XkkeOEC-u-|=XGTi=0rCplK@fnE^r;zCX?u&AGZD3;J
z2VtcW`t@#SSJ$<xw<S`nZ$Q+k>4!;aYfpUc$0dZ;>(Di3W@ff`u<|_|Wy#jTju@PL
z0daWt>L(-h)AC>DCrlUma-^9;jg*?xRaEBY%{ld&K0-*_EjV@<8Q58$uKRw8H^U?N
zZp1a88kJB~)m<7lvRmigJiK-ehW6{-cW{L<#Z)=(sHm~`yI8Cqg_hlGi|)G-SpHev
z-2NYv7WnPB+YHgIy+v^XTTUDl7Wqu^hTb(=7|ewLXGw=APoA{*_1WGT92~rvD3e-C
zut9r0JTB@|&ia-U26HBQPT11YlKQnx#P_FR61P8n`edkn8ueXikAyVf<(89xgQFun
z$M^e>9zCLVW4i8>qh04utk{*Dd+Ef?*q3E=c5!j>mHRj>RzzQ4-;aK-b2!q+;atr6
zR#}}o=?f#H^pkDF!^7Wa+@ss2CExbJ?>X9Uwc8w!^?o*nmzVc#Gw0=$%&DoV=j)5i
zD34^1a?Zm%<+exjPt(tTu@&@xG!_5<vG|u(w*RWb$<|ZicCEl<@H)L>mhaATrJ#n(
z*5pSH6P2=^;bni@iuK6%&3d@>Y$pNo^Mp)8$pz$_Re0m^<a;xwg~PU^&N)`I9S@cZ
zb?O7{CT@-vX<8pH#tXKTOl~8epFdJFuOX183#Kc~)T|Wf$<`z<9JWzavutK>9~vst
zbiVFW<7~+3yp7>Er4Ma%EO#wd8_9M!EVh?UQPR1MR5&{_sN~%Nd=TNdRGjm2?3;n9
zsef}gXI;0VCaI*rmJWk@3EOo>mAqDG4tDmMH2L(?YL&W;Aw@HBJ`_Rq)eHT(c*eG~
ziJ`TQBkNIn17yg_-5&LOemddMohC2MaDAdnR?_c0Q_-I)Dk{h)e$@wQ*9TI=V{12m
zV$Xqd-MVSHG1rZczpC$w)qmeOdD^(Q?QWb6J94Tj4Zn|ad1_2`jD<s2@Y|Oon&U<M
z(jxwH8g<59?Ei?2s^?3adxwXT-(We0fw7KDPNt!t@aLIJaT$!T>OjMpt4>_LQ2#MN
zaNo|##-_H~>#BCc2iiv+G>4yi%%U~rHd113BqSv9iH-J4zj39BKi9aYr>EUqYO;q;
zld4%cI|W4xKW^xU4y$`X`FvLCxIgcb=jg#cj>TnnlWVDHLTi6_i^X!PseI%R@%@xy
z(>gxm@9*zv$F8#^X=DT^2Mi^WqeOByWrPq=(yEW<E(Ha}R7+%OFBTbTUHirmMr1rT
z`&(VE=jt_k(`T1iv~9&xQo#JEId62I$P(OH$kiUmd$FU$PuZVEZlIIfZvaXCIeWI@
zOlJ0F6=GsykLC)Ra&=8fi{hcrVYtftjVjIQOTtig$ZV|>Qd3jE3=IqrH#ax4XH3Z#
zt96dt^!pPjm^~bu<_wf|S=qMnq5)aYsZmw2I@KrJpyW_!wQ-V{lHCm!OLRXMvs`(w
z@9FuG-_geR#y!0%8<-C7R;Q_ZEcz`GM1n3(I;>$c3k$xQdvl7ber8UTWR43tMc4r&
z?e;ifcmKk9lHNV;jV@X3sirWz0v2B-fT8Uj9WwK*fJBuvx7I%o;N%pwwYAweE(L`4
zCT%x-jmyblQBm=zt4J=vVjsA$lU9#S-hv0Ah4Q^n@8Nl-j$7`qI76-G)EY@#bjq!?
zB%9fX`rWYhH7XMOoqYc;1%<%MUzaZon6^Z4I}UL;U}m@$^E$O)C|74CU3q#kl&`i5
z9ym!v@omKz9!J80=&6VVir;jo2*O~0YZ}kEv%8xB-}(8**NDB{A<Nd9q$lANp^c<n
z%5|co)^bf@_h@>u)NkJ8FeYMiGc_>qG;`e<JdqX6XK#^LSV;bo(U$}YdaXa{O?+qa
zlCrf1diJaoNqw{16tz7izG<}AecDF^_y9$)!#Ycmj;DxQc5ho+S~{%mxwI4_-Eh?G
z?5r0SJC*~5K0iO-Vrt!=l-HyAh8;gYBR&gX(uq`$mX;O+v)0~{3uy^3t!n}ej+bqS
zpN{Sye`ZI_l6~bW%~M}bPeKj5=T%#goMs~=`1rEPPx(`FmP+QcJUx{@Kl}RNR6jm`
z9f_qxhI%Q7hQn;)gF#)^Fqm<y+dMB1Ph4RkF2?Y=PT$7DzD%lISX5Ha+Sc|b_jom?
z%i#cUN(Gj@YsNos4HBN)*eJUBwADVLa6ZrI&BNz?yNlw9WA{iOvtLMt11Bwtzo^~0
zX0sEy&90W^e5`7PT}y+uHh^4{`~1{p*Ud+jCS*=4Ut#zM2S;qGCSX2`77GR`D8_y7
zwQ&7(u^A{b*A~KbUq)u@ZlLc+6c41ASEtUy5mVu2BihcuTZUYA7V+`T8)%bBR=5HL
zwhbUs>=unQZ;FzbS0BS&ZRb?~(IVwk>w5#Q`*7h_y}Snt*V8k%Re+Z@Od><0oJS!;
zNjfZWyoONIx5kH1G*-%!+j0uS+q+hd8@V|hVfj?eelpnIe|3WVfQy3SI+I#aufxFL
zp!U|<<SM39a@oeb8s<?cSMQ0MO}{SO&A1m$?R!zO;?}{l$i2V3L&RcvUlgP6xssNi
z<qg-U^CqCzs_`Z$&5UN%6?1}Av4E>cl%a)zjUoK}!ptn7j?9sP0&7AXU{Op`IAkF{
zWYNfYq9BY#TLkjCLALW@nIJI~kwcN<vl#Vc6s^|Y)Shez&e3cBWC!UQiG1_g8~W9=
z$<Z!Sgco<S54N*CXTrb5=jU5zFV+Q+lS4N0VK?fjcE2n@bd%H67oOoG1anga_r0m+
za6-5PV;$BF>-Tq%XNVEdX<$<4qp-5Fl1n0qtzc(ktA2g<DkQk*R#8VsM+A!YhkG%P
zvg9C`|NX?d)veBgZX`^Li@9O=uj1oFQUnI6JmI~)>mbXtKkBY+1hgiCHJ^S&&`311
zF-B})U?46rk(y$=A~B?O+DJ`0T0X$fKUy#!ca=cI`~1iN{=I;g7gU%bh{*l?oTu-a
z441H%1ygC^CjLatv_erjqSRxL{)MFbzbZ8OKOsQ;KOKuBZb_2%MN*KUKU5<F=xtCm
zw)Rc{sUpH(-5z;@qVf~PZnA03#*clR*_iTay!w0m9fa3l-D$ayIR1C^n7epvXdRof
zEAgh~z?0>ktgiG5;YvpjIWY?LObP#fkVp>E#a4dRdAhWui2AOQlgNpPY$U!OuEKvu
z+2M{kMr7`S=_qg47?RFkCBAtxers~d({pBV(a%XhGe3utLO@`)y!~Ung0%#ktB(){
zRwN?Zq=GwYX5SgP^MI#3_lOU}xN7B}3n*h0uTdzH4-bopBr$ThZ_$}yJqAo{qC(a5
z>e)iEQYiy{jA3eJ6<eNZejr`FWkp0PJYCh*;Ey}utzA%fxXm$`BBSkmkPlF(OJ|dQ
zB9HjLQnQ#Ip7CPb-n(et>0>Kz-6U~aR+%ZZQJ!=sDb@ArdTo?aHw!X4L{TT(-YHe6
zc}7`Ol(;K!T8=o+G>r@*Czm4sj|cESEsY(<#9g*8%*Or;W}L{A(C*KExKpt-bEK18
z0Qv3iLmdu5^5)9jRz&jPr)ecX6u7Fp`u{>*${@q~*ag3yo*o3QS>GU-^pLn_s4hf+
z6nn^%+v>e#iNy`wT|FOJw6})|oB7%ABzZtw;O#~~PVDNrUX$jpq>A7)jw*o`y<GXM
zq=HMl3!e>4`qqo}n&`ub<z#so85vogKeUdv^l5IEPMh=0XSZ4C`+-?6qQ~^~9t=Ed
zU1en4$Y<Wxz%}MGxzoKcu-n*8{O@YVhSkY)Re$|)Kj4oy4{xn)Ib}B5$xBLh$?nS1
z1yhU;4=<NE@cuau5xV4p6_y0tqLQvD<@Z4>(FONNEX=7QtD;Bd&b8A{L&0Jj*UpMq
z+1iRGJ#R&&6ms6g-&nt;l%oVCWF*bMUy4D5zs9e;yj*%|nOi_pGn(}hmPC_b3Q<9J
zG;s6tO36|Ad3kaVXAH@w?>YhaFfcSsv%Z14puJ&f#rJJ;eQRbhb18i3t4>LY)@DRN
zqz0TDfR9Tr!MP19COaWXNy*BcI7AcqYoA8u#lmcAJpXQGtK*4<35(*2mfHEbId2O+
zxVK~)!i{V9r`NaA1ea9Ky-7(f90Ey1dO@GumY~E?>dE2-EJ<{${_i_`+sbn11;7?D
z)NpsVylr+N2ILkOe|8kt-3_ZRBuLPr<o~Y-z5h@1?7vd6{{Ij1TPAtwX+IUF4}?OP
zDb&Y$zGpFKf9x%JR)*_R@pPb{EdBR<re~6eqvb~IxVu_M|JU3^fM7*07UX?ctiRT`
z%Cne58}Zb7M`~}-Ju2U;T4`6BKV&j<!!7r%r!C4;af@%?OB^Dfm!4q&EahJf82aT_
zdKjr!Mg0f4);hVooPU3B^nkx&<qdQ7;OIW~Bijp^yn=#S%xvexxAV-t-RX+U*4}U5
zlDiaHjFnACIWkdDM1h*1RAy%`5iP*4qN+Oa`s!29%7dNC0F%Ljjlnv=kCdT}HRg4s
zip7gX6aML#(sljS@tV@%<uLuI)C|=^(_sOVb&rcT$IokSJ|uC*w6g$ZLx{ljUUge_
zkZ`rDb{1N>5M0XaJS%SAfu^aMX^)@y{QZ*8y;i<^S(W?S24-g2{a9OT>zZcHQ4;dq
zpLi29@d5szZE~&spq$`ewwH7QQ6*(}(T(j$+U^P-85u!XY~_5E-MAP0q0s^qe|UCK
zV@N@4T`XKA*W_Bp4w?nv%9ou>^OXxt)!~MjvMRRcluB((%4VOK4a7%BIxoo41%tV=
zXrl_G|K&nCZD`}_qM0i%Q}M6BVN=(if8mY)g-7Oz-2Q5#=DtPYA9k*_w83H^=_pnU
z?tT|suyF(tcgPtxY>peYt(z7+*xLMh+2s!y;w3ViWbt0QLZ<g+mlbzF&BcR@;T$I3
zPr^BBB5g-*MDH!=PXsG!c(uoiF6RqAJK#P<zA@yN`Bv^n*bKbbvC1i3c3yULeBD|)
z2YkKrDKF!>H{YZ}iX@St<Nr3TZf#|S$UEAdNl);)4&D%I6p3!`)<LXA*pAqnuQzj&
zw-MouhmY>+MOgPJ!-?)&BL)J>tt9u^*H@eMk}g+T1$K<BtgYo+N`^sc;#;XYVGfFl
zh|nn8O&m~Yjh&tF%B^rBUK``xmA8Vv5FEmwtb-O7z_<3eZ0w>LXfTYb1=T>bN(c8e
z)<5+M6|4Dg%=apqs#dx<e`uT>b_ronwx}8`0fh`~tJTP5r(_vb$o~j=1+D{({n%=q
z;Qs2~($4<Qa=@|+t~7WwyJRk{?~ATDP)hKoRw1f6T6pF&8ny$Reo^K)?_e92Qwv-t
zYW-HjTAh;CcDSb_t|H`d(oFr%HLdxcO!MV(T<z9$bmi#a#Rc9&OHWGCqF60MC7-TE
zU}u4AE+rmeD!?E?5P_QSQ&_Q)nd7iA$F=n<h>~Jlb#%K&U}FopG7v^0xRj5??W=x%
ze&*lK(p0y)?yrlLg6xz(y2HuFhAHKfBXijt+pMM$kWOWUm7_Yc7GgIO`*wVDA|Sxi
zvvjG1KFS%}zc+_NcpQBr5*Y}OgcL%CC@g(_L}qs1Z;sOlZlTqz+J*7kIrr+Ha1Q5*
z3PP$-R5KUWwV&-s%(0kk;M(7upm&`I>kteUPy6*L;Gy&qmB-E=PA+QyF<1{y*xYD8
zPfy<EGRy46PzL2I+4jvw+v+$P*EO2pyF0*bZ<J8r+L3W@2y1c80I>#?&Sg25%AKWB
zT=6K>>O+wWA3r(D!H_}ebIhm>h$9u){A~GGlQ&I^o=4Yl{lU-AzY0p|9SVw(-6_t)
z_)nNP=6?0iAGMx^i1ajbo>w*RQP!{NN|goX$_`U7IZa8v^;igVDiaWfJJuRV?u8N;
z>;i}yczmsEUyFXeI)7T-6_&O_2xO!K$LO6sP>7_I_q&n>*bN3@#V8)1Lo%@W`>(Ni
zM(UxOF7_~qI@-q^dw*xtw8iE9rR8z61FRXY*jXStK#hs>=QemTmmCg4LP9(}?_BrU
z;hU1OAD-9XoY_W9Q;>Z5VCq*(NB2ylcbv!G5YjdBP8X9k!4jjFeFv+AIvlIDKGhsN
z3A!59jT3#gEr*87Tm!BGSH5>H8dvjDpwULtFkNJicB)`W5=>i$%hQ$TyUriQA1A1U
zx~9``{mmuY%A1mcdsfDy75rd50Y<wM+6bl>q5!aG*%<JxH{Em3Vh$Ett-dNLBvcKf
z2eX0>x-B0P?J$>GUNCCCKM!ElnM~hxk%tfml_tuV<k{#UWppc99rt0b#V!s)9BUqI
zXijL8?<h%xu~H-SJT~@u;XOTa5N<woadoXzx*qI|l3VSSSkEKRn>Iq2?YwA2-*~Xb
ze86fj2%16z<|b&J#2Dil_u+l@`R(oA%8J1o?e{l+Clj!<>EV;QORXTu-0anGG+`I>
z_VkpF7m*JsH120ja0Z=i)c@j54oqp5)SYT!Lht4o2H+-#y#VTRTh3;LvP0dVsA(+?
z6@&WH%8#p_>rTfr-cfYjPz>J6W`>09vNJIItgsTah!%)y5W*mn554?;5wDy4<;z0@
z{{Ar6?#g%iM)IA$y77vgC7-OUED%h1?I0Z>X@>@U#2i(n$N^B7=^GLKb0RTRgScZ~
zl56a>QjV4|6fH`MZ6=S+KJ9>-dYWjG%lsHAA)yGD?O7Jxh7YiI=hCyY7+S+QnX_O)
z_Z66^3Skyo^`q=>3+#?vuCwk{C&b4GA=mutDZ+*JRw=Us_Fgqv<qAxP_yk1oOIRe8
zsk=`aCoMpQsb+Rr^(k8UcAkFc6p_OraELMeWl_8dQBHu54;9w0*mAlS5gHG>0v;jK
zH&e+>+?{LZ%(o^(6*IG7CDdgo=YpIDW8&*|l}6-tke3=0A@ny~XV!44a@GVdqaA!q
z^6j>_ahaKnknPwwIBHkx$jOa*vmil065$-Nn{dJH`P-IJ3PANzaH(AQ(l`!fI(?y)
zRyL^`sb3A-H_ggeKpHJQOh=$SHks;wKKO|z=&2pR&jE`%vA}$ywuUx2;!0yE^XO>v
zX{`Gp1x%qA4`s@H9l3{iyrN|GR3jT%w(>b?6{)QIO0X`0OovsB-&cbhUCqySHmTas
zuJ!eLOXhHAA0v5yaoet(O$w(Yv>|!S5$?APvAQ~T+10L!1CpZV<~Kq_g9B$YZxnaR
z!~gdMfrPEDe7}4s(T$YZvlw=zfa%L(yELR!V)ep!q>Ot6lf)@so)z?zhZd^MXw77(
zCK;$@3-3-PLy}`EUHD?RIres<L!8-<(w~b0{D!hgMl&zh-8xra1U@~5fH|=w7~Wiq
zOt~cAst{uHusC8Dpu!>7`^agg;j}le82~!&EdfmI<%<K7-iv|<yX)Ezj5$tQYj()0
z3%dQp8)g7`*;=2j%kR~=iQpxeR&tEe{R39r(Bq`|Jco*IAqX2fJOlBI9@)JJM<&}S
znc4k$Fl#$};EWAlEhk*gFnz#|`TpWSv&^W;%W-Jrx0o00=qn-WPhUMPIjGRha?^Fs
zAaBInNWLy3F)C<UWSKU3qjYJpvX~8#Ev(O{#p;jlP8ihx!WQlIhFh#2kPWTR`%!k&
z%9f7r<)-o4YBolu$Ia%5rt#=8S~Kk$TuUx<2^iF5$g2w{+j)GEF3s<%sANEC63$gR
z?MV9TR+;oRoPJekQ{6=Af$%dwazOzg&XjRx1INv%y=F@L27yx_y67M8A^7Cq(|G>#
zJvaY3L$4Gmbj%@4>O2PF)Z!u4H%B*X)4C6Jp}RwVMJR{w9v2auCzL9eHdNrUx2*(l
zX2@pH#JrikSIE+G-DLyh5@_RD%74OcbmT`YftXtk*hDGMKpHYS7a%C@v2Sm{=_3i}
zH1ki-uHcDsTB}E*5H#Q!h;1Y>0LpFsoOY-)KPw`Zw!EJW5Cv*N1mt-nkqC*4i$iro
zlwAhpFH{hWu;gzS824M;-{WLkI9L)>zlw}m)k}8J#eb{KLP88`MK1$34!yW?#Ea`T
zd4e2;>Sl$b5F7^(Oh|~tx=VVmfO|F}2$BeZ-IT#khexI9RyQ3hhZ+k`60iwyv0Mg0
zkH-h64%|Z0HX_?$YsDd;2dg`y2LgziTSscE))Xx$sMiv4BTORcHv8d3UQndG?Y1-n
za=qe-;e8+_B0@rDW@Z5Ejy)Rt3#qrklL&qcVN@O07u->9`ovD31=b5J*m^@cD1X7(
zPP$bt`@5|Nd`Xhg6|IATYN<-|=hEn4@gYi#dN1@vOd1-dgWEFd+}<YZu4QQO`M^tO
zAGejD!N$ocx{lh}yg#)d_yLX(HhQ#9R#<47p1t?3aZ;=4Bb}LLB!681F*{vcbe+2G
z$o&4(;vrA&vJo2R>>WpegS)JY+Zu_GQILKUK(mMaCY)OS@1Pk35X_Ym{TptnKp`wq
z{IKjdoAd>)g2m{C^ie@7Mo?6DwhIBo7Uy_Q9hr|&b~y)lwI4E)ka-%m8Fbrgg3xF)
zPs@yG6gJEBNr?j}VnF=tV0jsj^leE}Qj*I7;d%Cy#1Kf`-Q+<vwY4P!t^LLv#CUym
zd(QN_R7PM2PeC6JPooHBc(aJ5CCE$X`;EI~F$2(ob8(Mv$s$ZmFjF+^Yt+R2g6@H<
zCeBFxRQ}zF5l1Q2H{W7yPdeQ3nA)&$I`7K>b7gA0F!&+Tk_nfr<g~#!z8^u#3BDn}
z=}mqID}<J?m<;4?Vv+?(!Lsn0j&>EeEg7X2j0BC_UT0_L)$;PUBeOEaYAtwzRkw`5
zpG1JOrzafgQpKUJu8!YMc;Rxla_ax&$n`P0ku`h*a};57;G#ky*B&e|*->?24fFRm
zNVUe7mjhs5huS%i;w$0?LbBj#($9|*KD6iLD`SYkp35;A#HRU&EiU*v@ZjZ?mhzrE
z!M9|y_cEId{5?@gLH(GezV>VQ@rWvh!C<NjOw{E`@AVGe+_3sK4_5A#L>DsK#Vsr%
z)j7AW?g<`XFnJ<EdnZpGUT$y=5#<2;NlfxLzI0fAU;^5p{L>1a1mk7Trax(XE)q_v
zF=-j^l?5CZNqV#*ALsfHW?u1!e>3w)J8}ZJr#>EXQOE%Gh7CHb=Aal{E~<!+*H$_e
z8TM$!NC^+|>o&V{!NNo0TZsW%%6#t4_I5+xvxBSkUCJ{mbitSEvz?Np?p%v|*T1gH
zR-@(YoSSt0;HqDujQXjz8Z8#@y@d&fowmC9{q)lg!}>Y{)&sA!a_k;I)vjvel}jcu
zR@)<QUzu@*l2T6B%*AnT+NB6O%}74rueVt3ZmtIVp!T8O&J;Ey9uq#knd9J~ht-1L
zO-=<vBBrAy-ZkKPHs)TK1dxP3DJV=Uwx;-d5K6yTI7$ZeA;O~Jo!`#xdaL5YUodtT
ze2x-~hdch&`=UY-z=@#AQ487&a-fzse7MG3v8lzNQN{%gUEtX27utWaEy0aAPDEI?
zcp-LsA@pVagLB97_1|^wxD$?Uj-)}PiKGqRnQ*UkE-hUP6b$4t0QPE3=aVJEu4wL#
z6Yi9orOM6OUMl@Zr_P@ja0n#w_IJI9Md9V0o6^UugsLI;j@RBa^9`m~to_7pG!6*T
z`$cC`%?<qsd@$;^T{QoE4g;5C-2Vmf6ZtlOy5|5gnps-%uMeWBgGbT^Ok%!}Qzvr!
z-pZ*SkCNL+!gYJ})$@D|CB^Nh5k!QVPUdP(t2H}L(FiKo-CEyK=d@q@#RX30I1EFo
z;ZY`k`z7sU8!RaV?27=D^7KrVO;*rWI!xe(ca%};OraCN@XVdI8Z{A4zMNy4ob9?d
zp944S=~)B=5|?8x6!qb-XHd#EFrgfz)kDt_6pg~(0;C55(%0|KijVj7pLz!#o;>A?
z$F;~P*Zr;F6?=G^)#!t*==Qd@11T5*2VHxZOxlLS^dEEDgU`(cp}{?>W&k%4KnqP8
zS1qSo{ERsU&30xxlNCb_8wgP-%DQ)WZGaxL8JN{etg7xs+Zuut&u{>IgKW+Mx$v9@
zhzm&4QOHyw2vxH{Baj%tXBh}3>4v0$H2sdyFVfSH=C3Xe&|D87b%S-=={1DO!?9{m
zx#VwLPJ8}i+N}Zti@p)5T)Nyr^%%`6CPGptAbuVth*a!WV!N|gSO<PJwy0d{j$EdP
zOzO2>HOnU;<#~E;PP^_;7wvD)r*pV4g5cd?b9o9O@9>Xy%Z{U}!K*RE9=jK2U`R^7
z3?a=vxW(Do8{>w^gMeJ_z23shY+5p#gtV||0qv8|9s^qL1+&jGvfWcC0xX_EwLs!J
zM%1)u%!6y;^Y`(O+Wx85w0Cu2P{}|8(=AqFsNiWs$(lpsib4p)Gi%o*a{8`vL}7Em
z#FN*E3aq`i10+fg;hgGlaTF*BkV1OREitF@tI?iXIYfX`B)jKR-Z(Cob2dy`AXWZE
zL4V#0S>vH17N&~L@niZ|+#a>V+|WcjS?JnpCUx~~_|DpvO^`lPKkN%;?}5gP(Y+<x
z_}JK6Pk9&fihc!(1D^O~lyw8+V&>3^v|$K?$IQ2f>0AZbi4|%6K}010DR+A^RTW?C
z0M@Pvt-vIH6mWL%CFAFrYK<m`5_2Q(;HXlnB6I9>_Ui5GBJ+vyrhBcvt~-UUJ~F9!
zqC3!$gJ`m_SVW<HN_!8^s6A9<j&xY5PUSOOvI6`W23`q5Ew&V(+{S8gt1}a@L3}9e
zEY@>fn?9ABKEv3rBQSZJ0SB7_o4;rTmHQ=SlO(((VVqV$mBczmlk(aK!F3Q7fk(Fy
ziDMvIOl2$IoU`6;-~DwAg9j}v4QrCF+nwbFm17<^=^7ze_oYIScJcdu!}imW&<Zw=
zkZ}Meb%;bn1l0smP^BkybX~Tqd>H!&^z*oEekZSGsUx|H82kv|C@>o(;~aIejClF&
zY>v(1z$UWb>_EC8R8+T?=L3xZY3vr<o0XgZ{n8P^Wqa6R;C;&uy`@@TU!5{X0=bd7
zl7y6$^iZmVTOp*0w32~lt0kLgALa@!Xmh%WoPl27Y8iCV@x5hNSA?Z<R&F(OZO_Eh
z&tr9;P9Z}LM65qof4O~mS^!u4k=s&lw^MLWmJd{^_Tgbtguu*aR|+5;=>cvmYah`O
zTDT^Bl?sfNbp`oB*f5{)<E#Z)a!TooPCXg!1_CWWphRT7%}Hig?q{Q#I|IGYm!K%1
z=5`QW?igTHvUDK0KXh9A$Afn==#Hk2u4~PfA1s1jOa-fu@fzrnWi>WbOub*DYZPP6
z&yt|;VtpdpA>Lf}#r=h~*Bd26`)>^~d`WS4>b_!k+()lhbIsPCA;S%t6!uJS4!mWV
zvRp17xnvssdIflWV2zfvjZHzV-_2wY8f5d0dTsh&QQo@s0FqWP)f*3wIml(!w%o`<
zOT({ZjQ+8id^d)tajsjGE;jPkIpYCMas6D05vKT#AyWGXda|q1q4(u+AjCfogf<Gw
z)EN1Bh+zzrL?c-C4p0ijhFzgO{}Z46mn4a>xVAPa^`e&#pzEg_a!*&KtLx;{RGMV8
zz^8lq{&T(A*$WHCdjPu?BqV%2E|I1M(MVozOal4wIW$wqL!R#iEvG9>o%hCt(|bUK
zp>L@4!-o$Eu)r08eOfMQ-zCZeK}Q{$VjM8-qTTVLm)T7Q<O13BTS%y>s8aM>qX@`O
zEu681Rx+2Z&1-)1pwlLT&{%KGVFX=bbAyH1uU@_S5E_~c0)wzw`G$g5rrALT)H#)W
z!(h02(&x{2fk{USx@xEv7`ManOaAzgXc%5w>VB>KpbRj88u3ZcwW7pkQDvbBnx>#t
zC?3w)75nJzIer^CSJTTeCypP_vzQcv2a*Kwpk(Tk5gRaUis&^eDykU}oI3$cPpK7v
zB6?0z<qXV5jNa<jYK*UbXPLUl<ZO4{Ac34c-qVlPn25oH7pl4&#rv<C*lEl}+0B3b
zz^A6Brv3BJ`-_8xwoK$4#vh@a*#o*Zgv61E<tGzxD$;OT6`nr8J9TOnbozD>Kw^Pv
zSkHC{LyfW}`#w6ViA)jl{)Z3ONZ|uNs$FQFMzqBV`%s|)F@kjT+A>mG&U(1SI^Si_
zVGpEW<S8QNL3_yrIqVJ(j|z~0@W7y;;E0F}zz6hPTuQ5}s{~79yP!#k1;X^I=xjmN
zyms{}C0a~ELQY2~!7?&CJ3Ff!JHR^l@frtoH)=J$a(C}8G}8nZMs#i<KYe_B{BWoS
zg@lFcwQ}wV2x!10g&aSA+&kj{B7zznz>ifb{{FIygv?$9vpCq>uQCA=Be8&!-Fp-d
z9bMYv&9awc4>xAozh6y60v+h3&xkWl9es+@|4EXdz=LAw7zC9ncA_pIw7<@-BLQ6#
zvbDXP2C`LP0u+i4-I+m%Ty#fv<-UqealFaGyIYUEU$<U@blVQ$PlY~!4C9lz+>;p&
z^f4QT7Dl8g%*bS@Nc!v7uZaTJ6@i`8rEcyT_@jv(aZBn{PT1GbO(#ZY5W1!>ARs^t
z1;lG!(_xkx-ErbF-_Nh-Ruw060;og+$THz{!>$K=)TWkT%*;a#?)dSZl3)IChYt!>
zR;`!IEy{Gj9l=I)q2-x9WXMD^8a}?E$u&rlYBCRu&MJQU_KgbN-`_7B!h3vKque3b
zu4QSsR6NjTX=v7B4`5(dhVs3dy1Mzz)rn7cpT9y%xeVDGBZYMNbj551NS!(FYerpm
zG7|(}S=sg#nneS)OZoBR@u}0N8;m&`jX8K*oqtvx3*F%!@aFoa_ZGT34STcHkpO)E
ze%#Gnw60ncR;WsWahQdLh1KTDa~QCLPQVATC-5%axpz+%>SH@}0Wa2HIhvxM)V#J!
zaw;mHkR*Y0fT;?;{COYU0Rid#rut2ASI^g+vV;xl{BK;&+j&&8UmBU{S0i9T-pj2l
zx+pq1IeDM#yDYf-(yNZHxh0Ypc1x&TYTMnzjZ&jwy_TkEAM^vThzGK62wj>3x%vUD
ztgI1Nsivl<#RJzXU3<#xE#JI-J0EUV(GJlZ*{GPQLXG~!s_U(4D6}C<`$V}(FE^`X
zj!sxuI29IwVNXWb?%p2#MAy*+tXvU2-vbfV*xK64J<wxb3O$(A1il~ke+5a>Wip8t
zEu4u{=XS(qBnXN~A1G^S#c9}25=<XlzQHQQ5uW)ipX>P0plNAlA5UE|=>tsS<EKx1
zzy#IJQObin6Uk>U1GB>s{=}wK9d=<PE@|3TZ-dZGjkYNr=B5~kjL(xUZzDKr6F`}U
zhK0>8m@w3HcNDB-t{4pV_4SFRXUeB51n$q|EHprAXF?kSNzJh)WKwGby2F$j^F$<C
z!1=?+k4idm)oY*ojaAC*a)2fzK~B7`>iYd6=i@+V0@sY4DEVSNuYm4=aLWKhG7E7R
z8W0d*`C$_{PXk{qEEXz)Jo5Jmdpo#!@KQ^*rozQZpZI_BJ8G;!p-c<SCq&RPpv*_a
z8><hKQ2G^`jb?%WDKBPHIO33=@T&gcX#|(F@gAg)i1=`11?fZ%oA$z~KZJxN_4O&`
zn+z&IE>koxNC7kT?BYmy>5w%EkJaPp*63akSJbj>eZ4hGZ8EXTqnb+-B^G1fPN7c)
zah9-xB)}SgPezsnV1$;MTKMfH(vCoCaVj(r-m-)NsdD_IEpn(Bq2;o5f0%U#?1FOa
z9*9G<=uhCZc$I&oPK6H>-+ksr^#Vj(LQR8HuOW260t}_HUg(pCRB!I_3-2<S+;mH%
zZoW!)uD&Exsk|Q*;vo#}P@TII#R>Jry{oG3I|FwPOd#WTxI<5$u4WBh@6$l5=}6lW
zHS#N0;`F1P6#}8I;AJZ3iiCxQ1)g(YPfkcMfLc6h*xsI_EsBQh6_jVx%cNiWt}|KE
zXs#=@<~AXN*91#z6u;{FbZh9Z66Z5Ne@-l&@F(Lhsn(mE%&}Y5;#4@?28eR=^}PVF
zfahTJ{jd|<3Tt?C%OpUz&Yxf1eHOx_qnC}tq%|t{u;E}z@S<fZUG^G=_CbU1h1esw
zc{9C^+_kK$YCs))#*{~IFU`X8k=D`C$=Lv9%nHk6vkA3;O{@BhGQ)L~zR$5ge!V5-
zv1-eE(TM?z_QmGPSj$KYl;>tn0%GE{+fP1yC~6Lp6i9^}2-^aF+~_*!2jjhwFI3yK
zfVpzFLs#`ZK;=*)3gJ4>m7*dpbVpn4qp<mli58Bia&T$SmH)YS<M$>=x1Hb*z1K8c
zSb}DXzkrv6?udEdevODoX$l+3V=eRevFFoBp2X#fotFNcz95F{K3A?>$%9%~4&AH+
z1t#HZ>+9Xcmim6g>|*YCmqY4xEKJSvRebWQU$S$L!P4CLibsx=rxT|yv{n6eLY(ga
z&^40lo<#8#cU27=uo;4ax(zHg7VdezQ&O-S4(N(`Z~|)6Bj7aB<qjJSPGzUvAApXg
zpr#gaok1xE77aZgpBhqI$y|5M^9|bL)Q(v_dlqy00@2S4({=u&s#5|f{X=B;MMXsD
zn3=PyHy|sB29R|FgN|r~{#@x_og7CwsYvqveYXXh9pOb@m;yf{mbiyI)z#H^Zr@HC
z8ocQzl9-rCpz1Qy7Au1TfQ7@1I4-MOk&47i1J4XadbU}OFdG|;xnx20zrNt*X+Qk_
zjl*Ps3Z3ZD^#-uzQv^K~7g$>hpx0Jc+hM7TU+`<(A+bN$gC;Q2Kkff?8-WllF5tCa
zOF%GQrbf9+rfOl&7v08R6BF1&UJP87P9K07XoQBHNq&Xm$M_nIg8o356`+YZSl0?!
zYN-{@I~o`o^P|VA(yWJ#7y#kw+$7{2HO|K*z8bj)yCo{A4-0et^Y5q4LXP4a`kyJY
zPqzhj0VkZ^1F_sL`;ViOpxjjL`Q4ay+1+7#M72-w?{`r{_4CJoSrn`b@E_^<fX%j7
z98l^-%j<reBQrH(e;>K@3;Tk@ghREs04*-wxxF#ZuGd5~z4i@vC?CL4t|ov48L#_f
zC>XuL`FMMGK$pkj((k86p)|%fu9LuEgO{2Ik~l?t`}`pTM$SJ}y+|1YsX1kLcUPv6
zB~0Sn#f#nYit33_8XJ3idxv%ZzKgP^#bX!*LM%NxRPy2d{Z|kjN6#;(YhQ@@FqULs
z7Ak$aG`oKgwq8(E9$LRB6ubESn1y41jbt518J^VJJNg9stFO;iY|f&jCe$!zW`Nez
zn--SlUPsG7Rv30c`bFVK)ZhG;pg1>HjagBaBloM~gCvy^=-p(h7shLYJomoa&|04{
z@Yj2(n}>&2J?~&%&)>yK0=NxZ%D9?1`6L=^a|87fZE)1Z3P-JY`j8!SUitYzg#PcT
z6SHbRW(6ATu=3H4XPAjkv8Vq2y^8X8k1>3h?7>mX_0f5jj;TvSv9!0h2giDOS@Uf7
z;a$N0_?oqk<v|?*6p#lP5y5LvFPL#IzYlP$6bLxk25%|imWG2rew^|8{`bx7A0HYy
zF<@Gl4h*AZr5FZArNk=Lw^s0JzF{}rlTU2u+d=3+jOzi;RRzip?ADn7$En7mwxM2j
zfv-)CmX?;@-k#59x0;dC?_)r_pT5`um<*cv1p)BRK#)m+Q{SDfDQJ7hzL9%dxl)MN
z1{#s|NHlDYdH-{(m6+WyK~d9L9$+4_ZaO+UhmRBuIJ!z40>E{nTd`#aNyEiY#=K51
zm&`MvWgxIeTy{+UKC#q9B$xR^dWDQONTb&9fkt2kknj*p8Usd)yx2vkI^F>Ry+D7a
zGj2g*f^B1kV9;g&qm&mTog^S2pw8{jfZAzS`r+Jse{M1qOhCE{iT-as)kA0khMp@U
zCnB0DGrLO^fjf`a_Wb$tmK}u59q~|z!vRQLa^O)Rd(s>L^jbp_)z#Oh2Z<V<xf@zZ
z5(J#L?=UmBYO`l3<vfgyjV0!@`wUwr#xlkL!y*GN;!l$L?%f~!u!rf3N+wx)JjL4D
zTABnS0o31Ng;L&t!`rcfgxEMaHa6C>0}s}+^TvG^Zg61u_rmKkfMks1b6CwAgJzYq
zoSce~U2ojD0b>vf)P=*iF9wQo59KuMo$$sdPh9-F7htJ$Spc}MVc+NH1Nlaw04~9J
z`VbwR4Mgh&tev^x(!wa0?H8x-9PjoGD7|b4Ufc{+k-&tA2ok7{Xc-ui;J`o%G>_$!
zuceihf<lO;W_<gi{ZZ@qeZTA1uP5O~T`T+6fT_OtS@qYs3m3XbZHHn3&1p#TZ+via
z8X_cg274V)TL{%GbTo8k7LKUslz)9DrKI#RE<T=slr(d=)Rvx+vDsugJ5>NoT{?S)
zE(udfC$q9uk!feqKN!)4qb{ZE`+(KQ$_@Y-@+{jf^tJyzLNV9i^PyDz#sw70Es0+S
z7m0bSQmAJ8^`jZD3zS~DaU*qRX68yS!qkA{WaPfZZ1ieogO(`*xQo_28p&VA9P>lP
zmOKua6hx>H@gqjaEYfEIupo(IaJet%kzTH{mzP&y!s&~|KY(qRfilDJrM%m)8|Y9P
zi*{`a9C_}{87c4x5p;pTKQ-$;K%&I=^n77o$Tbp@j0f(gUjPVdF=>t9jtA*jvt`0|
zc|?7%(2RMtPj|y)q|`PWxdD(Ep6>7Omu%0aZ@_I6a_igs%eNc(AmM8@Mi`WkKsCL0
z?8IrV<pbD+(}qXx6a$@bshX88Et7D4i@(G8&YNaE%aLsP^pHXS9cXHygYXSxgFRm2
zJRy*Cpb@((O`g6SWY-q54uG`XF#7~#WExa&>6+Rb?P0$n1WzmsP%@w&T|GTR2naw~
zp#VIUb)vuV2duBJM>@aDR4{s9+)VZ2+v~G)?eTAcFI&yD-Gbwh>zP&V6~N}3c)<HI
z^N67FiS0!k;3QIR3n2s<!zR&e;Bkn60MHj$YnNp!|1{q|T-6$st-1IB0So(uz-^8}
zV-9dip7v_GmLqor0Emv4SM}{>@*dE2uHCqyY}`@P;c#EGDPOpG0*mnO274r0Y(}zG
z3$yk&vR%=1GslL3*aQ0#PktI1NR@BgF9YYX`M2>Be)#Ye)hxgaSt+S^gZ}fIJ&r!t
z8RbfC7R7mhj;*uVE~x?jc?dJ62&=)EBOHibGCU2UDFIn8ffVjlhjgm3IbaNsOa^OJ
zSy|Zu@G}(}S{q)sMDf$3=h|Z37i>xgVBXP4(82HPSX|{n`44jls|+ATvyej|X|$98
z>PU8TL)8FA;^r={E&-e!53MP4FjL@Ni%ClM0?46814dH>WZ#`ENlwb|AO}}Pl8KZp
zASq#i`*#{4o3ojX@P#$&F{+m!93ZG!PuA2l3&aw3psYTI?^Q>B#}}Z`FS)tFk&y^F
zBT&YHI&1)_vF0`%35(Y2D_5^VgZj|liCyenaBw1c|B`_i(D8~l`XGvesr27D=D@bF
z7%*<>85ktcX!P7raW3H53#Z*dTwyotx`PB=LsL^1G~0>sAzMx3Po2M>Vme&HN_63i
z5(6=4NK5574Ri<KDsSWEGg^)3bYPcOHY}l3&<_CJnfD+f&GebcqN1WJIC6m_i3u>L
zC}d3dahMEN)L|nI{`4sg)|3%Aq314N4*mM<U1eou($mN^Smm=Yiabu6FN?D#{y`P$
zg33ccLc-ww^-oPrO?Ah`{7>ALNlcZyYZj#0pS?9yyBNsAHh@&O+S4~ZyKC_4$9rTR
z07-uc42%Ogkx`?pFH>{%sDj_01=J3WEw-~TQN*045hYf$<2@76Am#<iK@?soq#aW2
zKP2Zq#<{wU*TIf{62>YF>z^s0^HB7G^({4CjF8IE#vSY;eQJ!ZXDRm-FMTHA8gMOd
z#`TR2d1xVq2ou;!xN!PWjtNP?7AzD-MaF1nY#3E4WEpQ?Uxr?R$w@yWpUO)BDRiT{
z4CFw)@BaM!yVkA4&BuU78ufB#0dsc)iOPTkGXq@7W{3Oui4)YRUD)$)9O-#Y0mKLw
z^uyOEP`v<Z$8^F7itWnEOLPYyVllp60!6hk3wt2J39mwO2#rQ7oIi1y$5BvH5<tbx
zXly?PW#R<&(tOHZD+%R$2=@gGXD9_!w7k5fYV15Jaa^yAY}bC}*JwsVP5KxV^eE-R
z;Ydme!^m7D<&i@`cxQ3|fN*<hXV*p$WX_{IU>%ANC9S-6fdP_5lztP--A!}!DeKVg
zWiCO%%4%!sd>3o>k|K&I`>7jGq6NykOvKXD6O1$!&cE~S*@HAB9tgQjF?O%vyKL|E
zZg)|K`_RcC2BMQmj`D*e1pSlWf5jg7b;*Q3FCzK|G}l`n$`BO?`;Pj&{v98#<?)EL
z*Vfe$hFxguW~8RBalRZwf~+#<Z7Zm$nd+qqG?GzPPlXH$2ZO`IRZ}-U)Us#|;0v7y
zu7Tw4;!*(}4GF;IQ(-N@X4FtCP*-iEe@kryQd~|;kYYjgU?wLobmbmNg|cJV7V{U?
zETFWanF%1?PXjT~2V0WJWIIq|olZ3ibEp6;*$T=qFAc?qgnRleb~z<+B;;o@G%H6M
zgJY7+9Z~$c4cDe$zk-15fF0|I774)62r?zc>FCi@bRel9#U3zDEVSy%le=ujihkEm
zjoJGK%049;5!}IAagnZ@g*&8r0sXbxu-kG-K~wqR!-rI8;0|}pZ`Sd^nhKqtZVH>&
z|Hjl|QaF4YjesQ(MV2QGrGkq1-J)2uJpeoc_S1aP7W1HG5_GsO2pWPv{)l=0-Gi2e
zC8w!^|7dj;vH7oTfqE@#WtArYzRHyovWg@2)1;8-HlWk=+jPZ7k1Fg7QbKpY_@uxZ
zH9XkgYpAa`Hw_93ibwPy*yn0x?+ZW;^lGs{Bv8r8H4_p=0hq!~WrMN<D!yD%Z6II)
z5t*+Og({g$g|L|8sa}B6fgr1U^E{vcA~_{g!=c_}LoMw$><dh|M9O0TWwhn5xUH=%
zF*Ldzc99#jA1kzUeQQs^MuxQG<pEenq<z@iZ?)ec)&W!$#ABK_Z{Ey8$F(AC1)^y<
zdf1dz2&4^Ip6I}e-8J!3Cr<(rDJ=uhJ-cro&JP8H+T*jX{bGJE!X>~mK(ef1d)%9&
z)C<`cegQrn3$!x@aO^gC-QJ|yYrpCd%@qJ5o88K@Q+Rm1IQUV7$_GOEM332sAat?^
zo;$7jsabDpa}zFV1RSWvBrYzlE8j>FID0A}bD+zRyGx+m5d)_+gzpD1O|K(`7A>?N
z3AYRYRz#fw(Ol;)5m4>;^sEvwfQZ*lh>Y2gosBtv9-t#gJR5+xX94fY8XITi&TfG<
z1~L^JRE)<!(`_n<ju>Ut9+cjD5;oSkgNvqu$zPwffgl|WIvl`tMbLBQfWbiShb*2k
zGh+Zi>!1VV=HPelVqh10312OQ2I3Jw2XHDBIk@w4=g!GM=3s|aKz;l6T!^Zvx#}?J
zJyHM&wlTBu;3Db&p}9#6CXmTUnKGzYijZV<*@(MAJh@0Npaue4JBWNk3IV)Cej<!&
zMN&}p99F*K!BV;vRG$EQI}D*v3_#w5EHk~xD&0)95M`%J>kdE+?rj6;mk~5nG_<xd
zf4M{u8Wp9HsHec55H0&N`~a9~@NL1kSk~nmOBTb`n$z=<vf*@ve=@6BH{UCvp7GFX
zyk~jw)4udaLb^47dc*12gSb|OIc{f{KE{Ip!xo(@-0CHCxFmzndDEc-Nz#V>OpEht
zahih}?`xhh73{v%)?<8f0sByR#Jsaxd9IT#UZ(X$Kjtr|%2D~$X-rbngGa%mDdsto
z$!jgCTR5C!EX8ust(a5+)47u93A|Cf8CSs_MGN7Pdl}j#S(meia52ZuPuJUVWXiP8
zKAEPG2=XN(=agf*b?a7(OHQ(6bQbG@jiauSr+?JCG5KJcjze~5nr;?xuSOuK3)ik)
z`?bj0z(_+Q3cB+4v}EoPQGfb3Q4i@i4vCJAE^IvJ<})5_%L?@#D1JK#eW|c(pG#lp
z)X{oD$&qWEjmiD;1<}wDksSP19*bX|oWIu-3S}pic&ZKZ8u&|X(36t__!L3W!g{yM
z!6_c%k){28rm9lDkvsGNP!aYARKXf+-0qSBSCt0a&;hrU2^dW>^l!N%MF=7q`nM3D
z3UR@p2nzRrsA&i*t>j>T2V!E(tMhLjpN0Y^4VkDHc0ojIH9{}>13#dvoI@|&edTqo
z>;Jw5w`s_Y@Fghj>n@O`*{tW}Aj8Q5Ojr^i_<cysIZH>BdbS2XG=*wFCqlrt@85@=
z?f!lS&DVn`W&p`U5ZjVp|NZU?;RIH%>2>SxzoP0-{1z+WjX*q=mq+i*=>`!GrQ^+S
zKq%^-V?SkIdJ<Kn^ZT3HVnm7uEZ$`G5Fm<+;mO~7+@!Sc+7h7TnEvgreTz0`p}?)r
z{k}kobO?>ghfSaC!p1wY^QY|{?$9+3>Vg`?;!yhc-Q3?0`tLf*nct@Zfll;y2P+)M
z_v&vEAN3Re_Z`980e}A1lwg)m{@Ww{M-t6HId#mZT$M~DTe3a>b`lZ#|N85%G}&bT
z65Hi0*mpt?O1aG3M_$`3kQ(3*pn)ARZhiSSP}!jpL8n7T*COO@U5@Y#XnI#@*pjk_
zcmxA@9=hj8m?i`NR`}&Cp-cW9@M3mg8jAz@;*dARK+6Nh!*22Gu{t!-ZbLoiQ*m$B
z>xA|vkP_ezk~|zSPV%6!hw8$opb#Jkg>KR*r3BF&np@lI22WM-gF8fUxVRz~ZpWw9
zWk259+}@^RU}&1;X<QBw%c?B}(^yU8ytqH1_bLTBdGwX&t5JdEL@B#s{PDK%{Wjtc
zsMWNC>gDRY?G!<={$+-tFW{Sbg&ghb>iXu5{F0GrMU70n%QC=xU8_~c&}h}%TDA{Z
z;^k$tlwXEr=|7iUCZjqHS?e{5lQC&vY}YHXyVPCq5R4iH9<r)MExfhal`&e%V?B2#
zW;5D3yS}k;-RA6_WF&z?O+n;WAP08@1qGEe6-c>W-Up|z2iiH}Vfic5($caz%vkG#
z^9LvRz+<zJ0$jPKrUn5)A<>!y4l<7YNS=Uv&i<mKdN2`X`Us`|M6Os;_)D&Q&^UQ`
zv%+|9u;IW>=rU3Z?WN%D{j<Ju?fhEct7##d#Hh|^`8_Gw`AV4zlOZdP`nmmd#l*`E
z4Gn?5HRP72S!kJN`+L#g<Y#0IiSZ}1FiW!2Vt+I@2M>1}mE~J#E|+CuDtGj{6AJrI
zx#H+?+C)L+w`*GcdpFZo2}iVYxE`f8Y^S-{_ZeUBPGH9A>FYNdr1MnbL58cSsDR})
zxdJ8yfvO%ycsG23k28Ex%ehOJdVpgm{raV&4k9^O>T?*Szy!pn0q3*_+)4~cAqWE?
z<JOd414t2C-24x=z>l{s?CoFO@nCl8%Rz8J+<~!0i<e%8Eu|BNEr$7YoP`z#?drsj
z6he-ds|!&>g;LbhOKP-cXLM@;AZ5?UYV-`H-eLJo*2%)cu#I5<Alnhb#PQ+<ngx&X
zV^UB3mDy?C3RE(K0VCHp%0Ahqu?29ormi-TQI>X9NZzAEV<J<*-LOM-;Z@~M@D3SU
zm0tP->G*R|MzFn@BfMGo_)OXwu1D`)`t4I~Kx}0+x{I)ZM8zskK)(<NZWW1L8cxsE
zZ*}-Z0&H95i;kDCNIa|}`Q)dhP{khu<Yi!YGaV@_hTSJLdti$piwf3HKG=SV$;tDu
zzp)qaT^9^0x&ur@3<x(yFeFI10Kygry_N`Iq9z+PEkwYN14t7e08Z@gI$19bDTC;O
zNa^avY<6IpklFvjw_Y5OhX%$MIWe0+G7%*QVbmZtK%Zb%pj=xJ^6f_e2&L`piW*e}
zwtihjceoupp_VlY8qkQ-`V;51dYYCA3`iB=%b07pe}R}oI#Q5H1+f^R1?uqiDkXq#
zH(-YiEi3DnJ^{!UAj=2WXtl#L0Lt1fZ-GEa;$13w1Xfu0c#Th^yd)arh75)Dwe{hh
z2ezOAz>bxR$P!r@`)&v$gvf?S{ek<l+_zo%`IQNG0DL0v+1KMx@6cfP$ify2Ht@pv
z0dO-_lFDsvg};9v3qTpZm1|_{i5u*5nSlXi{?ZDv>oo#`NfLtrFuEvOzk@}?<Eqaz
zH9dQgeL5SybOHJCuCK3`wy^k;@T#imD&ppZHz(nd+1+<>aRCcI5>iqgz)Kk#WlN_C
zN0<}Cjs!16V}cPta5J>kQ~+uZhMpYg9AP)^lY$*j5dgdsnwlgKum)Rl;(;4bhSs$-
zH+RDqYsDd*NWeT1a~~lZusO*9SShes*__LfqJG4QS?Z;Rpm?|74_^Xw0ADC3?ch)X
zO?1l_q7&CPbo_mMg6ef-z^(QHwX?{)1YkBh$Sb6Lc2b~zgQV~oG0uMc_+cR_#|b3^
zvDeJa&0FR_fEoelRuw@G#>U1u&H#wJK*m5G<m}nA)MPu*9Rp@XH{vnMi|dxY9DAiw
zXqpN8X(T{ek%xr`Efc9g&yniTxxcpq>0jL?z6Zh*<Tu0av|tU_J!|NnQGy#8fLMY)
z|16;Apsx`Owyy4~=j!=E{!D@XBU(`BU@;*i5jJWjLd0Aj%wDmi78D%a+RahP6c#{L
ze2h-!{JYe4-G{M&T{pSgxMk+_?lhR@6d<Z(+`Z?S%l<?oIww^<Sd1_rfeFyj8&Z4#
zh?vcMTqsh&Sp_}|jY)_COO@(+u#X5r2(X6i%~P5A>kJl}bLLp(oM5L=K6v@yHE3$O
z?&TwKy0(V6$?T>>N&u;m?q#2a-#u5Spqr>&l-&9>?jO6Y96)}fvvMXGTLM{0*7)`6
zKM}tyaEl;`=0F#0B6Lgee(wPz;RSRVA!@FKgv9%!9;?SPIc>wY`1#e5zyWoT2fUKl
zIt^tJU!qu8);yv+yx+zkP7&qq6R)kQ*G@|~rzGM@0FTOZ-m%fu)#c7shxA}!Y1y5r
z%Bf!T(}5xi_GUdsDi~;b5sO0zbwzhvmE@ED6^;kC^t)kGMW7|rXmC@5hfQrq_Q6x+
zEiCVg*4O>pGfTMzn>{IW`x(f|$$|1gSrZc6!vqQJZ)%59vJ8&_GZGUMlL4FIo?K7@
z5NLFumbP}I@%%kdzREYpeGnjn2y?GqVH)VOe;BYKvMorhJ!_MVpawI64hF^jNhFUv
z;=@3XF_<A^6m`7j<F7%hph}IId;ownqPLnQZ9s?h1!zj!V8{C`sKTB?aM&LNT0v_d
zEui^P*MoA{;E?h2=QC*aQ7n494tq^$Q~zXOUI0k#?dA2J3dolsXqHgaY_OV@&=tjQ
zIVGVE+u0_0T47+>4ch($jYbmsm4Iu)Q3?R9BPnYp#>c%E3BmaBsfpX%wA0nmxu%LF
zEwMo8U1kY*qGeADy#{F@UuZ%dMXauFP>o=;fX>c919}&zjABE$Z*R^M3%Zm+vl7zb
z2a-}Fl;rGT*ffZNkI+Mt5ExT0?R{alzv%hv3{nszx_60ma+KJN`|eKTkz}<j7x^cu
zxBBeaD-@*Re2?;*TitGP;S%%;#Jzrfa`M+NyI)RV@n<Ex!mqm>RA0P1V3aKn&29eV
zf>}UcJ~;RSEhSK$nx9Vx2?!mCFzC=&GmtH)(XdtB==~&k^C46hp-Trg5Y=GQVfrjz
zW08%gTJxpQEr_VDFoUq=N;ZIup9+lx?8}!gQv+&(6U9Tt=*~Et2TQ7z3SI7q!`cas
z{$m$d3;uLg53-ReFac6<l{<k(8TfUN>Fh3*5KoT6Lb~Gk#;pGfbge#tcHZ}umGfbt
zB(1rZ1vaZDw`rp6zD=$nGN4S(W$vNay#FCeA&o8W(z!yx0c9y!)wEc+m2ce_^b<wB
z=y7ilvmI7z2&oA!(_B35eosl`^Rox3b+j+W1?ql`f__`7Mt=SJ?bK(ilix`Gvdz{g
z7vs|hN0b^3;*eFb3w)s+(*86)F<}j|-;<AbA8-y^$<hXVV~0cq5kn8nfya&=6Yg<_
z-rd0cGhD;Z{|9CA4y>Qt{slhVUnlU?mZlBp$Ipb5LdeV+t6G39CYKT->~o#FRdW+M
z1;o*(G6mH#mqD9&)3OfzT+pp`;=JNM-;J2>FqTwk@Yk%Befo`U1O#!5gVSq7u;Kho
z<0N>5fmZ0CRLzCfT-Lt<f7g8oNTlo{J|27;;ffB>tuXB1AIWP=$H@2uIyP;&zm6fQ
zDZ$D2u9v*d)2Cj1plotZ%<zr*Hs%GoLpM}iVVrxuE0q$Bpx(v}SRTU17m#nLsMZeF
z1GOJUmzdi^C&wDT&9IaT5zq;(7X%&Q8#ub)d78uSiGqG1Eh6G!X~A2UviU83;=B>u
z6VS&E_%4YVPy|~hVUK+v6)4k*-!}TdYrDF@ZTiO1rc_0<{jwPJuTjnhRP`d=swHaB
zM7b(s#Mqio&$m|j*}K4SD_4BWUg7p{`sPNQvGUptqS9(UN7HeU9ewInRCKgv&ii;p
z4g17|S1*^L-^P5tSbXA4DCvVddVjAmlK7MZ1{N0S?6NiQ^@Fz;Zj4)e`Tpgf^`~{*
z!19PxhbFCfZa@f#2LkS~6vEW_c0hBJ)%&wpl0sMkVu2dY8}gy^$ad2I*4}qVMSZ_(
zq9!I*VxqB&iDQEZf(n8ZjT#*gQHqE-5DOyGbZAnCs8M5g5KyTW1VKOr24N_PMrl%%
zGIXMp0fqqu24raayr8+ed-m?$-FxocKW@&+@0|P$;PCx^K5u#6=Y5`uAz9NjXNI<Q
z6zZnFxa3`5U(YHt{1Cw98s17(d>%2RsqoFmPICPe;cq*+G-IiK!4+M7@N41RxpPH5
zb97&$jAnAWce_5H#zs)#m8U}Ibgj9n7tS4_)|j7!h=0lj&Ta#=ht6!?v?tW+GbnJ*
z(1=sO7|EZJKG2y5-eDM~4s79{F0bY8oYGr$F#9X!=i)648^i*%HrHu8$Z(6Fsj>T#
zzhb`3D&!~Tj7R7<3e2lYvR&G8`L}OlCb|u1DvU17gET+oP(nrUB6mH}Rr8&fUX=it
zbh?!5?BaAm7kU+ZoW3SrS6~wcIsV2g`|6OA&c*7uwFh_I>;^)Wt--H}`)=duWjK?M
z9z6nAFE&8QG7g=T7AW(T>7H&WqP5P=R|>*(;xP|>vbST_jC2qM#dVpP)mbi$2(H|T
zd*Yw`pXhFyD{xo2-C$={xzC^2kA{Vi;FM~C@*fSaBlIL7CAdF$p)+a1gsY4|$+s3h
z%XJEKKWg;I>_Fw}x(yN@#Rx;!{QVpEh63hASS1>^6s7HSfXE2nk>x|6(iLphA5cYt
zVZKJ~uPb(;j5TG}%JtkttTjyxsoziZ%l~H%-NdnZPX#HfF*EgJ(6)l2;LNF0&1lB_
z87|F58^AX`wtaUX=J6<7^RFx4*^6J?wQU&_15z&Psz$q+Y+P^T-K6q{6N`^&HX-2H
zQggRAS@ymzM#@w;(%=!?PbnUgRdN+Lrrj8M257h++`2X9)YaxsT!6Dq&ORVJQ+ym6
za2HB3rNVo$#Cz1;y7j1eKI&1>R{Mp~!X&;=sQ)h?A(iksAdd>V3U%Vqazvv-Le?D}
z&|#cZLahamZsctFWy`V~hh~I)z+diD3=noH6Q|emMwJfs7`PKFkCuyj*kM9LY^dxY
zrW-(Ql<XKJ0;F}RaS^m2x*e%_9FZV+^o$bav_(H<?|*a{DQxqeO&BI`pf?H*>>95R
z@7Sg9Aa^OhdbKeqF3XPvfgWwRXv-yIL@yB1!U4nq-=e&ZM(eeaftE>JZ87*Q#N>Yc
z`ZY4WIr2Wfgt{k6IU)PlX7d(3MG0~3#*O=6%;(nCW$x|wC<YMkjH4&wcVtk<gO7?2
zOq&2e$VDhS{TounSqP15P~fBDIE+?_>XxAeef|A0NEaw*j`^(pYbrEoz{<qn7F0&e
zEw3Um_$2(!!l_;V5!>2var354nD3f_fBG5$mnx*$7k?tu8(=ni+!*J#e@?*Q`Ki5d
zFBr@Wnu~yI@q<%82!qQRj7=!O*y9DEhaT;wu5@@ns6&%@Lfaqho(_sdV_u*w(&gqO
zm|kq&v;WD>;G?L%kJO|l;Ydj6h<gly?Mgi$XgtRE7yWthr#r-;1bm`}tve`CKndOu
zFN9zyG58P|4JmZJpYy-L!<Rprk{t$qw#JqRdap73%u5POM#XMJiC4>$2TBOapSw6z
zByCcJmTNkS%^b;HKcF4x<#(Ya1pSh}Am^aU@@31mV$dPDw%iDW;3$+2&+R2VjN;KX
zQC$6T<HiuV&(yWUj|q@nXSMlD9(}DTucei)o9|$=RVVUywPCSXMPA=$(?+Y>Rn?8D
zzhxE()<vqCIBc*;Y`tmitXDMUd4P4(oYjMi1gpL<-C|l&P&~XjP?I0G^uxeI^C3Bw
z^v~&>8UJxWdSvY=kZw9SZGF4SzXqgNuO3qzF%hv3{N}qS;(K@bb96Hvui3Pvu<tnk
zg6^txflaB3=|x*5CfaAkEiZSOsH>~zqVMh8bpC73u^P1`d7GMr2C|#2fIbp4ysGi{
zd?H|hi}@q1S)koWXKlaembW7LFdzU8OoSfs6rQX$sSg>Q!^-S9iGBO6cy|?|nYB(}
zdf1~NJd}`9;=1g~+D`!N`o-vugV4JL*D3yDO^6E?@FTksN)(io3=IGH>;&|i3~_83
zGqwfWsN6_1JCyY2s*hyx^+`xns}H=GjIj8uQA+RytynBJHCeTC?1-c(l3CI&0NtS3
zm_w;33JV;2aARm_hNYv|nDWpJR7F~`an`7b1DJZ1wm9yT?7=*&|NH0sdq873$5Fcz
znH@J$SwS(`XEpJo@BMtf=6@tL>TZhCpLR>t5l$6r#(o07rvWGS^7c+={q!q>5dq^f
zKe5qEz8rt@mz6VR<Y)VR^=YK=pMi4A{Cl3o|2J&@pHDs7X8u<`A-ITtO+bZLiV>Qe
z_n<^T@GjJX?qGjKA6+`oy40w83KqmA0BT0FVzi#vF51-!hUiTw%)HI8^p^Nzanro@
zJp|cH(d<qXe3~Shm?cdLmz}pZ9K6t6tUCpYYxt%B<Vw+GMbVN;VPSlAmL6z8pwpaN
zfd;Nlf|5*xcxMX4bOz?=D%HFyF^zX6^IZAq)soS-CiA=H=A?<?g(#Y_(?<*!)SsAM
zb1~q=!pSX2-fTb-bqAj-=KA&PY`Nl}S1yy6*Cldd$zA3*zn(fphz(ew0LLh;qQOYQ
zo*xlc7cUTo4G%;D+-tmaW<(MNe<x=`Y4jJ}L>UBjUE|f2PgJW>egG{x0mVgmWM7xT
z0!`(IUS+^St1%R(9TKo)M`!#XG#MEiUqg^x!UPLd$>nastKG4RA2~!a*X~!|y^W=y
zk-~S%oYhg~Qp{D=w=5+7ufsY=1pZ*54reH>>e8f-4}s%Sy2Mu-t4P?*V!*&)8W<KO
z_x%{Oym73`eNykuxUG&hAHO)NJz8J38LK6FT!;&)1p1B9H3W*zXaVcgwaOiDY2p2a
z-95d%O2_!HK-ewk-F+3Z=xTUWQJ4VJ1PPjd9*DZhrJrW7CYzH+Cbo+Qr>XGYet8zA
zH2&yG_wQg?U<OUkm0<&1Gs1yrlo}mSxi`e~DM3|C3`#9*Vxzc=Xm>>Y4c2tv6CV_4
zM?ic0Ji2$KdCuU&m_;ac!owE^iV*V^Ff;1x?R9fsaY5Z;*^BeKp4*RaTP}CPWD9b3
zr^1^#g2aq|Oc*=Wz8(Jd(s0F1m-$E*Cr-%sDGMG7KHy<86@5A$#}3rEtqh9EuM)AB
zf8bl@)Qx?xFaAB4ZzfI3GlzPOu%R(iVR@_JFss+}n_07>WHP)a$$Xe}U$p~_&6vBd
ztA3vDb6|f$g<{LabC@Oq#0^hOTuq%yDmE?lI+(`nTA>ByNI(+lVDNyqK?O4Ut7b?m
z6$49)hb}yAZj^LYh+S!0a&cToZMvubkum$#g8W6|@8z$)I(*G>f*|u=`gc7NiA&$!
zmt8-usBO6ZAW@~U|3gWX$WG-(gVED9aW-{7yzlDr3p+29dfMs^j#+0{Otqj|(~r4k
zxram|@qhKaBcx)7^oy2KeVh3AYuXBjhKKo<DZ-a)W*v;LHu9ji<qsN3jP7tx)ILtn
zq@i}xki7V`w42>ir^_5wj@FkE*BiatG0!`kl~XueA9glSMWX<yIlxmXh819qM_gUB
zCE$rY3ZU~VCIR~er6?|?X%5Xo!1ojYRx<&O>U`X%n-qQX$T#aTX*B1OxX0JYIT!;P
zf3?f9Ey@_3G6;pOz~sVc1_?lq)xDK}_UxBQj&&Bes2Z4_+#i$p>Tn4<TD`rN7*mCy
zNxj|r?z4>^sXx>z-u}9=v2ppGQ=+gSbH5iSXbh}jEAO*H#c2xnh^fy83&e+b6v?Ko
z>R@_CGY@=~0M4fA(+<`f*?Gg3p1liLre?w^13`E7i}iH%rYY>1Sbicq!sx*{_Uv1`
zH^dhGGOeh0^YizT6JJ`4xAOnIfW2e<_zf0bu^LZh_W$zz^pr1m-`acEaN0!w^WnRH
z57@uFdu)0myH-czw-x%Y8&>x-7-5BbSPEMGodZ2NK0UR<An(u`xd(ewR76!z#^IiS
zv1In1z7rnVpsgqDe_nZxXU5;RQCd7D9uSW1&8lWMg}J+W*00gBu2;?w*B449C;hr_
zn}0K@2lMem&(EF4HwdbOhn$~@z!3GwpI?MS5F2*KquBcLy?fHTa`P{m#_hdvy0W%Q
z%NLj(ulB3)GWIR2tbc=0-eM;1Wm)@&0;RNfKXMOZaY-TOV!*pTRED|WOR_;rbL?7s
zhr|@S;FQt;)sfbt8PU<vX@g=%M>i?@Ef13-v(mzh(<bu%m4mHj2nWB$w3tDR&OT%2
z#fyV2clKxXZ`vm{Yp$%WZc_*wz5~;aizeowmv7$m*z)tv<ywynCLeZa-*PqU#Q`s)
zxgEzi{dX>R4b>&N)ha=Prqy(eGjdG)gmArx%rl|*xj}=OOC<ps@)7idO4F@_`m3&-
zlyPQtJmI#CU-Z+3!`@fwuoGArYz+i}$whw2%oEkE_6l8lY(GXkwJVlZW)3teF;&Y(
z{2_`0yKl&(Mn}s7i`3^r2g>$*3t)nLG?&2)b<nx^iS35>uOU6@koMG)ZP%}07d65B
zsSpq`@AO--)}JF=Nq<SA3qLDjvP*VuiNH_8I3piZQ}Z)-kE3&5g~1pE@D70nXMZWl
z3(6k{5%SbU4-!;8(-(VTKaUT_e}4mMrba#!VkFeW)l<yDNLtl%?OQ;FDvq^jP_$W&
z->0Wa2~rnEDM2IhDMbpB8_MB+7iGU6=*9jD$?9T+`x1Y(T97tWzDYaDUa<;7AWgGe
z&(BmUK)MTI^g$Ky%7IJj_iB2SpzH^#-95=LY$R|`3c&cPVl~-2dYW$sL>B8XvIg|K
zNIz_1xZgpLWOHx`Zi08Oh_PRuo>KZXtlvYWaQO{Q_fFL*q%@)7m0e&f=cBy;&$9!~
zTXb?k2Up<?4d9`3a_Dz~U~e0ki0TbjHg@(TXKI<447MoCQ_fn?c_kd%%$SYFTD$8;
zVZ*AhzHO0#>d+uzm+w1c|6WSk6{K6Sj2{5CI%_<{8`$cs5RD|f<65mo2S%5tiab9q
z7%lr`miXg8e?SlEpXV{s75n~k97f2>|Ayo6r*EjDZ@cpGcYl=#1ij9)V|CY*+BCO(
zFHcMWx~W0MtpTMbPi}^qSX$OKdG1Wk_~Kj(ZDJjE3tOgJ{BWO5^x^Ybw%J#{yYDE}
zii1JZ(KTAOK=}**YTaO-W-+WQJH7bic-aNPJMYMQzO;{y5t$A;U~+oj;ZEHe`)14D
z)y~cr<TRf|KP~Nka8Wpz%d6O7<Y#CV)P@vxQ8(Iii(E{!w0|fav9b5(#X)mdhI1r*
z0Ib>@lQqS?*I&GN5vn-G*J8U&%@#!Ad$(@Q+?*jQi_m$l1G_6TK-odX(yGrZK%A=d
zQYy2L(p7oV`MCu0VSlC~yOtIe#b=bgE-Jb*McmDYNccq4;sopn1{pi1<YNnm(Xu&g
z$E33I^6N>qYQ~BAtuJ15N0uJ|0=PWM<$_~W+xxgk7q8lQeNO71wa07Y+rsu8(H&gw
z<DmYc_tGjaC}~9xM+Wpr*Bcymh|}Xp*&PUyhBeim(hVjiCgZ2hD=)T^DLO9+i>cR+
zgVNDlRpXwGZd;<u;_@GcD&9T;E6B$XN>PoJB+GbUG=juyE8QnkQ>(r6+8B|b_m0~K
z&LvNAT2_S5tnJsLd6Rw1;_+c7hR>ZdN4~bBb<W)4Fl{zge-!oY)3R*mdK!FXGGYGw
z`nhwWUxz6^QVZ=>@SLKNn35v5`C`SBcnMb~mB({UkV#Drb_@!#Ee;#o3^yRg#q|1g
z1MY^C(!!+iGL0=d)}vjByzVG;yZ*b6fY<a-E&>1i!AREn=Xs12GXI>9qJQN$>|bv%
zbH}L}3cDJxZkOi>JR=P=&)I4Yduy>cFO(a8FK1+ARJ=&EB6?KQuQs~y^CD6Q&VbP#
zT(nXoNn7N+SCdXXsl|8+*VW~KWhS3mdwbN;t~p;?^3^=><qEKd5{d{1on_?|NCvU7
zveU3dN4gUepuhq6;pMtz_Sc+?OO6^<Q-r76jP=8)$F|}VOx0)(C#A6aF)?}wT#&;F
zaVk(>3bD!?0F8!F>6zH2ZywdUEvk~B>Nb|QLQ~1)UWR0x`8GL~gImd6k&#1eq^pu|
zO-A))MH|8<gquFteHHWlrA!on$%*QPP)l!T<e<2>{GYxsP{}fsQL*pLd_z2wCQVX-
z<1$F{d~=z(vHr*-j{jnuN2+i8V{pJ299ico734NX4x<0-l9CdKWz{T^xZozdtEp;t
z6*xg!!y2G^^Ur$n4^r+&Dnvyg;vyF&tJ@<3&WO8bJ!OIBll<P>9^RD5G%A-)?9$2d
z8`=+KV|Gb_=U!Hg^-xCTawLSjk&LH(eDkn}97G0<EG^&Iw_9g@z_6HM-_3WKf~D^x
zw*4;~T_Wu?I~?5ymK*D=djZXW-kbKd-3hsX%sd`)^Ie*pSe7bw<qs#v9!(zSYyZkR
z?h1lA*KRVSGNI*HCYrEFMPr^-dX1UUedFACA4v>;4-m%O%C|@OpPtODSL92aXU{N_
z+*7k&zpi7r{n?8q-)a53p4<-YI{n9@s_L0RF@EnI+kQb`IyX5c`goY#0%aBb!KgZu
z%az})K`!Z-HN<P%vF`TM8Is^XPL8+UAqm*foV{-_rtac`oqjl6bAMbdUaOIhZ<!Jm
zZ(uTa(BZJ;ijPa2XMYi^O6x`Du}#CRZi~)L*%qO+VQ%eXClxP#u4?7U@l)rN*p6-1
zm)t8{u<o*MtMpafIEb-a+mL?P6T|^eSZH~qXn(U_cGkmVMtffDb<i^^uInghX{$L_
z^%&>bLn~^|uCQO={1%zlJY&cu@Uqa-t?#%&;H&!avfH)~A2l$2m2A@S$U3^WJb}I>
zu3A{<k$TEvU+*g^>(!B|?i~6bef>{U9~7o{4D7?dE2MGaFAgs&`28{`I#Mq@u&?_M
zE!G&BqO15Mj>$DI_zR&KRoSkI#!>&V@~rkYLE`AscX4s?fhB^=ZXufU%8>2DJ55YV
zQ#;nEsy2bX*z)FFc*oqeM}G#T?<Rx%La@Xtcs650of?j0*}9bwVw&Oh*M`sOwD}x*
zUvctVOgZh+`zFd%zP2v-nyMcz#2O#eJi2Ci{LQb790$J3P|RaIiS`Hg(S>3l>&Y*M
z8j?TOg`J;Fm7dY>xC?cE?JFBUPjg+ncyQ`DQ+fYA9ET%EUU!6#wW#V)9n={#<;*=f
z*EdeI>VWozS<2r-znF=JIsms0nxK9REC6!;`^)6L#5XQFIv1Q!KL*CKmHVZ@i=7|~
zvy?d%&K~C;e2NwpveIobAq<k>1%#jiVRTpqCb(=phkbQrpp*t^^hrhrot@*OHt<ES
zVr8%KK|(=mnZzOQ@#x*PHi^(yJt_c4g081(BtR!s><nA9wzl%Ft71`|Tr5Bx@MiZ^
zSi4nuB)e1;sEl8|B044}0rlZhCd7UpL%VH3+?Ub}?8ruBWN0&~egcYp?~KBUAHI4~
z_K6FS;Rr>5Sb+f+&bC1oQ`Yn|@4B<o7VVK!P4?<zA6V~quuHU1=CEFv`<MI9Ls51q
zw%Tk+q95YQN=oV7hM;2wg8X?9+bmW(3-_7-9fH!$!uaD-GudCPUQs<}iobB`;V4bF
z^&1;g#@*yS88^o_w&lNSCu<BhSwn-FfAGxHJzVgvJW?ln{338|!HHQA{h3sDaH_|P
zd)@|_C*P{qy$XP_#N@fE4<hqq!e@T7exn=^v~%-Z%l9$4knDt*W*>9prO{a;BO4|g
z?jB9#95nLBDZ<bo+qje^Og1nYYm6c~=t5$MTHhI|Gq{||hJa@TjOEWLjFqvFo&3?Y
z@BgB1y&VlnOOMnq!~4;9G=b}Bz@0o_V>MsQGavZ?x&8`WAQ&!T*1kKfcCfqomDb*A
z-QEzI$^oO)j{!pfy;hOP^U3$k{h^o&lbaCpF`O<UQY#0y$^2e6>b!!cUdL<%pWpH2
z$D!%3AF0Uy@&~huia%dJjeH!cfm@r0I0A`P$L5p5j#o|O;M9O2Od(>@4df!@w82ZH
zjOpHRbqhOZao|Dh<5i_)u#=PcqHs5xmOD^a)~$v6NRoOX)JCoVyIRL&gClq7?VrEJ
z7N0BEk*`oXAC;Nz(yFHw1CJMM0TzO|+67aQ22hmpp=TdRuo1+m^03#FfE65vN7Xiw
zApx*`k*8Y(vhb^M{>kyLg)944ZeAAY*wf@wF+09jI>uL^G-kCovbzz|Fi*o0$?o&$
z3PWp=XaZbvYWA3?R8`iv6!8<=W=0s^&u<}}S+ee6<RoVfiV1iM(pTC)9Ca}BhucFv
zW%rdN!I*6|##jy>u|N#Xx3Gj@C}7b55Wg2Qhw6s=IqrGpwT~+_(JZF-^zLeLy6%0w
zeV0RBTmInVRTdqRaWe6iV-HIcL7`a#mhxePz%kB$p#2Obebds?QgslhR6z9$Rh*Fe
z05uQzB<@iARUvo|F4Ps+c{egQ4?^9QhV2J4s6b(c6@&vHsyI_R#rqqX@XpVQx^1-A
zI<xYwe4!PaT(ZHc=6L<ZzW0|o!Pq@-I59~v8?+J6)Vn{HXZB|xRyV*cD+gN=Z1AKb
z;78)J2gvDZS-DVX!KeXkbd=pSVeoPWq=MYX9zK2%e>Hi8<d8};iR0TGKAa6+eHWQ?
zK<2Xm?DWdL?TD)R5GA=uE*T&^<bf|e@)elu9j5}(waOD~*laA-Hi5-Z2ypl@{wg6v
zYzr_|66^~BjdhztN@D=2Q@dlydKHksc}IUA(+7{IV5wB98d%|6T>x(5VyxtQFF5|!
zFKJ+1_u-S>z;$*$@*rQ=&UTB->NRVuptI1I@95+A>!g{7AXqRP3X%lPV(54XTQ1^v
z>37^n^RlxzY-lx-a0>iR5)c9W)8p9Etb54TH%Sv8#cao8aEEaRwbgsd4^*Yi<p&>l
zQ6{b6mDQO^4UfS?ip!{=FP-#+*K|KJG#7`oOuDAez^BtMXi(V&@A3wW3b^+DgZ3WU
zyOWNXME3RyTHB6!quf(RBvCo~*Y5*u);f9wU5h-i=h_wP=&b-wa1Qx)#qz@J((XuL
zfsn1l>B}QcT^k370+6w<B5^dpNX+pJ=R$jHpPhN7d}Mk-fu+2ECk#pqm|UpcX5{nm
zO_ohmqEoToPy-vQL&dr2J?>iak&dnrmP<nTwMSl+)_bdaC&x%k**fvgVXe6;Rj=cv
zMqy)p3%*+aYzazTCm{Vo#HYG83E9ffi=sS6{>4Gw3zzxpj-0h1hg6#cE4n4*DvTFt
z&c<6z?t2)B_pF30{{GN`ol4MF29xvUgr&g*F&{c_g@CS@-u?PzsFr7f00?r0CRo)s
z*4XYMHH!<Nx1P5$e<2RWDgZg<Jm)Q)e>ur4$6g$DDQuu&sd>pmh?)b?UJ8c;n_j$F
z_%o!S1M2Du8`h+vnrx8B6Kt6$tFh<uw=f~ICMjcwo=Vg!tAIQQ!>>&p##?(Vm4w%?
zCT4QnwhXdvDb0vnTuz#6_<VsGR})$?-d9@Bu+wmm@&<<X6nw#|sI!bw$dVw(!2Met
z!x3``p}lb(&ei}U5r?qx?4nDDlWX*;nnRV6i<(5ubMV%g1-Tn#wFnk(Tvyk(9WpqH
z72WIt>qef*cF)F=<fKR5<}Zf5#pV5t3$iTYbHza+;=mPM4q}g(>U!@K@8pD$_jJ&_
zQPF67W~-z3L>UeExd!b9(HpXI?}64YA!mojIL{HOsTzoSX0Y}^k#A}LenTTpl{>Y0
zlV`15NXpr@5XiO$<BpCMCYM(*icIW(({%g1V(zpp^R8a@ZaYA?2dS0~9l0jx1;4}!
zU?>^TU=ymw?grla$Y20Lpff_rdpXX9El??-U4kYU&gDRkWstmzQr@0Zs7z0=27eE;
z05%CRqkOzwESDjl9xHZ-q26`M!BDGnLsBzhu|n_@4&frJgHY^*gCr#5J#zGNJL26P
zjBZaz=bE^?7v`y}kTp5qt=8=)x9-bwL+{SG3;4DEez4NP%!jPUNb_!hqO}ucEM)F{
z2fjGqZrXvLUP;`3%`grQ@IDCLV{*d2gcQGBT!kH$LNY`0YE-by2^;E;Bo}V6qF{bz
zW?WlGd}(RD6h*BQb~3EMnztWc1i~eI0mip<*qXcFty^{IWi}O}?1pR_ywI4;$i&2-
zZV>Dcle?&hfMWLW4XlDV^7g6=6WCR_#P;fSt|o4BSsr}0^pL5v@t#I%ZDm=#x2e>@
zv^BoL$0CQ9%epsjFxFtqdTZle=P^6(PEvfb5X?^7-a1^Fgo**^0T^>HS~|{yj`xux
zM-1_|Vz!6tqpXeyvI?zXS)bs6nICk^G?{EHKMJpGoSFj(PWnpZ&9=ni?doq%ee)Pz
zAW3F*P(utRYq<(MGWrS}`FU6l>dS)l5ToL4!EbP6Y0MlFWg5bil|Bn}?zd0_i?2n!
z&@fK!!+P!EzCvrjk13c9ReD=eG$seb*<jO0D)}m;$Ie&Bh};S8hKK$<J1sWz4t*oy
zl^YrCF>;`9QPv`P<+SIiaK3B#cN9!ie=V#j)p11%!_!Sq;*^w?HIY0xl`D*j;{u!W
zaSK+-@8p;^>ph>X;e1%TzoKwS#fKSU(oXM#os~bR(f#y!I+6NgpEbtBs9D^0alX)g
zsm{{02VuJPm^U6734zmVg*@5*^AZ~CGr0#YYw?VP_Xu^t%-i7c`?N&FxX63=mLQ>1
z#_qyLSHr)jV1u~}n%c>VrUiWus(HCKj^>5QtkRIIM}m{~EkPkIfk8()JYv>e>TT?<
zi7mCv8BF8a`}4P@NX<tBaIrAS%j8ikCw?&Zpxy@(jocpV329&^7t?V6HM?4MhP!l<
z`#kbyvE$SK@<B~zDDTe(i69cCX}(ome{Js)W;^y3T&o7lv$V?^*D2w`HC7_k``OLg
za;-ML(e`&ltQ<_3lWkadq;xe^`-zc@FWCPxVkk4!=VkL&x6(RRcZ7ZKEm`knposX0
zj>ki%wGgi+faafM&Bmo0N9*^KZ1@R(-xCkuJ(obf0h|(K4hy2akOE?IQ5rsccGFyQ
z;N3GO_vBQKW$-0__HD69BwB?HW=0PFdy&-R?^~fow*nDSNxoFAieKA$seX{xUDIQX
zoHY^)<A%y3Wq!7u{KtkcS1=@-iZ;rMy9eN6<k@*rZeXDftk5XfliwU2PpB4^Hf{FF
zJL4B1JAsvi-jALkYg+u2hCS_3PoTcRhR;%Bik)865LShgsvOj1-D0|V4*8JP3M890
zGXz|!6kv_R;M5IeLK(}dlxgMNv??qI=OX~@_@xwKGaxCQ%#XLzcavQ1IX!n48hR>+
z!2MiC5u+Bd+O_Zc&flbbT}$~gEN6{W0Q5#sb_7wyOZD+HS)E`K-M1X-40J;8iWatK
zx$R(;v|p4vG_<F2o}jKPw0ES|L#s-2=A<4!`exz@5cW2Cw#r>R?!;5;I*w<ps|>Nm
znqvyw08}S^lcyf$U$skJY%l8L#iVwv?eQ>luG?0b`M-L|m60;xi>RE9vuG$42!wR)
zDa{K}M8BlOcCh}LAKV=-H$YFlx-G^X>n^%nRk>8bY(RaiXnvzY9tv+G+08bEzGGM&
zQvcerOK9|0VJ_WvH1rZLto8tAL4B2ij52BB2W+sUwB1^p8pD|k6s^Vv%6;BsRfLGM
zxJ9ab=j)N+t-kT}<eaM79$uQ*-jP&a62R38#tU!<ss)r$*nn7T6L|Sd&HKL}7rdNl
zYHWp-8+WV-Ctx#_!4#b=*YIe5V+%P`4W<f6@~%P~_iWQVb-Us)K3iKMIhI{nRn}#1
z>LNVWCW$(mQE@w66x+VG)={s{-e7TWmoCqwU8S+L16jH9d*9Q7S(t!jV6-i~>$g4E
z5~d+k!lmG}IZ+)<h%NFpjl3CR%ULrc8ux9dFLL^Q%oo?vL)qF}O+3u25{|!rOHT+$
z3q6(q#Q0UXlzJ#!(FFjNyapgda`>XNKJM9&#Bz8IS`h+OMIlA3!J}oY!2{TF6>EQ$
zuz2;X)rWV8Y+34ujBgJWybko(<?ZdA(di%G@J-l<O&|h?pu1WQ;Hb5`vqsV{Y8|Sb
ze@U}5o9Zi?{tIx*alDpbdDM{yP9Vy&xMGn_N^$Dq>?3)w61&;ZA$+LMY0n)EcJWbN
z_FFg?n&awsTRx2Sb}|yP99^>BXPnIdnQJuAStknOyO+z92P=o)nvY+x0`a61cC{-h
zqf^Yj2XjU8HxX%42K6&Oq*=y@m3zs7MV`ssbbWe4U1xL#zGHR*fz}}Si9T7`89rk^
z=gO_#=(xC%kDVrBb>yPy=zOl^^C(a5YkmwTQSA^gr327zY+=}RO%)f+Tcu&xG8C&M
zNz?Bb5Vfw{glC}O#;N-j4Wg4P3@k?4Ado;tY+IO}uP*x}>Twp<U~!B);Qd1d507dQ
zQ&lc5Jh;m&#ooWTmKW++#lO>oeygJOah1nBkpeHm(e)>{c^&7Q`To^~3KhFj^6a9k
zeIg9T{QlF}!_9XClx1crSyW3a$;y|(gkXgr_P}GFHF{?z7nR8(tk17V8~wc#u-?KD
zb7GSkZL8CUj7?32Ak7tmnnMs)FihQ86F1=Z9Dp-c&D${%Og0oamoj@0&$ic?l$9yp
zS>)edCp9dzo$woXMV666O2VV`_#!t!M?Al$H!cdW9XvY(l?9Ne^=G&vl?WmviJI%%
zQ6U{puXiG;_nawe)mIMIpUm2G&1v+0C`1wks0~{|%*q9;j$=8h51N5B_gyf$&3cq^
zJ3S*M$*01s=2)6iWQRlSkZ!xs-!7)w>RhwwV3NTYUzs_o<YspeuAA-vUd%h3(O{UN
z>n7J11}D>3U)3U;Gf)q-#0y1jJ`$P0BXS!WeHtP(g7;Ja;DjHMffbY{I@a6<Jxi$}
zRPbuczWSbt{lF^}NXZeD%$gl+oQio0DY4=T%7_d?7wABg$y(CH18+6}6+&E5&+=Vp
z(ZCQ2#X46LO5g&xN^)t@&M42-boe+rBs3g-VX^&e9I1A2cJ`tXVsbfsfgGEV<e01r
zx+T;zKBB)9eTz;GO2Y)aru_C$!m{w516LKSUBqOg1iBX&=aA3{LXm(m(sZkKq>182
zijsC`{O2W1lyX-=Fe1i~xmUiAEhJJa=Ir6n;{YoN1pxkejIiM)wzbp)7J?!rGYuWF
z11s+1$+SojRy~`#?gwTkRVwJ$^&Y=J4r8g?Wzx#mpQ;mcQD8{v{H%Vqw7u-_(sRl3
zcuPsL_UAGo`z9j~P`x*k<-GGu*8sL<3|u5HGM|%E7P+=mh4kuU#PaQdBjoy%k3}Fh
z2Kz9KDzI@%k@fm(9P2@vH@el~Yv42;!mx)Rr>c++xE$|FPO7;5x~g&{+M!-Br4o$H
z2hFz)R|~ZRAtW(<&$#E&_wVQ6a@}zB=?WSN_LTamc|kGDOSMBYfF*EcGG&6zPUwnz
zXlLZ|kYmk<Eeq)Qx8e9dqUHvpti;+s1fkTIT{GHV@m7L0DIo=DAW|WSY^g4GLKN!O
zc;q!bltpSo04lSwBj1GUu$4<@<5sc4^Oghq2^K)+SFx174&+_WfTfa<j2yp>5B!%a
zLjI*GkUL1gBlnUPht=A_Yos1|(~FV$;kgoTx4}pYfD#cef%PbsUV?-5-!I`_uuo~7
zbh?c5X_=ea@d|EH1Qn?_ho2-+lHd=zFhrW5-v^~o{)R8+Cq5U1#6(9^08}PWhOid$
zP?r3%1|q*NvFA}6Yfw#K{clS{ucP45#1fjP5B8yjcy@W133dW&QHQ;AwkKkNqE_~O
z1$$Grc~#7ty5Ye(>V(kqJLFJ-MD&W`ce2A*?p)H4hw#R<o02ie|07@xxj+#FKo^L-
z#LcJN;@{y=VTQwD39Q{8tm>7-vcoM3LMf$+OfEds72x!8S?W++VuaERYxJT8^a#nS
ziTrSfaQ3W$^kA%!8w4ZO5e69^2f?99>h15*owD&mny@3DkDpj3)%Oip7s40y3j7Dt
z`QE?-3J3*8({Zh4m%Y6`L4pGaCnJa!R3Zr7tH3v^fXg<|xoATy=U}bzr`nV)>W3ZO
zb!?qkA!wOpc)3%7N2maq$2*B)lX+(5|F+lplga&H1<YwJk}35y5$cNITYBGd=JYqz
zO{4n={AT;w@8EU2?Q)4syjuFLj<GV%{>o4^kBR~l5oUBMOgfc4N5Lasf7Pq^3mM;Y
zHb;)+LbvZaVSlU+4^tl07=br}@O1?nioQ>&-jN&rkfr7+b_(m?D{BJ0*!Ud%r*rf~
zA(a<m;N5COgHsQ3Aq)R`!H?u^BeVWH%~K-;$NxCWl<$wg{9lHwQ$vMj2k`!k9k7<Z
z>@C_{vMxnXW^yT%+>edThnXe6R02nZB?!z`mmpS>)}rs23pu<lUUK>hvgYl?XgKbf
zl^pt4G>rFnJXO#hw{t0CVa8O5&DKze_)u}I4j76C22%wtP(bCy#aOMYq{8ejz6Trn
z`-6%xRApmFLI?+)LP8TTFR}u7PndNVXQ*&EhaNeUypx`hYQM*wF<LAcnIbo+yJ%zL
z?o7}{zC|WH37nvUL*RG{^bCkegq8vm0oNxP1g_Ap4S*q`LPSZg3$jAWI&ef*L6fxC
zTT(h(^Z2`O^8$4vR<B;2ID|a}H6iGm?+$fQEkY1|C(O6jVRv#p<$YCK06h*YK^cBN
zkMCWmGwemV;RjgTB?#1z8lfJ05Z&bnpf_&n+J#i<MONZCG}9<bX8LVo`9`YjzxS#w
zp>m%fRV;>>v+5QH?&aeDIZV^eETdl>@iP+af@gi?T)g6`P@molJm~s)bIv)!BJCQ|
zkKOrqvvHc@X&&(_biW+%7<r5wY_WpFKEQxV#8%{WO4&ein|#Mj%??&~BDU*PK@z1m
z1kUYTnnDJ$T+Jx#(vwqDvqrt!2P`0L<5YD9lfE9z-I!-Cp|BAoG%GGc+FF*1u>bhy
zKTceL@h_7gfE8}2@OT+B#E^ftA+J70j&^^WAM_g*q7>MU4<d4f2+}gLz3%~0_Gv5L
z>aug+t(Ecpkst_(5MTOpf{Y91{uRKn$mC}PSi#rUyh$auF-8ud;waLr0dfTC$H&Cv
z0s9UF=+*$VFq@_V2?ZK4&y*u<ZEd^YFVl3<(d&T77Ie96s7w<C$II|Rt~v-xZAjfa
z#d7DpLaZ}e<4RQ7d4MbiH|!v&QZ;b~<A+VQ5_e`Z#)vXSVl3>Hmho?5k>i7FM{O*s
z;>$QsQw89!T^YrJfTY;t<+*y3KOy&G{f}<le`TP1vQBvCZiLG7KrK2<1`~=rMf<#~
zU{k9MvL2HQd)>^3u5`dDU^Kg8K4ml5#~JPzu6dJ2AR1C}WylbLAeV##eT5ut0EhXw
zljg%%YFUOI9bE)mz(r0#zmpaFp$^C6*T&YAY94$0XIamAmBU5hWMs`$9WUtsJEaS#
zw-UA{IM|xE*i8y|{@+8QF16;~AJ7*+ur4?mqiuo_`Ne8ed~rud#<!5Jmehg7@fgA_
zbW<)(sM(JuN*w0~D&hPx6I0Vb2BIYR>-H|7bO;ej@-QIGp<#r&q{JuzK#uj9`L5Yh
z*R_pIw1^Xbi&k}}Zw7_UnQwT%B%%-LwK8Ovu}t;Opc?z)9ce3&xvUA@2ITe~VexFb
zpLnkYAn~zZ%(kYfUJAeldE*rbroE(QQR9HW=I~%2<|#F;101BL$`Bq#V7wtO%q}8~
z<z1w8s9=)qH)=ez@l8+ib%y{#$R2A%LP|Sc?@r3S+&q(YjmB0e!u7aboql022{-7R
z1o_+MG1xE+uMz{|i`5y%=s|R}7L$g;`Px6cTj2;zgF<xT)je-h>8P4>d@!@A3~@wi
zwYt0r=PM*0cxdb}c@>z6KoV_UKJw#0Aou~h-MU<QQ|>U2s|=1u-xk(iE!5a+itp2w
zzPdbGx8jjRdY)fVj0r-ev<n*YbxoQemy_pd6)Owsk=L>E;kx!<4*IM;91GmV+5Xtx
z?4F0=<&LvNaEjG22wtnKg9N#!!x`bM04Tu?8l<C_H)jb@tW-(62(+Y`NgtHmuLCVk
z&ZtGFOo$ywO01!Jogpawb9~es@&jB^GlAQ~U~boFxx3e;)P%Se+`HHyAet1Bj_41?
zaR(rpH9|`-aLZVwR<r-+2bi#q+sxdw!PwadzHHCSRuaQ<WG;$4l~ka!@9C+r$w-vp
z{ZpD-#>)6+v^;z^HSfDK^SG!lR?Stl%SJeVuPR)JQ<eb7rE1Rb;Qcs<e#d$&k>``+
zgb;D?Ah&^VcWif;@9Phm%G!B~#(H%_;yOA)pm3?9Lh%afnh#zUVHsTr(24xGI`F@4
zXt*3XBPO6G>dvLgdpGgM<go;Typ0|WQ<`K7B;tYulu3=ytFTWO(R~mX^uuBDdrVX+
z?AjVbu_O@L_}58U2RA9!##QjgC8ecF>Vx4Yr{)rTaWMPJ)T#=!ie0EQ?On8?D_Q#!
zF2!{8F^X_~vZdjJ71;O`dr>rZ{vzwO5Rygi2qjliQ&Y?MCm~5*_eKg)qhPc(O;knF
zfC$BF1Ta-XcW|@2Mu5jqPX`Eyg&{6Qz7rEWrX7cz>{D;3Sm_c|hJa%{fR{K~VRY(e
zuM8hl3N9@vX-Ehgv_sGcg=DTg;BuN!(wqUIbA?=gdDaM>4Kq$#GCKWsL>ajzHoD}M
zk)w>v#2yT%4?+-a8@JlISii66jCRSh8F-pSEJ};r#3C*`7UFl1XaXtw5f++V>0+$J
z!^&~X=nB!q{164AKBh_d2L@sORMMctFd%G}kpq*}8{jKTjy}itIxpP_z0hB8;F4iE
zkXPCV5Wf@p@eP=1Vw{cJ5N<f~*G8deRlcm*d3Gd?%2K4?ah=Askhdzts)iG|FGMUD
znYJQ^G+|BWC0<mZEaJ*+U_l0f6A^dDd?MdI{c3b`{6Cv@I;3N18@ThR@3kuY_A<=P
M+c(Ai`16VX24f}-PXGV_

diff --git a/articles/AMR_files/figure-html/plot 3-1.png b/articles/AMR_files/figure-html/plot 3-1.png
index d2fbc338d07da36c544700e638ecc6c6885f20b6..103d9cc04349d501a9d7d724eada5e8c6343b1aa 100644
GIT binary patch
literal 28505
zcmeFa2UL@3w=W(<MMsK`Iw}Z^iZWIdDFRYvP%)rjL6DBpyYv#8W5H1Y4I<J}1VyAs
zl@h=~DN;fYH9;w%2M7>C2)X+SGv|JH-E-Dm|8>fLoqNAojxdnCdEck(y?<pt^T#D^
z4S^qof52cc0@(9su3#|hk}w$FiVf@GCzW@}Z{f@On_3!Y;4A#YR4)mFFB@;2H*m*b
zL_^TOJmEt#Rv66B80?u}b$#L{`f$&BJ#TP0q<ZHC{Mz#ye)vULbKl;bwoiBu{phSY
z-*axQ-Mfza@0K?^64Zh;{<u6!ll<~lR&{>v`V+UUCmzZguT4LHLR0JG!*i{_Ctdob
zaC41-TUS>!ll?U3M|@v{6*=0H?h_YLDXEl5q_Pug{TF6?zbQvr=XUXP?=kOse&1H^
zcjlHqCh?%}e)%mJJr5>n1CKEJ8oYO{E&7UixcT4xf)Pi?TH8>=jOTs?@|4~2-#t7$
z_#JW1pK1mbL!M?CW~5$8lW>i%JU{>Ecxkik>n_Iv-*b<*MQZNv>+73LSbu!YhNgX=
z$J*0dNhA`y|2N%3kr!SZ--B(TP!eqt0}2{WuF>oaVDw$h&}(&+%k^i@dg<`}VqVzi
zRrmICGG5}ae&&VbW?TB>U_B8fFGoK=KfFX;l$1_}o&){y&V%pT-(dvu?(RRuecu1f
zf%x|i<$v52Ugh>}wN0_kjkc<LKD5H35#znd(MB!=*>t^(ug?x%osA~?Tc;Dt9lLVs
zW96k_FpZucGl{`Q92h$4;;R*=vNQtoxJ~ING-~D=GOyOmnRxd_5(5`<Mf_nJ9p{T^
z1O_?Iy-{I2-l6%`S%W;=%bDdXOa4>tiCW&?-gV*Y&|5n%hrZo^N>NeKZSbka@I)@L
z@$3$hXVE8Z3I<DI;_B<8Pqy01xP3kNX7F8l=GT0ZX>3*yd%Vk=5<suu6|!uMQ#h*n
z*G<U5O}q>di_!7_zExrPg+&fE0cSlo(tK2b=1DS%@*^Z9hbbO9X<dI_M@Q#Sq~=1m
zLsor)vXZE%s6n>*uRlqd_O@7Xw}Y4EzC&tJO^$Nz4YDK&Y^qLT*{8{5jqt`E@<M?`
z?a{*CmYly{>9J<+`17Mi&+{Ef2e1oWHi@BA?w_}4NMN1I*$dA_H=&2jPTranGBekz
zBZ*z;v<QcRbM#1r(NV(AmQdK4R@H0Jt0qPidz5ca_C4Hw7{9Et&E<lNiwpjBR+iMf
zU%=0DE)5@~I&5X69Xj6sN~BMDmn&rj&Zmcbl+3n*(aWhjR+5JkR^y4(wCXR<bl(|m
zx=0x6{9pk)qUV%I(%r^J3g^12-`t3daHw44dW@WenozP{`hIM2NeN#c^&X{DUO4o2
zIrmc><>&+A9G@n7>3oKZs9RitZKkF@r)$a}J7uYZ{RaQKq@^P|JLOQjW!uYpd})#a
zjBR3fZrYbqJmP5;=S1<k=ORUu7wTRchy<z@sIto`6Pg_XH*Bg;j<sV`rlo#h(Jl%T
zpNrm+9^<eiYu|qMEK45il~0>$o-%k}Ep;+jZwrjpvP(pwS)H%N*!)t3SN;;~+Qow5
z&MhxF<*h!$%99D}CQ1)kT<xP%t*UJoiW*-wypu9{qI+PQX!3_Sb?iXeD+|n>^r@A}
z*Xa{dks^mzD6eUm+@DP>Fm?TO>=`mBw1s@=aW2`9P4M~t$FB?nf}bbe^Aq!4mN_}b
z!tA|a#;_4(sUxW{Ttxm@-SeX&a|>Q0w(sj-c<HS1y4hfzyGXsK+41>LfhJQFPI9Fe
z8JjbX7c@^-?P)qkRAD(TrCXJ!hELHLm3#7<-(5?;VpI~qzF?N7yS{jRMaQ&q)$s!t
zt}`CKdXwO1-y|E8=s#O4Q5(pbB;~-TUrDp%uo>cW3)>It!6I;}ad&iYSej;G&dxI!
zLglS`Z(mO^Z+~kapO-Oybo>>3os<7+O6}kaS$IbpA9ZmyGb3vBa#95HLWu_|cgi~(
z7Z(@fCBl^#I?pkR2Evr^5@;hCc^yG(uOdu4SnOQNmD)VJR=fm-J>@O`F<^eIz3AJ0
z5xj(pUu&%4|9s(!nLNecU0ZSVbsTQ0NThcm%)4|S#A`a0NbD_g=X+j!J;xGj-4GMG
zva+(<tMf*eLl(i%@2?MXJd?pgfM1rf`e<T{xa8*Z_~m7)DiJHmUg@)hahurf*>-tc
zw8vJaBcmwq>HHP%9;d=MZ?bQ+tb^WgV|;9pJ2lgr_4$vj^qKG=|77>>n19%{lH)&3
ziBAm}&8`=Tkkn~Ri877xr4L(u`hA`6aI}rIUCZmZTYqj;SXms9Us``B<J4cV<C>jc
z$-tHyUmkAft;0D=DaJBvNK0vuekSr_c15Jbs~%H4x8Fg!jPnszk-AZbue$IqGmpov
z?A9v{t!Jw->kD%R{%kuG@hI$r7Q6nj{YuWr&WiqojKD(y?VQ#ryZ3JlGpreX-YyK8
z;4KxxO=!?D+xDpOqOX_kVg%4vwJmF8&{xda4gY+>KgZ#}%X)~POu%GZPSd@TrrWI?
zI<K1((7@+)E=5)9W8+Kqp~#00TfElv^e~px9Gs$5TU~7EJYL3Y<~B6<^u(HYb*1#R
zUN>`c71k-l{g_REr+X;+_>JGXevL_Q^~ssoT+vh&E}p1cXF4AGrTz6~j}7J<N+t`8
zTCOcB23hO2=%XK4REJ$mDV%0Z#ilh+e-XD8;PJbAy~N8&FUz!#yYqsM5hfGXPJE7O
zu-3~oHt=|?*<tC?ye0VCY1TxponG<7uoJ~ybHWa`$=A~LQpyS@NOK8(%A#jAF!ysH
zQq3`<2#xak5FH_*_9zROuic!a@c_z+va-qX{hQ(&VlV<u1zwZguc%p;8Ss4vi4=A2
ztLZpljZEIP3_Y!k@(E9JyX}UirgQm*M+4?_NX+4PdIYDPO~NX%+ztQF2=LEHh!(<s
z^Niq8fLpWwh*7~2`fx*?Q(@op6s;)hme*L{IwLYZIb1~%O6^$BOu&Mpt#4df$|Mv0
z-BxC$IBtaSKDWA~Q1&-IscwLh9M!h+4yN3_(NnTzN7CP5!cIn*SKJ7&#7{>JpN(gg
zt0b~|sV#kd1Ow`O(s*nh1+Yx{r|Ou7;p}Sp*oKA}A#{z3F!M9(Mj{XETMySq!zC1k
zo?Vp=Seo<j_NLZKCe|~liHUq-K0mt;*Vh8#kY~mvp9}p8ph13Q^3e{dM!Prh&oxu<
z2d}2`7gM5b5~2$UlTNXo2<c-m02b^8M#eKsj?MciYlwR4TGzjLnvjqHICTQ>O0+W0
z3svampMF1LTKdt-qA^ib6+7EeSSD*?Vsd9nKO^jI2p4plU*DJdP%gH?7*Gc`r&%^u
zBV0^*`iDUy=b{vN8^ADZY4I-fk^N`ST?jTUa5U)1G>Me}&=DY4Ox&We`>8|`d!ffj
zdKlnv6vQ&CKksggKIM8-t8pWG+wm)pU&tmU1}=?C)QaIIo`!{mg-+!%$T&%CAftqm
zZ&?q8nj99Pt=Cu5_={-afpf{r<K-(3E7YJB$$_fSx?lN3pUIaq8+<1n%#<cRpsQk(
zUTxW$r2qc<(?P~lUkg7RE<z$8ARvu2e&_z!#SixF@0P2BmNoE}V{K`bQyf5T{Ekwl
zrKixq>IBY4Tu3;r07z}>$+(6jmS8IG79H+RRF-$~sT7fOUFbRQM6$Fik>pXzf*I)Z
zrVu86ZfLK)Y88UHFX!C*@wMTiq1ly-!tmeg3jqa5nB2>vTP5Sma?~+*5Z-UOx~NFG
z0Ofk$Rm`~uaHkIWh5%toa7_WGJL5z`tMt~5017yYmA3l&%#*TN@FqY;W^6e#7L~5M
zpl}ALsFz_FsYEM1$yp5Nyf9;r=`&lR$`DKyR2n`>JbiM+iOpvBP((!a(gXGG$sJzt
zeL1Qa;+3wKj{WA)=P5Wjgt(3?X|HqQ*%v6bi}jeiss|gJ;^W7szS76lTrR+Kcapn*
zp~utwC9!U^wUR<blp7(U1)r*`9`A~<2vax^ap2<D5S6)?N3Li8taEzi3_`Dah_bpP
znR@9x6c@?DLS}{b+ePX+l0u$IZkNZ=KQ~a}LtSvCjfEvun2BsyGGy43cnNs-=Oos9
zQ-We`@pXc1R$$;<vj%-}ApA{w`r)jANr%Eklo)jcl_s)#VG|U=3JKwFg1yVE)d`dy
znLMobe&=;Q9Im;`IKiYxd1L1vvat=PuP)MmLnZRahKgIj6&Nl@J{k}XeM)a^&*nA!
zaPzEvWR6*@cN$6h9lYXFLcl`TTt;_UU}~0L`cTvyuh$RzhHd`Xxb=VpHo<Q!ZQgWr
z`8ORErX}7IfOOBh53m%)_hVBp#ui-Lql8_V7UCSQZK`wNuE2>0@Ni=C&b<{POaM3*
zB7M{_rK$7y(v|rv%6HeXc|SxwNYrkWjLZH)^(EgCQh6~LL{Zy--fg~i*9o3tz+2sU
zc33<Oc7jN6ncw_a@2yG!EMA^4deP`~qf`*HZjQmA4oL<1E|77wqbfQ%&P7kC$%{OE
zyR)xRxd^_K{P+bXUV`RRtpv4@bkNFjeY}!9>;)`-zt(fhj`s$mG#U|2Yjgf{monyX
zoJR{*fzj(8vU>nWVVUABm*z$*8*P}gLq&slt=T4Zp$x|G1AzRszpvXg+Z-M!gLNBu
z88U1oh3h&%(Lr|~Ec)qz@76yp`$V=R{M#6*T245$4Im;wN57S!x+toC`O-D~MWd?Z
zF2~^*hs;`8lJUtA6~5iK)U1J0nJ7Y+#(2p94I)`PSR3I!ni6GAF7-t`Ndf?=3o$YW
zCF5@W7N)wgnBmu2k;tB~!FC+|1~r}1#0m-&r1F({v{b!&T{Xn~r$4<n$UT6Y%-1(%
zb(4mH1ZlX<C-y=!CFhI#>d={9gX%1|FT#90<NAl2=Cw~J&)U#eCJa^c`&xtJ^-fyk
z<k73oR=>5pSj7<6_ux0Tty6S4zi;4C*on*_!*{;L2yf|m8NV>Ja%(=}m6jNNxxwrw
zQQn=sx$QbBSxZ6O(lot&qKq}gWfdR4m0hJaI|KiTwz$*%e>>_#I0b|0DU?cd_t86Q
z)-`39z3!c;XqxUJEdCrJ0r>IZT_5(R#<$1n@9vy3z+fDA0UI8?r*9>hnlpjr5$+yp
z;OM779>H|peBRF;v;Pdj{NK;=BFcSd(eu*^hrB7S7fRlI`e|&!m@)dY%Q4k7tI$`@
zG5WH?;N)ZiVjr?kZcRF9&BQm>t2o(}yx~#GAe;EP{lR4<@}B-5Y4Pa;clVvq(9i%L
zu?N84V`15MeOH6yO-G5FB4r|0Ua4a$N5;l9bFVBKX6Zgm5Z~Dir2NC6i&>dz2j=0o
z>N{oabq~e&kce}bjJ;~%n}PKoo#-uFQK){rt99yNg?0vn<|q@dgG%RfPli&z3i9wf
zO>}<1=1=4wQ&Y!<iYXT_lIG53s0ZJd8yg+P{*44}^Kd^@Z1@ncU8|rI)Pt+(?I;2q
zF)J%Y_h*>fRS${#Di;eDzS@VWG=OqS_@KxGHs*)P{F`4Mem85aUSP1kZSEc^q9$lj
z##T{VI;sd@m>y%ws(0>zI@60$c_X*Wk+IGEjs6m3z#bA+v~!oNa446Tb9zLZ51>K*
zA6Gbz_ucv%chUR@h${d7HS%A61@q57i{C<8+uI{EGc%7Ef6VS{y&>7JATPs}oPxJN
zDoWK$Pti-?E=q+UE%G1?_%gHR4_?-jesS_Py-xFSJ(oQJVW2V0PWDw%R4Ko>3x3CD
z6@+n@YsG^TuHVmmdgAoe-P!iv*r4YghlmII^fwIrbj<kUr%6b@#EQvql_gvJV6%pJ
z1Ei@O&I;>IVWE7;ZnyI*D>RM@aKAnMi-1+6p<q`SNxHLM#jEL8K7lwoQ#IGRLD(Y<
zi6)WyrqSMI^!P3ujnE3z2Cv|$pT(D&LfokF-h#dyIXMoWxc_l@{Z~{&lgZ@LVcD!*
zZX9^IJQVO=3sBoW-gWW-VrA*=S%I=dWrnd<H93Y=A{$Ph9jc?>5nuipy^_3GC2tKx
z?s*Uh7AQd+|9)Qanq?-P&qUfnt4efAf$^J=%A&andsK(|U|(3xixU>ofy;}_GvXZA
zvW5ICE2uBj#Y*yteO@2da|y?NRhuZMoj>-(Hds4<{C0nNG;w9rj|FpdeAA9&Ty#`2
zRqSx*?b3HT$OIrE%&X{Sk>nLu=1FFWsvNp15$Np>)*^x(tf>+y&YTYCnO$q@kP?Nd
z4S*mdE-1OVM!sw&G!9C;@yT2uOMYdPkGy*1wlw-CtB^BUNJ;2%DOXNhnS0|to>7>&
z!>rJHB7;zFG<%9MKX29h(N!cK_;5-hz&q6_V>fKrsqYVZeFj3i5K8NGaj!<ty{yov
zK#lXCd1Pix8$gp1Do=hP;!=7-oYh%9o8c00_)VPK*K@??hg4@JpLN`4S4ydqD)+w=
z%=itx;Y9WB2w<(BKlZNOAB8GHk<XnwSSyV3#DQ?tz&j(%n>&neeOk|Rsy3+_DrK{t
zpH<46K(RsK;WUuS>R}~m7KKYI1uf{2pl-L0CHVI9sZ8G%_)dS+ZlAy0mB=<I1MXW8
zSLVd-?Gs+5e3epk0>-YQhv7m1SaMpxjA!W8Nd?Y4DjmC)rHKdhcnXMjdc_G~-EE*+
z0thTK{o(ARpHjLUGZ{7u6J6t>^CQXO!@S~)YIsW+v!dnMIyw|vu&Gj-Z7mU@57Xm5
z@uB(v_NOD)k2^j%%~__O1X;+~^~-jHVh=mv&~tEQF~5<Qp;M~3g$dBA2ERX&68l*0
zK+0@D5?^hEq@=-)Unz_-h5)NmKz^ZvOgS157l8(=XlX2+Ub8P8eG+e$xoMG`6_UXa
zp%2xeoonkw`3&!gj>k$b5aNQM-c>;%0<J=j2H+XUF@TmXZr}fq6zp9gGs@&a0^B_t
zXEfXEv|}|&#9z_Z*AJw}Qc$Vf1#kum?G_Nf)ddwKIB2tGQvh?9eEQIC@0r|lhuQW<
zB~oISYM?)=LF-?f*azFqTTw~rD!i?=H9}oTSw_Ck<Er+lZ+9hXA<ipJ_`?1(qY`KV
zE>)o-QJN{z6DtrTbjX|)hEOjBLRaaOXI33-)_6b>r?9ih;_P3-PtVrqh4#H;l@x`$
z!wqJ_b}1bYrPY9%5wS`I02}aFR7ozR+OYTb-(CO+M8m|yMETNh0@w}$9%muv-TTsi
zHX?C%XW3NoIMVt`2lq>1^9?WB*w~oPztbNYOV8r65te<@d-Y+`vKj7Li6uS2KtfQg
z(6)m`(1p;mUyqjAQp@f0%tvO+H`A-q+U=QD@?}pYnBRAlpBfSVQd``~1c~=RZLudl
z#LuE4m#Cr?G|d;3T}$_&u*mY$n$#U4keQE_Eq=dUPJxZ4p5XBIgH|6!FHJ9<IUS-J
zC&BzC6m;F=J@5=elB!HBe(-H_*jEsc<ciIJD~&2&9=hEb@a^6XL0_`de6Q2Gzh<z7
zo>eMZ4;nMxGNR***jLzCD%oW-h*mjaQT4j8(7Rs;1Y#&C{>l{>;uSsnI{`uQ`;k$E
zjg<}Gb<&#Omjz<4Vm6ze$nmcU2oWx6WB2XWQJ&X5_Obu>IFR(ZT*}!k07#CT!jzC%
zWX1u59S>MbONP?JRi^<Z^7h!oxmG=4g~8Q+z9eP*@m7uOhr)LE!tEf$a&=(a5Loe^
zH9a`w82}kJy~_8-+V}1DFr2ae6PH})01h&(8wMh?{D+<t>Zi9OTqJaQS}l;(LlG>Z
zU9yNOE{`3{1$b1E{!acZaKZZeB)#yG6L6nx6$^BSlR^+U4)WGnBRFnR^|)&!uxG5O
z=Lb`szb5o*#|7tmSGHs)CtKt$P@b_$(!y&mSrnf1GYw=8#coe5l#eYY&lUJh75u&`
zBCR9LrAUc(u8isg#aQPS)dtB8UCWIf`xYWDHoI^x`6&vZ*c?hA%W3A+WN~@uISX?q
zDy&gsuP!TJ0GBy#Ltg<ZLXf}hI@}Oj_+`K`3}I?>mUlU;FPl|<t6><j*IxJTj#ZCH
z=MU-`?XeQT7!WHKK>yRyvALvg>I(ychSZ$Jl)YPcCXJ=kJ5Xv_i+^uiB#oVIa0xt#
zWX}WIaW6htRHqtdWHR1;Q=0miY&9S_bWbaCnGR{?2`kee*V>#J3NgoJHvMIxcL%bm
zbk=4V<m1NTS^VYudgAR<q&MrLu;r8>WMq9YK=&PR^qb^JBhATt1v{in<9LNm-)ahg
zDMXB)v1j|WqW<4E%_cBPC4sDKD|j*NcdU5iRUGgU3jM*navw9w%T?dRdv#Y(LU0i!
zFY{(soErz0|IANKG~@NP5c3^;>OZPwX5aO(GTsI#EoK)n@Oi9r@j)biLi)&U4p5!>
zLx=x}8Om)g%|1Q*(84#DWTcgMDl^D(FA<>a*kvXfn@nj3QN8HEg+(^Cff%$xS6J-7
zlM4}AsF<^(Z2BI$Su8&7>YHH;an|+5Qv;7DN@!)HQ&ZYG&Xa>cXAUFhMuR*huCcLk
zd6Kf?q(u#=*%LAdc7VDb%E|(&Cm^nJn2DTdMmdL#qJM-$wRn&}4E)Dy19@ZIXTwL8
zTAH)kuIe<j>zl<_3Mw5T5D0d1u}Cp>0|&(5K5=GEw#(y>MVvBq1;`lmRC&TdtQ+K!
z;lpN3O+<LQ4pc!J8h)xFh8R>j%C%T&`?j|bb1fcKb&G{QCb=TGQ6VP-FGrjocD7P{
z<@nYE7tW_#!NLv&eHul)`GrJaERU5;<lHU?(#5P12$m-(hORI;)*}-W^@}qD?odcl
z!CO*m5w6_PqLAp>VK6#AUWX&o6ClWXnb)Jx3_DjJ@9A>P2e=F8`Rf_56X~bCJw1mW
zpKLsjKh2(NjaKxuKNl_*55%H<ehe(~eK7BaODvXqhk=cN0;hq^5d8{I6pdCsZxClh
zLp5e*pjxQj;rr)5cxM!_#|U#R6UF11vHmNQ@vGa<sQ=ZYW8M80kjwH7oy-s3+o3q$
zcGVp+pVji>OwUa}kyGREb7v8c)YRkMWfj#6MU(HB!=}yn8AHz?d8%syu;6N^$S>fT
zmmyl$xK@jjT3kciziij%Fa7nqIWo%(Md}Yrt$de~8}IE=;>J}>K?Nd!VbTi~-~-(H
zZ}VBVy}ev1=Ds{rP5nbMp++ch`VPu~KqP7cU_%f7$5zQ<os~hI*?Us{dk|A&O-$%(
zVd@=<^ar-BL7CATf!IxM&Qw&KEhZ6;ae#Pxq9Gn2gUVyAYR50^zPRclD3?)^MCv?D
zi5ysEv|&b5wc}$H+=mAd#qMBAkGUAbdcMpOOifPShd5MsDCoGe2)SUrSO_(0$Y(6=
z4ceJDkE%NnA(YDaWhajwFw13SWnMZK${hAMr&ghw9q=zWwbq4G{>nl(=fD6fgp+aN
zQe#e|NXz*y{iZktX50~+n{V@p;7@E-5{J^onN~fbRMk`d4CjJNLTpjv;@IQe>`Zwu
z&WuA+Gb$w??TJd{EK_~^#!aIv`(kO6x#9BhRz0&n{2OMt2aQfAY5-j3LSSpN&*!=~
z3Xk6BI-8P0AFdk#gB2}+hGUkLvr3K_=GorqNnk*-*dMOC;)5S7r8jEXQ045tBSeGQ
zruTXQ;@ttnt2q}9J-@*)$+y3z!eJ66vD%<mAUb+~>RR+trFbY@JP14P{8^x9DKO$q
z7&qaaVIo#dCupfvH(f-04Cq|(&IHw<z&}ROJ1|Y_j|w$`Ysx#5u+S}0+m^PvCLw^0
zj*N7UAlZadQLws(kGtiaRx@*}-Ot+tipZ|HNFouK^ofcEgiZ8x_|Wx&<?MWefx?XC
zD-co~NxD4=TwXKVY%%o?O2O%GxW)#xSh$etT$4I)55W3Z&x!7Q2cy#>=>3@Plp!8J
zs8M-)=Ka_VUV)r#8fIpciUsg>7%7XczU1`~&yoNL0Tm0WN782g)W0AO(KjPOko#Mw
zmRU>yV#*ykJ>~44Y?hhNw|l%jG(S>_;XhvBb%eGi7{2f*ik*KhWme{#6nf;7E1(QS
zbz<?bqp2`jF7qDT%i2af=No}SWDo+V7v6HTB_&$M?#i+9<yn~F+X2pm`40cZN^w({
z+EAAuR!=ND^&a<qj^&D4adV%y>eS!cVfq4XHUn_Mtc+!?*b{opN;~Mt*tS*s)XUGh
ze|^qg9!0FSDtoSU(#fR`DyUk2sB3#iRFEoGw)jQL8cOHGvRr4#xsL!!phPI{YwdJw
zx0i)V!Hyx5^!6rP!gS`L{@}yInKfT_n8j6#;f^3Q3zYW)K-<#U$SmX|tNn)Rna)4n
z`#L)%m@EN-aiUZ+r@9XOC`Zg=e-TbfBmpjmNF)y#f>y8-jc{;$Q-(ezI%s)Z3z(A|
zlZrPSkFomnK>+VOV7PMNF83*({LP*2Ra>>PoT(!Pt3YhMnnfyHTzn)|_VHdWx0XsW
zF%(OZg0K$5cvQs{C2|aA=Pvl6kiy^-E;9{gpc)7t&rzx8KLJ9~1qdSPZ@7S8O(g%c
z8pW3y4;tV1FOTS?AU25^6~v6%4TXKt_lJi81<d}n)DJk}w*DDaHkVQ~_AY&PSiW2V
zg++IO6{+AZbx`EqdEWJ&H6C~<%l(HAn_=<G{d}r5C`Wo}udeT(>>Yx&@c=o4%vq36
z9Vn{~U)!_-zL6c7EdPN}`2yDitCzac!#fuv>#)8cgIl&?U;`<umEY>4BFO6$#}(fu
zd{APUZ`Y>|M(8xwnJ(naEi70=BJrgvQKBCmEB=MUrf>+&0|;p#B?i?{atSAtA{xJW
zcb$ehEn)#7G_-&Hr|*dGUR>N%h(<Q}<xzti3B+n3j5DvYGF>S|g>tX4)R&Hz;LMwG
zB7uuACj`Lf0}CdXM*Z~M-Zyv(xUvkro{51l055UiJv$m*nPXKa?Lsc?_3Cj#A9dhj
z%(KGMp`!{*Bgw7J%^$j`B}31Rq-`4G_ArpFX$nEyC07C6M7FzwT&%*>$AQ`YJ3`j8
zgSFfuOCxAbV{~%zBEgghm(IEM?N4Lhp~y77p0dqrl+s|IHGn;W4wb4R@FMoL_ilmA
zI}CZ<fF>{h=%=G!U!8sA#-1lp-4j{HfCuEq;~K0_LLJ^2P%spShQ70i4bxkT<YEs`
zPvr7~TE4!*DLy{F3s#a_uWR+t$AK`1fg@dAuzt)un$K2Nb%XBtSX@;FmbwdMC6XoN
z$)me)Lti7K72K_%ltj_?LZXT?#4O)gjty-pdGP%>i&JhbUs!Vc$laS2oq711AOCp$
zWWAG`+TI$w=Fh*Kz8R}`;k{wm*BM<I$%4*TFAlxjemZ0OHjyJUQ+I=_L>~5jX}uSG
z>xjVj6GK&N?%(arK2d9(t3+qr5qG%arT*hWL|46vSdhR;k}(M<L+U+E?RH2Akb^Z~
z1+q~@dRCd@rk{><X3Ym7vIn+-{99|ZFU)P1zDNZ=l~8~!=lzIy`~|;H@CvG(Z<`L8
zmdtk;wF1g*)lpS;r#4O!fCZXCMqDx&{N4}<%Htgwbx_|F5yvTs`|`AiD7jQLp#n{!
zwo*tn&OU!kMucBefS#43myT#7EFSWC>EOci=g-N>QTmz23YIqLHj+*NNCVN@xv$t5
z@&*(5t83Ih`cs4tsKI(tfaGx=52o#xL@R9oJ`%Mk()|6-y!`wj&@wOLm+Q)xxz1u_
z@1}tu<_1;M5EKvZ>35HB2)uhM$+0tg57rIrwXchciilQWieqmjPz_jM*0Ts;^aU-5
z!}5h1qNStbIfU9hP)XH8=>s~`6%W7|&m|LlUQv30SdO?+xt_#{Uzy+?%POZP+`fW!
zZ!z)MT2W_fZx1i_GUFRdPfJT%cJLEVv<c>=s!dQ?Uu~A|RPT>6#$VM@mW9cvLFzav
zBZ~m5yY+dJ_G6J8hDa#|=3vD5>4WO+fjjCn#C#>768zZ)i$~5wmS;IEKZ){<#R0pN
z{XFomVW%rAE3=jd%fM<xu0vB&>^Y8DLu){Wh`~8|^Yeq7C$FWq8$*I)XWGns|DrJP
z_~gRUhus@QgaXRQ`zoI3rRlnL-Ux~8JMV;hM||G5HrvL@$q9S4E1-|>(%UScM_w;F
zESW34Sa(0BqoZS%*2lP<Z<xW@FmXq9@s4T@Vyoc35DJBe7X&DqHn`B^LfjXA8h=$=
zVLvJk8x_aj`-OkqoJXNaqtY&3$y;#n2w=&}tS<)V!ghDjq)V#$?DLCBJjE#~Fj8(v
zu6<rmPykWZv8UiD_sY}vqy|xRf*_s?o(Nt!ZY#pX7>Jz>B9z6d(wR_~dL(qC$)@>J
zmFS3J*<tE)qa(wJ+P_Bd<9%if>x;{T*=eeq8MyiVq-b@Ga!%cOQO2b1?if_CBCVWH
zb@5YPVWG`rcRu2Oo<<*v{3;fHOAIUlfHW2sPYLGzXm$z1N(I?EBi+^XeE-k+uu0ku
zSuy5+44Pj*L4fD`Dd=KhqI!U*x3^F+aSo<2oUhIG7IM=%m(En}4w!51%D$lv(Z_5E
zmb@pS`sRZ!n)5WGBrXxKyWq3%mT&{?W4QsW1qrFU-)<|m_#jh;oRguLPyg-H5Lrvs
zjogld#dZ)35mSr>m<96HK>nYM3Y}qkj=u)Z@H=QJ9@e+mFMsA&HY#w$=1=SYdS_Hw
zod0Z3fz#oY7;@jFhG<ekfeS@tM5R?;R(4~P!1uzy@j^c-m!5)*u!!TjlZ-c!6m}m)
zv_`@717NlN*p4hS3a#i_V!*<^t&xZp7Hx78cdtL;3&<e{Uh(Tm?!=si>WvV^m{YR4
zq*)m^my(f@!O*}Y0yI>KGJyINKZpvnU}x4$wKy&QMs_`lA!-&5E-pk3mD#7rKVVQE
z$aW5DMi=e%r#C17QxGA6tTq4*$7BzwU$0y{0uYQ&M7|gRj^q0aT_|PB;4(OmkMrub
z<1Od|cm2XS(4i%T9!A6UF}D5?QfgFJ22_XN=QeUN+PBJ(H(;;;u?UJv-)bfD)1yWO
zgU_yN*9yq@UZT0~dIiBqvK9z_pbXteCf-(1+7TqDgQkk8C^LZ<$Ob6PlK3~wvwuOM
zc*G1Bjwc4KFj0|EyH)4(ho10_US3|Q5In_Krb8kmP_c%(A$a1sS$@gAYM0F*Rh8q5
zD!5@$nvVwJIN;`NK0nxkY{zsUlT{HmMV=CfqkiFao3AbGaPay7tB<gB<56?628|(C
z6($;sC}D$-w{D=$3vg0Rc_GuYJ7C6nUkU(L0Ekib*VEf`pydVWT9ip`fYlJ0c208#
zDM=Mf*-#L2`J3e-vy~wgN>D9+KdMV_*Y?W;K7p8sY%MSvCJ1K1h)vA1P5%I+)8Ks(
z=Nd@bEo5>+sE9mraRIgN?glLm45oT84ux2$fKe?wm}nsXXOVp*H}~W~bvO}4+}uXR
z!-!{qO~(_<9hrXfOA=}>T{-(9C{HNM&)!DvoqF&>0dGPXXVu3koJ7B&7eGCZxNIme
z7XBL4esj5gYH((|J@QK62I#;s<^bZ7k!4$7g@_@KoDq498j6+_K?YgVodK8J7F#Xj
z6n9Ny0zgW64Ks$7hPt^I&@3}om6hA4eewj9GX4v>i4B}(A_tWLh#q|b=J;bs*BNm|
zOAYfjf=8&SB;n&SG@x`3FheC_J{>ZP4nU282n2xR>-~0fUpZ!3!8`Xg@-R*+5h8uS
zvD|SXfifS{Az){anS@t)6}Wgx_wy_TaTq~3DIhXa8Ez?8MhT@3$iQMu8v=KR(A;V>
zQd?~>-v~r&l`Pe<x5=xUnK7GrV+jl>qW^ez5bwqarQm}l*CB|8lXB_(_<=FIFxeAy
z@<MBH_$nBS=l7XmSE?>olIuj|97hPt;6cl0%mS6&hhu)?6Q3ZQ28Qf^88|fCrhyO<
z{G`loeOgC|qCjy<?J#m_;IauZvgwQ^CY|~}Z#et5={I1V{G=%Gz`nqOCP*mE*6gFp
z2hrtGm7e3&Q$QX!^Z0Y4<REyRsA}AMtJr*k9UiEvufs0!3!lk}iHX1e{G~+&uZNM5
z5s>w)(n;FN6m1s4yrQ`ltz6IX4)VAZP}9a>DY0{Q7F!*RoqMZxcf_0s$?w4NoA2Ni
zONy`@ta%z>3N06?tTpzZ`C?G$Yy#nb_MJZA9c2hruKh!ho#2mO_y?k3RrC$B?Hik<
z-at5prJPlrie6%SoXh4M#p)dDGN8FGXdX>o;Y7duAm9^B{$LTZF9yBnRR4X?teP6p
zJDlB3`I<%r`O9y~78N`v`fX0TI5^aSK1YYrc<CqZvNYcyGL7kj(hOM`L6g!%ML24W
zszbF}Q!Kh5#{3Dqi2Wfv;K<RVN3nSD$Q=X8#Nfb%7f+D3hVW^DQ;%FzEDE_8^UcDr
z(t#v;4fG0Xor+U%_simdV++4Ys`NO-?Pw)$XB2eo^A($7S8wCV&S@|#H8eCROy;%D
zhPwFggZxs9ygN|O<uewLc7}LbB!wVqfeE~o!Ohh+!m+pKgURg>!)F`4%T5X@&s{>k
zEnqo*0soH{-Vy4)#Kq5&(yre>BOeyAB}GeXbtP8tTsf^q8$%gWW#L1(6?<tOG0(GQ
z41yHg3l<Mx=F|=okLKIES2ywRg0{hdmqAnj+Jc_NS;}Ifze<<dvofo9qKC(9(~ZIq
zAjkG18|ev)b4buZwb}em?%EIjUGRD^80CsU+odDw7e*Adlb}*Lm$85dloGZ)_ey+)
zH&W!M0i#(%s6>KRF7%J|Img0Q2g&S->Qb_*rh|jS=w<G!PX#|*I|3;ieJ-l35k1`J
z492-iH;B_iI-rD}yz%GVAO9Nf%`o0<$)H(Da2s@KWd;#(<OsBrP{G*RrW<ut2i>lk
zdeP$dNA&!x0H?xna37*N7cqA`%=}%+%fs@J=i2OkyI%}(iNb93_LEA4foCpOt0o}n
z2go<Ek&9>rRXpkF>x+jnrfhHt$=AqE=UhCRin{WcAg@IY4-YF|sJSHHAF{U`G{&z8
zSc5HM5aJ6`!av)fXZ&R(-HdH-pKl0%-nFB6huBL6tQ)lDKvraG#!Fhh6`tOggxFfe
z*W&0sf=MPizDAH|5gQHqVI!o@r-w5??nOqilVFTf)ULTC+pnSah=^W({8TA>(nSZ4
zOkz;)G`1e!#*|e<zxlfXHR)Gm5XR!6x^pjV^{BWl4H<z;A&fad5roVenIFGDlt4zg
zvC~`BBUhO$z9MKA8b)Fs{`H$TZy5f>ediw6lyg|+h<Hq+`taXa)n8?Txx-WpFSiE3
z<@sX0Y5!*_bOX1Zu*USuV<1z)pQ))SbdRVZiVIAC-*gN_+H}SP{>!V^**)mRzWoWt
zbvKk!%uhmrPf*WcVXvDO<k!p9qdUK@iYB&dsGShDM1(}p%#NU^aA8HA&yXRx^M41f
zPksi(zjft(0m2DkL5Wq4S6CPJ0Mgqfu}~lj%`?^SUBa(EslWv6UJHa=(Z-V}`&baF
zZ#>w-wYVjHBKT~ZL#<FCUn=TUUXp%K=57lN%VxD6sI5CD53wJp8Q$YtCK|W=hqWJ_
zK%aDLdaPHk)+|nzK(@$Egau6-GR)Ac#`^4EnZh+FSQ-=Fz(9}0VgxD<U#iU#qg<x#
zF+ZQ)en_f(Gwg==RgOvKZo;s<HS29q8e^wa`rg_TcNa-0p{l9By%u;lf0*))v9sdX
z>hKEMLmaVla>`)rR1^REN6;HtDBiVxeR!%L*A)(B>VHA|^MAR|<{z54@3-SH>#H7Y
z*(VJU5)xc2x8B648^SVz!2QtK;<}Ya^E|IqR(^i|Vwq{0?!!#+&RnQKcTkA2v9XWP
ze_c*Kx1B;{08^9B*r0Z2bCSrJt5;i-aIL2tUj&(6a;Xdvk}6(F_}2bvoX?b;eWUUr
zZG4SPTNHb@u$jBN`wZLPY-3Y}+T{%*XF$b<<U6KZ<qT0|OnD8z9VFIRIj2L#p`tzO
zNpwN}vqV*nzx};({&#u-U2Es_pgs9})+i7$XGua6U4Q@|(WtC<kew0VMX1II2fK5(
z%zw)sx)%;Iz&Wp;3-HgTzkfDA{u@o8D26`EhAISg)_@n^1;~zPe|O>|Raiz|o<>><
zTxv}mMn(+i!B4bmO}&WdhtvzvcvKWri+UTEGfcpclUcSjrkoiBE%`|1DkQpdYfXM@
zfgMTWz-Ra`4~XmJ8%`3O!NY@EqLEi=_+%pU0x&#9r19~Qks1*B&D@}98wb~w_aHd!
zYvx8<5t*Yh-~|>39UY=r;pO!91wS#AK!fj`RbMv((9#m<S<u-KGmJRo$Ga7zQJZvT
z>FigORQ1gQJfXtMCXK&`CP1j+5Z{E-sGWm@*43+5QP~cf0QVdKggEE3f|QY4pJhTs
z{wmZV%sX?O`!s?-2zUyPpyLdHT0_+6N$;s%u%<%l*^dw^@X9T{y>SZ*3-5tu$s+lp
zG6iYu(CME5G(}2LkpjGQpGH_cfSK7pbo}>nJqzyP8j3)#v~k+dgJGo7Z@jYwj~1Bo
zxP*pp?IhHd1dX4wjEmeV3Y0v@+Q^dtd95lsxaR=e%652xA;3g}e{~;L8tOuoC<Rc5
zBP77-NC|fGCb?gv)fJ_1pF!B9GLF{|@;?^8FYMGSqB!pbJgOzIv!L?{bb6u5l8`z>
zgJ8`l=@klEtn}xwC`2G9BY|;4R3l=uwV~Goz)e)2cD`W)!1*Nt8_5rd^rZ<UC2iSv
zt_`<#=j^#q;cFi3Nc&3v@Qr*KPxom&r;r%m2|rwFgyiv%M%CW6VP4jMar!%pSvI>Q
z#Q*I6M$`|24cr>dn!Wk+6gYfAVk>{?=i&(hI{;-(y#I*0c=tdKDEa_yfft66J_b`{
zgH|N)D$7<DPB3&Z6H9~uU^G!fQHfA%6se`<3T+Eyf89es<zHxa;!mIWr8uyED8HcK
zCkiP5b8kr)c+(m7a%7;~8q?^%vQv9z(2T&erVRAB6uH~rl@r%nkmRqm+s|(&)V_l?
zH~aN)E`X*upmL&5`?@98<>%!=i^Vb2@Q$bq;GY1Qa!<lQ&WqE_)0GCLKCWmzQgXSI
z&3{e+PH_;xeS2BzBQ{bt2mhD`?hm@Dm)`EyeocP}05n=G2xtaI#lsfW%5-pYYCyIo
zv{%8rY2Mw_gPf0`J(N-AVOIP>k{o3$K+lUM(7vn%wI6!Q0&oP4;60FQw+ESy^p0(?
zeOm}OX0_On<1z{bI3#OaM^~4@vTOi}s9ABl5J3O0jA#|#mu8sKD{zO^z#B;zK=*5T
zUFps<Ap#-=6b)69eZFzPd^;2R<>R39jtY$GNFc{x?C!>`(m<S^p042ag&rWr&ZO9w
z7`NuLJLYjX4UOugO=Y+xY0RnPnyOM0BNK<vG<bS>S$|&mcD8VjntH%w=ZEA)hho+I
zkfpJ)F=3CyQF#AaTXQv;dZ~+#;n)OhP9S?Ju$&MCL_&mRIWcnirayRlOHRV4!qE0_
z=UlHwmP?7QkFEoU4Y_4mg?a07o?^r^hAg9AlYitHRkDIei_0u1;DHzW7QB3^8r)+E
zgZG%LTL$}oU=mq*Q&7Zzd9;HHDrX~BGX5Db3gx0t7E57T$iI*&;XAov<L>wW3RgHf
z=J&1lHFD*Gb@&Ao>=$C?GKlU+DPYh*Y8aA;`!a)0s8Ar_MoU|1szAHcl&62-3X@7l
zqCe`pa;B<JO-x+C;czO;--JNeS78n!8kxn)=03vul>5vre-7Rg-@A;gi}yC|um+kE
z#JExCLY=r%ClCRU$bjWB3i6$|H^Z3(0&*)D7jYo<fqodj>Db34%A^^z5c)Pan6|Pg
zP+`IG6hw$oECLZIR1~M90vx$(nlJYL4^7<0H^fgN5QUZ~1VH3t1B9gA2nI%dkFm#w
z867mC-DN(k%<k^)*%YaOgOih!i2fA|TAuNMB>}>c!p!Hb$T%|uRG?1El`a~Wgv6{*
zzrP7S<6k5D{A=gI{!^1tNG_)2B1JCOhS)gKmCL}b>i>Mn_Z9hMxOaLdWEJN8^N56h
zjssc{|4kd_`O_H~{>PBBQUGv~Ehem6Uy9C)@!ZdY`~$wb_akzO8ms9p6wJa-A)!Z$
zLsJh_A5l{l1uEr>S!;*@y?OIk8iH$qWm!PS5Hv8tcfEHByfVxXEZz|UVaYEXqfTy&
zR}vaSz^<gEWU&lH0tD<%vJ_;>KsET4N%mWu{stdxi&^7w2leNG(SlIj*#Lav->?ig
zz^q5k7S#ox=1Y0in*n;Yo-Ur)^7YScZ6qL;AC;#v=H|AUsAk&$5&nfi4`^>|dj?O|
zj^j__S;T2_x9$I%oi6`?z*o74zgevR)N|JU|0Ejz?|#e&H|E8X?l#2A+dwUkx}i}i
ziBhBs9BkkQf*-OY92-9>%T-J(%-e7AETLwiYSon)-Sl?yIGjWh-=3~dh10&ARp`+~
zBunsl5K_1n4$RJa;jwMI5%CV><&_LQ_fP9~48~HHt&s~5d5yo)mK$l74KcEajusm6
z(`w){{>M9Ib%V?PSC2;nbl0KYV{m#PCLXb1AkcxKK(O;4-gW_rX$EEfzIb$inp6=1
z<a#h1Km#(EVE8QmzOhp~Q9WJi>+Utt`BVa(%aT}!8hEgH<Pr;hU)XkmyUY3R02}~G
z={$b7>*p<?$4t3L{|i(ReKP+%<}U^CI3QX6XaAc2fqm}VZ9|$`qi%*?E{L&#BzmSd
z;cynNZ+v0;ykO}+@K$?5c3<%jsf_i4*fTu~-SJob&9Zb4A%4g}OG^vrCKnYEo}tI6
zFECK>k`&a$0REySb}6`7me)-zrM@%Fm}-xGub)E(*&TY%k&3)zLPTeMDLcOr6I7mf
z5_jCfjZhuD$3b_zW|p|A{ULkz*!z_Hl*KX?-=)_cj*FhnP8hb*+_XB-QPpvCN?3m&
ztK2>}53KLAS%Na1wk3Fd^Kl-(&g>mxUmsD8rvMb|(1ub|2}-P*!v-#C(Sko^tq_uD
zbq;5}1&;F9eH!7}iItnD8Fi|9@a8{PC_9GRaceP2zsf>q`~U7EAh5UQu08>9VbH<S
zDn{*vi7GFwEPJbah`<R1zdOLa=ZZROpWOm&B^6!<l^|l*2-Q44>;z~<`*jPKY|A%{
zmt3>n5XSD@<DJkU0=PP!OGlqc?wohuQ*jtQ2x#h9{QNkHfjc&Yj+?;Fc9Y5$g5P`p
zYy(I3C_sMzi$DP32|DC(#(WbQCd7Gz^!Cy}?(L}v6b!W^_4l^64iM8`@=O++LEiAW
zXj386ZDz+i-+G*Q0GuiW%909551^UCc@Yw|BIN1&ygB46C%HxOZhk1o^$$fN^oGTQ
zLBL3=2(+F_12|OxKbY65t@QK1Y`#xPH^A0cA0vAh06db}K|JrqX+A{i88ow9fX)(R
zN&%6%e6Z{vb$&GxOLJ-!rXNpl6P}1VFBZ7?4SL|Ri9JZdCk9Np?~=7|({e6>^9azv
zfR^CTVa~l#RW=_FNMZCDHS#p5!C(-m$7xej3%pTHNrI}&(`4~Iz2FhyjylZaeIfA#
zgm#kM)zo=~(KJwNsfj@<O5RiN>1SjfrlzLuhR>ZB<F&;I(D&VyiUA~nn(`3&14TEy
z)dKnQK*Etk&47ujYp?tLy<s7eOl=r>`E^SZzl4TbWmEqKTU-CI;QLk74Yr!DYh{jK
z5+2*V%I))u{XcTnSR_ad>5R>#ZYaP)Sv0P^Uci#{=04cGs<FXWCa6t|D>S13mZFRp
zOCd!@Ru)-Y$QBp@wcz!pH(`|_{wP8M9VQ1WKA*dxz%qiA89O_>TJXqUrMU*LL5*A?
zVt&7@%3@%_%%Y#EP-7ve+Uqx-fyJW}BoszpM}RtonP@!t|Mj@h2b%C0?YIr*w;*P5
zk1Di;b4rjk8FsJcB5Fc`O$$sQ@-k;JPO0%ghv@YJ=e`&TWXqHWHx=AEe;%m0hk$X{
z#B-T7JG(Uc8BF$HEd;@P<9=V2OaJZTUsdl22v~v>kXabSDwL>IWw)wYfr%Aoyn>FA
zK&OaGpr@P#-8qM11!t{54^b2tNDg382i9;noS?!oDFdg--vAQ8FB~$uUt_oH8SasS
zu2s9x4MP%JnAx&so=lVnwiB8S33m4OUs1m)uv;pm_O7mIAkwUWH^IqDW_vGC=jXpr
zZzCE^0pbX&)JY%qKlh@5x{=_CXrIxLb?Pl)qDpS4^8k2=IyjRk=3=ZIRO7N}f`JS|
z7LmNXyg6S=*+O60GMUCURbh1z7jP704n_tPRw?bifC%DwP<st1Z38MQikL+K9%11#
zz-SegvnPQYlEA_((}tiGvqFppCd}57WpwIDhEZWE6z@bUB-y?<D-SH3oToM_(dxl6
z#DrP_2n1-lnMEQji^2&+omc1-4N%m3(yRIS!`RE&m4bEP?41SM=Lv2z#^J2e9HLZ2
zX3=mA>el>olhn4v8^IH_@$pAQ-hj#05*3XJdoZCPQV4Zp#=-eNOmwJ^+3reAF=TQ&
zlNh7|5=)5*1x(;Tih^wp?59}`$gz&R15ibj93@f8SV3$G^$`b0L9PXxcuze@7&G`G
z`}}TG7e36wnc(~DXHd5(<N;(mg3eX5!4TEu8)ylD1FC!?KoTOCtV0Jf;~=w-do=Wc
zIu%;6xDC&!=@pNTl=SWCA)^Rw)0uq(Ry(rTO_g(2j)2XXWQv;<3$I%1C4YT^PH$A=
zo=l6%E7T2j5p-GSLe8M1YHfJX@&IBKkc&X&^lwS3DQ0F;(5+Jn{iWKozj<^6(S}Zx
z8G`PeI<V}V1oab|0eA7Tv(3+Udj_Ev(>OGh$oq`jH;rsHu#mPELB|7X<{6UB^1TQ?
z*=B07SD!cI8VG8zu5#eq060+=#}VVlOsu2Aczl4LNYtW%L`G_O7$&%#i}e`+=n;W^
zoh#e2QVt$Q^v(-$r#?{R%dy~>37^XxxSF`6i`oYcWdV~T4dg2@hBES;pvgYmrDr5|
z^$an0Q{A<<+Z%ko^rw)6TP)jjt$xe557lfNer0_jR4(Sgu20&J_x%xXck^cT+o#x4
znJrRSi9duzBzL5A;CHs-KbgC}|AqX+iH=P-*Zz9Fb5eZSCvb0}N$<M53<qnQgn&l6
zKT+X$2wz#8a`n4oCdDIgWJ?p^Kp=u|$>TQWxn$gk)4CRveas-|3_j0q-hTV`?a@~I
z_wP3>^DmMZg}tbrvC9b4OWI8&#oEY=+<#e7p>{Dw1}mhpm?SX@2IIL4Ho;y~(5W}<
z69`>e&z?L<f(AFEz`#Ihe)an|-l~TPK79PR4P^3s@MGP(d$&!5=A{ySJYdNK+X8oT
z&CsxW5F)9}{k1Yp>z_qMjYCUDEPv%COUu_qVLbp-O^Q_|B+dZKgnD1zfWxxm5C=AL
z#>0m{z`noulUARqT%7hBu*K|88;k0C`}Xbn@i{uZ6`IUU?8SpSPBgl7q-gDcGs%o1
zBO?uq+){j3puza6fk8CbbyKGLN=iDFF!Oe!)N!c1C9q5;Q{Y(nx+GWma4H-Rbx2XM
zxEVK9#&SM;?wntWGMmL9fu;7<*RNmwIKK*Df+e2>ukQ|q=d2y|u!n#?J3oJoQWmg%
z^X4RAc1NMN=-8J<uv;8VvsW%QLhUa2*DuvWI&d7I2J2sU0HN16xhD8r-hJ1=^u<M=
z`H^OI=<UpqmX`LI?*G-w+WMV-mT8Ql=es&rOj}k;V%B>WzfU80wi*aOK^@WdXGR)O
zz%m9IdI8g)_+(X@*TPLbMK?9J>q+~`1pdn7L21gj`h&GjK%4kW?RZ5yQl^Ql^(pW!
zj)`*?L+%TT8q$vS;-AIFI#i{(+&PK4ND{7MW*qtHk*UwOb=Vf@j<1hy(uz9fY4?+C
za8>>seID%WcnMD$Io?v{RxDg{KX$Au_l*(J$YqwnAb}VpfdxbC(OzMUn$Z`tkP6Yv
zq$D*-orGuQ<>gBYlMV^tj~+!x0Q+2-5?(KS|0Osj-hKRd9Ym!QL>p^sO?&_6=S)o#
zB+?+jIx~3GDh=ySWoZ>U_sLjP@kd5QrN4Uh8|-bDc9`120nA$?EoC#X%)KPw7%}2?
ziD1sn?GVa4OWsLL*U##J_O!8pqFJ3WI3i9B*5(o@D7x3Lk9;WL!CVSmD|0IZs{1I3
zi{NjsdRz%o$H5~<F5J6!PZ789Uh4zMW(Tp|CEi)S&eX>E9K#G@Y0L9>7<w+oEicvi
z<PC)?wr5zyk*LFW-C<y3xACg=#f&rk+<$a{E($+rbetcKb<uoh7-Iv=!!$$OR_I-|
z`>k8I2&A^iGl9_4BK+9Mz`%f37a4S^$5E~!UP&JhdF+6^AIzk~c{N_|<8NBiz}=#G
zB}0{8pt1=bM*<7B-)OBFMX!msWdSUE8kz9!?b{}Zp&m<fcA?^`rh4i5pK!cu)<|H#
zeEBlv#jD0^*a|~<<Hk#TL_`Avytz$~)O*8tlbjov{u$cWvDZ`&!=t7}nFShPvDhQ!
zJU(jkK4pPyKe*AT^O-uo{r0;Tjch_YEGO5IQV_c9<hf6uK7neK<(_*JgK_5DXxlI7
zH9LsizGFu^Ed4RiwK8VMqQjubIwU3KeQ+4OUqiP}c?{NU31ALI;a>wyE$e5NWlE4>
za||Ofr;}wv=|&3i+MqOE5DZb*QDx^rrJo9V5@DuF#JPISrNUvqGIGr4?i?#<E-`%l
z_U$itVsOaZMAy|T?YRUM`S8jyojo$2uMV`@=i9F=Gx4t>DHFy1*oaY+O?>W*2~K6f
zA>RA1HK@gb8w~C)yca%CRK@=~Y+xsuVK~zJaGL$aE?OB&`{m1*KL`oA%#}TOumO7|
zFtAKhQ`4}a>QF^|Cc4kbu3QZ;k~US`=9TrDxPDE~nSiSY-&8yLe+1}_LElspjAR--
z<3VZZmdO_wd;3i4VXSop3(h^*hh2hh;beF+_j4WrF*xI}-3nJk<el5V{F=PHynL>Q
z4-=gJf=8hDB?OwhAWk4Xz(l;={nT!2CS)n&hvPI_79^jMny32?9XZklE5dkbsL=1I
zVeNbhMAP^Aq4ZdjGJgZ8t|GqNY=+azEO*}b_xFGH?3o1C4|<rc>FEvYUBKke_^;~@
zgB1@OP#gam;AH7NqpPv-7Vv59!y1+c612uOcrpDy{Mb|>dg$cItN1no&N~2FQs*;S
z3($0^g?DpvGbXqDK@B<lA$`1R*Zr5DKcC@7cTbQ>*JnvdNo75~&U?woL4?}ATQFn=
zd~TZfAGdDZE-Y;Se15c5W2>aLuB+<@Imga^J>%7T60AH9$>e8jcd2hKe1PG{v-y4C
z`yK1fPlvI3#mgs{=i!dQIITsiE<b?fg>8Yb+|pnF1yWS#&J(}k=b)SJ<2KxM|0nC4
z>3$uNhxhK=Cwt@1wb&N$sb>{Z0*yFqT2MpiRmdJcY}zyrp2=(I@QV6;8=kfd*)VtP
zQk~h#D^N5J9XOEMI5-cw2Hf#Gz^(aTRWolZ2@v&@CLe@_$yL)q1kH2qQyijF>zoT?
zNu85k@NI1DW_iiV<MjqjAW=IlEb(BRwmQIWP69P&9Q1blYbdGY{Q~^`O9jPFzlFBv
zUsP1c>EpG)Q|Uk;>j2WHeo)V3@7}$0z&kZMOk$=yGCbpgp!P+>;wTpjhb|T=j?X~K
zxpD=Mb_5Ux2lwwk3*EM+c0x_2(`a^~%09Sv?{9GYM?2tDa=+Vd-u3)V$YyucjNq3K
z;XvfQ3yN&ljT3QlmNlEto;mZPJOqq$B`EkL&CPjWuZi4SWU=J^*l^zC)^+RFjY1!&
zV!%>f5zj;Uo%dftsRvO(&n$?YkMh<;=Lx7J(!dV?@ZrNN#>R2?`FH(!UjstWz^SbF
z?`5LMJ^;YB?cQxn%SZPDPNqC)eMouz+7`$vF&Ckx1a#-H24rAsg$m1R?%K8MYfa51
zZSAm3<Dyr=!5Fk`?yMJR0d>2r#G7_*;98LIpAnNd=-8i!x|^Kr%7dAV`etdv<sX3L
z3f_^udQ=X4UvU^7j7xlBFr~k*T@|SQ(|%kb`m>ajj$G_T_|pYlY+rM8a_VPcaD4MK
zpSKwHpl0KwKnY~@GpvD;Y`8lHe~XlMjCy=^E`$XoZ#d<l=#p*{+CFq}YOY*?`;H2C
zf5T$*aFf?Of6HI`NU)=^k=M4H_eaDJcn42UPg`rX$k~`o*=9^4QS_4PUneTT_de@3
zu69Jj!01B-1DB~1Qe930JI4heS=t~vxkCax`18+dsNw-9^0R~l^2m=ukf?^Md||aj
zNI*u?hD0$&DKB5MX3aKH(F|C9sNnIe0%`YmSf1;J#2gbuPWxo<DqM<u_RR7*b!aG>
znwy(zGUftP2`cPcG68q^mtW3<1x?@0Ehp2goZKHU)#eFJ%LH)H3d=dZ`2F|a+df!m
zK(f+COBn95|HDd2PbLIUCrX(~>l+<?$bPWj-ar@|g<}yT0niE1acD3qO0M(5(G7W<
zhK7cgpvOSh#=zM*)6LyI5|4j%qw@a2qen09*|W#f6gOVw3&*Oa0Z(*&AZB91(aOeV
z91euKY;B#oUU2vI!`hyY?I9-#h9p-NK}c2{cY549+6D%d6!7KdR52ev=C`u69D$hA
zMluUB*~epSCO$rJ>r|f4%uP5C@l_QQkN+_xB?W!r#*G`h3!GvD;g}jyF{N*%r281Q
zn}90>?@A^fj~AvA6IHXIsltJnpzH^_`!!?Z_en`f=LXPp+bUY4wc9JPZ=X7}W0h;g
z_BnOsr0C5ut)Xx)27C0i%a^~^9*{$bF&|HqFY%g;xq0)Z2c*LTB-AfqSW7kPn)FhJ
zz90mmBF0@wu`Xq4i;IhfwKnhxn4@>lmavtF>hiToiPu3@RWrEr;rBhSUhRd4w1qYc
z{JZB}orNxD$up=#gt~qNuyGSG<dEPk3wt~lr*8n)sDt;I`F?g17D!b9FnLiBX~V>n
z?Gu#z=HA9>+k}!p(~fN;Lc>HI<ZFP))+I2u&!VH#YHH47uho9*%C(U{xB@v`$BA6p
z<<FU|lNtw$^Sk%&pY=JD;P8WO;^HpPRe`)bbm)*Y1((i&=R>K>qAXF`>LCbVLD7_P
zfC}5UZL{U9u>HUz5!(cL1B02`i#mO~*TpuZ_LX>#gZV=j4(lDH(WEMmL$3V&&K+5g
zkxMCW-i!my5FwEPD>LN<O89_!-ZwQ}+I4@AxVZRP4UKWAg{8AtK-!F#Qf_`|^PSiT
zo5T<5x_YlJN4Y-m5-56%JbCr%l>`>Yn7Rg)M9+t)BL+9cVA&d>4b)qd3$-?6=e8^}
zRhTs`hz*gt`OCG?*>m-9*c(<kEF!v3`@3rZWy7~^-)<F{1zb`f7`+Z3JEjeVHWX{{
zNHVKipHqGF!w4KH94P@c%A4l#C@C{^oKbpX4nV*cwG*({aG6h!y->y#D6}&5AXjt)
zo5@r;dbA;<6V}iY#Lr_`VOjfUumE(ap7(Ez0Ba+^74@9lN*D767=@tUGYK|Or`Mn=
z)E`H|v|ktno+OhZk5N3drey(^fkg5PL=ZR;*&iK1gMas925dxuDq)~e?+l$daRTpQ
z<Zx8}3*ZIeBOGABti^&@lZDP-R-mwG7yyhT|37-U`k$uGD9lH)B`8g{OoXv+S=KQc
zh2bXc)|HJ9jg&=33OGJmD2pS|)d32HGTCGXQLL-BPM9EeIB^yVMyyIp!ITUG&V^<2
zp;WqF=^d^xR}mP6!JfC>-(Yfoy7!0o&3Vs#&U4Ot&Qq}`CFS^cS`6TH7zVX(+_!ds
zT!b958;s--um5u4JP{)p7Ufhvd_6*JyyI}r%1ts(w%LLII&kt4&Qkz)DeX@FXd3Q$
z&=GWWJ0kc9CehH`#lH4QuCa8G-;wi&@m1`Hex3zS&&gh(EH&$B*8yMu9wEUIpT#X+
z96<@PWYf}2-6-$Y=(qA{%+i~$mQ}&871pdi(rq65#Wp{JI0{B>zi8sF&6eB9EgFR%
zr>CX;Ff^3(?n?|XPCTfZ?TE16o2aj^ACq+V+fx8cEW~MxempNPk9MKUC)B)3&v$~u
zTXnaX+|Hk>R{kmJ93i&85kta7Y-9P-<4+N31?tJFMR5#f)4*6iE-t>bIq1V<AzhFf
z+j;cLuNvoTeYo-pJcZ`n#+xI~G?NZ~NV_1{cK>$+G<T!wZHJXgSuy9y?7|u?*JpOM
z4k$h0A)?lU1amWTlR{*U+$VlKH)nvn5M7{Vv7@4f`MnjcmYWp0!WtSJyx7%_v<%K0
zZR1`&LFxVc{WpY$ZW7$PR&*9xT~6}?WV*RyeP1F8b}G|n<;#zN+NilEs>wee7|OBk
z;uC8($9Zjt2*Xjm^3_TS^PS>Y9E@5w%7_dPAI30CwB*Kt7YC(9&N~-1ij@l={42mL
zvnyj}db;3zYwMqAZB*+x@%+70hjG#MK9g6PsNP!nq@bp@*2G)}uy0X4ps15gP3_1J
zlXUJ%+TkdKw0Kf-@+sDm+bzWifq|+pu66dhb&3@z>yWR=wi=dV%6Mmtz*NH7FiYK#
z>rum)Oz>_QF&@ZW&jR3tgAr9%SECRDpTn7+ey_X(Fm<w_VO%yF5D*|lje}JPjE97W
zW_|zh+xeQ?`KdyoP(y`<h3R^FM3{t6!D-@~N~HpTz)EZZ5>QMzrfMh^ST3R;*Y;+{
zrC6}Xt$odQyPZUq%wD9Aq}WpT;Nj{bJZWJ40(#I2hqZB+@5=Hg;u8|0_f92(Z(X0*
zLkzw%*0cL{5YT%|bEeFN93I?s9@1kiA=&8$(SRNvfTw$ZjS0kk-tFuh%brB`OAL!K
zKbdg9pc40wKObW{A)GUh@e^XN`5ded4XG4B6zU>n%N{-W<{jR}e0_a^_Cl#tM^V%Z
z!NK8x;_>keeU>3<W2A^AR*hyciuAuBH-XW9j63*@f>k07K7^h=ZF~D>%%P`}e4u?-
z8`$K_P*wPhSN>O1%eiw!ZH%@N-PF`>2Te=Snpk(_$cYhYx<sPIN>@;b5*teeFm6HA
zH(d7`Fja@@1e?u1%~xEuSS-5@SsQ|aS|K4)fZJ5wK3WM5^0_IJSSs-CCfRe|DL@~e
qzx_P1n7n@qJkqnw)1|KeFS+`xT>pJhCZjBaybx?p-qsN>`T9SjKTL7}

literal 28407
zcmeFa2UL@3w=W(<K}U)+Hl&VY1Uo8CI*wzB2v|X>DqVVqP#qO!R6v7((t;wr2?$6D
zpeRW19fC-&0YV56LhgRT%(>tHth4S~|9j83&b{Xx)_fm`N#6H;%HI1|_T!(IHP7?@
zAov3YgW<(p`0XkNvndvX;Vj$C1-~hONKS%pTzA#a{|4XTYxzu(FMQi#f8mxB1|t%H
z{>KqKJZFl*{EWf<cKW(|)Ks76i(Z#IELKOY?IL00h0Tsvq+gu-?Znk*oWK08|Drm-
z?)0;(c8^kocj#FScm@9y*G$oRe8sAydOYjrVez}gt(!M%A3S^H=)j>LM)bDr_+fb0
zhA!+g4Odq)AK$l6ADXy1y>b)pQW6~<nysoGrFxW_XA?<EZthy~UDmRk?7Otlo?h6<
z{z-ti(SrTM<884V=!XNp`k^Po#BSygMBn{>`9FU_p`tQJcbs-=ykVvrUhwHY0|SF}
z8!^4(Dh2g-R#UZ8ldm7b>5m>aBrPe}Nef!aMvBDzICwVxD$dT%E-{hgDd&cTQzuTH
zdinbGYfY^O4sWg|n{>ao#1RGt29)T%PVnl>pXwDUQTK8q%yM3t)`Yp>71THsr{7-_
zzx47M_Ey&2vx0%=0?aqQ?-{BJY3b+)$KJ}bzI88GH^BU}`;~O<O%}07k9=f5zuJ!*
zq|tL@&TejGf8ViJjXh|WA7XgXFaEQ<pf5T`O;IMYj)#Y5=svgTR}r_~=X$mU`w#16
z)N*hOKWp$=%!%|~yMO=wk(=*+&L`uo!j2h4?2$4Xnzr-xB(JpRC#0sPc4n~$sppNu
z&R9w@wccySiKsWacrGrhYqW$?!f;&dGS76$IQwLmY15mt1&iHQ)a3?W6%vVLqq+;d
z^|qc&M%cHpiHYhv0U}{J^$Le~8#rGvYl!I!qh{R9`X$G_K{KxDD^4Q%1;vM6RIoCU
zN~LA`Dtk`n>WF&Heyoj@%bc#<fIiB)rs9U5%4``YwMxt9<atpK6W1P_{7X@%vW||D
zMk1`PW_CC&4Frt7xTc9SGBP?8deODtn_e5OC<hDj)(87LyCe;J+l|@FAkTF0kXm@7
zwX9Ry4W0csO_k*#ml@(Ihr2<FGas;3b#nc^d4IjqWy6917Z+DHo;HE4it(H&Aba!4
z4?V+H+2r@N|Kp2dJVst&tBw^-5i&9|XfrvLqMlfs52L3)Z~7*>e?J=ow?v6|xq2mD
z9)$i!-)Z9%iWY%~mlyU;{$1*bFym_AxqVI}wR`52C>{--^AR1zW-rcfh=hqatT^@V
z4D6L1my@sSvRtCemlnQLj!J07M49DueeA;QcXngVRIdEFT_T~$!r5LH^X?c2o+nrL
zD6YAuNAbIg<XS~Zlg0bB68XxK>p5MurK>fiPu*kOiU&pFf?9s83&l;dT=2WBQ(nuk
zlCk6I&d%c2DHmm4+20;GJyF%z_oy=VWj96EV6^Am7KTLV7SqZk=1I%;3UY9I!z@MC
zlOSW%*|m7dUE}J()hC|@7=#KZ)5_SVE38%A$-NHIcBzH*iz!9U_+3_WH9WBf8^=x*
z&-M)MZg(^!;z{K03~k^1l&TS7#bZ1y9l?f@{FWTQX&qC?lp<#FltA6y4teLBE*+0>
zrn*fh1l3g|q+^aLa2?N{sPEUrSFc?y$XaEo`_CLfzpK_Xx)9HLsHw#XAr8-jq6A^6
zN`fftK>YaeBi8D7>Jy`n#b_9Eb?fp=Xz4#vh$l@i!VI4)F5O-zc=%*;ONig}&77_y
zYaXu&H?r(Go!hfbYns!Zt}6egc*$0}*kQACqf>)&_`toc%<=630ZjYNBJUlCt6R10
z3{+cm4uxuWU^<0MDh0~lTUr@jSy*V%3jT18dQD3?6x-!VA@#UUsoyvhI+fF)vX5Nq
zwLA}NAP^sPG~<?-r&w$fguN`cZl2H=%Bsin@T3@js~;<J;rqb3uF2k&Z;iKTc;)iC
z^vTKP4~)|H_k-rTOn9y(T*7H-X%U9zD#eT_D*63Bo`fL{JN&htpiOA)b+&J5G)}sH
z=#`9>HetxKpFvij^YihI6i#NS6N-nA+FADewbK<iR(yNi2Lt5?9&UH6mmf`Y>R0Sv
zuD^aLwAZ;s<CUtfk5%%M{pw*R^-=PBL`CaQbBny_bL%Ii|H~$A6KqV?POtVPQ)Alu
zg#ynJhDKFc9-5~H+$iFn?YGQ_XUqAGCOaX9nAOG8(!vW{G!=(?of_(*<eg6XuC3M*
zRejSLiCV-MEwAs}C!<?Ngtww+cz0AtheP33jy>yUzFoDDb#0WqRJbADa@UcY&mL{v
zJ<?@Ptb4jg%5f^Ia^$6P&@GA(Pu}2PJMTvP_;gU^qv4wH{4jc_uCtuj*C5(8MP=Sj
zTN$Z?>P4*hF@eyu>ehj{CqW!fZDSdQ(kFAr1g`m;$48hyhF@8q?Rb=Y!)%v`alp!+
zSB<NtO(*=GE9)?w{Zm+}EiE_IVKqUH>K(Vl4Eo9Z^K$L~dA9!VPe6<5-=psNFEWzr
z<=xffw^x&$@q$*#CX*5T*1;Ej4eg`PjNUl3v*Q<Q?z}t-?fzo-A>V}l>Jd)sqK#t?
zZ%Rv5*RxhuUiWwDnDGVdZN`n{>nClx-)BFN=5BFMnc7x1mY^9OalOWHGVn{=+bhnS
zZ|^`6yZPFZg0JhZC$FPlgh*&<#+T00XM~cQX1|D8@N?j!Zj$gWT-@A>%7MApCmRw=
z{|ec)k9Qt^9CW2B<<b#_4adVqV?2-5UDRqiAubzfXw17Fq;<lX5oDdr#MH6PWft1$
zx}gID*&WLjDG(f$JRJY7N3UddNJ+nUA(^}`y0ty_b-8w?p~1pxXxc9L+27y4`9hZ!
z$;i(?D5dRQGH*Dk-cyj5Lr@_}JJmGFa8aTEW?M==FK2hT`F6C}F#pWz`@hI4{O`v^
z3+X>JZ2d8#sTKS1e~lQd;_7dtwmHsMN!T@(NW{3`U7KoHvrg}^Em<CcTHda8aHKvu
zvZHuJMo31I{Y2?~AGS8q``nD`RTxv!f`RQePj(%xeslJT<8*Gv2$jh4!h3qkO+?Y~
z-VYXg%!%IZ1?gSZK^|T?UA;-m=p!)JTLBpu0t^IP5q&Q1d5EUMK?$OlBfOE?dQEE>
zfJ@B-ewCLV)DhFgsV*U}`YqeVha#*p_ROt)d9r)-eT$}g{M9OeMaRGUm`6FBLnt5+
zuf+cFo@|F)t_7J6{Sc_Z%K2555~Cb1uB@zFO}?JZm?<)!=`9!sc=PGe)*Sb_?|Fe9
zEgEwBapbumx3`Ij=AW4<Z4R{SY>4^PviEuuAlTH5!b7<BA~WjeKey{;s=>%a_Q;O>
zzG*Arb7~|To*3Tk-8cpVon}Z<z-2=i%Wxx};giSW90Bp<n$|KXrK$+Xy|-*R>AkX$
z7sU=Uw~GbjtkhJgwNf~Ofos=(fIX9a=E2H~7YM71`K2~BFLjK(T?j*OLo_eOU)|%<
zPh1_6SRVCV8(pRQvfL6by&^CddnD~VdwS+qI!b+N|3W}#2gTJSjY^cND8Eow7U*;d
z97I4sTjzP&gzgSkhb{-RmV&WWKb{vpF|x+5vUJ<ZbME_>TgC1UJ}WImvys|Y@}p5s
z=L}R8a1*qZq#Ca%r+Rt$g~l^-_KSKKY${DG6D2r$GB3sxqTR>^Q$N>z92(C)gNciT
zy8P-<uXM>|@j&?Vz8-*(gqTkq%ViHuk|1|{6$kvYu(PCI?rKig)WQxw2Q+ND>0#LM
z>1h!M;%@FyC^BzxthDRdX%K|%%Q&n`_6mQ5QKyFcE{DsH#XdM`TKxh_+cf=xlWx5Z
z=hxOKEEPK0)2?K7SkfGpigC~~l<7h4{j;;P?eud2Z7IYXugf3%8d?<Azixrxe|1UM
zE&7Z(o9VcIDB0RnwM9-)MG7;0!ghnR8$BXm{*yxE<h9HWy`D%R(-LYl1&h;En)`f(
zt_YWJFR*ETi<6)Kx=+fu;;Gj{oLC*KBkE9ymKp2_F`9ndqnUfTg*2wCqg5oobpTu#
zl5V9;q(;XtPn|NH1Xy_$lU}J-JY}+0@aWO5-3M{=@a~`w4d1@qCdg#roeY_)oyNh#
ze@et$0Dxe~TIsNx_gI8-TiChrbOE(q(Fs6upkQoKVc~oWan1QNBV5vmtjq0b)zqgS
zCZedkr85X<1SP#C>aY7#p(x&5b`zKa-PO=1*8~ISsP%2{cy$5M-V4n(YH>^IIQbgo
zRfLmy(t8Vi5;C<@hr<^*^R^C-nEz?nlCU2a<FhiJYdF4oS4)|8onS&C(aC99KJy_&
zEY7ShBKOu_MVt?d?yFigR%5lky`NNQ_{G3tm<(JM^<AB#%{Vcp^CfWPg#<a9`BMOg
zIt)E+2t)9keM8jZZ?6MHFV3sl{miDojt4!B#27%?Vbz|7t)iFENiH*D!osyK9R}f8
zj~YF4cGckOe0XW??5w@v++!UR*e{(!RIF9lR*$`pDMgf$NS9;iHvH1-K5S{k&DXn?
zNQGY9FW2XMZKQ!n1kwsyMcA)?(!4&3ho65m_5q(RMYl$d>~0K@=OJ3^aY1uTimxgT
zMa5uwA`n=^1odN85KD^NBll(K<{sL*`&ezI>e?K(3aFZw;iap?LaV3NiHc2wBO04S
zWbA%(pD7qM1JH#a7+koto!OG8)gYlV_a{xX$3hzRqm+`8()Z=?qk3TgGUclLj}A7i
z6CA@SsTqO}L~nYZ9PlloRQ>h976GJAhzV@Zf`w-FoZBCNKcfHPc!LVV+N}2FbK_uT
z*-BxjS~HU6w^C+L>Hb?e56}DhvR*Q@UY*=;1nVfre&AED4`bS6>~%o)y|$F862^=N
zbJB<y7o>0~)3a|9BBZQ`Ci_}?sTXs)tX=UxS(mUem~huW8H1I5(B)hwIMW0Ea}j(0
zMfl7A?Z^BLNHz?ntlfszSkrtXx^dj{-IkQLi-(f2xCz7(mnR6O=v*D2UO0lm$OgjN
zC`(#feP$pv`(kBN^t^Azbb|`F8y?8Z$cmqY*^Bo-gdzt17BkpUPN$EEHF#Z1O#CRG
zqU}RxB%jM^`uOTyGM>RW>3KTQ90dvMhK&}FllgU=SOyQGW4NsISKKUI@kr7rbFKg1
ze+G01(@kw{p=2^yQ_J}1cJWJN3p;KX>8wB|s5AL6wyjJ6kH<Hs?I>wK9QtOI`<&dJ
zPY=HvwWOw|;7IVd2M|4fk={;|QdTS4+&K6f@co5F`5C_TxDas{{15L8Qm-HCf}~!}
z*F2>;xXjM^fWfXF^-SN|*m!HZgyxYo8Pb)>pJHD>Ko!@x<SL1A?w+(@bj*h1n$`wi
z2pY1W8|bFDk6Wghc4eEw@2xu`>da!i7rWjVU(Y_IxKo5fpr))$P4J-5A56>-(YZjk
z!7WPg^D_pvDU2<T5*r$CcTIX3L)Cie)yepl7EA?4zwy)Ub!JYfjDdG3X#Mc6?=Ta%
z!1PHfpLH-u4T2eG1v>IR(^pE|0_x{!!U7G&TlTV-%71oM;oshs$8D9Mgc%<nM;s)i
zsTfVIfN8(rf~GBOjpeCOotYGH(Yd<mW2fl-zP^VQ$i6J**~;!1mkAH@ONac{%HF(`
zq84@)>t|E9wQ25=P4!qSb*ew#dmZ>y1dg{PZuPgmqB3&Md<!3Yu>TdRzTN9S^!&W8
zov2%bGCj?S1*~LWAuFp=RGpx-*lGMq%3KrF3L+G+<}4<?p?__<-|Y77+tGtW{6zA?
z!h%UDbJ40VuiuA}Tfh8?DDO1#^i*Z&VV#lc!<n^3WLgFgi=x2UH$a|?4E9-wD;Nw?
zJm>8-8Ob`sulxdDk3K=&*VosO(cKYL$bx(ujua21Wr%A;ypSV}Ysj-!snk_!L%z#Y
zokwvbRY_7W?}+M3Q?MgqM(gZ+oLsw1!_hqwy%(zjR2E(%+I8QtS6K%KgJ>t{clnhU
zE~`*qH4HsF!&mhd7jn{NqIGpZZ0%0TVs0kd197571!6_fcX`>uUq#^VQjgx7bh*en
zeYZZB<w4c8!Q4fdB2Pycr6GijP|>Y<-Fti)xLU!$3f=s+Ng6H47ojgL)`C_JFx$Dd
zrK~lE6Aas^*xG;?s-?U}75i~e4<u1R(_5~PD|rt5woAG<5GBgr*O17gsmff)T`Zl>
z>p`exSc<rO$+loPEcg55$Wu~5u-mnmQ7E2Z)0vW}zTfV1&zEuX523<nZ-6jiGP6_#
zK-i!H!)EG(v3D)WJ_zV?MWdDG@b*G)038KuE0a!4TE}Q6YbBGKZs&biBHFnb-_R5k
z+^0GF1N-6g)WeN@mK8WCU$isyU+oK4I$2&`ZnKAiE|PXDr~S_?Nm{ws+Hq9pC^S*y
zkA3*|yUPZ9!jHi)fhBpGUOrh+t&I9{@_pT|rF3HAgOGx2k(ixg9|%vVXwUDA<n|dm
zBc>~%QhGgIyN0-0MP!rfFGqWt#fl=&u(j6PMGNCvz6$U(#;E=`xqbU*(0O1#lrDXd
zoS)Ds1&9a85s~ic#z@kVj;uSP`##B~2S9MMwQR?Vl-o>iZUr9J-~4NS?*ll5TJ(LE
zhdoy3DzBxsHCsrZgfLh%Q+W8ihzpd|1D_rl`+tSnUJ^oR?M@y2=ke;{APl|jvK|=-
z5HqB#IgQq9=;-JiD_Q#9K4<`B+6=4&M5tFqY+kv~?{&L&)HW?kjy`8QL5;%ZnAKJD
z`^-L2S{aMmBP?u9`rYHp<DCU)W<hY1xRvwv3$I-7yY{R|shJD5BwD*ZEaLQ1B7gvd
z0w-vA&|-a{G#p(T47TfIt<eF#YNrX%q9W(BdJe7!m7e&Rn3z?-o%NvBL?I{xRd!!r
zUl)W5DuuO1NoQu1tW3BBrE8~7_K8*{sxrs1RjtX_sEud%he7nK67`&VJ~J}|cy574
z8-~?X3wQMl6|y*_WIXjC8!;zzJXnl=;=yYPKxaNJKtUoJJz9EYPfJUPT*+Ku!DL3!
zyuV!WUK~J@Rcx@z%9vSTUq)q7R)Z6Sw~T_3h+>$=T7WpON=i!LYVhBs7$tdbF)w>t
z*VMZ9zr6s3;|ap}vDdivVuGi)tZQ$R*TgmAJsTsZIvEs<3>UM*<8{*$ii#97eQ2rF
z2V$!~Cq}#K5@c%r(0BN<(^#cmgW1{FydD7a)M3u*Y>TGz$PCv$mk#Z>t6wC2vThBw
z-plnG6zab~@ETV)5_AasZlW=@I%TdJX;P<96-PSp?Us3TXg`D0-}BAG>l>$-1*v&C
z9%beyk|iH#bl7M<txkcmuE~`>$J*}6h%q`V30S})!B2v6Ve79hB&q^<_E{PL?Wi43
zhm^fsty9wcxNo<i`#{oY-&L`q>7Use`a1*sKxqCx`EP!l1d|8tzlqktu4&TkUwSH4
z{}n=2bANyTN=r{qBoq`7Z-E|>I4|t*+hixmNl)F$Zlp;AA(`N7n)0Tt41T5Avi8LW
z-!<wLx`@rx)nGScCFVj)!MESHI>M?q2h^wuk=Cjh7ELjWNzuv{-^S0&3)~0|?Fzjf
zvE{Gl&-J+hyiQm1hEVskP<%0LrPC$=NGx{tP`Fbm;wDU6gE_?FNQV7s4u--UR=K+O
za=W?26m^BYzZ^Eqx<YktOEr|4V8X~qnbm$pEK>jKh+N!^Vs~e}D8+xJcT}YVe}m?q
z)OHUNVAqsG$LM$>(blH)oh;RY@6<QH_Ut_m9z1v@ZP|>c_n^W0wv(L|t;1za>V{ko
z?BoFGEqjl}>rpJU67^KpRU&S5INYe@%AAK)&g)4HyX4?L*LC+sY^;G>Uv4flhsZnv
z^~nR4_19E0xmFa|CYTUYV9?ao0rilB0?i%bxs}Z*JRjPQ9<Wn$?tXdPI!Qw$+o`R)
zj&u$=$X0+}-Q2wfn!n=6nxtV)L1}5}*Qh+{b$5ttxrG%?9I4)2-7$bW$H$+JK{kX@
zgRPyI%lstxDe_J0pva_$VYUfQJMHFk?DB|Q=_^PDG6<c59phTMb_c#~b~?YGoab<!
z`aS;Q+MF26q19U_UAIlw#~9$J(CWRh^u8tc9cW(OZPEZmKbHg%@d|x<a<T?R11x*q
zsdaL@qkAqKD;#^%>(=i*NwRZyKZSP8B*mCi)o0PY>@GLr;WMY(BAIr-SOs>j1>$k?
zZF;lnsh=W`knPKP3&s;7&-Kz_XPa0p6I!%Xy+bN{yVt!S@17@UM7DRYecD~}4AQS_
zD-%WNyu9F3-~WgLalZg^U=G*-+NXhX;N;{)`G5YAq}QF=3xx+4`+ZrUT&gyxvb?Nn
z5daSm#aqmPz>2LpQ7Ie(<%?bWscC$%5LKyk%2d+I29+HJ!2L-<?8bsC2CiW4IQM0@
zIS?`;)W9F*7W>_#N2A>gtr$shLDf*I5`hwIFBbI}dR|QNrqrO{U|$`-^Q)%(@Cyx5
zJF9){ZCG|LLfY~wPzA5B%X7iLQ9!!HgL(tIO<7qP0#CgzLyM@Q9B$xxYm#CFn>m|O
z!br%>l!SP@9&%qPxZLAYS^5?QWi;ANJqn{z4N6jH&Yba}c=ktDg-GOAc$Ev0pD9v`
zA!EqxE(QWY&NIzbnH=a5<B7-5KM(f8;^u3m8ixMZ7xWbvw`<AQucm9~)Ep}s0=BgV
z$d-B~YVDQL(kxa<ocz?g^7$BgaUzW#?7KRME^lRs)(_I>+nGq@b?tSi<W*s{YqC?S
zpN4(Y+1=?*dg<Eg>DpFD<7e+C#^CSSei-n-6s_Q*e#uV6JjTPUWIi<WsXL2Laq1aL
zj+tJ=hkH&M7Ku_Slp3fbbyklCY2&cMBz@+d2zxIy*@(Cu(b0D}JIVXg`DVa9hck;$
z!t*j@Cwlc1|Jo2AQE`GIfIQHw)WQzZF@Qw^RM4B{#kEwHO`t3kV<#g}qS0r7Bg1TE
zVSG7GbtTSg?uk(?Y=oEsr_nr@5*TYS)>5StFnyyaLZb8q&XMA|K)c$0W?%m>l$$kc
z3`&3diJm#MvS*<7MEPo&Iy#D01Pp5nB&~fxr+$H_sX_jN4yXc!r0E*VbP*in_j*;<
zDg;tadB#+ZQyB;M`<MVk&Y(0%1y{l-36_y~a8_;#lpJP>5+6s9HwDOD1<=)2J2KZ4
z&Bd7ce?iKzn*F8ArDrRXOl~Cb1vB-GW25fROo9-m(-?JvI>n=un>Hp;_I0fxm!-%M
zUS*uETp}0v688tI=sNRMCutu})^0D(OCwu+4qoa>$X^{-l%kHEVJmp4ZBtNwz;$e9
zf$vwcgM1c}=0PPX(|dEurS5vR^o?(U(9n2`MOkbsptKLOZ^_W@dZIbza?t)$?0Eh!
z&bB{}W^b$n`ZMgvEdnBgk>w))^E>v7oG)i#`mANCh>!E7%K3(Xb1B`ae{0D~TjmJj
zNC{C8*~Vw`j>w?bm$_elyNC!n@bXwA3F8Fapuo|zWcHCH(6|cYZxM;QKY>j&_C#~?
zE7_;-e()(w+k|(_t(4K1zU0+<<@~U@D&tm48;#$k_C)3TJUb{0erjQpd$F9|qOlYR
z5&GV=HXH#>q+GB4nIMQa&Mq$LZY<b;iMdDD4QfxtZ1@|)^&N3mp5$VWeI@hDn?M=0
zwP8U8d5`YQe!Q_~dE8?{cEu1s5sb+@99oU1`_4u3sX*?Wj#-CIBF&2!CU`LN^E{ET
z(%F`(lkXwBK0FtDI2T?Vyr$bj!!zAD1K;haY;3%2<q0eJAK&c_j9*@!oCyxV#od*6
z<$PBEczsCvS-DJ9mK)Fb4m{eOm)Fmnam)8qSii&txWpETsAnkeRv9tdj1XwC;?iyS
zNjSgPQGKK?G8~H{K#=^%OIIy!^d>((OHaQT2x=wFX*&tr-$u3h8c8kK{nJi0NArh_
zp>WYvxAg+Q6?shVD0@n7PqrC+e_JW$>vgPn_LJ9AxmZ0gwev{vu<Bzq=f+4vDiQXa
z%G#g6c9^LJ>;Q(>ab-Ml^uu6KO`-}d*^3(4uTN1yN><52yre1A9mizYM!|{#FzWE|
z@wws!h0=A?&7jhNWx3<V5jBab!w^<c30JW6{RtIn-cf`?NrnwjzJ=+KCT;+Vl$2-7
zOFhqTG-TF0sS_y+(iPe`SC22S4>no<c$RZvE08PMz-*glk`}vvDn~2kok-y@x7!ef
z^_>8W%q2z5$fz10PahDi^?vK=f4Gf3!ax1x?ELj_$<%@&*qBK4!GriBF6IjvCg0&7
zrrR{!AWSzsuc~q@yDY&Bo`DI1utS8$`;h|R4rPEGlPW^H4I*`$A>_;?voF+m8dfC8
zCY4;%`H-$xzZk8aT^lE^9kg>V7jpcvQ|E0_(ez&{{fxq*_(KT7Rwax+%7~4fryiP1
zga@=hyJ=ltz7{z_o5rwU7hQ%m9f4hL3if#+wl*6O9ksr4ZCaZ!(O8IOT2_qgnq!l_
zzp?MbA~lG6Y<DKZ3*exey*T@K@2^#IE~MN@)$M6#XAF#hs)A^&QSl7S=jsSwJ$rrR
zDFYr{{>bn148c1|-{qQ?{(d54>QMs<UI*0~s~x_~4ty}<8@K99s5JFGhC}t-E3CB&
zE6JvVP)G50n~yigvt5e={JtMt7*F!iJoz;py_JncfIt)gK(4JX5EFnGgH1mEz6nQQ
ztv0Y~fkY^3SApv%x7`zJrQW`ltE@(+(c-mJZ5A8?L|ktol_;<F4xmLS)xM_ZlOq7c
znN>X9;{i)N24zN&9lOV<eO}bMS8{gbBnGlNW_r|F<#k4(G3YD--3H|~M9`lEU!lJJ
z$4$!*RF@tAy^v^_X$~I^_FAos;`!?9sUk+3?@wQcsjo=UPF=1wR|x?^<NffjAD}p3
zyF6);k^hFxQ^=(r6iqxyWkf~Kk<Im6_p^~6*^{CHaB0^u{g=S@<<3mb%+w=qWL&U{
z!g(=oXKwSV#f)i=l{Zh02}-rQy11B5v?h01Ws>FrddosmwW#R?jQDk`E2qUm+OZiY
zF$}_%CQyhmP&Y-nkv%+SKXOaik&A0%ls!Z0eJl2u55cbONf|+NTedh3QpyE3aIyMg
z-Wl;;Sq&9j(<;MUMI#X`BB>~tkdr}Su_F3<XSlMrJ?1br-v7mZJ8thLYc;iB#<DE#
z?iJ0dOMZDX*DG`|*+NjydQSYt?>m*!ckU26GB@+kuR`c?@0XTGe)dOrD_UL%Z58;*
zvAhBsSvONl;rB_yb@Adlwala#?yEAj`c4LvCVeMrlvQSd3{+vLcz{AIiac2vEJj4;
zwLXfETX#=Sj%{C|Fw}R9;x(rHuSE-&&_wcRfH;UFD6P(xCu^r_=6sB8J<d7=P$dkc
z9_%Y+&&4OUc9xdo%6J6J&$zO8fM7X5;}Gm*dwNZLxM2hmY+PbC8>ZdaumB-8$G9>G
zTlH|u_7(jRq<#}nLH+kT&*<stWkAfqR;jY6s-t6Z!S(RUd63Q|k?IxW{{2q{t<4X<
zo!`t|?awPjz#<eE5gEzQAAz#uA_36^jNCv}+$_VccnMPcNgEp*<S0A|PZ9<1hdqAQ
z9OP3JTNoto+LtHx$XeSo`^OtpV%JtYXZ9Qjm6qr`E5}j%Fd-q~`XN)gOKeF*L_~FI
z0)5Vd>y$m;XP=TxuLM<hef)U5{Pg=4<QxP!TeS+6K>*>8br`rA5QZSYGz7|#UxsLb
z8$IOY^=Y>(FW8J%Q`@*4!?0D5h~62|C&$Jv%=g6hgg3>Vm%tSk7DABAY0#Phnroa?
z3{#S(mwyaaoH>%n4`+P%W#74{2V}2-<?rlngH{TD*HPUUK=fH<iq+hIH`h2ip}c{|
z63RQYjk^L{^AcAsb!lKc+uPg2q(l2UyfKef6o9{bT$;e2_u$s;Bug8cqb#zw7;w<|
z;4+n^a@Eyxq^ymPjeUg}=7@MVs6!2HfnP*SE0p`!909!!fx4JXPvSYvU>-9@+Xo6u
zwR!@UA(CtK(sbzSbLO!et6tzsJVj#lbafra<HOCsS#N1&h3pP_Ha+Z5RySI?Dhl`>
zIHv>D(FWDqQYeI#W$-C4G#0PXG8-mg@pu8vg(jOVf2uB5X*ogxZrWGq!guVOpViCE
z0;8phwMS0J=<$h(iS+rBDeIO54bLW*^z?L8d`ek1zxA4ZAa)5#j`j?L$hpDF-LI0b
zCQr?69?deY1c$R#aPXko;kiwl-q}4?m252(9DTHr@2aBP%%$|Ju11n`I(B8`o&qQH
zxejNBe5;O|8$FhB$tbmL(Opf1JHTPEVz@%cuD0pCSTwJY>`+JPnu{Son|9A5{x6v`
zT%f2*a<@gkfNP24;0AgqCwubkwC248M3eRfN&p)IC}|k^<R{uv-L^Jz%3p&onAcXy
zw1|iZDREWcNS<w<F6GDD7gF2qeIf2XG+z5{<;-aTe4cd|;od;z?_4dODguvTP_<WO
zNJTxt3TnzW{UzRw-U)X1?)?-i^qI0+PgyV|=h^6qNZd*4rf3~5rF)jjJ##9y9;^sd
zC!`sbmh?Lv1pfwA;*fWpm7!E@j|w<h5ie>2uXtd_-q`LC&GC|yakuK&7yk0&>abZU
z!5CEFc^^YuEw&2y2bI_!AD|2I<^o69v)R}Q!G(%Y8IZs_?U_cUJx1@Xdq13z>-Tc1
zhdLWu1%VbAj+gkr>TT`EISQ{P5B<LN7_dsf^LjK*!)P_k57&fX!5&lLXX7^AolE-B
zV}7XW!QJ(6(;p<l2E%*7>Y*6~tCUAfMQI&T!oAlhf+5DbD|<gO&mfqF53bqPcAP^2
zhj1!5KM+1s4?8lhe4hn8Z{((}yP$Ybgf-MNtyCuoaa|FS&Zwp;GGKvwVc5!up^MA_
zeLk1o<>W|BPfst;2B=11<(G#-oEDnS^N+D(XCo0Y@M!v}!;-%86!%KSlMdfMqnm?G
z{1xodM0zQUiAcX}7`*rI-?w_|`zK|W-pqLt4Xdw>U+<}?GQKqbgzHo%;_s@#r;`oU
z(2-l&Ciy)Qno%!nBuk4M7}}|Az-bj*Q~=}vg&!5QOj|b?AkgdA{2JKx1{1=bJV+5v
z$f7K{_FZyudF{z=$@=eKo@~9uRSKnJ$GzNMkJ0D?;0>}tse%zR&7{yBfh6CLkO8WE
z7Z(@N#D$1!RKqL7!HAyUAhw}5Brh)y@U0OJb{Jy20kIs3j*i}=>f?nf3FJeNUt1ak
z9M0Ft9vKYxgI_rCHDO0zoiIK}DR|ER9yXCFKnqk%81|b(rGfs2m>(#ns)86`V9fc}
z2NlDe!=cLU42(BW5dA{LM2*NW@KGfukLO21<48#;Qb$PV*9?JoLJWr!FgN@Q>oBdg
z%8z>tFqQ<&7><ByEvOXgh|DhHv}?qlT{-5XPDF<O8r0|#apwZ7krzT3YRp$qji?2)
zZB%N}JnnvE+UH>%1D7_@20JB~PkrE2$ET)hA?LL;HJxZ2M0pc)S%OnERwwOdljrio
z?V4jwuMB)GW*<=xxw*QI_zT$zj5)AhmK4Il-^gN=vU)r{AQO?o4~Jf)3>$DRd~g`V
zOM*aS9b37M*EG45)bjK47Co3(&Wp5pdi3Pkkiy+~Tfj0z`Nz9MScNhyqUgF{=+if8
z*x~5`dw>+4Z`Z%pb0>n!Yh72KFe!UN5o0!q(G1D&?wp|v%atzIk;0+injzO99~EU7
zX)6DCaYU(;!o}Rb-6z)o|9Xcq%wD~;c0FpG81n$s6jLN0m_ttG^@`c$q;;&$DX@r<
zYtZ^3uPuUa2JOz(y8cxSRQXx=<fYSBc1PKN+TsCQU4DJ&j{g?5(ysco?HVhCY+U>{
z!7TB$>6pMQrVmnBeSN)(9iym}QM5WkS+j+rF59}VFqgifeyjkCoH$SlaO2sJ=>Nxw
zfLycMJLxo0&dx6|@z_1`jwZwo=#fChDnhgE>3ZRa_4vLsbIi>H&4)W8{zEUulZ~fx
z9ypkF>YUxMpl*5ZUiH%4;AEC1dWqmAHu0n09CFdM-7|`GwI+$>Yi3~$)9p6ZIq-)W
zdJ*4;JAE}$j+NigQf&^iIjWjW7qD03S=!Uk)7^a%Do3TIZx2eou%}kw6HG0AVPR-x
zGPB`eViW<9szlc6C^8W8F0elkBQOLn@|`jkQ-?3Y5NbF8rZxu5gs5R{dB&|Y(xxYm
zcR_spBOk)6jQ}yAG!Dk(lb(xH<VoMf9O5t(y%(*ltbqFr0f8|=H=S=`Wc@u@c#j0m
z$;qky)ZkOENxlARDDvh(7nHh|s5wnyq9kXT7#)TZ9<pnJa9|WFz}^>r3Wt7*b(4V@
z&ekW8CW$I9<Sl}QOu&NsvL4xmpkwL;LUJgIHLq_Jd%qq0jYW6}AFqPFr;Klv`gkMY
zf>=g%xEI0@s7Cc)b{l#9{MSZ^<rIuHf?g!H`dCxCs=h{5Wl14|ZSn*c8hp&Aw*6{$
z{Uyo>(<eYL$9BzAdy(Nwxq|)Iv9X-HVj+{xfUsCRs!YMbh@w;gPD0igi3#|t!<NOy
z*<~MXZjSRp2-}XdBqRY@!vfFmLB?^!gn{&DrK_i>9?XXDWgOSBYHrTXLt%QhRme*R
zsB6j<IS0caPAmDYB>G0ce7)#oqi@WEGFX(V%OR>&&GG6+&P>#_g6fo7#5yM=6h`6M
zCL+P={o(oq#7h+!=p<iv1XjlsIFSO7E9XJ_JOQg!bNk6wUU$DWGQFh3a~W}*$j}w#
zySBVX8NZ-rhwK;_|M#FzP4yN~&vvN719^P^Q^FDKqHKdIQlX^f<T~#B>R-sAx?_1~
zru$h`SORiaK$b)por5Z9qSa2LVjgrqC}M$Ag?RTI=|?z1k8ROWBTz0Ul=hx!^AqD>
zrW!G;_cGCi?J?8%>K@3m$VG~_9ZH=D%`Ss}5e~lCHiuvRQ#e#N=z5{)^ZPh}x&R3#
zbrCWEIb34=Uw_TyU=Q%$eUiKrFv2(jqKJ#46FB)^Kt_;8m%3~OQLzSw(`h(VM;r%M
zHKWl#1ya_@h*MwdUP(#7vhobU0uCaLdWxR7`x_|nYex~&tz2omc{{s#CYED;n9$21
zs-=}n&nijypgrREpckq;8pvMopR=2rIozWMBBPOHjA9~ajimD?fBeM$oNqtpPxAA~
z%E^raMO%gPlCVqb6_BCzP<~a=th_AFrq|Rs1(os6h%&@3!&0Wg2X}b<;{VG!mm=6I
zM4m;|vW$?d^;lq&{m<*phQ?UvGGtR8IHb%bHHaO0NLYVTi`$vL%t@!}gNY_U&KAs7
z2u&*Uoq4+MMdO`$6c;NAffH!HHV;96!X26@8aqs<>R<JMkz)Z8QF3*sF_$(LIc;PQ
z?Z0Q!>WMTmC&qquqPP78)yVrQsnT!nw2|nZurt9)^c`>~-s|&Vv01q;vtjWHOP_MW
z@akED@#eWq5%_CA|I4YXn)m8TuTf6;8LaCk!mSYbTe`b-;a4hu{|W{O{v%alRvNEU
z*f9=cap~W-kzoE_TmO&u;C_CG=PxT0fQ5bTslXM=3D=h9=BsSvaQzT8*j{R~;Ypo3
zQ0Vh5J~uCKSAPeT8%;D$Pg(rJ*ms5p#9Q{C?zJX;XDpWhNT!K$SZv$i{J`SgJwazX
z!2n)r!`2C#rP+bA9sN?py3Wipby$`+=e!O4{8zGxX9lfoZI6`@DTg@(1rGdh=fT+2
z)FluDNR^!$=g*%fo%h=dkkAxr>$CY;#y#Kg<omD3z!9RJJ{x|zEk!aiHU==wRfJ-S
zipEy%EB6$-Sj{oLjAUeFQ14zQT|kY~p6{RESN=DD`A=+J|EFGJ^9{?{zr6rxbN`1n
zZ~Qy<`~Oo1?f;&E@^AC>`$^W090k1V2m&df4xa=bqj+4yt{RytMn*=My8XzbfczG0
z8PU4?{XwAC>OezrTw5MRGQuRi1o4<{I!2^Qnj|=^Grgy+=pM(|5Bf1}V8a4X4pb0o
zmD=v*(hF~-YMZvD+*k&W3=#F{L%}>`n3)+DhdRSOnrM?9nGL9yAM8Z?kmd>bwcorq
zW8Hl)C=2cd!v=OaX#dV42}1%Xid1Q^FQC+j`eUB&I}bv0OqDsf$N<ubqn;4tC-0a{
zgBCayZ#-CKqQDS!0?|Z3f&d-Hc4mQxyt+E{FcsCJs8yd~(7>%Sdml9q@A)GD>2nzS
z%N9vaYW?`hbg)8H!9@^iGign_0=O3eQA7sApuNw}HwWg(lT?qY5o9=VoJdYI>;2$h
zX=|$q%+%NC`=kJ~K=&^Z2neL;3_h>d(BP%1uB&qD_g)^-O>N5tc+BQ4jGR58e5CyN
z6`6|OO>0Ig<hn5cw(WUGW7jOgQ-b1ZQ18ntx=vjnTzYx@b%UuU^ciBSkR|?hPajRc
zh2dz+h8BM3I*yO}Av{Jv+VvQ<5)(4?FSI6SULVM15*{k}U{U45{$ZchL9x*Q)zv#O
zUh`G&b909QXMROZ$WYlOTum+>a)tN}sJ)vqjT({=^78h@Yzh=O@rJyw?3o+{`8Si9
z*M&wrFP*Xo6qe6kEh*Vex#8!CGKZI{TeVtJ=0JJC?*0aPXov6ZSm{fCGi<W*Yj61O
z4Gs|1K_pt?ZS#b)8V&;<zCjo|Bev2g8g7tdra>8YnQTWK9cuHCLY^_8PbBwP0dov9
z(Mg#`jaF8M)CljFCB<Q3VfbLo8C@9umnV!L+Zx|AJr8+EGCempcd2BC^FV9ISX0~+
z%BUng=f~Okr-CvmOF+T8GN0J_g~tJ_Tpz6%GPNu%P*jo(3WDUMhBok{Xpx~kv0LB%
zG<56qjyfJadK9%ANI@4Uj29RaOrTsu&I)S0r1wF7)m3{`^g>}zp@Tsiz8+jD-#B)s
zJjVBcWE6LyB&r(2jp~UnVYleJwB2B<0I*m9Pv-ZV0u!<LW)k3l6IeaXL6TcW$_-E^
zX$D2IgO$M)H)SNdgEwe}<o)U4maamV$r9yfpYrVb4gV*%r;x9(L2v?~oXwt++$p_6
z-E?h8vN0~MuDg6S`e`{o*?*hv%rXXQPb-G98zZR$clZy?qU6S=JLUe<^ri+d`JwHF
z+Mq@NN7kJOrzdkQr}Zzy=mD-rbgqOl*BG$e`d)Kim}D~07h}IuVoG>0d{3Jjwkj$V
zJ<bqww?te{mbZJ8&F03-MzD?#tVPUdO}{RI?a9?=zLJn24%vPiFI9c}|0EObC`5E7
zMHj_sf9W<N;uS7M${q!d*SAZx-2v2SEKb(8cPYD_9Wah}!s@cMvYKS>Ed|g~*kFoy
zB&<9zi*yfWQN<`IN2*_e{^A-6Pyn++7Pg^*AI(tX1=wBy>5;yKBT#@blq~dSEKc7-
z2VD$-wV^Z71&A%gFN4b9hII2)sQ9P-K9A;Fbu6BnTl4wMmM*{D{x>K=oK6RIR2*Q2
zIciZs1wUYI?-;9PXwv%%0SpO^Q?AUdbIV{;s)E2f566QzW)4!(5e+ETB8|aKQOwcY
z+#Hdhph%ozx8fjSgWMm$2ARhX$kYaJ6K`N`8$hZrIuZrgNYvNkTIBux^PdH)^EHfO
zyIftQX%!X~xfQJ?C&p9*aT<bMMpC!F`@kcnEem=It5OYVDP$HzWON7(p!ar)H)>Ra
zC0#mX%pU1eozmC+Gznlr90D5ZD`2G-GC3wtNDhg}@^H?IBbca5(yo~U!twF_Hv6c@
ztqq751g|aa>>5xMhF+(5pgmzpH-K&7C9r!ENasEjTJ5vWH6FhyqYu~kw>Z6uCmBR=
z4I!f@GsbjvX%3wVfm*NnC((3bP6^1g%CpfEMnLJmNV5O$$3!##pS420dy~X{`!?jP
zL!d{|M2+Pl`KFWTn~%SRV_1Y;JMWy$Z<4>uPIop_omrNO*49ln(lYX9924VUvp={N
zp<?|q*#EK?y!#8C>U|?!ds2Br<Ilx47TenHxipeaA-ceGqrklDOs~h~ObFLtT>e7;
z@>k;MYIr?ITUg+!rni>|0Dju6#<?w4=r_pT5WOdqD{PT!^jG>>hWhH7vyfsQZ^ozy
z=t;JH5hG2e!IyU>#Fn5e1+WYhHU!Hir>2eo%1edRfwD2#7{goEf9?N6()>RTr2ms0
z$@QO`_4p@D7EdZ2vme-AdG+vPb<idpsSzn_3MtZ~C6bH;_rrB{buPE;l3$_Nh2cu|
z-`bd}lUa|5*XyZm<Vke82jHn;MnB6(h0)bu29h|pGb!GTy-5UW1t)e015Bt|U8aIf
z*$KRABR&0VuBb}|NoiHUpHBP*k!TzS|8kuo{~sFBfoV{BI|7sfVpe{S%suD=xLC37
z=13?gkOz6|R=#Z?hu%NDZA&8*grK;o6Tq{=IGdmU{{7HVI}de&3HUa+P5!>IQvy(k
z0mtnGlH+r>!IxMQ<=PD=;(=#~;+K}+`nQ1RzoikFA8@%ieEt!z(~Q$AWbeeyLuDiS
zj20HbMrGe_9|x|F=S5IZ5Vn-J&>Q$@XTn>+3s6ck&kfwyrKr_I_X?`6d^)HZZluF(
zggSeH?;k+{_+GC%3zbK@?l4fDZ3c1hnwL=~xHvlar)~lG0)EI&MTlb<#L~hgAHK_y
zyxU`;YvmPN@F}gCT1iOJO`mCtObgE;-;V-LdAOg&><0{q>{!aSiK2YUQ_rG~dpjZ`
z((3x;*ZGF6ZI7A46KV0e@k=Et%dNQa&e_N}J(v}@v4MbH{SCqL<83&vu8lOu&rO4<
z1P0!h@}nPD({T=b=etp{>aC;tGwM*TQHB!|NQ#WFHE5;q?aEwb#?1#G&PW3q>-4_!
z!S9IW+h*xCDiDDEKCShKkF<Mk#KfMKx#TCR=xR+c0i_#k6j0A%8v+JM%<VbrPmr8^
z5X>+wqNBYqQKk9iC2+bHiRA<4XE-b@OhIeo3wsdi6X0bq=g@zkPpahjK>*l4%IP1`
zAK`AJB{p#B!q@@U1K&Kv{SZ(G6tsSe>!dmDEvrEf0+<*_0L)MKJT?rDW^o)k6mG;X
z&FyD%IIsq3$qW(+xB)oo!Bd}mD&3%gb}O~5_|Hds%Y66(EjzyTwzLd3B){gEE;Isl
z%U#2~OuF01l9SeQyg?j1Ii&uTf8`wTQES(`Cyw@$--FYDYd|yDdhEl`xcO)(?9YON
zS)yDME~!NT8^E4_wPZC3;4TN$JCsp0Qw^mvUJEw|rze<!>1`a`U+Az3pVg(^ee{2*
z+<C_EM)lroUYO4;RQCb%C)5`o1^WMjM-Muw0Uaj@LNLC`1|4dKx^IvePF!0^<hCYB
z(n~yR<YV_-00D+BJOWAA=i5($t)Nzs8T%f>)<~kCQsY2`%<MpU2v$hW)|t=3LJdqv
z<4mPxuo0M!)<?U<{Bf?Swe<z+$45yt_KwfP*-dbwS`}&^LY-42k}|xt7Ft*w$sVKV
ztg>lp07gCY?~VB2R=Eg=@^$$Q5%-5STL=gWbMOW>{MgvIB+Q|4FvyRK>*AT{zq-*s
zuJ?rfyPm)Ke`D?X2E-{Ku73}=gLntNBy&<_?ER^gR?rM5PHnjiJ6o`y<(+D06i{KT
zn)m{07+?e1Jb7X{HaC_%v4&NK4EzKWFi@c8!YiYH`u&95?AD#{_96-a8W<;(`7W0g
zdr;@0|7$-Ebw;5sB`~=OSop&E5kx&Zk_>LXEz?H^Is>o5X7lTl-^BJ4|NqcYu7Eu=
z7thbZgL<RjSi6e^h;RyEzDS||rnUw3hcx%~MFXiNj1Hy{<v#EYEJ$@AYKLIKprsFv
zDQ&p*`-|vE4`AGJ1k@-14*<7`ag|tic3*yY%^q#I|AqcBVE*p){UgvS3Q&^;&e%%d
z)k@HD9fE66HyPr0La^v5Rlu3GSY!>(jNSsL8~}gVDs<T2Dr&hYtpVQyTFdC<7~mXp
zAWosgAwT{$gx!4#WF`2*x+>3aZuL~{e{Xq>%plWI1nV&J_3rNOUeVV942{n3N!QDt
zX^Sc&cM&@}cE@n5tPb#x!cL!|<{6-h8ht3pn@+G<Oko1!#dh)FfU@|?r1ujcg-N0$
z4P<I_cXtHzk{~>fct>4b-IZcHBZjRJ`}hwcqjz+LM1kDBhE!B<vRk=7?<++&TgW{j
z;Bj{l6BEI#hGBSl-#|7g>Sx+QK8DV<rH}mcsDm+>4kYBb!P-Y|v09i9(51RZvr&r%
z+x@h-;sk=@$P=U0yD4oGwjI!`+TsDmZz{VV6x{wmi4tM_rrQQNxhj2pI`0IS!bv~N
za8gK=?^1y8SHx_olwK-?gGZY|fqinU@Pd~=M|LUn(J1jR<Z4k?gtJnC-Y|pwQ2@v4
zI6<W_lDRgK>E*z~IR<&WemAThzzq~vZvX<oOdt*b{I9YgvpXQ~F^IvGJbhor42$MV
zlw6~DXO^z5ARo`36L&v934{IRNfN<yZs!mb{0y$z2*~fq%f5_yC0Ca!RqJ61NO>*I
zh8I&Ha%BU+N5m_Pp7${)=rWzov-T|kdml0|;|SCSRaJEG4~oz{Lb69-wL|!1&lox(
zsoN^EZ?NfQ*$h{W&stc1@&5ciS3Oi-?L4eK#0!3J+k6({z_ol=TAqSm2%ce1Md{aA
z)p#Q#q;sOCmP_+q&fO*6p6IBTA!vH20Z#-Xen--B>IKIFHl1nCU{Hnnmnw?E8QuWf
zs88s=FW9xwnU)Q3B5PEnZQ;R8d|N6y@<^p4ce11?6KXD0Bte(1d3)L|2K_eXw&W>p
zY3V=UHxfA1-ip}zi@-!8nx0fh=^-!XJq7la9yrTtgXS6ZPT+T#CCeHFI8yeln+S2G
zHE|xD599%zf1yCWCPMQR6m;moS#&I}8Jumz=EUZ%d}@>xSfmau^QVDJ+&3D#j5^1T
z>g6qf$*(65ou-0A>{RjCA4hNZi~P~iVGwMaas7~`rDfHo-TKcUTZ-e>N*8C84@^w1
zv6d!tw$|K8+F9rR<y$=2uh~R8x%%O~Hr>#ay0djJ1#P4?PW~P(Ac0+L`usVl={NDY
z=HH6nJ{P}d9i{(5SN-lKb*=|rvfj8K{(MvNj~_SQxkG1gtE#B}xXaZce3E)1M~cXd
zY8dsdzc26gt**-n+PgR@&Rk<d;M+3`8igJ!9DEhR_T0H`+t204O$7W4oyXsdjaeKu
zD$%#<$hdLy=F1v)xB04vw{pyI1bD)P`T2P>FoeV&#Kzv|i_Oly>I=QKrUL$N;^OLD
zdSPM)+L?Fm+=)=~$Sz?n>KPO|CmSxqJc!-6al;bKFWEN**Rlqj6>`n$1aUH@wH2Q~
zOY54esrf;-{E#*S^FGkQCl%ax7ElbQWMs5M(5j<CH$=ptLx-f@X7p(@eLZz#4*U3A
zVkRW{Q0&fKyJQPjcljmsDa4G{zKZMY>=dUIpl1>hnGRXx5mmYg4D-8`)Kr7D2jJ|m
z`<a8+856)S7S=M}3MQE$3MKPCC-<QP2QGjyy92gw@8daWNz%Vshj-3rfSGtdZr}MJ
zXAc%^in_P$P|_$(Z}1a;|H#3o==0~_XyYyC`@N`v+1c67V~u-F&COGdN_`?u82k4)
zU_P{WH|Vr`a}|$*FJ;7VD;RoghTB_T$~z^bX{VwS5fq3P8!%;&7t}aK)TVj9cJI;h
zzw@%RRaD8nF<k%_{S^Qd?>*@=3Q|$kH8nI?($~=46l~4M4v&sTI_TTjbHy%qe>znf
z{pD`cR>|U@ak{zpLglyPm*<CV>)voRzHj#_fyVK0!do)QGvq1POd~k(_T!+srHy$^
zszZNagrZvp_8^e#p?aK{v81KRZ5E@CA3t`UXg#=zi|gLEvil>;4<ByCH3Q$@xAHaZ
zjsSYRi|Xnwi&M9#UaqcrZxhTdckI}4fxyKpIOHIPk<LheO7#boOC1g$(l#}XPf1Bx
z0Gy=_zL?ole1879?Nl0g(!2BRh|5{4uq*N`m}=*Tp3JR+8OsWMHph%+Rzp_63K+j(
z!-fS22dN)EbXwVPVq$k~v51YXsjYp1)re6lsDD~+NWvfd<(FT-eEFi_HWQ_n1>tTV
z4t=tRZAXSdwr;v0$ntUYc3XX5U3c^A@|TI&m_`*jT74=oZ-}XT%n^I%D9wu`^QVkW
zw=@`_&w!Yfk8L|rCuQ4virCKY%#}MRFD>0VEW^A1xfta9bA#GCIz?UuRQ`LBbqP?n
zULb&GICVN0<S>4?I}s;^=<ZyraBLf+yzw}X22sU;umBF-!kwA0sRU2i$1YCyB&MVs
zf-d|U&=SxDvDze8SBNABi;!?#S9iiv4kP{MXdu;p$L`&SWn|jCO7FED>MQnW89ap%
zY$r5ybUzHePR+Rs)<rx_Z76~8X<o>0cWcln6s;eMD{lPK75erqcDgI4a`>={3OPLw
z9G!-6^g?xQt+jp5T?}SsBf3#Vmx&iJk8QbDI<Srt4LznWRX%;nb3<FZaHkWDqvI)I
z1^hx1#MG_0W+=@gi@`?hLs(<!hGL52;0Al-qcziSX8p<+a0Z-SF8~)O&(AxOzyK;*
zxuNl07`}uY^SQgwB^3m*l$S3B2n~MW3)4M1SL<^6LiH-|Oeby=9890H%+<}cX!-@$
z3`M>~=4Sw7VMbPR7&WRloS#LwXZCab%M)>McjsrKG!uh_<==p!DZ7UX3T^21oP;TW
z38~OtdiLBoSr4P@*I!_h-@UtRYHDg@X1p_&WTig1>Iz$^4GIq#=dsHgFOQ#YYinD9
zBRObbaI=71@Hi-F@szk<q3*=;MZ%t05(g*O1;QHCAt}Yhy-`0-UsMmSIbmF30p;8Z
zEKrDxohA&J?cGwb_gz!oy!n-zPxyGh+`<**fbu(Gl-`1t^bhyziqwI0;B!L%M!^01
z;5B+5B&wY9{rg>55Pj)JK2{xXLVJyVz^Dy&cb_VS-cz0TK1}?rBfFI0z>t$XXMB8o
zF5+;{u_QPkJtZk=_JFwGihIchk@J~`q}FsjIb1V38L>J&x#4cs7%Z<atWD>;kLk2H
z4valF+9cKW^<h{Dn0jK~3(sp@`q!Y6xj=vj-b-%7SHCzol2tTWvB&@Q(2xnP8G^er
z=mO_5Gt<)2LT++iHqsLs%@yVgfMzV!3j{()$WBpF+b~{%<7$ADu2@<ogN`(IMIS?(
z^V+1qSAP7(ix*g2ced#hxPWfKSy~_TU`}-9B%+6Yz|EiMivINkwCbX}vy20<<AB&~
zok#B4+rK*(EP51&rlaq%ve%NmRMri}$!d|NrY4j{u;Wnpw?Nl%9JKuVj#M1fOuPnL
zeFdUP0`LfOBi%W$C3aO7V3}};d>gbwz@F*2J&%V3@&iAA#~PE-GbGlb=2=eNId<x<
zv2op8vGe%VbfZ$kM~@z5_e_t-xE2|}k0aurk5(P$aoV*2L!n#jgHfyeEZC@~=saA#
zGg{v19Kjiyv}HWJ@7{gGC!+8coZ^R0oM=j%tU9Xy;WCp!?^vNt)(vT&dGqGY0+gop
z=F^z&qCAEAQpSv;O;`3=*+>m^(Aopq5`%Gg|NgT;aJ4}>^dc-w98NVLzULl0f@Zf8
zGR4lFJL5se9S2*ym9u=%-ZTCdP=Oq)G4f@)#3v-k>h@<C6q#gwP^DXc{^Lgo)hB)V
zIWXTepZjrjzpkylgp(0^w2_NT)`JP|w|$QuJ{*I4Q-H&53OF813i!W<?yQ3<Duzbh
zv{00`R+k(?U%W_yE%Nx!Kd=1y>+ir$eDpuBwy*p9smzQF*>JKuNO3y_1t;da(Y?Sj
z=`QsxZEkLUZ$I#BW<K}WC3NQjkQa{M`Mn}YBsDGV2yDNDl9F0FnT9tmUw+2LBM=J$
zspZ00(?ox%Z^+y|Ro;QRMP*v+wH26FO4|pFUww^zb`Bd9!T*(U-&}{!@bkCr*mY?N
zepWfXfo;$H=lwcX<iLRg&+5!V>dda$+S-!lqoRa!R)H^6Nl(pv-{y*l3;RIi&Qm)q
z@9_QMtINs`-+zniP>9y-ns)7)UKFp1)*KbdO-(K={t~j5Z&Czrz_<safCm2vM^1`H
zQoE>|Y&3}le^fJ9L(dZ{^2gJs)>&8^_4eUE^Saqak&LX3>&O)bLjvjp^eOP=^*g%s
zm~;ti&C?sui*B~bpGa&z1S#<J>C=Kr?(YD{WY*T!iif*@t*Sa9SFh;1R*W5oCxCF$
z^zGw=ojZ2C?d-gX;x;5mtK$_=*_h^ZNyFZw_5y2C_aQg)#fu#|#j&wvO2o*>No`;1
z{QP^9s-I|-extp6_ZEV#{30SE1(J<4o+#lGt?a2SS+exzT%ch5vuE2}rn_&zDKxDh
zv@Zbu^Gs*O<fRrD>jNHa1a7sp)Yo^@#*Im^G*;j|lh@FX(y~fM!#aDE-MJb4D^v_%
zjTMu=8Ac>#uC98;$H!ZOr=<Gp*UL9=-h9wOE^#1(x#A7TsjUU#a&kHa1qFEUp1z2V
zPJewSp#8};p}4T}foJ6dNlqCcxO~J%es@-&E%w+=f^?eXq;mMM`mI~H@X#1b1D8%4
z4pTG{cNmfwFc&S*el-y#dHC?PbLad|o;(S0?Je}eOrriG5<_{qv}l^}78>v@0DT-f
zd^pu<4R&my{lM>wGkvMRV4Q=S{b9=%%ah37={I7`1=Ce_6!zHyH1&MQ?dk($I=P={
z2T3-8Thv_*3S+YM-4OV#j^wgeDwur^A3LVG`<T9ks|e`!nc-d~(S5cZa12QjbQee$
ziE4p}t9S9zrL=Iiako(cEe=0T)D6~6^34BX>((2FhPL&EM-LvnqOBbU`|)tH*{eVZ
zQF41Ik9Hfv5Wa-c)7zlPRRbOwqTl%-;ilNr53soKV+PGo3{UjBK`UL=xbp;99!3hF
z&W4}cJ<|0HvTrf<3moI2c$Ky3Hpxqc)YsM31?e;FdWdk0oSk9LcNX-HHG>9TOP4HN
z%|2#O_;zCA9_);0C6B9r{`sfbG62^U0^uhrDRada3P@#u;Uf&FJ|+S7=P#2oQc|v7
zx$>M(_*5)HXM2><F8FrvCumiUy#|M~b?2A|gZJ4e8BVnLfrsbC9RbLdP}&H}JH9<B
zCFKI0bLs?H>&|c5wa_d-0VjiAb$9>3Q~r!dBtrP7fqg%s16pLy^OEhdZVD<b`ojiS
zhCUgyB5T=5P~k;IRJ?AUIB|mT7HVXuBpL@h;e?N3k777SGyd&c6eqK99+{_7TVTwf
zHn%-`^PSq&x_JrM6}F^P<QW#w!L@^Mbj}#?i`Oq*db&)?(@kFi7eW%0(a<fIvx*$o
zjT_jzdR2ATld}y<)TdZW3Ynx~Sn4=@5f1w?;0>dVHI-j76O(uR>>21>4zyS8tNMH@
zo!$XNN-cfQo;{*pjvYSS0@4RRt{G-Z2F?ZJ%9P?cV^=)q|LP|BgG1bTnmZ~_#Jpzj
zW5+=x$s!O4Ub<p%bqxKPAKXQBHNfHAJLO#a#l-<)U4#&l4M#>z0u~Rw9%7N6mS!#8
z4y~YqM}wa{33;oiYgHf*bWo4QvX;;McIr~3UCG>mMeul-i%P-)R!vrCe*Lv}qYDb0
z8eF9DcGvw95;ezph;8WKq$u<DN$-R2eqr;?>XMcSp%~fa_dx8?4-0toKdQUlpQf%b
ztV?u>aV(&qiHr{&Y%7#C16h$-w@{|-7Rl_&X2F(fBCwX0O%c{IglJ?PQU{H|C?%HJ
zZTx7vxl;L6<`xDOM;%IQT``bYSdj9oCAA<F_T2Jq**~!4eri9Qd+#~#bDrlp@B7|M
zNwPA5Koi~C)8m{=>1zhlRMcfrB3_70{@{o7ru$eT^vco|@9Cwkq9*s??*ju3?^j=~
zuV2mo>oQWPRTf6YP7jO7RNivXvJ<(gBV-h**so6--*V1SvWlH{w%GsF`M{9el)luZ
zKc7w9$z6F6P8UIRx^JGv@W_Mx7HyYWwc0e0L=fOJDNVSZAqzIE+f;1OXf*fEeA(X&
zRARBgHILkQnM4|PAz^*LG@y59Kfi68lvDqzgm-xrg!xcAv3kpMgCQESfHaO+GPx!?
z0E_zpx>S9C`!@4ZMa7*{H*5`Wtoj_;o$u}I>ptWKb#mlMB@%=5L~pvOSLUA=N8N(s
zr{I??{eAT8ieh?t`fx5|;**U6u>(@WJuDBdDB0#Xe@Z$VEhcvYO$#0j=S}j}D=g)O
zix;gfkPd15{8+&)z1eKu{s&}Phv2v8mP$X|M;?DYIr+H9<56mX6mf}(qf;gvTBy6%
zeMCzqfiAPyxlvxeg^mImrFM2UU;L=cbLDXmwnKu!+3z0j>y;9%ho4}RiKSBOImV(-
z5z2rHEqH^C_0ei3<m@mS(s{n_k@?yjr!0emv2;3*CV5oNF@`=yF)k6mZfrtIEvz_l
zLc)F5!ynDwN~yZKT4S?u2+(3%9Y&4j9VAB{6AIg+hbAVnF=P5Yma#w?;N0rM*1*QU
z_fT?bU1){Q$8pLzh;!?;w<p$|!~$7-Y4euRp*FK4uO_;+rG<%S-w_g0cXBVHyHYM6
zLD~~1BjdZt$;k-O$4sUi7Do&V!JmO=fK>vpz{O^>RpGP)2e`GhwJLha%~S>hZphl&
z+U{MuuH!ASly;#VO1Ck*4BSVRB&u6E4=y+3G_|kzs;a7Nkb+jPwUHJ_q3HOL1^%*M
z%eS7(`3Z+EJeZ##A&FxVQBek{kJsYkt)mix2jp$vM8o6897V%yJ`>B95JlGlv$^pP
z+f-8tl3YYp=a_XW?qIE2twU$x{?k%`d*SlpNgp;dt|#UR+R7x+iV6pWrWu^Gj^ax!
zC-|<pbt?DgG?=Jfzu&*iCtlU~5rUx<?f^2}85%k!ll6p!h3$@vq(J*FFMqD*8V~QG
z@@*r{j4U$`$2LTq5`%*7Nn<_}?ybN#m~;fhqDqs{1K2wVkVUb%C<Vj>ki5_m(r$0V
z<igPqq5QEDNMjdfKp9}9j-t6t-Y;KpVI)ZQU!gwRKy^<GW($s*>P@kVL?S^thlhqX
zW3olQ8GA~}Q(bCm>U>v_L@5&0`JtP2ahUY2Isc+j#Pj_JyZWF1y#8WM;OgCP=%qg5
OL0Ipmrx{WO7ybcJuXJqy

diff --git a/articles/AMR_files/figure-html/plot 4-1.png b/articles/AMR_files/figure-html/plot 4-1.png
index cb43e440db93c582107aac65e393662e62ecbc0f..d7d52242a7349b0cc860d85e9e08fee7ad8d75c4 100644
GIT binary patch
literal 78282
zcmdqJXHZpHv^9FPjbLa4jR{Zz10bk?1O>zZ9*`tKGNJ?l$w`pZ22c>tBN+u$P%@H}
zfQpC`l_-)_a&B^lHx}CWySM87xmEY8`|6#dTbh=0_FikQImaAh%zaN@R&qVv4mt{j
zvYvYG%moT%Wdw!t$D1{)@RP#ZEz$U6)n#ePGx!s)>C?Hc_+zcbIW=nvg~@~bv&^?+
zSf4`KO`)C<SGxYVzuDeNNqJ#$c(RP`kp%tOlV?^0a2IJUi=n?8p`>s|T$3UB$Ipvf
zcYg2X@O(B{Z)<z@B-fh19M_&Ty6L&5YQ-k`Q}i;zk+KGZLmOSPTh~$d@qWq+W_umx
zTYQ39u|lNFx}r%)VD!@yYbJNyZOfOgib8pFxx!-g@9!xGo}4@R$M65RCcb>f?{6>B
zZ~px6U(tBrMsAS2zTRBH_v<^#o4?p1mi_)dWB0Q4zrTI8a_!aM-?|<8@4mI$1*7+O
zH(BL2<4Zl?$swz78RaXNh)DC}$!4?6b%kr}&#_A!E-o8v&JESiFl$zCA7B(<KVm?k
zxLMp=V^5*<^E!;^t>1Z!tFoSHgGa1V%(J+peD#5E>54-wd9|+{tc7;@?7d&btetd`
z*;kTw?iA$>`(Be4$*VVo3>;4|H-5-uV@{R6T!uUN@oxL)lLtFTr^|TmXEz@`deqO)
zFEUCxu$N_V$|Etma|gfaH>Klt{cG&If8FnamAKu1Qk`e*_|2MB8#7Gl?GLb*JlJ!(
zEc~a->_Fy^4XY^@Yk#V475=aeo8ZD0@wfDuLihFh+uU2ryG9SPv!4w;W?JPf(9(S|
zUnA43q^xY6V%nX_iAM9#`TlR|aRUST1_mL90i%kVl#_y6$5fSSipzf1xKNpRu>iE&
zEBGw#SqNLZG~w1SU%p)P>@58u?boV>9vcO8jyp}gQb|x(zP;&J6hx%$S+@QSpUd3!
zf-|<KPMzBJ{MTNdq)IN|;dW41Se@y(9Un`QMke?DFTd_DB9axqQt5wD+o(kh%lx?!
z>nhEYH%7~`A4>FO;N;l1Phz~UzJ7kPHL0UC&?7A@t+J~+gx_TB{T8`s+rBzaU*C&L
zN`4#{6XQ)1vnA|X9A37#E;<i1W%E{!?~uH|O;=m#>hwrQMq1anWBIWcvQLafTVoXW
zk52bzc9w^(zOReravaR=s!6^g>CdC1ZE0zlGt(Q#ulMoKLz-FgMn*>B-a3<m&E>~3
z&6<tt3a&LAMKGMlM=PH{f8V$^HtP7$YegN``E(~y9eG<Ft|Lbj%wG??&rA%&mGTrc
z?aHi6(CB+8$^7ND!|2Tx-}okvoBoZe`g(d-JIjJ|W(RYXQw?N_-oKB=mFzfj`L@f#
zbl39qE6S}_UIM(l)taYXDm<6)^zwStI8Zk;-bcT+uV@4FiD*Xc>*9wrGL$qmACsHU
zcb+wVcYCd{b?1GygNSZ(osD)(%EybNUd_JN-R9yfB_mTV;M3l&S75q4`EnR%G)=H|
zm#C8}*80Mm`OmT{KEZkeS!$MCbR9v#Pn1XX#lOkP$<^bU`LGhTaTn!|961uJn*7Y{
z^{_<V#5WURi|;4H8r`kS;!l`i|NXYDa=XxnZ4zMHPP_lXd*|wOnU}Ip%1niAx*wQN
zM@F(emkB-AdCZ~NxF%ZnT0;tZ>kJ}Tpsj>6sVp-;zA;e8CD=rS1Xiij{CIsvT3~SS
z_Axt$yw$<M!5_wYs=asc8@J~=<9?5rx4d>Ler4TN(RoZG<Ju7)rsD>D*S;RU)LgH9
zBf)ZJtjDRZ!s?&z;?GZeY!IJpZX0e_3#^@$Kfui#*Xp|19Icsc^+s@Ja>zU~Dr%*2
zL9KJHU`yVO$<FMdxqk{b-r2D;sovy1vxxAeChow}ATihchan-oldmhg==-u%lPhPP
zL<>d^9y~bx!E#~Fp}=(Ha_tGmSS;w2lV}@ad97-Abq^n+i*dwd+q&Z)$_~df32^$H
zeY=Zy;4#lF5d*_u0rRj;OiWMAbyja=zQ(qHzlUjAO6o|~S_(zJ%3>8CMNwTnL?ubv
zRCMt3)6;K8aMgLnb@4IjCiR^f_7^VDLIlhc88}ti)jHqb+k*cSJTcVD;`I5Be9b_I
z!9;(<4!)}oXf)c$q0q;VNo@2MZWLRWv*6EpG1ja(XJ))!=Z}EKSGu=^?fSRX1mBBO
zNvLADsQHxNq;3V1q;`CZ2OaC4J>m%Tn&GzMKLSF9tY4_78%N#0f8VX6f3We{Sudpm
zm-!QuOwa!`EZ^b&^zT1b_jUy8Y*4M}8;_L>7o8b$UEFEYZ0GC6H2d2BozRfq`)Pyj
z&rh+3N^g#ehRZq6PSoZO<?pnKf17fnEb?bTW#@<9>7!e(e7LXBn(ur|xNWXlPE7f^
zgtuoq*YwHXL3Rf9AxOe|N5^E5-RB5#hX9f5|3o0dE(Q4d`quj$RJ57;M;95b1i4R0
zNG<MSl1i#UdD%dYZC^l_oOH4G&X<0ETd5ZsQuL8(`It^zm*2j9`^dBtzHmZnpPu$e
zbDFjinfQ9e)7$%yg)4nySZN1MciYi*r*Ey^cr08e+bU|>X`*prpmFqmlx5q8nXziQ
zbo(LgsHmt<_)uP6#r<+&HC0j4wMdnz{n$O*pYQKQ%uIc~;@i`dZB6z{B~ddb)vz+e
zcBmm$(!R;kL(Q(QE<ruV#^}w=<}F)lv#kvay22cL56;i@CE>FCE4v=m_tj31cBS8#
zG;^Nmd6}~?(`TJf>f;=J7^}hVTl~DYHqPY7yE~mriDh<Tptn9LeCchGQqF|gGMsG<
zbDmH`)``~6b3}eivgs__KJ6rG?V3B7(?=(`xe7;Y99tOTtAUMXahVcFgm-pbMtbZN
ziwV2_^Ik;nAy3O>$M8&bg-Yw}g9k^;9}8LY>6dIBwe<Sy(AYp`>&$=$ZbT8$#71S6
z^jW9B?bPM^AnF|!0Z!kuZ&R%D*A-!9<`<5rJeOF{&c-&CzSx&kVEp~f-v+_+vy*X>
zzRWI*ew-yoejkO)47D+J6i@BL(iV{~U+!$oG#5Yy3~Vz@br_y@lE}3iC`&PI%ve7)
zYJ(FyHq_e6C8qcBfo5rKqE;hUDsuE^;TsPxx$H2}zhU2O8>m)~WxCa3hI;TJRyl5D
zJ|?TakAD@8V8-xIFP=`dW3!c!?As?>ZY2E}cO2ysyYz;VdaW`{WR$CGbo%~Zhtz}g
z+uGW^aU4Y!WjhrDnP%mqkyMcGc6L>USEkN=O};W;GSpg7aBkMl^0kQL*sD3iZ#_Ld
z5{S6+W2@Hh@atM=8+Y_x&3t`#v0SlrW4M=<X8hN#0ri`OB@X+Q*p2c(ef)TsLpfG2
zV4yKG=^>J!((At`3gsJ)7`W@UW-*MZZtdN?OQk&UX<GQhhd(mgi**)80^Pz(*KXN2
zm0oat_@`!RgOGs=vjD2E)X~iV904NB4t=_N|NdC6)||e`EaN<m@rl0X<SQOJPYf#_
ze-a45p(q&Np{S^6?e}S+=xApB=}l#fr#TxryBWXAKR#|>aqT&dze7N27~4|voUA&$
z?h4jLoZa-8>9_WaX4NlbRt|b;k>!w$ZV%k{54MM#ypc+gNll-lb`ytgynb896q}Sk
zQ*w=!o144UGG9<3s9sb_sqJ#DsQJa^-%1>cWq*X32k_{a1>E!Tsjy{g?XRk;x^LA{
zq88@1;q0p`2HPgQH5B$mBn0uCHgfGK@pl<u5p@!5bs6veQpnZ%LMBwR^y~k*^?>gb
z?mf)D#Y1+<-_lL`W`_#uaR3>%Zx1#U!S(VEiA}dZLhaPdH!XhuzWeXh%!gUbzoi|N
z4CdC#37TxZF_d>IL3ii<XJ$#SO8gE!7q;y^NAuYw^lWTw%qi0W$MBE*K$=pV%Atbd
zPoF+z2h6rVk}I2S&b6PM{p1kc|ILH04F_1DSb%4P$LY3WUjdc^gqBdrR^?Xr4NN<f
znxD?hjaHV=%8AXVJ$wHAj{x8M_iMx!rz@TM4#ip~TZLzyrUu);6KU0J-5BmP^2sT1
z&pl7iDteyR56th|aeNG-7P*F(d!tw%cp(>V)>6=yn5&eao{ozfuf144Yf_(B+mLEV
z&u}{WcmC8P`4cH7-QvePCmsuu>qcFCnMH0Uip_?kWoupQ$?gin!YoX+#cE_GBCcPe
z27DcfN{()%XA=?8k`CmqZSxgNM9GZ>_VM)eq%{sq<PJ2X7B}q+;<rq#lUV=0$0in=
z=91iQfIaG*U(MolxYhBGWD^#S`zKAp4C`}jO-2_`SNus;byOHnxG^~>UeDw?ls{YX
z+O^GFU{$)z;XePrYO_?cD;u8*mK|r;&Hv<*oPi?UrIDo9YIrKe{CCZWMa^jFdeLZJ
zAQEjF!e{hza_alrTPw{9cK>_Jn|^Gdocwb2>#L`B{a=4fY8n{4KxO0u+Vo~Ru0k6F
z0O|G>TlkDZIvU?s%;L&7WZzSDcGD)t7qs~Iwi7GtCQ@f#YbQClqAm&b<{^2e+w@#$
zwkka&B`d3P`SLTQ;3!};Dvd)aI%3%$tF9uU8Mo%=HJFJr*xtxKvhMVj{jxRBeMMt&
z@E&BWf8m4#bSswgMyu6v*6PFSOj}+zI5kCx--_rhCuc~vChj6nv=yUo&%G`CZHgE!
z2Kjo$n)4gitl?;p-{$`G$#MG--_%LY2gqh!8ZV+h6vaBXaQ}F0FIDCAXg+-X+d&VY
zDF^wEQ@WG?Cq`baEBwq;Fn3wBVyM55OZKHAc9w7QB?@s2>e!x4oj+Are&vh`w^iJm
zWDP5PZ=5-En;_gkZAa?bwQD;^AMKN=qOnU`CG77r9#A6<42|{-$ph8z;Gsh@sHL5U
zW@#)9Th0SFXV<kRXb3zJv^>RYP$rLTG5=H4c_wY2?BnQ)C&%M1L`j7lw>SIP_UGUP
z_e7I*WzSO&W;&tdO<W#@x>OOy{l`lJ4rzSP=?56$_$n&2YeROk*{J0N?<TJl^M)(0
zN{WW8&!0ap-`K$@t>ELOJp1~|JN_ZRn(2LC=MQsml+Y$pQ-LgK$(7n=o`@30p)L9)
zerp{Ykz9i&6)#+%t8o!mb!b{ap&aNzoAtov$NphdJ6@KJJNORN>&YoNDfoDLURYe1
z$8kC3aDth@1uR1j4snN7HXGHjGE5mLXe@kj?&bURKz(!xj?*21<;Cc8&?$MPxh}dq
z5p&f>X9&de;ST+WlEdqOJ8DuUPFoJox(qf6mG*LQSA>hPC<zrFo}(!=JmM@$H-DQW
zxNympJ6y*qG16E1)hQv)^ysc9v=gnW=`8NCY~E@Lsh3eoi)POpxYv%*3Yr|%kZzLt
zo$`gzWuGS<h{M}t#h0=@PwZPhDSY^0F_jRLW!)uPz|;D+B}Hf3fj?hX#{iB+qv(bl
z)%U!hs5t&%r+HA4*i83xv=Qg`++Yry5M7wMGP;1!Of#<KXtt@AiB(H|@$=`;b{k=9
zr@uvj?b%H|O7P}dCyeS7weG~m9hiK2X`m(VIN~>{I@DRK?&r76OW3@E@VeEx1xzB2
ze65bsBU$I(vIo*Ts<<fNaGU0T%GVTs)i=Q`>h!frP^v13fuFOpR9<k`2L5YbKRZ1R
z3ffj<CmLR9-s8iW^g89_0OM`{Vj0q)`hYu7L?5Q@I&V8V-2LUbwa92Zl2qqBS{6p?
zHMPC0tm9}EyLnuvy{4Oh9=w!qybW6!?A#AL)ZkRPSp*^38mxIXfwU9z2NhpzjZL%|
zJnb+IKyZ^IaDE=hvNHL^Ny~nX11KYgEsFq8H@_Jac`{4~95ibCX(3qV`jPtfNnVii
zeE+*wSHd`rMr9`EErx#<iJOiBX>05_P2HL5)sQWlMpE^+Omp*=aDEdN`9@8rA60X8
z;TewqHc=!$Pi+^ri4L{_4ETI|?G`J)Y=m3+Y^CVzwZH3axb7R2hXhahG9CYGVgmb6
zHv4vgPjGg-_uDzHX&|r-vD&$F1Lm&0Lp-Xl-VDX3$TN62cV8xH8xVbY0CExDDSmqD
zhX+fA!VAHPLtFn?weIkc?@j^k)+t_I-f>i<E)8?)uE75A*?z_?rKMSxZP!c+UX5x}
zL(K=X+BS?Xm?Y%|3^jBx`|gjPKfIOQv@G%_lNa4Pl8XOWy@78?F6@L*wBDk1rHEmR
zd2Ot6BBN8<fAon<w}|>ap)qPz6RjYWKb3j)%ey(X9u$PymvZ3|u8uBf-9*M~6otY_
z3mSCf%FUTm!66~BLe`x@zVsuH)Z-d+dt>9uW_Jpht>bX-{}lD<(f-w3vt+HqG}kJR
zc9zdy&Id&H_w!SV$m##;5&t>Uyw%O}^?04eH*`JIQ~9%lr3x>yCJw1;<=7mlr;6AQ
zK4x~A(k-B0W?;C@o!ND6ID+<^-BQzWqTi^+b>!3jdG5&T$q0`rtulUrvm%xH7ppo$
z3l_$cfFK@6(o^5=IqklpX(Nk6`=%$twk1{Dq`n1FS04Y1!H)r(jvggK%AaF8o#krt
z5bC`W@c%IuLpcdU!>%B;UrFf8I!aU>P>T7vqiox~cM5E9%tgP>dh}>j(*(A*Lcsv3
z$Q_+!sZzB3+v{CV?C-ySe})y!K38B^nAxrq*FOiLul$0fdzz*I9I$H~c{$j&*{;#_
z18}!DAo(`np~LG60na`J^BUeq3%PCm4!*;s5|WZTV`5^2dUuLCWvO)nm1pGTfp7`c
zFY)IzM7v(}@#Duef)%4(l>(U$_3m#w^6qPaYXMO5`ohDgYU{2P`!Ia~F*R4B-#jtc
z9BPt>Ds`-sH2Rm;sR3*on2n;gwyT*TOS&8pd?MRc#HhmuEHXb68W3R6-<(T!+Wkwe
zy}7k!YN{g($KC!b{<d+-aRJHTTv*xJi*Vr|%gXo&ZEywMpG$}Py6rT>JiGCP-9x70
zPXD}J?czM}`RT1>qw0O5A{D^V;PZ?>I1Pge>rgkh+!1(bV%lqRQuc}P_JW0(IDl&-
zpF*^9-p2>CSnDmbf@())4@muV8f`EPpDy|goN{G;X1p_>_VA(ZH5BP?!25Hvy07|o
z@D{!@Z=$o}I(mQ3H<+>bT8Gp2YqQ`(=^44l;xh38A4WP#y|I0Txahv|9ry0t`v7ue
z=whOBoQlKXMmwKiUDX>)wI>F(CrVX(%V+$0+XrvnOmA;*egI!9KebG=CpyoBziPOw
z?UpygTbJ=K$Z!6vk`@>eVthtYvg^Q*jj;E}kLO5?Tt#EqdJJ5N1hDNbvsoPQW57e3
z>ux{L-Sz=R`xJBL>-=*X*&7+oOe8!0xWl+Eu#`b*7Tf<(V9!;_R!@#)188!9u~svU
zg1PFS6qNs&lyzVF)$E*>eKUj2ee;6(jMg?UHIbp)&)6yiM8kP1+g;2uldlx7%41no
zn0SPD-Ry^i-Nkzg*N7}L9mkR!tv|f3?cV;dH9ez>d5*Siz*r7-h<N?vUtGuL<Q1#k
zH0S4sTm#W&+x+ONMB}OJa}>dQ0W1eNw4FA2AaQB|(1O~gqoWU?-IK{|vXYh`#~~2c
zaUS<?m1{q(YkT9%moFhg)&@RZ&Z6Nbn0@hel$Up2+d5lwrZd#MCe5hYsgXs@<#mhm
zq}EvpiB$|qPYA~-a$}-9#3b>cjh4#b={vPaI%4>Q^0@|AZJhiVP*&4g5!?brG3HOL
z1b2n-8v2mb;8f~@IzJP>IL$I@si&v+#w*=F&2swtt&Jm3_FukoCEcu9c>VUHY?rd1
zg>|KH-W&BU{P7uAz-Levgx{TLFm(KRZ$Fo50h^DSxX;;@x>zeuftKs10hHDlYYh?%
ziE0YE?Q)pD=jDH^!<i*0JFN8L{C1JWxzU+ov4uU<9YR)-gL#wMXV0FU_6Ml)?5uba
z2O7<!;;|69^D!_#2M!*jK79BP%-h4nya{#m#WKw~w)9i_>C{L2K&A%xu!tp|_4>;Q
zHA_9$E@8ti!3fk`6kmDMZ)t>&sRd<v_RLl@y40M!Fp(R*A0M)Y2;0VP-n@Biv{I}q
zx6Z`L>Gd)5mU#WrfF68e4c?V-F-672aay@{y)`k9NbOKg($152PABa7IOrWCB#oJo
zQl50jabwf3uf)+sk!;#qmk>wDT!AKw)A?5nJ4BUK2^00@xg<Yu33!96D89m$KW`_#
z9+7Ik-sbK2{qNOfw)bx-Jf8WBBd|0|pw&syS9B(tK+QY!jK<(YK<6lXY+$O%0}<fR
z^#n9Nt+nW5N|Jp@AdQb3i$j*7E=eZ|rNjhpFLnOBQnpn`4Y-)`-dbrCNqI1jv`3Fd
z6Oy94vip%G+H8c!tE72)-mYe;A!&Qi12Jgan2|u@>g}!7aY*<XnqO1?^CmCM&w`ZF
zR?o6{+F2Q%1d!5)c7<FVNE7m5>zPlerW@DN#pXZT#>?p&3Z9(Z8%cz<Nt{CYa|5c?
z7i461DtOPe91fn34JsbTG@9kgRNYDS-!Bugwe#*0vtG1P+5-4!`RKHhBlzU<Vl0XE
zZ$f^n08j?y<lpEd48t5G3g$+<BI!%*=Ejp;*IO%*Uw8X?yQqjlIl1Avq_0Ypl)nuK
z*}mAg#0$Yk_1mqo+efEuCi>M9bPBXd>k0-y&y3fsNznhG;=cO|OUjSuPNMbf_8`ZL
zFrqn55HI*IzhASHbxADnJHz!B`~+P`uwmF2REMpb-Y%vu$ps5jFV;{fR#V;2eaG;R
zF^H3yh1nr6pWFn0150DkE<)9)NjJF&X+TanR%xw$*3xBPF45iQzG-kb`494n+DKO6
z*Xw^>h_bjBbXfOhlUXLSL;J&1_30>5XlU@o&d$ydn+X0Z)Yh~)C;fA>f7(PU@4C}y
zHF`k-#i}G2f3U6BvRC`&<3FThS&b0JZ#AO~{@74t)#@^vfHws+-Cub49n*%TyA_8L
zqq$r*Tr{Czaeipz`U%J3B1Y>&slfld2eW~6-Nv`O@9`~W^(|KHKBGF3^KMVmfS~~B
zE)wi0^7TmSWm(9ehfIr0CX=#~P0CW}J5n!4p<2c{jCL+<2}p#!CXD0*-QGd1(_F6Y
zJg)4Jr;XHi#-BsEB>lXRtW>}XP=eKE!Mue`t8V^TQCR}?xR2@x0NOD+?U`n}biH(Y
z_ckq~ys8IVF%HT^nil3V({otzks=DgyO4i2*w3l~_v*JSLbw8;<U|5@KeBhLB#_nx
za16kCk?hg|q?P%x0USCm$W8E%9S1rVM#C3Bc8$j1bj9ivxMUTIGpE#ro7E>i!FoxK
zY|pk5ezN1ZT|Ar7)IUFpeunQ~x|$WImhU)Fgqttt4=23KVaZ!3(WLQEV?lxiv5H&T
z_a$UX(Ll352TXGp`Xz8Khm_@anK1R}i`l5idgbY5$XOkBxwVnhEu-1{WI}=%)k<@`
ziL#(UrBZi@IHrN8O(Ln*yhRhuc?!0S$s(7;f~nq<l}<;%>;m-?LLpZ1rO&fx&y*lx
zYFU@_la!Zs>Fsj%{rgv6dwF=Ek)e<@_Zjoe_N`Q!L1oyJEB$%eN!jIqd1+U_>=gl}
zLvj=HYdk&=g-&O(**+ZloqmH_a=7zE@b*II$>uIbNkRsq?#X1fIL5_ZOiKJ+2+$c<
zqkTGbkJU?en|hvO`l=1PqR~o6gUslye(9S(_0vo9N~w(Qhor;d44kc*NK<h})h}p~
zATd^@|I%`GuTT!8Gdp(g0YF8<J5jnz$m;zAzoCJim*K}Ur}6Kyu8q3{FL7yR9j+We
zCH2;EnP#t?0A@4^Xim|W_Nm{<B&<g`x7I0a;l+p}B}vZ5uYAog)sVju4m`KbT^CgG
zYUO+sIRbG<u1)@&<Pk`EJ>scmFxLIWy|SxR!Qk!9l@iykU3;VII5}t<85wz-k2{+x
zC%42d8y>IMwDx`oOlmXO)bK#B2}DnJokGws=SRaLPdjUp=}yyr*yXNIs@HfWzLJDY
z#2Sb;@}AB(Wi-9MJ0+xT#!2F=tOw6fgW3sA!d){{cUvzsS5Sx-g8rnB5`6z>EUq#J
zP5MgVE#JCEmnuoL!gDrMWtl2q%?=B}xe}S+qh}#bsYpply_xxoOEm)ETpQS{T{R_l
zAfu5F4DJb?)-<C>(7m3gUA8<t{jV1wE$vh~D}&jbXKZtZX`>VkahV3GB9wJ0MMr!m
zz_SKxWYb%7ka_`h?FiS!bYtc1?oUBIhgO=l=4-j@PBr6H*J@_6@EY95mcHc4pE`v$
zEdZKCaoZP&T&U?H)GFkQw97@F4`waeKW>^wO{YbIk2OZr2&1)f5)H=}C<Y>x$UG6|
zzr;dcSg{TG^s<k+%Bf_WO`#7h9ah1OV}s2a)}3Wv(5?Cb<$kaPNs^$EDa#~m^T1}$
zo?J8(4}PLImWTLWuFn*}trZK=rt?4`QUJ76IhxYbQ!Ab4hKq^r2oNZ=Pi%hd5w3xY
zN{|fz*rp3y4GC(g63B9aHb3?{ZP|DJGxh*Q&F|sEr{$pnXgO4fLXu`T@XgJZ!C;PV
zR4sYNnRFr!;kLgF3PtJ<!_3!5e4InO+$lF3$fFZxJ`LI@sKdVh>!w%NCfIi&DO<LG
ze5hFp&ZCA);M&($nx!k$mtR3FcbXI|F5Jj>K5%y~;QIm3ZR-jTBbWobsy03S$1zU=
z#cg?=f8*v|Aei{o)2`}rhtle;*w7zLHd*`b`E=LUR{$*r!{GpEc~Dy+D{Re@!wMa4
zn;$M_u|D(&2Vo-jaND-BsXHTT$tlhKK+|R0KHT4ag$kblUSI#_jrjXQFJi27o~YCr
zb|iNzC#dt+*K_CgA4PSNprs+vKj=#Y<Kr+f1O%@7&u;psh*~-@M?VTZq-g<MYMX10
zd2D1+LF1C{FlvC6lR0ibST=gi44F$#bovZ!4Ea3n)E&BISEiTn>NkL#zqz*tgE)lR
zc#Z}cHVHgmr+P()mC;E1r#<zq$v5wZGn_INfc7ujb?3C;?;`HWOT-NS^f`B%J!<-a
zxc8M#^T6&8ZT5WH9*f=pfu&62gD#zZtpT*%WC6k9;2-ynU80W=d~cBqUi&n*TONQ`
z`K2rqME8#acA=TTYSK9mUYDc8RjmhaR}&%r2f-Rh?sXu#2?N`%w(!r7!cNDEjbd}J
zz`JbSvgJw(((zUX2K^5Reo5;_5d0oZ^=MXtCx`MTTio<_@*78XcXvDWrCH6L(zVS+
zp?Va#IbBqUY{2)bEt>`*n_p9-zIv_4z9LWC{O?{CL5m3N_ft?IeUIcszqqg8Syqo6
z8@Y*9;wscrhp;=QcYgkPIsk<1rxS64=;kHOGLKi*vpD@DKDq!LwLI(#*YqOm+qU74
zZNn>=q{V63=vO=(7J=UARO^0NM60D5EUQ$?bey}%kZ8ArxYh`O@Rf?0nz}yfVr(31
zIso3SvA3csT7lIWWx5214J0?9KA+Q+1sDaRvY;aXB_T+1Z5LPy(bey|u$gz8oXEJJ
zLjl08l(aOxYJSAxxdR6Z51-;-x$2sBW$vc##ax85KN~`%d?aV-gJ03Dy&B<{)1OjW
zHoH=7;hCUSwq_29oto#88}sHT8ij96#1mqVS|u{s^!)w%_ZN2Fs_f#eU*qHBQzyyd
z9Er5p3rT0hF%SAJBwBaft0>Ci^>C(GBxq(|sJg?S2;3P5o6U(*!`7ty>CT<0<(JHm
zn(4D!0UVDE4K-%+Tv97Mywt|bglM$|vgCe~k&)pKJoRa)>=$}`cW(usyp+C9v3*y?
zlRc)P{$Du;AIrW(Culi3jL(-gU03YOQV(3kMh$nFi5bkFH3LeK2HpIyx@YY8s6dSu
zJ9;201R4M8o*E>^8h{v3!=m*m4U=(%k)w#Lm6@QwYM?cDm?zhMsDjtQtbVFvQY|^I
zxu0=66at6OjZ2FrUqjNi(j7X6vT-L^QbNq9zkXF6b(v^a5j1VeB4w2>Yu)LI+s~O8
zuW)WSa&AP!MCkLK3DVP{3E*v?Ny;4*CfJ=jP#TJAJ=kkQ>w6ykP@wn$0I5}A9n*le
z9yHROMa%Oo&4`ssbgFc;cABM-jMp;>^Hn;=?<$ZN<+RH)Z>U^l{^i-(|8jX>O*5PK
zQPorQsQLPw&@x+G2h!_pM%t;3Kk4cK@`%Ju4k<VdVlXH_W8vtS1F|MIQ7bp3V?c0A
zn&A80(>!M_fl*A#L-;$twYckcgFTo*k6rUhx9|b?x}auq<yaXtvrG*v8BzLC$zpbO
zeDGy4ZyEZU{9NMxHX@dAfFg5h=^O8b#I0W7lBa!RAbmM|QR?ftk<!k2B%fX!wj-6F
zCmi#>K@^3~xApLLvyP?Q+2^c*vcY!n=+O&kK{hxZCx@5fxdV~AbtjH1+d=q!P!Bh#
z9^>Yg1#a@U*`uo!bAkPrUICyExc6=1`sfJy-5C3!mRPjt+1JEvY|@B_1NEOugU%5b
zYM!56^WnQ)?)c}^8>uwe$AV9hdN__9Q-O8}u?EhKI+zLkgM(v`47O%HAtp4j#ko{M
zH<-7I66Y9#aFP@1`yuDCFOr>Y0C3Xio0afUyEb^>YwzB@TUl6=fcbhz+l9JGw+rCo
zi47z-sT>Q5VPMCE6#deW0Y13ROzPql(4W`BJE7V43Mp(l{_A+YGrCDyBr;}cht<kF
zrlr+5>y7i_iL21YPSDEe%U2#Xs*2oI3CV<f<!P6;mCB*{=F@$*qJVv)m34!DAdsQV
zbO2|}5xwd9f(8A3qSjlOm=Z|!1y_B?_@&GI%(o2FP_i`e4)GgRodLKX=d>mPftC=q
zFCHb0w-(@Jj@u`KstGm>hFWRziN?OHxV2=M^eW@Cc8E9<AhfdfV@p?B#6b4V3o<K#
zSz4+byTt%5>V^sy6S1n%(7`<K-%mUK-c6G`kQq6Zh*#mR3)uj^FOeUGp2&g-k-W8P
zeHDyRHlV3%@#{4o9<ZZS5u=@9xbq8WTb$T7ECrE)z!38Q04Tw|loFYMDsMu_2$)p>
zm}<}#p!u8j&jwd*3*2-_J?)(9Y-8)#OR;$>j%RXmGIDwVVWLP$K&GuB+a7%#m4|@;
z1l8nIAj7-}F6-{TN_ZW`7v~8mW7x7~%c~8X#XNqMp0z`b{SB$?)Qj2<QJ{amMPx4F
zzumibNknj(EITj^rs#_HeG34Snd<NbLLqGRtL#!lmbC5vdIWnS=R7mE*8bem60Uep
zJd797h7gLLHpj!fI-*?-{Q$!C1;oOYnlvbCghRLqh#~z3&`K#viUCnT788W?I=m6x
z_nf}OYD5;_jfrb$;(3~WEWP;y9-=)i8y+5ZM2p2ng&SzCH6MlUC-hwsh&D(gy&$zV
z*bDx;m^-|`@EHCp<NLa+OFzVC|F>PDzkl)nh5_~e?XB+;V`+TH@O8ay5?O`7>+z^3
zd-u{!yflM64X9kSX&FU61%;S&y2l|5P)Mm1viR<XwiKN-g`&&A$oLZa-PWs(;NE-Y
zrbqvhTtPYcY4fH{Pm#fHFa6U0hv<+0vJ7X9fkjC#lw&s#i^6W<p{Pd;PqqW!l;NLB
zOV2+!uvKp-Wldd_aDjAP#dDR)HoMAUnB|nfivl;spQ=?<_c>b>!%}qcFHVLRI)p62
z`@vdebXt$%_L}&Mw{8!we2GN!sc*cbZ`^pWDf`EUn2*<&Qc`0deT}Sm#xl1{nkA#T
zdIKkn$4{6|?kX@S-+@pC-)(y*_GQ{GFHc1=Pem>jj`IcB6}SM%-Y6`iEC<0tIdHUK
zej*g7=Q)5{NskQ&EOQXWnsk>@=<ZF=SEb{Kf)kS<Vtm$VRPQ3CGg&x$n!+g+cXuAu
zyG!s0=*;ey;Vv9wYKrV{7Z-*W|7x6x_gViYs_Q~nYt&DX{f$D!;U^p}0A<%P**EQh
zIIeO0Kv0#V6HmhYa<{7cP(UNjq31Q}sfq%fVf^LU9g;O~-(G=gbm-E}pM$wWDj?I5
z6<&h*F+!V2Iw5ofHE5{kAr>lvMq#4@+f?7*&WkRNj+|5iexc_eM?E^xC$R3c*xWz2
z2xSR+{1a4c6o@>>@e4?19X2zIs#nob&rB99Cg4;0U^o6ad}kI(hu9FSs;l{JdzDCQ
zP<3aVPQa`w8cf9nWP&pO00>ybW!qPuR1c~|8Fdrnrxeo7k=Y>jpqZNt(TV+S;Q;|J
zda7Uc!7o^ic9p<$s21nI4RI*VQlv7Zom~P)!XFj#7p#u0^qh8vW<!fa;!VoVXO=1$
z{W<PP$hHLSJaq_$HJ~F1i84Q3=~|bN*%FJi()o2C+zaG>U?s*)@&uOpn>3Tg5j>O;
zNvj0hM$$|3+2DIx8k$he%ux73TU=&M=fN>4!HRaR&qFC|@>6etNPoAE_~lPYI^Zhl
z|Kj?LqiG8&T+5-uAIx?r)_;o)O#F3wNeFF5Px2f=WtP*k4`)3Vbq{QCE&6r9rB5dc
zw&^GA&%u`DLArD?nQ$I07a*)YDW%E~KuALft*ES?EGxFGxAvlrG6*;8nkPUC4_PIR
zO9KwMR3-uLVT~`Ma*fq#n8S<x%_)KK(CNnh$MBngu7n@!G=Wb6mdguPRw)ubyLtj2
zuSwH>3d1#4J`S*LR>T!ILJ9&T*6Mh*)VUAQ(RDk^cPtVjyIi1kj5AZe^e@wfl(R&%
zf%CwK%0-b1TiT#7$wHCe8-!p1)Cai~);hYf$6kA3wso;HdO!BSi;<`9XOWlf;{2ou
zk@%7%B_)Y~KnN$S$qR&6m--~w#Ex;tT1bTjU86e}<85!RFZ?L5@NSHiGvH>voXX;W
zuH+)!X6XnQ(yn}crR&0Ysu>M78u}=l3oYP3@$uD^ONT&GyX`#!Cn)u3K|88DG4(7t
zI;}dSno)w5WiA4pL_r=rjin>>H%N(CM36B6IU&}O!fO(>jss?$N6|v7^ckzdXH><O
zH&Vh0xk%P0wYkNy?+{rMM0^bzZdgW~sa25K#^HG}!l@$!<Od%n6SFo8!7;cFjNiB|
zbA-9S7ROV|vWS5eNzMzP`bF$zEhzbj;!cPz^*CP)NcQ`~oi0F6QM`679&tqcX;5-H
z?&d{%vxsTqBz+;x6CDEw8p5mSN_GgCJ>Dl5Mohu6=Yx;xdT%;;{cu570$=4244>cf
zopa5yez@;4+@{em1b<QAbXWLAAZv1Dus~6fkyo+z<dQ*WMxy423@Ha7XpMjth@B^K
zZ8ucUdDy6lsDVX0wJrJP!K-2^s?NJI99y~0FUH^RK%^+4rGy^!lvs-hFkv%7qeci(
z!qC`+<9Phodz(UOWD;}H1fxJeZ19Q)k(knurorqMHl%h2g4uHd()<r;5ReS8v;*Qb
z3aJmXNGvYh1au#9tONN1FY#Udyc_(6GMdm@oQt{M;|?Q{XlmK0gb_f{6Jf(DTPf2_
zD{paO7V$ZQ#EH(aJ<}9zkhaUTKD7$67k1n)C@6Ze)l~<h0we}*Y18w<Yz)rsXj?Z9
zII+iA2$J`L%eC2caXwc5=_&c~h7K?x^>;V10)z8-6z<7vOaO%ugPxGa?Uek7MUSpa
zrG`;>D|$K(YR(V$opWKCIjx|AF9M<~C0{<9t;vn1qNu1S1_#0sNQg~KaLepv(sN)m
z06=lBBCImSZa|${g}kQDwz%Q|S$BtB8CP1lu99_c|MW;b<61DlZOe@z5n_^^>lXm?
z3}M&q^ZYW3suzmM8#^-0z}AB-GjUux7Unk6vdD&=h=G{1BEAh2eC6=t(hfoxvQgoR
zuHZlT84L)uip6#A$K)JfI6D<Ol>#IHt7{iCuBnZ7)uVcx_NmtZw*dvL4?Z2o4*!dM
zR_*7gRq&T=Wn^T0#nV6}K$=anZT-r0(aTxw<6HXU(|LGge^RpeNQ9<U4xu+dfAu9)
zOJO1xjyu|d`Jvu^+zy0dPO3V#&ZoX$>L)#Qak7$TL099;rNZpt<=DEeMvU{yr<wlA
zo@z;SvE{>#sCb_3*X$`LWr+^;3J&XPG>7aRbF?lCK^q*tJ|r(Pu`xE)=G&zv2kyq&
zS+wtdkXL<M*@Z++Au1y`&0kC96kBHc=~PW56o}6+kYD`!`ZkJ;J;2PzOH@26ZPmJM
z58Ax0yAaa?busYV!Nidri}B&JopyHH-KxB&MP5~%C@@T*Herycz&4Hj#!Mb4QZi$+
z^Vl^sd>-w?*XOib3tU|j8)0)L3h`$~9j71mPv67&)t%YuLU1~K^6N`~mdT0F?oqq3
zE5oFf>m2r*y)V_ZoWH(Z4TQrn!n-|fTu!;|P9Spz;Txf8Bw@w|T#mYs_E1xHD%(LI
z7?}7R>K@h0g{8V23$3p-9Q`(kOhb!=p`ok6pss<JScb=KA=n3g&zkNG9gXMYA%t*b
z1kcD8?x#LHZdJa&ZVPXzh=PK;S~46`I}f_Z*A~=RF9^pPh{%4R*tLUr`cBz6Xgx^8
zMUcY^kLFYKq`{R*;BS`5Jn75pBy?w%?(5$;2IoTa>uab~dzaU~o}w8Gt%q_c4noPf
zoLAhfMSW)sVAoF5EnJljv~uoe3D5LyD*V}je)gN&>UCUeQ9sIyL1aIJ8KC(=9j#3G
z7Ed4jHj3ZYvZ25IJEuiB|DFvjm}4(CQJI*9J_|>jZ#!CZVp0S#tbzu#!$uhFd^=`}
z_#ap!aTD4x4+JzRR3sX~D8WQR<(N{-{Cjhe-B_Wq%24Q?o^bzUy(jC##-}S}A{>@c
zF0r8Bp)8>fVr7^XoDDwA7|y`@Y3`uCLNpW~Hb^2aC8=fDK{hHjx^!3_6kV9p2$fm|
zIs`aDgA56D27bGwua!m+61~xJ%9{P1lXL!c)6HF@MxbVvJbP_z*00U<3sPHNN-{f~
z0l|NfP)PJm)VJ(ga&3C{QafkikrI6Zs0qTQbe!eShROpKoL^wRQ*EDrMXQ}P(48^V
z=TZxw$JkN3+P+NN)@76lh+dReGIeKN$#@DX+*O>E?3TZ{H4jipEkW8)V@uY5k`6<%
zPRBO`01G7eJw5O4Sf)VYBzr)j5O(;JxVvgNfQY+62ERoZ@{g9NrV_xIoLB7ua*q_O
zW|1@sZGA+e5#K4yr0p{~{__>;^@4l%PO=Od<oW)s6tAj42F8FUp$h70pE#1~r6a|-
z`7_v?fJn_YIa%2{m|<>uig_{o=esfys|@CqgaJWFyEW519AT&s4bEWYX^byk3Plq^
zrD0AY(amJ*hoYijv0GZmYY*S6AT5vXZzI|m0vZy6-Zpnav|*GBO-C$pP0mkG&bbV&
z6uo;cQ$JRN$9O-teCL)e&k=@Nv@saQJF^a`XcK!m{A1^GEWX5<=TCo_+ly&6+p%s1
zP}=EA3%7Tk{8Tg-R!}8e;ELQt<JK%YB%m^pi;tm2!X79``VbWVNcuh}xyOPEpe!8Z
zkF;7AGXXn5uL11$_nRHrqef6BwF>7i9xwuwPv;~qyKH{{R+aHWj(-DGDN$1m)P)~L
zPQZC%71;_8<JQuy6mJm0NmxQoB6TEge-hs`N-{eY@l=^P=eF#RG*~%g!bP<*o9#3n
zYBrfc0TITG4ms919X!Q-*oQiJ+~o?o2vh;BQ95AP2aCY4#zvf2cdT(PAKC?CvtBB$
zo^2J!hluyX|G|R@4885CKZ-iEF8>PU5^LwA^bHrCSC{@K>xa8KQF&Ble3qP0U=;9`
zf061dr0v?DTufkZhlX5T@E{=h12%%`6%-U40)`-6iZ0Gs69@ZwZNnIyf_x|vLzxoS
z6~+stR0c!u4B*V37%k(Sfq%;%l^@*KY#te4X|^a_57&r_yzMedeBpNZ9W?Y3)_;TA
z2VtRIBOe8dL-ECZ!mjK)*58TzfjjAj>rDwbj)95k5D)<HEP*Ky!e~1E=oeto^JB{!
ztCH@#W1W(V6(c$Y8Xc)tr%@SsOW5YTp&o*-$|qZie(5Y|A`}{B*sJ-`6G$g1ZKT|J
zJLl3<F+>KDAkzSvC%G-2dllOXkc8lR!F;3t2x`B^Z&nfAS~Pr((P#(6i<q`-X|sR(
z7T|Cd7YhSJ6tOCz#H#=}!t$&HsgIbo>RlISb!wpxMB60{dicjT3D7s9!ouYC#e>Bq
znj{$X=zhQm0H(IB&XYa2Hj2vQHg3ie)a{`Q*5elC(i3dt0eDWEbYX2#0h-#g|6a;2
zVViTv)tQ2OBic1yUrz>^n}pGpXz^RCFzxDA4idA>>-h4Mx@1fauid2eCuogOSA+6!
zt*m{w1A74$$SH5M;`omZXuSPSKK`GXCMNHT*ME~I+C-!tRic7HrwFKlBcQ`ZdrC|h
zLw@YG?uvSPkwC5JlM9)op2ElV>4(KR4yGaR#(@AfOl;j2qP_F-@d;qt?yg`w!Jkwg
zS5{VL;c?*r_7gEoV+*gmh@ZpFkyO8PP@b-+$0o%3BzPqKS@kn#-Y%ATdwKC*N!jZY
zuPRkIi*4MxeY={mkBFLU7ifdh!}kwMDhY1dv<ZtC_4BLZw}n+5Lyv#rSe71M=Wrqn
zpLA7i$lKqwOV7C#P7k!LyU8#pm{LER9~%wgHaGs*j8K6=^ulnl71|I0vdF68A})}k
z?z(=SJO)GvVROi9xsaNK`|+Dvkp>rQYbbo9_z7f?{P+9$Sh25@`}p~5Q=AWRIw=3C
z;_`#I@8Ruc>ml^z!aP1u2_2Z!*>}g#zn)(g;O@HN%-ORCIXGf#cHKx>uvAo^;P)t0
z;P4SaEWxga#f~!2DJ?7G8eZGo@7e|C2MwPm69$8Rzy5E|o-bKi4zz4|EEeZaoyDvR
z2l3~~KFw-2BaKv4)Scsgr@R(e)uC}N?s)+df>6jiFX6w~sZw%s8%=iAhY7y;Cb?m2
ztWV8XRVod7`}3{+CPrVrK<=`-e&WZ5DEW0Br(=#Bq1ikY(`~7bf3RaGr@4gtuHazu
z|D3o3^H#p^#{pS-F2IH7&$c$tt9<IAvlq-bH*uZMq*68tyWcnNe*E#Ndw1_XXdK{X
zDh`W2kgdr$kzMtVy)42KhYbzyjygf({o(cLH|+n4^%?8F>aeibDkMdHSa>t*-9gsg
zcdbH*n=M;d(bS+)tQpNWxHHz>P4AkjuXp>n-@EBW*RCuLdm4A{iKZ_wmBD?sB5kN?
zyUdKfHONoTGlhTLXrG#hJ5~E|K2DXo_>POJrsFStT1Tq!#-rMBS_@-1_ZWMuvp~$X
zI~E>Syhg*;`9x*utrNZ>b!bce0VSq}>NIv|pGT4LH3mlA(i5%;f$j@_83opw{E4(%
zEBM?l-(~+r7h~7|ryeWtJAnS1iQs?xVF>?m)o_wVWC^1K2JDUkUgCs`h4K@qWfu&B
zARa%Bfl!R`u9D<tG`-{SHxfl!0m!%TPlq3VV^heg<h7NBErRFrHg^ox$AX*dygu;e
z?>{gb?nay$FpKrYybLQz9)PZgo*39J_!(WpQFalRU%yQ$qF>wX^&0&)WbcQBwgdzq
zpKNgS7}y|aMpbB3Fd$#Pt1(Q-T7pKnKk~c<??$@+`X<-#Sua6+AR9=bq5qh?;w(}J
zagq{#FJ~Twkev#GEJH-)_<y>39=3>7e4IC140wS(39e31VMH(lj?_Vm1mk4_o~N@9
zQw=r1Hf+?&MX`y&6QZ#rgNuu^Wo*2k^b8*{`8g{`2y9X-c`h2OTC;bfI7jAJb<JnB
z8Y}BXzdf_%T&PjcOW#_we7)~q3LG3;mD=krQ_enc<GS<e_SsWw44!aVcg`(*wEwW~
z(Y*)3y0jnTbI;kk%50xh3R#zxz3wWpogcI(QW&g1yGv6DJ=@XI5fdGK&c4+-(`D>A
z%gQk=dHDzw+^Zn|IJmi0h@=P4!c{yE05;ZjvHD=XOG-<t3GTx(nE+|epchkCRgDG%
zVFd6*#<ElUCX>0Or>CdUM?Zm;mkaO&5SzH873}Qn9<fFjfUb6i%l=!6z84)mJt_$w
z9Gx;jhsB9LWPF3$*Y=p{`RW?XVI)DcJEWEp`6VCooWxq(BTz8y?Y@zP*+v^$Z5?n^
z8lGwa4I>V{>5dcEUvU@AS;71o^sxI^Xk4vb69IwsyBiy5sh;?6L_~XGwCbA4lr@AZ
z_S)4xAdDUvg^-I`m?{=qm6rX_JNo&>#JIR>uwzLG>eVMY(Y%Eao)&ZmK~*RU%VuGC
zl@5Lp)7xv@(cVr(<ppO9Al5>kO$as1p+Z3u>YH&kHZd8;%=SSsF|kWGTwL-;Mn^G$
zcYp+k_>UN1<C`n#iQ~y_fS;d#27K2z9QVc;!7eE&@%)rrkx>qsP%q>KG3OjT(2`>t
zcfOj47k^}E1Vj<vt%HNZvPLgYPuPsq6SQ(wh?N$9x#nuYuLU@1a+1<~5=VfIx;WP*
zX8Vaf@bR$bM_9Y-(X`t@2qo5GbwDKex^6OGR8i^NXt#)!Fa~j)gJ#7qER5M=S7{0y
zV!JxJx=i}NCWjm|4Iy5r(DE0AJ*aeDNQG~YehJfo`=1+tX(=`~mW>Kvp9M}bj(E*L
z2*f5P)?tDK9JD#{h=&MTKGWo`SF7czUEPNMBWVMRXgtf@_tk_1!6O9@#JHqQU^v__
zFz_YJ4d>BpY!G}2aa4tn(CFvJ<8<bw;HiF4-Q#BzBJ4O8-2!|IliY`P)p8^2?$3Lu
zRS+L`<f-KI>|%>S7r$(lWUAM%PY~u`NJxk{%E8`&9kH34vV_^==z+9}BQlEq{{G&B
zqfCJht55O{w{G}T(OvgUKR?%Fm%VCuK24Xp^z}n4-P{pcS)Cy!V~C5G*jTBJqEivD
zc0<$<)HgIV0sksRrLJwL<C7saAmC(7aC?h>5GYPzYsOTtc(v(NJm?9W6~fc;A^P2%
zVZZte<9j^x&R*!~>(@m1vWN{XPLhWbp!uD3l||=?H6BAp53|xa5^~^oDq~%!j<8oR
zDFm1*$mt5Zv0VEBe2EE2KYf`6^9eXX%zx6s##2np3yx#mPw{uSFSXm9I(3s+&5^%~
z`mt<e)=ZkrOf+_<a~wM4Zxll0hJL#nZ}Kt8BTd8OMp$1~_q2wgi9tT@&1f_;Nt2^u
z9xWUX_4x2$w$-7F$+oA8z1@ZKlzrbnDQn(CB&t$dWLH1H?EA98^wFb7mBzvuLy9U4
zGhSDAx3ICTklV86dlMB&W(TEKGv7#f*>24YRmRUW7%`}11~D<GmxDUa>WSte0rIt`
z*la`P(xY3lva-Hp<i~JxBmN$=ZblPXzZfL{BNa?0=Ghs?`x|)mK6YCO67ytjO-*!0
zMqNR(z@2lj{qbA=d>>&4ug~1nFm0<1v*86-Bcm87e+Oax$I}erz7&sx=%b|>Rx&SU
z;F!<%$hp$k$U_~(tKHwvV5wMZ<&yRsY(LlW?%gTsc>S17e&2vyZe_RXzen%Ad{T9q
zX`>1vjl?t^9bFpQN#d&zcA3k9<2@28hzWFzW!9^u$E|s#y*<1|@#LNdk^3Z*TtEt^
za7nM;u<J7z*VCdE0{)VP57R+o`?1acWGq;-eebLU!xQF|FzP&5z6Webfii3NNUWXd
zH4==SMD_|qe&Mgze2x#g-7lt6Y%II8non6?ez!;A)0&zZ36@km2!cvRQ*TYkpy9{l
zbRwCrAe}c_NF`*Aif*~bm~&n(krZBGmg&LY+c%c)HhNEAEtz|nU=qOiUGEIKmn>)*
zlIt&odJi)Tw*I3|_nxM&CT?=#PBmlF70TYfDZ;m*&>@w|K~t;71PfWy@-PvN7e>Zl
ziAeM>XC9(=b$)KOa}*2@mF76!%P?{zUm=~AKY13jX(GFKkBzq~t)HD73fy|ac=CMj
zBFv%e{7e@D{r#!2EO!P{v9MI)c0HZp)sY-h-HA1$MS}TsU+3*S4?h?0)z(9qP0tY5
zoB{$Di8&pL8Yw=IS#a~opTuS{aGHzy_@|XCm3l89)-hhuz!$ee=y%<{_>k@~g-;HS
zu19^l9<TQ(wEW;&Ym?L4+v`?vlwML%F>BfI-TDi=GvF14agDVqu-xEyo47jV^Va3v
zyfvBTIwT{&-Y5*R`m>A9r5(nDTBwYko^*s<{Iv0O{%%15fywwz=CMP5hT9}QW^a+H
zu2~|%VLcL!n~9pav&goR>%DyrXnSg`&ezYy_Ogtc7H=n?NSybneS9o`<(kvpXO77z
zLgEP=YyKIqzG1*lCWMblo@PT_6?1b=#102L)jfi$Fwr7mGf4dvs=NQ1((p``zj$$f
zj7ws-JE||7vzX33?e3nxa=FL)optR;jE#*$ltazWB<ic}S-gFMhes|wJsmJl^W++r
zed7XKjg@ZctCf_O`)?Ht`D!m)zJ0wV_tJ$;XP{QMbT5GL`<TLomW4k%h*oZ(7|5+f
z1V|zTHZ(L~ykOxA8V&L~X(B;!b_Y0^e}0=Mw?D<r8DNFB{N+u-Cp@oq30howRGi)Y
z%!`d0DqxPaabmTK8hSTaZQ|4u705;{nCsxVH9?iB=c)Bw9+)3mI%7j7S7DVQ)<c+4
zz90vzJX_dvHor3ago^byB_*YE#&h(yMn<fla#_<NrMa`)@6YnBD-`QE>*zSM{g>5+
zLQ&^fezlh8dL{<B!SqO?N-S)8AE4iZ^x_Du(H4)XI7cJN3LHCYM4tJE@0g3f^Y})+
zK>016OB?h7rD&1lkxPJBco5UXP#j<e$SbJT9E)QcMDfrDSGL;92ApwC1_lNMcJpiS
zWn|O{&mAMA*3G}<Pdu90SqEfwg0N*`-)0c#WG=%$G&F8(%tqv&H-9V}NX+_4N3Ft?
z^D_{4@5Ej{tM5}*P<Ysa)C>f~XFyUxs|^!$QWb>yqPi<;OAcrb|HqHH4o=Z~;H^zS
z`EndMux3qCX-Ns;y^L^F(cJGieCcHL%ip;DDu2))>f_7C0Ix|^=bz3(;WCCcE#6pN
zU0sVA6BM*_P#Z8kn9^{K00-CPQ?1?K+@IU-<OS>_>LM0{U@evrOFY-Z_)R52WNaeC
z@3)X)DoiGAuQ9+o=0FM!RW4v7V<9+5)etP75iT$~9M9Rf3T!IwHK2z>2C5Kd!$)Mm
zPNK4FET@8G1F*$MV3Z3Z2m^k<u4S{#*S{|#)^uWYRJX8#MU7}$aPif3TY`BAyD=yP
zJJzurmR}KhRi+<)^589fdoM#@(j`#S^=&SyLTzm=d1e7n$Y*#QY#>Y+;|dnOyju?g
zJhnD2BSQrgnv$}zKUZlL25?@&*;U#$+>J5|@uRBJWg>N-2?nYmVaA}0U53{UaTW98
zg#;dcfVqxvmyt)|2lnsZe`%9=Y04PV9w(m1Mq->F&q(tR2<V*}ZiB18N<UER1sW_J
z;&yT<d(mrt7522==G;sox<L#9049&qsUb`qj8J)BrhV2Ien!TuMS6n65N}n&Cquk5
zVhh8*gm2@|`T9Lu7zcz9hC2mh!5(^}nIi^wJZi4k5g?u)f<;GXCvcQv4pbcI8S?|g
z=7#hED+@rN6dY{e!+Y^C8`9j79lLsarUBu+r)!xQnO_mUp#0Q#1Izt$t|z4@pQkA1
zeE*!U8o2CnAkF2d@(bQi1)UOE1b&$O(-0jK^Ux?{Q%oTo$PxhogN+Oi7u*8O&dH>&
zT6{TdG)6fS*p^bNZ_``XpB^QP3GBtB8_Ra@a%YH%q&tF#VZ{<;4w1Hs)%V2aKC3^k
zz&8gTWK$0(E9>c<UIkxRcP{YkonLmSd5E5#Z>-hiveqgK?GmSHnc7JG*p!r`d*{(M
z8YIQY115*lI5n}R9I3B!T*gQ4j;&6!5eRrvwSH)kwN9VX6;-1qSF+6|c&QwOBi+H-
zx3`MXZFHyg$>P6ZwxSZ#I!l|>uK=TbKfdC#<)zmA88ODi{FQ`mB(Lw9d)<8gfCI2H
z*>1+E{MQdDe@c_Ti*rMJ-*o=>&(!^Ht1`O%gLa9nj{Wz~PW~zJACFLcc9Zh&BNYE+
zSg}NH;%~V{?*;|`>vd-3vVR|;xSL}Q<h@@n1#!x+u^S5IF5U8f&2cA{{`}v3>%&~7
zDVMcYxP8P9Jgo2YKBq(a3xAvV*i$Gl#sdZ1K;ndFPf1Bp4cwf8%-C>kymb!*)XXD^
z`<yVP4s3;kb|itdbU?*GMp@Na&%i*6_FCJ#sma9F))o<(%+1bqEK~-tofesslM~cx
z^6&xtFtM>5wRLqLa{OMg?c2wJ!#GhP(mrv0??e-vc8k_78R*^i+G$~C$M3sM?<I#A
z4o*(xix(Lmx?CyxVezrU3eViQiSa1p7pYj+3fA$3gDUDJvyZpRB?v3|dDKdR(CxR&
zWh*MrGuY<N!NGA^OX@4SGr0VkURP43r7<#NG(Kd#u23GfzH0-*C2bb5X=!|W=f7P$
zMm$P;otFs~u)oitP!4HGU4G_hs2B4-O?`fKYE6w2KD5`bAxG@}xeF?eFBWg9Sw7j`
zX8bPjVc4G16s5oV#LB2t#VmV|JgwXr*Ps{iiZ9=BAFR8krgqfWL_dAvO`Tbw2&Xhn
zudc4d^VY`l3A+J3C8dwCi9WUme^?6YO|C3rmb+$hk9H<hCXY8o$>6KTJmV^ya`Id>
zs&8hk$4x9J8faRiqV^{~jmbVEDJf6z)>7(kW_NKuH&q=l4GvD(U*oEjE*o}jd@|&a
z2*!s#ddeTzCOwg1ibqneOS*`n6<@kG^boJ&F<(g)&PA&?uNuflC(L=tQiksx{12Y)
zzZtu0lesA;OI0om)K!?v#lC+1x;E(~QuhnEeU#HiloQzxre{bU)0nrvtFNz5!ssBs
zKf*{^Ss9@c$=nf@6Vcx|<gBUXn8QZwf_HD-D1Q{fkyOn3JV&@L>UOb#qLy(i+o!SX
zdTHNp44kixdRZEq7@(lBv+u&Y%j|5?6&)AeecyyPGme$<Jr+JCfwu-QO6K0r1)849
zK(!psf1|AR^!Du;-_t+{NM1)051gPXQ^}+;xPW-+wHi_~QU57?I5}DCy4Z_-KW@6!
zy5H8w2m=lQ`k$7rjJCI$Ph)X*SE0NXB93u9@Iv(!t@V@@d)!uij!cqc=zF++k+JWn
zThNW(@1nMI{kL#yg}#?1#!7??oml)eX0ug23%Wkoizdu#p05p;?kqKzG784Ua!8pN
zk6E<jAdT<#c|D(`e^izse6;P@oN-=y!NNJ(P+#|T!9Xi&Olp|lkvSaD<rMLtn@fIT
z<edK>q^JLhq`G%c-O4l4fM>)7QYp5mNtz#Y<9IX$8JZ-nJPh-|t0_K!LCpjpO`_7H
z+<1w1c;|u)O~-?SmW-J|2Tjader5BMb9Zwo-%_}UHv|GA{kMzY@6f*zUn7vi7f5V)
zN)B{D#0ipQS8V%`e#i?vi-R+;)VAX0rPd?R&DVtgE)IOsO)EbW7azRizz;9TLrme>
z8~6AGTto<&k&%%anA;;Vkd<kXaCm(>eBWB#UJ;WCc<8{9%kPE6Ix&b*(QxGVJH8Lh
zqKnvwCzE1S0SKZO<dXI0tvd*WsErFmUs!u}cByz`4YrM|SAGD}V58zQz5A~GevI&4
z9!rLku6VH8($HvF?OZtlE>>1n@L8}^$>S#o4qjXcpYJy$j~|GfF%sUuBpeC*9dV^`
zaB)GjRwfhyx)bm1yf40dsfPBgtgRhJ{99x~0b*z!h=_PB9MOP@T^^$VI}(P#bOo>x
z-T)PZ_+v$9x>*NW3v`-uY^&D`G)46H_q!bfvsy)Z(Xz7hdBY!e?x}Q!-GLB{WDFmC
z-!aFs52^uCXxOlHvgmOF8V6LuMV)P6!#QyLc<!f7?c|O`r@sF|9D#T4+#$Fe4}INt
zJ}3fY>xy)dz+&OhV=s();p(^u*Dglvs;=sk2J(=>w6W0EISCMe8-<hblsPgOLp&CE
zHf-$+nVsax0dN{;<8g*f&m?~SKis_uG}mq0Hu@tP3`GMfWr#$fL6Kxi#-tD#B1uWc
zL=urQB{GCcrVJ61j3rZ%P)Ny?OqD5xkSV_7Qg`?BzH6`jt-aS?``h1c-OqZS`-%Vm
zH(b|sp678K=W+holP5PmpfztxYDJT4ro}lZ3T`E?e3;fzLtN@+A-kN(B|b<o(ttNZ
zqodPF%O0E)%n#U(!wV8wCBolE0fBOvGEcsN)_h$8QWEAFO{x4z-%%GTBkrbKLq7&6
zcR`jbjB-X_uDec0*9a#PEJP&GH}DAX3J(+^W=~cb&)kJu3^3!^H>Qrjnv#xKS)~z1
z8VIGhGc&PL1BEQnv&yN!NciwW*^N4>3mc8JwBpcscL3%fv5=M|rKYCd<m|U>xdWXt
zkO&Axc`kS2iT}=mw;#<n({uiE>m=Yu_J;+)+K3SzDRkh~NmQl&V~oGIRMvc~w{tzU
za={~5ty<xRXDU|Ke3l~JW9Y0P_&RagE%ReXmKpb^JPav25JLjn;a>OwqCR;d35Huu
zLu%*>k<o%FzYiW^W(QJdod3b`1oFa~uP2B&ZW|AisI*CMLzdIfF@oH{V7BcK&KNvG
zJVFIzCVbtloKsi#CwEF@hL0aVrlH9);UMZpf;^!VNJ3dI*FD^PIvv3Z${W2$Edz4p
z`}Xf&tndEt!Grz(N0_V%YQoz83d)Y<W^Pg=-Dkx30{BY5ZqZ~Y6l>h~-U_}IBl?p9
zlm;-3zE(ki8QgbpJY*_j&god>KtEK1K}@WENLum<zW|%C8zCWaXiX(LGV~^rt~i`8
zm*r3Ye%>+JzA4ls2!%I5QSTue+dbZX876w~p#i)R8JPy(6FSdi<_URSbeO_rG4Ogm
zWFVJwAbUj9nKtQ2H*)Uf$3hh6c9#YaH`|5{yr^d#<d;Mk2d&ioyLWlX-yi+>*=t?G
z_oqLq-2-=`enRV8CNYL~<7f^Pb^n-oQsV%11P3S<yYe0{rbV~Ao6fxgdcc=dL!>!G
zZI2D`;KH}Z0^IH2QHzn2x@~4Gmw1TFZU7j|bsCsh-<vpZQgPx6XA6;RIDG$j1Z>sS
zCOoef;_52sG`;*FM0?iEa}JF)kS!Li*LcmNf|8W92HI#K_=6O1XtB!O-h5A+t8lhY
zw}|2KY<7duj|HP0#~4`JcP;X@8x3BRrHovRCf%<?;#Yc0wIYO6Zmj*bC{?$fJESm4
zOL0Z`|Liq41SX6kdRb7R%i9~YvU#0-Z_nb;8iBK$IaVIgdjsN!vv3+@A!@-8c_$Nb
zGZrzUML**aU=E!pyscwBPPe_|Mt9}nn<ty#7e#7)05Qxrjp9sN5WaRuDxH;=|E1M<
zIPW8ri7Iuv#;ys`)1L%p27sw}vKs~R{i>Or)_Ccxu_RGVO)VR8YN%JC>^$OcB+z!}
z&4sbALpCWO6{2TG1XM=j@>YB0$KI(j-G0wsTc*G(K5$(qBO@b!eVf+3AFVdlxl^e!
z<`<;aB5(5iV51jixuZpIbW!`%%FfGcS*!@cUsYo(Ew<XL<g}nWTfk3#b3;C3H5zWs
zP9gWOzI&1{D=OYV^9KMm21gdeeA0CUyFtQDy2Bs{T6l9xH`wjA--~C@ys(l-fzex_
zL9H1US11Xlr(D)q0Ve0FJps9PaJ=z!Cp(YvLYE&p(Q7YGDmI}<b<{s+3c^@^6|4!y
z=I)5136O0!%=BhV-2etR=buZ5T>4XVPJD)GR3X)Yei1$ue8G<eZTEiok63ZsCzvsE
z!Y5!Qs>dT5?a!xv$pW20Pfw4On@33*2&X~aIb%98F}qF$A~bx`6|f7;!&#yS0#qXr
zC(htyA|`q79FoMy5<M50k9kyE#`0UU4=6EV&2-Iy)93%u0+@7+nCh{mCs6J>Df~~v
zXl3`<vCB@OBqBWk1h|0OFcI1u(oDNmT?^edZx8`l2AzzRU7UNGYaulrfynCO_Y4i=
zyzl5poBUY#^Lv}9pp)1DB9Nd8c7;;hfQy#R9cg{N`%6yja_Ec4F_@g1x{nllfX;wI
zD4~QW%_#n3y}Q2jc6YRV)K>n^_-Xb2i4KEWTaZ;eEuQx~3^`MFS^)<UOrCk-J$baZ
zKeyxTCTY_2h8L3l;e+ApXc=SD$`3>aFwV5Si+9~U4K8Am%<~hb0OS3Px=D{~FZ~F*
zSG}x%se4|xWBwsMy_G?QkcGp2EaHfgm6er3pScBt1Z6Ig-mr1%rwmhfEKYW$MT+sF
z5wF=YdZnm4ZJj&ZrH^1ghvJ|jofe?Q<)-#{K9EM3e~*W7A2nJlBzLRO6^fegMqnWK
z^UcTg!U%YB`}S>j<Jv;{udrUbenvVHv}K-M9A7e$6Pgw{TEu5%Wd*J?DiW2bsji-r
z#}V`m=h7SCJp^k;s84y5%;i#UX-V_U!-H0Z;jqv5v*<br)|Qza>D_K*TSCv%68X(=
zEgsixhM9!P{ygxjDi&vN03d2002gVU17=iNQ`0~`6n-glCqkaBNeIs$^q_Xt9o>)?
zB!DXSqc=X`K?rCsrr8-;9T?k&^hiX-8O~LNB!h-;Kg3=$Z_<f0AEAoiA)8Oe*zj3s
zxmDf*>(|_@k7p!ulwon3R0;FE_irZ@b<Y9zc=M~!{VZuT#AYG5CwM4}4|#U}(lh%4
zdWXcM8EPMG0xG4-z^QJirS<lBe&QWJaw^zFeFa4%{=^NLFX}b`Y&=Cyfqg{>zttu9
zk>m+h1%kVF8Nl9d_UWH&GOg+SGFf&uwn?R(#*R&9G*j5w{Pg=j*9MyDNn&;Z-}Wc&
zCP4RQ2EApJ8KSm66W7!Uh7;D>cR5d=8j>Lde7#Tsn-xp9WpN_3FV@3Joqs&DNq&E*
z?VG*qcu3vjZnHn#_*LNMw|J+Of>!xg820cHTPyz@NZ^wRdJpA63Ksv&K%w9U7r(Jc
z-?_aF0zYu%h*Rr<^XDIv0HRXIkM!e(>ng7fx3znO&F5TfC@O2c9jmMjbn&E98d^<l
zJ-uG8Q<i#R2(k$zIUo%{e7&T*|F%XG`-^WtmWz_wdZ%3d*k-!8W(R8)9X2pXAtF#n
z#q-@-AY-6yp6QmE4bqkg2li@zNL!mP`Qp1#T<Td^3RXgm`5^xrgFeR_@$Cw*>sqz@
zdvNell{y4>`T3Q4dU~qL%C$g3v+cSRNozBgs`!g@`;4g%#I2G~g_IOf4^GEDfeU0Z
zl<EhOzCh5_FmH(zOYOI-8y^JD^~Ox1(Pg>;ZFz(XMVJmi)5faIxmW!p02$X*^n$ly
zL^RwB(d3B6t*y50#qN%zUmba{_JIQ|EJh%ZdeP`m2e28<79QW${Yf&mU-jD=1HX+J
zEYm*xfW@#KKvb)<9R<LWNPs4?5-_pQ>RRrIz*+ajICZmtfD-BP0*ssp)$T+KJnhTZ
zUfAdMtKS%5gGG%0Iogl-R7j?W-uMJaUFNTG`vY!Y&cQ$eJ<ax~wahEn*i;#IFUd$R
z#A`o|6COD*I7A+<Dc5pNbo>!VWs+lqy*I9%`3L{DS33FM@bBvvCMTjvJbVpFv=}IZ
zHJH@^*=)_cF~Sp&v>$x<ijmJHcyAuxV!eJN-;Ym--k$5e#meEs%ISu{!CPiJHpjB*
zF4(7D=y~s_rfNe%F7l*@_lR?E!m4d2_PsnM1aDxhh3w-MOHmD+goq^U)NPuM4mkhb
zM~(0k<WN0U-@8KB3wY=HgqQ+6H|_UjyP-RFET(N}q2wgVkkR?iE9SZv#I3cFi(DMM
z%w-Ne!i*sIDuj~X1rKT-|M$o)z+l2Y_x4k15G%1Hvn5UN70GK>t=;zS>HyLx(i=iN
zs!8wzib7=8<Z+;H$bBN+71x~Z_UkkW;j@zsMI%VEAzDnqnMyqI;0;%)*sUV13&tb@
z7#nHGB26<{4*fbPX^Dx&BzWQbs2|6Rn0D@di6+ZOc%gC77$CJ(hYk}JIw@T*jz8zJ
z0M|_O!~IWDm3GU-Q-9?Wla0c{_3GlS@lg8qV#0^73Nwhc+tRL2K)jBiaqN&|{_gb-
z?H~RV>TEtd?Z38Z(2ccsiTUd&$vlnA(o@`ko$WUM_$+=XBrF*ThtCMQP;M6_%IoRf
za#MX^i>|ArSHpdi^;PTYxG3eimRbGvQ^}o&!!NO|cX%JN)yp^$O@KZlCo`L9=Qq*F
z+Lx7a)(R8ej86p(MA!A0VbHIMa~d5@($7x*-X~PIw&A{#H=1CKG=gs%j)>}mJcL(w
z3{mkDreud@<IeOy#eMOFKP7n;DsCRZ5nhis2+v!H0ohD6Gy%l1fPe!!|G+@XbhQVw
z@t9R4@7b>>I|7d*Sj>6E9E*9~Ctj~V-_N3spUXO45o~|)6~RQ{y4O9O_vbHdCE0lX
ztYqqcCwTjRjvocF^wUyVmaf}c`ov{8NO0E+ThV{!Sulk5`K=G|`U&g}lz9&J4hs)2
z&$~T{>{&02KeuH~zovZU*LUhVZE|f>d05se!NAZXF1+p<N=i&z52Ui}L?#*g6AWdH
z{z!o%Ss2<!j-&wv$H#`DM1f;im2%Au{hw9)lvnP*ck##NH$cq)V3mmKz?`o7{iFwO
zqpF-VD}jh32$4Y7)n{F2B?&GM9toecBfN?~zg0xV&ORMA2<Rf8(Owmm@Yh<aGiIvE
z0mJkhVHSUjZfcah<-pwGH%MUYkv`FF@5?CJbs#FllJ`7P(><Gi0J&h=%w;|N9&$1V
zkW%Csq^53~U66@(Iu6}sg(#o(!V-Ry0{Y(7CvW)gY|akhyOk)oaq+k8($r0Q+1Zcu
zFWj`wtqKY~H2b|-Ur%_#XxE+q#{q4FgFGqOZsv{>pOiJyXL%MEa+vQ6-YrMI7;y0Y
zwB0{19pu9$2yWj_+RrUVV~@g*1PLM^A&9rWe!r##_{mGqVvx1r84F`B&+KQ}ryovx
znIz#QoQaI;m<m?5&$2?_53nk;vXX+Zo3{iZ$xaFN?)7@TUMXTnFkfb<+ZJYS=`HmC
zOv?JdNY)Tskuq;ey=|dq)~4EaapU;{f-mbhBs^WTNRv;O`#Nj-Jw>lpquD*2sj}Af
zhZ@ptjg8g<q5hIBH9Bi^8?Qsp!9nxTA%M--TSf50!F@FX`@d~Zd8Bzi*>{cz_>`6$
zoTff?Sa<36M6=#2`Kl}Gt>;LSI}as<(}gU(_8-dcceBYo_N|nT-?@)ZC86FLm=akZ
zk5-<!xz^QS0E~i3$^%uL|G$*#ogO!U?$G0?QP)4>A8b(keMx%me((bQqwn3M%|AbN
zsDHFi>wfAre0fIGr|qZB;!mpXxR9Dc<IWAP>$Z08fc+t<DWCZFvjWQsbEngON%19{
zoMl_mtu-_|BeS+Tp+hOdNrJ1b;C*vVndbCv@xmXc2D&>B{(5}8a60iu*vkGhckebH
zNf1m4FMq;Br8WA^HpNC6yR7Qp`fe~~^!?1_y0W$lwZAz(y;L=P@6g}{wm-*EEiLQ+
zO0w*KewzHhMe+6gP)0C9W++L#5w@ylz9|PYPAvQ9sBzNr0Vk@X&{DLeBZ1rxfQ&We
zGzv(fMASAiTALwV^EpHQNQk-N#^292X1j`y1C7Kvv;l$5sE6lM)4*8FFW9`)1Ih;d
zx0Q6vS5{O6y&h{lw;VVhfL$C;n)r``1(FGX2*6Nq)>tS^GL<0<fv|&M0f%EBsE9pS
z@%vk9n^|O|l?V#1f`E3owh&sY-U@+e)aFl6MUNs!mgw0cu>a1&rozXh&Qu{V8`E>2
z!$tmr8#jjVLHs0oten9?{jOcw?~inU??p{YJtE~5QDFUQ*;J)zZT$e!93j9~_oAGM
zf5EA&Uw`rK!|E`uxaJfc3#(vaUCTPK?cH|Y7ukR66#dn-JwIN8E8thbz?eWj?6y99
zOdU$;WMqgv@aj3JsHpgIY_2cYlPQT2YBcy%z%29-x`BxjlBh<A4j0F_e@@e5`+I!J
zHf@FXlaq<J>Ie*KiOd!H4|g)Xi`-FCq?ulG!G&BvhjB;%Qu6W+!|C@81f>peHa<Pr
zULqHF$L(i++bL)|^wD93gO^v+E$MXKyKK~7B!dQq0fV>`i0>qe2OxPH_NK7)ST_2m
z{r7_^ek@D5MkU?9KP<YTF?P=a(pyNHV~G+0MKqZUa{OiAoc>D_k|NrihWh$m^qV(8
zhcGXP3(`v>_99Rr=@MV;y)H?^c*D0LCo#AHqd1?>X@hFexhK|}<QB7z&{(|MR<l7=
zR5T_XC?L_)65yBUdeJ;YpVb5rH8i*<PMj$1TjHQDZxtSoKBhKO)Lzc%?(W_wBV!D^
zPv5n!BM0UGu;p3KbhX{SbF>9?tw|(@K36mq39;DBRMD&f(8r0758wXQi6Uas`1K)3
zp%HZhK8paqkjxQR-zLi)#8bl!ex##l0PDeYtbEew{_dWQQ6FjGhZ^@Cik&nB$U{0h
z7Q{`1NQ$2^ih?#UShQ#~w;ykk=xK2)f+$vq^C|LcTnO=Q2IX=e_exU^!Wx=r*v8rv
zKlU<kGi~5|=NMgAr%7b1t1wppuMq{Twyy3yaw~A=u0=)S03#y^E;B2-|72X|y$fS%
zmH~ST?o(Psa_g|yC*G1_PcGww+bq5tXnzk@6Is+)2v`rWeZSXg>soYA&l__9luiut
z2;>3uTzo_EsIcq?6@u0fq56c|esIaeWD3{-NLs>+{nR*_fec@z4j&KM+Ak}_|9TjE
z64QFQ(J9*Tx827V-<vrwcOyVqB+Vn>r41z70U&xG>J#FmuxEZP0c=shtZo%hA<oYP
z13fd_^sr@MSRo#+u7%OhI|5s+uPYmenQhNc!?6Krt3n_CUQ^ux@Tu*Ls!#2A#Znn$
zU;v&$e^NB$84yXQ_Ut88#iXuy0*}+0JXce{K7cB}K8v~?1l{HU_pBYw)eyFz%7jYL
z2g>nzi5Y{!vJyv&-Q5QXfcmRPNu~}ITU=-7F*j;}(W%w?9Ms**+m!pgeyDgGxQVfG
zONEFtfHau=npq?NO&kYouRKMW_d-BZN^5@o-h^>%^MK>5u@Q}|+R;W!s5sNEyguRA
z*e!?bEa)QTe(rtuqP9Dw*LRQI{OHuhOlmTe{dbY_7jC<KIxl1IDCu*=@<cZSDam+R
zZYK(=$Z>6Ob)6xi)F#q#5lm|&i^DJpZ%>y@CWF<&1LWrSLDQ*6d5gOL6fWkdn5;QL
zBd;PPD%wB*cO0mva13JQF-qz{+r8)gpE@9m4SVMj+J~@KRfFFFa)%kM5*kAz2rp_>
z;M@zFtKOeuLjc-U84R^+a@}J_j@YhJb#`_>;NH(zCi<l3ZXmRFZK5i$`O9=``9G6^
zzTwgrADNS&5>6sxa&`WCb*u2c?(~a55hA2?l5(U0X}-ftSazle(7Q}q2b2x|$}$vn
z#}{(2*}D}TfHurWc9-0;&2KgFkFnl>%ANNYB~f%<%3<z#mv<cmQ?D?Iq8{`XYcC@y
zizcNG5C}zDVf6LMalE`fKwAxf%O3cNJei{wnm*!ukUYD={2-Z$pc;I}-o9pXUeu}E
zAY=}J8G9+S1knuEG&kT63y#<=|9^_u$?gWj1T_3fy9XoOC^+VBH#SGD4*jH~fM$h|
z$dcDb=w>9&@ECfznWWE9lig2DOsv>>XU$@z5E=&+m5QJ9`v4XwgqiInGWldFUOgoV
z`-Ah`mlUbmCh|_KI$_$!PO(0-3Hjk}v%eGVITN`MVjd*+I{Yd{V5tuo8gf1=u&Mt1
zmVHxda`GuhbqHD`oJ|ZNR>$!KQRRI_83hPjAT8fZ%vRy5fa72IdU+Suz*l>y_f<;#
zXuY%$T$Wno^A*4gEx7ObEWFr=&jr*GBN}TS!MK{Jz};qNp4R?eUfd#qXCfE4*e@m1
zdX7#3jU_Ha=<7gOIZ$8kpI-YdV!kN?8BL3;)=5lnm`FW6?AeI|o%rW?jD>^D!@>*0
zv&Pv~lq>UmGvskRf=)a#Z5iOeSRU@aGr8wT$blB&s=B%EAwrV$r9&9!20(zXH<iu%
z&vB{06#?Ac=0O+MH2pvX>VGu4Jiu1}1rJi2ks1Y8twSRNE#gO~<{&cBr-L5Y0qmPk
znjSe4Nsezc@SeiX;hB0>Sy{KOMyiV|;VW!Lz{lRX!-?vt9|q_@HLhnM<scSuIGAbT
z!vCwdQ%)}P`(fZHCZ+u{ziwY?w&w7{S&!BcG=yjy7;H?IcAo|0MyxHSawCnf>dBv4
z{Vfa5-r1(Er4+y!v^*~``nm~+Geqv8pz(<67;`?KKpr}8ZP#iZ7w0KrLkb{-Gzdk3
z*9hD$meUhuL-UN}Jb-H$BonoZ8ruOf|B`|I;vPijx3CjjwL1XdJf^|0Z?4H4IRaL~
zwA`njs}ENxqJk~vjBLlFx@0pGQRq+h8_(Vczzv*k_i^FhS1Eg1o?6q5S1rKLPcMA6
zoA~4a)siE8AQ|UCSbMmyhHG;j1>9Q+W5R40Rsmv*CIbS1;LX+nydwh&$-W?`09;sr
zB5gpSDHs!JLnDvI0g`E<4IM%ZKjPeg9^9Uynf@X&Ck#RX;h0vabupiY3wmaf6G7Y+
zj3@$tbmTeL%mIA;68+j*s6%o0z_;X5YQ41(e&dr*^Q#bn=tX22M81xs^&npLc4REX
zh6vTZrkva*zU_}Gp5HfHdz*tz&3>L8Fdqe@ba7r+bj-eNej>M`MtW(`3`H&E|8F>v
z$9qo+EtIh{$r;Z=VT-V}Dy3gn^t!SeRjna9bJE-S;?SwoDp|&G$$piU&qcL>ohwXc
zHd!(S(z39=`r8)${~d+k|I(u84>~gza3u8XfIs@4!br!2z$RqY;Mno+q_|a6RmDuu
zU_Rh6P~df+l{C`}L)15g+7IGIew8pYq5!;|h%N&t01u=7g_47>LhN<k;|Bbk+3gc3
z!2MdS!;zG9sG{yZ*rmGfUCkuZwUq9SYmF`dc_2ElA?bUbc9M~gn)=FB%c_W^kSO_E
zy?4Li*_d*KF2y3s=|VJ68F*oJeQ^ya(9%6`dW4A*gI-iN<ZG+QjsBwV+72qdn{4_o
z?&?xjKLpRE5BK($0gK@`Gn<eR(_O(%z@?qrwt}a!sH`M%MZ<wU8P3~@E5(26Lz!W*
zaU<^Pu%%<Zp`KoFd~<U%Zs959Ygxlo@fX{dp-}{Epi}o(Q9IjHUGf~s>-)Z<X=RjY
zz;aMeHTBJF0qhQbT%``tNB>v&JfNXKR)quwapNZF&i+rF0E_d-uE!6f^`9M5wr<gx
z!wD+aEcmIfc_I<P#9||nR32uEP=Bz)icjUE&f3IzJ{tSTD2cg0|MwU1Uinl;s`k-m
zS9^u)t~216R{%D_6Xz?df2=&@AT2y(c!FzJ5^KMVWLJ5##o&iV{q~7FX^&>-G<2KF
z+U)Hs!S?B-aH+Qw>|xr?EmU)x>5=`~%O9tRbv-|4@acQrm(4S61r5zZ`psvCKDcyG
z95JXrH5DEtlH642pV?41EO9=1U42nl;)h?WSF)chf3lofC{HoRs3d+qlyFZ3AaO?5
zmW00h+b%lw&yFwM5LGP@RlWCW`j3q1*=5n2syefm%Vu(9XP+FE=4<X8&~=cCyw0zZ
zxUzpM6>~7nwf495H{9wydH=VR4*x%KFc1ul`!JvkYnU%?bN-5G8wYXQ-p6;3f=(+!
zvYAej8ub6|>@K&dL{&uGbxG94(li-eQGH(V(eD`>RLpi>ck4^P;ftD{3}PvDW_t-h
zbW|j0PtrFW2TunCWF0aKWWhSd9d}Oh_5WF^`WtC`*vx@w1jl3IOg{*@^Ok<6P^AlC
zbgtqxA3OdM#0J3klcOOf9RKZ$v&k3d*tChLke-ff8&7@si#1sBU?9}XT6Bo=*6B)g
zudI;77Y@#5p6fPIH=LR+Zd2jrwh_<m{6`DG-CoFe=)<`Kso_`q4;hr{pH6FNeR5&+
zewyRk3!^zYX&XoN!;huCe`4@6AzSD$y<u^ykcC_pZ-h^hCC{xkuIa7D$Jmr_22$S}
zevC_Xy}GqC`c_2;U-Z6e9(V`bt-HF&eAn5}l`AM&+{o*Xx-4{X`)o(w1DKupm<Mq1
z@fkDNDJ})@A&Yc@Svn}<=1m{WG+~CN{tHOmIyyUP(SnN<Z2>HF>zcaJnMzRz-V237
z8K7)=hNs8M&h8C;VF$1$B~Y*GFIKKz?T3im1?`cty82!ikJMtKCix6FhdY1nejVYB
zYS{$MC@gd|z-APVALqltO^g7~f*(vb#LOE@$Ed_k{52Gf#jr<YS+m9;HKu&ro8t2F
z1ps(TR@*!+xt9p%gt<RS`;O8!S3`)-i#mupfH4Z0V=L%r(5IsaABE~knBv`$beOwh
zWEiy=4s92q7*~Sodo=3(>xzmJym{Ns(nUyA^#pvKCx*@J+VFqzB4PR9h+N18RSc;v
zFseYHfa8$R3Z2x$mj(xIBS+yoxNy;;5~Qd@81}M6H9@ocDR}Dn+4}yF4X%@rIDE1V
zgq?c&oqP9StQ9P!p=C!Eqq#H#Q%nm+8`6!-fd~zu0i_GQJSUgl{&Pn@s=L?647|c~
zq@mDUE;i2hP!Lad51tj8^)KPM$AN;hwY8N6x(9;C7IHBtMs+udE~Et(b`=+gIUUR>
ziuWQnZ2$C$4p#*J;VZ~I0pI&NDXd&vOQ>-`A$~~Y7@>mQ3v4VXB;+#s%DRxxH=<{{
zr1UGA{p@~CI$#*bN(dt4kcIPvR|N$Jdmt_25fWlRiC&DKF8c-1MXAcl%a<>2L2C<y
zU%PD2ooi63v#26wr6*lgqVHu$WP!!>TDk2+41PIDJ9+7+Gds0~R#>+c`qMMAwd1QW
zTfsXw_gv8T-Ec&i`xCVL7~X8@ix;#nxr91l3%5o80no0Su<WURf~%&X;263DZ9aAd
zadGR=a_a}Xnw}d=n3?xFIY~f@{t);^<h_73oSX+>{pdn^NT<vNzRYun>rM}!ab#rV
z7UZIsghGe4R_iwbcf7odza)uTjS0SzCk-H-9fBh17>XGQOtsigs~>S=fjjQP=egBW
zRfBK!TO!mZrToic$@sDY2i1h7&_cezCYxIjP+M%f?Y>_{r34B;X6t$GQ1Hc|aa}Pt
zVo&S=<ujaj>-O=S+{G;<h|OW^;E?UWVSDzh*pwWlRW5HGz*o+DcUUPlutkzi$ZR=-
z=aG>F1qJ#oG*&0AKRy){IUPN@9qN+x&iz>`Z$a2Z(q-|WClE8NR=7_KxG=6aQ-8Dn
zRrmMrHnu*@3%sYp-=(ZE($QfnocgxQ>B0q52%+a+Ptz7U2-|Wg=aGd_3mn^rOP)so
zI(uP0{P^f&kA}qfnzx5J_kRBN&B*)~yyiNgs}y@eM&0ZsCT0&Je3(LhT;@9HYiDcA
zGS%1#p+}~ZF|0!?I;mnhF;=~O)uPWT_8~8SRC9TizhZC~3oE&Utggii7A%MZq;>)z
zsEskahZe%d@fmc@?ccv&2l>02bABiMNNdf-7BVo%L+tBk=<Rr{5WlhVLsV4MVp*99
z=b_HM3q9O{Wtn13kK*-o^(X8u!#WW+#KZC~X5R@958q$Fe?IC(=Y}3kaF?+E!X#pt
z=bo1*0W{dBC9B&Q#}jY4s=}W?$J1}R=RnZ7(`j-<kcxq&;EC_EKyYKA-NTO9C@(M1
zvUDje921#1Hp_Ar9af9zfsP|n&%yA?*+uR{pYA&<U)A-F&O7t@YMy0Zg*ErAV7ten
z9tfXlDL~-nXJo7%W2S^pzv|qYAr{}MNAh!OC;Q(eo1Qhm{vz`h<60u`+&LTET!sUf
zx5Caxb(=#}Z;_q?UnDeF=+;v^v&mE5WJv23t`tKVZ}2UKgh3@>kHTlIsODi~`vVgI
zD+Tnp)`wK^t^OF~Qz?$aMET5_t?tuwsr+0z%NgiVWV^!+vI|l?vfhEw`+;~jfyk!u
z>G{mY^B*vxjv~&Syj;d<I$iMHN`gV)o9Geq>n-q}y4(+^ObeX_VWFX(?4m|wyyklE
zn%c0GgK=KfH~slt@yIE6*rMa^*sQIsv0;~@lW_5pB}*hEBxKiz$g$8dt(D@y4XlrT
z?`Uto2jA>(t)Zs22S3eP^ImN6XE{1^x|xx{r1+$Ii;BsIcNgcI4M@bVQoHR_DImA5
zk!%S%xxmyVAyqq<uVGcW?56(J$MJ)Sv2ok6HA*7kL<-yU_TCc6Yp+A6!HdpYm#yqC
z-5xF2Y~!LZ!0|S*^X>bCFx842ZJvAur!_i?%>4_hL~HT;qXqm9bGHDuueH=e9M*L6
z=ijxczmMIy+*|pvLq8k9Ba9W)avb?*@o2B#TxF(&WsRAt@{dd_k8gEya-wP@+pAkz
zR=a<z<?!_N^TXD%Y^rN_lw+}-8nZ%IQ^tPb0jKvEUbpwblJTN=7if6=w27^NWdGc`
z!n2STpo}Ob3!U$10FW==^rD$nvNOm1{`p=7Gn!?!jtUWc!j`FBx~+M3^I&Vd4x<Si
zqQo$VvlvYf*APgamzC`m5Nt*)ri!83VOg<)e?~zm?hW_Ooh#8TFAG3>|L$fJfoo+z
zXXbOuT$<@C^+v3PznbCWr6wjOFDomT<|b<kfvoL7p^YTMt3;A9BJiNWHz;P`>KWH{
zpW3we);=#R1{%uo+XulHK@kytm<_WJT5H3f7Q=s|TT)ghN3Ekg78b;clsiD<m!DXe
zYzju%;5a5aGYcfGG4tdS<Zq@ApZfVR5+9Ro^Gy%|J8(2Wj@9lbTDUpS7%7~?OuDOc
zQzM=45-w@FqfW=9k+7T{XkLK}#so*tH})4CJfy8O3n3g*{JG`O1Ds~sPeTMWUOe+;
z9Q_u$Z~b%Pr)JrlwFn-zNpPSmasDT*@KIfjeRBnCirXy@A>AtLYs#`VW=;tfWJB@@
z#fbD?$Sovx?u-RC*Hf3kNG#RGS+C<$0jZO;1tYZR(Nntsc+i1-N2~FvsW`+h$z?qE
z>n)&lXAuz*v9+_?!l4Tr2P%e~<t@jzu0%(JFA~`<uuvrsQU&6$6Bif9k^Q`;W(jg#
zc^q4%uILgO93L_baVXUPR<H6yby!NhjE2JUkSv7Die>AQ73aQvkVBU=3kwU`>~Wa-
z?c?uHPlYUFfm8kOKcVZl%CShKi5y%RjMwh3xUM2YN=nLBdOK@$<SyZgAs^MaSw1QE
zw%)j$#g~&_{V*(8y>rFxq2bSJDgpPjg&e*VZB{}*orIW<-Qy=uc=yyEtxcce+qsrC
zRJ7m5NdEO{cFQ&MLzh}}xzgYI-ZltF0hIuO@Tv*-<*X_mrn1ZaxenH#Lz`*T|E_5T
zof5>3zkl@Tm(~9JcgI)k`~UebYM+pD8l;d+;2#`HnGO=#iwC!#T=nl$&_1!yg9=rp
z4^=g*cRqIPSZTZ|JeLmj3)NUUM7~bWjW@DS2)&#(j~k`H+#eyUEET1Jt$N;R54L?z
zKYP=C{hz<MyY+chmEWR0LI+)5-+I9#@lOis<MUEz953P4r`}IYv#YXWHIr3;<(^->
zcyVCGakFFl)+5HhJie8Z1^?<FCn$}7MJR@X%fS0?`4&-j`Ao;XBI(*fG(9thDPs0#
z&t6fCyQH6%<FKKOH>9Gyr)MFB(C#>)zb#xd`HFs;$mmZ+k?`jwB{T;ln<ST~928nU
zn0|0IK!5yL&f<HA5w?~y?7el~m^-{%=DHSJwNmT>S&{JYTZKwf()Tufi0FK}p#I&1
zB_#phPU*Qso2Gx4dmZZ<z3yk~wYpY)pQO~_MF|Ds0=#QbO$NK>jSdd7EZd>Ewn68^
zhYz?MA0#);k~fZ}AT`BTQQYvud+}y@w72&HIyqUa@w7dHIUoXQsdp9*x6Y?QD%%Nf
zmz>23<jN4$>9}XrA|4IvUl{77f0UVNwMI2#L#S$Ae*Qd)z`+;NR!BW~j6|7l>P$_2
z{fZSr+JVz~Ih|j>GGTK|!4K@60DWs~YuWMUxA!#EeueLR@Zf>%nKSZeY{gga(9o!v
z7EN^D`a?gY;<yv0u8`}&*OF)&_vXf}27S~5Mo*ng`VU-%GiBVF&z010QC3i2w$5N)
z!fS|V+q=5vQ9f6%%0mq7kYilvvJzIKbW~|sSuBF3(?}01DCx`SPU=EIWBws;3)-2k
z5#DrgWaM3wjG*8mWW$&B)7WGLFo9-h+wJ!#(TBkMth}~m+qPwZR-fb4!lu0h)XC}m
z`B?buaBMrlgmG|3N2Zy|xKJyiUF;|rE1W}~xw+!7z%t94M*c^}-<aSyN27%t%mp@|
z?KyA_bJp<$g6ogm`5|2GaB=MG%4g4?!+&Pcon7Y^<7g{EiA4f9sZLO~%Yw!(6_xTc
zurW!0>;dJ{L#SjqWiE2Up5GlStf)waj0`|p&$M>!TE(Bj-%dB^kOltdPv}a|-3haO
zpUwb}mxDI)Y^7noBQtj2bA$?3bUz@3^jf-&jw6Zv9xH5WY`s?6g#!tKG{%BBye}^;
z<zByjK5E;&(5#a1cQMFn(?x&9%dqBVCGGQq(Ce9IxhN|yB%*1p8B{zHZZlFdF!h81
zBQ0=_%^dlU9oT=m2tTeyF0QyN9kHSe?;`_uau&1g(9<J6UhDCAl<jjk&no}<7<eT}
zTuCye@%ZtV<w)nYn7&}LY{_AU{rgrYCPAb8oZ>Ap|Ae$8!)By+MzrZM=%PcIJoG45
zAI(!fF)`c)$G@gPk9lS7Zs+BbYM)S}_42#ZaJ1S${_b4S?SX|`3%7l#i_?wA3-+cN
z^!*5}Lr(n$@oDK)!fRv%qro-ln#tNuaC3NwH!pu(w=TW4^C<1#uwR*gV*Xic>l!H6
z6`Y+V#a1z+0DNHh*aNL+G!#$(JCFi)(QrhQWub#Bx0d0#A79iLdc^#&f%BE51CWY^
zVD{+)W$0cZM+gcGBu5|~F^V6|<c>4QwWizw!N@NQLXvO=nFGYP9@y7_4#+1KxwRdg
zfWR5GS37EM_8M_a@Pv&gy!lZv@%2F9%243wS%2iikL(f2d9rwssN1}OJpfrybMcrU
zMFd_MY`mDb-IV=^kJnd&JI?+)&wb;Bh?Z~04e9~q{rkQ2)4Ws@o`G^41S9F!f=>v5
zv1)`=B7gc31C#%FB>V}QzIYr*QC|)Y4!ZAKj~aR&81&1KwO{>A{ETzc9k;gVhWp>z
zbxy>6`n(QS_Gb9uV2YXAoz!<tO)Cov3nlD2>43t}Qy4;T6kA>ZfCn_#Vhs(A%ciS}
z`d}Z9X0IisKMo0n4*vMD2ZsI<jzh>4uHwSjQmm7!8$R@>Y75yxjDX69!prx}%-n`d
z1pkoJ5n0#6hnrz6wMcd*<b&5ugLFYMnkml(zkueR1xQYp34F4Cm&8h*7wAKpmt2a+
z0V;V$-AhPs4Sw+-+f0q%GRUBqtW%5%p4^Oo3ME5E-@#<<arAZ3Q0R5qzH{dtLy>Zv
zJY3X77hlBf_zAdsm120U&L;_|T-HhVP75L2xIppLISZ>V>*2$ReL3$ZRB`T^;^9&5
z$h*q!JHXV8S4?a<RSOvi9X<WZO#&emES#L1*5?Cxu^G<eA8ih${g0R`aw_`i_CzqB
z)PeXdLF(tH$1@}fhd@4c^z~iE7zdx(J9q97G$lcE5#C#Iv8-JiR3mZaurfdQ`l=~B
zOxEX4O-^2|!dbLc@4a^Gdr4z^$QO^Ilhq0I-y2vclG*908p+znF!q)jURH{o9{lc<
zGV<>{DYz<ym>oHCq>OG9`)Xf+?-9-PBZ3l*lGLAI&C6>5WJUaOE8aU5qm$xyNTY-=
zMm51hH9=^&Fflj|wAz-90TA&-d_a3+M_1QnY{d@L0Ajx{r|m8o`dxPKzMp)8rd+Z%
zTfWgA$T3RI1{z-_W}&x)bH_TMXgGN1SB1Vit;^u@AB#2N?zLN+=uh+0tYGle`|Nk7
zaBsnSJb1G@dz39F_@f8Z50*!WBsV-*{%?B0`{*uedA};`LND%xduMcS+&kgdhk|l_
z?rE`A9||t8Q)mmF=hvT6KVc!Z%FYtC>`9W3qpPVL8l+gAl-M`srOqf(PuuPOlBAz~
zKaX?uY6J5L57QmGNe4=VwuFkUkMVt(q`Qme5!X}EF<+|}7l%GJra!u9Q#m;idg7`0
znBPzQUxkeVIV-1*TBI6LNd~zO@}7dyJ?(A!4t%h>b&=!1?Yz`Adm5L%mu!3Xz}~__
zC`!g{F*OeGXEPuogtL;@ubBa3bSHP<a8cGt7o_(VYI<mLx#AH(m(Ib}NXmtV-`0)=
zAgvAE0$EVl*TWDgvCeXHVbX5T*ZT?TB{p1YDM-cF@$k?9@$tdHGY5%a!GWKOac^Rv
z^Pq;K*%~n&z<*t^m*d^Qx=qs3Mt9=ZZQjg+UfhRv3U#*j_Ln0fBGR632*Uw!44Z*+
zN9|z(mz^B}NrOWpuQK3@WH)~tnMoYBQt+CVFw9#&*3WX)3guMiwGs3!3BA`c8jH{R
znLt4U#_}Qm+!Bg~mDMDB%T6_Yy*zOm8X7e1$syL;0ai5dUbhH9joqDhb;Py9Wl3Lm
zw>Ln`7KyvVq^2~4x1J(izkis6qvH#R*fKCJJk=m~F>YrsTK3d<_pRaK;Tead>J94U
z0xKkR9GHVLKKpF_<86B@e{AoTWam8Htle^Pe2@o~o`_z7=NbXra?$L}E_A$KA}7y4
zbN$gK(!9Z0cpQFEZ1pFvS1PWG{3lmUqFXU7KL+84B9f(wVYRSLtl{(K&D#R88Zd8e
zXtoT~by9+&q5^REbdr85F%=76oY+T^v^Y%+Cmim^ExiH0i!d^eg3lj~sA&4J`gQda
zx9uY_eRjRb^;O#$FnvJ|_?i2(F(dLdI?S8i4Ra>_o>HGLAC0w$@xBNet(cL($V0=-
z(SV$5vt{GvRQ+rQie#8j^XMB!7yQTh|8gI88VNsCJBmFyjP2kKsff`Qiq#g{0_(`g
zVIw1>Mi?`V&$hQ86|-tyUR_;{^c(*W@E1@yIhxTJz8pr}!7rq6zn{K*IR;=43uRe9
zYQXInze0V|5E;h_x;$S5C;c6A(r>{@LlvVAX5rvi2s{gkJirKTw6y@3--9oWEqc1`
z=gjS;C>`CVySO@Mv3~<l+56hz{h5G-?;Jx%xLlp_=cfOtN)*@thrcnnr<ok1nOqvX
zoPi8UBkvHu8LHsqObT1vNL&k+vk%~*cPY9W!ISYgBp>_$ZBkmA!GjwFe8vECMUhhl
zp76Z&laRZ+g~5{+!MzON;48_Tf=m@!Nonb|fYn|=ca{Nx%Fscle@@7|b$5{?i~~<6
zuOIz8oY-le-`+yxiZ;tmAiNwq&+xgpxWpXT&<=(Jnnegi*s*3x;O_Piz2Pcdp%uq(
zO1nym7#CjqgS8*nupE)|IV2T>a47>ms#qtPAc5<)2MTHn%D}R%a1BoR%SbwIs3u6T
z@6*&=3Y<kJzESvb(Tfes8OSOi`5bgxSAi`0HpJqN#-WD^1fCAdJ$*y(f?$`W&ybmo
z^fxR4A!?>(_jPF+lDtJoZ)^3szkLhXsQaKDXiJDB=RdbAqy(6od!fgh0ptPebJ@|e
zGb~?jVqF_^sVT&8W2ov=w2?bubk*zTC7VSrnnI!F2TI{6(mH<4b9U9bRag)QHjtBm
zY30^Z^IKr#R`eQP)tpPC@t7WBg83%>tR&^njKP!H2AX_sw3ATgF9gPJZk{B;m;#4^
zM@GXdDP)L)@XTb!0@3u|^KZGngLh(CEmW&Z$(D4;ffE>}zTiV&MeSZKYFwyreeG_b
zQJye6<lN=RBqt}Q>8+r^Mr`@VtzyBO_{YV1e5sD~`})=L<!8>ywkRytp$I#y+SN*X
z=%L?He=1IYGIM<k41*s#*?<K&-DMt+2rmN{Nj8e`J>&=BNufCX^<d@+b@FegWH!>e
zU%vc_OD~xNJ`c3n`plpa47}y(f!g`F3d-cN2JH@BROWhOb_HPB=Qz$O3LSX1j*e`(
z_)cd0Z)BP*A?snUxuWd`;3rOl9Lu1se_zbqGySk_#iE%<0qJ?T?_#gUnEJ3%H;}oq
z&mL`8rE!9waSH{~wd!RT;5o<|jxr#AvG+fRnvajA!Z6)gN5`C!Q7C={s2GiCX~}*@
zX&%?v8Q0|JgysA5XLXPi%@Is1u+!UR%}$Z}NOnfTjeZ3uO=)ZEkv`kMA6Y4EY-|kV
z0GQA8{H80P7WDudy;_B8iZvXNE(3*81X24Z&{tvi7~$i;zsDQTP*yy{uTT{BSQ#Rj
zUB@$e!#rCW;*l0nc6jQz7)30l$uI7O32p#d%{sA1380R(G>9>EYtWo|Nj|oABTnQY
z>{2onX77m;>&f6mG;@_afBw9EsNP~biX?X=`CaH3e>B$Hn1Gtnu)ui*pwc|RuTj#j
z3_z2!Eb1BX6^dZFu6?`k<<&~N6Q@ntM2%9nJt$=ReyHQBy=(R>t3A|rbrX~tsAmo1
zA3=jz3K7!T_Zh7>HCX82!{3Ge);y;n@-Sgl<%`|fA%ig)K>;Qz1`p55T*%ij>)`yl
zW=eCatni(5hN$rQoUdxat+`J>ZsZl(87vTU*~8;NfwKdjfB@k=2gk<Pa)})W=uXg6
z+-{qC&V4+ugx8R?kkDP^yhaf@SKOe=S%xJ11&GPB>sNragwz!kiYU_5Ysfgc;qwaJ
zUNN{z<e>L;%W<VaVPRfC3PWG+Y=VtH(={}q+qkN#GN6X{1mjoI*2Wd>I(Dl>+{=So
zHcclYHLg8Sk-lNbXhn5he~Ov)>bkYpe<s>`wz8+;WBb!g^S2mlYcD6DVZkW9aZBRi
zI<`%%H6Ooe=}9cOP+s`1Fhexk_^X;)SlXnf+E3w{pI3cU1CP?9^FF*5WB`pjB^y}v
z`5D;ZB;9Xd^4Y6gkn6r*=;^}{1X&;9O2FVOCBD|j&CC|z)}4@9ZwBMbvtfhs;XK>e
zYH2LKr5+w0S8yi!_5)kLf|Aq(J)Cn7l@W&mA)C`KN{<PxTes&hjF)X^rqA<$p?z6Z
zwM=Z4e;`zicXnUUdAfj%4lMbFts;z+e-T<5wx<|<Ou3seGRfd!W~I#HWD_5Vb}#am
z=+9%vca7v^w%!z#l+-7m%d%>f5aI*_PyzV^2Ld;vy;S~Zc9!rB&mG!q)v;nnjV;<5
z!^8G}RZBSOSC}O+71n)WuL8f)@RpcM)4mH$j~yEv?ASw?cBEj=GM{ONqn&3Ue;Y*O
zw@GKI@7!Y{g;r91(6Nz6|7Za$=1>f!fGfZ2F8GT0{w`Fxw{Beub$h2YNcs5`(oo<2
z8>;+I3kvMe=n&b(=;5BA7XAzr1A@=STOp73poOp$vUXZGH#hBMO?pJmt+$wvq%B;y
zuoyd$hC&9ob>fDvq4mdTtB)Ih+`QmQQ`O8;d|{m@GA&9LmN`g3{KHK11toD<xL_Uk
zG-q!_<0lO}dpPg+)29qJFj^&Ty0hn0abeckqsIDA9*TxW2~XB_el>{tzJfKBGX-M?
z087OJ-XSp3oybV$u-@9(c^Iba3lf-3GJus;1zYcHTM-w<6u{iw+35k3kY%zn<2L%}
zU8bQ5#FJr?p#&XM^^=z`l|K}^u10&nqT^d{#Sew^gQ}trbkjhBF4AOoK0URnZU^1q
z$K~PN3mfy#iBdidhlQSsf64s&(o%7ek{QfRi?M?>@8lFaDkzLqTP!l?edGpTH6*(<
z6xj2IqqlIf(NY+SB>I4$fbP>O>I@JnugIm5b#&a}0d^Zhx)J-7>KdzVdvs;3T>bX-
z(x<xb%}%YRb+Wm-5%>=nnk0;Dt6sKCv%O$Tbb?jvrmWaUwu{Z^r|Rl_tzJ4dhBt?I
zX3^)Y<9&1vFm|YEvrRq{MvD>Mg@Ed5DUjV)`=Rn=rkutHWApYWX!7jX!FK7=rOqe7
z&WZt#LYWaRHTLKWN^NSmZL<z)AoIuQPgwyQmSVJuZ-z?%;cO5A#U+!Md=EYUs-Q3|
zhsXVAVjchmisBG6KM<?Y{;>7%f4M%*DZI}StZ(D1Z`m3Hd|rOsLZhx701YHHUL?NG
z%Uq@ynCI0lL4YbtVFg|cTc4oNP<luUpc>edtle4g^W!`MCw74Uz6l%BilI*%W}8I}
za+s(=v<p`qZ$dFjZU;3lP!dIR^9?9mE&~<fg*-D|>OU`40;n46+O>-*Nv><xO!70N
z)mr5B%=h3RuB07H7`yCxs<*iC-OTcP!VlN51{K%`PP*fu+`O|_$)phDs}{xa@M>kr
zWNsbZR`1akgHqG(Su}hXjv{sC*yXqh0PPzL&Pz2eTKnzWHz3T|`&)2B3$WxVkYTiB
zWE6=vm{eba#(jub($B~^-h^%haNy8LuNDrFSj?uHU-dXI?<g99%-afgOYPpx6H-B9
z2#)Y$sGtbBikc>XII*9U`F#xOH+xYZBRVC38Bs9+8|IkRGnV4~<#jm+*qRUMNqE-<
zxF);)-dWi)Wv3V|l@yMD=hKk2Y0gry`O0k%szmz?C+F}ioq0Lqw)l2-Rr-@fIzpiw
zu&GtNzBVnl2E<wr)I7h*!qqnrFFMfKRSX|^FCZ5CqNUehP7BddD4f)q1a6PU`wpZl
z<d6WfcP$JquLU0^b#-+KnI<hQeRHcB1Mj+Z<TH`g_TA_z5W17lkSk%eu)QGT9XttW
zD8yAUy?}wlPlub?D&<!TF?JugjmW`;$ia>`c2C0U(!hpj#kei!nZJgjXa4LrTHXtM
z6D*wQev-p$@t--3dIb~l?q`x2r)2{Q+GoH;Q55u_idBdlG%ww>Of97{Jcy7aFp%Zi
zYVpMo0#NR#-H<iRgGt#Ah)OWqGyu2Xg-jo5M>5?>O>%=c7TW<=5B~gFj{g$Acq|5|
zQ>XX5f${LYB^M5fB|b>sPEJA7N;u{dUJcki;c7d(9=0l1{rhIFV&Rn_CO#C+)|h)U
z6M!*6#i@Rlx%+7Uut1C`JmTWd;K&|_u*?^VXQFp$L>)7pwuUwI8W4Mm0^$PjhPjYc
zMOBrS!g$OW<#NNvXZXpb!@+^}<!s_FfOb7infI!_pCJpH_tGsB)2;3ui&Q!l_p8{2
zG`ZQdttD~(qt?PN7L5;iQ0N7Qh3R~9bxOZl-8h=9NY12v6X?vRp%6d!&I}%BYVkk2
z$%@e|mO0eZeuF>4nDke*fdx1fwaKX%-)~rHYU!VE+P|6M^Ott_y@fP{^iycu)MbF|
z<|{P#m?ucmzrdiNOE`w?Uvb}E`l+{<fMJj(@bU4*K!)#Z29X6tIl)<<arZ|Y<96^G
z!-1qZ&5{&(Lt!dWxe7=M1y<82)qaB&!^NZ44G-4fAkg|MJ|jC*%1xCDEMs_n!ECK*
zgKi%=*>B#}NwLH9TS^w1@2jp-IjejBU1QA;MTH}AsxH79OCf7axxqe^!R<t&(x$|`
zU1#K#gk<=*Jf3qR3c~-5*?rp16FB$rDqEk*HDIfeXHdn+sz!58nwfP#%tX0kxaspx
z8ORq=7?8Dg7YIwI^T4Kx^um=DuU^qn;B*Ww(k5KvUU<0BW#|5AX@5Wo?MNA~^Ksq8
z^!GCH8!y%y3Hki2l)|#QLH3B?D?Esx@NjRWxi{~8F4)j$9dGt;Bz=ZRdd%2D2KSNv
zPjuL!E;~;a<#^d=wD~lNu-rXErqiPIU&zS#9EX3IO}3Vn7J&&qef}H^HI#G6)u$HV
zc3!@IU4|3b;r+(t3|Z!HXxT*!-MJTbP9nFWp-@-x4y|kw7+CNH_yw77_6$VC>s1%5
z@Kt$kvxU|1tAJ+#nkt28kA^~3Nm}n>%exBd4t?m~c!(@3h95}W=TRh|HrLCE2tN7m
zo#0Tuhzxl*?j-lmot)en+iuKkKtD9=-3pNtNpCGVnGa6bLs|y(xV(J_YVb`*o)B<D
z@n=Kz72@!<dRlQeYJ8-C&!Nm5f=_k_GC)$5PyhN=CjP*z6(P?PGA`=nY{W^6JC!KP
zdZ#T#cJE$;T14#pG|WXhVFb15?AY;-8(lRG+dAh$;AXUqJxBUaQG|s+@xCwYWPZjz
z#CP~NyZOvEn2U8Tc;(?+h-Nw#0Qm1)+Wy)6bICBdxi#21Oa`L~Wv#j?Vr?nlJaIa8
z<X%~Dc%s}a;)Uk&Zvm=bdxqEJFfu%H#A?mrphHbkG!UB!JGy}JCxDaia~`4GD5>)v
zs9t!H;(?PK92%0t`mO~~5u(Aq$ip2*C(jV?2_h~TaKXhzYAz;$l=>5s2JuirBkbB3
zFPe+I0?2w061PYPoR;L}0n1<k--e?3U8>$PSZ^sH#U}kf^6C8FL+vyTjn!Ik+-WUG
zWiAg+iuA3y;KA>0_8xfQf)%jWS;!iE;6f%QvAoy+K7tp&>j5{M>R!TAb)DY~n!m3A
zhCWvHWl;~fDnGY7=(F&JjP!L_2VljyKL-V$(}@NBI)fm+%0E%r4At9Jgo~rdhdU=d
zs{X|aw4bO%xntrD*psanirn2PCQgY1|53mCyi~ka^WmT-&CAcAI@ZeX*`vOnPyXNE
zPw@MsA?tnR3aNWG#U0pi08`DOn}~y;2GX4yl1%?p%)Gphwp1*u1uqvo8Tjw-Yctrr
zV*HPvVF0rHPw}jpffo&vp(VI-1z-XPV*wVNKi@&5aCm^7U0uxIenKrK3v3^|ke_o)
zs6g<N61l&h&cAN^_7yWTGd_NPZ}n!|OXd-h9~lunMdVi4<n@9f;o%GBC@rmHLz)w&
z*>qX@X^MvrbHIZA8rDGv2tF3N*xFy;J{Fu#3;gHGd@7mbSZd)LPpDk%gTuMjYY5JR
z0um1blOWa*8WT7-Pq;ld-6Lk?=a>(B8Tyq@eBgTtsXwoY_2A=k-PLQ*J+=nI1JrKV
ztt)bON=U>&`qcBWU>8t1>*pJipN2@${DPEYd~%Xd;GL2X-HcC6lvGrx^!5FFQTzkS
zv}6aVNH1$zhKG$ck#^Gs$P^8TTmr--r~Jh*gc`szQ514kZ^gKd&8v@x^SK>@woc0s
zet}CuYW_Y%om$LYrDUOIL#JzHhi!!jR!pINcR=2U`-e6rFVXn>lN7SCITa@L5MzEY
zDtt8paT(gc9$K~RC@n8<k&x%z$QOfGwNV5o<$wO_Y#XwC0&QSE<UEQXepjwsISOMI
zl=h17bFoE4Tdkz>`4#{a=n;LZYNBPrCkOw2wKka@L?p#h7?+8MOJKfQG+|Q3AcJY}
zw61u0HA|=pr6Z?<ztUdA8HDKs>A&xRb!9uiJ{0sF#<P>`FnbY?PfWb3p;!cSN#v?K
z0s<@7;ejH-d=3nThC=2m3+s?~hyK}ctOt1j`+H#9Qxr~_XDSR8>pA14+aUxa5-Qln
zF~9~<4#IGvL?(9$P8^|S{m=A6DPiw;SVK4B2w<Trd2fP`Y`P&I3kp5UybVzvay(xE
zRlGt+Kn|j`vsyD>g$N`&&zv}s$h<CgSap~#3+|uVLZRccIDYm;>_~e#wardZTM8gG
z%JobK4m_wGpTVgi7#_~D0oMZE0*@Ved<lNwKUayFAMM|)OpB72EqtO~pvmLY9^1dS
z7Bsq7QLz(vwgNT=GO4p`mEm*v9C=gemKZ3?iJZc~Q}y#rauj%btGO!w-O$c^Vbh6e
zHNJrJZf`>0Is8t?W3Sxht>ZsW`vi=ICRgy^^=tGdL5-cz2cQr^t}8*9i>XJz=b@vx
z4DJtbDIJ!#mtplKmX|y5CjW|=f<`NjZxyrtz=2-t7l>Z3Kh8omTMB^0-1DW+%#xzX
zzp@-&-joeN0x_A79tGaM&48^>-Ps%Z_Y#sgAL`TUv(QaX|ERes<0cK+Z*s1BKfIbs
z=Ct9kPjtULPl^8THsTdxObs4*jW~)(VvC$gz6w3}gz=b9F|86?2&qM`qVnH$s-XQW
zb?f{QjC(5mWRLh@i@Y*!o7EVGT^lZDWXx2Q`1ePT&jZjE7Z+y-Uph+m=uvKh>*Fy1
z+P?f59kiQ7w*7~IOWRR7%KamZ_VS)$YFeCz^l8+5zE#j-1pbR%;}B-WGDa)G(s0Zt
z=MkwozV-5F#{H5vgM)b;{`0%n;k&=L6^V-M+PU*qw$1+i0i|#LE(q43G>PR#rBbK`
zl=tnUqp(7U7M(QRV+SteY8A+gIT%wX7-Cfn(B_ts;v|whz||Owx*g|MEPP|ln;s>U
z7#EQUn0K~!DIh>kAbBm^XR6{85=_7n;hm5}hfc0`V<x6sBdCPC*zP!>|9u_n!BZiT
zk^TVgc<|Af>0Sz6rTxnnFE(NQcI;~<kiqTfa_-;kurE?_1tvX<IJjPU=el}I6>S<W
zxHEV(ULk7smt2}e*T24s-Q}1hH@7Sad@mW$r5b?B6(@OrsvAT)P-t8Mvve25=Qz0B
zgXnjN+$t2RY9n(Y9D+-bOyp2rU$s(IHT+P*kn~d4&>iS2F9y!)j|dhyid=(8p)nly
zGO(j#aO2|GVR`e7Z=2d}$go2H>$O1}x?z0`Kp9_%f+99S7~^br{$(J~+%sKjP;_xO
zoT+H2o`!TTOi;Um5}HW3AWi|cjXXpFk&&d)xPeBA&dz*5w?>67zUT=(hIUWLo9fO#
zxzO><a~u$vB?k{HYv^|RsTNcbCQwA6tA8;VckYF>I3F%Swhd>>c^GFu50pnOHs@+3
zb%o;>m_K~jk}9{%!295alb)Xhck<@*Po(@pv%n=_X{hsu06h={5&DYF5_C!m3Uuh-
zprJ?$0*F2yvd;KoF&xd0%S#uH9eDM<mbx33!<u?Nkx9cEnqyxjR4R`Yj)iJMwnA}(
zoJ6YX+~TjEWe^tnVoF&-TOU}#p|3VLu@(bCh#Ww*0bIfSZ6Y%fbI8n2FGF4UaBq^?
z)!#3^#k}X6uh+LI6j(kCfl@RBI1Qo3f@=ZIAFTW#l;BC)LK*vT!RR*%K>gs?k7sQ%
z7VoKm(wI)J7}l@!6l(q~qv44|xFVpebp|$;Ql-GrJ5T~+gdas}W#DBz$T4xEo44c$
zD_-NcgyeWtYD#+{E~*&CyWO`BI{{vsRs{2RE;t8qGX9-Y%87+Yr??lIe|)+ViUM!c
zMSH$i)nTEVAToE>t^#iL4AzE>6mqLlbQWJd+xjZ`2Mi+S;vIakHyPyY6MY<sY7qn%
zFT^&WNaVmgm04M2uOeMWkWU4WyauT~k-wy;i;|{dG0Vo<<Ea2BBwWTfix@rif%Ox?
zGLbv=v=zx9Jj7_df0q|V|6K`rJJymH8eT}K1&dOGuSbY8?#W)qD|8(X3CKD{ak&g$
zG8%3QygTjMR^%o_J`-kBF+?K{*)Cf3eLz31`cNKR^cnl}1)_hMi2%x*)J#_>dPpft
zbNJ)Q71#Qq0P39bB<iW2T0(j(pGpPBZ;Gx735UMr2(=!BvjEg0i!_00ZT<$+9cpiM
zA4T&$f;9Ss_#W0<&l~TYQZVy96!nwL6VJeYf<Bs+FhO{c)nmHl&Wjf<B85OID?n(K
zPsq^F(n(7_g=G#KS9zn8PxBBjL@xoP;IZ_>v4jP(ULz{?r%6tGAvU_Kt?k2)J19ij
z=NRE`JKA?BasVRzQq)~<;#HQ>Em+Vm)v;-zhf(U6YoE<kB9R2_K_o>J_7wo)UD!CR
zxu?Wdxxf_{l?p$xTnurU?@mZVDD))zv8C+LxIoA{1fg;~@6pb8=3?VeI*pFGPLFQ^
z$mot7xxG8u?ft}{rG=r|z^`#Mh!L}q$TQ%}%CMHV0HuT$?^R<0-cv3DYw3(5dN|4?
z!%$V{I|`%mDlJo5L}W3R0TE$5#Bg)5Q+H>z9MopR*f;&OkU41kfKF{{la(RjhrF%r
zX0QNx1E^UgTqnhl>n?!$=^BhJvFba=$QlE@ML<5{C3DVF%QfPLEB}-VZ~x|zj|E~I
zG=9810GE22lZ196STyuaJ1<W53S2fLs{`j`pQ{$O;+OzHUWzmm<vo6^mllPW&|%{?
zwVGJgYv0#igeWI*ewdFmVL`$3tf7G&;Mx42%!?N*0$AUS;Y0W-mG0gS(BL>SRv;01
zfoYbtd3L7ML`cUBY~dB51;;=&cXUPhOcBjhY-}vpR(RLAT*8@uE0pPB{C*%8a2TOQ
z9C!guD&D~YAlU5GLx?X3r3BB5Vgs9k1lZc>XM@9#%Cc_N=bRCOPEhxOA(k{F8d*FL
zz2Q_xnz*3b#1*-tGi@C=H-#D%Step>DGm`~=dI*iAR7<RA~T*UPNgfJo+dcIkrB@$
zqRN!QFdrzzQBhMAs0mP2`l+U{0%ykzGcV{oVc^Zo#l<BdDanf~!2J;11SUL}Q2pM;
zXzuyfO{&|5aVQbF7P^4sp=CCb&*8@FO#^Uu6!C@wE*>Zm0ujV9ac?ogQZb5I$P9?O
z9Wa2;cLDe~pCoxY!bu|rE9$vMY@bve3sHE_^WmD;WJ7@`d)Ag)ejAsENarF2<$rd6
zT~tGg?h>3yC19<tqd!gx0z;fk7&c0JCjAR5kXTJEgS$SZ=v@+62?d&$Xwfu)ESP&<
zA)RyhVMrN@QGAa^op}!<|L0`Dfw<*fef?GFPNk(tDTWuc-ug5kI?{^*CU8MVM+Z^c
z0yK`xEZV_lFMXKp-hN=Mq|xx^PW$!Pa-K-t+kxIb!@*0`XYklv$$8EhQYq(tU<$0b
zX@c!SDO`@Dbu*k2?53cHWnH?I_l4e|Kn-XgNWn?~1Npgpq{n2nc}RVh&q9y=1}z|l
zPn}pvN`U;dH2T;w)`QYkZYjUuRRmW1aOMf-53Q{k)~%9}9g87GdjlrjTx=Dg6!1Hn
zB`n#FHnk1kedT{tCHl|-vZ`T=Jv56&b90B)Dxx99aZPY1R2ZzLc4y8!!}27I7dl6H
zwrpX=z7P@HU+nyQ`>{kxU$XrX3`Nt8_Adc-;zt(-nW-L+SqX`qjg1W*9UY0bIM+>v
zZ}}wMsHm)@orB-9qo6Wexo>0)jis_P8d~;e&M<x31!srLT+X6LD&6Kaqa2P@c&LNE
z#<Exu#JH1oUp3#uhsuED!SHhj@5qA(O-~)h_~X4Ar8wW1WM>As=;1y}LlIwb`Er)+
z;Th43PkVPFQ3#43MRr2Jx7P;vM)J?^^*r~X4`G7(ln7)LyPFp=G0_FeybIluGj3p=
zS|ikhBd@{W-hmCZPaIBD#qfV+E)=MZvWJISP*;YIam7W{8kLMG(j;v*^B(hi4mLOC
zy{V-WGsKMmg(<Y8ptRY8Ej&{Bbn??99ZSni6bI+`gDJ=s(IInv{B3t<@ZD;H?gPXF
zE;uhx=9U)1^1k;Mpb;QPG~}(ya1Fji$3d-?a*Fve3&4S9ylBZM&HIqjm5eq=FJtEn
z5ApFCJ~h>XFu@^gYH8Tv0iK$f+pRDXoYT$JZPL;odv=_j5f;y_5mGfIn0t)4hDX?r
z44bc2mU~j`Nq|GEq6a87UZJIUZ&ctWxzpGD{QTZn;e_0a!SrvI9ET2Pe#7aIFGPNQ
z{Yb{Ky;Lz#xvsjeqWLZvj|qGX>4~8>gJlbGK&`V9)4#UreW|CzJV8l{V@+J9#5HC;
z%^T4tma)Pw#K?%6vH3>72Pjb4XVL?ROxBrTOAM4`$jD05``5B(!VKz8u1J`9=Jom!
zpY`d{(60i%K1omKM9Ej<+v%JkK-n}_s{8@~-pWW@0byZg>e|eW#>NFXclpAzHE!Qh
z<%rmGRT7SF{($m*W{Hu@Aad1J)5ViWs0l@V0EI4e4gT;izKh;YP~j+~52Vx+^#0?;
zW`U9{t_Qw8FLSbfS;4Lcw3hnCEsPu*$1J9ZfSRcE>gMb13menRc~*!Ok>rF@@;b*B
zTIgs_46H-B+0k(chHrQAAmMNlH~v--b<+~;ZqnOB#6yVMtJmZ{%!9IoIpEg+An#4U
zv25G6(OYRy(V#&kscxwZsZ3FL(%??!StMy7k~uR?8V%j1GL~e_m?3jgsbro)GG|H%
zCBna-=zX8>`~Giz!@vHut+oDV+um(^pB~-r>$=YCJda~P_5rF#?n%a)PuyOplaM~<
z;X!)AC4L!>zlayX<HKh+G-z9&J#$7Mgg}Zb*-UYpfuWHq({!NT*mDGd9IPZk_Op$_
zEwc+@ThP{Eqvrpbf_CIRQQ2ZggF1Q@n(bHU9eKs%v1e1-J^M{AG8tmeKCZ7vLy5W%
ztqqtZ(IPhfT%<R^1*~On!%{mM0)-T+0}mjar{FA1%47ry8l^azn)2gm7EeC>ftTko
z8ghFDD_Ga9m}WzXii-zY9|L+qc|nNo#EhTu2R6MV*cns2OwxX}R&8H6hlDC;+YZhl
zm<ZrK0i;3_sUgB%1w@(XHkvCIEGU5HlHb4_spj*c;BHmO!{ZSKnjta%bq~=9Yoe<G
zqH!G!+<m7YK<#ti_TK<!`8v8A*$pvD^?D3z*Y7T>kSJ3Br~xU}D$kZC<5kj9Qj|G!
z=DZTwrw#NB0HHJ{?ZYb;I6FI|?2aq>7>teMGBl~8cJqO$oWRIy5H-_W3eG*Z{nhIO
z{%`>>$65`jq?OGbZ4_zN?1#6JvUrz~i3bkUR#>sEaQPB|VFDV>Q((q}HtM%q0}9ze
z!mptSoO!<zW8@jqJ9(qPLWT0?Ffb{_bW`1Do;$6B5yZV1o8k@9qvBRC@RHnKjRJ{*
zf;X}za9=bE4T>vv>Q+<+t+pYE4j6@-St4>ZubO9OWRPJZ#Y?igrMoin^($9=IOuMe
z3%?BAfLD_fEl^S(glano)-pP38@#{?;}jsI#760AA;^gZz~QDzM2Ap%;+=Tihsp^8
z@IqAcW+463+G?F?W5Dm(*bD3acB2WV<K~7|;z-c8?I=}1Ej6r9i53-(<Xre1Yjmr8
z&`r=7Z0+q^vEib>d<i`^A@CkNbm*@q%7k-AWai4zK4PkPjb5N$EKk&zMp0~l!GM^-
z$~EmkcZeeKYa5#0wa|0C#@uZ<ZJIt-cAiKxTgbUgKGx=|W=>y1&X0AZY}2+f+1`5$
zH4~jn76ghU#GM9n=i}A74eO?p;KGyQ6U_m)sc~I07wHNyQa%MsCwpsnGtbS>&!6AM
zE^NX^<oxY#mwyPAY&!V>pw{XRpIbL&Hsho}g5{hDwmj*~@ahR_ry<;Z6u~+pHA{g-
zfyzL*!|x<a!J4GMdRm=z=^|;ENqvL833Nva1%S~DfI%mo{#m+O@<8jf<B(C3eHl_s
zQf`9jxw^^Ba2CQsn?x9TX5ZUsM1lhP6=?^7s3D31gw0o+IMJb)$%nby7VD^k2TwE1
z4Ao%fuz@gmiDswm4f8&iNp`3NuA;+(P~ke}WtxI_91!VHj?$IrRWPKI0t?L1$qD&Z
z3%#YCR~`nA4uo^z(xs%tF~hJ3Rfr%?Y0|I&tQsDRZQY$N5BdR~2?kZ*FPT?7CR&o~
zXa`ZDKElImeI9~V9GgTT;0pp>0r(}-C^D@l@+Op5>n8+u`WNjouHhZ-Z`z1+<OtYM
zux=(s5@g5<=-{kp7S8C`*o?~{Rks4i7oe`F!w+5@Qnx^s1E-$>b-p3y*N}kjC=ox|
z#fEqDFqBSYZW$!$!U2B)S8s1tREmUR06Fn(19sWh;O!6=D_Qh?^+M(qx_>Qu^_;iw
znaOo0uI0<`b)3HPL)Y~nAPKHXol)#QpbGoG`@U<}uGUHu@?5dT87QRx!6TwjpuFKA
zKYQl>!8528p4PEmUQu@y7fy7xq?ewVMlJWX0P7gwipQbxtyJnwrai!+zQ4x`>{_Gq
z116qJQ3jR;?PnQT%5!0Ze9Na#Eu+Qq;0HTTKH9ASq7!cZ4*oSA=PIUqn~|SC*ZAY*
z;Zd^sCt9egj8p8!_3O8a-kcTUk7`!%T>S0$Ktm`>L)xh=V=iAC9c;ZvCZ+~!g9UGd
zY+v+ViTj4d3Wl66#goTG;-uL}RSy2T&GJAy$XRWf^|+~YWMxaorwBdYT}mg<tlzP4
z$1&CRSj$x_|5{|h7OC8>=yAcQQaHnMYO;$i6dql+m6@ru(Ar$LBX=mH|5C+zdiM8s
z<1^lMUdOOSY>TX1`@OKkq8qqsEo$TujBU2PXI|#2vSNf%-(tq-5+4;c`ldlcO-)EF
z?a>?ile2!Y=fpxwxncVFJrYSzaIGf!W4S+b`nSV#G{WdjmTJ(WFb*lGPdf5LF>Nls
zm^}gBS7Vo^XCdqF4A6g-VrCs|WF!E>$M6;*^!xkx1d<ra$gq-zh@%X2OB*^DnMd5V
zr#RK>UYh4ZCX-K5s~^tpLt{$<i{?#%KHA<~iW6qh<nQ2qNA4J*ITU%xX_wQM8yk};
z(>~XNSBrmy-Y*qc0wv6O4BN&KmCR2I*ky1y`)I*h{vL*5II8NxM^u(XiQ!#{gnx$N
zukRi8kcnYC4<j)bP&uhW;|c#ODosPj{5rYbd7jT;9^YJw@Vi?!4`zIIq$<W+mbz@C
zVe%CC+u0>3Hvd6apa#>vLx_t<uZXV7hxlsHD$uuqmYsdm098!{x+tD@?*N$S5a-qD
z6lrSxnc3S-ImO$<kV1vyC!b!f4gqmEoJaNJw4OzjH!y=%2t`0RNseCYwmi8XK^(Ua
z8O?l`7i$f0F4DuX^<FzR$*3)RY>r{H^VZ>?xj*+i0oXti1F;H%W!6^O`P%qk0tz76
zOf#5c=I@Idki0N7l4I2g&RjJ6_IpQKb(WW??ppkCJu<n-{eYF^@iwbJzwQiF37H5A
zhv$>g{PF$b!SXQY?Rn3WaQB>@=+)Gtf6`z%(~y<=F^$}2OkK>Q=0_cATQl<^ocTFO
z+=9a|k|sNcj8GmBJLJjXNXc#l()pj!8suMoZrh(MS<ZYRos(sz>9IJ`#&G<^qwzAt
zHQOUdq*Zf639>RNbd0raswAqdvKnEaM(<g(Q)XB}HdsCqQxh1|Z|GOn=7sU7o4yPk
zF7mB9az(h)G=vq%1@RbVfN)F0>ZDo5{c@B*FwN^MpVf$5ChFOq@fNP4RCNuF+OAq&
z)?1CHOD8urkJvx9i6cqBo^lEb{*~Q#k8Y5U+@mQAr_W=Rci)@0ksQ}<tF6VB3duPo
z=A!Jh!7SRF+PyFuYa4up4_};Ja*y72$=lCwYo(yt@#E|9{lZ&%+4Gt<rW*4N^e?-`
z2g0YzuOdA`91+B&wjLo-%E~wAmS^5)V5UAW%#|_A&;esl4U1Jdd(=!U7=oQa{w+J!
zO`6g#7j@j${>Xaqa+B<CU1L6`@A7Me1Nla?Xc5MI%R;zzXEk#CZag@oZTxOXT`+Wr
zo#n{DBv)lWkJgDbjLss==k9XtxKVIVuyjAOi^w~RIIbc~&xStvnA*L}lGJ0T-Sl6z
zTg&hMJuaPt4c8brF3Gu>{O+IkDvLen@`@?nSzmM4!!Ye9aprWAd%g9yNSBxGpWI+q
zsxWx?x{iadu^{iGSXk3<VdjQp(U}!O*EzX*7+N#`0po1Wq%v#vnLn0M1SDGxZfD-0
z4FDne(L<NO)B2~oAk&{ULy<c6&-~$2e~UI|{b#iCeVJRA7%1`=#dmmg_vtpMs;GDm
z#CC`U%fm+*HlXA=S!OlDg_WcnOfMM{kMIN;2e$qdr=lHHhy@>4aVjkMu+pEsY07@0
zX{5GFDL!VciLhFh4xL&lc!rvvsvaD$qq5hxWr!@RLD>7*i2069nR+V+4f1nueHwGr
z?nEG;i*E3e%Db2L@|}tZkE(C+Qc~H%HQIYcJ5og=Yof^|+PCFrVfw?>-^JL!H}s9<
z1u|;1S-d+N8_MI$TN|LyhV<Pw-J^Hxl&6NoxV7Siijr4MSzp$Vs_*mb&D!$Bqw{sv
zI?6(6V)Nz@yJMvONAV#4T&XLW*2RUpfB3%1TB1c?mn$dSswT5?-ZzD?rZssz{ddL}
z7(DqvWq!E7I!0?OApF&o(}StWEnJgMgOlS5QejOSoARcpV-sHCtdCzD%u~4B%Uj-m
z!^g4o$z@}{wcljj!+k!_J&ng8%3OYB=14+#>)#*1KdshYG5#}KdaLHOWiyYPmfux&
z=tt3!*I9Qjp3e9*$j@LX^ym4>EIOS2d3WZB$ECHDb$>oc>HsV!r(c9NAMBL<D_rky
znO#yd|4A&ET=$lL{D%M3_xZDCDU@A*Gs6F`t+hYjkV5$vm-=7&=*?W8=LN+BYzqUH
z+ViYbq7r=#(pO1F8D=+zm?M!Kbsc_<kIzxbpT{gOK>SiXdX6?lyCSOn#LQi#$ZupU
z<4)w21@*8JB{D}^LQ0A%(rWCX?G%C@9sCSFldm{`k*18<p>}M06m58H*8MQ(#KMOP
z0?-@r>1L{e0~|mCZ4k`0#K8Fc`IJljCtw&HjZR<Rpa?-&H1s;VmpoSM^>90wuyN-1
zzv#YB$-IoXC-MpW{<6rxQr2928)Qwe7UV$gHn#eWZh%UIC(A9nxFoNxk=fNFnzKww
zWB`<@=}bYpqZ6KxPA0&|_1A<mtpaS-RNKMU=1G#`4$+1$bpm@*70!AghaWJ>dxFVa
z6mp>8Vw%|*;$Q^}`Wx&Huv#IN2(C5~O?Wi^mx&A?q?GQwQLtRS2nkNAs;c2=;ZLB?
zt%S{WEXfGP-ReOmnCbJD`OIr~hu4iPKvpHZc;i6+Lz3M+csQd@g@N2QIJpY#y=ss8
z*dCi;3iuf?mEVE02q1S7U{pjBcqS&r*~P1@H3*1^&zrQz$%MGMymABL2T1lil5q<b
z^0u$eB+eBTel5xgOl3B~z(*Y|LggwAoktKe?PA?9bBCGSaf3l+qtQQ^{@m`~fMCF5
zl}qYs2Kw=bzN#BV<Mr}RAnE{DG}hSfyk_^8<4t4g1LqQIV@derSi85v0aN6YhoRl*
z1_V?CTU~AxlF3tWqwz8dfaSju{A3YZ%JwN86qEhG+PyP7{Ek}{FpC6POfVqamiYV5
zcs9fIANc|65KD!MrX_Kps{bcRY|u_VO?&$T32r$M(bz-ZEChOAM|=CL4xLD`FHl#)
zts2;$JKM?xgxp3L#gM5paY<HD*#CquK>P3<wPAdN5$7XfZjT?nFgzRVBej{+)$}3$
zgdUCwhsxafOGLX*yoI8t0xhdm>HeQl@V&+IS3%YAu@5f0SMtC0JO30XsT0Vmx(gSJ
z10@DX)q^oPo+_ZhW8e&`V6B{aR%Zo(H)ps)b_^x;2+3ap{<6?x$aWv`aitP_VHjZD
zCr|RsxpT1*5r@%=mZ9r>1Z3L?!f8Y>i$58AGIsJe?8z!IwX}N2<!^{wK{3RvaaEVr
z0N2YzdcjOutgemLTPJ%24UQmBg76c^-YGbTp`wE^#XCVcj2<-5mj|HBX7#reI`;tt
zPc9m<XnyRe);xCsO|19nvjabXu^$843(Nk5MT-_yp%aDk@7vCy?iZKmYG+y*X&P=&
zL<GHwf5m+iLx{+@InMA<ZSW9F(3}E+upjJapVi6V5gQd|g&*Z0`}}6BgOtV>m>UlR
z)``u`)CM@H0to|gq{iTV<Xhu$^yteA10tgFo+DNW&q~NX(CtwR&h#}krdv)TCKUN!
zHSQvxqkwST_-YImHmP{sH;Ajl9!Xt>;)EGlbE(KDv#aqLUjmw&zyKwI%TC}ezy-b%
zjbv4x%alrt9-X8vlUNQ?vVQ|J--|mOcflA#hq2c?ogV=@xPI*=p}$D{YG=jJ&yK|Q
z9yn_niNGr~&H=m69Q{;M9kff&X~dTr4T^-jggRbZEI<gjm>d|0gpXDM`~-fRR~k03
z+<+^>Qv{kmE;l$jvS;KXJU(xZ15kd7A?u+}ODS>hOxxz}3fh<6;Pk%f;GI{ULqZNF
zxI1{Ee(tVw$=-Lqa1P}m3~IM;Y8t+9ODa&FVi^5Jfthvy!z`%9vcrITX)90MZ6yZz
z#A=^}A@83YZG*pv;$wrkar_8Y3%eP*!W2Vp+d!F?XN7I$B<wO6>bd38hY=8h8aXO0
zO&xDye&6NQn?YBs1>?8i4JtBp3Np$cCqy5NVTVoyDGHleHy|(5VVmh>5F~yGJ&(h>
zopvB6K?jDK4PLgPv3m`|$9}A%Z!S^g=i~ELow^^gZNS!HUx>HZY+dhl^FD*qm1iYl
zvM>)PvPKN?9|2nH0fttFz9$8fO|&$JfxNMdA56kKsNAM+KqOyW(eLVF3CEkar~+g?
zD}=Zn%GQ18d!*n&Du5+Am1G;-X4|~r?5be4<d9<%(27P=jS%X%{M@{GJw~FjKXD#<
zH}%(IQC4AX+${90%?Q}@25$&F!sR1Xfa|$`J_~S)9uCgM^}h8;Z~JWD%SYm3*GW2O
zlMy)Z&(JE3po+>$A!Jb89PhJnc)Ng=Z+UT@9;M_M{FbvAsmm$_3u>Mi@)sosOzb-w
zaJK5KQ(d2pSUH8F?cUUXu{_;%T{16E`-Ct>a1G4V5GaQukR;oYlsUwe$pwq4Ve3Wb
znTc#~Y154hUFJTsSU*JK3Fw(1jSLE3)=H)44l--1tK&7;AnL3l^cD0#<<X~&-qF)L
zaFvyeu9DKGXy?^7M4G}y1G0Qals}~Ne93)~)n)1AK;9+D!u}Im)Y1kK;Kz6h^2o)5
zqQQ@hxq;-6h&_z;r;|!7FZEPS+w5=oCJD!lfh417nVc64&8G*e<$`V_r4*47hdMOn
zBcIh>3{d#(rzHz5;hpqv6xW(lxCNh=!gwF0x6)q}Cpx50(bxkp$~g+&vh~MFsgvuX
zf5u~l;zhTv(K;to*ScvLBRtfF*B%JjmZ%t_&$irw8(RfXa~d7(fbG!{*^$Llv_)Ds
z`wx<MbJe^=eW_-NV9S!dO;#eU7V%&1GVw}%O!Tyo#;wG~(<h%jKO=3ErHjL~2C88)
zHHyZ;L-H&!E=oO_KL@E0oj%S}e@c*>9A%L&dPk1N^sj2fiH5uAkGknqnLwfx_MN$b
z`$%Bxv-68TyiMihX{`;ubNe>QGeN_1<61Mfii`BUTyj7xLVob|lC3PCBEL8?M+uQ#
z<g#-EcIgXNQ}|3ccBA?V)H(L<>@!qk@+1}&JaAtOE<#J!1QpLBBve@Yd#hrQ8pP$;
z{lwqxYwXjfJ}c`NwI00uA{qKVxsc8)i}}5%cz3?|^x!-BPO{pBYPOMMnMQi?f*&-w
z+MU&<bNnYOi^dL;0|ylp25X$b$d)EMz)I$ZJC5Ep`!p=yz$r*axosnxs=9g)Qh)AI
zfiE<Ndm6DFPV6`YXKkA2Y`G<`!B`ffH((0-R6Sf$(Kuh(a}y^pAhr?YWp*K@+?T5B
zG*T^wXw3s}7T8lTJkrG>X*|q+S$OGX*S&Pu`_n4$X2zmoecv|7_!B{G8$`{B3-98z
zOAAMFd~!wSp6e%N77&VPXPY7H20|F}+U?_L#9^VocpKh!X5+xUmJyiRa0Fd&0?bGv
z4>a`kAK{!HhTV(X-7M5>Tr`q-fcTN2lM09Oni7!hL^2{V1$1w~R<N}!-r#e#9kNrf
zz9&H^BfsTIM8wfWQ!gNUU?!z9_N(P9PH3pAcHv+ugDwTNS_ID8BV$6dwKLOIj<T4g
z4?&-L1d`C>cMasg0y$)9Lx`{M6Kq}QL69LCMM$PYoDE+3D|c4SKU*st6xVPc)srR>
zxPxyOsAKpIJclK}#!;Vg(Lk7d9&zkJ80MST-Pd9}B7qiXt9O!<{1G`-u?x!pqH_6Y
z4kl?hTv@m6-@pH~!-*3iWa9$Mq&KOM!+3ZyNK?Z&1Ut1G%Z3=toX!KS%<M~RIPimn
zpx4901NivrwB~l)<q=>ly<qKK9Nq_{gPe)&W}@pFm(E-%b;USNyvV|=o3`tbHH>e$
zMrR-%r^1N@4V3*o2rDQ0D^PV5(228@tTsjXhdbM@12}RB9)SF1tbW*bJcf$qJt_@s
zaO&`^z)=>l)fngssWhz-a0>(|IxOq}A~R;;CX(hE1+mKM<$ob;2|F0i{{6ZnR~B@x
zN8i6&koALL5|VeoVcX1r%yz-ny@4@#k6<|)g{Mt^J91ukq&LLDe5DtJ<Qg3Mb%uEc
z1p?q(2_S?>1+nonCp=+oW0d67=rT3o&M*8?8+#}lf*J_2ftnqa25Q8#+^LCvW0Y7u
zm64u!<w<6JqkV7Orr7zNU%tfP4MopMsy~$dIOhTTKLJOq2YDJx10OlZ?Bh^)CD9y7
zLT`I}+Qv>Q&{RDZG=JD*Q=4~Mk|Wsn<GoLxB-wZ>J6Pf~%9WAk*}ivFwW_A(y<l-v
zRSWVETjSJ<#TSQJKq0o-{Dl4WyDYi^IcZh5mJ}>>&RRp!ewi5NtGdY)hP2j2eCdYZ
zkW9mPx-6|<ap(hdRobhLt5DxipKwHCA}@-4>5B)QslNShGZF+ppW$Rkv6~dCKAWb5
zU0&4#K?8vZ1x2-($fY6sBr>u{P3@6^_mm#KG)0QKRwcx1SC=xjup=F|!hv#N+_}F`
zq6F1-F$6Q>0Oh6u_}2*HnQa$cr#M~Z=fL*Xf4F!t`z+WgWxN@GNU~_c6P=4^->hW{
zjAb-pA4ETRaqH}#Mf&>Ku5tAXlyq^<mYw}GSAh?IP@?~ZUi#mGg#Pbn)BoEat*=Q+
zjD?hPOH9RnO-x3y^B|VXIZ)2`vltkYyypwu{ZXBhoK_sLpP!^?;-MnKA$+XKE(}2}
zfG~mf-o3P)uLk4~8r>)Wh$c2%0K6i`j((Ba+iy?FG9!BmTy>J@DHmWIhoBj}d4v2<
z4j~Q#XVsaa_xB>><*^c!_QoT61U1OBH|naYQStHoJFIc)^Hg{H<h4fO#Xum$V~bC=
z=D;D5yo^J^x9j2-d_;$tD3vz-cI_!ym3j>Ce2{!3Hi6XygoV2p>R!5!AVIA#3%H4I
z>*wta_xkpq<KADi_sH!zU%G$r)U&!OuqxMfKlr{jEh=YzYMn`bZ5pL)#r8pd%8Tye
zGwDTV6nu-jX`>Xef(}0tyQRWPKEE({-I9kLR<G0VZ+mN#fgOlFwVcVZwcF%UO!yc@
zOe;QBEKt`}Z_m{Sl^tU#4QGnp-J`Cxwx$;^@4TX2;%Aw*<i_zB+SlF>W@2ZI&ngB9
zeEl)j<lngc(DsiWYYO`O__j!YU$1=S#+AeBekSsrc~fWoSL|~YuBbd?nL*_l%xgby
zTzNRnWkDHm)3t7!*4~=>(covPKH)S-u5M`DQTe^mcl#$9Cc<?VlN(lYk1hzN&{bL`
z4Q5w2MPBv~!bNet{~s^Md!t1Ib8bdYRI6!Qn&sh$KFu|{s6F0*A4Msidm$i4+1PzM
z0^=t4ydoUNi~v8`IMK~T$$&ejZ`k7)9s10zANu8pO<TMpsi`hUm!-)lc|}%nL_G<A
zE$#Ha$5^X<;jr89)YRQ898(?g8sqD$RQKjh=+X{n7u{m=ER9^H=MQxItzfaOf8}PB
zz5M<>+d~{ab=>f!D^J-(p-FVklf;6!TiNYX(4vSHmMvRzAsV0%Cxv$KFIA(Y(+<IH
z{F1y_3WK&`_MdocOiV|ngp4aIfkR<@gs%`F!Y3DD^gb%i9m~dmEk;hk|8Wy%baCBa
z{CvtwK#hhKY2UZl_uL7y^=IH@(4G8Qdxm}g%KqI{nqRovMQ`s-UzU6dJ(#!Cxw>i1
zk*2?t&j#B1v$C$TPTl7}=VoUARL2u_$#VssHLMfAj?m|9WTF;0MbmwDbug{H3aBo~
z+3=qsApg(ffB)@`<$oh;d2dDkoKqb*WwbLao)A_ENrVGhg94l!mZUg=ZU<P!b@xCq
z>ll=9@esj-ow<j_SLEB}1so*heFXYpEdT~I2l?68;N6z)Ifg~aB$J#(b?J`-G*Q#m
zCV77vfapl#;mbFWHLJPlZ`-ovaq7{@!{`JsNW#w<r*ev#$)`8Sjm}1qJk+A|yMh7}
zK^!DOXqF4vrv)a_Qnz<PLPGTb#{jlef%lr2gk6gs4&~2UxAGWkkq)3x^l%hy&#O!K
zA{ky?92tM)AJ=xeDuWe`b_*Z41H}SLk?l{fAI8y-1}*|$OGwNa1AlrrP}x`6H7dj?
zY&yl$dn{<?6DV|9<#}b0NF`ATBotYdXGf}yZQHz>`u(YVI?oj$9cB5J*Buck2<`=K
z<boKX3BylC74V{xkx}$-=R9cC;!$9u07@yZtAr<lqLR`*L}uBMW)Ezqc!}Qw(x_l#
z0LNywxUCmF{nqPn?>Lg%sM8sswY}Bgf|KA*L)e#1<CzHV#67rvcdz|$6sE^fU@wro
zs#~^AkaolX4m1Kzh2Znkk2_87p3x!@4vN`S<b<ML2lrAM#0_Q8)x7t-CD1HHTna!~
z)bM>lGq4(by@teJ5_8M-4FR2EzPlofddS4Dp|1vQ+WP%9ps)*q#x(J}Q?eEY4==yf
zqFyE8?C?_h3#=YxJYteRplkkmRZnd~laUbXEUGN3Mq4Gd;A~%J<2RLc>A;~87n~f|
z(uBG3y5GM5j@$?V$vF|Umo>5I3^zIr-*X<T40W4#L(g&WIubiEyaP0Lg^!;~29owL
z10ls!7h)$DivO7IvYqqv03JmGT3NaK2T8Tu;<<0dij(QE$a2%~0zm=s))>h#i<_#c
z`A#AdZ3m9;U%!62hde{?r{9L4rs1x~o-xm#KQAvNOkexamKy~ebJ44Sj%*C-@umi2
zW8)FjjlG0X31@8|5GnjEgM03y#(pm-H-db(Q;OLjtRjMrkeW==d1V#$kN98(A4v1}
z^)&(slz^fkj^Anc;q>yh*^=uPXY`ydlxMQ^&yGWK(;c6~UWD4ozzwW?|Ibg4fz!d;
zLIE6d_s?la%_PBQfDuSG9OhxDA}$~^gtRG;?L5H`6R0VF7wTxtOX=Z-91h3OLsg=k
zNI-Rg@tGlJ-1Z2qB(JbLjC;^x<R@Wc0O&&}ccEv2<mg{!ZkG`M1o75mVjBc}ViQA(
zJ{#D4Ug`-a;y^yq25_}BYo`mI6b>mN(7k|7CR!q0(2Y;3YsB8V$?2yN>2ccWvVw4W
zBke9d9L191*l;&+-3sDvq-#@mCg0reOgG5&HGqo4i13A5^4Bf<pWpb9FkUH`iuFQb
z=*|Pj)FDkg$Y*|3UE7xH?k9Ns&KLn?r-uXA;yK*}Uo<ihKzkkY@Szj{J4MJ59VJ2`
z1$c-(#O->`%o{EhMvI}?S4An=L;IkHY;^F<Nb)!E8Sptn@T)hhju2kCP*<1VA8qXB
z&)))!<Cz-1Scz5VxwuSV^$^rO66N)E#xiig7fw0?UBAW-Wi0`m%Lw}#9WMSmUwFSo
z!Gj90a9EfgG@7QH0zS<_FiE~Z$|EUC4u&d;luS%`2&sTSd*EfFMCoNq7B&ISulGUm
zP6ihLm?!@E0lcdhWnMIaOiUoEywTS?!E6rmQY3zQRphSWBNa;b4c!frKWC<gizXKE
zprgO)(<xrdYN%2HR4=iR7BiLjQcw;lj9k=a|DNNPGXD;6T0!_7NLM}lX{z2hVfl%<
zqI@4s(ro=*8NCAn>bug@0tz~ZQlmT~PAm<Hx!n^JwP7*EHA0k~CD`dl9r~Gva@#2>
zs>(Tgz9P)>gk8rf0z}Yz&T-_SCAI&WRA^NxC(?~s(NZJ5az#$7oUX7t0gabLD%Sf-
z8G8XAzA>!DBCtdn{kT_n;@BJm@XS6LdUpBvjFm21x470}q=$<}97Ltlj{7}<n}|An
zxM^ylX?gGdUHAEsN(v4kK?g7q$|^71Ube>`^4k0hI8nwB)yD~J?oxr72z?rHks0X|
zq8?h}0P!YQwVn$1BAtsIo}J+()|)}F8C>f0n1N!rHUyMJBuUU>)|M$d#lmN64!Gbn
zNEXVl`4HMp7s2Xt>{B6Ce~kU&#_*)?N)yX+P0f$UmwzHLIcgAhB1ap_1Xc8IW_k}V
zT4C=y@CFW}G&*u|C$_b&50Gl=VJ#?q!P|hjUv9ZMuo`bFtZv-;77t&X1mPFiVgsKD
z4hsl10ww)|20uZY64o`Gzd^p^qG1TKczzT2q7TCt{te=KxG*?Ms1)xG4q|*R!q?uv
zeQP)@4D!~!;n1aX*5TOrpChj8Kl}KsBncs{&IJN5?E^&CJX^z%audDK94cE;a+q+#
z`}6rDoj=Fz_~%im;f9j_-}lYuT!%xQ#Y~hP*s2;xDgZPh!BjjC)E~e-8Il3QVY3*Q
zl5srMRS%27qG4R0b6bXj&hwoDb2Fk1APfhnYEln%$pO06XS+@zG5#|9wpVrUm7N3{
zFe1VJseozb`;rnr9F=4~gwdS)M#<cAgZU+PtrH7PF760l%J`l6xHIEwK`<IMUzy)K
zZ|F|OFu)JEswKbOJJ9yRe0`b$(|u9%Fg(Y|moE=M6($Q8H)S+NKu|V(9o+D4-Wr`{
zX^N+$K`$)FT~`>(735OhX+;rT>s&;i2)sC-5j$#B%nq@>9q6UN*Lc<&6&ri!=mxn|
z*SQqVdh9=F{vTWL1{T{?c&A{vKwB>r8ii2?{<snx=3#8iH~KtRX3mFD>M%*{#bDP8
zl&fff7q~!myw5<a&S<R@w|R>2K<UY=EW9Vru^l$^8@h$CF?<Ad`{}c>KuVG4jV5o)
z59!|#&HIowg7HTLsZAE|%vL0LD^OnS(>`7g3b;d_CCyuS>FdW$v`vc#gAFlo1D{3#
zz~$b~AXf8Fx3q__byTVj?5vjb0BhMR{P4L44ShBQnMRgW9FD6<Oy5W~L?XH&<q?E0
z=~n64)NIb>K}t<e2JCa_Jr0LNP6tE~LZE-qs$Ihl80~V=P%Nbo=nU}Vam0Fyj$31Q
zkBN&@14;ZqZrF<Z+l{r<UnN{wje#{dZrGTY8s#tVV*Mm=a=+3^8*5ZN>l!yMlt~9W
z4CCbJ#6(4c7U7&<pEL0;Xir6$gx?wRr^;O>t;j6gS}62n1_73+`&S6?7Uu!ZYL~E2
zuw4ZO%@_wBo{M*B=i=T5=9%My8n$PvE5<6!B^J4uElU3YVe9Ck3ms*6CiT!iP=zuB
zOc0yoJQUa@v*_a5<@dMq(8#f{{<xM_G{)KTbdtJy<Lly-4k%RhUS$#!y1?SqNop^#
zx;UeAO9!VW`=^f5VJ5IH>B2#kPM8BlQjPFRyL;#&d!5{EDEzxWS(Y-JE=_s3khlKQ
z?&&8qot`_zl(3#~$q;3?g#^OAplz%axZB*ntKy2^?1{ITcAGMWAoOY!E@ae!7H{6U
zb=W<D%J%?*E5~e(hn5NISHal!1usA1{aUbQMBLovWcf=HhnFAX3h<DORjononxBp_
zqWI~rd6OeMX(KsS=RVvA-n*MN@+=-=%_!(7ix7m0xfRQCwq0fG!oUu5|J;v>h75h|
zN8kEgiY4#fDPdWogbl}Jp*y*Vf3aJIJ5$h}WfT#l-tMFY{K1r~{}Y&Uvq8ttaXgd=
zhzpIO4m1}Bl+IXh{UK0f=ct2#oC>IrPasZYH+fJ2Ua^-s7IYbyN~u^nUIUpxIEl`H
z<h!J#q$Kt(oVjN*2WjPVr|%i5#KBD#w95i?D4aQf_$ohg!lD2&2ICGiB!F#X|K(TL
zR8)Kjw}e`Ih&2g_yP$BL+oK~Jom2_Wl0eusbp7dy1vP>W81c|TdcQ1@gd$JZx>`-Y
zq4ufPmhCA!t)wydqi_DXiKR*elLB&hxakw%we><?chJ89o+mNgGy<NhpYwR=`*%Ux
zfs^EKU^=P!xO4yDWcn9i>}TP}OYSPEDH=?(_)a%r@)w2iIYj4Y^fz*tz$5SLy7SvW
ze?TR1?AaQ*+45ALo3>nP&B~Qs;!VFiYT|WcQHvU*t^=T<j6e$h=go7R#v7Vyps(7J
z7L8*Y-A)-=1|r~4L0<r@Yg0=LJXtSH^l0QMV>FOv+89IXJUsSFG7%zv=3#csYdBMQ
zWie4CpNbjAzMO%Vi;>q9LaJJvLb?|3xH7+G_Fm!RT!&)>HwX=-ing})W9y6HUeJ94
z0~^}ghT<+d$%4&X6PNzE{<QH8x6ZpMr^bD@t021GjIcJynOE6eg#(-LX}}PnhEG|$
z>a&g2l{!a2e$|CMx~$x>9v*Z}B<C8{eT|xiZna@(dyN>@6%zh>uCF;!7CJf1jYCZy
zfcrWzt0-qgLX*xqf2p+mlJ5yw-^57Eh0|UeDcA^dY`4gOHd4f?Q@p7h#y<zfa5zl=
zbL&Im=D52zR`Z~4;Y<G%2Bf^}-Vu&lvdnnl$n^7*Y5EDL5j7Oo&#Nxi{0sasmZh(D
zOXwtlf15uf5A-J#IS(sl7fD?)9Qth>Q*81W&nSw$wA)J9LGC*Gkdw0UegFhDu6NXq
z^z+|-KS<#`IjDEy_9Z6K;?u|M4!n6NtA_jz-?loFG%uPBrA&vs0#+mdlctQVxmCRY
zuMJ3%u?Q{*9auZNxx^m5y_!8T7JG)QviF_I*SC!VrU^Bt->pDUks?&MCL`-+eimT#
z!iNTicv<M1cW!1|3?5V^wx6}X9nhh3*mrG0dw>4VK*#kUWrGSzH}7!uz6Y4W?Vwp?
zHD-QIE)vv#dF|#Mwm*<H?{cX^vf_?U1d*o&9*Oou_U!L7+q`<yY)be45s-A@M8u@^
z*PFnAeY>V~pEsHUlI{Yz95C{0)D@?nRlB%pDk&}RJg<!-O^!|&xSdC}`2qg^4Qq^!
z-rJEh@&3oVmY=U5#;BZoR!5@IY2SZ264-hlhh5HR5G^+YE++5b(S$pyZRx6NYO8$P
z#DYtj^9ohel$0KUjTu!1yL0})%IKX-Hz?wWbj+=6YdUC&Fknpg2nQF30JR%mPsw3o
z<xWhF08aY)`jRh;N&lJj2ChM|$nA4~_`ko|vpi1=-Y(OX5Wd8Nh`Oh>dm~v9t@_}>
z_VHi)Pe+`I0rbA35O_2}n^ZmKHGl8}1V#z^Y@uVyED`zGLo{rSNueyZ7Xa!WVjgdR
zz!trG$CSHE{Pd+afM1JHPJ(MZxOE6Yn|{;>5f21wwIMzoI*=dq%(~vu1laaH?=7_C
zo}SZO`Kez-$4#m^B>8#Dn*_y!Ef}wy^5o8a{px0sX>GHm*kK{S(in6>Pq{(foa^_3
zN(dS(#Ck)eJ;gjRKN8ib+6`b0<0&zE>uoX_fCQ`g#@uOidU>j%z&4iBXCBW=5HyW3
zQL+cSQOC0*dPf6=>80E}`P|HG!h!5lEVqjpr%tYuvIv_u&9%p>=iK^VW-d}hbJXm6
zhermFs;R2K?f+Sr6||bRCkGk0N{7xI%9b@s;w_*Tt52<MO?tmAA&O(cTEFdpsqw&p
z>wooX^rB)U5}r{5`aT9~cRVcB-H+_89z3r9Ik~P~oX*wV6kEWJ`^6(kb+JxW)mVeS
z_R2!ET~<NkXsziWKiAo8XO)H`&uEN15PAN&Bjx6^n#Y0{<KFWWozYUyE;71jm8XEU
zL046}H=1Cn>|||0nA@Mcx95?ZY{K+qbMx4^)P*CvxocD1imu6D9vgi0*+!;L>{v0S
zy~p$P`-=g}*4DU`wSjWEkF}CR8)z1pZMw{qYalk4g=2k|k)1TVh~m{8;gTbJ&T07P
z^O~}O3l_T_=kc1kteD8XACtH9LWqmpdlS}vi&L#Ka_-LA7FzflX6x*SitTpqyEXL4
z@XXcF8Gzc;arY<xMYNfjT!pRw9Ev`@WlC#yTY6!1lk#Uv{Lk=i|4Wbb|BYbuzl(E+
z{sk;Z@azXRqHT*X61ES7OU?e_GHF8g<FIRAZnrPQYhvlX0ar4xCI?!zkadqL`DyGF
zO5$gYLwG6-R@^CEG;?cZsALWm*NHCL&nSS<A5N!(gwYQ&9$~^AI&__AHCC-$`S$F1
z>A96N&@|2!oQs}EF~K0-g?J~?Am4nyYfBQZEQ(iOeKsX=jqpP1YR^?Ozs1XN;lol+
zY%|_e9B~3BO+<YEidErlGH)ZWp)rskHrfQ@G&;=QqyuYhKPp(FGH&_@`<}Z;M@4y&
z&i-(p&OHuq&3)=KA1M{K$yMU23BXi35(yHTh4F@IYDU3;0ivgFurX4h|1R$uMgKyZ
zW)Y<ZK*Sy9{6Pj@7^x7#L~-o-7dJwXvFw}|ASN1}R8ur%UEN^kiC(>4pyrBDYYkVa
zC~OxO7jGR!ZF-cxd43fEG(fCK1%?UmOolMt05C@b$A+NdaN#ssQ3V6B>YAGKC>qHb
zPY9QUQV1&NGiXr?-hj&sLgTc02aE>bvEhI#V)XKyGh1)-R3+%;C2E!A{qESA3d@n=
zkp0IIzNr)~Sz%C7Wq^s`{}qrf_~qH`gvYf;isIyku_p`vwvV|^4jUvKHJ9!m_ClG3
z%+W}od54y0%|)roLj&ZX^48|><k>WTA0I>DGz9w_K|}Sf+sbXjo@KYk9VG~;1BiW7
z=kY9?I8uyEqmz9=C!-@nl%ZjNn}lDH0xPLF&7=cF_Cvav-DgqIqacNW>@DpHJW-hj
zuI_(;+I=9{5-1)$KsZXq$ZKV@%hVQ6=Ozk9Je~bNbqIn>ZcE3|uVKBZ5j`>!$ldi-
zP)-H<E&^O@sg@@5%7sek62dMxtOQdb?2WU}x-kl|2Q_K<S7&H1T!vy{9<t2lgOpwm
zcZlr)G+k9fwK|V+3ZdsSLiJAwy(2i^b^svQzJ0{M_dkG5r`f%om<NN4)dO(rt<wdg
z3HSY5@1-rk-R=cZRt57BUkr)^i?`xVqdn*awpR;WLKU=1cy#8yYhs$YYO~BOt`gGF
zgGwt;@V10bd_9+3XA%HTEtJ_-%58J+^Z@?mJUtEo4-Dm}VCE_j0#98np7Q?R0|f*E
z0W&Zzks^{vc|?^%rY90h><0QlAtc0cIPvd5BX}M&lYE=iFu$Qw$vwloQ}j)XSrmB6
znQb28FMF0U4sicCZAzPYTyqJFHSq$Nh!{Q9db<2J0P1cD1=;aeluUr)Q$vzdm0c1<
zvgQV$XpQ+)bYerF(q0w{qr^eRfL4o)+`;u)+(rFAMI4?s^npsFt=f}VXQJ%Ry`ylB
zm*Z&3mM}g%$FIlpigBE&dd#I{uiqz)j!Q{-V!gO$A>rZlgx%p6>mGFHi|HJ+9WIod
zR06R*mxR<GF5DX|Z{1b)m!8W+Dh3q?>NOcxbACnlDKpA`lFf}j@3#eEQ=!*dG{5Y!
zYi--$HO4YIrVlBm9~Cm6j1-<?cnMkA#cVn7<`OU282omY{A;OeCCi2DX1h&<k2{@Q
zKTjew;gI!!&Z38E1HW%>+p}cL`BHtpUjl0=>sozB)^0BI9xXqzv6}E-p+M*cD<|h0
zbhX}#=B+!f+E3`6fI`62i_iL&dDB3y(r2Yaps?A8ji81uFNF8oQDvC{aj?5`Mrw{{
z_Qq!kqTyxc*r$n>;o^5^#yO)e=G-lRI;Far(<l7>`=f+NOyE=m*hQmlNwhp<Jb%y3
z3L;dLbFSc~Z`g0IO1FZF&KTna@>EkGN+v1=$=@wY^9I?-=qSAG(8!#{3y)4Q$!~#a
z$D+k%b03WoK+pUWCNx|$3?r792L5~SUmQ%#=1cdw5lt_xLhD@cs?@Rd4#xuO`Kx~>
zIAdTIxKATIiGlEU@pi8?&ns+WGP+o?f_u-F^%Qxx+FtidOfnRp&N#GgzfS@-|2Tn3
z{xI6Qs`xs?)bA9pAl{-d<^^Hzdq>=kS)6AIERH}c9L2u)#RGTB*b+s;x|28t-5fm}
zc`e#xz(x4fx@P(FGSM4v@eq+y!8C;Bxa!Wb`C*BP13pd77(U^uw&Ay)wi&vWu9bS;
zcS(UF+N`TU@3IIj4E{0d_;(d^^CW}?MZx{S81tZjJ?G?Q_G4xQlfhbo0aj=vKN%7E
zasOaFKh0vRAE-bxTmZ~C%W;B4VM8JOTrx?<`Qr<(yaIvKV3m1nh{e-^{6V-i<AU&U
z?~7+4J{gT!N#We2<+m!;*_&@ai_=a9%B>zNV8*VlU4nH5Mto)7w%~T=U%)#Y@BHIg
zD2?tsct;PAh4k{m1@`=*&rj+T(x>sF4`mn5e;LHgV{>R}B)Hg_<*x@!gfYMF3CB5A
z9JE&jjuq%r^6N?%fWdjK%-wcUw#RdL`Z~x=C46ka1)*nR<6obF4Ra+<nt)LF#t?b)
zoS(oKBmcPCZtf79%V@viC#HI*$Fai-VbqIzi?byf6Rz^X%ff8f_7~T27zQVSwW|`p
zecA2W>S`lwh5F+lZ;t>fD>6+thP1xPb|1PF`0j4czHqQv2PPIQb)V^DTgVm)1P+cK
zGV~J6=M&q&3h5Qe`{hmzu4wg|S?@2p?_wz|>$3g?up!Y2yCE6A5`_stQ_c_b2wzrT
zg^yTL7ea6|`yX}ZQt=|7Xlj<gW{lpO;5%R!O20Dr(%#;MlWb9m@{eDRIF!^O6oE!@
z_{H3%j12~)@*t8H>_rF8?);ZT%hjs|7nF<uTH)y#2_40~GzBB|QVn>5BA7AKVwS>p
z`nP4HU1xCq1@4+zn&R)NAK&)vyS<}nYBUnky$U8R>eJ*~Uz_KM{&i?sp~KuB#<gy-
zCDx1_s}9BAZC&)(v&i;{5~E#}`1^-T23SQ_ZdjuezomI~-NC;YeHL$?vkBHnZ)>_^
znl>gmRGi*KyY$3ATd%Z0I<lv#yT7}VJ94bSSEc;6&_jbDj0A{EY2N3zrG)}ZCb|_|
zD&#~O!%>)YWpsvA0u7q;el&iW#Na42{m7J1BAX3_jv5!2ST|agUYh$7mYka4H9Ev~
z`ovrm7KPGtsuYD_HC~<wf`6BEnrAhFmTLM!th6F;#Q@n!w0@7|Y#q7aA5KJ0P$HSd
z3A4F9TCmZP5C%|<WB~w5w>OwsVU#c?rY^S5<KCjXb|uxw4LZV$p2KNaNe)J=u--_+
z5Hb!nDYO{*lcaPtP0y+sgoGJ2@H#lO5eYLX88DO}I4TOmN4S!n?4c4&vxDb&3^lrz
zfzpTyuNr8i22pd7{Sd#yc_dcP2#c}@+w}``EGG>BroMrJ<k%(z09byAO$=9Bw_IOC
zZcM7Fp{APJ6D*^*eKtJk_fHSjjsyq}Ytt%lCu47L*vo(~TnYMX0uVhTyjbLj2GD>M
zKYrTzf%(^Fma&xJE?!gPMX!7?WWn^Go(axDN*bravs@ik`WPkU#L}<hN>?TABtPkM
zUU?#!qqFKlg*CpWS5>D)#`(rIVM(ZUf6E%o<Jr4ci_0&XX=C<s{JmFxB2B+tcE3L`
z<gipGV`ArSx1a<>Q{r!#i9VNFBqftLUvTCNP+qJ?9?0zfPviWDE;Vr;)9lQG;>sr@
zH4Dm7O#Yhlr#(Blc*ShZMxh+{`Uk)9;}ypLFMjmPwcjX|gJ~QWDugbSEyI-beezSY
z8>~Kfxq!-`qq;8c^ANSlyOyX)mO&sh`W{<t;pR;bN4$W*krmm<8YlF{T|*~^>~k=6
zSP~PGa`%|l1@UX!Vj?U>+3}p{RNzDR?GjLlfmnVG9|%k6?Ynj<9kvKGyKyRTrN3<D
zd}bv(y9|hPpBFG4b#%-I!~a;&8f4yJUy`R2!a2ko>`G=>vKXrYWh&TkwCsFH=d1S)
zaxvrk3LWZvZ{G=PS`5qx((UA@_;uKxQ7o9<Sg{f`HJhwewQ-SQ<05NaHP2|4O9akc
zs0fpn<fV#WJKXm4Txyro0+W+4_`+b0NG=)?Bcqan0J^X34QK|s{4SZ-*T;u^d-xU{
zjghx_^WGs2Q;wdzE_i3F07ark<6qByHpWQx&e-Y9q7G$%nBhItlTn|Ig?cZ<;PW5m
zSq;+U<$t*RTK?X?fD88k?+{qfW{6paY6_|)Dh(j9(o_e0U6Wxzh?bA-o8lEez-E!p
z#<Zg~?Gm61!kWf)rJ$91FR%pf&z}C&n3x^dba+2KVuYf-%ST229+h;;Iko3&7E|(1
zGO^6A_5b3ko>?ElmC5a?ymrSx7jqL2dT4u0Wp{DU)Z?H+!87aJrp!}@?ngY;`+Tmv
zq;~$rB{{YGicMsLy4`~=X7Z`Td{>o>|NgtzTyQ9LOMOGDKup83HCd5Q{K8{6-zZaW
z#g?-aCBNJtuedK_dz=5{!2O79rOzT9l|EbMe>SMt**Q;WZ0pv~#%|?ZxwlZ998}|Y
zCqS*dJNfI#*cpzv+mmUc?0Fxan74Q^QhHWqog1SZXuXpiD%4vf)WR}XYNd8=&xXB2
z&McSM=rOFW*M*!tJ}&(s*KDkilK$jq^#z)XeMZ&R{hwMsZq6HizSVKy@bd!7GOm!O
z>=O@;i@1s36<bwp%icHSN%j5x-Gw*hiK6oRK9_sD+JBqfm_tc@^|JpDf+NMa`Ty8W
z<nLQwiP-|zdc5?Y_=F$!4c_mNhNjrq@?h$;H3BBmCv02hadC5}j}D2ndx(E415snj
z2pU5|=uxzuS6QO?W_DjbxMfAza(Lo6eYekX-_d}HY~bVQ;@%O`&H<rW?(dk!D~r5{
z98|RUjsNN|^N_!cpQMLl%x(O={ap)l1PhrzQHc!U!@VkXW#!E@BC`ZHKPLWU{o414
zAtL{{8XlS>h9Op8Iz1eW$Qyg<nG>UJp7d}GRQF_GuG$2kd|LR}vnV3?(cRfI;-)(F
z;g&Mkukq2~Mt0oWGnH4CASfsuqHuz%LuV(};iE#7KEz7_%qhiSjrK1c9o;TdKV4Fd
zDz_4WDE2QxnndVx6-s$m1GLlPG-aI09q0nlM)!tJ{f;EC2<ivo<WYt6$#r)vVWmGd
zP_T3a+<{ndo#1k%48l1<$VNv;wL9Qw>rCnc(gB~*Lc^Sn=~yfZWYP^|YMWr}TlDtr
z!sFuWZx<x<;=ETNbedEoe3S14Dj57x3I0Zgd+`A?#t%}*I2ktRV<*P8zLf?+h)G2f
z_*Xy_K=WY!i1IT595hWroFuRvV6n2iQb9S)+@fl#GqK5zfm#OX@kS%C=@@8LXe57m
z=0Iqtrl{E5l#cw&iH@Bv<sd;2!bkP(^0q;kJlT<%^W3z4Xh;cz0R~TxBSY+uqN9JP
zOY~<b^#Gd-x=j9dHBb=mOVRGY;9&0il+(C0X*yxOLR}VFLsYhgs8oo48eS$7x2Us+
zcNhm#*FsBJGSQT=jrV4+c)kg+RtT^xA+u(wMlh8CA$j8nLK8>8E}|lb^mNc4>^@ja
zmZM3JC$mgQS=0#(;^oDOvKu&pF=T`cG*$e|bb-&|IPjM)U>Y*MBJ_DRRaHaW2cqjw
zHK|J$V|_*-PU3b04QLl!WR$U#(V&n@e?NK^q7Nd?A0Ur%7aj0hry}kW<^}N$YG|5N
zhjlLlO<OMmv{puwi`_OgYi=l@!y|;VV7o650B3rEi4Ub8xCA4FETs*Mi_C&Kkk*vh
zKil@m9GoNe5)nJH$|K6l$=tt}PXu<BV4Vq_YycXwiDut?d4Z3YS924`#*GPZ>zyWH
zZp#DiLL6;+KIdd1C3C%5brJ*$aA+dLhXw3yS-V21#JYg<SHS)IQBYEKBd?;GP-c0)
zKz?=}PlibQtzkX7U|MgCjMiwve8Y=mBQ>Yq-^V{3PO{Z`pWfVZ?2y-amhq%POOIV5
z?^p4~^A{{JLK*t7hfomrk5sADBWOE^sSCp(T!2fgWhk}Lw#^+2H};Z?3M!H7;T)7W
z?*g-H;*19$;QYsg?3(zJ44rt_q3b6_gXN=>H&d+{3^%m0e+V_q1taw4eOZQwTd}>I
z1vWfcm>cWdTLFV+?FL6$CHR4^j8?kfjErDu9gWF-nVz&U*-_*RL?1Dhx0rE=^ZA0P
znV9qR=`k0~$*Yj8GSTTWc`n#GONSo@iZs%UgC!;z0Se1edI?nF_THsJMb(ZMv@?T8
zUYBd)*>dwi^Iy@-%t9v1TwAva|H@$CtXi@_mg$iZ7N?5`Gof@Ge6{HoqoC_nb1&D!
z%RohXE3q^H#kM^KU-8;toH+c?93ioKMk_o4-X%dm36xIQdB8Q5@jZHV=AW8DGl9#J
z-D}+hd+z!nx%&c-N+!^3+*1|*l?mh)C@<b~g3TqQJL^^CeDO`s4R>_9)yrCB-T!%i
zv)%Dzd~>BrB1Z(X^E{+Ogq(y;`}eqG-$m?Rj}7L{Y`&XU;ROV^`Y0@HGmR|zK*!&N
z6hPVubgtS6W+a)$7^K0$?N{y2AA?+(NA9wfhj)$=wsdu<&IljPy!AUPLC2=dr8R71
zt2G#Ek4d>8Zk8Sn&Pz^yn_~yiyxyjoioT~iSwac*861ZXw9qMZNO70+Kqn$%w%RPo
zO`HA#z{>69@g}Nw(M5_OZ>8Yt0}_U)D;Gglbt63f<RoK8cJ}Y^P9FEydRz>KF*_~}
zO<kEA6%$i|V@F!qU!To=j7JyrFsv6U8Y*S$Ica-0s-uEK7K12}Bm$NwBh+c&$T_2}
zckHp~0pG81Sz(WP0zDj<xnQO<_6_K$X#ka#;FA=5#&HDM<5?wrM4c&fE%ca{7Rd>W
zKt(A0+|eyT2M668)6XBLFOXJ`$b%5OFKe-~4Y7TJ66A`0tKCX$TCGt*JSlKR^Al=p
zvKomAK~JNersg)!!pe_dgi%X-Rn2{9-Fj(Z0H%~)#roD0^E5?U7glRmP_&muC@>b_
zCyMoX7tG~U9lfyPFUMXM82?eD>@if8-#;=vnIS4y98{PPcI55Wz)K->zJBaxu297K
z+oeC*F~MN|_{||VLd<sHA=L=pDDFEh0l29BmCaXOGS=$Vo_F33%2vWk1fOly#*O_`
zuGzR!Y?@IRB^(=`SvohFPG@Ysd9kqt`gOOb@&H+2+O&OO{w1Q7U-?|{&AYFkbB@z-
z0zVY{38V#RV^NFQXlP|&ddLU>^z{>t6?ydUu%Iwh&A`FGV0Pm?qcsLBor?x)M`{ne
zsj89wx`W1iqH$KVaF7+x_LV<%f1P$A_ZVyzh*BSh9X!oP+d#i2st9s8ci3djrH6sF
zO%EqWF4@5`mgiENdS~S9oJ;J~3~^#Gl(rqW3CkTNa%DB{GKJsIYIhmDk)em!wlWbG
z59}Exoq)@M16$?LpA$*Ifs)RBb<As>I^=QSx%Tlj;+}zb0}PxVu5rTd^~NLEPJ~T9
zKE#e)p4a!PeU?zMEKYN-*a6#|6Sxry)0{Ms=ZpoAND!{@sLN*viBCB62@$rnwcn*#
zd?Vl$$m1$tJP{ifP_|30IdCL&xrRDv?5w?X^%dZ(qcP)e97z=efj{vP0F;pMIW5@s
zs)U|$E;v>r9F>Y0ex~Q72S4LHCCqww;Oev459BX^E6gd}IazEFu|syVr;wra9I*qV
zH5>e5{EZE}N_x3JkD3z50=ib5%c*ulXTj9t!K(^XG!wMx1McgDVrrPpmK2l`x;#AU
zdI(1!f|N)!Xx868Veqa6*u?6E`?8!933!os(i`~F8+6a<pkVW&5;lH~1Qe(SZ<Y{(
zBdVudQ1qT+cCrm=^H@z~03xkXl@Ou$qyZ@ffL6{A9eQQK{aG-PtD#Pjoo@9lqqFDH
z4UP)JXT|T2#WE+}X#^1kSW2#vzAl?#)6CjkCb#6y@v6DIo`9r?CMz7&Gr>5%x0O$K
z)?N#jpu`n@SL=W4`_uAyfs~I7nwA~);k`KPH$|6rSj_u06}Fx1rT-QW$?X(L(D+gl
z(I~#aE8M4HYO}%HU+Ab=Xih9Qt}YIzTw^r;|3Aj@_x9vJ27Q8BSSV7bjs1O|evOYr
zN3ZO6<~n*_6aoN5Q0uw@0f0>k6oWJj!4_Hy=HK!3^e4?WfkS|s5Pv$rA$#Oha0JUq
z)a)^fYRwu+-V_tDX9=Wsvh>pdXFp`uV%4;d*aOB;#K}C;?|SVO!9os3s?_N!co(+a
zGYV*7;ogRz0noE|?fEInUR~Q}7Q(8fJ#OdmbJ#hV-tcj)@-y=g)(4KAdp$n<xc#l^
zH06}9{-N;)t6eEXpfPS1=;!xEQlI6_{{-9EHjS$&DlYqCc*P(4B&N%7%Oy|)_%k$g
zT(|^y8xW2#`HncR2<0bQtb8R;e=6bFqMjmPLx%;CN)Uu*b@ET`V*5bIMyRK6`2rUL
zY;-;Qvl;E!F;#$)NDE^hVQPSP)YKY`-GOnOk^f)DI8-MN9hpW~=DD6sWc)*U`QM7|
zp~0narm%aIIVGs-=s*HVrMc-}OZjG&#LKg_UP)DzS|D&LVn6i32am%d1?=(($s-O9
z;XT5%@+Q9kC)UONLeZHUk_NKs+6F)FHgz1*u=HeBV+X_puc?tK_P&XZKnL`sJwhIg
zNQxej^thfxeO=O@j%VN+`Zz}}X}AB;>CZKgTwUMBy7)p;+dl5qC)QBz<tI8m+e8UE
z3H>|LdV{H?tX4xQ@REJGBiVP*hww;9oPf<q3(JB$Fk!I~(!=3v)KjgY$833U0P|c=
z=bu?kH2Pr=_Gq{uUEjNT)DdTKCMI=UGz~qcR3s!U|8ny{FFcN_Fqh?`ks$#XkZfOW
zHd}%Wj8rx?oI83>YGe$7&&=qFiA_N}N2`F3)+VsN@q>E+d6Cl_18=Ai&pa>4-$}Ru
zviunb>4M;4@@Xb=ept>R#hF_&%j%>2_W@QS*1j&jkaEumh7ZKUkQxp53y?bWJr+t}
z$0wQ$oE1dGwt>Z2U&}9d;>U5C@__?aFiK>_w^czoibLQ8-6tR*hUignQ+z-h0PB?=
z4wWLR_U#{*U#~&kp{l6JNh58KvWiLp?mB6yPy<Z2@V@u&lbbXIl@3(lI&?vFlzZ8A
zL{@DF19nHHAPSoVc!OORXaD^Y%`WP1dN^JqROoxA@5SG6=K;0dGv~&C3df$=(nKxV
zZxRPC5*C5?4-6ImNnkI!gg%U=b>8=<ea<vb83L9lh>-9sL$=BVgo6Yv){Fl(txJRp
zej}paY(%&>Z`f;b5O%We!k~CV+XNbm46szOw!3=-(Ibu^8SAqFa?e%B@t;2ifkPaI
zFg5k^dcisZ2?+`+g<c1ng6^UPMg3VGl}tuiU?#98x|>&8fnx!o>KII8Q_x;GVYrHS
zqZ)HJq9PND@%VE0`WKEhMiVO+U$|-Y;mN%_8G^fa`KM)@l{_ilpO*S#m8+lhoOP~5
z!wPE+$nmDqFl-@Q9%4m0{C11ywoRMz^T45KL=iRZYgrnafSrpd7>JP-EXZ;1+qFwc
zJ`7w_iCBfNB0`2(t=(efuBmrMBTnYhV?c%glRtsrvfA#Hp_U&o?5m~vTI#_KU9sNA
z-+Fs1t9G@=dgkuU@E*AX35V193kIJA8{rEFQq$7v`V&Jrjb8uydfYPvEpR<g&5FtO
z2>o2;y81MOVKol<r)Yb$(2WsVD^Zmd3Ci_?yj%kZkT~>}y|`s8qG!=Ls3VVzj9sVF
z(5&!qa~Eo7+nUF9zAY_HN4)$~V#=GA&aDhgTN84yGz_GS!GgkcM-4nFb%60Bb>~8V
zjW^@fjv5*T6Izq}R^o>;oIR}8CirtC_30P)O+{fB547OHkY7iJqOmZ1t8soKS9#&n
z^YQ?|f5-#a0Wx#x7&JXk$v1>%QnEX#c*Ei(0k>0+n9E%=1e169ALjm2QMR~HXWfUS
zxQql!3VV&~eC-Sv@yf!*ggCJvvJNlMwgQ1FL^2Sn5ROJ7C_?Q#bst~?jSi9Rx27*3
zR->hc|IXHj&X6U;s*W(P*XUX)6$$Q`1Dx$J3c+;r>F}O9j^^&2z!EMh)exhqOf-UA
zG_aq}U)6XZ$}TsaDyJs|wSZo<oq-H4Qy(*7dN`_a5U{F`dze?+O1mv7#}-Q_bHt7i
zprpG*T86=<)o&DeQ#P#=@ISoZOGVd9optst_hql{oqmvHVH&>1$$-GT?KJCn=1!X|
zYuL)k#GIHa?u2B8uG&jY#l{GU@xqUO!0Awq_Ch@2r_Z*@UD7WIqB?racsr7$H^utr
zCTm!d7m2YbnU}DT=)V)pqsm0&YCk0{TWqjxb^S7)`?8N&4YoPnm)#;OU~t)-k+b3+
zZfz2(*(gXI=tNE}R)o}}V{~G9IF*LtlvXVw6dnEW;fp*4I-m?8-~_0m<$<)Fmoe}Z
z5~?+dy(pLd<zd3t>TQhPitq}ZB)$Njc2Ubk^6QffZU9}WMlb5hV{_ngu2JR}kL#Lj
z(n)1*n)y2Gg4v{73Ut<KU0J*M?iR{D4@SYV+rM)AN<Mse`qSerpi4Rx#c@$y--kV=
zK2!jZwRy$Z4gnI72GFHp#^ErU@H#em?yoU{k&*HUlL6Y|oDPZ6=DEb~h1mKKTfhB-
zuUPAo&&cM8j66z!Z&kv@nY-0X2w7Aks5Q8A=G-th$om)Ha4l}x0!yRQOlNroJSYdn
zR+LHc5VW{kk9&*tppz@=qTz%{Q6Ra*poR!738R0+p0w>K4JQg+e&fS&*pRl*x^TlK
z!RI0DF?tO|<UuD~o}~O_i2soOnWYLz5O7#x!3B<yCp_^^pvJ7(p)kGjn!b2!x|Pdx
z7@};LC0WuIjh<B9_{M!AKz#Kn{UxsttUtwIY1Dp_-<<_}Aou;G7ou~Pbx-g1qob1_
zVjfAV#QkE<GzyqpKe-QPhxXV<t-;bj?WRK{z>sO-M8nBLTsH~-2J87AwXZ2p&t+=7
zX{1=BiEQlaL`;J~={P=7uMZ74_7`<%*Rh2V_MR9qXKsY2xL9Y9r4NQw1p*-E#~}N>
zmne7vYY>e%WOO{uqGK`U0pnxBmzL0m<pgF(G}t!?Tx(yn%T!z5vhV=!-A9^+(kTY~
z?n^#ADZic}_?>ajYPKVk%^R2uAFi?#i*=bdTC+G-w)kS9@#l&94htjT`mi;%h%-Q_
z11U-O168KSC@4ZY6+@UUM7;-EgK0*`>OM68=+~H)jKe$uf@ALn(xOLZ)S2ked-)tn
z_I6g)`vnC(C9g9O?*QQo0cbD*^bl&@e4ob&^6?o77X`>>5YP)M<UJ~p80K5t4fL!0
z$NN@{7Y&QYN}u0-U;f>BZ<f2t6s};G;_*X~O~cC+n@+%*@E-4gu^QIcKh|eNjSa&X
zS_OF~(5k|BT?Yhby!y56@4N|xgN!8~a+Mb*^Ws2NAi_`VVOBWJgvW1R2J$*8$v#?>
zXR-vhd7ZseADi?Fr%EquBg8inwGNp#JnPl}bWQ!A_xTjRI|mjH?(^<>f4k4d2nL~c
zqs3)tx>-v=ZkjSGo%L#+tP1-c0Wa=_P{&(!LT((7Za7wNVn?%diz~xis!-o)A@xxS
zfduFlu^c?WgHh|<aV>%n2)JnINZcjda?fXvozN41gDp$29f~6`U)$RS#|l)O*hC)v
z^Cr6E4O}|2w2#f+#3N+e1eiY|a_=K#*TH81KyXhRaVlsxfp7Yl&{mNrHSDr*xY6g;
zCFOQ+kdSD^L~Q7ar?QZw0MZUL@scT&aZf6bxc^1{m2gaS?O2RakAsu+emCk^#3mdy
zAP9R<2@kew>|cDN3uGI`o`9E4=kMPLvnQN<R3pIqsXrj=CnYzm*1*YPt|2p<j|QII
zO_P01zcmiXd=Fh1Y`Yt3(j7E#I4?NQ4TCWQF*74NVdBLI^j}X#LZGvw<Cns=-mF1o
zxnZ?R!QnAC3%oSN@9>_9plz%GX{Wwf_w)Lz=B(QPJV&o(2#VnxW%*L^cGU;Xlk5yP
zt*8$#tDC}k5t1~bfr5W;`;>YX21I19A+QBm@|n?ZZ{bCa0MQO?6RwjEIyym!mx}^<
z4m0nmAJ_swFb1==b@idGO$)w&=z8#6khvQiy)JK!%SnUfCK-=0h^*eebEgLMz2s%1
zRUm_>3jQJ9HAnR3E&4IST+K%B3iP&v5DtBWj`?d^(QZ@i6aQF@g5<{HrN4@5V0JM+
z(69a-2X9ks;CBF~goynFn5>H_i_)o6r*aC%zg=^LEVlZW`xrVHL8mb*g6n5Ol4(6U
zpx<aw#D`t^d+75wnTXBP!gSGF)#>^H>0VWMi+Y3a5<At2GtdwT1k5!}FsdLV^P@5{
zhKcM>YR9*oXgWDvpdQ221bonSv<kv@3=a>VU$n<GAQC%h9-PJ|Bm*P5r(Cu6lwU#s
zg3+s+%Tqlre$%{peb?VAQ!xSmw7aO}@;Q1d1I89f6>VZ*mZDqXOOg{C&DaiJURB7c
zzzK^Z7AE<zcTgU{XAcN1tagL!`z7+um2yl>^t;r8m4t)!`2T3`++v!(qc{%R7*mG{
zaT=x$jMf;Ot05Cqh6u`Ze=E2#6fbO691}Ou5x|M2mZ)<~LX@I5PGLkFFDOMvr+8_$
z2N^g7Ovgn9MP<a%&fu*^<<fo5U$g9Wd)bT+eXzCvw*TKb=X*O+jfpZ@S<$^9DDAV2
zsgZj`5eRBF=<;rkD{@h2cjI6XrIDk(JuG%n6r~RC)3~RL9f|HiRjyXGUgyicAK5B!
z=EkN$;xi6)2GG87>P>c*R+61MlTYbb#?rsODGkT-Dvpiqr)?=)GPHh=E;lzPdMzIO
zH{r~}7ylErOw`f4;QJRy+odvKVP?o>N=h9n6;U1xV2ObUPz1LvnSXQOKUVrg)xdPX
zkck2xf^<z|h_unDHlV;tg!(biP)fYc>a%ykBz8k`^5Wx7%#jC>xyEr5VknBPI>#DS
z8U_BMjCr{hXarr04k6hc#q}10jzETI+xxI_X3^G|WCsRHL&>&?u97z&6TYHSXy>Ru
zNas6sZS7vU<wQ&?Dn8`K_D*okKx1z@-YkjGVY6Jnfhh%3L-~VB@^zzGSgX!B#3^<$
zk0zk|t4I!uC2jA}2Px!7lIsLo-=se+X&->dVy=XCMX4>EHbnRVT6sGhop+kh!O1>}
zf+PMkO#Z3o9<A*a{T1l~=u%A<-g~I)iFie+tUiXZnTIFfykwmC0q>H~PFXZ2wUg?G
zZIg>jovMO#`aBSU-=Sdu*6-Jh@!M^19yz2@K(+B#rk3^bbO|ky9CNOWd}Z`LxR)g<
zdw59l77hPttWeA|+Dr$L6@P~oIb2~??in`!?gt3Ll3X(>mZyrZnvC8&jU=EGFrGbS
zrd-l<$=As}m}D)^vEv}!KU%Seb%a9Fm@F{Q9}b><Be=A-(;-VO8OtO~ea_su_>4Gj
z(e-frIGC~pC{|3xhDVD=ENPs=j8SfJZg~of5K7pq5Pk^F$wop8B;$k_10?j#0c0Fh
zze6DKXDYLtG@7u!0i8snH!k{=y;}X%lKT6tJAyueMNDRm#B<k`j!B3J+%9QHifDN4
zu0&l^dOIVJskHDRV%{|c0la3;?is3}?WDpQKIpYK6`Zvn7r91Gm;T8jK78QnvQt*3
znl9$RCL$rNoU-GCgB7j0*3)1<hh1<!z=>bk%6-TIAGc-`;t`A*Pkw7gWP==~cdMsS
zeLW|TKHMBQf-5=x)!S`yMWaqKu*DMYy^r+AQVNY&+1=gmd~qWOo?=wj(%P)XPO$U)
zOM}}2AUm|?qferJZ_>$lr>~1Bo)t8r{`^#4NAGJD0bWiSdf8PX%QE#trH<3LOQkdR
z?rrZqSl?luu*hNgEkk$MHD`5Dc}wf20-otrkgw|XRzZPbBGxJnLJ>;%b_Kod@0={A
zAXF+X;mu$NSL;!{F+QKz*n4lb2+ipe$Ts_a0;d6+Kwo-eMcwVR8qf{rHE||*I+&Ip
zcYJ>FNAgI0o8-qd%AqO+bM9D~E<GK>IGSuJwZqgcH<CZpoSj6QDp{CE!n3Clk0Ygy
z-w?hwZ>57z$Y<zsBxauUWy_Y?I|WNUe(~pT&SZAoGA2EMndCqVeI`I^6@o+BFHILY
zqlFrvv?!BE3}B9$vXkoEg+qo6ag`a-l!##U{gC3dS6B8|V|kxdltSI1>u=Lf<ATMZ
zqfr_ndyYlYz?Iw*Aa4Kb>M3u_B}-*VynEL#z%fPBy$uw<V6E0#zZ@CqZDR!Q-#`vJ
z^j2X)(W{Z9d`i|8=RvtLx#y@35TrM~x5e0J76}8DMklLYY^-4uQ}=ozA?7VMPrRU2
zs636-K^q(1KnowA+CU^Ns1vW#0tYud^94!K&y7*fi%X-mY$xz^aPxTTo?`D$>JH&h
zrQ_d~V1HNzoCE>Kw+$@`iI_zn!Z9}obNVaeBT3;FU2eR4_$8_8U?PCHuK~xw0=gs-
zo5gUMq^^c7Ok}DPlFHM7+9Hv~<2zMM;*)@_(C+A5cLD`Tm@7<Pbd?C7V@9j6U8+CI
zo|dGHe2ekg`86zJal(afu9jAwUTT)Thc89cJ1&e}S(ietOVl;lUG|=eiVC+wH!-ww
zWNriD03w+2ImSXrFE3lzv+GVK`abaP1Hy{ut)EM^5CJ>Afdb@FfLWZ%@*K+*XAoN_
zjPKoou@FCwP|gJ3i-wkWhjZri^>rP7L_chRbI7-LgV-z}07*tGFfS1SZOh9m#L>cs
zkaZ=+EI+Vc$x)lc4#6EZxv_IKTXD2hZswu-=X{`ggQ}1JJ%IZ^7vENSeEoG_`+QmK
QZ<GsF-i?c~&d_iA6Y5}&dH?_b

literal 78232
zcmdSB2T+w+)Fpb&3AGJ~0YwxHfFKe@0Yi&kkerbqB1jOBEEy9hV!D!%pnznMoCH)P
zNzMoe3X(wtB&S(N{og-R_1;v?)XbY2%I>D+a_{$@v(MgZueJ8~Kt}5P`n5aPQYe)5
z)C>R0Q79`yD3oRIR<Fe0<lk+J#D7*^kvRV^{)?~av)Ru0&zc(-R4pi!-EQQcB~Q9W
zbSad*6zadH6>h#5{9@yvpt!I&GFis*eC35hOZV-2V`-4RRE}Zm>8MxtZ)dPw$u9^#
z#Q&~daqGD+38npOU#a}V@oTetjqIk={(F~->%XMB)s>XZ&%YO#3}7y~dLUEa`Q65`
z5#J)6PnkUq6@Djr^h7c(;`5Kvo&9}L_?JrAjvxQ=Jtch08vVcj<8tJ}sbzn^zkYh@
z&cEMYUbngGKVE+6sSCMR^3`;EIq#qEDDSqhge>{{ecIk7>;HcHX2t*LTg$MmXA)62
zNxy_YrQS!@*$7f70pSr50b{u>L*Clkzio*6xVqc-bKE6?<g$jiOAp`9cf4jPq@z&&
z;oS6X1I5KrWa{g(^}B_5D;sxjaAQ-Bk2Q+V6dP(zl~2&jsf$(RInMZapGVCe%>?B=
zPtMaWoT0p9-DlKxUjO!SJ-d^8S_;!y_9RPODZ?Fn`>><x)Zy;2=`t>l%rD1|9rN_`
z3}cn>?qyn>a#Ksue|bRCU%%+_?r$4*|GD3A3U1da&9psU?9onxmNesU8^ZtHaq{My
zY57*uym|iCZxjmWX~u_VihQwHO;48d-guy26>^I%|7Z5JgGhK-n11bR$*jKD0dt>h
zhME=q{QR`t(|#5{VKSXamhj<BOZV~fGYC?xkTzRF`6g4PBP+|q^H6@4)rwrM_~y-3
z?^r+dl}(%32r{}qcpy90U7o66y>I<4{xXN5pWpJ;GR<{2iLL&9u`V^P*uB~ev(CBA
z^=sCw(f0Vet*pCo6R&%F^{ob*svX{%e91a0_9rG>XpiwLqRJXpUbc(hOP-%LO*d-f
z%$XZ4?Qe(+U{i<;@%7zaAEPY2@#Iez?<1GC)!5aD1WOw=#8y9-7Ky@cv5Sak%F4<{
zD@4)A`epyflGt)ks;(mN#F%=5+0@T(-RJJ%7xg6UH4_@I>wNOW>IVv09Sg2>!n>%x
z+s=QzDWoUZS#$RZ>pEQ@jn^3&f*Ka(d^@qm+c;E0Qc_Yv3KK4+o9G4LUwi{vS(Z_*
z8+JzvS@j<}a6pXDsG&~7=1aI~?zEUlFjr(2m$$EPq(HlayyMKTYI<dL?Z=k}8sf%#
zL!TJ_Xw5u5d|v6t+4rA}W3l(VI)!v!EGmVgWkX%BT)9#ga(WqwRMEvbi(Ac!(WaRN
z0oWVPm$h%S&lVIElnhxL&CiUla38<2YU7@|hg`XlxG^?PPAM-oMY&)RM_%m@%L9*F
zh|xTQf_|hYtjc~I9oQ+?<k)tUE>9%<_3Q3k=grNN+WBv(a97eRP+ypCU=oS-(Q-Qe
z)k*&#d(p)t>(*GoZTHN}$||C#llBfpL_`?jn#oFFH)608ijgv>1-@85*L0q@+AVlv
zZ>4emaXqCy0th{Ex@D)L_{=)~JxgOQ(XWe?m1(i;uPbSNeb#Nmw70al__5{`109p+
zjd6D4{e`&j(YWFCb|;?rto~?+fzm9;@p|RV2A=CpN0ZC2d^!^L2-VZ3K8^NIu?QCf
z_{$?TbL?_kiV6yLSoBns+t8h``ElJOS+{gsXH%k%*qxQ@mo_*Kr8e%2kPfa$|G|MZ
z;uW&&{l~Ihc#m|Dm1@q$dmSB#GxL+}kHo#$R(OPJ<vEF|sj10~U;2{%u=7i{jcL$q
z#YWLNnaIQuyPU#E+0dnhL*|(twqxBpqN1XlDrX&X-#mUuKl`?_XN*0VMx&WB_EZK>
zwjAJ6Dz|va_F(K$We<-gZp3B$c%YOHmX3MY?fn~_GP?X`v*Zqyt!3P`71PI>bgaEI
zEqZoVS65F@SXG6b7Be?DZz|+C!Tuxw=ZJOvwNuNEhKGk6jQ7`fcW&4%w47rRR~>@{
zpq6cIoNINEl{HE=Sx+t3!8S-&>V@FT*OCE-iwpDbS81$1_eIl0uiQWU*0NP>hYufa
zPbw)X>937=A~Mq(8Zw}jc;#MyT~w%+^E}_>OdR4!oubF51N}10yTmnYyS&tr^(3pz
zz8Ca6MjqX~c{7Q~-J(t>FK6!H(Th}!QTkL|yjrE=NA3~z3>jQqsYN^YTC;{&?d>~{
zUUt#M8@P16)l9IP{1LafFqwC{(RebWDY5d>;TL042sc-{9j~74KfjWrb8NaULYgUO
z_DA;VqMaPsMHkr?X0MyX263I`w)>?&-dB4tlBYs+c94hfdefT=6|{{XKXo3UP)<?*
zUAoi7;Ct~iC#~zNHZb~L`+R5B#%M&C-t^SeP065>s%{&Zl=SpoJBS3+o<C2u8#m-L
zZmw&MH)@QxT}ZJUY)TZTQlGJS9l4|=A0bu${?00|XU~{}D-Q-&M%2|Q>=v@TfD%#L
z`9w6nsi|q{Wcr(-Om)s3uFUyIdG)LIj!o0EEBRJl%QJ4z6|Pj-$!8c=DKdM}e)5N+
znwmd8^cWA1)#!KX5!IxB%L4=iN;m8{_U}Qd7ptS6JP}>UKvIezdsX00FJwOvt2!AZ
zY;V~UXydP&Zt^7>Wng(M7dN+T;0c=v`yYJ~!3_y{3(>eNBy!sv%|KCS&1{?DhHJr&
zuXmYbFgIomHo2|U!D@`&JQ=MN=Z|!C`LUvym@6q4xsJ1c-u7F*TaT);?60BBo*;Sr
zkQ`;auiCYW7tau{lj#HT+3_y>nI!}GCAA#Ky{0p;(KFxNm^^8;D^8q9kCzvo_m><D
zri*TSwVfem(l&;E{G?9+?O1zRknpAF2c`L$1Weo1+nr|P+MO~?z6hln){E14uY9~O
z<I2op{m4&Y$}D+QWh)1JPpJ%tYSPDHCLN@7<uhk)Yt7~@%*3pu+kV;mbDncvmd)^w
zN$Jd2j68pjf|6{Y;rfu?70Jub+u3C$I?h^$nzrX2cj6CV2(G-AV$@i{fs)wbA}T#;
zmm`+vG+#D~fT&1Sk5fx6JBEC{J}#MIO?O11Qku==7n=a9IL#cP-XT&CV%0N`wFkS~
z=V<ES2<q+P%K0T1q?^2WQH^HS?l`$aGeMwobbfYHpmd_QdVi(4NN}Zo+uKZ4ei4zj
zDICYK;j4SBJ|}Lx*WZz7SQq)sY+-KtWa*O~JcS8!3-hxNQ%o6(T5{}dN}XQ^1_haT
z9=X(TUFYk&Ted$$QHXB-e80+K&hljXeCDB|0(Zx@cvivK7dB@UY25)H9(18s3f%QR
zi#X0~CrPkim%b>S*KQrX?GukKoqlreCFgtXdWRk2vpaU|aFTGE>*8`=GnM(`s5aK~
z;kF~6QeOn>=yjQGp-@6t_AgoQdFAt#Dw;=e@rAsW0<DH*=jzJh;^O2s6uhZ=+wL+y
z2OTPvI&vw2n~#t0yx*V}hlRcH*7pwFDM>lEoo=l!K(Z?1CjoXXbC7zQBu@vf_4!99
z20w36sL=zwaO&bWZdUl3+muR{<F2^VgC*D=UYtCR#ksM{o<lm<+LMZQUH|fOW@aFv
zoWG-^V{~`V!!7r0ht8hk<5U_Nvt|v6dto!U^~bc{=OkS{zuw+n^~RBM&Lc<44=KmL
z=vvb&v;2gft9E+^z3$Rx_vvGv6UwnF-x?PX-D5+_lRv(cj~X|pl%#t6=dJ6k+3<C9
z$H>h&6NVVY81LN01^aS8p7YP1J+p1#vFcYCTacF)4ma=m)YW2r@uJ5bho7g%MsPgM
zJu??(2FhoLb0#%kHoRFcCY!FTqWj5H7Ko3Pdj2t^teqVS(aEtgo+a%P)M(Y@*X^C(
zzkgRRb)4yo=$;3>nV$cCQo(xY>^-ym^#Z<~0Ss>WFU>j&{hqYj59cuIx<{Lyb){Pq
z{X@oaq=2D&{#uZ&7^=aN((__s{lSZK*K)_V)n}+=R<^thD?9Nm$TVr#O<H(dA-DAD
zzvs^N6)<R(35cDbzgia7kyqJW7(IP#D|Ufr7;*b0S}oNux8?0bvr+eas!?PA;OFFf
z?#v2*i}W#|?u!o|JWzXc`E)LWpjz5>KifQ{uhWgej*`u-oN2$XUjdWBH#^qUN6DW@
zb*Oh;zw7#RjY{3n3R+N5Ji)3D4hG2pqbi1r)1Nzc4zOhQ3t~aXWJ{TUhN@BM$_`5*
z#V9$}JQefgD+O!E7S3)I_B748h3KFc3*oCwHu{p4YP&c!(kU(}Ic}d5IRhA%9x||@
zz^2{tT3a5l>h0zOzT@H#4b@{?dxm5fy8~%Q+pp%STms%6jZ;n<=3)LUpeHxde$7G$
z2f=;NqIS1U>!v#XN4o?j>>Yl7eZMqa>z}{3oOJ)jDoThx()Xzo)`LIoIeB>G5$`i!
z?`-7NE89-3iAl(f7G0P+JDj)Z6l66&)%m1*ejp)FbJHe<*R<G=RujvuCz5AT*KD2J
za~+TO=XCoUrCcr9{Kak{PFzY#>B^N?VviZ4P}#(3pM1CyaQcnjxhyek&&~O4a+==C
zK9ie{ym{^(`$2Bqt5@%)7$5E8${tJ#>HpN0-da)2OLsOvz?7e<F+oc-(r)ZZqlm(G
ztAU2~(X9nKb{!wrkjmWM`KtdCO70zZfvv9E`aKna-JOH;Ys0YH)82=p%@PfR(<KCZ
z0%^SMR~N6HeKY$F*mILYrc9IYe=Zb9{?T8hTzS4v#!k!%J*@3h0B9<kH^5yXgUJ>T
zG2E>$lB7^vXdXNvw<PY?`i*t&*|h1UKc7*5myebb687|581;F|&5;iEfs!mWdvD|_
zv>1n2S<e|57>G+qkOE^{n*06y#BzCHtN3x!bD#iL{pzc&4?VvzFGD(5ByO_ZSqnu@
z33ZSUW$0-UbL)`C;B-%LWBx`_M)NECRW`Wg)9g5<tdTa;-dTOl*+{SO#?f4m(LDPL
z?)%!lZMsM(m%Sdg_xSqyhNP8pdB0+0h?%fGanGpjaMYOOn^|oI1y9zYYu6@ysjND|
zIllB`x`#O14g&vg`IY9yuFO9i5f-*Ja{l(4(Tp_|icHNjmz8__K1g@CZP;xnvPba7
z<@l1IS5vPT>FL9r=4TAv-(G<-D|GW`2!N3#PFhKqIh9rsBpicE+b_B}E&afVtMtd0
z?D0?R3BFGdYLWNWF%VcL<h<|}(F1hgv5vvROMAOvP1pn7A-9f>$@$hy3-5YG^%tVf
zB9Z2<+Ec8dNe7kPjoNQpna#alP#4Mju1;n0xqQ!D$xycP1gmXsqM<0AB6Vl-E4=~)
z{=>-!GMh`VzDI|9(!DdhVo#bZrBFOA(K-9JIgCk;-PVba3amLX{PuaMNB*~;m*mY5
zIZj%+cXD*|j>_znijXIj80nMGcJwM*g#L1Yc3v&#!Nej<6oxYsCr=JJj+Pwko(G;Z
z?E2)DHQgPc7%H}&v?G0%-BU~Ld#jiwnm2gi&9iUT#A{@)RaBNmhtrJGC)35L86hy6
z6<d&M`W~C-BpSET+W7<!UhcT}ce7`=B{&&=#m>F*wGnT2c<z{a`*P!uu*1}wa5K*^
zZgxYZGM|?>1Q`v{%}xt)b8|-yVF}x7cB5HTyw%LL`Y%TK-CLqJ$r&l!)!iu@D%NG!
z{dv4m%d|}^$Ij~6M{m|+$K<3%ro$FSee!%O<FxW}RZ<xSRiAz?`T<B(I>}L88zDU#
zSfW@bgpQ&u_@u4*%l@@JBAz~6d2`1i%|Ev<PEAcQmM+^`lzZ=$Mz(eLJn)lHq_H=b
zR*<P5jh_?II_LIcYwPESpBpPV=}vTK()io2wr^xOqPDe;zujT%Q8>3}3U{_|;K09^
z__SPKInGXW&-?LQTbWv3z`$8<I&TnDRQ4C*iUHyVGoN+!rTXA>N%|gTKJN#hT={mf
z)uNLArex6v{pOS?+eI`z+NNBboE2}G2Bq&L2Ul+|tE3e@VPaCqv7T;eU(jm{7In^T
zSJi#Vwqdraz?(z$)v{F^mUHNAcMY=taeQoK|C5un(2Hh+mk!NL4x6?yov^;1J914=
zum2u{W^sBOO_1}m=;UXv1KvJ~Iz`P5PTD=>wpwj_0=wrMwVZ|9lS|QA$;@gW5^wgU
zt~jxc{;nbp89{Sxi^#6G#V63pj&;u~#i@1Ky+42Azr97!0eO5}D5rj%51m0Plm~n5
z5p`0}v7g*96VXFD+*)bTz|QGkzbqf^5f+N%@<bEnFKnMSov~LmDm5zydAs>-9?ELS
zmEs_ElFyfV&vyrya!AMDMN!ss;%>i@zg}EioJ~f<dho1LFShxeV4Ksdv2oLz_YMwd
zs(KE|tIuwWMYeBKKRAxVX!rfu`8m$zzJc1??K&PDye<CN`=eQ3b*P)|VpK%XX!g^N
zf&<b)gEriaty=+tHK+lm3p0&*(dwDzG)|TkxN625q7J{R&s+7^9hT5JWT10^eo#e0
zhQxAj)yq3<O0m6-P9au#q`4{g=PPx1A-p3n@c#iV|ME~g)xclyvh1LFpy6AQqk)!v
z(~cbmCMTTcrWkc6hTBaA1ErELPLFmyvT3s|x^VF#Z%u7&xp`k-Um^OLN2Ikxkn*wY
z0FE!0)k0&U+BlTnZbG29p!xUv-$riIfw==hdvV;NP$kzPQ#Q}ZG5NZsWy&*ufB(>2
z?R-~1-Q4N!T`et_01#AP<L*eu?tw3t&XcBXCoOhS3qk%o0zTdaG<%dTABgg(^<a}W
z-JX+83F-~T!)-a44K0}#*OJTdn@bCyh8xyLck?^XjhqkavL0?5Vz^V=$E#`A$GV@J
zyE-8c<)g4I$No`YZN#n|yYXX1d?#WiU*0YjQBqAu{n-hsicd~X4j1lGR(7$IFb69)
zF!m~&pxE0s7%8`}9c|n8(T&Nex3Li|^Y#FK<74feI6yjJU-Zft+8so+;>Wi|n61$Z
z&b#S6To?6x1lzt7__|y0dVB7djIoLn7Q5XZstOO^eZ^8C5bV5=sYM51!rzdfW%`31
zSi7*hJmAse-6vdAm{yj?bMw&67RK!@+LvFv=xb|Mrc~kcKFxHyYvSeiOY`i|a2j)5
zN-Mh{CB=`6M*e^1>#KkA=euQ2U8vb!Jqwq6e0R8}{;9X_%Xwwo>3NqJZ+GRk9#pYt
zX=+m0f8@xK^M1eq)2z{Va=Lt6Hgu(3et%oNb0g~#<8$^n3}Tj+mXAlJB5Ln@c<}Dq
zzyA@y>x}Vp&?USEH7vbpOt#-Qh2nf@ORnDGs?DMKR`9!7>A=GjuUVPXSD9iMv>d8$
zWU8M|1rajuu;sS(ueiv~Rp@hiQ4^}T^F}|hJ3U#(y5L@#u{^tyV9V1*f=+Wc^NJXf
z?b_CyLr0HA4Jw&$HPI>bSYszj=c@6yM|phCb(7uF=6;YJSL3y}7lNzXQPUHF8!vpY
z<=|*+Wd(QXmA@Uzl{FhEnKQHPi<~~CtbHFUgGE7AFzVv9Ko%^73+T0T19QOSJjY!$
z2CA<)%{ag$XS^Y#|FBl>+uVg2qrBi^w-5ajuR=rla3zc&YD8>L?!g~jJ-wNnZu86(
z!+JJ8tV#Ps(W6}vfSJnZ0!|kgMF6WCR0atfR=+y;av;f^i7gh@+z>rwcjdxZ4FC&i
zDu$O@WgeDtT!$i81vowaaf|f#OA|;xHH)*(i<J(Hh>f3Dj9HD<Mdp6yJM=uj?jdlb
z(28zQiI42b92D<x=)57wi>8C5vi2krTtN<_REZX@2Rcemb!1Pg2y3}^p!n7SDBV%>
z$#z~`Xihcq0R#H<n%}GK|IrZ3{+qFkB6HH~u)G-8+n=`=MxHodpow{Ge~AhqjkR&=
zBJ}iei|$?=Pm(N=d&8NWrcML#x_fvi<h!o70!J2&@a)I?zvGGlWNs+)<FT5XG9#=G
z`o|jl!0PL}B?TWpMxMU2(y;dRqnTfQp;CcDhxfg@A@5s1G~Qc%7Kyvs^RT?<)2AW0
zo8#@Nnw_1UskUQ!M>Mh&m>hpS1GyTS@3xWfvxF)K4k1(rX;1F;$J8Smga<p#P=S-G
zl(ihg5pqVjID)4$&ASvLq;`X5J|oa}QzuvFT9=4|%KDwWRn?*AvB7k#EG+s+e?n#*
zcflUAeX*>Sv_n5#Zsl=D_QlLLHlNb)^fo(*C&ClHZO)61M^xUNYDqU$zJ5Ixd|(K+
zye~fchaAA?Jg1nqV@~Z`&oHwG9v*dAhE%6{J4d`1xV6m9Tec|1s~@-e(W)wJKcN65
zN6)|zfkw|^fW6<AGn*#Zj!aCrYP;T-ZoHsAEkr#YKUPBY)I&?a1z!NpOzZmK&1zP*
z)|`WFbqolQUjvQOc)PmWE7tKMlY)>?L)B2g#gT{yLu3Rxf|M_rary#ZRom9(6v`n3
zMpvR!ETy>I$UtwJinfYcgS>iMJZN78xVhem7E>R~Z`Zr1&kx)?zK^i+r_A1OW&{K9
z4xmZ%=C>7NBS+F(Hy8eqWeV045k+RNt9I|_Yr*xvIs5uPB<*&dAJEEYq$j@~BB*V~
zO`$x^2Mwx_c;(y|l+vu3{ussA7k7XW10xkEY_BxXn9#^mc`_8-kzb3)?h{IAlh^3f
zkvDMZAYuTeSR2R74&3e?&h|9pOW?O#3-6PkQ&=}i+`!Au*iL-DmJ0sy+`m7ajmZyO
zL~{@0P8mfOrgVfSnsn5_xRd4`Q6zz-@@yPX1jAJKT5^|FU(Go{DMbL&{N0@7cSC&5
zx4UZJJ@H47z$*0RlU4Zh^}ktX`ui#W9SKW)-+_z1d(&Iu&NDftR9PXnQ2)qMIGy*R
zjSyja8X&EXSYBt1n;)dhmqEuxjvkw0w6ED0Bw+6}ovvBsR?C3~N&KQstJ(RCvx%)J
zhU|D#>!Ie7qaHWhek&`dWFWiwxB&7D3_CAopWO2VwKiP6bg(tE_M$kbX`aiDuG+<&
zBlxpbXp?tN{EgK%v!BO@+qFn^zO@|#S<1~5k76s)k~ligZZfztdGO41tV$GC+^`|m
zk8BB!R!DL43UcQmM}L1{qo%zibTFho(%o4ck@tU2V||}xH2|>#*LG{~-Ld3RkU$c&
zJ*ZC84!Qe`cadLrxqW}N4TUmjH8H44kUT96q{xA?>hB84dgX5pyv_gO<PE^=Kbbc_
zafuL&NZ{HYs|#e2R}?@t;LBj7qo5xcBI7K2AgG~xD_mBJXD-h5h>lJ>Am4cP*T*pY
zxbf62c08gb)hGh%^~^ym{)JRTp~sH>)Sdj-RT8Iq&<o=079>gWMBKbZc~3-;9a~xT
z-lt<dl@c`QOq;te$h3a3ZZW2XVX5>x3m<7e{w;L5yu%WNH=(;t0Pvv7`EY8OAF=q=
zE1UVngwLe4$sr!axcgi~l1Zr8qqR*+a&k04ym06thoK|^4c4+JynTf&Th(epVqur+
zwIy0*og?~SR|vsw*ccxd8F@&<`m-(y);&U4l?RJzf#F}$a`@t`b-_>4+#5TC>7<1r
zgLG}TPiQnUvFI+_K~lT)$&tILoiQNr$mfAfs0ELifE=Y}tuzP;%%t!JF!5}`Gc4vg
zLXR7#e|e3CBd;dux9a6tk{=~Himz;URZ7r|Te56rB>1ze!KBhU$S01|T|SwG-T@yx
zw6sOom2ypqF7^C?Bny>LD>5y=SN0$Nl`e7vpqKesc0fEUOD+R(LPwo$3$pFrVG)3u
zP~x!g?$)wGB*@WLPJaG|DR2*?*PVeV^*)0R_<nO97a~?4tvL250YG=f&d|B$bRJjZ
zmb4Xo6Tovwm11D%WV&#ai~(^ER9bU6Et-$@$0YdcV!?H%9C8_TyDNiDKNc3Q^axi@
z&=f<LaG{eHfL?<T%(iK@ug<Ll04wip%}i!4$rx|AWDys7v{U(Mr91slw;pr^SC;%C
zYk=SM%Xf%$PdT;eE1iDG*4fcgF4mHUbzY=x?*E*8EhqszLnK!7Q&|~Zrb+(Mx^M}G
z-N((tjxui(H@nZQ0DMY{<66rDDV067;L5^&Gf5DF!?<+{i@)8N@(MU<n}jF{hgj?~
zPPSa2^~N1wuj0h3HnT(NMu-pF++2g&1LHrwT=Mc5u>TJ)08sTk!}DydQ}l}2SRXGI
z&`<hUNdEJ`1NPK=kRIYuJl86E>!RdoVZe{ws_}@AV{~UxF$IEKavigYlAHrNG1Ej8
zojamMO3P_FTq;=P2xOBdMWN6+Vo+0QVIT_)P?nh|zZE~*KZ8wUp-TGmR)asZ{Ly+G
z6$5`jF&N_W{g3I2zZ2Q!8pzFybHUgid-n=RC<~9A(yn9aef2RudX+(O=vEK$@zr~D
zqAycRHwi@X-rINPjCEg4ICH`CXU|?j5A-V)LR<4M*whkP&clbPID2_4Q4Z6iGvA*}
zTY{Sa2(-B9G}CvGlyO>$^^lrmu!wM&O46ldy+^?F2m3ypn?hC&!5)<Kj2+&;pKx(X
zF-md2z#$l+<}4XtE1RP4PQEfes|0y$Ol07XpTRwKpK$xmQ_rTsTa^b-a9v8{XdeZf
zIa)g0l(_%$=Rpa<j4vjgSc^<=a2}DWCTIXNz1O5HF9b_e-spt0<}fSs&-9V^vRk=P
zwIFA3R)+6C^KIPj5%~1wvFz!rf*_cTfD>BP`B@XXE})b60ap4fZ4(od=?ft%w#yuU
zTD|dsMy?ydSfR^>vk<Sk)ei9R)DGHc7jpZKtm{g?qMQ90Xxf+CV~=n%ZTs(o_+}Y&
z#_&4y_GPom>Sp(hhX`pLEf+2xpP=D1-9>O8ivb#Q=0*)LK4XLJK;VgAPj5SesND+Y
z2ypE=%Mo-Hk;X(w#>pbD2EdE9{?Y2!^SKpf2ps1qmcB;<Z8C_AWrd?poafHcQlQ^1
z)i`tvSq2jPGZtLDG!AxI>Wk8@npI2=|CrA+uoq9MGS^w_T)FbyQd975^72*WqC*6U
zo*}1NYI=czrfA(qAZ+8M6n*BtGc+Cr;9mrmJBtBfUq2^>FU$`+gKM?|Q2e*|{kBtP
z_rG011L{V$8`?@F3IHq(&sv7~O+kdxESYTso;}<2CLX+9G$=p9^$~>+I!P}$y|Uyv
zhz;fAFGyPj{tH>L@Gbc7!)$DyJi-zD-DzN7HfAP(Ks+|wjwWMCDa11wBJ(yFbWPp+
z&~IJp#eR^=YUpTAZsuM1!F{buD2F~L(JtGTh_ANGX<J$^difoN4{92!N^L~2Q#3SM
z-vq^26<I*(#;43@M^CP6SzEyD$^p!lWUFL$6qUWz#3m>~^jFo{?s*)~l_x`0M;FiE
zF6w+%)VW5;`!<Wk;#B9xv4whNt=(^8^S{YJD_9m3Wimdsf?a*+cdh)#<ONyRk&%(^
zdFYKT&1Qm(Y4w5OU;@yLJ@Le0`}px+g1ejEXoKdh?X3<42va0UE?mSF>>5iLi{8;7
zrx}AWCjdS#Z|`ky98NDjlaT=$QxK_I&@Y-=ksoi3pRrR(h;&)~hAEfnH^UEOg~Aaq
zxh`q$oY{BtOiQ4}7-$kZMV;Ofeico?S(+u(7&dP1#Ja+-FwgMa4h;<*A1PuoLNWns
z_u>z~N|*msdpp<yrK<4_yZA4%E5$yw)P6Jog^&#;t#b79B!nO(KjW1aF{lm2^YQAL
zNxw<`C8axEUy?Sav2k!`-dt0?iJ@V1=2mg1Yp%a9^B*aekXv$WvqHHr9ncxH#C}vV
z>#-CfouKcNo-#jqLmhO+MBhC~of%B7w5$}KjD!r&CjZ(4_@dn}@1-V3B2o+l{5bpt
zj&~S3v%b0Mu}tssTrJ0i>7Et6RQ)8aQ#=D5ZnmGno(7dzWEXPfRk~{o>E#vc0|wo0
z+d$&R<rOHxL*bAbOyhg9K@d2U78MryPbMEs|6qW)A(otXy4#ZuG7e>Izhrj*G_75&
z^cH7|4SG7X&6k)PoxfN$8o)|oo~ho948H(~6d-Z(D#PyGaS$&gXykyPHs5VkIs~bX
zAZ=<5*n*d&`l?m*j2RR^Wwve5K}?UTCJs23I`QAc$-nboUtWPp-n=!nrla8D*pW-<
zAI28Iw!}j$Zr-N+hU_ZVDn_@|8$#5r$4sIo2X5Ea)}~qwC;`(KaF`bPg%mKaEO9>U
zZvP|mJhB9L;{-q5S)wF><B43ke&=0F?RSIZ%8l^RkOe?Er~C68M`Mw@4hfjF3XVBJ
zqvCUzx-rz6DGze2dknY~-jIfw@quw5P=-T?gSxzwHMxh=uYZ;!iOSYBt#P0p1P_W~
zgiFZ{_98A%EdOu|(hMsVfHNA=Q`Pd@lqOU68|%3&S;j(zo+jitWBD%0%*>2%`(f+W
z47k&?fBJT#c*wdGt{sRgX+;4(q~8U%C5LvUTz4JVih6j*2vI;&NV~53#k}-LIR8oj
zdEB82%@V>7>{7CciHSt_5DFv?C*q(0C`A|b_T7*=TuL)2KJ>eG#Q>8>q4yI)5Ni1m
zy#_^qp9xsGh0y2tsID7=-){(Rg_;AS1i{bnEyRK-Q~2r&CsSB}Nfx!{9*_HQX3rj&
zBO0zpfJlcM<dG0`49IgmYHnVg44<wQ#7_>vc=VKobhn&7?7c%IA}NOq$@z%IRbVG*
zS2guHpW<q|ZdUy6FNz1o)%IvPk>-2NmIG5qY@Y@IsH%ga`;@LueZ*Xvw=mAdGvXHj
zO4zvVtt!Z6$Cu4f$_YU@xv!ISOW|ap5}pG6|NJMg;KHOO?C9)VSFd!Gaf2I_iqJj6
zcHdQBlZ$?P)9l?yy7?UGRpxU8Puj|%pnJ3MWd3##o-#X!!&3mZjL-c01uBF0+G1(q
z&zc?1GlE%+DEau=&<5_kU=iH**j*qYM`9nm5F4lr)IF%G4FIToHb2yNpS&qUnBG+G
zs|bQ>;HO{MkQmfA_eYPsT85Mmg5wF!^AhKu>`_F$JYoP~p+?Ret{%I~jkZ7q&7YI5
zNyfL8K!yLVW8kc7UziY>?~`_>m3GO27eKQWh1=6_%`lrm${TMsN}y*8LL)$sV5tvR
zV_bS$G+_8)At5!OfqK~J2oD4LW_fJ>r{Cp&e3sv+frDD3sH_ogl#s*!ciUF4a@*AV
zNsS`@77);vVSg*m(88|>Q9>cC=o*_=h*RT3^}C4sQADE6-@W<wZZ9!g$t&}Tv>|p7
zyjiEUPHs3=k_@j6c`c5}`V5voT~xseKnx!j9LXqL->6*<p=>~D{qy4~W$QbuMvVw$
zF9Q%o{Xm-;`M(d=UM-X&^`%&!agZ7kG_fapc6aMjwthsH8-thk;r8nZmGJ}Y0lrqR
z#b^BfjzYQqe#ie`rqaKk_}^*v|Gwh?%dfg=6YR1or8pp@XJ0}&r2@~`xqr6~N`Ju#
z2ULEze<>y8Dhe@pJ35=~6!9gbR9Ya3gVl6B0ek8P;{5}0##NPuIIEo=>pAjxHPl<y
zef!QJNH_fcCGW3)FXVrVxAuR28QcleS&v?bkT^Iz-!?=PpPc_y+g}s@P%7x8fsx~_
z=EKGOlz(C+tekve1JB0=6%7S-ZrpP+l2ao~A7}nvbVLf{Lw9*mcX<vawu^bhu!i@G
zQ%U^bT1=tXGQ%@`=os9hh(E8!0sN^)H~3+<+_RCJClQQkjSP{2x34dwywC9Lu_<Y`
zt?RdK{c+q0jW~Xq^&@Lwt)jHS@on@GPFo{XVikOyuq*Ikk-Z6BN?CpeW@eeIPP0P-
z#B_;-_!Npd7@L!Oq%-oDQnqd$n+-RDD~N!A_>2zs8*ac_?oq^!K*_MY8n{W|=FiJ8
zDv84?i*ppb*0Di>`crgqOtc_PBt~c1jlN7=SJchQ66=bQgVI5DfS*>NP*GQ!8aAwb
zowBE0!YGdt!V64HIkXuiPA%!`1E33k0n>PrHJO=D<JQ;vhGg^v*%PBNq@jK^Qz(=E
z4e=T=o}OD%QTm)`TiO+1ETq=p=e#InN3?R)k@jT3Qt;vuk)s4FXV&Le!5bL@Go%3s
zPBe&*cJj#q&ei8S+8+;G5Z&evwUHq17%&fZW90%C4&Ovza)U|}iMCqO@7Ot{1}{tP
zoDWFl=*4s8?yaRaf5ZyI+q!KWRjA5Vph^hT#hS<=-IR;yt$Ce$c||n)>{JBoknoSr
z{Ky`LFukKbK}!qmp#rk$+IkTe+K^mud-z<>_Z#KIxB8Q>I6=|KVd+ESHHuGgs~LWK
zGe?t<Z86FT4TX<(SrJbKve(R5@M3I>Nwy?XcuArpgfhE{!6=eg0|cZ|+#0i-(Krgj
z=L4=hhJaht3;kYv&tiZkG}8pOx08>UL^TP=R3EQ#qGg7e{)c1IjOoS`Gs;g@MhAQl
zbHI22^e1<VIH(YtbC&f`G)SsdUzjK^slX5v_m8j!9)kZcz%*BtfDF{VD>^!<#x49P
zUdJlA#8d|!aAHef_fBi~cf>u{0EiH?uNRk==46|FLQPFgStBq{Bw5+iMooECG>f`G
zKfkC=`^EHOXmqMf(8|*UTdNGzNAfDWkWjNj+_lOefW>fr{3axWNH8DWMmR6~&p%dx
z>Ihlko6n2YDVi_AOJon{l8v1fcAomFT^@Ks88x9Eg!z17WMspx@|}ywLjx#8qwMK$
z<QjiYI)|RT8qR24DhEY8NW#&{XEf-1zHr{E=t}o#M;r@)&*)3(@$F+P`fM~8p#OA7
z9z1eHk_Zs5&OQ9~ZrMhC*yWrO&YwRYgR-WUW0y+c08n+8+N8%0p6}y!B=_a@jO|*C
zwYs}LzgS@5!>|49-nVn5l@^Dz&o8dsED_>FjI?OHofyu1fu4evFZ}M^<r?-wsi((R
zQ7#{WAIfDPH-s7LvApldz{J!;5PS+KcEa&3NndQtTL_1PLY@Si-!WZJfB$G=U?B^z
zj_d7SP#}0$@)_<=o+!irqF3>PV6i^*TlJ8OEM5#vV8xBF4bd_$Z-Z+L1(!pmP5k&0
zLwx7xDC&H<b%+<qVi$a3KALt^=&FtIN7X0kN)Yo8Ei55-Mx1CEc$0c4<RL}fKw(v}
zs>uY=fuIy85(}^b$o|KS$6uj??Y^Hw!a)kJZpfKziy7@IMX}Kb_S|{GI+jc{Kw3Lb
z_<AD`%7;7C!lvQ2PSR9r^~6w%Rp%ptZo&MfPfp4)N|HoqBFMUwGy5Xo4_w8~Z*7yw
zec&#mNaJho6^_^;JA}0S+-RxL@#CtZb3gAOj1{3R{v#H9d2RC(4kaeHxHf@xwW91g
zwulu^80A694uC0Y05D;t5@C-JqR@a>eK|;RvC3G@OnGOGuDTK30{!mY#s`+LM4@w|
zg&EYo?y}!jEh+FNjDR#W5)kKPumE724n)*Og`R&xlqR(Ap1>P>v8*`O>lp=Q6r&ZY
z!OB^EFW!_lgE|w1XrR{IU9+VQCVL1Q_(P0ukl;N*MxcoYd&iPz+}Y9b3mm|2t&JHw
zj&4};g`g||zumWc47WAc@!*z0bsp;^=>bg>z&2W01vpTIq4~0D<vPfWf9`VKWl{xU
zj-H8$%PpT7P#=bwXh_>MPKQL~du^x6O9sL1Faxtuvp|B+2c>7V<HRD12P(WgvjP`)
zoN)uVhbSBfyZP}(Rt>=|vsb{ffy8L|<i$Q;5mcFEJ)}mh0d=dyvbg+^zG#w)En}Ka
zJBR*atVb8HA;He-oIY*#!@}TDOXmg!j2bR>7CqURB}$<ffe@rL{Xi;u#zGnrr)yNj
z1WgUl3JoRC52hkvxLCEC741GAWRa<E2p<ZI0pzO811r!c9HP#k>zE5_&oB#R=|hL5
zj9|04u8iJhtfvw2aQ1N{pm;o}l|JwXL=@t?@^Lk_22K_zYYcC=KEs%Cnr4aabVaJj
zm5lG>TLxoOxwxb{C>gv*^LpY^Du*S!Ii6WER|jawPGp0fpe>jl=^w=HKqlEw1xu*$
zxG`^TRD!xVSxLLGwhlqJ$L*nAJ6#=uu4HBqPNz!<mhurh9GHjSuiH>gNpZYiLV4qb
z9G7BH%hEMR>$xFlS+v`xH_)c78rAZ*+^Z@bF79RSC^F#T`>?UIOb)srm4TDyrJ;0&
zB|Y_QvN{rqKevu6GJ)5?01D+BG))m$HgOuvHNl+^Z#og6Kwb2?a5#Q+=VEN|Y`3-b
z4wsro)533RPUc;Uqqd?M%(F^iwV%nc8P+_amU3Yi|8+3kZr@>H)ojmmc8+hc8gA2o
zKG2AvjXs-~9}%U!fW`9Y=SZZLc9cn<o@G|GXG}9{=eU6VW*tawM>0KGidz#yqT#T-
zL~*IQv67eaPAS`Zs0NTx5phNO(4ql2^1NBV@f_N}e}ARj=uC-4L#5u>%=C557NYgh
zEI;e|bbZ!q_>5-ZMaj68PidFW*NiUjOQW&uTxhJl!$P=5w{X6E%;i$^<j-vlqxD8X
zflN|1;$J_%KuB2_3Hpe34)43Cx3`|AFW0~sOIwYn@yP5XLBd7y5&`d5nV^z*hQ<p%
zOyUZinD)%?`}&VK#=g)z2R>J-e#rU0o}wNtxNIloatsc(MAjS5_JV<b^?JspQ?&C}
zqKu|p==j7ny_bqv4IfXN&7Ej3>rp?-ieUMswLw*T+CVE4y2CZFZac+uYuWHEukLAK
z_FJ<)d2_5qMoJU2U*F%Efh)AjI(r=M*a3f|cv+Z~yDWuBQ$221&iB-UsQb85GSNWN
zAc;nJEzt`~uuZ9^-`ZSY{VU&4X*l3sU$9r=)xh;Z<I@$AA+}2>mzkhaQJM~*@?j_=
zby{%N?<hks{r-}<!#1*!8rf+qK!#4AlFQIrvQW|gl#i%^n+me)KTNGb)0%*q*pEBn
z%d1<-KJZEdtE+LLWyvW)?!2Cal!{@jK3YlzfVHhmOu?O2?6ZO6BElf`h&`21NSa@z
z)^hsYL^6?}O)m_!q)P*8Du5!im|ykp1dsceIPbv&2e#RY#$FxIRrOs^-67F%%^7vb
z*1N>`iw(tPo`9I^&#kJ1k!^`CuLim&K7GZyZ5L={5CNReHtKcNYsQZFjLbB@7cXLw
z;3cwdXup8OStY@h69#2%;QcBl1#q=|L}P%vfyv?LEy9pL?Nk8^ftsXT^@DQi*%haa
zB9CkM^DqK)81ylUTg5oJr_^|}fB*d<+FEYMv@8ligt`)e8UW&;<n7HXCJcF=oGc+m
z_0g=<%)($?LBT_YRrJo=*k`m^V<yW#{Hpw<lGNaYkW9nah{fAn({_=ero>Pg2-0hb
zj{T0GG!ZOBgI&^i$m8Kd9_N+WmVGt*9>krwx3*il5TtK3!o0X=tmG&iMx#z)C<Psd
zA{NRDEYpl}_RWzi^FQ-98n$L6POspHq-Oed#FeEFwz5xVvaxUBr4WN^>TUV$6^d@$
zvAklm8oPz<6y#rDJnSX<ZKaI)-z_DB7GdicEJ`1F&6|MTVd+DCuLU|h070hjgHSv1
z8s|=RJPf&#3sVy-mFP7n%=-^dcAZUXbr>tBg(+*=o&zW0W%;da7?<rkD}5-o-%T<0
zZ+VUa*F-XULSqb)EGj+~S}}M(w3&ABhTiVqNO@-$i?-uU0Yrh9n4OZKHK&^fn@$t+
zGn7TrVt@gh*q52{<^HDqmOsCq#(JIwZgPIyh<ylmUKk5@sw9jD7|Z9VUPuZ3>3j4+
z#9KnYG=fjp4n~z<AJ%a#r!OKCc4Rxz(QxHXU5VGV`_;S2p@{NMtZdk4Vot<)!GQz~
zWQ=n`y0QI}e2wa%NGAqZeY>x>R++zD!1*1339ceND3<37G65HrVER|DUX60J6X_B@
z{O06qPyF6i$R%hBLy@R7ef0BHznfQVtH8Pzf>>ZvAS@9(`W0N{s6CluBSo4I`f5o3
zQi_Tj59U?Viq&?dAXXqOl%zX=@*#!^^oNBPuQ!zsSr3q+LP8$q4L0yKQ1OZG3zG%0
zY~?lOXeI!5GgIxrh?lSHxoSg*WE2$w)4?3kD=-3q?HBW?`uJdr6`HIV@G1mI%Z0mR
zNnV0pQ?U!Tqa32^o|Le0615!-Bhn1wdga{t+luqnf{eho>A$cVgoygfDw<^V9gxKA
z6as;c?FX#1dFwVzz;|_d8lcG$_q~%>|0(FleW1jk<o6Rp0RimMSb@{<3L!Og<*GK3
z)POk(Oq%T{35HBHqA(Mq50*^}CZv~OvUVs417#eCko95ra>@Yk@p(wL-!?3xxV&+H
z_;B5Es%IC99`Q^UY+Axfsuv_eLD*nVV^cO_cK7<WO`EheTQ^bG-XRN)uW9iM7oGyG
z1%tc#-DHdZ)9`1a_&ZteH8yaAge>o%)Bnp>{2v%een`6hcWPawK0cK_$zh!7Ek`28
zJ}gF|bK{8Oc>&51<-{u}XwMIswMW?4#5cH|mFv4{+G>){0!eaz*|V*huxGNs-yxTr
zfW+{d0`fdV*dcd!8Vh_B<nQ5m%W1peCU*#)z!%Ffi*}@?ao@hR3)f84)nDv*PH%R#
zKyGIn7q$_0O*xCfl9>|+g@rXB(HwvE;lcg;SVT7ZqM(`yfkVzcTeT3JzbztnPk5dw
zwKI*KJ8^Jeb|q!&HkgNO$uRKMt6^d5oR41eYTDp~P&p?i=It)(t)rvUWuD36^_WW!
zuI*!Vp$ZP%?BGCHN(RQg{T+|&-yiz%U`U`;#B|%$t5-e!9f!~&vx>ajfql&tfUnVe
z$Vjy$2FCn#HZbvHGM0r3;CW<ZSQ#IP#30YMISs0s-jz4Ja~6BCS`hxiyPKYmKMANw
zO29C-oY~-mhYz3Ue|uWl!_GIWrl+U(K>F0R#S=xHBQSQz48HjzCn6sHmra@fjjjKN
z5G2#%r$(&5&Z)*-Xik8zw!?MPj~i<XpbGR^vccck`f{t<FQ#g9A+EWN+>ofss<&#^
zB_5*6!N1_Ue@(2ooxjjRn($@6x+WDWt+MBN=+;3agX(JVQ|32MVia74&h2azH#g1l
zrD%bJ);cl6>#=Wl>@dFyg3$BKNan4LJ26uF=eYpt;*Z?rEYn+9BG_BMHSPP{SzRW4
z0wKql(6|d1MMy)F^lVE;`&X~Nn`<=ni08K-2OZ&l|Gtc&p<%|^HN^%~mMvTXOTDR2
z9((sVFILEkyEC5q$0zXEE6%w<dA7&G+}w~VKn*><lAEw%zp&8EhxY3`cHlB^EKCkF
z+6+k5bSd9Gv6E7KYdNor%S7ObP-@<F(3GbVs}A#>a}E^XyNvO(0`VCJJ?f{KX!V^l
za?Nc4u?wERa;Gd`a>)*5i|dxUonXB5V)E(@jui{acf*{f86}b<3?yb44^ma5%4@28
z1{c-1*3Ta{cJ9fD);h8Zv0CsCahm=4`akmsg8#SR_+RvNga5g=-uGemDKj#u8mFEq
zM|dF&F+oG+%$qakGpxHn7&t&cdU|><oosMOy(rv`M2-fn=*m3$;c(x`U(WrI$c0l{
zACJ-zeve15tazjulOmQNsmTZ|&}1Vq@$0mpUoH7|Bl03%MW3WAZa4S$vfUXrS26er
zn!Vb_sUC?pHw2AB+DbnUNvO<deu!hEK2mmnQ5u?#XDo#H+u#;@p)>6a^$F9TH#s$O
zW~opBv?qgaAMPO?l5XCd<MgI2TS6giMZ&k5X+l^lsvHQ->rd)8unIf<{rG})YkIFk
zf7nm$hqiQKfQbo!<jKj=``}Snh%n)Q!X|!t1}a}2`Y&qD;<e$5&QP}Dg~hzei^rZ{
zv`$O9lf?DPMPQTar%;h-mAZW!PqU>rsj0uJzqGtjr1_N<yHkCdozY?U6+4}dxGN|e
ziY$(+r?~H3qH3~QLWSk-<%W`D*D8jewDOc6^;=!S=t`H+!{qgXr_oBJ=Q}UYSddZt
zfI!~#qcqG115p<zae&@FV{2>6#>Q47JlPt`H1~BCvm?XSt@^+b%}KfsQA9yt93-?6
z5zt{`%{TPJU5t*7{;HASBJMrXp^z#~zik^BI1_)ki!i|Ud2nVKN2#2XQ!Z&;;pjxV
zj>fcB7F;1-fq`GXukI%gIUwMY-?T0E!-q4dH&7zg!5t{$=>s2{gUWRRsk>@<tuXX>
z2$&JQx<N_;YMv47)id=8PQ*`eFR&4$M~YtgPIP9H;4+BMWaY*^vFz~~X8?fpScLv;
zjvC+6-jx`4;G)8uB@}86!|^i%f995*3y`U7sN=Ufah<yUugS%<W89qCm^0(+?XP(Q
z{rzN!aR7n0-nI<U$P08x8Z$d!JQSug6V3ov$0X(J>+3(2lvIy&7TuYJgDMp9t%fHL
zBxAtCp>TXvT|+~EV$mLXCnu-kiMct4uAUwv%v#$`eRIqIAfMNsfc?;i$C0_k$k6Z?
z+Lzut8%3%<+*{}6<wgHoC(s}O3scG~31Q&S6`8kSMg3k!;nrQRr*lYDo*eVQj+{wj
zpr<zg6+wIfgkZ*tBdeW$qZ*1!g9|0=7Bt!mG~)B_rAcreAIf~{wtm-ZOriH;h^HEN
zhhe-cWT;R$J~ozP```lR?i(?qXNhKuU07K7yP&tOLB)&XpDHUG3CjqklQ^M(F9@v$
z#B`8A-x<scy(Y9<3$TTvq9T>H$9c}|(GDIl;xxmfDiky{;=(0-;HuGuz?w*GtWSz8
z#KtAY-%p_@ia@v1hv*`GH5^6fspFxkutkKveS7jZe&jP$C_XMcJD5E4t5%vQ2J;;Z
zc~v-8->tK;g@m#sE)O6<?&C@p&5V#$X_nRJ82IesmNE%mkb~lIc7jMHh<pM@@xTOV
z^anv!peyMEe?&YFD*C%G`@d#mXW#XOL6CEQ#1cx}GwtoN_8W|-qSdhgn8`!xH#t|h
zxULS_m4TohhnNr@9WB05WGVz*A0GxUbgx|_qnqMX>Vu{RUP;o&0R%x=c_eb+Xi*Z&
z7Z8HlwZ_XaJT5mqJ^dU0Ls)p~hzAE$VRkY0u>5Pn8?9Lr@?`gkZF7_0+pNm@8kaju
zNl7(8@EXS)ePm?h1^bD?YPfkSBAuwD%^^-T4YLCJEU@`!gPxKHD1gft0P~!RA+tT3
z*E(aQ==a)TBmu|Xf_Z=X7RX&V2srladPRm(0c571R+nztE|#EZU=Ve&RslTXcT1tO
z6GYFU@m{N7%>44>>jbDvD-30VH?j|P6t966VO1+BaISR`By|k9$W4>W^mFAG^XT?w
z;Sm=VQvv$VV3^cPmz0tc3GHjjl~7guzVY!A%Kg_LKYo0(M5dhH_w^Qv+NVteODkCM
zWR)6X^guVKCO3b-gQx<~tQ)1BMz;u$eB1)X(j>+Ba`Z#c*Mc1pcd-s@L=MPJ4UXH5
z_lPc%7Q|!wu`G2?NHSE6qesWZtvA_tk+}vv8X1L)#bDM>0h1Rj_X3Oh>M+|B4UX+o
z=SVO1(&U#Jp1JajWvDIZL`GazmYVZiClf0bV*n~mDa?r?2&@a^wMG}*1?4i|ypfTc
z=Mz@^YG<F*Rx7maPs*v<$-Q)DEAo<}l+<IrGT)cCZ!aZ=1k$^}7V;W=I<l1{8J;d~
zeJ?lPS|-=2t@94-1Zz}Dbqty6;3%y@3yfKlu=`KeUF@p9!P|!4WTA@Aey-f-T6|0P
zT!d?Eg#04pE><e~M8z{_I@6^>f?PxG$a7z&=beZ)K|bZ<f{s;ecM7+p{E8JT(BolP
z+V~eYSUVaET-u`7+fn9zRh$^^#Xoi6_wc}l)mFV#-%@frp!ID%W;d@O6_I={G*@b9
z-Mc(hZ~B4FE*agEv!VRW(X8`R^9zx}K0=HhlN8DeVQ<}gaU*rK+?#ur{j+{~)h3n$
z2cn^&^*1I2zm^J&Mt@H{rpTFa;#9-Uw|#W#)YPu+UM;>vn4r}`SiApZjYqWCuR!UY
zI4;afv_lqO8#aLw2>U6Ujr#>-c{r-?X;y6PMY;JygD#NRBxukOwj^qBJgI-d54{nl
zk{!obuIMxNrUUM4&tk?$Db=)HGcz-jXp1oHa)Eir{E*>?yK5w!hSK2?BU|xt9>{qH
z(JMWx?!Vc9^Tt<)g2sQA*w@$9@n%-M_rr`+#C+Uqc|~PqH;2+#R)7Bw>nSe!W77_R
z;E%+CCpuGB87w&^zdA%(9yU}IyExeH$OMh{?6O-1VVO2)%TaLaz_lG{r(1FRYinyO
zkH7uVH!?xm+c)({@k+{rt9{CX%sTDS@6*q)@O*31KkZTQK^~5B($05zcieH<zI`^n
z_I^pCf9-)c6z#BQ#jg#>pb>2$Cqwrvx_a`|B4UXkJcp>LmTAtoH<{TaSr@oQHqic$
z`GRtPMXrR|%KrX-mikKiFHa5poAKlfSO7m(9es1^D|+XElVXyRlF5eaJc?5}*gKRz
zj*X5gT)Gqkr>E=PuMzUjXP@QL3{}0&Ei72?JwCAEg}g=XCp-Sf*o9luX?Vg&Tkit4
zr!<KJ+8JM_FAd5Htn9oP{UXBX=g*(vr+;5P_!oT{iBTN=*RET4C-g2niK9;)ySln)
zXCiJ3D(-#5h%xHtMVUW8tYf9Z?jsaBvvj|CxU<`rhz9UeT;en*&_HfO&;8@IqM253
z&f9Yi$dmaYP*e49wDXBM_3OmDty|YeFidk^8&fGake8KZjvCoW_o=jW98MrAjWhE*
zt@cOlQID^l42ZmlFTXu<6pEnorc*Ia@3YAeHdz1Y*jT#9d(${1h>(tlFht-VW!i76
zAwG<GXqg3N_)E4xM{D*igFQ=^Sg@dY`>^ET%8B*(t)}S>Ee(x8k_4b)uZXZ&J=MNA
zuSFfVn1D2;3>zq||GWT1FfdhCK0wkxa%X?MS+P|*V!<_)tQul<{o^zd_+J)&x``eV
z5KzX&5^m~D^B;u|2SioNVdUHmsdK;<uU_rTfq6>^9;LG18`CGWjY~5Y3HfX40>pF6
zMh%<8keuYER&78K!LI&LK}P1E&6+-5UIwH{gEWOR6tlSh^fmqkyD2-%mc-3ai1LBw
zKBzu_5&*L-Xyt8bzzG^S<Bb>}<%9l@$K60Mg9mOU13R5Zys@BKIybSO;=x`ic)W`+
zhI%(KO+pU4JNFijJ!;Js>60%Z4jbS+Ya8PoU&q8}MXQcW)SP3`2*Vx{HyRl-%b$U4
z{;<0ua93KBl(lsl8C)ZhH7bPZF9)f(f$Mu>jvK@A69=0?A3}KsE~uSJ!?#xLslwUA
z6kqh46$eq`;(^oTfkyR0i{Rz3!yGrn#HQZAus(#(tOBzlQn3&3r=o>0#E9to6~oan
zF~|TBDJi_`w;w%4P8$I(Xl|CDUng*NyDN+V5zWnNfr4gdfPj)XB*3Z@Zz%_)c{0@k
zH?!z;iL`%g!BINm9>_uj6OWXV(rbVJoj58{$e1(LPn^PDym;~S-0~x(07N-qNX7H8
z2av^6K)HfkO6XMVcIJ<~MJF;2*ufnTo%a%^|7aXN;*cV;Bvi*o+qut>hi(1H=zJ{J
za{~59jL^s;L3NGW;c-K3wY>GIM#}RDCo;LVOXK>_>$|ah;EW0%?~Wl@9bt(lHdSqO
zo;oW$_4Uq9!5bks^rY=0+8(TW@2#OO*22sNZ38#W3cmOI_p4+G2&{88*)g%m;KLvY
z%=Y}`@q&XSUy*+1XVtC9U#n8Ag&nPSW<EdOyyOJSyuSNI8@U_hkGw%CGFQI8_UiR(
zvDXG-;Tym;?Nn9Evbm-(vrO%TTiC92$4vvrTr$3A&8^)Ln8jyr93>A{WX_t4l5$u3
z9DCH6GN?lR7~^h$bAG&d|C5hdj342VdobtZtT01v$F;-sSMki^199h5MgkR=!u=Li
zwvxB>oUYjwO)XW>)zwv)fjn8RDgP3fSddj&0^aw?ve5z8`qI!ErmAMv3u+UK^M@OB
zDQ1VQ8lE3>)rbcX7x@dyAIz=+k-t`aohxLlGW!adk`{(~UX<}S`BJ6?mi7w1fj~!X
z&w0oE_hh}0TwY~%d3Iumwv}It=kH&B+`Pn+u?Za+3%39s#Y4VaEX2>mt~*tV-k7C<
z_0Qi?PHh+aPXpU&%5Rq<e)ML=lK*@X;`0Brwf*yd%ZpugRh42NQvMM}bsn9am+fQ6
zY(*;Vv65nBN{T|<eC0WKOvsF-lG3&TIW-RCpbh8euib~dVeAoOkO{g>)jRytr%x(A
zH7w}~Qi1ILxz6A;S*cEWq9#95v$BN1cH)Qo(99s2(!#Q`vMd%fPwYk;0bDO<Y-}7W
zVekxi7u#R_In(W}h*q7G0E|6TCq(5DC9>J^IE&&pvaLeiDxhmahIlBRm)K#R3CXpi
z!|JrSxH~S8wSKW8bbRKpl3MuyV`<Hba8^kX7U*@T*|d&sO&+?l;7JlW!wb+SUEMC$
z`nG~9A@RJj$7npk0yBJYgfI>Xed;lfLF!?f7h>Fyib)+d$0dS!tRJ%|l%6!tkN=KW
z6w5s@NSzVT1OF+0@nRh{*(&eBv#{7-WnXtCcL?zm)~Be8*}0-YS7o2N^4P2T*TWmv
zvaQD#k4mbdq}_8L>?;05lfA?qle#KRD&mEzDvc#MO-YIIt$}r#(ZSGHXOyh0upB#l
zBq~nH42~6~Z3|!#V(brm7Je-MbHC`(3*zE(5EiAT=Hf)QUX+%Gx=UjT{3Y@Fa~2$Q
za&qX@6l>OM_NYWjItzX;F7xDhxeym^)$wmkUL#{)A6P#n&ol2;%+rk7^N(Jgnokn<
zU)cMS@5WWVPv6E$47->%Y~SGa;#Nq8mvhycy&{3L!@vHtxfDv==KqmUKZhekxnaT{
zHDOqkeo#SEv#nowuXHq<Jgj$YYSV0uG*7iltZtzTJRg&ilC~??njhx#LOMr&OXOrN
z?n~#L>wV8-qU;mINkv>9>-efU7XJFv@K{M?{9~)bM>=mra`GwCuFg0dt9WT2m)T!a
ztE;G78X5NpKfJ=q5?RqH`{C=iM9u>K`q$C{kDWuKquD^ftN7IJ<MffB(o@YwvfjyS
zyu5pNCY^)^K7>0yQkND@BaRnz{fOBsxF)1Z+E#%{f9>r6Cmw3ScOE|c9yKBsmshLk
z<9z^&sOs$@n9=BP`0z2w<ng=;Zo2yL@Df{X=fix$4fV(yOZ6#|Gc#8c_oyz4CUPzl
z9H^Q1-nQu2wj<qWN~!F==5g*VH)C3xD+}c2=1rFN)Z#mZ`OK@izg#`NcMO<cmsOJw
zbsK9a$fBc|vDnO?KQ=KWG*DzbRc&YFyzngST~YPO4bCo_+%@6DI!#@PoGU0Fj;{DU
zhlBj|ZrlHjN$G#>HIq<$BZ1mP7_&;zMRk;b=LY`$c+dy2JreUBCM8g3wTH++8`Ns@
zpaIO!JUrd?D0^{X7VVA5JPhKBptxH1{H+qF=I&=xnv*y%TBSlTPE#(pHgI)@&{cN|
z+ofRc^Zgrw)A8(DG=;*KSe*~n^1_>gCmsYPZ(F|Vv7e{szf&*>ldllf4o;UK0n;F*
zc=ae`Aed`3b{@NW;poNq`1l5(4o@E+@)(4Bsn8?5e$QgLYQLz@3NXhL0jM=-f+}aY
zp>EqXJpN<qrtHoAs7P@H#Y?zj&?C+>wZR`=SK&z)m<i1xy`WeN6NGCT?~SjgT8xCZ
z%uWvb{O;}IpZwQvF%K@}0+}_rAvPx93^5aI+_QGPlnklZVXpDctn=veV0|b)wEl=b
zL5ySJQ5*?iCZo{4JLPE+Ya3j0unhEyotLfz4hKDiDQBNLFid2yo;-#Coe7!7C;BFQ
zjn`9h|L`Pu{3Q54LcPsEl>aZ>eR(+0YuoM5P)Q|2gG#2P42cj*2n|GrBuNRCu}~<p
zCW%PqNM%SAWlqXaNK&RGA(<1&JY+oU(XjXTopY{pzUw;woX@r2ch@%khUa<i`(F22
z>n863+zNGoP%)`#X-ajsARB;(z(d3vq7~UCI|`h^0<1+s^1&rnbc(GqIT$i9IGBNq
zm~cxWE+LMuU|&A`{{1`uE`M@IPGB$)E<WBKKngPm3<xF*b_iR|h!tnIT3uLN0<0W~
zI|I18Dlx7Esv!)>$8N(ys6mClKPQYuXmQMt<pVs?MHNLjxdbeNFKeHSB<pV!Tza4F
zQiXXtd-&I`jnsOETYW9@&i~{h_a6Xw`o7G-q-F@TM$h3JckVm}n&~>(U+e@>so+8Z
zsy6L7^9KM-WMpJs2#D|zM-0O3Ck<xHfwmGOS8ISE0d1ob_TWXu!yq@dhq&+V;&kNW
z%P)KTHpqyb<gF-sfTI<RCnY9U-+_xA5t~kq_4~s7I8?-_$HLNbxYp#lfV_NOrKKn;
zn{9B*F#4F^J8B7YEy5akJ%UV{*e?(>pLCmMMFi=hHa*k24!mZd59qslvoEm*xfIt6
z3k#llB-zA&;Q90C{yoTc35-gZ%>>HD7h9wo{@w(VUJt2kfRq8T1Qg0{3)r*QS>!9=
zXmcRA*`P<ywk)vWVCCT}mx%-gB!oLeZi5UGoIA2s;G9J~fe5RJ0E47ko(ZE^q#6Ny
z+dwc_DSqM_k#XR*<MA+Y&){#C5`30)uQ{);`vWNk4B;_++a*7)WpnAQy1FmveE|5=
z0MNlhlMaAuvsD=3<f7(!h?2nQ>i{agH+W2FNFZn!6|3#t%XG*i`O%}j<SKy>$D-2E
zLHmW+@~u??UKB2F>PHZNxI36bee}X1<oGw@^gwzzfUCNf=Z#%PaR@Jh4Z!O`h9sJM
zD3q@ee4+z{r+`==k?i<N=b}{Mlb^Hq2N3MTpKy=0j~uxT`4x&B4ua8vyACt}T#+!G
zFDsVm|85H<sT<6xK-H84e$`<_-fx87gM5!vCf~nXp#tL;Ry(<#IXnCFDOhpua%li%
zgDvx2`)Q&r%kIgn9|AsLfrZFJGlUBA!pv=1$J4vHpmh(IbIq3f*~W&*N){^5pokQQ
zfGg_Z%a`6_A7z7(HUx6xoPr8ZTACq9Z3}2Sae%r4x6kg3_IcUi$iy)SEPa0WAT}%M
zG1(O@J<y47Su}qPW%~aCc?A;p!WDN7r-5gnm@G{*k-V>dG&}(MAkV|=5~d3nQI|o~
z<?mtm;7+(w03qHysbAf28?hhge;05n>vW9-IhUEDjczQqGeAmLF6=twEudf{*E&6~
zEH^eb*8gi!i1cv%NmE|z$Sg1WJu5t;Hd*i28K?E|xJ3W;tUSY4!eaiFN6v{8nBx)R
zjr9#Uk&r?&?7nFetQ;p2A-sbMwN`~tMu1Bv;550MvFOqxD4J~3l&<W4VSm)ZA1B0>
zw`m+lf7QSMoIKSYS0?v>x3e;G%@&@h=>xTTS&g#|Fk;ohHPyN6MGqDfe8T+srarn)
z9Vm3tSQNKs&mQ9AfvymtN%<bRpMR?oQHtr*Lj)T5o0G0U)+*0uH*USdGxODv&+&})
zS`)lr!Gg6PzU;ft*kFC+@#wWLW^nJpyWP=XeK3TBsd&Zkd@BEGzUZ}h*Rl5jQtv#C
z`V1H;v*75T_*W(C0h)9j`48LwFYwyDfD7*pB$h4PjFj;a6syXsq)s7FOrU^Y*07x2
z{5}5czZn8<+|zecd7m+5=CCK@)qUckZ$`k|HB0{t$XrMTn5bcGZvNyu!m1K3W#pU~
zESLWXh7Am03Y*f``mP?Vp2L7I+Nct~#oj`6Om6?tSnC4KVSHKWm)?(9bIx^l3ehXW
zGcXSqT)!Ty3vX#fm^&N}n4a3Mpv1<nRsp_w4B?yu2Y!$DP>ogk-YM!R`_7m%PEyAX
z)|lTaI@@}A0o7#XQ*%ad&v=Tk&sS|q;i0{VY$&aQlI?PZdH)&1WjK4E?++K?e}(7n
zcCqNJtAMhA5S?AVrf3|_>c!$5FUuY6sb44HBGE%I+}TEHOb_io$j|i;K3IHWE7XI8
zZ4F=-XD3Ri_n-$kqvj)e4d0O!(j5t3S!x?~c5^Ybt=jvOMX$o<ZI~EK{f-CCRvdsU
z;9t#?FrB^2$z(<6lV&?%8WMKFC41b^U_b<J;0A^kw^oAhbn%SKt}4WBB06Rn4GjvS
z@3u_XeI|<TE8P(rI@3E~+s&5#1S?llvvOFSk&&U55R)L#MBRPrlG~vnerZe>2+(Y)
z59((0=E!*-gNGXCR@{gF>@nhg207a0T%@hsV=E+&;@LZax<~l_f9U_H<)AVGSOl0K
zJQSUM``8BNZAtb(SK@_M*?oklKRB?=u?b=>4IQ*?VfWcNUxAvA7t2pap~Kk~?ITik
z$U5hqw>KlYHxDZlxM+*5tY}`M+n~aD(5qJmOx++I`I(^vNjJd8KD9q|X0gxrI(-{I
z7#5>HfmU|RTQF$&_uh(!ee+&GnETPVZFK71K>F?^^q>(Lu`udSC$Z^ycmwjiz1mX-
zm5b1agunp8)LjdwjyU&7s#K+w%Mp0^0owF<bPN65KBt&m0qX-@?-_p?I=VGl5#xfU
zIQbF7(I>0*7m%hMq?cN#r?Hg%@MoNMJ4ffyR^hw9)m!K7KXvL6QMup;ZXO?dFB6AS
z*zVc!gKyrw3tqA%JS#3PuEarIW)*2#<;#8o@Mvu}11pB(2Se~`c-{KAKe$IXK*>d<
zH4yji15yaHi@woWpACviKk-$aI;^M1&e86IPVSZOLK`=RF4+>Sq=YjGpkY9DC#@%9
z&iLnEH>}$?UirHZsvtt?>D3PmoX!Dk7Z8i|<SWkv?fcX4tCKc9OdfbavzPS#mpIeb
zC%D~iotf^P3E_Kpd)wBQbQbm1X@GVEV!>ySsw4grTj1var9m9<;JHuc#oc8Pt~<M>
ztOKz#E;?GVDzBj1^M3(Q_li|r2>F$eC|I&Crx`*MC<9pz8diq@KfJi{3O9g`p%b+3
zMDMN|@F;h@EBR*-ozZuk(HDKjBeUg`&6-KKyXlq(miIV<B;l&lKOqq{wufA`>VrFb
z&cfga4T}IB`7S5aB-vOaAS&A<Tez~}8hhBvXn3CZ-ad=UWtP9sLf4KohsgVpKhz}~
zU4#l_>B4)f*rZQzQ>RD{7Z6K?Ul3)z(E3aiv0T%A^V&N)1OeJ$GK0eIXK=334j6*@
zNjExs3`d0iw-`3Z<TfO|q-_MG{D94yAoQ?3(~;dqEZd+Td*Q+b|31<r0(0{{bj$!^
zUjA6~CyDd+k-3YL^)bYaq!X<!cM7$EugLRI7!xDVcO!2h*#T?-;M9f&@kO7d9c^u$
zpx9PH(U1VpSiH7IbRjX78ZR}Rj>d0G7FYppg`f(_>?EL(eHSLS=KB~qb|Fbwn(2x^
z1t@;Kd@7!F4BH&N=!TJ&t08nC=S978b*#2`OUHkPxkt`I(+GXgqKK_qwr)K{f-lhJ
zVX!WxoEr9(&-`D&+=~~3JI(2^SQ1ZQ2#pR&=SrO3<fF*|OUDiYFL!3Dmx$l%o}Rf+
za=<rlREdKt7_+l;EI?@4bt~?r=t82_0u`f@_{O)i7~hOr!OD4r$lrRb-5=tVCCiU6
zbwENT(hC70A)zd@BL#C9kX&Pdlk<;rn^Px|=|Ij;&Pc43PSWiM&)~v71&L$faj3G|
z`_JK8S0*`=Ko$#;hoqw`7#XxD8?v;XG@RZOxNob3?1|W=^`;n4fV05@RB;IFL%|Ux
z^E7a|gkHfjXgH7EGs+NVt%x6i(?84IyU<V$!Qqblo9IOj+ke>aTNdb&N%|r%xFJ;8
zVI;^FSTet@DTZ6QX7tdKXK-UbZQ(BuW-ZY=p*XC|wtonQ5U?yF9dn(T_(E`b#SWU^
zsD{3Hfp_{QPW?m_0E7xce4R;^!!HRo2zW{6sMD)(ZOdgM%fhP9r$HQ~3^Y>Em6SS4
z{ZdZHre>7y$={V!89k&wIqFDI8EXQ#SX6D=fAHXn;AsPq18E<1)(Z(O*jw^J8}OrG
z+GMuT@Bk2XDj?|D^qPA=>X&wJvhh=i9FDza6#5G^1gFt|y8~5WU@JCn=xNtqSUS;j
zhtIMEVC|4(@VU%-`cRaOZc)(^j;{|;9;<2x`b>W+l2Fe9*nkS_GI`R-C2kM@nZtAI
z3&Y)$=`hw;xv`ujy#Q~4)Of%63QCK&@fC=c>HmSGxmgtI$x{xn)BUO@C@dVrN5VYH
z3v4>4!W|2L<9Yde=_@v)oGm#h-+|l(N;>Z}H-le`my%xU!#ry_g1=k+H$uDTr-&d;
zO|m|5XsWA9>p^_H|M7z<BKLQoV+RffdG`e4hGbzD<3@{A>``1hI&{LN0Ux`8EN<Uh
z8Zi8RqAcq$WT<>4y`#nNaxf-{WEl9y!e$%du^rGh__Hhlm?YvPVdMbtCipk15%?yC
z+_)`}@#FqYuT2f7$Tff?ipq>!12Am#Zk8;6ya45Su)pSvLwstgHrDN{r5V>?QULBB
zf`##6RUhQ;&_JqqM>xL|U5F66BS}N(VPxWiw3%mn)zx8LohmGJ>W3r_cl4jhul6*8
zMbrNIIpd|*?8T@G1_DWT`G2nr{w$)2V(g*2NeGS)K=4G$T5~U5PghsB&-Wm7a|Ggt
zupJOTP+GlE)|cZ)Ld8$DT729afb%2~z$a^3>ah<sZ!CI@vfm3F(AAyFv$Q-OX)>yR
zCJSQ6lRGXa)=bBJY7Y?FmNR`tdhF<3F_kj<!=HL+5la&?GdI_?r|*Wvn)beZ5d2=%
z<zkGxf4`KNcB8*0@ZRckiaPLpbjz@($-RS8Jy)K?E0o+i^0u9i-!ny;4BU)-c=8Y$
zqIq973Nii2D4dn%A54-}lj2}{T9<x<?`G2EMRThEssN@+{rbq{{zX2=-)b*Ve>U3D
z1$Z{UK0G!s{35R7ubI2O>}ja7le+2|iK*82tn-2=rwNj*YkzNY{(NnT1KihH@*eN>
ze1C98i6c?r)Q9~aAKMrn;l!eNYyGop#xmUY%;SUh_S*aR6LkOnJ~u;Oz7KaaZZ%ly
z9>1qEyLw)=2js=6`fctpIehh`T4|LnjM*OKfLh~gFSDT~bMk==`<8<}Ni)wrdpokR
z1$90Q<O|kyJ|2GJnvlhc>jnO$L*MExtnuShx*os;s5)=I@2{s6Iq<9`zW+_nU{U;o
zj{b>mB*y5{O-)?XEi*i@p;Q9h8!VDdOjjmPBwZU)D`~!FFG(?bQY#e#yE5G#d7E<G
zs2<PT#B#3WOI^8Az{;Jb{Wfw-XGXuAlMeN?E^29Zp&iw?lwZE5fArb>TN?P&ov!k=
zgZHFj)8Fcan)n!72lGG1v)Dx4GCA@k^+?Iq%);zz;#GYy!!^^BYl^;DR#YF<c*E}C
zn7D_=L$tr|>~EqEWy8e#UwKLYr8oNj(HEDmLB*5}M^Ss=2Bi}3c5?08zkk;Dgfzd2
zLnB&j{%{F&&wG?N?Sxj{&ZI|#nMTx|h^N)#vJu7JcIw}=bbl>v1<L^DZd#iW4yfIM
z(V;FOfYrox5d2{5rAdBU*Mo;afq`qTw0^mxg-j4I8sUl}5^5!sNT`!!1eh|qU|QeA
zSxZyXokvfhKb&E*Bb(pBK<L+Rxtqb`A$Of<M*<{3olABlr92QxfznjsywrI`e$uGc
z1hzt)DXwQoLq9+QbEU|Saa*-dSC@abh_Cir`YpWHHtF}CtnsAAWm6%#SJ8&|;>CJ&
z+EB-Yh#VG#OqVFEf$o~8PoWvSRt3`btNQ8~K;-3g=LR$kga)eBBVHw$(yISE2qrC}
z8FD;Ef@Lg{r`-Vr{H)%m@eRf|Iv~>wy%jGfI!oh;SvZ<^ZZ$wVLfXi*`V@^MjX-PA
zXx+n{w#T5^?W~96APSo&)`nm;nL)QpkY(c8OSlY#B!E(4c}-cWFaYg6gro(yPRGC?
z5k^yHpg&}{cnbkGZc5fF%lrAxIvuPH;);9l;6bef`6@~CE`6NCC`~Ou2Owz|N#{l<
zKA<uqH5khKOjn8D=bO!DW`#TJ0D4|n!06Cfv7J-{NEBkhSOI&I$oQ_Yr_Qw^fHB-S
zLj8N}?d_dlZ#4`Zqo<X;O_K+RT#vz{JOCwEG}0lxJLfeUpe+M=1bnPTGL0w&hw&q4
zaZ3U--88+eb>zsA-G=`&z{_Z!hFd{B`}XbI_zzs)!j<bpRQ_V!C(Fyr51=fbHIjuQ
z>C*2my4f(p-Euap&0s#_u!Q2T+S+Y!6?{FWzPeA}2mg06(&)Z<_!e9&BBs)^4{5@C
zpz_kf&myvSFk>DdyNA|+_$?8`oNq*pPi%XLxnN>>{_|=8OtHj9i%bf{Vg@`y9DiqN
z!YDB{QPQ%qc3FP}nMh#cMh#%q+Q{gM6BBSfTnKT}5jyZl2f4J8AGRGoKt=Fg{Lh>;
zpYlB%xVxSXsjfZ5o&+vMkDPF{z)u3<EMeX4bBaVcjJm2i&9t1~eci7Wp8wPjD_l!c
zlRLpJsBfeD&pp7k$v_;4)V3y#oZm4_s8s$v<k#XxPd)G7iB`*oV}@w{453FdE6IcX
z$V=LWeA-I^cF>V^&66M7-uD%qdNRh26ppBR(M!o~vRejYqEJ5a<4--=TFVd79MFLe
zP~dUux^sKT+wbiNsh)jboHpRcUOEZB2D(ySr=Im}CKWogcp8P&II4Lt@K41|@O`V?
z#kC*J58fa0Y-qga#t+d``DgK6IA65Bm8K@1cw+zTGeWty>S}!cL4u#EA=R=ceY%lm
z5mFjp%*AMPGHj<@Z`Z<pDRx{+2lyx?&o!8)<5JhN>X1%(_i$Ia`*a#H0f8S@LZt)R
zxrERP-<AD`4z1ecyW=R$OL(4Mx>QaB=VaXcc`{7^+Vy#KoeKHg!%j-HopJO#^`&lU
zWZxUEPobK>J=ctX@SqB%*D~8Y^Kjq2%W07&mVL9^xxv5xXZQ@ugZJMSx(zZ?-;Jfi
zPA{$m10{OIt)h2y*F7$(DR|hCS9D#!$@Oh<IFAn#uWV9yzq=(fnBKASl$BNvX*z=7
zjT0+e!d$yBGtOL<apsbI#n*DTku<N9@2jf#1ls1?fiUTWS4RxENfonF%=i-AxGLHl
z>1oIEX|Gi#&ZsTp4vGalLcp~;2>8(?xR1mv*@U#ljG@^h9}1Ruew^_M36(GdSgV4g
zyBdx<cEo}d(p><}p>F7|xSMhjY&5oF$1v>z2Gtp+y=zo7G!`$gp^F;B+GkJs45XD$
z1<#)8)HAgD2p=3+!fba9JWnLW1lEF-kzT3W699;i3gLgp=(?MHj7{j#o|^pd`1ycp
zV6k`g=DQ$@l1|~8A7?>k`f_#;3gDf1a@5{kICg9*+d?1>G#iq9@vYA(XT!?vpEbc8
z*BPf_c6Z5*`*M+b*NhbV$Pv!qTo5&&5(C0+<F>{_Zt;?(OI02$)}k9+AqUoacB|jk
zA^1*xBegY({Us?mI33W2tBrP=1&nd|xBFMgzL(gqsd*O;;a-M|e`A40?C1t7**tsy
zt(r(SQC}191H#592q2~@nE_7H&$2Pvy$$OTxc7MDXzqO+`SBmV)Jk4WKO8{30*PD~
zr${#Bypm|DqS{;!DJ;Z!slr5f1EREgS@ZYPPs10+?@?9F71x{xnBdq!-Rj!~`kR7x
zNpzc)7*ta&9PV64FPh0+K{**eL`O|Z$}Fbu;N#q4XJ&H<$j>k^5r8<|hBNxmWw6xG
z>oAbBfAJDCAx?Z;qUVvEu<n(H{FDwP<!A%g$5``X8fv!Pw`eFREE}IYz9}mUDgU~_
z&w)uA?V#Ac9W;lu-D53T&7biR|JZ=8f|-9|jC)t7niMl3n#H@%Ov_gO>Kk#3_@9oP
zM|*~KF+>x`CQv;}9DlO}=Q~LrK78<fT)`PO*8~BLr=D(~sa0M2w`-P+ms$uC2FBRS
zkx)>_?u8x+z|#gJIyX(eKos7UJOVB}dHE@X44Im<c6e7d|BcK;A@|S@R0#kAvAo7`
z|NdaN(Pv*&s#a`Z`rV?wRf-@%cmAoL^KjSFOlz`h(3XR`W*AzD%*w}C=f*wggeA-C
z5#0Ws0AjHY98~03*((r}$nlL{Srwu%@5s&I;CsZ(T3YoJ3}!*?MN{ViS|UiRxNdQp
zU~OY?=?z9kM#Y^Q{{wwfL$88Ex@OLD1LomzjT~I`Do;XaQ>g*~c=kh0jp~ENO?hK2
z3qHe;<HgDQHH&}k(sbRoG^@X=y(fNAYcGqmEk|RA({)Izy{&CMemkJI2RLRk{fp^$
zGTf<iBTZ>A>IsDbb~JLvi+nd^FW2+vBd8e+W?uRUeAu{I8f^Hca@_96-#}VgW$QUv
z&RHqKpzNL*rY7tI_Kfj0BsQ%TShsE$nO9aT11P7M(^<{^XQ?6hsdoX=?Mj@r3#TYE
zb%wjG21oa{py8Fgqn{Qy5P1jRGV4#hC+!b7v=Ym^fG)c4vs$Cu$s-tig?1XrAct^J
z?}K*f19~zK5r2lif7(YVs^APl{dC{o;rtwiZJgK1>LkQk(o)A6%nvr=%-Hpr3IJEt
zq(|of%N8;@m2e>Nca-hBk$l3Vv1&AL#ue055v%P=;C~_mLjV3!lH!+u@m0_G)ej4)
z5Yy`+ECpS3ZR_BhM_LxJAN&Ta6)9#2UMF8d$sC410DNJxUid06p?`DXYC?t|oEy18
zkRJuUn;UbY!qe4AhJ8;yv~+2vNZ~g~Sr+W|{ZA+nx7+jF<2iNTYgxNDG4J}#&6y>#
zyE9$6NTW?fUy%8yy8QpB7WthxQ~$N{`Tu-_tj32yyo^XQIk5Kd*+4X?2rsd8mKTV`
z0F>b3?4V^hH3N*;1NcC7ZMD!ZgFrV`7(470`4pfPKFBA))G`kz6hT4*WA_BG!>1Bw
z{_0(h?x90M?<GtCAz&N?EP7Vl4$=!XUZr6i?N()a)L<&NrTJ-b`)SO}Qe}O7rc4Yg
z3?PtR!nvm(5p_V-f9>!-Ao0=iLKM+gpL~?T=Ly5{$XDsD_QCQsMU=uC-~|#TKsFqw
z2SxUW2E875@*`CIQMJxme*QPk1II{d9-Elh6w&dZrG+rwe#`<Z;RINzGQRE5!}sCJ
zt^U)GaYqj_Qwa{^Rq!onozR{D=mJ*Z#`2;Sb9Ff>X#)|*s`uUQUKj5sh?!p%`7C)Z
z49TVb)D<p(Ip7+7fciq;^=JfC!x+}0vIRrK9?sZs1De7`V{Ol}AjQQA#-uBG<cDN|
zC^*k6vs*8?S)4QtX~;|hG!KT46p)nIStR9yu1+DIu^0!FBLP!>9&!w0;+7Npf50;1
z`^Bw%Zq3x4Qa-b`G?idH0CH4p?M1X*BFmpTh@9n=f6zNq{y^N~OzMl?SGInU8fSv0
zzWhudIg_%)Juf2AlczLQf69!Opbo7wQ#(}OU9*?TTtA>|K0dGa)*1KK@Y1n7=f@eX
zPlp<cdihfug55)79;6mcNql@_J7S>Ex$g0q*zvDbLC$V#TU);Ch0;<+v#&CJ^1;vt
z@b0+zC*uV6ABfb7sF@jAt0Nn!Asgwl#PF-hoSAiU2SbX^2zbPcKFBehtm6xB>Cv#4
zjl8~AC2j=(kGon)t`)y2UQ`puj{iQq;r|jvkHD_YSfX~N8=l;%F}Z^o0>rzc!~5lD
z_n9eTNkYbR!Tq^Ckt<IC<xmY8X=H#o`1%>~k3(*mN_78<nw0(;+Rif-h?&R3JIuFb
zf=sdb09zRl3BU}T*T3Fca0mSm1hC`mo`4h&RUg<>CLV78`<s)N(hSL%fsPV@(8B-i
zzWkKAm5+HLQiHWBh)dUP*OIH`Q$c+lXvDsu>*3n3E8hJUx_Z=jQ{x=z1bV1~B+}SG
zrFB;JN?3YU3(S?#bk<GOV+qdHKYg3%7-v6b<ef)XFDuHJSv;8|EX=@A`;57$al6<N
z-Wp3a-c0t^j8TW-2Up|1<v7KpEN_w2x+oS>>zl*3bIyXJG|#ja-q!#0hYL{cVWu4J
z6`y|mtebmZ{m;}>=MSIwG^g?L*M7%WH^tA$iKksjoA~tUOnj^{ZQWc{af>KT_-qA^
zE~Te|3xl+63q7F^0$|p`RYL2s3%bLK=Z<G`G}`$2_?iG76{FkIKh|#|y>)BB$B)ZV
zHz=dxO&|FQT?j=%^Xj3UPzd>{Ev$4glOY<C?J+dVDp453*uE+(Y(nas<<PSd1HfQP
zxoD`fk`6!P(#4Ax_x^-uWeb{XwOO`n=(p#ix53Q9avtglwa6_?Ny#a200vhNBk!k3
z<I{-aHq9^RV2ElnUXA9%4}BveJMq@~p`q(bFz5aKA-FWmBU21?hSzW3(o;H*3_iuW
z&e@)i?iUN}_A=xI1m?r<>J_dy1xpESL$!uD-G?c*tve&{+oz?a*+O9f1|cil&T3Sn
zF{WUD!&#JIyH!+Jo@_hUM`0r1qFlI?p#<B$Zs$Gis~{Kh3Ja@Qx*8a8T)24A7KE%x
z&?qh{MpbNi3~)=~o^~_JD+Wf!s=86QELI78ZElX*A17V@UXO#anVxjStT8pyDLj4K
z!4CCX+nkqSl=ITS3gzD)IxS)}Crxh4xiV51qn_Qk?!2#z+oUw^7bb$@YT**|A^Z2p
z$=L>d2nLmTL8cM~u8^_gcIITOPn#}Xz8r<I5Mi5+GosF<g(YLWj$mPNu?e1?zKTw;
z>Sd4useJdD3B%{;G(l^!SQFF9kC~d9pg}3^G_)R>aGaFbRv2M6Ja=S6eq4BW`$bL6
zQ>gv)iAP3e1sp4tjveCzfN>RD&}RY#yHDVzxpU@3p*-mP_?QclyYztc@Y86&R_hx!
zd^KOs<hy+1k)t3}nte)L3zt7?>(;I7gH@ppUk*djd{{iOpfcaM@v-6;=Ra?j_fR2y
zx-aZzY5b*hl;KUdFvJ;MaWPB?NU{e{bsqILLTa&}pI?j9ym|AgA01u=9oR=fE>_kH
z$;qPUoSe|iTB@(Hy1u?X2CIGya^{SYO0~E1DA-)psOG}?(5TRhh1@Y<zgV_GR6I6#
zBQBs@vIF0!$ppa$P5az@h;D3QN{~dK&M&Z-f~ni`EA{6B;~FANHZLD_T<NV8gC6~s
z?j?+KAlABy!7<Hvx?G&H=Z=cfMBciEVM`93pXXB8M~Y~qE;=Q383}ZowDQ{EhO)9c
z9_C;HCMG5uTiX-|b{iWT(VyEXlVZianS8~(ytMA%2!eIAxiXZMrM(yr<f-dKW>%tO
z*MU3e{C`V)&!;XfE}p?!F@Eg$aU;-<se#*l@9qRi?i3fhgO5g@&EI<lk7!#voP*W^
z#5%5~_V&x*yL4~MRE$!9*;rp&2^*<{#mI7}z$y~DSn_qUeZVlHMNUJ>c1cJ?#G&)Y
zZ_B&;!Bd{SVH!Uo8dC=OKdLKde{WM$U|YJ*+1WYe`i2K+P^XRB+t~OEGg0{OH;zfG
zaV;$@E8Ck|e$gUBKcJPFg)D7}R;GDaEZA%OJre*Sn?PI+leXmrg_BlZUS20je}8X8
z5I`xVB;7TlqRddx3)`&@zxoqZ;oY;&$Wo}!ZQbxH;9;QUlymU{&bvxBm;c-CnUX{5
z+Krkm<L^%2zI|JNc-dFHoHco&8x31vLYHOtjTJ7JH;@pWdVkA3Sa3h%Y9SmFvmZB-
zaAJC56STr-kMaPWA^iCas(&pEPPVGgT=e3_3kD`8&$Kl0@sS=OKf}Z*6}z+FYb#^7
zU@!q~zRl_V;(SFB1sVp=9Mlaqj~tWoA6;TIO+SPPM?>NNq_XeC8NipY(7wp^wr5KP
zMi5AY{d(x=6bu13WhH^0NTxf!=)ro{SLuniac~HkT_J)W$G!;h2IzuD3XF$OCd5EQ
zYC@m`#7uzMyIHRU1_tsK^YGFRbUbMmuQ;X#ApDqXm>Pee)wx}~g7r8Yd5{lM1FXp%
zE_ydg+ffH^gl63rhhhuRTgZO7z+UhJvxjVd<F6PxyP8tKMKQX%;H&`h8if%pbKR?A
zWKLlL4?X$<`iIJDy%Q(+@d7X7q*<|APHEpGIC$!tY+gpT104mgu4I^jWMyX?qn(Et
zr~(b2KO0$FFTSZZcRRBvU=cj&Rj&H=A3iJvwzN5Go22AYl-ieN54B1M%?q}-*q-db
zpnTk0^<F^YVxjNJheG_69;C!x^`a;jYsERt^xISA-uewjG2c)g`&j5{_G<M=f4@ML
zE$G~u4|O*TVca>KQg(q)b0Z=`Nbcr>wr!v2!ZyoApSWgj(r?~{AwPaKPU^04pit3M
zz#rl?+aVssaow^7Yo5x7&o2X|mua<vmX=l%h6U2n(q2ljSVi-|4nwUgw$<s+IfV$+
zp2`z$dnH2BlK+#~@l7jmvovNJHecJ@=O{RvUAurdNlOu@=d;ha8M2&5MR1vgTO=h~
z5ANErV`;Wr(@eks!Ebe*Vv>+q(#9Wy5=*XdGmL>uK~ab|H8Ll5?wkvSF)g=s>W)9Z
zH@eDWzS*j1m4IauHT@AJu48|4!>q1br4=sDn02?21pHf(kr@He+nk2*=+KD3{K+Nm
z(J{IYZv+KhLK}e>M)?mp-zBqow(L<?pW`2@;C`m1031nL3a(?9D=IXPojBoLAEde#
zt!Z0yF&eV%_gF{HQGXpTFj{lS+WhzBs_`qqBbuThdS`8YT~e|g5rSAxJaQIyRR0^=
zv~3rft%}l4AZHGh4~Daa9fK#b4lX#$vCX(ojGX-b`Pp(Xdp{g%{PbxBrh-(gyqF9h
zl)k~i0<<XlFzBcIb1%FdK8)ved^@hh#wX`C$?h?I^{0!gYb9owUh?!bhL`Ew@}76|
z$0byLA{4J`DAs$5v&^J_FM=m=?SFb;)6IpT<hZ9m-UR@U3>tLmhON@C4?%Nm8y39L
z^Us@C>IeqENVVSU@)4w33?`>=T!4o%m&kiNJH61$i9!0=iP=+S%Ch*W$Xj>n=qz!Y
z8a{wH!iB6v8N|WnuV1f_K8hu;GBy?LJPL4kWo2bft=@hZNG1UvAJYgm{sMS%^uZpX
z1rTx(APxVJkhupA960Cb_yV~YSTf|iE_6lU?;rZMFUCIi;E#7IgWiL(zC|<?Tm?6y
zUvnLt!)9d8@!IzoC#R-B>$TR2*Q9~H7j@K*R`Jh0Mh7VKGu+x48ZV)uu6|iRDWpyB
z$B!RbVF4Z+H{QC@c20TiyP-CEI~m9PIwR)b`J9#`+|QoRJCf(Np`?Ea<K>#k<#D?;
z5C~rh3c?RIc7eshv6Fdx#cQHdd-7l0;VC_Lyhrr)&ZlnUi;l^;7x*mS$R~3<GIBMS
zKQFI}%|f$3m%+=M%D{gYerCTvUh{@|@c;8kpQE(le?H~qC46E2fAeot^~Hg^JxWs{
zO0x->at+g!H&kxNHIsjyg6g8}%y)GJqjUr}=xe}dF3b*dEHljxAHSLLC{18^*+<Jq
zv8hiSs6Sgyd95%1xA=qnrO7q+m^sA6_D`N^fgEX*(e5Je$1<(b^z(j4Lj3C>{T;l7
z7+H?UrUOa*L8`~E&TKAkY~<W@Gg0SSNn2ABg>#(3CQmsnvEwWLx}DiSBEqsqGhKQC
z?Ir$`YIVD!G^vW|r@AT~_-uy$qF~kIJ(}Li{DFw0G}K+)hbMnzoM5`FNE58Ozopj~
z3*&oF56$J645bwI%LYt&7Lqu(7i8$4?6L7y770}n2@MS&o#Wu(Sl$}sIfyWpoR%ge
z>3Y4xb+37rly>gC{gPHRjt(L8S~u@Y<d&xtjoyu`dS0F~81EJESsxWs!2Gp2QtNm(
z0GSVP&9Xmxb~)Q-ZO)JS28RzfH#K>{W%AJ0Al`?XQM7HRWhkH|MQ<vXL_T}Am43T|
zlT&tvl0Ygvo9QSW!Tkx2Nq}dY5Y%g!?(N2xlYExDcTYssfIb1BQ&ST|z5FjIMtMX;
ztfIKZ#rLIt)Sqq*Cj`vYjiUwz2AZ%WF1c>G0Y)=6_Vz_MGE`l(E8e|RSBu*QGsQyp
zoA9tEnR{$poIWmVu&?j+gfZiK$;Xm6)jW|&>yc~4bi-qtR#aQqlMCwTApR}nf=CF2
zK8JENgoTQJse3~}2bf|gp5{_0Pov2kpL%G&ywNqHqmU12AzsQl4$yybpV<cEkc&8+
zVj#9*m$u<1jJ}rz1xl7PU<HArIZrTHb@G;#sp&emo$tdWEtiqO(>Y=%jvYISt`<op
z@YOePW@%_>*!9#cg@>0S;=#h(cXG$zG?)T9B3vdY97TNt11}Ny7lz~^8oG@&&--5g
zWHAIIW|h%%U@&zJ)AaI-igu)C`<%|*@w9B?k>?5^5rYhL6~}iIPPcR5EOu2VTm<RS
z7;~;L6EAS=vZ+@0hEB;)Ln|v`XmY&Z8_)!(Tyy;rL=PoE$D+qxGiN<}wyNPt{Nuxx
z&HvsCONN4`BLYid;<|)D%7SNFUo8TnO^08nj7Ww`Dmr88T`Pv+MqbHW==#(VWgjUN
zV9r>f&$W8BFXrJS9)7m${Q2{>AWDwy$c!5MU?B;Lzy%^L3@#}v+pH%iEzLe#qqz6C
z>@awKjs@i4JQhw)KV*O3d_Pxb`#9xx7<`3X=i=3msBl`5*^*)z*vNI_AMI-PqS|S}
zU@3EkzArlRw1jx8f9J-H^W;uZcZbQjZXwAW`ktNJJeS}eo7n%^?)=0aXWjWwL*x>~
zE8&yo29&GHM~~j!yJ<xI%3`ja00S5&*ABop)Q&ZuTKHoWZI5ogJA->PG<Kn%+ec!~
z@UqNP3FQY=u?m&J^7QEm-3+UPN)@?0l?0uZnCo&ckvlNT9<j}H@cGwqEf|Py0G*VS
z)c3lK1z>`rp4Su%CO`o^q9_WA=9ehZi(FB1Aw#r5B4GO&L{}b$3pIJD4lO?nBV#~x
zoU72tEQ(rvr;Cd$a*}EbNyz?KmM*=FK_e7}z`>}dEMkzwjM!S6Z7+nXg$c*ccILjm
zW~H2VT=c@H27d6t)sp>!6q;-ZurjHEfV)_$f_-&Mb<S#)e{ASpNRbzr=Pu7F??#50
z_s{PjRRmClP7Dn6c+Rtd7(ZlKYjsJsfxTY~9>&-VOvbnYHwXqZs@`LTj|o|Fg*|9p
z6`_{#<$QnZ<D(2wn!m}=@c_lBd=NiLI*e7Rgu@YMEYv_RQGyhm#`xUoPY1+r_C%C=
z!rft>LZhq@228O-^MJ`mJbQJ!+S=&3xVQ*-F`M1JjokVqzqK*jegS^v<ttaNG>Z9t
zu^aB%k3oX^vW7Tp4Gj$er(88zPW+?JA(y++71HIRo>RtnnHJ0eMfg={VYrC2jLdfa
zK>YIp={YRb7#1zsflpIy+e(tICk(F~#I%H~rT8x|LF*v}lC9zo$jrzYz`oT%*yFj8
zGI-khO^)FcbsJ8h5d0lkv~CiC9R`7@_ma}uTjdROflCuO0jeN#r6~C9p0GO&;AtyN
zIA&1w?@din373a{>^WM`m;FpYUu_92vp#KOL-O<W(=%A}JFg#4JhdnCu@25XQ8=bi
z`C#c-RAxiq&&<k7OOf|QOGh^x5?FE!m<(&+XA}mYcDFe7sGk2Poe&I^&}dv)h4E?B
z`s%RPuVh`vbvVA9tBO@YUA`S-t_$5!nc3LcUBbDJx>-TNBg+~Lh8L|ky{_;ViD7tI
zOueg%OJO@m8SEP@XD><sl#~>RPT?jt52d*h`8nf&2|(>-<leab7PwU^k>dbod`hu}
zCG=}>=Ba7Il)s=ethQ*lF8D%D1|9dGbSQN$_D@Vb5Cw`741TbCxJ`o=C-VB>H;TQM
z!ZP|e$OBvAF{h)l$HBn?C;lts_kCy-uKaa5)<#66<MJR|6gy`L<Bl8MAJ)N_#-Qff
zJ1aHp??NMSY^LUxmgpx2wmA;>^4I-M%;MDq--eZFC+o0kiiAe!<cfYhRBLIRc*>H0
z8wRUbX%Bp=S>o{g+kbkfybH+qz$_O`fnm6y+39}S(IPV$Sj#cHXZlI6pld;C-1R~H
z8K-6BWBsIM#SRXqPrrnz2idI8U4F7+CjmA{G{w3+@zKN^0obP$!DCKHx%`PUc$UJf
z`)o;Sw9qMyjl2HEgmFae39IG3zCD6HqjBkX3{_q^n>;&bjsN6x(l{bz(W_Tob3D*R
z_9;aNG}%siE>h)pog+8}y-LHyF1<5K7~Igg1w`>CfD4odJi=B$58!eC{yL-yMy96s
zhpYQggz!E57&fUCY*m~5!t4{8ytZS$&_gU4H#a*}EOz&*>P-xkQ*}rL@50C3|2pS3
z3mb<I0sJ#huCFDNxiAiNG4LwnXL?&XN`i)*QHxb#APwcHu>jJM`Zp{pO5$3(RhBJZ
zzHV<73p29^DnNZ1UUO8_FQIL%Gg?xD140buKU6+^=|nMQU1*n1-#Mo^J(xvG(@o;n
zSHN+v#P6g!K#o)gh8<+0nC;GS6#2`)32Z#1T{WRiu}hdS1ERuIuEmR=)P~(EN|I<;
z<?il|$~*?<J%ksKQE=l@PEL;WUJI4rTepOB=M4`J7op-w;?Vlk{QbMI2&~<sP&>~p
zC0H6Jz|`hAOCjQ30lX9gSh9#EE~KgHs0(;1D4A2rG64{g$>Wpm+XEX`tZDxlemZjS
zy5z~klkzCy$Oy>9#tVLIxMie7El}5xlLtX&IAjVgkwwjWK(Vvdb<K>oi>>@yGI(oE
zZ#)(%eDDHV%Q5ZX>j{d?qo+R#@|TyN-%gOXvX0!-<^fv`2l%UiwR;%V0>Wd!<$@F8
zY&lZ{wTwE@T^cwOQywS;U%Y>xwtINDeQun=S4{KvD#aSe2XyU!^X7c+-jB4vQ44VV
zcp*PuE-e<FF#0hJ##P>>hqLWopxQ+K!~bfyvvSSC6`SZG>YPgfJ2;BY{fUpJmj5bl
zHr;@QHrNC-)EJ6dEzHG!eQeYEC5-sz?d@%c9zMsi1D}L%T7La{2GoL{fq`tu_wWx=
zWi0^ZV?Td31D#ZGad{pqRvsos2LOd#_M9lzYSETM03u(%-c30ME|>=Jx#Q8E2ZNIB
zTZEP{s@;-iq58gm5~ru9&qo!CniZ!-yus5=5d8W8x}|k@H0_#_eD&oEgrLRpg)<rv
zn-?t=)M0KN7#b=>+E~;hKQ$B>oA)Md^S?^Bx6B+IiD`QibQBYGyp;snq@e#Gu?gR~
z-mlLNLPEOj+qchsg4e)_M@<kIzG0Qwr;G*eebvMjUQ9MUb&|*&_g6VBNCjKa_JXn!
z5^Mm{o;*sPVRlxBOoQp|0K7w8eGy}jj)HU764Uojt+NA&Pd`pPW4wL>J8^JuFgDx=
z`C#|%-TA<GMR81@b9P2OK?-nygarOhh6vXn+ipIJ)DVdy$gr`b34|XeVa|$BOY_u#
z36WFSFqcE)w(NFrMfj5Td6TCZDs+Ge$Zrvn&<bea5V@41G`-HefSfZLt)<J}pKncA
zdKrim<A>SKOuM?kzS9gAs^?aN>~I0~`ZA=vcxB0->}v%}*73Z3{aOL3IYps2u)J8L
zatuQ$#K4A4vOjg|6v>r{XfUCG=XXGe-c<PhJ=+Ct_J$v=ttNKPZrDNVZ8$n$^?p;f
zBm>z6jMJ__tzI5GMZIs4iiK^$*z`niKhnD8dp_@wF<i9oQ5r4}@V=;71^30{_|E<D
zX9U!8=Hzq?3?hIaJ4!}Qt{7ad(e?3^?TyL#oq)gB@bS^}@bI(<C$0pxxTVCD0u)e8
zIF)T39jdFD{#8j{ZPQTT0>RjTiGiV_E)x|1m5(1PiUaAn+tf->(MT%+LQNZ_BlL6U
zs;*{bxoHm2u;s~RPEJl;KhJ>(aZ>_)9yKkFnml<`jh`)V#aV3Y(-J|3INl1K1Kl@U
zrsg7Sc#p=R52EY0(+MfOBDxeb6rh(heBl@9r3%YAQ6W9S-|lpz@lv(*d!}y$dmja*
zOEW<Sfj!+}ebDu1&z{wW4xCxV@`?ARX0_?amkMMAz;qW;WcROIx6bwuekcBX6$qP{
z`RS=~`+oJGV2!0<wF>-wGrWRn1Qy1sq4JUN?`aR6K331?ZYKhC&Y)~2sT1d$e?Y(m
zkWkFGr9jQK##TmF)}YJgAFSiGDj#gnk01Ybcn0g4mLg2Y47nM$s>NJ#L0dTf<OIM^
zSWDO{fY-8yO13)C<l`u!+I$B?4X0TYu$g0-uKf9#aMkMXKhMp;On<(>+JFD<+a9Md
zS0u}&w{e@TBbZH;2RN-}!5Zr7OGBX)=ChanyRVdwtqsr+kj2!L?+s7cC{fJ6B840d
z+n0!kWK%^Rye6r!lP<rQ%Uu}XsIwb6J}C&1i<9EIpuT<;dLmyc5tm-KpN<?rXomse
z0y1a^IQCs+tn6aO^Z-tWISub=8$rfMwy)gO(7ru){M139EkM0Z@_aCWn-KnN&Ys-`
zrVs(k$X1&>x($<srPDHau!(C{9x%K(`rzn}r@1TRGI=~k<G7KKV<ecc&L>n<Ca4$n
zyK7IWtz)2tIL8JX(;(MnMRRjAia7;R=Rk3KU$a>B{)Vt;calEeud-pv^m9$<F*dY_
zs102vZ#i<W<HsT0-lZ%{x$x{!Da7k%2m%l!rx&ogtMziQ2aOy6o=)d4wagB!EAko`
zo6(U5Uv?wlk0N_8gkPb+A?-t9%n!2E)&ejN-`D3(!;Cm5*B^L9x{G3Nljp1&vRFh!
zM7G!?+nxvf0<99`lv{yGAZiw)b7EhEW!Z`hQ`&W0Vsdh_58}TrSCjP&v_LK^w+t*c
z4{~4KyZ^=8oY|-GuK%1hqi%(3CB{QXm2Jsf^&X=DjNz8DrF$?h(^u?2-SkNlLknz)
zWg9Jr$xVlln_lyR7BYm5QQI%hrKM;*jsg#`g3rhO2B-~+Hi76IxmJ-Unl|?S{RKd7
zmm>SVj00@}+D{jd<)N`Dh=%WehJ_245I2nsn8BRAl%KEA40pzsY*b}DqN19lqT0I}
zr;8%$QycV}eePUOQU%}$Yeiv{&Pb3{Sb&4h$<W_`#@BqTvE{ulK(S3cI|G3o176BY
zocC%ELPsCi;MOl3J+g|GzHyZR&Spk@QW2ThW&Ezl9xJIhA8&6~@^dd;y2Jp1mg2c{
zS<LM0>t_op*LNou+>2Ti?0IJW{MyjE%_H~LvQ21zI~cQZ!<*dWKaa3z_1|z=XvS^L
z`w|d<Eb<sM+JR1FlxaA6JimV%)P}Q@ICR5AjV{bS77l0~vHD3{6>qN@MI0gy0)$C_
zOA#7<*sTgsLS>cXu%e+5p<1?}a@z<{7K1BwRM{>6Z~?l<E^r@(vKCN^A~vG;*H|ku
zxV;}SqoBNeF^;JV1|nC~_%Fu9tN{)zhP6fIV+H!k9XB>Lt-EuwQRsb`lPk?&9do|(
z8UplF3P^3DG}kW-F?l8+i_+2sS^yy1;-n}yHT{Yz<J6(`i%!N%+)`4>&rLse)KEV)
zd1LTx$?>Y_@4watmk+%175D>5M;a@P2Qhsp#Qfu<)!Aoz{qWnDOZ%~~sAw?Llk<Zb
z@>}%v`}b=IQHvEC$E;W4_tR04QVB@UcA&Vb$@cPMAU$Ga;xflJ-Hh!I<p=d|uK!t$
zBe^aDpKKi;3zs%*rtfQ963op&jvLDBqn_Y%sW|Z`8-hGH>#Qt`O2<WRN?CMp@q~80
zc2H)%gR*jFnK25#R975Pugl8jQfM6?vc5Tshlw0{zWxzG0X?3psADu4NB}enFr9$<
zxB`NX>IeJxRMqZ@m~2g7vGV;pj^_tLj-OgZd(PS$17cQ!gb<HmJT+`vwVQIpBA=d&
zl1Puj;c;)yL{-4mldqkyenO(t=VY$oO|?f^9BdSB{S4KjRo?*ywDc(ood~aPw2<dh
zD9%%|kW2CM^UtNBp=rrRD?K0R0M5x^*^#OaJV|PxQ8Yy8K-tl!aN%2os9X#}iYTP>
ziTW9gD9R+H6BhRdo2%vIcFdlE@l2eu3>3TQQHApwP0642ZvXrGwCnLc+m0Gq9P+MO
zR_o{Wx<-sfUB?rheY&wcsWszfa{0hKy;=p};Ld@XtpHWj4MapVGqAOh^)vzbp{3C1
zLg;1-lQ|Pt+26Llw+^!~IjBAi_bfI01V97bskz8ff%Yiw-pv4?Eng&Y++d*Im;Lvp
zt|!I&vSo8AeXeT@jh>~*o~c(fH&eRT%%Ms*axe3ut9eO&ZuQ8LdmEBhv)o8Z3mx}B
zl$F}@ZP#|U$^KpQV|aK?B;?aJjfj7k{BjqVo$bpzh*QQ$4%x*|EJQL<k?ZQ5Xqd-|
zyb4H^-t%)}APv)D$x}$DW!*4J)F97oHTDp}L;#;&-z4u4hME;vPygi4Ge}gT(dKt5
zmD#%01iE%OK5dYdmE{gB@dp|Qw8sRAN0#FtKgdAmiD#3geB;BHVx!~X^`2B?M0A!-
zGX@5u<+t82_xX#{R#HX94VjuN>om=!uLK~}ocy)3GPdn7UtqOM`B*n{0nnONOuuvQ
z1A?UEf|mX?z1gb;@7K*NSN&<4r@MX$yUb}mqJd5;$GPY4@9zP^I`!K5;wJDVfB>xo
z&wnjQIie@)B=DL@UzRmey<`moEnLg}unO~$@1tgu)I+wNxuqL0bp_gKNR~|o+Dgc&
zq^L-Xd8d!|`ysS;9QhKRj>!57xls=-HPrj%i<wMjM2pEk2gIw~YKP>F8xCU8-zJMb
zM%E&uu2V93KdOy@vcn3;eE7I7kV4Gtb9TlHcOXE@=-XwCz>6dyE7Hpwx!6{Un}_Gc
z(VGC^C`W6Hg}&=Tz97D%(71+s1lCMPCA&>I0X&Pr@B_32l%Tc}gIg8HR)>Y<cjQBx
z)Kodaih<M3EqQlNKv@8-Lq2INBs+f6=I)A_RMB&f8H;O&GnC0LzUDa4HlGl0E-Ri#
zZ#=@4BXgDc&H81kN~;7Hebv-X*|<Rlx3dguFk$`4^F8aY>nH8R61qCMOQ%i+-G0CZ
z3-*fu10+*sQ{X(BZuN;3)yLAjj5X0Mq{gEZPM_X{z}zUs3uX)DfsA`;R~TvvMO=D6
z$Pm=)iMT0*gHCuB)m$NH2<>a<v^tQbcq4bI#Qe-BXbK2u%ZP5HY#&1$%)jM$*Q~h_
z9<JZ%in}SV_;WWuA>pA2ka>B!?-)b9uie|fq3mjHXo&3hJtybkRMa7-Vr-A%z&<OV
z9Mc|A#x@J<>pSG8YnU#^>*;N1J(OXwAHc*afQ<PF(cn+`!LrQeEqAnWTXQoFo)rLV
zU|2Drf^9ht9G0Fs2c5?P*+!&nw(W10;y_ZJWRAZXq&yh6y8xIXg}$b4?3wlE1MWvp
zy^mXjgWzzR)U-llAvaaHj*ao9^YN8R9}jeqlbt<AJHZA?5~Tnn#oI+TbJe%7zi*@p
z3+yJ|$XvXUhvbdyuHSQy)jh6$r|_#R85_l2f?Ki33YMV8#UZ)D<GJl+%-FJeXXEvT
z15p=A2bB-F+Ah=d#EBNLJt&W}jgG~gDZs~|CiKQB8v}EM7N>W@CDyqsN?yOlH0Y;K
zZ@Sa2y~fR;mts5z(;6<KP2Ma&^Yc0%R~UT$3#EcQ`H#H0KKe%D)uYEjCmEV@1YYAo
zpr7Rfj>L+{_8igLQ?bYY*(fg&Ns(G^#eF>b6CHHb73dTo_9|+}A#rdcbM&SMkZSn@
z!d$e7l$(Xt8SwcObsn)o#T899Y@qjZb0qO-%Dq5)I=u$!X^`C(A~U$a&CoKAw3(tX
z^pCH9#V7uE^Eyy~5ac@yW`imwkKy~kg1VwQTxSh<>?q&DHlJKc<rf#vgPoml-?`@p
z>b#v??^!5Oz3SWDezq^C2q$WRaH+$-$$u6V$&W;kACX0+`#aRC@wd%9G)m&0o=IMX
zB=+2JS8#9)kD&9Z#^+awIQ4VKw{KU`lX`(lxM@24roa5eck!Gr@x?_&M(FP#Yocv*
zpK+Cifg9{SE`rcB_9`QN0?Y_e5Sl4R_rYtv`<`N+ebSwG;E5UmXW4lj0IJE`JMww?
zp8_2p+o{;P`qYtQ_rmjY{j_+fbQEbq;II+bzuogn7RpM%9Me40hDo|UT%v>P#))N_
z9n^-V?tcT1)oR>S(XX6GHt|E~GJ8Yc0GG#vf`?-8ODmVr2d)E2EB6>>$*fOKd+&_H
z2p*w683jD4>mRPWo0ghd=vNNN0kfSmPe>~tufYpQhFl9J7B{#rjg5_1e)G_IIWhE&
z7nJ}&z1sSE6XbVui*2v__!L0Ra|uA@UG00Sgu;V|GPOzkhx7E}cXMFpy{M}u>0yQl
z4-XAq-XEe<aPVauBr(9ZI;-w2z?eV%ri`|nO={lVma|C<V=EF+u$o>$!f~-&ZX!{0
zBKdQeh>*}jO^qT5*VvGGI6e7${v9pF`$hhndEM2#Kly~AezohK@f>5?@YfCpx6$Ol
z)3X2i8MHn6F+%Q;_}>au&<LPc>fsS>F3lv}<qzJ%B+3>e2G!uRXK=^;=AY`+%S&}L
ziYc^2(HG+H{o31m9ktq?{|F{D-`wgg1nwX*^W^{_cCKUngN5+&MhEN|Sa%mZQ_xGU
zg`h<ok2DTugAkCg0fF1SdhWkqiJNWnJJFZh?YjG)LR#>=bjO^3eGUCT{XylmdZ$i_
zfay!FiA*ab<(cfZZT;ZF@uI0mW($wvfRq5XkMODcmSx{T-k;1Ie+y_GS%lsukU(l{
z^^%5hlD$MuLEb-=kLsra>osW;#3{ISq$5SkHU@87wnyQijv&}9Ezrqioo;6&acMg<
zV?!@4A%yWS{25=(Oiu&y><%<NfhL{@#uJ7AkqgujTuSO&bTSEp&B1|#;e28N@BB{C
zYoefuJvTm-u$D>V&o%0F03Ke9#1S111!ZM=BmjjcM@B{nG6;((Zy4g~i>V5dR3s1k
zNKQ_UR5@8?Ag1Kz<^n(o%HHzlVMcFK5f4y+Of4gzTsc#DfPM(%mv#Jix<5R;0&b=b
z#uRs1{C)FpB*1Z@@{veJ={}TsJNFY`iD59|5rtwkm9~lhc_%mKz|wvt<qzT#X1CZ#
z8>FfPth<X*0P94am{lrv75r&5v9M5!7cVBPTsn$0WpRipLu@@;On=E;jkl%i=l_~^
zKIT887XS~ke|>A^j1**S1P%l?MMsg&I5-R7gEEk{1OhIok40~^v=8z-(mG-OOv?b$
zy?b)9e=d@x{XM{Z0(_FD^|^D>xl<#D?0OqlQ+>cj^6|8=aCQ9x-Uwo~lX<J5?!Z72
z7XN(@%1#I|2=`257%6iZUHR3}+qVQ(!XTBU+9D%(O>FA!0eU-#U2Wj&O69|D^9I0q
z`=x(gq_U0&Sb0XkzbFrAE^Px8QyHBT7LTEOOCV#2!d{TR9eCj&A}_>UUPPva>Z)?e
z5`9Jc-O9&RQSs1&fya&?sk=6`6gA=7I;nN*NdLcuyf)QkAb<dNu)MOJ6+9#FtXns3
z7!~b~PYjTy>G}3;{ylAhX?a8vFrzjMO{?8vS*nh-8i!>P*@ZXuKh4Uzi?Dg3CjuZC
z5L6WoJU*=wV{7Z{NQe$@T^Nv$^0(-?+?YxY_rLeiF-Lsi7)BE_uUzR3`}?GMRUr7M
zxX-xRF6@MgnlwoCqqJA>|75E$GBVQU3i!Lq8vP?9eUWY&A<^1d`g44|1@tq)xOlI0
z6(C_BA4OJQ;rqLOrR=(@=cAAZ9Pk1<8fE}bV`Jw4&?6sS!_7TQ<;C(gGA%yO{CW$v
z#NVZNqrK$VCRi9Sqhc$>*cY`%GJuzO%g^CmGcG^bb@A^Nc?n1B-S&VdtFo#pBT^2^
zX4(EfSHK&+DIZlT8rN_kI10sP7HGd^n|%prj9hmPC9IcBjf`+Cz4Z6T#Y-gb&XmGa
zCy5&R6^z0WC`L#XK!G^xfpbZp_wOC@@(P|<wt~(H6TyuKp2CDq`b@{Q7dJOQv+w2?
zx#H=m&-?cP2gFE=wF=B0w!YjHH*Oy;=E@2R6sD#(4&XkQgx>rYX-jY$saD`5WpD(O
zP+zROBS|_E?MQ=F{^MzmGZ<y7Pbq38w$S67ZZ;15T(=s2sUiOpyouaoN$Bd;tJUkI
z;L+7$_V=E>Uh98fAY!}{+ntXOrd!CY$1bHsMM*<DTzDKblv%WVLCM><g?J*UDLv(<
z$LQcdPuOvUF^H*564s3dy^jqc9wvKx4s0-Yf;gVU{M3;wEeKyPUAeLbIsFCh&Y(}p
zMszp_yyc~Z)+dbK@QRNudGiKE;d1h$A-QZtP6JBMj<gf+`p{!1)WBI@cbI;h+RY2Z
z2Rchmu`ActbuHFAC!Q)eefc=hY1$Sg01RFf(>^FK*3;X&k$|tqXkgfoUv+N}B3gP4
z2E@{%|L_7w1Bn;NZ@36GR4_(I(0(M%o-}_z4FEy!Wcsb#SUdCmti5Ykv{cSO-ynQ2
zl^)SWzkA=leYQUuWNtyujYU}iRGd&^F0$n<mjV%scY+~dV)IBacD*BB;V4E+s43jp
zV$X=fNKzk(@NmljS_QL^GG8VNuURm-0`6Q3*+nTFvS=1=10w^WjtD!@`4c{zy##A3
z<8<MUv)~mI#2@$#=_-Ib89!_{O6qBnE{G{&L7p5KeL~hQQcFTglHRgKk(r+0p`bm~
zK0ULJl;xnTMdP9B`h|PF9`AaZZL~XQXAM(~!rs#*`%Wuw-ZOB(8E|V;TiYcxW+x$!
z27bs*U}dHkglK~&MoWROMf!ZRt5KyBg<i{*W_%(VskVN7`)L`#U7nD=M-#z%U@B@e
z!4SQBm~=6hVqjqK0O7jKL5Zy=<iy&3IaZe7C2+Z0K2Z;nI`ZXRmd}8=f%k<}|1v2I
zfJhdi37E)Yk-%&J<yCIL>n&Z+9Pj2vT*)Zlj&{LgewLMW6g^Io)DgCdwY4?rG^yUx
z7G}c*113C*{n3(*G$CBpoQDONGY*A!QmE`mwIAr>h6`c?#%f=)YUN^E2pZ0zhy~W;
z1+oj7bAi1=5G=5peL?GrBCtjGo4j6JRI=qq!1FdVKw0&&?Gnab9A>5`e<#ciye4i2
zTI7u0V0kygj<)h=>JZ*m7|e>L(_bO4><0;#hC+i4otu8e!&3XYWL?HYNwmZ47e)ld
zk1D6`-@i{3;6T}4p)IV1x%ajas`p;vY_}cxevs@oAbuqLxVX6V5nTZm*-jH*U*95_
zo!k5zI8Hig$ao2V3xKT_#yuyczIv7M)?J<$9>59>YL{m4gUqVqS-=}x2)}CD$E#Vi
z4)QV3q9;#JvBQLY@4&%3FDG5$XF>swx`x*pg;xX07~A-0@AZN9((cd2xANLy-&_Qv
z&_bhi#1KBi6-L*jj-?6vV%mP4$PHR1F2e;Y-SQ(7WMa!F64A*t#`P229Yv%kN_At%
zoy=+yZ#J4NKk@d2z_{v2pmGRLV2Ra<^}PGo!^M5$B~o4wPQ#w&1={FhMlb0hs6GlS
zJ^lJG9NjX=B(eUJmDejP^W6YJpI1x_ty(;na90kcRvwF2KC&Qm!?`^v18@@uIu>YA
z29#a-G&upwWmdEew$xMHQc}x-fES|X%*ZrEV*{7D6YM7DB}-z5FQsuaY)J{<6cQt2
zKhpJKK~z)}$t{^Uq&@KHuhwJt5*G)uQSmdXRpsdN!Op%;26iTu;-rHTi*UfNt&(B_
zx1{Fkz9ik&XT7=aKaGODMZyDFct9q0bQh$h*lv&<ZAGeqLiw`pQ_yZ&anpn$1fhp<
zXB7)e@WKB6eq;A~IG6IS-os2!ay2q-PoHbmDj#IZlj!<mr2L|ll2ua)*-za|2Sc6H
z?!1;iaN((I2>&C}i9~N46p;o8fFc0M=OXF3c;(7G?2M~~m@$EEZ36^C_{ej1Vw)(F
znUbsiCO2S;@5b>?=iVc%LH8Vw#eYw&^$-VuSWMcbklZuM&rGH@ZUR507^dKMxdI?0
z*R!o&y@&$2Mg+lZgKjz508jF!7Ts|h7a->eAdp))Daomdq_VlSl@1#@XM%{Lu-@3E
zPI2I??hX&xVDi%=!#qE5G>oyp2t@Ajyu^lZ(boV%CSoXf&lI}C^25X5pBWvACe*sG
zK)zI0og23AJ~-mwhWjGbF$N5424Ed##EU3Gyg)b47pQwPy1O3}9zz@yw)LzC&}0E7
z_h2Lf;942sH=^F#3G&i8Sznwe#y~fdpFAmscR*8b?_yI^)A$b-2&7FQN1WJpGX;%t
zxN@%o+=Cc64=IYGk`j^8f%-y&MV!)UdDSbP570g*m0w!A00sVzEj2hglTi$!5oUxo
zJ!nXt7cbKNqGMgaDM?gd1gZy{=l&>r+~fwN3Sx?iilV?Nm3Qsx<~ueh{-nH0@9F8a
zAeCJLm4rY>zqLy?s2oZ0hkoAl^mG`ep^mw=z%^hyWK(D+dEuQ!R6jWw{rH%?%CQwM
zd)CLh)gQ3gE)*#9OhK++2>Ggxu<(`^M`X{*=-5s|x#)HMdNK0wyV?nwN&1qo8u-{0
zf!rr`8=?+%)P{Xw0%4zp!R=&>r>wG)1w&QkP+7r3{VGCUr)+y-b9c87%4apxT5oVr
zI6xt#l0o{?NzmTWpquCBN`PhrPbDx|u}v=^G?@21zFHKtIAlk{`=X*bL}CcfhVz(%
zWsC((WFNDzCISPzVV#O2IYp0plx+dcer#gGq$FT1L1%ztT|?vZYCR@d`6AScl}*Y!
zhM?-*|8N<YDF-?S%N`@;!W>c0F?V2epj|cLsFQXa;6v<NJG~^r%WJ@F{Ls75kPev1
z7h;<)2E8ot(38EESxWd_LpUBRGFBq|y#$kKm?>gT*yxkzqG#5Gy*F+D2+to<!B6Hv
z5rwgX-Z&B0;Iu-hD!`5}(&JdYS`4Nuq}c_W6YL8Gd}_fpwqygt61RZYn=XAV=WfU~
z2HMD%l^xZ#h+Xm6ZQ~VYJEJtoLc+U6s8ztJ73RXvD`*5-8Vv=Yby<=r%(a}@av&+C
z_aA*<jSgPU1V)l-_g-lfIxx`afeo-<h$T@AibmUbAFy}BRzC}}2OV)G-Q0x=#>Tv4
zmEb|sQV0*nYS?Vm!zI8poZRJx`Pq~QEGnMmiz%xg@4R0S9+idH#cS@WtbA{~(Re-3
z!0t)sGhPo6Iu&v5fgN+@#Zw0egs8Sf_Q$;>r$bhCr43AAP1)C!w4Wx}FC}W%shQI|
zUjn4wTLzuygg+cey~D>Fo%%AG)C^kRqJFlBZrk=bdYFCXkpghz2}=;J&d5ATIvBah
zTJC8grX$l)PQ}jEq|Gq$<FnB8(9`(X_%nd;v(EMNVo%bvsdrRI?^qBsEax)D3<mcu
zkSbbQTQm4+=6XO;U_Ld2eMC+7(mf3lJn$&-Y);45!k@+MxDwg$=@G=^xuxtY4@l=B
zL#2HkVQmVOY-jg1A0P3ocLDCD>OE<UY@s*-sLr>6ccWCkuNvQUh-=Sq@b`7t|B162
z4aOq8H;Qjh#mW}E6q`eU*Kpk?eRMYg$utg!$36wO;)Mxe|4@D4(Z2Mw2wGzQ29F8e
zx!1Ym=TbvG^&ib5!R>v8NO~>AQG7e0l2dbwJB1nH7rmt8{wHPS<H4XsdCWvOZ@+`%
z(e@_<sO*v!L3?ka0Zivn!{_SBE@YCB*WNL7@Nz=lU8A(OSL?N1*G{M)zZ4?ut7yd%
z`SXnkMn1A)0X#TX=B{gi0%i_%QGV^_q4P<iCCA?do_NLjD$YN;43EPYc}L4k-t@So
z3&f{R^2yU+YoR;M2B>Emr#6At(8ZAj{S@sVoFWw{DI%vNG%lFS8L+Y3us0vCM<@fy
zfqlw&ndq&;>Y;le1}NR%8yUMO)$46fTH@u|YM>H?49?9~?5L8}>vn;B`}y-~#BjoL
zAl<6|`}<H4tO7IdGDvCMkm179PE`7okycS*VdrY`|3TiHhhzP=@1hUQ1EonJLp>^G
zswl}+Bo9f3gbbCEF)Blu8w?Gir%*Cwo@Fj$QmI5{AykqnQz1iG=Z(JayVg2>$6mj^
z*Is+=W4-^plP{m){@nL<o!2?+K}_MFMG3Jg+<2SID5PV>C*L7(5tJT^43v9^#4{ti
z;Jx+|n>~dh{ilBp*k#xI`>lvwU0+5WM>l8>o0~7UuMg^RU-2bC?jZInL_#!OJ$wt&
zn*@AW1PVfNZ-oThrEDT^5?Y~KT8P1IS*D-suB;EKYo>dL0~>*ifS4t2X!p<%2nk^&
zHgA{0N*O_G>^M|&44cyYUFIKMQH4Fm<C|a7{_Pt#mHP7AGE=l?8*zX}|B|bNH_S8>
zibx!3#e+jb^C*F0=NXCRKJ5gGd^VH{43xJ<kAxJ1u5wQwllU@)!I&U2EqK5cqLCn)
zgS>m(Choi`S#>QLW?eg0wUbT|<jToGY=H@&U4KD~{SsinO+4;!fa0Wrds_@N9RmYr
zV^q{J+G$bMkJ%zdSNlBXN&B}~)-jc*$%0L}sHMfgu_zok3E>MtPAI?m{AZjBD0<Dj
znx0`0gudnGy|c?%?hYY@h7D&Y1BIyGL6%sL@ATDnHi!lA(VzeE5lrGQ9;Yflu0sD+
z3}fSslkY`Q(qX;4z-PnoO;X@Y9IA(}YrnI}{^bGy>Do&<LFRNfZd}nxbmv}(0rGa3
z<1NL27B7xW<2P{`bcwq_mPSf^vQGn!TFPyy76<g5fdWi3a_40=$nY2_s1#nIx_dbw
zQB_qnfT(2BpV+rrL#hIh8hefXC6rs+w_~yUK!1)_;pf?6Ik{JF(Et3fj7Fmin1x4B
z@NoA&E^xacH=kZ&+fg|Y2#)d`@1Ni|D5lRlK-c>9>sM0Bf68|{UO6IaKnJ-b*}nMv
zzpp63Ie^X3N37Yb`1AMggE;PCgv5WRly4zpZ%ctU9LTsuL{@<@CU)W5LL5Fs#(I10
zc1G;#096V8jlU9lWJ?6IuczmGG6bzoHsC_V{4vKaxpwWV(aR1Ft%xBP9UXzs&<iZV
z^YD-ivcM6vLOMOb6zT<XE?bdbi>U3h<FEvCBk~QS^~c|dW9~ypgrIKn>pnfwP3y-A
zOlU?#f&gO4ZLGMPjSzs|AuS;(f??uxSP%8<!6Qd}n#DYpuDc3_4XGTkHB=q{j&$-Z
z@A<HM$YCpVC)|2~R$|s2%i$e<`iVI~7hr|gk-!BGFCgA0Fy0Jz`h?@1_l9;W`m$G;
z(-B6Rc~kc43(q(xPfipv<|3o?K-cVUb9WS-C-CK5xRgN=Du<TO3h#7)$-^B&PVD}~
z-od)NmI=#lKblCwyuzhyK-VFllP+UzEkY=JgB_}a-E}HmM{an&S&A7OMvZ_}x0MA;
z-U7-Cq39AJgM2wm(LcAgUc+t_igjUB`gIax&QL(sF!$Swap85WgOQPu6L?<o9)GrY
zg0~3yH33vmRJ5W!2PAG>8L<e+AW?aA3?4gh#xw{N8gwXgDZoJt0tdGg0L|y-=U<A3
zhG;Rxl1QbD&xDK)5mCMzm~V_$%7%tai(9Cj!%zavmww+^Uw;xWwxcO$H|c=DmxbUe
z>_Xc+a?qlsR_3(B7I9M^TLj*S3}$J|(UN0;(F(GNg0k`faL_;Z^mswI5C%Z~Ywnz}
zJKN!4PHN_ZcQ^{sm=1sl3?PHpWb_XV>_g=(_Bura5YiK)QZ{pd(Yw!mTE4ky_1t~8
zOFl`J=zKi3f`#Rw$|mFHc?zcie}DbphWhvlij}njkQoU`m1g3-E?qCaieiNYN?sAA
zAAXA`@v~~~G!0Xn1`i}yw%iq1D{j4ol$$8tA*FiN@MXE`qV}&}N%{FO!xE(72f`b1
zD8_?L%;a2n=GM$8Ythi?$|Do$?;5_WdSAoD+|qj$+C1s0Be9!D*Op_xxYB>gubn2-
zz0KI@=#3H_yZ&4L!Ts_31Ods*%F4bvBb&z7?mrb>Jo>wIsz>G2sW55^uv?ajDZ{?z
zqDKjRQ{Dx=5qky??K@I-h`Ei*XkYR8`Ga#h9^(Dco<CQw-;p6U5y#y%+~j^Zx~Sxt
za=})^u;=e~tzyx55cB3(`_?U2SA7#c9Qo!kM`LA_#fA3Ci3(mb1@*_uYynbSb+2rE
z$IIIDTWFRFKi_TcBW`Oe-hxoxhK%*er@DgRXUjryQ4O!U&@H=VC}j%ObBsNTSpGaq
zNch2SX#lXZ?_+jX_mx?PxpZ4N>FFo${swlW7hLH{;1!99R)^gFP}Oa_SmVerP8kC@
z^%sVqoG-P^oj|&VUZ0kC@yb8XP@=2G&1M}dNDxyuSc#v}M%W;1!>Vxl@Y8evm6EXC
z_Qe%C2P9qdkpgj5n$WLn@d0dXw#7F{l9sNz0FN{6ZzuJy%Ve2d{>mCQ2FlqG%%4Ay
za5Qb#Y0nw9LZfk&_h;9-S5{StVopj@ZQ<~V_<8-v$G*4hDHF)sI0i_NxM;=WgD{2A
zg<Jv(<v2`}J4p|T$3^0`k(fxU1g9{m4>w%cikSib$S;hT!}K=I*xUH@JC77`DbkTO
z0_$zAcGGe;r(avIoP8s_lmwjA>0b0ABV1Y~8r$>}g;zZd32=qzm~G$g1%TEKe(?cB
zSRrJ-;mlvV`BVH|4C&NoP3K5%9hYPO6;9`olQ?LSnj4uoCA5fRYZ8nlVH2F%hX|b8
z2TW$JMJAr~th7p;Q?i>}7)yCKA#-T&$}t3b2NP1JufM+;WJ+84`0i5SmZ^_EP)%N5
zKH>0RKRz88lH0d>?H-Ibm{6n-l1$rY8`3qmFA41i`|uce$H<;})N1|r&+WkH-UJpa
zomwSwZ6X*sFUXm*K}glhG>P<6r7Qj>z<LV6t}N36kC|<+tvN>=gK5RcUvs@L`c6Yf
zwf*3G1<&|&0L~D3E0Ux>YvS~rnsCYAQ}gCub8_P^U4p{KS&cR5z$Q?C9wSpW+;bjw
z^79GIiR%Q!T<6|<_;6i@ifGfhtRJ>H5e=`F=!`{2!fF*rAIYz@<5Tvd(>D|eQ)zIq
zJf9TR32Sv~EZ%gPuZSL!MX7I76Rtn2b(9K0u*_FI#e47uglP~hW1@dWn@&7PsK3qp
z1a35)TQ<q{dB{H8HW4<J^4{=A^R4Q<O<Y}fY9Yka7oWzv^R2JGSiR}A-t_*WN?2am
zc`=Sf139#pb$eim**fqt=l-T8xn*}(wA$UnCBc<bPj7vnT0lf^HwP9|hM7Qr-*SQD
zh$BPFM1e8zHS!r)0|wybWmyn*dHWnT1>p+EyPq#SyB`>s03Va<mj=FAi?>~P_Ek?_
zqk5u=$_R_Tj3=9$84LX7vTFlYd<zmV-*vp-?w&%O4!4Ba+aI<L{dOx$%@X-O9ItRJ
zyC}v{IPR}dkSupuZ}~`5b<p-&HQe3x%e}GVIkfv`0;<6dh;ZMZR~9?Yb5hA@t^6VV
zcIt})>1xm`?gNBOz-YINc=qO&?AuoT)4*ibEicCZ<XZbLZmd)q_>Pvhrsl8L;r#0}
z&wYi7OG=kj&-yMhmL5Y$_%eqheaByWEt#uJ-e1k{%OBVI!zN^`-61}^m!u!TSycCD
z^jE=vEzWGSfm^*{XHLx*nOU3^?oI#luHXKz{07fOfBz~)=Fq<)icf9eYo13D%jwy=
z_i@(mA4d==aLYiTuG9LRQ5snq&5<Gx8+(vrNvC2{Yc~xjgHIlu_o}POJ5*{8;in;S
z;AE0$l?XzwKV16ZVlkoC`|jTMm^c>mT}QKZ$tQA#`aDDPVIY51x9^u<ukg<eH1@<<
zi~IhWI(LsAI3T!rQ^L&)m-QcQb5ZtMihXsD@~KU{v6WwZRFCpz^w-U+IUMb1J6<rQ
zCQ#JpZMjeRn~m7FtVT1J-2%G4CA^OkZId*@bj`4`kPnMh{+%}Yp*v9SSuKBw>1gtj
zrLw-~vUFS4Z?x&+>@%fsFJ%yMo!^tZrOs?`*6W<56oGQ@+9bd8BPVi8e5l&=u6*TP
zUymxRn%AqMR3PMB)6DvN*=wcFMQkz-TaRY^5v2)C{XR1}?p^(}?-t+_MxC*Vfr)Ya
zEEnC{bk)boKK0MyW}Uxf6?5AwpJ(_j{2i&NrdCk618*hJ%@{iPnScF9@8e&21j|?Q
z?_W6m!>-p)SpV2Mex~ka&TY%n&pr(ZG8pgryG#@o%71X)VLyD!ZXKoTui2u^Eu*x0
z4C=n>y<WF->BeSW%Jze^26V1#N{!NBbx3Y~=mg9D+3WeQ7RA3`#NWR|aeeUjw}Z1f
z_n$!E|Kxi6pYf}2L|kIE%AAfg1S(EII5K0KiFF0IpG;CCG2+CC|72^FN=OngPvTvS
zt=wC-O}VPFd%?_2TMcu&O1YiU)_HQ`iawg8Cr&7tkl|Sia66s|xS=OHX$a1~AY5ww
z{AzW14=O7qbItt2&+FYNW2->usYM0ui+m$=uBP~_g~FL}af;A!AKSO@3Mdq8yGU3i
z{eDo8K#_1jMz67P=g_Q0oLJ6snL1SsjR=exz}!MEs{UfLeVFSfV6qSg1bOHVv16yk
z!eMKBvgjHe`-$1NRVFyM+=eL8=@Agk2dYlBqIKH}pK^q*(g$C&TN2aa!U^I#5A~c7
zZLCemB^GirQ$UMIS_WXPj;n(i_p}>-YKh>IJ;R`Sq=PeQ@$tz%l(DKfVaSQL1qcJP
z$HoLd@Qq?aLuD}9%7rq0+8>_FdE_ztc1g&6lT{HDo0Q|F@a|<#_?6t;+;q_YqY>Wh
zT^tVKes_Rbk}6m}*LKD%<CK`r(gYZD4CYrr7OX1{u=3&0Ko!l*-Wb>}es6n}Nf+8d
z|HhtLOrN?XYoXv1ahY@^6-^y<H$}a57;H~pE%>qIYEh&T-(@|})Y#x*u(M+3J1Y-k
zk*P}oE={91_DF@FCqcI+0X&UelX-wi>85jv@lf80C?^^e=ogGP_3eM{*nPZZ@#K#o
zd*ULj8RGbp_QnFGp9qNbdFZ9=d!Mx<E=G3P1*$GMNMpZ*qk0tpscYjXg&uDXaa2Q=
z(k;Y{y6;Sq4u#vbx71k7;w4LDGg;THi9<*g`kOkm*Bont$0nRf^b_93l6rll#b7%z
z0MML|;x9TOA>nCY_n~5r>6`{rASx`^zGCYBMnu`Bvy%5T;@a%HVx!^N3(I7L909Sd
z#0YZ`ED7?%L!7KLOT~E6!qDyx<Q-ExF+QwD0GCP*>rXtx47kP-Q+Nc+kQ(hiiU->q
zqPTi8p3^dM7;&9J=x3vE-@fhhOUo}3=qb5)^cB_MH()Zcx!!yiqWH9g@&@Uo$Ra7p
z(|J>^@CQeQ%S-c?9DYEGK6KFffZUKsP>C5ZQG=6c1NaB1Dk{EPS0hw2zZD=`2)e6i
z)Pq{;QHLrnw+C&Gwapxc1fCdnyG;HH$sVj!lI^WRx1NYXP>u|CRGfdV1bU+a7x*X>
zQ&f2NJsEY?=vbcy#&0(ym?<s$^VKC?P6>=$VhU;?7wsQT)JoZpBnLKt>xf^iPBA<P
zSJb!dzep~f4)9|w<Ml@n>0|D%76MqmzGv8ToFR0yUw}DqU6F9uFbDS4vYX-~yH-*{
z_Y82%|7<Qt(JMwYYB)e-k`WgG=pFiRF#ETFJ0F840IsZFVzna5D7t_T^KIN1-rQKt
z#MFmP=7}vk17@bjPM&-`?dBgSw*6mj{ur>@!@_?*V+SVj^IY=3xU*XWMq4lyj12-q
zfKGTbY9b(xEIWvDf>bA_<K4_K-N}XxlnwIf+$DGbEh_<Xs1tG?Sc1ZwN?kRHj$sJN
z&M{zw*?)g{cE8a%D0*Y@JrJ_sr?Q#{+hI-UON|BNWJpRA{7S5I;wtID@Z|K{6|_pc
z6ck!l8)wbM_x3&wZ#GoS*g0egXAsYEG|X{8A`X69Mk#&Oe4Vm9@gBatR?DyhUw@6V
zN$!I9oJOl~c=$tFn=QkTfl}^+aj1{w*scSi%9|-7tNWIAL%tubf4OZnNfM)*o?TTi
z-kW3pBc<57N_lrD{FUl3DDGO%ZftWNm6iOT`&QVj3LE7Xhrm&K673U-HDQsJkFJWv
zD4pac!+*W7=gOK5#N`7x)Qi{1YX!kyN$ul=SkM^BMRgp#5E&8C0cBazZ(Hm{epHK=
ze95P1gcM!S=um0+jE%J#eJ+JyX+<;)v(Yz{X<+o_r!oE@cx(S92Z`V+@!4;#e8u)T
zW2?YDQ%4{pC?H{YdM20!k<eslj(HOGax^jWse-Xsk(4Swi2-!^xb_KRsA;BF!(GG&
z8@Vdjwe*m{$70E9+@@-0)C0VMN|PR|@ZRA5=8E=#X!nF@^(n$;q6G+_-2YO0-xd3=
zvaShaNRu3BWH!X0r(;1p$wg84p9KKi@((;rvNgN%O0j!VX_)UaHSUJ0kHmSC1VRL)
z%3+H+1bT{CVzL;l`XPi>W%=FMGwic$Q^1CRv;s=Hl;4?(>vPV&6e)dT!^eh3MPAw)
ziFFy;2DUjtE(<8GMy<cc9}NnGW+Qv`asd;?>p=FZD@ebX>7qn2{>GCfFc@}X(tTp(
zzxHWd@S^2;!lyBk7fFx6@h6|Fd_prZ!qRb$-`%?`D`XbjX*t3~j`>wmX1}ewrl6>!
zigKvIj-3`4<hGuS&S;e&O>Vq%tNj_<&hO|2=@IxW4lMPWQ{01h{V^W{t$5rsvG&K*
znC$3y<HkTKNiH;c=8eHQz;nhBy2pN=7~x;_R#rdIHIJDRU1T{r8n?O(;Exx!%diXl
z(N*;wyGx^;OA`2i-@ZC-N0GU>U3sTz-vWzW?5HneP*^Af@5PyS2jkFixKoImyKoD8
zRXfzR4s@W&CTX(Q1UMP@SwWz1jpth|<B~QswkdXDcq)jQp`x5Pd{(1CBd0$k!pCjo
za0U<{TE-Ig`Ez@#p0x*$E8A@pY&U)X;5p-Un{ng1GXYUVH{L0I>v-*%tVU#jYUw%j
zWRQdu96m#&B>3{ejriE(bg`2!aG5zCKAXRM8L<=H9B<9=&7_hUGhIRdZKsUaYH?d3
zx`Zh>93f_^8S{+3>5yi%D^D4f0~$n30b+cX|FP~$-d0(68DTt#B#9`me|crR`av>-
zXfa;GrgF<1Y%4>(*ah?O6ZZD@*KYCQ+_35WqzxP*K++)-lr8kXSJUce4s2Jy+mta8
z64-Hek->$xygcJLUU}UzuZkVze)6NGz~x%|o~b{-cxm{4_J)r!?z|8=b-gLNb_5BG
zH2S%MiQy+sYDGgz8EMZWrv_7{YH;F^<Z!0-;u^@-(6@33op+#VctcCXo)6gQKxw<L
zF|}Ym;@-$sNTs2SlIk&Jb7wKX3=sKzL+i|Aa!p&!kZ#JFi#b&=ympdM5qw^H3SmZ|
zMUWmm8_gXr?U<g8FByyPkSd>Z3FtW@*X8os7p7lcIQRWsB9f*DI}K}-+<Rn=KP71<
z<j{d_(~~^e=LM5PLbMD?2NJ$Wr>~%8q~VV>Ctw-C*q-c5%*SO0@U)LNV$_;}h5FgN
zoAjLU!OsKwfMNe*;9A0`KdnBd%R?hA=O9FZ3mmqh%0RdrDF_>2W;eaRU$I>e&O!O~
z8Xv+SJr;juEB0lzkp;|<!d9ZWM1p71OwcFM)?#e$OQ#c}&Vez}*&0+}zGUc8c9~@8
zJn$pu`fWj`sDloyDDy1x)$!gN0J6i{5DA5+)9OV|zPSGRl-rg144bpvwe2Kf7oTVb
z;NAJG0S+jpWGC}s&r7Sssg?vOz6k_YCk;u?2!cyfRfm)+5wj3V{QIFfs{~TTWgCc7
zh9s`zfMu!=zITrpSeangB_~B!o^*6=O(6L|NFfi4D-^9E)2Rwh%cMp^NoEIV3D*V0
zo?+X~!|;Iwb9YOsK!6wwk81S(_P=M#bb%XAz>9ab>dECYt=l07N%2y_OUI5L<pqic
zK{%;EAfc+k&@38PFz@V=Iyg|vdvhiR1hBzcN&{5^YHkc!@I?n<u@Azd5jg_~P@St^
z%K*q?=`!A!-Tz`q8?sI`HKV$^Od-MB2F|1ONZ~RhAF!r+hN9eqF4GrTeFS2}i`O(U
ziN#=mitsBX!Dn&^d3BY)y_RJlkg3L?4itY31ck_7$6T1y(N9hmS^p~C-3=e*gshen
zWZ3Vy5CKxgMil6km1;=vASnBI%T#`E!PKNZE4z6TIX6n$DoN@nkQc0>EpRn!!T3WI
zI)x$d1V=|l#hw2=J%{td=jb)qD%W6v!CxO430SjRu%NKr)_|&P0}D!z(j(I9Sof#6
z`ggY3<_y`i(VU(nZ`oQrr#$=9Q?qz;-_5Oh^K}-Y+b*$wcFpXZh%b<)%gj<M!S3Cm
zymPA!%=>vS#~&Rk3ya}cMsZaLL^5f(VV!)kfX&t-;j1mZ^IW5Z6O4I}dOpG(HC-qX
zKu7Wv8EJPf`Ck)*`O5=j-)Y?OefTg|^dNKBU6m=J>A%kJJf+OLUTSUMj`IEKmnTJ!
zMrWxa$x7o+yR|rbUeta_MV{~;Q^#@m?x<?iO7$rNjXNX}!lWwLXZzp^5=Gc{Y1boV
z9JgNYQYh)?23-$RFsJpQ4)rwdF7uuJUY@%wm)Y!!UQ}`H`#k1Z93XTL3;sut;FZ+0
z3XEod(sMIo$6UOy`LwXzEB|glifXw_WU2&op9-U!pZS-}TBE;6BjwcpjK=yuz=;0W
z|7v0WlGLwyb6q?3{M%dOD=3Y+VCe;{j3uM>wGt3ZsP9R90gyv}EKS=SJ++>`+{ltQ
zZ;<VYZrB@OP;I8gaT?tuV9~|I%X#`(nZctU;}q*Z(EZWw=%FnkVE7~9I-LUt+>jm7
z3fNPOgBjmniQipbmD%%N7nLL$A$c!>ZQIm=cWQK@6gZk%#phCg9^i6G_mF(s(}Ax*
zBk_G=8;p-E@Pavpf6*5oDwfAPky5c^)goZ@BMXZh3b{%qshEGE^Ps7{Ajnk=cx!;#
zw2@0P9l)USz^qiHG)D6G%Uh*J>e!M`M?{sJ{TMdPR&A6v&!CY}MxFX9heA2K+rLTB
zUs8Klbo8qQ<tBnz9Lyd+g5xgvSDx1H{dR&P5WOw`mE69l5&^)>F?SAsUa8Xl^d4``
z{PG(Y5^FyGxG>Jf@;*?xE~-iJxTYWVZA|Q4^E+a!)z+B_7f*|QQaf6bU$!(_JyU-E
zD*^W={qgbCy^&kyH4dD5+LRg@NpqGp{qi^z`8|)SM*9ltBiC@su*b%xKGUVw{4Drp
zYE~tm>9T<QDa1Og6PW7B5{TF9aT*w3`CaGNxT9L7_FzEdcJp2JsgvthW%Vx;XP`#;
zP2K(rk%?_%`OhqGxe^0+R+&TV6B5d^ixwPJRpp6yOP1*s|3Ys%cMQ3P<e}gH*_v8!
zDlH3v$LYX$lk9J)hS-|X#19BYzOCG<$V)@82hdC;${<w9GPkEAY`x+Rr_v)wH-0pa
zJ4{0WhNINFMOKMNqa)uM=m~_oCENC8H69HL<ZsU^*!I@bZqN2F1Ec9(-5V}TaH1^k
zyAXhASeRe%zP#w2GZ;gso3=2M{So=G&%&b4iw7a34yiVt1f4fdzW6EfAPx@+&JQNu
zotoN7QVR<1oIL8K9u$Bb?zFbw?YU2DbNTqtkjdNF9G}m^{g(@Xn`0|FVq+S|F~>vE
zqU?s<Y>ekM+xwhV>ISP`5}@<>@8-!{w|`b%@>dm7Qftb3dt>><`TxM2#jcOl>>pk7
zU%B#(XpMwFqu53}j=f<&><ql^)}8EfPH(OZaiQ|{-<8?$$$v3zdh@$aVS8Thv4&%I
zKl<MXA^iV#Bclxna%-aVjKUe*o%TVh3M4Yr=U{h+g7t5PkvHrQeUPm9@hJ$jW~dYc
zKr}=L(t70b1m~sC3?yTdU>-Q`1D>?coq4=IVk-n)Gx?QB<pxNFGc)<=Q!N0N_5e>v
z>^2ETH~#??1q%pWUO@aeOXTztbm0E|`$kKy&S|&KX}bPl_Fb{@eqro@Zov%h5dcQ(
zfar)CmxrEspo@G_5Gvz=^^vBZ{kV?KRvJkTJGr)0$4(XuM1ER!N7$`^i9n=mnq*#_
zo!%+%FIp|bx_Y$<ej$)od!U&wbd0H+H^&3W?80BlYhA`6#!c%&5&zyyawT{?cd1C0
z*z)+p?6{RXg{ncBKzpx52k?4Pe&P|q3UhNMMkkzUmF$+ek0Ixu|GNb!0GqUPZbZYb
z^~5t8EpQyPZ?Sk9KtIvplUPucLaKc~I{(K&%e)~c04wwe0F~IvLAE+dI&~6kY6D{7
z?&eG3=u-B-=8$&Q1!q(f+wmaAk8-(vgzrvTNodEs*6oaHzF1qF%D;Ne8dIPypxy@o
z34`l;B8ixUQiG|CTl!LeXtP6~B*7c;>J$NlkZ5{1qz&~p?KL;Q7~Fm6H8NKTWQM>V
zFc}kmYmsPpROCayLZ);91u^9q0PU<oNuB{8>-Bps$f5A40qe;Iy|Wqo*v{4*a-4a~
zpAYeBP}V=l=(f%IVEhSd50SxdGP7O!fXw!{yC4eCeylUP=#HwERtzG%oCYfnj&-WI
zu&v>6{&#qDN#;|Z2oh%i)Tmk@>r)u}xu-loJ_5n@r{di-0j(2<{{GEJl5FWM@l!#;
zwF!(UU@WLpXDY!xKc154JkTd&pzDWmZLLEY%6AVhj1?<T(Z{`b@xq?#8gSHZFyEOS
zmi}=rz+^lgG$l9lWMr{HFOp2~QBY-&-leHqW)o#d)?$^2=!xg9p8E#enb@)1VX?I$
zwJDWf5?QWWK@+dnS5pfE0mTF_)gF#?<cICOm&`n#^to8Fc8K_(4<ek-6q<$TrjpA5
zOTnLh2%wh}muv#|G?=mpzcT@ZWn&r#ni)4vleZY6V6C7HwFeBJ0bU1wI2O25;dV~%
zgDCmw5hPcQ;H97rCgY}Y%9vp2t&@8xWevYpC~$iuhV#xMgETq`fSdTdKzd<We-iFD
z_wuhn)auBClcu`vS}KbdUjqnd2)_(`H~o{p+f-b}?%^o`?%b5q#A#cGDd>}V3-k1o
z(JM*;9#uhXW(q-{(4VT%Az%=Mx>p_8=NNo8W0i#RM8ptBD>*S(H3rbQ8f?RfZ=0o6
z!Lh^O2#*rN*W@~V2W5+f^cyAU=8eFKq(>|fd>Yzp_9~Sh>Ty*%EERHmoZvuq1fT#3
zYQWb@5?e8HMzlFLJRkT>aIVq$+es<!pI1Ij4L~X<PC-+A=LklxMT>#NVWPTsbBnc^
zxBnPo;81UQX61}^#TR8F1q>U7g7oBo_YnGg6QF%O41<tFU6pRZ+<~W90lx#WK7-gh
z;(>*u)L+<{qiYv6h+R!~zY$s_5Vwx@<^DP>yfks!av7-VCHY-}-qjK+2yTfx;lstx
zfoT4j_kIU6BLRI&C%Uw{yx-02EBb%`Ut*{j30xaTH3}m6ZU~A5&XLAb<`EqcdDkTm
ztMz!SM%FSqY959Hjm7|3x=~pm9|qK)dacBe!OKeW0f#u%=>%H9m5P3y#Dn+hXOqz_
z-?dN~E{wW;S($7_?Y~50@3`wP3yw{FcQa=F5}EGLR@Ca_HX<86iXLAq+p~FV@#1to
zbU~@Fa$JqO{%a7G^S=dAmzfx-9^<yoL*vOlF<-or-B-rUhon0UYBLKjmBpv)7x83_
z(a?j5UDMLLBO;=Kb<>brHy({I)d^b-Pb1=%aJp@yjrC1U_IVBkvmHXbqbWI1u&;rT
zrq|cHs$*Rq{)x|+iEW+uvdqOtWACk;b5Gf67lZ%7QQh;f71B+UW7X#+?e;FdsM9X)
zTi5M1;z3zRagF-&Y!$v%&*1ChS{XQy$g~A|9kQIsCQ4W$=z=2vSC~T7O0sAb`V44=
zyI6<Z+-y*rcLPtT#fOuD-5g)#yx;T2<T25cI{p4%cxwLRMT3b_qJNgYPbEk|_j<dp
zFBj3&U|I4pUuNn%OqcijotgJxqW)|MS$l-b@x+4W)GlGe@Pg!R(h0b6qTea*`JtBV
zG<#xZfNfFlVyUPpcIv#=!_z>-+A0xTr69?=jf|{uc!XV>HuhlwgAI-jdCR;ZzuxR_
zLFGU(RSfmjLCrhd&14BrDA=>DI$v;d?McZ}_7Wqdm=7^1`}dTjhV5#62k_N<XnX?2
znrHl`KdkPWf(;)cCA=9t-LEeRor7Kgv=zeU0a4NzT%rIer);E7;^eRa*~R^n)bbS}
zY}vILr(P{4k-jaLdevEbm%Nub*8zScL0TUTV<6*0wZWI)eEv!;dvuwmpXs%Flr~*B
zZ!qLhH!_OK)lVj|kZ*fz&A?3`;@oLfrrvk(go7-P6Sq%t<IjyxLch!avl_A&$zbLV
zr{vby;q%_B*cRM5p0!@-IpEOgh}rgbGgxZ`Kx~Gopa?3L6W)CaWzgk3aa++%#=Qs(
zcW(F{$#d8K@f;Z^{*o2H<SSNTUyY0LJe;#Qx{A-EsG>t0;AEKEL0N&%6mEJ{I?Mj<
z@4!{iCi)uIn`c%9N6e!z(LPefH2yn$Hlkeg1QwA!&OUT)Q8~TYN~na6;U4j6pI+<E
zUs4G$gK$-Ta#^2X*bC4hEl>rO9YCm!G@hNum8+^zLtq%pnZosT_q=u-`t|@_GC&2^
zvnrfNC`Q&$W|AG^^!NVQ_5@3Y7g*CYwKw}6qZ_hq;v*U+tD<W!;p~y;u#5XHam?_w
z$f`d|8Q!hf=BcsG+TYQ%60j+wPCp(%R18LPmbnA&#8M3$W)nz;ncQbXv}Kga<MNfh
z{P)mq$8T(HO7vjoF%!Z$Y7e}4hlBZ`)o?ey2n~!OA?0y*JisfgUYnZWX)sS<%NArH
zdO=#Wy7KDA^36}rJ-3g0{-X<9e{GKRa-VaJnaYr?9s&MK(mVmt)i{h-?KvBv(kk!|
zDhv#V>5iGg^!sOYN`>H9vu_f}8B!e4Yajgcd0*Uw90;>z?@CK+0C4)kBst^*Yi$<$
z%u#KOICqL3JU|269N+T#U;nX9NXUOx!@I|Vn3Ujirp4Kb*1vnFIPraV!QEBOJ2nWb
zYtpeXtlE{Co2!e+`9NwXq`AxLS&d7!LqhLqW6_j-bk`$+Q}Ne-eULU#vj{<cV9S&*
zRXIdT@>HXzds%(73Ky*sXnFVg9H^hxi&@E#Lm0&l>nBd*eJon(ebyq=vj(hR`s7Oe
zx}*B~Nyw3vr;{Z2DygWGYYSTpBjySGg|?|Yj2&dZAaq~U>1a_|0!_@d{jjW)U_o;b
zAaXLbGcG=9ad$MwLPNXWPwRB6`>H3d99S|noK-N-fw8u_x&xP62WjTIPqnZW@kt?J
z>eQOy${?5=VW3Z~Da*zxNP|3QE1*3L8TXo>d?{n*iJ3~RLxK1Dy_;wle#*R**|TjM
ztj=f+5UG<91t|#DD%NebDwCn;9zzLDH6f@e+@P#9e`9_!1Z+?rh~GPS--8#sc1Q2c
zZAm^<a(s+Iv3Jeex25kG#*whli6bJq9$l|>D~;<?!2SE&S16)Pj*I}%7@wR`#H3dC
zHOdMaAu?eZ?SIjonz}<kKUWKmR2sO6k2bSCPrBTqiaBGL`J#M_$K?3ye9PU=e@5(C
zn*s75<gO_lpBBVCG?0Ic(o7KHR`_c1ub)4sBa$(6$tJXp&ss&ogB{`CU2rh3!Jm7f
zvjgsms`No<cFf=a7O>-t;>S@eVFESpFdv5>3)OAdEh!!yh1xf%msiH%+#QF#`<?Jo
z3<?h9_UsP&b#;kgIm#AP%v_x?`dil6f+t0|mI-#&82X?Zjlw_Cl~T+Z|2sFjd20Gw
z1~+{_VT|Alvu?D=a{J-XU$}r&@t8_)_)&BXIG~4~tPL4NK0F(2G8jgiS^dxJ$<je>
z<<!2nr2<rPG7KUm9%@dwWL5#_PqqYzjM#9wm%COYg%@uWnbnU;mO~ddU*M-yi9c9F
zi3Ss0$H)W>>yPZYdg#-#k^PLLakZ{Uaxdd`zcVHvB;dEIgIqj>JZ)oCz+@tYJ<u9E
zV>c#+OEO(!Fe}2ChTz!1wT5c*NID`pGOu}m$jToQATv&|J<YBquI~!oS$VSuMm|+|
zVCfhk<K$BYFA?oy1qqOL9O?I&tYbi6`&sGnhiFZr3!scd<(K?z;1XdPkeMU`w$J1_
z(I0hVS=|HMhE@O4KEjqv2TY0`#0-LsG1M0^_~z$IFWn>hrU1ujHDYj9uUcgc?46K5
zOWM?++#L$)b3BOZhPiXjUq;<&_thq6Bt*0Ush@k+wuZ3sNE@STN|Mwu(ja&>F6Z63
ztl~&!kq~UaZxI0BrUm#==i;|e&e6fCunQ8D8$G32Qyao_MVilqx|#m^V-|PVG906#
z7>*B}*1PPL-3YxxDJcpAi=z+Q>W2CRZ@e3zq%#woWqV)kR3eR=o^GP@X081VMWygx
z_V10-+zzGAaeb}m$yt7u?Dq<E1d*|4u_5s%8Djzxq4`foA8>Sk`e(7@J)fSb)7f+f
zg*0*0L)#jS7Rm&JJQ7x!RyVVrD4{kRUGF?5u=CAa&lCKxWt71Nbhf__hD`Pyk3CMW
zl~1dg{aMNi2J1tF6+{onII9NA4i!gzccqdLVS8*4JT=2nqfk=hDC=d>v6>HGWX21_
zA~GBPH46?rkJY-qeD?P(U-`cwrR@JUU?c}vo?>OoUY?m3{OsEff(8@rZWR^nJ9bwR
zz)OI~^mLJW^NbK4KVRQh&jO;b@!qAP0ebz69!214w9%6@)LXD*@&_{WnS<_s+x_Tq
z+|ILU^`M?@BJC7`uq8Mz=QRV~rQ&y^%D#K|A@At%;a`WS7P{voFTOnkqVN3=?YBUo
z{AwizZfv0N2T2+&!_ouE6?}xJeKX)SvXj495nP2J&Op%#Tex;yJX+89pk@d|%GTHl
zKv4jZ_)8S0VdyIRpo_b9E|rCWM(Xd5#I#?&Yh7#aP}pRyrRttz+M9Ln&Yn9NBfE{@
ztO+nvyjP5q0P@?mZ5w(0!?S4}NTY&4=l#y`zI^l^UCKfF=>Q|3-r!^a_!RSE4+&4(
zC6(UT_U0X(sx2y-y5h6WMbsp5n9bHywutNvy6|WG8vA950Ri|Z+vd0jHPX<4$Hm6p
zAh&6oLu$2fTGE=SLTe<k29`Cnm$#O+?kM$J-w0&~#D<+(^lju`0o+{WVy3>!Fk0)5
z4eo<+U`>kI?X9K~2Xkz9ER_X%RGVTrAQ=6Yj;LctDQyAAO4pNVZ!0nrsc+4{a2=YX
za47xr->>37Nr9Y!>$5Pw$d}SiO8vP5Z=5U>z9<};w5{8OZV?3dq}B{Pt?F#FEiS~X
zk{^#PHY{uduD+fnhld97z|0`yvP7^?Ps>)xO*tm7W3fu=&3~>h@fqx^`Lgn}&M#li
zy9KFgad<#{%BS{l{EUp+8hiZ1CI1yW^$TKyA~$OQ90xO<fYaJNsyDNABHJQC<85ve
zp~baq_9)`P1(}T|U!*&U;*BmE_;gRO0IP(``<^^HET#IG0qq*jjCkWuAB}$niSZW+
zn@;83&-i0jD&Z$sV4~>j3qak(eAnomvu~XB&%W)v^A8}MFL6P^+i}cgKyc1;?eAW8
ztR=QTta3}lHrljo+Wg|!bH?A%ku9w18NVY-e<jm8Exzg<KEx3pSLuB$*%K63otSkV
z`L&Vp%{T4V(Ib9}OJ@zXbI}$%as+{Cdq}Mzee>z1dDP}eip&GK5tlWyZ_8@U2G{ws
z|M@@p;{J^*{l9(=_CKF-o2a0z{g4p*U<vY8q}1M0U0XZWgVSCCXJpkzZ;1_{d8bZY
zS-=UqnO3Y5mW*sdp~X+fV#hEoHZQw)!2Zyw0j=37jR1|zC4Y^Au8Bqn><2-BQJF3%
z@dy)+1V6X=9wW@uU5zeM;lj%50B3v}do<CcX_#V;0s9<qU)6MUbTsa)yKzRlA>S!y
zX_05cvkj7)QZ6OTyfarncS`o^`V>PCDmltdjw3`fo`8*hXF7u&dMm(zn0V3%s-)Q0
zk=Nq(gE3SSpm;l$ZZs`URKfjH`4BH-+J1BJ!OO2HvqzE+8)Jv&X;dZj2$W;<v$Xvp
z|NL=;&P@SyLXa_%YtRGR7xy7tnhwk_G^R!a34cR|@^s9Pai$&^dT^KXo|c5>%q7Yw
z$x@nb!XjZGy&76zni^Ur=ZS8EZgkK`fbav4RN+Hr>(>z|ffVT*=2wG`SV=%!6fgwD
z1-@Dh>c(SW!GwHJOw6_E)#yQv9y?|RYa)B_`K*ygOXvm=h^C>nFM5rOMlodq*){6e
zdyvG3f!SpW445zgL5O|+M7yluPn%E%s1<~2icz*|taPPeWx3%QZ9gz1<4HdP5K|rJ
zI>1EGF(PpRUTe2+_?|Y)Rd@X^Lupb3F=0OIsvjVdqJ57e3kKljySHyEAdKALp1?2-
z27}kL=X6e8cMXcs<4%BEc^cue{oinBi|(vj_{vEtgg}#+Q~3NMIL<Q3K_9`q!OG9J
zC8>jWRG=Djf78OBfN;$h`pHN_tc93=;kq>33fKxDaL6T2DtOZ(dgz1RE`MZkBi0{>
zfno!H?;;!#;G^pMjc49Y`lY$$i7z2}qSC+u3MPUl14v77sE#F1jR_I<a?4uVfGD!p
z@^0Sj!5?>4+YcaJEPkICj0PY-J_7Ovcr4@<;5wB^&oczCwxbY^F5YVtDm1iGMdC3)
z-3}CrnmRfGAc~3D_3Gv!ZP|Xf{wx~Wa6o7!Hh7f)rpsL!=78A|4JZYbDGfR^AU>5~
zlGk9y;d2q?_*)U>q0Xw!-|}~5OpbBhQ)rxnBg+QTVM1-kcSSEXx`!u?a(RgKF=##T
z7u8@g$o3~_cBebL7fbvUIxKJ~7ft;*D}Arjs0vILQIHU;*toPbECG4*G(w%9g!^o|
z)an%gm-#pOsG&Q6!q)=q05j0iZQj!>^A_@&Kxv%)BemqJiwar}$={#C;e>%QLKoi&
zb7q=W>ggL)6A)!E{3+bd>>-O}l#2CspI9_qxLu_)HeXcl@*aF3*T89oS&N6oMe!gq
z1arm=q8oI!Dm;h{^UFcI7z>#i4YB`CtAt`5p!B%s_=oFF=e*yYr%*YIp1jJLOXc=2
z8S-j*Bd+IMP*7nAxf9T$Ff|W6k2nd{J!@QR`QDZ_+QJSiBI1eV#4f)x+Nyh)DgB3P
z70r=_rq;v07mYEbnVo2EBzgvh1mfBUE)BvAR24ov<zPwc_KD3<N4qeG(Zsx8f&CU}
zc3^NWSrjs7X;JwZf4wNYC(>CJQpA_{+v7B*Wb<5kSZgy*+nq6~#V_;V?%e^etk|R^
z=Hap=@0Px0Wos6=YCTq3eyY$Yt!93v>gQjh0U8TFL{iGuq<=4ZD2&bO!%5{)VOC1l
zS6`cLEFH8zq-XU#re6VEW{`B>n14)}<b}pss^$^V13ERdGuh~KHrx?aR#zJ_Z+O1l
zWiXuU0LLT59n*LU`@nS_#2f5P%IU2{A0vub5gY7`h2ty>KhIerET1;HQ`_|j9iZwE
zuyZqzryBAcvaq^dl&wx&_kMP3lVFj_Vg!Oi$Uamu;|PG&ckk0yW<q6yD6x7OD6*Ck
zuQ71o;j>oFd(L>Z(&cKE0_<O2Fn*HR;tb}}L5?gSurxJ%MOPc4QBObvx=mWz0E+zz
zjL`0bT?RTDQ1k7D<=IKZJ$rh%5L08&g5AnCHq&JYJL$*I&o+25b=1WBOO17edyY$T
zswg<zixLZ3S5MK|T+kvEFv#725}F)bzJ_~(%IpnR2HpWKgsM5Fnk7+_lcFP(EF?a~
z_1LE0K9A>KV89EbG(Oa-TZ<XXv&ns8&!L+Z$z?SP(8zGi2Kz-9gpjqQ$0n14ua|)1
z!Zqs2fgwf<ZZPjYj4%by&}+f_mNnWPVvk7RTIeA0q<NlVd76P@N_uP?@DR!7<h8rG
z9I}QCfJ*4Vgd#Yx95Gky!f1o3b6b7&ShvHW4Ho_+xTO<<Z$Os;1R5rcD>R&dN9ZTl
z5<*$A_c0rr42YZA2R`v+Pjdd{0_=K}Shzb4u&CqF#gy_5+J37uobM@1GCK(|Py}k~
zf9fu#FeGqSFP>|^V%fSK)TcergI9fU9^Of-BrI_pM%1q(G9TL1S@{!^lFnHxUs;#U
zR4OrvDG}If)-^|u)#R)V1}_E2n(w1W@tEyygPXflh>%$0p3k5((qFL7Ggq9z@u<+q
z+lhzgr@=ny{V%BTiOrjTL5=c>Q?q=g<th=ni87&^2e3Ep&s>Hc61NDFFexPPPP^7~
z8DAsVolnn|S`tUUw>5zA@g<zIu%B9+Spby{5vQO|1#57`s~8;%q4&IKl%HExL!h!A
zWpebbFoAS}kO8Tkffvgb?bSbXCer!WqZWnAbpp(@ljy+Gg`vl<$RxJ{G;{BHBz8WG
zAV8SqbsQ@gJDO*ZvM4={xEQ_}#$ib#Ly&lk`b$~|=KcoB2=r+MjKp9H@n{3*PM~Zm
zl0zC+eyJbY)j{-!>Ocb{_63}mxIc0lqW&PWe>gR)-r)cl@}YmREmy1&Sx`2VXz8@A
zYbaz4JLkEz@=xq)N#G9TcM5S#pMi{DB*WK2i|ATNSzV`Enu9zJ9*w-rG1Y_&ZH@hF
z7A<_U>D|_IG7DDkGe65{de9_5)xrGA`FZlk&%4zYM|R3D-^XdAV`0vA=_S*vbE>Oe
z%NzSI^xWd<x?`W*!s|XvZ!}{a+P$@E_)>i$x}w(PhqQlre8K->i+X!-;Bm{QKnzk{
zjbCw;Z!g0v6v6{KBiFZf!?o^YAVAIwf@$kJ4jdq4Jch=R-sMux#FNI1n4jg?CIVco
zGhWIhY5n0h<pmTbT`1;a?GD8bpS%W@%<rjOfJ%7-uh|K3&L(jOG)7S7=X}5HH|guz
z2W05YSIZxgCg#tm%E1!aFa}6Cwg6DyM?-HiXvL;RAS`yTS;ejACTgf*pylDO2wbpV
z_UhO9XS#%dOaLDu3IXT87W+AG&88>#r?zD^>RGxJ35y_HkbvYwmV;Q&Ffen0&c%Sz
zz=i}T!QB4+q~<vx-7;q(zVyR;dqgHMK(B;eX|OXog!G;S(?Z5pJj@5?{n!m!HYWYY
zL4GE<>*B?W@B8?K?kp3N_1C>{17aENzANr%jDgUBw5f@M4Y$}KY0@<_d6n5|^u8R{
zkVEizj{_LZw#)oMQg+wYGpA2)L5WQdaHR0e?Agnem)<H4VB8)V6QdAi;vSIz03uOH
zBD6CkaS81*<y7&{qn2BfI*vRol37Y~v3(fV5ZMj;FCM-RZ5L`B1m#EtG8+AQu_iRQ
z@A%bQHS<=}e^LS+d!?VM6xAjyv|H=R$oKn?>g&*3WVAy6$-%r}w*te$Mc@DFvp!y(
z`&X0td=2tJW{Z+cZU&~=|M+CyqS^Y4;wqr<57*0lA0ylBPn=k?Vs@HAp&a-5|K+b1
zD_T!+eN&sJmmRf~BjV@J3)qi)t*+92e7G^S1v+Tuu_8Spg1UzzqlJePK6&pe<_c}!
zu1eR?)?W8r;i^AuerdpOjmkv9H}2}{a%~I#k#a%ffQrK&4rZKIG<vSS<@-Q09MC^6
zNqk}E-2&SSB;4P>`$_QA<BywOwN>7~at*(Y4=q3b@(M04A<Ujv*3E-Q7vy{Qs7Ez4
z-iVY6A6&)ZU&4#Z2U`@msO-8uDHoDxi3MdQE{X@WO`@;wh|s*4(*&r*@64<B&x``1
zur0q>uXqHUMXXlc-}*OuTTW9>`JOy5Zp_Rr13!f~QZkr!^1(OyTbsKQ$kp(@If^k(
z?~$v}b$ppB*M;BEcjK!V97FShCSwixe;z*Mi{15h-L`fe8ylNymkJhERtV9S($w%@
zai^$2yv9yvqiqCw712OF5mT0t-Ij@4)Xo#q)7iFXPyJNe!7U|B{_s~JU^?i~sv)Zm
zL8xBRMyCR{J~-h&2T-S?g2EA8<Wt#2I*5A)a^w(ss!bOM2d)_p(ijjs<lEB^KVyc>
zE%NE{TqxT+?yH*?MD$;h;MrT(<+p@Vcw+G^mi(Vx$uHLRQYdn}s#3pY@LxWpj&F5u
zVCbbo>N`psrosb@Mb5CfPuZmZdJvTmQkG`i9)E4Odzq-p!md+VQGtG|!(wy_cSN$O
z^mMa*D9}H+(?BvTPmno0llgp#gSIIxZjq|N;;g6QLr-4RN6S7p4A|jpy3E_;9ebNa
z>vrWmr#^GsbIulwsoJ&ue9}7kw1!P(gYv_Ar$jm53RA0YPYxU$GvP?QF_|sKA?+1;
z{>z=Y6t-14$Hpl8zgEqWHXS?L{bK!m*Db#dUU`UqpEz@JZ$58qOxXvPi7)vN9fxiV
zXIIB$3U+=Fyw?!vv6tics{343Pmfx<?OjSy{hZ;gxox00*UF<J=Bs#exYM7z5vd`Q
z-fr2eXo}C;y?4ylTh)7n5*n;EB00;}p?J#tpSz3v+ulc3%n<{i8k-J1C{M5;dJ(iq
z4IHXYuv^cv?Q2f-*SDu(bz3<9iTZ>Iz*xc5xHK{4KzY9;F5yJix_4qT`>U(59HTfR
z86@c^XK6Ru_I|4WZrt_DK97jSV0ZA6Hv}9HC>ysy0F9$hL%Xng=l%1un{&m@xz=;Z
zPf|@#;gZ(z(lwLkpyf=<XqYcjhj56{$ixEw_*EFO*}LzU{YtoX?z-NQF>sm4m=-sm
zyc2N}y-nUsNoL8-q<{_+I*0Z2)T^;)x6F!gC}FD?bu1i2J*h|s(A50Z2^R+v8AK$e
zPEc=b<>#;Fiw+D!o8JXVa}_8G?716%<KM<wrX~zX5H|RNzNjLgf)%wNG$x$86CqG7
z%G1fD2;h36cA7l#Uzz6qb=N9`hLecWui<py<;$0eq&ff3PbJjwab!|mR#poM%W5tz
z1IzJ)oR{U0zFC2~9>7X7=uP0b#eqX<_vyTcGpL+(je;9DZZsb<-^vVx1Nz?%LJUQT
zNG6!TGj@KEP{&ZJ76t!%y{eff?74^<y7AEE%g@l+Q)wt>1sdv7`O)bsp?+W}S+aO>
zI(TQ~nvqQUv`oC4e@Yz9iPe`E_IBNxM~`-5PFqeGPOv0eKvjjD=ts-%%*J!140UP3
zBy_szCF)Q&R(&lYdGsMLX6wACBDF{>AM!&Y(*<ej4iUsW(@k=<w*V%kpL`6$q*@RX
z=L{3_Shs~g_zq8$kJqNficyIuwHEzcmp}_jKZ3Im9_tv4XAZ8IJ=9gb=UQKXivovg
zgRGCjy;mOn1SOIPz|RKR_>tBS@3f@TIu6=t5{C_pj3~^fhSBeW9x_c<dn~(v_^*IV
zS^!I`Z$tJOE>q436XNH-(S`p)1mBg0-jY$<lAi9z<S-JeG#U&fGGYdWF9|d1h7+fT
zF}o%Bi71i`iPEyGGV&HUGlWir@I`&Zk3AzEEnZyG%?0Fpn|gek46IQm4V4&7s=*J1
zfu_@93mVBHt>)SxbAIL!qC2sv(-Q}$;y!5cK+^F{33C!1@-@uslhG}c;<5Cc!^|t@
zUV#rm7>kQ;8hvvFD*gHnTey$GG3FklAJJvfBMx#oX&|3&vJJMmT@4xWL~i5S11mpS
zg0OW}gnJMRA*;HSs!k15t~MY$%>B{oARIzfez=2|do<v|gBTEiIzY&(StkAGh|qWV
z;}YWHbWe)!W<df<zi=$YC_+?4=dw^iW}N<$ZVe6iXmq1kuw_nbyZO0p`(0~1KA=Oq
zWnZEz=wG&{or9G1L^lUER+9Y6a%f)4LlA;Azm8UEk<}EHo}MmWmnDZx359nI6z7o8
zJvjZLBU{lc42mIR66;lXwnTQt`P`q!Jq*y(%ENUFJV;qt4EZS42N=a)Ai<(^s==<#
zQWl#C(%OVfbkNxQxSmr$(?<9!v<$nEUB6}h`U)L!_%j_&`C?rgtcJahp=8_Et%D#s
zY7>0IWwPC6msTbU2DGej0FnZxI&L_<b#<cawh$TBwadxwQ$AjomLe9gO;mK~dgFax
zU(r$5qDSYlX{eI~q;^q0-dI*`%p6_S$z^!$C^X_M=M^?EFoo7Xa9>u4F$vGOgmHio
zxziQpEU$O>_2UykSD3t}#{m$yDR934_%<gMOxk056^j`%lm1J>Lx?ijO0v)bFDRyY
ze{m2-j7=+>u7R4`hFIsHHbCXk>XW?@Jw&7NXnur4)7~~m+UV;Qb;Xu-*fCsNGtWq}
z-wID6?J2=MK|LVb7b)QDM!Q<D9~%`fo>^^jTXAU-kTes*T?7PQ>6fO!t5!wFO4^DH
z7{-xr9-~XB^9Mc{{Ag9Oh6{i0fnt(iNfiy&9a5&9|1-Ff#86GysN2~pW!N#)Da*J_
z^QY0rguk6|Y{V08E=Wl1uGiv_0%{$MuI|}N&Z+dO!)DfcfFp<e3;sk_3}I1k6%dg8
zvOOUD@!nj9P*Ky|!~*u!o4xU+Vm6AV<;3LNXQ`<_uh(rWePzJQV7#bT?7$OD4iM9m
z;&cBDhSZaatdzsoaSriq+2W(|$kn3M!xz?rgi=S;n1E*pRYJfZUl)*4D%c=4*aJR>
zTyMshf!Yb6*}`@dI~u;GYh;fAWH>)-z_$^Q=6ru4y=)O~7UvBhS&*3mFoTwFgX{8p
z4uf3m>ajc129I5qPLzc0!48MPf*|jgkYLekf#nCy3k0G>hm9)vZB7RI!!n?z@^v7|
zzik~5$;wv7%#GScaXs^MeC}o=P_th@IWc)#^8Wejrh+Dv%?pwWSfGR2zsr5Q8=LEq
zFIlv?1EqT<FpCI%2SX+Cp7bgaoMME5!zOMWgXi+c?z>`!-CCN!Bd&_w#n$QrqjxIc
zpXJ(?-<na4zMGeZjm<eN6VX4v*d)#!ufcMsj`ryUgK`JP{9~|`UlkXROLK!Ds|Nt7
zt1g*qj^xbRImM`*wD9XAyOD&nvc=jD-A`^25Kvb8Jf{~KwS0V5rngtt13ILdVEDk4
zl*O+^<~&7T`njr#cZUpNa|v*g=ro{>GR&DTVwM9c`T~B-=^f>a_86c?UUIw*z2g@W
zsssCrH}-ALqgmFJ@qR|}t0VM?%>msbmq^9n$FCg*D?Qh}UhpQLCT@dme<7!dfmQ(z
zT3Thu<ltr}AdYcKNf766jwXy}pr=V7B`x46-rXZ-IDj`wi&GBenmU_IXs%Sk7AJTF
z&e=cRAngNCn)WEcpR>7EEKxCMol|2Ff(RH&8nc7FUY%U)4rr4~OU@e(kiG*R2KjTu
zq3Tb`9Tojqu5-oBJ)s+~1EEK82J@#+7gg@53+tYLwK}`a@Aw~SOK@NabFUew`}{1s
zoF+nDeIED{T|L&mAB<8%iPVy*cQ<s_t|3l#Lc51uUjn31G#x2)1XG!S6&bcYI)D(9
zE3(&5xk?$R<wIfW)mv3<YoYfc>T3{;vN6NZPEG`5O-@5F1o;3HAo0ht|LM$Il}YDe
zSkL45i&-h7N>pU%+fXZ)<Np9i!5{{<4h$WM*~TE~Pu<9ufo9T;5C9=%17foE^FB{}
zSThBeUt`ZX;Upq`B-P+?%b=5MK?REgr7)6k`cXK6F@6JRM>YJ-h}9d|J&(ohdVjP;
zqX+=$Oqui7NY5P?;}f%024P=e@RSY<G$*+ALq~m$Fs|a0vK5iX3FPNq#3nA8S!Rmd
z%A&8^b7T(j!Ds>IFMQjuaJ!c*Ge-ZiUyalCUY*NouG$B`{n41nJsCXf9~-Bajd8>p
z`cFu7p8pck!7aU)7S((LeD|o)L$<80M4$T$xHI#HD@=JOAF%ccFOO8~9Sy&|k2Myb
zKD7;f`8glP<tyI*pM`Y%TaWS|KtERQdnuH=iLz>`sX0MO_;6;hkgsrVvl0XKbUm8?
z?+_oXb)WX7BS?spls+9auooP(epGr?+F#@WyWQ2Tn|E&4)~tO~KDTBu2!L%DXmohb
zJ-nERMFZQ$nO41el377vsoj=L8XU)(daP%3c?1W68*p^hx}ApJMXC<c%yoP2ww=x|
z8(1o)vPdtxalKmn`Jj~#9qabod4K0$u!ip%R|<wyJmXf6{QSNboM1ll><fr-VaN#r
ztl+Wtekj4>;DSrn_z!>$+E4qu?H7|!fe^rL7OnxQ5~r8(P2Y<>XemJA!3FOFKnbRp
zFM(&S|Im#;eohHzx*LBA9sV!GGR2==5S0nx+Cw%IE_L!zKtR{jsW=>eT?OxHhYxG+
z-+vYO5&)WV5PI6IYn!*N-qs#)axG3>Cj5Rl#pD6GF$uus08#G1UnZA>(H)ABn-L8{
z;`?oqY6Ms*R|{ADmH7R?io#7pW#t#d_W#aP0~HU~MujCtWopr@)Px5y2Qi`sGvMKv
zmfz}?HYxl{9?Q6fr#O0s?wWQ}54h-cWh(OrnyFbxp88^!R@|Br*)sAvxwe-*P7M12
zsJXpUdm0)`b`P(0XQ_7JScGBuSC{omoM<<LS}cM0Fi0PC2s(9!-!}r#^Z2+ke|7QZ
zHC?BQZ4UG23Q;y(J2@&ZOA#w0+5@7@t|T5|>MD|=?^z1lLD{Xuk%m?<8R_d*9@j#k
z;le&hQ%ly*8w54NW6y>WoNEUQ#%hwOw2kt&%c1Xi!NKAvH92BPItF?Kk$hqMX1XZ%
zcxbRn#SD)B^awDbyXZ=Si!RFbKSSd(h^ee1om4S6^0tw`3^FE?$8xANL{h|JyLzzk
z>IW#t$%Fx0m??+>Uzit2?|~3Uu;9-HS|vJP!s)!eX9H#7%R23uL*V}jwz?E_H>(8<
z+&hS75x|@;5xpOSg9xtfEUImC3d3wb%<9C`qCyog=n-TgKqJ6gh?Em~0w|6K0cR`H
zVWd)n(^&Z6+z_yVdkF6Wrc|*+CAKH59EdovNtzx%62k{P5zL*uF{-4}a4Kq^I(3TN
zJ0Or~jQePn+Gm4!;mH-X;T0GWP&1+PAVVPXZZ1i1_Q3s|POKw|-i161xqpM4^A-P6
zh5iR1Y`2}C7$}}Z$_@9ZXCzJv`#|^mogb=!x)gNd<*B0o$?JhGtJ(3Faw6O^z#%4X
z55x%v>rPZFA4Ni9R&yLmqYjkHDLLXSu~0D)6DCqz0-hmEWU%YR$A6>|V<}7m3o4wX
z$2vBW!~gBiSqP~1_~GM0TEczFDoW!%a*ctW#jO=^rm9M?fM_M2X!cI5n}}+I5ep96
z{PEtsM8ZYZ4ZaT>R4_z&cTRI%V&{^?CZWm(zBOITJJ%|0WHmH7eTv`0;R}b-@tnT5
z4Vsh&X5jV>VB_HQ-&7?sQG!Pt1s+N#Nem+X9oMhVoe~GkQ7*fz6T1qTaAA{#4J>05
zEUgGJ8AoaNdJ=Srl8ZFE4H1X$Mq&JgiC=QoA7YCF#xbF#<0}FqKd*3!EEMcR_Kzgo
z&uB9Vh9@3Q$e6j&WQm6FFArA>LTWV_n|0f{>H=xpk*uL=dAaFg4&Q%!JM4xg-*7v}
zzpzc;&L`ZC&%DnO&=eY>Yj_PxOA5@p&9UifMVaWJ_NWDk;Uv7gv@l>=`tdAuG@2lI
zXTZCBDixgyMxVv7)ltneT(xSI78D<~(5WqJ>|Pm*wFwS`kv6C?S&Nq}A$A}jO~<FE
zR*r$OF=5dU4z1|ZDbURm5F}%UZ&g5C(4s~n(aA+1-p1)os_~;ENY@Dkb;OW3VFA%O
znm07~$gdGxAB>p!y8$Dm!^8T769(1fqK4pH8)(&1JM5;_A)9i<!}z%tTm4$4@Rf!p
z9d3sFOhzsDo+VP!*JDU?_yR)ZGNIjZU&fV$fS+v`IlzYiI}mKR{a~AZACujyR~e_I
zfcE%NVVzu5@V@OrEWisWTlHi{XV4f3d!q?&Co@)KE-a0Lh<cf{x6pi%(Zb2K_|nU<
zA(5p(BRkj7roJ8L#dL<v9%Y(7^vf?$>mI{ey-vK>d1<5=FKIKi-aDrJizxo4O-<k+
zhkf?wxvZS}@ytx2m;1SHx{1rpd0p9oyVF~%BrC*WXd*lw{N7?-Xb)<T!>7Os0|Ds~
zRa9(uAkaPlhJvxSle<H(u`8+N-KxgU2roO~6WUNY5PdXeu`=!_uZv7-T|g27EzaT|
zdg<#4x()C26MJM=Sgb@BmJWk3+RuXG$NL-8b<M{2^AA4Oijhq?!S8i=@Nxf1elPyQ
z!FgjWt_lYk*~D(ZECpSkBE1?KfcaVL(4A3<0oddjRHt-PE>ktKm$?KH3a}}hDTx6U
zW{2rMuQ|=S%BpHV2A)gYcf3A6!$~2`;wsF34ue-rysH@Oeq3j?sJf8Yvm~Y5<J4p3
zl&dKs;uRh*JW@nb=T@<H9HeYmpMHwZheBEYo+h_XW0CP^>i6-?y*qcNRczh>MTs^Y
zw4-(dTfjF8bP&M04ZaUtB1|OM<c#<e85le&#!4-nSN{BHSc)e_t<e|W#LFw1-2@2+
zfrLyxJw2a2`u!1Mw3z@#@{+3dn0c2o4=Vv=ygqsa7=uCm%p7B06FQBoryehRwSWDo
zdDu$u#5iw9-sOFeFC)#kyc1);%_-RLX;ay2O+5SN=z`MVBJ~OwE@H>^iQVE1Sv4LQ
zCh^FWo@|w-($JsRB8-W6-0j*E!eQFdNcds&L3lQ1zO}IHX@~?_5hWH;u@jd{PRIUz
zl30T{jgqn?EoycMIJq2e^q@9uT8rJ7K#Ekp&(~B9J9ZD7Sr~Y_Nv&F9<2etPc@2+O
z*iSzA#2DkmLYat*q=5a>I=(YLYVuG<@9vH$lQFK5UoTgxbYPX$D(`NJ!0AKGri60)
zui``lCz6CL5Q!1eIOx=#mI5@D-3Gu*49G`9-aMFmDr3)zK-VXBhq}6CHvr2oxv&QY
z&9JnxC9XptUt{iui*0y%KAhM{5&aTS=$gUKsyV~qi2>0WeU@fq-=EtGWceO7$YpYr
zn2d!tR#-M1{%6Ie8}fTCy&YNMmLl@=?}C|LB-XW)i~CDiYkjg??$kMrWx)A|Fow>5
zeQ7_Q06%U5?vd8KMnCir1UEz#Pl%y}I0gy=p$)<7X;oN*92YizJkT!Cb;y@*4FTNl
z>>eK;*)9Pk=ycJx+5j>%g})Qgdf`Q5kRR>ZV;sNItY@745EtF}Q7tiYq|$(qSzN$g
z&}j0{J29GhC)xk@P7D__f2R5ojy%lHWWpOk(^QDdi!m{mO34Ogj(}QCFlUF!f&a8x
z3IAIap1uI4ucSnRW|6qiVYiY!a<9Ws+mE<!0#DZhph)!II!?thc#IQV%#Obdb}hjE
ztejV1JIHe*)8rjm`}-jwn5IVJHZrq0#*~dS{@J0nVTUTzV$;SEsxItw#d$7w<z2En
zk)a|Lh})LW>;u_A8n)1|l8>dmbA+8I@uSqc+~!L+Eih?;ipzO8Eo7eK77n;oM)>I^
zyk6Y0;5%qRgaivQr+r?|a-FW*JIj2iKr}=hxEkU{*&JMYI<RL|-G}O(Mu%}hz{Ho>
z?SqfX|Md{%@&2)4T);z&nqAl%8JGfJqDz=qcK0JpoUS!aGg++*=dlJp$t8?c1IGc9
z1@V1qd56Q68+~DK;2C*<uA!z@nf*CjqiNZDAWQl9%nCz={zNt`xTf&4<)PJv^rIyr
z!;|jW%V_D!>Nspj(9E<-sJyMha({G&6VIBlBmC7o4Y-ueuPfhRM}Ky;bIIUF%H2D2
zMXXj}6pIs;9s#~>&2jkiKSq6bY+8o2TiXxi4dGn6jAyz0YFWhd)`iFV&npFtOQ?LJ
zq8UT0J$K-Yg5fQ3VgT}{EBU9th-dV<BWPGt^UfG9(~rA_eJiTD<;XDJ=AOIcHo?6h
zxLSbYJBN$qmiw^-&t74`>=ojT{`(cu+3^7bUcvD-1W$;Of*L_gp)hxe!D)~0HlkVf
zz@03>6r|ttJAQ7F7AI!eX=UO{?7Ph2QZ(5{2+-3;Q7=Vr9?D<#9{9Pm4G5#%m`+$!
zd@_TgrWzm#SO1B-*jg}E>Vhr6ed3&<0EXqBlb6$vhMs6?IWjbT4`2qX(+TJpXmuPP
z&c8OZ`S@^drHI($HZo`4Idx}3c7Z4*@IIN+U`q6)(1!3I2^sIfgO44P`I~uwJ5Mk<
zhm%<lk*H6bu)`CW%+s^sDu8yht-#952Jt=NVDoN>9cFfm`X1|uYNQ52_^A)5is97Z
zc(3g@NXy`^96{pSy~Ya7A-As+Oqo~|r8gjM>2ha32O%LCa^SX^UT_#w?gkN{3*_32
z|5Dw#21Qv$aoqHSG^u4fO<qz{UnFo8Fhp~WODRFcMFkXX3&#s4uD4J`g=NAlEkTwM
z4MHs$L=@b#0Kvc;5-~M`+y+oVNx;=D5*3hT`<>Ns{MPiTiD8Ce26p$|ectCe=YKnv
zFv`m@PR0p6rlN<d@nxO9+V|w_ON?g?J12V$yjYn#H0OJly69-XjNJa0HgcxyRwSEN
zvQ;_bX@VozavVrh+pF0ak`(zex@ig4YmZn7qqNH{ItC$S4|7Aa;E*yOZ_7FP*LMcH
zXS>x`nJ;Y}hv4#<WcwLz!zaqd^%!R71ka*qqQN;x9W;0FFsG(%f7oiP^8-#7ThGJG
zuZR|mgk7t74y<fVjT<%G(PPI})o9j<<rCz!qN(6X(dK>8TC|NHv*hCjo7M#*ZWq}t
zTJ+2c^;6_`=#c+LH)Ho1hp8v}c)kJco{bb9`Hat5(J-I{%MA@lo;lh-|GfO>=g|1?
z;V&DE?ScUkBBvpf<=6UFi8q980{an7ry&QGA12W%$gJ!yCl`2w8nbBT(o0G<tfc>e
zsb}sZgVq5p3*X0U8(MO4)F*MCA-vplZhd)qxr|Q02U#q15T#l=!4r}<$kDnaz*+Fy
z#NQrW(hFO-x8=D&%;!nL%;ACEsHW{c*rv>*04$b_Li9V90`M%Hh|}_~NfXM+;U~8)
zt&H%~gvaGNEGHztV^hkHS~QFNBM??fqe*%uadRWOiTzz2X{w^(m6Qv6NFe~`g&t`D
z;js(OjuIi1p@cs*>dXwG%r4V1MJAI<)mzWaSDAG~boBf>sK>7@AcP)^z5kZ&frLEl
z{TgPJOz}OrYn)u1wSDW?ukJbpTs84CijPE?q&D8ibDF4tU#&nEey@h{Ty3<al{nfZ
zOp6%*lnikgP@05y{n@Nfr};Fp)7h-^UC0#5=z{i)RK<rnMG{U34{=N=9nWM3Hf{Xw
z?esc(6n3wb^nA^OhRd})Qh(Jg$GQOf`Ar?RiMR$zQ6r^PvckI_y?%Os$2pqGdxcLI
zN(Zle5Hn=}hG;B&eC7a@ixcEI$I}7&P6YW43l80k!oH|fPpCp5kWF86lp2YOmf8ud
zOcdvCT}=a)g@wJRmterP+XwMV9=G|clG4)d_`k*k_x!V3COx+6%&FIUGlEJXTMBN(
zs}&zqfH6mZv3UtnmkHb6derWh+j^_#Y8xR|4JeV!Xqf92FCOz?wiOPI)v|IUkT1Tg
z7qMg0%u@Wn$qFBO)GeszL(4=gDzep_q8>>N5=S-y`aWGz%LYy|66v&=zIR(%TOS(V
zz)FZWVWPc#nZ<GJSWn@E^WXVuSo5_!nwbw4%SW4QMOew_&$XYI(T0|wilk)0t0aD)
zZv)+|_!ex<;RW&Aky@YE9bWqha0ujn&A?IHdV70gr*i|Jd@wldx*&dr0g<B0I}<FX
z3C1zn`dIF|TT})Us`QTHz7ofnil#mZ|D5yJsYxyoc6kZ4M(ufT{xkMxFDs&w%Nx(>
zRjf$3{aiVlnI2*394YbQf&ttKPsz}&Zbhdh&Ku!5Sz_ak*G^=)g(L$PVTp)~KWSQ*
zdIC<$tCnz?3d?3f8v43Y?jGke_N*43=nEI??igAKigwk~Lu^ZuA_X$sk3Jp6Y~Eng
zd@_#YX36sX(4QHW9CZQ6(Fi{9sNBT<;8}uVzgedV46JI~2(mb2-|`!qSa0`=bGCdw
zji*>U1+tf)4aBd{#=4xSyxDO<rB4?lvPjj{w7paFKf2PLq*AFyPSTN?A(T#lGdy*B
zUo8P2!Ep@)sUvIoscij=DwRYud+Yb>`l2Lv$+JXcs<U3V>K#|8r1MR$SMMqAzBy9Y
zzlTVlU|^)Uhd4LRS~lZzNB*85ozW53;rOQ)n~@GkbkD+0L-BT6g3>XiI?Dr{kwP~J
zu3R|+Bn~5YiCZgYKFHFxW!?JQdKY5DeA8wGvF(5u?$Mp9H3oqVxw5YSt_@7{kU<IK
zhFVpcKkPtEan3SBt!}Uwigu{%TniG|Q^9VOYQJb_9*My{aGsRa(1=oT<It?2wvXG1
z`&o?eNrCEtoyT?NaLR%*r$I1{NAZb?!AJaPI71JYo!H#j_ANA3w~ndUMHt-=vod(l
z^G+n}NW`tcL8-33<oG}D5W|ZT{RC*8N9x+bD+MH^D`w-^7<@tj9O$WyYe+&6&}^jU
z03rx66}U4pDK5T)W4IRU`3TJc(osYpQr7OV6+P8KvSD8hT`hBknwGw8UBTL$@oJMz
zR}{OHU85ad9Z|m!6!0B^34t)>9EFeZTuDg<nsZk=xHQ3Ls$?<*Ptek`l=nssGzoO?
zMD4=-4cEfc<&z8Fm$nLp%Tz|>rA6KRT0j$|l-PL0H+O4?-Tq4y8ZN}#?yj;fbOrn?
uI6saTX#Kh{+VsCv#r`u_^ps4v#4@0%Zp|Ub?&1D&(LS^NX63vU`sv@Obrebf

diff --git a/articles/AMR_files/figure-html/plot 5-1.png b/articles/AMR_files/figure-html/plot 5-1.png
index b65ce48324a0d31d73e1cff3f4070af8050c3e08..ebf9ab80fa723dfc459c736e92f58038bc396a11 100644
GIT binary patch
literal 49897
zcmeFZcUY6%*CrZ3#R7sAq>2PVr1uVr0--Ctt3Z%mLhsm6X%Ymaiv$p9(tA-9qy-2d
zy@?P*3q3#x%;p7uGuO=d%9%6&oO8bC(gc#H?6US+_kFLmd8MtXOnsW+Gz0>nhN;}S
z4}lzi4}nlLo;(3QX?iyP5xkvvq^5iayn|y~u_g$-opMnzLO>wQp$GrTqGndCA&{#O
z*qvLt-pLDNe*UH&b@+XPSEo9sZkvX00_^Smd*7V2PyW*9D}VoBIPb=BC04GFjAK`^
z4l^!2i??L&h_!TX``kL$^`-jOn|!B}`bb-bk3UajUQ<0bcCEVVWw*NV>5Im<vDI=7
z@<LzUXktv^f*L!%ax6XqM)m>n^CkLO1-};)@yLG~`Twz(jcE1jXIib?Ea^q<%9tPm
zB$t4pI@z+qqJ>kk_*1g#{Z^6bMMbVYLJ2$~qN2Q~a(E$)Tpu=^AP@mzUen6u;M?iT
z?lU82iiZ}w=7+VtAHl`?=W&n!xfz_QfL}DHNZHf^+4|cIdA1c^;+$&r3g<mGa=~}}
zg73RetxQj2E}qG_0%;t~BHpEhd=BNl9LCx`5!l*r8>4~DbE19@P3o+cO;_Tbhcu>!
z&$s8AWW;&ANRswisz++ZuH%dcng`)u@n?jm=phfBfA!apK^nt<cPyMirizrbb1)Y_
z`0|8(ZtNoD!SM8skm8+;o(s^{wc1Eu0yzZo^)7@O^06u_D~p{&RJ9tjR;!2n?K3Xl
zUvU%C=-#qnJ?nxFxPvLzND`AZ#+ySR3YG7kP(ZX1w`fhFuqx96QWZ9JBvt)-4b<$j
z%hXr3RPSG}Mt>~Q@te!okCZ#|s~X32!n;(V=UEYGv#Yq1!o)JH&pu{gfFKOBb8zsF
zL3Je=<)V@iOEtuWlCIb&xt+)L#{3!@8WA&Y+vl;Ss`q11Ey;+A=IIrC)}Y-Lx)<eY
z(DVDfl7zc3I-8OqXX-*hucgrd%c8+Fs3_>2xZL*dn2{GhIbvF$peC$JhNhL5q!ZN9
zaqBclbe#KJR4ppaJCzUtx-=%9%5dRcKKdoX4=$7-V}g-~`R1Zppk_&(aObxd)vYHW
z%XX7HLVNb@hN9O${Tzs&ot-rd3fd=f1Q_XMX(jv+)JJsRqFfy4?smyS(WppiJ`Ezs
zO(xf*7Z&0vk*bO#^?MtI@}G6N9R*9$)(HguLjA&`qLmc5X9Z#@jkvQ@5RLL#o9&EJ
z%FvR;){la5sp4+=eEWV4DyC#;LAGqcC~<hZ@H?KbOeU^%!7tC!@biN&kGj?h-nb{C
zn9)_9*wKb}os|i`V0tIdK87f0=<Rt+Xklp5{FbT~W4{}yt*&nS_scJEKSFM*U60wH
zN;cuhF~T@tN6!nJO!W1YLKD>M+dK9g;8;~D?fYLl_=0em@nVmczX-AW`8d-aOG-w>
zt4OKee~S7J_q67VA4yifPQyA}-q5hWud-8xng^!c)70i&WVq0RWRUGL5tCt}pQ{;G
zbUFX(+c~6|TD2x-1v#rCWr!KNMZ5c{sHn)+OZ57~zEEY@@WEifVH5G|ny?ZM<{LQa
zR;yKM(Lr&@v)A+O&kA|<?A7mU6!McuB<F$2i4PT#rsA(GNAD^sHq{}W`W`@h9%MGd
z;GT$U(Suw!gi0M}YA0~XCUKyJESbNpk&iO(<B0506CKz3PkdZ$lon<jF#Uc3=d-mZ
zS~c&-8|amTE9#r8OHqNM*JbcDNDOi_rtdL)pZ(;Meaz%G8KjSlOa$`&r@jg@A;9tR
zEwLn0Cdv_A)rE&I+(4ccE~x<a_4r!!yP0HSF!cDXWks00D>?vW%7+IgBnvByUw?!0
zx_<tZ<qJCF(#Jh}$m*LDfyIVAXy6G_u|9>d>l)NAj?ZsYXi*&h{%FM=rlmG#ROvEB
zhU^<0+>(UhUD0Ocw!K*vQP^X^`z%f;k9_<hpIQ*{{ZXrf7$dmLy{@&yjs>@_HS<O3
zNG-t<OPMY|d$FVl>yn|533X-|PZ+i{N!)oaca#RA-e(>VFr-+>i}_u^Z==Qy8b3i}
zVc2A8%8z@No~8jVvs<?DjX^9`-nr(w`Eqf;p)E*WqPY9R-LOsZ5@BfmM6q>y@AM$S
zN~09ZCR$ZLutgG0x6J9Ur{gpA4n{1A!Gua^{8GiGlRACBKN5ThXXIXMrT`J+-4Za9
zrdF$B3-%>?dk2SFkpy+)Yta%`zu$ciqw7+=9s?%L@ej?~IZ<)_pB7?nV^5Bs88<T`
z<V>|+c=c^NcE&MdeJBN-^X18LJd5Dc%eG1jQ8BS{eRHo!nbe0KJkctn;vbqNV_hdc
ziNR%n`}N<da<sFPdi?ucb91wn?$0!-RCBWT=9`=I?RN@!`|bMNwl5a&n+qi{ef0El
z$}dQ>e5t!h>gASo1na~^<QFzt1-d5)nrUe{eWNM93yfl5=0L78ZgE{Ti1S6ynr;EU
zY-`LK`H4j>e|$u7Uko(#uHFmph=_<NJ@f#wwTuSxj%O+O1f)-0N2iyOqs|DU#~_$(
zS!KFsJ~Ja9dGA*}CvceC-0eYpuvF}rpxa#;mi@xE_HWBG=&DTQ#baMIG8X1@V-pLv
zB;dgBG{sV~*EMwrA$MfDh>bs{uY@5A?jqY}Rt;LDXZ2hrvNV!X8LK{<@)1;#37vi$
zCgi~@Q#-{i<|e39<`#*GiI>$9nU8&Gh+@*owRCWDiqyN^xnw0&#gU+XncG5IRO0UU
z5hbcqA`&U>wRf)d?6$>?VmYYSH#Wv(V^eSc@lnwLUG@*sSU0pQ!>0!&^*k=EYnxNJ
z|87bO@_<U_87)Lx7(@nbA0J1dgj)DFJ1t}~f=<m&XM?g^m-IL{AvPKD`pz{9_JPIG
zD&dz(8JGgEHyj)}+FT2(%0`*+G=wI)%1kbi;nf|XOicTtSsk$blzzMLbl5>Jf1Zjw
zqtlO=_c5<pbu&fik}Jr%1VoqG9Cq)?RS6yB=9t%nh29mh8B3Xk&rQF8<7hyjndO?{
z)a-N@!@ehhFwq}N=m~||?(J@eiAs2UqZ4g=x@PyVSk|C#ZpS=^FaV9p)W<JcWonk~
zm%xC>`YstbMi%UQ=v{ftYrW%8=}|ZQ82km$%Z2Hb=nl>6F>2<|<HXeN%dp6Q9#-vI
zyR34b_89xV%bIE?Cb4Z^4rZZsJ*F^ivYrUNaXD;pHQEr;_{}B2XGp!WTmnX?I_JIK
zqr9f~Q)`>v!WWlG1Ctu<sOQ9Ol)yWOvxR7i+TkUdn3Dcx$FXXUl7tPFoyjkhtTM_-
zU<LTte(?pjt7z)6D)WsjKI~}VLxQQLs85i_s-fM+__%3K$v)RS=MIqx@?PoWdoqaV
z%I;cMgTd2VjoB~ue$a1o*g9FSb-fsiLO4Bz`lNbkmTka2?^%;>E;WKxxzCL=Lsvz`
zsb<1q)-c&nW=}(Za$<eSYVk1BGf42M$(_~Jz-&^PH5kq_9GyC`3I?A#1*Nd#Y_MpR
z-@hEhh<z8dWTJJf6JrMImONmvLT1Z_c<2q5s6x|9mxoUgOa3qd2;<qG9OT>wc3b=H
z5{6nVB2w+a92B-PsZkT^ZXsWP|JBBwD~la{u9E|nlnnVnoO?G!2hB0$(NFTyLcZ;U
zr$v|E!`xWIcF}mCr&OSBxv0jD0?+|glY_@Vnf;f)Ah_8YPs6nM4L?t&rVGBh^D6);
zH=SIQ31anZc3Pls7-A^^d6t=;e#<Lj%`y+}S%X?cSB1s2{_Y21X|l~wJZNv%2VD0-
z@vJ1YC`kwcaSga4bfJJZTIE|jaQX%wax=zE840A!)d@9hSK2-UZiaHRF4vTA-z~3b
z$ied|DjsTfkL#ko-7<bAD5rcej@RTwG9pS%N-A0<d1Q2yDFy|c)xh<bR(p;VLr2qm
zzk*ofx`yirp0Z(p7|Pq)ok27a4h_5x5E3QF`B!*V276;#h{DT*snz+}*Vo5|V@9$J
zR(s6P7L|LT1I&;gdLH*nx7f|Ja;A-5$A!05?CWz~^t>JeOA@vJ=}A4#W&mPe9XRgV
zF4HF?QjmN8jbxOy{sZ?dEGACCEZos4{ey$vUw)8u0hY5^mBHVENy7GdZ0&^-sF1Bz
zw6E@zOJM8Gxnr-@+@OH?$rkVj2`d)x?rUK9|3M-B`agfsKfPLc>wZrMU(}sz{NULD
z8=9D7l!S@V+`Y2lAFSXO!X<Z+PH%NnW&JkIG8`S?bgD?ik{?*W&#i=P;hW*85|J(K
zCf9^VU847L2?JJ<nfk;6neY`whT4(s&Ga-F?XfN?tY-ZB%&X)J5QRlr1qw)3M+Z8$
zcDHS*ocH2sFa2iIf;WYVLk{Il9hX%BEQw+8C*NU*d&XjY&WJe<ulw09he+O{U01o!
zbJ=94Tao6HQePFO!?$B$AB38E4;kWksckR*%=51@u(Y`KVAKbcBe0T$l)ixhx*gUS
zh`^X@fKMjQ-!VbldEB2GP3=!I9MRaI2fx+oVHic|uR6!*z9}8=K+oav4Z2Tt(!$#$
z2`O#uC$OX9$_cr~2bMfwyR3=bh$XEj4T(}yQ!A@fJLi%5im}Ey---6cE%$Q?o&bl0
zAeJ7(QW&U8)1uc??&qvu$BuTj=p2Mva5Rd<07#>8Jx2f32S5kRpGgDj07tsp0SJ#i
zFm|;%zkfbD`1t?5N3dxfBSc%8b9A$AV4#NbffizfS-k4Sqc3w~l)*)@7kNAE@k6F~
zxv%bD6Y;Dc<J%D6i_e^r4HX9YQG}m0)R5`rul66bSbcT03dRE<?*b2)odWgUF^l;z
z^3U0?=+^GWJla^IXdL{&y{RfyJ|6I__0p4e-Z{QpFV@CXE2q7o8jZ~*Ujo?!!e&5@
zcQK0k;OzD310HsGG<MtpB<t`vc^u%$!{h43W8FtT25TP}>)~<x)_<M+ACE?luedgU
zJ)U(gxu&}#PR$r<TZ334gW0QQ-iI#^mj&e`!j8sMAb&B|OCkGy&KgwK2@Y26Wf(Pb
zHem?3lui}s>bEO4$<g$3S^4>$YXJ?D#Jv~3>oVtha}16y7alODQ>Z8}7fT$XMq0t?
zKp^z#?CgvNL{J5=5VBje$yRXjNFf>h?ZmTLI)z7<!Qa<8oj%*OHf{-bLkIB2w3_n6
z#>U1Jb1mMihqciio%P`KW;JBEW3#Y!x6`|&w7fhOGcw|5Ff%e8Fu_lik3f(04GsAO
zN~NU6w$GTmqw^(a-&vxo_xFhM*KpJ5DobdYwNS!7J1jfZch3IT%2o?BKpPW{ELYnB
z+<b3%xXhlFsR}>kF}>m~72h6UiaCxv8^2D89TiPUNvTjRUfGmds05s?=3B>oWFk~{
zHn~QWCCXxCe0=ZgV{8+FGz8zIL0184#&Lrm$C-)^$jHc;u0Mx$`bvDKLVD$prkXjM
zG^B&9Pj0o6Gsr}rhAkpB(N;nPDqd$>Tie&)AJG}io{|41NeEAHT$aJ_L`FvXg`#B-
z2O!vBcs^^=1-${#YCto1+VqtRukIwHetf>G*lY6b$yM*~Zo<-&6%C}td~_9K3IdOr
z+{N638J9Z@CH-3QZa_J!_=ci}ke5}Y#LvHyd83-i$DH~6T>|r)F6;=089(k%+u(G^
zLTHZeoHBCsj;}`MeY^+SQK&=>%B+t=6eXA?w9kkE<7mniE%x7DWrsR?qRlvQ-Y^7|
znFqIU^<-c_27?ia4NynzS(O(ihrRrSh+X^rqTk|TgwnpT<clT_(RW5MD8$pNgM-7v
zzJpw4M_sEMH}|4+x3R(U>*VyA!94+6&(XCjL#4eNq5xi<rlO*vLBdU4S-C~47w0Y#
z-yYks5O4o8Pcksvj7PLYIR(+;Fi}sE_w)5tMfPDZ=Rx3P<VZ;1<&SSCxO;iwCH8mM
zG7gBb8+yzEZXrop@5NV6M@q9WW;$8W9&16T$`WtE80hIGgwDTme>bE$LFi3TpDkZm
zPt?pNbj`SpA%7<mtz=jla_Z^=CQExYrComJ895^5Y~l0qJVwPXANSiT1Kg@cm8f6T
z4m3Y1%8inm`^ZZpyZ&aR3V|i>T9ac6`~IYL$@1C;X9>wl0?QZoJ7LHM0QHKRni?-u
zuPuLCS694yISS*Xs@vb&yJjvdZN*)uxAxpI$<FNjyUh-t)RG${EwW&PTdrvVG~=Hi
zHnrHB4Xo9Udo>WpXw<DbUQjb$R<X<X;1{p2-1yOOajlH+jJmy3+R&>p=i?8wK5yf-
z%<8AU=8kJm7k|%Giyt?gPK0a<_?ba?TO+}8u?D#2W(p!ryrcx0!U0x=W<7r52JV;o
z^|kD&r<eK79l!~0=ta-!=l63;OKWO~mtYT|=OxWBU%=At>+j!KW$Mk<&*xV4{Sz*X
zUJm>Is9cvjg&h~xKPQEabNBF&m2&8LT94<CUav&m<QNl6HDBA_rG(y1Mp!H?EYvzZ
z-Zt|Z+ek)fZP)KB!SG4KHs}@P5{uZ9I*d|vrvXgKHJEXo??(5W9Bg0e$kNiNu%=I;
zVAb1$kz7OejgOnhy^US(Z~ZA+vd?GxU6)89a29>{9T(-`+9kpmo)$Kd<1u#iWc^j!
z+hnB)bzdY6*322$OF-nQMf!CHI)Cz_fi&A-YqNUf%4BWPRg1`z??bLQ6(&7VL=F!R
zKLO*BfkvY<K-=q)d*}{y^g05Dk6HhWCkDy4u11AZrE#=M669d3GR`S@qV!FR?KXpS
zLmoLjqy3-v=H!NkhPMCwcn*5Q&p`E_1}y$pQQ2Rvo{ZDq^w25WNX1eg&&4y<k9HVy
zPkS-eYQkAB<j5y1ass7gp5q2X#CWB{P;mvXQ89`wFW@M03U`isQ?B34GpTS=z$nHR
z;)(V9fv^hIoe<RB7_RLfT<xKD5mz204n3BSk&h@gMY6<|a<N3=GrmwRN3=@DzeV}W
z?VFZuWZuu|T9bh#%0JSJf!@6qozJ#tIl94FMSAAp;jvL*SoFx1_E=iPoofxL!Zw;6
z1QKToV!{JG*4@*y(q}by;+9L!3A}M(**L8od2iyGC&<ciGQZlb@_ZV~TP~XU^I&Q0
zy^3^2llV?PnKdS?x;(`0zj1fUpC|84kli;eL?!O~QE2;7lD0$6ctS6aTeK&pVg(IY
zKGRLote#6uN^-_8F2<r1118c^TCL0zAG})SY8O`nAXKfuyw3M)&`Fr@H2wTI6t?Xj
zh?TH+jDuoFLy+ZBrdM9wx%@668KD(r*v|<AV%Jre^zFfxJ9ApzL=`AdzhDeA0nTuu
zB-Ybt9P~yR#u~RC9edM&MVIGt)LZvg31(BoJ*4_|1cC3V=k!}0%^a~V!rPS3J8=SS
z<h{v)WRwEbcT~T}Yf8?%o%mTp12e96$Auy7#@k=zIuG5SPi%Cz$^6RKHA(i_W_Z7g
zM#inJ!L&lzG?z@B-*4t3lVufQ+>eId7btA66$?j(ht+pRk{d5r!@X4R7a7VpBlDT7
z!Ni-f7*{;{{(8VxAUV+eapbSD4Ldj^^v3yDx^?Y8I`}@-?&_6UzPxjdZc*stiG#?~
z>L4#5AOLg*w*%W+T>7HCInBDiH3S&VIusD3o9$ULT}MbuKxfgz>mY^~0G`u{tOjv%
zdU0`)0-2YY$-xr#ef%1(xh<v2OOJ(C;#)Ezsbk@fb$cAIVU6eF?5l+VNv~W~A*}jS
zS(zxqu`d+^1!gsOk(s?XBcQ;`e3SIbLaASm@%d;R0nkLrrJMR&%*@O*YM9lT_6eqA
zw`{C8E=@1?cQ^qmriRh0$2*S~S+)>>S9b&9ATq(M;7jPVY#q?({gjxR7LoNgaE~Sk
zXOQ)PSf$3Iz*M1m<J)bNGkQk!3(d#m6AmMhMTOH?FrEmA<1m4Q0J>99o^jcyO1GJ|
z0f10$b}W30#*Pw5_Q?n@;6#Yk)zztmyS1A*JbwSdjH#Oq$6=Hbcx{Gdd^dGJr_TrO
z+2kZP!h`z(JVD3u^LqR~Kg@!u{NtbGnw*l7wOw~9*_-k0GotiYvp-!jtM}hN2Lvll
zTQoh<3B-H;Ae6P?7z<32S`O-pDgy{9(8Oe<9l;sVZ*NUH4f6`eDE3HtIe>VGS1wS=
zysw-v6u@B3HxHOgb$Y14Uw;rVA+Luq(-T`9n2hm~XFT==W@cvA@X;YC4K=MKc>?BZ
zb3JCepMKs#k2A0SxdIb?`gG_39mn^7;J#!I;r(Vr%g>r9ft1RfIQ}GeqdFpF6PiW@
zE|_(I5brfmLj$RylSq*vlt(ZgT!XU#9*~_SYGNVTuO^{P5*x^d^MQq&z+PB-v;GA{
z1Q)9<9+<S}!WKX%N|+#3Zq?VvZ_!@C*Vj~6vp79|{0^U7qk!q+pjP2l*~vf^s!8$1
zw8{vyEi;3QmQxV%dgc7mSeCczQ_)jv-oBkyLd-L6KIsVnWVE(PdIS5>OV>8k=Jk(5
z0rxv0jh%(<l%goP%PJ(XPBc#@QA<lp^*IawgxG-VF-DxYJFxki*m?`hUv6k657vDh
zeDfU4%Wk>ue%~YoTLZUkPwGXvBTC_yShsZc4-PKlrB9()Nx?99$xDHvmjb-_aRPBN
ziw3+182ODc|BH?oQ2t-DlsofdkmjEt7SSR+j&A<$1-Ro8oTWVtgqg!bf$KnzI6Ts?
z0tNK&c=iPH|NE0){shdh%&M!uhT?&iWfl@)iVv1ef${g%gnm9=9H0EHjDbFDVN`Pk
zPyX)mwGh>frN0AtG>HFOZ^54{eZ?W~Jv9vj+D+!iC<IdccjCQ9U|OVC_H3UixnR=R
zao?_>ez10!CsvwJH{)l4yN5`u4s~v3rg<()(_Tl>-(gJ}7+-q?7L@?$z!riNeLs&=
zLwaG;n>I_Q$feT(gMM@Eej&<oeoXmYtsCx9%mGb?(00)UvL5r)ts^$B|1w>%f*r2C
zmRzsDtT#B})qh#We`1|jWK@Hwc32DWA1$!gkLwr*84n^=mFd!5>}VozFv4HQ)9}RO
z!o<!*QTs?5KZ_${60kC+?Ed%pwB@UWp<ItWOLUbjspM!u3y{=DZb%%24<w6tiI$pg
z80rLUtz$t%m<WfS_xS}7vkH~t#Lu6z+w~U90YQp&zDHLE*R_^Xy*Dv2VeDKu0g^^%
z)G{%(2c#N-K%mmor-HAZ)D$XV%%zIZtN&~8Y)NswLLha^A`CKA_5f=7!seEiSd2+e
zP!_uXI?F?dm*TPTNW<!?`1DYHS=q+h@$vB*?g<t=G4SEz6vR>%7>BjcdH_*}_|Lye
z#`%m>b}UFxr)Ge+7jzc>E0nUbvYAt5q4L@SEK%8rs?in>pqjbBf?UvMy?~JCLK!n<
zvBbS0<Q@!T!XbMdHw;?N2++-wsero~JvLCsA67!ydN+R)_f8=L;qWwLAmndMOYlde
zV$B}GZ@@H=EP&iIM70z;Iy(nFEOo8NcZrpN`={{YxL`go`4t&FBft>^4UYqy9K8rQ
zq`$DADkfeyp#%G=&xkF@4ATlmAT@o4mzP%q<X(TZnNEjFU{eK4sKG-fdU{+7mG*}~
zivQs7E3=h69*QV1Kz1w$gJ+G8Z34*}_L|B>MC6h(&&elK?Uy2y05u+a49|v1X=9jR
zoFM)HIE-#~mm<Ah44q3DBHQ+_H^XcoBjZF&Fs|^;CSC3gc344M<q)7$JUAk9+~^7d
zg~E>p?UN?1)Iap{B6ted+{NVN=jTUcFxe%c2IYtgYk14#v#|TzvA%@}r<eq@Yd8UF
zbb8%jM?uGoTN3E!RDUzCMg8tcSW*gN048Pr6pMWTv!T4ToEsMxmvs~h>rMnM^#{Jd
zOm=ZyKK7+uK6j=)p8vH{Mo>Oe(-e}SBM*kDx<%{Y;OHpp@YeWn5E{*$jmOEx08wez
z<@PY(fR0D2X6mc$yh@Pm?(SZ8@$(~wHC5pM{MoXVN{O6xt3oD_+`u|%iMkh9hgjP6
zmf5S>+}sRtms-4Z`neVDF$2||$8u@%CilyNy>b#Zj^A=wSQsi{eTxbS31MfCRtZhW
zUk{K6klu+=aP}gMMiM(-9YEZaN2f>+tZwi2z{|%ay?|_9U0YIuX#c!e80VgfSh@{M
zV>RcBRpsbhxCk4Fl-Y~M;MVs*sf7v1rKN?pOpBLQRL~@Q4%<?q6Gd}1Fr1wZBqWF0
z<+Bab(kYtw7@%m_3H+`TyBuZ~(05PS(dSQk=qPw59auV3hCG+mJ&(9olcZM>w5vl-
zhcCVXh*Nd!DVlzOVbr5aIAT3@K768P6iY7nqFtX=|JhL|UyWSY_%l8*Vz|6)+!69d
zH(AzZ53qB-Tn{tsXfuae+^BT4;QT<LGeFr3fCbgjrTGxqznkW_lQ%y5&fZf60$Fd@
z>;;pF2Ef-7?TvanPIIRDua=eNoqEtE3>GcSvqP|jXMS<f43|u-O-SLu@xa<<+*Skj
zH9@I|P=Y))wPCGy(Z=Rxo22-x`zc`mI!z7E&d&U5zT7FAK{D~}brT(OpXSI)lM!kF
z^Rh6P|G70mh*$A7#l#|&PIPl$Jk2@gF<{n3ZQ>qeZ$B?DjonL2sMEt#r(Xivi_bR#
zi3kmlO3}0!0utD&?lt)4DZqH~H!YddpfhXRG%Bw_rehHJfU)jXRn|1nv<T$5xD7ga
znqy4XVKjlE_U7(Bs3i%5+HTDCE9MfRq<P&<!RVcsp+KQz1T|8ZFTQYbX$h^y-@sXn
zte1L@VT~<)^od0Fj$y=RLJ>sf;9K7ZI&6{aGsXj=CHyLh+K|O$6M7~7HFm*s3KWAE
zzGal0|7R%C6xe_dKSgD`M)*MRd^eQ^_!hG7=g-SlLJ6!Mo}MfzU06cwx)<Ct5|yUr
zn}!MvnAj)2`Y8*9BTJx&F-;I^0wyMY5d*&vNVh>@N6wcULgQj>-JH_W(yYu?0x_+-
zcb}52PXQ0JaxNFocur1jXXGL~y2?jy6H$daAVBKWyeiqfCC~t4Of``9vOT@RZ_)C^
zLc7+SK9!HJ)uun#NNI{sGE%eNb$U9`dWd65R>y9+)Iou#WQ^xE*(?XPBl4__|8SK)
z$Vw>iQfT-J5?Cjovbx(l^>`va-aQw^s$wiME_KkBy}i8&TbF`~p76^Gd$w<9kn5jO
zcUQdqS*hi&I{)a+aIlDj3XfQi{{db(6*&Lw6odh++6)P#tYTG*G}fvBQFImRU_Q;h
zI>WeQRRdD7FwE|`+{6RQxv;r57c<&YIj`kLJT*TzH)mG#==-m$C*>Gl-3gQhQ~eH4
z)RMy1&nMSBlnL4AlDo$j&!1yk@|5LF`gH-zDo3pee%v5P2q@TjFX80hw$CI6NdZ~H
zIq45Q*3`MmG*B_$Z`*7tvrBcS#dzR&@GAzc-+NG8C&(N0w%1Yyt)wf{F|Fnr{|x{B
z$TCosxv@ilo%~Pq|DqExbX6MiY_bVUk4h3iZWL?bJ@q^vgf1W0byz)KKBI@?k9g$0
z9S!8$F05DNKG!FEd;2rSCMNRuWRo%=b~b>{?L0;0LS++Mt(I7A5;}n(D6NgzjK0Yp
z{ev6?Stl!n@qbrVsF*LgCM_kk$AxP`$`u3l`4psTqmg`qB{%&iK+T)g(ztcE6OUQ7
z``VqB@Rn1#&ZjbX1T1|r&!;`7+@v+HdK=W`H@@awM|^_<;@zys)~WJDDc22N{=Vw#
z5Ot-YCtsH;$jx!0T5_0%?0-~+UeD8!MWcTBI!dZKP3ZZ%FgtsDVVpB!N8b{Te*+Ys
zAXlKN>VR>O)ccCn<&J&3J}l_z_XiZNoV`-z^8NLt!rliGq(9wEUv)s$PCcq|t+tEj
zpMVqAxge}qw-lM+_yz?s$Y`#4exN+`znlS?L$lKM_Y6no+t}EsFf-HI|I;s(urju|
z+9JQVQUcru4hB7MqaUZ?lWP0dmJ{RtL*-$t%qt&ocf%`ibd?6&a~(AFa-LaD`7rax
zplFm)3Ea$gE+;$C7Nq_uaPGeP>Dm%vC!eq`6cqlOf`g9y>Mh_LGeLm%5U%;QR(ltD
z0+in*u0)ELm;h}H=^l$GCV4Fku8>kwQ%$*J6D{HK_RCY=hq>{oX;5#cwv&Yl18E4A
z9gsx#(MReRm`YZd@})6HC^?(d`FiYI9l5oGb^&wa!#$oLbZLOnydt~)Pruq?Z(5je
z#}){bTu@HEIYB6NMUUz6#p8j^`I;ZdCjmP|qP(GGyuazm!@;8eURp~ryjDxmxu6E$
ztTE-Q0QrYTLO_^OhNLhtKcP+oGux(k$^s6bi2e;!yub-UC-7U`vBsjM^}5_+S97><
zs~|>yz!T%zXI4S<cSn;{VsEf2^|0d_KrZ1UMr+b%oRx$5x~(Guu@=;~%5;#n_VKAj
zEWLhsi|7Q@K*tz&rJ0eS1Cdq}+`j{4Qu0tA5KDFJ_Q0#hCnGpj+h993Fq6>8vVV&D
z3h|(jMQG?KKT!F3bppRZAkg_wQ~+fI<gB4)W;5<}?H$#!U4Bk~0zk{mk?YQ2q1Cw8
zVwn$nJ~*P@4w3p_MF@&tXJM>7v8rbP=o4;gSsW~yR*TKy-qU${3jr`&JVhH22l7*~
zbzeH1-j-_IzlHII*#O6H{;92rOc}^bk<9goB_UYQTU}4IELfw5vW$s#$%U&qtIIVO
z2L&tp6|W+@{s&b8MxENAoQyC45a&2Z5;YWx6#)5%0GvKjju%Hpo!SEG-$tHZmB<0g
zv@`di7?;*+^%Ev_@2UL26I38<Ktx1@i49jDO?s=ENk|-0hK}WYAsF3zN|U_%33WF?
zJu8dm!C}`SaHLsU<%?4=5JcRnOwI3u$_JErK8X(q!4J{NlED1Tug6T;&b@&GQSx%u
zF|zBE^Im&@QWYy4W&fd@$$fs>#O8kpr>1E@d%Q+<9$=x3MMIoH`_F3zSoH960(HkU
zIlJl{r(YbTX?GIh5DNH3V`JlAJ&)H<z)D2CIr)xSv*3F2eRecJhi?-|PJ<u%!6MS~
zus<?TNV5PhHu%Mfj)m$MFJ2%#8*Ut3)ko>2pmopB&+k?t#&kF~bW_k-{P3$_+mNHo
zJNW&-bddjqDs(_vA=)}oAbtKos?}%7XnKPBv$6{&%a=ea7UQQcv?orfFqY7JIgs+}
z9Uo0;0GN(u;s4RwL)U|}|DuCh+7`&=g8~Yxw^saU$A;_*knqQu$=Bnk&#fgMD28=f
zkFcJ^P3DSKf_zpM{q|Uh*Sd@aUJRlx50cVAVIe;*dmz~ax4u-VLq_I%u|9ll`1`_j
zGkv@qWFesT(X!S1$&G_pZz6D0!U%I{lPhy$f7|~6e(k^Xvi~FMp)db$Vqkh%na8y5
zJPODl^>{iURm>T$>uKcsGUw{^5{{I$N#}#AkC=GX%v7P0ZIGQMg6u3luXvA1xaJ-v
z3wZL#15X>PCx|u858M<KKC>2}+iR|zqt_mAG;JTRx+gVre{s$mt_JQSCn4Z2CXIoC
zVXwZfj`ZB1qGlK<0tMqod2h%&rmV?#KFN5Zfx%a-A0q=pWJ1A|@Zqh3aS_t*ZQg*A
z1N<HlzaNQOgXV*<nq7DR67YXzj)jzy?`>WJ<a`JC9cM3e{u_5SP>&#gkOq+Hk^1)v
z0vt^Nz5VT06{S|KfSE$>fpYaM;NtaRV*Vos6x)!;vnYW#-u)$F>5A|O2$>rPO2pZT
zaRTW{0ttS=hzC9mtl0fe`pKT3Ph*FZ_sVy=pzhvYoy0l0qy8j>-;jU@17J28NL&PE
z|2Ls#F`!xiGd?aiwd^xgZ0-EIWtxPnX9I<6a5dwrxKRLX6aX!Df16I6UEB7A4X8;~
z3oS_q6CZNN?nI;Fv99JI0rBCn$c}+<&7G&WJf0?q!lcAcK8XNQLukZDvkkHkoj)F<
zAA0`Ofx3at1rd;HO=QP`v{@%&wqxj>W;VOd80BI61Y%q%*Qp-B32Le!gN<KU*d1u?
zdkpfI%GaVJp3XS<RGHRi*VOp6Y-7_{7xDNYAU?r`iETJgMb*OWHM%nRa!Rc;kN@No
zOPj7#(t9X16%`Xm%t>`PWa5c9kXuWFO8(oa@v(6|neXPDkN#bS0n?zBwG7BM+7-<#
z_<Z~m@h8^K{5&MsK=I)Oi4i1|_PkDJ231?NBB?K2`5Z{vQM?xYW4p}hcO0{c)-FQO
zV7?gg*@Qa!&)xxZwAjbQvd(Wi?-OOq@~#}awf8$ltG+l+lx)EV7}bjbRZzS-a^2)A
z5Xbm%lR$_ABxvV*_i7sxh9<?e`AT4sVF$=EA)o=SzrC@e$by0b<xpPVZUTt|$rA6L
ziAsl5-#Vz*Ed#285A4%uehNrk8)OWuv{?zIG3*6oc4Y%iqXj5y#Lk6`L@Nj{jz`!;
zaA+LViQB3YYvCb7`(5KHp{;uTul9q|OvZ5VQ&-7h29&o>3cY@LzVF-@XYD(;?=u@2
zzi>5GB7O1qXpX&C&u8hvRB>*=wfRfPmA;-YkFVxDgS`HB{2~XJMBcSxcLT~Hn2K-y
z8ZpC3vB%x3I@j4FQyM?8)`!Slk|iV~@cARzJ386uSF8n!KSzRL$KNZSJTrGMJ^;~4
z9~{?rQsWp!cVh8)RhO@2xGV7#CmRgelJkuU+WF|jdz%p2uWFXz@>FcQCh6Lb`7Vc9
z<g-OxAVWf{inY0@3Rze_a8OZEL2;YxN<*Y^kfj`zbh9op+_g;Ct~?{*-iqYl1=0x~
zWY{dUkiVHhaVK4>I|jC2>oNaxM9g7OAZPzumf^rD>9r2wByq=Kci-@ph{kB$7gO&m
zD(K!{@m|qgjfcXJ;2&I?qPs*vO|2bY;V`uN6>RNc3@LPXckcjHqVt*W1p`Sgv+~Gz
zu4aj0zu9tE&#FrjA2Tzp>E1g7B+nSnZxLu%Wh%!R-%hey8m;PletT-1_G{1!T0TQi
zq!8dyF#inHhM+RDv$0Ct)4EaCqpK~wqocc7ye8!$1J(NyET0rpk<k7zJ{rgBB2Ze}
zZ-lLBVexaweVAq;yEXZ=3ti1OK#+CiTdpv|-h_>;-SPQ+#<2U3?_;=q1AJ_Cf>(YG
zYcyLm;#6Zjl4*=N+X`9dDFQZ4X-=pfsQa9mInzu(jqOeCPD{9RISB%I@fs-5m`ku)
z2@Nk<^nnBf1ZeH8RrqeqnFNbdI7@=<Km-<02E*hX6uvV3{PsgvFz_(uc_}u4hn>R(
zSr~3_!B0+QM9FRrQZR}+{Gqs}kgo0h=Ekx2QJ{jP)2P2}STUtAPq`d)%Zw|YQcGs#
zTPUL$O>jn}lpW+pEniE)PTaJ!irq_k%c>ra?hbKmI#C?Y<yeMF;1~)#-+9r%)b)E4
z49RqMl)}FnR8U!77!z;ymIOqyam0>mr;0Ph9Ye-$z-LkKAH@9yCmq`2$6~a=5pTNM
z4QjZ=OUueq5(`F_ps#iE=AV6|w>aAkHVW1E=Ne4JpS^INOiD^>6*cXob};==7()`-
zAEQ}}8h-$|ZsX)6ntqz|^z*OJPOQ<enNrD|_ICq5msK8zikYP#dk>^X^Z_@%aq%?v
z;_2s2N+a@M>b<vDXZTeJr_|#40s=uPnQQg-bQ2ETg$~etO;b<=Q1WSY;=l+xA+F@Y
z4C-YC7uJ=7B@wg!lg}<_4mwKUfg;PwY+37hdRz6dUDDLRFuMUUWIe;ibZYdhf1Wm%
zgBMriqoT?(Q$AK|hqG*XKW5xypWjY5<+}I?=z10>r_OTM#h54!1yHys13xS|mYoc3
z<!!t@S$sF_GD%BsIKwi0J_hPj$-ST77zBAnE2oXE*8DL!IeEMhvOERC8E=yhNL$<A
zJ}D2Ww(m4KY|5|^eQ;_fsCFu;0z<?#37=F1F%BEOd7h;m2v1Iqj*cmGw<XSO-B>=>
zO*2aFf6Chk7)>WvjnjCo4}$HPi)W%I*Ry4l$<DCLyxe()MZa2Cnom$tS}<DrpK+0l
zHSeI%y)Gz2Bk|FYXV2|gi}-WUs-2B_Ru$y$N&4yMjqJAL24va!MkTWpjoSG>M^h&q
z=1PG?!s9}vEO1m`k*F&-8k_43%n`a7^Y^rXmHm`gmZ<63cvHG&w7kVm2y`6#-ev19
zD^e0mQ8-TU8!#RF<ExVBvp~lCk1K$}KAoZHKI}3QRV@JduaCiC{GS<<uq-75h5Mf5
z4i4_j;GTCS-Rv{wwVegK@0RAriIA>Mr9)^$s#y}JUJQ=@;Z`}CghLnozxS%kpWbun
z&D#`91GCcF9*;NhCkXL8M(=w$xmCa<*58``klo3Pl_$aujHPgQFNiq~`tV`KJew`n
zbzRnrW{)IlAR-Ey#B6+fX}IcU(5Aw{m$OA|NO-i0n{CGY)y`vU;?DJa&z4t}1{5(m
z*JdIb??n_S%W}Y$^3S22+-0GQGM-l!h#n`WG9wB?;i2Q&fub%R|J(+~U`BRib(jS!
zi1AU6b?HvL=Q#S@<KcgOd@wBkcgI8{q9B+d19)mpXJ>pI?bl%dAd)eEc7VU$qn|DW
zB}v3K<F$_)?~1vO92`dYN^q*u0dD_JkBx}G{%qOeptcl|_^FS{a`fIfMJL-cp1J$&
zg4TNFht`PVJ~5VckAdEig<qq}6s@7-;mHMd@GXQ}ANi(|1hPLT{>;yhklJn*`hNf>
zcXXkz+xf321`%Dd?GXh}k4(EiN%p;$i&`9yQ8&N__4L#CCxdCfJ{4@vje&0O%=PC6
zk$sz62eEj7e%d0G77Z~!woWFmj{M!8YfxD46#nQokU1%~OeRlG(li=e<~4SodVT3;
z>Ny_ysb?2F?QLy+q?h9xry8dlX+mGTsCOB%jwh8%Ic|IlR%Y?}Z98BQL^28XdPW<0
zOdss}>dM++L0oI}W$iVz<~J&~iZbQ%vO_|%Tj(v6W&*c0!dIRE^x|*R^n&`S<=Z1C
zdCE@#-yO$i=Bo|%?#T)^6DUH-^97raHzomOD+&t4OeC(6)<QJco&}MapXxZiNrvW%
zZ*MRywrYKH@AXibHkawKUGe494-B|(%{ZF|kv1mi6Shz>n!e_0YHBpg){`@~JKK#(
zAS>Yv0=S%j>XlBx<~pa!sTX02C+S|$MxHSC*`d3dp?OS#xkW^O2!O(dlWeB+mj2Ak
zd8G>m3L#LyCl6}@p40Lrf^^J5S-13z_t@du!`vC7s3!CE^Ao2#{rq0&)JwtC&z_6J
z)DX631Tt9o%2P!}#r=AKtUAGEFtFK2W|C^Mm8BB=R<pjjX|BkjvJ(q(C|$LG?($df
z&n7va1>1I#(x9zL;84D~CfW$hh@A!O$~HOJcQ*(?36g$VkL_87hr7GKH(-hciUY97
z5#Mg6lLI%E5)oNn+n674mOFs4zP`Sxbhj(HnzyzG8um8g&B4yMAo_b~SHWg!#goc%
zdt1{DNh<g$oJ7`QCHFHIIKZ?KHw_B>Ka`XRgG#n85ZARJ&WrQ&(iB^p$5wq_<QCTN
z<k5Z&vm5e~VGY=-&ntNg&rGlKWBw7>!e~Yf)7zO{+DHazP5=C}Muldz-YYw;3oiY7
z2F%IrGvjF<ck63rzGOHrO~iSWBSo^zjJ;&v13b6TvoWVLQ8JaX?{3^Yw8;my0G38N
zAmQgIx+9|1Sf@|3m6B6YQwLa+b~XYapoL82s*OJVwqSd=W+q0BU=jBHmmfqw-&nM^
zig9b*a0YAyZ<u?1v%tI3u73kq5>fl))WoB3WrzVe;N=4pM36Kx$Tz82vT1tNCBMH*
zHaV-KDVcdt@yNi$w0m=MG<t^W>{*ryFn0xu%mjCqUT=_ismZm-#_rXF@&+$3ef4b3
zTl#iX3KVLO!rSdf4KNpDO*|6@{z$3#R;T|oBSZ++mz*^sKw?fSgwoCqWbk#V?*8Gm
z^%b2-B-pz%i}+D3)MHoar#CNX-aa|y+6XBXY2Ut4Nw%}I<Jqv<b~A0zyxOg5cLNF=
z@9FDvpMr6wtapJ@-8x6GjOsEnBLw+i1WD)8#pz0?JMS-p<=^aHWzk>#KeD3cf3cz)
zSVGJxxu$idUT?I_TE{Hdou9&D>Cf&0jeQ^iO0*%JjbWTd{D*<WHv18znrw3v(RD9$
zg8m>g@*nUPj}7lLXCPpuQN-k-)0z$CkI`CE>CK;2jnAL9rnh8T4A>yTexAv~zEdg#
zBsvacMHq9!>ty3xyPCW~k+{EGJ~470>8$mqj$?5lk#(UevQ1(W=}B87r#)Qhz49ww
zteORKpazunJuG+|RQY=(jRK99vVbwP&vLR5ML#R<I`(&XbytU(uPxwrf6pDn*A7dF
zFXey4QxZ!VAGo-<I6pw7d@4EhzN$*H0pwRwVN24wiax!r>KS%UPJx!;E8VnT#T8Go
z1}qe{unc~FsK`o72H^Trbv6j|?H`+7YB`qr0l6$gsz?#uxqdlK{kl2Vyv0(e?TIi&
zMOS6pT#inj1j2&$*f%x4s^95l_5)6j?d_#S%?Q8ge+V|WlYznWo6`>|4F|;y75%y4
zJ#D#I!3dqr_5*_$Kj;M!ldg45L0MUu#e36z;3(uL+AfgQQQW+E+JcJBR8U039Zc3d
z%kf8`5R?z(=z>ns<|^&eu=%rYaWB{3PVc7iuLnit$6QuhIq}lqbS2<?33h*~c2Trn
zzc<naYw?+s=jDJ6l|<`!GtVI7LP_9~q^~}6;nwE4940;x_`BCjeR_+%F<BBJ=koOw
zr~lkP;p!lGf(_L7z#o;6_TbVwbdZT2R0`q>Ag0dVP8axTSVy=N_WfxuR=I_KA_;n`
zDP}M8!Xz2(ZEv@%!2^;4h}$6jv`1rTFby?cSVH1tW4Yr<pg^oIUD)@0z?5XAV95E~
zCvSqGdOhd+eED~Gn}KF#^4#iNfV$-sCZ^i=IXSosjiDyS#&Uq+q{<N3p7DR9*P`6~
zq&#%wvK^*qnIfVo5?j1wwlf<6kFmE5cP8VbWI28!cqDi&_#nZ`pDSzzxIBnFq~He1
zz?iHc=(A-KSc{*4Z}rkD`!)YFzorc6p5`Z~cqX$OFDS^noLT0Wv<81+KwO8{X3(9f
z_wmm{i+u$ol(L1M{}@ZUTlil#(kGTxb{ND<bxw_FU9-)SfcIA<)80-DUJIM3avG0o
zu^0y|!&fyp!mqG)_M_53Mrx$lMt)GFi{~NO++}qF!HzV?$?pibiS*n53^(~`eg3O+
zf8~TB`iH>LDu-dteuz_8#pzyeZ+}hp_^&}dw>zcKa3OT*2!22vm3hhj?Ek2}d^mzg
zlu=7+$KRj-Eza}Nn(EK1K}rd7i3jA?$rjmCe*roJ*F0pspz(O4h594be{m;~GuBsv
zHG|X0!-Cl+?IyR}M;IQkEk9Y`=x=1d68D!?4S%t^5-b;@y1X6L7~PnmAm2^Jqt&}`
z++T1kP&HBMs7>HrlBcGUdS^!Hr9ba-Ee|w0Hs<TTSoqtY&f7*O-bZQ14@4F7hI^9;
zjsBB)RP9IN4s=nWm`b5bEnZ7EpHU^g>YOcJEj}pW;r8DSGpodr{h5Lt&-Ih0p-d#u
zTc*WZ8P*@6GFwWdHDpH|=T`B_qd_yCpDEh&R<-}trq#ayUHT0VMYjQ}{pqKl0rF;n
zmj-16>d!2ft#4ajiTgV%!yzKp?*?XYt0I%l;ETqzU^=p-kdct*GNRYlZG?F&j+#<R
z(JBNX`AzC60H;qjsWO?Z>#M|ST()O58Tmi;M|sjFBM+a2ncwzlL<B2`BzgP3|H~gl
z+QFc*XrY95n-_a^Q;&L*^QAcXnfBjKb6yFKJN%-VaQQuJWo6H$p74_11~0oSPo_=u
zh$({2JFOCyMA+d;s28ndo$3=V<%zPMI|NN$%0Q{um=jtwECz*XRDb7uIDy3M!V+s<
z>}=;^1~0}*MVS}EK5{@vno6wImU$b8&&H^GKHT{j|1708-a#pAUPd!3*|)*V)4R+x
zeE;2V-y@sFX#QYn|J&IAZjuprUIqDB!64-wmFmSX-27Mw0<3Y)VEd4UETJo7Drb5F
z8s66Lg{^yROglG!)H!QN?oU@qGk9W@%KyY*P_pF}qY2rNeacaQo%cmFhixd*jxCXw
zL*%oB_(vbN=&SjZu?$#sSwhaH3&pJ&zkeHt@G@RHuN0N^)2*)A4^K71p5Lw&+!E3f
z%u4P|>B(#(|8AJ*Y^>f=Dbz%lD?@D!ldj1Hw9fXr_ak(&ADa`<8lbY7>Tb#hRCne_
z3frK*TB4jHY)S5Al=uO&OuQB)Qhk;>&T>I8D70ec<FUui6q`B2d#~451{z%(wfoE8
zESWg|p6?+KCF;B)V-k8qI)iBm4K4{@tI(P7ob-H9A=p-^NlX@TwC~@X#<Ua$8|a+!
z*37Imj<PStu5{%bgDXp24cI*nv={o+K=m@y!j*ApNLa{*45^l!DNRl;Evw2koRqz~
zm(*i^+W%==1*KrXy7m<p;w9?5qg5ng<eYJSV&@Cj$R69+rY!fDhAs-?y;J(-+Bh~D
zCRRTp{dR-=n4CHP&_lm_I1a`T#*g2oj4`X5ka0b|@s;U50;R4mebBjg@k<SLrPzC?
zoKA3)^;+wam!8||q2EY(?4u*g_ioF_^iC#rBP^s!SE~C2cU>qQMUcvxk>q!`2fGCH
zn)lqhz++6wKRGCCX}2`0^I6}?EA1K8?!(`jg|z$3y>Is2eb#n7_RwTK-7lO{P@c53
zT-YbzHfvf2_fp-7UYy$Tc$96S<$`&cvr6x1mvAWZV*%c#LPy_YxGOw*c3aPO&tKXn
z++bWc>dbX7mX;FE*!Rw;^BpOwlGAlw1zHtmO@CU1L$jYu<u?D24<k&3M`vI>0=B@Q
zzxuo{x$MU6{6<f2#QA~m3rO@~OR^qaBl~lUo2;pasZmF;XiRBHt|xewMQ^*-CE1<K
zTR?;GL~xFAD3G#et~8_HZ;%EfvR{=V)6C5uHvMFQyc;r)snvY<NYb*-YZwfHg$R0&
zQ($kq{TIo=z)179H+SrmsBcJ9$c%1v`aZHvoNj_blWdP_kgE2XYVeU$NW=x+$sAaC
zET$(rVSn0Mmy+B03a8oc9G?%(r~jC1Szr6|YU_AL6kf(bg0{vXS!6mke#TWpSGb$Z
zKPLP7LT;-1GD>7DM2E+Qr=M(npPAEa!?2$+D#l4_JV>;)m~u4%x9Z+3Z(Bi;qWfYF
z$bfjN`KyO(S@bJw^N^U~hCla8<<n28&YJP9FK3nq+nQpgdFj~;Cp12cCRLP>Qr)X)
zV(*<-O8VneyIL$$@ai#zSXTSu70K2I`l&H|c(S%>RPNbf{s#-KC$pzX1~EK-3N%qG
zZZpR)d*0vV{yG;i<8D#={x3~ty~_4G?_%T<eSEv!^R_0uWfPTO>x|ad9o&2Il<sV{
zea3l13o04uPCsv<uMq!+=3P!58hZIHnV)hQ36TT~jpA@`c@=A_==xJA=8wb5M^D%s
zOW|WB-?V-dGEt-spJ*yC3D|evrnRKJ-EtGZ^of-1YdTUwD*+AIJDob`Dr;QrTP<!T
z7q2?$m#5&q@vhU%BmZ8C)ewcRH&;X*=Nr#1_ig!qg^EtAsznD@mgZkW>JD2k^O!U;
z4OR{Ju9+1S3hw7c+1IW~$hfz#UXk9arCk1XBd$cYLSJ^j#1&IlJGbrK<goA=gu0;U
zHiZG!M1^6(y=}4IY5xi@iLY2x<3q!fPP0H><JQjn5G${c#fBFJvMs+@-`*CTP3d>l
z_$?zB$44`Xs|(9biN7T|(n~%1R&HM?gQl>D%AYr&KC*~nR=SjD0!2d}N*;84mpvos
zCg!w1l{ek8|9j5AQ4y8!xRP=?EQFn29!}8b4*78*#6E|Ly-2Np6MmJ(9g>@1%s=)p
zH)Nk?3;NkWPqOg1lZu4}#jgKp>)O~H)`2_d>3#A#Tsi(;0+oyRD3%n0*T${Lj>~w(
zRGCjpvYWBcm?GokP2&|mhU<}RbYEa4a+`%13gQIc%eQPfkET(uYOu;@xZPu4Cg*<Y
zOOZ#ucRgS)gkkQ;B0DcPP&j3rci45Tc6zBgxiZ!`kc*L1&$Pt)ayJQ^N>gvt(0K<L
zw?u^(ldeBFrNdYw88)5x!)JlgoW}HQ^086jYf<u<8(R#PNgW#uUA(&FOLrgm?9kf^
zFEen<X4#;O$-D70IAP65?BKc}=7wGuwZfq!WNV{;;_|&KqLc32WZh;t?&Qq*?2pA`
z$17UyUx}DB<W_vRPEH_e$U6Unbk0)iw1kn@JshvUSgc?gfg<quuJp3m1A(VPS-z$h
zWTOnUaSwOd9$bkn%69&MA#P8EaF*+IwVD^MnBJN(t!I`H?ewCpOl|WaiG8+4`YdTH
zF`8KU>A%q!AU$Ml{}*H&+M=8^oy^ts-BgXySEKoJ$;jaLQQ#FJs$<m?{zl=Hm;XYP
z_su|l@6fqH`|t9`&}#M-G=5Q#<IO&{8?xAS?Z)3I{&ACn@5C?qC_M#}COgi*cr^Mf
z<qoi6nO*v0mqkBD{(Zm|INjt!ZnD{$keFcEk5mokf~y+sL!>bxQDiluA3pp&En&t{
zm%Lq^oOc>!f1*IGH&vAS2wEVaoh}C2r?^?=LQ1>Tdd<xS7>*u^gg<Zjcq}&Q`WrD=
zuiU-={yD?-R)Ip(Y96htj_UE9|5}Hz;ShCH#p<0g2-6v+lcQxRrHgt*mBeRYvm#V{
z)TN0r*sGbIj*MgS7x=?b+iLuE>pjspkO-^?`LcGw<_jOyX?I>dD%z^+S>?NUTA94z
z*je7oVL^El_c|PadLa$QE|Pv)X!5&meMb%3v*mn{c5yB=ttt*GB>`m%9VJ-rk(_6t
z$8$NzsP5~0e1tJtR(&s!qHstTRGDGPE3~fS_Q4(LbPJbR+VyR0YzitW2(;WWD*Km~
z&j*7{PuamH-O;m@Wuww1Gl5CGErPxM82y1^eQ#1$!A}HDpjfI?@0jM(0SB3~^8T_?
zy|M*tS&RrzJ4^S3R{VZ>ia*8j%sJ5KU?7f+Hb*dq2{z-K>7$N&9=}Y^dWC_ZI$N7d
zCR>Vx$y7@_C{sL8@&;>t*327i8}9-c)tNDYkYJNKL}hy3a+i~Fw+J+xUGb#XH+p#*
zo)eqon~=+ZNC9<}*mQ6U(c$_TH%aB0c-9QLN8#yGbp!m;F|q`KZ6b9o+z8w}#I>M*
zIm49CLb|Pf-15pCxRA-^=JKUUYZ(u|7QOa5VUwkbAvY#rvwTMnUdQJH{ETJEe#<4U
z?CLhs)aE6NIhpwdnfafr<qK0#c7QPOlkbFF2&Qg4qx_pSSTUqMSSOfl@+-jE@H)ql
zauT)6c^4Cq)&&*A*V&%Qe)OLlq}Vza>qi|7ru@IL_ugSmrQN%502Lh+oZ%foK^Vsf
zgHoh-Vwq7xRZ!_g5fMU@-pg2+84xry=@yEJ5Co|Kf}&t(K@gA{lqNOw5R#B@ZE)s&
z&)2Tsxz73HobUSbj|Rfdv!A`5Rql1KwNy{cJ%d!HI|6?~h^R80X&-HC4y@<SISzuu
z@IW^OjIwp3qTrpqJ0b`Y*u|#q?(P~uW#e&>DLb0f9Lb~lPFM)AD_Ts_S>V;%0CF51
z3@p}Fp{~j5(B9Oqm62qs`>=8|!t0(pFna(Z?e@Mt4OIi*2_)fm@R$JYrOR7k@g23o
z_F8TLcu@YFGyV5A3|{P?BES04hU8)}K5#4Zp2!uAey8~qS0fo@doIZ^17^{enjzj0
zE>a2!m7do;dU9M7oaxuz0asJTSL*8FFtj=!yZqwZshyeJ!Uqm$xB(F1$8{4h%2WiN
zi<k~S4S30gOGLK#aLV@>(z<$4|2@^AwQH12Xz)1zIn+}-b2Gv=7(AUGYKq-ZKII0T
zI%!a!y;6#Rk}kk39)ogSegwA*OHE0!*|XO^cn}IREE<}g5(w@;+mGGqk@FJNrYjG@
zPUPpqbCEI1482<IJ!Iee%f2WM)MQgh2tW%|fUw}7nIAoAEs0FmGSNnt<6UKeKZKpG
zn@-9jrv+g|X#P{}h`3wDx#r_s^Q)<7gG-6_Cr*!v5QI=1p6Wkz!<qNLcXHd&_UMO4
ziApCCK>YQjY73@jrnp(<-Y3xr3MvV;eYnSdOz_kn@@F7*$)lwn({prK1o8xxwT4|(
zDJxI%5dn~Mu}7{qZamxdwYh3Hfx7&D3J$J$BETFd;F8Pb+3&X@NIe@HN|vGbyQs(X
zwv`30&X+92IBN7wz7i#v-*c{Uuci+FNM#NUVGB6{_X-<-wep&>y{B=#_TxG8)o?N2
zV;<XzQ2qx!Bs}&dWKbxN6_g@hJ31|H?EdNY{L>FAz?n_$$Zfn>P`g8|YF*M$zQGh_
zDI{aDe7p`$GIgK5*71h5q4kSF6CZkeD>d793<QiNnCi%1t`Z&z1LT_*`Zd`Djo~cl
zsvs|ox!!X&q-cXL13jLkh@Ka2@YJu=y#e&hw9u;3q59R3{hoiHzmmRR`=+q0JzNCa
z9EpHAwo5fno(XaQUv%|do?WI}%Jy)=PW?f8$Q7HZx^9Jiip0FThC442h!Mvk+))!e
zrYO3DSq5M!hh{Q$*z6nqz<S}C?35%WAdSW}?)O*R93h6^Z?j;(79Qa>ePMRN1~Le>
zbv?jgwuRh9!O9Cx8<0HcTH%23ME|LL<`)ztN8xfGq_l-x`(8~JE(dCnr%i<fHRz=S
zh+yRkk1)C5fs)iBM&V|R|2ah^gWLM2cCnG0IjFxj!0h#JG>-K>02(R3v|uqbPU#XT
z63n)=xGSOf=ePIza{VFiCgK_du)E#5ckE{$L@a-5SYrv>pM88@c$>=Yy+frSu3_76
zI6JSac=W<SE=0WnGQkj_TW)~)#lr4K8A0oSvURbpnU_%L?{|Umt}(Z}z>;3_p#zQe
z)QetN16@)H`oalR$u1xUu#PrV0L;nIeJ(LKzwY2zJW-p7sLd5mK=Wqyo$?oH4}o;g
zpxR{(+RKLge@mWAkn74d&fV}HxnrO!;f$zFb-@{t#+8;`8gj?y9Br0=6eb{gCj0dt
z`^S&{nR|fwxgjj8wzl>&li`F3u!MSlT0~EbJ_**z?O_FIVQ@Iqw9p%cG5F@Om|$qp
zM;vcUCtkI)8(sxF!deYIQ(gkMN!wAoJNTuUbg1_WZzRh`SYH8_12ck}7BHUNxV*eP
zu-`uTCM2|!(hnVgTXKL~>fXF1P=SFK(cce?@1Ib-OE(+o`uB-feo$7GNIEpU(tJv0
zVu;e9853C62@2G>fPRmjnw8g7%)9K@Ii?t@a@c9=)J|)Ze8a7?{bKe5k|BZ2)y1iq
znE~zF^VY+&o7|HqAvw3d<U>_%d(34+qJ<7zvK>&b*tUJ#t-owmCi&o7>5m^iy!^ch
z^C}Z8vG}9Aa>H$qd591mA)$b9Qc<&PLz;>9nJcNL*5OosJJjGWdqOseSdqB9x-rK{
zhTi5<Nxj~Dv*nD5YrZ5?bz60)`F<KAVv+K*vr7K0&X=Y`M%WTg&k>(~&nI1nU-+^6
zMMOnK=XTAfd?<_D%WsrvQ&Lk6*(%PD6_v1=B6a}qCJT(7+G*5g7QDfj5I~Xm4pn>V
z{+L;V2wCj`dzRY#!qu1l{3dHF5YUWy?=MU0o=z(G&=!(N7x8b+c>;*7$1uvS3+{RS
zMghY=)*#kmF1^>2)S5W*&_My0FS)e2)C1F-r=k51<TR8%U#VUAb{fRDX9%Qm1^@6#
zxxP9(eaMYCyUJ^)=rM3Z&>M0z0$gaUv8Y@ow2P?W_BVly;!Aeeo2$_`Pl<E;NkAH{
z5=;l?&~4J5LuuC1`!MnetPXcJb=O||YEU#_%<jR4Rv{aDNxq?^b8G_FS|8-H{vUe(
zfrF)X@}?AY_-{(V-AT9H*Ep3QONUgqWE!<R5l%;8cy+k7xUBfVsop+ZEiZ<bup4Vi
zIDP1LEElwmwMNQ)*jyT#ntqc|-e|S+^NrzHNVINrnFV<J`nK&l^a6y#11i?&dk9o0
z-=y(E-Ce+k`6@+<R>^+<3wX5PR9<Px*O{5L5-?lM5vr&#MNetsOQdjS9yuiNpI{Dj
za#hU^7=xtgItw}k+3<^Q8@MdJuaVV0yOM7pGocg3lrf+wr~VFqQCXK79vK4-g{B+y
zrnSrL|HFhlN-WceA|0xC2x3d*c6-qrM;cP+d^H9eaVp#Yxo_-FDxXrU8U3$i`3!+;
zX1q?9AuG&CLX0ZVi)_J%gO-1g2IF@hvXV`Tbxd=|15=i=9a={fk41G&BYCw5cs%mM
z;*iQMn*tALs`B$)kv6U}{@uHSOKia1fl9-Sy{FW4LKvm+$u4=kEaVvI{3LMT3hI)s
zM%zNpRkpqddbhH~JWQabB!6Mu0sK)+7jn8yezey*2qtV2{OtHfxXHvCxXMJGQD>4-
z#`}9Bs;i(L6;4Di+(xo3&xQzzL5^hu?j>Vt*Q+f0rldFSW+6M^bQF2;_ml^bV|3;@
z_<dXA`6JlQjejLPR+V7hS2~Gg9bq${+6bNU7o6v_+gTyB<^n1#0?7s7UW5Q`i+_6?
z7C#yf>CfFU12!Oz2kluNJ3ptuO5AvCey|T7LI_HPo`n`}^`o}c+9QyzS$>rn1+Ln3
zL=u<n_PK34!q1-CX$pG(BPg!A=a=#W8;-u8HYHk6*cazi=TjVh0tOcU@dsf#)s4kT
z8#6v@G>by${z|QN{P*)UzkbSf<szb}371XO>7goa&zxKlp-zFjwxEg7RIw(X{WZ2C
zqz>U2#_Mt;iMN-)aoDz-&5BobQ16R~{}TX{Z^Q)rGWu`O_CHBZd3|0H6fRYs{%~eQ
za|CHSKVad^@KC#?$!CwQ#{kal6;3%HaM+EK?9|u&4<N0i?9MaCy&>k$m9RD#S~vYn
zUm{SoU8O11AX@%HsjnMYNTY2!+q5(yUd3iP@%<BH<@wImZJ%7?=pC6)(-RNkB5=#P
z=yxfZIH)U*i*$}%5<C~=Q^n%5bGYmC4d}m^I?_2N6i$f!v?@GbvV6$EEKwW5{RQ-Y
znysH){P9079hB2-tNLnR$|X>qv^d#FiTh<~vh?Pn;BNt8X}{FJh>!l}0z8z(?40R)
z|JRMx_X?=NI9bWXaTjco;H1pAJVo9{;r2q54Yx~<g7;i<onFMCh?!ig9o$)1hO8|_
zn{oz8J=gzE8f5kTHjExt8qV=%(CP@!wq^D3gmoA7Ih|<&PiNLU;E>UZhghp<MvBbR
zlmFO7hhcz7Pty1S+dKv-s6Xop>M~Vd_@>;1Td>_euuBv*u(R6b*Nt5s|MKAR*y$mc
z;BN3O2A}oMK)LPM5Lv_2O-`=UlwO=kxtXHSg4^jYlb|~GxXx#CT{F*{ipe_a8kXYw
z@9aevh2A;lmDj>kUK-)^VfO<Efg9n(2%d=M=iM@+ldmU^MNFXlu37!fcUkYvpQ=2l
zr+f(UL03^l|N4~FBrMO%!&MR0G(0Bo@v;85-8J!2NWkvsx$x(h_DP3pxAqNh*HR|*
z|0o>Zy{+poN_&QDcxoBQz{u*I8ds%<O(yN7Q=jSU{$g-W`yau)9uxH}+OFtd{(C&s
zig??3tOTrfzu;a!aOq^L);|O(uH6@QDcK67X_7<wiwQB;Zvpf|ecnflmhU!d*bm9L
zZ!1c1a^5AJ$*+`4p$OZvul)mGWSKupL@jGZT>D}ApN!69xCTd_{5PnOm4074@$X;$
zKk>0HGz+)KeP~&|H2{3yzbQR=TvH!-)`f?!4)1y-{&leJ^fw3t@>#t+8Ys@5v<Fv!
z_`A8pto=uuQOmZA7v~w62zMSo9-b2|MWWb_-47@e%m`a3v@lhvlC?*=q<Vor>=-c;
zF?beR41MwkHCww<_9=c|KZk#HxarBBQXsL-N`uglRIO`yDt@XDK7!S}YX!CX`B$Ue
zpC}A;Y}Ba?^A7_Y^8%r%E1xgy%T;eMqcL_k`y#FRd!eJEW`a83nIG+Ucba8SmYud0
z#~SD08A}u5sm)^>3Jw0dJ@zKFouYHA-q~9Su58s}M$p`(lZ&!rv~=J3{wvrH!$Zws
z&ofcJ+bwQhJ&8@ZG)~D3v%{m4cp_txWE<ATfiB=~U{ad%0*XC=!xI(m1%00b>W1gb
zf^|sTYyQy-b?}j(1DDQX@j1@GAmttNtZe>dsL9_>rIH$N3FuS=9C)s5jHgTl*+=()
z$0*tpzIen*zmYZEFDdxt`I#Pj?z^F(O{uJ_dYWm{)cA7lsAs`Zve=X(`=rg<ZDf<&
zdB;&bxA(S*0Y7ArO{bE6+zDd^`j6c9fF!Tp>7B8v6;*M@U<h{}LKs;F&wr=Xr;1#F
zZ7YWIU^=hc(?wh+<eDddrp{W0?$QI?$UFyi9v+?`fjxU_pt~_C`*FgZ@x5;zl63J|
zXOfA{^+~S1#mrD30)n2~A6|Q37ge?AYuj;KtntWg&&pt_gyAJq*omNk^@@!JC}s)c
zt~e6XqC`a4GK}Vf*bbL}g?C-N8@P7L$cWM#{V;Q)O_Mgm6WGz{&}sV{wr`;TGdUPi
z$Fubf>f6p9^U(CzGk+${|2+PBHSz=#(PR#H7?c287Tp|az_ObhUXgwHHD`*{W3?9F
z@3V7-I(HXnv2iwfL3+4dc!YCzZ>`zU1lgSD2lv0EI5C1ZWG;@2s{LSg;b?*mU&2ry
zP@-r8hrCgI;h5WdW!=D~XhPcenO&+e<gI;YVviAcu<CD!AKeapsr;ZgB@ESe5Vtet
z&&00o?p4mIofF)&V+p#p`EB;p5c*&Z#4#$5N+IokA1apV4?eph|A;}AN-lRUkKEJ6
z>;-oS<Di{mSfu0XCo!EeYb5t4KOgmMchpKf8e2Qi)#WpYCQ$NVsk?{WBMygS&NF`g
za%$#~ELO96JKsK0<l%FxB<N<d_dlI|_y<grn1zUW$k1VtQ6JHGdM9REZTsLLyMO9c
zoU2}6f4{AueKq&{Z1am@Lu&-%Jn!-OC~{fsVa#b@i`Qu1_SiSi0!|?w&1i(0C-K}B
zf3f)wfbKm~y*-~v1oS|%<0p1!v~t9w*&;NS67L}J)#j-9uiZRU-nJcc4JTfqTg|X|
zX^0&|1Bzcyd^C%jR^kbQTKfd~hPX)6yL)YR1gmCJNROe5VpC`T{p)swva+(Mh|cG$
zPTcP&mI%=9%=GU3E81m&DEZJQ5ct-^t-pgFSF4{-k4y>*jQdYz4$X$DYbF;1gG?ut
zG67(_o=zqS`ZBVwSMUR=KM6I9#aCBrtnG#XYn^X+`{BPrFGVk0f(Vl~H94uCqC3pO
z!W#;2A<y{bf(*VisNtvFaCfJYpr|$9x!=FXF&0{VM0H>#BM<TiABOclxp=fDB+Uek
z?c)N^UyuvDzboIpo`6qHyb~r&OwKkJI*Bag!21%o21Vp$|LuOf_Z#ZKA0d&K2!y;4
z(Lz`xNBohOzr)TeH=}PZz<`4oDEMH@H{hQ8f&c9fu394(VKkZ3qw^F|eAfvWAK9UU
z>*J7G6?{o;vK{CJ_SWGe`zPpXMYo$q_dzs0zAigVAJIZ%H4A?OUY~8(A=*fI(mUZ;
zXlnr7PV=O0&;KxNGdv3GYWuLd>rl0@TJgoB%AkP}p)le-=-r+Hy>qs&5rNlJnQaVU
z_h@gOX&y^=;%JAQ4Rq!4)5|tS)jpUjanHN>9ee;<Uf%0hF({LMq;>rGeBiKA-CZ65
zEf2YH_=;FmBak`grlt<evTU}CDyD@&lRNGV9+v6O&knHRju$0uV19}0q1(7;tfx$b
zTo{~pu?d7X2aVkaU@EZtuR&{Si3a~`c=Xw<i7KgvHRGeM?Eck$cTzyydp-O|;j>(~
z_l9csH9$p~y-p^L5c;aT9OEkb^$wp<Syn{vLo^vm%Z9XJ*&W^8D!HJz_I?t!XR#0k
zH2=$Q`p;yf5_B=T{p#Vk$o|n~d4+b3yw#UgvUP8SNBE$DunBaoOWXTq5(Qi#Vx7S0
zo$de-_tjiHdc-M1m4|@PqCBhPc9+$6xFj84>k}o@9P38P&SV~QQZna#WXm(aE1Fo_
z#zon_FJ|dJdbVN}&Vr6bY)c&4e4w+l<bkeH_G36&RQ^4CWDJ{GZ*MSxr>Yn9J*GDf
z8JUx7;^fsPlc394tm6@I;ks&S+EWkGKUg<*g2`emWv(jr3Xhz(h~33GZKh`~pfz8Y
zC_Hl2E*83CH23%SCuKL9&dkk0|K2gEzV?F$jk~+Vdl%Gj2wdf0UH9HMmie8a($l}w
z;#=#1+D5Q;mAR2k6t75VnNkJCWI7?ByAmV<(@=1Vp6BOJOB|gDd<L~1>wDisqF{<d
z%tL(}eAeAhA`PyMsn3?nLKeTb54MOM0qC<<?2}|e#S8eO;z;q(wVOY=4OHB;>&!7w
zg_jTt3a2-mUvW!(NPPQ!^96&lkQXY@QNxrD7|D0LUE!mTpaW}AT`7=WMxc0%sy|Wo
z2Q0f5pf6cq5FTr6@_LBq9s6d_oiRc@7~M?H_iIK+1&v+bT~R|j<pc^tR!n>PKD`F?
zGi5E{6PQ9yBS<FkYILNmRYj4*+lnZX_(JEUM?b{xfH;n$e8zl&OA;{c?}jB!^1KTi
zR$wiC(ESB=^*8y3pWD;X<jLp{XJjp2J*xzrC3edHaKg*N%uA(}$+|>KYr+u}*o?zr
z+}8QiNXr?qO3xcNZrJ0|Z-vvhTj70!N6JRBjh!k++UHq2If}Z)f?N<nf#vw@Q-3h|
z4O9TlFMZ&Cl3acy&Q&~r+LJ!$7rXBdj+BQtI2bVl?HwJSjdgXX6aA}G4w}-~j!)nV
z5zZxXxT{5Owc(XS22j<9*yM>z`zN%=DfL&Q%ei^?@bk}|a#+mxykfs6_5tr#s99{l
zI8TNS9@=(lr#Z{2bi5d9?QeR_lVkcbC#R<RR4l~!gk)VNCnqcAZ+m=ITe)#?A&{%C
zz5V><5-*O^s{|~5cWi$0*sl%vto5}$3BIgzWt}mRdz<8A*5w9HPI_J=Dt6r9SYGc-
z{P;;H#xhBG#BOH<WES4Ohj`ren5wd}!K;k2dOZ5xq2@zsHtDjM-(NdoQ+~vw_lr%h
z4PIkrAE`Su;u0iw0_c97+^-JZeh~NFp8joc_lqw+s7PQH#2;D7#;#Ka&|Man71Y!!
zR6>UK1WRR7FpjUZ%G?Im&mh)aHDaPAS<x}cJFWSCj5WLc7orc3lBz0Ad-5=3({vBr
z{_M-rj(*tU{(}lvmi>Xqn_>r(F9wP#m&JCz^EJa~$s<&wjT}F(y8*iR1J6K+{pt)(
zSc2Maj4lat1HAx6TJY%DdE0VnqVlYUhwvzQ&{&13wKyP%XZ44f2l?b)z6^h$<l(*Z
zbHS}!x4`WWs=4y{%^jJi@e7_vx}_G3E*0p~gL>;@bD)$mc{8+|bdqw7s3y}dp?)t3
zlmo;MSv^DD-DOW2Tx5BLMMZ-xFRvV^_mIqy53=a#&@mJOzZBn)2y`yZ$zrjQ+pyll
zVD`r@{+>NCy2LmJ{iXDt-^$??^S7Hu6rTsIi3sjOno=?-BE|w}65A7Fj#-Zj3T7E~
zv7N2rnKyKxPu$f<qV}w7I+<b22wt&I-xX1Gm@Ni8q~-`rYbJ&>5tnCs*#W49={tju
zL!u8>8nv;Uiy0I@rS`r598CIHa6Q9{R%uvk^kzgLuE-9LPgf#a$rDRwLFHI$TGU*P
zrt*pj9IA%a&|wmp6k6Q4$argNxh-sN5Wm`R_-xD-DR8mBfbj92=RXvzlu6-uz;F>c
zYs7HW@7gD8^$GNQ=&#WWoR|lV@9Lg95$OxyYx(Io5q*#^Glvfz0-qrbaS&A+HUtZg
zwQsLR%Zw!Na=sy+8?~iBAbS1ns33pL%In|oN<0XV?lydUSZsLP@Lf=2tMHT`QYP#H
zK`DFu__1aL0bEvtiir!+H%~_|#GtWmZ4*ml!8*45hX%N%qvdTbA5A!uy3^?*w7aDp
zu9KQ<=D@L|CQ>FwfQTx_+s3s6j}DlHd<CyWisNMm8MC5C+P8~#RD=*}Rs$P7bU@je
z@=V;_Ylw4q$K+Q<gZs>Nd@ler(3c6qf|0|qS{=Mrg7z$~NlI`&7|a4FC*)h^7Uvcd
z)*--y+t~Xi2|#}wn^B5(x{LIPr~M^gd%x+|g+@v2V!mf(wNJ%CYLySpJ$j)Ws92=d
zWW@>1E+!ZVsA_nc$LjF;4SMex_0_(G5{fK>l`NGj7znM7tS7lUs3`oaXxI-Xw1;NY
zi{PC8a~qE*$ZK+TtZ5dDAG?VE_!Hu(LF3fp4gNnT*j5kl#OJXTtG-{;1^){Oztr>_
z8HaASf@cE2nn=YRUAbv$gD@2gqsh6YO#OqVjlu$13j613CO1g2@}UY?JOebjs(}Pa
zy2%12x#(3|4WS@{2_9%wr4C?z&NRx3SPMfo?JDGg4N}EW{5xc~iE*t*Kzx`Bx;!|%
zsWR{`lqjb{!x=iyMn=<H6P~)mkpG41k1p8uqOg`D*n6FwonE-SiyGVfW#(<`XnC)W
zq(<c1M~ic1m>f+QO6JEcM6=`2$wKyEG@&Rkp==WBaFU=Bq!}!$dyWMK45`%ctvcGX
zS65mu-m|vGG1aVHB9A~-RHEWUXiQV&Ely^QrT}63IqeR|dg5H-)#JC-4IjtHKi+%y
zcPZEXgRjv3pA>fdN<Du!(X-Q2)B3KltdOj1Qu$bIe1~yg$E&Op-<6-VyB~A#!TBFf
zi~JNWlC#$$r}-=D$~jR<{E_sPjYIpR7k-%@{dIL>Nh{F%72SfgqItIQ0lT%k&(CP_
zV++S&7>~An|M~Q7A?YgusDY+*NKYKwS9p19;0IM{=oF|X)^krM&<i`*N7IRUGmzd;
z=yCFwat%F34$D26gZapOajrx3oz_-w*t%Ueq<Fq!$e|>$?MNYg9oZG$%jlW$A@9N+
z+G>xZ2A=G5ds`k){|Wh#Tebxoh9<(RqHE0=U&9-0l{Bi24VA$J3aF|5ymi$g%8BvQ
z<7~`a{Ae1Q6F)kFy}VqYnVA00<@L?ap-XMrZx`%u&OLy^nD8op)GyInGVlKzK2jq2
z++sMI6gOHLG8N@;T|Q!gFdmuIml^cONaLxkfg0tqgKROHfu{qWdahWT-1&3(_%AXN
z;Tr_?weEwF8vMu=rNc5U)>v^Nv%fny$a5`YL`QBSKel584ZoSlU+>%A+;nxT$L;Xw
z2gAktXd&rf_PWmM8->GLJv;bcX)ZBOlq-mzpe<=)%T}@vCuV2wev}!<P#tAx9}qyU
zHlWbQ_%N)F9f}jT*}mtjgOvM$HC60JVdMzXjw0N$!8I<vWg;?Hf-!<NI_GJgsA4j@
zt%sr-rFwMp&oz96bb8P|lD+s*8EmLLS#kZNVDov>so{{IM*S71&5jhqpIC1AWOP{i
zXuJ;njlu^4YCVE4lQ7r0j$8t|<D`$q;s+$iOX~xC&2JY5ZqNVDGcF(-Tzh;eX<Xd7
zhKUYXd(&gz_;Ry}gGclFG@7GG=IAb#V{3b;z7?gK8L0J1#+9$Z!wXLrD$xW?%*xj4
zyNA%E^wG>X774q73+w;fJ?GE;RcPXD`Mm#1XyXLGJlvNX-bkYIC}yBV6j{wzQrB7e
z@h5i#)Sis&dc<Lc6FBH4(_cBXk!QO$SG(&AT!KgY7p(c4K;#D~X?wl>Cpe8ibZ@q|
zv`fYv4A|<?BTCG!=3YOMi>_o}c(G6lu%2YXS+|g8Yvv%2$6~-crere@X>knX+ib{i
z|Fe>Eab~+8bMUNO2+n)fl~@vFx(ZEi&7z5WtT=21-<6?II+rv?@8F5@IF<!I=}0C0
zLfcDsCFuv3n^ZS1S=mS@F+aSHSoC7g-5PV1b#TN-(uuJ)&OOOV<-ARNOz1Kp-QY9!
zu>@~5MAHc$bA&^386So%_T7csTz!$g%$Qia5k;VWg7aWBH|~Xg@z3PpD_hMo)VO5E
zerzYz%rkoM)vmjyxBg-`@<RdhHCEe^7h5}|P#Vv*+#{qzJiI9)D7^zGNKT)Z(vylL
zHaD#KqwyRU+Z3CTIl3KdQ+?Y9c~+0)ro7onX$tgkxXb1);~nk9xB(2AH!c`M#^GC}
zuWU+E#czWSU;Y}z7Ebz!7g@<TpxB%vPLhlpc#Of~UuYV3@eX>hz9s1O538HsR9Em8
zF2s=`4~N<cNI1n@&DiSeJG|``EHU%i#&=sSqRl!`fzAJZfGgjo<rwO-p(`vlv@mvh
zv#(G4Y4*-Xrjm?$G*x%;fzFG~15_ttCq)m}NqdZrHCm*`X)W%v5JbK_TxZGwWsTSR
ziR>;J;D%ofohZ~>>_c+~>NjN*_4TREwsyaBiyzT)*u^;Bv>+e6sw6`n-*>AN*$-4j
zg4uIa;7{bIilp+Tb)L4_bH%=&7orkxY_)B18;em5uQkO}^%twr$LN|H*=U09VjG%@
zEmkED-z?lxg9^o;Q0pA*Vh7rB&Tl3MP0(A6#cnLAF~7;Gr3;Fk$eRCayj+rzj~3Te
zDF2ti=C>EIN@Kh>dg8qC!3u92k@cWfOW;~h?ocofM&H1J5AYjzSE;zDs<I9FrQLqy
zQqkyqyp}fEQ7Fb$*E0aGjkSpz-M}_2sB9jHx<zj-OOJtvqXo$V(1nnkkJmq9{$P}W
z)rlKDh7DE4GS~XHE>@{Nr2UA_xQgdwF!G;-sgsZJHPSK|BLAARbUcm!eB|2>TfWTx
z?h!rNz|$NriLNxkQ^~A*-_d&$N5|3bdW#k4TYA-7l3V@*oEjR9h~1DNYvZ-l$W)0*
z*)&a6a!d<NXmc7;-L7XzT+2Gj<Xk+f2YpO2tmI9sbeh-p&941*yPSvj*|=+v-@a*_
z#AXEW5xzb7(M0}7boxCsVXEhgLFYhz{NjMc^j)+gy7F<n=Ow&rNl8h1lw9pvGka?D
zTZVu2&;#3%uc)A~ID@u*QMz#j&x_t~=-Fd2tzOzqaxU-Z(6L5=9aHQH$P8w8DYXve
zILXq^QW~$SuOM@qQE3&1Rj9y`hI^O`xDb2%M~h78j~UbhHMp;OUYK`^iHYfy_Rs2+
zbqBsalp}DRF=JVi7Pu)4h9}qqzdx0;DCj19v@p?SEE(o5-9;y-r49J4ucJ9t^P3O8
z?xxx#_g9PQSs}5|<r@<&0~HHNFwr)E{3a>ZIpsR7)+;++)j(fAO;tmq>Z-HzRD*F-
zS)}&N6(h!{MxE6cXo^&}mNYpLE)E6|hUnI|LO)w~i2w^c?~%$w<e-W1@eS6OD7lTb
z2prAU55iM#xQaS9^cb0q53Y(|?ECZ2Kcny;pU(P_CRPLsE>AN`yC0yzX}DwD1hZC8
z6GTKrh#Th#XFHXS;R;9>bk$YKw-m#?$n-Lc0+Oh>IB7AwXYOH-TH<=6Lg=+?qi&_$
z1c?Bj+)jo#Z(fNM*$iKzk={m{)vN{D6;XhnU&s9lX+9{?xG=xeX2`j8=(vu~#^Vse
z=0Tf(YFH(srYofxm({)gB**EpSZkUu(Hj4XuKCJ6DC)IaIrDHMt{``VN#Zbn%Z}&M
zm5Oa)ZFKNpi=iHp39!nn?qU))REOMz&OH)vhB#R33~SmRdE-h^ci<GD>t=XWbimg)
z17|;4a47HJzt@b5x1aQZ&n#re6S^!iGe?K`LNZ|V^*flSS9<vHVY;T-VdxA}5r)U<
zNfC?|pTVgcP$yRoy3y;A1S@ml#ZBoIA;r+or8g#qo8Car*Zi;si0zYvGV0m*-8~kx
zjM4q@>AxV^I>g+&IV5N}%df+lOIu!k;q`+D5A>W1CeXdw3JS%_<RDyu=^g<ATSXNW
z-;oHfhQ>yY19Wh)b9Qzv>t25iF}3#8aXprC!OBv0eB_`<{hrJ|X!qxWM@FSQ8+5aM
zkkxIW+S1y}?jIc;om*<tbhm>dak@-)b!GRuqfzh|K`MI=M(prKK4Y%He$E4NEmRA;
zI@7!K$d{;XgBu)BYQ2%$8NmFo3=GadW?o(%87-B~8Uo2mg$aeLX<lvWen856mxv~r
z;zFp<rX<YSmczGXn4FSQ_4Cpp+BDJ*V`Z2ZvzBUV8{$*R7*93Am0D1M^D!gqd(S?P
z^l%~Sf^L8f{&K+z5_*KGs;gH9d3(Q7*47RwM^8v3YoGe_Ruw?bVy3$4$3-!2a?GKg
z#42>%_db#CUbzFy_86Vd4_h)kQ#EZu8Dpffk3N<K-IC(;ipG#BGw4LZBVN%wFfh=o
zH`r`oNg;8XaHQgo4qU_g77EU?ng~`K1Ev|Z@yfUYMaOiX?*61=36E*7j_vK(sd};1
zq7^8p9<b64a4J}l!a`=t#KgoYWn3GZ(KG3-L*!&3+z9B9qnI6k*sc7yrY2J_E-ns(
z-&WQ?D=AUwRo)*mvpU-!R@Ar*oue>uqf?UFli1dlmKN3Pj*gibqp<>)%j1iom)vY7
zmDb*vnq!_i8iloaFgkm-a~h<SAmNl`yy1WK>X#K}j4hDys0NEn(0P(%L8v8Od4{G@
z-G}1h1`d&_-Wutcb9jV#sL5VIhXrckMD^J3qHUv%n>_F!2Q_Y<o}L<5cyQzR;KO8G
z%+t9gyo<)r2Fh*ExLXiWadFUnmAEIdvZlO-U4v(J8u!fl66N$f^|_W|h*5~1l-^Kt
zMrWu3IKPSc+PUmey~L{P`CV0q_{?{Be9@RJ8$O2z7KOKFdYh)CgoKy|Ji>moz>88H
zZ89%j>kwJlpp*6H=jO1vx_Nc1u9VR^{N;Qfr%okRxnO;{;F<K~WX9ARMx^MIIa3J<
z3FsB}8q`^K3vW9eL>eaXD1Dms4Rg9upn7m7IKQh;=*7mD4NwBp`^LrP{jFOLG#ZD=
zj|e^OpRGNF-LYebtQjekO@UD@z7?i8M9bFwIeraX!?RXUWFCaKs+nKt&T+~}8GQnF
z%d_EU-O#zcjr1-JmvD2O_DA|?Nu*i^x(BUpf8)m2_!eq7tw>iYSCR}*#ZSOD)Yo@)
zy5RCL?3{019#IlY9Nh4Y?N*ywj}i@QDRZaRdI8?)=t~?FD(|0-g^p~_MNTP%c;k(i
z@rw-$3kw9<9Jl<gbsES#!DcEnj@1tVjdVf8G|A5Z&!iIt63ZV?la|CwyQx-FRkOa#
zhjQu}DJkn8=jZ32CRHU};?Oanl1rBUTy@1>Q$ji`Ox27~LYVEBVlO=_feM+@&i8+F
z0e;k&<oU{jw3#`GbZf>3DI1g&cTNs?M<3x|5vSndu6cOOFC&6u@hgt5-crewKyI@2
ztod{woOh6|J-it%<^8!`i2ds3%B{^ngNgOiFDCLLGG;GEbt|8qnQ`&_gC0__;+q{W
zuBijMJQ+>VUwi?Br>QruUmrh-&9KFV1avClKt7mZE*;-%&E1S8j+;%0-aG4FQzYcT
z#h>4z--}8a=^oMy88L_B_#26N4iELL)ce=SL5wyNv6Z*1HCttroq0ulmpFSl#CAid
z{IATex<yZ8x7Rn-#Mj#+4F~D|Kw?s4in)U+m%t1Nu9<aNP~*^wii#AQYd8prL)qE_
zS`{!=IOv*A+6Jpu`>)uKxPp~Mv{QM%5#}ueW9Gm1`IdI1FOkiQQE}SU4K^)mKM6X!
zY4ZtCG(Isd=dYw4mSGh7)l6#ww%Pv@DxW3gL^bC^au7Ki{?!MRdJQBaTfvSx;$6fK
z99aMI>C>kSv=1<jPXi(-aL)7fn}o_F>4Bd-GA(o&76mJhfm(@~1ruGnYSt~2Z52OC
ziL3SmmxBjv22Hkh?Tfoa9*o{Q=2puX2eX-x2K@}Z3w6xy`4U5V0rp4%vBKf60fQUU
zIqNIA&yd8r5mZoWb5WNujV8FHjQ0%^{YXC05!M9ES-x|D9GTU+Q0vp@l*@ka%by+Z
zhX!wyf=jvXj>k-UYf!Ox<l#BExy=nA1>Z<-u%4@1n}N)Sp94)8g%#^lYE^`>L~P1L
zX4pmx;CM78(1VK>)Aszqt*JQyu2N(8%F4=P<?hrC_8nS}6mTp9``+LJ-liHO{%{?6
zDvNof4$Plpj|J8L_LUxqfb+SXm2R9{pf4!suxje#WaFk(Pzh}?55ZbqSy?%qfeIM4
z`%Ts5lLrTMhy(soq<N45xEilBbeUa)53U_uQpIYuzXK+w6Og?+v{P(^+T3*)=T0vl
zH_;7Pd;9jSq=1$@_=Ce1(+TQtZMs3A>7V=r8{Tah@mi;pk~k~R`;UqEYISlHad6Ha
zSwDeqgrL9p$7!udG~*p8r_c3iq~Ejyr|)pz9NtH7SO{DHn(kh;FtI+!+iZ#T9lAO5
z*JdcVj>zs*e032O+R|S2)@!S3?k7}K!@Fh*wtRadVo~*^Fs8UcV_ilMnrhV1$_jlm
zg3VBYtu*1iOTiWg(dRIErM16+tK5R#KjTZBeQF&9cB5yvfB>~1e$f)EbN`wVz6NpY
z<?vchIw!4s{+J~C(S`=&jcqE9hZe>!E}$t|$AfiJY~Tp4fl)N-varn@J;q0PJ)tW_
z7=RBu@$zNHT?2CymmN=(fRM{n=HrHq)Yw6g!Ixm#Y%v+iG1(he8~ilV_2RT7$jp5<
zOlz!-rj}MP2W=|$i2|l#1Xn=OUCd}9nO;{2JN+J1=t%e0B%->4R;=IlWlt|9)64Zb
zh2cBaU;o%Hz1{Q#PhCOku}2Z_G$!GPAQY(K#6w&Ec_jI5MC$U}6C{!0dBpK(uo*-?
zQ>QTcEs_lUhVPs(`qnh`>#lFpi?shQm$|(ajD}}?n=slg_-$g*|H>EnGa^(KC7qYL
zK8W&I%*&p+$Jy{mb5`i0i3JwCQZUKK979PP@sxJW!ic~Bo>%*^Z0a7A-oyt>YA`jF
zj!LuI2wjw^T-wvgcDp55SEui{&TolX8w;g>(2EUVZuiUMnblk-3WTEr-X_(?b2c7?
zP5OyQA68TyWR-SmYPwIsMBGKicG`~c?2qfQp?+{hH&W25SAkoN5s7KX$e%4%B-aDn
zVzg&B2s3~qJ5JySYUkMJ@-4+b04Afe!)U+GG4dFDj>jzYZ?rI6Fz$cHr}jTZ2$4tc
zp>SYJ^ZD3IIrM|}iVLi<=~RLkL3|MG&(pw~eVYeDr&%N1EoIy$JAMM3f3*5x@}BJY
ze7s9)ditzY@WMOWIj^WlMKfGYwhiaCaZ~Yz%_hRqOer4x-5z3}UTm!yj$MxyIH!;{
zT3Fic<y;Wf<yOw2#bqpKbuk+dllpgbY)aV_G|4!J{U_WZUX^q%aM#CMOnX7?7Oe_e
z#I8bpOkaI{eJ*%GjolV<YVSY3zOyBsfiC85&F#*mq1oLj#!Z=^OjOZi2vcf1dn6e>
z-#)Z{&3s3DX?MSz7VGPWXe!i5$U&p^CHx&Zabyi4ptIal9zIlar}iN6mo;%P)Dv8)
zE{o~I3Y-JhmvA8?Nbyb?Okr!8vkl^id<S9SJwpQnZAr5GkxE;<QIfHZro8;k3;4i4
zR&_g-CL|bxXaNMxB3MFijqR3YE|YWA9th!lu(C97jS0~g=R1{R4L!{ZR_Nuf{i}DM
z`df!S>2lU4b!*^9=diWsbt@sV{_10IZ{H*C3{U9ap>mwQWPm#?uNkcLu~<KdeZr(i
z&Yn3lcNxca#+$+xaBkk$NX17VQP1z3WcAmMYzCFk{bOn0>H(6Y)L50p^ceUPdqze^
z#G4+I%8~8&Ft$vz^oTe1mB2y(WzhLTt7Gk-IQdu%#K}a?0?@gFpT&EgELcfH`<*L3
zO19UNa<?gU?)nkj35-IhZH>ir9wa~Pu|xMqA7d{|1pHdC!ikQKu2#gVzpqX(=mZ;R
z8OQqaCXM$9gBcq#V;@H1)RD<#5$6IS3H2ZZ7rFUIZq*U;uvBi^qETdkIvx!^Myy<R
za6$Jv3r%lhiS;>lTiAl}_L+rfo6WC+l$z0vJi$F!!HOj?dM4ViFoKmRZUeslV65X-
zaG1=g6`u)u*b)}I18lHBttmv{YW7fk(acBr9E!b*D*%Kd+O0h39i-K0A%AR+`SIwm
zOf)@Vq^}?B0WyuhbKjp1U)hbOfit8GhN=}@v3tx|adB}@(uWTpA|SBswV38bp6Ijs
z_%K_kv1-Wo8Aac_H@pAfLD$(qB7GSG<iF4)9SCXM@VB;ClP==efe_uTL6HY010A$F
z6L6k5C`KrCb4$7Ys&lfM9j;DF8=W$4I*l~*q7015`XO5^P*7S+h+mumZzo9~7U)G<
z0Q_*qxXCN7+M9C<_AIx@0+6?9{@@K>IP*1ZKp(E*<BdbzL9Pryu7cXsC(<;b+vHU}
zJT<?x)*VJ&V@f$cQsG(V>KhnXKVaOa`+$o{2%Ntq&T55zSXBeTORc%376p#{{QSN`
z#D<q2ZClpf{gSOEhs_6g1-vs-DgaX28XA2m85y;fc(1q?vBz&uTKo1oi0DeuH8O_y
z#~rB5KI5cqYA@#6rE<q}1~(1FP(pdl^jMPZ22sztJl=B*Ab{AZ^6^zh9j{JNAb5^s
zh`UqWs^=ctK1{ZlDn>;WU-kIv#-5898|yn;81!X~5;B&oXI|1>MSoG!rV%EE<1W`}
zRtPsV7GEujETBRO!MJgg9<IO$yBLoKTmjB00M6CmeD#J1B5k6oye8V>jrobzr@;SX
zvq@qiBB^?dUGQKhU^}!+yVs}H{ZZ-0O?|Wd<zb<u)8g(a*In^)5&^&Ad3e6gz|(!1
z1a)qHbj{_L0L^S%kQ#Hx6$oHGCu;@4yJuQ;Dw%>S1Q#>~kzm^$dtfo`E{TP+TvD%1
zLMLZrY;>V#jhn<l;;KWb3wLxvNlqz{J$VD<c(H_0HnTOzbDZ!UClwaWi!FwBz9G4!
zj8UV%g&Yu&{f3B-++$#9Xef#CH~JQzLyBP1pULyJyz+B`-}fg<{&n5mix>o6dUl~D
zF2p>>v%gGySwS=CjFba8$b3*N8g-zeRQP~sl_M$0E})XpmLJA>|1`z`3DRLakfBGj
zCtnO2I=||bgNS;8oA(%GvfN;?&SJVVF8C+1ARcbERZbK)h~P!Y>6tX>)K@WBYRz$4
zRWT^>1I%!|#r?rTN#w)z1V9Wa;{HXQlfhC;T+3{z=u-8rn>;446EZ&@Q4frptObhO
z^zbh#0QNb%dQccl*n?4lI4cbZ4!aI(H^Q0H!*&4j=Uuc^a2aP?v>wG2CzKXiJ`8(1
z>?8j-egPl5i{39RTww>j)i4k}^nJD|*u+*PQ^o?fFUM9aLxVMRW#H$L0)tL1W%6&>
zp~TVXrv)VdG(Lv#Kji17AhuZ3WA679u`m93tGLl<INv4;VYjM7YJL7#@WLA7*e^q#
z0+L%|Khc&>J(^vuK-R=x1FVgG%2rO>gY`Pz*w6?M<QQ>2Qrxw_V}E{D!i)dj*#GG+
zmOxIIGkXX?AOKLPEu8(Rig&vQ{Mkuy6~F&AqQa_ix7AivrGLyWB*Hvg`;DMC8QMXG
z8e{P$iOZ(Zaig6<?txeERI{lnRMg9faS==cBWUmI3zZ|e7?y}?nlB|*+MyL<LaYAk
z+|Bj7zCR&;11el;pDMi^@dNozDGBL@6FE<lRn^qiaHZYM1mhuN-GIrJ9$U^Vm`Z)g
zJZy(jtB<-jDkQT$zSbSDZ)8LbUZ#8*Mk1*}FvEI!QWNf<iatm5m%DSk@o+eT;plz3
zkXWe#u#Vq3&-w_-{@GQh(r!4HyWf+(!3CaK)N&gHrQtkm1W*Jjky1s!gxKP0tfera
zg!f<<Dd>Rfct_WvYDNr1>jJuqEQ{%8z{L6|y@xpVaWTU;V}k~s{62j+V7Z$w=)%6?
z`%MEgLx*F3Ka6W_(`PpkSWS-WgJ<zd23{ukBeXGjOF34XanClh7jCm+bdr&J<-kT%
zCS$Q+%u%_hTDo6Gjep`afChO?F#;6vN?_p*jpM}UCWDTbc4uxr?}>Y{v0<Arv-!?U
zFxQ4gp1JkXK?Fc0IG=vBOI2~LIy+uJNul+hUh{{oOv&nZF12}erT#8cQoRI}Ye<b4
znCAZ$Ti}TyIbInOXMM^#Vm4wnC3l?#Pk2>YOKaM00?<pZm{+63p_M|K3#gw)Xp~7G
zeE7A3o{R)W;euVo)!axXA{9PKLHFuFfzG>^FJEe7r=a%oE&#6L_eX!flaG^AhZ$dv
z`*OOBn|hoJ1grt}a6%><mhwumv(ZRtH(wjf;=@!TrzbilrtlNQfAwHx`(r+u0$%O{
z09NbT+L|)9815!>(w8X6munl!q6FJObnXPYLC(2=;$CXui0<`TpUidF?1Pj+l&%yc
zoWW(VB0|JkKKJRRJpk|5E)}dCNhYZel^bf#z6$FPLqbY;-@cTR)0;X<fy96x{u+?g
z{gP8tHT}T8&mxdt>BFsH(-r~jQgR?O6$!d18hT!HmJmF(1lUEIt!-_!)!pk4d2I7@
zoNO$0E3bjsX+m^81`AdD+75tv8(I3x#2#Hny8Va8eSi(D>T(`@+`TSY+HC|zV%9As
zr(ekcig6!4FWm?#SaFQAB6^s-5r0;?5rV%=931LWGH``747d>jb7dg8%m%a~)<yxE
z)7f__`GB6N>Yud%Alq?YS+LsegV>D{cwf_QRNmzrB0;W+v#JJ%ILxN0=TZEk5TAhf
zCEWl<=t<`HuGCs~5H{o0{1`H|VQM_b$+=+VE?Uk|0bmMNgfax`2HEkA=w#i+v)I}i
zS|!w+hJsiLsZxWzf?j@8k@Ew0DDJ&bK?Z3nDEgf70jPTP5^q4Im^m)prPP8tNY{aC
z^eQPb291>(+kTc=h4}q-<m=#JLvF`iHk)Nt;G-usW($d5tm+cif<HbNHg@CUCJ++<
zlCcqYr{Jhg0^?CYQUC15Pmj{l(pbJ=11Bw}w_|IQuDRpsM*^B4G~p}u@TxogsRkZ@
zPW<AV7TSI_Q(cBX-m%cUBnNCyGv4h5$zzEaizWl?ff1c%8RjbLfWk8=?FR2(cifgl
zNiepl1mvO<fq?^x0t5vGGo`Z!l*!^i8CW8W&~>F~Uww(O>9B_NZ)ulW06bDg<-uW`
zGblbr#zjU((lSSzc$mrQ=^?=dE9StpV9<N@$KBN!DQHJP%LT;MHpf3aq>uPnjXXq-
zz(XLm8g@*2YaA~BScU5;JcxM7E7&3U(MGdOscgAh>`>rinUmp`AN#2r0+}KysoDeg
z{69a7zcz8OvC!Y2#Qd7w=u$J^r5tdD#6+FLH-;^w#IoiRXkou1;qTYPG@qn$+nWLK
z%fw8FO5SBM(uCkNlvub!(tpYH%VvsrU~1>1B=8X*@b*|>UQ!<yfXXfj*&0GLd^{1z
z0@sWRR#qUmGQupLhewGD-|*PJeDi>aNPnv34Y}DnsjI!^BFHby_E~XlW<mcyi;esf
zWy>S5!;zJH0F|U!Rn0D@w~VPElnjCdb<R(qrn{i>@xL(n5tflDo*^t|gnc^@?MOk$
zCjFOR;Wko0U^3H)4W(O#)>5@mk_6^#AAs>BZ`R=Nzd7ykrVlJU5eTiKhAekHrEVl<
zw*2*C4lt2FKl`c<q?Je8exj+U{Tcr`88^4wea!vqKXA#Bk22zOetdQ0=`t^`%%cVC
zk3#9M6}n_J<Fu-&uFJ;ze<7I?H-*Y@x7V>g)%eRb)+8oSkka?7{r_;ue+u_9_L);k
z4HUFifAOD2PW6o~&E1{-b=ofRc~5<pMUyh}#KXl8eD&jz9s;dy3W21Em{=_vLc>;G
zb<XmpRzuL&B|tr?HfqXdy5`SmY`cQeB-+y>vnzW`6FoPNm0d);>we3fp}YdVZeSC)
zmv&?H7JEN`{%k9ZB+L_Mp<$jzdIwEx^Bdp2+}`+Su9Csh^E>m5({O$TEWZu?DXOmI
zUf%}lF|ivG`Y%aFKvfU|uVnpC1XtH5ZE;5o4Y|JxzJ(}@*LQA!5o6qxgBIc!5a2W*
z=Fsnj4`R{#fu_p7j)%1OdAta|+7HYj1=tW@V#S?@zJ9$C^naW8XEzo?0%#86MoNCE
z+c{5)ag$GJcQg1HB9MB<yyC1u1ki1Pse;7wS(!_Ovp7&&<v2)~xRqLvEN~&ej&rK5
z{u|6ijCKIIXOEvtwm0sw3k+Q6FDDMp%IXzMk&zrw2_U&K>cKYPmxMt7Zr>w}W;7=A
znghNQuk{r2*UEs-|9)D(W)^Z$-;(v!|4!Dk?8GOK*|hRde==q<y`(69QQ-fSVGr-)
ztvNY-b2;!MBm;FW>Q+AL4m8ATqRkUVUjmx~W+_em1wq~FLg6L_LsG%?L?+6)KwPgF
z=n!}aZZOvR_aIo?1I&(N<IP=rAXmpxf&4VHdZ|t16PR1biMkh-ZuAOfuDyj!`XFuN
z4OeGaUibR{Yd$>)!ugD(EZD0LfWcEC=9?1(n85(tY#DMTHpc(b!p+q{=^;pXBSF6)
z67-)rd)5*Rs&)+|H-9D*jEsyjQ%5uRnXDBz%LX}1jny`O=s#IA6J%>Sg-4xr_4gPq
zL}F~*cv-n+2xBaX<efL2Xgq{;mI>2NRHub;n6wYGzL}*TYpnc7d`R<|iY9v2`_!<d
zagRgkb!3Mg`_x=p$c=A&a+eg3HEvo4nX|2lSB|h0=UV>rvmv%))|XWNi5oJLe`4nb
zW_`tpFxFU{b=dWaz+`^ra+ahrxbW16V0;7d7#)vLDTi6egkwn5$FZsgB`Rd0xayzD
zwWvTfEEE9QK-ynD9nEKo3xmwG+n+YEm$!m>o3Bgx0Bleo@DBvfsHfyT3Leg@7TcHn
z9&gM(h*eucq{~QAA1lk1WY_`*(Y;=bZv58?3h*ATAyIK9F6cP-4ZFPE@rkGu%a0%P
z`~rLhnuhNGirOQo#<jc){4uZ6ZhE5eMwwFzl5aMRdbV&I?Nn-!1o19yzE;Mfsij39
z_nCE?Ux9rL@kB85cCa|(P9<A?PZA;@NYw5}98`T}QGk?AS``5W#gyvZX^=Sxj0}0U
z-i%t1w(~@~-_USrj}0@wwA)s1v8ctp{sACQ=>7VOC5<NH$Wbx$hSLot7wr>n)e8;0
zNDqRa$<t@foVg5Gk!V-fTc*1G=cW12JGv|cRx=ngS91Q2W%2ruat@h*EMOa?hUDDJ
z`^C+{b+Lm*0%OBoYV01MhPJae31=M;cLRdx^<T&7c{=g+g<)!d0Z`x+M`eTw+tN-N
zgg-3~qJ31x&QSr5&oE@nsbbU>6-(UmrlC2GC;eqx<y_UY4l<B*<E=5>vuj9OPH(Ya
z&ss=L-JqvZByOOL8sf27(Vsv&$c`QF6}M_i0Y&E{HO*DNsl5Q_EvEp8#S3sO3@=`H
z<TE`*niAHvN=1YE$=ysd%FBjj)~6A-g3oGN9mFAycr8V8iHiGBaAR_EGVhUq(1MjQ
zKs;x?HE6UeaAT0ghCqjYEW0{8ez9YkK-%cn^qqNVXdIX2lrrrp%C#(*F@KvTdx^jd
z`W8Pv`{<_qMQ6j~7f5oB18ZvI43crq)mh04QY2}A!Uwi=^SN71rPPsMcrypNB$E=T
ztSLV?8So;;vVBGmNegWvN1!Ed;e*x5nK6Kw&fEa(eJkG_)!R8~18H5-L<BD~uqn1B
zvNMxfPF;#<uAR1~t+xSo@B0FDJejXC#$WK=$jr*Kj}~D%*qq$lOjQIWx#N1u84pNo
zTE?A_azssqpB6lkX*UGSov<)}|FuI{9muNx)~Q5LO9{x1kHQ0Q^O&mJz}g}NsF{gb
zX!6&uD)IG@F58+B@O6`Xl<Sltq-x+b{(2nZ@Maa)zUA;c>JA7VOY<cr$)trcYHtGQ
z>kIa57eaZe4_z@r!V5)UV+Fpog*eV8wj&9D4ccAFsWE3t+)D_n2=Cq;XWe0|j(D))
zf(fYgZA!ph3XvMSg55YwZcfQj|8iLr!5#q6d5<RQNm=&It`_DvRT*@Cgo7|?+!S_w
zV`}u)pi{tEJO{+sRIX(b$Ym6Cr5`>Dz?mfq^Ipic0-s_Cs{Bansz(@+z)z@t)L0{y
z(A(RqDv^v`!~?P;LhqV`zm7K^+8-pd8R&e!>ygoUIJ-oM3dA!;r_f<aU|mwAt&Rl$
z0w4zII&7s|c0BM)BhW$k8G}WjO+Og@9v1ujmj2-1q0F6^z`~1_3>b&RMhY-pSa$Bk
z@V$*a7MTg7(4yt90Es14`CtRQBE++ivEz(`XF)7IJs0}{w$H^C2%#B6r)K{SlkhDv
zWh%(2Pm|^sImg}H{IuNWEB~7l)EA5fQ_Kp6N@AZxyw^OmCQZ%v9g;JX)4o1^hfs)Q
z&3HYoR<sUHiVUr`g;j4kf->sUvhJiWi<^AqZ)wzjXHWmPOnd(0+W(Jaakp}(*`7Wm
zO_ZUHS9Y-S#)L}LMO<w=l08igE?G0*BmKa5$^d&AnBRtC*}n8;;$~!sJe~j2>uW*>
zlJQ)6=vO_Kwv|R@;TI|u3pr<qpbWX6josC}7czgE?e;WDOgZ#3+d^?A7f1pgSA)7G
z32B3@-Y{_p1=9R?IF9T168%C(H8=C0tn<GMo^wq=)-ycnnUqSRlRtLff~VhXi}jTV
z*bFzjtujn3$L_%A)m|11h14@mEMupUYvu4-Iv%OqBpDdxLzWkLd0hSB#1^Ugf4Qvp
z8mFH~Kd$~<gENxZshvKFI{oDL`#XR3wCvfgM84JJ%5&8lUno|o^nQ-}uZ?1VbGhN4
zr+w^x{O#nnkC`Ik^N~eW$2G|Mt=G=o&g413)w=EE$@r-EG8g8s@4YGBJ-wJJ38mA1
z6n&pdbbSgFIz{UntmHkX<;8X8E~ZbJpng8dz3HyPChgtzL;L1#fgU?|e~X3y?|=P$
z+a@m={`=Dp=trAQBmDotPh0Ud{P&M-ySA9^DAWn`xvh<cAO9CF{UrMX*CTm_<d!rS
z72=?9XAg9&Fn&66Q`GUT1v3@;lRf=??(mKDH)@+21J-5wGne(*N$+)e+^kJkld%1U
zEsQW|(zp{})NPm)#<R;_U-;#=JQK6VO*RDzb^iA(%Y%$RKm1y%kTLOY`d@9YzEl+^
z-~0R5Df@SQ-2sEwhy}K!jdy1n|Gv2kUk=^gbKHGMi||1n?gV+A_*lUcm;%*SRLKYD
zf}1Rer#eC#9_AL5Vq)K2rA|Zrs5M?jhNjjg+ubK1Y;Tz4{V*#nEA!pem&c%1f9{2c
z@Q7O5Bwj~i(gQ1it@2#>@~#IOP}TlyUJK-FYfBhT|I1`1`ruKg{H#o9J?IV$zSZ(<
z2R-rK6*oRSF;Q_F@K4MV*l)iNJLPRn{{b`imjHKkm0?26;LuQQkd_!meP2}i^#biv
z?>0P>N>`@<3gq1I@HlS!`P0@Npxz#A@9cwFLPMXRwp0i=TZ!j?5dW%s?(pPp%92W!
zaUM@&LReguOv6KWJq=ys)GTdu`dq-!!P(haX1scfY|Mz&u}RXAn$sP!M<o(SBw%{c
zV&$r#!PYHUzN%i;sg8cuVvwH3VHoJ0V3<oe<`sRr)OQ1Hq}%ZZEBk15V`F1E4$rw>
zUasz^$jYI@mnc7e05?lKQqzQ4ecA932d&vut|=s&tJ|t>E&I6n0M{U@*Tpw=M9m@h
zb5XbK^z^hyJleh`7ya%<=>&9=;v5hb{**|*cD2&VO%9u@*B8L+CJ~pM+In5}*+X=V
z0z9zUqI!PjbPat7cQ-PU9`o+1qr?w?dNu~<W(D1oj|AsyrB<6h&^hi|S#E)Imv2Z*
zH_XLAO*zvP>kQ-g?lq2x{B@=8N9d)P1fW$vjD+tk1{n6%J^3CBoLmSkh)P7p!wTHf
zY`fXr$9qpc3MtJck_WV{Vl@ng_u01~jYEmVsw$h7YYHy`<GPbEVU3<-L;a#Rw#Voy
zhHxA*6I@u%-fR8NI-D8%E3_X_&zoo+W#=ocp4Oib6n07_Q6Mkw@cqnsPPSgshRacr
zfxzhkrX>!u&-W)x-^TJw`n}zeHI^v<{hcbb@H78Mch?!#WVWs2%pfYGqap(+2ttB_
zB2`MH3}WE}1VKUoDMAqGi1ZRr)KL%tL+>h~Hz^Tm#zrzI7>Gb55Ew;D3}qMsk%W-D
zgPytP=lyf;^W2*s<bja=?Xvdj>s{n)Q|!-+2eFZVDY`+oI!0yzKC7|h-$t7(wcro-
zDb5qCG)ZtoCFs_qudbe!jt7;33HXDE&RJt`Fe^`q1uDOe)mEH_2eqyZEyZc<L36US
zw6uOW(4{F^HS`?wQX@?ADshPZ3Q?Pl2My?5p_s^`2qE~E8fhD_G=1%;ouI)Z&<v1V
zP+2RGgd|gpt&EC|1A(u5G4YEF1&Nwyn<rbub0~ZaXgO{=?7w2?H^-Au-xN?gmo~7=
z^8zf#s$-}%MjN?V`*!Tr?9R0y?5#O{=)C!F;N)+Sa{4e)VaXOjuzs!DkhfTiJr$0D
zEvwRgbFW3ppG!5Nc5n55&>bu>3Inv!0<=S-o{c_b=^5x-Vl)8AulMiCw?tvTc%G=J
zXw&2yC%+MXKw0#3U*W`V0uB&anqY~5V2vq44G#XD6MC3xa#^qju+20C{8w*frmt7R
zi%Yo!m3bhITnWTcuI3R|iV>=|8q#(CI)|aHP*ARi^EX0%UUTAywoM>Y5FO6Fw+Koy
zZ-e~uvDv6Gk;TY0nh*k#9K8P+Y3;dGNEonmnXzI#Gti|z-Z$yvTJ1}20^HH>Y<HV3
z*EoGJ+`Cqlv+(7*G6^wQZgq(OdNzaZjH`hX(_?k`FHsdj#jg3p!<}6+RPlB9A7L-G
zr(219*Iky%H-`_d4x~p29q==cIqer;B=lDXF0tQRk86OGZ<kjolPUcHk`~;PA9y0y
zY{#_XdAC5hxU6U%#YlJjgHh<;<Uqer8JO4p-F@$!0dx%(_bwxJDDhy?*Dt`SV2UX8
ztL5V+^ekLxor7xJn{a@Bg0`UlYNK}nu6*C@2!Ll8Gw2Es+mwP%MP_E2!PkeKV(w^6
z-6rKwh=08lvjzqHQ*cM<s?4;{VA?g%>o^K7Ubdfsb=lJ{a?Mw1`3&l!f<||U%;hsB
z6KVMK)`kRmWwt{K^I71V?klhyO_qj^;_gJ*k&yS7YBsAky;eG!;PbK$*c!P&n!FHJ
zfzQ^D=!-TIR+J#nd-*;Cr4^nGX5LhSJl#5R%CTI99G4FV7)92gfYxBj8tZ{jF0@k1
zVCO}XH_C-zupaHvO@{aMQ$kubyj(yN+a@AW5C!R^clR&=Y_c(gQ_ba%OD%~*Xo#CJ
zp{IyD8s@^0hLDS|#W_dzY%6bRZXT8T^W9`&vWBi1w+ydVZd>^)Wog|Hq%_1YPvmJC
z7<3eCOnE}!@^LoTNAbVdbrmCg-WBLi1MPeElaPpWx;>|WeRO2eVByRzneWMO-biq*
z<@0+3LuAUSv`70xTsW}!RWrZ2gP4XK(thr=G0Yn0nXDh=KODx81yWSXZ>|t$q{!65
zjC@{-f~X+L+S)I11bhhKg7q|iz02@PW+sy-3QL2fn?igimkBcE>_{70B@O_PgaL5W
zQ#d2Quepxo;P9dtOpk6Yp&ZJS>uUHWN3VIk*dudqS9V6lU-Z>8*JF(YNB!m;ae+8?
z2i^waShQJE_W?R72`vX*u0X#Q*Tc+l1Y<Hg{vFso%2EmlOhrB#dd}nV1ZkE^98J<%
zHGae{c7|~W&x2zvsJrS|V<=WIX`sYv5;!0X09xOU$3w1)OnnJf<X!xE0H{eh|5(5F
z`)nM$ES-OPZW$cP2@C&Z3)@-5YLCBSec@<?L&?PR9)JgcjtFp|&geY~wd912Kc<%N
zlNG%-wIWA!h-Zz+#ebCpzvrc14qYqK4g4BaG67Rxx+3}?Q;q(ETG#)fp_{dlFxW$S
z-we2RM_VI5CF)E*IfmTP6FWpV)<OPW;W&H-{>ovyyzML1MB}>xmf|+0l@leV-xZ$!
zM{m!Mc<z;jU8`iy&L#^H8|)ythM}WbY&?G7r^T<OAmckKE_M}wi(+dDXa~P+DEcJz
z6u2J_f#WF?M{uFH@uto+en==GXV|uKzyv_{2GpK~qVM8!@MlRmEP!WSzW}g{lv`Yo
zg|3%svGJKMa;&y_+0w(_#4A7PbEpHn{FC{EZ-NW>2!8PHQ^vDx^Mj>_XD}Z@WAjCS
zotQLy1jLnuf->;{3iTi;6YTUI*~~oNOC8TE-LK`6)_|OGFyRdzooUT}s#Dhzo%()v
zfc@FN2IL<O5}wEDJD%q_l>7)&kIJ=F0<rziPJZ$JwT7puM{zqc*g`B>Npi5On^PR{
zz1070uwY#@&3FcU@~b$j`=J5ILZg_qX8^=HH?5iO#FN;(_C|*6=$X8lPFS&pED*if
z&F%QoA$m0c+%pBNEfUolU`V-ZVQv@g=;yX_#)PhBaNh9y8gIQ=GAL0AIJN>FgTC~n
zH1!t1%E7u&<=`&L%q^XvN2|9{Bl1d#$1DuJ+8$uS(i#$0#LlFJs09a%ZdsNoN6`Hh
zOh{kkF{rdT@8hsiU=V5j0XVBZF3X~Ae5vC{XWBh!OgMTm0TTh~R)5al4#d9<w1}|C
za@Nt)%ZYH|#6jbLj{zUcYU4T)ghs^RJ^)2Ul2&b`bzehCjalVzB-~Cjw}1%R??r0n
z`U5F&4&pfYaT3WB#&u6pJvH^m!(6o12Whm37$1GQT5S41t9uBf)yJy=pWEsnrSyQf
z22mk<J?A6GZn)y$SDYEzN?3UsYh)bMI3n`jr&o(vwj*t$t2qPE(%m%Dy>pacr$@a?
zyFF`mXxfVmr#$)bT+&b4>ZZV;Ath@_TG#@_=Q{8pcj!q=OGG|UA~QnE@i)Ms*ftIu
zo72_jS!iNLGYde$bS}M<BL}av47mcy6g60dDhaE2mpljz!Bke8+yaxiiUKfDVgOOt
z6^%v<XU|bpRV7>C2rCR=nAB#j+<!Y+6&Kd|)0|{ucM<Oz|Lmh)1(5GJ`cci*u2u$n
zM5c}v*A>PH0X-=<i*DZh#L6pQ9$W6nz0fpJB0Ye#xBjSg@)7^UvCI$(VUaEVLQl2d
z3w1WEW(U%~e;hWju`pp}DojGq7&`(20xiigBj(&Z;;h|Bss_p2)~HyQ`tELGAf|^C
z13HfWF0)e82ufov!vnJLps(NVE)8!YcS|)9YIe*?)+oefSSq2<ME*>#-fY6PYGSu|
zRi0_W)CBH#-e+yRELF~*==0hnGN;Sdg%K1sZSRqZKM0X>^51qcqs9!^KlLN3b<L2C
z?88<7+`^3B0BGPc!Usf5{Qc(k)ewr4m1c(()4OMte6Hg~X`t1eN9olex*!~ARNx9-
zk(r*dQwb|GUKoyScKf63%KQzsZlccJ<O#i?tk4HQ6LWEK8L7@JO+99p_U!!;ey!t|
z{sASee|D)$xJ%l*`-QvYF7|DUZVJy{3bV2-DR+ny2UONKZU&M5jG37k4!jElxr|i0
zzgpKCjXTA}UA|mS1NLe;ej%!eV?+y!I4LdgPwVwkONUdITo*6i3zmWEZ5EFvMC`@X
zF+g1KRb0&MN!}?B+i4@S_;l~4LymrwOgzv(R^dtS$$Q~u<33sILr0dq!_#G|4=zh9
z^vCVWVYg1xBcl^N3bMSL9*f9U&ji7IPyMhr=RmSX2%_8Rxbr+8SYsW;r@RkspvI@X
zh7i;^R3=OAw%Dspifyj1Uuee(t%FWHfzRi&;JLyro6zGNPiYzqF4$*<Wa35ohpM80
z&75lPq|E#T5Xjsz4vXLInr|8^0?Nt=xa5fRZhK6R0z`Z=G@ot;AFBTRLblm0vMl=h
zmcIe33qM9GP-P|xb68v|Ww14i3G6~IW>Bwfs+Uf`gW(`$XX1*OMcM{s?i&2}Jy*d+
zQD8TN{2$BhTO+@nyo%wkUUG{9rg6+~)OJ#~b*XI%egec$R<iJ+NNgQpC1f!&wtop=
zKSu&%eHxsNoK|kn%fx4=bw~75mVzO)S=(qBgMwBOI$<1{3xEz3aq~{CS10@Pb%Wyi
zoOr*IgLf<z?^Fd-dTU3)_N^=Rd)b&NrI$PSA(+Ofg3FJOa2+Q8VH>@^nt(fQtibg9
z3MMyJu#FxhJdiHgzUM!?Cr3HsY6zkkG2>3{w~1fLi7T1ip5a=gH3zCF4S?kfjn)e_
zgls9yQD=~yt_Wf!t3Zu+K2#Y1jGUT2mHwL`O{F|Z@UZiB7-Pw9@=-euRc(LI$&aWC
z4bkIsDh%c%i_E8udW6$Gra43xLns6P#7<99b^~|ozgT&mjgZF~o5Ja8M+V;+(n#-5
zGsr%PI$|o`#D~O96%t#$Fa8cN8Zi++bTp0fNI9nbuI=J;hx2>m(A3U4ma7^B5^@<A
zY)7S*@*6a(?Q8dDvX5hWY>tVHby%uow!LxUAt1M_E}S1k%#2bKyF7{n?=9`JR>aV)
zA#z?q%60;UZKjC1gOxGBkw6h;{DQ49`iC5p#OV%(nH!sVzI1~e`tAsMaKiV^+@S7o
zMi}ijJRm*Pxm7eYvIrO*NnIX+1U?|s2s5N2@DDvo6o`A(64KkY6r9z}wHysD5NMEM
zd!&|pXkltqm*Usm_{Iug+jv!(rTmvD=IHIgaX7ukeu!?(GgjN)_6yg6|I@$ZLa#zk
zL$pfqf!%YGWxmrzeiO`xV-w9lOHlZ?J)8ORMMVnpOdbMUy9HEo&mM+yExiJfh3jn^
zQ)W>7JmBzQrC8wd(I@2u`pvBZq@>h)wc`=vSb<9Sl0neohc=;Y4VV4(ZkJM6`H=s8
zVEYKGzu-U#nocf$TZaQ`kLi0R`yjBVS6tdceeM$LzY6_a14BdM>3Ci9w(&4Vk|d+q
zP2J=jR*K)NW}QDMUue|=Ax)`Tpi_5ilanL&1A8ZaLE9qUFDqL6hCC)rs`vngT*fa`
zngPt@>EahdE8a+-*Rd_%iPv<?b!j|-usi~Se9D-ftwqy@U-VKMhi5;Gj2KXko1oTl
z1%Rz(G=uo?Ty}Py8;DC*Oz63#vAgV420J~wL6srMqWq1T0<2HD)2QOyL;1Z&wMYej
z3b?Z5;JT`F?B8f%a<aUxr4Bqk$kC4oU{=c-p4{yUUzfdNp>SW%wb^YZp@;_@r9#Eb
zDJBmtjd?~N9%iBrTgZtO1BRX*nlE6z6W7<)*8ZfIauA&BB`g+uqx9cJJ5bD$DLVxp
zn#cDHUY&2%39Q*r&2(JB2j7lSBm!tKW<dD5cZ2Lw9a6yoW;i4QrJA>a49Cyg0fYPn
zP1h;3l2c$N0L6s*=bsnj)_6eZ4m$B1F+IP7(3CqAL>Sr+Kr<g7tKw>3e~LfZihg)5
za+ucTOk*5kZULrp^f_t;emYr$m8@|Yp5D!GYj4*@BX`b8I`@URSqSgPPAN=JHrz*3
zu=*hN7DYg3eVBIY_*_e+JVS5!=Nk9mNne~cu5kruwL~LrN701W<jq!1d(jP%`X90h
zp3jD6;dep0+Q^;Y3G%)Tr#G@L+oD^Nx+#nG1^C-2%cP<s%Ka6v%h#VKO}CWxUl&fr
zMp5WSS8tb+NH3mn3xUt60jf5>&wh&M@7*(Dxg4aTv{dgGDi8kkdFY0({^AP#RM*XB
zo5i*(5zu8b6|?AC{0Pd@5_8ofZ?v$NRuTEmI>kT%eBr^14QG6ht7~^-X7gF<GLE|5
zCW5mcIDQCx3hTYG;n*j&Lu!cf)F4lJ;UB^4TJD6Q%GjJgf?%)Ax#LzAGs>k@>QNRa
z*B6%H{M+~#yV$gkXj105OIPdtpv-0zRSXxW#Jr(mwaK>kE2i@_H5(P{@F^S;xKVvn
zj0S=fOeS(HvA?k2e!$Vb-Zm&Y-$-d%3)3A?o?^gV<e71hnmIQU|BLt4>2J{*7)&WZ
zbL&hFzQb**ePWFFh`4SJ)t}ec<(wDEkA{Xzaley0-MG<K)Q>0+Rl^}x^(@N@YPTP@
zRt>f+dnSq+=6qn4Ab18NZk+eX*K2zrYt#pFfWlTV{Ixq8BEpj)9k)_LD8;)z>|wzf
z2betEiv406)eH$*KwOL;!Q(I3by(=tefp8RBa?jrT0-zi<InPF5dDFNW~{ZqAq1h1
z3pYcau(iGw@G7aq2`l$kHf8DUSVi{!dA$|xyPv|66{b2-20=7)`rA}}WhQo=M>dNV
z?D>}H2<A~$#n@zsrY5Rbsk$Q!UCMesFbMWk1S+MeU#-bxGAKGnk$E7qc&cQy;hFuz
zo_zB+9fk>dN*CN9?oGFOvUo=R%#Im#%=(F2n6-Anx}x~&4a63(v~+vW>x)&YpClH&
z%Kn*B8F_F%vb4LGp(2AZfpT9}y>9eRz-I2RrtjHjy!>`e9*lZ^T*Dpy;8n7Iy+>nP
z0bjc{L<nUza8p?iym{3>#orVpRYsDRoS@4Ude~RRRsQWZU8`wtS9!R}ar7{`-aPA<
z3fuW`3ArM4x5G<a33r*nFZ;^WG0z;kjE`nCm3l_4!#9@xgO5)r7!&55<6;-v=dzPn
z{r4D)Ukt*|C}nhBAL9&_A@PQB+~i37V*LC=58KY2Cf|pOt9Y}rio3LK4ZeNtj}6bu
z^I4SY3;p}P5%7E%PZu@TB<_EK$ycAKupd{yB^~l`12gMEpxJM3=xuLcupdmIlO}c`
z4k&-e7y5XHU(BoKE_*RYU!|gzIa6CbhG~M;WY?n5?OG{`xIf*u@<xT8qwPSnf;V@b
zY4_jbC}qwQ+@$aOQ!<sGLJwqiPz3rrgJses>fJ|9cl`|h?R&KB<;qQb%-ZB(ySSt~
zH}MMqlLz<Y>|=_UN{#V3YUTRhRmj&iy;|#(8@?aTbYg_TDur*4vC(4)q7v|fDpu+4
zYcvyFAZl@kn>Cea^|*dNBsfFOM)Y!M2zaLjRY}0vj*F8HmJyeZIrs4bT(<5{8htrg
z8jN*k&qhG*<VPcD;l8#?rD<2|Jww6rL1^LJ;yXr5btEn5SgJYFoVtoDIv;E_wF_b)
z2;h9Lw7;Rem+_JjUoFJ*_><#RdU9c7smle*v*kq^!uaWZV2^;^uc%39J3+-+q3ubD
zA7pWX>1=mM3;~kwc+n127Q&^>gBoVn)m}u&Xm%JZf4D^}#n-?%YYDM^m3*f<vSr0?
z`c@sf2aG3tsUODLbhYQz`)@EgSi;90|MOttw-NrAeqM~`xg9cnk}>kf#Z}=Wp;J~T
J>rPxu_%FZLCR_jj

literal 49896
zcmeFZcUV)~w>BC?0Sm<nQY`_LUZoc+1O!A-dJ_x+QY7?_ifpAxAcP)~0MbR8p{pB^
z79a>n2N6POp$7=%n{3$ooZtP<x%WBe`Tn{0p7kgR$!c@VF~@k{cZ|jBd)jJ@$JmcS
zAP`2F`mOsA$dPvt2yGn$9r)zi%i#~;h3=82+AZ)3wvC&Wf#Bt+i@Fg40^thX`%e=!
zvG^DQxd4IPx~}h)JUis;XX;)}-lcf9-R0N+s^yaai#68$)9D_=^h1%dcb5HmSC8D`
zx$=Qy=mPQSgiGg~)y&0Da!&a6<8Qo}dKjO3^|9nZyQJ>tM`+xRW%zX44RJCpq2$cf
z5-o+81S8A{PlBa7znw^NJfF7`Pn2SOUTF<x)kv_bV6rWdjQs01|3CJlH2N+dJ+kR{
zrkK6HgB-+ib8PdIGu6Sux{t?an8znRr-}3C&70re*ht8A{QUV@3R?z&r2o-VK?A94
zEqT)U{w5tAUFy|9U)^g-+Yhxkj_I_vX8I^`<=-idqpb`7vYH_vy%sj3DUncRLO9v?
zbd51aNSv$CmwL0EdXrgCI>Fin`N&F&7Q!~*RyzfOtm%c^81vs8P+2NOx`jb=jc#6S
zf)_gle$Z``hkR5S-&|Vi`2bHDn`}$+e24Ag_5ajgDqfbbKn_W8IROz%@+x(LK*TP~
zNmBE$S*^1gFf}35&kIw=GDjfsYoBXcSvsGrKw(RYh1EUP5J-^ANmW`%PC-%8A{&em
zS+J()TnhD0rRbE-9fODsJ@ZeHOGfY@OZ=B7+oHNFSRoJ=k<3vDMBj|%Fk1b-2-?AS
z9!K<m#x!ZeScQ#Cratjw{3K*tsl1wTt$j8AR1ug%ao?=m>s!Ar<8fh7E=PFnnj+c4
z*Vi{t0LIA3ScqOZFX<2+i@^KO1ko~m-Yjb5ZZVk7C@LyS(-$zCOxA?i$F6)v%&&PV
z25g_!QC$4m;Q`JTC{I-8ClQ`rqpprg@7cmq=o~d2Tk%9{vmz_i$DZ0z0VeDMNd{Q_
z&uFSPUkrkG`vn%k%O!YKl+>6?^mhkCqC{jh+|tjH{56=Ga+j|iwW5O@hLWDadTopQ
zl6a5k^~pf0I6v2f@AgYPjCh^1wwjTheYK~zmtqdHxEDgw<8uv1aeDl;qVMkRP7x;g
zBZqUojQH}n;D>$;3{aI|^=Xb4+tUU-#zN=H6Z@j5o0|{yb9y^E7Ca=W35a<NFhgMs
zc4L_+`p2Dg9R*@d2RKahY@}5N_8Ke;Ufk!K(^o82x@-{<SgF5k$X84*inJR08ltJ#
zUa3JikMxiy_9r8D?XQi)r)h%hUMReyfjoGyGck4uHWIiuF|n)`f=w;N<+FiBf?+WE
zD+zaZLJ>}-SUwmc1)<sY^rp4-IKf6@gGrJalaL}xx(7QdS?NldAL5W+w@iqxu2Nul
z-iF7uK)J4FYZFdMR?4GW9ASJhYK)Epzzl)VZBqhclg-j+tH)J_LTfi+jHyIn9&V-+
zt7fE&vS6N`z)YY+7f82Yt4Joan-k07TKgwYo(vj9z}QyDbdgNpx6cm<p1jp&1XEa}
z`X*c=VGDkCjS-UeYg1y(L9?DcM!$PT)52oJL~6ZqYVLBc)-N{7eFFo}LTr*Wn<}?)
z!Lv6BLy79pYp^#b98HwTDz1289ln^*sQXLDU`3CL)`y}So#D|MwE6*~DG3n{1;1Xe
zDEjRt4z5<S?|NiU7wOMbr>H~mz%40A0+I>&!xFxGnc?{^VRVBA@&iI61$o#1Py?CZ
z@BQRDSmPPkIXtd?)_T?s87WmP119OorRezaWM2&Q$n^z(n2HD9|AW2&S9}{>304@l
z@)29kf9mykD64Um*WwpsMe|5N)x9e}Y>SQBm#YjkHWTT-J!jk;$O?XbBh4ifYYTN3
z1?z|f1}wAA3M0GX&5FF2CoQ6gho!Dv;~q^Oz<#kvEr_ar)b#Wk2M7(i?aS<Kvl{Ko
z=JN`XI+DfXO6|US*ODS07x#TgsJ=~Lg0Z$@R06-W3^GIR{>ZDTna%1wXXo0BM({G)
z6!(R`63zQ1?)27;(9zD9)OJUDU9!4fj?#1M(zE$L=5}kB0!LInb^NLiW4=~g40Zfj
z;XWfd{*z*(RYJV@09RwMu`Qiuo!wIdR=?XHh<PTML@~2Zs*Frhn@`A%i&k)smgNRo
zkTT-uL5#iJWb;Yq@;sXZn03X3yT+HIWgq>H--{K%r{eDNIZm-IpOltC^;(F#4t)(d
zK4N4}c|F#0`t_e76BGIAD}5-i5Ac67r}+HT(q{{4DXEfJ3(s)HR4aF(X!Svv_YK|?
z&nyb1;EK{YrGD!;$EQyfpZ<=2_3Bj=su!c2YEJX+A)LDU_<e7jhI9_j?^rLZXu{n!
zg9?Rm<KA}hTg}uRSV(Fae@s(8`{J@X^we(rUB3LZw6T)07GgW5_TAu2Y5Ix>99J9j
zZOU7qmtwy)@E9q;tywxea5L~4xKZA-ue?B9i$b^e5}HbxA@M@<L39v(EwIpLpw-t2
z((K|{*4g@-9vc%Xk-EQXK)hVqIMxy<0!t+hNxR-w<lZe@YWZ_v0*`x)JahPqaQf_w
z=0u|ChAbQ`g3A;1m#f=aC6GQ~mPfvipASP67$G$>2<Qfd$;U1uAQECYa3A#rR@IRS
zZGjsmhk_PCJ{vJN!5%fYVCUdKt0!_C{^EHjGP_6onzVF%a~j-0B4IEQ<iKVNUn$w!
z^+{?cj!Ma<v~1nF)VV^48?=S4Qr6aXo(d?1bS%HkF&)X>*h!o!(svjE15WRJViM9F
zj1~Ll4yj}FVz>x-XaD@d=MP&;n>G^OLw|IqLgk1es2@4a%<r<-J0D64$`jALzO|`V
zFi>o(!xC{Pqndm{Q-e@fS7%-ui*i_#C;m<zm`Tr$R)^{d)%+YTO_FgQ*2`{SW9QA*
z7jR4({VeuCu{@QCY8GvjR^x^IIMmAuqY_JRCyuW2GCVKlCDqX~Z#@XH(o=4@9Hl5R
z45E|*aZuO#=|dUU>uL8sOP>ofGYR1FS4Ex^GW>Y!g`S$9cDkZ$Av@my!l<->{J=X<
zYsoNYbyz!YMt>8db?o_QJASd93-aN}UlUo6<kg&1XWxwfl=X#v{KPK1&uHtQjRbDx
z@e{i+X#M!&O~L#XMdvgS48)dLWeN?23WRItQ7b4AWv7qMR~jH675y~ld3^+O_e3w6
zwe6)tSd3c{cCC@ySIEL@J2vVWh`u~;-C`5`O$fQf)&zvAH}@lS8$1PD`;fidaiB~X
z-Xl!f4&@2(s@_Z0BQ3ek{WGe`92JP_Us`5(+>5XCxaORtoy6aJp{+W@lsKx}qq8!C
zfCLF#-1Eiy<#v@_Cq_CN4BIBJ^=~xRd!kK+%R>c^i$MPN9`;7{B=zx5tcv1j1wp<@
z!g|UQl}&}tB|%Y8cOLKG(ThiCscI8w)Z~T3kh>5t2#Ug7q405FPpDmuz~PGf?vb5x
z6cCSZT|$HKm<2q%DY>~TxoY}Bp@xRf1e>*rC~VQoulxP&u<2w3t44)NQk$}w)pA<p
zq)xtmORMsx>x!vxw@;wfq+!lyas*QU%9)1`4CjC=rE2&?>VE$PTE4a_3&KKfq!Fro
zkyro(@~~P+ZIh3d*FPYb)GkF2@E(DCXn}O{eeW_*(!w|9LeWr&Ag@xf%h*@nR}Iip
zuPN%Av55Jy;o)HujQ3<hJ|0I=_t6Rr-1U^gM-?UZr64_Ii6UBjF}-{-P2XCqhn^of
zJ_(|^W~A(|KmJs1hCu#Yef<6I=dxRuPFf&cai;YfvANH};cLXDKv9d2mR|(1tUNy{
zk@vG#T8bKgEh3N}nickzmX;{GK#QGTjRB{jiqa7|%1i9{sn>sQXlZGQ?*)FZ#{e#x
z6Pi#B4=RcF**#PTiN^c+{tCqTE45pDQPX$ImhkhZm*J)8^*C%%<`Ejm%ReCO5V6M*
z5fO~YVLnnYa1_V$4kpAw1L#%uh=)~(JmXZK>FhZqULcT4rlqCzMK)pE5+rRy>Bw&d
zy;3Rjsi~>YrMynyaZIp4`QLAEnpZhwmzR5{W83A3csmfWpZpFG012m3sa?V7zBWNc
zvmb=)7d8^-Z(WK8rYk%XR{tH?q%N;<b;23`y>y}$wmE@lS1L~@G~07pvmBOz3B>oI
zqSb9c$SIi{C@1ndN)+>aP#pSp<qdr8v80I*2@cD^A&A3D{S9!BA`m-jhI)Ht-rnVF
zYfVMDMbh!u%ZljD;skP5cM@CoV$ejWc!25)PlXY=70IK|M+@;dNnU@eOT@#FdrF5p
zAVF6pi;p*p)_yK@q^F|`(C0>E+^As!snJ8y1WKZV>DVnCor+5AlLoM5u*!S+CSjBo
z@%+M69M({%Y<4V^hnr28XfHW)t$242wq-8P`U2R<%y{BND|!V6V^$YX?{|WGCiY#e
zuC7kol{gCN;_5}C6Y;%qd#J8sbg)h)vUEs4>RC&i@Bp;>;R~824;U@-T6~|JzrR1)
z!DTu|7mU&<z=)Vk?Z1L}po#p{H|xVVOsYz&!YE5t78DfBKY>vqN-_3pU`==hYOU=>
zCp^4(1cB$`&%XI0c&!*&V<dFWf}Bj<@UlOkXYueohibARG+&nj+4uHmTK2tfSOkcJ
zw~^fblL@wEJ$rcm|MlDVZa~Gl5d^N00QY=fNM@3Z(@23sAmQQ6wIHLy4^wqpW%I(N
z^Ox`P=Woxa^_%4sy7SbzEu*x~3**<iD;O<%e8<jq+L|LYJqvpLA@Kq40fSOKdwa;j
ze&~?cP^<8)&v%=~o(g8mvap<v%cPgal#_fiET(q4p11R<;Ce>}QvHA0Xxb0aiw$sn
zu5`)4MwHIe)~$^1-PrzC*Ln-Wc<`R#2tbtk+l4d0MfbO$dn|y6?Qb`(|Ig0<&(`QP
zmboWbL#}O*^DIipe_LWmU?L9EB74q-)wcqWx^sX0+=2NmjgwHGs#*85k3*HL;F#Ez
zJ1}?TWK!RG_*$ttbm_N|Z3>D_IkU91d)Ys*j<Ow5I^}&SQ?Kw~I^YFHtg7WDB~tNy
zT1Xo>D{EN&UZ9T7%*<3bGaI^iohA7ZTqa6F@!?J))4x0HosrCuP@*?kLJ}<S<R%-Z
zSS(n~Y2Bjgo3}TPADmq5NU=!Jwc-~oqJfgb;=;nhS*t56gJwHxw?-)RuX^N&PNb=+
z&tfUs?M)M|mB|Q)D3zUc#C+}U4l``?17h9_mY;BUa#x)|%EIit@lxD52_vK3!m(V)
z08;@}O%=#5^lJ0IUPE4rsrU(39+X=&HXXV>Eaz#arq(!zd|Fk_q)<TDzAPeH9E-gJ
zi&7vuMKzB1mY0@Ri}i0T_y-mwY`;Lv19GO(thrf4^tNzxcCK~)6PL80%y&QV(g3jQ
z5%^jOR@6o!Au#)%?LKvtNbr3XCj+*IM@5<~%ON~t$-X_U*L+evt$lr|)i;UU`v$1f
zY^%5`n~YEbmc=aj2hMvnfLzLdRpM!9$I7{)3k>F;23wgn=9~k4D-rW>;-G?*Y^|F<
zGEUTDXVXh@n4gpj(w$uaHUx=}x^;;edq2BH&F&%`9{PKE;~GpxL%u+9Eg!zN4InWs
z%;{2f=KX9>1>&`&zQ@Q&Aqwu#)&gJ_ll$2}bW!X^MUUz`k!7+(K!@IAePE%Q@-ht1
zEu_IPv>)@F&Q?j7Yaw2G-)y>}h_hN{RZH)zjg1<+0&|D=Tp?{;V7OLVQo<>xA-q2{
z%bp~6PgPGhw=D^j#*ic2R=qz#Bcn%svnWC7o`C^H-Na;|AlkZrU?32wot2I=EqDU2
zHLdiRdu=`YC?P><GFc&@F_LSyZ)nIY7CSjP=><L22W@J7dOKUcA}cr7ff&MS&NeqQ
z<6DiGe<(@)m0+9d-D~DKw7P!9-S@=>S*!U$9J9uK0B6Lb)v-gZ4>xKscpC}N@T^`p
zd4CrX^b8V89Bdq4To-n9blkR0e%6qNm~6E}tvr()_~=F=+`hSuZsdB~GI7mL?02;?
zK+e9iMU77W>tmrqsY<@*d{QZ24290!_1L!!|2H|$Y-oF{@&n*`o!#A~k}2B08-4-7
zKz2Dx(S84ZetzCFsqds7DIeMHG-5i3tP5w2`qSKKh8v<DLd&G+H{KAzlkZ{HzU(u+
zf%jAvix%gQaWcJ<ozY)CSmiJv9w_oV*X&&U;`54%n-AE2QeHuBlunR!EQ%I?IhdPF
zd<uEo5;tT#kqB9n1j{PUrjaLbYYE_*2Y|7IjElraV@*Z)M}TfYBHZO$ZXA0NCuH<U
zNlD2SKZmFYc~tZ{FE@9F7iI}PC8tLi11p=**SGPDvn$u2P(<BlJ+BzQbUfrnnW0cB
zKM8@FmM6w}cz7sZd)oP;hAfI&d5O*C9THD9U*6cFLT@J{Ff(BBT0Pkq@*LSt#_Fus
z?8?K)*@cBFx`g&u5eg=RG~|vmUkvURvM0|-vZTU*P^)#|Xuy~T{IHDsq)iYss-UH%
zr6}lQUVpn!?rpQRn#OY7JCM6(itG*_+?gTRufg<O7fDIo1%TVx@@icH5v5n2m@}+-
z(<YMPUGQr`?t7RYc&<_>+W6JAR%gyRy@8m?<bjK?A?)#E@S!uD$RGXv>|g=&xVgD;
zqT7kgu`8Z%k5nv1(<ch6<~OoSg~bKB*{ln}_8>Bn=sn{<GNQNBjIO0S7V@=gV%3>F
zV_uJoiz_O^G``7ZC+`bn^8;|%buac4&n=C-Ez0$c$=<{FK5nj!wY25o6az3{W^C5P
zqc>yv0we%q)J4AUoN8-9iv}(eGBjtt_8&kKv98tzg?AQ@wl`O|k=!DI{=JlKibjPQ
zfdUmyz15_qW~8j|&<g2ly+cjddKNo@T=@fAzv#6EVN?!szKh3Up%cp+M1g$Kb79q~
zL>S@u$y|2ctEXN+J#NP{UZB!6$DdbRTx>hhjM995VciXSYQr7ivR2A=Rx)D573g@w
z!#gWB({$HeaCBtj!m?Br`$Llb^EY;MVTHK>NIYd7shNwuz<|&77%~*<k?9o2<3^5L
z@6ew!=RW^)Sp}M6F;`J{mvvxcYaE%gT@(m8?e-JH{rQRQqt5N_Jd*ctY-ZbGF{Rc`
zhz}NI6a`-&A1Fz>hTPs}BT>n3_+F}cNf-%`HPWFcFJVFS2D<{MclF+&l*aQ)ZzGct
zp&I=&#ZLjKPD^N#qqP3<pc<(!bdEWuMDnHoUF^=wau60D3Z2W)9j3rN-Xfd|C+6nn
z0^hD&hw&Jf+IL$4xtR@yPUw38JQ!S4Wx%0BIoYG%4mG)v`YEPo@Z23E%R7Q}8H+nZ
zM=#upeq=?l%Su@rFs59D?O|Hp?CI*{s>!na?t-JObOnY0j?qI1UjltbJG*vPUv2*~
zX0-H4K<T-JPYnB#m29TmFGXKDQSIdDC{1!k9MXwR*j7gZA375AbWR$~|Hw?5yP!dV
zAByf9sqxk1m0^QIc$qHpQnY$~Wq9i>a;e4Qf&43VsB4q?i=2;9uYX`#*S5hnfzEok
z=VCS7el{vwW6Zl19&`QkK*jR#?u#WR>{VRA2G23_q<7QQY!PT*9=?z*$sMKA+0)Y_
z+LV}-G+tyl8-a8Z8A<T<+@J}**(z7w*BWV3<G1nEeWu4T;d?*|qFi(H6V}1l*!cZ*
z7C-Bu3U?D;Bl(onV%zrA%gG2^&1SHMLIu13xOGXz4Ck6L0=(xBXue_5FUIREZm|Xy
z(!=I$vmV63US6|t>P}pWf@eT^Wu*dIBV!t9c#O)N<b;sptizJO*ydwbq+#gI8kGyA
zFp%L3ktO!sIo=>13_g+aX-t%M*yJnTAN@>kA(`U8dlc5VQ4<&d7-3X*t^w=AeB;&T
zM@3$!gcQZ4WNJ-d3Sxf#ezu#1tu1kR0@F(a+?*_mY4KN%J+*I}`24k0XUkWb8TVNM
z(xLu*8iuu&NN6qo#=2RvTMPAf`uYAjVm>Z1GV*vAX(icI1l?;Hl$@jQR{!R#ns2Hn
z2D>S~HWb@rW4@){^08tF>B{b>{!4u(F)?uq*qD{5&7Rx4w24B_vuA{YGra_73cXXH
zo^ii1vCp6VvB(S%Yje^<#kAr3`NV%j#f&g?_S#(6XpE-{*WoW$Q&LiDuQ&%{u;r1i
zCy*;^Acp6P_tfTT8zVS^WyP-Z+hBaAid9TJp8{kKSKwde>?!*TuxG~f4z3-EDfsXg
z_TN@;4)AiavV`sJRar;+;qCdf-*845a#y9;=~yJFG~^=Yfy^?UObx*jd5^Fs>wGpN
zP{Znfy^kI%hRzM*mY3mcS7EzXvoBmdclf$Z6?J}g)(^ltr(neVC725Ohp3$@fd}e;
zn_wcD&`O(rVg#^S?GZW8+ul?pVjiqL&hyGqS-p4py37So=MwrDk*NZh7iuYp-V-NI
z2<4qo*1Qk!*-bod1F3wK6zn<&L}vw%(=&2&b3Fy9@#@=6Pccn4J(}`HgjY*Aa*Uf9
zz@nYVd5%7vB16CO1b=lxl+?QU)h7$VZ!M4|UV+Cj`*1jDyDhJh)~DipdrEg53(&Oq
zA{E{RZ_|3J(y}pvA5?nqnV3=WJkyhXzkt-Wya3w5{>F0b(BB`cUfE+x)(#F194rX;
z1M(98>VRm1-T!4@22h&V-_kAs5o&*X`TRdV@&BK8{;QPOZPVUeL2Fqdo^5d|&?AUl
zk`!W0VJTBB8(5?;idpdzJzlFQsJ7icbk<?$Z<=^u!~fok)w4a8yP8}wStbJ_wq>A4
zvtHByKY9CTud=0ze(YLn<#y_BjysQ)e9<-)W*~qE*$yIB7+C3K?yHtRP$#9w@II>D
zd1m;*U+V_2G03|d?mTzex}Dj#O4}5=;Oc5Xr+=E<nvQn6kxylaF%U4fC!7V;7;?8L
z|GMRIK$tUGiN~@u4wjyIKF8l<X}$YD#P~mu37VOYZT;N~0BX1YHOb(X!%*GRSnVw0
zZ&w^piAw=kvkj?M0W`C9R>q;XKuLVo|6qyMy|v>~rPl1S69?<zWj6?qU<%0yW_4o|
zp>vWmJ^5742op}t)>iX^WgY}xYX&2|KW{-K+kWjY(Ha>dZ&zL>_}?dR@OHxzo#D3e
zeZ7~WzsK07xIcXKgxb^du${6^GeVK+$+10{@p!Gw$B-ccfnW)jfguyjfZR-!k(Ry<
za>pficlXf4*IhuB&(!Kk-qw+WLm2u>lH<NCS9vBRBqJ;UuaVIJWx?K#X;$d=`+xTO
z5N&r}HL35*1iJQo``UlTuU)370Ia?MH`$j;tZr`wWn9EcP{L3NunXDU(~~u{mwQL`
z%YVk9W7m?&RHDWvKg=1q@)<jml$huQ01o3T%GroJ7*vUPOjELM_FFQQAGVpD;Qwf@
zzf>l2yh_R|72JFVR9eGlP|nj=k-N4gKYuQyvtgq~rMGqGi=1Zx)|z*Mcl+mZRbg3q
z`G`#S9-_Ndrjv31c<Zbk%;LL^#P0nxyavn>sSU6j<%F=alao_;{UdZ7kzM7h93?No
zaYd2bu(r15E4|r%Zr^nA-I)_-IPo}cP^;4c*YdO&Q2U#6>vICm{Yun8ioLp5&a*fn
z<szsFuOhXd;fIVZENZW&XxG|M(|q6_>dF}B7`JrnIJ6D50@!DO0BSQBYG5^JIzn+q
zt(d`_MWf{I5M;<es&Hl+`3U%GXu=R5X(cW$?v*Z6>4W>Tpx(cZXUn>YjMc~(o=YBh
z3P;1_@4rAi5fv}aMc@YG^ab4YGoA@P%#$@HoFfi0BDH^vZpfJuaMxM3a&S1@vx7uf
z_yyyefk~5EmwW?Cu)o^v0U`-Y@0_w|Zgh#o%1b)Lx&^QtP6Dtkk~8DL2x2o@Eeh`x
z<mCeZDW4Y!{Zm0N`v*Hr<ytZQ^z<~horA-Gv>z214ukC?JrO|*CiaQ;7MS_|Vw;cw
zHG%NP@mi4iW^}T;hU@B7B^aLP^7_kS+A)IJ_v<QKTYdiT_%|CO5gVF<aPBX%ZsY+a
zJKGJv9a+hN(d5btC=Ii|&dwtDpKHAu6?H(`!nU6f1@=M0Jrc1qYTUc(B)82`Q;}X?
zz{G%1vuM)a-@n-|J05-$)QY+UNaOG_D-t0i?A6F(igp(B_X*b|DYhkZp0N4?rL~{d
z<;q(N-<;Z0m5wX5Q$f$falXoZ6d5y?T&a(=g<}Lz8Y5HF)BOqBS=D~ZwW<Uh@(uNh
zZf8RTNBa-UD;5thLp8X5-r)1R*Rn+RsJs5dc#~R7K-~@WD+>@#U4XxlQ@+PDa$MVU
zaHmq=jkN;Xk)<aW4dy;U#H_^I2Z#}-b?PSCPTu5(utK{WxRxY`?CKA_!a}$fgo*(R
zR`b(%luCfNj!rv}t8H6nIbp=TW|J5)RR!s0#}~6P-Vp?<#)djNQOO8vZS>nF8)fk=
zYFSws5}O6UsT%Me-?rIrIh?93a^)q(#mhxjVcaYJf!SDosF|>x4J1D}I9PAKhB{At
zC0tRPkuiZuLF|Hs54GFwcvI@ot2|BJ@kc5HBNRG0IQTvjI4Mbu-z&G(7e(qIm;ROC
zd*h=1cW(c}Gh}z<E+67GG~cKg{=~fy8<^C*Cz(kM=)6J{Xzx{RR{?1ACe>4JJtUwr
z?`?iVKGZH%ft)^#(eu0owCsCEV`o<*kTv(_D&T8Iu+zuhr69t-PCdR3<pU^;`Xb)c
zKi{`*rI^^U!ISHu)xPYZekq6!{@G6MNGP<ltc(s22YL|a8v$LU<=sw6NZ}_Dk;9is
zCr2o|0q@;Ks}{Z*3ZUdcI2IY9lmoIcO5ITvK@X&VxhgTN*xZpNFE7u{ffI{q+A?}U
zv!dJ9*0y*um&|ceS#xvX4Cv_DIJ<_(z{>fq&B7Rk)w8+~P!RSv!K%rkxQAb75sHDf
zFFR$I3@=xuS+34~Lnj#_ytkBJ^ft{}Us~la-nIdOJso;Gy)y|71ii@&O>|F4spx)~
zvdl~<@Kmk+b8~fu%By6$vspl<l2LAzSTf95aaexuUTSJ;M(kV)MmodKsqXwM`@i7)
zT{8Wt1rjPb;4nM|nDt(L%QhL&t6rf8Ds_WPt`?Z{IDg&)iJ<zcGfd#WsS!w})&-v7
z5kzUAAC&f8j|#naI$qp^LiSR9vAjb2v;Bt>rIid=RksS)nW)Bc9k*~qL6*Kiby<J)
zmXjqVi+9_)bmmaqt%(Qye0Jvf)VdXk-7gk?qeUTVTRDl+)<r+#G56`kBwPRZk0gRd
zNs)75{&lU9u6X|eIZ6VnxtEoRr>Cbm5{4vG+wE5%o9@=J0SA(S%A00m6O$Jp2Di%+
z9~J^)N|skp2oxo;!=gaVk=kek4+B*<CRB;_?`C!Ar;gU&$sFh3QZ()t87kf-;92%0
zo72O}r(PQt8OGl69!cuEj-T7k`wpgDMSb%fmO0<JbowLcWC_Q%w9X2QP|%>G2Qw%A
zgeOker0nr~>xOZxba{yGxp?tI$Jb{SK4O+M>bEL~R}1*<heDxXAEB%-Iv*c&H#s=@
zru9qHSW0LFXRhRNwrUr8O?TF8PyD4Pl>Muopnl)MAr}^y)Kz{;(l=>O9nl;Z7{CC%
z5ueSe3>0cC)*!NIpjXJE#;*`}+n~U7LxAyBR}^F0MMa<uk~NMgwo`w+IV%_$SMq18
zvh<Icn)tpudj)J_#HXD-%^|{YCw=L&<{G<x$X*V%DI<eTv{tc{;M2aWTv2GYvk<DX
zHPc_}Pzjnq`X(OhrP>~Frugj439b$RJ#rP>eP3YlK%~EAVq#JYb#QWuY<yHCqfvqL
z^PC&n(->nz6A(_t30&oV8%rSoYUR4oPh)uP@X2y$Ou!}yV^JI69|!~oNii{(8!U%M
z1`I5s)qiIJ!DIytm?s*0R`6lfMTX~*OVhcT0l!}W{yq<s@3WGXt;<#Q$jz9h-~Ee`
zGQ|Ta2n)1Z1et2$F+aF51PleL1vxUsg3{|QRkr6uOugC{o&m<m>#s$~s39|}Z$1K1
zHx~+K*WNr^UtWeiVz>z2mRR%mLf%`X{v&}m2+wNB7>qh}$9i?TJLEy`!|G3lLgtU*
z{85SqHDn87a%H8yz)&wxRjZ(ySzYuitE<(uej*#6S@svb*c&@<`VyjdXks6+ZPpWK
z>Xb#M29v4hBJLpIYbKJu#aQ8q&{H+vTWhX1h26U3UqenJQ)#pH?N>WCf!q;;eU23b
ziZ+O6!swQX7Z-m1PNoJo>nVr7Hz5Q9bl`%+)Gb%3sLS6cRDh_k`(aYf1@E7+wRDR(
z_~6^~HUH_^*+CIf-ctFlIRHG36o1KLE~K(>_Gq#%<$GenU3fWS-UcQI+ZD1K=qLi!
zCQw~KDfzD5`a6?8W<~E(=Rjxz5I5gYNLi4?3G00*Yep6;7HMBj<?z8wxZ;M8J7z$<
zLq}1n4)|PgTgi!c;LbrMy*%Cs2oS8uRP>oAVi%myD?31$z7o@<sNgq<Pf<{@NX_}B
zBF!e%`22#G_`6w<tS7yP9olB$umN61_5KT_WFW1yJP42-y#l5e*X4;fyGUY2YT$A7
zy#61vvzwe99rLF-O(sG|wvCa$7T0Ta-2C`r>_kx-VXd=&RmHcIrjsk-pi#kUPliA>
zxVyP2{!UI3CB-1QJ4U?+=aVZX8J_D^R8;t6ecDG5z%<p^80mEc*V`ZbB=Ai|I`5Fo
zCk#+^oFu7lu{?GQ{tr7wY58lRcF*`?vZra;_UAk(!G_FVV0DAAuPUK|gd~xTcBs`v
zi{$4P76x7?luIU@(9sKpl0fVb=VcIk^#j9QLAhMJJu%=gL?2WwmreXwrp5oGB1$X;
zXpy4vZhNKKpl?)9$Lj2Hg}<mK`d@UOE?1_hz5*OhjyvLxGg3<Jh3BtB`*%|p5DM{O
z2g*B8rO2;@jbnhEZCLzj|4WGF32@|-4<oHkA98aQoJW!lj#71Wp<g)zRC>m#$BCMi
zTOG=ia|bGin6L}||5SYcD<$od`9My}nO+2%!fd<7NwU#+`_YdUu7pXTiCl}J=Gts=
z9ey{Nypjy7{7Xb=x6=V=>HolXL8zdI5%M8*jubPp2W=to;qRaJR+ahs*bk}24=;W*
zG3O93EZS=mIA7Db!KFF-^BZ}^YvatNT7{K@d7{G*T@Z=icm_0C>AZVa*`ownvCp5(
zRwEqUYd|;|=GTf91a};iF!s?5$4`4C4EzF*vef>co`L^B?f5S%9RFF*z>3gs0BeEh
zpPAhQ0~pnBga?Su4mq6$yxr68dB)v?^!vUORQnqA_1^`GtWSUIxgXu#T!TG5Wm#W0
zms>$>O#=Gw0ElX3D1-WBME7Q2W2;arMfZU(SH?qW%7Jw9F1*8$U68)ENMi=nlqcY8
zw_$;xrbyl6CamVe^EsDenm!v-{*ETD@z1R%LCUl+VZxB}K|~3tc-4OJWv0x0Q0jF6
z*Q%+2L?<Cd-j;|&t0&`pR;PiUb`W>O6i%wPvw%n$u?G4ENXaHwNoC+30}ZQv-0j}G
zYpZOp82mZD7^Uu0i?xQ%y<Hgxy=W7lo}nb$|JhBR3v?Cy?>zzQWdK}9fO5!vNt25t
z2I62bysUG%iUU9lGFMGC$hYvdBtVi<4fD+#b8)zhcc30q23j2?i_7g7+FNMh9$f`y
zl{LMfa-BS_d|=#!@T;=$AV1~6sq6GyD|#?NUNbb{Z9jT?u8l2tZ!Ap|0zT`HtPvnN
zA}2MBK}8)jXQ&FGD*R&8=qLZF=Fk6A%@@UwS3RnKQxBeK5QTdr(7l-cSE{dD`HS{Y
zR>#a|z%P7L5Hg_S1IdKa7bTq`4(6|}t|k#kZZ<j819QVAfVpW}X9HkIiGwJ>ns%=x
zu<wx!0E6v5Cg)#4X;9qU-RA&jI-U-SCxxE)A$z!m;&Q7bxnicElAA}|FCk$SR0_Wi
z;t)Ks*qxmnYf!eTOvqpIS4nBInSTqa?HNc#Nvb~R|GfjWD!iNg9Kfnaka$!{d*z#~
zpEI|fy5P;&q8bBxLB#|E7&xG*HY|q?G&fz8F)9WGPd+^_z_f{S#K_be&M5zFiyZS)
zkRbH8x?p-4H&4%9GK<Ep4dDMA{1TSmUNNeH*1Y4nL*E@0%2T$oZWQ!CrfI!@-E1hW
zf5>9ynpoJ1u*qul{O(%q$b+`T>ny)@lvaP_<{F)@+W|=s?v(+$SU>uIIs$~T1SAvl
z^U+v59&d3%ShW4(_5x^|nP5E@@)R%w9Rl%Aq$gf@4pc#&{(MjGKVl(3Itnw;WIy(9
z1_~N?%|^PrQE2SaP`?q(!bi@Bs}PiKUXP8SsebY)+JRE&mg?SC<vbW&2NEw{jEb3K
zgajpIdesS@OYB2kj0Kc+*OrX`_B&_#&)wSg{CT(H!}p8`-Pc*3hhKVs*j@G6^W&$G
z)87da;o}n1%(iX~DBT(nYd6mFlfBOxzC@q@p)1a=4!U12J)@C%{`_0!A3{UjUq13f
zt9v%Kb<5KTE5`o0f<xZ5sadHe?qnP>+1GhUxf>(zj*mUmaq(ry>W3B@9Q=f;W^?mB
z#7vDP#8@)Rb0@}}%Q{(h><oCGh5;2HjFoGJtZ=g2@G=iyq+Wcvd*#frw)#hlVbZ^!
z9aBX@4bB}48SDObv*ab1ROnDZ>y^rpYEYycP%98R7ZxaV=9ni$O~kCC<T02y{|VQP
z78tTBJ3E{7RS!&yMY;1xzzh&!vQ{uZrDo*qh{*_T(2JPV{zklpT{f;+jB{me?T{30
zdxG{35()$V2&T3WAEqaEb{pGejsvOPKYqB;E`$JR=nZ7e6n*Dk0-+f#J&UL6+0=a7
z2j%f1<&aR&!7@v;k`H)B*B%`bK(`P9TD8DqRBCbet_*VBSSPc5HR#PVLg=K-$X^A4
z$B{%L;ewlGyRFZcZ!Jp6$kf6?>uFo*yh<c>=4}=f=3d^oo#Z?P`cz;&8}r5O$_n;B
ziD(MwiO~C0(BkeN6~MMm&09v5_}UMzbqv+%ahNgBDfbr|)&43M#BbQedC!;jn)i8R
z$CYSGK$O&;{`_dwB51z&#unV{AdB^uTmR|J(|X?@Nva|chycJll=Osl{&-fV#PzPQ
zkX#hF1DY!kk_*P8?pY}w{V|{sG?H%kto7Etw0nm4h0X=)H&$qCAP~#B*5PWeSZdDQ
zSg-rWtWxzDEHr=B!(bLE;t`OQF!Po7`9|HP{o5&`d1_@yXt&uFG`)`8L@YhK8FSEh
zq=FOVdzA=(!A`}vvwE{Oc5q7I2}Odu<6Ay2;8=?7<4ur#4G$A6`1~sv3X7zhKcqa|
z*}3?oX`aKniW4C1QAX)A+a-H*{We<AAkXlb7;1E7?%TLhl+YSdQv??X%FP@D{r!0L
zUM<<zzkm;16>ngL>@g@rTcR`t`|aheATYa%q|XEU4uEWcLwq7}D+Em*dq+nly3a}t
z>(MvT#InC^-DF^;Wv1aV1J&(NZLsK%s6wV@XOs7M=Vefh_Xb@7A3E3CCtOubK~t1u
zm;cvKk6Ti8MbLrqPCFeg6*tlr`oOjH0IOfa=4?@>NF;Az)#N|J>ALb~pxY-chpW8F
z${W99DC~G&Ajk6m$yS(hr{=&hoxXvEvRBX!y~)scsGNoeIR9y;dR_3C6d-M<gYqxF
zM1g*&tb12tLA%W?XjnLYaVt`T{5ww9V}xO=z*J-;c3w{#`5SI|(xnePoKkCsT6@Vh
z{t61?{?Ht#uwVhX_|mf%R}dp1E4%){65?e6(tp`lRm;SZ^u?ECnxWE3;k_k-gwBba
zP;J)*3t|WesXm-8rx%97xRznQN24BtrjzoLvNG;7H@qYltVea<@q9kN&bhD!7|o2X
z2bhkTo*N8fgHm(%7{+Ak99TuoO$~9b6V}!-j7&^5iVf@Xqpkwg@$?_=KJrp_0gB1i
zhE1e1yL(D35J#WFkiQq$#zX2}*bN)d*n)hyHdJ>n-&=FvMwMaDXpu-bnyO$ib>b@$
zYjm|PlfSTH^7EbE^i*xr?bW~t7pL@@i*`=y^Ys?EVw|Gelk}OiAvL)LByA0%tvjM1
z6x-Lx-ErUw*a|Vo!H{7&$7%oPV=(#uhivl1-t7qVF@Ig=o!;w6ta0q+a;XZY)pN9F
zw+8HCeQfp`Wvw$j6}6qK9ZoiX<VO@d|0_Bl%*%iG{pu-pGa6W*+_Tl&g6wAWb_SF$
z5i|6W!tT;C72HUVxZ}9c)RJj_6?-?OX;R3@HYObj6#~CN=W33X?~c@Q1b9baUg*cf
zy{+`v$-z9e17Ec%Yef_UVP|h4H97W9*;_W(i3wqebly8m=>G6w&fY1XA9~2&<~(DA
zcY1FU<{fJ1s#T6&O#V6L^TcDCaRLo7*t<L&gY7)~_F!mjT6FJt_ZJ(?-3nrSFmQd=
z|M~bY3;h>u()nZR*avZ-+7egS)|`%C%rgNkqojxeowR#XQQ|xHAS{XINk<t+T@Cwm
zV0NWwnyK?YIy;}~gfG5ZB)nwVu>~baV(UFrqIGy>v+p~(mv3*I*~?|-HOK8wntuJe
zffE|E>22c~SJ5rF^Er5t3`3z;Lq#BJ?d@SFA_*CS02J(N;XQ$hx(4R_;6NSi8$Th`
z`ATP8r~82iV10_|S45luDm|W*lr(bQ+LVs-)Q!)g4PNd@sD*hkysbURFs0_+mD%&w
zTS+Wm`|65=l}D*Cq+bBYYqf0Y@%OHr!PD(<%hIg_08<~vYVp~1qM842iw?*z)u605
z%&>LVMQVXzVLL4?t=-h4?{RImwuzGXD0rIKchbGrj3Pg(b@8QjFbd$Y7`y7mWQLE!
zjZzQ$0Dc+bN4KOzLz|4m8;E*1ivTX`t!V(4YH-iS8@`DDZIG8r?>y~5znelH0AvMh
z<CQ^|gL~M<Z;VO<pq4lR8rRsPch1*MvV5(j5j{LfOQp31nG7RpR^c{wrGD1v${CSE
zJFK$YKVvlRZ3yml=^EVOQwBvZ%rK~W)B16HT~7?4Sx7V$q0E5%;~91T4kG+n?Mz|q
z&M%g)As1hs4tnUZbI9h$Yl!Wy_y&Q>^A0br{M`!xZprrI%j4GJg^<+PxNUI2?PX$n
zZDw&XqMWnPWPfTBg?v?IRGpp4Jpchri*brK#PsHyl%KrtazXpTYw;S}Cr_qSWB|A-
zEiRTM&CDn;$Z~k)(K~^DF>dg1OYgm-$bdA!^-i}3tQC{1NtsDsBe@jL0qolkYUiEJ
zepg-e7{=n&xV?W*(lW_jSX*6Hl4<aIz%a%L$P{_cORFJ!co9&}Uj3Z&Xz-Newi|%D
zCgAy^<V;YV$qHo=c_SXMi7ND)$UvL>ka+IIBJPJTuCaVAic(zq?#$oi>%|vocgP@w
z1#f3(hqLO=c@PB}aQQ(0--au0>*VMva7kO*k@#E6Ixef-2DNf87iNvuxZ3HtKl9HB
zWyobI?!t7eXKqFmyuzN`oFr3zL~zIiWHT8RntZZpdX+uv(*3}IE45`}80CJurgX06
zwA1uRw0jv+%C0TnS2R%;B~?*?PF`G#aNa;mO5)2rn~=Qe?lv(}p=?1;;ij8x1`KIu
zErBVIgA6}KYX~|lS?(Re7ixpYr9J6T<`ui<Lgz-9J^~Uy);3hv*eH-yTf19tVEU|{
z_CapJUbms|O@`f*){1nWPh9}C=+w_zQ;}LP07X>JYx{jU!g>NClLATsA%H{>Mn^|2
zXnDAe@Zb$>?}CX_pKX`o8}z_69&XDkQ7CR69yQ=8F3JP(hF(2u2)iM>wW;oS4KmZ}
zLx%hH^qC8!*RPBziWeHz<K%6IXzyNyaNRJx7geEnbuqmh+t3UkwsZwkVl~~`u;=aY
zXTM(R0e*8BK#8_z0mqzzfXZE6Shduw#;;)@g04-5Hi6!UL=i>c*t_o3wYhSq!OumW
zB_-kw86;(5RGB|#E!)WtM=relc-4&Cuyp=R5ENDbz~8sr(9mZ`!Bc6litTva=8bLr
z=j~#WjZ&!yyj&J|{JG7xp3Q=hQ<IlX+u2!ERdONuYR>^s>dyz35Ap|m;>VcG9pl=7
z?MzSv;d~&1Zf}`ztxZ-53_yVAJ{4Xrj9h$KSYT9~=~ERF0s)nUmNU7_S>ip+G=H+)
z?5_Amcs%2Ez5#F{i|v3ntIreUGIfOk8_USbBGL(1J4ZbpToy|ORp;b~Gd1HCzd2%j
zoMDy4nZg+u+Q8u{ANBseR1=LP&``cqb*Gnh6dR*m5hve@TGB!LjqBCnjEI7;m~QIA
zwhE2*Q#U0k1+KE4_h{vbB$bMa&egY4RJNeudQd8!gpZci&1fskmGCp4IKjzSCD^c@
zb|dX0cp`1*idH|P7(lc(mJTU7#pmAv{kj%-QtH)t1^t`elF#m@+dDZ0xQ8!(XZhND
zgMr5jbn2Hs1yjKM2LvE`<;;^oOc!mal^H+69tKu(_h}$Bs^?W;UFErhnAM!d*JWJ*
zXbLeg)|+MI$4OP%3>V*YKP`&;jil~{iXzaR67Du+ZGEAUO_UyV7C0B093e=6zke}&
zuTbKSU}0*bly;veWRL0*L5J!*EL8b51T22<(W{bX{0Et%tOz87yn9#tuDx`r@95|d
z?J2a_WnocJP}n088-hWG(wl({W0iHR!JdFI2aI-sm>m$*1uPN-qI)rLuo}Ia?GueJ
zSw!fRY5m-Qa-;=<u!?Bq>?hwYpSA(eRdMm+OUa-|MTS+`YnL%NYxYJS8bFmP^D!V}
zSR0soPo#rZ(&bGmHA)cOGLm=Gdp|}JlC2>XG(Nt*<>1Oi4b#<GhC<w_$|@Hd)+3=X
zw$v5T6bN$=%U5g3dQkY814H9)U<;(FV)Y^~D8G2|0y6uvXxz6e+q1}Uu;yvsr_)I2
z#%%_x$0BGGBeOylZVuy(W>T!4nR#`Toe<dH!qMJ-lVPC-)MF`UkA*bi{Taq2#T$x)
zwx@N=_7kd<f$U*5>)%0>;@naGSNA4=iEVr)s3kZwh-UQRsNd*Ez($s*@9y=cSTl^N
zj`9RsWRZAIT=Xm7iyI)m4t3?PgYulS9iV_$UM|2#8|%_lRa_(&<VH7uX?webZGYb7
z?0Rwy#C`x6zFCK}9%8;P*N__|2~J9NvghbJz-;~bFJH%~CkmUFY|dyiT+|v>>G7x9
zZsap_jqLfqQIM97xqI8QhypdGl+T+pyX*%jai&yeXNli-&~5uQU?uP@*HK=2KVo7o
zAo{T4D5d`x9hTm`vA)Vq;b`eQKlwL&?7#ivl)7?QvyHcr_&;n0aZi|Ls(iGA>i~ja
zkN!9nv{TsXaPa_iCL(5=gbv>SXVfRmxFIMtd^^}W=q8_T!x8tK7yIUiW(R*_QY9bt
zJG<U;b=LYH^g8)Yh_>VCg0-c%*kMz~DxsmWbB2c%CH?)S7c5^rJ_t=n4{;;9cRHD;
z{B|z82raO5FiX)25~Qd!7_D+lYG(AoCEBhVY2R4yciW(AU=mU}yv67u=yDu&QQ_cg
z7~)<W&D3eagnRna<Co2MFMou1Azn@VbB&AO8q6B#sE7F+>S)|aJ2DI%h_c%F+Ztx-
z;OH^fkz8K|X8J_Rd20+2Zdh}0ZaY@wW5{MN$+a-Yy;cL=&K~VyRWXSCzZ@+{n6P><
zQ<Ef8%P^awJVLvEe{<{8i5B$1N#<@OO0>=_xq0)s+}zxf7}WvzEx*pO?h-f8>CKq)
zaiRyd?+>v#AGD*i@aSEk{SX8g#9BZpJu1&QkbR-@-=FQ%301XG{`5R@CShiSRHp}A
zoug0g-rwvs(HiU!#ee^Nz<Akt0iA<!{;&i_ymD|Qf;Eup_P-eS<?3KI9h`deq^ECg
zM4|M=m&MhbZ9KR2wtlkGvs3#AWLvqaN=nKk=TAls?NsZv1Y>SbV87tMj8CuX{li!)
zivuC}EQ02mG-E-_(`$Of&Kc<=rH9{*e4V=K8D46>f4#&uk-I#r$d5@BWZU<<DUI2&
zdSnV-q%baQ(L&GkA5$>)SsNYP`M(h%>|!<3RF=dfS)Hs??tjg{TE>OjMR4JYi|~Ss
zK3_VAoo)r0Nazhrs3{28nqzZsrqK*`rlCL-f|7!CAoNiPq02Lsq6@o08~{ICG{C55
zM$_xK>aLB3>Q9Dn%>A^#TG%t5IPHC+?xcQ$t2uov!FB#_i&^v3vo7O;UN3Tr6&35g
zaT4HYu?7Fph`QAAS^6&D-L^jx*bLz7k5IPngzP*$AEXOjn49KyO!bucb8b&%NI}5@
zdZ*-nn8>;MGL&^A*|W;CH=<amV0pJ`Yemy@n1#}{h_A)hej9MrKU7kFh-%=dqU;a+
z{FIn}sW6uMkYmHAq;Bks{v(K5xZ!T=%A)ybTiub<Kgzj2GyZf4U&`%Lmev|DF5Ski
zENYJO>K{t&IvC0pLLj&=nhC}ecyx=->!bpYo^Y>26@{tplqIbK#vllt5v7x5=fq~c
zORCz1mlA0#(w63~-{n!#ryEqR30^&zgUo40{a+>{9FGT7G6rgH@~%_tMpXsAG@lbt
zdKo%y?aX<3*E3_FafVmnsfs|g&1DxQl&q<y@vw58Np7=mpX};3H;TIfbA(BQg1Iuc
zq5nK7SfkEO4h3;M93XbbpWkHIE$TR5AUCTX$$OjAQW?cm60AWU<E`T%_&wAr=49Kg
zUUuVF$>f2AX1O=fv>yscahjn&7akpWj$CW>SZCCgTjfI$x5|6h>-(_~Yc^zasN>}N
zC>wRxx?&qIURJ9#w383}F_C1~Zz1MU*l&<wH$U<ECQQGV5_3BCLCV@`YpnO32Z<_}
z3sH;BN%POX{J@WUt!12AiO_$J>$+-NMZvq1${g6Xy(SmE#Qav-Hf$GnlB8YrD+-u-
z7T5(&uBp(Qt4=U}s<5Z7wzjfd*tZ?Q?JgfgNuAeHHu0vHB41hWl>vT^j=?MejBP}W
z2!xVBN|n3gfBCd0OTM;(adoJ_sMhkx>EGfHy^T0oZCHco3^9}LOHwW8XR5mz#v8Y0
zpRK7REVf&KfmbxUp86{J;Y4BQaZbj(cLc_5qJ9l-oykRh!M5(<C{F*-kAr)7N}&~~
zhN~6hrZ)n*uB6M~^In!EU5aWyE&WiHA}?_IsCGj;o4+5?>Ra&7iE-@t!YZLChfj&Y
zOrs8p%2pPuHqWy%NlYnlIqPhuce^99q@2baWxr%M$;F*qSe(+^b{N`Ld<Ry^IE9ta
zZ%hi_ZTrZyf9d;|x2%#KUp#2fIvu5uH@(s0mJn{p6qT>2C^!67d}(v;Omnr}^EuCq
zmhJ3tq*(tg&v6ub;+fVR4gi6*ZGJ;v7<t=QR2{z(6;mMpr1otQ|K;S@6br3SvouPI
z4K1h`b?Ud1RQ3Iffxk6$y*KXxJu~GlPd>@oeDevM_>N?_Si9&W6?G;H=l_1`lVZW^
z!vf1)rMgG+L}^lylS+!`tspff);oIH^x-^y+zyz|)<3C*d`5Sw$mtTV)JLEMCjGg~
z`+?Bzk!NNLHJCQubktKwuaENKXc<qk2-$X4b;@gE$f$JJEz03iSkPqxv2oceYNnIW
z-8Dp7veQ2_VtwR&ZsXJW9EXT$XbX48x0`l~-lyy+TIdn{N56xUUu)6&=<mfAmL-N8
zyZp)Z5X!vCdZWb+m)5XVtX!~&A7AL;3bdIWT~@9`WE$abIc59o*p6d;uXismzUuq=
z_%TQyLa5+^5rx+!WbBgT_u79lnH0wmMkI<&=Im3NH>Bl4q~lX((7RO>aB?AMW#fqS
zWJ>oltw}}YWD#a7;&6R8zWrVDYd2b%%yyObsc#Nav=$neLfhG(4H4lM8Ipfpbe#q5
zZtK*@o03C3hlh_T(oQw~+bG!QX|^~=*&3qys#7&CES(uO7Wk$<t6N%q{yQyK9eZZ4
zrsq%g(Op{QW3vVi@GZeJUA?^!m6SW@3vD+Wr4yFd167?X<`ne#XgRql8<wL<`aXV9
zGVMe9AO_>L(W0~>uoj+z77(Lzb|eM9A*yf<66^(W;_p=I*PRD3hj`R^RMK2^FE88`
zc;Q2vN4Im$e`lV3`og}I+s@V=amqOLbW5wk=?{(3MaMdHE<sr}!y2%dZ<3}8w5ydI
zUIxaEl&DYJcPy`Vaa78MjVHEx&(fP8HDyXZHYj~5N+n};{gic5^D29LoBpBs+m^RC
z+3W-t?h7bo+GZFZ>L6PZ&+|7T9&NIbR&t1c>v$y!g0x}T;p{HwP>`uqefi+Xo|7d>
z8sve~+2MCwE}_!Ie1d(0EOd|l-U%w=Ka!lI@L}bgGP*gwo&Hem;hod#=fvW#pYKvC
zXi9Aq{IDVxAo?-6`Dd5RebV+8rY`;T4@6F{gQCGbIkfX(y!tZN;evEm^IDpxNsm%1
zV>=tpC4ATTM5<7keen-qyvnhA*pF@f5-PQt5Zg_l^?BTb`bU;jl9bcS{2K?!Jo*RP
z{aZc;wJY5g=*yT=eizxdIxSae0xf?TLH`D$?A^h2^{SlXMeI);4Cnp^YsG|4RTXP`
zOeoKbx?gnNy88<kDb+Gdqo~ST=g03N@R1ocCUZ!P$4hRZQmXA+oq5EE4}af>hc_*%
zZi!K~MV6EpY=N~KQKbDlM?u?M4elKk;8A*AqNCGgZr01bKjARs<jc_P!>A;_=xeYp
zW#fPQoSm;ptkASVNavE{U3A;O2V!?K%a?4-n3EYej}3}n?_bODhiZLkMhV932~H2{
zOdf@Y1)p+xCW5A}03~L#<NL~97rt!6`Ucy0lRh8AwmK+g;GcMd`mc2`hE@%*A(bBT
z6?i#N!gv6#RSSP6x4<c2F=@tgW8DYTUSz=Yh}|mv0e~{G{>f&cqTM43`c_p6&Bs<-
z?g$gPGXrXad!JV9Jxm-lz!$wjepCO;AlJYv>TxcRqk0Dhd=H~w*j;unkvdTBF##Q%
za!#QyjOSBuuC;Bb@%TA=yIcoH!K#2RgBcvX3P*cMf>YKPhij?E1JlO+ImT<t{V?{p
z$IMgY%$D8w3nLJ}fIpRyd}-k1<#t_}kHQyC#2ZcrD+S-KqlUhEH7{`Hm`=WSE#Pqc
zsFsNl@E_v{lvS0{yQA-cJ|`32N=-)K?dU{ai|>Rn?)(HdUUc#0NIjbhqY%v+?HWzo
zpKP`L;Ezv*f=AMo>`MHB77^Jjs&dC4hz;&Og%%!}KJ!-76b`SpPxnO7wBx3^V`@0l
z8jo22>PCql;J~Y+=SGj$Eqia7M&`!(+7=8tBPL|wm4A)rSfRx=dpXLe?f9MeCJ**1
z`04D7{DO@9oX0AKDOh_z2t*HU2FC<P)D_?P%>$&$mY}X+n$fQSWlvUdDR^uNy9l^A
z;7palAMNA2_|pG_-{?>JbsCf}V-OG^%T?z;gK}mdsDJ)1?%q4B$*gN11sMe!==dri
zZPXxxQlxiqbQA*u3MxodK@pH5NQVGpK}U*)CN(-zL<~K&5Q3s$=n^`Fpfr&ZdMF7w
z>p^GUbH4L^=X}5GI)D7m#~*Wz8lLREpS9Os>t6S|_gzR3FAdt+N7x~SEn_AJDLC_d
z9GjO&&K^pPJi&LjS}+FXe-%0*FpmZ0n}Kw$3TyQ;OvFB7JCN{8Ya3n?2;Sst1wyBF
zJMmV`qHlL>$&*G){Wa|jwV9;rR{_}ajqr*5^TV~9uCA%L!^B{u>J||f&a_ay79qfe
zi&RKcR#z`$I52ZY1`>}l`>ZvhdfX~p7cca}GL&R`PQPubjXV}JrR>*q*woO<#?b0w
z1IAVh-2rKW-VPZ<9FC6_Z}4Qrw;Er4r9xZMXx?Bj3|WcdxTEGU`)KYRJ9nlQW7w-&
zp=D5y$HSb(s^vqGI_T|-MD}KnriVB?_P@F(9*DU4y%__b-On;&_7Fe}a%F>mgbAys
zTy{ZPdSDZjA^=V=&|Y;a0im9IeKF?&0&j$XUWh#RLxrkq0D{Zfk9-1Aqr`%_w-X8F
zl(e<|$Vzl{0GfI!-9%5(R+CY13p&0-r1w2$SeClcYN1-Ft(X|>T<$ws&sH^$vX^`2
zW~FnVd|+^~q5VQL2^Z<B<9!n`_uTA~4noPr0e5WX(FTR*FXke<l)~J3S~7ng)V=n5
zQgUcf_D`=QjoJVb_w`%dIUkLHCQXM_^#}J<OCIN5?yy+7x9eKO)3<Nmaz#fD5%w?F
z1Eo1H$86DedxEtmg?<0T3*MU|`}aF<S}l~TnNU+Ite{uB#Aob!XAlaM6~k{954v3T
z|9hmlMffx=)~(8k#791wvUM((URp9l7;5V4T6BL;>_kYw%LxVHu9VrXmr;rGDyH!|
z&%<e(M0sR>vgJ<UsZ2Cz8Ot&iPO2F!QuYqnvg^e1+nXX#!;KuL3SPulZBc)*DrKxt
zI}|nr#)sj$Ld`UCf2bh7-#$n`_%&$_D%$-bH+kI1bM;3h#E#lT%!X{g|1KDiu+LDx
zz6-vJtSZL5h03$5&GeXdDTkR{QiFf}^~!m^&n`Ka@V%<GmW+Z2um3ST4Od0i#ec0o
z2<?(LGt*{bR0r<2XQyt-yWHvQn&Y#!CYvz9dA#dO%Z;}~S&_QtZ8yONLw;KJ>zHsG
zG#ETuy}5dCwT^JS)YPRDe7spEA$q+_)^eqi!K>Y+g@AtAZShQ2bqzSWtL<_QeUE)m
z3%KM4z|3{HmO)%>y#Z*zq_jo47%)B31i)?Jjuq7MZ61`qP-_tA#P|8HrernxQ`_h|
zKlZn4rejs{Ydb4%EdI+rNU$0JVn?Z`{ef?O`#-&#WCtsqY{ZIk?xJyldLj(VZ=a0s
zcFDN>V?taDfnoECjV7jXyWWX&Db$L+bai)f5<ZKYR;qMEu={HfEe{oZ{<ZD?Z&v_)
z!Xw>*zM;UYcIQd}63P0uUx0GYBEXtL)nVog;IPXJR1^0c#`z{OMC{>coLx}RtqpKn
z)(DGIs}r*8?CsSggImJm@v=zC#;#UKP*qLM4&Cuy3|z<A#r`f&I4j=R8Y9J@6MXJm
z6^Idw)EbQGbovqcwY)|!$w%uH5{%j}nJl{V`LPR48_JnQv$-#A5g$=_$;E1feb}j@
zP?TqtS$+P({)lH+tFV5R1rly7{cjT!B2_rwRZ1UJ_b~#MK@O{6CW4_cfE5aIfjR~P
z$F5t;`A(h`&}ml*S!cz;dn@(!@fnp`6{bz+p8K_O=d$)p51?{k1#Nh!>a)Iefhm3C
z#RNBNj8(n@IC)8E$P1jy)A1djz>tob=`aZc?t>~h;jn|0>?621jp{gs%DVk||M>mU
z*OcN80&6FCp4~oh{9t$9?L~;)#XFPSE9odBbJ#iLI62%*kuaJnc;f3O*ao`2@I~w6
z)V{;@aEN*Vai=s?&Gz<(y#g|q3)7`*mnpm6h0Bu`+O*W%`qjHTJD09rSqCp<o6z%H
z<8N?MAI)Z+T%*8D+?<y;P5-f7PUxY+y2$Z}*~@z}n5)+v-+19h*jsFioVF>{3h+Q*
z>0=j4b>OYhc27oQ+?s2yXq~k(tt8-e$jR!7gt*xaM<uK@04Db45@A5?yO-ixNC=&}
z@Zign*aGl6pVPSH#asjVK7U;pSJ^Yt(bu=es5SzREW(9o&uscd@Pyy3+ArJX%C2xN
zyYhX$Pkp`IzZX#h`XDqE9xAZ+@rU_mwRw72lyp|ZvGbO2LY&rOUPhKT8P4sfmtiME
zJCNGaNy6xmj;hkXcS98A4L+@&zH4Uny3-pkhU1XU$wR$P;Fw=`uGOv?wcUa21H<RH
z#Y1)G{FRm+^YK1`fm4JoiBNns`lV4TP-E!ZA-23-83d!jXwM{}(9I5CLf&1AxNR!2
z$yW*h=W^HsQ@h^L!*?~y?t3heXp9*zU3Z6bVL;m!8ELI1^D+$?DHIsX$LFYo(a$=n
zUjH6q?#IG^hFG~V#G13ERAgowP8Cg!12E65x-Sg3iNPzrId?O})H^)Zg#^$s`9QXL
zvFjZ#H<V{`ErHVWmJ0?H2nHQ=<etZ-nK~7hPL}5zZKSSzf7hitAv%<0lL8-fNd(Qj
zWo`lvSf!sI4GMMC9okxGEPAoM*9K}!%?Q{&Qk@e@7So8QSzH3v(j90R=tJmBGoX~2
z)K=*URnx#)*2=s*FF%hP%-bIGb!oLDGu{jLfgPX8OfotNzY#R3Qq8Ut^X-?fHJs2X
zae4KR%PoH%U!wV#e4yrfAUiX&6Pb2x^0<#Ct<FVt=Ra12=A&~fM_c#?tqQdemEPP{
z&VtGns}HO>K75z$CAkEK+`qPg+vIm2u#`)%t5v6?K-Cl@)D5NHl9o$(<iS6-BT_cJ
zVIh`KgZl>YPwv?2M4p~Nx9+iHGlT%KmRq3d9O%jS`TBT+Jb?A`QBUujhYCaB@+qh#
zZPpdEXZ6D@g{yqfbKT5PsB|aW{m~`wMMzs4-!MOTsh5G46rE>XUI_1-Q7{Ql(c{IW
zR0U1`?$%qJO$NFX_q7PAJgZVS`$f#ZU0Gj8+a<?YHI!`MgxV=8vi;rOd=GC;WnI1T
z%t&T(`uN8}-i5kXJ4gy0;pu2Z*`@V%ofz}aUGH2%aoi`XY1L9|Yij~FP#+VspXviL
z0LlX(%PjZ)vfCcROR&jjg@)En@O9nlEq3Ca=@%NJLNR#e3@OTy|Ks_UFH>8iq56`F
zkpnH6Jf1XxpxtF+LR{iI4p{F#;b#Q#-vQ`fnikIaCC_fkS%*nQ4%7HBII(wCJFKtF
zh&R}-7Il3lyOh5+>Ihj&d;V|N_6$wvpt40a*^(`d#`@QK%3j<k2G`be6A9_Rd$;hR
z@gE3?z27Hh)$RQUa^3Fe9KQNBD~o)%;S+!QS%)5;zlkO3bwZ&(Xbh;1``kBwWJw)q
zTY|QgZSCgn-)_y%(((8xwARnFz!TW``(~9u0H)J;gZ`)&xqWaf-N{Nx1{w#E-)mc_
z<Sye`)*no~@Wq#RQ{HpM#ILEx`+i+hQK<dBqn89eL3gvC{7fsVP4`)*ti#{HdU`K`
z$Ib}H5sRz#k_(>XtrRNDSGY*M6*toOEYCQ>-00v`$M~Fl*hS&a-@bp7sjF?goE}e*
z|ND~gFkHHTQyU;a!6z#E6(1D~q&E!xGbGcgC8GpT(Uw)tf>96X+3*_~rv3H&Z!W+=
zR}CBSXa4~B&ZW{eRlMCrPxpT6e|nv(LdwQ?w5bOxCq=dTv0kQZo9))z+JfB%*T43A
zg^qCts^!}zeSpA)qij=k<!2{cf?QtH-{=RL@VGj=I<Y!(d`>US&UEV;cdqp}R|(WN
zvEX_{#^+9mGd^o4`D^Q1jh^}^;dBb9uV%hl_+voCZ{JK;b#$0ns8y(*yTz391Wci7
zTbY>pPi5SG>knCb2Gs`x2Kffvj?j~Syz$;e?QQ)(vE*k2t1pkQx~sl9o#YMm1{Xd*
zjX1TDr`7Upi=}4cRUyA$>|Ix$I{r^#<TVaby{z+zyl#O!6MtPk2%r>~@22og2^aBJ
z@0;I_9SzIcL~bbiL1;lQ7TI-pX~jF6BEZ|N?fE;@L+gbU!g;vM?$!^ESbaP911d-#
z*NCh~-JQsOKPR4{SoquanX}@!uBmN1j=NyL?G6<$*<7X>{P)laI$M`5R)rdF9Etha
zAjY(XtN44KpWZWc@W>${86MkhqB|#f7Pz{&1$lD9s!m@#R}}q^#d4jzu&JqwTO#dC
zm1~c=zSnP1=gKpm@?8>|$oet~mAD_+!i{9hO|_2PP2=-LbryPi|Au2wPW-PWx!C)D
zNqiUZV-K{sy^7~r2r;@%`WtUhbBMnF;~!0J{?mP9YSXT`W#NLd((Nb2yym|-Jt&pq
zIswPYd}Ts@|N6=sAm#V>V4yG-c${}IB&<lO(d@?nl?@T(?x?tcE=O%<=b-Twu7|?$
zhtNrqb1Ol5Gda+a@a}Zibs1i<JFQe`&6NsE;0}z&I-CccyTAuIDu-^=EIvg!na7?I
zan_PS_u7EbVDu}3_RMtbn2$qE?FV(7DfuXcst5HRhP*slp>%FN1FVBLFzoX}Jv(%p
z6lw!n)Xo*v@N3-yMW%tiSB;mCy%w^6_V!3qvCZo08LVO)Xh9FYhuqg3D7D<-=f`F4
zsChs@`zM65M-h5|TJ0sT&X0fXB#6bMruG%GvR2PuF?qgMYF})+#`U*<Yz{w+_SEng
z_8Y$zkv9cp?A09?U3x*<&ko4FeCl}@j-(3g;CBZl{-a?6AA|(pBjq{gy<<^M75tiC
zpVz>wub*v=;#pua8D8{YrfX`Lb7U+?7!C*xsenG6l*;oU^nU8&DrO{=@3uE2O<Pau
z>9AEW1sOKR@$U6jp&u?(ok~D8?cOdZcS}B@p_F?*)a3-<7(YM%`Vt6l(fIG(<=4C!
zz3UxRMP;@h&~6+2W{d^|;P5?QP0x3|+NLYu?)G~s3SDmsod!Z>arJq@xsZFS!ReQb
z@VP{)K}UHMly9~Df!<OM6?gBQcp2sJUA3%>Sd>aVOGmuyR$mE80LTsLJx3b27Vclz
z)arWN_rZz*cE40?!-!$_PdM_qBXI+7n}=5G0vROc`N7czg}%wH$CQ<a<$Y0Tuk&1;
z=Y+p~R)Q&yDPF^*^|_Nt0$<S*2~9zJC7rrTKev}Ia$5o4+Nq@8lP~Jhpz7fM!4CD=
z=cuLt5TI5+g2r-q)}f`O!~=3cv(>nUn=uoj-tFh-#UYMyt*(HkoE{M6$`l$(4m)HY
z!4W*Md<)F%*=rw%_qvYIWzuy%(QDA+%5`YbKwSuM5!kttbo5>;x6DuLyH1pqfl$D9
z-~QWmH5V5(w=Zi(N3*qm?;X-WV;z0Gy=iGJW1OwaGZrU3&X&A^h9BhV$7Y(rzB9gQ
z^Q#Yrk{~E>-)spKhNv=FQwh<5rC}*Oe;g9`hZcp7nXR-fcL7Vq$JaL`Mrx^BT(?9g
zjQm1TVWud1$Kypd3atx`6pN3gZE;GVLC{l8=c(fgc|LdQ{r&Ef)>t2Zc>CHFz>em-
zB^PJFt8}_gK^WOpw&m8-krxi*^!EFq?m=l!iAJLVLr$xsB%MTJXDHMj4Yf`>wKx{Z
zk&ZJM+d+Qjyrw><1YOE~A}9IWx1{a1XI;mlIQ=vlyQWy&gjaW$y*2;BgI()^(K<`Z
zXZEMlDHeP9dif2p3%_4p3Z2fF`^|x8#%9oZ(0Ir1hHY=+P!!Wpp`o#?{Xxc&D&@vX
z`F6TEyS?_>u720~KCe`l!enwl4?%dc|A8{k8njW`>`1vxFxhil7Z>6Wu$bb#)G8~T
zBS2q={mX=&nK97B=zAa#nJOjTIJ>Mz+Jn*1iYp8V2+&Pz8ME8QveLzoOD-H?2bA$c
z4^INu6!cO&*IeGw3ThcjgNcX!#N3XU{Q$C;?ywa#!VCsk3h;*(E(~{Lo~4e2I?-AV
zM;jQxct9(?c}K}?Zi_BB8NrMgV}7mu%LcGTs-T7cc8VDa-MblV+l=d4gx!_>Z^sbr
zgF2x+`?00#Eya<3Mxb_D4y!#D-R;`uiq`z>?)JW_=5<$ByDop1B(5by<g2Ky@Cl2b
z?X3$zmOQ3@#4{vfHtAdHwFpKZ+@FH3-tPZ}Lom5{(ltDze8MEvaOpwS=E&SS2hp-`
zj-_f9{B38@wJSQCWHJ=XKZrqjtcquutuBCW%XLvW&i6UN`{NpFa0M_DgA7wMXp`P{
zKwGd(L>&*5KCj~76MQY)QL=FcO&O-rNc&L3)o`&A`8fIG-0N4NB>|It!2pZ$-!^mV
z2)V8N&xOZbV9R*Z?0<#krLT#?Rz8t^VH;>z43!v}$!`A@_)3*OFYn2pZ9OSK6L=1$
z#jb7uCj@&V(g%~b$yPZf1ctCFZ5zu3UOOzfqq&rAna5}G1NU5Bv0kp-2PR=q_J#U5
zgA6!s_tgr~6Ht^30fr>yTEAOZR9*U51*EOoM_kz={Ms|scQc<Ppt2RBc=!&moQoqt
zMdd+|F5QlO)7_o!B>MH#<9&qmu%2fqN+DQHFi0@8L9bb!wVdAe?tj^{7T)qo1TFu1
z>g;P-uzI~SeHx)I;eEpk3W}|o8A}Bfa@Btc4JpBInZZ7&-1Y7<7x+A@YUWA4JOIFY
zp^shCDNT*L4y1C$v<A>y`mQ?V8VUvZM-4NJ25-B(Y>87&dc)Hyzj>NF%D`?cG>iLj
zt^XOUkIfp-dm)L}`lH~8p?9X|_l&HzbH2Vzcete@KK(kz0eLM_8Kj326BL4dT~yJl
zVhst{&YTi}l3kBOyLZ5Iu)UpSqBjwVd1JkpNCp0k#b*n8_Xx2B<TDDjlGVDviA*Rh
zEtNNk&g+JKoA{|7X6X(p6<s^V1a`m*2f~I+4xKu1ugg43V*h?k9>1Sw7$7h><xBH6
z7l^}+@q_Y`H*;Yjh?N#NEp4=<&JDJ38&1{83(bq?K%B+<z&++BWfc`MrMOBIdgrWz
zP^p;vUu}I$1%Ur&iP!a&x`#;SheX`=hhMrb0B`Kj@%~~bYBiw9?!Z)&;{xQBy?Ar;
z*%z{p4#mGra$d7=g~kziq@`%H5F{eh<FGKkp)O&YtHUdZL~?wy>u$r0HXV2qc*vy$
zqvFPn^<70{YvV_DiMDWKp6GH8&Co9#PVbGX@i^0VYmo~Yuhu#bFo?v8inN*eeB{)L
zhMnyDUMSkS_~!YBhqnbo^uA=}9GCocCm?DZ$B4paUSsIZ@Eg>pX5YbINA#H6;K-;D
z8aky=d$2VXsKODSPRrR=mX*V?zW0QmN?buM?gF7a(ST@Wlb68-*F_x)*<_Pr2knA7
z$2>=X<ObIKP(gEh8ER^^*0m01;8FMD^UCfj9fLwMt`mIbaPWNgt`SRo2Nz+?%zxVS
zS7NE8nX5$3gy&eQZ`8hr90iXPSX4q(e`{OY95F16JGA7{x9boLwRJ<Fo_98Zv!!z9
z-Da$^ptV5d5jJJ)qVSl`XiLqt2<Yj8W|ZmQuFs`?UbNpC{gn4BgAwvh&v_g}JMhy9
zzRN6Y!e}9&i?=;y3ZnXOi1d$wrTF80vd+j`IB?HnSbg!P*lfUo>ekkCmx{bNPQ`I(
z?2hQ1#F5`?vFWSJDset6i^DhTPV$Wi>9Yl=^Y3UB(uAFgIkHNwhR(Mn>hOjUw~yTe
zU75f;!zECCh=ud-u$9TidwT-!O&*=Vs2eOoHEl(n>&;RvWVii1_`QKEJKHIX%a!ly
zaQcZFRi5JFVr{45NZ2@Bza})iTvt*%MTn2v!Ox#-i1X#aL3m{%CL0Ym3y}A#E7mTn
z3}yp$wC|AEm}0uUIbE;bjm8D1fl1ojZYY|i?|SE<weEBJ?5Nu2WbQVF$Qt$NsIy0O
zqW1UIC0;|;RtVNsAcRU(snM21?}UEfMXSNn&t-8f9Ix6QJpd@Dpi>L9e&1gBgIXFb
zgt0$+;9d@F5@Bx^MQEsxI;EFKs-PY{3T|Fh{lLAMRRFj?T&N`tl)=4W@#pLPxRFJ1
zg!p5uS&K0#$@XLp-W6!(F%iCwYoKPi=Ph<aOk@aK01odq7k3vItIPJP2|O_2sslLz
zkyp$1P@n1GU7REw|D4K4q3s(Kx!6s2E=S)!A&HLM4M6#}BX8U0;c(pxzOlUHH!6UZ
z#f$JAy4o;_8n|J{B-H@?5X%EX*p~D;rM6Z;0e1|BkB<r%sp0_UR5%x<fKkS^kP)Bm
zy16@q!yd~DqwlT8!F3-q)a-WooI+@vGUguG1BE^q>8X%aDsFn`fs{?!Q=jxD*jo-^
zPK(&H>??wp(Dqv_Sbbh!Ohq1qvLNQWGj($j2;YK8P|_Tv%XtKPQz1$2>R+GV<-Y~0
zOKS(hMHN2_#beVoB>8vl^yV4?1Pf-v6ol`t1fkSvg_J9w{yHTK8@lej5XUV-TW*WE
zK_5v#%%hrn`<Xuc%P39f0hneql&r>NA;y2Du!Or~6DBuU?j&q-_T0%eb+cRHlIZ<b
zK$2@QP4w`1BOnJH*qdNCzMJ&9eYvH0y4>kiBsE-1U68H>Kq$W8vuON5SK%6~Hl65e
z5m4zO8)AnugLLhhL0NUzyC&q^xSnC^RQ%}4X>Z^q>+z+eRk8A*A6RT9NVhpzjj?-B
z`wEon6WqbYT`Baxaj_G|*msi8C9H1=LSD6<V^kUqsDz()y-S3-x#!Bgw3Z9m3-D_d
z#P+UW=d*BlP4eVEQouwnI97FlD_=sPjeiOVV{Wo~(BdMOKvcUoQx5iK8G?~TCuK~N
zm;+h>*8in=p49-X)a&Ke1G@cD8&j)Y78yhh>LCQ#rf+O;P5>Kky?`07C<Ki)DObBM
zU@zT3?+)P&(ne#YSbt@0vE#-TM?7m8TLbIgTqE*-wLq>o1uf3LW!^%GqZhF4zrc}F
zVwzq2Ypvh5IJ?UJ&9T`mrHZY~Cm{ZUFrS`uEA7BNe1>WEt`q8lWr+sz6LcsL_fe^4
zXXbGRVqNcr1Tqwbr)lHs<Y+KOG<Gut9BRgrWSM$PaNve85x&;`{-8D^HCnsgL0oit
z*fIwq0KFT2Y?u=GKOW$-Zemvh1zri%l?RMQtaW}vPBAzkc{g#+T>p~IJ@{5=$k#qX
z9)xh*y7fk}>9vUS%YbXVxGP1MB?g%nda?NG4ux7Kj|qfJ?&xrW-@A1*RuqaW8?Avy
zT&Yb5?zKSea}30M?MzD3VA}T8fuVF$r<$bf{ltpSrQ4a^5Fsm9$T&8${}m^|%h3yD
zir*N!+_jw(n5PnxczV<3pOQ>}I`YRk?gJ0&6)xSPd`>)fWk%NXoP&8!nh*Exp8Q9*
zY#qoCfBoxP+_nqd@nOerNQzme@4Tr|=XvD$rurA@$2E$bdJ`KPE9iPRHXRsNPmJqG
z4ASjV>3ZXzwzf_q6K-V0VCEayDNl!`6W1E5N6=MIYQp(itlo=rl|^bzyeYcWXoqjt
z*sUf50t4#eY7x5MDzOzA^kC8ZJzCK=Q-7Ru^V&CWw4$fTly6^qbEZx7gZB6PEUFcY
zpK&i7Eu`n^Z)hQScv>}b1<W7p+)ygG@~B9D2bWYkD{^_?y!cPhb6@RxA&36`QjAhz
zJ9Hs4G+G2(sYS^f=4Ej6>Z87k#4ZVcwEkGhsY{JXNgkHK25D3Ntn#kI7Cn(wm+NsK
zSP56h$TaKCZKwwg{#0Ru)tQerM?si1ixN09XN9(<W>r~S+xN2&$FnLVo4xV*@7kgs
zeQh+x>;Ou_c+Le~BIWP=Q>$pk{hwMnI=u9Xp>uV}BtkP7X7jj^H+eSs=`yI$b)vmC
zKm0Y9Ot78`XC<l1=PkDI$#)4`b^qcRZFS0rc{_4$G%5f6cM022o~h=3-Bx_#=GHaV
zC9G@k_(Mr21)i^tj?+8+&qr_EjK(E(T1!tzTZ%%QXXiZs{7Pojh=+58LSOLEM!w7A
zTE85cVvX^0#_Ac&Eu+kD+*UdjUyiA|TzMWD<6ud)7N`7hxdLSr<!(GTk4_uNO0)Q5
zb?+udpVo~+lZg#|p}Nu1n6pwV4XCVza>4kAn9Z%o40?6(0?2oWCezUCzfhu4<@qh7
z{NF`ejQHerudENU#tapGoz`D;u028tBy$2L)jKz&GK}Al&(4vtECwnhby$n-8s1ki
zwDmbIo)O;6E3UVn^K{s|u3$s=!Q<ML(OfcmU4g<$9wswO#oQ*Mq(U)QF&^8a0m=$m
z+U4bpb(dOkz`D$_n;JJ3ecdYfU-ojToX|s>X<Vx&0?Vs=6Nv{`IimVT$Y8nRAHkOi
zEL5Ye@1EE&wfuXsH@MjDk1aNC)d-c$^&UR3@kx)#ee>JC@}y#i`SlBG%>%o6QZ9%W
zj5PU9`3h81Moi=b1IxQg-CnV$?`AM!!!*6Z3j{DkdhkM<+Uc%=T&*JC>txUox?k2;
z9Z8q9@0y<Sm8fnj>gK)!c8c;xW7kASMAuH&9CXSEBGGlYq!I%A3)htH@{DjJk91eP
z?uZRvBu3jhca0~M@Yc_-(RjIJ-tQb~FN(45VCaTsID#=;!iE?W`s{Yu*eLnG%Z`e-
znlvzL#yE+?bun+w79OMwUi~hjVetT&4?Ts$I*J~sc<8+`dy7AizvOKR!PFh>M0OU}
zl}DD1T&oo;Ez=K?yJxwo|Ju^(4!`vBvBe(b&!Lje3e3>;$EX&gxk{A0_4^9sZ(9Yu
zU$2`PCk7uUuZ3nf!@y|hY1}Xmx+;0N1Dz@O{e`BS^A#os_+nd@hyoU`_bG+0bFjrR
zLCO>fDgVn@U#XQ7*y*dII_|r_3-S!AUV{g5o;rAt^LS&F^%YFM3jyWtKwjCOjW*oh
zUBKi0$K7q;TO#A&lDUn?sBQdT^;~-kS4G^g<7(OW1<+}a<_hq}(oN<fQ4-@ds~ew6
z;d)%oyYP93<VprA<k|3Ushi*JWc5`7_Z{?HD2kjotby)bOC=tlxL{fK0$m$M{5(2f
z_i?%c#SB~dD<xXYWBMX`Aa+;@?JUr_q1*&RFWB@o3>979KhQiOpk8{2(z{Y8Z;`&Z
z@vY!42e?)T&=?)cee|ViUzT1>r6NTO8+4R1fVx{({#|F(@Z$-Xz7*wgObUTsV1=zz
zrO>5Tm_w64Ox5iL_d@bK`)Cq9epntGbc`~2O?gA%wz{oLvw5XUavZhdaPPY$%%mfH
zIAb2iNE?Py`C)(aQ8(2x3pLg6?|iG(O)mBVDhI1AOBoxDb~W(4h}A*crVg*8>t|K@
zkTF-c-O;x4=ua7zP28RoqD?s(bN2A%r^DrF-I(D9G)4=(w%q&uVc8l3I?tIa*RXnx
z{TDxxpAFBg3^_VXZ@ItL+4ID~@9B3t8&tgoD-?VKYn?bei=IvyE>-k>`7XFbvRs8?
z6{D?689bP`;a{p<-(TZxU0d_Pk_|kgQ33{YUFeGWv1XIPLtbmAHXKy`jzz`Lfvm$T
z7qFZ(hwGzmmzJNdc;$XS1uQbR7|#Tlp=-zVj7{bu_Edhi2C0va_@6VG1XSK+*B2vd
zKQVSL#d6{S%JFb{cC4on)>TPKDYZe4IBCfKwqa&hG}L#)0*ppPL}(wS2)f|OxM6Pe
zgP49oXMh`iQNDL-DMV-Xk%={llLB0d(xl9uKINv1?5`^pG8wiT&S_*Kefx8$m-{g-
zCq%I6ovSH{iHSs{t&9W()1{f2nQWRHQ-RR=SU@58n4u&6TSog)Wbt8wUfZLpZW1h`
za|oHSb6KgzV~c1-1%(2}LL_?Ke|=*aLWexsHn$QwwHozUUq3bXl-(YZ2MITb;pHWI
zl$!>E#|)(1MI|K2SD<wJVR(4BDI?ZZAo%M^9IGHYcrp>4W@F%g6Wat2=b$K1ubn>x
z=_omEI2$egY&a9eNK8#-Q;{_p;>{(6(4eOGi9(?`O07hqn1*xXru8nhb#?MarqLx`
z?6fYL6~5EU1%C+Ld}i*xX?+GfD^E;BggARHPoJuM6c+H3frcLCuA;9Og;{Kg*P{b%
zB3!_4D#}07K08EaNdNTd=}Rtb1FX5XW-7=3iDoi3LQ;cB9cMLE4FOw3%ihCd=G_YW
z9pkb=`HRkgi#Z(uY#@s1l`#k#yA;!kF5Rpz4UV+k(ympxss3dm)veSJyMzf}a&=ee
zp7IG9^usT<hp@XsNX4<9x1~pRC-&I|{DKDJRR|sCXM<kXrnDTsoH$&JGI1~M>q6c>
zW0&lXPD_*2q@?|ZbE_zUMBLhp<pf7eSlC}5>nTHuiilvDN{{3aI>!!|w*=uFJ1p%~
zH8lL$A3uI%^LKuHuu}wl<Em->1Xf#-@*1U=G|X}<U3&qN5UIDdT|^Dp_aks2vb`E*
ziX6>2_BWgc;OH4EV;*Ew4WUyBt4->gvL!)_@tWvg8=fL%!MNh+^SZh^y<>`sp8oj7
zeiRWFuLR|9f?rJf{P}ZBRAeMAXfOpD@z$wdzkaQTmwy(&$OB?4fx}nhm+xCnj2TJ`
zAdR`Y2yB#yx(wX0b3|H|CK^w0dj{`4;nAZ<G(l^Rx7DCFF>PhwZ-!qqNt?MRRXJh9
z2<Q^zKhLayN#p}B4x^1;n(Udc_92pBZ`e|mUxG!s>QmuDo3CPrrKF^=>9KQKU=Hr0
zA|icWaM$p_ZIJm$j$s+&7fB$eLF%en9~(<mgf;UXJu%P)g@~%T-vk0$yYfg&y`B^J
z(zH%zLYjiQdbiEj{dJ}x2ZjXyiuJsTy@X%f3|F#*D8bGEenPQ|ii&qBYD_9o<HV!8
z6$q2d%0T0osGghrO0&5j5mkM)DOafKWF}MG{}<7>jnaCaSvk2C-^}+B-EulshwUZS
zx=q9yQkY~8+ZT*q$FU%={CdMFrky`KL69iJm9mTOw>%{7+u>!4S-sZzhG2{fUg~kT
zYD}tjZ9xkjm&!)AR~G{|VWnyPV;8)M@!T*fF(u_JRhePzc{Pi=(!U<ltU2+f!fUKK
z)-<Y&_O7w9QU2`Nv#jGkce|D1o($^~Y18&4?(}t%PNsat*T2S5UAo|AZe>FKT2)DD
zz8Dp9C%+;imH>zuhlL5(9Z+uSMcJL5D@3_#CD~?WW@0qZ;NA3xhKBO`d|7i1@*E#5
z-8E@c3agDlx}eX_Rk25Pq$y%Ctcx%r5~>IETB@jBk}E?)mxnCA$vd!4#F{>caq#h3
zBJA0-#|*8gs;Zhi?1Hup7WI}tTR85w#`Co_F)n91O-F5!xHCSCR7$KN#jt9wbxrvY
z<<XvJxfftw!G_Y02O83-7#WZtj(@F_#qNH*k9Z@eqe6QA@|OId)H_xlC<-KAL@*dJ
z7_UHLQWC`FxYq<-S;|_Bwl2jnIdi3ab37-Sr)CT#ouRFZw#0Yt1^=zdQBdI&_yz>5
z9-SYP2zppyCoUnuVTJK(r8bA$kPEFsyvu{A%(q~q+vi<KvscEzKUG~>9AoO)y4a}c
zaUQ;%IMJydzq(zRybIhg_*W20orUhlkN{w4){rbWj@|g=nAvS#B|JXtHJ?O#PO<8&
zk}~}bu!b>}DwM?SzphAxt5@Z9Ugc@DoOl5j^lH7IMXlVzDgiIpn9{#<nLj7iGXQ(m
z$S8R2%9!Jg8@Vn7OSESxk1@yyO!=%O>sSuGnvnNkDIfrTvr)2YYR8n19aw^1A+T`X
zW;#__8?3CE){B(5(N^Q`x4RN7L)5jj)?R;@R-=$b{rRfD2$!wYatu84bW7hV9=kJC
zx{mK$op@|hN^_H#m#mA4iNWm`7ss?;V-|iRuTPaKmfjcSlF`V*d^T%aq@Dbm3qTH>
zA7`$2os!cceq0Rgdmf`3s@n>UxdT;Tsi<;a@ZhkrvUL_;&T(cdb<taRc}?hGH@TaO
zm-d8f_|j3xyIcI##RiAUh}Am0@`9f1R@#@<VQFTtQp=r{YlwxzPfN0gc2^}TV_hK{
zysP((8;3RCuT^yhzzm@=6yBI4R{Ib|sYQ!XsbyqhqIZk}CyKml(hb)vsy|)8Nb`?6
z-K7GYPN#<c(0MDl@l%&WPdc|u>U82_JkCv#%+!$mXwD=?qb>t^#R$ERl;Ezj^3>#`
z0OukPSNuIK+qF+7bY@0~PxA8RoieggBl|?d=idroX4-?J&GT$^NQ_yqN60U9!lEzY
z7inlk9Ub<Y%_TmJ#fdKZeDz)6Tvq6hz8i00^u1g@L+Z55Gr;-~*~KSQIHZ+u3C(V=
zo27kZ=E<0SSK(A6KbZx+>mDV}%;eovcY<Y$ZX1>nG^Rx+0F_7#STLA2R>9D~clqD=
zoS5ltUqNIbt;?B!QKT%vfg}%}d<ty}E!AqkY+epU3AWfJ2ClGnZenI<#O8HcN)!Sa
znF?og;Y9Rx8XUXmb!AEeLeguU^5KjYHps07U_qL@;!CHMJIPUc1dRq2wXxp29WYLF
zgi6$dEPIRtFf{dgrhMwgOvnp21Mvh~O9My&<=vA;S7}6F@^!dgF6PjoLpjcP`TBm4
z)4tv7l*O+0;m?RIm0Ed>5>z!RQlsGRXr{VLQv3mlPUz|BnFsx>0i+aRv;Bu-*=ufH
zkmZylf7t&FRoTRj6tJjgd*{<BD*VDgD(nwiOPAn<usnaNa-MXCwJuotXMku!eAm~!
z>){|u)8tr+&t}FVL6S$I<~U}nhl9iLWkRQ#=+*8;w0QUU9k*`I6{<4lC`Dq*_hv=x
zT<z@a?Do_8`sQbijg7C%9T(TI_yi>BesYI%usY>+%^OlCbv(%3B!m5J`ix>?c`{1;
zYauY)l;Jk%X>p^x`7t#~|E>c8&*U<+Zv_RhVtCVp;N(AtU#!u1Z`%oBBeUCgPwo9T
zw`CRu6|0<*ys*}oCohQDN@>dWzkPYj{E%MC@aJ$pe)QZtEdR-HjgS*M&JB{AZV%q8
zSeOzFx#1VU;8~^d8rq?K2An3pZKQ&$oiP$CuVb~zbhyd*#2v%ts)oMYs@L!*!xjj2
z>Y!SXY#^;?=0XE6F@~ZWwKer)-a$AGJ8RUI_dyG~FpxQI4$;t)VKa1Q%nlzSZRt}k
zDR^zEg<n8`j*p!aK@Z>@Ot2&*u5ZC=S5n8Vy&jGeeHjC^Skp8;JxodL+$^d^{aBE0
zk}Y_EPI#?>5a1*XH_Yd~4T&|it%A=|`S<BcQI?mNr3|Hgjpo+Sxruq~RMYxCWC0Ez
zKAfwShV1$iwZ59EN3q(Pl)y2&H5;_8lD2jb2PL7Kj|1mm2X0@9LR4bfCN}r)JYbhP
z+}LNjyaRdHr7BXiy{(2#pR6zMTr1nOU;UFcchj}oo0YlxOUgIxw|E6R=lfkl#yfso
znW}@_UkyLcrM9fnxWd&`RZG;CzHR(f{4eImHzMfPJwI+J3OMmS)jV$U<2s?QTmNs5
z`Ekw6!oBYxPH5T>4blItAF^YC$bIEJt&OoIG{#Wv`jPd%nuD(>4&=bEgtq!S`1MMz
zeC3_(mr4PU_+R-so)-&gw@jM8t3Ne(jmDE7M02~5X+0N3&I2%G#OX?fmUo@3#Ih-Q
z?Q4cUdFl(XJ#0w~5RH5mEtqk^mf5Vvgt#Gb+{%spZ9Fx#bb%;vWnB2HFHiL~XSNkt
ziz`0wQt2A)QfXQ*L)RkBnK*fNE+51EWX5w>{>&T;Q_3w9QbeWNxp+^EBj0#^Cie5+
zI7wCqW8*b*S)~2{@}2#EVuJAE!(u)`1pX(aY{`bweUPf*x+fMPO|^1ie+4S5c+Ui3
z<DD;EZsWc~Rc3;N%j&E+KuJWq9m%kcap-GoJUfOQAf`Ag?Uc8c6ORJeyoS<-tAQH{
zvW$+Q&-SRNPoFwUu3W<}GGnxVp?F}4ot7<l!uly9Z9Pv40@oa7B)!t#E5|8`WJI;@
z$V=P6p^Oh3LIw{BE6oM(eg<nf;dSH2jp+&`D~6b(x2C2h3tUxQyQRGD$M&}?8^8cp
zz%8^}L#s1E+}y=Tdc*{r(L{{HwQJYt<03JvdDcb>$Q4|O;`wShAvP+3S-4pXjMZ5|
z-|2q;6;?v@t)v@_DzA)KwW#Q5yL`q3<X^NZnwwsBhcnv~zxX*ZFfuorQ1y8<*&Q<h
zY~UM9c_hxx%@E3+h9m};I;@86m#1VIO6LhfQmu^E`8_bhDQP~2<%8u#u&ge~>_@)<
z7U0WdxPV6E0~C_TdI?;5<&>l^Szg$=N<<Pc_W4RVUnCa56QWQWhbV#u(g{$pBKh39
zHCcPI$tCH=b^KzNf%KS^|2b?nis1wyUloj1K!O3e567Z5IwGPUV!Sw1-nToNocUk8
zRV9TDJ$)dQPYAAqNWr%jIymmn2S(D1Vm(jd7hhk>SzLuw5OXYieE<OttI@7#x3}jF
zdJ<8DPC;d(qPa6eeN#T@wgE<K6<&P{IPLX1xHe$st!e!SLZ??uzXGJK(b5@}PLMyC
zM@L1iS1$)II^(m9jf^5C{e!XLC`g$<6FNCsii&elsF7dhFR2>MXQJHUxSRI^G{IeO
zsUVs?waz1T_^{h+5I`7Cfk0<~z01Vg3;Wj0SYt$jf8G;?K7IN$COdYn11ut@U&<@+
zChnq3a1W%fix7J-wYn>jqg#)%m=W70>F+RVi<2<&#FX~2TOo*PR{wFxI8Lu&@)=hZ
znL7I?eTeimTi5RV&y660B;TYLz9uQ0OGtY<JPtAcH)ITByEeALV0tMkFBC~4sv&P5
znLjW)4Yn<d9x!tu-SG2UF0!|`S7Yd50EplyLqK&*Lqo&Ng<Xy8B>!h~4A~58C{r#x
zDkvz>dHM1so)pMT8-Q3oe%KsxvSt&{{7|N#Grr54NDIeXm(LBM3*jb6-jcp4JfU+w
zDN}grj;6cTm1g<F<#6N<Cc9OPEBTf;*nDemZ$Igbhv#d%K&*2L(j+^&JL_nSb`C&T
zDER?$D~xxX&<QDEFAt6r2C)tU8QfEd!|B5N_XiAhcXy|GwdgRTpfR&MW!M*<W`js<
zEs?)K*1Ql49wJ9caIVS+@Rc}7yYE(Epj#mx;>E>cu$>4f4Njlv`jIMS#BPr%h9D3D
z36c?z0GTXE&k`j8+-clxAle>hZd?8$){`+bK6S-*E;Uci2OY|A>e{hL<c-fG9d-BZ
zLXz%ML86=VNDZX_I<M(TePe7!2<ICF7gDl5^6r%@55j}426z@UY+^kLYg>JaVOGz1
zu@80Xe11Jv$$InQ&1Dx_;8KsHUGX!Ou)Z`&;r~|dU9}atSBzHf<!B%sPLq29(9-m}
zU36q*)8UnR@Rdc#J~wq?cZh0-8cScFTAJ$9Bhz0*s~HukQUqfvWhi0Xcb2AUYtI_w
z$*jILvDI!++2uTso$DfW@=BFoqd$VUo(*spREpMMVdO<bMAil_9VB$p&thRIrNRR-
zXhjv3#Rhb5My%d`35gI|)zEw}%Z;k6_jGs`ntuWuu*@kn20D>=gq5e6+RTuoAi}As
zRdN&wiI#uR55*J|ARl?D&z?OqsA8rqTVl;6^l6-U2kPt+ugAfi#g<tM$a*JAl(TiP
ztPj_^@PsOqez|KOGaxZhqbRoUE9s4!z-g|XDSSr_CiwNmH`k4JO4zW8`r_WVAjvjD
z|J0h_C`*x5$2vfTn`WFf9|fUwIOMW4If_Y#B`$uLjTXlZ7a37m{RN#Qq$>hYsI*Sy
zrr)W`cJ4_x+^Nceu%hlw{riOVf*)r8XDK>=rRQV~@4Xgu75T#%>5*LC%VVd|m-+)H
z@Q2p;Bch^m&mhrcH*9b7s0C5*O!6o+;C@4In@cV6!Ee8{FhVLjE8H;ayYF`Wh`})j
zDrnPiG=K~K0$Cep0ooG70|^_@nT(p@;(wXW>svWO-|k7+8mqsLF0Jasp4|N8KW6lE
zODnYpYFiGY@r&V0k%%B+6mm1`%h<U&l)DKQw$kE!Flo7Zme<TcLkA6<V9c*9V<h*T
zu*6@&_D_YHIik>TI^KxxRR6ZRT{NT-z@92EprAT}YY+r~05UfO=X^+#5=_^tV&@*b
z+{zNjgfpsnVPOIA6M7?CnCt5!QW1@o*tf*Dk2d35<qvAwlV<yf(})oB3S%oIo#msk
zX2zG>?G%i$CJfTZ3L4{DFtJJWD+KWFhszDgYghcssabjpE^4VhEzvSJntX`C)_pfN
z8P*i?Jr^j3qqr1sEG6_b4S-bREU$)7jUu9=tT@XFHo(eq8I{YmBJQkFI43i)?I@?D
z(FdZMq3e%oVyoN{yn`07(DG$a)qS9!2FLtkLuo<831=a|);LPuWNroQso<rIS1>l#
zbQ;iR9ut^3)fq3HAu9C5E&&C3m9Ie|kr#nS?ZxSCr4ta1cs^tX;}@4QSTm%909dXj
zqFT<(HO>Pe#+XW_3Y?wmx12Cc&b-_=erEtu@Tfrq=l<i5i9`PF@rD8C_x*x4wesQ|
z-$rUpN=yst9};IKVwg`+04HG`J_Y(O#2Z!-x>dOOGvhUB3~>w0^vs8rpz)HqqWu<@
z@ss}nDo8~^hL}rXG8c57Sx&SZrhucq5A8VYdRUXgN)ozoPN$>|H-}$d8i0Fu$zve9
z?%I1Ny74|0M=uSZnLUzGDTyxaa3&$@>r4|*1kGFv(1B>^BDxK*K%a;6WHrvrNnX(q
zCaaRZ_5%GlzqX*=?&C6CAv2j6;<vQ12^=MyD85zmsq|hrilX2?Uj|X((`|;*Mv&If
zfvwyWfXKiTEb+Lrg~7nbehepBAhsPsik(3B()|Sr(dJ#Ym`|q!r`8hakoG&{7Z1RQ
z^`;>BBd-6F!BIj(x7KVW2<(kP0)P8M+NV#S_CjVEY*E@5Jn?kZ`M-t|mT@a7i1ol3
zg#(|_=S*lmTQ~-dophh#c^7;_yy?0kr6D=99K)QZx%WVe*f4zbwdKTPs1sDWBezR)
z3ORtL_9^*#J>Q<3*^@kM0&cMsJ6t*6Ol$zVUxBVl_dW!R`r!c9|2&%8O;v6ZjL}Ya
zO3KQ!C9O(qK&gnOG=pWy;|aQ})m^WhlDc9|<#2G4&48;XC#OT#Us@p?Nm<S0c>TSr
zNRn7hxrw&rra2R)EnI?6;TQJ+qDD;499K0m19aoC3!Y=?!UmAGg&sQ-=!_3ZPVaCl
z^;I!4gP|hP5Q&IyZfvYPK)Hhz1Q|<Ozp_zkN=nK%AEJ4J>A=$!Sqc8D`q&VL53#G6
zNd<flhxWvw5|HL_3Bh@lUo%)etQ!4zQ`-u=R?P1R=xdCXY7SZPg|ni9;FM(PLIR*6
z=4^r~X1qEHu!VTj`j=3wTe*v(_cN^lQ>n6bVHd$+P9U{@Pr_RQn^|LO3~-C!p%JLy
z0SbWX`n#3Z(XR-`P?~XP1mcX0jR&y93<SHcd>Kqb!uCOjA_%Sll~Ri{#bkC?l*i72
z-%d_XE)Vr{!DhiU`*Wz{{rQ|q1TngX$@$ady4+21BnI35D+GR3J%DRfb_U$WGEndu
zN$50fUETG{;ru|m=>bxpubF{AW^`#sifjgOH<Un$oq7uxp6As%apV=6KGp$jml5TS
z$)ff^>tSG?<-`OwNW&|S{tpTPIgXxxzCVOWIGK!o6$(SD{IFNvkS{naSl{TxXV1!}
z{k~(q^zl1zw0vz0wqqXdi5pgdl!Iw9_nBu6JoQS3<%Cj9rIr_?XuOf;G1q}^p1h+e
zq3N+f=r}eO&M2!@5Ec`&QGhTj4(@!sbK1DQ0?bCt7JBI-(F6VCch>tJStj(A=)0D`
zkN@R(OFxD&>?`kICt{*AcShYuvd2HGAV;E;p&Ky%$x6=uqEQ+hO8g^?VjZ>MDfe1>
zKmg*P$Kz9i6H0A@F9Ne5K1~uT*AzG8ghCAdOyNN<^Y0fC^^fvBPj~{+DsP9SUIG1u
zELee8u**grcko#Juz0Hj6o$NEiY8if=a8H)blqd~!tMP~j!E8AoUp_)TAS?qp44sq
zQAT|(^*=p#7DCX5zg8{DB3$tWZaz}w8Fbft?T+DO_VkOc3Kg}uFODj$frQZqNbE^9
zc5x^FuiYCIYAell<Fe3>4(Lh_DFJ|`p{B)_X-Ctmocr?<d2Mj`)Bdm1<<ty`<vSZF
z32CZHFMjW38rVTh!_4HZmAcZZ7r>l|uTs=z_h^wvYM|EgH%fBwTfD#`aq|^_3-5+u
zeZ!pfv+zVzo2*uh_8YqEf~oMYWHX{a2JFUIj^=jQFo&OsJZXBNx(A*64PmAX)gMMI
zf$M)P^!49?QC3myP$-8i&^EZz8v%(S8TNH>aA>%;;C<|I$HZr6+0p&TDpfOg0+P1R
z4KlE!gihj408g(%zTSB^CBzcH_yuK`3T~qZ(joU|+<uu1nNB7qzq7Z2`RT8vfQOck
z%Qpb2V<ulq?ltj}@A7J?!O2i*Ie|m1^aBl`B@*hje%FXrqHqr@gzd;TUb%YfZ2>Db
z)5%stLN~+%GSC->DnZ}zOI7pZ@`oV$a0{M<nq?oV#lW-YTSpGg1I}<GCQ%;w{lQ^W
zsO{m-TpO)?sNj?)n5s&aJHS)A<e;e7V~<A;bJj;dkn@1ghUGn6E=>u%;poU&KoFnh
z7o(9;Q6>K=>_H2eM3QYGQPIkL1TNAslvZoE%yTAK<{=dvUZw;c*3@!BztpvF>EnlQ
zi#Gv#m|lWv7aD(QBEbbO(9(}faB;@Fm#vH@(<_kr^N`I39viP@<srjkW@hGF$)0i#
zxsEjgQr`HTG!0c%pT^JgP@7|DDJvHrrQoDidQeAWQsRfl;kilR`o_ereVc-4yB1ri
zOF20@rc>Fu8UpFaz|-NtHFfvD0RRX=lFOe5lywH2#%B2vX*mcz1IObmv9Kkq?$gA1
z5h0=FE7BuhEGNp*)6X0(V(Vf}2aww*a`g4}`|5}r>o4P&8*017882a22nB|}AS=?d
zJrjw<Q-r>$oU!_josgPw5W2=1B81Ql*B?PC)crMKow5f?=)NQ<p|8H<riK8C!3H9z
zZjkQKOG80W=YNGr8?bcI$#Y8j1Oajhlsv7Ur2_==7UCQ_l!+(B#l&1eA%-SPSwTnn
zU7WqW6d*HTWl)ajNbf-cJFsFa2+eeWG*Bf&$peHY6*nxg&vd!|XkL(k^hj0Rr@r4v
z!&-JWlh|ssv)>2l8~|{o62|*zn8l0cHj7_A<U+Zh4$%Q;#bUIQ5zHX!!yremF+C|w
zYOH>;iHV%g<<J^uvEe_&EgsrLJEi$RL45rrIW<VzK-vt>!QhT-oj<t+vVO4?pH4vm
z(4w_nw)H<$w>ju!`pMN^1S4#M#HI@Xz_j?`X#NyCgUdvuK*8|+I%GtBV>I^xC2v?L
zA1ZE)1$dy#7*;`&!s_U?Go1Uj=hMU|0Kv#aPS<=e<~o2GJrJImWE0k%_X!<OFilG;
zKibLd7PE!dJmnKZT)q4(&XY)uVKHEiF2QK5{kG?hj*cO3kZic0pd-Ys?C}?Y6{7dp
zVXmF1ox^+G-QZy8gX_%#e_e+pUy0Dkj5S@aa7ua$FGq0utJ!4~pmT`RV9Zp>TGlTt
z+;LfF-4#$|Hy|;H-pmyM<yxSK`)nA&jYN_lTZh0e4?+LLg@nowsqVark$5ZJQrHFd
zt9k`PlZ&rxJl<};+hrHRmhZ9*k))hdD&46MG>RC9V-yn!lVD_XTTK7d+5k-!ts(tE
z^G$jxfYm)h<pCq*vVyKI=7RYi##rM@OHH`Q1s9#}z)`R8#JL`nBcP0_SO;g&+SWoc
zkxTGauR<pJ5u}H<K%L!FdV~RHx{+%K^iP~eI3cZ2bkluW9{gHhB!5Dp8B&}gzy|&p
zlc@F9d9^j=o?F#*X5ElPuX>pA%2Jn}hK>%&tzaV8y4;gl&|LocfrxIvx9qHeU?5W5
zV5B9WR+>KKZud|x$~HqkK&oAInl3BtQ;192V%Uc_8rs^{f<@vUeTe6RpDKUgimbQ8
zG^HT0Rt%9`I2!%pJrwHI)%{ih6!8%on$x@e%!_G+1pvy5DTWOF|EYp(ClIJ}1hkaN
zrY<Bq=@H;V3DW(BBI*5*c3i+FA&KCVA-?AloaMTZz=8l8{bh-D+vCD&^sCP4JLJH@
zVbdFqS%?IE9uPHA{`F>Qg=$`XcV%DeyO$4-e#qVGfg+r)uKUYkWic;k;(Oj`sW1M{
z^!Ca~LI|<LS?3rf0n3_&4976a2Uuhq^<~l%oz!U(lyL*a{g75v=0|IF_Db-Yjzrm{
z7Ja4J9WW)=I>f&#hfF?HJFner8*PAJgqp3h^S5^huzMYo{3Hnl2x*ohhVHuI{ae96
z8i{aME&~dEdg_!hsH3|ijb7JR%8T`M!ZHr~uK;vqR{>v3#m1t9Eym7y|IGz}$hH9J
zM!1_vqxX%dDc<=j;AHE8oS<_BW89N}wT2BiOx%2yU4^?f6l#T`9LhpSPGxp%GZ4jE
zj6h`*#AofsqD#A8F5u9GaPit$Dx5cZg?USvM#<vmU2NLzG!P^@bi{y*v;#T)ED$PM
zh~XSI(<!QKuYMM_Jfm+)_aJ53w0;zbMNmHgh|Js5)AM!g93zpaxpE~ieCo^`1Ds3m
ziUtj^wL}yX?b!q6gZ10wf`1Yjb51-eBmtn%vBk2kmx@53d4Qs#ZIz*;WWK5w!GKNc
zwXWq|M;1-}Aoqfzimq-|Ae@s|(YZ!*ys)Nu&8&;>!VCXjYNk)J|6Mcn`Cc=%D7s-(
z1cLO_Rbh~Gk}tRb`_b2N`p76G74K*MPAZbdd__s(aY*P;Vu_c?BiN=4Yt0kr$-@-z
zAuWcA#MWM9a2|!~w|?0wBOBf8`A8&!>+zS{cFU9cA!COaYXD-rrMyIGF_e84YgTFc
z@;U(xl6kKw2!+_g^Rj}LlEzXA*=lBI`&FD9rN`I5ufu#Jltc=s$j`rPrT^1|{{OmW
zyHP1sh;vz^ap!9{mh^T>UhZM&s{6Mm$uAJYZ6^-f$zv|@NLd&`8GR!vHzfwH(l)}D
zjWzu5{Aa;n9CD5?{tJEdAU8-SjBS0>$_Z6h8${T&KkjFbWNJkR58h?Fy-W}<U&O8v
zGABsjB=dtv3&DLiB<2?Lis6kmPzM(#>)bfa(Lp&IOn~E%!R#|}jq_y^c?XA8XtH!;
zS6VhbIA1x=;crfP(`XOj;`bbLB7&wCj3O+~fYr<?t8{B>Q7*Elc`O1fAzeX!LPya5
zTUW!YIrITi9p!xIia1A?Mor{DhWd-|5%<1weBEYc%J#Uvtq(t;GXqeqzYw?o8c^t`
zVOxDFsw?c}uiFkj7gv*;Jot+DbWCIW*$2O?^S{~MdUlufu91VDhT8pJ$<v9`0ZY-j
zTbo*GN;WnfI@Ec8vMu{t(zmSF7!w`;)ofPgsPy+Y{rew3{c}_A&#31cWli{(r@#DQ
zsdI5X{AufVP68L#anzabj05<@!QVD*q!jSKySDz^`u%BKp=X|a=PSUU|Eovebh^WJ
zGvbb>&e0gk<gwZXxYQ^2<{j;*p$H3?5aiMj>(=0(@Mc1F8&N73*Ni3V`2^fWspEvF
z<(UfGH<CfeDEE!onXN8U7gp4s9IL&z+wh6zeeYX8265_4MaNT|kkN%BL;kjv#-Gnh
z=T0Og`u+Il=ZR0@_KaJq#4d35KB}4<XbAk~!=D8X+zQq38ug2e`g<AvsugQBvxbKD
zKYl+rE9^4miRKlyPb=zNSKRlQ{Ty`7<j^H$l|$-*7A|@Y5gLYV=dp!Y%<jh@a;Kr0
zDy#8o#E`lpdi7rVOG%w0<4Mq-ja&=4nB=h1mSM=e<?71LPbVkbNl$mhVxG2)kx77y
z(Dx?DVvY?$dz=X!+JVcly4NBu|C-(4O>1D6HR$pk`=%ME1xj3N1G9dtVhb6cOu~}T
z-qi+DXYYj1WcO?g)VZt^?SE|PUB1UnoDkhPFOVd)dqWUBqj|!#&60S$ttV~Fb@J<1
z^*h!prDuAbBUW<LhfoA`z}@sB?G_@DL<;`xQO)FM_2!*ejLf(VcIn)KY*q~%pKOny
z&p?I}8|dw=IMvpH9r&C;w`=Qx(F|NT;(hgzJ?ag?OU->Cs7PTnz`n5niw#(mKW?DQ
zmoM&p#CH;Iejr9DbXmF3kB)n<7nms5_CpJ1i9)Jz%g2?9?<*fl`NIEo%SQ*rKCwD!
zjh*2HA16CI`g4@9z3w6D8+g*M(978Xjl)ZDpKHU7qBXRpVcWEp2X%a5^6cTF^Y>BX
zDvy5uBBC$m-EJr0H^>bHoyriIeOgja&%|nu5+!x6L}&y+I3h4jWK^jpH&xLr1BnWw
zm2h$V3N+4>4NL<A16{Pm=@xucxt6hc@_r$q?kdo?gmA4y)Gea?t!3bcH9BV<a-!Ve
zJN>tf3p<h5R0c5)-DGt7b3nV(rcx36OCX6c0C&QYlv0iBpeQ^VnC6?EE^wo$rwBz;
ze#EXsYTV%{w#^gQqbpywn>-*Hay=`)tQe@Qx#g_gAy+R!XneI5ew*uV1DEy4zwQ}~
z(wbK9wp@FZ&$B{_8v8%GyYhG_*Y>aNRJ<*xQe<fxM6#9aB#LJ!C3}`M5{*I#Y3S5Z
z5kn(}B*h@hShAFL+RPl1$z*G6rzVZDjGQ6MF!Q@co%8v9KEJ=-Ki=OT@BHEM@pzuO
z@9Vzq>%P|Sb?j<@m+ola(iazJtMpc$XMaC(>o>)zmZm+%`ZHxEA-~u4v;ZVo!}`CB
zmso3}9?9(DQdG34C<WzAx(7tY6O&0${#QpjhpHbo@%iZYeZM4^vCuG5lSv?f2R;Ee
zUqLD%8nl9<7XzO3sK-7Xx!_x-OJf8HmEV038I-bo0>y(NV@NkBgQh+vRPhoB`q(j+
zL5Ma0Ot?(wd?eh>AxzNV7bOvriuQ^65$8O#YuAF;1YJ-arIm3k3YFCKz>~c2XsO~O
za%BcV|AwE0*hzas2dQV*wqI&8`py#$@9|Sd7pN@BptIRBm_rF?Go^_AVp+*H+_I=_
z%t2l=S6k--Ipg_h$vDj()ZJ^p+?REHxhC<Q_cRmqyw;fJ@Q2ekoK(Nx1?C$eLL3rM
z3NIT$Dq^a#eWyzg*Z<^LhS_wSRXdsxr)ehO%iQU#w;`M<>;8onNOed!$~ps+!3fwE
zMBUcc9jMHlx%Pa52ul_6S+BtU+lJa}w?n$vT5%V$8u2gGQu`r^P9NE^q)#m9Ym2hp
z0yPYZFU<gf&VR~q{BxM0ARTz)cb2Y=6h`n)D#*>o#%AKGgoKEqCa2Eo3Mnmfbtq@-
z(Y6{L{<!3LKzV1%T<BClav(AVRtJ>Gl229nRog^YKYP3SYTf|2U(%q|Hdc?MG~f7(
zn7INgT)kO%R_-|YOF6x_7VI+C>;Cb&aNd!^zOU8nhqa=F&WM|GZ~L-))RQLjOQ$8o
zFdOLpebA{sn{_bAC7@^Tm{}g~bL$b>lGAfLmKZexEP&x|jx54{2*wZ}f|~NmoTb<?
zM_HQl=NhhDWLusBbWR)FrCfkes?fGO@1}}6_3X93n7%Hc?n-+;31sNHPUfySO%pBZ
z)rmo(M<j&mHTE>22`pWD{>|l+5d3)R+2}Gl)1h;u?37sQrjaO%rKWFC^uPyh@pv;r
zt1c_9j`SX*=Q$N`uk?D!qpj5Pq?(VbSM4mrT3jxO>k!K&cE35Sakv{Y9}Ncpa#q*B
zzwX0)NG<zdRcF5nG7)C@wDvp|uc@zp#odm!JEU&+h~1~|@kEl}0e%`Z<WgG5w<$tF
zY3?k+-D5oU$1+~y+%$NUr$GxW)}j`HG?)g=A_}Sr5-vZ&8q7u@PY;(1WiXM792X=r
zQ1+ZTL$Y|pSRKg&LN!1cdJ%BlsinQub?KU4y^(>G$qv{=o9R5kS@42KO|kB4L;hf*
z=GXm5@kq_O<t5<@!*(w-6?`Fdum!r~PCkWfkx}cs59J_1Hb*?1PFjWkktHOyUv9(u
zHHl4LRR;KzT$}8N!EV#*qh_73V|kdeRI2<O+eFNU;1yRL;P)}4&n@3>csX{=h+wS5
z3BvSzxul0ML|!{=Ob^_IGs5J+<~x0uI8H2$<<=M|T^@0Ma80Fb-b~nuuKWj38Se&1
zNDjex003peX@QqBBQPz`$d%{N_hlN(e#&j{mz!kce<j}A&0{WD{RZe#^4PIz!o}oC
z0_g;@4ymzaq`P(1*M{W*7|XN_rhhjlQ=xB!7}X}l+ehaela-PRtLc0JHRr-aYsKk(
z)cGWmfqmFe-)+)L1)K##vv{Qc)JPz;J^NVBzd)ME+-Y1}*6FU!IV?FGR!jN-Qd`}w
zRFC{~G}4XMuFGEp2hI=6go{aCyP1|G9r?)ud$BvOE%5XeEyjD}KA9~#`uN=$rE^q&
zXHI!Me%V3ZTvz`#q4z*WPl9i-tk1EBZB6|CrxQfD_hRz3+`psg{0Eh&|JGNxF^0s%
zem{@NUM8jyB#jA0Gs{J5^|Vt^)~0FxuGv^kntERB?#Hy$shI|b4~VKHtumsF++ro^
zPyO$|<l?>k1JJ@Qudr}W71-bVP`DPCT1&=0!bLod<yZtbyEzks&_rxqX{Y-IvYQGg
z5FyqSM_A?K4DjX08GQtNfZH^^t>erUC}GlthTAzsm2!lBMP)@_(9EcSz?BXFGZyFz
zP`>Zrj5Trc>{5c~Lnp+U>awP-^>(<ZB%g8@J$z6p$>dnpkOl1A;;<DsRaZ{1{=+7(
zwMmom2%PR?gbD!n@N#q{I{Nl($K^TCohom~)JSMKtd!g1Lt(Bkn%>YwopKltbjf<B
zvRdC^rF7wSKZ~9vQX8?sVq%Kwpc_GUqAPaHhFqy+$uwg;CNv}hu-Iqa*54WjiGXyg
z55n?Gc)uIOjWhRff)O2(Z9b1W9x>RQyc)q4ReE4vsKX%<O-(g%#+D>jm@{0F2rHK}
z<a)LAcg8i);<+oC*~zPWPBO11R(`jqY$7eZIyqKNEExCSaAET=bY7fFMMw8rUY!TA
zJH>c&790mhk*LJrcoJr^xw>e#<$!OBpi^}!m3tlEVM_f;p*NL*yV`>ur1+G_ESE04
z5od2&l|3f5Y1fi9*1I4~$+mJ)<m{mH)&{!ic*lJ_6)l0USLn;lqWImWvDj9z>h5(j
z&7iG60I846rBr^uUtC=5I3)yMn7b+>>StNxK9z=#)6_${sAtMpgr+aOU_vw|HF@1(
zhg+a)23%^l%O?mc7{+kRwJYS^j3cPYZjFOJly3kyZ`Coa6LUbqEGkNf$zlEx;s){b
zp7}EgaC`|L_ZkmjK9zPhZ0D$8#{d@ZVyo9P2}bDs9E!G;QEsgZZTP%snywuWB4V(t
zBTv}6d)vG>u7*YZxq7>gEZv%_SW{DThMM{o1sY{$`@n!O71h%N>GC0cHdZwj3{N~u
zRgF1Hv100S=_Qd0zH||;-gJfuo+ax?u5XtLVuIK9$jI>w-$H2F7}*tK^6|q5-Uw)=
zM1U`<gX>ioeN<IN#RFBOlQ~@l_)aN376;PFGkGJ2p?M5UbLFUL4fCO3@L1FY13d`y
z0>;M{kGQ`VX#g4;r5Ch%mU9f=F$V7E$iI8kSymG}zCPx=guOQVA(oY|bbXOA3oaL}
z)puwc5NnTJzF*@gfWmUtOGcJoWrH`i3eWFG&)uYq+A$L}sc2g@;ywoVPD}?ibsM0c
zT@=?Fl|BoNTj^HJoyhFf28CG6`^c$Z9Yp&_LmE?qQckljj`=24;kf4dWQI4#|DA1u
zH_fTf@c98y5j2nSX^-D66=(Vn`HP)D3{*81IDj7dEP3udua3Xa$hza;e=pc?B2(8H
zI{=t~fJ*3|NP+|gUQuNfhP)8G-;*xO3BIC^@K8oiJzw9<7;JXNMvdyW2H{zCaJc};
z*6bPH!*~mRbWv>3HWCG?aQ2d=g%^^{;x$c%x!nE}`Kq|0$N!8j_dwb|R7w_6Ng0(T
zqSC9)!9`h=B~Mnb_BnlDgDU%{y0Pmp5471LO4v+y4J6cTvy24<L7XdLBGUzTxAJ>d
zaj{wn7?U1|$v4CCWyK@hSS>-?_M|3PW#l2d{_D%FEM0<o$t$<z;|Hj{r4W9i&42HX
z|9<BUsTG$fP;B!)@wC%@J8&qs0NIq6plzAh@IiHf>^$5ys;K8+Gi+d<wXjoz6GX3^
zXy4E%;!;nE|Fw=%7;h^=<>lw?Y(Ov4iwYbs3xT`?x(jyT4Ed-YJ}GIxPuy%6Y@2C?
ze84(XL`DGjyvs^UL^54GFOj9&BTK6ehe}Ug7TKN$>6J6jNy!AYk@0b1g6&zb5pHxc
zPZFBE(no5_ytF=^2V<S1)5%;BV}9mFtbDI2KRS>3ljL?Opw8uKel<jF0P&a$2IUYY
zehX=Qzf}}A<;wo@80zqOiW!Qju4>w9)I~jMVaKX4PmkU^<a9V{+3`ZluSUz(H<Z1M
z^!v6=J^dYiitV`2bP9(4n$W+m;i`qjtz8@v1Q8b$0Qb!8G<o5Wp}fZo!%E$#1BAV{
zzWy8yye?5$CfVs6S=ua#RH)!KSi{}&HhE-htp4c?8@Ai8$TWmK{+a<h*YFG(m(J3)
zn6bH?V$P4wfmu|D7-tg*8&0PSGB=>ovd?J)u4v*Pw(-_ZE7SiY^fvtajz6l|%&_6x
zmqg-GW5t-xq>fnS=e3J$1K3iXOfxj@A{lpsGDC~`o_s8CT8lbw$GFu6y(3<M&FWHA
zNtD5;IYSmB3ql>$Z~(gP%)bC0?cVn)ZNJ;*+Y!ZukL!Cw`XuZ>Wz{}_1{CowSjrxF
zF(v(p;n<dkm)OQFBKFbN>SS7p%&Gxdua%Q!bF2Rra`#V=F(H)$J2s4MNkK32cs5r=
z?-i021MmM90|4<}X0W8+Z8!+ia#!$~3S({ZYEb7a=?d{DkUo~58&_Ap7&klSlqMPh
zdahpc>+}0d{oQl3vso@+9HDTHA$@lwx6c|XZoU&Ps7-sKI<}?Q_UG6tEpu!w(E65)
zVQ?fgYzj7k@vZu2eAYgg5beeB<z8BpRF8lCKjRw(TP^|3RO66r*A2l23nD-tjb1dL
zGc*)NB$Z>zx;gh}OguYX4D#>1NJGI<D@U89P*cI|G#;tp=2VW-g=8TO)#SoIo%6K9
z<9TxizjQ9$JiVjYh3_m}h<(I=l+a=G&z@6#C0kfHyY{XRMZ;cy+EvD8&6Iqe0<&nd
zDSyqvnuBC;5PmQR1j&9Wo&s_nIu_TLm-ECBG*~Yu^V@aeHEHO%AOO<yZywtP9v?8M
zsyf}<LFRwyo`W9Bf0hso>AcPH<Wtp`&K_a_+PI%>==pb>K)Q8;twJWWCI$;;sz;kS
zqoame$!J^wy#5-rsI8dc$Eri)g}nf=_zZoeoOH{zYi~r`QT~dJUwUt~4b1;l;k*X8
zCY@BSU_=R<_#B~<oRe^Ljp^L6^2ul3`4;<DX*t}Qkkh*C$1Kq|rNj1%cN`C74%|FY
zX!<_V4EEn1`9rI$cYzcckdv3^YEU114)5QHXYEdEio@0Q@#;?6ebQ-4v;MjPF#tbY
zp#8+0h$Zy#KnM@+(2!F-eCu9FcW7{86pIAqVf#!DJglk>ZxYTJfqA+2<mB<LkW4B5
zOI{s`H133@eCX`VEvvfIcv&bPfZcgl*SW%D==wvx3;)LoMdX4@tf9Z7W6)m3YMVqR
zOLzQUaGsFG-KYnlq0hQ_CD;!+&>{LpW6z4ePrwguiDmk6<Liap6&}gZTESID$Dy9)
z8*@NL{SPO(AH09v;L&fsq}o~o5viuPVp>yziKIG`63WdOQa*IRm64F#b08Tlw}bQ9
zNww+>*zr1opso<gMe;(OrZ)?Mou#B_r?Bg`f#YnmJ?{QNSKF+1fQ6$B(;Hp8`cNDx
zqziq;obiOTC0=tRUh|w%Qqv6w$Z^c+{mloU>0yo{E{uL5g=yG{B+b%S;#98+GVAfU
zlEyp6Dgtgq{NJi(c9Zq_qT(shIdHvTus7&J@9_{h5l`ORjtN;@y8o0x0EI>9t#^Zi
zvX_Yr&aBNMbyiI5N#S%s;=HV|8GXe%nl$7~*bpxs@XPJOfXGu@<wV(R>$MmC<@@fO
z*fFao(6(Z{1h$^shu9$cPfRtBISNlkdkNy>*NT8iQdJgR?k%nnPZARksJL$7?&bI)
z;Rq-MMDZA3>tohQwdJ!{BwJ^F?^=i+*e@k=*I!F}yT_%9P-Rtv*fx9U*f9ROvQh-H
z@+f@KKmQ`^*>Lig$)@;!ErOrG4GmnKO>}JrnbavhC_nwSNdB2XLZv0>YO;_giwLb4
z3o3?xe8aMu$H}jH{|L2YDss*ul*CC-$y~8~ulX`C!3$LkRReF4gi0K1N%>)&yN?2`
za~p2Wo%L(O&YQ{8)tT&SK~4I|CZ0P%EoCy1ltGBx%Q3s(8~)Q%eGN&sUaGIC%qGqX
zp3@sfODIPe>2{XOw<x(&ob4{hkUj-wZlSo}3l5HUx|0xcIvUrv%a<<B&sV@VwNICd
z^W`IObv~Kw*^x;1Td5xEoR3=k=O<B*VQJQD=KcwDd=aL^Nq+N1{q4Q*BvP%y!f}Q`
z#no{uvSl`N8`nSmUU02==xUmM*ujCjgh(adV$#4|kK<p8h>X4E&GC_K*ea9-Lk*!>
z4l9>ma0;|57=JDJD?0MysmbQoay0jW^hfv;Z|W%G?6F@E{;Z~w0`7%yemSzOXy;FN
zpxBcq47vB^3K<2j+BY~bf)twL`rNPzZtkCS)91Bh?B{ZCITPgVIljhdxe=MO&2>3X
zS`2pG#yp)$CfUpmM(UeZ$8fK$-S<^JS&wSM&onVMErMx@J{rD_tA`%Ar)BGwINH#^
zWr>G=#ydXf?JaE4{3@BOS8zzxN~zm%qD<vz*+dYfg>ra3&1={|F>ZNqswUgc(t&s>
zQ8Dx)km}lfjTPWrdeI_Dy!XvRQ<59zuEg<^%=Mo+8GS(~Ut`-PWg*#T)@08ptLyOF
zTXwrciQ?<%d(NqT>iN}EHhEYkR|C^<G}=S$-qT8-a4u>wwR5R2gnZ!RlWJra_TFth
zrR3Yk-voRQRJdfbq*Cw}%bF7F>XnMO2i3v#YG+x%-gP_M7WaCScT0jpx3ObIi*PkO
zy<VCtiY=v9t0?Fy*>dn+8&3;!U!A*r{lZ(Ey!X%VZY4U4%Iw<LtoGSBgo1BfdJr);
zwSzw`KcTEV|GO}<RK>%8Gu>$3F8Y#x#`ctD^V969VG=eq(E8r;bynSMQ9Q_J_u>v+
z)w3-&MObyHI&{-s$NOrZog{N5l-bJ!{DW@-tsUxaUXYR82mk#u{<W&-1A#kDEOFo{
z@7zFWV5?7CF^yjutE-g@jw9!0-WCq8w@{$-$8U9%CS0M33WCkS(tt}=K#DLjmZYs>
zo#Z*+<w@Wk|FqV_hGBBFv;+)nGlrwWyQwY(RJ{emTgZ+lL`nwAZj_EV{;3BgNB*-5
z5*4K3CAT*%hUv^dnIhv!`&IH!Mpwrh38$2haobO=eW3*bUH%F-miosShY9X>p2lB)
z1@C=jegG|?7ES*k>n*!+ww`oresBPmIHWaUYF_30NqVY?AuPe24xvB0POB@~Bs#jr
zD%yNh4GBfhJt55!A`O~wMv9z+h+{!wu$=v;Vs;Q}ukE1k?pJZy8|VpxI}tO1=4X{&
zvfEhQ`>9ybKt&&Kl6ql)BTxH>j60~&`)@=Wf4t%U>8JGt;ff^#HL)?>oW(dHa`>2e
J@uAb#{|A!mG{pb_

diff --git a/articles/EUCAST.html b/articles/EUCAST.html
index a13baaa8e..7544d7fe2 100644
--- a/articles/EUCAST.html
+++ b/articles/EUCAST.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -61,60 +61,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/articles/MDR.html b/articles/MDR.html
index 79f2cb5d5..d622370c7 100644
--- a/articles/MDR.html
+++ b/articles/MDR.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -61,60 +61,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -375,19 +380,19 @@ names or codes, this would have worked exactly the same way:</p>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span></span>
 <span><span class="co">#   rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span></span>
-<span><span class="co"># 1          I         S            R          I            S            S</span></span>
-<span><span class="co"># 2          I         R            R          I            S            I</span></span>
-<span><span class="co"># 3          I         R            S          I            R            I</span></span>
-<span><span class="co"># 4          S         S            I          S            R            I</span></span>
-<span><span class="co"># 5          I         I            R          I            I            S</span></span>
-<span><span class="co"># 6          R         S            S          S            R            I</span></span>
+<span><span class="co"># 1          I         R            R          R            S            R</span></span>
+<span><span class="co"># 2          R         I            R          S            R            S</span></span>
+<span><span class="co"># 3          R         R            R          I            I            R</span></span>
+<span><span class="co"># 4          I         S            R          R            S            I</span></span>
+<span><span class="co"># 5          S         R            R          S            R            R</span></span>
+<span><span class="co"># 6          S         I            R          I            S            R</span></span>
 <span><span class="co">#   kanamycin</span></span>
-<span><span class="co"># 1         S</span></span>
-<span><span class="co"># 2         R</span></span>
-<span><span class="co"># 3         S</span></span>
-<span><span class="co"># 4         I</span></span>
+<span><span class="co"># 1         I</span></span>
+<span><span class="co"># 2         S</span></span>
+<span><span class="co"># 3         R</span></span>
+<span><span class="co"># 4         R</span></span>
 <span><span class="co"># 5         S</span></span>
-<span><span class="co"># 6         R</span></span></code></pre></div>
+<span><span class="co"># 6         I</span></span></code></pre></div>
 <p>We can now add the interpretation of MDR-TB to our data set. You can
 use:</p>
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
@@ -428,40 +433,40 @@ Unique: 5</p>
 <tr class="odd">
 <td align="left">1</td>
 <td align="left">Mono-resistant</td>
-<td align="right">3130</td>
-<td align="right">62.60%</td>
-<td align="right">3130</td>
-<td align="right">62.60%</td>
+<td align="right">3227</td>
+<td align="right">64.54%</td>
+<td align="right">3227</td>
+<td align="right">64.54%</td>
 </tr>
 <tr class="even">
 <td align="left">2</td>
 <td align="left">Negative</td>
-<td align="right">1022</td>
-<td align="right">20.44%</td>
-<td align="right">4152</td>
-<td align="right">83.04%</td>
+<td align="right">976</td>
+<td align="right">19.52%</td>
+<td align="right">4203</td>
+<td align="right">84.06%</td>
 </tr>
 <tr class="odd">
 <td align="left">3</td>
 <td align="left">Multi-drug-resistant</td>
-<td align="right">476</td>
-<td align="right">9.52%</td>
-<td align="right">4628</td>
-<td align="right">92.56%</td>
+<td align="right">442</td>
+<td align="right">8.84%</td>
+<td align="right">4645</td>
+<td align="right">92.90%</td>
 </tr>
 <tr class="even">
 <td align="left">4</td>
 <td align="left">Poly-resistant</td>
-<td align="right">269</td>
-<td align="right">5.38%</td>
-<td align="right">4897</td>
-<td align="right">97.94%</td>
+<td align="right">247</td>
+<td align="right">4.94%</td>
+<td align="right">4892</td>
+<td align="right">97.84%</td>
 </tr>
 <tr class="odd">
 <td align="left">5</td>
 <td align="left">Extensively drug-resistant</td>
-<td align="right">103</td>
-<td align="right">2.06%</td>
+<td align="right">108</td>
+<td align="right">2.16%</td>
 <td align="right">5000</td>
 <td align="right">100.00%</td>
 </tr>
diff --git a/articles/PCA.html b/articles/PCA.html
index 9302b50c7..dc403e918 100644
--- a/articles/PCA.html
+++ b/articles/PCA.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -61,60 +61,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/articles/SPSS.html b/articles/SPSS.html
index a25d9bb8c..e9a48471a 100644
--- a/articles/SPSS.html
+++ b/articles/SPSS.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -61,60 +61,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/articles/WHONET.html b/articles/WHONET.html
index 3947ebd15..f1972e75a 100644
--- a/articles/WHONET.html
+++ b/articles/WHONET.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -61,60 +61,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/articles/datasets.html b/articles/datasets.html
index 1250259ee..a695d3666 100644
--- a/articles/datasets.html
+++ b/articles/datasets.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -61,60 +61,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/articles/index.html b/articles/index.html
index bee0ea68b..c5b9dbe62 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/articles/resistance_predict.html b/articles/resistance_predict.html
index 99b897364..40406f930 100644
--- a/articles/resistance_predict.html
+++ b/articles/resistance_predict.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -61,60 +61,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/articles/welcome_to_AMR.html b/articles/welcome_to_AMR.html
index be5aa2e01..5d32b1777 100644
--- a/articles/welcome_to_AMR.html
+++ b/articles/welcome_to_AMR.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -61,60 +61,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/authors.html b/authors.html
index 8fd13c84d..edfb8d8e4 100644
--- a/authors.html
+++ b/authors.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/index.html b/index.html
index f8a88e9dc..bfed27d59 100644
--- a/index.html
+++ b/index.html
@@ -42,7 +42,7 @@
     
     <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -65,60 +65,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/news/index.html b/news/index.html
index 3b01d087b..1e78372cf 100644
--- a/news/index.html
+++ b/news/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -132,7 +137,7 @@
     </div>
 
     <div class="section level2">
-<h2 class="pkg-version" data-toc-text="1.8.2.9098" id="amr-1829098">AMR 1.8.2.9098<a class="anchor" aria-label="anchor" href="#amr-1829098"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.8.2.9099" id="amr-1829099">AMR 1.8.2.9099<a class="anchor" aria-label="anchor" href="#amr-1829099"></a></h2>
 <p><em>(this beta version will eventually become v2.0! We’re happy to reach a new major milestone soon!)</em></p>
 <p>This is a new major release of the AMR package, with great new additions but also some breaking changes for current users. These are all listed below.</p>
 <p><strong><a href="https://en.wikipedia.org/wiki/TL;DR" class="external-link">TL;DR</a></strong></p>
@@ -146,25 +151,25 @@
 <li>Many new interesting functions, such as <code><a href="../reference/proportion.html">sir_confidence_interval()</a></code> and <code><a href="../reference/mean_amr_distance.html">mean_amr_distance()</a></code>, and <code><a href="../reference/add_custom_microorganisms.html">add_custom_microorganisms()</a></code> to add custom microorganisms to this package</li>
 <li>Many small bug fixes</li>
 </ul><div class="section level3">
-<h3 id="new-1-8-2-9098">New<a class="anchor" aria-label="anchor" href="#new-1-8-2-9098"></a></h3>
+<h3 id="new-1-8-2-9099">New<a class="anchor" aria-label="anchor" href="#new-1-8-2-9099"></a></h3>
 <div class="section level4">
-<h4 id="sir-vs-rsi-1-8-2-9098">SIR vs. RSI<a class="anchor" aria-label="anchor" href="#sir-vs-rsi-1-8-2-9098"></a></h4>
+<h4 id="sir-vs-rsi-1-8-2-9099">SIR vs. RSI<a class="anchor" aria-label="anchor" href="#sir-vs-rsi-1-8-2-9099"></a></h4>
 <p>For this milestone version, we replaced all mentions of RSI with SIR, to comply with what is actually being commonly used in the field of clinical microbiology when it comes to this tri-form regarding AMR.</p>
 <p>While existing functions such as <code><a href="../reference/AMR-deprecated.html">as.rsi()</a></code>, <code><a href="../reference/AMR-deprecated.html">rsi_df()</a></code> and <code><a href="../reference/AMR-deprecated.html">ggplot_rsi()</a></code> still work, their replacements <code><a href="../reference/as.sir.html">as.sir()</a></code>, <code><a href="../reference/proportion.html">sir_df()</a></code>, <code><a href="../reference/ggplot_sir.html">ggplot_sir()</a></code> are now the current functions for AMR data analysis. A warning will be thrown once a session to remind users about this. The data set <code>rsi_translation</code> is now called <code>clinical_breakpoints</code> to better reflect its content.</p>
 <p>The ‘RSI functions’ will be removed in a future version, but not before late 2023 / early 2024.</p>
 </div>
 <div class="section level4">
-<h4 id="interpretation-of-mic-and-disk-diffusion-values-1-8-2-9098">Interpretation of MIC and disk diffusion values<a class="anchor" aria-label="anchor" href="#interpretation-of-mic-and-disk-diffusion-values-1-8-2-9098"></a></h4>
+<h4 id="interpretation-of-mic-and-disk-diffusion-values-1-8-2-9099">Interpretation of MIC and disk diffusion values<a class="anchor" aria-label="anchor" href="#interpretation-of-mic-and-disk-diffusion-values-1-8-2-9099"></a></h4>
 <p>The clinical breakpoints and intrinsic resistance of EUCAST 2022 and CLSI 2022 have been added for <code><a href="../reference/as.sir.html">as.sir()</a></code>. EUCAST 2022 (v12.0) is now the new default guideline for all MIC and disks diffusion interpretations, and for <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> to apply EUCAST Expert Rules. The default guideline (EUCAST) can now be changed with the new <code>AMR_guideline</code> option, such as: <code>options(AMR_guideline = "CLSI 2020")</code>.</p>
 <p>Interpretation guidelines older than 10 years were removed, the oldest now included guidelines of EUCAST and CLSI are from 2013.</p>
 </div>
 <div class="section level4">
-<h4 id="supported-languages-1-8-2-9098">Supported languages<a class="anchor" aria-label="anchor" href="#supported-languages-1-8-2-9098"></a></h4>
+<h4 id="supported-languages-1-8-2-9099">Supported languages<a class="anchor" aria-label="anchor" href="#supported-languages-1-8-2-9099"></a></h4>
 <p>We added support for the following languages: Chinese, Greek, Japanese, Polish, Turkish and Ukrainian. All antibiotic names are now available in these languages, and the AMR package will automatically determine a supported language based on the user system language.</p>
 <p>We are very grateful for the valuable input by our colleagues from other countries. The <code>AMR</code> package is now available in 16 languages and according to download stats used in almost all countries in the world!</p>
 </div>
 <div class="section level4">
-<h4 id="microbiological-taxonomy-1-8-2-9098">Microbiological taxonomy<a class="anchor" aria-label="anchor" href="#microbiological-taxonomy-1-8-2-9098"></a></h4>
+<h4 id="microbiological-taxonomy-1-8-2-9099">Microbiological taxonomy<a class="anchor" aria-label="anchor" href="#microbiological-taxonomy-1-8-2-9099"></a></h4>
 <p>The <code>microorganisms</code> data set no longer relies on the Catalogue of Life, but on the List of Prokaryotic names with Standing in Nomenclature (LPSN) and is supplemented with the ‘backbone taxonomy’ from the Global Biodiversity Information Facility (GBIF). The structure of this data set has changed to include separate LPSN and GBIF identifiers. Almost all previous MO codes were retained. It contains over 1,400 taxonomic names from 2022.</p>
 <p>We previously relied on our own experience to categorise species into pathogenic groups, but we were very happy to encounter the very recent work of Bartlett <em>et al.</em> (2022, DOI <a href="https://doi.org/10.1099/mic.0.001269" class="external-link">10.1099/mic.0.001269</a>) who extensively studied medical-scientific literature to categorise all bacterial species into groups. See <code><a href="../reference/mo_matching_score.html">mo_matching_score()</a></code> on how their work was incorporated into the <code>prevalence</code> column of the <code>microorganisms</code> data set. Using their results, the <code><a href="../reference/as.mo.html">as.mo()</a></code> and all <code>mo_*()</code> functions are now much better capable of converting user input to valid taxonomic records.</p>
 <p>The new function <code><a href="../reference/add_custom_microorganisms.html">add_custom_microorganisms()</a></code> allows users to add custom microorganisms to the <code>AMR</code> package.</p>
@@ -188,7 +193,7 @@
 <li>The <code>microorganisms.old</code> data set was removed, and all previously accepted names are now included in the <code>microorganisms</code> data set. A new column <code>status</code> contains <code>"accepted"</code> for currently accepted names and <code>"synonym"</code> for taxonomic synonyms; currently invalid names. All previously accepted names now have a microorganisms ID and - if available - an LPSN, GBIF and SNOMED CT identifier.</li>
 </ul></div>
 <div class="section level4">
-<h4 id="antibiotic-agents-and-selectors-1-8-2-9098">Antibiotic agents and selectors<a class="anchor" aria-label="anchor" href="#antibiotic-agents-and-selectors-1-8-2-9098"></a></h4>
+<h4 id="antibiotic-agents-and-selectors-1-8-2-9099">Antibiotic agents and selectors<a class="anchor" aria-label="anchor" href="#antibiotic-agents-and-selectors-1-8-2-9099"></a></h4>
 <p>The new function <code><a href="../reference/add_custom_antimicrobials.html">add_custom_antimicrobials()</a></code> allows users to add custom antimicrobial codes and names to the <code>AMR</code> package.</p>
 <p>The <code>antibiotics</code> data set was greatly updated:</p>
 <ul><li>The following 20 antibiotics have been added (also includes the <a href="https://www.whocc.no/atc_ddd_index/?code=J01RA&amp;showdescription=no" class="external-link">new J01RA ATC group</a>): azithromycin/fluconazole/secnidazole (AFC), cefepime/amikacin (CFA), cefixime/ornidazole (CEO), ceftriaxone/beta-lactamase inhibitor (CEB), ciprofloxacin/metronidazole (CIM), ciprofloxacin/ornidazole (CIO), ciprofloxacin/tinidazole (CIT), furazidin (FUR), isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), lascufloxacin (LSC), levofloxacin/ornidazole (LEO), nemonoxacin (NEM), norfloxacin/metronidazole (NME), norfloxacin/tinidazole (NTI), ofloxacin/ornidazole (OOR), oteseconazole (OTE), rifampicin/ethambutol/isoniazid (REI), sarecycline (SRC), tetracycline/oleandomycin (TOL), and thioacetazone (TAT)</li>
@@ -200,14 +205,14 @@
 </ul><p>Also, we added support for using antibiotic selectors in scoped <code>dplyr</code> verbs (with or without using <code><a href="https://dplyr.tidyverse.org/reference/vars.html" class="external-link">vars()</a></code>), such as in: <code>... %&gt;% summarise_at(aminoglycosides(), resistance)</code>, please see <code><a href="../reference/proportion.html">resistance()</a></code> for examples.</p>
 </div>
 <div class="section level4">
-<h4 id="antiviral-agents-1-8-2-9098">Antiviral agents<a class="anchor" aria-label="anchor" href="#antiviral-agents-1-8-2-9098"></a></h4>
+<h4 id="antiviral-agents-1-8-2-9099">Antiviral agents<a class="anchor" aria-label="anchor" href="#antiviral-agents-1-8-2-9099"></a></h4>
 <p>We now added extensive support for antiviral agents! For the first time, the <code>AMR</code> package has extensive support for antiviral drugs and to work with their names, codes and other data in any way.</p>
 <ul><li>The <code>antivirals</code> data set has been extended with 18 new drugs (also from the <a href="https://www.whocc.no/atc_ddd_index/?code=J05AJ&amp;showdescription=no" class="external-link">new J05AJ ATC group</a>) and now also contains antiviral identifiers and LOINC codes</li>
 <li>A new data type <code>av</code> (<em>antivirals</em>) has been added, which is functionally similar to <code>ab</code> for antibiotics</li>
 <li>Functions <code><a href="../reference/as.av.html">as.av()</a></code>, <code><a href="../reference/av_property.html">av_name()</a></code>, <code><a href="../reference/av_property.html">av_atc()</a></code>, <code><a href="../reference/av_property.html">av_synonyms()</a></code>, <code><a href="../reference/av_from_text.html">av_from_text()</a></code> have all been added as siblings to their <code>ab_*()</code> equivalents</li>
 </ul></div>
 <div class="section level4">
-<h4 id="other-new-functions-1-8-2-9098">Other new functions<a class="anchor" aria-label="anchor" href="#other-new-functions-1-8-2-9098"></a></h4>
+<h4 id="other-new-functions-1-8-2-9099">Other new functions<a class="anchor" aria-label="anchor" href="#other-new-functions-1-8-2-9099"></a></h4>
 <ul><li>Function <code><a href="../reference/proportion.html">sir_confidence_interval()</a></code> to add confidence intervals in AMR calculation. This is now also included in <code><a href="../reference/proportion.html">sir_df()</a></code> and <code><a href="../reference/proportion.html">proportion_df()</a></code>.</li>
 <li>Function <code><a href="../reference/mean_amr_distance.html">mean_amr_distance()</a></code> to calculate the mean AMR distance. The mean AMR distance is a normalised numeric value to compare AMR test results and can help to identify similar isolates, without comparing antibiograms by hand.</li>
 <li>Function <code><a href="../reference/as.sir.html">sir_interpretation_history()</a></code> to view the history of previous runs of <code><a href="../reference/as.sir.html">as.sir()</a></code> (previously <code><a href="../reference/AMR-deprecated.html">as.rsi()</a></code>). This returns a ‘logbook’ with the selected guideline, reference table and specific interpretation of each row in a data set on which <code><a href="../reference/as.sir.html">as.sir()</a></code> was run.</li>
@@ -216,7 +221,7 @@
 </ul></div>
 </div>
 <div class="section level3">
-<h3 id="changes-1-8-2-9098">Changes<a class="anchor" aria-label="anchor" href="#changes-1-8-2-9098"></a></h3>
+<h3 id="changes-1-8-2-9099">Changes<a class="anchor" aria-label="anchor" href="#changes-1-8-2-9099"></a></h3>
 <ul><li>Argument <code>combine_IR</code> has been removed from this package (affecting functions <code><a href="../reference/count.html">count_df()</a></code>, <code><a href="../reference/proportion.html">proportion_df()</a></code>, and <code><a href="../reference/proportion.html">sir_df()</a></code> and some plotting functions), since it was replaced with <code>combine_SI</code> three years ago</li>
 <li>Using <code>units</code> in <code>ab_ddd(..., units = "...")</code> had been deprecated for some time and is now not supported anymore. Use <code><a href="../reference/ab_property.html">ab_ddd_units()</a></code> instead.</li>
 <li>Support for <code>data.frame</code>-enhancing R packages, more specifically: <code><a href="https://Rdatatable.gitlab.io/data.table/reference/data.table.html" class="external-link">data.table::data.table</a></code>, <code><a href="https://rdrr.io/pkg/janitor/man/tabyl.html" class="external-link">janitor::tabyl</a></code>, <code><a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble::tibble</a></code>, and <code><a href="https://tsibble.tidyverts.org/reference/tsibble.html" class="external-link">tsibble::tsibble</a></code>. AMR package functions that have a data set as output (such as <code><a href="../reference/proportion.html">sir_df()</a></code> and <code><a href="../reference/bug_drug_combinations.html">bug_drug_combinations()</a></code>), will now return the same data type as the input.</li>
@@ -255,7 +260,7 @@
 <li>Cleaning columns with <code><a href="../reference/as.sir.html">as.sir()</a></code>, <code><a href="../reference/as.mic.html">as.mic()</a></code>, or <code><a href="../reference/as.disk.html">as.disk()</a></code> will now show the column name in the warning for invalid results</li>
 </ul></div>
 <div class="section level3">
-<h3 id="other-1-8-2-9098">Other<a class="anchor" aria-label="anchor" href="#other-1-8-2-9098"></a></h3>
+<h3 id="other-1-8-2-9099">Other<a class="anchor" aria-label="anchor" href="#other-1-8-2-9099"></a></h3>
 <ul><li>Added Peter Dutey-Magni, Dmytro Mykhailenko, Anton Mymrikov, Andrew Norgan, and Jonas Salm as contributors, to thank them for their valuable input</li>
 <li>New website to make use of the new Bootstrap 5 and pkgdown 2.0. The website now contains results for all examples and will be automatically regenerated with every change to our repository, using GitHub Actions</li>
 <li>All R and Rmd files in this project are now styled using the <code>styler</code> package</li>
diff --git a/pkgdown.yml b/pkgdown.yml
index 99de001ab..e2a3ff173 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -11,7 +11,7 @@ articles:
   datasets: datasets.html
   resistance_predict: resistance_predict.html
   welcome_to_AMR: welcome_to_AMR.html
-last_built: 2023-01-23T14:04Z
+last_built: 2023-01-23T19:11Z
 urls:
   reference: https://msberends.github.io/AMR/reference
   article: https://msberends.github.io/AMR/articles
diff --git a/reference/AMR-deprecated.html b/reference/AMR-deprecated.html
index f5e6366a6..c4383dbb6 100644
--- a/reference/AMR-deprecated.html
+++ b/reference/AMR-deprecated.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/AMR-options.html b/reference/AMR-options.html
index 8c13afabb..509951081 100644
--- a/reference/AMR-options.html
+++ b/reference/AMR-options.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This is an overview of the package-specific options() you can set in the AMR package."><title>Options for the AMR package — AMR-options • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><link href="../deps/Fira_Code-0.4.4/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><script src="../extra.js"></script><meta property="og:title" content="Options for the AMR package — AMR-options"><meta property="og:description" content="This is an overview of the package-specific options() you can set in the AMR package."><meta property="og:image" content="https://msberends.github.io/AMR/logo.svg"><meta name="twitter:card" content="summary_large_image"><meta name="twitter:creator" content="@msberends"><meta name="twitter:site" content="@msberends"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This is an overview of all the package-specific options() you can set in the AMR package."><title>Options for the AMR package — AMR-options • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><link href="../deps/Fira_Code-0.4.4/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><script src="../extra.js"></script><meta property="og:title" content="Options for the AMR package — AMR-options"><meta property="og:description" content="This is an overview of all the package-specific options() you can set in the AMR package."><meta property="og:image" content="https://msberends.github.io/AMR/logo.svg"><meta name="twitter:card" content="summary_large_image"><meta name="twitter:creator" content="@msberends"><meta name="twitter:site" content="@msberends"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -25,71 +25,76 @@
     Home
   </a>
 </li>
-<li class="nav-item dropdown">
+<li class="active nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown--how-to">
     <span class="fa fa-question-circle"></span>
      
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
-<li class="active nav-item">
+<li class="nav-item">
   <a class="nav-link" href="../reference/index.html">
     <span class="fa fa-book-open"></span>
      
@@ -128,12 +133,12 @@
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.svg" class="logo" alt=""><h1>Options for the AMR package</h1>
-      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/R/options.R" class="external-link"><code>R/options.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/R/aa_options.R" class="external-link"><code>R/aa_options.R</code></a></small>
       <div class="d-none name"><code>AMR-options.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>This is an overview of the package-specific <code><a href="https://rdrr.io/r/base/options.html" class="external-link">options()</a></code> you can set in the <code>AMR</code> package.</p>
+    <p>This is an overview of all the package-specific <code><a href="https://rdrr.io/r/base/options.html" class="external-link">options()</a></code> you can set in the <code>AMR</code> package.</p>
     </div>
 
 
@@ -141,11 +146,37 @@
     <h2 id="options">Options<a class="anchor" aria-label="anchor" href="#options"></a></h2>
     
 
-<ul><li><p><code>AMR_custom_ab</code>: allows to use custom antimicrobial drugs with this package. This is explained in <code><a href="add_custom_antimicrobials.html">add_custom_antimicrobials()</a></code>.</p></li>
-<li><p><code>AMR_custom_mo</code>: allows to use custom microorganisms with this package. This is explained in <code><a href="add_custom_microorganisms.html">add_custom_microorganisms()</a></code>.</p></li>
+<ul><li><p><code>AMR_custom_ab</code> <br> Allows to use custom antimicrobial drugs with this package. This is explained in <code><a href="add_custom_antimicrobials.html">add_custom_antimicrobials()</a></code>.</p></li>
+<li><p><code>AMR_custom_mo</code> <br> Allows to use custom microorganisms with this package. This is explained in <code><a href="add_custom_microorganisms.html">add_custom_microorganisms()</a></code>.</p></li>
+<li><p><code>AMR_eucastrules</code> <br> Used for setting the default types of rules for <code><a href="eucast_rules.html">eucast_rules()</a></code> function, must be one or more of: <code>"breakpoints"</code>, <code>"expert"</code>, <code>"other"</code>, <code>"custom"</code>, <code>"all"</code>, and defaults to <code>c("breakpoints", "expert")</code>.</p></li>
+<li><p><code>AMR_guideline</code> <br> Used for setting the default guideline for interpreting MIC values and disk diffusion diameters with <code><a href="as.sir.html">as.sir()</a></code>. Can be only the guideline name (e.g., <code>"CLSI"</code>) or the name with a year (e.g. <code>"CLSI 2019"</code>). The default is <code>"EUCAST 2022"</code>. Supported guideline are currently EUCAST (2013-2022) and CLSI (2013-2022).</p></li>
+<li><p><code>AMR_ignore_pattern</code> <br> A <a href="https://rdrr.io/r/base/regex.html" class="external-link">regular expression</a> to define input that must be ignored in <code><a href="as.mo.html">as.mo()</a></code> and all <code><a href="mo_property.html">mo_*</a></code> functions.</p></li>
+<li><p><code>AMR_include_PKPD</code> <br> A <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to use in <code><a href="as.sir.html">as.sir()</a></code>, to indicate that PK/PD clinical breakpoints must be applied as a last resort, defaults to <code>TRUE</code>.</p></li>
+<li><p><code>AMR_keep_synonyms</code> <br> A <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to use in <code><a href="as.mo.html">as.mo()</a></code> and all <code><a href="mo_property.html">mo_*</a></code> functions, to indicate if old, previously valid taxonomic names must be preserved and not be corrected to currently accepted names.</p></li>
+<li><p><code>AMR_locale</code> <br> A language to use for the <code>AMR</code> package, can be one of these supported language names or ISO-639-1 codes: English (en), Chinese (zh), Danish (da), Dutch (nl), French (fr), German (de), Greek (el), Italian (it), Japanese (ja), Polish (pl), Portuguese (pt), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr) or Ukrainian (uk).</p></li>
+<li><p><code>AMR_mo_source</code> <br> A file location for a manual code list to be used in <code><a href="as.mo.html">as.mo()</a></code> and all <code><a href="mo_property.html">mo_*</a></code> functions. This is explained in <code><a href="mo_source.html">set_mo_source()</a></code>.</p></li>
 </ul></div>
+    <div class="section level2">
+    <h2 id="saving-settings-between-sessions">Saving Settings Between Sessions<a class="anchor" aria-label="anchor" href="#saving-settings-between-sessions"></a></h2>
+    
 
-  </main></div>
+<p>Settings in <span style="R">R</span> are not saved globally and are thus lost when <span style="R">R</span> is exited. You can save your options to your own <code>.Rprofile</code> file, which is a user-specific file. You can edit it using:</p>
+<p></p><div class="sourceCode r"><pre><code><span>  <span class="fu">utils</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/utils/file.edit.html" class="external-link">file.edit</a></span><span class="op">(</span><span class="st">"~/.Rprofile"</span><span class="op">)</span></span></code></pre><p></p></div>
+<p>In this file, you can set options such as <code>options(AMR_locale = "pt")</code> for Portuguese language support of antibiotics.</p><div class="section">
+<h3 id="share-options-within-team">Share Options Within Team<a class="anchor" aria-label="anchor" href="#share-options-within-team"></a></h3>
+
+
+<p>For a more global approach, e.g. within a data team, save an options file to a remote file location, such as a shared network drive. This would work in this way:</p><ol><li><p>Save a plain text file to e.g. "X:/team_folder/R_options.R" and fill it with preferred settings.</p></li>
+<li><p>For each user, open the <code>.Rprofile</code> file using <code>utils::file.edit("~/.Rprofile")</code> and put in there:</p>
+<p></p><div class="sourceCode r"><pre><code><span>  <span class="kw"><a href="https://rdrr.io/r/base/source.html" class="external-link">source</a></span><span class="op">(</span><span class="st">"X:/team_folder/R_options.R"</span><span class="op">)</span></span></code></pre><p></p></div></li>
+<li><p>Reload R/RStudio and check the settings with <code><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption()</a></code>, e.g. <code>getOption("AMR_locale")</code> if you have set that value.</p></li>
+</ol><p>Now the team settings are configured in only one place, and can be maintained there.</p>
+</div>
+
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
diff --git a/reference/AMR.html b/reference/AMR.html
index 2f4e561d5..377988c76 100644
--- a/reference/AMR.html
+++ b/reference/AMR.html
@@ -18,7 +18,7 @@ The AMR package is available in English, Chinese, Danish, Dutch, French, German,
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -40,60 +40,65 @@ The AMR package is available in English, Chinese, Danish, Dutch, French, German,
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/Rplot005.png b/reference/Rplot005.png
index 483ce6213f925204efaee6434ceaf7058a3d1331..ddbf55fa020e3bf84458e47724150d5b0f34a95f 100644
GIT binary patch
literal 13003
zcmc(G2T)Vrw{Aj!(7PfXr3nbARB2HVFd#*G2SG)eAV_bCA}CT61ylq?DbhiDKZ1bL
zdytkO(pzXD5JKMZckg@i=DwN#dvovHd6{HR$l2%Yeb)Ncx4yOa=DCTH9y0?k0|WwL
zM&7t?3V}dze?N4z;4cxQEh*rE*2O^YI(P(MhuV!%;DO%vhK)Z2!pQOW109};JcdAo
zA;|03%!5B{%!It*zuO$Uo$r?X?gA&r*OxD^QpK<-iLjZ}^prmP@ktm*8~4M4{nZQ|
z-7|g@u2S^*$DdFa9{XOqb^6*xtE)<9+39H81+{*(yt<S8t|h8z?55$!RPUpp{a*Df
z|D)bV7W;Osg^n#`OA9NsC^GrK{2ud{8e~%E;tO<ASTAO&8T-{`p<gm4lH%YnOmkzk
zO7)}9qn;nrTgzdMl}SZ!Y>V`y8a0vJ!Ue?y!v+X4cY7)~v_S^VhkiP=!RtI#?9Kua
zgjiDTqKeQ%HY4iSCq$1#aO?LQl+p62OTa#G+4(g79e=>O!9;Nbe=y@)+W2L_Zxpsf
z#fZY7Mt0RTF?my_<JW4?fd4H!45Z-PNR;$@!ff$enofIJ;--_pj1TQNl^hi}l#k0@
zsI9@9HZh4=4Z$0zH(r%nywzXKjWng_r4!nbMt_Tz`|ugfh`u(YfQ1eN4p&9+=;cdm
z=!%M(6G6k;(-T~0PR|&_4CsV5g`P3LKoH*CpBTGkn9ix?YB6dg8OdAyP|tixsUn1|
zp)}+4`L>yAIm=fe3~R7>gOfgyDn4#ZUWIoYZY{t(b*ans2;!93Ine#ynBWI#;$kLw
zcuhPVDo1>eN%`7h;2@WrV|2!VDD21_zeKhRe}A7bfwd#XNr0K;;AJh2@_EK=cZQjS
z^s@F>SjR1&02At+-}4$3c?o2WRFT<EmWsI`Gj-I9k|*n3Wd2|ULX_lO`u2^zy3e0U
zjk*WiT=ykfytd_Jo>CE|D{lXS+OHnT1~lI|HUGj$<CnTKbGF`r(8s529D1Box3#<`
zm0c)^Q)CTMy>z+G6Q@*HYt<t=A#%dNyyB!%>pdkiW7!(EXw(mjB~CH*nhDh(1Vt_1
z6CNV1caK?|!KYSi;)$RTU#7d8fmYcOb6cpjZu(iV{(KX|+|sSYQtpCvCg=S-YjmW&
z(zEM~)Dz5gJSr~<LRBm36gSVl?I;5;YeH<$@yW%uyn&ee!3-*2fF?i5>i&3_HQ}k@
z<t->ME>OG?*`|6(S5D*WrEuK2yye`Mw>VEfH+y3xn~&Qm{}((twV_3=!7=BnJZfyD
znd!q-B3_8;W>U<S{zT5Z3iowN@4eZ^o{JR9_Y-X1Ywsq0Qq=1Sidq0}`w&Ez5NwG(
z6mLv5uWGVni%T1#1nGIJGc<voOs*OD5JvALtQ+u=+{>K-t+@UAYu?I_qs<?oue$po
zmd4}PRd|C{vJZA;vzK7E{pT6lZdE~+*dl{eV|<-Wq!+Hxe~<g(jVh?xC&oasMD`lu
zY>>+Z(D1auwW-I`=|3t@*};F=(vvL8#HnX0fgc=;ueU3gx4tiLiB&wTbfx$hTPMCy
z9VC#4u-f!l4pWe<AMf)<_h*Lo&!uEQi!;5%=JL-YptEQBY3%ff!u`kgi==2_zEg$>
zSRKtoJV9fiwC-8Cjlbl@v~ujOu9mgBqYEGQ+2mpHXy>KqEFza#x9X2n+7_1MS8VnJ
zCP#U{-&O4g+udh0r0Z#BJv%CNmjC{KI>*#gwqgvdk(00`ctgeHf}P}Aqx_oAztN4r
z;vB)$`~0Kp{xVI*p3RznS!nf;Mf^Ci@|~}Vp>Y+W+(1Go46-qhX`w&WAvj_~;*3;H
zDI(tnmZUEZY<^<R%{XP4tgrOgSHEikz}_c0tO!p=QjMNs9Sj^V(5LmaS|js%yWRDm
zHQEGRjq_bwyEdNb*JOA)9?D*Q$UgU-KcULWxG&mpTEw05I+u1bGCyNWG4<hP{MrPW
zYmO_8kxtLmEpH&xM}DJ~D~FCd!<uPYo3tCerCqn&K7Xs^Fi&Q*(;XOGKP7_97>dPD
z4G7R9wkIG&mv>9-2pe4)O86gf=XN7af>PU-rF3?(w;M^IKI-Ug+$xF<XzjVwcVm*0
zSBJ)jbB875(n)X~a?Vw~W{X4|uYE~CM`I(p3`LGLRi;#Mt)C9pji-Dv`REX1yjN9D
zEjrNT^(pf^OXC=?EEWlNW1&UKfsj&~6Qs(wcE3s4doNdRv4|a~?mD(DkO}dftYYCR
z<mdP>%xijX1d-1>jiKp@ugAB0AEU;bD)X^t>dx`j%4h3MUY)wT{UD#rlbV>lVc*tx
zXDp0nPMaXp=FV+U!8uom{AKiP(w&wdZhvbf`%9ja&d9nEu{={xrT%)S5`g%$(EDMH
zDBCvn__SV65T7?3nKBu8e2H7Px(}wA`uW7(+vuG7(yn{sd79`-!|Hq0taHkpl*E~&
ztj)@|T4k5$B&ZKX=9nBvI_={eT5A4JbAq?dv~8vNkKuXoQA2fZ^sQnszNT+H=_Pi9
zpX2Y&C8&=qdcxY)KECmPVqkhJmfeW|RgZ96EXky;gNc?S)J2{y{Y@-KKcD)`LHWAJ
zgHt?uQ+bPA^<lta9)e2c`Cxk-{A%3blWzOP2aH;86&4GEbj>lsZQ^(rTDHV<{a)kY
z^1Pa7>Jn=Vm-*OQF5c<yzi!?xrS)<Y7WF*U)3=J>a0qWl@3v#h;-xoLpR~2?JQWc_
zsSc1IIT7=;E>()<7(Wnh<3>r)JMHn&4<MVwU6SfADrFHH-m^0gU|_O)ydt5!aC$qT
z6JEX+wYfLxSP}cuiw~rpD$`)t`){V!um9eR##nZGSMAqS=J#P}qXU#q?eh{)7DylV
zwvTV^=&zM04?gMfDr~;O+j44->Bbtp#<SkYN`?9`slz;q6Am67@!Y*FmS`9)FRH;_
z6rhHa(lRbEX_mepb(WTnoA-Vmnfplv@T<z<jIh7v(n7SHQ0v*fKZt8~0~))PSvLpB
z*rBG&7%5FQBG+xiqK6i1F!k-CZ~jB`vF!aFH{p|l{9!jmZu_sd-M6X41e7WvSU&I?
zV)^-MFU?7rZNI;qIvlvFPq(IAS$a9`xxF!|+(X#D@5`WPai;G|8#kSuP|OfvGL*Uw
z#dh2pHz}<na62PIZz|<O>fp(T@pO(<4NTSHB&sGL$FU7!DW;)4@x4JNdr2OmBGvUM
zOFVqy0H%qY%A4%14{_5=DiZln6cQ2$v)M7E2(*Pn%ZY}oY|*OUT>csHw#IRqF`E4N
zm(}FYXYgBuE!QX{@gn7p*F@k@_}5(Yi_ak$#>*B-Ki}V@!aMIpY1|&U9BsHMBvw4q
zI~J*wACVM(E*t*-UCW!A`J^kU_^0<>wTyR9io2FPDl_pkB03W}Ct6h>I6qP5H{7bI
z$_7tAu5U4y5$9i4Q1Br6wq3jV)X`OqGjyql9<B1+cXFtNK0Q=^#OEfdMBwmFXbIvR
zbyO!muI@|1L*Uc>;8OS9YUo@~kA~RjS)>>b%`}q87XGGufNj!exH+|4(qFUOM<Me3
zhFquSQ?{I>k(2Wn#P^fdT3}BNijD6y6yA$Yr@LJqJ^1>C&xzXELWveu6*HfvQd5@W
zqqEtu?|v1(ufK`a7$RHILowGU!W%OO+l|;0<7ZUfNAi2anH1RxNo7fsD#Ergy;au_
z^g8q8ayN*sHHqWj5Oj!FKU?%oN|oRoE!4&5_Bu(rBbmC(*<DjfeAiTdtAuerlJ}ki
z<4fD#Q00Lz=ft6-D`w~<IZm676t_liCz{IMe-LrgQsz+UnsIlbOynpT8Xd52+?JLc
zKC;L+JH%*q!!+$K{JrQt!-L1E|5BZ<Dw=jk+_G2Am`(FF4MLiJbu`R1rQB?I*54ge
zuee}kJVi4qhWxoQj*(v;?=VOnb>IP#Ma2L}s;DHCwp<iRe-V}I=+YU_V+iN>1+uqq
z`!Dvcv>wccMTr-@A62Z+G{c8%$f`3m#M|21nx;7f{yM!jUJ{1AHN3ySzhY)|<!+$1
zwzjIg*Ie7v2)#u95DfqYg`E}*ntq5O@8u&R3P%t7-pS`vtXg(bj5Uw(?X@Xx^ly41
z4rY|sSMsw2UwKaJY3{rVB24+TcDy6W$jB7HSk>|6D<k<)et6tBABV7wYBN^V<&QLD
zMK#Uqm>a4Yk=V4&@$qq%DE6(*vgQ<Nt8wFWCGQuhsi_BKuF8sviiN5XqcBO$V#_Q{
z*2!M}!Jkm0Z2y6x!XAk1XNjzEzbDd>zTbYudaJ5uXl!QIre2IZCQ9n@Km0zmJvZN#
z6y-u#|CQEya<rB(<yF~tH_hJfCxUdag|+QI?o1MDRmH+_FXUQOaOv3N#V(;>99m7a
zf!`s-%XqKZza^qAFkSv3o|btd6Qg5*86_Vk)%H&Nw7x_9WFl?&M5!&7)Xq)@&o+fn
znq4~LjwH?U)&mEx!8OeLLgxpZvN1QT%nN5c>i5U(^qh_>-Lb!X^Ea@{n;Dd_3jz3V
z@TiC$nOge{p47eF_vFmpVhWG^Jq3#*+LSEp%WkjVFY^|6Yk%EwCU}#wpN!Ym)U<3(
z1xlrO(44vGeAUyzVde;YDF78+f{jZ68dk=u<Ta7>pkXC%sH|y6ztvEAW^Wp7k$$wd
z;t#9xzSpg|S>Dc~cJd7_q~6fda<Z8Ipuv4FS<K*Q6obLqJ2^#-5Vz-z!*=_W`P_Qn
z4k?7}{F&Fpqgg*(aRyLM$A2Uc?_RD|jM;@%7s#fRs_jOX!0jKc=AgHy+`GKT5Gfws
zwbh>s3fyOwhdwWP*UiPObtMWk5<2;O!s(pBQSb>q{LU`aceD1_z4QpUFvdTq_fpsT
z*y!k9<==72HSZ16<4-WK=)NSi_40Nr@aiza-zRIpw=ven5C*U*R*v-{;bwo14|kQj
zGc{!~;eOA3e)k@zb~`41nVnY#oh&JI_(LzT4-**3Jd$sW_1*dNvkPh;dAKkyzjg_`
zljiZ{Ij61CVcRQMrH`SjuCiOJmA^)rK!}llNs@h!ZMT2uh@o;qVj0CxBfn5H44_Zf
z2qSC;1OznZjjS~64j?FxV`5_VGJ<BW>@1`?32H>09CLDCupSJ`iXdEB`0_dNX!mR^
zzaMtAz%1$|y*pj{yV8c$!sox{A2e>%IAuH<M(+M@egi^$pr)!SC-P)B%O`NsJ%3|+
zW-Ibw+sZNI?wT8Ts}BKd<6|4u7{~l9@HF>gXtBOOaV~Zs3mgt3X|r+54Yl`EB1@e-
zsLdJy|CpP*&@9(fQgXJgGOH0wIXU>88M64!+c9E)>~$=wPz#GNW^K+ti!|K<k!wBP
zsi7QUTKhfR-I0cw>W6J3#t3pTaaY-`|HDb`Z0K6pE_j_)ZK81$*kx80kJWw6`tQmQ
z@3kg&Ch!Fr68w7KUi!QOdgE8%V(&XFk6PgRq`miI;oO*&Q*-cAMo{G3U#Q`qayw3V
zj2GKbUcb}C?4&i&6o~j~l<_^Nn`<!5G((wYVH!6Z*5X_SB%|f7zT3|tf5?nHT5&sN
zoAor)%pwi?p`4ymTwN)Qu-@a-N+!H=10RZeKy9mO>2Iu7BxxQkXKTkWT~Wy}!c^DP
zjAwz?O%n(hy`|>`8oP15m2#5g5VDYBd~Z@^K3?8YWp7YBDL!d!s@A8Wv~;uiqvO{*
z$SjATnf>oq{1?|phlhuQK)@V8leX&S6O-<0GRZe@uXh!?=G}a^vjxWYabs?-m}21i
z#MVarQYN>~=h-!jyM(E`h#*LpK+rV#9jiOazASdE$O;4X0!O^15hZktgJ*@F94^6?
z6cwxggq?iN&ysiRN4e}uiVc2s(qly&<MV#?up1uSXu->vw}YTM`HuVcBuc}3vTCcN
z6}4!4k^w?Mry^8MgcL$vkc+yKz>zs34MzD-`!x~F*HVYZn$n_y6%d>V-*#qY`z#;9
zT+RGKb>(Xk7dS&>g(8W^ms~rWwn<n@<fvfS`u3t@*v397*C0)BWO#Vz*CW-4KjP_L
zUkR`D1najCm$F)a&&3MO9zK`IoDd%A#Lu;3?k0J9d3iZBJX*o^DwyRPWvI9h=PX&3
z*aq~1K+mtLi8*jOHq6j}I*<(7>yh%bZ~I&6(z@TUzj>hEe@Us>w%X&z?~WIwL|#RY
zzG9n7@xSojY})OAC2dtY%{EJ!PT*61u(FSFY8~iKk;YC{WvuPLJcML`o{8m_wVwz&
zIX;ZY3frs~sGSL%vUr-=y1vqtBt&^2xhpA73aIK53~PGV%nyk;+H;p2HEQ8y(In#9
z`!z}HLJr~E9eQ_L!vENvn67Sou^;fHoRIAdLX5J4e?MpT`y2U_6B84YV@nDxEDbv!
z3U`=rmw7MW`Oy$1<Iv$a;$;6OBJ9q$oxNx5BI8`4$EU39xAXn`uPlA}oTM2#`{R`e
zcM|^<KU<KLWkhu2>xe~8<kbgYVpTp^!)zUOc6KVhmN1UovWqNznC{bZ`2Li%rPs?}
zZjV3$><wAYjwN_{cr*}BNLzAsWfw>l3$}<EixJ=~qvrm8tK8uacGe4V*hJ043NaX*
z#pX;F;h9L{+aQcXkT<q9ih}(McEpuGFSR%M5s}BMGPTow`?`GIdx5U5^ZO%thEpXV
zE7Lbo>eco~%_#D>+oG*NPKdO)=XARiqkS*ZwKM*3AP4!npGWz@^nj82_IJJ`FiG-8
zoos?AzqNhzHQ&d}D}1Y61shJwDtL@|G&rOuq#+k}<@dZDs3x>yS(+6v^ItvRss^kQ
zuRWn{4x0^G(un#gmNt#ANQzkh5#BR0IQX)E>_H8?h;FgXQE%^Fv3zUoc~Z?$g0j1Z
zhsS}=S<G$&C|BfU7p`QehwQ|G(8eCg5<c8(mGC!ox*w5x#9z46#A^!A9a%~GJ@(QZ
zb`MNak2-nW!uLSKQLy@b^Bvqy{J!64xWoS}ru&qxzM9Vc(z#=0XKbIx5GIzQpK?6X
zXY0ypZR%*}T|L%VeWu0K*qOEN$vV`)w67d~0hz4Y`M(P)F{D1qt!=d~1lY-W@lv$n
z{0h}SBF^+u^`08;74Qmp0Qfpz!k_o$!&^QMKqi|9R{ODymjV}@A->EPY3dl38GES-
zH%k?e$)Tl7sEUwIZ>HW-7nx+yeO^X%jQafQ_7_*ADXW=Ncyi$x>hROS{MO7l^rU=c
zsz_>eD)pDv0bo;;eISTVV@9D|>8O!IC2jKXlQ7cMW_0-0AkPz2WHejgNTq0jUSS7h
zGfo@jv&5rJM`XTO;xTqZ<L3Oc+avc4x6Dcj!!I8=RuK(=*9#tI)n&%6^U!@mn4Fwj
zx}<4snN8L5v|o*l`^<v*8$D>N^z8-&RnQX80Z-f33$Xx8&robe<ikVD0`riT#sTtt
zL|@tQV&}s%K(oH5!_4>XF8=2IXBvT$24Q_Ysq(vO-0KdhorPa4BjNVF%wk4qw6?p`
z!$XFq+&QL_=SO5LFe6HB1?B|}ms=9M_nwwddgQ{76&Zx?kJZ{fy^(CrTs;iSWKiH}
zVf)j7OdkC_YFg)D={%ilQKXfeBlRUk(6>V5b*p`flqj+=*_Iw>78l=IA!PwxDOSw^
zs^WdAe;34m{Rh2(G^Jv4MkaGOLkyDNKw#F%In-zV5AFP)ZaOHjLgk_aVSxt1VqiY7
zDCj11T*5j{6nWQ5S_9{eN<#~vQUE<P4KM_FX{mt9-E~x-m6!&^(w}9*o8dfg!noJK
z0$`<o-%a?U_&{9(`Vpqv9NPZL?n*Olv5bGZX)ZIZZfbpBEnUWI4<4j-qF*Ra?Y$Z^
zX=szcT)Wb|3=XwSLBN!6*+RCc_*2}b;V21JyN?Wfzgo9TGh&pyf7Qy4*(MSk9qRKa
zt*OQ8^)!)YiG&;v_cJwfRnYXr!(c2F*2AF$I#01U(tlPF{E}gjNYEb&!qbk#dNl_8
zq6!}KEK_b#3z~I9!Mn%q8JkE$kJWHzCN;g1s86`aSfRHzE^4g0wf2dk>vonj3OS|;
zBY~M0T}-e?U#o(8inj125>ACF4x*+%S3!Oz0%@8EFJQv3dwBNilA|+W)Jfq@ZUx7f
zqQa=5IywFBT1RVSkASpe>wZ)O!mS_$q6s5P`&vAr1UL^YQzB3UqPO0NB2!F3BXCWy
zpSh-T(JRtxdgi&gDAJT>OdsNQ;RZM(pIrFOd*?rvN&jhA|K|WSY6`j~ocRp*41%kU
znJ7LeQSb=_ZZrs7Ian+0dz2cQLfb-h%rwENqE<gFij-=#U9mBOYBF7*iDW4jjoy~R
z-EwB}y+*&WP9H8cP$z?SYp{1e7_EEQ+9`cBo=^Lx=v#G(%!|b`#~{D@$=8LsTQAS1
zjVbT_`eMJ4iz#!e4BQ@mjT!?3zgfG48h^uCXOGf2gjF2E-gCBozR??B{d(i`ExYC+
z+$}OG^5|<u&G`V0)BCx4PWE)P<qcU!oiTD=0gJV|Z;G0)SUJ_+-}s%o1oiJ3%L|PK
zpTZ7phF(Jtu!R=FVL?zK*rs$vM(&@j1tUmIP)YBmCkTOn*D*{N?83-vYvXApmkQ!m
zd5=ncCd2Kw=?zAMoGAf!J#RMam#o7ynI~YuJQG|K%tXOKhU3#J8voNEP&o<yeg0aJ
zi=w<3+x<AufFHrG!&Ai_P#_FUq|-l&Ay+N_8{_<6IR6WXPL>Ikm2=>zQo&Ibp}5ge
zkXR@=Sw=58r@Jr(LVJex15-GyHT^Ne1p5s!o4?Tghd9PzNw6%q5rpMjG+rEFMGK`3
zcAf<aLOK&5C!_n9!Dni)Wx&;003}1-oVXWanwzXcGHD{{IR`nz<ebu?T*JY`CVKl1
zkJyt%Q_M=JeO<wYs$_FRkOx<C7+;DOy!Q}8{TD66#ekbfWsgZ+PtKXmF@dmBXTnby
z!l~qV&Ld5KfhVO=LqIM133>q-0}^!vC6LK+=TGOjJB#B3oX?db@CxN@DyPCal0^gb
zD<GS&b>?XrbtZczFFG%JCaTR6XK#?Pt--|_IB*U(0&qF|<9MAVv*F>;vmeBertp2q
zi<`G_TF%}QLUPFDo%38Dqh#l{WfuNzJcHuP&w&b?_C(s6BKs@gomifR0=1CFyUdL+
zqC^ZhG2&qQ1>Quzh5bIw5MT1|O{j>pZCg=h6}P)MLpzg23m6$_@V|gLU<#N4?0`$r
zoW5`D;%YJj7*sgwBpL*aNODkc_+d{8&@x&AwJwwo%7_X%RXG2s?YX9-#9jk}`Gc_v
zC*}%p1Lt<?eXS_c!s<NA3@`#%u40QHj&$311);O9Q;eZCw3fOD2%6*AVXEMB<p!A#
zg=iN8vvv2w;bybasA{TOQ)2C&sU9;V@ne%6Ew{;}v9X0NXqzxFgE&D9r0lzPqo1^Z
zT{dAlI;$aMeJ=@}lfKmlotD==>#-2W4vdZlpZQfE^w`~#i(0MMF@{!lkL6L{oOWK5
z$xoQ}Da-RyBp&tEwl)V2{Z8l^%hWeJk7{N1Vk$1s62OYDyPa_Oiq91qAcVahXHwbQ
zx~R#4oNftiZ#2&htwO_&imxo1&G(GYI#IR>@AEV5%xefa@laRYGM@Ua%)5*@Wgp6C
zJ9iF~^Qa~B5{6&~;<?-M{)@F;T@W9R$HY~s@pDMij#CUcMsz{+lWfa72|X=NEb^!s
zdQv)xlaPK`ax-~U1U%pKX&e6MjB@Ga1;}m6R>LPVPzWS|zwc}?y%t4oaH+D#g3<QU
z1**=j!d)lc&?-G>G6d4XDh=q$F7z5o4?X|sb`hOK-%Ahz;BY|xK<x{ATj?Be`?}=5
z0G*yM%>{_iYdF$Le207~^ZGaNP!Uzz-W3Rc7*W!xyF65VsuY==BUm;#@G_6hh&4oZ
z&c1hm!wA?Ok^*PrOp`3nqc9K>r>u?&gLSkOCwS8fVE1!>u-7B(Xgpqt7GTa-ffLQy
z2kG?@FMV%CXB3%$%0R<5TM%z?C59a5n*j=wQ5pe_QwyHJP|Ep#3o%xeFnf);b?X81
zQhrc4#%e*2W#=ZFcw~SGz)jlrl_;|LYBq%Mi7{E!;#r!=8*l~?1ijuZ+pAdnJX_EP
ziVQUCa5NvP8J!IXM(=8GL73uZz{ChYwF*t54Wz!;8AIl(gQ!#{>ww*lfWHbs)zcK$
z#c(4j(jaBcW}83|x^#1O#t_Pve-9a@Yf&u0bam0bI|&=;0Kn1Wivehv5)+6Vl{mYs
zP91wW<gHc0C)6AOy~<E06r8W`;{4z{1j6C;cQ3$hifDmU9J%-YIlKi<RExN&f7fX6
zk_3Sg2=D>2(b96wwBcW<cELxv1fqRDI!i!<fuEb%jwZa8D!_}EMCk%fq6ZN?69UBB
zG>ei*Qx^vB$1NWQ869+0SQUU)6db)PZ9O6w4FfJ`X77>vV<GvAJjzr=Gw#*Gw{v7(
zP3&9!jI77wepP8Ps-`Pzi!D>fPyDK^M8tijTjv%-O;`LB-x}poE*;D)x`J^b8gM-u
zB8Xhxrn;lKw<>Mj(;xlO$%rJps!cpJlRROY@w<{-?o+=w+FnDc964>buI6#WW-`6f
zMN#jVe3;QQY`t-2cZ{CT$8YDm;Za)AH)IgPqBKvmpdAEA0FZ`q;-I=j!_Q)GhgL1k
z_Le_zu$gaxgg5?A8w4}>D*MU~vId{FhK5&B1$?k}p7x<fM1c_NI?n}o(!sU8uM;JQ
z&pAI(jO%tG>L-VO0CB=`5sl}wkBXH>n(~}F;u*f|e6^YZ#G$ez7mx;Ng($(400TDt
zWOJpbO4leCc`3F0^PLuX|G>;1s?KH26kq1|PkB<WbgeZbfU@bq1xBoo>ADLOM5rkE
z6v(ibbE!dEm8uMf-`==7MRR7gaue2DeCHN;>QOxvksTK|LGJpxsa@v+B6T&#9@=C)
zRs^P^w1-0Ab>KYpe@%d)Y*}%K;HC?tso0+e{@Wa~@Kt{%EZtk$DhRgF16-B0Fabpl
zH#!V8RT+~-r%qigSCMfRxCho=v<g9=lu&)>ZyDiUvhKJmZQP6n_NGAKpOec|Hbtnk
z?g__BWc2g94|WA`C6{X@18l%MS>w8Ze&7IIJ%m4vI}o_<#(`f^pBIM=Jj$z^OJI&t
zbKZ*~tL;wM3)+=84fIG`MKJ|`wfEKs7F~5&-nL*jKO2zSY)6HJ!k>usj3=JA3KJvl
z(Q?)jLnwxw#WI@i^tN_3m*<_z6yzL_=Dt=*q}&2~Z3L9*psE5YtaY`V8#j|4)brCb
zZQXqM<Y=_|=8QYIlR+s>J~Jd_sE1;cA!QY2Dq1~~Gv`^>;G6bp!2>TeAp5rQj!nqd
z^6Lc&gegzl7xICid19w+dV0uFPUT|iXPJU&j}77CcVGoO4#2Kr@AQBuxeOL!{1`2j
z^K0T-Y+$7_Kk5Q;>+Q-q3$oA`+bL=pq-oi|&_VxI&;DO)GY?6W9u<k|$!C%pl^ZNf
z!UE*>za_x26Tpu%0Cs_EfDh^rNCD=Mr^G>g>>(RaKRSRwpeQ;MKLwoEf>7A&PSrI>
z2NGx!d*vUf<L_88rl@-U!FN6(9jt}fUzrLI3Rnx+x)6HM?^h#PpbDxd322!PWdLrA
z4stmNlB~#(K-KQVtFhf*@j{zgDJl`je-0&lq9$T%1Z`6Zj=}DdW|7Bxm%)XZhHOv^
zQfPEx{+~%$+TkHe1e#ml%S{aLlKJp8YA!>@L@M^-XZYoZyUg9g<Ap((lw1Mi&o3z;
z$wQr<&9t5)P+kF&WB;xp1;Gbk-HmTNg)eFWtNjA73O*ZC2C~5l%ij_=$@Raby8XX-
z`>G6$Z<I6K`9|WyFsoWFWU^F|B~`GeGY{V1S=Lv7oJcBi1X<veIp`#9Tr0`}l?{kG
zOTE<pdLC?>YdlPIoj}X-;>3xOA8yZJ&-`H!9moPT>XUy!jn6k4mjLHEz+w)eHF40W
z(vU@K1ec|XgpH<6^b38mlhvbYS91busRc7o{tx^nlOmw~w2LNc0U*DfuIBKPM>Fok
zlQaj-JHVt^SZ7P`jM}H^EGe`FS<K>^3=POe%`yMo^8^3Fg3GedaPxoky3vdn%IBYg
zwY%W2O<HJ2xzAv0zX#UPoJbGU{RBJ(B=wVX^g*xV%|E`vKNZH5n0umfLmm{NQg>27
z|1XW{v!20yrJ9$OsYtj2)(Do)!Ld?cw$K7&TD{~4QO+ut0j!5}EUAcZ&dvK2>;E+Z
zvg9w7H^=?|G{Wmg&`Tm{5%dfw9b9m?a7wrm>NRlS>)^nfq%Sg_f!M=tLQAQRsN~Ta
zucg7VXQv1>gCalx)@<5^s2)(C(0bWy{H>|qfB|<4m<s7^=1h?5_@|EYraLZu5&$iC
ziRjsJ*d#BdXM}jkzDUO?Qc24nU~)%xjYEzQTYF_?p0awRwc(YceVN__W?RyFa!SC0
zeaxX}`joeZP1b`5*H-ww_)>!*a3aO3)Mo>J0c;gGwq9#FbKI@=qI*aaIC|vS=)m_P
zt=OTGEJ3lZ38-@3ey@w0=_M<AVg^XVJW0JiUd2vUiG=iYyQ5N1qpq~u`&hv&C#%J~
z_YdBC8P>LJ@74D6Qs?vxog4*O9l%mMXk{YiQ`JrnG_MVKf_=D~uI#U2pRw9D2LC+D
zH&(6mTDw0pd^Z6I+pj6MQ$Be5LM}{gEPvq0uPV#2wlw>$gTe9c!-cl4EPc83{jIMu
z?MD_xmYrL*rB0Oo5s8dnWnLR)_G7Hpne|!v`P{93Gct^hjSGp@ci&wz@Qm;r-z^??
zpgWE$7gE{%mPw^V6-j%1F}ndw>qD#PEw+}kxnNqvrU3Or!C;Dr2f_U3BHgkc-Q0ps
zUdq2*@BUwVHCHnusTK5UU;D-T^gmR~_83da8@$X!8+omccMO5rD73TvSp(Y?9}Q71
zH}tZvSo(6Bt5B$Izaq-8&Y3I%%#K|sz21>J=Qr8P>aqQ4r6V<4ttpF9Mms1N!5vl-
z6njuvxGswOb8ci5W`MesBA0LGC(}L;Y)4Nj!%kCa7us3g+6Q!iiyv7gc&u5Oe!MSz
zvvi(5cQi`1oPO?W_B@@C(dQz&d)IO=w3|Dpt)^LiHFeV4Uh&)@+k5dew3F&bF`*?b
zSt%$^$rG0ep5Q`;>PMD@%S>WTA!{z}vd|X$zE7&|s^w<m<))dRZC@3g)Hy+gicDoP
zQkt2}=(o_DIfJ_<Yg<e4y_X+h4*Xj%p+TVtZVRh$j({O$IyvIzx!w0Sx?-v!jbbJS
zdJ2#!n4E;e4T)1lQ6g>dVNQ#pFY`Mx?GAuGP7cWN;2yiykr}&*k4%))4hlyE#bO#f
zu2%TCk|mq26rKKK8^etrU{`{BGhLwFEcSB{cy%t^Ra2y^ZWEtSZ$fqw33tvzYZuv=
z!%x}L(p@WOEPs!O+P#ST$DT{JF`GM)vJ2I-0<!~Rm2i8TEXV`uS;K~xER&2XkMXy6
zJ#i91({*X7br)wD{Cp1MN{-gP6V2Y)=<&nR&BbY#fosxpkdIj~p)$)`0_OkN?+WJf
zqqGD6ppcTZ1gO0Y7Fr5!dN6v+8vfpQu(Jy-gCcR1oNwLYO#G<+ije-R;f3j{F^myK
zfwB~uItUECTKbrRZzxpEy`Wvvnzaj#GZcY2@@y_<!gcd7$HOy?8eSR^sV~079}!i`
zS@HSTrMtZuZVN;`-Q;O`y1E4mXJ6)<!P2Ru`1%^kNY*fQaT1VuC|0Nwe6!>pTI~En
zfH~+Kt)9Ow+T5|VN>GWzhgCg#<VmZUrP#+4t*lcbRmCJiFL#mxR7Ydk$KRhsH`v{$
zkb23VwsY0>kW!WiAQM2)r*q}Q^epQ;UG!~Yn$bMb5v@_sz~TqbbyB`EnM`dhx%3Qz
zX6<lXt3HYR*Ly|&SM2<UFNHdlC{Wt2R{%@9@qBdCG{rP5kRw>F&H?MzpX0)U!LR$&
zSx}bZ;id=FF&6HQtI*YC*{f2&H9uZyXx5>tjrf`u!g0niCqvWWOLit<D}{Kh$*2z5
zd*MM=z~6jCA;|VtzPT$EtJPy^C@*yp8;aAyom{R>ALsyVEZ(Pug8HqxRIXfY5E4(*
zPXlIZw)A^$&bLo_#tryL<J^S>X0mS3RXj@U3J4S&dovo|xI-hE)lA<!oXSv@(X$J1
z(>a0CW{M<T?mAw6EgHqgK9h7XsR?hoLHA0sD&lAO^BTiJ5ju_1zTIB`^S-R*{B?<J
z1gH3ffnFb5b3I1y<~<^L4v=a~i;bU0e|9<89ZR3#6kwW$6vuaN@bf7%63rFDdGHa!
zV;y&4bt1Nj7Jv}ym5q1bIj`Rl4AxX1PLG9znJ+15$#H#Tue)H|&y9ZKvSEMS+Gp+a
z;fBMVBVtn)wPR0ZF#;aAa;G`togj{(jawJFO7Nrcy+?!&Q3lwwTvg`UL)p&$2&7ed
zk@u4tFCLxQ{g!GsJf_^eo@FADjlrpxH;cZY2d&{UFB><evo{e8$zrQEd=)ys3n5D2
z@3z5D;B@GVQZ-%}4g%r;Cip1=|0wX+<yE?D6=_d!38(B+2n*FLB#)Eugm89G6Txm6
zCWprk;h9l-C~p_<zE2SVclwDKPTcuw=0fxVVnCpvR(=E)eu}7zexGwtM{hebCf$TM
zKMXViydP5r)j}1OB8}T3C!10AA(J!D2DMwRc+6kHanr2c&n1iZuall~6)C%NoxKq$
zP))v!*TxM_C@evXKG&4eiZ9*I|J;*0!~kwiqi6le8x*N9a0=L>(*Sg$XV<HN(rDm4
z*I0GtAW4%?Mb%UA-M(e_9=Jt#+(|S2x?)h(Z0XI`=o%S$(`(>NbJ4@5D5r&Uxa(R2
z%R$koIG?!YWIWXNzR;3x^z6Oq)&_I;$d1*%GrG&~IyLl2hRZ#!lrr%H&9Ow;sgr0y
ztcR9y$mMKh-ZTv?O$DhhI)ccjR~tmH7pn|o<;ZNe*9TXz(38;FKV*$R;5u;kp7xD<
zLJbXPVb(K4SDg{FLi&&-cEVIdV!51bOGv=LawOs8dmRlB`!&XUW<~$Dz{UY*VmGhT
zm)ZD^D;|>!!Oc&qAKm$-1Sy3l3KwtDwjK4?`qaPFi8kDT(UAiA=+^#B6t9>Hd}x_K
zKZugpdtBtsaj~*{*IfB7q)4qmWSw#>f17h%&1PPLjbJg(srezDp0s&6m5RiXn4^iB
zUgTsc|GB3tF<2cD8kOyhn9Bod)l7Ukr=m@cyv#@PfFSx-#OAZa3wE%%p7!o9H4$h5
zRh8EGkm$R_3)ya&RGK$PPf{FnOJeQ1_j2nL3me~S>Zsf|km`n52H{1|egHR4^Msw9
zdp76gPlteoio6eG^EDh-@TAW^fN9C8rbVD$)00kQ&+*?MvyssCE;+}a5gq_vd&-6i
zql#ry>N$}-LSQ4;we%foo&veijs&%6*!Pn%&fOlBmu#PQ=`^-`hdOwcI1+dI`Dq-y
zj!e~WPJA*c8L0P0VL6p7z!I}aKxe-{I)L-%hws<_1D*Xp{2Aa$VdbzFd3SbeeERSI
Oq#$*Su9s>#KmH$UtI)0h

literal 12930
zcmc(GcTiJrm~KLV&_Rkw7ij_lD!nU+5D-wh^rG~pNDC#2Ml1*@O7GI7N9i>Y6;z~`
zAT@~6OArWw5V(im?%dhgKX$g=xfw!+lXK4ZmgjBH`FM8I%#f9dp9us4vBHe5S%5%b
z!pWO~9yk&+(vbpu(7T%$UIRXX*S>x$0{CDIHo6@G0x@%*yum}$v4<egSrF{nRjY{X
z_37|z!BA@V7Jfz|T$*=?8O(VR9Y{-K!RmCa=)AQR`Po-<gIJ@{r%Vi5S5CdW!tG;G
zWJEzd<i2%L_v*6b5KG*-moqS<`)RFI>Q>I5h1BWx_MVNtnd#O2h_(4ZFHh`t#?QYq
zn?KfAjFQD+|I;5U^-^#GZvMSn(zQ<=lOG#2C{6dv8sJ6A6|fc%EXM}2lNiXCK@o}x
z3MbwBV-c`xB!QP*rV&pWnSt@1EmMnawF+<K3zo%PY;j`t&r$9@!g=7BT-kzIFKYN@
zb~9#OLoj6vneNF4OJcymiWrs%9<_6ZLQz~Q74lF|h$U)H$S%>I@B~Lg2q3)BO6sp)
zZb%P(S*1&~7#XX-Ql7JXER{)`tDH+S_*C<3!-Z=)h;|HA1Lmb!tsmAwhfXqNa|nb5
zDy_{_`o!O!&AF|RTy$>al+JHk5QNc1%@DE(fxPwUfEJ-BC^*4m&|{3o!##kioEgZ9
zIdxH~C67LpLuX3&w7_Y^TG=4R?`@`vD7x$WyBv4NQ}y?PtQKwFM^cqlrt=EjUTb35
zK8WC<5tmwAZjfnSJReoO$Unwx$HO|Q)9nEVyCigBGnMn!{(vI1`ANIunC(J0Y&@&1
zXAEzvvp6j0VjS^m8`GI?!$z{w06%};Q&y}bKWTCta|1NXDbR3{Kg`G9&O>P}e<}8O
zTubGtX}FitTDQRk!?uKV_fI1ER8U}$&>h{4G@Rx8<9No|7gB}YLPFEC5-r&LE1Fw(
z;}Jg6+&Gtb)*d#3J3jpVs)3Av%Y&7^1yBW}b_)X1i+-t=$-yx1G2FpLIc{Fs{HQ(=
zEk`>3Aoyu$aCTxTC|WpZxMg3tHUsV+#e9|Fsbi>)<!6sc2k6g^s|<a*pxL*fk<ahl
z+u73`oaJ)=q`2;XeaRZDZ^z!_t^DrmK6iQ9`0n)J?zxm~aOFnjGmQDBeP_oNq!Vfq
z1@Wd_&Fb4mG^7e1t9HlTMMQLOvP<|MRK~}ZA|cE_WYgSNe|Q7xIq*}bMaDlos@FEQ
zlhOod(R3Sp3Q9O)6ZiMkL2+#Ca*MRw+X~H$IqUO&wY3mXQ5|A8JuQHHhGsuT;p)7}
z@@UgcK7lVs{r(Fv`Lx@|;cLfL8b7-P-XE*Jpkno*!g;_d6uw}#0_gFDiw%&_YH)S@
z5#p@xytHeelE0$8ZiujCH0sPKilpfB;NVM~>DSB>vDH?we_iV~WX_x5hMwt%qI22N
zy$PFKiEru1;JNTp0S-16-vT8mRD`atfGbm#cE?rRCA70`lnn>m7c?uibD$!zC;8WZ
zz3P#_9GY5==Xkgwg8O=I;q_Mh(&ob8Y`n+3XV=keDn^huEPvgw5c`Bfr?r7bCrjD%
z-zfW&snh(HJlz6U;=?M@Xb!Jf`E3uqmkG~H^4O-p{x>Fb7h9TMVs3{>6NTNdi&bb$
zM9Wu|4xak$jCTnhjJjJs=9gB_I`ESviBWpN?M7Q!&%FjF<^xKUl|(P>IiqhPl7sU{
z6AT)dMeTy<!^GBRwUNR3b`9=h;a+3$|B`StetcRlUb(sSbip?w&n7D7BngdBg|3l>
zyu{}6GdH|lTo=uB!VnX=+7;;ylTkOk)=kLER@I~@G%hlqv|67#bBEg~!DJ?aJEeuv
zAc%t9@M4HJm2sdY;>B34S&AYOUq2Hx0JkZ*PI$wnMPkt)MNK81_nK^8NZF*}=H)c|
zam##icf@G?vvzWW?(Ooz-SCNhXAizo5H@kz{FK?fW@NBZnCHRTzRD2E<m&U_!DnA^
z`gPh#fP(QlT0*0lK@)lb*=#J|oG&Gh@U=5wsZ+T`uB2UL&6^0}t=D(0w)+hOA@t#Y
z--n-yZM0|m<F7dx^o=^)ZDj>TgN_0ceXb9YRKJ9x7hBu$cF%+LUO^d4bI?S}oQq+=
zj@03XW}wX6-p8L^G<nY#Y89%mk=0^{Ruv+1R_S}Z=_<3qNI9g!VtP%BGy+<Ae}b(E
z9Dxdc_~cKW^N+K`g@Sryxy4WD9*XJnE_TsqHEknu`>HfLUgYrcm<ipCiCVp@*nq0v
z%+KK(c1OO1$yWnnRfW4BBK8JIEGFGUaQV5M+LiWXqNrw8@WdDQwJS}&77bj5Vf-j<
zMq4YmR{8RQcjh#M$XE!2RNB{ZCUR6=c1DQMta+3Dz%TFpjkmjJ%iJdubr%lbHK^lC
zLq_r{dKuA+(ZW@{#mL}>veZhF=wTPx<vnF<IHsX=z>lSq@oKEOnm%4<5TlG9=yMd6
zp3#gWQvNt9x3M0&C~b(KXF2uNq`$MGUq`l?kv|a4a(%Yz!E>QEe>8uTD06zkX(srp
z2h{n5!qNGK<$w$BKFpZ)<_Sliz*OFVVpH<KB%jG@$pUwC6mGvnS5Pfu;L~&HmFEMG
zzB?`iTI!{bES81Ct?-~uNg`=SOwb_vXXSpNQuNtiuBxm7yjZl`*X*2U@8VL>XUpi0
z&dA|$-s=2s=fXMre%A|*$Bl)E%mTG9YJ1Rc)w6`^F@|vrU=yv}1fvx|{hS?3c&^;n
z5vHGoLt_1PwnCM)`EzTaj!VkdchZx%8gvfTRiafprBxBvnjGDxxS4{OsztLiE`<F2
zGR;s^^`g~#J;2L6tA>?f)t_nQ(i*rT+mov^Upt)hp(|YhH0`HVRFqOQb0^=+F-|x~
z2G!H&{+`ue(_<`-4juE%r}GIp0Ievl%~cU9K=9QwFT2^kIZV=H;gDAvFq%}L_dwSA
zv|qfk5Z%|q=%N!SzVlnx#IIj_yEgAeKh<Th?J{0Q2SnC#(_N6!V~<dCtN3U#IRAvR
z@5>G7gz%ZD8`^FmYn|S=Yw@AgD!P#D)26d$1R8bbWGuHbFQ*Q{SBx1}Rcos+r#*8t
znZ%TfIrhFA@T*J>UhcGIa1e<bI+%cSswHt8;tkh%3ga++eFeM5W+vWIW~&JdJ-L1p
z(E(RhH7wZOKo!}};{NW_nFHI)P{9<B76lHSH3}%NFw`<&VX95BuzQrFZ?sLT6?I|1
z<XCu4t8<Y{Cp(-j_w3GZ9hjfO22W?~$2HmYJD(q&r|iAQpVIlE%PY`es+8yNLwM`$
zyvPY36}V%nC``88LCCocUq+j5P)?2cEcohX4Kx^bs24ypGdj{6=9APDiI2Tw^vt&h
zE4vqcYg37IVg}<yN5*y8^M%Lr>t1!BpdtQ;%^g;9l7dT0;=YuNomX!>cJ|QZgfCVw
zpwv*o6N7S$S#ZVS8uQ62q5V7XDqRU$MAroZ`CY<$+*|L6>VKf!)1z-NA__C$qnk1$
zb3{MK^m9xE4z;5&(jmf_aHUv=0EI4{42~l6VbXd0?3W`%6K+QnG@IC!Ahm-QU~t2r
z2VTbq9cf-Fm+D|wvkYphc4s?0U@Ul&@v}0s`NpjNAhnSJjK4A--I_Y^$?R0(3n$&o
z*b7xq7G(}nQcc3dxUPNNkGgAnhFv8JCF^95h8JTWXBl;0ed{p3S*=3h>Y%L@-|31n
z9L_acD(Id}5-?Nyr545YK&)y`4K<+eXrLB&mTMeHtEz*f<mZnVD9`;E9mG>l_3g#@
z+sN&!Ts8k%E*CpOJwk`>(#)B_I()WSH<unui~Ho<g=UKHG6X(M{SO<|l}i@u=5L!G
zXtb-&6>TIJ-DyuTE_OC*dD{0qyjl5=t;s6ghy?80@(^BeY3Ped@`yDbE?Hax2TB!}
z2Ge82VT>1%#lr3fJIkz;P3*DgmRqf7?@H@pZj|sVD^q*b=3Gl+<hqO&QwusXE6Xy?
zDePy7si~=mx^YWGgG$UwS!rKoMTLo1oSb75X7B4Ou^{>-e9dg<e3I@_iEMS#uV(i}
zRll*y4j0M<UT5z!m-&=+ro>J8B(1++2Nv@Xu@TkxG}r5f4`WC_z3wf<hJ=KyAR%mO
z4<_n-@&wd^32Op@O>W;&P~oQh`=bphRDM|Lu*m+<^w7}IoWd_BYJH>om**2@mZgCc
zQ&Wep^4!Ppc-6evgXNm;C)TUm@Gm^JT5nd>ANOh3lwW`}p>u(ScB|V6ZME#v-gX*M
zQ~I@O7Xzo7zC96%B~Ouw_vX8_6+;$&^c-*J9ghUnkJ_X;MQ?pk*jb-!EKc7~5Ks-Y
z?qJ8^oujH|8?n^&el60t$6nY2^|3$irw5Z_i8)-2KheF=YFqq5q*L94&|Z;PME8pr
zQpP<W&+(wx?JQQ${@nJXZ1s>M3UzO`CPypsZ#7gnf-s*_sd{wC15c=ozhCbl9X=De
zxD+3IX*cRo_&7;ye&Kj_)&eT@U_e!M(!bU^!wsfe^K<jtkApm&7`T41@6F|X*0Rqk
zA|nG48#V%@{*D06JGhC|)YPQtNwZ=LIS}<=*RQOs?6!l0Ll(W-M$^3H?&j?AN`d~9
zqn(DyKmqDPRuJi@cVR-Tor6P^LydELAZo1lMst@Ce5pX{aM>K80r;Fmnhb0W*h!W!
zfsc-j%{T+6PBz_}H$&`Ahp(2J=gnNa_v`b#F6n(z&S;Gb*2M|FTs$A5-EiNpMq{*S
zNZBi==`NPpJZnHVR+CDWJ1DV`3up*a0$vHi+VDQY$4B@?K1I*os>YcEHC&y~Fg@Ux
zIm+FV$%y#`EcE4Sv*wAxy}iB31_1%<>uHYnzeSLjR>7SM-RljL8>=2&FVtHR$49n#
zh@*W6y~)MR@`~yS@eb+xGi{M#A4-#Yl=B>aWvOkqCw@p1E8jyWsLn+EHPc*w;Mmbn
z>zA$)FhO|6B_+%ry#FUHsi(3#y2qt*S4CvDZ7wZoEicAz-1UJ6DM}f?J@IN$34idl
zzVeG!Y2$R*<P!NnVKk)tdo45@lcDIfVPk1oYod`;t-d!E(q92Y$!z#+jMyxo$Kdut
zucpt(hji_G6Mi+8Ng6BfbOob{ACqomT>kU$hLQgGj|aP>wn4et8`Z5FEl0m!o9A+E
z7AvW$BHlMQtMB%>9NnKEHrI{W+e92w%(43mSq?|@BFE&bmX?;i!clAYx|U1xVq>ej
z%hjF;g^jlX;&go2n~4$u7n!U7dE@g$@c!3m<if%NJC@i`hbHvJZftZ=Hf||&kbl_(
zweOBee~M%EqxrfES6fK;kf#A;R&6+1)IHvx1}+-;{EVw#cm9Q9hXCTpvo~{euTOLH
zFu3F7!qly0cFi?Rh~hj`uT&aYvTqGPIR4(Frq?-fdAL-y?L^f6^h@(mfS+!^uIL52
zo82)7-u0&W#2P^WXwbI5-(?-z4pu63`)}m=?Z;0)968mTi~slts>-vSO~XxTn?W2?
zZ5#Ho^~f}DZXf^bU*_}5-kR21tsMZ2e;LW85_?F-_GYVZ|Bc#c_WFI>D`z()q)&bE
zPq%QC{|`%Z?a;6JS0Aw%mkuk-$;s7A<wgI!YbX>tGUos&&_~&5-JGp%St<8Y49JdL
z$fyBKC2H8!y}L23>11iS?RHb)_J<<8eCyi%p~~B}Vcvm(8`=Qo4Uwgdv#}KHT^$z1
z_N}$<at}%K+{2o0uceMF0G}y){zw-w%~V?M^d5QAUj8Gtb#0N2K%P%RC~b?fzFghK
z_>PwIBLf<zNuL%sPkb4ofIYl_92Xb25Z&)W+BwO~_?hVEjCFuTxy-fR-y+?=HmseY
z5Y0S#0##kbU;@s?JV#^Ka7!V-ITZmoJ_6eg*A$~fwr{komRNiafsb0-YG?Vy{I(xc
z_8S|iTn#z++oP~2q7^}$h~7R=Qn%i3X4G)Y7yvx@X(0gfk$!V3W^cMMY&K>ud~e(%
zsWnaB3GN>fvdufZyz>f3&2yP2<{L|MiQWG$l^fyS?Uftp*?vRd1U}z|ZCzYuKRj3-
zLS^`r+{pN_k7nd@c5c4^=e;HH=hP7A3}Q<)RE9^#<bz1Xuj%_easrN#e^NyJ0t3z3
z0knq7+ExqJMQr_=DZHLwPhF{~37nal%5K^0pj@xEd+&ZRdZ$@HGi+@rE2tx~hDX*W
zy2(cqzbd*7)&S6DE`L&z+9J2%*qtGh$rEtOGHWA#4krnyZai{d7|sO@2{`*GkaQok
zvoc!Id{cAZS8XZ(5t}w;trh_vG~g9V+5h`0Xg|KJvGBfjO$>z;Aiba-vykC6)>E%@
zU(PQkCZ?xFr*tb6Bi~>4y&oiBaP<+rGBsq`n3PVMc)K?FnmM)f3A;!~%fNgMfNUs&
z_(()BbZ5}8pHKb%?_aOP49%*_%9=gM#5vv=>Z;3Z{Yi?|i8+{ZbaBDLe|>%?65G?S
z9QfK)<u>(*vRRY%)9b<7`|dvY;a2yakGFTz+@PU|TvN>j%KKt_T_<b(<EzX%$Guqj
zpUcGV(_exN^ZqtUP$}&8XYxqIPwaUi$JD)`$;O~rfyNp5stLOenLz1E5NPzR?NxD5
zcK`ged3SeL6@XDF9)O+%ad2^?ZrF^nN8XyB<yY|^AI*q)-0aMrv44V&<J^mGo$OtS
zLX`N|W*W<>9Wj)K&w1D<!f<b^wvM&s)zwwq!;5{Yc_;|<D4R{=V5xBNj)GxyG*IS!
zzrGYr`we#tV+Fv#r|ODBvI8-iopzFf9CWD5<{}wEkt-`JL(LPtD$!)(sEJGVtJvL2
z5xMUjRq#b8et!Om=v!ZR+cugPbK~P9&7{n-$+6YDuYzJ4_p&{)QX!@;-Z7^sg5`&8
z{1(vS;bkh(*QlIkxYRsIZTpAfUKHSk<3`8ri1m8>K@L!EHK$`K>pa@Yt!Gl~j*M3m
zd#pTzN-WCWB3f<-tbt9w`^C#Okn{OJ*{-myfx};FyTZ!(Nk|EpMfaJ1*HaO1*a@jZ
z?Q$QSN)|sY;e`CBh4VF6rm{td2a985FS6LNXwgm4{V>DG!;+1w?b$jf=puNQppMKp
z=&R}B5Je>HTZ$yiVsDr8qGW&5E-Hhk(jd7=*hBPYnwO)TaOeftil^^nUeP&vkPB@t
zy$#r>^Jw`L&KxC$>gKF4OD+m5KTQ@cD9xfDyN0j@=@!8|&Ko6LagILVe$WNVXBS0J
z?*R8mEx&}sfBA?D|NBA!G{taZ-Mesguc}ou#W<jzEwp|x+f23BVKjn)Xj3-vMm$u`
z>5tVLLoim(y=4}{{+lnHc5d&Bkd2F_;?_@yWZ+ar_rl+4-dVc`pDNYGE+Qq$y312%
z!HtWl=Soc=a*=_{OJi=*`I6Qx4vmu=XJIQC-(@i3Q76=Xx7tcHS=?H$eg|0!+C?kS
zHU}n)hsus}2d43y35R_te<@x@Q(g?6ntAojrOJp0IM=A2mRPp_0$YF18c<AhloRm5
z?)iUhX(eFBNbm)i1r3WUESb|4WRm;_1hGplqW$-t0)+hUo;m<<xb9;RHu^BSIB*C`
zmxdke$5m;P4Di0G!bBUK04@w*adln6!AcijbMyiF2#gLvI@rkKqG)h6s2a>hs{?gp
zi>`Vab^sCh&DW^&(z)@nHEv+Z#yAM#_x3mnlJ%yET8um7Y?Ou>l%;}Z;)rfpmjuRj
z502S!0o=SeO^D$izP(F?jK0(FI<J$eu!tw3tXaF&u=+P|+Z$~vHz@rQvSvPU0)<y8
zK^@N6S$}oR?21JlJ8|UjF7slI&qRsUI?Te*$^>+LS}k>qeNqesEj*^sC_eXJG#`{p
z6Jw;LTGNM?Sy4xXy4ghrS{uaIr?<wUj~0L8=Ua_@6DJ}frlqrj)G*?TAW1Fa)CY@t
z37C+z95`B%=tWprIzk{}gU3-|L-AX0#9@Cd<iKs*<HBdm)Gqov`%gihS&73e_^eGp
zUKfl2t~^<$z&<t(IF#i6|HNhgIUtlvBg5%Cz%LRuL};eyq6L;xtZpTXr|1KKvqiZ;
zSaC5p1jGdr3%)_qXI8BQ1aOT#JoPoooW=y@LL<xI*2WpZlunmzdrk8-vVW1Uk&pOG
z!&as&yJ1by-OdLaToaJl0N?uHXAGF11^})YYOnBE_vuk};*koCe!Rce^~xN7ia`LZ
z+?nf%`NVp9LF-z&jDg2;m!xy6|B4C{Tv=`jm{=;9KUguCwxATw5v)t}A-p;_dE5BN
zc_YGgyd_1tY-+`j#YENra)UT{YG^4u(7ML=Cp20Eqe-O4oWjTw#q#OMtijjs7sHaR
z-~cq8&D2)Z1EXATDpeQ%;sFj`2V~p7V0z-1?*;ZMSAp@d@P9FY{&U{=CxUMR+G=59
zhYWyFmj<M=h17r%P&T>)afd_zRsh~uE^@2UsnOVh9ohZai;^!GCl}pcN_haj<cBIn
zDS)MDpqzRU`UELl|8wtoS3;fEd>n+mIx53r@rB^bW~WZ5vJ`3Ea@g)U$)+Z7xgBo0
zoe;uXtx>_fzRO)tJ?O0#xvb_?P8z%5JXE^>h+PGGnr25>i(Ed@GGjQhv(etbz22y#
zGFIi(zVN)hC(Y&NbCQ)dfTVdFn(*t5tf(69{ia!iM#n_jxT+{0i^UQAC`;>BX)Eib
z&*Yqh&*tMa-g`gFhOfUVdM{iQyX4w{{q<|G*mpC9Sw5qc{FrYygHj*be%)Vrf8-Rk
z%ik`Ul-9n(q*!|B>kS)nv@Xt6xw^P5AAIW{6Bz?0+Fdt42NaN7a$v^~)Js72p#u*7
z4^t!)9dQfy(wGOhvAM;0Bn6@XLD1{Or?I>dFS`SLnS%2sXyUHo?i2W3DPh7=u;htB
z=a(fYKAbA57ZrnAL_Nm&jLW8qmsw6Q6P$6mI4X_{XMl5cRdfXrgZcbki<eExaN~GE
z@kj;GYm_O{4^@m7CPt!z0UAVTqypDeirhvi=R~)O!@d{ZL>?79?N&`I=YS={ll8Ll
zRiTMJ{9#HM)BW$S#LK+Sk3c#g<H~;H?KtK$oyp==ul2MEtouun3#VOg{Oc*)xJHiH
z<E-bbny3LqV3F1Qo1i>aLHZwb!XQsbH>VlymanvZ)R;8Eh)}Q;b|wHzYm_|lhi3xX
z#*|L`g62Ai8L57^e4eHAna+{^QYZqfmKH2oEdmllxxDe20Sc940muxj37Q1A*LIo8
z-4mLH$|oG<vG_AqKHWeFlU2Xa5f^ZMkzFyI5kW$}R(s(b#qBh9%)wHR5he<uR=R{>
z<JP;u-~a@?MHO?4aS}=v!iXOr3cZDkcbJI&&n7Qa=z$N#Ty+Vyb=7tEBQt^r@CXL&
z&64pZ&wTS&>qTF&x2Jo@WxRL3#^R5cew(u>SV!TfFNN%fD9FLtBI>OIjByLA0nI;0
zUQyNtZ<f;^hfuUb@E7dcVi#hm7*TwJ`T@q&c=YHkR<{wp6{64w7N&c*Jh`_mrc>X;
z15pvMO(tdO+I?Ar3h%G3>@(|FC)3Lo-2@?^b|Q98D2)#N@%}>>+--D)ywjC&T5Z0j
zqOW1j@qx{iKrmRQtc)G(t=1om_oppDz=VE8Uh6?~+Ub-^ZRx^U`a;{JsVIwYH00^1
z*x4{y)AEwY-!rd`N45MG6#VTEr{V+RSP#glER%Ax^gW3__6^xT*|jW^8)V9Eg8Iwt
zc!+{To=(q&A3;e;bTbmPTgv}5QXgcwaf=X32B>~}p0Ye8R{91McuW(tVKN@H(4h#k
zXtX+yREY1|%=4HU|N91lreaOk&45mi*XBIZq*3ukvUvTirE7x8;$=4gAorp~Xz<tf
z!oe%w?O%z*w67F^j#z@Zh}^-^O8ydaMSRKPK0@3~gdRqyEA`n}#W^aMTG^BV@W5x9
zhl%hCEv$g%atDp1HVOhbj?)82bItv*;^_0qI_H?N6n7a96vRf`B`>4bjHOQwMduuU
z9G}fbdSICVX3wG#kjNH7;gmN}W3ofraseWV1Ci+Vd-ht4EuSwC2HofD0Mfc(FRM8w
zJNOn_NcLu$IBeR!K$tkH9_M7<WCpN+_D~^ho<Ebg!9VA(B^Tuu-DFpba+I4}vr|||
zw*hj;iAKoafSDJbgj0n0yH!R&FA4-o)F2&D8b0@&GERPj1Y)c3ynKF;!Yw%<&765m
zLz>6Q0U^%e8s52I&<LUb4D@tmkC=9mhLA~!qWh<6P#>gE#tW#eq2Z6=xF-;WyR1<{
zWb&q^F9^hW`eZMF-v5nKgcv$_$_s@}tN9lrW#uP<g`x72+0h;j`HbU*G@*bws{nHv
z;AlIG84ORtGUYte8TvK$BBUW|pC_|M9L6&^)Bi#R+QF86gJIU`d_SXx=a-{d`UvQF
zM)05FMwIy*w_Jc7Edih#N31O@Ma~zx$)YyX3KA3CkB?c#(>M-FL9Jk2=}%ff+jyhE
zbRdljUeLp^h@vM;5v2%Im&fWf=RlYjywb7Q?T<L0$Szi>u;Y`K@1AyfEefNh7K<hM
z#oX(~1#wmfC3`yFZTZ~mWj$w9+`aR#Bcr3I;V$7{CrVPAfzoq8q}uBGe+_+^Nn2zx
z7^{(ErYvtCeL;AtO!(b^K&W^@H*-}`!^GvW4`QNAX~QZ{;iqDctX}+6W5m}Ssx|5{
zyZzzg4~E+fS!G~RwKFbX<`6NF-N{NN)prQ;lSyablzyp%3)YV&g?A`Nz>2;?FFhND
zYMP_9%B)}$c~qQOLeLa3i`8T(edG%O-z?-YO|=-UJChnqQvpk;qsgl02Bo$uhL3E<
zw;&C(a0hUAhX5u#xgtUk!BKFrci+ty(agu{llNZ(h`U|@ayC^v<4ZeNY7~#$leuMe
z`jc()$U1OmJZcL)OE}PU1bV4=1N|>k!BiRRr$8jZ3P2ra$UK-!7-8p!H-Xk#x{mQc
zYLJZmeMYrG>kp8L6@N1kL~-t}j*tCLu>Q545gs^3h05Ie=kh3C4-omm&kA6Ccp@!%
zGOIoNqsic>X|I60nH4JHdR;vpz+`I=56+y%wrk^9ZdC^@dm5HpWcHm07iHbX1O()n
zJ@Eg=`0$Pd%1wI!K4~PYdrnEt-OpF)CZ57s<@1sAp4<Fu8;qcfU}%!^Bg1g>xQTO<
ze6;@AUQ=CHJfwVA*(`8lZ%F&jbcYpapl444T4^nMG9N~a=;3Or)W+A;3R%Aa>Uilj
z6+!sYcY6E_tANccrG}CuJY-c)FA@=cYxAzDA2g+08@A$8E`ihqbr>)V_~6uWpK`pt
z>>u>}F><vlEnhvba9~|)^!?73l_+EVs9yn&*!WQ5u6n~L`GfW3RXassFM=GZ8#(hW
zRSaekX=TE$R-F-ib1VRT7AWhJWnG}Y^e4LdKulYXHZa-)dE(>0eb(^bp0@rA&Emf~
z)yfi-L`$NlzrNQ9?V~5%C_fA2t73Z^vL)^U0gFsSK~MsyNSrm!4aNhE+v+>UEy%A1
zI2D`&I+y6^$}NgSuum~gSzgnOf*3CFsj@|cIWeb*mvI9IlmZ1A9`{4+k^Ao{n!G>z
z6me@>^K76@O^1AOSh1BH_z(-UL+wjP;m8L&h#*1=(4B+ZQoW{VUG2P>fsW}-`CSoW
zI7*>=!-bD>Y(yCO{*OtmP1qykiLbThH)u0u?11e8cWvdg33@CpK(Zg^p#i4Y!0!Jq
zCPn%(5judOHeYa_ROv4bAlVieiBfTw32C?}T-oh;k*Wv-;OH&js5#OctO?OaGUB+;
zrrbFhpG<0sn4hjO3i4C2^lHLQv`zGC?;-1gEZKlXf(akZ#-v~tFklkYg^lmEssWpd
z`atrufwaEy{)^!!lPJn9N8wg-(NI~c>A>utLP`v{kKXf^cBt_QtHJ57h}{1Bf^uo#
z=0|&Rm`tlLqbstWuB)oj?u0A2qD%)AIqkUr*Ob7&LEgWgla^$_(N;rr!2DwDB!LV%
z*%_8>TxL5&cdY|Fa{A0{>({UbdYu%nscVGwL~bBaH3Oj)!F1D{toL%&rg9WFq}7;m
z1t1oIDBA{@5SHNEsC#MLKf5ZIQcajIR_dP&Y?8nfmpjo%uCXZsGM7vd0Q8Ztu37t6
zGUBlEmw-)xbjJO^&4c_0O8<Ly<sZuAI*lPsE6pe57n~)|3AaYO3S{8lfJSZ1M418h
zrqk&xj;uTMo}kysvZjE)Pso&dTmugAAx@WVis_JUymAO=ovh@6ai$R29NE>I)W!QR
z6CFJSyYA`)YQr`}?)L#tHUhnbvwD7YmB3vM7Gf<mv^Pf<kF~Ku0j3_(6G6tMet4B_
zXa9jr#D&|pEWh7PZ!CG=JNCMMIW?RV>3^`4$t_pxjrcN=s)HLE&%bNipWXhKq`Ige
zV?Y7<4a)_D1zd3c;JMq7M~V41Hu&&M#VU&Z@}6zYZOuh&U-W(Fn3w=dU>$$|jp>o}
zM1@b~SS{_ElFvN@3(7x}10xsrkM}n~XPust@UfV3cFYA8?`6&KsaA43<rve#z5ZG<
z#krM~L(W%@PpgbM`dw?>keHb&UjfyrY-}!DZnu?h@eeGwt*I~e)}0=)!cUC|9!ERA
zCoV4z^*B>)zoojZe@~J9wdXKaopXHn&1+ZsHbvHk1WxtZC>>h<5;XIm`(fi|Q@AP2
zb70D1GI>|4B4QDO=ORJ8bjB@l3<O@BIcGUQX9ZRPOyZn?1-~5^)d_rfkzWBwM6ZBE
zDM$~BVGwO=%kh!;&+VaXDaoTr7+n7?I4=+~Wmahy`bD~q1%h%pRegOo+%@Zas!d%0
z63P&&1M=q)z|VwOu`-Ra&mD|oB^@zGFxP(L)3!yV)Vcc;9qi>>Z<p8Q^lc+_dF|t>
ziLrs3^)oy({pFa~D9eeAlGAoH{x^^AYe?WPYq4fuc^Bj1DthtVhr9I^r<*v=FpYpT
zY5Y*Jw}*MQ1tkj75BfnW;*Kg0F3f%Md2qMq!n^dl=`?GO`$Nxd)>hB!#MHOvO*)*c
zB2TJ!uG;jHAc#*OF0C<W*`n+<WTW`vIA+?E-@EZbT&nKe%28q+7xlBd)PJk~t2JV3
z7gs84W#V;L;S+afRKBeAj+PaLqApg;;<QW6282$T>0dARL2y<T2Ay&yIvFe|ze;5F
zqYpF9PfdIWX`q`iYI$kqTXS>@&0gdsV{@zfk3$><#e)+;F{a3uKbnXVZt0|`cd$5Z
zQKxvESIcSEV+2-B=6Y|Bp2c5oqZia47p&94oN21*3=O^ZoS3lxLvX&OQ*}Xa7x9RQ
z%AjW}yK_s(%A4;j?Z-ZxOefS|**A#q<d*o$4^s9*F4gRw=$)xe_<L|6SeAHo=<$OA
zVcZ;=J<A1>MiatW^$rt{RL1ou%NM<78bv4%#B<m$TC`_{Oh&QUGeAL9PV$Z7=PUW7
z)B@SIyb!=;k-Fh2c=)$7xIeqvgz}>JC3GZ?mjN0OO+U^ZAwKy0FXhr4!}r(Ttr=An
z=Sz;1Fd(veu<PTMm8XeXP&qW-x?b9oubfKF2IL%~G*7s!O56pd^?SPt5~Bg>I=xUw
zXneO9vK<NKsyg5J4IO%@+~P?-U5@1?^5lm<K+p#B&k-_xhh{EkUm2ImpwR&au85{&
zy3+;e_*?bZJZ3q4;CJhHKPT&7=Q}qChX$)okuAoQyf|mLN6QB;zYuRD+k#X%Vh!yA
z4mei=OV&3P_nvj}0$3_*KLAUD6*yF+%^-=D!3pbts3h7FE)w<V)Og1SiH3^wyU3MT
z-Nr^1r#b4>j9>f2Z2y06|MBROwX3U?NR%M4SZn?OvIp@)Qc)c^r$Oo`4Rx%sOr8Jq
z$5S{kPbR(!H^9_uGJVAqC_n2FwB9cQYZ>PnY{!)PX+BN(l;Sl;ZkkN)8Ar#u&<aBi
zuGPnaf$h17evKcs>33xw>-E^chI=l~+7qG)gPe_7{RX&jStW%~2tB6UDachdpY06f
zkfVvC>8wg`BCeBZj5}}kG;aHfMhjM5^z)FqF#czxm4(H&Q<{R&{u*p4MJ26Z-O-u8
z;|BS!1k7nB54Bz!$9k4_intWph7V&Q@{3-R96pfP4z*Xk$}Y9jr1DL%UlgYgkV=wz
z;|K{A9Yt)}Eur2z($M_g6R<3!i|}PLP~!9He%qih^9N^xnK9@18$JA|DsYR1b3I5A
zNLi?(3Notjj~(M7+nJ2fu?n<lTR+$YIb5i*`D`WIc<C|C2qev*XP-aVwK9j9aWx=7
z39CJsp`)|ug=*Zsu&RK{(YzhxRRV2FCO*EE+|uR<&gSbt>>S6V2hLn4V}!3Td=Nq<
z6#85Sw}}>%ez=N?>@XUybA<ToGd!-jlhBVI`Gebde~-6o<s95!3u*phq9g}CzYU52
zlV#>0#~$b_XLuYM1Q;lJ6j0KzN`ywMKc6!CX0);&c@?)is09UWf`%C=SEEkB^YRi(
zV$P5^y~%LgYn;vd2It}cT%#&=6&G*?tJT9O5|gWGaUv^OgIGnQ3=_G}Ro7Cky6=6n
z2*r*Zy~11&zGLHmDBc-6p^e=8Tc`POK)*xVcV2>EOSj@(uns)1r#!B+4eC0t_wrD~
zm%<8ArlRkC`oJ~@0Zxz`KL*VV)8q>2A|4KK3BOU^nDWlugkU9hK)k2mwW^?24jMl_
zE4;nlCdHqyR2g|^%;C~7<PeED`up=@d2``zb<d88_w;2ko;#q53jQ9$J!^RyvvWHy
zVoey{kxRxa7)0_PlkO#+-BFo8YBoz*z&p3|=`(5%H>G*EGR8!%9b5mNDV-R+JDD~x
z?_jVbZ7#lZeg4B@>)9~uD-1h=x<mrcEfKF1%+ZS%)7(p49P`7F;rrg~&FOIb-Gi&*
zwd_t#FTs`7?{qQjBCHZf>?zWukp4I??`)ffbW2{l?xjF5@mZytLs*N2_uKW@&Re(V
znMbqUs~MU)fR2}4<%DiG>>{&JpBPtL6EPjz>kjgFPPth|>2`KZYjkR;%loxhK}&-+
zuQXyg;U80!!0ET<BFp11BS%w4I7o;i6oxS@c8t7*j!CIUvoBun>}Y*ajGIA=mF-D2
zMxr79m)i%oDkpW?b%$E=g2f1$Lhn>P<VD|foEfb<MU3g(OwxQS)?;Hx{);(R9uVZ(
z%GRgg!RE%-VYHM{icA_NxfVBiug{%UjJdS&Vx_vMpNnlGdEzdpLN+3m!)%+;uq)SG
z&0THsLT5j6=2?jl$T3T~mtSwm>B^G9;s@SfCb!wy$7o2E#%j^KsuXnL>zZZPb`F|g
z&ftq~wQG+CkM~I)ycTO8<Y?tZcRnm~v2jqaW<M0>L3&U7M0Y+b8uEG_z6^d7zHokt
zRHD$ug_dJVImn)5YRbZtoeYEP=VihrD<_Q>#3ldhX|K$Lf=~@Vcbrt}K6vu4BrpTB
KYt?$L5C0DZ=dHN_

diff --git a/reference/Rplot006.png b/reference/Rplot006.png
index bca5c2ad00fe187c237e56c455aa71e9896f5f9b..f67fd95d62d37ca8df317e711fdad0316f8c9a00 100644
GIT binary patch
literal 12528
zcmd6O2UL?=lx{*Op-EMwi!_m{2&gm#0Yg<<=tZQ1L_unRL?b9tlqy}iASLupa`6Hx
zB_O>AxfH2EdQ0BVoq6lc%$u3@=FPmqO7dr&B&U3P@9*qy|A#jZ40V_pco;w+5Hsxl
zJ!23EOgwu+X@Qo|p|&L8Li<Qh=N@nc{th*p!+{Gu;=ZLX2*k*K_5}Z!4m$yXE`eb8
z?wST>tWSqzWNc4Uwno<mbT7Zhyu*BQdfz!$s9x|>f57<aeG}s-4>Jj~4ue<C0mUWe
zpA+}|mox7_tmiD#%*g%Zn`c;WLIcByu*1BnzHOY45A1w5YZGp*yZD!nPmgYIedy}5
z8C94)v>EN|IX<$BY`haM0{hQC^1t7LxPb+tn46V&{QbozS5~&0^lFENlWYxs(A;0C
z;`yl7<osA5V6S?5c<FhH$7h|Z%}x1??@Y}@#BEXeKN|dtXP)ReT$fXCvZeD%SLg-f
z{Tw9ZxEd~8%u?vp#2DnN)AYS93}Ly+co95BEk$bw?p*lf<kRjl9EZMzH^T>Keu%Ro
zB8i#~7f7cn1VZhvx<u!9WrCzfLnF`clziQpjVWvu_dFov&5dfuRm2F?gCtRKv>;@a
z(_OH=${TF>nz>ny$3JAGra)@5T#5^3OwR+g|E-5cW6t-@;XCjsw=_X2cL$z+s27i_
z>v`<c(lVUZU>Qh}D&k(;AYKO}yKmt1<tO3P?#9~0r$Qq(4JXSXLy0i%+NU9=ONy1F
zN7i0Vba{?Tevh~xtNTb350{@x%rRo`mS&*`u(v<n;;D0rT^a&+Y0cYz3(j0S1O=+{
z_&@R*>lnbQcSR&swdmQ(#OKS5eIRK~F?n^aSR;=H1$ZVqBJ6pYkDa_M#84DgUPW~t
z{~#|5y&G$K1M^{(qZirIH<I1Pv_yM!T4MMgec8+n1aVo@juV=B0#*ERiKN~@so<G`
zfq8;foVrxMA#&!Yr1UGQ1Kvu<aj?zGZ<*(f_oq9RGJ~rd@y|5kHkdyqv9jxMP&vLG
ztJ&<_Q0F*R712qr=-iO)yC%hdS{6g@w0F9&o^CInUQ8()(`Ti+49_%5B27K&zBKs_
zLp)(5c~2}Aai-i0H;N-?vGQiQyXt9F9ND%%v(Rt8E81q5lcsAslW8ksJkE_@={0BH
z`(>*$#s$SnBIFU5_;GH7K^v}~-A{1}ic-hj%H%+(<DjeN4HX(b8)EG<iQ2yCvypO_
zH^KOk%+htW_Unh?_(lAmZU=uYZ{BGyT;zbx&D{T$!^>q~I8GOz+|;7nY?EIlj~bqA
zrOCK=@LLet^Ex6t77LSi${RcFahYlUPCD}WbHit_Lvbc*T9CULXCj`J?Zf@}n42W5
z!9sR-Fcn|10iVb;g~NwB=kdn_SZQ}kUt=Ves`9S*1fg`fl%H2o%rmV|YO_y@t9Xsc
zVb|r9UI)S=x)Y`*w1d3l^~6TwX4D1ZEd5eu>?OX7L)t-M)Q@08iuk0O{R3LCz35I;
zl%?)+F*rkSdDq);!{D#+DCw78-k8&pQT7)bf_N;uo%p=cz-(DAF6Gb8)(g!zRY$e3
zsW)|-Z^;6>2OAT#H?Mf68K(rero1)vr^N<W7hp^t%FwA@;-j(FJh<GKA>+#?^i}b7
zVK%)!jbe@y!PnsA1J=@)O)ecf>w8^DDod&|#z$uzDdaJ4(JrCZkyyo4Re<&Z2e7|b
z4bq)8J)v4xoiU%#<Y-53YC(6qpmbK#4<0qMzStFWP--VnL+I(EoF$!u0-PJ~|IIeV
zgDO*YCFfqnx|K%|+-pb0&q%11K<{_*O1SKay+ewO+CJzudBR-6n=8Z+zOHw3_$vHZ
z6GcBwN>kP1FF1`#EBT;v_p?0DuLF~4)>xkMe4e9DF+zen<$TcvVvV6nKuEi+>hq3P
zOkS4qpnv6(-|!wLFVnX+oEE`5r#c@0T#wB#Gb;P%(4DUBrcvROq2(^Tu%F88;SJ)^
z*gWT)_%(GAzShiC#pCDJ%MHfwF5Bck5+cEGlQ;J5FmLava1W>XOd!9&*<v>YqVv^q
z-jnq1K9x0Vh)7sIaN=wo=5&|_)?nTqR6d=fJlVIW^DKbf_#r>*OhS0iwO+Z~SudyV
z9RJdKj`58Th@P;gyD2uZH;A9cWEW!)ZCkq!c~7BBZ@O%?Zf4({yyCFGtgu&b%J+-5
zouOj_liinL?f?u9R%1p=cdeF0V(ChsaqAzmz`bB0Mvvt_@3VX4&A~a^IA<Z9ofEfX
z5IHY(pzu#HFpg?QEQ3z@O+W7&%44FUWySl7vcSyQp5SRGcgiWz7HXfE+P!%ChV+U%
zTn!)Iu_!)!vXs{LArlI>{O-@5vou7t25X=i)O;9&iz^(DAvvq2keoQVCqd!jQgH{O
z97Sy9ay4>kgq{l##*=Lddl_%@CKjoVmp-Y()16RDsGu7AKdIU)OlqBLS>`hSqQt1i
zQdHa|{4bpdX1&cizP4`dUw1i!a-!^z?KZ}s;ow^SiF1=*^XBX=ICC{hC+{icQbq{V
zoQ^5^Eook`g2Vj6Os~9mo8qMw7TrVXr?A#LIRYeyc9$m3IUSab992}`TLTK+1Z34r
zBQ96n(ZpZcv5XAI*nPj!zSwmQ(@i_V)>gw3<#DLI?pOywSoo<|7hi*0zof^BJ?G<x
zAh$oQck*p#UfxMIjP7;qG^jlMrbKBhlzr^o{;(@G>CJxb90bklM6i#<K6-zsAf9`a
z!uBHnLW6o51&zsiL~%Qi9w2cFp|m;7zxr%Gp4=@Blb-3R*>`2`9$s{4r#OQgUoHLm
z6sz4%kx9xs{R8un&|-+RboPJ4xdIMP#a>S77R9?ltpx?5nP+229GP6KGCmgrL|-Y<
zjIxG(lk!dNoJI*hn)fMat<O|F1461=@?o4ai^YQf6@&zTh|ku{-4nlLYpDIQ9(^nJ
zIektLhoj)ARJ&S5FZ38^&bF7@dS^LFzvz_FJ#<1=<Xup!jrZN<^Wmwn@SohwHXOC-
z19!$aTQG=)oB^#Rwfz%{_&WHh{>YB_y)W~};!0wrHIHRP-i`l7zUEl4K-;cGb1t_v
zGTXA87a)glde!Ql+87p*(vQgd4b-zYHC~?+@)|+DXT2P!vz|DyhadChp-*>O;z+ji
zuaJYM+h1BE*s@DU_B1fl!wY!WJ=LAq*43ZW&xIyhnx}T_mghQID6p1?QRgguSPi$-
zYZ#jA#!zNiCjuZU+GD?;U31hb6YARbEf$ih791dl9lwsnp<CG;XM7FPE12EvwO4YO
z9Fhi;F}?n>i8oK@FtY2lC|UNl4|b5)%PmSetQt$+?Jfi!4Ws=*%*<rlz=2i!;=EGz
z1Y!6BCOhrq`rOimW34ZCVmndu@X_(}fgk#PkEJ?T$lsMH6vL1^Cjlc3vszzp7kL$L
z2Tyys6U859a%tDP3bM7)knZ^5lChTDAL+;9`kVV~_}yrPz82~XdTA}AT?%Cj)(?Ee
z*v<15MWkkrtFp2`_gb|xXh&gTrweXs^{+R&CJAm3vQ`LmapI);9pd94@zZ(*tNG7M
zYvHPI5DNL6j;VY!Yx>KXN*q*AndV$|>&3iQdi9%y8tW4uckft*>q1YXmC*BfirQet
z+_}cW-cediQVQ;JvC-F=sg%Zplku9()!q{qQZMHWwekn7;2K+d8?M!9FhdjCElzL$
zKw+T9mtM8w8z$?HOTzY{-xT3!Y3y{Z?&mJk>xtf*5+$P5EDh>&-4tOBf;13EZA?Ef
zcJ{7IxOaN=sy#iPk7ZW(N0+xIF{o9eF=TTTR#b>40|tfW9`^Fd4P_%721-7~Kyv4b
zIttZe_84UoC;U%tS#u=M&e9iseQa-b1)hEil>~J|Rkx1ZCgkaLu69kvS;ry%d|JN$
zSd6cWHTZQeW}f+H>j*PSIrnKC$DYCL{VqMZbC=h|%SFB3Tj(EzlANkNzSYwQ>zjM;
zcXsmMu`nmFnd|;GNcRbJm3=H!%;k~qgipMx{6W_&M(3`*4U!$U^3|Z<%0v>Hu610s
zognk?rp)<2IGdaFD&h@`SPE+1=IF&AMI08JdT6uD+Q5<Vb_Ew87V$$emHMPdOz|S3
zcu=y41eg{p0;88hiNbVIU@4d})zQSm{uLp+koDTa$tTU;rsYqCwWBR=KmGaV{?9Ki
zJs&DJXt->!n_pPek&^g7=mbwX5)5zuD%NCC-Fc}yWZkonV&B%$r@W~`@d-bk#7+my
zo}L0Pp$`MvX3~Ay#&=?Tkn?X-7ap!#Sy>sU14@A=LCm0O4oxR8hFlpcW)ZP9fopY>
z^(tETCPoT+2;H){sm)mBjj=1E4G;6QrBme`+t+&LJ;Jj!7iL#gQ^BPresiz5)|!Z|
zU2Sda$uHr~amUjNzG>oy83*fYb7f^^lz`iJt*x!EW*WkKgElzC4Ur*lrRy3V6Y=P6
zg4*$#Ueoce;-pvgVitQFh?$xB*Dc>geX5&Ve{#5r+7i-UC!b2Ec6BT+O>Glq=eGi8
z1ZI8a**>?9s<$mF>51!qKzJZ66mW!`-<!KxJqG<HRyqPTPv_$Z7YAjJM|{?s>ek$K
zqAymS#3;>-)fE&Jd`TJj^9@Hpwo1@%D73ZIKxulXbiHnJBfYk^R>}3r^XJdQK@D4-
zygHqcke-ZN)f6xtAL0srsMtiQw5+VPv9?x0!FRETocb$*iTn2;x4cWKwc7+ehZx_^
zfR12h&^OVPss8?cxq{l-=e4rET>>Fq4kx=yglu~j#8;t6zM-+PgX6g`kw(GGIgukV
z>f&}fiz8E0HrtEoK8ov&OIh;_wbSE4Uzu>gtQ6012%u?LtLm=%aKrsaUFCvmojopW
zl!^QLUqvQ``rHEH0{G>DJlrtQMS^Erm1p0P`l5HhRKwF_TjW-UwQHg3-k8(=@+^+x
z<KgM)({|R?*ZSyZ!z4U}g{k~(NjW)loo8->tlYm-9@O2VlXLh+ujogyP{`WE&EK9n
zd);lrL!$v76lafBth7}ZTg&kv^9t;^OS(dwfa+0WVPT=f8G5t61l2B(4KS?H=}#`)
zm>@9)ce1cJ-x<^Fv)Gf0^Y-@U$9)q^SDF0r<M*)HXLEiC)@!`#D8WeWFluCEWQ*6k
zW}ruZ@|RyzAprApz_Vg`uRZ$nZR#8l5~F}KmCc_yR+sgYM(1sf!9`wydgS5n2?&b{
z2?zLCmrwn){~%b*C`;wh-r6{?lm(WUzgphBF$!2=%|n%<&wtiM{e;KoF8ME$6B8Z)
z)JZ|FCk&k1Oy^rGls6ij#;ko>6_7J6K}9OtJ@Q-`AsdYx^Mtoq*4A!qKNYw<>wz9z
z+A;JY-c{ekwh8K#6ZoZ4J{8Kz_GHWH-egg2U0tmDYH76{;S6Ie;NenUz_QiD$tUx(
zVaMyeDKfTJ*59wcAy^~Tp7j#azV53<X6@R+9YCK)8z|&siukr@GjKJc9SfX~f!Ea5
z7A*Fr!^2N^M+Fh&!=)}9>kk0zUp+4^t6j?E{g(TkK?({AzXB(VGW{nEU9*A~YXE~3
zq&fnq;0J(@Ou|oB?ZcNO?GL~6zABWyp)xN>Ip})#URXy4bicsZ8)T%i^(Uz!blo#<
zXLA;}OMJ;fMjZZLfK+9tn0ej~LAKR3UuSuxpRE=;-z6A+(tr0Q?R3rIV!FM$=SV5P
zd8x%iU&`rz9AW39UC=^OWkDaj(qAxiXW(U4;LkUik>~i2U@jApf?<Jq^Kt6rwj9mK
za;duL-@}N#aaSKD1QDMe$s$Phr;tyrM<1v9McQooE?aIsrvU^@L0VcGg)vp0pt7BH
zMtf6V-wV5T;U3JVrKfuq8l=~C$uzHBmkpW9xaF&e{P{*)EumG`Cp&a6bz)@1e+P(e
zzl~o-*=pteHHQ&h2C3Jb_nl&IEhZgcGXrLVHh`!O@OF2HsRF_9l9z`kP0yODV{&6n
zg>(Cm5Ke9dVrRZi>+J=8z*%mqWf}^OkByDh0Y=sDrS2TD_BnNP1#oT7KJ0N7IJ1y!
zGr%1C;Y*nTJo1AxeuD-f&cWrc{`x%kLr*rd@$t9#ExlwZ?wuhMqq1tT?)>JC*`<Ep
znN<cAsGgbo*LOmC<n5JXHS&UiPg#$^XmUBFLEbxb_p^Pb?{|r!HNVZ)eY+1nX)jsS
z4zK_n_udAkz2q7fvXBJ#r!1*cf;9F>pWukcxUkbj``}*%DO_oav!v)Z9O4_~DPKaU
z<Jwr|@!hcufMr4yTMvG|#iar?_#^D}Xd1iGwA$B3K3c7~^0e-4oodkUztV5Zvph7~
z`|5YwHo>sSL}?~)KHz$~LpbTBV6^(ta^8|*x}x_S*6~ZZ$CD=lHy(d|7jBTPcG}e#
zoP9>~6+?&LM}c0;n?KdnDTV@=kT*WQJa?Pi!DZh9{5&oz<aoUvrwVwmr+K-}I3-?B
zrqKa#r0jIp#!~Yy`LKk#*5%x{<kq^nI_F4!|3G4O_UT%eyldZ@y;thv<kct5>m$h3
zvU<Z`f8rvAR?k_gxUQ_fRnPF>_n>oB7#<E-f&R=Q*}<FDYJF7z<Hd40E&DF^Wlmy8
z?LtCVhvD|8&Od7Zk_YzJ$`t#sLyj$jq7VRMHUM9u_wbY=-VqoUpD1Cn;p5?9wN7kk
za{&<4>HTnRJ`UCzW)xTf&jB|1l^hYfF@O>MyZ8f`c$J<^8TmBKMflDyndr90Z0^d)
zqa^<T_IqcT6iOAU7oSgk5&zFqfMw4mFzzLtBP@R6`APuI--S72SF^z&{1=ok+7_LI
z*C0M6nmZW2cn*s<^&fu5I<ka<qiWHsC|{H(Dws&);PtEk7C#iy3iZlxor=JFo-fsk
z&sTF&>wo8JBP~!M23v71SxC$P;{%DXkEw`nRh(Egk_qveL-3<v;D>s{RO%u-euMa^
z<xOKEvcy&EZhU^#r^39C^0XjNHjOa@LVUhmF%KlrfR8F8%XJDwq$QR_nF;S+k6C4I
zjH=tQTkw!&{*_F_h&!iZTdrt!y28WD@@ai`GwNAg>gC;{1;z^*_I--Y)<%aC-}6xF
z$#T<~c-x@P7~u?isp8KQKSDNlCMDG}b(37DL-#WG>rPf9zU9fGjuY$B(#jN-_m;~>
zk56Qr-7B28x>`tIU?Ir`l?)}x+o-pPnr{XR&~+;Hetroxw8q7piNBIdetGlGnU<Iu
zaL?*ETM3occ>Drq>(4>TRwT_{i@=f|NUIVh(c0|{cgxd(0)(!@t4RW5baUw=v-|O;
z;Nc$-8UM$>pPnz)h|gyzP6m<f-ftyH8ov^Ooi~;~%hj?Uf%M`NK|oUXKb!NPWOPgp
zuy}Td|Lj+v!?;+h&mn+nm>g%IV1wX+B=+hbO(L+X&A_UBAT2a&ASs$Xh#GY)t(`EY
z8d#5Z?G7rO4w=l_4#H8F)10GigW7<Rc^LmAu<-BbN!I%nmZ?`A?)ym=S>mIv*GoLF
zN8_>A>u*K&-zrXY#2j8Jsz-Rb&<(TW^XTpCxP4KO+~aU?;R7qsCKX?j>r^4$#8&#j
zD`roRjJiQ0*?MF&f8zVV%%on5(&=_yK~Zfx^MadENn(W1s$K1#ws6}6oIkD#W6=Gx
zgSmIJx3%1P>t}WMfStq&UAeqdf1P!TG%BSxxM(Y3+_wun*gfpx^FSJWqC>Qpz<I?{
zZ^UjWfn^iN`XB?+DZ=!}$-~S%#>JhV{bwGvf_H13-F;Rj1xeFI_7%ytBF5i5R#tcv
zPmeG{o<1s7QtQ%TTh4aPzw4Qu-AnH&Ff%Jyhf<WCS37X>yL+OWQ@_>2vZ_t>K-n}Z
zP#X@_FuIGp)3b~hQ|>*C7Xgaa^h7OuEIt?m`Lfv2Ih}N03uw&)TBBLc6hXc&BE99G
zXYv0vHvgB};NR(Wm=oU&5{KmFc12RvQ_X@`;;-t)=WhT2BKZ)i{elgU12GaaQ3-EB
zZ23z!L8SPkq`)=k9Ldsw4WWQdWbN!mb)l8eFwq+M3pb?*4bteP(S{#c#ZSM~@`Rh&
zr&~4KFqgY|+R9Y~Ex0du0A@Y9`!dXK@<DxA<Ah#?RV}ikU8{(ycfrFXRI`?vB<BG0
zqa-YE{$BQBl*|9q-|ls7f;a9{S(_V?)?hc1f-LIQvyIgXJ>7yYy#$wL71dSFd1jOU
z2pE-whxpiq%)dC#$aM<+0MN*=5@48Fstlm|A1%CNilf$t?9pvVS7vJcwYl&F%w$`A
zJ)|7)0Ld<<6?olVnun)(QrWfQ7alw-@LkH#r=J|jpW5sPzgH;~sufu6fwSq60)dQU
z(6RFkAV5>|9)z9orfC=u@Z=AOA>XN3{6{w%>ki3ykz!smJ<wA1m0wgMu$6PeRrFQ>
zp?3VMy6xYw&40j*|Mpi=fV<73nG<9HHqr&ysEs!8{DCsb*`e%l_j{4z5MZJZCa@u-
z6Rd}xg(y=Mi@XKquRJ7bd#C3r5#~UJU`NOaVRWccsMIBMVl+|mzoV^L8bW%z6Ghy6
z%}hXD(vT-_o(Q9zbdfXJdDb#IRGOnLH(>E2HVWP^`j%9Q;xH~DnDH%OASE$d6u_UR
z(%=lkCJ-$CYOxg+K!X2Ak$EnvUwuvvbiX5_byRiXe*BOUCj)U8{cGTq0FnhyQW=4o
z{e5lYMRq-adl*(kKjPhwG#8oI=%S17{6p&=c#}?rZ`voOF!S8W$8=8sol!|n{O%oV
zKZ>OT6l;FNCSIfrt&#q5^X)Va8kmKZ2sW-a7l9cIKDdGsjG4y8AwVNm_?9Q1glW8}
zTR5f#>G-%}j`<PF?=~VvlSCxgUMb$S&)5yTHqFrnnx%`-+Q-pkP@h~pU?F1MKAbUf
z<u=O1v~Vd%1`*^(;dAhGaNjwQBLZP5T2p-<tC#gOo5rQ$Zp_MQ8#R-RQ;0WS8erx+
zi85xsyIr#`7AAsJ;Sx?ESMOGWo^;E%WKaU3OBdrryk0z^HSg@KWmgPMxvWLD3vKi?
zmz773P2h0Eq4g0Upm6C&6J3Os1|l~x=~=A=*t)EGHyO@6=^95~@E~QTQ+CKvG>l48
z@Q|wpm7wyx>#D>-bTg`g!_c=W8GCrkV42O{{E>2ncD+q9SabA~?ykq_NV#>`nqzuW
zSt}idO#ba^XY-(LFF(3ghsj>eYqQdsk+@IxE3$THe;|djmNceOf$(XL^<*3-l{wL?
z2OSDx?j!>ixWb92SXN$rk&1tU*^W+!9{1WvCW!3n0!D1tJ^$WWlz$(r7eBPhIZ55B
zhJeaNAy7lM_^AyUfZb-w`-AYxR23Y3gwSLJXs{c<boD_BfU^})1|G&6VpwS#ARnks
zxW~%VR{?XwC5Iq07Xl?c$CUBO)9_dkSg2+Wh%8SUE_Kwo_eB);gXc;O{m~>|7;gkr
ztNEeoEQf~-L!vO_3iB%{F7PDe3;igS9jNDz&+M@Hnob7q7i#IOb=7FDTwFwj2+X-Z
zwVMr?*L4X_Jn=jRpo|N{&u(eJ;*SzN8u@a7;dE<lVjlp3QqZqP)V2TnZQcXWER%i@
zkN*w1rsMBGB>Mn!2)>H-9KlY~<D#5_WU+>pVHc%Sb)-gAbeP|e)QQh;%Xt8zfDbP4
z8{N8%?w3y%fq8!H7hB#&g`j=WMQDC>J^Hj6Ck2ZSuGauhF-6lTf=1}ns29z1uK;b$
zkCwoY7Qt!2wNn$>@v9)na)aza>xRTX_FTcDAGZ-s8%RHPA@Iepkr8lrCKCvO3K~N?
z0ctDg5_#kAsM173psgFnD3+_Fw0IFS{hYyfdk}%xje6}tL%Ohj=I1Tk&FVaXTw^q2
z+#hJXMwu%lV8)MtrL4N+_~xqdtPbusTBa1ErYYQ5S(3%hfi}^Y$T&@E0utj~02Qw0
za5j>PqsdPGlQO89(HJ3PO9iX*hM-D)Q<%`Oi)tSISpG!buPlN-rg-R8@yV$DM&ga3
zce=ATj}ww9S9C(me$Fa-c{DU8n~uxGr5YAc)PN7xW|dR&qX4(FkNzJKUE=K8UbqqW
z>*BrV_*K!mt)=DU?)R9`ZnL5bb*CMMK;3Mg_T~2<GwFJ1w|@G_uG=JcmUO-5s2@Z8
z<!&A0brt^7X(PDM#sFqqw7@_#jdf&wu{H0891j+HzlxgAJ07g+42?m^{mlX8AiKL;
zr!9vXO`i=V<Th~VB<C<yAVW+#UblV=0<mJw&H_lHA|I=guke^BpC;Hk9O%XeXWD>U
zL{^{jVjHMx0rqp{yv=eExTiUx%Xt}QY#;xbPUYs2H47hD#klbq;CZ~yXh_W%k}drS
z%|-ixD4+%Mj0S;=f&&yTXyPRZR2^4n|Lxu8zb2`@Q%gr`0q*tx<398k!$-v~(k}Wl
z%ADAIzU+Q{{u5veK{AoxL?g>DMqmLd^Q5u9OAZMt^NL`rrU$(EA{5#d`9#K@sMQ|z
z%2Y<e;YK6erP+g<6wEy-Sx7%t(&HTl>XGnG|H1P)88mb8fEz21V8dSKnPHe3dE?&+
zid^EZry(KXQM?mlq_5yFquwjueSL#|eu-P1v6^U6C;2r_B%+LBF4pZuhx+r{tuS~#
z3{WG<2uj;k+BHr{{xP@eReLyWAgoh*Ma`XlV&cGUQJpeeg&1({{*h&>4J60epjk2M
zWhSByUe(skRokz7;l6_#FyI6DVDN%rdcd#7#*Ez)YsQ)2yjy1Fd-?Z0imqwGZG5+6
zzI-1h&QiAK*5WRth%@RXT7_q;9(iLovmoCGEUfhy30c8=YtL##ldNm`F}qY)Nvwh#
z|0kn5K3IH{4v^f>ThY@w8wgQX0+eT{K#S;9QDHvQab}ZZZUE^}|KAZN|Ed!`FaWDC
zjqzT*s5--TlVwc4_yyo^G5}i55FaXgIy<U!v`;{bU>t2*ys0CQOh*DoLSCOxUu14E
zSf<CZc-?^#mC@EH^`!p38T|X8PH$S3*1}i1whCTu^C@W~CF*S%-eEtc{O9Qy)f)3M
zymd>Wf&AzizI2(a@{#%A(q3Q+`(zNqhXmIt^c_z)W8#RZlhY}etcv%stWW(ev8rbO
z_v+i5ORfYNS|*2Lx)MO%2|C=AQw~Pneiyqj3Xx42>oX<}tIT(TG@GqHnx4yx*mwGD
zRA!P~mmFFJ2#Wi<+O=yCHQocOwXKRr`*GB5@bR<2vjft|Fu{f!ga&I6d{-2Zm4Kxf
zuOWeVhDK083I_B4zp+b+1S%cHkGgxSbKP>>`0tsr<E<fY(Vx-d`2DKRS@_l&O`N%O
z3RN?t8T1l;4WD%9>bKl8)1ArBagKv$e&I9-6$Cdz8euFxaqGV`akE7>u>F|<YVD%q
z>}LD&Q=S7gk`s~kW8I(rWWMQVx7aUE?6Ql(u4a3TQ->;k-rT3T$r6~bQfzWoVMule
z0e?tzozl8PVHg3}tR9GES_?CWsj4V5fUamvO#tyc<oYiwCjaFU`(O3szlA$0iyf~c
zdY1Gq44F_Ju)w%30h#bHkX!%t_+uUZ;D~*4{KA+jp&=aN^1Kk(V}T=3JE#h39(qaY
z#wP_<=cK;-TGQ4fS3CwjDz^&8gVbf^HmcYs1Ld0*e$X<lo1Q7k1VG8#n>{}H|E1*c
zFY#E3m$~F}5g~F`g0O(9oSS0kfnjA}@!!u@lnt>1kI<Gw`oQJj6*L#B@>`M^%=pku
zkLBRDLkKambdCj_fYw1tOWG!iz@`hVr~)CxQ<ZtO*PSUpS&7jq^CL9mv2RTvi>i{{
zdE1Skjn;A%rIc6FlDz1G)Go=vUqQ1Wdoe74ytDc~K>qI6<ZmWf?ex<8WInGzVId<j
zNYd)Im#SsblaPSyU-FTo`EHUr$9nMEG8(BUW-DZ`r*HD~Nd?P=#Jo_H*{`~D&59So
zY91Z#*|$`CbuWK;)y<1Gx*nHOVWF_U>Z+<(QCaEld~3qT-M!3yJU*77xh-te_gP|F
z#V^pPu3&N|bg$W!1@vRVEqqwK*Ax8K>E(r1<=wqk)kUYyLANHv4X(C&3^#Axl>|mv
z88-=kQ0H9pY~ZIhk+R}#+xTp+TmA6xxVWjwY{5;v^_jEg>i3a@{rMk<MrsLmC*LW_
z-U8;*)^#_?4c@~gELp0%be>6Nrv=?3RBC-Hh6RI+l;`t9EUpHvp<0%9A8X6%q*p1W
zKWv#!vao_Tw>z?^M(DyIHfaz3);_g(7JAS7?haZz0TPI?8+Je$PdLdBe2WP*av4MF
z(ke{)Upoi5k{ZTPbFbdSH`?dhQ|01E!%2nxDooU2dJR0ov*>{&>9WUp=w|5i85%DR
zUiezLDKo<`%iPh}D2I#%<s#Nq^sVH>%Akf9vDq&@THJywgO=&Dmo&EfIn)RpGpnj|
z+ocT3=WmvMy-|zT!N1;+Ez*0|`y~NSkC{*cFG5~kwKjiPi;qJK-C@ScV;L-zJ)4Zl
zI<PAR<V}b$eF`(b*1<C#yYcIcSDgjQsr`$Q?xK>rp`6)m6F~oqIpWAHz0eBy>XIe>
z%~wBtA~MmgS0CuXoe5tY+9T`56xY~^4Z0VGwd7HmRS`+Q)k`ikuEZUyV#8l~{3=-s
zYKSLsFT)s41xrr1Gv230W*r6f?b~g_vX^o_qKtACcD&np+ls!ni2tZ`EW?-65B=a~
z#_!>+VO&47I#%qnNw0QHJFc#@Ah+e1Ef}U)qKo|=W}2>1&Q`w_wEQM}>8wtD{z{$V
z$i2B<%i&9-DlH7iB<w9WO$Q-_&a2gxXimqN!&1Ka;C3G0oFgMKq+=mkQ_|8@{CpZ6
z^e&dMU;p4mnf5=d)VEjD=ujo)x7{WHar`)B!;TIJKcje={DeJF>?P#54U5gW9)p_P
z_AshaP*}2`kge$_vM1`chMVpE-6wt0d+Kh&d>}ss(ktEb!?1ta>3-sV@c2di^lBu&
zB0(mXaS<HJac<&(3VRFTlY4$4fNc)?-DJ5TwX6uR%ngzdiIGTT7#`-%JQS>9n}H7(
zCI)@lBlxxjEd$tjgjb{_fu}+vAn9O&nC*QoKI$Fe7WFpJ8qHLun^t3{rbXxMN7o3S
zoTF8g6Y6Mr>j=+{dFfdKs|>n{#u{Cx@uEXAHWbCvB!5iYe^?uy#n`I}yoP)au{Ha7
zrTx(D*YXIJ{@1icsrJZRtMh4*ju<71F%bt{Vlz({)0i&vQ&IQLc0)=~@<h&m7Icsz
z@i`{^WFmfL7`4!s*v5*>C15xA!yLN4CtH|GY=eeRgJcw00AICPYqeX?cKzQwKwHcc
z4LP?K;V%x+Y|62Pl>}$g6+-mTU8p>?Y3}*ri6N>A?eK2z8>GP}Zt(aLD-MJVR#C{0
z4iX266the$=~KfWP7nDre1Mb=8?5UY$6XURh#Q+n;{~Cre-)uKo#yS{bh>&$PT*@>
z>vtHjOb7CErTEnis0vFDBupd3=ko*JC9U}?pQVl!<?9~%!bHA_ko^h}P~ug<>*3E^
zZtq!`$JA@D|5@~{rjnkdvG%lbuljt+c?ODL8mb%3f=<OyAe?q;bf6TMVZ?QWaqL7n
zt^c=&J`&wUV>duJD*x`SCScRvfjptr?u<~S<I{ouX+UqE#d~kwuE+`G!#I_gH_hW<
z)Xw!x%@q=Z$?CtMQSo_5(F5hTqqOmy_@o`V3)#4gv2n2xyVRw#ZkM_{CGKv2VO2XW
z#l=7#g*7Je$ld5_K(4z=no4q#)m5Mw^fy@B4;o7Ae5JE#9j-kPR5b)|I`?XJ>fvAW
z?Iu?tqYNO3%cOO_3Q1V&%=tT#b9zU??I|}gvIjwygJjkN7eCw(s4tNAqG^8eZ3hOq
z5)SWtXszpJbC_z0gp()n{a6DSgAZpOOBse7NaEviyNW=OmBDyq1eX5!AYX;;r&F(2
z9@0DE7J?s)=1%txlL_bXGXtjs_-GY^ea}jF%g};}7Y}e`Yw*5peCwX{F6XQ;_3Uko
z?27%pFOjT@Z#r9{$g6vS;PrGx`>$#K_U$RbzBkCTxuai~Y#p5a(t(qxRlPCO*_zOv
zhHA74`qizhW4XDHHQGdLOqNR>9ewweS+OCF^%N`59LkqqOj5ib{krS1G(y{tl8$!{
zDh!E3WZbEXlxRchrn;~?ifXJ}hghgu@>o!vFdf+3@Djq>j`(HM&b(2z%f{ZS29F6T
zBKb9Z4xsH~!?YT&Iai9kISuyDIfg%mPkdFBS|xW|Za=$(u+Lg5rz*d^7LuuN0siWL
zGY78phBe}8$AoO{hz2F{5SU0gUnBWyPSL4Ed)w^sw*J1-?TzHGSDX(MulHUww+~9l
zxAT^Bq&#*}cL;C4YdZT@pa`m3wl#1X19f~iE@<J#vGc&;!n%&drUCTOCOw%BLXk}?
z7ihp>1*?Y?fl>^aBWHj3{!NgZhS!uH2J`~KYOyD_jK(}L(Al23w-JhGHCGde9)3{z
zw~h9~4|qxZ?dH+J#Lt%AOV(<jE{M;-7O~Omm{l>zb=3O%ZwW<ajI^M^eNVgqHaP7S
zUL2o?XV5V6zP+CQaS*7ZzjJG9&O5Hwa|=;(OjuOL(_!-b1$*V!b@0Gp(?D0PisMMb
zy7z<|L_v(ff-Kv)>)nCL?Q{`r!-<zM_k;*K6~3n8ld=UPk0_B4sH8e$-V$!?&FBe7
z4juA2+FgJ)R%k!K1_-DA8<U`VHven)<AdL<2?Q(Xm5ls>d086u{8DUgNEp2*Lt|V^
zvdFFjE#m1C*j1F&WNZm!rYMo5PZ5Zf@jN`4e)RHxgr)Ks4k#T#DNkOWi!nR<#|4;{
L;k|MVhZp}1V(7+H

literal 12462
zcmc(G2UL^Wwr&a#ib#`=fC?fYpwbZtf{K8Ebflvqy-2S~a7Wk{K&b)(B27Bddm<{R
zNRyJ#1Cbg^kP;w3-p@Y!p8Lid_uX;tIQNamNcfYL#aeUC^389~KRmr-q|3@IzzhO`
zSoLmQHwA&fgySb8127Ub)Sd!d815P9UI(tg&z?>T61Xt=-MSk90<mx&KfymI5eFcU
zC`j-6HS^FntCL}Ggo3hY8&-W{@=c;*@?tO1I!!!`MW5#`&2X~cVHelZ`uZ4kf#m;`
z@#5Dr(y@P5oxS*0D1#lm#{r&Wv}*jNbP@Nsib+>nl&Q%n@!OBB^#tE-Bf~-5?eOOC
zhYo#Rc_L=^7Kf|G!-13*Oi*C7m|o(){74Cq>OY~*DHwuOSbJeqKRnzOl;%;YE7g*b
z&+>A}MNx~knEI+|dXy4uC4#drDHkqY*%!A*<yViLTY`wCbo!<(uPV_Of>_>-Z(~#M
zZmvLofKs8GC=$x#?xwmnUMyVFFIZukie19G5qO-K$ZD4^>z<BaS1OgIpNCjMJwxuP
zdPE1H5~5YH3BT1fafRciBiHIbVx>)NanRzkn>1U6^{Bf<B!3)6wi#(3h~c+pY6aWT
zmEFycVkFh`t0*b0wYp7Y-Q7&BZf~h}5YrQXe=hQ?94v;j-7g*@OE>c8{2tRwhC=8z
zmN&1f7DVbA3qPCTD_$ks&pv4k&0*J^!0-w3B3C2_8cFXm+O-(tjPru^AGr*5W|?H!
ze$FO6#YeK~5_?xZt9?aGfCvSP2+0|iDx<RO+<``fmYISNRVZNzCUn{msX+>>PJ<H4
z-6kG=c*J{?cqAlf^?hG9sO?r_vr|+D>=9QLN6z%DJbI@ee_rXR*=<7PKJg0pfQoOV
zA872pPt3@BH2voqTf9csB0jR=22u8cv+At37q1|a?F+OKe=L9^*6hQKX@@fL+v20x
z8<db|Ye>7dS-aI=Ftw+c3G1DmE-=4hQp?cx*&sp3z~!EKur#AaGZIveeyQ?;oiX$C
ze3YEhLF5(%CFI@CA?T2J-%f4cR`4(m-X%1qjCrVJhAhTwvqR2WW%Z?!@%Kog<4kD6
z0Sg&AmR59%R~Km<e<Um@nB(qhrd_$O(GhN~-BPC?nXRpu(%gQv#Ua019yOd<es*QB
zCA&(F-N}_;>Ex(}=r7zn;p*9ac|t%i?QrHaW?Bosb(c5J7)<32t+3NCm(K%ESFf_)
z!d4X~;TeIdGCJbN^Su^|%i%mct&bmyym)4UB&iWdoFyqxwSJo7LR@jr&4U@L!dpU6
zL?)3Vo#^y|-7LmT1^JQZtgcF%Q)V#>G+nRI=d$%1MqZ(}y+UJ+)HSEozV5IPiqOIb
z0a%8t_W|O5Itr1-L!D;W*nvhvciNAp1Ty`-ETh_-y?62#lzd2KVQMw)S8M)07V8-y
z)`?q-hmkiI_q6a6jW7I8$zU;Dy;n~gs$6;>_xphHbM<>~RH@tgMmfEZ(P9vH&6|ER
zsLJzx()jC%%&#@vwy@DLMyf^SIl9T|=vU(E#+|<JFqt=ya`A={@=C2T&Z@G@*T>ne
zBiY0vgT28kX^O^xHET#Wq^?4(+eZC6iZ7%=ML6Ogm!FAbcQs0DLa{#}UEl136oLhR
zepqNylRfJ-Vs|1^3%{=Vgq^1^8Q;nFF1Vei?$N24Q#tqa@4`lZVkyxrOdq#73w)&z
z={v%{(yO^l&P^c_==~%#wA@GPgFWNNaF!|-XWCjj8v3O=<?qNkhMOUGVIMh*SZ!Se
zt54M0F;WsRmOr&%t#;J$N^`vw8*d-Eq8WHlS8h5cyYsqYUuwp@)NWN7q#O7tVCU4~
zg;lE|^DpYmHd!XdT{-vAenWoZ!~M!iVcA;LN)<(@M9}PqaZbcivGkgR(Dp=n{d0}N
z(t2^B)i<SwX3<v}E5FT`**^1IHa|F3q^6dQYg3vjx|s$^7}$thNJ0tTr<kF0J|DdH
z+X{X-wX)z{NKAWtYUV8Wa}?(1MhGTE!cJ^9{ryPf=Dgsvwn}q^O5#2`yhD!uOPkG%
zKztD~!DP$QEZP}3!zy~yu0<!LK5I4M*P2-VJ2lbP^R(CZvtI?O!<yoE2A}V9^y*D%
zdLRu6U#-Xcz!XkIA!o;4r}9nj6X)MZ2!|-nM9z8%f{1wwyHcwQw{RTocTSu=2%O)7
z0GqRsjj<vA>XqS)5m>3TnOd6Pm%>`g^_a~)2W@YKBY}?CSt=H7Z7abc8%XAxVOEqR
zz@taen@)WaTQ_In{Eat80)BZ)J1kJDvndzGknm954w%b)br|w&q*JE|8@UDl`<LyJ
z-6n4hbVkZxG+tusO-A%2qm@&*Sjk4ifO-qNX4wJ<;>FqfECgTlHV5XxY+wg>49|`3
z^wEwx9^(yN!U4|a<LXZ9LfZ=-0iJ{#PMWK{A5dhHx%b^w9TkZ^<UI-z&Svv$m5~+;
z0IO_4RCn(~^k$APXy-*|xfosW{4@QHsmtiitbpN5uu%1kosf>EE_THAxe&AnE%Z6r
zWu{GibKVo$L3*1!&6zgDa%vgOCgD|Rz=7BzXpTV_SMUdzB8{pk(X9(H>`E>;&h}xN
zL~dF&*m;vUg6?M6ITz`+&O7{DdF+$dRl-|0m73zqR<@s*Fz165PD3M!?^pMQy0gx1
zrN%#(aqX1(F!!zDplM$20sG8*;*Yeal)Md{=*;^Gj!EP%XE!~Ku5Tmt853;?AKmt1
zIm4gnyxRZ(*bEI4Fxb^bxJrz#^(|C>$xNqwEPK>6CS<gWDZ94Yv1w<nt1*L2`#x~w
zr`_+GEzw?arR6&FHVUJ+dp5263)T`{&c@c&xvstYv^Rh$P5DL3vyRNio{X3WsZF<u
zve=IwmKp}9#(T4s9R=A(MlIfVRl~~sud+9y<J%fS*F_O(-QH-ntvR#<{*t!als3Lg
zF}AAD^IXSKB~OQDO&U{MB7ZGXH16%8HY9zadzc69u%WyZDxUoTTW8Y3AA~mi*v)7q
zEMObB6d*@K_6xJ?KI}nyDQLDM{i8b4Qhz+5yOKGP#@SvwalP?97Ewzf%aelawfzp+
za$QrK>Nncaau-CnWIrmuVt>bq?#ZZR*Zx)fKFf4mSRmrIhI7CQq(iNHjeFi>(-VEz
z)ctPzWqQ5rOo=(`haGvI!6UOYb_d<`pp>Sv2P~&QmBjmPpJUYMvZHm|bQGL)Ja<47
zaGPc&i#d09KS9fR1gQ$=7_d5L9VYMK#w7v|SXZ_gyR$u~BE7Y!YNl*1pwc%=;$HO;
zO`rELH+K7lL6KSHk6Y*cF3i-aX0WT(FlB0ZGHOgB&u&e%3m8_4dubxBmLA%9U)wGI
z=F5rjdE}?Crl?)1f(XjWiq>V)Xpl2IAXG+n)7EQYpGB$*C)wj@;a(H?#ju)!=p5ak
zY~JDQsO!7uu{r~OGR`$uuv$)X+UsfoL2pygqL~~$HT=1-jC4$LJuxBu_U_p_S?5E|
zm)pB`-XomtPWg<PLypf=1CrcSrVuv;$(Y($r-~lTb4HjNYr@!((u{!+`mnN#t0E@(
zY*~^{^dt0C7}pv0hVuuvm*&xiZRue+aY|~USGohK0NvjgDyVYa<37~MSwR!mx;KU*
z&&^fdTIQ-&rntyzA{kbx;qn;%Na-iaA5TiGz#?8$t@|e6)m=F|e)D(0%ncRR)wPWu
zG3QFW9Q_q}@-(I9NLA_0q-ATi;=5q^_ymt{D^OwWT`~>bAJFaP@uKn%g<KqNbWZLU
zr#74XrciqK{-I(sJd@o|hcVOFmeH2e6zh&giP}v40PE;5!g%Vgd#`ZC!6lVwdbN(B
zxkG?TM&DE&9o<y9ah_VD4TQ|?T?kn+S7HjDK~d;<^?hT9IC5U-(HoLtI#X9Ulg8)s
z9UBZA4kR_K{XlIIBel@CkSNZFJG&xLA@23cT6;H&oLBAft$`_=R5rGjU^pIIRpB5O
zDfvy=w|yh4?d6kvhxgg>V6&1HCn*a>VfrZNjk{vS=ajE`29=9>@vL_D>NSJ@Q_8Gw
zwz4eSrmvQz`SPc1jI7?WAWyz&ak`%RBg|~gmQ)=dHZec=smo*D`!B^L!3<SmdQ6v4
z;(Gch@I^gS$O$LCM9zL({9{YDjrmO9ldZ(~t4d!Ly|)$zx+RRW4-k<PKhou6vm(}-
z9HX`dbbVoXB<&D`IN0cdM-ZZ62=k}m&zlZ@cPsNN`%FBR$PT^tYj=YP7lI=WX4fLy
z8>SHvgb)mMveI=#x6aeY$H=>JCT1ZUd9<gjvDL35Xq>6Enw#&rT;eu~B=#szPqZe_
zg{{`%7^Z`8_N2Lt`i~5P7q8C#c~0}|MCA@n)%>V#Dv2LkqQ8(8vewHojr{FcKM@#e
zuv05oku|Om_WPH}g9Kl1??cQW)7aRU=~7^`M{humdf4w3L#5GQv$;C4vMR(E^0I4d
z3*&BiaT($IyO5#b;ev(UtO!l)qps7+5=-Hhy0vS*-4k;4>aJM<y%ol7S0jEBwsnzf
zb?sYY(-+}@JP1A!tDee$_XnPB5&>MbB_n63<&MaKVq3P}c2?n`N|}hMfjr&H-7fgi
z2bX|=fLOM?Z%GmfIagf1yD_$v$ssnL7x~uoC{34^m$xYfkZ_gL>tLVuz4^-<8=#tp
zJp$Eznp7diOWtF(L7twTz>&NSdomO~Z#`^}iHR|A?R}$K*59ft^yo7pC-V0<cV5TH
z4a1<IATwKATR0_m?^lxX$v8OeF+IyZRLBntrVit9wBVulCJXT2U(bD-O?ZlX)ugDX
zINW-;GY$<I_ilLlWNdBa%7e~nKnWzFkcmfEp?1NeE}3rtW7&3&P|zR$fH{u$_xmjk
z6`SdP5rJI_Ucs*`155m}saVOVIZ6l{&r*3b>%i*}Iy0XhmY}x$-ahsO|L;t|5VmqH
zE7gX-%k%6M)d9PIIzCc)X=zK?!R|)Vvo;2Q8QX`d%reGyYAHsFE6o9iuh)gHs8B5j
zG051k&9>gWprQK?B<2%N+suOAwI+l35BD8~RQw1-$7`>*thb*7%eIm`T#?(Qj;pMG
zb17Cn>W^kW!yfizY&({hmnSJr_<iEd$joH(b$3_gBn%qESCJh|5_SV)V`}bg+asAN
z?pL#naJBXIg}qnTTc76XCLqM+5tPXgOj})D(9d7rl4zlTmb~rUrXq<%%Q9;dU{~r<
z?(v68<6iPmr|-#<dGKArA3;2_cA*E`I7b>OFJ5gqK!bMs^j{*3d({+OA&!rYc`(;a
zrnvk$X6T}6PHd}eS!?$E1#BwsadfaJp-DT~T*wX@xtl~xcrI*f>((@@$m`hvtT6xQ
zxp;ikCst)Uzh2da){`^c9}VQ%YQL<E));$uduIjiFBM@@d{57nIQMB#>;T=Uxqp02
z-}NI~BZ4^BorX)THk$3`ag4X?u2K8l)|>0vHp0z^T~-v1B!8r(?Y*3gSZ^x=m0^zd
zr2#2THy8TWk;FLVwE$qd&v5pib#--1KeAK<=F3L$_+3dYLqkKgsU1CutlCfRMOjKw
z1Z;G-JW)7&G53>)hezSzJ`Qt)^F>m}X%`fHb2s>rH0rcq=L`NCcF`35I<WfT)?i-y
zX4bm8(u1!rM-C~|G$OaOW!bC4#xk41(ZS7QEnRp3`~eBRJ!A%}s2j0q@M_)YRPIfe
zcWp_Lwi;0f^qq7?<2}w18Kqs^ZBaJaC4!8^6y+eo?e5<vx~7MT0JyasuR8x4CLkzi
z#civ*=Wz@G_55mqi{*JY<1#dk_a440F?j|@5(*#jw2uatITRNb&eOE#YX^;5)@`Tv
zM?PP^-x<kH(~9%-@F+>wjM}fby4FZMSaA<n=(VRT73EAxML14nN35v=c3Q2n^=X<b
zOET6ByO$fb8JCc$7PP#3E<yeG_HN@NVrNgLlJ{6|Pft&AtZ=wnsJ*?tnY-uP(D5w$
zn0xKNQz~0Aq>7fQ51|{gc{aAT5(R@xI?E)|_|8N?pL;qMAGz5hvcBR!=NVF3ULU?(
z-Z0g?0)Y1vbuwhSyzfUuw@kxI_EHm1Vr7hhY}130YM1WwGPd<z<*qqV!9B+(l{r$2
z&EB-JB3!dB(Pw34?LH!Az)~H;y04a27u6L|<yLRo#)=?zC%BB&AH`DFF|^jm%@qvo
z2<Bb&ks)9Q*ob~~&;fb4y+J#irbT(}?7p}1`%3uu2d|y~H}N#y4CU$F7sq&q-1x$a
zQF87;w~2c7PIxy=GPM8xE{#bCVDLA9C({aE_$oFZz@*apb}R3^jnl8prS@Lb8A{R+
z1EMj1aA^og3I~<xhE?#RjRc9)!FcD*LgSovw_jghSpnEnKHT3=#>hYX(RjgXJ>d7R
zZ|iBzc6(!|mAvq?05og?l16<(P*+2KNLRM{_c+I%R$2$*`^_XCAG%s%Q5s_xx$$LT
zt&K9Z`MWHwZ`ki>Z^1Y0BR%gy1c11k!Q<YP{xr<NT){CUn|ftE7$M2Fto#h5t~MC2
ze#XScII|ke8?^*{>l$1?oVaEUrEaeeaeUU8sjCQ%@>mE}%R1t5c#+$euI%eDxz@6N
zbf`QPO8lPh`OOs&mP;0M@VkhZX+*)_Pxu2+_{`OkCU4XqC)M0?ip`2N0f58yofg93
zZoj@X(+~Lk(dFWWb*9Q~saha_G!j2^31XLr*P2Ncuec<PTDJ-_ebl58ngv7SyEf}r
zppBeBem71148i{a<o*9W<vW245|dHw!NTO0Q1(=@V*4V5mDXRKxgbl$ESNf{3LT!`
zjgALw3~qo1aFrP(=2w@d{!tYSaey&^aZpzX4ssZs7R~f6<-DHh`ZjYFBHNt=@wKet
z(M#NaL9b<>?Oqp}oFP`6R-9_)KLR=r?gJU34N?nG9M}SE686RCk|f~65_*L8;!<IB
z%E0s-R@J<ZmtHUN`@4+z$Gz52J}pLPWetPG@B)t$gyBaZKmTlZPmnU$Dx`iO2sSeL
zO244qI<8sG&9KO-$JaxX{k46ClDM9Rf$^T?cb#Z>>ZR#PtVUR&q1YS83HNP9MaOWT
zX*q9vO4q!9r@{KggURvDn}8xrb1Y;Qz`UDO&IJRgcbL!8;)uKC+w&Ifww2>+qI%1<
z9!uaHtr=+fzEugkUZPa4O*qyHyp5KlYw}4H+m;$W<%79+X|?*9#Th+QdUFG|M*OR9
zx>otjz}$Qr$Zqo4!6Ng2duN@PulWU(`hTNhf7t~D1!mC!#7#^b`>##~8lPG}GO%E5
zuhu#88L}wZ>aF<4W-&df7T{FA;8v&?>>_LjqRDU)Iwgj#5z}kCS+|Aq1-0L1bVf->
zlTe4zl~60tv`lW+N9>~d;0XQCQmaB{w?BR66kyi;tQ1)Lo%tPJs7Okap`Mgp9Ua-a
zBi6gq&MC)jR}O2g&q6pIt+RQXdY0^E#bWM!4mM&u?Y#Takc!J6`#vzWGAGS&M3Lw=
z_IQ%{&V=e!KmQi)r5V`&%)ge6Jfv&D6Its&jQ6jT(91HB0UziRE{wsw*uZOXF))Z+
z8opO&KqgIxk(xTpx@=m!U)tqey(4hw+5K~>c2s1;x37(R`i5!1VvHv)t9JBtu1gri
zi4>9TXj)M(R$NmejxvS`GgQQ_VOVbe8506Kf8HcnOmDKl1`>L?N~20?23xiF!JQ&z
z`U)6*Zv#;w5`hM@STSeGoC456+CTL_Xs5r3_<zItcMQR;=lmd5*8*u;gkXYWG(wMC
zT+cM*eJV&(ppk<QGC?27wkt~(bb|N=`xodXnj?mP)~IvsUa8`1WUw}3BpOE-p&Ft$
z4cuHIes{9nGa+*2!3BxUgY(dv3aJ#9knWOnX&#xTiZX9~mpd|Gbxvy@RwU1%J;$Lp
zgMhx*!kqQh(Vb^wiRNwXwL570lfHO&yAq+ef=sK8PPs~97Cvl=+At@!0bB%W0C6#C
z4EurxquZjX241e0_ad4yL2-a1<*!-7-(A-D#Uhw2jvCK6>@8omkfEZCq3w`XXc=tf
z#2c1cFcO4FwsTJuOK}8TGmSGoJbql*S~_FIa^snp-ttXglf8>Z$7NEpPMz0d0L_dq
z{~s03e?zhV!qm4wu=6=h=068~#1!zZO8P0#5kn*w-%BsmM6sYs4<;uR13}E`Y?aWi
zOqIq<{pt(HGGp>t{&WXtgX2N*upJ3BnUBCp;1#b00M7#eJSe3Ck#356VHeSV1G?-H
zFQW3cut{jO+<IUH`Q`garlwv07SX>JXEk(EK%oJ#TNT(eX4|xw1TN2ss^_l)y`@z-
zV=Br?@DGJZTB?8T^vHxAuKM0wii|<pQJK(cak#k|s4I$jf@iW5q?@|i2-j_}I|&xB
z!YCAbE|WY2e47K)I+0VstJSrX*-{qOgXHvc-+x+*m`-1Mv0B+0YTh&!vFe%@)wuX2
zjU-R;B<;uHXMdR8JYj>peRs_9_jk|MuwkTPvUrb$MTt9CpyoOhZrscW;C!(Sq_KVX
zB7mX7fDK*7Mb7D&8UwBd6s9@%juX`XBoO|4fT0lLKWffbF}!>h7lDcR{z|Oa_rfXc
zITZf@*%DidO~npkdnfJE04TJ{1+|}4;Sc3=W9WoLfNibb%IYPC*J)Ex6o_zKjBQ?+
zD+s<ep8<H`HZ#2+@~w;j0_+B2xK0$;`{{9^mT5m4Po~EjpbZBWLOgE(2L(TY>~7%6
zP&BlP0kqkY8-J=wnCzpV&Y_}~5e)T{c)1p{kRm3@@kHsG<Le+K(<E0r2oAY<^V2je
z9wrMxFbE080eKU5Az0Y&+4n8=K8S2WOdp*RELE4n1~~#B6rzKbpus=nSG%#WQj@%J
zj4$X@H{L<ePq3=`g}446s6Uo=$4``!?i1ubP$Ssu^-~9PqK)6pY`<H$P=rG*e4lvO
zAjO0A4(m+Y6k(eYB3?!t_8VeL@U@m5h3lsIU#CmuLVA{6ZpI01jRznbd&cHG)??g#
zWuuN#p7cR0(@G7Q@LoMV%5CAlvRh6{TK%y;wO_<$C2LpfGmnTOk_AWLE8~tAtf(xs
zor7mL`Y;L!i}-gL(NH#l%ZMUr96m6wx^{a1E?2;M6YVLtNR)c}MOMpOA3$rmF5y@S
zC<R2*R_R$%>zH3q$kmO!`*gLHW72=`l$%|-)Nm<KdHa4bfu$@!!Gsx1RO9TP6;Eop
zhUESJMHA_pYnOAqzz0V;Yio@u=Pd@+{1cewJhD?nWUT8J_tMl}*m{=sxKD^rGj#Xf
zt$QE|NPxNMZDINW<Wqi&o2>39<}4pDZ&e?E_TblCA7eD&r<EobP_(>z7IZZzd&UKR
zHnA1ufa!FxVk@ar*aeG{KQERpHnIWP+@T16evt!6!whI>{u+2bD8vNthTtc?&?({2
z=YEJa7Buv=12niq%N`i_coL4qfv&P^^ATj7q-N|YyqlZ;;dvSandp$}&NddWv8cIQ
zi9^c3?K3zu)-~>`ih!J6qNPr*VLu!s3`Lx5W%SbB4f|SY{<qJ9Tlw%jd^Z|-$l6Y_
z4k@=1^M@_nxVHGwRfnKZ)qT%h{X}zytR|scfEy%fgHJeI1E%<x$fTW11^~4<wVOjf
zaj3|=dyY|DZ`q7_6<V?N*GqY3dU84TSJ|Uj@coubAs{WrXlK4H0$+8FYe^!PTE_s)
zvo!Y(Tj+_LY1jlq+4Mi<o`y~r5hZoIIKw*=#oQH7jnPFZ`N92o$U;>bmjv-G)b2#F
z;t=UmSVJtG(`hF<^5sjFoHu~GIpf?{+4u;b7S971lAjE6;IjTBp89xtJ=-4$L_t6v
zMyHz!t=wd(Dsi$x4qOB1p9N|EGnCK2p|<c=1<@~i(CwVrY8C-$mS6*b!(V=4q%+16
zsvzJKH=hMQXcSxyy{e?n6Uv6?;Dh}D*Du8^B#TL2226A4juDz+(SlZ~3po0azQiEG
zk=&tInvwE;OyoMBPzRu;2DpXDpRA6c*6a9FQHTahI>$3BuDzj5_`1(s!k{-9u47}^
zwo((T26v%5A=jg%f*ho1+i6$DSrzktDkI5vH~YR1WQV`#<Y0vgmzI{w%n`mP)|Pot
z$)D&AX_TW<Um*lE|NNc8?&QM3CG*XW8@gbmv~Gl{vFT#m{`S8EOmXTXwBe+|65sVh
zRD-@3vF~rbRB9ySwRgudBMAgz*FOF(K<R0vsnCR=5h;TRUpdcNf`r3qtJKb+q!W|w
zR1VgLBae=+{)&yO%4@<0Gmi^*INXEzLngU1U+T{x?@0V;TR55zK~Q2ZySU$8cJ`<D
zYY!RuxtRLy_B7&t1uXt)Op&W0_2i7xB>+C-?7*#wv(&Ke&FzNCr}Tw~U~7x{g(NXa
zlO{;x8Pbu&Kx6ev&I;s{RpJoo7c;@-E@d1g&i(!dz?}2?!?5g1ia5)601@9$w9_EW
z3``$kKVt8vNb4r%x1X^BVRRX%EwGL#J??VAfi*>~Kx`GE+(1xW{;~rG${3%2A;JIG
z*tmHFsL%KxbmTa-+kuDX`QVU70XO=i@^{>T|Hc8_Ac5|P`9RY1;3@W;mci52GJ3Dq
zu46G;0lXe0R#R+hGKDrtcVNsvo|Lb7%txaLyjT?i#K}dWox3rQYt@T2Qo66!5S7+!
z<HUx;TTj!TM%`p}w5VWM4?{ZB#7k{Pq^u$mhj8WwASA;9iv6v8bPj|Pl$(`)>;b4w
zSqt1|K|P<^`=yf8N)APKtnF2Pqp=2-Db3E>iOJ<-Cp*IV@%(t9*WfMVdLXKsbph^0
zv|)m|7>d(<0CJ;|B7<LNq&h2b9s`T<^KO7W=>GrmVRcSCluuxCT3AI8FH`&&zyeu-
zRuH2bqLE++`W=`LWFCrPXwNct2Jj#jp~uU<r+SI!wc#QYlCeyOk<b!7j)JH3_X=ar
zgcW+zsW&t~0bt=Iu!b!(;N8rgmLS<=<tSdfe)Hlb<-Xde=#dJZ3woxi76u?1-02h<
zpyCfiPXiT^gY<{2IjkVC2&+PHoz`DWX9t`Ej+6+d!S8>H1r?e9rBW+ErEoggZ}O{3
zP8Rf@01+9t0IEm!zdU~n4U~Sxxc^T~HUknWT6Mb0GTyz#qW%<Mek-7|o5ykz1PD+<
za@i7_dFxqV)C3q6$A&uTI;m7C`f>lwXbq0teuW`YUciA#2x`Y*2NRlp8hjFD)Fbch
zO_-7$k<c@}3uq0lho`h$XwG3k(Am*V&<&;Hdvo$^Wn)2c_HrhP`M--(4f|cmdxz~H
zSE#R<Mi5{Po?%WeIq1SddNBA>{^L3z1FizW1h!$mO|89WaVyarJbVhMo45J@!y6s{
z&qM#On*D7N9XVHzR}^rK?PSnV)K7XqE~CRlSy6w;9b2bx8t3>ZCtr^1SQ<(-OHw~E
zf6Y`zgHVbJ@8ErG*I-%>;#p{lVy;cCd;n;;0%$gX{%S99j)z`tp8Lt55L|MsdOg5-
zDrK)6E<Q5{+73^EIL|SCu%G<TcLqSV{-0?f{5y00Z}4%{aeT@D$898GvJsYq6(xid
zZWC<Y!pT=IooV_B&<+gHl5k}6Vrk7(JmPR-Ci7Lf7d!_zKnrlVN@yeikh%j~rb2(E
z&r5`atTGmqi%hveNbC9+O3QD&9gkvV2`trhtx~^eA?J4oTvNc>cF-<<$EskkPNqDP
zU9j%=dd|g4$%QLF97iXdNqPp&t8E&tKLjk2-jxfj%K5qrf-c6#+4!$!2Eg04k#HO9
z_#Lp#Pw&&g-WT^D%sunQAVNk*2D+YG4ncEV)+((>udF*7A^j0iQL80DX*Dr1hO`J!
zYd>L_pdpVb{L$S%qd1!C6S4jTdG$W4sD1p7t)`~{=;G5jn}ey^tGrQ{4&|i34`{lZ
zE~TbYX7em%_w(nTh#WO~k=%x7#kaAv;qhjVVOZtiL7GV6{<f#2E_KPGblJwLt`jbj
zCKo!tx0Q&a6?t{{|6VHUCQ`<2Y&64XV&TGf>UYwRY9Q;3rX2ku-oy2tLCtLW)!r1B
zu*W0yWA$aU8vT$nWZMn|;Rh^&nGbxx&?uG*w1t+3J)(2i+s_pI-H{szJkHqWKk0u}
zF<e5icNjeXA^o4;js*H5|9A|^+cPWZHi*o>o_a$Jj{H(0ZJ6uA$9%v$<6(}3c3Rfg
zCdSdPof!&atGeN3^MPifc)=WwCf%RD)#!AXg!M_eSaJPJ>I}15J6p-dyQMFwxe~28
zEHXOAp>W=aatfMKBefEX)w?lYOs@&P8G=-`PxRymifHkn_A^x=>(Xi$ZVMJWIt7p-
zR^=@{U$Ua3BrQ)Y*_4)mx{U6Q@zL7#6R;S)!Xo9uUjYd&i*y&4-VQpIgBhFZ`^R1e
zQRP-7>Rl$4SF>_4kFcBE?+Z$+)0|aidThA_LoWLTia%d|mySJom#DudV)5K=yX%k#
z-=bX?Xt8s}?nWrvme`Xcw&5d0@7Ieu#!>JEK<Io*fQjN4*8z`x@C^O5*#ifzC48f$
z(*m8x(hD^wTS8Vo%hJ1h+X<qPq8hdzo~g-AU%3rSg|b0xt(xB(N$nsPnbsF}uWWs$
z1Q>^wmolHCDk;6}$gC;3R^bu~Re4jT>Spi6;b--PwEW`KjNs;yYT-88_s5fEcr-aW
zo#ABJ1CuOsH-1sNkMUYuGthbgZrhk_GNQNcgvQ*V-FM(lW^0bYL$=0<RoHE?ra|*d
zqcLuk*JS5_bAGCq<OZHGQ&p=tcW?ZB5@?dUUrE2up-oaR2?_A|K)WV51A6O;s<bEp
z<g7oTF?0CFlRx}BwW+9eq`MQe<^TsHvI05mFNy5(%)E`O1Pekb5;i6XUiSF6>aXH6
zN5h|)E*nEdpcu$b<sJL1f%M}ggDkxD@5Xo_;R}GATZoDsT=-pn?4;|dDh{$XAjL-n
z)~SO;^u(@V+n!n`kEf%94(!7p?(&piMEu11;mvS}N{XMKp`v8{i7w6^y+V{XY8^_k
zsK#<8|5hv6p#<c*ya#?8hn1J=3+bzf$ef<ch(1?6ubM?Jd~f+A)md!j3r1-7^o)9k
zhy2SsA=0P&`X=Nv9KO>o=zUp+y#}@3!sn}SspNMk@|b<UvcmA=><TrC9p7Fp&SUZY
zidGON=lQ?6tQD1d_wU&FXOR5YUX(C!MMt&dPD*O&DA!8sS*%NAPdKTP#r*hQ>ersN
z$h0BEE&4Xa(aWyJ1dA7!u+sHH7&x>;RLH0NoDy#oFRw7Y88q}RLN#GgJ|2#V;V<;C
zefKD_5RAmu%N%Ium%=t-!#+G@*M8+FOG}<CNp^L55WO%d9Ln^X?cmMMlx40`Z+xN4
zbZ`7{$gQC@32b^TWz7*b@@I@E$|_pAI(7A1nSgJ?*welw_xn18IsD<hRaa?q=~GAW
z6LhjLO@@aYM{IXO4_Baei58!jup6o#Ee!a}x@`qzIrla}2NG2V@e3*Wn)fgHNnbjz
zw048wrY(wiMV@IgQdpHyV<4wuWmLz?!NKPuMHd1X?iepJ8A@z5>wD)h+|or7ryy(M
z7jaGmVePLjg)R_()1ftsy9rS-J3*H(He=L1vlST~nWyLk0&aKX5cS%1+B39IpRn#)
zr_a1d==HdY+b@Quf)4Yjl7z!Dm#?ARd|7!@rX$85!a(s*#7knBcrmU8;wBz>Yx>qM
zdn_3YhkH)tfTo|v7_bsvEJW8m8~Vxin%S%WsgfyrtcGHb;T}%@ph~v(8WTWckzBh{
zIcx-9C_HX0hf(PB0`#GD$N3?-`Or1(V<ma!(mpXdfuB&u7i{5Hpor_A?nkGG31iTG
zm7AuxV<Aq)_nZsT+iCDEE>d~I9_h)c`5ws~=PT;P85cpntG#lhJ*Mp4{zh@#ib-VI
za9~UBM&GNx2yDrsQp0oa2bsKr*#?Y18s$+UkmvsDJ0>01+_$k5CXF61Hm(tqcEk5i
z#^mZov}v(*Opf|L#cOj`F(|XVnRRg5T>5%c$0Nw7aKY}rq)T=kqvP3pS>SuJNd?g^
zSUFVIyGPGa-qrI|3Bn})kOSS59)u14u;IxW{t3pVR6N3=oqxyJyUndVgo%2t<>$@%
zS9d>Ey2y05W1(Tf;}APTJVosZnO=0ffw8T5lgXLSs_f9zQ)UZkt+i|VGvyp^T6C)x
zqlKqXowE0`-x7VEz5uKux#`ujfN2FfFG_E!kXLF%{1|6MOU&L;CAEz`O%+;&&z!hI
zg|c`*_Mw~aN1(Tb(J%Yz+{0AJeFKq)(a)Kvmkqr0`p+-IIl>*c(NI+UpN0`B>{{$6
zA>C7S8p-S>OKzjE#0Zf~XgLwKE{_W%#~#B&g{y-Pq@AU2A_?TO`pt3?)O}RQVkp_J
zUV|yKwT2|)a(F5e){&YJeXI1Na8-E54Xi7ch}G9N_GVbkd^?DxBcR;cBl2e0^xY!B
z&PnqvSY32put=Z$x&*c!ofo=ry^l&Ys(;lNNLWW3&Q)@rywV*S-2sc?7*0NmzJ3NU
zYJi)>xLm==Jz8uOqa%XnMgp>ct8Y(2n{77(vZ>B2q_@`B2q-8wtRA!`x^nNO*f!^=
z#+FB_dHE+M*j;XtmXJQ>fOEv$UnERMZ~5a1!fY>tkuf2xpn5qi5S(cKPN1XMH31qS
lVfXyM21|c~nG}HrT~1CNdYTohe*A9-dN+)&S7<vu{$D)wmz)3q

diff --git a/reference/Rplot007.png b/reference/Rplot007.png
index 9b6a491204f0a1c1d7890d67910365b5dc4f3b78..727d42bdbe58f5f6406251be7bd5bdcb3d9f37e7 100644
GIT binary patch
literal 12678
zcmdUW2UJt}oAyOVox#G4iXtUgP!ttJMQXr?fHVb^B4sopy+|j4!00#(NJqs&QE7%c
zAjO30I1mu235gIwKzd@R36UVMZ=C(kp5629etXXT_uI1`IV9KIn|pu1w>;1DK1N?M
zGuSLHEsh|_W+TJ%76>9XDEj$+Jsb(A);)w@>u(qvoQL1=XYpKh1pNBJ*U;7<K{jp`
z{fKol@m~>SH)3@Dj8#Y)ml>K!9Ze_CnrEIsqZO;ZZ@%;U+Jo*-zPoI5Oi8BgLX-aK
z-lOU-50HAVoFbo%wWjracbNT1T}JZl#vjG9Ja<(j79Aw*NW3ie1N)-YcfSmMelj`V
ztM_W^!zL!0iC*SJ?HVXiig_}{nqJajvAQxR8P2h)YY=4rXeC+<LE;U*JBA=Ter!Nf
zV|Sh4wQEm*%vg`?Uc2U_82Rj%#v7`$JY7V#x~5XGbK6ndvcDF#BM4bzN^C9i^Dh+$
zp5J55FY*aDe*5z!%RIB5_H=Da`n`A8+tRhImzM<e9~9G`U-PNG(&c(Ga5=bs$pJn4
z*EZ7UvQ{fwTS7~Uq6fX$LAX2}QKE`zdSzYWNSdx&>WkZ%K;~zzE@n!f@_fu@vw7yL
zJah4TXVcqXCzfjIewQwb`|J0g*gXZQcz%J?p(Mkba)LH>78S0~3`$i~S3msY_pgXV
zTmP{SNv!Eq)--*hD~sRn-{D-O9`xB}d9fqHY#FsBYE-Z!nLzd*(CuB=h3wY5y5>vg
zi*rx40`nciX7%NC!))r8xjuX$rzEd9Qx)^u(`U~r!k2===UNnnbFC_BxP{MDeyNR#
zgzlH;DBNdNISt>_mboUE!^3MUJ$qlq$*8+8EpV}4hNAdGQS!O8#jXT=c7ooDjeOY5
zFAJZ0vtD=TUV{@R1B&m(_RKEQImOjx9C`^IH(i^D#w|>Io$sKN<s;Gp`{&!W%{^#^
zmn|(V*=5#_q53CW+O72U?@d)t>+0|Q2^PG_q57(X&dh1rcsd2G*uRkQw7t^YWoZ+V
zsEBzx^e)OYFt9??cd+3yT}~&Y)A0C<O9umc>%x~}6)64vp0TR8-aj}r)?@8t>ae;v
zo+*<+#(bI$VV7x7y?Ljz@@1GXtMG(IqXl6$OC4(nTjtYk$DdByG)ejQI;TFVV&SVD
z^rE)Nz1K3+@*i(kOF%E!umvaDHQp6E)$4N0O6pfgi?az4X+c|>q)xa~56<_xWai*~
zyu8$sO^)nnbz8d0a6ux!+7dU&ZdzaA&D3Is49Agp-Nu0(XhHVWq?(%AA*oIIFJJDh
z2=nqPQgB;cSthg!d6Ox^-tl<?iDc(3aOxwxdUff@juZuM?`wl(t1DL?Vn5QR1s>z=
z>D;O7w~{+-<haagYe`AT{KCS|0cr8Y#4%}RAl>13)5Ls-9z9-8J7DaI%f;kV-u<tP
zRcfu^Tj!RRNfR&cmxnJc)Q}VX7=MRK1kG_B8Xg0^IFgc_Ef~O=?=y&}7CTg1SzG5_
zI+#tPU459EYMi`9R%5qOK7nw^wU9*83e`c~JG*BwtnSmv#4N{JECE9F#U;bHKOZXz
zjFONqKE3;Sg09d|rS8j1{K|Z0>!{x$*)1Mlzs&aiasBRvSgAcF^a8pg&J;f6+GOWg
zVx45Dz{fGhgZ-JhPZn$$dKVCcSnAhGgdi=lt5A~s9$)X&*4B1WVQYSIG3~)2%YY?r
z)nVy4rLEDf12v(fuXC*o=HYe&2#w;Jxr<D{)}!v;-j5U@LN2Y&%C8Q6!8xa3SOFAM
zt*|eD#HzY`dQufL!;&dNbynExv5w4=#rbj0`@<y<0!9}54HaHKeJUMV>&P9by*5AA
z)@tVhF@<Fl@*Wr}Y~K<U2wyI*M)qVVGIZA>BOMnJiCxFf7ZnvrYX=ps`6UuRmy$VA
zR5g%H$Bk@RjFCJH=k$~~=|RMue)#aAT}{aC)TXdc(TV}^!!CT?Zn68vPS!G$HL1;7
zjJH>|R<(QYKJ@23QPD8d9NpeSqq&d1e<-aL;A+M(OV_%xai=oX)+Id$qW+@RobOEX
ze&c|F&~XSXcgRMPkX`VaMjU-~t=x-oYTX(!$X~Oa_P%GTIob@}bnXe{G~T{xvN{q$
z1`q0lZa^Z}k;9Rwch`yb#pSD0bxTtyb!BAmW5c;l1hMh$XRTe2G^}4E1r`7MpAqE9
z?np7@kB0Yo2WLnsNW<)2$_CwsI+0mjuI$(8^Fu+^Jq6Y!)SVkNUX+zt%!b$>J$f{r
z8eGlHFDRf4)Z*j_D*RYfet!P;`;D@kx5KlqDl01)f=iZ`!xKAiK&&5<j?1s8=t&Zy
zUu=LlVYA&0aIcDsuPSVvTtNT&?mCnR12(SK@yeAeZmhcScxoz%c1~Ykzc0aJY>(^z
zxxYvRZhFnXx#|Dn!3Gc98YFTmk3cXwe6_II%{H?m@BH2uAvyz-WsqS{PkGr{=h2V;
z*nmXVRFs#$DlILY2wdV#TJoNfl~8y7p*Qr$TzG@CvvX+4upd#}QQaDqV4$4ZTB>TV
zoSMboKaTUQPWak3c2mN;>Y8IoDoTss6oYAzIwxWK;AIqsA2zcXW@eK2oIA<mW(3^S
zmhAKQlL@}LKXQ0BWPqa}bMPk9ei!Cu>}}6YRSd3c_@tpk^!YzaRby3?(^wL`&d_iI
zE>psY8LFp1o|r-#E>1_C-#gT4BCdBJzGr!mpPOKJSWNbQ<D8@7u7-Ps&k9DG0^MUR
z_=$!Jq(F(5wM>Gl4k{sgigi~t_gbbzOXPI>_&JMU-ABoDMbjhlsrPtnUg?9c=L!;4
zzv3hMT0^(|>*)J0Zsh-})9<`o=c}-_iy6!++I8ZVYj2rzqtZs?bb?hmf?r)0sNuhc
zpjW@n&n<@am0}D^FRn#8hePgPapg^Pk+_u%-r!vccCxW@*XlBB)orlh7XqgQN~BSD
zbJD)R`8M^PI%Zy8Ua8|xqZ#2N(@AAz$H`5;M0;N64+-RbnH}Rc63VFs2E4(bYUA#6
zp`pZm{K-A)5N(bng2$Yp!MAtgKjbA6AGqs<=%CiE`K5+#s<72fMa2%;utuycQ_m@x
z2d!NRl~7f^;<U<lvTVI~_C*b++FtpJwe`+&?_#KzTcdydbLfwKe5QAm(&59CFSemy
zM8(9217ZV2V8EuF^G=%*6^fTHKfU?<A}6OYX;BI@mtlNp;oLDPD}}9>PfEAp9O_w_
z3v;km7O+^Ng4lYrs=WH^=hq#N6!fz$1EkqKO=`gf-8-3C01w$Xe4E_#JgWaeYN~eM
zUGi|!h$n>jS%~s3RIF;eV33E0z?Q-&XyT6S!o;_rZj?UWMmaR`_*j!Hg`C_mZj1}<
zTB8B|rzv)qs3}4B%_EUWHy*A>o|6yRAxq2nBAe2eHlqkK(G?>9H!_8KdKv+|DJHfF
zs_s9-)&KE>gB578=hcP^TcKyRhc4P2$pwI9mg-Nm7hiwZ;fZvl_wUGhzw(GxJQKgf
z)i1dQh_`x*R4@@JCd&x<Uix1(hJQ$I`4Bb)eEarwMT&&yf&Qa>?C5OBNRak?aCGaa
z(wkL#03Y70yL8S&1~;gD!=Y6S0pcQha(g-2m?#gmWgj1R7K$HZ76os<;#XDa=XU?-
z>#4igzCRQ<#KRvvd^m;vEW+~^>FD85L<X~6{N9{n2REporltnI@CrO)h|bi<%|#6E
zRa@I9S?D+R#qfpj?#K@R!l;qx2R;&JZF!0QdqcB6^YFi4$a44c3e3ivPrT*u-;d90
zih}C0!dA^OtS>tZ);6@G5&nV0*Q%3;VE=Q-cYt%%$|}1yE$TV6(|^dS{Y$r->B~Uk
zHy2zj>K5`j{&qBO(BUM*k@C-tJDmz|4>tumyCa?ZeriZm02s$*)z?o1z8muG@>p})
z{O_j!|2Ig_(2!G258P1*B5Pf?e&*HXta8_`z0g4)xa-2Pon`BNF_<i%PbWj?ZZ0$G
zm!H)E;InAkh(w}}dWbS@naf%!0t6(tJh(xxQ<RE_EORH)g9i@v06(2b(hC3Sk?I8*
zb#JOy74v`*39u2b&!c3L#3}X<^Ft7x)qtn9F0cdAcm^(@QPO%M12@$bDYjO@2o4nY
zdspo^Vsk0kXbsXyxw&}|a2NZogl=NTxJNLtb+j8;iCj8#D6}mt>N&6ntksvg)s>~>
z?4OTK^cTVn48daoQAnpa8$DC^p-Du1Ij45=<OEbthfFy*&kD{9*UgMo^&{F7TKiV|
zuX=bCOw~ie0fPdJe`M;27&37ku2NOM?6nL8g!;rK<B(Bbne%<+nwdY#Tk#9ME<pI)
zn`TUP0~{P3i}B0CQS{}irRMa&5_Qgl>c1vcFZSG3Jut1B?C*aA@}P0lPqN%m*FSlV
zC4ovHM^8434%Ca=s<1@O8G{MY$w=l6j+8#oIgs}G?d1H6PE>r({EO}6WJtj;Va)+#
zHIJMPsF2exnc<bWX6Z^*jG~=RVV8mUdiOaG&%Ozo9w;g(CS6>+VGFe*V#Oa2TTXHP
zaJ;sH++six!>1k{xG3XOlO#TF|FoGutg2}8IFUaz7mz&EGaEX61J;6DX<WMB+zLuN
z9c#w7zj`${KVL}%>VbTQm~lp4XSTMs1_FsQPIe6EU-25KAx>aSGIVTR(r*7|ckP<L
zovzb8)V6ztRX!};qxNr(jSGq1SwA0pjP>{gth*wRLwlKU`|Y}&6Xg`MR=3{l5&54)
zsH#W+wAh1D>7{w}nYqMHhk&FD38*4qvaYkA8ud$@92|;zU%QN#doqOHRoc^-76M&N
z@_6c5T(iLoa&mxzJf%gtK`}ibxlYJF?A2Fp1g%8^&AZgZlRxR2I1nyyfSb7_V$TEk
zB`?z_$M**=&yBY3H|lb=0;X;3u+pNK>H6-s&7tblCui=icl$Ck6k25Y&qc|=hs5;m
z>FKH9)PEG`?vdT1eLVBgqa9pEmAR>@Y5eE$n3x#q!dFV>%1E;mT!dnzm_G86BfT~H
z`~dk^C`<6E0S=HGMv|A6g9G;qUSsru<w@7h=QT7!e!c~?)1`#GqH9&`gov*gN6yI0
zx;jVECIT;%?Y&To<-cEgJcF&}E;tpZX~_e<K7$6}u8C#X!NT}z*mrt4ivYQ{e)>S_
z0=5=tk!RjoDwX;wVs+&y=nubbk;}j4P24{X3FTYUEZrbVibC7hye<Rz3S5mI1sAD8
z9NFsN*RsDceGlZvB^r{U8%}z3^jfY^D70t;&N6(KmRdK(s&@jydZfo5K{UJ}GdIYG
zU208L=~$Eu0bNSnR{l<gn#Uv1JN8#ZJOlc8JLoE8yRYq9jqTzO|E@TGLft`lxCb|s
zq1LEFidA|sPTu+;1*|JqSw<)1kKvRx$n(5b=?3+F-@5}q=Yu*{r}`>dXN|<595`<t
zgeW4wlMz^Ps`257I;cu1g<?8x-3mYoKpXJ%v^sb0?mu5es5D`K3O&Dh^X8ZfSx?cp
z%Bw|oqs<$BUkeKXF>`l=1nsGsN4Xp20KgygT=Txrh&HGY8g?`U9=85v*Tm)2s&E_V
zxg#nb$aFvP1mAyeQfGILap+Gw5JW#wEz&{Rn(dN6&U$jf#U1e8Q&_m+ZEKO|iX2|b
zAANm9|2)84mi}VM;F}~q$`?c)z%~kpPzgY&-6y+q_CNW%lp!ks9s5*1w~;gur1>)K
z{C;0C#P<U2*P?;~<E2yC+3%txc7~sYRg-{Ki&&;dxPd?+d@F(9ZDjYx_l1&@(n*kd
zl1;WCkr_%Jh=_jo)rI5Owe@s+|2XJO27rY+^=(<dQLBS}xF1XR@s~h$>&Y{hCo8T)
zPMfFlQ8S+!@AKPy@RqM{wIwsSG|^kg%~W@9*hd@NwO@D?bNT|8Q2{Hq3&WdUU6}?9
zdAO$^s&1)PcxF<^IOUJ)@2o&?idjSuU;AdE?EiCzHv_t$I#&mA{lY*N<ptQT4PlZ3
z*{u2qb}(j>lfn1M>7O9jj8YB|Koa5OpgT!ROUq`0pon&$?Yu79H+NUpODCm!KKH(^
zaPLv_`uN8SD}UGo@_Xrq)usB?0yPhsD<#)7rtt=LB*|bzuw<-O_450ucXqFDnVPi<
zm+||zMpLbi-5L(64(!t3X_S1*t1r7lZ?%RJ2psQyYimz;cQPgHLcA=YURY57-pv+q
z^%!o7)2zKk1{ec9W1NvgG1E3x%m}$*otK)jS?1IPr=)%=K(mT*w4PN07%z<tO3_ut
zG~ctpTb*Gsn`C@Mv1+Eb%(H0Ao7kK5`_&Rh2ObGuuP50ckN@&w0LwXQ&;$y=_8U-G
zKt?%&Wr6_o@JJ-R#3_PG|Fq*=rfztAG|~>sTq_tT7|)Dwj6VO;YF-p>5S;a^%hM1P
zwsRpmphyuwQhx4Hru&_o_`Qp25}@H8cAFMsh0V7SGSz(s?8{nU%W4c|8U+3R=In9M
z;j(U~z?DLh_?jR^493QGh{=Ok2`o4I{9eAl1+*BW0~;W89-fL^5=@s^nxn5IQ=$4(
zJB*yd`48t$z#eQcmT4e{<!?ifgR&hW`S(A7<F@s&Zy_}l`L996x(RIfXabqyU3kNo
z7DS2)15kK}?tcgRVK3fw>AGs;gZ>P&9&_)kFle54X#V*dqUZO}N$Ala!uIiV<{FQZ
zIk}kqk+lQ$n>ZtJxpYn-@3i^CP=Lf@@jk*JKc;PXwwc2pLuZC5cxVTfJt~W`mn3DT
zv)_*TY1YY$qvc6{D9LMcHcE}$WC~nxVvl@_-X?L)sCWXhCF&g^6T0p`%wIQDK_vOc
zos1(U-%X<ChUOQyCy?hIJK%M?xo+BkM3*bI3j~1_!NP2aj`(+LLQAY;P(Z}?VYd6}
zepz0*L*x8VWo*Wc8d>}??t+|1J~-5SPWS!$_u_Wu7EO9rL;kUpyy3k-Y@Z%z(ATQG
z+uVBR=KLW3)9QSv5X*ISbp@2Q7nNkUaE24~+TD6VUxf9gEDrLqYJMZl#by%CyTtl8
z|9W;0df_7m2nygI5cGD|Di3CZcCYQHOQq4+sr6r-yEto}%=~!r+nC^goOk{EDEmy;
znh+3%p@wwAD5E@Jviruvb;xrAB|C%;Vqjy8Bz9$SJn+lRb<lyUY;ph`-mWp*eiTRz
zYuYEc7sN*;Bdy8o6rLbrRX}J3Y`M+y67U$%-9XjJu4^5IRC%Z{j{BWF98XnomPj)E
zD@m9$@uH^29&$)L>Rmk5hjH%Ub^rt1fOJr~U>(Q9Jil8b1_~zVy+mXGw(JK>Lo-A0
zNjU%Q&z(s^FWd}Pju>ja`^QUgb*TTcgz2Dax#>Mb`MDvWa6#1x?<<QBx(5TYqKL4?
zGT&*n0_rux!P?15#fVMGOSn;L!=5|8_wt8B<pnbL8~fs}c((MJ-K(Jh8K2m*AxGy?
z243h^3v&eh5<L&v5!0t9TT1oDvg>(+R86@AvgG~7i5J_LwNVxjAOKPb1M@%~yXhaQ
zEq3g?5Zjr5rJx+kclYWo)6cpPchMmj(7s{v%dS@-aVL`c3F%UC7ofDh1m)X+ErX$U
zckeZdXCBjL-c`Uhp8>JK*!Vt15XnFpqldztM}4^1d)HE|e_MOhO^HQN)nd-?UB^$K
z>;lzhhE{Y<EqKZvgyi%<nk{MO&yDx5NCRida~0F^8!`OfA`krgVfr7`&Huc%F<>Dv
z;YZxQY5nSPv62&(GfT^$I>d;=(uWa6L7&gMY{0JKz6DBs{6bmOw{0=@tBC>)XWiX1
zKoT`9>bH|E%Ax>`Xj{}jKtqTcuv6#KieJ6*&BilC{C^)tqgKA(JGceg90l;#*4Fk6
z`8P)TN}YGh+K*CMmkvG<qIcZaJZ=Wt3nB0C|9T&vvCy>*>#H=9K6NH}_?zy0b`N>Y
zo9*PS=z!w9bcz{s(HE<yur)fFpC|!n%U7e&S0haj@?>T3(|?By4P#3LiQHTrJWT_p
z0Gyt~S4uKGm}D40@`ORst@gCu(6+P-pgu*pd3p7WwLQD<SmjiY*E~x8rsMxN@8W-c
zzyHlA&s<&`0>h1`n*O;NSoWb^m>648j^wzC)dWv}o4|DFE)_y8;f?F968|oBF*_QZ
z!e;LsFny{3ob!im2PP=ydS1cduyFwOC1D=wN3>W?vv{wXR?M<&@+H+-Vl>dJ7O`*f
zx(iJcQ~b#n98Ac4H5*1$nu9X^vcwtlG>yy20W@0|x3_oJ5n4Mm$7Nw!^W27FxC@Of
z#|oy3em?f9Ri(aWf{#We!26>{(t7xs^;=|Zc&eZP4!cS?mZ<l<BaUYj0AG^~j}<|$
zTAbi2s7~1%GCNY)LpAg5l*B#fVsx7(^D>KJ6ardPe-a7=?))ft2G@zs7y9!1de1?b
z-iLnh@L>-2vBvF}zipPe%2Q1qzss5q&W<|0yR$ed#atI?t^y9qO}yY3@Qif7_lMxr
zPzIZim7ClnwW*RbM71<ODwKi}XE8lc3l&gy66P_-d+vf5+Ew04G)c-RWWIJuaKtY<
zI|TMzu_OBBwCl`&2vS#Q`9TCAzNxfKZ$7)n7{<egngS@GLs;pctfBsWlWIYaDTf_0
zU>l>eqJWn3HU<Kk`$<~l1pw|s#?FN3<QElj7tkW+M<BE$3A>X}P-uad%Mn=Huuz;q
zI1MU~f)S7Y-Xw3<IStvX8nQvzb#xB&pof6!#|^^?9%w5oE0Om5s`dWDj{yKLMHBZ)
zN?rlXVSr;>RCA!ch3RJ247_AcLi{t7+@;N)oiYV<efX9Uf?yQ}HvB{7*1_XVk1e)H
z-ERzlEvum$D{R#YnR)K+;UV|9=O-9<`BgKar}<~&38FOsm<t_JUjpWr0D9xOQ6W0T
z=c2X*E}^!_>lVVG5mvBW@Z<ePkRWBbd$x<WK&cDT{cHF(h;ty4Yg;(IjIxKfU<WA3
z&wrmJw4h@Ts;EGh)ewqPOo3sLV`bFayHuxY=FR3uQp~lkMt~XN7R+1Yd(@jAizeSk
z?8=)<h1x-%k8L|(;#*j!+SHHb6v2!*<96W+h&I<Dmqu*DF|mt{D){Re5i6l&whyb0
z8Mee=1&KcX;hB2J5eE{rRG5hu5{S^t<O+OZRrQv7J3c@?k}If(S>t^AbQrl**d#Td
zlKJ(oLVIF&sSxm-TB^NYG=?9i3PnnD47?5L7{0n!-_1Hf?X>1qmK0`X)M=%$X72rA
z{i2p{#cGhayuk3OLPy&xZ2bY2AsshGjF0<C{2quDyEmhBJ|+oUP)!N)(}tqa)Vbd_
zbww*?Oc*W$P5~h#-M?KE#)9#@V<9tx-pqF(&#47WT-q$F0T=})l0&{WEFrB*mY}kn
z6Sd7u<L$SjfPiLEkZrkQYu9U7=<T=<gGlx>*4Ny9{G4tc><=%_C+uC4Fd24z&t`dD
zdq70N+4v(nEOk(H5cjy>?gb#mrF#5w%+=Q;B5Hv+uG@@6N|FPR5__TrcpV@u(K#l;
zRLW7xLYd^@tFB`oo?u}NOzrG^{@ZWs0rb|mE2scIdIpU0r2m)+8mNzE#|a8wP6b>B
zrfst^hl>yPodUYE3pagHVzSbJj1p&_`Zu!TuBvdxY_93EN4E=27`szZb4*9#2Q^D(
z7@2*MY2v5pNK&GlQmW*0=p69L1Qf2k)W3fc)3gcA>pZusgB*5~u^`tGeHOG~Cy**c
zn$06+2PMLnrF{(hjZDSGw09OOOSy^jm90zJuT&=Ae*!7<R@+lG&H5P6jtulIsG<i@
z3D0tjQ=dZlK9c9$=UC}Gbaq~}o=m>TXA(^w)}$M%2kD^HMQ`2Q-Pr-XCiJ}O8W8de
zVRACTP#|EYiL^EsTsikeCkYa33^ZFc&{wtGi@nGfm8)+w#-vr6R(d58q5}Nn4pZ#s
zlUi-=QQP!$y{?$ZRb3X%kLJgo`8@qLt*C$i6ihL1b-mO-kYxBc)e+kgCY;REFJRtg
zxM(YiKQvVM0Q|xmM(8P|LMhRs-Y+%x(~PPv^YKIbz-!ss4W6-G@+&hNL=(3@QeZ(-
zoJ`(*l{$^UaARX*w#xmH`po6eF3H(oeK?zPz*MAI{QAQ-9u$1k6+Py0FBO^NH-Cq&
z@o@@U#Yne~3&2!^ln3=OH#e8!e8M4fa&EuzqX}>#P&ca7BwE%l9-)*9>^Wnv(R~Xu
zFwELvB>=Mxg-Wo55k)A~UOWDw?DTIk-j{Sx9VD+Zue&Db?IiiKHrQ*?lCVg`=JKML
zL(Ef3k19Oq?w|gO)-QQx<>;tfLO*OY2Nn3$tAlpjc!n-Rvr7`BPwoKSE_OM|tGMx_
zA~EfqL$5k9qY7TfXI=+#mOL`Q7~kjn%jwfF0CyGmtbAQercUiyo7(ztVb8AttpWDR
zi?|nRTE#B0U586*UP43@nNw!tPbP%{sqlvb{gDE_y2$lpzMOW@h}B`1^PGcXCbz3H
z3dT;%>fmt)7%qCiHj&u*1t)z5YN#8k0%VOX+-r#b<4Qa3yOHDNGzIC#5)Qo*PsW}S
zymN_xr>^b^OjtKrdAam6NTA^BC<710Wlp6eIB-1Z$`07c9PD^{aEy@sG5&b^wtIb$
zUCOmQ3ofW<lDixG<e!YOM|3AO1ZM@Ny5nA|3(;9(pZ$m@p|iH7X<9<D?&+)WsKD~s
zRDSQ2n1u96L!VmyqgYie7(vuLKmL)$&byY&b#}rp=G3hSIm$h*)s9dUPITyGfivu|
z)TXhM9VT>_YaOG0P~7DLG1n$eO`5zwXERV<xCvlzr#wGi>a2v65b0*cZ$63FLjAKP
zUo4&<O?3j8H_{)hjNV=*ul39$moz!@T5q1<am|JCs(@DPQ1Hx~WCTfSSLN-%Cbr)Y
z%>>vSj!1QK2%Bvb9qu9<UJN!1X#D-BedTaC9jq+yDdWomgIuCkg?V>dZDM%Jbm+C%
zaEr2?*C8Ay{@keIDfls<64wwe<Xv`l&dD{;EY8h6kRH%&lpL?Kr_Oii&)C-T+9TlW
z`8HD?|L`OWw6|57bE-q`(saFEp0pQvWg;s9$g2eu24lEG@THV=D>Hg!PdH?R{nQ|7
zZJ+cMckQ0{HX|UqhWvrq>mSbI467j02WLa5^%1Kz9NNE##v<AmC$Fc=wdr<p)R^s9
z{n`Qj^yvu~v%b>fUU%EE2XE>HoT8fqQd*odcUn}P=#5Xk(Oe!E<IJWKMDXTnq0I~E
z3<i`ua~1b+BaypiWhP2*qO4V!Qs&$m(C3yXnd43J%A86&NCocJ10@+?24`8oqJtc7
zPhC!O`<50N@9uqm7S_SI@p;?d$b!scB!b>Z_!oN&%=7#na0HQB{Pes(IAFY+o?}Fa
z2ZF~eXP`Se<1TWc{Wma5L4>?!I*WqoE53dpY~Iw!!ok78%;#=PAkjifH|!0N(#}ag
z@Ei^YdM&jCY8u7YtjpVniFFJaS{FVn!@2~{p}(THRewG8=mv)!H1P&snov?wC%oub
zLQhf&W4*bZl@T^)Wb`DVQGR*CKz}(=QdZ{spGS`DxIYQ4s3&uUmq&FDW`$S50OzSN
z91Jy@wOzXO(Tgvn1V_ZV+ShD$w!$2Z;N1Gi4X_|gh-sq4hBE|@Nd`SV+|Jv}H{_kn
zb!LC918Hw(k>fSvROKG9Xel`B?tqETbMLvf(&n{du{B?Ld3rr-C3JQh_0{64)=QuY
z!77<u?1SrgH!a|ZV8Qy$(%Eoq;+=%vhd2FToD#MBIFKkaBUXqg?8F{EmtmcvXl};b
z2fBr{Zdge5BDJR{2j5;{cB*&m$e}#E!sNZ3j-3s^tOdsH;s)j|x724RT;c(C);N~H
zB7?+L>CMasmz)z!P)G}X9t`xzq+qEcY|#&Zg$<bgdhV>-Xh8?hKo6ZR&lg((XIpUp
z?Wi3`?XqF;_kPq5W+WHEk!cie<>Z3N<t&OUcR8t!`9B{!2)Zh`t-vAq>AniDb~@Y~
z)DqI*osAX|e>mqq^wtje9Ndz;Kfj~|yqwk!4(Z-CvljsS#&W8d?e}=}Z^ni9hw{p+
zkI;v?qQLuDic#|&Jbl!uu7+6~2pf;%R+zD>x$}!4X{S(BW4*H@I})01yEiB9Ij~*)
z3CukokB!|r5wQ~X+PQUyKUpsLF-Xl)nAjiI--WgZFOqJ;a8q+z0=W%>ybTPcmK9pc
zRgk8lL!c0Q1KIij*|qvOM8{ji$>v*~R=TAy;BPzuP|Hm{4+dyLO}f^ku&+KMBG11)
zFLT|-9gl5}+|pg<SyI61wk_^Lf?c~5Mh9ii7Py{t2g2k9W0hN<X5ZM>gp|6uy1J^1
zAcqNbf52U_QmT6^rXa3vpd!A`W??-%Jx{{W?Q&n8zuap~N2$#R9*5ph1NKBNR&}gd
zR|_yv+Z40fbZ^oz?ck}Cc^TMzD4w=eP7H2hF)JRUv-J732!rgRn|qI+6>@+kOBuP1
z(|c!kM4vYSeD(pzg(*A_I1pdWE`|}L<s-h9F7}k*9|~)^r60S%Kk9YLXCQ4sE5Q7_
zV}7S@HvcGcmx6GO5yc^tk#SfM8sd%15>XW)n&JoegnKp!+S9GRa<`_({cAK*VTIgf
z8lH@$nd`MQs5L!?#U1eME1!Hd8mr3nj+K#-X;Y)yrwGviM_^6!_Y)kXn4#n8W1yU$
z0Dmc1d$x(+i)R;=mUfxIkkE@ojPETSZ)&<QpRoER!Hp9RmfQ=;q6dgbHh~cbjY|*G
zRtWqo0Kjo7%OK&DZe|Dga{Rd!^xR_38@!+P&Tc32^R^ug>2f}CadBh0&%7@>pyDnt
z+W6}Ai*dcAKo7RSQ!EVb3w+d@B+l&f0m9I!f}h>Ugz-(fGGih1&B(co-bdgL;nq3)
z#SN;$*{Gos^6+K?hYBL?P(b_QLdw_PkuP_i2gu)H1u-k>P7t%A!8&+PgCIMUx$1SB
zWKNlYmljJKx6zh?EpB=oD^HULv$VwOj09+CgovicRLgZl@A`QEC1@MGnRgN%FoOYM
zQV1^@avO=%=8AeSOQwn@vtwgp<@Eil=D{`K)zR9~hlOdl+Ks<{PZNGDcS|wT82UBs
zk-p$Gvd;&cOQIPmxJO)uv9*rji=(Lo>jhLnM(Efsm>+>bVgd??27B>cwr;ra;C6AN
zWKlDP>fptyb7+#P{qpI)7J$Fe`Ni&H284P=WhEQ%SMwM0C1{g)AtjRyt^$$MalhQ+
z)(2!1&ZN}l$h7KyH|nd)Sok`aROMzB7nGFHMH6Tk)7LoO>krb--nJ!5^84?$!{4|I
zfdXdmcsYL0a*jWo@lEyiNFVtE7UHxZZr^@PEZpuBY9LIgy0eW^{II1qbI%%qfX-*E
zjd%F&80jGZZxPm$KK65i>bw#=02~a$1#Ut8;<Ng$>4?>(SoJRgRzy7vVwLJyDj;8p
zz#&98z`;&13}-jq-{~Uf0@hl0H@7ap_9vA6=?=k@cQz__f~mR$lmt^yO!CtFVxX*s
z={gNyU@>c#rmCN+`#7?fZl<XGDKex%`<bd0I9{=`YMvGpz>GBAvuJ^_hq+Hu1ghTL
z9)=>81x;}Nl|)^~-_Hw?Hvwd>8Ha{i3K$)RY^D~_mL#LkTd4er1(&`WrYSayHVLo^
zq!YFi04?z4=P<mhk513t_8e}Niu8UjmF2W5q-#yr%!`b%y%}Z+>bM0@@X&>W<e=;-
zB8sAp#_g*Rx!TphS5gbDKoh-PFOY3@^jhVSY21`X<xdT#)j<G$31&2haDI=F>l1-9
z(UbPAd8Qo<1`Cc;E10q&+p#dXmRme65uo=?+?tX7zq!3uB_cPx(x9xt6~KR^K#cUw
L&KIBk`OZH9xpd4Q

literal 12686
zcmdUWdpwl;yY?fcR;|)1B8gQgMG-2(P^p}Xa+(~5Rzl9?Jj1ZEEWZ+pB!?Vw$~lR_
zFjLE7a+cG?7?i^}<T!>HhkZZRZ|~1%zwh3EyzgG`yWh7DA2sIiJm2sAz3%I}?yHA4
z^fY&F7uk*=$WCpo%LWL-Gk|_?<AX;6Iw&#lgYS;6=4JQ^f0i`Jf$-x8H!V{S1lh3%
z{pM+75?2u9XGHt*MI+zDNv7X3qv4n9v-<f=Tvc&c(B>;9XYc>xrU3Dt(Xq6&cWEi|
z`z2kp6B}PgzxcJRMJfK{{_7Dcdj&u2+|85L?0&#g{!xUc_K~p7UO!7+`kb0eb}Z+t
ztj!o@kh60r#vPMcWIt;2msXtD^r9cdCtIIBrBEx(g9OP8lQ298vP*OGX$0A~`+ZeM
zW1IqKiagzu#E1O6b<<~F=Ao6Sx^!Q>I8srVGWo;&)v$f1-eR^O7Zhoj+X!;<&A#`A
zm2;0MtCOy|Z{HfUXX(>Rof-1<nmck$Nf>k9t-CsY(iBZKcPYQ}@bY1g)pAx+E=`PZ
zO~}IS*PNUj&*iZ!NAAKTp3{jvOI-2gjJ0bn_7Pmkv!b+5g@LP%;lgs4I2_J0m%2{4
zlJ-fDW!iY_p!fs5NX-2qE%e#c?=`Z|bTZ~5xFZR!<Gmg&&OW9d9>r2pQXRd$c@u?H
z%P(%X;A^lpyj`J1HJa4-gJfcg`r2${T)%bEi$A`+5v93T1<Xw<AQyhg+dT6t@76N`
z%bu9^6}E$eLz^p+eV}S>c_MDYt~oJ#WW?6F`|Xuv;&L`+Wlk68ULzPc5nY!)MO`Oi
z<~|<jA-N`JUQfuBv#z~Y{rgU{Dtt+)Q}?T_;{G=r=><)B>_PGMTgm=&57m}Ok4*NK
z8}60V>v(71uKdzb&f3d=jWeEG*ago{S?mwAQt_GU*l(AZs;{;rF<qAR(mURyz#^MT
z_OX*8jAg`n&ejM??|C@dN0>HN6~ms~*KY1wmBF1N%TBYKyOZ|1a5b;nF|vvS_>kjI
zic+$(>pD6*UIcI#4tg@zR=MF4d&93P#7=e>)3oH9nwn~bt>MaTE&YaL8ac^<Yvn&|
z-c;+J*l0uQCzAc<KWQoqYRl*6=TFv(oVoX8sXck^nNLATgw10)bSwQ>FMWDA9s}=x
ztsK0oKv-~Me>@U-m$*KnNVQk{J`kMOK!HUZkyKyO?m91Q-T1l4x@n{-L4istEfvu|
z7I&?pM{Q|gKa%2RGR1>6F>&b3F|JtXar9bSVmVIrmd6qBWS?mh8=H*7Pql29Dk_w<
z+xn^q3bC9Cx-8q{xPZa+>)|VF>ZH97FF(6>?5H*@-ZKefEY?n}bZ2OFPpPvxned`q
zK;A@?>^*59vFE0-v2oU*r{{1CcDz|-`kCBS^qxyym^pj(Rc}VU!*d*72CgmT<mVeJ
ziFS3{CFh?qFBMSzeaMA7#IQqx{xBs7&cj`Bz7Iy~M)d`*2ZW29)nSlau>k>9uRgst
zGdDN3w3N|CD?%c>w6yef(fGuK;I_>I;a3H>hv>8i=>{y%--JqqCre37?=V{&O^Y<S
zawSA<?YlKvealNNfilT0S+`DRYDXU%tL9hl`1tgQ;E#Nq9>>D5FQMYirqaScN{L0<
z5nZ_}1&qmP$K;Y#>&?hOM2vYeT<yJ@M}lz|wuM$ZxVV@|#h@#M)+WP+eEs3&OS8Tf
z9wUvPt_a~erYzkLL9Kg2QH{ePGJ%VduwP$WT_y!`6<hkH6U4X`69tBbhS87Z9Gsjw
zvXY784yy-TD{uPr2au*HTo)XEEF+3SR8tg-%q=Z7`EdJ&iubqH#Pc3=U2*4}<CA>9
z{*YmC0x*H;&2Zk@B-g)CM`}Tkkm5b>;g}dYI_Byrec5bmWHg$kzM9=f<T`H&w(37*
zRq7OXr|1RWkHRSzgZZ*^a&&eG$*88wi4(rb9rPP|8h`qi2XN5m>4B^C<tZPkbaD&!
z(et@@^+3wi$HJ-Bkr)#%FE2BSfBEnH;!_UHvGy#-wUs6GWy_1xvSSmW!9u6Z(%bb^
zuE)g0RD69T?YT4@%gXm)Hz_){XI{^F{kpYIGC2=$qnOEg>73k*mgs4<K@RQ$g1GHY
zs^mr9Kd4p<0wD2Mv+4@;VR~T;4+tTxn3>&Y08vF37K3<@H@_l$$ooy3gaP@tZGh(7
ziS3nBI}o?BkDOoiaboWQ8rj{=Av4_7aX`?KFy6`=#>Pi!?j3DyU$q0M{e{cR_{pA<
zhUjI3>Cx1R3bVSqZN*}nehnHQAIJOimz0zw5f5GU@+#T$@HePDOm_bP{5HPeob2q`
zccJm|A~l1vV*MH?_Zgnt$4)1n7B+&XE_~}A)D9SH#sA~-VN-RTxX={1(xCt0N`FQ#
z|L=J40W=Q;(U}fc>}!RCuYzlyUi_<k$vG-E^#b9d=jV3YZGDLF1zFm&N1C9{PWmwi
zBUB28&hlebH}6D(w*0e6A;_%@2rqJ*hv&!t%?ED}bAuY{>$h9dSZu+n0`p%lTu@7H
zDVFnBOU~jPohHn1scQ*M4PtFpyY={&CVXm&?mkWpRjqgxdM7et-rdq6^~USh<-c7z
zq(5@RFOV#G(2T!)AtVOz7R?&)YTGGLTwq=+yx&^&K=t$@!OUv8BGgD~xLdeZSi5g}
zD}J<+(ox(J`Yx$byv^E^Ce}vN$`(`+DqIZi2#CzMeVWi=_QSwkdD9u?F+|1|M`}Aj
z&y}mlf%oW4BWQ__T896$ZL<VtMeMO0X4zkBtG~XQcw5C_(U37B<Z&&1U27Jv;)kPt
z7W?CiTUUeMu74}Lzhg7s*Dvz$UhwJq{Q0%|>X`mZ8P+1#*u*6B`SX2PFIvRG+#`{y
zb2qX=Eg!&6?gHrNU0qwG%K9;(3O5Pw|C=}RzgP$V;mdElMIvZ}0C?mx9wXOInZ8Xo
zvf)8SV|4btckIghl&&2;|E;)1N?^PFL|1->)K26=;og(RsqTZdV_oL1ovVxeg&xi4
zHP;sd*RwtZZ?!9+&bR3v)lRz_-Uh{gYhDQ;DO8niv9eaUE_Om%n(g#sa*HPa%Wa!C
zZ4bGW#2H{O;ji!$sZ?G3aaq~LbfVAHA<<m~`D85#12-y^>WABJ2E;C<-niI6Hg@8N
z2LuHLlNz&gBQov~v@#4#Wo1nfEI=vHxaSg8z^3ktW-&SI^ZM)0Ol3sE_gVC_*x8wx
znXkQ=0B|>;GoYwI>7T|L8w+AQUn$P--e{tNku*%UKL3;eLqk4p{;pclt-Jvu*_?mY
zx3U56CcS&x@jVZH@h4&Tp$SPp)e@+5DNrAUWwM9f0yMF$NwL3oYVP4)Nu4ObrG-fb
z){QxjA>WDj$<52V7BT31jt^62XlXfRkKRQT36pMc0@{Q=;dHHUUof4UN?v#}A=Z#8
zWh+zr;uW2JNj~nRgG@~xR%>+%e^R?#Jp8yRLK}2%N<43P_Uze;<?&o&D=Vv<HUybh
zKDuMTO)1Z&;dxAsD1uapO!NMG4E{R~!`}gnf3<}NrINeDYMyCHDC3%0>=VL20(93I
zGvN;0l>A?}^CI&G5reNYGHN(;u(+@T?ZW_8z#9Na@d$P1j~&a*X(D#60(e+bvnsnW
zioOMz-(^m6>Fh1{(6#h=AH-8JmYJZ8i}*i*^V_^RL2AkR&_n~1F0>|`_^tYj_h8vi
zKk@<L8CoQ;eIB;3R9>$e><GDpp@iN(eVp1qZO<PCtqWHo4sA@5L-ycjj(MMeq7vOD
z)EkWD=I7<*y%wvluUBOhjCtg@`T;xeJ(pbSQRk)7I?Utr_~sdrrj?{?$1wXP#Ue=u
zXyG|IQs*Pm0EqNQhBiRSWTtZa7dTD+aGI<(I%b<9AEqkQP3fB-KTsfGvq>G(%^1%t
zM#hDoQ+!n|-S{I01>RMfL#e+O1Lgr=k{;Xng5hgROG7SOAx5VpL04i=U4<zB#s5+Z
z`VXOJ_d5(viqZ2^=9!(jSh86cw8$8pa<c_cHJ#d$FFlVN@&*O(em{B^IOD<;(|oQ1
zZsBO*56FK9tPbtGZG8>f#ES$~>+~UFD&BefPTop+{``4CJU@bjhE($OLX!aE@{+hb
zw#*7#vkL%ng*z*YAo!9>9?@N)9Bd#5i(h2=aDv5GE?}(!N>F}^a?0G1tpKYXV_|O|
z%b_C5rHW&sjp9ij-I8z}eI_fK7fB&)A0f|u(iBH)+^g<r>^)Els5+4t>oC-h>7S-o
z`mrnSMgbh0^W!ZGTwhxTjxDe~qz5;}UIH~VH1;Qgj5f`wZN~V2p9%iMrz!qyRC;~A
z&a^EkT-jK~AW6N%(ebzMKxIsXs;{Jqh={y{x6w#G2#{jb7n-7x`sA_P+>(D>L(f{T
z3Gi4VC)a8$i7<ug3wygC;9a5>gQ)D6)1!;j^(sBp*~{8(zwbQY*=1^HgYyQW6R#G4
zZ=SiX?uoOsEa2hgyDQToI2u}yvZF?ZWtW$PV5Ox`cCuKtaC;hIX30PF-MY@H-P;6I
zWA6~$8m1Q;SZqJyNx^n9u$UBG{HPwyK+faD!mYXS(vr{YR24DZUoX6mUh+)TEeLq(
zQwpn{d`vI#HBe!A@x&4<nNFCliuE$3xxcff?ZSC?+lZ+8z9Fu$lXF33d8|&7`enu@
z^yw9or40Gl(CT04*9Bj}*<^lqNw3ZwWcOtZv6r?dVGp%QI;1i<C3MU7o)SmgsR{Oy
zMc}ouj%-7P!1v|Qs4OQ*z@F~lk6)IfwIyK)X9Cy!=^W-13(okfzWC3AERCOc-!L^b
zy*S|~C@?%ceC41nvnceSiuWjRaL*FGLo@?t=X@FJ%G`sIXKP0?TAe349pjXnYupp_
z8itt~ng_j}QI^N0^UX<m*+x42sxudwUn<}IxEt+(s3t%~cNM!?CH|bV*~oH+ZtP!u
z>05Vnx$3^K^64Dp)TvXGweD#kG@v^zd`bY^0P|r>OYZKPg&8P-i{ixZztpkV;jaD~
z)uH^d_g;>VI~<L!dq9{OzXdW&#y44z5sdOlpyXRx9DxueePhMP$3I{hIPr(PE+{ZL
z{Pc#SqvOSinn8E{5yQqJBSXWq4di)v`Blm=^U>9`o?^SmQrG^ex}uIkE9wK%%?-Zp
z^<lz~V+NX_r|IaPhj&-4E8EqdKKPIV!s$1?t$F$Ro$r6;JqwIwr?{GhV!yw~uZDcc
zz|2#{AbO!yp=q)0*kGi(%i=A+f<+`52#PW*(L+Z?0}f+qC6B6g?T^58E?d_=9?Mw*
zX>;o%*MM3@Saj%keG_+92rQxle!Sn;xYP8u3|$-*zY{^qq8AU1!uw{Imzx@JNxr_E
z?HA#BLVmnTBH`YhstBT#SGKG+0FHxKjRpM6X=vxb^@MZwE8k~xa&sFj&UudBXpw}2
z<_ZU`uzW<MYPR;kWYRj55<q4}Z-V39aNvEeNq!sLm;&{HwJLYU0X{&|kJop+BfF=H
z;4`vVTYL5FnKQm>C*VHFm4y$8qp6`Ct*t4S52s2OlwFcG$(tFfCh;Ijz3}0w8leuu
z^)Y57mzUsaiQz{N=~FF1SX5sD{8Dne-BtL`f%^t@+i{CNqz^2E5tfluMopLy`|wH7
zE<d{>Tz^Vq3+%3tm46W*zgXUW1zTS=Ir!{iJ@p@Q^PL%`*{@#xky{Qc`1vD&-?#CF
zoX)Z6C<&T7wDxztvtMQ6fQ?s~_91J4L_M;ijbtTw?g`gdd&&^R_lWBsU39u#zySj@
zP}_Bqo00cl;8WLWfh(cls%46=3~Y@`Ut+Vs_qo@VXNr?Q51*Y|$>Y!TqT4q-KNYvQ
zh8j2`r*9)5j@}=t=blTa1aKXIsmAq_;2nso%YOe{ti>Q98H?A^ddk|xwoOq0mVboo
z;Cu~}_3rEiP%N3_o0+AF7ot7_v%*WhcGPN}V+CBV%h&^ump(opa5eRY){(1^Z#X+U
zV;i@^GG6Lq1t~iBq|Xl0atjJ%Ji1#`H89BnaKpGhgfbHEIA43f^|-)x8yUubvc=z(
zFa5R9`Y)A@!SHEb>4P5f`|o`JtdqqnxA-yhPXLBtJw0m#v8czfJX6h2c5nfdH-j1t
z1mazCe^>`afx5c}=+i+5VVm+LpqYb#Hs;U`tjb6#W@&q^{|b@Xtj2ZHZmaN`m<KHM
zsfcJ{8T$E^o0*weG}%G%FV`wkzBri9?+OnO2Yy+oj4Nohw*U6INaUQ;=TyBy^M71T
zL#Y?bq5D$v*xcOx>o1i#AY{wXnyy+fgX$awi)cD$>6wM97`;K3YB5*w#Z77NdoTn$
z8LrgHI^oSTUu>ZB;MZ3dbuCHYiqbc`f!QL-^2Ff|@;sJ_Sv)^Lm@3z7QRC(rWE)|d
z{#tBg4U`pnvj1XDc57hhYM_Vm-kBa_Hi8ViiQ<UOfaZ9y<hQ!a!JXTW9#2zVU(`=a
zrY<$PdUxN2b7?UYfNDhFBP@^iV8up*5Ri(gUr98<;h0A^+HAeZd(_As{gif8dzHJ`
zvXVG561)jPF1VQTes0QAh7bNaEBcp({GX}wCyNcjm3y{rmRINctLfK*)v<l^rpa2V
z5>Mws!tvi)Skicm?00{P%tY#BXw5T&B1ZA&zN#lvA99@~vIi{N8-&P7LX~P+<T?M*
zZ0K=}kJF1nRY}<1p~M(u7vH*SUaVsLhuf{~(Px>Vbc{enMEWbbbjgLEZ_HS<DJs?E
zu|n<;j(cNd(@A!zY<Fmzf?kb+#YtF}C!^d=ZRq(_s<ewaQ>P`Pl~Xb9KWf4w8df8(
zt`ad=Ck%B|bnDuzZ*TN+TQYa1BJ>hy|ML-sGk9h;ut|#!yd~#}mPAD!XmsP&ITybG
zHDT-RQ)hQ?d;RLws{sxe2UDJE&thV{a;;AUs7dVkL4RDx<K&j$i@=z@8mzVJS2d5c
z@cgZW^45~t?G(vJw85Tt_R+aE(o#}GN)fr!2RPhS5e3`ER6Tx-7Vs6d)p<i~!_~t-
z9gWt;(hN=uL(Qk{^1?G~frbJ_>KMB}HvQbYI8OpK+yJ*#I>rvlZxVLP&d$~bsjpQx
zVPb4N1{Uf`Kb)r(oY{q;TVP^Ex{xPJqv$2Womi_*P4NJuxr#t8DKHn*;PXnCRNrkP
z|7wQ)za_*r{JD)wO1aBZ=`(E!2l(DdW1Lx~uX}d=CZtt5=48;s`1m`gZg~v%TQNE#
zD8P|*j75+jOUcnIpWeQe4*~TP%e>!kDk?oKLRlw}tr!RlLtwj~+!omHY_RWYh4shb
z7=S;mJ4b<{$$(QuHp_w~trgBN$kvPHEct`=95WOEf@Bo>UqRP-w}A_!@zlyni_Xr@
zM>ThnI$Q}*K}m^Gaboca31In^hF<DAVOs^t{=swEyK0un?PIIJr^d8b3zVLNO=^V0
z<!ec7_=){CEDh}Z6Mbzm1FC~K7o9x1zDixs0_^dohwoRo8U@rt9f(ciu#3yxu%|j1
zV5&31jUC2%EJOS&LV+2K|9-&bxZf%u9upXQROR$9^a5NwhTvT{p?UbH)nz*MIb1zv
z>DGNwO*lWmrwptNJ}_uNYz4Lhr^U_>bWR#zr$Uo@g5@$BT+GR@wrT?;n8r%mBax!#
z4WM_o0$k?pJaFzF;BDN5O!?3rZG*#4TcM-L#}Z0-P_xs_tfQxATsy$}G5l3RkXSwV
zoQ~0YwN>crY#BVFO5WPLaBF-gYlD>8*w}0-qzhu^e_tIiMizf(r%Dr^JYy$$*KP8J
z`4C)vDKM&I*IuL)H!J5FWq|GD1%3u<-MqDF&@tWxdmIk_*MG%({11iS|1S^j{x;4N
zu;DwbtNxmzXb8iFoV>hegGx}13_#rtWFt);f?AiqS`pgSWuiZ>J69PBhXSZW4)_<*
z%Q*Lw8H9Td4s+>58oOsw8T_1^H*dDig*0a6HjkkNx>}0ayopD@R!dyha&jxRzdskR
zwVrf6cIz9ttDo+jVG~e0D$!T9;NF=Wn8@jmlP1#?=Ld{G5}3gfErfs6+yNGyw8af8
zs*CI1*!VAQWdj`i9C5VXqbFBI+)`XO+b9Ri`S}D(+~U7OJMVt~pS8*VyOnTJo7Z<~
zZn)z6*D%hXd-XpG8`h)dnG~33II_1r$QL{eXayf$o~Nm=75#M}9%M+(*9ckN0}ejN
zU5!`udkah`1%OV<BLJNooruu^g%soPIT1!8BgwJWzS+`*8T{j<-e5XgKk|#$>Xf&d
z0J$GOSM;oa`Eo%+jx+XwDg5$;DMAYP*v(=eoZmhF?1Z6&_TFRHT0z0f$0pyeKLNV_
zMt0^?IhS4~4mgSEyk_)On2x17G+EbvN)-6A$LwbYJ{%Rd27!^Zu=__o1xzA-JX=Km
zu2vmQX{v0ngXC(3x=3vr#|7%=-Fob^>_5Nx<*4`|5HxH*B>)C?Q`-DM&f2So>Y~qr
zB@h+)bSgD3uQ3`mqU13GIjtNR7)xS|jEwG%$ivv;phu(JQSl=IdZCvNwGdt?d*wu6
z7CytvD9$_if|KR}f4d7pKc#8L`adqRrscqu0>;7P#>SmfWqJ~OOk>$`{l8T+5ag%B
zXck}(#+2;A(yM|UsrdG`;hq1|{g=L9A5OYxwe8h977c~Hg}%5kCW`c)>NR~dW(!8c
z|I(xggcC`UlTk>}lGx+m?953gx{e+K!!a|nwp|R`nLs3s;Li_yr|4E=^fHX{px>a9
z6kek-acIv&aD7ER{pyl<r|5;r&@j$`cAV_1A~UO7+USc$mr|cR_z=Y$<3mztQuZR2
zzF$Hrz@F4K-2>qH@NWjs?~mm&rMF%?7Of23@xug|$CLe3D=^r_8pj<$%tL{KM+ze^
zEN3^2UU!8;n-s*HQB(x?+P733G6pd22Y>MT_3O}XIq4K=S<tLL-Ro!<1CI^v#f}v`
zs06Eyj?&U8m-$_kBGLeJpmP%ptd!r0)i*R4-@NHQBbXWbx!fb`{L)Y~<{*r%rcOty
zua((xNUr@Z$F9c@nL>vuUtM4{J=n5CHJ9o&VWQwSceiEyfOX?3QtzE4{P>BM@jw{q
z+rqg@>Qt<Kk>FBMrwR-ssk8sBP1sVi4`mR2lQ;#t2-8a6B0G*c_nU$T_(*;x{leD7
zh2rEmur%-{fiSonA@{Eq*m1(;M$mbxDGWgEIEpU4_nObML^nQ@L$`7zavA;bbWnFA
zmF#AA9!0KpWl()B%@5|lI%d%uKJm|P3}+J4maxYKy!k6>H($bmN(%oe{OVcoRdya!
z?ucF<6GQJ0CW#YbU~FZ={HHB?8T=Pme&yk3hN{`J0nBPpf&@==nMKt6%Ro7Q(+;4y
z3xw6oPr3#&P*zP)`udL}s@c;@5zx3<n8y(N2<F=fJwiUt2wFb4pYRdcj(9i&%UvYO
zuy%kp?0Hax+VZ`fqDrmN%Nrcy-q$}LCUwM8rhXav_!Ly|g*pfCB@Y1GAS`OvhHwx|
zSPL1-_dA%`Klj#q+o{qy2W2B^gAVuZ-O$hoHpqT9v0VPV&bu3&NYqCNlcn5hQDtAN
zBiwU#Zkl%klTo0gYJ2y&kPa_0MtR}Jw^CCov5NS;T0AE;^&)q1x}|)Q5#jSCL})}*
zntsl$T_csz&}8A!dCD_SBRe51VQ~1yaL?6@g%4ZBN1;q+^46dt&`A?!<~{HE7TTpD
z#=^mthj%8KWbDqJJLAuLWbTu-JefVuJ<@Lx#*_?6tTq^1yq-!DELgy5TeTUdyj1at
z_hXOjr@1$NtqDV86*ye7YZZ5z+OHHbm_sbEQ8sUJ7xe&nnE>gL*mt4ekrz94zkNl*
z9%5t)a1SvS?lClqyv)ta8jZ0yn_Z$xZmkxKh!{)n?zc7trPnFWPjW+@dLv>S3qsr!
zOys<nA4~FevIoyeW%#PP^!c-|d%)b`d1a|wEikp}legZyhjL;6u|xI+s#))rsJL+b
zS(l4odC??EQ}as|h1i%ofkO$7Cl($icqq{+4{3w8A0BM61s!}0MoDKW35@L|Zo@cc
zK$i0Tl3|t}YUctfb0CUQUZK{HCsO;pRxKJlOOrsaf-mUc=&0LQ{pw2CMvMVsM$*0n
z!MDyjMdELrBTF%=tMlzwI+>Mz#hLoa1pr&YBR%eU^S*Qok8n|19oc!#IoJXtKo*~C
zI*Vn(y&B)cDc*O^HK;EQ*BQ<y^|e7u(y>&`2LcY57$pF+jfa<YOvr<!Ek1gpkKVq0
z>pksws%Gt@BHiHIUPYE$LBQVaA@fz!0s0!h|M&?K3W4^sUu#e`mR_i?aVWs`-0HNQ
zI@h$%J^dRiiLq*PHt2dn=oWvq{)_y3hbkz=oZhaMl4}&sV>w7y#6mUxOzpR7LlKA5
zsL+C%qUg!!$~V_-)3<vxc_;wKnEX^4dRIOhJW~M+y1VG)=OT?*oOc}Pm*}N|=Mf+@
zA1gvQ2eh6uv!c_h-vzJqZ1ss_LHYPdyt$pVpdm@+Biye|OA<zT<b^djyE<m1d@#hF
z`zbh9av-HIV38R`Q7%ohfbBd7UJtuP-HK7_-0<7w>;=t&a(ubIa&uL2z+OPx?ICv`
zKOIzxN`LfdJJ?8hOe%SrqB5;i-i(2rbwK#{%U^b-u9m|6LG_%u^XE1!-KR8(fgZq;
zq(LQsnk+OQ2Dvf_JD_A@;P@5wmnTX?ofv7({NO8ghKdt!b(Fe`YO+}^^rNvBuYCIa
zB&TnUd7<en=mzuv@>uGN)z)q}fK8cCR!y~V3}wdE&~bD_z38|su$J=8N~G8pb|;)a
zzqtth=ch-4((|!_OR;7p4#ssftp;z{-KR%19l&W4!QkHk*}2iS`qZ(&ax&V<qou?Z
zRLDLs(jcXEIV%aDV+%1c==ZwgD`IYkNP;elv7N}j@08}KQ4J}Lvw>?XiF6jZ5`bfU
zDS176hfLXp<bVa!=3(UnN9cI*z?C96`gy3w3Qw^L;23KYwAAYY0fdh43p+S3)^>+m
zSNd-y4xAaF@XbI4c+VnAZYk)MNGAImL?63eul#(TC3JPAT74xL#%%@EwdFkP;U2!1
zoEweAQ771c+d<o8Aw!bFQber5#}X)k<@qr*ID-zO4E4@m0n6aHeSo%z+@0UnV3wna
zhQ6MkG<}68G$g!=ow-hELAdmyW?j<@ISq7FE=LnJ+*qoN&yp@JUo5!CR(H?;_QT#?
z%UX5KQ#?ZV&A)gD&Z=!BV%E+{fIIcCImEx%u}t}@*o{I8TflpDR8w7fh_o&+@nDC{
z4f(it^5-fwkm{1ONHsIbB;Pya1~fAiQvFNS(nvy7It-n#uEFkP3#<}Md`f_H>BYj-
z?tGymBa$&J;WxK0F#@V?s+={S?;4;B^YeHhbBCzm86CU4R6H%H9W*)&nIMZXS|>y)
zw5&2in|XRK?Q3M<%3u`n<MxWJn}iGC7=?>r%mM-Y<zvBt#i6sDkwpw)p$wl^#Tc%*
zbYjX>fZ;=VBNL-CRi>f){NyK8(E@*{f=mE@x@xha6O*IC+;xyH%bDeTwvHU*_OBD`
z#!E_kXYYVle%)Zo+I?=QZv0TcXxOIR?~i}nso0wW89)6fbXo%q*l3Uz*<#VZkfaGQ
zHP=9{`A#@h@E9G2Fdi}_a4FCSBoo~psI9Mfpgm^1pGwtJU!8BnY@)e)0vwYP^aBC{
zM(jvlb9IV#+6Bu`(^*Lbz2L3<U9eg2z%tHjzy+>ybR_nmqtMW5oeYDXGWWD*rNJLN
zJ32;latj<5pV-0Y!))LznEGh&6DC^?7O`3qXKWi^1gpJQ7YQF<V7Stu*L6?>1D7_u
zkBQ}p!WMrcD?4l3i6sCMIykU}05-6X`Ic46#m!)|r^38OA1JOeyHP<Q@Xc^ejI)a8
zH=j?hu7Hd67|1xJ>bxdP=nz3*Sx_gvWiqJ|`ykT=*{@QjjY}lytYg>cZ+rDx2R;Os
zc#gGsFLs5?;o$|!0n^?P8-&i0Yb}h?>eGM3*CTw+>BYmVX|@2;ndRl(y#RK$kWU+d
zD_4}p7M!USHZ(Q0hb|@uQJ#@FyCk`Y!585N6yqWMVdLOs;Q|0(>n_TM`%2J~xU&YK
zRfbZ8H5z|o%dXGKLdFsX5BtwK-6Ed{J#0(#HkUNFA}mf#dNzm5E4cQlMqfMDWS1OZ
z3z4@cO9P9g4g0OH{k;3*N|KhuHBcx`KuaDED%pPdBP265HI?~&$G(%N;ToU|UyTrr
zGhNJxwUm#9IoHLiv**sm7q_UXIQF~HzJ2zPk2}N5_v7hl2&$4TA1~p>Q6^^#0`FLi
zP8`fF;sCP_(A?)-sHKcU=G22UUqId_P+#{8lKYx#=IJ-3V&tRVHWwBTo)U{pfI;O8
z!1Nb)<?Y2_BnF*M{;b;4&|Y?!)yiQufni;g29@5MXGK+;@EVQVcDoF4^K^S}uM=4C
z_F}#RJRbEYvV^U#^&u{VG-Dj@<r0Q|A05A4@`ho>2vCPJwPibQD|)@<be;?HXexg~
z>>;+-M-u0c6uU4hf}bnYz9Y-II60lcTKd_HEE-AK0Ml0}kto@b24}6ha(#^p%MH}@
z?yFD#Tn_v8Oq)r@<cos@^mT!YK7p|%eQ-`73Jcf2E)fUph{Ys2lQhg|L`dmsD}dO9
zU%?KX#^St;M=ByFvPd3st6o3$`1AC3&q507UIBTkqH!38cT9`*)&6y$4rOv|0tKPW
z)l2^{VEKD!%FQ=*(UKFH2`*^Ip^v?FP7c*oABuB5pWH&9I&gV~7)TB^ZWm9KIWghW
zS&?t}Epf-B<Z7||(t{Ky@dy3h%u>3QSEq5jicbL=CXUuuJ3m31MHzA-bq(Lgx}|cU
z*t(_mMWT2Y&}YhG)$|Dng|6+lme&_7V-gRt7QllY1HRdjW1I_|`<?e>k2mc>f`|)u
z;v<V~-&^JmqswMIZh75_mLNBhVx5`~5l^!tnh~CzG?<Kd{`r$;nb|%JFVEu#TQ>24
z*keSu&?lXGZ=ZfGOI?v?*Y{09uTF}RyK3b>8v@hs^57)(YZIUkK}1Q|!xYS{!ZRB#
z{;>7qq`)=5m+rOuA?svHKY03f5OhMXrnB^+73<e9cUA0l3of86J3!h|w~a2Yybd|y
z<8jyasb)%qA5<{`GWZG@k58)?+Nx17<yZTjl7c-%V(#kx^Nt+Id$8OuLAV2=n6bH>
zurb+$FqsN1bT=<fq|*Z7)V>SgdhfTMhlqep0Xm4v)PpH*&7CpVeYv^0(!RS!Dcf$(
z9}g3f?E=bo=3Z-x3tMX32}VVb87-uK52Lo`7E<EbeCt^1ats7Z3t*lE@exuVUKb{5
zkS+eSL$)#{WQWkmLCB1scd)nr1oFp22Ia`(z}z-M-Qb8_kCVRwQTRNt93jp1!J04l
z;>M&}!A@_4d$(Qo4_G2J`~YoA?8@Pv02R^p9-ZGwRJlt?zw>?Oi!!K9V{`Ldh%C>h
z@RM3fh;pko9%!Vwh$bfOTLl*;Wa)bm*t0Rjc_GiGAo~X%ehqS?F}Q1tK`MmN*hn%^
zE$$@AYa*YtPgSi#k@A>lH1)CE@~~`RHX4mZljoCT^LVU<d&pGPq9tIyKnYPQrY-8I
z?mR@^HcD$OE4cu&8BLuUv*`Z;fM0E=up*|y5k5R*LiWM+<A_Vc%T2Dt_I&Z3O1)^0
zpsozd3hoN^0<km>dFF*7UQ8={!y0>!YPSC=<+&30dZk<4Lo3=Hfj`<;^ez`%y8Yl^
E0Ew)c6aWAK

diff --git a/reference/Rplot008.png b/reference/Rplot008.png
index 7a9e6ec0554786b3bf0d74ae22dad1e283495ffa..9a58eff0a3fe0c8666b5b9ace4a196d98ae53f99 100644
GIT binary patch
literal 19061
zcmdtK2UJvTmnB?)2$E5fNCp8#lH?>vRFR~TGl-IN&MHYsF9whxAV?~5C~{Jg$cso0
zMMgl9fJjbru3z{3^Zl!PrhBdF`Dgtuw4|VJ-TU10oU_l~`_ywybrn(~dLk4GMSA;|
z;yn}!XBzoScov=s7-)P04`(e@RTSY7{_HE%1;N8P=Ue)2C=~H|<S$O&MBot$#e%x6
zc=LgG%IbuVKh^fJ?8fllKmRt7P(#f>c=F}F<^wAHYurTClr(}*kC^bgv~Dt2RBA^K
z=@=G1-nMH%D>X5=@}!4e9SR(@{rKVqH^r5qm(FcxU7gB0R{aBmK1iwE@+|MXyC>%@
z!%WUpD^$MT^Z1eM=<wIo)hM<Q3KllDjJZ2+CUD^KJ|*7Uh=^ObXE$8VM=QTyktAuc
z-gYalS)jL{YHVQieKSTXHXckad$dtqrK;$D)eOa~QmR+bJLit~Re<F49@<2wPEj!j
zw`!Fv<%>?S32z;-q9Vt+-QedD5gg)6xZYa}-PL=uk(7jI1Z?^^UcY{=x5XAm7`RiO
z_FgrCPkb&)Xd}|}D5KYW%C4y9EPuoA^PC!~QBhGOQA{%QQ~q0|lfEmwDSkg|#MdhN
zH(D=f2;1^A#m;qdrL~SePKzENHyU^C;w21~=iL!vAhY~AUi10$=jQ#zwAiYtz`D-s
zq~teq42r`-D5~dTq)N;_;=avJtT^_X3ZNY}a#PE+!C){O`~JCwh4`p(Pprc?560Re
z%jxDPdNr<AUp(lgy#-!GMizF&-8xfS#Lg6pXYFH{RTl%7{Pfk;;Qai2LAa^{!zwZn
z5fQx#YpqPPx|y&`nudmv85wLxTWP1QAD<Bj+6`T^Yc!|xK3K}xJlg8XEGs*2*%703
zdUC9#rx&q?t;)>JCG5Fzc+K<YxQ)!>kfBqXu4zChf8!pmN({rtP+F6SQp=9Yk>RIL
zaov{(&RJPm?X8%ezEFy!5z*(|WVBOVc|#G|MnQ76H%pcF=%-iHGc;$=zH$`Z6R$}h
z0{Hcz3bkZW^W}lOjl*M9FjuNKZm%^JT-18QE>o)Sa^CBy+hV)#oLk7n^D4D#S8ge~
zf1%)tOG=`Td-LY<RUV$?zw^=P^C(4bs<P}i2?@lgjLJ$KLe|!iQp>i5?&QM#!`*dS
zz690QITyRJfZYi$6aP)6d>IrQ1DTlZ%I|&++S8NWrdK;cy$QECEYx_eUWJ2Qqbbi;
zOO@8QA1%8)?|b>6!GGV?<s&|A1nDJF4u@Sm*~8JE)~y~{Cz+F-swUnW``=%E=xRFN
z!On8Y?%#fOa+!oqKyDRV)#J-R7LJcHr&Er^qO&|W*dzy29{4pskwY0)Iku4p?J3+%
z5vPp3agd7%*VWMplVdSko4}D%S0_WIXJmXzicd>(I&R9g`TXUJZ3WlT?qOjX5u=3b
z8Ps2ii8hSrK@Y~umoG=}T#MC)7x!z(LWmgl*SatDWpCfv+wTdA`TOs?ci#%I?w^%x
zUmY#qOrAPAD^IvO)x;c5#`x(kR)vCnY?V_->+=h7@7|Fb54#L%>%Py;<z71=#Z(mb
z>8rp#?7Zzo^E(&^Z=>n-un86HfUTTKar^pFY|8%`#CV82Ye<W^cY_H^&dTaCibYDQ
zPR(I{Vd2xfD=QVz&Hf4<nc@#0&Xnr??Q3FVWAkN^!?3tD{Mj2G-P!G7(~g0>`=ze)
zcdCtwXbyYpyqxy@Z}{)eC+Jl<GXAVx;p2XRn|^YLRpoD>N((%=a<H>TT!oF6J>Bow
zJ6;b;t4PJJDGSqMn8c0(#ojp#_DP<ne0N73?bQe|UcWPDr$hLoHfC(h;BEHvf`V&N
zj8ZhalYaabrF0p++%$Yf5i7+_jlCu=b`PzrX4)?~waV;{TfVD)psgKBz~s#`ziwGx
zR^|+AGV?y1>8Uv#VR%dR)>kQPg~_?3?3pJ7OwHfkD+mT2?Qu3;QD693x8BYkEB#c3
z$>)sNq<5ysgaC<Rrq;v9k6YF}Gb<{nh-vxm>f5c3l<Ji|Ja@NWk>mWwhTTc5^}G8H
z`R+q;5kDTd-otn9NtK}_3m5d>FoVT7`kf=&Hg&QiRz0yT^=oCgScT*K&Bd?pTwuep
zv$NxM#q*XAF2MSPI8M~gzzz=YS#0qbHV$78+^ri*eRzFksIX<S!4I;WGsLgiukW1>
zCdFZnO~;-_UK1X4GQPr}W}G9B(}Zo#)9{-_K}<8hlk_OdCcwohFlnG?SB{cnp=d79
zFW-O#m$fQ{d`IB7`!f{Yro_BO;L+c2mAByZhYHv!d-LQd^E~GxA|i5)ktkPGRAj`*
ztB!`ON~L78Gxd!8hWD4>Pa-15!ot&$EIiF5KuH;QhQy4GjqMyA^84on{-kF!)CEe)
z+s!D)o40SD3nOE+oiH-nnEqH+=PoZug4f`;TWQR7{`oXqu9KVLlJwNMF!OZNra<0G
z38zWv2fDgdenFBMov*kI#S~eWKdaZGXym8g=$d?lyEU`5?>uUiU=9n1T6Q9w{#}ed
zSqwTM=#kzcfuKO==V#RmiZ__R{;+lLU^5<0HqyCn98S*Mj!$c3R;R%fO8}8*lZ?Hy
zxUVkmzbBv^!;o0k-4ad?H<?{Xs6T0_&TE|$TVGpCpKT*Qyv;e>I^;1?>*$FVBp(Rk
z{`~c;<4$Tnw$h>HM{j0aT-+1nJiY29EBT)XbsNY0E)F{8flGdIbmV`u*CFfZer^Db
zzKYx|C?9e+Z{pb6+V=UHpna+)eR)iL=HGA>qqI6)`tUc7cc)@8cEht8K?fIX?d(cz
z`Z<r_riGi+ar5&#ZPIraYC}%H5paMCp2PId|MsP(p_%<EwXO&|=%fLXmjfE}xF>b4
zY|*D4duK4*yd^BlXdx@X<dd4%#DH}Y9Oudvn~LrWI1}U1BJ}F2A`4Fr1d=7(IGKFD
z$0;)pz?%yC{<c1vArF%C@DMTap208BE5$_>++dJ&CzSF1UBcA#yH7RxfRZS*#Il2|
z{_tlV9P*})4$aTTLr1$nxVr4SiBI8r&pf;7zwS3>x9P(>$ROeRF!NFXg@>N2qr$i-
z)H6sfA~j2X^6Kg_0&4dOSwpgIEY`*_vr$4{DHOs;DC<1e%HsQl)#`tJ;mLq#wHQQe
z%M-FT9{o~9PB1Pp@yer~R9-Y1-3ph+fg()ky4)Vi%+D4VK__4qtesP{NXV*aWtBxI
zZ2cEhU}|~7QpX8NmmZm2c(WhF#cAJ!)xLlK9)V9x69H9YkcW?tX4u595NeowzwqW#
zPMT<)NpaIj?T?|tWIA!@bO0Ls1`UkN-qSdnkS9c?oTeHBq7ODs4%dlULn1onX7h4V
z&j%d*rcBCyZYIDTLNN;|=e&mOam8CHucFU?|4k)(&>LG|5M~}h!0fN-zA~idzCLMu
z&9remsmgxz`erj8c?5(~WJ(G{sr%BsQlG5_(JBo5WM!W@=%^G<*gA>x;P(>yr2kg;
zqhS$!DS1N5$hOv=pcA*=8n?&2#ip{G9|@TC0!Atw3}1;}l3cIfQuOff$h491+o7VS
zrtaRs35GL`&c>sNyY-<>kUxZCu08q!?3D8mi}@opZaE^yu*EDs|HXPcjVpvgC6<XA
zF6nm7T?NyiV@&ljIzNO$eqd;bsA<Y6TCHC=bZXtHvY0e2;cI;)Uv2K_n$kdA$^)Ew
zfFK`&TH*W%&lHH^p=JSQP{4)5f*QF09M^BP5VV`@%Tg^AYtXyK`RmuOR>*Kl(oD2S
zHk^#=fIE3dRW+0%LkpGT8$%)Q|GQVk;>+tR7O>{Ca3g5K%!QZp&FL)KUQi>)E@AS5
zquOIDyA8r0u3(@ZQO0((rONoVHb22LY+x*|o+WyZMpNH|>Wa1amVPti!-o&InUN4;
z@>?zCbSk>Qd6(jTjym<5H*eVJqhCOc&|r}9eE=}zXn(7%gE{CLNejh{v!0Pl8+Ar~
z{W#id4Kr-uR961Dho-Hq&A~B$X6wS;ekyHkZ8k2hoYA$v3f?Iib|vuB!ewWw#J;Gn
zm!^zXZVNeg!NC54+^e<W^2Zu8t~CV?KX(s9DR>E-RV_~-pUl2g<)f4*+-##Yjo9B>
z6iW)~R}*_K;kFO~1xbMc0&#S)iZht@njXuf_Z%s!;xR#c9J`8WlC1yU#v`oFUcLK5
z!0F)>E#xruI#Wfi%vXHD9Wji_?kA-;-w+zHhP)MXO2yFnuU?vrVU%hGOi|d8ZSz8o
zg~g&&FH>wh3@2q-;#||gV6oz(N7;M3O{WfOeo)ZcU?oGdZD8XfX9JsP50ZD=--$x~
z0D+x;CMG7TO|FVLj$1eMA}}g?Au;<(y@}H+@-yd-54JA=Dy|x7@T=0^H9b8@pc8X6
z>T47kzh48{M2VZ~JQ7_fvu%ZbVH`OIX>U!~yt_Xq3I%8~ZlpuoaNm$L4T3dU)Zu)B
zv1j6jZv&D*E(zp~?RUtYuo4jw(W_k&dK4w@`#bk@Pft~)YK!OvsKgcwBCpj^IrLjl
z9HPA8julk;?W~S|$7}1&RB3~4$U*}P$*G<)lIv7>Zkj92|K51e>G9B9Mn(oFj<oCd
z&zv~^t6P1EVtzaJX9YaGxdy$OAEyO~P6i&XRX8b6ZTQ_+(xMNL%|=J-9*=aO45T-&
z47!C-?0tWw@h$H@7XT5QTv>@l=qK8S3JocrE0(w|sv}5te_<I@VbVRJ;dQjPIppQ5
zYviJo>64X}RXzQbc-pllsqh#&jk#Z4{2i9DGGUbL$|zJLB%w#^kBtD`{G6y`my)`K
zGE3rwCSqpyuoRAa&A7Ir*DF<?(0y%;8a6(ZD&;>&w5W*eos?Ht*SsyVl1=cd^>8Tf
z<LzNBXeXdam_z3m+1}&VmqL~J?K&DkQ5v#r5kSth+K7*YNt*6OhOliptCE(0^Ryi7
zf6+YbxTvl+wMlVR?=QgmXtaEP#cN9w!Y5Nl3u1(4pFy(&g%)<kNWG6+=DfPpnlK6T
z4F-)H`y5}ox}Y@~S@q5N^-Y2NeRg(suNP*2e}504d~($O(b3WC9@WQ_MaFf`?H3<c
z)TSwNy@G~F==yc%!?h};4TIav9eg0RzL*x&I9mQHf!~yj^$Zz<c(AUC5B}7_P*qYP
zwkas6{$M$u`|8!F6u1b6nolq#Q|o`Zla;lI)VJo(aqOGW>Y2^9J_l47p~wck8hk~x
zSw#QT6E<r`*^gKMsYB(@4SvImEq?##y}f9c6xPMx#DMG%O&y)Hs2_b<#3ljT+KOy(
z%3VLPRpfIWu~kgW(%wjB#Y3e89xNffTlvijr>Pr80o#MoOk7_-IjSPJ!ETt(2rwPo
zC1eW=K#0K-)!$}*iNQlb#%^Uk-BW>30d#)!olEv3;0im);`)!}#O$~9hE0QH8sP#T
zEs8w;nj+!7koee)?(FQQI1R5s$m#Cs2{V<_ZHWc1)sZKvd^Kk_y~hv!x^H9@1+W+;
zcdYS4r^v|Wow@(U^iBVr(Z`>=yVdHPP)NUwi*g<|_7X{gyyViWL<<;#ltSJN{-qPK
zrIKdS__Bx*?hzn)9UpJN{4GNA+Lglz(YM)GOak|<1g*cZykDuU-uOh^c6vN@dX6=T
z32j_ypZK|}>jEAD8C6=qPNH^Z&EO<d8oC?))v%Fg{iY66J#vLcS${m}8HEf%2m5$1
zf!DB7FI;#XKn28h8#FJi>p`b}n}CL<(7Uh_s?ZeO{82mkgQLK5IRE|l_&8^{)P0}T
z(vBns`anpZp%n5|R8)j&{UO6E)EBtasi2Fjs@zBVN2oU;UGwLWv;?pK8pBfamb3Xg
z*=otHaBuBp7NLkzJx*~6+n#tl`!E+e9fNSS3$c)+wneK3H}(ey2QBtCX4rUmRF8fo
zG*Lk#<(7Hj)%lm0erFRMop|w;g7Q|Ry4`>fD!rZDA?Z9FtXE=oGt;Ickf6XO$s@P2
zs7M_Fhcq=cHBc{9#(h@`htBywKM@vNaBAFNc%+ei>H5P6ad;u%qxzGBm7y)Zn(yaJ
z1CI}syDHrY51>{IJ&$-ENKP;OImy(-gb#r~!fF?gLIy>Q_eOD65-eYlUZuT$-wjF+
zD#jhpkPaqSPRZ0q-ASQHo35{~UsmVZmk6E1P?2%+FG4CUm1Yzt8{5n24+mt`B6N+4
zitcPHB{+AHN6UYK^vKVHk*7Ft{)ON9(*vmSbND~KV?-ms2)RIe@DabpsKJYj<~7fp
zfa9fjTbrau6V?-a#>0SE81_5gWZMXkw3Hfbde?WVEnq*bc`6W^8w+7$S}||mp|iMO
zuv^~M)`r~E(f;=S9Ca?UCD*dIhig<~cA_V5@o@5QgJs1+m%cM4Hc7ep`KosMu^o8u
z^v<um>Ip}0X{MDhBF|hjQCd&<moIm)7!(TyVR~)tSe)4VR#Kc4!q=^r3NJW~L(u{_
z6jL7`*fdoe_te;=m^LX};-2cTM~7{V5g+u#D((3h+=ORvW1@ANrecA!P`QD+eA4)C
zFCEAW{1$<v20m{$VKqzCD|T<fq(5xe|Dq%Mcb4zpYoVs~P%4>P0H)78`^Z_<fh{q|
z%T|B);<9SIupj@o_yuUQJcMKLONv(XngV6oqb~MaA3$x>GB9{S&7;%2zMENHU9G(Z
z&6*@zRX@Hq#wD9s+!>Xr91S3yPQsOgh?uxHU$?koAvyawz#InDw@ejsBfm9_&GyH6
zSNLM3AQUgVfksQ-Q`Mqc;apVTJUotJ5Yg18zL8ek*&A7TYn;lYUl@xScdXi`A9?db
zLlP^?cV_lVW$3JD%L6KF6Q^HRRQLw;$DTDtG0}qB+KkMW>O;Tw{bQnZm9(a}ec!M6
zMs~ITEq+hrqRR0~-jzFjRz_%|;h+VcEAi~Mq#byBdZec#(1HHy816d)8UR&z*g`F@
zce;9K!1>p=>V8ZL+KX-I<+ef5{xH+gx~%7@t<x=&$I|XIger#~(sq}hE<9u|mOcJa
z%w51;J~&h#_)H*Am)TGxB4+R>&Uk~L7n17%=zfgnH57RG`LBw_eDsSNw?$kOFbzOa
z_1pH(ZOUnDL**O$*@RsS%eILbxQCR#;Wz`i<4GrD<9F<Sg;4Qkoaw^MM~bp+=%H!O
z=*?X-;7cHe=C-Klt0!;M6uFXC-`I)z?+M}Vim^TqM!tluI2V9{=_|GD7%HVFJ&SJk
z^#}epAm>Bo$6}<J$l*AE%p$uaH+Nz06s-L<a)qU=tG7J;*~dQ+@NTN~MMzB|C>B{+
z=8G3Ea&vRb>FZzg-ChoL7LRI+q~&_2p{O{}D^W{O582~Ujz;2j{QKE=OPTx{O#V)h
z5L)sFvNk5B3GY7{n*T@@|3)hK_hR|~)C-z<>+oJHR?{rUI^C_gH5zo1ynO$)&w9Ds
zVo{7oq-0FrEyjhw->GYos$s@4*VMeyRZc|?Gre4d&sS>CJ;n>0o~`F2+m86`Gyb1l
z66Zdn2n8*wGq_JG;yr4NoNJ71_|smX(VE(WagYr^G%(5IuNsG?r}iv9OH%ejqdzTf
zeH2#1I@JEjHr7dx>f*6$l_q#~=Z|kcK3u6iCRY6*b=H_K!4mS}U`2;sv;uR}aiA=2
zEmxsczzyaQ3SPb(?iUk|A6K^7V5465bT_W5%)1c4j;o#lqzMZZQ5L44W9~@?H*zKr
zx!5(&5AkU9hPU+6Akna-t9J%~!&{j$#=}~=x&$o(_Ek%sbwFQ1v*IueAs%KvP#mrn
z*d)8=GNU*(x9M%*uvAOdG`0Sj&aKAin=o&ofd}OwmI?*j!AuNB+Ogh*e|dSCBDTZO
zNc7!bj<>}bl)0%~zbjY)Nl|G7<#Ekd5k-Cz;fFr{DK`I?MiAqd_ZGS2r^54W)Gu(g
zBVKpCjcr(cSe%P@rQm8ugQ>l0O;iTRBkgaCYD-pM@}&Hvr^KAc8_~jzP%<=ln%f<M
z%E-1U4pRtVW%K*5sSV$LR<Vy)IWardt%<H|0$(Tv5VZv%tOXs$gi*o6D{Wui@L=my
zftTyU_@UB#77~#vftrTy66vp!uL)VUliW=b&TFWXhZ_IMdKaY*U>M-qvkY#vg+kyD
z?S=|Ie(!uGX!V84uGS{08X7hnltMUJ+kFk0LA2=B*4COw?HkOjhjy-edLeYVERN~p
zPYeLpIA_%`y1KeDSeK?kx*YuFEsYe?!L5CNUcL+5Jf#yc**1mr&z7zwllH48pinO|
zkPN2m5WQxAavrnEVeObxvl|6uR}p_-`*11-qy|>_1;?u6sv4l#8*U(pR^<#s2Wqm+
zinwv>-MVmMs`#pX5+{g7(Qu0I?S_j`%TwGZK*8dmLMXTau;ud_56^6RYZ@Cn)J-{7
zT>>Ki%^Mp<JISsO@9XOsBBZudO!W27c^i-w0}nGWFd+VG;pLk*NGi1!wyo1qRz6G8
zA}7vJY23BAbyJH9DnezoJ*5A&9mvuKmOA~yK>9-4hpD!g?5g0{ta?D{=p*$Z6AqsJ
z)5fI+821M{I-i6MPblKPUgyor%e$$spU7@mTUj~e%L`48{V+Rj%zYFw>&9jWGt&QQ
z)Iy+r6DF09Q0_<iDAcyoBb(6apfS9ZeRlPMTlw)elCC<*Hc2tVw$CYWvuz~Hx>JhY
z<Sr`mvNR0$J>Tgo>`w|5IXWz95mxK4#Vl;QEMxA5-?yX?Z#>Sv-{B%bTw7gDhcwpP
z;-GVF&bFa~fud!jBe58(uz}8KXp*0jQcK0tXeZbY%8E&YuYPQU3IC(7Dd$i>uvNt#
zFKH-&JbwEBpAzz)&PhnkS-MAsZ&EB)Xf@#|_COn0lLtn8x?>K+u>^uxUR(eNFP4-C
z@(gT#8*5Shs3ohyp8N$Us<uc$do8FDk|}EXvmGSg{y<(4weks*TFVj<%e%$>4!b<P
z@dVjx05s^fmQyPdM7ra#+Ei_hRp~2|^(Mvae~{OKxK=Eo_;Xx6!Y9_k$G2Q-j0z2I
zT&%)^Ce)u#?Zy`PMDN<!R?<b=tX?m`AuR%CbUWB>-v)r;acjjZlCzuD?3nX66Vwa6
zr5)>J`i1*#Rr`gllj4WU1PR*pW}P~pI}oKe(io6s$*}}m3n%G7h}!S+2%deHJLpWZ
zTzgD@Q?dCrRWqvZpauWrup{~BuceAa9o~NT?{6Fb7r`k($@#o81wEKF+>tkYol!md
z=Jei9vO41t**_HI{{W_jmW5H^9bG+j&&}vlsU`o+yH5-wUO(eWa^WTEp;(ksx%XDy
z>}TcfJ-$q|ZDdo{aa#>-MviPtXA$MQ61wIATeLpuWs^A9#Q37f4}_14J}Gj=WX6@O
z(Vyk_yqQD6^z=WBj&5CSR-Y(I1{H-~^<p#LS(0Yx1);cl)gLH8lDz{zm-1Ydb9CfW
zj%A9LTCcxAK+Ys>ats>E^0#|u0m{D>Fvq=dw4rb`O(5GmUgIX{xvJ;CHxu5GAB0FU
z_aeE>&#{&S9C{+{=NZ(vTaU~h=)qBdSS$S&;f*EH#p7fj{?!ZcTFlZLI^xYAnX$-*
z_69N)QJ?ToVdTtY+S;3+i1`^3)XzhYxarLx)g%$s;_+T|_(wtj{nx+Oznc}`3VV$+
z4rG=;9ORpDVXwQAmgfEY{Y8xIY|FWmH|m)$y*M3RSUk1ZXd{^>W0`0DA=D#XetR$Q
zWPcGAEXVRKumQQY>3e54fUA8wBn;o#zI%;RU}Nwq(uMnuYLx*OK+yOPB-1=a)wl>r
zuw7kJLIG@!Rh`!hxSW{TSquBg`kKz)&j`pZ`m@!}`?_1)@;N^QMJSY*zn<b1ryBG#
zi0F6%S}=2QBjhU{y09leRho4ti2$iHQvR6ytK`Zxn_~&@4VD{+t3rtK-)%CcN9c0L
z^q065YIk{%7?Fv9cmh@})o0;i^~9AlsEaS_PfrekmLM_lUAlb5l$H}0;p|YsAlSVJ
z6&NTc$)5od7c0D3VNXYcC~pb;If*G-#)T1y#gu1=qSGxlr3h(WBmlI*z3FG<C=}>N
z<$>L0L8pPg*Qc1l7|{9hvH%Glcp4O-#vJUflcxt3`<@)_Bc210!nKTzqhS@U8C6q2
zZf*xL>^aDysAh+%i7>n31{&j9cbso~6k8Ba{SWs;7RJP>VLd16ypWRtUJDLWaspZU
zc@9)XJ&Y~2tDBn(!l%KBpk6XB^IpCB=~E~%(l}ledNds^HrZV5G6!`s`qL+QRCBUZ
z<MYK-KeG3lgia0HG>CH|e+WqX&5agvOJLzq!5vI~BnZz2Y)JnHV{Pr^YiZw%F6BIZ
zjbo&#84S%R^gxJ}VMvyZO;XUO!Gz?JD2eReM{-0$G3*B}i-(Z4#A8LrZWPE)7AQVI
zTYDdEc9<t|x*I#W?~Q>R1d6bL8n8Bqnub~qx}3>P<Zc{plY~MgbiMig)2Ewv>fR+r
z<1eV9+}vhZe7E`oxPhV&I~O6(4ttD)vicVEE>t?`WZv6@y1j*lVjxeo8U*%cC;{$i
z0Zlvh<oIxIIls6at(`x;>0O}k4IC39q7Gvr;2i-u8Wt4~1tQ)zpz!}rlVt{Go*k7A
zmJ@H-qlF6JNJXmr<;z4RYhRl#-#Gr=kGv0ppH5B^B7U3^gUzg%*~{6lt=-lheJS~F
znv9<)C=nCZV=phOmOSh{zfy`_YX==S7WmD{B%KEjLh_4hiDG3>yfYVTCmg{&GvSLg
ztsoyUqBi#CV#V|uXO1kffA08Fe~zJPyjVSll^Z{#P0B2OD^$3=Lz5u^Fa>uTe7EI`
zO&T70&3#B1Iu7RH;fYl-g@mDg@yc#6Ai~JouQ<)&aVYOQsC~__5V5L|P$V-x=FGQP
z&DAFf`3DmCDU?BIfjTkAAGA;GyR&Merh3#E84+QiLHI;f(xd(IzK;Wc#zw|1#+zSs
zT_7l;)wyWpo9A6IemQWkt4dnIzdSVaPm8-nX7|PLF19ePQg{CKOKMsxQ(2@oWfX)7
z@S9*8?jzaG_>0Um5_uoZC`$E6${%%qf^ZK*bTw^KXp`?Ii;~J659$G*Ir=aW+#O~h
z74CseC2~Dr-S4Pd5H8#M3SKoxS>B89yh?y{g}i_qu(qhGHV$Iltnb6^?ypk7DTC%z
zYSBjYhd1WqTUc6yzIi5#WSY;omW3u#Byn=1#9a~rmm+pUh!Fqwy49Ub9|AIl=3K4J
zbo5nFlAptF;R#mj|3luQnr(94g|RAxeHa9vLl|^jz72)TkKK&;Xn~x~kKgeA=5B{c
zh4##%cBXY+9lHJ3!Uw6<%65`o^{%soptm?@^%l2E5UAJ3J61`p4VTS?J|4R|AvlsG
zm_3X+!P_W3@8DxrS?^2_udPoy4#WvAT7h?oi4x?ptC?_I*`A@S9RhJv+1#A9J%$l$
zeNbA(FFp9dzl!(DmC}<bVKs0DWDjzgUnY|R252x}cMb%=A7hw&k)|0&Ng!QZJ7QXS
zD|~l<Mo02DiWiCkw8hQ3y!nYSqJB@WZ~tttwa~Hct#c1QdQAxlh;+xHhCCP}F2$TF
z$+pq-VDykZ#ZjylU2+}dEnr7^FHD{ruKF69$6ew_?2yof??qp%F^h4ZRpS@*?7v8>
zo?`#@C*1>WYBV|<Q*giP4ESRV#-H~Z$hMEUJ}hWJE(ws6w!@|?fz7aF@5O&R!{0u$
zTC<pnT|HDSs}s(;U(O!l`K4(g%*hwH7!diChKr1~G&R31hXLb~ZKEdY2@zlFGN=4|
zsh_~G(%z;bAs>B}Dd4vjO70qs>fuusz;3_|g0{!Io1Z~)r}Lvu!R`BOn%Yx3YHD4&
zqPG=5ET<4-$kV=mAMb(o&Eo|M&ByCs9=(6znvNz4><^g`Cjp7!{roVOkkExNbB>0V
zJ9c}=f!r@%4pC2jnkiY^nT5vMQghg2@A#x5US{V8+JQ9$2q$V=ww<ag8)<?IG2-`l
zsEFR*alss@)kuJ~EEjlIX{IV;9tJYQVvRq2_1Zi*2s+*hT5j~I^4}i4FDuGW=dluo
z)T!Lu&!wX0MWmkUG;}1qe%-vdg+V|0Cy)>KoafVw0C-49P@tiyKotUA4MdLi^`Pew
zV?Pkf&=|&Ub0OJD^Z<>RT!wmE5ib#BDgqSZNIE{;4F?_)tT7}l&GkN8f5pX_Pmp#B
zkRVACm?=6b&ud`M&;k`Al$bWdYkjgB<o!bOu&N2qOGOPk1}lsC0EJqCh)1D7yH<ZS
ze<8_^JyEe63f~`)GgQ?WDrni>EI^V!RJx)G2dZ0dyaYsk3uNfQ50Bi|n8Bxjh)j3;
zTM={C3KL=HPy_G1v(8|M>#cG!{k?{zMFc#)+8=Bnz1M4#BF}&=rvs9`K)`|f%3)5c
zLmA2p)MmXpPhQ~4+tzi#@sj&rTQ<x2Mg~g$d)4gZuq9#z*WCu9J5AR6j9~0&QNnd(
z@`M9}gXR#vKl*btUL|#7rZ9H8h&(rS{L{+?WE*|cn;eF_mA8Bd4`8=i*Ow?v$pREQ
zgp@!hY7aZB?Qp#*_Ps**r<Lv5{Nlz>5tQsm&5fcLAwmgQv=Sl*Lkwj=1xG=7R~X(_
zmXgo5nZ*p7w19S9u%x7<^i8;1vgf1lipCFUT5dP-**fk&aQMLPg6q^2cwA6Sc(>oa
zj11*G|3;Tnrixwv5LCb)lMPaAaqXt19}E(PZq*R1de&V!W(R~c3=TiAhPoSf7<)}v
zG1`D0&4uV`V2>aLe`0C8X@xZ<2sX~3Rb~dAo>YV6RcbqU72)_d)&_M=qrrU03O*`8
zP_T<p%T0&taEOJTwAf2d^^|hQ{EwLe_hyuJHSbzGBCrTB7Ss^iZBc7S#^BS`ek!6+
z=MF}%ijfmgf1iTe?wUab5<w3$-Be<X6BcZ<6)3NK^#p;;VdwEJQYn}Eig}I#_jp~;
z0NnXRQ|xjw;X`@pS`zUGXSPq6$I$qwg;#dV8lMg}qj~wq?-lx`;Y)gj9=uwr)cACr
zcAVh%C6b^~cFm}kN?WD!Y_<i5{Q^&YF7t-6(=@$Cr{cn$vx^IJlWzR>E;X&U^}w{j
z%U4%d#^4on;$?W;_kh|i%rvy`9|UHh0jlKT$oyw#yXv3Mx-0FDJwIoOOR*D|%lgu)
zu<xjc9F7LFR*M%C2UgB0tmfJJV6(5+c?CtS8w3U3frL>glrgs{{wX|utdYt}`LRzz
z#<i<g<&i=WeQDUy0ol@EPNb7`x30tV(x?~teVG`O1??0ZKOe~hH}Fu+vu%j<EC|~M
zOA3VE<$ll(fP+%-6dTk2f;#Dq4GhW1U|Xr)huex+RL%-e_FgH_EjGzn<=06u+I>BR
zvp7FL0~Kdz?&Zt#-xn99`T(@_=3Sd-+9<Q?az+G5u)IK<49<`iIImdUDL@dMx}xc$
z)o(*APWuWtOuwL*gsmxFym)~WcS6<;u&>fH#V&epM@QtgP|C}7UL=U?-+ASoQhkT1
zb9mM%Qdq48G5D^ouFmx`vt^K}X;HOK8j!nTO|eET9usQK`z;IzBDwxgkZ4fW_xFz{
zkmQ&Xz5=LF%&0hHv0hXuEBpHBUbOqKTRt3?0N3}rOi%iL4B0<djRN$gg$4LfXjmm$
zoAI3DQyjJeo_n_pjSv6^`Ux$t$uNRgzBYLUq2T$fP_nz@Ztw*3D{n>YXh6<+7S81R
z1G$n2gBif!PoTPcOINR!b<wZMWo6E+;pH8}JAnU`5E3StC#b!Kjy3q;=#hs6{b#Xq
z-vl0Fr@$;bSBZ6ONjp7S)RC!zwuMeM(7$|ZHALuHgU=Pby>QthRw|{B={KJ_L)(U}
zMM+RnVw%7n1rpxeq~Chmhug1E98ELFfC+m+Tn8MCh)_gdEEgxh+*}RFym0Gj41)x&
z@i1s(p=rRnfj@7`3oKXDhEoT~#o)CFpwc_t@=U&<0s+_~``B<-iUTa;i(XBKjG(m?
zfjfLfm#5Rw+Z13gQYFB3DfH;?5=Zx`@%n#hG0JUyrJ0cK0fgASHK(9Ru$vM7bGpnw
z$<gV!k+2MFlk8Y~4zgu<*L>Me!OjLVNID4!BTo%x&Q_<OLqpVQ!%V$R5{3M9?Me|I
zDvC}J7qBL@=*@GnvXi-AOSgc=$;3Ww1Y1irbb_G5AX+_7wMsNSzP2`Ey8;#vSMJr~
zy@uB~3`C&?8j8Kv0|zRKid>b@F@V_#aZLEN_@KZF@UO{Ctkx&MZ3~>fPs5o5M<k=9
zA^DEG_p8|i$oQ<n5|!(Iss(o);tE5dJl7|qaq!ME468#(t#~Z;;a82jMsDvx6k|?K
z!6(uIMR>}3f3*CuCHPySvpv=)WtQr}*kO1&1)V$M7Pl9cm6F2NYEOZYr*O%!s;(D#
z7r$o2yS#l04%Mny!^cP~KwY1A7IAgnc52rIDpXtBJSlto^c;w#9a(M&BG)d*83!-;
z@0DS4xnq};)>AG9(DIM{lmRY35xLY{KfwYer<XH`e0KS1B`x7Y@!^XvIp>eT8Twft
zAlQ?7^|HE~T&UC#rT@2z{O?Ie&i78p^!D9}H`<p!Y>{d!-O@^Lo$HKDnZ&ag(!jp=
z;)pakd|dshbG<s_!9dXD@5D!aRwO+vt+;axYr|YtS$dW0%$<i>+;H1Bspc@{9pQCn
zQ`fDVB5oDEx*kl44s$KxUb5dz3%Y}*M-zXVaf~y%Q;&=8{bhCUKI4woxXiniZ$Sfe
zf>fbshrx)fy>D`gTwMPNPDtDagnw=#+$X@v{M=-*El-7J03TPN!9<baN3twM@Wvr!
zr9Lt0C{Px`k@H*o>1T0s-O}TSS3-FxP+#t!e3Ima4)(NXB@t0^Ab<yjZ}1F_GJj3V
zzDaxr^us?&BhcAQu^pbg+S=f}n~oj{x_lu>RfH_yXy@>l(0ZqeR#Vml?+J>k?SyX2
zw*mP0rRaAwKWZHtPQ`o0*rqv%%vjIJzo$USJy*PE`jDM8+lCE1;OD{}hRY6g3|Bt0
zh63rp;wXMEADVf>P=-8evhXLGgn_K~PUY6ZFGhk)dLBH`4mGFSUg^b+H6LO620dG^
zCSh;xM69=*G?NN9l{|;VF8gFqhn=|nsinR}{g4F3g50sU_Jp8{Eo^Ak?|m#G$6eo{
zwF77RTQZ|o4*F<=by+YgRIX6sNS{%In8E)aod^G3%>18{8~=$bhGCU&d1}R@C|!Hg
zzWqm8MAX4O!ms!)Y>_^jVO;QsUV041Vm{o94pNAw#?%|V@Od%WQ$t2c$;kDOOZz8N
z#s7v&v);v0f?15#YutqZdPMNLMcDLbizG3Vg%{dDQ^TWMWWUY_W$E@XNa28x3rw4s
z0PsNrrQqWu0W8ra-hV?Sf{ipfI*NFquRCoo59*aaB1ML4uH4Vo>xb?)>Y@+|1*H;@
z)i@3+V*fa|e3Ut%E3_bmf$j;))?o;g#Y71S0lP0+mgO|T2M$zXnrm`}pzY^MNaX5J
z5hi1<U%x*6weyMA`>~?=-NW|}=UJHg%#_y#e?i=S|IWhHYd*$6S6kGQ_6l!`GOa-#
zHSQ<|a?^BiUWu`)wl<i1S=gD%jlr~l{P$kYXc7KEZ0WU$xm$&R^1eS@lmpsbiiYsa
zU$0)_C3IQU#rG^KAZ|H1!n(|~+)0;nuon>Nh?R~+bid^egQuw41#n8V1A6`jup49;
zD#*gbU_@Kh<bGhvf4+2rt6vzL(JY_Lv6v8w?STC9!_ssU2M1o*)A?B;@-g)`5Z2u9
zDTMn&EXs9Y5I~yhDNQW4h#CfL$U>(@Nw8cZz)sxa4zV6s$iYPc9}9#VB|*ELMN}vw
zT``J+C{*)O<za6*a#SP1$L)5;#*FfgjM+A0Mbys~;l~Vx_yggBaTms9f&ttCEAkvX
z#VzZ*T*m5JRMtsD+}u#*!T66FXzJ9_(HYY4npQI;1aV-&nzz*=CS{N&vMsp!SO8}k
zuPra|vaLkv6jf{o)V%5IY9kY}w+HU2_3!wC$64=M7$^nheL)?euP`Jnf!`$biF280
z1+hU&q_%CV>+@m-^R3}Mt<@;H=I<$<vcv~qu4+vZu|v8nFtk9;paZu=BrX40&7hY$
z0(7kvHvJUO6$=_n5inFRu^SA`N(A`a8ajU`3O&Z%Tfc1p<tOL-$$wlG_@})I=<|O%
ztneRZ`2Tgf;s1jdKz^wJ_X!F$<NC2eAA;LDX+e*-DjO#$th(EvIcVs<Hk1#~y7%G(
z#frRK;GmN0Pe<>_;z{bmrm#-ZG40Q8rE@Eb$2y#9456uG|7;85?)NUeOkhAMHRciI
zi%4<5hhMTC{=J#`wRX=(gHLgHP74<wFy`1v==WUV`X!l~i(OGP?zrHREVBCQRWaVX
zF?BR5MmZCOY7;Qq)sRCZsO3N5#LyqxW+6B3T%?0MQAJT%r4L2m8hxm3Qb}FV+uV6U
zHSEeoh_7g6bGoqZJEx4aT(*UJe<#XJLww~bq}{OmGwKskV){*hq{H@dJ3b0*S=j9e
zR3HK3LHobA68slN+eepk(+JZEGj|Bv5<*d!`p4o^;fG?D@udP2v0kYKK41y31{;c`
z!8i}Q?Jnwu^E68N4`dZ|{eH_I8fu#!T6r_*n!%VYwzED(4r(EMX=A1wz?bD&2fieR
ze2687VccnJ-h?g>c_eLvX#OH2<u6PM11*;UFDhn8QmaGHFG@FOnG#2?E;c(X{53VO
zh7l5j{;X$UM2ipwGZ+*<8+J`l!4mFgX5UG2IS6ekrz|^Hp8p&oC3N9Y)6Iy}AC0dO
z|C3G9?Sv<W&*Br$_0HX;yJCy89rXE%9nLpbR~&P;1vy@?s#Hz?z}AH+{MP8#kYle@
zC9b4A(G(J59ay4xWZXC3H+qt^dzagQGenBeJgQPxb)Bf~)w%bk_>{+$4F(2W+>>{W
zddtOp7Z<STH!GWLz{k<n_)gylYS!UR!$w18_}wou?llwf8e>3T`~vskqet>vhxdkX
z`z~1;ySmNj7{?4OPvi+*7h$XlI<!!n-Tcs%Ro;E}`vPnKVn_F4YAo7%K&Nn0U*hxM
zpY2^FGKMr2rDC($RbtG*5De7ZSyB6uV4#Gj{}$@G>lY}0jYa{C4F(R-(s(yaRwQV&
z9jA#k{`%#L#XQhf0+BF=NyfI01_ixwW<fzgn;6FZ`@%v73@3%fNq^{n^X^@AZLOr@
z{raUWo<HPbdJ&(`bWYd$E*OyjL18gq$Prcph7z-80Ob-KfiRco3AZzJb=Cg$n>V9H
zS=>Yj!}9$zCE}Fi=O+i&AtHxx$Q`BtGqSVK!swqTQkoUnD8c4jkudBr;aR7+7Dy-w
zc8vdt<BTzZUl^GNLO{X4_yrQ(Z$Jm5(lLdD_<#O)BQz@3kiRCLyCV(Pz`%zFdtrJd
z7K)OIB6B(z0a8LJQUiHHa9@Dq7BDgjhN@INJw+c3o-F1rFWUkk3+WPz0c8hFi8G$q
zCae`wW%+2OnPS%rpjOEfg0};UfhjYRmf)#%;9hAWSsBQXp)pU8&0{pVzmIqq=H?>f
zpGYn(&d9)pwh1BQJM6fro@U#8^N=Z=2a`v-P12g@a~8>Tn7dK^J6WtJ&LA!5qy}35
zn1KVBx!^_2zACooSX+S`Q9=wN7|y$IS-`Bxm<N7uhE16I62G^S>i3fp0G#`WAy8b|
zQ@v-;sn|9Er-}@P!IQ{<1DG_HZWR_c9^8q#B=$AV02H{{r^I{|U<i-%o!bBEg-j7M
z=4oqcvcM4R1r=Lfy%IV5v5L+(1I5CR3g4jOp09@8JLzZT^~nY%djT_odQ!9o-dzU3
ziEykE<1S$}XmMwOb;cMI=gOW5SjtL}bQLdv>xXHso65>zYX-oc!`u}!M4J6D8eA_R
zLm@B<ivhP7GAfbcF{EE<r^H4HTP_1;0W0m=ECk_(03$i_XLX^!fElCDp1iCuZh#i>
z-*Z+WyP1%XFqaX@_jGs#V+>=&L?H1nm6ew}45N#Rlwn#3VV$s;2!pu~`v_{tWuIZS
zMurt775}@h_U7B{bEuIjr!)~>5FLRhb|}I}ftuHbjHf?N^*Or)^b!;>`Zkz<jJV`j
zjRRD$nja%1&PX$Xh`jqb7V}@;L@zjul;D86&;}}A-?Cwu0YW@u?rm3LFjuc$1wG59
zg;Wd?qbqGM`~T|XLa2m(^qT?`FcVb{1NN~W#0>^%aSjM}WdS-xSg&L|jK?b1imqXM
zAX;!}Y%^_n+i7h?LnoitGVQbIbFN^CpD?KAxD(Y4)7t02gKLyuQSkt#Kpr55{Gelx
zO;9tdYA&;RI>2WI93!6#FjVP)Z3WVt2WFLk^>*6Uf4}K%Bn>L%&}a`3u9W=s8&AO$
zZ2>2WOj|h=4H^Zl4c!riYk`;WH|}MKyMB)-H0ZT{fK30*iZ$)4jd>*H>+0%q4HcVC
zEz@_}b|(s{zz`?4XiyVQy~V(@<D~SsOz0#w;CL<S_@QMUoPTbiWGj=iyjxQ^=^w+H
z-S%w`Qah0&wS4TF<F=FRr6GE8XBI?sg~9X(4<1az+>ma*oz>&VhFobd2lYQ#m>a<!
zFFI^n)TBOO>*#A}ixb>2cl&TRsnZq-TZAm@D~dx;E)V9r%tedMf^=T0Z3ox(R@mlV
zvc^+8M;SOP084PHfuoG4q^3bqmZu{Iq=%~R_3nGUl~!Fe{)<vmrP}NigwsI6eS~T2
z&6#lKF-gO|#jTk*6(&R&0aEN#r$K^<wFJgC0pG4f%L|LIW^>;e3nxs@AJplz#biZ4
z>=~`|;zydWmoHydD4=T$=I7@TGC%q2vP@;_KOB3TVESUGi9~2oha&3Oj(^eQ&1OD@
z<`xw>x)p|0Mjcz)jb1$L-p((aYpNM;Dzt3kwS1l{<NgDglm*^O+0n76P%<<$^v6__
ztcl;62r_#zQtOew)~J-Jg$xEmccx*t4_RxzE5Q;-+vuW=K@gRXfKYozfeSL&M_`vG
z77QHX3Umy|Zf&PD1{@+XfC7;GnlQh!V1Nwx2ma1vh6c$3L|&VqoD{UviwwE`iVD2+
z8Uu)B6=|jp;D`}BA`IfU=`@X9-+{FlDxK<EnHt-i8ef@;FHI|;i6dd;4E}%o%%hT2
z+D1m>*$UAeLz0K9<;YYcqPHS~3}m)?(cwEOtAdaQ5gUL_)6GC+;2b*8Y>l)S*pV>G
zi--EPwS~;)LhwM1dk>WJpGd|U@MvEuEQBdpCb&f~ATkZanM&G?fH5OGkby_QUJpSW
zi%iVm<Ky!V-O=H9$>{w@W72n0=<0@n<br$<!nkt_sW-fB|4E^D*|vSxh*Ob{VMkzD
zAjay5%kLO_edm63)=*3UTiu@2pXq4HzmZw#-p-JJ8lBj<f);7Ya^N1l*!>Tx{l}@(
z`iXqf!IXG#)3E1_GPTEL10+JKgu@{lpNKxNoMXG&%T08Ex5Rno_<eGEen#ZUc!6X5
zbm(6^3YJP|sm&F&&O8Yb&bQL+We&AkP@4DrI=a=cS6VuEQmuA;u!Y@v`UIQ0<&zpq
z>*x9Ps{}J4YdSX(leBj*(C@1t_;6Z%O&rzZIXG>DHurk1{gwMbga&7DyNTYg4B~NY
zCcdkrgdaljxwG?0_oEhQEd@nIv!7q#=6<{Ll1@Lq{0noPv3)9J&yO%nO-1z4OduVD
zp=Q3}FQ=iQq2tq+*m9R6xx*-VtzGu|b=+qkgX`<-^%=#6W?I6_YkLVTuZh5WfZvgh
zk+JRjcaF1X&z8X4Zh--YXl`z<_wDB9roR3SN6AtjP{l4Zd{BtD0yOm1dbB3Uj_lO4
z$i8WhqknYtIoO20F2B9+0Ji?qkJ_pilfA}+X;~Uf$ls|Gyy^^DSa_5o?t;t6$k@Bf
z?&q!;0y_`}C7{3qaNuC*nRkV~Mtp7x+>X?sSeC*NE_5B;esR*gyu3eFRur_fC_^X&
z=%c+4cX&f6fRfM6&5gj4$$dGn*u8TcC<`^stW<B>T~Z|6FqER&ZIKMT1zOtL_-Q=}
z+tR4!N98vOk=SIA@+5h$dFvHyA46_l-Zn&F-QRi)Lo8G@G!|e6di(ZmM0?==FZ}4}
zXwabAU_fEx=Y$l4Y#>9~wH7l0IYP>YrJ#(Fkrb%QFm9^hE&M(whX8nrujqDn%geXr
z0|Of$8McpHdwYmgD<1{)qbwVAGEl!W+eU0_Z5?q#EV-MCEWC4lxBm$q!L9iDY8TXB
zXtX<^!R4Pn)!%~XtOZvaaI_akrSyJh$MYq}6+UuGt$+X-d|ddLm!y=G4=`iH#&(Y?
z^vjno!gER8kJvI^Z!5LOTO}6g@Q{*|ze1zGZ+hp})aVPvLd`+G-97Adtm?65qI{8|
z>E$Kn;qGqjy1ldW5{(|1n$qBW^*mMDM;?vtgW0PeFk{;5U}tA{g@RC1oFTinR|{QG
zz@e>uyLi~nHS0z6_toX)JJoVv%H>2QZT1xk_K(EJ)A$}Aco>mJm9DzrnV7v4nLao;
z__1|MA@S|obU5feKR+o_Qqr~nO)*i?KA6vaxj@gr@B#j#oYqyAQsE*ZEc|U`gpQID
z|Jh9gNG+6Ej?H<?kx??@;<w1c*$;f--O5K99LDZn(ljwKF%s&2$OK8|a@~abOO7~`
zVy>M2y!|w#Sx|&B6NjsurmW#J9~2e8Z|xJJZoi}oK&3Ow23r48r@hpfXz!8wkW~B~
zy->7=IKz1G#bhgg!UNf9*b)F+qx~i<N<pqS2a}a^-9&C)UT=zh1$h6)8v~@4&fQWx
z(_kVyckn<v`h>A=XxV*RiNoMCt0>>jF81>mnfsRgi5;Im6Y`lhMlJTFvD1;j#DR@%
zY<E`wlCan>5LdO_9nok6^}y%c^b$~Bu3xi|EbXKz`?lm>f?eA?KJ;oAc|<QIEF1zA
zE5t*fy$li`4)NJ%)n4mNUsGiwD=RBQ1UX-w10UJ5uK7>9>r*1kL+Uvf;7+R;8XA65
z`s^Tj2|RUS#imV9Bfbp|zHy26ZD2b4j8(FIpxoyEAZ;EDFU-OYWMLbJO$~Fae{oz{
zqwjmCB0v|?YmIze>k{|+Y1F<I{k_g_mk*M{Y@_Bi{jxJqitg};GQy_?e1h@5a<q^!
zn8+yz&A`I!mK0vvL@G9o|JfEJPl%J;9d>dONJ38DVvA{SlL<JWk|(4iISWSZ85r<(
zN%#l~C4rin8sI?F@yS#QT)2)7*p$eJ=%l2i6fCc;t+jzMp#&!LyC-cWw{O1;fiZQ<
z^~nZ-#Ji9Zo^{N1atGCJK~vEYMtbS5w6s`A0u{81Z|}V1sQ2GL55fnmk~|?5EiK;O
z)I3u7dta5@Ys+q#aZWEU7s{9H-(DRRN|E%iRd*BE5u0tJqcfZ~Ff?@8oK@1h7B6wJ
zDKGCFOjv*Zk&9<(?+bxq8CB}j9G?D<N7A=~?R3cm|N8Vlf4f0Wd=;}0{?+&r^662x
MmDClB<jtP`H$Zvx=Kufz

literal 18894
zcmdtK2UJvRw<Wp}2@)hHB?u~DAgD!-B8VQcO3tDnAUPvRB}Wwn1r!8DC~_!3vP2aD
z$vFrTi&P>xy|q36ef{q3`~Ty0kJ~+Z^ckMRv8b^3_pNWOx#pa!!Zg*DsVR<8AP7Qz
z{hHzp1R)wn|0O4dM|``hU%(I2+bYV6@Du*ox>Df}KaMzF(|17-N_zBPqRtV&T?9FS
zTvxn&%QI<V#LI_qYL9HC{oBGf6N%FS&RF>=O@1De01~AFp1P}%Cu(#*9m`G;G8n=t
z*=FVR78&5R1-2(O)u+zhdcXcbhq5Fi)cIOLTwoXJAqmj|mp5ZvbLvHRJc_=kdY<qZ
z&5IWrkf?6IRHU(9bV;frjhy>%XlUr{%Nut+0^sMJ285S~=UD)ePoyFb9c%DIz4N^j
zZh6()HundMGpW5_46OB;#?Z*^uDmQ&QFOg%hHzc!xQCf@Dj{NtXgo2Q=B>X-PxqS2
zs8GuBqG92kumwR{x~He4525cm9!NP}I_gcyV&q7)>nF#n`Z9o+?5N&q-49{EU$Mz4
zE;DW7i^W|lMag^T=ZcqmbWNk;P5sy!8yi1=`SK}4D{Tcn++w8MO?+pe$m#2u5943>
zl2vVlIAbO=KfI}XW%VYku1;at8=pf?`nYxd5+lWr=|qRC>gq?^WHyW{_tuBh;;elr
zB)i;2cmr!+dyIzZ7g>J7v=%Sc>?~E3ZO>=LruX#pe8R7#=H?zn9CsE=>xHRc5r~LM
z%{o$**h;Oxr(EV25-Q~ksW*qmJ*AJPZ27)e6h&`YJp;+j%={F1gdyK&%elL^_jX&7
z#Pv_^dux4Z@OF3a-rby$I|!?KurZOIXkR$peEWj*>bFatKY~~^wY7sTSTv=W*7)^T
z`$~!04F(n#UX<P#cIhv?8-Z)HD;)_Is@bJa_TQH{$|)VX+9pL~(G+Q2l6U&_>Dm5*
zlV{JJBgEJ2lDJHNvo2JfxYRpca*q306b~&qx~Q9r4*rzdY6rimxn$XwD_4)g({rqC
z`*IuKU5_1K-9iuz*{xGNPsw2;MjtH8NbmjbVv~ziUwh>^DJK4EUAtuHnxbnbE#H&D
zL4&99@qAb;HsM=VZZ0#TD9l=%&LQightRsYxharyH$}3Y9QDQv*zPZlR0O44^Kix(
zb}iK$_z^l)g!C7QXy0n`XXf10kkQ`V89PuPYF9L^`;jk_lbx-K(01JGPS7zjI^UaT
zn(S0Tzvs8squcm|`%1&t7w5W49uz0<{^XkoVlm0*^j}RJW90JRbsC%+^V_i_q;c(K
zTtA@S-dGlLUmUzEb2LcdP(Wzo?bfBN>5``q*DeYiL_2$a_xtmS#t!y3F8J+wO0V}@
z^wOE!y?c7sug1-l3?U&QK>|)HN9%QkhlgkT<IAHfD=Xifyq?@{<*PkA{^>CiFg$F8
zzm;RoD(*Ng-}!W^16I;$KJ!DJFqN&rOxtTiobeI4y*Z7iiHQ+TlTGi(E-RlA5NOQv
zTRTV2o&R9NeqpGT<}n#f$+W{@@sY{q7-c^{S*rS4CS9Y4g@uLLc(LB$>aUi^!$Q@0
z`G&Q>@9k_fAM7t59706xheM0nBq>e(_e3ZtDe0(4=_rE637!a|t*wm&?C$RB;I#Gh
z$kn=NsYu`Ay(4<19LJH=jtrcMl9E!BTc@4wMC+$irHFj*H5=6g%#jFYfk)}qt?OyJ
zc_u5~RhttLLTKC$`)-VAGLp#G&y=#5`dpCR`pNKnu8T}Cp3;B2D|w}2Y){ZmCcY);
z+Ns+z_Pbm5qC41+cBgnGUnObg`9O^8;cQ+o9SSy2fslvY7ZO4P^%J~l$URK>wNsA<
zimWEf@wqCJXYIo(E2W1W>#55&Mjzx*#$&Ux2;+}wEO-6xo0xFsmQ7Gd@2=Ft9+C$s
zl;LwL3}9``b0<HMaW#B@d*y=rLO<`=8TFYr50*Zq#mI&$bNMog4Ld#-kztPDrVl$I
zC&vXB&BVm?Qs#kslL2}am0qi63A_;GOBKu2@9f%&^9l#9@}$OoIl@#c<NaHnpRq3G
z^5gL}Z^9(k0jvKW-t>pF2>a(ey@EqO99dsWy469N2~SF5Kd+ZxtCI*P@?yFBVz^7Y
z42ww~OP|RDf$F_Q2Sep&bLUN~*`+*|0>_q{A$&EBjE3hIZ5E=XmTGZ*CJ|a`SGgG}
zM1~HIFz}i!4Sy7egWgeKp=?_Y&wqAWMc8N4adXda*tJV-bIGG7v7onVV>IHmlt&$Y
zZK94+sA0AvEq2;!P*>p3<v--P$nlkqj!r94{B3kVSZL@VC8^`Npn7xu@MphjLO8u=
z-^~%z(czA2TxOu7yIy<JjE9Hk2y3mPq9VO8`utO7$e&;QBlQnQLRL1L{r<MMm4uY~
z*r^JY<`;VBgH5Wu2XYyXhBz&KoGB^Kx!>!daGv?fSha7WoeP}$jLb~y)%{{+$kx5o
zTA_nQ41xhcw4RH&K4I5cP0uP5Bj+!t^=kZN0)mep65q?gj_pp1d9Dvw(S<2$-n@Bo
zD(KVP;$W$RX(vYnRsFqRMb8EXYy9Pcsn~8;XxNt?PxkqBJ!|Nzr7z#YSPj?7tC64k
zr}y`EemH-8l9*_#hVRMJjrBFTd6T#cW6pXtcC0~^s(yQ9LDhoup6AUc$;rvT-gF%o
zvu}Vh@+3C)@x;V!yuGgS#%Y2nHu>aUi|PKOw^u?^Gcw5Kc7M>s`W?cRmmfp7)Y;it
z^Tv%wgM)+Fy)BsKk|Fzg+q|l{X|@wKa_VJ>-}pna{QCnJcL_26ipItqy}8B_H4p_5
zx8kskMKvA6H-<_bl$S@Vh-wLg1J2$RB@ScJ6-(umPp>s>FE5sk*=y%;NV&7eo@}=m
z(Mgvzf{MTv@3l1*?=%@D#;X<q?}TO>S&hT4*yFaCH*wbAh^ft?NMNM5XVb1bbkEPv
z3)^(_@9uBZD66SaoA?lv&Ks4{BH6NNx*4hPDCMl#{nc`P56WVHrPrlV-wk7SaYw|<
z%c~5|Pxcm6k(c65Ja7W($Pcf)UiSGgu$GV|al0Y(-Em)9oT0P*m0?L74yVvn%q!-r
z!Ozc6n{G|P9r)eN_`G3>YLfe)_3f4MPq*>CIla#1xe>5#YOeTRweu#GdgR;<kVv?%
zMjbot?Bb$iZJn6`g+pxAGYvU`QDd-Zj%NDdsP}t$Yz!M~!@(VxfyL58{4xGrPU3`2
z1(&KeOt=GSeRtNG;JqI`I&{Id_dMOvkR0!+si}zs+gv)?WR2N!LC96H&xdf3-#T!%
zx);o95yI9?a;c}B?B@96i4l)6H3^5lT;uD$o(FpyL9_}DlTL)+T^bRiRSzhQ`;<=5
z(khS>R_bXYtloHC9rjsm<q;Fp-5n>B%eQH~eSz@(3Qbt9aYeZONO_p9sc%n0soAGP
zXvPYMSklteJTcv#ECi)5N2e4Azp>qmk=?!qm*r~A0yUr6p?b^mtDRb3G_8VSniioe
zdH<H{eE0R=i$l6TE1$@8xFEy#ziVx3lbB)Gx_L7elcOBNIq~`F$um2Me8VK+-5jDQ
z&TjAijgq_Jg|5k&^~E7R`-(-;g1aruw%gUdI}K2vx^npfX+w^3(8DLTm8n{o`mVhw
zn+QJ_NUPA@({lt0S(I8ACa8TzWuUJKfA`Yz?rOWn$C}W=ScbC!Vh*E8>oKrlP!88_
zBl49{6L^>-!T|mKg<dLLw(aQd)@lw*)jiBvN!QsE?n}dGCJ1GE`DPE-ZcDo_=xJ$b
z!A@N#O0tHX+=K!z$g7^j4;A^eVA=Q~x`@VS+s7gr8|s268zY3O@5wB6XU6uIIi=yK
z=Spk=VF6l*fU^(^fUDS3&cuD-u7bAqxp!3c`Rk3f4~ZKe5|btTep_68OL~8EQq9Du
zIqEncuUf(>03?d1=;>yXT%yHYXP?k2guc3D6^?E|!|e-DC>!p^8}lCJlD*$kF`ZzW
z%`(v0c|*&@WOTmdSYEPOLOL3fQ2ozrTq$>_x2pmqh~D^>kfUKoIV3}`o#JVVI^K6b
z5lL96;gs=)BAe~Iw7L~Y+e+r>VzCe9Xd*R2kRHMZ0xkkRnzyxv&~&^JDre(yhf41C
zg@IV-y|}|G+4@Bd=mUZGJnO~L<F!>EOsz7YKDk!-YGY*CPxt;%i2&Y@?sX`n7S2nS
zs)Jj~WZFEALA19a#rA5gbO?DSia9X9fB(MM0yc7NnFU4PB&AM>l>y%mmYU6=&#kRY
z0B~w@tn(^=g|iBq1;8o2m%y8@kt`TUJ5kgw%g~js)?!m}tfp*cdRo-0z_iFRYE9u_
zeaL~Y^^)36Pw6+j4&?OP>x<_g({mQs4T)wg)$FecIZeILO<y#eJoy6lPVeyJ%LG8^
zM-Ux3h;XC<P7$0bIQ6X*7Tm`Uon?-|VRM@B5Uqna+s4!JMm*A-G2bx)cWdw1%y1oS
zhVY*`b3H-S`}aMk+3(z1w{G28jBm2zy+bkHAZ~L$5oyCrC;P7J7ufWi&02&-$9P>q
zpGbiE9}2KNj4;{Ex0Z97Xt(>OMi?2VXRWu9j!y5X0Od&5j#B|KCPIl`uu4>BHXG_0
z?E&r5tEy-Npg$H_O_)g!Pd();n~wdG==a+KX*Jn=4)J<ec7M~tb)laQS^(sjV4D}*
zeZF};X<)ti{eeOfg6FV-tacRu>z+N==4cLE-*k_GyYZ)6vobPjp~$CL8+;vvD`pd{
z`h5cpPgYqUab$e8T~5PGZgPI!29}0;YYV`37Y0J_XmWdqfMJQ*{_fWGWG@Qt@9$Mu
zy8HT00&MvML$|lR;mjRLNdL2n4sZcNRln(V@=T)2%O#-~JECjkq!jBtH%Js<H&pWD
zR|_;H&{3dQnviY|ZHgx2>CDz9Mx{>yM8ul9x_$5LvA((3v(lUzoxcFhV6h5)YmpWv
z<PS~v=5@&r9}=b_hAImg)f1mn`YcXr>iJFK`~AYwYuz`YkO@Q4dgEMi$!qgRh?b#Y
z;%|LBEiF1U<MLWmXJqhCdJlXXDt9}lF11+9yb(LA=&V5E2#wcL+2m0)mBOi{4G3mg
zW-;;Pp6dAmDdlV+ZGVv!jmd*W5ueH9W7qUcq5JWKW=Ts&hm`v;B>rd>p(@7m+z*Q?
zE_k%IHHXrjTp=`bS?q2S027jj)B|pXKcQFCu6vxHAFGk;&DMXMV^}&kpwzkzcovFc
zM)9hfar66llW^1B36_lP?8Aub(lA?I&Aw#a568~J3irjj{sN1f<_8-~jgVH4AmVVt
z7p&Tno?kAu?GrR|>(Ra*!=P(knhwG2FjCHRC79|mfOfPEgCs(ATr~SSS0|1b0RUOc
zwZB3JRV7e?dv{h@u1?5*OXc~C7y7#LV)m=|`iwm|s-X-&U%l~4)>rZk1UDonc>1?j
zm&guQ<qn9Iv@S+(wI+&9LNZa1<}4lk`8+-z1-ys9uP~J>MKJ5@vM!HQuq69!pMf5r
zXz4hzyI>_pjG&<XdY7*(SDV~{PqxuV$VBLWp_K_7T5Kybaap}xz<T!V*=(_Uii>g2
zC+&}`b}{}*58{p~!c<q1JVw1H&^{xOR!yTvP%mHJb@uyZDpp~6pY6F1I#IV0tDwqU
zfN=Oh@NDp?Ugf~wvMRs%{Z;QkiCrSAjCXNQ{2IQ`@B?NUGRBY9EyaWVy~bs~4JiU7
zwLa`dp8uXo<Z&@|*5IeEbiExzJ$mNm=CNI`U;WAB>eMyAJ5`{0{Ohm30(0?olY~$>
z-4Os>40m?-ca~W=Bwc9}(!-$00qF2Vn?h&^a|WS<WkT-)*LH<Q<`je;24B?P(5MNm
zO`&=sFK3=fRe6t{h)GR)`c>E{)t^&SVaW%(GZS%!_SegW&u|+{Ma@9Ohh1tDzw#|v
zZfw3jLBuwp*sECP!8)2`POC|6Uotc9%ejeLTU(n5k=uRCfBrlp9MB2CfklGjD!=)9
zUe-*-8Qw5a74uv%E3j(kG*3v^tMbO-MwM8MPw8aqJuSZP@*#zWL-LMs=*!s+C}TNW
zAv9dfDDnY318rg$lrPIZ+uUhrOU^N$*LzZ;p}442QN{72<3mzd^!r5n@*`-6{_EGT
zJ04Skm(1%91ME6K$;O|$9zdk3sCeqrku^3)=o`X9XFYrEiC+Sf<Jt5NIFJ6G2z}_<
zAyQ#Fx~GS!{^0+F{U<9<X9-0C1De`QSZw;P*Vnikj3k2b=kR06_PKGn_}-}A#fwzL
z(3C&TGny-p);m9~m*Xit`jdAx2VnP!z<j?;L(vTm7o@aRv>26}UShG|F|7p`rn@~!
zjy3fYBu!qkTclfC8n*y?*%NR}jT1LN)Q?evG-rj&9$E};p8VpyrD`O|GHhS!c7;Uh
zmUd7dUP)W~#i9W+zOe%+X!3J}=0q@}e)zA6px!W+C1}6lz$m{_0VFeW?UP_kGqG_W
zN9JADU<({Z?Q!#@)1At*;fHF*mis-|wtO;ja@>AWYfdZ=g^3*lv<sm8j6aJslW`wg
z?N9PIkN&2*{>cLU^9Jc(zCfJ0_UX0559N`E@+n^y2|Bq(Vb^1M%||P}&McD*4*T%%
zq^>s=Afi;n=XA1b(M~R%OVX7b8dbeAM{XZkuCFaEhM45g&<q)831dy|*h{|cZ)8{{
zoKM}-ZiEb&@4Msvdtu;G)>gXpL>S*21aVy+<>)hVQLF!pARhQ?KYcU-D=#v+!sb1A
zmyti<ji2A-qLwz3Uro)x-NZEhaJN{O51T%+%5BFouoQWHLq8XkY){!!<XIK?Cr&zl
z%$}*uRrF8lKM3=a&{FBbG}~li=Qmkh3gi0lZhux67QJ5heqSes8VZJ>uDei3cxu0W
zEcw01h^)b+oT!pk!Y)BXOb&W~`CnZ2yH#SeuQ#6O_+`pGs4qMCXc@0kJohux>4U9=
z_OfU8i;T%zIZX#vIp63nyIhRyQ<O=y$ZRDpGc2{&E3%~a-dKu))M5ecg?_uPHj~(x
z&n4u<v8YrmmI6sp<jLRR?1qr)=@F?&YMFYJi`N$zV%OG&JjI?#f??n5?dy|r?w)tA
z02D|Y(Fm9A#sTFIdM|eY;7TGIHBC(&D&pE_9;fn0FB~+`RWsnI>I2fPqr2e-gU!Wn
zKx{+z+T4@{vB}S<ApK^4LAzwwUJQ5ypjQ?RfMtL;{NX5`HN`+iqUbK+&E$H5wiaOC
zLud?qAX^~;si{0afBsB)_wMkE7cXW9iue@X(oVO%PIQ-rw*A4(EZs^t)43b!$<TxW
zV7@1_?kW9J)XrdV3wrcLjvBIm8FBwv5C4DY3m&StlkrDsa&&WzK5=^b^<Y=7tAc;E
zdL}^KIA#AHg(jD3g^{)>&T@q2H^=h=4MoN09Gpb|UvA?6$9;*L`-NW7Vgv#Oksn^X
z+F&UI;jpJsH63?TyT@kesFK#Rl9LQ?4{;9(90J+~I~L<jX5%Hzc~AehaTmr@nsc_?
zzCta&<*p2w`|0h;2A5Z}@eGh%T(aY^SpArS`x5#s_%#J`^NQ$H{uNJYVXAsGl*ZjF
zemLLVaL(WlUla`sVBiP#gPioZ-<Ru0Ld-WbYB6eACcF?UkYKam9AeK7+k3d<6x^bd
z-elzEkv+PsY!jrweWJye83Hw|-#4*4zx{TBUcGz&&(*CI{_vCScPFTW{{(0IjLW+?
zh|smaLsQv?h<Ycxx|$Ops=GkFFfF`6)Kxq5fc{%xuKxOUzx4Xy6-Epc0LH%y!2e`^
z`(GJtpc=?`kNRpBaA_P&t`@H{DG?#$+?|3h34JkRNmAbk)pSp#smxMEnOP3ebrox$
zv~ls}k=+0Lw*FDq{>yGFrPV>X3?SgJ-_Bz1<k<dt>Fj7##hN%Hh54_aUl1gw`7uB;
zoLfBTC+}=m5m{i(3vM@3Ahm#71A!$F(YKuM&U`6su3+d91QofgW11)lHV4{8GXC(+
zJEK7L<-5+^U}Ryjm}m&=b?w495gJ*Dk>H~o;UALyjv=&dUoL;Q^oI)hD?Gf=q&kD7
zMN&(po6M>IVF>`hw@gew@>AvevtJ}9)5x^!rQpk3d-q!w-RqY(q;%TxYmz$f7qJRe
zd*7%<cZ`~C2di}Nq=;cqP*8Nw*Y<Xg>^O5)=oedU2GH7G;p!}9NmS{#=k0?x9J9G7
z<1{#cQ9G|+cz9=h@k+d)F@l7rj>q`#o<!uyAAbG%HTcCN_sP;9F`u#V4<YxCEi0q7
zTU1+H2?fYpZ9E27+)9Rszkhcq<)U?;WC4nhsIcD(3NgU<P(_}Sp>JQlTm}-6pRv#w
zJyTkY-8}<irBGJNbJI#nN(Sy0>8DE&Jf*V;SnFdzA36Q}OgChAHvCatU0wEI>s51e
zZop2^z%Lp=YX$UHbfCGty&yn)xKBR5X7@zJ+6xqB(A#a#o;32Fdv6`D#Yj?~dj&&5
zp`_&q5)y)P<V=skY43Tnk2nuh(w>TQnovqRy;z6g_^8l^QERppQ6?wq!f*^&DUfHq
zVZb3)Z(8DhtB9{mlYPz|3%jJMem^Z1N1x;^_&R&)C+kU@FbiiUS1&IK!H+-b@lHT0
z4}WwaLVyldgtGASODsxVrJ15QVBsY@(Z<361HjE<&Ii(*s)?f1$oCH#TzDP<_<bzN
z|C-D%&Rcr&)L+fUB3!w%a-D4~A&eOcwaIU<Hb_xu9o%j6SQ0Nzn<nQR-gaub%c%5_
zmS@q&Sc}j}lBP+AZH|#OcF1Bzq#ul){_Hl#7SElU&RTow1&=}io2r!Ds*1ltcjgj1
zXk7AY-J|RC^Zx2SXY!3H<%@{a6f+5b63~D3N&OoG|0fvte;^kK4sd*q`;*TR<YpgT
zk-mnuHtRuVd|0~mb-SUr{C`o}ybA5Nr*o_r(Uj&?wqXk<5{anJ!VczV5AN<rsde4o
zXqj@p+>&Z47~fJbbED7gUP7F<fN`H)Y5Z)dmV>xX3w4*fK#F;!7nMhEOG5gdJCo8w
zbJplcF14-{yd<O2mvTEEz9eCy^MUfGekHSp4MdsNVp<P+TWY7AKM6->$Js?=v0sLs
zh7m6k$zzVWW$T=0Zo$SiPr?amv5^`{x8A|1!lj*$D4};qPx2IGa7ZuAWO6a9<ZV(z
zpC8AtLnMDi0K@f{Ukw)kpr^?CpXNDw!LW4b^%{Wo(NKDcAAv{AkY27_9z|yVrGdT{
zOEx|ej6tU$J2`KbK*&E1L(!l*ci^Jm>A{u~0lqgX+=>xHxE4(7WT8&U&fE7boLL3}
zUV>ps4ekGlyzoy-xO&DiQ5WHp%TKQig8zrtTv@lq>3Jr{!+#n*e)%AO_4Ua!s@#sN
zO&b>l)37#0W4OJ6ftj}<LS!C3<wPMT%?)w(co)%XeZ9bt65mJuy*eL^1qm%Wuc}#O
zK3Z`Z^?tN%S`Re{CsGwiPY}_ZP`)VGHB9rKzwVW(dFo{ITgT7tQT_f3YmTSxSD5ri
z5+m4Dx*?o`(@ss9k}Kwapi}tFTB|;ip9nSK*<Gq}5GX8w;ei4#r?bb+!y|d-4%Ikx
z_-Xn1v}sqP>Wn=`f`F>!%<=>M24~Awo6jlyZ(ac6eqk!;nU8Y$ZE{y{R4t=gcGJrX
z7ArqOxHz-+(HE$+ngpH>tW8%-Tv<&%U`HI6t2QPy{I?nGtGA{_m)TKp`GR0cg;GbH
z4;C({zL92&ky&R|msz`o)B?Gcf;|grL8C?Ib;w3QzFM^O?G)6=_Rk;lUDfzBr^Lar
z!1U$Gg#an{h0u1n1NQP<J~drN5|9Gy1~0%%<`?%W3JQ0Vb4(7b9t)ZwrNnF+mw}#`
zQ>)tmrjZ!g+}kg~Gr@I8#a{?$ms+mOPDhohmOE6?RwwSVhNNOVvfMzAvQckBsuv1d
zR^~n=8^n}?MA9NuQ`st|1OQkWk||28^LA}4jYQyV`~I5kAaGAA>1oLAVcMjYrBz=F
z6JfEpw|-4+4vp=*XK!CsR1^SgFYLSRdfvF4vEv>i+(9jyRT6iO9Ln@Sbq$B>lbX^v
zPRXr4pDf+RS_?_I0rsoF4X^VuJ<KZEmswD^NgLYcXB%VwTxrTNO>jl$rzYzZ$D=u<
zn33-wGPo`dGJ)vXTq(r&$kIQwe##l8-dLqQh3n%(XT{xn^{vbY3h#QZwu))#>szah
z6<Cmg8bOTI0->DaP_!^wb$oAo?XfxQOECvdAZaYWy?h7)<8OQ)jeNb<8c;(|L99Z(
z0qN<aKvjU4DeAp89|V`e4hjhpFyv5!Hh<KT9!pdwwis7oiM#G>1>UktqPj@BQernG
z473FFFVKGI9njHr5I(5H3+h#RUI6^y)Xd>wHrW(~bSBRWoI6*)Fi->p{=>|hA10#2
zMr(l%3rk2ic7Lpz16hIKn*efKS(vq<+zp>^KO&jc+xA+D@cCqn*^jy)B%qngpS>ks
zsJYM7f8?F9wszwA<R{pu*vn0b;F&X@fbrKdHH`uP0A3D`fh^!{ROi|p-KY+M<sC~{
zpNcnCAb&`GTvV?Gh<bKtpn>RaV$hI=+6#hS=bUX91|@@ErXS%xoLjYSfKZP;gJK?R
z4$vBbhRu5UO}x$4^x(Z^xbdeal^tR6IkKw3enRa~wZ;rhojGe6_&hFvaE8doaLTM~
z<MYyhLlXB~Nx|(VNw}cE%*bQJZMH9uE4O_9EXY@^_N7CTB)&b`y5@_6IIy8W%I+_*
zJC3B3lrW=|AMB7*J+&Buzy?Grb9yyMl8oqKrx-eM9iw-5H8;SN@(G_;6CK9D_iLv{
zQL*_WY~~62Fw83;V}Y;iy5ntaedaF}mDqI13pA_Ktwo#_MuL?qb(E+xB-<Lo3srDd
z8W=QA<=~w^=3961;+;zEN51D6iXpYIKpl6snpR`%C-1;P{h<2lwy)mkCf}KxGM}J%
zd|nkv#7<SRZ_lBq*e$#EH`aJQ62)Mmvq7AT#Y!^!qG`$cj`X3?k1l+uB?sta{rG1d
zKq^#tEX(q~KFMPfL;Cv0;npTMkq=(JN7sYzd||D<WYyLHT$<JI*^c^zxJ^G6NJmMu
zhgjSe0wcmDi>pKHxRh*YF3Lt67rlS2JjsZhNUdwI@+O?-7peTUKc+No_w#y0CX`AM
ziF$T4$q+7$!=~}>B@IlsCV~w4=gEqT#!V;3XOjInAs*lqi;cK4=;Rm#0UP#Ci5hrT
zG^5Ga=M|hcD9-qD@tkgM!$e~Q0sJn<$F|x$HeXvnX4$w%>IjL00GYxJ6iOh0JomR|
zAf_UYzmjx23-|oQc@)H7O^E#<N|p&Q8KD_R$bpY1)m_K}@c_cO<6iNX-%TIh7Zu$3
zLXV~tkhwuJz1>*}X~XR2=choTfhJuGs$32ts>a~IS<i(aACjCYNNG5Sg^l~@Y_(_-
zZfG+FH+L;KC!}8nQlU_<L?S%3I3?}~pWjl$qWcw;v$)GjdMl>+=_Me(udI5qe@ut!
zf4Sv$a=DRU^BWc8zPHu}I=Ev8p=!3Lx9z%1cc~rA4eVdG3MG-04pGP0*O<!Jv#yF`
znv$}oP%bCIYStjPvXUJn=w_%L&(gVkcj{u*nh;sX@qjf4I8v^2oy;|6yy{Yk>0zf;
z;><wMpY41fAyPx&08nD>+#KB?P>iM=O2IM&q3yv85hZPEt!%EhlGpt99Gtq1@SNFx
zajPYnY<aKUvWGVyyNxH*v-|1yZZ{6B`3Wq4{(~V~xNbI5RWcjv1k(7&xLfrB`X4D$
zryZ>0FC7#e;8ff1w$WyA<TlP13K0ft_MaAv+?vD{6j4Sv&ZMkze2lWXT`B3NqFFDT
z(wtcR(Vp74&(O^>JID5;QEsqUluePG#}l`lN#pa(^`N-H9o+z_a-qL~*eb~>xOo56
zq4kBLcJ-~&XG?-=1Ksy&-o7T}eI!W9sC5Zjcb<X;AOQhf!2X&R<81lp9V-tIYHWM6
zFN0)X0L>7dpXHwTU%m?k%?N~G_x5Gl(~OTsHd0`>Kq=CUzNH|M-=`z+Bkw9C<f4{L
zY&hDS#3^OuZkb;HqF`qyplM8v3wkX5!c3U_mTtXOLdk|d5J`+ZD-E~vA5gI$4WY})
z4X)TfVbt;4G`7W(Msc2o`-XAUW1L9;@nVmeJf8Z;An_M_gjgQ|?a0`8<fkk)hdWT-
z%}vCnetm~z*DCU3U351E>EjHYV&)qD4>xpmLqKr?suVEH+b3%tiShAP6()vWAbei{
z#jO%#h#S$}F-Mz0&`yOE$basfOLmlE>w1b(9)avAdq5xFPK)^8wJaSVP{D!w_F9Ss
z(Y$pl5USMk=g(6L3!fz)(9_{M^{rk9-EE2c(JD5kx}D(&3R&+!L)39GRB<Fm{#l{J
z+g~ftqk?3j2x1XPoR6X4faqZXis@`m7K>obt}{OB2bd(HZF4_1ZEw$m%vj(K{BZ66
zTj<`!X6T!kL_b)rVj0_P5)+2|16?HxR0d7-;iZpc;2dZKBjLb1OYHi_;!wzkH{KLL
zo`O7G?M4&mIy8EWjkpZdr`fI#G~2)D9$#vgW`LX{YvQ}s!z_aKW7%K<ieM3HdVc0+
zc6%~?>6b$RkkD^KX)l{gPu$$1pwl|%25|(>L3>hE^}0V?Mkl|0vkLMzTC_oZ5q6uu
zRSB|i*{`qXfZKk&I8;iEjQXsqNgwR4Lhq_qYR}P|V`$Yg2$`7E>nGhG6UWNtWQ+wM
zn_e1G5qn+j9^MwdcQz+qE2?oHPVL~Y-khNP_VNNTu%#pc&xcL~%KQ4tl-Q4i!#as&
zzQXPa8kd^^B-vaqt%(A8VQ8D2XQZU`YSYc{U?%kXRd>{PAIgMX{Ul)=oJE0HENYIp
zfI4=hIeox`f~uIJ_xl4NHy=WRX}F><KPEK?0eN#aE#`Zct`Mr|oVb&~i)LkzGW5Gz
zzJ3KB!p-iHwW!he57q{QF-A88k~5?3pAsC0JI-69duW}YRzifp-u^1?6Mk=71NjL|
zoma$@cIi~o?X1Hhm1}_|n?r$uU)5T#AxM+K=Hp=6Drv*4&vp=_>K$sl0wbM4OFO7%
zpbyo$^%;lw?QQ?SX64pF4fx?+k<(SGYwX5^I&vUb!LA9Fjp*LyLJb%zUP`#|+L!$x
z1rU+s_4BFnGr!9hxO;vb$sC08fbOY5CCv>x;*~Mmgh!uFELE<ySq>e*PN2p<376?{
zFoOM6E;ZgU^bBh$dNJJw<gBlG+#*Yd>F7APAdX{y`7Ht>ZA}nSOOKC>OD!(OSPz2#
zOxR&m8eh;d1#4Xy0AlE;1RWin;B&cUhcu)Kik;;^5GIP+6>_3E2-bucktQ@SsKq%*
zAIyrnFONnU+JAh1J^6TEY?<Y~+5n<nA3)LfuS^c{ZSVP^ZXmU7U)LQ(_|9iqq!vUs
z{JXi#lD*l40ccBV4)&Iv#%qaumLH4}Bj`DnO{Te-u4?Uc1tMSoPL@=;$Fg9>al5k?
z$N|`1<n{Gmxs6s97c+wO?KW7%UN;=$(t2}0X?Q&{naE@II}z|gMVwsVBm#?JGzJ<w
z*NZKlhbzJTlH|X?_449f3eYSbkx<_7i<SpOo-##T9>o=_xp0tl_6zg_kZ3}|=XxJh
zRR!|hU-31dR?6!50^slnq}8Oz4dgE?>=8-Yd?EcTZ(x}jBq;P`0@2f)DhEI!A(cNg
z*O16%($v8CO2zus@nOn;v@1}t!%%PCP>Ee{o<FLO{5L40*hm!m+R(7ZPk2v5q%-W~
z<f}+9Y)rKK@4Iv^cGJsUfZjnd2d+-+e*cr-5xffI%1dK^!L1dxu-1a2s#>1b^dArk
z;s<yFAPcarPF-qZ=75PjK_`#V9s?DJq<VkbmNl5DbSO$i@Z+gD<m~Y%2d-;&04!ib
z{F@<<Jw`R(UPFgQ5IDa>qhZk_2C(I=>A`j%>U{*;0^x()UZxTrj5JIj8>&gPtBYUi
zzX=zFdRgD{Q;@<+f(@~+oQnqnFxj#-fdo970Cs5@cvX=n`@tKq09R}L_RLsG;o{>N
zpDVXl!23G2iuCFAk#I<R(gvHK0KrCXpAbWY8LnOiA8?~sHGw*kQ|1_CXe1$9g&J@*
zz|Dt^5iCRHW8CmTQmE674(=L64#P2n>mKzxu%F-F$Q(y~my&K+x3ryGtDIod1w-U8
zcFw)R2{3Icv@+l)$%9Hp{U4H;s87%QtXn2D4b_k+FWmjgcrU?n#NwGUVA*CNZSA+%
zvzF~|>_)B%In{f0VYmZPKP4rCCaFUY5mATH;h*c?%I#ekYS3$YcT30oj?+l50L8}-
z2s>w-^N)(A1Q16{SC<S?>wQnkMl8-sWw0E|^E7mg3vOu){C=>H@cZ~*!K;X{RU12K
z;Y1+b8+W$iZ?@uxKVCv}GamD6<wrg0(6d}4{OrKrc>KI3(5+o&V;t4%5%BR)gLzjj
z{Wb7-ap;TetJZYz1lT02J*gh*85I>3EdzrDjDS-SED@Ob?(9{-)}j{iR~R`txv_HX
zF>r2V&p{@%Hnw^4i+r=7nX_nruhqeC^`%=d>ec_0XhREz&L``ZRA2%I1e$JbaG(x^
zt_+IWd2I4-kUD}XxdTj<2=&Ys!-0B#jlciUMkItKhYDM5<;V#-t)Y&H8e%8y>v+md
zmn1yZ^;m)*I75G)2SsD~yOz7ytaxoiKF}He!6f=mSe#86Gd}HwkY5w^b2H~Cup-od
zosTPsEW<m6NWr;kK2Dk+n@Y$3pyi?<Jy{CoCa;r)EnH}gCq=@4)TjR$cQ+om#hDL8
z-Oe5oP&O15+UkwP*5Nnme|yrpr#Q&gaMjIR`D`hGtZeznOHW@D$m^r*Syrwh#z}M1
zU~0I;hlj;pC0enPQq^!}bR=bIJNS#^%a4vMhnTaL*aAMS8KWs3va0~C2R>pYa8WKc
ztmeHz$f)0rkL;$~&JN{0);!?P!f*RWLK6!rQwDaAT7;_Jd(SD=L9zA-W32)?c%zU2
z`lDhy7R;>|)EGXQ(f-AMry{PWv|LhI{S1heM1+?oar1cmL$mKs4c=;!lau~ns9Tqf
zP~-_EeZ0~znScCKn8A|XxAlPNt7)N!ABQR~R5ub&5X7Mv)tOf~5cZ`^u5j--w9rrD
zMjr~39c#*(50|`=1tQR8HIJXo_ds8Sr>b@p8=s>O)60_s&b<UrM<h+625tb7|K1Jo
z&sO`tf?EAQN^a6M6w7y+Q6Ki2c@0!9tDK)Sug04sj2t6{eiuXtvw%T&uQBjZ&vHS7
z)<~AsE4WRf+SLFyu9*4|8sP1OtzQ8_a=R|@h}{Si9PY`hiDZtCu-MP-?O^8ji}g~8
zJO8Qky^5BRQ6ykQgD$nMhVIOpL<sOa^)PdC1PR^t$tWEF+V_dBX28p_7|BH3Eo~+H
z%S7J>>oairl=Ahm;mw+p@;Ts-&mbYm&|)M{#6embKg%z_5Nh*BtbV2&;HeqHR-g;s
zids|60MGsaYZo&pGp3h`D#@zWDTo|D8i7wYAa6Z%(Ey80O-Vr{2%uJn!~dlqKfFi&
zaM4q2_R2O>)6=OZY7nf=LDz+vu^2GeLei}OxnJq>Wg?JzD$<fOK3zBhD$4$5vmDHn
zV7BW2N^*xT7rj-qW<hI!Pzy1_Y|l9`c`cX;_iGBVmyGarVPH;yTY-BB7f4I(n^n3~
zl5!b5(zG!WIx0s<IlMZcylFnVJez)`(K5$TJknE`T!ypM_#8~GFjCbqMGRDDaD$tl
zor;IPU@I4q2X<SSRk8meA%WF`o2=;^0tc--XrX%TJ~-US1m?ea=H%rmf4>Wi&=3yv
z`A45p(r^L6!o1E3uu0OK%`pD}W_+sT_IP&*C9M>^_YF<{@JE2fXyxl8?n4r-cO^3^
z4x$B1S#+-gX8yCb9k33eLo^I2+?q-Ax~Xvt&1%s2!Gj6bbxVZnsc|+5C8b>$HRw2@
z-Aa71FD00i4OdPh$!AMMJ;`IUlb4~Dld+e!+k{K<Tz$pLf)Kw<>YCQQ><?O>Ub*X;
zbk$dn<WB@ZwKxgVB8&ro`LL7X@S&H|UKe3B#_hKXP&aRITXZW>4`6y@p113RS|aIq
z4U08UsjpQb<^F3w|3699|2G_Y5RAXmm*)~Af$5)$^+67wEnm_Th|eW1k1lI9sP!+o
zsm-1VL`aHwcb=jE-@OFVS;<>H`w!&#Y%{SG#(l?a2A{K9sP{U5TsZh*fSb@7f^czq
zD}@mpOa(Ql#P)O($<tVv>?+xR!oK;qa9%=BMf}EG%~}$6(p7*DFEQmg+BE6r&yhFe
zDZNzG<m>#)D<yUa=1IT<grq=!zrzPT*(rLbV&+sW<+eHHG_4byF1b3VChiHu)0S-L
zLr7!jAKE13Kk#{(F(s-tG{hFu3?mV!S4C%gxlG)|Rw=EGod{m2$nL$t7IhVVc_MJ{
zQGwpK0)sFoB7`%h*<FSwAo-6^;?uu2ef{6C=l}gZII14<ocj9WCB~W_z%3c5Q8VBf
z!HL;%)`AyaK}m^(S<r|a47XsQV{YXGz!5Vj9RR}XW9VwZU>?bO;Sf0GwR=~CXi;((
zSo2^o9|D{I3CK6?MGr$Q#xf(pdkNDJTP=4hm=R?_3hqX|z~@237M{u;k4s`_X*Lu<
zqI)Gxn)M7LbwAA05y>aUq&Izd&IBx4xI~u^_hG=U)CkOuoq%3e7737W6`$acdT)1$
zppZ1@S~AfdbWV)La_nK~!T5e84wP5x1nc;RDj{zYDLeLD8!mPqI=27hQtzP@7REz$
ziro(la&u);JbgpkDvyUYU+kq<xHD`us85_SJ*|*&^TfC9;+DHb2X5_in!k<6brBxX
zuO<R%l#CvNfleffKNHt&<~ZJZIy>pdn9OSH-O18<JM<X*bxC<{&3m#nIR+;2lpjBS
zoVA@TJE;7@W-B6Q!bAetGH7YlGMnU%Sd%0RbKQHJ=<fNR0tRO{Vh^9yLxGb7^fTMH
zS={Aw`$7!cWLbKihUOSl{i1l17(2A!tSIm{RFqSVjE1uToR%mdg8EdUk8c9m=grz~
zAPk9tpr1YHS}_9F8hxSUFua_Q57;G3j8(L?8JU?UfTS<6C6WjCG0fg2`f9bHgo}-h
z4Q<4Gnz{Q^Gc#!bf}T<dc@KPTZB+tufv@j&ZTsH_FCbY;Yyq{+mPZ56G=v(x+Gpw^
z2>!U)ps@slaIrC@5ft*nMHU-VA~5HG&IF!#!>v~Y19qVxVL+}Q!a*$20&{mXq{w7c
zm}+Q|9=CO7_FwzFJXB@BqjNU@+bmw1F!$lkR|p*%>e*f^grwwGGv;0a4IOZmpIcgr
zj8(`VVzFl#85#R?Nf21)t6<%L47rm53j{7f6ba}6g$dBeNN~raB0DSVak@1irxIIm
zE0J@<u$*0AIKbA%+W@#rAXjMtN%(VmdN7v>@)#8<$MHPnX!gs1Bp8(njF(}a1!BnU
zPNal{$>UEaT#W&C>kA~u#)6O2u2ki4$5HTas{^A||I`4?^sm5}9^JjYQF9<5SU$_6
zY;%Mg9sX0gd-wfG97t$<U`t=RmNVD&Ap$JS%})(rlrT@P_}<f#IMiQF_!+Aqc>@nU
zA_K@e@h5Sp_J-;|$~L~hZB2YVm*@HpZZC)R;+@dY=E>;po!-*w)9Xtk_-i@Op3xJ)
zZ7y{KxM2O;p#DU6^Vl5uTMCv3Ds5*ecI3zhB_29p%94E72TE)kC!Ih!<h~NzpL;if
z7bsgtDDgwNB$%W}z|%!=NN~7=e}#%unpHwVqQq8PS2xXJtQrRG9xobzq>B0i%U$Pg
zSLgtTCkcux*plnrQCtMa66S>Z+;i?>D1fxS_x;2W4w?a&*hLNBD7u0HmL|}1*(FGP
zH&@J2_63g5pO2VKa%ls@diA1#h=>Tva7wz(->G2MQb`j2Q!;RTe>Vk}E6B%TJ`<3L
z{0SIQAVR<zUzkpEhzO|LiXI$-PAZh`Z{rETrSlBMl4jb#txNy@eg%E70Ok&|OP!$E
zgbhRUFyQFt7>ECsl;gF>I(z;qUChaRky#@EuH4UbhYt}M`R}a(1j@`YI0Cc07dSn~
zA7<OU&tjnn%H9+4`gO+;J{CBfGB8-vk{{Lt=6l{Chht}MyC<O_**X&Ex@+aVZz#Fm
z)nn7z&sV56L1GsDVEUES4U+;1Xxz;cEVFF2Z`}&rTAiWn!ZgnJWHq9bTk{4m2*?OK
zm@BMy6R6$PM%|z$7zX=xsa;Q3j!W88^UvrL%p$#4i4QlfaL1MFVD|@eUf6rv+Pq3X
zW8yt`+G%C{vLOyw1J8{S4|KpcD;xg~t7~d11eWiPiDPp%YRR(3-QC^&xjlNeO@5OG
z?#=rhdxe+wWBm3PBllb_GDR$sGjM~AbdCzx<@#U+Zb&+B8(E%Wa(gzVas=ZmGuJ_J
zIHH4oFF;n~*xr33TVVY3D%=IYChrIfxQjPEuyM-S-Sxpw#Qu93>hHVE+{P>B-0CvC
zNr`~juy?FaRTmGFr1db2^UN(ubuI3^{-fjeWkh*Z>j^mq=qLmXuN(N%8DSTTFRe4+
zl2~`5cJ6xYiv79bG+U7+p^k#iCFP;*H9R55!M8<+8u|rbV~@ckb8`rn-#xs|^}N0a
zbQHHGPT&ggO$NUw_ycQU0to1_Y!-@++2X`he*I(~RqZTXXOE5A@wwgsV7r@QxME=F
z?+(X}OdFUcX9Z7$eUEmPa^I?_G#qC-6R#;IRMP?exCf2=f3WE&(*#kSll$U(LV>@l
zfRu%tc8!75{!N(!2$LK(6-B$Z#$@@lhq!!y(}Cb^*E0a)^>Fne+F3e(nBrjS<M%fN
z=)=suJUb1tuc=N8c*rYZ{)<(_=2oYWj~no?=pZmKP4F@&h^>-x9ou(VJqe;0bGlGv
zzyL>q>I)$J85;jtT^hd?Vu)>+H0Q>*Kfko5hN99pZpb@q4=A?iya!OPv3wd$@P>A4
zSt4-FIla+)({B#qq7QDh`$Mpl8z^2ZPDG9#KI91m6Lc*1Ux%L+5_0ItY)Z3z?KS-h
z1{!JL;Yd_)f%9$cmebJ43769hsV;Gi@+VAOK7`o4{q^}7IFP{l$RQ~(+6rk0rmil-
z#Je{LEb|7L+e3o}uLkc~qXVcX;L8HQpp1?%f$17f8c>I|FlT{!8jV~#l;D-tB|7Ea
zVU$tOGDkX!C#`wBxQY4*mEqhw!Q{>)I0n9KS4_;66#d&>x$fDtZfb1omRc@Y9a9nD
z*im#RJoqztT%3r`A*V7N`FCW*c<e2eYLy|q@^c9^?BW5S%ED+p$T``DrAI%!c0ZO|
zv+pf6a{rS0@VRYIP|_oreeE*ehet%fY~f(GY{!>yBA-Wdpd3MIA6i5iPvHma_t_;~
z`K@1pEN59m&k%Lhu*Yw5?N}*%g}^T9!Pw|X5N0jJ2|36ua=C3K8S!4{rT*nI-ar<e
z6C~GZ&BGNgf2Vwxs9W`2tyGDnYB)RVv?uq^uiy2aUUoBA)H?hy5R-LRvm>JJ>}smB
z)Xc(`>F$7ebCsv4)omFr*0Lh2nwc%H0;`#Yq}A1(J*boU208Z}9GZcU{odCXugiJK
z<z<xW>Py=o`&|nQ3-~R+prGJv*_UW`iD!LLS6M9voQO@#fJoom6i-i27sg-$)e=Qf
zJHpGCFTY#x^72v?`V6Z+Tdnk7`>W<1Sf3f0n84EcQBEX8?)AAbA=y{19v+jxR+4bI
zltX!)5Hi9&FYs!n_9UV|OvD^PQg}WMhhyT?c%ulc=f3ytnCMW)?cdg*!(DlH3fQXm
z9PKl!VDg}}Hhn;tAhEHvEpVRt3wrN*+c0T6J3CfZR?rIAPF;W0YFUr(%j3RI4{R(*
zS@FMQ5q-SM=sD_;`g`~81=RllVNrzL{>7;y;PAN$z#Ii`6hXh<kRbrDuRRNdpq<zx
zOu{k+8^JuPB0L5C=7Sh6xw_?zG>{@tA^Ydgcbq$OU3?fZu{4Nk)Y=1H5yy#!BmCiq
zvU(`4Kl`1y{~<!Hjg5*FEF;)rWrpqT?R-#&Ds3>w&xP|q0;O8M8_85VJZ7K3bOX-A
z-X@_we-XZ%05bMFxzeFU#+c?a>_@vG0Iz91f9`}lNw;?WJ*zdpxTuzRjyVF{JZx3|
z2XAg^zi(?(r&qdgnubox!r~;<L>``mgoIR(UwL?LXuklEN<BHa=+2Xxy>_}uhcPHg
zoi8dn`dPX)Bl+PB9Ii(=2K3jb@YkauUl!flZ8(o$u{RtXgaDwiIj?PO#HL$g#Ko@_
zp6>!>;93`^+-K|JYYCUD{tkc=;N!xZCo!_J>L5kGvp#m@k=f@L0-{fgK78O?JVZqB
zOhQ<jE*V<n7Qi<B{x#L|{1~gn)+A{q3}MzrMMZ6|DqeY&l=M!-wpY{2iZ3cM@|};S
zn3z}>K=O;z$2d9Dfs>wVXHxpoUj}zI3#+fF$QX6i8pc+^TS8=&C3InZWknb;wJ-Qr
z`6@<o#TMtNGs?=u-@lr^7z2ikCqs+8T8!6e5C8J63O>;1S=v}03-<Ee`V|Bz2$ZL{
znxV)O{_t&L#P?u-<cUXmzhawe)+$_|rOh{y-bsQZv1NAcChc!i)J+FYI*oxPM{x)m
z^ouNjeuemCQx_csxf3kcF9?TE@jN;~UX-iRh1pyFiPN#d3UQAp9R1?-rDlv*XXzEE
zttBTMvohB?`Zuz^;m~hr^54mjk%)&8@8Qq>FoV23j8Uuq^q34X%5O5d_f?CjuliLf
z_4Up(myOu(Zm*r2Ipz|@!N&FpMm9QoKe4vZh>eq+KNd9sUw9(^VEw$Lq-1B)=fX(9
z4WHoHz<CI!v4yIL+WykgPA>BNgq{(;bfa;mJ^8ZY?A+X!k5@0#n&AxAE~F=`CFWEs
zQUhVkCF_IG{Q!uIo*Vw~bX#Gm7(joCt)%b=tjoX?CCwy$_LynoprWRJ@AFo#<UX6<
z{;u`e-bkpP6CkyE^;iRo4L)nV!aHA*rp*^j>*2^dOTK<`>IhuI<HwI#IXHs4=2XKT
z>~3Cw)7xXC1YaM}mMoX-%=QiC^*TErK~;XYuz3AhAlT;2A<y%}*O`F4x{58Wso?@_
zRu4f%{UFP7o<WI@hD#Q8&~dk1Bqb#Uz~=nb!JQLGHTV)6N;q<whK9D@mjmY%wHj7u
z+F?rYM9alf*A>8O6L28Z2W?-cM%`Ct+hB0CdU$x4xFxLp{KAkV>1LzuBD^j%(a6eb
zI6gQu<V2WI5@(N=V5@omo&-vE`&<UG#eK`+05*%r0<YSTl)ryczr4`Inn?C<5C8kW
bu5hK7`%*qI3zR^=mg%~Zx?-+^*`xmlm0*DK

diff --git a/reference/Rplot009.png b/reference/Rplot009.png
index 6adfdc5e490f102adf2600ad98979ef2d90f376c..f61f77cb49ac1927df8d5d68d9a82c16ea1b87fc 100644
GIT binary patch
literal 8444
zcmeHN2T)V%w%#;B5%qYEAV^a{1O${OB{V@XC?HLc>Ond}sL~1IL5?Ct5K*a$ib#_Z
zN`SyoIS5D<h}58n0YWbcE$~)6_r7!Qojdd1yqP!ezM0D;J0!F7?|-j<t#5tb_viLS
zLp`ote7g_?;X<F&H9-)jQO1*F2YeFVLr8&_9S-O9bm0~L)^uvH@UqkU++{xm;oQ%7
zGIb9}Y$Av-g4WeG3re3E4)r!$p>Hn`Rr-e$4TN>8gFY+W89yTOdx?3;UDv{i``p}Y
zd3Wor7P?KWDW76{N^nlU@IET;bxV2B$JrfaGVZ`DfVoNuV=EAOZ`*p;^z_#wM}5z~
zcMj|z(_-2pe&k3)#Dg(P9ZkFC`v+C#{7OGR^1GfK)UG0qAf0((6ea|ji03!~^XBlr
zQN8f{A>8#DE6Pa(@f2<lXpoDV^tY-l*o&;mI;d+M4%u2G7wRF%SC#lkCgen-1yjUI
zuSGz!SnA92a<k;42HlzJp{12#$>`xgQuq|9zP=r`bb-E@T5l$q(lVm)^BY$6l^)Z_
zsNm9;mKOi<6btvck-Cr>98tl&=UB$|0k`!T40g~V{LzK83F_2`?J8t`b2GCEcFolr
zQ&Urh!F}Iyv&Anw%ofiIGa=ez>tK{}pRwP^WR`@sS7VM|c-Z*`zrG%la{vBI0>LiH
zzeAmNj5}6>J9ct&se_m#cHXyLC1lyv+}zwrRy-Nq=y1|W?$Py`n>K?x5oI<RBYsp+
z=gLIaTXPGG8exthLione(=)N9l}72xUG(`FO@F<3exHDVfZ0B4Ou#8pm>-q8FxFJl
z(xOkB%+134tIm9D&!9+3N)F{DXiV`PIyC&-P4?l=CpyC~4AM#y)<$Rq@+_Vr6SzA6
zbE;wPu&I&yxJ>=#qG@Kp;@4)cm8stT>RVigzFnUg7>^QuUp-_dIsRp#DnBo;*{;%m
z(j>4|x{}|I^kT*CdcTub+hfJTqCpk%*?9iRt);AON7+*f3d(4i48zLmy#oV=NJDdL
z8X}|-_6Y`V6;SpbeK@#B5H%rMjmP7s1G2DN)yZF{D3lC8`(I{zE$rJy2!3Qk3p2B?
za*r-h@%G#6#Mx%?tPe#-Ngl4Qt|dkC?aFV%*C%tu)${$Db_Dg8I;o@*hr(&Y<gL{v
zO=nsApwH2>bQ(!>mQD&9;|iRsqgCL&$iD1Vh2foj*O?JRyOH!L8ENU>$BG{QwBbOH
zFYj(YK84A2_wW!y<8U}hX=!tmV8=*(;M_>kGx`S2rI_+D*N8gis_@FLE~J^ZjrnnG
zp{jebTy(OmLnB#qWx8KJRR0urtXoH>djCr1hk`<01=owAfj7NsW|Z0<$?LVN%nVhV
z)?A-4adL8^QdcNM#ziG%Wi5_#e^eye<Orx;FArU7!{PQC`l~S^GpQDHQGzPI&oU#{
z-O$~kb9K2tzrE!KdHU+*%Zs+Q8GL+vJvn-N(cL##*hSIf<KyDEqC0nX9XN0xuc*kd
z%T=NLavFR6ZF2C8-E1{EGe0S5fB5R>7^{kFk0KUg?Hz@(E*w>K{&29+y3&xG5tw=8
z-mi98dI0_NUaa!$BD{mNDN9YOCk$qWdmn+sbgi#lv-fX`7qF`KbyDIkvAnRdva%~m
zt1e`vdv#%KW%TA=$rSy(k`m)G=eF+q5~f!C2eN*Ja815Q^Xg{Vi%JZ?`4RGPrBi1w
zZq2W}+L`MuEK+fdP=w`P&CS<d_{kU3D^TchadC{ei0<!##8>d>D@bqU(~PL)ma%y$
z%yIw1QO1>=F&-X5{>#^ywE`cUViHtvO@w$TKutl-O^9o*-5v=h3!FJ(&;Lx;o?z4L
zXhL6K`N->%{-m^2PTHz`;(p&+kEzk*bXiB=86Mkb5FA@9eFYCiF@SRL^yo8Hbn6yX
zU-}@ApLiPEQ;8u>uRMSLoB=#5y~Ppc@c-kJS8sXMt@Ne0hK$}|FAv|i&bV~<dD&h_
z;o&b-n~m(8${!KDd<QO|iXA~tr=HSlKoEa%)~W+ApG-1t9Wy&r9b9t_(IWmP;ff%;
zy;*l7kxa~gceqiz9=EGjXg6}vHJ;X*v3Rs0W{-f0+@o^AoK{MIS*}UmeYo=iJ?+fJ
zaC=z%S9y6Qf^5MB0EF$;)5Up>&9O~mg<AuThT1I1l}O}&;UEl{tcPa5u|<B%)pyk&
z4@Cm&)a+QGH)+Q5#p$ueR}>(5_V6yHhQ((%#n8fBEpgsV_i{ld#E>I58xUNJ0ptI2
zhkaI&v^i3-?vW>lFtx_#Q)N7T2Jak@Qt%v5khZD1*k1km(oRGx^?+4mC~dZIyfu{q
zb@uV|+Zc0GQxP;Q1S5aTgBBbb$9x!2FEo~Jx{q~NROAU31fOzF?|6Atl_iu>l9{b-
zA+(QXT6mm*+NHq?&yS<O-4sK&rpgl2Rs2NJWiB0k0YqD#f!%q+KMs^8ZbNmhFHc@o
zKb!hy>vZV5F(1kg_*yf`-l>%*JAyW-GkDraXh7oui<boj7m}>_`$IB`UlxDa`J;cE
zBGHlknyULAodLEd(wu9u8HO?In-Y$MxqN`SSA~T*M(#+|LS_G+>>n;BCU)m+PLqw0
zdt`^&9IDW!+C;W~v$Vo<@Wnk5J@<x4CM%qK%B?*DuX^(>@}aasw=Iu9P&d)=pGZt<
zc62TC*00(4>^GueuyLCnU7<`4%Jk3fv}4(t$Vs?=PE;Xa%FJ`9%2+36Ps(zmTM-Ok
zkfCZ00Nnqv14_juvo#rmd85H>SfgdSMZ<MIh}l%CwTS)}Pk0w{SLQcbICN$27D1Ji
z2M=0Qnj|Lny&GG&WPhGa{+G<B|APeIS0b1VEnty8PY{}C<{2pMRQzQP*8BT<*q}O+
z-z_~888?WGy>rLR#)f}jar2J^3nT4aSu=ePCy;}uWxtx4nTeuNMyI`NF%E!0fYHs4
za<${t!sSkfx7oC_MfBn=6S~A!3B>@UR32Y|qIQ)`@x#9Y^w@P&vjMj4yYlbD_Za-b
zJvuX3DRu9h>p;1iRjDI7cJNYx<rBA$FWf%AiS})e#_1ynzMzvi3|g4m+TyrXZBPji
zla)1c*7+oNclUA(NkiJM)^Z`p22kba!u?33jMRQAb2!N&IJml{wN(t=+uO@`$}2sL
zMEq2873Vpq#3hKbOe%M7<Exv0!<qDqak1rzt|x8xcF8edp!ns>bI`_11#`~Nql_}i
zCH0A>#>V$VCHC)B|8ceWBCWvBkWu*iZ~qcT>aZieTdL(w?Ep?~>(h83>Dfm^H3Mkc
zPufmL)}#t^oDSdmNv>n*r?|8I_V4om<G$yD^2n)^PuP@`n_Zie#2D23%R&%!orz_K
zYws&_K2`shmx}EQfk;n#>fGAhm3UZ36wT;#IyyR*W5;HqIv+%DlG8aESs6yMKnc2F
z59dC8dW2APy1VvvTQrq#9(csd=jT)10U7m0_Yc&CR*L2sWobxCNqy|eGc~uiPE)7v
zMzjLIRilEf7oX_F0F}_Owi{r3yjWixU4i;q`MFL~A#hm?de}|?#HUYBD=I4X<p?Ky
zhnHL+^b})!Z4hc?%J_sP``>lxe`wl&zP*vo)SgW0VnVcjYvvsV(46Xb$?AnyRS!W}
z=IC^$k=H-p1g6eVAyZ_{?P`s+ugUF?Ig!Yy2l5Tj9Y~wsPBFyLa4<OoKNhd15V09l
zXj@ZJ_KgL}7RId|Jx)&VE4DATr%zcVJ!2OP(kias!p~NX#qSZ?gG8QAS(;<ggfSWX
zmO!7uc#PBrtB&237e%MqM^r<KM6q$II-Nt1T?Xw4ZD!CSP07<lfzQ*YjvTBSw(1?T
zX9uEnxHWiJ$os^@P45E7eveBctXkhgkkPMSch5KS*bfy^5TxNY+@GsL$OqZ6c~r3u
zJ2R54_MUgNIDE!q9r#*lY9%y{Vl15^pz#xeM9YxVUPsVsNRwY;W&56?lfP~d9!Oii
z0J&CK<_tG>WqkKMG(lV_&C9dD^o;htiG?O13(=&d!w8ZsFi_wI>~NwhN9`&%L@_SC
zm<cnCH&01Pk(7`yzI^#IRR}?p6;%=<a}6_I)x%x$opjK3X=6dODifDDg0{Eld`j0W
zUD}x8gM0D~_1pBh6_|<OQW;RR(Vnj;Hudmz|6U^Tyh*G*y8qLsPbH>I$i-3L9aTj|
zMP=V$64q`+5{riSsFurzzl<_ii6Oo;&grQMoSP0{Vc#=7{{%sf`Kz#?{JveNTl<zK
zZMQvK6Nrh~%Z6w@X<g%+2l>%ckKLYjBB$TEb4NAo81i0xizV<}44!!2hijYO8W<6{
zA@Oh|S0czeBDX-&C9)~*Q0~CM!1OrRA*EyY&iSuiL=uf`O115-c8A<G>}@OEz?$S4
zTAQ~c9a)<8w{=hFnd!?rf9PyUk-~Wm<J%u|L1LxIH^B6ItTA=ZrL8NDWf*#FZLZyE
zbjVOVgdl8EE|D9wxneV^n6Wma^DIc@kE!wvq15T-2!7TZ4`ggx(q1D7hju#>x*g7w
zE()B}4UkNgQcwo{yk`$k)yUI*-!O07y+8}WdQ85&4B}qNSCA>;;K|d_JCcAK-q(L#
z?myXW$jdkz5*?DxJ(zs1y#}s#z2&l56ept9^#1ZE2+Jkx?!0DTRXRc(NaU=rOXT))
zt|mig;g{Og#LxgkcR`!JFv=Q{cnfRv(su`lz^#(&)1U6q`$>adH3q=_MPOaipQdZx
z@(11MK`gTEZ-rYj&e8~HL><{>+b%MCCYF0r%6{z*3?)ON;at0p6k6e4{bxF|@z`X8
zNONs{Ann`kMxE}Ll#!V&=!Ye?^xR4}R^aPZA*bgoE-tn|WJe;s4wF3^{>zL2cGXXz
z&JGWL)`6){`0fY-F%5<VmRjl;uc^m@XmP-2LsdRN&D!i3P_h5E?qBogXu;YN85+s4
z6n(ef@A85yd)`-QO}tzNL%oJ5fN)M7386?c3JAi!MD75sNlDax+U5EZyLtG4<7sQ|
z#jLpvP`kK4k>9Z4hmJBqri^DI1f{V0mE<;+*g%lTaGi-LP8p7XhkegXB*c=NJ0GN8
z*LH0^y~P=&1y;mgK(ngqAw7T@Mb?d~VJF*6*dJO5s_mut5q5087fF}^er&pLpFD!R
zxi8;9qR$2@LX|-RfivQ?&4fgnCNKS@koywS<zS&86A8Ob?@y@o8Xj<a3oemuIp1jT
z(l;kf0`xF7&x26-u~7JsF!4S^#K^t;%EBbt0ak75*im&@+xwqkWy}h9RAINb_%y<*
zMDs8tLVrTq^hjOJm^eU8B#v)1LoM(+l^>|4RPB!c9-#jT?f=jIyV1!!^y$+}P&4ol
z=CB&7aA@3Rkfvn7$s=>$Po}_}8R>M2<{q^?{(>Q8rt*uue8LS3;;1u&xs9<rQi2Kj
zuU<+1aoU+TcG+{4*FpjUaKFbL5+~6VuTNWg4ptc4WZ(Nh6j%um41=U+P_+5>^%0-j
ze)uER$iYxD72i`WfgTWLlIfz(tcunP^W-0HJo<!v)wRt?B^0EJFN`|^#tqtpL<m>(
z9&PCCJ0DrYL!#}}R}0>b+qscX@6HsrLs2wTDIPg*Sx{FR5GYlB&=I{;z4_*sFF&gD
z{P6H3pS<&3(MhnqRD<TPxKa#ivS-wuTyd0pbTLaa`onki@VeeGl85!n!KzU2_UJ9#
z4+%{NVjJVDHF^TI#rN;5TWpbHP!)}>)vIU|01LBAmtN$V=Dw<^Pzqh=1von1BQgr%
zstloXof^tM<W+U}gE<q@`Fz0-0Oz3azYl-J;8)ANoud11zo)(3h!Fl0cqAlB{swro
zx|a&{w8STWEsh(*Nn@DPn;m;TeL6U>$l*=f&w3-N`Ps@r+tVLUNlnX+TDDhr)+@#H
zEZ6RQ?PsXH?-xAEU-oAWES`mGu@T&U`|9Yd$TW<z@+<M*TJR5RrBi~*uEZf49N-tx
zoIEWmMBaQ>M7TI0DAMB}MTI~eXplv_XE_$&H2yPd;t!XuKS8~N)3Yw~jWsv^)(NPo
z0yoW)`pCY3KH}LJ_Np}rmyRVXn~2HhsR}Ob{OIoK{<47K0Ah5-$X&n3CmsA<TXiBR
z7W;|dS$lo=GTiHLz1vRqxb|9*C8I$~!aO0dUdbfSj6(4h*nB$ek`?~KJpa<98fh(_
zA(_DL=HivpP5O!ThTNmOqIm?uR+Y|EdI)to&rI_9tsPrsS`^R<XU;@}Y66E$P)b@l
z#m^ADjlLZJV31D>L7NXLYMY9q8sX~<tHd<d(A?A0le%2C+=ylEay5eoq2o{~6Edt9
zBg9lm{6jPpHcc!lGoQ7FXQ($TzhIfj6$J4<{`bD!|FApUs9g(ffOhJB?Ha@3%P%el
zAJq6bcNzFHk{rwesGy5=p|lEkH~s#e$Rf%}J!JW{;PX~WS5Az+zCIuqhWW1dhsTQN
zdF;bVit4vOvn1wf(3efc0M39gG4zc3`a}-Uy~iqkFIJAR#hP{kR6&JCdfWOoi}9g?
zUauT{e-*qRkWqbCg*$A$e`C9&drnvH%TEfhL8HtD9FmFIMi_!F7&?8@;E~)JAe!KT
z;RJUEy92il0WDL|q?I{PkF5vEc+@Df;BuKW1Bm-X4jea}=lK;(XLk^w>s<*Eq8&>G
zP1>29Q4JxiUrTu9ohJ^FR&Vtc+2-%nSk=1P6ps_k6ZT=X5pLJmxW>2xt4c2cQVFxN
z7$^7fPLK}ej?MdRLnfd1js0{SW@V^0qd*6S$mjwL@7$9_7!DCdY<mO(hn^gex-`*h
zzg0?|BVp7He>>8^CJLVYr`JE@sRJH3r&ex?58`i}Np4<3F>XuhQo(D%+$#%w-Zv~O
zy)v9TGNsXcithI#sAXBaMy%QSjIhHl06^27*x14*3FS?G#vayRLi9I`;%mu?ts*~2
z#h`rlL@dXNUHYmSmq4y>ZIU&!I>+?SpSQNr=y#14s4<faXrZW%$hE7}Y+$b>+t!c2
z4tqTP{qDlyRE%yS2EN-eh^a5M_NqPuw)y-{x=X14GmY)drR4RLO!1AXWS>_Q{oIc;
z1WXZzRkii@ht7d;wIx56a(1S(+K)g5&eqIn#0hEISyy^_`dGvE18I9Jkn7+Ht(>!Q
z-QU{M#L%=MzXULr!De(Qc^D<^>QnwxY-4%Yn(z0s^b-2oGt^dA#}e{^QsJRTTJxV#
z!VbZQiauX-K`l!{P=R#!kJqh!iRFWv$i!|1hc9>O;iq2Z_vBm14o7UvG;K<D@z@1v
zyJQ4BkO7;Wd$1?WR^;s6i^sW5AyBh~4KGC{rObJP+C91{zz`;!V$BfqV6PWqw>L=A
z4?c!%IFx+ysr)>#q3<5hU)bhdJEJiYo;FuJHnTJ~!;-C|oUPT<(^J(dQ}aQEJOt1;
z^x=`b^^i=n^ySibPIA1oE<K^@r4CKaP;2lzIHQC^tINw3LN6y-<Nfnm9pz@@PK|gk
z1P#^Ese>}f%~b%Lc!nTvLc#7<O$2?t8l2YRM&GmtG}ubOUpP7jVN;_oe;fwdWP3E(
zCze~<_qOiS#&DkFmZOj<8xS?y`Utv>WQwZu??)&72f)htByIURyVujrt20~M3+hL_
z77M4=Y%aDAgM)0dkCmaMyjJHw!xZ?-!aY6TcG22!(>a=Y)fF|k4W*eFn++6E19@{c
z()ffRcGIWQcU+&G?x(%4O8e%H>RY#NP0u7*ZRQSvkvu)_sxaViS&zMHatv%c#tBYy
zl^#Tyz_(nd_?N2U4xPe7YCZ2&5p%j(_Ve2vfrFlvP8cXpSl0>;vyV;fDm3~WmISI|
z{2}i)Y^qj407++Zj2Sz2UiEb9rOM~keau6d^s?Y3XYuAUIbGA5=%R`W>cbfik3;d0
zmrg=-l@{D^;c%s!=GOsoHI-?flA(5vuris^C;+KoNAv&>4-iiMV#2z6?1mzi>%@My
zZH$9wNII}cDXCVwxs8Z?>_CT#I7xFW)naMTswT^Wx*o9fNWDYt8*T=KHs!W>W+zB&
z@Of}B6UG+nwce6}GAsa>7h%mAvdei}g=}VQ>@(!qg5zqN8_aY%rIIq!j_WX@sxYS?
zW4;^p8op@@yPPeKa{b#?Fr#J0_7!Cze0|y2*4EZ_=`O#DN&clm_oW1fyP$7n39C;*
zdLAaHGyIJFBD_9q#f4_oZ><#jw&&sTR<gD8QH15^vN+4La*xc==-U9>poSF!XNNi~
za?ne3d53o#b+Gm!+jQC>{cL4L#S`>jBUY`EHPcVlauIGg#$kk7_yzW>inxhf{K^+W
z=Vyg2qSF$vP-cpb?-RK(11bmZoztBoY!o@SJ%0Y>%NK4C<uZJ6Q(4=axgc_jf>1@N
zb|L84i|fi$2!bRseha`Y?d~H)qV-l{>}^A5aEwjy_??Yl$N57We^py+111ab>JJhf
z*z#{1xnLYph{Z&a1k_U)4Yb1+eS;hpW<A&`$`>aX*sq6;_w@`c>Tn6As&w+MUfqqT
ynt9~5F@4|a+R3~-TgPXavpoKvY)g%9vzSyxOYXkr$@o<Rf<9xYTXgzL)PDdK7_mP9

literal 8531
zcmeHNXH-*JyFNh_L_o(bAUKLh5g3s!Z4~L!ks81O1VjiDI#R?@Mo|zDBfW!k=@KBI
z3<gB0LWIzy1qeO#df&{<`o8<!_UD$h?phx!IcJ@mbN1T%-EVoG=jDm6wkjJ72MdBA
zZ0c&t`Urv=qJNq9!7u(_3DI!c_lu^gGMwRW{aRHZoc6n_nPL&-z+w6q)zKfYg&=1T
zb>*K8z2j&4{ag)~w|5svJ<~3YE@~$Gt&cIJnJ}(DNh!DFH>fUka5@nB)bJxF-`lah
zTejTD{q77&&)$|`nQ3H}cOrdn%BKb~*@2JU>uyD1XS{?jYgaa2*J8!}dUs`Uefzs@
z?f(67jM|F>cN4=;-7dD5vLB?_MqQvx-yV^n#3P7O1&hf+IAN57P{>b8K?godkSx+9
zoVrcIQHTQ*YL?;E4_X=e!;bz)W8lwJ6ia*R_0T+31R0eHqrdUO7!|O+O!64;p$Kq1
zzq2^n)LZ3+S-~rxO{tnAm_OXd)?rLoJhnY9RjVf!^Vu@6;h6D6k9|_tO92BzGqapf
z5xm;w%1kFGraGlPO_iD+rQKl@F5f-8(=l0^=smnY!L!%#X~Qr0<!*B$V`HxmC6bDJ
z{po|m*eA9}8(U^~bm%*Od;9%0b&IIDlOj~`F14X}W5YN4<;yn&f@P#T@w68|7rPJ_
zJ2kJQrQ^e+1K1Xs+n-O)d@DNVCljGYzn7rx<-jxAtBus{cl8+_djGIMCrQDDRDc$V
zYI?W5xmvYOqu3WFi91Ga_Ou3!hjGaaPfbnru}Sp}6;#dQr<adh&~`U6GMX)ERlsH&
zev;psuNOHUT1n&y?30j`#6M&@i2q(6QuUlu>b4@~g5vs<s+n?fAN+(CjCN~P*v#XU
za_QUODr@)YXvLkboN0@5D$iW2yyiS(wD>5#QBCQg%4ZAkc-gAE3&Rbw8`MIK2RU=I
zW+?bT-&9vFxf(->=ajhfa<esXC#t5et!;?6(onE#R>1*t1%(hrF%0+D=5HC_ygAO!
zCEYefpO371K|z5?)pV#R!8Ovt-@gVs+uT;Ovq9~ldd>DtK7IPsLN=jAWV3%RfRgAl
z9y7hI?#9N(COk_WjneT<@?Yt>*E(fEMMO=Dc_|yQRhzR_{Yg7ZS<_=+UZVf}dnRJ_
zwmGI|Y_^)#A~{E{>_fR=cuFf0kf0HqR913Y`<t7Emic5%Ejo(tM#pqdiIIW9IrW_E
zY&~%fBi_SJ0|cwufXMXT-rnhAn9VdXMOV$v&W?p~2Xo|H#Bci9FL@_vt%^9_<r%V+
zpN13{yVKl2P0yx$qA6M5)U>C)KN);V@1XqBi+F5rB{s`*ZXiD=Cue$@+MVB-t{!D*
zX<6VZoi}%%a;2Bx?-xQEwD8+ZE+OwnCf-&19$R{&m$uZ0ZB>sP2;B95e)7^)a8rYe
z#|_Zv1P%_4uUR@g>KzaEF!8F7jg1jKtcpyuu3x{NlbdV6e{|8gd;C3S*vB%2<hL=I
zm7AWf%;Ucl@#>;!s^4toh0XOR%2!WVdZ%<3pmQ&`NMCzzt&1V7XYQ;K6}hqgaP|E8
z^Pc7}NrN+u3KO3_84y_!Ou$o@K?1rNC))b)EEjt&xOR+|3K%3-`d;d6?QX#ND0OhP
zJKcGv_>HMtbFO1vDyF6}1vM*X{a%CqJtw43%X#Y_R9LgFs#T41x~bUbH&^338lk$>
zcrJXR*QxM}h)#z7ox67peS9i|-zTrk4HlY~+#a(a{r=>L1ZF_aPVChA!~6RKcei_}
zy<{eIO#;#uH+CaPNJyxwtu6VI&Br&e%-XFn(K5F&jlB=8xYI?^ctcp}9wzp$O&5~#
zAW**~DenF{OQXksc2;6ekqt2iUNW}y$q=dCTJ#JzA;&E%-$;}*ZB^W@{up*T7qw@f
zElxE}PuQ$fZE$N`>ezD?37e0s{jT+FG9-^d|5ZnFxwD{vK>Io+sZdk2rnF(Xa^+`Z
z%mrlWcB0dqE2OqH5ez+3P26C1<vjf4SGiUv+F<=b#rO*9K4gh2K6oEehp5&e$jLMR
zcxcS^G8|gAMuM(l^Q62!#UVVh?rKkzKlnc5F2oEHH29+KI5@oJ-s>ZHh?)O*Xf__-
z>w9#x$U$6?VzJ_U=ib)Y2(>s&+-Cs?1Ytd7{l9Qv%x9Vq%+^xbrM3G~FX7SrNA>xl
z^kfstxiie!!d~9lczZdIhy<|<eM6d~x((+_5*hkOKij4)Gb3#Re=ZzFU;Tf12*RKW
z^7D=4xJmTF&=7i};+;!Rv0bdBbL{w+xC@kcE3f^CQuvs8(B?8VD?<}E)o{Y<E2K6f
zG+J0ar=XyFXM3~E`aQ$-vp+!2tuL%%M3RY9krk;IMMQ2&bG_o7=`GVd!l$Ks=1|+(
z!bq71^(NbCS;NRua3lJ*M~eGz(39)B<TbI*s(}NQ6+(i75aBV)C*^}FK8teU+mGTQ
zL-4A<xBgDBDUz^Soi2AKyHgUprpyO7UTJ7*p3iO&uy3WAS#XQl{zibDrV+W1^<=X0
z+3)0wjPJafY{du)0W2{TAC^Z~r$t8%Ei5jo%W2;~#PX=uV%90BReFR~*ot6LNZ80J
zcj?d3OH*;2A9`<A^vk2i2YCvNa&MMNf6C9xGqkhI;GJwsxkd;`F%TIc<6rE?W4Dqg
zl9kTX?Od%2et#zXNRoKM4e3}+D=Khnf%ud-5-aPGBYF2*G8jxVQczHkUK6tovT`yr
z`TRCcAwdJ5u|A8%qXp6ogpI(#d1wDdjQy*IrTN#iJmSO;3=e{SDKc4k6scsM+WsaU
zn^Ra=!JR>bYVlP0Z2l{n-#@zazwz*YULq%%%?`Hl9%T_YxrB+g<(r}J85k9e`LDg}
zYRZdrQ65q0OAnODM7+<DAzQI*y7O%4|GCRzG0y-G{IwxZLqmJ48UYM_lF0%bLe=xF
zR^#m$chmlP4`LmJ{PXai+Muoi!H~;uEO=XVX5Q!oq-JhWakoe8Mx5jg+W40!Y|Edy
zV^O%yKK|b0S0d<~y$6oJ$;?!-y1(#<7ZR}9=IR_Nc<+IuPn1h-i0rkqhmjy|p@~(N
zJr9CDH8&esT8gOmmO0`0v|d9@CNGQ-od67Q$hyDboy@No=;+i@6jrArO$nDi>A`We
z+Z}}b1M7uv-l!Fse1^PVPm^0Y;l{#9kW&y65s^$xr)Xx8XCn$xo-LDI##WFdTLTti
zliXJ13LEG+NY$&~1IOz)RQEvk{@Pni$)TZv^HEJ}8*2i#50sQLGBP09`~f8NuU&h%
zz$<Sh&E<lv#GtgZI?dj>retcxq^j{oq7}U7t@BNauGWVfB;^?88X6fz*A%3tUOjf~
zSY-OO5O#qGwKa*bzkD$s3zezcW{LU6uFUPgA91eIpI4)GuBPb5HT(Oqucd`6bPP9y
zN<dC+7`ZpoQ=-~|ix3tT?)sW-00=Bfew!7@z<8yckExq67s_h87PD5aFj{fB`4?aB
z&pz~lVd?;Oy1Kf$)6RrnPt%j37+P6j<Tb)iJW)1_Kjrda4)uTT<Y(-I60rReObAk=
z(Sn3?NN9*w&j$lg??BTKq)lj)b$(-+dMvcTz5V)$NdWB~SxEuCXe<iRZFAcv4dh^R
zaF+n!Db3ua^19UE{SrRy_%}d<+)4Kl<Y>C5)vsgmop=xG5_xdvW~4hHkI64mgJ0p>
zH&zd$ya}!>xIZ3aFYdQ;cY8iW@hc!&kHw}7nvox$b7q0FRbnZg%`afA*mxB8Pu4+_
zE`4uw`M>^BN$~;aaK*9jLzI4O_F?h;_V!l(aMi8)ed_w+95eWA1mSm;*>iuklH%!b
z3PC2~<5BF7uI3LstEW|qoMwg+0gD%{96jv6_C2Jh*}TG~tD<&$Mudk2pbL(6-K2JR
zd(H!1r;{@lMh3HD+b076{$^v@kf7k`%XQ#EU5WC3r9QOV0P^J5N26HwVFQ5TP6T;6
zaj@=_xc&L{?CZTqkmWfFw$8l#t{ym`hK2@|s@QF?xVBcyxgn@oQ<%WbIF&H2p1({k
z#{j^g>$e}y;hl~ni0|`w)J{FmPBP9apt5U@VGj~?B#L%)X=$?4$jVAoU3eg}xTvVe
zDT@I~t{Y=@MWfMPUr>lrVMj_W(|rh`zouXxRN+j{1ge+|{Gi9~+GEzq#T2lSyNu4y
zt?@DjnA+`fK_Q`%=9L*TY3v$G$<lN;vD*EU?^Bm(5s_C|ZA*;b7ajhKtGk2&VJ}#I
zu5n>^^3M;8aS2$czf&;SnI!K(fBk{)54&yLb2Fz`@U?*_awp0WirilLTOSy^zJ9e8
zEwrk&T=1@L4uR>mjma@2UbYV#UU(PTln&NP8EeKl0j%kl;6#wULiRx+tfx}F27FZG
zq%b!HqdVedut(WLs+=Sdgym)na*#t@t;x2yYjHd)16E8KW1SzU@hjo67ds_vS^fIN
zEfg~JFdns1z3aDauBR7~c_S{<KwIXzL6-K+;(J)T?F`|_O|0MQtdY99y73nVuv!F-
z*?Y7xLL-B)HAZun&4-Ew*v+dMaOlzEc+|kVdp6CSOxOV~1aa4GNj+?VAb6FQ4<COi
zk_=?aZUQ*s&-pt|ZZAWEuZHaEhFD95M~eMCZNZ>1cIC)s?vZNC)yL0jVh0B~2QG0d
z%Iw)%>eQhVF#Omnb6#~id_NoUTc%FZEY-CGS6IA4yP;66)m!25<E~F4G46Nw7ZjiA
zLhUKLz_4Uvnw-v$Nwrd8LUpxk4Wgy~n=8|EI|JS$&pD)Bt#ORf(His&SD}Dga7H%<
zw#Z0`i<^YEpJQY|0j69rzTn-mOi<Kl)AVR)jii^_#du;5uy)dHe^m)?b+$kE=~EUf
zraefq)F&TCxxk(K{v(`&ZQyvvm>I?45^`&5)or+M4VDLPl0QSPaDs~C)-w5kiCg#H
zy?b6?AUfaQSzUbwP(b_2jCeE-=S7r5kR=wGq&)-W&T%T?-1!098+JIhQ)20Vc?IkI
z=a|dOD`bq&Q5HKK3|jcN8^GEn9NLnn?I>J<VTTZe2NKk;`;Qnx2Ck_N*!FE0S@?SZ
zW5+-1$#ENt#e98s>T>D*b+>$URr%j0T)VR@T0?8O(p&9Q0<3uab4)#%^oPe}B||WH
z`Xpw7EMaa#3_DK%HpFWm1YA_FW^Fk1ldZRv9fEAVyj-Wavk(q7VQJb*@Xp4cF--}g
zPEFyR*dcVICBuW}YE}oPi4gsh<+mU;2=}tiLy9ZmNrF1pFUbmt_j(Hjc$N}LdK_EF
z#LfkHUY9SbDWESM*iye3INGC}FMs&DeWFh$uVY;hswX@S#>k>x0NV$Zof=BG;^nyt
z4=xGHF|i^+D2D&9he3wEZ{OZ{%N4*!i?4GSZ`b_FvA5JQRtl3Cj}7QQk0A1|Fevtr
zdNaIxwn)uV$MNRe+}yIj-2h>ew@MLe%2zz6=cyt<TZ@7N3V%A}(6fZD@^t+0>wd`K
z9Et(ebRLp&Ekq;I(r4_L{K_|*$aIhvvW%*Al*OV}=y3uv0t7(?cN%dXC{ow{vIjxZ
zUZg7!H4XQ72Glb2okdpV>r|ZrKK0P5kK_KRATv%@QA=Y#xH~)NV^%eT{BVYXW7LtX
z7ke2{(b3UGwXzSgT71K`_(nEYRgx6^kFq>66P{Z{4sy$x2;IzUoBv*4cBe}a$eLYY
zs7l&Xm9)R%6VKXHILBQTe*Q`E-N~unGGD)~m2JUlCoUhFkPHaKa8VUq2?Q2d?r>@u
z$+8o~!6lFk)c#{eL^?MFJhQxVD<`+0!^jvtMnR*HwzP$RvYme?V*H)r{Xuj<sQ{wG
zg;U=w7g=Hr0McuEW`1n9s>kXyZb6RvL)-JMoq_qVg|()*Ii*%wvEs?CLoaA_ZwgTS
zgCIlB{$z46$YpGGRb_xlIzc<PsHnYSIfH9ZtmX-}g+$c(@c<(OJhd2qgjiP-0`-9?
zn>t?mf6qf<y@&}*S53a_wiDFxs>lBNUwxVPJSpDQ&sn=my@aSUo~%iXvgq|?cIUL0
zM_x4i?+G>kGwP4KW)M!;Jkzl=({Z5M$F0SKh-N@uknnPKfW>=j18a|8G)Y$DO+9_Z
z(|wtKYc&|{&s60((*c#`D3rQ@EpO+3yb<8NXx<Ec0gwxCE3U}3R%|Yh4&vJ`bG42T
zTZvr0`v~I>I$rWBFz^>08NWo*@S;#*b!rH=uUh0%-JXLJi_M~|GXb3~jvpH@5%LeN
zJ6o$OL#mluqS$HVyc7_a4Sw<>eB)(;K;b)Dc+}&yz$OVv_xH+;QkLq5{$V5b>LP%4
zW;Wf>Tj|r3q`-n>LN_N=6*nSY!Rm5y1lc$xwIW|O*}l}yI<LOGyiDXee412&v7#%S
z;`8+k&fsCt5ddka9Mrdq|46ifFm>GV!6`CX=;@_^;&>xL)<{A^qNlU91E<Ig)f+wx
zD9aaY$b>w8i}_s|`)S#9<+HcsRl<os{M1|j!%woo=d5>Mp9T&S)Xn@8v~&L3hsE(Z
z6sfB#8$yLH9)ffWx%s>6H;@anwfNFNjn4rrOJ`H3W!#h#DcR6{=uTVmhI*_AfG&5;
z=ZN*14UiMr)jq7pShTTOc(B|u02Kl5aHp|P(eyXvch*K&Lh55>u$jyJdqWM{K+vRz
zKwtvkPCCO~KG|q<{``56oVsn!v?hRn&$3Tl*?9P<I4n_mFK_`AgT<M@nl5Pn<t})#
zw*r&2tk!5yR#xUaQ%1@oDkbLpsRcrU7I*5tcv{xI(KkJ~VgC+8kzMog=cg{KDDJMe
zj`<`mOE+R;v_ASWjuxG|e0#!ga5It4t_~`0>5dS?E4Z`I++(yjqb<K|m~T<3??fsX
z3(P~b8~ZA;5<ZKz!WQpz!7~WU(tAUZ6Td}i%c|FIbQRLYF-oGJHfX*j-20UddeFW|
zIe{3tu|cXW=<PM5-eWnA5;^nT)dcz{ZFV?x=9f4r^YA^%A&YW4iImBy+`{f+yXHKi
zfQcNF#TmN%&cyAl6e;ez<gg86NG38X>5xF#hv)#Y``C#S=HUjeYw~pQ)|G_=LH017
z{QIHNCSVIqF}cWRIfWYKVR#lCsgXf%D$p&Euu<(G-aEXwpwc||4LJW1LZMCm2w-Fq
zv<zg$iS3)jtM<1YsO4L#jO&k)wB%}Npx~kXvJ7&(`&7tGd&I_YsAw2fSU!A}7DjwB
zU@RCtHSdhwR!y#Qzx}w}g`8l;nS74<Czelirn%bh<uv^Jlip%G%~0xx?{9Z@R%@L=
zqvVkHEtPcc<yFUzgk5>5#dnN#-!b-(=q6h;F5yS@w}e5V2lwlKeaWU2OVOrtKMvrg
zBKV2a<Ejx~6fu;Rgji^MfgT2fcITVp(1XpQwRt~Yd+Y#>z>T$$aF2zC6S;YLT2lS)
z2Duf7{U<YU?w<s*9Hze&372QD`#<NB(T5EI#h_v^po)W&^GeN}Y)PQ!Om7Cy))12*
zNDmn}%bdX7%|YSr&ik7mzRzE(_&!0_xP$F?{^FW9qtNXfH&<vgL$jILldLJ7tYm0x
zTy;R?U7ZZ254d$dU~F>>(I#-2wv0~4)deC?RY{AikRKYDQnRgCPjkD2xSwgHd9`<u
zW>)7c=sa!_YQNWjy+snVt9)qVmAgB0yDrN<(70oR_E^pC&ZclTxs(7Mn)fgwPKyt{
zyR+R7nliM+JZY3xFCcym&R`lU;O$N5hxOGx*n<{}xhr841%L_C@X&Zm0(rb#+}PA~
z@{W_!m6||!5_){`WJuF6DHmW$v!5@wx=*^}yH?67*i;84N%-&`j<McZ*xFJaw;7tn
zCNj^7QhDzUDnkF_SaSll1?V|_L}))1lQi!vT&o0R-P4?+`=$XO#_rllR1M?02RN;V
zJTp*3MtgJ_<tt$7AG7_@FvM!B_YCD()eiInvFf3MWC;jOn4xQ_2-~u~l%?na+|)&4
z#A;{pgjctzeWfn{D2VEPH>CQ%!a_(lgRT?@f+vmg!kw4f7n*<tm-Z4?%3-U<%~sp3
z(k#gRuv0773j7nDc#wdRqv9;VSZ#X$4R|ql59p$0Aj;8ujKVIYEtxv&w<B}E_b&NB
z!YeHmuaY~&T)LjM9gk&U4BMXR+QOH+P-5i+`|sZd<zBv`#=ezXe)+3`S&eTQv~gf6
z>F%=q5AyHipWD2UpiRY73k+;*Y*v!6+C?VQugNc+IAaIP&rx06&XN<Hl6dB97$+`5
zkiK>7<U_l@->Z?;*~8NZFuU#HL4|KAt)TMhLTe90dj{eWfLVvt@L!&R?~2xL6_;ZM
zMJVy~_8qiKO;U9E{o;Ui6}q)JLXX%NJbtOGFrchK7Uo6QMfCMkfo_NJ1bnNQap<_e
zOCN^bJ5X2~ddObemS5xS5b4yf8u;AJ`M0lJ5(t7&=>H4gRZwWJx+2qd8iM+RfsT9z
z*`+GKp%`L~KSpZ`v`Obx8c*QXJ*ba)4WXXe%0Hd_3OY^}wCS?ny?fUX`Zo5!qpKSf
z#og@5&McwBPbQ!}-oCX?Gw<U&#Owfk3qM`ly!4S1Q_9lt_Ex?7&N?Nxy4nKZI)47c
z0Uht1G>Jh)XkwaGxFm1`M+9~aIf9KtoS#Ces9?KsHKpwm$$yE&Px7Q}_<GtV?dgA@
y_z_9gEc58)`lE;T8t^9msA{be`9CU}JBl(-9uM-Hd&I+%5%ufZ$^}=ggZ~Agg6Mt#

diff --git a/reference/WHOCC.html b/reference/WHOCC.html
index b31372716..458bcf687 100644
--- a/reference/WHOCC.html
+++ b/reference/WHOCC.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/WHONET.html b/reference/WHONET.html
index 01d681d8f..42c3ec6df 100644
--- a/reference/WHONET.html
+++ b/reference/WHONET.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html
index 423576579..5f4369839 100644
--- a/reference/ab_from_text.html
+++ b/reference/ab_from_text.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/ab_property.html b/reference/ab_property.html
index e80842638..c813f1fd8 100644
--- a/reference/ab_property.html
+++ b/reference/ab_property.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -182,7 +187,7 @@
 
 
 <dt>language</dt>
-<dd><p>language of the returned text, defaults to system language (see <code><a href="translate.html">get_AMR_locale()</a></code>) and can also be set with <code>getOption("AMR_locale")</code>. Use <code>language = NULL</code> or <code>language = ""</code> to prevent translation.</p></dd>
+<dd><p>language of the returned text, defaults to system language (see <code><a href="translate.html">get_AMR_locale()</a></code>) and can also be set with the option <code><a href="AMR-options.html">AMR_locale</a></code>. Use <code>language = NULL</code> or <code>language = ""</code> to prevent translation.</p></dd>
 
 
 <dt>tolower</dt>
diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html
index 7ef64d9b4..55a4df254 100644
--- a/reference/add_custom_antimicrobials.html
+++ b/reference/add_custom_antimicrobials.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -153,16 +158,15 @@
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p><strong>Important:</strong> Due to how <span style="R">R</span> works, the <code>add_custom_antimicrobials()</code> function has to be run in every <span style="R">R</span> session - added antimicrobials are not stored between sessions and are thus lost when <span style="R">R</span> is exited.</p>
 <p>There are two ways to automate this process:</p>
-<p><strong>Method 1:</strong> Save the antimicrobials to a local or remote file (can even be the internet). To use this method:</p><ol><li><p>Create a data set in the structure of the <a href="antibiotics.html">antibiotics</a> data set (containing at the very least columns "ab" and "name") and save it with <code><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">saveRDS()</a></code> to a location of choice, e.g. <code>"~/my_custom_ab.rds"</code>, or any remote location.</p></li>
-<li><p>Set the file location to the <code>AMR_custom_ab</code> <span style="R">R</span> option: <code>options(AMR_custom_ab = "~/my_custom_ab.rds")</code>. This can even be a remote file location, such as an https URL. Since options are not saved between <span style="R">R</span> sessions, it is best to save this option to the <code>.Rprofile</code> file so that it will loaded on start-up of <span style="R">R</span>. To do this, open the <code>.Rprofile</code> file using e.g. <code>utils::file.edit("~/.Rprofile")</code>, add this text and save the file:</p>
-<p></p><div class="sourceCode r"><pre><code><span><span class="co"># Add custom antibiotic drug codes:</span></span>
+<p><strong>Method 1:</strong> Using the option <code><a href="AMR-options.html">AMR_custom_ab</a></code>, which is the preferred method. To use this method:</p><ol><li><p>Create a data set in the structure of the <a href="antibiotics.html">antibiotics</a> data set (containing at the very least columns "ab" and "name") and save it with <code><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">saveRDS()</a></code> to a location of choice, e.g. <code>"~/my_custom_ab.rds"</code>, or any remote location.</p></li>
+<li><p>Set the file location to the option <code><a href="AMR-options.html">AMR_custom_ab</a></code>: <code>options(AMR_custom_ab = "~/my_custom_ab.rds")</code>. This can even be a remote file location, such as an https URL. Since options are not saved between <span style="R">R</span> sessions, it is best to save this option to the <code>.Rprofile</code> file so that it will be loaded on start-up of <span style="R">R</span>. To do this, open the <code>.Rprofile</code> file using e.g. <code>utils::file.edit("~/.Rprofile")</code>, add this text and save the file:</p>
+<p></p><div class="sourceCode r"><pre><code><span><span class="co"># Add custom antimicrobial codes:</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">options</a></span><span class="op">(</span>AMR_custom_ab <span class="op">=</span> <span class="st">"~/my_custom_ab.rds"</span><span class="op">)</span></span></code></pre><p></p></div>
 <p>Upon package load, this file will be loaded and run through the <code>add_custom_antimicrobials()</code> function.</p></li>
-</ol><p><strong>Method 2:</strong> Save the antimicrobial additions directly to your <code>.Rprofile</code> file. An important downside is that this requires to load the <code>AMR</code> package at every start-up. To use this method:</p><ol><li><p>Edit the <code>.Rprofile</code> file using e.g. <code>utils::file.edit("~/.Rprofile")</code>.</p></li>
+</ol><p><strong>Method 2:</strong> Loading the antimicrobial additions directly from your <code>.Rprofile</code> file. An important downside is that this requires the <code>AMR</code> package to be installed or else this method will fail. To use this method:</p><ol><li><p>Edit the <code>.Rprofile</code> file using e.g. <code>utils::file.edit("~/.Rprofile")</code>.</p></li>
 <li><p>Add a text like below and save the file:</p>
 <p></p><div class="sourceCode r"><pre><code><span> <span class="co"># Add custom antibiotic drug codes:</span></span>
-<span> <span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span></span>
-<span> <span class="fu"><a href="../reference/add_custom_antimicrobials.html">add_custom_antimicrobials</a></span><span class="op">(</span></span>
+<span> <span class="fu">AMR</span><span class="fu">::</span><span class="fu"><a href="../reference/add_custom_antimicrobials.html">add_custom_antimicrobials</a></span><span class="op">(</span></span>
 <span>   <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>ab <span class="op">=</span> <span class="st">"TESTAB"</span>,</span>
 <span>              name <span class="op">=</span> <span class="st">"Test Antibiotic"</span>,</span>
 <span>              group <span class="op">=</span> <span class="st">"Test Group"</span><span class="op">)</span></span>
diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html
index b13431ed7..02f4c01ac 100644
--- a/reference/add_custom_microorganisms.html
+++ b/reference/add_custom_microorganisms.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -155,7 +160,7 @@
 <p><strong>Important:</strong> Due to how <span style="R">R</span> works, the <code>add_custom_microorganisms()</code> function has to be run in every <span style="R">R</span> session - added microorganisms are not stored between sessions and are thus lost when <span style="R">R</span> is exited.</p>
 <p>There are two ways to automate this process:</p>
 <p><strong>Method 1:</strong> Using the option <code><a href="AMR-options.html">AMR_custom_mo</a></code>, which is the preferred method. To use this method:</p><ol><li><p>Create a data set in the structure of the <a href="microorganisms.html">microorganisms</a> data set (containing at the very least column "genus") and save it with <code><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">saveRDS()</a></code> to a location of choice, e.g. <code>"~/my_custom_mo.rds"</code>, or any remote location.</p></li>
-<li><p>Set the file location to the <code>AMR_custom_mo</code> <span style="R">R</span> option: <code>options(AMR_custom_mo = "~/my_custom_mo.rds")</code>. This can even be a remote file location, such as an https URL. Since options are not saved between <span style="R">R</span> sessions, it is best to save this option to the <code>.Rprofile</code> file so that it will be loaded on start-up of <span style="R">R</span>. To do this, open the <code>.Rprofile</code> file using e.g. <code>utils::file.edit("~/.Rprofile")</code>, add this text and save the file:</p>
+<li><p>Set the file location to the option <code><a href="AMR-options.html">AMR_custom_mo</a></code>: <code>options(AMR_custom_mo = "~/my_custom_mo.rds")</code>. This can even be a remote file location, such as an https URL. Since options are not saved between <span style="R">R</span> sessions, it is best to save this option to the <code>.Rprofile</code> file so that it will be loaded on start-up of <span style="R">R</span>. To do this, open the <code>.Rprofile</code> file using e.g. <code>utils::file.edit("~/.Rprofile")</code>, add this text and save the file:</p>
 <p></p><div class="sourceCode r"><pre><code><span><span class="co"># Add custom microorganism codes:</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">options</a></span><span class="op">(</span>AMR_custom_mo <span class="op">=</span> <span class="st">"~/my_custom_mo.rds"</span><span class="op">)</span></span></code></pre><p></p></div>
 <p>Upon package load, this file will be loaded and run through the <code>add_custom_microorganisms()</code> function.</p></li>
diff --git a/reference/age.html b/reference/age.html
index 4dcd31653..2b852ac93 100644
--- a/reference/age.html
+++ b/reference/age.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -195,16 +200,16 @@
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">df</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    birth_date age age_exact age_at_y2k</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1  1970-09-14  52  52.35890         29</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2  1938-05-10  84  84.70685         61</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3  1969-03-15  53  53.86027         30</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4  1980-10-27  42  42.24110         19</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5  1939-10-10  83  83.28767         60</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6  1980-01-05  43  43.04932         19</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7  1965-04-17  57  57.76986         34</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8  1967-12-21  55  55.09041         32</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9  1961-05-16  61  61.69041         38</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1964-12-10  58  58.12055         35</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1  1954-05-23  68  68.67123         45</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2  1996-11-02  26  26.22466          3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3  1991-01-29  31  31.98356          8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4  1931-12-19  91  91.09589         68</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5  1946-04-30  76  76.73425         53</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6  1990-08-24  32  32.41644          9</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7  1982-08-27  40  40.40822         17</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8  1987-02-17  35  35.93151         12</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9  1948-11-15  74  74.18904         51</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1976-01-28  46  46.98630         23</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/age_groups.html b/reference/age_groups.html
index 77f9fab4b..c7c75ee4d 100644
--- a/reference/age_groups.html
+++ b/reference/age_groups.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/antibiotic_class_selectors.html b/reference/antibiotic_class_selectors.html
index 527677d9b..15606a53d 100644
--- a/reference/antibiotic_class_selectors.html
+++ b/reference/antibiotic_class_selectors.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/antibiotics.html b/reference/antibiotics.html
index 4384981fb..70dfaf880 100644
--- a/reference/antibiotics.html
+++ b/reference/antibiotics.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/as.ab.html b/reference/as.ab.html
index b937506ef..a39b16c33 100644
--- a/reference/as.ab.html
+++ b/reference/as.ab.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/as.av.html b/reference/as.av.html
index 2a1d70652..3bd42bede 100644
--- a/reference/as.av.html
+++ b/reference/as.av.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/as.disk.html b/reference/as.disk.html
index 86541c350..8c62d00bb 100644
--- a/reference/as.disk.html
+++ b/reference/as.disk.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/as.mic.html b/reference/as.mic.html
index 7229aa081..9b4ebf4ec 100644
--- a/reference/as.mic.html
+++ b/reference/as.mic.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/as.mo.html b/reference/as.mo.html
index 33e0c9236..6759926d5 100644
--- a/reference/as.mo.html
+++ b/reference/as.mo.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -186,7 +191,7 @@
 
 
 <dt>keep_synonyms</dt>
-<dd><p>a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate if old, previously valid taxonomic names must be preserved and not be corrected to currently accepted names. The default is <code>FALSE</code>, which will return a note if old taxonomic names were processed. The default can be set with <code>options(AMR_keep_synonyms = TRUE)</code> or <code>options(AMR_keep_synonyms = FALSE)</code>.</p></dd>
+<dd><p>a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate if old, previously valid taxonomic names must be preserved and not be corrected to currently accepted names. The default is <code>FALSE</code>, which will return a note if old taxonomic names were processed. The default can be set with the option <code><a href="AMR-options.html">AMR_keep_synonyms</a></code>, i.e. <code>options(AMR_keep_synonyms = TRUE)</code> or <code>options(AMR_keep_synonyms = FALSE)</code>.</p></dd>
 
 
 <dt>reference_df</dt>
@@ -194,7 +199,7 @@
 
 
 <dt>ignore_pattern</dt>
-<dd><p>a <a href="https://rdrr.io/r/base/regex.html" class="external-link">regular expression</a> (case-insensitive) of which all matches in <code>x</code> must return <code>NA</code>. This can be convenient to exclude known non-relevant input and can also be set with the option <code>AMR_ignore_pattern</code>, e.g. <code>options(AMR_ignore_pattern = "(not reported|contaminated flora)")</code>.</p></dd>
+<dd><p>a <a href="https://rdrr.io/r/base/regex.html" class="external-link">regular expression</a> (case-insensitive) of which all matches in <code>x</code> must return <code>NA</code>. This can be convenient to exclude known non-relevant input and can also be set with the option <code><a href="AMR-options.html">AMR_ignore_pattern</a></code>, e.g. <code>options(AMR_ignore_pattern = "(not reported|contaminated flora)")</code>.</p></dd>
 
 
 <dt>remove_from_input</dt>
diff --git a/reference/as.sir.html b/reference/as.sir.html
index b7d1ca7fa..11852cc2c 100644
--- a/reference/as.sir.html
+++ b/reference/as.sir.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -156,6 +161,7 @@
 <span>  conserve_capped_values <span class="op">=</span> <span class="cn">FALSE</span>,</span>
 <span>  add_intrinsic_resistance <span class="op">=</span> <span class="cn">FALSE</span>,</span>
 <span>  reference_data <span class="op">=</span> <span class="fu">AMR</span><span class="fu">::</span><span class="va"><a href="clinical_breakpoints.html">clinical_breakpoints</a></span>,</span>
+<span>  include_PKPD <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"AMR_include_PKPD"</span>, <span class="cn">TRUE</span><span class="op">)</span>,</span>
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span>
 <span></span>
@@ -168,6 +174,7 @@
 <span>  uti <span class="op">=</span> <span class="cn">NULL</span>,</span>
 <span>  add_intrinsic_resistance <span class="op">=</span> <span class="cn">FALSE</span>,</span>
 <span>  reference_data <span class="op">=</span> <span class="fu">AMR</span><span class="fu">::</span><span class="va"><a href="clinical_breakpoints.html">clinical_breakpoints</a></span>,</span>
+<span>  include_PKPD <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"AMR_include_PKPD"</span>, <span class="cn">TRUE</span><span class="op">)</span>,</span>
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span>
 <span></span>
@@ -180,7 +187,8 @@
 <span>  uti <span class="op">=</span> <span class="cn">NULL</span>,</span>
 <span>  conserve_capped_values <span class="op">=</span> <span class="cn">FALSE</span>,</span>
 <span>  add_intrinsic_resistance <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  reference_data <span class="op">=</span> <span class="fu">AMR</span><span class="fu">::</span><span class="va"><a href="clinical_breakpoints.html">clinical_breakpoints</a></span></span>
+<span>  reference_data <span class="op">=</span> <span class="fu">AMR</span><span class="fu">::</span><span class="va"><a href="clinical_breakpoints.html">clinical_breakpoints</a></span>,</span>
+<span>  include_PKPD <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"AMR_include_PKPD"</span>, <span class="cn">TRUE</span><span class="op">)</span></span>
 <span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu">sir_interpretation_history</span><span class="op">(</span>clean <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
@@ -238,6 +246,10 @@
 <dd><p>a <a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a> to be used for interpretation, which defaults to the <a href="clinical_breakpoints.html">clinical_breakpoints</a> data set. Changing this argument allows for using own interpretation guidelines. This argument must contain a data set that is equal in structure to the <a href="clinical_breakpoints.html">clinical_breakpoints</a> data set (same column names and column types). Please note that the <code>guideline</code> argument will be ignored when <code>reference_data</code> is manually set.</p></dd>
 
 
+<dt>include_PKPD</dt>
+<dd><p>a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate that PK/PD clinical breakpoints must be applied as a last resort, defaults to <code>TRUE</code>. Can also be set with the option <code><a href="AMR-options.html">AMR_include_PKPD</a></code>.</p></dd>
+
+
 <dt>col_mo</dt>
 <dd><p>column name of the IDs of the microorganisms (see <code><a href="as.mo.html">as.mo()</a></code>), defaults to the first column of class <code><a href="as.mo.html">mo</a></code>. Values will be coerced using <code><a href="as.mo.html">as.mo()</a></code>.</p></dd>
 
@@ -281,7 +293,7 @@
 
 <p>For interpreting MIC values as well as disk diffusion diameters, currently implemented guidelines are EUCAST (2013-2022) and CLSI (2013-2022).</p>
 <p>Thus, the <code>guideline</code> argument must be set to e.g., <code>"EUCAST 2022"</code> or <code>"CLSI 2022"</code>. By simply using <code>"EUCAST"</code> (the default) or <code>"CLSI"</code> as input, the latest included version of that guideline will automatically be selected. You can set your own data set using the <code>reference_data</code> argument. The <code>guideline</code> argument will then be ignored.</p>
-<p>You can set the default guideline with the <code>AMR_guideline</code> <a href="https://rdrr.io/r/base/options.html" class="external-link">option</a> (e.g. in your <code>.Rprofile</code> file), such as:</p>
+<p>You can set the default guideline with the option <code><a href="AMR-options.html">AMR_guideline</a></code> (e.g. in your <code>.Rprofile</code> file), such as:</p>
 <p></p><div class="sourceCode"><pre><code><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a>  <span class="fu">options</span>(<span class="at">AMR_guideline =</span> <span class="st">"CLSI"</span>)</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">options</span>(<span class="at">AMR_guideline =</span> <span class="st">"CLSI 2018"</span>)</span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">options</span>(<span class="at">AMR_guideline =</span> <span class="st">"EUCAST 2020"</span>)</span>
@@ -507,16 +519,16 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 50 × 12</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    datetime            index ab_input ab_guid…¹ mo_in…² mo_guideline     guide…³</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dttm&gt;</span>              <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;ab&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span>             <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2023-01-23 <span style="color: #949494;">14:05:17</span>     1 TOB      TOB       Escher… B_[ORD]_ENTRBCTR EUCAST…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2023-01-23 <span style="color: #949494;">14:05:17</span>     1 GEN      GEN       Escher… B_[ORD]_ENTRBCTR EUCAST…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2023-01-23 <span style="color: #949494;">14:05:17</span>     1 CIP      CIP       Escher… B_[ORD]_ENTRBCTR EUCAST…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2023-01-23 <span style="color: #949494;">14:05:17</span>     1 AMP      AMP       Escher… B_[ORD]_ENTRBCTR EUCAST…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2023-01-23 <span style="color: #949494;">14:05:12</span>     1 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2023-01-23 <span style="color: #949494;">14:05:12</span>     2 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2023-01-23 <span style="color: #949494;">14:05:12</span>     3 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2023-01-23 <span style="color: #949494;">14:05:12</span>     4 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2023-01-23 <span style="color: #949494;">14:05:12</span>     5 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2023-01-23 <span style="color: #949494;">14:05:12</span>     6 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2023-01-23 <span style="color: #949494;">19:11:56</span>     1 TOB      TOB       Escher… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2023-01-23 <span style="color: #949494;">19:11:56</span>     1 GEN      GEN       Escher… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2023-01-23 <span style="color: #949494;">19:11:56</span>     1 CIP      CIP       Escher… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2023-01-23 <span style="color: #949494;">19:11:55</span>     1 AMP      AMP       Escher… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2023-01-23 <span style="color: #949494;">19:11:50</span>     1 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2023-01-23 <span style="color: #949494;">19:11:50</span>     2 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2023-01-23 <span style="color: #949494;">19:11:50</span>     3 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2023-01-23 <span style="color: #949494;">19:11:50</span>     4 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2023-01-23 <span style="color: #949494;">19:11:50</span>     5 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2023-01-23 <span style="color: #949494;">19:11:50</span>     6 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># … with 40 more rows, 5 more variables: ref_table &lt;chr&gt;, method &lt;chr&gt;,</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   input &lt;dbl&gt;, outcome &lt;sir&gt;, breakpoint_S_R &lt;chr&gt;, and abbreviated variable</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   names ¹​ab_guideline, ²​mo_input, ³​guideline</span></span>
diff --git a/reference/atc_online.html b/reference/atc_online.html
index fdc259f6b..8ced05302 100644
--- a/reference/atc_online.html
+++ b/reference/atc_online.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/av_from_text.html b/reference/av_from_text.html
index aabaeaf03..eabc2da7b 100644
--- a/reference/av_from_text.html
+++ b/reference/av_from_text.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/av_property.html b/reference/av_property.html
index a4e0617a9..987dc7e67 100644
--- a/reference/av_property.html
+++ b/reference/av_property.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -170,7 +175,7 @@
 
 
 <dt>language</dt>
-<dd><p>language of the returned text, defaults to system language (see <code><a href="translate.html">get_AMR_locale()</a></code>) and can also be set with <code>getOption("AMR_locale")</code>. Use <code>language = NULL</code> or <code>language = ""</code> to prevent translation.</p></dd>
+<dd><p>language of the returned text, defaults to system language (see <code><a href="translate.html">get_AMR_locale()</a></code>) and can also be set with the option <code><a href="AMR-options.html">AMR_locale</a></code>. Use <code>language = NULL</code> or <code>language = ""</code> to prevent translation.</p></dd>
 
 
 <dt>tolower</dt>
diff --git a/reference/availability.html b/reference/availability.html
index 3173a7543..d0b189859 100644
--- a/reference/availability.html
+++ b/reference/availability.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html
index 069a132b3..1f4f7be0f 100644
--- a/reference/bug_drug_combinations.html
+++ b/reference/bug_drug_combinations.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -178,7 +183,7 @@
 
 
 <dt>language</dt>
-<dd><p>language of the returned text, defaults to system language (see <code><a href="translate.html">get_AMR_locale()</a></code>) and can also be set with <code>getOption("AMR_locale")</code>. Use <code>language = NULL</code> or <code>language = ""</code> to prevent translation.</p></dd>
+<dd><p>language of the returned text, defaults to system language (see <code><a href="translate.html">get_AMR_locale()</a></code>) and can also be set with the option <code><a href="AMR-options.html">AMR_locale</a></code>. Use <code>language = NULL</code> or <code>language = ""</code> to prevent translation.</p></dd>
 
 
 <dt>minimum</dt>
diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html
index db6d37bb9..2a5a9dfa3 100644
--- a/reference/clinical_breakpoints.html
+++ b/reference/clinical_breakpoints.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/count.html b/reference/count.html
index 9d55671b3..4feeb81de 100644
--- a/reference/count.html
+++ b/reference/count.html
@@ -12,7 +12,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -34,60 +34,65 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -186,7 +191,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
 
 
 <dt>language</dt>
-<dd><p>language of the returned text, defaults to system language (see <code><a href="translate.html">get_AMR_locale()</a></code>) and can also be set with <code>getOption("AMR_locale")</code>. Use <code>language = NULL</code> or <code>language = ""</code> to prevent translation.</p></dd>
+<dd><p>language of the returned text, defaults to system language (see <code><a href="translate.html">get_AMR_locale()</a></code>) and can also be set with the option <code><a href="AMR-options.html">AMR_locale</a></code>. Use <code>language = NULL</code> or <code>language = ""</code> to prevent translation.</p></dd>
 
 
 <dt>combine_SI</dt>
diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html
index b1065d249..589e11a70 100644
--- a/reference/custom_eucast_rules.html
+++ b/reference/custom_eucast_rules.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/dosage.html b/reference/dosage.html
index 95b6c1a59..02155f3c9 100644
--- a/reference/dosage.html
+++ b/reference/dosage.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html
index be8d870cb..873d89269 100644
--- a/reference/eucast_rules.html
+++ b/reference/eucast_rules.html
@@ -12,7 +12,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -34,60 +34,65 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -186,7 +191,7 @@ Leclercq et al. <strong>EUCAST expert rules in antimicrobial susceptibility test
 
 
 <dt>rules</dt>
-<dd><p>a <a href="https://rdrr.io/r/base/character.html" class="external-link">character</a> vector that specifies which rules should be applied. Must be one or more of <code>"breakpoints"</code>, <code>"expert"</code>, <code>"other"</code>, <code>"custom"</code>, <code>"all"</code>, and defaults to <code>c("breakpoints", "expert")</code>. The default value can be set to another value, e.g. using <code>options(AMR_eucastrules = "all")</code>. If using <code>"custom"</code>, be sure to fill in argument <code>custom_rules</code> too. Custom rules can be created with <code><a href="custom_eucast_rules.html">custom_eucast_rules()</a></code>.</p></dd>
+<dd><p>a <a href="https://rdrr.io/r/base/character.html" class="external-link">character</a> vector that specifies which rules should be applied. Must be one or more of <code>"breakpoints"</code>, <code>"expert"</code>, <code>"other"</code>, <code>"custom"</code>, <code>"all"</code>, and defaults to <code>c("breakpoints", "expert")</code>. The default value can be set to another value using the option <code><a href="AMR-options.html">AMR_eucastrules</a></code>: <code>options(AMR_eucastrules = "all")</code>. If using <code>"custom"</code>, be sure to fill in argument <code>custom_rules</code> too. Custom rules can be created with <code><a href="custom_eucast_rules.html">custom_eucast_rules()</a></code>.</p></dd>
 
 
 <dt>verbose</dt>
@@ -252,7 +257,7 @@ Leclercq et al. <strong>EUCAST expert rules in antimicrobial susceptibility test
 <p>Before further processing, two non-EUCAST rules about drug combinations can be applied to improve the efficacy of the EUCAST rules, and the reliability of your data (analysis). These rules are:</p><ol><li><p>A drug <strong>with</strong> enzyme inhibitor will be set to S if the same drug <strong>without</strong> enzyme inhibitor is S</p></li>
 <li><p>A drug <strong>without</strong> enzyme inhibitor will be set to R if the same drug <strong>with</strong> enzyme inhibitor is R</p></li>
 </ol><p>Important examples include amoxicillin and amoxicillin/clavulanic acid, and trimethoprim and trimethoprim/sulfamethoxazole. Needless to say, for these rules to work, both drugs must be available in the data set.</p>
-<p>Since these rules are not officially approved by EUCAST, they are not applied at default. To use these rules, include <code>"other"</code> to the <code>rules</code> argument, or use <code>eucast_rules(..., rules = "all")</code>. You can also set the option <code>AMR_eucastrules</code>, i.e. run <code>options(AMR_eucastrules = "all")</code>.</p>
+<p>Since these rules are not officially approved by EUCAST, they are not applied at default. To use these rules, include <code>"other"</code> to the <code>rules</code> argument, or use <code>eucast_rules(..., rules = "all")</code>. You can also set the option <code><a href="AMR-options.html">AMR_eucastrules</a></code>, i.e. run <code>options(AMR_eucastrules = "all")</code>.</p>
 </div>
 
     </div>
diff --git a/reference/example_isolates.html b/reference/example_isolates.html
index 301bd3f70..a4886b388 100644
--- a/reference/example_isolates.html
+++ b/reference/example_isolates.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html
index 51407a37f..c9a33da35 100644
--- a/reference/example_isolates_unclean.html
+++ b/reference/example_isolates_unclean.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/first_isolate.html b/reference/first_isolate.html
index ecb98ed6f..5e3474a94 100644
--- a/reference/first_isolate.html
+++ b/reference/first_isolate.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -34,60 +34,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/g.test.html b/reference/g.test.html
index a1fffae96..30de07a7a 100644
--- a/reference/g.test.html
+++ b/reference/g.test.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/get_episode.html b/reference/get_episode.html
index c725a9592..111fb48e0 100644
--- a/reference/get_episode.html
+++ b/reference/get_episode.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -182,48 +187,45 @@
 <span class="r-in"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/sample.html" class="external-link">sample</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq_len</a></span><span class="op">(</span><span class="fl">2000</span><span class="op">)</span>, size <span class="op">=</span> <span class="fl">200</span><span class="op">)</span>, <span class="op">]</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu">get_episode</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">date</span>, episode_days <span class="op">=</span> <span class="fl">60</span><span class="op">)</span> <span class="co"># indices</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [1]  9 29 29 17 30 21  4 56 31 35 18 50 62  7 28 28 57 37 53 46 15 23 14 39 42</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [26] 46 20 62 29 54 18 54  1 61  3 12  6  8  9 43 24  1 61 37 11 42 28 36 12  8</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [51] 26 61 55 60  4 19 30  7 20  9 13 57  9 59 47  8 17  1 64  8  2  4 60  7 34</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [76] 61 44 20 61 55  7  2 32 33 18  6 18 11  7 10 18  5  3 44 23 54 31 18  4 19</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [101] 25 17  7 41 43 17 55 21 30 61 11 16 14 57 52 41 40 23 43 60 37 26 12 12  4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [126] 10  6 40 11  2 45 30 51 47 29 25 58 10 61 19  8 39  1 24 25 58 10 18 34 20</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [151] 63 43 57 51 60 41 36 64  4 61 55 22 19 27 53 64 51 15 55 23 22 36 45 57 48</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [176] 40 44 49 57 38 40 29 32  2 11 50 38 53 10 33  8  9 54  4  6 58 57  3 14 60</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [1] 31 31 27 14 48 21 11 37 16  9 51 51 33 47 10 36 53 38 14  7 21 64 38  5 54</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [26] 11 34  5 38  9 65 17 33 31 40 13  7  4 60 38 31  8 11  6 53 24 16 61 44 18</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [51] 10 36 11 50  2 63 15  1 66 35 32  2 59  5 36 14 32 35 21 44 18  8 32 43  9</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [76] 60 37  6 30 13 59 38 14 65 53 56 26 20 59 62 40 46 62 10 62 49 60 33 35 27</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [101] 17 22  8 64 29 20 63 39 19 20 46  6 60  7 29  8 14 37  9 34 26 37 66 65 28</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [126] 60  7 54 57 57 52 23 20 28 60  2 14 19 39 55 54  4 10 48 40 25 23 53 23 25</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [151] 29  2 34  1  4 20  1 60 58 27 47 28 39 63 48 14  8 21  2 45 30 12 65 22 42</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [176] 16 41 63 50 63  3  7 57 65 29 13  5 63 53 62 53 17 40 27 19 65  9  9 38 31</span>
 <span class="r-in"><span><span class="fu">is_new_episode</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">date</span>, episode_days <span class="op">=</span> <span class="fl">60</span><span class="op">)</span> <span class="co"># TRUE/FALSE</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [1] FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [13] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [25] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [37] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [49] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [61]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [73] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [85] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [97] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [109] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [121] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [145]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [157] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [169]  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [181]  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [193]  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [1] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [13] FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [25]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [49]  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [61]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [73] FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [85] FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [97]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [109] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [121] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [133] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [145]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [157] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [169] FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [181]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [193] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># filter on results from the third 60-day episode only, using base R</span></span></span>
 <span class="r-in"><span><span class="va">df</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/which.html" class="external-link">which</a></span><span class="op">(</span><span class="fu">get_episode</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">date</span>, <span class="fl">60</span><span class="op">)</span> <span class="op">==</span> <span class="fl">3</span><span class="op">)</span>, <span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 3 × 46</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;date&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span>         <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> 2002-08-28 390178     57 M      Clinical B_STRPT_SLVR S     NA    NA    S    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> 2002-08-14 785317     51 F      ICU      B_ESCHR_COLI R     NA    NA    NA   </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> 2002-07-24 F35553     51 M      ICU      B_STPHY_AURS R     NA    S     R    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># … with 36 more variables: AMC &lt;sir&gt;, AMP &lt;sir&gt;, TZP &lt;sir&gt;, CZO &lt;sir&gt;,</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   FEP &lt;sir&gt;, CXM &lt;sir&gt;, FOX &lt;sir&gt;, CTX &lt;sir&gt;, CAZ &lt;sir&gt;, CRO &lt;sir&gt;,</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   GEN &lt;sir&gt;, TOB &lt;sir&gt;, AMK &lt;sir&gt;, KAN &lt;sir&gt;, TMP &lt;sir&gt;, SXT &lt;sir&gt;,</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   NIT &lt;sir&gt;, FOS &lt;sir&gt;, LNZ &lt;sir&gt;, CIP &lt;sir&gt;, MFX &lt;sir&gt;, VAN &lt;sir&gt;,</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   TEC &lt;sir&gt;, TCY &lt;sir&gt;, TGC &lt;sir&gt;, DOX &lt;sir&gt;, ERY &lt;sir&gt;, CLI &lt;sir&gt;,</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   AZM &lt;sir&gt;, IPM &lt;sir&gt;, MEM &lt;sir&gt;, MTR &lt;sir&gt;, CHL &lt;sir&gt;, COL &lt;sir&gt;,</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   MUP &lt;sir&gt;, RIF &lt;sir&gt;</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 1 × 46</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   date       patient   age gender ward  mo      PEN   OXA   FLC   AMX   AMC  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;date&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> 2002-06-18 012595     30 M      ICU   B_CRYNB I     NA    NA    NA    NA   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># … with 35 more variables: AMP &lt;sir&gt;, TZP &lt;sir&gt;, CZO &lt;sir&gt;, FEP &lt;sir&gt;,</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   CXM &lt;sir&gt;, FOX &lt;sir&gt;, CTX &lt;sir&gt;, CAZ &lt;sir&gt;, CRO &lt;sir&gt;, GEN &lt;sir&gt;,</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   TOB &lt;sir&gt;, AMK &lt;sir&gt;, KAN &lt;sir&gt;, TMP &lt;sir&gt;, SXT &lt;sir&gt;, NIT &lt;sir&gt;,</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   FOS &lt;sir&gt;, LNZ &lt;sir&gt;, CIP &lt;sir&gt;, MFX &lt;sir&gt;, VAN &lt;sir&gt;, TEC &lt;sir&gt;,</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   TCY &lt;sir&gt;, TGC &lt;sir&gt;, DOX &lt;sir&gt;, ERY &lt;sir&gt;, CLI &lt;sir&gt;, AZM &lt;sir&gt;,</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   IPM &lt;sir&gt;, MEM &lt;sir&gt;, MTR &lt;sir&gt;, CHL &lt;sir&gt;, COL &lt;sir&gt;, MUP &lt;sir&gt;, RIF &lt;sir&gt;</span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># the functions also work for less than a day, e.g. to include one per hour:</span></span></span>
 <span class="r-in"><span><span class="fu">get_episode</span><span class="op">(</span></span></span>
@@ -253,16 +255,16 @@
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Groups:   condition [3]</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    patient date       condition new_episode</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;date&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span>      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 822116  2004-02-24 C         FALSE      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 144549  2009-09-01 C         FALSE      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 982666  2009-09-06 A         FALSE      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 5DF436  2006-01-04 B         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 023456  2009-11-02 A         FALSE      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 725063  2007-06-07 A         FALSE      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> FCC668  2002-10-14 B         FALSE      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> D28985  2015-12-21 C         FALSE      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> B26404  2010-01-02 A         FALSE      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> F83217  2010-12-21 B         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 0DBB93  2009-05-08 B         FALSE      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 889500  2009-05-08 C         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> A42180  2008-04-21 A         FALSE      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> C56827  2004-12-05 A         FALSE      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 672020  2013-04-01 C         FALSE      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> E19253  2006-08-23 C         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 28F906  2004-04-05 C         FALSE      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 119392  2010-11-01 B         FALSE      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 105248  2005-06-16 A         FALSE      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 000090  2003-10-08 A         FALSE      </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># … with 190 more rows</span></span>
 <span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://dplyr.tidyverse.org" class="external-link">"dplyr"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
 <span class="r-in"><span>  <span class="va">df</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
@@ -274,19 +276,19 @@
 <span class="r-in"><span>    <span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 200 × 5</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Groups:   ward, patient [183]</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    ward       date       patient new_index new_logical</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;date&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>       <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span>      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> ICU        2004-02-24 822116          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> Clinical   2009-09-01 144549          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> Clinical   2009-09-06 982666          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> ICU        2006-01-04 5DF436          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> Clinical   2009-11-02 023456          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> ICU        2007-06-07 725063          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> ICU        2002-10-14 FCC668          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> Outpatient 2015-12-21 D28985          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> Clinical   2010-01-02 B26404          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> ICU        2010-12-21 F83217          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Groups:   ward, patient [175]</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    ward     date       patient new_index new_logical</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;date&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>       <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span>      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> ICU      2009-05-08 0DBB93          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> Clinical 2009-05-08 889500          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> Clinical 2008-04-21 A42180          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> Clinical 2004-12-05 C56827          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> Clinical 2013-04-01 672020          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> Clinical 2006-08-23 E19253          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> ICU      2004-04-05 28F906          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> Clinical 2010-11-01 119392          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> Clinical 2005-06-16 105248          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> ICU      2003-10-08 000090          1 TRUE       </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># … with 190 more rows</span></span>
 <span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://dplyr.tidyverse.org" class="external-link">"dplyr"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
 <span class="r-in"><span>  <span class="va">df</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
@@ -301,9 +303,9 @@
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 3 × 5</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   ward       n_patients n_episodes_365 n_episodes_60 n_episodes_30</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>           <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>         <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>         <span style="color: #949494; font-style: italic;">&lt;int&gt;</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Clinical          118             14            51            70</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ICU                59             13            37            48</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> Outpatient          6              5             6             6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Clinical          113             14            52            70</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ICU                53             13            32            38</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> Outpatient          9              6             8             9</span>
 <span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://dplyr.tidyverse.org" class="external-link">"dplyr"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
 <span class="r-in"><span>  <span class="co"># grouping on patients and microorganisms leads to the same</span></span></span>
 <span class="r-in"><span>  <span class="co"># results as first_isolate() when using 'episode-based':</span></span></span>
@@ -331,19 +333,19 @@
 <span class="r-in"><span>    <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_data.html" class="external-link">group_vars</a></span><span class="op">(</span><span class="va">.</span><span class="op">)</span>, <span class="va">flag_episode</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 200 × 4</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Groups:   patient, mo, ward [192]</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    patient mo            ward       flag_episode</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span>       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 822116  B_STPHY_AURS  ICU        TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 144549  B_ENTRBC_CLOC Clinical   TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 982666  B_STRPT_DYSG  Clinical   TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 5DF436  B_STPHY_AURS  ICU        TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 023456  B_KLBSL_PNMN  Clinical   TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 725063  B_STPHY_CONS  ICU        TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> FCC668  B_ACNTB       ICU        TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> D28985  B_ESCHR_COLI  Outpatient TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> B26404  B_STPHY_CONS  Clinical   TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> F83217  B_STPHY_CONS  ICU        TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Groups:   patient, mo, ward [184]</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    patient mo           ward     flag_episode</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span>         <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span>       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 0DBB93  B_ESCHR_COLI ICU      TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 889500  B_ESCHR_COLI Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> A42180  B_ENTRC_AVIM Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> C56827  B_ESCHR_COLI Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 672020  B_STPHY_EPDR Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> E19253  B_STPHY_CONS Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 28F906  B_STPHY_CONS ICU      TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 119392  B_STPHY_CONS Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 105248  B_KLBSL_PNMN Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 000090  B_STPHY_EPDR ICU      TRUE        </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># … with 190 more rows</span></span>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html
index 68ce61aea..fa11b5ec2 100644
--- a/reference/ggplot_pca.html
+++ b/reference/ggplot_pca.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html
index 0123385b1..939f29a1e 100644
--- a/reference/ggplot_sir.html
+++ b/reference/ggplot_sir.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -238,7 +243,7 @@
 
 
 <dt>language</dt>
-<dd><p>language of the returned text, defaults to system language (see <code><a href="translate.html">get_AMR_locale()</a></code>) and can also be set with <code>getOption("AMR_locale")</code>. Use <code>language = NULL</code> or <code>language = ""</code> to prevent translation.</p></dd>
+<dd><p>language of the returned text, defaults to system language (see <code><a href="translate.html">get_AMR_locale()</a></code>) and can also be set with the option <code><a href="AMR-options.html">AMR_locale</a></code>. Use <code>language = NULL</code> or <code>language = ""</code> to prevent translation.</p></dd>
 
 
 <dt>nrow</dt>
diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html
index 1e5c3a78c..235046eb4 100644
--- a/reference/guess_ab_col.html
+++ b/reference/guess_ab_col.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/index.html b/reference/index.html
index 8beed8bcb..11976a733 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -310,6 +315,22 @@
         </dt>
         <dd>Guess Antibiotic Column</dd>
       </dl></div><div class="section level2">
+      <h2 id="other-amr-specific-options">Other: AMR-specific options<a class="anchor" aria-label="anchor" href="#other-amr-specific-options"></a></h2>
+      
+      <p class="section-desc"></p><p>The AMR package is customisable, by providing settings that can be set per user or per team. For example, the default interpretation guideline can be changed from EUCAST to CLSI, or a supported language can be set for the whole team (system-language independent) for antibiotic names in a foreign language.</p>
+
+      
+    </div><div id="" class="section level2">
+      
+      
+      
+
+      <dl><dt>
+          
+          <code><a href="AMR-options.html">AMR-options</a></code> 
+        </dt>
+        <dd>Options for the AMR package</dd>
+      </dl></div><div class="section level2">
       <h2 id="other-antiviral-drugs">Other: antiviral drugs<a class="anchor" aria-label="anchor" href="#other-antiviral-drugs"></a></h2>
       
       <p class="section-desc"></p><p>This package also provides extensive support for antiviral agents, even though it is not the primary scope of this package. Working with data containing information about antiviral drugs was never easier. Use these functions to get valid properties of antiviral drugs from any input or to clean your input. You can even retrieve drug names and doses from clinical text records, using <code><a href="../reference/av_from_text.html">av_from_text()</a></code>.</p>
diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html
index 270018201..b158e0a5d 100644
--- a/reference/intrinsic_resistant.html
+++ b/reference/intrinsic_resistant.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html
index 34983a296..6b9e1d938 100644
--- a/reference/italicise_taxonomy.html
+++ b/reference/italicise_taxonomy.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/join.html b/reference/join.html
index 3f6d4f4f1..62dc9850e 100644
--- a/reference/join.html
+++ b/reference/join.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html
index 2cce07f20..fecdcedaf 100644
--- a/reference/key_antimicrobials.html
+++ b/reference/key_antimicrobials.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/kurtosis.html b/reference/kurtosis.html
index 62e601e01..670be4c82 100644
--- a/reference/kurtosis.html
+++ b/reference/kurtosis.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -172,9 +177,9 @@
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu">kurtosis</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="fl">10000</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 2.950875</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 2.977128</span>
 <span class="r-in"><span><span class="fu">kurtosis</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="fl">10000</span><span class="op">)</span>, excess <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.0330772</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.04861302</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/like.html b/reference/like.html
index 5e280fc24..f837efd84 100644
--- a/reference/like.html
+++ b/reference/like.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/mdro.html b/reference/mdro.html
index 739400784..c5061a439 100644
--- a/reference/mdro.html
+++ b/reference/mdro.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html
index 4112aaca5..2b1a4de27 100644
--- a/reference/mean_amr_distance.html
+++ b/reference/mean_amr_distance.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -191,31 +196,30 @@
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">sir</span> <span class="op">&lt;-</span> <span class="fu"><a href="random.html">random_sir</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">sir</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'sir'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] S I S R R R S R R R</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] R R R I S S R I R S</span>
 <span class="r-in"><span><span class="fu">mean_amr_distance</span><span class="op">(</span><span class="va">sir</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -1.1618950 -1.1618950 -1.1618950  0.7745967  0.7745967  0.7745967</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -1.1618950  0.7745967  0.7745967  0.7745967</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1]  0.9486833  0.9486833  0.9486833 -0.9486833 -0.9486833 -0.9486833</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7]  0.9486833 -0.9486833  0.9486833 -0.9486833</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">mic</span> <span class="op">&lt;-</span> <span class="fu"><a href="random.html">random_mic</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">mic</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'mic'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 0.125   0.0625  0.25    64      0.0625  32      &lt;=0.001 0.01    8      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [10] &gt;=128  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 1      8      8      0.25   32     0.25   &lt;=0.01 &gt;=256  128    0.0625</span>
 <span class="r-in"><span><span class="fu">mean_amr_distance</span><span class="op">(</span><span class="va">mic</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -0.3704641 -0.5436561 -0.1972720  1.1882645 -0.5436561  1.0150725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -1.5768826 -1.0015510  0.6686883  1.3614566</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -0.2344009  0.3848258  0.3848258 -0.6472187  0.7976436 -0.6472187</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -1.6057520  1.4168703  1.2104614 -1.0600365</span>
 <span class="r-in"><span><span class="co"># equal to the Z-score of their log2:</span></span></span>
 <span class="r-in"><span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log2</a></span><span class="op">(</span><span class="va">mic</span><span class="op">)</span> <span class="op">-</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html" class="external-link">mean</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log2</a></span><span class="op">(</span><span class="va">mic</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">/</span> <span class="fu"><a href="https://rdrr.io/r/stats/sd.html" class="external-link">sd</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log2</a></span><span class="op">(</span><span class="va">mic</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -0.3704641 -0.5436561 -0.1972720  1.1882645 -0.5436561  1.0150725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -1.5768826 -1.0015510  0.6686883  1.3614566</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -0.2344009  0.3848258  0.3848258 -0.6472187  0.7976436 -0.6472187</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -1.6057520  1.4168703  1.2104614 -1.0600365</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">disk</span> <span class="op">&lt;-</span> <span class="fu"><a href="random.html">random_disk</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">disk</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'disk'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 37 48 42 45 41 36 45 11  9 26</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 35 32 25 11 24 50 37 25 45 42</span>
 <span class="r-in"><span><span class="fu">mean_amr_distance</span><span class="op">(</span><span class="va">disk</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1]  0.2132007  0.9949367  0.5685352  0.7817360  0.4974683  0.1421338</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7]  0.7817360 -1.6345388 -1.7766726 -0.5685352</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1]  0.20559684 -0.05139921 -0.65105665 -1.85037152 -0.73672199  1.49057706</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7]  0.37692753 -0.65105665  1.06225032  0.80525427</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span></span>
 <span class="r-in"><span>  id <span class="op">=</span> <span class="va">LETTERS</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span></span>
@@ -226,35 +230,35 @@
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">y</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    id amox cipr gent tobr</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1   A    S   26  &gt;=8  &lt;=1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   B    S   17    2  &lt;=1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3   C    S   22    4    4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4   D    I   21    4  &lt;=1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5   E    S   18  &gt;=8  &lt;=1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6   F    R   26  &lt;=1  &lt;=1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7   G    S   17    2  &lt;=1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8   H    I   31  &lt;=1    4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9   I    R   21    2    4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10  J    R   20  &gt;=8  &lt;=1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1   A    S   23    4 &gt;=16</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2   B    R   18    8  &lt;=1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3   C    I   23   16 &gt;=16</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4   D    R   27    4 &gt;=16</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5   E    R   27    8 &gt;=16</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6   F    S   29    4    8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7   G    R   23    8    4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8   H    R   26    1  &lt;=1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9   I    I   26    2    2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10  J    R   31    2  &lt;=1</span>
 <span class="r-in"><span><span class="fu">mean_amr_distance</span><span class="op">(</span><span class="va">y</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent" and</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>   "tobr"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1]  0.21379008 -0.70858534  0.29772954 -0.27497563 -0.22745796  0.09238219</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -0.70858534  0.36816222  0.54713702  0.40040322</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -0.21933074 -0.41717151  0.19830380  0.53628817  0.74510544  0.05006051</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7]  0.19790396 -0.50063133 -0.63808424  0.04755593</span>
 <span class="r-in"><span><span class="va">y</span><span class="op">$</span><span class="va">amr_distance</span> <span class="op">&lt;-</span> <span class="fu">mean_amr_distance</span><span class="op">(</span><span class="va">y</span>, <span class="fu">where</span><span class="op">(</span><span class="va">is.mic</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> ℹ Calculating mean AMR distance based on columns "gent" and "tobr"</span>
 <span class="r-in"><span><span class="va">y</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/order.html" class="external-link">order</a></span><span class="op">(</span><span class="va">y</span><span class="op">$</span><span class="va">amr_distance</span><span class="op">)</span>, <span class="op">]</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    id amox cipr gent tobr amr_distance</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6   F    R   26  &lt;=1  &lt;=1  -0.99208370</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   B    S   17    2  &lt;=1  -0.56611233</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7   G    S   17    2  &lt;=1  -0.56611233</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4   D    I   21    4  &lt;=1  -0.14014095</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8   H    I   31  &lt;=1    4   0.04301464</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1   A    S   26  &gt;=8  &lt;=1   0.28583042</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5   E    S   18  &gt;=8  &lt;=1   0.28583042</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10  J    R   20  &gt;=8  &lt;=1   0.28583042</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9   I    R   21    2    4   0.46898601</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3   C    S   22    4    4   0.89495739</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8   H    R   26    1  &lt;=1   -1.4835846</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10  J    R   31    2  &lt;=1   -1.0659501</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9   I    I   26    2    2   -0.7902446</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2   B    R   18    8  &lt;=1   -0.2306810</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6   F    S   29    4    8    0.1788009</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7   G    R   23    8    4    0.3207300</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1   A    S   23    4 &gt;=16    0.4545064</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4   D    R   27    4 &gt;=16    0.4545064</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5   E    R   27    8 &gt;=16    0.8721410</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3   C    I   23   16 &gt;=16    1.2897755</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://dplyr.tidyverse.org" class="external-link">"dplyr"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
 <span class="r-in"><span>  <span class="va">y</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
@@ -266,17 +270,17 @@
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent" and</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>   "tobr"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    id amox cipr gent tobr amr_distance check_id_C</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1   C    S   22    4    4   0.29772954 0.00000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   H    I   31  &lt;=1    4   0.36816222 0.07043268</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3   A    S   26  &gt;=8  &lt;=1   0.21379008 0.08393946</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4   J    R   20  &gt;=8  &lt;=1   0.40040322 0.10267368</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5   F    R   26  &lt;=1  &lt;=1   0.09238219 0.20534735</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6   I    R   21    2    4   0.54713702 0.24940748</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7   E    S   18  &gt;=8  &lt;=1  -0.22745796 0.52518750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8   D    I   21    4  &lt;=1  -0.27497563 0.57270518</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9   B    S   17    2  &lt;=1  -0.70858534 1.00631489</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10  G    S   17    2  &lt;=1  -0.70858534 1.00631489</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    id amox cipr gent tobr amr_distance   check_id_C</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1   C    I   23   16 &gt;=16   0.19830380 0.0000000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2   G    R   23    8    4   0.19790396 0.0003998324</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3   F    S   29    4    8   0.05006051 0.1482432883</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4   J    R   31    2  &lt;=1   0.04755593 0.1507478663</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5   D    R   27    4 &gt;=16   0.53628817 0.3379843756</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6   A    S   23    4 &gt;=16  -0.21933074 0.4176345348</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7   E    R   27    8 &gt;=16   0.74510544 0.5468016429</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8   B    R   18    8  &lt;=1  -0.41717151 0.6154753058</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9   H    R   26    1  &lt;=1  -0.50063133 0.6989351238</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10  I    I   26    2    2  -0.63808424 0.8363880331</span>
 <span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://dplyr.tidyverse.org" class="external-link">"dplyr"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
 <span class="r-in"><span>  <span class="co"># support for groups</span></span></span>
 <span class="r-in"><span>  <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 95f1b6aa3..44d7ae78b 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/microorganisms.html b/reference/microorganisms.html
index 247f7510d..3e6efc56d 100644
--- a/reference/microorganisms.html
+++ b/reference/microorganisms.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html
index a956107ff..22c9430ce 100644
--- a/reference/mo_matching_score.html
+++ b/reference/mo_matching_score.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/mo_property.html b/reference/mo_property.html
index 2084f6bd5..400944021 100644
--- a/reference/mo_property.html
+++ b/reference/mo_property.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -379,7 +384,7 @@
 
 
 <dt>keep_synonyms</dt>
-<dd><p>a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate if old, previously valid taxonomic names must be preserved and not be corrected to currently accepted names. The default is <code>FALSE</code>, which will return a note if old taxonomic names were processed. The default can be set with <code>options(AMR_keep_synonyms = TRUE)</code> or <code>options(AMR_keep_synonyms = FALSE)</code>.</p></dd>
+<dd><p>a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate if old, previously valid taxonomic names must be preserved and not be corrected to currently accepted names. The default is <code>FALSE</code>, which will return a note if old taxonomic names were processed. The default can be set with the option <code><a href="AMR-options.html">AMR_keep_synonyms</a></code>, i.e. <code>options(AMR_keep_synonyms = TRUE)</code> or <code>options(AMR_keep_synonyms = FALSE)</code>.</p></dd>
 
 
 <dt>...</dt>
diff --git a/reference/mo_source.html b/reference/mo_source.html
index 7dec12884..d92a43d29 100644
--- a/reference/mo_source.html
+++ b/reference/mo_source.html
@@ -12,7 +12,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -34,60 +34,65 @@ This is the fastest way to have your organisation (or analysis) specific codes p
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -162,7 +167,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The reference file can be a text file separated with commas (CSV) or tabs or pipes, an Excel file (either 'xls' or 'xlsx' format) or an <span style="R">R</span> object file (extension '.rds'). To use an Excel file, you will need to have the <code>readxl</code> package installed.</p>
-<p><code>set_mo_source()</code> will check the file for validity: it must be a <a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a>, must have a column named <code>"mo"</code> which contains values from <code><a href="microorganisms.html">microorganisms$mo</a></code> or <code><a href="microorganisms.html">microorganisms$fullname</a></code> and must have a reference column with your own defined values. If all tests pass, <code>set_mo_source()</code> will read the file into <span style="R">R</span> and will ask to export it to <code>"~/mo_source.rds"</code>. The CRAN policy disallows packages to write to the file system, although '<em>exceptions may be allowed in interactive sessions if the package obtains confirmation from the user</em>'. For this reason, this function only works in interactive sessions so that the user can <strong>specifically confirm and allow</strong> that this file will be created. The destination of this file can be set with the <code>destination</code> argument and defaults to the user's home directory. It can also be set as an <span style="R">R</span> option, using <code>options(AMR_mo_source = "my/location/file.rds")</code>.</p>
+<p><code>set_mo_source()</code> will check the file for validity: it must be a <a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a>, must have a column named <code>"mo"</code> which contains values from <code><a href="microorganisms.html">microorganisms$mo</a></code> or <code><a href="microorganisms.html">microorganisms$fullname</a></code> and must have a reference column with your own defined values. If all tests pass, <code>set_mo_source()</code> will read the file into <span style="R">R</span> and will ask to export it to <code>"~/mo_source.rds"</code>. The CRAN policy disallows packages to write to the file system, although '<em>exceptions may be allowed in interactive sessions if the package obtains confirmation from the user</em>'. For this reason, this function only works in interactive sessions so that the user can <strong>specifically confirm and allow</strong> that this file will be created. The destination of this file can be set with the <code>destination</code> argument and defaults to the user's home directory. It can also be set with the option <code><a href="AMR-options.html">AMR_mo_source</a></code>, e.g. <code>options(AMR_mo_source = "my/location/file.rds")</code>.</p>
 <p>The created compressed data file <code>"mo_source.rds"</code> will be used at default for MO determination (function <code><a href="as.mo.html">as.mo()</a></code> and consequently all <code>mo_*</code> functions like <code><a href="mo_property.html">mo_genus()</a></code> and <code><a href="mo_property.html">mo_gramstain()</a></code>). The location and timestamp of the original file will be saved as an <a href="https://rdrr.io/r/base/attributes.html" class="external-link">attribute</a> to the compressed data file.</p>
 <p>The function <code>get_mo_source()</code> will return the data set by reading <code>"mo_source.rds"</code> with <code><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">readRDS()</a></code>. If the original file has changed (by checking the location and timestamp of the original file), it will call <code>set_mo_source()</code> to update the data file automatically if used in an interactive session.</p>
 <p>Reading an Excel file (<code>.xlsx</code>) with only one row has a size of 8-9 kB. The compressed file created with <code>set_mo_source()</code> will then have a size of 0.1 kB and can be read by <code>get_mo_source()</code> in only a couple of microseconds (millionths of a second).</p>
diff --git a/reference/pca.html b/reference/pca.html
index 6a2636621..1661ced48 100644
--- a/reference/pca.html
+++ b/reference/pca.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/plot-1.png b/reference/plot-1.png
index 9dae527d7269150510a0f0520e22a718122d05f2..996d171efc8c973107cc027f847cbdd56e570e1b 100644
GIT binary patch
literal 26292
zcmeFYcUY6#wl^AV=yD;t+=_sLE&^BqMS9caQiuwOl+Y0okQxC41PH+fx<t@~CM_x=
zkkFBuKv0wzs)C^f0!knfN)SjQ2}$moxc52heD{3!xqsa6k9(f|J`ZhX&N0UvW6a-Z
zFApx+SjzpP^a}_Cl7m?Nc^L%S7!3lgzq)x7Fha|Cz6ku-boGMepTJMxSv^BQ0YA3f
zuyTn2f%YXyzGPCTzWahe`$3RDPdh~C&W$06*P=X_VrY@cR`9J;`!}@x{vmtMCdJ=w
z?|)YO;rG8@*jdK^b)Ua|qYU)QKV%gyfj8e@Z+QLYsW*0Z*G_);J!r#@?P+>1_FHYP
zJ_x?s)3;165fxq!rF!r`m$@Oaz2pKFo*Of+mluayC}Yvsf-?3YDR2M&_J2kK2~;kV
zD$)y8I9hYC<vx2SsIr=Si~#IqczYh7;^`xBr8%pNYl5!$?yH?!C*@&CNk&`pOY2|1
zElNj95B_T){D~e2<eUq0j+6m`_6~0#f?5v`yXAQxL*O>>99BDKUhIM_Z7Pm|apb84
zO`bRc!(8oS$zUS#JjPy*=qid~_<9HJoMhC5j!Lg9*MV0YU)dA|kK^#DTk*^I-X6p0
zd}<rMqP!d`wC8lNP>yWF<QN<Lg4dXDIKicDSqP)f;s+hIOUw~_n9i^BxVfS4;mYv~
zr0OPn&J=YEwFpNl@$?i1a6W~U2Yuo?48o$z#>>Bi0bhEEK=@_2Z(HxU#mP7?PfrhL
zkGi`t_5Htl<<K~uTY)3b{m#_iuuHb?24QehZ&)O!#X4C~#cIik{<Wf<TM<UMQZ4=g
z!MZR7?i|OYWJDXQZ1(eN616I)xQ5lm3YkqdLB@ui+1xl5$&cVOXFu2H=jg8;_bwPu
zvIzaL{{4&5hFaBLQR4m^YVk4c-^KLp`)vd*b=+)=OJ*fa1Dk^Qor*rhz;j|VU6nQw
z>%na9>{;EE*tShU9xFi+CiEac>Iojx-$Tq8!&cuaD`G_6$*9xHThX2tlRu!2WWBD#
zV;@#~vXi6Q5*2fmjizfu=J;DJ9Z4<knP2)?p~{BjEP%77;fcj(X4FZ2XY%uU;cPOp
zfvz)2WWpwf%;ZilxRS*@J-(_<YwR=rjW*s*Gq)|#{&~v~fSFXky}VcO6k5vQ^o#MI
z%zTU7=Q&a7`R#^7+tJQ8GqM}rX8>`u=IsZAx<;35F@3R$BOhGd4)ZK(F_Wv=D$i?d
z=rm?3_S>Lr>XjDF{W0*1<Gc4Q(qZk0ionfOW@B5jkNnA_MmLJv<f7#r*a2(%;|c5O
z^U)z%{N~+&iiQf~YijS@W$MTML-;Lm(Fb<sR8$fxN$@sxXgsLrhlP&MTG?oRb&t=}
zUFSll>e5p$p>E{yRZcL6pwJ?Nu1IqnzdBK|G>9WiWsS_{1|2mK8jYQA6_h2d8Ul={
z*dycH8`<?eRm^U~1(@H8#h%4@qW8IF7dhQ<`Id!LoK3K^-z3a08TpJ4oBgck*D81i
z@2O2sh%)iac^F@@5#TYhM4tElRvSrggWPOm7-uKd!+(zC*;d--@`iFj=`Ffh?hPe?
zx;(vNacL~v#%%W9lL~AuyEu{Kp;~JrUN5JR#h-?V3vlAT?O(fCr~<`W5C0Pbu4H0q
zt^cWoAg*gIvm2q({u+brABnpW{YOn(|DAd=)PSAcIzPl3kh7T0toxBr&nnRk%&D(i
zZ1J<GlkGB`mZ1*Tf1f%l3T*$u_F@hlUr)z+Fx_+HqU&dC8$a+|0nV>vX774I^ndvs
z72W<r7N>C1WGK2LcQW6Y8MplQ1J7VXWD=ijMBZDx(Aa*Q*)xMmRjlpKp_szt`fL35
z);vj_P4(8aD5_nZxIss65P#Y}*Ap?%${d{0O?1;E^tdRj@NtSlUcG2ZLmAG`ZEFAZ
zD{~_;)vKtzdlQlNeZ+kw_87oM<-_jm@4N!Jntk$QQr8~AsW!{U{zl~o(p+a7dJSUh
zz7TbmT69woOSwLD<H;<0=HBJUisZHaWD&}!Kzus3elhD`()l4WVm9(}myfm)+z37F
z{7>S0x_t4-eZ74qlY3=xgkGc2t^}U{KuK7Q3_G$}UttwMf}<bLnX@*4Dlfvas)UME
zf2Fsn<9L;^MZ1Omks-DrSwB{vp{zXMlQ*@*lJ}o8d1Z`P&$Rd)<R2_%8j($7+qt$~
zM$<3#>h{s*3e`oyJ<fVvo7O%iBct4yLucKlBbs)RPj)4HbiOY`dDqQzE&PJT6umkR
zJikX#C}tW?Il$lNBJ1~jH(ho&`|3<v9dZ8*2&VFi==3H~WYf5-zxbH4->>Fn-ojpa
zC^o1;=VRXV-Y8ZsQh7_C-@PO~_s7?D&8d}<EZ6Cc4`De~LYxt~lp`1?GQU2Rt4U{$
zh3ZcZ5xNwkK4l021bxJuiw$cF?#N@<`UhLo99YZY*6Q+Gia3MhvM)w0K5f;9wXZik
z8zC!s&L)XWd&2wWa{Yhx0aW%MoUQW&T`7tUQ{I~JYM;Fskxxjh<o?j(ce!f5PMo-E
zrkYm+qc3d>ddmATz0pY(TZOD4O?Jm>r35;YBk3U-2!&j?*S;1-ZpYbz#f1<<v7d*N
zkeiGgkN+UF?EYpDw0rTirK82r_6|}R{}GFzw64QmDdWOZk;|xwe`!!zM0wtI6R(_y
zF~9}feX(^bXznJ|$=^UQ**3e%N_?8A-1ys?UssNG<8XwBzW+FJH~|arP_&j3HLsp8
zutM5IW&3vxiH6D%`*J_eaqjqcuPgszgo3pFw$10*ovMA)_qy~fqQ|s?n)Rbt5>;#?
z6|V!~C;H_Stc>w7o^47EIbrO?)JL|0UL&ErFnZN>8krkU=Qjtra4p$l9}3g!?my3W
zgBF6n8X;=oe^8>?;zM-<EN-+{^}^z}i|@0P>pe&M_SW=>v}y00X~GfrcT&Aeq?=AG
z<ljmh_Ak=kutOf7Pp`~in#vn8MDHW`^q}>%qsX2X`Ot|UdDrL>-;c@ZF4cwSDBNCt
zKaEsvDa$j`T{^|Npvy(F8MCRqLl!|?n4H*lHQh*nYVt0dEzm^Di@N4>BDx|Q>A6Tt
zXc6pcsc*ABIqqNbEPVmsl5@<Nwxg|<(0Z6NW_i7CO5@}Ko;Hl@O7NntrQTI_%QNvA
z-_`9&h8OYUYR=5KD3GtlVPV`}b^z6^t}3JZhftA*dhNZN?W${Rak<&2C#y)Ww1SX<
zEzR#+)Q$D^GIWa_IPPQeanaSQa~`nh3hR9T;fn6#fa^=xxDyCREYYb3f*0078!eXo
zHk*g^v}m3b_zaOgt?7A2;aqy(x6#(_VNxpMzbtkP*XEo0k7HkpmJV1#E%ZE%l$T0a
zg+aj<E&OFgvT4a`=k;7*f>-x&RK1a)otvs#he8HbgvGkIRm<ko?)1vZK}t?<CAFs1
z(w6c^URmFqbn8g4M<Lp|x;<x<{WR#WgUkow3Rq9&WY|YZjklDdUMSQ>>L6FHjmIdY
z;EbPtDOkqh;-iUuk?9JK(C(qA=EULL)RLB$LGhX6gWK24U-K`WiEbK;hqrUxdWS?W
z;O&CBT%?zgkKh57U8cK`MK|;j*vTDOd7~2#<Lsi+@HwzArZmooJL8{NDfDZ*ND@*b
zZ~`ZTpsWXxH>~^2sB(DK+!c@6NUY+d_(QIbeyj;BkG#ZJBsT~49GB0EXi@GqoX!Hk
zDzK+fMW5LaWpLL*GqQU*{^l8wNKWp}$W3v#=k-v$B$CN=D`uNa=LV_O3nSV+)0*WJ
zHqfk)DB`W}DFK_C{LNZq9R{;k%<8-wZ>N?=&=GHVPA#@x&2PwrPqZ9h-QPXqo#$;|
zM$Jd&TtY1!6_RhHpssJuo*tg@X5=R>h9p;>Z#5Y@f1{zu7}c{nKscs0Q5G}0X*Nf1
zJ?|1T<OXqurrk4a5ZOwo=hs^g@R;54q^Amtq2!>5PwZMy>uu*3G9bggdD!MOKjfT{
zmDma!xc5T;Yc177N$dXWj%^O(PG&*F_vXT7l``C%+b?fTnp~4xS#{tTDDP$aXCn{1
zt6hudyt7`haEIy%u09MxAUMyawJnZ!9wzD!#!AfLPrw}ZQalGLr>86``q_U0Sfusw
zwdZ&Mwz#FF2@0Rt&7wUh$Hl)kE{Iv)or1D#BiH+vHFvbTdkBN!ovid6!-d<dPKC>#
z_~w^A8f>s$p_2*+h(zO>FY*UxjVfZSXsTK*<zHR|W&-39RG~6Gs4h^Gq;J5Nc&64C
zUj@KHrl<z|7~;%p)axq$QITU%x)BlAW_jDfrJ3Yiau@1QQDR<4iD(Wx!9AvYn^vRb
zH%))}MXgUYXtNW&Z_#cRbv?@^3LFD%^RX!Ow(cf4iEEpXZ4vQ^Gg-?mi4))6CNigX
zb{^zz=Jv|Xc6yQySgmy=vfDBZ+zn^#<y7ZUXWJ})STy9G5Ti&Rw{yFzhaWyeHZ}RT
z`G^)+bn1*;&D6L@^K1TC`Ig<8$8SwlDA6CbH1jhO*`KyhEgVs<W~4A})4Jlp>ByGB
zpJ@l+_W!%b5@*(_B?)`KwBGkw-4_{Q$^(HcD+@9_e$IANn~;QDAZI%OBme#Uzm$MP
zs0rye$gBfYJGEpW|0<-m;ZXa$O@Z}8+YBn=i;Y8v?<G(C#X?X8nI4-#w8^v7TTfuU
zV*{3wEs~gn%3^VQCo#`rp@03+PQWNz&;OvX<f!x=?fhw$%Ic8=z%=~Sh$D~4G&7k@
z*3Izf%<#DR>1^}bly(JWQpO3JkooAx6;AS#_9mAzt+7*9lR$dYTGzOo#~u~3QDWcB
zD&Go3chI&?#MT{Gll2^dj{!Ze`&Tmhf6Gq)|2I&K1k}KFAUh70bDf}$nU0xI*$ijk
zegikWZA*0hcs{F#ip0ap7Icc(CU}c-(F;C}#kvQTl}su9Rff&wx3h++*7`%@F3LO;
zm+F*SDmtheD-l&f=^KIv*Ma0Abg5|@>vhPy*it^#PE~b6J!UIQ)<1rBgRpj)(`?Vv
zE?03wBH(|t@xB;SA8B}{W(Qjo@T(Gs39`so&gXm3UnTSBqSw*io0RcqN(8pSS?>j}
zS*rL8g0Jcd+b-6T@O%zLOtzj!-Gnc{Pm?6mpweGt*k8?pal#yGFBBRQo{YzPmG|U~
za2MN3r4yXP0D^>Z2Z6%2h*Gco<G?)!4!(6uv;w=cXhcDE+F2F*w?UZLA(ri(q9+tK
zt?7`ZsNs~G5cS>DglayiLv9i}2;fOZk|YsS`E>*FA<y4_o(UxfhvgP%k}LH7M*W^g
zP3~K$0}K~hPdOfH-&|!J8!rBf#XDEBO|shAMUdgqs&xYaA7@c9f~ZouhcJS3o7F9+
z;3c?u<_4j{xWu^jz(-*Z%PA>&^%2$gq1Il>!Xcm}LYu6>3k;6pZ!G>1R9~E5NeL@2
z?<v#TQ+7f!<BG4#3va>NI4hBg0R&^p{fkARKEjE0!=-rE`qzm&QL#uIe>m|mPoFx5
z*Tre$LtXa`R=#Yr#POi)5>JcOrr}F_B(x0QZi&vE{EMZkX(0JC;n<GF>K&;i@#SF|
zD~6FvoYy<rB|=K@ux{-<m+S7~&p4jyL6S;dCl$DS(Q%v$k96<wC{%f<>K^Z-5+Xy?
z4MaRMH?a35@{4{xukT5FSe2pVgFO!tqBz7l<Cs3WZiSj;<i<7SNi_hBf#>M01gX(p
zIlCoVdP|+s^Z+HctEFnDNlo=IkX!w@GU)yfImz@ZC;jgHv$CWcT|XF$!^=k`_mdB9
zkbLNj1dJlq7C!wFS;UG+GNOx)u1dy1*B&I!FAmzz=V;+KOa7ktpf=$_G_bCPkzOB`
z3?!(f0M<Y^qP8se;Uoi~6hnZPAhU#Pmf<KReuEMAr1rg_mqpW@KVrFu-LRwm=S0vt
z`uz7KwG=q#zK`?-N4kEK@(Ma~9&q@;Q~3fYcS)ZAQ3HYF)myKqyOWxtCf80+=wm8G
z4bnp<K>(XE1@h4+WYY-7cgfh5L5Y3!K8+hQdFvb{0f1Y7H!z}KxN;i;2!s94DalLb
zM;@Df_D?PU4}$FfsD=K2>uuOQk?!z6YW;`rNkj)SNll1~&2u*^j=2dBh2PQR&y}fo
zA{+SboTsb~mNq^%*!HPpUhCnZ`nh$HPsk3Wx^}LmV{g{$)Dll<9?}Y1fD>E{B|(ue
zIOh+X)cBne6#;Zgax)n%<8xTIFkINm`!5U6RW1wHtl&S5{$eX3TGOF7w@Lhl^@N)0
zq+nf_=QUQ%7g2<Rj`g9=<=!xkFZD=Kd-NY|;ZiNPYH0ws5XQ=8uI5ogl-7;=I~GA<
zI2)Yj2`V#4>FqdPvmPku^)5!)PnzpUc)kMNxu``4?k(!&hy`5ySK?EmqrXjyc{LF7
zCN%}(*v}`WW<z)k+~$_6IANV0m$dv?fC3WznMu-BKWTOP3RHUXTR9hNEeP$RPWeXE
zaBkHrL4MuTU;6BLIo*fkA9q?qG`(z<0Aw4oP6=(;Kbxu5m{LW2#eeUs0Gt<Z1DqD>
zK(-!<#jQ`88?R{>o)MH%dAK(1khYc@Z@G!%V>bcYpRnS2;ghrp_je#YIImd|omzl6
zot{dtW^hrp8i&EBs#c4el19Doy{^ddh#!R^L-I}}`}uv|rK&g;2VvW0ADPzB>U!3+
z0SS>B{Z0*tg}f1?lPZJhP*0bPt?NMvZ4xPNC1^}E!#ISbITG&xPt{piq3AP)!U?A(
z3~Av#jryE<d2xAq7ZgERlXrL8eCvs7S+4FhJQ~YeFxw+3)+bR)AG{Y7lyG5A{ujqc
z)|@qn00qdlpmuPu`4i*E9Z_`YI*F<is9<2jK_}IxuPeqXPri|rIG3{WejR1rajFP{
zm3Odk%+qT$tK(;~-cby(<(^f?sOu%_Gs#d_+RbLM;;FPIjZ38WpGA0FgtC^-QZrD(
z_rYoQDlVHSY0)JuDA3$hzs1nhD2MtAQRt(2p#0iX#uu{4%;Xx9_dfd8bLCPKay|{r
z!txxW%58AvUP4RG13kXbl_N_%gCju6P<FYfDJG!pqL+jsNQV7eTugWlQ?1(z$n}7y
z^`AJ6{~w9d?Y~5OIp>*u1^RlxQ|eQCiu;C|1H^e2t{$kDVNwZXdCw`<7diA6o7*+N
zY?@uyBON_8d9R|rJm0}Xzji(cXW*3SkczWWnQ@mAI_cRec*F9?&pPqSJ&)J28LSy9
z35xO@L%v&<UcNb3AK5afQw6T2+`BQ6d~yRpc8nA=c8%tPS5AqGmk>$t@Oc8%t~~Rg
zW5vb<zOV#2h|eFXjBE39;+1l>Dy4x~f?7}H(exW|PCYfsaj>MNp5OXeByd1^AglQ$
ztj{6ETO_2eOoi}V8?Yrk6`|DaD>eih4ue$yG<sqkV(V(8;YWgF^_UF%lZUPkFKT*)
z;Ks>j<x!9A_S_{|-ulZ(y*0DZm$RN)fcM!p%u8f3mDi2A>AA<v3p4~h#dNQ+*WP~G
zomEA~WnrTIH@<H60R?~lPNj5eT?H#Zfec3ikS3kKucrq&%9n~_&s;K@uVz8<Oh?tH
zBcgJax7;oE1-!{gfFMBmYR2RsUUeU$i<WGv()v8qS!i$Bqne?G3x#^r&iw(98Zv6%
z7R__;*S~vfd{CkHN1PAG0ta=1_9!yX$bDHPU0kp@D8oL{T=ONiD(Y^PP2grXk<Z;K
z&~&3O{A&wR&?Mqikmg*sb*g8&Uv4XMxA`E@1jyP0=3tBKDa*e!#(0d(gxZ$ny9sAC
z+Sfih2sR}-?n|0QU}$@K@bRVmAt0UVg5%xBaQ+s8;ZCi|O4A7$`ES*oT5gew0I9iC
zJ{yUdC*4nljN~X~I7Od@zu(<%dZXd2)Ohw%+C5)*quPSUm@D^mZ`89+t0hZ6xbdgb
zkqIG<m5Ix&uN`ixAzph|1yBP*t%Cr)DCGcY91PO`RxBw~08i^cvfiC27)K4mEy6Dw
zM318Ip*jKLbMPrEj&#D_@4%th4ra){__rD<1UpU;hU<niNOt(?UUtAr)s<Spa6$!1
z-K2!O-V!Ix_pd<r9GPeybO4HhdwIR~Aqs&~bQ-!_wT@1+sns5P7lzFV#F}50^b<fJ
z!dqt<HpG4&ZxA+mtAq>xFI2AKkaHR-CNVf6R*fn(|H>oDyB0kSsVy9UrS}d3*tx&$
zD#Vv?ZG*oykZo$c{tJCo>sr;zW3ct)IBU*&dmak)QqofaBB6T#Q?^dF*-heVRwf=Z
z_$^k*dH}f5I-x84yH%}p(%dhCb9-kYR3xrOlZT247hDILqEsrX#KwftK*GoSy)*t7
zF~3D84?CnIXYEYgcmqE%(N}q!Toj7XL}NHhr|5fkCDaF^IF+mwmLjz<Xr=LHHJeN2
zIbu~CrMR5n;oYUK^s1Q<^O+GD2hXE$#liC*70P}%&i?CN012T4YgdO>lli{8O;WN2
z%CQfm%RzMy{?~Z_^NWFr<}mmyOM@B)C~EcLA!*z{@j*ATRiFpdCf!25NB=Vn-1MmN
z&o*0!is0EV(wq#GqciX;o#sF~0T-iL3b=X)Y{|Cdn<~;xH2~{SJx-0cho=ULt$d_P
zu1u`TxZcp~AQ1evAXV4VQ9!%hMI&tGfQ!;q(|~aZ>r1$fYLBD<)4CV1PLw{?-23J6
zpP1=Wn!wv=KOdWXQbW2T0#JBMAwLfysK*7Rp8Uy}E->hXL~+Q}5~>?s*K)5kZ%#Nf
zryP*0VBKyUA1<z=n&505VdYw56Ncl)m(471&pH|4xDE<Xan)$JgmgllAF$VH>ZhP!
zk|!Mdq^C-Gt_0XBot2R^7YpNLvBaTKc9NN`JtwDslPd*!c-orv{yj7G^?VFV3<PPR
zay2i!obs?g^Y)L?y3w|K19H_qc@X?|FJrrAc%4*(ebw_G)LX3Nk`S>MCm$kt(*txx
zK{-g{8`H9B--87MYJ7LHd6A>VakzL`ZdMU%2W0&7tQmU&s>B%aPDvt`gk6jR(Zi_T
z5^j>;%4$jp_X6J1A=5ep=y!i($rsg`MW?YObQ99mf#`t-gkA;(y#^O+QGPwnsn|f&
za>fy<K2kROl)Z^9?#IJ|vYKQ3;AIr<&R6_VqOo6JOR;y^u-d0j_VZ?CLPHV|-ieHS
zkXLX>X4|1aOei#uW{BF}Fw2;&QcJGAt_hV7a@O*pdN>MCbATe#x}%?mslrT;cUI<h
zX7x>RHdtk+@xjWpPdGw#j5o)&Q|oS_ap4z#zUwU9T?Xo;{cS4>W%fBgksMnSQgZz}
z-PxmZ?)S(ndwh};`558x4tF~to8>jVVuS-0y`tHD)Q?7Tpq*Ol5;%|cE#{9dVlfrt
zr^X3-Ykg2oYhvM+lt0A|W8Qt{jr<=<$XBQ@jaPe|q1Lq(QCaYi_Fm|?`BGNBDuQ_S
z$8(lKhR3`63!{s!>r>pB25WW;R$*bBuSNn@7c=jiz~OEF`XW+&X$H`oXrBB1+)V+C
zFg-`I=eW@vIwKp1U%vK3wFr;*F{-GklGt<X)t488HMx#vFv;3Xy|s&8&491Tf&uNz
zG*&YeVox(ieh>y|m*;=l9%Ej^Ury<AA)=^yGHj%hh!tDB6<?9}IyHd1k@?DhJ?dr}
zOMluQALSHlxo1`x7lDg<q%-g$?-IPoWUNHD*EC|dHhB2Gg4gIq1@}vB+WxIi=I`QU
zEq=V&hgxIB|Co@2MW0ddxM^1=Zh`+nb@9)m&J<x=7MpfYsj047RYbo((jhpB!Oqn5
zniFW_@w-I3Tk254LDalsMT}#}qF!9bp`x4P0(#UJ__02=Xn`F$(RyoADc)=MqUYpY
zTttsi5sl8R>n!)}G+%ix&tXAHP5M7{k*t!UNUUJgU$ShKiS=sJyv9A+_z#f&mC}SL
z+qPdv?|3h4lv91bdcBsU{i}w2Q+3Q`&Pw8xQcpSk<YhAx3ijmMR1=HHQm6XM&qTxz
zZXo(Q!19g{tiaA{tKwVbqG6NW*UU-|OpICdMQ@Fgh=a{9M__Ct>*`6udD-=zS2Cj)
z@2HL*D$1>%J5e`>zMy5aXEF4HL<V<MiA{ql$mN{zIGu@mxP|ac!pbvQv+KjY!Nd0y
zJfn3c#va?-n$@yb^Vu#}&OYSbRv<n*Qjs>>6W(LQTxpdpUy!AEiur@8zeXD{+6`L1
zcZe_Aimq49od69ztJ12Dv^jIijZ>G%5$m65ThV?sf)}{W#lcF=PX>%Xi}CeZ%N$p5
zRLy_g8#GTsFO=jP!^QHJr8^{-*7-Dmf&H`Fg;rh-PIIPWV`_`Qs&?+AEDo02go-@#
zLs(q?^n2XdHh!@Jly}{^<?A_n@@S=Z^Vd7r<<%QBlRHw6K6Fdsul1<$v;%@^Wcg%-
z{k%zvL#NG3N{h}0ix%&Izib`Z<?)3Fh{Ah`mAp4{-fSlB0{#Myox3;oY}-cG(VJ4w
zP8f9#T8y7@5G)-_2^-xEpSwmH)LG`LQ{k`b#><DHrWPwkt2f~6a+f73Vh_D<&wgWq
z{8ZTt_?Mv)uB;`_S2Gq4KMw%tPGD%gTultjt{Eot&s6q$sUlG!vfH>u6RMgsVgNgI
zOFJi!6!y|JvZ>d=-D-YDE>)aDoyWU54rb4Zub(Lvh!WMc%^TCsOObG22msns9y=H5
z75sTQ72aYAxAPp%*Ku#LR`#ewoFM%WATsehNsV`fzx^z90Wi5DJ{Z><y7gxXY+EPb
zlgN0l+}E}^`bgzmt1H)W+^*RtkmiHyO?Hshlv>MmoLb#D@5)^)c>`2}(!kedt^*e8
zkO?J?>nZpMW?3$31Jct20g2+Gr^^^VX8LoT{kEA1cKMb_e7*+~38&tNOZl1m1!%}S
zLLE{|LtTWIIVF00_krzm^p==+j6FXY4j8}PE@ZO4V`yP=vr8(}%AWFC$(vjt(7DRu
zgs71z>U-J^soPEP@SRgvlCB!k_}N%V*rhk{o1B;3yrOq*eTt4W5RmdOc*Xj*qg_!A
zEFBu!b@Y>TUF8cvUxLT_yLYQX$DYSH!@pjmRXD+vf0kH*<N=WH*_=5shT4x)Em3hb
zE9AfXk5H)7^~IOJfC@w>UfBT_O01MbtU%$wd0L4mizC~N)R(Fd-~9}(oO-IYtGV<N
zZvC+`$(xXX6MH?E!27F&JE-;dfo~<;?-%e=_jG0BF_Q^1f1t8Z__L`{c{@XKF+PCv
zwM2j-zWf<yIrX(XX<vOF$lk=CFDdsz!r>Jq^8!5c@2V3dx7;ur>u$g`MR6!k0)N`+
zt_&c&DZ+b|ggP|GKlrJe(w9|!i$KeM?d`d_3Uf!~D`TC$Rq6D|JRmlFcuq+gE+xph
z0s(;J5TL!A{<lRlrP<$um=3Y$l!7w0Dwvu}X1AVKJTd{ig|}km(_Q@y^iy1gl5QyQ
zTsiwl?+%Zq<TpgG<x@)LEAXDK$YSG5W2H@A9I8DsUw=lrd#eTz?uY{5{j4ZW-uR_F
zH{`WPEw*K$?#<5*d=oxr8Q|pq^YDNzkey(7n^OAl_R;cP3o0w0ShF}1zTBg^**N|`
z17G0SZh58w7R08j>&fnVGlGG{D?bPlG0IQ!pR>L{T9&;vYtxc%{MI>nwt;n<<<zNl
z4XmJlR5jv?4Hk=!zY^Qw(qQW}ez_SXDUMYJnbz`qd=-wPQys^L)+m95ZjO>DHF~6a
zt0JY-I9w!_^#WW0&+XL+Z2VmqJ2wy&v5ua{ilkbfQ(1oZggm*JXE)|Ed^Y);8-5mw
z&FW{y;<bk}0(Y}7;3^<Mt+$nnaEA9)Kiosfbcm|(d_DI^<l`EAjl`L&<R@gReFpCJ
z&4aH|JINkKR1xk;pm$tqd48XvZZy^ziTa~>-|X)ChO@Sa4*Lv`c5lSDGpZwnnVwBw
zM7=QsHO5nE_Pd=wT{Yu1_RjAD++=kpprFhc(K2>?i)L=6#Gw$EduHEm)pn0mG#S{p
za3d*y@vi1kLh><_8xyCP`LPStoeB4VEG$oj`GZxXY`V};!u_O1efwkE$<iFt#7Cmw
zO0a-CKfB4G;f<s?WVx@lk#yFb9W&@QLyj8_BbSW>G4XB&17o}JZYPv?CY!7hJ6C;S
z=u?W(bXK}Z+;?mJveWz>wKiu3m)J=M0UbcseEa#qM}Gq?!P^op`($ZsWB#m@c2<*E
z_V0<4=ro-25_uVJ*GAPGbdB`(f{#P67)_eFqmca{x1`<mP814W&+?FazQDm*;LFSD
z4$bx6`vz%FK+gGQ$x#l9BgXxwA+4h;`7!8CF)r}-YcxBmhh}{(k<>K)A1Mz2#@qN;
z;C>t`fNh?&=dgI30@Q0zrvTdUvwpwPr)R?K+bPZ<h86{sl{B4ValH=MycOvknb0}<
z`$S&kGoYz6cf0pzmNA>${HOFc5bc_s1m0W9fDU&yaS-T^dE<J!@je>!537TvQ(9jo
zOTxt)VYSk5(NR0=wW@RTO~y}*O1bW%_36g>wu_@PH+cOKd{P7d-AiFt;=@YG=4%$*
z1yh^4f^ucC|3ZdY<AGDZ{kHkx{_PvvGzh16WNzN_J7NF+D}SE4wpu0JzM)OyuY|XE
z_M3jN-9BvR)V8!(dH?RQ;9K{>cjsb1bayvH7S=??`B3cBi!fd0%1r-qJjqj-&6;81
zjFGI|1yN+)VNkmRG$HDM?}h~~U1+P(2tnV$xa&?B4Qe1<w2=;I0`>FH;*H$%au9p0
zU;bmf8YK+Uvu_fiTJciV-QG<CD<J*5P3wrMWiPSWjHct*C?|C!JA343`h4K?t_ia`
zw5a_r^6n&4n7_L4f?n;<y!}k^k=EGd^L&<GMpcyDyp+<A4(k`dyFp<26Np&HKL3Hg
z@1J>O=8ThRo&SgBP)7)=<W-$?N$_U)#%K#ih?u*fH}<l+@YCP7M%t<i4Itc#QPq>T
zd0a@o9YyVD6G!J3z_NmdQAs8}pD_ywHQ3M20H<1o2Tq}SFD}*morX0!^QA@VLK!Ab
zo3aYwmNATsC{zeNPjodHdtoU`uTHA>D`$#zTVq4eJ{aQjC6aQ*hvlFd*+>lI5k%!N
zqe+P-HLl77pA{#p(LrcaGs-ax(Uek%XEYvGuZ$&tQGKz}mLq8ANZ+MK3!A04^bmF$
z0%1_tRTES$?Fl9*0m2+Jp!}iT?DO6Ss%cd5Dn=@TZzX^vUC?FCD{m{4r6ZpU%8jCr
zp{0&k#}^P#Z}H1VKi`kYx(%Eh(4Ks0$<Guy5zvF6B7H>|czCB|al!#W2O@toYPN7(
zso-Y}qXdN}7(i6e3<fv}yy}VevaT*{{~3-I7asu$@;zN*V|b}nOEcg>bt65BZOn|F
z%4PdlDKpca9x`-U&EcjFv4N)1t0<pv)Z-;34GL`}SlYkSiM{w-hek)oPq_cACOE$W
ztW8EQ{pW?s0@zu=mE<CBelyN@^Vq;%Twxk|hKr0lq{lN3Jtjl9EK-e*^M4fb6zcEG
zS$&`h?>Q`XD}FAmaXu>;_-z6i%a`dqE#rCmiu8zydbJ-$3{Ouyn$89@ZGPnDIQCg|
zbBeA@`PHv{wT`&rk;|w|OW=8_BYfKBKQGu=T#E1K=jhi7(W54v%nhFS9G)oKbKNUg
zL`M}7Mw2Uf1BbLcX5i|S*MW96c%un*WB0S&vlu~;-L%lnpx~S9h}{HVG|6U(452?}
zP#`u|OBNyefq%eko<<hfkenkBt=DZYEREDD+Z9BC5$?1{poA^Cxr~$$gBRy`7nU09
zlx0B5c_7$wqz2+Vx?X+t()laPmJpFC<p|oH=GouQ6{`>DpWwrqLvv32#p_b)Sf>&3
zCweb}@uZPr9oiS^HZZr;sog6dzj)_my&34^0ZTCDIC(Fo%x%^=sd1lJI4;h+pw$Te
z?)fW7_jE#3j146GAz0gbiAWzOYZ{qT>fM3|4b49hs9$O!`A-;2*&XXOZUT;E>We%o
zn%3=HQ-PchLi9xB_ebi2u5^2@yio83JWz;%Pq&Tn%*!}_KQ@%02s&lEF9LU>1-J$l
zlur;F+buuxvrpV>^E_531?X@4W$2N>Wt+czZb^fpi^+Y$PzlkKn#wm789LWJli_%%
zAe!`)Q3>`szr+O#YKNp{nrCVQCbH~a_2C-8(o&!J>xpZ6xHms3LKbov5C}c1`u8=q
z2IBgL3CU$lI540*Vb=W0^uq3ek;f@C45J%jj%EO`na;qJ)k(L@LY%wblM#U#)ua^I
z@4hCr$354NsYR1&+nLAn--FrbmmYwlf*Pf@#nxZlE<EFw*Vf;rx}$$%NKwu?{g9H>
zsEp75wC!gF8-8A?e-B8r$p4f_;02o8vz-f>j8ywx`E^lqLbGRy)u4n`?jl$|gs?-e
z@gQXx?LH8B1_a8|UPnxxgp`OosmFq}<Uv^$sC33F$vwf^E|p8}No(AW3E#c8kR>`e
zC+Og87$oJw(mZe>K-07VFk+x4KV8ThC(YND4ogZIxiPE~2n;~0XEqYT)nL`qlG0d?
zXrZLU6m+F$Ot=u$dmw3VU!?B9T&I_4>9RzZf&P+zf&mOE<+q>6`t6{M=f4O3pX5<|
zEW{w;!8{bgzFxLVBC1of$rE|yH1$<7SV6<(;!+v7*R~pY`-GNtxG!4@@4;8r5&M^O
z!Q&T}?t%^GgzRk)&wR#haO(9EsZ%^<xn<FYtcD@Re3*2@5KSnwt5^wTkk4-ygXx|S
z`sGGcBzoLR@qO%{XtG9Q18-;&vvy%lYJ69IUH70n_7s0>fyd($^6a9^x0{$?gg-h*
zWA(ar?-~1Z%OeI7TCJDdMnYRREfUgK9zf#j&gbVrRI(U)ko>0%Z%DcIQWiKiJ9Hvz
z+eNd}%;vy@Qk=LCVxJtXMT@!~$32APr!$(;HV&(#UxT78=FA3OK{XJe-M)0(^pb@B
zm9`xdA@pO1XO6Ug5RZZdU_@X?E|GvXRiEF4=)DjQL3it=)kbw3q~n7QN-<LZqzrx9
z!@yhg9&BqHeyQUha+<F;oWVZj-X@}G|HS9&A!!e$a@+2uKsq9QW#BlrCW{dUG4CUY
zlfl{*Ro2q8?cEOS%{x4EM<be^50-~4HPVGlbwt>luH@rqmX(zVFZ@fG@t24S>!ln>
z%O`>u{aIIDinuzm8=`Y~)bsqzfyFfs$O4A35|b@Ge%DrjK_nh6dIW@bY7`I5+D&1%
zGzgl0pHZh=DU+MYuxnmffTu+u&^~C>*s){Mh;jNh;NW&+h=f<wTIXGYr8#ix&$op@
z9_l}Xw~uz~h!WmF`~E#35yO8Y55QhQc{i59@-}7`n@1aYQ9*LP66t@Q>Vc$4(A3C+
zLcq8WMxMEz57M5Dc0?yp-KRh4Wl9PFl`5(>;eXN|6@O}kn2$xO{hWe8>tce-pO^Yz
zMh{T}^wK2$?Na6)Gy`}6;ln`LSo7kK3ts%oJ?4Tr@Gw2nEo+;MXZ!audfB!L>n)3(
z9`*=e0jL)7btg6p$s4Pm_bVMnF!SpFG2wfs@`Kb%N-ne13T~!>q%U{;d*FYSM@Rrc
z=Gp-Y9m2J)Hln~G<>$lU==Zy6(rBa0P=-F^W)+?ZCZ#hDKo)es?0QVxehL{Ar$@<e
zcav6kZYF}*H_db?%9y=Iej=~AG0$AaD1ahMqCLY>Y<ahr*kApaF%N`8Z{Cby)f=Dj
zqV$q95im6Bflvy{e?Gh63ZQUUx&QzQC$yJUHL4@tP>1ronUoVFwlJpXUOvaxLO7!b
zPtvbSw0>oJ%c4H}KDZY@ih=+zMuIGjflb1NhZJ_lip8GjLCzBC+0qZJx)JW_wA2DT
z4z$vU7Nsx8(-_)lcLQ(5STyl>O3)E;Y@hMZ!`{pVkRqOd7InfiIDg4ZNtY>q{DUSp
z1S-E+?WNsp<a=@<=BPFEhBZ25_GplMs`b+7=jA)Ck1(VA)t!a6cOYoozrejF-=5tG
z|9D!qb}U1Wz-Qqj-fUC=Q<&&cnAL!ZrjhULK$+3nVq4Lt*X_CR0T&l{_lsy{jmBuJ
z0aeY$)h|h^`DJePHiaS94L`m<tnneIfQ^n*a+0nI@!(#13o>?_>0)ci>#{8-Q43u_
z?LP~wskzEI;DR!upn6=eK>oSp?Sk2hz~qdA-Rgp$o~LEs_;glb3WqO0kJi*!B_D9{
z%3&x*d@izh!l+NY)(k~!2h-|~CGCqGau^v%!>%zjFHnT`uL_j_JtlSAVdLG=QNL37
ziD29L7;Fm*hYttnsZ&Urh>6FgBs3!;cD@$6&1~f(z}2C2^X4eD$C+x+EJmW`-mpLQ
zVf4{xLiXjEeiD{}O}d7(NAHw3|1q)mMyrf{fZNW)Nd6g!iYa)uLfr__!(Ykxm9&o2
z2d1T(E`10d`8@XB2WW42M=p#$1@h^o`~xoIXvPB%cE3sF+y|hIlnZ8GSo#Q@UX7yJ
zL@!cMP>2|C@jo`UZ<nmV1ubM2+XCjms~_F_>KOr>M(~MZZC!Ng)q++O<U+=DDf2;E
z%ed^$ptn2>Lz2r(Wy(J+y4lXNG!<dMP>+aiJ%N+?NBFQ2q<qg4rT`KeztG4``7p5M
zAtYYs_g-KX@Zv+KZ>GmU+{0`GoGtdrHKyL6drfcd%1q4()9o2)gNzSA&&mIn?tol#
zB+|mBI*7M07TXQMnyVF9^u}3Gyd(OGc;MA9_f3_{Y{j<dF+$}0h`uJ0Z+YmBPU31j
zT9Aw64%=V1nQUL^&-9E5&}v@kn|Aw5v;(F<%7<%%1_z90`YpP?u`9-H!&9-Ml_JUP
z@(bbo`=<Px^^ZpAg{Dzsr;yo3-Wy=c=4p%{p8PEExVxo`rRJ)Qwy+9JE-NU71fO4u
zI3&X|KB{3<_NqENvSwxdibvhet6q)I#?Q-Y&TuO$Td>z|>I7dvyV*WJ)_=pL!>Zmc
z<$N`)E#&DYWDI=ybCI@nIDOCRbfGO^VkF%r+wuL7sHBfaqx{Z^CkT2|x*tqmg!!xe
z>s^37^|s@t6!UI<-^1<m{Yfv~zv#*LSXD>-nU7%gXM&AT$#L~oio>Ju!Iv8dD>GAd
z>Wz-e)=&R>h;i3B*Yaqf2$sb-*KOc`I6n82g%<3cxm@y>Er&F_-h;h*M~WPWvKdFx
zt3|~bKU*C-AL)lkF<Kk_NKY7bJ2S#^z+Po(RVK^FU8SvjXIcA`7s*u+bHZ1Yg^kPO
z3DRY)LBi3g-yR%0^zhr>3`QtK<>{<fJ0}NXKI|SLjXjF_^J$GAb`ORTw0t-IDM2RC
ztGNjv#jQSq*%%q6i8$NAn3fJhE`Iu$QFdsVMK_^*I&|bD{iJUT9goS)t2SN>fV6xR
z5D4iRQpk}`|DW?MB9skIwOx7+R#0y|L4l%)h7@ZPCzyvX5b<t<sN^w#GDrHgsHw<z
zN;LBI5DwXRb^xWH7{H?%3#fmf+t6$0&<cgYZSolR{A-qIAmO$|%N7PR4MkVZ#-1B9
zH#r^4e1B4qEIcmAHrz%Ct^hNyAxNe~>TasPJ=({<XRHX^_&cQsT%@@=2nH}Hp-zd6
ztJ5$tp2`)<A7Zk$V&=|=$D@0}h<G$D)h@#a+2ki&?)#0o5flg?PcuNjKTr=XBsO-(
zG{e&oZ||Sj4LG{fggstv!m6Go4)&4v2(oq|p7Zf|hg;LU)hhaA$4-rd5yvSC8rRvT
zS&`T(3qP-&Wbe#o^TF{2#O6id%%#b6@}N4xHG4I=jnRVsp4zLeZgkViC51CH^Ojlo
z4k43}ZMJ`s<lU^xX|6hH2g~#Q<zsC@naDY6jNiK!R8Ud3de3);dwYlPF9){`ocq@v
zP~l}ht$~1Eh)Li%9g@k0aKA^m1spnJ{o91c#qa?1nN4X^&e(jdIL~lT&@(4m&D#2v
zdi3a_sqmN9hg6H9DK8DZ&&`$*x@0w0<Itpw;c;jhc(@3<;A*}{#}zp61K_}`15RFL
z1h0MA@YV61G4mulbVzfEvsEMU`&}^8Z?$f(*~_lLQ{S_;jGSPPuyncPxuCCxb1%vu
zJr{@TEUX(%5uWRa)cs$2(mI8(T#-55g3?^G=H8Okr*M5R7L%LC7{rVooid9GAkC*U
z1|Xi<Q-XQn`i}5YTU!NlZ6j^a&Cy<UgbSKbKlV6Zp-}7>7T6)1P9;{N*|}*3`7Zb<
zSK88}()l|w1+`SwBA0rRuIk7f9v{%;HII#-@ICIE-*_4)^WNm-$BgnoeD9FzvJZN)
z@?pm9YvVu^w7he|OsDgXZd*!Q86@6;BB*S4v-EKCe181fUW*ij#Q^Q(5o~6z0?3%v
z4#`I|l2>fI$aO>c>TcC>Dy`lmozU}3{paak`BgDRjmOsA?7oh-upxvBiv$1@cP<uq
z2?GmLE+jE_ULv;WOAObm-E13ST7L#M7!o7S$po?OW?*C&ABII0T5Kd&_mJ<AEovc9
zj61UjeVRjRmJk$#%;9V$nf35tZOWO;8Z-+F%6L)khfMdea|KQfa6Udx1onORZa)y?
zG=+;B6!G<CA<xZ27S0HhAi<wSOPupDu|Z|uUOgJ|FCI5lNy@>*HDrn&rlrp?DO2ey
z`Nx;Eo%)!cM|Wq#L)4K|z{X@BWWgT)aU<<h_dwb;*Ds*GAWyN(4#4?_r*=$^Te;Xp
z#G=KyF|*}t&-4`nkam`w=O55@g1OQDK#q(i@#Qf?#X?{9d46nJ?TZ}!K<xAQ<><ZU
zYLpQB_%*|lsk)P2s=;2fg|G}p<e8;V$dZ4$K>W#}pm^*!By{M47>YhJ;6gW5nN@yo
zDChyszajeRuC0*x1C(*_@DoPFA$+iwkuJp^vp3_!0Rf?UO@Tl))%87)(G*JFR?Ot4
zuRzbeK6FTxi0}27Wznra(+27;VfA^>MQvdHK-4ObbDH^>GvB2>K`0awE#nO+G|ZY!
z$5l<`_oXX}f&#%n_}-rC9Cd`XHZl$!<QjYdqms{P^5h>mG`?$_jd%#ab&t!t@9|gZ
z;`}_u?L&1>C?Pga%^8##d3vJ;MJ@1PEIozu=-McfNd)PrrCc<6laHO+`BGb>5$t<B
z-L1T8&cEXFbc~eUwsh0UuJ84gFGi7?W0jj`^QP;v&eSRb=om0qCcf#FgM>eRC7{wF
zT65{B5VZ2I6t}!(_)v%?dfYZ@Jsoqs=3hp=RbSmm-8a^g)G@hyLY9p#xO_XIAmx20
z#<ZbO|HNoPzv`))kru%pEQEZ1y$>>@t4|6)&-7%~5f?jHgGX$e2xy|duMCD0Z<Or>
zyb^RiAFKM<zwpB0-o{UVx>19h`$u=nH2J*-Zkk8Es2>@^rJl~7gKmYGI~)@8EE%c>
z&l+yVp6Q;?I~&4P6!@55jM*FcW~Y7K)Z3$^mk0`RV>IT(Ws#g>+!|8Tw<pxE-{(a1
zc=K`2T7pR`nSb<YVef?tz-@aqSLGtSE9gmU<kY<$e61(2;nR*UT%Tlh(1V1PV}Jf{
z_^80832haHYSU*hYrbfLx)FFv81|gUt4!N*iRskgesSqpHqA6>WDWqTxU3b!TzJwe
z_iSPo!+iZxms<kw3nbEysOn13xK<IFP1)YIj`;A9S}eA*$9Q_)8LL;bf;bfB3A+Bi
z!>$$!d8$^_*^)2fCY!wY_M#(V!1zV+gZ~Z$PEsp8T@LZqA6k|h@ODF|LOaY2mYZAN
z9_2p8J*u;4dQ~!6c?^(XPW<+3j}gBtnwtqjhMxjO=g{2n++$$i+9NS>cr-ihW(0_G
zuIllLr8Rw*_+%{&1YM~!7trJG?mnhEb7MlrQIB&@&GQ$Rn!r&P4M>}jU|~ojZO<yS
zXG_EQuT+!nkR8v0&bbN0M&Pp+V}pKP`(7@@*FHWm|MBuuwzbgfB+vRO{d!-kze4v_
ztV@8}d}?`Cd)QE7QeklKE(li@4Fj+|sfe_XfEm4MMejaz$0}n9I}|-z+_|q$HN7Q^
zaeqs`Nd=(B?DXCXJzGJ?Ou*_N-Gr@;Hwh;x4>4=*L1j6k<1fcWljIL1+YH#UtF~g`
z3wl84Ly(8=2rIR}+)X?Kz$9xwoQt=H&^-^a%>ouIyXqZSPnqw}_*M0m`W4KjYGqA_
z8Cui&#RtCBpJU_!7+TXeo}!TaM7%qFjj2DR#p3m?@Wyn>aXmzp*_uEni61$252F&&
zU`TPZ*DxaaqAhI0UD#Fg5Kr`y_ev=kl}(9nEQ|5uA9+0xW)gYIY`o$nS<&uh&3V4;
zA(&3kr$3uRLbh@OB2-xx7q8|EIx{4xNCRCMrGdDHHj|_B?wpUwhi+k?#||G9_zYMJ
z40B%*US8aS28%PkV7>5nG<tAr;s^bK!PW~v12Qss&EKIfsWAT@AuA;n+84D?O>C+<
zyBym5PrM7DrDBLjeI3|&n6-+u&T#Zn(8TzwqmP&Jz^KQRmV+VZt83e7`@L{4Rn5*3
z;+xU&<9k+%T>b{ZS%ac#DB8BrB7C{*5ZlW-{!?lBFG$24&g-%xNQoF-1_0$|bSsLN
zgY;m7EwWBU&Muv)degMIl<xI?_+cwDpIG7pxB*aCL&zbo4w2a)_O17E3e%px`CNBl
zefa5m4n1YxrQU&~SQxwR%*g9Ql&9%L-rg#?wv%@3llAY_x&PH2mezaC=P>Z;C+Lr-
zi#86KbV5^FkZqjWfZc+w^U49JE}VW!TL1wyAa3MR>|q=}(Og)UTJvMT&ZVW<W9xO;
zwF(G|WQMY8d32++7w57{WDtQ)l&l8AA)_M3rTJ0BaL-BNQK3H4?2hG{c@dhuRw}kG
z1a4)m#5D&1uDK>u=x_dd7z(Y)kB47D_aE*>!L!9L8r6N%X9|tADQnsXb%T(v0@W5$
zUHaR@3}@rhR-;LAH~6%wBW=u`^fU%0eGMBxgnyn=aS?g2d)6-*4bb%d6(ihFL%@5j
z#qZ*RbL4Ohr_n+Eut6wh?GL`!Txi<Tw4<<f>HdMKye!e)0F=f1n4H5AdOsP{x8v>S
z_r_hXsVMg=6S5v3RXxV9*8A)Mk3&OW5y|_q!xbm{GO^Z&Drj3EBF#>(9Lw?=y4Ljq
z0Q<)-@xlzXr+uH)rWZ_BSOFDf*&>wbLLMU%6Q|yp`d(m7s5-j*G3{PI(4J~P1P#)f
zueq0oKf%sobi1^S+fZ1yEXXkezo}*P^14&xV=vv8eq<lTIyZ-q{a16^D`^7VwagBS
z5(cvWAG0AZy6%;>e6y@g4uT@q=svG8Z-AQ%2Rv05NuM7$CS>{gm_~h^PjKvr?Mj#@
zg|><nFvO4#+@72O9^hM*S?1fA?};0tF$@_+@sL+jGd?}zGKX<7ep|E7ok7pAQrq5A
zl;?TC30(gmj1U?$r`HequN1z(Z2HW?)jv3)a4zlmw@B=kkq(w*<AE}9@UQ&~csLKA
z?&Yk}D2aaOBYitwfR*R_J_i6!=$~7f0<NdphODj#Bjx|2|7F+h(=zna^{DfW$*ApQ
z{z@00S*YH#bvu63A7eH))n*j7CgKuW@XCERP;FARcG<U*obvZLVkL7*1QcOjB_}l6
zqD{b!jvWg0yh9b8gXlON-!Gp$=1<l*_5*%Kwkt(!_4M%6AbKsdqj0bu@-!8wagJYH
zdIAOtrFIuWt~J(cth#rsHSw&)dt)wj$iso;B@&%lI5)o4H9*~C`e(WNe@>G4f8Zn+
zdsDy!Ok7aU>@{xig&A4k_h;?lNt_CO)&~9jM=>_eR!gYMbwc%deP68*+v+1zoMxZS
z5SeeUtwRhz35~q>E9)--C2jPz<qf^0!M~VnoA9;AYi_ai$ASXplxpoX3mgUXUh9$`
z4gTBW6SU%p1HwV>L%jil-QF~Y9YkbKF+c+;t3O&79cM_{iW$|z<Q;k1qNO4m6ta8{
zi4~NAdGm!{{eEX$Umu#<TWdmzI3li?G~-r-E39VR#J_z9$`V;FUXbdBuaUi5^2Kz=
zeAL^=YlK)t3n8Pu+)f#O!nKw?7V`@y1pk@$M5%M%0BNLH_t5DVg)yYdKF!Muk4xii
zly4FaQtU5tb!&<qlHa(}h&#ra`kF=tz67s<aJP}0Nh^Y``NQIhSu^gY^zeB9eN&*|
z>+uKHKa)cl`?ah`uX$qRWU|n*nno6wy#8$v#VNtcIoA!pMgX~a%nS)ubKsbpvA^&r
zMPBKk7!78#3dLGi{{ag6%GlC+vbloC^9I5MHSwLpGrSZRa$VYv^Om+B%Q=AsHu~F;
zO#+ptCoIF^9rx<h8!gXokGjyoTq<i8kin~{%je|Z9v!;QybE~!j~B#l=w%@2&e@G#
z3)2=3fMb<#%w5eSOLfGlGm=*!XD>D1Wz*AF)Y_vLqGAyi*(X?0xrdalP>2^l=>)+w
z#)v1Ij~R3?1t_#o1>=T5RIERlr!7^7M{tDl0!B1@lb1p3d*$|0Y&;tB-Um)1iz=Z5
zP-%m#*oCvT^$|KwL%5%;V^Cqr9y}4B5cT=|IIYj?AB^?s;_*Y2AWUw4_0T7I4AGp@
zn|hrY!y-nZ4PG5BeXUE0lDwL53!fH-0iz`E2bEt8cS57A!f87~2xkiyiXM%k83)qW
zqVlRjo_;RY#=?2`PQMx8tdw1g7Q2@9+)H~lk%P${jujb5>L-r|U6}FBVy~R3)1B#C
z4=mU$<;(bLgBvdZb?=VxCnpBy$oBki2Yg<`KDG#^z^J^X$&WqsNrC#RMf#e-o`N;7
zQT0-8SjB$lZiRkSp3Q}7o^gk(HKu|YBUk`l*;?~PqpXeSQH2+w-&fNbt=Sphcc+<v
zDcF}s|BQ;Sur4ZG;l%o`P#?fUn69aJAc7F;0GRZdCW_Krjqi91HM!hV53_OkHTYvt
zCEX|Lv%sAiBe>Qt4BM*;5pxvlDo0C+nt<PtN5}n6+4X-lcV2%@WzilNWpo@1mJ!4P
zjwE1E34(xhD-uF6gd$x<1VRZ#S}0*;><AJPkj@ApbVN!frlBZEKo~+5NWw@U29!WT
zkpRg(%skwG;6B{*e9rl-efHXWt^Hfyy$?*s&e&e92slpdFB=b$`5g+ph+b<aOmU9d
z7F*;35tSce0Tn=$)B5JHRuI#TVj2AFC>#?PO-!gbXw+hg`t1clq23NAJJZRWtEHJ^
zzbe~%3xHfq-I14eO8!oIRbwRP%kyT=YaBYClCJLFVR@njSp3<d0KLB;@z2K$f^G9h
zK=+3JXq`^zGMOGjU;aEWy6Os9egI>fZHttj0$ZQ`h+3b!w1qWd+1X?2pz6Dtxi8EO
z({0|C&}~EZ?#&vltIxlXi!q;>rg}IgT|Y=-n#Er>hN_mNB5_Ad?tk~33D*q-4jB8G
zoNy9NDpTA!gOcFFRotpd91I84+np0@xMz-wPSLbv2{xM&uc>C2D%xU6V&-ZpB9yJ}
zyy{S8<`R=kmc~uy7;GGT&>!HJ9OEI?c;u}+Zk&g-Dn3<~in7VwK3ABp=NOm|r7^vI
z3)Iz07;r}`j@g~AB~JKxj@{N+68&^$E~v%hs!za6G<afLtYv2JTMlt_V(7X6bMLZ|
zy{fa}tc0gmns}1DI&q0*d<^{cbxDjof2UHk=H!vPeo*-px79ZXpdP*5CI*X`!GRdE
zAMHGk9da!h7`xWI@J@=^Bq~Fb+>o|ObZ)&FHMU7~%Z7Qbyvz`)s@3A*S`<9(X;SNZ
z($C`VWo|QXKRRP3o#nj|oz2Mb<@W7!YVI9axwBfGX$uvyp0u?7U+*{SwdCRN!?PIt
zYUOhj|4Czb_6fti<6dtHiuk`nvh8(B6@LxQn|%9NA5|+!1ues0qwBiqnQJc63&al#
z%zp1|z7TTZsU74k$)hfSS3Hi$RNw}pJ4-dU&kgpf<bL(&_@Tfhj&_66EBh&@{d#NV
zo*C$2>vlvU21(2hpMraS6o)VjJ2H`F@N$OE$&~9%<6_!C^C}LgiEa<BQEo{L1dE{0
z0z~x$U_%&2vQ=tN%t!qG5@tKG=!xbf_B{~;$-z<Fx4x5TWZgGk$E0uZUy>taZyei*
z5TyB$!JwXtbu=nzp%qmea%j|882&0Ni3+Hrg#<C?Xuh61Rv0x_$R}s6l{?Pnp#c9u
z2>vixy^R#G!=1PMNj{W8{<Qr5{=$&<^=Y`0jp{o6xn4-C*Xy<6>u;XzAFtV3yzDhO
zuQ^4jlUY+Da`yqH_$yUE#bWEl%z=B4;64f6w(=clqyo23{|Eg2Al~^~wpa~;Ea2I#
zd}+#Eos#;z-&*6&h{?j<^b9f0-92X?>7i*5m>fg@Zwx7qbBs%?KS6dV>r5s}4$atV
z`jl5lOFlKJ9@z<mS|b8YaA#5dcbCv*3Ptwl10k)Nk6nF48^|C`bls%pqK<`}S?u0F
zvM@v8GUD?NOqz%FZ`~E<L`+o&2t0UpFiR|3-%-V{h&R<T-#y4iYt^6j>mI#%fzqV&
z+HgAmn*V>Eg?3^cHt>(m6FqKsQDf?Y=a{(whedhG4c|=j`8v7&KDj;b=Q$Tro9f~v
z0B6wM_y#JDI+@;Givsc}6DLpCmZP;>Os99ee@vjY4>}2;yDQMYtqUQ_nfYg5Ce~H+
z=^X8Jqod=1CS!|YZxUfvFYZq~nRBXT^`XWz?E+ZTq)Lmu#!=vsoyC?>m$|1QC5sP!
zIuA5_OIyGtw$k;Tzq|l}i>Y`4ul-bv!M2)!iKQrflVjh;^cx!0|2@bGZ(tkMzlc&8
zB~)hP<&)a!L-c}~U2|!pXsz%%o0W}@1dzh0rH-<0;zT*a!nExpZz}$a>B)3;qnFi(
zYXSu20PsklrPNvk9B=Vh-zol>%TgTPGiXwDa8q^rEtX0)C@z=<ZiZKC(78ilrnMix
z9(c=ifhl?X8j3Sv>htG6faJ~;=EyruXOa`URfmFpM+<=i)8#V;0pBZX^~A|+M8L%3
zZhvX<skA&PECiSBI?F>}mYKsWOz)U_yTjBikM)#j3te}A)K4S4m2hTG1Q9iTdQ7CD
zQE%?MK06Wgf~dZt(>)jY2`52L+1k4(g}O9FrRwCA^6bAf+PvvTOq84*&?3=`DlOO6
zV&4huSIy^d(wXtN1k`0Wl{<6&;HnjZjkIsS8JgY=5G58LFbXCP$tuasYjChvL5k=;
zovB4is6exa92sG|BK^Sho)*u=K{w89q!ph|!>L6<0y%~w&9-cnk*^+>UWln<0LYqN
z`6m~#WQWBT7VJ}0os<w3j#}~8LvcZE!JYm%&jC0yfY7eO_F0>MjgQkEnjSf@Oa{tl
zm1RCXi%XnDc9G3LFYzsG_iu#P0j`c#&Z4iJ=%rAqHkm^hWfP8<C<g^k9IVML<>|Ot
zf38&lt)zqeC#9p<B(S`E#dn5%yC{Dtsd8&<&*2wAQGE#xW6<?T7DTlUni!mF2G3Id
zvUZ21l+x$OnQA3nhn^Pzh{NAmJ8%j0*-73aKsxrRq!e~y+70Y=gZs0aG^fXqu)3Z5
zYMf$LHS2qu{cuZ7y=42t1z?ys(*|?Mr&y`HwB1W}{hke`{}HW0GoZ5x=FNfg42PK2
zKkOkpV!@MQp}8BisOB&1gma`_t#Bxko=7%vm^ErY5%io8**BY)bSzwiA0K3g1Wh*I
zb}FuW=G%zECFS8MeoTTmw-C>lK_oVHW3o>KLABvF^#0s$nSES`fsAEWhaY2s46Ruo
zm^>^MlQ}7n-CVK}Q|SxEAEddCiFVU6#7{SBH(FRm>?{&x?F*2maMy#_UBoKgp0-_9
zE-71gY>6}|d2Hxybn`IR9VYL6(q1WXs>@eTa0DEsE&@9|mOoSBdn9C5(ll*jIt17i
ze*=dm<ybh8YnMAv$>AfexU(T+iIHR*3=YAJD&IOBV^0Z9w%<SgQ?Z>qL#C2u2@_X)
zOR+@miZ-Mzj;G!zHEIww`%Y2az~(~m#~{Sff#}sUMaZMyO@u(i=(d-O=SW0sjzk7i
z>w{zUf{NUWr!N7lmjOPzi#tPcTokV%`Y!B5a=PVt`xNu}E<^Z(4dWEp?pY#0i^oIS
z=VcI$Xd@z%^RV%6hMOoevs|Y-dbq{&dlh49LMJS`q1O$!AoxulC^EDg$XFHpufbHd
zy4~`-1?cz0NYhU_@PW9MP+cn-qKcqT4b7UAT-_~Bw!yHk{NbWME#apvU|$*QbwUMd
zd?0nM2ny`~89KAh$%?3m$K_v(TRUJdfb_a7lvVg6sBQ`7RncYYe0unxTG&{I?5xT%
zODt5mgUIAnWhvKCO*){UIIPXJyH{E1r31aMw)rPSHN_3C@<FwgrRZMwlU7vi_2p&i
zl=P`d?Ud0~|Bfk8I)H?qi)$FHIpkzna$l-o0^b<DHEWy0qqBSlyQH2#hfY7Dd1RVZ
zylwuQ#ydLyV+r4jg>Bziz-Yr%DiFh`VAVR6Kj!wv8IdfZF}Q@#E<?L7k;3$T5BK5u
zo_H)EecaETUb%^rSB}ZgPpi6_1l0*_1_E^pDQOX0?kW}1(K?8*M`{8w<yNG$#Ebd7
zS3<Kuy`QlA*Z9AdN-bmVM$P}_*6?EAR|nR7ICJj``uY53+Eo(sle(uz-yjhKX8k2$
zZP^3Rnj<l&ENHWs)kqy4q<zkiRdSZQ#+?UL9F>bq!dfQow&nrp;5d-2a<gOtCYBX@
zvQ8-?S|ke5m+Jc&j4waHBa%|quCVSo!XgkjQEf$3t5+W1nZ96-oRWLL=~_9Eli<y}
zpxj_K9CerV1vjA+N`B6t6>gi%?i$%}EbUa4+biYsur)96dRnT{w(nE;p|z}_>PW}T
zBin-Nwbo^^<elmr3AVWGG{l|#A%%+;#*ZCb*hYah2TGiMBr&(=J_?DE@^fk<tMONG
z>ecmLGvPfpm>SFTn}e+ZOs+BH4ER{nS?oA(S5NmJ%E+m+-eMwX+0N4Xl>zwcLOrPG
zMQC3@OA;&z&wnd)7<y&w=iSw_BxCaBUjAM_)p&Wks>PwX9m4e69MgOr8sMV0>Tdia
zVDjl|30uiJ*k=1jy4L#MCYbm~DpU9G(#p3PaxWOue9=UsW54#Y2*=L{)j33v@aoQ4
z$~o>|Im|>Hsq9-P6kunb==sktR!f29OZk}mHjWG=h1piR(9+cBv_zaGTs-W|GB)qv
zEwS^ENqZ6!MaBaw@0MZP|F$&u^A=(|7N*7v_~))a{Qy$`PE@juh+vJi!3A@lV4=Bu
zFpObwC&#YwUyrtMhVd$zTvC@lgXA2zIu|a-NHxO9rMAL(3aC9D!M!Mryw5b~lh-Hy
z)={^}CiyP}0E^TcPZ&5c7?4YM4I#RVTv*08tr;A;S&EfnNEKd^ab}tx0+wfzoCMU9
zD_0Ze8CbBWxD%>W+ZaCl{pG^dYtXW;8Ka<4y~ik?N66mXH~%Ics$6DeRFYwbzaGXC
zT6PL`uB+Ul0L0WU{>Gt?#=Gz>&HS8(&wcl>6?ZReDQazw&mY1(@2M@9@T2esEDfa9
ztu*TTz-WUjr0&yDWIls>y2Evd@(i}!vB2ztF`PW0d^GqLfGzDF96^h8i_-=|q1B~6
zK9};cQzPxms~ilYbdC<m2x6|qarCI@F}WjFKq!F8fVv~M3Ln+Uny8}7>zoqP={oB&
z{plr~!X$_0$c=0(S5N*$BUa&t%qy4g%Jxz8H}Ee@fXPOj+%NA>%G#&+gQ+22N8p%s
ze=^dEsXzM!28o&@dW#~Fo4=D%5la%sGP~=Z4qMviULRO?K)tGsAt|<4m}vWcH~@(p
zu8sC<J;f+=rXqF7NsA_(6hpr!4@v&Cj6zG;%HA;d>}&_jk?Ic>Fj-h?f)XpXJsSD%
zP^dkL^e!n)y~rKA<zcmQll7%Mdsl~~0WY+xTul&Yuc!^L#(Uw=w!>+5!sQ|a`9hrl
z?aCGvyh=*m1!NwZ3?D#Aw)f|0cqng(sF-f?T>R70AVecp*vx^Y*{!@>V<L52nL2*|
zyvrta#jhxBDKxDx2Qb%tP8c*RU9n_yn1Ix!fmz0{+;f;|68S?)&K40~7x!1Sov7M9
zaN|c0E`jlnRL5G?JkFYBvxtVN?u~P1&4fkc97?+om>zG&kR}ClKCWR2Q+wAAC(txL
z<@d{rbfV(RrP8&zg%2Psft~JcRGM!Nk-SmE>oI3dGuIqg&9m0qJ?SPL=D4xr9S`;C
zE&sTur8LEQuxQO%#DyH<`dSBGT_F_D=jm~yjXqBS5oy9bw<O+!)u`R@Z4?sulcjM7
z3CZ0SU78QKx5Y{ylmOMiV?*S-e(h9XJ)F}_T9hT}`=Jg4N=<S`Xy#;g^A?cbv3?3f
z@IZ+?mi5MoWb!%YgYQEZHL4sxN&u0o`mvrn7B|q}ql;w=DZgB898<p0yyh(xJiO!)
z+KD-uC2OuWOc(p=o%><BOZ0Uh(Z$nmCvbbJuuQn^EQ1~4k^H&AcpX85hEPt=)EoFo
zbBGd7pG$}2KG(PC#Vu-9umdwh3H4_Z@KCX@vg19fkAr1-7@*Q`zB3pO5j^6+mVliY
zRiCvwm}QNUIlOU7W<;Us3BPWUexK#gcY9NfyAR_KHly}Wd#S)XTJ|tAlb)AgOizOk
zZhpnS$U2_XVDcoUs5;5}@ZI<urk{=k#IueA@b+wfga>ecvMSj<d3gjxMl~Qd&FF?E
z$6Ot8Gur@^iwy@hiLSu7`*u_*aAL0G07{Dl;=#60d1t@A@tK-wW(MA~(bWI9mmC`(
nX1!tEv9IzV*Wv$TJ1rF6q^53mZQJ9z@nYB&`^&W#Z`}JI-Nt^B

literal 25832
zcmeFYcT|&E+b<kMMMtrW<0xIn04f3s(wk)jLsjV=ML=qV7+MHH9bp_0G$6gGG)aKa
zI|+({p$Zs!f=DnDN-z*22_ffBc*>mjyz8v*pYzA}uIFBhwzKbjUHdA(tL)9=YnCSB
zKS}-s0)fOKSAV|&0__L~fkbcZ+6jzIq`q7Re(bzuX7W4m6L{7yH=}?byYF6ggn>XZ
zae^<Aq}lIYAkZ-o<oAm<;n@r1i1Yw^R|cOAu|!|q`Jqk!&tuzny=r>|dJyws?@ha3
zSY2K}vo7wuuk<Rs_2h>ayN+H{eSPfiMG>)I8h_q?JMq|qZEF1w&D9R=%IISxxvm##
z-?QECV{bn)H!>fwHiTVsLo7n=^Dy@eTm2Td3Ay^uxBoX1a0o=X=TvuucBsXF(fgqz
z0#fp4RP6zQVlIzT=znUg;c5ZZ!?uC!hHrMO2w8|JJGd;=N$E)uRl!KxUqAnIK+91S
z6!X53ELh?5M}^NjKz77wQVD6ly;iwpxr){ppG2=FiDB8c3=Bm*VuNJiMr+3ES(d~7
z;P3tVZ*QPVC{C>bYMYx6e?$*g6*on&R!C)7PYep@cFmxe!%2VzT_+PbFX?~KFO#qs
zqcTeH0hY_lrpxer;+m^?KO1ESeeLGnTEO{o(ZzEhbR5=nIPK1lFuo{>g=Lf_mf>x9
zFxFpmDAo!JbB(Vn)~%ebjqZ(Qa5Kem#BRGzdz!`u7h&JtKYN&zLDEXS#t3zy`oo$j
z{vv=-T&c3+7vFCx2h>k~*03z25FQ5`Ig_IgO|=`@(n>3H&gn$=6!e$QBda-YL_1n+
zSaooER51LL0==}!lC=i!$f3A>I&dkdK4&v~lc&onqnV1M%tycVPvfLEFI1jP^Iv4p
zvSW|kJt3<zv%&Y<b)cABU)K7SZJ$)4Quij)&CoN!zW*D+r-OahlJyN<&U!0BYYl!_
ztHP;mlws5gw4t){x=xlE0KPZsY%qVU!zlmB4hS?ocoU07idUOzg|tdpb~M|x@9le%
zz0#x^%74_3nVR~ta@P(%B<|vAuE*0M2iy&RX+XjSRwRnn!-gfe9<9M0@Vx5#F!uTF
zjs7`&!tgiLSrNu2Jz3mnh!r6JIS1a`Sqdv7WCl~RMs%QhZdvisxE%mDp)dVI@7`~Z
zeA#|ey!y=kHAyHo(S?S{IBbkB>Fpidz{Mt&p~90jDDWK4+W8(8{ZZN%<9Qb!Asc>e
z+ysl^bFIp{pA7;#;Qi2DB-%P&g0=*82>E<LOlo-vN16C>aU)c$W#ap|%PK+{pjctX
zyY4TxyFcK^NzOs>>CS0!5i;9$|F;RFTc~cG8UAF#_xdV>;2egR-O<M9fl6{OH%E0@
zWLRG#-zI19?8JHn9U<vmQBGnjkp|{n{LcskV%o;_6kA800T`*`3@7;K)%7)6Wdu`<
z`oE!WwsF^EC3>|2(w`xw=Qk_p4Y6LDn-LwOTr3}N>FT+&CCQEE<h(n)ex|nZ8`}xs
z(Ibs>drCrk!{_qHirj8nf(!X}$M-{$-ZFhZS-K4#NR_4>$aZ<jnbF~~?d@}{`y+2s
zr4&Z=N^c@Z;!1~5sCMz9CEYoc1B)kqysvaKE#vycdqvMOcwe^@Vked2jkD#Z==8Uz
zhcaPKS^M`T1%`%4POrVDce(!3AY<#=Iz7d4R5tuv7CD$O{<TcGrG9K`LdGW>)=Ae4
zjEZ=M%09O#HmnoY6T{XTZXEU&r7T7UsxVuB1y~;~^nP@bA>+Ft)^ikvVr_VfNm*sX
zD9K-4pl(Ok@5i<+UErUiW%EeN!`d`i7-uTVIB>;Vt>3wQHCDZE>_aE3vm!ZfXgun!
zHWcyzcYb^GinOaibdI5CgH!c<HvBS9E_;BVJ?u`ooo|_8J(7NLqa}uYeg0F!SWMX8
z?5fgHeN*<+*@^ESQTG5*OBp`U@5aYo^DA4ylNQEeSKfY@=+~z0hV?~EB*0YkXxWH9
zZQ5mZ4k5~zzefiDh~aClx2{vxD;6i04$E=wZ|qZRDXyL0$dKT<pUvkpo;VC$S1+$5
zG>v7ibnFdB!E+?Fq?Ut5{9(@kH*4D#NX@qdfx1^aB)C>M{P2NjeO%<7c9TZj$ByVu
z9n4tu@u^Q0vh!zr>{WAv7e3Gn#9jCoa9Nf76R^JZb_p)wEG>IxWHijCIxY1_vyLa}
ztkm+1dLsE=4(v!^M*p0h^w?STP~|*do>X;U{ZwWp|DgXuvjfXoox{%y?kb^A>U5T?
zeSv>%^mtcUw^G~q8n7&nHydISpwYR_+c`7#f$8UdP-9s}Mvlnp>5dB{c`%9>O?u+$
zQ%xoP(&^aWv%VL`)>@CKsX9b)6L_?K9iOr({#Sz|R{rT_`cQ7U1dVd1uOYr}fp$+x
zMT<5XFX?T)1V-t3^5K$i3BE@9`3!_J3OOm6J2S6&2Fu)tmBJwF05ttf=yWWvxj&z9
zvj<R_dsciT00qCAx9tHf+*9@Q6!}<l0?W1Mj~^J8tv1ecod09^fJ-TD2$tCEOu4DN
zf+s}WC((QsrWP3CE4lIQlEalb^}TA0SlAXXUwP7$YVnb&Qm9QoAX>$J;TumSc;3C+
z@E>oI`nCRGLUEffICHTv%t7()LUX>eepH{s5oel{-asf`^9ajr59myK%n?9LeOSxD
zwa&PamCNGl;#wh!KYG)(vO=myI33xZE8e2f{vDe+Og+kgS<Bi>PWICI;{H+ovPz_k
z^wa|Dp-=C&@@X9ur2Xf8p2`m^WxhV@(J%@ptN6EShS32es@O+(y$yu@+%2wkwdp3S
zQ{4Y^P)C`Rp{C&?7RHhL@ts-Q!`0@sun;3v3rQ4|7jeY>4=<x)=W`6&-6w(ATd>Vm
z&L>GO?plt%?^HxbW4~YY0e(Cwr_ODBP`a`2J7esf!vt^K<(+_-$2K>R$Mcq2PGjtt
zPg>xv-<IqRYHup{M=XW6&Gh%oA-cssg;eWw8rI!k2}4KpzfaQa`7qI?sk)t}*UQz}
z2-kyoQ#a|sfCVC$EL5Miq5m3GoS(<kgC!bO-7K3wp@3A~(5esX)0vq4L#H$GwIx8l
zJ2EhF{h#u5xWbhRs#xuab&T0xR!*u=pv3`@LKc<HBQMm3qAJ$qb{k=uIukr;nh2SZ
z9J+JwzCs_ebo_TDc8nj5yGD+Z@*hhu%B$pG6t4~+$sAZOq07KJS}wJ7n*1YvXT6I>
zRnu6D)wMo3>UBJE9IMs^@b3CaDm;}pE<FRFlfzjcA-Q{UJ*pZyUSqec4vqbsOq1ra
zyit3(`!fk0^OhR(jSZ+QLh#@@lIQ;NY}fhSutc84ysBKt?<}R6=hesEF>XyAo1Pd#
zUT~jhCLy5%0!*06_SDLO1qATVdGZ6D5KktM;@z?V!<JEjL2ClC#{jYwLk10;XPVKT
zuduzN{7sFuR*ybywr_eyc%cf%VZ{qmtg*eNdShUx-{fPrkK=Dv4<>ugleIDQ(b%Lu
zR7qr8c;Vt{)zZGa;DtY8$FlchRi>^Gx*37Hq8Zl6mgD;5AOhfdB%cM;wC)TN*;gr-
zUdeBuN6aVa6>0U3q8u*oLf$-z3WvASr!Y#;Y269dxC?bFBF6Ksx0@4S7$2K@93Fd9
zVf>LldZ|RkmbX#BaGD78N#M8X&;mjcUAj}<Qr_8Yx(;nJxR0fxK9hZdmc8;+XQXK=
zdxbJ|U{iYJN<a6YxX6!1$p^J_n^~;`lXi=%wAESX*9=9Pi~orU9ax83O1o6huDWNO
z%}CK1nXv=+`Z=3R4z2-%?ANigKN+_6!D&0DoY!@DA9oU_o&-U2VP%WqfhwGT#21yl
zGW)!__o{swW$bd!z15(Q4xI0;9%}z!AU-{D^yc%Ju!`rO?^?04bw-?_&c<!dEPts|
z=<wc2Q3J1sGZs*S0c~_tgfUG*1y$h*&^2yv%fBPXukU^q2erLi%8tF@JKRZ8y|Zh>
zn|#vmgquD2^>pl2YygTiLmEnR{h(4$xY_u~>rCz4v$@sx4c#%D5f5j0e$~)F8d8mL
zvaOFd#pXA>rvieNaj%z#H)a*R9UqN+jTFR&2fM;WVlIz$OAfbee9-0fl|?sA@3fd_
z<*YrTtB{nj)pggVEbm3LUeL!$&UJS;^&}1P2L6k-nqME#wJ=ya-1xf)ADgM>r+*Ez
zOXxjtjz0d$)E~<WbX{@O1;>7C45E%V{bgP%2ot)d%iC;dy4;TFwOgc)d^Mi(7m3+1
z+Pnbd=fmIIVv}6mVO6fh$5)#|+MS+uo{s|mh$o3jrkaL(vBv4uSuMj^3ZCw@#~WI2
zw)adbX-56dsTP0J<t%0B$19J`fXBTG(~4rG1vyx+D3DOta?)~k9{Ir67Q%6D-RkLx
z^|xw&8T^4PxQQG)B=z8(+?^jfQPB1$)%Z`#1Dp1mQGf6~yHBm~*`u))(9~Ya_Qh_J
zvLv3CR}$n3WLnOf1|%~qE%bUtL&EMe{Ly8%)kCBt&5eY_GD90)Agi>a_50{6x2y>P
z!u($zY0TQ0X5jPCN4yh>vtgsX-zP;u(r@=UhXNTp@B};oz#e!!uIvuq8WH^UpXdKB
zf!6||+P#Hi5q5D;&JLd=h1s*QhUeY_odeLB&OpqBYV)7ZqUs|3;A>TTL}IRl->gMZ
z7c(X}GQfnhbygaid?giF^H77;{<bxNMLO(GdQEYVF?{O~Ru<%j^!TF^nCOt3k6l7c
zaqFN{c(0KvFQ4pSV>!;pI6o#2R4OT=D)qgRzWS_#Qyv?`Zh6)r3TW~1Qb=0!y~*HC
z<>mxs1iN_(d9!U#1-iq*`mkU}G4cogS&aT4)#?BL2F3xw-Lb00={D)M=n^=_@X%V+
zW4O?Y^|E8axQiu0*T{HIC7t_q73sqoAsN~5Z@5-0(}kjVe*81pEex|+(ez~}*WIUz
z$Dj|99%2byJ#K=}kNGbLr5TW_u>}9n25v1KmW=anbYXp=t1@ALq(Pvce+f$Kv}NZ;
zZe-A>No>1L%?)W4H&<^atAe=cZ%_Di7Qwz`blt#r`mk`mUT#d-_ws87A@uM0Qr_PU
z8#zhzQap_Kh?mS`lt-fm`6ES%Mm0^$TU~<M5M=iVn7^b&+RUeGVOO+tI(Cp|u*K$!
z&HRhQ!U?6?B8)FNnxyE3i;1is5;MZKL-SMMEqoY;m+??~enq`QI5YIPV5U0BaN}5K
z=4M0=@~Nj#N=lY(&EdE#KX>l2&Zc1-E=x>DP{n{MzHD!aXNOrY`q-KT1!td7py4!x
z?Tv~(yTgw#cDUALzhz;x>_;Z4M-}a%7|R@e!}>_dNz&G;f*#4}j=u6Bcis^7I~>cl
zkt@k{Z!Mhu>hB)3D+mykJ&=UmLt&c6MLa1boQ_v}!~|qRHANU};TG_?Sg*q>Ed%4W
z822fv_7NNQ>%OC9YJwRaUXHJBayJO{8zfH*OfzDsk#q>tZG+MYaTR>r_LthjFa)xM
z^Cb2un@aa4)nXYX<u;4{4ujLyBry!z4w@cC4mi12Fj*SdWOr~7OD<Vl@TZ5__LhKm
z?HE2;+bmLAV?FZG!45#8q2+q^sPSdUd)u%e)-rvpTV*?FANaG^2}_KMAv4{iYzmOD
z!_9h}O5?(B#`kF3TR;mJwz+A3xlWpq^x4Tcgb}lmlf8KcAP|RW6P5kwTbxt3pjNlg
zqu#$zkK)#+x$Xq@fIqt$XqB&-kNX78!22bArM2RLR*@_9!-=GYZ}~}Xp%TuapROB5
z?P}{&0H)%wiRm@$p7~wEeT;Yf_+TLT4P<r<=qUk@!*w9x1)iA0UgDJZ&66KycFzh_
z6Jy%eEMqu)MeB;tB9A^9dm)$xQtH}ES!7Y@4<1U-q@4Rns71Rxz>p};u0DhKXjbn-
zX-4*$b>ZaOk7E~?GfUQ<r`m284BY*8a9M?tRXjf8<Rk={w&Ny%SuwKxraXLYg|Ea(
z1NMi!he57*9TqMw9XWYB@H^g#_pSg**)_8$byKL4&j*fuCd;bI2AA$JmKKIJhnuN)
zgiHK;OY;A9kN*<}5NWO;rRjI{;U_C%_mdUfdBH+C?L)_;JqeFwS)WLX$a&T&&{~YE
zNn5wEz9*Ble6XV_sQnCOf6`f@*Dm-8?5;LBaIcl`u5g0ppdcE7D*PSAglhR$gttri
zFCqUw1OG~a!s?KuN>Z-Z5h0i^=x?Je!f(Lq=`TqJ7_;&L+A@8Uq=<#uxnujVZf@i#
zM3>M9?A}nkj#R|!mvUM)){F{tk%NX+kw`dQ1i23zq&JppPi}7VIBqLkRH+?sw(l{+
z`@*o%gr=1Oo*2@n0r3K1N6@*kg<?M)0vxK6G*l#uDvi7gWWo=Vk^Lp*PP_%`_jxQ<
z>&(dAaFziHTh#G`6+0^{&~l8cIpA|)bFXOYIV7U^eZ~;h8UW}{d({j&3>&-5en4EC
zj-8d*g!@hE3BeYy762N$ZMgSII@U@3f$2nNcn)zyOl#6+@dC|?WpLL;u&~FGp8;lA
zMdMKSnIC%l!#7l69hrvwNOgqJK%3tRHonIt7{g;}B<3O8W}rP1{!NU-buI&6bB51W
zhbI{AasiC>$H5M#8h;zB*NKjy$4_VzremX3-9wK96e=zoyl9%Qia$#9!Mc~?n-(rP
z=nKM?^I}#F+lISe8}IX^Q*6x<%UBn4ACKkPv`e;mHVuB5QGRQncaJjaAKto0kh*#V
zCSg@70T5nIBTcC^@A}nBfI_&<i2~#e*1Bk-+>ee*yS!Qsp8eTuq9PBsS*jcLM`iTO
zCTCW;fVp;TP+0Q>9P3Q>8><Z%6EphpCU?EZCv`**V6q1mPJ;4=p|wCN#^NkF^dpyG
zh>>QBZAgr3`~Jx9eG9K**lQ%%e(mUaQQ4_R&X9yB`6h;!`cO5n9e=&mub?u>XkFJo
zm6-^8ujE!KHTijGw~A-?+7e3*P)E#@rcD-ZYQRLURS+8rRgrU5W*%9M#PKgYk_|Tr
z<8NhP*cdrnz(XmxTR|6Da8-33{w0)w-F<<Q+WZ7cn#WjKjGJhku~?p_i;}pQc&(le
zqyxOKpg#`lUJ|4|ChLZ*(G61TOT*d+nX!Z~CeBt*#D`skJF(VOe)@z{gKuomWl36C
zguUwP#M0ceK0SE>4G~g;xfSslZ<-A#*Iluk5skIH!Lxex^nNT~2WVqR4o@3+QaOTI
zF^6yOWUNJ?;1B6P=<n!Cq$9)M*a(ul4ZAeRPR6>mIMRYO7@L6?%mr<u$p2v8&Z1fO
zy1x>dQyWl!3LdN}&<_KiidR596+BA+69zhd3U|Mjqx(gO%b-%v(hF-#q%5qJ{eFz4
zw6D<JK;B0Y9gBt7A*`y6YDrL4jK6UEAH%2TN!U)Csr2!%G`a<ehKaLLd~#3F(FcJf
zRX_6_SW+Z;jI%v1TQ~Fu>l2-TA>e{EzicJLp=Fg1hgUH6_C|vhF754-56FASFC%%k
zr`oY*Poj{5HDX*npQ-yAxf>KVnZx~9H0w^w*SVi6qT0K$&NgwX6~X`$bGa|{%=5b!
zS*j!}`y;p|S7bQ-M<8~@#=SJUuxUZ_lMo9lm~{x{Nt`W!Si=WaEt|txe*lTK6ei0a
zQCF=kBxYMh7GWH8J@3ix|GL`j3sl=VB->xzwz7wx#+xlh8vR%>(q-(4{n~3CbS<fE
zWUR(y)FM9FbEMqxnrq2TZ`JO~;-*V88y7z}9P9wa>~U=xuLpept{W?_TLr*b2ZQ*%
z;i`7zMvlAq#l;$^4Z)cg%ql4XQthRY13&OJ9DHmRj^};(LQlvB&~13usUJc84jM%+
zxf&qGt|WZ>PQocS0|}N#ZGz15Qc3yHft{3kM)vphB6>Z>UZck*V|GS-df7xViIEx;
zmO&X?uGR+dBoUhap8Fg~9X-Ohn;6eTog(1NTL38acvcyn`5b5C3LUEj#DMiFjbZ-)
zybWh>um5>;jFgP=vCA8MmoAs*=gxeAnepI#zb(~njd8~q>H)EQA%xvD(%Qr&Gw=e8
zP=-dBB}GwB2=Yh3PXOC88o4W4>kAEXaGHKjYIHsd<G=Y;m=4}ODTR0(3zPJoH_V6T
z-EX!H`$xVNb9vA~gh8-L!nzF%d(ZbV(FZ$)F-p52;qWr$UO})xU7*ny?Ww}Z1Ohp$
z5AUV4W5q1T-OF>U->hsR;6w;_KW4=kBaAAapK95)A$atX<TV`hOG~#6LCcp=8#!}T
z2sa>UW{1<OX-n%x)F{x!F|TFl-`KL@=C=SN%EjYylkXzrGW?pa3n8aX0bs+;6N4O9
z=;MC1;if$E6T-ykVPLySfS4*CudG?ss-FA%Dzu_;lfW-6CyK`1WlFZ}*7YVJ>YkD{
zl2$OqwrHIHzfm=^tVKl}q8UVCcz^wUeZC(C*-x(#&xT>?v?5vl`c;9}K%j-6Ja@FX
zm&&TMes}>DS@Q5V#4Xl0x~(-g8pr9m8W9}{9DQuv1^DL+2ove~bEH75wPKQ-&X}Pq
z4*|c(;*k#T(`LA1-DN8G3D%7PxRhwIrP&!d7|h4;(y&1z6#?z{r<ba+S(bf5MS2_p
z4qLE&B}?`>H*(x^YK`-%6*|jp$@QG!4;q3s1k9-noLw{^T~?SlM|!WwC5(^16rMwa
zvT9z(gpE7y-ogZ9t+jt(ZNQ=bTKT`eQuYSXNT-tyW06_f49ECWTe|(I9@>V$(j`cw
zx^VnIa>$1rF0ZD6fKto4D@>wdEROo`WDrRVbAwG6)+oJ<G=kxsa1|~P^SE*w#XZ}P
z(AX3K@4M<LEJ7+`D^u?@EZT7KyTgRa{WS<sR+sf6JL$ZFa8EG62sUXp+>K`)LV)o|
z1ePIW`N`=GrVA(ZoU?1=Zj`L`nn)fJ48-UF<}u~O8!O-`k_JvZ5r&esy@1+mjhuOK
z_!vEq1UHrznplkJf{btW5z7u;Z{`n4k{(9Y7FyCb{#n~d^M*m(12dbnk~N#W5`5KU
zh;Zo~Z-BcX5^&sP^s;I3>d2`0nvIbY&k{}&O>D(QI<WY4%_jvjJ$xi#k9Q7-+OU|!
zwHh7XKHO#zryH+6S$5NIwHrgD(gYVwK<6oYDKo~Bv}^a0RWjdiXu&uJ_@-_VF2!{b
zS8h<tK-n;IR)x|Ft*rEZ5ba;Ec<tV}4YyXE1Dt`*mUN;<D{{XBb#7T=d9E&eBbWp^
zF)XYLOOMI1+*_Q@YmE%^`>iZ%-JN*1SWiljQ>C&_t>J!CWRnu1-8nb{iJ)EthSw{B
z3<BB>Bppu!!1PCy?S;~o{<*{HA^SqHjS=N6f~8A(ufR>pp~GhBA0XU^;{Ls|e<h~-
zF?<&pE0NZV`+Fqj+pQO+q|L8q-Rq^x3aXhbbG)p^TL;j}g0Dk;RGE~0`K$W#lL3dc
z@3}TLL67j3TDtnMxl{H0&7UghDnO<@UQgBSxEsW3p?d<=wsyFuSc{^q>g%BD`*^Lu
zAB8x2pSal}>AwtZkW<3cPJ?64@0_00@yszI8BQOYA5WMnjlM-ahtpt&gT!<TIn`cA
z&b@+R&CYX&j<wMdcUT_;1OB!~tqZ?_j934pr=_~S`?Gv}Iqr(b`zI31ZcUNy@QqJ+
zRk`Kx@p#OT)TRDnTh!D`op`of^U2Sxfh^R>H<&l8J$da}FMfJ*HQ=*V(hYLQhB2#;
zE~v^};WKpdiG&?z`zvh~bMD;;YnhQrbM3wuvAL2r6+z1h`@J1?VtdQoZ@P8#Pu5n8
zP9sia!Xwm|LwVmwxM(;jxVwCZ$L&r#GF3rqj+$)chGQhM29ly{M(}I3vq;H(5`ZlO
z=kTB1j(*L(PA)$Fhkh}1zk=#LH&!~mM7&!g+o?68ATkt9D3LYvH7bmRuvnrT{o?p-
z6n}eY*12JgqH)|&Y@2wvJ8Se=hc5p3X9J&t@1U<up9)n-txIs6S6c8jw~a4H!YC0m
z<>tK}S(D$4wdjHbxvG1#O1)oy<!#K7&rerHjh02p)BD04fDXmlIKUn-to?feJh@g%
zYkQ3?6u0!T!#jPm-39?i#pf_ANS%s{W_CEb9^Z8BEj>AT&02EXis)iV?s?Xa_y089
z7l42B&yk`%rto$P`g!SoQvn4|p_x2VPLXWh_<?Anf_QKJyotL*HT^bPPzU@MHEWs+
z=4R`c2}@a2)nd*Yp~Ej+0EAQzpbam^b7h#6<7Tqlbb?fMs5KwD|4ovwL*^GV5&|>j
zy9+8eabS(odVGQB4Yy-8(u);nxf6FEHtng)zbo`)nxwxtg^}x**ZTh4+LLRYmTN7s
z8TG0qDu%WE%;Z32F+;_?(jf8`Geg02Q1hsRdU!EZGW3T+pPnCvSNY6ujJR3SbdOa*
z-vi)jyLfdZ?nWz0WDALv?f|gw6FQ(<t!SH<qpJx$b)S*pNALx<+yvE_yt-H-KE;P)
z{cY{BDw;Z*bFtUhqT*(5QNgF43uCq3I1oCOnofQ;D2rU%+272!;d%l4JiTTwmmMHv
zC`)trXK~eRKchz7Q2KX(TCok`Jjthi$5w7F<^@373JGd8-f0i@cL?=RMqu+wDd%b#
zH%TMLn61cTe^d#$5pnNGSw4bACyV=kmo*^Ko|DE%d0_*+qi)s)$alIzVxf=DiBeWD
z&URK=<6(26oXMv62V{&<X^?e}(JdL_l;*eTB2ZEz&}_xpC1LS3#(7ZSro#v(Iygj#
zNI<9xyEqeF5c%o$#7Kn-<EHrhf|LJNjlMnpr|{w&SERR8`BwPACba%8{0%hm`#*~Y
zL6Q+k%01W-F|o^2NKzQIq2-B3Z)TsnbW1Q4<ND?w0Z_>g5D7cL^X7Lvor~exc3Q0Q
zf>>+Mgl%HbsD>bIZy;_az0P$psA#fcy`%3rK@y6-Lg|b@kXT{G>~XJE99s*&zxCc)
z`euedaQz1GJ@?us?$3hDQ9wqj(~T#NbBo4}@HM9e10cJPRiYGF@widT-v@xirSnXg
z;65TyOw}yJusf_5>s9|X&=bRM#vZXS*qoOq3D>PauO*)EQ?-eMzL?4dO0GDJv*n^u
zab(Be^Oa)u;{04t$2xFbOf}JFD<GCv^vFx%U4Yn?K^HeG6$;Tq{)xszc1rB*5zQ~z
zbhEACEhpf`V?;vfGw<`&8NnZ*ii_PYTP0h}!~dh{_^*bXC;9eb2PyYiN9JtLj->s<
z7jFo}X)kkb(4qQ_?ge2CbU5{|CZA{TXWsW%i8`$k*>kmg7H|ZXWY5JiFqYWpJDC1t
zMV^rQ=pTX%psd`+AdLCr3DBCcDjQ%!NV6E4*7jDyHQq(m^uIGXz!qN97fHb+J*fv1
zwQtFLkWHLtba!k%7KKfe_;-c~*uZPc%o>J!PV;?OU*0Ox`SRHfb&|R<CN7wFiS@c;
z{b8HDg1f5TTZaUHcepXU*6WBGT3sqNY0Rj&pI;swgy?c?P_;91LVAon0wsK_$07vB
zKpXb(G<v>S`NP@BTkU+d^iPKF<Py$1!MPAwkm{HekMrvuuTXSIc>isb9C{|{u!-zS
z!?Vtr&#ui)>p){<AjwwI&OLpIMj_4C7iPY1j)G&ix3s;S#mnfTe!)k<<F~golaW|6
z6@Z)zembm^rrLY}G`a|gy7=--m}W2~!g^6oy5!dm_Q%+<ot{0Pk@r~DyI+QuVqIoW
z8+?Ck<q|3(HfzXkwDQS;565zicym^a!8H8pHYKN0AH$W5R-kYJ=79FBB>3p(AF|ZG
zIp!5GHk7xy>IPILK!gZ=YP6ngH238Q;fEkX3|SeqYPC#@cdLkk(gEnmk)@^6=cV+a
zQ#RZ`pZVR4;z|u$$hI5v11rD?_R=Cc&1Lbie4I>OBjHN+-M71ycm|A#dMM8lq_ptv
zkkvv#vZWJYX5TC_Q#2?1%VQbT#&<#cBvF_C+A9qNGMAWF@P}slO+_A}k#AvmoPVny
zu(q?L&FWaV>Im#H9@$z%IQnm$$FNl~{T2Z3$ptjz)1A6i7RrK*=3vu4i@;6Dsedm5
zWI);UB2u%|CGsH*0ju(w;?jz{DKp}4qqQ{gaCQZoW$TmlPq#X2*XG=wFp0HS#g-e4
zg*m{JiFK*_ZHg`amBa%kc}WgiZq2*a-qb?F4bV2|S4c_cd%hiB65>k0n{@SX)fz%%
z?`>`dJ)Pd*XT_?e&nIui7wJE)Y;PG_)7swT?!F8cIz(trW!hGs+#~CZ-%iFUQj&fB
z9fxH0tz=%OC95$6PA%ZQIWr-gTyaW}QE5!NENV)9Lk!W^lh<;vX^AddsBk<GKm`@@
z*+XHvlRRejrYZ)1*4L<PT45`e1bhUdO>V42L#LH&-gQ^J?n~XU4jr0pQbrBVB}ez~
zh1iqklP{16cpQC5N}C~9G#(};5d3GpC!??Zb~8OP7{%EO`5?Vb;_9*E7tH_s{o>Bu
z58r_P`T&l-?tTAt%-+b1@(0KAPugE-*!A{`jraDJgx~HSKCsI==EAYxb|2rJAr=yX
zPR<@87}>FKp<zv!P&fnQ+RQ=b73aE<YW>P;{c){Wy0brUzp5?vqfHFE^7-lzdZBc^
z1$KI_S?kkQZ7BY-1jKm!d{xU%zfPMPF`+c)9`JgrD3!WoEkZe=H0M9;&><^cvjvdt
zcTTq<Y~@=c5@?HVkUa7=XQ5Cv|0vql!gqwoHGHXby__3{HRZ&M*KWnm%fKt#5>t*=
zM>r)}X)a0g@1zl@=Ln`%VY8br4z|==Fndg8Mcf1{#+*BU_;c$?Vt3>mFFDG9SSiX3
zf-wICPhmC~W02_Kzte><zpT;*S}o8Fg~nsV+~@jvmLV4CutLJo2^zZP<(p6;ivhbV
zc824uLT;f)>^H+c{-v*2C^u?>)>3GMqI1OSwzhZqj#^t5ctk$J7@d!9(I<Lgsg1hw
z6^H<|(KmywD(+lh#<fNxQ|4PrU{Xp%gjQiswST#=KM=DM;F+vWyrkM1_}+5_yzYCM
zB_#cE2oOIL?2MRubW=hYweA>O!<L=E;)0Jox-x%S&b5U|Z6$7JcUbHQH*$k8Caz@&
z-`+a_yxsbIwN>);R(*FIm>+$LNLU?&KDoY?#2gy2iP3_{W>SYB`^=eSFd_4{*~3}k
zo~{kpfYuCJt3p{za;elQ^ny9F3~aPiXcBSf@}iKLbOb<A4t;z6G=D0ay4ET+gK+8c
zPX|N&tB(sfXj?e<=xqSPjB_8hr5uk8K-=8&He2b6h6bRix|bne2j0ww2f+`vTpr4s
z2`BEDKPfCRzctiu50}e{{`?(~vX@&<GKr=rI}(B2fWEvRBA_3W=wuedUYrv<Beo-a
z$9*A73IR9bOG&%8LC=rK>!5mQldsU5kRa`?gS(3R7N^_HrsN^}AiKl)SI}BT(Jw+x
zIQN-ZT2nRSYo;OWGkJ%?%MurO8m-$w35<p_e36E3e6rf}!)^MFuIToOpx-ZBcGInQ
zR8>mnQ7h`C2A)E_H|~dh+`X+OIm`x4FlQnlZh6#G5ZP=hVItsDIwf0e(KtD1<ZcA$
zEF;l$_g{VSX4b#vx8fILLFcKX;3>c6gW^rcyeb**>!dFE2c`Y|E$;};J32b%zSs6}
zMJNJ2WyP!m%bDt2+&oLnQ;4|I^u6$_9C{(|HcZq@CRyz-_QKAtZ81-%AHidQ<Awlt
z7;4F>a2?hs>*|Jv@ELG(u~gn85WT?>y-?k6sIxZeIT6&|6g8{l4(Up|n}uEosMo0Z
z<_)Q0;JIJZJrjMKmHq1)8q%$;3^X$Go_)A0x~*ksg#@lQXQILV^E~7ww3z&QX#65(
z=1{BiA-61Q2BiGXYkkn@wM2d9hZZ2>1vE>{hY+}A)e$#D;6yJd{ZHp9&Tx*ARo=Zp
zh@;E-yJ=s5)7hnad2TK)JrKp5Poe4_uQJoEpL5F1m>VhUD}Cm>e?%*J{S-%b4iM(A
zA1`ieS^N>ywPD^rPez5JtI?CSWjlo$C!c7W$jyPcJqJXQ1!$J}9t^drvX9^T7Z2TA
zt*~W^F0tHplhA0KOWI)TP7Gb~kzX$aTUjzYz)4n2XNYAYNMB&DXWvDNw)kkK_z>SZ
zNeu=!qg!-{Cg>JbA{iZ3Mfz1pPQ8EEz*tB$8Uc>D*Y#iv+(pa0wljSFqhwiU6xNHu
zc~?VdsT0D8)KgKr-H-7pT*;xg@YKaEfU?cl$^c*FZ{NB)q-(m>6#CD=f0ajF4H=|6
z?FNZf?cF%Gyr-4GJ%8=jv$@WS>qB5f9r@`_0Fz?D{DIKR-aSN!EG6`WWC#RJQ*5jO
z+zU8;QWJ5bhliMcd;u}haFIPbekfVLXwmrmoxEMk<*OKQeE{HmoJ(i|wD}+~z1ln`
zO*h(z$VCSPy(sNeY($}<S^|1ykdQp?q@rT>-AfaJj@5FT%W>;*s2_0!^#*6EH6XdH
zQXj7=Bs&oBoVDrwDDA0@uUdz=M*)Ne0}M3i6OFWNhRKnrFbx~ovqkxdx~B<DCBk5+
z`fvcFmF@P^vRa!fZ~;mB-p7l9TAsavZuglT0$Ch=^P9iGFhMf%TQ%Cp>)W<)`#%H1
zwClf-hx2$4$a4aVrVKkN2}Bn&Eb&IqwNqie8GJ<E(LBTnu#YiRf|`n1h%G}{M+JBY
z5Uvw~&!Rqt1PyYhzC+w{sc*qacS?le`GU#rWy{VJ$t{r|W}K5-l!z0z@&qe`G<GXk
zQkEc;8;z(Sw7Z$adxQhJQ=zedJ$eY;qPHgG@Y=%*U#Mg)-lG8(^NcvQ^{I>G6?BU_
zu{SxYCXGf-8E9D(2J^F@QtPBDL^kZ?k*4UpGPDl@Ee4LTK>H|0IH2c~^%aQa5Uq+L
z<dFHt2=>k{A^qRl^u)t31xD)Nsat3ocr2sp*hD_sC5sAa?Hph20`~_t^f&{CFPI#l
zEeKKFrkpOx^}H;qF(fxtO_5Oxi8fl?8G*{A4uZ3!PWmUMTyGfxd`$Sp9pH_6SkFd0
zB=^~ELdq#gOUUk6L%qcv5qx{b@E;=z5#C+5fdA$L0==%o2Y+U?69ihep8Z|`s?R7q
zABBjBhk-&E{2BvdP?7a$3KYoxOqn0Sc>s#Lp^v;b6lNNc+xI9k{GYG(c1A6+AkhXy
ztw^?ew)fXJ!aPv;a*h*(|3xwv6$k*YPIbmt0aeh-rI;|LCAI(+_{1x~ETkB0<cJdT
z6BAKcxVSR_WMsj7?H1Xo+tv6RaWQ#gpvhU-o9)>lLLoYvGs|PUnzX$tw~hd;&kNVQ
z_Z%eb`wNsV<o#aCe+B@_{5SFl^lcWo_x2e?7VRB<;71{4s!TjZs4=NbkSe_8G_hS$
zWu+muTcG0@aT5Ss0k>KZ*xCF$aM;xlV^J?bNNtN%Xfu;3W(8S<2b#vPGh@59Ug-W0
z_6aQV{{zz30>=LI5f}j;Qzw1}>m|G7P&2{t%5_2jN)HCmt^O%B5wes<eF;gFL$fC!
zhUwH9h^0l9|CchM2kKsq32TylN__&^hs)rHwob&Ov-7AD5W@o}gcnv~(jSI7{fcfe
zAeyLYH*l-LN7gpWzkUQW%$Q4~Pyw44w*OQ-I}4F@Z{!bw`ytE~$eQKYey)N}18^ad
z!S9p@z~UQOe!m$r^Km^88bP4+2mvBFplP;C1Z}Th81C{q2E@9_yIqgoQ}~1|Dh{#k
zel23d5t5j04igwkO!<M&Il!65Ak>uU0|`xZ>?jB0t}@5F__-~8TlcbM6x;M72-hL}
zb=~gB_c2>#g7}@60x+<bAH${r`M6}5L241KL4_DJe+FCLsSpvAxqk8U3$T{uZaad<
z@07aM--s(~6HvlJ{Zw>dy;pB}<JU}=@aaboW+Rv_iDsptqu}$yWjd=LFBcEgJl3IQ
z!>WLU8LR~fAwDfNG7X`=jFZhQbFQ~63{1XDlux@oQ5OgaF-2?bCvuKJHt+Cx?0!6V
zN$+``#oM*T3E04uBl5)J<Vl~a!_DCJ!E6>#y1JY@<Bx8KhE`{Jb*Cy@`%ZveqQ1Xx
z<j}Fn)Uf0Y^3zfrQ1$B2hk$I8aNLoRN?mDQtaIL{Lw#CO6L=$fV@6I@o^6?t>i2Xd
zSz65m$@vbUoCD7Tp`>gc2`q!_dw;||8_d@tipJh1D-p$#CvQnEW<EffF>4M+sF;Q%
zx9`(ClcfdCy_BqwKUj=XBpU4_a*Dvk(s$cL#(eUSMT2#a*Q;~;<s&{kEmf3XUtMZu
z<UZVQ7=Ai1E2S$U(k#~fexxIsx8G=EIbi$~`Mc*4*L&B2TiLnbq^r!_<Bn5kYFIO+
zxIS<q)l8)k458#vsmC2r%0$H`O7W2+M__$Av@>Lx!(v=VbSv$(6+aZczh+Xp=tXm0
zO0%a)=ALD{ewC5Z{OsVIr_?&}v<P_nti@Y4n);Vg`G>>yd*h4mb+C=~H)p{1?qOs&
z!p`s`d@KS|F8z##fF$bkH&#q;&;RirJS8U>2w9&^qcOWx^P=Jvpf-7iO2oDK^5!LD
z*kEx#aruJ74Y%a>emzFZsq;QxR_CNR*U|oA3!|0X?A5tyHO@*88!fG-h>WyH%O!lM
z9^kBsKrDM!cPg8Y52v)f>0)VM@M%<8S}tPgp3`~9TJEY>nrmveN^<~HIkV+Dk`o6h
zZ(bYsukRy9aKt2dJQf17#7WNExcyUaMnevyPx_fj-csH5iRKiVrPOOHb;Sh0835T|
zQ$=#YQ!XZso$~9Js=P`t+ug)*W;BI5d`g6_a7IBV|7DfJ;;(aJ-<kvF-We|JF}Z=i
z14lSGOeLx79<7rn1{Qrx^=;7iDwsHF0v?0C4b0eyjD)XzDpoaP4oR+mEwpmaqY~7b
ztfsUfVR0YNgoPUOKR0X4s(;XbJyD!w9+FSg^g2ynn-jGzU(OWsksmYstzVxtXO`@8
z?ZlLF^1`|4I`ek;sW&Rf$VM`H(vNA8@s}Ce#o@%1@%#|rG($u!pVQGP14b&)cZTAh
zmfu~zI~xAay8tPNzTQIPd=^jqQm3#UQdnB7Ik<ITvUB{ztbKoB+kzHaTHRa1L{X>q
zMn_^#=N)+gxOm8z51f@zR9Ij5i_bghb@2hNz@<DaqD%f~=NrEc=zxhi$@*$qEeZ1@
z$x$I8y~42dg06Mr>u=63oq;}GKm93K1!O)Z0&zoA#}kf>lAlozpGwXW!X^0JmCMd`
zMLk#q8{An~`Z0VZWjzuH2?^?=FZ+PkqZo`tJ15~$gm9JrIp1nLlE7A1nJ>Xd6dO+y
z(~?_G5_|RRpw?bXS=1lkBunN9#L}!PKSVF!$)aan4T+sq>g-NFT12+bTCm?EsLuT%
zn_w@g=%>`bz!T`~T&fI2HoYnbt{-#-VN5pEGe*$fp9lVP^c+_qSUPlU3DVcijE=a2
zRrc#E0xK3}Hxn|cUEqo2><nrvBwCSZ7^snG8)y;Y(xt<NTKAET$Tw;dcR&o2sq2s+
z=lnCBoO`_-TA_U39*|U<fwkpQ{N*5=X+(PlD>Cc!%9|5i4(1_0jxOHPto=K$OZJK*
zC!uq7%ClyJC#|IOd)|Zd<nmirJjRh7o9W18?S0J)@W$!T{>w4!1~BdRsv#wVI+xMo
zBi+HC{bXcFNT+^2?xizaT)ZbQ?PuDK81ZR*315NPcRs8t@Ap=V)Khmf`Qvo1#Yh>h
zkD$r%%W`)}4N^UNy(s33N8qXi*K^I{i}nJ>-5ezqW;5uzf0=Sx{(^d+VRVp^vR5Gx
z(c>XN{qi}cN_u{1=uy9!aC@7Mn=jXDfXu4#_dC{Iag)#UrDXN<S(>h2MbR4ww2>9_
z1=vKf@j7m@w#8B18K9s1+Sxq^8pt9QaY+wvsPGZDa6B!GDrczFuQybnQFqZR)ltrg
zQa8LcUW9YcjjJ)n>FR5p3t8`iN3S?1XXjJ}qS8Ymm@V7Gu@~Pio=W4&rED5f^oZo@
z4Hta+;E_8cTy)DBA`(6M+pIyDFJUp4stNehEO${<v@4|4%Ib)rs*Wls0E9ws?%r@d
zB`0gQ^41i;a)YwhyNu!+*sWDnr{(W$=8QyT!soFoFMIJ#nGiO%b!dOfnW7$bt*ZcI
z8{5YRZ39_=(4rGy(xC%4o-V9{v($}5D9CDkZS@81$xLe8^OpTt;q!O8x|GiE%$b=_
zXM46<R5K%p5h5O<qv%=Kia3`9wMOUJFezRg^qjACyE5VK0NHVXEP2G+UFNnVk8ZC7
zchLmr#e5O39rIa8TED^8UdYTJnkvsn@UzlDjDvH~P8|(q-VGwZ^H9K%-sUqrWLPyk
zRRh-<IpICKf?nBXWA3+oZ_T3S9Ij=3Cgu9xhYO}mdKZt|@6Z|;xQG178#Q1r(P_M>
z>W(X5L(2}6Yp!$m_GWd?+=Zd{vj<VBRcbu_##d;H?Fs%Zd8Au=;LWOXTmjC>$8jaR
z6#5H%Ee9a7YgrAqWC!6}KiSB`6v*j%vg&G;?^$Q@uDE+iOVtt<u0v4uwRZQ0j2G3<
zLuHfNeH3&QW;>J8Y9~`6K)+%na?Tm76MB`R49T@^<l~b4etFW2L}#rIx<f){b-S}V
zJ(2mZs~OoxUR;U+!tR7Ar(1)`4}T9EY5KATrU&{ve=E}D#^bxRHT_WQBvX!oMjp(x
z>qAR?a)6mcBWDdv$fz168lO`F9O@gsRywYF;a7;;keSg*B3!x73oV;_+eei*3szLr
zImYuuB!R=s9e<(IBKh2W@Da<(Ckaqk*XnUA2(ysF2bzX|?(E8~_e7p3X$E)#eVvR`
zr?epUJO@hGwd|LX#f{2eH5T!8oXwi~Vf$uWFJAXU0hc5TQK3IaA<<I%iOxAI@X<hL
z^b{malrnRtx_?w|#WQ=RVvnZ$x?k5?OVIO9n%6#xyh*`!5fhnsdvL^6v%uR8CW>PN
z&1H2A!#r=wL)xN4o_zIc#HnDn7_jlO8et!{Ko$24HA`JY^j`bl)H|=b2SUl&EG$m4
z3h@=4UCN;5I2u%_Lz_POXbx_Edj*k+3yi#lb~lx{zG>tX)O|iXhw28QoCWvKojS3$
z8F5<qmP-~oV3BMWX^rL<ok2Is3bpS)76u>zY(a01e<RGMmu6!>;Woc34)RH_H}Kr)
zDlXviL*>ryi+D{g{aEsXJr0w?V@nvkU7><exzs-32p~f*=p~!|H09tmnthjxIkPQx
zLf?OU0r2{$qipSLxLC1EW=lF%R+QN@v5OL?IF=iEf<Bdcn;n`-Jdi|O=GC2;jf-cc
z1Iifg8Kf1y101XzX~0YV(f{N_GxbXsEJ*h<%M#|iy|)QhhBsPlVv&~ot8x3(N}^d^
zrj2su8PJN|&GPHvg$pa{Q}#Z+xAIEH?{M?|72wi!UdM?(L;D}>RkISI_!JP7CzADb
zh!;<Fj$P_H$wMgUl%W~eQ%3L3W&!ZkX{6hoH1HnW@9dee5?b2-%8}DTZ2_I7849qb
zPlJieE{2w+1zWzN_y)zlaAX4!pxCt@KaLDsBVEi~0>YJ>^Qjf2?`HhhTu6D@r|VkF
zV^^w2eeA}sa`Tmnf~D)GZ=RZfd7)e5r~>bQ33z`ZSW6Ba21EDTF!R*D4-cfw49Pvm
zr_WliB&gv!IcGAT3$XgyO&gIFI^p!3uADd9A(&5>z|@xozRVh5Q01JSNoQ|Xotjl^
z^I2OWXm#6126d4f4I>#qersg*4+DAND-$<Vhw5_ahm7T7odUXR^fBn@U^Fb2O3t7{
zo^w?*e}9<%CsaPdadxxa-w^7S#Cqak!0n3=IlVnR!+r`|FrUC|yduxzrMR=a3n_JV
zkAnQF3E!{@p1IuaR4~KJ@hJGz{d5zBF^!iTygQ_S<@B`coVt`3s;9|UE^g>vXUeII
zXsY`S<8tvPauX9c8|3rJz8ZDM!>|QL&L{Ye&sUG1%AcQ$O|l;h!#qRfCb1&wDM(ri
z=<v3d9;~X}U{d)rzTxv#u21RT$-8m8mmr4EsmEKnPzz>*`C9zfz|(U|3Jz8iO@ls~
zU7P7%fzok-W7)UopH94JaplT7<lOR7HX2TcR_kf*zl!rdAv3>H^jJWtLL{J<sk59Y
zfey1qS1UzWrC0v9SP;o2<w7jWQoeJ{qi2^4s-kAUMdbeM@hi8-RLU3CgV9WE_iYYK
z?N@D0ov<#V&{+|#oIqUlkKu!k_*U0Fcc8a%5EQ`xbrt{QZfQTpaa~|&6&G91^OD&k
zu=eYF%{Cj*j0dIs-;Ewe7S}32@LqWd*nTfy`|BUQ-CcVlIV3!!@3lUkl(BK7SNRmy
zT$L2z`xSLD!*O2SRw@|PlL_$P$ZqfH<j!6moJ#apkCI)ofEYRw`5BMv6N@G_x<&62
z&L7M3Y<2xdSbNwIjGiAddYqR@r48|`;6o`|DfGBMP4=E*q^#1Exml-%^-9$2Pu$Uz
z6J`R414y`G&6vG?^>8%YBQ@z)<eVhEJn|aaUP0#^@y)5Ub1KO7u@q*Lfi@B4v+lq0
z0X{sjLy)ZGOJy4FqrK0VBPhUsRm`?kGU;^Bpq_*5yUGurJVA6tN9lAYy+>FySDt}|
zpwkL^(ju>QZ48)k1CGzCaow*nzn+<by7$uW8+Ar<x58xGPUed<M$h&)BagMC3^pE`
zQnV~8DUg)Lr=%laUZ)20PA5&Z8tY|<2eNoY0H)L2rI59A(X2tqewlj{KF^u5>K@#%
zeLwiOx_<g3^}YtkC<nwX<l<ItxovBKgiL)hT1yM!e=;>Ii8SpnigQbO6K|tCT<kM!
zqza^p%Y1d(u9c<sQ}LxPTE9)rTSvRPWlu+Dslc)yX{QFMAnA!fECJI2y3Wo>IGX48
z>6OVty0lkqf8fLoyS(VZbT7DL7Onm?*R#%ou3cb|XS(~Vi=8^Z+dBHiO-=E?t^gh2
zvKvK>CGC}lj@6rjJS4JI@`Lg69GI)Po&(-*9@3v;reSHt97s8=*H6Ftx9}0SSv6^-
zuzsU!S31xOHJt5U!k<d}EjO16q}Dn_LUE5?{eZb3f}Pdd)bPTjB%?R%x(Y{3inqt~
zSulISa**}o-@c@de+U5_tTAOB&dAu*xkpHOmf@qY9*RyXT$tM9>?`m3b?c<ze@^l8
z|6nKP{dHhHbhai9u7(ITn-kMRM^(HDiDTh3>Dx7PpF~<ZTxIfa)bZXgYI><G=26E}
zM%YK3g7#b}4)kjl0rj-17RbenKNED{JHt20cKRq2w3s-NP#ZQ;mD*8$T?($|RLdYo
zn!+t#sV1DRexiG15EV%H=+P?mJ{ltXoC-xpogf~D)G6vH5#y3u&JuIfD2?d3`7gm0
z!<x`f>_)D9BaldCYDx{hvN%P1Ed$AQUPm2bRHi9LTrr)B411>t6wBU79pab2CU$%3
z^O0D6{+r88#<y7=r1BHjd`<f88o#OL-e2ewWjCfwB+rC7@cr`p3bsE^rSn1_vih1u
zt2sJ>9~ZxX33*j{83wNU`-aEAKKWr-uR~lvHNhK9ofs^d8Dgk++HFpKLM%K{n%(%S
z+a)skapTg?3sRdE-gqQ>V+|~|?X3KMNXW$wy<(YkwJx^AA>V^%dmbiB51R2Uvc!GI
z26g6rAb6wuOjQe}RqLwMZ^j}O3YNJb^C2z*C6{`eA-OK+03qPKWts~UuIUCPgqGP$
z!K;G54UyR&QgqN*Yn2vdqO0|T2Im`wY0yP70g2{6Q;R&{{l-{NF~F3`apSn7H)`J3
z)%YGKGFXF9pg($v)$)qX{u9j3xdvRqY}2(-oo+_R^Zu0VH)u^`=by@to;M!=nSJ&8
zE7lRKKKHe7F0e5o)vEcf69WYgFb%my&r!|5tH_GE2Ee42o^8&rb}QExTGX5K;SDQ)
zb!J8<@h;*@t))%^z1lkttUYZO$x#ZtW1VMB>~h0y(8a<{%2r#QIq?soDwupda6}+l
z-TGHO&PDWy{CeZTMHj1_Fe_$9%A@CXitCnwldZ7lYC)+~E5X$lTSy;-Sr18+7^ri$
zqepW-C_`kOh^S(TiPYdH9Ol?P_oKfAxLQq_#<FoFy35twKjC$VW34pQ31U22x~5N;
zP3Q4BXZAalyX*=!L9_RsL>wo|ByYfR8-b@_z0vxGNwN<4$cJlWSM-KgSJLGPL=xjU
zm2b<OYA{}hgoMxPz;VYMdI6Ko>APA59K$&Eg5CRBYh-!gXzK==T_nvIDKfYJ&Nv0j
zqUf-GP0>3`lq)#%d)P&sg-^jMtJ!On^w=Vh=9Kg>m>WnM1`|dnHpArC-MZe|Pu%FM
zhgv%B4fyC^LGcXx#C0J>a;XJePZ>D~pCwUOF<IK8xRgr`h8R8rItFQl(u!7B`PneO
zsWZc^da|(JZKQbh4W>x6#R``a_PeOyAHBsf(IO2Hv^#E6PV=wD_pSOPMNuWMr9R8l
z9_Z<c87%^%(q`Sm?J2oN_x5z+U}ot3drP3ssi>Ps`k!k#Y6XENwoFn6_XpK<^P&il
z)OfkTx3yTTJ%X&3-EO%uTvrtJM$V4rIeeYwcA~4CVpw6?s0G|tknBg=nLfSql*E~i
z+nG`umYNq9P3Txtl>c0{n%z7c)n{7oIFLF~UYUJSffcDq3ac;JsJK>4*ckXT#LXo?
z0jHo7{?kO`@QlY~v@`3O$hsW`|NQ5(0hnGhx=PjX?|Ioas8TG)snzp$ietFdMq>(y
z^FmF<Vt4wc_biJ2`g+iyoGq7))gQhWQ_qQ*9TQIhZY-U@;}sKDziM)z)uRojS)6de
z(OiO*Vy@7}olALU3db)~ir?rOx#Z)`tc}U~Mc&-#n-|E*dzUQ{&K(+-G4+q}XCLFA
zf7-tst&sn)*{oi%w6c%2{qa6i30ph%I#gm1|G&Ds^1r0gsIS$G(>Uc=?`S!i(+C=w
zS(=)As|vW}%D7>hx$ESP3s~0Fq-nC`lG~)Xi@6dmpp_Z8C8m}rSV|<O6ru!(yw~3U
z;=RA#`}y4GJkPo3Ip_QRKIalx@~;QEpK*KQ-%WO5kpHMy<6?Jr*5xZcG^Eeo%O{0~
z;Qr6vBzVs$4m0XPMiQLi#OdS)<i)nX0+Cz)Rf;J=`+HmTb`>y<*CT2Gdv_#10_5>l
z`I8Bki9<NYg(#Wr*N2q%HJ>M<5yYXmu6UYjhIkIPnc5^w9#>|&EQ(tNW)}d4^mREw
znO%7#phW}@Y44@)B=C*|K0f)cl7P8<6Qt%uiH|)9F8|y4p^Cvs_|p+Ue_}{4?XeOA
z;JYXjmbHkf1rqQ5kkeT%kiKeGLnx<wZVa@&dz=k?YJ2CR9veG9jsdT2?}n3?p$EKc
z<H-2)UDUQeIZyI(Dx{(+m}SC8c)%-R6xELI=}`r*p&m8N{EKE+-aXMFV}jJ6J0lx4
zdefm`fpJbKAiL`x)#^cQPp|eEsW~-vvg&IJi7cRookuAO02t1KPaxMK)|WlepJlVk
z+>Kyj_Mk^q%E`*9(%VQBt(d7+f1@KTlJ|6=&riCHrMU`=@l#6~_%SKGX?_!m)}yN#
za;dfdnH%p<a1Cv7bL9v4A<TYFsaHp_(%b@^FpW2fR8cQ}jH3z}H=j8^n0ue-U!oLa
zrqshboa=VERL0N68&*SrFUqgYFGHM2P&8_s3y^+@OOysB8~iqBYnn+r7wO{LkA?su
z^$Ao8L$hgO#?)ftPxH_Tp>4~C>U=os_@a{F1UWch{>S7gmZts~8T)lxH|q|*-7>iE
z8L>MCd8LH@P1VdB(?+q&%v4r|Zq{MN8x5O!uNljB_7>XBPnqut8!@_hWUH2z5$%rp
z+8>!3<DYH*!=Y`bSV_4^clLINsMku4UW{*-e9CC?^r|BQjy(-lT1AeVnZIZxPt@)r
z4xy}%93oGipL8hMTlD$6XRY0bp(;KOMX%a^eZiR5B<|Qmv|7>)5O4eqfToQ|w%aes
zX?v8}{5K~n(02j2xGJ<=NpOY{%*>*^-$+bF2<_X23g{2Xj_Ef}60zbJ|M0tKA4oNr
zWu=#o(+3^e1O!6&07>|*5^$dxz<qw73ycXWILCp#RQ+NXcCN&Nw9sRhsi_uj)hp|O
zZB_$yH8{A9{Y2>%W0@Lr-y9^*=r7NJdTi^2qc&rPV-c;Kd*&;&oED!V(GrDC_18&|
z6{!dB^3+M_@#{?_^1)mTl>zj);sNTX1ljV*6(5MAwBE+9Rw)LT9_ztlTGcg;c4$d}
z#ZL?2ewF1OTB<UfwmjV1<pvg%*Q@EP&o!cdL|r`_ye;ylh!VG7lqRnn=VrmNHs;(t
zEbv4gQc(7{H3^XE_R)NxYX1KdU2H`&UHIA$3aXt6in@fWYw}?}KT0XPg<?I_m!0`N
z-4Ig2xkEW2C$KH5v)hXQ;cKqB%jcvZ<E9zf)ZL9PY+A=PYorI%KJ-+nqc^ij`$2UM
zp>I`Z=}-g_N-cE}&GO?_o3z{~kHPJQf!O!FBCuxVP+@VyftiNzrG0@Wt~8y2{e*a2
z#Y@LRud?EGblv8c)r#X~*304-ch)vnV8UAU;6QwzXMV#+#NkEE@AIgowQ-pA6=YKN
zHNaacwQ*+&@d>-B01t|`;=<*ibR(|n)G>rN(?YAD{+(4lE1i9}8{x&QI0}xMb^vx<
zDPY%<fgwYCVV)pT-}T0tQMxD?jq?Pwx*MtB^o_gS)dm#OHGcrQ<|CzUKB%;V@_t=o
zw?dV;MpDpq3^@VsZMHZS*OM-GSac))eI0f&%qY>#w+)!AI`UdHE0r8!NY){+n1nwo
zG$JDB_cdx&bB_8ty(4LYBvT;X!app`X|SS3a^;Z;-Oh4Z?zQX7X8c9cgv+Y!^(N6M
zB*>+QDr(k3Jv?IcLNbik{jr+ZIef>p8JRJ%ckYLBBTaqD8Dn`jo1@31dBxf0uT6Rz
z>@(*NPJ{R}-q5CB_B6hUm%VHr>j*AhZyzI%>?;97h4Zi!t)OyoZRHXv8+{3E8-9*$
zd8Lgj1vRY@bLqQ`4XI}t+mKL7S_)!vCB$$=w=JCX)kV4%5W{I4!O4cTf_JsVMo-(0
ztASSX_RRQoMm5en%>G%_em&dg{&)<|g#0-S(zG~;ov{u^E)rNYim-U%<6Oxt=sG=2
zP|bfOi+Gu~a)Ps0Osd3Os9;}}P%s6vxD6j;+t6kzw3e#xqNv$+Yzolgg^n}1qe-qi
zPuP-@6rIi=ncf8<37L5l<$ce318~RxHDA$g-4)YVS=RZXnQt>;$WzgsB7r!KK(=7P
zOX1g445w3l0>gNIz_`RN!e!?wH8X%~ZPB_|yiNe&e3Cdb6zqYJH!*wbl!53>`n`Po
z`&(&Mire#QSexqnO-@)M6?V@`Aj|YoTwB-iH02l_Uc7F68kxnf>0nK!xhAwNJ=N23
zw6u^9J})cX3?4}}Ksg)B=?&@OH|o^9nbjvQAWc%6c(94VRYw<<<IV}z3O(>oCr27<
zA;+)v#o@GH!^=t*$eua#J)RDqqhXjXwaEX{TiFH!?ty<TKe$1eMx+lf9sN5oYs?ob
z;vG~#1?Dajc`}|GB|5`(%MV<ps_ctaZZ8H+%RVL+ndaB63TK}>Y`WJIOuM4<xT}u4
zV>z&tne(@NO!=z|9U{yVOPfJs-hqV`f`~lHtWif!%ewp-#1*J;jxpa9T2spZ6DD>w
z&6*zmu;klrEB`}q+BdcLy1#ThV8&>$I;h}0ZUfog7CLJNMcdI;JZ?T_d7wd!Ise%H
zv1T^$(N}<*f7*$q21hjRTjq;690C6Kqx`2uLAoX2M>rd5K6?J!upc@6g1kY4ukz^#
zAVr?;5UReQ_@aGHm*RZE@_IdCw2N?IXUA>2tWiBzP-Y@EhTQnibm}iRvvRMH(ii;4
z_tbRrlH#PRu?BQrct@Ca85~o|W<JZoOv_S(v7;7VqiWbRVyq%F?Aq1EhMrPG@9Mtf
zbt?ucRUoJWsrfrv;!IYqKhbzJuDdLiWS8>R-1QEWc|8j0ahq&6lv66|vj&EX`theQ
z9yz(G_g`V=->7~$kiTiJSe-8FI67hI&L1&UJGJF69CAxqGXYUG+u!kVxI=_kwmX=K
z@D6hFO$+BJ=Z^1+fyI8w7%&H;{H0}zO}jN8r0&8Dn{&lPY8q@dCzj&f(U}{*jGG?N
z*z4HwsW+YPNp8YAr1TbELd!oIhd%$ZY%1wN{}HuBo&1)kS7-Z|HLx48($f-f>v{^w
ztLNwWAAIe?)3jr1Dmi&6aj&CYQNy`@S4!r!cqjkL3S1NqSciOb`p7asLGPOwjD269
zZ&$(Tt&@Jo{npGdjz8uWBdx?!*JA|&0+dR7nb<h6lOXcLWbAnRV9P+(W!S0ku+3hJ
zUq(j?G(10^KmwXxv9rC?6EpB9Q8Q??IC{JwqX)ZjjDD6`M!FQo=q@N*mSgCOfRMnT
z8oVNPzn#{X)y3SavwGYtSp`$gDH68xRgZyOy&WpN^(JUvXu!nWD@AQ3r}SD0Vc}nb
zEXg?*36xZnvy9xs@GNH|(F(_$KJKy3#pqkco-b_umf|D-gU<0;!I;qfJ+|G0>lJ8v
zI5@G*>8)6sL_+L{vyhguI1I7>iAn;FyrjLifbD#3uLne=q>z-jFFA4A4gHs~x$8`s
zz9gP8^AgVcG7OC_UFe5Wk*!0}Hq@kS;iR26l=EM^PxksdkYWO^I}PUmBL<4qD$%z9
ztjffpn1gc4GfvT>;w|;TLM2i<lzpHPP|`b(<$F>=NAxLlD~9Ab%u?+DHpbC-v|Rmk
zmp4P=bU!7xb^*h{^V1MO@Hb|QNeeph60S1Ae0Nu1@#atbemqKkEz19;`*Lc%(V6l1
z@3{t05{8$Qxd;oh*`ryIEUn$9b#4aqnB9j`9R=&9ze&_w_W$Nnot_0{7oz9l-X>0=
z8dU(U-O9cvr^NwvmEknh$qMR`j~(W=OHw8mySl)Z2_>uo{2vq51BXHuJEk9J;@*y2
z&&h0GXU(g;X`WubO%N?<g|1?)c(<yg3zZ|pgylepV0owA3!t0Aj;mY$2+$LD$V-#s
z`6URT+8t!byP7;E-3bGTR7Fs&^gx%XeAAS_{y~|m;|mewM!Zowbz2<({1r#xLc>Ml
zo2^Byz&v510P<uH`r)1(-*VL@r9}%dS~Y?z5WQ!J>FKV<01<Bke@cdkz{|VwVS6>E
z#(TApogX#__5Ygc^4NA!SBKF2fq}P-M0B-W(g+%@(}Cps(^5*f0c9rr&rLmIDtWn{
z=pDtAT<-h<m58s6Ah)_a!HwFaNVmFSAjmFt9_E_C7>5>y0g?+3)Vet-k_R^S@IrQF
z=P0=eD^TvCS>w{K3?)XwXo~F4T`9Jh3M`sau(mxi8Av;x9N!3Gc<md7ON2e9;ABxB
z-uMX_y1x%7E1Y64Gc>shTr5R8g1@aARB9sNa&{-LJWT{s9AOt0(;@bIvVZOkd|;CM
z$+&t*GtHqX-5$Q5C@IGOKty_1H0XO}zPE=91$#+?XZSIkN>ziG;vrr=4^iE{o2M^1
z$IvDkh=e`nbhGV@N9|K_#3iha1^pnzvp4Ms_}6wr?wx95kR%I_7EPUHyd#)2EukT+
zOH@+;afn>%!1qqn<7Uk0M!2+-<Eq*uE)Xlo#GwdT3*N4^t<1hqs0Uyl8*qM<7{syx
zc1;q|vNH)@=o9=f-8z^wR}GSo@!}g%5FXZ(Sw?+-ml1b#XqQpoyRU8#`vzy$cq)cg
zXwtr=rR{Zc43Q9*m7x27N<cFx@1FyR3uL0!bv@wg+djg;iY+%0Om8Mg1)MWm=ORxz
z_CVz2&n_8}e<(1bm267h&pl+dAnySDQvGnnM!_$;H(j4wrzDi^MeN^Ff0)hM$TME%
z`$wxDOEs^yzb6J8k%MhJs+{dx<Eg3A!zepW>gT<lkyBXtcnoX1f6dl8&`$-o@pCIv
z<7$VC-(JhiC`?e`<6h)|QUStsV&)J%)pHsZ$|&xGT8=K~oAAW%7R#0rD@)WnpKm~6
ztdYB8U0ZhjN-IP^s7SHa1Ml1Rgn5SXC%V<@p4h=x-fjMAJtMKm^7SSp>x+>-<?LY8
zmSFDMOt><82{%(W?4aCmc2-Hy(bk%*1>87wrB0chh3f`u_-yYKJlGO$h@n*6o}t;o
ymW%JYur@Z}U%z*j1D^f=fAuQJ?#F%(RZ*fK13t+DSH8DQgr0XjM>=!y=Klg1++dgh

diff --git a/reference/plot-2.png b/reference/plot-2.png
index 20f64c824bd0f2ed5ebff25e7c8c2dd944c4b6b9..4cbf045163fb5e3bc439d4a57365bb3735013927 100644
GIT binary patch
literal 26402
zcmeFZcU05a`Y#;2j)RQjC`#2KfQ$kvB?1CEjA%qasZs+XO-d++79dy#$C09;N>x#T
z1QL3H5Q-=mTBL+d0BI5kp_l{`lDyx<bI$MF_1?SgAMd*Fy63K&wGiF)*-!aAW$*ap
zhWQnVAEbW(fj|<_tAAL6K)b?0pdGjO?gm;$(_XCrzjog?z48a}8~AJ*6H&mgeSuf)
zLqH(8$D*HYNwcdSAkc9T^p8u{;n_>$5!oJL&htXMFfWVPi?KgdmnvSe{^O_JKOVn&
z2Vs2h$_KUI{{9s={k2dNH+}Ntfs@nm<|aEHe0llFLg%pkN2Qw=kMF$EbadPOo!cLo
z>lvW3M;phq5O5nr9>%d?aWm?|a()9Rd!3(4Dba&CM+|aeEVhYh`@etuui=1fZC(*u
zBmDGw+kS3+RL5^Qwg$umrV*$#UyeZE4g$G8UBV_BJ&0*j`x*vzPjD-sR*Y^4c7XIp
zXf{$}lA4|$Tmc4xK-W`Z#VxL{ch-x04$Uh2f<S@BusZQd`9O1%c_qFM##*5ZsN<Ll
z&ru`W=$Dap2q<fS&ZwAI7@dh&zG2`QisRW3O4yyP!bLh9liuBG6#ff-J`jls!NT$K
zwmA;`XjVP_0Ufg|THYxKW>8t@!&;_WV1+iM&6D(6GuBu7ey^laNEBZ9Hqx0jLjOp;
zlK4f|eb~yNH1am<Q{3X0HM$KIZi5=GF~W9@;C$1k9BS-aI2#ohN^UgCAAzNRqmRTb
zvP&eG&+V+?#fG&HA_;xA4F%5Az|op4Exh;29guIHJ|T0VjgJWnxIHGR_2`Mr>O57T
zEw7QsFhYcOz7C1Ta9_8y)?Y^S=xGaF5MOrtUYK#Aeq4;#7lykblFf46uM_U>jZdR)
zWa99v^XamLP6fi2SIs9|XQGDV=XpxnH{IGP$zOa)iF~R>$+|=02Yb`*Frh@^s=|?P
zLP8uUhz1F}GqLzk0xz7A7^oa(3Uhbi-B7IL#q7mh`?;i;hgAVAWTd`;_oGNcLrI}C
z_q$BCKbpo&T=;txM&rKvym;grzk>cI?nRx9?qIan^!RJ;*WJAOcM0R}ZgAGAD;owI
zf3*BK!U*LI*Fr3(c{0Ca6(z!xz&FO*eZwkP*<)+Zm{-!W7rjX_&AJ*x7bgrPE-gFo
zg?-X(3T92nQto^lXD~7mJn4L|oArPtrS_Hc4#D+rLq=kS->Dk&Lfo5bj@<U17Wd-C
z6=dR5%%QYwubWN0xVTw)oyc62@5aU^QpbKGU8ATZZe?MYVw6HdV9t8{LMt^jtC-m(
zVXAX_q_NX;2dShX6k<gotK~8H&Ze-<9%TQ6b$RcN9c}0GDqb?1<COLs2&>%2Y^KYV
z2(LWPkzWZA(SZ*FO`i0-6SoJ{6dFGAfYEq!yzGSa_~@DaoOpVO%zMmJ$aw)lrx+Sh
zxjg<nx}LvHVrtMNr>8VvRt^%83$ynU4%)#gTqm$tq-`4{N>-pQ;fuh{9;T{h6*I2#
zEsZ}Bk8v;!L0gssmDIsylbrsQ^*YW2{=^Lf<V=YCvW<l453&Jp*2l$f{CB%XC#;>T
z-%K=WcdC<Mhz^`EhUr@q(Cr^|F_II<$|iK&S34S_PwUg4mEHqLHMA79tmzfSH*Hy}
zWM$j%U5)>$OUu&PdFrWW8mXo(#28dgaI2n#pVk$)A_{iRJ7_)Re4a*BgfRSXX>xoP
zWl^P5CO2V*`Y~|h@Nz$s#TWbU5?B3-tGM-*Rl*IF(^IcUh9S)nt@pBa%^Q9TzuD5Y
zc3@XznD5?WgS=ezis55fjuDK2%G_k>fFEnVvJ3-uRK?FqN%fcdNlZ^;EHu7yG5|xh
zGI*V!VY=47YZ_K(K5^D-MX;eaKeF^AjS*=9XK5c0>F3z%i!*YH3BsKHtwy(;hrT@f
z(x|?`dAgr_dVjcve+D$H26d}<-jy|}&As48UY%DjWQ?YLv#&m~5H0m>_*2_*(m<b7
zHqW)Ct#^<+%<$(w3wzPEGtx1d5%4DWkfZ!E4iXaT7iMqS@meM_y63?(hY$r#={oNP
zb$%*1X*s>S-l)zzAxk2hFl|7R8$1ut6!_yUEhLkO?Mn<^LOGmaH^(6Yq;!i;xH@1b
z3LKH4W96}&=li=WmONbn)egZbMdwz&q!6WKh2^{26v~-<UHWd(%4Wtoey=5}FF-t-
z937DioE_zf#MH#=(g6LzYq=cuF70UkgXuiN<OWQ-?A;-$>}gllOC^5mkKu#F=5fy*
z|KgGCE?)p_^*=63`JrrCt8vGt2{NAd3|~*>^JF%i=RVMvcJXL8qVGTfRh)1vWj>0l
zQie=x@2?h4qYWwcrTi9CZgxX!^&!Xl;tvXH$FOlPSEBc`4FYo-u1wd&X<H7irO|ho
za$QE&m^~TQ^bu!@Jr$-^Y=ErcOZcv;6Lkk|xf;N7pQT6516K8F8V${LZJaCaOn45~
z8Cnh|oF52nD6m5$>;PDIXbqv37gXzggdr?6M3HN}G!`14XT6Q)p#>c0>5a>p;pw}`
zNwDBV-s!IuVbHv)fWIo2c!4iZ{N%*G)z3*abs6lbn?<i1`tIPP;83B5j=p;c7B|kK
z+kdOUxb0s5EvNSjHy~uFEXg@=Y!niXBT?tIPk#({W2H+>2@C3`wClY~Mjld8PUi#}
zn&GIxoWb%J$LdEnb7^#6S<v|(1ehN{OTTFN@w4RQmLJi#=x~|$X;y>hJ$o|qw8zeS
z8>DLt6Z|3`x2PfgdFe~M&kq<`e1m{tjvKsXq^U#x>FLZ%=i@vS_%XuWT=-1JSwCtO
z^35`8Co7(jOogirakL*Bz!oKct_nZ0asnwM0Ig5aztD#zF3m{kYz0le2?&>9I-VOK
zFJ}9yjC6CpnG;*L#p-T%0l5~`R8Qs4JvIEHKKHRLYN&OY-f*nL6B#h)l;>0qA8xBQ
zBDwO+Hw~6kY@|v*L~0`1%KLERdGLsgTIVBO<pKc?RZ?Il=@>YspnePqXMKrNk{xhB
z-zjil&X=DKA6NG!6@-ldX4g5aDs%^dZXhnVbR{l3@=aQ#YqC~L=`N4`c|+)FNnzoJ
zL$?F4W%>Tcqqx$b=cnwV1d2gabTol>Q8=1l^J%x=N8r@*&inwZ&{@dDb_@)~WYGgE
z*wcV(=}HscoFPi5g=S<P8W={+o5fd+GZ%910U8ow%Qsa3H8}|0ajWBA+yPwS;fA-|
zS-RZfx9CnciV#6AlN`RNCh1VlB*}bjv^vc*jPhvdtW%6Qy}v1Gpd7@13i&K-c0XLc
z&)tQ%7%{pO<r@)&xP;9)8&S#~-o<MOVzc%fS%Ou!uF>dHX-h&$s+_cE%QA!O%c@a|
zuI?~PU(#>T^E%{SLN_4ge7E=by2~xjql0y2l>LL}1^oSq219E}VR=KPf4q0#vk7d4
z4yo}w{yh>e?(Q`>LJgaHV5o9>p~Bm&59JWJRgvCbeK);&13`;|HtMS_5NLk%E}FHk
zrjFR?RV7}HydK<?DbblBEj*~h-Ocz&9B4oVM061xKf_5`898-tCdy^h<;kqlugadN
z@;8+9(?#%-tYT_+l!XEr5SQ#O&G2ops@xk-;Z65?rAO1c*vM9Nw9&wqBx@t$tnd8t
zA5R-3=LwrK$ALKMTTg1m35s($vr%oRp@2(OEA;p=ixfJzPL9j^yQJ!u->C%K#$NjG
zv?N;%dv{(DRYBwHx|N#b6y;VqGu0FT2696gzvTfV$(x}aU~zZvG{je>p`fh+P*cQS
zfRLOoNNLYf|8L61VF0@1`1&Df=%9w8$e5PmCS!%0h7~7{Z<Q(WWBhk$FKbbJ88^5a
z!wm^z8H<&1NlmCqZujan$FI9g+0pJSl=Q%~!M6&1;?$gD(#Tpnj*tqtN+<jA0=I<z
zs;ID*gh5-6n?Iojca5g=JAF4tI)wqvY#EzIQg@AeBtRol=J&70izp^`*_rR$Qbn{M
zW#zj3!?QHu5&YggloEOS@dh{B;a9_tk<x|&9z#uVay?>yD~gUb%!<AcLS7sqn`-r!
z+RnX>fy4I(1@yBf)~99OGd2A8ews^~=ro&4Y~5vg*RZ$kTo(VTz!pZ7DVblQTbZ^B
z$p{yY?eO4Fzr$zd*Y?O9Wj>-eJrM9@Xcj7zM1!)(aKxtGyd3gL+~a^1R=~G1?<a91
zxtwpdFmXtEcJfASpjJ|c;{|trIBV@-_v%30XKzu6vh1_Br*&^m^{Gh%lH0(X=UwEL
zykjn1Mrpdev0(rlcJ+r)=p#z=n^OoEp6zCm>D^4!>CF4c8n1xB`pq`V>0wA{6A{l%
zHyhxzsR`iy<*{Kv^xzl`nBbM>pCv#|Ww%kr=kK{T3l^mx^v^4N)OG1Ar7aXB-Hx}>
zzZH#OP1reqQg*sI-Z<q|e^9~i797!2SE=6>Ik@b(Wz4#4$`(eope05b4<uyQd*~XF
z^Ry(bv3URd+zEEKtKkg;w?l1>uepDxX&9A#`i0i#?lKK(aua0(Ws`3u6fA^oh(zK$
zZe838oa8<qUBfM>pP;HFzHG>uyJ-9=j6%mJz7%H37{-yjC8#AU19Wq$(xNP9E^d%D
zrH1#Gl_7;frW4d)4>#;36c#7U)3y7nIw@7FGEy$i<JhSr^~DHJ)|+Z0rzrmxGb+wy
zzGk9Oetc!3K(Q)$qR%l%1VxFC2q4kawD8~9%kt`y=iJ@<tvle>;A($d5+~+OUFC@t
zqzy}ox<@Ah2kC5WurdxrPQ<;aCHQaDtnZQj9o^`&G4##RQRvRM`c@q`7~K(9byneX
zU<D4T71f9j*y}+X{s}}Xn?jajR=$<n`%B_-hkm-9OI_(<+Sy9kqG+wg@5}t=tB5yI
zy+7SmuPYl878?%tba_t{=LPKL9T{FS`;7ChIkIdO9nC7GXU835!|^V?frlE5o4@>~
zOsFrPlHSOqpOWz`%e5sDxh-^v45!uIk(En(BE~hqAOH71rH2^yqO;3t9!NZ&Hep4`
z&65H(opohm_KPP>egf=V^f%>eP2XGo`|JGYwJw<>ZJj}kB6Ey+5!+@D#=_RlZjarq
z?`iGhVXJWhca_eR`F-qi62#8PKH9A+Y*&>y)jhL5MCYqXBBTriZzH2Tokh_O$Pa5k
zg;!l@;@@K3A0G5*BOR}Z5Hqg2i-Kr*#$KcIFi6`1_KS*OPG@zVl|hLcdpSzz<!4G2
z^Nu*?U*~q7cSr(aH5yN%Cduk_4=WDr74<bq$J8$x56{ca-lAm4EEXgMpB7IUSWS0U
zXs2;cw$*(cyCo$zdrRD0JdMFgdbHK(j=U;kC!IMWYCPlSzBBPOMBzg~C}X^(OIVLH
zY%dda#6CH+5<nX4tYSu_xR1Nz_#^qUVmZemqCy}}5Y8h-+4&8+S%`Zy?<7@xpwiD!
zYC)Z02C0TyY?c^ApyP>CTw+1|Ek*H8mjJdlsjohxQzS>(Nz^~7FdLzR^G#<fIlCN}
zaSV|Ef1s;BX7PLJvyOJ^qA8$tc^l_ZRz+aaOwh~7txzdZHUPRfUdh#<7K}QT5*xX-
z^tn;kIZ<<|mHlfs?L_!3JI*Dl0ao9R;Cl<tq0`Ihy>vP1q9=@le*=`dfZ9dd&nx8z
z^Xv?YM)cp`DzFVJY<L?f+&jGuREkK%=DLhWQvdX;Kott+F;79RS{4DHbV8FQttp=#
zU2>h<B{x3O>}w!st8M^9p7_C7M#yO@x)1<)&ykF!5-($g5D%NAD~T(7_w6F^xc*+!
z29IYaoEb>Bo7XvgX&b1iJM<cr0Y~xV*E8CR>(cy#rv8))olUi~&8eq5fR28I+7#qQ
z-nEEXOSW_Fs16_Qa2Y_-)^juY`rTy~O&uongvf!b?&A$7CP|@hRzaYl+g>>c+a%Q1
zw4HNn^+vuCH{W=h2B__smAS6R;FcphjZi;(qp3sv#AHq`FN{AzhC6wj5OJdE?!NGX
zd2Q-yMRnGUNQ6!5)nU|KqApP2oqv{VlqSV387F-Kg{Ux*w>T*&8eqMh##o`@8dar5
z|7!B?-2IPXn&E^&eavEFaC}JX$(^Fk48v{A0pt|~-k!IPwUC<)=T*#zZR44ebae}M
zeuUs?b5OiW*_9?Di>5W$(AJI-HGmRzx6?{EX|-&r;$l(35;U2iI5{3E)EYDlv$-s4
z0JX_O>_;ONunnFA4WW-#&86K$MGes8(c_cj@{N!$w}ABHAG)6AE$RVfoCNp-e475)
z_updJnVUe24SZss{QN&EUj2(3|EWuS*GeDiL+N?R?P1x`<C^bpHGLZ|=RUBJD(k{w
zTwrpy5GO!QiGjha%M6^*$mj+M$NhlWuWOP$6ey8XO8>pO2SJs=B$d?tG_)_Y&*->p
zEetQ<e{!e!&aB4zL?RGLz*5^MZW@G90TJoH$6L?i>;5{tkwb@K)xR8F?W0N-1OE<(
zvTAIkw&~xGO-}&&l&K8|TkEbM9@9yR@w={1lwg-b@_3c-iKD_N!^7&trE<|vCMf_E
z>)9wZ#s~;o{MKgyL#R0}x1Yi<p-t6Q8zmDK25iFW`f}<vyWkCUhw3pKJ7?-Bja?lq
z(JT~Ok01MJizb#?f|K>RFnM(4kkRU)oX`;;th#%Lb71o>(GOOA2WaSi?$(S7Cw9*T
zSE`3!?Njx-LWPa{<L8I(Ek-j2WBesi8!dpnr4eV)rjhMdx#je8u2+JRwf|37pZ^ys
z{4ccwS`On4z49&vvtR6CLglwH*?Rfhzv&CqLzpalP-g>>o#$aKY!vXg5*OPXqJf9l
z6I%t2VfGJQZoqTjYF8VTL=F^3{|*#<V;;*@5cJ^e4F+8XyIB3^nn+r%=Q;tPm>3?J
z3ZUYxld`Jv#=YW*kCB$Fyer>>os2U8e(9HRskQUW5+Gk%raM>oOIe3u8W8NQDyp+g
zn(vU^crCZD_H0bCTdk=226B}W`A>~SE_2-xL)6~cASCG(L|P#tSzl`18){B$kpkG^
z7A<Q(qVyrl(_*4SwSjGT<+W@pEI)*T_sJkTBJjh5OrM&fk*Ncna>$Ju(aKHgGs?b>
zOj-!z>EMtsttD5;LYK|)CXiD%9o>t4KPns^@@H4`aJ(JVX285aOGjfAw#TZ00oWwu
z_`<4M1z1U>fUM-+rGU&*#CfmB+-47He#r<s8gzQ7sJleaNbk3iD(`YZd)xuVHg{nl
z1d>o#eyE<n9iz9~NNsNd+z^q4D>Bcl;(U97`e<-L%Xu-QD$yYD+gtx3LEK;&{81TO
zr#^Af<mmp;I@I9lv2h}2D6cYjWwQBz{DoUBnNuK;_E^}APL+O)0;X=cZV}TRP$O(F
zxA5NHbaH3CBQu8@?a9!@NXk_Ul|{7@VEO2_p7d%rBM35n>IIc!%arlCFtnPx@j)e2
zunFKxyjob7_TW8QM7c9)QZt(Eg$kua4pi6zJ~14y&t9c;C%{HQLzhF_@^08ICWJ7q
z;L3z<z~bz(xhs?^A^opYVlgD{6S<t%wQRf%CmR_<phLVEu)OGKTVK2dVy;+t0(Hjr
zzE}`i`@#t{UrdK5!wN&NWfrNIs22r{osr@2qzCE@JgS9{vAk~i2^ilBkw1Li&Kf_G
zMi=REtXzjQaK<|NP3lKyg~ExT0O5W5K5795UYu)}bA!KyKHFG5_0H?Nv^{q-+2J9G
zBszR>D8{^HX$&)OXH<G8n=i4r(hxPn6^)ABckW>rufBr&0Jx#>q^43I51wC6$4r#u
zS{oGVRYf9Mbh?s+X0+IfcGjN?{J2!s>7fRDEpBK6vOUqq>ObDO{r`(j|Fc*D0f3O}
z&+?82B|(DYf$778eOHc(V`#wAHDp$))D{!@^-#qmF<=lAMQ*T~j<VsI99`7M;xRP6
z_u|046uU2c_%AHB@u*L6TX%yz;J8H68mbz*BbM8L-K_oZAwO-~CSE-~Mln%*t|q<K
zAMPl?NJVw^0C0LLCWtxEwB4OjL-Jr1(_5%dv3vZQbwx)4dDpM7a8wJMyzEi72M}XO
z+FD?{k1jZgJGESzcr<z&Hp^NP>!M9y#?#ZWD6F6+^7CKw;@ESjs`Y>azC=*WMaRub
zH{eeU8O-!heZ58-8tXoOnirEZq+K1X%y^9Pvh}j%U~`QK%y(1`ozoL5<@9E1gIck8
zp~;L2Ia4JkJ3fMipeA79#WYQVu%1q(Uco5f0;(jm#b$Gz7K;Cqw+h@is!LpA*2~x#
z!JJ)4I6cBhMb5|XBgmn9>f6Hj@#8|$OwAr!)J@Xcy6H4K@g`?3jUHa16}g21_dLGK
zx%vbqi>in@1l$!h>Jpf_)VEaoQ7dK9$(!!^c{Cw|S&!%?RHDqlpn+R8GntCQ1vnZM
zMtW2=Mmn<8sKeAOueRkZx$o@FF55vfpw3%2_MCC#gvoeL$*;xHO##*!**jOeW^-(k
zvXFwqii7(^>z|2eOVH|Gkf=bQirs9N>#<l^vET|zU>3jGP|NO0!sv-c#kzmKwT-z)
zCg7zG(@etjR6V9{Zg1;_@XE%9?o0oC3vgL{ZrP63AHpSvCxE?c#{+jSKQGr3$NPZP
zch#eu`F^aw6aA`}f7Or=5@)Qj?(M+RJFbW?u0XL8BSg{FFz`8l&b@1L+|B?o(D1s>
z^&wvnjaawm4O6QWsp16#J7rEy^0(;eUJQlNKN>Z$u*@pqyGZ_3mUz1CdaJ|hsj?7;
z(eNLQ>69u3h^3migZTcIX1Kz+!I(j1E`Qo-G+`v*tFnY<Hpd!BNoz-fMT25<Jd2Oj
zhvoUAZXe<sY7ZKEy>WQwXTh=&$>3bWo5;3eej@!Q_LSHNhkSq|Tjcq|2v1`6%#T2h
z6{Y0>7cU+aZFqIv02m8}oxm?wwhD-L8vZr+$8Q-yM0>ctZ~fs1qw|A?L5KK>z)pT|
z96~eqSkHIK!<~c2C^oRrp=yGSFmR|EUKkTzNf*;amOeEZJ?+H9Wql26t7XFytvk#$
z8;jq=PM@FCp-Ofepv)0>5fpj_)(Qj59TiqkkNu-ya9t$9Q}ZK6H7kXbZ~PF3T|6*%
zeX^F#J0iPY6~aijp0J4y4j3)K6xcOF2#nQyYq8Z_9{?skog1;Z>kiq$`$8X`%QZrY
zXeD-48C6YYXyo_`KcUNF?&0{eSDeJTT;TQd0dIl~E&0~0JWmSDfgi<sN_{M;&-SEp
ztc}hR80i?49Ka;cn4Bqt@s-x#$74ub^a8q!ra35hkgs|-dD$}BW_s(=WJ3jBB_1eO
zM7gj&iE<P}d%k(zrv4F-EVcn7W1c%AA*h|2;8mU587p)2)MUwtNdtV{#L^}z!$2J`
zXsE++$KqI?^L+mfuM+cdB9`)^ic)A;*?Lx`s|s&_Zs2|`+bpRZ*R}D2?h62N_VczK
zu3=n89E%>7rHN61Ar68{C6;gS1(>I|o||GC9S{ebx`8V@q#5fn6>l?ffxs^wc2{e(
zF?c)sYObGDzPN+7E-ly|Rs*q>%&W2}G$gqeZw@!5Cs~fSZl%Y)_$WenH;Iby2}1zk
z1E(=<2FpP?k@O-g&ZEOQn5^V{ujjHhavW5;f2@$M45@`CMfRK6KFn?~;#C+e?oimc
zxXF<pdU<vS4a27rv`%-e$W^eXgap<TI!b$IGxO-?mVS?r!ObEYR73_LsBUdepIO_O
z2H2-Z;t`x$b|@`nSplOU<5TRVv$Ou`?|z`6N)^M^Cpi&wFGPtD>FZ?;y<b+gH{pUS
zvc`+oDMlzD33_U(UORK6X@BVn_whT7KsY|vHb)0J7Rf&UZOey|7)YQ?P<ft=EIZW5
zlE$+Tm(QgQ<`Ub!=4!VN_x+Tuu!j7X7TWHrUpMS2&2E~lU<ZVhMlOx5^R%Nhfb|SV
zL(N*u`UF*agh)4SBZ-A0)ITLmFZBPk8$SP|*x$PD838<HWC%+lgkn(=Q_J;k@tcQv
z?}el4PAo-784vM_;%;m`q#p8$#uv#C1@?)-%T-DSDdBR2$B27D&{&S!{}wf{RF;-}
z04bnIKva<v(rseEA;tsufQuBYqwR!Uboe1Yt?vw$lStJkyc8FtTpv{5-OaS8I%7wH
zdfkom&8`wvuPdAPS`zwWYU@H+5mW;V(OQMVhS7eayEr&IMc+|M6)&tmGPI2;Wk>Pf
zdn)Nez}|34-CHDIw#b|T)}KxH-e{&<=&uEei;S^mM}2oQ$1$)Bq@bBzfNd-?q@8g*
zEVliF35Qo=2Az0j)LN?Gd8_Ef7RXh?U9BgO=ETc7v@G7tueNMEtzv$JDeH4xGys$a
zm<2z*mVN(utGFQJ`UDtYl<AwN)0-h4aQdum6LQLlH}>xX8jA#+V!jd9%0&7*UAq8?
z!!}fLAJT29m#HKZSsBsf*cMqx?|F@k1O$C)a~un?Qz#K&xF2@6ZDO8_!(Gt%IT6*D
z5nIxuaEBa7?{{CHp*i=DxYIAc%yWh2{U%6E%g{@JIEy4Laj_31bLWZpkpESZ>Az*F
zCA*wW$71)`aWLk&{zm@G85P(=o{TV?J%mDWsVCOG7vT3X>N@6%-{jC9TdD4mB&^H1
z;P2?)CgOOFN6YoG%|OoIN^Om$+?8TJmYRFeSM0vT^e+2k+(&YkHaoD6c90UB{pa}+
zdVwdS0?SLV{_bOOX8@)NagyE|IC*F1hWjz7bAy7vpWhX)F?1MUR-+q^-%oGa%_E_3
z{FndU1NfeXjne2n>EC$$PP}y!j5s84wF3l2YH{Mx6=kR292CPnXh<7a37_f91RUHU
zK&Wy>-;e0*RvgO#(iqJHqhaIdn6O;mMz=Q(Vitz}39!(vvr3`rNz6Q@3a0m8Zf_fN
z|J(?r&^!y-;JurvOXYOSDSj{T4Lt%TN!l1``sTSfTaUXpm$liz97z!YqZ}Irg@4<W
zKvyBg$c+<i;aI~PIp@MUu^23NK8)Wr<l@Zd99;Y^#lyfe1$>FTC7(W4?I<7G*-~dX
zR1#^<`itK57eYK_=?C}S^)`dn6ISCz++ljYuPku?=*p3&hINUP8c->;&A+Ecc#Hpa
zDaf*=t{y2fFkCetw#25r)xS;Vgdte?(Lj08YO&59OG?t;w^sgV6;ImtNgUD+X>xS_
z^}*KfeIl_;Sj!o`KkxqNgSd(K;2-mp?*jfF@NjOcP#|)eupIZUW;EJJC0XxroUWm}
zB&_eb@0fUIw|-j!hFDVfmw)JG*0POMGcLG*{tl~tJImQ2+D}yQ0$pzgyybqKh%HGQ
zN@1=;v=s}6l}xhUKaC7vbtxuAbbp#Ila{gxvqu~}wK7Xpz}4*y{qu^o2H<H<tqAA`
zsEAWIAiQyH1A?3JVqXl?Cu5WI7aflw+703g<<pRtTYjWPE5;*6<Lv7dj=G(;WvbhR
zkrtipoZG55K0h|>KWyi86ESZy_#PKDkCB<!XcZ$n&co3ua^bBF0c&1}bMY6|s`Mu?
z+i94GRE{0oXJcbw8av_aW^kJQK*z`w`P|EPBJ0>_TMA6bg25@d?n3z|l!)#SE`LJE
zw@GhS|2DbDsHVV(zX13Q$Lug#gmXxjwy1ap)KqMvldDYUMqs4!-^5&2_NH!QZvB$;
z82`PF8@tZ|(S+P`;`OK6hz;h<CG3H4rqN(SxQC5+#`Vv??V#yfYh)t@dro1*;hrlx
zQN|~mS;+REtTCW=VIJE`z5s&z$mOe5;wgYe?W(8H;}#bJTJO`{s8{}FSGQ(=mawyK
zypdy13i$K8`d#k@VnB+|tr#~&Hh@`%E^@L9TjFJi-GKavCjX2^9e}ywcG+G3!(x}n
zBMPXw&s!a45*#lWiiS76i9fv3@J=u0P1%uQI_Q|F&DHXBkLWk(%uOJ$<<sMiRZAAe
zcn9!&vFX-K>;YQ>Uf#yw-g1UlXX5u09|H7MSX$@ILIFV(%-~g>UxGWTOb=UEdg3b(
zehyQS|2pGQK-&*tpJHWf;68f;27d^XtgYvw>2M$nY{Z)XH?XmfdaQ!IN!6D58sZx2
zC(*nz2&kust|k$v@lrz=jR4vZI;$F^{vA?6=0|vt9_9#8j{kQM)JIiRcNM=y_B!!4
z{vG5d&J4y3DRT|Nf!m{hZ_k4a3MQysI-lp2segu0P+|YR$3%(Yc>sY(*hoKL4Pu}Z
z>(lUAug>b|N^Z`-)<|>|K9d-R0nauajSG=|^d!t824a(Ly$4IQ<1piz*Y49VQK^bl
zu{SI|1;}CGj3Ng|HN_&ks~IKoXOP9Sz4V(E4Wc81{N9T^3h)-hg=#D_Fl$iSdUg*+
z$`;mpCf}|`%$2~Mt=Y}UPt9~{McbfVfh`2XwgxJWnQ~$oHQgNgJ2s5g9Cwj}Q8N@@
zjx^yk+5dEI$t9W1NOKmI`cP(I#vr1aUs>zkjWf@vnn?8!J5Z30$5JdOJ}wJuaC=9a
zVQF39K#UU${rn%!=v_kXA1!J!o?p5j6YYT4dVf+M0|hDnntk6V%V^m)5ewXQFE3Qg
zYv2Q%$1CeRFXj1S^D?U_xaEIKc(yV5M8lV)n#g;I&D~r+*@<`Rzf=c^58_0H#~m_D
z96>Gnp{OTT*hb5F+!0J=y%#QP&fYP<`e~=z-k;;n?7wNUWAC|7;77*}-q?u`d_I*r
z_4=>vQm`MTE=3>qcy?;9(@#I$`DxqX*Pp;{DSMT7o_nsVd(6CBmnSHTY$UQhnalKL
z%rb77G9SSO6d3PNMYpRW+6g1609Lr3&`O;&cw4@eia<FoSJ1RmXZ5xFzq`CYOG{ow
z2H4s+gnrNq)%lzlvEC1MM0UVmcp8fY89Ur({eXihR`w>m8@pJN$p5Xw3(}t!u{H-D
zMRxowetz5}ZD!W9qLDhQIKcOZ<*@>8+8dHyRg0#BPCt5QvJ&$vxh1kg61p~!xyZlf
zv8gCg<uM>@&{{{`xmC8gdS`Q&d%qYC?m1cQqlKwNxWhLq!A!Fk)hC9db~62wFW@ab
zT@KaH-$7*l8I(<IW~NCOisPrRh40>ZZ`?%NvC<?>*Ji<l;Wtaw;3SJ+li4teMX-ml
zzZeK_?KMl7G(+2gYvE`lxYh#=SFSxn-j;eU?|SgH_JkqZC}nb%v>$MHXh@DQ#)Txe
zuSxcz4BbU1aBbFlF*%-$uIE1lQx^Ekg$R%3#^p_gg6n;ygyzuB%%{n+$>+>kgjwYj
z#Gxu;g&wJ8wF@FX&)0J0L-o*OZ@FV&yv14vG&g5v1>u1+U#m{hMK+GFu)TWrsY01v
z1xxFi$rsL%Iq!xGPZ&VR@V75i4Qx&BDGXk8_UF9-3-!tO`vN{a;(OT6x9yIN;FPB<
zyg#x)+dZhXSp`OEGU7U)DD&E#gQC$=$`NgqGTT8~d6UkoFx8ydbfw`J=QNmUDG{Mk
z{<v{pOTEu<=-PcS!D4MZ#eCA&(!`KbC!?hMdP<K2uW8V^aFU!*F#eZn_-%0bwp+4E
zroj(=Ah&;zm)}Qwmz_BW?6OjL706?*d;ivqyl8vHV99|)@J-I1_#9fd^b{J9JCh1^
z8RG#0OuD*OrCN<moVX4(ft>wq$_AR7ik?}1q++y5)@Aiu6^zFZIEM~<M9{7B^cbF?
zChPhs3HYb3fY1sbNIoJDxGY+4g){_>bY+<0V_4XbUZ{#0@mPsI|6AYs$Y4Y0NEZn7
zTjsSDmo#eaZK;K|YB%S+870+;d#K~Hp7*@Kx@u%oEA{j&n{;h!N|gjv^Q?Z@a%b}%
zg$oSN$^a%UBKHRw5LbVtX@}Go;9()37<kYIqSmf^uW8$!AJun)u3KBJ|3UsyVm=ER
zkbDjeQC)AyNu62lyZe(!F+LlZDTPxor)8QQDKs!1(AKF5Kow@bf#F?W#{0!6ck~jV
z&`Gvwl0JpqmxFB4dDma0hr-aXYrz5NJ)ULDAH<`6EGpXh1^HVC#~&C~u0hUIt_?y{
z-h#<Sxpx+|WW;f7XZ5?E>xG#!aAXHcK0o{l!Uc^~iLh*!7#7h2(xJ}$0!lJpBZIHJ
z8T(@?23oCsOZfVh`C501ZiIqUgm_SKkM(x;IH``!%AGh#HhMF5btxV^!Bmr6`x-Z*
zDX9)!EAxSf0Y1YSIGaN19p3K7iOiX9lDJ|4lDHfEw>WbEDHj$UtltB%79%rATRU}<
zgnJb!-lWhcWhXPaT(7D{U)oQ6Jdp8GrDonhG3|zb#ZEl<jA+=!j?B#0ZX`XZt9Vjt
zsYjr=!~ybrzEgaWeDMnO`+f1fl>cc%fA!fx;S(@VXM;Z=?V+<qIy)-%BBf_q%~o=U
zZ@f5cny>RcOY6z_UY~!NZ*r`eM?E+w9{BQ7KKL$kApDe=n4_8BFW^CX*B*#b=4C!$
z3Zkz6eH-qMR?1vd*}9H~nQ6lIj@5G3U+xA9eq8<YWGq@%n-O<?ND95xTqy&bBl&o<
zsT}3n+D6=B{pA+cHjvTsJo<vyNJ7SgHl+w(wCTHXK*7DUc4{ED2w<i~dDZ>8n6u=E
z$x$Wu>p-!BP%s&)l9O`9fPS+tpyQ8h;HtNlOUEYP;Z=?T+9O^*9UK8SM)0k#JqUvt
z16%BnjsDhCJHcOuFg&k+$fA9FmnstL>75XJu>`)J+$SzY#3rJ@r%wO3;r}~73jO;6
zK*su|<@LUIOT?=Eu6r+yTxW)Eyt<YT?tnIA&L}|-$h=N8O?ILp#Uu7#e-IYzk}{J8
z&CQ+pL}M;Lo1sG0udi*WR*8)nkPD3*{yTbtxz1rgX{j@6LE?f)Y$o{7C;^6s*yRe(
zkSTBDzmRUCVb_C$m~aczwZS0qeM77?_~%hhQ!(u*<9$j*xY2WTKj7{3(Xx3nillP|
z@8ahWej+Mj8>-ES+xAaN{nkOb7CgJ#0|#9z1>;TEw!led<tk(=^pY>}zP4=6OtX*j
zLX;(Qen6z{z{Ee5n@1anHfcuGg0Wz<6nHo**tl9nxmKO58Hvhz2LVSowIFD?DRDOJ
zI(2oWnmLy`d+KhAunWvn*<=A|32fv0A^^>iP*qIII*LFBx1Xd5{k~VLZh8M)&luIe
zvpEZ<m<4;c{~`g<=ilBh(RXc4V9M-yG*|!*57V|*5S`-mX~^2$iOtkmhW4Pn`I;%z
zupcR?2jc@u%|zWzvd<>{Cm^Rf9v>*9HGl!|enDH8QrTpj_Y`$sOuFO7Yp}LAjQND%
zfFo|8@!xVAf9>jgC>BHKwtX~q^sDlyf`bE%>SR)K_V9J!tAqdtB2EAtz}u#w0)T){
zsn!v#5I-R+>Tz}P%L?6bq-hhHJLE_+FfmA1sSQC(C650p?o|hP0oNrHr7?@IQ?g3L
zc=u1R#QtxOfB$JneOgIZy({WOVBn7Lne+3>x4FX9nOcd#h>o~L5hTX`sR+RI#MR(X
zbcsYi;uiWL+G+Trvq)B@pTXfp)0D_0<$6Q%Iy!seMzpxO^dGmE{{jB$KTKX}=%wqy
z7ttk9wsNf=xeA(AF!a58-3nbXr&*$j$`OI+D=M|ddF3$EH33+`Z0%1l&jBtclFR2~
zKi4O-O2Me?nO5zNi3ZLF13a%30YJ>hPhyZiJ~x?@@)is?TYKe~J4QN3{*#m^qpL$k
zCT}5RA)+OY|NLk28Xeqd9T5`)pjxdn`k`fF%L!=WGJmX>$A?05)6YeQeRNN*$FYux
z9jd0q141<9+S-@)b-Oq5Ukud95cE=el0|Ll7>^H0u771Gc$?ZNG(l%o_ldFj<tcFZ
zC?~#sEf2hz14ceBc88${!4c83VU6cJi6SCm6O#eJGXAZj5#s5LAATdI-F2Iq7^*Sg
zJ;1BTck!)qZSD6C45|#D^>jw?Z=)x!1M!G#>dbCcGClpkNqv7jbnT0=q=D9B`N*%8
zqrG(`Bxmli(Uu!}>*VZtYFRe8O#u~^mNqAxo#{2z=t{M7d6A-9X@M<kf0|rBH~CPq
zd+M<Q?e3^&b3-(5?V-ZfacHQ@Fn;cCx{~fUQMyp=_d>{&)c_v0IR$+@3|(snH#)ng
zdZ}zq?~uH3=8A04A6Wma!xKUQ$L<KnBG^B{rrSI?c{)*FeYD!&x5T;P%ipJPpFGl?
z(^6EkbM4$yRg^ceI}V&benmdwj%ez9c8GaedV2Cq23V^9)@(eOU^JKEUhdxZrp~r6
zglc0q3p|{Iq6tb74hc5$nKLb-tmEbtDntI>XzhG%mI0CtCYPY23R-^?X3w}>kwaLA
zE{_e1j)Ze22CsgUQXx-(H~*Bzj-4VKF^9?&+hjwoi+9cmbHOzVxL{pfveez;wJETq
z!P+Oq5FlTv9I3VsWK<2#z2C2K9c`ghi%PHw$eW4xAFZCPnNIYr@~`;vV88k&#GrFJ
zSn}!`SsJ@#gI19b`fyMeNPzYn0bUH@i0V>KftS!xs$^&1;@~JW6ZkHY5~N|(lS9hW
zFyi(k1q1Jaz+`uHJ-ofkZTp-s`SUl1dB{H6U3Px=W7@khMY6vPZXKu=`nC|zGix0h
zl-YEcA*1?wFCp$UGApE8x1glD69bpD5<M_<g8r!Sw;SbE-m+4h6m<M3{|b86nHZ|Z
z7^%W`u{$%FZ|hM5t0iEFE@v#n(XabD26-S_h1`I}H$<8$MKtW~w%FWWln7U<<!{dC
zP23Lgv>XrlYLvWG%O3G1s151fix{ANt;quh*lD;`U8Uz50~lL3P7GrbT<1S)tHOR4
zEiXy>`ueHE_vQJ=$u>y#lCF1i_p8O^D1zrOT#5Wq3Rb_itMhds@_=*7%<E_#Tdo$E
zPXl|I6AxZ*-IA=$YLEm+`~lzWh6<wN>&&mMv1n@(Z{rIy9;rcNepR|O{%E$};+ff~
zS$XD5*e}8ty)O#gz0g^Np)~t@m(dCbZVFnV2Lg)JD(FZk4r?EJ1?^ZV>a#(+H8d#@
zfg=3({}<pefC_GERUWoEIb7Jz0Mp=(Bctb@;>`JPE}>;}f?1uv>Z;75N)k&{=Rg0-
zdWtuVD8Vg$0z>>nOFvd+ZX&eC?Uo;;e(2+7U53x?R>fa_I<o~%A2>}+2|l3K3o1_i
zU<L57G{vyz*V;4CaX*G7`p~&rY?N1XLrO^D^6|QdR)$$>=%-fQiZ8cWSVZ(Wn6J&o
z3my;+$n&QV{JV-JV)${j(zjBl*h4#Gl*lEIi>E)N)MnYzhjQ)+RJ_C)CfDJOY0tZb
z6mXskhbX-EVlRK9T+d>RrJ6n~n-=W;EIIkYiP_*B%pRr9aAcP_1%63C0cGDo{IYy9
ziKH5K@q0q|{(!|=dy0(XkI6&hIWyX-5b=XP(AC?2$N3EBp2IJb(Ag=$dp4)7<`zjh
zvpABM@@6QS!j4(ay=E#7azxPff6m9Vy|+>Z6heOnhL{HPz$z-bN@V}vP>m62g@NM5
z0H`74w!Ta5OqC*Tv|8D{^0q0O2ad2tBb9Z}lG$LRt81xXg==fgV7o-x(i0y=Luk1+
znF5aZ9jKe>o*?5=LY7*fv|*%SM;f4TGl{BYn`AJ7!4GgmSD>TLl6mN;U&&R*1C5$W
zhDle0@w9a~c#{Z*Lxa0(^goyDO!o~$==zwo6*5ACrALMe7^?UV6_r{fe)*7PH@3r4
zKoJ<uBDh%z45+?xbSKAj4F*jt-dcVK?Ut#a=~NxeFHnwH|H*&D3C%ljGX||3bU^Lx
z7T(|-c?Wvz9GL}eNS;whj?yA8KyfnB5C`hL__-2`akj3OB%iM1t|b{wf5Yd@;W*gj
z(A!!X(RX8O*`L52JLDnquN#QeLU=`6Un?yuR7)-&tgHI=;h91j37VTcEO>xT*Z{xP
z3}(kh0?~M9co%%Us7Pp3dWxJJSI9Gj;*Q;z_thMow{TqbS**{Fd*|Lir>m>xztlJX
zxN!YjisWR)-QKn>Nk}1Q=<K4Oqs-Yz?oSAHl}$f)WM6$X<yEpJ+U_A;&>v3h!Mt<V
zf=APJD6b+G><EYRBNnVw7!44LaigA(p~-rG!R`#-ij+wMza^5-mA<oem#^&!$r#%5
z+`;Qf)1iH{z}@s>t@=&_-%pHND8^rKamc6bIdF4A{~R%=r}~SgMNSVuoo#19Q9`uz
z+>YL0v}v+z&!nNY54vs3JXJ5$E^S`5*%4y$G;>mpEgSjeCq%dD1A@7jlLNY1762!2
zYgquCByr{d3RSw=ymPtzpb&-|RG>;`zIFOL_%@oK9Hq@@bQM~A$S{3<WX!zvOsEi`
z{Agk?n;PshYzag#>iaMQjoCAC)68q9G7~A=pz8Qy-b1h?G&t~DR5hE9v>9lf|F9bN
z_ApRN%uSKxhCy``^~)pcC7}!FbJDALA(nZ1k{u%-$aY8soJmFc#YF8KSIinOX{|h%
zcc%{TZ|19K;{P!!6VNoyyZY`6$H``v-Fc~8wrOqDR_Y=>i^?g7%B?f(<&@YH$=UWr
zz5|Eq{XZ0DPLe_#CGwtCcfaBlpF-V47ks0`ig>YL!nL(dFsc_|mVYL`#QCcg;c#9A
z-3}s`AF&7ssUAYH`j?PyU4=n;8beHs-}JJYf4*7%hp*3yqcurDkT?ps7E5-SBH^B%
ziNRxx!e6LoGfjpb*ol*<JC&Z@@ej+{YqbQ9DsSytt)``(uS|sf+p${J93oCy*^qzz
zOO?Mv=HE9RSzQlw*gpKiye@$P(RFO2ILdX)<l2{)ru%4haZhft_{9@E{)J=8hWeH!
zi6trg+t<GM(5hDT`dNku>X}vhH!%pC@uO;Y^Yo4_lOhiCsN@ZPdc?BTj?UC-SgB-5
zKk8=P=5F0<@c|C@1>Imi1(Sm6+3TPE?k43`lFa~e=-KE(<o2l;`|)&Bh-!tJ@BE%c
zNV$jUwZ}1Umn^ggdKU^0<z1R1X^#96KT-@oQdwp}1yh40sJd$TfMnYvPvtkxoo&wQ
zPwM$qIXs!W8e#r+Oj|X&<#Nx=;^zzBiUzSVd6(&Scsmu7r(*@?bP_}(xKZyIUU-ff
zeb+%p5^j5OwWfW|7rm5aYplGq)*hS_?Sea<HyY^ZX;W44?W6Rp(G7dSi@4EqJ}}&A
zxDJY@X0`k^3p`<QOqK8=8XD8Mw)H9>;;-EQgz_3tmi43S07or6HZgJZ?6jL-Dzs+<
z#-MBZ(1t$mr=FAx0+?2jc~lyD%XW5C_-Vwjx88S6HnYtTF}`s+xUtSW{z$DwPCpXv
zG82P12jffWnLLo1YvG#n_1{sSsn(n(r$=@`put&&9t5S$g!c7DIQFKd=i;+jtQ=P_
z|B&Z!cl#Oyq8dVJU(@&P-+a=(mj5`Q$FJy6-sM36%FfQ2G5d0oWrrOF1hAdfjd`tv
zpY!IGY7y;^r|q<9uZK00gi>ahLwQ*qETJ-#Hel=SX>B$Cxt?9L*y{D1M@8JO{1i>E
zV|G0q!{-tN`{fjy_peWNLj(F91&_f}=4EPqD>W%ZzVdAAPX)@l7qDUO<1c1#s>#C>
z7!Fa&Gw92=)b=$w-xO51$ELaj!J~KC&OU~t>=EE-FNjLeYitY{w{KWv9!Gqs_?ns6
zbD_2VN%Tl&@Fra|dCQj>HmDM@b(E2LxTSSo8R|0V81u(9*}3tP^Pej$O>|)?ckJ3v
zmJyUUH&e2xoymHuMGcKYFZ9z<-|p*o=~ckDZu?t4c-<5q!#kROla?h_0yCiSr9e78
zbJ=<*n<Ab-V$c9O{o(67RX7HK^>md8Yhy-E<<05!2kns)9U}*SSTs29J6%;^F>FuJ
zBqx>EDL{tajP21J8-tnJ%jEB94OqC>pFlajegSsN>g-)?gZ&x0p@Hh`fl(^a&rTt)
zD=OQfrPa%{-J`KXHS8!1E<Zmhr))yi4d)qzDn$g>rJo$SI=)d4#_~`O%&<`_!JuJo
zaM8+T)%?@23@Ui;g7+G55^ldu{s*svT&fMiU5gn_SEgmnlVIbXLXTV?0HtqBq?YL8
z0by^Q)TuVpt=>W~yt5Tv4V3J1&uLiOB}nHfTAJK0E=GEyt;+S@`>2v{LS3?{2fVcd
zM%&=r4{C2ak=5mr7Ry%br>VgNx)qL&Zr6jzl3rM8UXR-SVzApg`Rv_%;BeiB21;3t
zw!$D!;SLwM{15Z#NDov>!l6CuX_Im~CWu4zr;;Gmy>v1FBXC>_GO``nH@u*b*VoI4
zaqnI7UA(|5vWq{I7s0f{A5}J4rxgTzAw|%5RDU*XuF68<OxQ?dvmo^v3GY`*^Es+w
zlGHJxU(H6=E0JTmv?5-&KA|LAY3Q6%_z_DX9>orNR`F3U1o<@$E~?1iySUh}EyiuM
zN5;mF=kPf_%I^Ip(G{-Q(R9u>A6<_-oqbt;OTYXmlb2tSdA<fv!#TBPeZP2fvo$2t
z+#Fizw^&l4Mn0>mi0$qe30atu8R(a2`7oc8T26u1PTAyewj?DB!v?fBNAv)Hb~wLP
zB#~2b2|X7$MOoqSTuuxk3qV8$fQUIdE~V+^Y>8E6393E-UteoeSvcszONG?nqD4#e
zj^r^FvfZiKhK%Ke&Z8XJNGJ4SZ===POiI=cYt&%Xc|N7Mn$JHLWgM?z*FGrc6L`}j
z$6mpwlvcWW?2f#CJ`fWva6K<2^D0x$Ij}w;Sn37w59qJmrYllr`s~fl_<833T<_0^
zEnQvfxi&ukHj5*7Oveyjk^-x}w^5-Q@^(2rIFWJn#dNuzLy#`nJqs@QLyNB`^3!*|
zZJfRaz@~x8)6WI+EK<aCF4c?!n=1pPlRgsO+z>JlT3i>9a#YUI-YX0}%}W{2oUuw>
zqe$1j?d$oVd+)}z$LlTjjO#hcy{ff7_)fV~qqTSA+2$jb=s~Y42Gfj`$bJ0VVzyI=
zKJ~!Z{d}#^A526h)PKzXyZ==#vk3b%V(LoNvin-N8sUfEI;@KC%X;y6CllQLYDc-w
zrX_@YW<M~=x13E{;4kD2#TdU`vR@cDNz>_m1>6uO0SLHY2MIdR6_OXJh?vNddJ7{x
z58`Qj4~P8gT^sW)z0x%<Ii0;0kAdii`y{ME@F_;{M>^(xZ)o50?1#Gx9LLO)tM4$r
zz6~o7{8iHT+oHj1lTCg_wc^O1k{_o(cs+|)h}V(J*Ks#gOCBq*v#nm=N~`Vxz$4_A
zZSr$T`5V=X`)JTyW2&wXG5Ann&=;#5mzUj}WdhPt>A?|w3I819_1*$`h~d-E_}rPr
z8&bS}et!hq<xvd6X!;v$?k&J@PG(HAVU6WPZl@sI91;I>KF4mL7MZZ-OtkW)0)z^S
z%7M_PCg+`B$H@AmhNjaM68F`HjduG4#!@h9Ty;;cRm9rQV%D}rNh^~q!tj2J&T0V#
zs?#6ly-qDM$n^1ANaSg_B|BYaBuU4ND;w$N;Yjfg(l<K>F)Xki7<UfIp)Yd$9}f*w
zRw52`nda3!+2}+zM)u;3ofoYnE$77+{4l-`_2RLicGK_P++Dn5sa%(di-7f|9F49o
z@3t5`+1Q{M`DvvqI}=(D07BJjt(oblPkW3#GGa1Ia|<F=u^BMJnry=GDMHc-g0_zt
zz+qgNNmgPH__m=>-%tV4rkazRM$pCuO<S3*Byz(=A?!P!Y-5zeFwNb8Hc*rK7sV}=
zwWu794e{uXd_d?5@s{WIn|<`k<x!Jk<me80b%}b+uJtiI&OKXTItXCr{(Q33(p#{X
z9bG@Dlk55M<XLV@yB<QhqS0p{w7O0U6Yw*f%f$IjcXo!}rpJO|pY9?jGI~ac2EOxm
z>k)b7j^%6=Q(n;zZkhQv<+$^*NF|^jqtNfFEbv&rb)`c?v_gQcdX?~kpaYESwYt_)
z=ybEz`O(zKMEM(`UIV$t6&EfWXU&Tun5=k8JsVp9#9VI;5zYtz<*Q)MgXj6_>YSo&
zN3JDn3{m&-=BF&v4g=xg%~S-=RH!=-=;)_M771g$3*PHyDAl)$FvgpSf+>Hkg=fY5
z<2>Fm{PY$vwXPw%S9ko=)8A&*KP|;P^U*Wp->cFbxfic|Y#sVyz|ckm?qt4}d}wIy
z<hz4;%y*7Lc;8<=T+>nw@)B*VbYbJdYs*38?tT_8wcIPQtgE^z`T&AtF@7&<t_)_d
zF!&0`Dq57D15|-aMI?-(7VhRryH06sKH#&jIa^3q;Ox&`iyuhpt^F0akde)$c8_+0
zjZ$ZTZxg&jPP~&gJTaYZY?L)KtEzD4K>Nthn3Avl*A5_gyoekP{$n6!R7jNQk9e38
z!Lg-p{f-{>&{hp>DdwN4&z@P8@FPh0e<;XP4;5+48+XHfK-^QL3X!k%Lg%BGKG7FH
zTedq_Ff*#ssTu`iT`@-v&GzZdIoFXL`p!oF(LTOl0X>#e2eYLlsHx(oldi9kiVYs;
zo|y|B?QPYqRGUSAP1O?`k{K15PQ#fqDwcD97Pora!hh3wBCG%;GYW{8g&qB?2XJnq
zk?`Dt7#5hP;k*R+fvUGZhPkJtwvp}*pgz~t9q=n<B=_Enm#+yJ*GsZr_NxjyU~rD*
zzFw9h`J@7)_nT|QEkE}88VeHjxy3_yW{0104x0U?kL^1O)|Sh!zHMj*98I%gRoF{2
zivhBsE2;`Vd-YT%Mx_5-Z`Srz^_W05()1K^ARfp?9Pcn@dmhT8s7nFb7>2L{2T=Mq
z<-)%xje_T<#~@_RW)>J~Yra`Do^-SIpP0N^g?QLF7;U9rRlZdXCbs%~{YCp&zQvn+
zCK@HR47-A;8`&QIxyD(;=`*{a^4YybU;j?HT{fJbC#f895xux41?k={oLc*8mRtDl
zOS1H0Wsr;c@86?`&jW`315(jfuDzzCZ%XU$S`Y8c(jkX<+W}7;YX6_^&iyaRyZ__v
zaJRc{<-K-qWj47h#JD|`rsk1aMO~P(z(dZ`b-+BK3sXZx+uG(_sq5nM1RlEQSy56`
z^MK?!7<j-^G)15#NSa82LWshBx%~s*U%tQG{Ce@=aeW@|&*$?#yq@nj)R55qpTYa?
z##7(!NABihQ9MkLXvq`Ky5~QGPq2+qWRF_@S|ajW{$)M-X^Z5Zo8C4QSI$H;6^V?i
zhz>~JVK0>JZTxBQ77yQfL2Cjsb}F7We@c{ooLMB7G1B)`wO)DxO&th8;3!HcclJG^
zldTt0VLgklIUT6We7a`oF|%?(kP}r_LgCNT2~M3xESQHhXveyD@4Y=<Zf7_lSiP(V
zC|Qu@!K~^VI8Rsp1h{%e@XGKbF~+uafdj6_3I1M9ySTgq{#{T57c&ST576Mz6{og}
zJb}RDozUtS(h_I&h8`xUdWtwf6-GWVjeJO-H!U@Ic)-rLUB*)WC$iD}XVvR}r_S{M
zu#&wWrPxlE${QR-yoylsB2t|gV?C1=9U{KM2@rhzX-x&A$A`~Db@mYSTiy52j4)G0
z!IG@I89l$HysD{CX{#HUeubw_`vazZk}JMNckY9y3%9Vm13Mip_7<#b;T9uZLfcL;
zDzn?2e-sX-<zk>0l9z)e9&-0~y$xCC*sjkQjy03e4dl~Pl_w+zN<sN3Yn8m#<?OGa
zu^@@pH{PS}+1l#sOrOHHm;BM$+{*N`6d}63ifkE3DM!NjjRX?A#5wtEm`Gl!oWm%A
zF35ty-?!L3<)VV&nMQGe=)%ZKf8RPObry(#G&}Xv>vgvuj@ty>*C7#gVY=B#pU#is
z6F%7@4`-|%O*FItP0VxQQ6hvcL+ZspTn!Jn*_H+46Sr?#+fpxr=HtLF7td5!ph`4E
z5^^7oLnpKRfyL8kqc&fG0Vr2+=7g$+0da!E-d3Apd686u2x#5ux&T7@E+mzh?gzit
zH(0tKpQ66h026{ZtG<*YxEHTUKt>RnS3*H+H!@-uP$?JMd=X4axVu&xMxNpJ*DCkr
zr`a61wSWi}*tp(ukUSadeDJ8CWxGs>?{p+jf)uw$4!o%0U&N&4^eZC}%!cfKv&q)N
zp<r42k(1pdL;eC8325q({U~Zm)nG0}rKX<v?YmWT5(mYvdQSi+mQvA|;a3y7E(+qP
zU{4xz)7({#oo8#uYWHaT`m~n#YOnA01&7(GX7(3+qih9|<WuZ4oXxe`pMVTsem?<Q
zc%~41u!;ja?5LCw#+KmwH=g^t^C!8wg+R^AcNDLl-ISvNKc@Vb2o|PD9s{K41Y=go
zona*jgqTFPS(-gh_VT1-_eF`UUVN9T&<pFDd3jBcY+(c49_w2TtZ7l`3MVyFzKN^p
zCM5jh?GuRZ^&9pJ@0PAeHD}f21VogB@=g>#l`uPd8uNm?kzDQ{EM`oj6`A@oKySWW
zBkhXBYiiIQ&+A=Zo^O37u_qx7Vd!RK8d!+*yl~Ac*IyG3^p?0F7CZry1IaXccE`;Y
zJmLi9P-3@iQ6=;>E(pKl>eT!xb7_vLLn`@Q7aIThKsiqH0nq!TAt{wJRPpw!5IuRs
z=l-Zqdn+Zv*RDZ%KOpPWI=qECH}-xa!4Jt{(+=T4@@{+nqr|1US4YEGL38>8mnB_r
z{%2Q>90~y|YL?L96B~iySEJ+pge~tTgA~@oxoY}6V)>-x(Z+n4S-;(w9$S^pdtZH^
ze}7%76v@8{1ag{lWRGW+(V7|MlLCnK)pCQ@_Pr_h8$gMFuszUwD9@DuURY!2N!pR>
zS43qKJK$II{8e|KxO=)$@vMF9f^aKi2e)tKgYi?TiZ{KVxAV?boXYry(Y1M=D2%E%
z8g$zgHPrZvfArwSd$E4JSuc21d-u&CujC1*EC*Oa554zH1_f*zr``acb7>*RX3sF>
zI=Y@+HbcZ#ZjMz40}(`6O4=yBlovyA2B<-TJriAGYEpSuz4D125gRjJr+tS$&yFJ~
z3Bi*0)fC(f4==3!c>m%xxA`6<DM&+%ZT};e-p+F*)faUclc<81n)*wy^N!Cv>Q+#W
zvl`-t`F^0!=0^qAu`^?HG_Vy${Wu}<{CV?OM=S)hbyu^u+x4-axy>QtnXgf#4YsV1
z1YK(hWm1(5=<rtLPDpch4GY*5$bGH+`iwsdWSNkQqVH}2!6}o2inqxJzYW6`n|1PJ
z67#sgw~iphTSm`>+mnp8%Bkb8b7?0SeQ!<)rfzoAKp$8Brk1p8_rjI^pBA$udt#1d
zte!eE5;wE+)jT^v&kmSxi2GXk)Eeyhmtw;&Sv4mnZ$sdqmjnMWnBLLSh?yf!V4J<|
z`E<sPUq$Cl&mrV8t~ejLB87<<AT0sb3lz(>+m-m`NfRPSaE2+jLVJ8ZJucRn&}Qez
zTF59zC6ntQ!<Xgc_O`X0b5ua!c`14&?@La_%HR4NW1;fd=29Isc)3~NRS}r8F3~h@
z^XN0?3Raw_D^Kt)j1CJ{5Y}gnfcbqjC)lKujIpk_<<dgux%)}Xt2y!hn!GU9t!;K_
z{SP?bYqPw@K<AOf%E-};QdDk?%K;mGZTv;NJ0`ObED1r=BHYV9NT$2Y4aS!}o-At{
zMNik7o0i-flj?H62xFb{yM^zJ?e4wV=3!{)5_Yu=0D)7L3&ZSyY4T;Cgs%<8)YdmX
zZ2NYk;ds@m%6(63kin=lj)}%~6aAW=CG+=Dry}ddxJd^mbEaJR=e>u>9DR7cCbj;g
zjYSl;)EcI<ud7)3ilOT0mlqRB2HqohNENIE*cvLNT4z+}Veq<jy}8hS0FUqd&jeP7
zq^2JhFZ|M;JC`Op|GI}Qoy*`VkY+si5lxr}*A=%fmor^zl-MS`#yekGk?z@Yu0U8N
zK}Uk@yKzIyHsvmg2UwFQKR;i|8J^948OP|U)1+5C(F<!GFuO=)s-Xq{>`QH>GqE%y
zKEO&O`Jsx9vf~71t8b&=Qx+o@)mhE=WLVrA{1xW(Hq@c+=@4hvV5PLWW0ehAA15PP
z1MEHe(_++ye!^k|FRUfupP~nY$2LH|&ioqdX&pOmrYJ$aF<-&k@l^^Q_xr_%^ba83
zE|NSJ6It(oz}$+*v}QQbyIay50{k1hcJU{St8`C3FSyLoD<zWcka#(%B~rE4bLC!2
z=;>cY$Q61lgs2=+sr_5~yS)g>kaHx89)FP}1}GUMLzJ7~y?cYt+}Ay?D~GxFZqx%W
z7-dW)II-5mQ01dzuv|qN7I3O=xHrr^lXlr_*}aaWA0)k>h}&<%(c~9_IPja)oMgX}
zKHwqsseGU0@jGGMp4F^5Of2OgCb&So_truoVGDNcba0rBVj$<jit;G|=U_9{pdCkh
zG&Kxszy4zB<i+7}ujYo_N=)j($@!O}W#Wemg-@mMW^U2g4i_Y27;VNonja6BTE{Ps
z>U6xDnjZmTJ!e9h_;qX&1HQBGsFoFZjq6+HC)n-peN46=kXbk9CV@K&<qFu<*&5#5
zmY#oTM{vr<&<~yw`9F3J?3@c>@Fhsk+LqN)=BpCPx7JZ-82y)bi#&gPB^8-|7ieyM
z(h<7pEzRY?WKjQpVU@M5AsQ50HV;Q7y$%s&!|gE?;nkM47d;sXQ(mfu?QZ)pb*ggL
z3-oJ27+H>s?|o#R%7ILy(`U*&;O3iZco5WvI;lLS#M%o!^#=6r!Nnr5h@Kd7Y`@=L
zzLKz*E9K;A9<l>1zdZu-0y~q^!sk(WtU#C4L&W~ezjie@qAnM>JG%KYd<r)MR!4nL
zP*Oh+Fdk^<u5qtj|G0`-$##v+4;IPa)`m8~us95%TdeIUhqc|$%uf!MPS|<w=Cc?+
z?xZ8r{IJsSH_bqq<l>r^uqDepLNk2?NDHGFJ))Zz?8Z+kqc$b*0+LrUS(1ktt}`~H
zIYD`SWDm!-@ss(A>SNg4Soj7^M0xgfq^E%WmDADlw;*%ABbq-@1v%rQg55IAB_j6;
z`oiNY)!enS!OESOsNcnlUGvd!5IX*$#kTKYGmQxS?FY7!?yz(pw|J0-$Bj|RPJ+;n
zPbpWBmVw3Z{tKPjZf5QGSQ~4?g)cJGJpMr&dA^HCES=GAfQvmbgQ^_Li3xGO=;l3-
z;VHDWdlE;Wq4lF+{BJ|q`hNsi9Zujlj(Qblz_9NWvcuo7MP3p?(~(rs%KNU}F#6Zh
zkuKby>B>7)o;4-S7_HYv$kJmQUslI{xyp`ld)ETHv+pB|_*p-oESPHYj)@9<I&`{J
z<Xwv>?%OSE!gpR8>rWFR3gp*focKnmR0yLC2?r-Dh}h4B_3+a4)?cO2z{r^B?)x^Q
z%cicD)w}PII#AS03d6;prbAdLx)XV5K6+Ld^?JG*zaxo8rp2k11<;e{Gxfc>qT;x=
zIaNnYB$%mo3{4Dr{7pxbLnaO8RIh%rZrNAl0L>WEe99oPWUNm9ye!$nA9cMV7asx^
zN5Z9reI6qKXYf9lUa4Q*3HX&n!AMGz1J99vmIaBKa%byj5v&x_@}gFMoN_%t;Am@D
zgAC!u7;RC=8kJ<oLGPw|XuY{9wxm0T1KOh=<fqD~z3gFC8f67h<84PJy(f+zW&L{~
z5znRm*ioI1{`PlNrunQ|+35P#^`x~pakSrlpg-WO@l0}_KvPVN`<um$gD@@r40O>y
zCo_8fMd}^y2sUNV!E*k*LUVtrK<-S~a>E(EqS@R}*ZR;N6k~db^2yr4d0BKU9tKGf
z^S<UzBx2}oA;EkeOw<*?nkN^E*%^XeCHr?aKbtF7J?RFln%|m(%gW1KzNFb=#c*;V
zSA#wrES-!P{j7Xw-lx}_2s+^h=Pz#xCv1sU2WnabX?W|i;bi7g=baLfv+rg=!-tL-
z1#hHG7oN@@Oz-?o)utLmnj1QL+(;`qm=n~Y`r)nIOfVb;BWhYA_G+Sg>}pc}N?ECC
zH&qM|9QHdI);sXecG*!l$&iKnxn4WQg_)0gN9caGf3%jk)F-AYnth&M3UNoM!rBNE
z*a}qQ*XO-O{fm&CD9RPcFi;L`TobMnAGL~9Z$xjDA0qdn_^<vgRC59P+KW3cHs+@-
zy5FK_o<w^`=T6-xs*55adFJNm!=J)|<<`9OjMMubv`cS-?8)P$+S>VXxKzzP*;@{j
z`={16H|F;b{CgntE<nHkXz@(G=34ckx0ATZp;?5qK!foeq;{0F6-q+aR#A%32y2<-
zg)S>caeH(9n0frqP_l*|(ReqkYT<h9o%bxFe)*)w;`XWbN~CY74a*b5Clas~^wdFe
z28I;$B6DmHEkp2gYZCU^uoPK?GGMq$J>H7%9ti4bXph>iyZGWW=)(BSs2i#izgJoL
zY%wlCKa3S_7;~#T&xE=sWiI@!aS;8IU}@LD?u3Q_hF{_Eam|hG3Y#){-u7n^VqIlP
zk}!b#WKxFLJdV%EHJuUsJhvPYVLgG@K3&p@F9!=uwxkEfaX%8;h7gvauC9V}Ik<J=
z-Z?nOwIn12=~j?<T(NTL8QeugO8e=Oqn*JB4ZKx@E?!Hrv}AaTv_S?s;+a@C;n7rG
zz(s2#dTaEn5YqhX8z+V-MKIkYJZ{yOO2+M4oIw|+AWG$!{@P5?WLw2`4g`u81FXI<
znxG>ijzsM1%`W`&Oyt(eL0jV}U_vU276K?F!9MCCLBPWZ)+Cd8mJ1sx5y&<-5U?vv
z4;E&CvU2%PfXThJ_#d*zy-;V7Wq<*bc?|a=JrTD9k1syFvIu9&l+lMnI&!4o<;$!Z
z<kIl)^Q0QSszcSrE%yeelX2vipmHh0L=)uy)xkk*wIwYXDY)N?ha{+mQCFYKg1;KU
zj)Oj^hU|fgn(2ZtoetVo`f2FhKMOOi=i)++z3HV0$i=}~B)5&G*XWMF`p0v!s*%pA
zcAnqDkB&OyvxOb!m;Piw_nt2xB$%T9sm}9uP;8(Mpgpeti6p@R>;KN@El<_=&iX`~
mtGCP?@h7$ahn3ws^ueaT-@Uwj^pEu~14jdoG=2Ha_x}Zl7{cTL

literal 26417
zcmeFZ2UL^U+b@b;$3e$&MwC#-kuoZXihzKC4hlvDR7yZfRHTNEp@k4EgX2gU1Ja9%
zh$N8ETLJ-57`h4}Ku7}8UkITkfrOCzCeFWn=ial<U3Y!=tb4wDvKAzH-@Tvr*-!aB
zWp8+L&B|Q%8~JZ!WMpKamoMIsk=Yh5BeUhs&h6k8ddkZM@NN4Y3-gQMJNQ~NC1Jp~
zU4fS!Lu6zWA4`5VB~C25%gFpB1HE{`Hau&N5mDjp=`tk_vvaqK`SX^{g`MRpEwN^A
zl>ho^=Qj`D{&Hvc?(2Q_n;wVyc$d&OWtu%0_^E>7Gqio{hnD-QNq<~Cf8KQWFON>{
zR56?U>F}egT%lX;P2`Q?kokH$Au}Y5K|>Um1Ntb=)F3Mak=x!ib#7{Ko0Pl%{p<f7
z2?VZ27uNYSxuu)9e4Ns8vY)8{2L0C8H^nHj)9mEHXFA=5Bj;|f7<ycwG#^5IYgnAa
zXz5Xw8TiB_)O}@XPhyN@pv<jTTglQZ`&CDzN=y4i&vA=P%&ah0N(myl9Qq1PHNx$H
z@p1DGjHoIR9G_^%A)Y8=y3~8}*5P^)IokMujO<w@swUp3e39iU^5fzLSA~fqTm>I%
zBn8-k+hL~;!@%ob8xZTFF60Vf);J{xMvdS+cZw>;F(bBehhIg8BYOtN>}5@6F@%je
z14^Vjcjn01RtKIfzfj2u?beJ8^5zPFYCOezn1b&bA|O&nZ=Nk4^;~~Wg>`L^-I3)$
zACM6{%VWrLp3&?B3JDGu>gnFcS-pfH$x;hj&pg^A+&i`yBbzg(L>20uns1eV_db5L
z1HkDh5!2Zw5uu&0LeMV-L#8e3WxxZZ&$<X$mF*92)TbKu9}3%8TmsB8&$Xuc_kVX#
z6TcX*ShNUBQK;_Bty<><J}a&Zh1b96fosf!+-ctN1EM|wkApi*RVpN8YU#7;b<ei+
zXiSUSVr7Du`whvt+>KZo=F4`(&|D9u=K(Fyj=MaYHS4}xQ1AV)(w~cpG~7T-yk2c?
z%3!_G<H)9Wr>FsnO{+w2(Y0x!+=}Qa@F+ID4x&$Ec#feRx#_aJ2sa3+^&JzS?OFC*
zSc^SWL1$Z)hXlNCNz{F0{}{5MWc8x%Ic?r!R=;%OFstW;KJ8g#?}Lz>>iZ2S`3J+5
z0&#&(f(bSz41)}679xxu?_W4!FcsM(IDTLSHESTM8Z)Y=ijB8`T<zySS9A<7ag8ad
zzU?vjENAQiP@_8K_`^a&?d{sf@i|jTvTzgeh}tZw4?E<uGuKu3UC(IFY?kL$y&_{$
zn}9i3a<}=FsKZzHBOO45X>+p%mIj$7<dx^jwU0H=aw=t&12vYZy9M(B4#6YW%;gv6
ziA=gc`H#7km@q?TuM;|ci;VfLBiiR=WU88N<b1_lfUfLIfEnvpQvegcIb0Oky1iB0
z5UX=EvCqYjOsr;+4zCZr?S=Fgle^S<mJzda;(5UEz#PgH8H)66ri})$O6;d@x;kZ7
zv<>Z{5p^GtGlQ(Sj}|Q)d+p&Q2=dov7R^O&4im*)f7TQ6mZqn1H1KUbl~&x#M;-9R
zAdqd?=v?14m&imQZqEjpuj8!8M+BXZEQsZ1Rd-Z6otz9M7lgIz>;c6$cT0|+uidaw
zdpmR}(%)5d*Yx+g6o}r|<4-+O>dWd}Ol8WdZ&!^3oY0TPAnDttZk~C_ZyY0fP7TpZ
zJmzg^;N|A{S4ujD(E+C6Wxi&!mU4Ze&4UgIZiF2Ivx>R;)bmjeY2=(+-G>Ju%HiZ!
zqaG1EHrE^KK8l+aT}>UFi|Z;Thw9zz*(IyD-FYQax%U3emZiCEIvP&Rt7*W~-2!y)
z17nu0(d&3Ei>114WAF+q)+x^gV{~?iU+8vDXnjGoKz@hBJpHf#w8VVYB7V7Tj6-xC
z{_^J+%842N+%|N|HnN;f=WD}06OEa2N}X69tyDg~D%5<-7N)K^RvnmO$gK?iMVn9T
zOVm8e--spV6S-m4)m&4iyYBvJKW-)IS!6;$L=B1AIatbwhH5_bpXOHXPN3i>EMEDx
zI9dQVS6*I@hb(Li8|e7!jxZAVE1}L~AWx0gZ+)7pSBDiSj1Gt^Zq12gXNOM&|7zM;
zVNcPj;?32#Z{p~kINMW}dEcevMNe1CO2qI3V`YcHb-bJcrm>+p+|n(AgALKzJuzSM
znw7RGz>i%?ay4qzdxUxVu8!?|Jc5Zj(6_?aoq%b3For?C&q#m=CWxr$)D?*Khzn<X
zSf*@v^(^Quw6ijSTZ@`@%gp@{aI-`dL7kF+5XbUM`?U6FEO9<yJS=?-@1!-rp9Fd>
z1g?&vjR)vfog$suLjleuB2x<i`BoFpT5~1@kf$dEES7zbA>S>MfAB6*V<0RSDNuUi
zCf|SArGtb^)SN+G@4%S34dD=BMtZ~z+->%tsSCB_0H-W7#4lf5c1vhoT*`ibfcQ}O
zE~vc_091v#q<-#bo&WpNs%4{k96|vUUD}@<pS8G-OXXf-b8bLt9BNioq!(OXq3rNk
zJdaSIcx~sD{j~PUqC2R18=7}wro3PFS&B%3;J0&Ys?h;1nt`PuUw1nMGpGDsh&fs_
zlui#%Uw^7ZdQX*7^*V34<|o~BW@+xx=k42_fa=-aQ(0YS(R@Qgi?H$L@VfX{8YNb)
z*p5UIBwDm6hivCq6P9o4YCjA!Wc}^)jRiQFMyti8e#+pdmkIbBTak<DK=6`$tH#ac
z@>s}@;;GN7!W2zT;9GYh8>cEXF1;o6z$7~HLI&t1x(j8kKR@;OF5&hdjABxEe*>cL
z@vRe*twca6ni;u~UJ2OSgbibVP9e}gU4QDiNx7tYbW57G9p`Nh>${80^3mq|`|CDz
z$N_B*>VyzNwin2jAscjr$(`YwVzR$v#1b>K1jiO4l^jIZAtb!eLnD)2n;zm)dY4s3
z^uq)-@v@gt!%+N*TTi$iK!SUL3yJc1>0}5=Bg0*^Uoon<z<%$|z+q+0L#P05d#tKL
z|4r=Of}0$UyW>KgOMj#_rvlVdTDi?etBlMGpe#01kx?(`iq#3!8@oW&f9xxyskSN$
z>=rABquLtWJ5DJ0klwXiGA4alR!-<hof9s}u899A{bn$xxF&Qjtr^cka4EB5G_p;e
z9&nuz)E_<mpn*bN+IuOw=C=3aLVf>iQ8oR&16`EEgo4I=(vwPA*(6gm5rF3eq(MW=
zW3P>(@Kg32mjaHS;?g=iML`%N+ZW6N1y8=eVfdjex4Bxkv&b;3wM=IHvgYoO6#p$E
zj*0!@%#)Lqq#R6Ec7SFgp&L=Dt;UB@$hTPgJun-Ow+T~=OUc!d{aBHSP4?hPNNB(g
zBGoNrW$=WaYZ+$%D6)*J#DV_q`qkfo4*B_^hW>c8YeUPTvP<CHMhd20xEibaeLoNe
zBBDdXv>M{6|6jJI)1_}4GEYp03}cO-_MZ_IY-=^aK>hHc*Pp_tEK^oUY;5#w*Bj=X
zWu>lLIc+|Yo7MLnPBSu_GU#K<glN5;U5a%iBiN*x2_4Lb2hp>P_jBW_q7+8pVj$u1
zCB0L^a#eIgG!%_s+$|2BL<s`H<YQ!K8i)W_KdIl0+VW<9|3H;*6)PlV4t4V><%VcI
zg_up+uPM+!SJ=gpnHdM<5{t5ABR*l#V!tMy5tSHlLDOwu4wTh#dH-Wo{Wo^nv0$Q`
z_}vjEHX=}K)`X3F7jofV5j!ygESl(4#xb_w#j};ckaQVA3_|^CdXVC1xM*a=10zq7
zX`=*+ti98ksy=0Y%ZP5`@j+BjwP;z(0rF+bH}8$QwDv}J(`awXiZEWbucnv$y3%yU
z%~Plw>_)JWZ`dqO2OhT^_j{SDImmwmVDySwQ6rrBepx+M<Uk56^rO8i39>i{_{$<*
z6DHbZ^<15vU%Ifac)%CV3v=zDd2EOe_);!#NwaZ=Z}hYV8mbRJGA`NRHvj~x7*P|d
zhafR~@eM7TWMpD?t;X*;fnIn8K=4jFoyQDM3Ina1X&B6l4{-PB=?9CLyU^S&<8GHF
zgV3e#Wp#?4*S=+;<bTbA+*|OB_BCA<$;xgTTwlzrZ5w`eHUQf7Sm@wU4sT<D`9>ah
zxK8j6yNHR-rq$c=bRK<#tfAJLW#ht}k?aQ>FdcHPRhA+uiaP>)Ms^$WzL(uQJTl9e
zcKh_bY^4!rruZv|d5!mFMlUO+R)x0=F#(+;Sz`>@m!Ez%G#X*_e)lSVpMf8&5#`fd
z)Ya9;V>X;2XJ9l!YBq7~T}YXQQ*%Iu>eSi{(p6}`mT2Jv-pja;dK;p5mbpHbnlcA5
z9AUsj%WAX2-UDJtrG_-V*~7<V8uyi4+#(ZLQG+|IP#4;?F)RO|FIA~YpE*#%oRLes
z6=z3>qfuNI%;hgNXL|;IloNi$m|4OEULn21GPndr*Xm;ckvb=S9;*teqDMf^PHIzG
zzJcUiznVjIv-%2lc2kreH_cK(JOnu1Y*>H)vrV{lVg#6QJ^pTCvyAyem`;?D$aGdA
z49Rto9c4wfb_;E}UDPeO9%VDMVOOQ>Vb&m(z9&y?2x*9|_mZU+FU$i~)Zb<m_}#HI
zZlNa8O94_JsyC*o34gd6Aq$^nS*4!otL)UKug32t7Ik~CesnjQvQI<^n?Ec={BfZZ
z2CX6w?m{dkcMr`f+B)C^Xv>}Y9#pVq>0H0u40}rn=AA7#{E>ugu1iqYi>l}MQZLEg
zXU*a%IOD3ekopK+o*(1^out><S?}IV%Qor%4!iExOnbv(2;j51uQ0yh49_z~@e!QZ
zOeCXrbPDZm`pa@dtjx5gtVxpHZw0BHJ|qlxAojcENm;lk+J*ZC(4vQ5KAXY@^C+N4
z{@1QcdIyBL)E^ZG<9J<y53wZy&$^gf*=4baQGaR}Y%i|cC+)U42e6j+e;4+XgY$>+
z6q=6n7S?q%qFJNg2DjH1V=J;BV}|?UnZ1UK(&P#Z$p6=`&{szI6QGOawQTHbT|xo<
z%lvIJfj<n8fX&hgXU}bRm3)YK@;#`r|4J`XZ;qYZc@%CxE(wRGR#JbYKBsQ{*e@+!
z8xQkr!-zSqBDCr6r0K<OU<!}2N5QTgmkK~T@vZUD(zUQpW4U;9&wd@r#m1^38&PP-
zmSIe>v92&=Fi5Mz_p9LimOBZ`pIvW?!ol(&7;bPptjAaANVS1@aJu=J?kZbZ$^4QI
z#;e)K<vm_C=|diK#nHS!gZz!|#7l0;6phX(e(MeoeB@^2>0bmt_pVfW*`bwn?_{!Q
z>4?H?$7D<0$j(-&f^I3MhMi1S9Gmpo(NgF0NIEuV7vxCb-P*OELdy{xC7%t>Z2J*~
z-4ZHkXE$;BQDe@+Igm_u2zxm(P(w<+fAKwm;y)ksSiifJ>NM3NxPKidsn=v~z1)w}
zx_2@SZO(n3sW6s6rIR)UB=vZQBa*u(FQDvQ8c!_709tzoc`tzvR5}h*cmhu#BotZ|
z``a_}B7KoUQ$e&TH>3Etv~>^@u@_I68POPSj&OD|iX`opR0A?E{GCOqz*D~y1NFE_
z4`dQs>yYH~K%vH`68To$B0P!(%mJZP*qI_4${h(sUgt6aq9?|+5_<*2e3=+Ui^dMK
z;rvMF%CJYJR)p@NhHAm|&N09IOnYnpw-I`4y+9AlWvhhf$%ItoUBnI|cU74#_-0QJ
z#%G;o^joV*{6e{8$+sX1<-q2&N4YqrEgN#X$lQ*PaCmU1z_1X=;gdbXNOXq1OGgUn
z%OA%D*{rqfjCy|w)llP2T9Cz2AmT_o5+57G&v@2C51z^ju}X|y$%DDHq>$#yI`({R
zn_PVq6Bap7ZK_OSm9_sWnH@v@e!;2-f_L-u(`%D(5#!>^!AW%{GqM0CiIPETSHLI?
zzD{26A7Mo5DMy@p36`-kGS_y6S2sU447WKW`4;mjO;*wf1c!Mn_!If|G{w<_p7Gar
zO0S_eGmH65Aj?t6vWK62w@7Hm?v>m)pW-#JCm;=h`GEUF@?9puSTeG|mX{5)mwHKH
z-@XO^`mnA_iif+MbcnekXidU`TAs+>PAb{Jm7t``Vm^@?);UCrJIUO2Bu>h>!nUns
z`WW9_VYY7HSF=^?{9ft^GXH8IZPJnuZM?Yazg_Hq14&nt|GPb?|HVl3ox-1pfu7B;
z3mA*~DKK*|7iD&EcUprsj_8QoN1s~)EUA@{v!T|=9-u<A*lQ{R#{Fa`2TJthw#}}(
zUw)%fAQ7D*W=eb0-sstexgz%+UwBA;sv9_b1P}8M(lq>}j?b(t{SK_pZo&31l<gB2
z)LpL(cvL#?ob8AXe^eSXBV59f7;JwwU9ZLDlp$R;)%i2{bEBMAzX|S&Y&qn4Z!+D}
z-_FsnozB|e@i;6@5-=dO;o0v&g2Wu7mOy`Q9@W!ae(%|D&W#=FnNGH`azq|-nkO%J
z&w@YndlVFNIY%^*C>l6mTd{m@<>g*BfG4^lB@Ui6s)mr7(*K{$;6K-%4e7h6N2y@2
zlZJ=6bDNoLWF%LUnrN3;=wH@yN{CaplY_zE8pB*{>g(!QyyRUH4P0#kjq#S;+9Cj>
z4Q%uQ5#_uf>KfH(kG@9$_v7GL7+&3$<_CQ)8QXX_t%+n5{kF21frI0f^bsZ$E!nWW
z+#0|n(N+rbCvSt`5q5<;8`5LqY?L1v%-tBQTE@}sS$e4GXyoJA33;AfHGU(?KdwMW
zgD)W}Gj~S<I9-bC`V@Sfo9T#<g)hfd+ZWgqvkWI^<=?SpJreioYV<6Gg^U@Wmdub?
zwSRp;4nlkxNgZCsLp+x3v$ciGM`iWw4EriWT9pjIWCx74yKCEq`bo3SLPf+JhIY7z
z)o{0HZ6nMjys!V@?9IiOps36<89cF_5Tf=%M#iwwUwG;6kN*0!hNyTEOlfKT4^_7=
zWz^GVg4c4+Db(DdpiXUbNkp}GKOPNZ&Jc>DD{c?!%iQ{~mF&a;fJcrf>*fN+@*dHp
zz+$(4gHS`0Qxx1Nat;h9^|HjVx_vF1Wn%15ft8*;IY#WkkQGm|kk1JaZ-|8Q^N^oM
zw#6u(ZzOS<Fos{halPREVPU|%zm_W#9A^jkN)g@=Ugx0Ow^O=NL*i%8TEM>hbJToD
zE?s{rt9;}B{;-X;DSn*iIX9}ij7&(;<2o+$G;O0rhi5YV=TxHe>Hg-ap#Z}<Da^;@
zJ%|aLt-nJML8yZvJsby7f05|jU~UhB%|!+n%wigq6YPGH2=Sz)L_IA4)=~HxU{H79
zpW=4oA-bI{qQ%%sf9njkcm|j+Z?(1++cIwHcDqw&tMelz6K}PaZRhlp2$2rlggx7V
z>U25=tP+6T)cv?r+g3YD+1*Z#Z_SJ}-j_U02Q7=~$fy;NbgFWxCk?BCd@9D_bhn-C
z>_UUbxIjWNX4mP5VL~!dkO6k?D}ZupLI2r#7@mb!f-$NMY9p<=v0%FFA&`2&&T!aB
z*`Qmx#mxq={E^I7m6yE9y!mG|{Vy9}{&SH<Dz*PUPy^s5QlrYbSCKxFXeJ!YEl4<d
zK&E8ao4OmPZSV4zF3O3kjX$yZEl>koq85W?rmpZjmxD*>2F^Xi<@$$-#A}t*5S)n<
z@-SE|GFFto+rz5XTmO9O4e?;aIhhd)G@1)OqjdA$q8t}=F!eVXMsegj+*619JM1af
zT}tk|<$hk^aQ(|oHfc5fvX$)no(~h_hxFb#nAoD7xbqUHOQFM%sifijR-^eKJi*;b
z8nTu;w1`2`0h<zr8*th^=gQSh*4iXDvrzN3E>VjgDi<-2d3HvNPps$W%6ph)b)kPe
zJyG{ou3_P~36^)vr=8)0zaPg_<h_f`J2!?+K8MRR8dw>!E9N6<Btl)BUr^_%==$HX
zC82RpN4zIHYN_opl@nS`Vh~mju~{6<woNUQ|KALRZ%a2bQ*HSNgQSV3O#dnSpM=vu
zF*To>gzq(z|C-TnYsm>hnQ_U04fQx470{$FwG=P%0(TEp%NEw@C!7SjsIB+{`+~%K
zQbd-K*BTK8a#sK|>O&l@hizCN>CbHgtntEx@UL~03u5jCHv`bZ5d$0uqpgq!j4ev8
z*(P+}erjrbRJ7y?F)nV$JwHdkGN`WeMkWIJAwqv=Bhu})SQv~?<oVs!l(J!-&2~68
zS_iPHVRm{&IoHsE{$f+So^386x~^_ex9@975J9g$Fh7L@LzLdu`elCy(G{dOmqr!g
zjOdP%{<chGvTs-;%6Et7+(tKcY~Ds>$0Y!|POd*nZ*G+|Z0NCrwW)eVwJ1t0Ism71
zLlK_sBI+M*C<mc3Gr&4s%1&?T^G%#5R5x50Oxeo;&aWA4IF1uLGs@}$xOD&yP{wnL
z{axxqLgyL=zZQO-hro`G4I0VKlr2YMh_?^SZweqZysW#<U8bt;Zdtp`{i}A0bvig(
zN<S&A^YU2M&`Br0w`_Uvl#8PgNuW$M*E=<7J~^bP{|b<fO8C`n8(BG^=Kc`5T34fZ
zK>z^F2Sdcq4t&k@&)k{*{=q-a0zi(B7>(d!2kW!|fp*~D)O;yY%N#g(d<1M4bx;w8
z<iWb9<ly&T*-bbGlI9=smP$=V*Xv(ZOq|ZcFgW)}InSLM0h!}-aG9M$Jw(ExLL~`L
zF?mdfO&m&bjv>OtZ@<X@rU)n!RZ}y~aMA*C<gH%+_p*s;2=Snrpx%QZ`s<mGw6+-d
zu_dEP7*op=>V4vvhWP3EkQyL5oq<dnkp*{<nj*#gxifBEBlW$`!sX0q{%^HB$0O9!
z?kHRU-uPol%HnTSlqZ{Szt_pdTf#}Bm9~8&3UC(IS+3AcXbqn(SI(Vjg@MU#G`cNF
zh!Z=BE+FYTJXT?(U9;?5ivT5khMI5Di-oZe_87>=KrN4wVB298!U0~Ew~|s7_?<u|
zSRp4;O%%%5i&X1Gdqv5kPCA0Z3P?w=Zi6e<rfp<C^BMG>Oto9ZZ)+WXC1|Ui(s~OP
zvxEkmL1E<l@CKY<rCe$znR)9$%QxY0J=#XwZ|3O2+%yCw^!3fq3}H{DN!dsmCkr8l
za+|3?hR_?Xi5$4X5W14l6{KWh<K6fl7aG8TU!xnUT|&e+VCL5>%yI4owP0Id_wIzX
z*jirCi9@qflS7FH|40(vojMY~JOjw##Ai?&H~@z8gykBH5A@;+Y<tq^JUo>W2d-9|
z(!G#Fz_`_JnKz&{c(pkwXCW|M&wi+cRudbFgN(nc5zh6Dfi;fI{0;ON?&*LB%vM~W
zB~siilVH#4=c-$?x6A3R^P`Ku9lfSUY``<P;z61nvFOC`$l_jNL*#7x;4@=;5eLYy
zlY7?>8fNj~5wsUFad0^|-RMN|M#E?qKR~`eGn*f#nOkXqRGuC&`MCg=>oAtKe{u+>
zb!c{|mKUE`Lg-q}2Mo=WmW!UXZn+gEV8n8Pu*_b30kLkU%)oX}drS%Nv;Fuxzz=eE
zeulbNT1=Tj8WbCHdT}QH<bYD^HgnQO8BQU;<=b!~p`l6;4?GE>2N<J@CaUom*3z{Q
zz?Ie%!+$B8+o%X=QXhJ<$8cIF4kDs;7xMFLt51pZZ+k2@>}!d<)~vzg$AOV@xCD5X
z7<E@YH);uo7z~zk4Zkht9C)G2RZnJav)<dfPx1bJ%}Q0zs6Q2Ov^KtI2?kn??IB{0
zNESbj=Agp<uGCrzOhhxT%Nex37L2hv3}t<8@x2oFEu(a3Vr?py^+68q9PK%*1M-b0
z;2NrLd&eqgRqhp0ihICdd3a|u=dUT&lZ}8K0cv6X&9I#(Ovcle4Gu2kxVV(uDgv9d
z)yr@0iLV*?y)Nzc+ZitR+ki}ocOrVujWlB!PzRi@8b0p2_{SEo5}!L(kR5u{I`VUp
zy-Q0~&BD9gd$*D^ml}5%m+UZEZJJd+8w9R#^nOg3jg|gHvP+1pB-ZZO>>B!{I3s5x
z2PmW-EpJWjQQDDXQpwmiZ(m<4_^Sw=1oUL0H|tNi%g)aBeHSzD=$2(9+qAZcV)C_r
z*0|NLRd6IXz`<(dvm>f{5N>p5e$7Ze`haz@Rkk0gZuE_}_y&U*8KGk$JJW%a)6-bI
zW_pmEtu1Xk#3*{{oj6(l4W~%Sgugxx)KWd=Jq%6ohsh$aHOv1bdz&LC|LbBREHRr4
zR!`Q=ma`J40=qSHI^m9UVGs{yL)olWu;_Vt>rJb(<7*#e^^OYvlK<NEAjKUb(`U8t
z+BjvRapjX}W#2R^Dic6EUHe)!U2xKQ$e*Mt;+%N-<=0Zzii?kWny=kyj_=EHVi4*q
zkW&DU`lMWxs{5!;4%}QcuK!2(%Wp1*KZ}#odG!3To8DIPjyWRJ1SR22#&;;q;>`M8
z?v!G5jkJ5zs0IQtKY?04nS&t<J^%su9-YpwEuLE!K{zrwM~g;)r*Cl2S|sO4WNyj2
zX?|MxSj*eLe_onV`__U<fm2T<OYd`<WPpsuDf>oYPY4@fF27&$Jw_G8CUH0c-G13k
zI-}%02<=a&hWHTgtC9gSDPZsr(Sa}zw%9TI%;y^EMU9j8jZR!NRoPw-f8uTTp&(!B
z<vl@QL?ip^_P3Q!h2fZv=r@s0+%?&RWxpd-DewmNfk+@q0S0ucST&K;m#3uDZgqp}
zmVPRU2O|XP;WNqR5V)L&Aaj(npU+C~`TqHLHSn+A8o1iPx8=A1;ygUtCi^Tsx_$Um
zH6bChk_Bh8Ufq;VOSs~;gIoa9DpV@F@5ZsI5ATn@%T+%@ldd+<783F#R$JcVOZQ_^
zs;2(N1hv3}4JY#A9KI^xdO%T3n052F!#18ha2s?4atr{rp$Uc2xBDBA$i#mIwr|2Y
z)TS7oBDEQBq4$x~GdaXMedB9dLwW~_bZlzK<c15WDOx!j!3ul*mABB}K=thCtYiiK
z)gZDEpl=h<ssB#0Ckp|ONOQ|N`KvSp$&gw=N)sV$bmnx}y0ftP2=%MZj>my<!kvH?
z92OFsK664t*m{Y!Ul%|CH6;Y0v|0BP2O7eL#sB;DvRYmU&So-43lzFNH!r01@9w4U
z?V=d|eyUd2P=UQ1*scnkJ4Fq%=VZ}&W7H`#oHX;e1p^JESg^K-kcWB<=0b^f>RpP<
z0-znpS0o>zLvUi_JAZTiE^QM>2i!Y?=h8!44!53r`>@F$tTZ0m(h?k`8ABuCq(`F@
z63I;l_mH$;xsGFL$K@X|3}GhcW~Fx1sHRwcX)OA!pmuQVQ7kfouUk}(D0Y!3IA))(
zU85nV9e`Jje$9MI?F0WXC(D1FebWImlCWZ4+{x~3e>XZyq(ZAS2z?aPvh#`5-a@zi
zJ@0CtgLb$wt>xX9{WvgWRM$26(lL1EsT+#E@ou;f`Kc}UwapGb%pTiLRfM^`*CiDR
z8gWl$Zk=hhgHz`PYJ+lk%v)Z-!m5+K9-}Te^z~j?t}}9<j0{BLer1+QVjHk}QZ8&L
z5D=*aX1L!L>)%Fx`O7UwXDPg>WgC?Z68+{JPLKz-5F3AqCJIs{wUjkJ+IvfDwp~4m
z@nZ}O-B5RlJ1FTLmE?d{)Kym(d8N6N^4K;2KX2<&{wmU8zEV~@oF9L(UlZc7814J0
z)S;dZV~p+zb3iK6zwFOly_o6}>PThVyX=<9t3^|b{gL<tlZ{n7TG@oLaIlBBn1~_k
zZfs)v;f&O?@7<`b`Jh=vC{Y|YRTf;MIfrz$$(DmBfMgmIs&O&ZvBXEBi(sN%Ma_eF
zTq{{77j^Y~b{!uE%(dNwZ<pT5HIx{k5;mR~X@xiK7CbareQ1}usqsL#pf{K47l5MM
zrIt>(iU1hD$kPbXE9wS2BspuHp8gju8TO1Ri&sxHtbPG!$dug1H?$4?7QzmIJ4!Qp
znf(<&3?HcFJ^Z`--MF>bV8ketfL?0HNg*KZh20J^KLbH=`@0){_2aQEOl^E>kCC|f
zN2*l!C!wuzXS(;=3&Eaj4%n%<Pu((E1-GmB`gd2ILW;Qe^u~sO3)BMhf4G6e#P_nP
zc0D*S4d#9$4KXrluS-1^a;PQi@64R-&~F6Jm6+GdU$a{S5VkqW|4g}`|7~%BTmNCH
z|2(Dnj}Cb}GN}?}hcc5#JgfFyT$QdU=`?6ZEx^FDmYa9+TkcBz09yLBY0`Kx9t;|E
zh{phvBq*$9Pr)!Z(65K!3>?_jX{>e2(Kg&8onR?J_B;TrX^c=CZ-7%&RfK|Y4w$4q
zg*;#?7v;dwf&W^3ir3kONOR;vz(ux+eRFhvQ>r~2E^<NM;EHic|CEKK(71{e6p3B~
zfb2?bXsEAj(?ZyPz@p+1wz&F9G$#XL;`1-^o0-C{O5)H(3Sx2yZusxOx!yITTb?q%
zD}?R#A0D&pEW7~1ptzQ|_8(yIE<t2S^@dD`<N+T-dN2Y1{tOo0*cg#}vz`)p5urs2
zyvmI)ceBd%6JF(F{)uX5Jw&Fu<2b;WY66CCJ;Fv!9U&5dyj@ADvq2IG${KJdMs;2A
zzmt{s7-znWv_tZI=}dqLW)mlHO1iWEh~SkMn>Q&Fx(k&CN=|Sp5egxG+$CTZ;<55g
znAGz2G^c5Y?;U{G-{!5(1LMGUY7P)WohWycYWnPEr+o|Ebk=v%t_>fiq!R{^Ev~Pm
zpJM#V?6kvsa1Hf!nC1+!dUsXs>9EdX{Fn!u?>Q(<31Te4n$)O%bkb+<U}LG+1Qn*F
z-eE`EE7lI{P(@w-Yy08v?!q5_=l=A<)@^sy=YEg*d3AsE&ZWPWOuyR}z53Jocd6#r
z4@Agpx^UsoOIvNvKeK#y;K{9DdFXnT+FE}1a_daQ8;`Vk!VCeJ2cju(Vzec<v(ToX
z!OjLPpchy()Z0jr9eb^p!epzzh7E#_{-x$;M~?*I+Ze~UCu87P3sgsdRqZZ_biFz{
z&qrLY9J-J4;;gA-6*9+5RP<IHJ*}~<8BOKf@z=3KUqjw^8CMoI?%MCKfLknmRGFeV
zaNM3xnCtW%kec>yA8jA-<&JewQtk$Hak~i`tm>ClQu-Q?-h59kuuY*<%?EUB=_gf{
zMOFWkW|fX@kLIxsTD(JR_x7Pv0XZpm5dmw(y}n;J<WS%<U6ZI=+5O3ox71R&`&v=#
z&}nNX_AV;G@qsiibjT|_D!)-#7PVSOVM9uo#m73}UT?qV0lQxo?jz@3T`FNNnPSPs
zlaUl>#|<x2zjPh*_)>qJO_;0EXB?wP4<e;Vp-2OlP!DAeJ4Ksf`2|<$f37Gup&qOB
z5R=nsTRDS{*&eqQru-I2`P%`hWDRiOqfb~Z0nDYB(1wh09=hG%V(F1efn7BJfeK9T
zpQ7p8$yAAO#|qVJ!>~^3YoXYu*g5#o<wBK$*TcX2eeg_Lzx@zAX0E5Ak4c&xCYME7
zZSV2?Rw6x(G`0O?)D3JkC3n2Go~2kz=-kq8BpyrlmgngXBshk6WAkj@c&}1yc(wko
zLc{C}!p;)b-biiLrT^{&y`y=yxvAsv(CDLWAy|bfLg(HjvXOf?vlqhL&@ecxIwQ(T
zMn~IY&DGX6&Oj=mJr+w&-X2#HpYLO|>RyxUtV5}+&8qCMCLP|-(}U*dvn2&~<Ii3O
zO{6cBBytgdApNu!CnY%|RL+mUKqRgEV5`;E=qh1WOZCZ*W)L=J#yxeJYWn0+gMhw3
z^vS2uYHf?X{_lS{r}{k^Bz1D1rD{0ex~$KMSvem-Q>}gRl6`M5HEH;C8y-up;V;iX
zm7a}5Y<yvglyq#I|DCNIe9}2X)i&APqn{F^@8$3+3M@&7is|*JFe@W&pSPYwmj|xc
z>|!1-Gl0!~e(_-Ue5{HVbg4V}6S>+d_$F3QCBhp!TR^LLN$`4`Gb@kVVc<p)@j5Q-
zP&~u-sF;t1_@{+Ra`juIXHNaBozBU8T{%jJ`sbd;>Oo!q9Pd=&XV^ak@5{u@dmn2;
zdAJYmWJdDTB3#-lWW~?Mr&ZvWm#S2DTrHKROXtncJRcD`xJP`NKVU-8PFfFdCszou
z>vyor*mJ*hDJn}S-P)t`{CpH>-y5>j+RHrEwO$oHi>0+>70@Mpvpw>pPaEt94U~ml
zcL`YQar=Suw%a<GqGD}Tdsyno=i!!Cn-<bka<2r3w_CoCHfXEbJ`4Y$1Io-kt<HIP
zT)LATqmyx^v7s79YhW&Cs;#vqL!M-(jPIzQxUJNC4K>;14mCEGs#r#*9ACJVlZW+X
zjqsh4vIf2uOdnsU6?jRQ0edVN@=d5~rd;W_Yc`<3XCYtPQ85{2&;?_Cyp18byy+_l
zl<dk=31_8HAvmtZ!j;WZf2$}0-KQU)wDoHx^{rE^WcwDRd;t3Oe9gV&t<r7p^XC8K
z3x1Fgk+MZuz(ZC*@pX?QEah?|DPz0chPQGK$v$^1J>z34y>cM_=+`|C1E1u5WVmwB
z*Mah9uDs&q#8*qfF2+*(l9b2)JCFV!GxtAdgkNUpmgz^iSC)=@Pj0!tA7!%F_l2~f
z?BMl1xo>e!#oDW;+eyb+YH%6~yIg@)WvFr5UBnio=vV9x^)<6CNQnX(e@z<6Pw2ps
zDKG1ye^zZG5nl=<lDl=gwoNLz|CNODKS%Wc8BdX@Z=hn!u@2Ik{%@r62I5Ati|FcW
zPgUg99j%tQAc_S~s{u9V+H~rrRa{A<O1S#eC1^wT_#tS*v}pbW)HP}Rm5O!R4U&XH
zOagW!J5F6~t$q`Rl{0PxWy-foTl#OIr%Ta7)d-7;wOVMjuKN!h%cS*R!Mp&2io{a>
z7?V0d-%rm58g{sITfF4*JDY`V$*<=lpv?5~8Wm;sbA286GekG3k9=F<HzeyIlSt4o
zF>Iqkl52I_nz3Z#w%yosC)#pUtS!rk?Ct%5w%8p7>93a+Q6|-3V3Dw=ab_2j$0#pb
zpV>5#l0-h)#!QBk869ooLURU%^fUEG+r}V-Wh+^lbg{e5A5^2Oi!|VNdFugcwb!w5
zi}WPEK!uVqqZYwRKD|`)<a)Jwe%u!kal7=yUd(T5P4tHPObtrbcm#A0H2u}+Ae3N{
zUU&N@l!;**Wh}HLZ{-kT0m&+2ew#3JWs_c%k?SW7I-t>~+8(2Na&yOZWUHh{0MfR6
zPi6~4-PiuKEUMOObHhee@z*~<UH7$0<Mi}14PO@QSVya0FvAEMeWHyP*#l4am~4_J
z3X?Hg1B4B>jt%skv+&&UXlS?B8|gk!Bl*t}KNjtHhl$}GMG|&cyG?&gPrBMPIr<w$
z%(lP_SaohnSmO1b=TNJwOAxneX=0SN8|22xYAGics8gBnKuDx1^X4lsPaoU$b(aWK
zzVzwSzx?kD5)J=9k;jlOd;7;-7SIGsZRMQ`ediS=f<CVjUN|LIiEwR3GV4aaV&mxL
zg&VEa*sSLgI91FTZv^wde_A<+wB^jaT>)2@)>Y)r*nS0I4apw`g0KIrp8rI1mFVlw
zd=<l_x!k)5Y&|qOZM;sU!^Zb(3waEL%YdV8M%Z&_K@T2wKR+0tvUUrbsJeCoi!QE~
z7U|R9?qaq@I$%w#g3B`|IKwFN&uweaoYe6xr^=*Me%B9IcR$)j$3~qMC6Qq2wQu<$
zM_N@PFcRRDrZN{_1>OL0Snha_*5##lDq&ZGZ?y-VJ==D@KG!LdLUF@R6~r-J7Hd?9
z0rejwCeYZK1j2x261iq+{rS<naMqvWPSDT^^8#mwdC>+P%IrHX{@l*O7_}|3&q$e=
z{sx3jBM>^(q0xqIOw0aKtu{mwxpavHS1SLScD6znsyK(APp}lr2{c}zbeh9?i(dc6
z%{6Lmo9ZWukLCrnmY&XTmW77KIl^2he~#<*cJA10u2vg#rA&}-+HOS$X|Dchx~J{%
zV};Y~stSJ&vtFM+9FjEtC3(!YH5jW9HXP)5Geg|bHjxU?!4n${j)r(qUbY<5vRWE?
zxX0j^$BLm2c)!eTLLR#VY%nFMkdo$LA(h9m3c=->3=OBMPO)bK%sJ>10o>qKiLk@M
z`Wz<op&P3)TlV}l`dFFiL-+WwH+tdW^A8Z!T0+Y)Pu1GB(pIHRKYCSVKfy7GY6qM6
z<2d1R`wvz<#M1U8EEypd@q6}}D}0hn9rVo2o|u?W(Kl%OO~+nevn@N(STH>|aH^0%
zT>(34I<?@*Q|ve!n&ayB{IS~FU)|Y;A>+@Gq#cY<X2g^`cmxR=OjltQm$p+>YB|r$
zmC~~f!>Wd=97_;1f^+0dK6FVBs^sGKT&T8|s%RKGKAo57U+Gu=WqrA)M^<n6PZf30
zyyESQvEwhz6@o7ESQxQAU3WKuG+*a@@6#pd(u~U9x25gbNm*6kniNoTrNvc#_Ris>
zztwICW|PM6Lyhg+o`<PMd<Mmlvt`fOTVA6C@SO2%$mnl!YHcv=a9+!?R@Jqp6ip{k
zrd$$76ji(W<!K1JlBU+?mK1fXz3cYoNpbea6}DB#E~XDJuRD|ZX4tSzQJ%2;Se8bM
z<*2khhq<O7Cz6AQ{L)`@e9Pw8J;uVhs-e?(E7sLoP_L1yr^vtPqV|aHYMmlq23n>~
zk~BC&l?ZPd!Nsr2vp>pYf?J-zQK?;vn98Qz3GukW2$?29d7_2KCc`(NIa&{P-!MLp
z8#9E?M>TDes~E^ix8*Ach$<+FApCZR?r}-4)H6^T2M5qC95k_YQy($vwbA@}hEQ`r
z|DIp_<3Ev6>TRZ4wd2?Wnc7J}j_H(2tp})|9Vfv1rdSSDLzt~nyYkrrW*p=A1G49(
z1=hJNfYos1z?{NR=6Ia9-h;L@@!_yuel=9-laukOHcZ@gnqpE^dG$K~=#gV;w=J;B
z7SL;YxAnRE@Aif+7pYCleZb`3Qv(Ll$5S+`+z+@sA0M_%rZ{3B-t_4x5=3Js_3}Y)
z`}%(YW)UfjXXEzLaY=WzbN^6C8vmFAim>~NXz%7~)mr3Jzw}EcS4FQcU=^M(vC}UF
zhqjAvDmvM8zq|y<!E=L)3A4Ye=y?}vmIK4dv~`1w`Qu?tSH5_Qx?TxugW4;-o;m+H
zzLepfdIOvxA5)19$D#HpW_(t^sUoLZm|5&{CHPl0z9!4DL~B9;XZZX4i)6mX)A7&A
z%R9XqwQsmqq$EY@<iP{0)z|Li6X4#jrJLR|E0x+4v--!M3=&mq8EUgNmhHoq$B}bZ
zr^QLy(u!=0=4aTaH#5m9xsH62*eZReh*fT2JuG_^-l2bHqU9%b)rdRoG$anpd(?*z
zUk|a^?S+=9<MRT|Bjzk?xBM)DIhp;T4p><J@W37ICkzJEH8c33v}G5g^2RGIg?)4x
zVCuCg+RXoiac+;Qr{xb<^5awr)YqIjQ}cxtKS)XZm-=<<jfN#@)5(H8R>4ofj!Ezh
ztPOU!VTtdgreE#h!oRw-wvWEJq(X!QF{*S9<^AY{&B_|T4xLXL*M(N8>#MiXz#!E2
z80%~lS+4#jepRKdU&ZA4_!H=S_V^<e(v9xYM3smz&YFhPm0(*e>COAe)}-N=e3Bou
z`|8q!it^>9HWiv**+%x!#fXboAuk7Wyp5$EfxwoxGVZ5T9?Gri?tmf;%=~hbdOx-p
z`&rkX$W0!1t*^7btWP9dUNaFsPM%$<hR~BjuL{yQspFI+h3jjKMQFEjIa9ZCUtWP~
zM2FhY!kP-7b#V=v@m{|od5OaNOJ(oX;8_(tl{J4X=E_nj*e?kdO_M(1-ng9!Km%1h
zFC(U5)rayTrft;O6y30Tcy`79yoec_EuEF-YK}@vHG^sZ$unLcFv>CJF)Q+B4yuB7
zW3aOsU}TQhrnC3f4E=6>!{CUgY+;x+wl#sa-k+Tz5N{!2&qeG<=4Oo#9Y!{sC`WHp
z{3uzXw->m?oTT2*Eyn1e{mAIU6SAvC87rs64`l%(k4^)6zgb*P=@Wz0q$omZy|*iU
z>Z76!7S;@Gtc3rQYh94*8Cdm?5<b=04Z1z16a#N#v*o^m?P-Ye<PoELbM@Zly{GEz
zuIvcWE?w)|&dbyEBTwhr=5+&OuvW&C1AcGH8<xVo<0F+To(P!fgY3AfA9EvsI9<iO
zsyoJ(esjGZ%%3#XYtOZf%Ff#)sYP^h><it(+#_<nCnMb|-tJ;{<(OIzUCds_jue9e
zo3*J%?&-3j)Wsj{vdJ~@=2S+w3aHTKQyB%uI*raK2Q0>FiF6cxsn!w;QC*81qTCW+
zbcZN=c|$Bgkx=CrVEfKdA$^HjmanqoO_b`}o8n+>W*p^sv6q1{*v%{}o7aabVaH!2
zullm7e4r@4$FGI;-2U9pZNeMOX_nE%+4;YKMdP#iWA~!_=X(@)^hJ2}t64QL@hB9L
z>9&-2WLjx(&R<7jyS?>QI>j>wD}P+}_HoN)<_2d++`?uSWSJc2vQwA1i?CvRfIYlJ
z4cS@kiA)|4Ukx_PCpcO!RPD?2dBA^PC2xG#FWXd|cpL-87VrRgAxf-LZMjsWg84jR
ztmBtK>p!tlNdKef92cLN@QKVVObT_)oGOH8pHa?>0Bp8sye8(0cJolj=ru44x>xTn
zrwq;_;8!`PU(E4TSomc0@L!tQ3D<jG0+26YEA`|HU+(^i#DxCN^q^b)(dc=_E$t2D
z8}k*(-9u)s_D929uS^l}y8Cl4s0I0Dro6e+XOAv_5s$uU0u->sZgO#u+~<06tSeB3
zdq<m`XdZWL(MFJ!NE2&tisI2Dt@iEF`l^d(I72mikoDFsmFcS*Hz;*D|F{E{*4e)E
zl@a@d)V5VoYQ#L~7SF4+1|Hi26ct9)eFassR#<<bHdI`Gd;)&vS4e?(U&!>>bFNZV
z8~dKo{e@mfMrwyJc`&7N`Jt@*V3}&ZDdh%FT^=!WukT6E$z|ptzn{(g`t^1X4HWob
zfq_^cE9LrFNM?KF@jS}psiw@n#7{q~g(nFXBm6;(K8kL>^l2RFr6lN^rN;OC%*E62
z`|~c%+o*G0Gv#XG*>N;XI^S4FGxM?B@fYDy?rW7$o+bY9{!n-K+HPYlSV|9~Mzb6@
z<R}P^U&1&!ZWPASPkY0QHTan(oc^O>E0$R0*Kx_So^UJ+e-1M}ZJD_X`K5e1BjMAz
z7IHj#$i}hZ%}av<chCwgYawCS^-wHFcj(oicA{9$61P7$Uxi{9u|X-TN<72KIQMw6
z)Y?VQ_~|f_@E_Rhgo%GkcsBCeT1kVAxZ7Lj?l*+XOV620T}i{y=Lh|u(e+E~%0oVV
zaf5YjOVmB+>`%MOeLdaR=;-$0g0l5_d1Q21*?O8~wO17=##bP0E+1Te!=HJtV!o0h
zHfpnz?bEl+dq1@X^-o=zT&2cWd+|EQ#FvtQ_frQPMHDEux|*7Hh}x%rhKL6079Gc<
zk=X||gLCWV5z9aG@l(a(Ut2FPyo)+}s?8GBAx;~Ab+pP|UMV-Yn)Q14;Fms?$L?JJ
zBEzKNBkre#bY3(c;+S<7U0fDLlb=v_b!<R2`=wXp5;Q2)*6D`eevCt2w{nqFSPDmA
zcr|-fqYgyh#8Uxxa;x4%p}CIGjbc6tf<1RTF0|{9u9m4us4L4U`r;LZNyaZzAIojb
z^x@dMu=Za{t5w(5k~68DNd}9B4Gm%#)`E`czUl)E8Gy^^dhdB%NJZYM@InLEL31dE
z8}*3fWCUIzR@q^8K=k=$r1j^srwoiIE+HfS8f#Zc^m$vKr0Wyq>fp!`IvW0ZyMBj<
zADQ><FNU+BYPDza!k{tZ3FG9hE>UH2<*6aeT#A)BWA$6@p5@<ke$1-^GPmW;_a@oQ
zm$=k-gmM>aJ6qQ(23LKdYeSsicEuK2W~+X<-u^s5f&5Sj3<s(ViywSn`$xHAsUnOi
zCyZ{hK{I}~WH)MY!_0BR%swe?`9&LImb_AkH+$0a^9mjhw0JRveU@b19u%dnYop^F
zzi`v!6(_klfZ-pOALW9>x5R&n$CC9Slz|iKYaZ?7Yc4uIQG2WpJt-}lKLoXE19leV
ztC*`r1lb$Dpw{>)Q)c#8C!gjFwi}-VD^e|3J0tXbEw1C(?C{+c534SZFWQ~tP_xXq
z>kw|&gCO2a;S8pDW2|!nLN)+-(&DzeX2tK{v79o(@Qnl1!eL4Q{@@AZ^7K`5{*C!V
zM~Rl6?txLvKmGZTBoAUS7?4~~Sw&6O!|U&8pUU7HBli=#mYGEVa`9;*+9#&Aw;%jR
z0mr!wxTeU0+gS&Vj1imxgXzV76`@Y=_!D_`V{2&rKoR*=kum2^k!vcICwAYS_(kB6
zKetO~mdISJehN(i)t(l?j?+DqrweLiY_9i(-sO&?N8EBg-E}LtRlcZy4FBYzkb6`+
z0E}5J(|szG%<<RB7dxt$26U`K_USTUy~rq4so}OIqXb+B=?1U%s2&-&oFRU8nA=^a
z_V+S;q7J+Smr;HyW9t9BjFxSvqX_tS0nBUly{CQRa$t^}q2xV+XGh^kFi$>@O-hQY
zzcJr2!~82QsOM#?dadz8Lse^Nt^Rp#n9I{%akmfMByCjh{i;uX6yw?URNMY^?z8Q)
ze!9lZoo=2LGY55CSQ=izxhXfy@JtWT+>YkGsPigcbiA^ZUZi$VkY2Dgz)37;h>a?`
z!C#x1M4RhEa;xpyiBZVakfR~_^QplJ?91t6Wd=8c^xMWV0yZw5K`Vlx8F@M~>Oek-
zn@V|?rfm`hL*&aM;Ehmgc>qi$Ec~2HtUHGi>}c`d=eZr5owEuajwy3WimJP@F?gi<
zo<YXh$9t~M1V*divkS{@3{+eB?ZcI2*@zY|&TCE82seBB#zx=lTTD$^H&mzEhTW)m
z@(QSvKG27(vp>CY>$C4H@cv$SFSSVYjc@AYH;OvOiL=is$0iIIJ?FswNzfj4Ze?3)
z9W>VQB<QA@l|)PB&XM=L%wz67SF6nytAfCkBR4W@RpSb!?n#JOj<63>C>$dHA_C&$
z=$LeA>VX)0AnLx7?XvOBmp`uGv8^9$vOpwL-1FDhj#LC?sBvG3i`qktd%uKcRH!1%
zwd(b_=DzhW%a})ek}22wLobpR-h^j?9BWBA_UJ4CH&<71?V+w8KkH_I=&`5E=k!vO
zX?v(qlVMxPTLX1JnEz%St0-LW?mm5!eoBg>!LO)-m&GAou7bX?7SDYGY7%)5veeq>
zayD)9(ll)EX)CcicuyvTIJ28~;kkf!mp63}=azgBT~gLv?~}|K%D#!0FxvB+O)mPt
zKI87xpR)s#-i7SL2mAqd$y@h3K4HzWwJ2~K1{vKeWi+(2;Pz+XJz{$mQ?j<xR#DM!
z%9uGt%xqL%YjAEErSD^Oh4<&Fj{&w-+|C2ly~#miAy?cP3>{jbD}__?#Lv>gY3d`0
zMw!3&kokCZv?6dbH!{sY>cL7LSs)y(6mB~3Yp>j#r#*mj+X5oS^*bw1+6_z`^VS6k
z(@mKHlYLjeToTuSxf<JP$jXM-F+|+apqeQj7biUD_7q*;pdFjQgeB24D%8GcU!@u5
zS+;t8e=4`ad#)FKoQ{Hb(B)}0i-l_8=IC?y`y#L2Gu<yuihVhyngIxB%lA_s+Xcw|
zWNPhcPopOKr2d(`XyDBk?H&;Tg+H$AFCgS~BbLwc@v{dD#Gkof9{SE}=|O$+c+nMf
z^`Y_x$5U70HeQrjWw*^G_6&8~r<zeML3Zn(`(-^Fw7Sa99k+1w<78^zL{)pTo#U$^
z*Fr7&H#iK`Bevxgo5e$KIwX=lQ<!G`xq>&teeTzAcQYMgw|_C6zm)@WntfF7G4ShB
zS=(xsYvI(}!|vcQOZ_t)HfJg#_H>!%*0rs6A}Epaw3jEig|oQjp>sqLP}ed;xmRl~
zTXHRfd<&*Nm;0DlR&-F+ED=`}g5KVvUk%!*T2$mX7>Y8r1QtgX*L>7P<FVDBUOz17
z#m{lv$qCCGTR`A*W$NA$EnG6`WVz7n&CKezpQ+tmy{9tncB;GI6xxa+$AYoSxsAH!
zr{Y(mv={<RrdeD+W}C3AbG}P9Gok0mF=TzfWfuzUD5~Wk-Pegeb&UpwqE=5OAvLLC
z53<<Wh2{a~w8d~2T8$``zynReDoJiG%od1TN<(&GuivSNPVTmY=46bQ6ooQF)z+S<
zz&nTJ7(zYj!mdM83W9VME$fIQA-DQlCg&aL7vNS)^V^5$Db3})-VK)bi*86kC#m|z
zR4R7(C?0x0^DaAcbOPL0yhZZ!I%53rUa9uby*M?+WvEwVu9ao&;$LX2Li$~Ed!nhu
z(Xg%tM1OQ~wEl!YuRQIS-2PLX&zAjQ5_jarycOn3`*feEEYjZ>TAN^Ux{Y7XQ;7VM
zM)$LTOnt0MCSD}=t?h-*XHm!7!4fy+wANi6v+_urQs{avveuem7ByK6XPCuJ7Fsj<
zLG$9`)dw*%cXH)#5j5RH^voP1hO0e9I|%sMnC*1qqn54H7iRm5m(wP??LqoSY)o{O
zts|1o;&FMK1s`6q#$IgV+M658qN08(Prr9%=&3cGRt5C|tHcjc85zt1Pxmo#emy!G
z+*taOtoNkc>t-w2Vi)s`+e)ABhC(FbII2Ics{5srJDZmG*oa95D;ykKTu$KsU4PJj
z|8$O9NRRT%NsUHAq~)0Q>siy0+xC8}k?WPnhZGw6x?yG6I!}ev;ywKXSo>SQxo4(D
ztYy<)ldosF`{kI5<}AS8@?NVM+j}3(N~ie;$4#M1Y2&enCzx4Fn3{8uN$R-5)z>Ws
zep#tWAMeHuy$v`}QE4p_yy9Gi+PdgUKwl{_qE{{A|LE@A-;zq#KJHYzrafkR>e|zo
zGrAj5V@xem$@5IjsHkY-fz0xd;xS}I(L7?~>~SWwbO{=mQYm=O6qQhsY1d?+W~QhK
zLI=<!l|lspg?BmcU-16${_wu*hjnpXi^au#J<occ`@X-Q`(8%NzKRkkY)p2I{$`a;
z4C+hZ&&=z2ORC})ibwH0Es%Y;d1EA&lk;c8J^sbz;Eu#MU?8dGTZ4|yfU=o<H(cki
zcO%FT9vPIaql1p~?yWOCl#^MxWE>jxbw7ev@|p$LnRAPST}-O|o69EFUPG+t84hMd
zMd;n4V2>D`KX00=bD1NgpS%}H6DH0>p;6)P&Qj|vv9DqrLa58h+O>O1vkIXi$kY`H
zBnzIsGsB11L_7)PnRCUB7sl);zSd;4+*g5xn%`Wh=BK}VMeHDg*$fo0i2$Qq<3IbF
zo(14e^HAJCgd!bkKG@--ZdofvMy?*}^z1%O%{jg$({0ZlB{+N5TGylgU*(zpSB!~&
zcn?hG=do#;tTmaqR#IexiM_CYG}%NhXdH=ulMw<`TdA24RG3WnVmDfT3xtpYS}(_D
z0jR0et&Hw`QAX&*!hw<$DA$u_GF=3Bb>OZ;J6Z3Ef*l62FF_s0z@S~52jnW%0zBLn
zt{B};1^R-6iPKp!JK3s41{OT0hiUD=9pNpKYL9=L;R}qdsRbH*RFx!4OPq6wbDePd
zeU)B9ajOO=DD-!BLtZqF3hkv~_6Oi_fC(^AzyoP86eJr1pdjj2NS1#Ww#8;g;pZ4~
zCRjQ=g&9t^PuzNY8^Q!BGKigTc4clPkPf(=0K6yeyK}u$s@Q0aRn}GA7e~GybmT^@
zRYg@xC}3O`w)MSyVHt3XqwM>nFwF-9T1;3;K$ewyj0pLrGJP7NRaNO7rROdqjPVbM
zlE=O;sU64kaNrSAlVy#L5@p6-C9gk)c!S?cr0gTdRMQ5gEVyP*w6Wk|#!nuqJf<>b
zUQ{t@#Lq7mH$sd+i{JRFrNj<<>hb0$%1-mvW%UQ5;KkTjLQk2>q`qz_OqTt;yo+!F
zn!8rsmEj${aKI#jmz>8rCGG7-`|+43;?)#q>lGq@_4NW>_=UljzXu-uB)YSW8ugPi
zDC1XKs?P%`D`!h?`nTxJhLPd^*aQ0{dTOpC+|~@4$>AyPeKETuV<I1UPYPF1lAIcL
z4w5CMB8A+aaRmI&&jazh<n6Fd(ax?+h+O~W*-R9#_9@4s;aLqt-2*WTG`^Ub?|Wc1
z1um+PK*JHfA(;4G@)Pi9vYv!2V8{>}HFO<h3BYV2YJGO|4kR$Ji-Dp)YZ@*{>&vFG
z`8Vb5n+5gWJxg_XLTl>VDw5cf`9$YIM-GbjX~If#{3|sJ2&C6VkvX>}N18<w{_8_5
zRGV?7%q24p&+#~?-kPf%IAOs}#{hEr$SAAeaqhuckLs3QD^>(gzmf}p;sc3|=a40V
z!Kr_QCOr4%%*?^i5H%Uvsf`D!A<kEdT&^P&5g-X@Z?^Ojt~-X-O^lv$LsLD7lK+S@
z1LezO`Ma0KK4B~6+*y5XnT{zE{6~peE0G35)LRg-jL+(yo)5|cgIuS}b+4051>w#e
zbv594Y#`dUp@4%Yl0l#k3qC;X;}+cB3R2u#=I+V$yKRUT!<?_WQs?^iiUbXi(J-LC
z(eP-*^j<Iz`+RB`d0+<~z-_(B*euj|=Z7%UPD@pK#uE?fd8p^nhpx{~AY`>LIPM3)
zZgS>HN2A`EviUB%+d^s?QY6dcmiJ<wK%UWBND%S;p(H50>-(vp)Cgd|VN*J4%JL}z
z?PF5s9;aQztdbm+3&Zg1v(Z~SliT_g8{d5<i0TYFrtb~BxW}lekt!<~toKrHn{}!K
z=$U?cX!63gRhIF$cFEJiwec+Rg@H!^g8xfQ?a<M^BCwN9oTvxB;`Y;+%-I(GgY3Q%
z6jggj3nk^RzE<6|TnC8W^io`Ve=XCnsWrzm|64RMzShO;)NL?8#M_4Q5g%+DA781D
z?Y@-1<N;<`hml?uo;~p<D=yJm0g`^Lx!)5-FSYGBZ1qs^ScwXWRWB09g<CS`#P9qy
z&a^IFvm-BXbtXVNaK6)`RUa&F4%#lpv`oIW3yY+?o(tDM%;y>UIkgLy5}c(eOeP#x
zPaLoEgJP$!6iEzRR6S~S#Vkmz{Sz8&TABitzL)cV8LdYiu=RtkitD=X`5%O{tWYvF
z_Xs90g0^&9a=~P;DD6g)y_>0DxGN4!U4vq0S{U{pSNb;-zhQ{WfJ}+^4gSB`PNoC*
zp2;%DmV%=$PPkNyh?0LpVQ1Zrp?Sa3h;dVQwsW$%B+M4Ku+Qn*92vesjnlW8uI_1s
zG^2QJka3k|cBJo%bU&aSI$fW9%hNqt%ZY+!WdjLx;kln8umYH`nxIC~gpHcX9vX7q
zZ)fkzDJATh6b)T?e8kx8@9gWcJ-e7KK?Rhd%-Mxd{*d+iQWwW<@w@vH$lJMEzt?+F
z?TY{e_=%AB%d%C`!2!;BwLpc$+D^Hwpqf<*V$u$$E*q{V8uRm|TMfd4*6W5f`d%0I
zAGa$C5LQ=ePSLp^t_b_PYTXwJI$MkEGqff3%PXdn>)eKuFFY$4?kSDI4;EitG)_Ff
zfc)CE%c*lfUx?#p=3A#=6)3!-uWJ~-GSj&9tPLT=WVl`L*xbqm@1Fuil)P@FF2IBs
z-m<w>vQw6vqJ1BmU9g+*9V0xdb&{R#^RjIABsbP|6w{%KAStq1!C{a0sx|r6z@TJV
zK#~>&I~aB`yEetKy|iQ~x3zLJNUjEnRSPa{A7AfBk|s0=pQo7T0&aKiU^0J<7|LMF
zPFyJnlV@;5I6!aB{gJ)kZN<tj(*z>O6Y1ul4zkQ!4f6OZ{KW9WixuL2c$lz$J}1zJ
zjJss62P87y>U0C)8%-aCa4vgr{GB!-tjesyek{{di@bCJv+!V6q?<r`;bAe*Rxa0!
z{y2+anSD7SNx*M5mR;~Ph>qXhejbG!jD{VK*)jpiCFbT5(ZmO1#z0uZhH{eR>Y?D3
zNU>cw>{{}r`T8@wI;!O4zSP{Qo-}`$;xSJVWd!ZFvUtsSqdUWkBH82c<sBhaH%yS3
z<>8*y0UgK0z1ZeZl!bb}kYi`1R`FJ2hE|42(z4V5r8S#cZS->G+Gh;P51uS9b<C_#
zH|UT%&}ts)t^R<1F_anBTMIc9QiTnwcQY6%#Z07vQ1EKY-l7!mwzr3XJeV|vL8t~M
zHxhwB`bfCCj5r>T|7s#43th)&HkRHOz%B`#aNqGz#gzbL=3;E^jkqhwv^s}ue^?hG
zj1yUqy88y@!pAX^wBHb_B@ura|JQU#mACpQbFz-rO*~LitdtF0<duQJ-ay!%{7EvS
zCt#_?d~wVK+3S*n-y-hN(z;>LiH^3s-x%n*vAAWA_PkeT7j{P!b+fmyw7lpSMA^FL
z=RumLM$%bJ!*tjY{L2&u$4^4Q?xis+XTx>6-|)9;B{N`Xr2xI18^!CU)*fU9X~WL2
zxmEK|g<o)LA3qKUgB!ISBQWP(5*&rUhEGdQ%=zF0UiW47aGJW@E<av&Jfo{K40Ph3
z7)@WK!#%1b&uY#!ioY5N#a}{gQH3WPmTsTwpw{#TXj{AxBZ0}<1Z>w}Op9FZMXrkh
zhBB*q*@J^=V<rYSk8bzEdK7|HI6T`|Ya6KenfQ5p76Lvp=-Fz1*o-Jyg=Tr1O?s<w
zVU2rExpAXtLT9!OzH$g*Mee`d9P8pgHxqWy#V_9vF$Ni<6$N_*fjpjT2|fQSfk-EM
z{i73&($ID?nH0*GY&40(J}GQ22T8Qu&bCq`g|&`x)dlMVV%SVb_66Q=4=TG{m~Y+!
zZV7*A7C=8n->KRhhkKOAR}Szi;+V~>XEuk01vRoYy=LyuL2BBKMX#2jS4R1M?f5t}
zYq6rDo-)N+tAQK6x1o|GolqFX<+zl9_VZQXpwc2BJhV*AiR&4~o4MN8C1DASmeq1I
z_t%)Li}-G(IymKT2BqJDbp&5ZXe6nh{VXE)*OxW+3GcTUE{CMn#ck<lKxonblX9Y~
zK#!o*6eOc)HYy*s$!}Z258ng_*S=kS;>Z(YMwZKP{?q-|4y)js1lqUX5g;J(EB=5D
z&q7T!EG$~xjY#dV0axmRX4PsR2n4DBAz6ydRic*@7arO7XIilOn0ZtO!bwhyym(er
zFwwFaDaIwYzoRFg-W*AC++pB#1Z;M?mW~4wP<8rD3c0eIY8Jn4B=e5jc*-<Erhc{l
zQM6iYp*~CTQw(xc;L=Y08mVj|2K~v9DR_#&{m_1Qt{tp<Zq#&QUDwjJxp4%|1XIvb
zbpnjl?u5`>n5FQ?N2tReSO}`aymD6AD;l$b);}ByWnbr$Q+gJcnMqNA{+$g98}jrg
zX6rGkM^Bi+AD?bd^7UTJ?DGJjWphOcAvj&ihanWDhGy!_XrO<sN`>Com+``S^*%_A
zx)XeW8x3TLJmTA7Y#=U?ii@b+MDG-&Ox$Y|<?BT7RDPon35fm39DKRcjU+x5RVBV<
z4=}0Xl^$zo{pb+ct}m(5qgsi^r%qSeZ=9;VDGA+onSmgj3or1Njam;lO3z$9sM*{S
zipB)!joiCVy<&PGxLDQxN7*kEk)$WL;o2{+f?)Z53-roES4egYFRvC0?he0#(mB6l
z7(3MqAyfd-d2m3Guw46Yzb~soaERe@V?|8YmC=<A{^K8Bi&m+iCt9ixl@WXu%Ob^V
z_#oBZ<}vR#4h9N#tUMi`yOxUdI;Qg^EkyYk-(er9Uxu&Tl*lXHI9~Y}vx$FFhh=Vq
zuEBt9AWxRDF9LZWQI1e_@p1yqVj?h<HCFd6{(g6(07WzL2QBZTh+@xCT36=vTwU&2
zE`{hDnFEe!O_`?;D?W6gWH1OUD#?xnK0|sHs!SYpYTx`Z?yHIc%w-OO+ICEw|964>
zE)BB(oej0(b-f5AI->6m-UL2`2N?62{fcv@ic=@IymQ0aCws=5xNlP~H)>{TfxMPk
z&~+(hbhg7i&OueprBsRg5sRyCZKKnc+4)lSmSz%mG{Cy<T=PQRnOW5#QrDyTCii3O
z*VPtqovPf)A<e%s=i@0$ZfMQS6lkfVNpres=-fS|F5Ho~-+nQD(bloIM{y+_t=_Mh
zWWJnXHzEsW96t37h6=E{*BGdNqQ+TX#ZacGLOsQeo4lTnY@1X9qf^+n(|VoL{+B12
zL`7Mr20^uV4=)=Ok}rXydaTL@3LhhRdme&hC-V}!<z=i<P(AeB96OA*^D$1#RDLRW
zStFT*5ax1qg~W=>Zz0(nDz2!-Zx8V$NkLj3yWUv~f8h4W`x2D|lxuk9pfl-TiGyju
z!W~>$mt6okGA{curUy*ZzDR=Ch1UxofervThXr-S^p)&pn2UA~+?1?acqeWwy%~v>
z&ORuhRMVMadJ0l$2Q?QljgI<#Eaxe-+NvFe#ungnU9#K<S!fV8EtSFRZrP75o`CDb
z#6{yLJ+~xg$iXKB6><EI9+b?p0BpmETzgy8%VDKC_ADXDDn&jUTs750r7Fk8iQxIn
z{CM7{gYS_C(x^V_WK|Gcmr|;+T8s*w)xH6%aYV%NPBVY_t~nR<F8T3FH#!^_!@JPs
z3C1+Y>s%28Z0=rph%O9S!js#Zji+kXSZh<n4UP-CLGC-^Q6c#oJQa}0?Q8aaEL-v;
zm#E8$`qeBt<e7^8_~U2LbRV%b*9P3oX0T!<t@L`q72nhHHbc)Wim*TsIjv=xbKU8a
zzuIP}$`=R?dFg+_$<fKZ4W$K90dR*2@iN23{n435F!ltGLI^jOL3!Yo3<l_ExzF$8
zFF)=>ZC{A?x;!d7DncPxASU&^&GMm{hxEUTOxiv^CrYF%VC4;X>75k%(b?Tg{uj!A
zG9D_O$9jF#HJu4gEoa`sF?_av>i_t$p`R_<d0^X!@AXkk^Rs`kTK^-PPJQaQgJ0X_
S&isoYhyNJx!@bkdKmQk8Z1EHT

diff --git a/reference/plot-3.png b/reference/plot-3.png
index fd47f98f8a06f7d6b4da63db82380138ae2f646f..0e15f6077a52c578549b8eb51115c5a09e7f0599 100644
GIT binary patch
literal 27461
zcmeFZ2T)U6+c1hpkLas{1qG3E)D4ItRl0y4MA=jY=^dn(2neACP>-TWksW$fq)2Z9
z0trO~j5O&GAVCQrH5f<`V&LwCbKdW}^WFcOxij~__s&0mW;C0<_gd>&Pj4&FZ|mJU
z@-N<hv9Ylo0X1(Ju(ACfz{a-M{=k0t&UoVMRrqVaz4ome@HhO~RBs4_zYgBlH1lO+
z<Bwqe+Y>wU!;y{c4>s_|^}7M-OOwIr_xw;Izw|e{nw#0AuX|;ONB-+ApmjU$#lGxw
z*>QjHMt_VRc_!ub65vJp{-AuMg2^rdehD`)#YZ)}{`L7^e||B#Cb{q2i$i-Pj&dcw
zuw;K2mlC?IFjS^AX*hS+XrrL8bYX)|RVv#Y+O+Dat=H#ltqr599ArWGzyJH6gMsG$
zjRfF~G9P!8^o0-`w(uFdnYVk{*utf1rE!HEbIKVG0nFQuc0q%CSP+y;9>=gQ9JC&B
zGcRm)z}4TF7g3^|W_#Jf+Z0)^mWMEJqun&1Ibd@L=+&kS0u8l2#4HjGAnH@dV5zQ;
z)gqOFvUVl|9W|xYbRbq=ddPgo2C_^cVTQM4UIeJE=?W=c#aW>F67K0jWMRegIA^rS
z+vK%J078#)2^<#QybguwQIKG%*^R5gK%gr$R}-UR7*c}J^#LJ-(53;@!0jC^e0Z_Z
z<?}<(I*ar~bftP6!klkYk?qNYe*GC)w$lh^_HC{G0R6Qq9iUr=6A~kN@8z2kN#kIs
z+@pX9*OzMA)BZa!QrJ2NiwprZr&IU&`le+w(WIxU!&nuZc911~5tQ>+5O<-LxkeY%
z537S5X|=OGVCll~X*FQ1zJteEMFuxyHGEmnStL*=YsBUAtCbgJIotH=mx|*;b>-(0
z)xJ}YY}jo?2FT@U?xTI1Jh?{Jin}HjtbU4yN(L$0!1(&n-p!M15~V3JMn44Pgg2|i
zN0f`~3ArjCQx9)DyYhrdFsUGh53XAMct_%SR061qeMXhg+#n4Ko%EH^t5j;!o3t+V
zyX}t6Ku-)zrz7%e=dS38Y<~K!X+Nj1Vorqn71x&=ZGRqr?&n(r26rIy9gZF(<Y?aT
zuO~PMCo3nMiyL>{4x5(@l{!dB0o3%oZAQ#o9@Q?SlF2?;3FEGY>`si`FR>$AWDOXS
z@S96%Z9-D@LehDLOBl-~*;1`aqi(;#<Cvt}pDv+gBXyVBI;O%#okPmrKEEsq**eRv
z`UQ9;Enac_vY~-KNa*Zm2+-eT5(C3x3f>d)xSCAg`d-t;1V7+u8r?_MfffySI-hxK
zu5wH}yMEbFW0SPUF7>dn@o<xi6~fFRkvz>pz5LN94Tg}74u!@g_+q6k&0w(Ive-E{
zxbiLfu_5p^sM;`cD@A{4O-XRRf2STOEBsEs3FV8JC46((r02XnjmM2Z9^=j)horvK
zvqAQJx{93aD|egt?40J10WfAsdZYGIk4+p&M0RaOtD<3D5%dS?!4>Cl9HI+4CPJE7
zi6mL8Z*Kj0dx{mKvU0uacVH<BcU<Wh^O+kfnZJCtCO9&-QP8rlNlxQYzFWk25_DBj
z<D8RHTWQ;XTYE1MXwX(&=)c+{ko2xpHY3|<MY$m=3;fYsX!5RIKVS3Pgo#Y(NaU!$
zOUaOHPvAV}p{--#;!bJVdp-32KpUWYW$Ln^AXsUF-x$Pm5nD&7M>qatIBJObQU5X#
z4$QG9RGjpU(WPXsTXW<T3jBzcT}vgj&_%4iEg{*-<)MlDQ7q2Cl6_Ii7p1<Kb+=+$
z2a?E9GNahI&oUg_((Ct;qecDKyEm|@iZY<}7rUM!_jW<+W8)TXvx7r7RNmU_NeJ%P
zL|rFx<7|&($~+_+LzfIs7!K{lauo42Nl|k(w<^MPEsdI@T$Ebq1aT=^+7y3#Z@=UU
z(<Ftr-(5Zzatm5MRY%i3>E4_3*4M((FY<;-eqe0Qnm86-Ui)I{+UrzB6Qi!N5~rT$
zCw)V2OKty({842rHl-r#_;N#|KjhJdAn!9*sX~EO<7(fZQIS8whHP~#Oe`IQaj#GM
zrs}OW=4(eWgaaa|Rad^#1=)C{WCQkugFdcpo3X-ye`x$Uvjz_5{tWmjFuzPxx5)qb
zHbf*zsII|jrL{nB)H5M_)={m|%zOKmtgouI_jbOlum940uf@Ab2@17NB{`#3KLt;s
zA|9kPm_DwT*zb6jnv%BO7`YZU`gKCCp!6}Fn0inIq^zYjhPvxW49@N!;hpX6e?Pec
zW1v}@hQCYpC{hvtDFr}psTr~?brTCr6U=3HUU5tp`L7NOK{jT-(R(k=>Wp;FuiNI;
zm$l|`DJLw(6xMMMW6FdNFEl0(t#c3(7?`>}qjEzP%TkD?&Wr6<Bh%G+=wAkr$1m7u
z`UFDb=&SK{8+t}1_!J<_vci8rIbi2iXYjl;J6SSpmle*>%2%;91B%L2POF7`MNB<h
zCVE`WT`8)dP$iX%Q*vRpCbhAi!m&1lt&NsNA&t|?8^lL`HWBk&)ejeAE*dGHPLqC1
zw?FEo7uUs?n<b=(%_bYIStGYJt{Gqt1q?rkv<dlj1vdn$CZ0RCIRP}}T<|n#L>dnk
zSL-IZU)E^-WyYHilgO0qpf#H<bTyTvSg@_>Bi{s((${GZHbc7x*QvfGSwrhXsE}WA
zk!#vB#7E;C0gT)rn+N@!5{I9DeRFb-m_Qq`>i}*UMJeYyEzbisT6aFYHWYoCTPTi8
z9~7~U$Q|hISAYr7{F_)p*x+8aD>DQxPAjDgI9E}(DcbZ~pf?@4vYq;4O|6oETaiWi
zIdabyX>)P5qRuyN+aLbc$n#fy#%SyMB%n5bKQtE5_pG+Dmf9F7*m3+gZW!;+BX8k1
zALb+IeuVDqY6<hY?H+*1a(<3bZO&+4jYTA>{S5k-AGFxerQ%Om+_8|MJrl53e3wU_
z6_}Mc31ieozoHd!_LkLe7*|~aH`+rOw`171k!2p1Y0zg=O_vwl-kENdWsvnMk&%<-
z1FppLZ%Vb(0eAN;W1+F8*y@|qD$+JP%wodrJ))Tc#Q8ny&Pbjx?Y7aB!LY87MJ}P^
zO5NkhJx%w^f<Bo~w!V%jd5#!~RJ0iCDkv-oGHm-9JM>XbZwo5{ISE!8%R7$_=lMpR
zF<APxJh(AE7#6>C@Z?&(%0~~~ZcHINzxcYlCG}`k*>G2&oo+~mKxLv>cUJC`2^h!E
z###=siJ2htwyxhUQcf=#_X}{-C5GuyZ#AeTZsF^+?+lH6?VjJcuHz1c*m{4xQc9%T
zLz8GyCx1^FNJ)rPB<rI@<1*Z~#Ea*ZbMneuiA4%K*$+y+8<aH09*16T%MyYuMATUp
z%lxvA%y@uRU2@5aT$6vGfV|nQ)RaN$;%YeItacIW4mE*sQzew=0``_}J(_bl>xITP
zwd{9<aQ>pIkzx7}I`vRV{~zQR;Kq(|dYD$*W>tN0?dv@9=E=EQeafczqTGCC<l2BJ
z^rgzL)&vMNhQhQfM}8}nDP{q|#wIDJ8s|U%SYJ30gj5WdywVt>Kkr7yo^6sH5-bSy
zZrf_IkjW=Hpt|X%pM`j#Y$xX-%%TB+uF<EvH(u1B(7$s%uc!PB6_BpI{kTy=xcY{=
z0dShEjo4UMqm~nlK{=x?V)8dtLY}PuBG8mdsu3UYtiRD(w*E6TSfx<w3edV~#=Fko
zgh~G;dF)2JeayH4*4Il3_xlmcX*?IM=%Tht3vBOGMs85tlMfQEPjaKvr*t7TJ%r7h
zLjLz_C1h_b<wr|uW8MkOC1?Wg9AX1fiAVen5?J;hW~!Sy&-b&1*F=j#chfFz$>(e4
zWY#@%50Sz78eR?XwCn~H&*FCE7p^_c8AnOYtB$uO2Sb*G=^!%rL$by?BZbtv#nWV(
z24U)Bf7odIST1zx-#s0Xl{*u<v$q)%f!XD@f`kTtx1itYjWwW5_s5rfFNe<1RMH5<
z7e{LO{bTK*hZZFU(ATGA{NB|h+}6=j<W?ts5(^8;k_gy1>DXnHv%$V!HJy|q8ame3
zy#<Ik{lv8)as%GtTdmqN5=ptD+t#LUbAK^~BwVW8hv@)c`HnOUi4P_Y|L6krwc=il
zDr$7&PKv624snPyILIxuX#;wZ*UXcuheHh_KQ3Pv^`BK$ZR;pS=*Jb;Nw;;>mg!bl
zAo_o0agl?QxMY<$^?Yn0hCv8cP%n<J{;bBig>lhFVKsjHs8)=G@DRHJKrdXUAAJ{$
z#{AKSrPKqrgvQj`dT5JKh$3#TUr#K?-r+Os)5e+zZBn@!Ol!3%j{(@>evpb<9eov=
z2n*ML^J!kyiB^K2Ur2GiUjlB|mX@7hV*{c2J4~ejTb>V^|9JMW-2Cs)|GzMB)7@=w
zkL0P$3{owmBVRu357M+KPXT%Pasd{gvOQYT1FPQn+eB92rt>P{4M%vzeJuzfY}VIo
z-XWxT4tL~8@$bx+n~#?0S3E{ezTL}wH{5&VhOQB7lAG85OOUtA3gJ@Kut1bQDFEoe
zm;??FYzWt>;-W#hSV?i_NZaReQ&#3NOqV72!S{wsxN&s4%HT8Rqi`YZ=TBQ2{=l`B
zyy+<bd$i|BUeHxJoEfO6UD3&kDZ}@SC*RBSyBnzM+NI;(GY=9nNCTii%1GEZwrT!%
zR8c}}{T+5z)cHlXb{}~G;7n|{OupBRks1dS^RGCmLnZ9TmX7Z7?=|&!7ar^z*pYW5
z#*s$34iYOPAigZp?C_#2$Z<yN0TZfbNT@|Qs3QrVpL3z(3+@d_FbYCVG@oNF(*9^4
z`MXN(VB_1)QA9kRT~3ghSz(jZ0|IU#$OiNtaR;(!^#($fi+S5NU2`>tpq@+16L*lW
zjk(v!Ucn+kXB0e(m9bmdcWM8N%j7Y}<AW?g<Kez^i@#ag_gc+FZm@O%Rvl_^pDnvs
z9X5FPHgmq_0BDN8l<YrG6u}|gwnk_br<k`Tzs9?mn#U~nHz5ibCD-?}AQeOTuW}H;
zd6l^BYf0O0w3oF{o#J4<EeZs%lM!bu7e50CkLo7kR6X-{GY>qsgbiPxpS}(y!Zk0a
z!>dFM>1BJF`OtJ22$*ks4adi;smih#7GH@eAIGeJ^8|-Qm|PSVZ+~l*`ZpJKrtTy6
zS4iXTLfxQTv_j&hTz=hOOZsQbS-VVBIY{UmmBoDpV?bExq0q$_sl>Ez58Ys`DF}M$
zNXw9TSZKO1CW2EGg{m2RBO;3T_Wj`au*F4o{WovQt1Pf3#=$<)b2{bsE|fW>4TXsN
zTQTEvw#&yctv@1xEHUi1HN*)7hCyNO8RPM!XwlE|e95F`ur#^0=eejqoBk1LQ|17+
ze~k+VURm}zB=Ue~4Kmn4bS;iw!g0&g-fJZvxj1_Eb~Xzno-+t7WhwgWRPQiL-i^OF
z805=TgjSq_z^&b6Nb~4VgM|~dRsNTw=`T1KhM>4uv|`1WWW>!~@!`$hPoCm8cO#n1
zti%}5tNFK_Ax!@I*7vr3EcuI4J4k4;Fmywdo|ZRsBb!Oh;tlAZe!sPk38KzE_#xe{
z0GsD?x3Ye)G2gzcHJ8?NDCPk3Y4U;Nn8(P#0+h?lTT);yt@cv8P6B>ILg5_qsqHy9
zJ|?8((nQc6<91!cpsG<2W8<WI0+MZqtkJ%^xFU1@o6wF+^@a|dn6ZZLjQ|Zmpj{-s
zUf(r(ESVH`x0sq^K!23Cmo=ch^Yb0{^24y_eG)8n!-j@HJ7zN5wQy$Ud$3snW2h~H
znPp^)k~v8D`d$fl7VOclM0LNH2Nd7r?R*Bx+^U<#bzGRQu&vwcLh>SY7jTb30XW)%
z)U+zs4ovgrQ9&oD_>A8l%y%SLmSFa0N2d=nZ512asW$at)<t{#J_+VUbFxA9L+j;K
zr_i(Ic@Je-&%XB+hzdjvlYabo-N<5%<ZJ$`{V!mT8o)*FAI7V;i4*=u+HdZxJ8O>r
ziwxI4%1;P(K;VyL9_+I-Fa84*Xo`g+9>f1}n(Y74NtkL_@{~XtNyBxP`!{;c_If7^
zfbg>lGB};q4(F$vENa*uA8XN;P))q&0G&Ki>}MNkg;8_a6?3+yE~rR;%;HUc7Dq)X
z``!NkcvJs3(CY^0D{aO2(n-3^{r9>u>hc~|GfBes{TmtGbtNRkDH*}~TR4s%)?69j
zwlfn0)Kox1$H#N8J8P?wHx><-rmSFxl=-UdMBpc0=0$Vze<9~*_2I<K|84dD*RXni
zOd_*2_n(DDg8-4Ru6a&tF|hq}|IhYs0KGJ}nmR83kNq=b`caYz_cnk_yrzr#hJ(jc
z6>(ephnZ?7+_95`(4wq};|F_meRiHXn7vH_YUSPy?>$n&QbV2ACuEHZHeLf+`aU+0
z44RM22FZls)t=+gt<*DMBO1<+lXWPgeNS2MGnHsxfCtp1=i>~mq50yR7RFhIzVq2W
z@_l}YBsiB5><yKP)QRA-fH8;B>nwi$GS<$;DViplF)oDzL9a{HgxF3r<_gYOJ^F#d
zaDG7SKf-=ZKI1s1D$n5RgXN_Mdh1>~lq8ULS}UjZ?5>83imu;>7>fZzMTQ!Jnv%Yq
z^&ICA>;L+4>Ip#?j&?-11vabtp8l!suS3y{B+pC4C@_7KSL|5f0B87x8NfNuHybI8
za|JnEt}@42S?57l)o>AL%Zz*KkwF6{?|K+oP)P)afQCXYwn-SS=u?Kcs>}0(qU`+K
zSK5JFv}5!?`Oq}4?dAO;C1Axx+yMHQe<XSBq^N+)rY4-UvP>tbfHc9rATX{t3-qxX
z`~W)~;X-9Z7;*;)BZMoNPyfyjo9fPv0rW)tg3aA70d7uj><PEoj5q@1Dd-xlmf!K7
z+f4(nyhT)-a@U0}!MW!Y6>;s##g`-6kj;f|#hN)JQ;$Y{v}fWAx~i=4o7STw?^XPI
zCfA}+%lOA2{R`2Xw;@jh;7;u2j4<~kh0Hiw4BqDZ*_AehMy?UBy^X`byvSK)v_1Pz
zJ#|aOFt_<M0#?KDtmQefaZ{Ja&*IY1zuv#5KGl7Mr0{3f9V}~r(v$DaaXl|C;Hu8a
zO0<vI5_$TLK&+m%$-LYtl|Hc$d;#}4aubfhM|n3atn96e=ieb$vXtRHq=soyT3{Vl
z=^S}udkIzP2jv5SHnRjn8@_KrRZd}Ml4#O*Re~VAyyQJykpO3^xx|BlxTy>ei`>x`
zh2UPBlzVica>4_=+KaN4FHP?AyuEKt9pn!Fk~saVmuQz8kSRaqe%VCg^F)q^TJ@_m
zbMn)7WKnoJ+vmJ@A0aKlRlOQy4ys8lK09XJz!cHZuaqJ1RMu!AOe11jMT-%9S@LOU
zdYZrJvqJthFpiBls<d73V0%n7bl6MhhhM{4+%3Z;pS+S^osT=BZLt)FN)(;xcEgE3
zLLCqwGIAGE9%v*ItN$7vFL~FKdD21d-IfjUY>c)!ifnb`FxDLHU!1ip?O)VC>T9@k
zce|m#POl?)|8Z)-MWT&zlr2^yp*?G&nF>ox_?`f3=&=R+aSVFtc}kk*(#&<kR`W90
zvufgC_$de23@L^AE+7ZwW}X>{i99%Mp|@%sPbwZ1K|05BbNsR5y;l6LNx&q^1?w~a
za*_8LQwVLRm_T0Y*SkOY{^M|(9>vAI#KiwQvF<_1UadJnboR#<9cVB2Y4x)0Y_3jp
zL)2xM2(ja6j942-e*qUbk3w4-^ZFX9S7fI9KYu11bDlS{=yN2{kRQN9Jnx#+67cQ7
zh_c!;xij_mcIJYTS77iLRa`|1^*l2y5=ioB%a_^vK3IX`IzC#F^);=QWZ#nN#<ACA
zWPjc4@>0yGzMokGXp+xpG(nzLR%;1%C`A8N1Wg$8jxR3`y)dkFd=x0vaqP&)yR<Y{
zkyvFbG6cj5ehmUCBDtGQgL9VfqT#R6QyG@|xfySR)FZ7b<nvWuBi#x+1+HKTs=3}f
zLK;2~E@~Kf`y<K9+S8nfj0Oo@P3~H(wznWLp40-^=-<(@tN&KdxR4YXP#*DP?F44>
z_+L$iyulwIz45SEI7m3*UP}GZ#b}JN$L738I*kN$|2E51jfWHVkx5GDmv0OyLldb4
zD98qk8|7Pvfy+w`oXh+92?cp%(DqhY8>EKafOO%~+2?QW>s}`2WV}Fo(h1B)?iQ+7
z>Ft&6k53{L<ra=t%82@ZYqa}oQ3z+3yD1RP3|bC<yc;<gtM57WQwOHy(0oeT_f*EG
zEs1>1WW8KX&6O;Yc1jvg@X*oWG-?#!EL^9iQAtY!V>%~D-y<v*L#iKmdE%8(YH8W+
z%MT*iFZ2x0Gp=>}1y{Jwt5^=U{`7gY@lBweY{I*f_<OrTX`VqmCcq8ibX;LjR*sb-
zuelO#aUW#@a?K=5vr4cv6Qtl^fA;C?U-!#XfSvfQ%HIV=qp_kEE2^GBSnF=#>w>zK
zhV#F&YI=4QmNOcwJ_}Y6y&jI}M%KTd$@nxGe*!Z{lME&H|C~vj${>e#O-5r!Q$HTE
zLhSUX51cy9#B1m66SC)1LQH@<)waFqr-CQO&DT~84xfcv1$KJ@zCP)F`RhJmu~uZv
zQl=5p>0JiIo?Z>Yn#P@rO9v3TlmP=`nnl>QZsZMXGsV-vUw$i9UhFey7~eZ=nhxi0
z%h$Jt&YPE64o|<<XonYMlf3V?s#khmx;E!eiBj?Iv9RVv>Noz9Iuqv=iDu)(VU<ez
z6_4mC^r`!kW-kg_lwn_0B>yMjiXUI!H64b6!g5(h%6W7BBg`DtDaQ)VeZM7B5r>jx
zWIj^s$5fVi-*z~!czrF=US_{IOC*>(ET6Og3CAfPHHu}#20-IzrDqzZclrcogBlX>
z^0G%+NQgRq<eK4%E`?k|E9BxlTvVlmtI{e%@UI5><w5CvZaaZBVw`sf*DZ}4HY!Tg
z$cxkUYo2<_6<E)BSDtq!@pzM17c5G9dGXAyE_fC1lI+oxNbBhC)nh#FQMKd^o_;x8
z<YS5Fyj_Hk3$k$U66N1APAWy+%^JxhtLIi{4mvKq)xHu{<)io{1z?YH!ImY?ip}mp
zF;S*|wncf&IPYijrpJec4?wSuvTOZ0oT}&oIuUIrA=n?3;gLmZ8OF>FM$xaicXf|Z
z1~|(uSOwEnODA!0xz*NVj>Y@PmLjl4fYhNS=-Z+UUHf4H;12z=hxDCR7(R|lJCs-R
z)Y&wx3gr-C+!2)~ZFt@AW6)Gah`t_0b5<C$5F>Fo5WCs<B^4Qz<gF|;Hpoja;1c3u
zTF2#mKf;#R@EP%f?}jf6!wQ=K^=y31i(HcdS9B<c)Xfo%Snaj#S0nPw{*Ko2Fi1_;
z$2)RoZ1RiJXE>1+Kit=;CnEfKADOcHSJz~qg&#&9JM<eO2y51h=pKJbP!-tumg*iR
z)2(uU2{W7;o3m|absXbsBy_JRrdMdp?%85D(!h0vfa-$j8UAog6!&|<b{4U6B(bN-
z<C&DSIny0@0C5W`UdCMp1$2E(B2(Wqp=s*4o7Dtk*O8&+Ir1wNbjZ~<^$9uQIx!p~
zZ!E%CAd^On?JYSi(xN=_0Lm8*L_tm36bW!euVR|^8W_nLtze{yYKb2X#%`8s|G>I@
z+rtb#%3T_OIuYL!^=mdsB|35GDPV<6uQ*}m`Xlc8G(F|bke+7_c9Jx0dQ-Fnzqc4X
z;$;C~lEY9dniCnr4%JF!oWOvTp7k2-XdLXAVFMlUVQMQ%-%MQy&W0ff1X$J31X}>x
z>l}K@$GnYZhog$MS5>PAH(u}Q_|*RgJi;uFCQjIhRydVf?SS7|4&Hp`z#Pz3;^C4(
zgi}rO8mJG}69#UA_}gNVIDxEt^gHY=Uw#Eb`aqyQB$4};{`qIH#bU3Z(B}0>pCj6n
z-Eeor!XGfCqXvct3FozzH#uL-zw<>JE1Lb#KGOCZdT9_4iBdZ7)XNbH<M%syW#u_o
z3V*98UBTtP;edVE@XNAB8_YQ3>+hu%^$!)`x!Bkeqw|hq&S)*4On6iC&aVLFt6ssn
zFLp>*D4@y8jeMmH;oFT7Nx~<|avjnsoCn6El@8dN1$8moR@h!u!NYAXRmaXd5Yn`l
z*LWj;7w*dx6=+|F;C!K&wv8TT<e$Y8rc{asRb_pVr;V4VCe?7{q;}2KKdX^WUM23~
zcBy(lHoj!O;eb;x;dQB6tArHN;}Tj72zN2qw2dZ!q33b3xxOMIfv;Yh+8&>jT7Z3j
za?3D`)aJj;Q%L=QOzt!-uuMXW@*U%AqaViU<bWyB|ADfBW6bt0W6?(c4X2gf*w(N2
z4^O|ph|`9lZ-xd2IpBti34u5gLTk=HMSk{em+oxnRon~6!%dPS`NkPTpsi`x^N(gW
z%y}(Qcr7vT(iv;=92hFmrq?Pj6leieueJXY!#NuOkIE1~w+^=pfHAr<O-B`8QJn`i
z{?6b%)~aM&zy(`$EO7Y+<{J)<8Mz~W{W5|~<T0}{yw=RR5L;q~-BNal0&a)@gC9?N
zla7x`YC*!UUtA3VJJ^TSiEpw#_8@J43h=T*sOJ8u-JHpnm%@UTtb9ZBQCXwxuYq9R
zDFvjk#_0%U^aQ`)ZvN;y2y+aag&z81J_2msg9grzv9c+*T}2aJ6#l?<Koe=C<YOJI
zIH&xcZ1v-fzqo{WIrJzIxobpCA8R;Og$?}^dV~Ju=GwIdm;hrkxC)R1kwGk!-S`C9
zATH>!m_}(-2FDO}Td<D5SDRX)EV8NPzVsN(yt@!=329rPWI#E<OeH+vbTn18vFx9S
z=(cGFsg&z^LlIQ`-LB^Wq;2lIqFw1(fG7fLb23OOuW`1gMR!v;E}Dk20X7-;@{Q>w
zpd3}w*%zv<#w!DXRs{$L2(48XYvB>jf&`scu#a4pgdbU#BG((xM`o!gnD>gw&sa}|
zEn$TC{O0c<xF&G9+D5XB+-N}eM(w8mB)`JkS1MKw4=ww(`p#%=%SmfFg1(#mv;82O
zFvajMK-~~r;}ETu$QY?LDnJ=#{b0?jgMp>NIRT#qQj!H`J(5UGN!B|I%yx#>a+sCr
zZUPit1VhFRO$@k^<4I2eiJpo6BUQ$LqIBEdVSo3G1~uH=Ty(H;0m@F@n?(?vD7}ND
zPXYkkYp@iIYn~LsZGv(_bysoGFs4_~q|Fj3#|V)?7ed`g5}sR@ON0f=$;@>7jTN2P
z4o>uvNyTt#e+$Vp>sxS>*Wg3u^XB*Ng*RG@1%W_w$VT^$A<{5H3XX*wpf`p~-?63k
zCG6TvH;9dG?TS(MYq-*_RvR;tH~O#v1r-3Cz}OdFru4HlpF4c6#X=5!?sbD*^9U>-
zaJ1l`D&EJuU~7JPIEY;W{q+-o(B74ZIojWu>ucQ3ITvdCNZwoju9jS1p0dgyje#^>
z%12<Fra-pZ8yV52D2f{cjjxmshiUnEK<;NuS&ZxQg#+9cK13YCku2a%VgF-OKJ4Tb
zurcYL=VNyk@@~I=9^uqo>Kls3O_GHIuR~Ul$HnMzA>07;D{y`yOs~>#kp-;nCl2j~
zt`D4O532>Z1TVzHaWO;9>3~$_l?%JOIpXTZPF~bnMgoC35sEtyX>SQF$$_0*Tma~U
z6bewLPjMIZSCOY;Sqmpm<KH2tR}{*aF^!uwF*COj$)V8heP0I>%wOC49X85^eNTpY
zlv(*q+vkCj%Qr~BGV)>v*Gn!?Gt{PdZp}ObxNBpS^7DM{k=x$pH9GOJCwAq))P6;q
zDn3bx8I<Lp?V(MoePX-yws61qn7g$6^9Cv0Zv7@D4S=I_W&!8QKf_A3l{eqR&}if~
zigYbn7%c=pXqJl6VpepzCikH1YIOcWph@Zd$wyM?e>8ZX+s#QFab0M!+A(%R$d{29
zb7>O2i{o%<rt!6Dd-ol%#<QBN>EjXk7h+5ujM4Y;U4%1z5l4hrxno<L+r^e(*8=PY
zrg2Scxk*borS2l`mNv@9>q4TpZ%6@3E7Iq12AtWp${p!AB6f~R3tO{5WBklsz048$
z1QOn0=P}rrN?PeExuo0_(wg(#IQwgG?)GFuakZiA(v0r!3iq_GFo#`JH<o+XQw@r{
zRA*f+LkQ#gz-=VN52qPbf(XgA!gaY!fC;&@46Akmob)G#Two4k%TN)<MGO$?8bsUl
z2+q;cZ_j?OdAw0-uydDl(Nl}QC?B)LN-@JNnisJ`G9uSuY}Ms%dnp&@YdMhTrTq(b
zbC%&xnWAE&CfMh9YdPGbZmq^L)jom??ye~Q&!r_8A@t3D^Rm<5CcM`s#Tj&9Q%`pX
zuR6?Po9|$)^uEWb${e4`W#c~@#oe*5uF#I`jYNKlcVPk>{xk}fTOS_-XkMU0^-J!~
zcLO@~<Oxd{bs}?LPGyu_Y6#4^bGU~n_kK75B#gR+HMRILB{N*=$^uMh1A5};J6&7f
zSmqlxJ27{J1M(qE<9hwdSNo`8vdAWS-P$d?$HO2jc0UWKa7WLx2MJ~QR>pS5o7CKb
zFkAdx$P;}PMZoMdhMV%lSfPtO1G<MMBbX4f>5eC#dpJ(=4on^6|2(zy@?OXOnArTE
znPq3Zelnj+2HrHw{u;y8fKp2$b*4;26jR+V;#5JJc7;2Q0E~##Nxfn4P7=Q;#a?;G
zDy4}99|BkXw_F(Qz$GpB;RF{=yfQLnF&W6xn%jDZ6bcN6sv>xgF()s-cNu6Icjca+
zeRl$rc1ob<M*Sbl=B>Aeqm*T-j)&m1eKDM(p(JX)WTEWHSmOgplR78wgpqq*y5FKf
zJ$=dwkfnEL_|?SUrO!K$Srt;A6O+d=Md+)hyuNmj>{`3Wu9$!|0yoOlVTX^o_>mL=
zKQ_`^V7vy81Qg{oKGZI~20BFQ>Zn1wQ0vp<{C`83vmYVhuTP1G8*t|$DvdVRZ9kax
z$4FG`gLh(Hqi>-xmvCw|)`Cf->^-{`BQcp<!gW%<i)}Ptoop{5-bRxJBDT|9IicN}
za5r2%?*?l#S8{O2kA%G>Wucb_rcygw{tK%&E4aeUV~7rUzrGa%2x|H`LhRCY4Zdls
zxAC$8@_wfq>il$^|6M)RfsJHs65a$*ysF{)TYZBVuG4(^q^R4naf(*(2$k{pFsmqQ
zx&_<I#`lAv>HxBc@8xCWJi)+?{F1bix>=;PEGT=DRil;6gx^{*awC5El8XM`V70XY
zQZiDvt5LfJah>;h0+s&m1(PK)VnhF>#HzbIr6P|b*W$->1Il@yp!<W*VaG?7|4+Qn
z^g}h3TY>|iHC}V%@n5h7uu36Cm(VuOSh6T+I;*EIjV9Fq<8aP2v6L#hg7#y%n{YMB
z3id0|BwcV@6z2tdoRqHpBP^%NHZxD_O8StwyY3=&0J21rG65n;v5j5iBi_Esq$b;m
zGT5AZgT{UeO>(#wXX;tQ*o0u`ImP3WVf=(kc20-*kz$r<%&FT)rX4A1?O(?M2$z6v
z#+iRyKNxr>vuh8G7{FB#xXv4^U-4ptwkv;Zw$Fw|QozdpuyWaX7g>v+|C{k7^HK|2
zl!@D*o+jlHSG7g0YX-6Oic=Y2QH>|+|EOSvuxvqt4;~@&0cRla4yOYrNO@vRp8*>u
z?lZ$5xH9OH1Z==XUghAfFsmAAoK?x1yif|S@vEr#2p0I))R~JU*t4(1l#Fl#ftFA;
z7-}sw*9VXh)f-xr^r!Z_4!|jx(y9{6`<915Yq%FmU=~$9s9O4Z*ZoO+0`IlS$Zf|9
z(pBkc$Q@LD_n)8Kf&=;9#Wb5>Sab~NNpLv9gw58><zh<%vVgJr+lcApH9qD&w$7U{
ze;&!DMXJtvTDcJ?Ztw1+^G~L;n}k>HUL3cSA>(psj&O9u98fNH1@_&{3s5?OYXnQT
ztdYM*tlQ0TaT=z9KKjtI7K$@*>oZLvgBX90-V%X3Uas1<5%TX^_y`;?D5FElhX_(Q
zBal|a(;$sQ0Cjl}R!9Kn6)u(*U_&v(k$1?M9-t0ISbVV^Mq5#_Cb&0p|8(B*+MGG(
zbN%>ruCoXIVuSnNnG1>MdW&ksHn@2o=i~Df<-i+q<!@co@SX_j(>iuh!z%mwi+C*!
z5V&z8PV+`6PNHe8BZ!t#Eg}<~nZoGm%ES`MBkowI?yHMty-26b6zrU``>>XeAJJ2=
zGvH8F#c0&7@rrpM`$%5&`S=M4Q(C{gALY4{k${_km973~t|LtButmM@+=qZH6G*2)
zuSAlg=-Lk&9n`Yl_zpA;E$w=HO7CP~FVu_YP=Q?}i_yP#)tu&=&IcQnA5XI;@wix}
zxmx)Xo*?IeOm0U;S|>mMI*W$Q2gqQG@#{{?a0-Dn+h}+_Z%czL4k1q!%tcXF`oPkW
z!5siM#NVb~3p37{d8d73iJYi!Dh-btnUjZe@TCzxb#X^k5JiVVH`4#KELJ-OpMZd$
z#5|BXCj$zY4(Xq9XyP=Xvit4QqB-x2(W8yO3b(0JG`f?K{NK$rhpmJH5Jp21f(}iW
z3b&;A|K<<T2XKh`s?bKJJv6v*%TM`l<}>^bn`~@h;|LGkvuq*B|36PHW5oCbPn}Y@
z-TR+sVQ3nK_3+SDiVAnsZ&k1`Bl+~P!R&yQxJb?D9QPJY@qt@kMJ7gRW>Yng@R0dN
zbM~U!J~AhS$*JFd2uSGrtcKUH3kBYR!iv3u9U=1H>#{lEGf7>=d<hM3a|)E62(4pn
zn`ynyGxqKt9yeM2W6es{=W%I1%fL!GWflnT7BZOC*xk8O{}9)aPJf}mKOwkKBZr&W
zi?<|rC@bNn(UxQ0hgpC|*<}HAxm;o+$l?C^)v|~9?xfdl)%Xqe#`dS(LKYy#XHH**
zb~}e;zq>^~o#dyoXnIW>#%yoN0^|H{nLhbZ>2XZH5tX650C&2v*~)6eD%P=YgS%Uv
zOUdUNbjdJtV7y!L)ac!n#LD}_nE+>|uUIjHMOC?~WzZ{w<P8#DlDYuyeBhn97Nrm9
z)%Ve1@4#QeAq|c@cFvS1(h0@S$sd5Eyu(XdGw*hWR(hO>R$%CB)F%gJsTrBsQh=`e
zL*Y#KHt3Z`>I1FOByF?6Og=(omIIx3CjTljA=D<Gf(ezKJfgSHTtRlGZG*wn)_I@V
zPtfe-?-aB*=VpQo#8>&$0qcJ)%kwyf(?Tc@nP>H3Syq?65zX%xY+(p}3WhNEB0I)h
z1k(HF2+`&=KaUrUPZK&y`pMUg8ra`8Iya|!>Qmz4xrx7v62Ew+x!)*;D`d<i<n$gP
z^&vrF#ehfC6O9WFCPsn@Q&{gI+DVY62j-~0s%MCv-+Hg>H}P2b8MXvnn9Py!&08*&
z3Qeil-XM})@EK>`1lSo_3E2_{*8OyS8LPlf@;xL}1%(t9#QGTA`thaY1p4avNz7|P
zVI3d!ChS#}A46S&;{?@Al3IUSrky^-4|+i)DKNCwHSF5wy9cL3tO=QfPh&foq+0Yu
z_DrLMn|QBI%0+v9Uj_rrOdutI+X}ce$Wq022X1AoDNvKbakf7T=CgnmOu-HcBJ0g7
zoauv|DILmlAWJ)C|JPr@PP(*btAc70ZNPt`TJa`?k;lc}d>0l7-C{P!p`Z|#uF-I*
zA>#hh+;UZ{%H-81zrZyeGHh0w?d$Z7L!aW^qfj;zOKS=7e=RB+sTD;}O+th@`mMvG
zp_)Uzo=?}kwW?6*ABjLrk$v4bS8P>E$l2-GDgKUz!fi6aC#x(Msk%o!`Ok-PnGi|B
zU|Y(x2+k>LLK4U?FgG{fq;iU$=SvD>1l<4p$o0*HG`6pa6Kt><5Fb}_Tt`5^#>1D1
zMGLi`s?|pLGo?Ssg954Z9HbyU-#{a%H`b-$JkC)UMo(yU0a42F-TGdG<EeA`QO6nR
z_`abBbM;_oNx_cE=L0|FtdpdZ)-w!mQZh$SF3pu#rHdXRvrJ9xGWTu$=G7Xrt`PUn
zy~`Up@<V_2hwdZ4Hfl&a?CNV)#gO0fveBXxIE^1N|5$&Dp2t0BVeyW5r&>+cF^7^?
z>GRivu)xr{J7r-5d@XcRA=x>)g4kWifT<zOCJZngRY^R5zqUs;|MJh`JVxMr^haI1
zNC4+sJim%9-JqJ$f*KdF<`2d<ol<=rIc;|tKXQ;TU^sltO{RKE!jEqwNWNqr*#J6J
z*ocs9Z=}9EYqKmV%r?wwT(m7O5j{96l3%-uF9p;F?1^bPN!J=s#2W9}DQvy##+N<g
zWj0hpiFUt9z~o9Tu|T8Pol3bFlUD0uBNC9*sR0TkOG^id3W=ibLNbMNbN)vcMMZn<
zOlJP#u;+C$KS)qEW@M_iYzr3Bh_$Wn*^*x}TavE*_`ZG|`S|9`(2L8*JuM7oUr(Z?
zZCsmb4ibPsi-DCBduvo=u{pjfTFAf~OJ~eK;-4rTtE<wWZ|@^3p3b~5kxBYcxct>U
ziaK`s*My$Ao~gstq7>st8~jF18=1B{#3CAbhw_`PmARJBJ%~NG%XR${=#@ZW4?h2r
zNwf<B=&#Q)BO+t@j%xZIv^s^yCo-pj0K}bY#$-I{yMdOyci;oEOcAX^zSgRrn|#Dj
zbtfZ@L;(b~Q}pagvOE`E&xs@I^urz@??S5u^Wv}T$4+4A&NFMWxOINcqOE{?x4idU
z=0lU7fU2n!_SwpH<6=wy8(QbqKE=zsKqsnvtW)bP)1~wKve7JnjI~$AW8+c3o~d$x
zl?|?86EEdpfY8<-0&XkHI$nI9BbL~RIXb~8Waz)OUy#Um_Vis}F`-gt66u2n3EI+1
z?A{(j+fwK!buaVcrcee$b1y`err#IFcEA70-!;(!s-}MpJKz>u=oL0~*de`|Gasl|
zQ_7w!>R%bJmv^c-@r~7vHBu8Vy+um|t>Y51_GZ<7Ruy!|V>}ZYJ9Jltah?zy=;Vsx
z@Nmjo(>PRs(VEvgP&m$^gUBIU7~Z_^1^R5P&(5P<J9+ji0+-Njo-k;4vbxcg%@aO!
z5&fgn*$YZ7<4y1Jy249yHAaGu^ZwYQ^x!d|*7aHKPsWJ*I26)U$CwcF@Ar|VT57dk
zmA)jHZv_${%B^4B^SY%?#~QE4GttGfTw|vAFaIx=<(!Bbt5oSqzhv+@=8c%h^)kKF
zOVvwHx|g|g<ol6k3`6?RH>>WiMuKFRTp3d;)f)6Sg*3Pf+@L;0oE!PlD`ljHSo%ZU
z_E|36iLvpBemf}aim~>6)hfLI$b5t%_nYGwWy9ImW?pM?mcf-9STv^0&DZR+zTK%f
z7AGr9=#hP=L{Z}$nh41+jLQ1b6BrA0NcBAVAZk4EmWL{`IW*YB``53(I;<BDSi>EE
z_3&XT>;n0^`}d9m);zC^o;0i^lEifxhrpGr$_3H|YOjJo{~fUxvxIrw8#`El^Ha4*
zWYamgO{K@Zl$`d}_Y$mM%cTahQev0K{paF8IzqotpQp0zW=;)-&Y9Gzd4G7HnF#G;
z*nck5Px918<ouZ1zl78Xs(bWC<f=a-aP!MSdUCE|<(99Em!e%>pZ<yx7>X?*>!mdM
z7>txGc>3s;&%>@2%EM@9Ii4GFX3m5f?8nuatyoZ~raW_I^m?ftrFdb>N%tA-M0lLC
zh!|wA9&gk-J?syqH`~oeXI~Y?$qv<QCFQ9JRPJbW&pbTArwoP~423y;P<3+EHV*S@
zzx*QG8rb$bow?}pMvs)$@HEu6bpg&j{b{uGC$VN|HWQm&NNjv#dP-MCw(V_JQV6Dy
zSU_jC`<mVoupFYRx%ajjKu9?%d{#EPak-&zt6b5)Y{b%9+V1d3P86Ts=Q2B_-YDll
zRXk~<a51dDeLJRb!MUtL!uDk6CA$?|HMe#V_klC_0>_68UwAd%>~68>ZEEnmVPjEP
zljMJLDk_DUNU(j^0DEfV?x#s>S@TX_LlHT(;rQ~t9Bw(!OcmSlo{KTTWoKhd?lLBd
zHG+LtYX*k+FttTn^XJY6n-39lUaYk|9UeMB=q%iFQ#&(nvJ*0md0iq`6U4Rlw%~Fp
zNS>2m9AY*<mp7`2IevW?EL?Y8>zjAr%Rz(BbC_|V%__k8E}fwbg;}+FFS>H!^$JGG
zO5iraaAycGIpVsJH%pnX^Y8iEXvC;G(H;lv+#Vvoe;S$`6Lh^fskovncfIkjg2<&c
z{c9b1y5#YA^{twrH^Fp%L(gFPmA&b2?wk);?C=^qK*%jz=4|-a{dQLPAy*{gJo9#9
zrZV``GJNbLo%BjpU?aV-sa1Gsn!kG+ak_71YJH{mj6vQVQ!VhL_|79Lr*M?rSm^v@
z)}0@~%~hJk=c(T(7S>i4_Kc4gXWUhj53Z!OxXPDo1@-XK9~~(wI)<UvI*Y_q5YdHO
z>`IZ2Dh-~H#YRHV>pI4*C7YdnvuTr0;gF>{{V?5Qn+C}1Qy!mgm;R&zUA-uDoxXm|
zbLSEk(~M3<m4c_AVDuq<;mr)$c3(4yQ;vGe5o2@QFsh1&=rf-M`M!)WqBdy;ZjWU5
zNHK<Z>A&54A8H&URx)>^U@p02=nWE?|7*hG{KC#Dk7}Q!V<>*;M`NyK?BAHU40@%I
z<PGwqe_Co${E?r_Y<jg$i_*Wxi}KZ1CLY5m_ota`Jakw>qM0e>jQHL*Ucn}~$9A_D
zz&U54taN`We-;5%zxb@?=4$EP9i@PDAIle^y(M^J<73k%{Cc&(sndVye$Ac7K7)@{
zf1jnnX08TkN|c@m+oU=7T<IU<6=|V=JBDId4$*!XMk4X7u3FQg%kx5+MEN<><d`=D
zoew9%dMcXh<=lEkyfGJWz4^Z$On(Ake=t~7t;N(o2s%+onL8n<PYF|sa=-?f<HOW9
zr;m*<9-LQ`HDeYR%U4Um6!C9m(A5hw+-2O|vghh)m|4mrAirDY=%`1HNT4S^)|7#^
z49?_!sx0bATA3c0>@|1lKEP}&s4thr-od;HJ=STj_y=~P@9`|(q2gex82DIJ5p?yu
z(DRLtKvN2-xlv9HOmU(6fjuiff1a5nb}7Sy!~IrP<DcM0`CuRQ65RealT@mfflzor
zl27eK2-RJ2?3tmiq=7wI$fJ^%mC<~uq^D!=4;6Sm=+y@a<ds*JXv&d^BIh8uG8f+c
z?R1LA9#M@u@zwEj`)6}tR<r2WCTuGRJ+EDYL*MZ}@8sBJQ8kp=LV2$^otbI~*L5vf
zdrwe?8>((PZNC+5K!==jye<i1qQAC@f&@?g_>n89O!<aYSW?++9a0}K=|k3{d^tNF
zt8Mmm6>iQ>r>uoW@`D^&K4ymd6~$&MROB2<OWf#?oVxTRINYkre-jE1-tq)}-UPDm
z@z9FM$FN(;6rL^S4vVMxzWg<oZf}X;(4u4(hj^%Fke)UA_x7xIP`ct10Pe9O!<A0l
z8nf0qdYcAkg1W*4^fd0sAvU#&)!H5@0GIR$RnWvG+(S`ZM}x>_h)vp=UZH;K^cL8d
z)grNd$*^$-ggQ7}4U=9}jv`L6;+QK}udL~>T!_rRDlF-M?rNc5GGbf;SK>*AU}&;5
zJJ}hUki|I{`8O%KhM)OJ6jpp+zdaq_aZ%Jz4kI%&KC4!S$A~!=zR1$2;1_6T)~(<I
z)E|oSLUq4M>JyWZY+u^x{dD7RKaKGG7S;4mhrgMZ#q@ke_~_{%e$+Xjm)yL<JcnjE
z_HHSbmSrvK%P!1IF@xUZ#9KLsZ=`M<P=z;Wqo|B4V?Xt=ZaxD;FYfm$E1cZO7aXpq
zV%7yBk~l`U<BLPof3_NJ1vw0@+?jl;D6_jYmGf)*>lY#`H$VIC^_9kP9f4KAC1Y2l
zW6&jb0gP5d^a^*;*7$z7EswmFW<0-0HDYX^*exe|96$pBNrhXP$2^hxUv_T#E*5T=
z>=|zwK{7B#TU+KvP2C@lG5LV)%!isl_VR;#cZbUO%CTw(Ro`}3X5FP)=<&g+?dEoB
z29TAGJldS++k)9^y9W1#G-{(Q%L*hMSKrPRK^};OqUcBhT6(!H5m2uG*HM_W_HHib
zu{GzF5AP#i?oV?@zASNgrwbv9r824CC)ST+f*q-WFK(G>A<Uz-hrh7O(B*rIWm|&v
zIy{qZ%9|<J==jX#g<^p@PM5WuVx6a)RoKFvIo@ejKJ8SxC~qEF2Fx5CL?pDpx#F6o
z#d;$~arN#Q3$aGnhq6h%-_*TOWyJUk$S-?ccD`sU|KAJgKyOuQ)-K4^>}aOsodvzB
z+8b94ht#m`yV;)Koq?5gs{|4UKh@74B6=;@kl*Sv@nm})Toq45_A&(>{);gxO=exO
zB}e^zKH<^dUF4>>XQy(bGr$$dtMTajJHB;;*KJs@My=KW!AjzY%y|{&-{D6-9k9m#
zTm&LS#_-o|<n>Rt1z%@Jv8W=c4*P&D!4%ByQ7sMUI9M6OLw^6eXTJQ^-$xQo{c}3v
zKUQb?z-(-X{`ut&7;qNm#4h}OB;kMdBzQS8o9TfLV6*zc+LLCL)K09@cUF=z{1^d!
zPrqZPL<Y;M^Oo;9-J*S_2~Bzd*7$!JVHn)}u^s%s4hBSo6on>3&fdS_8+dznSIv3C
z@P;{cd}3qhSBs~&nKZQ>h=bWhL<nv&D&b$Jb^)=Plmw2gU5&2$C`k^k42;EW1qIw<
zFkf%Z`)_zbF<>Ms7pT?rc>wK2|GEHIv3o$DZoXZ~89sfGK%MyrAT)h`hI_&urv<p}
zleen{7899L)e5TO<A*R8aqQ9xb!oteHYF24=$xKm5wAIZS*tQ%wDoGGTUGwp++{E`
ziPR02E{$&|i?%C!mg&`Tiu5YO3IJx)J2!;_kivBsx3xHcPOBbM#?cGql7z<Gd0TqI
zcg5wwN3J7lNHRdbLWN7@*R(#=`fEUFD1F3|D39}&dCscAnbraUbL5=D?WOp6p8a)j
zcV6KlXNo!8qzEJ8Pq^;b)L={<xb=<^VAN!|@tS#??Zu5p$Zaq4Aqjy#T7(fJc}1Ni
zQKz2oBmc6xGlYzF3Dz})+$BD;a>*Xj$1!Wjn?^*X>}7+X-m%<)-P3xeT;sF+&BDIp
zmcj8Wn?|S|K+V)~XW596X7xKOvn&R;yNzx2KU=QVq>LHW2rArU33>B==g6njnLX!g
z1dWE&6uYl6K?<k#9w5vIo8v|L!>-!XQLsJ|KE#rM(F;I8KDk$UX>kqA{4&@?uxnxt
zX#NeFvJwlE>2M%t+$(q-nhyV76a0}`hnNbaEaTq_#6=7L9>6)W#D^BYwo<suIBd30
ztuv$_ZIA+Yk59hnIY4OhtzDn}!05WlQYmb++K-SwU<V=faZZ{>FLU<r->V%7EL^ck
zp8hYYqN_z272!9Lm2;K(?n@ZSr+TyzcUVR6@E)Ectg2l3t0=e@T!pr*YFCY!KgVJQ
z+aGX?IB^UNt%1{1xRdTn2os%6<{WG=2UM;8C=8tr{GGL|$JOJQ0i=_WT9DTr)(dB1
z_L0{a7Di2L6WOdAa(o8~^UAe_f@)=dFmHrMcbsz{K)ON^^3)>b?+9o1-J;W(@O@;D
zUq%rqs{y-Rviv5a!O9gk|2KNl>|V4SP2VS^e)G>Mr|ubpe>BktWoT*sJ;pI_V;6`&
z&Ih<oOF&ELd+zM0NhL=va@{}WhQnAT$9?2oCkJj*&Cu9A!}kA*md2ZDQS?DL&E6NH
zKK70SF0_93z2Be+H&~U6Gmpqh7G!_|NhC59q6TwJj}GMnATRJHD@`a+9Xf=zOeMVo
zn$n@$o@Y9qh=NTvJk#%|>usRvv@JnqCfkU6t}_<GT*c7<a!juboY3<v)2kH0vG!_;
zhEw@*6eP-Y^Kq13zp6(9gP7uf5HoURnJB-K!P7b%55T%aA_dMM7nb%U0g7+&b9)93
zz^6!f^k9GK6cJS!lwB&0=v~d3j4tvo)2u{loulp<TGl^(tkW~qzro;m*np40$${5~
zS9@*_4s99)-x}DS;Aw+ZlMK@)zE}>AzqjD?=ZZGCl1xEwUIsa|DfnU?yh?zk4Z^Kv
z$G0?NP&s!DRx+OIG-4_#J#W%Mq+Wj61Fi_qD&*_ITfCDrr|qd&(IkRHDaXPm!+4k$
z^wc;xFUeH-63$v3kA3B~kpU}&nZ)(uVX-{rJ7<TsYft+`%>b)&{h^Hnkk6#er&d(J
z`TW8{$Y~PX{=EasaukgG=5P4KgtfPXpoAI=$nthI)}2V(Tj<$wcr;A3DE9kR3_kS6
zVz6Y2;XvAc`%#x~j>_<!Axv#OBSv(GJp)bQ3BBw{S}3XK@aLbWz;n;1Oe(_Mcm;L?
zY`yt2tp8lboLbJhQyu0_tWhuWls{d$<Je6jes4gD;__2HC0tJY<XuX$PDgn5h29$4
z9*qAulU6vsKVc3jK;8N>vnl<lyO5Tz!*d?~J%BT^zwLoY9HMJ@zl!j-AKfzqCVO`c
zs}yZ1TfEzeC=4}#h()D4?+VGvw#3{8n)X(WO7Zg<*q*%7;B)u(FbdmAro&BQR-k}t
z_GBj%qKdoKz7&RCWI{dxnB;cN(Q|+@UJ*E88`UA8X_o|RFMHl>W@B6IERrVtX-dC0
zL);0<oVW~@dU=I?4@Z^REF;GkXp4CpRXfVj+}k&-t=qiUPAxAU!*tGM1DraQVV@n!
zFXQfjJ#X>p<JXJxbL3TSaL@cD+dcqqt}CLD_P7!5ycu1Mf{iWw(+T%JYYn+mUjoc-
z5Z3ODGo~=*8uUwBrQ{5fh%O^F{!T_LxaqoWvr(;5PzM{uCX?Exv$!7EB2pwS+nW~A
zjj0Iz@|_X%geq=k$kRkbQF$gT8<DGH;(g;Y<^!6~VJtDeuXs@-X|7Y!8Q7f33%v(P
z)Et5fpULrxOuC^|v3-yyH#_}$buX1(GntL2HHf~kNiw(@BObegavIwHIa{M`a5GrE
z!S3@8&s-j0pq3~d-D;knRLC9F5OQPO5w3Y5)Up|dMKvNiT#mg}zRP{B?xOC|`HO3g
zaLQm=Q1N;c%!A1+K0zsB@8P@6SA601?Dp2ENwuDc%Fo+4l#PWfCniFh+;&btOK`Gl
z-fUcA`2T3{N~4;})@%^ea)PJ6!U@6B0H&A}1Y{6ODS<GSARr<j1&D}%%mzq;QYBhU
zdV?Til$itpnIl6$#mE>HnTMzZ!W1Hb5R#CjPweisy5D*~dcA+Wy6?~A+}wN5J;VO?
zx4)e=BL9Y9p}?>`25D}ubL&5d*&y4g*tw`H1DFC$=dG|Rf~I`$CfL=mVW)vHyUkTK
z1W!Qc<5hVhaF!i=Dy{60NhaL|w_#<))&mvC^k_s_Mc&|e<W;LZQPxmHQ4bZQV$V3p
zhcrb&`|6Uy9!A>@vFxhE<U+WjOPH$@?Rw&SJ8(f4t+pYRpLW8kzDO3b_k)|AgfBHl
zwUj|LXL8eIK&xb<86!@UVJ;&@dM)(X4~T$Gi+1dV0Sxu}DAq`lXUu|*081;1UZBTA
z?LAR@II}c7xQ=f@c9IQDtJ*(TtIlv+^M^fj80`7KUt)+Do&5na5>zxGhdE<e^cGto
z0x2Snt`*L(Bj%D<-Pc3}%%Tj%)>%bPz}ivJ`!%)<_f42sD+Rl`T>`;tIRgYthCDOa
zVL*Hgm+TB`*bAW8lbvpau?A&><}z$h8^#4rswz`D(;k9*yn}ey&`YT%+^Vbg2lvN&
z2lrFe`!b?^zWr(D27VEEYJp#jFn7X+C$5n%4v3@gP909C*VzgS)8_W2Go1#o#y;Z2
ztZr%5(~;GoGILa$VTyVnGIODxSnXVM+H?MmfTY2d0+F{QaJaq@FsG3d@oU$+QLp;K
zp6sT@cyGl3M5j?UA4svxX=~9^m0n?s9+jkKpLc5)06S%n0~fqGwQDM?gXh3SMGD@7
z%@^5XIL(Os4*ISh)k5;zT2(vA*VZkmjEhXou65}hiXRta+JB}rR{5_iN{vN2Q^?#W
zl!})8dB!!3QC8_Fel%Zr!3mJ+`=_Gym@Ct@H^|6+{a~MdE0LHaGM}b(P||TX@YbQ(
zX&vt&Iv**j8U+`rOhzRw6!u`7REg9H{>S286e;rDxAuYMN}ZYI0P=5~mG|;Y<=ZY<
zGra0F?Q^&7yRLfypuI&h36_Oc&MI_gH7pLge~4m~g{EoePqZT=^#f@SwUb{tKt{zG
zM?~=_QzwGSt4rObxAy>NoZ^Vq@!C<A0Z|`dzn2n$;-S}C#rn~qmr#Lk@mXIKds{P#
zChV86sK-;-qZ8`#Yj=vtYW;^$hDkE4tNo`v^PHJ6{j%QRY-dTy<spg*0@=w`C7n{f
z!~HX@Y;Ta^S?oV>a4Pq7AY;P`jpMPg(GT7<rCe8QH@Y&!=yq{bY|sk-yArSr@IiD=
zB1}&<pA92Ly%i&-@+Em8`ChO)QCZnx>VLN=$=FX8=YSf3t2kGQl$B=9E-Dx4)YU-E
z$9)SeU5@=WV+;1UOTWt5vaq#gfb8mYbIeOzsR<F;ZvkFa#wVun@9Pt7K1~}Tl+@+X
zK9vBxSKY5K*8hv!5tiaxV|1iN{7C5(1ITP<g2tMqhvDf^vq2};t*AE@AOh_(uOk3I
zq2B4K%<G1y+JxFR{Im(ScUZO4k8xN=r-n${$W7Cl6q@QO4Fzjf+OVsOZ|X5rP;Jfe
zFxcQ$fUs}7I;%vHv*}n>-mNZEKU3s^J5@Ik)6!GBkr)K98QKFXCceRGcA;ITq_@B>
zk3jUn71_I)ZdRGn>AX?3Y%Zq(EdjMRRTBKBFYeH?;XR<8u7{&Ao~9Pz`=``1^QGdF
zeaJR?94P;I*X1s(4D6}b*}HQR3cnvt#`|!epn(Ew8qxW|*Z#8i6$t`ty9<ipTK5w9
z)<eTX-3hBZU_Fyes5xNrEG+w@Ee@2T?wU~mi=*t5_$Ypm;{dtoZL6l@M^>#c8WKMO
zq`>J~QnQHg9Iyy6QBc9mVVNof)6#XyJEluK4{K9V0Z_XMS{(b-a4nFiF|=(EU-1kd
zau`qdGg=BuUzTn=rC|7STiZpH(k58-DHcrc4yaC1vlYrBTO04S0`)?-tuP+BM84Ph
z_5~=5)fRodMdU;|TcD-dnm4Wif2RY;ttSF93S;}$OC$k8)rT`CEdxPfT^#lYkd*8p
zRF;GT*<h7?LXnWb{VV^t`{S^wZe`FhkLu=P{G$7JkN>2MG+K&9H`s)lLrtfKDy`US
zcKOn<!LO7RS=ew|g-zymSy-Dwv(C4EIM<r}VusJe#p-tI<C=lFr=z^5;DXiHNH<6t
zh*Mr#LqKPE-V-lp!^Zh<{T}y~2hV)Y&E1R3$*Sx1j<JO-;JgJpVA3*pgA5>F&*YH9
z;5!h6e9nC#jmycQlMV#;l@p=nY6J~`?h35Ur(VS%TJkpfD$?LuFc1vn+_cYh;d)4<
z9W%(F93Q`~G+8ic?4TN4EP-^2A}MG7|Knfjfi`1}UciYJ|85P%D5GvcP09v_03>Co
z@SLGa72ZvVs>*ACXHZpb;%paynP%WT1<vCucuNi2%yVfNNn(agW?O()O;^+T3&431
z8RU<SOe=k=_hpfO-Cl*t&76$+_9!f6^HrTP8g?wrUe(~X=A%uuwUTJ{ec4`I?9ZKF
zzSGKjaWQb7!_ND#!IRrO;*zK0;8;(0u?Iv^A$dWE5iFh?ZV^W}M`AQP#g+#;z~g53
zUO+gyXl7_29*pB{*-M0pFG#ygR5(~L4DIb%B|Z%JNL1?=Sk-pNQ?MS&X%;!SeAGjG
zx)G*wO-8HjZ3vAZTD^1{K)S)NMSvyP|BF;PSd+j4pG@TI&&Y!X|K?5_tEn|i^8zdh
z>}pjZu|$HBZUexgSG^lOXq@huJ)7Hry9+pFE0LcKjsNa(2P`pLzTG8=M&K@eY9NT#
zO90f^kBenpuE@M}dJ1CYo>Y~|C7)eaP0U-wLBT=T7-x|un(Mmx2Uy!o<XBqAz}im4
zK2?-8v=u($XLoWFxbO)rj)1iUZz5D1#fuX#U#}e945+73E#D5BnvU^@rVTmf4o7nY
z?1US*mz4`-amvpuWaxVAD;Zdvk6YKLom4xvXs6PM;PL+69VY@LjZWI#<yONfeX2Gf
zR$BZFm$M2rcNVSiQ&3wrS8W5VV~>SU6hmwc9Fw@U>ALdB><=|N!Sjq78mNgwS|(#(
zOATKHoAOKnQ^2YrL;`?|1$&h{2xN0tMIkNR|JJ|(vUdr@>j~e(RB9xz{)B!Ev=J5|
ze2Cf%$g+i=Ja{Uxf~?)yXV9^!>Sr(jdI6%zB`laeaXA9=Lb7O$&N5rHAEerM5={5T
zbc)ZkLURIDUm?!61E>*KPA&;p8pO2>)scYcXHqtAe~=6z4$|W>y&a(@RD9wS(`7kv
zDnp<`@`f68eMeNG*qCdNHUX8;`ejH?mxG3ky;6YeuBtsZ>+!MBv2<%l!8qZzl%5XI
zrOkLlTv`DUt(kbCxy{fUjGY-!NeB{xp5I6AU>TSz3B|3!kXVoW8;Dch)tB__Ub_#k
zv<$rrg*lkdF4<-3ESAGrNcK8hKg<3J><zd-ROYIXJ2-&JP#O7v{w~%3SElg)opajG
zH5qqSz&*2~j+m8m*I!m2o5n0zu_sgbIUMf>*F3fTZ+LRBg*WLaz|N^BJqI}?4~He<
z$}UxFCw07{H<@WWqL=*CuxfH@!C{CSF_`L}K@o&UGgdSVXDgGDo@U_zlhoJdXsyk0
zwq^iXU0-N~Z!P3L6P3anvI2hHv-76H*heFL#<km5DxYz@K-=b2Iav7}0IS{)u51}!
z`;=(`&`O(_ap*GaX>vSnBh(Qa2Dzz{0w9_ddpr%lruo<571^tQa1?3LGcV#tmZPQP
zB3^;y<<n#fC(aOX$9AOU(M@XPzVnN5dg;)WCqDMjj;sYE2Xp2UGSs{qlg=G^Om7)r
zM%l3EfsY{RDDXdS)_cw1PBhF-&CjfN`wqpi^P~i+mwD(*IY4F2hcom-Fb16~xJA>a
zsfSd6Gp#s_UJYlcqRhIQI|`DTw1sz&c@ODY*;ByRJ0Fg>WJB;Mr0bDNT9p7b1P9RW
zahianHhr>ELzxt2RV}u*?V;I&v0YM<w65rD5lx*ab0Vw4fmVQ_E5!WNxdS);^gKRL
zu?grg@!I?icSPc5kB^Ij>6u|l$;2=Br-#~HF4rEO?pak#J(f`Gs#W=Hv>lN;EpX^-
zQiF1G*{3IUh^8sHy!iMvw{B8R`p`LTo_EzC!|OSNT}!hL2tZS3=f^=W#o5lK%%Q(k
zGRDzP5rQuo{5kvCK;SP>Zlk`M!H}EvqwN-anhxHwX!<i^NQqHjjQ6568mr;90dA_l
z6qPupva6v8UO8$kOASJqii6&X1Wp2XE#)%P`6sG=oQ_xgRPve51oKtilijJvY|7Fn
z`4?bmb+*dKJf{Q@1MfziX}$-Awl<e`i17BImfeE>HHAg~c;=~;I+HB{XvM*3O?yF0
zxa&UF0ry-1n5|Wa7Tp%Po~;)fXw@%;*!tNLQ5+_^>wcBqgFijz7yQyC+Bx&-WWzo=
zXwe3FwC=$|B+=L|5K=MvsOeRG1+ep`S=vz0uxQACjdT`LN(mX)Pid6nEL$kgrgu*{
z&((Cxu<~YFJ))8+0RiKrJIR26-rg?xj{q5#v8UOXgZcdi<`e`UQVPM2e(&N1k`7YA
z-FQol)i+tA48KR8ne%4~h+!a1f$8NMN_}gU8l&22p-x^u7<XAHg(h7|qu)XFE2tO2
zZBx3s=+h)K{+L4@?@G4-mz44Ox%WB`qy|EkY(mMB0C!AXRUuLpWp1VIXWcaw>O({G
zXAge0hD@1>ETAFH_F<{CY9LbQ4m&_B`<p0JP>}Ky;=xnVqi}09_scxo-Y)bvXOk!)
zvP2+Dl8aBlp6`mKb~9e5%$RJ1c{X_+J2z$=S&3wq7t^F2&9R!M*S5k9PIh9QDg1sX
zewN?esJzN&F3tks3E#gSnq_-y73CyE1@Oypp@B=(8;s941kb0(GvO%}it#>d#xr6V
z>N2ayz11%qy#&J9wqhU^G&!!M|GS{Ye04x9CCu5GN7Y6=_-hBTKE62a5}_V%@O8K@
zWN))4vU=!YJ%urfOmLI)Z_-iQ`|9<QpmcryQGblX9tGSi8Ncru=L3OTI_i=81^KS1
zJg0T))y_QWTJgGz&-z6rH(cz~8v;MG(TK;lCLU?q&$5@-GTF;eC&+W$-gWo_a|M6E
zcq^w`!<E-OQ5-C2WPGj?4n{8}9kR$sJ*&-&aXl2+H|^h&5y{K@z#++j_m};)boWcn
z4>a<j4=&=Zt7ECx#h(bwZPN8rAW^R&uGXc|KN6VYgCUD97XwrCPP9hTKPx4lp1i?l
zG0;G18#b`kU{O1fuKhBot}sfxj6ac2jVIK*=u>8gd|z?$29{HlK3<#LHi>G!=W&Ip
zbKG@=^^hR67St!l$jXidKWFqchJ^F>!~21NuYmdwH}~q%9QqfJz?2EEu+=r9=~GW=
zVBcpe$eXwpFK?N3{%(V9PKgWC;`zAfeUhDF(qAlyka~6{tKNx5oj?>!I)|Mfz|`$d
z?9OwoQ%W13vmaj6e`-sPe$nK;<t9t}OZ2UXa@Ae_I#d}}vxiwXvXRwr6A#~~FNn=&
zt%XqA&k7<!{<HIhGOW`7`sUm2Y0ol3f!QiMjUXCy8f_^im^S;;#1OkZMmNKW)8e)^
zR;L#ta&Z#(-LKEM$jRONrY0)-0mLeSC%^pm81C}C8jWB5DvazZoxU*t>tLWoZOaig
zJj=_DIB!vV-L%J{>s*vrr_%z;!PC3a{ul@)mEORv=qdzUo>@LPs^<Hlyb!)iE4b@N
zTT=DU9B)+1HT;15K7|Nb*?ta5=jRlElP$PQl??VNnvVQ5Yb&g$=PABk3bttJdR!f8
zf@4depJ>5(L0y~7$l2A~`KOgy6Albe)N3ECWDzi&`}F}W_MGF5S`{xUM|sEUJxd7K
zI$t~xE9-;g+hRHz1F-I3C?1ng0v9-EM;Fkl3~n?06)T^KQ2rG36=&<(WW%iqbNulG
zKT=L9#fLm+grTxn2NKy_Y1lQ1tKxq8zJ5U_d&1HD&hu*|X0pRf;YI_Zqa%D#v4E*t
zkPUauyn28WHt)Y`@J@ta%-q?#*zJ>wa+}JDudATuUI?*SIk}&5G`G7Z8ZF&c><bD=
zu0Gti>P|Ds|75OF{F*+GH%QG|duq+OPdXb$lMRh0nB2HXzn~##MmmXuy3seNwpu?$
zdUWNy(?@2$jtJ-yzqPgnnt74_OTO{CPjsr&She@|lfCofuJs;@@f-|Ts?SKnN+l$s
zP}Vv$Rd-SRrU%pax|eO*`{SH4D)`}ua*(Ed@ZY(~K*==pdYnkW>*h0ULA;+&6<p*5
z*I%cx9`sJzJDO*SZLN;ZvC}Tx<NWaI{Qd(YMS7Lbx`B688BK?|?|gQ#Ypzjuv2cGa
zS}S{yG(HeBS4-1@#dTfdosfamau1<7^~d`2h8Am-JB5CK^UG};wn?1bKslNE-lL$S
zoUm{1*Sqx_DM9op4Ze6__n4gJfubjAGJ^&h{KZs%)E}Qqw4)WN7Re*xqgiy_)%2NA
z>rpMj>A-%ID1L-+f0DpB9Ia`;&=Nk%Islf5Hkcc<abj9poR{{-(oMf&P4Fs?X+&U_
z)X%a?2|`>#E$Qh9CSnvD(n1_zsF~HL9%3{R)(W6w<V$retUs0~s^`7!ol|TSD2?2e
zMuPB1L;?FA%70+(>rPD2{1gj`bU3VCNY+43y49h(A4U?-L|P*loq6yKAI_tUsM$nj
z-Cl*@D}K%tqfpCi&bNbx)$=R!wQipS0<7+`uAv)JW3XG8LmecYAO&}mE!}TXip3$C
z_ERmj-P|Pq909D#Yd;|8J}u>WIB-|trU$)gEB$UCL~F>D*%K9BCKFeJY}wf(3dc}D
z;I?akBd3b;wmRpH!cdFgg2Em&Dioqb-Ws=8=G}CpWomC{UziiFMq694=g+trB&<w*
z40=?A#%=gP358so;5mZ(#k-f;jq%&~R-@XvHH!}hFgLK@IwjDS2Yo${6h+(>?Hg#U
zd=>_6gw#=85y;ox(4te_n33{seo~r2FfDhkr#;(7JU*wNOu3rF@Hz-4@}zZ%Fb_aT
z*v{Wl!?S&|{-n4F$c4dUr_8UOIihA!qLJZli=xJ%<<T;%&?J2KXMstnag3$XR7#$)
zy07f#dI~zmsv0$nz7)iL0apqfVFW+lLo1~$)qB4Y-6ga>viN4lJ~)~VUA!r{q?sYw
z{Nd&P_A$_>VD~=*`V_7B(j=o~*&{2Qo96{!ZMg)SULA?32`wBmwR=yYIdL2vpd<aO
z?Qp-Hjp!K1V)O=T<Z<=iCYAcu8}}8%l}u^*%oT^3Ta*3y%v8rclX!&>OMrSWJdb^y
z^O(L(WAx1RWQr>s*L<(SF7qJ$fWQ7nAZ1X@p_{5Ch3M88{=?wB3<l>fl1ijY&RX)T
zSVw0+r6D;eHouI8qp#O((S4<hSTQy^4k$_kt!zd~i;FTU2r|QMaMNUZQyrGXa*mi#
zA=yz8KNNm@JP{gK?wo)&{JUxunjH58mZ+kfyEgrXeYl*L=ne`YLw|?JmOP|jNg`SF
zMaojU$`C4?nKaTbpaSFF>*-<B9Cpzg4sHXjXAk**9D1*d^vGa$yg%k(?twWrz1(c9
z?7E(S=x^Pyfw;cWsdMFhxfn23amI4)dENc7TDgNU-*pRBaVoi^AXYZh22TbKi9G3l
zsPd6C|7XVrdF}H<$rr!87Y|`hd{3AEvv&V`KeJcgXE53X$9{hT<axVaE6!cL`)}W7
B{=EPI

literal 28235
zcmeFZcT`h(w>OR>qmQH53yOda35YZS0jbeJfdd4DfPhGm-jNQ50P2W^>PY~pQF;fd
z0-=oxhK@980VQ-uG?W-Z$nPZ1+~>aQ-n)K(y=&b+-mIkwr|z@AyMFfG=h;mIZNA^m
z{Kmt>!w2YGH|F8_HJFEIx81+?KwHKWUw?zX_SorZUx&V-4@0vd9QxXKPshTKhez-U
z=ijcl+3$`#Jm+|T>(@+z(^e+YFC9!nlvZqdckS78_+i@h*4-B~X<E<rn#4cdYod1e
zp_Yhjrrj?>zkt_g1h7geGHtz=`Uy*7{LyEOZXPpw_WSji-!qS%*!|+^-lxA^yUBaa
z!C`?KXNuXNWQ;WwHUyb&P@1UAV<W|5%jNhblEQAolEz!VaLN0B|NY<LK>0RCjX0;#
z9)dmaTk|O%+W>Q=Md(hiW`^MwQ}dzCe$jDL`+3SQ7AO1vBtSBjSBCTJsy6VAyEOi|
ze>RLd@bT~@&OHV8^6<1Q!CUw96q~qX)zpYHST#8!3tSme4fg|=!BACVIJ{1hsDv)_
zww?~4B$Gdqs)aJh6d?EhOgNkf2YOax5RHG*<)=(QO)LRM`~oP7)?Fqh1Eh3vVv0r_
zlJ*tWoZ*lJn*wNMz>Wq{De-VG3CKtxA8EA5<M0~_fV4i10TTvjSoobRzL|QNG2ffu
zyjaK|9gTsWU{cNm%1SYTY-Sv`!xRg-N$qVA8<i*a02G}%8)>~dB}4)_3JBA95ZvyH
z!{?MvvY{t#xwx@{Be$>r9{Vnm(Dn`+>b`5nI*ClwjAbRmQkiGxH<pw(#|EvIYhX{h
z1pI1YHiCy|_F_F$9iruh(#Y<++e(C-#p*hLDeF%(nmi?JxJGrC#RTg3h9M=xudE1I
zM>M*99ZduOb+)a@ZEGqXyjrXx9mQ1F7YnfhS0kU!V-;A4$v)@8Idu;M+WhX)pj*gh
znFB}M<+gf1UD>hbg=jRd7;*UiEbH#k)5~Dmn?Jwzc21KAA1h!G=?<D5`jdA{H_A!D
z4e&ajj~e*Sd(6wl0B!EZ&H*nZZQ*=rf36YfR1^SH+!&Rvly71RX6bwrg&!HE-lqy>
zQcZM!ueD(#RtLhw_mLK12%WmJR7u5F%b@NNzPF?&?pqkwtIa;4iCQfB?;QD%*zElT
z@;^Fy+TGa6T6*J*4YC6HE@0^>;Z%-V3SuR|ODE*OPQ(6aYBJv&IDB8=t^J56+6Uem
zSZZNxs*<N0FFc~YK?rT7%XFdKN^9Y>QIAFUD!y+B!f)y!zZr<t{oZY(<$u0aHMaLt
zJ~f=j_OHm~^j$oCC(81b!|#LW9GK}ycGq8%3PiT9uU&_I|GQwLj<J_|8}SDWPHy<!
z(>>~C-jG|24=MZAdcJ%25<ZD+p%8#a(pq54D51_N8OQFvFpUHU=C5rv{8>#hWB&K{
zOxdGE>NkHC*ZZxjR+KoiXB~5`n;`>d+|_IrFm_7PP$N>3Sysi!3RH*6K)}9FkZAQp
zydA4@<h75TsT|B<3UiidHY~oxIl~J3<)|GxM{O(Y9+D+3zAvNxw3jg8i^pB>O2Jt~
z<tbI+4ScC7?nB+v>B`b8R_@4-l)5thzAzQXcZJTIV`TvFbz-nDEH349;8M&`S#RAU
z;(@Kz(g=If#4lYjhSsDrmfmWqPD*o_>LQ^UvbPW7m99@jZRDnyrwkv11a~tfOnZ1;
zS&n#2e%tq4!158B(Y~_P(}Xg?!}EVQe~VqNyu8Gv_|=JkXUP(`&#?bY`JRU*$ZQb7
zX0HW$Zi)tSA4vk`wB8*QqKRLG#jOs^kMgY^DtWg0BT@tDo~cos+j}XFE%PCt8s%_#
zA8E=%w(0Tbvh%t1e2oVST$`1<^<GH6-^J%MggM<G_>iu!+wP9b!=SVH9G?k}#_^OC
zq~7>Z0aKOqSt)XB%P_E*ujz@zT{mkEtIcCeHs<nDvF@d7k&zlUC5x5Lns|Hnt<bz&
zmYDlkuX)2$`d}g?&;WBE=}^t#0pmf>Z?}~Eesra5h7Z{3j}PCmS0UQFZB?aDV>TlA
zPo+!?jySsx(h)qD?ha4?%5w{p8B7b;1MxX96H+QU8`u7h-gA$wUgg=5MPuE;k;V8h
z;V-uSoKzHdx0ZfC@75Du_PTf6t%v>gb#D)*$1`=y5w0<I>6Wq6shuF?WY*qwt3pWc
zvnpP0B#*D3QkZAw_tvc%Hy@<1M+~k2ppD|7lz+_Phx*D7Z`v3ZOJl4<5DSX!dXcQI
zn);bdSROpY<=tDR;<~WtX*w9Wb7*;>$!m`{>UV~cK&j77Bwm3Jv*p<}xbF8Elta}m
z<}ahe-Z9<Y<x`?gd-hIS)y69+{W6}XQPVhIjk%3%@IDw)q5y;`IPSyI;dNJt0f1NM
z1Uk>FWNZ-@ZY&=-E+5Rk*MheAc!1;+mZ*hc@8&r+mukUd3s+X{PL<xMM1rTp7<AnG
z-Zl--rC}jt#~!UmcgAmyR1w~VBB`E!nv)v}^QX3^9wHG>6vW+kD+vZHImy*Hn$SO7
zxRVZcrDfyALd=kpm2-_0`i-l`i2cEBDL5I8Fvwa0q!jRJ=yh-sm8e)iQgs(-8}n+Z
zPRxQRsrQE{i8{yeFR&n3Nbr&{AVeO)reVJXCfPh)jI4irXL+7402voIW$hPMw_Q8E
zDIyeJM>@qiu=cq13L$51TSA`c_gR_vMPaqal^=}+<FX~c^m&WN&lZXk(=Llyx93bb
z7wAE<Q2wvZS;W#Vo-x`04w)pBNM6S&+&lI48|+X<5M?fMb4}QS>Rc9CpX+#P&RzF-
z+&kp4rXAzxuTA`qbCW;M_`8C_o65~WAakR`uxt^nmC_u2Mb^>vN$IH5RTk#trQx>-
zJ*ztQu814)9GNTB1JB~+vOIfn<hK3TZBI~Q&$_xVX_@Wng8qE?jau$&%F_8QnNtu$
zr*x9P)V|CT-$klXq(&ups1VsG6uqGlVOUU5mdZ-SvDv})nyeylLf%nvC8dibg0)V%
z0tC9|TjI$PzeXz*^nyPPl5C*fsCv`X9!+PuE1Q*4GvFH56*`WCAKls*PusD1JK|X}
zT%|ntg75pP(SUPmq(*>yk+tc-P6^+e0OB9isO(APaLQKnxb&j%jFfm~Fms4?t~-u4
zm6bd73fZNoxL!2AaPJLbrXJa)(cd?}kMH}&xPz2M20rHlq~<MuCUaKu)<|BMNIoB?
zN1<Cqt@<@3Ja4C;QkxsHIx(A1ng8}xGE^it2C#0fx_7&_k7);QkR`TnrA@sg&nRH5
zljd@+2YTkKHovCy^96V|5azaz4TQ&x^NDu~Ne=pMOF|re^#~TcD<XkBD=xnnIl1D}
zoU%5x<VWd>^s$a7v&1WpISVC_M2J&4u;66^<|%(ts{8;#ys^nl$wj0PHhO;6Xj=fA
z)@SWftrcQZ?9cY@W-y1-5biC(wU-Cg3+J|D0g|figrb#+Ul>yN=zXWj)oMTpBrgM-
zR__DaHY|K0spa8G|GwEJjrkS}Gc>3$>{zKuZg0yo7Sap@mV^t!jJtoxcrH}-*`wYu
z&3p7N!yLPN>cgh=0nyc$qj<&^j!Zuy{;b}O`Pf6Q)*SLmx0a6b0e~C{_l2?;e#VaA
z33?PPHY?9+kV;1a$6;}XzN-<-HI3Kbl^tS)JyBmubkS*BvpBQCH7$K9$=BPAW8F~*
zKQAP>cKdCcqd2ihrBl6CH395$7hx#$@KPb;k&rfx1QEo0h^+e4Z`VA}_!5@BCqZl$
z3my@zb9(R{qqY&QbQvDp{{82b^<Toz@bEZ!8UV(g1qIFW@E2P<@H;bpfY6JI@3+m6
zV#=aKcDnyLd%1-s^F@;S^;7}u9W`=rnZanZr#aP-HoxDc*~Ai5L+@IJ0<rwdG6<u*
z{rH@rF!t`23Nc7pZl<MH#Lm(8R52aF`CN~!%lV!|aac7G;$6VY>UwBrcOpzi5Ax(T
z*4NqI*=h%^YZI4!Wmhn7K-MMVsl8Q<q65u$r)5C3E3=)k*A(H(iZh1NVH%F8YMTQt
zLHEJD-0jA4^G-%3d1r6L!Ji@V?e$MM?K(PD@DSveal9YA#N7=_ks&xn2QKHRv<}T1
zD7BofmR^d%P;u{9-x)j2k<@43EH4{_3Q5^&O&E&~kH&;K1d2qbh`akk0pTmyAst2t
z`**nVdI|3O;3-s?C_>0SdWIl#t8gR_#$1y0KUReo4e{t`q!D0G#FWoDIx&^7p^`+j
zT&j86`}ept6eZ3X&WOQT6sy7*2B069_99S8NMOjB$X#HL3;l0D(Y=G&kT6C(Up(1<
zo5(I%p6BJc3OaOiHbNg;A4pX=KU8!dp5lD}zt8_34nz=BQv7-R|I);68-NN@(bbwD
z275M%ymYdSyLzu$Er4804Fln2RSwY+CtN18cfUGj0MP1S?w0(^`LHK&#;yJK+y%a*
zm1^wtE#vayRnD=mmdDIdCpf<%_Ho=1^jWyD&zid-7W{idegO7hM2S9pRED@4Tmi{>
zK&g(eBd99I&lQ#OE}d`EGKPTfZLpC!I2liV4|9%F;A8|mwxbo--z}skkp~*Hhy_3o
zl1=skClrVlfPrE1?0(K6El)iS<R(>@)bc(#)sM_u0oDoRXjpA;5hC!<@3CXKIQqR$
z*}J)<^E=#p7^DeWN_h4dR`^(_u7YVOS3f<_Q~SsYzYaGyy{aeKe<|dN*l3g4ju9w=
zI@)Af3s+Vl4mFMlV9&}WE^uxc;i&g8h!$IlGXyMRU*lClLIrIr|HTbXI>qDvke5nU
z&opkby*l1|t7C7qFb7ZJ>4P67V1s&f8C$zNy^C>&ToA`On_ua{g2kvcx*?qrH9?!P
z4$Cp0Ih!L)wV*pl7ve?^x~L|MPK|bF?SoQeh`G-pUfL!oU$X4|#0twHpg{cPbsHP;
z@UoIuoc$4A;Iv?w-rhlK61kIaqe0SElC#<Napm=QG+%PW7E5BbH%{&_cOygoQP8@<
z$8XQp@qGCRh~#@dUpPB>Bp&lp7aF0UQW~KBWz86uisiD}Kc3gSHZ=H;5p_kPa85d0
zeRG9XXZceuS_(y5J1+@g$=9Uf>fAeLI1Fzo6oIydA&9wLF^WSu@!ItNWzM-nc_Hvk
zT6h`Mhu6sy6Zf^tO{KfaoM+A?wsB63Fg1J_B)6&K>jLJ%lrIo>fMK(x@RW+zW4{7B
z%ESa9xBaXL=Q2Dem~dq=xJ5(!%yU>=##KUAx%jMl`hN)Lu)a!!DftI3`K_VN^(m%U
z-6bO0ZdieHW}ir_s!*>!Ew*F16HCw_LLft9ke42KDpzvq-c9frHq@1Lr9HQcC*zD`
zIp+kP<Ktp=B9)y|X9x1Su@W223T0kXFyWpFxPY}rp>2E1Q3%U`vRG%rh0156N|&pc
zSltHndO<icCrnTG>)WWG7BZsehfd0|TKUcVDrS+!1`6mYt5U9+ywtAs({SFl<0JK>
z^>adznhnCH9lfh4L^^pWVMJRJet~oSS4>#2wzqvwsIz9nqHYL2MKQ^=BhJ#F3(B=T
zKi8^yf2sthG+d*rXwsoHT*bJNLm1B#;Q}wWc=wU|8{Qg+>3bHDUnI~@xsU0ab|}fH
z{8Tn5pcHJRn{`liDlmA(aZ%+G+SYCbdsAl~cKgv8?iExF9tN#zq>`J_vlwvO3=|U^
z6(Lf9jQENg3I^?|D8`sic|)-h$BzWQ;!u0Fwm8+Fr>I2`$Ves^HI4)s(q#BnZQ>zJ
zrasTK18;ucg?m^M@GZ7|1XNQYieXni_fg)$oD~OcK(a+6Eob#l4*tl~a!FA%#0T6D
zPNa}GVKzFnb|5g-_RdU|IME-(iVFUVbC7@fX~Il^J2-&ms!I#_nS(GvT+8~$KXn}C
zJK!Eywf4BB?PMEg7muw@Uam*<@Z9&BH%(l7X#SvU+L7N(lyMiVhODK1ha}1JHa}Md
zB4o#Ka*ycQz+Nspteml2VT$+<TlZg#-~W~kTqA7o)*TefO_$-_q_P$}x#l`THaHwy
zOpFWXpBLb8BO>AB0qe-`!?09rvFF|5N5)Ry!}1=#;q2n!&q9jJ2OFmNpX2=17chM7
zf3W!fTORs9=(r7GlnZ}8!V)g5hMxcSjf5Al{Ch!nX~a9!&&~+9F}PA@3zvJ8beHfu
zu^DDD{+D?#K>HJxRDJ0r=Ye_btHd4mPyy^guBET<5q0No7UTez(5KJnDB<S@M{M=1
z^F?A9AQTTljtHdh4Nw9*q1^hGvpd2OGVUQEy5In~GMPt-go%kNql=GlT~ucx{EQZr
z0K-!Rkp@U#uPw14EV&fu(e=IGA!)<CxR!?>xJWXZS58nIXw;zU(h6bDusV!2>y1cs
z(4b-t;mJSB&~on4QGx(Caog1|42)SD^GG76>84tO9;(l6+pqp(Zk|OFRn>@_jkGxM
zY%=(*q+W{H50p|GNiWIqZ?1FINYawyy!>-Qbvt|yuqIxR6CisOBgwTIua0p%*pUR<
zue-;>YK%4VSvR>4Dg9zPoq)d8F>=TS4Oy$J%-3A%t$uG+4XHKlu-sGCSQ!Mf^nTA}
zd$Ann4n{eIz^mg0T{}HqLdYwe<FA@=?MK&x(ff%My2NZcxSjo)y`{@Qd;ZFmJ%21O
zHP}k!Rai63#w&lyao5i%MIsG;CNW6_@`I3X48uL(-n$cjnvfOrN2Fmk+v6$=BKdLD
ze(v*`i$gY*nl2R^0Dz}9JEpvR?r=`v<bMsb@305CXPaxlf={=72duGy!ycqM`h12|
zG?#>;6keHk!L}ggwvb<YVmrr#M3_3eecIKx4+U)MR@6Lxvm@y!_yvpgaJ#F<xt4i#
z7i4L%*zF5MZR~{F7xR<uDntdTn2pMR{9jp&f~o6fB#esmMS*m&5cBN`u46o@b2)az
z8_w{BKygS&t;7Vl#PvBs?DH9cg{P9YV0e9B^D&DluE?6-fCaZN_X8T2sS{W*RFnuM
z#HkKcpS@hluZE|h6vb0Z)5evF`+(AtLDRS>teOH*(LTms4U(kxe@hbUC_z5gSkX#V
zm36E!6-y8&hS@*!SNq4Ox#)howVFnz8zYM7G#qlQM%W^I=KN2!@NX;MM>>rH!la1I
z0YtxQI(Z$ow?=3&_-Dw)lm4cU*L%ncf-%S`>xS!rFMd`BsyNBTt9~S4pjQzDLUw3_
zYjR;PrdOXY!fcSViLRa{)|cx)U7M)E!&<bLfyE=b(BJC$QgR=Ovb1!DtCCltIE$-b
zDsE70YyRs&q@2RyInG){Bxpk}O*-Rev7)8J;TcyT{~L>5u}9|JOI=?)$9G6eMRPCL
z5>LwDm=8gF3|ErMe@TWyo+cc*oD=f6*TPiA7{LD!IeS!a@APl00hy8VP$ZmD3)$Xn
zhJ)s7xT;v4`fSivv*=llfsu&dmARyg0)!ICF+fuyIT5Cm>oGxZ;}kZ=Bb(5oA;usA
zNqYgC(uJhP5_>0uJPBlEIK2DCu09B|Hg{6Vb1?jZmA%`?9fA88C9#l+UrHPJ4Gnx<
z%C>Wl*tqAI<H-*I>v$S^#~55jfrTS*hCgT~rfj>*o`NH{z?##^-GDTb!RkGWma#3y
zi!zF=(}bUfKY^GL5&MQXw)DLPW|p(vlp1R|vW{?i+9cmzB2KKmmeQss{|-J*ns5*A
zIqaEa=c)U9hzfC8E3_nmtao~+sqO+%Xm=ZW?(ob6=7H>vZVvUu{uRzM+xuJv5qiMH
z9lV@7H8I3<6X`oel3nRiu7^UzfMBmk!-{Ii(kSm_pU*{`j5VGo28siNA`<g9a?=>~
z{1GodUC;{`Tl&6w{%Kh?C<$v-O_$3G9f|y|^#Z~u=o?$sCn`CjVXhU3^tE1Mi@hyO
z;kiG?Z|_h63pQ)gl&AZJYl8OJl|=Akif#o4WQr+k5XaryLbSk2P8RjLacA75?Xx(X
zp(x)OFc)mw9UMMY)O^J<yujbLj8X$zcH1gL`Z|_PQU|MCJ5yjI>!ln09Bwmg!DfBP
zzc905G-pb(C#uP0JF2VES!3c7X#fnBCN85WMCL{t;j*6Ix1LaoOX=jNH`XCJix^(b
zkUQqK)0JP(ORa~{)UE@fmBD6v_mL7jQ@dCbHlRcM5gWfJFf+4K21UbjIJ>Jp-GH{D
zv4J+(Lgde>$OF@54(s7Ig2vD0Ux+jfyKTJhV$ln{q!v_cIzJsH02ZkGa~Y7nMLbLC
zm}s{{Bs-K@PtL6yeiJ21Tnvcg$Gnu>eUu<E-YLgSZjvHewfRrBJ5o;Rt*zcEpw`w6
zo8;^yW0vTpgVgaq2#-Q9SL$6G?7m>2`v=A;t1P@QE!?HUd9?#K^{y5qz#WG#R~Fi6
zO?W=QjO%>6g^-zfTLvxttKrsU7JjfSSFJ-WOS~1`tUD>7bylSF5ihkvA)#V!v&(ra
z!JZJP@L-%uO;MK!U=Bw6=#}j-)_M%#b>(LC6vc;vw)QE`B4(Z_S7tvhxg#83hlpad
z7ik|2P%@>Cq@Vm;8^cR&l(0`De3a$f@`QKq3OifF&Dsj}FsMOy@^RLIct_2LL9_K{
zbD~F66-(VL6&|2N@LO)b{MdzAN_G0CQ53YZbh$Uzv-56(dF<)Fnw06rb3}_p{9t;x
z+Ff3spl(e$=Gu=>zi-vPm@bhPq;=nj<(Q6=A5Kne?VyH`fLgPqt>l6RM~D75X*%CZ
z@AP)9%riP<Z5rxbXWW^CWq_}C*^Ux(ka}w|+;(=`$~f`LO9S7VQQs9AMY@u7?k&@m
zOZ7(iJ@RnxH|k+fo|FG+t}xL~UAa>4dX>-FVdFai!?}{e{-I86&#^Sx?E60;r9pOp
zTJkd<cmCpd+H#ouzW7CtMn)fGtW~v63U)q&*u1gS6_#PRnhxQ=wb-;I8oY-O=rQ!w
z?WnKu{2V{3y-L-gNhqw62He-`Gy<BFcHBulT~F0Za?GC?A<x=OUr_(27r=|ZFEH#O
zy<<>p--uXU&8M0eJX?HJrImo2DL-jfxV73P8ppJv{w1lQJbvpTy*j}sEMVUZL|6-G
zG9sJC{+A!LAmh4|L0YHJbw)y@1>AQ+^tR`8J#SR$-q(n#ta3b*3x;OPk=D+PQOsQF
zdE&(W0$ldjt`(A*$(Zn%Y7h!sm6)d{`U=}5kcVM-z(V(K_fb-N^pf$B+JpUJVGo0D
zKKw?%=y}M}A6J=(en|fu>E0;i7hbl12%_j^$CX}-e^{m3WGFUjZ*3J2NDbX<vP4qt
z;IUAI%tfiZJToNB0(6(VV@#bHy+6k;->H}DK}Yh>-{A9s)Ii|6SL&!tX~vz!aC1;f
zfhF_t)5yZ)YdMmmW>uMJuy@Baon5ZWv_B>6<P71Xc_^n%q1umc#&y358J}+76AUp#
zze`V^iO83EX*PnJo-`?7mKg2%SaH6!!M9Pu@0hcETnqcQ?qB{F;paoH&h<cgKl;(*
zHR<Le+b|i~bCC85Mm6;H)2-O_17bTS$O{7KnXpV>ucWS?bxcslnOb-7Y}#n^ymj+a
zx>2?EoM`mk0?}ZE`gP`Bh>Oi1`5W9WIufIpj+93Ebh2p_e_07H^+AFUsp3x`h&WrI
zP~iCFOS%1?Z6%BN>5?AOBxH;FpAhBFK?IsA?M(&h6{Re{&FPL8$!$xL3c6Un3y)4~
z)Tu@%!xjXAz>+j}C5a)tUT3`_%M`2#Mw_)UejHvc+DD3m)`(q9;)OWwMDbX8TnbFw
zdRV*V{bf;)pJNkE3u6>Su(S(nHk%!g*LBkcv+)7ql>yC0%0a#$zWVt7OQ)yO>09q%
zL5fvnJt-4PM61ZRZ7ERn%y>jvuEeUT!i`2EtHqx!!a@L0*lfvqg1JWBx>J@Cjrn}E
z)MnDdw{MTTWVezWlX}aTk_F^TFZ+!DX$L*_t9N6%kZHt2if7j34YKupt>(P!{H}pE
z*=j=S^1CUOz>as64-eMIF=x9I2BA9PmSCBg^_1sTW&ogpBL|_&rcx`!O;JZfUK{8n
z&u(qt;9`X%8OdK52^uV~&J|_})7PDUC}wCA($^VZ-wxlio`O$e6t?R0?n%OBn~x94
zDDJ^Hah5>BQ#6>W!@#dK7p0A3tW)2~)BERY;URWoshGX`JtKRSMs=I3>zJd8#608S
z4kQ>P8WJ{!q)-w@6>i35C~ejx_)ZS&8QIvGaNH-mO)zd6IoftQNdXwH4UQfkOQBsc
zJ;pFYUf@J8Djo%@O}+F^n2e!?@Y)R^3}Gpz8kRv8Ha^;FRuziTvZundISJ>%71rox
zV>_93nbXM;u3O<1*CndX9E~TRPZ${)tQyxKHs^Q@C*n@CWqQ96zueV_D_5HBAV%-k
zJ?n<(Inxld$_g1c`t_|m+-RproQl@$Kpl#1lb4t+LtcnvNjS1aA%9ga>8X?a+Q0U*
zPDznQd|j$@wGRPZOpWjf(Q*oJcg_o$sZzQ%W^FOxl!wb6(Qi;X$nW<iwqs?FTYx-J
zwpy*6V>lV98~sE^Hc`o>t)3oHqiA7CcqC7!W<NUlxw!;6tYLK8SzKTpGU4UAG4>~C
zWx(xT{?c~~P(;k<I)7wNJE!X_tL$W(9Ya}(I3%8$D~OeQ#3tilt1>9cYcxE>s>3Nt
z-g?xz*b1fSF_A~#+5mR^d1a|D$-?N_sf7=ty;EW#NH7Vmtl*b)?@Ffe4(;d+!vsW7
z5`<GqoR6WXFJVwY!MY=94+6=+803Y@mSaGc@$l_Z(YI{%S!;pXg08Uh_Z&e>(Zif%
zq~)Q9E>M#Ofnxm0Ojw=zyWY7lo9-)RsUwuw1I-pAe`K5tw(;H$;&Z8W@3{YE01CK)
zlLk4kyWXqm?B!qY)b<=q#`He2lY10YIy1l)8aMU}Qz32uVL5lZ3f7z3_e`e7o&KGK
zzSZ{Q-Q6h|=Z-MoTj@c%&gdn#o4#~~0-Ny=$hw|yhg^&H#@H12MJO)Z_?-eGX;bkx
z)mo*wRo4t<nO0RZtB19eV8af%bI9D2SLV8WJ|6y_anm)t_ciG}bk(kb;iG(UU0+8&
zq#0PWec6H`0AEWz*<IK*7H-2{7*G4WeFVz4LL7D^fF6MM8kd6*bxef`Qxfo4=#B^(
zN5(U=9FM+$5m*u|#2utNQ!4oMzK;MIpQ65xfT0CBK4$enq709Yt@<aql6(E|upl7W
z6zvGIb!ak>-=k$cyRN+K*obJ(pgrcmP5NEi?WRe-cT(hvTpJ+psO`f6Ykm);lTv`H
z^IvZ+ojLSzr5SCQevgB}_PIR_ntQA6xJN)DidW{qyplYV{zd3m?BmTw)!85>YfuKo
z$!~U7S6_c8E3S9>Y#KT4;yfa?B9R<Ux1JK2HCk;q8}rZ&*$&1-Dy4QS6{n5uMbh5F
z@UG$p5v4!oVQj2JF9arptUA1VeFHLq>DG+*Rkc-|#H6UtZlrtF84pX!@$gzN12U4p
zv<2r{+~gO03V9xYYD*1`^$l=_ZO5E8Fq*zoD!#n_36lzLUjwmd$CGnHrwQW6CE8yD
zq(pKoPNuKes7Y?y8cfP9ZeXtai?lKuap7g0DEhTxwnyUSbaIg^X#)cp*HfEY6F3K^
zF4Xf7SJI=F5vrfsDrc1FVxCetu;Y8-;FEW<3&EV+%RF)HHk~>O1g<7P;cMq%3`L*D
zY@`{8%ZE987>v4jwi)U1{lu+vTqRnW0tqywlYP|G1e*NZ5T?mk!=d#jgi?IZ6CPZ&
z?{oM5g&^&m?av`lsMir>dGC5Ttr)PFIb%B!_%qpCAG!4~$SQ~6vx@~mrOWd}NV5(_
zbNlLXB}Z(Q2m~mWU&KffEky-6Xpf~kbk7gEjPhUCi@e5Yk3#~+O*+h^E(w{tT4y-m
zfxWwmZ=(R~li`OTE=a0EB=i>I*ykY^vrwrCh8GidS_oTK60_kpcg)M}wtJzv*{w!P
zE=t*{W4Vt6wNtz?BPkGTK{au|B(Ilm6p1s&iss{$P|pC2nm$4~pFm?seb{HUQ48#d
z`i<o7;ZF)T^wkH24OKW?nOjPVu!<hXs)-VnjE}x;5MwxiQh>C9uVY6_Jrf>cQidFQ
z4S1ynQ5X<Z!@W=^ED!Qdn65wVciqC9IV|VkILoky>&e1J>%u`pSe-X@Vhk4$C~P$_
zkA|A%<Whs0X6|c$eeo0HDXD^lHJX|Y3lYXw<WdqfJfTH<4b#r<T3Ah*Kst?VVJChf
zgjwCEA^ZTl?M@B$0%L$c{m}<ZXTWQ*de=$HUh&G=U2U#Aa=Os{C67TZWVeucFn{mU
z-vex`uY`Pp7z!EN(5lAkTy&PlUT*0$q4+?Y9*yZbzyHB*bU5Yn&#D5?fqPCJz5<>?
z)HDB4$}*7;R)U4NfGq5U1aVVy6FvL%>s?m^9p69>xVzB+#OnK6{GFjojfoSd9KTnp
zSTGcGwoCsFbb9toPf8GHp;ibVeGWA|ZdGqwhr6!eyF7g9G*}$60u4$f1U864Tw|k@
z>CM=cF>Wo5hrd!&uA%F%AgqUF0n{3ycS)_-cSqd*>5JLcpY?)1um(yZ%8a3?as0``
zS^}($nt(VdAM6a3M!dm1v5+v(IlJq=*K!b-lb6~Zlhb1w)(G`W2S^1W0Zfx6YuT^-
z4b$R4Sp1GapB0{Lf!$xC;KVIwwaEFoI~{wm=A1$z-K?!KrW22}UlE^&vU;kEuiKVb
z;Jk32^YZnOq@Pd>bU13sdGVHWw%5u)P%dT8XwQ{v0q%#*)W!U;Zz0r2f>;C<jOzf}
zBCI|363GZV%^@nnaV|9(sZokI3%Zs=5n5Y<sx=gTpJ=Syh1Q1ie$H3%_>yB^#^K6&
zpXL&~CO?+`4aU5JdwNc6Z0LqO2?@X#;KDtvCwCHPC!{`nQuzO^z!gDZ=c$Xc=$<0g
zTH~G6E>Zaru@Jo+iaqCSZL^NNY{~Y%UO~XxA_Oz|@pq1C<++vc#37IofrT_3URP`!
z?hZ*f1DGq7Pz`59CfM~I<x4VKqMx}mT$)gGs`kY6ON&gd?A@*?&GC`e^R+Gu#fJn8
z0I@nWVIa^TxfT7MpeO^-Sc!IvDgPDfTn|cud3UXJO-hN&hX~6NU$xox9Uuu@+(2nY
z*TO3i&C+s@xCKxZFHEGKQz<Wm)(V#l9!fD%H+4nNMAUR9{EdWVm%z`QTuM4tPstX1
zS>jB(m6ZEg+%eKGeItQJAN!44U|h(pIZB|GL~o?YZmy>MmkMCS>jSqVZA~x@ueNsf
z2!_kr7PsFT6H>|y*6#jC4gsnD@|3iAhwgaKaK%?UPjwl8eR#}Ox#HuUP}^i>=}P*s
z7(0K`=vQ;df!;0KUOm~)6-pJCPy0xG5H0SOvNQk2OGT5X({*Xv9m~1hA6^zCbZBD-
zng_3Ale2e7|3If>c1H<#;!4GD@`Yv1@pF6wxu6GxL9K1EW`4K3tG20Aq1S2e#XrFX
z++D`!aJ*ir?H^p7121G%qQG>^E2Ko4D#DfUg&XK}$P?jb!f<lqbag-@F`<Dt_6L$%
zbvAbD-nmRX0hD$*glj3)UhY|yis3$45eXuIB~&t%=HThrl?y)ujwUyBZQx*eIqa<}
zC77}-anwGBTUs)&If<QKRf<~g=@JdDmDET(u}J7DRoH$T8JL$FGWxJVR9S*}3_L5m
z&zB=?ui&b;4$c*pTHKjePHqtW4q0(7RG%&BezWB7+O+x4^<Q{;4PqbQRZ^vlAhv~g
z*68^f<aA2`HG((F?Kw?r&_~Kbev#+n7odik5RM~%#Qo_#<O#$Y_y$ln+F(zj;VPVi
zY)5Z*oguvYEJ4IzC)8O=jzxZLE`M1J|L0P*9P!EhvN&De)_?u1=-o<ghu6vZjYL^}
zd~=lVcIFqFF3Wm%+gSCd&961ToR!zR%(?Pay|Fu)Uk*VA9Aw=->!<M6IVY4AQKJME
zxU2ptaC=Ji-)n|>L$1yUgmQcsvA4q4dA{aLO;>4agX7h4RdjobN8=H0m8ra=Dq&jv
zjWL8XYN8TFZOfxTwGuPy)QVxR^n$LQ`lb-vHLmV;+Ca`x%(SEZhHq`p|6Bz?K}TUE
zX1%K~&*}OvLS=3+wC;6S(yu=edMG6DtO!KrsJRGQED0*V3S;Xs*0K(8h01(qETxs%
z4-4;;pvG6cIrCxPX^j^e3-f{3b3(s-{fkS)t>+xw>hyR>?}N?PT&C!L>p9t84<qhJ
z&wAHhk>)U{yrWS9szbVCJrG~a&6^Z+Cd9;$T+ht2P+v0)L5-*2j64`xl;H;Mt-Yei
zIfrMB<3BAL-zaWcPa%j(;KWOEC*URMo0etyoC_!0et^Obv<bL@>7jyTK+1|Mv=JO~
zianN%6DM5kfw)Oae(q)+^gfcH<(Vh5i-JI`dq~&r+d`anP@Y@q(BZYUmk)Lh<Uy?)
zrBGF`A+PIeW9h%EdO$9glOJ%7{_i?E@}UPvr$)fqVNh$)SjqTC5A~*Nr8nq#f7Uxs
zt%iG-miRG)Z<G{r7tHW%=4uv}P-Yf(AB?N!BG)`Tnq%t37;$+Y=U7Hr(J;L3b<j>B
z3^nr`?pXy9YW?Ejsmd-<2!`mJrc9tQ)?fpC6i$?J--3!w0gbdKK!{|_?PTC7`U6^O
zZ70QR%xah+(s%VROT$Q9c<4XxohrcDq&RfOvzplK%AonX6{fR|KbkS32ebZl;oL=o
zuIHK8X0AUKZL`?^62D-2Y~JPRu5P|I*qIMS-vQXm>-eX-mk(W>%QLI{p83N4=<e%-
z63>tBgZ=i$)yEeuw2NpT#IQdP6%5_rYbXrbpe${LW;i1n>o;$^H&?rx1&ml04PWet
zbGyK;zeLpanLMQb$P5K~n@Dv*dgD>FM`xaMji#;i<%H=*Gou~ipBN9%bKE*kL|s30
zcw#2OCpxg<_-v8De>#*kpGolUZ5n@4)!=Q^l>geC!!@3{KT!Kf%@Q4kXR{{tp3}ts
zJ|jYQ0zPEt22CWyJH**d)!5lAe*C(b4s51{y)539mv_I*&`XsYO}`94#!N%7d8+Dc
zBQ{i-cx3mImoBXnke2dNg%jicCN@IWQ`h%yeU|SjY-sH~eGNM?>^DB;On5x{(o^ea
z=Zt2`nbuU3XX7*2Z3~}qEA*0@Vkfqas!w}sg5uf`o=cIU#|Qvz0M?vL)&O?23mL&^
z))iAb`cqihN)=brZ287g$PuuhsK<ksZg8dgveUrDvom5qQ{`{%+(TYU6up>s+?*66
zK67=_jl(Z)Pkg%ivosog&!EutM9KuIxscA~&hYD8t#4lXG=X#)vH(v&dx0#K*=8MI
zOU>_n#Sp|U-yJ=T(JwS2Bq{ZAU2$K8{eWOHqh59_-M9)(KK|8i_031Fd|u9eb|e{~
z;W9%>{Hq#PIQ-bnp91Okx`3DZ{RU&I&4pf)>G9KLxA3Pxklig@ZA`xx+DN0~Kd)9}
z6m8YM%2Ki;=>HHB^o@`t*)dP)4ur>lmK}T)?Yw<eLy*x<sOHZqeAnY#$Z3jqG^=Yh
zn4Ow!cK>-ePg}WRA;UkZMr_z+?PZ4IUVSN*VQ>JYP7<sA*&NYT4{-x($4PxYg-9k#
z0h_C+AUw=lhZg>Pd<RAh`uAEJ`1&Nqbyz_#7Pw{%$CRyBvh^&QKA%}mJ;E)gm1}td
zG}46ZGgTL~4QO@y8LsB<{wd9rl+3%tSeT5iuLBqa8y(K;sp)AF0ST9gC4BANMQPy=
z)T=-{FIS_%-Kv(&tgcfc+}hl$^QmW+=5zh$`>hs6Yo%D)G;Y*Z{tFV>Utak-jC*o^
z0mN<78+AcH`OJgj=ocZAV5W)T9)0OLqz`e&XYt<ohTIb(T`smV88CJcD$FqYD_8Sg
z)<X;geC@zB>`E%x2842^+&xUL!F|<d^2=tLflX~t7~%qv4OGYzbAYBv@i<>Qj>w3K
zGP*CXJ!rX`jz|B8#0UZ>1wNan?x=LDHO+1|nKp8-RDLk|j|VE&iR3cS!{b$1l32)n
zu<Y-1x~d@p(29eH+y~-N$h!VN#r(t0yx}&gNDM;HGaR|a74=uSq9?n#kM-(TZfikH
z@ogPiT*1lkaLtC-eesPb3@TI`+*@;Ye;3r+Q|)}7c%C!T^ed2&NG=a9h8QsXs2hmY
zr9sW{>Ey34rEFanj1tiqOHd@fP}sVy<9ijf(WNl~jSTX0n3C9NBDn&>hc|Y7mtXpj
z@{UbGD9=kWp~vT>Tg}v%Yl8^UzD;fON=7{StpOu0FF0+(sSKtq7UBiUgP{sT_SIE_
zRS%m^foi`K$H6-wRG~!Z1e7#O5qrf(9d($gP_bvoZ}#1bLl1_}CX+V`b}miEFA+oa
z4AUQ>;Btom7UJ;o<T31>RJUCfq}51RubWI&TUy4_PTEA^Ip}Gm-d<r%t!<1;>sGMi
zn=+KOY{re@YfJpY)V(N~FM(QlJ7R7^en$mR@E51B1lQ;77TyLQ7nWx!$2Ku0V3FH8
zd-Mov1vQAF{rQIH*q9PAPyWZgJNFHIn5bK_Z9|)jCNg)jtO=xy`{B6WE)TKya7Z#E
zLKLB{C)uHo?U=pm3eZni=H!v@lUe!9VOT@1Znj3<Tb859*4ywrQmo0ddROH)PZuM^
zg`69cA$+=Pt@v_lD$F@($@)ZA@ZF&ag(2Y#65=`(>!(8go`MZX#3sI5(P1-fwh|-}
zhN@s%0(lTd)!Q&l_hql;@>8q0Q*&!8-XKAlC?i_uT*$i=X+nJ#1Z+r+C^<3EEpZ1I
zmR$i2ko=ef<VXWG$62ch7n5r*cLcbqy6J>cR|grPlph)_Tu;+#0cn6<DN9U9zuznD
zo0c#ZPp<Fr=}G_UGn|zNW847;jIA`E<klZ-P9Yv6v^z2s0__VYK3IKf6bjN)_;pqf
zODNL9>j@v16b;#a`JLyO`A3@Jd8p^QrKjocz8<p^Od&<qB}>_htqlr8W{!zVP|pEH
zJs=RdQJt8hQtR*|RKY!&qq{9Gah<e3M|DZNDKE%NnPv1t`L*XK>E*qY%5ZjO9P`kE
zz4*TE5BfppMzW&J!vdeTCyOOHRPa(0C55NyIWV;1@qrVI)`l%Ob+t(`S`rFlSL<N@
zaIMd7$};b)y`>gB7;mz|_S(blC*fw-K5ze#WmGD}X-$o|-;q5!dF%6t{qv@<ORsuJ
zH`2qR2vWzHh6|PKS9a`HiRd7l-vDdR%B%kOdNtV%!Jpfoj|-<QsV^lIXu)k>kOd30
zl6$g%V+64it2JVEs7JC17(-^t!#aAYKmK0a!l;sDYn}TW>O<E(pEHdhaOVV58`*fX
zM`ryL^4GT=+*X98I_PO5&?V(%av`b<ti@d)cF+g&{q&sK`|o?%=xj8F$r6`u@h@L!
za9(#Ie@e{UsVmUB*sRpqt4-Y;Ys<1`ib5@v4vc`UrAYgF!+yFrADwUghgQAeTPyTt
z!dm+#%0RVTC`R|2^{o}ey{qYP=E(rWlu1O9KBx1?w#xn!h8^Uyo0UKu@7yyej&0Fi
zA4PcZQY%$zZPNQi+{~7K{2uE(oNpr<+-vA-@SRz{dP*P`U6KlGMN=ga&W}#=rkN;v
zepEU`5m<PmJ}Kr$J*ygUj8Kb|st<&2o47`e-4c2GI-mJ2_m?|Rg9@a-EKhxfZ}T~e
zO-ZB|7oXJ__X2blS@=MH<@q7>+6P!Kv;fjKgtbrAPKi$vS~Sl*4ZzhN_nD~GV#&2O
zRAH2c?CjAw9rhl&zQJJBa}_g%!^`4}28a6J2ZrvJ<&y&*(*MltPCz|hMtzXIKO*_d
z8IyzURHX4iy>Arb6(;@f<q(Ks1&F?y8ZBt3e6er~&ymKfvvrH2l&zR^jpt^%AFF&E
z_MfVqN_4=V4?y%4(Cajq))`)(>O!f_mw(@!<a>WxJNfCv?W{0wsG@nm;JBB8FUE-e
z>O|XC%6hnm*kr8DUA1@m;55dwe{MB>Trsb{29drrKBkm=`{s^9@=EB=HJ|9c$UdQb
zS6AP)4<<!!i5>lojj@uOtc}aNJ^J%`y+bHkG1F4b2jyLix2p{}EB1;;IAB0-lqY)e
zowYwtY>x_Kk3Zd_ODye#6_OYJD186*(*nut#75lmM4+D`AxLjyhgxVdb%(_?84&j{
zZ^ZzK%`&80l}PW0K_=k0*&CBHV$Unw8^wZqCC3PthAvQx<hL~l>NCih?_HUzWzqcy
z`3JnSnGVeA49vTuguGMCu#l8pHTnAAgwo0R8LKkSS?@G%AQRa}TQuC)bVaUib(Hf`
zKk1B~D*eVEZn92)Fvy%+h<ftg->a|y1}<6F>4`kT->gDtOnT!tz2==A{njZ)>l+&>
zo*nd0O3v%bS8DPRZnEne4j?aeZ<L}KyiWRE*<#MBH;Q^&AJjgzI_L9U)m&j#O%0*5
z@vtE~&A!GYmNc2{(7X6#^!YWL>cv`h|LZo)Qvtmx#)JV;PWF6Hdw)T$z?I8H%gXJ>
zuLZ|8EjHKQ_#dkbm_QJp7?-ds<cDy4hP|}xiE$MS-fSXp-d}sG2{ClGRsd8}o7`oa
z7ZYx_qsM6au?^A5OLcZ1L}cc(Kkc(iGsVJ!doPYj&LX6BXz~RbJmxw9J8@mK7v!jd
zlXFEg3z^-jZZ*3^{cl5hDe=k5r&6i4L43$%a{T$YO~QtI8ZT8qdws+2#$-a+^YN2h
z7WT*X(2UAwx~%HsXQy7WQlpb5N+4T!-x9<`joVDsvBosoC7a!vwMG4I_P0+Bd!Qf<
zEiqz$%WUGM-kW_Gq$dnJkQzA?ok=k*P<mc__FS-2_gmZ2VzT>@Mp8U^9t*kW^S}<A
zNUsPzn6ZY!m}Z5(U_XI{lWfnrcS{(D$G#Sh`OJp0mR5%^kk($>EsdnjnXFBV5-SI+
z-n+fz1EBFnGu7sB*bpJ7R*8SS&Ey#Y7GeRiB{v(h*VcM{+ds5+{<-p|U~X1$#Pdd7
z&o2=pMNmld^SuD>`Xfr{uyDsn8}~dYpfX@g!-8~u2cFvF>WWtFZ|v1~@GJa63(zj!
zxsk`Wk@nJ!Oxa&ad$u@k$5mDJLxyzL+7q|IDcr^H^@DS}lY7wZSQVDdrvY~E*UxdM
z`vP5Kht2eZR}y+!8^%O}4S|hrQf|$R8Gr93Z1l8xlS`%j!<n-dSxw5pcmEXChAOv)
zD9lm6`-Q-0xcbnxYL2iNqsRn5XGQw<eWW3-Mi62GPm$A1gfF5YJ7qt8UbVSd%+$9c
zNIfk<<<exxO~Wo)e+rbaU7Nbe^2&=N-MMCkknW&ol*bKKonJ=Vj+=?P$Ws(Tt{OGD
zJ)CK+S1K`jHKj{~S~Xy!3PVx!YXzcaQ|d$U7~<sU_c0S9Go8;vZ%t~-Z{sg0n0yI{
z9iP>U92}P#x*VoQoJ92cgAMxujnCrjyObXr+rGT>uapDn%iDyIzxcfjl?vWk>JRkQ
zK-C-fJc9Dj@4@dpkiPognE?v1KEXzbdJo0N;@wC%RmZ@*gSO?69%)xm1_c6Y5LQEI
z&T?CJES^4zRdcx7*k}xIOk8S|+BjL7@_t~z>Z)g7{@}7|fc1d$)Uf|d=*n+jgWdak
zF>ZskA8)H;_50&th7w~K*t@dk+7kg~#iQ(y8>zsl`Q4Gh9$@cWEU}?C=k{Tb+u<hW
z7y*H?oy&6^0C{de9!roVieM)cOuzapV%GjB^d@LrBHBQ~&5BG8o4Jg^-mw})4;;6j
zk|);m_z0<FlU&nP%;sZvw;1Rs4Xc|S?zDd%mSL3D#Otk&6%AI(Nm035u<8t|otr$g
z3wjfPO<_oO=kiv8!j`p;f?TL9@r3<*-obJR?sr(f&~501=~a+_CRH#OcY3p6;6Z_C
z3HvJ4-L)ME?nNT1Oz*ycy1Z?zrI4w84XbDGG3`JBV`TW}-qia`q{OYLfS6QU^#;G_
zO0B{1+P4U*fkMn*e}H@QQqS#V$;M3<-Er|WQG3SgU6zyWN>yCCeSa`sgPKYnKF}14
zuB~)$yI+>s?LFeLdvLwIfSt|iOtib>z6tmmB;r%|W6w^Ys0yb;rDY5%=odezN^$&X
z3*iiITCfUpZs_=dN9&@&?W1kLH;v4Jxx_){^KBVEWy<`guB+vSfm;m<S*KoMr>(rs
zW3A-lg!-xK6`?TdtJQ!4MB}`~^FVsx=$`z$Y{_st>y#x_nrfb|hKAbEh<1kx-39)s
zdPW-X(r2Hlu|!E-O@j3b-^`n6N=?8d<aquh1BxE|U@|&163j-Q#xfDHuFbn2jtPV&
z>{9-^iyPAU<^=><Pv#AJU`_VBH_H$Gep?m1gIfwysP^jhStKPriOH8}ZJ4Nn(y@&n
z8q(0~8cG3$qd(eA))t!7CU0ymZE4Maes}UTujtLNuRk`{*<?@q!v0*n4eM&R{u1OV
z2S(Ia2$X#FgC&f@c;;;DzE4HLd*TdzwG5e+w-mOVxUqA&vA`#!T6pHH`O8?5Pu-8y
z3TDl1CmGl44a;XD2UTnG>B*)~-AgXo#6waqJ4yT=hg2$H#%A`&Vzxs34G}@fZi53N
z98YrfSjGzqn#{6{eLUBQ7_vtrQv?`%{&x~6`xm@4msX7j8+^9zmRRKFqJ<2WE7^_~
z8E-M;@l8FFOX_)}m&{_gSu67MOUeb35|p)dkEu>>x1%g$e;p<~?l+}O51Xuwz=p&w
zL}@Nj^y#QXAhud44NrcPPP+?~E{ulT7AikWJextb*}EQ~56T2}2<t0^_ZKUx6TRWe
zIVh+l!{yU6AS`S3oVfy1G~{aG#QR?hXotI+9$_a`iE*fIQ*}vt)h&W5@yXc1hm0!M
zhl-AwfrH12^}qELWj&|WGFRBvy;QS9i$(eer^3HJh3vs_*(2i;$G<`?bQ*88yp)2f
z#C0`~@)uE_dt0_s=;UR$&v_T7#+<{cc~Pt-NrsP+hClZ_o``^J4}-SC%yD~>sP6-r
zY91!*cHp?`tdu4+9u0l`Y(eoex^z9IFQ0qcWS5}IC1jy*`<rq=8Y|Gl=9<m^MbTMr
z>uU(;;N^bENMx^`i>O;Kf~2I{>1c6bf@pA=$6Lz%J5N_7fUsN?NoEM!IHFgfc3R`(
zT;?`aA{tsVTlEkf(62&c%Ip{SsV_i}fkGcOGf?v`2};a5UD#>l4-j*qUL{O;cN4_~
zCu0S0ikr(_jUsjXtEf#7)|7w*r&&vXuFegedQSTe^gx6?IX=7pRv!d>w%qr#2Fu_f
zSH~al_$||5co)@w&Go{bjoAyt*<qL?CIgpYM3&j-k(uYT#}b^DR$Fx_%f7}bqz_58
z|I&p=7}6|(z#74St+ne+EHYg|`!h?>g_`@t*_K>Tqxe*<7$b1}8js)Nm=5LB(S4**
z+LszGIL0#^%je038+ckAqB*wIR-Iv9_+gmm)_~IZ!3YL-GNI+Zuc!0wh`MvFdV-c%
zwaeZ5i$3(8-p6~kxY&a2pNC);9>M6TLK63)=1-!hb7I*D;x#}~v2O%}iut;cAkJB^
zh|`ddJVN+4o+LVF!bMd6h9`J<<o_Q3wAK3iT`yh#j9{Gpdr0EaKa)64f4>E$CHn6<
z!I%GxPyAoHNInvhSp6$<M47zgL%c%$j5@vD7_J~*`b!zYa7h1#4bO`N7tGMXP&lz|
z&({4vAb^nl55`!WIe?0pR<B#V%HfFZu*J^-qyLQ${9jqGSS`pS|K&YY_=h-fkg`3I
z_n?XU7^T2B<8~CNmia~FkjGn&zVW<NJxUn-4#KGV6>lCiK~t5ZfVBH7C|81ZxpQ_6
zz-(Od%bXPkAvJ)ybJU}Xxy<};R=lgVVN()TX9J3W6Usy+AZ-YZe~w#nv=iz$u!jYY
zBtmT~P?P`G3N)lIfem#q{n^z&EQz|TKb40Kt*PW&+k_??IP;$?&=?EMIrq%p^Pjj?
zor=$+dl(6sJ$8BB&}1l*)&#5FTnQ%~pGmJ7Kutcuj{h9kvBg2TcnMH*B0vM{K+jNV
z5M^99nooo^=!<+d{kzV_CwoJaDU|9^YrudS9?~v;u^h{Q22#y}!lu-TS!RFN*}B1L
z&#7lO7zzcs_6zehurlee@U6k0Rc5D$yxvR*`t21qq6<H#X{H?d2|7XppsNjLMoo9G
zPYNHO6-oV1o5!tjL9zL0w6h-N?O|?x?2<Dqcs$6+h;${B@<)B#=%Ku0+ycQR31|sh
z(v($#&gP(zMkEO${Yll-9bEH}3AZJq#RHtSva%6;!wv(QE;+E%+ZTQ^d0xN2yhbhL
z{m#268UiWLh5l_<<stiQ{bK9c`u`E{L)H%(09|j)Yi)ui8E&N0niW2C*ZmSS5aZmb
zZK<|d3Y<K%(5~<k>3mMYIlTF@Dp+)%D+#uOaw(t+loRWLF?{RQ%0mJ4fqzWRiS!IN
zUMkV^=lEP@&%a2cYn6*VkD0xmd$`7^3L35543!?bycdv<%HRr=yqo4Y_Xp3V(|?Z^
z|J#^!wW?zFDo)_D)i|;gfBj*Q!ku(VrVHmUo*3CP$9Z^`EFjDj2!w?|m}yr@BX%NX
zNjBKF69UtV_i24AE$zfkH2tL!>+$<xo`^_E=<OijF>u=s)C9Mol5qj}dLCYP!PM9*
zo(WS{AfmvP4Dw&F@Dwr?lpKC~*B$hRk19d$s|pQ#$TVt)>c2@#JFM@x*f%5(Xs!Z;
zpfm%amrLKFm4f76kCqI>L(OCgTMgnwOAs16eS$rkkiZUyuwPB9l%*J=9cU%6;H}+1
zC8?rry27=z<#yWex{H%+<Q~s=M@xIpexf`opvP3ot85@-TQT|W_TaK=d&t1?xJuHU
zwEmIyPk}}t%YD7huz!x&{p0`-PiLdZWcbxXp05xP`)s9x4VJ_xd*H!TqrnjO!b9(r
za&^a~CU+{1_)IjTytND8PgSLvzxQw9Tre7n9rbCCOhn#jD>tCng)ajS@<fQEz3SQJ
z8YK^QAS5fIMlfj>s1v}d$q=oK;o~R46$pEV$slQ^yI6$Uft|H<#T=9hv&Q*bl^yif
zhS380Hj$z$B>>(TQSPZjsrB4nR{}_BZd<YFT-~oSRw7V}^=T%?B1|1qrPZwWjP=1}
z0II0B44zH@|JwVmxF)l2?N~;19AIWtP>SdfKsu<1p<56`C{h%pD@Y4PYJd=6WWa(D
z4OJkZR6&|l30;ItXi-|EtAr9T1Vf1-gq$7cf6n)vtMmI`d{^ho1sCwXJMX*q+H0@%
ztY<x}=;xY7;>`gX;yILNov)O>BgMAF>B=p7Tq5u&I!)vqZi4G%w1E@?{z-K{>A)9R
zUBZJo4}epLZ@y77XRcW0_aC9@?SLFd16|0$F6p0p&xe2B7bOpJo!!tlZPSnBX+wRk
z$WzBto4M~jco({MV&xvcs0bRu@4G$xN0+_UMkthR8^DRR;z|YIDP0RSEG=mkLTY_H
zb|dQU-3GCZUSz;H1=wED$}>Ej2JHXEM|XZlQ(r|KfZwyM(HlQ+X+2+D$l0?rIXK3<
zrx)PxevCyp^6naOh1Sj95!+}uyg@eI(4RmjOl<j9`syr=Sv%RIM_xwf=IzK^@tx)=
zP;6=cN<aaxxvUhgaTRut^+1nvKk*ah#=_>SGZB~R#*)tOzV(5p*~?(V2S}^bmBGyf
zL82})3C?nGaHuhb^%FIV4g*-}{UtD+?vw*G+0PXYx12${;3#eR7-n;(@cih9E;`Cs
zqpRaK?tYnCyw<ekyMUP=V-9XkLCxVB3Lz)2TW+mFMURA3c%K-x8pI#Z14>T&Zi~6Q
z;`Uf6ze6DjLoSo($9j6HE3nBH8U&!}+Tgg48QT~SPWA%qvU(UPC^=GVB;{vKS+)s|
zQjjT!$`B6T+H%OnsG9S_{nM|!Yenaw)mjmQEo6J-FvE1!=~>4^Vn4h>bty^9IloyJ
zWuj8)rX3acZ9T$|K$?vrHk}|%5x=epKiU-bRsu4HE+>HDXE=HkWtpIFm0Hx>c!c3r
z>Bn>u-*O*CwB;(m2u~DVzd1`_T{q;;YBfw{yES~-Y%1K6nk|J+;I=WPw8J+D?iXOD
z@9HysBMvp^<C0X(gAR<;<$S_SKXGk2(<?PZ$ho=xO|k#40}#kB{9K`CIhH0nWM&-b
zxFqv4@7(q3+FVg#FO*+t<u^H!3Qgth#$S2dZ}3n+1&_LM&WLf`(Al|itjJ__I=h2~
zCcQS~E|`kU_Z?09<S16oeXGui4E`{EI|S11khz(6xJ(#bD5hRBXrTtL8xJ_W)h`68
z+MKjY;Xy|Z?-7+{mqVQqsm~{}z1>y-d9~JTO0H1Mi~Up&(<(<=NN%pM@@JXl7R#~2
zO5Zu+_|1GJ^A5U|K@)f@nE=g#%$3gt)<c(dlt86O=Nw5Kw>|wAt0CL&v-Y7{UK_1j
z|8Hf(aLR!sb;J&#MrMR}uL%mu5(O)a0zxQ2hAQCGb~WUz1m>SVrem2r=C!#Tl>i^G
z<KqiwUpU<L0a84txA%NH^N3(`hbbI}U0e_NKkybn-&Ew=2yMi_h`KKqXZ?}&@<yvU
z_W3)X4Y}!Nzs^Pk1gBx&RcK3nduo_<rudK;q<!3duWHEl^-|b}h?(=IUtU`0hgEum
zV8tZ_Q?tObk!{3W<ys|gyB9~^#c9hldiA;bnWSDd11xJc`SuJ#OP{B7J+R7J)(vZ_
zp~`sUr`QuBV(S{1E*l<H+L)0{9iypGHBH!2?&u*tF9qc$#5X9S2RpJP#KE!_7I@D!
zXV?o3Ps55#Td#96cKN2y?~m%*OEkTqC&2K=e$tg0nH?Ms-Try@7^QPaS5&HWtXrZP
z(mr_xY`+*n>*)(w7D4ZYKuUxP?vZn6dwzC&#Ki8a<pDTJ+dOPrN^}lw{*mYnkelNZ
zH|qin!ZY?fVqWi$+YM3E&a^#y5E8?_YlVxZr+_M6^kr^2JV`#P&F`_RGL-F7ynVlM
zcKz46{a-EweO;lj!`EWC`I9+*mgt6zqFdu;r+U(deS*R)TAMons~~2V$p;9ehVDg+
zlG%|y)_Qk*0xVNfcy@7dQ_T2tixLWIskw;UTHf_qN_+k_!e4o-(L8ZW<Ytx$Ot%yG
zieVp=(+Bs#u5~b$I^R|KOr$KmT1CL?$k1>D0{mkld?npmXyo)Q*06fgYY&_+K2%uJ
zl2L1he-%XZFT!7wK$F#zrk2$W^~o<SobNR(KZApI<frhJ%%w>s%!#TeZY-#<2{Zj>
zRC%O)r~Y}{>L=b%)@__(<}VVUO`TuiaGwDjs7!D&-I^G4-HZ+`p1LW$M9*^bC|+r@
zGR?1gLhYc*KHz!wxv~^Q?Zl6m|IuL|;kD%~tSvCvnn8_+a)v=FY}hp8r{)Jd$bR5K
zY^7G7&Pj(0_*V9YE$ivz4XpsYrH2dfmQ8@S<hka4T}L6WsP4#gOlX|y(DvjQ;99yD
zc}EN<&>BYo1wt(woC9T2095`5=?JvPiCgt2Y%zmHVXu$~!rGGFoyVap6Xt+*!W;V4
zKnsAQH2xHaMQA?#UiDjuOjy%7DC{ihAg)=H1b7(wBm1Z6pc_-ej?-9DboVGl(-Pl6
zI9LtrQ;@I4;@%%0e2ARu2@_E;D#$t-ZOIRLYD-HjK(JBeG?hawFk_d%H~rN`6pBNm
zoU**%od@>FPsU<C?0Dc9!9A5eXMP2}w+pDAN|V$W0Z4{@);I`5UE+O>nbi7Nmyu>U
z5<`YP1?AzWa`gB!4LKuhwp58oi}s5y+i?*8txX8+hS-Yp3tqAf8Y6$3&G&XgrM$KG
z18Gfco#bUi#nnJtkl8|zvGPAy7Lfm*6=w8Z#7U0}?an};p^fN&E(CMf?lzrCJ?Ja2
ztFN$0rV*gh`~csRf0DPmIeiEPZ^7B%C&=S`<tMdgl?{>*HCF3rC;)JPdIorwwKAwj
zt>j*{bU!$=32x>Ks4&kld`K%9jK-+r|D^)KOoL90^9P<Os^8-xE0IiPd(H3pb5=_q
z<@<l-K>^;Sl`qcGs^U5P>^Y`U8f4j^Pd4{Q*&n#rM%#>iF&no$aOV7PL{%WQ!DaIl
z8eAm#UsY|0+OpC=y}<v^lVh-cF-p*}Ot%K8c=A^h<}7HUsQ!4{0X!0<T}-Z{X-fpR
zZGw~I6CXcZS^_QGL|k?{P&W0;jR@vn1X%~<NJ@d&Gwqd7N5D)r{*_z}a;)6{%(2|@
zurM3qMO+OoJBzxqX9={pv$XU%Fg4N)lzmqRY$fn{HLrYAPmHw%1?nyeq?N|mmy>Y=
zbR*gD>!lx>?r<6^jfaPBfz<>bwKGCJOzPKVE9IhdNrG^vi6L@_VE3EMA2n3S^OLPh
z`WRJ(9>A@<4-ZWmBhpK*?8<eT+{7K}{*iXI@6R<y4b%DMUdKnRt<7&+$A452z&Ye*
zHL<JjPmbsuqpYe@eBZjZFHe~iZGJ|&Rd{i``Cr&TyF>CPe5+)MhzX*!5#<k{pF8(9
z<P;Y@9Ebq)>BLmHZ}T6sN=3O=vR&eP7_K;^hX`f&fFMtSMA4q(;i+)NMWlo{P9p7_
z=za0gnL@#hA5W&Hq1wj`4{9*^NS=p^ieg&Io-Xo-f*kck;^;J4cU5@c_Y90gCN;rO
zN*nie)a5b%ukF8_HtbwFuSc3;O>UM$J>=LIOS>q5vNdGA_6Gy+tzLa!mYoV^Eqigk
zLvwSS-tcdNq~v5!>w}Fxn^n_In2$$<fR_ZeN*&<4)zQc4AE%!3`9SvEC;UV!rmp9(
zApK*ux6_o73|JuGNTo+2TlC?8_FQ2S7>+v#wm|LgKOiCgIRF3i@6JGK9B@xA3g@8V
z*8F7C2LK~Jrp!+0j2SbLFKtr@!nf*jyxOk$(KvV#Jq2sR^jGfQt!CasnQ;P}@#;n!
z(<2$|u<GlNKt8b3D-Ad^OH#vxiHh>eKdZH=n6WgzKS^`ll_58`?2o6vg>veDmp3pK
z2ktxOimp28C%BL|pZ{koaQrFtmHLU@ww+){jbdApw=2J}7&4&i05C9dOB&IxxGCJE
zE{QC|W|Uk}2lm$t(-|2~MNGt7Kb#Z*d)k8W99mq)_YQq{kVYwtrNTq~H6<YNfWT*<
z-5t)=t$B%FOPSrBkpakK%f#6PCj9kX;O)!ac^QUR9yQoi#s~hEiMo%Nn)V3*1sJ_A
zj*glcH$DuJ2GK*A)Qn2c{RR=RY088-8{1I`MGMGd0M}GSZ8P$=r=cvQ;*U3RvPo%s
zLLIH&y$j-^_w}i_b;s@3j+xXWfE43Btp*w02LP&C*WqQx`~W=~KgYAtkF49h7vj<?
ziT4hu>)JH|dYi$y25qW4EDZrq<SXwSjglR+@X;RPMK6jr@C`l)iM3YTna=EZI9(X+
z>747GvK#UWaZmKQ<lHjjB|RG|P<Kcc0)b{(n)a_|0zHlnX|!s>hi1qtQ~pJ>&@g{^
zppW*g-4KIlfX{p55~{azDK!(n{OlqG5YyWHNUm;~$4n7aKn^C|%d^qDt+8h>h!4#M
zn9&X_q^3kn;~yOd-zO0&+bUI-98(q8VEK_f7jesjT;j%Sdi)+W8?cb$7OV}_Nm88Q
zl#Pl0*%*?KCpdKMYBH#6T?|gck^NvQ)d4rO{5#hi=i@6c67vGwG*7vRyMMSXx~<#*
zobiH$KS+&u<|tIvPaRNM-^4E5VM{a%)QJnVJE;FySqxXV_9ftUsSF(ybU6)Jpk-2k
z?YRk+E3l)n2H@w4#Y(aU$WzHW=Sc!u6F6W&*y{<0fy`s)5xCxnJR^;22y7bCu7&bT
z!~R{~1k$b<kuN`Gcz~g5U2&8d=||@p-koxCNmRcl>e7ngJECn5fU|>>Y9kREO_68h
zP;Dj-f_P=aC#V$mHV02FhYuhE@09*V(6;J77U~7`4FU0RYU045n4@6Y(%{a~ScP$*
z=$JNt=^p_7-`%<YtFCt+uZL>mBJ1b|`)rrLY1~1SavG&cM6soyI`UMr&W-ojWhuz$
zgC8Y`zQ>7xZ0u*bB2JG_j{76+Aknj-vBo#^$~8g$y(KgbVSJ&2H0zkLqVIG%55%ZM
z{{INrh-}-p^~!Z{;n#j|PJnG?Wg$dO6BuI*;W*xn5j-<2l|!w%A&ql!;eJe6*=VkM
zMBI?D6CaO~7}wK-RP6@))WETLvyndc>Nx*c<#ox`LJTB1vg|@LHil1qtb$gWFttls
z$F&vf!5O_DsoPrD*BCo83k08#e*u?dAW4?*@qoh0t){c70k5Qf0IiS^09fk*I1Z`Z
z<`38KMyZnwH8?lDYry;S$zVXk#zuCG&zfZX{fUi1F+c*l@EtLcq&ES>jAQNCm}}Ws
zW99(VCgjU@1dbv*_R3reg<uN6TeATzk^&7!m4+u69YfvO(v}Oiz+r=vf{-|dEMVYi
z0@3EupO_JXgj`_+Qx2epA^NBSt%brksD84-7;nU^Pg54AN`jYMiNsdROc7Tm^JW0+
z`VQ`uHrEM9-GHyy<&(7*{DN=-g<_bU7uE?sO;37pHU?f}5iNiIiXi{l>pd(v_a&H2
zJh^A7EOp_U*NpW7^H51e6UZmbLyk*5`@9e^h^%sMN_Egy3|E0B=f<Y$2`RRu6?5(E
z>}bd<Runxj*?olq#hmW8>>w7si!Ly-SgiS?;Uo!Nqx1TGQ%k+MeiPQy%1;XDzgt^Y
zXbza-qxMO<+Na4Kays`%k;R$_#Z=7CIJLPn=(v;BT2vI!h|+x&$|*En!fMTvj<wp_
zGQMfbv4<b%<!XM(9)_V7Mjv3*Nrff9Zz$>l?S(_l$j`78Yf7)UcP-BnK%UdN1d7WL
zWuPFSY+a&kJ;0V$r$eU<%9u90pADgd{od{n5l!&VV5#`&*S_JRkn2{h7xeW<9#m;P
zjC1}<^UyfdyihEbs=0K2#=KhN`tK!X4#E<FIZIH}`U0o7ZeHT4pSV7yPlIE}8Dm~S
z6sZ2u**6|ZZ-nQ*8ObcB)jNw1dL$p1?i#?f4E>C?p}aXTeJ=M>mSy6vl@kE3S~>k?
zXSih_r_P>McLpwDnjJf0yedRUvytwY82+y*#h>1_`cRLfYOWdn!Cx%KlD!Y(#qq-x
z7i7-6oV-3rT~#u7a?*I-CTNIP9c|Vpg&mka9iMAnZ7(-a4s`gux8e%55C`t#;ZbL<
zpZ2&^x)ykqjM)N24FnFg@eUP=%Ll!Q5B)Ed21S^ZQaK68X)*(FlS?mOV_YS~<#6uW
z<I-iv<a(u=rVEB<1t#xD5odC|TNetKZy^@+B3p~6pa1qEWMJwT+-VB9`I0NqD)^cf
zP&X&Fi4r3yPg=)KR_oakTgiD-!j`7L1sW`TJXliEnM+^4DLc0`nR|=!sVY>lF;w<p
zeo4?1lb!QtLzK;z>uO1q$3>MGU{o36WK&imGvDZI^zeh$h?(ck&vw>s%3ja=vX$r8
zCVxWY5myv@KGbV+FXIk8^w>4ln@M%r-QgI5eT~jZoyAt{qe=-_ONK&+`on2XI3^x@
z19RDeRsW0GMZlI^m!uT)B%Zf`cj~ah_?R$Bc`MFBFDce<LlC9m7t@m(CQ`8JCTX91
zXJNs2Kg`bzMGGy-h*xh1G)0ql0xCN$&~HT0AB!6$FPwO`U00T*MPb%DA+jR-J-5a|
zH6*4{-TN;;*jv`D^wtt4cIWwT8_rI{x~=3Lu-1AmSDg>SqilyUYY$x8>RzJX{q-$X
zT7Vb!?pr*qB^N7h!NV9%%^bBrS(SbW>BLR95Tidl&|}FjO^?E^RLUNzz$zu7CS$aK
zj`Bp9P|Iu2A=R5>NM7EKmoBHfWuL$g%kq;j&53m*9GG@nuhS`~49Ys6$!y&OalMO*
z1a3u~dMoIwP-cFE4SOGV{uk#MMR=0(63hOCm&WhSAzyUb0R3)m)59%z@RoI?rYL4T
zi&9&`j9M|#Sn|!F8**<+i=;P~zMN5Q9LlO1%Qwe6&D};BZH~>jy$zZ9Vyk-WT5<ng
ze|z$aLg-|o#<Cnq5PvRrxe+tT_a?Fx_<&hYrUZ1zd^L%XR=i<1PDNfB0=}e7k=v>w
z0C@scRqSC%Su!5yZI@+Ycr(`i&G#D%PciIjW{O50uIVQ*L!_6c9a0+1ekE7XWQ_re
zch>vu9e9+M8_$o{&5<g(qKvhatxh^_!5UYJZWe%5o|1CN-#KxxM1;VZ0;(^8rZ{^m
znX<HchR6n^>7J`8LY(}<(NZ}x)f(REi}yu)G}V3V32JmM?yu_l$;dG__AG|}_>pVo
zg3Is74ZECqWOvy%7s!naF<$wCzHm$B!)Z`n_LAV%iFBZ-T>4Xdl>Ugq;%qbfxSO@q
zs~2Y34We@sSB4#F7zLva%$+z)YQ?eF(d4}C#7s<7<*6Ch%Tv9`O*TC*(xv}gi;!{Z
zSMhTjS1K<At=5+$E?i?Y*Usjo-+EYYLHTsT)LA?KOU69xD8;Tdt(cvut{ANkaREG=
z-4O2fMS`<cR9LBnOQ+IE3%V30-*&F>O-2&lb+`o+oC@tdq?F%krkkk88B-jo3tu=e
zetHa6iOfb;4X@XoQAqO2i4Eu*tZTL-`vah-2vpFv!X3$sEF+&niv*flBNI80U!?14
z3A<e}GugnMtUHAjI}#q&ugAEV(q>g-N<$5rK5)c)n^Dn4nVli7rMyH@t%=q{>uuAi
zln(tubj6aNebKK&-j3Rc(H@xXGT1rJXCN{6K%)PCxgCnNy}%Lm*jUiXPUm^m7iUph
zkT+BVYLureCQ)Q&rL!>eWnLuh0fA*wjZV~)0>S?E{HRJ%RMZZ1<mwu>$QbI2(CDd>
zxN!cUv~-sB0<2b<gw>ieD%#2O9ICaW4B)(SV+oaah6rg{5z6>Y<UYu?jC9RQ5Ce?r
zqC7?NS*Uk$e>~LOF7ABq-fkV`ytSOX$R35`tWHDrnDmmL2`l0%F8bvM9#8W&Z@T1)
zyzKSe%ROS4&F<Q>7gz5+&LOVBTsQR-WW$B<BkabL8=}{<>%E+{TQc<oX7z+joqLD=
z^|herLW~EU5gYN*>ep`<RG<&jRcJNQh}XPHF|MT3a5ODOixRpyleQ)0z*wwp^l9G-
znBBf+5p5kv6@5g&=Bl+`O3)LjiFEsNz8m-rl_h_34F&%vLyriW1w2=)<6u~vH*Xi$
z_bT&s-pb8AOM==c;DnRApqd`v|L%2-xQ!<6C5ltJy1oe=yXK*+b6-e)s~NETzK;XH
ziOS8HAhzgp$29`}@pLYv^VrU_6?0zAY3G2=B_cu4CkT3WVg@^hOqX(UsYUAJPRT2v
z+F~4dH#R^p@YcctUzCo0l!P|Kp+wuFwMfrOeiuWmp;e$`$|IBMa-gk!4-x2OiE7Q2
zmt47^CpT5;<bO4_Sxc^W;qFe|u)UiX3(Cu((r8h0QLd$&RTh3CeS7-*XAgCf9OzWD
zbeu6N&XSnOSi1B`6Ut`p<&sMSaxdA)vWN2e3rleEMzUjtFB_1hmlt_RMwFB9{?79;
z(V{*+WruIF1(fc{AZhL%<hK{6{>oD_Y`lT{T44J`P`+^cR8M?rT(yOdTP&`B&PjxU
z*J1^)v;=iG+jK5iSq;I0+^54A@ThOu9y!lUOTy8LA>OO98^nP(Vi>jlkeBLd$iBk5
z1yFN^xZL|-(x7rjL@054=j%t5#_6g}kMK~tnm$$K2Dxqb#beE;;?C~OMsqu}iYlyE
zja)<FFvJf%DK6hJpE~ejY{d&Fr_2fuPTedYL1`Cza-Kcih=#>B^y=20lzI8FUN!5^
zcAO{1ZFN&Ulu(;rspn~emfSktt1&-V&<4A-4<OAK!H@SJv*(`qVYR=`uKd0AE#<^j
zQ(;_l#wxqJuyFGmFO8id`nkV+EkM6$uatA$$IW14!3AgKdT!e9t5L1pp+-0duh(s<
z&1Qjbx<b!Hhh?9n1qyRb<ONS>g+5D@!bx0Z*1L+^)Hu0%ILu(w=pl<}B}yg&*?$bu
zzJHe!<hlA*_46!we~;YJ1t>XfR8wYbtvJ!H^R_Xb_za4|HIsR3o(wFs7{DKaN-_p(
z8gtui1v-Hk^}=YCjj%~`c&N-6p6YFUGbt{`U?LwXXXyk9IXUE*z=ooezaj#4=bmoH
zLH%*r=GD5r2&>=_<wB9g#+1I!BD%2EPRd6dHEJjKgcSwtsZN?p(T^7@a&u}$(8vt=
zj<<~|)vDUKNkogDW992J6=c1|K*=qMUX6u8yCA-{5k$?v5dre%?PRwf9gdJ+QXoGt
n$##PS|HCi%cmMDB1h0ud=}djLjlEUPe_^<x+2x9hF8BTe$*FmS

diff --git a/reference/plot-4.png b/reference/plot-4.png
index 64aad2ae09a24ec54748149f8c752c77e2d9d86f..855c1019b9b3651be7870921fee8232e7413c769 100644
GIT binary patch
literal 38130
zcmeFZcT`i|+cpTIBA}q6qS6$p(iD(hMTJn6-ce~H5|G}($|DGYP-3J+C4$t@krF^r
z=^z0@2?Ug82oQScnH_w7-#729nKf(vdDqOW@2r*NkaM!n-gmj~>%RBiyt-|qbBy&o
zD+2?=F^KLB69$GuK@1E`_m3O~-w;yYFM^N5_w{vdfKPC2YS%`B4;F7-YhMNi-e~%Z
z5jM5t!oYBW0dhmjEGT_$G%%ghf3JBdP`*}zP2c)B<LTo!P9=x@BYNQ<(SDyClS#8a
z{!G@GDMeMb^&-)bnDHUa@l)Sl@(td88zXA?{27yx7URvtn$qI5G5)c0oY!4(!mWoL
zx|=V-oZY5vT?3}mCX*;(4JY?I^uONzmjr+WGy~Fva|ORT-4YgmvAY;~fPukUT%q?g
zT{i4@tKrK660q{4$R-*K1A}v6x`E|?gqCpjK;6IR2_KIvg5S{(%~XL#w!fzg)-ez~
zIF0HfC6ee~GTwMAiVL*D*?<m!*FYXh=e|PM;Fmq&@{lbInx?yDgYzL%JRhg}qS5LW
z*7JhA7hBz?XfM!*;KY(;4#5(Q#0|j=0g^P_D9OwkcOQ}@h!u5@)_&UsKdi58Zjy-Z
zhpQ*A7NCV7eKXz=tWd@Swb^za3cp}yqMR*`8y*4q@Y+n=t6A<uzjN1il2ApWUmf~7
z#8xm|n4|{r(i={g4*B@j*yI?}W7i6~t8Ov<w%}534H_#mGd3VYLr?|7FS}bFo=r2#
zt&{KV$M26h>Y1(vQuF48Cpw6;?GHrUU3fO3@IKl2@Hb*~vA(RRFRh3(4gL;SelcaO
zkF&fpt8~*Of@dumJq<r|t5sojDaPQq%xK&8)r>PABYOIhHm_d9)0)hX{Guiwiv%df
zuM93+IisMA9+TkIJnEl&vUZvYz5q8n?v#<OGP#u4w5}c<E}G~Unq2A-psS$pqb7T6
z*g(hjuNK&46vUS<7)xkH+d;a85@|=jpV56gk+63Oa@r*%<%bE8#Ui4@ImG)1+%@%n
zTI59?+@FK6CL;*%ebFZUsogg}xE0Gc1xHUgseR=ay0dJAw~O|1e0{OdSU7=OY`5@4
zrLk}$cS5<tbmVzM0Y--$8A^-^-h-W_4ueba4!GtKZ1#=8`?#`2>mi;Ghj@LQL#%~u
ze^|mV-x%V`xjU))eL&y0tizM+@w;F`>CWs-63PC}T#4dUJtc?BlxDde2Fjo!`NWp4
ze^eDhyZWPymcN1|5ALw@OP(W$M^OqF++Wmje9KX3OpEuB?e~=2f1ILj+?`3yuZl?9
zo)zb({1He7dp3xr`<U%q?dQqJYVEyOs(k;HN*jG?xxx2p<od2>@|OJ{ctAGVRR){k
zE66L<`|KL7(pJbk#w67eXvR>mF%$~lPMl@%Is~cZZCf}kG@k_q={rbdU)&Sy&DuPO
z<f~ryM_tN|>A1p;QkvvVkh`LXtvk9%<oj4~<3Jthv%Kl(e+;HCWS-x9LreJQaYK7Z
zlTgNFalLN#z<bm2$z#a7uN;NFeLkbz>?%2QA5NN}9(-w0vItg^nW$xtEe+RneVXfA
zFz+i^k<9X2(bJLe9%^1#C-5;?CoxoWx-Gb1SjRs88KQ0FYRYd8o-=JqK@T;csePJX
zqHOEYSuR~`8G7MSmT<m+Picg~uhl-pDs^`EEnQ{p5)SF*dhNaZ59l?SMim)7>!rg%
zz5brrrG<y~ManFebD@nDw%pKmkrsu1<-`6+bsWBM53xM*G%?@#K)|omYBY=PEYC$l
z4aB_h<4<X}#;#RY4UD~YIos#A97MaD&0;jX|Hun|u|PH!rRl*JVoT$=&bL=*8gYjB
z`wZ6K=!sT%WiE>d2|o8vAlo95=VLs*3y(dsYyQ)Pt2um;mhGzn20v&#%j_*9Pv_7d
z3~paGsTuWI@ceV;^&BZ-Iszf*sD#d)fKOkKmdod@L@g9!J5i15!MUpZreb@Izb+$u
zjC{pJI8()a)M#_XyMod4{7c0~o9r$J1^xXXGcHk7%AMJ>UQr761Fo0p_M33MWMuXr
z_=NcKrfNvJ*BUH3Jw=n9p;PL1CE-6>5x!n6!7>vi%A1C`Q_=pX1^ZvW-|;TvNyYd>
zR+5{RQT8}9Sh0j-m$rW06dd6k!W1!6T%?^`o}1e0^!6WC88xR%L!bU|mVG2-u(#9B
z+_QdyW@IO5%p3X(LPG8E*H(*p6kPbGQ@T84&FL1ddzF~L$e<Za?G`0-x2~S$5t7}T
z34wf<;bf|K46$r>v6pxvmoxs}Sy(Wp`3$ne^wMKC2`N7-hgrBVZbqZZY3aFvmrIpS
zcyzf)&1Bj`q<Z$NPvp{t(@0|SRBIZ0ds_QJ+I4^5!GISZU@&o{(}<p{Rmv+F&OA?e
zHNL4^M`Z23XfvAwP*1RDb{<_lV*AjZ9EcIEWVro_6#(*<iLjNe(pPz-iRXI4-X}T_
zBmALFDmQ*tMnW0$Q9P;5+vmFHttMMuJCWDDf6M=}%X8U!Bm8xqR;+q#s@Vg9p5eu>
z9or6l(-dHYjq+52?+I`wJBpGZ5U!Skq8usbq=Z)OR%<8CkAEy!7R>-yGfdaj{ewYt
z(NtNQs3?u8^K|CQs2y`cvZ(Vxv0@XF9*B13Dq>VEZz=-O9h4$twf-c;pgpabH+AgY
zxc_bYt_fz9wF9XgS0lAuk1%6ie$i#Fx==RB7i5Ug9dHfy^Z;$23cC#kJTz{JWPuwc
ztiJ9z`*}&xaJq8oHUao-16K896@fJRgAym$TSfHh^xn)(AUVA~QblXJ>Yjfy?2P`F
z`q$+qp=%`$sHuc?rE2Ei(YngAOL;B^FGR_(m`fNx$F$rB^e4rf7^gqU`3bt+B?Q%<
z$a2F@m~<6cqB^?DvK^Y$UTJ^-lfDkZf9yv>z3O9?Gm&soBBG^9@<{w{^)7#x<>uVE
za|wQ!rW!5$iX$&xWo<8|gPP22j~^42#rtPg6oz+A)u15=0fc`r{O$Kd%j_aU>3xg;
zD@A$M7K36r%WST3fB4(B*R9n*E*j<+H$*0_Sk-1sM(79Cqa)bK>n*`N1IrJq(x2gs
zlAOMT)=q`(@kx=A<?up$NxZ3!lD)ACCa^AwkmTdm6>sE{|E0||G_!Np5O%n#s?zj4
z=8$vInr^&5WI(9PM<(1eqzB++)Tv8!9>FfC(He}))_uZX#rHJJ4nHa|LnuZiZ=c0X
zrSz7ZE!Yq-%99{1Oy>FitQm6|CC8|T>1Lu&5Y0t|Z3VHs_kvv@y*#)PA?iK3hcyME
zV=ng02!99L>EfNEH~A(iKXeo`FI|QWxYq5v)kQj^c~@6^3rC6xzyIL}d%NW=EBRI-
zl)iI_Fy3^ra(Wn^nZUBBe2!C_w>=9I!}F%A41Y6CQ4h}Q0?il`AYG>91>6p{Hpy+O
z5kL~t2lSN<zH~2F%RCv=7YrBSR5h%M=1JwBFW#9F-JJKf4lHe2z6=+>(K?{N!jn4F
za`659-8x4T4@j)wHQjgVe)eg(kANY7qfzV8YXjqklMpANwPZ8LY>~HW!nwvKl90%f
z%DB}(3kkH_2)QM7zPM6pUmjfeBs?3YA6&tUGxLVXc6=;ox5G&ySoX~BEPkG8J=hr9
zau9#L3KsaG`bG-XNm=d5)>@#Lc&g4TePZr!1oF5<>Xu>L#RgWHC(xW-@}=CyVR=W_
z&rgrtY4c|b6IqT_zV>4`Zf&Vr-}=AAwPapD-RTmoF6@YpVCUky*BF-rhS0XqD>xC(
zuoVHTn^6P0g5H0URzK(m_wXT)Eb8G5uj0FNnW3L`oI0deAH&*}F-&z45RGrv4emZr
zuaW;5H=|D7`v>Gz-tWW{Swxja*Byj87k%G~UOrPMm?0{A(L|n2SJ@G#A!DET%udtw
z^9zHc6$!7ooCaNE(cSkuC={Q}BUj<Aa45`a+c~8zxb(X&F=<t&nnUB4Aa_ycgMIa%
zpsRmeb3MM}4Z<VqQI!tvkPN5XO@e%1H@qvg?<TJ6;H)KIie8Nk?lF8?Bq8Ee$2`Hv
z2_qoy0>7pAP>|H5i0e%73)6deYLp6Bk1OpAj|o5dbxiOYVv|wmt&WiWr&VSyw|0ZP
zb=&0m%)~z1>_UiHaJ{_KKCZA%y4KJc<(;D`)$?0c<Q_ZWKmD4n5-n{qRR?JT?kOF&
zS@?CgZs4*N+uyoPjp_<}hdwpv9=YTUO(Y6(T3KK4&0iakQ6sb87;Bl^elO$nFjJ7k
z0X&yyWz6}hlDNwO9aI|+0wsHAh-uIUx#Z%}a`2>a>ZS&dwCrR28KmvDMnd%q8ll5%
zB`~o4;CA{q6_#NPT)BO21tpXroQpK@+MI$DQ{^1KG~O3hF0PaAJ+qdwDuSflc@L;J
zF?lt;E+a_7e85%tU)s0N9R6?Lm@hkKi-WtOo?UNjs@piq%^$A@ES^z9<Zl{6Z$R)R
zgY>@*Ig9;|#N*#@{-1nAF`02fgz!!VFUjzN$1>YJLdQ0h3iSmIgAvjsRR~6R%K`_5
za0_CkNf>y1(qy~U{7D6yX<CmOlS^je51A*AmR_UpL;3{@YOTLe^B%yY?ztmu+e4dg
zM}Nmxy45~Px|I72-7F-TT&|6ihG*;7+`?VFntVx!x)=H(#q~wURf2?jJbK%CU1Myx
z3o{6_-Egvd?Jovba&QuqOe{B?4(dqod#r0eVwNmthMU3f6~O%((AuU}0W$7m#g&Dp
zK1R^Tn{b9hkJSoh@_qegL0yQ2t40m_qDGGYE3{a*5u)7ui|;p5kMQ;3J2F~N`P_5e
zcPv`_uv&-P3J~-mc&B$%T*ckn)xGHzJ*R^xE?@T_5P2CQQC6wed~de|ZUII^5S+Rt
zCZ0$g11K>0V5+;MlG<Ec*gG+FcGmQ`wt9o^!#Q~KdpKSNEt%ry!`fz4NsKG-<ntcd
z*OP9n9Wqmf5uzcwF5NhFn$f{)?e=2qLb%M6pLR%MO)#7t;a7y_I91Kndtcl$%g;>S
zXp?op*5Pp5YEG`f1=}z|U(Y^oD=+3Cr+M@9ZTSpdZig_xT;L&)JgMHS0g>L74M7OT
zn(%q|bIPA>iY&fTMQAOF^y00P+GYKR!tVc|XYRL<*~BqVu0A58HHF7jHM0vbpT<LP
za3hJm0mX8-?dOhPXx(;@wZc_%A+`IGW8%4$%`PIgUSdIA7J*hAbRoMx&g(!VeV3^c
zJ1p4Q_8_iQM}U#|$OMv2_0+^6S<oEw*DUx#UnnHz5@L=fZw8-lKUn%?G?aQ1;W@|P
zC7{g`d}6%tBIkqhXB|BeXm>H$*H4pf5F_)&2a77>N<AgitCr7?YdIL@wH(Z1f=k5|
z?-?9o$~|-fZ(M@cKt3%^G9);B;n!qjn6fjXyND*My_=eba4(xFTp5y=f}DNFLlWvu
zBH2Q`L=uP#o)ucI9mM7A7i&BB4!4DA#JL<88h0fJ``z;RwQ%B4Xqv-CS-+^X?>@Av
zB0#aic;8&KbXKyX!y`-YPtj5*@UY@cN&=~er;D$2f))8otWH8@sC>oc$NX-Z0moei
zO(i<Z^M03?eYd-#$|WRk@JU_79T4?NqeIuH<=YZv99;w33>0O}wu$RRE!+(x<Zh+z
zTfrH^y7o$uFew1pY8+EIbI)ht#QAnXSAU^VLHEq{jI(a<)@e^uyC8(?)LMAQqzl`r
z^heWDNWPmSTvvz#pIeQM%yvoFR&CKb>a$3gUE5)J5nSo8M$6oa9x|%!7yH!6!>Khl
zB;jFb%g7n5x$q~|TMJ1me#My@6}Z4eC7sB`)ufbqQ3=C}gz$g*dOIvy+uYo&_};rH
zy9>)^PskuAifvp!ynmokV}_`f+#Zt|7-B~fGB75SEcr6#4yq*i79NkBnp0NfK=Ea_
zbzyw{VA5UBL^;#!aJ@;Lf*W0?V%57rgEj-;i>+9+v2$0U=WwJ?Q<F<kTrgffO)w78
zr+RZO39iv~NJdRp--W2tmURH~tYE-W|FXVJ!;P`Xd8&CssmB`xs&HnpQp5O5z|t7I
zyJbr{w-W2&&sb4HY`Ke!);JF&M<_vqZkdAf_mQ@24M(i;y*8B&+sy3OhdAr(E^(q?
z$tkrm%Q$LMe608rSfrGu!ZQrsT`;Mki7hL}^}N*iJp56L5qYl1sk57WHE4dfUau>u
z^HFN(Vu$|vqz3c}O4lfP`>F$PWq6t>f%%k@;<a|00ezfVw4ci9;E`KmLEP)^aH5}X
zgVr|nY*}-gAKRO_%F61m?>i2kz-<VrP2J`QJ=%t6pE_o-h+oo$A=<Tqm$LI8N)jYz
zD*ps|q*b55tBso&X$oKAhTZ7yb*46{yGbEf@Gn!TMlAtK@P$_<4K#hN-8#jti~`S`
zn?q7b+H&dWadoA*=JT-F!n)t#v_ei37`I(zthw^b2MGp?V-b;T=Efzw13=i}Y7&I}
z@*G=&fx!iOy_G?Cm=!546v=LVMmgUPJ)SS=cbGzg$0xH~nQVJ4{($-MbnC$*+wEb&
zE0Db0@q=<~S(8|kY@Pg3Tax0ThOo%@yA2~Xhi-4bm<&Z2ben|Ue|uiW>4F1SsxT)b
zG0K2aB`mX@eu;GU5X}*GOE-;meIsGrsSaHv!}#9nHDaayWhrdCL}mtM?Aib`TU2GC
z^dm0yX0@}w>_|~k@F~<$LGqO|-sJ0*m3D5xw;pVpo0vziOwU!~Q72N@S(KRD(c_A>
z`Q=_5wLfK=LZ594br$0bQ~U0GVTpXGciy9r@L`v!EAaB!ciaNK9z*IFj>K~+%R5IA
z2;G{>7{1loZ^P4F#Z&8VGHFRHd}l5PIrPKbVJ~YZxgi2G?xou&Lb?t*3;R9HYr(QK
zTNA>BNT&`F2-hK8qCwccrQ!$&-WhUX7&N}rUYMy4aXH8^=pvFj=6;dj30IQJX|bza
z($k5eIv*l)J2c}jl1HBQbDlo>Fx0%#(w*Ce@F-sPuM%u79cuXeocg>%MD8)0;P96g
zmzc1$Kl2wiqnTR-W&5JA>S62dDwO*-*@e$sR$PK?7yBDA_OY@ap4%%JbP;H*wM%4D
z>^!9^)w(_$^THT68Z#p9^~#{J$@9%qRDmZ4<OBQZQ%4P6uy5W`KudXvE-MYT+c@U!
z!Yu!6(r!FoGbv{F4U)D~`Lw3@TuvRyV5_-Jji_kfxqQ9m1nb`T3DTQs(ZT#${g&k-
zd?~Mt+OPX53(F@VfiF^A-?Ynf7I=odltj5pY6!<G3pbHeQ{V*H&z04iN{&=Fu??rR
zUe9d>Yr@X=rmbNAbP?OlovUpD0Z|I?cB8{tBA@d|J2TY<3_0E~IDv8=i)0hTo`&oE
znGiwpUva{c1^O9@d8~Vb0sha|GEVROYVMrC2Ly&%0SStW1?`LyIq4Zdqwjh#%Zfk&
zIeVO|Xz}igPWD^Mai<?p!-g)-y!+<kk><p^%ubO!GfTnCXMIgoIdr?$sy*wN+6gI6
zP?mFgn>CJTX@jP$TP1ef<_Y+DX|+WA%d4jALsj7vGoLRHPzJ@q@4`dj!`Rr^sW6E-
z<%8w4!iE{Ec7>MUM>kb21ZWK|H%)~>sg_sHa&<|qJSm=f!MYx#(eYji&a!dP8QPEN
zsBq`FhZ_>I|LliAJgBiOkQ1qX<TS(0C+FZKNhx-W>GM`L{y4cl@yKIS`owaDdBVBa
z!03@@`T-Xt>pCSknuj8!6@lE|GZG(gfIOhex_w00FxI@3(opH-av*b^c5(I2Wm4VQ
zS$G?RSAB=#G9xm|4^4e)#Z<RmtUwK-ODvp~j~W!Qaex7RE~$W#fn6@*P_)1ieqRf?
zYr6rDiB=DjOg>MlS6Sw~bX(%1^303_T(b6U87^w5xl%*XT7{3i9irXo@$Sf{WtGh`
z)*VN0E(`?(q^}JL_-68m$nJS5An%qtKh5WNr*-QGz|=#}GAN`tAIM5_c{wS{o=fIK
zUT6*=d!_Lnsj(IQho?0&VIZ^#KQz-BegZ!<2bum#<JZvLGISl&4i({QJGiKSs=66r
z@4BIwjdd<+ZM7g81z2}CSa;hyhct92A^pTHNK(>0erg3Xr`gGXH*9N!Y3$uo>rO<X
z3sOEXw|{_4(v6Wr#XH3Ed6;KT+sKJK*3)N}?vqOhA%p%qEyEGf(Q7NSii9McTWrfi
znTafU)oOaYxF09~#IEjALdlzd&XWIpYp0H#hC>rcC+r|5`bhqS^_ID&Q;@i%0<RA~
zF2aV}&(JWTjQkeLAwuB{s}F5#P%R^JsrV5F_p->iGJ}x;eVoyOq47yJ_9>R{Ql|~p
z7S~WP(DS3#mo<LKF=$<}fMfK>D}q(@dX0A0<h?Sc$7H7w^`!Lp+*4y6^TF&e=a4>V
z((3z;!r@DWF4u(mIS5_sjS**s8p;HBFLTO~r8!k|&ze})auHt_`{meld$l9gs)s=x
zm4^&fi=0cqi^x8XFQ*CKVLa3<ek{X`d=Yt}eTsFj@kDM1rYSYBEYs2VQ|!DVc_<Uz
zWdsUmq^~Y{bwRk(tp|52TdR4CL-!(ccb^5r^lENT>!7)vUw#lxWC^M;-8rddoHWX@
z&GWj?h*0yguE@DR|ARqe!F65Ud5T=SS^>(kd5C>^s`)e4bP*pndia|UTlo7mp5Zfu
z=0edQs_8+Sd)#?eu7A?GU51^uvDCK*XWvLP7!bnp(3>(E0*c{hHpC|VZz5E=t-Hyp
z^x*ZLl3S8`h@pi6<T+7HhJU!~QKzdBIpMY;9P=8tpf#UUY&%2tuKuWtsLbRru2Z(o
z$rqqL3T?{T(QbZsjH6>vORb84G)`X4?r<w4y9kTq3#<qcit<M}*PkW$sUKphgEEGy
zyi>mRD7ffplitq2MNd$79y$ZDy;Kl65TgD)1S%|>N|He?={3DbJ;(R%fs<M!`8Jmd
zR&9?YL$1zR<Kep!FFZOW1TT8EraJ1vt+KJH_1vEkBHema>y9xFRZ!QJq$EN+uVcS2
zJ(wMmuLNF>7s7^z!Aw^j3e}MpD!A5?>QuV)3*4$MYQtTq-@J~y)bc=dtS}1CB^*yJ
zD<dz8;g<Gl@8FW~<e--~m!6LLeva;-Rm6^jx_Yq&&&$<4F~+grJ#sRqUnS3*5C4qP
z;4EbPm!e(}OYT#c^lA{GE+6%WhU8VEOzrSPT5(p*+IA1rP&umxw*#&mIriiEVViK)
z!<J2<4KG7lLex#OMaC~93C;5kZ4t=UMs&D{yz-4NzJ2c3b5)VQxCBY0V*h}}X_Z6K
z7mv7g-=*~VE}N38VI6KaRR%{b6GN>DE>gXFYISp&b5QI+R|+X^Gt<D>L~OAAr=-0g
z@Z?0l0Lm|2r)0fs=zz}9E!Ozj-5P_&EeVg!`Nw@iJu7B<P>nhMtUYn6rs@0kng?y|
zkM}x4pwl(xK6CHyd@!60aBd<0Y}_(}Boz!8i<VISA%)Q@B6kejU^pHKr%U`&r1Q&<
zV)5|o8(*kPx}2qY+0Av~c6$@|h8)l-PXl9jc-)j3G~dz-i#)nr<(br$RprNIl7Cvo
ze4KkxmgjQ;<9Fr|Tp^w;c6Iuiu`7x!?#oDKg492q@G|PDpBT5WxblX}{nG(k5%Ol*
z5po!u&+N*T^UCZCS1)2z9!9D|U@aqhYFgVwxD=$>wwuz1RQvY4SMle1@uA2BIXmIo
z>*WFz{RhZjRg3!57I($Yp5m}{#IfPY9@?n{HP-FR>ic4<D45axqL1pRcB4MBJFio}
zezp{fMKlS8Wa_ofN2~2b6Bdt}EN(cQb*a?_LUOpPrjzC@c@AUSEdqHsTbi*;EY;kG
zQ9}w1?$WFvFtIJ_w`|sR)f60cFHM|eLSEB>a)-2t$Q}-kL^cMgD8XQSU7114@;CXi
zCj!Wu=WgzmTtb#6K|EC2LLxM}{OC+Fq4>KDwvg}DJwnr|^mEE#A5uy9<ukutNi<uN
zXFgBT61DjPh>ZF5hNpILq2*}Ib^HtJk8r%rfY9||t{0OkcCK?)D&gixTUkuZrTttz
z@dLkU>uLb2sN#*AmvJfgK>_>kT7cC=IlXLVf5Stety|9VrqU2v*=&jR_Skj1cjJ$t
zk~4h0>j%T6qbGkMNZ+Ym$Z<W*ZKALwk|pMVOw~Fx@m#M=^W~h(6h}?r7BjKy)l{*q
zf~ECI??SsuWd3->LUO3FUTuf2hEzFgYYyzGT0g7Mv1FD92L+F@=?^Fgj@~}h&4TJ!
znv)_?rry=vz^zqOj$QUu-*Db*)@gkmx#Ovu`Cg6BI8`aoIQiVIFY46G?MJznlTxxH
zpNA+zR#gpMH3Ug~9>L{}8?6_EM<h7QE2t`#)92L<af<VilBtM^M^)!Sr$)pbWhNq+
z1>;^_9rI2qTqQU^EezNmPYQ0}&Klp7hb2Gh*nW=fypDYDo%g&zI(hS#+tUg|Sc1Pp
z9Zf!I({Vk4G{vh7aj);#ZWg)d-qftaL9PB!72UION;QCdidKBGnl;lyR;a%s)zV~9
z`H1N9asAfhj7L~cm{oB4X-{psRu`|U9cmE07E@Z-x%VN+cyQV8V31Br*a?msK|AkS
zI18!|-4^21jY&rB9zpf*+&#LeE<$y3)|eI>WV$l)y4X{{oHqE}-flU<0w;74qm6Z!
z^b+q)k~i5(VI|6QFR%e-#)IEJrP>A6BK*2IwdOk{Rc?@TZk|73yQIGH;^-SuQo68}
zULZY7l8`-m9ql=me2(ebyWQqBteBos;&ADMn$MF{0hz*O{3eU(?kDfre||vgjuL=j
z?K9)cc*8vBOXGOQD+s5mn*TXMT5);03|%AU|7lVzPhdH6OXV<C{Onok>~7V~qf^7%
zzxwnwlDQa(HTPU<cOAb*UoGJkUdhVn-SdNJ4LS6w=uAidh!UW^J3<#tW?4hBbo?bl
z1@TU!c|zG`UPj^>8EiLSD$C$}3i7VY=XAHLuruQ+9fB&<Rs(dY<#hWHvrJX_OAeOD
zR}Ke>w;J3JfEjq@8Ok)f=X6*hsk#r(3Be8^37(cM9YJ2qufJwoC>yJFInc;?nkV3w
zk@zU82{%`sI@8J%9qn>pPYCYXdT_*tcXrBhu{{iJ`$Q-=z9J&2DTIK;2e3`57x_Lw
zp0=wbg97k~1#;r5<uo%(j%Gz67QH0n^e(lHC$?4Q3vU%M0YD1T&)+3P3!1`my<VV~
zZ__!X?T~Xw;>$J!>CWcojx}uWaMgGCg2mjE3h!6xIjwU#5;N|f&&Y6k6ecUx6P(w#
z#qw2i!T{7%j>%^F4hhROa?hX0Rj4k^=zy^YY?o^bzfm@Defr&M!zCmKmO`4czdyB-
z*b!=$Q9O?gp{=#nMS}=%6FI)k?xBHiEiXen9CP>%H90ZvA1dM3qAf#F8?YtJrI7kn
z^uF+69T4+3u>@<f6Dj2wT@}nJRtGW^6;HRObN>Pp|7pfw2!$CTQwRQm?PfXN$reBG
z?GF>ux6&1y(QltR#^|6}5~?59?yV+eKA9QEa5Av2!{6VRe&@JuPbgT2!N9<CV*Eu?
zzH?WczKmH!(bK9z+J%|-*BJz-gOP+4Q>CNF#$Qx9zid9jKMWRs0BAZ;=e=IqQN0u`
zhX{SW&Yx7`{+7IdK}!i)L_ZOPxLI|3;OwBX<V^*Lsk88No6Kot`j!759#I9q+Wihi
zJvwvbAUncLym#&I7@1SN!qvx84J;2#X+uHL&bitK9^d6Bw12fx%O!S=AvtM(J<R?$
zC$;HoV)l>HQ-DMNJz7lY)0q3m1^xY5K>B%<#LXx!%?`fc$;2$%DyZ6Ln~I09sp)B>
zj_&TrO6X`!p#Ptrzn-cre4hB+nWiw~QWMYoHdWGg?>mKRTt5-763^xsIGLzJ{6pOq
z(OBt_JyzGjrMan@mUhl)-bu!@$hID7LY!rC)~kY!?oEQ5qBWLRR!|t*ss&zbD0QJM
zXzBM?g8|duHHR1!!35ofJW1QiNj%1pHr=qdhM`K>l>b@vBvkTR*x0<aG%+!8nfMXc
z+>xe0Nhf$U>{d>9rolS=?8_i!uAN2e+q+5pA%EUyWmJlZi?@T<ib}zXN@45|Nk<0c
z&9t=hCFRv{-|aUBY2;L|?BuE~2IPH5wqGzf9NxyQ0DZKzP)l*>tMMnhQEJFV*;5~j
z941eDq`eWF!bULMCLSL@bliNVD?=KH%3i-=tYMqBN1^fAmO8fz)do@)s=$CE#v-=o
zUr5(2eqEbM_w6dX=7Hb2*z4aZ{PE*QB^MW$J<SW_?SA$Q$^7Njxmsy#jfSyjB-M`|
zJP<zLj4!lhehvB&bmvY_)f3Ie!0q{}v8~a-u_?EjOb0QF=lx&|KSQ!YHSmq7<@q$P
z`rJA{60TR!z3<bVDo5YWYx&v(0~p6h>US>3y1n1A(hex(dA`9^DSPExxHkp^OqwA-
zr(KC(a0`4A+brKMi-_b%tWxsK^HbURR5u8fo&@4hwqcrEds~|iKd_1EEFG9bLawsU
z=mCawF#5LKYTp@)+Py7ux0sZa)S`d}s+;T20A@@?VbrfTCJ1MUQpXmh)IVxTxad*$
zi4F7>47*-Ed@FrzmpV4EcDY;F@NGfFTEFEOOyroSRMfNYi@%y|yWd=$xPe*DH~HAM
zzBJKntTy{8D+b$U3b(0(VzCeV^E4h2D(8#6>Nm49(XS5yL-zi9{FtVaKLtw-c&iZ^
zLHVeU2w3}F;@k@F%?$eeitTq{^TKHDF8tJygFZ2_u@g|)%N7M2ug55;y1QSVUQWb&
z4HmzDs)oGj?oLv#x|@=kx^?UI*`DP;S+M#dpQ$$E?cF`dix)49<=t_UHTRlcR*Y79
zrcIo626lE?d3Uv!W>p(7U%RrrEIc_gqv6;Pv707cJqb5TllP2KyxLP>T^3UlFdyPj
zXj8G!-PxIOJJ;mn<7OV;j$#!`T^Z6;>3z~dfmO)|6NHp~7v@gZkEZJCYEF;7PfLr|
zZW;aJVFKu!%#ghI^d5u$u*3Z(;uMIdm2;c#Me_!vVLc0y+`ribsGd~f)2s~HoI;R|
zQ0n3DEIk)+AN6bN*M~ixftGEQGDDn8J5x@21#%?ITgDWQsvRF5u@YIbYlsMs=+0E@
zE}y+t0t5_0`Lv1cT3{UJx0w7?XKp+Q7Pl;tP@H%zyBGB5eKavGEluTp!2EDvXi38o
z-&n)Wl(fj^?p~{Tp4nz}uhJv_CZJfmnR1O>MN9TmPR;SyZ|(Ad#JXU&G2d>rNL`l<
zUmu?XM&y&#FDE>GM~etnD=}4u5>irK!HZv*)6&D1TbVrY1)G`<4#e2a&CTwCd`k*G
zR;uPx8DVDW#xOjH%*=4*&lk32Qz0RtV7OUd_Q!{nUTJ0ywF<jve+>2`K$9Q4E+4?7
z)8+<tx7NsOcv~;SmF?YlerVI715ZXCBx&8SakR0S^}wN9kAa7|bJc_aZ?3Q?v@!SS
z&odX1bgU0s4h1$95;W}FrIa%RJS-S8_<P5njIRj$^@7Vr%DNQ2gm8@54ExhoAHE%s
zCgajJ^}Zcg*otM5-O#T}0+hT6{4Qc|ZNO2(=JxI4?)lL58$7^rr<b%}9#!%JGH+n^
zt$Z|OLIcjt=Lf)ZJyRuc;U9pi3&ps(vToq`Tt^OGr*$1`)tGfYzm&|Ie*?yxP*~|T
zmMH>>?k_^y7ClV#i@3zMV?qdA=mB2)d#fNNSKd;uZd5H{*v=z#Ab_gr?(2m7=DW2@
zYIX>nuZSG0@UT1<I=VhjAa`V{ZI8HizRf5B;F}LqTOYLlMVe3(xlY4qN3*M8&}g(z
zOAWdKBj^*>9M8K=nj0#k6^Csun<@v)Df$6P!Cq$b%Q)-S3hAe&TYy1MB^j#+%lO#~
z%#gl(DleBahC@9bqhl@$QoS<E1Cpj`b@t!Cf6rv@;*zqnasfCK4KV?dzeHp~v?!(O
zrjh+de<WGfdEN(Z6jHalPI&gUoo=BkES$n}C}sbLW1A~8v_k7LSCytJ2wL)06_~Mn
z##g7QWLbSLyq|;@%GK%Q<fL@`%r(t%<d%D|m?DVXUZ~}=D6$K-8z{2xt|b@MzLm23
zeC0mJRcY0Y@n?qk0_$EWX=%*H-ZrJdf3(J*3jjvd7!VM12U6B;Q3zqTyzxGoZ%aR@
z=F2l?)T7AUxxU<94-_y}$GO&5XC!U>Nd1<MySKq_Baa%&J{l~pG|e`sR+X0anxS9#
zxjfa*JWp?w4(zI%lS!9iVq)rT0ff8)*1DHv8a@_0lMyR!QSjMw<n!aE!1>|+NYL9|
zROH?!cb+AtK7wwaARYyjjoL#0{Cw}h^h9$k3#Pth3aOdN488TzXto4^lkJ3(1+Wt~
z>3Co#56#*K-mRP$+5PkeH7GcPeCYe}+pzuc2vcomhO+;x3(z0oO7QaJJpf>{4fK%3
zDPmMtNk`e_<Ycqz#jkAL9L7m4O7}u%G6JQFY$|4da5)71+L|X+aU21GDsqdI!<{q~
z?Ho@TsrE%lNl1*Wj?wn0=qF6fnGdB=SDkKkUUm9m2Xf4v84b!O5G`Nz?ga%G@qFqk
z4E#sxk(ElW&cx-+*AFUTzkmNu=W+~N`Uh~0Q2|JbxFs>H^V&cG>zGuJuS8FGP2#tG
z!{~f_t%@#{2@sy-CxHi6HxjErsBjGYc0xyjSncap2%^l<$N7$`p05Tx<r#P+HrCiQ
z%}^0rq)+U;%63%%yfo&B*xF&B-R-V)CDThf@n#Vbd+Xo=mb%!)L94Z|4IrpWlqCvg
zgs%@t$DO_UTVs9s>({S1(7>blpYNN}7v*d#AGL-rRL{KB!`<YvU;P#<U9YI(7v7#K
z8K}CqG2!@J3Yd2qEjK6gyf6eXlyyhI4ED;fML@nEE$#jLhe1C_Mn2uino$S$*MoQw
zk`KBN`n4U<m(Vj3R^CoY!iEw2RL`I9w8+YV3t_!II{dpGbf$Cu+C#I6Uro`pz3+`P
zD=82YC=_a<R(ev5@XE58N%OZ%&$P|dkNySRG~b3NwBliZy<@e{)F_B!AJ<FSq&|;F
ztA(zssy_Jn?hYH5lxkLaxnJFAyK17Z09_LJsdfZRbuvL)b*R}H<^@Oy(b6G!DxQc9
z)lhQBp~bsZS2|8(L)NyIc2>Ii&w<biU{+!MDVx;Bm)tPv?$c=EhnulR)a_mSk7J>6
zd&F~j4%m>`9Ang;)t`zBLV@u@%hm%Bf#C1M!s@i0m9i#;If##|U)iL0_Ucx;YB2zc
zjSK8+E9bwyV9PIuj)sJyReyWx|IX!SXne;k9rbAzxKb&=E@p=i(nm5Kk1C3b7pa*F
zK*~ijq3d(Lgeosu$7xHu9%8->xi5DmU$D@wy7W$gJ746^l2aRCHsa=atL-2varH+3
zj@+5x*P)EoDl|ZngnhL&lB4q+8}=5QU}^zI7mhC)B4wuj(Ixl+5?uIG+Pm`QG44Hl
zg~vcHe04S-lgUG_|JKxAidE)gIEi=#;%md`$4<lgxn<&XCNNghuS_;@xx0-~6d~VO
zz<(kgzc^&JD4oc2>(0c4oE}N3Q>q6F8Bq;d9B&pRm9|UO&OhfgI$IOIy<QV{{@PoO
z$G^Y9M%ub3gJZr8YO_8+94Kz~sd#0nnV;r{zIX(#)O>QYfc&k!?)RPBw{PPCP$<FW
zJcs3)*TsmH2N{lQge)HTXQ=TBQv<)E_jrTgu1C|$W2b=&Sx{eaIsDleazm}rSu%`Q
zWqk{HpuI0Kc*fJ9vcMKvJ(Ulf{@x5+NZ%;`PS&jnaJlo(Xe~9StV`L$iEbRa?)4kL
zV=oDjHYQ*+Is8@enQqkBZ8*@OzYj1>TWgK5LkAeQQz58RJY_xrcCHB(9_!B0M@TuU
zq&%L@&52k6*y}MrTw!=W*R-w<RH`Q!F9eew`F`9W0Kl4Tm!fz=foCb)uyJ^Q{Y6yw
zj5yoD14;n5w!&56H@aTk7Rm5~=WI{+FY&4b%;osbba_@h8Y(rV$Kv<z-ra3aSMue9
zRl<;}#wQv8V26DM5w8S(t~cP<*B38<EL3~}MVUztU8siDQ;DJ4#wzpW;V%tzr+M@z
z%ZfOqS7Mxhygql0o>X*!etYhcF%5hrffQoQx^n0kzR2FTNi4k1lZD|z#OnB=gan|0
z)T4AN4S2HA4v-6`qqS8m1r1^mCWmWtpPK`?d+ZO!kiEM_#;j!<AbmF9X+68u|LKmC
z$+N@9pdj<8efu(JsK(#J9HI=|QDFVuKa(@4$Ra5u$8W%=H^D2I{sQR)SUk(G-1Ds0
zD%gLvZwO-<Jzw0!P7s5DaNB>s&{FlUP#bM?q5nmw5fSwhg+1yhuZD3~fteeh065JX
z31)Cg!@MvKSoI3Wz&}6l)%1R@eJ>N7DG;Hgq||_-L^Npp1Y~0#h&iLL+`V^*{(LSw
z?=SwpW^`p{tOQX089_+P&-5@b3^IqS{1F@XX#BwK-p~`Q^@tbqX--k{|HCq;Mx<97
za0{rDTbZl1Sb_k1^c2!4LwR=?7$}MDjKm)-z(v<YdM+2c@zEVHr+CL~o61Mk6D!>s
z{0)HVz3R<2R9S7}(0H@~d;;}3WNmgG!1|-LAKYFBwB>ebR9O`U#IFFza9wbj6US3;
z0EH@r;X$4t`b#&zl^TI*>{$)DkAf#&hL~t+eZk*YF}56X;N`er8(wuUT8j6o0x7I=
zUU&3LZY~I<8}vXr*{=R`c!X|#N=n3awWZk&KrD*e0Hi~UX?x?(I1bk>w~3?H<_QY-
zF*j_5yL%t`Onj)wn=@XBq<!zs-7eVfQlY5!ettZW1ccM<1j0#XHt6@x4+q4Y(iw`|
zsNa-Jnp$YU8UQ7GF-YWAFDer|o0nhzV5oFz{lkY3*Z_|8BKz7*1>fn8`lSUGhDO_i
zK~H@0ZpED^2)Yl{P%1n|shN}3xh6u!B9koUW@hDv@SO;n`h>^S)YNGNi1T}-9Mtlq
zkJ|vp@^sp3i;IiPBWZiwTU1Je7Orln%*_%+_mx3R{Z?M{(?e|8<(`CqTEPnNK3_|z
z&v$80t&o<K%sAdY{~qM$_A239ev*J4W6{qWcEXoiMGS=`W6Pn_9mr-qx>@gWyN9=U
zhPg|tq=Qh^A`jqg_#%W*N$F|G4)#TS9;1wz0@E8)ZIv*%Ot2HH3_!>$?3Fh0KJbJB
z3(X@Z%`HiSbU_KEBv2_y$xcd7R<r)G^Ku)H=LSm#9DQ1aE&&=H7Dqq_I%JA7e~39P
z4kZB6+ZotkTy#nM({P1H=$>^IU`q}D3nQBqfa9(6s)ek62ZHChhgq3DAHP-xy}az>
z!RZKMOE$a@<i%7vcRrU`xe55qz!iLrkid{UHv{__y0B+~0Qb<qU~MCJ*9HzqFN0j;
zNxO7IST0xa3qpxgG_9;#UBw)JUIALy9poF$7xD-6VSsNLNSu!V;uY#^FEN1N>DFxP
zkYkh)At@@l;R4v-HQEY_W>%r`$sv5LUz}3Ey@1P53FMP;Zb{&z)0yrMQ`Z33JPq)Q
zLM*hc;)?);DSceNwkiz}<cwWVFTmOZA{`Eu1|Q*j%=}^AMS{Cau*e-bKl|@7r(=cO
zf#1Zdg_)RFa$*2w^fc1gT}APSEq+OB0J*)g!_*K+Zv1}@eB{E7PU|HCFt!^W0aZ-S
z+{y1t0I$i1(iFU)euMX-B>{gKCIDo(;?|Xp1X!(70sK<ive0Jjlc|k#cM4Mv8j{49
zF6VCYt@ilII{^3Q14kY=pTsP)T?<+L6Iu13=L1E$VF%~k@#d-ypc^T)B$L2nD&hDw
z8I0BNH$V_s;mK)f+)L4~1Bt;ocpyV#-Et_j?qM0@_+70iF3}vos@LQB)Izkf$x9&l
z;=B5=?-OyfHjq{a*zH(_d#`JnnspN(P%92VRZY;^`AYpNuQ3N5_^o&zB{jKl04*B9
zARrk9kv<~!Fu^O++%MUAYih2)V14p}jmx6?cZ~SnXHZ;#Cbj`WABX~^@HZpV;jqyX
zr)I}F>k7*Pt2-nB+~i7+fugtSC`<t$y$#(`ku4Xa=gxuH+si3za8@Q@ZjcNpApx;~
zr4o^nHsyoAsM}2ROnYNSgBIK|+n?`2z#eB;noLkb*ar-8(ixY(vFg&dMS2G5NS9nR
zpy6ZF;Aa5hl|W3A3<JqOECf^sjBEinYf7B81*NhvV7#@cklEZjAaLy6f`rUh49waA
z=?>7doOho-{UMyVbZbXM&Jfho8thA*UlD^Q;-CY--b-&VX@U9AZy*>uX92Q_ftO!S
z@^3Lq6aPL2n5Aodr+r<}<9k5ynTcqSZ?UUv&y_{KwlfJ4^fCJO8)V&{5g>;q3etlR
zm)WP=7qc_JwDOJFdSNRz-B95H7B5&&2LJ}XqE)N240219J7BuKAc1HQImV;t-Sh1g
zr`xT|8i~F_J>i|V=z6TCR|;kAVODZp#4xvfi$bDf`GX!3-2)oMF?Gv)V%5G9iQP3u
zCtJl+<x7ALJcJG2yaQ%cIzZ1%Zbohs#!^q8K0QBL?rus10V8bZPfr$R>G#I1-{xt@
z7?|0>%(z`ja`Fa3!rEt;PI^K?*@LHQWi!mBDVn1J11MXXHbP@(v5Adtr2W7F)MsCE
zLqAi&+Yv*~KVuEFUfR|`L(!3LFCf#3`mNdgOwhL&C***gZ>RRJs$22=8hc4`+zR+0
z+AfvZ0Hwa$-J4@f^9`y2PPC)4N_jl-BfJJ6>sL$1aR0?<{)j;u=<6JRu4<@BmTng)
z$GqYXU+qC1K6>^_NMjV^c7eF4=-@J_S`v99@@GNKfi~2aYbtG98{kPeeC)iz;$|2l
zs@u{6xZP=b-+ALz7PN+-b&7znCebSf1%R-2hNXEJ`<OXEos1^a|H&>WFnFJ&^!-xL
zgi7SzP97EDNgRB&<q~XU2)Oe7P<uM}qt`&X{%c}@pI_Y8owS3vIwslxu1o6!9+_Sr
zvAdP(Rkw6&9u!pEl9D%+QvbM(>hA05F+ITneXp+u9x&$(WHJ^8-Y7|KAWuyl$TfA_
zrKb}xi{_x<epjZ(FJB4BqYOOWQrJ}-Wj_xDi9=W^05g;s#v$Au3SEGLdgLdwTwM1t
zZh5<(4Ux2~z*uS~pD^=T@nqJ&d-v|q+H8LnsLLvKpE-Rx+${&xefmQwAooZB9Jlk8
z*#bbzE5A|!|IG)9W^X>o!PP}X+%WX;lyxhPmI2CS7+~C?mg?(+PL8+qwt)>|)jfyH
zFN%Q50JjCwB{(=(g#!rt<L)+<e+6(nV^(8Xm$qbT17OSTfTCQh2SKCV*Vi|2W^yta
zlqfcHe#CJ{hV<OzQY!(~$@Yx+UQidPda$>%sSQFHH6Z1(x%@_0eJOD2y`O+tAwZ!n
z+<$w0o|<;!BdFQfiWq0A7VHZm&x|w!O3dPb)iWP>@;M?*A3gT~uJ#{&*U|NDmj>)%
zxD9@evWZSI+7crAm<oX$Fy*&eJsng)N>cX`RB<pmFg{K?4vsaB{2$U3PkJ8lwF>gR
z`e_G=h~1{kd&dM0ts!NvXb%Ya91~fwR<PSo^rY--R+m6#^JtY`OhKimr{hLOMt%c1
zNb<=&_{j+han)HMXGlvq;7~Ed;9V@r)v(Gmbouy^a=4#gHu*_MF389_$@oGKCFscK
zU(c91_T+ol7^9fZc`npTffU5_2aI21wL`W$>{nqGF+`zv?a7y5RtDXqO?4EEdQSHP
zGkpPhLJ9}~1M)zbl)(}wjTTUY2VW}f&x%Azn3|aU289_z0x(*GA^_fyY%&v=AL+Qa
za~|Axp`DInw{2|bF$PSdM0I5CHwrE>IEx8eGr=1LJ?MWhTwr?Re5~Pre--3l5KuPh
z7c}qk1G^Bw(Wo_LiR!Kr?@ihN^S|Y-@U$_Q_WQdQAYm#D%pjD0U4uiDk|(qrA|SzV
zDY)Fwvz`5}=tgVkofpZypDq6Ti5k2pMNYl<G-E>D2-vA@{XQF=$2SvP`tSDaQXOeX
zv~$f#K4nutiI@&BI(+->3Zb^8@zqiFYxHpEIhOZl4=)rhc#Y~Z#jDEo_UjI+jL{16
zr!j4Q*BFBSVUWIG(VG>fr`pC$e|l7Tdw1)ddqo=HMys0*T;Wv=$s%_B|H|n9a$orW
z*<OzSZ{F-9YtPsslKTOz2j79~3C+V-)6klS$za<a{dkvtsWB}=9pVW#ZGr6$XQT*H
zDRSnx2BGkaxe`+qz+~x1!nGrd*#k&;Cfx7FYl1=*UlnBd0_i*i^C}bj_YQ_{XX{A6
zlUD1{Rg*j;qf#UtP&%lAXX|buHX8n&Cp`Y-t6#Mx?p;EIf?kalZV7Bj+v6}7>aWc7
zEtqPC)L3LwiwZI5Xj2ELe;b|RV<h&e*x}+XMvh7K#s(&%UEl!^X&1Q;;FrAw!K&^C
zG3Q->_<3}8&A-1WJp_JHD>1ot780m?IZhTP1EE4f<eZ1b1^+YT3+*Sy4Y3XvF%%;i
z%r?*Rzmo7iF3^}3(i~k!eB<n(p)|?%x5e=bH=-2Q^sYN>Cv}Lg($;L@!T%1Y#phs<
zsh33yc>>;%@7_Wlf=vJ0)ZZsmU3btB$-Vw`bJCi0Y3jc;CoHCze%%9t(pQ%6QhlrY
zRwOrLjmifBD+qc2J~gT!C5^<&b1)*Q&lG1srdYsJ^=-u6KY#^!Nf@x)@N;L1nY{@w
zGHQVV64$h>YKki|v*h{vTg}Z|Q3`76seQJtR)<1ARJ?>kU&*3mAiH44TY>9;<a9<`
z4&7Y~e+a43H-V(PQ*~<`O*|o(5WD|uVzQ3mt0Pt1#OWe0cyU5_g?L3sqL4K~^W<~5
zU!f3C1xt`?4vAL~J^HVl7<8f(WY=|VT-8doxFKBaX?$ZD1v3Z!by%=b^5Mat6R{`Y
zTOsK)SNUf4ofAy(`yE*nI1`z&`syj?H=zk*c#ZDX@H4Q>jebNuI}ZTheg-;6wJkr<
zASw`}v-z*^nI#X@4dq}3QEOYh1{PrJGyTwXJu%*?UM4tYjR}Wx?*jz`gJSWN^Zu?i
z`W;c<z^o(v1f0^Rs{fucl%8^0%yzJcty~h_-&F&e!qLhCXO`fZ{uxw_j0Hi%lcfoo
znyJN?ST2YzHbzMZ#QtKu5n%oDrt8_8H#C!VbnYCtvpV(soSpa0GvBq|9F4tl>qG+Y
zNrR~3=hrS?6m*_n-P*a6V##GFJ>xg#pGWZ}QU>xJW-QGI!ZRFaMwHsCifW*Y6uk{S
z73moIzh_~%ayDOUf3UaOScxfXLl3tZyB;qekK6C^gvfYCL$^g03a?>j?;o||*_FEA
zam~sHRxCN)eF_d{ob($TXD@*pIlB9*?5*|(+n7iHLe_0oMY>?$B=7d0SnL|BmR-Z1
z>E9P)@z&>-;T?M$wGbOG-n;d2Odb2ZC^&F{n6ebe)KwAQSWRy_>O|SieRycpW%;`j
z;FjLsf2?uE<iP$afvCijfB)&m>*T+ZzKcHiSF@Ld{%YZ@7~{UV{WmuuFHysI>z=lM
z^vhta@o$XN^x-g^Tr8D~v{Q2#S2E@cQm21o<$JJB;(KkLY2F%BhyTQENPpGgH~T`v
z%P~spFQt+^k6;Qi?d|1$hSBdYcCBuq2X}`5=HdLmL#Y3=7iyn93*z(ek~Jsj!WbXF
zG;VL*Y{`L%C<!s}{|fz4fXQ?S$JXT46b##7^J@m$%J&!Vy#Dg?rb6*c)trz`C?D;`
zta<NN@$7EDec(A?-L*#2JSJbnk}!i4qBnZwyv1VCYm_JT7qp+>f+GWuA#FE}+N+gp
zbw1hJcl=uhjVh5k=9tWCTFR_-f48OM8})TWe`h`lNsNs2&le_KE5JbSV7*-99X(1%
z_bs{jB}>o@<=*@bCCt^IEc}GRZ!TiVUld<Rl$V`ue*3(EkW(dnZ7B2CgzSpC9K9cR
z4LO1LG`Y^YS){owc)6Ftt7g}GVcidX?o`-&rk$%L?;CvMc|qWHDxH0(k<Jyj1s_^g
zDAHqnLuLN)DS9i#!jG){m^>QeTxZ(@S9Y@P7aXe*)vnD%?*<i{dJ(g3uQyx=i!^qF
zMj59ML{&IDtx@|8xkj3A8+k1+2m=u_90{87(ofQ8$-|qg1O!KnLKi5?u7OTfyg?rN
z0hj<N*x$){IFp0HqL#miukMfVVL!bjt_14(Z+Y-^4(&sAk+!>9YbC;5hn#Y{Ej45c
zVHl~AsZFPV>W12@wcOi7OkSR?nRl?IHX4m~-Iz?P47El(hT_zMeC6eA1(JOCo>%O`
zR8-h3A_6;aTiHi#_zBpdMDJ}z%BB2fb1pOKBI50Ew|CXe3)=HXgCaj{<=baAj7d@X
zZFYzFrr+9EtR&JOQK?{eA+$$xg`x3ndBnso-p5JY^P%Hk=8jiOF)$o;`4d}Uj|nkP
zI!^KIADvlL`Y~N7yGDDk9PrI!q$Xsyd=J$N!#t#JlQR|7_RiC;m_4J5w^8xf>3U*b
z$epp;=;5Y34teYTZ8pP7KC9Thime~z>L0cY);`Xr&-(WF7+OrI=M1azu6&$D<kuQ_
zbr*IO7O7>-z-O`IxP7wi{qy|g@m;+&vf=taSjRDLb-pp@NTQb|t+r++3Ke*?(lTcq
z8_GFWP%~9dv{1{g$qQLtEosUdb3eHgfex`&k?ylt8x-d9+P64-@%v}^!hTxe!kBaI
z%NaSA0L;?(>~3#!rmcP8cz0y6a#oG!+R~_Mgilg`wYgV8W)&h>USZQ~W8um6eharA
zWQw~>7dQ+#QwD40S)?(K?UKxMxy&ee3-*O5Cu!zq-IjsSWv_;t{@2&Edz;;`bv1X^
zp3auft_p`z)U%Z<w=~3ifpo#v^<qNt0hCkdTF-1wKFwy=ENPwxsvf@gq?hoqUq7#*
zpv&<|{h28^$<wR8$2oRCN=2#Jkg(p;9w*0%r5W3u4ikTb-%khkH<(lN@P*Fc--v<3
zsT9XZd2eVz&UD!Q?(kzYYYQ>kA<QMX-~YQ@;-?phb*;1iTYGOF4t4+bjayPAlrBlO
zx)N8(o_%YRB&6)ykfbmujhz{#R78<|4TTx|Hny1&Az3EN*oHx}&0vTz%$V_fOyBSQ
zz3%(DpX2!b{<xp#{{4<;j&jud^WNX*`8r?cdA{dkyeoaFKBO#lUhkG}j#iV_$6!Xc
zUxCHEg}P0u8p{N=yJoSIYtkH!FR9>!5uj53biDvKO1g=>v_F=Z4e!C(;s)ReaFtK4
z!)6^U5KweofCV^0qKIZ`#Y16t5-jV6x11?C#1!IlpjE9ls6;}`Z+DuL$^sz8VA1_)
z^CQv-2(+XVK5m;1Pqu_Qgn>o;!XcmZreO`N3EMn>aWGKf!DIFsTS@QAnwAiR99?NX
zmJ_`90x33URnc=(sP;La6VNcVw0Qzldw;K-ZRFVp4nmxjLJ?trSu!+{faKHohe1}h
zXby>sm}4!``4zwI6V@<nOW7X)j4R91EKw+8K7Q4qi`H_n!+aXky2bi>B^!bOD3^dW
zRIvmK7U{^eL512ui)qal8LZ)ogRg-5R^O)5GP^WZCR8X_4iH(q$JyjKFhA@~#&b6@
z>TK%R^D$QSB$q-MRZWE=1k{G7(tbt+v!-Kn6j*CqW)V_hrcPXiqQpsIZELyoA)!?#
z#XFMdm>K@eA_eh6)>2@qd%avyp_C*&8t=JJgi8LsJo`QoWY_)(n^`oea_cwA^lSl^
zVDC<pu_}<CPLV_~ySf?b{DUkL+nlCo(M-=I8mLgs5KeaDnDmO_A}1S^&s;^a^}N78
zE0w!6*E?D0mN$?#m}NJ8<}I^JYpwMyCVZ1;^qf%6%`8LCHWUf$r4_g+ygHa6A2*p3
zd@BJg>dyxRbQ?N0Z2qwWUtvfOKT0+6C(#lo)F|TDwo#9_GAk*SsQc|ms=9oYJ8?hL
zk<qf@e-E;;v1W`9TlhU!<sR9oOo+i{$Gxs#pZQR~nGu`p)&Izd(uO6P)C;{WCkn^v
zVrHE5v;A44TV;RXBuFBhtS27p#|i&tP3QaGvXd1oG3ryHVmjVdx=k*%8(Z%dJFXl^
z;dKUu!yC49(~p1cfDZ^y>=CyE$$)^_Q&H0^T)61;9i?E~8rK|b>)jnw%0k4vj@#5W
zn?L0l&R%%p3#=5dX==aiGrt6Y{`%^7pmhz_H9<M`woQ|zy(L2eio;=P#XlHJY<}!Z
zJxZ2Q5+OH-8;5m|O?0tu&pjl2cL0}etfOT1c${-PaEzSp!~*GYM{sWn;l=7VY4$Q0
zhs_buvXIN_A-`shO|-LX?xJ?bYh}yqo`?do6^nc^a;JuvpGsTj>|tqNvlAdx;eMY8
zYTq+hd$QC9bc=T<=Iu{oaakA&5Z$oWn_*=Ub1bIntN=)-z7?abnmIsx8S&fuauxsz
z5#gU`QA8R}296idP-pelIHLGn$0w^7@^ga;hO5A%5pfpQ=O1Mm9vWmZ=VrJ%AaAS0
z?*P_1P?{t3emf{CFUE6OssIzZFnEyQDR6;Ro6YzFptTghQ0;xnuhu?b{o`MJmaoR3
zwbZm!!g8XqAq-NoIAhooqgkZoWe5E*rc}V9$81&~fkU4`U`iy)BP*zd1jQ0bJCy6x
zKOWYcLO!T-N=U4$nqD~*BC?xA%zelpdQ8Ajtf2q}=V~IQ6*uep2k%bc1S~o%?Kge9
zhlmCPw0vUe-!+v`lF7a-gE(M4FE?Em&^svb@AT40g`$E08|$ois!(+QBT95SX5lN6
zXG+tG$QplFS`&Ao30<?Jot?&-jE%}3CT156131y@PAoh0Pxai>;SZ^_Eub58@{G6A
z9jI^4@AUyH$w8oY4;IMJ)Bz4l0pH)7%%#$VaONshYV}j<PvWSG>p#e47tmjb3k5$w
z8zCOWfvJlAS12RInADD}JC169o1YJk7hfN@QUb4n`&b%w#{~J*6a~9#E42FuQw%vb
zSqBP$n8>Pk$;us6@LUCHS$Qse`H(-t$zC^SOwC!6wJgt;x9mQ+B!E8lAd)SF$@jYx
zO`cBRt@PD(e;YryF~x!)j01zXQCrc8Zi>1v>OSn$$ZE@(V%@Cmc?ZHg7%H(>08@24
z!{M}vRl>c(HAu5>2MFW&_sV_CMS=yK_`4g3M*yQ{498k#XAWhTm^wOm?mce)e7{hr
zDZoR}RHD%AkYdN@xmBWM0YwvsCA*B%h$Xnz&D9++d;pGtXWa%%mK*Lha*o_n3<q(o
zW}PdMUk;=<S_@<h?RqNAIE_)G8r?0m;dky!gxpTRH7SV0FR@x=c=ahltMKP`x+L1i
z%V{4|>18FcUV=-6QmtItv($OeZE=0)xV@^)+<Y}psjuYZ&J%1VJ_OrAmKR+yps>d|
zdi>X@Sw)zJC?&vROqw&s<*^`3#@&RITjm2D61FJsm;3iN9`*<zP+{xJ9r~DH14_3v
zy_4w;>AQ#$^YYfc1FDHq5=biol_%GS*JQGj&7@h5B1{!r2n(pMwZM7^hU?|ft|vsl
z1Uuf+U%$|(zSbZeLR=x5s!%jTQc1=tluUvAB8G2htJmVA681S8BSZE2TZ9oX0$sHr
z4Xd}e&MXJ`?_)Pen_&C4UXO?0O~?oUq|HObA7^6lBfSBeZzclLb}-%4$EI<~ekYuz
zjtQ*}NiBqWL+(S?rS18f9!4;_yM)udjLj3?ZnR9qjB4ER8SQeY(szi{Zbm1~P!aQd
zI`eV|vV0^Y%uye4Rn`=;6{d1;^kCzJ>;lB+Pedo2Ays*94F{?TMw{*4r?eQTf4;0+
zM2oh<K9AZBq5MGUKpE1)0($q|#!?&2EcM4%gr5{!nw2oCaek(l99kw&p;cP=n_lo<
zg*eNA9!);q@Amha?J4!CH1m>P7&UuLWL2-6mOc@)eTy=)=S>lHyHy)cY&BhKuum(q
zn1t`a{5un?H7CtxhjssX;@d0y`o)x}xPV6^n<e&m|5;1RU2JX$``n7RrD&{HB_t|F
zeSO>?X9=FHq@;z>smE4nds~?7F+qD174X$%#&NX{xVu*CSW~naW;*#v>;43d0@nET
za$8-^&|V%@OpIM-1Z>wV$m1GLm(_fDBO0}J=dO~P4}suj?Nt<(J)mzRj&hl3h}HD!
zgL+7&M3)k74`f%_w($as+c3)<1#T6hJIrS`F=@pXrNM5LTH-Iq_8m9vT}FXL_DHJ(
zG8Nz#7SD`V4F~Xtoosa*X>%9XdnQaIwi6~6jtJO35v4CTW@{o;pp1{v-kdg<0)pFo
z0=9TM6dK+z{Cho_)jlXPC`pHzpB-+Y9{Tm!rta!V$eN7a1hi?(E!z9pv#&;Gflu^(
ziJX8{3#a!Q>W|tggtf(YNQR}`L+HZ0L^16&jDQa9?D9y%iGXy`cH=B+t1hDgNuV}A
z8^ll2Yb5Br#=G@Ts3eIA{7P7(rvC-#FoP~o4P2Ol_1SFv${H0aGL$cl-ljQIa1X;S
zQ_yIr*(Y^tQ1(!n7uCnb&bo(5p$2envc3Zq^F5c7&<OaJ4mQH;HF;@{%%EXuyzI_p
z$kIlIoC$&XI6Q%|fAV&whF>=Xp?+cX#|LSb)f=V1Looc8qYnl8j7fcMYS%ws7?<dK
zEg|~8!^~HrK$^pI$`X;97Kv2w7TnaUG#Ux9OptvEx~1zKyp%+gz-=Zz5ZglpjX=Jd
z17sDuF|N8H%i*FG`w92CwH_R4KXKNXGHhZ+K$nnFaH4>zPh-`iWICv9Ww=+4g|IMH
zFMu&xhLOV?x++SH9&Beu1Z$=+{2(ZzI|5Xa6wH;OV2LebRy?M#05|7^{M3YGvITVf
zUdYqvV<R0m8XG#F4>EHa3?LoETAUoh(<EEJ%&0KLGy(N$`0^I(n@KD;w9}*(Wb?Ym
zWHSp@+{yKI2riBj{=}jNFgJj*3*BZh+v4&o$0qjH#KuN9{Eyu2pV6$rTf(-D_BGx)
z^_xGh_+_zH;%osx?rx3N6QJ-Ofn9m8hvveO-`v4D|ApOWdZDze7uiV{I=4o(CKsaZ
zmJ7XaXWC5!-0znCG_zNq&E^GQ6QRZ44h-A|^8mcVVsfNsB-v8Eknmk<A?z9@jcEKY
zyABYHX`Bqg)7o8)$2&lH#c8IA$i#IZPQ%SC4gVd9P9bhRWNdhJ3C519iQ6YQZ}I%A
z7ok<=;7vx6n^?fyO-91s%L$&J2u(3FwY`J-T7NUG<fXQ;q^jhV_Eg&Uzk3P#8h0jr
zL^>uyK85dI{0Bwh*$XUa-`O>QWS$Z8yGePGO7}VOcb~un$F*n5d2v_97`42j@a&XW
z&2ZXC8*cL77(}_t<dvj*evmj~vGj+PM>srU85f1X0yYe95kLxgHVek-B24~1@u7PL
zfaM>6-}w|LetRM;7@z3}zf<trAuK-xxJABi$}Es&EBJdL0=QA6swCJLvMVhc{QS4!
zrn`I<Y8oCf&(uTde$c9H@UJOtC~gSV`fc$!72{w5L$T5kE3<w~9oDIN_I|f#1%uei
zFV6v{@h8z7he}`S4Ea0jJB0@#Ex_k|*LQaEnu&brChSZM%8>HnA>%tyoRj+7uY&W{
z8?;i=ioay?1t^SmZ6Pg$O-R=2_1t@unT<s{8%sM6j=L66Cmk6VAr3We0v%o@P8Ukd
z;4A;`lqSYRzK2%^`$7nUu_5y}8LNLktLzJR^sB~dzs#W3?o9gqosiMHfe_Vfy?BV`
zo*NkZ_iZIS`9!GB_VW_UVKHEss%z`Tbq7K(%s(uFw@|KaT7?3<c=-9WZh7aB=#{3E
zzWBwCyaOrGF5<UQl?}7Gq9R|T;KF3r?isjD@kIsMX>FVzg7?@2fMK5P1MqCSCr3HA
ztpLR7H&B0kc+>_3Z<yX|)R?{#;*1jkf-bixZy$z*Ciuh*kNk^F9w!_{>;_C}`e?0&
ziq+w*v3vb!)zI>Y@u-;X5-YPDM@TTmJ=z<1W25R!0Q~5&i7zly1fgL;n*UE~VHO%7
z0~@e?O~tnXVI@CwZw~RG%p2=?Z;G}(<fOE$W|=cg5^$pjh>N&ZxXRvoz=dOh3=(9$
zK=)A}O6)BbwWA8gR|EF0*SoL*hh^T3k_hM?r>7;an}(PPLy6rdRzkFZlW%(g*RRKe
zwLDr6L*?b$CBrvK3_4Ol4r504xPlXp)vXjBU&%M1R!;g?=VddIRb9i$mR^Mk^d20d
zfFcXWcvgb1EC+FsI{{4h?K#d){!IEsbmZ&-Y<pZt<CX+(_4Lcr4ZU$O^t33F+Ig})
zUJ0pR|3=0sz;vQ}GC+^^$^Z$V(uqx183}wv)Ejl}kag>&$F=GmlHm^o>OMb;_IAba
zNZRneX!ttz9Oq+3az&U``<3Aw!G#j`nM&&;e(<jSe~TlO1iNTc`%sn;s{~YFT#Cp#
z!Q&34-5(91ZAjDa{TAs%SN1pABk0wBh!=1c%MK%FrIdjyJ1Mp7be2>DL54$t2!Pwc
zy`50`4T0*erF+D)mR7&0#Hw(9pvnKz?qdpK0N*Z6|0U6XFWWI>nbrLvXTW~E^q8nd
z-%XxZvz!;$2x2u!0XQZqU+alo86JO;Rq{e<P@+jbo~6a*EdW>pfrygdCeVg`@8~m%
z;v^oJvg&T$1$vcJp@bdyo9D@(62%8-W>v3Qu@2`IpbG>*?~qz(Ed;H?qZPK!irXl<
z7P4*j4Aj?}ktfT8K{X{>c{E}*A^AV?GtuaVfeqG#nqmLX+)Vn>RXud3z4i{}A6{Vj
zA)tsEq^(d`TL81>X651(-2sd%M|_NIW-Uexc4bqS<uAjYz59QkhY{sp-LkjB2dr<=
z&cuvnDu_G(4nKLeN9=E!KXhU41ZflEL<;!lT>vC2=nt!DKgP1XY^Q7VWdrq5*&P+3
zyY-oP$ENUizf)KTvKc%cWaU8NrOs@NC{xehnV?l&uRhka6>rztrC^Q<OpT1PG56y(
z9kX_a*t=PM#$><(nCPKi@G<@`>H+H`4eY;N|7+j=Ii?#I`P=C2Ok}sKxAGk*iahz-
z4%@WuYg!L5hcKk^7k0uaV@VQ&-rn<@{OS_unlsv^4WQQIEi`2#4>wc(i@$6IBwn#_
zZGU-5FbL9_E6NUYt23u$1MqQCzSy+P|9qVQo6Y)OhwH>y92DW?$Vev2AQa>(i`;@u
zD0LC@O^oJscKYVJiPHl+Il?Vnmp&7=X5}V?mRq06p#>%`WG`@24B;SbH^OP7L<$a{
zwl5u29F7WpSOa-fA~!0jUY~=fvClQQLJ;jjzO5dQ?&99bOw@T!tnQCqal?SoKN}r`
zhvq$KWOmpQ%?jcmeZ25Zl)xH-ayRtnfbb?|z3jYxH2YM}$tg!oNrbl*!-^t15NB2-
zVU<}AxGFz3{+Ys3`ySe?>_{b07q01VPZ=O9Cgry`+-coD%#~a`8;r534|;+$t?O;y
zdzAHS2TJNG$ZT9tV}qTg=ad%9;ds-U<VYDn&P2qd)>k7mB8=r^pO%_(jH!a-)oeTM
zDH+6Z;!3m!i?kk>WGDadLWCnb&k@Wlhs*SA7(tMq9CVgeU%TAg7&vdGe_J<i%t^PN
zcAo+Z4cn6n80zpz39(x1GK9B9SdLahaDXbon8a%1Co`p6mtoC#K-rQOzQ<C<kvzU=
zZy32MkA^2Y;v9v?S;0Er!$(&on$QDMKUo3M5xIXD@BeN|%75k4So8c>Xz9Nq+bRmM
z-n`W&;&Z<Br_CP*;?z$#%d<H{=y}AVXz#lxCriVdXgh!o0A)v3EC1-QFyz;n|3H`E
zU@LwUS6EuJ=TXlPSN5iMJO!5gaf=Kj3?Q7{lt!EiLP0=Ln5}$2a7S8!Ts{-)xaWdp
z3^Q$o(Ywz87mN*Nd84`OwxjBI9s2`m2iR;*^K0&u@BQ7AwV{%LQQ+L>I(*VuxbS~C
zb^O2eRQ{J~^t}Xj((>0-+Uw^KmY$u@5NeVo)#9=(g{`#VX4VRYv5xIx!q&-VK6|^?
zd_o!k0E|VZrEZOu3vEOk4urET;v8>*SWkfjzIFQyd=lTa9%bcI?37)`ipZ8<ZTYgT
z-9$@BrB4=0jNB6<^C4s`g-x<UZcsjjI_@=vojEMkJIOf~<WkL>w$w_`0yw%l0#oYk
zkf|oVUMP(S3Int1p~lJ=_7B0b(~7dq9c$B-tStqKoUC*Uf}hmX1mCR*Bk;+8&P2$l
z-&_cdTkP=U10)9c6hGVp$F7?UC{@|pkTULJpTJwmcmjb?su%sDH=2k_oY<iBi1|Hr
zhp8F@8DI7EA_d2mEs%F*10~gD^Czl-HhN6-mmZQf@4;a6)2(dqp`-#?R8|8MpaDKf
zGu)6;OiYoszo3&hHh7Qp!r*z+WTBJ#cL$`0;HtOy1HHYe*i<=u^<?&D7YR82P|{9Y
z3Zic=GTf~dM9O9M9y<#i1`@%xK#rtK!#jlXlDM(R02PIw2C1-~-muYtN@f@NG<CZo
ztpwnDnyB5D7o!JQi?rbaj0m+4pxg9EJ2+gN)%>9WR%o!?4oK^g2UIAWRKHkRc%ubK
zf0dOkfZ^`8=8D>I_LhYMmo)TXL1_DlqF@4h0L#o59|SfHZ;pDDQ;(kbd6yMNWcz1;
zTK50Qg^T|_T61msJ27?-<R}MH_N2!7fuUu=`G!{!4!6RB`b^4|_pW?MS_4-=dU3z_
z_VNxk;(E6x0MmVFp%>QySnPF~DYvtx2=6;3lFp^+7i`-?2tbYmsCa00&Qkx$xOoW(
zxnQ+9DXf4W3_yC7GypQJdI<oy9ma-Qq_q{c6s_|6zR0s6dYJdHx#$a<ncCyK+t0F>
zq0pIJ1LWs}kKd{8iOql83pD>b&eH=B@f--Qo%qrttfr))a?E1c#`!m1T{3NM{5515
zP#T^ad=(OfiqWkC(mk0tL&UNlnWZ|%`Pj+dg0EA?h}Vq8p!CIMb@)2m-O{J$8!P*8
zGGw*%Jp`9+?c;-;UU37W5OIxu&xmBW`Yee>$ff>rIIB>UrQ^4|L#)Rw!}HQY#bZlN
zGs1Q5(YDUws?v)IsP;TWau3RN@1Uk~_U>beT49v^cAo?BRCF#aj`$i4?RqXg(1C?B
zeYKdo=|N0kM8e$fRJ-CaKu1HyZZh=Ylf|`iezD6qV}y0D&gKT^5{soA*JLN}WNO{P
zY_ZJ;FG~C-xI;Mrp>gJ`o46UqWbcJcOUz{=Wh^n{iVEBgKD4z4&hDB&(QB~*n<WFg
z;6CsYa@iV$Dqgb=UnYg^%tM1rCyHZAtQ{(f*Rq4A$<^Miff#Y``mK1kH@oe4Q<6QI
zKCB5*f#TJ{c0*gTy^L=Q*hR3l9C<Qyt_+h8A$5e9`T+N%f$B8-Ny0XHdw#Eamu`W=
z?vMIB{Q@Ki6=XYu-NFf%6#3CSq13ocYA>~?D193b#EiR6Ku3kq7ifOLz>7(M|4epZ
z8u>`WdynaJTwU}NyPKZag&OGk=w7eMj`dAtWJ869;zktds2}mZo7W`d=;yGyU3%|>
zF9lB~n>>Q_lbwD^f);DCciS@>QZZe%@??{?UFNc}VVJ4H`jb*3dUs<{6=JFqKEp5x
z(owTEG$m0?YVAirRqUi+%o93_yG2?-Q<`OBf#@KOMvtzK$u+b6T|kcWa4(@_9aK^}
zj&>s*u@0L_m+iwgg;g~?k#y}X1#YF=3m0G1_Cw~;#`W5ZYy4>AL^yEm#mO@<V*t2K
zq)#VgI8Hnm*zR3?H#V%vm?aW{r((d1j2HdzRI-Y@^)Tqn{Kh76oqk+Jvj7wF&>r1b
zCh3w_Ugk$L#?39Kt4r1bl@+Y)H<jj=h=!OgoaLIXXXpF|moS+4G}`JHHo1Nj=()|K
z>k}9+pgUhV`+Vv^5f(|tfUu=2%#U5xZI}RiNUvUdh9hM9lbmDSL(pzxTuXfKBEkx*
zi7JDVRb5vqU-Uk-dUXUNp>)CrA)!`6wg@A#&1rq4byqX>anA&tOJ_Cm8{86L!Q9&L
zil#_tg8%*vSN|UM%`jA8kOPFVjjZ|;J%(;A=fjm)<s>Ki8UX>P_Dl<LrFkc-r+&dy
zs{zfhN5&!ZT^^bRNGs5T$@vWk{OJ5-@9V7_^zhHr@6woO#rutQ3w-PxJ<%n_FF$4t
zbRgHoom>OFYs#)Vt7Z;N7NKh*AE_hMdh3%D%Rd^8%w{m1qAZaqP+8d@xgKZB{wf7d
zAoAJ<|9IHeV|w_bAi1o8W<i~rPW5haoJ?A1)re%2m1I^b3B%tnzNfBiA=MR~h^*m#
zRoTVizNvoW4YY-Bv2=TJ1nUAljMF*#MdG7uk6{ERv_AL><(;(8aASU_y_A|ohgy*F
znj@lv!tj|1zC~Htu3@QbbMc+@C4<@-Hg7!Ku=L3cYm}Svo9p&CoSeeQgafL&b8e1R
znkaYT*z9*{I+0MN^|4dvgwOar48a7ap5`FVtioYT^u3*~7qDvn|LaHdIb=of4Ip2G
z$ISu}+{r?3ol30sV1}V}K%oSP;X+v??)s2>Th9~`pT$ARD3w-z&aZPExlg(GC;oLV
zJukQHnw$BfOMP|wUcGK#hJ>i@laenp{vvly;cC!RUXOh<PX!hC$Ncq__iE(uzN^P=
zIVxjL&nT)xGl&JI)F%*2$na1YCVRffod~?xX5M1B4BiNJg!DE%sK=%-?x1UbRD7#_
zll}3-qD_f%=^QLGVwbDwSvj9IJCf5ACJV!|MIEWFdMPZhA>&h@a~0}tkf1cfTtI8Z
zwmz)o@#SPZsy9aH!h3bfU;D10_tkOQ@H+5?9tk{EZfT!6aH}gKjC+2`>_{(8oil(_
zonxNUhig;*R&qyhRRc4qsAK)9RMG9I!-VU{49}C#G}S{;6L6x=p9Qf7Q|L3ak3Ys<
zcbvnkg(toE<XS_l(}G_NL%H!y8{f`KPAc>}QdbU4=+eNyP00VCs1NnLR3MeT^fXj*
zl2gn~H~WzVq3sWSJ&eH8g<SbK%G}M?`7^6cCyn4E31^uvAp3EsiorhbL`YhWr=k6t
z4FungUGh9_h2LrEw<jdsDd1`I0)L62=h2B8`~|pHBjh8;YQ1G1!T~|S3vQch>Gq37
z`xA{;<vEb-c=y5C3PQ%it)`zrm*Opo5;veQ=Gk(|Y5)A7mIK7kvS(u#9yD4GZy%Zp
zX>NqIM(9TL#0Ph5JLiOg+g!!LqWV=OB{n5mrTjAEOlp7rt~Q%&JUHd_I3aLfc^VTu
z>V7rz(dD%xXK2c#vDYG0Ln`i(m}%Z@b4?^TL~eTXr>)UyyHQI`B%To<HPK<uai9+>
z50d)wbPRILS9w*+*}5gf+Xn;YSYpq(z8g+|e9cjU)RX!GYn&x4_3)c6w+2t(O}w~K
z)3c49AMTL?=USU|&joN!zbWusR;!UqRw5kWob{BINcX~TrSgH>iHeX8TvGn4WP=tB
zKieOXKhUZhZgw?7Dl2iL=5rbu*HBWULd_gi2~!hk%%9zbGrpRuh{7d54{|14IWX$l
zkS6-2t^N>xF!A>cZY*;*3D&*X_|gP#V{V+!gNikqV-$NiF|WSqHMlOT)yqjQ5c{lj
z)Fx`zR2XA1xbr4oa7T01yhY7>BY(7y#fVTlyv_!TnWpO+Aw97_ckiG1+$g%}?_iy?
z^T>Q#9XFz2NmlC*3n255(r~UQu;cdLNxdF~aK%_8%E8=^?8jq{Y#pDzN?4Cl(^7R7
z=(p8_n$+F>vupoUSl^@>$)*tuj)o7+>0Jo7Xutg?**K4qSVFn0C5KBfr`%uDaA4~-
z9ol`NsKPj3uWL7XD`)3!b@-LGFmu$9At}JbX861W$=Kjs*gP<5%9N$B9H)fg@$7l|
zDdT3>ybgua-G%jnTbE`~_>qV{S}NB!sJ0!{s$&f7X>XCKM*Xmsr<~-`<aMHC*U%G{
zVrr~<-vVW;!@qlQBzkqGqf?Ihksreuo>5FbsB`b?s9Q5B7lXNDU0_jf$VR;}1mzxA
zatW&nELIRQ*vy>gI(zr2wWHDjoj<ltmhaz^cS-8W@{F+N)IPfZo_+!owDclK*X7S7
zc#@^a@{)K7C}Ti|Te4|pK32OiKzzBjMh<S8PXE4TE@G_ely$fS`eDj1_OwH40PnQ!
zuY*%hrWPYRO_N@i0=nZ$d*dhOKU&&MPoT6$=+}-UYv~Ht$=G62RvlgJ`&~osOudUV
z`XG_9GbtL8eqt%_oBhc{l8?_XwrNB6Wm#XEsPZpXIAcHs=4?cKNf$INSMS%qNc+=Q
z)<rq-K#VO_G-^<@v+`VCW>ucDLHky^Xkoy5rKpu_qltI4YvE8{NuXB)c*dPpBZ9Cc
z#v)82(i3RIocK3?iN<YfdDwC`y<Id3OzykiU8jHFFm=ZFL^o&h;oAB4r{b{RjyLHx
z9*Ktqb^KUvekVsU&qKjBIMQ6`E0^Ix0$DY#DC35WARZls>$g2?*<(+2hwBFRqeQn3
zyKp2JVZ>Aur%+Po)~8~WpLxH@s=88C0!L0rBir^l>H^UA34pdwp!ABgkG*wioahs{
z-Ik1B21j3`W={)Ik<q^e`mYc%(`U&?YiIA%d>*c(*6dS=l%3VTytx*j>C){D8Bn@M
zZj8IrBTxsNY=BX+o&qJ%>23ZC)XQm^aTRV?lALFmr)y)z6YbYdU@`2H?n>0M))@G)
z{mt*Vf4?y?2<kNmG-*dtDUO4{wItwW76{ESW=^E~at#s-dkv<_U(?4`!R^3-s=e3c
zZ}gUFDd~)E{w(5lU+y#A!C9^?{DCIlNPecbN2ECq!Js7(rI1<v##Vc%@sn|Oax;MS
zHFsCxW9vMwKW|nU8zBo8gba{Zu_Dyhx&Qf$UL&YiDdEo*3C==RY?CoWryehC+4r`T
zS8_EBB`{HoAFq7C;bPjpPGw^r!th(~F1slOm^e?zs8~6dFZb#|;rN-G^ITHKZDP~k
z)^!*4uL(yWV{GS(#Yv8XA6b}FbOwe0`om3b)n&@%=DhF+zw1c1U5?oU9s9;}ul#3-
zW7&Zda~b*Il<98H5bo*WX%$U02{p}=Z|~0eSgrrb;An@7Xx5dHENz&a;rsN&ePsE3
zd<B2q<jYFl$mr|AvvCMi0Q?&|-}<8h2ledQxUKF`W1(zz@}2;7)yhZ(&j@6oX4R(3
zA`a9HHU84OKDOJ+`+hQp36`KHefUqe2_Az8n%XEtE8LolOUaHMOP%e!(r{d*^?V{j
zi7!ouKfOABBPz0B?tLaO9ntH^88Fv3vP2@t(G_EXI%-6nAo1$CTg6dIiQneWV0mwU
zNyr*ooop_Oywoen)^v2s@z=<WseLU`eI(fM?UiKw2+7F(LCIa)#J*mIT*{x;e=Gxl
z6u-J(B^H?8zmV#_rc)9-h=ZxKO+PfX-TWeLjP6#g*Hsm1X50--tq)=%8D$bI<#PQn
ztW1fv?it$m1=6>RX)5Zq`q}(|@swk>?A1`fr?gj%gE2nWNO9+Gg(t;PY}FUh>rE#O
zZmz<?yIdJp>)=$g2#m~l66f@AGv@Tow~sYf?|ut2PdkugU3YIgyqfuxgAshYn)#7q
zHr)$Z0yc+?jYqY|eM`Xwj_%u1TTDzZ5F}uTJGmm*gJB_WmaD;bckdT1#mRflXn1vT
zwCQ8o-3pdNoOQl@)6gH%Ak1ype6^9he^#7S_38B<kjHCzb4?qM>Q}8D=8tZsa{KHH
za$V#zT~J%xL!{Y_I3#lTrCtx`@HIDqucI%_i4TyS;I(7);A){b6C`-&CPvLwwJZNo
zjpk}zVQk#3-Ey|{eto|Uf+}+O(?8^@0sdyD{9%;x_WYQuD4BB4^we_5yH2Ek9yFvt
z?gQ#QD*Fir1-V-P(YH9j(ppa;2ta_dm8!lM7*$T~#_#a{X}*FFv*pjwUr(wV%zEGZ
zrJe|!mB$_i6ae5@34o(owFsj1rl7Swr}Z@~RFVSibGVk`)-XI*(#?!ic8pFPxjioF
zIMDuR1cJG%Mo@^8nuu)_(al=b@%6S~?u>;Lz;%+C<X~u;d9N$V@waJ>|1=oJJwq&&
z)>SI=sJilQ8Ai!x(2lI$>R0gBK1^u(A#ffmYCicW-!(t1__IF=?sxp?6Uswb3DN`Y
z=rt8lz{b0@zd<Z7V(d2ZmBwPl`J19vC6Gs_)!GTk*+DHN|9z|38yGcgcKO7^@7DnQ
z{&WS`F<qRJXf(9HEEdD5v413KBXInWzze=gjE)~O=_|!FF@qG1<%`aSEfZ;|Fg3$E
z*>#s@nZ-$;WJR5|`3eHMG}P%AKoIYE$NF~7&=)0bP~fP6-A_s$+sb*+qEJhV&7*v^
z_J)~$hq5<Lm%1|Eb_ksNZsrCMBZN0VjKaWQJaCU7w<G57%h~f4kyB!pF)}l|!Vv@4
zyvW55weF8g4tyZa_*!+}cq%;A`G@{yNLOmfhDC+zStvg$b*bK3XDEGuf;FDnBasPL
zMz>6Sd^Ml%*c)_755hf=v0C$wiC^vb<@>DphOKn0Mr+rqE?f{t0LU~YoTO}=x{q8w
zE8mhYWxPBzDDB$LP!fmh@w>O3KNEPWd*G35+a{?~<K_+FduIZxlCXl_GuR&hX)T31
z5!aedP8Ubq#`SVqaVYHL=j1;%e(W*VDx_s8XZWVk->A=vwSKZ>gyi8;nC9^j;lPvA
zykxr;(l6JkZ0@6Rgb7o1d)z(XEb>>j_+1L$%Wz<&#)DBZE;=iuh-|ibHg`^B^`Id|
zTL>T>p%YiGV?W>zhZh1D^fXubT9~M!L?@3lK!T-=;{WfT|4;2ek1c00XSmi37#?DE
z_=mIjYT`kXxDW_jefHs^Eqp#wa|*<R55L^#m}(>&(a2LqZnay9mNKc?uGKzilollX
zPC93Z9m&CaCJQ>%ygtkx755iL$2RHBz^GD9-=|+~71W#;W5uVB{UUnh^=S4FZ{{Av
zJDvk**M7bI4*SRXi@8eKd-oIgbzVQ7zoF`LBJSBEdXvTEeAYp+_+kvEC${=QmT#=x
zfhJAb?m2=h^gvVkVf*Zkok(Dp2W^EpqdB|Sfw70)&Pyug?lk0g3(Y+7zX`4`3VpP+
z+;=fVHg~hPsGMNFhP#%2IQ}ZiU!k;o!WOC9ihbxJ^a;3y@W)-FtCuWnyX2Z>dFZwi
zDsMVI*M&K~Cj0R=d$jmx4?YHsRVzH~){&y^!U?X(Xkrj;ALV*~{G7tQ_<XgdTIc!_
znEUYP5hIVU@0#Dd*De5?)qSI?)@)qw|I3g~TZSa*S#?^Dlv%a1FWmMD1IghoO_FRA
zdoJ~+EulcF$Ov?U8OC4k7V#^}faEUZ<;pdE@MGAG4bR)a8a}qBWQAj8V%~`f)cc-T
zwYe-9ATsT@{p%;$kZm{Jmy$EcWxSlBC=e=TyL)04e~jM$NpngefpRB;Pqk)!<>%xC
zOlElS1+Z25JYSHj;(wIrmo@v9UH=xI>5KDucj`obmaRzc2TyRz6G9pxiQcEb)MA#f
zD|=VM#-L*TW12d!85cI|`3@wYP2(QcD}cu`Hy|U>sS5a{YuODV&72Fer|@u_9WLhs
zQ=$D*9e+{dvZz;I%LK09r#W5HI{Vc+cXv`?>#(n?OJyDcb_aG~e#0+bO@KqsG{xqj
z9N=AAIg?I7#zg~Ve9!s{`--ogbuP<5FH6*fg{~wuSLObSBaYO*Po&OP5pBNSs5KR|
zSJXnksL?dMelT3HPA!P()(qm*HXWHe*120&p#M-)<H!%KDP9o>mOXBtUdABih2qeX
zI*(FASOn8;xzUhQ^5l+2wOUXp?-VHS7hb4Patht&_nL4?vD+ZOY#_1u)6qfIo0(<v
z`4#voxmD$<=*Ss{cZ;vk<mni2gMORzd%Q%TkjtM9q6vDAMlUIxofz#3__7#P<1bNU
z#cXqH!t$=zDsRgdYftWZyqTahXIC(vw66a;Vg7DH`@M-Zn=hLAW!S=3vkkwp>UdV~
zPT4n$H-jT*exAvn&nPem(=BvHp|DOQ9;M?5t<NE$7L(pWgEGK27hPTxhJ~=3y<$p0
zgz!8@sf|3KbFZZX*QK9|+<e)?xf{n+XnNLVZ|Lo^t(MFVlISCxpkd0<e-=PH-Ynob
za~~VD>*<|4mz_8TP`+>H;-^ZKj}c$W{iWVH_P9lO#~du7aw*;Z=b*R(Na8jb1tSXw
zu8_Ema?iK^SVf3Hz(%`4^NC!wl9yXPQZqV^h4$v??x$5!Qz-X}NR;Ap{>eweZT=Ee
z@aZm^kK}+ja4Zcr`^Scey?=ur(G;28pPYR}@P#F%zF<Mh)AGwb$2A*^9lJ2;0xx00
zQOTRvbKG%{WKX%!HWqgI<!>qETjRq0zq(q=1kPY(%gS=}_5@7`c&$(ANz~H?un5-n
zq(emI^*T5Y(P%Zxu<mxzS7CyT<;dJ9*H~ksFRn+4i%u<%sU_W9RYS?S0pT92BfO9=
z$Nn%|%`yvrj6^ow>sFI75?ltlC|+3C*Ug1K!!vr0P`nJTHxwH&(Gh)}rrh&6jq$&b
zip(Y<8NQ9G8xT~MW!uWZ*Jo(K##T=OUA!@wOBpw<v(N8cF8=egy_C1B!O^DZ$hwhF
zxq8$;fH8#<AeqL|c0#xy3)9is$)f@8Gl<G4xqu_>^~(%Lsn3mfESTR<#aU-gWy-v%
z0$JS3-c#0teA0oZZaVbS<5kW-dVFysgCO@BA{7|Y^`=>c8)@2mMDSopUv@^_rypu%
z>=FWAS=IGtS1tPU`~O@_9t(jDAC5n^9{F=Xf|Jp8LjspN1r7$zJ7KEiPu6*7O)blL
zM&#wGSs#{OrhUFxZ*HwM*R~OAVfJoKaDHKOj?!EaIW72O*iG>j<$Vn81(;gW{4Vm#
zQ2D-<SLUNp^BVyGp@M9uColdg%xb!7E@IM@mC(K-v!6&ebOt$B<)gRrR}2q_FNFkM
z)0KQltb3z4*xG9Zor|YQTzT25(KqzPKS`G#_#IYR0-Qc4o1E~1r9)&?tZalnXSfiK
z{^~VtJR!ieP+6L!m3&M{=CWkUo!TDi7tQ|qgTWO~j!zTGnl0sgarwI=3vvd6O^-N&
z*H6U_EA@u$oSvOhJhR6^>@J-DOqc1~<um?s6TRhNOnZ(j-ABgVgTAscO=&}0Z|Vuv
z1aVDA4=YBMuL)Na+jgmc+~u7qUoO=CjQ?5t-cs$e_QUgoN9=FOe{S@@>c|_(!yDqJ
zuX#WHR(S5GEyHxmJsHk9n7}nRYn2_toX^WnJ?+-#`5SyJm>U_DYl(>l?d9UqrulNW
zmo+{>Fnjxx#YxdU?ek1|=@@QJ_C*|MOvMZus{iDADjZq0^hnWkJgMTT;@om!{&-zN
z@oeT%cuZ$Q??(g2W4L7X`~K<VSBTsWA89FOlr3AY_ygqir_`4GC8V-M+3C-s0&fzi
zS2FfwXFs3uYVQ&61gCp5Ys<AU-!OmDb{hnnf<Px@qra8H3w!iM`Uql+tA9i{cWN?1
z%q%ufU6CmJpBGK9%uquDEo4G|<-m_N6&TEdGVl0{OnacrdES0r4u2e>cQLK}>y4C)
z*B3Uw?f88TjvBq66DKQE=y_{m4?aaf5IQrX;o7UR28pC{@joVSGfM$UM`b2R2S&Gp
zMGjn}NGkta1LkJge$R2f|IZ)8?j{kxz-XDQBf8A$j0XSX3+_%&0}zE?LlXx8@t?t-
z#FiDB8rcc9i<{ky&o??Rh^}L6dg&VtHVgeYob0jEA^sD~&764<Hkj}0Hr5pSWhS~=
zAP*B|;w9K9WhV*g3KPE%wui?&SG&vGf+OdR-_RPMb4=gplKui3T>s&$yNc|ajKiMA
zu-D}{>zl?531I*7ds5BJicvN=B#j+5xiy@l1;U=Yt1jU^jnag?>8HOVV?@GA7wg|C
z-d8?k=G{J~buHxE7qE!+HIk*1YF0sMidV3HoBqy@b?3YqBWo+;=OkY<(vR4Xy7a;L
zMUX=^Q{haOdeP)H64j^N(I_ogI`E7UcssOOBJMMF^XKF`H;72uaBUGTHD7r>q~3Mj
z5}dx~Kl%2~=H>%kvQ<A^t_FPeC@=WSkk5W%_phD@VaS7HJyn{E%%-fj`#RTA55guh
zDy`X|=SPy)BPL}?<m11##1?<XVNS<9(=4TMLd#NeN^Mvt)+n-g>mc&5pz+K|q~b&2
zNCbRXs%z|Cy|&$ufic<iT~p@?yv8+Gw;#3cvE`0FoM=vmj&?lM)k4o_X9PYdl3+qE
z(D+Qpci(PR-Oj4xTGX5seBIWse(?R`qRE%-qb&l@OkN>_lK0~i^NZY{TE`D2rNg4*
zDM+c^m21I>IVmLNn5C3R64k9FR!#jX>6uaPB}-~q%*R?eH`?$uFUM5SJ{ZW5WD*)d
zIA^Dzr9w053nmB!4JR&i1y6-_(ww|P?bpI1d74g+>!BjnCNx*6u~&=}t*Io<hfD1<
zKbiR#9c}k_e45C(@UHIcsd}7!lA*DgQ*PNC*9%tC-lt;Z=yzH?gOz>%a&cJvybOAo
zrQ&snp!IuYTi@vV??b&4$gk|Lk*tLt2-C2PJTDF%6<cgtahd#@=WR;dx%dkMH&<`O
zj}105f}odk7uz}~q4`<%)*o%HE{KT8@XaY3<MLwl2zM$43a*jLjt8EXC<yiqU9D7O
z+dab2_3-L78v84^Crq#O;o^MVw=c^>&DbKD7jU&@B@vn9>F9)fZv$!%voAOm5so-4
zGcv3;UMppydbA1PqG3@Ryd4Obx)cd&Rx@LH?yq0yY*jk4!Kdu$tpA&#F2Ylt*e!T1
zidRntJyx@n7#mb7gVNmz?8j#y_r)3wVcm8<aNPyH8Hz*X=q(UPnt!2n^?fv6n*Uhd
zQNJu$-hCVQ_PFEfeP{eVhTYU?<V>J(i%{cwJ{}du%OC~cOgds#T<dqgXtS*>sE?=D
zzej;1f9CD3v~AiSpn6QhbnTgk@YJb9shUOae#9cdG}x0YgJ0~u`MP5kUwU!7Y4jV;
zv9Vu2{9clbA<0eV$O&ok`p*;nl3pvuyUXLY{I7?gYNiE`Gv4nsKJ`UtHy`PnA(s|r
z1`2+>Qgr`zqdYpRq9)&nATFXmUH9TiN;K!9WcY_@;*Jw8y?RBU`AUSs(fC|XZvFUn
z>sD{$M*tNeDcQD@^C%PcBm?wh0ve<L`i%@kZ0_u~fejl|$96Yx+uP*4DI7F#nI3<k
zhIO!NTU<YaX+EAq_q?NrvftkE*IX4=-87(t-0z){<+0wqbp4Q{?tSe?my5n?yjshD
zjqN$xcu`AUy*Yq)vnYlf?^HCLKC+hCi3wm6wi@iU+`}i#WmW4W|7D8U9|F(TXgows
z-R%J2R?j}l;77nN#B%i`92KOX8N+#g5@iUiV_avh+S^!haWjcw)#%8p`jIm;Ynrpj
zJe#KhlKo(2!CHgbyDiiAxijC4;}t*48hK1&7qY!(b*r^Ufp`C2(ek9S-^0T73{yfq
zNHHC%;(F{$@7G3jd`~RSW3acq<|yZfWp-B#nC{;+rm06-EW>S?FBCug(Ja?2<Vx|b
zrZk5nP&m0~pj2<aaP`>^N>Al=Xr-rQ%g=ZN8Du)A#Fe!7<i)W)@!_2TZ-7*t+70t4
z*jrwQ#<*HMnK0mYwj;WMW%1>&)#>Mz-3#C6dpAOhwBR@FS{dV)M+rd=MTf3=GDRxO
zGFCF_(HiHrsr*yPuA82M2d<K4m~3Z_jN^HnjBewP@$eK8wjw0O9Uacd3ASHY+1B&r
zei-kpd1`TkZ>lV9InAS-YgKmYdjIr^W85=$o!;(1vAe0|CDFOG-S|FhPN(a^XGotS
z6;=!rq^0I~c<L42I+^pX3cfZwWMP#dwjK3=@dOUxTkT&U8*-?#6|m8dgj+0{D61y#
zpww4ATF^TIQ%p{X{~9I6kt_5ZjqWft5E*%wd~i;)MJPUmvy9W}LVgw^0%JTR%&pIz
z6|W;Bp%AKa`6*L1^3g|qGe6hWBu)PI3hq!{+sP9eaf-E%$YN-=aRCkXx@zZ4$OcP7
zk$Dt`9U!5+u-)upa(uD=tBr%>Zjq;S(Y#_#IcUxPiWakv+miTsn2a>ZIssbKz~TXp
zH@D);xyL=920Mx3{Jy+=Oo~^wwcc0qlBij5u`$G8HV$0agEg34M+ZutNbG4~JjNHs
z<t5o)FUxU~iXjaHcI9*|$|d2uh4>zEj)^U+>*0m1cTQrXBaQeBJEI@M3fu=#^0sKj
zTj9bjmM#V~BzMN8v@Nlk2?Hn6dErCVL4<vf*&w~?8{N5hNd+IV+o1F^_je<X>^l!y
zz>#nBwaTvQqTELEpFzCbQ)isV>n7Cm-new(^C)-DE%=$e^Sf-RWIJ46T_~4FGsT|8
zZ-)HH`a`>{m`m?4BkcRz28qMEdP@xGtsRLFfrmRk<z(&+2J9B1)rB5_;{RAX=wuXs
ze-hk0b6w`M$edkE@=r<i{?oW=)60TWlaIflIJRuKmcyR;8f@jvb}DNuVm&VBGU7QP
znCFUKEkC5h&rjt?054FkD8U{PbOvE%kJmp_e6d`vE!waZ+&*-~8yP+sNhy{&w6=QU
zwd3NpXG<K+WWvd5Q{#9C7M3jZSHSVM*yRS=I3?vgQNG1<-}Jek&yUgL=g+nLEX0VK
z&zZKYO0VgGwDZSR;-O@AdD+ppp#H5+`${LQe)JIb+$dT~E@+Pq*qvZzD*n-K&kaJ8
zPf5k0FK=-)9+*DpWP*e=pKB4Gg`ONQ4Ht0v(SkV>6S7?tGoMt$u95QALMwXp^d%>b
zm1`{;xpAjrghSo>G1@%V577~d9|l8s2Q8m#h=j|mqSEk<uY4&#HzQMt`X+UM{-Q<>
zPX|xgM=ISAxc)7Sd+W-zFvk$x1B@eqD&;CJ+$lc5+l)oI^>rNg?SL+eg#}*K73H33
z@V;^!L_IX6&jb8s*SB@SlB+h(0aIRS#$vI4VlgupTIy<Lj^6!*%*LMC;V$P~RecQ(
z$P8NvzIhBMnis<5Vv{IO*K{eKa*cqVY7!_um0=uvS{&_yT+mdns32sDy9<WfJRDo=
z#OR+o1U}7*GrspUqxpc7rMQJJmq=%b_IlSVSiXwV8V#MQ$jt5F2iJkv1YV6n?Rb}s
z_gsSfvlO2G{FX2vfR*nZq_(*2QjkPfF3L1Qx{YXj>v)^F18#ATuPtkBj5)K{|LsfE
z(<Tob#vg;EAXtt?%O;uH@noa(GKcgBv?s;&^ShsSx=wnOgLGw$=>9hUHi(Mh^%bWZ
zX%;rP`%zMmp#kpsmyqUTa#XjJ9+-k4t)ADV+nIa59?b0;4l)idwKvqZUWon0e|fU>
z3nG9ua6h-1jLG9KU5SzRUP|$$oxb^F7>5A9Y_t_5=NWWOd!oBKUXf?=&vH;#*gIfd
zQbywH=RY?}1Ujc@0)d`COjb)jWOfd)p2VL!Dii3#uJ`6B!YD0Vn^S>JkJrKU`W)d(
vO8JssvmehZXVzB!uKa(0{uk^3Wn1!Hr`%}l>k7ZUYf?rArdLZZJ3sv&^{~84

literal 37733
zcmeFZcT|(#_bwO(#0IE{DAGiF)zG_&3ZY8xpwdKo?*y?RDlL>4swIN-PUxU0Efftx
zfKWu5l+XeRoqNLP^PBs<Yt5{=|IA%8v+lcA+FQ@rXYc(y&pz*)=zF^A3@6!6LLd+Z
zn8s}b2!t*W0-<?${1{k6NO`{kK8`)qQojv8!Lp@R2L~S~JT=UHAP|ly>g!0{^r`~{
z!VQ7lzG)PgzBn3?bl!NZbu}l)>Ef{$*SL=gbH5Z6<QC;Qd6Gv{YBBGdvA(Sp&8r9I
zRy6ae%&&@gs_)X8=xhItJdym4=T*e<Q%7IFi};|ye(CKTCC<`8R>#yYPj_W6-7qI4
zZ~jGIZsX|Qf+a=sus#3#?SET<YJnQJJapM{?7Fte$&iTyS_s73Bs^P$x@ECtjgRHF
z8J9B$H0ppE0%<o7LKRUrP#5m&w1G$d{i=TUH{Cy9BiQdmu>SKE;(b1`fVxL+-_<@A
z@Y&oL1Can{;R}Zm`TP=p-87JdZ4_=;^18*Z7ora|w=Hnz7M8`lQhYw6RZPr@d>lL-
zPSXc9=pe*c*%}kCmr}weZwBw21VT5#$QXAN#>}TI;GCwG+JktZp<r$hjb=fNA=i`8
z&tbo2@537SIGn^79C|2B2uE0|(3~`^iC6z_o&!N%=QaasVV@S}#>XL+Ylh>8WoZVh
z;}#GDpK5G!XA+IHW-7zcU0z&5q|^d|+-&qH6hDrK9v}mF!+4z4kIkp)=7zdu=NJCd
z=beOb=#y7Bj%pz<VjoF3o3n4Z$N!RywR?LdSM)1%bl9eUxGlHd04DHa+FTun)Ss6(
zH+aClk%Ue`%&2$B`Ub)HkE7g&!gX`*=Gsaz*oSMs+&H*)XNLZjQEqX2n;}2*@i*T$
z80Ta=Y0s!xqrcgZj=(Tl!mmno``Fhun;*clTCOL-YNa*JB^1zU9Cp@*d&Ao5mZuI!
zfh;L2yvnU7dNV2%rYDea;QRf8M(T9@J`F6{Avn3!V64<6+|NGPvlTI8_g&`TRwnxA
ziOeV>LZS8E3uM_|dh2DmiY?yJGj_^fk!6BG0xnf*HND^C2LzbgW0Lk~j_(LC|7Osz
z5Fx3?@Fzfu4N(|%p#_#G->f?@WGvn>k6>}wj1n#dgAO|O4|E)B_QB@-maRyH@E!RJ
zIrpd7zvH!hK6be+h)fntn(NQcCeB&EU96E4*Oa&EB>SX3iU@E`-#Bh<!10?neb2o{
z``)OINL0lU)D`-#e3z-$7G9=Vonu_do~%U8S6w)~++t3vpf~q}^sd6cbkHwMP1+Gf
z$!KiltQgtaYOU$pm4n+UAs^Xa8(xDZ@FiG&HdW6Z4-$8F(s+EOk07sydjM57yGpDx
zH%JzA2;w93I`bSED^FC4HKGp`fep{y^Dh>3=JM8{<hJblM^FS%oZW4m>+O}clpsW2
z$LaFbT(z+yu%{n>VX=_qLj6un-sZIbv_+Ohegh4oYCA34hg=EgYV;&W6|#L@!S`f(
zJwLPPDwbB4F=ZW%-9G};v6^QVyC{zpme6wy#Xry|Gqr19^Gm6OXQBt7H`#Oc78HIJ
z)+U>xr96bZnc73;<BWIWu|X>aGFgT~`^^tZ)1T&$y+5Mwo;)^kl8-~&r_jT6o~9nL
z9-%fm(t4o>Nh;m&(&$siIll@EObFmuv*qL~L>?JS(C>UD+niQ}cNG4w0nG9~`9X9;
z@BquP#-x$TJ=@&cAU?<GDby==vBrx3KC64FR%!&g=PYjBXXt=8s<vjs-v4sTx>=M|
z=}6J?b61rdgWV@;m@6k_u(bZ8MhU(FNn!OrjCwYFQ-3Vnj@rYX4+~5*tMtr8BA*0e
zAj>iO^A|lLWI2kvjWCsmgL+?e=GWx1CTc9UV>1g_7-bosVU%)0^NuFQu&04xG%`V%
z12-~>n9+)o+OWc<&&E4GZ`to-Cu6pl<@hBWNb<>lnApVwgl5kpy`c}HdGMrO#vMK1
z!GoJj3r2tM-MwKTfb_LF7I?t<DR%W(VBf~DwgWYc3<OgwGe8rd&Q&ej>b!2rC&J$#
zePg-`N9aYWdgY#bFEPlgJ1Ut7G{I6AO9~O_;p<6UOLkEk`BpfkrILLoq?Gj40+A%r
zk8>W?Q+%kgh;ez>g^PS{c@Aso{2vh7y@MJ9BHQ0w=g3S^8}Qd9MH)Cr)Lzy~wgLie
zElywyF^f$*$wbneb&)i76N5lBu8+kZf!wItF-;=s*ax4EMO-@YDMTBfqMr=cqVs3V
z#8Pgg8m`=fGN1cym)JF!n@~-6VOFs~A^^rbN6I491R7;y$_Th0SBPHXTiF)bz{BV3
zTti^lvJNsqqYuKgP`_<IyEE>H8?Uc=@%hBYC0sIbi0pdy__Dpt!UX<f;<4*VlXMa^
z^>ZK(=27#Y&|PZUx35E<w}c29@xj!1!>%wF6tX?w%JI6z$6Wo=F#{`unc(2@QdGf2
z^y@J$Ozypz^%q~WZ!Pt+Ot03FLq0~S<V1V!sEWeWwpNF)%w39d3v1cuoGHmNl(=u1
ziEe7Aou1VS*2djHL8hNTTRuIrc^!_dfAt|LIR>{itZjTaR&Nd=#=nlQ5=kPq{7e_^
z>${eM`aL-@kOmzq^RIUGSzxH{dk|I?+M18}Qby_bNp<7&GJ|JC7C%k(=2RWbWi7Gq
z*>ZuZ$puQEzN=78hvK6&yVTwec5`&0Ug5*<r@{cl=$bmU<9!Rz0uHUsF>OLnOia}9
zpECF+aXO)TRCie>+=Ca@xDoF20;~62JFD+@^&VHOhNak_@*6nH*#R!mT-3(y>8J`l
ztnwN7b95hbu|=!VtFpZkxiw9SizLfG!+f+J;4HE$(KlLqxjY#9<mGEVWl_nLoHUgO
z@Zx|WjMiztxv4TYzteN&(S(}KMD4ix+ZQn0a^x8@T+Vc8ND|6DCm!YYw6Lv7!8Krh
zPXx6rM5%FYvT-a}IO<}%lZp+1C5rQj6HfA0zPwtj-5}H8>H<9L)ghkO2y#n)!MUPr
zBJdiI#U=S?HKU~3p6%_WrMC?$>n~xRv1-oGmbf9)+q}9>6sOsf{8QOajS+-q7by2(
z0-s97SKm&R>}#TI>Jx%}!zG;GYh}`7-xOc&80Q@o@#$U~$ZV+0zy*qNlPuz<G?2d5
ze?$fPighH00cONMq_T_V#wh`Lz2WmhE0Y6io=MoMw5ia`WIJ!JNoh;@jMrCQ!T+H1
z5K9_G3Oj`%R#gpoe_Nu?Yp_jN`#mg!;nL0ym?lg0U+E@#bKU(|g{=y(uJZF8nyLKG
zxS(e^b=_SAdphJyv1_PXD`DU$B;q_zGZ^2pQDcOz@gg&`w4Yg%=-1k4@hvOCKFQK2
zkr-)w^7?1(2z<xei}P<|_=Uai*ESDdaQh<&6Hck|3r|F28}f(4r|-a~83~&9!9F%3
zbKG>Q*jX%XxJ1%=hW3io=u&_gyt-wZ_2U$#+ktR`aUmbZ#>HnV(d}iHCBXcw>gj4Y
z%#AmUFIWHQG<u;D)%jnD*4Em-zAlm|Wn!QQV{i!mFl2|==Iu{dXLT9Z&<cp!jjBaQ
z2LuUrmUaPPdZLx8B0k;kr(xK^*L@9HChiZz+~(gj)cHcP;bw*`Utu;oHL5xmJ##LZ
zIO}V1bYo%Jz(iCFr8SoO2Z=f(nzF4EeWghg`oukFa^Y%j`!MF7V|ApTey885P=PhL
zf_&>_jNew*rvl%48N*RvJFrCTC2{y_IGPL0+PDWHvP15*;-OFYWrsN5tas3k*B>`I
z`&;_l3HEJX<yixp3O?o|Hn7?&t=sY&%?y-#Z?>L`2h_Tf6q*;1>uTv6X~MJQR<ZvM
zoAErSe}Zd}{T!f2_;LtiBKAkab(!yc|6>Ph>(G9ggguJrNnY>OHnmq}jlbl6g;`CE
zce3Upd5Cwe?;>fZE?wZ(8+!!Wa&l1BeA+wH^*SPecTaz*MNh8dIo`2?YGm~BBQ_97
ziA0E&V;9y~pG@F$e%D2-^OMZ5QI3}6^Gm2uezNWG(Hmx}si27)p~Xog)r1k87mdGV
z-@<9Yba)%M)NAJuvAVFB4?8wE0ey0fWvSk+%`|ihRQ26$QaqZJwEz2$HVW1s{<>^D
z5fO~7kdE%0!8;Zl2K$p;ZBx)i?pN=j7;7$PF)@con;84n$O2&>trZDG3ifpJN+vpv
z*X?C_fxO9>j{Q*#U>#k`w`VTQF+iUr2SG6waIwB}ciZ)tu2L>R$?Ta`#6EX!O_%(E
z4*%P7ZGO-7=&qnGdFJHNs6xag+o}|8?p0dULCa4lvy&U5rt&};Ww~kUl%3jCr^Y`x
z%R2`95uW_ZeCF+e=-hdjn0+u-B4IrnJ<D=h)bRriwhrp1ZwrGXwzycdEUwJ?!St|%
z=r#j@noiU_`9Hhdx1GlNQkis#?eW03tXU5xTWaVbkin>FhuTBO0-GYfo%uf#82_`E
z_>cWc;vVzoc3HIASBrPB3HY#E<KnaWs&3=#>u$kjHG*Nd$1p{{!o>9=bQ2h6goItC
z*2p8&fjZ@MQji53me%`wB0`g||78jBF5P(k2QllfIPuZ}O6+^aQw3qYzC!|f2I{al
zjZ_KeG_)<RPNHuzn&zRPy5`azMNXvK0OeDqj}wIjVA<jCE3<4%voI?TR&V@-669^|
zc*T+`?#zU0BYkgbSdSQ`NEzXr23AFpzHw+8Lo<J<vu!!K@Z6_xYH#8%P+%v`v}zvi
z@T3%GBIIMmo!fhzEqR>FhH?=yB7re-Vs2N_9DK~Ye-lztTRm#DM=v|P>pJEv%G^$;
z-cc`eSHYDl_b)NN!S{rgOViT!w|jBl2O6E^T~!TmuP)AezDDl~I`FOSTf+F`*$a2g
z2ln?blKm_)U%gkkbA5{R8l5d3#4hMus5a~k6F|RPv|kwgHau&CoTLs}L`6GtPKY^v
z{prJlqv?gmSAz4SB5i$_Q%*da!LAR8#ru|^%lR5|+8f?pFaw7ASmq^cv@&=SdM{g5
z$Khzr9~u<?u2>Y&KSFlE=F|K7lPmrvE7J951`;r7hv4;X`(REki8&S*E({muT-ZSv
zZaAP09S&`{M=JP~hrWO)ieL9V9Tet1EiL#WEaQ9!Z908>#YQqqXxt-_zK~(Yl)J%M
zD<Yc*D6!o4$&IyW`ofJmzW$AB&<ioT;-OSV0#&whi|M9pd6(Uok@+5hj+3<bIFg_d
zA&WFhm<zic?;Ct{PAw$H!LE445$b1|`UI+~Wrw;UUJ|mbGFukM@mt4U@QTFI`bztw
zG5+!L!*NoD4lndQ5$!u3I&_47e|>VycPA4Y8l`qDOd56H_Xck3Y*kj#eeO!ZujGya
za+|?D|1xJq!A%-L2siehP(c5X9++%_HeBPIyAAV$8gket*g%z0CR)LgPuBfC^8KK8
zWXCj<fytdqf}~NYloL~tmkZl|UMhTEN6)h87M$Dl<B0o(_Pw_B+hg!F^SEh5<4c3^
zs=>V9_n54CT)7@7kMZ|?luuW-Z~FvoF$^>ZEtu_mL)ei;aW+FDM5z?7q&V;W{oXEF
zhv2-yC-vd_INjvA(IL$lna%{Lts@4jEhlLdGDaTL$I+r-_hn$Ig0lp&8mZ{YoC&H!
zPqVP?f%no`HY~5>2Hz;Rb1XUiqEkG1KQg5UMz|GM_o{2kfpk9Ib;cjXd1nqc7HoCS
zsa65nR@%B<*GSH+76`TKyo(S=%G0a1vy-5xh|VvJ(<83a8%`*~<B;}|3k~M{pOx+|
zC6atAI8{q=0SPsC;R)-B$&I|CIx+EfFMsuQnSAeb3Nhn+@1Wp(MKXKxA&OLT%kjhe
zN2;~Il4`_uRH4P?ZcvP(ZBaY3i9=p}Ag(Y`9<S>sR93ZYG}XJ<U0_!#@MW0S`GxjQ
zQhS_Pv{jCQciU=2Wnqr?&N?fMtHa9DvVt=8f*xBhGh?>n8oyqTP7%t>O4<z)N6g){
z5GSc1VqWnwRAt%<<??jw0tc=fH{I(jd|(q^#o0pGKT1dVejtao7WQ$Q61w=C{J~WM
zXVOmlgv6VHxUqj2{S~L|-Nkm5?5*`&MlFaOa>pI?R=kt7<N0w@yvrS-M;b;Sgxuh-
zo=T!fL!U%y%<bp2PfFdr%sHWntfjSgyd5X4THN-#GH%CPUQec6f9Z>)#8BcvHC;H5
zyzbI$;GMyJw~vcl_ojaTY8$AsL1fFJ2}SLbGMZlqC3*?#wQAdb!*y+Ml})lOn_Vj^
zdp|Kc$h?Ygm6-F23Ldn(SLG$>@iGz<t8QV_6}k3D$8^*JC^Wa9KL=FGue#RZKcz=#
z-<RQxw#n(>j0)?v;$bde5L+&c-&^Ayz4R&k02#R*<dvycLC#=vXwqi<=~*AyHYLUB
zq9k6j_KLEHvC&X2@)?panY;MmCiDw@XHU$C=DWYmtl7@`Mu}@NF7q)_^2(Fl!x(nf
za%fE2mAAM4TYyW5EUx--sXhm$V8q`XFCkg<j^i8ZDl_+@1ZlckAh#-#`A1hAf&xWx
z7W#X9`gneKZLfyB@4ay029Xu9OoS?H5u=?Scf5HLsAHC@O&dr9Z5U7OVE4&HUp>DI
zS3ZBmO?YrpZ5T^J)OON7d$?Mk9w(g?HWrsuvmXj=3;P_2hXoclDxo;7{jb?qbyX&K
zh!Guve;hj>q}>}CKWTB5S+KkpV+*jlZ#dk-Y*>AoT{JOra#0+gsl0d#dwQ8+1}QZ<
zTy@JpfKFgHSIoJf(k+q9D0iUt1v1J4ZAc+5Y(r3`S9`lXgQC$cIe%|9ohl65KU+1%
z*++syzqCB0E6!Jd5`TIkL$AW76m-aPw~A2_UC(s8S!!e_<Xo~O_%hJ<Lo7v|-$fQ9
zgE<F!^GTtF$-Oc9WUPGzg@?|T*+ye#FW_-|_+|SZEDTya8u;mIb4X<521Bu3TD+V4
zm-!PXk5x7Z6Y?u!3d3YtQL0pb<t>bkw6*#g@^zRwwVLi6@&44qJB>Q)1M#~pH1>!~
zkDJFhRdy;e0&;oinP0P{Z=%%(WpW^o`RHi;;U<gFv%jrBv>!>oa558VAL91?tH2!N
zouy>VJ^tSlGTLAC1{&#k^`kUT9?QNJwfmXN0Es`p#kT0+CV?ND6<ObgOMUeh8hk2W
z&sxOsD_H0jTb6c&*t>?1reTNhOIJpZ)4^9>`s$L-FZPe}*@v0BjLM)gJxetMfC=k<
zX-alEWUUUUD7OssmJD#6Rp&^Y@XgTd;86eTx1FADOK}q1v`g!A+mSWf+5O(K9pslO
zV7|3`y)ztO*Sm(OuoJdld)3O0*wiidUd?`~SGL@A2QhZM<Erl#Y=*weY`Ip$F9K5=
zsaj=y=|PAS9emS^^s;iCgENI_JS==N6ON%A&K|6!E4zM8KTfC>+I%jeWcHx^X@agD
z-LSH4RfoJ_DL`J0sQQ+W*Uz`>DtkYdZ0H{OB|fnINKNk<_I3n*E`3I5ldmk#@{_U&
zkL39%j`ic#RYG!JW`a9a%S27_i^Rke1GE)RdIog?Ub*SL&#j{($c-BmQI|ah`MAPI
z^xuxXC{#7;!TZDMo_b9zLYEmVZ42(lQJ%8T^PnR1Zp@g30ZKu<qODMeK80LbJEucI
zB+mpQ!xlqZp0<h<Zg5aE6WDa|xk)2l>CS}*e772_G@*8@?uj9u9gOJb7}StwCXo_3
z`C*Zj?RQL7Ajj{9?-sK%&K3m-kR3UMm{%$enBHsEpnV>G))J<ve-52&r?)?fOBg%#
zkYQ!GWo%^b!H~_T4WYoX$*qY~ka$P!vSW}z{3Z+gEAG%Pb5Q=3^wQwF!_+PsVAk|K
zxS~l=y=*Ml_oP*qrx3+%VU-pcc5A3Xh<N|5jE_PN%gLT$POSa)K9(9M0?KQ^DY47X
zJE?24Y1BUQC*nj=_`3#Gi6Q0f#RrfPql6Q0bP<;jAEnSmb@q|$mW54j{@i~VQQY3V
zb+<B?83~TIvH|LYj^zTVQwQ)}?m1bM9TPFbN|d=-!f=Yy)*m%7`Id|b($Otj^Dk=&
z-t7)v<muZ9i|s;N9=c)n!UJV-zd;+rFJlK2ZL4|<UMXF?4sC+LC8DXfiwuQoCQE2?
zFzW4i|FSNQv&IhYXC*3AG6o*vS7}@h+zx}-Rgd$MSi4h*0srTjsw72p4Brpdxt(#!
zz~sdBfuGUH<t8n@QQp3*)vrKZQI(sBkblvENaZ&lzdeoBP9@XBxHJ_k6UI+fS;Qqz
z?KhQ%C>;Eu8k}J?(^ZiMqHzTjDk8#k=;=a~@1|A;=b+)1y&q!&X3w*iH?1ss4|3MV
zF=I*@iEi|FJ6as+{|vNN!};Yi422p|gj4J(_QAj06V@BM3Wu*2`c?8tpC<G&HaBwd
zHA(XB2{TJANH8nqvKyGzv5u{m_zGCYd32(bYlZ>vu`jqQhyMlDXi2Zw3JS0O5xO=J
zh76;Ha1=L|@8o{-+1#$;mVtnBPFt_fuZe;SKQqxix|CwUxvz)Fp4?8mSJhs_Q4+Eb
zf8X#lhzX{kH=~YTuz&GEF!4lSrRnau2Hiwu#vQgdzjO(;FO-TO_UD&rHy0Raa1fWJ
zw3G_6OlOA}*H~Jsn+#V5Vn&aB^F9?;x^Z#%0%5k0w^b=UaBKf^o{{6<^aY1uyPXC~
z9_{>F(I#y|Xc@W&s>&l5c41R!D#!t;lx7y^q9h8^=KJN`(iA#KJLDge{$*{<VyQvq
zgjohN5tZ+{*H$N60w1~FS`WwqlWx6&d`><O#Wke+<E~oUI|in%!JEn$0xB+Xy|&A#
z(8YmYAfKPaM<~i4WhOVo`>N2<)Vm)EQGBPs=Ni@+*`gV`5D#=<iEARd`)pN{n{uYT
za#j2#tZ!*o;HL?IIZXE|DmhIRe|^{Wh=bP-1~1wxzo38f5k^gqE{@8&K0|Gc;f<^h
zEN|dWLDlX_#jgv96#Lj0bu_i=BX1Y?*~eYA%~mhqE%b(YWQ~73-(Z@r$FD)Wv&XyK
zL%Ox_)2%(;en@%s@|__GJ=~Q6lHZkurjtRLUGeAV0uQVu=B_UUDYbkE{IVoJqBe6d
zd}m3yT=tn#0NYrzz?|7ajiDatb|+cb?NM`9^Z<nWHMMTFsP&3(gJFwM+}@z1mXVQ}
zN1WzGy7}L`I<R_X)-rBZP?<T2$aL%J_4iLqvLe6quZ$JHU&3(eA?Hq1Tg{NY>X2o&
zj?in)z0om@_g2{aQt##6aNMMW1@pM3anx_=etB^G>yYH2;zs)Jx8TLAB=et){uJXW
zn*o2+>&gHoi8_Hg%jL>)hhV$Lt9^+^C&^G>Eml}9R*N@dG(-+}@e?AKP^nK&h@5n9
z5@s$@X|aI2u=~N*6_OE(j5}?gJ*GJy3HuLS(5XtF)hlefP!z(~#>vJveA_+hg%T4(
zfd^9RXYl^#b33tbdnmR$ZN0u^!R|z<n=|YOrjBN*I6>#m^PyaEQdjcrgS!Z`(@g+O
z?pO!~V%mGXvS}dbOQ6mH3TwU#UP$d?W=y6H#%#wXYer%CuHYK2g)#4_t8x~*osExu
zY|VQpYsnb$ys+H<j6G4F(cPHtX;8uo;gXC;%Hhlp()4g)Cy(7#Yx|<iGZiF2>=u~2
ziL%UXbLq9j@y$J2Sap*xk9zCLMnBA1^_vOt0!!POd2h<@gqY;+h47A%lcID_|4guH
z1`h;>`?UwxsXws&E-*UHhbW5OxF{4jmsmqy%#3;cryb9!Hc&|MuD)G-nCzksJTGV7
zJefI80&oEXMhY6RtAGt4R`WIj>(MjfJ44H2qzX!4u_>089GA3S@&T^rT6DkHP$+D<
zX$v7<mI?n|^)V_GY8OvIo_K>nk~aTNEJh1>=ubURaIfUdGMeI2L<lLODymuv+5#rE
zK{WzQ+goz?5$3;KBr9utLA`Ggc#Lu^^kB(s_fDz|OVQfrlAC%k%TY2<(EcLP5T&&Z
zwYLoP7|AJcv*t+(2-j$AacGUL>@!8yNR!s&)_jIjG}wf;<&(8Nav~ztjL^5ZU?1(L
z6m}GV>^!^|Aow2Mqy$-@H>0$HSXa&Gyj)2U`*!DK=B$kO(sehzX39ei&Ty1X=t07X
zBHD^sHyi@>PLSKjWtks!U5r%V&1SYmXCJ9;%###?`OwSep~o2T2CGueqSQn5UWPd%
zFU_?*x4X4A_wl}7<wMw0`=e|}U@c#9evB|#qc1LB1$<QMw6;SoBq23w8wd8&9E#|(
zx(+t4p;qHeqoytmPlCdc3q=m~;QXRXab8!+6;*TRZS~JNiPVsfRYNRQ45*)alXV8s
z76ZL$xl>JYuxZKBV9RN+eLduzNtCg}q=!svIgTtxps!z_x$KTe4u7p(((ld{3SADc
zJFhgBk^qel$||9tBqY!4R>l**LsfU*V90Z4%er4v#xFn*u$<6_dNlzdv814bO+j9_
zO)!WmhU?^OY*~@B?lFJ3On7X+xVXJQZx3t>pKN42`bz1W0n|aj(u45SgwyFZ;VgAj
z>wnz%BB81CVbP_$NpS2^l?um^v9nOyZ!~99xXuSfN@E~-_D5j}Yvsrl^KMETWVz2<
zRh<k~|H2tDksU>}%x-AoU;Klh7$U?^2XW5(xr8aAGAj~r5{4^PL!McdJM0rBqdC8W
z{7(%tG6@xDVZ(v$?0y6<9_ih<5#?+50qv7|Uubmtds<Om*R|sb<S=W;)4w9foOsF$
z!RgzNu^;CCAdm#V)GjZLFD}{U&)g<H?5WbcQX>1yWH>2q4!1gRDK@)cgxs+&kI$WQ
zh+K2u7%TkSB3FSpar~|#Jw@>1Maujh#`yH~aNEQ$E!8B}BV*eS9P0LLzeZgz<=`cK
z%;;P7h20#o`K72g6V)2QbMWps)iRmoO-T|juj<H(bo&#B%7uYpOrQ<Dobe|Hi7BZ2
z4%O*S*W)hyPVVAW48Urm%PeQGLm^O1#S5krrK-mQ^V_u_`sZkS<S{^Jo$I>HPyw2c
zIr!puPy{z*XIG#H{hO~GB0eLE4o91r&$IhaaGBJqMtvG`FO-OvXJTrn-No{Ti86a)
z13n{dzetc0-04!#!2@IlMuJ<8<xY3vPIb?F2NUn1;>50Jo@ov}ZLTOT-t?pWRg8#C
z<RzL)s~Ib8#EGTqjThPhL95yM5`Cw<%eBv}@uA4=0)2+!+<mX5jNWU<b*kIjj1vm&
zEV<_xzjK<MLbg+F{mnb$g$ti#=>QZ(s=G5&&4uao=@hW+Dc{Rnt-z|hKQi`63q3Yf
zMTV*>3=;cfQ1|Drp-x<|n?TNGsF-9U15nAm^w4Gfjjv>2eY^mP)4vftYah8xD3~<6
z+Sg-)Rt{mQgWZY1yP=ljqwL2*iYm!NVtx4mNs-FI4dR5?!5B<c8&2dhbtuRprL+pA
zaN&d+nXhdMI<i@j7)!;CPP#K(JL*jcZByEpKSQ8+S0eaE-Mx(Ah>cunh*k{NQ(4?#
zDl-OhE})`oVZN2R=a=^%DB{$;<iKQy01|6;nbq<0ZYZ6H<-yG!wdW9xE=ee6HplxS
zTk|Q8_S11^RVu)Z9}vh5+Q5m$J1Cb)CTDTVaiTK69Z3pLRoFKo*PzzaFB%Bla!%9i
znvn|wlO70utoHFr-flnYvHyQu<Xez)Cm}VWXO6Rr=IUlIP%S_msr)dN@oa6=qeE9z
z!96eDRvfS>r`mIe8}B!jbyz_D@2lG0=cyABhfBNu|9FqhnR0rr(3KS#x8c*d`X5_c
zwzjsIPo6rJm1o<uPvlVaXAdN}VK^$iCR-!5@m{ulwSEgtn|u2(%&0#>e}cfMy#8Zh
zFZ=*4kQG{%fZRIT&R9`{n)s!{-#1e`ohCG7!o_U}jv)&)jjIZU3W<t_W{2->V>XsX
z>v|*UPQ8+}){DlAL7J%)Lf>t;La6CM%M024jbeBMDZjW;!4I1vMxTsCvL+=b8(TJo
z@16CT>5^X<u=Hs7<bX}NDrt?k!hN{&T7=q&o#mzj&zvw}Vc|9K+GQl{>JmP9VOeiD
z{+!9aE9zD>HS#|+Y!QEJ)4;^U#BXnVqkBAjhe#q+IFo)6G2>%Omh<y>Y)Y%5Q3&D&
zO@ymj1nUi>48?%P!0uG(LU6I3gPo>>kidzr&smp7YRboZG8A_TZ5p=gowCBn!{t2+
z3%SFUE(h^*wKIsdwYB<JuTH|b&#XAqibKli-P|Alm^td%muH+$p?Pw{He{$ih!g>g
z%pf5oWX^G%HWV`!ywE)!MzMa7y)c*T>cL#z5DtlVTayHlF+@!BXw0pB^z(xuuk)|Z
z`WPF(eq)ha7+Kdr&sjQFs}geLDRHwj6x$Nvf|!NWUEEu3Jk5>ZA2W_)`?#egiMA8p
zge4@3dDfTb`}2`Zm!S53b^gTtpH*&z9IEYrLwrg3s2-Pz%vD&crGv;_X+CA2J5=t3
zuO<(>^a_iKiLLRdX7#fE#TSpO%Z_-q-~w?vX)=Y4$&G(ri4>X5-LZyn6Oxd~o$d8G
zpH|8^+;%))DNWj?>d!RNHe5DSs-gx$W^IXL@{kPMUZ1S*EVih8Y?%e$-x7-A@Hfjf
z4bn77lX0suDYn^*60@vgU+<P3mx@m+rB21`Ui@&R-5_Ap4<GF_+e2&sb||tDJ;f@n
zH2qVZi{qk)vj5V^91?wlJKog7f-N9Tzs7r7fa)JXq#5Ja6=9>be#wg+qMcYQrJ63k
zzJ5gZ;kTCqQC#7h(#H8FVPBUidZOau;RWX9j`-7;WeP@9#4Op{xOX-u<Fs^@H-4!O
z6k1YZBO@bMpFe-zE9E-4(t1jwe&rj(WtrRQZ~solsfy1HS1!o=&Ht(qP*sJ8CJP(4
za(I0^+p@nJ2e0rH7VyPoe9}Ui%;7#^Ybqc22XlkHZWUToa>`A<kqmyW_iP<pOP0K|
zmk2IBppPhO+TSLq32eyyefQM8loI0yjWNT<s`Jd!v>Qt_Za&;|?#pIa%j>qTc%+Xv
zwFz=)2O*KyY8a<oRb%jnxYUT7(JZa3+qso?d4V_61z#k#I8;76XVSoL1T378sH>1W
z{)$D!3LX(Vn|fT#W2nsGPELmZFc*{3N^{*BiPVM%U1pSsl^rAPAe(ll13@_Ff-yVr
zt$jiC83d+sNskp^!FM)3TpTQA4IY~5K}pVbDK6KOm~snzY;0_r!CX@DL?FQmgLf+k
z`cB|aNlBR*4HysW)|H!hcC*q2--vnl?sv`CuV0<mq#k*awq~=&hlr#=9yeJ11`Xum
z`fs{2BXe`}C%nlOZiT}f!+gp39tKtCZV``1l%-KGIO^}O+`eNwK_E#4H>|c^Hrxwu
znv5cNKDoiII&6O?p$Z8cGF95ef3!1Mgb-CU-WV2=F^xsy%^O3vR<%+@QR_XR!EBl2
zg7TjEt?h+k+ly7Tp44~;Aw4tzJj`|bJFBe{b8V@|c;hI)US=rlZ!Hdo`wB9VU;T87
zrGg4&#3w6kZv)T7!oouI+R0Qt+qO{L6u!=4YUR~aWL+1|G~+s4kvjn*Sbp<|8c^UN
z<i)dvmY6ZePeYS$X`Vz#oH_n%KZ6Sa|J5P&M@Z2mI+Kap)NQo3@&<x2&+?9}^_cM7
z?%ff}^D6i8ParnFZJ3J~x)mh_*40T=)(q?y9S(OZG<vqexi7YUdp`f5(XK7lf?Nfz
zGLR<kGczP+Q6cB{C6dmRGiWM7@oQgOh|aTp+bBkkU|nF~{&+VS+!i*q8_S_cPDDvM
z?LX*1@Cg8KcK*9IjpaJ21OLuqT47u5IU#iC&Yd)s?Y|$}ZejSmGnV2&Or7GLOEHPc
zLmPM>%tYtQJX-3tG`6sq5BeRtzq{36<vQdY&+E4|BL64DGup~7x}rw6m=L)33Zt`#
zFSd=79S`;K3`7AJb(0U@-Apme)}B)gSQ=@neDw2S$fink1>fob6XgDnr>yPX#+PM{
z8r-cB7LU1*fle=&^^{iecVpPXgkT;`d*mPx%p7>&SU&FH*9>X4H*Qd^>l9@)z_vVD
z#I#C6P*AX8HZ#QCrrfbRs4if63FEgga0L8d^PYIls^b0o3H|=LO!xNqD*}VI!r<iJ
zOsRcs8cbvk+vqoCwGNt2rhrBtwEd83dBNZ!8?-j1b@JrNDqHFX3G*`h6##QPZe#U9
z&319hG9S?BsXB0FPCU4nszN`d5HnW$&U?DkvNh(iY-n37$6mnV;Lih;Q)W$(V|S_x
z#$((j#}eZ%JKQk+f{~+E_{{Nj<=vGQ)dVzfR3H1lXwHR!!aDg(9~Ql#(v?4<keWP`
zm|F}gInFq(>961T<$I0NMEC}la5GB;x1}T@L-8U2^0lx;eg;CRo%#U)G;-Rt2VeX9
z^Gy%l7Zel>2hC;#xCeqN;0XeNSihSa^Zj_$oyBszJII<hxX=9E>C;oS&g#0(>l5zO
zo4I$}%WL%o)!LgkpB~F_EzVL23npH-Yqb)A9c<&9-1BXl!cl#zfkB0f`91EQ5j`$e
zvbmp}rS<Mg5!9TWoyUB7l>;;|?xXGqG(bc5`uV*4l&EFoU+I*;PNN!sIz+}~O8tq$
zmc7c95ii3W-G)B^K`uSwB9MAj;oVoF*vszkkcoa&pm}b&_s3bQ-o5<DefXDRTgZp)
zsYJcIev5;vslXNX9?bS+NIQ1D{Ta~u<`SE{HyZF9OqIv@eg-{f;BQqT0W<#R0ZNB=
zu1m5v^uf26XAdgztgNg^FeY9;U~rtC=L}Ui8}0)GC>tSqR1bF5vqF|?Q11y~h#H6q
z>YQ6jzErH(jZ@v<yao#<b;}+MH-u2Kh;G2X5>2~nSksNUzS&lOom8d0i6}1eXvu??
z%fRVIS!}Atm&c_&#_AUUOp<r1J)Y7t+4R-;%wA?x-CYrobsq^F{H*^mm61zjmm+aY
zC9C)r0_5ey`GjPRAyBmAf}BG}%ix7&i~Nx?0plJ%F26pn?v*)UmjuPd#W8pWcDa=(
z>pFksYk@1j^+7DC7S`AA2Gt>hc<{GBgQVLH65yqv<|Fih-fucG#}Np`s6@j$&JDCb
z-1X15Xk<qC_MBG8#{9-)&1|M3^vv<8jRm~NV)GLkxTo+q)`c#Y(r;>;F;Cg4(`=%!
z?KMamJw3*7K|4)`IPn7?nKUAu((Ys}5Ix1>lC}v<+;!U4v>|w7jRcbJj!%#L?2>}t
zyi)Ds{ycxsyyAguowUM)wU-#N=p1l^!c)$A$Ok}5!-qifUJe8CSu_XIebS#lf5t<`
z76Ahr?m|iSF8z4ll8CekcBU`xEHEt|D@Ihf4wdnjW`jhh9{c(8=k{WQPn{Bas(ZfL
zT(1tcdf}XnA(W-BAlkYa_wI4#0rsgFr?l2?{^Uk8HJ;UYh<&AYZL+tTnupkA-FGeV
zMb<eQIIKid=&gdbb4(Ig@_Z;n!-{{{N@yRLUUsXjyrCQTs*gYveFd*`bHtHxt(gl{
zX4;5oP)Ts%G3w1!wj~Q!iv;<EgWO-^1@a4%77hSQD7LhXQ_&ZM^hTPLGc@4qvlCp^
z$ypr>=c7xb&L3x3GRZS?-nj~zSFE9ShSw*CZ_OMZjSTw!5p?VWAYkRZ&hx)=V~K|K
zZYLnz;Q%qul!LGgQ+@Q~9b9Xh0*JV<PKH9|etEA7VGj5)(NJH1f=khVu@Xdg-F&)F
zk9n4>x$LNKAF@|vXOaB;6l<nIzDZ#=*qk;7veqZ3bg$NJs&gdZLie|3qcm}VbM3(j
z>`VXw#+34k**5Mz+uhyuFALv1NqdwmmZ%~m=Qs^;!$r%2B~uS6Zr$iFJP87GpNjtd
zAm&rQ8{`BAnOB+4(%e|X4b5n-V(KbtI*=JeH+NtQ{2dG1T!T*$y|ekpX^#eh#%_iw
zA#N8%-)rN7*XOj&^lt&3vM5mQ)T`pS`scfVgk@EMoxc9hY{1`?&k-+524(Ps19BA%
z2aspSCz(y$-6Fk?Vz}(Toi6XwqX4oSS1?F>FEYJ5g+flF;=BIwRGS%=j`g`dmvQ*P
zUIwd}#jhE>EnL|iaE)Z58@UvmF)zh%JCJY=a%1=Jms8wVKs-oXhT6B!1FEdIxzYlh
z1B(?wqU)9$>vCAP4(a)97ChVmB&?i$z%Ll66r$tzm#6B$4lWNr-i>ju-XjyzqH=&9
z@bQBw{EndEA-{V$1&%5bLNWcBo}PXVUUbwKAQ)Q%f0#1<esM0S58jw3_UE_w|F}u-
zhC~BO!d5yU!fBI2?B9a?E;Pc_80-?o-Q3q4oI=tp&CLD=x>WSe5=5#|?b2uS0AwmM
z!)Cu>NrZgUV!g##V9)d!sU`Q%+BP{aZEbAEmhpAt%S;t6c=rWB90g=N#zGwd^zVb<
zFFw(97g5uCZmVG78@B!{1Dn*}DNg0!H$ew`l$`@WW7Vn3tujtzagd#+Vzr7760!M{
z3f@g!dj^Vd&A%bIWq*%oIrbDMU4XE*geo^hDq=R67FzW4;8Z|2&o)UBhbytGSR>#L
z7(m6GdOg5yUw`EqGKGT6T!wE>M70%~m40YxPq-QvM6ASThpm2R)g$bf7Tbj7^SX^x
z|171ySq+i}ao~FW+G-nD(?yqBPmreVjC0&B%ea2#fl;XpbGNFsLx`VBgw@7qty}G*
zZ;NQsP??xt3pzWmJNb`GfU{4cM2M}64|5Iwt?B6?0_9siNfExk`xuN}ZO=ZKj~TnX
zg42=+8wS|@vf-Q?s$SQpW5c%4z!>g*u+t0I9d#O|YP$}Q-ZEE!RN(Xit|!AK?>!|;
zuCgyK4OCso7Lg!;WagrqF1HGWZTDsEum1wNX+MbC{gkAn#j22!0OIgQ`Xx!n^%%<F
z5y%)Qz1UOvxLX36!=(`)D?J{9a00SJDI;FM2nOr809)JH3wcv)eDB^p3!r#6a<amO
z-y|)8JXUZ?Y#A74ZGUg4cwxNhKmq3C*pqHv4z!gW6|y)<Gu>$;fVvhUet~ym0QOZE
zBy2|B*Vl)JiejRwfLHVbs#)PV@s$yAZhwseGJ-U+LJn62+Q%*gi(!-AmcfzZ37a?@
zf4Slw`goUADd#dBM3w94C%E3mjJQ|RcvC(|R82t$^S4Govba*6b=M(^fFB+Kk#2++
z7$R9(SfmCFJLim=0W~oQ<S8qg^yBr(!BV^8hLx|U)Pd;R62`!m>+4O(cL2*8D+8=|
z*aMzrrT*d`z}Su5a`(yB82G|S4M{}OvFop5@cQ3VcVpS*zmCoHyT}dn*5<Zj3w1&P
zwxnv|uD7w1dPIurp~^UwgPrjMwl)jQZC-*SP4j*vL?ZrEX5gRbIG~>~TOcb?^>EWT
zSM?M^>q1b1X|)@nYD;x*y)Th37bvrkH?jeMOBR9g=|4iH0SN$9_l1;*Vwe$c$NK3E
zaH^&YEG&D6Ek4*=hUXKwjEd{mIvoN=ywtbIL;_Pa5Z19Ff1(9cVp8ycHKdIB;jQar
zK_IRen_Bx75EjSWRrWV0C7X7Z>STdOSGN&AQH5UUas#C&1+5CCXVx?Ke5)GoTwb@q
zk}suEdU>7xe?Wfl?#|80+$MzqVmt}@V%?jsr0Dy%gn!p>?zu0>98&<+0I*pBq97dP
z6%I-T3ZB4_I*bZ`-+c#+EUa&tLJt5`+K!ri1O7`Ucxx_9SC&bn%6a_>V;0bT6Ucgt
zXCAQCjVgk><zM6Q_jlts_p(e1EjIRmbF;-=lAQU06|Y-_8_e6Z=w|sfbcv+MlvY$!
zAb6)|XVrJFOai)`R}T1r9dlvY<>Fyi$e=B_iL;FA-%CF~s`p}jl`{b$Qv>;|zYd^!
zmf!N&ULB}vxR(mJ3|4#DeE}7v;2%`0{{#|#0fqzVBIkMbGSI^6RL)ikRQ%V2N+3~2
zf4o;SHOey*x<(CdKq%(8SjDXd#SCh|&@CBSSy?IEZ;mKd^qXIQt(;HBebUOe^68cy
z^<Ny+C(M$J0f-YRL(@!hzay*x3krU?ySZxm2I#3_zyzB-@zw#|kNWfaWq{L|Rk@n~
zYyuUpt0o0z`uL^io`8fc)^7fJ%@wxRAy6E&{!={)PUVt~#45trGGI6tz+r-7V%?TN
z?ytU<Xbc>5DQ@uZ1PY$>rO`O3Hhr~=m7j?P_!#;NkhMA+gbh$6!ANWWp?(13;=-WF
zqzq~v2QRXnhb!jh0nE==;%yfEfvS<vl{%C352d^z&ZZK&=?%k5nn!6@{tF>9gb}(^
z7tUkc2xJ&673$DgBH61%YHR2Fqz)WJ)V*<QCWTs?VL%vJSSXYjeA2wWaaxzWI~Gdn
z7Nf?5q_A;bMHryqlp$q=Q&0MqJ;<G@@EszijR}B9{t0IOu9rsPJb29yZK<zo1D5xF
z0OUG3)^e6U8Y-J6Q@b`=+_*jet8S^*<%a|4RnC8qORcm)Ao-Ff+v4zmQT<$4h9B&h
z!gmPc7my%xj+H!6`$EH{<hlO$=f~o;Kf8DT7^g8nc20r0ajWFy<Sj7Dft_EUpVF&2
z*x%uxkhVfuK$)tk4rrarYIxO^W*QQ3hEFV)W!;q)YZr>@7Qovn9p=9vb21<7Q<@|g
zdObin(XSDt&P>3d5q2mLpZ$9in7b+myCkkc+or|=zz7uY4_7$DGeO}stEI0JSX1>q
zJnpjWQX=4SEY!TKHU@adKH!bv2O85uiEMYgLGsht{QZ<^Ukdo%Pnu(=2kk7WJO=|x
zm$56B#>EZfVZ9t}l=muNRm0Mce?6-2ojtV~+V~0(KXIzyaQ9L2Wa{*v{JsT=e@h+G
zhtxznL$C@#d~~gekq7iN6%;C;>&~2yyWP7yQsa{kYLKINE!<NkmCZO1<jKPXO-)Vz
zCg7e2e@E*mb|AFt7uWmrY#)y@HbvboO16(>$@K$jO?&R=$F^Lmkl!12DehNZC@7cF
zh_<zh4j+i&O{jn9%uLV3SwFK>x18AkIC?Cg%)K$xH_o7ed{Yk?&;|qHz#(()=G)cR
zKc<Sh38MBf2w9%h+V^L`p%knq^@J3FsvcK=fw_-Pc=*09s7ihY48Io$>P&nS{NOqk
zDbcht7P4%8CbJRH<cHs1vB+ll4VddCCnbeC<tWa)wHpgsD8v+91O_Y6m~Nzug-m+X
z&Sev*Or#KW*@d*W=D)MIZw=D-_Bc!+W4)8AzK2?vRCn*7D?o{>C;zZD&zN-syM@a?
zynA<l=L<bo{b%^WzMTyy6+}-s1E#mTL?wS-#WoF@#Xy=M087pY3JRL0YEvpqWTk`p
zXIxL3?DDxBk}nWMq0*r6@gC?>csYQMg-{jSJ3x-Zf#<fJ5-};LE>nwQ4AJ?31Nx*p
zkza>x02Gnq@{DrVd_bYu*vM!Tl$Nbj8~F_Ur8+X1AkiJvV!#+E1~?XVgU%Qmcf6s5
z?yj~Sob#y3MYKyNXs%|C1MXO#sT?w91W@(|K-pDrkx`fXOJJQlJ0Q0$2wy<0#>+rG
zKyzXT{m0_wj4w#Dv!H|lzXGEcgKORs&IK_Dkjw#O1brXo7q_VRn?TJ{>rd!8H^=Mi
z>(@ZnamN_gL@z1@u6z_REvhfn%}}_UeAK3L2vXA2{>payi51In+Sml&!0d%5UCx6g
z6EQ9W1<uOuBh?dXFHW0h%x!}^4&KNF=&@RPeu4D@G5!PSnY|&|vA{{9HlF?%UI@ai
zGqWA4GUmh`4a6guSJF+DbkeN@*B=GUAWWjq4@|M#nEB^90B>z`bCkz)XEG76g6p|p
zj=}*|!~~9lMy<Mlj|g@{AVO{g1#LBhVmerXQfK<_b1LB8Fy1Gy0k9gJ@z}Kvw($Sl
z3UnCa%yHVs8hNR6!H4@q>~$8aWO=A(i~KXobGY*V;kQwt_>cc}F93Jj7MNn2dgj76
z$|KbKw0;+Hk~c%b89~2w*dC}|0s0B5=$X2VhQb}(JarcEPjc4WNp68(DMq7{N_uDh
znNMnW2;*Y`lTn;PDZg%ld8H}tqxCPp#~_vvvdV$W3BEPxWX`F>N1EG9=o$#Qj-N&t
zB&<{dYR0zrXSGdfAm>u*#IE7o>eRQAf|97#hH#%cKf7a`zTE38ea<Al?Qb%~JB?Kt
z`d?<cbmKps1^>UC)cVJg|Ko$>|6kdeYUdj&uw*{tgejzvxN{VGoR<!22If5u7xP$%
z$MzO)W#TVG+z`T|X0c8KcOG_-uwH|Hm4s*fXQvxI+|6k=Ia<M1xGUF}#&H(z2^G3H
zV}#9F<&b|Z`SN;T-*<tzOE8ai`RY1jT-<;i?*^Yv%DTrYoa)|S;Xe9&&KrsTas^(D
z^vytj<YU3^a^-2|xhT(=FT^2yE47!cEp1^;==#=w4uF(#9vS<kXolOk0v{LddmWI3
z{*I_}S-!%0bYR<qH>Qf*bj5y08o~A{yY}BByy?IZb#hZ%7hxEfaGYe^NmzhZu%P{D
zJnzCk&%#5v+s^*hX|TD{yQ~2%uH#tycXbIDPjgzZdX$oFmc5Ot{M5hC2i;e_9U;5X
zc`I`#u}ftAV8ap-^ly*by=VgsmnY+A)%j4Sr1)9M#IApjxe<F6oXX>w65PVz6D>)o
z1gGBl?>`PShrfh)%<C9=$~~7ss3iK@rw2(TN~s%!EG%0iT0;yJ|Lupo2$-B5WZLD=
z%|Txp7;n?C;BihypFqrXYB!SewSUXl8tA|_?2p#c@JcgaNxm>e18c8;j~$AEcm#(E
z=3=@^PLglC#PE%tcT!<NY{OEQ%)9^HXG-+NBaJK!Uw?vc9lA`eZwUdO;i*N~kgNYI
z33;Da4(xMDL?=LRjyWO}VBm?e2M9KeJW)w>3k8v)Wg+aGvT@Pl-o<~;*sKB^$#D@;
zF=*j^(-Owop2j&YU=ercpLpdCkw!=vWna}QBJj<@P+Lae@l;{jz_YK<BDRCmXU}rZ
zQtx2mt~;IRtCKA<@je)F%#k`L{p@P<YqMQrocc-KGsripuz8+27p67p13d8!uXyM5
zMla`1axr%KTA!=>+myDxC^D`&^87fY-GF*-X#^)lu$UiuWNegk`tba({oHL$)wX4^
zAQ_NbLtGZA(|_jC5Rauf?hYsA1KvcIe+M_-oHo#>w+(T!U{(dg@$bq-!oN2XP`iBN
z;qevFkX_Y~08a5CYy8gs=-d)7?-iO8iO+%%x6d#}dfuQ-xLF->{H37h&2KHnw6y8U
z>gQ?cxdp%0$VD(iIh0N#Ti&tz+`e(z?!$>QGeW$)y!PA~>%Iq{JNuHeqZ;Z=iSv~U
z8^M{uWQ@t)fCpSLM7gdnYrEeAwC7FIKZ)x;dhv@QT$VFX<-HQMS@%u^;=i&I{0yb2
zzaqYvF;M^f1#->6WTmK3&qR^YJJ#^Uqr<x}E~*|`-VH0Oovhf`>rbYaAMYPpJM3xu
z1qPRib<}qD>&J(Wx0%6d>q(UNsBOkm3;(PL04o9pwD-dPa|Pu&YFp+1IAp}$ZC|`F
zJk-MD^8LoE^luN7fEhh)z00HyO;4p=bIoA`&z9dg+|sOZ;-CNOxT{Hh#Fg6Oqlc~-
zk#LS`=l|?6EfswskgI(EJaKnX=*VF<`(Jzt8L`?UcOMw~<-7<|`~8BZ>(F$(za1R*
zD}vXWeKMgdq10BE_|n!Fe5=bd%#Wez^6Q&F*1tv5b(nLsbegf6^<LW-zdHAq=e<6b
zdI(r4DQSm)N%9{f{Qr*r{;zGtm>MCW|9bz6JF<s^&mIgMXW`p?LcfGbT%qo8%iEZK
z!~9|+Qxl{yAuqs~VxA^L9e{}IgB5>|rL>plBNrz&CG%A?2ck@u^OqV4Z(H){b-3V$
z%DKbWk>ZE@{w#?MaND&CVGPLM{~Wwo5q@X<($dn>iGB~JUx)en+QXw`TZ-AiTU{gx
zlitta@3*_w2W^cpdEt)RyJ7uqX|DK^(ptZbF_e6Gd58bu_%)wnUg;UXI=nHyRWR?b
za}>;L>*lH$ao9FEICe2fpMVEwb7i@dDYvLQ&V&V0J*3&-@Ce8G98=R?rSV~VK97Em
z9NVm)Q`wK}2r82Pltajg;N$(B(Nx#<i;K{*;6x<vz6tA+4%xRRD;!3+*~zpEqs`~!
zg0dS!nTkDzh+7!Sb^PLB8oQ@@K4o(rd3lE6$z`1;8+b&`46&$RB>yApXgj3Jw8^x=
z{bvld9arWt`RZb|(NEHCmBB>y&zNg)(0_sjhkT)|kzzndSPN(7vWlag9fm8^MZ`bL
z-|fovq{L&H9-N%UqhUXFPXEn44C4D9;ia`pAJQ(i=jyDbH`)4L?Ha##WO>|JB41@>
zD*OK8+8n>g&3xU0#!|vU8<8BUXGfMIC(@4(zfvw#C>)k+wra9cey`vJTI#0OAE3Pc
zWc2S5ilwgv{bHGqDxs!$leFC<OTFtW!dk|tVRk=Y{&S4lZSL2r3tR`G2ccj1_~^~p
z)adHtia1TJ?=`#&cH8KFGQ(NNR3-9zFJZnoAY^a9P8lA!!5V^EcrUrp!~r@~^>O0p
z5C}u3ZuaoqRSv)MM2hOlnaT~E7V9Y03E#95<COF4WMX80EBliILuQ*#hb2~CHiD1v
zehbm^>y;0UD-9xP?~SL4CN#AY^Gx*?4g$6};s~VuTG{kWID^(A)$Q(gFt9X^RRxFR
z8*dI?&0~@>@89X-w`3Ci2rp_>D2&V7Z`ii-bKl6FALP&X%Cfdr<{;I~o95N&aP{Uk
z{wNB&FP^*arBi!&&o84n=%sDJzGb5F!{zbHmNw?J#omwy8wbV|&Xt|mFR}AitV2Bh
zSejr^1BA4Z{*}&oSf5>#X%n7za}P8Bomi`H*{>&66LfeRU&*%@j20N<gSy}wiA1){
zQQpCZR<(SJPMmU1?SAw4!971mzo_}EA#&lGiTyRk#rc_aaY04yYvU`UG?c^ky!oMI
ziVOd!P}nh68sKrx#%A&vf4+gl=P~2G`;}t>5&OOyVLJPZm?GW0#(?0^0j<2meB1mn
z{fsgD<<T~iHq!$axBu4OdjK`nb^pQ?5wU>Eqo4?=AUux<QIXzkh&)nMdJTy5UP1|k
zfFP(KC@8(CC?x?xFM*H<3IYNmgcb-O0uoASA&?OAorL#&|GszTn|trf{oi}PZ~hr)
zoN&%wd+$|$YptDgM%dnwACfsR5xP*=T^L?L1K|@DhPx+C`v%3Yz^pAXWi%vd-u>I)
zJL}7^g-)|f57tlb0bgt8juBdN)aM!{RJmh^-A@rD`_)mjRFWn>rrLuIX6^I_R42wp
zA675T;lWD|qZkC!C>#GX<{_KFf>Q|>(MrK0J2WI&BZw@&HbO`8n(7!An4)q*7W+w~
zq*nJnDb}sH;IHx%z)vqkq8Oo+VL&`B1t?epZBa@|upx*dfXCy9@kWSDGiQS~udD?2
zj_g{$agt+52KZbBixN6b3h9Gdi6ni*`{LI!M)f=ln~XKWX0{}Fm)OS{?ba0!UFliT
zH=W;cN;avltT6_a?o`oM&(Cas*Moh(24VBNjQ`NFW!rB*_qtVT_CIug3@@$`!)2)n
z)cnO-WCX>dFoJs0ObM6|QHtTBULDS0hbcQy%>Lk>LR)i80k>5NQ1(6H!#;8u0dWKd
z*m8I6#^n8?HNRL-iUZDKUc6B<F=nm-vjpE~;P>~?i%9QX8PD7ZR~hW<#i4*6GK-+Y
zRXbfKSwStNbA7ZHJe`-sUZvfbh+AAtY*#<o`f~_5r$}7$E<0j#+_49yYm%opoB9%G
zo!yk&c2UCPw_HR*Prq65k4711lF?q$F8}^Icx+ir%ZP0LIYiBwH1+$RZ7&*$mp@xm
z?CDSC?`Pu&+<oEm6O0Kx7y(#MXF3hc`x}*=7Cputq%v7Wo)Y>ynWm8Z@avQp$FkOL
z;Cs5&;bT`Z?Co7(t;pN7CpTm#fuxOp8Y&!$A%);Wof9zcX|#Xc1OOY#Ht8CEte^)D
zp(&$iH>>k#M3PiPHuWWGaPI<pcXodrtbSDM=1$?pBZJh!r$`_5YdnWEMq&C8)EpZW
zaTGVd;PeJm$OvJC&?+q*YDtsHM~%Iu&5;n1q3i{j@4w;IdvQLCl-}3Yi!bk+O0Ajw
z#*SGLCK+ZpwfSe!yD3qW2sCb2gac3hi5UMG$rVr<!QR$mHLCThe#d4pP|d|%S!&(j
zZPYTxAkbZ$IiOvPW&ZXIHxZ23vBPaC2aHxgAGUbR`3}wvb^%xh=co1-Y7OasCvpN;
zL+Zk(3XK8hvoFd48c{nxGP%TqKY$Q0OEppBrD<MhC7WnzE@u_+tBZ4V&ZgtNfq26S
zgD2xo0-o_Rpq23O{GGdyToNgA#%U$ImUg|`H==%t@bDN}nm6r0#B`uF-nSq;sBM&H
z8n-kZ;i<xWBuD!ei%b9q06K-+1aw5xC4Xlwx?#QxV9sF!_oK^RCtNf+Qbs9D;e%eQ
zWxOEtbMC+u9W7sd7St?ryRlGY30X_0^>KIcu{eO!1c-!t<`Xr)WwAN3&;o=nf)mMM
zZwA{oBVd4T!4~ykxsfw$C~YSWtX#{QF<rH@Mk<V)+0|E;ks-tLX>`&pybe;0Us;Jw
ztzr1eH+Ao{Zpixj)*@0K8Ha=;OU)<M6HgY3vu=ohcmr1P5<jDHO6x83+tqQl+C-cH
zB+ed>J9*NLhYaD(VB1#2v6JT~Fm7)`*w@U@Sh0csZ|E^*<ehy`fpKC{cp2IOl0CQL
z;M6+Ws2XMo&mU1K*iH8aKD^~73L>#cvARd)RD`WsiMTBWHt~;()n{s5>aHcfuB)6}
zI!6~~wvyhtGFIJMB6f{~ZTlh_#W;;{+V&Ve$VA%Mit5*0L)z-2&EUs=w>x5<4d-GT
zBGz=aWTG*syFc+%;jYqtxM+-{e@ds#;Wv)uP^Z&rL~aTj_?7(uFp;cg1ox;N3|yLG
z`2#h9-z*&SSr0SKF{|tsp~+@IN-H3$X?aR3VD+E$7@s4dY=;Nf3s0kXR*~W8Vznq;
znLHp*;r%zmcg_$0a=ts1q{m43eTa8j5!g~xel5&fCfKkjM?wD9Zgx_}qBmX`9%LyF
zhE<_kY+Jn$;qO%aXND?~oxc+{)j-+Me-Ud`8&t8XZcbmdOsx=u)$>js1!nNec5HbA
zUhqpqkQHQ{G%ereRYpjo*UAi)KpiPKH+lq&y{ik40rNwY;N=lkCZJ+DVoRF05?>(s
zE@ONB6#E8K*S@S7Mi@n;ukEG&#<WIpyJo}2ZXgxLHF6OxtPm*FN9ryw&buF3_OnYs
zdYOB=9DM<IFW<HM*o?Q-iRlTCLNOCq4f;LJn9}abXeXVCb3eqIL%ARDTU0sy=JqH`
zFXxPM|EYTzh-I!7quI9AvzTbD@c@JxZ-VMj2Jy*AQSJ@)Z#3e+0pf^70B%D5=IyY;
zdS}WTSB4h#(vmjJxh^!Yx>6M~GbET<JOk^!dx$OYsfTefTbW~~lgtIjulfjZM#xSF
z>6g^UC91KG2`Dp!@fiQ~KZwf&=YH#G{vm->GchbGRnypr#kcJxP2F9OlBc9@T%M73
zBGsB{VF~kQrr06Lec=Fp&qJCW>OIZc(M1D5h~DPYo8VGgOASnWcm)=0bLB25HiOXB
zZ?@$4HOiR;CbU^&drKpzI)dyp6n9_b{De&jAc~CWT!G@9l6L)1HL``dAsJ(t!8ejM
zAVEQ?>9kSUS4w?4jn1E6#O?Ed<twu}XE@A@BoGclW1p_ddBJux_^b)O_{li02ONZq
zZ6cSs_jb-+ewDcW!R|)`Gz(ObXp>}FURx|Kd}g{*zve@s?rqw0jd&Hmd?GrrzNV*N
z`qV=(awQh;&zrNR$lb$R)*y&9G#j)Ekl<EnXdjm*P?Zh~mq?q!hiUaI%kUE0vR_gh
zd)F&VP=?=k?o)nfD{-D`)ZW`@lABQ^-bXta%zSIZNsdBb$6C?^OtsWY1W=_Toz|7e
z$R3gR4X?KzK*CwBIKivp8I=II{W4H(QEBaDS<6Vk*IvLCzwyAVR{#Oa0pTrMBVw1Z
zWa>E-{PT!t;AXH;?BWo*deO|Qh_<6m<+@nbU?N)W>M%I0Obi_WSHpBty$QDAhArsw
zUlBop)g%Ejq`!{QBPH`1X@Df&w*j&@tlMBNv!9d);Pnk4Xvetu@a#PNN9v3^bVVGe
zDe6COwSmDh8?|eF?`m#t=6cd=sWs2l%7~-eD_X=Ljh~TmUQ-dUwfIEHMw~7Bmm<f^
zOc0iuqOoGCWE~*FyHEnIFU#GTDA>?CEc{#>JAI|$EM&A3xv|8JPtB+3;zN=1ONujx
z@MifG*TpS|5E1l-jx_O#T_Q`FaQkFcCopY#jPl2jOJFr*Y?nGVG<oq@j)ka}Moo3Q
zeqWRASOL^QfgU(PK@KVo|9C59zie5$8(;aa#T^Xx?v?z~*g6g>Osw{CO3rk$p87>{
z)L$|WF`)43Hxq=e@P&$xNiJY2(C^T1prh-fC3p-%)V)`kHNVfVlWqdUg6awpA5RxQ
zr8T+<HCrVdGCyzdDs)vz-;~(&K{aq@GssXp)bFQP5BB~T*2ZerXWn6<#KeY;a5S<U
zqwBKO6$w>&#V~?Ik!BI&g$CA>A-Tx#dZ>G~r)CM;gE+_=<3djZX-HN3#vUQrvc&2@
zlVp)1Ofeb)nI-Y#JrVPIU@eqxVW?L!=4tAgZ8icibp@t*B}D)gf5Pi;<Y0>14}D3&
zca!A%*ntd(0HEytzt|HD4B5wdv3A7~!7cIsR^@Rk%q$+u8@c$+I&|%$N)J2xzIYxm
zX{NQOm`f=pJt*QJi^RqAH7>0GIfRSenl5YRx9=x}nqt-cfefC21evHIGff1FmXzdY
zKJ@LDP>Y)Zr)7oAG=*!xc6LbRcr)t7jnDY!fYkV)xu%$Rw9$V-zPB|l%I|3AkTFuR
z)W^k~<}vpyGmSU2+Yn>Q3hCGHp#&&fWDOXW)RazS0Y?B8_+rGhen-J=PNYQCkH$(T
zlDOz^K`}HpoklXrs`bp@iTs^frINc<mn~YSyFc^y02^+X-^4uMP5+oqgZ&;snO^xj
zog}D?@@yTcTD}MTZ&Oq7JbkoHl03d;WUb~vCPF+tLB$uk@Pp+2FVY?(%ooS%(P%SB
zVDepRG|3a6K)Qi1KxE?=k)Z*Lb|Ik<vRmhWaD|JQQTzqtNl75}&y}|zLJa_|^P;@?
z7rwn~b;Wur4ljzND-5Rw(ZyOlP(*W$H|@`CC)tSzj#Jo=hpZ52T3{%ih)O6ewel>K
zL(1hQu+?MY^jC?A?CMJ0H=$cuShnDC-%tDt=zeK5UCz0$kx%<56}oEc4BbvkzodlX
z&mG8ZuVIHW4&>zkT<C-3Or(Ynj|15R6KJW`2&LoxfoI$Sq~#MOCFASdGInR<(#~_d
zb{}n8->aFe#<=}}o_e`da8Wns)`HB&O|tKLj&-UK5VMB!nPx%azY8xhkf%x<DQ1~n
z`)G}*Vx;BwW*1VWury528L0RW%z}xj&ZN+P!!%^Ti;US4>J!&Wvx>;-zpKe{c}fa*
z*3=hH8e65!PiF4Tm|X>?5y>3A>Ul09tq7}p7j%$(O}~aH{NqrhnjTh64QG9DzKm2w
zQjGI@cv`&Jwk-EjB2o;y7gPKgNNS4Y%mXAixS<ayL)uE0UK~JzJyz)%)0Cg2ULZQ1
z9xu!>v8niVi^Fah*m!subf`+RTopd^8;*KBsqvPE-J7#^oV%y(V}k=F9w784))@Z1
ziP^iNR+4+QfwWEn9Ut3+iwLh(4PljvH1O_1V|xxznn|~SS+0_ciLCg<o};M%2t+p!
zgT>2@1>OB*;X<JpY8A;lv$$K++^|IKUbX_TA$KGEOC|KRT4;yh@v+WpTm5%zdpBgQ
z+?az4`3S<!XiwEv^wbP-urZ68Kn+-F>p@J07jU0CuV^y1ly2zWcS#3?PUYUHuEkW|
z=~ivn&T)bsYMc=Oe)!SmVauvCRSjm1(Yuwg)_oj4w}nZdpGwA{+yPJtvHu|<W`w?<
zXp|B5em3J07xBj6G`?YP#(Zo6m<n0bIPTJSPX~f&NTBD$iYS&WnGt-0)IUgg2QyS4
z^6+u>sk}HZPP@_sY+sRRc=HM?DA{c82*{#&_%Bhhv(HuMpi>y0b`j#rmUb-%$5&yJ
zl>4~L>Jc<z3Yk~~9DNw`{O@7ScH2gsuK2Asw>uPcAS{5kE=7O13#a$r*xN`z&}-b0
zF$j?b#hD$OP{XfgrUJz^0z<oQ_`dMviINdZjXVH=sC}|+jJ-7Ob##IFmbNvg!q&Os
z?~O}P+7?MEKT#rcp)=Gx85X!$y<4=80P^HK#GyXgGeCdu!(ea7yOo4~K;O^dXIwG%
zU~aYv3%A}4T}JBySsxHOPXd^V(X8jm17vWludM89Ru8st)qi)<0xX;cSc1~;`54yJ
zJ9hw{W3t-}@K-X%Sy2i};z&VcB~l!rcu1N3U>^?onEiI+7qi{l*0=N#R8s<waDnva
z86o|@S1uBZ70HvEtt7eKP{hG~((j*H^S8S)!6>zF>a3M<6tm#0$$ykbZsX`iiz6eG
z-g_6=DPM$%@{0(n!Xo<Z2$9S7%th81T-Kp#?Up6eqg-rO_#EaZFb}gXaPVvY&$l={
zRllrx8!ZmCtUQ+yu9VJI`x4HkmD2igZ{sZgm6?+w{8dL-e~NH)*xf_KOVt<08grF#
zYvvecgAKu^5&H5udr)i+H=i3>F<5A!E7v5mpnA7JP<Q-aIb_N48FCD-?~(@xe9EoO
z3zZ8$?9bkXgfvi#2GzP}K8{+c);PttB?jbiyUzt6T*R?Og1bHZjxB1t_r()7YF5ZK
z(~#~iClYEs`ycf0=;74hLMBRX75XjWrYMi;Ym4~B5#nTiOKr^qX=AE)+OhH4%<yXH
zDGpOI9hnINO4DKQX#MXpIfREatQN3iG3&h~`8cmfk4Nq@W=T?P_g19Y@Jq^0jMbq+
zlFs3oMwmw0Fi8j5BU@2q0@bF}Mb0)(;np~D&>#W{48ZY-CKDTMdqEb*Koq%16YxTN
z1MHJCW9X7|t3dGvd>pu=+q;58U_cb;m)Te_tSKattp1~}9#dus^j@{6Z%N5eu42fn
z*A$2USbe+J-L^k`xN&bM_L^iSsN_$P13KP?s{APwz8pVNzg9<D-qVPq`L-aE6NW}H
zrSMVKMQ=vOV-xA=R$`}@jdh<IVVKb5cTb<GUM2>#^SWb^R;E}Z25Egh8L4CJEL(<p
ztql7I_U&Y~jZ!-9Nsrf&!<Rr8(9fe*Bil4Sh??18SP1tAf14;CNe-d2P;n_!OU|RM
zTRZ6E+aJJbJ`IvP)EV+UlZgGLXji%rg|4YF#hPYYW)mcrH7eVW^XptFV_T$M^q$5z
z-#|#LluQUR#|HMcJ!YG&Kr&}1Y_b;dWF?Ew^c5))82k-XAF|FxXPfP<>$rwsaN&}r
zMoIgaZmuz`1Zo$~Mw~d!eTM}OsUgfvq|L~vgc72Sr}aZ47De1!eO#!E3W^p<D##dp
z!H(8;C|0Yjsim7l20A@KV4qztaYhdp`ith2{DNIUuuJ?$4fy~1zTrQsZ~qzHOHa!H
z?)YYZ<gCJ15w6Svu%VcpoE=2WoME$Dp4%V|B2z)=mp8t4B64j41i9-Ap;syG>?WJf
zpMuL3N83zv1k`6|7y_JoHzG)%*l}EXVr(oAF@J&88L}ReVRc(^ntZ_)Pnkd^e&iz1
ztpRIbXxkkAoV*h=T3f$>8k@KUE1^n~XXaIc*S`b0ce`OeciUbkD;NWO!D+4`1_M6M
zF)IKS<>0t=4QPbgsC-i=?9Jd+zAB>VNh*`z2eT-YwZJ&+;QVS~YlZMu5k)WS09KsI
zo0M{AY0LlV7Vv+jDF4UX;VkPgUHp|!d-j-h0*!;?Le?>$S;~(G8;=(Tn&uXUm#Bsx
z^S8FP(cG;j0e-h3FuX{$0VpWHpDR~mm6Qs@A6$d6M+0qj8<9lhj-5ElyVTYUqpG#r
zk9ZvYbS>y6#aX@wYpGG#ex8_VY*;Lp(X{cCMA_P{Tg#ljorUGCnQwIwx-uL~)zH5_
zhBAR0ja5P00=_zA6wgp9*;8z|_PrA^=h9L%(kelF2so4$i<x5Tpn;Srd<}^YpAe!{
z$2D%IQDlF8HWcVs0)0T%w(NlJ;w;%qp28%y)n()T5sUg>UpA8QCU~QKl<aqZWE;Qw
z>>@94G7<QsBt!)5c&QkH(T`TJ-ycHe`!W~WLrp0DvWh=p1{6blE9VR}0D>ir165`A
zfDB+j4a#r3n{)#2t6$?1Y+3!Gec({s{t&DsP+zV0B1le*DSiuM-CR~R`o(>xNz=&N
zc2Y$xb0LIYGq>h!0y<SD&AMzSdKl;d56e@0k;>jW04iCZTLYJb%V^27ul{YW3>Qx?
z@<w;~*O~~FYAV8Z8e@ie*?x?5LSikbRS!1Ht;|G_coF`wu54Up4q_d`c$!YT#;e?R
z3_pfIoeEQ9k;6gDU3(8*h(wubsg$5JpM*4-jD3JET?=AYri%}-TDTS%uQ&B_9)6Vy
ztPvdljPI}i+m670hEMt*SLFY#<^*)hIr7Tt(f%!sXMFO2>KyBHbh+X(+)~4JLd8%t
zm>sIjRk>_Ue~1EUF6;P4r4%x_N5qbuqMEz0uE6!gZzz{QO<DbA7BY(XF#TV?DZW2n
zTj{C@LIRnGc#hv75%4K)v;RN^{Jvbo>e{!oCXx<9$0TT^ZKx7~MG-Bs?=7Z<2C;;I
zbJ-U>IM9MHGnGd9Y>O5ojw8B}IrlIsGVH^K5epw1Ocjk$MZ^JO%UCDkKt7oIvlX9-
z7@VDDC+L!YTFZcnbFQpy&jU_^T$JFt>aSdbo?M@|@0%(x?QyGsQcr5xVuF`M*;qDK
z9l&*C?`in-*&cVGxcLDOH#P%6s@y`Bm{tQVZ*CKu8^RDmX7&`=u-0dHfn|xW(rH*T
ztc@pl`ji?%$fTg_R4D1%20IIg&jMVD*kvQ^#OYaRukCI(DCHZ#ld?Tc&iOmEYu%LR
zn6GNyhQ;9@?1_i#KCaJGc+=eV!zhd7h3!73W3T6_G62C<BUZ+DR?TQ%K-(QzXbTZS
zY25>pDJCNXI~b!s#emdNe%vut*g<-S=*(iNxwHLU+lxaLjwVy6&1NATxV|^m4+5X5
z+#wtwl=(IeuH%KkjffR+$Qpr03*B)w82dWzhMMctqCf5h_o<blp(ay3+s|h~cm>9^
z5~g~MxJ6{8tOQh}--jECr8`aN;cc{q5xFF%DJhalFAA!4or+PZmDd6{*nQTTLWEOa
zJ8yJg89%eo>DbODj3+>^?pMXqN@-$uQD$|y166+yb&8X$QF#RX9k$qkXJFQwnVSz#
z+v{em#oN?)Ld4JS3?2Z9QWP*9o%IMhqeVJ}l;O19N{4q$`9hMJAtfV24;vnHD~Obp
zE`g(XXSK`<4yw=A@&d<Dx{F4jbB>-s9a@`uIof|?jh2e=CJ&((3uUmh>gr$4#3oZ4
zbnc~D<Juv=9Uk{@-J^@en$@|NIs^6LZ_$2j1D=Bx(u3=%z#)nz(5$hv_ZHe6`hFuw
zAKrv^Ya1xhDNc5$9av;Cox6*?stii!MDYbwc_%-8`26>paCbQT@$=@L(m6o{-88lb
z`&yyAe&kzIWwbv+d~+dn;4``S7`RW!y6VFLIm=FX)7LKG-X2q{T95Jat<g?Xp-1BH
zG{<(B45$I5r^lvAZl{re^ry<tWFfpghDVfYJhI_BYqjs(X&n@QeKNTxv@5MawpXD`
z_0kWj#H}e=yhJ|529YTK$-2Olsae7_qkM}>b64O;di6=Z5o_*tyWl-JUTnk3NV8ds
zgTcLRbMB70>?{^P1ywUIdKf_n3cnm>t?vWXDqkg3LWV!c{i6cLd44XVsqQsw)e&Re
zM?mycG2LlU{Gr+L?u^X`ba+dUe=?KTLckQNKmHEg9cE=AD1*s2^ca+GY+X8l-#Hpd
z3DAdEs=|4uyevdjW^D(W2c+y%-DxBIC@&4B>A)v#{NKcT)-~?5_qW#hMj4x7bhv-W
z36!fHPdK_A^feo=qqcl(e<o6(>njDkcaEQBgAo|jiuQ(_!9A7Y$fnTO{-dR;4o(S3
zD{<s&_T?zMxR`3+aW`rMHmnIa{o}2#Sqi*JB=NQfBEft0<&Orl#CbKXR>}lvY|Wh=
zcq1$XTq}fzyQ6|VP-2*Y=WN$-v3|IRY;0DIwxa@%@6lIOVu#{FkKVFBGcy;Ep=46O
zRFF%fYcc~`YYg`E3)(a^WnN}LDc4omo`AKM{~vvhPkbS@<E;<}kex^Y6RgQtz{ByN
zLf`?sOU4XiiVj{HfpivAQo-#gS6v{Dh_8k=n5MQ^f{{0D%|s9)c;+}UJGSdSo>z?7
zL=GiSS9n*{N8Gu(C+hD%j=gwu*5O@+u<x~}_XH&`)!lqfNIQJEw%7OOp{dJ<e?Ap?
z$Jxen&hDV_y_?!k_g&&Xd-_Yng;bf;P_i04i&WGHYd|B=?(<9J*$`R?a$FPET~rD-
z31Xs2r*;l32R5r(bC2>BbCz;-b0wTEc0K|U{bfj8{Be|#eN4YKo{&c;4G9zsJK^-#
z+orurHVReaaBrg>sM9zboaKb&gg@?1L<xbE6=TV_a7Wn*9`94Uqu?X%bHL}w@^QOd
zE=WB}2|1se3RRc8{r!}3w2u#1%r{hE@=(G2qN&O3Jr&3WZUMPo5X8u;9PCJFC55@D
znj#aB4*UBD8%oZE>2|EG9)-wpr@s0{zTl$jG%7Y`))ubZW#G_9$$7ko+=M&y(|hU=
z?rHzp37u)1TPut;0!~hX<OlZdz7l53m3ScOiV%OnX6Bb+<DfEB-q`v1E3S(4k!K79
zLDAG1EK)u)Ba}j1EKvcT75a7H*4H`>G%A7NW2uo6AUdes*_Sf`+jHick&_6v=yi_U
z-Sj==%Rani4!3NUfKwDPJ$J-}BwhJEbeORt;+%9rxg(a6VP8bm8zu`AYOEmHJ)BKE
zjoivdhYk#>7X@W0xkK&%`_uvU>ESNJ+NT6xDifnV$27np9*Q2{m#QSjGpff#FQ~oi
zR(`u#cNCTm@%;QxG4%dD0c}QOVthhYizS1nBquGKr+3tny{1YcqUN+xK!OjhEvoV=
z9}C`FrZ%ZjTRW!6y|Kq2AKs9ic`ua?_0OMNOKRiM&EWQy)`$S>ZZ2Q^N_gj|bcA>i
zJ>X?DzslVjT4`Eab&L+2yD9FDQFMT$p{LwrzJKbxY@bzs!S}i^_9X3c$;h)4wQUQ3
zR*D<s*(NQVw|hFQyX<uX?&s7WUUzge#CT(?nrdKjqVc-KyfFAvF?rsT)G>RGB?(^B
zZ(54ug<!_<NboJk+@bQoOlzTJr#@84kT=@X0Ya-jfwahYf-KPX9KG}|i|@03^$B^e
z#Nqn^T3L#rFG~k)+nG-1J~r^qK}YYt+2*^qr8F=EG9mMS_oU+Z<}>gxjHk_3<4@mf
z&-x*I<NIqgbe)OYJd+n<E{H@@Z&7pc<$(lCA^ifysn6ynC)Q4a%x-oPst4lcuAnaE
zk-C0_*X_wly}%T;ccNMNIaf87U)`E^*~))Le+Ml|Od+S2m0FAr&T7B(oCT&xo4^;V
z{%N^m9JvZkOkox}EY?)ZWf*w2#+4&3&nVcM>U1)RmZ1E;#@2X*QXGBKJrcvG6`nQX
z2PQ3xOwij`mvDKvUfFOq%AuLh(W)OhH28zBR&3i$l=97Ieulwp{}>Jw6MFxI&$bHz
zdpF&={}uBy&c7|&w;|!b*@j_Uik8)~iuB>`uMLOHZz%gy^d^R*+YM&4%^tKaUShNk
z3p98HO~Be$U*Z_D8y^fn9Ey^ishn*bgybaxGO|rj>M``;DprZ3R&hw@b?r@I&7Z*&
zYVFJ=UR(2PW0f_ip5%9g?x{zTU3F`JDlHMO8;745dUs<{v0g60U6ldYQ$Ao%1>nr`
zj1Nn7mWQ@gi{Kh%El(;|(olI`?h&aMF?{69Bk}*#oG9%Hzh+vN;?EU}6H&F!0@sgT
z^aeKyDx*JWx>X5r&nFSeJcIb~jl4Z0O~i9Hw8lMvq2c6%6&l3;kY(my-TL^EP`hxq
z!LuYT-zanCK`|meBrcU_LjHYf&JLeXC=bPpGplR)Q+IKCOj>%u;-%-L5dp%w-vo7^
z^D8E#6Xx1X!5KvgB>w4tY%(4LQv5FQx_V!+o>!CsFTb>2lFCz-pg$|`?zt@Y#m;@|
z@}r)o&IB>)!C2K1Pd^)V89!PzU@Ntr#RgbK2ZcM?zLcE8CBr0m%<LtcS_F55D4#={
ztRy_>Toxz9-+1=2&&|)Ur3^&}Kq4X?OR+Y!2PVJ&be#C>grn2qL81_c`(J^#7$<N}
zivdOwr`mok)+8*qe3o?b1d}u5V7tDouk^IW6Rpby9zCAD{(+>s4f>^Vx|MT-rVC1A
zHRXUH;_(+CpYaWOQ0!G<@3Q^K2-Gde_*V~bHvV(k(=NhBnj&N|*3*JRPD=mL!}Je(
z$VlBmN7-d(Tz53XBhn{j=L|0KonNY8_>+4|vWMl`#^ZKi0Vjgj(jeV{B(woZ0M?b{
zG`jVkcbR+tEAdkW-lLt$^DedRkV~$L|E>t6I!g}C!}t(>JiK?5s}AIr=kmk<B;=CQ
z-mbolwBz~<v7W)2_OK<cnv?guOpi7;yew@~oj4=WA;&4CJ#AQs2|{#)zjyus>eISn
zm}4<E*ia~wn+imdBR%K871I)3>c7Me<GHEi#eY)-i^NpzX2%GW{t`T4Cp`!02Vyvr
z7ma)wHkS`wk~btcoV-#kuIQ=OY~FKtU^eGC{rfA2yy@f!kz23EI2zBQnYECB+tqbP
zl@-v;M~`|k@i3T}Ud1A7Uctr;6Rqbdaaq%qp;g=R%_yjiYi$XnH^5EaHs0j*rOuTK
zu1$6O2Z#4PO%vi2ad#s-_pV;(IjSjpNghPVo6jH$WUdD{zc|eSKATma0-<OB3Q@Yr
zWcK9)&uEW&HywB26;{rY_}V8Bliy;NH!c0gpIV)CT5)CoA;kK{S*c(JWRs{Yw(jbd
z^Ucm&via}&Z}B{}D+0x>BTd!fY<va9x-6ZL9k*~$^UO?1OHW<wb@T-IZNX)lQ0MqM
zyM`ROa#&_++k7fGv(Mo#hmf?8kwItuW4rnu`FcGuvX=U!A`VBnH_WXz;FUc8qb~p7
z{j}6`q-+FP8xr<}=DcL9&-~h3sV;d{$ieBGx@Tgkn}e3ei2ZsZNTYjkvo%yBz3(>N
z_Tg%Y#xL-=>m8S#O6p6m+S>R(l?3QeGsyIAU=l2NA=Zep>#G!o5`|wkJZ=|Qhg1>z
z(P>vQ4|#qLPxcf+R=({r7j;Ui8`)c@3f&1UV#dwvZ@$onQS^4+kbX61`SsI`Z)kwN
z2;F{=ku@BQRnG{}SaMib9*x8C+}nzCBWEL4AJPPUu&MXDVHlg;XpuDYi60039<@o0
z&`Ay-9(lSPa}@q0c=8F&;IwG%g2bU3E3@pDf2=~()A^FA^Nq}M)vtBF)e;QnxSY$k
z2;~xyLEN!i4Tt3%7GPTzl8s(EoJ?J&``SA`LiS$IPw@l?Rbz~J_cM20aPrw?!9e`P
z@JbM88z<m^6Oob9T&cZSkY@E<H=^efu049?evnkW_!z<IKy>GVICt2qoy9<v7D4#Z
z{NnsSf~iizd*)S5QlMNOTqDfHcw>Dpp(L-HC&uv8k-_rtFH^iizY5TAONLOa$huK|
z1ZmQS|BELl6GDLAhP62sGh^bJAVH@$IYRnv|5}}e?lRXYgB2Jyh;A&SVX_e1A$|j{
zzajY=fID-v*>lTvry1}Bm3J0FV)jX|>HZzCC(HAzezAlSboXUJ-*qor+7%5T8mNu{
z=*r5f+|t>4S|9of@04SoOBguplpC(yj?TBpY^zNp<PiX$jL$&{CaDDdae-;jEO3RC
zUu^wp7$>o>W%Q59CR<b~+;+9@^tmjByK58I?bIgD;f~<WMBh;;<!aJPD_=wZ^}F@i
zH?GrlCe)(k?Q*ylbT%s*4o%+wgp43_gZDIP3kP`-uK-{|ph&Ihn(+DQIh;RVsx4Xj
zHpke$?Ze8s%QwDx%2MtzG&#ePFE%ZmdEfTUlh4`7_Q1CA=6-uRapZ51LKolyk|cHi
z>)-#EIPiX^m~((rD`Q0wuYug>TgJb6{V^rs8A@^F)fM;*C9RBO`+)tJp<AttysO%T
zX7fBfEroto*T}Pck^gp*6Y;})zCs9Y5qJOTtoW7XZ3ynAL<dUGOQv;hA#KFPXzRxi
zJv(NkSmO99$s@0e=lkHs_x*%yAp%z$mpErQ2asIxlyY}-6Zfh|`nCP7;C+%B<x5vb
zk^SKur;;vUQC$gDE-!uJEqNRNqQQ@m%dwn9?I$HYcH!3nE|ZEoBc>w>k>_&Y-7jXv
z72a*5-+wik3JBEmtSSogU058x8j5_k(Tyr2-dn-n$~>HO6B7tRmW^4|O0*=pTGSFE
zu%|toAw5@?c&9c(#Kvb+A~N~rVSfVvs4x8erM1FeOcPAo(qa7C0w3w+`}!FW@Tx_{
z#(cZ_lE_*7@DW|NFTFU~S8Dl-x0O4o`0DlB-~ZI%Y>?^{cde9=ofoZK+8b_qgHg+!
z`*(^~yvlyvlX1L;y7!YcbGKodWhRlUIyXCK|L|})HhJ&^?9RITePG>N_Iid`p(Cxg
z6Jo8Exq~mCdD`%3xjxL%kLim83wDdWLfa7&_~LM}Nz))stXnzA(~@IH-C}1?S$TdS
z@%8o?w{l~?BFhf-Pd7!ACtS83^W=n@+~4Sot_+Vm*n67twd3$18+XC{weRaS>d`y)
zLgBBrQBSld?miO*;Tv5?1@g;#JcIYo&9A>tp?}Yhec8m%dr;k!61t$EYcq~~BgeAw
zV{urHg+10cGkywBzwKSiF<9B-P1GAl-j?Rsy1ve2L<(>W9+gr<?^Qk&{Lny4+I7ut
zg3Ko<Q^FI@)yOmCbX!zl&(7XDt~%a2&Q0O6AOrO`eV>3zI$rnYHRExy<JGVSOBpzE
z?djZa<&eK*$eqsk@)xf?vL$Qm0>Tct`$xX`WBX4Z*y^@82mHW-x_zdV6ccO+|L*5=
z_7eo>EKjoK5EBTySOh<0Up$OD`XHc<i_tj}Xt;-HxasTSxw@Hx!`_?@`tz<9E-G*K
z56mOGv^%=s0%Rm!uwOwh8#VjBoKUH;ynn(UcrUDGv(K^dv+<r#j?YrB2uFgC*`GyQ
zC288|KcPf@!K#)cD5rw7{uHS#e~oiUrZBH88#RA2bKloSNIWXV-3dJ;H5Rl+oPCho
z`fzN;{L|KZR8Qg4Y4mDN-HGM<6YfB<Ks1{1)A4;<ma{fYqs|V4>9L*XyCjTM{TTWH
zV8*e2X@KQL*H?rhcsY9Fml9Ru7T>@B3X29E(-v$D5&U>i>eKN^rZ!WE_>o|BtJHo=
z(Trm@!NvO!18fdX@52h-T3?*JSAW()TwL&V&)>^HCgyldciHsB0m!H0uQDHco|`<d
zQ>d7j`KC+OpOoM|%U?w0#MK^9r$VA7@1@xLZW^9|cxp3!XF>amwW6c%kW~!jHnXmU
z@~=DQUYc*eKR9n;vQT<@yX>x{E%Aj>k1h|X?$pqS$ba%>O}^t0u~bs>Ad=*7*sD8$
zc+!skQC_%NttwOCAw(1u*S-D58Skrh#-6tD3vT$=UC?`He7N5i2NUVwsU8`)$u$_u
ztDe7-HesU;-P>y=pHq2LncFel$5gOCc>?poC;$FSP$Te$sIImBRY7W##hFw4H^`Sx
zcn+pdctUMD&L>ZloIOr@BKv2~1m?;1PcQOD#U(<Pq;8e`WO2p29_HqgokOKmGoAiQ
zg)?&%QuT1byyWb|3D>BS)7}o-Au<LhemTD;K{b%i#!UXWvrh4{;3FQ3mWRa+_)AZ^
zy;L2ne748?W!>{*`pa^HKOPXC(MF9VKU$gS4L~l)lD-voJNR|fFmg68Dr(4t4lRl`
zE7xM^+5oD*IV~Wv8jR;7YJXRzyYf(2#)bH1_eiMu(>V-CrRAtJ@2#FWn|J0qk7|P&
zc!K#Szqj*Tzi#hMOBNh78J&RuiJEMZq8T^*YKF0;6ytsaXRyK<*Yex9H5XOBVT*s>
z%jP5I4}K`V%tt)`;BCUtqaS=kqNl8nNB_+E=Jz(^C;n6->9sYc*~XLpx@`M{i!t9Q
zrkr!p`uaBdpj3HI`n~ZoN|UH^#*3koDL4(4^Yo#t9kG+IVKzG?N1Cp&q3yfEcq!W+
zT?guVByH!&kN{B3@xD|<_l*{(?_9~tq3S?JuL+av2j6QQ(1jVE{bZlz;{HIR6Y#m8
zJbQQ}2L)|Wi+s-WRNJ=22(3NgV)#&~Hf*U~P0IL0GDP%F+%v(qiC_LA%!K~nnzQD?
zn5#bW*`RXS55=_gVZGPA04Dl!LF3LO4qobpZA(l~Ts+6p70W9ZKGGX{nK9>%go49L
zr5VjuW{;?r71*1_l3yQ;%h2CE8)<yD{MkM2#?X44`9SeBa*o-o?4dz2SO)TT$JhGH
z1}E9}ac8+y^xoi6`{rib;lzYtsaLs$f9>=-X$-eV@JwKJ9KD@36R$?i!{jq_%9Ve8
zytgIq-_h!9di+F7V4CvF8olbN=mDTMf3hL;P(L-R@mNQhj&WPh3C(cVp0kV<#;-Gu
z2-}_t`bTA7tJd?)X|;Jtep3b&DM5OLm!;-aQ;t)j3Y!`)RT9Ds)vrLe2cthRFW$lN
za4#O$UA9en#jtFhf&lfdx4x_4Pm+O+_rI~{Bl0vme(&^9Z5lJZw`F#ViIVx4N#*$w
zUAXY}SoKze#E8{t(VPb-lobl#<b)1Qw=Yu=>EKN3)QS$NVCm<NRUaJaI2R0l**C*~
zZ<zAXP|O(eh#FtG4W4;rb4}UG+nDR24@Lm=jrX?TaSJ}deSBlK=}en4qJ53%uc<)5
zffg<e`hOpK-h+mL4Q%p0Iok-_8RCBi`U~Fnq74b?ieRJmR84%;JDR|W)nFSyF&!#O
z9bAvB%vS=%3&nB4o&+(tsez5!p>=uC#|DV8;yfuz^=RXY^9vyp;{wU9$G&ZX$&#)|
zJEK8z9B{q5Pc_n-ee-Jy)G4`D=RM5uDs{K}b(cMN9)6nY#0BNiTci}9+K9;}qJK8=
zSJudxv|bk}=b2of-t&^qo`XI0$#)d|0V0L?mWc&B2QZ6Nrj@vagcjP`dsknca7=if
zGM;@e|H;RnTWCJgN#ogh3+;v07THGWg3^0XtyKp2mbg3A>r8z5!ItFA@|um_oXdbH
z@x6YpouAJZ$w*}q4ORrSm<2!mGs6wT!dHVNmMK~2aQ%&M=UTSItG0X2<tP<_wL1ko
z%WQNrpi;r-bUnB9s!k<-q}Kc-KaK`bAT-U%1A~83Z+2!nryy#&f-&B}GeVuKvV#6{
z#m{)!bbJCfMCpuFtqfDJB<4K|T=Tew*6@~;ytW!9V@U)RESTUJJaoS!KHe4E8)p(h
zFAR1zMVgh<rYyC5;P^d|&!lh->E~lzy0Q1`p1&|Z+2$0j+i*I|1S3^nqVGl3u7&iL
z1bw(oeE8gRA+P+hFQM|PIjWXxoWskmkAgjSK36H+!-N#yo?mN}HNh}(Dw@m)GaOA?
zPxDjx{9sOor(=Cx>c{q($=HwipXM8_q89)D-Y@@AM%S&g1oxqRH8U9-`ob2W((Z`S
zKBgNp4YNSzbv^H0wM%lji)Y9<5C=Z!F-M-1AlKqJmg8LNpMt(w>2zv4-@+<bcB@H{
zV~)Zs1R9krh?)8z8Dj3mNTKM?0;S7M4ZKTa^(x~K2sS6V)l1!AN#H>LYeyfC5!u^<
zPmz4>B}7lhfcf@2>(%9ntsS3aqstxPH-q&JdH5ybWi;w`?)3V{JpX7FtLjiOxA;eH
z2$PW1<GHHgi*(t_;99)SEQi19ikOw^v`D7l&xPwFcJ65>Ic4zK>fX<UrfP{_y?q69
zr5>(Du|}Wos(9D_McK9Ng#|(kvhI7MT{G;gW%xACVMQ4qH5HXwePomRYp1YWMcOs*
ziDK<mUH*sMsq=!h1P-nI9AU&rRolZXI}h65N$6u^io7>F9lL2I*R$-v1Rp}u6};Z^
z-$^6uhrWYOp}1^wFE9-eqNw}VUQ!vw!&p2lROmu&XqENG(VxDboU0d&mQ_}UOC5Ux
zzWPhdO;1WE+0HZ`^Rcc7<D3$ZJxnif*^1szidw?tME&SoTZ@SjK4;qlKPSoAIf^)W
zH17a$k4NqWuz-4EnYg#MvP00z08Eg_TewYP`{f@`@qt|z@J*WvAk~k<OIz9z0jE~M
zQngZ_otMT(|6`>A;&a0R-V&H9YTjaiv()FQ)y2DS!iXNzq|&iDajnaplf8b!y3sN1
zboKg}b>2+gKDbIyN>tvoOxv7sf>9C{xi1yXed?ITPU*9er_%RS<K3jH=YPzfu)34+
zb1Z#DFv0zHY`yZ6ok<~dVK;D6Uw1zIfci=0)E9r)+}M11MN=d&^cfP-!&>vw^Azy`
zOVH}r9oo*$9>wf_-!$hLA$z{f<cC}2&LH;(zV{jE<0Gl>iyR-BCH2>Q1RYlT4!5Y3
z^4F=_uF9&AiPS=tgAn8X{$k?^J{#iEpT1Q))CfwydKRB7Y>3!=lvQ!({gfYfSfyQV
z$EMU=Iu!F%lqt0pD&(W9y=cuHri5nlAXF-=+)=Y{f`#L;8E<mrld*BQHR-|vIY%HN
zy6`e*5z4nU)hL#^7Hk7sg6bWwei^XvD|of8)Iv&<c4k4`u<I`@_vfRH^rX@?p|<&v
zn?GdkJxh=Q={D~{NfyD(zH1G4G`I#;lvq^81Pm;3x96C^A}?<BE1pPnSIy#INLm>>
z>$7)m7HfFlV}^1;NeWJtV#+&aEz=KAUcg>}>!jV?KcP5*D2FPIKqoZXS0kfWu%W3c
z*tpdxnv+Go)^Ih*-mm>#GC`KZ@T`6ZHDHl)>JRYa6;oQzNzhbF@2bTgB2j@)mov1&
zzAAQ}uN04WkZ@HO!~K#^7w65ge$%Z=8>OUOn0n;06<I<#3*OGi)!s<&!#Kc?v`NCr
zT;YmNSSeq-D)l9S7fDxA+4^3icl$P_RbYGh*N^kA=@;{Tduk(Z+)myj!>3z-;y`)U
zxFbHM;CdqSbYtDk&&J1v_^vW@n5y<xskbOhmAsz4jnV6R1NdmrQd^Rg<Z(W}`kthm
zdk2;;KQlaNY?^gyE7oO84*?NfUj4N*6`2{)7s24uLaa-rTi#wGb8nxsx7ra|{uZg)
zunBD~<<vYrhUM5)KJ#`+Eh;3^C+4RnZazXw?0!!*j(IyJb)VM0oD_9wF;JNQ^+!AS
zXx|T7H~bxzQ-%dv%MXUBSP=Kty|`H8OBQb0qrOL(ukM{)He|hi{*p!OVBR#tYF@i3
zE)t*<bSEYE8g%c{{^l2A<9iY=+#TndGoQ?Tvn5UtiYSoo987}OpaB%{ey`1~&7`t}
z>UNKywt{&7{wIF(NtZ30_mn&%sbH@*gz8KudKY#X>P)XS1&bel-G$!vqZcOTdDXtm
zxrWgb9z?m%-@kP#v_<M|ktz!~3yXq%GljT?UShLT`I1j2BnkC#^mG@7+W65Iu5OL(
zbAEhmZv3*3qU#U;eyC7LNu{;=x0%O+{jX=WqvbEfmBPb9TVBvc!ZhMOEuEhT6)csA
zmc&0kN17Xp(N{HZgR&U$oWQs|A{-cMRTXB*16%sbPp|Mi8v<3E7>}o`I1<7QN8=u}
zy{^X5^XR++Me~!rQ!PX$?`Lwk$n7a<a^U@2FoEZ!P>0BkbGk=*CSFUrZj5nmYtBh3
zw^$o)CT%lrLEo#6>fh$tb~mUu;}S1=gFzkc-X~slx&Dv=&0qdZZ+J)~dR$Lf(LN-J
z|F};Fj$!2PG2a)xXDD@T|E;C7@%Cy99WioEIpvV(&XRF8dyRR%8|sZ251gSpkCr9u
zjDVMWj0v>*o?Cv#HJ$KgsL94Gw?Qmfd+bK=^V3<68~Y!CSFLZPouVj7bd2f?6D5f=
z?y;{z4OWA=m!pyx=915I8?6TBw_<x&Rn8Go-`6_>KIi?`vUl{`i$uN6RZQdybsE>?
zfrXrCpxrYzz!`hA=}#v2q#xx_^wR_5y28{|hZ8-r?h?y?T)#hesyT5_2w$DmzNsgg
zPcPao<8_%!Qdu9ipv}?u$$#B7JNDz@<`}Nl&5LZ>Je;g{)Cs{<o{awz%7<!sjvL(P
z@JcN06`f<55KlvIK>5+)!GdKn_WROMhmYP9;nPyL*}tuH{S?gSriKXL6x!>KFqrBw
zp~W|kj(JfB1uZ|Eu*Zd5NmV}OtR>ubBHGu}@Z2XoZ#D4M%_DZw6L<cwBCFC_Hjhxc
zMLpkKcHU;gq1pUn8#hxgSRClmrDR=I4QmnW_4jf#!pNv;YH!+qaFv#V!*2_Z++rbo
z$-f3|*TExlx`z#-(dj-O%Bblk<8xx9hjEc@fltNMiYLQ4)z7;zEo7@etgI?2zklw%
z?qlBSQ6b&2RrT*vEpoC{$NLRQrJZHIuM#EHwg}OcNj2U$Wc)eS{r9g4oY8l&9`FN8
zLVAwQSu{%5j;87sOCHqfQzK?8zyEr!-2v=g2G$_F+*o`9L<rWJbd8)MUyNcNT`BT$
z$rMfMx9B;0N@9T1s%A9RUXfQ*=hv)P^rfgXr)F!;#c#nh=E}W97qi}~brASq4JUhu
zuh}#t)HAQN9~m!|lDr}5`i;x}D7G}`bAw7tqPs=yY*k;E9^r`8q}ARH?|KlM5k*rm
z=Yj{h6J08elCDI)<0C3m$%kaHN!@q+v4`x5Yx8}epxftdHt`uJ9SOX>uwG=JU<&MH
vFz1;g7udXE-^?cVUtj;%a-c|)De&q$$5`R_s_(2rJ-XTkH%qVEJ^FtD=vdG?

diff --git a/reference/plot-5.png b/reference/plot-5.png
index c8ddff2bd446c87ed5e4e306ba56963b22d13643..fbf19b0d5654c55fe9e56470b0abcf7fd66be4a0 100644
GIT binary patch
literal 38019
zcmeEucT`hd_hzh!4Mnhk6p<!XgNl@Z4I%X22}+eNH9%-qM1)8}G15dLNS7Kqia>-=
zqzj=)H-ykhq|b@(`~B9;teG|c&&*wmB;4F`&OXoH`+4>`Hxatps>cp<9fm+4$KYyr
z^&yZ0{t(DMn?nb|9&&uzB6vG!qoH~iyn}81PE{y)V|}D%<_&@Hgfsv4pr@AXArO8D
z{O&D7|D?I$fY?(;gTy6k<U4n+cOP%r>D}beePqgV^1%L+2O50u9k4U(d2s%Kea~1U
zC&S)S*T?dW=dA<xJkM|r=?-w{?!SBDpzf_bDsM_ZrYS7&5n4Dd7!8QQS!z?gy}IzT
zBb$aFvu~#iRhsPm+oJ#f^8YOWETA^TgHgBQMF>~L;@%ah6^o~AAKT=EKnx!S;Eppb
zq!tr~S7(hSd0$FUvkLk2aB0rt=Pm^D{x+%2=RXz<Yk2ePz(4<lNvJ$I^Y8v54pzj!
zAHVBs7BM|zE(@8lkGaq0?_q{__rQ6C)?$0$`rh!hynce3xilpdQya4yhZpLrBQy-O
zU%+S=4$g{F?!)Dbx4nhhu&eP#p_e0QtO*w>H{c0s^0)Mj;70_t-dxr0x}E?X7CaX3
zQ;oNQiwg$xQ{eC;*q1Z>@L(YxHyBs*9F<R~?afuIE*H#o;m#M<UV^^yS8wX`;U;YI
z@C+fCB*kET6VWJcMU-kS)YWj{<GVm%>s$GXnT2Oxaw4>PZpO9BE$8D0Fn)IYBR=X=
za&1@s8VRDfhK0-TKjrU(E{uxC#5d|K1mf&$Br@P)QHyV#B@3MMCi;vc@c9pX`(jpA
zV$X`e#a>>yXCV$>N$;TsRf}fbOLF^@m7-byUA#$$<JFQodaYVBo`L!;Y($*rNPzMM
z#$JkDe=N}D7pq<=*-|F>p;i@@Y?C{|m9Wfh$Np0L%%#F$p4fq%`m?sLkh?SGu6MHV
ziRmS4Pc$lUSDN0<ddu_lJN_MvH;wrw<E)9M?1us{De50~vR<Wdyc^S}-+{;32hKKi
z_30T0`wF_fDr^;b<re+i5}tl)h=pt)_#=ZnW=t+@C^r{&ihKA(Hs+bL7<$-$;!-RU
zvtW)c!(8kB*yed9wk-xec~30V_8BnA;z6Oj7`eI+aKW$CK8KQq4U*K9Ql!2V&x}uY
zLC4?b@RNAbCNV6X?1w6#Fd3}d#1Z&9FRfh2-?8~9<AjR>x1GDP^*P5NNB;(4++_C=
zrMd9*^pc6pS<H3fxzXFW3*1BTs0i7#gjrkJ6#v6h!(`OUnRt-}y97UyUC6J+uECG4
zx(?qTKWY~Ce~O70+dvC&!oDU(d&~C{Zvc4TmVhaOhIBo6OQPBeU5kNdPq(br=SgNo
zX%;z$yk6Tia?otCD>bko5cn=)w_X{!;XK6?0Wg1yl6D0s=M(Jt;PU$VLOAkRV$18^
zbInzl?1pm|ALbL_%T4KV_**;S?0VVgVN;{{fV#m731#c9i%NuZ^TD_a2kNKPXHR~d
zdoEQFDokNPdvV(ZCiunryuZDv!0$bDUWBoqG6tQH%hWJCJgZ5Rv(k(9{A)Aa$9bJ2
zu-cGVO7dx~VE8wC%%>O*lA!TWV0?`*<uY!+TE3>o*Fs(@ixAIjSG)xqV)M#3XK@pU
z)}aS_eQYh79KJ6~L-IA38*)@#tP*}^TllJot;7rL{z(;K@TReq1N%O;94zCLCTYey
zimbjP37$)*8AvbDe`-z@a=R$T*$W@^A&+$1ylj+aR5bj$_QEp-pDZ<{?Rm-j6&}m6
z`B`wGqNh$#-Aoy^82!4-xEki)?W>)+ahBz82h7?fFBJ7;Zb*A)DT_K)4`DfWmqn4O
z8s`f3FL>cne8x_rVp-K^Pp4@rqu0>h5i}28`|2hESj$IE3Bs@lRXw5XDU|S0HE_X;
z6V^->N_mE0nt1%u8>E-4d52luwY8UxZnq2arKkxlD79GslGT?an(E2s(ftc+dN!|R
z{{n!A2>`JDWGP=8sj}49!}x|$^^v*`Nfxqk_UxK5Ei}W)5l;l`h>{e|U+W<Kxdtwf
z*S_8RQgOZg_9ZDQc`vIg);d10gccX;GOgF%vA-a8&{o!5@c^`0nP6unPgM6aA7}5j
zw(Lv&RkX9M)MWUp>z=Ou8MIf3uz$MNqca{)WUYWNpuxDl+26zrqMVJI3H^MS5+mW5
zr6f&sJJ!y97(KNMJ%Hh=l1LPCN>?2i71Z)Fi<g$^vr|;^3<!=>#i?2$b{o?*+YS|Q
zeh)T{kX4+;Xnt5kCD<pY$7<b6X+4`2BY`OyNuToySh%SCarS3aMlBv~-;{F9L`>#%
zLI0#>mQN0ex_f4<^mYaM>1Mg|m>R|}Kq3;EOT8PnX=)<hLDA%y_-ifx$RzOv-URoW
zhpBIRM$$)`c&BV}5;S)7vZ{*dXdX*}iNLx9`ufgqec}FeO+0H%HT@1w^Y#zwm@yrO
zrm6Qm=A|id+*yb7PSrG!ZDovz?j`<1ykxt;%Z~b-6-1=&&&tfoT!LUznU_#$+__iw
z)p#9y%&ySj%2_||oa>?#dGyBi+*bv~ma>NKr?ScaWxo)kzxIoKr8J;d9t3|X6fA{x
z&Ek5%L%5<WB;%=j)ddzGaBSg3y9-$z7Qa2W99X@JUKwPofZFau!G_A$_*b9y=Z)sr
zit>@<L)3Z;6F+IQHcSwBFe!d!e%e3lE^hN*s9@|Zn`+sDtVH71F}yMGyS$Ps6F2Q?
z0Y_OBXT<>2Rbtz{D?c!e_2uz?X$a(#s2^#_dEQU_(N&^GpjBbpEH7KZNXk-4={H+k
zzZ+S>3Q-`si_XDNAc}XXS=5AAesd#|3w92b<!eQ?=4ozumni!a))0(O3MO4H&^tHo
za$)cvUeRm)g%I6BU2;~P%&727SFiRg7$3E+0w|V;N60C+^=$dlja&LTpZm-FbNNDY
z`~}yT-LE<!Wz~F7Hc@6MM4&7{!sW!B^%o%r*q<7nuZoxzr_{A<?XI0s+qh6qWq~`p
zZwJ5A7|5L5PXB%am&Q}16%soY-l%6%*+F^b^BN@baLwIJHo##q0hk@(ZftIfrv8`>
zoK4-F>*L&|2fK)wLU+bl8_Ia$T-DhY6Ar}Ym<|+1We@Nd`huN`ySZ@k&bm9eJBu=U
z_+f3?VC19(d?I#$7kdkitd!ukgY9<PSM;AHRbb|qOhcmZckBYcO^f;DP6x!o6Kv4-
zae}3<)AKcxyD5rIdzk3_qLdxonoEljrUzW9-^j7*JJzyEpWlnSz%CFcZZye98WuM5
zPJ9E0!r2IaW-DWx8KM){GbjWps`B_riwwzxO+VgWdh@zyug*XNqC%Fj;yBDNz0p8L
zIBU8r**0!K(4$bp5@2=do_X25kt?!3m(IAc7iaTMRSR2cPWnGeY?$LgLq!4`&NyMN
zW!a>3N1O*p;AdTB`u4zY#|oV<hH~{Th??zt0TNZW<_U(%0t<!S;sS*2%ce^@VTMLs
zwC3lhuvd6V^S%Kv)vj-sUN*8V@m6)ty}=nubm<<#teU{n<0=FfgyA0d{A;rD%~JO6
zSR{LG8L^0=81G9rohCF&6{O<7+YTAW!<&dX0x8`ge9TrHX0Ha!?L+zP3>J%A?9pq!
z0l{X^`i1>_ywmUz(3-t8i&$!l;31DiQHJT!E5=t(amZb=YBoPjYOqv+625p~d~l&@
z6@g!E2l(L~Ew70#b;u}t;Zb?HshH>Z<)2ZF4)D;}a~H2+NcO@A5(nXKkW4~<&4_n1
z%5~Znz+9M8A-cBV6i=d7K;aKjTMJ=$KTK_`oZm!TzPEh$zv1}%Ui77_t6$H;g9Wc)
zC`JwprPi2=Z3cVAXv+}k<4zi{{dlnAuR<QqfR_iGU&m35-6J&FGvD3btj1_AR}nOy
zv{r;DVq1l5lT=$Hl&Tw08X?T&FySPT)sViEnC^<q-fxHbQCKZq(4cEwi-#sGe}v8$
zq?Niq#;0KV?m6`@T;PgEd!lTE%a3*Pf1F9uUi+8k(f9c6y?`T{S1?RTaaVi8uh(MW
zToqZrSx3&}1mE$&Gkt{oU0PNLUyGD|98?IH3i5|v{hFA~WG2zECFJzvlX_(^qx_YZ
z&Fhb-kGba`pQU;$%`cixBcI6fQ;0Vv5+xWeu_jMsnFQ&dr10V)fc54pecYrjj<oEB
zyF1$;IQZwTLv?pDyeG!#7Ti%|190kyZv`0n=4xW$AGkxc5y8_*{M|#MyW(k$+1bJ^
zcfgXm+$@h$T`{Xpuh*=^WzJdT3X9di-jW2@B5k*kLdlmF#zcnfzRA&Mm|d;^%jY+r
z5bWG_tU2v#tT63y@EsM4e##kmrpBz46Ke00(N%jWy!N6{8~pv&Z5;XYaFedq^8!I0
zF-oE+RWf!U-&&tHM%f1DH*v*c#P)=;ptnV+{5JoogoG-N&4%Z7aA%>Fbd9TQYVtZ1
z9VzVuoFR=MN{x!^F`3?o{q!nGJ9C`L7LNa;L%u)_r%%sb%vBY~Jww>yJb7|jhDNIN
zy1a1DMmI4^@g+*_We#S(d}7#gn0kie{8uPJp&$W{X~kB!ZHTMX<tcLqOBN5aw7iZA
zc-?AeC22=zctGJednoJs0qCZnL31}>?$1g%7eBTkSK6D(Kw+9R-evs6J_~`*6msVv
z?J)1$PepF4b{!Y$Plq80B*D$01_jo^0w}SbPiR}Eixp$uvijLtfTD$i|CwvB5tb)Z
zi+CSRx=7K1)|f^L=Dk_1#Ai+}G}vazXNr&iizsH$`af>(1zMW_imjiH`-5{McQZ41
z7l6q6qQo!T{Wt6}ufTrie<i&BkJtWR9Kaqgo>hzw7i>!?(dxPfHxRVCK!GH9>8Odt
z^}yCHY)J7_Zo^YsTs@qg5r<x@B*+cWQ>N9$^d=e7TZO~s%BSvGTv=_=-RvBaK4s09
zI;-J;+K&DxP{infpNtnO+P1ZCf68?--m2hdJ6dgKo1MB=d!^y?7juRW+LwJhm$iTB
z<cjT;_!xY8-daM-T){DngIcAA1Nq2#>OqdpkRzdrCv6oQp_|kz@FPNQF#~)CLRKeE
zqZ3;&FVr_$2Q#KH^YK3S50kYs=3M$JjXpH|C7mz7ox~Wc9ljSZBzQ?WcMjWEH1G7P
zS4T&D<9bJjsSgh8w%}x?63Ry-mK<#q#o;w{^(T%t{jyKd3<_U2w1$$YND0oEnozEL
zrl%L^d`@}FUH0p0jUmxK$cUBLv*V_&d8ikpWjiI8`r!%FI?c{gY3oq<<U_ZPN3pC8
zpKHkz&7w<c`VWq-xyJVNq(|=)%JaNrAx!znQIhOB6mCx4BH32`9SzUl89&YxHDa{m
z4Gl69jG{F?KHCb`xaM^$<OY=q?Q^@vIXHi@6j{*zP2~vz^<v^>T=3D|B+5-i4pE<K
zqn1Y!F_U5(*7kh=F}&A&>dl`;cguF~xNMtCk!brpa%<F+x6~JfH_awuMY;{HiBgDX
zC%9em2v+*aINnbced*cwc+3m6KrwY#S%q=%K3hv=%xxTg1%ksn+T()t;QA=sNVi|n
zReg{Cu-gkAn((OH<zD;1$1Fm$`1O;m9c9=ki^Dv{*R0|JO8|1;P-B{uy4|R(*m)v9
zs+E?v`NVH|zH>lx6EI-1rQ`~thS`Cu2@#%78T;VaZ8y|}{X%L-&?nM-jY7LEkHS~(
z4&8&-mD#I9Li^5p^p@UqcpLT}ucO$9+<D)BL7iN<(VFcMrn9D^uO)neC)OwA*;J-j
zteWKO&q0{dtr(_}2ekW^tJq&2gM3D}^xSZUK<uhEIcN8*B5`E78i_Z`tP!ui%%3A%
zN!R<rMl|avmf?C%=<8cyITs}mOefHy5^6$6R?_mRc{80#J#k{7A(l=sWl>5nbs--!
zTb?^Q6exvV4Jf%U{wDA6v9%wt`sI^-E0R#yyKe(`{I;wdULIB^YnRQP$9bZ5bvE5~
zf+07P)E4(bPSo9oKh3R^PB2Wl4?~>t(khEDjj!0IJK&m4)pKVac=5@0nw3@JUF>TY
zJrb^pyAG$@HFa;d>oMFgvnrnm2}T95_VLt`nT8vZRs<i9r4m$`(XM($D6W(-b>u+-
zi=DgGP6A$<<6Y}C3^$@zIJDQ=y2}^3*)EsW`Q^}3!$yj6%&HD<={7AQ*d0r$iPIJ_
z?Kr|NF>2{>TL4v`sRe~qg}1;S2;vZr?+Dghl+o+Hjw9oxyGKr0`Ie<Pl7?D_th)Sh
zj4?-CN?fDfxT0ED6m((C4^9<=iDv<YvdQr-H>U`%AsFl$H)_*3Zn<EGuIwtU>9lcj
zEtNByrf;#sNp_f*ECCICk^W_!efrZwmadIOx2OG=<^7U2MJy#2p6BUiusGq^m3Iu<
z3Ocwfj^R>rFsm7@g<BbG3n5bWb1rr_J5g*VSL;kW#%$S5^nc$7Bz~OJ$Jt&qNfRXS
zxozr|Sqifz<@~sIo(akmnYJ92_SbN@+?pnrQjwAhePO55P6F{BoB=~>jssB-?5EAB
zlP#zZB~ZBIi(WF>HW;OdXhyw!ad$9I6DlQ2X*@(~H+-nrylCMTt`f0s5rDS!4~Si6
zd!ENfLIvW6MCb$j47<0ZNdyER=H_~6Gip@A`;x3_nnn9XSUq-?o<|i?3mpE~YOXSG
zpCC?&7WLfe71FL6u4@*2mTW)r>N8^E@aGytVF}~q*ARc9>Y9eCs6B9WU~Zj-gXmke
z>ar_?sIi`2bEH@78dUD?Q|r`gWk+%=r{xh;+nTa_X?&7NvvM}&gZ9b(A8i3$3OYE@
zZ<+bxm0MkVQJ)-<Shg*O`cT^bXaDTq7^zmNHXYfUAxMo7I`>ed#az7S>8#pw%pUJj
z#u<0VfeL8NWIbxHm5Zcw9OZ(=!(Ibbht^wFL6L_FeGB7j#*3_gR=!JaGkJJ${RmHi
ztf6S3kN-3J!4|>aTo%4Ru>&P<uJ(#CDoK8;-VNS~nmP!SO`17$!US>7Sh$bj^G>ti
zbC}WS7!=m@1jG9%g6I+I(=gG;Jro5CnIb?ViUe@M+;0M;H#N9;(}wblE0Uo#FgC-7
zsl5E%IUBCTjkxny^5fyTgPU#h$u-^PA^OkgDWBtmFUXu%;S_HdfX}pSy+6@20wX5V
z4%!P}b58pX+e7meSS!<*q2G&aR6?M2e^(#*F36xYSFlr>xfZS;<dA0=f49_`i^y=+
z;7b3oc1<yYRvOh=raWZSSJ|&>CXHo9)G4{R(yvWRNB1A*9Q1HUKG5{bEk-z`o)=jO
z5fb2G9kJznC8yZ#7j2JVr7j&7D%*A@7Zhpb`d)7^Q)VLZi%sZQDz{sss15vJ{P&~9
zC-T+OB248gVyz0kM^p(suf6F9iXfMsv&W3hA?)2xc~=PeW8DZbw^=Kup{&XFeRacu
z4+2@g3yNM0j%ha$jiH}ul!|BgL<oqyHy2w?+3eJidROP%!HfOKi@sCeqK!rA4{NdI
zv|h(G1W6qCZTRz9U?BmOI~B@?+P+F%yW~|@x+U+Dtf4r_Gt-stU8AP#fJdQfY$AlM
z)4avwA85EnxK12;KK}Y@g;D-(*icotWHDhW^3c@y@J>YEM$G)#M@0n;I-3Cky*ME;
zu)hERvnz#Y{3n@@{>;W*S&CCV@SEPq`X94tmdgbXuGN2(|7u~CR%IfE{#>1STUp&d
z;Jf*PA0L^X<I4Gb<f+=sNyvTWZ)bsiY3R_d6jb1~k(iG=w;(fg{duiV<*-s<jzH++
zQX;RVluX7$K~Mu4wr4L6Q7ao>un&Bpo}oHb3~in?;d~b>%xiymcqGv7XOXQv2BG|Y
z*W$iMJp9uO3ul-~yfVR7wYzr-c~iJ>hW_V5uB$C|J?tzw8~=lCZ?Jt}W>bBNu1m7!
zLihb=z9FS4$usa;{EU6bemR5rQwD7%Dc?@@(sd6c+P&a=*#oSG{D#nB&;uLUBdre8
z(oI7x)$F(<?OwEgCf$8Elx-<ICQ5_Rv-<bDEV+kTP}hlP`xB}=V*J@B3YS-kn`&1c
zl+jc8y<e{d;RHNXBU;&nCGAG)jH2yX)GBXHzlD*)Z(xo1SJrhUWNH`du%AduyEZTp
zjFZAhQM<QDAN4ZiKHoh+3d8;kH0L`wJrvj1F(!-jtrSX;F0OfDZ%4f8SDf9sm7)4B
zc56~2q)Y+saCG#k9j3;`{>6hw8eiZUj$>=k=IW@yV&br`l-s~WXzkgL^LHhX0X3Fs
zf))~lw>n0Xh-aC^v&4wLm31iV(E?)r=OVx52QOM1jNPV^f6YqCOfN`Oh%hb|qG?q?
zPD;Uf@*HPEU<il$%4{g%a)K#3x$oPE37<LD<48{T&Xw`&&!dTIb8Gb?(sTK%?gb_i
zWn$2o*1Ncr1);;Va4YD-sBwTtfdR6yE=q*7_4cyfNJKAB--^eB0`jI~2K}_#=C^@U
zT=%GrPTV=aXClaKtc8MyE(S}lP>Y4%|9Au)n|Pto<%6T@)DA7=ziCD5%6?xxm@kkO
z)oMCaWjCHGnB{~i6uzkA&{d+WzM<BY%DKK#(;HY8PV=;7_#D@HDbihLZJ$gsSen=i
z;U_Vb5`TzB?u^aHoj$dK!ZS2`mP(1yvE@qG`LC6tZUv<&IRW{c3U!0N+9h-kmH7h?
zt8_DF7HH7sG#iz9&oe2yrKr1k5$rAkL+nWXAlTYa;K?S8rdaGb15=C=I$evr57Ukf
zkS>U?kgPQn{@M)+8?d~Y-G%{eY3lmLgekehLfOgK%C$#ZRw@}LKK8h0+O@YgTXyOe
z*=f!BE6Jiu160YwDA+KW${A@FXuTtbHKj>oFX!g|2>Bd-`Yo4gZiH*PRg;_lnZ)7r
z5S5=)`p&x8?Q;b*bc6PCqx?>Z#d2R{vIKg{yS~=;Ye4WKwKxOYissRuv<=L}@0y1a
z<wsm2@Xs;%G3S(sJ12j?+Hb3Ad^2N)x`dhju6dYi=UXhzkvJL$n^o)yjP6r_rGUIc
zt!^g{p8hY022=i26;sW3K2ZvDU#x9mNnQlrn|>bnO1;6dgm8wtW-K4AuP>!JO<{7R
zDlet=Z7l}3MY)fYUa2^|R$kToaZb9g*t#acAT3;gA+S1o!A+rIiP5ru@OaD7$3Jdn
zgs-OBHC4hi9D>?HU*wH821due^~PrYkX+yOHp`6H+>6BG+fEKD=y#&5roRg+CDR58
z3kn`8XpYnxg#TVqru9gj&{M3_Et(R2meWLR*ln^0nz0^oeo43}ik3ICpxD=p?ICWU
zJed|`9lcjTLl*xCbU!h%5%C#iCQG*@Rm-wigckT#&-Xh<$xt>T1sGe({pKSUBdH28
zxmcW_!Sei`Br(5*u@UHk3o?MwidgtVcANWs9<FK@D(!0~62ml{<{z0(E=U-&xXAib
z%IV1(<C{8dhT_Rfn%hru^z3sIvD02L%}lusOROhaYZ(^N4%!8t1q?3~(_V(y)r~<t
z7_e<s5~k?oD51u$T5K-fK8^a2Q5>7X+R@df9h=uRLikF&DB4Y0H^SkQwns+Jm!%9V
zbmkB&Zzznd)eNE12_V*?goI_>OOktbQ>(F(^uWkcBwMAA2XI)1c}Hf;YBfS?Wg=qO
zLXlr)3CA&NhX5c8o)3syFV>W%B00G{Ob^pi)yTg~x80RWQ!E1W<IbZ*BOacr;2Hlz
zUQd6*mwHWQ&^yVGuvC`3lXO#o&K5<P*Lo)|qrHIxGlUfe->CV=n*~c~9c@_4KBtTy
zA?r<m&xndY4>`0g)b0fd3;sL*$iBjc+uwW=!)v(O(R2Fn+QC=EL(kpwe6v6NcxHzc
zkYGT+1uEU1pI}6nq&Qyk5%(^_xG|(~R}N=3zuw%ywE9=GkTlCrx6hAF#^YTi7)#EM
z&4OZFmkRAvRr;F)qp$D3RXyiv^bPfJ5O#7{c>n83Fl9*QrJi?#g`<t!2uQ?v+Q3}l
zhjbcu0lcQG2C%uG%059n56q(RC=T_M$|yD-xPY@wKI%Vf0mwIqf%8*E)iHR-+`8S9
zWvwI1F)w1&?4DEv^sRKJxCqS{QP<DnIv}$2(x4PN@4~f#j&}xmGgi?4y9Ft8be?1V
zcbzU;rr;xU>y%f`J00bqr(Ed?@Yl##_$%VLb>E8CD@9FWTi=SQ%Fa=E2F<Qz@OW{`
zMXei$m}KPT9S<C@(=8ltDy(Ftyif(o+B}o%8yFrJ+7(!XdRCcvV-~^hZBe={RE=xh
z?@TXR<jwFK=<`D6lxjz`y3+l0K5{{=gJ@+Jdp%#5wz{2fb2v;pRW}Ix8W^08pN;Nc
zvB&7&dhQTG;Bl%!2zP6Uj|SEhX?k>PMbia@_I4m8_@rbMpVbuPkg8J?p2tN@!PWw;
z)$@Fv{c|V63v&H78qMZS;pui5n_iAVJ|p`klkI_QLa1ql`(YX>?Xa75bSv#wji42`
z@wAl7_8#6P&C)(6Zu&-HG(A7Tka*H1r4<$M1TRv;SoaP$k}@CfLP&S9K`0}L0@KqL
zB!uqozRfVrj$Wi)d)9mUc`p9b5w5ltsyG7<`Q1o6VT}^hPqxS(DK()2Zt4B>s!Js;
zbH)#kj;q}>r@~IF4r`yvTeh&n<Qe7md)vJT_t#7d?h?~Kpgv#h@OW)-`%1&$m(FOI
znYjjT1y9h_p#%{(4nX^h(L7UJjX&BQUxnnVV$*-rTIBr+kNb{YzaS>M@-zY_9KiWJ
zI`h2%TQ^pd+}z|=HJbg68qF;ELvFq{Ck4TefXYT*jmOg{p>sEG3tu^r)y~Hm@TAm~
zJz@{8xux1>CsJd;j=egf#6ng0!g<5V3!Gfdf}T&I5syR^0NGuNxNTiB92kh;7V29(
zM=8GWH8E3n%l?%jZ|2I_d6{+jfCcn>O)o(V@k8v9)Fh|;wT&^UwJFh(I)P)&zqY=9
zp`CQ9wy7Fkv~6~S5w5)Nt^h#yVZ0oJ<M{5))lG0NTvzWLP4K=L8*`idv&R-kMvXPq
zDwV95615i(a&B9mJDC>rSW{rwB@rpUart;eP~TOb>X1)1wjwUHm2%&sZiD1seKHD3
ze5@lI4``?wY4<zmt8dPs``r>NGvB)@kGC8BW@!<VqB2ba8RvLIX}f+egJ7A}dNzTt
zWpyieMqjGW?!FuD0&9G8Pz1ucmwG)XP$J^4g(J=r(%&Mu8UJ1I{&Bh}R`ZGMh~GYA
z*ex2&GogE+Pu`_uCZsuzq9t!I9XudV9D2~)m&|HeV6o0kTIIDAE#8%F*1YE%L+4ex
z$jx3I#c5G5EV?ps!-G}&4F&e+P3z82scx#*V?3D?7SRKDxDj>Dcv!hba3IjB0};DN
z^=OQHYJ6(|XHUv0;pZ-rGV9&YoEk~vTy)FojpGFh@qXX>NeokI9x}IvglhBoPz8Ho
zJFbEsXGi=B0ttLRA0m9YlPlE%RUAH)CqrjuO1)CUI$gMJgQW!uQR~W6h0!u!xoIay
zYzMN^HE#!HwT27|V>Nk%WdaZq<J-A~p>NDi*luIwq@O@%%3vq0ZE*@*P0g!2qYiH0
zoN=sMQe7#qSLtw-C;im1H8VpMhvl#9(0;~(-Ms#%1wCP0+4jq-^isum!~oOqVMkwy
zz>h%GTEn6JgX5YkdmDZS<7~_PGWPwgRQg~1dIXZiV8OZ2)^Nx7zx;aP14D{=p4~Ap
zIq`;9hcuLuI@^5gv0cptnY;J?EZ$^(8Fu(3GuwdxnLYG7hxy_E)(sPjr9i@5Pai$V
zhA|ZB{%5{FY`X<1CnuK(<=HRxqFR#$Vpk!Ld1o*GH(WzLTMyX5AX5(V|FOwU8)i}I
z?{+g@0NDGV&Fs}bHU$6KU1H7z{oUSt1P8y&cI-$AJ-mPKsc(D-4jj<wFR<Q?uMOTz
zDZ2ml$p=m1CAiyQNrm8vLz>l7@fOjoJ`#{OuQwO!>ddPAXb1a0=}mmCuC88Up4taa
zEo)C!NJWt(3Fj*we0!mei}tjhq|14IzYuPD>d=1SXdZ>K!qhbfb(Kh#?^p9HaAUrb
z>eH2^YsbIryx1=t_Nj$L@+Of;`^L~+U0vDKzC4#DI@u$2G~cR9#G{H<cA8VjSb5fY
zplE99@y+|=HH;B*D|%zgFvfP@n<TBo>r>a9dwy?(?yRIDgVv`KMBE38KHf4jGy4cG
z=L{~V5Y(4z<~S6z(k5H#-0N(YVcRBLh>#stQl0Cly-Q}StTeN6O7ij>luu`9OY~b6
zxadLqt@1q~bXh_FJq@|O)IPrbPrrK45vSV{SrnHVPJN4vjI29y@Tr{aXwXX3Xy|V6
zt&E9P4RBBQfPeu0vqz`x<l`T4%3X&vQ~L7AjX5R-?!Xj&J8K1@!H>r3pP!i@swf@>
zcH0bQ3?pY?E3Gmn$(~K;bo%nGHi??VcUtA{dtW|tai4uOd?0MKnFQtoid5a*-78i`
zdvi>rxfT5vkv#G~<)y)!be;Hwgqh*c-R-=4XkM7K!4)a<lHXV0X#4D7h+0#>b*PnZ
z=LZ!LwXRQk>F%}Tuf&aSB*;4VJe)aj#`5#@lOs~5Sll5-@Fr>I`%Y1E*cmWH#L`2M
zty@pde0uNDo@6$Wos%!0j>qGFpf|j{yq;`ttZn|Oq~=@9ss?M*22082HGyk<1lNJC
zU(|v+chO6>U?rH^U0{y(OPNVtgKWxMx}yd1iLxHG;~x_cfzl~ZgAC0qX*fXn)SVYc
z%_t@k5Z%tBd`i{BJy`~5dQPo#Z?;=7eZbLLZtn9pbsNcV*Umsb7u<h&@com@m3D=y
zcAQM+!UvVez&}f-<&VZ#!7UamvZ57aWo6G&)^wEC@#U&$UdP@ngPD1N)tPL=tmTHW
za6YBEJ_`>wim?f^aW79^9K7n%H`kJFl%vE8pvu9?3G1p3SP>6D#@+XT6fcR2%9*;J
zc3aUGw(&b%YwzPP4fdpXUqB4=E2gi{OnM#Tx?0|hAmH(Wj1<(oQ^;H)@p!IP6}?W(
zDEn&u`}e;nFOG4K`0uRGAR{@_+vGeO-d%G!ys=90&MpmYGATf8r%gmF<d44M6sM~#
z5>Ii5{j6E6KeCpoiG%(qaG7WJuzG8m^z@9Rx#6{x8-DXc=icD&Uw&($ug}id?G`i1
zSG*4VG)dM2QL0Aj&$mkSu$FFVX`xrzUyq^Y+vP8br1+2H0EBC5^gpV1aB^@c4g(xe
zrK=a~^Qrv>=o?q`1X7Q*Q>VK=YUKDVjBL-DmpTt8BqYez8t0ialKlpqM8b8RaV+@C
z#76|!xGC^P%%SZX<O}t3Q*~m>MEY#&2S$qQNr!dhDR>z+#l<fQE6d9%ITnO@T8OJh
z^(F(OAtfga8kuXHH&{0u{Q$*x-D6}+oj`*(78Vtpm2qf&9)j?8Y<uUs!Gj!HBKq5?
z(cg&+A&0eQuJucu=?Hl9yYPI6uWVoQ5BhVe6a>!>^M8CeyQHK9Be<4pl5220KiSY~
z)NNObO~NdFxC)s$Nov?yU&(O;f#9(q=(}41Z~aV?w{`~{E4<ydZVrdTzk(*qkNR|L
zB@?5#83QKP!D}}kJQ!QJb2BzBZt5y%;B1_@iJwnAxi%P;JVi?AH>(X=zoHQ@f%`LG
zwNN{2$?J6cxj$7*S~{UP8jQ%PqX(0BceV!y3asl!JZjgc-!Hl$iXF6aW>Qf3z2XJc
zUhPS8-a7>VK;&=zg(A^!q4q5&cmEKT^M?y<RV7gaeR&pCtyHD$4oQnLMW?S%4kYk-
zHk?!#k2-d9uFQSd9FF1Y$@{=9<&x|Zd~tr=(t@zi6)$NqE-zB$Ln#{Bsj{vM5vf~h
z;>PZKe6xYCU4ZrA(|}IlI8Td|-@c63GVQ-tHmC*GXN|4C&i3Yz6+zH(J1u4Tb9JRd
zIdrh%a!kho1^V*x@)zL5JK-1Zyhti_>CYcM^*iypoAc^)qQ`Oh$v1a4nBBMwc9ogg
zk5{_iOMEMmQf3?Xdq?e_#3}B^mGw!Eg)3u0*5Su_0*#%VoQ#M?c;$ZffjZl!*Em@4
z#;UTES<z&<lvU+XD`3%m?aLqnP=F8DpE`Wxh-~oI5)pkPLC7m<9u06T2JgBsvu1)Q
zEe|<DCh72ne2~;g$N~PAze^QHwZ*a5&1b9tu=w!`21*dUhokJFCBajbfOOntL%e^o
zd8aK~z5qUq=MCl-OyKu3(AUQ(xDS;-MbYO1o(dY}nfrd@G_foks%ZcG;G4YWyQ>tB
zI5DHa5RgYktL&OFx2li}^Mrtv={;!0<?k=Y$Hx~;@+|{=Mr(sReB!IX*uE>UtSGYm
zs2-<2U%NTa4SYIyWPZ46(`~rY_si>ZG6${w$DXtC`2Tv$Z5tsoQu*RIPo>z|qbc3F
zX2k;-A0iL(wU4$G!H+h`P4flzu34Ka?g+72o@~Bz-F?VEzs9XYX>%@)g)eCF35`tX
zmz#<;c;Vm3Xb~^4v`oJjReDN9`+S)#F?vS<IX@>Gvh^p_5X2QCGx^OVBS!`z8V>S@
zlsa=f{9&er4Kb8ba~+=tsdZ<t+;c)&kU}GjDiwfmtF!vMb0U}QUX|AfeCHzl_=<)=
z|M1tREPO5^T$O&bjb$QVXix-9t~0?pB%pH&yn28{5b6~VU-kY<$s>$ZzW17Hu>j#F
zVobqr?spxtO*+cktG(A;`Yek<qLPQ9=+z_hmu48gG5|AU#2vLbf2CB{i1Vk)2qS^i
zCjgcOsA-&I{$QmqHSGRn|Mm{$-5@O}8IbB^Iq&HS6%`fgE~9RjN9NJ5FB7!>OzmXz
z%|aq?l{yBb|J@<|x{z&s0&Qu<bikySG6-J_^wXKu`^jI}=}QlH$&cOg6&|t0F=GfH
ztOn9BN0P2nymI2u>YI>Vp&bTa1lM)f^7}2Zm)K5oa>fl5fB+)2_&Xha9E)dW`c#nS
zHX0Ge;H`)xRpr{t%x>Fh;k+9?s(isd?Hg;<(N7@mNx|QNo~B`ZHYl0O$~FxzJ?6YX
z%k7gDkRwAC-ZSS4o01<|F_RxA7NqVC(ss9wG7Pp=3i_V_hAcoi$+yo8$xL2jMmfg5
zZ<RwsN&!IJq6}4|xUPkmmbnhl+Jlkv6*D{K05Y2gK^}76*;*Hgi;II%=k(Y$Tp>l>
za|6YyzVk!tE#Mm4o-^&q*D|yc{n|^My03aos)0=4O8Z$jQ3VLdr~;3I-_W>sm8X@s
ztlMBLqsKt!SR5Y2h21gFi6)vS%(XkCtt^~3@L~Q+sh!f9B#;X8fmc{{gKRlU96W#Z
z;g4DO;YzE1fa*f->#jFkdp_xDg|qSPY@(w>tYl9)k)y0PFB*++2dPiKeIzC-`m-9i
zR}9PfM`6<Bw{ts^Qc@$^<B`%MT?CI&RbkDyk0@E0DV3{}Se+dT@Ce>>M-O^$u2E&D
zhrBzsJM)5-(VY19+Mu?$t%`Qw``%2Rl*S?*R{oRVEQhUHUtcHl1^!Okea3cYaUpcK
zY5+Lpk$18lO5N+JBANH1xM+&wFE2U(VwVK60*`AQq5x52E#x#%WFIcfU7YIMnEv_m
z=W44eKN&$M;EgO*c}PG=bi6_W%8s4A{EMY|sQ%A4Hs#4oDSZeh9Jqo8hz#omGpV6F
zEg-s8>l+%bqC9fJpc^MD1#hG(_|9IXgO;E~l*v&H^YtHh*mw{KM2ZQB9Z@>4HA7A1
zN}-jCUp)XJvPssfZd&iIuIB+3Q=rzRAzkC+DsiQe)KRuQ$d|>wFk>J<d}ffU6pUCF
zuMl4^4&7X(6hBz}^_5M%tMQC=P`92c&ov)Fe%IJwt3Tu>i0IuPIoq1NnJ<U`+DwiO
z8Z5D(({BY)<AD8#<Vu)+zW>^;=~yDbTPKKz+R4Ck&oPNLpFcoJc*>)C*1A#k>yGV7
z!kChv^<`U->E*v~=v_K{bdaDsrIzN|94~E+u;>zlSB|~luJs4LTqbQ<zW5Aqifgoi
z;+Y;GGo)a&lI}jvGiMf}MckFCuk2=fvPu^Kt#^++E$Pev*@w3p#h7oQLs4WvL5xUw
zK_K1q-3x>%D=WXnVhwl!6p(X2V}5RrK#KalJl+?2_Qs=Mp+eh8Kt|WGB0BlItuh@x
z>#N;5I+vuS*+3MY&Q5`$5T1Qzj(IETHU0&0-ZSm6yO^EDaOKu$-_{l;rYn&PJ8>4=
zAk1y`gXW}wd-p2Fza5`)B!|$@h7pXFOEI?fl()9gOTOf-!G98(_jGGn<QZwD5P+NH
zBOrmby}dG#_K^Ixd4gk!e5RlRqdapf13k4(0ZA$10|4mTsp;vR5R?)Bi9>$Be>|YJ
z;$%ch9(LLU0_iYP0W!)*@2wo`koAulc01n;jg5_U!CzKxZmvE!AZ6{)4H2V9@oqhz
zI@<wI5F>@Q#OAu44VQ0BZsYdj{jGEm%&(;Zx9uPHBfCl3M3Flx>yMqkvG`}C2Bl2@
z%EGt0OUX<<E-fjU=j>tfv#dh+;gQ3KEj=tq&CL;;N1$QBI!rXPu<>pUgT@g<S36Y`
zrl+Ul;ayh9;a+0F_3;{G_mS#~N<p4SU-q0t-BiJ}&O9*yVUhl>ysPj1t*11Mp}xNU
z#lG_KvH!t%S-|fUPF<R5>exmtH;Z(X?E)dR!a?vvCPM>~1mrebu9+;)D1KiptX8%{
z?%~%13n|6Qks(8$`1}MJ!7~Y6J;%A%0ELeN3YAg_l1uj(a>!FPGUlRch$I{YS+bA}
zYlBE?(DLo-wKAt}2eM;oFnzAfjohS@qPYHrP3h13W@fNn1(aJp>bzVL&848``V^A_
zi4FS>Way+Kyz8H_Ca8C%?yP2tNLS4jRe*a9m|I#_ljqGk(^b*M%rL55O+L;qQ)^wN
zbtdX9&Ie{u_Hn+F3<#4onc$&pKT;hKYy$8D?UJ9GnmSqcW`3}gv=Zs8*xv$zyhj}V
z829z^k+c(1104s*AW0hbx($Hzkpor?Js6w5OVu%9V(ygy0Nf?n{?S^I5ZdVa%8Us&
z(7L_!Iv_c>_ej_N8P8<W+6LbCGT_<+c2n4nON96QS@LBeE6S-K1}r4{EiV;P-LwA#
zL;L(H$O#@+z>I+8={pVIa3CEIF18Ps_BGDZZ|rZr;QBY{SP<N$brP@Bl0bK@FMCaX
zKZ)r{yAy5%R9k2L^CJpy^*_qG%#60Z$*?BZh3=MO8~}5zaUi%4Hw7&<o~fB>OO(yJ
z6Mp=pi1bR16j9Ow#S}pnYWn0dx6xq0t}^eL&yYRp+}K@QBOXN_yBTyG#3?sSi9J<n
z8(E}`v^lu<u@x}VR$D?#J;#|d9)*KHZG(E$<HX`(<KvCD*Jn~MI%oh3M7}HR;Ukq6
zft%%wglxJ=BC5*`%*+P73x}X{{WJBzori9BjjNHEqxMYjn)}cVn<kL~ssq$5e8nZy
zkOQ!ftZ0#eZ6K*Is4lpOl{N6UAAtAGaLc-Hb-%r0n_meunW`VPk9N4AIbloDn63h}
z!AKDxID>UT^a>AOK;319cKdy+rqy>Wkp{jXE6K58VDT06dvDIh01SU(MwN-cwOO~F
zwSMa(+n=OQ9NNE2C)7zUr{}8y-M+02f_2A0xu-3b$cvhL10wz^8I)5bYk=HEdW=-B
zzZcIh9~Uja6;E_BVaAL#^FIsI>42FSz*6>mDEb=ZTUOk~fRYSfVgF5_acTtt_EfgK
z7{FnA%|Pu#FQRE$$lMh&?mZqMvg6eOV_5F?nppR)<g{xi%jdcGe2OU`Bx!U3ciEd7
zvO!tG^Z>R1YZ3ygZXbYi>1-H~@06Dyi0_<7&@&R<<zX?`FItI9*KIA^6GlT&19Tdh
z?JU&pdna0X=TBzm>=M;`46CCgw+-q?p)x_c`DVo#zqT2pyTyXZ0n@nRWYSipyKq-o
zqG7PMgSr6muGo5EC~Eb$=R8o($3Z;%I^K*GsXKRQ{}Uh--<J6=jZXl%1NB*7o?<JH
zTLVn*jMm%BV=1hsgv;qKqf7wpCX9HIB#&%ocT9tU(Dxo*qrMYg_hu@DzbxUx*CU3y
zjg5^}Ah*J#yFD9#FBp>nFgHh-)OH*&Fu6r$%AE+?cl@U>fU~lq-`r1ezK}m-W7eVD
zYoQ($Q}Jx_zOyAlU=%gf)xDVVe6{|F^m+QAOF{Byo5ok0a~m6U8cJPHPp>&_&%WB(
zu8*X9C}Y}3jRfP0<rZ-ibL{-IwY38}QedH8Xt9dQch}uEhJkynforU75tVleZ)_>Y
z!2(xJ*8%6DfDom;4!+u6-rZT)J$(k`Peffv@W)LcN7+DFs;y70sUkcq-1BmDaqL@y
zhIvZ!*`_FN<RXy!8}!*;YB8cFFc=7zO-Z_)0^n$$R%dexu9s6X5~Tp&c7egU<h&+1
z9M=I^3EBdzmv?PzrDL}g7{>*Oxk>?$eQ$*BY&nPSZjN?XmOpx+Lnxc5G3Et1pz7f9
z^MC$$cfe%rTHjtVVHUMIMgwM&yUhviHIl$c36d7Ze^Ph1W)?ZcjeANLyws+>zcQ2B
zdpB|!K^at6(%XT|@x`nzlcf8rR=YHI3jqhHX9})+ug@+;6~DIbZ@W9_hn?F2EajHo
zCY?r9?*g(KItS7`dIR@X9XN7fIgq~=!{E;SaD6zuxdPC@D{GdTe!C<76H$3eAYXJZ
zudEQ5!Hu_yIJrvcCIXdJ-nhHNsIz5uV(QP*M5c<fa*VA|r(4@SCgcAIG+npMxjRK5
zm@ZM><?}22D(niXA`lFEBajk?hM-FLXBvz`$VcSDhzJwmZ6K)_GbOj_Zo?^^){zn?
zgH8abtAC!dDgEAMreL&TUrKd7koA@F)<H`userR}mew#`98q9#5t~N^HMfn`I1@yL
ze{Z%C4;KhUmH;i6MMN^I!ATmSKqp%O-e0njUp4=(!gZji9XT4zSYy8M`u63Rc)&s1
z`~DV0??qlgblR*`!5|bpb91i01+yb(q!h<SPz%0R(%_Ooe-_7(Ym{!srz>j!PaY$Y
z(zWF_-(DOGw+deCMIyjUJM`<$_NMaAC@|GR%&g|5`8rdmG8<#gByVAt3^KOtfp!XI
zmF@;0LniHNGld}@tIf3wDsMYZU&8jJvm8D9nOIa`U$5m+w@s&w5u`VLhc)qT(#`X1
zDCqlFP&*wE1FnL&<)t0SfxgRF0M&-<QQv;6ezh(THbPxirrR{;?3wP%6xJ312b<rY
zv7VBGt@Rirm=pRIE`XuKxv6YnYNpjAtdZRy24L!dL>MNU6jVWUPp&Iq_k?Ym1GXRH
z)P--J!T1dO_9wg5DS`hO)9uNqrDl=TEmvT@_{pg$T5DUIWDbaY9iY;j91N0}vLcAa
zb#m0e-EBr{6`;B9E~B;Ev7oS*-`v(V%meDYV8p0oP}rNFVh%?a(C(KrZjssZasca0
z6wyw)k*gAUn$ILle<}e~jtme8-a`kU*6rRXd)WCQTnZ$$P8MGOU$PYy750Gh34t_d
zdHLaw_u<UagGREvFMUJcSGcLozBj>AmA^kMhyZFcS-sMZ!ZR-mB$&4^0x{B_l$3NI
z6f^$zvYI(|Wq`6D*#$Ld$wSiCrlzK~AWm_&-<WtU!mMW#NTqIM`Y;gUF5f}Oa40WL
z{$08Z#9^>6vltN#qyOyBy-pH0$}ZV{|Ma+d56JsOCNfyZ?Ag~XAIVd5i))i)9NT|@
zihr#c$QGqci3`fryM@3nItok+$Cp8xp<Nq@{_D%p!kycNfUIU?&MmA1Ry}amqbdvY
z{Ys4b()=g}gBb#WXy`BbWuATvl)DQaSNzX9XRy>|<y$|fB<>9r`tRyNpm?}yI&y|N
z;Ce<ctv4RKavnWMR(Fo4fQJBo@?B-l7=V@$0ze{s!N#ljr%IEEbvk4D=&L8Q-15T!
z2r`^32pTj^;=ZR79Gi!u?cdiYVzJn76|C^TkM}@|x`5b~pPZha=LM;06Bo^VC>r<A
zy8Q3Zj~_qEiJRolF_}cf8ns`#%|{V@|NVTEb8Gc^c$|<?496QoVW)6iTBP59)&k&1
z1!={_(_lJ>-xGwcuSG>g-ZDJQ#hOs`evgeQwORfwG+tT5nN~P2054DrRPC~aPhCR)
z^H?(EX5W2G>8iX@S32gn_WL<=xQSYz+3H;0<+pan{!ahcL9n@F$k$&JdF3!E@vAR}
zuvHd9+jcNjed{hk_%7!H_us=HkSllZR1RHodNVOf6&x0K%Eo)xHx2pHTiRmIVF`u!
zQ$kU1;EU>Aa+r`eK1O2u?zI{>3r_#@a6;EqKbW(c(>acTj|`#{?Su^?RrB#K-)5cW
zY+wI-`LILx#A0KEuQpfSlbfxCHCO(-AEFnOt*c*l3*+1C*|VmC@%_*94j+DI=<4^O
zn>*(!)q_9%JI8hN^vcDtLmusSU8`*uUU=79*9n>Z`!sr(gz6L7H6_ojU^|R&>7y;!
ze}4~4JdD`Zrt>*P9i!yk;~OE}`JdZZ8pRua(6#Za#a|rHO|QUL40>-3izzkzGxY}H
zKQd(}AGUoL3Gc2)SO_^)<K-}Y@C~CbDR=<JSv^}MD;>WF^X^3)P14DEww{Rx=5`}l
z!ijLZ=Fute9GkrO@Z-O0<iURM+&V#hFzXL{)kn-c#^>h79p*#xVAItD_IEJEoHHRr
z;x7vE2xq0W`q$LKH*R%#!8L^1KBs8NuwVM;*#COnQei_hjEli#;Vo-v`C*!D(<?$+
zt7%H$0KB#pJXOv44drH+Yau=#isj!2DYCffFuv)BJ1*GP$9%>|>&3)Pu1R}&h~4u+
zO!lVbOyS?TMBPr`H|NY<L%mDz!kBn_`4f<vU0mO{9un#<Vb&Edey!r;{~Bp(DNWH{
z?|#Z~hbO?zDR->EJ=LDRbF;{5Toi}$+%s>j+X&n)o6pn?-ve1R3(2$DUK&F&zX)3S
zl|Fa_8ItksCRfa_Hl<_^E^}KyF7oia0Zj8W0R9_$FE4LS(cC1;<t_mcB3kJfB<z*2
zh=}9vy2O$FP@cVAulIY<*Y7$UwTQS9`_DLrZF4E>{A=X@?UZQKFRhvlrG8a?-hV6@
zCdgh*Sxml-{qQds!aRN-WB%X=b2EFzTTLl7)DOV;maJYp@y|}k0Px9K$+(`Djj9?v
zMvyAbd539j=62%FRs1*A=sIo(v461x+2m!f&SKyu7EYgw{CB?gi&8lA3SL{o7i{(Z
zIT7-6<_&0>2Tv$pLKgqO`{qz@?IhRA)a1c(GXmS15k2i+hlgxJK&!7b6DwPSbN|aL
zz|!x3PyDVAxXC}C`89wJ`RC*R_E#JJA3ZoOfdYBM=?s^@wX?<lV<>Q%cmRUwG1br1
zWQlPg3)YBaUG09%T$Ygb0-rKiSDTsu-?-V8p~+H&7RC5{z;9`?VD3#}GT?*2!@^$O
z58Z&=8sCw03hVGKTdq)o3{b3|hx{2xJsPw6Fykp01I~I;_!)comq*`<Qie2H3en|8
zU2(YM0{ceddt|$BK{)rYA{uO7ubp2^%P)gAC&elC9N^#Vjl*Nvvh%ivk~E1JFOBO9
zKV&WZ)cXAPec3HWE_h;weUo>szqJZJw!CojW<G1b;D`4ZmO}zBEsq|)d-Lx8J&h^{
z?wXox2bggjxW%!*LB;q)Y+~*!ey@{$4=S2@Zn5*fIl(U|_)Kt835y$0&R$Nmd3&zX
zQQLP77pTOC4qRm<$<K7B<}2ayt2fD``+lqje?3VYEqj`e99CgKkrvXG<&nzUxaE+C
zYD7bx5VD2u;<5a?--zk?Sq&-L2c3{g%R7s^rxuIY7gCg7ELIEeSrFRSyYDzex^g8?
z!E5F5?nR1RW=Mr&=!Qn5`#y+ixO-p=8d<|v-u;36wklF-og%ff$|qeJwXX}A95m|w
z2+e2d!|jcvoN1psRkwozC-res-~V;4*Rf%LQ^9@w{(sg!Ton2T{%0@k`Mb8{iyG@c
zu5nNO%U{pAtiSi~@0Wzdu>Z53;{WPPSdAXfI-YY~qVgm&CfwgXxtO1qo@0=0ox_W;
z3Y8_g=JHwxf2{maiLa~-bn!)(I-O!3c=oAQr2G8{#nO%L;kn)3*#q56w|Y_8SU<UG
z^WN<YbX^{=fm^CZ0`u4}58r;Rx*k$79A0G<ihw<m--)b+4Gs6EXOHrI`a1^q8}?VP
zAWhfmakS!W5fhzkCFNnptJ(6|Ga2NCEcb4^iWgD6dD+PJo<GULBpCCe5Dm>Q3ZZPy
zGt{lD*)pxYqi2jn4Eb`Svqvs_x1z7Ih7xr69Iu+CjHS!?^8H;e_qhk$gz+A?bSg(u
zhHR18I+<7bZFK^hIArK%LUxEAF8#YA>GLyYTQYG=MC8g=rr*HMBEq>XB{UPp`!{Mz
zJZE;Cb;!pdh~l17=aoPsG4x&NKNIJ?RdbMDSX|?st=qr+y3wk~bdU5^Sr~WZMVmh2
zbD_0qx%0C^K34>K`bMhA7B$uWn3TRjkOX!=9<+C4oovnn<?;CBH@Cd?V%T*duysD#
ziX?j&{jliZ;v4ttk6@-~USpVDr<TN*VJ0q5DgS6sgiB*L4MTh9cVC@?92EMMT-RYV
zs?5V@m0U+!QJpWUsj~G$&N<TSN;x;Bdq)kqSw5{jn~j}SX8Cl0I^2}aGCBu-QlXG2
zQJGLdE(%5|uZ(oJbb!U#nNddl6$4uG^Mz<jA8D8IBhtb4CQ07z0XqA?a4qd~+)?_`
z9>ySMo6Ik7)gT8#?yvQuKCOk}mcp6$s;Um0xmg<Gy5Xr)96J`~9eIb1f*_v%lY_AY
z%TzvCKp=ftTH(BdddTW;NTo%{=Oy`mgwGL(#5q~B#JbS8=XULV$6M)cISE?CUE7VN
z1GJFfVcU!u>j}Pa=f64ktjL~)t?kj-Im#b-t$5)Z>GCxjuGmEB;7@_hrS^fX=XyO1
zkts?k#=(X1LFO|bW_euxTYGOF4`uuQjgvy%mLl1yTc{*yEM+W}kc8}8wq$FpX^d?w
zkv7X+iWp0_FoPj$7^I@?+t|&JWf;p~jKN@@%ecRv&;5L#*Z29oet-P__&v{eUcJb5
zE_0m6c`WbaIIi=$dKpqv;$@ZPxL0W*t!#Clo^pM6D49@Y#*Hq{7c;@ssN`=?VfuIG
zrIfuHpds;tU>#&C0lSLTftAstq{{qIb|$-nnEv73hoZPD&N1MDNlZ+aZ@R1$r*)ZW
z+!HmkMb}i<I4HO5x>H7oM%ZzbCaMn!GxL6QrH^5Rt~b^)@#~8R`}qgkAHa)7o&!Mc
z^naXHD#$#9v#^lnwB+9BQaH&W|N2HImJQ{Hlr|d_&S&TvQ}E^Wm{DPKx%^A{xO?MJ
zt_%j_!=wMO_cl?>t_g9$M<S^f1hWQfQMGOY!=QhhJcKJl;!$yh%eEM&*)Q2Bf`vVL
zFSmvko^fF5-8n_e?QC~RM0Nc{NWh~62dWTvJ`OZaEhn<FgoQH5$(8R)E+<%Yq2dY*
zkai&Q7!*ZNvZ|jf!g5;(6NVU$ppn^CHu27o9lUxwnfQVzj}-)U;n6`BlvtuNQn=`b
z;r>ia*;NCONjqaqRt+@aBW&OVR@{PMN-A15^0aK5W%Ou%Sj2+sSV}FWDF<I$_QKYp
z49!|PF*5s&;U`AQwKE8Sr9;`HxJG8aP|X_fXS*@b1O!^mYkTY^b{^Z2(!S^{uVkw1
z{=uGIMEyELVm@xM)(sYmS?+ozeDoif!Dg7s6?LW$hdrW@0b{6ahVPr6g#c5Xc9Gw!
z3+ioD^alGdRKQY2ldVn|3064DB0gBvwHXpdVZ(w^D5^0*PrqqW&Y;Rrx3Tti#-laN
z(ADcj*(vS6F#+M$`^1*?M;{A_M~d0)8bJGO(`!Emc~LB|Nft(AJ<!ww$pW^BPBIYJ
z46viTBSHX=zQ4X)MbpM$y9D9<j%|m8Tt1wU=lJs!@J1CQY6)i<?$%TAaKB<hm4q*F
zjKQI;Op5#8#Kd)5^GKa|ak>=-14k4A_x}l$oZ7$M$n2aSPW58JGSIBUv*L=m1B^SQ
ztl2R!ZmH(0QlB66<5*YlL+I}OrW_K*c5udVB3#+Dkp{<-^QQ1tKLT4!!&F4Wa)R*J
zclW1d5?P71&YF~VY4!#hy%B;MJ|7t%fGM%*9y3YJwK>E7N1$tG%y>S7`wn~nC%lzG
z^&Wf#TX~SW<3D>Ffu@#FSG`->H-AP1G3c~B2CRnA5yNfwBM{-*j2FpgNNRZQWq_uL
z<W)~|UtB3@Dop&J*S$eg_Q01KA_N=Y|D0Z<Jx1Itt?sE`XwLSJ<fTE5{sU<>7Pv66
zt4)bXUQ)K`LdM}bN(WKtb{Ibwe1<uJ>pwaTDL53lno>~)DPHv1bzcLDICg^=j6y6v
z#_6E&LL~><ntQpQ1BV2Z!Ax${nI4OxDO*)K4*F1Mu;ZT&)Uf23CTpFU4Kf35kZ*U-
zwi4lV384-Pp;Qx*5x|iK-~%lBQqSdua-v0Eye3NG6Rlx*S<{};WbSUqOe$>SAu=?Y
z!)s1b=wOJ%e}-QDbpsO^Unqk333~>0mE=rzXPj7_XaqfJq)A})kT#^Lt-lBH2|a!p
zZI8wc*FUdiOjp=+4;0{Sqvq;Dt0Quw;!TrG!K~Z+*%v0JyPSM0d#&x8kytgPEDM$)
zL=MXPLJ$g?YLOgcIQtK^fDyB4kI_)Rr_UI*@c@uM6lltm1$%h^f`c3&2l%TXb&roJ
zj1G%G0C7RNG*bogr5G22<$*1JL-hVMMYpnb{dkbzSp0gA*&8coO1B)(-?oH4XNEBB
zUdd~H7Hr^n`9Nmh(x_QR;iW=#Q`^cW6>h=&43@8Nu=3bw;u!Jwk4i@GLS;ax@=Otc
zZ>quWvTlpCIcQQ6uztuX|G?!ktO+X2cHz@L4dE2`pOQjDm`GHWfw<gQo3Ah`47q24
z7h4{9Xugg>SxnOy%U2lVz<pyw`kk(UfL-P3eRgrd=9G`N8)?={`39-O)Hx!kZ)#tj
z!zGpa?{=eDgG62!qzg6#bGMA;Y*Uh#_?4}?Hop^VQ46MB8G(Dr*dCS*M${Ygmj)%H
zK|EbB%$>VsXT5z>AB#3k>RPPL+O8l{Kr}Ix+pv8~8{Liu5J;7vDVYtQ_FdQJhKeNX
z1~pLViaLzJZjs~B){!s^X;W^<m0$`Nwjz(ljkq>@3VWk(m4GNz3_d-g3c__SH*|tK
zVhr(mMIe`EjS>z0qTVIcbLDoq!^<kW0V_-uOcR~hc)6OFivEF=&%_-q<wH!6Y1fpo
zfRN;n-0g)&v@Buffrd;LTsF$Wf@=G<6o#@>6xAsC)q9vNOU@Jz5)~g`rn>^q=2;!U
z^p7Zk>X$<|Duf#Bcl#R^<Hf7RnA+yOQ+6H%4vU!YE@vT_!WDfyz-5un<X^<d*ItJ#
z>Cqek-j?u6l}9dkty+(+Z5r{2{>?6ab9!&YwbxYqh~Nqiy<IylLXIHn<V{76kU0r1
zt}Gd#+b|h&Fg6bv%hF#H22CRNGe2T(bTl&zSziLLtwh%Mm1a}F8irtCbtIJ8bGaIR
zCQFIuW}fQ5SG;zds8;)6t`3IDlWFh|-3sI`>;|MYS_<TYKI(@zSF6-z|B~y?DTVfh
zQeuc(fcLywz(Q6wp_{6Cs)X86qKUn>ae-Q3791}sekpTcF(aoAp3iDAQHB-gGF`|A
z0=dKpBJ*{_kp9*2omwtiASk0|S%l7}05Mo%>uh%1;w-m@bB|}?OYiiJVaOr7a6Unz
z6a?nZ(`^o~3%V|YaNJHzUX9q6qtH-t`YnTiGFQ=0EkpGO1`BlA>l~G#N~T?D!=bz3
zY7EmMp#W&t902KQ0~HUbvu&WMTJHX~pt9`km&r>BDN*=_kxs!b8^oBx(il-pKM{S^
zWj0k<!OCwgv~LMrUI}7^e&J9zK&{h3Q}Lv=G28$a^S~vfB_hZwUVXSKdg8f~Rm$%s
zVma}ee$%Li2hWCjx$aP~7SsxAM|m;-1(^I}XPUykUO4>XtY9U{x;9%ZJH=|U&N->k
zMx{XB_<N%AW%P}{mgU9ml#tvS7&s|pU)q@xhXDKJ>Md01%SRAZ1*t<m+6|P^O~y-t
z9znle=mcl~D#SUVx;iosf~2VEl&`c1|6)+^6G}xgVWaY0W06ZUNjyJ^vW%2cHVzI=
zK-1#@xLSmQ7-neX>E;IqB}mHEm;PnI&|0V`u*#(+jsU}AyZR=wm<A}6d>s?>BaTS!
zV_4NjBxl8~ZwoHXVdkPrWtcp{I%!kG<ZCX&e;e4{D}XN#rRemn8ev;fE`m$I2qcgP
zxK>6gz)G@E7&}sO60nG<bX+I&iML7G$U|A&Lc0h#j@U|6T&fcstwr*44jp`%vA~rH
zOihg|5cR>8$7VhRSAm9#fanPSXfzzo;LpWpXIS>VPy%WS>1xiuJo}*yak#d{qTz}f
zV~TLI5rw=OSMvrnxJd6qTO35u%5~8C!1l9zuxJD?v=YY@HxCTHp7a0eWrQBRj0T$t
zv*wdanSlp`0H2G`0E^FUj$q3N-MY=Of2HK)#h(4&OqY!TgaJ2|`QtTrL4Dr#7I9_1
zUZX%={T?F>VA68}xC=$8?lqW&nX8osc~BOKPyglrUt4E6LMYS3B`gKSt$2Uv@2O9H
zVI{>8-TPL3v6}pUqt3aD+ftB>G(imyM_a}h$8=}0;EF&~;<lCy*uW%FJ{Jxnw{Lbo
zJIIS)zWJIffOG}4x7l>yUj#C>SnH|^_d;E0;{5#w>^|oyl;6pVjFq|Sr3u9l?XYUD
z8{P$WSMD){i%HKBApZz_A*cpbT2uq0#QvM~`&pA&#A|@}3FiNM>&|g1MpC1s+T;qq
zClZ^8Zy&W>&tP!DVV;9;B{cM-3WM;%?mv^<82@w7VE?a@EJFJRpg@Vk6k@1;nXt|1
zo(%{ZEezGcf*nZ+Wczt=?ZHcimkNI)^oW)8Ev3B#1EJtuHX8DROendJbf9G7G|>*!
zvNOC6ly-oR`TmMBPzW=0s#gO#C;+<Q6$kf%P7%%YpW>uV!;I48|GjoeA+BO|J5f70
zQrD-p)`u!YFr^l#TifPs--6Cv$1o-YWW!}ey-p=aB{ZZ0dW7JC5Tlh!eO>7?mlMnv
zj|M{PD&blFLplZ;IJa=?i#U5TZp$aUki{%1`s^+tOW5)JSn;+!mU3$Rr%<pfWll>O
zP|M4#fEU-e2HN|6p_A6%zWMcNz#wYWLRqwWc9Lb#NnWgBj`h?;Es`cR;q19{n=<~|
z>jATv+3o}A4B(^yltlvcjU*4keSSqQP8XziU&maovqz<mUj_wa*zMkgZZlO_N~=?*
zU|!#{5SA=md+SNtqo9V$IVH=R8qn9m>%p+<nXFMuUOW{o?%uW`x}S1ma!>%KbzKm#
zwykL*b0rIUi{<l2S7raI`K4(&U-9+D*yaeEtX5<2&}{5$uLw!#+^5*_{Yy2milv#H
z`k{*e$4YoK#~&{3f~JDezv}?!-{AUB7>jak4(n_6V)c7SK>{fDUjP+Av2af)?n#I#
zWs4|1vitcD1X3I!=C}R?{|t-g{0|%}@Q4#9iycRrAfjppi*S~nXm!A!{(2fzg<9^d
zVr?5m))EXDN7I;4fHc`82!5$5z}-)~Iu0u$76Z08)h<m{?Er?G2>^Bgl4>@dS!0m^
z4Xj$4{oskkTTtDvWLs1-z;y|Cbn4gOSW#^4r}&H_t|Bh95&$NX4C40<EAWVlc8Q6!
zifrAF!Sksa3<TZSD;RVjuS^3!>s(gS@2M4TPf9`7?1F3PCCN@+MxqL$Vd`y449O~N
z?PezR=zA6W<M~8_tB7WOt1KvG&auD4P)`89c?~_3TK1?8?y-xb(}+*!H=pTyH8B*T
z@fZMQz1*&_Q1j<EMv)|Bv5n{Ww`&#_T_57SjnWkUE#%?UOG_MX8g^<QRa}n{FwQO^
zt^XG7LpYtTm<&r#o5?`?bV2<86mkIZk2rqji)ipQ(yQcEQ8pq29Xp%{lpXijC1gf@
z;OfKhgOW#LGXN=aK~u2+TAxT>wfd)9(6|gz4@x3bl4j~`I%4n||D<dQG?ifCi<@$7
z-WRoft{g;1HOZ?PX3sl3NbanpqgWG{xDy9SSpeHJleVzp`Yk$U;;1<Mx6=P7Ut|q0
zs_AT(C*>Cx+VyoJN-?OQo_{(T6e`$UE_E38%{J}}3>-O*<hP&}RsPdk6^&S{eNib6
za)C~I#eJ#MEeQ^x$n*hEb_YI|x%z&{U>C}0U&|T&ff<Z>0@S~#g0X>@!vUYNU@H7+
zstrKqfJ=%{XUGVX384D?W{A8g&R{SjuNCgdQNfBv<S?YFuoM_3*BR4skgv960eX8U
z3yRDCGZrDaHdBTE8ETNQ2q4r*wmL|5`vF0(4dpMQD7FV%fQ>gruofPOXH4dyp+S9!
zXNDI(C&;y;X!{{0cYYHb`19ZK@UZ~H0wt0Qy_smx$A+T%>DzrnCdq#OJ5O91)wI3Q
z>(5F!zy=dGfGmiq-xFmp!+al4AtQ()FXD#d{an4D!lwL-eM4f=OFEwW9UP`NBq!hx
z!I3Quq9sWz*rd_j?e8cGc6@R4@^yyObI9565eQ~!Q#(?3(_w#5_%+0!{(w2pAvr`5
z_8Ll+FbkSOuP^B->^Cy$dDt<_fo<=N=j%@FN@HK!1m3I7O}nwx5$)~ygR5v6S5<tp
z4()Yq_iFTvq>?!dvPO|h;I^>0f5OXDq75XIMy}!z95YBFMjr?HOpgvhtDdq!+}E_Y
z1Y~G|yxpld)L!{^F7X*w&YZir)AFGSYW=NLn-|AC7t0{xIi%jTZI&0Cc53`EQ{kII
zb1E?JA1gXo(#4vWEZ^-dxEX(E5p@Cd&vbmH_?ZmSz8E+`kf~d;^^8bGx|&(Y6$5G7
zT4KHw$eQ{J;RrD5;2i;jONjR0YLIYivVQ_{imTSYMa=(aR&a7Hs?wW;A=uvULklw{
ze%D7XKW-6zvgxQf#t(yoX^<osai0Hu235007du(?#SFm(UZJl*+O+s#!^y7iA?!>8
zoXn(=t=mu!AOqxWprKwFbdOSSWTS$|J8i!%Lp+cD!GcB9>WBq@OMgs5T&juvLfba8
zsT~*AD4$beayVnRf;S}AdIyhdRmsE9J9%>JQTVOKtkJ8dMQMKVb1!(ks^*ktm!F2w
zZ|A8Iy7<RgnJzme54^PIt~75jOS4SFon6^|m>?;_W#_=i3^cMI4d9>ZCvF1Alu_x{
z@X}wL2WG|mR%7keQKYMK@(<ceqBfR>N>cv9%YZJ8c1t9ox~k&JeOcubDFeGMx@tJB
z{~*#(EdW!+Om-hqN)GS8nSrU%Zqd6jc>8+GeW!2rN>?6_qLNI#3+?R3i-JaPf*JUA
zKn-+jFv$-ojuac7j`r9e#S~mF2MVA&iFY|U`<tSRan@<&6O8OoIMl=b=?It`cchja
zD|P0893aJf;1&M6;>7e{m(B`+GD^5nwFO0wll(^nDJ@hr1V}PA;zW&heWBm>3nc3I
z)q;C~v`eG)aiCNfj}}w%>6l_IW@8Ger73PnE`1KXP3a{HzxD!B1GD;#ZFhDaQ??&X
zF;$3}sD0e-2crCN%;<gSAb9+vwEFg@7WK1K=sZlLTUl=0x(^K)Dy}Do?96{>5i?kk
z(5m^CrTvU$eI#Ohv)kYcse^yKIVGSKq;Bd7gs}jaaTz<fcjmtfiA?{SZT(kK)zkV3
zOW{Z+XsYeh00S+Q_yBd1qA65EaVOf}ly0<Z4(-42=)R?`3Y?*2Ks~S4KNO=<5p*BO
z+}4u{(yh`gcGsV7Gt?3&&ekf48L7F-b$J)tnP4vB8D&U^XHhT7Fds@UP&h=<6qA1h
zClGtENBRh2HW(#514cL;ISh2c`{M+b>ZBG{>M^?cG%V2@hzq>a#mFU6Wr1+-eFDNU
zBjt0fiH(MxF&X!4KO>xq^Fy<M1b06%4~atQn2i#~Cyt5sT+SqaW$9;7%mbKla90eS
zY8N+Zq*gRC0N4u+6I-+0o%JtT+fzhk*5W`^#KOKYXzV?)(uoIByxzqVoA~@<TeZ?(
zDRmZR4Py^9aegT8{n8xtc7j!%1=l?WW=Kp4hwW7st!;_|(XRyJV$VvHl;fO<&HbOW
zop~qrE>wE&v<m}yG=};}ptry<m%6}bgvI?rmT%4OJ7O}+#zD%AX}_FF1ai@4sa2Su
z+mzz@4R3S08XmO228TPASHr~91o0m433j&Q4cnVfX!D$9T#V|B58$Xe@y}hGVPnt9
zXk=)<3j70bhZ?{tmS+HUdrmGP4e&x=D=2{|L&G!fMDh7J#9-nVY}efun~m(wAJ{Y`
zy#_DJu!wAjfwJ9Fz8&aZ5nB1nun=Rezf-@>|5sMu8Jh9`;!cGAPg=^2=f*y(<T{3)
zS=sH22ND&GIrR1opla=QK?EfI>VFh^_RU)|)(YFq{Aj#vyPgPQ>EBnT@LV94l-zUL
z7HA@;90o+rlz|Bo6A^`sZThc`_P@0I|KeLFD%^WGyQ7JJ5mm6wk8GfUQ0EG#2sndr
z3#s9-CC~iOkK3ihGg#{4%#fiO-4c_#?Nb3?9%ppPJ~Lr!qhW-pu9-g;-|%gH6~A8)
zwV?l%jM>+Q>jM+EGQ!9t3vZ9E$z@4wU|)Q#-xQI8g^=Nlq(9He;`grR@yWyLRYBI2
zajc_$J-3c&%UumSNDciv@w~nZ$!;nSN$A8l?a${h&I_opJ`$fg92x|emoRg9O--S<
z{bR?LC-eerhG9IN?vhTgAf6(-Gpwb+AN!T{o%T&R6dqk!^tpM1XoV%aY^a55&<+f-
z0dU)eG+_07G$3jaZbkwsT(TTs%Ebs$x?o0aMnxxHb372pQ3~&Sc3~hJ;t*~P`TZk<
z_6WWEJMgua({eGcbm7h!8CX(PXdx%d#%H^<Fzf(ah4kJvb+jQJ{<U$uP-B!jH6{*k
zkshr+%-?f_7QngtEva)K@2dJJFZ7v>QkU+iZ26Mh$e&o~e{MJbu-TZetE!_dF0W5{
zi&P^h^G{zFkK~cXHon7PKX0(@jXi&TX}Buvd3_+!b$c!$)M>(NAWx<XOb3gX3unOh
z?EQ-(xy68iuA_Vwi+zDfp_A>oU!sduyzzVZNiuzE#<Ok%!nW+PI~<Cw-vz^eSajK@
zL*{nSq`pAc`rnH1c?x}>wyq6X+D52b+cSRI4#o1Br`{K9jtqR*H|M6$4O)ap8#o?r
z&{c}Z;#E+TNbFIT!Mc!%AhM6O{-I<zr?WP(+)ONZhafjxrMxY%k&G3AO(vphecG^o
z9<Jk7ONvjDK)|U|h+8hY#}y@tAFJ{Frd)Li3wffQkdYzdYYioX>O+2sj9Mj`2XVrS
zM?!x&+n_1Kpd=#jBv-7KjOs=?k;^p=KK~zG>T7~V-&=Ur{9Z(NmSs?8f7esATY~+p
zp;T0T=mh-=j9&&5-C0;XQV(7Ds^`yXYm`>e<Yb5vE141IH|Os}XH#5r2fHnhda(O&
ziX+g&v#7pgzlU!4@H3fR;JKQX^NQ;f;sUtRyJ7FGt>!4QkAM6yWU4WP_&LBkUra$1
zU$Ff>v|pukyK8>a9>un(Q-rNHhb~AlQ;z$WuZ4T`c;Pz3L#;yUe<5DJznY`sABQQ_
z!54Pg{a95l;>s|QTfhop@8r3Nn2tZ%Z%Q(^-)}08OWAL-D%#<XKp;okOOq&bYvy7?
zrlKac-UgUL3wy6M{Ky>-kH?&{eYD@GlpPz4@u2suFu3AhWr&@2`<Iz`nfR}YU;FOR
z4=)Dk@5x$A5h~Zp{WTLHZpl&(w!ErE>q08XU%^?<kL@aY@gko2aga4!kd=q`W6q){
zJmZu4dw7rJ``yzjhgestr8LU@O~oO2`q%OMaGy^DVS+i}eKlp;nEOjAEZtB4GHsQ(
zfhzPJC4K`bR+<h9mgwv^J47YU8}^)<Psh@So$<+76q38bFRi*rfA1n8ABcu@u@Wi3
z@#jLCg*U?fAphO2@};IGtqdsHmKFugSt~Q#^L5**?MNx*N=yBc+T0-X1mXMpvoo0{
zx>>&K&<6@1!O#Z@;Gg)yUqKiPr7sZDJk}LSHPG;~x}C@9`G3}rh1O^-WhS<K38alV
zEZ+dRY$d8e-YLZn%M=2b22LAlA(?mKTKou}KL_W829e7~X~yG4HJ%k;w(n3@iLqD>
zi=@TiQHYBe+7R1Je2&sF)vy$`upO8dq9qZreM=M%P3T<;M&AxWLK}Qo9yNuRZr`SG
zV@FXk7FP7Xq|jZF$XdfH4clD^P={=z(T=natJ-G-+9p9f=LX$ygVTSuPeDe;rtvcg
z$XWx4_~3L+P5!SiSNgy-e%^$Qc4yY9sDZ#{;c7mZF${Z6Ox-O68<GQJLyLpvF9ZeH
zL(yg;4T%H4g+{uk8!Ff=s0Cgoo<-^wnA#vc${rC3r(c<UG&eYRJ=jo<1ev=YV6WB%
z{1sqmOQsL2Zcwp`8Ni=`hMYdvgMBKdz^Yy~uYzE9H!x~yVzqetju4JrA+xlhY0f4X
zvbHG5c!9=$pBw;xwB|BhNV-QQ5;JLodt+*a@9D}uR+OJ+{q;J~AF%wb#Q`b>gdTLY
z8=UzQn4*K6a{pamh`#uJtq!X4miP+g5wI+|A^mY-l50mGq6M-}AE~95SiRbAoWfJ#
zUDrgGPd`pLayY5>X~|)&OE--EJk6gSe$GS5f!*bYz?<0HmtSS|wEVmvq<rJZLkv>{
z`-v|vJ|2GgQ~x-}9fuqJERT7o538p7;dYEbV8w6kPrB|>o)~sf$*SZ!HIfYg;TO`E
zQ-V;ac)`<MffL&gZ4&HyTDrNSI#GpnXhBcVvG*$4P>2bCAO|+r*48wJ*!wB^0lEX~
zW}&$)fGyCM)vYkk<-$R=aVZaS;(GMhIooeMASWHI%s!`ee;wBL#&u`A%t$%wdZfYt
z0!=0Sgf%d)f#Zov+OJ<3rU_*iIGLKZx0!xd;#@o8E*5aeQf^WySD_QWw{|Yxx#w}3
zN&vap%^DoQrWTD;xt<Q4pXlJtuBj+^trAV_FTV7rHuRB+rtjcvO*!feJcii+DKW<y
z%wEm4$v$cRt|-DRV)Xo8)b6YNnAgWHNIdJ8e<fGbbzIxZ<9c<42dibIkKVmhx@yVZ
z9K!fT|Ed*dz!hsMe<O2aO4Me%;u<(@W79)z`_swa$0rUo@$cHQ(|k`9?P5-4PL@Y)
zEDdKGrZ05kd>;o(ke<bcD9>(5%yH@0TKzhCLi`+cEYX@Mz{f|v!dI%#&(jG)vnWZ3
zXmsqDpBYmRwbltW!z9K+GURd;?<{sj1ZUMDabIn2#r)>NrM*UOemDu`SQ>w`XEZUV
zz1LA^!BWW_$D*F$gD{pMkEtB>Bxe0K$@7O21+Yc>oIma(7W#{$*X4m4f%V%@y`Cq-
zn)a+so0pHgH_CB*P=0EOzfY8wW6M4!oU0Zxmy+l-<aBQD=FT4u6m|D0`Z;^0`-(D7
zoFm>D;3~rrlk6w3<3f<}RGwTKU#72e0qaXcjj5|u!1<k+SFPw{uZBK#8fjqE;}abE
zHx(K#<Fw|ke#qg%)v>-b3Q=2tVbBRTewV$t{J1cA!{ug+$vB^{SjxH*)Z?$syC$_N
ziOSl2BcGzb7I*Xw6-W1s%0azM^o)y6!kg^Ct#l(P@}X;YW0c|tet~r9t47-i(fLlv
z(Q47fF9yJFjgO@;ER%}ml`}8J+)s6@YgjhCku8g2g@cx4-eSu|0)HpuF30wql)rGL
z;ljBGh~EZCe#zFdi{F}E7u+zjb7&R+m79A4`c7<e)A3zLjcQ!kJ-@lXSyMHAt_01i
zTTTAt56x4RPnlIxoUT<e^s5!phBWkJ^oyQiCm%?jzxt>3o<Pax!dEfF2|bU~J=5b8
z!`@e4GUHQD^y+-GC)~3}k}htm9v70Orf7J}H1WcjLk$GZ54PQz;O(KYJV#qgPQUCb
z*AvZFX|8+WX)PwJk#?@@GY4<RwWhvmX7$567u{o#d2t!9->rRjW!=2KzK3@6mcZJ!
zI_1ZC5&`;V{h{ds`hEzZHCsg97h6usqXxI8p|3JnP1o$by3ZGkpW>OS6cUwLDi;#`
znOFFGXx{v01+S8YFT=+^c7aqfTTkh<Y0STIiFeyVU0bWWU>(}msEABdT}}1}G_mDF
z9_~|G1iw;Z7`y8G(_L3Mc{5-vqdfH@8R>P}Sd*46tvi9g$q%eEgv0$_A@6GC0$Moo
zsZ%(^-OGaTvPYq+fNZx<m~GX@YpEE~I%Bm(4v${TJ+74no(1D*33fx;vlWLI-@>8p
zYOzrH5*f!UG*6+1V7WftywR+gw8uFbSFLf9zP8Ovial#vYP}xfviJ`YT^X=Ko0+^l
zmtVXY?n!__iV1w>nIbV(A0)C8*-T1j!CkqDK|bi-^k5D7TpGfDh6}3PF8@T;@f|t4
z(YWbcX<3BwaWQif>A>H-_ISEzKw&k})EMapu^sJ?{O)Q!iVb|A;OR+p1cY*+xY&=T
z^|oL0jUO>YvNah0JutrSocu_tk5O7zfZEYIc_Vm>=g?-VKoLxQN#djmCT=4@mXuIQ
zJjD{<Jrc*iej9c)vSU|KGdRXv_{*o6pTZdzq3LoL540zG{8QD5^b%1GXK<*kp;}zt
zjVX{`^%5+7C2G|Gni>iFOW=p!%0{s4k+=tID#vPm31=j@d(!iS8f<QF7TW4on{663
zool1^62BKlz9S6OlovRC`|c_<f-S9+M+PHQepGx}ULyYaFTH{`#bx-{zVoT=&k<78
zMjdkP-F*P&9@G86|HznAID^72A)m5<V_DRMMD=l$Ft4avb9P_#`a=i3J=Af0Rt@6y
z%?RF$6UiO@WLPsa)uXb&ZQs$$!2_(xGv-r_(TOdyujfXeC}6s<VLFtCi5dC3AvoM^
z8v8hyi#YHV>=v`;zoKOCddhfVRq(93tJZwnnfnN_k6|I|KOR;IpIQ1+Vc<#pWc1sF
z>JRNZ{hyZL37CeZlhCHUgmd^lo>`k{qX$n8FBy6)QMY+Y?xb9Z$DUig_9~>*Jybph
zZ}^~4-2k;`|A~IC7@S>hGuy5a`kQpt|MP#Qwx9Gzud3t2cKP!%HcxL%m7OVyi^K%w
z6xgtlKFX_bY_gXKXJ$L!a7sJUs2%mjOwUatk!v}n@1ek1b^F-f^y>i=X6cYUWzbqt
z4X^_$6$Oas*e-=~6oNI@C~dU=d~pZV+k$^Mv;YE_<41WfcsDqf_`i4X%-AG4I;|JG
zVO-fc*E!j%YC4%vEWs>QI`dvGYuYX`K^&sYJ8^rlZ?oCC*<OSxyZdenTSFT@EV4Zq
z6UyxZ_fGV<r6J=L<`mpQKKGZ(r>JNbkWo+HjVfpg6xBd=>d8??_Y&N?H`|#8dx;Kn
zxdx(jTJTG!n3Hq@<s7eMfUmfPbjI~6dyCemU$!7#h;s5c;TYEzQ2txhCt#ZxuSF!^
zOB?CG91`*=*Ov2Ji<{6$|2IIN3vAPBc8yz<D$Zh!Pe?DOxqDmc*8qOSx4iR;L!Y`k
zAsmvm-okC$_c-Tj^uTF_s|~s+dCKWbqtAg79R^0%4CC+NeG$x^AI+$L{2IL1pkVD<
zl{)jCvl{!nz~g9itYM;xqP9s(e*T9fcahO@iKVJWgWmg@-<k<4+S_^Tgn2t+_g8S7
zlbhlQ*nZ9bis{#q)h&Ug!B4N}@uj=-=azQw4xGdN8J7ucg$`g~%TXpGnt)q@B~V9H
zWtWIv+MW&l2T<kvQCi)(F5UVgrS2aDesF^W!s6C#Zp|lg7`0r9UC0W%Z`xULBr&1)
z;!<`}r!@UVk)hUt=v#?hWV!M!=}EoSbF06MlbYQC$1X_z9OH*Dc-<Okc*8kI$s=SS
z6D#;7OnXF^G$BfmKiQ=VKF=K?B&oi-ekd?5z7sO6qS;@?p(UybbvKC@%5ZnHh$pKm
zy!0f7uH8rEW!jE+om)x*)LF7%!#Ja4FlNohfI@iE>hwXtR-~vbIBq>*BWGZ!`qK8G
z)=a9g!?l8oA9t^AUWrufpOX8LN4VXf|88$N;*oWn(Nsb%S8Pwe`~{U+X8|g3?*Bcd
z;!vBe^SjMDC2Q|%MR|U(;123-+QzL>qa0sxO;T*DN@e3?V?jlA+O364ZC?yR_Hd!$
zpIeRl<xP{6pszOjF}7;U<nEk`g0kr^N`n~NH0O64dZHu!Zx#3eV&h}iz!En?%`V@K
zDUKNkbdFn3<Lm+IyDsj{zCHMN0En`<cdelCCT%IgRem#U5B6f9K|^O7raiB5#ldPS
z@lSOtDa^h&PrcZxeqX8|tUY6U&&MiTNKxIFjZ}i3;_=XV(|R>}PyGU^<4BSs^5m+O
zw6%seo42Zk3l<#kS~Z&3{mFnYL!}DU0vAguSAvQK0$K<-CHXrv=iX6(PDSJN64!9H
zter|R`39tbly=5T0AnQY&Xy}zO1YbkU%j+V&~YBqtN?#9XYtB1!-E8i>;G`iK>3cU
zySuv67cq}stTjuTg?zsI*hv8z)67Tqy#PU|3%(Cr6KEAS-)B-}Rg9CH{N&`lgzmuP
zE9x+vEsqax{N<Ng?!C`*JGUl))I_K_hY&}KFHNZ`=mH)c*!hK#;!eoBjkdE@1(Lw2
zV^zl%-i^UY^Tbb%57wLA((pZDonNd*bfoTsp6It3O>2ckcmEYHJRdB5Ng}txjs7R?
zs5azk*wxkr^P5xUh=i;s+$Q64R|=;3x^k2NrE*J^7f<?JdA=UwE4JzFbk0w}`8&Z*
zYJqI79i!s?H8ho|K6V)z!u4{k=ClOb@RmDCFta_256+<<giwKo5QmDzsDW<1BmIG#
zk3EZRYE{riqjCu&0L(5(Sc`&ty&D|WZb#jTerZHFmzEcv9RYP$#GPV6GkN9gX3>bI
zeBud4ahZdM@_6ch7TyT<@Xy2mHwA<!-hjXUEVe{nqWe5mwZHxWhE68vH|n`Vj};oy
zk|qNuWRJGW-aN57@5&<##0Uz@AL2EMo+>db6K~YQIm5NeJ2gR3c`l?|G*1dSDsR}L
zG`p@4uCB{%1)wWUcEddU^UEqwKI@j|i<7FM=X`*-o^`*GNVIteScxN1Hd%<kP~iZ<
zqR#i%CK`BVpT#(tVxPZ?o$^!q<w^YWw@H;h^u#`zuE~e^rHx>xJR0sk*tg}L(O<=4
zdPA0yL3BlxHET7;@fJyF%*KMHau=#Kwg{M9^Orb6hTWbAbwP};TjmtT4zlq}S#0Ya
zrRK#Wf0Y})tLL}Jlnf4{o_9frxNMrMyH-5DizWVe{aJG(D_Y1~l>n2LIX6~~=z7zi
ziMdAo^6NY!-tnD5aLFYXNwal=2O!T{Kk^I!FaN+aPYL>l5&gV=VFcK2<X^hthvdw#
zO_$I^_mdh3n0(PwvKcHFMRT(x?+9k2*kgO7A1Ce_HSzzrAhRYwbe8n_vqU4)NdA1y
zkE}sdhF<s}bC+`tIAP^ls(5{?)B6kR)0TNJrXGtJH|M|DL4M5lBw|mg#djM~HZQ-I
zy&*~sd^DvRop^{Ixw2BI`{sf=l}CGC{jcoyhOF(SOiQ9u{w2V@o30i()z-M*ClGZ#
zO_X!1l?E$p#}yvM0N~O5C2|LUR<}!;WhY%5&dJ2YOV+)0!xXx{*XL}O^R!;S+S$*3
zg8OJyoTq-Zz#h-$37_+srK@hb(2IS!jh#`^v!PKoF9-hIdA?=cwDgevb2{vcAQl_N
z)*+%*?F{v9l7+#TlbG#K<9Q<_D`<ySY@2qT1has7>Y0l41>QgAUU8bRi6U>I^$K#4
zi!zPe>=GX~vY;GATGhEbmnKe4WP~rUXItiBBO4Q=Y<_XG*cl3}?g}>*^(GxJ-s&3?
zd-Pd<JY$K717goh+avd%`&TUS>%Kkv-~ax9#etj)md7r#c*&ikYsKy)G4u1AHs^EY
zn6;<{=LnuP5I&T5NP{UoX05(JZzPG?l?iss`67$@huV#qbKBL)rUmhm`e?z4;JeLR
z4r2q%p*k-*3?v7&-sZfyc&_%)+p`5ec(zb<3qJjb*pcPK%dB3^#Vm;&md#DgsGpTT
znwq<imG&-fSYO~(MRmR-Y}J)5c$Go5Bb&G5%l!mr-g%0&bez2tcyWHqg<UlG_@3iu
zotMz_BYTawM!!aBy<$D=4(q1C%3$MQglPEt+=1I~bIx=m+A{0DHLX5Ut`ezY4>@GR
z#KtUrqJDbs*ya(Rv;Jc59nbh!b(Y(7*ZNr)d-rwUP-hD%E#2{*m<J*?vkdp0u~Sz+
zw!galR^b>pKvLIO8Np<KB=pYQt^Uo$!-WHudlpUgA0%Q_QknhZdh5?v@2Qqt%7hQz
z4Sv+~{=KQy^FzXli5eCzS{D|5n<}^_$~Jc`_m#rbNcj*sc2G$)3~?sL!6v@jH{jUH
z?RTN+J~HIQDWnJYYd0*R_w7P&n=rr+^og}Vzjs}sg6{N7cWRBat@YzfTi(_nGWqbr
zz_N{8j>4Qww^FV6<Z_{^b@kIV0J$gWn&)+UpUkJ)`-oX`sj`l-rgC|3)Jfa{auRZ+
zEML=m^sr^KaOo?(#XPcdvL7$KmFg3V^1Ifk(^mNNa5ZmrUg^%{6`4Qx#n)JFl^s{a
zYUyo1qO-QwrS6`@lYT2SSUY`zg><<1bj&51XeKXNY2yCGIqNi@(i_?<UL303`CV5g
z^abb1mWM?+?{leQ^U0Ep;w-O)-PdAj7WwizvbLM$?N991kIER-)=hc)3fkOi&^Xhw
zQGfmpet{XT|83diXV}<v0q7T*m3i&$+$grgb_PSAD37O0Ukv!$itL8+PjK9n?gBM-
zvHSV?#|0Q>&N-d0JRWduvW%uUfe)7!UcepQQJZAGAIJ({e>I*S^=6vN`v4~GbEU*Q
zN!9Lf2vha@%i@cXjT*;iHAKG$MS0d74yZO-9*<ob&hFYM#O2c;k7$6$)olp;_d-q3
zmi31=Z6=Df)7ylgV$Hebm9miZ$tzS(vp5v5n<zJeyCj`q@SNm5*yN(*esw^_rJj6-
z_APqUp$zS$dvFE4qzeC*nm}+smDH0IMX(NaK0ygB8zBiNZePvRJZ+eFAAQvmCrx*>
z2iT$AKqrfghFHF<w`U`rqn%t6Rc4Yq?Df;A*}j`5@Qfg?Qx^679;J;u9aF>kK%~XE
zaQIger6#glcfinf@e~t3<(J?2CDG}y6r<-;pw>L2SvEh9u8ziN+W~vEmYR-1tt0R7
z{j!Ppxi^DC@U?kKT&JI3jvR0@WU~6{hTnAX<6?>Z?e86m>HS)Je#t&DxZ?)`JA4u*
zFdN9(_q7&W^d_*2Np7LmdCTLDz?&2^d|pJn7AY??64nntwxx1j`E<<V4x3IZ_}Z)Z
zY}C<g1@iP(je6j$VzJZt#HvVG`td0*T7Ch@KWX|W*|#ZwI@@o+F;9I)?(|*01XytT
z*_cc5vAtgrSv#MuCJr9g_M^7CcBsqGJ7>7<etW!6Y9jo;Vn^@x-Cdiwb%wWz#hw$b
zJN^mmR@@L4uV9s?-|>96o!6^&T*&hM<&nqHE6#A<iPIB@SV<FVXSZn*QRlg}6*a4O
z)s{;FB{IJ`M_;Bdd5O9kKWW9twi4b8LRG<cM$vx7FSu|<)bkHpoT4}0)V4)Th4Xg@
zU7x5_ZICD%jC^})bq7xI$&Q0Srv;OO!{T?=E-YAXEBO35$^2@8=aa{WGf!NKSVWk{
zgx^!P)B~Zf$9Ef15m>h+^6t^J{tkr>gLS^fTK2T`vPO}~kdrN2De5zH&v(m5CoG#H
zkMJwB+Pz;1{Br&d3>shOlC7XjIbW8)R{LYkw@JQKX;&wcaDpB=A$hGg>5F%pdhr8`
zTMHau#b|~wJ`<>jq}I9ZQa5h*q08KT%lG&+-*NAP1LzmGM={fg*NX9&tdNY;cX_)z
z4ow7?!|uiOo`*erXE?A^1q5xofaWbv=$9Iv0gItuwOSdFreLg_rtkcZjuUPyE#9qp
zjuX8G1PBI>344fM3#%4mvDa=SOS>Y&nrb^(&)#1RLpV`)LWF2gj_uvx7Tc=b(!h0U
z@Vq)Vr0=4S)w{i1+urtDVGX5*c3SW8f^CxPbheI<Ua!~eQr}eCz?*>uU99b6%UYM~
zdSmxCr}ZcK`Vrl?3ge+XKzcDNGlQ>ZjV@ezsZ?;{$%J1lRhb2RC|<IazSOfOAH@~~
zEfWdQ%eo@1Wkf4CspH1!FX&R!W+UiFc4}5bB^+u+w#2nR^pzn9;oMrJ=Uar^Chy~O
zouk6uDlDb=VXBUQN}Zdv565ehH13e%=3FDXv`MwG^%_dn>QR@w{N-eh%Ev8SLuR$n
zC7SY=PrA544ffd1)JF;t>WhqrSNk~vJH_9gHH#Lldwcf0-WYQw@%VxUTS!%Di0UM>
z@gF*cCge#=l6PIh)0OpYyEN#x7hso5ZMSO=S1UOVn^}BUUz+{tw$&!+p2VvXl46(k
zX77B{Pi&~y%S7dl+oOiq_}tp;8PA|kj{=WXmv!L@5#rjUvW@YMp2t<s6L#IF2u^}C
zN@{meyl|4peA!8>y*s_jq<|daWqODpwZ7=iFcY^{n_MS@v6lDzj{v5A=7%V27e{hS
z7+a`zwdY30(KMkXW{VTEg*)<&<yK<C$^)LAWkFnyQRA&=76v+qQ8v%dJmI<6R@$lj
zYuU6g;T^3Ufpr_V+tb^QPw}X^WQ~)TSdxw6$_`DBO9VK~`Y!BZ$MFZg4ia6SN4bk%
z69Qw|;M2)viiV57->f&Z-WHCxTLJ=mzMJqfcN=<lq&q!Y4BhabW;I0CR2@(mS#1rk
zSGW^Qf0HSiY9<t=PPk3(XK<f)ss!e&?t4cz^jY23&+zgP{9l2p>~-u{`2wUf$x|Pj
zQS%p?WLK3RPV?b`-1lUr&3Q14dEiB2SfXDV8_!@S(RjJf>-V*1WOeM`V9(=$7o&Ng
zJ+9$2{lIsD7d;Q%=c@AvhX!?g6>j6L-2{D6mGEr13hUHI<b{?xf@fRI_99tklO*>-
zmD|@jrpBtwF4Te=jN*lL-!`|N7XtX2xOv7fTeW-s=!9C-2^a7Q6&Il9k>#r~L5s@E
zrb4pE?m~jXDdA||^i;cdb!jaH;AH6=NO+2*x5pG$T5@_pE2z`SUAE~Q@rDWvmA|-a
z`D9Dxa{|ZN<(ziVBQsfF!e$8!b^ER90Z!3C@T8x{ulF4eRA(DwPCX)GAaJ5exabQX
zHyf_UbyqSf?i)z~M?d3p!W|qq_2!`s;XdKV(Kb2b^bLD--VpT)QGH1Y$2Lm6i}i|h
zyGHr>)A2p@;;v+gm(v#WL`M{xe$kE1!gd}NIahZ2xdP<XQ2I?&XQ2IS0sY|_?hEP9
zL$c+Q)aHNpjOg7#Jv*zbSrTahytRS6=eA`O9qeY9$QO0Z#$F$Csg>KMkStQ7;dqRR
z-LR4_-K(!rp@3{Fo(>KlS}03+CzU=$K60(MA>{j7^l)BUVz1EA9ecNo&zVVT*)w3)
zi|BLCqI%xmJ0Yh6&QE5X=2jw>z1`^PTrjrPi@+--Qcv%G+xcA2&1W0>Xti$8=AHTL
zUngNN@b>1CXLW@bidm3^iI~c!ivQV>nuFP4E0WG{_-pZvch?Cq5vbPG?u|ytoP}vj
z$*SL}&B&N?VIFNh3oLKX8stjvrNet?jlM)K_Zis7C&vH04Q8v83!;xRw_WJ1j?cYj
zot=!94)*G+inFxq>Gax3m5gB(HwzJK0}VbRuDvej*)(#x7J4u9v!}2u-qu<cAn577
zr6}kVS<z{vX5n{vHlLp%j~>1n4bTH&0xKn2yxnCI6>{e~bO}<Y!L%vmt`gYwDzS@s
zVE&C4*XHA=Yp=@R4fGN0kJRPp)Cf-SMs3;9&Ogp*u0JlDp)2rfwC%YN<>}>-kat^I
zCc@Ox^0xzH#kD<Q8cQ6hi67hlgad{xDTI8i>%`vq66g2^`sY<Yo6ilrd`6*fienP(
zO>b@`5Y2kl)N-*m0dNhJTy)>M9QEV&;!eH&0ZmSwBXu!17%up4-H>Ifzdh%mDmWCk
zA>D)44fty`wtTi{mvu{Ap(6FN*0<bPY8cb~&bcb%Fz!uVr$Ds{^``RU9Vf#6VCOzn
z8}TR}_w4e6Rr)QG_vx5!uV%M1B`hnl6XVy`WP&o3Yz-k|_#aUQNmacm#g2*H5&r5v
zcgk_%+(zban&B5a`nL00tQ)aWaT~LH=@RYe-5V1FhiCYf#3sU&6AA2n-XYOGov#S5
z?f8a{1@NyNcRU<`Hk!i6uLZO1>a3mGU+k+y*!j#IT?LlKQOo-0i$<is!9{EoS4Ln{
ze!X)KM12aXfzb(WP6>nxD}D)#d;ELw%1wfahtWD!Z{CZm`sy8+Vm55eOLy*iCz1?m
zl+LCGQ+}fDQ&Zw%*0wM)+i>|i2t}Qxn(0wO{GqS}qvt09jQ7LIX`+C+RVBXLs!u<C
z>RhGgyS-|Wd72=wTTXGGQ_v<^GhORHy}O6tvUf5RIh!Ea<(OG<yX%{GxgU>p^o5SX
z7dh2>mAhgI{FZwkOGWyHjP_g!x9w-^Wogxqf_&d?<cLuII$K~Y0_+~}hE&NXK}z-*
z)x#qC0<r$27tVXBmoKd5UF%i9CE&X;aeStbiHl{gbT!nz>2{?N@On52)sMtdb@xOb
zui?FA_L-P(ybt5QhlY2bf$C}7O^xIoPj_kH^YM!-CrR9uyQ1;XN=t}Z`h?BLBkl<s
zONQP(yPiZ|;K0or?<X5w5t{Sxn9$dkAJmwT$A+$T=mBc6uHZ;J9TSkTkYN6mxo7Q}
zn#q2+<iBzUaQf*-LT?1z8y0LFWhbY4aqQSPt#f3jumRbT*f9R4xo&Z#H9J<|Ww8yU
z(AdwU#dQ5kGVEkKyY7Gn?iEq(TK#RWrm^A{9_<x1y}1{)Z+#%t=*EWlzvx$YWisnY
z5>Gw=!Au<HL-&btKF7n}iN4Dw;m?oy{i^5LRCUZKWD3<Al9uKXyRhMlR8-W=^A*>#
z|MNTjNz-k-OMCHQ97`eh4hXw=oa7?ucxkrcXa<?gE=rJL_pOV#{Cs`6@l@3FOB*AV
z@1uHp*mzae#ZJ?Pbge>r)Nz|%*oP`|RPMgz719X1Rqdz65H{VncR+dqyXuB>Zsy*j
zJt^svjc*qi@fgs|RF`btG7~L1nA_SZ`F-w2-8Zv?LB?9`JwiW%t$#rakWe@1Q}O$c
zZU4@~6m&#Hb+$P-5`j^#3?#o3yhJd70`IpLJaA#>4)Z%nA{S`}T*s+SI==avhQG%i
kbo=k0|KD-Ioo7dSQUfBxlhkr_|F$b#ErVOdHyxh-4-8{K9smFU

literal 38005
zcmeEuXH-*L*k-I)LBUQHLT}foASHkTLI6RL4haebq)YE$0|XT$G=mfo1d%2!)PPEp
zA}B4?Al(o;Bmn|pcDVO`^L=a9tXXS*eluq+AS*d1XYaQ>&-1=}A0lq+Y8*du;Rpl*
zIgZd&(}zF~_(CB2tPULn?+hiRFM&S?t+X}Nz+Z4|s8)x7KZhS`ntDJW+~LfhJ<(Ik
zXb6N4f>66|;F~-*;uptZIM}>w>67GgA?d?)qXTMt4upH1xO4l&9Tf|^*XlRbxts3X
zz8S=A;Wm0&z4^fTvj!@SNP{m2;&1IySJ{6ba{9#W{ify@s#Y0pxjKh(v;=cJ<(IaT
zV`GI3X~b+X!_zC1eR%fW0sa49|K9?@0xIJRaRzfs4==Z*x5ve)Cz&pDJ?P(oK;Buj
zA$*t?Qi<FTHPn1(5v5wGHoKzg_hLq+p1}fvOqqFC?fch+(6If||Gc&lVXyh;<?D3K
zrM-W@RtdAhTm>)hxx%}-nQ#3*eHaCS+~zp|^?Kcd)Ym}#fcBQ4M#b!(X6_(1b-QFS
z2_`$l3w8?OTJk>}a-U|ZLeZv_&iE^OUDZb0U!gc*{e(;oD3YH9MZ^nAzP^I$GC>S+
zw|d50G3koH91+Zay%v+1EhwqOpZZjglHmE0@<61dFa&c&waXLXsx=s=#u?m#=|OZs
z5O|xY7hV{pxV5~@&Z)(K(}l(I-uI(IFTm*Qnuw6%RJIrcV|_0~fKAkpt4&8t4#rk{
z5F6&(rZ3iMALX_?!QY?Kbh<9S`I3>_EF-UJc18owX@iMC>BJgvO<ig<Lk*ZU=j8Jv
znl*Y%wY!WjStW~zc3Qv0C19qTKgw<`M`k*>;l{G7V21_D&ne#(X$r9D^SF#ySWJ&K
z;I5A4ZVjB%7qXmYiF`10M}kB>H-vfaFE#JAX3|{7Iwji*JyDO$Y@mAGwxzGan@%k<
zI^FxjtPtf556I|n_sj66q*;$DiGt4Eq4GT)dDp-pgJM4<he<`eXNX3ooJtzIO|?dx
zM*GkD+mj#U)|z2t0wh+kH9v}8OWGSeq2CMFVH+&=e%6aiJZIbn&1b*0B!szrYpRIT
zRdK^zZv{EiWmIe{Un<w9Iv{~nb17YsRX0_}?svW2bpg97o}k8FqwCzHl~*@?d*Q{o
zsn+piT?*y=?r>}>1mZ1L_>A^mG|#fLEp`qR6eWD%&fdQ$fgR(i!Ij*Zrmn`jHTfiH
zJ7Ew?___MvYeF{_f<KAbYbP&D#+@(zYG3)*m%~3t$$KI~*m41n53g|XThaA;P_8!3
z!YZ53)p`&e#`QeT>4~g}eU8<Rh(3{{2JNPsy?G9VF=DB!9)$5biDMqVK`%3#V2EBP
zEUMzFU|9mf=m*VYXB6F(sbgd==p8^Wg0)B&I<&qtAGp@xeY4SoA&Us&P6*;Bi6Bnf
zxP-g%UpBJ4a#zqW@-j{hE)qNWN<$m*bc;>!lL2ov$}rBaZctuA*|N)8sr)=i6?^dj
zKU#bCOvYR&YhlPm`+V)nS1`P`g660EpA6RTT(ZL7<*xfbYxnIye}EqFN@$crqW%fC
z+fn(yYT>oP7*fg^Z8(DP9eO82G!Bt>jUo|nw`&Y`__Y(3_{7?xk4;OyJzX-Jr-o`B
z)@y`oe^Xg+mB$;06`7TA+;+lFMwmzU$+H$2md!>QO(WHJmX$R+;5T^PA2jQ+D3m%Y
zqhhfVow|Rn#v))%gV`?Q^F`QJYl5TVX|&Km&q3m4#fI@j7A~uYob}f<x}?J0v*qIC
zZgq*SC`q)iuu`O>@AP%@)CgFWo_1v;7RzVf4SKnY7>|z4#GNpmy}O+#*TWlkMYjVH
z8Gvks|GwQM`L)_2OBk8iD20B4sZtZ|?nG~Ma9PpAwa=FqX-0dCOibbEHb~M>@7vS3
zwFiqxoRzTONJH#TZAuYYFjcWq*vFcZOflz3G)Wd(lPBKawyI={V2?f{<tDc+!oTtM
zH~!+ym&-!FZR5#OFRZzCBXEu-*?$o!mBZIkRp;|jLF95rerzP{aMpt<Z&ClnYvuc0
zrNh04h4#@~({+%Euo<~H)3MJCn{bNr<Ndf6X>H`7w|47QFVPvhC&pUyMV#WPirC4t
zyTt_(&04JG4HvWO<nolkP*{Rqigu>IdY9QDHMOO))RgpFin!m^G^IO7sc8>Hh5g*q
zvk*w!*~{Fh5VbBDtXk7i7+hX2rqXM)Z3n#qz3OfvqunKxSX`%_^yjmLv29+9Z{YOO
zrdT2@TA_xKtBsw#gKCRi{mkxK-6M!q8;fRblW!Dx`;rTLNk79BYX4MwV9I?|BUr1q
zh!ccOTI)+VXO4-j1ggBHQlBIfPT1j|Dhrg0eoD_&9@E5lS4+I`$s?=9Z<-jJrgoF$
z!63n*a!}5A0WOu||4T_QCq*0o%Az#@D>2WWxT2w9I-0{$XwF|5t<4$YEfPDgFEsc}
z(}kX{CUo%+^`RL3ahtbbE>62T+E+yR*N2+7z5z~Iv;D75M|WFS>#$9GeuB=dvqqWF
zJi{pozE0xi`kdHwlP1Kbh0um&CO-Opc9FwVOo30%C<rS;Lw|y=|5Cz~T*@rdk<iDM
zhQNw`%aK-k-ls&3yY3+@TcPG+8m7(m?RqUbT&LRP6VWd93To~{IIBc--L)E?#hr;^
zsw%F%|BDNourB=2_HS=8&4FzSpEnT>a5rN1jf!IMkSNJC933RU;GSnW+){b=v!wYn
z%Nt9<ABk&kbof`!VR>1^92Tvw{nj@Qeu-<%C|1mF8@j{_fqZ-Y`vBm{MjovBj}(^!
zQB!IN*OsBYh2@9j`SW__3dn@Y5gfj4jIy$_Q=vs-{i2?iS`{qn+PnaF*RxGHosH@<
z7B9{36-=4@Of;ABveWjzYkMgCfhs1>xQKYRyur~l!do}7oH_evqSBVffOnuIS!_#G
zRKk^;w#SBjlc72221j#bUv!P3Vv@g9+i)22xq(s3O=0t{;b4ATwM6b>df>jgy~}*3
zn_b@fYx^TeJ9Uh`2*-??;Ln-1eWP~Q;(~52C`TTPIX`%@d$P$S*q6&Io+5`#Wn$3@
zp^+UDNa^RcshJ_)*PyxLu?7_;UG<oRCXoQMhB`uZ-cYZqL<%xt`*(Vv-x!XU!QWjX
z6%%g0SJBkv@R|!xtIx9d>uc+Nb4@Gxy{Ice&{u~30Xhc-N2WeIy{2r1u(pjfnRFz0
zrnRj&3UZ5ebIO>fTVHMdpkpfPX=-#`|HZXgErJ{}q50ZwD);O4Vz6cK!3L#5HPeu?
z`l{=dqr?>9%W5}D-5Vnr)|K5*M_mpWLWneW%91-ARw;!}Zo0A!lC#;uO1pf@o!Ifz
zqLq7EU&_dJ)>pVUQlqBXCC^u^1=^d8^LW|yc2d&wxns4CH1R=O-*@=*LDng&TUJH`
zACiZjy@=pq(~W(DU(}zrNnC$_t5_($(?dSy@7Ux4nuw2Mn}6o;8?l>yKk~~*H51XY
zts4om&67&rE`F1xya&Y@hhK|nR|!-;R6IXlJQo~JD1pM0o|tT%SS;df)5Rhh-yaX<
zLxdz=(IRQDxFoFU#u3D|lO}IZdshmNIaW!~zo1Qp6Vbuub4#0|kxEU@>R6k=n$`)q
zc^Wewt@yqkg+N+gh}-yo_Diw(7*S7ddtW0%{E&K7@YZY6=?A0lEi{C#`(|iY3M)34
z5MLUvWqKZP#3n{m9<gzGJSstg?cDHKD{d>kj!8{CA592eywIXk>*dfYa1=3BL~cBT
z2uV0Ea5bq7ErilOo2?djH=Hm+^k@lknx5c774nlp&rvq*6Y9wWFvNp^t;I)~I81GV
zocDyG(TrzR^xsPA^`6w*5B@y;h4Du?h;-++F9f|2^q?;ay4S{6hO2I?B3m2izTeRb
zt*a-FW6q0r=M1xBp}MC-seT8rlRkRb{if@i%Q~>cmqk*Sgf|~@EJ|(wX@GC)*3^&D
z-Y#$GUobfQ9h#1>kuD7QC~d4Cp}Jv(T@@WQlhxDrL6EIV$+Q#4h5d(7g>2auFJu<W
z{ZHE3ew3o3IbEdN0q-joytSPE%GCyQQ@1tXiLACz#I#0NP9A$rFWTR^iDO8A{%y^X
zkP5_=uWvtD%clB9VM?1nq>Np{!3_(1B1E^wdiKe7sq&DIh76O6+ZXq{s&whOHOV46
z2qJ)QOq2hE6W#;Ros*74V^jrM+qBW_IUJY=6PK~ex;}z;G^!2=rU;OfNvu%TI>fBV
zDo?<2NxN^Z?nGCDY*EyRuS9}pP1{O<O_-C2qcuFQ#I$RMhg_{|3zI1n`sft!SEA<Z
z+2+l=f+>=;G2vvJZ*uc9%s}Da1uptiAGC{}<!NjD2&N+*!Duv-9WKOl#Pt|<MeQFK
zch$1N1Zp6XVpTRmVdeJ~UZp1;8P$~6>GHYMXD(8Ltjkj55AlV2Ph2uzus)&8?_nNd
zY9`K+__lf~X#Cr#U|HP8FTsRxXi9dmo|JAP)?l7?j{GX7$9#G-?&C_JZuU4+p51rv
zVwMY!&>!0+do;|V6JUq&H(?X<MhTU8pV-VP*x@=h!5}ev)nw-%#j{E}LT&-2I-SF+
zHZDFyzE~($*kfo-HZsUOL}=5<w%W1n<tx9ub{pgmG?lgpLtxRc!|w)QbSSc=yUfT5
z2|fNAVbvE~NulpUOh9kN(xB;<tt<VM*&u9YR(FHdPJHRz7RrNY0ynHhNqAZJ3H5Dz
z(UKh^B-Rs?c?JfbOVsq&uw_?LD0&msA<6rlYhRGywtCiC$H$qcF+|MT%dHe&3Bwsr
zKgGYfbQiP!kH6wHOi92i5D5L%yJ)ZFot~bFZBSPAv)N=T{ClyA)qg6r_?dbBe|xBW
zD;D&^tUb@%z~Hn8q4vd@;!y6%+Y6$IMod+flG^A2C{l#vft{C(T6yyp_oqv^o7nAy
zP>*CV)RnwV$l3KG;$b}Y4$28E=yCGY-Jlky!qK8Q4Jt!Uu(enX7xJ>*@3v%XG3Vs`
z)^l&7bP;NC{$kjHzAC$_yx2c>w=!+-Us4_`dI{yW&aX4D4RMYG7261GDD^DPMTJN+
zpPUelUOv@2S11gXj4L&?4bh45ysoCGE2ecpjC9oTZGv`{hW`91T#C+sY`&Lx<8wBq
zB~FA3fQs<c%jF7FeO<MI?0ht`O~gnUp4&g9sO<dfgI*Rs+v^lPsFb(6>r{pfM(R4^
zRoA(vZ~b7)+zbC{4)!O|rZqz$ab=7{;Zp*W+gzmC6EpTa<0ek|0SWahHcF2CBWH2J
z5mv%CJkXf$MT4m)jf8&R56W)tzBC?_8OTO)!t^wJh#JK7U5k^9@w7NHs4KHjpXasU
zH6X<ts0vq=$XcjVQ6{J(5=rQI!EVku6GWjYdF`c0nth9kxmPHTE;2uqyLu?*_k7ay
zwVLASG0QIQ=PwjPqsouiM+qi5g^uU`QT^BMV|3vYMG`UdJN;&LVfF3`U&&?0_Sfpz
zYg&T)p0&l0axTQJ#Wt@unULpS_vmG@IN&HBC2~SDFJe+7?nFBm7>=q$lQm+cV?BBJ
zV%PFSN~Fa!9fZ3B26Yo|lIJhn{<Do0%}F<`{iGwk^ez()Q+~2B;9cydoo<b*s&{8j
z>5;}&o7$}b%Pj$p<1uD!!^A4(2jVoIlv`fEg?umn=!GrzI+9o$)GuStC1xqShPl6U
zcCDG)UJYgQ8o>KBSLIm?`%av1{W7ibgs|9|#F1Zh=KC>@6LD+Uw#_l+KV{LsaK@G<
zl;cY|wGTdhhh{#-bVL_#d4{s*Y}`rED@{;Nc!G<S9QtJz?6bFM4}^M`bV1`d<dF{T
z-3|C7hNy=^_S*w1!WZxG^us4)K3qMrKDM9i=WUkpkkIPS5Xrw2m$J_}U;@3E#uoUI
ze30xH$L94Ray0F#u~{dZ_U6w`x-X)PtIyRIo~XGAGx|LBT_!Zl|IY>V#zXRk5tipi
z%-lr<hUjIr&@^XZbML733j9tVW9Tj~<b*J@Rnj1B*~Ld*Y!i$ZGKn5~W)Yi*&bM)?
z!>ilE68On}Z<3pD9g{XU?DB$HuKQ>voi1AV1)Cr-P8iYVF|#*w%EJu{;T_|arL=}?
zl3eAUmdm99QHDEOav|6<+Em-gLKYhrWUX<R@sV4$tB-XSMWoBW5Mn*??X^eKdLG=1
z^R21Pj)!iiwfm-18<fi1)`()-!ls=^*(FAGY;W)fe9Qg;gI9;Q%H4-zQIA!jc<IY`
zy1lVOnbO_097wO?RJ*p})?vf0YgpRYS!`;2=%sN*ldfB^#W7X{CloH7^FbTSaw;h=
z_kE@|G{_--)S;2RmY5^u72iv3iu@4MI}ODSt-gRt#v=H6=Gg1bE<0u9hqhN%_D7;a
z+m|Q3=bkO5X`+Zn&1-eoy4VGSx3ZHg)2`wiU~pqW5;xX*gPT0M&DGjZ`2NU>#v|n#
zT$U=(3@v!~(`~I*b>IW6`6liL!bN$In&FhA!+y;?-Emhd^655<dA2#gE}K@rA~@(h
z@c5w9B{8TbF)F@U5i^FCRgc5k<;P0WBD%DoDcpBl^%5!b%f5oNix=4KjbSYn?=p?w
zODLG9FGB@f%ec=7OfOC;MU72eYE@=0s!gP=!Q}MjO73*D{8bk^>L2uA@6=*Ez1DR4
zw#(-m<;fZWYC@!WbAldbEkPCQ6-jK9GVe;cG`>}>n(SS@^BQp|Jkd|K)&EuNS87#b
zSBKz1`dGq}H^-vPK`4Bon8AZhzxUp4sL5ney<#HYOSsWQIz{!$I>pqw`pYLrmwrcl
z9-&WUEknJc>i#8*`OmsFtya$uwges=APhe|KIvT7QQSu&TMq@t!w1alEcQ}%>vAX(
zy4vXAfuZ;!4h}{X8y%s%lh;bO`C>6o4^^m{v%sR<W8jYqN)pbtt-bzHKAR(WdHNl)
z)-SGK-n<Rv(=P?2%lr9hu`5rvLhl#K-WDnHREwh?Y=!>0VCvN$J8&iON~0Jpq27D#
z=!+R|Y-$a);l@>)rotN7`<DcoXOd3g=SPO4Ct>iWCm0_0=gqDmtS_2#WGgrVwptg_
z?C1k9x(wecikj$oE6%;#bjrJBjhdJLQwgq_WlWK~Pc}aE!mUils+Z{My*P*LTe<tb
zlonSuCypHx5i@l=qbYAZ%@X>KIxojVbh&M-pD`Ut<oJmaHgl~m9wwR;^*57aj->eP
z!A{n~<&62^rV}Ggk8aEmk6_6W@LvYE5E-YdYHtb^jo>czQ8lVs&1hlg$R93$B@^sk
z{ito)lAJ3G45hy9MxKlP);GXcqNn@Xua5oBnZv}1Q;~A84&42=VfMa>Q(8{+UYsSG
z)12(|vLtvHAsZRrlx6!OF9E}!X*pJMB1a`X!bDy^Zsgsci0ZwewJP3j&*#sFvd6^C
zq0qV<9&&=dvCie`Hx5_Y!VagP`|48s?^gj?E0Pfu(_ttQLzVTLnSvwV|2h2e%n|%d
z@EYdD)Z$=uvrSRk#Lwr&-IK`%K3zY=N{~XA78~#1zohi(4}D6>aYI=ErCjeHRGjQ8
zSw}RRVNdjKmM`V3MV0biZuJisv5F8hPWNbQ`z(l4!?{)5aJ%_-Sj;?~X8|dYFfg=k
z80Wt1va{cD`+?7~XC;L+D%)L`=<gE}1N#d>#&!{#jV4ogsn4u*m8GV%{T1>8*C)<m
zdA|&Y^*)cA-S27`x`3)wY+aE$vMn+;ebN!H5a={iSR8-KpY45$2ISGcW7eRZd_q})
zgqibwjvAJ}Otj0KKN3XptO{C_QQz6?7!9wmu+{nXz@<2~x)o}>{o1KoxpnPWyiRoM
z3>wqoDsn~imZH|FDm>-mb98eH6yMjnV}8#y2J!KxnFHK7PPyE=**&m-bPu*6wcFZR
zZQy3#>UEx>Pw#%R?Zu=0v;FVueJn#|(d_?fm9I1H9GgKDWS&I_@0T<0;JEvvG*y73
zmwNlaTXY!j^FEh#|GNSxY4W_+i^dltN9F|zm8GD?uMWUeZTr#hZ@CzX#XgLqa?^X9
zpOpEQ*S(@_#Wqs}X&%tjh6>~0;J$tzrS`sB4x1=ks`TBpc@fOU*UZXxdbya>z;%pE
zoSZFv{e)dHP#nBR&`oC<dhA{UGid}5ek^W6k~4V07tgv6LbZna?twf{OPO;pJLs%V
zZgHTu`zv+a_bWxY=!=|O$<iKiMEhqVTJ&ls?W#G@O-pbSp#e}JoI9AkKiS6r4A9W+
zLz(!1VdUb^1UO?x{L-v$gL^nZ`?OZEUu9!p<a-*wVz)JyuzBZ98}zSXo@#;C!Itpz
zFb?ywn1Cm_x$ST2N+y$sqnng)i$-gi6;Q9wsr58+1dOB4FrhNE^1J_BF%PwNv)?Qv
zKrMwDeX?BKQ}L#C;aNGq`^NF}0Q{_Sp&lJNuSpxI%%8#ilB*2oSUNR~{As;>*#OJS
zA@m(yRMqXzfRB3x?6mjPS~-V618&rt;IBDbpQ0<eZC?DU{p7Df8mvyqaqp4i!W?og
z70_FWm5j;OjfEpETZg<dbX<RHVa(cIHsZ~N7!jBx=y^w&yN%E-dP$}pbldP(;pkAF
zT|hsNu(#gK_h-H^XS$(Gpr2T<1q5PqZkNGbBPbRhdqyviWT@})dARzO6xPC_`jdIl
zzo_!$%1E*qaiiB|UCnXl&Kl|w?kL)yLA)5tC1$Y3;}dh(uv()h>0FV}H&Hj?M#A0B
zoU`ePHu|;gZ@yvH&|J=gJQ&Z^_5i=KcIV|r*x^*xJAI2T@EzF>Z-0}{v}+Qb`XYC@
zm2%9>Ki7%3tVI@x#QtnCd|)}Sa~Aa;Kj2l%E1(pBS3g;pNCC#=#}X6kTA)VsHs~>N
zu~Oc-^aEK9Cms2R9wfS5AU4|4K3bk!uEK}ZM(8&=(=&=<15wRs-de<}qSz^vlJK0u
znQw(`!9jhVaT`Udk-<8Pm^BQgoUm*Q_&d%szkNxL8m(dsJ!f6K7Y4V~i@Ty>tN-&m
zZ<bTtvu)(B4;}S+GbZ@%344&%V+6lKXQ0OZ4$)D<{SY5_i#Fa~WP)KyYKXVVLeIYM
z3$F}>Zw^scz7t<XtYnNN4JycaW+w-f_r{;^HipG2bMvpFl>q+u$STBb_xe+FY3^d4
zch^wQT;Q3`;bGf&*kjyNm?~dSBI)AxS!|ID{rk(pjgXUMA6#mN;B@k&h^IDVhpR8=
zV(ga@34850YLxyQD$x`c(YfS3KSZl{RgXTChDX`#{g_uykW8?)5n^voz?$S?+oOf|
zgyKed7FX-9jo-5295U@xuE>iO$VsHo#-9`Bw>c_bxeP7Zq*E+LCpIHK2bjvbBim|Z
z!%ad8j|Pksb|x)lk_#h=t2-8setMN53u+}OTZ~Q%p5@`EetdX9YfT|?ig-CdwSQFC
zTEh+@Ke!o?Q!gJ;I&VC%^h}u}e-DIN@V$P-jcGCuQU25uoSOQ(xLx;i;7Pj_?;F#{
z61`@{dP+pyw|dnChg?*$!tV<rb`m_JD4WfZ#G`FI*3`p@XmiQ{NA|#=`kCT>j;oYA
z13JD&eRFC9V)Fr<fr?LH_~g;oh}MC{dC8OH0X)Zuf17$S2THlMQr7<{S+3aF3N~?b
z2(i7>0w1s{EH|N3--$EfZYr3(;fYNpw4s9)ivnJ-*a#`bj=yaek8v*TgqOoY;z?+@
zVfu2|ho6iH9MejZ9^}eUvRO+}IOh4)n4$FhA>lh8e>dCRdQ9cX9<i^Pe;9J>9XCC^
zsd(X5lH_IG%>hhpY~|hh`2yA!Xz6_CkGoub_Md(RZ*(-{wnfB4gAZ+A?Qn-Y+CIfx
zV`N{I#_n&4!SEQ(b@ogjp&X2Cei-VKf7}?6ez-;Grs;xh7$q<>xp|TMKD<-^kI}S^
z;LySd%=(mW?0Fi+5WDQl7K42#i5aPH5k(L*J0^NNFbIPy239RmD+L)8F2_Spd<P~z
z-K7334d9o*b?DrWw7eObMW0-ge-u~lpvz*-wZW1BYxU+qS(jM<cxD<}?fhJJjzgPb
z7Ve7p&0bORyb|HWd3mA#-@O16T;$5|`js$DY6*j3+yD#MQRBSHA9Hwm+O)H|q98t5
zHgCont+X$>pRnRL=u<<mQFhcVPxWcD08T0%^0{Kcnej?f4kJ~}VDvkyr=eSPJ`Y$5
zT_Ro=5?yG-_pSbjRMKgd>{~sjzH<zbHE+{8$XcG7aqHS4rW$#!s)yyVzmDZe8!4q#
z6diyaZaJIh<$p6SWY8b)7*{16-iue6#yQZneeYrq_spSV;5zfg`IB=pjw$@ufw}oq
ztzMhj^+9Ki+zAs$Z1=UKTc-zwgUbRiluaRoVLhrd9-(#Mbdn&^JC5QXp(CAdL8e5)
z@V8{c%g#!k^bPJ`akL2w|4j2T#L`xapTkhZyXCYG{bt!oZSukEO%B<X$x~6WSM-HM
zjnvb(@0i*K_FCro(>3DA?|;^Eefmj7w+yoqRlB@Sq8D&|yuaByPdTQ@oGj!$+ebW(
zaR_Zp5?Dp0LP?_Y0gPe$_!sdNrGyvMeJZA|h2|13^3l@bp5!2vteIIlY{uYr0Ne53
z>ik3fLV4}>M^bhUz+wdl;d9~Y_MJrKU$|e+^pr_+G*#rvLANUcd6d-W84C|N@>a~z
znEd!WLJ63N?~U}JE|}Q?t%VZX$CSaRJXZO<eNk{zv!mG6%xoP!((q@>s!f4iT3Ec}
zpY#;zQ*@JPLGARQa<gSN@fd+sRfF5pEF1wkD_Tdm^{%V)W(i|)v9E@x?+n9-xGyn1
z28lLn2}B%i@*U0ya(zm{9OIITJ@?#bMlVw7Yf_?`m5OaKto+T+^lcfiB+EbFhd~@-
zIUS@K8U{lQztiDYOir<gpswqrOAqS8;e{_wI!?Y^!@mCg<BkY}U7a`j^43Wu+ZBrY
zDDQRbq~;a929dLJa^1jb0$j6g`VT_XIWZTvSoURWlrBfkdkaJhW>b=z9!a$UEclSC
zeN3-y>o2Pv?n2-4JCWP9$($cvo*HZtl4?sP9u=li((~Koy`RW_?4M~}5}8`HVJEo^
z)o2ujyJ=3AWL@tkhqrJlm^!94=PQ=lyFR_!Ij0#6^(Cv9E>df+9f#{%+A5;5ooADt
z;I^NGYK&JzTrO|7n0bCzB};bb0hZ|A2(z{bmgrtBDNvOwZ26J|Q)QQk-C)P}d?MHz
zc}Yj8nG0jxAcPhuJ>fg_9;=&Ztj-fzqW3-{`1N@OH{%MAJg>e7i`y8<?zf&<?et<p
z+nr;jvp9|lm+}=^3<+Lw+jhq_0sC^6e_kQ*rV!MZv&H<IkjN_W*(iG|=#(s-#BLX<
zW)Y_LB78)=*>f`5A6{_Gs|vv&iYY&dc#(bgFR4`rnZafZZwKhTIh7ONKB?E1N5z>H
zDM@utTq|~wp1)a;NG?X1Iu$0GL?y^Eyjyg>U(rp!R5OpF=qmqd8293=_exl+YFBfs
zw(003Pb%)o7?SZ1l73$lu%SFrtn2$kwojnA*}Z}lc6j*%jBa181M_;*KIw;QpqpCn
z@RfYA-p0sDKY`8Q;3q8wunHHNTGtq(F3$B1*uz^=T|{`KHA4LfVSbEiYJjj^`MLok
z(0FYY^gqt%3t@rjtDlw)YZp2(V^QToV{GOD2z1&S>*##HYTq8k&D+y&^05ml%ugW;
zfVWg;w-M830jkUouNkZn!7>@-(aY;k&O%z7N?_$P+5YaTe5S5#$6`*(DV|_n^^WVG
zrsi`|1*#bHo&ToCBPjZV0)#L2z|*f!1$(u8z}g--em`1KH8Sd(_GEdya#46>59EA7
zLS`0;_3zy^T(@P+*;EGpZW1$3f4wV9nXe%KWe?8(Yz&`|{I45!tY(RLy5-)-U!Hx}
zK%s~wl-b_uGM`0awxr4DcYGsmTKeR^eCfmc_j0)4DSa7T+(#kMiLWfYPJ=k-p|8E4
z3<ItS3)@+MOS_qu;_qhb&Jg{{`5Ml{6-76ytcQ8!f}*(m|D+QR?tOgNy6L6;q#d55
zPaNOV^~%x3zXJIJQ@P;&YTv_m7r!3m%j)Xtngc&7lNl8i6)T$*^4mHZF<_?Ld5B6H
za;?Mr`9<_bSDBSQaP#9k@#f*qHcgh2p`;u?(+`2#txo@tJI*OnGq%H6+*#P#Bp2!t
ztGpbyF&NB|#>Pg&zCz28hCI`fj;g5ya~GF^;;HAJ%}IzFlx&KU#$0Eu+7NAZwS{d;
zl7|mjF`X5aNG=#E6Ty<D2-hGi7l7bbbnN}qtMT>T=fds&VszW1>{S2Rq;KE8@jm%_
zuTQdk*u6VineirZ;o7#ozCH$NW_IDw!KbFAC_#O{sN*W`Rh}gG<=Ng`bC$i2ks&+u
zQC?KV!?Dn6@A=tsM?yic^43zlUniV4Y-pF_)z^L1v2ROsq{x=N;4S3+Skha03!gpj
z<|Z>i?;k6mpiq5fYt%5;Nb<a*?-JYmP<dTGvf5iOF(F}EasBsGNsU(*u1X^>Ntu=Y
zIgHTD%ngEQHkFSCoBREGuP%(}`W_?TS~(smZuCsrwg2;2!BMG-dp{1GwfHOqKe@2O
z8rK=K53R6A*21GZ+VLc0{po(`+qyrJ<w^z(jgekPckbM&<)xs}=mT^LnMR@zEbDrr
zH@}rR4|$t|m<SnUn%%W-8}~S~RM2-1-$_A1LDTFM@4@q6p7}GzDL;~~nlI1h76zZt
z!?wf<6Jj&-^76Vas)psQiJyUpxE54SC*kg9>Buz|ZhkVzDW2b$NekIts%Mj4F$;5)
zg4_%Fdg8c>oEHJ<!>w%BkxYDXMsjw#*?R0-s4#r<PsU7^{KDMa+`UqQGJUS(o6ATQ
zCC4zg4=yJsCvEV_fRrN;b<L{ZxqPJWTTTIpkG!$^@N$>IQc`@pxN$J-#G#kcA|k#Z
zR&0$~Ic4&;`|{0c=Z)L!Z-n~J`fn{aa{?D0ZcC6PLlFd2!2JAJY+G-k<&JH@$ou#2
z=jaR7g#&KQ+_Il-e<ab2KHk3X#LTVZ7p_#0wpKeA*(Gn9uY>ZdFRz8%n@b?|G0q#8
zIrM7iUK@KBEN+z7FaDKR&k0f!urZZLzLl!z_s)_qtp4JRSHOH3;h?lx=|^`(x4FSG
zaX!S?dw*4b{3x=plWmx1;-&+V;xAWXjo7b*Li2JdLM<vTE^f}F&556YY_XWoQ`(p;
zA@}5XXoMa)jr!i!7J$27A0}(zI~LjuqTu3egIYwO)H6%jL@A_M4sg{>&&N9xkCT&=
z-L5+H`2K!*x#QYZcm7P@!nY`)>E%IhMfL&SmL>V37(!?EgP{q^n?tUy1I6f#>}2=W
zfig!zt+=r8OsKrq?C@AKwW&Hpx^fa@y$^tmnBk|bX3{wv?S6^vj{?uZR0A$?`iox~
z8kT=%uKM)lnc5NkW>P|;gu`qKEA`z8BgHk-gHz*jZ0zuaiOL24lw-~z6$^V+#C|<J
zE0P`g;)S@TKCLQLe*Se5qL)%1HA?x3xjI;4r>9k`)1IbW*9sg+*478}qLMBSm%ENG
za0u&eX{<Y{tE>Al{ph*+L)Pblg7@6n(ZJOosv8>{A;<RI0a@G&f*{wpz+&KH&2SXA
zf`g=GZB5_GR9iH%*rw%K&fV<VleLP2&yFnj8Jd}WT9OA)VD#6yV>>inLPzlSCM9qt
z#pj^9%WBe9N3+_cZ$~99L$)ck9@Fg`J|Jej)e5ZjxGwpvObYB!3PX_P9y1*msKw4h
zxZV_{piN72fOM@-4jh$IUimKI)5OUMSp>LibR9EG$jRiCc8vE7s$Zn*4|&h^ff-K7
z3oH06)O=-BTh;|<*KSjXV)s5)>EZ2ofB4{2xgw!>H?!0~UbMWDj#)~dxhwMA9VC&7
zN6NHekNmSz7X6cL(sh9{f%o~-j(X31`0(LzIlyu~)v#lvk^tI*GW(ymvaT-c9SV!b
z<kx<x26QINdH4c;C^tusSGhDLy%A2u;k7(Np!IDHH<tsILSN9{915`vXXPHu26*n?
zU!18-U>~T<(1=cj2W_q?OPdx?KI=$PU_;ghZm5n;g&#ZLb)T8TQ!_KJjJ0k(-lLWY
z-`)KA#6(4N_z>Z#1WP%?@l8o5UgeN2EKUkp0Mf03QTTT5s<GBqm3aceLbPa=@0Yb)
zD!CxB<tH_fECtDAhx<NuU(o9nC)YH2Vf@z$(k!0zBLId``-`mENPqxeVGJtWr(PLb
zxHYaUd4(P4><b=mh}ijfJN;V}Fv%7}P&?rmGo0!q&2h(k=Zc!c&Yn5LsBoWZwf3hi
z1bO>b?&f@Jytsk^xav=8z(S=xAjFyZfs*V|;If15Z!TFcg`P#NYt9dr$@)cHCRT;9
za#x9+J4ST|xZ{MVTJ2EKc%~;+4#4W|oEHFx%K2oE4nK#kOoXb8^Ppz|-nmnGXDdFO
z%l-QkZUyfi`T5fBu!=?+AUaEne6uny4k4X$I1oV$CZ))_3|E90=9^Kn$H&LTKok<?
zA{q|z1z-I%Wq&(O2%#)CEhytgTW;pvG0(X9suq(8n0B!SOhLIf-@IH2kVU}i#_Ej5
zJoj{>T0!+w7B;00-78!nTg!z(o22Z-{#cHnZ3^KmGI(Q(M#wc@oLPcJ1WP>&ppE!Q
ze>5o?@7v|a6a^pZXr+hUj$K+1brytM;XskKM*hM`_2@lX+$z25yxtIMy+~PKFtt<I
zoWJ?m{D{ve{+&bbCvqCFVlFVA3?H!4_Ee%}!7W=(%s|~`yN<x(ckk}i?=Wa})-0@C
zzVEj;<3#T|C@nW|7$drv+-eSx)p)KH9?T<7&oyiIF&J4u#`;eZ^qf3X_KhWp3x=db
z&`hSQjzZAUsVImdohcVs_wOCaR1S{Vnb3HD@c0FZH;R5M^#=|d5TrVqm%AKSWUNor
zLrK7t8+ANz|1AgUT(dHV7IY#KH(T1RTRrx<o;6@PR!@mk4REy_AZ>{`#<nvx=nXW*
zZyGDj%B!@cP;GJj`gKIMZVJwGw#Pn<P{ENeE-s#HfN^(s=L)F|p$^z(Yt$M6KEQy8
zaCJ^o3R)-t+?A1Ql<%M%!q^f6=rD37KMGhVABl4s;2xtd4108L+tU_C*p6Me@?dgn
zV^vR7RMbzYMk6AzA7Ya=P+}K7*OSv7558k_wE$WD>DF7B{KZoH?kiReho+{coXJ1a
z>w8#ue1C7Va?3kWDn~%K0VI0|7`9!QRu`b(oj)L3XjMEoml1!uK(r!EcB{-r>Fl=y
zdmoPg<yDOXWEt|ZOzy$YcfH6e&&pc+?ksB=mto&^pf%oFc>K5;vi0W=!RvAiE@iz8
zxSQCStrivW(9h4$JVC+~HvaA@>k67PcBC#eG&BI^Hx+LVFlW1eeR)C}zP;9+2*g&7
za+*hnoB+UB@4Ddao6Pu7mMdGFDSFZ~kXBfUQ6^y3qR|D_klWsWfSi<~=9`VRj0_JC
zb4M$#d=C)_zqsBR!szzwOojUa3G((lBQ5(Okc`7#lQ9|{OtA)NL9rni4|1>$(FHUf
ztG0}-GC5KsI5^mT2<L3439>Pk)y-@Xq$c(pV`@O=-j_#Zn95K$kk*wsFHWiW=@+8#
z<3L6b>Ekb?Q3I78GlZi@jxheb0)$u~)}5`l<JcU<-MqD$wlnv~u6h|I&g(ZBBcP?N
z?Ez@9l!UcQU0%yDhov1I1I)SRy?!>xe4^=9$YNjq5E-)uR1?pPV3zO30M3#wU^u=V
zs5SZK5Xiqm<87@4Eymm^3h(dC1yo@@#&fw5@Y&ig4Ym=SvpK$=Rl%vF!fisy+fg~C
zS+r(o*Jd-<tD>j&kw7Lyyl0Vybb^qk`~sYFQ`>g<GXQ|I$2eu?)_`|YD1<r&B^_`c
zE1Z-W%czKV>PVJ*C1}5~GSxgaIa!)W$<Y7nT&Zh%E9S-ohRKv!nT8n8iC4106cC~F
zCP3~YfRcF7g%6;Kf*+KB(|Nl*X$gs84!z;vzSBRLNlXmc*+!m`H0z-P2vV>CzB)aR
zrs4p=eG0p~0cfu91=Zih#pN%MHCN6Zvt1mnyj(=b@3F~yypN@T``W|b+8C5sxeIau
zxM~*2X^#{Do2%Wx!21C6)P4eCWxPh}w!`zzHwNS<1IkRttYs^2+v5nA#A7!s4iSK-
zT$2Z{0W_)G{{57#6r`Fh$|^E3&6)n6BDa|*Ag^#}n!w<O`>)MmFWccvYkZd@m~tX6
zEv?S4>E-$8hQTIJp6utN4BsE|TXJk&K@z;01SCZB6x?@@Z$e7ScJsX9V->#s+90|!
zfHkhs8oyN%U|zWyKq2vC6PDWmmwE+Pd9#2@tzlL&KuVux#!8+BU!Z>IQZX}pulg(u
zw<&o3`HKWh=J=P}ctZ#i2y8$N&?M{7^JkM;J5>@Yw)4chzC7NiH#bl+!@(Q0c4sFo
zgt74)5JW50Tixlc^%EIs>CR<b8BpUDb$t8!cX~iv`7Ztbs@x8^epomy(B0nK+dBd}
z5=0xpZ#f7M9j6i`%|B^%U1#AwRmNg)r3DDcT=Vh;B@W}kSwdl*Dkd^A@{`{yah_n@
zJb|GZ@2i}~E+_O2lrGN3{8P#6l_7d?O3wgwG)3MkY3|B`O9e2PW(ULle=!&!@$EDj
z`}vN^+Qlui<iYzHL@<g=x00^NEr9YsX&eX>Cnc+f=dOY|fE~hZgdVTYm2{$_%84Lq
zTz5b^rj7zbS0G}AdKrH*qA!H3v<NrnnH0UBcz#k~ux4ZWZEW4vN}FA4mDCW3klbiq
zWrjXtv(1vRvIbCIrre9%H+1CaQCdG-yb8Rw8!WXCni%nhkd`N^=Bpm8w`Y<Es^DIP
zkqJ-<mV3_+-b$5qMJ<BKmOaPm+S}XrNM~02EP8LhztCA$48m#%Y7cTd%11;^i|X-}
zy4tC1>}|$W<yshK3<>_)Z2RO~N7e9)`F`o7l)}(xt#I)CD9B3V^K$N!z0^8p(YU9u
zW~Fr|h)#VqSU-@^F>xg}FL3h!dS*({uo<_=n7Mm}z5>k2UcHdDnIyj9!IMefWxk@A
z-v?TAE?jkRiqBlcE%f-9`)umleb9;zOyT<02Y_O<+LN>b=GeU%b>Zsllam5Bk0oYO
z=Z6ZD6#a_N0Ks@i|J%-EF%c0F(rDdI6=R!T7<<KWbB)vsE`)977S_(nfpR1Ql&8`1
zeppMd(2?S@#Iu%xMU~Jp`)=D=$3nb};Gq0y$WD-TVUgfBQ5ndx+}!7SoOV~Ab2X(4
z8nV!EZ2#VB&MOa=s-@%AMF3Pdlob)yKb{sd5D}A-D%b-0OzlTPAf?y17vHluWi|jj
zY`BQ0rJj*S4Hso*P9BghKSrliE|Id+WD-lS4Hg4p;ub}K+=xtCMo_D;IzusAgS=Df
z{LN~gCJ^2`XHK6+r@6O^%7Wr)k}@~2+IaKTg>6OuwLiI1HA~-4GTH5$^NOHybo}<6
z#>ms<3&WKq(}k9SB=Z!n*&am8j#o+nlZ-|TkRE~|9Zzj`Iq9`O6zI^|7U8t5Qot|f
z!Swm^K;_w^`}ckg*_z;evGq|r=}yK?{OBjc+}gJ?&JJ4*9C}>-qz{R~%1-^CKlqGz
z5mJHv<O*y&ZOk<<<Ke9srOa3#U0Gd~oo9}~XEez8YU+MhMK4eb7HMK+ocd3c5mbQC
zEr0@eR@-)`EmSY|0etWM711;;WaI=H_W&8N<39@WXz7RSXb&Z~0~9$0F8!a=3(J$W
zyMV9vF(D!X(*wX1-z1oatfqqT8FBCO&;W>KOa3+@D4Fbpx@=1wr+`SSor_Xf5CI~u
z`8uc^<ffSuRy2YK2T272ZDR*CRP>_S!YF8OOa`rwg@?pvmUJowc(23i3f_WsE~sHz
zED7`dQjAvc+$gY9O10ZWQ#}((d;#|^?%(_P_sjE_vm{Y9>ka==I_RRe6^*DG6V1_e
zu3PKNQE;mXLm;u7nf1$!y4jLSq{oLI3!m+(X)Q)FO9W5bZZ8FFcWuo9{Tn#Yze%8N
zQ{w=7LQz0#2QGn@Mp_`~E*!5}>(X9S{PXUqw`JgRV|3K{%hRnI06LREdxQGH+!asB
z02XsO4!p=L{Xvy}x&}Z<cpU%};e4Qi_f*#s@d0T@w{PFR0<?mwJgCrGQVj!0T_55L
zK;A9@{>j6}#~Tg;LQG4^&?+bpx|Ou``Qg|NJot_gW?d*5wWWs!g@F;Xg4|qblh(s8
zRFP|e`rW}-eE$5f6O=4sic4Sig@9!4In9_G32KZyuCm2!qfB8EvM+}LZ}7m}C%+Q`
zRjt3*h~`noITBoQ;R78_uQ<hPmYX;!uC*J}FPPP(G3MsWbC=|50)dcG3Ig4Z=wP5D
zc8EX@d8vl8B4ggZ<xEx%362k8tftvXTU30mzB>eD44eF)cO&~)SOZr;opTPx%$H!`
z;IoTW<N|v@--A4d_CEpa*tWA6vJ=n{d0Kq7-$xF*T6v-%GZR_}B&iQmVGDH1%_LJW
zYlDu3y?9{#oie-5)GcP4EsTwqf%HB9Pn-;F7v1~%<Ou-TS239c%aEXcpq$RHUcM9$
z_@$<Tl=aq(2pU8LZT&lUepP}a)n7`eI>fb#l$F4Y>GR+%g{)g|z0*dl-2rA<Gi|rU
ztR0HLB)0sAE8M!@m!*9#!u-M6zGv$FK_DMUq?Z>591G&sy~-sDD!nUh3acYfG-@*_
zG~<lHu8Z|)K-_+KFl6CE(~>O8Ti06C8BlQ#F7@2i`$4Sn^Y{v2QGPumT4{FYmj*8-
z+NdwJ<{J+53d}5nD29c`D8I%cym&#ycyPJ7Kx{twQ*OcL7PH0X3tDUQoL3#zZWtLE
zG1X70Zi>8i2&l!(S&m;Q-2};<TIn=UtP3Ph=W*sQloudAAH+zN7a<UoYQoKtmrzAk
ztJQD_8Z=n1ff)N13t-&0YnwK@Sb+jxOS($1@bCeSmkFz5eA&m=9xxSeY~(atvAhI^
zy;;B<dyUTLkR66zEz>tIjqLadt$=+Q-4rr`06LFs2X#^vH(X<d^%&+hSl}HzFJn<n
z`Run80I3s7z`=FL&i!?-=h2>hRI>!YaT|CVv07k-ivHWLLId0P^$r@K!sZZ@Z=QcB
zEj1X6<!Kc7wXFd9e4hZu(aJDu;5J0iB`V<$Fui|&EH%+GcmoHrOj#y)Yh7DUd3)_c
zUp^%OF=>LZH9uL$>>-VV9umm@7E3#J{K9-8&|o6bTPZD&FMIMqHq?PaWnCLl#n>cw
z7FniUa~o#?bu@k*_$;UvfRHvQ&b-lfc8LZB78{8mKiidnq~uMJbtNi)BX5Gnq-6!j
zAy-#I!N?@D#%<~7AaWeq?@a&=G&%=p?3Dr8jAEV`aG_N4R~1n9rRZ<Z4%a8ix-#qm
z<pv)I)d#cXa__J8=4fls8}gq7f`!Kr)bfkPB_+%eL;_9s`QFcW*P{z8Dp#05KEJB|
zg$-r3@3o&))t~pg!axg6)~t2}<oT^mrx_KZg1mHfI|_iByT`l*=*chF0FqWN3fx#Z
z3OJH@AZBWHbyeFK^bRLX?NTV7z?#wCl>h>D044Z@zvPm054$@Qff5I3y4eIcAdJgr
z*v-|<If2>MYz8X<H6NM5z(kM<fi~z0NM^k>CCg4AhaZ5(4>3poLu(bYxeHi>(NkjA
zd6s!IKTrYDuujc|WLtjcX3QUkNtuHoy5%J;5cF1DBFje%fb%s!_|jYfdwGL!bddlZ
zpr6dknO&34FT;3$%HTDiOv439z)ZD%AjT_W$eH6NkLxh_X{`uLpSBfH<@=1>S~DT?
z7J`xx75~C=^Rdq9V+V&c9g-HnX3%RVt~m8Cg@HyvdGKsbcEN5dofIWJB31GG2}dfx
zi1h5V;7t!E)CZ8FSPnLeQqOX=sZ`1ei;KVT;J|<)6D+mZWP;+TtP^<rGQd2xZm>oI
zfpmrMF0{P951NxDMxcLK3m}05gobhQf-iG@M(Jhmn_yFnnARt#C|{0RmoB1C`_%`!
z?Z0~g23_rbfb}tzad!`or$F@2WGOHo2FT<+Oe{_m%)w`(FbuJr__nGSP`nf=2;m_Z
zpQR`d&R`zrpCbBN`$!m`L{H7VXab_@MwjDzJJ-suP{-ZR`HsJ_IUaKF7_m4;kYtK*
zZIi34zSY&)=W&Gsy%o2H@g3bg4eSZHc%iiwi8&a$9e~fI3*L%Z!?(Yw9>cqr*fdQ#
zLrFG>dNg|trpLI;3}O5McFz~5C3I<b=RM@MQLwfT<jdp9d)UeR?Fv&{SedVlc9qoo
z)SF68yBiL`J)T=VyMY*Jj?HtlF+uu3O~I3py9Zxb=V`6hKE2`sv-@5_@ixeR-hXrs
zCmt6^v39>wU0}sK`_EJ6ufJ|-Ui*gh<(DBA`3Qv8S=I^v`%RCIX*`joC>^EKKSIe4
zJXG2ml>E<CkcNjbR32Gb*{}X$nw$K21=@iTyU$yId;R|SK9fI`y<}C8`@9|Z59p6x
z;>4oS%B7qCjN`nB_{bhEzACMxGs}Zw;GvNpF{P+3R}6R}G{yj_k3?{TXG(gY@167z
zclFQi?(hd!@(t%;s3JW_Wn(lVGL_KWAO?326|p$ax0%RD=^}Fpk_&<*KIr>DpR|gH
zJ<yCk0-KoiPVkJ&tQWlc`g|eH2jM6vnOJJjb&OnjO)}=+`TTRu7t6KZp=XT|Ri^ql
zHm?IV{(q%O+dpBWe9)IgOb8n4`tYHh@-ox^T}6aEQeH4{kF8e^P&CUjfBmL<YRB8|
z+k~C6H_zW+s4opx9{B`@`AAY5-A`#eh(v@LNfn76AC<iD;QL9IY=xI`LlutYj4fg!
z3G6=HJ>2-dzV(wAyHeUT2XpcX*Q3}B+qn5Xb~-(|zl-~3>o!V1X<>JT7d)c@w!>p*
zE@Lk_2{zqk{ud2!us#SLcK<t$AP~)c%-wdo$A4MYv^qxY$(`{Cf==x|p$bmq|7w0F
z`1S++EB~CnbR2*EpU?PIe?2XCl8iS<vf-B8I<)`qGgPJpzP|O>7Az}exCrNnv}{H;
zzW(>N4JVGH$6`O%ab}AD8?JO-_L>};djIH;?la8AYj8Z;W<5;oF+WB1p_i)vbFX`K
zsvziU+356Vo(KMYZ>JvWm{V@3Z%JN+EA<%v3ja4*=U6nC5?)0p^%>4u{~PD9bE^N&
zK_%>ezkK`m5dMo%{vSR0W3ng9qnEc;yAUfgERSvbE-OnxUT5CK$Y&`$hvAT2ESN}m
z9_Q<xThsD^d{}9F)2gSt-qTTeau&6v(FI8(JCQtsAh2$O(BO5s*T-VkD#9rpacgFg
z)}qle+Mgut^}x0Tro&gF_Dk2CbB417e$0KP1GD?U=C$X0pcj<X^VCsHRzZBED03Ji
zsX!U&OTm<l@btuFs)`#MyuENh^hK_spCjewfe$yZI~ltV+d&?k(_H%YLN7m@mj{Db
za#_t$g3urDaE++-ip}ltB4|O?W;7|rr3H84zWf4~LGIKi&s)=I1edOYt}f5;JzL5F
zG#=prBw=IcDi#Z!WQ}-67qoyzTsM8pEAd_Z)Tv1!zV}C8h)Cr=KO|O#I@5UI`icFG
zXw%A%!)m9lPx9wg468l6_x|wf*C+T+u;18Q*=b`-jsCQib<S33<Z^b^rVczUS_3{C
zpc~X#u8a(-s@_D7G5|1~J5GMLi#spl9_@nmz0Cc8C#irS^YLeJhUgw?=HD-xhrwoK
zaNN}Tvip46U;2B&_B>$77<RCg5gh-mzXCbRnjiI1MxtsL?e1-#Tq?-7&ebrgyA8+5
zyT}H~80i(%njj64Mo4pfZdK=C+~HlDe9cln`&ljZ4*r2GJ*3v3R2uY<@_2TkdQI-5
z|Ian5T`x!OyhqON|MWR`{IV~hsk4;+uzZ&7Q~0f+ke>3e>T3T$iuu0YU0=L>^T>x0
z^${gg5`?0Z&dz))$)=;sQ*Lkfc+&Uvawka;J{MIXDYe*3!T6THarX;5$5O{={m_PD
z8fv5n@tYpri|EzJwf<Dzv9>`J8e622=97imE(ZqM4u|jFMB?h6#VvSX)^?9WTRXK+
z=(1m~E|#dA-=f~@UFMv%STM2cZEejj3L!pN`?RXJ&3dC~x^=*LN9ua@T+wbAeLf{4
zS@lTp;R`Hvs@iXB7KtkyZZjzfa;@z=olkuFwq1f}{P<(nG$QZiFD4Mk=Lf2_Mm#U_
zdlzLYy$?Y?-`h*v7&7CQcXs{gJ-4zRHsZ86Xy+4DJ|AK)POT7fgs_nk_C<pSOkXUu
z^#+|qHte;C0rA^+01Q1SYjeS#-aq>u$)oI_I6p!eK#uC|UTFD=V8F?8jx5?MkQ>5z
zPXYolDSc&F9yC57zh%V2;0h~D+rZW?O_wLs3GTT!vZU?DQ0nS<L}STS9+_HZnJeD@
zJgZ0jt54N-aHnSHLw<jJFa6*JGD|isAn5~md_Tiua^972M>vR1@!_g;HnuGJ!wz|b
zl<1bf^I|0dOXW2d?sOV0^6xF(R?enLJJZNy9%VQ~KIE#wV`i{?xpvrhW^o5>{2L0t
zYMS&rc{XphyZ6p5+X<zNLD;;9E+uc)rF-adZl|G!ZgXX+|6-ECF3aVaCp>slH-D=j
zFTg=zA7!XRu{Ph|$;*(^FiYwEdaFRIT3C5-a@=|MChV7=`Zy;WrI-W-%F^{5U<&24
z$9tt0(8-dw`o@B2Unsj-E?{$Tad1P&i!l3U_GT|3_k~aQqIxf9ZnH71fVHqAk7$X<
zx9C_hzY(%$aj%6ue*VYCd{D&S#IOA~>%ENsZwD%2M|>?UM}odA@6Nn}NA*B)bb*pt
zP~~Rj?9F|zc9HQ5&oTG}wW(J-_eE~G5iPmCdH3XZW8Vl02Aw$t$pf^d+4|nl-cI9Y
z*0rMD4<A%KqSegG=4Rxw7%Zmv&GpX;_8RBb<uAa0H}9sUiJ2-(KEddJwD;X%O>OU*
zAWBgLyL65ss35&cJ&J%x6X_sLq<10oP!vI>9RxvoZwU~34?$2+DWQcHs+3?bgqBc3
z$ZX=dzk6opnVI{~{4sZK9-ar-*=wzDt@V}nd%u+(O3A^IK(Dl7fo4^3R<t4S$Ms+b
zf;(MU$Z;>>MdHY`x~oQ_mGQ83E4b}SHK(ztTTMt;D}-dsi81{x9j&8q%A*M@aQQ(s
zwiNpobyqS$r8FpvP>(f3>5SzHP<tA(SgZFEg<4t6v}+D3td(N@p`s?d<?9CSroBI?
z5_dnZpquT-Wmc2Y;XAD+gap)2lsm@i^6{G;jPB+I2|o7;BPdZr2K?4UG-S@@!s=}M
zw=d(_Qs7@pfM%!YI1p>tI3XFbeVivWSj6aQs$OX5dG;;|p`>F(PV7;*^=F)h)XJ-4
zphCt&6o-nLIn3Ip2CNn;UhV1$uRlIwME~M6WQB$nXg*e0`;Og(m>6S_zf=kiz6nI&
z(NG21=DjITX{I-3u_~pZ9`oXOxs3ptj>dyZY);1ERg}bj2q@a6H~$Q6i!&ij;2K_H
zUmetng$k{bGO^<ri-DG%eC*Y$mM1$RY*xelRY8xE#5Od+Yq3aH6NDuk*5W6+7}BmO
zci8-s$air=Wca5}`;}w&@=>5R0HF-HwT{^=L-+Pid7KKwkE&NJd(-78LM9D%8jTQ-
ziRS{MRi=&5_=zfK0|wq&E{b;5@_Q|GwL4m0Cx!E=W=C);q+f%3@*V^JfEC7&jk9Q|
zBXFjocDCtlC}%Hk@E9mHd~1zwoLH`gLa!QApa-3T-G_(Tq$V;Xo=o`u)16VYGCNzP
ziN>a1aVOYw(lr!68zW+;Gkp+zR1?$gN67D`@$+87>8%bR$#H6_)dI@<T8PQz93_8L
z&&)!Z-nzQK(uO2bOOLdBgquXPu1e6b+)_Q&c9mE`IoKT%&=P8EoN8gUGmioMFz{$F
z(I~5YTdkzY7T9Wr<NP|5x~0duK<iu@zH5i_f$r9M)`E7ID(K>=8twHbyWl*0{LjVX
z<}_9J8H8J?f6);8A4>+v8tY{>h*ns&b}ojt$$Ly46=0_#uvFh9HxDepQ8bo+gb0KE
zXj+6`)j?C9fSq!KsO?z-RE38&2GfWNCf?Jnj#I3hrB{yM2rm*d><C7r5htN?>(gls
zXX>fFm3Ka-#Wy1BV5VQZP5SHfIR1HepLQ@wFdi*Fq8yZKPVGJA`1aS5I8|fs>6J91
zEIoue8JpY8mp4M67lWgXxct#+M3u9Kzpd>Fo8UZ|ltvV-_|fDzP&X&}SeV+6mMyR<
z!jiZi6IQEF;&T?{ojxMxf7e+>&_Ewi0fI5=Sue)NK^#r+8I!yD*q0G5RJl!0XV7!4
z-9^2EwCXj6*$0tc5MU@GRsouUe=*$ywdSs(X2HO$N110K6{uZ9*pxj*2g>VI<JMKV
zunvU=>m>Y^{c;p@0JOnS#VpGZUf1$W7F_vJ+3y*l5St$1N)^`hw5rIE*jgVJQ_|Q4
zEhr`-hSpVoJAY$mD_VTH(1qVgJhR6tKgaJTeE%an9Bmt$hdYC!u~$%ezz!H~-=-?g
ze21iO2MLZ6f}rt>n}0XD%<<xFlr>~_ygj;=+8wY<k^y*AxQzp;6YCEND-(hGG4K3-
z=Ez45V2LNwh#F9mp#?n$=W&DZ88(-<Q4IZcImuqXf$cirx`D^n9rgWbM+GodR{>?V
zPIva?bke2{L3{@6h`S_?kDlWqwF@|~b>JCok}_Q!70h*50egI;MM!rhg_%U3mHN#G
z1Or-SK`=mB8_AX_>t{*KPf&aN^=#zN#WrX8v4s(wCrlSAQg+nb^6d4ZqDyWN%SjEB
z9urQ;83;_YfqF#P1`gN)_<4PxEXmVU$tgFXnpwtB<taQ@-Zr&I*;1+CptWA)_+gba
zra4hh^CM;y3WLUXh&AvXv?&=xNCmq#StOYa;Y+PDVAS~!dPxQ(a5-ht4Jo1T#_Yp(
zhw*`^F8@h?_4)$&K!>@D5Ir3dHObd<D^1HCXZBS%rxZ|3P6%H0*~X?iG+56*S6WPh
z;!9beRubELK2SS`^7F?6uwA2IRl;_&oWnv{!J)pzWFpoPD&$DCG$P+8tfYYxcnal@
z@3W~p*|zQ9lSVWOUfrgX#97bunj&OILO&~;P_uTfb5Z86{7r5eB{)k26Oys?BV>)f
zon{bdqYmtsRPVt0Aj5HrbBc@!Oid%21+%YZrV%Z7OG=Ge3<wiZfvWS_BP3ONwlg&A
z#^k~L7*jhFV;Drl-1izym3nCTx7R(X->yPc${Jv~c;SqvFwFegS^&+Y6};%6@iH;Y
z5QN$PP%8p13bL;U4ZRi%Q)OuDF^0i-nqIc=Lab!nRm#5&pJd2KHf%?Kt;QWCRI55p
z1(UkbG9&E^p=0$N2qW^>4!i8*m~y9NTq@8maFR=(+?^jo6E5wA_H$t9U8%zs2(9j}
zq4bc=DTLKf6xn1DKOvxH<?+!~dTnG96@10=<hJbdj^ITA=7~#^L}u)R$E*!S-M8Y1
zHCRstF~`j#umasIgHc#}hB9wiyXTa0lajTUho8(wDnprveQ2#bd`hmvRORtZfxKOK
zHO>4RNQv@-E;o27LdAKxAm`8pPaaH##6R^F2394$bSh*tab|}B5jIp`{`SlqC8F}^
zyITF$+^&WpcW^?h-(H2MbYsg{jIecU;hCc&UYBJg(iRmJCo5N%q4Mj~wvZcl^Xe<g
zs1YW(zeyV~C|Rkrh+xZ)q;>4g^vJ&~4pwZo<j0MXWmdz_o1<DAYy6A5S71;_U9@QB
zthr^oR?c{X<h)Yz!DpWCbaHBPeF($K2nx?EH72feVpf<gj)6vruzAxDn!(+^DzUQ8
zO4%_OKh;K4Whlez<J%bYi|?vsclus;T28yjyyb_*b??S@oHE6m<#L5+Y8Y{P?Zm9n
zr0;~SmJf@i_bdi>Ccx~1s^xPWkIsoHhygUZu7<rBpXtn7I%m&YsjTA8YXXOPf*F=|
zbjHTZG#T4k!#F1%!@V3x{d8kgvM6>C@b(uSru1+b<L_oNMEmOSN{@xt`s1;Al4yU=
z3!#({1xH0s_ZLHTXCNdgv-Tf*X?E=u-_liC(>=n(T}zKGp<|d62y}DWq)|&AG`gh)
zh%yM*T=TJlk%N_UhZ_P~22Y=4nZa{414s%wowFLUOu;ZnWvNIxkF1iPYtos~ils;C
z)Q)J~N~HrlB}dwWczfi0!{x_nRa_;ui4`b)NfKTPjvJ^%0!q(N=kx7%On!S-`jtO1
z-<A`Igx4f-+w>57B~moD8zl*e0WRIuaUje#9Q1MhcHZhvrR;6I&KUXH9%JjYKsQe<
zrfH92@<N1*8NSl_#kKlWsr^F)RKB_e40`o*(c0qzHXHT)TMSIN5O{trVDXl~Ji&U$
z8fyZxTJYA(c1GCcns-0b01<bni9waiacC}1BjQao`rPo&^mt9wh~vQ>i{F$&l{KJJ
zqL8DWI<Pk3tw1l!uYqE$26V)Ms8rD6_KyJUiK<06$FFlkB-6{k1W#z^>>8VxcsgOe
zOw3x77s5fu($L&_eT{C`6-qJ^&6xCGv^-GE&HChl>V+~Gqv?Nt$Os79>>I}J71_pF
zkChr*o&$l;?ZU5YQ}VAJHfk5f&Lf%KN{t8gKXHQsYg;^{&$BFDan$g^Y;zp<pwKg8
z^S=Sioez1bPSjR5ag2XcB`&4>9P3Zq*=#cPm;rzbygI0Oz^Ohk+&hZ#Ej5s*y!<<4
zs=EN|pWtb4yP1c|g65RRXRedr*wF|qwO3OPAYOTz+f%G7LlHqPgo~(cXbi)@p^rO2
zw5vdkKz0p#j40DywvxXGGhO0nQ-A>WchfMz%H}unD5zbHDF+7?lRv&Tq6#2(KZYfs
zai9+2X=_9sDt0g*fcBEOuB*mWfY8AxEI$wL0TlS(TQCQB8j!#A5v_$wf8z}ODXBp@
zy)v1~+_@+qWh6sM{lDg(z@PnN+wfYX6@sscodqEr>q>|E9fmJmX@1aEggOIRgtChK
zjal?LTmaS3NwGG4D~#et*`wkkl$EJ78V2H|QgH6?z;BwTCNe~oxFhu3|B8f?9#tD#
z*LP=gDp%V7n%P4&47Lw>CuS)Y?mWQ7H2-iQcc-Bd6HNKNeQafU4janF-<Z+C#t6k)
z3LOd!MVcr;ER0wy*HuO^yCr1mn5M6RP7q}tX5^o~Bvq6hblZPq_cclfIxfu=l=FnF
z4;uKho_gn<*Lf*Ua)t8s@-^~sO|RBguU&cT%67y-0LYZ^4ZHC#M5o$64|W6nsv|GC
zteuXZl);hS3VhlKgII|_R$>Nt3z+nAuB3<el;|+MGfRQ+)T~|~n<&x=T_K}^M8@VE
z!c+;q+>(oz22=rId2enJ;%_XQ;i|TVM97+%uTKG)6O9)bu(B{9)_((_>VY=tJX63p
zRKci`EufE&fhXZhz*hbl)9?%<R_%^6UbzAHLECCqt7#Hk>}l~7lNr!JeYu7#RJT;Q
zcJ)3DO(ha=kYI-?3jhxPQuuKUZ<+B|AsE>{=2=G*6{9Ta4%70BC6vLLlxI^T+Z6IP
zd9vBs%$J>}?-Sa3KU}wMW@voQd+7*x2rWTKbZ-xmps<ph)Ut?Fu?M`mN(s4fZXJ6D
z(E_9&G{$bF;r@P^UC>^WfayF*Tt6MEbHh){Og4~FNJ*A0F=KXqzKAg(VWQ1@h@VF6
zW)H8wldQr~m0GH}e-9UELz%nLc6)`TedsJhr2}}X%DYr0k%nfh`|(vX(uOvP7C@!#
zk~mi8e?WIIS(_w{iZk}=8$zu)G|b8U6TZixUXKvPQIjZDG`Z$X281`0c-OB5{~n9{
zmt8W~cZ{ZBtx)Vb_h&ebJ)sRIycNnb&$-7vVe3P(483Sbu!#xlzXseoeZtYizjRhV
zOL4~kF$nq7-G2bWqp0*dfiXJ9v}>6rSx;sV-&)$xej|<+gsZ4s*|Z^{X{x18FsKg9
z!w2HA<PmfjgasB~E@PN#l&VLf>R}b{%K43fsi7N#@Q?xC-yz~BGyDM+2hzAcw~`Fd
zgYX#4G=58Oll{1^@~jwH0ew}1tF3jT-qHbK=HB2zFa2;~eR^*$*a()<z-tT#&;QPh
zSe$?)!D~@K(%Z3XMr(JNA_|Eq!KF$!Gg1*LjgXXKM{C|U=4rcPG``Zqc&2KqCEBjC
zB3sgZuBC<a@3wMcHn>hQSE)YeAz}G4*X}?Xk?-Hv^YVtW?J+lrKAk}7)F#dT%Z&g^
zjh7u!2Br4+{aS8<I!w(;|8uWoByrGP?^soaQtDJ}oB=Fvr93j*^lp&_U@>L?c`u(N
z&dxv3jbs(y3c&xae-1GxITE@d7$3Z)P2&FVu1k#^k+E;ma7z92N@YO@+Pr_$=7PuL
z7?Km5NguHnQE!7+TgwiHuytu*WU0tM6H^PqFTcuy;Am_tRtRM)37)mEw1nqnU9}4i
zBSh1UQ5|*FB_L6QRjix9-5loP{C{qT`_Z==iNbhH-8ui2?LF_XsILz)2i+%3W8cs0
zG}i!3(KABrh~00eRixv?u{(_&FK$%LDDMq^oCNZ#sHdi?V&Q;4>w=seAzp4(^aFuD
z{rOu(!pnf`TG0LxViX)ih3+<NsuiEst~*nEbOnrZ*GJM_@^{P|>i`^rg5#9NSt%-$
zL=!A-*g?7~i6gQ8^MW1<U4o-znf)#{_{bjTP^DNnsv5<P+BF6;Ti)Z&!`W3&Re7ez
z*MnQY+gLCPIG~T+9w8C}NB<;H2j|)Vww>3^eYXE74rN&^Ym8Ts4Focl`mca*0@@RL
zE}ULN3!&LF&rrn+_mluQfTe$N4nRm9XO<d>SluJb`4O)N$hu=VddBy^hsK^gZXY|{
zezHwvc3^%JNngJFC*idpLV42#>?})?VHTsNe_~Ha7dF~kvLcqVPgq?OJ8smG*}rz(
zRfs7fwSNqI^(A}oznul>g(|KXOX5U{6@T-iT5eXfq6(HzX9_jv$-$^zi@?XNMOpXu
zg`-8?zp%j8XJlbaZ7>f>cw^hi-uYYrZRy8|JdevVx2#&d4vb{O&Il03kytfUScR1_
z*_ZGJo2hoC{t?Wn=2!P$y6gdprU{{K2P4P+8zZwnd2I20X+RC35)xV*WJDMf__X2(
zoh#2av21M`1|F~g)CJ<`VnHPUgM|bLa#OsA6=<!C6-U&VwHd1Kn;~*597MyOnvWol
zP5Pq>Ex-|{VjEDNCI|^Q<t=sr_47=*EB~xC{OrOP)Cd&DD#cSCoY1uFO<uX(Xywc9
z<KIuj&HNJPJ&phX>0=mWYVhF$+MMhHdI2&{>e#Il(30b->1YAoqzONNVMC;w9raen
zVapgufuAPDh*RyL{7tyOcY_hqQzB+J38e#JHGx&KSldvmt{o45;W^qGVGVc(O84cR
z$t)jYZMOKqQ^E`?37Tr6S036Fq<tdbxPKc_qU^kqYp(M6WEJdn>@*R38HI+(n(RVm
z;e+p}5`d7*LxkmCfILx|U3%$ue2pf!PE<000^&`uv-dNE!DfwSz@L7{`L&7mllA_b
zZKwS(t~&RBSI1^*e!JKI&+BK@==8s-C1zZ)q$%*CU=cC^!kN1ZS9t-Xv%t%yj48nE
z?M;&e_JF>N^gPsZM@oEn>W95FY(J`-wmaw<;V<kCZTB&l3@;Lxx91nBI10+GUYCZ?
zDGyTjxvK8zbE-N2S8?+{RHbz{_oXeLT9CvEUHOgwtGpb5a_2>WO%Z|k8B&g|wNJ3S
zsjwv^h#E{&fVw2^@CisApbVRSwkwz*D@4jeeeZ#&)(TXTEjbgQR#mAzvL>FTRv%zg
zHm&M@=jVNZ##-(p?xS9%_x*qe5+bobe%h(%!^N$Cb9SdrzTMvK@UvpWbqcI)w3ld>
zl8UfMh=no!BQa1rX|k<d=2%HOtd2$2nvZsz(kl}|>*PQo(Z-_X+4|&%Aa}x^3#skQ
z)4T!>0bk|k;o`>)!cVrjE_g`dD7C4fKzd}1PJj9rt_R`@$;ni1c!;o2a-A3Vlm)0A
z;8o>&%wqwRuB`DO#SC>8QX*rPw407~G<W+Ne2-A?vPfgcJ@xzw9z_>R)p9KzpfZX!
zo($6fYP72YT2hKdnrsKLSFUcgKM&&UjDeQOHcwDlzbf9RUtNzxK013^Er=^XZW?=L
zc&NaG*2AT#3&#Ld)(*3dx#!D0?l2xBcvuTr2o;7Lr{*gkFI*Xz>v5cFe7amcZmjY+
zOMWKQudDnOHI{SH07CCprxE>eU7O>Ea|T(`+INp<(-CnWkd7ih5;~|H@2v)E#Kw@U
zsR^7{?nK?{iIk30s01Cf1<@`OX5wEK>WvUOwk#SJ#^aK#KHN69x_xTt*>76--x4OF
z|2iV@A2=@Y+r?#I6VGl(b@=PyWm(0jupTJ5iwbQXVm0V_wwEG$jx8#@h!1dN*%nzQ
z?p!wIBqK`R!6=R0u~KwAxwuhtfg*n#U;)Yu%E0m4C8orbQ+4J)^&yP^QxN$dD-QqD
znU;Fzp;LoV*g<TSi(P$1OHE75RQ2Cj1Eky0N|Z75uL!LQ0?1+bjuQL;ip@gxKyW@V
zs)4Yb1>jDQ6JZN`Md(-9_Z{LKP!iRH&c08BA6v+uWAm0moDhJAaz!qz+XJZ~X$JW8
z@Cr6=zlFLrOsB`c`#^6FxLH3|sy&Fuz7epFE`ThS>@w)=<~&t`fBQAFcKDFc*=$0!
z@A6>4;#va<LXv2t2LN<-p<%GfRg2W86XG*r>I8<7BZ~XVrgf=@QqGIx{==)!0Vh55
z2I?qH2Nf=8!JY{#C0NjAU;9nG<8Ng2_4s(fCrRAgB(gfYG1{BjioXt6lRFXkkIvRr
zId+zH(cTyw6RMVD<!l6}9v<NyUOo1?S7p0scCYyiK)y~}w%Uz<xNp49gFkq{NndUc
zZ&^*RAo)(`Wc%C;^FR7RXHt9QHkV+Bz{5-Gy_t0LV|F*Wf_gEc*jHCW!SgO-C9Vy4
zblT4RO?YE}2Fqd6s;l)}jb0CYn-_@}QQ2vTfSToqwC|*Y<=4ga$UQ7ddmT9>ub=iL
zqR%R)V+bUHGF?j#r0*>UoiUY&5s#1q{;=tL(OeQ<(rd^DXysJNq6p<z^DzbNv(Qe)
zc@ZfgAWqMXZlL3^2J2c+l@i#&@Tp$l{T&oA$n<!79ul$d3G_*xY`34YVs&p`8ZNpU
z-SG6VUM;nQ<M;qg3U<Re*p8GM(RYOMCWOY;np3Lfo#(}2Tmfx?a<u{xeeF>ae4rIU
zQg#3l5cPi8XXX0z&{VhP2Cast3Oz5zvQXKoyK<PVB86z<11@mO)Mi`-S|-G9?TL~G
zMrxnje8JJUQ66(9wZA$O<XnUqbcEniEvpH+dO59zh+dIW5lh=SNvOeq0%hxNO07u6
z4wrmuiys;_?$&}m6z#9gCakQ1w_nHauJmDN$3qAmVUqq=g_NQPDk>-2L2K8v{8%iF
zvKl+v9gSHhx9YVZS}P_)2erdKt1+l$g-ST_4ZlKkgK^IBFs>JD{JuR3BXgfK4q^e<
zA7JBg=md)zA14dYSbmI+JJ+6p<rRDds;dOoYM3*fP_0{e<UV!OioHe>A9LX{1P%@S
zsT9dwq4<YIokhK5uJsj1*+B8?3(>}nS$4u}L$Z)<lOf6#RH6^W9xh*E{&O_;6%^fH
z4U|c?uxFsFVI$a&cTqJ~S>xa57^e+qY~uma$)sUxFc-UrRX=WYa6buO|6yWciOalr
z{L8r1z8U;Uk90nzo#X4iPACTUc@XxYXTD|LxrlPYuRuU0Cgt0y3dM?<!Q%!@rsJpd
zfTf9d-b!~$dum#-lT~mQ#iV0pAsU0z8OZyfo3BvwZO);0>4yO3yjb{6>HVVmf`bR7
zBRYQ{!cbcEQSTops!dmEM9b{&X0E-QG=M5pyogmW=FMQY(*jL|OH;!pU!6APUBkm&
z=#gzI7-I~B9yMpJeRg->^1Rz)C|kB}rBwM1vDSAGft}eFqGFzKGuFyVcg&q>aI-s<
z2R`<iim)EbmWQ@}do65y@s~$g_W05X>b*1{_&KiF`y_!tc+0HmrPEuDZZ%eTluqST
zurZ+w`)Y(Fe5UNuv2*&*^|G;?tXe%WPXjUpWnh!B>nJkhq9YH&9dvZMr^SMh2QFWf
zg<$ZD)!Q;lKE7G6ASA<t@^zz`>ZzyTYWeyg3xX`lo@7N#PwUw>sIPAEBs5`FjBgs6
zNZsu@Y6v3x1}mW-b6bNQlQ1i-(4=CLj8P8ss3=H_kd4I}#fLd1VK56QcnO+fMYbeB
zu)wi&roKtVoSeX{R9E*^jMFS;>3ZLy&(!o1W@`o-tlOiU_bm|QoP@7Mcy9R|#C}pv
z+BaQ;?O#JRqr4?a9+u!hm7c7UKt&BobI`J&%id!U#c0P`%hj93-DwLjO)LqjU}ynF
z8d6N*wFh3H{`N2qAL$(*-&}Zc_U>KZKiVp@F+RYr;>vxB2M=itn~ntDY%8Xh%T>ys
z@>XiJD1X-0T4B?sG!yy^p^~xBc5oLW-~8=N<Qk{H1;M8RFWa=Tc{sZC<!j)$<a#}-
zU>A1S9IP!w;c3c0*ffd-=U^dg?A!Yl_-#ikB(iOwR}JVQ9doz&*7jbea?3k$mFXqc
z4T4JrVnuYOze#omDUNaHK@KGui?76EU%dj$bY%dNzDU}@Qh;Yu*(MnhB6<*b(f~6Z
zvut9<oj;nUFGup<dcpMW0&O$fsRy>T=T*goy5G1xc&UFWPFdY}sEJANkF`^b58ixL
zJIVj(fyYam$0yF@vwYS0i%G7pFGEFlm-w@|71}~FGyxHIw-w24D10zB(*Oafln2im
zl+<jsJJj{xGQ_f>Ds(KD#0^vZK>FektqhKLaGCKu%d<c23`=C{Z=lRS$Onvb2fDsX
zlvfwME+xkBw%=M{B0m3;_t2b@M)u|!QhnyliL=9|p6#=#d~;-muXsfWnoU?x8RQaM
z9Ivl0E^2TdE%o(kVc@*C4LK5>JS?}nuT`L!qR|V&&i8W`IOFEIci4iqX#&TJUdigk
zV#kU_uxt>6)|NMHs4|_iUo11$hAX3X#32S5=I>o_HuLiHU6B_04YbA#9tC?BiM{y}
zB7&u{m!9<8YF|~>VY9EkwlG%1Zo{;Yk}F3Ew0SioTXe2$v6g=0LEXA&I9)_N<7S%c
zlD|A|bJAf)=l18kj#H7M2dx)~i`UIb!S46FoNwdMwRSm*<3;err3UrcGo1S#ojw|a
z+$nyw?{8mMe!Si+Tt6x<D7a!T_$))@My8_`otv&)X!?GIdK37tF8&Y_B+NOkZ2izR
zc_N3-Un9^6{;Kf}+ZXP()=Ng4;<vGEQzd+4z7FxY!;C-}t=kzch7gejnIhwrX3&MH
zQME56x;X5pIK%?jk7k+ivVZM|PLaj!qewEUG*;=fV8O$#&ra@BqQwdX&DL{GHy;Im
z9+VdDsp@%r_<HYZM>=@8o1)+D*5qQ`ePPNoN1;(~%Amlv@gj%oOy-3m(&apF*)saK
zdMb-@jUV`pV&8p-oqjh_JfN=vS58Q>8QqgYh|OxQ%6%+knQfzgs~@7Y<`0J@-TP7d
zM(jm-8d2bWkHO4kZ=UoW8Iapwd)fxA@+q?FBU8mupDO!DCMu(b(4rs@1Fa_&m%yD?
zH@{QP3v8dbs~y|$X6&1!CS_CqAStTUE-gwas`Aa4LO{n00XW@)QC-9GCXe%#L1h(F
zM51tQBt2LX*ZUq-#})V^sc1cJ=#sdg1VZrEvy30QkgGSo*WT&scFb^sudKr4{Wk6&
zvFX?`h%O}{ne<yqnB2RZ6Sou%qh&xPZJTMuexMRX@$?lL>7`Z~J)c$%byvhFT&LnC
zYT+6GZ8-_^qgz#w@^`V57l&SCyJsh)gneidF}f_9;_*H4Xl-Z<f3vc;Y373PZn}!6
zYv)BUZKy8W?eSj0`Nbj9t0<T`|I%Azofdb2e0SR$ck}bYDj#kkYnU(Q-0d7`qE%)*
zyyF}PDT&XCN#6SGNFNZka|Cnp<U?70w=0cz#{%-L_<^$7bbK;6wk$JBnk~-~To6v3
z6Q6`os*VLmZ8d^VUe3|T%BILNG~ko`Xg977kvuOOmAy;j6a!D^A|>VXzF*PkRr#6d
zfOOgyy1UgTXdc?tAq`1U+)VQ;voq}ZSTb8|8IF-n31d`D`vWO)_F|4dE&6;rS59`D
zI?A92srD%F$M${m9L{i`cMxs0BKaunRPg|3j<bge=IxMFW7)RVv0#>s#2A5C?zSgN
zzfQPKnI6e(tX)`p62-@)M|!<s^QJ3;;G`4>60hR4l_0ruB7#{*E|ypnENA%?s@yQ2
zmG-vm#u*K5?JEucIww5$7auamzuaQE<jCRM#T@5Ud38{1;5+wpzUlTLu8mjS`0P`&
zQupDIXM=lgEFt;TA-ArvfMol`eHCq!xA{6GI&W3iMvR~5F*djv_~WAW9ECfet_f@S
z1mfdri5`9R+0h)03VbHz?vAwulya=H(ubt>epEHl2ODztdobd2V8X~P@u`<y`dP>T
zCE7M+eQ=Na#9k&_g}*3{?~*(`iWpF_n$&>3LdQBX6^|i2^k;h2PnK2<jvZrbE?)l1
znR5q}Eqdp-^+e4SH=fI`;#RR&2n8nuap}G4c1-a?;}sW}7PGK&4ER69-(lu)yXN#<
z{Y#)rzDcsmH%F$9^khOC|JpEXeeVU`;EU8x!RMjgnlao)R)CxhdZOMUrRtz|fp&(w
zGN5;^J~L>PT7xxqx;UlK{5Cd{TLtIGzTj5|JcT6Im|>uh^u>>->6Mu1Xd#D;^4dl9
zXHw&E&WY49#u9cL)p>XwN3Qqum3S*cb;h;6;IZkb0#YIT_M=Ak9N+4me2@%VdwLQ3
z4P`Uz6q^ov!SyZZOZ|qQY(Y6tN<22~Q?K2}y|#)(qHT*m*J#Bn|3S88+`N1Hz@5Ue
zMahRD1Mi06AH3XKa*j6lZzRolMAl)(<m<~a)Zgj>QtQ0>T)OU#y)?ek(g=-lLyXqz
z2y50Gj53k*@<a5rK6<jNza2`fQ>m_>TZ`#%afT=w7ktJzZG9=;{~{Ku^0xYbt>Pf;
z?NLF~`kP^o50aF<w}_;PZxRnfYDVkD(<Ri^mP}3+xEUZsA8tn`ihJsqO<rK;$cb|T
zas8U%)0vSEV2B%9=!lM(Vd54(!?AXUMn)jE-24%#+V|mjra;#poBNNFi6=YhU|UbO
z5s8VOs3OJ2u`-<*DYuZ~Jj)p=r&wUsb-mpygIim^614#JdT*$pmRaTKg!scF`_^0y
zGiQOn`kIoCQ?BR3CKQO?^x_0Vlkg+|i%GH)cB6Ky59j@NJd?R}0_6TYY}JeznHz+V
zzC2esx0rGZJex?L@L<glTzIeK-eOWweB98e_@gZA%Z1a$?43?1cloWXo3Ss`9{?If
z-;`p^pehPPPhqT2HDi<F_u$(}`_qZ{7JdAZ+XL4&b{Tz&D%It>ubf|Liq#MrpwlRX
z9hnv?vmS@jt{zA8DU?`9Y;m5B(y#i!^eI#s;HTXHKP`OOyL0w6F$z`4DWIP5VC@cr
z%vzEkOOwE(utOlQXuPv?V#@opf7U#%jDa!ky)cKfU;Ot#m&k*7G_x0dMT_;r_NarR
zA^BF!otqvV6?e7mwZz<j-6<1KmseN&(x$yQQJm$3IOX?#<utd9vWC>Kil&_2?vrSi
zS#K^?*TLES_l-$;r=#MK8qNqAOKnO#PEXAlZ&#zfdBvL<5MJp~NO6&6)(<v_Pew~d
z+xUvJvx&c?3#mxg-9-~pe_eM1qZX*UCHzLG>+`M9iBF^#g>w>*5Wa8ZQ=Fe(B`@Z`
zs1&ZsOm=YQh5IV+Yq)_W%<+R<KTS&D*W{V3-)X$nOKFi(H|Qz-`Z3i$6YXnmIM8}V
zkgwi$5lAuzzS2ny>jp94<r4A&bf+`}8AoqzzAd@>)hEQ)s&|Y#zuo>8fLpXOf=2hW
zRa=8HF?pJ92D?d^DaErmY^b_3VzlU3<5LeJ;l{~xry(jO20UG#SARHVDZMDnsk>R<
z-3=N-hzMemPxNZq)8cwQrLvXh@r2!0R?;|dFWB)vYdG>EmIH7&PRGb<NL;k5snI#1
zv>bNDR96tN71U6WkdsnYE84YPW2%vVD*k#Li7iOaaIE@NO49ez%}H5v|G)vzkL_I+
zx@4o2{P?_<x-x^$CF?k>cTTiGg-#Yfvc4Jk?@mFTHFgm2FlB!oM1tLt=h&G5Phk7Z
z*S>C`*QXHg_NG!PKw3*OKIX`wuE&w|^1G(J^pq#7V$3JXHo~s%2z+-K!S{%=K-<gF
zcxm&S#Lr`O=FsTtsfcx-5)#C}eA&x^D&2gm5D*RKPPI{ZXV@*iv#THId8esdzvvCJ
zW#}MKAz-mt0y_K20zjug1c&U6Hd`vKZx0sMmu-Lbaj*1gl~2&05>1*tmNRiHQi*Pw
zce%0u&A!nWG4TW6I^FDVzZ!Gl)zX>;*lzD36&1ln56FyoJ-ruS#X<HrFM{Pm($hFJ
zK~q7C<cPnlnk*|)_9MyuJ!e6gomAz=1Oqel&fC(yq@KsKRZoCL;%>9&QjK9^jZTjo
z=uO^i7W(d35L*}HtP%xLR8vjtdCWaEdVA4e+z-SK?7WKs<^yKxDe#{au$ThPz|I@+
zFEc4*S0Pv$YoyLzEEv~VJB&Z_8R#EYmRLy7b2X2@E_xst85J`9aPFoz`|S!H($&cX
zN%O9KReh_*a~vfznU{)!-(W)%HMw&_1pt>Puf7<&-;`Xhvx$q_?g-;lP3yYFr@Sj>
zmeVNlY&7r;5r`QJQDuuhPRZk^*aN|ekAim@?XOg<+BprQN6Qusdi)4YNY$jV;sMvc
zfXFh13XGcyO&bMpzfM;2&0jtRZL<>3tc)bIW92n|Dl&1=m1{sZ8`uKaz36aPXH1GA
zdmm$8FvB_IK5VEtFyX2vKM?5;6gNJcBnW>$sG~GeRGGFycx-3k4%xCNM2k_kt7vi7
zQJ4r>{#%WVYM)DTG~EDA9%`~vW+`j@>DBp4gLqQr<dq?L*8X$Z>2OJ0BMNyj+rj!v
z{8kNd|Ax|YVNfg!zhYLXb?53R%88q^HgbE<wgX<rtEv%a>Y@KmF6zoTkGCEx`@-}5
zb-<V8gi{`Uyebi|Z^SDo6OWv{(O4GqMh}>$+UHVTcWi!TKgh)-p_VN1mg+y)>4U`{
zZP%L&9H#m%)jM>yBxqsTaC6lj*(M)2E!4PFqZ3tROwP66Fg8*Y%r&_a1RujDc`9z*
zUIU#+{qlYJ<A=dcvwP-~p3>5fskaF-+#0*c;qN?%90neN5Y6)Z<2isffC-B;hcbhK
zFQnEgD79NvtDED=A{@M!>VE`TMNSq1GQQKx`ZpP`!}>waCRfiF0D;u#T9WcfD+uJZ
zrB!vaNEk9$UqR7anZZ=%k(ySrgu2Ojj)DZt9TH!p=sP3hCGk-Jnm(APxnJ<?iEoS=
z%tR%X21c9R|IlAx!ADLd<U^03QOMV7-Sa)ma)q(_=l6~7>7z%X!a+QF1=wm^L5r92
z>)WH<fy}~mM+^%KW=YqLLe{Uv4#QP6(!$gTXw~Y`p2wC`qdH|j07U|gG!3AVriwit
z0RhsrwZ(aq4OOpGalvRGq4_@Bm40t+m0a>2^6(Tm#;bQFG#0=4jE3govgU((h8?^Y
zJh;(OUgC%R8+`Lm?%ORUzQ>vtYeiTxp6w?yHD0LD*<;<Vk3@evO8iE7Aqjypv8LZm
z_v*zeM_R0}pA?}})@#3cLd#`0a9$yD|0j{w?|KNWd9;||vd4T+hb$9j+dB1$meFKt
z1n)U@F`UjCn|X$p)_zt;e}@X~_<8keT_{6%1!LoTUY52Lu^%MaNQ)xTYfraJs+C5c
zXSArGQE(vmSnkv}v*29NqVI2L{@2g{TPt8x#cawlZT$EshGSopK}4i9R-gL;G(MQ{
zQ1W4>%n997p)`u;m%(<ab)vKbG#&}xO<6+z@W=J)?|4g=J>k_#x03AePmA6D)Kqci
zF#cRdmR4oV4NX3?$9|`7Xg*nJXFaq^Kq%2Eo|vYcJ~hq!l;I9{RqX3HlSKEJ*Ks*U
z?ufoY$f9<FRN!KrsF!;J0#q4%dSijd86)xiHd-z|Lo<2Hbl9i=oILT5!>;E@Af2kC
z3kQcgM(8C!O@-{A-O!(hMZjnkPCy@3B;L?eO&4xG@%~1c*BK`eJ*a)`M8ioIdfn?G
z-^iCY&dlHXr6|~Sf4(Nuxv|h6Q&~7$ocAh(W$(V-zFmg_|5y=3d1kMf+sh{Ha^el=
zOMV-V-WPhvoN7ODm^}Cv0151-1=WYJNU*pD8%U1s*M%amW!>Rj8>RcC$~59=zK~E^
z4GW!&$DOc-zITQbc9E{!9gZ8M>SLoQ%lDvJ;e@#>9LcD%eQHq7Tim!bzi^AJxL~|e
z(QP!zb7csEEaD$8N*y19EY9*RZFk?wAxjP)Stk1}#@3~CmBsUeWL1UQWr1nu_gz@;
zF5S9YD`=&?77*b64FrgL>nQrk`@)~YcM<c7RVM;2HC>kDo;Vp0oa}Jk)@oL5u{8IH
zG7Y6ka4fVGHn%eV)AEK+u@V#JMR-E8St64cgO|VEzN)?PgJbnAwEA(r&4RURHnc8b
ziJSu-7U5HTd)N@GC`&U-`}7{0B~ju2&6SFd=8mB{dH!dwV&`X0HX-zLmOD5lhB4KQ
za`a7H0X6y<t#B3kE3(5&?4U^@-3$#voL#%b0l3j5(@(8u?uSG}Y-rg1cpO+YxqY1O
zAU$fP<S$B7gc$aP?7TtTPA?-XV_RmG&Kl;c-T4Y!i`)UZvV%QRH@hSv=lsgbFRsn(
z`$IiX+^%gr>2%zMb|7f=q{iTMd%>M_O<T+dtCw5hjC|kle4p1@=AYZ(L;~Ar0#v-t
z*eXvjgopjn7bgFPz=ZZ?+IU<r-O7Qkb<Dw;)#_6&*M<4DfExxxa}SHHszcZ}8auM$
z6jNofim_VG?iOQ;UH0o}2$#vtL3y4AHX9|X!|Q%F!D7W|{XSz&`N8V*wc^Ri0LPp*
z-F&z~(VS~mJZRf*!$Wmd%GED^9evZ?%7RvlG)fS^AN^;xZnKs5RKDI(S39sA??Ne<
zyAAsrx4;ONS$*Q|B?S+fY}Tha1+j?SUGdXMrFaJ>);w$~<{LYR^Rrxhk!fv`=o&~N
zFR46dU_zKi1e=7Bm{Rykkt$&6aZJ`L=wn@)$j%6lYbi%K#Dg+mF{f`K$}H|6X8B<J
z;H{thvjaXpg`z%l#tTg^i9SoMK9zjO^BJCq9sbnHj<Ze)?*Ed3n!M!Cwi0-D<Wnm{
zMPi^Ho#@Y2`+YZcw#0O!xsvb%b$m(QEB{gO>3#XzvNvL1^ji#=fw$fz6hN5@q_&s#
z)0G2Pq$}-ir!>Cu&px;4K`Jei^h;g(x-H&Wx@7la%od}(Ec%D`<s|>$>_1{f65@tG
zW#k_g%cYE;Q}@~Z?pUEL{?mTWiQGjMH3j@sx_|gUn`{xkL-n+As9W4^=ZY1aD#QLv
zkTT2C>5nG&UG!+3p*PJEn|cwoVGlN#x0yddkDphPIv{mL-ey&oR&63Htycvq<#pLd
ziBWLt+|EyYzr*>zW0u)LiVBa=FrUgdz!vP@e%@mjMNDiJ3SSJz3<do;@2A+vS3Cad
z{gusWFyV1QysQ5bdup(C!r_+Sn(4tMuj>o6@7B&2yM4Uo>rioqi*fqm2ST%QASed-
zG_X*Fv=eTdjxlSrU#ees=R=1AQ&x6u2iHQ#r5<3FmMQMZ>)!LGov%(^mHKX#wGr4X
z{>UGc(B@DeB}=?rTe{WyWlOwMyqblqk%u`?c{Q(gcR01#vsbzDnaP7SW(DaeYWRK4
zR{v_Z&569b^Kb-K>+D^31R!`;vO)mm&H-9!DI-QY0bUTodsF-3VE>8v);j;kvBS6h
z4U_f84jVUjPLS0Piq_|Ks`aiZ@W9@DH@k;l^rzo@p6qvf26GShOS>gwmiz7*=Nw(z
zk)w=Z6SJNtJTXpqQ>y~KmL@Cz;iJD06UyAqm<aCRLQC!79?M2@wc5^Z@TxMa>&P81
zv*e>MmcxAy!Vr0SR%##S*n5k1C=C6%9&uY%nTZV+;BH_-cUnhC1v{esiB|6mzkl8S
z^OWX$shQAYdNDsQ=fltzg|k|untk4PzBX=GmR5l_LEt;ZeQ3*qiFF6-aB8FYfxYg#
ze4=wn0B>8&Skk&eKON=F9%zx=-9fy&rIIOD;wXze!5cfvx{XXM7Ub3HWun}0o)X3c
zd0yAPJJgS$LB!35Mb2^$A!%~n&iZqL!`$jZZrNtztc=e;;~MudR7<63MC#An?i<8r
z9<tsKVUQjGTNKqSma%VTs@%|oFe?P`Yd%S7qH`l$8w<O#r)#$#&`DQ#CEm1TrK@^&
zJRt1U!cPKRel6;}f8W!TM8Df!?wvKz$~WMte*K}XREgorKYaJ3;1Yb~q{u`ld74Yu
zGbOPh2g0l6uhxquYNNJpcxmJykaQ}svyQmdjQnK})|;e}Zls3fclV$-#y1a??+!cO
zt~oR+oSEBvh_XG}mskf|e5j%2iA}No$Opfn`Jp=Z*Xf7A0>>yYXY)uTP*@IQz0ki@
zF3L-b&{baVGuyZ8bgEU9aV|$^Iuf`#8C#81$0{<UnHpJCS8teR+huwFHB)AK^Y*T@
ztm|M4{4jNgS9;RdvaZcQeJX>ATb-<USr}O1v$rUw>DNC0$W_lqpXYgc6}ip9dhk`!
z(KUPG3*XPM4~a57@Tt2+9ROK!|6^#;zFy{Je9mCIPJfu(QecO%idt&e%>`b8vd@?`
zhTvkXdnT#jk-dNM+zpK&&G&*`ELNKDlkKCO_Ghnc2har2dI@f@+{g5MiZdB8inRCv
zLw*%%e|@OmsoMC&Z~erbw<%#MK3SxT<9XO8>mwdN?!MkuMjee0y%>9gE|EB5nat7&
zOb)!`e)=g(+w*WxQ2!^+-ixh!lFiro+!1pA13DQcq1CnuD?LU>baX4J{70d(eLKvH
z(~U-gt&#}+1WwKO-QRC>0MI98@0wnL;^0s3`I*S`4hk3L9YhPI^S#67Ns%Q5yRHS(
z4_t%72@$aK*_l?!ZCO2K3TZd*LBQ$!o^FdQS!vm2-z5j^oZUNbVeiTNLrZ^=O?~%;
zYLl36tQYo4x)=$2WA>{2p%331o~A0BO2+x@KjwBuP6jHUnP$m6#dVMEe4|1|^W_AZ
z*;z+&QDl6Vr|c}{n%8+}g}}wc=jf%UnAc3bqEE7k*07R^T?wo*PGFWnVMiO~(dBfP
z@b#<h2hbfdf0dQpe&~E$B!iCdwGd+mrmR%S$gpHXBQ8W(o)M-ErWRZLYm4DBQnh{a
zV{f+arW3BH2zCE`;WdDs*L;5vXkB;6*ril=v-eSFW(BNJ`sy-+#8}`Jxp1*lG?6#a
zl#4%$bIh-x>}9u<{`^%xsrl$4a&y0p>!-E+v%s!jBf7m#aVL*N>+~iEd##4HaC9jq
z$u;psjnbjYbZs9u^!{StAegC<oG^saO8X8^&fehnN4BA6bJ6caRO$m}*Mzm8U!K2H
z_>32VUU+}wELB}bdzCvjhe)jN9dw7Sj$Vyj9L>=qer<GQVvTRf-#oIJzc38ivYObt
zd*<+-`cxca!8<5+(`ny`-TCT?u=l0Y_ja92U~0V@4ux5Cvz&0sK)C&7=jondu8N_)
zIlf?z5!nP&tDymp!%Y5IdS0Uto?gju7wlF{-RV7jySt%}^J?6$3eOpt3*XS>c*)%c
zfWr~44UG=}f;sYqz36M(=t}`YlYDb2Gz!uwCW&MkZVqP_dJ)$)m2-Ra&hmlCcPU8P
zF<hbt%ifchTkq;($3`&Lv~8stE$s7)k^2^;+b?pum3f78G}*qPdl@(hz*l32B=6@N
zaPC&uy&IdZY#j+x!JWuV$?dxi7BE*%4cR`8Jb!e%$|wOrsoC^Vs6kx3tRI?K33!Uf
z_xF>qmxs1y{;{VHOg^mZG?@=S!nQM4@yHVhy*0o6d%`sBf3DgnvQK0aZw|rK0{%ju
zty>u)%kT3_)xQ)|>ne)d4WoHFu-f<}?CPGTT_AYgs<ZA~|M{>pjI38$vt1HqUyD84
zq&&dGZpIFJbUR(E;@i-fx4gS06qF+aP8d^ck73FZ@hq0M?7EXjhYOicc7m($<d*|P
zo@sgsK`)^`0?NdV>nAH|e6%LfbqF(k7n+*spm^Lto-dx;lUm5UjJyh$uae%-oxj(~
zxM5Y$jo@A0^*cyd63#!oZh2W4Zw^e>iGBiB9U?nfFw1|8ggBs_jNHoGk^11?g7J{<
zHRYfLVyyHwR>tK;4A=5!*mC4R$wdAtK6QA8%gZ}?-9;V0H1lpSBGU*{5~241zq_7j
zjgXV(zP!90%uXQPPnL0wpCWv1MDo%e2^6s01~6Vsk3dHfT{4I#ueLZtu}9yn^%mLt
z?(nYUx}9?gm%X%g$%EdU^~AZ{YLZK`vMWcViH37<ze-#9MjFpjQ$`87fi$reROp$_
zi4?6myk~c}lwbeI5%bV`rCpfR<Pv0?Of2Lg{B@)32^V0I!@g<!zCkk9a79KgRkk=k
z<Qw~=OfkXDlDoso581ql9p{$IPqHu^Rd0r>bUtj*mk-4AE9PQ{l$}$~drTe;FfPY-
zEj~~9+@3Rd4dkP4wK!FBF53Yi?PU;Ohv(N8l~8$ZrpB>b?W^GB7VjHPlcV$4YRLFp
ze6#<1PhTQ3qH7f#8=55cti@n^dg5-s7GM@TmuyLY#0KQ7B^iH;7}|QRWT5%pQ2#jj
zlA6h;4ZRoec#^%tf^j?3<HVtL=MHm0Izzi!Q(Txy=jtG@%$60);YTq9{wS^nznZNh
zBE+<9Wi=Rp5Z!QBZ`C;v>71_YxuCwGq`LYBs3N)Us&^m~{-Vf{h4R|*{JtM6Z^XvK
zKMtO+leRtSnc}@}5FX9w^R1n0kIOcv{A8%s#Lb%*cm#>!5NT=E5^r8D>l>dbzMT)}
z9QrCx&YpFnIg~{GI)`_-Y%8(?V$9jzX5_{QF?qLTi$(A7bfiQ^ix8(8j3S4IqR+|i
ztluO}Xqtr%DUa<nGfq?#%4@&B$e|MUpvgy#iexq4Ka$j9BP;8zIvIPSN7Azwa^J6A
zrzRLTG-=urjeC~Wfei8c0RQJz+O{sE-;JKA^$l@+2{!)*DuaNWK`(iqys-S6hAHd6
z2N1H$9qB-;ce?zaF(O=Wz$sJn(Ps{ftYJQv@IDn<0e{XaF4!jiEyLe!{~q?gF8*Iz
e0p|;cHx<u*BWvu`tsI}c(p1xZP<h|x<$nXNO?SKi

diff --git a/reference/plot-6.png b/reference/plot-6.png
index 7f2dcfd47add8aafc8be0b2dc5b566137310a2c9..04ef5aedb2b1a0c819adff588275d4bd6469953c 100644
GIT binary patch
literal 36797
zcmeFZcT`i$`!5{q4I6f(NEZ+^H0h!!gx-5mfgml25IO|qs0WZ12pAAh2_U_9Nbo4q
z0%+(Z2}%hNdT$|j(DS|Px7K^_y7!-Vt^2;~?zJGv-m_=VJkMu7Pnnq=rl+IEdhE(E
z2n50kQ&%;BKo0ssAp0L5IRwrOC45)_ABP@ms;PocaBSYIMS+i_Pu0ymArOw&dw(p^
z<BJXu2sZ?#s$%5(Ziejh&d_+5x#%Mxv48*VeIH*Obmb0zaq!G>huMRsUe*mIa~5~*
zf1a<gCGb?eV6Dv);(5%%xGqc={v=|Ma8UpBne(_K!jJDAxSG9xKXzyL_Ii?L)p{nG
zkIh$+D_UV+br$8FJzBRzQElJvCjH-k|5pM)0_s#nbdCZdn6lKojOewnFq<9ddAiF2
zxf5yfZhjBU5c9JMM(79l$I*dP@#p_a>P@v~rEx(Z-B0dsBK{*nZp8z>EPsByzjJ2f
z;GeG{`|gKa{I|%lbIEyoC5GfhsH}VE(7&&Bt3HCw@}&r5*W)grlgy$E<H`(3iD-Px
zN;U3&FS9Shtdj*@V}>k`D@()081Jf~C)E9igq4$2P`_UCA>+LwafbXom=!E8kuNf#
z>@%;fAWV^;p<}QgW(!N=nYs*HhCb=UqdPEObx0Rs&jM7u<ULXVni8|3c?fxcpKpo}
zc2QH#5^j|jBWX_ZM8mK5(}azyuhibO`|yD%i?}{@gdVI6i{$S~EYpS$2a7FV!3sHL
z;<WvqxQjU~OSNA*{JV$vnzjQRADxEf1kKgtVVVX)J{ej!Z(TOzH?AJ0lt=ZIlYD!O
zuV>%0!Ukw0CBKif<L}WZiLBt|4?@4UqRkq^YO~DLVy?6uc(XHj+qzhqLW}F@v|K`+
zthaZW(OGC-=1Nm=fGX=%t)^>`tdDM?qXd*z%XLfM1aoehwjDT~QefmylN0V@mhcV5
zPj8;`Tlt_tdVc^}|0{N$!8epN+vg=lN&Bs%J1p$)SN2P{95@}$ub8Z|IWO#?_vDot
zg{hu%L&8u7TN72<I%H^YAMKYZQmuF)oKG=nLbhK>B2>EXWcjpCW+b)cKt73eA(Kt7
zeWG8xT+w0a)Dn5x&@_{>{>jtptlvH8bF0c7ln*Qo79t}&6@cwC<(WF*TR*LJ&J5Mg
zOMQKGY5bQ|%Iv!1Z_(+ESO}MnsC<OnjU^I^TJ*Of+1**3g^>Bf&^ARr%T8x(TPH!M
zILJC^Pyjp9CBpLV^(b368}1p*#n6?ifaPbei`s@sy-%DiPSyx$9aA3K_NS&g&Rvc6
z=Y=Xi-Ef+Xl@Tr-yHlsNzfK<(f!R=tP2wDBZ*-9xvbzd_*mGkqbApCcKTU5M<>luP
zVl%pd#=fk5jgy9c&e27SGM%wf*Cmi;q}n>h0qlrr$y$tg<x;~rx}W6d#Wp_V^6fQS
zSUw-7+3F<fZ=yvCLO>%Y?(q<5wMZ*Q=DeE5mga)C2L?1QZxGtq_k*)ZJW*0~L~c|&
z!Q6n`CKhLScC7xN(Zy5zjFC9^a^eih8@*;Q6ZqQUo3@(YVf9lO^G;b)9&;gyi-l83
z&~JZq$hubV{cdqJoj6mbnxtN1F7Wqs=5wuwjV|y3cJ#@Fqao*7h!?I-#VH9-aYxtG
z!50QAFPm83hq_Eq-f{;S!P7q#m~8Q2N5ZIYdg~PjO%Ryz1e#klbHi42{_=-=3e!hP
zcuwR{WuLutTj_rP8}={Wgmq-689BPbJ!b@lQU{^z`~6qf*I{M+LZ|%7KD)DyK89H;
zEFl-0yY7=NYXqC4VVvXQ+A%WCm+LA8KZ*2QuB-Ofb_{s4e^2Ql`)KD+n_t!2tXFJ}
z*6`P-sDy6^Eh&!0Hi<XX8o>pu>kcaUeMHMuk4??R;nUTI{5QuIEhAQLM=P{gd8cI)
z{JS|Y)uQCtY7v89+=XdI`}_wuYg@{)5_Ups!{8l<W>2S8__|H@eC7?p>?Eqf8h<Ly
zE^X<n9+Pg6=T>1iWL=m55RABW@;3y%_t})LZ>i@bJ{q9<#&clIX}y|mSV){D{>HO3
z4%)>L#i4XgLVLr6|CrNE98H(g55J_Y{F%4@#=8e;FG~8e5QO(uo@s$9hkP?;qR6VV
z+5Buf%g1`hpXJ+U-RE?xA@VXbIj6Wz7HO9&O#iJRHy~&m!$MJIAI`Wk(%$nQB!)-<
z7QLgrh;JIb$Df~g)V_{eB7OM;R&zaWQ`At?KqA_XOMR<Ra2^h`Q;fp2=1F10r#Pd<
zms^(qY8rJ3T<Z4m%S)(B#9W#;Ot9bglInj|{<;c9>s(u2>@*yY!zlSBwlc3Cyl!bH
z6DxquY{^q(FVukFupzWpmrUtIH!(ZMi=lpnq1*O1+hh6MR%H{WE&8T%P8Q`^;WL+|
z{>2_kwd$^fSUvoQ693etv}ehiW4iFtGVpjV8k*vAsL+;slP66sYPH#LEUwAz9v@-(
zbnMY1TjaVWouw($%(bO#pC@NGo3_T*<EG!VyG8<eE!McjU_ZapMUIj2L8GKpS}Z~z
zjXV#N(|})3xX9m*KDuk_ynSn?+{Deu0F|~B`tEap=L<Xz8Y+`mIOQ7c0fEG@1RDdx
zBh{FzQ8%DpX8`O#kSLpLHXZ%VrCqnh^=f?cu{68jHu<m1bW2QUu`y?K)|PC?yu!Jb
zJk}YX<zG^vH;)7-2BQ*HckM|Bt^pZY&vrf0{Vomj?-(_!tYX$OxUczj?yET8vI8l7
zKoUg1YB|6Yow(`ozM_Kb%cHcC*0bHlY~xE()?MFjLm*GN8;(OD&f#v*#H0Z0WAB>h
z`63OU)z)PF<6hw?jC5O)o$wi0yAa*v(n_n6+eCy!t4bh44Z6O~1udO|PlWf0(h1jl
z`?k<JtLicxSu;E^_h!+bBNcowZOo+K;_<qP@FAC)%t+Ap5j{ivC4gjgQXw>%gG0~>
zN4!#%ecPB6>k*y54C6v&S0xK5HSHu=|DBloFg>x{uRxiDJuqrEPIZ?N>a2dY<d%0A
zvH+KeVRmk%!L~k+F9z|K7ws|vqU$<anQF0ghXijS$&8~>T<`9hR|jkN;r2$()g{J<
zQxHf)`&C%IJsd5Sa8%fee~JrzL9ls<e~ziViJN_=YN@j(TcHrPBlVK%SEUDsTg%B1
zAikM9`zEvaVhM(NtT2@r0LPamK?pIlq{U{+t;-n6{?UHhylv*{?YYWt&th?h1EbdC
zyh4bs{XC7|oQri+t(-<Y^HfPcug7Bwfn{p~;s!@QZ1#GoOYP7%rNSB<vxC>oXgATP
z4{gr#`GzfK7$v$sXy5K~PoU+1V%=b6rCO>%nu3i>Qt)VO@LS!XCAaLl?SW=Ns*2L)
z+4f8wq+z}hPR^UHJS$2|uRS-77enQC-yiJ_fZ9m+o(b5j^g~4lhDqPbrN?!;e`)mk
z7TPBOpWhghC1(X8lJj6v%Tg&Fd+x#Ihlj)=5Fypvv3eFmx5TC$__pFN&lzbyTLVR{
zT^CxfstxT!mH`!#U4W~VaM3Y%uyHlu`{-f}uBqgE0{31c2KY*|-wqiZL3@?s)G!2Y
z_m?;sYtBNEsT<J>J}t^e2(LSfVM<LpOR^Wr4su4PU0atzxU?M@H8f_k?Na$YJRmGK
z5Qv`P3I_W*VMgHIYhMNY*YV=Sz}D)!q)S-y=WG0}0c{74xG%TJD1=G(N{Y8A3Ryqq
z(1hC+_(KyLOx343thyBHLBqw-sfp&%eZg}{Ev|T^POs&Kxc1`YH|6Lkj5%+QHWxOs
zZ>dq_G<A5?gFv%dvQ6Gpjc)RZTe<t<S?J31rqTOQl_vp=`6668e~_?*w;?9C&kO&r
z3WXqF&RcFh{0mpgFNMJx!#kBuv{>y1MAnHrD0QXpum>*T{MmnXMJLs%hT8YGuC!LF
z3;bQ0{Fqj!DzFihpcO*IDq*xIptlCI+NUTxIDc4hMH8ZA)=40nl(nOBbSve?BP_QU
zw6l$X7AiC{_{xjI5b4=O4NFau6WWCDlguRlmEc5sR9wB^jUU|O6TnaT??%EKaxHG?
zm|v%v2zmtpK?aJkNiG?5?XSCr<+eDjl4W(!m)=Gk)XH#+D7RZ^^;?cZgvbCI!K{^`
z%i6P2EzhnfT5fHZwfHS3J!f}UM~}<8uWwvEF4w@GuwoZJ8=y&<*AHf)Sk+~gDgv&(
zbI9x?u=X>eIrcB^orxapfj;lPb-DYNHN3v-#^v}}cYPQte#IBhj(%awU!Ld}6}MPQ
z^v|`SlX@k?z9ED!s85f0?kE8V>6x3?Stnt>c8!ucO9yE?&E8An-iJI@4c5<*bmP6A
z?<A%z7#t~_;@(rj-}m;M_L&ougldH#^A}#0{MI~dJDTymPLa4}eq=(~t;n?=$un4P
zTM<}kSF^J21k|-q%Bb|GC+sk6i$|r#56ZBJTfK0Qcc3^q9J^#2>idc4z^Gmc)vDbY
z&K;8x8y1~X$8uSpz05PE+ZyzAQ4y=%BQ2;dV?e5g&g)L)v;anb^PAQFgdk>!nCic3
zGLZ5H0`L2@;MPqw(8Tp!9;a-r2KN#i&|0Qh-(i!1!z~98(Q#O8Z5naBL&V_)pQpW8
z@~%~wI9*)zxWiU`B60AoV6$s4@-1<bBFYwT&cg;ty6&m7bY6k++1XMMcCqcit>k8_
zEkzh90-Y(m$^A(0nXGr*Dj5G=E_TIP{B8zti9thTrB+d=W=`^ZhOoqiD2x{tgdiMZ
z5?_L2aWW3lKTG#VY`z_a!r&*o;=Dc(MSK0xc(kMrsq~olXt8U3rpvE?Lx$g!^}qV3
z$M$lGyjN&O@*_(^BdvR8bQ1*d{YTG@*Z%ed!KILu)BmH8>wi`C|KPgb!$ayhXi3qq
zvUM{ju4d;5Z_fGFcZI#1h$G(q_tC-*-z+~4JrD^}QgAN+NmS@bYyJYAm6w0N1f6pK
z?v@=kv+Vh*t=MZ@*<hl)C}4PWjap-mNEuxcE8$d@3ENUGC8efuTgT$!_*gM3VK`QF
zaIB<?fh{Z&{VDfldi$3+^?9#4jAJoz)8dBHD_nrXmTjBHMUy4kjomdPw3`#RC;pXY
zlAu!xvD(*^l*Zu(!&Tte`GTITI%VUw`DQU0H4-h`uP_xlFO>GAbH@OYc+U8l?aBF4
zlG3}L3$qHlyq@?Y3p_(>371`3hBGI!Bhg2zRQaXa?AUjRu};*_ESN+XDiooqZJ(CL
zu#U>;Vw#ww4p~PHpy=po<k}O*M83hI292Q}L2XT#E&q?G=jiaDR=<W=>5pUkK4d^u
z##E7=MWokp>U3m)K-gM5zkY`}`Xo%R!v>9CZ{~{idfb3?9GnZylUx^^{2d<iy@a^0
zogTSg;Dx^IlDkAc%~gKxcwqck^$kS$ITzbmNNXF8pi-G3@GLd$=`awlJAo$cB@1Wf
zYkHex8^*BMY?ogtC#CXc<F(T6K_#MfV^)$d6Qa@2!X4n*5BM2x?`}V^aFWrO7Lzz$
z-dxjz@v6enLF}h$&Smakouq0Z?f_+>ML4`jEqCa+7lyO5hwBb~HYgk@c+{AhzRoXI
z-YS&1S$7swrakex=>7D`kK{Gq{CN)$J%K>}L5NHM5UJ^|fJCoNo6o(#vKXh)Ux6}w
zYTo6$nXO97T!)NO6$5<Anih?AdmE1nHw@_se1CE{X>|Y4;$X9A#>j+^iELVnDBEd_
zq@s%7>iOehmD(}Vm$@t)tgqfNxABfU`aq4}dM<WCq47nwgIH6OMN^!4xNr|p_7Fub
z*gf1~Kcqy+mi~56=N$RlSU<Oy%G<A_uKT&&pPO-JXk&tc1r8cfAoSfIA+2q%UY}2P
z^Uo)eSb~+}d>}i8y$oeO?TTHp3Mo}-yu4_R7p<hz-GAkA{bNl<uQheg2qfw?F>k_m
z4b9QlDyo0Ro{Sw!xlxLkXFAbRvQI${zJ>q;PqKuv=U$!j2#l$dDfZKBaUUj;v*voC
ziB%`j11mUE4M9JwPf$SzWEm3Vm&p9t9xnosd0xt`c`Uc@$QLC0z9oJWVdvL26y>I6
z)SgW{Ou&B5UTd7M9<nB#rTdDf$t9~#i%;!$BRu+^F}T>~Vm1LC+vaaKcJ)kxK}%D1
z#GHJT`fLteUo?X`zB{Ckl78n*MkT0RzF`la8e&&6ZqDlq2(1f!u<9U23s!^2WwJsO
z)z^EYlC4tg$(`}l@ztfciw>#l#!v|ncD^4SLHrDB_&c|Fkdj$ON3EzcV;(0XdlI}N
zi7rpmiB-oNGvu4H=B!p_^evOjk$C=Pyy5lbD?w%xJN7&nB{>eUWq+H8<unoO;+T~8
zJDQ*kwDYAQmMBX48ydiBZ-~Va%$Re$*>a&J-ig?9VVHaCn{k<Z=5{@;e?IpSf2yKY
zTwd`UxtCW=-&M4JG!|7%>=!Z+ZRSuVV<++xj_$j7eU0;X0Oxe{S=ga~(F7B*rlb}W
zTe7`$P_a8AF?4W~Da`ivS=)N#a35S0ee{)o&&61;=W1zf4lh0}`t9<wg@m`d#<O|A
zJVVv6C0lPN6{-$i>Y9z{^E55!hu`VjF00giQE@U-6J_^eh1wioeYZA0n5DNi-Zu{?
zp%eN^`AJ7x!7269H{0XPKv9pDF2mxsb~2*yYeuEGwX!h@#H%7>ieC!UNs31Y{mM0!
zYAH6*&kr59Dy>OU%b%1J8LKXBx0Kw9+-*M3sT$0tgQ!h+V{AZR>Pcc?aFEuD)mQq%
z3{;z-Ha_vmZIxorU*!TWCA+Se<=D~U()=d8>5N*0ZzPA?MzU^={rKasLoGX#=P;YG
z*C@Lhb9IBL=-Pv3KBxV3D@k>4A8hW8*M(%O1HOPI8a_fL{u$oq=xtMZC#3!4+{VG#
znTJL$jqvN`1~Z!LYJ-cKv-!Qn!obJCI-N&j{iKQdbC1MU2iboHa+els#v++mURxsp
zhy{;C#D;EE)2XFNx<l`xidYi`;}dd=jIUDk=@==ny;)P6K3r=LUm8qalZ-d#S}OGL
z6T=*>l8QU}bRfB_Um_(Ps&Y$a;Mjt|ACWc*d+)Nf8~E52+HOW_z0XS}Rv&45lgx=m
zM74QlVH_j0Mo{97sml+brA5q1b=zN0Mtz{qdoG06KhP~|4cCzSlAfY#-b8C9h7{VB
zm!{%0&uyP+l+`4gyBoo+XTFq;zL9g3s~qsIo^k(J(O;OA5MGb}0{tAc%@9g4&&yuZ
zH>qEvuZq9~MJfimInfWynuN=OxQ7a(!?xxp1Vk8+G|A1A3pUyP)y>_HbMxA+Mq{pf
zQ|bzIVzf>V>r@&{)Op4G;SDemkomCi^1l2?HTkIRgL$?_qD34=WR)>Ry<&pwo-Sf4
zfG$w;pT6w=Lml9C&s3?^z&6;|`G>ptl9c8=wl&QJqy5v$qjVz!0^J+opL!zXP1K49
zUIbaSmhCU*Rh%vhWkLDKP9O`QQ<^&fvu1i<@);Edjr)ufH*uQx%>?=<R~v6zvQX^5
zIh|fTJnP(;y6(1Q+dB$L+%e3rdo5s2y>7C_3N59fJz~-$qgxbDFNYv^EE4V3JS9{p
zi3#hKB&$*V`6to-DN{-wvI+Fvt#bZY@Bngf+q<utS<z_o?QTTw(1gi-sD#F-t)@2@
zt!50C#k9*)urakzFD4+@PCX2++_-f?HKebAna=SH_f59qSD|{b3>*8eID!_*SM74g
zkT7;#jDQSGDd8Bok!Vi(IzO)`*ckkFL84p3$0r1NIJPqCF^xJabpX<Qs~31X4c1uP
zMLst)_Tbx=n$Q+sJ-BtdFZ;Rey+EMLrq(^1So01s!|mee#?`F&qlU{7GhYUqh`=kf
za@dh=1=dHR8TSiY)3p)MsfaEFlzrhUPj;aeFb-i*l?<+P!~B_N)XWiWLiYCxnw(<f
zZtrp=9G&qa2S0705^LP!Rjv7<rK<c>R@i~;mZH#~$}L2t$Qs;cPfaw6k)W}Kf+~Nv
z;+~Jprc}<QJa-PresK&tet38qnVEcP-B)Rg-jiVOYCN;U7qA&r6O<?DY5X#!)6Fy2
z8T&rHj4_xM%$^54kMz84t~jmv^(!#`o2kdPH=9@L$B)s2Gqq0taQX{LwWKgU#czHd
zyNRW_V@J(+VU;a!h9aP@j+xFa#4300QvAZ`<pf8<p*TNldjp>wzm&pcdrBw^#QedY
zi|@1EOoUe($RAQEy%r45>GGoQsy5kq-~7p%7N{(A;RzD)&|chgqynbL<IVlC0#_r}
zv|c<nN@&<_!HHK)gdg<bcQSb)H_I<&gYj7)w(UX_jR@jhy0jBDyGm}vS6<(y1ea1-
zd@YY533UB$o3IIGat*;s!1t%m&@`HQ@(wX(V0c={@9=B6w!F>l=XEI6r*?#iISkfe
zCNl*|kepkj0yfh#oq$eg&?_2i2559;6Mx#WBXiOtaR((@+!b_s-Pds(Pq1+-?uj**
zwtGW-CW#483C)LglKuIaqk4`ReNCSB!Frc?nGMqd^x)FbTiZKzg&h3Tgk*Bwwrc5Z
ze$O7&k9}c%(MMVi9InS-98@ll)ctru{83{<ZJ2GJ6`B%vlsmC?TlnPAW9)i%sQou1
zc5Hc7W$VhLyvL}+&+^USU&`$G8KxwzF~+(>J<?r#b+j>c_T8krvoOGIpPz!IvKRzn
za0kddghMKE6S`Hb1-W@~r&p#ddUo8GXtQr5>%e$H(VUN`0vz?oD%q;5V)VpTRethd
zca1K8Lo&?PwXjXWz0;D3Qk!FWlB_s7xov-C^65*Qy93-fO-N#>#qE%_oP1)IFxkTb
z_Ye;8$HZ#g%+`Xp+qIM_bKJA<eYp#R2WiMZRWvE)#?4C7cc;$LZ+F!&3!4^W9zUPn
zU?uB#?9@gPqj}05_Q_?0r-*MaZ}6!3*AjismUZgLD9<ehuJq-C#JJyPu9jZ;Yhr(X
ze!px{;ssxsmlc^qYVuSUeD>qg1;R50cv@PgHEDa58o@E931@XK9LeXBH%le*udHFt
zEItnku##3UxhgYzK0w)<y~~oX-jyMbDTj;jx5stsPPj)i?&(n_2j6BF5s7_NSkjFt
z(Q4*R2g~sdAGb8GHUITsiEhJu`M>Kr);I0x`ylpb_7WQv7Y$r5zu?pe@^-A5-o83S
zu3-)=zU8<>mRG4>^PtV#*(NzxN}<?14vCTBgB*{@;-0b-{Bii(jA)|REW`fAxAeeP
z3p;Waw5y_+XwiTkAUY{y#V78Ex4&n9d)E)!Z2yjvcm8rnsG$(;Tr_kC{&?u*)17Vl
zxoNNPR?+80(Zsyz;|{$w_3H4*b|w<1I8ftroalGmWCpz>u1J!MKT0uq(!`}TBWHPi
zk*G;qHqYzN*bXk_b*$MK(Jk1f_a%%vB!pf^4;baWBt}a<YTQB;uFJ~4T+8fIYk7l$
zS<J;{nvn*22^Vj3mUij}axu2%9aE8nX!o3zfzrI>%}i)#dvGto^ee}9_u_n9Jf~v=
zGIH2)*Y+`ovgR)8RQ(QL>9wqQ-wRK_&tShewkTw!V0=y1G@1zYz0oIOavfXizaaFJ
zJcSbdoex|fdi(dH%7+1Ddoja6Gm1)->*S;-@uYp=q%ZNL^A1JO)~*qLNVe}L<DVL3
zrK#S={!%gC!CT)gKAOdhCZB610+QH8bC&Ni-V?QO5qyjv=?Ve7xQO_jq_nl9;xGJ)
zkIKJLKDB#m1!ju2*uUFqKS1ocg7<#!imf0VP~y<XV*3l*4vf!uNH)iQOHG-SF%(PX
zSlBWtZd)DYK#*(OHC8M{<j!oXy%rzmaxLz<@jXgn@)_BdP@(XIoF*ti_s=E<yjJY}
z2b!D;%XS;X=x}B9(|g3JT=sBtT_e}_N83A9t3L{v!7~~5C)c$xN&(l8610bLv#6(G
zO#MgcxWqh^a%ML{xwmVL-V~5uIlJ=u9KC2j`NhPu2TrlXLHfn!9XCb&Il5d^uEC7^
z)@4qoBoS`}{;6y#0=TG{$I4X&uv=UZZp|YL`yi)1V~8i(k>PIB7?H~6AlP%csERj8
z1*I=F?%7L;kJ<jS7GSn-0$Bs_d#70Q`7BctUb!OY;pc4Wpg}q+<}k?K-(Q<5KOjND
zn>cGq+LJC0`8E-99bUjJ9pLw+y<yDf$hvIwDS>{8gt`}q_V}HxQfxErHmgPeUFpZ!
z15-tP#>kHEWscXp4VPvHCYft6JEr=tidPlZv^0ZHhT-|Jl*!=@(8fJW-?;n8{zF@F
z4DRr`<Eh^Y>2$#qC5?$DuIm-Uck434+jp5<O`~g@9+nSy$gC3+X6sWs{RwaS^0(Q}
zSq6|J8A?#Kxp^{^cNsruKvIt1kRN*#-mak7Swj_G=A4#^AmjIaw0*He1``zb#Tt9d
zJ1Syuj`jr~%>+s%iQmpGQQgU>;<uq3p?TY}gNySz0w8imz~uUCe1@K$-gBVP-nCjG
z9=>Ot_5%DFkH8nmz;YzDk-z;Z-OMq%E`L<w_OL@nb$`~}CtRn!m~91NMmkZl&qFjg
zb1J2`SOZHQnZ+vAwgq~WwIN-G?CJeRjqq{lSgguL<ZwX(*27p-g1t9*M_tVv$!9H4
z>TPE<p_$H3@Wdc&XP}l&YS#I7p5vS`Rm1vo?a(&4ep;2)L`e@NY9&9f><sp@X-4m8
zxx<U0^HEE~FZ@*^F<!s@uqn^n)H$ayMST)4RHc}FE}&z&HLtKnLq6(@PPn3sj8Mf4
zOnu_nf&@SLifL!PMxYp_oOE^k*^H`M%6`7tsV6SQL|=JhlHuTPc~CS)P=AD^LtEQ_
z|2K^s>@7AuSG%GlD&exqfem?VBrnDqqb?T|-{UQBd$Ez?G7YOSDmLGg`8HnK8Ev`8
zBU!}OO6_9g-=i5zl^yMuM3{2ehq;V&8+EwTk!?lZ-5^DZd6ZXLv*?dsR;TJyiN*)T
z&73+)IGI|zC{)UtNB7TRq1mU{z7}(6X@(|8lXFX5mbT}Zn=pz?czL}#<}qzfKBF>E
zwU~HHe-RZy7n08WIOuWxgVtUu0qv)hzC>91z1ZMv31Q{II6gIP`9h@T_8lwp4sN<v
zwQWh-EM)!$)V3jAtdgsoamb~jh&W+^@S7QO;B*_j2WM^ASYe{E<SQU$d=3P2wdwCH
zJeoLT*U^WAX~Jau#z9|xPp4vzhn}7;FXTpj0=Y)xylWnheid(E*I%+L57XTB`j#z$
z!tbwZ3}5{+Z`EJ9QI6eiW6DOf9#H$msv9F1Y$veY6m?H=>x{vS?6O7F=grZ-`fdcb
zKdz-*;B@$w<ud1?uoul~I;Kf0@RoXfV9Xn3460dvq>Z0VRPg3HwJ1BWYPC^JHH=!j
zo!UyRv9wz1Lm$O$ulgjP6(8lsu1C$EjX#gs)Gt?syP0nnhg!9I+8uUIDg4<B>vmfI
z;sUQQJk3H8@IY=WiU(^(&+x7j8rSkY8<2;8%<(h^e}6+bc4Ti%82s8HB~RZ77KoL2
zI#E6VtJq(c@9?7FRLhJGK^ty}Nyhlnmg4MtNtV~xZd&rOHS~9i#}Q|8U)#g~DkvE=
z7xnbZC1>(Fz%RJPnTs#2R6(6v@+!Au9w*)mfK6Cs3?{0wP@s)?g~~<sE-kn>MFAUZ
z(oB4cH;@-g*5{LuSP8<4H_-E>t%TWd75l$|jJ6TN2K;OKBjNV=Kql{?#~Shmb|Cbg
zLuzRxv{Xzv&Z}}~KWvs?QM?rHt~${acMGa=jdr57D4i9NsG|v=ivCnV6>ZUW!M3L*
zm>~;F2e<wrzOi%MZ0TlCE9e%q7TZeJvW$wmjIy++xZ4=Hx_Jbw;rpWX!}x}pd<ENf
z-k0|}_iaN9Z7bV38C!2>JbyOgp(EzequD$8p2@|oE`rOuMP|iP-B~R}BgEUg0bih{
zS!&J1j)Icbt!N@ugoeJo7JVx%LvPo~BsDTz5p@l_9@)kpnNYp&VQ5eEyuI{dt-(;I
z&{|w1q>wtx77cYp+Dmiqs7|<le}?7KO!mcf;^w#q$vSgs8GDN27PQEj2)Ygvm`rf#
zfQT=Bv9bAweq1Re<uxO9h?~mFf_^BzSf++&-v{~oQJmD?EzMj3*TX3I?2sM#JMh^B
zHNN~7cS8t7=g;yRZj@4@qy~v2-0i~QZq<7Z37u7n`gb9B5Wkn?eDb5jnEO1t59;F<
zEQm4RALItUZfw*O1tQp&$)_O=YvId#x#nk2inYF<*n8anACEg0cfrCXZ^a)^ytrTi
z<Fy=rI@w%1{z2~(4>eNAzH#q5ECA-5I{)C$MecdwJa&>%{GSz3d*?{MXGwlvJ$7!-
z0sVal>HW{wL4{cB%?_5Uz54R=R_NhVh$#$wxXL5oCHVRCsjt@k`}aM>hF-r7Jt;Ri
zp_3wq^ieV~H#avjFo14F$<IB(6ngg<xeb<83tT-J{d0K6$|TQB-<iOrjfG}>;IsA~
za7g**=fYdYIu&&XKlFO%Z&ba6j<-^v&GoB+HY`gKdkM<g_fI4(tgNj$q8_AQOOyOC
zJw5#tiA3H`bo;V4Smq>w`u65BE&8f#i8<fdqsB%?=h6b^sxKmjD)MKBJv$lF)Y)98
zoZQ@qOQrIH+l)^&HFA8gSan?UmNH9<b^H)L#iPXY=d#Ank>z7CwmY$4p~t7shgh#j
z+U;yCqUL&X`$FseSGQ&tK_8!Z4^_DJ(Tbh!bVxdpaKcw2y*-tr;l#JucHiD$lM1Cj
zOgwrA+5AE~&rNvy8BC+=g_b*Y!N0f^)~_jRCrdLV&mA*POh`Z|Y>ve-r5ronp~h<6
zq<-vdF27bw|AE+l_@q_Ld<CRg%x3;h`{(~ub0?~9xkEk@Q9E0)T!A24i2S{D_k-SQ
zkEyL<wQib{?O}aR=Yj(3+Jc$Ea{5SM<$g%fax&CG<4Z7OD}~_Dme8P7xU<=b+U?Yq
zA+I=`9{(`$3v^bQ2s~wOwYmCriXp8dC14FSL-*(}X?IFp*Q*nkmzul=&mMc3!|${_
z(SDuLZ>g`ZZ)ISxbocgE&gB-tw3!J^K~9daQI<~18Gr?1mB;AX7EPHs&`*Kz4-D=7
z3>U9->`WCDXq9<3)(l_VJb8O0BzUjv<d5`qPZxLaROS;}H1){W4tm}Bix&xIHa1Et
zZ|$P0r>C;g5Yr}b0py`r<;#YdT25~#15^Dbu(WN=E&Jw}(CFL0{-&%>_XK>bs@iCL
z#mcVKU2NAhH($SQ!|)m$^TyXB6a&^2ZYiOl$$1$=^MTO^D~o|5r_ai}J2;G=Oq=Bj
zT=+b(2jYkeH+H9yN_<XRD^sjGvg7d)YXCUg;SmwGpA54)3}18lPe%Gs{AfRegPyq<
z1`>u-1uf-91-l|w3}$o3m{-MY>Z)}7{5I3~YO6$$b(V9MSGsfw4?%12wK&|^ON(-6
z#lAA9E*4WOkNdANBBlvdJ(xL{hJ=pl6lGK}SyQ}z#pm$Vrw#e#&OQE_IGp{8O~5R&
z(<v*(`{&F+X`7*<jKQ<{!l@`;LUF^Zh^bmyYJd(_!cpganki?{$`3@C3*}6jX+>gW
zr-({Va(kx;<gDG^{Jm@mlHO|ooA&oNOz~J!x{N7b$hBSFSdJ(M=h0uYeT-~ucDr(n
zbG^5JMToB@NGcBm0`T|u0zCOBZH$B#pqSfKdHiaxrF(5gz{bzlmPRHf(oecS{ph2m
z0Z0|%x18gQWRyLI^!U$){Y4s3ZKMP?<`XNa!2z94U73D|S{0=cM+<EU_(j3AKvESz
zgkwjN6#Yk5TJRR*{qzYQ6cmh4w$#oilGAe6-UCc|5pOkFRX+UuqNAfpL|8ZpzfK9z
zmPbx|{Y3UzuP+om`U21D&&0{JhN<$%zXN%5bInTXCg<_srl74eIRynL)p*WyhH;Q<
zkn(Ob{l)d~qK!>VdM!%qTVP;_DLAc6{@@DU-K5=XZEekobBn`dHs=Jq4t9hFLkHBw
zQ}7nZ8l~5o38L5K=c-2MsNi`N>ss%i2{7VPuG%{G6<Bw2${>c%dz|XqnyZ~3d~-!A
zr@hW^`Q+xtXmnacp$K;OKoG*R62Y+AvCB2dcUE#}jh~nus+`~LHq<2tf}!hPX>M?V
zVmsR9`yZ9&f{w@H6r+?)Sfdn$(@TU2wja2@F_Sw*V69Cw%=_u~3EFu`*sV~Ikhv;f
zfaGP2f4m2+N%`@J`eyJuc=|!KB%oTI><JFZC*Nz)dkhtklapiRQ_gwo@z;asl`Lh(
zlre0&H-9MniR56rpCuuf(dfMeh-vk2WlFhAA7*iUP$dWOcx0q5ff~FWl;CX36RSSi
zDpEKj0?WwAz_W*(V3(e`P+Q<ORDn#B_n9*W)*-+U{UGuRGT(CHT9;gDEH%4k@N3a@
zcXp}3ZlL!(CFn0(ikNkcmvzxY723y-AJq$O>R;^uNhN|x^0!}e1^cf4P~&1CB(Jb4
zE`DWWZvS&!94BR#LX+S1UTP5}r0QTRw6-{?6@gJNxrcp6Bi)*!IEHe;D6vaZ3gWaX
zd-B7!!fmKG2(;zmDbT(Iz~6z}t9`aysJUvI&1w7k0ZwJai#a-)U$ZqzajpMw-pE{O
zAz^o`cG1ayZN~2-$MEd;4_{omGPP^rZ3#EXm97IMMwhN~eF-4gQ_@bPF~`cKt{_(X
zXNY7em$eqRO0sv~$NLexC4gS%lBM0IZ+ZOsO1CI`vKS1yLH4KT*T>1q7_?uaHfz=2
zJ83Z5CFkhn(qEzdz-?r?)qQ+?XIUd>^$_;ro$0+OyXg2dxprL=wzWx1JEb`KMncT2
z-XBQ<%W9MvD^l7?lID8G#>VTP4|BO*Mg?}fldZjBi{E+s^md)Y_xCEPwvYe$`LuhM
z8VvLu&Y%71(<ixIGokpEbqsabt4q6ScW1ftH7BxLKhC1Ub@PMmAR3LPPM>)F<wtt7
zl0Rc(anv>er_N{KGWsnN7F3{-(cwDgGg#;6X@<W0;rmG%6))P`4!Y{Bn<P2)7<T3M
z(-CXl2XEdE;9zk%Pj6p!>nkAFRD05t_Lw9a?bUXp(kew~=p9WSQ^C(m4z4Y~k!E+z
zklverTp4IsMuU{>j^8BQdwr7e2?#J5w7#saPJvYGGw%RcVJZZ4Y?Z)dxL0mu93WvL
z7TLrn@nYsBxI6%vb@%?FNAt^_%FOCMTU5~9z@+f-@L!H;L2HL#H9JdfT**p7TOu6t
z-g!bdZbT*|Bs>@@b1Ho_`TYZzU%RLlO)6%pnlX{=K37jKsGR_serD+8<W!cIm$wC2
z+je=2Ua0K0dVMM+0N*6zeKOGI*3zOMe~*7C@i;ef&r|9@Xzf3hp<L!p4X)z@x|#Z=
zxIVnx2%w#qLa(s){rU1<{gSO`yA(4XcZT<Y)g_ecNW=3K{%pIZsL{5zHeQvA0AN!y
z(7;bBxzSa!=M>2WZY(GP`_V?&T`jx_I)vUL!7WSdVFNEO+H9!72Hgpa3Bj&DEjm#W
zRcD-Q(vyIDdfN)4fd{0zqz4-Wh%^}v-kv>9CMj%9rWcyIA<A6`N(QlI5>KYqhBTGF
z-962T+F6_cE=+C@fbM#y!ZJ|`;glrpR+9mgAo20XlP7>J3+N)5!d#<*&G=xzQw~7j
z^(e;HY9x^N4vhjvpU*Ifl|ydjJ8;B)$swnAmS##jJDST*wH(<Re!vs^ea~J6tW0`f
z`BJ<{Fn9j0sHB_tV$YFfjbNohdkgV-mOFFBd$y_e6R=>rgk8pVC%cq$Hy2QRR>F@b
zWkOf9=a$#=D=9Tk7e+qQ3EOkE(xveB!TO->H5dNUwb8MbIPrL#O6Z~4q6-e0CWSU+
zJ@j;6A>mqANsafc|6*^xWm^G$YZ`UccjS38TLxhw$%V6OZO|n*qdzGNIIXdugzjZ|
z@0sO+PZE<mi+dU{MYp!KS?{;8nP^Y$GeQHCy&7wpWT>mSng}%O*3;9|DB;-Q3TiyO
z`_|kgG!8*<lX@h2;L+O5z+nv%DMM3NR7?yZ;<EJGj-xT=9DNj=T8}9sn!j4M`nqg^
zf&!ubjn^Q(ll8o^K0CCI>MOLRnxTOR%f9AzL@RAgLSS;g{^o@X$kW9QRZ@obP(_h)
z>Am`-@V>Q$MNP3wUqNTxQmY7CoRgaNC2GIlf*=2Snkgw;=2IPn^yLI_=)E5SH$8QI
zG+Oy&UO~YeC*i?xf3e-R|F{Sk3#mbC1I+lx5)b*NcDLu<%>kwOP=nS}sK9j!e?L?U
zZ%=xF?gr-W4gO~R)^x5myfZZjk^UeO9}IZdQ6R2~k|!+o<UH%^FGIp2B5Vj8S|>$i
z!l(4<&wm8a-S1WQ_etHU&jW@}2Ikyf^s_K{3RqZSJ}2O)IZRdDVkga3-k{oD6LRUz
z%NED(s`A8I09TyvY*@UJsp&<_%Gs^S3Pd^(7qxD>y=mj<$3nEEYkyJlo}|W#zMM=J
zfB`7dnX{GD-e;P%-m`<xV9wp2^evS6aJ-*9UEJQT4EZzya?=LZjACJIj9J*W)4ojY
zuLhFx0g}p>`5Q>8X-`tmW-yhBPP-SeL5XII%b6h-qxn@;{XYK0pmnm08;EjDwNm6(
z{{bD4X#;M8TGUm)4}72_^m5lSr}0+Iwx$6v?WAKwn7~s{#%do`2=?XmDW+P91Rc1e
z-kS52`osZ;orN!_GEOP)EW}0wyVDLv)9h{3?hYOB@YL2<oTVdJOfG6wRaDIfJik`Y
z3m=o0mX=u2M*8~2wYD|w$ufP3IB;Y7gLVq6@{a}V$u)IPx0hxn?a|y+{7;38i*jC*
zPZ!@@mhkQ`68(*`!4CQPI{ne6Xy!WBF?EyiHH=MMakbm9(6+{Fig$4eK)p}17#mo9
zy1UvMj4dY2z`?=c2dLu83cYWBU7WD9R1V;-unUN~x-WM)NfIAh-7}B+q)lbS=wG%(
zS+nc8)Ghis1Yma}TX7Vv<$cX3_9Dt>I1qKv`J5=fj<s7}CECZ<An66iaqlI|0MT|m
zdd;y$0pue$41o7K1>lj2S||IsX=rNpt<(G5^}ny?TULtgw22e0a>F>}yv#5N**@S4
zAI&u_RQd$GFn1fTANQ5{lf{F((3#rF2)~sdYBVD7C-VnN9n%0%2s$<{9+`WBB?EkY
z|L5g#-FO`EKXB(J6~O4iC*OSZ;)0k}zX>S<;Y10TuipX?R7DR~x-&Juv7S^HU{s8I
ziXO-Ys%jI^di&mQd7?Zq-sHPPN@T;*t>71vxqK;rsqDT*b1~gv{>$w$P4mFnZ9;91
zC(agDPb9HDcyp<ktW^$N60z-d0yF>M@e5_qsyyOjJm1f(RF}(`%a6w!KA0_T;>=GJ
zw+&v2zixOX)qgs>VAGkj@nCDFQ-qzcX#)PFW1yRL=$gPlo0k77VS9jhCLV-@reJJ*
zUVTO}!o<fLtxt6|dG^t)s-`lNzpVli{?Z)FE1m@SBMa>{fpy$1Ei02;xbuz5++9!S
z&$g-e7XfxLu9rW0&*Iv20=7Z&^Ydfe>lPn8DBW=V`LxOcogi*IS99Gk6A4dmM@xE5
zeh&q`GBE)$pRNDOWGrbbb5A+HK!~Ic$celTXhnG?!4k2_qohs62Z^I`^=pIlh|p6?
zRnSk~j>2)lg6P@e(`D@Lbsmc&pF=D2fl5KaZB7}lNxe>_WoUf#BAbz|c=Z@sc8#E^
zshJKyOkm>Tbo@aK87pH0at09q?h8YtBSBj$=Fb3n)T9x^{gZoUdl$r|?=VivXImz!
z%lkxZs-I=hzqv}e1@Z;~7E8MQ<oo+Qh;TI@!h-JA0N-x;g^-AKpbczCM!3u0?-b-u
z&Ox>WL>%rM(ysk8ft9@KAZly$ks*6^g`XEODFaq2?PMGdSS5G=7-S%mv0i@Y#HH_Z
zz))A&plf{Qcc0ZbuyQHY`S6|1?x}RAZYN7R8DL<wz))E)0Y2bT0{pkkmklB?`2z+o
zK1no74^aT?D1vGhP-w3nV^hVrY2rV7QY!&IL!ow>664hVLno?&0PmbSbwzTr6tG0|
zuf_<vmESn#VjyUeqdJM~LuGiKb~|}Dld*=*1yTlHxm*2oPA(_A?6Waju7H{1<cnff
z8%rEM!?!YkF{SVMT_q(YPj+`US8YH|p;Tbs4GDnyU%&Vy1DM1z$c>6;3<5`KF80xv
zSL9*9tCpsDW<NSlUIHfj2|XjxW^3%Yc->RR-)DFc7WlwTCcA>~LN_q5?ni)s$*-9Z
zugF;GdcEgN1Q37?o<H=XQ>$Z^H(KFYa}yV;4h4vk#_2QcK?fv|&MxhGL^oBTTA#Vg
zU{)c>H9;T_uk@NCIb>Lpe=9Qbiqzm1h#qKThtsS;;^r4YAKTj&0Ew~P^UdxI?)6D6
zno}I4gq8|{H}MG!V2M2jj#R-AKmq^_vQ5l`+swVdCE)AdXSi^dHpa$1h)8xBo0XB!
zQytn;g`TF)E{Er-(vSA6SV<FSzzpL(9R#X%yivD*)qwH5)3M70Hg>lX2xa#4WSNX3
z7Q|bP08~S9x=I6uAjoZ-9c2^Hx-<m%F9;9ZQZ|rNA-OllT$Arc0;*7+AjavctXj5G
z&7StUc=lR@#6coR>qyS|#_|OLd(!k8B-!>-PfZIyzbKbQD+BtL0MYO?V>yjEBMTx*
zPLLOw`f1_=5;J@$(g7_^8!aXv<k?w_7SK)I*}Z5R=s}XDz4QDG{3WIXR{)53vw$je
zSp~I|ycYjD$|eT5@E?c8&1l()OP!h-U9od}#z1=jq$9lcauQ<}2%&S~6ptp3_4vfZ
z+SIi7AYvUTYyruKxo<zx)u=a-)7_MT{&UCWWt0jp1*6iT`7)Cr_h1nB^r8{SEh+<N
zWOo)l4hA^>jMpXrymb#qEXbsROb#IkBooplQjoI1f`0&ZY#1YVCGwSXPfj`BycF&k
zur?!WzqvHdsDHtU?Y?XjoK<CAem?R+#7wZPd$1p0sWc5ZSg%iTDs0T}rD)y&Alu&i
zpyve`f%)?<Hl0f{dm-6_XikRB-k{K9R4h$6h~B0nyKTpA<L{-tvz^Ir^4{1{_MYj7
zae*X?F_2Zq@*WP1tgPxXK7A5007@}_ef3#05g4SMVp-3L6^*QRn<c5>lAgY7!>p5q
z#<{(NfFD;b-FmDlK`Rbp6U6U=REag}SC}CF$9t7fchDpx4A$}u3~-;|XimjfAnc^i
zo#~Ox2tXV@d39qG3|qw_AR0punb)==96=0rF~ze(wi9Tw$#i|5iYm4xtJD!Pmy-^l
zKhrQ@fta&py6PZMHjQ@&j+jVZQPeP4-b?$Pe)Bu)0*u{>T+7P2X)suAIuCCr%>vRP
z{EQIC-@bk}%<7%i*0p=Dt_F62Y~c1@KElcnKALG~I$Jplh6a@}7N^TO0L<;R#pLHK
zFGpUnBW@!=#3iGfEIoS#zQDr)QnSuw10e4_qU=7-Cb2i_9&D}8qn`7npmsOyHcJkG
z4MaRjg^+&i?-afhNYi`(UVde&j61~%1UdA3rxeGkGugpXXQc(dgAD~C{8yI5`LZi;
za_E21JZNdypr}*Uj&Usi70COdy_~L$YyZ<)Dc(J^@KYSW@vA$q{3zJM6V@qmN99r2
zs}tvGwJf#oV{jGdj)axNu>2rWN8*Y&Y+ij!9Zl~jfk4JILl3dy!I-DhJC(LYOCNz}
zM}7qf6?u91!bGnhIyI)u_KF|aFCsD}b$)|r0|!z0-qG;S3CfA$L+*&b_!I@5>gqyk
z>HG{=a;+1OQywm{aryTIAgE0AM~p})KcjJxnZ~CmG-VB=Hs@8--(lF%=5du+To*r?
zhiwFdL!g6WybPt;-#N6|zZm%a1W13m@mMX(9iHvh1Mag!cd+f<2Kszcy;aYW4)*_>
z%7=tR44psTsucUkJo<KPjr<qTr9a0}qJgMTt?ny_*(o6x#4CsYEU!P?|K3bb`tSB1
zGbK6ugg`EO@3_NqoM+?+s^gApj?4TvPbJ%A3DN&tdd}OB9Mr|ZL|`77zn0x)*!-8e
zstkq-lP#0rvixWzuXv<3cmD7Fkf282oEzB&#SJrp11@4<O6Sje0niqiW)F_TxSYT=
zA^pIA@54bMFcmSwBd3{kWxXE?6W0K%WCURZjX~jTGlL+oTe&>Ws~C6h*HS9~6nRGK
zKbx>eyrOV!;qCuu(=Nn%=@+p7+58BWAzo3hF(h5cxX_=E_A10ZsCI|`?j$56DIDM_
zT@`Lkj^i&!dllgr4!v};=+t4DCjX5D$uBj9t(uzu5!9c1Ag4tEv@grRt_Wodg_QgQ
zm3Rv_yb6RYfy*(90h&o-PHA9qcoW#2!d-e43SjxtK^m_tM`dj(#-Qaix0E_(@0>gb
zU?_AXKwbD$6_M@tR`K_lE+VC6{RI=gu)`V_==oO=2(%)a80}q~<PSx49$A>%2Uek9
z-p|Ksa#O;0KCau6)W;GJb_9l8{e<d{m7AHsZz3M(1TIyw4DNJwZXymrxF2S)vQPFm
zaW?x%Jo^=aQutu2w8cklzjN};$PI<r^OhEN@RM}%B|TZ!3t%6Y-ov~1?;lI-=>Vm9
z&K-NKvmf$4^XSXFwH?%wr?;uvccS)g>vHHV!kFE={6`x78H>x3{|e@u(7w8Vzv{i;
z%T@h8?mU0YN||vv`_%8i_`i}==V?ej-gbWI-0z+8pwzxEu6qC8DWpdnY0qy&=SYZ-
zE^uG@x03e@XGYrbjp(Sd-JmzP))Q^HXEl}nz2mf|<k&wb4kks}HuB$7Ma$O!M7iX4
zgUl!v|336#*1Zs!?Fso^vb1~tf68_3m80I?CExll_dbd%pRp9oPF|gNV~IDF5BV1?
zkeLHV7AC&+L)yTGQPF>EIQRxI&!6Le`euOtt?9Erb^LG6wf%p%u#pEy4ZrxVN6J1n
zarpU#UPZ8YFa`%l57KJS$X2M3_+Z9hHx8IuJL8?`mE@g2PdfuM<O8&KO+L&Yt&?b!
zH2-Q$uN;?+HlfvCyQ5ts!eCL^;!O1NXmH@X{L~L}tV^{qcw+!#B|a4vC<E}*eI#1F
z21<==4!wmIYw_XrKI@~g<$`CAJ<Lq-Lg3k1x(^)ijeT>bH|TaqNfi3%L?Fi<bI36&
z1?|;=5BO9DxTwOO{INWFrLjgE-Qx~~Z=QC%^_0s8(ks>_azftF7TqG>y2$L;vR=$u
z0*-8PHP0eIy4A<B1TnP5x`m|d3%hk-=M_X8eXG6Z;G@5_LVSYmjI;C~RR|uwHdt>@
zW5%sC{4?*xeBr)K2J_N-`)7SrE=suuc;S;{k%SJ*_vOdu+I%FuHMZY*zFM2{eY|&`
z9dD7T>yB5XwnLoH^{V_2-%0WR67n{fGi1Lydmn2W)3l3xy5x*^tkliB5Pfqs)icO=
zZPnnvKZ*W&@o|6n`~AXm$Nveb`t{GL^Oq|<u2!mCKn^#&ysw(@*Ns^I(`WY`J$;u1
zTi?;|iB_ui@|mV6A?c}>owQN?=<WO*PyD(eHK1mCXg*+Wy+QRTa38=Sps}~@7y`Me
zzYio40R4rX{X;cc@qgaN@$ln;KQzR~52h`_@m7ch<Pd>@{y(mY+R_xG6pLAN<h9@J
z>);(B+sG7f>IdnrPEHG`4Zz)3zfLoH#(SD_b8}3M)~Tt`JsE^t@2{NR*W28cL*p7>
zoG%D|M8P9gPxS;zyLzTfKVKfw%oQ{`oM^zmSL@Bo9V28;>cEdL<eaoZyfbBYTuE`5
zJn7LB9clF2a^8Hjk1{S_zSa1dv2H{)R?P74+bK>osw~7KgPB}8oz4|jUntWjdu&f_
z`I?lDbMDm|A|)_ljeqT9|Bh-!E;b+8*6gv)L1lM*+O;ogPn156I@RG$leSF#$r>6=
z5Z=4vVg6Cy#QC40YjL?Ehu2R@%EwhsBbE!PJu@Gj*{v1(z3m8l#Tu@hT@a&^56{YQ
zj&I7iS21fwSEPoSb4|m0FC&7O9p5&wZn#p_^PUlwuE5M@U03#dhkrFhdDeE`jdCw3
z#a!W=_<=b|rvJAiL7#PeYN!2rrbG(tYU}d`4)U=;)=b6o5Vg~HD+X$l83$=Sxnc)G
z$hr-+froSACT10TKCpJq%eFEihpxH}&At-~3@VihY4W1SZil8!>i6)PT7Nw@EDqpw
z)3Y$jO3Obb&~KH_!6>}V^3VtfF+dI3>9_r@=RSKX;q$9)wV{b8L#dv>GH%Qa<x+Fj
zqQ3XV<)|6CU;ZH8Kec8Ow3_cuvg@qd%yBWV5jn*<!)_Dgz3IvViR!mRF6Fxq%o3(<
z<rX+t)@|P>kjJ^|-V?(h{Q^o)6Xw(8&MqibZOyWFq6S`V+ZxwzTnbY3`W`$RmRM1w
zYl2cVusxNEQtUNvEus&v#m8--ljnDhVFkB+pY{M$$amunb*A2Q)((}sR~?^G+}z3)
zV+oNO<#B0SR|{P$H1gpXCfKdAFRvAL5O(jX+1jp8EHh4RQ|-?GUV^0+7vi(Ii<d)D
zl&S|v2WTeqc@)abFU`XIR1<4i>csH$<(_(4WtJ&ZKSg?;hMq30$nf7&-@~R7)h|mN
zd7LCy55|}0Ti8%qVF9PLwT<G3E%J0tPv_OF&O#UOMd{y#BV|K<0(JtBv}sl~za6pm
zj*k4T`ssk^u1-vQ@6Vv|=GEVfz&3V*GP@i#ZPRnPCo)GfHzK-(yqrrd<TMR>gw#NK
zuU7V?$;fy8#`>Lx+I@*c_a@KaCfmKu#{h6=|9ByW|Hre%{|U6`OwBp|@a&pVrRzJ}
zBk!c|f;T0BU}2xlh2`ZQ!v}NAQM~KFUl`<~*3ia7v0|u|`}~FPc;bCwK7)7VHygWX
zgDW<Ygvg%<dkk|=MRxzLBJajU^WTxe|C6kp&0Q8!jmYV8xpJldt-Lo6YbxpbMRBBU
zM0!#{1nd^&MHw0qP==&!8<18&g%A-TC^An9geipB;?On-G6*t8MWzH2=13AIQIIj9
zgb)G&WHb;0gd~PdlC#17`gzZ}&wal8$GPX+^WFTR581n_)~Z^=Z>?2TF^g`I<Tt+D
zlqI={yvl9t4|pstpZZp^<BQE#*l#r6U?mvj+&?4)<34bC4HIj3@{l-qxYh_dK=78{
zgTu?SM$5>K0?+vNeAsAI+~&9*r#*L2MR<r08@yb>M<AkG(OX&~lwrJ*xEeBTy%Jjn
zX<B=CwsxaWxw3neTiNY<h8;=?mp!lyV}(e()WYYB8w%`v>a(OF%$^J+`Ls%u6**CX
zn{9Lh%WjLhPXnH6`Q|5lLv}~eG;1_1F6nJP47dh9#GMBE?$7G)uOT)~)KBafYV>${
zW%KFa>&shs@;kX`u&Ga9WB>hGq0+E+veTwOG+$9UHZCEZ1_l&qlAdR1q9QHN*cS_K
zw+257YRAUa-7j+^)bdfxXOi`#+)_%@+P#)A4CJ4YRk!dm1EUu0=&rL|qnz>%V1;vr
zzHKL~;pRWXw+TW?96kE6S*|(w<^=(`#elBb(>sptyC2lp5Ht4g)3Oa6<7>Vu^*(31
z?jb0rE8^qnc${Vl`{boBJJA9>%Wg-0o9QeIien+%=nD&*UR<uu!+ab2`L<(gjhlp1
z+AjYs5FR;?kUM!{_y)f4gAHest=p3z%V_^<B=X!BEKs4R+rQcD{{bkd4cUd3#vldz
zM3N*!g-sJM)RXb;5@dP1aPOBT)P482P58bx(J0D+ut)znVzc|5;Ct?kcQ?;o%MJa9
z<@mqi3_VXp_qJQ{oEdsP=MLvRJ!4bE7EXYk*hT1+T*h{DyVTHN2FhA-%sbBSn@=OM
zL77U!OZaMx@CMFaKFdJEhtfdTgv;<`$;JF8M?n?m@UEFn|KFzYHF5vQt0KTzDR+z=
zn#;|1vy5%^48gO>6P4|7no7CZ2E86t8KCUvbILdgcnXXZyzfX8$>p7x)RS@G<g1sw
zrnKFeu+J3Fz_2b3uv3IM*(B=_OXlHFH%rj*7vm8`eGTII&5ry*#-*7YQue)!_OR0e
zyPgdFp`OO5zBry?otS(Ypy6fY32_&m=VwxsoDCHk(}|BR9^7d9vz;elnq0j0gkwMv
z-Xw91aGCI?Q=#hSJ?iq@uH?!dur|ws+9RMReKCTJW1I0_#63gWIQn<N%N9uj9_v@9
z+p>e-9_)<1Cg}Fw5<>qzyW9w%ejC8Fy|0|la#T0#G>O!#83S=ZuX+s+`IA-9dl!0v
zn)aVfy!Cm3^_+s-q8oY|moVgwEsHB4`_k{pHJga<dn;=ow0T7Lkr>^eUAfP|*;j)>
zp|51;=pS&~)ap<O!F^699=q`F?Q1DYkQy<Iabmlnd#UKjB<X&K{WYdZ%szU&P|=}+
z(DS*H6JWE*?**-2bIe*fM<1_$eTRK{oG;x99P4EDS{_Wf@+X<QqzjiI7tsig6{1oj
zd$C4HiYww=gBzPCi1zdhrk2M93htzEn6}cYLtA*;@UmO!LDEv@@LL$8oN&?_(OUfn
z^yKfz1n~r@fb4JAVlD{8bk+uk_T@1vlQhoGVz)?ot?p>kI_m*|XT(0^)W8^9<ycb#
zI7u;fP)}ohdIL~g{Y;Ye6+^-=imCGZPB=FM<EH_XdE6`W)$XM2R3-n&om9+KOEf#~
zv_PA#Z4w9`kKRs=ESCyY(26pxX_)8USGP%~=94kIeNHGu5f79?<>oq)DPbgWx1+OL
z+k46T5Nz1V@g28ot7M64;W;+Y>vXTcgycMluyu<;^RI2>S4@mX%~<V2Nv~QB57k{>
z#MQ)$dReBWQnG_gZ$$HiE27x@$EEy&)#~B^g5&ywLxW$CM;~Yk!GoT7lghE1l}l+h
zIIrSUPL_c;Wssf>_~%7$pX$ND(NUB!MP*cim${6_R?zHn%1^B}izOmb!{0XAe4`hN
zJl80xo?fh$-?7%4;?I02ZHQSPm+H`IFwOE_LOx5ED(UdP#$%jNKp7Ch6)kIwYvldG
ziK|aNvLV@J4V8oA5*7$zg@^DmOp8Rr%vaWPwsyhEAShyni*+hi(trlUnP;b&j9@SV
zFLjrdO>1-6(=)FHcz+sz<5>n3G#_UJjS(3aB%niDM0OF>a6Z~CL<Uz^$I!~ym3PS-
zL*3rBe||Tc0Vf94H7>9@xf~rj&slS<f~(W>x>Gsg#^|VNuLvOzKS6msOoK(1fG#Xd
zfXy!VinXdXj-dtB1-@0oCzrjvw9%P|4d@gsozPwaf_InFLP*3B9?_Xs$~GID0U_3}
z>f|!!CB^x=qJ`()lL7)vc~d3Nf4B^4o)ujkFLN9XSo$0^ODc%=6jXTolx;q**yUN?
zf*N{|t@q)k%zJ)C<7B4^+|-pkaSn%ao=v{GY2rXv#ox_t-2;>T+=TY5HjZ@2fr=iA
zxqaU?ehJX7xL7xvhnF0B4jw23KZo|C9)8K1)^%L=S%Q#;%|oE*mtV~9$s|yVik*Sk
zpueL#lZxNIjPjp{c>|4ySH<&my6cF@yiFHOLW8qn>T2B#y!n0z{<ljxc<xJH>-@m+
z-f_!%vsZWHHkVK3vUM{`{N37@R@?sLt^Yju<f@fo(`-1K@aXoAM|+ptjW0nrhL+Ry
z1zlo@U@96&<s4R66g}k};R_A4|Fy~c<snd3EpgZ!5*AdKV0TLlI}R<#PT_oUU4l09
zyW!TI+{}>cP4GR#2;+bJI*2H?#0x2)qD03FC2cyJ=*z=0aqkk_udgxid%z9iJLdI|
zj|WFljTg3nvgg~^*z)flT|u5ZA9TG*5-)7G#(n8U59~$y;hDrGXNU)48m!H~e?R?9
zf(F0;6fea8cklN<lf2^msIb5<q>~gEW$b?+BiRXR_)1pAVbUKZS8*TVG|QziOGA9T
z!aqGMs8tfNB^sAUed1(Ue3m@k6ef?Sl?T^fYch2jgI@m|U0mzFE&4@ZMJGCqSwyYx
z<FoqLy#?xgG_yY6N#Ze?yNr$(GiWN$4V-xl`~w2{&!7lfFxb|^>))-tS<Jy01M`{4
z6$X70?gp-K^ACxtOU`aC=yL+9_w#X*Q*q$XOcm#C&z<owYpGPX-GyEbqTZSmz|%PY
z6=$pSxDlRswz*6A`8}t4BUrQ`FwG>`E1Lnn`RLFV-ZdZim}OJmr922`-Lq$_d(kus
zH+!O2l?uHA!qeVw*RDT^;=|b#OVER;S<II6xP`N5U%FSt{YGJ(Dfa;{f&$B|)LUFu
zCh|JtA2_>#LehLg7le6a-RFw~>i%^ZD=HAfOVEXbq*qcT<EL>;q>K&AY|b|G8e<3F
zlFSpxi9wacmOYRNQ1mXpQSrI)MT58gkNK2=s=;x~QnC%w8R?B&xdT|DZ3Ub1x?tuh
zPc4Q+VV2PefWnuw%)S%e&8P$p2>5IkzJN4Ol>kNdyc2Qm>?>dpL?AgZxKbU(1zF5#
z-<fQQCoivW%6Idfwy&dj@D0c6j-+vT_yk;hJXasbz9{iUV#S_N7j&E;CB^@X?Nk6!
zda`;A_L5sAxK}-3-=ec5y7x<(XDA&+4VDYW?aW}>eZXM<!1B;0wVMi*1q!c;1t{8V
zm8E>BFEr}k<sKF<1mk|U6TS#cYgns71^T}ef2G~S0NAwm8M;7`#?`EG&?w!v(edrt
zpFz(V5-%)O%Lj5!1MVO?59otvZw|W8#JSm=S~1Kuu@F_A2E^*893wFTh4@)%>^~zP
z-^w;%E{){iNWCl{6W27+H2ncies>*e;vda;G(R0ocg4YbS&>6aBhNYDj%~bAHt0T{
zDY9Zwg2sQJ7WuW+zKA(VPe$<P7LF@k%~>7~he0ni+<LhQ2IU6ZKo$Q<zg)^%uWKUX
zn9PPvN&{OWc2dvCJFlWQXE@?frGC|yg)#a5e@v{^Rqf<y2HtA2vIw=6Y)TDy+;^A1
z=0&b1W%InEoJspX(D?~ir9g09ZnEH!TE9tOa5#FgU(8+7^<!Ij(2(may!m%ajWG&i
zn{mu-&;Q(hUx{d@U^*n5X1=W4YbUi>X&A1*3~Vw|RtSC}L-tAA565lBl(%bZK&;Px
zwYJE4urs+KYS);LdaP${BwP9jw(kq|7OuOZ5MhZDg6rsWv!K>VUb)mmVeCQ7MVmpx
zeP6g|R6MyYV(Y~%*C^ltn0(z5dULdsNnX1B7X$>(Y<Aus<VEGn6!ESF^Q(u%`{*rT
zR%UMd+U6u42?lx1!&$aREjrR|W)NA?hy?6%tcB*uPS5tW%}X-m0zANXq6^@g^WR*g
z7w~W=Wb_YCe9p+MNxTpMR=<=OzNFj|@HF4L^Q{=7`_RYJ+$ZWT?1{liFzMrnGg!zD
z*cw9T&LD~_sBhTb%DXJQ==TDv88A(E<hODacEJ}$Nq*gSZJ|~I&eda{x}LpHia-8R
z?fLF~tX##mufXh_kj}@00dM769S4sOgS!kIkz5fxO#!H^cM`qU2`rN=CUF+=`dj7d
zI(TrUE68t;EjiMwo!#E(FMs6pZ-Cyo(wI&V<H~KPvL;qI=J>G^=}VUiF}MVHA2__6
zP;*a`%t`e2smU*?&dz%a<bS(%rOwrPR5~ypIa8`#PR=N#d>n6aM^6+QV@BmFjs&|1
z#MsCW_U6dM`Hzc%vzWQG25w$vIt?%!t>V<s$Y%iiQ3>WVa{6#)1T&ha)BcUzg34Af
zBecfp=CtE|dYj@L1xuWsV`Tx~5}%H>E#2Xi(hCm>Na4w+_}-R+GS_l)Vj-}!SDyR<
z+Ymwbl575b{dMw4F|Bb@5x#+Dg_=ziZOeHq2yF@BGp2Fq;<0IayuRlred#PCQW%Db
z0~^V0o3F7Su)q0iP0AtA&2x4%%GiOs9?X|lY=Rk&JKL%F<vLDpPw!ND%oTxVkN(o;
zf>(UAg~z$-W?9U1p~{@m)`DbuhzaGpFM#OQt|&MU3PLCc+#Y9#637L#RUBkP{+e%n
z^-bjccsdbA!nr%<xi?e|Q#xO4Jh`1`*G#F7typ`(G3?QQw<(1;&x2W0Iqb{@0fl}H
z2b8_qG+-X`-!Cm*)%4r9vZ)LQp8Q?P`mdB>|DT^T8wOMFPwyI(HWu89`*EqBPb_}a
zr!03f7W9XXqk&%(j^+X?oLq(acB<~jhx7svAqY3}VzFd-6W}nH9DPIZf^!H=J<G{u
zY-Gb4tJhlnd0(L|pU3zLX1-n9Dwh<3p`&?mAM)2fn-j?yLCfmURR{FK_wxSI;Vp6=
zr0<ui{yc|+YLpUzo+gcr_-p*i$lAW*)jx@^dT?Ds`y3<>8P4*X=paz-|2DPuzrB=_
zoT3Y&>@Rx(HiY;8YofQIHaQWU&(O?H+y;Ge&LC1_&G*t1K$vodo`d5Ndxw70)GUV>
zHf?Hi!1-nhx{(;;D(Iox%_O7k1s~}5=#27`FGc_?O|d5D@v|DUVhNoGYf8axCMhcp
zUi-M>MvUnnmx}4za`N7yx;V@9xBS`0(lyEY)s2B~@A&GwOx`iG&}%gcIpwVY&inbI
zJTB9jL~u75o4MH2J0qyxq#!8(OwnH6CL4PudC8fyVa}{@uF?6pVi>~J4Q5F&d$q7t
zIXq_aZZAV^<8oVY_P6jNUWuMbFv-=U&pDsybkJ%D73q8Fq;9NhA+|K%Fccf9QorfD
zFPm?aio*J&Lt9zU0&<*S$~$1i{UX{2uNZAm0=dLhe@;x>FzcloQoMnYHp)bJ(|XBs
z4h5%n!u7?3ZO`kz;#%@!(d-P#koqjfzdE63lW^_kd(;fxozp1U9pC^i|AK_ME_K9X
zH7bv6Q0bxk4Okaq?c}NjUT=!0AHp}~jbXe6hs0T%jl*8Qo<_AkL(khSqF`Wt@(~|^
z4xgMK-?VI$+hDoE-P9Mu-Y0B&0UE}<$@EPOv9NEWXamME&3eLHE=V3nqV2wCW3&p_
z1y|&jfblyVwHfX)hiBMxcnW+B^?L8-V3a#xe|um}rk4<!!4Z|4Czs{^BM^M@e_R#v
zf4+_UPb;z|XuSSy+gEOaKikCjnk{D$qGZsBf}b+uEd~iu_JZ0&qG(16u(3+FYY&Zk
zeG7GKC>}Qd6`@qJcrnlDSH#B46@wcF){f6oaqrU|mkgYhY%EXnaA?ZrlKh`s8UA0e
zCc<*E+^{29fWFEtClDYmXq;Ki>o4ghb^B(DMCc^KX<W6eTFadb1ZFrGxB<}}u6{KJ
zxiR(4H@=D@F<NoT&ZH|h0ZLB@^b)#ajlf9`_1;7-kBy^9UHKeSJam3&VzC{sFpEf@
z*+<uOZo*>0-F)Cysb@DS>*0dCEfNJ>F&yibH@q-=GX}{QGv>QBn*sy_-ab4HVJ)4X
z;cmF`e#PWQ`8b1aBrn3$fKUnQ+E7a_b3lYn^sO(Gz{KsCRDLH7<A7vMr*BT)`Fo&}
zHhT3*pK#i7cvEJ33z%F`03*-k?cl9S;O7X)WUP{E4Q6FK5F<2P+steX_BFzkD|SLq
zeU0PW0rH%RM4vT@_c_sPuszsB7W3iTwc&jRvF-9};GW}~bT3_S)29jHhpJaJ%-g0q
zpEp+`a0!Hy6@`*GU_@apfp4HupPe^5zH0$MNI7FH%1-(Ix2_iVrE?9+0*-=#I{5U{
zU-7QysTwOi7`hZqksVxnR|pUGYs!?3ibo~KWPwxu5k(0G_1Dh<mYfa@!<I2BnT1?k
zSuSr-LN5f(uHG-U@Z5OE!7Zx4HYEIq5LrFqlNp1;en(%hfFt6>8A9L<h31;$Iro<W
zNr-t6DdB|FiPoB+p~mL4mSc572wE(!e;MmJ_d+SeuZi!zf@%;c$-==-^W)*cL6Goi
z8v_@xd1nH&iu2u&H)t~{Rc2mnHtV6+!B!bEma~10S!PhOcsvw~Jy74|b&}%!N4N8t
ztcPZ(G!|Rzra3nJ8|HN{vpYhwVFd??nTDOy^=W4=7_N$E<F0d)o?Ergap}*K)nCQX
z+V_;vRq|NiO}pRvz)7b>XidcRQ|9kqwD+0g;x(HVnvv`{_7!x7+Xc6(@j4%PB#I`+
zUjF+P3Jy<@4(tW=p|WjVWj@9AH39B(pQCySjZ1$i@uY~E;EFlJaChX}mTmF`l6QOG
zG##~XM7Y3t4v(8Epxb1^`+KYB3|FO`@4?g%XTaF}{vIcu`%PALz&;Nlc$!2SanCCG
zxe{Cx623HUH@KieCdbU;5<E}~Qb#vuo&)XyjTK3ha<{LM0|Xh-fC277{x67}TX`I)
zu|i4v&62m<s$dj5o2_feWrBCwL#te;yn0Y-UL?b2L#_j^uwuQ^j6BYWf4`#<7?WOm
zSiA5WwuDW3$PKxeSNReYFHy~BgRLSMwsxbHLtT{b1tgzieGW{6#~`XP0SQB_1(hqs
z(aU>#*mz}R+L2WT{LrZ;rjPWew)|rbEfeE@{m?Cxi`AkR$k5XX#kHtx^)vI~TL(8D
zc+Mqyhk@T;tDOr;0&Bb>CM0)d`5g5#3HiT8Ua6Mll&2-+sM|Du)mYDbF!-E<V6CYw
zN;!y~Xf*II`lf5Vd0yKX;ODlsEU4kDO_=TKc!uKIt17eTH~Tl9-`;hl7;5EODDQ-+
zo~Y#G#!B5;Daem|P5{-pMmSo@q9Ynz4P~f;g^kYXC!2cQ+;;|tHwy(bJ;87|qI&e+
zrsuw3S{6rT-qSPre!<<vlEU2aC1ZT_+=;SIsrNi5?i!Ne4S(E(tQN#T=?l09URiIc
zyQM5<`uC%oioRh1{_A{(E(*)Yazlmo#9;VnIoUyQo6{RVODpR|R1i^cBJ;XB)XCB(
z0nleX^$Z_E;sAJZ$s&BTav0Ij6yquIp<}^vLJX7TtvcygOIrKLna3NH1S8x)2?wBb
zUN4V#1BJ_hV_AWLi!P&H(xFBU6vu#LoEmUz14jU8Df6=hb?voSLUP7jlvoQIzejI5
zSJ%DlGI*<gv`wSHOEV2$k;tfNVgf5@9MB6iqN^u4sot_)X%!P#Wm%&RcU6u;`SY+@
zn5n8o1d9`|J;Xb%=b6}88Pk8nKgv#UIA<u%JgD8UrkL4OOC&T>*UC7*2$DEFu%IZr
z>{cnrtaS2B?hnp!GyqhF75N;mr2gQij*Jy(PBDZ?m`>~u&T`zg;E!-qCugwAP5Q?g
zl;?PtNg-wv@ahAe$rtHvId<O=%E$2d;+kqqOKswEDf7mZCF4C0MrY(+7M~N9?30<^
zbP+tYTSdz_D*>PQr??A~J}#5hWs{+MH@MVD!&fCTRt<9I_?kHq`Pky&MXcE1K;f8c
zt_QZOF41sp#%?vbBu3y%^p5R<++Q5lrIPC!WxR(CiG}-7i4m;an5_1`;4HX7Kw?B!
zE;$2WQq^YY#!?k-(JA9*LS5Qcwvqj3x^DZBXZQo}@4I^vdyK53)Vrz@l_Y>}-|Uhw
zPkZgB3}2%(K`Tk~^0P7G&~F%npjmtCOk)Ve@3@&zMK7Qm?x#xhGm@P)b3)gUm;WUC
zoo9)SWYp7|U<HAqGwv3iIA}aR3fgDIoVhU4c4`7RdEmUxs|?$L^9ufv4bCehC$fu5
zZqqCnJyE=Kh_2x*K?~A)q(4k2(FJ(Z+I7<fnZ;)fOwCK4;r+^X-_Orc(bd(u!GjA9
zT|%c~6Cmx`HQ57PPX6a}E`L?F&rWgPw>0vI8kh1017n4~08mOe1&Op+&aC=Di754{
zmd%Y7FlozVb#XM79r$szg&>YujLC)2=CWHla%RvxtO%6=k?bZDHAG&IHUt@u4pCzb
zdDfH}A_~2zMbJPYgg;FW%2tOy04VO&P)*sgqJ%p@tKzO@@p$iCu8+Ajg!M7(m7+2Y
zty^!47Q2qd=IG9=kY)F|(5sfgY}rJk17Xn#?J78SsIjQBe5C>Tbr{F%5iK|yo@{AN
z39@(V5(l!uIogj&jQEhd-$SpVa=Pi7j6IGTiL@Q_%&I$XT^!k?^nrG&w+_HZD%DAb
z_2A_Fu9o-j%YKZ*AUki>sY|WJUarT&+E3O7NW~-PlB&5%igkd<JFT`r3~ou$^fjr^
z!Sy84mIK>SEL@_obr&Z@`*K^raMU2JQfg`6@-T@uA^W+p*5&5Ug|{Ow=BO4-%rciD
zyvQ`L6*4F9R74buVXBZHLGhw0*`e(%YqFMIilYU6FFb1qVVvO+AHG>He*Vp@0VUdA
zu>!9PC;~g<rO=TR(1WDo)+F_X>UTd$9QFsw(5@x7p47$cZn$1C)@mX~7ik=f^|NvB
zvH|P*jf8s0&~k5Jy?ymA$wZKh@B4(%_S0loPy}!zJkjuqH^#i>xci5nU#@khtX+Ll
zo<viZ2h<@zfay+Z?Pry8&t}(zWSZknmK!@T_?3QJtmd#{80XPGbu2O)9#Zp8rJSEj
z-UkG_&qgEjhSt4Ev{IL<(WPrUx-DY*;v$eYVPNj_oIKaco&Wqau<a0|%284K91-CG
zKvip+qNc9Yv6}V_w_kzss<YktNt}kB-gNVfczT$;NRuphA92^J&1duxxT?3hGncao
z`)i<vm$3M%C(BIMg8v8%>fGQt@K;3s=x5&b^9hNxe9i-T5teQ|dzLc#6jb3{M*}m+
zyamzyM&e+!y;}m>gvh6oBN*%V9)7#@da>fxCE0t}-m0ycP9aZ8A&#TsldMI2qt8v5
zK_+ZqPjzG(OG$e*AvdogE$@VH6IX>b64ECf-LV!6?tFY_2d}BtJr0=<UN9Vf<OvZ5
zkWt9AXrPwfZao+Fr0zJxp}f~$h@HglZ+MaCV=ASnd;n*jlC(}KT`Tw6TYcscayNJI
zl;Fi=buXV|)#jTwJCG2!L17H32k}&8i^0O4NMi7*5E&9V@pXX9JsVTXa7d5-?&T8B
zef9eN^S1PcY&~yGvDkBrwU+onO`c10^8kqZYj}pCdMqwD?a-5LCXuQ&GOs@%W0Y%S
zd9n1dfa-Uj%P62JFijGx5dA``p$JJG2RoarUedU@oinUsVh$mdf=7R#ROZQr2-j2h
z^9L4mXO-ZY#^&c1O!36Ph|28I8!5}Gag9xQa#Zy6!dcG-(jv%+cbkvfEFCmc<epN}
z3X>Yl=Zsolwv4M})ED*gcCSgRQUJESHYmJ7!sjt_SAsL0lT}LD+&cPl^qNOg7-dHF
zYc+H<;{Xv4>YIJ!CFDgL_x49gm800Y`r0O8c#ol(`0g>cM^^2#!wWNl*KPX?yac50
z0|myGMa;c*58zA<w)^Pf$$XFphM&-<=|Q&Jwb+8uS37y$ZNA{Y%fXqj<e-?-E&aM?
zkg>58hOC<3E;I#5!cK3@(e(*$AD6fgn_s&}J-S7MqsNOihC4r(wNhhz`wfS=VH`uy
z+5*&=EPm#w1!!$y8G`%ZkNj}GdZbtVarWVf1cc<ejjY1bsiBZbzTj9(U#`DZZDhEI
z2lR5bsGn-}b3j&evcT&S_p}Ge9%eRjg(qA^3rhh}3%Baf5)X>Gx)0@f@@`@Mr@^kP
zS)iS$WtOM)8@BtZO1Rs#SF$x2m1B6sP_Imh{Szo(V`gK*7fBii7V2(CR(sa?HlscZ
z6V8>!0D<1I?k9#8b8^ro4aJkmG@1!F+Vc^>Cg;oyJji?Zc|jO0xCUCJQAn}IWOX!&
zhR-~OVU~ya-*)qH2^n%&$8bkc1L1Ht%_SbcF@!C*>&KDrzWH(MUnASM>Y4ti_~fU9
zrV0~BE*#VK*xRz>*MOF<?Edul>)@-qR`t8S(z)>Qrybkg9@)`ycsl9EX)FKhZ%XcN
z-)eYdd%m>N#|(8O?37VhZ?_RY781}CLj$@YC|)%!x_3cr2`soxqpl=~39ZM2R`2d6
z4w-QUgIYFqj?+)B6ruA|y@Ni;u3wg6zGrbr$4g}9$I(A9GVwK}fTx&-kGvy0U1t2S
zE_Jm=!`^Dhlri^;ZbLiZPEF47<1?AVWefYeS&(+0Z%#&y-#x+FH=aj&WN*8&q9wB5
zRdayD4f|Jz-X&{ix73M%PZvGIhIjL8XcT-6&CX>B*Wn|T_nD$FYNJC&iy+9c7u;&i
zH9DGK*KZ;!Wql503*(TPlFT5k<F>#&{oo;URp~EOyK`bf*NdQ4s(%yylG$8)p#_JW
zHdUgfeOP?`$)XXHnxfj}e#YT!D6%N>_x#kVlfOu+?E;G7-WNpGMyS(?G}xz{q!X`d
zhJA?1Csd3>fbObCdHr>)67IV2j!CkgwpR^D-FjuzPIX7^!<is#p4P0-Hy%%K<ZPw7
zN!$<EhXy|p9cFeTZNO(F*e4))59TK?!oVR%LS?H?*ww*;y^(1ajS>#=H<svm5XVtF
zfkcb@1PA}?kDGHeSYuMLYxE!yqOQ_GvEm8&)ivzXi6nZF>&r*MF5ED?R96RE)XyE!
zur(ips~-full#rg3R}f3yUiSxi^G4npDV0K!Foj*B|IEgE81=+5`)M_4$w-4^^vWi
zoIN8K6PzLx_XUCLRcbUGp@yW-%-8(oNp`)j`N!zX`d^iUu<fGvh?G!l{tdOQRQ#*^
z!H*}#&wV)9>R#asP=9L0+WGjzcmTDqVTEd!f+)(X`*o!b4d;P{%s8YPqYqq|c0Z5@
z#2S54mxW;#L5!Q7=Re+;^d6<z#ci()Nio4Gn@8P>UnP~lM^ycy&hUe2uF=XmPc^lM
z4u>CXEy!f2%^a$8cjJoYc77saCz9rl#Ok;gp}m<Iy}qoV0k0fl*U{upHMm^ma3^aP
z$cm@YSJsd~>tQ>kCBn*n-0aKIbgO3>e%P3pKaMck=X{uz3ImbqWb*yGLWhPxU;#X2
zeUx`5XQ(*8sKbXJHwSm9J}>ifT^o>dC34k-iuiRx4;%ZT^r6-8+=4$AFh1MlcdT~M
zS@2x8jLXdozijWi$Lq6Ml+Db12?OyL&G)fX8^4Ok!xzn027=<ExG664CWr?x%?}b>
zRWGL^w@&v1M2P@yNQtM{w>Ly+zeHP~Yws_FA<h_-b;iUzn6B|+3QyG!i?Z(n10tR|
z?cyLg9H|&|4oiL{WD4U1IFRDO;`~C8;yMxD$E8SCrHJ;wjeZVN`~ko2{^8d+RLf66
zt0^GCCTF3;-&%T0{RursA*&&I6T*5&o9UYAce|k)pNqO>nKFtKfEYR`F@S$UKzv;A
zgVrF~pEk($-5b>CKYy}F-f6mlGzo5@3<L(Yu&ofZz=@OOnxufaN3iJR`B!CLwf&HG
zXTVf(X)w%dQ6LwR39kKWDa#)lv<N4zL2CTaoFmz>CLv)><cD5s203idaGMTOukp0%
zOFDUb&dreEy6>%tc%k3%EXgj_#lWEVC1hCaTvPGU$p9wnk5V-)arjPwWK`FA?9g!w
z_@sLAOUuU-V-0}81vajR4yg_d^{L^ST!G~f-;<L*!C+akTRW!pa2wMl$1@{5pD)zi
zQ@g9B%Y-$Hhe6tDXHgBi5ob`*f8Ez7J`b96BrzeZVWN9G+Ba=&z|I)Nu7hz%$F5a`
zNj<1}#G2?<g7(7LeH)jxeSQU4qOjiV!)&hk<B61AvwEQB&8?ilXcrzrM80?MuTL~l
zPn$5P^6{=u;(vm<W6yocP2#^eIy&aVi%apNpo^FT8^T-DX@YoyRCkm_O$Mi;%Aa*x
zl@*{H^Ly6Nt~P3qAa~F%!o`pGgGjUjJe6&Jh-iebLAbu?CLqy@K`~e|SIBJp4@n62
z&!^1<&BIM=17%*0z<?ZJpJTc}f=5eUr$%!RC)D)M{VVTSs)cdKBcnO0n?VOUHTGz$
zCRxcJ|M6H=xAE$@R&h~m$|<wfpfK9*$iB!lD&^i<#B`CWG(h%AZz?)1JzBID9cR|z
zX7tt9BNnZQ`=H&bycbI8vjWanKim`+dQjunL7E?QbQF@;!&4_y5A=>hYSOCwg`-C6
zmnFfS{z8=Dg+@YHqisT2?WlFKy-S{SD!sqd#`|W_fwT3=&?6nPo+abemxsH!zbiBP
zo~PTq0HWhgbU1i@bM@5IsdmSidu1$NqW;mkzs9QV79At|A*?~-;)_S1(HW)t@J$4G
zI1|Hsvteae9kB`ps|Y3Er?UP-(dzW;qwqqmNzA)<jdt#+`fB=H<JG4pLUNRfV{J$9
zoQqr1hOfV$zwfwGZUgCwX@H`P=F-C%5VtXwb1Bz^gX>|Gay?_b+hqC@kJq%wv{<9*
zb06Ko7@^U23%iarSDGc;9}}Q$GnhmmJZJfQUY>IyM%JP(mvh!cfggYYw^|xV9mTf*
zwDFN*CRI<u(I58BSsPgRF$c0;Lx?-nM)j9(r;e?j;imfoqO_~^=dBneJtk|KI(hwF
zY8jFo9mEf!Do|K$<DO2z(=2og9V>O(YTv$OoX|YGLQIXtSd+)>#Fis&)XrmcXW(TE
z#O-P(zVC|{?xP#paS|Gh4qrere|q8;A7n8b<T`B}?v;u-?_v?)2&xdmLA4NdG5$El
z64eBC3IO&GXF!k$S8nwq38xV@3dXSxql`At;0W<RMo<ChXREDIVxny`5_Bzo0vUsV
z{|VJ+<EX)0&(e~aP4`!tzl>FhJJg|^nw$2X`6}EFd9^q!HTUWJ{@Amf1;4ewT)!t?
zPjBy#x|+zkda9%+y`E<kzqF`|xzu?mQdvuUA=R{)`B!sug{ig}p~L@>mp&D&gSZ`>
ze*EZkfUVS`L$h#%CcCCrd&2+)PlcXSZCP#JO?d8kNkgaUV24lI`nA;L;>7EtM1A{E
zO1e#lgY8OR`nO|a_R&sj+jOvEpb|Pz>|mJ}C%C~HC|+Evj#gWvuWjb+|Ldf~{$Jtb
z=U)5N<iqtxI=Tj3pY$Hcnc}2-Qi1HvVgjnSMN4G2@?J~RF4QFzpL+G_RJ^VG<Eh?x
z+yOa9ON)-+-<SAv#*D&q2Io%o617fMus7<-&Fq^ES~Q21ymW(}YIkL=`q?Y~UpJqW
zPrI5LnhZhrAU_S;9sg_*^XBhSUVrl2H>8fXyruqR$GDkBPD_J$UjU)&DD~L%WrM_v
z8%JQ4WZS5jN1`KAbF@$W<OONZV+)sUHPWZPSKYJ1_Er?^@EHFkwaKnt8(Td?F6(qN
z`g|Z}?o0OpqB5=I^U-f2Gq&k$xhqwk&y2imAm@$p*eRL<2=)5-Rb`#_^-oCn#@1Jp
zo`&OJ9f_r$Udz*x>2?I9#Ts@z(J8n`vs)R_GR>=$O{81J6Kq2xTFv6f!#=5pQb!JT
zfQs;>%7YdeBLG5EO^=+?;X<XTF0EwVv`mZ}snvWVC#R%m``c-!ZiQ6p;SQR2L`B0R
zvS#<QNBhtXMV%od(ZjZ(p+pE+JQ0c0@2w~0+)6BnZVa;?R@17d{w{mFGg^p%GN0+&
zXxGQwXmZtenVp6C(Hu?@6K3eEpK~}LV}*Js2bI}2+x+~g23vjmQ*aD6G}+9uqrvEN
zgFi4VyDZ@6oG9ZZI|I~^UQczptbk=~yQk+YrE|V1VLgD>GhRJ+cPF^rM^4T%vTOXD
zK;HXhgG(y3XF(i`#eTP{I(zd8;nQS0=q<BeYCN>y3JSHL?<+=J7T;HNH+8Juwo*ev
zhkj1>BQjv(NBPBvQe&gqqz-wZAS3z4hq!p_l}7NMMhRoyXDTm!d}1yxZR=G#&U)g}
zQJ>TU^}o5>@+U)Z3H+>@FozQL-*^3NTJMe+Vij`(WY6g|nOnS>c1MU4FhZPwOdfzu
z^MyM<fetY;1;dSi4J)7c*GBeyF4}P?0*CSYojiHT`Uexmr&PaRA@h5d7KKTZ){0&E
zMSiy<O}&0w6quKxGPkh#$IVrSFcOvymwuu-`t{S;*<DN0gI?Pgw7_3q7DaEnn#Ngw
zlM-Wpl#;MX8gtO{Il(i{<CcsLyDhrV`LC(b^!l4VJBpN!h}{h&g)WOaf285f^4=em
z93{&1SBUCoRX<U3R({x^?dKNz&dKUXhh4`7O|gjF6|cDW5ajsuuomrnbZoTt;e(;=
zS4}UR3a-p0Y^4(IR`9m==cB2Vrizh2wAznEN2L<sV(Rm2%nu_v(-#4oB8wO38?c$4
zG>ab(SC}93_<$TuE)$|%ThVPhA_%{)Olh^Raw1>BXQ67gk+Ja>#XC+y2hW8?ch(M`
zXhykrI&Gua=9?UrVe(s$>x=#}tF2V<_>SyKCvQ;8?EuCx2um~J?LvpB*aC!f?cD@y
z3$N9%!L0G5*TAZ7!d1XF^h)iRrT0tA`rm=|Q=KZadtvURkjHUP7#L-7GETO1(<y!x
zWV^y3_IUiMZ!nLS3mA~B?=I&~NnyhQY3pz9hMU{EyA?KQG1PkoVYf*S!3gTEke_Yx
zb-5lC_C%z1ZmRnH=)4K0Yb&rC{|71ivCtz`3FbF8bdYAdr59WJ-oh+CCB<$lb@s-R
z@8^427Aq@~h7&~lEE=+3F2s$Ia2(|nrQAN|#G0+{LH(LKa_wbYaxU^0MZHk2_Oz0T
z-L}?QXFuhvkXO!Q^907<Yo-ZvKpGya*w`!qv=twRlu&YxlUFCQBwww#;?Z%@*28CX
z5`GkX81tHvAa_QkO%>y*_WnOMxh!6OB6@R}f2C$IXnPbEa!6u_t6_Mrf4oq=HP`0l
zcDYE$naa_Q^(|~QXPKt*LEOA}(#4|LRsN)bZFRHzztOoKA&}y|r%aQrwoz?Y9{P{j
z7TAVdDHM<2K`yXLSIAWp5Ij{%d50O$fvss9pH-4mBXajX+j3hn`!qczrYWt1-+H7K
zQ8Q+Ehz3ryw?HPZ=Um$xTRR+FwSWCZlI_a4)~rY>EAMuH6Py8~qjN>wQL-v6A5ooB
zQR2H?Mp)X&3z8&VcVC|@I2Ae<Bm)e2vg^Dth1B)wtzzvC%d~ad5MU+!7Y1oxH-4~f
z*|c^+90=rJa3C$Y8m+iK`GDivIGp8A`2$bRWx(ZAWd1D{<VGo_R|W>InywxgxuKKb
zjT*mvFS1ot9mKY-{FyGkrkOp35xuuCm{t(K5}yfq+*0s*>};r2{Bfvz&{&Z4s6n8%
zSMY?gw%E6m`Wo)@v(;N;okP2T?Cx940Z)`zG<#lRPuxQ~yYfJXFL1B5nTK?Zy-d`~
zn;!88?w;y;<(DcOVa0{rk7x2@ki0SN!tQG$kw15oHgIJ%9_&baaYHHaam?*!;+33s
zXjj#2H5c_-;3z$qnr_wE$UN8tJ894X71lYZ^QHEkynxhLb1(akUSI&#l=IZTPM;_v
z@MZS3qc`3d$OT$}_Y-)yOWMl~>{e1h|Eb!LqeZe?L9{J|wfa*&b<W5x1dYY~PPXwr
zdHd8S`VIXy*TU|Lz&$OWmx=k3{&btf<PvvzJbE}co}_63J-Nb%o>k2|spng}NT$6%
zG4z>17gAfY7{xUcBFBBXu5HSfwkF%E?qWq=`u!J<ysZ@2DrQ4Fe2ii>f^(<r6sEw-
znJ2McOg>X+Iq}pVP*CK%-U~>}WwIJQ3FfpVOCyzAXzNr>eHV92#Wtv3iUXV>JG#<T
zH=HkY+9emLxWSJL#nwH&<*PT^UTeQzwA-d4D44FE5m~-y3CQqFU61a(<BjTZyreOP
zxz$E`>rOx+lxG_vuBWz(>ac0vhko^;7eMbU6{@c0AO-=Ez(x0VL{54Ne#{}1p(U#o
z@wK(ZOsk{AAB4-a_cP*!wc*PoQ#gyZKbi-}aY>{L&gvt9Rn5a%Zf-LCyQV#%4ycb?
z<m$l7C*=C>2RIgOpHoAxHpQg_y^4Gb!f<0$%I`g&pf3|&o=;6kwa`DT5*gm<+qF&g
zykq6UQBfL|cy!VuHK#D~4c8zEX}y#0I4Jye)TiFFHJnshmjk&Tcd&zkVqu<CMZwk9
z!n)bI9g<7dwj$*spyBX}<XY!Dk-{ZwZv-0<14feMLuuj2^$)rz2&++hU<(&!|LOf3
z)8kLwKi2CMaMD%)@c>(B^S-y{JHcyz($lB7ECK<P7b50?zRORVaziQ`+ThB$==i{f
zO*!tBefQ;gwVSQo!rT79Imh?tf5nea*Dz`<Qd_p#$<mKktw?lEzdYu+HB>OyqE?K8
zo>#2}ppF`oi}LMpsPn@O&Z1kvX%php+R<lgN5fM}w7tH$xqdIbox-|lm0BIcYRaEt
zf@1#xrN?=xg`E#VWVL*BwK*Kk*eXW;!^YJoryq-6mGP8R({kB>=w_#8Q0kN+9U%Ia
z=3>g~LMS)f!KHAUMab6~Tm_i`f7=+or+9l{1d6Ds3SK!t2WTq%IIc(MPxg}40!O6-
z1h#qcHFYS0Yeb+*QJSD0l{n&8i2;|TRqRVoT%a)xI5Hzhnz2L3?D44is3oa4jYP?k
za;_Bg^2^c>ddh}@@w>523~(FWE}jD=e-YNeAhN!<d7tZEsA?K=GfxH_8JJLnWw$$=
zHOhv&{$u$sNk8{vWQnTc9?z5j%)s;Ay{+0PD3GvwpW}hNNR+CefzWY6@rzA$LMNM7
zo{rhz=69bPpZhs}dv2UODIlprp)=d?C*XyN!#Pvau;GvyvV(f9`uM1AmiG(Iz$_CV
z-a{A!|Mika-c;4@eXsB=PZ%=fP&3e=1(Jh0*ujJilZ@Z-%e08S3*)roTiAK&0pvZu
zKGGjK;c$Dt%#f$ukper#(5jD)C)Xj@?P}2w7FAoTL*1GqdTi*ouOIxiUe!I|Wu*Q9
zBcpu$Pq=gR7~%99%w6U-1?7w!mGmpV2Vq9DGx^|j*K)nB)G9USM%35$&*&tdh8~%|
z_{pr~xZm@VJS{Blz1x$j3MX0r!Zw|jKShaCqCX9zL00Tim7I7OT`vt<T*azKs%x>X
zT#fn46_aM5PAl0i3$yZ$Gb`_C{VP<zb<1N>qIbm$GU?lKuKW%RB)%O`D(Y@ot4s+e
z`9GdD#d95t2RyUnLN4uV<21?c8}h(6?NU)rpCI|mn16lx_!(p)wl3OX4mEjp*tGCG
zq91GGt=*v*gN%(!E$)dP>DnF@k~Lp97JNj$Ls?*er)vI(gC=rcuK#hv)a&lXDYgYy
zi5!ngM5t!EOlRou^-X$sd0FaH{>Pe8q*EM0sy~V;$FmAcM<LtfJDo3zytYwiwNDYR
zsbfF<Oq0%6?Urx1(Ni1GD=Y_Tow?2}RyPegz2S?MXRFImzq++M&{D<}W-S)Iwo+~V
zNaS^>`|+k*NrV-QYy9e_#54&#LHMb6VV_)}Rzlepxk(F)4jwH<TCEeN(->h*K9{=w
zTPp3uJ3ixhRAThr#`LLAitjCta?1`viB0yQBCRnMKHHs$N{uz`IFq*Ckr+<vTt~)U
z`F#ncmYY3gfMSJoPo;F%A`f6(H+_<^?IL!nd#AX(_0u6)NEl(fk#|^Jz!s)`SX^&J
zjke;swtTk@NoWjp>a@u4IU@e8^SV8wsO)^4edjwW%iuB3J{8c4MyC>2SExUErBo>2
zSXnPXyCsfuDX`91EP}uhOr*Ehhk$Ug;3(0{29)XV{)MvZoaBtzW<6MWpZ@l#d&*Rt
zpYVZLf4*_yE4DP+?D5n!okI!+pXmHMATD_IR$Me)pYz`PgLU|k9OFBts`=-HVX6?(
z2C{U^r8Ro-c1<3@t~j+kNWGfBf<n4<HY{{&DZN`gg$ITYw4i=_!r1#s|8vYGSTEZ9
z<!I5jRK%x3V14T}xaM*{UuQP&K_&3(sb*2K)ID9N9)EXp%r2_{1%2hXGYAL^AZ+Zf
zX?FhRn-Trq*%kKV(`qJJah|Hl_)dS+kEDh<u1<T*t@BnJ=WhGH-qOPT7^^=!sG5JJ
zAVA(o<t=LX>rU5VPa|KJO?mz1?i<%Lzucsp#3nFpcP+;0#|zD)t-NGciPV5gGfrd9
z#YG2Z;hw7-Z~hXneLE?Ee+p}JBkD9&I*-q4_}SexB6Z!b4)p`CQb%Eqd!&ALBPb2H
z<vY3;Y%!!A_qe5G7&c@VD%<;{R)5SsD`~5oP%oJXJ`A6CwPoi{-PJ-M!rZ{W|Nrvw
bWyrP-S)En<GkdUkgJ)YS``-w^dffSMTT1sG

literal 36805
zcmeFZcT`i|_cs`9=%av&jjqzWbfwCp0-=cXPLvWvnv?*c+XbYBW<-ce=uHSUl%Plt
zprM2skSc^;0wF-&8}<8J^UnM+@61{=?^-kOT?=w^bI-Zw?6dc0fA&7-+{k-(wORh*
z{09UAv4C}M8-qYc13{p}4#$rHS4L7kt^yy&9Q3qr1E0V_y;U0ye4Kc!W9171@w{UE
zF~!ZSIfFo#LEzh(rh)Gl$KdZx`uFK;+xpOB#~vTKee~kv!$#+9PkTSeqgns3eq2y=
zGFN1$sOY>mlTsh{p3&`ZM>lhF`^0k3-_g}Pcl-8Ht0U*noImr}iRV(=^mc1aRn7BY
zFHc08)1i{NsRV7PPfw69qCLcC`NrAbZTesT{+9#*3EbRWPB2~a-q^_2&Il+QAbfq)
z6Z>+54Fq~rfe2Azh^P(m8cmR2PY#^c%PG9pS4mlXc%2RceJ~gA4mkWD8O~p6Jo)$h
zXcS2L@98;_Lv?>o?|m3_Vgb$vhh3?jjBBUQG1-Ga_qgr|z~cQ7=vag`_z_gCL5tu6
zhJbgC2^SHwHn<XSX-23(x^sWOCcz#NRk*H^B5y*lF{hTE+D**WF?Io4fR&AmL%}G$
zAR~eZn5??)m(XWI@Dfl!Y*wHn5g94+D$gj3qVxJ-ZJVlD*|}Y9u!&&3^gIlVbGoN(
z8706YJpV5^r}Is;GlG=3S%{whXS+t%thhxAZ~0KdH`Yc^`5Sgz&)5XaBIuF8Q3YwM
z(;_9y8xUTqmMf)}QQtw|{1E~~&FLv?sl2D%;ej8Tgs<m1?4;QBPdOmak5Wfe2G(}X
zIZLql0x(?>Ukfk>QSSW)t;{s?P{ovx1MYy4>=Vj;qhQbT^7I?qN@7gTc}<0?&+pZV
z(&xVGRkkb##;MZR+D)W3j=r$b$QK+-MoZ5>oHc>8MmTS7wBd|}B7XVR5LAdV$b(d7
zca!`%Tw5kvKjN5B9@#lAgzt#ETP38oCNP`<PXJaeUdZD6eB$g)k%UeCV>7tSY0Zy$
zyl^^yUNK&O>@tkUIk~C;-4OxL78&F{*^J-?Z*Ui;A~n<8n5|*Vu2aUy4`x^<lHZ~H
zA@16|${gDR&4#qlg^oUEURdlu@l`rP@#GY`V(KRHNT4I4Ar&4fn0PmWSkUeCWXnQr
zJv2!GB4yt@6!xFy6j#We<P|u1`CKO+k1vn4LUe>Ito^eZCW0Bg^*zYE!#3=|AZm#`
z+pE}q1ZB_qQ<Xa%opH@f$2#4C2JMs>nt+WO<}=m1$Bmf|n@G`zNW@)=s;^66A6GK<
zX_KK%T%8P3kol}L=R^$H`olCNs*eKlVzhvF3IO24not0{LDOHic;n*Y%S-g{RainV
zEK4DV+m95evsfM^%eomu7anH?z-cnFUrIjsCs<YajGrUJ1=`p21Yq0ODG;!SKyrsI
zYj_0SI^C0$Y%1McnMJ;WyzC{$n!K5_nl>pm;;591mF*$^<ZYEsmY1JaX0I2<pl^P1
z01FGW2P0dj_`thggvWJ}Ls>imOW8Zr*tR!m-RrFJydszk-{RYtdr+Xo70!ROEy8*+
z*h6KCtc@IUZI@5<%hW&JZ@`aij3VO8h=NPf>>$w;q~!Il!lC+vFkTqEYofx-qzD`3
zJb<rP2k*YcYZ;9dZZ^w)VaIV4)drUreHH7J?6L8ouj6i$<o;z2?qknZQ(LI?D(P@f
zabe!HZa>12m~Ntx*FA`z9b_5Kz{+wR4%*tTVj4c1!mKttQT5V05^cas?Ot#5O+lNS
z3$a3+;GL1yMJVAp>Z*kE#QQnwYJv=0g8#Pv(A@dcR;P`r^=ch);2=$sW)v-SUF}yw
zt8`<n4phkQQ;j;@0fDWap8uKHfYBZaqE4^bL~n}3scK5q_Vl6D7Gm3NQ<f>C=f&ai
zL_@LPqknlRkDor_S%$<sP-)DOwC4(ft?NJ}>4HYa-k%L?>l)4_d}UcBA<!{-#{zxV
z-gP%$V*YIqBTOYj!Jz^QZJ!st)H-6@3w_C1#rhHLlH)=(J+T`nS2LE4;>Mx(F{<nG
zTkRXnajKADG~Y@B=>j;@@PXBYNfqQ?ah74jwZnDB{;i&6l)i)$2k@h0pBnSB-jFy&
zyUB!uJ8_$AF9x%fd12oKcvcdn@gT1<mStm>)!7Aw^d%Wq5J<L>6F|!kl{<}7YgZ5h
z9Y<s{QjtI4QTQyHrg~>zLf_Y^!B>c|wf0&g!lgJzXIVFcY=@}Usp!yk)#bGvF_bEc
z9d7~uinCw~HAxV8^Jf5WYpfnZiCs|E4UI+eyVe+z?yPHQKj;`3G{{Dx+hyH)gz{6Y
zqKRI<#qQYNsWn`Us=3q(>@n(i&J6^*OTE?Dj;w5_a3v+Vljzsba{afkL@r-!4{ymL
zN(>7N^87UMJc-)VRMe~O5T+YR&DTR$caI`^w)3WR;#8Z};iHPT*HzezkP#ooyTn(H
zpu(IVDWQU19cj~O5yqiRUEHB=i$ZR3M2~d*so9Wx;T6gfGM?fd=eKcqz)WKP8Zyh$
zK3?8VXa|y2)cTodT_FSB5A9&{J+=}9-bHkrBH1=%W7JxhKsS%=n4brM`Yx1+P@&q9
z9RgcPo00mTvl{d`OB6H&G2ZuNJ40frk}L6TC0_mNt>%HM84GjQaU1s}Fp8hYyP}Dw
zzFyn0+pU+sd2reFYkW~@T5+svxv)EzfEwAmF!<|=Cqzewgo*HImW4DHA~y5TA7!7k
zWjZ%-=N1&TKK_<dxGn(e*QyCIAXYL)ll%DUAyCAyLo_~X);{%+@CFyy@w~gmw&_k6
zML(;UMZI-TYJ@bs7U)6cwvZlYSxs=y_OyI7GFWeT@C9$X_Re|#Vl!>{9wCx<zGXMb
zPGTO06yzRF5y&e-s7Z%CA=-Ke|L0MUIyD%`j@T@~L$$TRoL4Z%JjL!N`91vV=oUf{
zwijE@M)t_yoG+f85gYvF49g%Jwko3>gO9Y~0xVfhT;_6AvUAZ50#oUQ^cpmuE$_IA
zu{e2dJZL&p>i`mED~>6@^-)yMCxm{kqhrers~F=A0!_rPu!RCo^@Qp!2+AcE=IP)2
z`45;LUw#=(!ki7Gw+nj^K^`|qbAt#F)$Js1<8cNakM;8mV_Max9{p~3JRYZK$pY4V
z>!^f;n}>RKr^@S6(`7gk<=dtPQH3;I5cjr?V~r(YJT!JYu{;j%KIqWg`Y6&c!`5}$
zx6qXEMKme7gy<*L`bRGZ?=Fd9+-gT!)Jv+-kc9+AA$n&mxlJNrBBjK78Ilt0Q8rs~
zCDG|Aul>@<7{4LXkhes^0Jo*1(U;>vt<)jyyH6&1X`n*dp(Z)lJaaK)p4#?d6GNfg
z+5Vn09bWNO3EH&-?f6NU3Z;H;q^?T4!71%+|91Oa)lUcIsV{Eqn5xkA_lv#iRR#m-
zVd}fPE&$j?oVxbxA_#Qu)A@B$L|$>HV;E+imvY0upiVlESBzfa>&9QQ@!cfPE5Z~)
z7(*%u$NyAs<y{S|(;;M9J=D9zXoP$GkSX3n^avPhOyKW;#8(*;xTNBUsha?2tDWY1
zKHfYxcm(CxO65K|VjI_Lm_XyKf;m53%gc``ng7fXkIqvDC(D)G?ZDqZiEaPR#2Y8=
z2VcB{@{vlEw}zU32#s7{mSy^C6S~fF@_Wnh?426sEWIK}O~u(qmwiu!WT8nv6}oym
z)msk)=5HXk0+;5BU*KnLT%78iy#W%SS(xI7#&hO*?zXwtrAXU9*-7Dzqp8gy=)?Hd
zAk~IM^ju;RS2mj(uXM_Ze@DpS+5$XM^K!`2w0zt8><xwi1IJCKOW8XF<|e^(sk|C|
zp=>baK>~gTXVFeGo;6YHz;z27=2v}D-_PyRlh=V3>6e6TI6u9vA{y4M=lpa$r~OFV
z`+1WgCn@&Kkm2{sU55Fo(=Ne_$2<&Ag3Z)t*z^f2?vZUrK0Z^GrvG!#9*NG?K6r#A
zn$#p$#-bAiB=wBF2KD6kgzkqwBh{rmjQ81i*G>5^51|qf-|m5|b16Kp7bN^#xgq`p
zCp>S7iF6ft+4_=Zj_uJvt`7XLewN3{a*x#x_(q~<gp!S;QZl7Hp}aXKBMq^sX0uBx
zZ-;NZdy@9>apU^?s^E~=7d#U&I=eE>lzP#XZ4uo6mX=8T07O!Gv0wJf$K2L6PUOvn
zU#Aw{Ud52+vrl!jRlP-z>jg=Svw**pAe1{)TQ&D2Fr<7Uw!FfitTZjE2BA#l#u-A}
zU)pk=zig0&o@eT;wSvl3e$W!Hl6#wp2tvmn*n#Id!l@^uU#z}k^<^mQkt2ajphn7s
zB(k&jqA<A1if~8v3(m<k-5ji}ZEOK1_dbMpuqU5buSk&RkJ-aGOEpofZz*s!#FwMc
z(uwFa$=(t(D=(w#Ph-bS^%Low-)=cck$bZEoQ7xS?2nFDKh8woa&GVgLqDT6M7F;)
zZ#yfRjl2~YI}Uc~sME+QP{d_pWB_f5xCL(D0{VZy=I-p-g@J*kd&mg;lSmj3xzm<J
zi$>p(Wx{O>7^IY|He=PxJ4l8?kAsu&_|bYNsQ`N^b^)ZQ@6hH&-7u^0EANDRSH_$M
zT2!^5nBr$n-P>Bo5BPtPpzrcXUqs2oJe&ClRtI}e==G5PEMesY0`NEBFVCJoXrCEm
zS@1x9$R@Tp7=njC;GyL?3!cdPs8mxWO_~{0f?IU{ELd1q*%JD4KLqU6=Kj@%`ND=w
zjq_9drC(C>sz|Fs7J*Pn*Fv<*x#bqep&$cd3&S(~j~*Ws#X5dRSf9sUzp%W3GQq^n
z0YTP%m3!}3{t$VfCc>=<0A79{|NM@q{(kwt{BiS1%^l+k^hYKF$uVm`*xaUkKTvx%
zq@pX}*5@(t6jMPt5@Kw?W`5)6nF5xa*}MZMUWDuT{FUTXaCnoao`(|(WnvqfH9|il
zTk;~EowtfUaI^+y(55mTw1)?6g?nbH34^f?;8rl_)p-*zSz!OFYaO})@$N`&L2_nS
z(;;+#@^A;{=4?n&lDrwAUUt94F)LbR2d-DA#_?G)#;Kcl4U7>iO1N=xMQ1lNQ?k7;
z*)JU(SEw$D@I#@;%1i@<;E*c9fF41Z(fQ-k{$QD-w7FGv@ugc<9zQ5k8nScIAn7uX
z0A%TfX+=>(k0kv@L7}`mydx7X+4?jJTV9?1E|<T}BYxW6;*yU<`axCc`slI`{=nwq
z^gw@Ry35grO}645N+|gv!BCzLJck96cak;&w1;z_9{sn|>1kX0LdK)4xFb4bSg`WV
z;Euyd8`0>)h$KuJdPQwrTabs#XAD^xtRddD58ge>XylXA6J5NA>e`Z}WF0;3G;mF3
z#!fzoBdAt28<b8i6>$9vM{X)DR`zLiZ?J=}r+Ctrwntl}S{HFJO_yZl7aS86kOCR;
zB@P5<$7YQVbaqV!VP8RbKWlV15--vJ7R_h!aQwLVQV>|p3HtGZP1P=fHN~$8?Q$q-
ztJG|unTZf2ntI|oM1*~_P~4YSSCHq5T<|b!N>G=7jK~3Z=;gc`Gp!f+x#YKKNm<DA
z7iIyMmMUA_Zp8s{6A-~)Vj?YGX^gg|M&m7m6~<ZCe&}Y$oLnD^=N|g8n<hrv3pr;D
zr!0;c@wS>!ZBj)FL0*o?o0&Wh@1AlOek6=ZdYU$FITIp<(Z8urND`P)FjcJ{RR<4W
zRxj(oUVC0vw<y5=CKinC<L?@ZVm9fti;XLdWwweE>&MEqkNi*$^NMml3@Q_Wkl!-=
zM|u&>74GpugBBed3HP|Oy!de6AUQNl=(#CLa$uhy&{P&SQT36{N64(hdm&OQpdC~Z
zEnFw4XSXI1Z5P_%<~*h?7>Xz?t@vi$oEr3NAz@aCe87sd>WW+xR4Goe`g9c!JXqxx
zbc}>^rKRVwfsVdn_*FuhELQ>dqIbxLI;B#$$rmNh`JW~MHb^~N<_`BDW9KnQ$uyar
zS8(16tjA4VM)b*p8-mQSe9i-)x;rDqX@T+h7ICtmfw}7CxvW<R6f+L_C6Cg)QZr&j
z_?H|gjZ#hnFG$bd@~D6Cy?K~|BEH5uw{7da%i5WhXLo|CQ0)lIA;tBlOxH^h-Pc(%
z58SW+z|vKp$y&GL2~|oV`AJqK+sbg}7|ssc?)^%<kOA3ADq*?EL#al&8;cV16$Nq<
z(rv0M;!aR=#ecqcs|k(?$#oCtSxe2|0KW=Chu>T4!Y=4yb!Qc-?V+RR1<@)^Im@<N
zN>(=3maqoVjRq6J4bD)V**&ML$?D2H5*tAoZWVKG$hBz&|MzpkM)7lB^K79PTuqVn
z$I9|-38+F8O((zoLt(lzv`Cl0CF@9KAqU{LxoM#apQbUd+!D<O>PLdUWktnhRjI8@
z$8&HsM={ql<*M}7DadM0I5dwpF^2rI<O8W6_q|35Ei;xUsa_83PQ4dtv;H@7)9F33
zPeiLi#ydhiS{#t~b1g!Rz;i*HHUA2|e)o0`BFmHu1i?3qQss&8B+-Jn_U>mk5i(;9
zfocKgqr8s!6&VK81YF7S&o_y}V0uv<QLubu;o|*zz4lgJ<th0$yG^roi$N&)&T7a(
zPwT&_s%qzPqoYd{pMQgBge`W#n5{O9K1psdN^mR@oo<1h#*mi;*-!arhj$9vos8S{
z9fII0xqtQ^qs^XQyerhNt}AqJr`j+>62ThKK1XbI>bdjjuKBJ~C}BD{@}=WB4Pkq?
zvSVA?1@Gf{;?h0UDD{&NYk@}#51Sw%grmvPf}8>yStk0kdM|GWix0vp=)3`JrOVgW
zGu*S_S7O!~vYH6@IWr-NKr&rOM4p+wa2>OOJ$lI9|8cAILl@@EVKH^RyqHP4UwEMh
zJ+FR9*<5mdTUM?p$_bjm0!s>x*Q=*!UFYj?Uo>_f3tw)#n0O*0r2~KU;MvsLzUppJ
zBrN1ZXKBDR&wc$WY0sW?xoYyoH_Jaowxy8BI!d-$jPt;&J>P#bqMZ+UN;(~~rOe7x
z^yl8{-@_YJ9;(x9alN$lYDuTLC%nvR!E<p-9G83|Ng@8m#WP56-zR7;_VrQMnIQr8
zopFP;xjF#c7ROZxsmjCErb3^Gb=Qc$WX!E(z&U~C8({K|1d?NQtI%ID-Y;Pi^69!u
zo(egpy)Zg7zB1itPJd2uL@IUPVBi96tIv9k7;syt+6%{*B^RyGuilYQ466^5oDXbP
zN*lgIcucm;s$_MdZOK^C$42jG;hKdF8V=W`iib(bRy2x{&$c?wM4^;Q3fKkMk5u(a
zrrwZFcg`p=ow?c=TsXM|bDR1ZI~B%6zIcw!{S^S7@NKg_uip#+EqRfNG_Jhxnf`NS
zAwjA4gI%)zeOaT!%}T2CZ&$1L4J|Lm1))k~4Rr%U0-9ZN6?Cqf8?FsK4YlnkKU~VM
zx={X%sj7y1L)ikkDY#Zsa8EV&=RlYZa`H*OUI-!U>Q#34&E3}PHcX_;zqQprYqE!S
zfhtWdjng{-o0AS79te1v9F~q>thr3_EnhRLVRkt1<iQQkyxkvtle(QXvtJ}{0q;}k
z$h3FC8rsFqU~yp{;B5W1n^oho!jXjeOs4_$ijjrN8naUT8^SjtVyx9l3Qm}(KNOE6
zCSNe#^Ge@8`gUB>`^}3p6BT57U>bLxcWlu&apT3VZ^vlU(y8*@MlUxi|DCPx^x}c!
zxHAWeJEh34(Ny~GtVs*)ohl`a@i>e<m@dlMp#}uAFoAsDC;;sAv4zw;9L(heWrCJ1
z-#^XPR#<?1m_A7#ydnLgoVaWwfRW1KQfV7Dvtjm$t5|Neo>B3Lr)ooZXJRXtr4xcz
zTMx8LhBFO3W#^-N@5*wma$n9X(U0ipES1&F;yXtY%r?@t^6V6Gx>elj`d0ZySZv!7
zN}Z=h^|aT6F{KmtEb{OYMqp(VsQ;tW{@3<|kwZB$sq#4M?Z}dGSuovj_T)R?nR=Oi
z=7D;-dmhqe_e3#!5jOSRek($%8F2?e6@st3qEM(0spB~#t;b|7%m%5WidFduCzN$<
zN<tT^zE~^oYv8eTnfpOFqK?>;HPgW!?d%oFRIBY?r>C#)HWkrG>&x27_=`f!?W*C?
zJgx6^ux8NF4AcV^IWui&ODI2b>jhuuXH}ZQrD<3Kcr0%g*5LWHju~*3Zy4@sc{u^u
z<x^A1@)UY&5^tI#I<Q|F+q!uFD_NZyc2D*D!f0cg{cNL0;s>Y2o44)2TXwN455|NK
z4zCC&HVj&e@s1{==fMl*seOTWNMGcngF9`F_n+u%q|5yR0vs7G0!I$2Bly@vk!at3
ztTxBo5qx+VEIdzt_3KLIkRXmH;;k5_rvJe@D=RG7YGeBrJUnuuC9G>qT&{D_fSDX-
z^mPiCKqY)^z&qp320RjBm>yzy;b!+-&jDddiW+X^kVCunbFH`|G`xP;p+Fep-trJR
z^DzIM26LKoMs3AE2Q&99vWTs6P7U`iNzxk^j*-JMr(^&+^1(xpj7v)|q&Z=(3;Ooo
zE+a+~Q;%yteP&O;I1DK+KXvbA>g&nQF@a(?Dey|?k@(K((DSc6p5SSHVX}_->3HkW
zH0RxGT5Gt&HRe|_4~7#E2jcSq>j$*50c=(3?wUi7|6PibLNd0gjjWncBn?n=(aXTh
zNMogS$DZ77q(11{#k~Qg^0=khK)n5-i5!6$Da>+!k1CYjRIO~;TRC}Zx4I~8cob_W
z*q8=}xR<o6di7Z0aFW}g@~#Z;&CvK`%C<uDDoCsT)(*VCPnu?GclS)5ZovBs;_o>k
z?46IC5K8VWgi9bPIgX$4*zX$cg@&%o#jPT|&&$)D#x#;0cyg?_>08(B(SaY7y>C^q
zb88S2smADH{hK-ZYa7ntyr>l&uT-i(f-*EeEAU5W@5stYhbN!Nx4$gsPZkSfUCcU4
z0)H%;r~K&N&iBgSk=QHYi5r@mJDIu}w!iJbOnq9Yxpcm6%aOVBneD$`5RQaPgH^50
znGrM9^SL{;(vi0$fq6mloaw;3B80q+u?7;SM!BdN+WXKk`>L!QB%I&%CAxR|pgGCo
z#nc>DeSUFkc^{gaV+~+v`pSkT`4GtI3}gPN(j2`bd*q_X)=>KVqhG%6F60B3R!4sj
zY+GPvxHTPfcuB8dY+%ozg`?Up%{AaP3^zFQHdTLjax+}EwtPGB1ZUi)s9Q1&)uBI<
zE$hal2bIgIcRG-fHV-*A7IChl^S02`=5crrh&$=u8^VD}Kb6==D&wZ+yo_^uMTIa&
zzVgaq@_Q1k$dnMIf+BrEC%6u_a^dZU@uwX@Dg=&pm0+(c>Z^pj9fVaHts_&r=P<X|
z^tSiywY&Wyzo2Zli6xTSof6C@5|}(oaip~C)u`<;`<0FQbHizO`2^(-1YIKy-<<{L
z*l6^?RG}!;H}&1E@g8?_7~8n%JjTVUYSq9wBdxv=l`6<h!>?_4>&D4VV0D+}=+b4e
zC?PuTm3<guDBeU@{p59b3jSxWQ$}yGuDW<|&6G>YR%WySkMn-r<!=myJw?I%-~{y@
zV&nh@tpkvq=lBo-@%&uMG;Yjc4!#?_m}i{0t#+d68d9@cK_6GX9LG9}y&gQ^Y&vqx
zTJJoYPe|IoNaSYo0EECl(8Jb-zpV@*tf+Nhl>SwQd?D_Y6W{O%&3hN{P_#TM`OnHZ
zgN3I0yoBx-53E)kc8;#t5?q~s-H!hjW*{Wu5|{ZTR-UqW8;A6XT#O(FL_vyCzwVPs
z9_F8j)<4<@A<7|w^!w52p2d~vnyKW2-HI6|@_{?jiWu!gQdum$kTA5Vuj|2I;<qc(
z3@Hhth6&4c?%FjIxhs3(Z20zfS6c0LXYVtln2J~(`DQS@douN#^!Wk3pdY(bqpSnE
z<6)2busUMU$X%RXDQnuk;6o{wqzj#e^<l;M@d3w~hGO4OkDBS(sL6!vtjW1qz0|4s
zfpk}U(SsM{{Bs*jpt9!-xb>#B<G<Tx?qu87_#}>|p39wk*m<dS%A|-kFdXDzpEFWK
zV=F$+!}*`J0OxV%1`q6HYqN-YZ6-u96GW`HXfLU;g|s!=p0P%WF}C@11;F8!8)3x>
zCxkpwkmrZ`1ovMJtj+$ztN+<W0)BMNT4ZMzgKV3-&Z4JHRv$WhWd1YJ4A%X<+~tbD
z#bb*5W;hL9iWBg*xiwO}!6w=#+Dt^>_4nLU-0FaZ?+-hK%gFZ3SSFxx;=HN`wnU<#
za%d7hRl_)>=Ed!<^Qe7#cx>y^##H5-+gL7qSNU=;wM_Cgv5@9?4iY>Tv#%~|vb-`z
z7v5+XwkBvK?S;2JJBNY?Rl;yqvub@_7ivR~-o^>c)QTopMcYP|xTSmC0VZl|q8v)`
zgllAzws0Iw<K}Xw->LFt?S~^c6=Yw!YptMbI57B4ngEjp9bfcEf~G!$Tvspz^=a>r
zI#@^+EIdW;-GhPBVqW-&5cF|%r>^fu!!a2!s?)Z?osJnXCP=iFenVylShwao`9G_2
z*<F;_h|9DqwizK|VhxN#%su6h9DXvbOS(u73qe`TgQZqBSdy6`0hm5fYH>Pn^_vPP
zCM-qULAg8%(qQf>qleF?DtFWyL9Li++!k%NmBS+m4Kc}zVscaY_@KIC{l~QgqYRsS
z(uIG+vTLCa`g{3f>RNS!EQLfW6DNDDLXlf$77s_f2k)%S%_=vis)-_Mxc5xKO2U1<
zB?we>K%xAr8g7*Vqd7^0KdijsHnc8XWFG9XKitsTktp%lHOH6s^eS<G%dOLvJlGip
z3F1pYR_s>l-;~MrNIj+_l&;?Ks5X5?qIuknQwM9gW<@0mPh<XCK+b4K)txoiYxb=5
zs_w3b;&e{r{et~0`vg4UG$mMTk{0x-mwR44+3Hhs4R6q4WWhffRfP9YwS$^H`hoJ&
z&)lXOqv0>IC5gRGP`%U}vHb~f!?eY=5!(ZG<JlG8J5i|GEL|_F$g@c4PiN^Dh$bQW
z&E-qR|04T%v}y%6LRzN7XBdkDKmyM6Uamx1P*}H(#FeFS_70z8Ryl3^h<$(+dLgLH
zs<S>cBO9;J(C9Yz;+d4z2D`~0*>+9?AbowhG;*j&25EH%h~=tOI`R@9FFt@v!zX@^
z6(qt!Hub^?lcJK6aGsoZ>-G<I*BlY`yL*S~jE?dZblI-w$lXn+ZtzS<8rAWI&3$bU
zEbbZwLOLsbq3ei;mI&2IF1aCyKliU6aaWsBawRjD-b3yNj-4e+RFXYrjGzwK6yDa~
z!JOZgYg4Z**TrFt9I<}&VPKBtV$F0ZEij@-reyAG+<t~nb?chx!NAxw0vD@)d8`Cy
zlN7?i++G(TVPI?yL~i}Y$O`ABR*J7^PYh2ktl{OzvE1I}S46yf&b32zU1t|4!S;J8
zyhFE^!=smMi1B3B%(3RE@Q=vA^QRdyM1P|idgWJrL*DLgL=vIGL#(Tn$tSStA{dqZ
z3rGVqPt*1E&bUM?JujqwqkDrs$Wa=nFN-PRO+uc*etqN;iJvL*Ph(9+T9w~{*0&|H
zcjWS8NXyTnAYT^++iG+xq}vbG%|y{RM!We9P9lfk80TFpQMo3nk4LS$Rw*9;oz3Zh
z$=<QzrvvhG1<y6g$<=J1jel0NmH$ZC<Kx7B+@p~pBFqWO#+m@{9_Ykl_iPDc>udtB
zq|NvEclAa{X+y2fBQM^|iSWlNR@*5{BC|9chJ!EyN*>ON;nolPYg!(Wr)1?~(`T6T
zlw|CXhFIH8B^{es1V?z96Uoc|UWkWxFr|T*YSeyY$??;abHjIQj|%c(B(Y=!8W$FN
zYS%WfwB%G^%T%CPIC&H*F{0d7ry+4Kgk`H;RxX(<=R4^&esTV%ECy3hRCd6>vE5N4
z5WDY;pA$_AM<lz{Pxe3(@U)gJgjTyP)93DmR%4{Wo)mFFE^6P2zmXh3>&rZ4qlRQT
zu8~aTD!)D3Nr3Xs%jK!O@ehcnxrj0bsx5LLKa`$y<OFu+om^w|-sjZR)m>)3MoLpy
zk4MtVOAynbp6U_;H~~%mSu_J$2Yl71Dlf6ibV`Di%H9KT!TQKKPy{=J0s$4Gqy=J<
z{9a9_H8WNGIE|c<^E6C34T`s6cyiDq6w!Gg-8KcNuPw6EY2YR#9XM4F6UVeq?uIBG
z0e$|n(kFu3`ZBxxf34_YRQ&&?BR=I5u%1aJ!&0L16?$xa>~A7|dJOUD)2EIQrBCt6
z)3r>4SC)kGoEx|p2@?<~qOPFaQ^&;n_f*91D>`g{L=Jorx%XSVjLRQxo|*Wa&jpGc
zy7RwF$ot=fkq3CrMBdh7<CM>>uy|MM*t&cH_^N$1;YV_EvS5un-y~(k3&&OKH&1Ay
zTAG=edAqvK=c+7MXBspBfoz2vaXDW{K=z$4Ce*6Z-3qxMChtA@tp(xNE|}^UdHZVS
zHYs?;W_?V3T6>b5?p?P3b3^y%#Ku4Ikl$R6so9|v@$TI_9f=F#>tGX86BG8lyu8Ti
zGLJ#)TK}b4{dDE6_h<OE%3o_`-eI)JB?CSSx2bd&)!4wx;W`6okJ5nR4lRq6_Arh7
zygX3^`k}nF`~KaVF3dBOAeZ;<kexc?>pOLarZ0w10|I$e`s6V4T^=RRQNG}<g{_+<
z_VowbJ7KgXrOZ$o#cg9y{<6zE1vhh^=w_@6AD3FtCi+LFwqb`w+CAE}yAgXo8EwZi
zWSZMNT;W!=+;0Kd$IW)7g`1n2nwprKS03z4#nFj)q#QkArLtekcNWia0jP}a(zW}Z
z4I_sx*9O)fu3$!4MC>^n14!77bwI*OHCqEVJ)}CgZ~C<kaWS#{n)MFxadK}?kJ5vr
zSHlH1Ri2%-FtVb^_!0=@P8YiSey|#X3!8HvE*CBD5f9&+;-mXEu_%R{*MjHPGkVJ=
z2Uu8JLhwrc_BSgGG+o`L`#t;D>aGkm8v4sKX1aQM<+=0c`*jE{F<d6QWmRQmW$5zq
zO)4;$yS;`QW9{O0Fc{3JK4|MDLmM1hW5<?I^gS1o;KNBDsu+rrV(HnFp%!P5;`QzI
z8OgI7OPC(@{i_LWzl<`qmSzS>#dfs^q;6F8nb+w=xVWe&{Ntxj4sB<&xxJfB4AuHa
zi}3mjr})E9vkTvKf4>-#5wwsux05UrxH_@Vc4Io8#KI{bScgVWzW`dOF<9d}$Gd`~
zhc)>PPy092d#VLfR25~y;|&G~O92h7Pe%p;66>6oUX`E!D6NOWrn-8w6X*-ptiU{+
zqRfYockW2i5`O>qE46kROAZwj6zm}uSgf=k2JYNdS*hE&k5jLy5G{px_9RNCuO-fF
z1?5r9*27sZN?6`2`SfWrfl*uV_OdX%AHLo$jH;zA*N)p+TknRXE5kB8NOe@nR6BpG
zoSDwlRBV6pvRh+HcTKvS%iaBG>4qI8+9IZ>3%1Y)$Cj7-^?BAV`S*Z@)BQ+qION2P
z)s&1sZxj7m6UHs?H#KjL`Sz#@<KN$q>+2DZQ(f2Do>uUR?h&`?Y?({`ywEi}cOKL(
z1I*n=a1Q|*gA{<N)YRO)5f=BR<K6aFNzh`=(P7}a>w4cves1oLYjf0jl~C$TYOUTo
zc6msqR!pVeukSyX*fjP&k9bs#4N$w3#zQ(K$8NEy_2mPHQKFDR!;QK3zF}cA$rkUj
zu++$rmtsF2mW!ktm@al%jCO>Oi>mz7RpDhWA4VOgQrZARNDJGY&oKz5EW#Hy=X?3!
z3wb%}#|yfztuq^$m?Un7v^dBRPcZfO_ul~&;G2T)T=&vKNFAVcRN4w|yiPAwYR=BL
zOM#7rN7+v+%Hy9pqr38)W3!`2hMXkF!sdb{c6heuwT%o63<Nl1g6=N<BGuEtjxBHI
zXF>{rHg|u0aWanU+Wkgvpnto+0Fhs^wYRrtFE=oM=TNZhv76q$)9%Dy@3;G?e}0!+
z%C`FRUT$VAHhc^Vs|_FzEX`Zk!)NNhDFasKzdKm!D6JP*-A%~S8;Rk*Q8u<Xgkjs6
zPBhFgC=}NT|EgZ)F;W?-xK?^z+`Rn#b#I5xSRNHhAfOgrb%5bk4NHigTq%ikRsGM1
znps$+>Jp^1`SP^1c~gw6%*LgA*`^M<=8_}U*WiMMK^Q9h=uIC6u3T=7eKc}AT$vv6
z#xuunST0ks+<nNWSim4dwMH)=&dkca6Dwfw_S(<Ag@s2%yP{WYd||}``#;&j1MeWP
z0V@p>7ouSSq=90);E(BHO)pt43h=HB6pgHZ^I5uVmMUe}B9J}qR#LBSSRoCJ>96%p
z=@oIXvoqjYB2MurZwl2GOWIU!zEyA=I1h|P;4Oq!EH}KuX7x(1GA3a>FT3U2=Y{@)
zDzUW?%C8@e)esV<Te)Clw8ppg!o`c)0H`YLwDN`5Id!H;s;Te($`l6_Z8gB5)bhtv
zoW=oh4i&Cm^kqSHJhF=My~3}f+?lZQt#Za!;`z^Aa*3A)6#v#fD=Ujk+nnEM+@o&L
z%bhz@cnd5_8Xo`p{!vNcQQxmxuLkPuFS#DojnQl5F3OHKyFcWjl&xjhbb|K5*gwna
z8^b^s6W?aL^klRSe;K=*u3WDSU!N{e^ce1Q?9Db%QwI#PlktR`7-(0RzqrO2@utvR
z<(=pYC{hr)D4+|aK{rfO^jIi;(Dd>oDRg_x&AQ^zmL}k+u+-VKVCEZ2#!HvR+w`9u
zrkM05VxfL+qYZXi9upfK^o~yIT(T*dMNT@RkKumrGxya7th6IGH+nSq0y{)=-q-rf
zbTrDj-BqVfn{~ZYNVBoCv!g#@=6#qR7&41Tsz_RuO~2L=D07`?e3C)`cKd3=+mW8I
z{DJ~KfxPPKYN0htBclnr#lhT5F3;=h43VyWZemUQj)jrq_6R8{DF-FpIs#`^_EwvS
zv0O^ag5W5vOxQpPgzuNHnHUVpkLv|cl^+4Ptnw~um2@-=6Sy{sWf#(~zK1w=>e6H4
z>d&SeKm`hw&qjVz_WSi2I#6h3SmoLaMF@=f^%_`Y_bSd&?`C;uqDQ={7=!2Z2<S)x
zF!Fmr;Gn4(r2}QBw)i-}an)Y|st^+s`&1viZNE3<f*Ly;d0UabjiWDRR43iU`pATx
zeKAISaq?nHmDl*e0l-O?!n$wFsnUQkj4{+Vv9hvS$IJED3WHPagXS|$vi0GBH|S0V
zbhZ=#5KDk446ty>PCJ$$U(DAXlU1u42mE<`z!;49exS(4OGI2eIyp6!_&#L4y}&+r
zsUoLP{U~eoTyTgZt#R%=KOf4%xJU_K2Qt<M2vP*k_vVX`y<4rZJgs2D^Aqe+qcEZ!
zL_|s|ti8r_1U{Fp^i7;@W?{Ew2<B7|*?~v40s{MPhENVzRxSSY=_)6*7GM{aLSR6L
zZDb|ghRU3s0W&1C1HIlSL+C*b)z#O(Yj{*LdW;Gn!EveRddjfd-S`HmwM#aYy~wik
zQ?bCEs|gFH>uPJ?*qL<+cU6|k7XhAtnoF5Y(B_2?J@QR!j=o6jLZ?5_s~Wdom$~qD
zb<i=cVzkUzFWtPzI`lCBz{Ng#y>;*ngCd)%49@80sFpVzQ`iQF{B@bIuZv_NB;1bF
z5K5yFi|s}>-}`pMDO<p`5y`RmE4Nq+0WOtiQ9_aiYz*J0Pdj~ka88jk=0ew7nas(j
zC#9-yBVqv>sPwEnQ}K<LJKs(n0@Wjq9Ow)N#RnIDISFcHeKcI|N-qhgA7Hd&xv90N
zu-!ZuU_P<UPeTs?^G%xrJp98Y2F4no)I;Y02rW**561zFg#<^)Kb~mx82E(m>qr*o
z(lZ7GNOZO~uksv|8U=hCHQB<R8V49<fMeOFg&nMayrf}wfm5_$*45kFyBt7g_SQEX
z8@7rIr&Pi}0r1hEijIo<hZvEXI)rgiN<{-W5VDv&`;FajFRQvbP!I4c?d7h$oGY~}
z4I0pa;Z;DbZ9FcWeDGGrv84e~{_w{qM?GEL;m&t^KP|OOb)vSte|)-At7E+=(FB(|
z3op_@#;vhyK?Gd^^EIGRmS{wP8!INGrR=a0oB106)RD9itN^cZ)pXf+7Ay3vB^TF{
zY9ICIhb~p*c6M|O{PserkTB}l<)vJfDXtMV^^)m>4PE7}m&q4z2EF|)Q}^#5G%?|O
zYpqNQ3JPjxiLbfjp!$%K5QZ%Pv~3?{9CGZ9z*snazo?*~fV@&Om*ER2rY(nmutkw|
z|7`@PlIQy$8u}%5>$(^YIq3T#G&s>KBXBa(RdanX%}nq}kwb!xlODkI2{ERGJURTH
zCqQnA`Up;iN7LA2xv2msEDC^QxReh1rS0Hwc$PqsZB3c#m?HERcE55aSmL>Jf4&)u
zU@r<F;=-IUDO#C72mnVvORhBI8yoQO%ZUD#W>HVQr14<W4DphcCuoX6A9(z}YbFZj
z0i<>x2QVvTPgjc)d%G)OgLm>^a`bb*=P=JAmc)Z?oC6SmyaaI0v$(kU?k?TYgJXQK
zVHGYIfU+PIDZQx!5n4|Hv8T`COw|Rq?o{Nybm`(8%aw{28!$L>W%Dgixz{?w!KcZ)
z65v2Ty^;WIcymc+QWXsNHDbZg!`}6o&g~S2j-b!KP1K*5WjGUi#7h<~O56uIGNa8V
zE2R31BhCQ6vN^M<ZGQh42e`VJrID%?Re)%_Syy>3?S4bsxL8u_@o7pWSppTVy)C{M
zKBYxPMZplJR#r6_2o?~cViMZUe<;hmiv+G3uLM@zT~m1Y{eHcZBK@7Lb4TZV(ofZk
zk;<?Izn^`1CRIb9CHSU<-!u$&1MVwfswIZDotKlbyALRB@LHsBM&Qz?JJGy%^(~By
zO3kBsraNDJ?q13_D>x7W108KP$+_#%R|3I}H*6C0ECBDe^veskPq8oYaqHdDnl%qw
zO;D8Bzmh4dNXIIp2Lh)yItFB^T*=^i<m}OS4#tj|X&r_o>JS!>Ccmq{{J#C_7Jwt{
zx|*&J$FN&3pRp?c+NW;J1z=YiXspWno4q&)*3Y#TK<SG8-K{h}|4_iCMD78cgQjDg
zb2r@_ZPN_|P@#Ch%D2hiKQ?gzzU;tIb3@S6n!tcAfd01O=X-N-Hvq1Op#e{8c9ZP(
zBcBq-r2+aS??tL}6r3-_bnA+KhqqmMCBdT+R>x3Sd%{UiR}x?cF9DKcict1n+|g@c
zVFe2TItnn2JRsPMZjIwB0pK7YDari1TzX6+0~dc42&4mkBC|DyYuoF$BWqczAlhq(
zZ}nU7lwcR~9bdBXHT3IOP?kod0d=Go0lvupjaFvQ+|}=AC^Z#IRw_TAvuiDvbxL`p
zN?7^qe2Zk~LIDO=Onn4Md3Ui#oQ&JQ3;anX$x`bO*pLwWb~H(U<51fvMg;HUG~Jd!
zZ`kNoNd<7bd&`Gpnw*j{0FdfdU*eo?_0N}FEfj!{_P##D-wojAt_#8s!1@DBVWGt4
z%@%;r`&SDvln4&^MFMtu4G7CdmH@X~pIu&Fz5qac&%+BS$+MAtIs}BEo~@E|M|rn<
zxfSB&DUP!76iJ&7a(}^g=h2I{@r2!MU~IOYGV_Jp1LzlN<AAoJv6`LcxhpGUFy0W_
zlZqbmA6e?$-yX+_75ck~C58znE|s3HB$wdAzI@PpR#|9YAJhZ*7+wXZw$nW@o2DcH
zCCwqy*k7NJlpY3Xc~7;Bg^zl-s>wKfdrg+EnMP27cxrc?a6EK((<%@^cx_?{WPBPh
zLA6~##ouPQY9QKYYbuE1_6;F5<QDe|j}ZP)e(l(h0DvrMl-SiOCb7u!C+IMo3iOqc
zITmnDdtRp5TkE`_>5G3v3{*R=3PeF1R8Q@<Qnia^SxTdio5}!3Q9+&0L9rsx44BY;
z3k#v|Z^JxmW>Q#tH0XO3h`vnixKx6q+u-L1^_f2&rUQK=jTc&B7W9WJ08U4E#T)c%
z0?=2KBp7f)V?yy~wbCC{4pF*QRu+9aC9C!f5XIG#Q8^~*R@L+angwiml-e(9lD)T_
zV&hp8N?o5}W8uEOx&gq%uct>(F4%=pXXe5vLoPWw1fb3Prk6ZeZ)NXO5Cwpkg$&-g
zH%vx}^YQ?~L-B=f{@|MHO4}yz1dPf65qhlK)dU?za1_}6{sxOZP}4pne1D4_cd$!l
zZpu{xpu{G0X|Qx^!L7Jv_UFf2uSkP{%B7`B+6?rtUQ%!+IBhVf&^Lg#hA#nx(95#S
zsh!Rx|L{Ay(qp7J`u5dQ28Yqu>N8;u+utJA;&vAbR-XUp(eGYrV$%p|6h?)V1pr9t
z!UO9wa_i{ma3Bv9Mb`m2A%orR6*`UKON@+-%NPc1y=)%f!8NV}0isVu4a%V74eD26
zi~qye#a9Bnwszv_NqbI4ryWZzy@5a@hbLt93%N-we5NBgi;?r#psX;07q^|&pM2Ri
z_AuDiU4t~mbb8}qHoUGviLb>YH|7<($Jf4U@2O@6b~8U9a%dgge#Pz6#t2ULWA<r|
zacY!QKq{W>zsi+dkk=l3eUYGprm-8U{wM+B3cbatDF8uw0a2&{<fFjx7#r`X*XqQV
zkv`abR;u~}KGE>OmQBaF<>LW2-vK+ovt5jiigGy78Z9`V=GAZmpw@4e0gt1>a4ZR-
zdq7g*y}6au&;%pT(i6!pY<~^NrJWpIkdl&$&F+Ot13q(Tfy`*u8l#V20I~od<#2iL
zz0CM3O&bLA21?W%RM;cc2Vb9`1R59KzOyzZ4aSFmJEI+FFP0wb*Te7!9gvW<W;gqg
zwL3%~Mw0><26+Db`Qka@!foPhirYE^FT(nAjfjB%%?nUeZ2n4#=@EZ9KAx*jW}$8T
z6VWFyb`;pv(%1u-`9VOt4*&-cw%WbjEpiDEq&0B?Tx1i%2>*<b-B|7$KGX4j9gmlF
zfwWrGLSG(+5olq8D2q1&+Yy5M^-p<`eH`XtInV7X&I5NWh28K9g9~63=Kz0p3`kD?
zss!>hj~~I|)RH@Q?v$nnkf^U5V{bqg8J@2z*C><3mf7{-c)_?isqW2Dm-^UUVD(AW
zeBuUQFuBx2ogN2Jov%P_64DHaYt0r20OwMZlHA1r!2&^1crz0-_q8R0o^+@mK;P;K
zu9(!27?k@KjDQ9=F`64R`7QF|DL(bO7aN1XBWc6H_`#P-TX|_gK)#`Yk+Bn%l(fUm
zWdwKE0LuB2LFkvy^vh=jdorK8wAu{VuG$9%EhQ66a9=%L7%f0~w{Y}?0!af4%F1~7
z6Uf+uI$ZX~yCY90Hgwl<M##Q2S9=grR5#<uaSMqHD*DEqAytP#r(>%~L2g7K=yrPu
z_*9Ex`}$pL^+BR1fYPflfIiN6|9H6h_3}J38*d$*1;DkHL_>ABqX#l3EtaQZypm0Q
z=l&ePN5gIdxzA(5p2v=zdkS#T<y-~;TxSHyZ@)zQt-_Dy_CtXb;rYF}qLAO2$~GPP
z!G2FN+PC|L%PG19uoQmyb&oix(uI-Ay=ndF$3r9FhLLVYiV=uisb84+Hf?)>S+vVQ
zba3dtcH>zVs)ayaF**ow74Zq7s530$TDxbSAjk>&@Y&4VJVoAfG_XK{pWz)INY|}?
zMTb<J08}ipNBpMdgUFYs7-?{o4_CoUz&4Ee0L1VvI-P$Wf*_@AmI@Hloq>F3s#XkV
zC=mcLB9JHMfHnbHZ)>19s4@UQ(N%;IRFiTokzbce?6V*gh^->)e<yN*tZ>8&jyetp
z7SgB)0@!Hlz0SH^oV<yFtpIz`fOX4fdl(D;8As{uTg9gko5HOt8sMOt1QkR>vXw9K
zhIo|@)PQsWaW};;Q9r^0fz{j9B(My*KTeTMIRX7MadE%Z90@FrZ(kH;udDDn?4)R-
zcc&Dc#@^w2#^G;L9|V%}v``hH1-^7vTNEKLpXxmH=dmCG8N0O)i@Y2u6$P1vRvamR
zQ~DqshxAF!p=*lTn9h9Gj}m|HJ}U7hf`9ME;nKX~`m{D`hzI`jfBh5-@}d}c?~MvI
zm9hHl?S;Jr#P;VvRNLB=q}ilEZ)73VeUe~iB1Qk1atX{A&(mFN%x=2@yiefIdjS@6
z5Y{=CTH;_nH}X~h`Ng2te;%GIGR`un>6a`o`m?l|v1j2H!4MIaTyA9?iGGQAr@L?&
z*w)M}Fs)`lP#JQQ`dh4V{uJ2e9O^|{x1`woo~;u1-|LqZ5Pmsm16|3_Hj?x9U<03V
zr@y~coY#||*3QFYv8{TK_+7)n!JUOfU;`o>;sV(EvTHsJlAfCK9|8WSHe39}ozqG>
zoR{;iKQ9B;^SlMNyedCmFPlFCme$Qjt-PEb6af=wYR~*F@YOp`c7(foiSqDnaC}Da
z`7qqcpA_Fq!fbzjAF@EK9Q$y2^r~z8SLByq6q<x+IPtOY{m#gjVDj+cZG3=SndeM)
zm-6~JYPy!=Paz#9CeqhlqqQuB?3vG`tt-hUkJFm_BhM9Rf(6FA+ORP{-i0Xr9Tm~5
zjW&VVZG{iSOJB<VDgOD^$tgte-}c?)J<XVlU>yJb{Epiy6oQHG<QMtd(wk?QNVb-r
zcCsJ-xf^tv6Z-70bZ$OzIOgRk8E=~SfP?wfkKc2)z+Erm09}!c_siYdID^QSl_;^6
zp#Cj<Q{qSbNjhw=E}(()Z%gmfZbc~3)P43s?vbqjbFG$fZT5PYC#uT)Z_&YJU}r&t
z7mr3jw*~*-YwqELPLi|%odwVRI>G1b3oq~e-9-|y1Y#nMUSfB!0=9bo?dOMx(%b)0
z%>Qm1KVw_t@8jl^tN*8a{{R1Uvsin;;d`e!^^}c%hFtmHft~9-3PSY#Yup;5bhUK=
z{O)p|^3CDbXm%&ks{jF%uJzlM-^P6N%H_uB%e;wx589l0Iep-ue|l@9!q+hTHRgmV
zCIW!H{SsAKiup<9X=q8R=W&MY`}~gtp858etbX=it5@wud;x2a0KM=E04qE8*qNH!
zCaqLDjqUYwZO*)7LG-VH{8Q1P3&j2hF<eeXb`|Y|S>optIom_D{=T#Ud>Sy<#3GwZ
z$2yM$5?`9~Ml3ZtYV&+Z*Q=syfWuK$rrSOHQZpeq*Wr6?yrtn6tW9Zk`%TirrY4Ci
zP)37pe!0(M$~Xaz|IvBT|A{vrXaooF3R{^I;dB#Iv))ZD%<)5q6eVd9SEEIa=VrQJ
zUH>q7h*e5N`NQ$+A3i<f%H@C7c}jWx^;J2krfW@YBGJm86aQS*yM=*u62ALk{0<wG
zm~HKyF)|$MF3yj|V-pgTruM_?CzFM*cB@gN6RWOzhLLA$7(Hee{DUNKMVNqTNx<*k
zKEarz2Z4li|5(#CqeH(Z^&=wB{;@Ruq(2kN_dgx^^D>S9hpVW?q}!&OFImB^{BB=2
z|HPQRQUR~Y{=Kd0g<0)+OjVHa*2jhQh2DOJez$yP4@2bd8_zA;z?j^o=lf5_uJ5p!
z<7B;1ru#4$jwRIdo|<1+u+`SLg$d*QBfmS_#8EdgMN<i?w%O|JCw7g{YsfEaxzs-H
zwkIt7#up*|#@dh^Gn_IIz%4N6=a{dgYfuOato;E!D|8sF722t=Skzs7;C`*e_Z_f0
zmT?ryFs-bsVfi=qDi`i{9U7;bjuq|F7Vrzn&(tA95_@Xbap^>N_)8_0mVGRvKtzSm
zgk6I++pu9?G3O`_JA|w=+kP9q{{(u`Y~)wthm{s@#@IwC2mrSQvkZHwcWii#Jf`!-
z$qhWF1@mBjZ@v3`fl`ExP*{aOqqxMb@15sb4yRmqWTBCuo##DB{O{7OR9nK2kHQbO
z@Ml_pJ>h0t8<udf;^MI4p^96ei1X~LQse#o?HiPgh3_xtwz+2xTGX}4!4u!~YzC|3
zZ4L+OSMk7AK_JT);TCFv{dWo*odKP>uYDRQS6dL=Gon-=MOxj9Q5w~-MrCfPZ8HjP
zo8#=3cpelATg2s&)W2GSK%XB47jy?rR{1R~`O|NlN^RH}3tgQlk-+I4YK;AP3r;<#
z<Y!uGDi|MH-Lvqhro;NZcpVG1S%>M5C$l6MG5x3*`GKZ6NE|E$@R9o6^N`2>E~87`
z#BuLQR$K3xd_53I(J}}2$$aMgGfIhB0N*IiWD5bO5$7P`S(zmz!JSmftNotRe}1b#
zHW(P-a5KAWUMPaQ>(h<=ZPS(f?uY)0%e@5!v!UGu8D?VvVZmBk(*5%r-cor7em42P
z-`3zSp(igf{sn3=5bCRaXbW?!hcB1c!p>f;ba!k44YkNpOY!pbUsGfZ@coz@&jNz$
zDzI)_*_o<gRZy2X^L_g*{L~v?GYl*cqT<8Y!47IX0^CNctHm0^ps?Nr!ormH{Pxp@
z?E+y_w!*tbB&+>9Tlr9z-QSvdJkVkSL0t-z`n97c&nOMD{Pf#`@8|!&W1zoZwDUh5
zCjM`?VhMl={jV7diZL5}hjQYs9Rq!?=IZexPiOWg7f2o2p{JXV`Sss%$HZBrdVHc6
z*_4d;u$l4|vld}bM~vN1SHC%kg%oEfufGl81k7Rr+mO5a>JdHv<PelEciGaeV&UHi
zPqX@3tG3#^>SpP7q3%x_H0;z|YGxL7q;oTB7OsN)4>L(e2igFtMLN@>j2W`3OO3sD
z0rWT|pnx*|Yr1}H5zkeOP4V#lqOCFV@zz)|7L1XDKj~MlRrUZ9ZRviqL3=V^%usdc
zTg<HQjnexam{#iZDJbtCrR($Ptbs~)?z)Bi;)A{%w*xHGb?-y=7uF`J9;c)WGg2Vc
zp!*}L3*ufQUi4SY#n?9<-ZW`CpWR-hxVhc2&tCwudbLcg<AcCB{#$!*9@SK~^^0Ol
zSt^_=Pl>2Nof4t20R;g8Ns6)*Q2`Y~=t5AWZ=zy=5Ry<uDP_?~BM1?qB3%Lr3M6zP
zC<@Ys#?S=<N)rf#AR!4PgxpQ6bM6`6efPa@yfNMz-@Eta4;}2i*4lHfHP>%;YtPO8
zM8K`+OFLv;W6KedF>UF!vzQ3nG^*8uIm>6tYam&h%1PDlfs@1khkB&lJx(&oOCZ&q
z@^D?x*hWJ)r}316I*4pmi((dIsnKnFX&PU&%&~xuScwbEf@OdH;GT^EV}14o@8%qE
zrp#WbMmvbcd3#yE+AutvBgxo1i-X}rd?#X~naZ(%(pbqRmNl0zB%z_ehW6AtxLj8q
z9Vr^&VTCQ&!djf@L28il!LB=fw$-QTR1(}_HH~J9!DAz3(U7h$3UM&wBy-$)es{|(
z&A&;mq0Wnn`7(Y8Fn+0x%;`X$)u{l)t|+5@l{%nn$CWpOuLC!8Y-9j#8fsS}DnHhI
z+&0({^1@wtxBrdnCPBO^9|Lra>~5I7&lRf=7bQ9?MYqv?-_db7*G5*kn<-y?iXrS~
zj{0_k%#}U&nz=Mu)t3)*5m5^%+xZ%ICohl6S(IIQXx4=)WsA~#zFX_zFF)4MoVgur
ze^ZZi35#V*ir*!r2mI^t&7wOr9-kG=<yV-Wi7bwG6;XK|e*TM~kh26q8R+!CbHO7Y
z7*@j+5B2T_yI>-b={#>yEzg!@p5m-Ty~bgHT-e(J76U&fh7O#vs#EHo*yDp;v5_;p
z?=bKUvSMLAr3t0Vxi0T)wv3Z#5a~j($@%?XmUIFb1LIzSL|*!0Ym^^X78y`c`A?up
z6=H|a`gx>x0vkK__?~hO=Kb2giXz<I%>Md|3A6X+1Eoh1`@p?fv%bh#HS5F|$8!tN
z&ItbIv9ec)_di?x4+c)=%k4%J)whxDL5|4l$l0KFB_|R4{TCOGp@}Un0+L|~2a;=r
zAb42cKo%(PT6F@8Y`|JyO$x5NF3MTuLsR+?2oZy*xH|grN9I_KG@n<nV|t@)Z1c{q
zDVihIRHHI&O9owjTAl~Dj&AZ16n70R4$`2^{biOLBznKftlBQf;~BEzNN8tT=w3bC
zQ@8i48l9O3PglPXFw%ErK~~y*mTT-qO>r`#oH51x+IkX^3vtAc;k-l#SS>9_l@tCr
z2M`iPt(5QgZ0fV8E5^2>U}r>ys`{|b>GK4Hb|oAlXYXdhz2sLaEac6rK4VwpSttKe
zkAWRko)Tw<i`;HNX8|aWBI5CA=gB@rHGNMpt0$#rLb}&eClW<IapD0)%@0blq5Fip
zI7Kdw+9Qf6QFv$O;p$uKgU5-gF3hmKi!RnLhb$`?r*9m$YF&I2jwYV9oeihh$gEp5
z=jv@_ApL6}^hOJ!Jtww)hFdD4n*dWKR^|e)<NZJ#@+W0EYfx(crZT8EHa!1kVs7#*
z3qc+1&7_4!(DTwW-Fv$3lt-UyVf6QO`;gFvlqXuV`K$yI2@3nF-Q1-S-N(yPl_;D<
z%jCK}nDS^R5rIUQegE#w`dLwL1lQ<@VD3HseW#@~_zbEW!~xfDi4)s9l#@GGT6y^M
z+CuW76xGIPDW4ZcGQ6?PfrO1=ol|KvJ57=zfD7k1?sEBNd7cx7nHD_sK>Hfz-DY{1
z3%!2GE^=;~&YE+9ZQY<kRu(7Lfr7hxn9kPoIez}1^F~9C4-s3SMh8psawi=vzg+}X
zN-4IpLRx3PA$dqq4MQcqyzjpScQb>xSGb48CejZjipqKGEUWYTcB(j#qT6U=1qTQX
zGDr7S8E)MbC%vx@&28Yht9av|xKt?;`|4OtjA&L>NJ7H;CraI?e2sxm1W6HObbehj
z`++?c7Kr5k9g6vs4qMU3c_lEJ2CO`~xn&JIX7o@dbWrWi=Q#zr-RM?FwL|mQQ97Ui
zaK%J?-C5?sg+^I<gny%qLG<rn8<l#wVf@Y#vLbZH>1dH-%f8?heFOF?_=F%RqFJ=B
zD`fAYmuYX5O1D$qt%0z}IVUKrxmR8^W+Ax+070;?BHMf+A_d(+Y#-c9i00O*sn+A~
z2xVh5CaOE@__OD^Qzil{#jVCV1DS(`Z>acLJ4f+r(<Y<f8>I<&OzTrQ$37%-{i(d5
zW!^Lt=Y;X*G@wNOFbVF;`qLq)rRmOJQ^fvT(6wUAcSbrdm2R*1ZvaDxQvvXb2mJd9
z_Xx(<j$3s;<6VuDNrI{MZ`94K@Kt_u3!I{%cQe|~atSXpE)-NWlyg=+cT91m|B7-y
zS8c3ngN>}iM_fz(m|ZH+EXmBBDeEK$pax4=Mi4?NV7mQWu_*S>xkn%CJ~mIUuPv=D
zmS(O}p-}aJmq!DY7G2$-Yw+UwIb;e-_L8Nj8;?jiSZU6PZL6#=9m=LrO1t)*!Dg-U
z0~|QF6lYmb<jqzkBzq?%A-4<KxAWnYh4Mx-8W|gYOA&dV=&LUi0@|{Vl!CZXC(OxZ
zp<P4^hd(F-E0?=n{TCZvO5^?J+84^Y%JSUhVWQdH7{}1PE)B(p<`x=I4RXusS3~zc
zU`s6Pq|bR|mc}WM_LVlj;N`r5kfA@#p#)dEf{p#&m<(tsbg^`pNAJwk862Cx%z;xl
z@LUr?IAG{LQ-k$~l-x&pIEk-RtVRonJC$%*7+4xh<9*^euoNW=CQ)l*A-e~t%;sES
z;-b7i`1{L#Kn>@2W(G!GsX(GApo@epota@PfATo2ZHERn9;{NnxA{z*r2H%k&O|5*
zhjQwQm;_nJT13E(F{M`WE<1K;T+8x9>Xk1_>YF6`U%@2y%~F#n+_PaT1lZ@rN#zqy
zC%@dxDT2tjp?`VAX60WR|0i7I4-JPCR}Zj$Jsu{*`0Q0KW;y=&p>a&_j*z2Y><ZV1
zK;(Plrx&E@znFJT%kxdO^6y;$jr5ouK3{tM`p;JX*KW8t=~S1&p(JH=tZ_q(DVl*_
z0;zjXxir?%qTA{*cB0;GG#QmQEfnJ%uI$=)V0k&frx6(&$ia5$QR{p~zoBGFtOMu2
zcs1Sw2j+eVPT5}xr$69(Sq0!m|7B<Z-Qsr*3r62YaMg#^_Z9tpW3*V&31IO7^W_z=
z^#JpQ_U|T^zuK^>`ODE~HnQeJtS|iS1?V)|Ux}!Gzz2*V!v2!I%2BF-QDn3@u@=zW
zElxa^^zX(|x{WLzxNfqYKk)C@G4Gzrf9HL(Ger1^CE2y0;QTwKj?Q4~YK1LJNWyCs
z2<%B*7o1tt{5ICTe-R>1I^FLGRWDmGXDH@Z`G;5@#5k6Q3vldW6i?LAe5pK`A6s?8
zuMv$DO|dADF^i}*7eGwcTtpaQ=ul0br`&bo$!E;O>Ht&g`Y{)QInkC}`$uSm9T1@4
zlMJ`UUOiJ0?Ox^2x)+jvl%!L<&fJVac+qQXMc0lFqq+jA^CPA}GnH{^qUNFt41$4*
z5;}i=Eg(9Kc5`3x%<<b^bCcKPZpn4d1oNfGMJZ>rNuOUpG|D&A$PId%j2go}^I;OB
zj{es^9>>PzX*|#+vafzE5*g1M{cBvUMQw2xPnAn^BAHC5;D~OG?Uid+M8YJXa$Pt{
zK<1ddah?AgLN+WTzYBB(<GNI%hgNl9NKKVvuSs5OkWtb60XVMZflP;j!ZFwHnTJN#
znFwyv7M9~2y?#Dt#q_`}7u9}xo>Er33&^V+KRT{ed+gc52dyA#=F&cpN$=_T?`gF?
zcER5)=23A1z&H1{3j>N$)rpQfx*;_$MNVt#hX&SJ%OsJ#_6XxUrdEA<boS??->Ni5
zHJ?88#a#UEZ~rj@m08vJp_QcpjV$RR?uB*FYhL=N^lDm-TjxuMcm^ILUVj;vPfu>)
zctE($bGK12rLcW>;Q#k1{y*(e{Kxo4aarkWnX1P~*f%M#-Y=+H*X8otaEr671=wF3
zFxB~28lmx*(5YP<RS0_TyiA(>`+K;T*5Dh75<|y-q<*`&KB_0xo&n4n>G2q4U{odf
z^|#7*1OHD#ra?oS1nY-Wl*yNJYNqC5-pK%X)mwpb*z!!^!aCbVhR9q|MtM~FdjWNU
zs^F=Tbac*$@t0OWbK_&qXf8ji^p!Fl<INqq*eJX26B2n18G)4EZvkJCzuw!RF8Y*Q
z>T~RiJ|X)a-6X(xtbGd9fT$OfId3FNEjE5S6G&UK&?I<J#CBdS+?$`NU(UakH0c3R
zH9w>1*|SfX-F#eKwzWQzEIv3<bCl@V!7vuS-V2^sjRM7Fv?PndH>OObztLqgM`%k?
z0(Y#vNB_6GJqG~i(yXSFzn!m+9Q~rIVof)Kmnl#JJ0$zQ;tIX^UEDCq@>P9PXp2$k
z^&%Jb`QhNr2o{&gaQFHRc`J^0iDcpedvAsgM}8zGY^)9Pdtd-7XQSc<ij>7e6DgD~
z58tc!WjGOW0M&s`Jfl7T%b1J2i<sd0s}N6DGJ0v_q3t9A#p@dVm0S6015cmGg$|UG
zEAPn%cpX84uY5)efPjiX=lZe1*)S)h%*k<Krvsyh80=OX7q2Gb>d;?IQ#K~U6|_kp
zM)@FJL{6*<E}W4ceOE;NuD|M!fewUE)OArMuQ#<hPD?O{Knt;-v}r&@<Wl6+>$p+r
zcepy<MxM^QEb(RGk%YQhX)14lh0fX5fC#1j-n1L)s<b;!*lgyoqHK`^e*Q}?)^ur>
zpT-w$@A^k<BvQ>8JT`xfgCm1zQaq1FboytnZ-8F1EWq4JC&jV$kkp~@bb?aEZXMkG
zHus~q2u@^@I9GD7(y7CBZO;KZRAO`O8|x}!UPzaC>>SubrQD7`2ExKB<cTYyIv`9g
zE2~y&TyNDqiwmBFfW1q~<vsZ5KW}&Z$KCf|HiPhgJ2dz|O@$28c$**pP5}h<_WIfJ
zom{vhAYL02?;5-jTYJGfV5==u1H8nIYr(dS?LRayqES_mzrW0LwSE=N{>o=#lQz2V
zS$y@!C8EN#*H!&7&iXXq5&^$*s;9Cvxyt`r8J7S2{4mBh2mbRcfYN{Xua-922<C<i
zm&A#EkCK!E?P}q_7)GgcVYmT-KqBXw`~q@XuX*LBD0@}8D_ngxbNIq0mK9M@8V176
z=dpyu1R}!gs?tQ*F!^aMG-t-GCtL}X0j2ALm8OWd&6~GVKzQfVYxz!MyqQ&k#EeCQ
z>JY#ZEtY`GRX+LHPY<hO;C-n(^I;!Cv^WE%JCR^ddTDK_$W{m%8=MdE0xKbSy!uPJ
zMzL$ykqp9QfU=+|MC{AxXpudT(Aq<cYd5@Q<A2IwCI*OsY{-Yow~Jx%G9V8kIW1R%
zFpJer)qT&Vxj<dUOmSh4u;l3e_da>nOJ8IuVrzYSWdBon$u=pNRC`sF;TOCFc;`6M
zZj@0e=;}sFCLN#q4*l-Gi=^S$ul8cFv-~IIgx{r8XKLK`NtazTmezaM%<gNrI0i?&
zwAi3xd~5Z?fhE}K00I0K{wx4Y;3s1^cTxQ}!AkZcHUY^mx=(k*XFaNwt2I@cuqyY5
zDnMY9jn3W<uGKLJ!2Yjm4W*Q5yU<ZsMNLwLnle6DzVoZ7EP;+H-)Q)a80rG|qWeF&
zv(YPd^k*PRgV3zhseO0SkhI`|nR)wHOfCUKGW6aJ#I)w>F{~Yl;ekjBFI(SJd7`8I
z=*D<gMCe0FqL+Re9$(b)UrwF#|8$f858=T7?E#NT(e68~_{S~5>VW)Eq7ndt?E!GZ
zHb@IoTk2)+CzP-@{0#tWT!?@oPCn6pmS{xo0^FjF_4|U3nH!mhjS$eu|MGbEUkT%i
z0SS*4JL^M>oYAB$EPv8b;@P96O6(r|xV4^*a6}S#A6Y=WQ@cpOCMw+MwbB;ecrQsQ
z?s{h%>*(4Lda<;An{3PnE0eHni8{qzsV3-+HH$5MT?Z38Ga&NoEozZq80?fH`V@`m
z<t4{r!#0-gr{CW2m+E;B;$#{w%;I$j(!GMvq{%wYg(FVj))rWOIk|N7pgDw5z5J(g
zKrKgLtI|sH0PlMQh;6R7@SgBqk?SVkJX@U*6k|~wg{>-6aRn)x%24uf`Ce?~YC~}M
zdKFKfv;}`~!dtY)yBF7zclS5s#(*k&^Z`rHcGoaC{5KzK#l8t8$^-|D5qG+7Q3Q22
zEN*S7UD-Eb$OZ!EK~EHatA4jO$4xxZPmSuS1CIz4?dM2_KC@(t%G(7G-;F9v6(91x
z2_7mjY*^wQ50zMPVS_sG#m}jvA(UEPBUK@F92@4C4n6=iyu-|Vr&C=$0KYr?o;80N
zsy#BCxBQ1-txqgq%dL9!4e3Fb*mS9p(To>d7Jmp@VS-spJ4&k@H3v-@S*^YR$`dvn
zCUAyj-9?<+VTfp6#d)%yKj17UP#Aqz>*untWd}wU?*ko$2dmeX0AjKwxePN|_alu^
zeq_94U5coZoM(Umr<nQrC`}B)ti~I85->BLQUI4(2$!NU<5LRsWKcw295j?4HA8gt
zdIfZc=qTP2FL~p40N_Ui-a$qq^fQ0mg`2DXw6%Gt)QGfTw)B{wg{t?B^6%W7U3TAi
zkEqLDOrJx8=~Y|gJFFs2<RBEw6thGO&UEcct{B41taCAbeDSk$+!+|_nIjD2%`75&
zkt{4JK&`DGIBug0_3GH@2S8#oVd1QRtcI1oA|V#Z!U0~z{2N}{Hd#YOZHvrkrQ_T7
zq13&j@f;6W`1qekB{1yk_$)W3LwMjoL-*&;^%c?Mk962_MRwim=d$iq)+1HmMX&q8
zRCSma9rwCHP^yh$>4V%bcTnH)&j63oA%enZa)KQ^DljuV9%iQ)18~#<s+nmSBbU#B
zmS6IG;?=b4S9ADhRf&?)+OMcB3t*P7$ji+-n$rLkG4)UMkCJFiHqO&}X>0Rv;sIxP
z_pztiP0#kKzjxtBXziF8c!$PoT|V|{l2>}T|J5E0vDSN!989sq*j3Ftb0G<NIk&GN
z#oq$)evlL9)*d+ml3Vk_J%T8qt>CsUkQS=nx<@=`QlkQ{AlrF`mx#VaQtt{}aKa!v
zM8~~o;?q$)9csjzwx6L;&fl@AV6L4@#FU=d79Ur1PO){qu#=O}-+J$OUjITMRlTfl
zhwH`-$ukqY)}FHT1mZabH^31Y@7K~l_sP2g6VQ`B<A4y?4Oy;6Rmi{E_c{^~2TNzU
zlLxM86fT#0mkP8Bl&K|^<k?9-TweI;$Cz+rmVNT=BA$+_k0&J8_(sykrfb|<)<<|=
z)_ax+9$Oq-W#LHc&koJKg^kWL?{t-dXti+B@y>Nm|C#Ho;4G+8d#jvHDJIXpcdPcO
z(JF4ZUbj<e1V0VCCU>sb=@FtgNGC>PD|UKr46ovx8Q}Uz-r3K>cq1i#(1(oB3Q;sA
zAcDn_ICEVn1ROo+@FV3;S2q7&=AZc=(JnBR`D^~p1o&t^FpCd)<Tx&qXy|&YROW&h
zyrBj5lCSNh!Kf#>Gvx=I)rs0p)=z7lsu<EMqG_HwNddz|&-o(oz8#J%4(~OK<_vck
zeO6mK1QO$jW4U8vtzu_dX+!zY$Vgo+d+jBL#c)QHdR?WHYj31a5Jp0<+A!-|Sj7`w
z3Xn|GIn$$2ezW}v!Iz)K4xP7f7lUfPZjZ8;;ps+W6lw43U}OLl4(pvC8zP)S6R<4-
zl$#b*nLoCPx5k@}YYBs6Cbzc03Vl0z7$siy5B77eVBYCT5k>sLc=0J2O$@w81&u5v
zlQ}TCFOWGz5RcN<jWdJhErvr|KD2Y<BbsiDjJW~k2b~6%!1yApC#*d?fAr4f-Q^?<
z200TA93_45d7-W`s-+gwvV)N`wGW+4T;R;Be3Uj@U$L&rmFsx6KPKd8CDtpVo9Ttp
z`+JV!W*c6<Q|$J1!Laq3u~(3*qQq4pe_rnICafrx-}$aNFW+||o%AYkU|E9SCq(4P
zH#aziN|<ZVWaEW0hg%)K9?b_aT)pNq&U9=g)TF^za5fUL*9$f_sF1w%YQC0aZCl5G
z9Gxxe4znP8^&jMC`taNr{Dons$>h%fGg?kt=7X#Z#;t|F!#nRJTHE@YTQ!b!jGPJn
zec<J>_LXkN*k&I!#2D_m(TxXY;pNyBasJLknJIpsz?VM~*W=sC471#)g-*^i<&r8B
z2P(7S57mGqQkdDN;Qf6O>m7`M6U~q~aoE>U8oBw%-BIRs>X<pb=8@x@1V4mVvXe`)
zs?_;~6Fgv9!Ym8PQy4Qr<^*p)E;*v_q-i5rT+E{o34!9GA0vrj&h&yQ#&ullIJ*9Z
z-I&iVA7;<uS;>;0zuU<3Awnj<#35L$Z`1Ty9YP5IWaMkmoJVjXUQ%Uai=%%*0+kGg
ztaJqab>g*=*|+evYiDYuf=+CY;*%e^CEapK2+AM&wa>OzJXa&T-F&7ZSXCd9okGWf
z7RqWDH1EQ}3+?+mji-xTWE?Z<OKyN$c*8|N{EQG{kdVdV=kUStEp^u`z&eVO-4px|
z%vXGAwm|US?FQJD?th}+K|Lz#q-|5I#H;;C4E|WKPIUMEbsbXP2_41aU=eUcq@hS!
z^$oml&}EfldC!NZoLI6pIyr)U5_z0kfil;E4y=S%;*(1@_4ndE)(?mUyBk`v3}?~D
z`dp@G63O!G(aeeEQ8*pZEx}VgXM;kJI{VjuUVFy-FtMz)p0cD9TGpX8QAw6_!;P#R
z#&mR+(ZLmQRr#wgZ5Zn2R@Hc7X9x3hNDG=xOf<D^-;dCDZ4Mre>Sw-Ow|02_po{qt
zzK<EW_uyrNao7jH>3|hfL_JeBdecN0eyzf!MNNY7sTdEWc{IzqKctkzo0)*;?s{5R
z&KxY8JBbWks6c6@kmlZ|z$1hkEBk8ReExuXv%<A+Eq0{s;3&4d^M{0GFsfS-PrEX4
zBowI=-RWIt8@eRBZ2i+pTXKD-qjo;CflLPte(nmXv8mW1ZNe~aR$heqq^{P0>g3*c
z6D{1QqpV&Vd-a`oQ7gLOLvC~7avkFMY6N-Jfh`>OnB@MG<7Mx-+>$tKmbsOgr-o01
zI{&^8FKKTs0clC*1f*KHbAb&qUxCWqpfwpDAv<Y$iFyE38k^m+dzeX4BNHEnM$A5?
zzYhkT3cy880{Zjg>)rzzGm17F+3Q0CuSVAcyFvX@RAflRxiO;pDJ!Q1S2Y*LiE73B
zvMlPMW5(dQFaT4M!W$9??sh(vXQwuMJ}T>Aa{QnZWN9mJG~cCCINyxZ@NvJ16+44Z
zRx6&6F^QGzq#AX8vk&ha*B_uwBRd}nDkQ%H;RjZ$Ya<3e@7!}}{NCvG2^)F43*`OR
zp4aGsoCX+j0FVf%ynB3|aEw%}<tNaSsjJUuSXf=xw?cJeTYy6m-reh?+nQrWpUMaA
zp_LW?oj#B_xE6{OqGUQm$D|X}M?!tEc`iVp(x?S&6%C|Q!qy^B7M`2t3ayp|g*;jo
zq(jZLA5`g)QlbIOCW!2HGr52DadZ!J8u)_)L|88;#%e^wqan0xnxeEe;9gbfn~5A4
zpXR4=f2EnHr&Y6mu9bz!Iqrw!uKF-TFw?W0abgX_ZpGYozL9oKCSUvrIE`>HpE<gn
z&wzV(sD}(j=)*-4>A%qo2rPS^31t*X>sEcB(6N}!z-a+|E`beUoJN3ahtAALcnrno
zjm#h8ra-bxtuD>eh?LNXmpP+1Q&%;y4GfxeWvOi_wK*CwTGsLbpG`noD>(r`^eCZo
zF2a+JPl2nPzVAye5sq#5!Li=^Ad2>6=d;aaz>!^R+9gs;qO%}^&09O;Q$C7<$#&gK
zYN!on4w$yg*7fCj!bVaDZuj{t%f>>(_!MgxI@!VEBOt>)8#0Wdm%p42aGT(7^}yJV
z_0NCJZX$)KBPfW-c}IYW1E*vaV`n?bCmMRfJmVrw!cTd|Nup@&#1?b<)A0a6h;?S)
z&nZ%bEn;&(p$+@tj;y@|&=FK|=pWNzC#KPJVHSnj!)<G&fGX>L4#00pNy|zIhu^|#
zKZ70WZ26#4*Y=829uKXfmktpw*|Q#W^&}DD{23i;JdtMZp%Fx+T7u1kBlMa-hK@N|
zzc}Q7nqLxj)w=GG>eD6^8~3RIQD@!RZ*8@<UmN$N0RbFq6W*5Y#@s0H<c82Y9u0M@
zc@jpWPgqVLJnmDUJN}DT-yTabT9h!n${0U><ET(LDs0e*TpstPZL7!U=AYZbX`;bl
z7g=n76QS^v4MPwj9$H>c27?tZ19i;%s|96svyY|ad$I|mSWnC#fJ!tSr<Bh_^=!aq
zK&`z3q;XS3t}p#MIPk=RW76q`BTwERv?d3`Uy7n=S<VVLTB3s-GacKpCXZo^#l-#c
zmh$@%19uVWD--Oa%a@a$|DksE?|lY4gEI`<F2@%pUI;vI0Q1#!?s@P1;?n*5J$Cnd
zArrK=8^l68Cwq_7zxAH_CJ#82WEDAvmrabInKhCc35wodJBMyyj`|BQVhd)C44a1$
zgI?txp?omr3o}jy<JUEsqze3?3nSY`K9)M0U)|tbKtJ2GHuoLQ{N#IMM_I?KvfLgs
zc5%X*_&49=z+sISqZz3u8H0Yl1Ku351t_lZb)5c-A*za?KJ;!-8dSFiaq;QY6$Ig|
zUDzYzSG?X`@JlJ0_&&eKMHmjG029{s8`K^o1fMuGMkjZo=gdSUvb~`Mc{gPNPfMcK
zIF2^^3#k%P-8>^Cn6Te_?(Xd-PV1uTC%$`IBy4YJJ{KZ`<@VZu4X3US<m%cUwlHYb
zAl@Z(O(%7@iuX%vbO&xm7fl)%By9NrW>-c;L$)Kq`V|^)S3({&%wC9VCt}3X3vpM4
zROT`Epk`=Y(r_2TC_wl);@Wu<Cs226G~?<Od;mv0WOv7A`=<MLRD(t~OYrWaE*FA=
z=X82T-sFf;9*-InGm9-w(-*Ccr}kStF>dj-cE4@jx@VZOez6jxGh7HlzKW8~XJLW=
z4)EhaqUVH8SqXeJLg9Nr_Cdv?B;kPgf#!m1XzjV-nSK^%K(B_MQK_2#1YUp>zcGcS
zn3135J6S%_dH>V|X?L+`@^`Nsw98aX<O3;r*dcAe?}|iH=w>S)2_neMjukC5YE@kT
zR-{Lq?Tfd}52am&hBi2mP8XtYu6#3SpL6tXj~Ml{k^GT?WY&)QkH)$`gJ;fbsx_S2
zZ9Km%fyz8;vNm+rp-`0yhZfL(#GC#}O9&3YOFy(%GO@Ez+U?z;;e_ZXg#Zr)aO<vD
z@~lvv)0U7b!>~a?>C(ug?cZd31M{O_#%Y*ez{!rt3wD0Eo$DfcrsyC(*=c#K$X?a>
zO!Vw%An3-NDGOV4>H@B5SL83|A1{42=9{!r@1AFe<r}hNU@oZ3MwB*`jjeAb@C_IX
zvpDa`Lw+wtmsJEuD9@rDhV9+924#y&iZlZ5U=p#JT6I5R2A+|E13UmqsZ8t)l(Gb>
ze1PEv=ERr@;<2Jr?aOd#f#dFS(Qw1{vFbKK?Y!I~H^=owQdfx>^;6t3o_Z}f6rv;5
zA+@3G;-@O7uH2`@Mc!;kj+Pg!ByOL@)n8ORstKyq28KVz?%oiW+Atx~Rdmw&>5;j4
zwf4YvU?X)0G3Jb2bI~Kgkv|XnSKd7ca~GK|zaf?uMY=k8WQG#bNf=6KwIfE+Fn7?m
z>=JaKo4Q=W4j_aIpcE;nG7!Ua11LtI8PS=(QX_VE(e^<NfMPJcO;B}wlBRL2_q+~S
zT!V{S&e!VknWjt+mwq~{plXH@>h3x_cqDf$PW+1KTEnd}JgOAucK0f-JzD=RJhLh&
zw32xY&)Fc`rDvK%P4<bt0^y)74TIg<sg7FXuw_xl-sJpCbJh2Ok_wWQ<aT$L1N`ui
zDqNom!Wug5rM?(}+8uh9P+KM*n#PKVg0uMkqOj+!{z5Ye3GZ5{cr}X&ipAu29uPaE
zC6Lw@DsN)Z?|ukfmIe{@(uW8SKX&!&B@sQ|#jn?qj2m+3T90e5raAgv0bH;fBI_TT
z!c94-i^E$q*GJLgo2SYydI0jE7r7x1kgC-qWpj_cV2@UHpjSB0hk@x*UnEcLueE9W
zbOryn`j84t!?mgKwZuyE_lu^LSw#UeOZ$iKgOGTBMVXw39Emq8aU69{XHJLmq2^VW
z6~5`f&XZllo6kqTSr3iMC_to|Nr!?6!N!=bnk|~z*|*j=cAg=4Ir5t25>j9OBC|c9
z@1*AX4`{#4xzhx%DbY|y*X(H{Vy_${UeL&<vi)=H`H9rg^P~_V#BJatE5&>P_z30K
zZJuu}hq=K+rDHT?WzWo^!7P6Puva2vPp(ag@-$JoZUu7Qx4sc&bHM?9vJas&rkR)o
z1z@8Rgme-DlHkFpE%B8Z;fDc9h#aoJ&Y-u=akDytjoe-&owuF!%$_W<3(K+L^0(V9
zWKE`Pu6JWDczg#>^(8-x*Rwc2mC(_n8HPvuSI0HrCt=4w(2klj7vVBT#6Ot$f{wZY
zrqIc5A<yQ@c&6sMaXo(W!SM|CJ;Pyuop~iYVM0iN?6HHrThyd}`oXNpTiYeiax)+m
zg6xkiyhtaPim`-Wmyo$ad-t1Vd^5|#zDQ}h)A=Ip?AKMdzn=oF59%t+n+CemGmp`Z
z!aUE$U(<fDz+Fw%1*h8Fn(|K5vCr0c6_!u0Egn3AWtst?e@+$x@SI?_0h~}mC;J1%
z%U9vMX9Frj^@Yb1dvVsq*Vk=RCXvnwNcbZvd*=EfnHh>d)#aiw(CyP#9}3D?I((Z#
zwB2?&D8Y_$z_FF4y?Sa+)G*)q)TtLHyNf}16j0g4Br|vsHDxwiQ&G1p-vJ)8UrNd_
zdVub!?ON5_76r&%k>7gojKtF7Rm5H7?$MJ?DYne$T-L(%D&pK&g}uNjk_7^&TX>#C
z1*B^V&{fN%x`2ED4=dtBq<@PVBqi??615Hrv3=@QP9*-A;~kdqcd&dj_6+XD-Sd<H
zS25y6%+-<h+WPx?t2SwHyQM!e9Nrb~8`zuf)mIO0Wn5gMYtCZzrzE!`KOdnud+^h@
z%kJKWk$=XAemI9k+dx}Rl^998GJaxBB{)ayrha}x3w<=u4Y9-i!}oKkeUFPQ=D2Uh
z!n@sVYgO~|^($q3B|u_At2U%e=7OYfK7f$$1Hdc~Qx-g^Nz^LG&i(u;hTf(DTKZ%G
zO97{m|D3ncu|wSy7NxDtW!SbXWGr1@5$O^f?}T0SpLk`;x}Do)f9J+UTj6zsly9}t
zmo%n#Uenf>A%@nB%-f|C8Tb71(w5%y><m&yV2>x`>^NzDM>lV2;-pRD3OXDkuce`1
z#B8dyYb(y6=&cHMaT#~OiukGzY4rJ0U0kN|Z?ms8J<qnhICBkqJ&mcm+N7&<N%LJT
z9F{hzW{{Fb$eH|TaHxUtXsLKv<^&Q-d~n5%glJ1SuXSTpL;yT3lSIlx_W#QS#r~gR
z#QRCEu8#ek(6p>F+L2rfvsP1smMEs_z(#II{3On9fiPKM2TGe<(uL~R{Mf3RR{wn~
zYye`RQW$+RZ8T$?+Y?N_l;xFq2dK1t$D0lTk`b}=kK66?2$O6Bc#c2a@bVo&)rY^w
z-d5AAIW?t?k><?xR^fdYQOm`DJ>~blzGLN9(&$f?M|L)f&KSm_g=PJoS&-95r(BKx
zyjl-XvZw&Q<8v4lk>;Ui@q;g@C6^=qY^R+zIiabx#0lFi+U7m}QyRm*1}H%~U0d2V
zYO-=LdL?IWBQzQ;Db-DJK)sF3++wil_jQdq=v`MM6+bk9Xp=HZjUj1S!=PoY1PR?Z
z0cF;SG5$&~rqo87tE)KHI+qsnk5-65{ypZUr4e0o!b`=+jB{~h`#TOzmRRDjf7+q6
zk)y5QX-{%rc{6Efk?dNUIbe_S!RzR-?z!VCD(a?ozt}j>={n9#DQbuvJJah;TOIu)
z%IQh9Ivr1w_G0MNHpB>O*zV393P?l3lhkC~TT8~@PRvI&gr6TiaJc6AMa8}CC@~z&
zd1`2@U*mU^;bG*)<w63DE?7_!rdi7?c;2TN@o}`{K5n61Uf!g&ouQot6i2v|Vrkuq
zF<GGp$%YjHA|c+iRG<!()kk*uXt5Oe>?%98&I@&IbBu(wU}ood#mw*9eSsNJv6j3a
zza*0Rzo4H>19#4^#t;bKE^BgcJtEIev=B(*>=)V(&hSO=bip6WN8A?r%R3mY%dzJU
zAi;wtQUWOh(AA{8qC;sh0nO`<xpx3Y@~jW>Nw!N3<u-ZpfdhV%xf$ahXRzs;J?(jG
ziAP3@(ht`B;$<hCKoApz*_Gjr#ab74oG`EPf)BEb_`!;gET#e$XQ|&xfy&N2{Ro;k
z2$>R!x6eXR^2k&iUkx&>aq7p7B;E`vd^?<oyM3{C;@bJYo9%vFkNO!jr?$8tPM*-)
z-JVw%7#nHs`^&;^t5S5<CXUd=YTqDE&Nkq)PBo(5JdWY+Sj2*Tx6bQY8Q4frTRhWw
zYj0Dd9gfr`C`lXYw|++UN%y|3U_oLfSK5A@9L=b?<-e^^U2iqaNbc*lVDM`?$uf8N
zd-)NH!f=VAbwP8s4!`vG4cbn;c4(WZkX+tjEMmmK)0TX(y&&ZHRIM)aGAah8fB5@5
zTu<;7i?EkZ$eZgamzIv&Ib257*D=aRe${Q!L)}cHKvwHtULWckIXrb$W?OXTIp!v0
z`gr=9=SA;Gm&U@mS{*mOjCx;|xO3h!ce2$kd6p%(Md`wsr7f&%M>*`gg{{n*{xc)E
zEHGBGmQk`jBK=J0#dYATb8{;&)_TI$C><DG!O9pbv#UM5UN{UQgmU&sIj7Uar5XCW
z!XCdCG@YbdHk|hTuzW1RQ)aic6vRMxUO?Y@Bwh{AIN5cSRKGCz7vnk*#mA4u+h+kJ
z@P@DavOWBJEO^3}g8AWa`RUdzq{@NAt1oxVMx@mjUPK3y!8v6l6~vx+r|GZwT+`6M
zyL(`t9}x^>z4y<q9SB|T-zJ__-GVUr2miIMPHAsLiA`Sm<|klCfyLpt)KvS;^<2Nj
zfR%IEXO@;Q^iveOY$mct1pymKFhM&<X|#+vv$++W=%3R@0$*TL@{m96K7NO<Kc#MF
zzoiM-LmJtLYL~IOu<n16?ua~jv|J5XF<G;|38=UZZ(O^anl1loNdS?p^(I!vU)y_@
zSHxhym(A^3)N6HgLFIn<8Cx+@b42jq(sWh8DEq5L5azrT4+*5Sue^Kax9xPwg&isj
zg&jV1>8o4n&B^Ov!=Ic7gtiq?u{5<?UO9fh(hZXtB~+$AUh=!{Y8g)sWxhvfCv1;O
zpPXPdq@Pdc)L#_-x@~Td<}`(bG+|se1Yc3?L|S$XdZ?{p=EXkx(-svO^Q6Hc5!Nc5
z8*hJ~5y0>{p9f5I5>KJ-RvMJMda-Ksrs>K#t@O!fc7`)@1wNbp$LKK<QFdC8_j!HF
zu4^__=dxz11%<0u13}0P+>Sj}<m%$O+3OM?j^ZANp`o|hzHP}d4XwYL&JllS@GBW%
z_!wH2G=T^j5)O&2pXG&pH20sR9WkO%9kU-!x3a{f%wrU_9~7+LP<Ju%$Xvx3gbjYx
zxUN!u8z_zwtGAG4Y+MSCpZdl?*aSM$s#qvMd9`UQ-4@1kevGrd$H<#(+wO}k1wNgz
z2JsqnXd8KB<gk>Qfx1U&_5SnEV5jrnicPa8UX`?nmITafDc?&g?9rsU)ASrYI;9_z
z$*|M3vusDP#5=+>Zwqx5H8gqVF4YRID2KyvyqMioE$~k3i)JeVU)KwAme5X;cZ>>e
zuPU<?T&6G6pDp?QK;fE=u0cWuCf^22x`j={&4&`qrscUAV*|RYE^SZVwij=y2ZAK5
z5SW(&##Qix>RSJRs|-c~H31vzr70fp%*iG|e;P-d+l3J(8Hq#OW>!SsqfW)M;Rb%j
z-IT6dD&f7PR?kG5U3R`$XxDW?(^}SKkg`wbDqYP*R%xM%07qeOO>%J$jV*7xcL$9k
z2?c@WpD83hD$WFPA+SkMaO^ZKky*4=JKgwOs^CRN><3Bunq=D&#ygF9bLGs)saCwk
z`(S5$3%LCiw}Ov;bCM<I9+;+mJ#g3p`2IE56PNc9?SJBvG*K7q`l>#9e<IHP2LhR_
zfK1|GRo48HZ&e2OZ}9UfBE9~{xNrV&Y+JtfJZJj}o^88-rvUv++`zFG-Bn@6woY+Q
z(Tg(;icg7%1iGiB!LSR>e+%fnP_TaI`QN*Uwz#byFPY4kGfE|b1Fu|F_z?z&pp2}^
z1-tcN0oT!CsV~8~vWP!HDCiUpiK<?PAxfwO(<P1(l#M_<E48d1y7_CBU2W|;G!>^c
znJ0d`MI~f6P`K{t?ty~KJG#xKGdTH=64-%hlxG#D^@m3}eYgvncG<cb`ly(TCl+Qh
zdNNgw9~a&jf&kUZ5s>EBy0i<L57_X7bBVjAi%R+TYMFPq_h-YPq2`#kYd@t$il~Ys
zzMsiv%iPC+CRBZqn6_YaSyFdtQWlqyD$poQ>}Yr{5lVceI<|VVyp<w1aSRpXSiEJJ
z^rW}o0mc6W{cX6@c@|*C3rpyH^`hLs!@PT}Fw>I|6QHg!j0;z&!D@d2sg$4G*bUSV
z`GsmJ^^uUaQ#<Uk&BmLv`8F)EM$rqI_x9KAfs{a$Zrrw<bpzj<Q?hHnU4RyBo#gez
zncGXS4~>H)xWY5dqw2}>{>PQ*vW1Be!zN*z!%}%r-Ruun9oxPiUlAIlMAAOC>H15k
zxZ|xAXtttA;{L(T4#Z|R9Ep2JOTKugfAcC5xz=|K4PrOwd*<3alvD!-gJ${@j!bf^
zO(!&M@+C_~2`PDkE2SbBH@&_vp-MG!|3L@9%R#7G(bkX%G(~5B`ZS*<W9}1T`QB|m
zILMdtozxGKIaajmT3{&Ogj~Ol*3lHM7xxjDDG1l~R~+|8Zs2G-@h`X^>h!Msq${s6
zk3=hy@$UIu!ugEveH6p8#@}NYY_#Id4uNA`-hvYy_(sSc+ukQ$1)AoFTe%9f{)dlA
zkR1KX7ff=X9{)oAYwIgLI7Oo7YNt<XFz&;%U0qH3(ZEM%ckOjLm>Y@K6w%48r*@O-
z*X!QnoG~d@FKwbXxVh+mbEStDsCM)8X%S5m44#f7oq`r64&x`kgA5}krH)!vTH~X3
z*?!M;LbA>L1rHz~($|aHxsyw~_EwYFJ`g10P@`;Ax5e=S`ukQ3$S}?Hy>MQa(mFrR
zG~UF?%?Pel`#H&Q<dkFV-26#_erqbk0tf;6#??}hYxY$b5W8Mqw>9@XPf~s|aBt7~
zSaVIaGwb*u88oYG{0}GuHTKL#3IT9u63nYVBJbJV3z!*=F1077+CQW0*3YHkeWSj<
zf7T$y25dHUb=I=jIPh6<t}dap7nb{~+*#2xzs2D7ACW1OlH!vnfED{R2~>bP<*Uua
zmM?p!u?vr>R9Y>$n>EU)x+#~kaeynwRdqW5GZ@sg>0xAwU-|P|+Bf5T)onPlxE7gu
zVaID}PwHJ-;6tuCk>6MN!6#b<acyrikD+KjDF9m9r=cOUpv=#>A7?+U0BpoiQI0d{
zz6-<VMVH|{1T#PV)}&};3^uK(6Ey<VwL#?0(Z<4nZzgJpj7ar5WB)X&_%GIey=m_I
z`$j2;c@H@dbf&<yv)rco4TOefor2tKt;wLHjp!5$o+vE{J)|Od7oLH(Z&qz{xhnD9
zQm>(F!MLtP=sUq&pIh3c+G2bBz<6#!89?jw4Stc9d8o~X&7i@pC`11YYjNyM9oxx0
zv*5eA-Y$?<y9V|$X53CDFX24mmX#925FY}}&*NA2s)XonyvRLq#<*3$OkFoI2sda5
zJ70S#ea$t^(fqv-Y7&)*`mW*e<m~QVYdwDH_h1Uc;SN(5t|R1lQP62I`mIgrYpscQ
znQd#x825{d=mSqOCymf-MB8L)M-}oQ&O-^`Rd(8sCp)&UmNm^DQXs;~;|&76)qIXP
zy>DTy0X=$%#J3Z|HYB0pj&s|YOn<%AsJ0sp0|lj*v0-iR>#YnQ3LMg8hf!zKj3i6-
z#=fcL8aJ2L@-eW)aekeBTMPkC<cXeTwA3JaE`_Hbq4?SYGX3q3=+L$a-k3vn{L-t8
zcaOcYCpQI(<5vym8s@*^tfMR+PJUx>Xr~dMC47_d(?IpR)xxP7e6L^M`MY{})8EWB
z^Dc=iG(i&BL7Wrp2@^Oduw&vNVafTmYPx6Y5*mqWqtAEfs<$m$kYvLLU!#9{H1KHF
za3%U0q)QG2e+$2<hc6V!MmA3YYp!zQ#!#agIzd=dZyBXXQ){<)`0cGR`|Nx)xcbue
zP?<?EdE>lXqs!m^He%StUE(~pIbfEJ_0deBv<0F6L8H&`4O*h@E}z?|%+&q$rq}#W
zF@{{KW}bU~u&T+vce17LGH4Dhe%;g*w{YblXX^df@rc_Xjf(VdN8};f`H@S1wykG-
zp#};&N*c->E?IO@-p*{ajP3vGt1u02I4GrU@wX`X0Mr<Ly`DO+yV@5b`H|z}w6~jW
ze@C$4^Y(x0u00C$D0hqj$6c)=mE$LWJ$fV<*6_$!Ya1Ze+Hk;jp6NGFuRq+n!>)lk
jT?;h+-{sd$R;ZfnDI9z8<a7Q8&vxe=ej)$t{o8*7M9kut

diff --git a/reference/plot-7.png b/reference/plot-7.png
index d66ce5306dea0e731f3bf7ece19fbd8fb795db05..895fd641cad9c924d6e18d5fff0219aededcab15 100644
GIT binary patch
literal 36814
zcmeFacU+Wdwl0cow|1K`p~MywDnV3|3S|IkMI?!UfGDU)7LXi@8CyjVP@)P_0wNhC
z=g>-MKtLo)RDwv(nJUin7Cm!k-*fi9vuDq~`}{Hfx_?yo>Z|X2!&=XJ*0WYURJ<U&
zb`|?778aJZXMd4aW?}ikmW5@x?vE?*O0$2+2>z_lm6MgmU;LXrolV7`pKkx6VadY6
z^?>}p%&U9!1`Es2EN7)pUbGDwXtnd@ypc&6iEx;@_v=-c6F>f#v15Mi_shb*f6S+O
zWUs}IY_^jp?=46xYJ8s?An<2f#SLoTOP{z=bG7j&xeZTbb_=#_@D<td^z_e<-%GX9
z=XdVy=6|;PY}K8Hu5pRId+Tb<)F|}xYV!H*@$r?LSKT|sys*bdKPj^@9~ES=j(kTR
zz4CQozVdVU_c_dW_U-=s19|YvzGXt>?}>9i*)Agwl+P8cCVw9*e|Lrab@AQ!cbvla
zp^#IroRYRSS6yA5u+315a=>BUUSWLx;Kz&pSu9umv;^6>xVTtZmH0NF)2jIWRSDiJ
z_@(p5|NTqK3cCYzxbL0Xf&1aK{rsnJ!Ik7XHgEX%uR~j5)=`SvuIq_7eWFG$A$Q76
zSg%-GUS9qY*D+D+cBg6axo=^evXnWc<MRW_xP%p{m1${dPbD3!IjIYyqeC|N$NKvE
zbbaJSA9^`Ygqe>Pv6UQ1$T<`_dH%&wk?5G1u&k`3jIn@xX=!PFef{^rlE*HHfBCiX
zW}r!Ee7s;<yYsyGY?n{xGyPzVPW!EbTHAVp9ZUxjOSr2W8@K6|czSqI7dnOYD}#RT
z8NX|E(QEPrl`$)9)mPhi-AA6BqHR2Qx=^1o|LN&58`C@1*71`^8)P#ldgXNHl$A?b
zGA&nXFRm8K*${BW-r#(os6bkaMS0h3dczfmqSC!I&Sd>6Pq8*zy$}7G9I`x4ca+B!
zsB<-`S%ZxEVOopz)W9bv&+?w}(OMOSqRgIP4xYIW4|FHKRabxbu;JKXiV7J9a(TB*
zOtjk{c=K&ubygcwS^e?jq0c7IpFhuPz4^v1Q(#R|-jA4!U;gx6{{YY8jO?}kxO743
z!k3KJJ2mpl<1O($_nMlTmNi(t7cSY1&&_E!`0i8j9v$zg63Q7XJ8&;9ttq;FGT83x
zvwMzGI{2P@Q&UrK%}cl%hG%l8nquDQ7P+0AG#c$Fx|ciM=1@=~>bx-Xc42mWWoo4^
zUw~9aki_BbR7~kw_32O<_f<Z76}TtH6T-s6_~?Co)v`SD_4$Ko&Fki@7v~!Cy_;_&
ze>x>6Czl}n;>C-59A-I*2XVe-JesFZpT35xUEVf))jp`ICp#`XGt;hMGF~e)so7+O
z@_@-Tz9|{p>WQP`f41Qg&`jgE{&XY2cD6gfJ+0ERQey9G`9RUgFxG^8#$4mI2V5O-
z>22(_S;N`GdA7q2?Huk~1^R4S#PVk=W_lH<?3r4E>m(;WWK1;~&2ufzbb6KSc))q+
zGkuXUo$vM2ragsvUXr4p8;ji8O5(GJ*w-H)DR9wqkE?ZJkA1*x&zII@7-QS@MAj{>
z(khY6d8ASpuWsFw!qseooVA0_v*kK_%1gLxv})_?r`Ro<P11{$WVyDuvk7tEcEUao
zHfc)9`1;&vg@#@4W(Un}ZcePYx5wPywOJi8s*k;8YU=6g>LJ?1XPWWlzIw_nrIhq^
zSt**AqZE&TfWT;9OhPf1U?*0O$K2eURD(^G<aFUZwkuDyxGBz#EttO1sb(|s3CyPY
zo2w@|u^CIB2Ix3TtdpAgeA209u3U#<aYRh4ByWC*L*e+SG#-=V>+9=hT8Ew4G)+=f
z4^6GsnK>0?+wstRICuICrf6Y6pW@!2LzT41)atUxwC(m&>uA*Tk}s|b6cggcnw1)M
zs1B78wj1l}I`V^F@uP<^33*5T`876Tx}Q|_ghfT|`&tpmmOYRpXOVq(-O*dWUl8~Z
zpgd*p=8El?druGDIG0XynvBu$_+j-{X{?j3@!8+IEfOud%WQ`-dphR&6TFtM6Tg}}
zY$Lya|Nf4N8U+PG9jiLk<!!GO`3hq-lD*3#f-KJEceZ0DOC}q1c!ct1bW_sO&fwlE
zD=Td~J;XYK9flsZS~m-9pMO^6xW^xhX`R<~+ciaLiUUjYa41~rSMj(s9MjcVNyTK=
z8)**?4&F=DE7{W@C~mHs&$D{*%X(_#U}wEet5pMgiIwE!3F*xjB)WaOQofTE(9TL}
zD9K1~)OCML)_C#gtYhBo-*-INr3Z^rH6>w+4I^JXm|P`AmrBzTRC_9{cjgcS%W&Az
zIMMesZPmTsXi`_xTdn1aJ+>cyJyJzwSPt^sVa=WRD0fy8hJfr+=aMQKg|a>W)6Kxi
zPyLyeJzKf$zlBI8=m-RqZ}F7)`t<sXo)<P(XnJyXZudi)wz_2s5$E%<gf*xCR*~D5
z(y{Dz`q1R%hA=NFcI+E_3%kl72_-ePhMpOp3rF{nmC`k4*YiT`v(qCEF+oAh+V9s7
z8Ra<F$cuS&dLD~!t}@N+_TP5+=6m)M{X@?(KL2^OcR8-QnY~03hJEx~nAaI;X;oF#
z7OMtr+^MlBM}xMF_Sh3TmPJ;i&P$QU%993fzE;tA6e#pN7PEg^$?XbjVVYZ?nU~gP
z-`{ns1~<A)fn)R?596SQ)cE_u^8=Hxp*%c{kq7fR&Z#&hW#+f_-sNP%S8C3^VUIQm
zc{<OF8~xeod)(+EeOkevI&zmMwjupvJLbQ13~qp<v)F@UM6`Y3tC&xB3AY1(oAdmj
za|xMi&$6;I=aM`0ZHvVmW}FVgxi<No+4s+u`wO^Z0bS7}Tk`ts0iD@%7J2oI+Pb=;
zhkJvcYB9!Xi^o5kxP@PYjp&ch7SsqnYGs&Sv&r}4Cw<xre(HR4`;euUrl#h?ObO+J
zAdgk8l1%1eadGjkZ_b0M4WV&yaeVy(um5JEWDBxAD<!8}uFs~_TyP&uZ<o4R@qCMd
z<YcYOH6w)>uReFznX1Kws*|lZVQU7g?OqD~5O~Dkqf38_adL%scd5_b^|oEttHRbF
zJ9^aK;~6|zQp;py=bs!uFX#EipN(Jr;J4<u1TqEOjw8QapMB+X;d0O9=4RMWfBtsN
z{OH#}vv~6s`gA)FEG94SRI5#UqvLqRN-Z1QNQ-5aq<ND;M9~AOi4gK~*z4DO;GDDR
z4vg6zpXN2wnSm?yBGc~a4!L7KTqamY;=^88H|@h#4!(8kmaD6(j-%DoC;bxa$n9aW
zUY>vng=hD^*t}kHLNQ;~c<ZWrwqKv=dzas(XE=!ldQ9gIJC%0B<wmuS-j%p~Fpxf7
zb8Vn8@ou!<&%}l$<t#GhWnrg|962Jbt*wp8k%6@xOE6Nmfdkx?&hagAi%Qhok!|-*
zg*5kWZ<}wMUf1lj{#9OHUNc)+t9^f*5vL8n#@=%M7gm?@`}sGo8V%#2^PTZv-@-%x
zCfk`*F6b`HZMVx`P@5eVp&I`Nb=2^vfArS<VnvhHMNMo3xApG&ti66E*BwQEcq-Gp
z*{|YVk56a)KrIgt@`9r-5x!C8Yg=-xjTH{btUkjtm?y&2*qC#IZkE;iy41qb(lTFf
z<<YV`P9+IvrDzJC^^erIWQo8;;~iqnjEs!n680`;-M0eN%51$Id^deF7G7|bAKb>R
z#vzA;H!Fjl@$1~FmdTCR$+}};;oRot<|aN;uzX&-oj?xSd`nN+p;&Fr)42kh^>n_+
zRlo9av5~B+tu1`C{je@<0q%|!j??YfP{vLvdar`4jEs!5bPj#0dGw)zBX4|0huf9(
z;q4pNZLYOWHU5-zJu@%QnTzMQ58Eq8EKh$(rfKJE#c-Yv5*MCL%4EA@y8}q9ad1ut
z$gOmY%(IAyh*Nw&uErf$>R{$V$a*Kfp`7tb;@9=M%lw{{ck|M!WJ45xVGQDB8E5Ic
zVjt|x9_VSRw=bFM3*WWzmfcJi28s}$`Fyt%uF=!q-#-I}(x-p1eQ};TTCZtll`X%=
zr`I|fF4Fdz;<O(C#)~EbmabIwxeNJ}-Z`c=0%P1P@R(yDKVF9~eMkBAL#=ri<MdrF
z8-6kttrYKk)HSB6rtQbiw(8zf5u-~Mv)J#B`TcXBs+|4#$z~q?aC&`FiPTi$(}UMe
zkp(CkIJo6`-dtZy|NMsfTL*P6i+)i0`Yu->wtNY+?Ois!Av!wRtww<&x#vRA(}e*b
z#{rR{yF3e5lUf=w<sABEZ^*$B*B!kikuudzUA%7BmSeX&_WE_=D3cr~>(m0?c#HJg
zbGHpUPiL2AniGswY?P3@m6f%##A|qR_n#!KoZ^G5>NS>gFUxkE9W#|pN$@(qXH#lw
zYH9axocrhl$yMvbU;8${y*kZ=S93qwKKKom-kV>DII&4A_UlQGb*gG>_eUO`Zn5;~
zKI^lm=v#GILvmTjD4^6)4XOUvWMaL3t$Wo@r8iJ_m*>+5&9b?QwAT?-{Pc){vUKdx
zs)0VsAsB7S`QkNu-VEB`F|e|lbeVR*Oee@?I`w5$Skra0^X^C<%@pc>Bd{iA$yvb$
zt^<PY2(ql)esM$d$B!HH8IuV<<wi&S-gcGxbaD;b%39bpr5Mni6KjdThx^gA$TVuy
zG_?lof;T3fjnjGN%U#CD6dCueMQZtTbs_vhR@QLbraM}ft>oMQTqbx;Ciex#sdOwI
zSAWaM$enD{(kiLRD225dp7CCZ{ZZWYT|i6jW{#a4{fY+d>m<4!FFAsAp<nuY$;!h<
z(JQc8_x~UXDImH#U+%FLHzw+Jy6*IJg+CF)4NzqewqR8Bdz#Jixz<a~Tphrv6EQ=s
zu90zZ9+FdyVGUVW9a%3dCxD_oxU3s@a3lKal4}^7b<CORa5wLGxYv4q+rq1}K3Vsa
zy7%~$7Q#hve;SC6XaZE{&@@hz(X#9I8|`oxvR<9k+}x}O6mLF1m|jvFrK0!t*PlkQ
zgmW&22ZW#BQ!{vk8*$oY<J`%5a~SUuccHweK^u4Is?JP*loKivNN?HrfXCT(Y=P5|
z7($=+=@&L_+mmlrocr$M^7Y5xGzV^XlIU>V>D_JS)UV0(3JBOX!Z`8r9zA+g_qRX)
zVE*=}y@e-Na$TG#xm~LyySto9@7L)pSib&|KFvwgjuY|d==;M}g->L;x9<(Q{q|+O
z#Kq=Q!b|IN)fROltg(Bp>ity9ukU+pawBNYbC=`H+y&D3U}R&fCgq<U+P(c4m9eml
zr@?8uHQ*5ih-xJ#t-L#{uBN4>rKhokhnl0Ps+w9a$suOi;$jt>V9{Ii{@Gz4;ptWz
zo&5fs`JwEFtN>id^7_mCwAuIi_3H27fDcDD+gMv~wQjTPIY~(a7#qLGWz!;@xqzXK
zH_tVy=|s3-{b8Dl#cY$^Len)}eL8h%{k{X(dI`74m`cwtn&_=rDdG$W>PcWl!A=t)
z0J@CXuYoJADyw&Tiu004HPASz^razRD%ocpMecf7+jsLS^rgn10f_It|0a6kIe^9S
z#f9-iTy8*?ugFn^x(@W%tgNi&fWpa$&`@4(toKIv@0FYmrMm+P{nV~(2MRON)~7np
z)zH>ucr-LLlyrJgxG^FNFB>%9n_P=r3a{)z54fZ>A%f_H-Si+>pBUr}Aes)~CU><f
zcDoUpcQ_3@=nP<)kw~V|ET0~lWN|w#7%-L-7?eb=8SoN%FjtAUszW0a61>RmAoT2b
zs^|I8B<<5pSDtI5CUAD6f$a->NQ6N4b?27{Jf4(=FY6O+M>`^68_BYtLWCjy?d9E$
zmv`5>DTST!%j!x>14=9=J9H+0aXvqz_jRByzKev@GAb%6kJv@_6Aqve%b;^7&+PA)
zC0Dv%1N8JT?<_8Yee_=#@Kw2>5wpSi{ZrvlL=hzf;xuSk${!#5eP`nJQg49T^8*MT
z6LFfN2v`ANd4c;KJD6w|IAOHQN1+%FVGBaT6FDPqR|^}~MiR43?%%wSRaTn75D}5M
zx-56Z>2*sc|H1hXMLypw60@_g)Ca2^VLtc|*`TP=>HI~>L(ksZFV25nNm1{6pnS-l
zoZw*6kN<Cq=-DZp-im~hEG)5-vj09o^Uvg($d{csr%INM0peEtQw|V*dH?=>S69#p
z)h#Vr9foCIj0pjsQfVhACuwQKbhiu)_L<9c?+&au1DJ-FEHc4#2;ik~tX@MSqn*5=
z$;pRd_|Iu6*+s?1J_2{)>iX^5H&?_18`Ume$(oAD&6V6jHG80fm3>i7O;%bOkQ7Xf
ze@5t-GMF#C%o;*5QdUvf<mVT2WiB1CNig65`Hr+SQACftyrJRYyJ1Zq9@sBrWo7lG
zBL1joWM-xWh%WMy_Iyok?L}O?0xtXdp=YiiXd=R5gMpa_i&;}8Eh*_oDqH*e`n((F
zjpPcl{SgxH-xIWD%a$jl*8y50LfgKi+5g$czrE)+0Lob^tl&h%17NUMl(Ym9%~o1v
z<pp~04xiG-yt!(tdE9gjjeGxr1KB^^nXA#p^?anI<m{jXWv$4$vN&-GVcFnt0836b
z0)A6EtBXxTW8>4DlUJMj;xJ@MpNu!J`hl*o+;Te4%4yKpv|6(vGkY*5JwD+@%~+HE
z<ZpugZ}+j2x%vkwMe@o2A}jencnVdGJ|^+Xr+|21clIz$7?#t(0KWzo;2oZwvuHo4
zzC^Lse*-A;#8&D%bnYv-5S#Zw63MkUPUHuLGAY^rF4FdYaCrX0K1Y=)vpp7lc79LN
zr#syp1~E1ny$Uwk^G}L<<`w%tt*7@20xN6Q(}s+7=;I5$G{!`N5jE*kEMwyH>|tAs
zB(_deIo|=vbfLM|6nYJ<%~+>b(KDaJvW8)DE5Yf|4+&T5&jI>eyi`_%!E|{t4341f
zW%~@QTRTrrkmZG_@~g`~Anw5S?Z2aWw)?W7nVA<5JuxZ>mO(&(FV;nD5H1)oV4PBY
zhLv@RSc)pF^9=yB_3-e(OXc06nVFe|Tl@mR5UgFhmNl_&E$zs_`ui$~VQL#1WDBDy
z7XT=B$4BN|ymX0ahGOR&KGxS?>ToNQ!?%OF19<_Pf<P<a51WE`g_X6ssfisBfqb&I
zwpJQDRa!bFC4~^t#B2~~{c8x!2n@m>mZkJ0Czc&9Q;t|OI73(gS6B5+i|!R%6R~IE
zDJ-rxx@|ddPD_3k7KWB-Fk9YjaU;_tFmOu9gRVipY_0vHZG*C4fLzh9j#eks_%<sm
z4d2$J%U-0~_s=DQx=o-+Uc7j57j@S}M0)(N=<6F)i6BhI#*sb+Gi}P5x=){?D3aHc
zKkfQlM{CP+ej6PY_KxFw$&0G0yXU`odag6uF!9E|*-d?NMG$NZ4DYVbCT3=AF4J%3
zDh6HOm|eLGi{53H!<a}r%Hs8bW}zBCo)^FDwQ(3s%-Whm)1Z-Tb~=_aP0R_y|5Eni
z|ALM1&q>4l>qh>mS*`!<aPF7WJ|VmX9MV|n-p--yVNXITg9j!Q6d^YWJlyG1>YK&P
zAi9KZ7hY!O;NXBzQzG%|Z+|wWPyx8{U$85H?lHmv{C<-wWevHEM{9QO`JCI~c7Oyn
ziGZKQAo;k49ea-9goS|Zo!)YS4+*hI=t6NoSUK(e%<ETr<oZq{KuCvsJ!Rpxw6vDv
zXi*}dGND$mJqdR}UIK>b0Qctk_3PJ-YRm+nBIi!t&rS%<cJk_G52m?;i=N5?ZzTo(
zjOOY()5)_a9-GQ^P;2~HSw{qQ@{aTJ5)xIa<Pi@AVOX$zU(~gzpU72Qjr4GJu?h1K
zg&9IjJSp+DPEE(d)fJHq?Va)_a!7$92dfSfXP=QSq%cOkY{x6a^Z=Jz<IM~V9um5h
zb#(Y;I)xZ*Ksu2tV&MB5N#-cIBP5{oy7!{m%Fh%26*ng%9@JI6aCLQaTSe~;F`@$F
z;3ea!_F;}os;a#T>$T@qw6$9Y55-JL#DIO0zR~S!`%L)=Ue?s)fF<4?`+DJyQ{EqJ
z-cK?6D0oXpr@hChb**XxgK#atz1Kv#Bl;N;iHS;7`)_m8Ek?Aq_ww@*l<owro}#Zq
z^!_GV%6s|j`kb^gh3^f(8YUv9DMlu%6G7*r1gAkG;cIVy#Yi^fGqwb1S)3i?eU+SS
zSuoj3U8FmCB2K3(i(G^MiHas}agpivPE+U9l$5GXPW@stSCd~>c5ay&Wg>$5c9?-9
zK!wU_pVFwIG3-nlDfC_-$Jul5a)j23_$yzYtEsL|tW4(;K799Z)$zl5Getth-n->G
z5SR2?0~BimbsQGVUl;=&AfR-QObJVoi0*f7u){OeUe24R+o{wRn|7yB(6BN<r~~Qk
z&gRHT6^~a@cvu&uUYw>Pb(JMWu|8ry@vTd)wd@mU8!S%@mTP*OeN1h)?+XvHHgR5F
zc&b?S#Lzz+C?R59Rb_J$kb5HzG@i$)7r^`V>(>N$WA?%l6NPzso0^-${&2D2DVTJw
zE0VZ>K>KhcX#4v6ck0&l`uzpQM@2nEjOyx2(p!JPc%WV%v9ZAC&+GetbbS(u<t#CV
zWRKt7xr@#BUxkG1kVvvassiqu1Of)e#^<F(Mv&S%RwTQg!z4v%EGp>8`z;TeN&YRc
z;(6fIwaJzvw5k%!mUf4sx(^upq?WYgPsRuddrsZ6|BDu2Kkxh2hFu<(({%}x4Mna$
z+3vhVAA5b_1-nYbg+Z%UP;$tPBi~W((r;Jg&{or9b>LF$FLl2rEArVcqp;9><NGBF
zj}zzn6`@W2WYEc3$17NOSx&w1w+a$3DY;g3^<2DVwHQ?14d5UDryz3RY|W=n`#ukb
z=u^MXaY{CsRG``)*u1LL=oWpSLif?(!!|rQDLK|xXqGKz8`_7w%ey;>;c|6#n)(z`
zSjSuin@d)(j$mKMA-3Wiwr|Mpcrom#A63rW`+RP!mTvd+7wN-^cX~e}a+dTSrx05}
zNa&}B^)DchfgwAb<>a`HobBg|Gv^DMaiRci>rd<nMquSNl5ZBl_DH=~U9u)GST~Ja
z0Ee45>S0!h1OOLFWq|M#utuWw>2W)Y^B`Uf4Gqs}ldKEN2_=uGE@FWd#f1+y>2(x7
z3>RcZn^)D1H|Qi0=?2TejsJoN|0JOYbHM*=T9JQyis+<TOxLR-Vi&RU1WTRtlRy1_
zQ!Q7FR@JkURu%Bd^WA#NaWeJZEn+Mz9|ECdx%wF4xh~knaKW%wmo+uzv=MB5xcB$a
z8~^s({~@u<|KKS|Sosl#aSw?gwToR2Jv%Qg4L9D1Jfj@I{6`u?_0pw2h4nG%ajve>
z(fgSW5Pks;FrFR-g9y{P@sf&j(kE*Eu$Gz{8zPL|u{SGVI^jA>>lzvYy3<-=H9ma!
zY*Tj{&Q4VH%pcAUVZR2qh@mfI;zKg!PJ`9_{t~)gRNRol9-nHO$Uz$+s=-sXh>+qh
z{>7QSb2*F+Bk|!75Fiy@dt*&xl0{uYnj#-FYJo?Mncu`ZS95xg&*(z0>>i)9dnA|!
z?RUal5IfhzQ$=e7Y2tnMYUb45fO5aAU%d}Bw9DVPaU;FQt6kMbyIrxY+ak(V@>oz7
z!)bV6VDre-<BFR;4=n@(Kx|~K_}raA<xfTA(={7L$$jSFmv2FDeVRQj<YAc*Z?-L7
zVA=5HKKL2<5EAQQMg7}YkHlkz(H>gVtn!=5rAn5WS{JDHk&%%fXwHaYWJnkm+oaQ{
zV1x8{>l~B1K_F*hVzTiRLNXbM2_7Ek6Ih$D?EYnB&ooS%5fzs1iU0)!PeuYW;$8Mg
zKwp8hmlA@&ayS)l26Pb)cV9lJ4wK#MQ;C^Ipacrp?>M#0d{NyH-}Uu1os2cpcn|Af
zJPu9w4Gf%@p|W#uG|Z+|{`L>(xBds0^k1lezdr)Hcq3{x%gYQ*SVsbtZgh4i8!HnV
z0{mK7*iPd7l}j?rJDFbU><|a;?Y@r-+tpv~kbmnyj-B6X`)-jNWt*AGH;(TkZjrnH
zXDQ*xss01nYQKF!b6eY$0aYeD^k#+3dfO6DE)JdfrUqiqimr%|6R>D+mi&yB<l!!u
z#nk>Z0umB(8zH^B6RaTqapZ)q%t21Vp2<ZP-d!)Xjc63~fSSFCbd3lMblbVep#>{q
zzAYPuSOwauEu+ot4ADLE&o{FY?UH?vMcF=K;I)~F2ntu#@$vJC!&}K`uHIFda1rAT
z1*{^%CEJD3l1}LIJc;y);4I#bCZhxol9jNA{AUIkra`$+<by2r)sMf=(UyODa7QI&
zalw`hIuXD?93U%Q2<W+~CPKjr%ew@)cTzp+dZ}Unb1)8uuCXh~zKjLUdFR7PVY9Xz
z^&DGst=qr-kXU_&Lk4>&2%5RXZhy^U<dZsW^5?Zd<9^xCT<$-o-WT{hXjm?g&P3LU
zx4*o#1;ayISZH$?_P}?SK=PtnAFGk)yOvz!OP+V!BP8A57ZuSF8RqFGkk%O5%oZ;n
zd|U$DCeSHwrPTZYVdb?T_j&gR)Y!j9vw);EXCT|+(KOAxF@Rk1sJqSrFE4M~sHY86
zx@eX+d-FZ?Ntfcawj)`v$id2rw3HNtIK{|eNod_*-tMd7$18I1I!RF$B2hy^9diVZ
zB}?hlm7KRv9A6lDM^M#M3Co+tIitl;Pr_pBAnB?H=~}6M<+0(M5PoL#^t0do2fE=z
zGeV>wf=Cdkrx|OEjbk?u^wj~S1@ji$tsDpRc;x(Pj$@@Ed`lm#bK@Sl07;=~K7GoR
z(tIO8yJKOz%FD}XKu<|kRU~rq4p#uy-Q}HR;0k_GoN$>2&_Cx-S)8pZiHZnj9|mNt
zH_#>tem7O->M3x=>aigyKP(K%o9~~p!ua*4#`7$EUMUkz6t*l^yY|@^%tIEr6HY8~
zDds$}{*!KkVg_?Y4<2`O{@zTQh_aI4sMJyt$){KrG^@QnYhz&$P7P5Ar%j9ba?i!L
zY<Z6#QG3G!+_cw`!wOtgaRFqMNZ$X*=b23x#CK@qA&c|32TUV@`is8}^0v!Lh9R{m
z9m51^3|Npeu$+m-1|tEgPd8TUdU0ojujl-c1nkufSWXT&sJlh-vlI5)V(!>LK};2q
zDhZG`Sm+1r3$h`T+Fg!Kyxep6+qYl*q0a35+YizUR!TkQ7t+eOT`;=1&_nU*CXl<a
zn|Xpq>M#ru3bbH;8rJ-B&-`zp?%v%7HLtg1bCLsGSjmotY-TH54J)fT)_vi0-gGV!
zAV!??KD32h&bV--C=ks$j``5|ttFV}-Fc~cc^e4*jL~8apYp@{m5)f8Y^_+!2FO!+
zI-wa=S?FNPcU=#~4$1fK>5R|(`h1&+;l;!yxW6rttTWn7;ogp8?Xwqx5aP44GF7F*
zC7d-qGjf8NyF8`lzJ>5Df$+lpbF4WrPS_%kpooocmaubpF6CrTWVoY#363`+%b3@-
zE%AQWk8O2rlkql1L`Vsulw{;!9nL}_x=qYf>pbj<gc?(;%wlBr%ks85gmQXFE_)#f
z?JPdTob{`B4_^PnoL{~5zhVB<*6Z3JLnc}u<Y4zQVDWbAyb*m&q`Hykn45B&(tQYN
zGHzcZ!7WM4g{aeAi1x;&%})O~(w@v92U=G>2<kk%9J_WVVw(M4?O{%eE~b4RS<E8p
zLJ3tGdBL&dhnx12xCBC_*Nrv}@`!{r&2mLZoNP+bfzEQFPc7d~hu)`VjzuVQqx&&e
z29xhOXzUhz=L)jvXEU;)Z!02_IY`J3Otr>VSBSN9B2VfB8o8KkKZl`AeF9d%2j@Wj
zL6V`@qL?f6syO@zy)MDokH6ibT;Sc-p*+T1zd3!f0j3yfKDz0&rmE_eX5(b}AQ*&q
z%FLZtaQpYI4)>reJ@1d(?$J%iQxI!+w3L=UPm^+LJavP4ugdUb*>#sRcb6jrRdj{U
z4&IAMm!+jKC_LN!=ZU_Dg)cRf7J9At(e}{_Gv~v+yq99tS;NkF?4Qz}7{frxfc=t|
zCR#s|h$dh19tuNtc6MEoJAJqA7(dCXo{Sqt1P(1~esMoZi4*0Q`=xnBNM@8km-ihi
z^Jh%>g8=F_7L8$&&ZgzvjWi$v<p4zsKR>@4v)R3KyJ@)48yb51uqCWC1vjrEus5qd
zBLs+ee|e8-!}8TcN;a6#+e-U#bi+=8EGZoXnR7v_jU(-+A~>h3!fPidEhUC{<m9Py
z=~<Moue)QkdYlN;Gnalq0H89zCKXm^nuu68j<i9nHd?CSoYZJjT|U-b4jK&{5YfiZ
zv`yy)Xf=S<C%IfGeU}S6%}t08Wpu94YL{Wt-VE)k(-pDCml>Q0o^Kf&1F+mU5LnTt
zKmj10h~Oc&^ofRTOzsc2L+&p+E9>&Mb}x%Tl5mQW<I9BQPioKmIPzVdFEdy;r7>=k
zLWLO)7YvE@n`b2YF0oYy){b`^sZ~GMebi<+DlAOzg_-6brkf<nr9i}$fRWsWou*@q
z^m$QUpeatx%i94e$8{KK`O#4?>T#*rQiU^mdU`3TsYQs_IFYu$(E>pXlk-AcC-rJF
zb}C%RX&<bh$F<A^izB8j85vNO$Z4-QA+~xeKVbp<vL1OqCn<eIdqz1!M4dEsruY6g
zl5*FBu2RTq`=b;2Ag%~UAqW&_h1F0bisf6}AD`71O%&4iGegHLpwuqf6KJg5cSyPS
z&#MJpiRr1m;d>rqMZCGw6>7bPei4xhKLX!GM1VGV<1ds0mL0qfUnr=Nw2J^FqTO;F
zG}3X0MLL={8D~~Rv^!v{xJPbx0x#&s4O>pizDGiLAhqF&IS|s`hPwKCf=-C;I39BL
z4D82Fw*&FtY@CQpn8s7yL`nvtq6`$RtnS>2RhtX8hKsm6RCim`sp}-TVq~5+s;OLo
zPqgSdCm7%keKcXgfJrR6R5GVZEbZ$U=?QcNOUv;Uxn`c~lbpRV6qrj6M=Ap0`GKlF
zF+Z{E-E-5qKyDKEh;R=mX`@;#c1rV#e5X%~6A6d|`6>GpH8lg@XFd-MEFasvHZ9?s
zO?ytrG?Lu8{EOBy3l652`8G3CT6J&sWNO9p(<o%6W04@%DC(bykC@QxZ{C{d>8ifP
z#hU((B&py}I41@hx{FRVj4u#Lh!N*u=njeKy)!)EiW^e(&Q&2q898CsiuBV8u3+EJ
z)wC}c`8Jn{-s+o^P0=#q_cIm^TzIm<W%?3$PJzz?C5%^J);B2j4-5>9+k{c*)B2MV
zgJRbqfpnVEKOM}OP_IKhw=b|_Pr@+bs`9bSmWF)=qPsAvPEynf{koPY&h&)%9urgK
z`^zmi2k6PaGk9s|0cx~zo&Z_!@m3xjSr#wY0<)(Rl9G}JdWIz9stk6WDNI**#4UML
zW2k?pymr+VRZpQACP7ty2@u2yDk=BevSmcQI5XNwq)tRlwGDFk1YvDTpQ0y^1Jk9b
zYr>U7$X2DBV4?V0fyS+-WiCVf55dh>8WI?9@7v_|Yxi4SU>aW+3t`?1)f-qxA@>$X
zMAu2gSp5n4QWEKBWMyTKLvuJv{5vnu>Ko?ey!zxS;hT!t7KFG*d@!Y^<8`2^TcBxH
zR0Fgx5__znnB91h)X=afI=<UDl9rNEh!X}oj!$5NitU<CCXiaBL2DDBlq|uVqh$vY
z3JG2Yn@03DND{n#+_F?Bv~P_@fhOPP4kFfBoTV*3B-CGSyyhUZ-KPB5IlR1r&}C0b
zQA`P^{8V@+&|l(}{w1=|<}|)PEv*=;EMLb&4JuY&XUAeyTdr)WQo!tTX9xQspU2<-
zxZ(US>wfxi#gC`f81DN0yOY<dH#~XAT5lyIC>SGqyj)djWbaGPM+F`6E-wZ7PpY$S
z`0}#lx7!|WZ~xT2r+oJ**UXCZhn_tPX3(F=bDYXLTsjdR-DAgyeiakbX5Z6Xr%H>D
zXAIdOgdxDcw<ba@bQc*Nmn@YhJQ~NoKGj?GWeCdfZNxkuIb!UnN`$r7<{$%4kY2)3
zR#5}XhTq;>&D1QpG8s%lR6?HSW-OXrJxfITZ&Q|PuC9)hagSWFtPr`^Yd8LNORj~@
z3)Y=yHoqoF(qaioDyHKQ1S+}rdZSb#w!S|6?ERS@okiI?62$%OF?pnfrWZD%!hYzv
zh*6mqGnhF+-|tXx3_;x6JAwi8&2DZj(IM&S=?5jvI|~2y)6U;c^Xxyr{;U7$ex1={
zP__N-o0Tt)r5|)cU5Bv+<3M~y=s_p!Q{O+}W|r!d%k=L%y&l0FA!C6;C{FOx>e-<l
zEuq(tks{HWt1F3?NbJ_ltk`0CY3e8Wc@PW>hiS$RUS8BaY*l%E{vne15!DS1YESj?
z!BO~xUas`(NLq1-&dV-EkoS{|&+$)Sm0#B~FWO@M@$B9rlBuvnfN_kwedJ2EnzofH
zYOu05hLd??sf|n0`7OJWxT%D^IYSZ>$n+5Pc@9*$qxXayLzJ1I1qZrm;lN*i+Qf`r
zi38XwaCx&?&gdB!$wmyeUc?J)H>}We@!aIhFXL5@eAbRYa-BE?pFQ%?#eL*;mizbI
zT9?!rE@giykh}QFtok-UGgLp#9X&culXW*@nf&uhXAA@#SYRUp7R?36rINdX&%TnM
zUx=Dqo|v9yb8@_T^=EP|a{m1L7TTc`&qLNi&}JX=+pOZS+%_p37ml3WDs+XDxRQlw
z>Y@$P??B5B&--Z!pGRtn%s)v*m?LP4fRU+CzRzLms^C241Ma{`l?jti^wsiz_HYH=
zWsZmm={AJ%toaMwLPi=(??=}j!TXIUqF7x+foC^VV=Bl_l+_;pti70*O{8;1>degE
zi7dP}*xS4BeD+GFg(&!3w{#F;BmR2O5mS5rio*vF9*ndj>j3E>qo6w<&)!?qp)xc0
zE+4T2Fz#oNX1hr-`}+PFK}vsJpN7uvNJHJ9`6`ybok%B-9I4G&3WRF;=C|aK^?(WV
z9tRal7UB8lNA{7jzgvce`)#%_4PgR?kizJs_&YpdzF_>T126NzzpsXiN?QEAiL&sH
zB2A@IGub#u5!?JBrX~9ri_=v(PbGir?>{iD`@>RIB?lk&A@&tsbQdwIFZ5B6(wJEU
z_J0ZmC9oVRE(pcW9BCv~Q1IH_OSy`d@mL_a!+G<%70*Qoes^^x6*^j2LicWq@j~5I
z0<|~?M5?Fw)!+Pl#*3Q^@k#b$W=-eS!&pZZ-4>%yyc~B01I9NZN!JNgvB?q9JWCOX
zYp|H6iwHqP>=UAey(Az9zJG)(vj|`Ub;SVovfKh10?~V#hZnxdQtZ_-vhyHiAww%I
zy#V301x=fbmrI2rY(YMwKYvvXl2@%=3j_qNt|1Xg=3f@!jrF#}`HPIVccNl!8cJ^_
z-|Jn;ZTqsNn>pavt}Ao;+fUmd?-m%89}^REuobGkp{209?-GjgP^lGp&<SNfCKe1r
zvxHUsKtB311)tPGgu1$BFU`?n0W6Hr(P5H2BUiq9QL5WlrEG}+IME85=uoh1W#L|_
z(TA?Cq^he*YQB)@TAAWfQUt(sV5BBz6smgBDL82|bFQw0`WUsLPTTy|l9|k@eGpaI
z<MR-Hb)P3hn;ED_;^oz4EX)c+0UTN-L5czVHBBF$-=p%kcjB5#@wX-#Xo!|K=m*=~
zJdWHv#|yK$iC%M}(}pM9)xve3a4)z-5e-u3!jvq2>+Ov?yOxx+0U4W2BqDOfoICg(
zxO@O9RlquF={0NC6v7J_DwiNW;vw9MQIaW`m6?q3%41<zXM6fDyv^+W{mD+JF#FY$
zlapHnO=Htu1!a37;=&qG^068h+k#wV{kjxV_R6fE0@cEQIfakp`&?ZcO|u7eQS0;Q
z$xhizhLf)fAY0W*7OUPxME2D#$kCKQuB_7S=6$2DHj2pEUUrb8uy1{R(N^0<4x6@!
zFB{(4H$2QU)iYG{T7J{Wo)bmiwM}|#6Xqp)cw%B=5|5I(b-5NYKv~Myov^yty{fG)
zMy&*9KQtjhh?kedeTK12ZV5J-|8oUuSxEsDDOV!9s%^6IC@GDF5u;+Z&5$yzZdCVs
z&)ob!Pww}AJvA{=IPfXCGvonxQ&&#Rr8qZI!vO9C(r_Y_KA^ObT*isksNW@Ry->||
zD#PLPpH>!=d0V@dL#K`?$)Dz-;OBtSl9klP3d;u3m@!$eiRcdGqB(3+33yhdhE4A<
zv~5~b4+?x{uH0=oSh+;+d(9p+u1rFUwtk^2>%CqOxLJR@El~1mcFi7Rd9U~Up5*G;
zhDD`Rvep0gn1r0MbJw!M!*3)9XQM#tkQy`ASImb6_2ib!I{2czn~@gEn{N1Ra8Tg9
zLXddk-+r1@{;sOIo7w$#PHh1-H_3ndre#m1!1;DWu7N>|2wV8h^H{zY{_7@yk&=X5
zIV?3*gqJrDLtO$8Umt&KXZkHt^GP^QUS0(In+RU|TU&r~unyveTwQOJ`!iM95fr+?
z1Qz614SdiMRnGP$0^5mnrGlX(%3Ka#ViPy6rg!b7Y$t%3I19qZ%(V1KO?#Gz<unWt
zF*M4a<E8)bkud{mBk`8-zs2zD?4%gB+U{%R3l3=Bl^wsV)``6MhbM^P#es*Ry<i3=
zq>O@;X`pVB6*Xr}8Mi^i3sPX>9m7P;kw!3Q)1bStB_AT8C6qIAN}ANKmY@_u_v5Rx
z-B*@q_BRA2q2$LkI5^lsh`Cr@*80-9bB+zGN7v%pm-+(aSYA7d#HPb2xlfh_8de5;
z#=i4J|Ip&j{L0UZu<4R0Cs<vB>XC%9Tr?uUKZb&gY4vTC;efvRX>ZU5f_5_1m=5X0
z#m}<!q(Z%rlmv-`U+W~1FX5lC$hc{9wGp~48Rpm&_I`v&vN_8<BNNCHf+`_lm}}9d
zT86C%48)Php80D_)9~BAc>$(}W{LC!%0ecdk=EjV-WRSzzU`*&3z)kvhTd^dPC#$$
zCKXR9nos_sI`U3QG*Cdo+3*J4Jg}C%xv9xe`&0#Tja%r=C?~s=`Mhgi9St~kznaf+
zWWeTBPDL?lA)QvamX(@1$+dXFZCCw8#Be4uC0-0^QhGKv=~r=cbq(UahYR)Hb0X_x
zpPhf*)o5^QTU&#*Ni?NWUXGT%-X^?q{{Fnt0p3t(MxJ!))AQw(jaoh^7OTLk5LUDM
z%Jop7gp)kS*G!ASSa(Wi{0d|dN*~>$ltnrFq?Y}~&k3z+Z1c^SU^QGPvUL1SJe?7D
zs4M1om`NHzchHq*M(>QTm_3pA{JsOr7A5-yafZe`w_jmH!yGm6Rh}0+C7kl>9n;Hm
zEq5}e_eS-IoK-Sh06-ykYp{)C5GkyCt8<^7Y~D`|H8q=1fW5soXZiBwTjKY8f;5I7
zg;3u*&n|T3KcAbQcb|&L&(ANb+ohnRqf<JnasaAVvd37BjI=T>Zg?E9PbWhqDJjVu
zs@N;JS+}T=y+mutkKM7cVH;Giv9a-sIU5`wA1^&b!&Y3?eicgOM{R9w>}nIH_+YAF
z$X6sQH0~^{nc9{eECBV!lan>bMy}#vAySSfJreKe9Ji6k?aH&uc%X<&-97J9D6B0X
zUMat?l?F>qMyhvkP`+yh<Mjx9xolw*`>|ukjI^p>!`SP(3uSCqE@(!g({^WT<H?D+
z9jY4>LYYhOm*gGc@c_oVDf^%R6*?}}l2E@y9*T+WXNJ)Kh<1U!JoTlDf}{vLd)qU%
z>B&hyM@Kyn<imb3f5dv1(p;vYncM!cv;U8}h7~1$taN3=U(`nOAd>5jW|~%)iX*RF
zY-%Hw{`$OYD>Lw7`6sv0Amj0WNFI8m3bI_a?6$~MPt=u>uYXZ-b7w#~3L~)~P=8SW
ziWDHCR3Wl7Yne+QDg@jxwa;U-^Yba}91X!o$rP?wu>vpwppw_8wDx){%j4_cUHRGS
z=&@s+06lSV)gnL|gn~5GUcsWc8%Wm+0y2eE!LZkYc{3e|MdGctvpg0zt!B>If37>o
zzjIsvnFb^OI>i!>_&c_OVXW1%ZX)D@PSXSRR>v8r|1L(a3}O=ZC<JqoC;&|jeg^qs
zq|MA+ocL5LU*JGkQz%rY;}@wUD~i;Dn^An`9zrV-9B8P>^l~R=>*NcLSr0Ulh9{(0
zOc;+5Xt@EPl7zCB^KADh@`okpg|b~u$H@wD&*gxnl-_kK%k^yl&VuWX4ZN|1&TUgg
zMFpg%%qv5H-9w`~LtR-&qJ7ZIkycU6)pkQkY?;|UxCv5qi!zF#p?LG9JbAY;|95Q=
zrM~sc-n8d@<X>J@5}W?q`V_9(EO+t}sY!&MuS114cM*=nY|M^S-MdNE*Vb-cyY?L~
zo6zdq{c-Bl2?6YlZ&iCX_urj=#6byEj;0iQF^tc_F@1zthm$C55ZAJE^7F4v4BQ+(
zcetfQL3z63Ec3Y0<b9O_ZgPDy8jDshYT3)9hQ4bfrE3>ZJdc(y!Uqp-V3x>w0htR;
zf$BLa&`J0d27}=tW&PU^Yy_LGObxQ<IUeX;aZ?XjbHTZp+tf`a#Qo54i^O-3T9T5g
z+YKK-DyL9Duc^lGu><StKBeFF`xEj6%)HwaL6+nRamZ5<=!^pAQRT|Ib7MaBLZ3Kz
z&^W7~M8r@&7TO6lN;r8JLQVJ?s4K}6SP3^zpW@=IW2vpi;Zqf#jMLowb}5`beb=&P
zqo~!8REytHG3``CRpM137%udp9jV<WoWdU|*#3iMX3H@~E^>FIe}*oA?7BxREc?4i
zDc?#S$2aviYHPu0a&6jsAr=OOWz9}2wvh`6*RjCTT-7-y1QB~UBNtgh1Yx8)uUs(O
z*oSsy2q<`X)x(DElJ*lXH%%fLWLMPi%u#CiT<yklld|pSrXw1+OuZ{DC~#r;zNCc1
zT87g1O)<oRz9|@l&y7R(Q8-LpoZqkJ%m0AW;+Fvga2!s?t?A|WlQ9XQow6W{RghD0
zbC$EnpgG9+N<G*%L^n6R{G8$YwIw%2x#vo%Z|U__?L*tPZTqFjd_i%l4%Z$p1mz(i
zl%V2%Saac+Krj-vwliNIbh<2G$2XfJCzPA=O%EBJMAFzMj+&AGaNU|Uz5{i)hZRsA
z*2J@{wn?l^m#zU{PR3K2Zti~H739I@zdCMZ`lv{<E1WFbU{zmTObi@*?B&$xTU2{{
z`@eemDg=VeCstNl1{KWA%*e_74ybL--BvXErHQe5{>)c=r4&ubZb+ns^79AU$8#?N
zldKmz8sB6n_q<N6MWZ675;s-E8cro6RGy>Er1*Z3yzb#==NIVhi>Ra{8Bme<B~I)3
zeVGD|FPkLD4t>d3<4%G>wxRNDXwYjjmN=cUNOu>TPf0h-5$TaC$SF?;8$_W+q)@kg
zrdX_l2ui>xlKS?w2bggZA1!O<Nh(YUX=c=dT#-x<(E<}44AyqEIupX5=&qG1z<Oye
zUQeBQXA362lZfVt9wE_wvTxflYhx&B1%-jCXHdep&BMbgF_URYSe5oJGm|bv+J75D
z9S`&{sn6O>-f@YJbaz;)UtA;mFURzcbpay-`G0^f{~FGJU^t_SFQYOJ=~`0I+J8tH
zb(`N*e+r-TDZNg%xvza3qA69N?rPdcQW@Z@7)|l;Shp4_qsfTx>HFf4<hzAZIb+xX
zZajr;zeQzd*9r`y6q)q37-i6OU>GetKc4fTp&?MWq4(Y>giD=nuaFWC8F1&kXZ9Mf
z@+2*1&Gg+zaDI)3F>!8EQZ4P$P`6hz5WJIbXGTXf7|=i5lgxZFvS8#1JiE+%4YY1J
zHE}bysAL1<(h>h{9aI2;MJyC3-%z@NfkBjoNGUlug<>F{7@2}hAtJJ6TJU5$-KI)b
z^0Dre_2kc@jV?RsdwiOGsOR;w^TnA4KLsAO;TeJ*YZ42P5|ejBP<oM5f6eaIhc=3-
zC@56D<d8aeP!aK|3D|Y;&X*=e(R8c`7le@N7WMn5MmEMpb3_(Lt{ACLTt2D+$P*Gz
z%|kn{fy>vQ?5o3*)>duuq%K+&hZ)vIlfznDTk!-&hdhCm$6+-!D&#Pbn|RXPtVy0g
zcPDdKoQ)-xz9GAD<!(<}SXXAl?fkM2nFa<(u3o3!&sOdk@^m^!3#|V*$5uBvHTAmV
zctl*BiTmAK%Z6{3^^Ct`RG7b+FsyMS&d!U6j=1X!<BRgI3}ZKjujs*pFDbj3p6+nV
z@?U@K)K{^{3>sDWGh=g4i&4$F$&wji=W}pomz7JZ=G4CKO)a)yZ)>k1JC@}a;eSdG
zKys@8+jf6+Vm;HXuuR_Q{TObJb_osFTfvC4v)5l~cwW_`clGMaJyxXd-W)Dl%~5j6
zR=tcxQXk38SAZ)QA*)fX@BIHY;QN=w{7+cSI`#hV%1ma*y3HZQD<MKDB7@wg^12W$
z_BIk7CHS7p0ZR#RQL8N@Qpm=egEKBJC^+$Dk+dQt3~~q27?G|(moHy7RNui|&9k)?
z0_-G6j!mv~`_rHPB;FSpMij)4m?AFpGbA)P>5W42pO*ue2{A=ct4$+q$gCsg(1mEl
zi)4w2Is(g%R58p{+T?8lheO&`5`k-++!F9{&-&4JeAW>SYy?0`1JmU9kD_R42EiLQ
zD=QJLsTwiah)DEvuq6e3;Eh0ak9L-r-3bO+0q&LCVIVQOj(I-CvsSDlNYhbKWoMG!
zDw_0Z;7reU2<gyIX<@8fHHI0Hz4T@`(mo*(tCe|(m$9|O^gJ{BFflbnIv22iFDvbc
z_8p!}K?-8RV(1oDs;Zqnt=-k5$q(R5LItT{xFmJBq&~t>5u*+>97-?k87Dyqf=omf
z$ng*c3r4@1@rY9*i9Cs7+_`h-BJm+$eVS;vS7Jj^=nR88WNReOAz^X7n(+r)_=$L*
zslS9Ppr-Hy9_}x(3o~31Pi@sDoM5I})Oh=smmwh`*Qr6-0a9=g@QS%Meu{l97t<20
zAFY|16ghbCO;LZ8yUlM_%U{>7HlmBAV1B*LZj+`o)h_7~5&9p@Jjvn#)HYNcuMa~8
z#DAa;OG-JV>fOnx1C={@L9}|QCO+@l4$((7xtlffi~-U%YCajtxV3nK)^d0Pq`GdN
zfvTnOF0m{39jIu&q@F@S+s&Fc!&Shdc!I?E_9^;yAlGW<r%oTeov@N9<(g=a5g9d7
zf3F31mO=rqt6+?lL#pw78qA^XA>EtK2OrNY+p?Q<A}h6YM~;Sq<h)Xmn|C-^?CaEZ
z(3rh7;du}SrIYVK6*3@~i{O-ymBP3qfV)N=NJi+gWspRP_noMu)8)w?*V7Ug-gotX
zR?dJHuo%r<*oe@OXs|h6M4(xQCwc}!3s5BFtdx{VlD+OLyxP%rhG>&d3o#>rpX;v_
zWQDgkM!6rbl|@T4R#u{ouC}|BaD|K2NGPuTqD3AGUqijDtgT5gV%p2HJ|d7pAYO>g
zNr=$;?p>7_MQEr&&J!2jY3LmewwV-~x~gYeZm*<Gn7yCV(dk37qlNc=D#`*@&36R{
zbEmsEo<R39<J7uKtcg=jQQ6tq#anjEVGTY)_KBA_D?5AjaBD0STbDAj5@TWxAe=JP
zQcA)+ZZ>5lEg+FUi8GfeJ)~Rc`c{Bw7rz|v;UG8&x+sYyK?9sOPD&~_R_zUv3lQX*
zQVTV^MUUaBxgD==0yAMnszniXEjX{EcDCeZpkV-!TS`kK>t6y{pb@@N+(sx4+zd(U
zva*t0jVeNRwG@M$XC5_nxTFi39!VbhVG;I;O>&z9m;qkh!G`Bd*1zwh_V+o6+5{at
z^3eCjqjDTQ9YMW19A>^<dl#kx;TtC!#K5Q^pPJi0O7dz6&XX~iyWeG{l<S&^sbkq6
zF8E!WtE+_NS7~H9=ZEtt=#r?DG2BGB7$co|qEaX!GW7>s){*An%+GWyu~Cw?CQcPE
zl6mhWM-lxdKDg*vviCyJHd3D8vBaVB<!2$$$V+mpGY|}N60s+<_YKjvqm2z|sk`pz
zx0i2)0-2mIE`1e_N8_GAG}_(N@$04wkycBYo-L}NcbG<Qo9TZ~J7aQp|GiL#kaPwB
zyxiEJse*bwd1HPFh0&@px?q_}UbsFhNP7FWZLUPNj_fBXu|TV}E1gKR0$AQ7#$<OM
zGDC>duiL@zb)u(`m#Zrj;ziKAU&L|UL~%#jqrm|@JFH&}21b*-OlUpmd$0j+Rt0k*
zM2^A|Pr@$D7|QBLMjI(XVTY*!cJL(75!kdqY>TCXR>s8mlhxXS%&<B1xiZp)L~%np
zOL-hP=T9_hMDyqBDn41OqKEQMa$*Z~aw{Ud3tmRyHc3H=z;p>_c6r+}Ys)*{t`>TP
zCQEg=k=gq6ye*{vpQ|faAAWX@7dI1zA04iA?Jf_H#lA&?F@e~FeGx%KNs~wzfOH5m
zF9-PWVcYBr3LHY?O5)nduxMo6F$kdm+BuN?Vl*yh3^}|a<o3|5j?|!#zFH#jw`$-T
zw^AC8Roz6}oYRK3smF2s)pd1Sn7da*q;6>{_&5s^)DurpMIVC%qCfnR{^<l%At4kP
z>5sIv6B84g=a5tU9xo%`5<-Ekj9?gF1@OPeAd2W}+FAlAKhU6#)uM`ypx|}uTuohF
zIEBL&dhFHo-rimu!|ahvf~(%D73fg=L&<~P#{pMH(Gv}Xzn)$g!}v4ud7fayjzR7?
zd2=VJACU&%0~PfJs^}X=W&=2)N&V%>)_j0*zljTiTMnV133c2&BSVIXA^wiVeNiuN
z^~*z~3u6hWB~(_0)rar;NaPcc1;9odYMo_<a&w14%Cf_A6K^%4U*@|{1a80BlS}hk
z74V1>?6v;F%;-G^>E1=&BB}gSR3gtIpCa=ULO~a2P`ahbNPn}DVf>Z6g*LCGIB!XX
zV2H?Z0FshKsKTWSo2KpCdOzP??@8L~@`fJtoCzdVqNJ@CD@sk`Lz+I2u5O@6nc=hY
z#$M6}3^;~ZZ$s^K9e{*y9p+(Db#Q$|^!kB@RB3$%oH_JEArzu%G18LnzJWj>uU>{}
z?%tpe9#aSPA7noOr&u&^{(pec=lnKu!GVZAaNOtv%EAt3)JGJOtTi%g!WLb>izD-^
zk}gS_cSZ!;?<eB;Dyey6(%X}C1|@!&wEfath#8q2n|A&d@_=+B+Z`~E!t99H{M#B)
zZ6>8j4Ia~wFz*OP8#j`k2aws{M!FP>p|E;KopR;T1DP!$IcL)DEbKCvZ%7AcOGGWq
z{u87RH983DL5F3IHp0deY(xFwHmB{lYC~6^RvKc2o6J85-KZyIBDpR4a&FHnnQ+ff
zePH^^JK%qy)aQZgas}g;R=>OMB2>+LgKc@8#{=67Nv{CJ%!#C?foP!MP1Lb+<Lh@{
zx|lg79O!V|cPkuyFlu3JNy!=NgY<|T1prQOtgBQQ+g$v!%W^p2S?wwg)A`pH3-p={
zv*^0fku0mdPVIib>a`si3Ot0uDxH4b#^ca4Y6St|gMc52FL{0b*+!!3M)#bHR+*Y}
zE#$fw?P%I5V|1=&iojsfQqfza0=m`8qy4cJM<wDS@~4S*icQDSyfjNN>sx3e@brqz
zE#a!sHkw%jx}8omr>Td~OxU>hMM}T$yF8?*vs3esvv&%;AAKrjW~->&L};XOXO=j<
zBcfj&Zi9_n(gH9B4>#KPwfg6+GpxQF!e_JiF^zs&DJn7GHMj3+fi}yJp~@x3ll;)x
zkbWPeJ`eR0fG_cAJI=1AdJJaJym-5QA=)RZ-wB4^OoF6V_Fq!fKHXYkTso@w+;MTf
z-3yKT625f;TNA-w-cCOD3m7>%y(Wu{+->$L?Nq6+^5c2??6xQ=Bi<zqJW0wzmdolQ
zCzQ@;1HX}(N<lFmY42E^V>jM`m?*l*;=x!8vW<`uLSER3o*=fQVLnM263H9#&k=?W
z(6W=>5(I^k>V2ZAMu`V>aAHKQMv4GBM@x9Th)R@TN;fmvk8{a+Lf90fw7@ahuuaRa
zuT{|>c~7z+nH};_0KhGjzhI$-F5cZuT}gEsQQiLiB8M7BeNqN>dWZs=6vAQIbdt`=
zqye!jnC5T8!(K$}ftn#kd{3?1FmKv|mEs`x8kya50j~#n+|GICT6wqKwTPN>xbXa9
z$J4kr6hEG3wuuaqu<U@qE6PejL=2KITeR&BLrXYPCP*5)kR}zR;*W^5m~G1mBxXv^
zFe8fn76)`A=bs8laG&!*CzWE-vmbgW(pBXQR1eSw5HHX<Rxad)B$!m?@$vVhc7Sxx
zC>&`L^Xng`)jO%gc|JhM5Ireu_Iz4?=(oT_r1>aOu2L82oyj!1F`}h2fN!LWAqpCX
z(Gu2!bPtATR4cW6U3Vst4q;6)k4M#_E@@oI%ZrFtIb9MA8+@%e(QC63hzb*lX(dxn
z>cmiK+YcOoUXln6V`i*(#Zmf=ZbdG{M=0lVlqF4Lc`^$P(Y^SVF~n@uAoJoY1dOmB
zOf#?Syvs#7+strv-7V)|2+bE8E9)X-c#)f=B4Ogm_aGaGq|hT3^fcF|{n+q#OieIK
zu#0vFXl+Dd={Bdu?>kZj_p%~|jqVjQD63+37)(_|M?@J62mnSA01yWRiyp42YQ1$a
zAp?486SjQvWWS3Kym{9lIc%pudeY-)wEZH1JqdNl?;}8x`w;1fu#uoINoM;S+9?(D
zuP<}0kGR&ZTCUfYG8}lngw^ziIG9(`)tdB5xj1UoJwE$3LK#2BFp<p-?=0kaiuCJM
zy^)z}Z@MvEUxnlV-yQ|GASSVRyI(;<HL3DT+udEcXAG5k6ayRp)q$_jbH98p%9h<;
z&7gbWH^tg$>e)|>kYD;6!x**i`lEsfj3TL5NP`n^hkv>451#^hl9iP-#P_I7M)8zP
zVH1m+O-${m;$>JpQmTMfhuPT<AF5&koK!Lyj4AoyONNhX%kyHq6Yk<>zR%Ho3CSJt
zIixU-69O>i&tVXSrCWjxTvj4PXRfYqIo{r{&;6N)P!9a*m#PGIKNOKEJT|mj{Hepr
zb?FRWmEHZELz2IIG<xN`%ajTyK$NGvp#y5$UxnECU8=;Ui<=AZ#imQ~l<ZTG%n2^*
zCPlr-S9g+ruB75OuKet;h?odkbLF#NmjSgM-~gJ8L|;<7d~wW{PXHOaRcb>QP@;f4
zdho{`ai}fbk#fRa`$n<d<5?Zwvja?%oyBY}3K5@Pt62?x!9l=Nyu<aWs7ccq+H}9p
z;!y~M8G6YL%-_{O$UE)1IFFavSy`J+vrVc4qZ8(f-r-jb>^}6fmsuE1wkUh6s}wfz
zcsx{h_}ElmpH@2CpGkrGU#*37Gsn*dlpwXLhcu}9$Zso=8XHjZSI{{yYZKd*RKD*a
zXxbI??AzbZob8wbxa75WaDb2yK9PB;rYN@}^~nT#jW{b0Y?@U5_#yk;0sZy&Lod<g
z%BI%Kq}m5`aX(%Y=<xnh0>al0QiPF%f()Twzl*hk|12Rj3iIuY&deTD0M*1IsK#6>
zBF%Ks*!?0QB}T0D8Lz!dPM<}UZzpP<+(`L2bV!bUDzv6pP0iYOc~P8H>ip{tQIRu!
zSoKkQZ5Y;{Y~_&IX8tw=M3~ItkuP47sP`dVUr4E-&#^9~X=SEMdWVOp4U2J1lF8a4
zS-5|NEw%*g)=7HSS4Dr#n4Uf64(A{r5?Ju=41f?_zEY?3viAqtBsJBN6xI-ELe$al
z=8w#PBLM1hN(cGEm2+bznBy4XTVdz1=H|jK^LfIIu^PRLA*+qFGkR-D1_ry$1Cfdt
z5d@MtWl@dtqXxa&yZy3(XWSXP4S;7Z=gL{EkIWU2xEZ5HdcuZ9MC@h8SXsV>q$$rP
zB8BgGZWR9zieoyL7YewTDm=_r<D1N$>93L4Wy-lI(J;rX;RhguwCu!5pYY+F@l`q^
zJdf-@iTQO7o3*Ey-`&2`pxs7w>}fjpl>B#X9)t)ar9GsOck>0&VlN&ZL}m}c>LrYv
zzIxXj2(OsXHvU;y@4TTCH@@}LzCr%Grms++uRN~KQts?!d4NZAq9h<NjI@3R#xn6~
zR*brQKdU9Az81me;+W9l#y`sMiJ>Da0k(sctY{+fb|bL&^qkd%MXg56jR2L6B?`iK
zoji;ynG1BjZ}wxj9MeaECMbl2vlq%PY3N<|kWSI~RRre^1mTdMry)SoEGhWNWW4fz
z0GmkbMQ;LZdfUQ7R@T=0b0^n@!MX?zAtn%qXj`<uMPJ+w9#Cw~<j+GxLrmw2ZdSvh
zp&PL<)4%+ngPz%*<RfUBbTo4PmVSfwBenVYPBq0QmD|qt=f!+1$(ej(wsFMtGq3?s
z4e;~U@0o`F#gTvy^4l8hYFqE=^l6HXh~A`&dHv?xcgVN9zArO;SJZ1DR)X&q@IyfE
zBv@w48@czfC?SG-J~=Vr0~nz~{CH3`;VBlzDT_?vg8Wp$y584c{ra@S1GF!k<)m;V
zOC&t0*iJdy!mv773;v1}5sw1?<&CJrmJ!5WjU3X<MS449u>D-UcUqh3sc`$pwe+g{
z-Wh)#ok~SuX?etK#QzARgopf05e(rg6b&D6#t%yrVqV$UwC^Z^_LCsM7$)BwOmoQ=
zH7!=J<72-N#S7`_a5*55uUm@yTox(v258;NYSjLY6q%6rYI=~akB+=sUxIr7|5e;~
z|8w2G?>|wf>{LdSsA!R7i%@ohlFUe@7m~e4OA`q<5g{XYAv?P=BO_&xl$A|p=I40U
z{r(re-`B%W5zld5=XIRNd7Q_o`>UkNtzCk#@6Y0lICQ)rZ~Wi#gEn#_5l>t@9T}6o
zOZrig;{YRTcI`sZm`*U>bH-2ESLagw?`7>)yH)t_rT><px)z`}IdX-xn<Lx5PJrjE
z$;&Y*oV%g~k9gscN6nq*u4v%AQ$LXZ?1`k8xY*a`$_4SQ<Z~mZ1Bood>)-4}og5lt
z(WB+PI_JIN+@WilT<+(th2RuC^U7PTb0y|wUyFu1^GiRwqNt(wq!BwBlJ22P!d-7J
zCJX}HKg*Go6%$9CF9Ie#1Sw6z3^;v~zT!(?<d=1jw3njqO{a$vl=!Sd>1q%FCOivl
zYo!KC(W1>oa=~Oj+_8bw3ePX6{+yNBQWpi17gH!aQ%xmBA2$_nlY<t_0_0}z0b>i3
zgLukGNCGpj(QE^fcS}Ryy4K`ZU;Ufk@-Op8&NDbRE;C0qKr5HR#6k9_Bjf*of#<N3
zBH4F_04nz<11V~F8MUbMiS(WaXnYi}5qfRb{?*wl@$RP%V9^clt*|s#8V-2wDDUw!
zZq#J;)d}zxGsi=}fiDgl8y)q-jXT!_HFbsP#Jh{IH@2t<xFXm!Ej9~G`wYD*9Tpm0
z@phl%-E=m0g=$IW)}wdPAD(>rDRBA0z1Y+Ji~?GldG`v4YJJIU&@m0Ct5q|4(xg^n
zU>qyj@O9s&4WS|7D%AyB)Hsdz9Mk!<$k-PvBKoPISbo0d2+gPEqRGYcmQ7qO?#1I7
z-<E5mpwnL-yLpqEc!%V2c>f^=-(1@sTF8E>2(+Up4>u*tZGN7EFknJZ`qRap<qSOt
z2p~n~Z`|ju#U&;l;=ELP_W9jjxLS83WvIBhcEd!qaB(+uLMb^Xl;k9&?3Y}x{ae#@
zWJiU4=x+Hb@!R%6OLZ&<Fsz4#m)HM$$zA^4yXo+^N`0tkt}o5of>L5uy316Cp54Ib
z&zb#>U(Av%(P|p;49okduc~@aOr`Gk)PPn~D85PHD8E~?IHH-`)z-vjQ(^AG_Q22Y
z*yYO!N-rL9Q5N;IVTjt;!{A&#I3|95lgWrYZHk-U*GnDV9=5A6fPVz8gZtg;uF2F_
zv9YmD3yvRiM-9t1T6Z%z=R8jxFFsVzIp-0>Seel=<3<tG^i*M0{A%EpfACRTdhoo+
zzC%UQ!IZZ@rcNncNU{;@wGZ0sFJGtSKf{_EnJ>HiG>7k5L|Av00v-(#YbAYsuBK#z
zy9j1G4jf>jW9B7iY>1dwvV00Zyn&8stB8zjzW3@%%Nx^ModNi%@JG9)q@eJOh%m5m
zy>w|O+}pT`!^H(H8PEOy8BVtLwDtB052}YME6uX`07%=lNMG#d1-`PhS3E&%RyXO0
zEVT8J>2bKyn$r7lneO_iWvO20O3$msRalo(6Tn#J=}U6%vjowWZN0Zfg|dAv+kWlI
ztQ&_qTD|OAysx-qjQ>{WemwAYc;+h$lK#pOXZDi5LQ0nYf{79@-<1cZwdD}}hig$|
zPcjoe_wLcB2KY=JtM%Ze(BEchC@6@OcKkQzb<FdKt#T`}5{=h011bkQ`jYDk?Bpd5
zzPIF;mzOUsExiEAeq~J!*@sY{c2T9bug?@RM$&Flj;#X6A=Mql8<8^3JFrsFFi!X&
z%I!FGXd|qbezs*<7Dn8%`c}k);uF+K5{CLIuW}AQGHjw`>E>i*p6qJbR-$GxH`aOY
z{{8n6HbsNeYL!oyC$eTCUn~RNZwn}>FH!&5Laeg7x&*p0g1~MkK9rR3SRHI0c_CvW
z9Mx;NT=BH|9{+Mh^XxVPoT}*>I=UW2t#=@Z<9;h=>F<Z+@&+OSwL-GmGPicSRLK`|
z6=gnuHv5fpY{=b?LSrZI>~T5Yh@)uYTdi94n>Qj?!^3b4e23P*!Q!aj@mmN>OemDn
z5Dc86rIxk}qAK#tAIoNjo6{(bdywlMciToonH|T*06gY85hZ)y$A=IQ5KyhQ3AGQB
z@n0RayJnXVP}$efSM%oq3*Da7b4oXGb(NYOZNK~BgCfag@+M79d@rf|^hqpt-;N#B
zQ~fnc3JMCzt2XV~jOWjv$5T2eCEfDbF0xbi#peq@>ti+xQ%y`wDIGsf>ozxL-F(E=
z&AT>AZf^};+$q6*wTwX_A$Nf6Dn{(CT%__y^;B0^7qRPA4Ql65K6cC$O;rc-JKh^v
z4_LHv`O%j6G6t<Q4bL?6DVN*mCoJT@@JL#foZ_<0T%B)N;eICX&CAKjnRQ_Dpg2lA
zZrlFl!lPV}o;c1eS<*PVJ7?M1S7srqqwU?Mg2^cUEn^FxoMc2Yjjmr8L9b8G==Fkx
zxVSYq(^Q~eX>}xDl)1UNSj+98i~$anBY2u9wIuKI`ARtq*l94lWG53DVd%Nk=XC8?
z`GBbc@4x{Y@BWaf+>dkR?A4m+E`1ypcCWNlIiQhq<HlQ?_Uc|(UYH@*NtR`EeYUlM
zz|Ng9CdFP%t5pM<xwTf7)?T4`_CxavE5vGqduxN;$&tJHRgUKEj~}yuYhL5(>RMS>
zr*ir<s~8ufezw(pAg36cZ$<a~{hbE6@8`GGK25Kg+0e6^|I1kd(aL8zl~rXiKzPjC
z#YGa;t{W)U(dIjien5C&;o^FLG@C_8NJv-yb#>t=F<?R$MWMO!He<Q>&1&s+mG+Ox
zjD}@j#-;3ftLm&A7U|cmdyfU5cBPINz85sWGP~hkQ6GD31GZo{N&+%F`1uQY656Hg
z{cUHb9t&^SutCYmNgQ3#yO*FJ7TmK(5pf9lJX5A+Gn1{YEg62xGzz#Ea+CPBc^kMc
zOo?nSwhxs!GF6ppW4A^aC9c1pKuudFV-z_4`#d$f8H(1UzaQ0vu<o7z`_7IhLc3g0
zL;vq%{$Snzi~lwx>A4_wX3r*kG4V(XEV@;?uV24D?xM$7ht%Ebw5D^0X9M;-vIg4Z
z85!rdwzi&1IztY=E8uxxHQJfKWlzX@JPZ~^7n!YaQl%nqKvle|x;kDs)wcp9wpClx
z{KM4mwxex_gNE1hMK@An8YxDcMa@Gos|UxFP-nQ7S~t%jGqtl#-^BK;T=v(qdU}Fc
zrlzJbI*y0WK&qveMcIU6q#v$MAz7DSQj)_c->D2r*4t<}S}$SKHSH9Zb2xYUqWg~m
zo-Q4752De}B1^vjop4=D75ub}Tx&wq@8IOrShb%m+;`*%uTh@E`vCcsd&s$SMGbfC
z_{H|;!gU!;j2Q#)jh2K_vSw{#kSE9KUwk5Q%1JQ2x60g3$$qL|i<DT9>k07gA!<QA
zSP3xVdsh=7k><VXMXq&H?S?iQBnL43TW@pM)8L7Q{68AcqD0+it$sR4lM^#6x(af0
zN^J8Da;}+TW7Si#NQ&8VKd#UURWHIPGgIv6j9cPGtdDU@?<j#~u{B2bl`XEo{L&>d
zx>$UzTrvnhkr;Hmh$A}#UI!oeP2%LvGJkx&dvX0@{6&8r8DGq6q=$)_cyj8OL6M8)
z40tXmZnoE8a#wUru~T(bm55E3Eb$J_V+C9NuJjHJ&=BEbS8qCvw&iAy9PwP(zHcAH
zKy73Rs@)TCtIx~yz~kEIPcGZ@ZeSg8NvBVrzES8tr*Ali66=!#S9+g$D)&-C4;U$0
zSy?6bUUPGkX4)$0lb$Y$8^RZ?f_@;9<1q@?XIwccd|XpN9wQccr`_c89EW`?D^+do
z_OMzur>rGF&vL29TNdZ-4N(Vvry<CJnY7D21ncKfA98;@i3epygTP7nz~5TW&=~_5
z2_MX`+%7BIF`9-SQ=-4Yr{t@uvmY_}J9O=+K2YU34BkW!J>Utxwr}4~V5@S-y5N^j
zs%$;t!NDW#F!76*<#*B05anuZCuUsMRDX8yHBPNSr!O_ZA!>flV2DG^f`#Y?6rKk}
z?9`@XWp$<9SfH#dV1S-HYT^PK+XRJ#4(6UXf1VTQhmJrq_#>@8{rrutM~~jfTmqj)
z_(1r}hvPgjFp!>sK^f0X#J*oM!&XYa4qjXL$r(WwUlJH^-ntd|_^}TnZ&QSPYGx)o
z2^y7{<<?~B`@MWANUn52Vd0#&<3I=h3}uR^niv{xJ+mjoVXFt9*SYJBlZPvKJj;Sn
zn@5<VNjAvw)lbHrl6wsA9>)i4_Kw&P>#&u1WSoLmnn44*NeF+!F=^n0cug+jvCe!Y
zY(6nD_T|O73OIb9hA!#g0iBBYmoMp`K7IPLC5@k&hURmO5*<As3n%9~Y&a?!+Lneh
zVBYs2w%`tIhnx0CWFI7t5d1o-uZG7XpFa-(3~f)amT?@q4O;ZPVMU{Lzm-YK>(|OK
z9Pnl2_QjKXfw?vG%*+))$rG@>(3sKF#)`vncCkMCDA_*E0y+(k0rPb?!{jf+VD95*
zFDuK{L%RC<53nuyanFTrGm3h89JoeSPR{#i3A>0zeOX0Ink`XoV4cbExuzu)i!gMD
znh@BvtBvRR?k@M+EG@bAyQ91ouHL?LCkBnr9Fo_!A6Z?pA6(7^S|ual!eC<ZKhC|C
ztQbF_kaQ*%i&nSUSs8!H%*>1+K-Su7@Sua5@8-RGs=F{-aScJGK&D)Z^nB7zBZ36k
zgZi}NGRo-GK6ul9EQ+Khx!NW6En6Zw`mswIqyd*-=Xk8HEI0fD_f5tuHScE6HEY-2
z$B3troiiw<Rawd5y=(y}#P4O|l@+9>rq)bpE^xKCU0HI?uwFuyRvjeap+tlmY9AN4
zrfVITJHFJ!F&6{LRy%vni;8C0IDI*H;`x#NC|sIlM`5Jw15ma#R-gKZ@?0ikTSfi0
zk)9Lh5>6y;L^2v)HoCODd<?x<TGp-$(^m(xte4)RWO|1sYjF~BvF0D4+bn>r+TNHM
z8e8V5g~HdBxIDbiD|55`Z}V!NjaDntPh#&pjE>&^%Cj3YDKA&IytRGNDEcHQ2#)Y~
z3}(p}d2)55;yqk<>zR{)vd5s$EnIdGIAJ-kj?W{*q?Od|ZzeDb`F5jY;bhl~&lcqP
z+RNEdEiHPd&z~1sm>u0q+{3<Y81h00Q6gAw(J`mweEMfj)Bpwt8U)Lp5OoCQ#3~%b
z<KOAYpppf_9zkG+;8nX^Q!gov7waQQ367g{wm6J{ZXhg1=jF8}x#3COgndewQIX@Y
zt_MV$;Do8&W=GZ`b+aq{@xx4D_imX@5BYn!iuVSjCN5NA=a40<a3f<~U0+-v4ENqA
zxs8Ayu^8-E?MHNG;FaZoJX*K9y0%ta>Q|!(mkm~Vg9o}NQZn_LvaQ?WR>X0@y>e_$
zw!!ryH_ga&@EGkI61UG@^#rl2s-_m05PQ88cqj1LGpnpee%unVKx#9`&o%WWHJ>qR
zT6iu;qGH^Ji`9w_7Jv2vFb>tbG430#KMGaoZv+RkLuny#d00qLP{GbFz4e#Y$J$z2
z0&2O2e(SBDU%~rM0bXsuaD7eiG_Uk4h&0``V@G;O5^F(UUth!O`VAW_lf9ACUOlg-
zcAw?L48O~baMnNswOj;6R<}Z9+^cQ(JF3qqMlx3;5t5{hPU`7}wPaomdTZO`TR%J#
zo1U)S+uy%FUR%g7c&IJw6|o&+9$^ec-r*n}i32k>I$9x<(x|>?7^VWd<~oh|UF0?u
zAD;E^cI=<eOW)_d_~7~T=dhl9dZrG4pcEy#9oUu>Kf^<1`bNd8EBTpA@R85?+JRm3
z>(?(znK({J+uy$~4Yu&gIBCYk#i>@G-4i0XcW*{sz|o^ef8fa9*qmyz#jUWYYXMk$
z3!!Xb@v`jnRfHEdLV3KaFnLVF=v<0n;E($cA7;3_+jlfx_Ma_=qM&KdZb;;}%DSAw
z@wdLvSQ!3p$82rWT8cdLIiww4eq=4UQWq7H&Ae_MD_%AGK;82#`pLS|+eW?>n{a)n
zS9ood-(<aLZX=(av)Y;GsDY*R5O<_aW=E90_=P}SWksHg<W<D~Lc^#*&`jIL#H{9&
z)S0~oA;H0_edG$&ZJwKUufyw)?}Gv%tF{nZ^arvcB<3l)noY+ixQdN6{U)6H=#6{4
zE?(^whhM1pUs$KP)8!6}8PX!^m)@>3Kn!b$gV(zi2E9{GO#FLhkL~1-V?lj}c@Ap&
zHUQZt8aJ}D3#1qqQqrR^;xD>sO9fmwx_aGUSr*dt8+r-Nl%A9|sfj;w%DdL29{Ll2
zBD&=qem?%_1{*wO1nBq~#3O5GBNhLxX<;KTt0hfyOiO5kh5;fDUjD@Kup(#dNR+H=
zgIOUEePZTVSE0LB%Ef2Ot$3)7o=L;?F-i@yfQkQhwny~%i`yrxGIEM4>qF*8eRp;c
zrW+d-v_1fR4PS2lGU%{FNJx!f8EwyTZa>o69xyuUon1fZ5R2Ecd85~C>DAG!Vaw$z
z-Mh@&vKLk^95`~aV2;B-Cf8|8Y`@-a0fClh-8e>*2ui|)NLk1_3>;mb^cqGaok{7~
zA?}jLXjoZ4cKyk+k!&_xiblA0fky)s0r+z56=Y7Wq*aF20n5*^Ysnx&NM7&u`7$GB
z)78<Njx9Opnrd7)fkwSBLt#--HXH<Ni6tC}C(14g=a!FG^$aH!{lZ4pkf=$j{`PGf
z66zq(gzYww|5k_NYRJrosLyw{$q1E%?LJ5i)o|VK&?u+tT|<gHR+txfD-#sJ5jEwb
zXXcC_4wC~G?)mwxB|b&UH1|sTX45k=9#d4jS@uiwU=g1ImOh^Aj}&qHOmanTF!7jN
zOLXIcD$x9Nvfa6V^=g*B2aZU>2VPTr0s^HYdFt_OplWCc_RX913xuK*jddJb8&<7i
z&g?-ur3>*6F!R}gIyS_l*|#3Q)2C$t<5H|Um%B*h4RVBvAVC4_p(6GvC7m&Yj$XXN
zTCcgmK_-QbgF}$V=EP?OWdRe-2{zWi5y#;sMm|2idJD0J&XKv1RvN$KG(b%A^Da4!
zk-@>{wz+)odq+GHu>ua|f10m;6)$6t2jejGb)CYf@TaKl&GSaC9&(N6Ue`3wV^pJR
zo%iz0xx}ILelSlBQz%rl{>TF(q%IA>Soy;v;TA1pdeRC^yl4n<s}>)ifku5@BcuI0
zy}o%&Y0dxs-FhY=4d<Lj9gJj0ySmt&v!1?Ku9J+Ko_2s+>chS;yYC;?3c(aj>}4Zm
z4K1yD0DhXpKL8u}%6c+-F4fra_YtY;rWHB@f#xWZRBdhT`UEX*^2ZmGw3Fg~o9^AS
z=hWG=eGQU+zx9jLE5M_~4?t1uWIa4K$J^rsJ<FB-<qe3uKoRE+>0U~O{sI0AbK^FS
zp65ga-Uu5P{0v;cvYq*pptkDl32}k76G5OCn~s30ScagmvH1&Jv$`~|ZGJ*Q0TRW;
zFH&bu0wH`laOhD1l@C~+hkYDu+Ws93m7I2d-$E*ybv)xy<k=hwFrR7NzL~iGqjVkl
zG^;}PHcTPoIB)drF$1*^28m=0cS6M=-uOH?SYYQFD0Gc1TT_hk<37hETnoGO5UWJJ
z&giiES0n3wi`9IG!6(zQd-haOWD_`c&t>ZGM{Xbe*&<E_M-(E--=x#BuxS5IIk}ne
zfAQ=jJQo}?Ek08NOn`KxL7GPX{POGkRQ=?pcw4TiEU!MHF|YB1D^x%QAIvz*@0)2b
z$e+ofG*<N&VKP=Jnc*L%_YFK0Az@R#vb>n)I(3;i9QrChi2p@v$N7N7A5<e6=?i8{
z+1D@07JWVEksQhRXybu@S{7dTp%)&l&kO`<tCV~M9!;9C2xjH4e|`Rt41cnzEcNxe
zD7kAqH?I08TlfDwxJ6OY$T$Cqd)H7!_H;3TfVz%WWnlhdh}@$@`Ltjw@v_cwx$;!z
zbB$9V<q5*rZE&ZGqMa)84xcu8ear(Z5P7emtjzcUrBQxANp(%lHH5N?$_qr0E(Q&!
zN5vlc8r@Mc&b6K2AJdQ#*ESca4uB!myU|qUp3}CwPex_t6-^52xeJ$x4`A{nolm&N
zD-8VHf?MX}?&psL#4EXk_X_@Qje-`NJ=pUT$A`JCFMo3JY6Sguw+f2jJJTM`DMl=Z
zCET(HO{*<*OkOk($LBiIP*YFf!n=WXk;U9Sk$B5EHFVYnyOQ_*$L0mx=3>CB#P;j|
z*yiiY%u&C?ESk%<uCSs13dPiqbptmuPG7ndKkfA`&(SjAtD3TMO2wbI!vP&T6%ia9
z{B<G1>WZxPzhlqh%O5;^=sEUKyz!;XrlUr8nRuQ=ahv##(p$<?bC?(-1K(y~c<#mL
zZU{(e32;L#vN!9)$>lbs$CYsH;G@Tu---%bxf@z35RW!YkvU6&P}qKtn3_*zQ>gW|
z$(gYd<hWC{I(s`iL$=Vpxo%aVNXudi;1Tu)Y$jAL>yd8KGcoyUC%v9g3O7wT>s&F2
zlZCf@x#IIS&W^JA1RF@p(%z1NKzSXtjX_>9sgZIwQ8_Jivfu;Gm`T-0<P>gXkUM>g
z^hs)3XrE_lHRtY;DIuq@?rH{EuTBZHf$CHUE<6;w^Nx;(;nTstY&}Y=mca2%pt7w7
zwU3)|+?TCEe#$tHiRF*sM32+>ZR9yH58pp(0&}=+5sgTS{@v=e5B-l2H>ZahHz?H9
zM3Z#rWcdXMD`j+rg?jf|U#@>^dy&Mh3R=Gg$s;BI5g6L5arTH$IM{j`ICxA?%4dg8
z8s_;*S!khT`$){DKq}?I7xD!wDk`$Du~A3QGM_2h{+HodOL!nkAt#g9Oi64WPVVYp
zBAQZ+Hbh@`6FRxGX?0;(c<?ux3&sPb@BbI3VrlsW1z$GB7ruZ0zKcoC3At|k@0}*D
zeVc%tHiLpy(6XBVTf-r8!Do7Bu!j!P?Z-hu{C=Bp_)B`si{Q|`k35RzWo~{!Rs{7^
zGuiPGz^i@!)3iRQo*KAy<=B{jd{KKba)*WF&3u`+`M-X>k2pNl?+<7^_IBT3Yeu5O
zQ)e@?9Z2?~wUa~ukP@qP1=+#z-%*dJCfM28yHRB;#bUQbpS-@kFAfl-0aOv<){6LV
zZ8<hJqNaTtuyY=>(2*<<!?q(^0e}M8o+{D@hlHTK<n<1Bq2;grOvX87T9%KG3N)JF
zzI~+txpAw*4$@fi-H?}*<Jw>El8-b6W`qM#2cO)w!xVPB4gMJ#zBSkgX;!VGU`5Us
zriJhAna=XM#9R`<V32J!X(uqlnPZ7&e^AT(g|r}MNG>TZPQa=?yT(k)t&kULP&$Hv
zNBXNLRdZlyC|z{F3-@Xzg3PZEf4EOIuEUhuq?0&7DY9bX7H7cmb4rkXVs1_ao9+$L
zB~sY@vm-Yn^u<n{hA&@W?tTZ3WVCp9_&JNOFIlCX$MzYm3=a(41^Fy_rbs{epM=9L
z>6bQyOV}{oQKSk{UkjE2G==_fMOm3C;&{V1_%0||Sl}pK8>~(es}w&oqcpR%;o{aq
zr>yz~L)76z$W3hbd$S0`X+NB;`Eg4V6W8%lz%A0ebR8v0CX{9A*RQ{+81uMIP*!&9
z$B!T90cvXg8Nf>+2UWicu`<fI%m^h+3{KlH2$FrVHH40yJ%=Pz>@Y0>WJM2BjGmtU
zaa$rn1PPmRu62sI{xS#6-}M*8-&K!)a@=zBmM#2zd`E}q0E|F-f+Au-Vh|V=HI^pn
znQYQp!s2}TbYO7s?56wo@BeJi5%vj}asUE75b3_Wv{YZ@>F$AIyQ-?H0&-)ju%i1$
zU`~TM#2>BCE`!8m_Y9pSvkrrNi$BM~5B=PFKt<!Ge+X#GQ-o{>p;RF?HMPo*AJbe0
z54xTNQ-_MYxsxl7@i!nd(H|kl)J)opyRCkqWFhBji0>?i#_-T!f=)m1j47H?IgWU|
zEwMdMHm2x%2k^)=#rMG*wg;)Mcz_E?s4z*}9md|vjzQI)F*T1anVWA%Eep8^Qz)he
z3t~kjHyM8GA`=_3gu1VGd@Bo!4-FktH$>Bu7SD>BQmr;^+a|nIkWoT)8pht#IB~Nj
z`?N5sBJEQ3=BrGWI`{2M3M#<5I<3x||733Bbp%DMl@}AWUq_V%cl?)fr%a-01nc;X
z*RmUm7e~Qo2@2AqhA<DI<K(y1l`Vc;hkrSRnfofnyaP->b?Owk{W_o!L=mV2vfF!b
zAg4oGV!v=)x-;LI<hLJ3Mfv~yY4J)8)jWBGhVRg{L02kjcJbnOtXwb~)1&QMP~JL#
zy!9p)czM#>eOKEug^+Y$)BJMii8zs~jv@}!n)g8%>@z9a3hkpZqCxlgI5QS_SQrO6
z?wVvvPibr4MC1&MR4?Ds_Btg->8|>sI06jlsrPkt%ve9Fnwq7+rl(Q+^)nZbN7nz=
zWpanOP1hrIjKw0<H+Vt0-wpL)3Ft=@nFCrguH-v<kBnB#k_6<@qf~ZwcJ&BzK!!)-
zwSk_g;D`MV<vrw5bO9BE50|Sp<K;E@=?r{&+S6w-w1J+UMC}u>oaKY17BIyzz(GLC
z0O*p~QQSp|%|~pf25L7$A%T&RjM4pfSx%lW_L3u_fd-xYxIi3yd%@!0vHnkCMD;id
zM4$^}z6`#(SiyuYpNe)q)<7P04UPDSWFC31t+<UDJLOp^x9OL`U%zEu-3TSy!K<xk
z3`1F+i2%QF6jII(-T;6Y*xubitm?se?)33lFOE&Qtt1u#8tQ@^4S$4%o&7fQMt2l2
z(sl6vL~MU36InKGyXnaM489?8#qQHTPg~}G@3$rMSd}OF1gQWpEb`#okJ0NPR=9rG
z|EfA%ufZ5wbrqb?D{zNGydaPzP?OWR7`<3!Aa4tJ{LS05t>G}S8L~oVk~5^I;#&jr
z@joTrUAt%(XGduW@D9;Pn6k20ar{vcu#9_BVTLqUl(+!>ne1*h*2BcJ*c)V>i0c_l
zZ8m{CH!fUtANqA=hg|>|jcsHZ5H)uwZAV7069n8}H$m5soHBqV39WYZetVzxi{J@<
zcIHduIgLJ8TJk{5Of6SZR<7;g(I$WM&+G}DG@`j00`?%XOTft#@c4G>XIk6<N$et*
zyMdUToa~kq-Vy7IQCIMj0ThWpv*&Z6yR%itTUIa=={Y5Qv9uQ5?{1**S3%adLH6Ii
zeVgP-!FrNn!Z+7CrJo(~<U+*lVhX|S2$JdB|2_gb|Gla|{~(}4;+_9}KC+Wk`~N<o
qWq%HxzuyehJoxtw|9|;!t8`U$!w)3P?)@2fsdnsya*E=GoBszJ;m~{l

literal 36934
zcmeFa2UL`4x-N=st8JSa6AA(?h!Md=5Cz&6L_owql%xcaEFh98x@{4OEkRVkQUu8$
zIfH>9q9Pf|K_r7EnWBpOe1$Wo@7`<Ao_o%myY9JbdaW6$;;+BH{|j$;p67kJede^>
zsvkLjWMgAnC4W-p92?sb2R63F`adkiJI!|=f5AUX^%do0@Gt(GIi5$sKg+M4)U;(|
z<Gw}yx5&STX2iz!Gn>53F%^ffp;pJBEk-%gU!t8aJE^VxebL%qe|f&@qt(wpY!Yyn
zS>*d!Ch6yvR}qiCQ~y3#_Cx!Ut-m|u|Ndy_cYKdhP4@jEB6v(@;|A^QpMv%-|82{6
zzZp*UkF@iK1*L5}7#L}sYxZ>}EHH4OKdaqoHazZuTak`V_^yrKC9J{yY?s<2#QMqI
zj=y6G`RU}YMZ)CYUlf)*EFwRgQ+U3D{QKv*?{vw(?m=t6d`EtW407in|Nb2HeLne@
zZI{45af_Ir^k&|u^MzE%3Waz(j(rZ!3u7<ev=|@fSF+1l!Pa)SviC_^+Ce@(uM++H
z@tg1a?8oo+Rg2&FPhWQPM{8OMhg^z=kABLgWm~TPl2buO{PU83FrJ3<kI#56<8|40
zT5_Vc(Y8|3;O)KrXSKAr49fg`Tdz0om7;&FEbR%=<EcD4c51zM$+3x#ZEZYh#+7~s
zeuuZWF-Gj=TwPtwQ_Hv0s{Gv!31xTuu91DU^U>^mQHu+oY;s=r*w7k^m?C_9xw*Oe
zlS3b^hf>N)>$R-7CnhFR@C_x)cZB_<jBhukYd*VpEX;NCV+wsFPwxEr^FG{1M5SiR
zl}p18+eVz(xzh5H(}ev<PH$<tNsWG>lIZQ(*;$85=eBj!`MF+$wtUB`y1I;d*Cc<1
z&C$s&J$vN2$3DN~y2hxKo_Eo{RwcXNb#}Z_-+OPsuvMkxl+U`;;#)6QKIBd=V$MFI
zYjIH-L&kv*D3fW_#5#kiW~-u4DR@p@gS=lK|Ku;tbRa{cJznFw^MpSSx38~nO1IyU
z(*0#y*4ci3dpk2TbHa7^ZT*ycwZRoV5g8eF&&v*piA_1Yy%=nxx5>+Z9ZT`&ce3>#
zrRQfVyVSHSRDC>;G4eV+xRwstVqQuxKa1!RU7q}2@7}#*Op4eI)J8G_m8CSbv#(yV
zuGpJ3l+|9NEM1_nb3L{~N@G>k*`ot7Pi8HOdLKG(R8P>@A49v4?_h16TrkP`B}eJr
zzQ@y_0+m<CRi29pvX<zN3=EjGZ+D$?w-dgQ9wTn_Cg_+IB|JP_U~}>ak5k{{$a#y*
zkC#9AJ5SW92bLM7ebhHIGoy;?=;)LTCKnZoUwZk+BC3{wfq^wfyEw^ypMmzy^-klh
z)zt?s7VS|_8lv6%Ey(HbkGlGOhmKsu-M!x7_HBoCo%*8!CLHTEjZ4P+YnIuGc4ai^
za$$;H^JhMX-Wb4wkNQm&P8FnFsfuuGcI`dVew!@Eoyy+sKaa1z9iLp7+dk=k^y|qj
zE?-_OPMTjsnLd6hOng6&+jQrmW+B~t@#`OrLb9jYoR^tvnfCBRzsQ|u(hV?cKG@yQ
z3|*7FC8t^rp77<|z-~pA&%gNM_hXVH>P54vsV=o=<z(@cRMD&!8^tZSVTuE-YZ2Yd
z($cTLCb~>jT}R~gn~LaK`pYw0ys?Vz<3D{#dZL+jSyoX|QAWl*z3PN@t6f7$gsgYO
zlPCN?-u!j_k2kMaSontMxgGxeE|g;j)y3w}p+gBNMMp;S55C9$eVY7Z`MR#mW=rp^
zHm5!K#I<YJim|xdx45rsRDX^N(e=8qTH?wX-Yn*TdJ*T|y?awCrRc>^{H4gxXLqiT
zNKD-8HcxLayQ^h+Kb=t>Qn<d1+g`iHu0f}&v2hdGW8IiyZx<=v85gOILfV_;cpMF@
z-I#MTwAcLp3D&LN8~1wcI{$QA$gWKXFQ3wO><LaWEDz`#81TY&#}3?2Va_!=_J#+R
zaXWPUcKo`@Zz8*T#aGpjRZ8=ok3Kz2OG~CO25I9p%5vBzc;eT+V<|R0ZyY8+8h1CD
zCHpd_+9?GMc@nCss@4Os(c80<v%9<&@tzmlXi%S^Sw6f@`DjY}<S1pH&bpjK*I{c@
zQxnd)jJmpdK67@^x;NauG%0W7K{}(QSBe9BN$ZyJfukP!dOIgle!xPUyl+GOHB7D9
zJWc7Vb#D=KB6+Y*T~1EUdZ;l)o)WlSX<*8rQ<_QhcXnx+oou$!v&b6>;qmnBy1nD!
zJ#Do|Sy751lQWy9rfJ>Y#0Z!+bNqO->%}tOuyn>;uXIuAb^1`6a<%1X!PLl9PS4m(
z*W3FC11HP2*uLd#zN;YMb3R3Ux?<T-?qHI9-hPA9_45<Sltf0L&|?lgm;JFjLMk>K
zk&u|Rn8z{PJ}icDiug_BJC1o_X689Z^#rJu9RA{6o{^Qd?++XMT2wgR_ITXHq2MS;
z97=WB=KR4cC0-lKzvi_wMkWRZAK&8fYS4Awn<HubJ}f3VS<isp?Jq60TB7gKr^&;;
zVFx=TN`1LF+UJ>5{DI{OlT0PC)<b?012H_(dHqjA^<moMEvV&&UCr&x>o#<39Zico
z<A;vh$?^B}jERlCjU!pm-h21m<?aV&N%B&ul%8PqyW=a;m!3<q=1jmwKJd_?>-ML~
zf~hvHX3M-`r-_`x%qG+LPv$j#etwIDxoF+I^WvIPjPaTV@p}4jmY&P>D_-4hM!aF#
z$7y(opq9d+)QY8Av(Gr=TptIO@!?5-cKsetTEv)Cyh|B>!kgHTRjo&1Oq74UBgeD3
z8;|s5fBVdP`+%NXyl&!cPJ@YV&6J6FJ>P+W0k`IB^PA>NxvaK04`-+s4d>mGCpIT3
zDM`u#TPN3W#!QcX!KR>|S=-Q1W?jY|(!#9JV;<-*P>9m*KBUp0ZL5@e)S@Y!CuGOY
z=!2C<4{9>orV6u4uMZCo(*mh;+j3l|J8y*;GL)2*<UcUp>`9h!c6OGZ-5d3nm6Cg&
z=l5QUK4dr(bpG`1;ml^C%kLg+fX|qAzhq_=|9-&RbGmYVuJV}VWgPH6Yv)J8OG1x2
zesy=Xgvl-c8no;D(xbMiQy*`^9=kTZ4HXmC9C53s{UY_{w>8@;l%*Ntk9e~R--w*P
zVFwE?P*KK9nUk10Z^}DAT^87rW7~@v$WLjyKGkBY{Ap~))3ec->Z(8XPK@{2=;Y*N
z_SpL)-DGHMT>Flvz~ahTSXjV0^UObV9?AK%|CQ_T=NFs)!+OA;_z-)=$;HLrH7r~x
zG4}SLfxqOWXVnwAvU~dtx~9AR{pq7cMePSgy-W0uPSl<+AAaH1A{F|sl#V(4H0do|
z*l0z65w`c<JEpVkrX$yf9t~`I4I}nIa5SskjTkDp(X)Dbyod!5bcD_g^3-!%7qgdl
zFLknvQo)u(DAU;eb>*n*sPiq0*2N}QuJqAv+K9EgjF~p;Tz#>m{Oe}puqrERiT-|}
zqPee|L`+p&u!VeyS)p~@SW{XZrT7F!S>nhM=efb8Cr=C{AHMrQ9#v&w>xE<Mbv|Fb
z#>c4QZi%m0+a|b+tNtBUGhcgH%m4IrIOnKC*^#cSBF3;Oy^mo%@bu}^W66{nB{8MD
zS{E+NS{(VH`E;<^%?@VVCi8M}^J6Xeh4l1v87(cX^o$HySfsDX&Kl}F*9VlX>J@RQ
z*nj#)owL@*D^GkqELv|^SIh4ZsOY6q+^F;rA)N=>)U3F;-K)hr+}-F$V1T+;=uQ7B
z>ycq*7BhA8+=2(Xw)RU>G_2*l!?u@UlXl?o45}hzt(z^fN~+{|xnXnJ*=dNfN}{DE
zVzWO5D9#=&wEso?%$eraRHKT39uE%>JC~)$jm2M8t~xeK8)Eic2)<YyDPJD)PI7*3
zECv3NXKEh4N8v8n#xaSBItF@hLFQrhZI0!`WFPIfA8B#uj|$Q6c@qpj{RD<PMdK86
zIKAeyfZK^1Vjt|RZW-ulv@b+Hr@w1k`{r`menb)CeUEN*;UM|c*VkwFMR*6$=jlcB
zN7`J*-tqXC8y=7p%J?!jR=LJh=jN}E%c8i6eVIro96xaXl)u>$X>3Eu?{Z!+2bni*
zxKdATujy8-b6OVDLyQ)_ThUU_x@1GS4Z>i!Xo0Mv=@Q||=7vfNbIfq&%d0hH%Sua4
zrudf>-(L45C1ro=M|0|jafG<K?a|LJ+?c6O{q6k$-VxVJI)1V+PQOSjcer&1r$5YT
zAa=h=b>t^w90CpMb>6v-Wq~~+I(g|?ZF-sMJ$JQ|41$lFvCLR+B~`Cf;e<!*`RMHx
z>bj0boA+oIj5KsIUaWEZQ=4nbibB`Q|N3SRJ3E;WDZRCgT2mH1J_p}#`m(aIry^uZ
z{E4fp;eq=x@$rh5Yd)Xqhn2qkT(qZ!W2y6kbC7#}WaafX$6iCn{^}^#5$U04uL64Z
zBxUQLIac@4m>Mpq{P5^u1gZrORag7s$Vq5Qb^9EQC@3iKsf%o<Fd8TV6-D&cQKzsH
zo#mN`itinDw2{eo4gaXx(88ekQ^L|WpSsx07^>D566<ghE}ZVN+hy;YJDS#7)M%-c
zOAXK89{fyr<+Z;JCvSCAb8~k>+IvowpPHMRIE!Z9Sp_u5=LNllb?f3Db&#`ho*nOJ
zxTVyR*pI|oHhIpCS|-;KJAF$14pHj`m#%fxP2(X?_g+ZfSqOK&#>g=?twJbo{D=NY
zxJ(U1x10A23}T*6-YuFc7?p)7p8K>zTHbB)ex=Lh=Tf=SWg{U@8sT}Pg+~e}60H)C
zN10v!@?zPJ3O#yLgOkg#3sz(`TCO)+ES@~Pp8jjBKWmL=e{g81La@U@(w^gMBoB?i
zr#m1Jei9$Qhp(;ha<7(E!G@_;d%Yn9Z7D7@U*Rs}<Gow$+Y6NSL_|a+lMc^*IKHL?
zQ3fx9P^+&|W;cJ`s_W2sQ+y=5+sS36+t*L!k}qQv0^xpfE`7}>hn0rmp}c6_Wo3x>
zIEC}S{ObR69&FV{VcjhV3(Zc2itgQhI&2f!!EaQZWZ#gZ(M`k2DMtFR0a3qY{+E|4
z$_3c@+E&l&y3k6p;pH)f(=w^9t26xF#-EgrI*+vE>^G}V*!Htzhx=lu{QD`}Z}B)?
zNu6sQbrU;%xF-nN+9gD9o}PU}Lq5?|Hwiz<$wu)n&lf8)x2%?)`(ijlHtUwF&ll0&
z9<1C}Ns`frI11v}ab1lD{@g0m;~Bz1Up2nRVTn9*Cj+Ni&Brr?DTW}$jgG|bd_!Cj
zo{}SEuU2L$69)A`y6e~S9m`yFk&f6`Gw&JrZGPq|MIAk>tf*MQ*(~+-HGj#22M;oM
z(yY5nS80^Z#(m3QM9Bw=T-)Hs%_Q%_S1zu<u$xM6=~dHuMbeCzGJ89_4fahY(Z{5-
zgxzS~CEV8=x8^Wjt`JVhY|q%-&F_EI=EGpobeFet%ZT!>shf`_Hj*5<zjI}q-5I34
za2xFgr9PW)m=@7Wxyp3xTSYqVXL9IVJKN&W?Rz8lb02~*&J`O4J(Wa18lDjeYW`Fn
zXh7>KA-Ux->Cd)Qy7tl8z9+{uRGpNte#E~vT4c4p(RpJUe)Z*YSGT(rJx(t2+LXAs
zxUS!p@py(`Z!&qFiRkx|Hj<>X=g+6r+fDWwOVdBP(;bc<KYkRmVf}W0*~;AZb0aH`
z+bx{8C%G+4BI{Nk8Ttvn&^CXv$&8reY{q1Bcjql$znd#KP9QDMhu`!rm~7hfRGG?y
z9Lq{YeT`)d)@AdpEjA~K<FG84sJ|yoaTCOe<K*Pj$1dHE#1la&zy4ri7EhP4hEu3z
zuF2eFR*|oO;@x5^x3N&<1IxPnDRTyb$h%BUP4yAy`Qs=^u0HzZIoUb}wJ~RVgmNM*
z(%y;EqNN$Tn4|MdVax1J5At0{nbX}>EiGG~obdG`u@Vkr*7R#RsZ_P*i@OjU3hlRS
z$#y{C89)t3uxYm8rQha*+ezG+jXmEL7oX%s5R1ElnrpFCw~>|an8deFzcrRx*Ink%
z>lOVIPNX%WpEATN8`ZU~Wm}v_3uy2uWie-;=nvG!TEE_UtC*}L;tvcZU(GcHNqXFZ
zt6F_uKSe{(o+RvSYzdMfS6Dy%Lm=oJqTXpa80-n=6iocH7Ud{&pRM@Z^VfX;5d<<K
z*?S8yJ8W#vCc|F*E72r!&wl_&sp^b;SN$|9N&xXfg-)qE3GMhIGz3o|V7VV3d$iQ}
z9h@vuRT&u>;6gab2h>$G;UEz_%gM+{QT+jPkhidTBa)JYSfCE^DQ`EZ-bFNyFu~W;
z^ZonxeSLj9ZDoU_=+j7SBfCb2uf`#{&=%rgXGdHOm`3%p*KS21UO48S`=sK}<LK4Z
zSrv_2nVBJH)g!+}O0J@+Dk~$ikY19n9T*(s4|S!tJi2@L?#_+gMoBTL*LbO)KYu=;
ze%=w=moKnf<`0@Cw{4BcL9t=IDcXLq;XS^#Q^1KnO)#4fG)1{cQN5NMBR;}n?Ogw3
zY>a(&;)9OJo~grOc+LY-Fe;_dA$UGRmm$6QEK?W2GCs<}_fM5F>-<OP>dwx2wEif(
zDff%9i_@DPoAT+%o>eoQwUQkZv(d@;CLT)B_Z^ZVhYpQz;$7sRjL4y!>-)*XLfn%t
z^xcURjYCrLmqrRNx9S}U)f%sJ9kHArh^L$Nrsq#;+ND<HUcF6_(|@##{|v79f95Sz
zL-JSyw=uD_6l%`3&Cy<1CZl*Hv@)=Un)R!KJXknu<3(6}M}j!mgq#({x%WT#Ui?Xm
z5!>Jk0Zww0e>GCe8rc6D*!wTLMVyfRA`^rXe4$F}LJu>YZiUkNW9bf&*3P`91!b?M
z$AiiZodmh44~O&^X^ip8>G5Rxv|*ZNca8V3@fU1#Ai0>fvreOW3HNFJdQpDLQx-1A
zRbnX|PV>6EQ%Y?Phz+-UOg8N}KR#fTc+JjE)-FSP3&wxqcVU;#6(qF?&htsVFK(@`
zjWn!BXtAgRjt+5Or~QLflA?eH00P@<zj-kngx9I<>yV+TC24Dp*zA6Pg3Dq(J3Hc|
z2ce-A+Db-<`KxMc@fM(}3l}cD(LQTbemRY#hwN5Goq-<RFRtS7cp+!;bfEjgR%qSd
z6DL?#S4UDucB^?w6DuoWVs4&3-3}|n#|M)nDIp=jx>1h}fwS4!k?~f2{HRGR%R=(&
z=}9m;AO$STGB6OZ{5Z@YvgdD>CNNMO<3m~#loRo$`oo8xSV<K&oPx3VRCQzHy|B%h
z`Xye(^i=N?YyFVb|Lfy3cbb!OOy=Jz(L+jUniChU>maZ^Cq-2*`NO5|*fR~mrx9(P
znY%kgq+IRh8*%+UsR9@|0m?QXWxdvRi&GUe8}SX^CEFIR*rcki9zyFE2z|GGQ8KMR
zp1vaimb>8C2Sr#F8=sM(q4A~1gySa@J+s=~*SSxr;PUVDxw*N&`=YB>ki*UH_bwjO
zcyG=(xt7NMMeTF`Q|ISqBW>zaY&Fy4b&QNl7e4C_I!#*sA*@L#2ey-Y|M#*T|FQ${
z|D1D)L&Suye?EKfh`pttPHwv3;QJn%J2*pRyA%kDU>!;TIBDLsTYSci#qFFMzHj{S
zHZt?V7NZ~~JG%^bj*E*wuU$o+;-6!8$c6vFAOHW?kpJ%?%l@0`B;k!htcGAI3H$6@
ztmTdZh6GryPoON|={rfJ`P%S6j}R-idt`Xx)br-5Dt6;=J2i5tq#LvEiGL|U*$J8$
zhRFQRI|-A`<ZCx=#>LL%0wFAzo`l3<j-T(--rE-*M94$}VId@A4!{b#kve)K(MWBH
zV)24O#h&8fb_7oX%p@aIyo5vhCyav?+=zzy-d?w(l)QES)OO2!TIm%NlU=qM<Qv(d
z_Vaz$hKLtoRwjrt;AiGkIv0X;;L(1bp3;nd<yRND$d%8}Y-V3H`n9KmpHDwNG4p~!
z=<;>j@l^yJ78GI8mMb&Z*}qtVs$gd)9E(V%<4jKoKVO&L{A|OsT9Ui5?W;Xe9Fjm{
zc3?-qOAfXn<ssyNKOH8wn@}z?b4U7yD>aS7H{AU7QpVJ~u)_zhH(t6mk>Jp0bm0D{
zdDTVYu13VoRK!{A;ERZhdow*=?dlR2AMdLy{BfwLT`VCscE3~sqBd=zhHg<SUR$+T
z1dRg&?6d<>hPnRAi27=4YlBiAys2?@j!jPX2g-Fo?Ytv?b^Q1%PQ5|P82ktp3LcF0
zJ}(6^<Zc_6+=lF}ai1LZI6psa)$6wMtll@Oc-;5ZyOsh<YuS#ywlx!nH~M<suW3v%
zFbL7Pew$#~rva=^mx>j#uiJ6j$J0|?&sBu1;wRaY>go;dj8^cWX69L=h4iPUFaInz
zJf}K(^`I1^;&NJPG;e7%1rC90&M;mYKf)P(*XFi~%3NMVH+Go~spxs5^Q&xpEsgfn
zB(lrSB&t69@v?lAdmPb^S-w^8LTPkbnr%4m5&g?ck##${%v9+4kpv6eL}Pa$;z)5Y
zc3oZG!hQ%g52zBpgYC=({_PzW=H@;X!bL|xgg(qhQr8W*#}~e_5y^4)vsbIVSzCtb
zdiWMo+xDG^*<52)U)7U9VY_(4CvyIN3$B=eZ8jn2twpiMo}PrdS#V{V*vvjWk`jDv
zQr)fq_E;hIe4nDRu`$@p5O~6KIy&6&KJ4sOI5{Ar$Q4*I87zs6OnO!pVS|W!&e0Yk
z5qZ~e#h#3;EY>IChXM5BlhxI0;iOoftg92S`XnYMmfW!UC;qdD@Wj&JKtHgc1+e*y
zA?#T;5u3S69{0DoJm$4w4<EIX+^rhB#Kp8DF{>@^wW3So(<l19u_U837uBk=tx?Dy
zdVTdx@7RVM#x-i_gS@HWcb7?4%8tle!Xzv&*(tF|2q&70HYse?ly0&g6oAx}B&~QI
z&Ii!)HI3kv#~zBbWno@al*GMDA~=pL-X|q#H}4QS9GyHO%p@LLx=n58dZY)fQ^MUf
z>QjoVH&F|7DG6=FxlV^w_F~t!E<Q|tH5?5n?|HN0C@r?w-TwHRzUr^J>F3PS87iMA
zYg{eQ#){kx%%OKg^<<3AZt)iNT(JRsQ#Kf8K0b2Zg=|I024IUyIre=%N%?^<7w5H2
zBsqX8MNq~1rGkF3c0?!1MB(2KT?bR}Y1Y3YMA#qX!2Zu~@2`W6Iela}eHF8LKQRjK
zcMOWZ&&L|l7^4(Q#*~PtXla6G8jCmh9LKt)$9vD^4m*?{tZ$#K&+76!GU8b@&ls(O
z1b~pYr|usRAOLwrM#kT4Y9uB#6mtzMnSiA>AuJoXPf*nJTZpmg_CJs#uuRpzSYbh)
z<KI5`pAxJP2YLbkOrD@xg2vgqcIxW$boFLr>vuVUiLE0HGZHY&=PxsYdWA;`<C2<i
z1eqBup^OYHG7O1PtU-g0y{}2l(^WH0-{&9it5eqlui}8zEJz7dNRA>|kVh-O&bYv9
zX5-yDBomT?gVz85&wJx4bbe{7t*-|*$9U9qIgRVO+e4g{ZeKAW`^^l6<GwvAIyx3E
zPDqH#ZIqOJfi%#kKiR;5C{EFcpM<MpIlcMTg#X|gkJr}K1+%=S3s9XWj~_qwsZ)74
z(p`hZ3V0KFK|l>h*g)VLZQbW@$Hj{c#ae6+jBRYtiT9H>iLpM8WmHWm3RSH>V{lk?
zprs%!Ev-c$sI}>>cSG+)nFmAtDmS2Fz$Q0L2X9^?JbjL$M&gJt+n$V48#3tW_1>}=
z?DjzU?T2sv`eJ%K;N9gwS_%U&hl{ziD{e2aP7vnM&d%<D6tX>R0^%!L%c9Th+(gyQ
zEqWYO*}IJ4bp3c0W7HYhOarX|#PKJkHvon1V9+9~vJ@D3nyRY7=F-6zo(xCZP{ckz
zHM{GP<1A2rsHOh1eVUY&=yD`o@L2FhYvP7Im~s#m6vL4K@-j`77>o@t6AUpI4Gj|s
zVOeO53jQ3B;hl;e_^a+HTK}$)iiu4Nz!Fd=<c2@RA*H;arB#946j6z?ZgCb|-M35>
zAY%D+dS>PcY;W>7aF6otADfy6d>-gRaT4qUT-#gh2dB-FPS+0fn_F5Yy%zgHbi7?N
zMI!-|4^IiNE8CF$xUzMRoi5V)x2^O$cp{{uIf^^;GVXJTiHIbQIf)KcZY76@E%M&~
zjMR*L^8fROkeDDg_cyfJ!Q`^vm#lb`I)XjFfGdK?21g;N$UW-Pd&Hn6+uBG|46!d@
zz3<p=1Mx1MXwcIeIq+rxM9c=F$&2m$e>GPVSmOY>heE3WnP|57vtQlqAw;41TDcHP
zM|&WN<=y(8ep+OOjT1&;&ZK}eEOny~bBy&?`U4FZsAGN9{EGt57s9cS4awJ4NFVV=
z@coJq_bjT&s$fDbR(%<{_SuWwj?a&P-_c0Xmz^82FY+!A+#x4ZY?fTGamPakeG<$0
zNzL{-K!KxBaDjZpebwpm#ciIRKw}9DK==h(wW4qdiRxFaTE)(84c@DaPzy(fo*ke2
zELKE|GAAEo8@zJzyS5QRNg>3vB+4z8L<jsHMj0FZ&};L-%f*N_yF=UOE(>a=8pbHH
z9(mJQkE0zr08J$bLBOILkS`T7hONp76a+ZNnyC6VYMHawJXP{0`V|n$x8eoKm%7(-
zX1ZtP?agfDE5uwDV^egIaOLY5<Q;pSp1RK>0YLT6oD*eT@3zwUxt8F@Zdw_y|Eo39
zM@#*A+q+jZkSjl$i^8$76Ef8YltK~&Nu3M1;LjU8_IBtLx;Sb>N0w5uQLC88d5FDe
zu16R-5+N_OL9c-kvMQdN88bjsN|T-+lV)c}cG)#Xop*T~e#Ace4J(a0>AQ86!-|`~
z&VfY5w_GkJT8Tf_o%RItD8R=@h)hDD=!3T<)PD&PuduTtFs8k;aytk?V)!`ZK)oxC
z?vux1i&c?aA1<VoDVUV=`GNRo{+Ry9RHO3B!GVD;u-)Dy#UoE>#mh@?EDPu<<8e0M
z`E4K>0n74hqLux@U1pS361G?z0+DV5Ub+S%_LXtekzr^S<jq*a-n1N>t__>9{3{0-
zNAa0Yf!&g$j+GJ<6E}rj9;+C!meNNYyL<<BKsz>u0b)wPOWC@vj6ON(-Dg!<1Ljx8
z_5#~Z1-7Elx|j7{fuUmS<Et#i>P3g3xs}*{AQudC3JVJjEm$|6&I+Ku$b<0i%9Sgg
z$Y#VLZCq+TA0_O%%VZu|(A`{uKC-#9$A4NhI`lCuum?}>_I#2&Z>kG)enEe|0F2vR
z!BHgQi!<AahK)#M{%?2CV}1=WbR6%i!UfP_1oYsrMAY`M2J54NXKY9AOR;~*olQTr
zJ*}yX6~%ri!i0ueWNN0iw6#fHq=V=%bR^E(?l9|v{AzgeH7*^C%fDimV)RhR?a^!l
z_oS$^nq2YRQx!%5p;Y--ByUG9(jlgk)_f@C>J-b8>auN0!6LZLb@Tco>GSkt@wvC8
z=Pn_%s>?*aPvInFFc2#sol<xm@|TCULh__dWK+X|rzLi6SAMt!ahT2=KIBE@fHWd|
zB6^}?qJm1#8;Fb7M=%g2#Ihq7i{p5_PD@Y3DwoWItSc?wLB*yds@E;h<CvLKI&X56
z5>oQ<PJ*JbT`b-`vpGMWm9;`@lvyU?=p4G=;KWg=GiVvft`;$G$YppSk54PxRFSCI
zm%I~e--5GF)Rh{khVpo(O=6bXM8%n-#?n}`nOU7&+@=bDtDiTV=?!ksQe17p%pKd5
zs(6TX$9?JlF@K2Nnzz&n+!PUZkSvGny+C0NuGeXmQb$w-7WVDV<7>YNX*~OxFopQ`
z@-x?@A3^?C+LB}2ol?f*O@4kFYRO3ye=eW+d2hcHZBF=xodjLLx+3X1$n2J8@&gnO
zq$)i)v}-%kB4j>Mx;kqBZd(@4e9r#-z|3jR1xg&+px(TM8)HzfY;RV}+Pb>q*Sijo
z;Xi-2%dy%Ks+sMbBX~TnHIn0Je7MAtYU4E%NtTubwl*dy$q%~U!;@gwiO2vDwFAH!
zgZE*FU!7x3$MdT@9iP+5S7;W3^7q)=;yRqs&_z_2o}LKb6Uv;-K+A%_^@GUyl7{Jm
z8D9PIoP9QwJ%*0a;n#QDO_^Q8<~<=(3@hcaSB1qo?Tp42*RsqpO4UYKG%%tb4>%2y
zMHqN~-kI0W64sHYc+{fccH86rLE}>U!m)5qPvU%hI7Q-W<NLa?%02-9Jw3_d3S!0y
zs^tK_pJ;7}NOyX=OqvG(SnH$Nyoq`(0PG2qg<!#Vi9*$NYy#oW(y8xH!~my2l-MKm
zL=TZ^wq3hr+Q$SI6h5gTiZq;Jki38{F%+ft*tvjY0O0bmuC`X?)96$BWcO69`c${F
zP{^Ql{?pOvl?}HynN$H#f{v+M`{MCBXBV)ro8vVLR=HA>KRGH*&B$_^w)~_zkYC!8
zPBGa;1?*%Ka^||(!BH)$!C9=ouxr1f{BvibNd=?be=a$~A3`0tS^_9Rdro`B%iNcf
z3|;OB#F0daOmqa+5Erj%Y@g|+@&eg1%eQ)VEJb?l$1A3$yZ;vPtEw8g%$_B2|19Ve
z{yzrfz>8`NsgE3(jVb2vc$EmwwQFj)sp*37J^H01BYGhAa};B)z*qMOJ&r)qemC!r
zy01@VRp#20PHjJ*BJv<lotZDbO62bOsQ0aqsC}E6m9zFWA3x^2AKZnI!Mdw>df1A>
zMTLH*8#sfv>r|_lXzR6_bIDI{A=2{nbYM(0bQ9y_@->j^qfD8tAtVivMFPhxCXyAR
z;URo|3Y4I$OeEQQ(yw;D6dz1Td!utglc*nve#9nZ(T7*Ul$i<wW{_Wm!@H$m;Y0FP
zvemzYTCLz1CG`@K5D;~Hi_tGduz^CFsoRJN!*uJqbaS<PCT2G6E`(mGlt~}u1&+n@
zB|HV9lH>^q^#Uy|V7x@pPf8MUl0i&ARSMk*4Kpi}30BwL9HMO_U&LUk3645gK=T07
z|7c9^ZrT&YSFxxpa7yrKjyt#p-VkB@-JAz+0?G{o4yq$Pfq*X<XcNg}4pMj9xqzRl
z9}F+L^9Y;)pTBgg^h3<)eQn*CfCQ(SYsc%M%D>Z0NMD<JduL}lr?f@-W5N<gFU5{p
zfUXK5loFfnwgpfoJE8{*Qf_nxsfAeI&jxlR*Ep<>z+d*g?QUx-uVrwym2AGNn#=Y;
z)D~!@7>}uTiAshoeUN8=?V9-q1C>}???Z2qHc)v_V}spjbIa8Wu&@X|+R+M~taJWV
z3lQq?2PPl<^+Il(m>QgOgFr~bA{5HBQe@`b5O(QbyMBF=p3=$hC$8^1m59Uq;kOPC
z7d9UU`i#1%L+<X5#7~?RgoL7O`@VxDaT7l{?6G$F{+iW1dt!0Bl2C}^cWnxZDQl1S
zcCsd*DAW^?BHW{w3%lNFL|u-q4XlYtJ;NL>Ivbo#v7>US3~+tlQ_IMG2cNKEv%9*!
z(yQ2jUJ(RB<+pW%FLZlI*^7-15^9q3^9XW0F`UE-b05z=RsINSHQNjWmDHM_(la7<
z)0RlHY-<zoNyMVY=50BHyPmqYZz~Z~5eN)`__#?8BmJtL0?U{Cd=Do-1q*x5{_xI&
zm*09S3gv7g-h%|aR5~xp3~&S*^4c<V8`8<$*<#l7cdbQu%**_8>q%_vE+U%FhVUkE
zPzw`Siu1!kAbEa1CznMmRQ{I-zW2^+g2N`YC)RIvMHjCUYxzm6<?3;&sN5E7nIW7A
zqZn3T@MzpapaUcJCGu`ie8BNxA-Mx4%n^%P*Qu(Pz+P@#bM(tEg4)^p2o?i%yBDcW
zfQa#yVF7D^J^Kt7SwZ#~YE^ic**;IFAOM|vQt28Gp`x@Wuw^g;M8!H$C}jy7@)qFz
z<@aGZx(v9bdE?RduRg^L&izJl8hN|&u=~}`Kd)IHvgq*2M@w%A{<L0n3%ib*hWfG9
zmnPV$OBRJ}IQomjp&wWHf48FVWxx-6zT>)d_H>TF=gy;sIfjQ{ZYV!o-ol{s7o~4K
zWq<iVtB$H<a)Vy*HM_cKX`PATIQs^b#caPcsc!2ShUg42@_M(%grML(>ZLb1bT_fH
z%W>POoC=r7Si9i7Ys0+eUN^x~==@+$r1RN8<Rg+SJ5;%$O1s&Q_$q7dLg-c-nYt{W
zBvWu<h_#9A2_gaGQ&cHQXh)WXQ6RtGd1}Q%CD+pAp+%!O13LE}N;+S!+GF2v1w8U8
zZ~`?w3sKRd%fXTz1;E%>)SQ2MYS*b=o18_xdUUxugwo+QAuP)DOxhcsFNJeoD{l5f
zh_Y2OD?UE{?Lr<`n}pFoQMmWi)WihCPFB{k?n7&<&b>D9P!$Ui;V<uYJ3psG7oWaQ
z@}8DZf@{eriksg2<(DM6dpQmvT1MN9FaWog<76VfA-<++9~ZZQYdQe41Q#8z$1nx-
zdu_sNX}0Vu=or_NLM=~E$mmKIE!%SDub+ZfAxx-@4B@VbsU<<;lj!K^eU;>cq$;h=
zR?!22^XeJ7kE{)LBGp4daGYpp3IA{!B}E$PSI$BXpyyE8J=kV8MMS+=O_hlC<a+l9
zs*`&h8pHvrcf$zZ1}m4@cQoztJ1<fmQ8*qYtQxoS+l6;<VIwRSs}v+YDR(z1SwT&P
z)A+<h$>&fj9#Z5%LX3cTRs<AdvSF1&1Kg+=;m>dg;!`A8w_<;+pS2*^dX)MtP~5CK
zsdXp}^0BIhhO8HrMTmUgXs}4!Uxvx-r%@S#PEJmu(=*>H`d;jEex3)PCTnG7RaHi(
z(+xUb-XyHq%X!x?H+X#@0RM(-pQ*v(OF_?e<H6`~R=eMMWl0g{U=eeV)cE5yB}C`C
zYL$^NYs8N(3rzBxk?f)6>Z?`O$N*WfCz(xXl2jHli2H#W&$-x<_Opl|PfrYDFO!I)
z%NG#-0|Hg<Vto3nT|7TzEUlRI6YHsc<dI<03-gHRc5MPHpZi&BE%1mVM^@B~EJ)1u
zIow{BZ`N)<Me@JjgURRE3>9C^8gG8UWHOJvOG8|q4M3tRGVt{7x%s9V))hf&lkQni
zjA4R2=JJ8+mVhDn`~Fmz+qeo*8ZFVvbr0dAV&WC^7xJ{XJLwzpiPjbi7#tQ|SSUqG
zQwYySgqnaTD|A=^=P&Q}U<v%^r~P|$x3Ru)@l|Ek1GxKqBl!*gR#^Gue=I1kCjAy=
z`c~iY@ZKqX5*ZumZ6}}p<t|PvqeR$^T~boAM>k;sC%edPw8kMCF#+C$_-upnQJPHW
zJQo+2!JD96WVQ*<KQc1%(tu^$qoSf(qF7_8O~(l)MHNuMl(5SoC1QuQvDdb%Q@wdr
zj1T_23`qvpj<Bo09v%O1eE;>K#(QE`5C2@qbbr9+n8f}CfWt+;l9a;u5$Pf)XItI_
z;4ef%-fdq**C7UqNRAtEcLUUDA*<FH31AJu^qfu($25|+)T?G5l=&$NMK3!q6Z~K{
z^y2_05SRthhgd#s<uO<Tl$p#6o>IPlz^Gts6dvYgKdIhp`DPyz$n!v-d?~lKF3$|I
zK8~lO5KI5nmL<8aX+<$aqCIoN9<}vPLmxc&`*P#7I6q&sZ2eO!#Pk#IDUevMnbu|v
zQ-LgFk1!=?AJO)fk-EieZ7ezQk&*0|MWn#<uU|5P`VVU`$B_5w>FEt&>(Fv4U4@{P
z_R~NW=p6K-RxHga(e0-UCgq(_R#ujgVTGbaGoeMh>=zD<h#8~kcTpKYs3gr02bR@M
zs_HyF58Hk&M%k#BR4*y+ATH4+M|+_lmMumT+kszgUZV+_4GUf)g+Dy5<5i1#LnZ7?
ziK59G&-sJB+#{6a%3_3kgFGlLk&%Hs2pHt4NDpFCNhzZXfOugP`KkAKsa5pY5Nea1
zoxxyWn+2cb#`ijsY*OB^mCgJfA!Z*!t}0XC3T@M>Rq<;q^78W59Vwk|ER)&@37fKE
zYi*_+uj?T~&*CbGWbN$Y=cj?gA4Dw5XSW40$4$l-w&{2;SBcLyIn%{Eq}WMWB=A2z
zVloLdh>}E96{XavX_Ww8Q;Dq$Ttt@qn!WuFofG<i?=%i|yz3pa20Wme6LAeY#mk-J
zX9HcgnI87}nT(p#N0vLpdXl<1mUg%Qz*@ipuDko!vi%$!_jSS;qI+n+SEsyMeMH5;
zj<x#yzkZ|`vP`P`K+KyB`SEIrUb5aBWm$`6p4EE2PJ;ym{h}1F@jMhoogqbYMq$uK
z6HR&kXo`ps>I;Cl;7E0$*exmaGt0NGJSSUSi!c%h+KCp*JYz829?+XLun1(WXXUsz
zpb#-pMxHeUk!ZfD4oOI_S9Fe$9D#`SYwK3!$1Pqh{%D^oI8?QW1=I9EWzyXqhm6$J
zA|%2pcB3ZK2hkKtnHlb5qj<QRCJRES=N%*hm>q~;Q3F>h<kU05i45pwKX!KRU(XZ~
zreAV`HM!YO>LupP=3|;rJazuB9v9`HIs34tM<aW$+t~%@uvCifO{y3TaN?W&0K<xs
zVqPb5=H?;bfa$mR>wcA<t@7XabeV0%&5Y#fa1>Yn^_Rx)9;n_c#4)KqGEXX5?g8?B
z^4Ac<`O_UaqVmNNB9(JMJCby(*s`uTK+jI9vQdZv(KTw07XS6IH`mv1wC#NlP@%+|
zqYO&-?q2D6$MNy;^`9N_!LB`s5}Yl|_d$BTb$;i96y;?7ncFu>S*|rOi!%8g;p<7I
zDG?2hI=Zwv59^}R6t$8*3PCKJh49I3$tWkms!2f%sSe*5Zj1s^8bH4b$q!oprUfVs
zLByaC%tA8Gr0urBhk$F^X+x>Q3XoskT+s~zz#p^Nh|^;LPka09lM|{o6ARlT5y9Rq
zxMfo2fT~zh6D}ijNJNB`0U2}^-|o@nCKm#tn{=N-zV45GXCu6kyytEg#Nf#%nw?7$
z5Fa?x$eV0)6j%{bzWH(-B~@<<pjyCx%pzD=l;^oznnl^WT@J?@^6v9_^*dUJh}!_X
z{&M+_;yuav=SEb?uh@RtFnuQj1SvnO)E#0DZc+<o4g}1)bhB|W0UH`^D)xfX8{dtL
zyTT@f<sQ!j!n>dXGqb9uX6)g79tx?tBD{q-YsVP-7FMDoh_R7%CfQWvJ|=g#q7J5_
z;I)d0xw-bJ#rAqs=5I*a-inM9#l(qy-LvQOr3ZDFNUyDHXb5R>3rL;39$L>S2mb2X
z^4n(S=BIj`&%+YlJ8kBR)LK5|&uqlIDurTZyc9@7ij18Vyb%~hDxTEFKC2pzPDx35
zQx{nY#r?S)N@9U@1_&|=h4OpdN<m+`O9ozUWQ1|{Gs=5xVq3EKm`-zHH#z0vCc^Xl
zubD`WR=mvV2WJ;3wAC6&#|<~DfdSDui5AQ*(Prgv+~18%@rIPcrheYFIohYsocR^`
zZxwT;adgs#sM)}cCW&X=pE)I*?siTaIi|rmW!GFeaN}9Jl{D@ayYk>tx`4x8fMjcb
z3H|_u<Sb)PV)sQ(>4R~JIXNF`w#!X0q_#YY(c{uPI}>|FOzzw?=T8)w{Ytsg`xMC&
zS3Zwi=keN)A2*Y%`+=Bm`<yzNoqQw<vb?<gQ(7YkO`51}ZEd^N1Emmm2xL(sNeybY
zbvK{BoiD0X6|*Cz6b+Q>>H#3U8IDFuNg4~V$D0lH^_|4;w1BEr3v5HtB7bmmWs|zp
z^=sE;Kx@9-P(Y18q~`ykB$Ilm(6~-5PFkK5)MDqhYv2@KNxX<#PwCu1VAhCBFHVjs
zLw?#*xPd(d^o1No8)vqqzjq|+y^_2Yg+Q&ax_+K@B0`Xibb7T3`--S)LH+b7iwYEI
z6*Iz7gGE<$-bl~Oqr3VibDztJU>WB}`5wW!ljGw-b58-1WPF)ES#;qWI@l7!k}LiE
z_kiu2(DC)oZ}QR;-}He8{?<(6?6=U{$8=}I??j^#6Yp%L+fq|={wwXbxi7b*1=YWq
z`9$+YqsVK!FG&_I2*KGdUTukBnT4O9c-So&C1y8?l?<?Linz2SAGT(fI_qq+t;8W@
zow@%UQvUNJ{4b-@|8xtHZ90fZ)rfi;%rM`erHRRH)p#|;Ja~zB1<uLAm%;`Kq*}6x
zz6}sij3_FSzGF8vHANN*u?DGl;}Ze3UjmeIou*jE-u_{ycW$lC&0J&yMf%~yA!$Y2
zFc*wa_u`+QE2+G&*h;xRGOjdlJs;F^@%JBK+w?mTb&%5O$cmFF12^b8(mr##+UU#;
zfDPR(ww2@q1aG&Vqm}K@DfR6AW;r(hr-%A4SMHymmw(^lpEwBr?R3U?M6TEw0(Zyi
zxIj1z60)TLf+ZB1eBgExpBkVXm1UMISWaVS=!4}B!a1J;NF`O<)I7p+6;im(0!m9b
z^tO=v^t(FNX$sPN_kBL8s~}tiQO<6Xc5^u-DtdyLIpAK_U@=OekmMkADsgQ{qymH%
zGM`4%&pi2rn%iRYdMoX>H7-Ac#V1W}z)=yx2c14fNzV;ZcBTu?DpHZfD(&>f@Dx$|
z;m6Xil4J6+j&)#z$~=E?aCUO~QGJAztSwhxz+QMnY}d5?hJ)e&uA|FqPm(9*V|qMV
znj0={(6tSe%GvRh($dnHcrNxF2hBcJlakpdR_w?Zf?YMTo*t_2lj{E1=vGanB;(Fc
zq@(D8r53qOu#4u>0+Dt@T3QPSF|y(!+hlTA>N6{WL=}p;bS8Dedgy$ZL2q?j2Bl8>
zIsI#eaG6PT_(q6YX6ba`cQ5}~ZVi~I1f5N!VvM#pe!5fD+<c_NfH-$@0?$-z?myxL
zS7MIDg}96(PMDl3CZa2qoCo9Z(t<gR$YW@Ov)fGjn<qdX9z6Q431CkIe(XQc77bNO
z|Jt<;D7m~wH!(Adw-M>DX=~F>mxdHF4lmNGPH=4)R_2_VG3A1icc82nvDY@8m+n}P
z0Jv6kSMY`#U&L#6B_`*5^VU5XR&f97DFNfL@$t5n7hcZ3Z<WhE*ZUL=UJ`J>^8@+W
z8kwR2@c3(-<~dJqR0*X1j)xeXAq{i5Nns?Kiiq#~o%0!llhftOZUNUe-+pkwNP+AG
z`H(yE$%V6~V9UjjR%8IjE0sfF8(D!xezek5s3RN&B`q*h>?sm`I;cz~3fBE7SY(wP
z1}F+?j%I~`qN}9;+9guz$9<$v#x-)@L|uw%FJQVn3QHSBPa{o8kb}l+pUnlDzj4^#
zN}eBU;yr1MI!%5z`bR`fUX#&{z3nHf8ycde1^Sdtfac56(>^CxoJc*#kUxhf>P#MW
za&|uNxW$UD>FT3Bl)SBG`k|^;tgBh%R9TLR$?W+Dwo}y#;m(J}_O?>(Rv9NG<+$5h
zASfZ^Mj6_1tRuSKqd*T>gbA2+^{<XVzO1Xt?h5PiD251e*Urk#-nxRIs9e`~oC&04
z!?6Fcs}swNQka5TnF$D=lw+CMvd4+is)~tHI<hl%)$vj;Z%i?*i;G}rW|*o5$IDXz
zoo*afMr=S{fPIT#^bZWM)*q{Ij8{>;2C|G5l>RJKE~~g{c1~ZN*ncsl==BzaU%g`j
zQ;Bz&OMYVpsSUIQ)pdItyy{X24;9iY%qJtTx1V}6mE74PWF^zGO*EvUq@6k3j{FP#
zTkc#HVV%w%qE!E({WZ&OWEjhQpFiVLcvY`W=lVy1PE*z;Z=P)bu8m|7=upE!_5jjR
z4rWnw^bV4DPB)+;oir05`x>b2!;1^}OV9ar(y8@8DF|v#a5LFn>~e2!q1>GiR;iXO
zkj%TIac|P=#BH{QS_(LHEg`PwW-2LuaJMCB?_xmcB?4Z_NqxO@<x0%$5LVUYPwXOV
zznqXU&!|%)HPZJ)%+#tEzF0+8sb!(V*2QiAttI~Nqndx;;?KWFc>kV@|CzZ!Ecr`4
z0zp|gglZ$hUxh+y;@2unpvRCZu?ZQ;;DOcfQx&xuOvgm@ALke*SFT?9@ZrNFmVtvt
zQI<|JlpfRSXcbC8`eceaB=x)7<8vh%J|hO*(CT)=iBfMrB2rx`JRW`Mu$)O$CKc|K
z<S*})6uI6oMZ!{EpPl|m1h#+nlTjqEAqh-jSiGgAs-<NH2M4_;!5>5uqp{%1CMU^|
zh%0NNf`4N9H89^zREU4bZIMUH(hr)}#*i1#q07>1s;kdogvL;&;6+P|HW@Cmiz}3{
zqjH3cJt}qT1`?A<#Z7+-pWzd2y7DBV`xNK#8B=&J-*MX-ZK2+l^gmchBvKsTcTtr}
z0Kwz6m1ct^#B>_2rv9we^^@K8>#K7+fzpu#7_Na7@XPX6_KvA0Y<Gp#_nwq<&vClA
zr=AL+ggiq3$cRuA6{skg?0$$4o2VfF)$xK@;J=$@%^Vw}DrSOqPAZuLL@#)8AtMei
zDp8!{Jy|CIexNeuT-hyoQn9FQly3epRYmts&v)*1diosnh+>*;P)k;Iwvu|FZA~>W
z-gAdVqh(`l?aQR_9N~E+X;=lfD`Hx^RBuACgFC`SO1evW%QD5}`eY5tc;fgZ31JRa
z7ioKapiW>f;Nd2szh~@1&~n1b{in0J(()JR1^t7Aimtx=s<Bg-i8E`M4Ic0xE}J~>
zg>Mbh3Q|jCm>JH8FW~bY|BlxG718>?eFWS8N-oJ6AoQq(bbJtNy>^~RRtda8TGnj2
z_TgAcyBl2=AzO9NLgJ#Nv?QNIq6F2)0a!6V1(dlMuhyX-r1uJ;?4P0GT9?40&K|j#
ze%`o>C<M~pOYFhb-7!!6ytpj0w_4`(+=W1Nk3PUb8ajv+6V;*60>WW#y1fHwh}IK;
z|5C`h%7BZ`pmdM4IM6ark##{E5r3RtRy`ny!PB`aBnom*jk)bE2GWQ~;n>+phr{+M
z2k1at2F_ExJUvM_t+))<L#QYyeV?y*SMWB}U!I=#_Fa1EK8Cng3Z;3dQB+H0wJCL1
zh`a4R1#t~&LzvtD6!p;39Y=|LOGYMtyjoE;o)zXi3Lw+B1^x3jvQt)BFFOE>i+(R7
zBYrUN5y!5|6dOy8-3OP({UrtH|6ZtPL}PLX22A;++b<%ZCD3q$KgjRhYJpNwvo%0v
zh*<`}cZC94g2bG_ArOrsO(8GPogsoj+FQjDvpb*+wKI7eMn8gKVquX;k`!bt>C$F?
zStn9`MVn%-P>|D*tD#rU{Y1NTu3pNL7x~I=e-TiH1Op_EdH$BD+&<sD3T;#rMzo^d
zQ{;Y!R5E~JJ)%3?;u>jo<;u<`s=h(>$#VlODw!!xT$_iOb?xm%F9u_8E58uoIu*-p
zHoPVsjAsDvRT76*xo4pe?*p$NX;uf*ZF6R<K7x$NFqt&gi*V&xuApPh!%jmklj;Ra
zj?Rt99ugBRil>YLzwT+Gq9L09G;;`YT;veY<M~Z9Pdz-E^GPq?XPilZT)YiAq0cmv
zM4Iq;j$c2qw^K<^oZ(7<U564DPhHq|`R%>hWlAQioF;+CG@7Tq3lQQz3n7tg7S$gd
z5~-%ztscY!x)t#{LBLV~_w7~-5<;rC`y&<Y7K^Snm)*GnVY~p{7GSS>%7gKbfWqOG
zV`EK<dFi;PD7-4Pt6lUow2E0X+h{Cac00#~v*;mD#LQT48EJt8S+$wVsB=5l)ratL
zWu(C=JA2V=U9y5Gi#o3Apn;;XBsuJ~EGrE!9$29vHZFYKwCl$<q?h1Sz<X(4dq}XZ
z;sM3MhLA96u%oiPS`b(x<-f8xE+Gm1bGCt!8=smg0|6O&?Qkrax)UrukVq0bO{=4z
z<YE;lYDi}Le6pHdmO4olwnN@v$^Oa|0nN?*IcaG%!+iBbm_}+FJ?p57C@fsA9wdZl
zf>f!ov*Y#6N=r2^(0831Irpd~kp3ip>`@4T0OI9dK5{5pTU*IT2(hCj1N}rT^;D7p
zw80Ba3A{Ok$vGh(Aw3>Xi=(ctm7O9mn_FKvyMINmS6${eWED8X%hBF1>hD{<K?SCi
zZx2Nu6=(<`h2PfcRb7(fYy$9^ki<BBXtasx-JxAIi}FaTtItE<7fEwQh#Z+C_~rP;
zArv-xdDc<tSqh0WD@^%1xKRMy$u)&aht=Yhq{Nv$e)Qb}7eIdfuE%CSI?+NkjN!CT
zO~<LmcM%FgMrH^TteVK80v?^imA;;&zYpM;64J&IpV`%7Z$U(>M1YR)#{eC2hz5R}
z_AX+zesQcV_D_&aD5TAUt8Se}MZjj#@)i_pelHEeB+>Wc-9CTBvG&5wB0xC&e59!t
zQNwmYQ|rZQ+lWac_8QxUpY*nSDICh;e`<e!@FT8AGo?WB&Og=>q5R3*1^I|q+<oVT
z*2+GI{ss^KpO!qZ!gf*`1Wu&dD50UDa56&!Q6gkA+exqF$O`#j*dp@iDD*__wijh4
zgd(3EaZNMRT=TJX;0|R!(r1<IPpiV2%VFSzP@xKPumsdv&{f&~ui(i_NihPXsE`hI
zp_>-@xFZTax&TV~dwP-@1ZWrTkc&x=GLnHqV+92oSqyB<9zuVjq<t43pWorn_wIpQ
ze7PYC>}bncQbjK#L&P(rDf13MDnw%bt&*MKU8D?@TufA!%2G3ixR~fWp(ELji|3&A
z^<&i|^O2_|#pL8-qCz8m$7E!*ZOR3zK6D&%wrEEowtjhl0AbKUwVuumt33>45{jf9
zM3y>@22|lN64j_i6c=l~9BR=8r+$h&DVW|BjE^u~-QXh?$o<hSn3I4nGBQw+iJ)pV
z`;R>rir|k|xg{w-+!~jh%&Yb)zK4{oK;uwMDllM9<U;~?BPY1Icp6dvK&1q%(_?A&
z$I^pDWioDz$(l{`gYo#4l8NqY3{AxMa*w59F5{UA24G%pGRZ;1HDJCs%-Vt=#Z3s4
z|0@MmC%BITMA|<xkUrE>?ed9YjDUT8l@bprOvkcY0z@oLy11b+$(1V~sCJ|!0S!xo
z21xO3D?3R2JwOxE@i$g_P=nrLRu`v=Z$o8VpYr+hfy@M=-ugh?N`njK@07^jG&BDt
zkRP|T|GOBUN|(<Ei8KZxMEQ^a1=3Uv8xl>n<+B6EP0*BJ+!%=Fa^z`*PZ>8xqcHLU
z_^|M}G3x2%vmcD#-*@R(Ovq@V^Go<iK*s8Ql^B26Rp5}QspgnH2p{$bA_3Ai?*kPU
zP6Afmn6xthQ!6V|j8(mhi^m{bJW6^JdTKB<E1gOIVG@_f%B;`iXOikJU!pCLk<ke^
zi>bE*vt5d1i~xVuf9_mAq9Z@BZks@eRYyghCP-eID}*Y_7$AZVEF_?8CA`=Sefnc*
z><xpLbt<|-)XLsKA(lyUe>^t9$1;5XfTlW`>wbr9S8MbEEg_H-aTBP$Dm%7}dp!vy
z!1e7&ZCT}LiOK|0*H8K;6IhDqgh<ySNKYlvePKdxog;Vfg%OXV_P86Z_PlZlDvVe)
z{RAz8lhr5Xv^UmBIozOZC{&xZoo7syArw^<nxePQyJ=E7Q_mGN|7oOdI={<4JXm|=
z>g1hOt!~xhvkjBzMp#CKqvU(@;qdpn%}tZjO@ijd%$Kf-<Cfy|V{0Wh`Pobfn<Z1k
zcc%;+B`$jvTx)z<O=aL-;@!RQ#C~W1io#yvP9aaVs1^{|jSv>)H#|7TR^8C<cmr}I
z5la^|@K*`Y%R2u$hz8AkHV@Fws1X$(5lWHHnXI}f(x8rT0Vtzj^`^sw0fQ(Qb>z1&
z-(5zhmZa>8?d}vA<S{X5@R&@{F7wk~t_3!2p)WukXRi%_cD>A~{g0Nc*jNnt)@GdC
zQD<{(OY0X)IAm;XZP82WaiuHxtZr~!@99hmQHX=Duz#I=NjyWvCWq^53u*ca=0q#?
zDLmnk_Y#8%Tf@WZ6)1dsZc};fC2QQi+z7DKfqa+jzg1sVgVbW%Ec#OSq|B>CyjR$3
zpG|-5VJ>ar#4v;JpN9Nt^E{1r{_M3GlBOIwG&gai@xnl>$H+4I@r&YbT8k12n4K}T
znITa;LC4op4{M%{HORbx4*%T<?fpn6W<EaBMH9u3%Gq(BU>%CZQ1pf_k{n*|ZHcaG
z>#Z&4O5)<ovJKvPY*}MePMSOtF2_r%4O$JL%QWYg;dc&+1`jeS2O5KFX<trm$@8d5
z>*P72<{Vb>`O5vR|1sC8bel{g?f8gvh!iFME!QEvJmZ>dZjH6TaFSg^(ht%bp-fS@
zU_FQS)!&#hGALCbuqqMU5Jhr|LD>lqCd5nu9%h%GezB&DFjJ&MF#^ehU9)MKq!xe%
zL6PZ6ku|DOPGVt3A<px$D&fHwEORFBys(ruab}p2Zi}R;Iw6c)hRyU?T?`5Dk(DJz
zbWTX*oZD?pqwLwa3VBuXmc2TAn`zWPu|`fHt3af>?S@V4r19yyDqZ&y>suilA~GZ5
z1KCOIgzBDcmQW0m=On%5SbuQ<k0pn%0_rYEI<}K4t1c)nOo%d#6$<HC%C5Bc`S@q|
zW5Lbf!NOd=mb1Jtz-3a{f+->`?Y%KIhe*#yanwO|lTL-C=N-S9`R)cfwT`u??}%Ry
zHYjTPU{Dt4Z|k^w0II)=N=UIORk9ofqbWE^bHrIw(b<<tnp-s4RA|-^S>hb}HI9)s
z3?3>J0n=R#HhWZS2nNr(5|X8gDZp)Wb}FVhuN+4ZZH2IYL1Fe%JXA(KVpK-o<P3F=
zNY}-sU8vA$mpV{8yu&wn9%{^T^7Yb)=;lZTQsl(lfZ(=Ok=pcQInMjG)D5+BVt>Cn
zTi#|AwJi8te`VN()U9Ra8RqC{Gee}u6#UVNUOlLKSOX*qMd~%p+p~E^^gPx({?H=1
z`%}>^X9g8RpRvt={oM_F?6W^ug6EAF9O@ieRz75nmQ*B+kC_^g`O;G~IG^En%U5Sq
zJ<j;m;p?aL!^Zf{HlRR!MLVA>{z`*udpB6Vn^ucPisqSdf?4N8EIjYjvz*H_Kg8C_
z_DsCAi8Bo6FIZ;MBo`24n_W?W;27*21V%;-wWBKv;?r#=FNy~Y=v~mAziMdL{A9BB
zB~#@zp;F1Zo<2$|ei`h+uL&y-k_EI!q<rAgFqFJa-uCoF@Su{IBp6!@69-1D45=Y%
z^U!4aLuGt$O04#1QA$5U!Gb?{OCApj3a0Cl-8MiC#c5i8JHFN}xLABgN8%$Q%m=?C
zBXfyOui*iAALQMMZ^+_$Y%*bJ(sh5okz?O+a!~oOYU=wtZ8r`L^G8L}CVJ5w1bQ#%
z<sRKw%{eZyjQ_uS7NH_^>|oWnp}s~EXDC2u#|FeUBsAjVW1`Xh8u3c_9Lx&*c<coo
z&9mj*j}!X(`)@bsQ6$lP2)cn|(#ac3pV}16mp=V1xaq|1!`D$K!pAB}g=Vvh^cE+*
z!Vl{Gs-j<{U>XKm_QzR>Rw0HYy^^XzA0^M+d>Nwa6uY}3QALSMKXStO#qUelQ)VOW
zfc2uH<}Te9{UwS4mhg~Pz~pa#kdobHJPsF0jgisJWJbWK!Gp=i=c=<A36&S8?!18I
zbzy)<^M!O5Ezj|nJ-Uc9BD<JWPhSC3ME-;ciX1e@xhBq~e#leUW4y+LKb9HQ15=UV
z6!Mwp&(+5zFUPB*cO8)xmZIwS><>#chDmR8k`NR7D&8Ar%W83oLr!A%7EpOCM@?G4
zLh>gc@<1Z^D33xOWM#yNYNP)&q~;t{=6LiI{2`DJmB}|#Vz*#3#AZ)mGjQe+VBsEN
zI#G&gjqtThAVeE8g&Kp~5LF72!oRIcXhh82wL^NQ{7+L)&*=}hq}HPvs_oB*2k$XV
zW6J#GeQm#M`|$$O1ev)BL&KlVL?2-u`UVHNYLhRmHqHr-Gdb=P$s|0Rn=1{a0Rv9G
z$%cDP`YM}>nr297Kp0yhZYF=gfF<DuDO^vNP>B6>{ozyBn4m+J(orjocsIYD3@mLZ
z!e58*)Zo^IQPv^U|3*^yw3+B0TVQMmeK!4)qwrbVh5FYDqq}2U5L9fN4SCuiy`^qI
z=YjJtUqwINOH^ZkoA<?$pN2Z_;k8|{V?-MO2ShC)N8cFfjWZ8yvN!qLEUOQH`ZFee
zY+2f191hrp0z}N#XzeR2MHSZGMgANKsfPZM!YQ>W<A7*zH$zNxmHEErlToEfK!h4G
zwFac`*ZI?j1&HW28}b(Nw;g;I+$^aOBjxWd5cF%0(zI~Ck>qc!`-z><^pP{m^7K|s
zH13bJKK)$l801^*XJ-$LW{3?DseE|M%5T#f#3c9ysqQ1;A1R0=+NHLJGqD#u@>)9U
zYtaaMeq4EeJ%p;h#5uxWk}?+(JmD~yu&Q&3aep;yH*QmVzc#gEmv6K3{u`}w9;pl*
z^W>SDG0d@;P{DXLN!q&(QfE$)E=#Z&E%=2s3MxxLeXa+}qy_CH&tT#X#e(=C1;iV1
ztwrf1-ncfJ`eAWE#$y=mD>!qk4Tsc+6M?*ZZkUP_vXZ59X1~MpkU#^3@dhU`cZDr$
zqFQs0)%}IlUpb&ya)C3{gU;j%T`C%65m{}Z4xdSO4@;qKZ*NbuB>Sz}@*My_b(6^^
zp##LbB;X_f)}my0$?1+8-RR8U*1@^dV4q|&9ARYX;YC(UL=Gibjy@2x|JiKK<PFAM
zL-7Sev2A<@qD>NsO{46emox|<rI=CPdWw4|jUP!rCWNpuI*0EEswUnObiRu;Kqh`(
zLyRx^f9&MNo8L$s2qHd@qVy9GL0(eny9N2}%`(`;E{q5h;P*0XV1CkSUM=-*!bH;A
z-E}w8{iFvK{;%4;G#tw{?E2P#WNb7`q9`I{h!mL>jmi+on0cP15@k$65t7uFdCru|
z5D8I2GGvx1GL(mW>u&G&JC5(?_v3wzest_j&+}aOb)Dxr*SXGh4UF@IYb=Uvl;0s!
zC1aj0iTyBBG3~pbv}Gf|UhB}4RxbCiEQp^MNbR*Fa(N9B#pC2;UYK68k@9N;w7F+6
z?eTW2t^o_!%;TkB&j>Eg=zKX2Jq)R7L9xSD1oaL{o0yRJgdDR(U^q3~@J^2-KJe%N
zEc=(sx@ZDU0B+<0kJmsXNsbJVYUaPu6UusT61I?@3XI<JN|GIkwxhY1PYpb*WJ`M_
z+3Q6bHz<#gjjLlBhtt?5&hDwYV3&I33v*#l5<(a`GK8qRV*!Kf(kMZLT<k0LrkhIz
zwC?M$9%=tdW1)mhBr&O!`oxiPq1|CIvM{rZ+^ZPND;4#HsGs52SFn)^NG_1^XFEs^
z9Leh_b4oX53QW;k(HzHO?kcV*nDaNi=X~|~dL$WN(yxlVK!c?_$b?j+A>c#9zj1%L
zC_MO{S;FxQI0~hyBZ}%;5}lDMUR3$0A3r{O_*UrxI-5MC9QZH!fEZd$QUK(<T+453
zM;rDgioK0jWwH&QdW0b;xR%eK7ZPoamF%iS4TipCYj7PBkZdZsUVNRTp)mG`I2+uv
zd+IL*cMEYN>))jHIEIpM{(+o6`|BZ%+l&}~YozE+X^WBA7ZT>3JqU%o3dMS^Ppx=_
z_`{&ue3u)MVLdr%0S)|K&4h9Grg0;Rb^^(2QpNUy1iz1%9S(ugVq;WWPxR?j5^}Nf
zwJt9O@r}($GRQw#63SID>xKpkyw9_TPVXq(`p!%(ByAD7nqA=?5&Zo7D$Sq7qk#>N
z-rRjZs{Vcpi*kyVGL@v^FPEbSK1v+l>#LzD?lGIU(|hS@jNaXoZ0c9yir<)}4&N~E
z$=UNxRCw!gvGP8K_3ytOct6q0vpg&(F1kqRqZEG5kaTF8SdpPjIGYcPQzpcHq=`P1
z?#e-oTwk{(J;Qvk5y3v3_bH*6T~kf%6*gk7eedBXsi|UYsc$bN1^K{ltO^z!q3_>o
zA$ad+6*WE*<r+6$QXjd}>>S0bPr0?b$&fvaS~Z;W=*5fNvp;`QNzARH3&Mj7y%bos
zZZ(^8%_^;r%a7Q5I*Ci(HSy<)c)<3J3=Gl3pJcB7DQ|J5+qjX6@TDxcYL;XL*K;=(
z`p#*yJ32bn)z#%#-#h7S%j2w}q4C6v^V>V=iZRv$Z#gIt;rZc_wR4mRDZ8sjju3X0
zD{jPUyho*2w1!!Z-{qHI-J1Ht4>|;-cQtnv_p&5TdG~yv9=w<(n;5q-Oy%PsZvfo$
z%~Lq6%tU6A^`C0Wwya7I{M2za&a#&nagGvSP-s5BFwf$iP*$KP92^odgE(kheTxMj
z>4=}-O9`$jOhS>4E4;F@Ure3Ox<^Us^9<YN4F?ar`(&Z2oiK90&z1LqR^YvRTZkie
zc6L!ia2NcAAvi)}`BZ<nli}7V`MApzTHYLw8Iy_bvhDKT3#8$T`J#G?Oi_f)b`gU+
zFJBz;8X$CQ$Bw_fBh%Ayl^EgXTVh}2vG4wa`I0R}j_dfjl%a9I&Z$pnQ4&09;n~Fr
z;xEz}uucm<Z%(=g?4~3Z7f_W%q=!EnHU=}%QSO=F5;Qe?G4+(-O14bI9dQ+@=Kjun
zi{pNdt0@1dh{(uD&I;=QzNDbIwY&im`|TH}y5sj*PmKt8FJeg;CMNl^<lHy#I(+{m
zI5;?H4A_U*Newpq8iZW^7+a_fWZ?LL+IIjIRPL20-#;WP9iOsxCV^OdXmjV==+x%$
zMCoR+*W)Ibd7(8uz=p|%8}sAsDOp*Jgu~Fgt)`}?k6ymyuJD>a;3XbmSmt)=*N<<%
zFhex($%)G|Ghg!ALQ8_g!emzv2CT?CrWlhX;!ab}oI6LRJ!#*ig4YZ#@Dk9`(ecMT
z$XWt1lp7>|N2f_sr;4-fh-K&0pzX?A80RJa8L;hQBGyvVA5l}IeYyI_8-sBwaoT4x
z&t1khudA=8B5vIN6@R)*=1sS4AFXad(tg`YK7+PJpEQQLd4rh;ljg~kH&?}X3s+TD
zrCOQb%AlK|do@zlLzeynIM@Ug7U2(O%yZufd-LfLUL(iJFOql{Y6#*Yd*1p7P9E}I
zlDOzSDfv*=mG0WLYib%AcVl|@jR@#Yti}(09aQ6S94fJX8_q>at8&a5wgue0c?lHB
z`3f(|1LES5_wEJs^%=*%a~AOKdf)PQaq9#AoOg4&T@}Q{$H!MSnpan+MlwgV^z=4)
zHX}_5b?@FO+Sp{Y810-8ZjWsL{OOZ1h|$(wz07l^oQkVE)U~x$O-vrQ%qDCNDK2cr
z21)(x4-@as5`(<FJO?1M`vy~%`}`LRwUw3E!@0(OPEr<Dl9)x!`Y3YJ;cQo@e$n1=
z9?kN!RcWWtj;^caRutE!B!!5%IhPv^PIF;!%cu`}KK#jTftx1La;>U#*@}-Xh3i}J
z%MokaKKewD=9#ToOYhypKNK3`$$ZY#9~pCyd;0kC8ej({GqcosS894ze~>HY@eWRN
zoR^!)mBr=>QRhfKbsN+o+e0I}r_NFNi<Q|4@p3bstPb5J*1a!%_w3zU<xjO%?r>e2
z7#55ziL2W;(b}*5kVi7M6MBc6;t$#?9z9Bxb+ID&M1m}@j11ci8m1zOu8itC*TKT4
z!}%?%mBN)A62%KtxE~|5WN4~`Q8(JU*%~-1*QWEpmMvQz<>WB)^Yh~}uA^mclOHO)
z-RapSXvM_DPOLCbJTG#7Vq?LhkQMx~ZRm^F-{o<H+H_|QY^lLCOLdgF?+13ck&`1z
z-g%&Q{CMH7OWo~Vj1J4v4!RuDZ=xqhHg-*8Hzs5kUdw+$k$<~g=BGGEx11tAfk_SB
z;K&HMeSd+jOg@$Np~zOhy0$j1!c)h&zfPvMrCF(GpPU?LXJ_Xx_$$rer&69gS%>&U
zPL6&kakiM7?&U!|mh`^h=BywkIkI9e?0%J-+Q5CPmFM<Uqm7lgg^!zyyTS<V#hw56
z#Zx2WfpYr4m(P~X<v4Tyy$%O&_<w$K-F^&V{`-oIFu5N4KfiUWPF3tR`r!Y)22<|$
zVFLWW*X&8HA1JW?d;Q)c@c;F8C;2cDs<USc{z$uw-f3=EN<4k$%&y4+DD689!&6`O
zXLx0#h1@Ft*yuE2ZegL6tkZ`<_8%^L4qLDOS;4Q3_EstcFefqw-MaPApZq?vZ0*@b
zu`kQNbMMwpEcjlyWaQ%-k*=G~Zm3I3Rj9{Ms5klkhx|FFl?Xo`4NXmFPv0d<N96vN
zn5E$S@3~L2`uk1N7(zlqxSpq%JkQ9ej+3+&b?iUpGWlhzM(QSBUf$8R^z8_vGCnZ#
z!pKSBv#Rp6-x>-<{7v!S<Wq4NP?6l!%Ovj&`!?qA)dUF&pA+*2xpNAty$zc-ZTgUJ
zp}h>i+EMD7E?$MTWV%GIwomX>L;BPV41&(jm!g(A=qcR&(1l`@(&8bzC28=zsgtua
zZf%hRyryMfr~z=f4Y1OO`30S{Gx#LbjGWT`;9becY}s8Z3D1SK(HcWzIrbVaTO)k=
zPq^b6P$~&_hD6U{HZ-(qKYn{*0N(zf?U!|hdSU1#b{IcMnKs+FU1S{r1Jd<+nV-L%
zzkRDNU(Z99!1MOr6Rm+B_}U8>E+D9g=oN^r#5`@Wd=k08wEe+mHny>t{jF2agM5+>
zQRpbCzTrFy;b?6!!PeqjW_F9EsC|#zKW$ggjCobl($e^-gr^Mu%L9}?6@l6HSqF)!
zljqOV5|*8L{Ic#-6QA;E7&&kK_~G#IkUOKNrzctZB<Mk>QL<eXdoEJj3l}0LpAo`o
z%DKAgmp_ha@rc%;(1-aEw;oRR*M!^(2@zMD!mS+N`d;7o=lS#N=hAPUOILPvl{}Gj
zI1K(AYi4F<xI^#PEtVtEez;ON+Fd5~`0-;ou5f*ujvOk2eDHNcO)K9;VjEC=$q}!x
zY;p4uYmXh1T1)Qqy4~FvAXC%UrXftt&EpKoP6I*aHw2D1L%A$1jY_oFkZ06S3qx{0
zfaXHo`}fD16Xjd-%rxPbjX&$CsdpYYu!&DKyqZa2*&pd4#kxI<yJyYLX<Z|u{f#Au
zF4xjCsSa@KWo}FITM{J5^e&Qi8QY5R$5#6Wl<%s}g#lV_@+3w!dksIZAPD^;o1@Cg
zH(OlcO_}33phCPt)s!LB-kMMs5*++w2nlB`K`tyC8{4hmU_~7rX0L^b+kXK;Lyy;=
z<)7Ni=z^#q#j$MKbp6SbU09Li`eo1P=x7fRD~{;ZA5A;6Q&yH8Bg@oTMwNDMUF}(h
zd)ut6E>`|oq2Q>hQC@TOyHi_C8l5(1(G8Vxn_zAI_^~Bfi3S^KZnA4THqy3dd&JJR
zqal;5v*$ymzSP*&?owB4r=M>^L+PW;V(;BEM$jGo_>AeE2LGEkZ$w>2bx_i5!KW}E
zI8bB+2zn<n@|s<DDes;=w16cynb({=c@hVX)U{_=34z+Bp}V;T=N49nh2JG**RNmS
zQEboSAK38E#*G^tvHSGrzf5(PT}Bhmyxq|)Ugk?Ij3y%E<3k`yc%&4>NJ~q5<k+!w
z+#Y>Ypc-jT$q)Z?MkvFuY#X0S=qogV0v|ovK|WoO6>l;8IMHgGSInc|wK{he;HRs|
z7TZI$(r<;Cn>(ERNoD0r&^>kJTSU~Kj9RF+YVds1wG_<{L!Y%6jvxr7K>A#n!|vsa
zfT0%6+~l1;v`kE@<DWCdY&t}J{`?+krHlc<VZnZ2!|U&xhENm{;7-F3LQ`zb{3HwB
z32Bpfh3QFyQ#3d*w|5{gvf#9>@h5hmaeACLfEnw&nW%<(GVG-}`kib1M>}#;m=#Go
z6E(AYv9TP!jo8SkuHfW~1sa$*rFRH}$_^!i)|~r*s)Iw0wA0|N)8Anb7*_8U$jBuG
zdF)MQ#bpp+J4Ho5jC#TOnFaT?Np;pD{>FT6i?-01?x|Q$%;45ecEhRLzl2++l>i;`
zh3vc+v&9?oQnT;H!~`RRJ;-<liAz5$Nu=2F(@I4QUKr<Tt-J+7i@TTBVoCaHy?@!%
z+iNs9JgnCigCx?Lo4HC}#(0xCaOD@SCKPDcqIe6wh-sCuQvWznS_`^)^C?3PDZ7H-
z^G~JA8aXE$6lrHrXoUc%h4}VOSFRqsPz%w@zL?0ncXyr<iq<Tu@A0$IT-~gA3l>r7
z-?17I|72v?<R###jmhx_xG(Irh8*bv!3>1Ht$n`Q8}>wD<lwK?=gr%<?}j;h&7-3?
z))AzzOz&$rCf+v?CWCd6$73%1yl9Rh*h`aOhBcC<1bfgs1lZ<pWbU4|$}XR!BaA^k
zsKkyB$BIim|G<K|?<-Cg*1kf~bZcoCkNNsmD9xj%LXR;M@e&`(!sIW!ht9ckfxmuv
zz=Dv~FuS~*Q&3QlvBreSD6IDZrQOii46Sr>r0KW_9KI9{3^?Dtd&l|w?KXHv+_MWe
z4Mx(ci{!)3x6Un}n6OFVGjdItQ3++`ZPYEf(k7H|Hu<&W^mls%ox=0(153bvG><MF
z)^w({pKG!cSEckKh1`Nqn3WKnH^%1I;O)0HbmW<Bq-Ns&YU*Nkc}n&vd;X_Yn;G!s
zm-=2^vx{H-guP#Qcy-yC$oZ{eJmoRXqPPQ+X&9DaQw0YH-?@9&B+ut^UVc6mftGmj
zlH|f@do~a82!+Wr?IbrVLqtBs#3oTlsV+2h(DsbCXKSwR6%jdjeiie_t$D%0bi4sS
zRe!8u;M=Ky_d#^vEzrx<NpI^%R(K4c(JI?jZ14Ue5t$|z#oG*a-Nwvwd-3#?VJM4Z
z)vA_uvBC?F4r1nh92muv7TQ8opuxe%u<)sQ;oS09T)GS;ueTe{=$nkwUQ}HmcQdBR
zfB(LXc8TUZoQZ>t{C+C~@v(;*h}WJnyt+ISRIrTpv1t2OMOt1uIy!ojco<x~a2dN>
zU?0JwAad&T>C_=?4&0t`;cG26=?o^AZzDHs$UN6&x`O8524d&FeeWm!Dezla!r#Tj
zTKbD5+<woR2__-{cP)FJHfS?nSg6DUW4kBr^UGldoa1e%50l%jK-F2XXaD}~K*F0K
z@H!}aeG%EIbsTWH2KuIIBm#h#m?j$J;5#bPYnV9>+BkF`?EDg8J9!nd@tyqq%08Gw
z??-?=rEgWJ=eV-uN<P=y)z!z+%Au%Y&1{d67UnAJ`JA0`7K<MJ6aAM92EE<W@_5s`
zF>rt3c<2qc&FXJeLCQdL3?_LJ*L7Dt#16jcdcfx~PYElD?84LS{);!0F7120H-H(R
zb=#4T4e6N6=O=DmstQzJb#}Y1i#xRdTUdohkoH@>5QWQ?=GW2CgkZReHFE?^iNvq|
zUtn0+45xvYT4j0sekHfK8btTG>MqB0#PzwFuFB@`)8AYU2Ihbs?n+2NPz@@$Xl0ew
z=*lJU^~okLMQkOY8!gPRtdzDZBnSC^s<*bw12uR4Jc4pkYbj-MTB<bMZ<87F;I`t_
zT;vY*Y_f@G>sKL|Wht(@)Qg$zCqoY&JaDu92uB-!|G)rBp>?}f*0UDNV@^(a#Ew#z
zvHm>Uyh5I?FRK~J_^&=b<(&aZ!j5{fZWWnE7sh{ltJ>Dfqg68!zR2BuSfXu8QDR~o
z={bFKxr~g=8q51{s#q-qJkAP5pZN@`?z_$fETPQ9$JYQbdoZ_1MIh_8nmvO0)C2ku
z+|V~RCX((A`uYWAx7n`;LkWVZNd4H+GhrguT}8tE0|Qh94HLJ^i$nlLR>K(F2_Zj?
zBZ%IhU&^?$vT`y)HQ^5_Lshbe%~1NxA?MZ7^lVaN(|X*Q{U>OAoSD>0STTIlF^oa(
zoam|GAnx41Z#lck0&EPKa6St8Ku=)aa9msaEm|>FZV6ETYy{mz_>*O$p=$fKOP4O8
z3O}HqD7GYJiqxI|d%pI?^Tvt4+Y$E;!swFZ^Kl_M|G3of>gn`0vKS}&Q17c?Fsa^k
z$VrB*=!qkaj=7!jYf8uOUwI$*$AY26ARLE@ikO|9<rHb=v>(?kB2VF*95CF|_R^(_
z5ztC|_Us`Dq#bH`uQ^e@Km!rg6DPvk9LGOPN_B_luUo%fof3aqf|=#F$X{GIGC$SL
z0jqEnn~_uINl8f&adGTGJ-c@8dXX_TIB1&R;JQITb>tA2oJUT**^<%4#_k9D0#V7{
zi&F%BrD)lW?Aqz~E8|%&O{`wlqV28f^#^V<vS(uXkx^q4aR+JVM5#vVDGgaJS(myA
zpQivLInVz<4zPweXx|e`Ry_OCv$JD2Rl@8^c6oU@L+G^8kt6<d<#~Cn!|fac&kyDo
zua3Qqiix3qpk-dfc^TQ@Ne4QEYf!5LsEn~NGyD7d6EGzweaTHJC`lf^JRY7`v`X~~
zQ7tkdAq>IZWuk-S=+UFCZbDn=to+VLYurTEeO8;+jKD6OWmqO4&qPO;H&&SCH{D|2
zj=MoxZT7*^R|WW?)73xFS^d%_Ucz|vL-MwEcWT2m6u3>;r9oTwFye1D=jl|!2@^Sj
zfO3_U_yN%qVX3Cp5%nTBW=b<s5Y!AogsBYZ$m0r3i4rZTS!&`<RaIk5il$npv-l3x
zUssl!zE)0s-#gh=ELCP85}Sf(Nce*eBG)o@zfnvgJT^M}mr6~U3Ec7&Z!it~QvCc6
zo{?a3p4+6}4KqBGXq6!LvP&_NUka^v%e#Nxb5ghPLB+~68Et-!j#`Gyu%;w^#g+pO
zBOf`bC5FN14ZX}jz08BsFB|#gyIr*;&23lXQ8;}mvJJs3r!k}!djtiK0y7jOgcYC7
zJa<U)gfGJ7*P6Cz`6Y!T$5d3}gq0Ld+JsL9eFwFs-`$RyoQ#H#&5uesnFCu&JM<An
zwq4YZzB#$-=2=L@?;Us4mGie%KW87>%ia|FHhw9%xG+E5?@bM-_0@tI97cqBDgtjU
zKAI0dHyQ#asf?NW#YJMR)JuHC&!0aHW!HS%V%_z=$5$l4;RU@e|Ds%5MCRC4K#;pn
z<9;Y-P3abWNcvN4kk`qoTe&#O!_)M91d)F|L5^fD1eoHIKOrJeL63zc(*AE{8+Q|8
zFeP?J3DY`e;)s?Oy{y~BXnl@P_@7v9%?)3XgG*#N^OO*5JBLu_zy+BgVtII$Qu@L5
zm^u2coXjb@7;O6v7t!x07-G+@g7PNVMU*}_s>(VKYn3%XvyKa@xA+I{8pb+tg?>Kt
zMJwsg^7ERS8n@^cP%UG>dn&wn+3n_R+N$DOp7yAuw`n+!Q&Us-<Ac`#*^%iBFwC-0
z&x(RLHyCrjJkzLCv2s<~yOcq7tT;W@c|WlXI@Ub4y{%2%w;vZ;Iqs9*i+jX0rx$Ss
z9W)aVXnn{U>|{Q9l`KIB2?^2v5f~qfxOYX)BJJhutW!4S@o@%rZRHOg$x1;_MwVhP
zbyc-S5KFP!uR?=l-kzD3_8RpZ192-Xtk7#+GXLb4!l>cgPsh6-bMUsOYHp8`dXoS7
zQ`+g&k<9{HqTZ)Ptv!_D9|Vs;WR>q(y_0~!@#>w#Y@^B^K}VUW{AWHTPD6Ak&KQaF
z`Q{90H2H&e3<wA~A`;87eCKJrbz7UGmwsj5O~2LLMOA9umddfNQrGeKS>CgQ>LDXE
z-4Qw(sjX9jCv8#{vK>&kcAwhK!Xm>%@<^X-<j`@XzB=B^8=sMK)#4XS^jb${6=`og
z+2$Gh@F6GXA-DIVc}q)P5TmItb+t0w4Q;7Zzmpo@y`F|fQ|kSf66f9EnJ+taqlQqA
zojpWpnt@n`iU6vAoRL9~mj5>Y)$V{smJJ&=kf_f;F#agNfIwV@*!`CPgHq~ro|^Gv
z>(;F^2B;?3?`q@n_S{A@l++r039xpGOiG&m#5#FNQ=usOk3ngvjAa<4)u9sh<$_JY
zAISg(ZjObWo$l2S8X<dT)VaWUR0P^(;+!&0)uUcuK>8ae3FE3j27>)*tdp+E3Zm)V
zL{g}3mxhMczftPS!Rt3}Fo;YtY}hd4qIP(<kdQ{4(JY9nSK!@sPM==;h*!pAqC*TG
z?Q2_Ob!JbHeTAj@@zfR*^k{;ZcvO6R<iOq#kG@UK#@NxBhW;tf2cD5Q)XG7S<=WV9
z)Dd^0qxr+~wktj=DA<BFr}DXT+x!DPopCR#y|ck^_1m|av7St92iBp?L;cHo(B^)X
zS%c{TEG7)_2?B7{0kiyM160~;?w{8WldlxAo0*xRpxYZr&vqR)C9KEKdn(0`edMpE
zGKTyD_hRh>wiV((aFZGI$r}LU2$%x0bEUqpY}q;HdFs@r*49=X1A|c1M`Nq_DqFX1
z{RJols|3Z6dTrGXphdEKs@A+4J8yZ}Cyj@eSLfnIQbWXt9!-B(+e5Zz#q)dhNBvCe
z8XA5&>^eSHY>mnUwZ(3(wTK1ycB)QJ2i&H<S`~3-o(lovg{u-HxF<{fIRufDKjc)-
zUmXO8*8Ao!f2ockh->7n=W9RwFmnchPD@93T}$8w>_1A~CWDa$NWqR0I;wJ<!fZ%>
zfk;<mZAiOe!!<1dC5VVVq^L5D%^E2vD1dlw&6<tYNR`MWyJ)!0Xzc?n>YIvGHx;je
zpSUp(#1{dUK<%n_(qS$D6kKJ6df`@Rs03?GDTw2OD{b$xI<(;5K(<QO60Njl@Ty{?
zqM|w>h%W9LpeG%nXo5XF0kt8ofB^OD*RRRzusEQ>-NDbl9{VhzezrWhagG#+P}hHU
z9sf-J7|Md+H2hsnP3_L6GhxaqDjKIR6R+TN!py}L_UhG<G+%NZz*WxyhoJ9V3p{UF
zbi8QcG8}#BtP0FMJ!S6Rz5B#(M{EO07r@ja8SNd&k?lhM9t;;pa4%|@rW8ZWCVBEB
zRh*^Rb<s@~etv$Vt*IN~y?1CAKYud5IuY7io%Q0yc@6q;mG$}qXi>*5-AD}*E9J3y
z8^&QV&#(_v8AbFjiA)XAkf7l^Yb`w0&)G)y3$2%D9~KlyV3ebJa2EA<|Ep_jNk`*}
zEWX{lpVhB^x+3}MN}-EY@yq7J6s^*+dxH1?=gZuL;Zk7jZZM4e)zmi)^M{_ap|{o5
zeE<;WB0TOj4BeM;7P#m;e>SC|0t=Y>?Ahg&rTHI=i^XN3Fu^be14ixa>wPzKrDGcq
zx%r4YBRzff>l-wV&;gf8o&XN~tU<2ty;6={UK;^aZM%vdB+Ne5FSf&8jT+857$e=@
z=q-oBD*mcZ>pYESL@oLgLftyeae}*W_m}s-v^n+i%1W;HqMgl`f(`Zc^?2a4jEpLn
z)`hpJ=JI0YDiE%)9GAd@9Z~h7zDq7!wr;(I(?`l``bI{A&}`TaISyk}!l98LVJ^7o
z{RYs>PGMmNv@W(qVKMyBI(mbHDeApoQ(aT@y1AJN`-oM-k{Ln+d4rceZ16}7P;z|m
z@%h=k^xSf+N2-R#mZTD#V(#2w#uzQ#(9jUPiOHT(1R92KAmIRIh9NYMrLkuyUyL4s
z)h5G{OmwJJ!G+7H!D5PRJ8~|8mkLTs;sM|ycL|Bm&|hg)Grx?2KNP=U41@x26FFJe
z@4IYuO-*;f*_Rh%O0+j`-h>(9HO=C;pZ#n?05-|*jHg3>W`t5uTX)bwLv!1p+=B(I
zo~!r6o{sE{Z`0HJyQN*>8Ok00G!5H)aA@coYT6nUMJ@Pl2>d2Mk|geD<d(aIAF$Gh
z!MaHa@*O(FaiofgcPh{7qX2OPWxFlAq}H)xJ*ol|C0j+v_C+L98ahMhYk?tmo9+p_
zwwA_AcGu3lqiAfo<QIY@c?9Ul9bR`3oM_bDC2WHlB_{b@!h84bRRTXqkgEhB<>o1e
zxCK=L2O??!(#01+=ewI(B1atlZLmmsL*^iYH^CQ;ynlam%;8p@3VyE^l{6J=PeJo`
z2;2uCBf9%%s;fBrwNrQF0F2Ks7VmPWGDfcqwaF&HP>VY@&@}f}|M!pTqn_(1vW>^k
z1^}KXAMZ|{)I)yGPEHQ<-_L3X@pU5CLTxCFytWP;k|HfF?+?WD%Sh?K8a3JqE9(gK
zw3N|jqrbR{9t1&<O^)IkIczH86&N+o6T5a^p_W!&OD>k=A<*YLdmsQ7Saok7YCbl|
z4r6C3XukqUWDBAxk&%Y@0qGgpjo8)>SXpIsb!F(T%q!bmkoCI+1Rm>VU)-~AUx1dt
zPfIyKC+QyEn`Fz$j*iE7jwPz>*kL<NOWl`seg3Q}=K^*Td4?i(yHoAp<)z{rQ9kym
z+##5nY(rfs!c9)(xj+qIj3zAE4T?(!H;{~uAIYVmh9$E+3O%>UFW(UVNuj_=J-y)j
z_gS@rG>#o>-15v46zPAj*^`|AOZ_f#>G1krX|H`>r5c-;R0-Lba*rIbP**;2EKAY&
G`u_ptA>N|^

diff --git a/reference/plot-8.png b/reference/plot-8.png
index 60ce8e6b214438cbd4d8b631e07fea18de8358f5..38cf32f6649f959645681692a9f29c77214f1df4 100644
GIT binary patch
literal 56739
zcmeFZ2T+r3w=RsmW0wvpQ3M1<q&EwK6zRQ1M0)QXtYD!CNUzd6NR=8D1pxtRK?o2K
zLP>x~4K47m$Io}~^Pl<mp803?IcH|iVf;SxVv?t=d#!6-*R}4KDoV0P4l*93p`ke<
zcUM}ChGvfk4b83x`}e|ca8d7m!(V$JD9B2~-|(|`yT%{>I^b|u*O`WfB@p@Fj!?q9
zDGkk88ae5k8XgHVL!KV$np;it$|vLO^3~q$e<fUa_8|MOORvu!KD+-#+WsKXCoh=R
z_s`*!WgQ(1R2(&K+;D7g-0}L+t`BEZ_W!znlm5(}-3K!o)by%3L@-k8<4Rp-*6}|w
zeKAQKM}_Jp6E^BNC>KZLojZd08|ioaIWHQT2O$NSyZ*kW(KvFz=<n+nswj`Y?>Q?I
zHnQhmzkWo2=Ly|EpFH)y^35r<mw0)j8b*s*8|g8Gi;6M#Bi9yMucWbRZ*NfxW^I2K
zpCjV(UWZVukvk68q3|@0mgdI8w^306m&cr{@|_-se+<*m(__>!*gxZeG5hnbvlUnW
zqw|04^^n+;bFJ|keOkTm2G&LR&j)C-FP^CV*DIQ1X?K6`{MYLnLFYdm`ul6XSG(<5
z2yQjyP6YWO=MIVLsnpj!`MUfUP^e1+0-A|p?%3q*P4j!nl7{+~c9DD+A9{cN`c+<8
zxi5y##HEqJ{1PXp97lq~lf8%OQ!v|9@<6G%vX0|28IPt7ybXVt1a01+L=u%Uva(2-
z@pfvs3fsuHZ`1MAIrr+Z5V11PMKjE-4X)x|sKiQ4waavBz!4@cII^~pXT}*`gG-e-
z7C+fY>&2X`BsI8suAdKZAcr(h^31fWUo-PvBSbriCTdz$R<DrpMOjH+@^nTX@6^=P
z-p0k{zPlZ0HBe?L>a}E%F%Wmv_(|_J+HcMDM@g7uz%577L`DItx2N&&)w#YKE1ETi
z_WoOo$ptIsZE>18)zOA-v6?~>5)$`CDscs_B<%G2CRX*1B#GX1xsw^`=~IKw9ldZD
z${a>-oj!e9(_Pz#-X#AgGi~6(Lx*l-n>#uvIVTJ6CUG=v(Ekb26s2p~&VFkII_DP7
zEP6R(3CYdh4Jos5XX3wn`SNDgz<tQ|qLh@Bg?^nkrJ}|B;lmgS@0A!q+kxG-lu!HE
zuG>}O=uH&m<X%?IH!?kq!oagqG%_;!NsWw+wdAc|{q<&}uiDi*A}s8<TTL%K)i(Ur
zN@WUPzl~r-Qj#5<mhaZwbkV|G?V?iN_Y%|AopxesO)6`BVsA{ELe^^cPA0l1iNMVw
zh+aO7_uD8a4|ba!cy37k_;{D3uaS<9&by%Q>FZ?7cD?0Lwd=Pnc{w@0>FH@>Td{<`
z@3U?jWoQ+ZV<kh#m-QUD&^0NSj?elNXxT;dpY)rUMP2S9hj1egvxKd%`Yx#+VG+Ha
ze>{*}zfssOGMP{;8_BC*p=h4y8nrSv#8fuO-yxo+TWZ!!aGHL_UHC)azV39{W}JZa
zb&DS<#Sd2JYRE1N%^cRt#NKnQW@cu@&BcyBZM5%cEnKgF+l-C`+~Aw7ABODUI2uLu
z>sDVXn5IoGy`7n{ah>_u>2+i;Eps-T-|Ed^CZUkk3f}6e^LaxM^Hvb0#=RROPmksk
znCxp+U%q^Ky5)PkkaC(#uo@ia+seY?qJYbkO5NH-ys?Z?>3QEIX0pj)<XRt92f>o!
z_gr{*`qZhr;Sgf!lwogWZ_1n|Jh?%;O85g$qTC)Yi3D%;##~KgLc&aC!3Bk=sp+ib
zilj=rA-&gUc+~9d3gNx-^73V-t!ILnMY_03y(xA_n1!$Ax8{)RC`NFAY*>|gHEsn5
z4;~zMtI^cZNY~8M%DERWsMU|N#~6+^26uO5C`5*br;0dFOpSbc>QrR_e_)k{7Gk!%
z@-|<c<}Nd9i%Y@bYut;xmZpsFP5*%P-<Bkyzd<2KcTc~?&1(7i)obPJyjyG+>&WdZ
zLQAno`UsD2X*O-HEed$A*pNK4loHFVdb#pKkt;K6V|1Mz&{We!%gCcb->FpC^r!N-
z3*Q!VpL?sZBI>`=Zyp(UE#=ybTg_b6Br9p{do2HLEMKF=!gw=wael069}m9X*Sikx
z%s{zy^s85|;L->#qOPmIjd^rSq!UG5OIku$#IyowS#m!oF-vs{D~C+-{Yj-a61BT;
zzc{WtJ5XNAYdKtOd^-}y$;ru=lar&RPiNq8^G9oa*1h-%XOZQ}WdFI!1j>bt@b9bh
zW4n|%-Xta(j$WJ9D$t8<2t2yMLxu&Ea`S0^i4fk$`J=~>UF9^kAYOANT!HsMwLfok
ztAX!wuhtr$N#pLz`i2)ST*&lXoWNED(6U%1D4Bo!P*^ipeUPe=$jNx#^)SOppH~4_
z>Q_Jiv=OwXE_O(v3v8d0D)D}$71IvFu&CvJEHE%IXsw;$(c3+PyMo#5x{R(DAMpxZ
zDC&<;mSiaNxu?{5tY8rp-bDXrO)ayqdyrykYsIbCg819Boy3q8OUo*5A%Xtj@Gd+&
zd}kDf5T$?JVdMsB4fzHqCQD08ulcWZr|IY>-Kj4*lCmMVJ16bg{5RjjqME4mF)&ED
zcrWguM6!^*(MMbRp=wR!OHrJXl19n?Tb@6trTsQu8?%)K3XA3)Ni6eBrk?o*Rb7m|
zu`w~(6udv$tw!R$0l#^B(3H#GFwuY~o@2p6J(c!oYYhu8uj-C(0~Vr$x9a&}nQ*)G
zs+>%JEOckyD~)3F`Tglp6Uj8%FjL`5-SrW#qw#H-eQ5t7udM;=qUTKOj&uc!;~vZ=
zf3CcT4h2`<%3JeY&yTv?m*+FN2FJ$h)o-nnBBP?tl(a+MFySdOs?)2gs=C>V_a@g_
z{Ybg_<C^dK()*}*LEGHdSL%f=Jjo9`p+t<kh*G2AEtGr$gxr<<-Ii)<;f=ve<`QU(
zpYJzO%~1(F$(J%jx6rlp?AdOK`7bAr<NVS?&iM!Nd5t{Ym+z;jsMr%KwUvGDc7U`E
z)9CLc-0E{Wqy0LCA3o^tn|&`%3S|`FI*+seS^Q2(Z1%dGnwD17tJkmDyX?~Mki=h3
z{Ec1T+fOP(JZIiF<r7|><RH3ZHWDu?C*?!wd9L`uYcXgbBHg;EPB34TMbzcDCo6Hn
zGHK1^sby#C^YXzo+0Zv>?5&}^mw9-W_2*izV9KKEWLX0j0;M)T=?#~dD*RmLYVekx
zh8)n4+l}$xeqk#G$=?$eQP=O$hUJ;k4tXwb+e%wpCVqR1(i>7$QoG?g|Ld!$)A$R#
z6WV9~xdCr9DhCpdRey=;w~E)VUza~@K5;KWSQW~YMr#yL<eN8V0~<ycf4(;f)Z`d&
z7S+})YmcyC`TNvlk*5Y2Lt7#0vZ}oNE0pDAd!pCsXrSxpbGlr)h>KR({8+mx!WkYU
z@Tas)f&}e{Z6qi`*AIU+E1iZDtVr5k!P(zz4diG1VYY!k7Z(@DsKq?00|~I)r{6T1
z^TLHYu-YMI0ssg<apFXNpu5^!HL0+a_<R79d$n;D^`+MSI^B0Uj>J>%>CZbJUgN+2
zk;Zp(z9~Gtv_n!K@_u(SM>6yK58Uh`BK?z@KFK}{FY!+P+Z*@-LLjTxC5!k*dISwY
z#lSw=v1{qq>nm1-mdKX&b}dNn@(K#Rtiy2p-1kDACt<Pb{j*q)9~c(RhIkTnnR+9+
zKE-~7@%lFoxwwdkGzNaNY<Kdo)o^W<Bz39#;BqUPcc|F7p}5lb*Gmq));)Xn+-wbC
z4Avi7cnqb?S|d{<%7FXQC6${uA46``fSey5eu6Zb_;&F-12-p^^Y19~#{I=aFNM2z
zUsy2rN(9{c8WS0r0TCi|eWIlrvUmXjL!GrhjWstn*H6otb*oulnPn=gYhv|QtQ_{T
zF1Ux)(kknSu=r=aAx|zFLf;S)o*pC@xXf~QvJsD7*<JY73cH_?@$q@a4FS`U+D7v^
z`jTOcT&kotXXmSO=O#l6IK$*m$=+W33ALdimo7N{fh#N>$&DF)q$;cF?kocEH2?|!
z%NT9n9y){jL&f$G7y}iy@f}{|syFskgs2p`rCD4h=}&p2%=D|jrn`cj8>)dK&R1o?
zTQy|IV3IG}6vDz@D;s&zY8W@>m?XXa7j;RV|D=BuvTiw))m{5&duIAeHFb2dA^i{Q
z`S%&RuLWfWy?(tIYh?^aHMVU69HThv01!?3?D{td^2OLI9wcmm<jYD2wqc>j*AHzF
ze6lRwBi_ggB{J0G@-Ub8;k9n0YQ_t7RQmvgLr*4o7Yf=9()a${^&V0tk|ib~EtW+E
z^TmmKmMm{p;Oq_!U=u`~^9ibHGL~x#gzC+OR#!kX+z`Zq@ddX&yFeA}9eV!gbCsoT
z_VKBv4e^&>{>c>5Pbyhog@*P?PzKWu@qYNYjyo!~Iij8kFazt9vL@Hc?a!a#Fd15K
z0D-aPwGY0&Vhl=8wHvC|ZACJ<j%yT;{+R-Y&4ZKl3r(=PW-Ia74~}D~$YAlg!767R
z*R)UGti#c0zy3HTcYqNsEiK_FxP{f9kL+$;lnh|?ntJyp4ejjY<Rep~tqq0a#L1Hd
zlb)WQ=SVg~)dCM=<HVJ7y@LL<VJi4HBLz+EVq|e^5iv2G`Ovp8_5wmIzj;7pkk_Al
zS7=y+WPsx@u8GQovfA=UE&aI7#migx$-*a?p6|2X)&_o?)hXT7(lUqDe{<d<kf2>`
za_P;755j<T?okGvLs0+{bFBLc*C*obQ%{&&<w$f1uXdZ|%DLP>6YJ~_ZGcv+!-<Mv
zb_Et%*>AH-iK69Pqk9Qdz0IX=<#Vmxp(?cLf(aa&B8`7)Ot$jf-`I1|>v%17$~MS1
zo;h>IYHM>1ySUKJxir;z8y<CK(ouk~Q{qJke4S{EH<zF&tzcyUo~QvHLdoqr%G&eh
z-0jcxmIK3e-mcqJiY!kBls65?9Z?#B+w;&o_}J+Y+UV27=04n(4Pk!yt%BY2@Sc-&
zbXgFdM#|#z&z38V>b#yyJP-+oJATf34eE-DqvLzC?=i;IHnCCG7w#vzymR;YBabuk
z*(QQlkSf+Ox!00_fH(k>2m8&gw?~Y*{(Tl`corr}e(RPQnVD9rzen||U2<rdh0kVn
zbLL!Th=G+{gxS&q6s)DB6oTU9<5M-PcA2;2kdz!YH|n&<P_-r#oI~o@iQJ@2Yr9JQ
z8n@Xlh#VhYSMor4o^nZ!Ae6T5CwmS(ER9UusSwQzh_qmdm{(-gS7<2NJOjzf)XdE4
z(~~_!JO-mAx%RLQQj+THwowO*^`&X-VwLkG5inL~wJRzA1EiYBu&{S<6Q+ZOhF?sr
zu)7|9J~rRPMu|0d9r^fp{ddDr_EL6Z#;f-qxiViis?~|$P_771gPgLw_Ky9{XhWcW
zYlj3S;i6JPHq<Gdmup>JYPS6)cZ7yqBG#8lbkFN9#c%YPK#UV{l|>bHLsqj25N1$&
z^269=iO{qj%xkkq<Y-#_n^4;Mv-u(M2BphOL5E6T+~z%3m8E7|Zq?fY9nPiu-hbLR
zvxg6#4Q83PM(Glof*4Mg)V2~9US_4FoV8dJu*Sx$uCB^cboKP~zUkyP%(}%NXP0@#
z(vdC~;geJ|WQUm@_MA-AiiMAB>aan}GT7%0_eOHIEkUHzibeYlv{=hEXeguy^6H@_
zV-a&ZT!XQznHB7QcY7ac4VKf+3KpWkq8s3&K$eD{9ChEMeJ7h2QH*5ue_<%|ppk;x
zP$HKAi;{Bl3J>P_02C{CneIwSvqe&bF9g1E8@H&>s)H@`=g{Qe`pMT<24Bs^&#!(v
zkoHJTJ5>5|Xb~^TXbL(l)KP}J(?TT~OJ*Q}6s+0gaWgil2m_GHf+woi0qHpp@=2AR
ztyUw8`=IKmJ<tPit7!A;hwS<QW2XV854gMmb)ML(jn?$>@yQrFY>Lg-J%_FtAAeY&
zTPkDH7<8a!2zqLPf1+7iAbXnTm@Xiq9~{X(sZaMEwF1nt4vk>B*Rs`Yf2k}$Yf<=8
zR{*}&Q$OAjd-I|hukZKWkD&MJU$3~4{+&ms_y)ID{tsBE(2V6H*$7H<H9&`+3R~P*
zabTD`)M8?{vQ+ODbwj{^%g1-IO=x{<gAA>A&g!a1qI0_lu`A;>1XPwEbzOIHOeVg~
ztXo}KB17M<>P~l-a^L6YhsrIxFJ8EK5s7Y9gv=On?VJ8`QaNa<tti#;@N<(dFOFOF
zWQP@g_;paOYsej60FQY(OqTWK-HlHP{#$FX7}NneE>}X6q|J2z2PFp(gYr6nkR?ca
z2oM>-_+tReXF{gV(JnG#<a_W;uiWZ%u;CjG!SWX579ssEl%gK!lQUqI=Q@rx5{GIk
zk%Jy#lQaPQ)Y8#Wq=5?)SGFSl4SC*I{Eabjw0{XC8Wm5)&OcEE{|5}xmA|jQT>1~`
zCXI&7d-12}=(J~LDRV>CUfNbX>;GzF&R+fh{?Pw?#GwEEG~VCK`cK%zo3q1p`u|3B
zWluY#HqN?nOGp^r4mhj|h`7Lag%ZPWu5>Su=_IlB{mHz5qino^4e$PtbeA+&vZSbT
z@bRa6kGzuK2qUIhG%@6fQ^f$2Vn}&7dsvVW4WmDh)slGiv6ppkuBMYPGZDJbAU**W
zS~$NaX93(WOL%D@HOQ`d`XUmDK+C8$1RUwjP@o%fYUNpn5Q$7qCRY~n$z)_?aPja+
zw5zg7HPQo>pn~vh2o^I1D7Z009(Ag+5*?3Gp$kof9(doqt$kk1l0F|YZx`K%Wd$GY
zW!w547)P5SAH;{1G;r%TRqdBStxtJ;K~B7@)V$*$DHmriA|k>l<yQ;wx;!4Cyv`F5
z9j%?QxQ6gZv*}q``lR~pt&QSeVqQzxn&zLM9WW+qyGnNKTBDNv?Y~~L8~h;OyS&=r
zzm<j%dXTky4sax-q^F}G$I1`YxC3e%%0Rffb$~5C;m>%Z^W>@MEyrF>ZA~t%=+=w^
z{YtC$1QA9dy99(&?CH^feslUee@D-9AG&DIp)(rmrLD7W)^JS-2dGzJlk@iNDeCA^
z{~zPc5x4#JN?*&oThUwR?VbqBZXI|g$P%B6H;@G&@G+%b)NLlF3b>!(^fyN#T_aRh
z1l-~7M#vob*PxFVDC6i{CPbqHdS#QE@^wr9xv9N*+S){&dGSFz_j`eJV}OISY!)Xw
zil!ZMQQX|E0|*ty`)gJ<H#C*SJbz&h50pXb=XK%=Zr4%q`>5DIWJ5%Jyne=_2)sWl
zacyx@TNIm+z?|2f=swI);)9axp!P!prfdia2xy_t1N%%`)*U`R+F?u?+8|@>dtHGq
zC2lW-etNnukfn&PF^GZxV!j)6#a2M_9M_5uJjx~rX#42E@IE#l2ZIN%mbN_W*#Y|v
zd@v|1UPrjhCiJd%N(e&dt^IwAFfkFJIZSY}>`IqIA_Qr^sdZ2@T(?SUl_l4{=oyt;
zMFJMfg+!Tr4W5Mj-MhlW@4l8*d&~escQmrFJ4c-Zu-2V(ak;p9-<onCU;}&Zqcr<~
zq|9bT|6LikmhD~Fd4z<tk$R4re!P!0Tklc%-E_F(SOM!ORP(4qc6z!rU=lz*T~I?<
zoW$U0M1Up0L7mVtcz=<DLly}q;FXZ3bMIws=menbKt<!X@Mf0@dIQT1daqPgbrcS%
z6l&_~NQU>PlF*F84)@C|fH;3Nb^ld>=oADqaj>ei!+$S_VoXZ1-?|)-4Nf&f@Q3U2
z<sOKHrO`k(BIM@6PcN*o5Uy-;YQXLFKyV&xHk2#r7uGxrt?r<nq{r`+Aii^Kq`sn}
zsz<w4DVS|r9%ww91?c5w@37s8cHhp-$hcEB3ybv*T%BQ!TW_p+5`$JEaC{Q^i~aOz
z0RLpJ58qE+^PF=LU4~=IhXYP_TqgA?hcChU57hKW5U`UFFgaK1mcpTEn_hTj$Q!+|
zu&@MVfyHpGCnLXE5CY=YfyA+pQce`@<*ESy@`!Nevd%HBT2|oD5Dxv!6{CDO15W62
zc%v2IbO7_~m05&!@QK*`yjnFh_h?pPYjsS^sS<!6g2pur3@!tM4v5Th=I48@et<fw
ziMQCp`R;ga+X%)3;e?Vyy3(~zph%@uyqfrZpV=R{?xmUj?&{rSzypHN)*Q*311Qmk
zP{4%!*2z%c8001p+@h0NR@OsQ&$P(Kz*>HYij@gw>~*=+d(CP5E_?HJ`{CNm+d1V^
zX;TYJ<O|UTCpz5}6cmCRMz4l{mu2%kRhCaVb!1dA;@I7?Op6yQlU0-@Y44R8KFHps
zNq}g%#|0tKtt0WkCF(e8HcD;HAI+mebl<6^-Vb3Gc>qbq#&#RXs%3p%DJeX3n(WP}
zix=a7WX2He>Xx<g3ZTW87Ra<HTLxyOkE;gGoy#6kqB<LjzlfRvd#(5W!E;m&G=d11
zt5<2qqJ@Yt+Wu>;XrHYLV0}q47ePtMRxzipFJrwr_QUh8u=w@mrRZ{xI#dJ2L{Wk9
zVDx5rx$~rw#KJdr;*WPvfdbq#YN<hfv+~mfQFh(}4-8~)j{o+S*KB#8%hHr81~*Xa
zSz2Cz;QDanW*ro=&mkV^%{|c#>*DPnM&}*EI$3N0N%#14+T;m+d)ZLd-d|r|3poCg
zHgsyfASt9%HJuq>=CigSAN-jUf*)>dIs%@1)Ri@F-Fl)VI*l>}<zu}`YHO6z)sxFC
z78@JuH6AAGG7)Re1$qv&JM~CULKq)^#$sJIK0bcw$`$4C>-RrCvZ{;*_5un0U3(Az
z0Olqgn22(lel@_3SOj1N1qG#~<)WJD(-4sc;F9XUi!|BJlkL?|YY<WzW4lR0Z-=9n
z=Z0`r=1@zk;iM6n=%kYH%{~L?V0>R2TUK`t5b+j+9~?!Y&3Ow_73eP((D51Hvvvdw
z*fQ#%!@dt_fn{K#^58~V0jUR?4>bEzmF);~rZ*%z`o%J+6K)>@%7|C|*Ipy7FQ9%!
z09T^hn~TdoZXdM-JbbfKT~~9KE_8F#r!7%T8)B#qxm8H5Lc}EmOfRUpPWw}FS;}k}
zQiHE=EA)0`4K=m%a?q^UXArT1VO!!?I=U-?+kOlyJPrEG9!BF0IPl&A5~Kpi5OU#2
z4Mf28Y3+QAfik3nb|h7qqgyxDNkzb`0<D4tHh8LX5I8WBy9guSowNd|nIwTEJ`<V9
zN+jo9%KbEcE%km+wkr10Yv4LR49q^SR>i_AivoO|cO_INnU73f*X;&>ZUXq;QK?y9
z0Fr}-MH=J^EzjsyQ#kSZ6rtlULqjQIuSoE~X-OARN?G0XC%5(w6TdtR;wv?4D`}~t
zuHbf{Pj<X`ck}69%@2>Y9UUEYHrzVz(8U7Rw0Dp$bZN;Y0t#M99iU^)7yN@JKt)TK
zjedT<1E-{<Bp5GbPs?C^y}+;r`)U#?_fBHK)hotaJ(u4%MJmG3{%i+KuLv#rt%~=6
z{gb_h)6SqhM)#=e=vW{`Sn(Fb_S3lzXh}@Ey1Je;UWIP^k<jEkYqKyQ|58_yR(GxE
z;z3(x5vMQqi9pqtU{_Q>I4UJeO5~3jgAn`4=vA?1Ia+*ct;1h0?cpv7v-SiAhmBz>
zsfhe}R>)tR{Q@wJOeHq9Y@e8>JDlx-Dp<l*Ois8!fM6$+rQn2f@lYpTG)|~SuzLja
znE_J4t^o5Pi!p5sWg{<&ocaDon}Fo2`kGVE_Fj^B|J`e}fluQLfO6@%g>-kI9u50%
z`^uR*XBs+wJ>BHGQB=1Q2U(QP!573OQTMrg>INmy#u@_0PnOLy^imD1h<v8$-D-Mz
zxz>eMGHd{BzS+v$ay4O;@G5iN6~$!t;U?%f`6`w6_<E>xe^^V_tZe0fya1~sPr2|P
zt=r-RpnPio2&)CzEf*w_+$T?-aM`jOI8S^(;8lADlq={?K)F>7W~V2M+^%t%RwKC0
zeHf#)9&T0fqtxKns2foHk^Df+O7=V0-`H=SJi9pPjk3_wi{q_ZQuBp|ZU+hNJzKs*
zwnP~y*=QrLoRX3fcwV4M)h%@?`1MgCz_AN(#jc<<K~B6aY5Hqy3=wcKt}WzbS=*^J
zhhJ~gA%nc(lkWmlsX5D}r<sLDAzE*Dd7qJWaQKmjP_@?Ki>QT*b9h?dEOprIFl&nd
zPu0U8Y{{r}Ziy$0xfhnt3~48=Jy={^1eBWlXxUaUUeuL{l3RyFQ4R&{MWZ%TzERzP
z9<2+rA9Ro5EfXgtr_&>zRJel<sNbqNTjcQIm6m?Afz!+Ml6VqGSb2IlP_=HH>TwBR
z2pf}-)k~M@(J(uei886gwE?PB1ct`3W+?N+hoAZSmF+H|X>tLz?~lQ8;kBHcN`OOf
z{|<SWD@e_-&Z}IxA1Jn1s7qimPy~vj9{?PKs7rSF@Tfz~;Gh8pS2rH65QO5l`C0sk
zdaQl_@ZGO(lauX(U)b~&CUWxfR$zg;YR|>^QN1vZ(9OjIgp3CS8V{H%=JV%UV{SFU
zxLHAnNvJXcnNTLQ#DD-=#<fHC@mVqdS!{ffoxQ=TW7;JS2!vROjg}yKzEnMSkBpMz
z!`qvaQUxYiP|gufZUR)T;+D(0C8!bWQ?`K3ihu1lA1{@H%Av&s^;ZB$imK-Jc0tG?
z1Cwx7i))j~@sD@yi-W><(AH+4tT_w6_Xy)dU=U24oSl&~gLMU^B_@(vI}XCT@zj|!
z>pjU5-WAG+-D-vuW3PREXllv;xhg>^tNGivtB8u&Vmnk_ev276tP(7sw}D#7z-Ewe
zu=V2;6LCP54a8-2o0V$5_)2dw>@jvEnF@sO1O#jOulc5$5X`mmjL=(Or};ouUkvr%
zY<LK=J_6T|D#*)!Z%-6!`||Q+fg6$>AI&~f8}jocL~7?(&(F`BK_a=x&)+SasDwN(
z==%kBYJ^)tPxzcNw?Neg?ug*W-QlfaBb+v4y|{na!Y9ca1cL5dO>Xw)AXbU$&+Db_
zqOEtY0VzT|&<>28Pe|ZCR*#RHt7D;v(cruzc6|RZWYJh?e%PC#<Vfsj^^_@rwJrHD
z9v)oz3dhf0hTdSvFP{FHfpK_71pBjrl<^SyOKdb56i)8*wpxO6bW<|GTVI%{U2HFH
zfu>mOp}v^g%zk=p@^D>&)1fn$f9peM9RJ>=NsnOUyR3;JoOg{VzuP!2@%#(Wph0|%
z<_DjGspI1P{85K0g9$*ewAFZMK`SeSOk3W{+oxS?3IWWftE=mW6LA=Mq&Wx6s>p>o
ztP4mGZdz!<U}4{uY~d&Z0rg?|@hLO$Pw;s4UNXpCl#r<FvV|Kmkc+SNbdHFPovs{o
z$NQQ`>O)`BQ4JC*X|KfN6P*a1e>85h8f3(R<zPmEgQUirNuqRUAIImef1aYQF7)+$
z;fnD1^E5Ek)DpN+d3MljY}N;Q4>~j)(bDygF7RHj1k~Y2(k{mXDzLB>O2}cUJz1NC
zGR<xRrH#AnWbkhL>yjN3*dnxj`7F6>6J%hVo<DH0DYio(G)nqTd4inYf_&oybae+j
zww|e}A25Qz1b)dpy@ag@_{YsCXVFA0pT~Y5R)Dk=&D(@-?O0??YQGi;L#MX^Jm?W-
zp;<dC8k72t7FoSf@dDOvgOySs^FI>OLke*f!%wgWJgq>GT)A~$JGuJFlbxh4JgbD4
z#qW_%eyz}yO=m<Kg|}(B#Xydg7?;%0OgBN$Wu{UhSI*4B3Q$U2an<jj23JGxntQqk
zXfHTt&S`%ku>~yygM1JsRKkIP<UlU1cfjaUhxlR;bllVKhH&RxAd4%g=|o4$-ZnBy
zLh!VNdzAsSkw?}odUGQguUm|CD?zXo5CB?XB16WiCX-4tg*{0HmDeABdm~>tO7*&e
z=!K?Vp-fblC%kzxQfdcIf|j;6O@LUDNlAskwLZcD=#Ln(i$-CD4(gXYd|6&=(|;||
zZNTyz<&%07{z$cz0^9Rfr6thGoRh<P%bIFdqPNJ~TOE2pJ(mMZJ^siPFtVGqCq(pV
ztoJ$v7IPYYU30b)QjfJr4`E^JnP?UqB@}m^uX9t}AJ#D{a!5E2V&OCv4a*u^GU<Hw
z4TO`b)YfVmJjuyL3e;Z`BE29u1zVt>2e)w@XoK^|d|en!T$)(iEvCCN<UwA})y&&W
z!iAIHsx~(_pToHcGh>}V&ewSG%1j>^PVB}}_3N-Wdn7@sJ4U`T4Y^lh4u=^HV0uyr
zwk&^p9`6owVn_1UQr08V$}G;D2k%8HxQT<$z7H>e-eSL(_A2p(jN({RXysNnk|OfT
z$}|u)RayBcck?1ZTW!ca-B3hb2Q0JTAAq@P@Ip*W;Vt3agvsuu;p77+FKT!u;T<BC
zY2QxD-Fn9F?|fb30^ewC(GSp#hw`gv#ayOR;_a&C(2xXlnvDQ%Lrszk_oB?rx`z-|
zfUwAytR?2=xj@0*dzmCrWC!KG+(1vys*S%6=u-j9E@|X?+0UK3dEmeStRpt%<}Pc$
zfrTb7aH&MTWx8(h27ElJZ#G5@n<7>z**w0dxY`#Wp+{Xo7lO*K4g8U|l~ry>712TX
zy7PphTo;3Rg6gzyt{vvo>C?J6HYq=!Zz!L|iM9g50$CG48<{yr5feE{Wmg1Q8NuA0
z>*80S%K;_vg4|Kb)f7Bs1)5GvSis@H;9uO6m}SVdVS|P+gZ3KQA}5gPoEfh@d;y=&
zzs9Xy*aL?grR@7U@EJ%bfERIswsFvUmqTGq^djc$X!WKT3NQ9}bGSaGUeZd8NgdJx
zec}S=cr6JYvhWt7O${Fih@+qO0HJcXp?zt8{&D+Zq0ad%=z(_hd2%#-mUHt6RRJdA
zux>q9>Tz{Y=!U(?63H0f_313c758l=*^9_oZqa$Ga>y+ndi)fG4bIN~W{!$;FDclx
zNCmZ2r?s~(OhH(D+ww39l%VjP!!z7(payB)ed{Up`QEDc_E|o_#IYsQwofE$?H`OA
zSK<)8MrhdMHiTy!)K<_f(jX^igHVT<Pj+2&uCT}3UHFyk@$04h^3y;{HaA%{YAYX&
z8P;3zv+oD~$F&yf7~*b%v+;r+3|O5nqV2Wl(v!;Ko#sr#LdR>tQ`IoAk1R^C|DmbB
zen}%?7{qgR0sCPCq%Ayjr8WiWXc33dRi~abE}_*tJB$Z@_Z*_T>JE!_4nUPmc10fU
zt5Amxo-R8Kb)~qS1awvUEkm+2`UAU9UB@ARIjn_m65?3GE`Sem@)2U@Jey;)dj;t1
z&sH^{c~=9_bOrMW7qGqneE!JRe9m7_Yp*i1l|S#E?h)uSNb4^(=N1vs1!C}=)CT0V
z`@t>CLTYujP~h}ila8;2g-m;tflc+%z)LWX{gSXksQhzeCsxbRpoPQksO-t`xx3lK
z3vHdJ>=XfWRCg=_iTNxaczovM)||Z0-e0uJWXUqlOz$$)_V<CQE1%#X6T5c|LL7W>
zvv>>|Dkq{7K!YZ1YHDf;+yMkZ&O}Et`dyOywNAWg@91x(QwZI8v(+n;8Pd+{4vmw&
za)Oa;JQG7vl>OWHYe{dn&t+FEsb&jHWGcp{!qsyM2?@=t=7x_H*kNo2KXmHRUUv<t
z1c~939<S8$D|gIl{KUBgze8WVxt_Lc#b_sXFq3n%!KFm6#X|0@g~f6jD@$4&<PX;m
zhnPJ1yQv6teSLxvz<MrQZeCs`^908L_%8wL8s2E`@v@^$`l_m7NKAp*3SmqJJ~*be
zi!XjJCMLOJb;Hwv7<tA#U2BUIv=;gCRyynjXPm8P2yQ{j0{WtqiQf9}bh+QnN_C?(
zs||RQt9U{KAtCHD1hZGU?}nS)LF=Tp8^HP%nEE?=MNKANeStNt>3lIA$mUmkqC9DP
z2D8w@joiqc6(qNsf-2XOCr>V8_3bS|+-zxWjc6r=<7O*lQb8ZSa$jtCdu<RLnct{6
z1<>1sq@>M%zE|eW%MA>-qx7~34s1Eg)7`Ql4`~CG_gb0Z%(1b0l$v@zxn#Io+5^3A
zzyQEATtg6@?!L1*`S~F_`EYRBjtBwZ0W5nOn=RXsw)x^c09tu2G5=e^zj)DE0O(GK
z=l%gKoRPr~>%fbEicG{K8Wci8S}zq+s)%wZ1<m->ch%GdU<h<u-%XINH%@(}L%-D!
zWO+uZP3ziTrvUS|;vMUi?RTirTVs4ap*;iEFt)_~ngl($?@I(~=}d6PA{fNlICR-&
z_FW|(Z-iqM{r1z)h2@`CJ|nW%>M67puG3w2KwMKn<V8pw^1f2@sLwz>cJ#<>d0+xQ
zO%xZ}DRx@r6&L@gCT8SzBewIiUXbGWkXjVAy%3GLBqg=|!M0}8WhVj5)1UMTiBqD@
z?i$hNM&E7#6ndMK#G7;3_*g+_ZCD6|WM5o#1!5`zUP8dh1%M3Vy1`c*Wfji`uqW3A
zU^@@9Nw@+&&jLn4n@jO+6Z~pGiIyABb^sa5?DSC0a6*D(3aOUa!Gd-`@ocU4Ve~rc
zvYOxcWOeR0ub1@~e@Wc2Ywzx%P`E^E)B~uuK3LCCRK6YB;{+?D&U_aZf1#;dr2Z&P
z`;(*HY78-3>v@PCidc;h(=?I^Y;m)0`a+sE;Nk-MQsYk@^5A%IJ2;36)Of*o2F6lI
zoXTV|JgY)I6Xc${?6Gx~AR*BS#it=|ozNzPFN@kWlyJQ(dGeiP{7z^Gdy~Zayos-J
zlj6hNMn3Lvo%(ST$bB`ad7x=CU!Nz2QbNVR^XK@{2>?=Q;YX3#vhJNpO|VoLZRP5P
z_zt_eA|!u^)QMVGMUd+XnEHU~ke2XT+P@Lr39Z*Om4Yu?sK7(J14{Z=vM7{s1lo7p
z9@5~B0j<_LeGG8UJ3?#pzRMdQcOBJX4}<I0CfKre#02rJcPDS9Ll4wH>jtY;5daOs
zA4B~^{HxHwt=~KE!Qmvnh+PLgiM8(c=R-UCI8dl5=-I+hz=|kAm>t!rC|qO>GmMo^
zul)X95iD5}Q=18m^w4*<dijA%d^Ml)9nB*Lq~*;F6R_h~Ob8u`6kk*diNrL=n3uh4
zC3GQ9VW%48q0~cDchLU|7gvc)zzVUK`=J<tv|6)6+jcrl#ZkTs1XWaIn<|NX!Z#;B
z6bwmckh4(h51z_Rt)_N?cgfllJaIc(0pYsCQ+x{${bft2&B`1exL(a{HYLoS?lMbt
z>kub0+o?1jPR{0a#n$iNmqbFT{l&&lmv!S~V(1+TNALP2i6tuCi@kP7t^dWEgA9Ih
za#COF2I31dwiP-?UPmmf<pZytf#5#{%M%NNbuT0sTQHA(_URn`FjLZ+Pk>zY(>%%0
zK2&_+MfnUv6<a~f`Jt#l%H1t+y=dm~z_XQpd@~UmAxmH`!ycj-Kq^8stWt2}tE;Ho
zkK_i+?8eG0f3ojN>~wclXxKwmkC7e86^Lw54*qggV|@g}mK%jT3NueYGzdW?!C=Oi
zi)b4d_!Xmh4f7GR<vdfRyFRe#2dYCMk1g~gzC;@Y<U_*;?I4mnf*1u_^w`+g>_b>3
zh?NEZ&g*}u1EWSBsQzYm1TqmL5*NSyuz0d7Ld;_kh2b>u3>}?53HUxc5Uz~tCO|V_
z7Weq^Jwc=!#lf-hh3HvVONF@$NmD>)rihskoO$^xa3Cu%`I>_8rT#9sg<0&K5gGiJ
zjB*oi)s+tD@iI<)k3H#@@Yn~u6Efk5y%NaHfPU~t!^l7*y$K{7MH!g@20@#c@<ez5
z+7^&;StN^^z)4{bQwa9pljO^sF=%aRxrT5Q3?feH%c7p(it5eX7z>r!8%zP89A9`4
zIEn`-tB8a-tPl0{S1H2#fraE-zRvs42F`5)GU00=A#vF>h_U)N1xgLOLZhc=y#Z*a
zk=_}cwd-GE<KwM^IK_&Pd)n3A-3C;y9U@3X#)ImtLp?R^2Mo!IXXrK%D)#^&Yf^w@
z7H}W-%<2$?_7i=Mmh1<StMFs_@jB1jc>8)F5g`vA(iMa(Ixq~4oNmUf=O(zXR{FQ0
zQVeYZ5rzmZ^y+R#K$gS;>DCOAN8TVXtE^>1cYTw*ho7QiIn^_t)uEHNmMB|&<<{DZ
zz%9g{=GKaVtQ`B7DB?41as(Dq7ZeW$0PV_nU~D+^ZMVnRwu^}{5yCSQa~nvQ;`K7f
zI1|t~_gayGCaL&k)14Wt3r*Q_HTW0fk&~Kq#yc0va%bKpCpRJL<LuK(Dl0^J`97d3
ztf1~(yl~;`q?G2Ucy7c<;#K2Y-&Vl%*bn6aAHyB-8@vrbk_FVEp@Fsg=ypDGBv@LJ
zNyx4Y3rT%|EC@Cb-n{RS1i11sQ0s5o;YJ-+YmK%})Hc#Dem->NS)G)tz$f54bdtwF
zV3Qm-0iTJatp~^KZ3)O~_sq;P@jBuMx>Z6!OVG&P=#*vCB`q#2JegYre^?Nz8w}DI
z5_D{aIh4qmnVEyOKH!SQdMTG&Jw+D1bLWm<YfpgWL5_ArhCF8i{X$x-h;!!hCPJd8
zMQ`RQVdzb`P!3Ne0B4U^9wBe>T)v#IhIzgK8mO+Vhu{4}U&KCfbLKmXfr;y~!G|CA
z-cVYLdOSP9BEWr|UJ*p&eTV7qQ>h?o8dJ<6x;5=V&6Ol^F@>VQ!5mXu$x^V}9yoI@
zYBh2XcHrQ0W8@SR4q0!5ZIWp@=6sz$0e56+XgAz<f!Caj#BAs?xw*L&ga#dM>kxnq
zox^PVNvuz$X%0_3J8%+U^OZdY_-s{nD)gGYal6`aV&qzrA(hL=YK97tkqJcLXD?~9
zXw*<qNs;tjzspslPA#+S-o*NYcAKw|33LtFS;$Q;?wYCmM-i5c5``!J>(d;`3JSYo
zg&k8T+7n3>v%AaBDT5K8?BvI(m;Bd=$k-A%@d5|2uv7{GrIFGjloo2XAktOAZ+W|7
zESC36ym{O+$PhUIaCeXxP0E0rmNRXNmV&xdX-9Kf5iD;@(6N)iyK|Uc8O)(z8e%Tt
zA&VkQ$D_1p;0H)R{V*tj7-n}=IyPU3wt&JUxfrQ6Iq|l4nl5L?*BoHUO;QE4U(glo
z$htPaqyU-#Rq5PGCCu|Ka2}8v?S_T24#^5nx<M!PQKT8}zSX^_$6BU3(-4Na*bb#V
z)Uk-P5M&c*dRD3!3@<@lD4TeQG%UV$^;)_}a|%ySm9XA<qTQO+2BWT~h8VsAYj^yD
zg;M}hEORsz2Q1j+3gR1vff6vbMU=@V-6I?^j?73h`ko|#`@=+)bt~}$FWCrWG0?ej
zooTZ1!0r4XUwGQ6VS8!<2#q$|${{nb8=1mrN7cxaLq5DWY12#CJtUBhj}<C$ca^g_
zaM$;Mo1O;GktYz}4b|8A!r49Y7$b1AD^=2nka}cb3({&Zq(_-!Uc9fD9Yhn&z?7N^
zmaL25$`)p(?S?5ce$$py)_N~GBsabf7^NJqWd-@&Y-Oh3?Ax1j7X<`*f6lr^#Kl>G
zqGN3Y>P{)}Fb7?UpZ5<VN<^^Q##CD9W<_*Fgh{4yvfVm`Y>%kNKqB6Qrtv#+AwU=y
zjjC<mK=|W7);g&3$jO6seHsWb1~8ZCf_D<%84B(*P?Nz7_q1ZxS7fUtlKW}YP?=?f
zCoJQ8Pzx2|df($_GoenHZLZFPI9Gq5S_(WCLgC*5z%gSbz5C$n48D~J^vjMmPQ%~{
zjMm(P>=Oi>CqxcE0;rLLxhiR!wLEsv$UxU3)`1|H3QEt&U;z3PEDh$!S(buP<)FIQ
z+sSGC4XRpfpHK`(lE>$}*XRsgRDU=TWPb;2tO9a)QsF)I;~c+9<6(Z7V~U81G6N~z
z5;{Vp+^^yATV`%EKUE37c`cJ`c}4YBU+cjcC~v<AVNda{WfODz?gPtc0x4uL6m<=#
z13_pG1O(&T5bYaO5+tR@L80Lox3KaL>Dxc-z&h$P2k2%I7TzEB{krWy&zGjAcp$;!
zF<Z+;enk&nXeoC7#K6TitRJSOI3p9PP1*^3I|5Q^jQ`johOU|d`6}tow~uDT1F;Xe
zmX^bP+!%aFdxHR^0b&s9@9#$j6d|=*iK5^eRzICO0<B-WMOXSufeS4N_-^Q=d2jpm
z)oH}G5Bw6S1RC9=@Yb_5h#4(K$@BN8eWsPT<whp^DX2W)wK`Uc#IIZzNZ}W2hK%3~
z;N8dm^=?a;TX5~}AP$P3%I<)?kpWU~HqhtQ&?;JsGQ&G}e`2o9J~J`$cM{c#mi^!7
zQ~Cafq0ax|*Z&8`R^ddEpZ~<~_OCb4Jox`LJ*$BXxAtnQsinfoi=#!RLV>vpPlq-a
zrgg9m(wz~1YzF7#fUSDlJ)J-LB9#N^#UqSw>omtFC&8%IhrB5v;WBi+a)|c|>~GAf
zOV@3Ff*0EHTH_QfJpmRa4FRj3yWub_25vWWL#MPda9&7zCkGR6<-eTI=|NfwkBGP+
zhq&OlxN_k*Ss*e4C=AzF@kARK7&IKea}>``%>432`fF2@LO3#32aY)5)Ttb0JUg&=
z;Asv%4Sjyzs6}dCg>PsvOvfqG(0G8SdKY-Q3RXjJ`uNl?XJF~oxgWfW0;hsC+K^W}
zakvkx>dC$Zt=oJsLWQjtO>7sXvhl~aU5szjn(WY&!&-CYD3S9h^ZNRdbg9i71_iAf
zR+TtX8=nChBipmQSiVVlv0hf>x+XinfR80!g8?b{O5o>v*L?TTeBsTs`1^4F_l-IK
zJ>Y+Jy!;2jg&2L$f2B`@QfrH^H<a4^{S?|ZF@z?B)V_|{-oO+Od~0s*1Z{o^S`reN
z>qMb(S_adJDxwX~xOtcVdToaLd)mY-(TEYlO1s1)U|bLSyVndE_u@-TUnA>0c>y}+
zF5vhOb1+Ejx)89MuXy5Yk-ivyvN6Q(r;b2J4`Z<1AmIH;6z>9A57yX$ih(%wVI~DB
z{)jytks!bUGzDguUN8%!0yfs*EVQVAq=fvW_~r2*7)G2U$e2h^p7s^Q+yYIpai3xF
z{m0NufL`+s5#C`86|D8@kg$?GCm5i6E`SL)$aaw--2Z-Nc%tHy%3Nr&0r6ju19VVS
zED6*j;=D##0;^b_Y5T9^(qvN(48zTYX+Q;pdmF3#euvHwC8t+vcCWya>F=9}zE3DX
z`G=>29|VqwF@^GeL2lr^iR{0>hq1?nMgZ~Ub#0&EI(xts$)m`=00moMWswVq^#zM)
z=SAzZ{uS^V!g)!p8M*_6Bf@0XA?{3zlKw4<_sxGzY*}56n_BQoM3!1m&L@l1R&O*D
z&?5FL<=|5WKim>TyzaIjcnt&6L?mql^%IA+JFK}NSzON++F)d3LuXNXH%bLS9yr3-
zGgM@^yypFEa^t??cwNyECc*q}JbP_N2VxPskLsNCt;Z55_j6W!&B4ncz_ODj<xFnP
zw!(gOP{&bwsLIbMbm}mH?)F3|+!hQEhA>~UT*C|F>41Zbzq-`^oLwp2;KcS4wr!%d
zlUK?s64g63&?^GGydk~?PobhJr;d8Ga0zFkLneB@Ris_u5xJ8kWU19{+>=K>zHEtJ
zV^tOE?WwO0>LliWXY6+?`#JW@#ZixxVrszK=lE(b%`pXHM`b>ZIQqJTGhuy<dQLBP
zTUw?Ro}o%9micGVq<^ly^VCtvWtSoR+ED?Py)@4>emPCQBS-hS&>xSquun0)Ez{#d
zPxFXggA5{Ms|{Erc1D3o>#asqal!ZfukThML=!2=dv3m_9VUUG-WfNY=7C_7S5k^X
z5hf@1-Lq+`237~G>+<2CXykVexFQB)f`c$h4%(K2?zFu~(@DMRJpL_A)RzGfsZC4!
z9|m;-2(S`O$QB)ab`ljyO>qMw6=TRnFSs)xd4PU+Fub<5mNDd#7ea;IOH%{AaC38$
z@nu?O1=Brv)B-Dssr7!L!jSVJjF3b;zr`9BAJ3iDtqaLIltOutkdPqi%UL(+{Vbi)
zyW>*sQ@mU--?@$M4gO;^G@)L}BM3Qynug5@>_3V)9EC|2IZ+_Itnz2g+IFIOb%ApN
zT&BrYVewljfWd^g6s<@0b*&k4^+MP6Rzk`?U}V2c5QzNQSDm_94-X6bmGUBPb92I5
zdgx0%_8x&!ezB)_>qt4;)fKaYrdORjxf^j=3FO{~hQhDaW1^ngz{cg?{NOs`=0JWP
z!C3{vkPOUXUXVjPHqV!zKYxypV5l5$iu}KpsF553BVdtc0h?wGFc>RtflL;$hl-yE
z+Ktz+`VtRM!L1?iQpm$I=>~#<b-n_)Zsg}|j41y;dmzkZLYOTjDBtfbk^Q&z;ccZ`
zk|6mmPjxnA*+pEuciplJh1dq;z{i5LO{MkFb0fYvs$Ji#TPwk7Ppz@E>%c#!e;;y;
zHdoDUuWZb|>T~kIek=o~1uu}N9`xn3Y?Aq~#tp%$sGcn8yE%N8$Dq^ZZ$i02)igg*
z3&EvVd>@!B@SRp6`#22wEVdc<O~U{V;=qdhxAUj}H_Y_kwXFU3!Fm5d-TqH5@X@w8
zd3Y=>n@kPGn7fxYPtab9rrCR(6J?;W^SPQd@A;nG3#kredbKo81*cx0XSL5(DVn|c
z{E6n{^DUg`h4e31TAe#%@&4@D<F6gY&(oblp%lW0sMJqatZxa93&uBJE}9(s)ZplD
zR-e2!H)FO^$B+NBFyhbugT!h6YhOC)8q&~cG}GZsPbD6)KKa$r@t|;GIEr0J`{3@+
z14bkJ5>f6hI)Fe@&U-5*cTNBLTwF2A`3-pQx2S2@24P{%$Jn;W_Et%F>Y%Z0TKz^}
z^Vq_kO=Da3`m1Q#rYh&>U@~OER-mSd;|k+W4t6JKXilFS*>nF;qgYY1@RqF(?wb^*
zt+?Xio}{=7h69(s7tqig8}Qz@VufoS(~36E<q&;1mAf}u$1otnX#E#I0T@)nfSj$L
zX>2=SjEC+cTJB$&gQl2s=Re!&{2zQ7-Mc4c1xRYJbmjBjrfs5N=sd((@5svP>)i7Y
zNo*f+0gS)%@uTOx&$Op)@7=vyY@@%(OnXP~3J3<@j61#it_F?xlNu&v4YRK#M~<p3
z#L0)Z5~?jK1h}|bN^F-CMD65iMxkfmA$7c76aG9lucH>&dfF4c=Yfd{4=zxFVh#r8
z3j%jdrP$f;Ja~}$D{at})fsM^#=TVy!iUS@7<U>PE8c|=BCnbs22VWE!mPM==VLEb
z^$%~iPB2d6^wt%!@#M*rE@?juI(OfO5{itOd_8VU;Qugp=hx)BNUG=ixduN>vhPo6
zE&q#eXPO@0ID3AhkL%qqN7y=pYQNii^bVR^=SG5Q;0r)v)`~tC+H_Lf`4efmg|w_^
zpt|~25(j=RG%$r{v`<Mwa&@ZdNaS;z_|CBABdI{Vy6JakVC|B5dqS9#Gmj;rT3<VS
z`|*t%RcCrtwB_4(E^Sbc*A+lt1_<dbYR#wq#V|5O10`Qh4Teokaf~nhCxQ094EJbR
zI5j2Z29&F)9>B{>4qTTm72B{W$;%%MpuoxPrd)*fNd4n!%?UpH-E$aC5nksigI6zK
z=A^x%p>g{}|Ida*_tkg*-<Pn~{=3`<-n>IJD$b4m^>5n8pO+s&+vLA8*xpV%e?>U}
z&xT#u8C@Cq>tq80Ax^KYD;9<%F9(zd$7~uvqa>#W$^>*a4Fg72HnqIL^UunW-bg3#
z5nXZV(TUcdHQf34P9;#bWpwl-uFvc4DO5b_&P_w}tnAlFpB`oQW?t*n-k%jLJyMy^
zdtp~HnzM@Zsecg=jm@h^|7`m8-|uCMsp}3cZZ57a5Rr^BJYkB-6=vf!t*OYi6-F>|
zai|@k_^2cMq_VMIe{ac1>QRpi&6A6omi1tS*YX~Ed?Tf6dEd^*Rz?5ZDc<!*LR?((
zt<hJCey@S;o(Pk!(Z=-E>%=_k!FC{^1z-av#w$nv){04!!^84m8on8h>^1`<K#WCA
zqbYfRj~Da0XMQK-=s#ZcUo-lDKeSA{gS-b+Y}vK7HDu})F<tQP`}>bAAEuf{>fXTk
zkz9DdVTSV!SFOqm!B9>D*L7+A+62=D4i03_68v-8h{>mH4ixvPrRnZ-$*+ThH38vT
zYbG<uAH6;5AUy7LXKqf@OFvO5%T%akvCj#U9}0f3QxEVjdu#=WpEgk8_`D2;fa9Hz
ztDQN!Dto70_GT3fV(&8hHMd~!liRihM_r{~y)x<>V8vr)wdNW!Ho6H;&18zcnk)*)
zC4!N*Hsm@rLX&C6_THT|D(9Ur)Lta2!2o(mu6kv_+PbbDIQqR=!0G0~4YdMrOavV#
z2{A!6Q4&GU0XdFsp(=TEoWlzoTGobXu~30dGcj4aGPs@x-KL~MxyFF2B7gb!i_p+$
zZjq|Nu`H-`1AXbhR*BrUu{MOH-h8G{MVpzn=}u_@Z!`(Kpp`hoJiY~M_LXryg@Gd+
z@Sj&!SVEiFLz_(Ga9Xp<qo<gdm=;`vzpy<4?wj;gG9f0$LKhcCKge<60;go|jCW~@
za97BaJU4@h#Wi9!Bh8~k>0#AGImB)yhYZ;jJbU(xv4}%Z5V5GKgLDKGn<;$&?2uIs
z`&QLK$N*aqXyi400d(!J`CWgb!T`13gZnpF(&WOg@$*}kCxHpyDHK}1X<!A^fquDe
z_MIM_O4C5&Aa=FqB(NkYJU9R127U&xNfu}(nqBf0F$Drpdb@hYsp?6zENsK3>vq5h
zWwfrXp@Blbp!Vk)U;Ug@T&zO-dbq}}Qp_Vj*>`nwf7aBLh1}%IJ5VBD=N({cxcbDL
zb)D*md>pDN=nGIu<wV^ts=vQu?x@>qCk$n@RT3t69i>sM7%;vE`WUh=3xCe_6$Tzr
z_=~Kuv9UC3jSd0A`PD-7Y)9$wRffx&eg<du@21b(L8C&u!+t-~w1Lb5+p_h-rbET$
z-KlpDnLd1I0rC;zC*r05O$+?Q=gC^5B5<oWs6Ht75KkJR<1gytF6y&(yfX^hLeSN^
zT!$I6lCxibubr}_8-?ba``812!Og7~I4qdK?w+Z02QTNPhO4Lc?<RhologKLUF(OD
zO0Dc2b=cixLRs^HLZ1<sf*2xf^x5ah2GAUuQgzWG?C0m3!d``(X6O6P`)?(EZ!MJc
zs6A6*^p__I1kRCHT#GS6J1O{F=4jY=<?EL(J*szTXlh>6*4&M`4|8nc>EP|;oWX<`
z!MsU$Ix@_fKl^t{zYyPDJ2}7R1KU1jpe&JjRC}OQe8V-a*zNrr6CaKCdvq)!Eh@wT
zGFk$f>lthXh}(#t3RtRgYdUPj-|v%V-8sAn;{np)F0dn&w&fN$A1y&t^FpSyBtY0n
zg8?3Ov_I7~_R~V0Q4#PSOReZFwQ!d<fxb&12tOnK6N-u-n)NtgW*G_8KOMNHUL%>J
z?Xi!1<7^6OXg3fS$@A7q>pms*OvN1CPk&E=*J^idhR>hfgV?rLn-)C8%Z<>W^?-pO
zEkFM<5D&WGqKN1tX#ybvPkkR^_5v~Q+Tk>;?calcSUvVT%BlOF5RBf~<zlZP84dOZ
z;P$Bp7KKsVqj>L8Rs-C^-y^0RZv0==y8N$diT>~R{&{r&wSDG)kDH2g%T^s%4@0)q
zH4K0~^J1shRiJG`nvd$ru#T&jt;Gz0|NfsUb#HV>i-V(Z{dSvRS7rH<d2u98uR7!|
zRC?%7x;Ba<n~rCYUYt6;oOS%Mzkb1>yF4YUm7sR^SaEvDU9fm(T+;6E>!o{4*34NL
zE4jNFw3DWXb%*_K2VrL57)z>yu8FgT!k=E#o|ho8v<^tlI_3N<!8Wji9kuY-#v*6n
zZNEdr16iQ%i17H483R<wl2V^DB>l7kUjOMWdx0|pS6^4fWX-n?u}QZY(${(ug5xvI
z4Rv8p@uW9Jd680TfeIiv)%^?v11XQS*omhbi0C96>1$v8ntk@t2dgGi$>gGt+#NJO
zUrx)qLc?|3^S(jy8r2Ur7-}J2JH^7nvUx2kIk^a_V%!JWLYvZ%vZe+C#48@rk4v&6
zk8R=gsR#Zm8|*y8k-!}Aus6n0?4XH-jv4@&latfsoOgxbl~NF`Jenx!JMhcua}yMb
zUC)30`sGZ{*blodBAdER4?4;Mn7QDp1GVrpOx3t4s;>X(1ECO?!(A2kgY8a_u2t)~
zZk|wrk|BE9woQ^5TwRCTU~0s+|Gfe#NhASM@~_4Ak!6|tKN$A!=As_vrBYaF9-YYC
zSBnh2!CYez>~D|_#$K}`)^od|iFSNswC;2sgGXPnIvCxxZ$>h?qr_HV=Z-rAB8-yB
z%8!px8FFyBUcV(@j#I2GmkFiU4CK)z4A6APMMBdmC9b<hsabM-7U{2lC9_Z9#M2wx
zl+iA#u7Lq_*haV}^c<Q#!(sOwckiu5z+0Hdgal2>iXz*`CektyCQZ~~LIv5r5ZNJ)
zdGK!zzCj<u`dcaPI;XI71DGCqYuxRTsRWp<)RRUt!m#Y0AJ?{}NABQLhB=g8@MFW&
zzmQxP?Asy$25e-&7x?y0*dyS(-I>21;1z}Y&{b7)bbBdFsuvkEXAyR|fozKlIPiiT
z*x4(<;f07v84A%d<H48zelCUg*@Y3F0)WcM6ahTbGT8VAYOz9inROp8Y_){US0fX<
zz{@hv!Di2Ye$J*olNjNF?LJ_{`p*V<qOb`8A}hhpzu!p92;!2Llj}SpH27y*-^sbl
z|A74uDWZow6cpeeVW=lEF|nYmtZZZqSnM#bWnu<M>BuflgKv=z4*B6tY2P&rWfUBf
z#1OGL6&Uh`!KMOa`cs%0xj8VXlnbAlg6*BMNhkh60%wSqM<iT{Z;haS!)CkLpuGHx
z9|z0#dh+%fTcWt98kkt$gC4O2%9>pGrXNNoi2fZ|Qi=;(5El~igWGTYkwn>82G0Sq
zYZ0hKKR_#X9shP*P7THwmnt#Td!S(S!VaL|;khgaCEN?1H7IZ;BVI6&KJ5^7;9uWF
zQ!#$9NnSZY_#F&9l3>2C7d(ScLYuyPnF5a<Hd{3f_DwE5RA>SlO@5k2T<B>a4FCBl
z7U<qJjBEo0{nV939q{ph_2D|4%UxAfRZTtR=`}d_9DzT(>pc2>X!lO7XchJVV;#et
zG#atfJIKi7!3ZcPY0I!9Z7wqUsMm*V2co4*^Jx8|%4PG;Xk+`v=;%w+gQ3scKi6KZ
zoOs(|Sc>tadGx#Abv?aS60L)KArXHinl#v#H#j(09H=*YGJ@b3KzO3~@j9g61+O}-
zXjNO-&w(|xiFl1BrA1NdyhVRW1~MvsgyEVrpzLzJK4j!H_aKc5o?bLBM&0@Iu+nG_
zIm_hpPz<}0Ij`r%L0}tmEmxw&mj562-aM}6{Qn!y7|bBX7=uELh@xoGred<4Hfh&t
zY0;kcJwyx&C#_nwuLpJ7S5aCNZAUv&skEq0X}_ND;`>|f-+f*8bwBQZuKT(?zT-PX
zozD4u-k;a<d_AAfm&fFXxd$|h+40~o8E@GqIjbXN6hfGNxoJ$P&vxQlWV}zO=d5Ol
zKZFZ|w#a(#;#~DUr<?7i!arS^XyaA-My_+_kC*f(^Dbpce0_d=<*yFk4@ZQ>b%Qst
zAQQZ|HRx<qG5Ud17EQ=Wc}`%cavLq6nOQQ-c657Zh-Xwa{4U+oD!+gKm0JeRMY-e_
zkKwj)ht6Y;yh@=E;|{}s?*m?zb(MI0;So^Wo0OW9SUV|h*Y`)r90p~t|J9@Z<&S%V
z4*FUh1WA6?;rH+V7BUBFP1S$=igI7BLcQ#$kp_0Jb=GIUABmzHLEDDW9EymzfwCmi
z1M2Zg1uibGM>@pShx|IW57!1-h!t^ZX=yy6^C01@RG9yKQi?y9FiOa;TN;QMki!=D
z5=D;KJoK9zLki&e`k&r`yl`7I_{Ef3@Y%UYL$>ngpLX<svXctc&vpucy;{O|vKL)^
zS(e|P%B^+0c<~|=wgo@Wdw{V-EQjEA7Qf)R!zi4KIezC)GO1z{wbr*PfM=vEK*t{!
zPq7Da6-Hu8B=*3}aNE6;unx%qYf#8xP#anlfTZS8$Q+ot5^*%2NC(2qc36a9S{>k&
z2aYKIrlPz&!M<ff%KG@z+8;ECZ7CD1BsIcl<1{8c6SVcZA7*N#=JViZU2XMn(VR*#
z&WA%P6)a+7^cDu&q#zZAxm^VDrlqFCNQ0;xZ~tx{17}v8d%tQliho!aB=YKo+Wo}*
zr`KrM|63QIM*<P`R|V$vfTUd?CgCy$;|MWr0xjWJeGtMK9Gpz>6G#t_+Zd1D>($Ui
zQhfX=48!V(y$mEl6(4@l+PC!mU%&ecILd;!cmjTH4;cLoP)!pr<7GcB5dt3+p<=a9
z!(!U_?bl~%Y4nR_Yk-QO_l0Pa_ge^<>hLXjjGvf@hUrhW%|TEK8rl7NWmn&)xW~A$
zMBG31^_~aR<<7koJT%VMMV`^R9?-2Hav~Qb$z^G60D26!7K_Z>=S~nX@5l8<&hT@o
z?iuYR)@`7EHo)TqomYZ#oC0yFgip852e<^ZCP3F04<83aM-!-_OX1`O(v^Bv6$q%Q
zBxR%Z!ru{DD999h2@8G$ud?&w&+AY9-~#MxB?C&RxD`R411Z+7a)0I}P`M4d3FiV%
z1)yi|qmU8e6A%0sSX8QpbBT8YwEXOol6Jc-3H-<O`_n7;I|m0JVIbU`ardAH(9EjT
z!)xL|CR+B>RqK0@E0FnS9X_+&$R`RKWK-M`XRr-muCK;x1*n^t*gy~x0sl!8kjW7H
znlmlKNusgCc(v90HA*9x(nN#g2uIqp<S)^a+v74o>#77cULCH#bfYf+pM%g;*T>4U
z=fjU3K6y{jx6`5a!_|<7$&OO?oQOw@>|AKa3O}ZfU31Fq=CNhxcK;Zw_-flVs8XW|
z7z8!Y#>0>5<p{qQJO|>^Mf6034%LA{et!cB1u~-D>~+R42-jQ}#|^&RjUWm7`TFiG
z>>xED9K8BrezI3sVHlGnCV1~W`}fO3_63Cge#a0I7?HjdUkL*gV%oEzQOteDkxX8R
zJO(KUkPRWSoLaD&bOVQ!8jT;!{o5~lcJ7p>+JY~LOZ>i&sA3xWq7pN-R1NWgmjCMm
zp^!AikDx|_vrnve;Sc);=nV-Ym(bFlH_p@#yb38?5}t4b9qRc_w|#un&><7UqC_yU
ziYMVGDhOUDr51jg1j;1#lV0cd0E>hHObnQssl*>T3%2)R#L$tbE)fg*+VgWST)3dW
z$qOe-=)#L%aFb*KD>}pffIC?TI3cm2MCF06Kw%FSxg_z%qzYdC_=0~l;CdB3f3J>R
zOa5<ZQ$HP$e)p3u4io}~2}E*zps7OBm&8NMdsa)<VBnWM3EZT)2Z-m(x%IH4A_zzg
zU=SzZ*zUzon}=9P&O8m5fofZP*+y0?&Cr{(7nfEAT<N6rDpZYKC`yn`Dm%Yk59xt^
z%#Ra%&b;SLIS5(wHN;`MNU4RA0@P>4nBc^cD*M-ctBO{qS|tmLi)-P+=!EKcExsGY
za9g;on>HrD1$C47{}6LaVrB^Ngj9e;euZm9M)7jSd?<pD5Z}VElju_6t@^jr%!joN
z;7r|*G^7-1|Nghy@$$3JiF5=CZ#L^N>gAgbc>NE~GG0$b^lWX@VQLYLr=QTPh-Gb(
zww7vOQ|ehfqQzrZgki8ntaM<$w%#=vSDlz5K8}idZLyfwBHAHlRsjfFA9ZrAeBgnj
zTnZs?z%PP}q-L9ai#b`<kaBa|{b+o45??{>zD=a5_avuIQU6%KJ_;gt#r0eYD!>{&
zM?piCqt-)m%bj_dlyp7UDl06u_l!<*yYlYX5&66aG@^7IwkLPBObv4lnK-OSstjG1
z{hJ1flmZ-o6(BROEf$FlMfRWoM?+W%bm%yFV}R?(mEq>|h<x${ZAq|{M*(?;V9NG4
zA&9}}QV8%mQ7926DXy}o-KmZh`Ko95y@p^SW7lGysp^zH+6C+k@X{xnUB$*|`n(x>
z8wY|LHeT5*$Kg4F*t<$$zW21pU*E&_o|rN3N<536bzbLxj)T>ZG;b~#Dl!ZXe123T
zGUNuL?jYi20Fx4<RRYCMsoC9Zlv+5ZBx*Z=am-19$~bw1KT<_d!Rq|^NMi9vSt%Q)
z|8JK8LWB3Vh>fpxB^D0Sl@wH#`$crDZ3KI@iylEzw|6Wn1#deTX7KbDp3Oa(wuUy)
z#K=<pd36$rjM`WNmQ43KUx!QS5j{7(f6v!<7w_HJ(s=Ie<Kxt5Bu?%_hYo#nEI*q`
z-<lDbGoX9x`#H%(0a0umS7$u_9fa#k0bn-Ur(Q@-J&a7A@*910M;;;Ww0K;&C^_&2
z+5mc&;Up1pQ*H4K^de<ZtG!2aAl$S=u0lfJ(L&-D9jrY!SgaHunsqNE=^r#CnJLM7
zzv>&Yr=@1oqiuw*!vugbY<2-pIuJMFfMeigbxJ{@5ezj>3TmT9+&|b_v%9vV8$wnI
zh6aepH_mh5GRcG3Y-H8;owP$t9VyPx`&U%yS=(TxNw;d&tm>IId%hRR1uRwPoQuR^
zRq)=nck3bvW_xX@go_$2Z{#*Qr6>sFFu{@WI{)gxYC>Q|K}Wm`a1+N!T+nhy<xiZ^
z!J$tD32(F44$ywUnI_^`R72-p*~1Y#iu4G?m;H%6{uW<m-keUsKX0YiN~jnHA)}KK
z4g)C<t_i;($Cn>i4Fs3arO_HXW((+Y!D$|HT*5+NgC`6Esb@>iS4U*cSP&K;Zg&3Y
zO1uc$-6nL<!fGHWi-~DEv|hf+*-Lh_AYD~f&EL_R`I}0b8`Lk0#@s2I^u&>k(SHu8
zmEQe+Eg(pEKZgAL<KP|1CoMFwYK7cS4PqHgf&j(uLV=Q=E+{cvw6qx-Iig*(7GVL(
z0lx(zBgui*4c7g3%m(#-VwDek7rFS+{ekKrfs;gIPrR{F0BI}4DT7T(%!Z+Mrs3B5
z=Uxpg`S1o$FCAV^#A(e0A}@@Dcyqt-w|UIZHlrj77Bp5e++1>iIJ|Qs<`RZ_189%f
zWkZ(ZAS04a6YnY+g_+vBlc+EYCFf^s+Af!ECF3j<F=V?4*@;6s%F5T_xRRjA_kt;<
z2E)%!qm#tC{m6Djzkuk{@0aI2zsn8OgY}4V5#h66j8I=klQk0jo;Md8lVjW^2VhC7
zMaSR{y!*trVwl<x@BTh?r28jY2@x8^ed~q36bYvuUA4x*I57YN8lI+EsSP6v=efxq
zCQQ22b0sK*DGz7bq{AKv%G~)oAXo1HTXGH_6wBj$sno;<i{t(KQZn3@)RS(91VjKC
z67bUv7PCtv*NX(dJ_aou42_D`b^H%&J@5SJp|}m5iyj3BYZ?}r(ilm^v({;{FHH9b
zkg$=buIyi6c0Frd&HFZJxto%H^zPU3;Iu$ImZtu_1%JfjmL|Z`eg4*BxMul}9F@A8
zmwt%>HKFVthdPEDoCzqy?B4!BNsgsIS(XRN14{6>ScZ{6j)6<+iy`+3acncuppL;D
z^i@})Z`J0-tjc1>x6e2cr57>z$4USdF-ss|?xT-7PLYA+V@#{89{zZz>$vt^B-5LH
zn+C^$@Ow<RbbPY9ZjlJT|KeV7Dy~QUt7SWdmJN@p6c7e}N-Z3wk{}lNj3pO-b-k-a
zJs<({tos)+LeVaE?b~7E<ras%MO`avTKBI5s1`DZoA;v+)%a`_pcBciq?E!NrKC{u
zB<E+A_;tu~`1H%->gwt|>rO1=co<`uh+}c^M+^Mth=|UJNOq(c%k?|G(t<?kSls*h
z^JkO&5Nn?G{V}<{^2%W9^d&qzWCsK%M#jr(-lSZL_j>;P`FmRvli5Q3#apf7S$O0`
zL{T3%_lEn@*1NTImb0il>Rn=qBekHN>haRP)R^3~tcTC7)Dk84zux<+2bo8GLN)+<
z5OuKGARJ_3w}C~`93T3AP|)TO-CTR~m{H>UvQ}X&`=P2Y*5B{V_6z8qVIrV$@Zdpe
zG@6q=TwFcc(mT8^y@Toob8iER5+1R7D6oEfVwQ!bpB*$xIR7Ahpf`XR{RCV-lmWt6
zh2hCGMx$%srSFKWljNp+==;yg3Jj85PUBoK(uI!-4fs<p&>}WTSI3~98V*ymN7*nR
zKO!RYFLYsp{}pvA7?%ksX?r2|Yaqj+`=k0;P(<w8Q6`kAp74XeCs%RgU)=mZ2K1NN
zfeh8cqLqdmCFp2Kv^F%rBbB|jjgI->-wv<t`Bjd)641GUKzS8jyi(8_N)zzTRWckg
z>oL}%$ZfmF74lmB?DpU8*YqUWh6OJeM8Dd34=w5nj+ShjIMU3(Gr$D-XZ7hHs7mv5
zwqbuOc)G!N2P|*ZjjtQtEspxPD>EQ8O+p^PE0{uGwUCUrZe4JVBxbDG)<nF$&tf`u
zkPZsm!mkH4V~>)Xf=bT2J7ATKHt^bfXXb*fHG0;~UXh4~CeYx*1P;puY9S|+w_MDR
z*o3z8J!{nW+1tW+FTh0-m4svIFloW!=-=P+6Dd6On}9*YbT;X}f-~`o^+`_~k?d@7
ziYZC}Z~v|yE#a6`B<X-4s%;Rs*_|cQQ1a`MfW&(@%nm`nQ|atVS?vf=GDT{BvWB>p
zK7<cAoCe7zGswH{pWoD=+X2LnlR}_s%qm_vp}yWtnF1j}dGz`BH>CX#$O-Ys1Ae|T
zd}4Z*bXEW)wz*<0i#+tnUz8HfVIWl8OGp*CxSECT`4F_kL!<S$Stv}>O|y1lhs7R1
zCR7y-jXmKt>gRu(!Mia*m$W0iAH_7h^fXhNpGVpb;P5j)3b6*%wy<s7$W_>3N!hxL
zH^9Wbz;zZweIBC=7qZK0M7FHdP&_RHHB05X3tIhum)}!E>VEr}?I%g;xr_dSTMwy?
z8})MhMaU6$^*8WZ8#Wj;Wt4{8eHz_n1sukoY2+sAbL&NCKwx0teVXa<vQYa!!5OgD
zD!EK!O~MQF9E_aFX#Diz;S`GQqhHS$bUZq<@msaAx~izpFF*e^aBCCGLfwtTrea(`
z`dyTF3myQFqZ?xn0Nl;^;$(nw6KotK@jcKh;7x+jrj=p#7Qj~`k-Ni&s`lYt<wSZr
zRUjxTDvBp0ZtN<(V~93<1V~id6g0})MzXO<&3VG$MIu9q>N-79;W2Vdqh)x0-ecEu
zw@m|b`G5N?4|wR(%6@glFdlpB8}GtLS$BT?sXab^51~-JEu4?EvNY?Z9kYA7@wi<d
zcj!cy*n68wtrg}B`qZ$~7b}sz<6G^DE6nL5mO5GW0d8d#1>P^Vzv;5R;i|(IYwrEx
zjR2HIx_Ns2*IEYrr}M5bHeQ{ksZ3rzX6_|@tPMq5BU!n1uJ|Y7!z1Q25)I~j^4+_u
zi5WMlX+_vHG#E7hjVMXz6SE!jv*fW>`rry3ttU7lG?c3WqD`r2Yk=g@P%0n9I6*aO
z;C7?2-;~<gz7>@|VQ6fUU*6ehiYdD@!kOLFm~jgBG*LZi{{xnVA46-TD_Va`yQ0ke
zRi780W;}nm4xij$p?%SAbL?%7`K;Fb<r+H6`%Vp7ZhLUsRI*;)t^V8xvCTerI%W6Z
zk+PckcA3B*IIcXS-{9q73kQmPAgk2s&?>+f;yg0xet0;zSyyQO8}g<ZQ_$c!Ra~ol
zdUeSgl9I``z>>96^|(~FsN=QHDv1J2Pz1RIZCfI#Lp7IJmec;m&Wr@BFnE?xdY->u
zrL$WYYuCIvq$EGt0Lms6t+mbvRI=;0SpNkFk!!{FBFk2;+HWizg8np0V25t!k)L&g
z{UIl~(X4jItN52V=wUX$y^MUsg|oRU`*DTj=Njc`o%h>*`FY>;VYqG;7mA~L?rE1@
zSJ7p?7w5e;dxsBR?IL9>00;kcb_FsLGvn|9vH=L1)AZiXl{<$;@Z+UE^|Sq>w+6az
z+gcrWa4(X}peOE+rp@e<df@o$Ji?ZWZTscx!awQNC2l!+Z0dmAgTH?JB_5a`fuKl~
z0Cv(3%VTO;=YyIacxK0;xwYS~oppbGK1<;95z|$~RW!sMA+2Ahh=-!Cefb8tUZ7#5
zYE~|Y<49+<vW$UV%5oP4BbkEtIq=e~T^T;=oKIwJYUUplowsNrm^58YU-wIW`K}&2
z@SJ#ldo0zY<}x}xt8@!zT>5Z$4cBIa%&K3G>0aVHyYa7wbANN-QRlDPb7snfgD0~g
z@hhXGykc%&{#q@kE$jZlz;pI2gE!WkIqABQkrC#wWnDsxZj=4-k5)S$>_K%6HTiu=
z>0omB@neBhLJ1cBktGR002(lUTa|QI4p}oT=lYY}O&RMNjGTNFo$t~C3cPwQ!>wjb
zIgYUx-CtHgVU15DaH`1rqh^3-sP&jFXsZ0O5bXOrf$1typ|7Ixf3Q&fHaln2si1f6
za5pY0v4ThM)D<>5>FNyNmay1_7R7}yVe}<SxJbL3g|5rN1a|e<WD*J-2}aE+usu<8
zOmm=+jT1s(ERiikh~7vUZ{SUASM1w~o5zP%1}6)8Fm<w!0jR`7>`TK$D5ljd6xQT_
zL1R><fIfTM7Y=+We6#(6=DpZ0a7l<Vn<qJvdq7QHe(G^7cXMg28&U2O>;$Mfkahy>
zj0F_V#MIiU8%gbHYbP5j0PK->0=yT4Ga&=5q&ga$S0CUK1BjE>eQo{m#jgk(d(p!Y
zhRmyhU&;#3sP9|rnBP8R`Ba&Mj|(N=sbSCPoX(G%ydsHX8+D9G?7G7E_U!q^ebel>
zGO1L9n1xGQWCA%9H5x&>{&U6sic$^SW$>c710uh<N)oW~zXQl_!kPN-0<xlxU6=ns
zDS+NI0VoKPu0M<gxX@7GX7rKD9JpROV$OYfz}*|U2&|;8v&{SIMRX|e<^iszO=Nmx
z^G>Q%b;mf>qi5o%ftz}$ebVDYZfBJJyan>G$V>;z41&SKBX|J}nc*{p@{P3?oI>f;
zwB6H3DK4uv@~XhbWMP*LP9{EKB2)EGp<V`L^rVDu0PfEurF=^P6y9g!PKP%_E*uL`
z(641p+JtwGp;J*VHZi>)yXQU@C1xoFu*|YxGM4PE<G+~il&?$pZsb;S_Cg_z9cePz
zU5pg1I1CK>VbJ_b_8e&nLGuI0>&@ficdm8jY?X4?23)TSnl?W_f1*$ZSp4`EAD)`i
z$4wzs6_RS!sxWzzSkl1~w=8%X6I>8;{EDH8N}wC)-ARhcJ}()Dy)RfXql|Ndtpo%z
z1}u!dU1ZFAa+_0ht4J}R=X!M2$!N{-ZWGXOVXi<>A^{^Gd?i}>p;c>kiT+O@%eC1_
zQA@8L8SXgkF;!>6Jfqr@!YwfM+zg2;oK(@o$PGUOd(iNNgmIFJTEfBO;b^_S)SiPd
z-5^^_u(Lo?_|t{$5lfbuIQV`G9!omENfZ8iSa2hVw*qW@MYs!`EHQ=I-3D7t;#K)~
zhVgj883r)8OzuJjLae|@86$~03<a;<3kXZ)LB9y**HuZF8n5gDA|DEumfuIRsh10G
z^JF!tLa|6D=JGJrL8)1b8&YvN9Dd@M_%U1;JEJ4{4^T&vNduY-a&}>i8v|^+pG#`;
zA9VI&Ol&j5a~zwgWxaP2^uY$83GX}KzI_YyGhf&B>r4L8o==~CPV)f)j#x<+_dI_5
z*cMomzj&trEYb-U3Lt<y0PnpUcM-gttT8}(AoF5^yhD<=!&Dw^K5_btj4bPXi}_64
z$B!2XvJwoR3_?5&Dbz1$Zhx4%2+^I-n4hA}*Ah#YnRyiJ#JQ6wyRejKt9fhI83^<*
z4o?PuSrJ3ZV{(LzFJ5QF>H#oFzwmM;e3b2oi7GiY0BeYTmI%J`9O0uHoNcL5JSO0H
z@xldwH+<hP_>>{L^lq)&dF`qdsvfLcFt!)<gZ7YYN3%tDu?L9y)2B~C7a)uT^y7kV
zQ?|3+!i8sSZEd|<ApVYmamD^N+dBCi_!ob&5+Mi)UQ}UW%ia3*#uE<~c*XBOk^=Mp
z6>z_AeV@*&e}nA<er=b@#Xxfxg;1oeB#0r^=0*e@ZEE`YlI74Ot0cts9Z1tj!#PHp
zy7a1yxJFTGNjrB~*LOA|o%p8Kvma^9&_nf_LiRlZ&CG!hm8a2qnkHQaS8PLuX@jKk
zp8!Tu$kLd3_>ne(b749wD<@AThw$}n#9TBf-wZ*$0V8gi9#bGsgav_!R3Ck8Arze3
ziOF39&Ymr*z=76mWl~g=8WfiJ@GFtk$+0D?w(4OquiQ-Lov=tDE`cO&;wZgY>+ww*
zzKVzS+m(Jl5OCc<^^CRuFw}>SQGa-a#C<wefY(>e3ZtIiSG^HEAt~xn{t?K4w{T{J
zo1wPBFvC<$FK~bT@MCH{>^ceG-#w_z^clxPdz|FPOWC&r-_pn=7aNhK_hW|_71OS0
znISiWh0njV9Sfw?!~&ny^{SAz9@dRmpR0=k-Y{UX@j8dPpQst7;ZgPm<}$WOZ)Ce5
zl3xQx+p%amf~}vEJVf^H5`QgN62HNg0?yu6zeX}sCetJ!u`7C{AT{MArlhJCWIYSD
zdYPFY>szNtvpxUSMBzpgX$8x8#40n8B8)LyJyEIp;;y@0wl);4_c7bw*^8j*_tYd@
z@D2bpKKHMye&AWZt^5CG+~A5k^5^IOa@??lm>T>TH>f3*yDJ6<l?a^b9_vR8BJ=b^
z-{x^quvDsXna8PxHz{=SuZ3g7q8&{tL!j2O-Lf|gHndf_VOZdoDz$s}33VMiKXmR<
zK*EhN4}ogU89wdTMu}$?H5%B2YW5(6si&xFsoH0|4E|2I6!IQ($Ayt%HEW#lA%Pa5
zB)()d-`VXUCM`6~BC%X0BHE_pbStKjWFZ9s+O(}ilg24&zkT0ZULT1cw=7Q9!e@8r
zDn}q&yKZdL6GRFHhLdFPva55(5K*2Z7lCIN1(T-+kSCJT!>H_P|9L)<H{<@F<`Y4F
z^!#H5--90=f$f9JBX`!He}a2NphDQ;1R~!@v(*Tsv;ha0z|T|I(xUU>nc*P2+V<}#
z<^c-oS9fNTo-wW|>UE7Efy;%kDA=!)qp~n6Y(r^|t&MzXGZ^~tP(XCgCH)blr*vl<
zY)^9>hEfsT?+;*FlScMzLg?`5+#}?^6>GMTN$M^)j&4fN-*s?0)~wBDe~xX)oQ{W{
zBG{9f4{$$!z#vE@rnooMXcVhtzY%5$tdc`aXIRi%ybe4ljk#6glY1B+#lmh05;WWw
zv{t7G^@1}WN@Ig=zU(E9Wa`yAml$u6yHV*;!8>@L0ndiPMW{~=yNvv}XJQ~yG5*LR
z>w}jGL%GhK;3$|TaFd6S6dJoIs0m5Vh_?A~`RLK3B3(jtKezy|>`k9p*4wg|T>UQ1
zDdI%a)PND|W7s^!L+HOa9Y&!87j7j?1Ta`9+pO{ta=ZLFu!;cTEEYpZ9!T*qIO(-b
zL~2AWT<5t~a?`Um*wKIPk($qj%k*Zmx?WqdP#&LK3IAZP<XXS8#9N)no9!Tx`%Z7d
z4@1w0aYZc@)VhPorO4u@@W2wY>Ap+$7XVmzKkjsR8=1{lJlt#G6(-VwEyD2xPDHNp
zUl?XLVpZ1;3j8GE>M=jYXt{OB7^5Y?C{T&ucLJ+*@txY=oFXSbO`1FWQaeu%GI_?K
zQra`x;K*)TOG80UAH;fZ(CaV3qsE}L1z|dAo6DDjTP2(YH@yp^?Vdhip7GW!wnKja
zHAJFSNMQ3G(OtxxorNW$fQ+mWc%rcUryu0HS|D1d(T|++I!`=!i5LaF%>l4Ncs|L8
zqm2ROM2KWmFi7fAX*R&Bo0L?DnIr@MogogBSrSYMbWVnIII-U+Ao%Q=xHjBSl^AYD
zh9a9Av6bl!sd~vFD1KDHy6M0X)F(<1nOE$)xXBA8&2ijR88R`~n;Y3kG7yscRPkE(
z2rSO)#mI}*Cx)$pI7|XQAS6t}<sb`YQ5p4NErT+Ognvy_jB(VbuwsnhxtN85pOBcx
zDP)n!FEnt6ns!WppC465Qd?>huJCnbyJ<Quc$MP%UBW&*1Lz&<N4hHMj$bd}3`DoG
zwpO(Z_XV$uAIy#X9b%Dv<@9oc6MxVcTK$(VRlWb}c+KIDDp<TS`svYYI$sCqo6WwU
zW|ZtY;QIKdHs=7BR>#RRn_VjJRq7jE{ilZ8%D_3h%)9Jl`8SU*vrd89c0s~(g-H{(
zzyCh|<kEe&4fk)!Zn?i>AOGp!?q5DWcJtcpidR`jiq>s$W!db1S$`A9mUWkpo<H)w
zY){2+8A~1-T)%qc`rYl%b|1;Wz5^|{`B%~Mrwj&CQ$zX~{W()P+;$3~HXW*diKp}X
z>wEG>tXa7e!{%u7vd<>fcyv0fD)hMW>GR92_Auw`=oIDAl?=a%JGGKMNkWP7GHfS|
z-Wqmjj5;^Gvy+xS)THmrDZ4_1>?NaYEs0D>Fg~{}16y&Dhka8%j3sTHe>hYXe8$&J
zy>iF;jMwGj1805REcbNYvXq*<6k9msDnG8XKfKMUMy|=a#V>V!x@=q3P<Mfn=k3Iv
zWC<>VaBpF)-YMm6Q#CUQY2%$;kz@BPgF^m^DoeGUJvlgi&sOcjl}DOWZ^hSCe`a{^
z%$b@RiFC0{Idi<>ZS$MJa0N+ydjG!pZ5lsINJ#Yd^~sc6(~CVnH(%J;m3+4HP;c8v
zm7tK20XEgpQ)7AI^q_#{U~0&B0SGJ&IU&KKFyppCzUl@Kq&l~qnW`Be@VKGU#`@L0
z_h<pEot>SZ>6cRvqv~JEfWNU3IJl=0R4OnTu5D|Rcwz>tXjv_-7R7)C${G7Z)X9?K
z1L?{;d{)PRVDg@<ut~!!Y_Y39L!sg%OLa`QPb`1lrr-NE3rk&(DIEU#q7`H8&9E9L
z?gu*-nl?Mra_TOp&yhDrFh;$woy)VBZRAQOjm9_R%*Y)9_0tePxDr!M-`Lm<4$9nq
z&rmJ)AOKV>S+V7Qi$(5!%nm%rVqQUEVcLaC5)Bl!Q&Li5cNn}%Is9m1J1NYUDhtf?
z7tZy|d>9;IV`m?28*&$>jKB;{?7>0+DH7W>a%BRZIbVyqB*rx+k5Ooum?^B*o-04?
z&R^d}UiHZmM}#;f-hfqZ{5*ZK`G>h~;!}6}h%e`*4SsGj!0WEy?ZEaC=|wu@kv4o1
zhe=V}jr%m7lrqJ|#Fq1Brl+sATVx!<AbDv!BDdrF5e&p~OPQ9)Dz`HiNNnerxS+FD
zMv?+nldz4oC97dltlO}vdLJBgpBEKMO*hr7`S&jbX6ym<s`}JuTsljqO05yB^%E(a
zYM}800-3=ysU{_}Js4455+>8?xZ6}tIHo$zjquG|$1ie3<F!mBS2c@q`^jV@T-sn0
zYR|^PZ^x;hPy|#%^Oj%Hf^8wYh_#_iOs(P~Yg924Z&ltuY~KI?d$Nr$2rd{BqzB<i
z6-GT90@8pe4%LlZa${~zoh;;;jcV8K=WmQxG0`2BVPTI*{5h|@cBfOv?4U!BaN(r9
zn3&iI3?@He9IHSzd;0RvCJZnHFv&9+AVZ9IzX|&2Ol4l>VdSt}VXDxtI5>eDQjV@K
z1V7$M#GbT0W-K$B8zZK?b}dvXC&H|=L&nBAXJ_fW2Hq7Enf)nr-lgY4S!IiI9lw6e
zLj};=+e@L6LtgS!3*R46I^{lT*<1J_Q8VD_i5TI$z4AtSYW@5yjb|HvYR9Xgis3a1
zceBS_|0x(K1<Vx7E;vccQC&OPJ5DM4dfr|r(i=~x*4C${rf$Ij&Bn=PWLdz!vtaVI
z<!*`(Hk8Q)3nsAbTKLlXP%jg+F;9+}_eZi^-W*-gg-;|WE#0NF)pfXdX}}Pyv`ro^
zG}wv(xaMwnW~^a{x|^~_<o%VIGLM;)<X;3KN!0cL1#DZ^zQqbn!$o@AFp;J$4FHDx
zQFU8(ZWX$n<-Af@koF(WDh6Xq113Obxmp>cl<;w?GF-(RDDS`j8Z00V+nm+f+?=D3
zse^#=oBHBAD#SYiDl=2mRZvhMHFRl%vXHHV!&>byk;@<d{Yh~v6K_9g<zybKtuc42
z1J%abe}9TE`iL67A3^VuQOca}n4k7o8X)zpc<p~U=}7Kk=H})Us<MLUE<|g+oT!z`
zf+VnHfBSgHmy_Kx@A4Xe7m<sE<OmB^ry_XfeV&29K%QC^DlUw@(-+-`Jvp$G)973%
zzEPO)NYn$VAF!FUS1f#y#ea^IedLNTr5!<Rja>dP7^a_K()w-Xk|8Y1v;nHJW@b9K
zW7cAg#Mm2?1s?8!WCf8A*C7l@9%eK*2M=Vl@N!JPI%y3)R1A!ba;mo2zdH{hTsbNz
zk<Vm#mFWKc`-vUo&4_1_X_1Y~q!YN8SW-i5Ybj13^w_jsE=#cv#YNdYKPolvd)j@(
zw}G2n?{gZ=@$p!^{TqdYgM%}uVXDYqlBbhA)KgPaCYn9Zn!TPs3lkP(H_<upF!9gf
z-R11<!(KBOo>ozJ$&SF03f1DMlnu^`A^Mr#FJG!aJ`8}Ux*&KLeh5qb3m6>+;jH6-
zb8~ZuV%tebr*e`ihnW4>XZ;<qO^}tjykn+YI4&uPr^{OOv;<Ry8XXg}3!hpCdk1nO
z+qdGiik2HsuCES|iMhK~{1Dly3=AnK1Z!J~W2QC4i;c+zVDd4{I*Q(G9@*)LEe#2W
zvE~n1tWnXD#OR1&aw~RIfF=3i-KeILgW82Es2Z?-I0n=PKuxMZ(`M!&dTn3~_4ilS
zxR0l8asT!Kxm}(gXCM#HUA20_h#ykXdY+WR98pJxz1G^p!$+`J$Qss;T$GF^!$%vs
zx6Ms>%uigkb8&XI?oY|=Rt}$1u?G-@eL7+QpQ^B#^*#x=^ob_ePx|9mRk)NCANGPO
zh;}`c3lTYpLbCekimeZUcN^SbGGci5<m`~T=75Hw20=KCb8kT9UOyS>d_sY~lB3ES
zFdD<>wio{4Usp0}0uSzl|LiN2fse3en+s(mn?@sp5kR6m@_5b4OeaL=QRJ^~j=2vU
zBM8ujzVe4QYh)zi6WM81T1L@mmN&9*1V{z7B8I<o1vx|a{3F<gniYVX(}S_0JHks|
z2*e&6hI|cddUcT{MX{Jm?H%^7v_8NyvJ)2i78qA&{Ow_?yz!5=`y9nC^=D49YU@5Z
zSFC7BLUF_!i@dL;oaJ~91~<fB*62oaZO@A!^9AC9Y@m(L5M9S6CXBE|J{z>jy0*4Z
z@)@AwTAG=Sz6LC$34K7G3)cik8DC4z5n*Q)e0#~4$~@}W|12dF(G}Ng>G@HtxHxcN
z2)mN1=O?A+jn3s-I@diKNeiFdN2tfhYRjE-u>FPHYq>{<_G7WdClk}@7hIALd=<p@
zVisaOH9eh%H#GxufK$|v%cYxxge}{}7|V_ee_ArcCHeKAtCedUNCweJOiL4xntFTB
zx>~e{vwY>rN4#}!-#);6LkNQbV;JTY=ON3T*3#0FiBUS#d?_jfvi8XUkI4XAd;4xc
zo#jOXFc81WS?*9U8u|s#g0Y2#LV}8nsi`SQpPAZ@A8s@`c6u`stC>xW&mwY^&gEBu
zRThjMd$tKHjO*&_1>e-U;F+7jHUK%QHrOypDFf0RQ;OgqJtLVBN66UJv=W=)!+}_p
zP@dJEDO<c*Qn}c#G<{XzZz8rQ(d4G{g3kea9*&>;xoSpfX(>6)9J8NK*45Th$+pXz
zD5GRDvr)*^b##Q+DTwbUY^$uciToiq3YC27_c<vU&D@j`3}`z%Q(g9C(lf?8Jmxvi
z8uPX>#5?Z_aO)P%<dq(%UF}pf4Gvj1*uFJb`|_Z=x|%!{aiR8_a73P)0rRs0VKA#%
zikyGvr)FU-U8iI1!5O0+c-b}Lzg5BolVX~c(vz-v|7A}qtFJ!fEZbru7yRhaXm*Vo
z@6E0D8gm03bNhjwhpV~#o~gRI!aTp7JX(Biy|PV+>cqkMYS@W;+d?^E9NleVNi3Yz
z^W2-(y$lTv^-NuN+wj%tXNSOs_S^lrfP|sqWgC+l2IQ@YimToeojE_p^t&?ZK8bP$
z47Po-F>?ojh%qne{)E^$DKq-90FKTSD&mpQ^2Ew;K{Gss<tTtcp-t(!DK&H69prW{
zN^^7b$PGT%9a!valJV}s9eN_Uba^yT-$j9XFmmE#P(AYK7G{jDl9u3KLa1LyHtdrc
zGKC-oN#kZKjzg14QVL1<YKI-(KHGVQmfan^L1Cp@?+|t}b*_Yz<&n8Hc0n5e{+L94
z8{je5<57)5IT+T*QrIACM|d;Ib_3GcDV$EA;n?6%#HFO{C)^JKFrcpPd<4(4oZMW7
zu}e`n>*TsTT6h(SyjNntIQIi1K@3ys+)-qk!{liT4xZam^9832N5ir})MJ+6k*_;_
z3C9sgjxS&@E}*^E1Wt2>r*(~R!B=l>R3So51TT2NnGMvMTjBIqjE+*2b?^gmVj43G
z?Ba#Vr&kmv#SAEZlEhBf1~bH-C>+(IX9irlx!9C6LZ`Xy7yt64Bx1q=wbHJ$&VQKG
zGcW=C26MiXCo^!t{45xFUODm5rDnC=;mx`9hOYW7HYZ|R;DSxcxlMjDF=UtbLM35v
z!)7y?p+jfjPSwXWv>F?1e6f0D&Ay?&z6z}1c|a~B9J+^!nDZ0!a~(671y&<|1+=%f
zbISI7B{-i=Ern|rWsT^k`}9Ohw+uSsEhmMP8p*a6v7AjF^RvF3=>G#o@mQ>_;+sL-
z?nWN2k(w!yN>(-RH<7nHDzT}4T^_Q>wr@3Oj8h*P@cl$l`<A^#Xn16_JSi<H=gwSX
z<5)U<e?BG)N?nsSGFleCBeDIG3F2_!<K~l*o(NCOx#!hhI_7Zq?9{!mFhSmg!60g-
z_r=<dbaT~-r;#pClLYworw-1xHa7)l9G5LycV{DC>eOLZEpL;W>lx#TgG*V|dbSQZ
zo<wFXJPhz?63s*)j{o^{O(c7QEz3oml_iI-u1&UPhZ3s4sH+2%wMkH!b1LjWkS%lV
zmDSTsYHl>rBF_+cv_2u-my7m<_#*NM3JQ`Z$Z4*RHplJDb-J1~b7Zd`O5`D#mYE(L
zUf1!oaGj*7#+Si;*PIW;*V9q&&^z4IGc(uj;|r+tY}(+dlGD@paIVuUaWJaKW8Obu
z{KJQ?D4)}7Bw{OEmYmZO9Gf|4K3b^7oZWZU;&w&`_fQheAk)@cDxt!q=}Ftyk<sk#
zruhWB&C-3+>m4elJ4C*P=lxW4j+_domX>yZKGzScM6tsL!$Ycm@4AAGx{l{1nF*)6
zNa1IZ-Q|B68ySo(Eo%^usp#cIh7hfnclXOvF)TdH@vUfub(<Zd3kuLzILTxYpdr9D
zQGC<r)wQnY&P|&>lWb|moXG|!t{cPjnl^`yPykA?NZ;Gnl1>a&q%|tUo@bT3mE;tX
z_<M7U^{KgW%lSiC>EfdLQ@c>ZGG@_vIvyJq>Cd1_RH&-!&`X&lGB+&uCQUkT*86MP
zR>jLqTh)-S$W=E9^^t-2dFsA7&sk@c5in-6D!Sz5<wYky`Pv{lsB0JGOn*c=(i|v=
zlM8xQ(Lzh$-6LkRh)e(Ra1q3ei1<$1T(;O~vFzDH9MurLj{dr6_PjFDIR20=8t2%^
z^DpzV$UUs&$;Mh{uS8Drg;#P8=`R_Qn(LHWV=~FVsH(X_zX%gh@|V}x@Gzi(-JB>!
z{+mVPq~}V<G9ZsC@LR>NCq7<gYlsr^3d7X3M=v(L(}K=>(Obqm%0%a<BIk9(jaR+A
zyo9#N=SS0p+DcSvtdK5cWp5O06c&!lT+UQcc5!hD<{oQqs()C(R<A6N2T$SR4P9|I
zHl@^~5)IfR^-fUbv)y@ClY|WKL(O|)y8Sn#@=ORt)2a=pkbQ-w?PL>F*s@x|K2H;y
z+DMP{(3Q8>5qkalwQ-7-hfnZ?3~ORSDhkBgHK7?98Eh)bIshj&xR}Fi^@;scpn{1P
zX!7#Ri8B%T`S}-UtgK9jz6a!_w7&Cm(V3f#&^1ecdt&ow&gEmWmigY_7#3UJc63O|
zHLQLrpD9-otJRpZ5(T73RL;6YZC$r9dTiHmRhnDAZnk!#=~g!5{BFDaM$;kozx8(Z
zW(#dmv-4=|cRO6`l5#SvglWu~->BY4*L9&~76b<ele!80Zgt<+JHJO1{OZ_$#KXe_
z?YLU5LPD-MbNtVSh6cZvtNu*3bxVkg8+6t|465*R9vDc;)fE>P=b32k&~wPQx3%Sr
zQX8bt`5csielk!{h-Rgekhf<*C^1eG1foAx9Ou^D#C<V)R*o%rXc&VV-o9%Kau#n)
z%ghwk*)Ujxj+tDkwzjqn;o^W`6OIZs&;c&p&bN19v{;7uORDoQn;Ghsg-bQXeB$d(
z&);*__6GfE-yYgwvBb<P2hNK5eD-J)d7QK+ywFs@=aPvD>aP9+s)CL&N)(Ni2UhnT
zJh^a$Hm&;~9$}fH1)-v%;`ifu?f3ubr{I6%mxzzmMCHj$Rnet-g&C3D<mENS$M%SG
z@5FfQQ<pQU6Y9M5*g~gVl6P3H2(2If>4=}-lrE(uZ%D;WN>+Dzw2Or9LgS=;?Vos5
z+sVx^z3?MK{4a7Z$W?f<0|O-8ZBCyakfiE_u9qJ~>bva1yabb5gK~y+3H|v4i0k5{
z6*OUpVsUyJkjUf-Npf1RJTQK-A`fHSS19=lYncd*<>2ZFVC&Eql+{6p%r#L+GMuK-
ziMzSEnQAXk0M6-nf4h>Y!fk*Jp`xE7JfD@U5!VMb!}!vrQw|Oeq?IAfNH%6eqktO)
z32M>kA{9PaWi%2tk!SP^&%(=`$XO?~VhCaj8?3@KK(|~2^PXU2^M^7q=@}V@cy&?$
z1aqP?HEnd{9PASmy!$P80bt#9L8(mW9RM@xzR(lrv9??&bs@sPulYI8^bO*fRW_YY
z_wrkKB$F?_s433f4bqJq)hu@)V>4KM&Xzsug;T2Tla+ei=!+}CMo$ACs*kBu6RI!(
z!;Oy9$j>{_TW$!zYZ*j&AJQBcsAOeL$W+c25)_QCPajdv^4LFE^VrzTOm0lm(<Cto
zQ<xE(tmSkSEoJ4pihHK>IpJB)ehKD292;Xj%hcrd;~sB*BYe#{rKC52jg4(g?3%OA
zql1^;CMPnS>(QS*b}>&Z>9JID+VV8d+12}}bHgV}Z=I8{b8zSZgegZ2v98$b*$hE#
z+HziuaKs_@sRqIlmOtLiV-#wKJq(&^F4MBS*_<e(U0xcT+;aKY94HU&^voeMb9qKi
zK>kr;1>RhGPp@~%$KT(U5fNr$Vrh9=_J;08L7^Cu#OeU&)v81?oLE`yHo96MT^J|7
z={FUMk7>qqKR{{pHqEB1pwu>BCF)vYG|qwRjk5GYQ+NNWgri4Q>)*Z8c*-d7dE4tw
zU%pKBjE#-W3H8r(H;Yh-cnaPhw`3~tHlg^~9JDdq<2kVh&#t3qW~zUoZ!0N=4qD|4
z{pr)W0#`P1d$Ur@bZZ)OQ|mpP>5IC;J{qlBmQn9j@9p##4jg^XqFC5xsUmy~J-7`v
zos#t?L1K0$_NbAV%?ilGk`viA2B?t8hyYEsn7H^T_NaZr(wx_T{en@%?}2iW{o{hH
z@`^Lp`;-W6UUH$UNcTaG_v9xosqFR^FuQ7H7~be_Nz0N3kiMHBMX0m_V2ZzY{cbsp
zF8gs5^B5YaYiFS9G{P7nDJ5!KVebIa*}I96(cO$YVPRnpPb$*fHaPDO=r^Z6h$;O2
zl19mVoAXoTqK8v@6Pv~vEl<Z~Rh@OX9iUt2vd*@qNl1`uh0n+{-mG==<UJ;nsrto^
z(|hL$B^ndS>RYo)T<0wzx8?C0k=ZDt{iW+<`-X)?5)S)lAZe#o?yOz1Y0HwK{)d%a
z+TcP>*38e;kf(bRD1bIST+|$oL=dQJ<hjM#FB|%jA;_;XD-3jWbk1g3eK46&?Hg3c
zpVc6vmEOcR4hy+@07Ii}C7PR?9m5n5beHb*bbl%Xio{(ToKwnz8j$9xu^S4bx&1cA
z<ey-w$YyIRoQx?;&lKpFx-;#SfVsjQp0Vb4`6A5mi*xPm?YD=7b#$1ACg0XtCD#T%
z-X<Ll0Oxl8c>RZfKa$vZ8ZQRs18BLOPyXf7+sFHo517Q49mtJ}+Sx@0Tz$inTD$>_
zZ`r9}g7L<^UUJR~DOtm^@_8GX(UZ|Bcx#E0|I8vV-sf~0pgV`yttasze)p20c}>Yy
z)XHvTq_w8<v>tIS;XK=^KeZ4*Xhyfa_OUwfVI^)WFinC6jG|#Q*LdDWZa-9O9@OLs
z%7VAel?SFTIEr&!vvMJk?Lx<?8)ro<Y(znKuxUJNl#*^n(5sKPc}Ad?N<QGAqpq%w
zE~Q}Rb;pc6_0xV!C)o^iK*hofNci17JLi6BM;j>#Kuw+c3A5Wu{F7T@{?C_tKezyP
z^S$A7do3MH*wwO93k#(XsyEa`V>_fx39IlYa9}mM4&mDwz_83Ek`{ySvvnN9yfkfI
z+PK;Ce&-X-P?q&T?p{OAG)7=jkk8oW<mB+5e}4T70^~xR$GMJE;bzUSC0FI#&um{|
z-O*z4h#)!nmQq+T4#e2NuaZkH@IHanK@A#n9$NSq5mdzb#Y+Cnk4pZ3;~M7w($)N*
zYjFf6c@E2RnhJMJf-!TgL3ZShuJegTS!nH}RU(JxloFOVsHg+KddwiDytlWvUw8>g
zN$4}w+_k6KO_k;RRGlSw{rYJPRnX7Jyd!kk`-WUbyu6m@`$Xu<+I?%!N!0B>gUozu
zke+}-p3-&s1?IQ?LvDXwDQ4fi^O_jr&71pp5@`0J)s2m|tjZE4WCm`W^v2<&tZa^{
z;%OnIs<@cHX{&;RYqdO`6OA$ZGERKcpXcS3vr1AJu&tolXC|GkUdbHldf5b^_5BrY
z-o4HVCzh#F5>yhuIk$EQNy&ECM?`R&K-}=oRIlu_ZK0;;wZ!=BY|$vhM9IwRZS|a(
z<!s<!eDCp)G5^)${ikHPP5LQm?`BT$OlaOKo`op^f45!dt(W=CLo0QeA{U$x4N6p4
zS&l1_o-Q8Y0bDPw0kN)KvR(<n9|(GFtNJ;&Ft`0<TQo50P(M^BSIvQXDJ3xGobNdJ
zLd+jspD{I`%e;Wl1>VDL^>K%2`k^irdE}RG&f0xv1D)^O#h3`y%@h4I!92-&`rs98
z(DOtWXR0V~hx$d`RXaDQZ@~0pjD%_*Jy)S@9U8rqy2~%}8vR^S$`s8|#09CJcU0kJ
zv<!{=`|Nbq6ZZL>SW;%I+Ig!>=+GfUs4hxW*0CJlfi*#0)=u6_$CNxg`UY}m0GB@G
z8PnkoKmsZ#kj!s1rX{>Fot+hNJbUZO$DJD7jE)s)(bnf`72`PmMxXbNq3*C`C=qFj
zFARB=i6SheY`c|eeq$8!(1V74q|khvmf-rHjeIOBdJ^4>>W7`z{vV?h|L1xBH^k=u
z|JOoAIc3$m`&HOhjm3Lzgm$TqTC@pOl+CLhH$S1~l)HGbb9t`$Lf+K>eRz%rHamf8
zPkVUG7uk{F9EdU2P-*qRhDjF4!t%_;FaE@TT$&2p*bof3-l3ry<o-vdsoA>;dz>(X
z&9HZ#P;<zXjMbur{D7iYyWh(bGt?N=SRP+DdORRB4MY&&OF`J%SuHg`gRy=$W@?on
z%L~UZE@ax-a%-(45PQN#<SfTTZ_dv@Gc&VCV#w(<^%E+T_H&EdmJWPe+75dL15|?)
zDjBYS6ti?`X~7&9^DE)N#aFxg?aBl=9`aN(OhC~eC1+KuX_@<RPC`lgq<C?WNkl^9
zvifG&|4?T{FQw=s&{C*mXvbux@F%BqXV7x`5)Xq^upwXoaHlB#O<k*!>hC|6EdO>{
zZDXT<H(IjZsnqW7u|{+;AOfoZd&|j6*y|<~9~a!*(o&}p$vDo+x|cLLYyyUIY=Gv@
zcxon^BbMwH%1pM>$)-J=6BXi)mmx#9#Nj~yfGXK}Q=vWax5)%$qPZz-&WZdNvJ8UT
zq0AJLg=A%AU+Mz*B%zI-k)ig54npJe=9i6)jcQLB^sRM&QqX<8)MaPu8_M!WPop38
z6w#U6&jjD#jfpksWiAb-WUiY}EL&H}gJ$5^>LSHWUzb>(uBm-zW5=QG&oMJTF+pRV
zbKH|TO9r#R&S_c6DJkaO949JT@(wm#>=5$P-t$_^Lg(pFf$-q{+H2WD@$t^V!YzNj
z-rzjoR>x}`pSIR$e`jgj)4ypbf`dl7*PAGwVW!{R=IgaG|IkpuX>4!i(A3n@@{R2#
zFyuX%iO+2&Drf8-vYk+M)!{ZWn`JiV9e;`1zBDeb+Sz=Q(9@5m!`Gi)OdB%PYK(Z?
z*JKgp%_EuG;<HuVA>Ts6KI(RUfpP1%FS%I-&TZCX?{4Q0rhkpKn>hZpbRddnJZF0M
zBO~98GvtjPVpE-z=z0Cf;z6p|Dyft73~)kU-b?K&EzNP!F)=pN?(E4aC~%(ETeWy)
z>gLz{;s4rCt9-=rasSAGhfrpIs^(fz?v$HbROthjr%vP#T`ZrL1gXaB`1JgcrHRh-
zC!1|5&eD0$zq>ozw+sKT@sO>#PVDPl&47dZWcF+M1nRP%7!bMO9D4YCi(HtZlh3TP
z&5IXXt?`eEPU(|x!wN2+O2h}w;3Z#{yA;GU?ppmwVBF<oitMIC8}m9NGy@Lo(**tN
zw8!OBDU~c&SfAq&0-PMc=>$TDT6Q(~<i7h@%@|#AX42UM;H}>Q1|q3^`7BOQcXi6_
zx+FZ7OO+Kc;Y~gCjyGp~eEjOpG(zq2X-qu{8WP23w00*S7+;EvldRx6*uTFMglDgT
zdHOwR#Q}YxtE+mXZd4fO5em><q`a1|-d{dc`5w@P<?Rm6E-qx3C7Zg61i<*sXsM|&
z%3HuyjxXJydK&LyVV(&BAloM4`Y4=Vj6=8*<}bg1e5bZC!}j()XO5G1x6StKG&xx-
zLef8i5bg5uhBUJ+5A;31ZBdf1EL}G{I~yeB;qIMumiY&T<*wOI$@fG^;>!s<>1@}Q
zEHWsk>VvC5Ys%n~(_o&i%gxEDeS2p5tat#&3!HNXlf`{fcY127FNe`3UF|$%N2(UP
zt;4*Q(T8IxZWB2(qjYdy>}ZuWd}oL9dFS1g$?scICoW&t?XT-ElVfffE+}9)tMLY`
zXm>b-KfLT?fO2xK`p^>}n4(HqsT5<{uXs6>G&huy@ytRu&!z=`f+E~B*%&1_H34b@
z*F#;ifK2qK3+G*^pLD<*TuwHH0W3quw7dMw$*q1$2@eUO0jtmLFyHj$gbR&K_2JQ~
zvGH*-kl7GN36ykm_K}?W9Ac-|wf+d}_kDRu0v~$_xsCHZd&jpE<U6z>XUv8KSzQCG
zq+Ymo@#rNuwiwv_13u20$;AauYA1`P3-PvSuJh9!HLx=XM;pdRj8g3}7b8S4!iVDt
z+iouXZsGGBu>RFCAPr8~-B_(#1<6}hV<!4C8R)K*{1&5gvXAKZamxQH$vN0WhsBeD
zx!D5$^lmFCe3LLQ4C5s8w2^9uf;1fH8ZaNpE^7(O2>wnIxQ3QDPhuDup226viu!42
zZgA*XgPK-D*5kfOh@ZedbJ|U@hOu2NrX?B6RNorMh5F+Gi@_ipi0S2&%7jFW$qrIT
zAM83{s+V+Gs6hZI>s4KcCWC;5o}K&%hzZ2VB-WSH62>Jq;2ki?k05h%f*4V#5Mm!D
z3?K5v<m|xT3(Y>*dFRMrnmnI`2e~-jCnG@vOz=|ioY_Fz(+$_5(^SH(aRM1oqZ1Th
z=z7aR9r5ENfUfYZuTFIG=14lPd-pDoyazgOT%sgsouGAbkp>;ClMu}SU!??<{@+>N
zv5;Ymw6ruEn~BGPaZnSegf2;;LXH%R6~N?LlKJ!-0`2HbOaZA_J%)U$#mEN*#uuyH
zPf(+Qb*8$weR*2OkEJ-+K%ba8Eyq7R6AFsntzJ4%m{ga6!NF~x?Z9|S?bjty&AAPb
zEuL}m<U}xFtCWsc`1*3=3TK`4OK0$dK$*Q9!9Wp^GiV2tb7}tWgnnJ4s=x;z5Am`B
zk8Crd9-bL4=^Y%bhD52aK%M1w(s;sl@_e%NAO9Z?R>(18UPR4Q2{y$C&T?c`y-t%v
zqER3qF4c_QpeB?Wn=|*3Z|*`w898iO!F^&N20X*8o-1KUK#!{&+R0GNhH{1k%8UAI
zcRw;y>Xb=@Xza|=e2@|>lW|1oc>LCk5hWS}-`@;=4nE@elnRZozHoN%JDa+)4#*m(
z5u$zfx!vt=67QI-zRKuu_ShPRr<I_XoD>up-bo3mP>S)RYm=3hp5@>h1I_;cj%(AU
zC~p&;l`P3h3Gnwe)mb#s=}(<2v|eES#w-8d&X>q#V9x*#4@bQVL)i+jsO}TyBA6^q
zfWa2fEx*kE-rj{mkltK&uxyJj5!_0vjyP*|=mr$dl5YVGL<poPeXLA>xIg3FUkLGf
zJ|Z7)k;_=}=4JEdL|sVs{DCS8AV&~j-R8x*llmQQ6Y`jHdhs?^LnDKgdG|hH@8<$5
z0>f%UI5{?fKV%J}iUDqwR+724$1D?EJ9l65*C3Qt?YOyxN|;}O0TzCa9L>{BVRa^R
zz-~|2+7jaf%yA~O3!(27h~wcz^H+`Fe?K-h7I$)u==&>6t+6IFHYUbkQ|{UPNOv>r
z>w9$C_2YWb!XQ(UbN^Ptp_VXWCmg5?*4ZF|O_SPS_!J&a1kUl5x_r{itZLxz;KK2Z
zpuX3KR-_5CD*>mGPg|^C?P$7iXMbYr%jUBkx_3kj*}imYMjxzGWNKc=?|GJg7*wSf
z<o{`&_jA#!Gu#1{U78z<z4{^gc!h^Ft(Hj$*Vf>F&_?XhMmvGAm8mu@0*kuQjLdv<
zH`y)x{Vk@?(mM#H3c_v8E-_*!NiOZ`jH(?619fwAb4MF!?m`%OBxlVK>V*Qv_JV@-
zb1Qx#(;kBFHrtVbK7j=NIK&LALl%hExJx|yTS()A6B<LO+nws$dbfC7EU@Q=6>j^@
zkCO*NmTL{cN|lN@>*kjUHH|GHE1UoJ;hmz?A<T)4LN1>GRq6rf!i6014PQ-3@f3Qe
zI|QW`84D%l&o0}jeV@?i746pCh;-CO(*a&l4zcn~YAq!hE;hD#TiSs@-8+YVT)T5F
z`)dRDZl0Mwai&zRd}=~7`s?p3JMZjtJez`vksP?Mqz8Ehf@l}fS`sp%P<tu>6=75&
z+yP`(Lbk#vgYb_#y2hZq-`vebo=R9qxPme$*Q-T3?=G<FOiU`!lPol2MpDyfLAD!O
zKxKE!1uAwaG@2dAcL)8k8myHxNXvTe3RJ(s^BsVqfWWjHwXKdWz`5Y_v~Tx00MbPe
z^bl*rK1YzVM@A7S&7gq@g+JK=!T?zyuz^kd`lRH@13!yG)N6!nh+9Ego>6e^r7+?r
zyxxT=2f`v@CJR8R#%7!O_}^~M3KtI6%;Lse@}>yOpUfl7pnfAXK|MXaFi=0WoqJ;z
zxLP2?^?+NV`}*E?_P(+O5eI2N!N=?ZT)fF^O=G(vb1JCDb@z}{jf>Y)JPLp0>td`p
z`SzasLp(=dvgp0i>X>Rwr_*CFWhY!!s4%+E4<<e<9@9)eSa*(t@t1ed@)pwvxMA<x
zg>MTQ$}|?@&jMIBov(PZW*dmKBT(X2Vs6zKkueI^W%6OWzWNb^0G&gEH&lyr#-_k(
ziCFkElF@Eg0+SwF_!e?ge^VC~Y|LOFBKX7dNiz(SA1AQOdG_lel}sY0>^mJQM$f~j
zfLm4EmJBx*zLg~5?95Dum@Y!btbpu)zaE$@Jo~lCx5Y>KaFcHXxs-41$!yPOvATMe
z@pClB^PF+9cP$MS=lU@C;4TD(asR^Cf#RJYvtdoBU9TMgtS-RbIWsAiZb>sh{ukcY
z+?$({f^vRq+Jl|*N2|pv$q(!R{Z1&};!;Wi1KhvWc|Cc!p+fOipI3L!o31OBtJG4a
z9p@0(ZwabJOQ5m*5Ia-QP%~iS)nE4lSmOEEONXv*Is@iIYT?`zXVd$8lA7#z@DRHx
z)UEdKB0q{%x+^rjeS6T%P8IF;@Xduh7ykNCi&;z;#!*f^;vGSpL?q)y&tN_!tg^FH
zn|QUetZo<pSbP7fP{w9w26$12JxKK9Ru^+j>Zi9SThfl_-dnD03BqXxd`3CTA=dZG
z%ZSY2%?ICR?jp#Pgt68z^Xe1xOnT3UbIAz|!UHX@buuz_<Lo(?GSsR0;$U5HyH>w(
zReFwYzd3%N@OEYw*f1dRI}7z#QRS0q_=AOm-f5Q88yh5Y`9ezN=5#z-&=T*Wkg3e=
z{(bCOLzqaEsHqDP$b~XeDqL{cuiq@~^&tus50#aM3ESPSo?9-nAy!D5m0+h+_0Qd(
zcdnGYK8LzLMd^mL5w7eb<^cWD#9Q1^q&ZYO;hat-?Cl4lanYK`r}XBTdJ7Y5I9ZFL
zODj3ke4nRz)arm5!xVgB;2!>*Am&MX(Nj${I?Cv`F2*)6H7zg33VH9OPHh%Q8FY(k
zFO@s)a_{FWFSg{>7oj@~<Rnk@DkodjUE$weaE(Al*+nY3j@h;j18`Yo0B@5aB{uBv
z!#wsBDezHWZKLzrDZ(%M5vLQzoM<aaF5{dGpPvjrpJ05zKXgnV{NLAT+0-<~!2#S&
z!JST14Vy7)I$IF1H70F`mJW?+=;pG&*)3g?>0jBEhx5Hapauz?uyMgTqZtNCUodyZ
z>8^li=Jgj$q?RsGT9-1=IhCY0%Uw9r#WxD7xB=$;O&9M;@MGFT=0Z1|EOm5f&`q4i
z<e#b!UzdgWGC*f^!$-Ls{t0O)oQxn9O**{NRywVK#&>*+01m!0n?On`!x<Oq_4JAS
zyFs_7w*AWT&y|4ljZIBKgr!cgE?TmpJ}Wp9_qF8{^b%DY)U_Yb&?E8eV@K8@`mZgQ
zU#w%VI!k3R9<`6fa+YH*PACv&V3G6e(*v6b63})|kGVI`BKuH^imadA8yE7db_9qv
z=j7-Ls*Jx|VE^DC!ea-wRDFGOuyYC*QtR8nD7yqAO(`e*Y3uC6R_RS-&_xKgOQrbQ
z@28m4P^>xBg1%HZ+fukSwbrXO=Wr@A30u}-a9^vCZpZ+hv~3}Fjc1|KNH9t9q{r$8
zY@N-5B%li~R|zvNu_wvkr^ET62DA&Cs)J<<qL3%`c?FFV%ov<0dM{nKRL-=I{_0|^
zom<DAk-&t{Ll0P<RC!5QKOTfmqL4yexK}3+)!#vpoci(1ukg{Rz?E2eFrYP<z;9$;
z>C1^0#W@VDQUC_{Uml3YqNpg8dC{|=n56HsqyHCkbiYO-WO_CgUducGL4Pvb*y2=U
zEW`OJs+R#t&2VS5>}rlaq!L1@vBfdWG9Dor*rp+wc%oAffN#qARO?I0<}?JJ$<`jp
z0K$V7ZXKSdJM^59*KyiXRJi2V>l!z%$u|6%<bV6Nf8*8|MTyUsj+&c_r(39=-nhT)
zfv60RJ-yL9{dVW-K9&C4J|~lJs~Lsl9C=BPXw)p@w%Gm`_bt{_r`l(V%O7p0yb7Bb
zo)+cmxt=#YINRpHQC|7lZNmK~l+HUPB(`GBfFie_2*NJ7WS_e~n_raIVa;^u$hKKQ
zoWl>oG#X3zmb7GAmJ!V_DvQl5_a8jC3KN{nQp;5*+x0%#zt)%4>2iOqFTe16Wsg@Y
zE$GwKX3DW+`geCamZ!kbbajMb@k1|t*wkr2KMl_nA8fpcg5TJs`uKH%Y^*TIxq@BP
zJGr=4z-Ic$kt0{)O6*=iZF36-fdtz@Prug0TscAIl$_i#_?hYV6#nt^5)bfIhCvA5
z!O#CYgd>7Msb`{sGfzr$dF~YpRdD5f`QSujBYnq{%v5Xf6XP#nWu*nEbLo-SqFNdn
zx3K$<Tmo#nKP4~kS9OZ}Vq>FfQo{ulufP2I>rtSzkD_eXXf-#~kF7VMv%x?GHc(4p
z<9;2+OIZ3tu|Cgl+HWGl#dMayo{}4wl-+5;s__Gt-fW*illt-08?05y+2yV!GBx*C
zoat_@KJJNy-x>H_O8ArOYfeVZslWbue3Q~s=f{=@(=inLOM$)qxync*>Dja2VI8xS
z1z~+Hu&(R(?ydb}<+6GcA?TxT<9TBD^Yhc7oA}*~vJ25nDTVI%AiT=1dwDe`_Eh-s
zy!3m_nz>u%pY6YBq+ePGMfTe?qths*UVjPk@;Y+`&c=qL*e<}LIQsI0$;SDNdey-%
zDM_t94+K5}Zz+YN=|514Y>JGG%(5R~!}sg_@pRTKp&xhSjGCGk>e*8{vsIV7o!)^o
z@^tCD1H$nS-zhP0eQwosc6Q3g!fs><i*~m4aqFD}Tg=r(bp)D})U)*jnwnFs3k><6
zblXS|GUS$9Pu}JH>o5KrI4b5aeO$$I>&_kDFE>IZQnj;xiZE2jj)>UqYYd~5o48?}
zaHb4|P3%7L9e15vT*zjWO@fycL(O6Mz;Y#3CpSlYpJ&^Btfbj8O*wW{<<4ro3b=aS
zf^kD9s*z_R*IvBw&$MXc7gukE{ml{<h{bK>-`GE+r&=3Y>U7!P4cSWjJF*BM$z8BF
zKD_v6y(2pE&`l{9%~Fy+jL#tRHZ&P#b&bOwVbj42KeN1tRQCi<Rwzf-R_)8%V5z(f
zKHo)fk_!1F#^#--dS}8Rvhu{&xZZ^REiW-~rKF@}^{RJEsxPT$YYQ}Ks*B>1w4_?+
z8S=lZ?ePks1<5@aG2&de?ty6S>>*j!+X`{ghK9RYUXA~$X|p=YYoFVagJ!<XAs4G-
z>w2FU)of!q0_P}+|5n_Yhf}rxeSaA$2_ZR@si@3T$e1A!M}-O@NkW>;nL;FElu{HT
zLm?ZPXEH@NIb{fmZ3<<`sdSM0z5MR3>v^94p8KDBUtQ-qr?YK)t@ZtW=I`sx)4s~>
zFA@<NssYAR)#*4oTH1z#;cXIrF2MN_8rXV?+w<r?XJ%Wzw=gZ^6*O%WYy2UoJC*fz
za!QqH!D8E$2aKjg7ySJ86Njd{J$$C7rl%vIN<g=`8hByzTL-zKzyG|xx9@~xkHPnM
zCn!Azltus8Dfzp*#{0DKU-u79E(`A5`C`3_XNv@QEx}V$j)j*$^idfrTx{b;=J5RN
zYHn;Zsbkr=@lM+z4%6OpcQNdI&@2Tq*<8TEY5*vp6C_{~Vrp_t<d`rA76KgsO_lTx
zu>Bc7_Z4l@Xm=14A-A*b+Ox9Unk&FSO$ahBTj?Jxm$a(n0`yJ#r}O|L#U#72Cv9$U
zG|NLmRFqxCDD8z=vY@+so7#m>pMA|tOgM?;10gI_p5&y_=N_^3@^`D;V@KrgX8$^!
z?%8e|mi`yBlGWamI~&*b=f8PD4+d<2i^C-JY5x!PcN9-MIpuj)oOE%K#cvgkV$Xj5
zyrE`w<qDGCeZ)4M6=YZupem+TR$5vz|BKaUmT$8TRHV(pTGvSp<ZB`-Zl73R*4NJi
zvi$y^^oxsg!-m2ficUiW6q$@poLEN)i->4K0y!%?yR*vYs-C#*sKcqThQadIFI-CO
zvw+9t`>l9eR(Kp%y>k6wr|Sc$(@i)M5(rF59fY$$vY})Jc3E~Q-2Aaaq>`Uo(YWWu
z3)$!9MT~eSr6|tm+__yq+(|<kfv82i7Y(LZnGkGM9|Z?lcLE_92`wbH>7Lv_Cg!ko
zdu#mF!Cu)&6meQFaM&l+8`s!@B<)gR=TdlT%&mR&6V@&ze)RjA;b#uE_FdiCc|cT5
z><-kve&zH`H(FTTl5zsi=}nEl|6Q+pv9V`>AtxM7ZkS{qmyGQ7i+Wd7SjYjZAuJ}g
z_t7d2FomY23F%ojs2KN*G)JFBs<(Rq209K{Fk77`mNkB-5*{2&F*HjMlaiO;N>*MH
zd;UsL3c7cX<L(k9M6Ce=jWJv4>nv9u@&9lN)@^UchN~_@g8)l=t5m^~g5Etl?@dd&
zu>C+a=H8oN+Y;HcQ+YBkoIfuD4#ZiRRY-Q&;5Wy_y{P5>Vh*>FmX>DzR)!06gZG4#
zX%pR9UJpNP=`3%Z{QXbv$J<@X*6vNY6^y%X#Kf$<V9VZp_9^qz_x<)L;=u<zw0M!W
zc(5_9%AJ{kp*HJ8_U+i%U@Vvs*!3WtR82(cY<zrC*b;)MQ=D_|uOBS01{Qk}UDAB)
z`DGku@SGc>0XCVuogj%EdUC9eLz7j!qvre!56kAw%|Kk-fv1MA0+oCNXdN0CFN&{c
z;u2_KOa9*7)w$|^6g%nr%!EjB({>)7NYK}Klw9b+x`r^@evs(iJU_V?Mfts<k&)Uk
zAG@3_mg+~l6{7NrC^R%Q<buZfae{RjotQ|4vjU7&8;E&UVBTQ>yef~iarpEXfjnqd
z<t^{=r7sw8DJpn&3`|T7h}i?68Lmg&5FVoB!t#3Z;l{jN(gxCg&d$a%N|-O(@!-O4
z-D%P0>0LW_IxZ>(J_!B&I+UTfL5l77>yANw(Es`Wdh7nw{{0d<_IU_D{@<?{5sx|c
z-#^CI!lB|B_WL74ZNqshfB%%WwrnE{9^LO(yv_gDuX7|QR#lAY#v9X(!w}1=sI4Me
zl39g!e>!m`8U#Si%;Qw#KhFx<?~kPPOy`5R+0fja3V%cO!x0HeEagA0{{ZB~-V~lc
zCi4M|lmv4r-LiizZ`0W)Zhtq2Gr;&18_Z-Tu`~+8Ij0;KW#i`FsL86z%R*kFlAhqv
z4I0}22#ZKb=^7f|Zn>nN&W=u(2Kj_IId1~N_Rq|R?Vz{h>^rT=n%w1>$V+Jf@lgc-
zn$qQHgmFjvM@7vCSm~L#W$8(vSV6nu$E6>$Sdbk1pigx7NN!I*SJ{RCe5r)((Jzqq
z2&$^`5KcH!qKaZJU(_ol{XhSluiL<{@$~I5HuRjEB3~yE+HTX`%;4+kz^{Sas>-|e
zRFr6R@p#JOQx+zNn`diQqhQL;01$<-ZayfMMg{~BxJ@Hq&Ly!_X!h0k*R*te2x*Vs
z+iW`_X>`E$6Mc_kQOr-xW^U9kHzghkK|uCsCFP5e^p!zXq86PlsWSd&<P(3jp9hOX
z$QfAW1~83ee7;}w#gShaVE4aaH{_m0ap|7)Q3dE!bicaqqBJN<Ed2bBl_yP}o6!&s
zAIjx%$LAptSNHnQhc$P;TINMIPjtG?4yxf8H$mJ^NccSLpem-UysfUTPFPsD7P1x6
z5J@;1vbMxnV#Z63!7$W9W<-D?aPiIbw`W;dT6%gLAxj&GgWC1zEf@fQABa&0Z)xhy
z3G84Z;MJ=`NQ-c>6hs7gGcg3Z;T9;#h`sMPwNv&I#EkJtpWYYj(K>KIjW<dcG0`vr
z8G1r!_wKJ7He)$ISs(DxFf3HHp>%<hKrrd+>&wc)al@+0`wV1~ge4>zaR^^mrX91P
zw;w;AoiW`0JS$6-&_E8=mLW5<?1@&+DZVXiN~Z4aMT!$Mc%LfIExAhc?1%t7u3@BA
zU!CO0s~i<3H=CXr=DzZwKh)6F6rRhiW5r$J{Y~_q{x1B1dgLWpsT`9CK)817*g=C(
zEC@3i4;3zjhlVm>@<XM!%yp07>dMs+Dqw?ujKoQwp><PEJoS?yFaXY*{28e79Kg^r
z5cbgs=PY8MrjN|yl0*CbmgjW|4bXiT`_ODsS9kshru(}-&MjL6FZV<HVbS}VjE;_u
z7B&%b_}wKcf17kCY+4WC?&9J|bZ54y)Uz%}`t|E=5MeIj;Pp*7&xob*@$v964h{~2
zU}R*xjR*ARjp4K7qU3VM?70r|+Q!O@!>z9M$<LoN0b8kKY|Q@e_b(AcL)Lg3LUS9G
z1mNT~s#5t5^FGEIVrEkR462PP;Nx+@X@fR@EYZvGb!+P!O26buMZ@ea@CXGl(X#2@
zg9jE~_lR3MK~Mk~!azwQx^XnI`oW}=LeoakZ@IMP+U3DY()3_qN92<7Qyr_?5XgGl
zDrYwp2k|PwUAxvHI=!i%u4QHx*VSA*G&fvNjaGu%d10onX-WLkj=T82&O6lVIo6(}
z&{<kGwz77BZ|MnIVuy@O=hO;lS`90em6bQZSvT*OI$bS(S>1i=c;d>YO`C%6OIU=T
zZ&TYTD0uk3KPXBv?I2efWc)-ira?|bL?qBEx1`*!iBHv2Ad*Yw<;6(YE3I?q(&0G-
z305n}Nho`nDf3}4$F-`tt}g`CuL_TxMQ^ORMrCAY#|Qx&t}gFT(CyEvcGYC1*L?bR
zBUV8{TupRzbRHPj1@4}U39s7P<Q_e!si~R6iZD~%-R<<P?2K^z!{XVl><5M>CU)QE
zPlA>Hz7kTALP>j3Hf2ZlW{|Ook&zwP(P_0Kun!!|D=QLB{Gg#b$UQV);;#A2%p0Oq
zDmezO&7a|7UjiF8H98Ns++(pCg8GUNZjq2sgN^dh!uUOZ-9<%{fuK(}7Ww3+JiS?%
zG|hS}q@8OaHx=vFt?LME+EUN?CtXX7fdSxCJ$d%*)vo->Z{A4QD@c?GVLjFwyND9(
zVafIOx1c*?gLK2LWMX_A8t!~Z0zJm0&QUIl1!0AUZaNa5+T|z%(k>Y|4=y8g2?9p{
z{bvuX8i=_bH#P0&$(9jG+>WJn5ZX&879LQ{@@a7vUAE#H-25VDouUByPKL^=-AK)~
zB?VY$@%to*t)a0|GquyqpahCgOkC2*9nTDXN}seLaKJ1^amV!ch6=9*J0kDAjLg;I
z_@}L(`%9z(9%ed9p02)lB9b%;LWfCz91{lDANH63(wBYNKOj`|5)%=$1Os9gFe?qM
ztXQh7Hdk3iBj&fP_T@&{eE>MwOW~Qwogf-Xm8ga{YD8vsskNSr<Z+6SCfROBGQP(^
zm5-@qfTWbvX7Jh?K&y}%9UbjGp#zPq#^5hZygZ;!J}VkZoF74CMXe?Ujfx~xRQhMi
zA5vS3WffgU8LC&lTg=bm8?gh*{bthb4YD>;K787eJSkFD;)@7#%i)J|b-+IvnVU1>
zyTXK3R;rSr5)l9hDjKqzJsO!xCkCXE!PJ9`=k%pd9)bX&%{7&kJmh@`T$ARVoA7LZ
zN$%h>m>a>^aL{s~BB`{oxD2r^%RDJ1C2sKU-Z;pal{)Q!`rxi&y~~#+NijU47HF4w
z<ed-2pC%_0IB!7^^z`)BFowe7;x|mPx4u>d1zxcT;zLJrSeDU`)L^AgfIyJ%7xcv4
zf9-*ToGz4YCwJt~>})hhuVG)r83NmR?*PDC>hnXA=>121grcxu8xJc>b?vApV=5un
z8D41v?SC8!u7<DIWW51MXV{YD$rowe3V)U)o-TFL9PpT@9;~1$$RDhSxwwG}+>y$4
z9|zos;!n`ZG3&r+u0cj7g~Ew~m|#Z1go1Anyjp=5mqU@cOfIOeO&68z4LXM+i?ecP
zZcc6@7|4V!KEx{b570D)Gr$==nJCvZal<&11188|+<jsEu*?Ve(a%|T1xi;3^0c46
z4I7J7xf&TAjh%03Y-}U@0TNb@AFNg%uP!&+mIH;?W#hvTD1eh;M+gC!c*d)<gGF5a
zsP_;Wp6D}9UsS(3lVH}3UP{~eNSo}sl<zWh0+!`&EjI7FQ7SX2YVJP%bH;J+(R=5U
zI~^JdQS2*>yH5rdX}1IsQ`)}#`0=zaV4`Qf`5`M2s;sJ_ArQ#8>Wd?A(tx44{op|e
ztRxj`dmhok@_5LvT3cxelC6RoaY&0BCU4O(`!#HM(s(SqST>{6A<uu+kI=xvX&-FL
zF%-@NV$S*95Q;t$<1kZ}g$|uBM~ks0Q?YM}0IbJ~k^6s_4UdflgJVjN5y;eGT$d&N
zx^?jbcB<y(fWUiVE(`XKnfV|Ngb>Ny3>I`)%P`io1QgN&5O77z`y(AE0ub8uSCu{y
zdu^bv{}L6qOQx1hfFm|efNuE0Tm5Qb{L|OR5!S9MK6Ma4pb@i=^!_b3=9lk_#<ota
z_+Gn~fBAzlp`oQki(|0Yfqompm6l#xDsFfFJXp0IZ5MIM>6$2H4#S9c9NuQ!YX7PF
zJTK4C_ZP+zEDW}Da;}FT4|#oWn|(=FePr}J>bUkYIp#T>DgmS-wDSyxJ<KA!4r*%?
z&wie%<x5iBhiwxKv%s`nfr*SR^wR^eaQJVQ8Ov{D&dSOXAD6eTB?|f@RsI?D9APe)
zL`Vc(Bidw-r|EUb2OAW;aS$)XBs;}8vmper2`-~?k=fOoPD4*GtZ}j3`CL3SD6>w<
zsI^I}(OLH|WvKKSFrM3{9CS);b{}c%%S_hdaP4lHq@$hPcP?$*xX}}B3u*sfzRyop
zaH3SiS^^C98MFlA08V~y6xT%RVa@-9ClHV7D`*5!zhMFqQc;{qieWP7vy$9nQ|CJ_
zW`}U|pRFG#y^oD)4LC00sB<g3%kl0cQ=;}bm$Ie1a$-?RH+9C9%$unW$1Y%t3CHRe
z^z(!ISkTWTC`==usMu?xgcHJqfs~xxfEQ&g@|PGt_q>|L(be*#9Zc_+@RpMUop#2;
zcggj=ZGF_5{cdYBnD@djy)>vUF3tr0z0QdfTcIzP4+uDd%_qoTuqm`rzRZVp^InMG
zf319R8OSk(ikONVdd)nMDy<YJ{jwy()6EeDeXT}KiTd|T%OwW9E~8Z6l+khOg4y<H
zLFfK{uNel@`{(1b8jP-AzfN_v$S8K!eWaJt1{Xj>0LD34s7fYruU5#kPlygSn>vtb
zWo6~zKDW)&$A{#}a>?2<K>y46cX%!h5;0O%ive|zts}>9<nj~h+c}ehpSzIp&i{mp
zjd;dZdK%2}IF}mO%g(rE=F*iUC)Z}F@?OGik6x~T5^dGwt%<gxvF{%Cu03VQ;u_1`
zSC*IOaQu*pO(`rT6>{WhkX3Go00S#M4L3KpN9?dDLexJV^F!P*^6kSA`P4Fu*$K+@
zsT@3X;DnLUj%w4ceX>d9ZBDyICZp_o7_@S{bTL7>XE1P@2^V!pU!Q&T7y9W_ANC-*
zP*PHw`}$Q2UcvkO=P;yZDjXs6w1;j6KWZy;8V)?B9GlqUnKp!AuPvhjJM9DS;qQg9
zG9&$i(-x7>U%W6*NZO&QIxy8)Ng1NJrlzIQLsbyex9Og<D3g;la0KtLASbf7oOXO0
z#PwHDVp7sfp|8Y>*>IL@VvAxE%z}?J`=m@#YKcY3WxmK8<hSQpOP)8PWXPrNrwYYa
ziR$|?F*e8e(68QtO275I!qpKLnG1jPPkyM_7sap6a^=bucqdL0*X&u5tA@f1`~w|<
zSa=KU!8zGPtK4Sz`!ABsCn6^f4i;Z})FKxsfR@01=|>^ieXX^yx0atsaYw|~0q+YT
zFGZDDoUL4lIy|QMsG@^;M9Ee{;vH%b^5T}IZ)RL9$r4#^%`as_Kth5Uf~Fo5W3#hT
z`%4N9AMAbY77NSgoHU2z-A8fTc^N+DRrLD2q}j=WfXiChKu9~cClxV1QmjRkV%U&G
zkxco4{pkCEgx`^&iV9r_nPh+Jq=Wn(ig|1*ta$VR_|UUMwbyODEp%MeFs}CM_3I#5
z7i4;GV0g{5yZapx;!+p*)V!@>v#77Ek;wvWx&yijF}!*x$5Sg)0QG1BJgVVmSax<c
z83|A^=0OK7L0g5*rp2*!m?+z|KRPT-)6r2B88~7Mjnh@$Td!1o<HSy&Ay9(m+G`it
zj=Q6RyOSw^erg2N5}gN4G+CXNzF#8im?Q=L@TTyx*YHw}6Ua?aco*$>tRB5#Ghb-#
zw3{-7{HQo7Omcf$qNY%$pZ|#gCh@34S54w6atJ}!+|GbG<=xomC~Pu+iTm&o@*c);
zlHS4V6;gTBEgD0#QUWkEK}$=^%Eopbp5S>mt=;2m-??M%-u)s;65$X>9Y8y=p}!q_
z><x;<JZjz?8ttMpU<{Xtd;}PJ2m>f{KYlc0^Rl9F78Ao>VG|h@)dZM1&AZ499u*I9
z5_c=i!6@hWjv-OmT?>&T$jEPy=~c8k#R9@Jj4J2`!?#9I-6mr7*9B|QKp5>taPS#K
z#hz&4;`x|Hc$H=45CZ5o!H`owLYzv|+FAf^1Gx|@J9{9UMtDCopKhTI(rX!2P&P7v
z{+cJo7m0TU>1+u1!F-tSrQfjOER<lMQ7BEA`A9U`XOEA|{1)^EBhsDuXLT2U{?tq{
z3~d&bmS!a*S+Wy`QlIiqB&9b(Lg=1NaxgP825P0Se$0-X-RXq;ZL9ckVPT<>e=h?y
z`Y@t-m%b++WXL_rO@`cG1#(Qp_M<H0DNQ~>jE=mcbiIYihz3#5yyVoQzbn05N5{vz
zA9dVAPP<lKM6VrEu>r^<M-W#iOr#nL&*4<)8Fazy-nvb_+lba~zSt4dM=<V+>jFhE
z0eR@?(S1HkH7b3K1X>4sBzN0I;lV0g><s|M<~2n&q5afE6XqluIu0L0u0@7HI5OHY
zBWrmc7N-}wEICRkU-DhXT9tFT2eX-uT~(|wFrDnl<ww1M2;HQ;e+mh$pyU&WMWZ0n
z){I81SxT&;PIRHjN)<YqNc<XL^yV<_MpjQ}e9_UJht;G)<%6^-pxIxqt<~`K@=8Gn
z>xrdKmf%oN=0#Vuq#^-DjBTJtAH<VIjMG`>YLAq^Hz67U094Mx`27}XFyqXKW6-pS
zNKo0ZfvP*XkM+}Es2nl<IzL~BK%OkVA|SyNTU=T?+naZ$7fL$StG|4(0AGFnHRC7-
zvzL~UK?MPP5h(+L1sePf-YmF#_j(*&u7m3*mHPTl-4rXuI?6o$s=fW7x3@BMi4nDL
zMvf1SDRJ{c20+%dk;~t89Nh-JCk-PbHdx(TIB7xlIBb?~q7;sY7N_pNABBROh-)9V
zrDBmH>)eSs?{L`Iy5{EQv2*1hiEqZV8q%rOjkFN|AP5i#IZ*zZ8FnCV_bVgDy1(@$
z$#tbIYieS$Te8?=_~kn2<J8r&c}2lYqabk|Inc$A)x5}Fl33O%tUL{DYsJ-f$smez
zzyfYC^f^<i+nI78#L&_*5|OZZ!q+w5F&ab$(4nEv>MD?hAh%u*>j&>V0wTi?w?llL
zko6?@J{&5#Z#SOS?;!2To*{;{a;@VWEgfAxB4eT!=0J_-mc6aL{RraBPlHt|kW`(S
ybXvO#z5l10{D0M!|JUos<Q0*x|8cqcU;ana+KZg~s#nQ5V7-G!v?*HFf&T@vD=7m2

literal 56841
zcmeFZ2T)XN)GdfPg9!yBsGtZ4isY<<1j#vrNX|JYBM1_tNs=Y!99pu%6$B(Fi7g5=
z&_YXSn$$4+sCVX{zh-J?Ud=zR-qfSKWy|e8=kyo$UVE*zzaS+AsZ+;kj+2p*osyB3
zP$45b<UvMu;PKJJ@SCxi)L-z|;m5L467V<tY>L<V!(Yc7q_v&N$mjx*|MrE?ESr*%
zT_%%}xTofk_;bS3M^$}yZ`u7^yj_7x>d|2SqRYpF`P;7^J#h5+_ZtV^hg?p(v2}VJ
zUChNu9(J5H?D)|ecKm|R9(>rRcUtMNMCs*|ulMhN*=NMUJ`tISVIa*Y^qE;F^kn&p
zFoX-gpBKT7cZ*c>9ZN~{woBw}Ir{s-$jDAR+wjo+eNFbxO8VFSKmWKJa`oehzrW`U
zK6vZz%YSa1t^51(bozhkn_cfZI+oIs2NG_#Y_RhgH66_Va?#M|4B`D25pQp6|Dz>T
z8DpUDl4i*D`*}|5(hRtekzGx%sNgoCj_ojwJ`r^=ETgZlujKpvN6cG12Y$b;^yQiV
zv*#aR?9Y;q<dkfoA}o%fvzqt){=i+Hh;N7feB&on51w88^OEfG`C|ruU%pai^1$6d
zj}@xtYs~L%<A*b24f4!7<5{j;xtE)pyD(l|ZdbP~+4+`Je5}eglJmNW_oq*v9x5sh
z&-JFe4r!aSu&~H5C%K>wpQL)%NLsWXt#!B8awOm}hU9_b@aGbrT@;f*l0s&7Hm+*S
zP8F^a9Tk;<*_q|5*<PxxH0`+1xM+iE&{q`Q%&2jhPYXD8iIs;(eb~Ue@B+II6MBr!
zPb%7abv`>qV{ez_+9$(|+|oS7x5}=O<mS8l>7tWhlDZYThMboFU3Q9}POP3&kcx^*
zRD68i74ZOzt+jbUx1U;>qw%+mo(=99`Yb=_DT@#dxbG;KL?hsoL5YD&7m>D!nzj1&
ze3KsOgS$UR%GuP3?QF#r3t8FOM{Z)>YAgLnULu>*kqaZ`+40<#X;D$<ll*r#X+*p#
z;3+fG($W@|f+>tcC=;5el#GmwJ_~McZi>1#QMRp4OE4J!9_;~{!>(Q3VtY0ULW|st
zECsEB41QD<^=oS`k#FBpl_e$E)wE4xu}5t4-SLwen;{`5%iZw?Fs}*k7DZP+Gqx<c
zT`~@VZ_kOI^s??*i8b)bZR_YbQQLTji>uQ#R{w(2o(|g1_RgI<O?K#-aXa)T&Tx;0
z`+7pBSzmv@ypd6AH%5e{J}E*RH;&eB@bUa$E44tyy61JoX=lSRW%%2(6CAf&!|3w$
z0$)xg*f*3`^%m*Zmf+}wJ$kacPn<Zx76pSgOCn<U3U$%sBZlxS*F{7`3Ty*c<Etm^
zQr1r#KYqW;8@ZTZs>GIX(AxI--(Q_T$>@Q5+Z;%NgIn|@i+Eq{n>?o|oC*{4g6I3M
z33tq(CpPKgdQRU=M=UPUxvRIqcl%k4_xgxAkN3JwwH}AQFJaINSBQQVV_54}c=!~}
zd5x18zwJT=5rSHwcKLOakuP85^gX6tZmo3OYPPDaX$hfDZ>9IXX+aIE8}2hVsA(Ab
z)s4~j<V#diQehaKkWTT(4^Iye;O4oGKU&UUhG6YmRn}S$bJ$`UL<+nZe11Kx^nrV_
za&ng5kK0_uT2FtDX#eu%<cNWvUqir2>NGx#57(E^)*2bzk>`9ves4OZ*obe1#@7{-
z3!XjOFQbw!8M-#<jkm-!__DIFM2s!Qj4f)wOswNE#kU$Z?h_~6Q&5qSq^e4nc@>e>
zFI+>bLxsB5%ilk>cXmd@du3#Ts_Z86&M-=9nXdmFNq>6aNPw#V;Q)p3JGg->Zb%+C
zRH%CI-cy+G;Naj2FM?I2Wq%8+_prXZ<<GCBI#o7tupPMFm-OfaU8Ff;W#NxEacQuV
z4`UN;SnZTKRHT2&WqW&lQ8k=iq=1Dkp2s>%z<G8K1_!p5Q|&~1clU66G@I+kLl?CO
zTLfG-qg2?7@x}$Ml1EQxzr-1f;0JOeHHr;v3Ci%z;Bxt}Y#y@WI;4Z9InTGl_dz0s
z<#3Tcs@(vqC`PJKQ9Y~{Fkv?gOUYg&g~wXsvtSFAe6sL}m#E*)#=Ds@w0*HV##gh}
z9j~FR906Y~Q6W2wRv;}?Ha1T+TR~%l)3{~s*Hp9m*T{3sG}yKv;g;VEEoarr>nP{d
zeEnLtp=+HGd<vX|u3fu!orh=83iti>Epo3B;@W(_FGu$7&+_h_nAHXoLlm``pU;Q+
zgJtFAY<gJ}dFpJ=)1S{Y&P>>$sktrQsIRY&*;7yzk1g^})VP+n8~AO`+#`0+IK|<L
z8z))Cyw>KPnC9naWc<TzKRG@+^yZ3qNdNpvs$qG)3ANOeJgx`g8DUq9pDFhK+Qg<@
zg3VayKlSorUS**JX$x08#7jpW2pj2`jwmBzGb<+T!Rsf!+n+8PzNKW-IH-qL-sRcu
zFx)*`;Ullmd%AFy3DrvVXHU@~d#bKZ*m|>|yerR12;;LFN=_$eB4Su!5rIOX_Qy2-
z2ovIV_;we!X}G%*`({j9R<{53t%jWU@A(^X5O7T<U`vVmZ)3&wav|t%vNK$~m@N}|
zUAr6Gox<QS2En=?mN=tKaCrDIgxm`*dp5iYlYZN_xV{}&A`&~2+VuGvg=Lbi*RNk+
zfZ2?lu%n<FF3>K&DwuRHBP{jNq{oj_kdlUJDC3ipm$=G!<B{lRonZ;<TBA^hvSJ6O
zqi{xw-jnIuve!(s=0Y%A(b4yl&1U8EgkeK;a^w1_^;Cdg(*vBaz?px)`k`Z!g0o0~
zjxqEn5m+&vyKrxGjD5qVS!QPD%Ys#yT%8W<p#o()JG-703gKT*Xt*s-SIDJ^`WDPU
zL|rKB;5eZ(DWvVP@yiI(Q{xP5QW5LTk4G3zvfYb^3{`5}s?*J9^YwI@g1SdHaqHW2
zihw$95y*@FTm30lt4Pvx;=5GFYrmQ)bV+aDzRkfm?ioBkLJ{_rw`ocQy&T1&x<7`E
zgJbAR99JP0c1+0U^-H`qIb2=*;;5+T=*;imzrU)uWZ5gInCAI6bYR{<=t%Zik+Jst
z{!h?Wr@dc#TO_kJX^U{>T+yU(`a07*h5v@QmaCx4+_zQXfzE#6)z-uPX%9|VS4)M{
zUy+iEVbIso(OE!`1KcoT_YM~(pFua03M=a%P?X@*1BkEstc$9z7YIVa3;(*#=z22R
z9Y)~PC0@C15n^IS*{~Y~f||Ls*Lif#rk+F`>YA*}+MIIZjOgBuGsIyT6|~*NK$&UB
z8{3N)FIvy{WybS6rg^RZ%#C5!Wrfip4{XMkI%5^`6)LbUMR^uU%)Nqt_hz{CuOUus
zihn~4hkhPpP?<i66Kh|M!{<@59LkZ`<p&D1xh5Q2>2yjTKlspF*g|DHQufJfiQ1AX
zxYMk^8k=k_vImRec?^X)_q)#ZMsaQ)9-adJrc%2JLF-Le4{8q|1|zv2<}V|I=^7K0
z76m?I95$lK;E76qb)%4FnOB||3y!y{3If+2b4q-tS~36dWZxES02UcR;<$6A@3vc#
z*Fx@@*R^*bEc@EE6PH^Vw%Lg=ik&)sYrRs>t1Lr$L#Vk1-a8tfqPl$-f_NG%<jkKx
zZ2;KhR8$OLrwZZjG~VY#khyjJJGTsw7M>%)6Ul<<;i7%-?mhQGWe5le#0xlQc@dWi
zXFC&ck&2?ZN{u@+eJ~>@y~mhSe99{Il$1_a*yQxSA7p2EHMUqKY5LH#trCJi<@xhD
zIXTh~9z1~5Qc6E5BhZ~#ib}h4#D24Zf)|sEe(~bPs}|)6;~Q!r+S=L(43aP|D)3&a
zZq?ZZXrN={>*iL)6F+K)&bP-j+#B;=X=8T9Cb<udn%J5?*cJ|I;Y4Zzvb(+#&(L8H
zrKNjd^ot+7zK~l{p$6-u|NE!cJXQlYZ~^+&)zvhDE;$CCGn6ScGv{(s&sI(f^Bsvi
z@3iza{%o#<BX?AIVM)pD0<T_aRt+rG3btQSY!G54Q}nf8qY&=tgxn;AmmKtcmOl-h
z+ICwU%>*F3T|i7eu^A?`bTnQ9Q)(QT>^Vz~^XyI*sWfRjyEsxV)f&za+cATJw947y
z<?{0@XOp*@K<aG0$Qi6mMmXp1K)JwpwvO<m(@Yf-B@aZy&NwcqmoHy>K@ct=5GTEh
zyDi!wBBtbCfV)x-xk0w+9w5(7a??4O7G8(^pUr1z+p*b5j}ycxU)5^q7Pl?|tX8*p
zr}0n6Zy%n%YW-eV*7`=QGoGh^XLGf%gSfi7>bkp$wFc~_94c};p)(7h-d8VC>!F+0
zBUH$q*6nT$y>zjnu-X}`(*eO@AefTPWl&vnen3@G9Tu?-ukOni^Sh+&{Mr-yUOM-e
zDKYjau6dpfGCqlXrzcyl4%&;HXJ9Da#y1+mUN0q}@eSKu*>c3CiNcw?tC2C9yJiok
zx)^lN71gagwDz27kLID0d4OcF7_sf2<s}O@4I6}YrZy<MIH$v5ca7OM7DCK>$a=|h
z9G<FVLGn3Kxb9ZtP8kp15=2Z0S=VH&;PWpp9gV^Io)p=dOTQx3I6FPP5#m-{1jBUg
z`gNV|)s>a|LnBqTx}WdxcqXe6sD76&?;5Ur)D72n_;_f@36@-U4FYF6%FQ~Dvr!YK
zBNcTzAYrRR1h{(I)Fp(5r~UU<Q`ghed)~W2BkJ?MatAgb`?PdNU!RHyVL-K)yrcM0
ztI}mhN5^<k-<qYyJwF*#$BYVK3<9iCscS8d6a;3@yDpBZt7w;vaAB8_G;?Q)$);W^
zMvyk_%~%qzt&V!rsrK#44cOu0F+}=QT4`H;#XP6K8#V=kv3T(7*Mxu0DZYpfVZV8E
z;Mt)Q1=yKvRj7Re<N56EL>TUvNcVUeV0?-Cohf1%ucf4)g`tA33mFPYf-)#b#u*=<
z9|`pBukhVopHB^>57x2K)N_1E!)JFh*Jk%eZ@Sd>ctv{xrzC(KbQrw|O@yt~3f1xJ
z92^R_>V7@jUCS2J_Qwt8Ykp;nVVv)kVyKMCmWxaK{{3-hqCo#vf_sz5X*^Q~QH^33
znNHSYoA;g{*6yGX-FVl1s;@nP%RKDDEd$!JPK!il=7oRf;_^q`R$+je59Q?tON^Q;
zY)0+?#8*<tR#nlybDNJ()6ka?E$F^fkeEm-qw@H18q{Uq`xShA>bmRF5R}m+gctVR
zoQWNhABN51hMz=M&X(T7WTmxciixW2d4LfwPh4y_V-q?AX(0>Z<2=Cc!KH~>U(^WT
zF#yp6O)pOjHv0P`(bfxMPTB=1oq8a!7GPIrJNsv14T}i4VO_`Qm>5<@M)7(tLf<9(
zx--3tP_i6vRaF$3;p$#M@cUFCgQS^M6ZfMJWH^4UGLs;{-Zs~yAUxq##icGc2b?O8
zN%7zHluzVW2HabDb`>DPXpL*WUF{+-+~U@8Z)IL$aVxCx0kplL?O0XkXe=x<$UP4j
zB|~~_(GwRm5+IZ-O#h9BN#;uC=P4<vWupCV>Qvl#lt9Gz`)7_?4dz7)_-r_QSchnC
z8{PT&^U-R>j@Vo1cEHtwCY}(e&MDrzcduu~dbo&Zd3iZ{sl1}1;!UYqAYe;K3lu`h
z0KPVb-D@ZO#_F-j-UWBy<HaSSeXUCGi-|BcKBEZ$mInokf}y<m;ob;|lt#lhyWbLG
zJC&P%=q0``m${ygUJdX?pV$U^b=FPztaci;-nJd(PQiYZSb>oKZ%IZC@bS%y7Y9je
z{fae9HMk~LOogQ?yKdFZIg7E21|IhqeAkq<R)fg_8vq_3hBE)W+ZUcfcz9&`Aapks
zahZLgf>MwpSUEa0(Fb2oTtha?O(FY9VQbN`h2R#>^I@<wtS0L`hgK2Dj!dgYLbM~4
zJ(?Rxt&E?nZ_cwvk0HA@-?%mGN{1+<VK2g<CM5%d;XJ>1oQSvE=V1;30d2VPAnd$m
zJ3bUvMxO3(QLTQ;N{~!KBaobK4fZjvCskYr)_~~tPcA46C1D)YRa8)2B1AoXw-HmC
zJ0`EZ2n(5qPu*wOKWyk<U(~Q=#jaB!ec~d=0F*eD_88G?%*^?4tA&YLv|Y{o^-9~Z
z^@Bf+rjvYD+O{B6xqSPuk6o+eE&$KUT@v1(jf+ba=28$}w>pq1dl43tBH*?1+15~A
zi2Dd3;XdvdX!hl8D&$Q|SlSQe<OYyofZ&eHO0er|V@{#%O)z}$1C}*ZG=n+Hj7YJI
z$^d#q5%Syck8m;ii1p{yznV_KX2>cixQVm>(HdT?cD72p+-wWJH313>9Mp3IdFuJd
z^YK^@$w8!6EjFm<h6Pz^HONX+dpXy}Dmq+jR|}$55}^G|i0vx1Zi_0wMhyaMgWQyc
z*XAvPpHfl`bE_CVlLrR=E_wa)<{_LsbO(PYR5G%6D{s^OP)ub1nN9lp>&ue=d+I4C
zpfiCF;Be7sI7ETrwW0x?wLj_k?jt6T|Dz94{---q{r9Ex{(STQ%q%_|EjJGbq7VM?
z+Kp@VPf3JA&F<w@bC`m@079S6_D?*g`_gzkIp6hxSYu`l1Ze{;2Kk_HUMcS<v2m{+
zK8x@f(d&$ZQoUhZ8lGG3fJyy^HPM(X#T1Uf=G0nzL#?3Ee!|egVE&8)|3INGAGNWS
zAT2!8!F$ip^!#o=-%dgfns6JlMHXTd3We}!d9tw_HBbVs!4kV6CWf)sHx79yE32-f
z<Mk|E)G>sUhTq{`QT@7(@78>FdskQ0`Us|Z#%a*yGiNFXy^vHG?XmbBiZy<xX=(XD
zgO5-OsOjkBVRqNiJaiAEsf-JJ-><Z@rI&?Duk{AKLLNNRygQk4NF9xFa&qGK+wp+)
zx^T;X^EKv<r>Cd7U<w7j@LazF3{T|}W^dPBYZ*}Ampm?`rWYl|e8Cj#Q$3g72O2ja
z4iu{ChwtrR#k}Cr2FuMAlU({_9zJ}i0@da~y|?>>H@<$WRctpEw&5VN%$!qo-oV}r
z18^StnU7to^+u~~6K1=T@*(&jqX3!icNh@ewm|}-&U*)=OtD(XT`@Pc(;^t9@=QBo
z9?Hn{+WT+o0u3QBZGM-@IM_sYqSk%*<wb)W`$m$+>kI71YY{40>A=Z_0@#2F0GTGF
zk8hTYEzSlpC*MXGKii*^^<%7&5fRGb-e>g@vQf)-+Ve}YNNtvQ*fH1Tudrj~Gs7sm
zn-|@NUFJj~=UaWMdi3#GJbCuRva+%Q-wj|J7$^NQwr-3%wy@%NNjt0EYz4)3jUqe0
znk7-xB3>>Z?f}|`(JU`3^BL7Hd2}z+xU;RTbYa}NTjWZI)c4i#lq4bdt%1rBo2+;5
zE*JRT&MKfda+2z_8>W75cUwb0yI(tV_txI~*?8-wuwg1=$#8l)javBxULH2hqLcZZ
z07&1iFOD6t#XqMI<-fxDz-vz!6M=AxdK{WsB-pJ_&J{mZVg{Z}AE8^FoSmCl@lMs;
zmVHdfl#iIFRPIhz*^UYNYfrW^_}j2UB+@}39$*N?I27tElxB%@J&$$th20t;&OIES
z2MP%);<VH9gNt8Fn{`BC8C%^(<ynuEnW}{sOB0kUeuSu}ZlMK9F$oFzP(>P`klI_5
z{eupZN(y!t##k3M?ZB_5qH+}&zfZ3(;(%SDs7`{rAuv!EZFh>TNGmcjG9CCw1jyD*
zqhz5(zj;#)IM3*~IO>Y}spi0rDF=(Wo>b*<7L+YvVNe!Wa3tL|?h<aDqUA5q83RK7
zuFLHQ;&d**zTI<O8Xv)mJwJC{t_MPE&iJ?<@SyOA^1=lxfZ|*5gsvl|v4fBP;lYXR
zZH6=D!gDMDBldE-h-t(ok@by6ftC?F12A(fZ{LJNbQsY4un%UMVrV5dJ6Y#>#DKw?
zvWq9tUPnhqV$2T+l4-ZvLBLr_zUw1KgVngA+9tu2P);itCy5`YuE(cf0mmUlDbqY$
zK&8=-#3a)#yeg+f$W~0TyXOAm#}7iE>@7=pI*qiiGfphGZYhfG?Uc`UrxXJoW#i=R
zMe_0U_0<Z?{(>@O*tG1XnqC@x!bJe3e(c<Ji#F7?(Ry$6%IfNjQ#CwJbZl%kjQ2Ke
zq<A1XaF*m>Cjp<CUUS#2w8|`Q+|>ce0BFR~M*l{DEymGw0O&C++*hepGl<=0<(7RJ
zGN=rwN&^`RV0&5RTaZ(<1Oe$T4~1T#>@OsFmqUG8vS2q_(N;$o(p3&CS)U&&Oo%Wk
zs$HsiVhTa6P}DRyU@KoaT~ZQdIgq0)AE?3(=<PO24pOeQV3f{l(O?e6J%qeFV~d6=
zou8JRvCRhx`g<K-ioK`1xQ{)RunrEe@K5$uRIC8vz1(Nh3AYK!O583TK%vGapsN5%
zP17An`sh*MbG`o=qnHIik_GJX(7ONT%*DnT7?aRWGcJ@Ipysx&u65ldz{(Y)P~w}Z
z83#-1?!ku(%W%WR#Vc;GjZTZ4lr1nGD$q`kX48C#qT#X10HP)r{xcdB+1ptqK#1$b
z+Iz46EVVN!gZmtJ<+b_xsE`=)Xao@Kb%n$nD5EE8+CIZ%-Jjg}WT*=ny$=vW5fmRc
zSXkQdnRUo-5MDv{R=pY!jaEYiaUeq#gTf*Y3xBn?5HO$C*w$=<J(J8Tw!7G2H70LT
zsgRX(alBgf*s)_y{Ipxm47;Z*NKjXN2<~+bDnvtswuEJXa6cT4J5DK=3dSM#AiPbc
z<ka~K7ZwnP<JB2P7YN^MzzSt2It3TiEU=Q``xCt_t<=MkfUg(X-S{C0#VDc(ow!w}
z1nV>eGDlMwj}H756&uSeLv#Dl-O90!_Vzxu;<~hb2M*6gsVXSnzyAz)?cB+FZyX{F
zNlHrgD66APLVAH<A#D5z&xbr@)t7<jKm*mz<|1o7;yNH3V0d>4#r`rV1fN%y15=J9
zOr#0|Y@2b(?)^tRZZ^DsexNT?mIug^B=<>AB<CdX+HzYB<dkGp@`D5f3AiBQIV@?f
z?L{<F!SL98Rf3cb?^@BtA{nG`Dc#{acZ&tKE6HoMlMk6h<%@a_PZ44rXfH&i@PdU`
z>5T1qH<nwFxBglR^aP-PvO#pwMurO5TNfY@aFAAxdgj)wHSR9<XNO}5Qw<F>zHnPa
zN?<~iJ{4E%1Zn@|SnT{55W8BoqZPfBntDc|CBAUu=R>Z7Nm~G{ugU1wxG2M9HwUw6
zaNCZmDT)xH2;=3&Gl2OthdfulaCOfCwuR*iB-?5&ZRp#yUJWa&JOWL3qCk23@0fRf
z@R1X>@@)VE)={4zmUUa9FD^)GWBm8(EB#=nnt>Fs&wj2Kc(GMjl!wMOHme{7>!yYR
zt)e}n&k^hS>*JB<lM+m#qLcU=MJaw;ECmT~_>&Zj<u({S^3O$bwm=XcV<kccJmMoS
z%oqT$NNwt{0gScL;uxaNfNFEW!B7}cvtWUIUJqjsGq}>_5S~=;O^gS8eaJ>gKw!jX
zr0kkWL2m`Hf^5kM%5PtMugCElWf-IvH*B#!5`uz;FG<k#_(%)z?LG^h;W~+TOoLf&
z-sA>kc1p`1*7iLj76^V`OH0dp##BhwO<45^H_-#l1L%zeODJhx@<RGvKz9NEqHMi=
zW1Qdo%iA+U%TR1pJi7JZYu@NB`n`=-u>$3?r}PNd@!l7|OBi;aT2By5jzB?7Ii+1y
z75*)B4O@+d$hm1d=ImSp3k0{?4Xl>wARh9Bgz;)l+nKr+s&ZhwthbTT#@qq$LENN0
zVb=5Rp2kRBU0u$n^MzGN*~>&7*!;0`2#KOsB!3NEu|w+uE!~h&CeRO+&ceAG60+-I
z^PYMy3Uxl9gdxzA?pN{nZn-q3hu29(Av<Cy)MY;lJNw`%zVUbAqFXnnc^Ok{I75M!
zsoB@Tjvh}#GI<URp&?{C9{a;$*NOjn0Vef8HM<vW|E<z(Q3ui|+nn?y&<X0|0VkEA
z^o_1MQv~GFVG#pR3J3BvZwVxI<+Pj$Att863e9yALU<4!yy=a`%})EBUEj@>86-Xs
zAfN@<`~ZAI@LUo(%gxR%kI*PUNv}R3?EMN9J4o9mYTR;C-|-V=dwctFn&w0S=QYT3
zoBG6#Ta8*Bus8dlMk<CPdpjy9JX{l~rkx>{Mt}Os)bOStRTOBZ_gikr02^puX>XyP
z<QI_v+^dTJMl%C4eNcK!BBTh$b&@oeDjs+o*SyH*zq=gnFRk(gbCh*71tBfUW9qlt
zxeRS!GlLpNSJD8SW41yPlMKR{fOdDTcf;yA)Mi>8Ps5s#yr?qb(*)c7Vb3}e8?CGJ
zNR$e4y;eAoFk{u+oa+I8YFdxMY`%IH-*ubOsSYdsdf-)jdq`81$xpXhD2=Py+S)kn
z&||>3BtYqzfXJGr5Z&?ax-(-B6%=>@MuiD@ty#QO&I37QnMDX7T4YiZ0J&J0Vjg54
zz}%D$3DJ^ACM4k8Yhw-lsv}GS$mwWG7@DX*Udnw7Ij!(a#Nucr7i`3#8i3R~1Q15P
z@r3~o<=n|L{Pxg^3vZDf*wl3Yhg&UFK)e9=&EOMRph{>6b;Pi*j&*e<3Hh#@1GKsh
zI+s=Vyh}XHMgkO}eJ2@2>vR}BGRw|P{@yRf*^r-iAdJs~uvON6Q@@7$oAr5Hcsu(0
zQS<3?vADQ6jZ3h9cv5`V;~>rQDrEPMVu68Q1>_kAD>@zm=y6+{$-0qj3<MDq&?Kz^
zq#{qo5!*36J^dE^4sl4thg`KakODlJ;z_sK3ArS}K@(F`#DW2WW83G?x8X~ax5KRp
z@6+aM7FP{{`Y~Duh}8^;i=l?;+1YpqDDCmt{buFruRc*3BYIZ~sdU6<79wf+_vO~w
zu*N%;nc??8&2s`%z6u53go%mCTcFnRDP$i$`~nilmw$rJ6}r*;{QCH0@rBBSpC_Qa
zf*K$d%tk9LuXAzrLyY|4R4u2W5yP`z4P^<m@=M~<3P~3*;fx<!x3`)NfBH}B)uWRZ
zKFQwiC+zw`!(?m&9yPCN%ehOSRr=1bp0j+9+XW82IcH&EfS0qt8}mWIawe$!y3{Xv
zF~4z;EU08=!2&{|)y0LXhcI|$Ob@i<O0kbcT{(8JEIYQjL<V+{;KIKQO3oEfS1THP
zFi?q|u`Dl>skzIigP0SLOs8%K3E|4vDCFJ`?|-vly6+uQF?V34!d7ZwB`)L_Z?Rv@
zO(=M9=Q^H+2?`L~5Kd85Dq+~|`;1QocFf#~)pyhGA<+XPQCXj>nyuk=DO$h{YUQ@}
z_LZ?N;8hEq+$UT$Gk?5_0yab2@#~`7IR*xX6=hM+N{{8o`Js3DejLjxDd~CmMf%SC
z{Cu>XG2lWC66Dzgz#fe=4WxD5{BbC$eTQadX0-X{jzzs%=LdnHXL=YB#|!P@{aSqF
zD+AR(%Q{#K^4WdQvv#%92z!)7n1JG?ow+r*7z;w0uB&c^#S~Fw{6~zgU1|GPt<6M#
zBgEEq-Nrmpm1X~R>)I)~#cxU92uv*EB2buXq$XJ~d899UE|nTJAI`V+kDYBKt&a_D
zLE&}K76%p4>b!V&I;_!IxFRR)VTBK?%u(?Vf7R%5^cNe7F-DU-)Ya6c(TV-@72i*+
zHCLh$vUEml<J<F`f6jMS9Y21&U?aujdl0KV158-&$MdILrNNU_IS$(Fxdsy8<U@76
z4-rbY^8@e{7b*&a&N1gfVD}paSxGB)cYUr`99B&z#tArfuldYalk#Y~Ss?t5R#T``
z&c6i-@*S*18P?i55>n#xxQfG^6F?@_U^4*b3-G*D-=3Y@bm9B=S`2<0sZ2153N@fo
z<W<%yCHt;xSK5s9;cLJ?GY4X9WJU=GTX+D-*>oaa171r`)xb5-to6c5=CSDckevj>
zrqhAIXEwUl)ow9+dB>F@U3<%4-#$j$HE$CP5XrD37^!ru1+HBAXg)FB78wndB1A-1
z5fOdZP5aQXF)`O`CpcvM?9W$$-NM=v$uB=bxb81K&kk$6<T4l|dYsbSw9=yYKB|Bi
z)}CkAxP#dvrBv3Lb<!Y2^}dct*J1sk6>z7p77*9)ct4_0BO!m(eq>~1$SJ2|hIGtT
z0q&AqV);02({mRjNom$vM-4!NYz2stU1-{ky?Yrc&l>i2y<yI}vG^n8v^|LWf`C6D
zQ#Fubgl{Nyd28-Y%iK~eehB2&C{UO|2iO)tU&FAZgET2#wFp3T20UWFCmaG^0g)lg
z0wz1h7ulVYhJE3{t~8<jA!Q%b_wRjhQM+SuHEwHl8?z_+tp-`Q9RP66d)xpe7ntB$
zLN7g^q-+pOJJY|h{G)J>TMTp-R(5tojsn2d{BUIlg4ZDEC_E;gPbWFHPy;qbHBXh$
z^G&!!W|U34Y;JPb6V01m98*um^6AY)9+>e&ou2xFp*^K8Bj+*8wgz~H&$_4?N|QP8
zg7rhHq1GrHo_7JL4izAv`R$~1!6;BR^C^mJmjn@q)xHj|?)-$97&CSlR-IKj`^h?O
z`FL(+Aax1S($Y#$3AiRAD<nRpYNs?gCB^>Q3`G14D0aA~MLmX>nRZ32WEoxtmkVe2
z=YpkT?&%SFB`AKQ<Kq{civ(%OLAH}cxqv}M-76(;>=vl0L+e1hGTb?asq=KUuHNyE
zcHi3)l49+*pYhx~8W|O(mRnOPUm=P(qIk@^Xl42s!-c;S>+4}tAp|NDQqbPRTE@1E
zj>Uq~4QskktMooNzv>WH5CC5O7lf#VTW*r>l~y^_PS9|fg#g!A^v^#MwqHx`0c-5_
zVP(?yVwW&sQfA_jSg<;CmIZncpta0#0lXkgvV!p0I@ee3&r+{T3MRXaTHYHTv*ES=
zCxIyMvSSJ4#s}BQwh{*v-6Uhw@e?P+V~T?<{y}6JKseo4`xKv*pm<(enlchXPy+$p
z^WO1Cg~QZ+NO!U@0-}t)b&<9+Qs)rsmrl`As9w|5rlRrXpr9bs`Cn#+Pr*o0e?RTZ
z_8Ge-%=VEW005t(<n7O`P5GW<q^O-J0`pq^a?2$s%=-a~4P+}aAe#y)PoPYpx*b<;
z+)B=NW-nWel&eDeHYKvq?j4mW2L6bWn0kz^+t05?bhiM!9y)Z0m6f%O9wZ|~;tu1&
z5C25!l#u|U{6PkfZ%<3sKh`R2{@uCRXz;X10MD!m$(hHr{X9|x2?7EBG9sN`z51h}
z>-MMZ>khzopU|EZ>K%pHk^+DZhYt>S*k<no4Wy`~3kN*T`}_KM3B&h{KK0!v{AGqL
ze3Sj5jvZPF<(s4`8Gmu?98+}Y2PgoXwQK<hDIr*t-s9Uo@Hnyon*x}MC}4&t3n@R+
zs~iNAYNi4%al=9ZWr2;6k#XAG@cH3W1<KXCl6kT2U^TKH;kqi;$C>o13MN)b2#f;a
zbyD($I+d)``dy6diOR`SVFw9%eRRjv>oWx6n?ez14AHZ}VXEnkE0kC`X8~7rkMOU-
z>vIzpc8mjR$ES3_*HuZ|b))|gWK6^gci;&54K_Ae5W=ckz~KiX0-}cj<u?eTChNee
z!u74%0zulzpfo7NZ(C@0M==5-Uyid%6Ku;`N&i$gwn9I}5aOP+B6je1n9Ob+$W`TW
znNymXoeiKfR6kn>$q=rm-SIT5Yk3`9b=n;>*#^r?7ybK%U-a8Q^-wfb8ev$?TaEi6
zLTCpN-IVu(6we2nrD+RB*jv&`7OAxN?`{kN!J9vBU0kn`X$z2Z9#CK({L9RCc*f~v
z;`)>X8!M{{IC;4JcYVNqAdR4V0Dg2D699IkP!&L*ykSg2+hgq50SVqX%lV+3z1vZD
z_Xs<T??ByX?B@OG5gT4y*+Y=zxX?2}B|sOv!-3xm7Ty&Zqt0?_X()axf7&Z_uhgA8
z+*@PAN5kn0nw3V{&}s@v)4=lqq9}fFgOBi_?SSi<^un<(PS)!J?sclx1F~yKx&jP?
zM{HWR#ZtgVb)s6VauCLZ&V}3HeUMwX=_g>5!sh&Kbb-bY_1&_7IcjffQ-;`oEn5DR
z66AK(A@BI3c-a&-W2mE0?X$1f6fSR;Tl8L)>3g#8z)p)1dJO2dCK|$=XA}cx6=*y%
zrdY)s<?$ID7$HjUt5jC4Dd+HKmPHhRKG~R2w(1Qx+3NP+H4dF5h-!)indDlja;u>Y
z{YV5&Yk>iuq!z0Bjqk5f?OD%%CA+I_8SW)R7WccQN-=2v(IY%qIHJEAc%~Meb%m@W
zFn1oP7n5nrp1V*cZ!zJXlgMU8nmphhl#}7CI7rTwF(R#ZZQno==FqRP00)BAXhkls
z{iI$lo;g|KeaFm7+~{Wp{P69sv_pfTEIPKpn(AoQtGUJ_25$)pFnO8nY%CA0Fzjte
z&0w)9CAP6C3h(M*=yW>N>GYX5!xmP)oS>Eliy&mEYth$Ert!q{^>M<I)LHGTvd0)8
z%$a1E0aPgv2TCwi6d=h%JeKJLNhMzl3OFl(_7JphBy`SlsklMlG+OM2l9ATQL`8pA
zgn16{zqg$e%4_$vBy|_$2;J`b?6x;P*}X^28>IY&gBF01IyeyyystIWV~}}$hCIWW
z3hWF$&eFq<D{!In=bOtzaHGP!KR(v2R=;s)<u$SafvVR;eA8e{dt^Jd=%&l3Zqu9g
z5*R?Wd0)UC9?Xx$LqkYF1Q$+cHd^utlP+0IE-H~Ogt(^>BzAAlh=}w;WNnJyngOg@
z5p!T7k-IMin<|>H-?4ULFj;8Wdm%V4IU&LgVX5bO?j`WqBaBX!&B$5q<%Mu!xDX<*
z9EcI}+j;MLsEgV>@ysvbFbTO{3)z+WAfuf8OE3ox-w`uwjXU0M;yprjE&xOPH8#W!
zcz@s({G!pMmP_^);H&W)1`}N<KzlFxg)E(qOw7!>jC4RNJ45EX`e+2Pzt9A6!}eme
zUzKP9q7KH@duDq2vmM$RKp0f2L*ONf29T4Mb`^N`9{aN&g(7~$p@&kSu(WEAV%ay$
z%)~SoaLMisQ=ws_0Rlv|s)|<WBlP1WGSC3%!K2PNx0BdH)d?h$mme6#Hw%bg4B2H6
zMk3Sr1_wvgEZ?bU;Z=pO=*G6U<`t<OGkpjIajHco1*(Cu<NmiG+eiki194#@G=tly
z-KS|gpRVNiuy3@|`dW0SG7f*%H@6@h|NaL#@)Rl~p>ug@HKaaZgMXvReH{orhxp1f
z${2#$!)?}=Fm@9)>NyMwjfeU{U$+i(!ZVytX9sF_ORnR~mo<TK(#U(qbb@vQn7|#1
zrlkFng_0EHlLXaOMqX_?NMf*9tghHyQSfUR+47ynZvornSUDQ}0tyU7@fidxI#A<k
z4R(dhCZFCZM3O6<0q64<^JIN{B$-wIq^@Qte0y~wCXgt-3(gevd?XM`JiV6`85d^>
z2_nLTi5h&V$sXU2m4hK)RY~blG#iw1+eADc&~&-;Kt4v8(EDu|rBs32!?qF;xQujv
zQ-201M}FGho^Sw7d@WQzTm(A-O3DNBDY)aiwYGc-?XC+l{HdC7D4^FzmSA_;AhK5o
zCm7lj5dAKMR-jJ@N;iAJ>a*xV7%ys>SxCkw-q(W<BfoH@++2z$3#Mv>l?y_CiYsEO
z7(#47lnWOwUev|G+P2du(zC3eg#wdS(B<=&c%BYegWLZsc-Ge;3Yw^?WEia_EdIv;
zS_{_UMrMd%8R|~`)X;WXp;QAr)IT&bvlaacCv-SyAgf+-OML1B^eE^FurcGnq&I@L
zPXPmb3zab#t>q*o1E|6AR+$92Rnx*4AiijED<scH(1`E|=oQYapL)Ps-$6JEYR<<m
z2!ftA;4R792CM3kP-3*E0VgU1638fV1Jb$gq;8f%(n7fy@DsG6K3@y;s{6(rnomih
zHlYGwwP_Zj_iqgpn{)+LO#{eAHCX7*;7#B9CoUnuI)p{27#UNDf}L^Z$(CR+vWZR*
za<|hWouBtZgb2^@JpfK}KuxI=VI(=hWD(xY3u1!O6>_{EhzZs`gr{5VQNK0KI{*Vu
zX!G*%4UZzt0;-vdo`@tRy6q1FYn-9q=BKEb7#idDuGfGgR)J?jR!_7yi7@=U>QZjE
zbGP>!CN9^7ff8azf6DIUUX9zLRWc;Za;KSx<^Fqqs#;nWAilx~@ppB0@<9CH0z|Yj
zp8(h`uyU~x$~=uEV7CM5oNrK5Q){HammZ>pO{KF4In-Ap5r8Bo&{?GhL?C^f;h{Et
zmb|+N7N5?~lU6;TYoO5gR~-hdXYba*>?CCiL=f;B8#~#oiBhg->p-qMI?^y<#|P<Q
z`I58gS-%!4uE+npDk0+F`lr4sWSp&wc!lsRLt6mqHz!)^iIdjR+O=-BF9}G^^w}2p
zvXtQt!)KtLX352!UEduTJcxhGY3vh*+1S{46Am2*8x>PY6%+(%IXP}1to)3ZndHPp
zfYDJ+U6Nw(H7V?d-i?}WunhB}<Umd$6)`~Y&z#gmeCQR+U~AIBiL^B-)#1Qvw8^e|
zY3oKJ{~qWUFGGNW3o`@N2*3!C=phCx#e$^AA-QipoLbka^C-pe)@`FpN=hD*F0{j3
zTUJhOK4m|RKUkbm(zUp;;emjN#>G$uu?7|F17x2{Jh~mRA<S_5(Gvm@o+n@;1sU~v
z#EevsZzGKQg4k{*-73!30cW1KhTDbYe#iz7W#V;qcJ=~N3ZK2ffN}8JZnQmLkNY_U
zW!=oO$Vo?hov=bCoI2QzqR}HZUUR8|h(k!tGXYUdp+-CJ;EQxfz?RUoCg-<D*h*LN
z(z`%zB7W=%j~}6DM$EX4S#R7>0>H-<J(24N4LyE1#kf14YD{YU_jX0yjbk%_S~#58
zugqG@v;lJST6G2Y8`#c>?a0qQK+VvjTZH;&mc(TEN-VNXbSOmDls?-gdCta-mYKdr
zjDn3gj3<^36cs)bq2&4db)H!=7GUQBWI$C?#3>03W~7>%&n6d;%(%l!A1kbf6%amH
zL3DHa$ubBHI>5tT8Cxmp2RiQw-*(<9-}4HI-~;N1t<B(Y31*=hI7l3qp{1*xf;Q00
z?UU|B)wA*M5b7v3BqZdCYMjSbCJ>E&*>8?zWA4vS*4Mfk^WhWvUos{-*Ft01x5Q~!
zkmXtTj+z25rBm<as>O%OU&3yJf1!X_id4_zAskT%1MFNT2K!r-&oI9LjP(*+=3NC&
zB#UEM9*QaqP;^ksq}-n*TV6zChQM*8C@mLZhx;7CIMitb6VO#s1-)}m&J_fl`@+AE
zoI%tE>G80up1I)MLT&yQ*3S<IemM}6I&+lMzd&Z6BckpZ=kXqQ2CJRf_<P4J;H_Hh
z&<RlKeZ`)gzRN1~Y#0_S-E4#uJtt%>ULaZ_I48gpC&0i66v&=q6nKWt<3w7BWY<7Q
zjeWO>A47Koqi20E6<el70S-5y?aUD-5vY&Q=Be8v-s{}JCsk&>+yMVt;o>2hkGKek
zgvhf(m2IP<su~I#9Mt<PkdDm&K$w9ylyYGJdg!34RHw;Y2Rr4a?JZ{J)-P{4o7eCd
z42XQ6|7mSa0Dj^vXsvO;7O1@+5PMT;Jsgkx8!Qo*YA5V~E|CMf*_W@S#+|^-?1cIK
z>`8qdVFJkaT!k&ZV_6gFYxoHd+-V#xVg}~P$H+s&(*PgC4cZMKBt6$267&SsN0>kt
z+j`0RNNpEfPCTZtP4qaQ0(#8$!cDD5&{<*x+1U~V+iFm~k((7b*W6Jhf052g`fmFv
zn9}T@S7%@gcEU)($`j(U8|R13opxMRC<;4|xl2-s8|Hf}Mw(ZYL{)<x@;ru3>>1y9
zD-``S6%z>k9>--K525NUtS{s85jj|3^03do818LpmV-RWiq};a?S_@E&bAH~8FjA&
zkS%I3KS;3kXTiaPMyTp9uyo`h{mH|p5%C*Hf-I1;kY}3(3H6vj{AkKKo@acCQv4~~
zz|@10j(HY<+-<~g0=!%n+-Mfcl5$KwRT`x#_c|Wx>=M!E&7?%==ll>vX)gC9^~PN)
zzm2IQxBwQw5`^m40%T;&qYwuR5(*K&2==-&8EfmJTRyBka5DXMVCnT=0g?R!#cx{7
zRT4y>0v2VYrlAQt{K^)*E^1&#1_q7WqUT*a`4Na)``#c8ldkQ+=v)OF1Ns5Q!3LE(
z@9^X;;WJ~nG|Iqxk!NIa9WDUxe){L#4?)~3Nbu9Z&Zge=tpZuOP`_3Ks^{LyVFTU%
zDR|@MqqE;0RNg-Fw-Z&#`9GT3{zFsefBf}-hqhICQuz7L9=AWgCHrsho<*8la~i;h
z1I+S(3l;bYpq1ba6D1`j^bu49Fa{@s4OM`aTt)@j#}XCHPf48@k&%(H>UsCG@T!=Y
zp&hywdDHZ?$;hIcOyBa4R@0E&saI*)M$G|W(1x~GfS$$k4rzIitWnT1TDY^bLlbu0
z3k*dAB}U?iGdzzn<wWme^jLIa;!PQZ)l^niM(%TphFb}^#Ei1;ZFo0L*bT1@dSyPa
zva^bc8lcpalrG11s5hT^py<0r4^3596AEn#lne|EPD!29uUzzg0RZmtezFhG58Bgx
zbE~!f<bU$}j~|7%DqG&_oKdY{f)14J1*htTwT6~YpEiG5>+gogb()p056-&`&F{Sh
z7GlJvO3=UPVVQxdjH{PneK=-xrTVn|B-ToQQnPl#z&>nhYfB`pD5ZF&QEDLDx3EJj
zICif~Efzis+z1OeD!cG(WS8AIAN+RO!({!pPt<>DPxPN<^xwX?^6tqWInB~S1y!R3
zWRop`#cPnk^4`6>3rS^YkA&HKIVh>15QaFJplZhmdadPjfgV6wa&PPhSHu+<&ccnf
z5#B#f@?<Nwi^*fS*svJFVNDZe!p>cb0P<cQq|AS^cOZOMdajxw=HN(XMUDIS??*p=
zZH9=k$j?HtAlHNE6Ly;L0dB|d5(wFecsiPk62J@v@J<cbOT>C0y7NmMctIpD34+ra
zA@;2o26EMd+2U>gPFN0RLGBqb?KMYVGlf`zLA_Tl;zm?ZAg^5lk34WZ1L;x}2!;aZ
zu$rD;A_S~8=x=L>(iymGh;~ITdc8JCxeh;fNgC#7>(os{dO^RwDgyvU>m>oeNzFoD
zK9dZ%C-Zpzz3<(6#b40A0o{OFsDUi`OO2)+^=m3JNU3Wr|L+%7!k?=Pq+p0)%FN1w
zJ8rWh+M-M!h`ImUdwSkw9muf+Pa<kp0euY)3HWm^K&%A;dbMRxh$AnygYvfarOqnz
zuEbmh|D9fZjVwpC1PBMJTAU9ep_L1B0u+p>H*b`l_e#VPao}LRYAkn3@|^zU_9);C
zukvDGeTqIxr`_V<=-NU9H07b~3^FPofvx2Qdk2EWz50=1z}|xO#gi$8;9G)UE%~|T
zcVifZF-LG$@xDN<8zt@1Px<=sf}Q6uV~{9*wUVRph-@WZC~|DEX#O-AS*6kh$?*PL
zAg-^m%=urfYkXTXu~c%dF2cqe79Ch1lQ6CTDxe{fg66d~o}-%FEZ}&xj^5s$W7^lT
zL=Nxxz1&+088tg8iz^HL4FQ7L3#hHKV$jfjTGN@7kNBKD<*MyF3R|0gomi-)nX(>@
zKjW(EJ2E`FGP2;5^m)&5AKBvvq|F%0C)n3n=0$q`bz%~1s|@)TRr=bW*kW?#`aPAc
z8?Q5A_!ZW#@C~{A=>{dc8=6ZzX3%ZQ3!Ps3IzZNb$SV6)!|cTarZK;trL8V|B;vNd
zdHIM7^e}(>c%Jg*E8g|<&_H&1z)woX$*Jt@xpUWKfW92ESggr`zzR_sl>xAAa9;TU
z41JZ~Tf|0?)DQv;U{=|P4M<Z^74Xn0s3F!|RJ)&&5Ll)lEQ;_Z$U|n2R&zTf^E*)f
z4zeX#$`GqG{QUfos0(%}usTMmttWk4txx4Z7B)Nr6N{Lb*c`=lY1g**KM>`?va152
zH=Ia(1q2&dMUe1Z*C+-0{lL8Jya<Mo>E*7!bjYetIA|z9pXN?k_=p|YNA@d+cx)0j
zuq<j9#ID1M2Z-N3K1XFh>t%Y&s2vneA6!A?5CnsA&z57$4GxQ-uf`z}xv96hCWFM#
zWBS>RAH4JJw{>FA)lDvRShJQEyFS?T&F|RO+4aq7_?gd{H3^}7&&Jdcm$d?Kqc$$^
z^ssJj7z%*Ns8l4q{mlLf+DUZMk3*X`9vx!7G={jQE3<6>Y*I4h`JiJ;A9zVr23Txz
znzq0@!ER6oI}!LUo&LWuifmx}U@?=v_hKdFxqj#ogBD|9Yx)X+SM%WekL{mVgZd4M
zagEd^fYQ0ZsxN@+GuY$b?R#1-DegHMngqcMS(5BMdwmP)N^W57arkr03Tg;rxxj1L
zCjrptw`<UIbrx|piQ<5>&JU#b`iw_7ZgBm6xFo5T{qN%+&4S38StgVbxaIvW;kxV3
zugcJRn}q~ku(!+s9ux$+Y7m5K!hC-#{#0KRWoH#2G2~~!(DnL9;JR|4K}aJ%6lMtr
zey?GO<n(Syz-8qDTvxXI8_md`8Ik|TqbmMh<3BeS{JHY~%}#{>`R4!YBSHQ@8E29|
za&C+op?+_f7#AmZXXMO-qmNEU>^D-@KA!pNX4*jBwKRuH9lZ}_8o>|F6kpPDu)oE6
zzTo(IWxF#YKR=bJpD4Zi`p~{0uLwQulb2t-Fe&@;1s}e1=ekK*N?RMXL3iu()*rXc
zH~kuznVHQeooBuOPW^|hqyKiC{J;ECPXo?asa)B#qyF^L$Tp3MLt$UGD#~Pf_TkgJ
zT4N`6-qlWr6|L1Qk93uw^-*swH{U*g^x(s78ndSr$*AYu=YNm;?MwS^k?G!^^iyvM
zvTq)h+^x0^sN`Y!H7z`!rV(wxcR`pf`aIBNbt{W6D>GG|?rquc|FcP&p1A+J@gJ!k
z{P(Pw|Bf#?@=x4t24oNVt$MATkxNA|O>S}vq#8!|QzY_>+5OasVXt4SUOrut5!RyA
zPz+f+hQQN3rIfKagVY4~X`8Dr44k!tlJZX4c+8Mef^%jPb-<ZKw`x7CykR<s1vSOw
zt;Cd1Qc_YKGr{As=oMdrhj4IMSXD{<FF~SU(~o5>EqRY<ytR*ykAxvt^d3o0TDqhJ
z^^0w9o2Cu>Xnwm<Ai#(_s6lcCf12#w?p30GlV=N_s3|0N_SV7uPw$43$YWP7R%#6O
zQnfd7u5Odgc;3|hgeS_L@%b5rC2pH?KSe%kwb{ef*DZ_f7tq?;6{el{6)_AqYQ!+~
z`ii6!F~W6z@sl6ie`aEM<?7~!h^FcsZqrF`AK5~{6TbZ$BtLK<sn>=mt1+dIUi+C{
zQld1eXT9Wl(;qDllr02*_S|$%rt8<+7dh%zSKxq}l}(?R0izAl8TXSZBHkLu9ZyPq
z|0)3a8^zX+s_IzUzqU<kubfNlr2+<l<Nde&GN^9s>$wIn5P=LWivk)PhOW1_H#nG;
zG{FKYyNJESjquiUbqD%zgmxT3M^AYpsxj;aFmKAr8NS^M-Y_{IR~_JS;IlW2ZukIT
zdp6|@DL(S(f#ytTZ29<06xIC^1swsVQ~~gfBj@s=C09-^WYCn1EdH(ZmotAi;J3QF
zAO1a`iTA<(e2EOp|H$JoQ&xj~1#a8G6I}}y_x!ZR0FMQ?g+?%W)*C>X|FW|Fykdch
zu%prM+QZ`H@7R<xBsC&cA&g_o@3=kapJ7lm#!>xc;SOBr$zkJHzYiXO*Ci}}gWLZv
zU&am|(n1V!=m|I7!WeltR|^<3?d(PWo#j@<beE@Z)|Du*^^w9)DSPqn>1H218Gb#4
z-JCz@1cYM2YSjS<QezYc_q$GVAkzRpiNt;OYHCp+ap}+OfJB0*CU;JXgK@#MevDeo
zuP%Us!2nPAo94gig?!X~4qYJ9sG`PQuj+{<FZ<T1N1A`@)7uwwjd}JzeS$FZGT)bU
z|J;I1o_wD@4|3?%&W;ZcBI@VnYQ+A_K~N?VjelvF59X<(GJv0Mza4Ve7CFac3y!i$
z1FR?sB`IPKf_7bKW*XM96#{b#6o<zIZt?PJfQ9s4CGH3<`Ge8)afn|hV|fA{Hm~pj
zHzXl;qj+a|-l>{qr&h_(@Ac>@X2>EdRO`A*vhU%1lD0I=@SOmosG&2d?J2QW>x&h#
zx%lbkeQjCAhNWq(+NT`2gGlo(f{a^K9R3qZyY75vle{~piBjJU*Hp0UJSd*Z2FWe_
z8M1eN1$!q3CBSxI4f+%+Bfq$z=Hu8lShJt^gWC_v?jgXggWzaGx>$I^@Ir9H5(rHK
z({(93)68HN%+Avn6BE>#tcjh-@D!&1#Kf$tAAqn!9I3qsF*LMp8OZqkSvQ!z1jK2Z
zG1~AAAW^ox%G=|gHFE%#%x*t6q~)7abyq?IRH27MBkY+uIXQ!af=UY(rO%x^*U#jq
z2&`2BTu{rCuJ<b>R^;N`FEQyYJ!(aaKB6gu*C6y>-apBTu&$^KXrd{*cQL)Rv=mM;
zLm{ouz^YLPq{18mjVfMTw-SsTGKF+BfWm$|A5NwF{ewLQmq2{asEoC>wZpdAn#Bgu
z2Ctzg;ThIT2g2L}Y=7;aKlcD7i<}O_itdM|%}*^Y0g1!VtOmvo4P7ZFzdl_k>5Lnj
zKg~EDYNUtr9cX(EKfVqQKJFJ?k(2YeZ0g2UN2(CW>H;?f11{+T9eA>lN33~n&O#=$
z74lHTH6HH6&9|Sqmu->!C_(y#Bn6FJP|V4&lD0KeR31A%nCe&CYIl86{XXoK99Gp3
zXdQ>S{rP_3G_tC-VMh<{KiLl!$rn%fUI71%gAx|ZItxH~d>w_lHWV2VL|6x!rkM8|
zSW#V~SL+Ol!8@LB+i`<cB_s5aj+WL9dq6L-P{6^o%49ZsN;thifqH*3v@2P}dL+T8
z86W=McmuMu$}40370H^Ym6a7H&8Ta(vGoXm;7Mj%{4(Wmu+^Bj>4P8waV0phM#9A~
zJw5&TK6_v$=RKSEM^JqO;ZNJ`=btQWaW+-?JuI7p-OY$^oAgu;hVu5e&W?`PbKe?}
z?E8ijvTq;J$nElJi?hDa1barnhMer3(N#_<ixRM!%A(*Xzm^hzK`-b?ltDFYbDWi$
z_}kHaTH#<F_SdEl(j~=Y2|S6BJszo<yHWxXIS=`{O|cIzfONdWQCAX(PZh|drqLmo
za5&w<O-2Pe9hycj{CV$_&*C4v!$3cgM%mRaR_U-ef(ts7it|4B5j-+I;m{keAEA6}
zAdYEL)pp?s$O#XPTZ6?+GMf6X(TKp3L;vSWA2weE7DOXmo1pkeqqf!;b@N?!{QRR8
zD*;Y<0jog(&$?!{AKaeXxFuwZ^7jELWMtp!BtPhO0Tah0vju$`v_55y;-ETNe|&Zb
zS*M`oxPmhV@)OvQRlxT=2<P!24gAyrz26WY9`cjuf7GL+8mK*2c-kS$Bq*<~pn*ZX
z%O1=Yk|9*7aEu9eh47y@ljS_&{4Xji{;SHB|0mb|UE18u;M#8>5KIw(KGpo@)~$P}
z>02xj<u}9%7TaFM+{*I>-r=UhoK2C!c`qnj<ot?2<wsZ@Lx^iewzqgV?p>5@Rr4af
z9;Z!W;W%zRmi$Z@*}N#&IBY517l+n46s1)jM+p@mR0B5;f)ifEO$p_`?LW~psnDB?
z&t{Vnm|dyW$^G_h-pKBdg-<?(f1x~vzN+S=s{GHd>f*QTRObn%fek%Hs?0ZYZurpS
zM`bm+YkPhJ3mQss?!UA3ME=ab$A$jSmw(3Of9P?jF5n<LWlj|UM{(xqx8NOa^hgu9
zFJ$+#qzCu~mM`An&@|bjK}yH4;9yj<Q786(gOjVoaUiAvJRqr8eQ>x%Ey3CJ>uA^q
zF|@`~_X_qA8_we7%=Ur<fBZk9;pf#C74vtyUPi*{ej}QU`}e9e;3G<Y{!mjL+8^OY
zAk{k;Q!r|);;fmH)8TO4i=KG?ZsZVD5VHn3A{pq|?HLFAwH`lyd|p+&@sc=j`GeQE
z<3UmH7!_S~_-wC@n~Hez=Cb)K;|-w`R>#nAh8eZ8ovd|EXZ0PuoymUIFI5@yC161c
zw%w0?gdSr@kBu7Vmr6)Uu?Y%}mQ^vz%gM=ocD~O<uSg%xY+}f`EWQ9)s-ufAgB;GJ
ze?Q&w_;xKPU<Kvm?u$Wa0F*TCYxy-WX0PgQ+qLibH7cZt>M!1T+SDYOm$eN1@Mm3d
zNY_7>4`P1K&dlV(DTgRQ8hCQOUv@8nl<nYPCGASTcFzQl!RJ?{vs89A5ZqB6@wEn^
zd0&gB7Y9&RnsGRtUo&DowimlPxPfNYgWjsMXU{T48@R)Hr@AUq#;*_v@MezSHAB!@
zbu3h042T@Yo$@?+5fY%1ya#}=z@OGk2947*GxIJLJlHJY`5#&MfvEDT&W!~*KD91#
zAg}RIXeNuER20^Mzbl^tA@hyBK6t}1UsfyAZOHMj$O#7PvuEyKKAp3?f8y6RrKMpL
z<m2O7Yb_Z&hiaif{>&$B+U{Rp0^hUj$?(yGSC^>PH+>+DGoNkr(A*Mwyr@{hi*0vs
zl=wEm3u`1xOX__T1I@?w2Fb@tGsJE2rS8?-;t2j1ch!AuPht(Y9(*rVn;mbEEW3O8
zG;LNY%NoyFeBBW;?!<Y4XT!Kbq+J+}>y!l7%@E|O^Su}O{>G<Al>G-L0g@aFwD?-*
zS$>$DHV5hOg>Eye$c=~pR?ia0;a%X5V}ou^<hVn`BqPJDkl2$U1C0t%8DMkOg_DrD
zYPJ7fmLLDZG)D>~(iAvc>h}p6V9-I%@(K<Ojb^F`n*qRbRUMsp@Y7L8P?Y@}En7~n
zn{QTt_YAad6%g5mKsN*Lt27D{X*L{T3juz<w3WUe45H2Y$kEcsk3^KDw^l9KEa8+X
z5XTti!EJ&Z=n)Nzym&YtKnu&YYY)J_m9)D{Y>XAv{+sm4lnNzJcy!3Zi5zfxVsu<w
zE*OXp8t#DyjR%f-LXNj7H}9TJwfS=!GNn*S3XcO%pSUfJ=YoMvAFT6vzdlsL^L?Ij
zxB){GF1+#z>Apr3xl-AGPqERpsCa^$2I{p~IShhR8W>WL<0eoU2r1pTi)(aQ7+~>*
z2XF7_=!Xt^7MYc85)G7GqR?j5!ul^TvSBRPSB$|AgW#41BY`{}aO(pAjF@Dgx3U+g
ze?C)lb8A?Hpz6gi0caFi|7xsLX{GKU1fAGw{2l-66FI#!PG?q39sn~e;dfJZTja-K
z<HwJoft><5pIH-8@FrSd9G~}t-f~nb?(Yyhvd%hE2WNGbBQ@VtB^+A-%Rw7CIt@0r
zRb|pj$N$3Kdxk}su3N&WZD<wLwoM?SEuw;gfgBCpC?E(*P@<@SP?9NfY6IFTphc3b
zA~}?T<fya(5Rg~`5)8y5C{Q4I)>GK$oc+!Dt~2M~Tr=afx7)2yRK4%>-eIkEuX|#~
z^E74t2YuYI>?br0hAT^-<o(2vAcivp&q0yry&zqfWIhH|7c(=Jn1-4LE#|mnv2cgo
zdapHOrtU6((UcYTIIQoyo+}pbJ~%m3xrpOhb<x52FRY0d&5D<ei}cfKvvt4)8n4Qf
z-YHP=Ic3^$u<rEEUk6(*e7ap|C#igFO2)qH#C^ShyqyMHLt;MlTq!zqXinZE{g&9<
z0QFP5*qCTwcsNgE)=DKE@%()$mZDjhG)1YFT#;}vq+*Bv>kHIP9O~o~Lon3<kxd+W
zOhHiLhg)FF7Nw-MfFq3i==EDtg<JavevO><lot5lEA`1?>_vQO>vE0<eqCNqb>>3&
z23JjRGltUo==C|*l<Amhk1rS5{q(`3uM?+>w*@V1J6~*+A>U%I@yEwC12%n;vkYAW
z#WN9mM*H$##PN;h@p8q!+No=>grh#Rcfk<&molAA?AzhZ-&?x<#<MTU)v3ZUiXj60
zy$yVZ?DuTu7q%gh=Y_$KyLM@FT>En&B?yw9_lCVnO|1C0=WQs|bLkN5NY?T2B)@-u
z2GZ)dSDiag++VFfi=Nr^-x}>T6{FrxXtSU9VxqJ7RVO^S+B!xtlWFD%i46PK>Zd1G
zIWtoAHbfMCx+P?xPt3ud_YyCMe!CxEvh{rEmal`&nL?707;pMKOrXAA81>fw>sLg<
z+HT5M2D&=qjoTPMXao+;>QV1(4BtQReevQ2cL)etE;S(T;RakkUj6qyIAEZ5mc$=C
zc#xt$i&lEM+FZ{ug#-D-!ifC3c?LHVLz>Tf5fmD7rV$rgI!4J9=D&UnYnYM0{QK|Y
zG$O`lLPM*ub@j5`e(>bP5mM<CKo6@zmUc$P4`{>trip6_53XT$yQj?eIXEvSpNy8*
z#jN@4M738RbWS0}pbl@&0?M`Loo((Tsz{Y#Ki>PH7|5V&U<?)%6_tj;Ej+s5!TT_j
zS%V)5S^c(Tm1n+i@~RkcWRe*^M`FmL2JRn&&#7Nsp^EB+KT1Hr>F%uLW`TEsIWL~p
zplRrFDxQiYBSUN^oi!3YS5V#=?KUVy+)qF5n!mCSG$JK!k3d3QgJD;pf+%>#B39%6
zy3rnEdM}yyAO~I&LgXb!-!>OWz}<=l$C<p#YcDCED88EgmhQ<CA`a>6UVV|%f$I`M
zYl!puN5OfV=0VL*3m0&1kv+3?)fP=C88A7c9SJnqv)BmQ67ZurFtth`lbl?~-k;yZ
z(S7g9hv#}k+y$qP>*$N24orzT<}bK<SpqZ*n5D3^Ntvf2ypP|d?&^~KM+<-uj{fb<
zfFlN=j)Dw-?gAHTn9MyS6LI)>dS^^7LK+(YxFYycp#Lq@NDk;ydtsgNlvv9{G#+6y
z?_d9W1jZ+i(BSvyz5Jt<C9re0<<6jBNT`BW5gHD-4QiQ3Rd?5s84t{+7?O!(A_c-&
z5!7u4P_@(T!toF^z@ryKOgnK`Vf<!Df;Qr2vVM0)wR|0>yy~Iq$G;KxAPkm0CPqbq
zjNJ<B2jOTCi|{Up<OOCTZ=x$a6HNK?iXlKKxc3+U3*pa%`$Ucx-VTNyb&MyP?}X@u
zJ2I2xrxxy8+y3Lx*{6g7Tz+#5{UEVW4U6OU8+ph_1MOe8>?grOd5j|aLahq77zPBc
zFlD3T8uq})KnJ9+;9@`ArqgjJG@xq~BktakHir!9I=Gef7?z6LBIl~<`sD1^)$c&q
z6UQ)Ut54z|15tG#(Gge61c>Fxi1wT2idQcT;F?EfdXXXxaCo(>tkRH5QZbv<12gKB
zX9`sVZcWAZ<A+tJ(u4;Z6_1WBJ}2-GG#j7~=LOnhAdQ&KOl;0qsvKn$&l1liExgrd
zaMc3}TsAab!DLxL>WtCDu$83=PpU8l6AwR#atJ+%<9S)xz4jp;z&tRtXCc|^aPM>;
z{E$n4P|}*4T)db7wGTcvYD`@iaX^Mo?5zoRa+ZmCUuvDIL@(H9TutIxj(abx<O<Uf
z76h-##Puch(AEbK*eKD6mpCf2>2c`OpQ%kHWDPV1Z^Erv1nQ0a@>W9mO0KF>ZGj8h
zPfgsi`4U0|F@3>v|M1B(J@L0CuzPL51w;#5wr}Mh$BrdK%1C@=VPu@qF^qQ^w*P_;
zzd5l~9;uSScnisH>(<5MIP0T=Y5Ze@!LRx|F*Ar~Sqlylu!K>A2w4XxAK+S?EmCcu
z8i@x!SBL=>t;R4o0KzK>^P}4MiY@-fh=DDLcM+v9X{yEb&Wa;lKJtNSX#?7?Q#=;j
z-*$Nc9Zv!VGQ_BTni#B62V_j_2Ms?fus<k=OgMl*j7p_J26~)E9%vBreX!hBhi?#+
z1#w<8OryB0CL;Sua{TaN7&8M~mB>gVWJ&(IsTmMSp|l_tJHyVh+d^_XgY_UZ(xIo>
z$>1Y$ZEEy`c?Rtn=1PeCFDWi>Uxq;j4$l%DN%1hm1^7|N)fD;6zngb~ql2w_m;-$W
zWh!xDBB&%PnHr3LlVil{Z#qrJ{Q%aSFc-mGH_?%!)+M(0zPvKp3ez7~HRMs5cOROb
zEzw_pq*1ZS;KeG1=_i2EH`#@z+uj(*<?VDV_jxL*<KXZNqfKdW*<eC^n+Jg`#X!&U
zepA|6oP?{r3#?J+5@Qa8MDg#scuI*89qw{?>wMZe6hH=+JZB~ojfy?P;2)zFC7TEd
zqB$l^^H50#MMk!EJY34(OO_rW$caHczV~eU@9b&#-2wuN#FrdXA4!;j|0zTP;g-J_
z9`$Kd9@Jj}n%w%0qa0Gv)jFL8Fme{`j(z<25(bxzH)S`zb*yaO;JpcX9opQ6b2%-D
zRF)Vs0){V|KVO4zv_Qy_OZ@GSw<2X-PLYQiNH(R=lWJ^?KM<1yVq{51LNQ+vhudxe
zuE7<)L!{zHUB8rhw`i3sCFb(VcCwZMqW@HuYd4DF)5Lto#pJm;`oO~M_KgE!nIaqq
z@5?V4LdfNbm_<H2ku%?-sH;sZ4MG35G@i3`nOaC-;5B_GTuCu21$=%oI4f}C{sX{o
z=6KG*X_D^XYK1<v0lWc@cthSG%-WP`(9Y{IsYenmB02yL6T3p%ufeiq|9%-zSX`~c
z^xi?Yk@<s^l?4ya4u_`Fn#Z-?5-^N8)nlt?Z)f)m@@VO79q}sbiML(N6nYSUs($V%
zk`fvmamECcbTL2ytDIzMhDH}!7EY;@=fJc1m5_0DE?>U<m6e`v#Up+Bs6k7r-#_OB
zC-O=il+FF9hRH9{vzVWl#Ha?b*6M*)dfnjPGtetO!~6^9rUSpz99k^1cgxEcy_h6(
zIQ+dZ3DbnT$KzUCL+t)YB{CTF?HBWjAr>)92V)}eJ`S-FUxa6QXYZ7{jv%LMv}@mP
zrPx*1^k9DQ)#`k7bv#lt<l<-7`PdxCNF4?Q`IY2cN8b&1R=^8^Ct`@@n&~y?NgS|=
zz{ie{%zMM1^q5TxVi;K!0TKIlC09uBh4vVY**rs8r`0sqoIuHM*iSLyFx@BD!7F8V
z(I#*DPfqqnUEPcEau!$-3Iuo-7?eW0adD&1%2IWNGg37HBp?qh+nS$6bh=2-#C#66
zTS3Mfv0wr-g*T9$M@ts1x^{USj=qzq%+hcom$W!YM-q1~{JH+&(WP?ey4E$4K`dNK
zc~YmqE`tanSC-xd&x~+IyyUg=K2tFwLQ)JsMPdlI8`1Q=g8%CnGKEUUQ+=W&85q=h
zfJvTX(0AT7-Uz)wBNRx;7GOzWhB~yoV(!z1;=BCB8~_;|#R`7MLz6tQwAR~^a{g5j
z$J=wRB{FcX>==dVSY?|m`*Iu#AP(as6~jr5lw6oYK<e9ph8sV<C!}~f7DH%{$ZXSQ
zu9DB+cELTCG^fJ3Uw**^r2+KxlYrRin4c%r<;DCJ>-W3^J3+<pF%yGUK~Q`_DF=I=
zBslI-Fy|h$Wows>Bztxrc5TEz33b$VcpVUdCB7>fLzfSM9OS%^_A%~(nWeg^X`JUs
zczXh}GcG@**y?a+Bn>9XQ*bx237YCbty71E4{@ZMM&S_BGSCRHo=zsST%jfu+}n<P
zjOGj0`VIl;tOaoP(4#G0_-FI<@xE3o)wk~vpY%<wpWutwD1Aos%n8Ir!WW%3{%XU%
zTHePY6o7Sc0)|%l(B0A*;^4>FjW4Fy5=E`C1pyXw=VM6=>`=-nLFPXb?A=fX)x!*0
zBc4jk=P;pW?+W9xz7J9Suy!`bPJ&FsB7|fy`PEjKN!VsIhp2j`4k#SeB0I@4nDNeI
zfoLFFHh>ikuqKd$SdEzQlbb+X_lQd{#0`S7YYqesU%mYgr<r8xS`r=>rfXb60eFE{
zg5f%o*Jiki{ZTkLx`paq|Fdn0LzJs-r~~mRxhedg-u@cSOACB<GbDbV|Bi5>xhDrX
zx{UvGs=MySrDd_Gb^zw6!?=Y%5so~*HG6*iu-~6OSzPevp+iF2DeuT49f$_C!*sfc
zoh$~N1eHh}?X(k9?)t%oaAa)=7HvTF1!`v!&Po)0aiH?m@>41N<`9I!1V=|w`3Jx3
zeRFN_erKc$-jz;EVWmOVI@w7ck$~;T-HuVTOD{D3t+V4tXXk}@4=^K$zZb4PjR>L+
zn3}BUb@0(ILRI|S{GTF`BS8)y3L{+-SL#HdG!Aka42BoiDm5<*?ETZx?itNc5w4@|
z2A8a{J>K|?!9tGu+qhMbQ(mcq7u$35m|JU2CT=A0!n5c{cj|pzRxLzUZA$_d$H()N
zt2xPdjg7Rlbe<Y>{rdG``ty+raFlSJ!Vwk3MK&}eA1&g1!%FL@7z(n*@uyFR&+5#p
zE)Vg3UvcrL<tb^86c<SGo25g&awro0@feeF5;G%#n$lm65E#awXqvYeX+h#g7kh5o
zv9|S6c8zBY6e~ZZYT11|k(|uoEq&1ArVlvNj!^_|G{0nlhYcosYX14e931Q(9@8=)
zcJJ}4h939>g<~w|^^K2=5u{1+%dbRs3&3&vtuPr<A-{eLMty?V*M_k*rCPKHBtjCg
zIvTg<CM2KZfa+|deT1GMapY*z{C9D5xSLLHETB2S7=wv-a+Mh8lP(j?S>nFA4^JI8
z2<sE|@Aa7Z#Z9C$H~!gqhdD}L0w!&7`LYJ%L|)5tPawEOVWyK<X6u{Omt*d3^Jc+E
zg7#f+YgT!Fz3Q56J2`4jx>e$ueZ@)K2r5=$I1CcMF#897*)hE?%Ug#Ax8CF(D#1Pe
zUeht8G6yqT(LF+Hp~f8{`oBSyRMkb<yL#Ch;Kw;9JUmdMxPJe519<CI`ao;G4(a6(
zB+05MpZFi2RNc&&)42j>SdbT}A<a?lboE)nO@Mrg#Fn>}l?`yB`Z7Kx217$)dPh<K
zK!Ri<)FnHl*e0pS7I9=6j{Mp>96EbfOvYaFZ96Z@+hCN{@L_NOdPzz-A7j!1hrvh;
z;5OXk0^zad`i~gzH=YI78y7EJK-_m}k6WzrQ){Q+zklDsTM8|_ZA{}2txL)53BE_h
zxYA;NW_pptoj^eQLTX+7$z<rkh(tg9ZpAd*?&|SrSh!Y4vp?9WTnPSgBucta4z|OG
z0=ziG%|mEW8#rGkv1Tfo=;9&XBdCH<A84UpG})*ane8<EMM<d^8DSR`6n3G=%SyD_
zaj&`T+3P<-o}>sl_Ar71#-LR-J~bn4_vNNqgyb6cRG2D*I5!PBWrlHJ6B}t689k^X
znt@cK`i{e4D5w&PlCoE<9p7fP3vCKbQ&W>4wXDhNX<`|G0$^J=sS7aunUocLBwLT=
z-x?kVApegY0=l)16Vaz}xWPy4po%UDVNTWkj!qdGlpWLvs^mr0ijT1R5;+ETz50so
zi@h)yRn&ZGf2qXmjn{`qS6=+RZ{-f3@1aYUpRAaRT)g=DJ;K<4s+m2?NJ!XK(Fal|
zdKJcXk_*(9WkXz`(##p()6;YJwc6<%0Wm#7q7>;;Wv`z`!S|-f=kr+}?b|R7Jb$FK
zsImK1V4N_1;J0r5W4~*SMAfAw<@xQZPNLXy9I8Wx$Vbf6-5Y`xW0*kgBV%o($H3{<
zTt?%@3#JIDhCN0Px*Lx6KE|-+HUf_Oj+mR0<{qtfJ<es{v(|K(&}6sMnzJvOp+$6g
zSctNXn5V9i62UgIR}$NIsG0q}xB%*wi^U!{iUQ~A%bsKn%+1Y}Uz<A69O@dnxl(wg
zy^t8h73nDh?xi<#yd?w$m8+Jm{N)G?BDO6w%r02puvYD1m5QfV;PSl*J5WX~YhoNK
z+cZ&VbfeJ2D3_Oe@MgJ<z?+Y`&uW=XizlR=yBzN8S?h0bIo}|^Kev{7Z{tteRNggz
z9ez;JDLpjB{ntY?+~warx30N`g)CDM%w<MDZ7Dug$#+2PqU7(QWCDzM@~Z^zA;T$;
z(DNpuzC`W`A+jUljLy~&nXf=lWuPP&^_m;^%7Y_GvOe+VsY4@7TJPl&EwEiaOXhFk
z|CxrtZs|;e2JzI|`o6<;=TsjFl2_F&a?eF7iX$~1D5x4dGJA8slROv8K(*-r+rk7q
z41^WJ(@HkSKnIZC*s^y;O2w;>oUzk<J;w{)WF2`v<#np2kyAUwIO%(tf0^95`y?Is
zPPffzX*Ax(UkjSKugVp1c~Z;V^qPx#{p6hS;XgjwU&!YQ^E5F^uE6-=7@nAi1V@3O
zVx3`idHE?+@oX_wtb{$L<jZdTVu{}Fv3BX7a5K1}pO(dBn1pMaTd*FOKiSg}q^zJs
ze9!2Z(ewbAp_^gqOBNFdsU$tdoCUdjg!dr!QfdpVo8ph4c}%toCEMp>jcuq-ui@2X
zfDtAxDeNqR&GzYr#$UcFwt3hQbERc9Lx(i(EpE8R0EtA+@`<+1!36Bmp$~HQU3W~J
zIDh)hX5$@r6>h4r)efoI1$J<6ZdLB057F@(Ddj(h140>gzx-=71ftJdyGO3~C&%nN
zdCyVdEFiu&_Cza+m)0Ts5TlbNj?lLsVP)=GNZ$@e$)jUpzqD@#rPv#oZwI0>?KK$0
zo<REWc=&JMgr?zDt^G}kv8HX=bSTc4MpV#I{)b}-00b`tS(b$2H4JcHTF$5CF0xxD
z+~p(XJ#<mHH^RF&w`PU+?me8&I}%C#(Ti<~$z_X?^Z+QD1Chn#SRUsCkZShqS;FMa
zM&l@RgZPojikdAA;6SfcHR4)WUXkosueN>lvZ;LweJh8?g;sxhG3ee$xv*<a;Cc7R
z*|8<NPkE&+f2y$k+b`AKBT9-dqc;8dX2n44yKPstFI0E{snNsgrQI4v+ij<*liZ;m
zEJ%kp+4$JWvl_~o>94v*CcemYOaFFi%J6jYZ?-x2jtBmKP}27H`j2+G4ursj%^mR_
zyso8)EFgZ?;K=*PRUW^$o{icb$mcYUxv^(YXh}gHvA;>>4&QfZP{Q<>-$OF$bGL#l
z*QC{Fix}NtSE40@Mc+TIV0#D&Udzw?I6kHFIx@{vwhMMeK(Q&{5`wyLJqv+$=FLtr
z$<{x3r>FrXVSp<``#39@oeN}!6e_R-puPUAfxbb^_S4^jd}G2&#O{Lh^uP})K7UI<
z7fyPr<U*-5U}H!b0QqYEXZ%!1j$w{-i+_LKPb{#X&%j6&5Nh7?3M05Y;_ordcPU7$
zKCU${sk$hEo-}#Jvwe${-R}eeg|7{sX-x5MovEIiKIz+%R$;4NR7jd(kZwR63xUG|
z%D$v*#7sBZu;<KH^kWe7_)LpepiCn6)5roI@UPf5wZ`v4%+oVlz6Q^g8=an(u0gGM
zWhX7q@Aid_K4cZDQZ0!}I$=iG$|bvL2L8TQ=VI9fLWu>2nQ4znbHWVYCg%IJZ)5`P
zss8Sx-zMBi<pZ~oRFaeN3=((}$i^tFNs^IDgRbl^Zl~`)9Y?NLn2b{zkb1(9;-vRs
zh?p#9ywWZ!ymr}7aBmjH^-)E4T?a_16AeA?h1|El*OK)!2n3tb$I_qLgaG#;HhXB=
z80d0{5h6k#;XYiyy}VUzzpGfJe9~}i=!1uPT~V@1erC;G4-F$R--89`UoJPWE89Wd
z1ojaS&>0xUrSD%qhLJ@qmW|z#yC~LZu`C#Ak!0H!*&KwLlXEus505c|qft@)Q_$&=
z9WFwwA+q8GSl%dlgCyWQL6gQ~Pix-q0V9=ic5TFLjlXAo&p`K=FFhFgen-IPkH(h(
zV-o<Sf$q6Oi3~1+oIPbegM)95D%lm%*rQf+W(rfGD@)&@9l`#h^o|@W!g_-(#?aU!
zq}K-X%SP<G@=lbv0{^$LY28?3c}$5*RYuC_pzXiCK|~u!Ac_&-&Ey${=Rr=b<a3<o
z23S};Mq|GVtOmj<!JTou;i!f@_!vI4+A@J({yVs|;EbaK2(8&El-RoQ9-1wVl!`kZ
z##!L=bV&1z0#yhH1+kr!`m|AuqzB6Lb#7)!ah0EdpBu{f8r*J`8zC!y61{w;bjvSB
z(K8kseXwNUA?7Kt+Ttv%NS?b814_~(5V8sty7e3JN_lba@ZJkw8@uXu<0l|Dn<EnB
zp<umA4~e$ggI<$3Nw;~;%?O5&WewP9v&PP_Ns>srwsr3W{ST0xEIULVJ%A&V@?yHa
zUK4yv;p~qU&8mt(F33_2JaQBanGvcMii}hum(9Z}ih6jdgIx=2vue^Q!vab=hD?_l
z`TO}1`%Ko06(X8v<iRW6r;>;URBKrkikn)3W%v43!_<p-CIPEp;M+RCIbRup8kV@Y
zBbLGH#1Jbyve1ETNN+?Q1?G2PBe$#U7P3bK&Vn^4lU2y~!%2us*eFZ?D4((p!!h|n
zW6{1Auj$ubJ$MRN%o5BJ(I|w7A8;%5kc5ay_UZ_u=+6%d$nYHw@jlBtUw!%+(C(08
zhghhR6)PsDrh>qFP|WMVE-qNa$j~IxbPJF6z8S7IRJ|B(w3q_~M@ko9{+IWy9&9fz
zCU!o;@kA?4*p9)XDJ$R`yCIN<rU*VB#B9>EIw}=Fjdo293(Xifyixf0_o2kDCxtzB
z$DocP_L`HeL!R;X?;m&bn+K}8=@_A913myd_=zwBOHm#U!q<xQOK7wwLExBJghNU}
zdu;K#a<4VqJ!r%v|KX7Yok(2gilK2PR+=TPE(mgh#prPwTW#)$H^h4&g%^gF{N}Jt
zfpbfOmMYcQG~aY!MTQ*K7;iHw24S{CiS!NE8BF`lf#cw!?oV>O@fl>`nX$llG)%l}
z4V+rn6@)tjM8SAyO1eR^pHjws&!yG|m?xU)_L_|cq~FwW@_b)18ZT}bEJ2W|(M^Rj
zM-uKtV~##TB)R{f1+wIK%wrooz1Zpm0^<RSjY|N};2?1WaA^!`&r9H?$aZJ&Jdd$N
zEwt7U4K7J!<T}BDk1#Qwumev*^xZZRobH{wXHT+kdj2(_`Nr7BcJRvq6NzB}d-U>w
zK+X%cX~-zlp*90jlOTsUpp9?L%W7tmAJ?0XaEb_$rapil*tm{!q5SK^E)Wp76gDQ`
z+BC(2cXZuSKKBQJ-BoDF_}ZT{=qb_ymFLl4!1a-P>1jvlNV3QQWo<mpI}8!a`^n@4
ze(eeQuSf~Ya!zZ)iE?d`P1tnMl<e~$i%NC`dDcxr{qc^}h_J=)7zSJ$!cR6bW?Fql
zWTz=F$_n%8NfG7&XqaGU2xC~df$;|0QYtSqQap^si4vccG?<=RZL1!Ifog`U*8U5{
zkjHWI$?L=B_O7P1nvLp@4>EPzX!=FJ>imuQ{ThmU3f>6W!h`rg#m-TZ{u`M(8`fk0
zu`YseBjV~P*iQ;j5pr0e{)!=cU=Yy=srZDecgmYCUOu?c0JT3<0FAnIr!>R`w7xeY
zbk=`GmTGXD@~r{A^TL8e%!BsMOUfc<@*rO(9sw(6y#UKV9GozA^;dG*&Kwk&e|IG=
zeHhPQt`;&^?mqD(CFw@)5KhXgf!BZk-Gg<t@4ywq5lEQi3;F1i&`mhlgeIruDbt=I
zDeE#?K^x7F)fcMm((+xakK}+a(md5`6J~mluwmC5`s_DfJAHJVVrUpenjxslPkwNy
zQ*AEt$cI;QlLaHA@4o1YAME6z?YQqcHhKLYFP$<kfjh3i?$Pg$2r2sPl15~U5Kp%R
z2$Ap22wFl)#0nH+$VSNnfpSkQK(m6}p!ew?3x(cH^b2DsWUropQXitt`3n~i>s!w@
zy`#TjWVZI*&lE|~j{dwgf;-&^;wn<TkhB1fC=VWUWM4IrIuZ1msKRw}(?VbStouh&
ze1dlG-b2VM7{p=8kMD5yk|Y0U0Tc{Bs(pYDv>L!PvPO<y3DYm(0Dgs1ys(7!IBgJq
z*Cr+=N`k!p4gd=J@JxXdKbkcKMbssf9%NO?6-!J2;EM<_MJrl0^0@ZhC?=n2nv;ie
z$l~N70qFL?XWxZ4xYozyDa23$&hRG!*Cf3m6#Z963JvLG4>vX$U&5Kv8jL}K(o43D
zTIFM88quo`j$Zsq@Tj)~v%19_9=4AJr;K>0mf6yrO&buwVo_LruyTs=tL9Pm@VnZ4
z2CJ9xS7a@XG~;yp%p^!NV#nLZphpwX{WuMulPr^0iv4Jdg$QpPnf90Kv;9B*GZ1%<
zhC%Sc>EQ|(L*B*hd<ei|lh3x&ef8*9h`}t>Y^1^@X3^#|&}$LL8?ucDL<7KdfKR{{
zAA&rR`&zPSTZ^1chqov2*<`@*&&4Cv;_+qJXeW+7`Y;qg=dM!`MH5l#pM(W!1E_O=
zM>|XReSzr&{Dg>1Eojgp0vk3FgyKNw!Z$$!;dtd(HEMeluHd&7uRq*bMbdK*phgXB
z$pZcJC?<vqqFjYWmKn1ks(3MMQtbwwAQ%@1-gx+($Rb8oJzg--2w}x<5Rl~L<Qz86
z48?;`|2J^He1BFG9Y}5_ko_%?{9Hn`T=%L4a9{QoEGC4YZt0>H`-blKmyYvQnob^V
z#HpzgeEpaC&2+sVyXnG`kWR>sNCHa{BaRsmll72_;T;mG19V8b;IJD3;~kM7Et_X!
z2rUn7<W4LpIzxIMoB*=P3{2_Pc{T=a6wgp3ll73WTKGYq5EziG9YUkBt`UZabN+=2
zs{)dRM_$c2v7!bq_b;f~IwiPuO^D>yqh~HgZEg8{t7p~XUymuiTD*{}ql4qX>Q`$v
z@om(wJ#2ia<MpocC#xRp*}n3p)d#NJyu9WY8n{J0kGVIGRgaqtq@_jlF_?LF20`{}
zk$FS%4O-Ez<B8Q}CLspFCq4R3PjpEXhvIm+e0^lSbc^#jHOY_HE@w5FR<y=z3U-2u
ze)IFrh`SZS`Fn`{#um5!r_Zvp&2I2Iz5xRh4dWv_8{r0J`H8ZVT`+UUrrU@Bqv=l@
z$eyv;VUf85L={NP{nll{GA$Acj`1(J@)bzeL6CC96&EXX+PSE5l}RfM_N6eZ&-b2v
zgdR=l!iCSe3WGL!A>D_?QU|Itv$LIY69s||C9TeyMN+k`az6SH+E=PCAx1mLyTwL9
zLQyxdAgh=&xdtYXL0??&SELBFwzeim-iUJ;a5_Ed&s0o!bWcOR|9;>q+4I*sA*wjh
z`)9s|x&|vy!PBnXZAdTRH8vYecWgb^8-8hTV3p*yScO8(fY)ikf6}K_(#yJp_E<~y
z&Ix#A&aLRM;r`y891z#l85~r;qE5cMLM$mknOi#D<}+DtJLJD<q;Ij<t_0U5VdB+~
zIE$aX7=9J?x>kHBom*2=Zf37ctlOt=LVM<3uJC%3ZCagl_>TtrlE-`PKRM>7J((_V
zR|rau+B^8cF3A1Tu%pXdc-C3t35h6o{_LXB*W?wVn>-mOS}s?t3!a;aS@lK2eopi1
zt)%X^2JsjDImHc)CzMvrn$5lwd*;H!keC~7Obq?7-|BiNU+%a<t=9FmRu;VgZ4`Lv
zJjWcy$GeY?f6$M)l@yctaKA#7Yl&dCxrK#puT~r5SYcsd@8F<X>2!4b$ILme)QYo(
z!4i+HGwSN<>jSZ&vpz2@?-}rL6O4g{!qyKJkUG=OqAS9_stg5w6at$NT<`NK22}~#
zBGqNa3Rj&_u=NfMR6?^l0upznu(g#{m2iGP?NnkQ)O-k9W;U(x%?irO%K9kGmOeEK
zVGoTZJmB+>L~K&tztXM54UD)6l9N>blTsQ#s&c0y&`zs-^2=x$wm`|hZ3az^vdHZK
zfQAnsk&du+W_`pqb_D0N3;kfzIkhE6#rhXmdwF@aA)|Mrn0Z5fFjNULs;U(VClWQw
zLA6K73>6&2qKT6*wF~YCML`x5)qVI-K$9cD_aWQNEYvxH#@U}Pw;d%&;Yzj2uk^V|
zy3n=!3X98^-<pK%4UHdyot^DAF;a4QXYT7?Fa$oO^m$7@c12iZw;SK!U1Ci%j@*N6
zk>#0HvhXY29a3?<=>9rZtYK6@>6Wi;o>K!?vvsIrgQt9beXnN2R@U$1JjL_Ciqowm
zzkhQccx0o2vB=2CtJ&E1;WN)QdFSC{4&ixKaGpXoHyjv4{4sbr#V%K#Sm32Sv7~-W
z!A#;w4?(g^M>}r-8@a+Dm390&7d`RT>|b68Y;X9{RdS+Awe9WE*+s0>39t+i<f(W0
z^w-W|n3sq{T5Rtp)pfrgHw`yim9`n{N+v3brz`&L4X4fuUZNx-#^4=Q!Db{XdZyn@
zi3Vu<00vJxm4^<(wo@Pd)9xVU(s_a9p;qoOZ;7*ZPoII#-A{xFYOr;kM(>f9(+YK-
z9fH3pX1q2G;*%ZX<?eBITJowOw$UU-JF&iC(wC$0sMgQU0Xxiwi@N28%Aj{lc5rm;
zLXlXF&ODI62_;m<%k_E}Q6)6EI^$NI^LQs|RaVT>&uv`HkrI=%xS)dcTUqU%Q<vey
zxBVMorb}E)vZ)J_tbb=ym*+-t+mwu~5A0KCp3+W^cwAqr%RIo**nIP+0;IcgqNE=k
z8xv5<f(!UP=QDVL2d^f*X012n4BpZWd8!*LQ6!~mW^jsplWxPpHoM#ih@J#U*h6)a
z`64g22z$x8(Xbt-xwDduR8I#4++qt@8-LfTtsfj5+zhLGqAb<Ux75+mDH_Y9?@;oS
z?n^ikdLWj22m4FwBYiCTN3Zftcbv<~$EWE;9drEX(asyqXxW!q<8KBUcCo3V2f{d7
zQ{<V!g2a<FSV_rG_|JX!Cx0*pL6HNYnYDtm-uzttlP({*4pNXTwZ<+m`&hF<VRTjR
zpSQrarpIPphpe7Wz6Oe?rPf2(<#9DzCHB<Ifsn$4W_-uLoV0`0vIm{>Gc&2FgZa+i
zJ{hO6sjml~ct<jLu%EZg%*=RsJ_tI|@UE`zptBp`yZ?Ap#v_N^yE_KNLhX#34w8{S
z=?|T_+;EjiHD2ZHFZt=_^A?a|cmU_T=7#1t+h#kKDoFmvuYnfq>$A+v9ZG7_y^jm9
zkwaxm!i!%yAt5-4N+^`S>D*P$bf6xqoaW`zRLc8BZGL`!(*XL^Xm2BBBoMSLi^dCw
zVv)gY!Xb{`NzQplIPD|Es^4^t0@-x)IZ+;*Y^ApGqH#{O!mn6#up2JsuR~Ti<6VbA
zkKz1vj%>K$lKom@5lEifeUJQ5Z^006&p16ZQ%uh?Q%ipMp8pq!jg@ls)2N|RJrAzJ
z`-=wItnr&EBKOnP)g6W?g!8CLshLOW3ZK(j3)}dmFpfNed)d_!<8Rn2er&Vt@3Ux^
zcM_N-S&LlnlAC=@ri!)oklmcCtLrc<lH?U8Yy1!3@gz@m@6Zt4JW_?i!=>V8*q%bC
zKDSBIEeYqhcK^&Xf=XWjy;v1CyhYAUSI@oWb^35`XO#1A1_lQ3Go_^C6zF6Sq}e;{
zJDRF+9CrNY&p-cs8;upTAXfwU$*h4rBu0#!BM-KSa*lJg4+m#I&d-<Ww38z95VXg!
zu{+4;&uX?v{^$u2F^4y+Hhn{cNRaX-2`hbaon)cekU|W%2N&Fpu$g?;12{jWatqH!
z`BKj%Ls&o=6d&a(h;nYrpaBW%zqU=`Zt$03Ecf8PeNSo!P)627U`0-oD>D_t&iDJl
zF-?@~6wv@rXMP&PNkk3gNh2#z0ecujP=L2tu)}DwHe?tQ3i^|oq^=HwI*(R7U;s?Y
zupl6W-=&nN?a7A^rE{}GbMiTs*4EZmpkE_wUi{pIC$O%j<{rA+Zs?RM0ooUoM>na-
z5S3*xIf^d$EcWm%H^Hgz?df@gZwLvu$}`U$V8jXU$|I>3j?YyH;=%j~NhFNUV9E)8
zmC5?T-7Ac~AHfmM{V<HAYXVdQm7BdC6hLM$KfTJFoSMo&)71s-l;NN}y0+us^6yQ5
zR+znj;2d-r_19IVw)oPa=0Qr6$LRRDh(z|+^bG-15ZXtRTVF9iwrf>76^%r?cgwVw
zAsL6`DrbJGzSyguM;1)^Da?NF-0~UGqZ(qWC@f>xiR*eMtynB^#VxD%)g5w3?n|tz
zqZPbzoQP~j{Sp9Q584;GA(v{qI>|jOUu*a6fM(v3B#jzCf`r$Sl0(ShnfeeyU_hXZ
z<f^Kws^?Q5+(#;N(Ya(mS=)`#BbAdUPcB)qq_(v+67J`FqG4~(_|9LD*~DmR8_No;
z=zRWU0*Qey!U@X&yXU5(=MLtyOCUM2?=mLd_oFttDu+6jN-jQF*kK44($cd|x_?^B
zeyH%MnVdVfmAolD)`k3K=JAoiaLh`j8mK#vocn2Q^u~e#2Pb?q?;Ux0dBX_DTY=6w
z()KuI^`1f!m%;+cj2cCrPEwl4w0-kKEQ-|>G=VTd3g8>+>tT`JzP@sQ&(WCYQ{-eR
z(X2g~dQJFIwI4o2prmSSZ0v_id2Yv*73t5H@T6!&GSlg?hpDk8u89~@k|G-mGMY@o
zV-pk2pTr|jc}zf17sXEuV0F&6+>jrAs1Jajq{h>NLA8yI!593b1o&pZ-UzsOJ;RUj
zm>e}PM{C6Mt2)<Y$-6={;Tk_mP8zl%p$K>`WrUdua+t~cBxk=t$0%~qDp{NBQ)Cb5
zri~ll?2eD1p`O(IK<cDW{#*!#mxNg2Sx};pXJd<9YiTZBEEDMS&a&n9y+v*Kgxa<f
z=_A~wmky%1fk5vrdGz8V9d&Uj54a3}iLfi*or^O(d_!S+n@GXe7lLyn_0`{w9r+;a
zwm%b}!!&oda7gATgas$%sJXYV?47bU=g1%BKCWc<_nKpmb91G<W*A<PSGlBj&5c>l
z1rxn+a>31(@|E31hC?_-6&s4aUhl^t+XPzv+(}+uUL_jB+xejJ_Vc&*3JM#!D%$Gk
zFz!;WJ=!#OI|Yck%k`i0K)y~#U|SKz!7D(Y1RGXIe*27-{Mb|f9cS(C(aoY<1aK%X
z&$5i>#GsHVJLxr9x323Y8O{jcU$kgZGN)!p;idPGx7~*ur8FL!H_&p;Ob>??pP=E4
zblOKx=@Yl<%Xz(V6wPs)LCRSp1S>l%5lXc^86*l&SidN4WW`ar0}F`0?9jSBGDDOL
z7cQXl3nxfY6CM|PFkCtXjy1IhHR#|~I)#^O_qLPs(m6QgBT=vodfY#tMGho0BJ4I2
z!F_*sq?2LR`}4P_5zX~JH{Y-~QX&rq+0Cb{r>92>%`SlOTVyj}zY&jOKXq*BbkmdQ
z=TnnqDdhf~IS+&7TtG%6QR4+WNfsP|vL`OFxBA;#Ql0aY3HuR}uMOf~U!qVd(39Rn
zh~G}GyD->F_pyxVN*(wE=wo}YSQGJz!*T9;bX!pE<Tn(XX<7y)(WIuy$}Y6baUvgI
zT>_f2Xebr8xWIGV4lc!AF?=&U9eJ@E4D&GsWd|$ki{X}8n}$E*)jXAgBmU4!^}tG;
z{Yte>O`(<IEtjuPQ5_uxl)}Qpg$Bm~Z)~=+3`$88PFTuz6Xw`>IQG<TEWcKweR&+9
zraP#si20GX=!ir3n~(|Y8?<yx;5BfSnV+9Of^^YMnq){759Uns&3@z?#zx(J=$-W^
zCnsrpLSK)Q*a}O(Xnv^<??sIXMbst+xeojjNLtx1GDB$>G1{d@bN_bR=q;($C5ykJ
zvZmpji=lR#%TdfsO>Jmc%sO}6?5m@L!^&AZ%`hR8-rI+0okdn0D+A1;PUNlEW^J34
zk*>`j9na1<c((G|_)&3}md6+LseCxJ4_>uRR5_W_3&+aw;N2(ZLJ9!TQieybicvW#
zIXf`XU5og>mne_hLO`~vZFa!gD;(5Gg%D9tWml4CM=Z(GV2t(D-(wH3S<fo@ss0Gy
zm#8aG96frJeOTzTee?qW5J<h5i{PDst}g9KwKecDG$!&HimX)M`A?EaL6IUjH(Arx
zCZAKJFTASrq4#oShNnbqlfM>g|N6qgxd!=xV)tQ%#|5pzTAt9Xc{k@{t@rR)cClRa
z)Q4_6OmD`cnkb_kbQj6#xIz*!+2&`5Ns&&HG~C?W9N!z?=r5j|l>#}jw413)BfG=!
zDVN%z_4`7y8Qy3(48Tp0ND=)m4F9>x{hZBQa)$6!^M4M3c<oyy7^F-_A?$$rchOXY
z%do;U3iE8TOE=pbnnmalM-ys~dX0S267x))S&mWyCclAG?bGIgF$hGomiXeT8dUuV
zF3GJrc;0aFW-U^ylu5aL!_o-tF%6#FlR;_L78d2$7knRb(5Ig!wPyB2!GlIiehlM3
z+OBi>F@KoNdntNf`DNbu;MYKJ_DI!(2S2>x7Vqd_x97Wd%>&Jc4vTRz{7Bdl72OlP
zVSH>X&CX*7>d%ihL2NOBI>Tpg$4tD}Wc;>NBjq0rgXxJH@@Y8K)sISFbpl*msx>cB
zqnICGFa)gJCwX2!+F1Lf@;Oo0eRcI6nyd)SYE5K!c9thnRXEP<T-YZ4?%lg5%^8Us
zva{mMuJvu&ZE0y~p7?J3QCHV!!-FUb3v26>r>`64hlSM|U3E=LjEULqyUums@}=Xs
zOIVt9Ikul`)gLkzizUQ1zw+mcK4o8FS5ZF0*-Os|^;AZS!uUM=iBUUe!MQx?(W_Ul
zZWVIvxm;aR!2PS{AC;Ar=Be+P_g%b_4vRH30D}6Kla-m-@F1+u!qPJJJw-)D<$CIE
zON6#{Zk85F#r_Ki1_toQO9{m){&#pJgx9{pS0!qaZx{2N{(i@!Rq|0Ry-jgii0{}Q
z%DHprt|e_U<*^L*@fA;|u0stst4NK}%q{5ap-`E(lMJU^3+-}K7}BE6u9ZyJR7RT+
zODxPa^_b9<K`LYRm>Zq-#y&^hH_P^KrW^j1nM#j~i*s;rsL#8TsqOEzYe8SQcQ)6`
zl`9`-WY`C_1iHGOvbNR$f_7HaYj$~sN?{?D!3527Dy^PkWYo%PZfMZTW&HVP9y2o|
z<KU)@^K_PzpGNw*=T{yly5yR~4!?NDNQkmP;TdZ5aiZm2_CD6w_2SvtSqJT&%%|}}
zEXOo6BO{|cq0#2nR)eb+Ny$hCpX|})y%bA`%UdsEsQ;7^73DfrRY&W#h&7y=Vh*?+
zQhBu|$aQc3S^wmBh`SS=>y8~`tzRvGzg|SJEXKzXCzH=NO~k~+V$n5T6Xec2mB_zm
z@x}$L``g*4?9TFeeer+iRR28h)3W@>iB(tse=qTW@U^f=qAp-wMV;OWP^yZgHpo+D
zq28!OEJ{Z^-}&GwS2_ByXH`>009g*8K-03ahqBrPsQ8)g*X(nq7_Y(iwF*7#>&^CW
zmsfPZ4Gqw<E)*k8CyCXF1KWY&Q3~hK?c$+mup(t3$CE5Yj}kq>Aa=e(p@Cw|k|3C(
zqN0!E<9A{J>=4WuuYADV#spl7BK+OIY-dhO5=dzTiQj3X5Ib}#=4&V{9YQ=q1sqQb
zfht8F%Rq#zK;_tHSy`q)U?)&2M)2oCEu-xwFqkANdS{Z44NmTR``9lTsm_i6>;c~w
z;7~2*1@R@(KRfGOKpgt&NTE<Th1e+4nYM)Jt4>sGg8GZU49G($O8Ar!Pft`GU%D0M
zT*&eS2u=0_<x~MB6pU_3EcTRn<~WMqZup{|wKYJFLa;3^p9<mOK4l$ZN4a$Ac%u2s
z_^&10)3UeYo<7}QL*YzHOvAe`ul~qgf67u-RaIyU<Il`u8<RDnT9R?Gb~7wp!63o0
z=64FWTvJPXL%6xQN2PDM8U*aWRFjgzaILSe7fo`rOe*cRKIO9cX^N}6@4|CEimz{;
z<7JJGegVmGoE8CF>Z8pJwEIh~vEZx11&D(^L7BleR$#m&6kWGg+Z>49a?9*iZQIsu
zH#XCX=cbB{I%o2A_38@8Oy~><zyq@WTB~!Wt*Mq4#a(||6;U23h&de>$4)^6JPZ>Y
zm8L&@IC!NRd|dp~BXoM>gJ6sBea1(WD=Uhtf~I)tX3qOAPa=T$U+esO4~<jK%Em8Q
ztfhha{H5WPj7;r_GRZ+*U0n{EJ((U>k-YW%*sQb8pMtANE*7BkTZL&>C$xWOXPMO3
zHa6Jthjeo}rC328{@lWAU(?@H<d%$MAo9(7hVS<EtcI3d&Yg&zoSk|5IO^$>JPn)y
zz+r&oONC-J6!wiqp}x0`9II<<iz0RxRX|+H;l!bGpi(;lFIPj4905Okeu6mxBH90T
zUdzK$E73cgt6#NvgZ^FM;&bJY42%FQv5OqX-p#;u!Bhta($$~LLSaZSmR$6Cr0O??
z6h)}D8X!azKI|=~Bc8H7h6T<+)cVgxu2Ykff@6jK1!b&9+1V1E8ub*f>xLDbuHhFi
zmSz9Mtv!;jCxSc@QCq_F`*^4@l2ZJ8EF;$LB#w)4h|qX*iyqPrQtN~BbPuZY*ctEu
zFAa~?NuC#F>C!*nyhT@~O8cE@HS>0TinJtJY_<7m4wuo@fO1@=po-2s+3mdE57&Gx
zQo~1sk_l|#M068yh>@2fo=+`|?3-c2e-NJ!nB5|RjB9WDN<btb0|r0`>_AN!eyS`d
zmzXU=ft9yp>$*u{)>xwf9XWTmPG4qm@u2HKUtiJ(X33#eRvvU5kB`t<k{P;YW@fd|
zruh<;JFlD5HRKvj35V33?Y%!nHEA`og@^X|xS7UvLms#Fv28_f+Zha<Z!>ZE`E3FU
zy1)2oai6ZgaI?Oy?(|beoAmVdm*uZ+u{N5D=srt80>>ZpbM~zeyu;nM|LtIZ|A`6e
z=bMAl?z6!aoeiF86bL6P%<VZghPl37)7VsNGY-)B4LJo6k|muyUyMG8U&+uTbBo!$
zQCyNz$y9MLrxW1P>Il!t-tEH;A*5eb(v}I8C-JU?8Tx3O%8OQX@B1LVHgjzE7>~fJ
z;O)N<+39W4z0(^19@P0q3ve+~<P#eLzGE0OyRyxsv=s+tFOcy@-Rt=zwsHZljliNN
zp%h?13_Co--FSA-S`Fp_7XN^IGZ9`q5zb$x*FM>J`RU8`N3Gc}w^2)RW>bOJ^k+VM
z^sfR+<Th=l)@um0j**)>@=Bt}HrE7=Hvv1HcMKqCBk~J_MQK$Y&;9HtV~QuAc}9+W
zrhQ?)lip5e!D<HStTk*As)e3=e~6*0`5>qx61iP4Y*mL70Hu%#UZIlUcHqLL5Ev6;
z*=Zo=UBW770!)*YZcO(%`1+V&Oi#!_0qajUW3i3Y^w(0x#D4PQI0QGb?0*#KLH}ie
z{{Kpue{WX)@3|J5r&Cv)>p=?>7N+g7tAdkrx4*jF_SlCHx5X~^SQV&D3T|#5p0$OI
zq+bE2Grc+O@ZN^T`ub;|Y<VxGsP5O&?q%g@rWG6NjJ4H~G;5hqTpS=FD%wN+m8F|h
zI^Gz>()AAu3sa;bYJQ(q74+X8-0=R4*H8w>%GREZYwB}-e0)#}*ao#YWM;*=k@gHx
zLrL#?k&K>h?P=1F6&A|vUFRCvULROQQYX~-s%Y1`#YF23zDl7d64mZuHINDbN$YrG
zu1T&*=j<zo;xjQ<k{)XcYPrj8>G9lqySx6u13~g3-c!8Hj=35p9zLv94=mJgd0|#{
zYR1(t&;_xdY%M92WYVfNHfld*Ah$2q+n|T;{-q&v|K`oe(&P`n@_;@N@H2YKa9ueF
zH=Vmr(PZrDOU~B#TK;-XMc>d6-$M(IT{PrM-y?_)V8Gi1vv-Ra${uOB69H{zZ2pn<
zMRKh5LjvGCdXwrYyj+)MWMl%A3%Tm`3c?A;5u2qoH`sU^1ovKZ3q#M3hs<;-r_~w&
zK;NRjj%{0<nEeKqy7)Yu^Y(_g+!teiG`6(peG|(`PEKCZps52C`{n26Rs(A<0mItU
zv4(Da4x2R+8AZ~($KFy5$Qyr&-C%cJ*7+Rkv@7(mwl0%=G{lbTvT;KAFv%#eAQk3$
zc#C7#@&(qsyFx0Gc^`x<S#rwStAcOi#^bq1+GFi<k&1?f7?jAdu;qn~?IBk~e3n)4
zJwKH@D}R4S(4qhz>6z8KiHujXvw16=bs8yhvmedRojXL2Nx&OeNd5v85|2S)k=5oJ
zof|``N*=Qx7kl?5zuwF)BhP8B{$GX|{_lBe|7*DA|NLvgZpH#gGY0d8N<!HG!>Mvf
zc6j$gsR5`^-+G))qTW$k*fvL;YQEH;K3Q2(QqqzBgWDpIu|32AaR3DhTO>%@+~<nY
zqYV8GDP913d`XD$E7#I72!aiaJW1DtO?(?ACj){>e`sv}2UvkHmH1$D6ztf@2Cr`$
z2)suq5mf5P=AFQ7RZydoL|=dwg4s|+j<<J>W&P3ukd;JqLHhzqbZ$AbrKN>*Y!XU#
z_}f&8srfR5wO|PTG3lr_+3nO>+wg3RQ!CMkGLl)zel-qsh*RZ4@V*sqbN)<#{12u;
zS5MN|fV=hl8-1tATxelAJ3XN^yZM>+0hHqWN{AGA+dABh+V<@PxhFNwEhZ3Ke^Tir
zr<mBPsJJp7_bfE0P*g^~L!C5aWu<1s*_RlHuI_Q-5&G?afHMSY0A~j8-`%w%{|voy
z+`}XENH%ch%M<bgIE!8RHc{iS)+w~JMKAX66^RP@Q-`9izD^A9c`!3sZqbMZ`F~<!
zTK$=pR_wk7arjmtniqB+`VY8SDt?`(Q>5ee(#FQdZWW4)*0;OtMS`gOZYwl~=FdW1
zl}g&03q}%tmj;uw*UXUsEf~VI{C#DK%|_M&Aj7ox_F8=U!MxKzxl+EJbCw*R!9c_p
zC{&Vj(o$0`Gx-$Dt({M&S41TX<*J8Cv^+ZBK4aZ15IbQ{fl5r&y1+aPXw`TZZ>_o+
zUzM)Fi4zyD8@gq+i*>3BG(33JXWqLp+-G^A_)x*IS|cMqU0prBd4<^0SC8W_#B*zD
z^2Yk?UuP6jzDdWcP~2MgDdW%Czj{+M40&FiNc+w$3w3IL?l)7hgap?t?&8BQKj(Gk
z{0!J_{pVLY{jAEh0;bh}uo{@Ayq^Wf@>-YhG_>;FlDIUK@Sa)4V@2a^c~y^hu}L9r
z*sV%WyjirBy57^z+!DU&!+Zu6zP{A)y!zVrInCA7nHkoY-irAPQ#Z%;Yj9PNURk>J
zBgzP4&SKEbGINW~8QoQqDHc;k(RZYVFO=QmOQvqx{%B@yTq0B0`0qP%y>E3x_MLy_
zG4Tlh`<pW7fO(|5ZfeEGHL<~!imG(Z3t7Bkg=&${Oybm%WM6w0N?jZlC;!b9zVN%d
zbH2gw#{tJgyyU094cc{?zbL*ncPq?j+;QjrNPDhPs>;SSslL7w-Nlj*8cm?DF~$Q#
zl0K~fU4c-mY0aZQl!AZCqKs%|ef%&Eq+!Axyi!_D8nAo&?#9_W4LC&4ckcufsJ-d(
z1_L*@sgBSlqKY!3Fk;Ag&2=dY4S`sS&dgYX^e6HCMku08152j^61KajRgi$+hU=KM
z$9qL`iof4lMpH=G8z~*XMNCw5Wzo<mi$&-fv9ttN6g>y5KIHJL#7RPCZtN?R6oq39
zWR+hjFF$|jYfe@I^cm>GmHNCk@5iI~EOT$@r<k_aYj}CR<YOE~eYX?B8X>4Y=TYBT
zX}{y?hVER6aj~CoqWvaBzgU`S=V|^0`|?O*5#uYaOhdP67Hi*81ddjTmYUs`XF3%n
zYTjw2Is~zvnc0BZfWG5unSJ}z8H>J~xFL1ZnT8>pQp`ez3ED#jwIn~K#_mMG<RG=F
zn*y~6{x#Alz0J*FY$lHUE~P*8d0Am&Ji|Y<PpZSigU(6;ol-QEvZ}E0^X3YUl?F2g
zmgn|@0JHs?as|vy(I;_82aIT6c!!){yJlFE2<r_5ELb@f^Q3Z0c9`y>Kk>wUo50BU
zIDZ-V&Ow%GJNY_}82JuWS}B*z9#CWez1-3YhRb1rz^?>YOQ93{T2BHf#@gglwqe%@
zh=zeG=PKEZ;l&Sp(&vAY7U_WmRD^%|iXkvDw3x2sHS{4=s7Q^Lb+IcNjTO-<7^=<v
z8~mA%$NY~?{;gxi2zKLokSGj&A^8PyI~-~bF{0TaN*hA+tP{DLCEw3O-si@`p9(78
zUyAXy`(XRJ6DktD&r>JD#rMKAB)NP(IBUP$TtL2*<A3Hx{x?n5s+q#x4=Lv6Wk5GA
z3>1m{WEz(piaq297e;=Y)@KMyiAE2wS1|uXznMaKq+@g9t94q{u_1<G*!s0gY1qkY
z+=)=e6nzbr`vAqL0?Mrbej+-kZEG`Zxsucqcv`Itxs7B?9Nxyfh$Lxf1!(+OC04%4
zEJEY6MUojzovs!RJ}Kp5cj??ZnsQ_~qUife5dkHLADq72zu=c>`$q$m@zVlC4n_My
zG{+b#(j{+_aBn1o^G`s=bsschJb9B;hCv7sAuG|=s!47`#o$IBQ6frZ!wdY7{aCb(
z#z{2b=_4%hFp7sAN|2Nku+F<1?lp_BRWt;Vj7;0b)BasCwIVU8+P&)_k+2d}6=Yw2
zV<-BL26&@39-U{Q5I~xv@5~Sx7m-ua_%X<J(%#;l{A6O(aJA5pN^PLhVc7Kc9Noji
zgWAf37$X$r$_^!TV$3`S8Mh$RKLq7{dDZ+^tJ|Rj@((kFrA%KQh>@u0hS{Lm^9Dh`
z*gs)_0FOy?^8j<b#EoeA@d^Zld%}QES0k0}#Iw|Gptm=XX$a}Q+<OWwZ;%Srj|-}z
z+IR2N86E;<;E4nA!YAO#H|VzbL5@ZA@_B_Q-2VTIL!Fe^xL94mvDrM{9YarQw9DWd
z#nR&P@YK?P-13)U2E!1ym?<**No0vz1i((;<is>0tRQ!UHd!=Gr4eMKx4%DSN<Ls^
zk+3BsMq+7?^MI@ze9sKWE#lTWZNOvstmYh@kfG%J_W~q&!ic4xWLaQ-fUloS5zuYL
z8Zv=jKc>?~%4iq#tJ_#<PMk%vjZ@68AUD&2)(9vyH#b}8t5SKfVw}j9xw-r1+#eDj
zl3pgW0uEdhm=dRGCSvx0VleN>L!r;kePKHltH=NUUJkK_JdQ?dlzJE@t00;(xU7=h
z+9#2S(!pDI-8oowrgz5G&20n@#$LW#KHuE#_vRSnbRPv>B2y{!iZ+M|hpNH+M!@sn
zz={pRZ!r-YRyfuybYrvKaq)Kdku^Q%D_82!i5j2a?#LS#35rDEXrdPYiu6<7nS<mF
zKr$AAv9Nt)ivk8I!+%~YaFL+dhnMmO;t@9Y9kT^U)&;4A7Wrp9-{ORB9>e+<oOZuM
z8z`o`3b|NP`)eCB86TbH?>v53-vY@m;X_EYpPee6lYy4S9Vsv!*xyAAbF&3k$bW14
zO&ffs-jixQ`upEIfl(fW4AdQaR_UiYDb~9R+wfe%oU2Q4-0KH*vT-n-QD(!-z+jsc
z)Y;W`Q1W&|DpDm7ewhZ#nlA4}TyJnSbUTF`-{y_!{=L6W-Jf#r)rryu${PIstw<wq
ze;xke*|%RfVvhZ|c}zOF2T)awEw2FRmraByy6jtE|K;AXw3-V9m~@$HX)j9K!4*y&
z5Ib8tM8rriIG{U(lL-l_FL#gn=ypBXs7#lWlVcMnkeWRDG)c5=Qk{XPYu~9)i7X?{
zJRaIn!EAy7WA*!;AkQA$pq+Nu?P3bz<T{n)l`7mMa4=jdg-7?-8{Qa>*l5mdX4=T|
zDb}hp3lV?pKsuz37C}lh0bz5I;k65;9+%5I?Fi%aHcU)N=>RU|G?9u2>)x<PD8$u^
zB!O1jWe#gd6l4%p@AhrxDz(+G=(HnC<B8d(Q0r?<cZJ8rHAiLa3pBhfZpz(fySMhf
zc|zfO^S6O3XFOtdU5LBb>bAGm`DgE%mzxSwPN9>5+h7>!K!~1f7%5vAXL!(Jhygf-
zXdGjim}%;C9}b8nQh7219kGL+forHl(l2SAQ1(a-JgZBuQ<H(fY&Qz7YRtUeC*snH
z#_XaXq8<+wH9r+3bGGo|=p+&Jos|9dyLi*ZfC!w>CX3`$^bf<3%I+un5Ykx@OB;QW
zs(nyQghJGcpkI7Bqf$H+#$VulFasNaF_x`lClF|ZA|k}CItFFzy1`;_(}Z^eP(#K>
zhVcX{ps#R&<iZYfD;LQKgOt4~Dm0C&gTWI2esk2l6t`O@u&mO{1Saa^<%95i$c0}{
zCt9C6JPx=f!q5ZSVtA=60wRhDs7VY)4!*(h|3D787)6^)rvC|%vTuL)Jd7vJkgQnm
zhSF|mnRd~bPYeT-MbZl2pRcOLP(`)JpuI~=OIt81SX7yvM4XOGCi%iZOZ-2%aRVMP
z$N&AtX~eChd_+1F#N>XVdsnC#m$`xh1dHX!KO#nu@s*xFvo1xIPUI@=ze8UM?ffiK
z-d^^%z%*^8eNgEIRX41)v2uYkUT1Gzd<f{vj2DwgCBduU+&K(mhYEzIlvA0dWN2a+
z`&%KL3I-#ieP#|a#s{)6A|A$jFMg57{x)0y_P4>wtM+)H41Y3{#qwb~6|Qg0Vmxf4
zYx|tCc5-r>@ECG4V*ga4w9`tm`HnXWPrF^1n*G>Ghqq`ap5!d{ixXwynk0uFZ3Yv(
zltz@ihwB8QL(*YtDTP{Cg(Bmt91+PU9KK#^wyT)_$Hs$u@ek`g-abpwV(D66h-)ac
zuCKdqZrQf|wyfd82Sq7-?Mu&VLa~sGqC4PXp~BQ9RkQ&hw{|EIUEHmfJu}jnB<sG}
zpv74{)jCAuM>$F*efYY@?Ck7rgCSQP+Lz}``8L-yH@+l#tczv)^`D%3ZpSR|9PSA9
z3hGk4Y;DcwK1-sXRvO-X1EugR!|?XlL2l(j9L6S4Mgjb|wwuSAeOj7FCr{3eo;1gR
z476TGBL@Q*f7C?XIQlft5XSy}{3>0MsjSe5qnT;1GjEd5lii5VqgB}0g5QUDSmLIF
z&2Ic0{C|0etQjX`89*VMP`?s&q;AN;i>SBBtQAcbtESgwpOv<0$J;uVl4fxqIg$27
z)!MaAIh#n7@AA8J{JB)|aooc*j($0e>Yo907J!J8R9dohf}TBkw1?aqK`lx5fB8UA
z$WDCbo4e2N@|mYyGKH#D8a6g*G{-Vx%QC@|?T12VyGHX9q2>{rUHDcIX-Mfbwx~_<
zS(?Qe#%B`dlU!4?_t*8kTB9^`CsdnNq=B3FsPNdrL+7~+Ejrj^2?WEqE}Nf3Bl24^
zpkrV2$A-?K*}<W7P(-mnIq})q%2sp_k|Yp2!rw(VM4BJyrwUCLNm{6tOo9MNfhkD!
z&3e&aRYY9Vp~)gdW<{zSN-G*a=1#sTi_B(>3bw0M*Pe||O=7raq!q3oFncD<0!vna
zj*KX0NnF5e+yTVDhCTLYQj`d*H_^-}3MTf0`wj^{5|c|SMqxV))=-uR`vlY??i)BZ
z?Lae8fq78f`{RvTtVwhw7>L@bbO6MUEX2UMn8qE!@O3=2OQdiDD_c~Xafl(ooSZy`
zE7j+k8qdC7A^YpX3aslZ?k6S;-Q&2s%z2YBk=#Q79l%d!nS7Hs$y+;@@(g3pUxJL1
zlNM=jb{byyHLsIC7m^;UOR$XDS845OPh-bM1O)N-=dN}(f-T%diW-`N*QK*YV9-|@
z{xwTfjm9%@I1ZSeSfsQ=Md2;};c;=XS1g}}g(bDHF@QRdc}XEAGjSY@Tv^%9XbAmf
zowJ&VY=G|GDRk9H#4tM5oKBK7QDqm;v=s9u*7~$IZzgU-tJ;uiLPlV<)(ym_0(+u@
z$#e*4Z>>~n0)jwo2A>3;M6hOXZu)e4DTC_d)Qx%CO7zEJ+x;YeBT~|1kGrUuX>^f4
zHXlq?qO%T|>Kf4h*m5v+U`p}c>l1%?`Tyh{+c<P@Ew!6eMYM~!Gn-{I9vYJKv94PA
z3>thm78u;j{i1CKXTmDNO!G@&REvyI#6i%iFFfZp(CVUjT4&4BuhxeaD(OGysBK=K
zeM-HiHZSJaq@}!i>iJxF(p~csBK+QJTz;BN<x8!#_35iOsQLD5@}q`OHG>DSR*N&W
zd6RSgyt!+u_||PMBTIK$U*X^8)>b;y9TBm1crbq)L?XY4#CYp%*kpmxIK28Njzb0p
zkrGEU%g^mQ2N~Ti5s_m@jx2mpTFMh!)9b*I(ymOI?EHIFTy^);*tE*)G1Q=z{dW9@
zg@)}Nn{gRmBGA7a(NDUZ%EX_(&5XBS0Q-IkTh4C8IB@DQleClfo0R{Gjq}{7LOJ0F
z;`8?H2^diwO4Nk$=2|k#d?O~?e=k+6ZO~(+OLZEKwlA?U?Wu9;lFKbwoKV;+>~|Ty
z$Z<$pdp$gz<hYCFB3!)gwTK1CQG=$$0`{_hmy71HD>kJu&G^@>xwqS|V~0QHaSwV=
zE<PO=fKldiSh;bDk;V&F!#m9&ABwc~?<_LkXIt|XO%JPJ<BRdcaObj>D@Pr3)F4M$
z(&jO4pkL^^4uQ`6iE8YrZBkO3Fc?6KoIADpCrp-jJx2w52kr|Xr)n<et>4?%zQs3k
zWf!)~e9Qgy7<ZE{qpPGQuY6#m)GyW;#(9GAe-2jnWn0_*u)w>HOYo(u>o-_SAK0FF
zqDfaLQtbv#>vmDmUt#aF#jjSZ-(1JO?&Kv=?!RBXdISrQ`=aK*V8NR@*4&p}FT0$Y
z{4GLs<I0})Vg=}6M66aUTUG*ljc=%0Z*M<b4D%)bzpYo9wHtkLsJwX6pwpB6yKCje
zp>CfDdgzzQO-jH2?jX_Yo_8ukM^p0}yatb;7(4%QrCvjQJ-J#O-tcPLE-Skk+tk%}
z{rc{bs52O7AmY{!;^LNIs(#e&SykO7(eHS+$o;u?{km8}H@1iW9rVaITR{1*t;^07
zIPb>@d<|A~1MsN)6yftq!y8U6>qnB)(w3;ty;QW=FgI4OHMlu7`Gem*p-(Ip>m}OB
zYZw~mfS*^6`@{i!f9tPLKldjxfwyU#J^R;qyO&YzfZ1O{)urfC)cZNat2hOBjMZnt
zGUL+-)*NP-!={OYV}poc;D_TfW-VH3lnXohIXHNYmHtO<XBtlB+P?8e2_-^8wiTs8
zGA0?bk&QBBPNq;KEJH1`N+gsal|-4#79sPHidZO7(#n_$SxH1Hl<I#z_Pam4AO9cT
zb#yoytgPpG?)$p0>%6b~cj{<abQ_XC5ML_`a35kl=Lare3?<rWh{r@nKY7dH@cAl+
z3qg?9K_O1eXdFXYl9ypFqpaNHnF<!tApmQXeclkraV6V~In3|hzb~W_stl{kK{s11
zEfyjZriJO;ZIZ*ur6A1ZAjt_tE7k3IG5N}O;4(>ftO=OtQzSHjtRGli9`UiT;JS*<
z{u+w-{+0q0i7C(r9MR0Ed~Ae+Jk~C#J1!D3?q0X+6lPUi<y!BWdnbPEL~*;6m;=_S
z6UGAF?2|;m^(asU=s0!mgDE8V*O>6Pmke5d90K=KPvJOSagE}_!pMw_jPWJ@;oJHw
zWpD#tT6T0B##a`I6sJ5X`Bt-@>9P6vjRkUC9FCzqXg4d%?jBD}OXDI+??t}k8_rwr
zaZoe1|He+{)q=6F!5;_j{Xxh9h9#>vb1L`XV-T4yT)FacL~s;GX?znm7@W@#QKfMZ
za*p*xH`_;6y$_Q6<6l~OwY1t#@16eHno|4W>v!E%!wXk(`)@w9pMc>Z2XV2s;#FcT
z_8W2_(i6WPyy<&YZbOauV)o{oe^Q&`C2_x`am7n&zYgBSNRGR?GZBAMG*kT@9Ubl2
z!0qrk8gI)gD&huyzP$Efk;q!6a^10a=jgIo&6ao28NNw@k50M&>$`_rg$)gSD>LyC
zoJ2T7J|_^d=deOjznkTnNL;vZfgpNHT?}$27z{>F&G9W^-#c~9-niMnYh9ewHQOAt
zc$!q$Zyx(mmE#9jgXMtRdwp}NTVt2g8C|^R?Y&31B+OO+Y<-s!%_AS&uZO(s=?$s^
z<&Mw<K&MGX^WG!N3CSpr&3faKEgqW#em>Yco%%@F197a*wN&?9Mxu_6j*%0)Y)})<
zFL4opIK^>KBxXGF%$ew_Yd{zc4b&VTk|;?{6<)yMx@IVl%QAyr{00fC=Wc*(etv!v
zBeSiott52**zJQ4)~$$K0`TF*?`HmQ>REJjb%)R^UO8^ii%^?oB5?<R)x)l?*_D+7
zgo%~aS>TIaFc?`Fy%()pTYGCC$NUrcHxGXPy!hWG%N`VXYMXiT%K#GfDfB!JVG-xx
z;LwB<^WA($g@B5<=@a@ZKI!Rpyr?KG<wL+|!gmTwxmuW;M*x^Y`g{^$B+!)v)Y}eK
z!9R_JxMl;i1MI5k9q=oBx%VM30z2$XKevW>=E$<s+xMM{)K`zqM?Oea;VUM0w_(Rg
zuhy~DJzk4<tkQ%dcUG3~6>bn4Sj<DO*&?*+-Z8!`QQEX=8H|$Tuj#qCY;rj9)spzS
zO}oHCQc3rt@{tcVrlzN1QVKr0&6D5AGIW5Q*(fuVc558=IPxAZrOM{rcL+n5*uP!e
zw8*YzLr2>!6N%d$Ny+Ky@enifI>&(q(}bl}?+NYQyGwlDYVWyya>CQze)_@W*7%o<
zHOz=DDyzK#bGVe|ARNbr@?<J_7t3r#ocwFx#hj5(%ET}Z;AUlKFTv@ERK`MMV<Y*K
z_ZS|)@Ryd6(S;>;#F&sFX+gk8IbEb0_0Fe##MAyA?ccBGo^sXLDX5t5SFxAn<?Xyj
z|1n#ZIC3Dh?AaGTj|Zzolzj-zpV9}+7P6fmch=$@oAbNJNv)3_HNdN58Wpit0V5l6
zBsw-02sCjX0fEuVYRX$T`;5S$<)Jv_&~q<D3Hg*DMjksaFR#${!yJgU8#`|~#*1-S
zF<+O{xb<{(oe?5iA3xT1a9HOxSQi7p&Uy`vb>QSBjc}Ogjeq#~yH-irX20I-j6ldR
z*Yf3By1HyoQ?`g`gbGDQM&=b2=>n88ja@b|RaN1oY%4D`Q6~Z}+4d_p4laIZEY+=T
zZH)jhnOIv#V*=3LKVw1E)`8V4IH;3fjpdJvP`gpk6Qq48&_yKxo7&wET?-frsjbMl
zzmV9opy{i0q{B(YUV$Z^VHw|T7<R}nsY~(FHVvhbV~ck*@E)dMyQtn2A1?Nv*CT%B
zn@t=3eO=t@|6l*67KlaUKd-~*D0O)Ie_q?^+z$V~Yr}>VXLdZF|GW|nOCpm0eOI)8
zZcl^3zwgr7X#Vm4_2b&>ZyF?LT1aMDNct|!`6e?F@?}R4T3C#HUZBW_&iP0r?Inwi
zxXRw+>MPlnlIa+hOqgCc41B`zI_iZY%o{Li8i5h*%r*K${uTJ9b~ds{YJS*VSh_#$
zj~5Gv4<APE=8WT`gWU9OSfBC3uqnl*#ZyP&Ca<YRpVOONQ->ydD?eY`$LCHjb?;3+
zFst;SAmg7;gB!C_NRGfk;G^3QYVl-tjoDF(dKYd!--_SjmkEx(xm_!4+0DZmE26>|
z^6lf%^7b9CVR$1hB?S%BfqJ>aQLblgO~^kV>iQqErPyv4+rFKT@EK}at`4I)hxk@S
z*v-o!^+ZY*ip*t@yJZ`=p0OC}!-zUM{{B0$#@A66pKT36KIEXQB{O4_EkMx4LvlI<
z;N;amPicVobsB5h#?70_5I4wu_dws>fa2bcS9gJ8>h@&Hm>xg;k+pk_c5z;-Weo^Z
z?DrfJ(4%X+kTu~Tcj{LtQ4ir+yhb#Se8{cxB6z+VeR1%w9@I2B(4_0owu%fZ?)jOs
zXLlCF3mxeVRwoy!>3K-7(O|mPKYdzkS_@3qQUV<NXqX+SqmyrHI&swdhPvSG;prMB
z(0@2l|FaS|a&v=Kgg?P(?=O9Q4zL98gv@&3Ha_oPPd=N(OE=~xE=Rv#CwKJuMvibX
z4GaQz4-Zp>{EU;|mqYN0DlRU*sv#}Sg%hZsotxU9LPI5p&dyFDz$JS->~V3WrQ3;m
z5?=%Bx;>?7cXbXALdjx@Xb6K%$$BZNC;e{&lfYcZu`C5b^VqTNr>4X(_}$s&xRRn<
z4Gh)1Sv0dua8;`s8XCwH7S=KeFlSsqb2@()QbjI=fpqSQ-u%MCWdsK2ptEpSM*^A5
zOvv*ldSFeCIX-EOl(MZ;zY%a4c0&2oFZGkZ=8wqg?ItHr2RXqK&wvuhhh{7i>pYk0
zJCbw@Fd?m57RYpmpw_$2JRhU=)cS1$hW{Cy2Ii1=0RI?$?%YF=ZDNo_yg{-VAUTL^
zT|OQ%xMp#;+T{c>4l@5E-&0Ao#nu2D9CS$YM57=ZwRJcoRZu+vv49{ByST&@zxs(a
zk!LmyI~NgD@M?!DL4k`dR$6n2M%!_D?<OSg-u`zLs4i_ajur@JWaf)>BuY0&3+PW|
zmGLAPKkxSK@>*|zZmo??P2n1zm&bwgupFG65qocHgD@wLQ3M#Ib$i>oNp+=I$!*(8
zKng>V<p{qFj^)cw<J+vQg|J5*2Ef0f%7@N@>3JMUoWdUXTKig>7G^x}>eUgrdc{<B
za=C_IiwbOzIpcIArlx$z`IDEQUpg(pVpAvp<D7cj^eEICqLN7~-oAZH7AG+E@$}CC
zSrj)o8~m#w56~0czWkYZ<;rO3cB2$ES3FdG1B2bbjAokM;^F0Wnvs|L4uy&?dK^K<
zb^rXNmx6cNmi6n`4`~NCZ*?Q5i+(ffh7F&uq5xV>uoBu}JGTD-Nj3V%{M;M|nn1WJ
z4_5nV4V*-uy6z7_K|xN6DvhVhIv8%H*Q9?Vw{cEwOP0N6g!mvLZ-nL9QBKISDB~7<
z)H$HFmk1q#nVX5Zxp)(uzMMEi%7mutUdFe8#`S!}_s&<R;5yjCY`gEUfB%t+czDaN
zCO9CXx#v(jDlN^K;bI)&4#k2vq~NM}>d!y_bi$1k826b~LE=o6m#wgoUVzSIl@KF6
zaKcNd?as{!n83&)%jgRY1>ezIz8LAndFB)J7MuwSBg)3YBO)TPq;5{=LU3TMu%Rmc
z0#Xy}^3I;8Ursdli7P5HTwH8aT~R*pVI!_fA_2>r(t+Q1N#~{|#QGgOUbw4zm!~K_
zy#!Fw3W61C9U@@=DhYD>@8Krn+#^kdHazd8gq+Yy<$%fS?LSt8hXKf+1xHfplC>+@
z>TvEa5F05h1iT;WaaUaZ<&|x57@8CFrGP#Gb8vk_LmeZdxZU#xDR=uHlg{iz;pSJ~
z(^MC~3%T>9d7vrTNOKwb1V#<q;PVO!%G9jP%~X{D1&oH_;I&a%VDwa)<0&4#KR~LT
z!f|8@0z4f!E$QY{!_8czg}t-~vJ`!N{V=P7id3(bc#4oJ%`L|?E8a&h1q4?ibX(?h
zQ(^+Jc||yOJ3FU)DtITUpfn-AnltRGeHwCw$YohSm0Vdjt_BRAFXJ~h0Cs-bZ)O$_
z`qvtQXYJY;_`GE1=dWOkV8Jhhm};N5OTZ6{0_U}NjX9Mnjpd@rHgxu5Zv>$0eMwQb
zkj#S;O40nOt%=V8<wQ_0bDM;O=7W<zr=V<d1_cFiCxMi@%GXKl5PJ*uWQC;yk9@B_
z&Wx}Agi2Z1v#q_|uexZox~wpk%z&Vh*ev|O-tg|DN4I_n-bGWT{6cQOQ@I<rsDDKI
zHHNVc3}Q-PhlTlG?<lAFcUM+sCIQj{2a#D`&j0$<Z|o2)O;=Hh#mBt^t35L9!PN{+
z7w6|cL;)**xRr<aN_mW)dHYNA;GJ<|!nJmEG-HLPR}KKASM7Mv-CYu3(=3L2iCgCe
zy9X!jJukr=j9hhyFu=@)AmzKZZIzV7Q-?t*8cwXsT)BYH%jz0y+Kve>X)Jv)Ik`J?
z=>x<Y9#$5(jq)E<gG!L|I|dCiHMb2709IUDVH1a{J!V3RBm5O5F)1kt_mG{q`|F50
z!*x(fVaE=O(2k(Roa%5#+0wHy(Ua78<4(-EJFqbGg*#00&K}o^${FxC&h=v+Fii$V
zbKR=_#;;7FK`o~0jcqD-{W@g*ylpC=^!4uW^YeR%u|=oF51jSpr%<T!tHjfcw~V6C
z<lq5A=DDrHcAZv6j91DhSO_Of64x2bkcW_|=rqi1kIT(puA4|EY46z+(_04_X>%^=
zYqr~ukM(KBA1^V@N<<8p@nh4b1q|&22U2<$-{ceW6XqqNWn<u)Q!93{YVkP0I)o*R
zho67r<>X}BxIZ;C90)NoYl*AOQ1#m@MY5X(W_(>z!k<lDW#PejFz*UaTXfB2KRV=5
z6uPe>Md|U5=k68Geb}zBF<ML)pEvU&-Q1pKRX}Iraqs*z3$LAq?ruTC2NplcO~<}|
zu%v;b34k!@_DiAk4Npazi5FU(Cri?L>;Wdg5GvJ(IX-@#JkemBw8?VPZJRk}E{rdh
z5i8ejZyKTwpo#=ppI48IlvS-}(v-Z{^;LO$``f}8Ne-q&gr=6(=|*3Jv4N@SX)P@M
zgd^aJM!6aIM#Q#uF9nM9E=*05Q{~!)6(I`)0F~XD!bIc08v&eTA&|!C;B*aR4u>*!
z6ZN=65eK`@G?&~PH!^Xaj+^e1UtVqz`U@1Wp1O=JeNXyb@+~B%PhB_PfklP)(H~?M
ze)u8nq(RbNmzk3|CTCscMIpqtZe5PSNC&#zcj}E6SWS(S%Kj65k=T9Z-(O^>h0o~=
zD_(UNK>0I|SHWm#dLF{PlOk0|hjq!4CD&!l-R)jk1t{!lCNaN^a_A}_2@M#kF>3Y=
z4-XfP!h>m9=C3FgJ?P2RCx5Df`A=3Fw6W*Quuh_pK2(Ugm*HNKS6En)+6{+QX)lF|
zXCc^0l*%=i;D6s+wk7Or+7(?yz%~6O=<u{|4wh~{QC!2+?5yChQpXq<0Uo8*a8*;9
ziw4wXZaPhA_{++$6Q8c7yD4nFeDvc=|Jl0NWDYdqAYDfxczJu9X&Qfp=v6{^dy5l#
zjc8uI<3C{|0<8IffQU#e@;hfJu>yKdsbJJpH8C;n%T(Q;uIny7D3}56+9P+HNbS=I
zgEcOf3#w{L3nyKJW8;nw+pJ!*CKAX;mON3#qE5FbxeES#2b@3pFvh=Dp6v_!`)M2+
zQ(|(AsE7)vNu<5J0S~8Bn|Uu2JbZl46(Tq}MT`mxD@>>(dww=Xvzpn?uwbv)G$~aE
zA%~kjBDr`?P^3_XEnQr>khWLO)gjC9|0If10$9o{NER4Jq(}odMN^0i7%|zXHW8AH
zF3pp^1fjp6s#)7TdgTx<)|2FrWm0{^&f6`2m~KgZC)H)V$z$M^K)SjTmeWJ~nMKl`
zaV;s;lnM2KvubTSj4g+5XJ(2`*X%Oy56o`AVKQ21TLm#umF9CGeepI2ktN#2(>T2C
zJ82;*DjKC47#Dit1S!pt<qK3}1SA0g89NRl5NHfjYil>Z-_RQ5ui!&EAwlEHP4KZm
z#Y<;Gvn)@w;$g*9e>k??{!w|?&0E|9Fr&NnnO2G0uKM{CN)V%5r8I@^^!|SPc5~+0
z21g#|OD@R2@M!RT`*4|srKMJJcO#Yt9#)R<FqpYUJ@!e)K#zGk`{}i8^U=1(E1d&o
zTf0((C}|sR)Z`Zx3A`>y6<)iKyqf3DWSttbnl-DTE(b^cYXdopw!sHo3@)S#Kadt5
zJ(hum8-jTQ92}4ayzed+^?H4O!a^jMQ*5oESLq1OLduL;_;2b;499+7`PE4Xx7-0c
zRGwSq8hVF#yUUlNV)qR`+uEvsO>d?);_r?)6cqT_ECf=b1RxNQ#P9>|eI_AcUxab!
zEu&S=s;m5ADAxA5;@hFfyN_v`x`coU8>Z{uXraXqqM{3t=Oe4@V*=#d-)LCCa0ct9
zr@|w74)m&2`=AAAr4aiw82tlAYE7Q@o=57#Sd>HO<nW+;;ZUOvoYm08{dlO{D09RJ
zN9?C5`+vOJOG7RI<OZ!;BtSk*FbHd}YZvY+HbAYPG}=>LZXn)97J=lV_OZX}sdFD_
z<kJER;MjE)9kU}J&zMN*O9A)0P_m&kj`*ld^6>D4p<3c4E+i(NM(mB6F)LsU9rxh$
zt0^@MSiW*4KXW<h(wLl+DA3h8m9XpERYCFf<kcM^bpm~DRBTA_O-)R~_G!$sqJpx%
z7hF+&0$p(LpWi%hA2f(k5S7OJ{wmqhvNBY5oBhQGA=rSk+yaaArZ3-OM}mR3$Nxaw
zG>gI7nT>$MWmQ;39%H35$smb9OiyeGzPAor7uq-!vr!Gi*oRF)yRneRqDqLFS>4TG
zyhR!?2gFDi{^r1w#x$zS4y4)BL6Bk4P+@9n%9441>30O&K<t8ENKfY`re<au;T!83
zpi(p%bG(rz8BIR!EnD=iS6T9~A|@||;mR6pK26w|JTb>+jhIG4OiTmTL*LOSYau)7
zh1wT)=RhKhS|!?yxzO%!!OqTp{wYZA-hMi@1&?Weqk)ZuAgkVlE8F~#f)O8nw@lbq
zUn0wi8<C_a<M0_LPN89(TNSCzf+kT`Hnt6tk}F7?q=;otG=9w;LKUrQH;B|N@xp~L
zh$QE#uCcSSW<n%Z4xADT`))aa3WvJ?K13%Z2U{|&3Vf%B{(`ESef##4EFtv3$U8T2
z5MOh)w#5Arg&iNQER#&^IlUjerv_U~9@JX!b5^rBdly!bOEN+~FAw`3{~a{<JPSz)
z{uOic^LXM*pnNot3o}CRf%Z0g>N2tn*WbIB@ApYGQt|Thv*Pn3;y^bb^~0fWWH`in
z>YqI9p02Ef@)GPvDzf0CO-)UjSn69gZ`{a=;jCkCPr(Mphtk@by>4!95u#^c;TeuX
zkCl}bm3edvfa)A5b{f${XJThJAR&r65c8GznwE_L2eD!(Bx%f^wMeonEtVPL<dijc
zh)rvu<S5E88QT}}<gKoYSiIFYkP$|064#9-7xF21U8<A^e+Q2lqrdY_rN?oMw5WxJ
zh0R?ls|5tkqA_S0VDQ?gazPkAjX2+%mY|(gj|mll{@gz2RA7D&VWsA#k9k3?5d2$H
zZVSXk(76S?f6waJved&Fs)^ntHI1z#RFzXW^Pp^Twjk)k0CuBFdTNduYibfJR;-vp
z^NNbySv53qbCaoXgi02r1TwR3@!Hn~vE~8QwT1y})dS^l6NzX}oxyQPnOkMD-4;wE
z$g3MUPhK4#q_1i8k~+f!MSss8Eoe%l;)(P?0NYpG$fd^5<9sQnlGi{zYD78LCqk@n
zVMpXd(cRD{`ydv}=UX^`zwfIkXnaS5u?8lX4l4h>1_or|0M|I&0MKsHymcD7)H;~y
zNx{w_4#grYbPk~~l8}%H+k5kvvT*=5M;j3fkw-_)%>`oXFpuFLE3CKz#WNiZ($I5k
ztOs73Q{UlJefdJWzAHok2q;Yp3w}gFB#ylB1wu+a1gFVBWOo#qMvA(x7e?d=lPRvC
zuo^+dq=13^HyTqCL{wkwV`0gt&eNTgr@c41eEG5?T)zU*9QX`Q&?P6Z;WCXj>_g~y
zSEjw^fiiYvX99-(Fy`@9RX<5cwcMzgtCGiLu*bMqZob&3qDAiW5^t~rpqrs_qE3X1
ztXowK)?3>?iX`v6{wSg!o&FNOo97`d2dQlMBrmkG8V!)9Ha6#QKiA94cm6AODTgmA
ztEm+L?mMC_i4c}q86c|){f4~3FtaN#PbZVc#84mBMMYLI1U}E;bW5Z9LVNycQgiBG
zA4CVe*#}u8G#w}ObqsPxksrk_Onk98$?N4@vEt#yO$G#k0`1GO9fOJ2uAPUiN;dXS
zgmwj&S2ZpMa6xP@l7al6SIW?T{po)t36rZj`TE~0&>v3C1k0ta&Q#h&w*Bt;%UGvK
I+cEtA0Aa&@yZ`_I

diff --git a/reference/plot-9.png b/reference/plot-9.png
index 77ed1c778912ed1d99a09668bb346e59ecfcec08..2c8a192cabf14aae1d94ff146a170bccf37b4ffb 100644
GIT binary patch
literal 26089
zcmeIb2UOH&w>3U$lq42{1r!A$C>8{Q1(2>Hh$yHiC@_$K0#c>-5{-%=pjc25u!2%W
z%FxjWA|gc*1{ekqL{Q4mhc?WAKV#m!x%d5lcfH^D-MiMk-@UArKw#!Kzw$ii?6dbi
zCl?OrX)pX<_<IV4vQTG_<{=7Yt`~(e$Ku<0_zmOw-2r@?XQ8XDiSPKD*p(lMZ{MBT
zbIg@O5jju(nHAAFc!EOtk)ory<FME5UZ(GjBkqOj1J$v=^uMB>{YHAXzSScUhxb1$
zm3zZ@uDB}v@u-)=Lgj0Nx;v$}>>l<QdhqzMTl&C*$bma*#*0tSyGMPqb@!4(GN*rB
z@XeeI>#-TmhhNH`H7_4~Lfw)^AAU3J$2MynrN(8^Z>@+`Xq`wsEFWD-PM1QtU~ss5
zHt#1Jv01X@2aTQIdCej}EZ+NUKKXv>z-%+}J?OW^1H6~6xf&!ye(~eoZwknF%6Dtq
z=92Gw*8dZS_?_>WQSoHmdTHsfckkYLeR-VlJyoxLLIs~0lzPwP?tF^4u&}UrjGnfp
zrhK&Yscq$5!{6ZGdEamO$MYJe<n(1T#G@;<<fFqnBGij&I?0J#`r+R=<+)eOk6ZLN
z=Q=l@h$<D(UL{cI%MO3~^l3Le&D^hjNnG%jYLzYhc|ECKeOYuII{W0C%4mhMk<Yaq
z&92p99CoYv#*G{A>Bp%J-mLbMbj<m1&TFcV#@Jf*-MZU9Jbd`@ps}%V;LLEGg!sLp
zBDHw^IMJ>cPqW^PYPEw04+`L==6;`b%|ndLydRc33(A--$4@6#1&n`qi!&i-aoeNm
zq@3%gH?NLwc9^=8y@GYiF7?6^J;l|NBcGQnTei&k*(|=2xk9Et=&Oz%eov>f25u-z
ziO~m>%sICG!vVOZw^R*X>79dRViCHsg<BV?3_N?3m>5Ft_sm4sut&IcthZHv4!gyd
zwW!ax*}1H9EJn@WCxf&3=Dvb~-{(7bUNd?*@ZrUM>+0BpM~*BL<Bpf{F8<~g>zDES
zqdmqByF6{QPff)UAEl%?xW_GADZjNTA!W$B)uW_(As)ex&z~+WS?N@8nd!?a%n*1R
zpJqNY6qORb-G6e#-rL(d$uh>h{#Ya4m#J7EEFj^+G4~rR?PWL>$A~DN`Z=`2EMTIu
zai8RsN~?^<yu7^LM(a4H1y>?mQ9M6@Gb!(HrW++wDeC_IU`i~*%(w5F|6r*QW2=?g
z#~vj!pU)3-)rx0E)1p;dvgs!V+e;e@$C{GLEi2>H)t$N~@GL&P<h;^Wd-sWBn0)jy
zBkj?n^(H1J`P8_{$;n>XfWc6!YRjIq#Kgp2Ja-dSM-#Q`eUh~{y<saAvX%PPX9gp*
zrZOd4*$&#%&1A(zRxW6neYDWWeU;z9^TB!(r%s1AQmHxOS`nhm_!Z&o*1*73UTwb~
zG%%=*PY>kun3ok#R}_~-uGKZ}_2i7w%L>^oD;F+Y==^#^LP<p6bcf)SI=k7`d8m9H
z&1brm)9)!~Q*-Brf|FU2f0PVW`1~rDJ#TJpOR6hUPLfFN2>0quFKZ8)vncqXZj^kk
zYIIp+wDu}z&5`c665`^5(#A{Y3(45m2Jov4Xr%g&HCk(Xdrx@y<53D)-*(o@=t|GR
zlPKwad*AZ))eR2G1*eAmTLmWr=HSxQZhTWP_b2b6j}1A_mYSNq*$l_Q;W)0!^-aeX
zySuwzE<Y}QrCig&!C{k0_NG{$FOSDx8x<|ScI{eaIJ;zten}SHEZVn_ZsuEhKSE8o
zr#>ZgpX3s}aqvr0+RH9AcCKk0t;I)5PwDdm`LjXh+)27+$ICD|x9*ywPk!EE-<%_d
zRcYUpxgpm3)6L~r8>y_dQKpN9%)EY4nfQ2xtWWica(#=s#3OHOYJR{?92{dfm366e
zCrr89T|bq$R;$u`)1z>@FU~EjknXC<_hjX;il!_khB^<Ln~UAtCn;~=@B=p7g){tW
zinP{82k*DzdDx9MduSD<b%ftibhdb3S64bS#S+t3n>had{d-NxD;MnLS2o(qhpP;|
zDiBG^$x)Qa?|s_b<J+H`Q0I@`u`gC-JKJY^d{BCf+fb$^NY=n6vjRnVho+_Y$S*xT
z9r%#Yb+KOJi&RHyOk0Pm)zuBt%$VU@8n-F67fFwBNR0N=Smri!N@(<Qgx?zWy7wfo
zc-kgoN7xq!a9lb>JQ{wHo_RbG<Jr>Kt)C+nI6Wvi<xsd(L-Tl9gjk$Oz%M3JMybvH
z`Y-qvEWhN>DrDd4jVnL?bX}aEODYRNq{hDZ?ymEnCjGw{#MuruBlbSEEDNt3CCe|m
zFPeX%AiVF`z18AR*TuLUl8@12y-#O+VFhx1`*RTM$kr5`DjshS&7JY>P1nv~R~zW7
zUW-xn^~hk*Y;0^^#bTT1$`udCu(mR2mvpzfz4wo<o+2TlTjfogbkJxIy{l=8Rm{~3
z8^ykSnRq10CC$3(_6h{L%CX|^d!g+QuIRlkoT>6Gs{JB2$!3XRx!658^jMOHhuH6o
z)vVydx_e$Uq@~bTp;7(X-crxvDQ0ue$=djBjVvs`Q0q8<d2#W*d-t{@BsvW?hlGT*
zC#Z8yI<5$s>d%)=<L_)TR7<n;y?XU($@|9!_f2Sy`5(OyxI7)RPg1GWH!;ez(izVd
zZ|hY5iOwhmU+yZ;W@qO&a%Efd{X_5FyEp!UKk$aHf0>A9X4VKb@qRyJXR8w~@fqKh
zAQ~lHxj)Br>b&~&2fdVQlcDC*;`Ev6E_E@^6x+!?t|zTnjUJ1gooqr&EuA47z+|C-
zcKE8BcI+>8QsGKp9tlo4i5y5VD^$Xwq4f-t-A9l8>6e|33mdK<K76?3tdQ9aUrw3&
zOnRMvlT;i%UA0P`J1y@oIWbb{6tIO}H1R1qx{u~P{3!?5>zY=r*m1F7Rk1I-KOMJF
zOS43oRj?#l!KtbuXX5=qw!>YP0#3f|jLtI?3Q10>oUAl|Y3!=v8!Fx|BtxW}OJOu^
zHuy=rU7b6u&d99Z!6)**+ue>|KPffU(fy6;Sc3#zRNnq*QidEY=Isr=1f2JK1O3h>
zlWHZ$%6hx%qRB5w4lPdFox@7mWyqX0AIci)A1B1#vQr;xuuQkAyd^os`J_;M;oP}%
z!NJRw+-!PtyJAzq3vm@*?HAUy+nG-pFA!5x8ADcHFC`U<T%=|0R5W4b7#D74ovW(!
z<<Bkj(|2C;+d9O!UMhnnOGv;F5C|2N4si+?jvg%657_K8*1LkBfKZvd^;Xrf%#UyC
zrLltxhF(kM(oL-7v*VE;)F$6Xd@|#5Sb<LIfhJZz3NGUe8-78$j{ag(d3x@Am%fg0
zWf`Q5dx?nv2ph31#9zOD?P6}i7i1mdZs=HCU*%wp^puTQ*msRBXJT;dSY!8UZ+`XZ
zQ&%_yN0|Dymt2qzzgcP3k(%EhBC6_BVy!lOGiS8k#1e=l-KF#OqokxzHzj7bogG4`
zva`I%{eQXGG_=Bmmn9jch4YE|{iLTh@lk8m=cX*Xj{baBxPXL~W^0ju$7OBNlP(t>
zxSIJ`0S;<2_*<-<Q*%ywX;Dh$OJ?KD{_^9Sb64CEVDWjcRB*hAq(`#8MQ!~4gGNSC
zQ%y>gH+TU@$z@?n@U&^!*hXpT${sHV_s<WbJ~h#)o=gvhQL5=plQ<f}Vz~OuxL{d6
z?qvs-emmdhWadE;WS618XP3i<hJpw>VSpD}N-^PUb+4C@T+wygRd1<~khW$?o_kMw
zMjXd}x|%y(?J?!F@<F!Xdq;=SFLk*dCr+GDuD;XpQDRNY5sxt?$;w~Oi(K~&YPq*N
z)Z)YMXT!492cFHz9osELU5b}hhL2A6q?PfKlB(bM055T-&{{%}bHzyhKxoFJ@3(t(
zX|=c7{cJl2CWGqS>XF3@SvEI=*mLe-F^o0Yr73=XaHSm)D72}mDd%&1v<stnx>Htr
z|Ni~={SQ4peE*>!67TzX%M+V#YUwKXBG@Y*^PhQm=&_dF++rLcY-dq+fCEP(mwH!~
zE_adyrIoGYTK%J{?B*+W))|dtv-OXb3Yj;zA0;WyrEg1>q68MG2$F`y0^zOe$!dG_
z$R@wWt?}szW#8}j+g9wVFpd@|N@6imPZbj!FTG?SK;s$$+leTu;CT^`qy2?lae)E(
z0or2zZmG;>pGS<Qu~~X^_!ik)UlZxc=?JgL>Uwk2;Mv}$v=p3Hw%K|1R4X^Y(V;oY
z%x6Q3cUMfXS72D1gIG5$B_*w=<iZk@m|6KAeN9>!)t*`#uZ0W^4K?>5tkd$U?a~VC
zZR2*f&|*e;Py5!oHcpW8c-tA*zH3rOjx>&I+>~jmf3+$*bHj8VmWXTLmoHzs8#B7}
z3>)RDws+m;ZHaX3`PqgUuEzP{I>UGQ{nsZZB}ue%+^uNIxxN6`pC&WcK4JQJoHVIl
zT7-h2$-prqyGSN*vNq1RuXwcg<0ro#`WaOoyc<%vWT4*y!7d#5?B4zz_CPMvUyO=h
z7dqN*IQz?sbGxxcT+_4x+}d9x_7`(!Zpb?r_m9^9!s4q$mJvQL>SLp+nQWZ3ai*_z
zCZ&>pzsb|%I@85mHdiS*+t0jg4Z#bk{==0DWuGde$<C`P^ql;hKvQ~IzvE-QuIkoK
zdhv|AUyn(vt@gu8Wq=shzVDVuZuQY<%EqgUyhh$?n}}pc^qa6(;tAHQn&mXh)VD9|
zZJK%Uas@|I0(;UeN<$u{r9}j@>9eRbN9yz#tvE~{^-DUQL~!tWS=ope{OW%B>BxG*
zDVCEYwf@Q{x;(?G^0lg)^p*0G_R`Rf-{;HPk0gxN)zyW~PUXLKc;m7yR&Rpu-D5k|
z0w8Rjw_y1XwAKJ$z?PhYm#9WYLIB-iE4EmbkW%N&yoDigoL*}YV{;_W#r~hR#FbN{
z%S3%QN$SVmp1&so8()h%SaaLC<7H=)LW`HBU!R?MC=RF$M5n3wp!T!ZXq{0<!EjaR
z!TVaBoK;oGOQur~qs&53%c&tsW;J`0SD_qfCoAPPQm}(?1|nW+0Pk7u?=kG2o%fW)
zI#B>``BDGwyXS>dgz~bk=&NlffGh{8PeQ!CQla(YWeM$U#g?F1b9g9VRKI(^L(QEX
za!xG;ij&><C1Ui-5GXAH*yIAH$9G0uzmnE-V{2z;r*nU9*C(qMGyl=LT!$r+`)q2L
z6D+h~#pX4n(g+SF@wo$--yxfe&AN&J$td#x?{yow3D3{)eMO>=lzhi}wfJS4>m6uJ
zK~f8uh|nakOMSjx$d)snLQ=?LB!tLWy}2Pj%Bo8;B2PM{DzgyLrTsadn0>8MJg{--
z+4(|Raccfehj-mQVq{eI?xC(FkiJ7p*Yt38#N7FUZb$cvxQkr*q-#F;&VzOB`t_Hn
zu$nRhot>RkI3u-k$6sACoC;w1wuU05t73(C<Wya62Hr_AD?`fi!UeY@t2zwlOcXdV
z36RlnC0OmuBBk}BC{&MT*&b0rmL44Mh$t)Y9&YcWwT2^pjdyFS3u}wGAHFE6suT_k
z&|RP65OuP*A)Qy(yt%o>^5w-9azJ-Sv1JhTwUERvE|(LpQtC-=07_FDUoh;%W_m_w
zi+ZoQE^96drXa2Tb3$DB#Bdk6&ifA@tRdyB;`G1^%lD5C%GthugsMgRJpKJax}46U
z?H@07w-yItNh#;DUq*-(?^<ooYANi{51cfZCTL=VL3~MbuB#=m|69}n1XrvAaCOH1
zEW^@JYF~N}A3-jXl*%2kzWo=DWmuIWncPE2kh3TWR_wfLV9}i8OmM_X6b^UOhrL_#
zT5*$t;vXP&WY_G*&lJj&j!kcQKm3~niraUx#nUc8voSMDScUWbH#)qlt0Mf$ouq%@
zTMnV#FzFWO9ks#P>feKo`DYm&cPY17)beu+SAx?hsyRXFI}Da%?b@{h0tn7lt<{uF
zU4(utp5S1h{^0%5^2>6>DUpc<1u7w-p`b#tv*oetf0dfulmuY3S67#jl@{bFl!zGS
zrbO-D9OMBM5$4Ej;iexmG3l9F5#+I9<Hpw<wQ{SBfH+ExBkF<N)(xYhqsY<++CqZH
zHcCm^v5T(T@NUOH#XbGA7yBQ}fc?9j#bMe`A&stU<yMxvcKP3}2r4+ahrXLSIK`D6
zi*Td9uS=M3D)3Q9-hvhxxzh+(KtuUkug^dAe5rM=nm%?`W=@ddh_qCMZ(@YB+X~#o
z%rk3+dH3=^i23_>yYBxh{_(%~Sunq+Y7}kVy0wJViG(1To~RBCKT&agH_ef0QOp?&
z{qW*^nOxbbC8*A&!wDK7_~i|VulnR;OV04|ha(o&lG;_%*?CY#X$g7cLD@PdG8*mg
zhQ2y);#9aTV2aTypI?YNrU>;8GJ5aZk7GvW{@Y}Vr-#5wG8|T_c%32y2R6J%;>%mN
zZtY2^Ro<o*nY^D~x_#JsS>6JQ@mpH5O8;ZE?3xoAQ3nqnUJB;#LU6E~lE}7g+vcWz
z8}#4{0=MI4#;t93_wL@k`)t{)zB*K4fG$q-q8O`rEMOr~R188Lmv$|>!1Qb-nF_C5
zO_(S3@#p*m-aDfn=aFZR4=27Y>Y=AaTv)PB3+zm~W!cLd=Z<^H$(um&37{&W77V^z
z>0BXAD*<Mm@8-c<>x`bQb_0Z?aDJz=t`v}C8SdNVy$|n!0~kbAub2wvaQ()OAxDx7
zOOb$R3{RvmQYe5-3YGQyF#n*Lndsxg8woK%HxKkj!9r4^Md#~la}~GnZf?B$tsvT3
z6d!V@+MWdmH$3@e*L??sa#8P(mr>d(I5vAs85$bCys$2IxqyIcS5;rzULFPCl&bqp
z!Dlxm1y@fXm9Xp-YQDU@Q_CuW%kzJwxt9olBImJNzaHVO>fRfA8S|VBkb{zsri=HZ
zG$EllkjoB?z;n{_2e~97Ai$X%4Q+34mn>WH)6W{3ngswz(xS2egebC%o6QctU%PoU
zI9SF#m7>#SE#Ire2q>PIne(F<@;lgY!Mp``C`#04`CfzV<ABY=xDJBm3e>l-LFk3=
z;#YOcpD~%;D81Uz09CNK_`TFr8L&#X7M`}Vv+Dq2aPqhfLPP=>p5Z5Rf#z*+SBi3H
zAr4PCb%dq72a%DBTLUQ{cUQ-%&Zo|PvgF-ow|sWr)!Ml@j>`u}&63yGq&n)1%pHc#
zG!2;@J9aFi!$G9mQB{WAZjIIMc`FLUW(Lj^upuxQP?K-p`1Ho+6MOf?C~YGoBGC+l
z=VmkW3b_LI$IjC>fHTbX&<)&agjEVGc`Ab;wQ-}z#xVU`scyIp*DI|!Z_ZKIU)$R`
zv+!1>)qSKutkz41OG`_0ZzCbLEM9o&XXRlI<MZs6lx0`|cXpg<e=U`E5vac8_p|dm
zZd%7)ungZWNw=<u`-y;)%5Vb{la&Ij^Uo`#tA7{_2=Z<9XK%|djE|SJwYBva*}B-y
zKQFarEv0525QQ!$;Pg(0&#|kMlRmgHnI;jUs`oN8w~C9`r<u3foEE;ciJ9-|#4d_)
zxp2+_6(+$b;^OV#g={iYoLY+n1O~C0<fqP@Dh&boL0BJ;pRe&YfO0(6m_TJ(3<In|
zGuK1fBs;lWZNA{9;NW112$iRnA%p~`*qoOU7yuRDoL4~9fRzrq4-Yv*X#jZy(lm!z
zl$Dh=*nK|&|NT~5jQRW;Z$)NO6TbDLBkmCLvw$<kIB3MKb~ms6>_(W-)Vwc=UMQ39
z10<Z|7b{}UB*b_a0}I^%eMlab0$~F!-aXJEH0^n@0Iv~NYhY9iFJx)h9ct7Y2rec2
z62uV0A`ZpycniBo2z80A%M^r<rB@{P+qy8+rh1o<>f>ru6zgydf)Dk}hEBU$_y3<P
zSaZUjb1BPBsc*F%C6iIlf6OGqD(rmnj??Y{k<3O*nhO2U4`c&@Fx&E}I!>%wo$I&B
zICH(Not-7YtwbF<w@5@7`1QD3PyO`gMW_<u_uX6!?j#gdY$-st5DCI4cuHSgmbjmj
z<0TX;K<=<ck5cJ^mIptb5eniL-{SQPeKUUYlRG1%6cKatIb%cpSCOqd<o`r<`!AmL
z{tY>g+`9ig1@eFH#edG*N#JbFHS8MckdQpRc(Yp@-R4p<ccZL;I=eG!7FBEGXm#Kk
z)B(sxcbM5#%TYy0Tq$4rMEGZGdyZ3y$l5mMS!6oo?$qJS2?|k5^S+@p{Rlz8F~85S
zd-EOLjWRN096k-R!>HZzzWI3_$OR7;!~NY${veQ?TK@ik(G4f+<9oiR&ZanADnCwi
zASt&MH7AxXTedsRtWdn26Zi@=%-8sjpc?_&IXL*y<Hv-jo^A9^rV;Y|)vK9KPamMV
zd2N+J{ZhR)?_KWfH8F9G2Na9Alvn5XNp^UC?{<)Cz2hi_;%F+b^EWR5fS8`ve~9Y-
zKWqL!=bHa-RLT$rVd5$35icn99qeMR|MX~DVAwXhdQIRwOYrT^zkj!`1SDlcyc|)c
zhr6mH@Yj;I=Vv-<<7V7exxSG*Xlhz8zlyh-@AN6{ewOdZ%p3n*sOSlE3Yf+blDHI<
zqh%m8<(%6~tj;w3rE1A#6HkE`3I@whZ*Iy#(S~f)^8~gWd^H@55pUc7@DS0Rm&;jb
z_JXE-3q(YqKQvCS4`+qi<D(rzz^;|d;aB+q4vJ46Q-}m=l6tq7Lo|39F0ztvbBI+(
z(=B$JdNSQnZa9@l)0!7AS*N$@StAe9Z<S`*4b<1wb^5+C1PpZ?40G}$Q20Jj8ZK@X
zDhWg|i|(4c&ZWzYfbV4kCn^Br8yvadT{U;@+O=S%{HCITv$C3SQ2;9uy2~_>GfLh(
z{HZb&yn6o^gRhv)uw_mQ%o3#!bgo!@e)n)fG>=K7=HTF0RvGE|gBAm|0`zMM0RcJ3
z=A6E<l;q@#OY~Lb#Kk{V#W4N^aX1VJol<u?RR}u4MWS=z4%XUG89N&$#~G>j3H{$t
z;O(lZxnPyzlX6akVmsg?;SrS{?IgE1NF<6K#C}>KLu9((;PF1Ez?EgIT=x-8&l#4O
zG87*}{yFmAAcVo}Q)p1n-e!XV)h?OZ5)ti7VSt<iq6Lp@Kd1;uT*osj1^Jav?Wzae
zyb90Rov@|OmD}hgP~(Ulfbh7>)dT!)!|@>Z%kRI9Lx#W3+NJOq6^Ucs=XjjQP*iaN
zb4>c~@bw>P8%%b7z@x^^wvqp~1!f1b;3~siq3UXar;N7K&%0L{&C3i2P$&QflFYCc
z+R8ZUBUvAIKec;tP|PL01V*7#i}iq(=2bAS8xn3WhQ4810Fs#ZNRb~jHK)hR)WZp#
zx=5+}&Ujr~K;2#b6BWldJ2Tzsr6`Eop_PV31dQBgj5ZE^aL9LiQJ=lYZGK@hu|iRv
z33|sp&rjKLW?tzwy6KYE$$FXvs{27ED?kAcZe?1*(B6wHwn)CNsIdI~x9^;>;b@AX
z9@?n!iBQ$fA2_~ifr!#pq7q9^x%Z6CXg@h2(h?6|;bYV8-IdnuScYoTANUpW3zL(R
z-yV&j8YvkCJs94y(j!HB$+u4%lfU~0WeEICSb@O<tnJ;el~md)MIM>ZmT$8?d{KDY
z#_Q51f(G&XF06FSS&b@1jBe_>-_*5o8)0m7h#2_jez>j2ir=!a7I{TEY*$OjzQu!T
z<}Yv<?QPs5E>7eotFok&l**W0y6#6%B%{E5-#Z4z;b?}{eiOaB29I<+n|tjkL+h@T
zyFG5zKVY+a_wKg|`f<q0;1=;hWMz#*qj18oS3Pj=ssGM<_47PWmMF-}z9!nP>ozu(
z=ha5;-q?Eb&?eKICvS}lh{uEMGCWa{E9>+f>AM=Uf6Lcb=eQA<76~kZ*37X!h62vd
zpUt!i{2>+fwz&8hI0}(eKJtX_-0vL<Qi!g6IbXKm2V${Vu>7YV&AdCWHD?ov2{$eo
zp`&P`(n*WZ)1a9f@21m!%a0o`Sw{+<l&Wv~vHJT1^Kl>RkH;bGAQnKlZTw_Rj!#i)
zl9}&CPo=Je5kcFpdzV;<e!$QbIrlH`q0Y_gBO-%ceewn3e<5zA9H{NsSL!PY+fL7K
zPcIX-bDxHPMWNNtl_&zvuU4NgIbuYui$5U>>GC#QJSZ94&hR_Z3PIe8`j5N~4kjuI
z!>@?<@gga^=Cr`aLt?a?<x5R+5l|ZrIi@fmv%IX;R~Lcp<RWpO`HX%+$w_Q$bw`X!
zVW)EK@as};Jvpdo@;j59-=)>N+8M1gV~bKg_%@wPAb_ti!@7(ZF;e?ES(ZjA#>-I2
zg}|CcX#e|x+!^IbUdcppyFsjspL}a@ugVCol>=SXlE}jBii(O3BBz?Vz1=PM^Rpnq
zGEh{x50`*vFg9K9S01_6xqNMuCc=x0iLomip*R%#$$>rdXW<x!u(8e&q!D4H(m&M`
z+mBLA%VW97;|_T;RX5d>z=PL>6rJdF)*nqL#KpxFv8UJd5>$^b4Dw)1uC~Jy7`(J?
z&y#*;K26!T_j;GH1#}wRuZvnN5$ILCU<KBv;ZwqM2PQa}%!zTtwo);h%Ff1J=N9nP
zip-O>7A*zdgD~rrA{Fvf50{UxL~aQzAdljS)JN4e|LMUp*S=8MBE_PiYiT93_*T`V
zrWcKu%4R=qefH}d+Wcz_l3<*l&lMuZ!6&trykz8-S-@97WW1!*yp1G*9XWDDd0Rcg
z!wZ!oJWFTI1UY&~n1VRS4`0Ald}Y6#u6@;{lf!d|KC%1gKhH^%_`g!A2M=DReO({Z
z1_*#M)V!@A>F+;%y15EPxZ#w!OZnQ|nLzGT>o9c@Ih01iHFl6EY>hm9{=F?1)QBuS
z7KCL{1@8>IGlxV6)Ufb!$DqA7*>Z_Piyi>C+t6Q*z=mVaGL@NOU2QnU!jsG$i<=oq
z@QdPQ!J3nus%=#0p6YzEw*KRXH`aapdXAZ!ShHB*HZR*j0?e(O0ntV$o9?e4O(?qD
zF;F-reOy{GN18tKK0)mo%Y~dgMJWZh4-d0ED=qiaEnFUQe(o6LsJ~tvn?!O*;z)<k
zCfoVc$9EjhXq23qfmX(oPXz=H9y+v`J1EAz{MX~cKsQLNLhQ+Ry0T$V#m7PPiWY;9
z?I7Ws&?CfpmIE`6D~B))sy-vHJh$^sEXI%)zI=pdm|zETJ^ebR%)z|EF}IosZB|o(
zMEXO#&5-!YGvY3*o$(CiPNs1~z+o1?y;Z;v-eR?{0K19A<M55VJ9($}%cT_sCJRJW
z6p29Aka^Nz6CN?q>A(UK0u*cMbxIv?VLS`(sR{B7#g#WW6>|tP%~*DxcbM+QO7<7N
z+K-j4|7$C8-7j_n@}ReS8#4$>8-#0WH9IJ)zF=muN94$2a(NmF?^e%Z5n8RmG9s+A
zBFeLbn64=lO}=jmK<^Bu;X=|8W}AJ9-(eLTat93!ud`l!UGvAh`Mq#PW2QNf+NWN^
zmb(&0tZMczykq1Q6sRBXBb=#gAC&$*Usrj}4lF|%)105^qMMKJDLT)K@oik}4PsVJ
zrJ9=FW_{oteq0@gFAEomwj<cE^#$*W2LkbSGqU){fUYk8^`(3Zg_lOC4DHd>GzG>a
zEZwK6AHH6VCe4pO$8TcjQEF;9j1=;c`)@5Jp0sAKjxaey0N5m~k$wf)Vjyyhba|k-
z`zk)EAJ=qQFTpiU0ainpHcm#ftF1C7WWFQEy<_ltYB90jQ7FB_fDu!~4C}jx_lsMX
z0Mf3+O&>2h&%+sazWWDtt<(R)x%l_>^@N$@nY2qlGv`Y!;yv@4$L>d;MZ>-H;k;P5
zQ{WV1aHvAoiRKUrzu;6lE(M6I-?MlnGp{f54UwEEKMpFb+0Vp6B9MV#PTN-EHd?6A
z{DXi1u>`@yP$04Q>$SNl9bedj)S@cX9}dwtHemAe`i-`e2PCw^-(<n6%oO0czbS5e
z@eD|mA*O8!af|VR4};OI(~{sAMM2r_A5(7&n&aZ|hgqG0@KA`A;L>N}nESBtX1<9V
zt3{PQQ(-<6`PU6eoD*66fNQ48*Y<8}4D30dScprXkolVMYhU~FNO9Br<YfEvV@#mo
z*rcy9tN_ns3|2n6qm9jY*RQvZCEf)BrME!v{ufaUi0E||<LSrW>$e1)UnFwtub<8f
zGjYhvhEuJ;;kjcF#bf_`0WS->K7)r>6RuEV)FR^c(M+3zBY;sLMlDd9fcNdZ1ZO16
zCBp;8FOiBU+13y4zE>WAbOyuz2lLT>dm_V_^{vK^(Lc4puj~g52f#S+<-_3gtJG<k
z2ac{KzEbn4b3g89_cMtw4>~%<gl_YA-=55OL&0kKLU|$7M0^qyl9p8e{Lb9##D&f$
z>h+_Za$hFFZzPluB(C-R6Dxq7e?!ULcDjvE{Z%3R*&KeiFQ=e`2hBE__F2kdFFTt$
zd&sIoK5cl$y6M1rK2hPDy2aG)jf*8%vz#=Eb(!axBNjeG00!J!kNbU$)U2udVx09b
z#nlNs<gIZxW0A2qW#S&@A@cfT*4r4?Z*Is9lJHId@xKaoa9IV6WyF<&2wGWjT{;v7
zYb{h^q;N@xfnIy}?ozHbtEHnLv1j_%;^HkYetL6iSyajH)#920rf+r{44(d_U#4fR
ztCUH~Xk>l%JcoyGWjz%t84`y)x5$22yJhR^WwO^i1h%YQx^t_c>9U1OI;(=7H5auG
zPrVqIf7RTjJly$4B`%^&Mbx^e%eBfoZcE%Sr>bClU^~?tjVHK4qA9v_T4D&+|HBZ`
zuegf*K_l(w83*1guKwX9cyRc6f{h|~dRQX5(!*;C;0xRt?-j90ZL?T#afm`+7ils3
z%0bo0&*k^xhoyp)L>~S!QK1B8O^)^qr-``L?7(9+>qh?zzI-xI^A!+)9Nk1TDww*f
zONop92AojMfbfXF17CBdPx3w0;|`F(2FY{LQ~dB7v7~l<yrLhzMknORS5)w2sdQUd
zFoZ@@4(-+~A;=EydA^WiU;m!CbQ}J@T-n1uU;?gGS+mu=v)EuT5=0v;$S3hsK#I5b
z_C(#|BROH>gzHzgL?-p^oR*iv{Q(1K#5P&1<-PCop?aSyW96ML4z?LE4c|}?4Dcgn
zrs5VaUXTj9zD%Vp<<X;HAW#n{&ySb2U!F{eG3@^HiuB(eKnf?3Rs~Q6>3rntpAOw`
zlkjrN?Yxc}M>=~0{0*yjohu>saxCwQYW|Z;p_Ae(NHh(%9~}<VZt#xys`<uMoP4J|
z_!~j=@DJ91!{2&s9-l^@iktZyN3_$L{IFctE<wLI-kVMu4g|4@a|P$SlV$kzVGb<9
z!^E9=MXDk=7LOKqInRvZ97@IqJHVor40TrAM_4NE_h<Q<!&SUAZz(w%mKV;A*fF8-
z5E~Eb5I(hmUvF%&)>CT^3*HmCCN^+pLXlN4++Mye?jmtK0i><}$it5|=dmyV4v34H
zd}0C1pT)DhLpN4McgF=%1H*>fT5@%RK|30Qj`+3B5etR`R1@*$d+K6x3K|iI4|R*o
zd|anqu3tG&$ntlhouZ|KNXY5DZURyOTX9SESM5)EsY+X6Pip>9MMqhLQx*qE_>%by
zbljtVJ}sOO)U8*tw5>j#2{_2~kY$AG-ybIa9M)CCe0oARsjt<iCuQ0NJj>lb&}jPi
zq62zBUd1YajE0O>&sP8aj=a>TQGq|1A%#zL&X>vg@wyDP$({knH~%29tWL1hu_cz}
z@%CkqDA{a8qUR3qbCKt8e+Vsu(oao*n1P9UOAZD69w0!h!~1Uhv>MiL_}E^E&74{V
zkN?>}vQC*TXM;^2>1aZUK=inmWUmAV-%$2+B(C*F2i_J#dP9@O(!ATMdpr1EkIp-x
zliC1nQ43efmuIL`ZvOJ<@y#pPVs7^zByXC&Hv36ePgt>pyQGk`*Rz#_V=vwjcOB0v
z+a4+#zB^QKTVwx|7mGqki%Yy-OjE}TW^gbntMyVnk-A0Nuj}iV=7V~_gQPHTq443}
z&3BAY2)J^0C?uvOhNP6r%nmXHz%30j8&iH)b!?H+y^41)QBJ~YU_T9n5K1~ih+Iu-
z*x+Dbxl+K4m2>7T=uUjWy6n;0AOOv_ROUY8FrB`PeAJIo6{R2zL#Xpcgu}Q8AHpV(
z?YK-~ho?@n6Xo#%bMw}o8Lh|3$-M*F%+0qmQA5h%C?te<u5ygHR?$hc?fi?5wb50}
ze(Wo--o@65JRWs|+!E8&0secl?&ywWw_X<JhBuh0G03W<97;6N8t89GmjJyH7pF@~
zJEk+QM8ck_nBB5Qr+uCJgwFI-G1qsKd66H@T^ZJB9xI4F1UqhhlITtz4z5{2jf_Wq
zS%O-RXHLxO1HI6Zn3Nh4983VuBIVvEsq4Sux3bTX+r8As(jz)wFbNZf3MPI1hLe*A
z3=;GzWo3^(xg+)ZEAF_AB`I_4=^7$b3J4G~hWHNUPQ1Q~?mLI4oQ8DE;fg<a@oXut
zXTCitv`<k4NcFyA#sJU9na^Os9r)|kAzf9&WOG%^T5rB7$xsM*)CBJC`#-9&h$9F+
zQ06OT*?kNel~G?`FPQU}6oUeGxT4X1OqS2qD)AiHHuy;{OU(c4yMradu{lq?KKoK4
zfW)hvf0S{S2*Rp;nYBX|#1v@k5Lmlkp)-oyh<~xK&_W;#vH|o3DFY5tshqK2Z{4`}
zfy5>NW|+yrH&}dm|M<@>w9)IC3G^WBWlv2_(r5(Rz!E~MptY`xK}f|xlwJW*8&(iY
zg@^i89(ul`Ak`BR6YDor?u-*DB|iP5#R2|)XrpO5!BOsNDnxLyXv#cEJxF>*5yXxR
z2oxyCAXcW^)+LT3nl;)-*Xk)t6INOg-8T7&e<Xt>U979j)_|!}boHztc2bge(I-O6
ztd`MKPhKYy_{W~tU+aqz8^rc?;GvP}F0aukKsyeqO88RlYlVeCX<jyYikt$a(D3=3
z79n##Id}}dFEru(uH|l<>p(dCn#H%*=-l<>C%=3aNA5K|1Va@j+4dVo`g!YVJOi<j
zc=7`#P>XN;^S3fL@BLDBd%J*uGuXOP)CJ%Bf6F`cz4@X~#|Q^bT3>i(kydyxEkC}x
zJlH84xLs}NazqK~GE7eH+_gwdt)ONt?{h}>;A+tSvI1BnzrX{Ga2Zw<Grmvt#@9RV
z@V(k@UR_0Dk^~!vc95McUZx0=MEv&$@;Co8ks55}oy&v630eb&M~-xgJu);hqA?~R
z*A=gNl-zdaEbl{ovh*IN;sYmwXd4ijCPsB4-98=Qy|v)XhOfT3Lx1atJ@m(>`4d<D
zue0>ioqaY~PX`Y@wKFqC6l$N0C&urMhavjmOV#-K>=qPv*#<qHe}J^iu)lp=qJe}L
zJ;lW6krMB<HVJxj30t%Aun6zJGq3#Zh5!5Kwc(HAXYU_Y$Nz&%`|o^+*CAAmyK8h7
zlQsgfpGYa{g<eg|sj)t}lkXmkPtuE9;{EMg3byy7a2N+MFBC2B5CZD2gpkdOpKe&f
zz>^tT&3lq}{C)O)byU4nC`xDa<eH}LXQTlI$P|sYQ=4SXi&O~7!ml>|vne!$w=}2L
z(_dMZnq#VxZPEaiu<C3kItZ$(pOZ~<H>c*bhsZnRgYVAy7^y3c?1+L<lrYgFAg+lO
zknF_{;<YDaD>^FQm0I(5LX8Iw2{^K=qh=R>Wgz!cv{SY>108zm5Q|oU33f)!)M?&|
z3XA7)BJI+oi`kBz8iqu@EYp!(Q&9MqQT}7Pk!!$c6oEVLxVXi`#QfN?6~sEPsi`tE
z-oZ2`-3=5CT_68v`S8Y|lQ=9GMs=;NjaEY+%Tcb{yuIg`#DiCNwiT+iF=3oT11fGP
zTCAInN5koea1Jk6ehLr$1=Ww-*zToli!x1MFbaYWaq0vIBg+7PSI&FR$aV<*T$`Yf
zA7_`EYZc{v+Ba{X!~S$#xX<7$B$`{P%p9MGz{EM+J1gt>S1os1mj5lqxDIMUcWXs}
zwylj#_dqtzB!zTd1Q+>F$ur`e??hL0-dgA-F|&BN!dkSVOJ4b`aqaW_zX6Fs9BeA5
zmGbU|uLomvGKiMk+o$`)RkS4@Jm9p8j?Qp~@$t<!mxEYZ#yZ*dH!na&k1pH|Q5EiY
zUb}tS-}U_^kCu$PzJ1d%n{^`hL_T;`r@{IVFlW#CH9FH_?-t>lM7G)-_-f~?%x+A#
z)YdEznFowZ1N(}8X7jE2MR|Ej@{Y|7&-d0^yZTdk*X@#Pnl|Zo+Cpl>h6^w{N{P43
zq<KMw*`4~W&t+*7mEd_l64RXzvvoa4vIyK7Hyef-w=vtbDg$e)Z}TBqI#F<W1^C||
z!(@=>K-+VYfdm8~qB=gUL{}(sGVyTH>Ufnw%`G#r0jte7Z=ms9Q}YdW5^Yr3qc<3&
z+j?<vQZG2Bd=2+&adBZh+dVKWPuoK;9E@aR&FRGN348u-6y${FORrX&TAF)^tg4{m
zZaMyAN$_QmI&^~Nsdkb@G-o1hOwdVxDf*vAWEhK3-;+L(8{6y*0hoz=mR~VRG>j;;
zV#p+ml7rphV1KR@s8m~%GT5UNiS<cTHHHI?#|ogen%5$CL(Y=qd!oSrU-d=a;%z6T
z#IkP+i2A|husv<XcAZ}oMo8`|ILK!I!Lhr)dCptpm;U*@^4~f5{_9Hkf9?h4PgdYR
z>AgryZkRhF3B~oGVZE=6_emL(-o+G~`z<3A-;^32LQNugbKgr2A9MW%>97Y4yD2q^
zE#u0E5J(Yid-g6n22#`KNY|UXzm%2yqNaZ>D_O1T*@7fkLn9WP<I~96!(+0t*$={e
zgCGLGvhuBdR4?tGRVp(pNd8r)3n~LD4nSXz$Tw@wJWWmFt*~cgr@UIWgLN74`Ve@P
zyL3+2p@RqCesGvuz+4Z~44&X<AV-TRipI*(`091*vtJr)MWHuyrvH6S+m5Z24K*tR
zwZAZEPUmT(jlAOSe|9xW<^Msb8vpM>ZL+I-bx1=3h;Sz!Z$oTC$b;>>#3m2${94Aw
z#=S!%hf+LNw{bF-Ng0-q9?4u&8Ez98N79E!d<HL<8JP<BPmM`ioaMbZfAj8Ln)b}R
zZ8EtZBe7d~{dvR>L&(`?zu`(^Bk@ANxy4v-W2p?!5&pB#?3Umh?#vX}?Uyj2ixBDz
zL?)TlL8Sat^z)OkL+GA-iB8W@>PGTW8fIzyURTy!01GWEE>1d7ne|5Sndq#GSt=la
z9#$_}{a2DE5`e`Kh#p>Vh)N~F0{gRXqC;C#69(`{j4wRm)@DDBrhsW80s=&;g8@js
z(F*z#z$}{g{{*uqN@eaeWZO3`76|;#Ir<%>Lbz4X*>5k;u>YX|_d#*^&6a3pHQ>20
zI!?Pavpz?@|9kK|w_wGgzXU&9cuTT*NR<S?BZa-54@_m~9~^xBpP9foFF<arL-zg-
z4TY$RZ7T;Pbrz=;ju~f6Jx)rp^I*h2dRm~O7g(KR5j9IgYLflAHc2h+XZ^_X<IRCJ
zF=GKnhi{L(wJ^fp_OhA9bWZr1w!Yt<KBdV%uk!Yaj;ypAu2{QPy4v<_(`3to#AJQb
zt%FZ+C<+Dl;Nc5x&CUebjUFo~2M+wNFzQGeAAARc(8;&)Fe>S3c)s^ni=@QF*ZPf5
z0W$@;zkEx%WdM#HEx|^b1)Sn?Smr`a1t=%Cp{0g2I|q}N!@kWL3-?6LaHh0D9xG)!
zq=`8y!Os^Qd=wqh8BV`_WfReL<Y_|XEwhx*qqA6qlj1gI!KWh0=v{Tqcl^Wozy93`
z%Li2hDh;&tc$gGJl_W|&cxUK!U+SB92Qq%wGp8ju*g$XlT6Du~I{M^C;=_V#3=bYg
zJAy7r(-uY(2tshxzbwVWz~iC&BG>4ohc+xx0skY1)03o!t=hgh6?TG0j~;cumm|(V
zbekAVQ9{ag)l=4g!+&hfodYVz7y5+bGCdW2H%Xn6e!LADJ!xubhew3BykCZ`w1lQ5
z=g^c%G&L+k!F=98vLNd6=Zvt6ZD-xKoE8e&`4jn<+-V`7@T!Y#+xU2%y8pU?>WE?Q
zY@aJoJ5e7soS=xf0dkjBV`2{KPskI*SB1JJXe&Uv0cj-%V?`!uVKT(wk0F#jNzsmZ
z8;EU9K!7)e2>zc9F1t0f(Le3sWirc2VWX65?&``GM4zL1ZYNP|!O};ZX5uiI8TU=A
zK;Rkmo~?1d5N4~1>Z|9@&5PSmqkHonuMYU}9~7K_z-lC(QXZ79KEG-)@qvl+g3RvJ
zc|n<p$;od5V~Hn~{O8taG&A+0u7+(BQ=!C-<}B5f)WJ3H=B>q=fcQUpV*lY2{!GIN
zK$M^L(b&`hzLaNb4Ou2-fdZKva5=0Wt9-ie5+-9gX1JtReDmXp)Z&>*GMJ#_UZ_k6
z6qD?RPRgal%IfOEuq~#O@lIsCz`EO~cJ8~aNJchY-uO+(j%V|0E~2BTM5uVY6oPN1
zOj?==O$IWt5UHAigNZv(>*Df*7Z=ak^Z4sPrN}iKJT_1sysD~>@HEc%a7c!rNH}g{
z&@)B9{lP=+>JGb{R=3Ty*%`TpY#O2@U&OZI$&UFLmPG=Bsf-Y*_lf^kaMRH?^Rii#
zAji?OvVN<{K$2juDW@pFLf#}93<QaQtoxG8{Zhn)m{8NTHB?j?`^iAB>A{G=i!k-(
ze7a>vM!FQIqVa0pqE*Xb>b;0wL&5aDC=eBFRbH*Z7%TwQ_TQH11ry&g#&|42VoVDX
z@)775optL8A`II7)B~nvK7g-y7Hi96IfzU6#=LKR1ZK%<Eb%y(msT{HXnqk-GDsEf
zm^<Iy*NzAe4u%0J2d}h*w2w86Pdq^;*aQco!_3R@+Y#|k<aQv865;X!S;#!mx*GrH
zwYZ}=b{=@nS4N%@8W*KJZsG$Dq6LjS28^u2V$)OSj9?|Zt;2b2ndxWE$Ph<ZD|d!%
ziQyu|PveEi$g_3?2gA)HYlR}3<z4L?V!8o}WwalfM#IT4BXM!C3IWEsxM}0<LdDDB
z-P>s8t)Z}FV0I>)7XD6tFy;kA9w-A>+zvEwF(KB6=NBbu31Lfuw3*((e4}(-gh`*v
z_VW1*yE3srKVrY!{b=5LNu1(Jj1M3Uo}?9D4y~mPdG2<m7}1c9W<Q?46i%yOx2NJZ
zIu8E*sr!DzBGYg%-7l1k7(jUKz|_3NV*NWpaOwpuS`I7Co7>yrJk54w3S;=qCKqff
z4X3w?1=1)K2#lENP@j=N23|}aKxwFuP`c8ce9)*$Hf_&-o#=$LDsBiYFyevH5rYq9
z{Wpk<lO9sZgi{nBa^id`py!GUuw#~`gR~9`9_%P9Ba_!K8jaXg;<2%qn%FqM5wLW7
zyDQN*L-G8nGo!wEP5P-1zmGT3ix8&Bs0E_bCLJcn@$&t0j%bhK;*OYtbl>Z4)Br9?
z5-LX7^u(~`%=CnG_DXDR;)rA(UWUu6K%NkSv*ZGq>_SYQkmHwxP#4#flGU$>IV~O7
zPL3owrMrE8$Ft63^<d;1@zn4Z6v0_ggV=7y6w1#LF2rD1%wZIl65Bgb^R8aI5O`-@
z#TuQ+rKrxxS5x(>uVW+s!-`g8BCwDc0P~p_#)E}Y^)!2LZga0k|L$<t2)4VYtvS=(
z->1F$*OncvbAw`cK_hp|hqZXC*rwZ1XqW-h#4Q&{{(ZM@OeazktwFevp1a%~GS=0<
zKNeQKK_~KbUy{_ac13gwSF+3C&R_Z@FFuRLtqmy6d)c?$QQwb$R!|EGxo%0AqO}&9
z@GX8pnA(<r=jyH({(i-povV9$BjEU~h<M2LEVQ6ns+&-{W56zBL*-*!2DT-99YmJL
zmQn#HOK`AI?$b{|2`&-yb;n9>jjc)L&wQggo1%JNpn#%5JBgNGV!c9g13Z2SMG;5^
zHY{1IW4QT`iG3gsL&1il8;bmWH8pRe_4$ugN?K#bF(vEhn|FBSAsuq!(Zm@Fn=`wJ
zT?%Tp{f1@u1?aq(c>=g$%nTr11$^HY!kh=e4L7zd`B;Vp=6#S+HHsfE?INEXf$^EF
zYz}GjMx!T&a>NBXV9LsbfB!UoS1-fJ8q1uB|ANpS)egs_1sNVf%!EX+M3Pf>$!(Tl
zK(r}?L#NLR;*J>N9O+jWC(=6E_o4KAS+|54AU(k&%BsQdTn3ZHJvd%Trz;Q2b2+Wt
zn6%t1S04<MA!d2f96*L`lCg~W4s3MmmT;#6xw3>rC&$N(6p{-%&aHGL!B?=y{xwOF
zq;mwN2Qf0inx;asVneorNhl+$bMi3Gk;v~iw!2#S!C!I_OtmspIw4|YbFJFG4vFYV
z*|gAD)fgu@oXw5Nc4`%cB5dUMQ9|qjVY7*O3-N5Qu{uDWXbK+m)<c1|iT>JCJGG}=
zw5w=a=s8<B=@MlQ)xLYI`+IhdP$_qoeMiwyAll<%0UgaX#O*zt76{9yj=1>a#KgUz
zZ;hMFFllmVicB<(B*SvJ9OYsN@>j@RVV;<R4LqZ9EUcKAhkKtglhTTAnbQzQ4SOb^
z^9R)Q7U^Sf+O)R}20T8-xT&Yin!CHMK$BGPl{QW~nSj|W>!6vX#13P4Kwa<l6wF4-
zg*5HK_ypm@v&dgQdVkT#L)k1Z;(1wvE)~1lYELw1l%t)axwi<}^ah*~f!zb6k1<Wu
zz?=-eIz**1KgaFTMU@oU%A27h$#BbNt<x!+3XEJ+jvkyETlHHYkDsEtG3;^D4cvI#
z+=i8|>{=vJ_%fz1LS(z6$)w|5R!{gCZqf-Y_<xvW?(0M+2~d@?aY%bW=MIc)(v8CS
z#9=y@i44Lo%dSkt2q<4N76yI(HNV;__d^2-C;s}%)IgF0Nl@es6Cf@4u)|&HBLgIB
z6ZBb!rCTSeJlkPss?raAdyupplkSuJ!WOTsLl|eZgtS3~PCbTxK&n|Xe8@1u`^7?q
zl8LUk*4{~imC&uz9;VP@<8B^96c9xW+F{?gTp++~Wjhs)))T@!iPMct9{kuKp<jM4
zC#LCLoUjzGz9N0Tf6>|ZOip?Ydo*^kXd&gXQSWanDBTkXADc1lY>jv%S)lnT!~r=Z
z(Qf?CYt7MV?~g5H<QP$_V1m*?s2h5-rFsplI=;gW#Occ%%Yb=vdJ<k+nsK177Ft`L
z0@<u1g47TOAFV|>Pb7!UxSR*@e5mj3gF8f5TzsOQR&)(>4P`LIg&g-OvdD3u@@iHO
zu9tOSKbcMWFdpK-wqx1$$H?T#HBl97;q4%!W+Dj;FE3LYOBd80=ue&iXTA^9Gp~Mh
z$)z^Ije9C8JBsRYjP#UEZ^ugJn7crO>!L?RMXiHweR5Rk`t|FQ(9g@cewaOQ1<86H
zs_9p>?bz7LZvH6bub{G9hY5z<E5z$O(GywG8JS{2lSFRc8xG(3v>I`MFA_<RZc;u?
z+v2aT(?sWTM-tRK8H?auB&$3U8(|$$j*AM9mq&gi1Jr2wU=vJIiXYCrKRi`6a&~N0
z{(&w=3;V{-(Xz2sw5%e=5w^Saqzt8ID|FbJPLdh8FZ|Jyke>p)RUBttjG0+h>{`Y3
zNMrh>;*eyd9Ltbw?T4wm*RdxKIm&R(`<G2i1~5265;BOBi#l0(9^WYij3$|HYji}T
z|AajGmD1tb71p;eo2z30m0@-IL)Vbt;}}j+neDL3gBi%>?9awSF9*h8w0jp0-++W-
ze`N~5NEeN5H=JllrOc+K;$E1;TaIbTmo+tA(4?`gF@E_hC+o?8$jmfuAUqFy#l=@Z
z!Fme+oD~CoM#oWPAsX?mI;9ahN~@2%W!rkQs3z7@=!+j?O0-tc;d2C1q@6!as=ud-
zY|xXWXvu31<j@1*_(LHp0^Hc-z(N01r1{L-h=BL;{-l9sFx9g#sob4R-yp+)#l=b5
z8oSQeKn7K!sVTGEoy;~3m^dRg{WGyj?ay8bmk;{Vei9dlirUgIjORW~A9R}e<kUXj
ze0*m<`)vbqZ3l@}S9(ZHj!>J#h&Q5jR_aLs?X+XsUsz-$Es#@y_9<hPPZ%EhnE=!)
zJ!B*^Sz&?PR}vAKK8r+%nLmy@-37B2M$mdkt-3Ffm&Bq2gh77dAaSCOuz)yLnN0v?
z8fF0_b7Br#=6GAi^+^MR?;C}Tl{}1MobI|=2lggT`RMz%q5=eXrciBREO&-U2SXMJ
ziFR|J6-^$Vs9-V{P+XkISn`#!+fnoj(pd{4^L@hWJ#V4>&*%~6E9Q<za2xFNVX`6<
zE-=83nxD<wmW-jI#VUY+l4;()Bn_RcO_#}OJ1xjIICA6~e@HBXmtdESYEN>8aWfsZ
zk6Y$5n1I=8!MIMgHHm_XFvyVcV<hEcwN?lA-PZSp4vtZg^bZ|N4|$Gq5C$c<Tk_DV
za33>ekP)M}gd!(ObYKd47b06dAk|j()39N#?i@RNJ)C)@iy`W}_g~+DFNk`tWaIF}
zE3>2JEVa-`_gO#Cw)Rn7jD%^l0mf?+H(j>$3Bfn$7raHKxn4CXVcfrI@(z-Du+7YK
z18K;G`vbg@WcylXVrJ0skk<%?DWyWTm|e(na5oRrKE-NCzu)Kq#XB%`Y6Bh;X>igk
z!7G}-I7bhWhD`(~L1H0p#{L-ubRbE9xP^*^5wa#H5ie@+&=nRn!nH>{0hs%Ig-a+a
zXwi~sM|3|}>T(`$INLj4S|mTIik33=V5Z^7A0hkXIs(z&!yk6toD3Y3x&Kx;Py~an
zCp;9IPwh&Y>g#><KJ{IFUf$E0mlAj8JKu(hQIBPr@N%`MZqp%8>#9BsoPEU=n#%l~
zTGV=EwTbVP!Ro1<x@9s}k8XULS$?o#N|<7!P9o2V8T28(#DL@|u3B_u|7PKoL8FdS
zouttf#j(ZJW{It_9YYu+(*4eIo9KlCaF~V+g;<je^WuQYY!i%u3!Osr7=$YjYZ66_
ziE6(xjk!JQB%1J=1E%_TL!MFYgOb&PfB0kHc>yx5hmq>fUV|-MAxnn6PT0M>o|b)a
z(mpA*cYfHi9zS*oxgFamHQP{!Xhcl_e$wyuA&1da-zQ2K{Mh?hn5!@Xn`i2NS7D*{
zqbZyMJ6l@|R5)Nnz=a8Fb6^~%O857`Q(GykFxpmho%Gw0nodA~j1Fo?*F!i|UM=nn
z2iEoDQ3ubFVe0u~G4j%*TzC*ZMs=YZL8No*W2vc~mQ?qml)^H%BZ0kdd`*m)bczOj
z;4|PcY5FI_N;C^pg*RbTywdlMARQ)7wE_0Bo;Cqu4OfVG2RFv10*ekJh+$-lpsZQ*
zmn(hBq%Dg3<T0`2rT_f=v=t)&n5{hGy&C-4Ce18&H=9=!%G?v={}DinDJjEb8y6}x
zs@uRV6D$+oQy91uoA1_jJ8r)>a9z7e>!cyhf1X1hmyAd_FV=!S5i+6A)7xSH=hsCw
zM^{(VR9SLodn3;JIQ1lA5p)Z~$aF`SH#-vbI4DjsnqNNb3;3ps1h9&?ERrPI&Rxd=
z!gZyrxqID_bfhMk?Rs)lI8k7Bl`Z(GN@e&BZ?OC?ha%UcV-^*URk`h2bphbYef%hK
znhaN<jn<SjDwAQJ)6P7PKBmx<-v|g07EO`F7$%FbUl@T@NhjnKseDP4CljCGU`)^p
z!!yfHYAK#fDt;|X8uZ8oc8M*xwnoRk;mK-JT%pr}VJVtyxTo(kmvjXOcw?kF>A534
z++++L*9ZTz0MsO9q)X9=>V72Zw{~isP|IWF=vX_`S2IUnv9nY2(q;wy&WpP3@L}S^
z5Of?``psuL&4((8J8*|x6X5km$$d9KbYs|fwIvKBNESo<e$k+NqgK(B3%gWVjcdIo
zfo+%biMSoW1R8Cy$&rzf%VE}7g*{E8xyP5IWL%6N2GCXq2EN@bR0@aUP}z#@F2|<_
z&a1ykD@Ltl0dhgQ-<4R&Fou)RgZJQJ#mA8WSEL0!R7f^Fm?n0^9kzt5{1%kH2;=j?
zA!3^m%jY9di1d7*wldsQGHRaO5)W3OK0S6u9o<AZpmc^?%|Wn}(hBTzFzngfTr%Yn
zZf|~G!hw}T#-@<k_^<C(4(?Sd2sz64j=1|1+ub5C;E5_~_Wl;=sgGTO1t3K8d&|dY
z%`O#=Y1>Ah(Y8jTMk0O-<j>m9vOHKKhWSHm^-i8V*^Hg*@clG;G6cYpIAru-aQP3q
z*Ee0j38pc#v^3wusw#{9w(TLvIFkDnVI@+|<f>AAC%M?}B4mOlnI^zI(jt?WCtF}}
zw~D4p&$+}&zr=DlTlD9t_QRP}B}-Zo`<mooETI>TWnnlMjNhaU_)b>eR!j)sQ69MI
z&NW;oR`@~T3$qm$abE;oYKRw@ebU}SB2w)Ki**kc34TtoS3SLF>6=;aKFt&TU3`1c
z?u|RX+rDd)V0IH)Z|eA3J%bdStXlRBc05<!J|QbGqI^;7zP)_(8>@^3=U;4%t~`9?
znpMWl;o;%t)>i8*<IaJBfw)xU;P~2;XtQu}b-j|2k%1R!-M<`A99LFWmQ3$4H8Y#3
z^U5-o2D&&V6P25n_xkPIgDClEjLPGi%X@n4^z>mKZuKdQ?R2?0;TMZ{;{9jNoEh;c
zR8dj+$l<8Q=ylb0aNY7#xzB&TM5EDER8>!?h<1&SkF%#7XpG#PoL5XHH8w8J)z>$l
zo*OV~JbuN$nI%*071!e&$IbQ+q*I>JA2}2c9X$aON<>;P)GeR6rg)K%U6ygHiUcJn
zu40iH;eCR3e)pC4IlK7l|H$L}wO?B@mhQ#M!Pol34*5*SVqcJA`0B0tXWw@9YlFjq
s^}mv_nUp&RW`ET&?tb%6e(rm|u8G87@4R{@N#3usTTe4}=g+7AKOOTG0ssI2

literal 26275
zcmeIb2UL_<wl-X7H?|Q`6cmAO%pxG7phUYx5m6D9Ah87nlq4cKcH0ONRYXw4Qcw{|
z5(|k2f`|kqQv?MG0!5Ha5x(cV+H>d5{QpgB=KI!N_qx`MfvPv0v(Mhoe)h9>Upab2
zdGUhP3urXjVwD3*$7r<g9ci?=Mn7=kUzj)V58yXWBUNQ3{ElA}d$T<7+x+tfPTJCF
z0^a0*96{}aXK1t^X(~!TpK!d>+vF0aVV9#gP=5E6)uIdBCyrJe8$Nxx;obhlgMuBR
zZ(n^3(+Ogv9+C}lx_;CnWb<uC@R=B=&qg0r)_?p_L1*djoCcX;XAf?8^rP>Oi++%N
zdpBpSpNmU`(<JOv-7AyQY=d&i@}VXdw^|3cnz)^ka@M)*tnFUE++RvP;DaM4V-`|>
zx@Y@cHuZ<i>py%?{%~M3hdB9t&%ybQ9OMtj4nC(odiB^G1M+vz8_NdfkUu=Q;mJ#W
z|MB5}<PuCzagT`-^^+%8g@lAi7#I2<3k`P8rQ`d1)ZWEbeNW?lrYDwMZ=s~bv%qVa
zR<QfGpPl0T`^RB=hFj<EUf}f$KDw(T%=n=G67nFMfBOG_1>IjJTopRr-dfIU=xVd(
zV#UuAzdrYT{N#!6&zCq0ycMTD?wTI1Y)fZa`Gp!dZ^os)3-z+>Ef3w=zP?O>-CJi{
zE+6&i(T4OEclP*rk)mL&fs1AK%w&(i>drdjpnx}T-aL+r^H-dmDCg#W*Vwq`_@hHt
zY|D0Uv}al}9>>I7ksqyFn>*kop1)8$yP|v7P-l5iiFlse61jorU6s*li&pR0bVOm|
zQ<#*c&ZnV?K({C<zWz5NiT=hx@>1lc<Qh4n4I&Ec`tm~mRg#g<o;jryH6AO-2sE7e
z5*pk;hJS33)6}hP|L83g=qHxBX|&m;|8aagc{az%&(VJNfzfn#=L?Op{n@rDqw?L4
zqprI}AG-D4B+pers9bL7ke>TQyG`9G?|pgh(~;@vQVPTOxeDq{=>nCH4*L}r7K+M;
zDBVALp!*5l^z!A)_G2e=obCMFWS)eLS!zu+lPx$Fw?`#=>Pv~2zC%;mQf`6C$#!v%
z`o1tM$L14SS`68mRi<~;wR4<DHWbNxdA&Axrd)w-@o}WqP<)Yq%*Eg~1Lw~Vy<8Ox
zH(_fZJ$m%=mg(JR#WV8+E*?Eq{X|1x(p^+1_3`=FKY6FRSY`DltA51uu`0p}j>h#k
zzT3yU-pzfw$B^Nay*RNXHZfPH?o6n@j(knF(~z(IT{XEurR{y}mf4vl{bMz{*6Z0Y
z#TBbpua@erY;4pUFA#Ts*Wna5tD&o_8@Bz-!K@<I>oG}sIfKuo^!83xAiFQCKfNwm
zz982+t96VV{i{|c12>;1pD8hFaT^POO~jiS8`#&MkuZ51wxslYcIRCeqyC(+z{$zU
zB>{M_S(%x3!<qFf#q=_Y-D~x14>vk>MqDdtZ_Qt><}WlmUKHLt`zcthaQ?<S6@7yq
zV_7W)Pfxuv>T7()8r4rVQ}1Q8xJ%e}ytQXr#Ux||h8s@~e@?<}3nKUV=`<N^YV9q(
zv_xjp<S2`un|tBPtv(Y&oepMsF8w*uXX#EIx779ZbJXd@!5fRtMn}}(U9CsDtI5M=
z;N4`koj&uhb45p{$*Wm}f0w;}n@wxJc8bYiB_#_+ll|ZjeO3<7&Ug6m;cMlcYw6|6
zd-v|m9xUW1=Xv~nXf98V%b1mIwz8npx`fQ`C!a>dvU-<s$qyfm(|fl4%Y^HWojVKs
z#B=%RlU32z6%unsb=G)H4GJva$eXNAEWEwb-00KmYlBtC)e7)uVO-zX*jR36yiMX_
zSH*+=?|Jo>c1AcD$W3-dsq5<UD^3)v8GU~L5DO?AT2j!Hm?ub|8IBH~Nii)C+(>s#
zE;Sa{%h<L*V0FmW&TTmF89fPEMuV*d<bc8*XeJs6kdL~JHA&Z74|lyAER*erUFk;F
z)!Ntw2q`MuzJ1%MTz<@7S;%cicTW#_<NHT_;6>*Nxt@O)s9|6rNc_o{$w1jYGY2|;
zuzcf@Lk5P9AHA@XFSy*d^*!sn>o`)a6_u1EK^8z-x(MfQd63+f1HE~(Gb$}`EYdec
zb!)Cj*2`qJ{$=%}4$63BcstLsTj;KTOqP4h46O{PuBtl0x5Mn{_GXTGYn~Pd-EML2
zi5FFPIX&=-i_u^`{g5UcdH$mp?}id_3$x<u>-pVwxW2ynlb1uYQ*`yrglq2km-9ES
zb5n#1Nw2rYN~`1rxerH$%Q5uCwD@H0E#A-erj%QySWEB>$=I7*x$LuvY>$_PRMx(t
zzQ-O@Z9>I1A|fISvHy5Ie0IaML!(WRR6l){qzR8n>Fw%{QM(Jz^gjAamK`XZs$t|-
zo3tDgdsm?`W+~Ax4Xe7x-gb%coo2jFNa|RlZMo>`o+}&g`aDz(-H;f4IDF;KojWZ@
z6=$c0+Q!?0)C@d&bX(4i@m-q76&P-nxq17oL$OQ;IH+AiZzXlJ9cHJ;iaa^y>agg=
zr(2mVGxlGczkDi=A9eZsFtn9i*T=`#*Vl3Uqqnq^)tNJA1}o_^J5F+bNXY8j(!KYY
z)QPt}b*omaSRrxt{e!OR*jmlq&G^ynw^Ekp&iU@P?XXCmjb}99P8H7?evfyI3Y6Kh
zL~&|BijjC+Q*$L^23K&c;`j?L8J$VZYP@0$^@_hT*L@KEjePj*?0~moV`SPfk3m1f
zbu2ZH$w~>wfe-d$&pK|KUmtCJW^3I3CIl;4o#>v{v~;j5D(FbVG<z&)?~>i8gQsh`
z9>XwJmX?<Ck4#LId)!SGfA;D6>BV)2V-O66%4Efzri&D3lA7FPV#8{aB+KN-8ke}K
z`F@6<aekls@iKoyFT<YIkTE^V46coiu-8!!Y>8H**TLS`3b&R@xA>=-*RDL%Xw?0L
zonAG@kNtUSxeN}iJ=E}SYTlQS#C$=QUT&DQzv*44M@>)d_V$=8*snBI<T0h}Jqpk3
zrju>e|M9GyogFLiSzDm&29|o%{xn&B+gGas>MiV}6eLaF9%$$(m$53JG%wE`Z}k&s
zwzgdX>ncnt5@fa))m5l$jJT1CrxGj)6}L`nv34H{f8Bn8n_J?-r`L7xQVjwwtcJ9~
z*K33FwQ|3N?CO2`$_Ve``{c<JL|lu(5k$##?298ZB@yrbW)HaBy7rPUDp^`uDk*K%
ze)?nX%t&I9vci{ZUA2jZi}<$vSO#MY+i9*DE2&$%nP<V^%Z1|W5^GB>EYwb(Jn8yj
zuFwI4**2lsy3@0PZ})5!VRy#wFsrz?r25_6=)2Ba&I;L8sS{6>mX@Y|{P=R1ueydt
zh-<tM?XFGXs$zFm1oP&m8oS;&ef#={UB>Cr4*kudg4f5M&*e&O=x;DjJlxP$6x8x5
z%<Gq*tg=|?T}+0xD~?$ZF?WJiRBvv*f-v~nBt<m0rnago^?TFGotfQ#aTCNzZ+9N<
z(oVNH+33*XW`h%+`ojUOIBjl(3YRrbpRP%q|M1*#;Ke*$0pnLeW@Dc#qhA%Zobnd9
zJ_;WzRnN$&&(Lki+X;Voi(~bm!Bqy%G4c63q@?!4{laChH|&|`iJ#Xeiqw7x*i?r1
z0j7uytX1dLzo`B`cd|QHx*_*;@%7Vg&0oHJA&5+BcQcGgkj`%GOfP<FZ)-Q=ru&Ay
z7#3D1HQ!Mcz-vokpa4Gs78X+c3wg!-y#<{&mC5wPZ4ws`^6?1~(JI^s|32P4mdkiI
z!_OJR`aZ2k7jOB#uPMu6WBI)vp0(?mb<&4r9+koyUX|Np{o?oUB|K)PRx%NBn;*E%
zOmr%1XlR5RPqVB&{9+R_w>Y`McN7eNu53%IHSoh~NyAqGZ2@Z(*ZOd;^>y7ZP}`iz
z&~3|Qo&9<o&dzDJRDvn=CYOlWObr$l0oSZpym+zA^hoVSW*#8Jem6HaKR380zlSSV
zZq=^1Z`?jKu~JpGH?t=}SKDpy%=TL=&A-nZey=L}OKzZh{Oq1*@%JTbB`mRot>%Vo
z9V&PnXn+N5IC3W}duFOrF|Rnu{Yx-?9jo$)tSGi)MUjAm-Xeaft>?_mqhRz=6~h72
z&2{gOn`tNu>eMv*55&8*jQ;U;eRpv5OFfB<6c)pA+x5Y7?)9;|?vtMbCIy_k?yF?Q
zj~taw{W$l|<s||Q#aps!)RhH7QccV6>c2cFlNg1^sxkBi?3lJnmg+-j;<e75Fj2~8
z>>s$yHO#D-thJjOA7qtz>L(fP^Q-UF6%H;|!s-9w3~<Kk8+xxQp2m#6F=pwI(}KPB
zOrP(_umQBNes7R`x_CcRaU@1<JxjJPBWmx*TFT4xbazJ@=DF)?pQ+u|6PI#*bbPSQ
z7zciRoJmQlgFvMT0)Aj-W~Ps$bws_Ld0#U^o(etiyY!xNG1lwbFQr>tZB5@;R!-Ko
z!@nNSny#fsG~TrQn9H)FcCbQ<qhB+fctLET3{Sq)jve`l?jy0E?95d+`>)=)g>%W8
zuxvs2sSQG!c8K-)pNfNnYkRzNn760LS`<fHJZ61-v@<WBb!}Zv?;6-!NdVC7h~aGY
znY0ybnMd#Nz`>)7r5l%;*Cqrot@Gra`T<}p9sEZwy&eg5HY-i^nC=<(nC+jni+rCR
zff%g9zOE{EcF;3*ag$xG{$Rykt~6N_twu&46(L3EG%w5OeiCa`aqnjdtHun+;djby
zh(o>|9UWK&feLaqtUcJwaYUCjBARgsbKTwDy9S=mm0lei85vn1^N1kbUYr7g6+hiK
z$JsS-8>!yapFO1Gnwp!Ckg)H0T72>8?Gkp~RhvXy`lZ}(6JuZnn-Wn*K7ezB@M|js
z!F_%!3#wv{w~-X*)T_${<?f>?V)2P#wGsgeGlM}JYfS(^B>)oF{V0!9uQOG&SGO>L
zKQuKlt1ol>jaKfm#Jm}295EjsxUj)iKk>rdop+W;DF=y7?!IH+a8`v4eB@^wxO)Qu
z{-5NK0A>%oTsRn`=C-8_A6*hq52vn{M_E(N$QC;e`Feye-mxYJL+{CEkJ)L_nuYo;
zz{^Vs5NkaDYB4wWAVNa^J7uAq;=#*{RxRUFoI0N+bYDD;ya9r48x}kOUWY6lvaxMq
zxZ|-<;-?RVlZ9mWrUc(&v`lqK2U~{fT~P5Z+yw|~obSUu2*|QeDgUr^6OVM$#pB`A
zB;PCQtxsv2=%ly&{$}><_`t`(&uShr^EFI?hFV{E@n=qSgtz$_P8nsj*|)nfkAH<h
zb|wQ>XW*R%L-VHAAKBf0W64N=OP&Wowyd($93+-)x$M543YkZ3g#ms*@kOId>z4bg
z55^}Xh^-XW;Q`?OJfML5!b)zW>bSn<aV`C#)QOk8xliA@Agi(YbWPdbY!=?wu{Wv6
z2+z(Moi208UCwB{pnBrO2@Cs(@C!Iv=`I2;V|@Z@3KOUAxQ*5e2r|OP;we93=C>+)
z{ZnLT`E$ACma2w|1t4-1z2Z__W7}C4P;2NRf9~A5*11CCd)8cd{S(O{cD4GfjnqmE
z)PIy@TvQ1B#YfzG%<<?#tdQ}?mtOC(9Qq%}#`>)dlG|RktIfa8lF^hozR1toTfp82
zh<e*faf3A^6KDlE@N*jKAZZN@T9{<A#J-Jz(IpV=NSF+*t<tEKIE-b<<CAkdpN(ZM
zcHuQ}{Al9!bS~#2AI0g<YJ=e|QycoQkYSRhN46Sd@8Vp<w{)#?p#R#yUBXA?N2>U?
zpFW^<=NBvp^_FUi(-(nf1<0|{e){Xr!z`wBpxl?&y9V=@8#iXykWe41mDn7Y<mc3q
z`PC**gDbdSwGZA7=xJi>JL(VrCRZ~2L%HIt>*?*cL}z4%EB30oZ4r21Sw^Uod(nSK
ztq{)UXtywR)jC&`{|fU$zWZ-J5Owjt=YOcvR@_}38};njv#K++h!Y@h0GGMBlk8bK
zisiJ62R8&?Y0hy`R#FlRB?Q4Tt?%~eXlQ9EJ2^QiDec&?LrH-yZnJNXDKZx&0PyJ1
z6$4iL0BEn3nyBh?R;a*I-^JFt<Vo?bUOgo@w_-CsRNNB$%l!}i^Jx*9$Bze39X@Mi
z6^Q(#)z`BOn6G;{_of;3!2c|w>c6^N|5&>0|NALUBp&14GZrGwV`P_rE!p$#Q}W?=
zCuW*w{c^%BeIIpLToL(Qc(am)mb!ZP7F?q1I}UMulQXtbWw)hMu!R+YziCw4C^vCG
z6q}3ieB`MTe*4FBegFF5yEVG!_^wBSZa6zx>)0I=-HLS9me2<8aF`HU<XfM<TH*)S
zBDY0EM0$6j?+OtkBytkg&Dq+yu1<@D6yy{}lY$CBYzelvx9fgBL~cNn*>D`l?V(sC
zpH=4PN8><w%#P89hKBJzhdh2@4C7d)q3d8l2A~%bck5J3IA;>806rTcSN9;D4==TP
z$9Fban8{;W8h)~SJI$=<`IGL9s6L-#p&au2f%`1-rboHCVXjfRxw-i(IIe2Cu}2SY
zND(#6l>>OZA~HuNR*kLzUZp9<Evs}<vw(fwPYPqt$_w1tEMD2(r+eXB$m#G}$Xhs;
zH#;4s=;5x1P-g?@yMIIQ*`)p_8nH$IRUlN1d+U-&#3!^QquE&`0m#~MqCK>&kl)&W
zu&pSxtCxClsT#|g5AtpPlC8*+gOFOR$GWWr5=%75X?5#&G1RlG)=)=^O1w5gxMgZT
zg7{kOz+?5OR!REAAy9G3IJ++{%`4hjtG67BD&f%AsGWRTWq#Qra*um<O&dNm-N1q4
zleuW7q!ee6lUa}seuGP4{8uF<e|~AJC^r&02r^YtdV5PU+E0YykGnDixd%*A>Q8s{
zfx6PM&;?SA)6bF;7M}l`G<er}2%y1g3iVzZ6p3tc*D8h6+8j<ad!$C!hRtFqW3@ck
z3=cne7DiW!MSZf54`DrSgoZL?%C7#jS6H|;M9ZN5(;&dYcK(c=z@H??Dc$XLB6dqj
zYRi1j>m{eRM;N>ns&nm8<cf@ods{99Z=yL`?&N})8?UfS@PPMX`l|HZnflM!-LZ*<
zh{Ek83lLAgzt_7^I&Y%14$wlnJMTjK8=rAgA0~*P+de*KfJAy?wI)j&tEzYi103Eq
z^<kc(Pm?_}{#EC7`@`m0O|0)LM1lDUsT)(`DHxNT-H5yIOZxbS)Q|dj;^;BfP1PNr
zE<9T~Yo3Oj$#JlNS9f}Uyh|b$yL7OI>*BOEAK8MZ@m==Ex3-Msju8m9wl(M87(l;I
zR)4PShQqgcf)r;Q8(oJ=MdvULa+=vI<8-|lJ3*nJO`IL{UaWzLriD-{#pqA1Ji;&k
zDPC5$vTAgU$I}uFR+qX5{azJvmhB~5LQD1`%Yz~R82=<THrPMv{{0sZb{t5uTLpJ5
zY9zqC0}#O{WuZ;6!^P`E^QR_;+6Ie+g2;jWT$yN?=i}ozR4UC}6Mg^wPr}XY{fsoZ
zy-LL2-*N7av9wA|OjIlEp1(Tf@Ix^U&m~xA`R@MZH*|2kmJM_aIVpP3F6!*tx9|FK
zq@3wt_c$Mv+}z5n77aD4I|?IRt|liTEPNO2<w|Ca{BSRe(PA~k`%%|A2ayP?;lisq
zpIR>`XQk)-??xM*5efy_6&|ZCKw5`21UL?(dofR_ac2Ql(zy6b&<~Cay?#j+>-Pr9
zy^@KgxO4Hn3OBb6cwEa=YtWi<2+jC=*L`n$n}_83G=W+`^Y)U^BJeEB39j9-bK174
z7DzHNck-N`O~DERJV`pV#=h<pKg;7w@kVmWPNtX?JWvU~E^$*#fA#VWhkO9!DKs<i
z;e{^i4t4E@V6YEf$9R(-k`IAc8Qs9Hs;Mb-$Q_CxiyzCRyYxK^+ji=gSSBecWb|eB
zL}q3>`*p`9CI%6L&iFyGuf_2@JFQJ(N@({4=Egp03(Q=5P@i*{4?B?r4oErQW%)9&
zn5uf*w6oO=>z32?#dsZ;*Q+0B(-qugBIHN6To7F^0fHj~XVJJh+i7sPQmyFs?-xd5
zXYo$nqzqyV;?M?sf^wpPCA|-6b1P`^K*A{#Y#rgyeC6XS@1m}EQEi0Jx_j{J=EQfc
zlyt0oxyXQ)zRzUfWz89sbqb6)#V-o!ZokCPje9}Wm@1b4--x3Bsk#-}p8pDZ`rjA%
zp9s+Yk*4JwlIeN(L3Xwryi-g`-MKB1N4uBNy6(ZX9Sfz&7f#L!zCFF&eTLSRnw;!W
z#*BUZ*ayCPndV%JE271VzR%WGhc6oLuC~o>FE!^^)t^HXI0{q%1S4v<s$wllKP4nI
zy#xD-3tIeUA;tXc=5nMVW3ic;8SzrbLX)biOv4Y7Z(apHnfTHU;OOk1oSaMuR6u-`
zGk{+XQXU1Dd5lM($h-Ywp(5Xa|2oC;aMkBoD?p10j+CTtj1bw=FcGm>SMB~$vv;FI
z^Y)m=&q@OxPK3{qzu_H6Iq3gfY5Bk35k0D~#_vxpz(23#{@-t1{&y|Np8b6){%0_G
zq7}1n)6Eqkn)xZ^3j9bX`rE)Rnr}rmGC9)2yLP|74-pIyzGO*&gwvN#VbazH<foUp
zgan1ybw#Zx6$Zgc#GhYD?(`MY5JJ&ZVdz#svj_0EG3pM403l2bk`BhIDpI$xK}~JH
z0$r`y1-v?;yMoSEJ}St+vOEf~wWN$XwZGch`FTnQ7w5mewzds*7k?spm>laD0swgj
z#7t=nJf=se0x#G}e&`V<_p=jq(zX-^2?>FvBsBp-c;T9kJ@JfIVDrFGL-*ZQ$QJn&
z-CgIGQ9=)7Cq|2#S?Nr%+|jheefs+Pi`MMg_NJtSBtby<{%dw`N97rb!#LyztDfmu
zMNLN^iC7JIjAHN#`ovpPfF~LF)kT7GyAn{2BUOiIww;C7*Q=(F#!zpQ8l(GNHme?a
zz*^zRqnq;K=X5MYP$T+qiEF=7euhn(R09L`92+o3fR}f6+h%4!7=*Msu!ozYf3m0B
zDkHFu<g%c%QO`mNTXRgY>)s{gFU1?_y^#y3+ejG#i;_Im$5H%wd`!&QB+L5bGx$7U
zn3T|Ssnf5o0?4+9Xk|V<NL{_OQdBTGeKTsXqzIv`l+TMYL_m5XD(u2Dt6I}d^f!_8
z<e6SV>5myPF`IM8a{AMDmu*xSydq^f-$D8C;ij3<IbAF9_CT07GZ%G9;E(Uc#Kp(p
zjHBb}YE@us?jPNBYUlHtn?!o1<QlCap2K@*pw2@=fX#cAjRo@*27U{eJe^YSQ|I{k
z@<{ybLL!t{MKZsz9cPb)w-IiA6ntsmO}g{qGq*-%IgVbqb;u-2ErX`3eDdTix+2XN
zn~+(xo~HY&{{H$^P4-tVBf$i-=!VLwhtIXO%7q|@ndkD5-;`w3#=m0T))Na&Wvn|q
zk1WKdM^?KpF|aYniug!2R8?JFus&4peQVVDo*pVZ5mL}Hu(qONDR;lu3(L~x0LOlh
z606*a4k-qgfXt4Q%OW>wS6A7aEqR^}ieT(<p4!oKw+?N93@8iP9*C!%<~27mglX@e
zV55uk(@-;F6zuKz?P7gZ_Z-$|dyf9h^J9dp1V_3JHk?;{k}-GQ5`P~bRNt~nN9;(2
z-p7YjT&10Qaqh;ulocB~Mxh5mF4_J1?8Sw;+tb!~#>Z})cKiL9?rh5=uU}q}Y&Cr*
zc~QMZdcnr4zg>)pi|ZZEZ913p+sLaXE|2k75~p7Nakwlaz^4nG@t4J}f0O3uHU3VS
zR{9>se3Ir6R(udV3&lxj>Dlbd`e>E!B6Lmm^Mha*BluCKnajUQc~&wgj?GiF0x6H(
z$38;f68<~D+7*?XJ{gEBE`Y!!mD5AFcWj;rYkdDu)%fKf^NF%zo}lBeI9D05N4|Q6
zlrvu`Qi~Wuf)Ei0cYo>YrmEXnq<Dj}mPORqU$Pr^FBabhb&)h}<|9dDD=t-;sUpdT
zZECV_0eg7bFN%8-DBB&l2?)mcMo35j2w)-Z{`gRBLqo$>U)q7#TsQWHk|Sb<xs9u{
zuNt|bKyh)jPE;s4J;u<?vb~=8NGgX`yGt?I5Jzkz-Vrs4z^EVv=M<CgC{D~`ag!0J
zwNBWJh0oJpFEXgheEZ@1d)jY5c=Z{pnhe<c?fj~aa~A$5$mO1NaabQeemu$hJ7Vbf
zR_x<)oVR#256K-t=d5t~N`TfJANFKGRzheZ!qTikF<Qc9wD)!AP=Nx);_M018ulDR
z)g_QhhY3Z-?=9r+*b7d8s`R`G5%F`|MQ-9L7~93s4}WfqO5R}JEIUtKON*DAn@~uk
zNJ7GkX@s37sm>HAY$adW6NCql7LH6#-hM=GFy9gBE>&195zE@@3~Gp*TM;!PW8|_|
zP+HiCqMM=9CL%JR?b&Fj@;ob-)MLQptQr{^q3Zi!r(lj=yaqnUkL+Gg?nsOkynRNj
zDrHJtnwfGR^rN*B4M-IrOy0$Qt4`{sToibVkQDO<1O(W?Q5HhQv!+PU#eyh@pbbjb
zJV|a!i%F}V(>%h;9x5Z}hY)eD1DAvjHG7cSSJh*6q6iGzWfSS0Ox?`lr6w0SJ#rA6
z*fgAtE&%nUBX`~spCsH`tY*9~sqB$&WkC9nZhz(LU7q4DBtmn!4_vB)ysK@k(8K|9
zN`!?`Yvdy~Pa?8O`CWnjT(*_#P<RXc1tfU%>0VPJ=+ccnM6Tpn)%D&}nqFQ6@mlMR
z<43M=%;n7Ig@%xjEzoZuyAH%#ZoRqn<T6r+AMS5a6c#2$8DEhEpUuY~t!s0Iey01)
zcItJG#b{|Q$^@G>j{4MUEqyzUt-9%3o$?R|Nf0eSNEh0Bc~Si*K0@ieN69!kEAs1u
zoDgw-E{g(Bn2g;yzp>FKhn9>lZ$#Sg)_%~}ED1<)hl0?sYBhDA%uA8;vPsG9J*i|1
z8BV?rHG<HqyGomg0em$m+_8rN({ZSKdJ3e8R(@TQaa4M`y<g!Z{J!>sC)7G-(s3Q}
zyeZ;mjJPOv;5p|TU-^6IDbeY#J)0&MHT(ELhxkArXC*b4`qtF(ccuekFbZOT5TgZ3
zmdm9y&G71-|M~qa1a`_vC0{Km-B)nn2*jOd=^ir^tWm|zJGR$HQTp}!Dka^+(7?&L
z?=5(+f!&|yku{BiW6J}1coU;g$~{zAu8{vQ4QWVNDtn}-)@qiZk+ZW{+b#CHsYhwj
zd^eZSMYFa8$)n!or?<!VeSM2Z_+}HTOu?fKY3588z0@Swes*U1U)>oP<46NUqSlOA
z$Q8#POuq|qPpxaGE__iJ{y+^i!v}7#_H^bfP|}eG6a+W_^__!{$ZQ6o2Sb@*_XqWR
z%(B%t@#wJFQ9{$3P{pP1Q&IxwHpR}Hb@}Tx8SQJ5btiJAMDfh7NP+?Bvs0ae5KSt~
zpx&GhBC8b-7f9qspB=C&$}PEWe|hxkSP{<YPa73i%}%$>3Xr|Wfrf<hie%ZYsM491
zi86;)P)hy26i}jN#z&Ta(C<{79a3yVMO)Wzr<3%>H=CpLRmF37Na<B8gxakNOS|Gl
z&3431bIA?t0Zmwl${<nl;hl^?qe)oQJv|*y$&+bPhM}C<Jz%(TIC{BVQ2wmhwT3L*
zo}h{|synD9tVsJ?$#RUC?j>n3<Z&lcj2cqU4nh+Alhbn@mzhyUo}GpY^{u9Zq2FZ_
z<t`~7qE^|s;uU2JOZAry)YR4%<<0cu6{21iJhlYF3!=S<1dy%srash_1dD6FP$H_5
zkp)FW5ok?r2;0O)Pf^!)&W=<(&GMM(^te6>-GO-HS2A4~Tisbsr>83W^QrfKq*h~G
z5&!7DMBsL6NlA&Hyd3rY*=V?L9Hih;;8fqg{znAdtEoZ|0vT~}k&ix~-HhXm1tX#$
zm<Rl!A3-a1kg4O>kHXF?T%H&B@hSsWlR^S<mUYl6v_a6~hX^S{95awyFq>=D_6K$A
zsb{*Vif3{FF^MvNaSuv?8C{WnGT9!0k@nvf@7TN0uW{?^(cO60F|uwiRRgH-#ZecU
zc&+R!zm9r__`j=5J?T>2k@^TMB%w#^El^Fc2U8nwqfI{VX%$~Gr<oKdU>vS({4GA6
zbPx9^%N|!KSpvwbDCINqrdeIp=CP;Cs5`{eu0MMYytlugybSW4N~;ziy?n@mpqn)=
z3Jd_Adn$5}db%$A%+rgTkE_Y=obLeH=4(<=M@?a5uK+|7DlRo~C-v2-tYi=41bhaH
zJZ?laE-btR<)86VYxm_SZ)=n4MS}+%;lYM)GWtMxRhL?QhxIIe>+Gb`TUk9&CT%=B
zi;U%89pqb*rW;r!fSxtoeoK>`ID0gW+OEHH28IxKJkZzBNIS_+(9cSLT}{2?MMLZf
zd#s<HYWu8Fk*lik*kU9NDlSm+iu|>wfZA}~S%=fbUL&Scfopar|DBtWhi|cGw;;r-
zu)(E^{^iS$j&9=d-TeV3Zw#@@ySh3)QhEP=;Yy`%3UVyYa&GRPB@<8-iQP(*MXc1$
zb{sTGWQK1*)The8g_<c}3vl)B%Cv*$e$}vRRCiD!wG&94wi4|-B_2_y@ZG(6PON_Q
zQ9q#@nF}`E2B7|Qe-v4r@0!6q#XQY<T0AptQ(&z$MzECdezIxSiNdwZAkVXbD69}^
z*BZzSg9t$l$$5)#-BeS*J8$rY!@k5p7Q2i!yI3=pIKZSgB^6%wshu(C-g3|E0?XmI
z6K|@lcxSRA+i;5aD?w|&f#~&iL8@vD1+q=YEz|V6+>bh0ij(hxj386cS0~LPG!jR4
zI@a!n@}_k*#i8Fuv(|3rd5fvl+igDxiyid)<qw~qGQzwbwSNDqLBB?|Lo-#UXMMco
zV&?l+Etb`7hVr966N8tknr1gd47RX@O%86^Zx)xaW%Y(Phxzt%nv~rcnw~y&hw~Qu
z&aGDt9TTo{9fRX#6X>1jXWyB)Q<B{wNhbxyR%0QycDN<!Fn~0fxAm=Ld2jr7%1S2V
z4Q{>2YC@00au|m0+i+=dqKi(<{&EmSsyg$;97@}c`d71@JoQR>HL)E1_DHiO?0f5o
zY}oJ)W&gkK=}KF_n!0G0{-3=fTPBw+MmfVC*Rm2)L`7Fv^K1O{Korbrzo{!6UTV*F
z{05>sdE`IHBSl7c!?V(95CY^;kjsfTjzYX532*AF|8-sSG;!9TG3h#y1rcjuG~^!R
zh><*`?*iGI=$0+M247EQo<_1?TmTpW_wiRkg^0VnL>YMpI;?B!RASyx?(0Cr_3yK}
zxsm!2Fb&$ESj~MI1j=fgzruwBA=f$aa{%y77A^4M4@D?s{^Oa0f&=M#4r9N~6Wnxa
zJ@x9JkA3lUV5VDoS(<+pYg{xap4)u0r;p^}qJR0&JDcEy;?ALnb161$h1wbCPq{+y
zx3#hP0tJvgN3lAP`UHZKZEqu@<6RS<{(?>NfK3>&L_b(!ICJ4`gsk+NHwSZ#loCl}
z2F}E>(08VsA!i6}8*#PxG?itadG2|wc&c6U7*g*}q2c?k8X7x7Gk>7ToB)>9nA<CP
ztfbsWpMV}xY(|mODjZkhQ*d?k7m$@A@eFF)t@f+39$*t5Ki=f@sM+-P>Um{u-ycXh
zv|%}r<7T^6+kOlQ4Rx9MS?Enom6BVGAMM`Wxr36&95j{c61wI$evYOa^op?qM0L}L
zySziB!jq|r;1MJ&N$%5=sJYK2HFx+|LGHnhc@tm<peUJpKwtlJ;*KSIyp!vDJ=p?h
zAX96)`&~~CQIq~#%||Pyh0SM&)n<7hI4S6p>L{lh4l7XG{XP!CQox~+8yR+8(}J&=
zTyrG;kw^>>URLw-8$&?v#e~}3`27QwYsIIHz<hwjI#D;4;E*E%-{SYzmWbk_KcXHH
z3L&J*_dBWKASEvC+pn}vWf|eU?%36wBm(b36w3tRW)iPbF!tU;_>+zc&xm%)2zMML
z7Z*gZe*Cy@a0l$M8IY-v<QZWKZq8F*Li7B`vODDm(Im2;l=W1<hN#dtzD*y4AkcsL
zeh~p#_6r<zw}8?@WFfUv_sO$8z0@Z!{tQq`e4Q6dC3u5fojWx2;uf<gVsHB9)u#z;
zC0S-VGdw3tA4;B6Y~bKOQ7!f3o910s&Dqahjap|9?mv}9K+e}_G)$nW>Dw^%Is?e3
zyjlIg44Od{l)2}IA3<N)_;Qp;0FhT(rm%#7DaSj3dR~r^&W%5DBr))@$5Da49^>8~
zC@AQO@sfrM*WoCk0KjoYO~{)`-_R5?j3A<s-GF4CLIuqL%SEss-QEk-X`GA?O#HoO
z9~KhY+@%{ME*_f#JiX#W8e@u3$Y#oQkTeErMG=r-e*$5Kufiqi)>6NRP3W8<d|zSe
z*~$hB2ecjKlU5AJ55LVL^30rXP9D#@Dw~O_|1RkF+5j~yiMl1pd8GSm^9<o5n@7_b
z1&A3ny3rJU)ivfU`oZ!njG4+tQQ7?$HyZM%8+CKre#xPyCY~G*AS<wfkcZ^%5+(1p
zC$Gkw!}|KVK_nr&W`MWlZ;Bh{R_3xO*$7%rV2F`^V#v@GitkWne=Je!eU&9My#R8J
zHduP!Q%kTudgxa|p_{<&pS;(+=l=@mhGlb-Ir~Xt+%Fj}ZzYQI_j1YeoH-LuKZou!
zFBS}BWdGMZXZjcn287UdB8=tchVCKAaH^G80=W`cRqM3rKjtrA!2A{u$_VJGJS@E;
z9RcQzlxd%pGziX@qr?#Y%&e?QW`&jrjk>#F5>Lqn7pFGlw(qO1iPzQo%eIo_FlYGK
zDHm*mzD#;jQqninGfYqqc)GEd_F|#zk}D0`Pwt~%;;B3m*uTDmJQ@iWFe6T0xZm#I
z0x3iGUz;%Wke^<Tl7_`4OhYi%UMxbp6^Kzgcdum&UIHZOK9tv#l%{&}X3W7o<~T)a
z-ZZ1sx{v`Nr6(u@K*9H0MoD>Xbk$iwQUppc!^YSj9Y$6;BQ@ArFR37fNi$c3>-tkF
zm!WCQDd9as1xmixTb!dRmmk^nQ$pU%<Ty#L5D<gNVVCXo=+ViNZgw<&duMlw^g(Kq
zyNK~RWT8GFi#D4Tg!FH)umv|$1R7^4`Z!Fiza>@HBX1Tpvq&3929j4Jl>3Rr5~`VW
zQyI9hRuJVj$l&3Q&(x(S)3E9!z`}<1!SE<GEXB7YM1Z>2?X9Ffw@4dXKp4HcbW7c&
zEb91w5cSRF><4MSfT?nwC(DI8oioG_BZzC);pVnUS=91=D2GcV?Gs)s+&{6diCTsM
z)0||bSD-?WP{yT{`D(;!u3+;}S5XqzJI$)rG7KR4s=<fiZkwq#k1z@2)`f=SzYvng
zA8jefcG=NucAh)&7pIB)1Omt*tSn*Zlio*YtsJ@?c}>11Y<D8Xzp6R+v=FWVO>GbA
z5K{4rN?7{c$FlsIz6Sk&NmxeAIIYoc^6~d`z}m7bYb&dnk}F$$6;4u`o?rL$sCVBk
zHCf1EZO+Lhs8>f{r=oU*TA=5;+L6@i{m0sA$z%MBEaAx}O=Xm9t|TW547CC?^v%?e
zJZO{V*W7Db=iQuUbU-{&W;2$5LnVRVe9W;Y%94(?I9hQs<eIduNWt&3UtL}-qA5WA
z{o=JhKlqR7111;!mk0klz3boR<^QB^<p1hZXxzy3G*Y>~PRg=@2f<7ayytQf*7w7a
z9*U6Ji4LX2i%)_Mhf5Ve39|}Gr?oK(S`zM4<Bl!tW?p3hyAwzS$G%Wdn$~sOeffac
z$>eo}f@58DQK0u%4Sv(5pZ6Xmxe5^+ky;s{L(nDs*ldI{!Qm5m^Bs}@w~>M#DaD})
zW~qo)g1@TRvj@kE2pvJ{LV#A3XOD@#yZRF6VqcQw*5=KOIFc$JaZ}<TdvaW<7HgVJ
zV#{~gB+#yL9eCl@Z|K5GJ0j#lI37syh-mwWEGzXT3o+4wNEiv%jBL(^sQdN@t~pti
zN!f3v`E$e5qAJ%(vn38I62_*pvmahuY9m^4B_+A3fmd!iUuj?$PS@vIcz81l3;4@-
zQOb-46Ob=Z@S|EWa#Ei%K!H^Q9Q~IU^9u`8t(ZWinoLq$5*F4>(5pAKqi%ZfK}*~V
zrhd4zb)Zk)9~r2l#URL&&Z+hbmYgqu$Nd(HW!x)ur<%E~^QN7TT4sK_y_$$32u3#Q
zs<@Bx0;m6>4ae(9DQxPJ!j0JYfcS@Eb3B&-)fO$KUz4$XJ};Ys8Jvq(SDA$0{LM;%
zZY}wtwEY6%uT$&GQ70kw(D0B)YG<yC+s&t4{AEM%s-yjCPenG1hzya7#`6v2&6)e)
z<(?`3RQaghKn{7=%C~=N0rHgg7SLNhFcPU+msXva2>$@rDzXeu{rCnva@Rl#rlgcT
z+F)*(UY|Kq>d_kW__3Mos8xQr>h4C6G?smd$QlCprR`JR566^9g=Bt5llh5W&h>a~
z<EAv|HeQ-mK-lp(F%i1#ZRgkBwWh^fEj5|f*uRN1ubn(uA8$O*^|>rM2GI4wu+k6j
z=}9=5vO!oFZ5_2%hQOSJdY2ljt*#z1%d~4qy)&`sc|xhV)NsT)75lnT@`~kbX-&@?
z1FK?&IHz2g0K5j;mBvt;#HTx<g$l3)4H&GHOlo&!wttx0j07UZq}11kPzSxK9soQ<
zhlipnxAK~I(P`8Ur<!8%xw%0b8G#YjMl<wVx}qYoQaxGw>B$kIUnK=QLS~Ti_cARi
zPaNQ#Hd)+EFc0badpU-bQdk&{yQKhqhbJ<or-s1h1<76THuhf3zmm}CB%MO4AOxPT
z<<#t-K}1}W;?64UrZ;>DFmoAfzsX=^Ki;PryiqNX%p&Lmi$=}@83{lRd)RJU;0=kW
zYQIG4>2DmvagB|QZNqBxsSA-JR7#nQ2x)i|)}Z`<h08Y$+<#x^_@_$Ye*?uodUyso
zD*vPHg2dh5Ks{`McRi@TpMLh-_H5ewA0hVu*aBC`0p$lzI^v=H>^-jbC$Uj6XvwL#
zk6`h5)vxqxZ{B1oa%s(}_1>~&i}UcSgW+%184@ksABQ$b-kYJ#&stWl*ri(<5;DiY
zlZVfTe(lRdU#HoeW`!zf2S5^3of*EB85eYfc9zKC5YVYu0e4Ee7legDSM5gU7-VpZ
zejFL8+5Fvs-{539#$?~1W{CP|vTl*HH3cgFR}=Yf$(#P;jzdGe8!$-bSp7ikrt)0v
z?(O%?Zl|9>O-q3u)ZNjsOlxjI<&N=;*II#~rD)sk{ou&qsc&%7UgL)J%zdhHX7y~c
zzCq^pdtKYBD+yD!XxIPXzM_AmZ1w*Mf`78fjw9>(zX!WRGpS;kr@#-6vla!uD{R1p
zL)0eTnC?39g|r$q0^8b<9*28BUplD$6LGuG1Gm0g%1tB+7!N~6c-*=0X7k`Hp_y%j
z?~rahzn2iCcTq(qMR8UA4b+#Ge3#8_^Eb%(F_0OM&Y$pE9m7NXc*O%jU*F6;LB8er
zJBQG~29;$FO?FyJN~C#0mL-JVMly#0a16?g3^jAgjhK~tayd#P2Z7`Pq!199Ci76Z
zl&G*UYMx07u~eb?-rBheADW(?LN7)ygjBd+^SN-62rK17_rZ=6qwX1a0<QHK6ZyB&
z)cSl&WUZL?f~Nm{Vxi`eKUhHsT%Tgfs4pkoBw=z+7r34r4t-#w8>33Qw;U9-5t^zV
zibd&{>c$GUXrt+@3yjv@NQ>7A?7Q!?nN~>;DMt(z>Ty$nhw|ohSA`Z9ftcZ?CToQW
ziDsF8c2!r$(1g_7=5;EqXbQJVp#!)}1+juSwKNJg_lMzl)4HBtee*_tcKR$M?}}^t
z^GVNnSNFG9yX>n^n7W(XX`nv+8&^o?0-DUxnRGXY=@xb0L(iFWSFEsB_>Nio(eg0l
z$QGd?X?aifDYNHYZ}5Gy%lgevL{H>?&vopx#8ZW4jtc%a$^n5d=(`fpD7Qy_@i-+=
zKrVtY4Qcfz0Hxf6CVY*}hFs{vCk-&xwjL;v#u?`2hKI~~sEMIL+Q|>mCf205h+~f~
zS(_kvFg?<<;c@7=rkcn0dM^n;1f%Gmwk6A}JTI<72P!Cd`<^-u#ionUSfRVpswq>-
z@P}Hec7|Bf^Vd)rGK8Fg+Jx#@E#yk<xJJ-&spZ5MB6EKYqmaCH=<V&DBErJpJD*n0
z#OpE&Nxw8VkhF~wy@pKop;g?4o$Xh;<JfwI5Zmt}_mQ~?q`Aq*2U-Tw$AOm5-Ai_V
zx>^ToRn^-}zJ;vr`Z|?^`uPbTK;#EPEt#Ha4U9`99Hi+bsEjCv5G<A5Xb;<j&1pQB
z{Ngx{ZWT-BoGyY*WJbtq@;tj)bsI)R8ka~1Tw!a0UmGfMK@gf2ft5s|aTV;;pnDmG
zn(cq(v=-WaiT8ae`bK<TkpC-KWYDGPy!}$`>TL2*|AtofKORi}$&=5>WBxsOJFzcj
zA>RR*(pr`4sC-<k#Lfzk(LCP>F7!>`B+5fGSb$B<NmHC!2swnXXa-cX7GVR))DI;k
z*LD%ZE1(FnU}8-eNz}|T{yy97apcQai_aocOe}yl4@nnk9)nR*J-U`2ikp$-y3g6e
zz-${;udy2)9VY#ox9vIkALgK*L}F&!gMy#u3GfI_cPZm6qw2XnfzvZ}HC)!m>C<$#
z(aknHX%Fhjzzs`l7hNSKX#L{yt7#9fgF)GB$4wT7b}{1L<(J4((dt6lFFbQZ{%8z)
zO)@I*txG<mN@ijZ=`bNJ2>n86i0<ti2X2`sJlh}$7K4mDAWA7QyWO-0`J-b&H$n50
z0+Y>ij(Pc{7)ba`B>j;Kemk~wU{30(p^nnQXXS2UC|#$#V&T;L+4a;Ez)rD*Sj+&h
z6rqtyv}2@k4zvh45esOWlc%TK;%gH0`N<FnGRou;<i>{6-C9v`Zxt<H?ZIR^Gf<{f
zmnCMakovI#8q|w0wP=*IdqVz&;U-te>^XGze@bivzfYg+HC5y%LN=xBiL8=zOJ?ge
zWx*@t)rbm$%pM?PlEiI7_84;G(T$K|4`LvY1VApO0Rtp`nM<m85C;-T!lOgCj=kK%
zLG%3War?Spgd(K;M7>OgZM9;8l@BIU9e=)XuFu3IEgm!4KyE{~cGjCHu71_pY1xnp
zB{B*NqI18S>*L9LZv}I26P~zu-dgPAuc0_bW(8n)frl}q0F#t53CRs(rP0h<fRk5p
zb0b~0k6+5S2dQp_FKN{!bT*~<9gbEEASECPFb}&vUESM?5jp;Im+boS+m_O3k&rr)
z0Rfmeg@$tyh1Vzi(4`8*E;ZHEGOd3oY}-${?56%gGL8)drVSYn1u2aXH#a6asNF$?
z9t?EIj>-?g1I;ZaO`aK;!z3(>Ib*yxLrC{bxm%wt8HsW1=DcAE4)Hy*WV*n3Lv8L#
zjC@HtSN+5h<Gfl)@8g-0n+2HPw}z0e7-+T&1t`7lV^~^_b)hA!i%eJ;Kg_^v{u7Ra
zD$f^I%qKIft}NeJNEDVZEvOlUAZ&Lc8ay!^K{DsYG(ZGmPc>JFvjT{UU%2_si!3s^
z1iI@sRG58G4<$p^K8-Es+oN}S@jZUh2}mRe5UA(oDj1+pfzwT+E#eV<#U#lIoOhez
zOq(JJ(v$!g^7ez;0u{BuXiYmHNPlS$4a|HII9tw4#HDef*yH9V;=owdU9{<7Y;rhQ
zwv%<uc%XyY^I?@l0cJ#IA>a)_BqplrFR4(*_2RYB=u6ZCWZDdLW6Y0<EkI34j)Se^
zWcbNfHix^qy87Xk6+~v#te5%mj%^ueI)1B+7h4VZekAX-0{j{QRu~C{k#m@>(gtRw
zCyHnS8>G>^O}YT&@XAzG4(C^^`?{yW24Z<wAwHZYMjMGvm?&0A>Kzal`@qnn_6PV9
zi8dML0D1cL*h<(y)(tYCafa10BTD)UNbN2v@|UT((<e8mTz~Lwq(g;W%?FB~`KM)W
z6~CdDu@7>J6B-%?kQr%oUl2B`jf;uKA=k-JBSMTISU^#<;>;R8K7V~$mm-#X4_$Wr
z!=;5UUZQ-{)Y<8QGR{Kk|79&uQ-CfXMJ2bR`klSLJ4a5Guy3qWc{ka8xL}tW#uc5C
zD?`7wPjy;CY#Tj6q}sSKWN5Ugb}5bTF?D5Uv?LQ%i`TEGNKpv>Pa*z(*w4Z|uSN&M
zO4^;L^$I=u@wHylg|xZHiF2lL-o&^Lq+fUqf*p~Cp>`uq2AeF?s!7Lqz}^d|iW9Ue
z6Y|^UvWC`hc+R{+G{09)8py~=gyf=ID{r-=X&H!M9H@*NXJC{MZeqe{mn1PWv?c!}
zEKCUKZ$?Trzn}_cyjc~H_`rH7NGeRfHhnnOW;L&%^xPQ2_EiJf5pLZ#Fb=C&DfZ(q
z>C#oo7fH-sMn;*;U%o*JpqI=9wZV%QJvtn2FXN7Zj~L~(0@9!@jZ&Bo_Y0kw*HIRc
z^~y#--!eBXlK}ql#!dxZglE5>_Fm2>Q4a1R$W<ZlRTALPAe3K{&C`7j%3vrC-Pt8E
zGo(061no?>tujwy9uj1R&e~R@4kj4{C}kdN7bcS$sWLU@G3#)@Be1WMOiLtBiE8~m
z@|5SFbI1x1NjKHAPR50jHsOJfuSh5ef?7ukUcVteWhbd343#S?l9n2xe?lX3A?fu;
zm^nL^Q6{HAo-vp{wT_HHbL@9%;YY#F6xF_b)N5NYz>i4HS=(n{HdiJIy_+7)(?^NJ
z74ry#h_G4WT-9STl9af#XoZm&K*^1ndLAxqF=?Ys0t=f2N}8Tw5|Y2mcEK#$=7j9a
z!=Ie={gW2bF8;_t>za!Srkb$u6`xPaij(ReM?ua1c*W-dilZ3zVZ{g{vl}al;9u{M
zgv_hIWpXFx=6&**9rJM5H3OBNd5T*}BEI#jH#IQyWiI@&opvpH<#VLi3Xpv-9J56i
zjSAi|mbKrX?;4q3P+Aljbvs=bTAS^-+LHbus3YEWxap*w)3|_UqrQu8Ni_b2jV2S<
znIXFM*cyx{DaIbjj11|M54~C}omyX8aer?xnf=$~?h2WR?9P=in475oj*NF!+${~4
z=A9gWYpPIrF8prb^t#V8<7(H}A^UQXS)&Y&`yi%`%EaYKw#9@Q-;eF=E1qWc88=oA
z)<q22oAj<^J2f?7ky6Xa;ZOs?o%eK_fnf+<Ea;deg@w~C>X+fPSj#lEU?S9NvLG!T
zGtfX&&I_U`{%ZRcGK-2T`!=F?X{?ASA4$}1BSSX8gr_xFAv#K!*Te}{e~Gtt6FIW$
z;~y`R`I_v|bx{E4nOn&cHC;<b1(rbjRHm9#cmNrtm-Oq4-<Om-vC?V_iPPzr!OIqr
z=}@S>vYsa1CBjI8wVKbEeAz!dU6=RyQMeIeoj_m|Olu?f@TAJEBaH61j_1hROLmHl
zPK!Ru6+~Z(LI0CPChe;8!y{pE4dV3jjqun9g@q^bH!37m&LE2CZCjflFKYUG0o}BZ
zh^LFO>epQr=<sx(h~zREI>8kU<c(!VQjpERoI&BtLEA&i1es1U)_()h_I-i4X&q_+
z^_h0&$#|UyadC0mR9RQ|E>XeF3Z-+e#pyD{irX)!NQEDU|GnLdRuQjUGJL?QIu*kp
z1kTBzY34f2ZM`nT>M-bl_l=ErAg29k0*ZwYS4hmP960+&Z(BnhcpAPzMS<ROp9T2n
zVCF*R(*hrgHb$epVpf$}_ZfuU)4-ep3W1oyQH%oj0>u_6i7**reKGXt3oQc`XU47M
z6_Du{1Cm~!ouJRE7@%7Hvh8|#XD~X6J^^f~lF0Up5D~yK$|_gsN4#JLC(+mG^Uq@F
zBz=J;5k!2#$lJpZ)J0&;Ue>uo5)h2RTaPOG;I{go&WsiwH)pwQUzvHK&#0*-A-9mn
zT+Y5dTOgpR+1X(1H1tF9q;{NRMIwV?Bi;2&al|MYQ7hUD;8Dx$jXrbqD5f8h>QsEX
zE>Q0MCgK_*MitRPA;9@q)NcI;0pteB5VL5qComl}ZEwXOBy7pm_V8AlQprD6$KNJ{
znO@GoJtx0n*EPHO`kcm;Ov`l3RrXCtW#iK!n=6K<yuNSRxo0;WEQux?T@MEdx;0|}
zq(wCFtaU_9t{6iOZz?HWz>6{(9`SHks};Imzi2r{=9t3!tb(>J8R9!A>@GYz4H2-4
z(mG1(KjN_Cu8w(2a8gBxEpN|^{m9Nok}{HlSr?iE3$3i>5?y5MIXr7frqpFe2Ekzr
z1#z9oKkTBXNqy=R8@icO(UaLot&Ac0J!jWKIwHyW^YI~aetl6)IRWb9lQ2?>Wm9%A
z>Vop{uF*PUT}x>QuD?!QL7zx=$xg`KR|*#8O@!xNA@kSt`=TBa$KDLA!^liTTqKE(
zkIycQQ)cREVj^F@hL-+{GiR*br${-9Dc?zY{|I*z%f_G>GS|VYUkDneCH;FHNDCF|
z{X_t+FL$pI>Ff&3UCjmvBTyP`jw`hEs39BSmL<>|iWp8DL<tvonq@E^mor#(+bGa$
zw5&;YjG;YwL2k5W$XUy+2{|BTDd$Kb4`LrcKQ#65K{FmLg=3*hm@O!da2)K;xfMg-
zoSx<3a<!3Zo|;=trdT)DZXch<jL{+~N>hcwU&t=eM5&)}3Mq~k3!_<6C&L^H=3)5i
zk%%6E$g>POvWZ`g4x`$fmSz8$OgFYNBw67pwnH9UKg*Jt0#=1g&1n5&`4Jy%JBg-1
zTq|<Z6Qdi!^5d^f3ThG~)pZ_lmJ1c5FzP~@%v%H(HLw%Sm}Cac$E4ek;eD(FOTwtP
zMzq^mux24EB8$;yt<}4wEs1frHmkcwiuNFa02d9SAU%aDdA((NI3|!mD}?@#?y9(I
zmZTo@n7%$*q|l}*d1hrP5r%c-vQioW`b!({Vkr4OB_{9PbLB|Q7BDe^wfD=Q^+>J9
zbgg9{9FP7f7pRDOo+jRu%wSkGHJD`KWH6fC3F(AcLLjX$$n>IPbB!y@+|ch^CnZ0e
zfI~KVd6N(Yh~_BqW{`(eIIG6VH$Jh@z2vHBcq!2_STNJ$2QaaV2TKyKpvm$IVg*Fk
z@Yrrmb?9r(%!^192`GX~`0BFlA?&)RrGN3=J}(wh<Nj^@@$I!Bo7Bx55Gd^Htkp~(
zj@~S*sSY1u+~LsoA0q09j3AcI<wn-$LrwC}tTE}x{G(xS<s&?UwU>pyfZEqJy>Z{@
zc9F4?4ZW7Orxv9}PlF+n+>-R-<rrFez3a_H^K>qYko1u}GSB+4g`%|?u0X;rBYL8y
z!sv)iel;puTN8(_+LR{9pc_~l(N9IjT5@v}Vr?MXTHc<m^9V@^)ecA6PLQ*ZdD58l
z{H3G7rwzSiy~kRni*bM#GRY+Cj}o8XYMGk2*=t${dfhx|=iyK=Sf^WVPP^woiVOxr
z5&f>wx<%-hRmgg*;c#<LU3J=&^e|>BW*#?R&D*9YFrCe?WUeGLPknqaHiaSE(~tHV
z_bfO0k$@^rV}NNP^4|!kI_|})5o@fy3u99TY6sB+l;&5drYH}Vj3{m)_emBoSw--U
zez&Y;1OF@;tY}dV*dYGqtOj{LXZWw-)s?z!NgER_b4Ic`Y0lmbhS)Izyh-mJw{;n3
zN&5wXvfr8DQ+`^L0sC-?Psd~^-t;V6?iBqdP~ok6g(i<;g6_%W2d_k!WBE{XEaVNJ
z1R>7tw^H<63JX0ZN5f*!q+rTm_LF{G_;WKmLt9(hI|!X#{j!<oD|OTKvM#c3)8n1U
zNGtx9@%^EbM=q5G&JvoUfQw9wB%_*La?)#tJG9MW!p0aPi4p~Cf(o21d*VBH(kf(0
zc9KZ7R3IORkwfbhb37#zN~$%ccWm}6Wv@CkLrz#o12CU@T`op`wt`*k4;i}g9-YC*
z>J)Gyjd30aA%N$3`Cy%j5utm?Y+;P0=LR(XJo=1?$9U6&c6WpkdcX)X484Zpbkeqv
zhCMQ}T}cUiZ6Wb6r}D^LdV<o9>VgVFH@gh!`5=vua9G$RG8Iv*nyuwAqUFUR8z<;K
zjyZ_;udlnZm|rR%!QQAf|4P<duWH{Ik+#_{CF0^1m?kDqCjSzBEAjkR<?R=epphX6
zyA@MtdqR$8GYVhouE5T=ONeziZtXDFwU$rh@NA`=F8PdzMfqL%tjeP4lDsL^Q*6Y2
z(Q}8H)kqY8D8OTxDzZ=d^GOJU3;X!HHPQ<f62ONJ*=+){->H#_)~zsNQ+UUH7+(Y<
zBNK?$*yM8)XFk%UM_Nc-Kdlwo?CR0%)LDS!V__!-fFB~Wur~GPq3PNfavIj?Zqu?m
zgjFU(#w8&0e0_Smy1F_yVcgcM-c-sD=Y312V`k}DBW;8i-wo~?J)uPDO!|k9_TP2s
zTSXL$#8^pxedBCGf=HVOA)PU&lqWZjj7h}`V*HxGtwWCnY)MRqJi7l1a{q0BI{H=4
zF1A~<HIm?;1>1}Qi;Qy9M+%2l)4BwR-|8Zi$;;K5MQd!|F5wmLeoX*Zk0L==IreAR
z$Pa9ynL1e?bZ<}3j`rPKk-p7;?zm)x&o!^x0mA(JQt!9+R{a_scjiUILF0WBv}MZ`
zJOg$p{1%dP%s=P!!vj*e&l?L^N1FK0G)g-5jOj+mM2yLG-syDXojsC+`E%-twNXS9
zGA5&-U}ecOa(8P<>!(kj_WtIPrV}tdJ?&6FHa1pNU9GW*i;JOeuA~&NY5x5w>(8G*
zUkMKn54&}1qH0`GUv@e#IeA%UXQ#xrZ37zZ(+?|i1`@OHG9n!Am@0NWOK&H(Q3h_4
z_Doh@-ckUP#B@7LjIoc8M{ByHn_GaNpP!yN+xXnMEq#4`kvj2(<KyF@f81+1Wn&`&
zGQPE|Ytzb=D<e;A*?04d`j!wQD=V?l(b3kK83mxvnI46cHw!*~Jk{9T4BWc^(2*ni
z@v*HdqmISozj*PpoT6gU`}gm?3o$w&DLs8<!2Y$GT3W{E&u>fK>UZJUnyjp>!GVFL
z%a<>wComWc>D2-It9nKrpV&e!k(DL;C^|YyGq#|z^0<FM03`7Agr*sKL_|PQQPD10
zSwDbgJ^=wsx0Y+yu7%O*K@kxV8QIzXEEY@8oOMw=dwo!+Wv4)AeUMXvr4#Yek_$?Y
zSj8?v732}NU{h!b_n?by(*67QtrLEredzo$@9TeVBoe-c{JQc@p}6kPa>Ax>ttycV
yN=;HGn)Xqjs!;IwDy#hakNvyX`A>hKo44)#^+Nrfy`ALVD*KKoCH(U1@Bar#ten~a

diff --git a/reference/plot.html b/reference/plot.html
index 39d9be033..83aed0bba 100644
--- a/reference/plot.html
+++ b/reference/plot.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -260,7 +265,7 @@
 
 
 <dt>language</dt>
-<dd><p>language to be used to translate 'Susceptible', 'Increased exposure'/'Intermediate' and 'Resistant', defaults to system language (see <code><a href="translate.html">get_AMR_locale()</a></code>) and can be overwritten by setting the option <code>AMR_locale</code>, e.g. <code>options(AMR_locale = "de")</code>, see <a href="translate.html">translate</a>. Use <code>language = NULL</code> or <code>language = ""</code> to prevent translation.</p></dd>
+<dd><p>language to be used to translate 'Susceptible', 'Increased exposure'/'Intermediate' and 'Resistant', defaults to system language (see <code><a href="translate.html">get_AMR_locale()</a></code>) and can be overwritten by setting the option <code><a href="AMR-options.html">AMR_locale</a></code>, e.g. <code>options(AMR_locale = "de")</code>, see <a href="translate.html">translate</a>. Use <code>language = NULL</code> or <code>language = ""</code> to prevent translation.</p></dd>
 
 
 <dt>expand</dt>
diff --git a/reference/proportion.html b/reference/proportion.html
index 561731ad1..af1d51eaf 100644
--- a/reference/proportion.html
+++ b/reference/proportion.html
@@ -12,7 +12,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -34,60 +34,65 @@ resistance() should be used to calculate resistance, susceptibility() should be
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -229,7 +234,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
 
 
 <dt>language</dt>
-<dd><p>language of the returned text, defaults to system language (see <code><a href="translate.html">get_AMR_locale()</a></code>) and can also be set with <code>getOption("AMR_locale")</code>. Use <code>language = NULL</code> or <code>language = ""</code> to prevent translation.</p></dd>
+<dd><p>language of the returned text, defaults to system language (see <code><a href="translate.html">get_AMR_locale()</a></code>) and can also be set with the option <code><a href="AMR-options.html">AMR_locale</a></code>. Use <code>language = NULL</code> or <code>language = ""</code> to prevent translation.</p></dd>
 
 
 <dt>combine_SI</dt>
diff --git a/reference/random.html b/reference/random.html
index 157dbb414..f6d66f2bd 100644
--- a/reference/random.html
+++ b/reference/random.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -183,41 +188,42 @@
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu">random_mic</span><span class="op">(</span><span class="fl">25</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'mic'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 4       &gt;=128   0.0625  0.005   1       &lt;=0.002 0.005   64      &lt;=0.002</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [10] 0.5     64      1       0.125   0.01    4       0.01    0.005   64     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [19] 64      64      64      16      64      0.125   2      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 64      0.01    64      0.005   0.125   &lt;=0.001 0.25    128     0.025  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [10] 0.005   1       64      0.0625  1       0.005   8       &gt;=256   128    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [19] 0.025   &gt;=256   0.5     4       16      2       4      </span>
 <span class="r-in"><span><span class="fu">random_disk</span><span class="op">(</span><span class="fl">25</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'disk'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 47 13 36 12 41 13 11 37 13 47 39 16 10 46 22 36 16 46 16 44 47 12 40 18 24</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 27 23 46 49  8 14 18 19 42 37 46 37 26 31 39 50 23 50 44 45 46 37 41 34 14</span>
 <span class="r-in"><span><span class="fu">random_sir</span><span class="op">(</span><span class="fl">25</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'sir'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] I S I S S S S S R I R I I S R R S I S R R S I R I</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] R R R I R S S I S R I I I S S S R R R S I S S I I</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># \donttest{</span></span></span>
 <span class="r-in"><span><span class="co"># make the random generation more realistic by setting a bug and/or drug:</span></span></span>
 <span class="r-in"><span><span class="fu">random_mic</span><span class="op">(</span><span class="fl">25</span>, <span class="st">"Klebsiella pneumoniae"</span><span class="op">)</span> <span class="co"># range 0.0625-64</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'mic'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 2       0.025   1       2       64      0.25    16      4       128    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [10] 32      0.0625  4       0.0625  2       1       &lt;=0.005 0.01    0.125  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [19] 16      4       0.025   4       4       32      16     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] &lt;=0.001 0.25    4       1       &gt;=128   0.0625  0.01    0.002   0.025  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [10] 0.0625  0.0625  0.25    0.125   0.25    0.01    0.025   0.125   0.0625 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [19] &lt;=0.001 0.002   2       32      64      32      4      </span>
 <span class="r-in"><span><span class="fu">random_mic</span><span class="op">(</span><span class="fl">25</span>, <span class="st">"Klebsiella pneumoniae"</span>, <span class="st">"meropenem"</span><span class="op">)</span> <span class="co"># range 0.0625-16</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'mic'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 2  2  8  16 8  8  16 4  4  2  4  8  16 4  8  8  1  16 16 2  1  2  4  1  16</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 2    16   4    2    16   32   8    8    16   &gt;=64 &gt;=64 4    8    16   &lt;=1 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [16] &lt;=1  &gt;=64 &gt;=64 &gt;=64 &gt;=64 32   2    4    &gt;=64 2   </span>
 <span class="r-in"><span><span class="fu">random_mic</span><span class="op">(</span><span class="fl">25</span>, <span class="st">"Streptococcus pneumoniae"</span>, <span class="st">"meropenem"</span><span class="op">)</span> <span class="co"># range 0.0625-4</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'mic'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] &gt;=4     1       2       &lt;=0.125 0.5     2       1       0.5     0.25   </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [10] &lt;=0.125 2       0.25    &lt;=0.125 &lt;=0.125 2       2       &lt;=0.125 &gt;=4    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [19] 2       0.5     &lt;=0.125 0.5     0.5     1       2      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 8      1      0.125  1      1      0.125  0.025  0.0625 0.125  8     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [11] 0.5    0.25   0.0625 0.0625 0.125  4      0.0625 8      0.25   0.0625</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [21] 0.125  1      8      0.25   1     </span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu">random_disk</span><span class="op">(</span><span class="fl">25</span>, <span class="st">"Klebsiella pneumoniae"</span><span class="op">)</span> <span class="co"># range 8-50</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'disk'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 27 43 17 42 21 27 42 19 21 17 26 40 14  9 34 16 49 30 41 31 40 43 46 45  8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 43 49 26 44 44 15 26 44 43 22 42 10 37 42 48 47 13 31 48 35 32 49 34 37 44</span>
 <span class="r-in"><span><span class="fu">random_disk</span><span class="op">(</span><span class="fl">25</span>, <span class="st">"Klebsiella pneumoniae"</span>, <span class="st">"ampicillin"</span><span class="op">)</span> <span class="co"># range 11-17</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'disk'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 15 11 13 11 11 12 17 11 12 12 17 17 16 14 14 16 14 15 12 17 15 15 16 12 15</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 16 17 16 12 17 14 13 11 15 15 12 13 16 14 11 11 15 17 16 17 17 14 13 11 11</span>
 <span class="r-in"><span><span class="fu">random_disk</span><span class="op">(</span><span class="fl">25</span>, <span class="st">"Streptococcus pneumoniae"</span>, <span class="st">"ampicillin"</span><span class="op">)</span> <span class="co"># range 12-27</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'disk'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 26 20 19 18 19 25 22 23 19 18 15 25 19 18 22 24 16 22 26 22 21 21 23 23 23</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 16 21 25 21 19 15 26 27 27 26 15 27 18 19 17 25 25 26 18 24 17 21 18 16 17</span>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html
index 8dbda6b67..e366d2e1d 100644
--- a/reference/resistance_predict.html
+++ b/reference/resistance_predict.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
diff --git a/reference/skewness.html b/reference/skewness.html
index 22269d46a..da0cf004e 100644
--- a/reference/skewness.html
+++ b/reference/skewness.html
@@ -12,7 +12,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -34,60 +34,65 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -171,7 +176,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu">skewness</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html" class="external-link">runif</a></span><span class="op">(</span><span class="fl">1000</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.08484826</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.06738265</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/translate.html b/reference/translate.html
index a2bb5c358..6245f7466 100644
--- a/reference/translate.html
+++ b/reference/translate.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="For language-dependent output of AMR functions, like mo_name(), mo_gramstain(), mo_type() and ab_name()."><title>Translate Strings from the AMR Package — translate • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><link href="../deps/Fira_Code-0.4.4/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><script src="../extra.js"></script><meta property="og:title" content="Translate Strings from the AMR Package — translate"><meta property="og:description" content="For language-dependent output of AMR functions, like mo_name(), mo_gramstain(), mo_type() and ab_name()."><meta property="og:image" content="https://msberends.github.io/AMR/logo.svg"><meta name="twitter:card" content="summary_large_image"><meta name="twitter:creator" content="@msberends"><meta name="twitter:site" content="@msberends"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="For language-dependent output of AMR functions, such as mo_name(), mo_gramstain(), mo_type() and ab_name()."><title>Translate Strings from the AMR Package — translate • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><link href="../deps/Fira_Code-0.4.4/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><script src="../extra.js"></script><meta property="og:title" content="Translate Strings from the AMR Package — translate"><meta property="og:description" content="For language-dependent output of AMR functions, such as mo_name(), mo_gramstain(), mo_type() and ab_name()."><meta property="og:image" content="https://msberends.github.io/AMR/logo.svg"><meta name="twitter:card" content="summary_large_image"><meta name="twitter:creator" content="@msberends"><meta name="twitter:site" content="@msberends"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9098</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9099</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -32,60 +32,65 @@
     How to
   </a>
   <div class="dropdown-menu" aria-labelledby="dropdown--how-to">
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      User- Or Team-specific Package Settings
+    </a>
     <a class="dropdown-item" href="../articles/AMR.html">
       <span class="fa fa-directions"></span>
        
-      Conduct AMR analysis
+      Conduct AMR Analysis
     </a>
     <a class="dropdown-item" href="../articles/resistance_predict.html">
       <span class="fa fa-dice"></span>
        
-      Predict antimicrobial resistance
+      Predict Antimicrobial Resistance
     </a>
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data sets for download / own use
+      Data Sets for Download / Own Use
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
        
-      Conduct principal component analysis for AMR
+      Conduct Principal Component Analysis for AMR
     </a>
     <a class="dropdown-item" href="../articles/MDR.html">
       <span class="fa fa-skull-crossbones"></span>
        
-      Determine multi-drug resistance (MDR)
+      Determine Multi-Drug Resistance (MDR)
     </a>
     <a class="dropdown-item" href="../articles/WHONET.html">
       <span class="fa fa-globe-americas"></span>
        
-      Work with WHONET data
+      Work with WHONET Data
     </a>
     <a class="dropdown-item" href="../articles/SPSS.html">
       <span class="fa fa-file-upload"></span>
        
-      Import data from SPSS/SAS/Stata
+      Import Data From SPSS/SAS/Stata
     </a>
     <a class="dropdown-item" href="../articles/EUCAST.html">
       <span class="fa fa-exchange-alt"></span>
        
-      Apply EUCAST rules
+      Apply Eucast Rules
     </a>
     <a class="dropdown-item" href="../reference/mo_property.html">
       <span class="fa fa-bug"></span>
        
-      Get taxonomy of a microorganism
+      Get Taxonomy of a Microorganism
     </a>
     <a class="dropdown-item" href="../reference/ab_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antibiotic drug
+      Get Properties of an Antibiotic Drug
     </a>
     <a class="dropdown-item" href="../reference/av_property.html">
       <span class="fa fa-capsules"></span>
        
-      Get properties of an antiviral drug
+      Get Properties of an Antiviral Drug
     </a>
   </div>
 </li>
@@ -133,7 +138,7 @@
     </div>
 
     <div class="ref-description section level2">
-    <p>For language-dependent output of AMR functions, like <code><a href="mo_property.html">mo_name()</a></code>, <code><a href="mo_property.html">mo_gramstain()</a></code>, <code><a href="mo_property.html">mo_type()</a></code> and <code><a href="ab_property.html">ab_name()</a></code>.</p>
+    <p>For language-dependent output of <code>AMR</code> functions, such as <code><a href="mo_property.html">mo_name()</a></code>, <code><a href="mo_property.html">mo_gramstain()</a></code>, <code><a href="mo_property.html">mo_type()</a></code> and <code><a href="ab_property.html">ab_name()</a></code>.</p>
     </div>
 
     <div class="section level2">
@@ -160,7 +165,7 @@
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>The currently 16 supported languages are English (en), Chinese (zh), Danish (da), Dutch (nl), French (fr), German (de), Greek (el), Italian (it), Japanese (ja), Polish (pl), Portuguese (pt), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr) and Ukrainian (uk). All these languages have translations available for all antimicrobial drugs and colloquial microorganism names.</p>
-<p>To permanently silence the once-per-session language note on a non-English operating system, you can set the option <code>AMR_locale</code> in your <code>.Rprofile</code> file like this:</p>
+<p>To permanently silence the once-per-session language note on a non-English operating system, you can set the option <code><a href="AMR-options.html">AMR_locale</a></code> in your <code>.Rprofile</code> file like this:</p>
 <p></p><div class="sourceCode r"><pre><code><span><span class="co"># Open .Rprofile file</span></span>
 <span><span class="fu">utils</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/utils/file.edit.html" class="external-link">file.edit</a></span><span class="op">(</span><span class="st">"~/.Rprofile"</span><span class="op">)</span></span>
 <span></span>
@@ -171,10 +176,10 @@
 <h3 id="changing-the-default-language">Changing the Default Language<a class="anchor" aria-label="anchor" href="#changing-the-default-language"></a></h3>
 
 
-<p>The system language will be used at default (as returned by <code>Sys.getenv("LANG")</code> or, if <code>LANG</code> is not set, <code><a href="https://rdrr.io/r/base/locales.html" class="external-link">Sys.getlocale("LC_COLLATE")</a></code>), if that language is supported. But the language to be used can be overwritten in two ways and will be checked in this order:</p><ol><li><p>Setting the R option <code>AMR_locale</code>, either by using e.g. <code>set_AMR_locale("German")</code> or by running e.g. <code>options(AMR_locale = "German")</code>.</p>
+<p>The system language will be used at default (as returned by <code>Sys.getenv("LANG")</code> or, if <code>LANG</code> is not set, <code><a href="https://rdrr.io/r/base/locales.html" class="external-link">Sys.getlocale("LC_COLLATE")</a></code>), if that language is supported. But the language to be used can be overwritten in two ways and will be checked in this order:</p><ol><li><p>Setting the option <code><a href="AMR-options.html">AMR_locale</a></code>, either by using e.g. <code>set_AMR_locale("German")</code> or by running e.g. <code>options(AMR_locale = "German")</code>.</p>
 <p>Note that setting an <span style="R">R</span> option only works in the same session. Save the command <code>options(AMR_locale = "(your language)")</code> to your <code>.Rprofile</code> file to apply it for every session. Run <code>utils::file.edit("~/.Rprofile")</code> to edit your <code>.Rprofile</code> file.</p></li>
 <li><p>Setting the system variable <code>LANGUAGE</code> or <code>LANG</code>, e.g. by adding <code>LANGUAGE="de_DE.utf8"</code> to your <code>.Renviron</code> file in your home directory.</p></li>
-</ol><p>Thus, if the R option <code>AMR_locale</code> is set, the system variables <code>LANGUAGE</code> and <code>LANG</code> will be ignored.</p>
+</ol><p>Thus, if the option <code><a href="AMR-options.html">AMR_locale</a></code> is set, the system variables <code>LANGUAGE</code> and <code>LANG</code> will be ignored.</p>
 </div>
 
     </div>
diff --git a/search.json b/search.json
index d94c69813..df3556be6 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial agents, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"How to conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables.","code":"library(dplyr) library(ggplot2) library(AMR) library(cleaner)  # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\", \"cleaner\"))"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"creation-of-data","dir":"Articles","previous_headings":"","what":"Creation of data","title":"How to conduct AMR data analysis","text":"create fake example data use analysis. AMR data analysis, need least: patient ID, name code microorganism, date antimicrobial results (antibiogram). also include specimen type (e.g. filter blood urine), ward type (e.g. filter ICUs). additional columns (like hospital name, patients gender even [well-defined] clinical properties) can comparative analysis, tutorial demonstrate .","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"patients","dir":"Articles","previous_headings":"Creation of data","what":"Patients","title":"How to conduct AMR data analysis","text":"start patients, need unique list patients. LETTERS object available R - ’s vector 26 characters: Z. patients object just created now vector length 260, values (patient IDs) varying A1 Z10. Now also set gender patients, putting ID gender table: first 135 patient IDs now male, 125 female.","code":"patients <- unlist(lapply(LETTERS, paste0, 1:10)) patients_table <- data.frame(   patient_id = patients,   gender = c(     rep(\"M\", 135),     rep(\"F\", 125)   ) )"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"dates","dir":"Articles","previous_headings":"Creation of data","what":"Dates","title":"How to conduct AMR data analysis","text":"Let’s pretend data consists blood cultures isolates 1 January 2010 1 January 2018. dates object now contains days date range.","code":"dates <- seq(as.Date(\"2010-01-01\"), as.Date(\"2018-01-01\"), by = \"day\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"microorganisms","dir":"Articles","previous_headings":"Creation of data > Dates","what":"Microorganisms","title":"How to conduct AMR data analysis","text":"tutorial, uses four different microorganisms: Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, Klebsiella pneumoniae:","code":"bacteria <- c(   \"Escherichia coli\", \"Staphylococcus aureus\",   \"Streptococcus pneumoniae\", \"Klebsiella pneumoniae\" )"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"put-everything-together","dir":"Articles","previous_headings":"Creation of data","what":"Put everything together","title":"How to conduct AMR data analysis","text":"Using sample() function, can randomly select items objects defined earlier. let fake data reflect reality bit, also approximately define probabilities bacteria antibiotic results, using random_sir() function. Using left_join() function dplyr package, can ‘map’ gender patient ID using patients_table object created earlier: resulting data set contains 20,000 blood culture isolates. head() function can preview first 6 rows data set: Now, let’s start cleaning analysis!","code":"sample_size <- 20000 data <- data.frame(   date = sample(dates, size = sample_size, replace = TRUE),   patient_id = sample(patients, size = sample_size, replace = TRUE),   hospital = sample(     c(       \"Hospital A\",       \"Hospital B\",       \"Hospital C\",       \"Hospital D\"     ),     size = sample_size, replace = TRUE,     prob = c(0.30, 0.35, 0.15, 0.20)   ),   bacteria = sample(bacteria,     size = sample_size, replace = TRUE,     prob = c(0.50, 0.25, 0.15, 0.10)   ),   AMX = random_sir(sample_size, prob_sir = c(0.35, 0.60, 0.05)),   AMC = random_sir(sample_size, prob_sir = c(0.15, 0.75, 0.10)),   CIP = random_sir(sample_size, prob_sir = c(0.20, 0.80, 0.00)),   GEN = random_sir(sample_size, prob_sir = c(0.08, 0.92, 0.00)) ) data <- data %>% left_join(patients_table) head(data)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"cleaning-the-data","dir":"Articles","previous_headings":"","what":"Cleaning the data","title":"How to conduct AMR data analysis","text":"also created package dedicated data cleaning checking, called cleaner package. freq() function can used create frequency tables. example, gender variable: Frequency table Class: character Length: 20,000 Available: 20,000 (100%, NA: 0 = 0%) Unique: 2 Shortest: 1 Longest: 1 , can draw least two conclusions immediately. data scientists perspective, data looks clean: values M F. researchers perspective: slightly men. Nothing didn’t already know. data already quite clean, still need transform variables. bacteria column now consists text, want add variables based microbial IDs later . , transform column valid IDs. mutate() function dplyr package makes really easy: also want transform antibiotics, real life data don’t know really clean. .sir() function ensures reliability reproducibility kind variables. is_sir_eligible() can check columns probably columns SIR test results. Using mutate() across(), can apply transformation formal <rsi> class: Finally, apply EUCAST rules antimicrobial results. Europe, medical microbiological laboratories already apply rules. package features latest insights intrinsic resistance exceptional phenotypes. Moreover, eucast_rules() function can also apply additional rules, like forcing ampicillin = R amoxicillin/clavulanic acid = R. amoxicillin (column AMX) amoxicillin/clavulanic acid (column AMC) data generated randomly, rows undoubtedly contain AMX = S AMC = R, technically impossible. eucast_rules() fixes :","code":"data %>% freq(gender) data <- data %>%   mutate(bacteria = as.mo(bacteria)) is_sir_eligible(data) # [1] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE colnames(data)[is_sir_eligible(data)] # [1] \"AMX\" \"AMC\" \"CIP\" \"GEN\"  data <- data %>%   mutate(across(where(is_sir_eligible), as.sir)) data <- eucast_rules(data, col_mo = \"bacteria\", rules = \"all\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"adding-new-variables","dir":"Articles","previous_headings":"","what":"Adding new variables","title":"How to conduct AMR data analysis","text":"Now microbial ID, can add taxonomic properties:","code":"data <- data %>%   mutate(     gramstain = mo_gramstain(bacteria),     genus = mo_genus(bacteria),     species = mo_species(bacteria)   )"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Adding new variables","what":"First isolates","title":"How to conduct AMR data analysis","text":"also need know isolates can actually use analysis. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. method also takes account antimicrobial susceptibility test results using all_microbials(). Read methods first_isolate() page. outcome function can easily added data: 61.2% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 12,236 isolates analysis. Now data looks like: Time analysis!","code":"data <- data %>%   mutate(first = first_isolate(info = TRUE)) # Determining first isolates using an episode length of 365 days # ℹ Using column 'bacteria' as input for col_mo. # ℹ Using column 'date' as input for col_date. # ℹ Using column 'patient_id' as input for col_patient_id. # Basing inclusion on all antimicrobial results, using a points threshold of # 2 # Including isolates from ICU. # => Found 12,236 'phenotype-based' first isolates (61.2% of total where a #    microbial ID was available) data_1st <- data %>%   filter(first == TRUE) data_1st <- data %>%   filter_first_isolate() # Including isolates from ICU. head(data_1st)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"analysing-the-data","dir":"Articles","previous_headings":"","what":"Analysing the data","title":"How to conduct AMR data analysis","text":"might want start getting idea data distributed. ’s important start, also decides continue analysis. Although package contains convenient function make frequency tables, exploratory data analysis (EDA) primary scope package. Use package like DataExplorer , read free online book Exploratory Data Analysis R Roger D. Peng.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"dispersion-of-species","dir":"Articles","previous_headings":"Analysing the data","what":"Dispersion of species","title":"How to conduct AMR data analysis","text":"just get idea species distributed, create frequency table freq() function. created genus species column earlier based microbial ID. paste(), can concatenate together. freq() function can used like base R language intended: can used like dplyr way, easier readable: Frequency table Class: character Length: 12,236 Available: 12,236 (100%, NA: 0 = 0%) Unique: 4 Shortest: 16 Longest: 24","code":"freq(paste(data_1st$genus, data_1st$species)) data_1st %>% freq(genus, species)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"overview-of-different-bugdrug-combinations","dir":"Articles","previous_headings":"Analysing the data","what":"Overview of different bug/drug combinations","title":"How to conduct AMR data analysis","text":"Using tidyverse selections, can also select filter columns based antibiotic class : want get quick glance number isolates different bug/drug combinations, can use bug_drug_combinations() function: give crude numbers data. calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"data_1st %>%   filter(any(aminoglycosides() == \"R\")) # ℹ For aminoglycosides() using column 'GEN' (gentamicin) data_1st %>%   bug_drug_combinations() %>%   head() # show first 6 rows data_1st %>%   select(bacteria, aminoglycosides()) %>%   bug_drug_combinations() # ℹ For aminoglycosides() using column 'GEN' (gentamicin)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"How to conduct AMR data analysis","text":"functions resistance() susceptibility() can used calculate antimicrobial resistance susceptibility. specific analyses, functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() proportion_R() can used determine proportion specific antimicrobial outcome. functions contain minimum argument, denoting minimum required number test results returning value. functions otherwise return NA. default minimum = 30, following CLSI M39-A4 guideline applying microbial epidemiology. per EUCAST guideline 2019, calculate resistance proportion R (proportion_R(), equal resistance()) susceptibility proportion S (proportion_SI(), equal susceptibility()). functions can used : can used conjunction group_by() summarise(), dplyr package: course convenient know number isolates responsible percentages. purpose n_sir() can used, works exactly like n_distinct() dplyr package. counts isolates available every group (.e. values S, R): functions can also used get proportion multiple antibiotics, calculate empiric susceptibility combination therapies easily: curious resistance within certain antibiotic classes, use antibiotic class selector penicillins(), automatically include columns AMX AMC data: make transition next part, let’s see differences previously calculated combination therapies plotted:","code":"data_1st %>% resistance(AMX) # [1] 0.5813992 data_1st %>%   group_by(hospital) %>%   summarise(amoxicillin = resistance(AMX)) data_1st %>%   group_by(hospital) %>%   summarise(     amoxicillin = resistance(AMX),     available = n_sir(AMX)   ) data_1st %>%   group_by(genus) %>%   summarise(     amoxiclav = susceptibility(AMC),     gentamicin = susceptibility(GEN),     amoxiclav_genta = susceptibility(AMC, GEN)   ) data_1st %>%   # group by hospital   group_by(hospital) %>%   #                / -> select all penicillins in the data for calculation   #                |              / -> use resistance() for all peni's per hospital   #                |              |           / -> print as percentages   summarise(across(penicillins(), resistance, as_percent = TRUE)) %>%   # format the antibiotic column names, using so-called snake case,   # so 'Amoxicillin/clavulanic acid' becomes 'amoxicillin_clavulanic_acid'   rename_with(set_ab_names, penicillins()) data_1st %>%   group_by(genus) %>%   summarise(     \"1. Amoxi/clav\" = susceptibility(AMC),     \"2. Gentamicin\" = susceptibility(GEN),     \"3. Amoxi/clav + genta\" = susceptibility(AMC, GEN)   ) %>%   # pivot_longer() from the tidyr package \"lengthens\" data:   tidyr::pivot_longer(-genus, names_to = \"antibiotic\") %>%   ggplot(aes(     x = genus,     y = value,     fill = antibiotic   )) +   geom_col(position = \"dodge2\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plots","dir":"Articles","previous_headings":"Analysing the data","what":"Plots","title":"How to conduct AMR data analysis","text":"show results plots, R users nowadays use ggplot2 package. package lets create plots layers. can read website. quick example look like syntaxes: AMR package contains functions extend ggplot2 package, example geom_sir(). automatically transforms data count_df() proportion_df() show results stacked bars. simplest shortest example:  Omit translate_ab = FALSE antibiotic codes (AMX, AMC, CIP, GEN) translated official names (amoxicillin, amoxicillin/clavulanic acid, ciprofloxacin, gentamicin). group e.g. genus column add additional functions package, can create :  simplify , also created ggplot_sir() function, combines almost functions:","code":"ggplot(   data = a_data_set,   mapping = aes(     x = year,     y = value   ) ) +   geom_col() +   labs(     title = \"A title\",     subtitle = \"A subtitle\",     x = \"My X axis\",     y = \"My Y axis\"   )  # or as short as: ggplot(a_data_set) +   geom_bar(aes(year)) ggplot(data_1st) +   geom_sir(translate_ab = FALSE) # group the data on `genus` ggplot(data_1st %>% group_by(genus)) +   # create bars with genus on x axis   # it looks for variables with class `sir`,   # of which we have 4 (earlier created with `as.sir`)   geom_sir(x = \"genus\") +   # split plots on antibiotic   facet_sir(facet = \"antibiotic\") +   # set colours to the SIR interpretations (colour-blind friendly)   scale_sir_colours() +   # show percentages on y axis   scale_y_percent(breaks = 0:4 * 25) +   # turn 90 degrees, to make it bars instead of columns   coord_flip() +   # add labels   labs(     title = \"Resistance per genus and antibiotic\",     subtitle = \"(this is fake data)\"   ) +   # and print genus in italic to follow our convention   # (is now y axis because we turned the plot)   theme(axis.text.y = element_text(face = \"italic\")) data_1st %>%   group_by(genus) %>%   ggplot_sir(     x = \"genus\",     facet = \"antibiotic\",     breaks = 0:4 * 25,     datalabels = FALSE   ) +   coord_flip()"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plotting-mic-and-disk-diffusion-values","dir":"Articles","previous_headings":"Analysing the data > Plots","what":"Plotting MIC and disk diffusion values","title":"How to conduct AMR data analysis","text":"AMR package also extends plot() ggplot2::autoplot() functions plotting minimum inhibitory concentrations (MIC, created .mic()) disk diffusion diameters (created .disk()). random_mic() random_disk() functions, can generate sampled values new data types (S3 classes) <mic> <disk>:   also specific, generating MICs likely found E. coli ciprofloxacin: plot() autoplot() function, can define microorganism antimicrobial agent way. add interpretation values according chosen guidelines (defaults latest EUCAST guideline). Default colours colour-blind friendly, maintaining convention e.g. ‘susceptible’ green ‘resistant’ red:   disk diffusion values, much difference plotting:  using ggplot2 package, now choosing latest implemented CLSI guideline (notice EUCAST-specific term “Susceptible, incr. exp.” changed “Intermediate”):","code":"mic_values <- random_mic(size = 100) mic_values # Class 'mic' #   [1] 0.001  0.0625 32     0.0625 32     64     >=256  32     4      0.125  #  [11] 4      32     128    32     0.125  1      0.0625 >=256  16     64     #  [21] 0.025  64     16     16     >=256  128    0.025  0.005  0.25   1      #  [31] 0.5    32     0.0625 16     128    0.025  0.25   1      0.005  0.0625 #  [41] 0.025  0.025  0.25   0.002  0.001  2      8      32     0.0625 0.002  #  [51] 0.001  0.5    0.25   16     0.125  0.005  64     1      0.0625 0.001  #  [61] 0.025  32     16     >=256  0.005  0.002  16     >=256  1      0.125  #  [71] 2      0.125  16     16     64     64     0.0625 0.5    1      0.005  #  [81] 64     0.125  0.025  0.01   0.0625 0.5    0.002  0.25   0.25   128    #  [91] 4      16     0.002  0.25   32     32     128    128    0.125  0.25 # base R: plot(mic_values) # ggplot2: autoplot(mic_values) mic_values <- random_mic(size = 100, mo = \"E. coli\", ab = \"cipro\") # base R: plot(mic_values, mo = \"E. coli\", ab = \"cipro\") # ggplot2: autoplot(mic_values, mo = \"E. coli\", ab = \"cipro\") disk_values <- random_disk(size = 100, mo = \"E. coli\", ab = \"cipro\") disk_values # Class 'disk' #   [1] 18 29 23 17 30 20 29 20 17 19 22 25 29 30 24 22 29 29 24 17 30 19 25 23 26 #  [26] 20 25 17 20 23 27 19 26 19 26 19 21 19 24 20 18 19 30 17 27 29 21 22 29 21 #  [51] 22 30 20 23 27 30 27 19 24 24 21 24 24 17 21 20 31 27 31 20 22 26 25 29 31 #  [76] 31 18 20 18 17 26 25 24 31 20 18 25 26 19 18 23 31 27 18 18 28 30 26 30 27 # base R: plot(disk_values, mo = \"E. coli\", ab = \"cipro\") autoplot(   disk_values,   mo = \"E. coli\",   ab = \"cipro\",   guideline = \"CLSI\" )"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"independence-test","dir":"Articles","previous_headings":"Analysing the data","what":"Independence test","title":"How to conduct AMR data analysis","text":"next example uses example_isolates data set. data set included package contains 2,000 microbial isolates full antibiograms. reflects reality can used practise AMR data analysis. compare resistance amoxicillin/clavulanic acid (column FOS) ICU clinical wards. input fisher.test() can retrieved transformation like : can apply test now : can seen, p value practically zero (0.0000002263247), means amoxicillin/clavulanic acid resistance found isolates patients ICUs clinical wards really different.","code":"# use package 'tidyr' to pivot data: library(tidyr)  check_FOS <- example_isolates %>%   filter(ward %in% c(\"ICU\", \"Clinical\")) %>% # filter on only these wards   select(ward, AMC) %>% # select the wards and amoxi/clav   group_by(ward) %>% # group on the wards   count_df(combine_SI = TRUE) %>% # count all isolates per group (ward)   pivot_wider(     names_from = ward, # transform output so \"ICU\" and \"Clinical\" are columns     values_from = value   ) %>%   select(ICU, Clinical) %>% # and only select these columns   as.matrix() # transform to a good old matrix for fisher.test()  check_FOS #      ICU Clinical # [1,] 396      942 # [2,] 184      240 # do Fisher's Exact Test fisher.test(check_FOS) #  #   Fisher's Exact Test for Count Data #  # data:  check_FOS # p-value = 2.263e-07 # alternative hypothesis: true odds ratio is not equal to 1 # 95 percent confidence interval: #  0.435261 0.691614 # sample estimates: # odds ratio  #  0.5485079"},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to apply EUCAST rules","text":"EUCAST rules? European Committee Antimicrobial Susceptibility Testing (EUCAST) states website: EUCAST expert rules tabulated collection expert knowledge intrinsic resistances, exceptional resistance phenotypes interpretive rules may applied antimicrobial susceptibility testing order reduce errors make appropriate recommendations reporting particular resistances. Europe, lot medical microbiological laboratories already apply rules (Brown et al., 2015). package features latest insights intrinsic resistance unusual phenotypes (v3.3, 2021). Moreover, eucast_rules() function use purpose can also apply additional rules, like forcing ampicillin = R isolates amoxicillin/clavulanic acid = R.","code":""},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to apply EUCAST rules","text":"rules can used discard impossible bug-drug combinations data. example, Klebsiella produces beta-lactamase prevents ampicillin (amoxicillin) working . words, practically every strain Klebsiella resistant ampicillin. Sometimes, laboratory data can still contain strains ampicillin susceptible ampicillin. antibiogram available identification available, antibiogram re-interpreted based identification (namely, Klebsiella). EUCAST expert rules solve , can applied using eucast_rules(): convenient function mo_is_intrinsic_resistant() uses guideline, allows check one specific microorganisms antibiotics: EUCAST rules can used correction, can also used filling known resistance susceptibility based results antimicrobials drugs. process called interpretive reading, basically form imputation, part eucast_rules() function well:","code":"oops <- data.frame(   mo = c(     \"Klebsiella\",     \"Escherichia\"   ),   ampicillin = \"S\" ) oops #            mo ampicillin # 1  Klebsiella          S # 2 Escherichia          S  eucast_rules(oops, info = FALSE) #            mo ampicillin # 1  Klebsiella          R # 2 Escherichia          S mo_is_intrinsic_resistant(   c(\"Klebsiella\", \"Escherichia\"),   \"ampicillin\" ) # [1]  TRUE FALSE  mo_is_intrinsic_resistant(   \"Klebsiella\",   c(\"ampicillin\", \"kanamycin\") ) # [1]  TRUE FALSE data <- data.frame(   mo = c(     \"Staphylococcus aureus\",     \"Enterococcus faecalis\",     \"Escherichia coli\",     \"Klebsiella pneumoniae\",     \"Pseudomonas aeruginosa\"   ),   VAN = \"-\", # Vancomycin   AMX = \"-\", # Amoxicillin   COL = \"-\", # Colistin   CAZ = \"-\", # Ceftazidime   CXM = \"-\", # Cefuroxime   PEN = \"S\", # Benzylenicillin   FOX = \"S\", # Cefoxitin   stringsAsFactors = FALSE ) data eucast_rules(data)"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"type-of-input","dir":"Articles","previous_headings":"","what":"Type of input","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function takes data set input, regular data.frame. tries automatically determine right columns info isolates, name species columns results antimicrobial agents. See help page info set right settings data command ?mdro. WHONET data (data), settings automatically set correctly.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"guidelines","dir":"Articles","previous_headings":"","what":"Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function support multiple guidelines. can select guideline guideline parameter. Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. “Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.” Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.2\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance Unusual Phenotypes” (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance Exceptional Phenotypes Tables” (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization “Companion handbook guidelines programmatic management drug-resistant tuberculosis” (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"custom-guidelines","dir":"Articles","previous_headings":"Guidelines","what":"Custom Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"can also use custom guideline. Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() dplyr package, recognise input method set rules. Rules must set using R considers ‘formula notation’: row/isolate matches first rule, value first ~ (case ‘Elderly Type ’) set MDRO value. Otherwise, second rule tried . maximum number rules unlimited. can print rules set console overview. Colours help reading console supports colours. outcome function can used guideline argument mdro() function: rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline(   CIP == \"R\" & age > 60 ~ \"Elderly Type A\",   ERY == \"R\" & age > 60 ~ \"Elderly Type B\" ) custom # A set of custom MDRO rules: #   1. If CIP is \"R\" and age is higher than 60 then: Elderly Type A #   2. If ERY is \"R\" and age is higher than 60 then: Elderly Type B #   3. Otherwise: Negative #  # Unmatched rows will return NA. # Results will be of class 'factor', with ordered levels: Negative < Elderly Type A < Elderly Type B x <- mdro(example_isolates, guideline = custom) table(x) # x #       Negative Elderly Type A Elderly Type B  #           1070            198            732"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function always returns ordered factor predefined guidelines. example, output default guideline Magiorakos et al. returns factor levels ‘Negative’, ‘MDR’, ‘XDR’ ‘PDR’ order. next example uses example_isolates data set. data set included package contains full antibiograms 2,000 microbial isolates. reflects reality can used practise AMR data analysis. test MDR/XDR/PDR guideline data set, get: (16 isolates test results) Frequency table Class: factor > ordered (numeric) Length: 2,000 Levels: 4: Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant … Available: 1,729 (86.45%, NA: 271 = 13.55%) Unique: 2 another example, create data set determine multi-drug resistant TB: column names automatically verified valid drug names codes, worked exactly way: data set now looks like : can now add interpretation MDR-TB data set. can use: shortcut mdr_tb(): Create frequency table results: Frequency table Class: factor > ordered (numeric) Length: 5,000 Levels: 5: Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant <… Available: 5,000 (100%, NA: 0 = 0%) Unique: 5","code":"library(dplyr) # to support pipes: %>% library(cleaner) # to create frequency tables example_isolates %>%   mdro() %>%   freq() # show frequency table of the result # Warning: in mdro(): NA introduced for isolates where the available percentage of # antimicrobial classes was below 50% (set with pct_required_classes) # random_sir() is a helper function to generate # a random vector with values S, I and R my_TB_data <- data.frame(   rifampicin = random_sir(5000),   isoniazid = random_sir(5000),   gatifloxacin = random_sir(5000),   ethambutol = random_sir(5000),   pyrazinamide = random_sir(5000),   moxifloxacin = random_sir(5000),   kanamycin = random_sir(5000) ) my_TB_data <- data.frame(   RIF = random_sir(5000),   INH = random_sir(5000),   GAT = random_sir(5000),   ETH = random_sir(5000),   PZA = random_sir(5000),   MFX = random_sir(5000),   KAN = random_sir(5000) ) head(my_TB_data) #   rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin # 1          I         S            R          I            S            S # 2          I         R            R          I            S            I # 3          I         R            S          I            R            I # 4          S         S            I          S            R            I # 5          I         I            R          I            I            S # 6          R         S            S          S            R            I #   kanamycin # 1         S # 2         R # 3         S # 4         I # 5         S # 6         R mdro(my_TB_data, guideline = \"TB\") my_TB_data$mdr <- mdr_tb(my_TB_data) # ℹ No column found as input for col_mo, assuming all rows contain #   Mycobacterium tuberculosis. freq(my_TB_data$mdr)"},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"transforming","dir":"Articles","previous_headings":"","what":"Transforming","title":"How to conduct principal component analysis (PCA) for AMR","text":"PCA, need transform AMR data first. example_isolates data set package looks like: Now transform data set resistance percentages per taxonomic order genus:","code":"library(AMR) library(dplyr) glimpse(example_isolates) # Rows: 2,000 # Columns: 46 # $ date    <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2… # $ patient <chr> \"A77334\", \"A77334\", \"067927\", \"067927\", \"067927\", \"067927\", \"4… # $ age     <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50… # $ gender  <chr> \"F\", \"F\", \"F\", \"F\", \"F\", \"F\", \"M\", \"M\", \"F\", \"F\", \"M\", \"M\", \"M… # $ ward    <chr> \"Clinical\", \"Clinical\", \"ICU\", \"ICU\", \"ICU\", \"ICU\", \"Clinical\"… # $ mo      <mo> \"B_ESCHR_COLI\", \"B_ESCHR_COLI\", \"B_STPHY_EPDR\", \"B_STPHY_EPDR\",… # $ PEN     <sir> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,… # $ OXA     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ FLC     <sir> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R… # $ AMX     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… # $ AMC     <sir> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N… # $ AMP     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… # $ TZP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ CZO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… # $ FEP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ CXM     <sir> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S… # $ FOX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… # $ CTX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… # $ CAZ     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, … # $ CRO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… # $ GEN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ TOB     <sir> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA… # $ AMK     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ KAN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ TMP     <sir> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, … # $ SXT     <sir> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N… # $ NIT     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,… # $ FOS     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ LNZ     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… # $ CIP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S… # $ MFX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ VAN     <sir> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, … # $ TEC     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… # $ TCY     <sir> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, … # $ TGC     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… # $ DOX     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… # $ ERY     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… # $ CLI     <sir> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA… # $ AZM     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… # $ IPM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… # $ MEM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ MTR     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ CHL     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ COL     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, … # $ MUP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ RIF     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… resistance_data <- example_isolates %>%   group_by(     order = mo_order(mo), # group on anything, like order     genus = mo_genus(mo)   ) %>% #  and genus as we do here   summarise_if(is.sir, resistance) %>% # then get resistance of all drugs   select(     order, genus, AMC, CXM, CTX,     CAZ, GEN, TOB, TMP, SXT   ) # and select only relevant columns  head(resistance_data) # # A tibble: 6 × 10 # # Groups:   order [5] #   order             genus          AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT #   <chr>             <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 (unknown order)   (unknown ge…    NA    NA    NA    NA    NA    NA    NA    NA # 2 Actinomycetales   Schaalia        NA    NA    NA    NA    NA    NA    NA    NA # 3 Bacteroidales     Bacteroides     NA    NA    NA    NA    NA    NA    NA    NA # 4 Campylobacterales Campylobact…    NA    NA    NA    NA    NA    NA    NA    NA # 5 Caryophanales     Gemella         NA    NA    NA    NA    NA    NA    NA    NA # 6 Caryophanales     Listeria        NA    NA    NA    NA    NA    NA    NA    NA"},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"perform-principal-component-analysis","dir":"Articles","previous_headings":"","what":"Perform principal component analysis","title":"How to conduct principal component analysis (PCA) for AMR","text":"new pca() function automatically filter rows contain numeric values selected variables, now need : result can reviewed good old summary() function: Good news. first two components explain total 93.3% variance (see PC1 PC2 values Proportion Variance. can create -called biplot base R biplot() function, see antimicrobial resistance per drug explain difference per microorganism.","code":"pca_result <- pca(resistance_data) # ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", \"TMP\" #   and \"TOB\". Total observations available: 7. summary(pca_result) # Groups (n=4, named as 'order'): # [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\" # Importance of components: #                           PC1    PC2    PC3     PC4     PC5     PC6       PC7 # Standard deviation     2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 9.577e-17 # Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00 # Cumulative Proportion  0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00 # Groups (n=4, named as 'order'): # [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\""},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"plotting-the-results","dir":"Articles","previous_headings":"","what":"Plotting the results","title":"How to conduct principal component analysis (PCA) for AMR","text":"can’t see explanation points. Perhaps works better new ggplot_pca() function, automatically adds right labels even groups:  can also print ellipse per group, edit appearance:","code":"biplot(pca_result) ggplot_pca(pca_result) ggplot_pca(pca_result, ellipse = TRUE) +   ggplot2::labs(title = \"An AMR/PCA biplot!\")"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"spss-sas-stata","dir":"Articles","previous_headings":"","what":"SPSS / SAS / Stata","title":"How to import data from SPSS / SAS / Stata","text":"SPSS (Statistical Package Social Sciences) probably well-known software package statistical analysis. SPSS easier learn R, SPSS click menu run parts analysis. user-friendliness, taught universities particularly useful students new statistics. experience, guess pretty much (bio)medical students know time graduate. SAS Stata comparable statistical packages popular big industries.","code":""},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"compared-to-r","dir":"Articles","previous_headings":"","what":"Compared to R","title":"How to import data from SPSS / SAS / Stata","text":"said, SPSS easier learn R. SPSS, SAS Stata come major downsides comparing R: R highly modular. official R network (CRAN) features 16,000 packages time writing, AMR package one . packages peer-reviewed publication. Aside official channel, also developers choose submit CRAN, rather keep public repository, like GitHub. may even lot 14,000 packages . Bottom line , can really extend ask somebody . Take example AMR package. Among things, adds reliable reference data R help data cleaning analysis. SPSS, SAS Stata never know valid MIC value Gram stain E. coli . species Klebiella resistant amoxicillin Floxapen® trade name flucloxacillin. facts properties often needed clean existing data, inconvenient software package without reliable reference data. See demonstration. R extremely flexible. write syntax , can anything want. flexibility transforming, arranging, grouping summarising data, drawing plots, endless - SPSS, SAS Stata bound algorithms format styles. may bit flexible, can probably never create specific publication-ready plot without using (paid) software. sometimes write syntaxes SPSS run complete analysis ‘automate’ work, lot less time R. notice writing syntaxes R lot nifty clever SPSS. Still, working statistical package, knowledge (statistically) willing accomplish. R can easily automated. last years, R Markdown really made interesting development. R Markdown, can easily produce reports, whether format Word, PowerPoint, website, PDF document just raw data Excel. even allows use reference file containing layout style (e.g. fonts colours) organisation. use lot generate weekly monthly reports automatically. Just write code enjoy automatically updated reports interval like. even professional environment, create Shiny apps: live manipulation data using custom made website. webdesign knowledge needed (JavaScript, CSS, HTML) almost zero. R huge community. Many R users just ask questions websites like StackOverflow.com, largest online community programmers. time writing, 478,396 R-related questions already asked platform (covers questions answers programming language). experience, questions answered within couple minutes. R understands data type, including SPSS/SAS/Stata. ’s vice versa ’m afraid. can import data source R. example SPSS, SAS Stata (link), Minitab, Epi Info EpiData (link), Excel (link), flat files like CSV, TXT TSV (link), directly databases datawarehouses anywhere world (link). can even scrape websites download tables live internet (link) get results API call transform data one command (link). best part - can export R data formats well. can import SPSS file, analysis neatly R export resulting tables Excel files sharing. R completely free open-source. strings attached. created maintained volunteers believe (data) science open publicly available everybody. SPSS, SAS Stata quite expensive. IBM SPSS Staticstics comes subscriptions nowadays, varying USD 1,300 USD 8,500 per user per year. SAS Analytics Pro costs around USD 10,000 per computer. Stata also business model subscription fees, varying USD 600 USD 2,800 per computer per year, lower prices come limitation number variables can work . still offer benefits R. working midsized small company, can save tens thousands dollars using R instead e.g. SPSS - gaining even functions flexibility. R enthousiasts can much PR want (like ), nobody officially associated affiliated R. really free. R (nowadays) preferred analysis software academic papers. present, R among world powerful statistical languages, generally popular science (Bollmann et al., 2017). reasons, number references R analysis method academic papers rising continuously even surpassed SPSS academic use (Muenchen, 2014). believe thing SPSS , always great user interface easy learn use. Back developed , little competition, let alone R. R didn’t even professional user interface last decade (called RStudio, see ). people used R nineties 2010 almost completely incomparable R used now. language restyled completely volunteers dedicated professionals field data science. SPSS great nothing else compete. now 2023, don’t see reason SPSS better use R. demonstrate first point:","code":"# not all values are valid MIC values: as.mic(0.125) # Class 'mic' # [1] 0.125 as.mic(\"testvalue\") # Class 'mic' # [1] <NA>  # the Gram stain is available for all bacteria: mo_gramstain(\"E. coli\") # [1] \"Gram-negative\"  # Klebsiella is intrinsic resistant to amoxicillin, according to EUCAST: klebsiella_test <- data.frame(   mo = \"klebsiella\",   amox = \"S\",   stringsAsFactors = FALSE ) klebsiella_test # (our original data) #           mo amox # 1 klebsiella    S eucast_rules(klebsiella_test, info = FALSE) # (the edited data by EUCAST rules) #           mo amox # 1 klebsiella    R  # hundreds of trade names can be translated to a name, trade name or an ATC code: ab_name(\"floxapen\") # [1] \"Flucloxacillin\" ab_tradenames(\"floxapen\") #  [1] \"culpen\"               \"floxacillin\"          \"floxacillin sodium\"   #  [4] \"floxapen\"             \"floxapen sodium salt\" \"fluclox\"              #  [7] \"flucloxacilina\"       \"flucloxacillin\"       \"flucloxacilline\"      # [10] \"flucloxacillinum\"     \"fluorochloroxacillin\" \"staphylex\" ab_atc(\"floxapen\") # [1] \"J01CF05\""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"rstudio","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata","what":"RStudio","title":"How to import data from SPSS / SAS / Stata","text":"work R, probably best option use RStudio. open-source free desktop environment allows run R code, also supports project management, version management, package management convenient import menus work data sources. can also install RStudio Server private corporate server, brings nothing less complete RStudio software website (home work). import data file, just click Import Dataset Environment tab:  additional packages needed, RStudio ask installed beforehand. window opens, can define options (parameters) used import ’re ready go:  want named variables imported factors resembles SPSS , use as_factor(). difference :","code":"SPSS_data # # A tibble: 4,203 x 4 #     v001 sex       status    statusage #    <dbl> <dbl+lbl> <dbl+lbl>     <dbl> #  1 10002 1         1              76.6 #  2 10004 0         1              59.1 #  3 10005 1         1              54.5 #  4 10006 1         1              54.1 #  5 10007 1         1              57.7 #  6 10008 1         1              62.8 #  7 10010 0         1              63.7 #  8 10011 1         1              73.1 #  9 10017 1         1              56.7 # 10 10018 0         1              66.6 # # ... with 4,193 more rows  as_factor(SPSS_data) # # A tibble: 4,203 x 4 #     v001 sex    status statusage #    <dbl> <fct>  <fct>      <dbl> #  1 10002 Male   alive       76.6 #  2 10004 Female alive       59.1 #  3 10005 Male   alive       54.5 #  4 10006 Male   alive       54.1 #  5 10007 Male   alive       57.7 #  6 10008 Male   alive       62.8 #  7 10010 Female alive       63.7 #  8 10011 Male   alive       73.1 #  9 10017 Male   alive       56.7 # 10 10018 Female alive       66.6 # # ... with 4,193 more rows"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"base-r","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata","what":"Base R","title":"How to import data from SPSS / SAS / Stata","text":"import data SPSS, SAS Stata, can use great haven package : can now import files follows:","code":"# download and install the latest version: install.packages(\"haven\") # load the package you just installed: library(haven)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"spss","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"SPSS","title":"How to import data from SPSS / SAS / Stata","text":"read files SPSS R: forget as_factor(), mentioned . export R objects SPSS file format:","code":"# read any SPSS file based on file extension (best way): read_spss(file = \"path/to/file\")  # read .sav or .zsav file: read_sav(file = \"path/to/file\")  # read .por file: read_por(file = \"path/to/file\") # save as .sav file: write_sav(data = yourdata, path = \"path/to/file\")  # save as compressed .zsav file: write_sav(data = yourdata, path = \"path/to/file\", compress = TRUE)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"sas","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"SAS","title":"How to import data from SPSS / SAS / Stata","text":"read files SAS R: export R objects SAS file format:","code":"# read .sas7bdat + .sas7bcat files: read_sas(data_file = \"path/to/file\", catalog_file = NULL)  # read SAS transport files (version 5 and version 8): read_xpt(file = \"path/to/file\") # save as regular SAS file: write_sas(data = yourdata, path = \"path/to/file\")  # the SAS transport format is an open format # (required for submission of the data to the FDA) write_xpt(data = yourdata, path = \"path/to/file\", version = 8)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"stata","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"Stata","title":"How to import data from SPSS / SAS / Stata","text":"read files Stata R: export R objects Stata file format:","code":"# read .dta file: read_stata(file = \"/path/to/file\")  # works exactly the same: read_dta(file = \"/path/to/file\") # save as .dta file, Stata version 14: # (supports Stata v8 until v15 at the time of writing) write_dta(data = yourdata, path = \"/path/to/file\", version = 14)"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"import-of-data","dir":"Articles","previous_headings":"","what":"Import of data","title":"How to work with WHONET data","text":"tutorial assumes already imported WHONET data e.g. readxl package. RStudio, can done using menu button ‘Import Dataset’ tab ‘Environment’. Choose option ‘Excel’ select exported file. Make sure date fields imported correctly. example syntax look like : package comes example data set WHONET. use analysis.","code":"library(readxl) data <- read_excel(path = \"path/to/your/file.xlsx\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to work with WHONET data","text":"First, load relevant packages yet . use tidyverse analyses. . don’t know yet, suggest read website: https://www.tidyverse.org/. transform variables simplify automate analysis: Microorganisms transformed microorganism codes (called mo) using Catalogue Life reference data set, contains ~70,000 microorganisms taxonomic kingdoms Bacteria, Fungi Protozoa. tranformation .mo(). function also recognises almost WHONET abbreviations microorganisms. Antimicrobial results interpretations clean valid. words, contain values \"S\", \"\" \"R\". exactly .sir() function . errors warnings, values transformed succesfully. also created package dedicated data cleaning checking, called cleaner package. freq() function can used create frequency tables. let’s check data, couple frequency tables: Frequency table Class: character Length: 500 Available: 500 (100%, NA: 0 = 0%) Unique: 38 Shortest: 11 Longest: 40 (omitted 28 entries, n = 57 [11.4%]) Frequency table Class: factor > ordered > sir (numeric) Length: 500 Levels: 3: S < < R Available: 481 (96.2%, NA: 19 = 3.8%) Unique: 3 Drug: Amoxicillin/clavulanic acid (AMC, J01CR02) Drug group: Beta-lactams/penicillins %SI: 78.59%","code":"library(dplyr) # part of tidyverse library(ggplot2) # part of tidyverse library(AMR) # this package library(cleaner) # to create frequency tables # transform variables data <- WHONET %>%   # get microbial ID based on given organism   mutate(mo = as.mo(Organism)) %>%   # transform everything from \"AMP_ND10\" to \"CIP_EE\" to the new `sir` class   mutate_at(vars(AMP_ND10:CIP_EE), as.sir) # our newly created `mo` variable, put in the mo_name() function data %>% freq(mo_name(mo), nmax = 10) # our transformed antibiotic columns # amoxicillin/clavulanic acid (J01CR02) as an example data %>% freq(AMC_ND2)"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"a-first-glimpse-at-results","dir":"Articles","previous_headings":"","what":"A first glimpse at results","title":"How to work with WHONET data","text":"easy ggplot already give lot information, using included ggplot_sir() function:","code":"data %>%   group_by(Country) %>%   select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%   ggplot_sir(translate_ab = \"ab\", facet = \"Country\", datalabels = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-full-microbial-taxonomy","dir":"Articles","previous_headings":"","what":"microorganisms: Full Microbial Taxonomy","title":"Data sets for download / own use","text":"data set 52,141 rows 22 columns, containing following column names:mo, fullname, status, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, source, lpsn, lpsn_parent, lpsn_renamed_to, gbif, gbif_parent, gbif_renamed_to, prevalence snomed. data set R available microorganisms, load AMR package. last updated 20 December 2022 15:14:04 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (1.2 MB) Download tab-separated text file (11.3 MB) Download Microsoft Excel workbook (5 MB) Download Apache Feather file (5.4 MB) Download Apache Parquet file (2.6 MB) Download SAS data file (50.9 MB) Download IBM SPSS Statistics data file (16.9 MB) Download Stata DTA file (47.1 MB) NOTE: exported files SAS, SPSS Stata contain first 50 SNOMED codes per record, file size otherwise exceed 100 MB; file size limit GitHub. Advice? Use R instead. tab-separated text file Microsoft Excel workbook contain SNOMED codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source","dir":"Articles","previous_headings":"microorganisms: Full Microbial Taxonomy","what":"Source","title":"Data sets for download / own use","text":"data set contains full microbial taxonomy five kingdoms List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF): Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name ‘Microoganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content","dir":"Articles","previous_headings":"microorganisms: Full Microbial Taxonomy","what":"Example content","title":"Data sets for download / own use","text":"Included (sub)species per taxonomic kingdom: Example rows filtering genus Escherichia:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antibiotics-antibiotic-antifungal-drugs","dir":"Articles","previous_headings":"","what":"antibiotics: Antibiotic (+Antifungal) Drugs","title":"Data sets for download / own use","text":"data set 483 rows 14 columns, containing following column names:ab, cid, name, group, atc, atc_group1, atc_group2, abbreviations, synonyms, oral_ddd, oral_units, iv_ddd, iv_units loinc. data set R available antibiotics, load AMR package. last updated 30 October 2022 20:05:46 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (39 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (66 kB) Download Apache Feather file (0.1 MB) Download Apache Parquet file (97 kB) Download SAS data file (1.9 MB) Download IBM SPSS Statistics data file (0.3 MB) Download Stata DTA file (0.4 MB) tab-separated text file Microsoft Excel workbook, SAS, SPSS Stata files contain ATC codes, common abbreviations, trade names LOINC codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-1","dir":"Articles","previous_headings":"antibiotics: Antibiotic (+Antifungal) Drugs","what":"Source","title":"Data sets for download / own use","text":"data set contains EARS-Net ATC codes gathered WHONET, compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine WHONET software 2019 LOINC (Logical Observation Identifiers Names Codes)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antivirals-antiviral-drugs","dir":"Articles","previous_headings":"","what":"antivirals: Antiviral Drugs","title":"Data sets for download / own use","text":"data set 120 rows 11 columns, containing following column names:av, name, atc, cid, atc_group, synonyms, oral_ddd, oral_units, iv_ddd, iv_units loinc. data set R available antivirals, load AMR package. last updated 13 November 2022 07:46:10 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (5 kB) Download tab-separated text file (16 kB) Download Microsoft Excel workbook (16 kB) Download Apache Feather file (15 kB) Download Apache Parquet file (13 kB) Download SAS data file (84 kB) Download IBM SPSS Statistics data file (30 kB) Download Stata DTA file (73 kB) tab-separated text file Microsoft Excel workbook, SAS, SPSS Stata files contain trade names LOINC codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-2","dir":"Articles","previous_headings":"antivirals: Antiviral Drugs","what":"Source","title":"Data sets for download / own use","text":"data set contains ATC codes gathered compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine LOINC (Logical Observation Identifiers Names Codes)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"clinical_breakpoints-interpretation-from-mic-values-disk-diameters-to-sir","dir":"Articles","previous_headings":"","what":"clinical_breakpoints: Interpretation from MIC values & disk diameters to SIR","title":"Data sets for download / own use","text":"data set 18,308 rows 11 columns, containing following column names:guideline, method, site, mo, rank_index, ab, ref_tbl, disk_dose, breakpoint_S, breakpoint_R uti. data set R available clinical_breakpoints, load AMR package. last updated 21 January 2023 22:47:20 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (42 kB) Download tab-separated text file (1.9 MB) Download Microsoft Excel workbook (0.8 MB) Download Apache Feather file (0.7 MB) Download Apache Parquet file (87 kB) Download SAS data file (3.6 MB) Download IBM SPSS Statistics data file (2.3 MB) Download Stata DTA file (3.4 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-3","dir":"Articles","previous_headings":"clinical_breakpoints: Interpretation from MIC values & disk diameters to SIR","what":"Source","title":"Data sets for download / own use","text":"data set contains interpretation rules MIC values disk diffusion diameters. Included guidelines CLSI (2013-2022) EUCAST (2013-2022).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"intrinsic_resistant-intrinsic-bacterial-resistance","dir":"Articles","previous_headings":"","what":"intrinsic_resistant: Intrinsic Bacterial Resistance","title":"Data sets for download / own use","text":"data set 134,634 rows 2 columns, containing following column names:mo ab. data set R available intrinsic_resistant, load AMR package. last updated 16 December 2022 15:10:43 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (78 kB) Download tab-separated text file (5.1 MB) Download Microsoft Excel workbook (1.3 MB) Download Apache Feather file (1.2 MB) Download Apache Parquet file (0.2 MB) Download SAS data file (9.8 MB) Download IBM SPSS Statistics data file (7.4 MB) Download Stata DTA file (9.5 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-4","dir":"Articles","previous_headings":"intrinsic_resistant: Intrinsic Bacterial Resistance","what":"Source","title":"Data sets for download / own use","text":"data set contains defined intrinsic resistance EUCAST bug-drug combinations, based ‘EUCAST Expert Rules’ ‘EUCAST Intrinsic Resistance Unusual Phenotypes’ v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content-4","dir":"Articles","previous_headings":"intrinsic_resistant: Intrinsic Bacterial Resistance","what":"Example content","title":"Data sets for download / own use","text":"Example rows filtering Enterobacter cloacae:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"dosage-dosage-guidelines-from-eucast","dir":"Articles","previous_headings":"","what":"dosage: Dosage Guidelines from EUCAST","title":"Data sets for download / own use","text":"data set 336 rows 9 columns, containing following column names:ab, name, type, dose, dose_times, administration, notes, original_txt eucast_version. data set R available dosage, load AMR package. last updated 14 November 2022 14:20:39 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (3 kB) Download tab-separated text file (29 kB) Download Microsoft Excel workbook (19 kB) Download Apache Feather file (16 kB) Download Apache Parquet file (8 kB) Download SAS data file (92 kB) Download IBM SPSS Statistics data file (43 kB) Download Stata DTA file (82 kB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-5","dir":"Articles","previous_headings":"dosage: Dosage Guidelines from EUCAST","what":"Source","title":"Data sets for download / own use","text":"EUCAST breakpoints used package based dosages data set. Currently included dosages data set meant : ‘EUCAST Clinical Breakpoint Tables’ v11.0 (2021) ‘EUCAST Clinical Breakpoint Tables’ v12.0 (2022).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example_isolates-example-data-for-practice","dir":"Articles","previous_headings":"","what":"example_isolates: Example Data for Practice","title":"Data sets for download / own use","text":"data set 2,000 rows 46 columns, containing following column names:date, patient, age, gender, ward, mo, PEN, OXA, FLC, AMX, AMC, AMP, TZP, CZO, FEP, CXM, FOX, CTX, CAZ, CRO, GEN, TOB, AMK, KAN, TMP, SXT, NIT, FOS, LNZ, CIP, MFX, VAN, TEC, TCY, TGC, DOX, ERY, CLI, AZM, IPM, MEM, MTR, CHL, COL, MUP RIF. data set R available example_isolates, load AMR package. last updated 21 January 2023 22:47:20 UTC. Find info structure data set .","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-6","dir":"Articles","previous_headings":"example_isolates: Example Data for Practice","what":"Source","title":"Data sets for download / own use","text":"data set contains randomised fictitious data, reflects reality can used practise AMR data analysis.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example_isolates_unclean-example-data-for-practice","dir":"Articles","previous_headings":"","what":"example_isolates_unclean: Example Data for Practice","title":"Data sets for download / own use","text":"data set 3,000 rows 8 columns, containing following column names:patient_id, hospital, date, bacteria, AMX, AMC, CIP GEN. data set R available example_isolates_unclean, load AMR package. last updated 27 August 2022 18:49:37 UTC. Find info structure data set .","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-7","dir":"Articles","previous_headings":"example_isolates_unclean: Example Data for Practice","what":"Source","title":"Data sets for download / own use","text":"data set contains randomised fictitious data, reflects reality can used practise AMR data analysis.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"needed-r-packages","dir":"Articles","previous_headings":"","what":"Needed R packages","title":"How to predict antimicrobial resistance","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. AMR package depends packages even extends use functions.","code":"library(dplyr) library(ggplot2) library(AMR)  # (if not yet installed, install with:) # install.packages(c(\"tidyverse\", \"AMR\"))"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"prediction-analysis","dir":"Articles","previous_headings":"","what":"Prediction analysis","title":"How to predict antimicrobial resistance","text":"package contains function resistance_predict(), takes input functions AMR data analysis. Based date column, calculates cases per year uses regression model predict antimicrobial resistance. basically easy : function look date column col_date set. running commands, summary regression model printed unless using resistance_predict(..., info = FALSE). text printed summary - actual result (output) function data.frame containing year: number observations, actual observed resistance, estimated resistance standard error estimation: function plot available base R, can extended packages depend output based type input. extended function cope resistance predictions:  fastest way plot result. automatically adds right axes, error bars, titles, number available observations type model. also support ggplot2 package custom function ggplot_sir_predict() create appealing plots:","code":"# resistance prediction of piperacillin/tazobactam (TZP): resistance_predict(tbl = example_isolates, col_date = \"date\", col_ab = \"TZP\", model = \"binomial\")  # or: example_isolates %>%   resistance_predict(     col_ab = \"TZP\",     model = \"binomial\"   )  # to bind it to object 'predict_TZP' for example: predict_TZP <- example_isolates %>%   resistance_predict(     col_ab = \"TZP\",     model = \"binomial\"   ) predict_TZP # # A tibble: 32 × 7 #     year  value se_min se_max observations observed estimated #  * <dbl>  <dbl>  <dbl>  <dbl>        <int>    <dbl>     <dbl> #  1  2002 0.2        NA     NA           15   0.2       0.0562 #  2  2003 0.0625     NA     NA           32   0.0625    0.0616 #  3  2004 0.0854     NA     NA           82   0.0854    0.0676 #  4  2005 0.05       NA     NA           60   0.05      0.0741 #  5  2006 0.0508     NA     NA           59   0.0508    0.0812 #  6  2007 0.121      NA     NA           66   0.121     0.0889 #  7  2008 0.0417     NA     NA           72   0.0417    0.0972 #  8  2009 0.0164     NA     NA           61   0.0164    0.106  #  9  2010 0.0566     NA     NA           53   0.0566    0.116  # 10  2011 0.183      NA     NA           93   0.183     0.127  # # … with 22 more rows plot(predict_TZP) ggplot_sir_predict(predict_TZP) # choose for error bars instead of a ribbon ggplot_sir_predict(predict_TZP, ribbon = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"choosing-the-right-model","dir":"Articles","previous_headings":"Prediction analysis","what":"Choosing the right model","title":"How to predict antimicrobial resistance","text":"Resistance easily predicted; look vancomycin resistance Gram-positive bacteria, spread (.e. standard error) enormous:  Vancomycin resistance 100% ten years, might remain low. can define model model parameter. model chosen generalised linear regression model using binomial distribution, assuming period zero resistance followed period increasing resistance leading slowly resistance. Valid values : vancomycin resistance Gram-positive bacteria, linear model might appropriate:  model also available object, attribute:","code":"example_isolates %>%   filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>%   resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"binomial\") %>%   ggplot_sir_predict() example_isolates %>%   filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>%   resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"linear\") %>%   ggplot_sir_predict() model <- attributes(predict_TZP)$model  summary(model)$family #  # Family: binomial  # Link function: logit  summary(model)$coefficients #                  Estimate  Std. Error   z value     Pr(>|z|) # (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05 # year           0.09883005  0.02295317  4.305725 1.664395e-05"},{"path":"https://msberends.github.io/AMR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Matthijs S. Berends. Author, maintainer. Christian F. Luz. Author, contributor. Dennis Souverein. Author, contributor. Erwin E. . Hassing. Author, contributor. Casper J. Albers. Thesis advisor. Peter Dutey-Magni. Contributor. Judith M. Fonville. Contributor. Alex W. Friedrich. Thesis advisor. Corinna Glasner. Thesis advisor. Eric H. L. C. M. Hazenberg. Contributor. Gwen Knight. Contributor. Annick Lenglet. Contributor. Bart C. Meijer. Contributor. Dmytro Mykhailenko. Contributor. Anton Mymrikov. Contributor. Andrew P. Norgan. Contributor. Sofia Ny. Contributor. Jonas Salm. Contributor. Rogier P. Schade. Contributor. Bhanu N. M. Sinha. Thesis advisor. Anthony Underwood. Contributor.","code":""},{"path":"https://msberends.github.io/AMR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). “AMR: R Package Working Antimicrobial Resistance Data.” Journal Statistical Software, 104(3), 1–31. doi:10.18637/jss.v104.i03.","code":"@Article{,   title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},   author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},   journal = {Journal of Statistical Software},   year = {2022},   volume = {104},   number = {3},   pages = {1--31},   doi = {10.18637/jss.v104.i03}, }"},{"path":"https://msberends.github.io/AMR/index.html","id":"the-amr-package-for-r-","dir":"","previous_headings":"","what":"Antimicrobial Resistance Data Analysis","title":"Antimicrobial Resistance Data Analysis","text":"Provides full microbiological taxonomy data antimicrobial drugs Applies recent CLSI EUCAST clinical breakpoints MICs disk zones Corrects duplicate isolates, calculates predicts AMR per antibiotic class Integrates WHONET, ATC, EARS-Net, PubChem, LOINC SNOMED CT Works Windows, macOS Linux versions R since R-3.0 completely dependency-free, highly suitable places limited resources https://msberends.github.io/AMR https://doi.org/10.18637/jss.v104.i03","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"Antimicrobial Resistance Data Analysis","text":"AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. Many different researchers around globe continually helping us make successful durable project! work published Journal Statistical Software (Volume 104(3); DOI 10.18637/jss.v104.i03) formed basis two PhD theses (DOI 10.33612/diss.177417131 DOI 10.33612/diss.192486375). installing package, R knows ~52,000 distinct microbial species (updated December 2022) ~600 antibiotic, antimycotic antiviral drugs name code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC SNOMED CT), knows valid SIR MIC values. integral breakpoint guidelines CLSI EUCAST included last 10 years. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"used-in-175-countries-translated-to-16-languages","dir":"","previous_headings":"Introduction","what":"Used in 175 countries, translated to 16 languages","title":"Antimicrobial Resistance Data Analysis","text":"Since first public release early 2018, R package used almost countries world. Click map enlarge see country names. help contributors corners world, AMR package available  English,  Chinese,  Danish,  Dutch,  French,  German,  Greek,  Italian,  Japanese,  Polish,  Portuguese,  Russian,  Spanish,  Swedish,  Turkish,  Ukrainian. Antimicrobial drug (group) names colloquial microorganism names provided languages.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"filtering-and-selecting-data","dir":"","previous_headings":"Practical examples","what":"Filtering and selecting data","title":"Antimicrobial Resistance Data Analysis","text":"defined row filter Gram-negative bacteria intrinsic resistance cefotaxime (mo_is_gram_negative() mo_is_intrinsic_resistant()) column selection two antibiotic groups (aminoglycosides() carbapenems()), reference data microorganisms antibiotics AMR package make sure get meant: base R equivalent : base R snippet work version R since April 2013 (R-3.0).","code":"# AMR works great with dplyr, but it's not required or neccesary library(AMR) library(dplyr)  example_isolates %>%   mutate(bacteria = mo_fullname()) %>%   filter(mo_is_gram_negative(),          mo_is_intrinsic_resistant(ab = \"cefotax\")) %>%   select(bacteria,          aminoglycosides(),          carbapenems()) example_isolates$bacteria <- mo_fullname(example_isolates$mo) example_isolates[which(mo_is_gram_negative() &                          mo_is_intrinsic_resistant(ab = \"cefotax\")),                  c(\"bacteria\", aminoglycosides(), carbapenems())]"},{"path":"https://msberends.github.io/AMR/index.html","id":"calculating-resistance-per-group","dir":"","previous_headings":"Practical examples","what":"Calculating resistance per group","title":"Antimicrobial Resistance Data Analysis","text":"","code":"library(AMR) library(dplyr)  out <- example_isolates %>%   # group by ward:   group_by(ward) %>%   # calculate AMR using resistance(), over all aminoglycosides   # and polymyxins:   summarise(across(c(aminoglycosides(), polymyxins()),             resistance)) out # transform the antibiotic columns to names: out %>% set_ab_names() # transform the antibiotic column to ATC codes: out %>% set_ab_names(property = \"atc\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"what-else-can-you-do-with-this-package","dir":"","previous_headings":"","what":"What else can you do with this package?","title":"Antimicrobial Resistance Data Analysis","text":"package intended comprehensive toolbox integrated AMR data analysis. package can used : Reference taxonomy microorganisms, since package contains microbial (sub)species List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF) (manual) Interpreting raw MIC disk diffusion values, based CLSI EUCAST guideline last 10 years (manual) Retrieving antimicrobial drug names, doses forms administration clinical health care records (manual) Determining first isolates used AMR data analysis (manual) Calculating antimicrobial resistance (tutorial) Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) (tutorial) Calculating (empirical) susceptibility mono therapy combination therapies (tutorial) Predicting future antimicrobial resistance using regression models (tutorial) Getting properties microorganism (like Gram stain, species, genus family) (manual) Getting properties antibiotic (like name, code EARS-Net/ATC/LOINC/PubChem, defined daily dose trade name) (manual) Plotting antimicrobial resistance (tutorial) Applying EUCAST expert rules (manual) Getting SNOMED codes microorganism, getting properties microorganism based SNOMED code (manual) Getting LOINC codes antibiotic, getting properties antibiotic based LOINC code (manual) Machine reading EUCAST CLSI guidelines 2011-2021 translate MIC values disk diffusion diameters SIR (link) Principal component analysis AMR (tutorial)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-official-version","dir":"","previous_headings":"Get this package","what":"Latest official version","title":"Antimicrobial Resistance Data Analysis","text":"package available official R network (CRAN). Install package R CRAN using command: downloaded installed automatically. RStudio, click menu Tools > Install Packages… type “AMR” press Install. Note: functions website may available latest release. use functions data sets mentioned website, install latest development version.","code":"install.packages(\"AMR\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-development-version","dir":"","previous_headings":"Get this package","what":"Latest development version","title":"Antimicrobial Resistance Data Analysis","text":"Please read Developer Guideline . latest unpublished development version can installed GitHub two ways: Manually, using: Automatically, using rOpenSci R-universe platform, adding R-universe address list repositories (‘repos’): , can install update AMR package like official release (e.g., using install.packages(\"AMR\") RStudio via Tools > Check Package Updates…).","code":"install.packages(\"remotes\") # if you haven't already remotes::install_github(\"msberends/AMR\") options(repos = c(getOption(\"repos\"),                   msberends = \"https://msberends.r-universe.dev\"))"},{"path":"https://msberends.github.io/AMR/index.html","id":"get-started","dir":"","previous_headings":"","what":"Get started","title":"Antimicrobial Resistance Data Analysis","text":"find conduct AMR data analysis, please continue reading get started click link ‘’ menu.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"partners","dir":"","previous_headings":"","what":"Partners","title":"Antimicrobial Resistance Data Analysis","text":"development package part , related , made possible following non-profit organisations initiatives:","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"copyright","dir":"","previous_headings":"","what":"Copyright","title":"Antimicrobial Resistance Data Analysis","text":"R package free, open-source software licensed GNU General Public License v2.0 (GPL-2). nutshell, means package: May used commercial purposes May used private purposes May used patent purposes May modified, although: Modifications must released license distributing package Changes made code must documented May distributed, although: Source code must made available package distributed copy license copyright notice must included package. Comes LIMITATION liability Comes warranty","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions — AMR-deprecated","title":"Deprecated Functions — AMR-deprecated","text":"functions -called 'Deprecated'. removed future release. Using functions give warning name function replaced (one).","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated Functions — AMR-deprecated","text":"","code":"NA_rsi_  as.rsi(x, ...)  facet_rsi(...)  geom_rsi(...)  ggplot_rsi(...)  ggplot_rsi_predict(...)  is.rsi(x, ...)  is.rsi.eligible(...)  labels_rsi_count(...)  n_rsi(...)  random_rsi(...)  rsi_df(...)  rsi_predict(...)  scale_rsi_colours(...)  theme_rsi(...)"},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Deprecated Functions — AMR-deprecated","text":"object class rsi (inherits ordered, factor) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":null,"dir":"Reference","previous_headings":"","what":"Options for the AMR package — AMR-options","title":"Options for the AMR package — AMR-options","text":"overview package-specific options() can set AMR package.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"options","dir":"Reference","previous_headings":"","what":"Options","title":"Options for the AMR package — AMR-options","text":"AMR_custom_ab: allows use custom antimicrobial drugs package. explained add_custom_antimicrobials(). AMR_custom_mo: allows use custom microorganisms package. explained add_custom_microorganisms().","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":null,"dir":"Reference","previous_headings":"","what":"The AMR Package — AMR","title":"The AMR Package — AMR","text":"Welcome AMR package. AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. Many different researchers around globe continually helping us make successful durable project! work published Journal Statistical Software (Volume 104(3); DOI 10.18637/jss.v104.i03) formed basis two PhD theses (DOI 10.33612/diss.177417131 DOI 10.33612/diss.192486375). installing package, R knows ~52,000 (updated December 2022) ~600 antibiotic, antimycotic antiviral drugs name code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC SNOMED CT), knows valid SIR MIC values. integral breakpoint guidelines CLSI EUCAST included last 10 years. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen. AMR package available English, Chinese, Danish, Dutch, French, German, Greek, Italian, Japanese, Polish, Portuguese, Russian, Spanish, Swedish, Turkish Ukrainian. Antimicrobial drug (group) names colloquial microorganism names provided languages.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"The AMR Package — AMR","text":"cite AMR publications use: Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). \"AMR: R Package Working Antimicrobial Resistance Data.\" Journal Statistical Software, 104(3), 1-31. doi:10.18637/jss.v104.i03 . BibTeX entry LaTeX users :","code":"@Article{,   title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},   author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},   journal = {Journal of Statistical Software},   year = {2022},   volume = {104},   number = {3},   pages = {1--31},   doi = {10.18637/jss.v104.i03}, }"},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"The AMR Package — AMR","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The AMR Package — AMR","text":"Maintainer: Matthijs S. Berends m.berends@certe.nl (ORCID) Authors: Christian F. Luz (ORCID) [contributor] Dennis Souverein (ORCID) [contributor] Erwin E. . Hassing [contributor] contributors: Casper J. Albers (ORCID) [thesis advisor] Peter Dutey-Magni (ORCID) [contributor] Judith M. Fonville [contributor] Alex W. Friedrich (ORCID) [thesis advisor] Corinna Glasner (ORCID) [thesis advisor] Eric H. L. C. M. Hazenberg [contributor] Gwen Knight (ORCID) [contributor] Annick Lenglet (ORCID) [contributor] Bart C. Meijer [contributor] Dmytro Mykhailenko [contributor] Anton Mymrikov [contributor] Andrew P. Norgan (ORCID) [contributor] Sofia Ny (ORCID) [contributor] Jonas Salm [contributor] Rogier P. Schade [contributor] Bhanu N. M. Sinha (ORCID) [thesis advisor] Anthony Underwood (ORCID) [contributor]","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":null,"dir":"Reference","previous_headings":"","what":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"antimicrobial drugs official names, ATC codes, ATC groups defined daily dose (DDD) included package, using Collaborating Centre Drug Statistics Methodology.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"","code":"as.ab(\"meropenem\") #> Class 'ab' #> [1] MEM ab_name(\"J01DH02\") #> [1] \"Meropenem\"  ab_tradenames(\"flucloxacillin\") #>  [1] \"culpen\"               \"floxacillin\"          \"floxacillin sodium\"   #>  [4] \"floxapen\"             \"floxapen sodium salt\" \"fluclox\"              #>  [7] \"flucloxacilina\"       \"flucloxacillin\"       \"flucloxacilline\"      #> [10] \"flucloxacillinum\"     \"fluorochloroxacillin\" \"staphylex\""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 500 Isolates - WHONET Example — WHONET","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"example data set exact structure export file WHONET. files can used package, example data set shows. antibiotic results example_isolates data set. patient names created using online surname generators place practice purposes.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET"},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"tibble 500 observations 53 variables: Identification number ID sample Specimen number ID specimen Organism Name microorganism. analysis, transform valid microbial class, using .mo(). Country Country origin Laboratory Name laboratory Last name Fictitious last name patient First name Fictitious initial patient Sex Fictitious gender patient Age Fictitious age patient Age category Age group, can also looked using age_groups() Date admissionDate hospital admission Specimen dateDate specimen received laboratory Specimen type Specimen type group Specimen type (Numeric) Translation \"Specimen type\" Reason Reason request Differential Diagnosis Isolate number ID isolate Organism type Type microorganism, can also looked using mo_type() Serotype Serotype microorganism Beta-lactamase Microorganism produces beta-lactamase? ESBL Microorganism produces extended spectrum beta-lactamase? Carbapenemase Microorganism produces carbapenemase? MRSA screening test Microorganism possible MRSA? Inducible clindamycin resistance Clindamycin can induced? Comment comments Date data entryDate data entered WHONET AMP_ND10:CIP_EE 0 different antibiotics. can lookup abbreviations antibiotics data set, use e.g. ab_name(\"AMP\") get official name immediately. analysis, transform valid antibiotic class, using .sir().","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> # A tibble: 500 × 53 #>    Identif…¹ Speci…² Organ…³ Country Labor…⁴ Last …⁵ First…⁶ Sex     Age Age c…⁷ #>    <chr>       <int> <chr>   <chr>   <chr>   <chr>   <chr>   <chr> <dbl> <chr>   #>  1 fe41d7ba…    1748 SPN     Belgium Nation… Abel    B.      F        68 55-74   #>  2 91f175ec…    1767 eco     The Ne… Nation… Delacr… F.      M        89 75+     #>  3 cc401505…    1343 eco     The Ne… Nation… Steens… F.      M        85 75+     #>  4 e864b692…    1894 MAP     Denmark Nation… Beyers… L.      M        62 55-74   #>  5 3d051fe3…    1739 PVU     Belgium Nation… Hummel  W.      M        86 75+     #>  6 c80762a0…    1846 103     The Ne… Nation… Eikenb… J.      F        53 25-54   #>  7 8022d372…    1628 103     Denmark Nation… Leclerc S.      F        77 75+     #>  8 f3dc5f55…    1493 eco     The Ne… Nation… Delacr… W.      M        53 25-54   #>  9 15add38f…    1847 eco     France  Nation… Van La… S.      F        63 55-74   #> 10 fd41248d…    1458 eco     Germany Nation… Moulin  O.      F        75 75+     #> # … with 490 more rows, 43 more variables: `Date of admission` <date>, #> #   `Specimen date` <date>, `Specimen type` <chr>, #> #   `Specimen type (Numeric)` <dbl>, Reason <chr>, `Isolate number` <int>, #> #   `Organism type` <chr>, Serotype <chr>, `Beta-lactamase` <lgl>, ESBL <lgl>, #> #   Carbapenemase <lgl>, `MRSA screening test` <lgl>, #> #   `Inducible clindamycin resistance` <lgl>, Comment <chr>, #> #   `Date of data entry` <date>, AMP_ND10 <rsi[!]>, AMC_ED20 <rsi[!]>, …"},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"Use function e.g. clinical texts health care records. returns list antimicrobial drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"","code":"ab_from_text(   text,   type = c(\"drug\", \"dose\", \"administration\"),   collapse = NULL,   translate_ab = FALSE,   thorough_search = NULL,   info = interactive(),   ... )"},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_ab type = \"drug\": column name antibiotics data set translate antibiotic abbreviations , using ab_property(). Defaults FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed .ab()","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"list,  character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"function also internally used .ab(), although searches first drug name throw note drug names returned. Note: .ab() function may use long regular expression match brand names antimicrobial drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"default, function search antimicrobial drug names. text elements searched official names, ATC codes brand names. uses .ab() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(abx = ab_from_text(clinical_text)) returned AB codes can transformed official names, groups, etc. ab_* functions ab_name() ab_group(), using translate_ab argument. using collapse, function return character:df %>% mutate(abx = ab_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"","code":"# mind the bad spelling of amoxicillin in this line, # straight from a true health care record: ab_from_text(\"28/03/2020 regular amoxicilliin 500mg po tid\") #> [[1]] #> Class 'ab' #> [1] AMX #>   ab_from_text(\"500 mg amoxi po and 400mg cipro iv\") #> [[1]] #> Class 'ab' #> [1] AMX CIP #>  ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", type = \"dose\") #> [[1]] #> [1] 500 400 #>  ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", type = \"admin\") #> [[1]] #> [1] \"oral\" \"iv\"   #>   ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", collapse = \", \") #> [1] \"AMX, CIP\" # \\donttest{ # if you want to know which antibiotic groups were administered, do e.g.: abx <- ab_from_text(\"500 mg amoxi po and 400mg cipro iv\") ab_group(abx[[1]]) #> [1] \"Beta-lactams/penicillins\" \"Quinolones\"                if (require(\"dplyr\")) {   tibble(clinical_text = c(     \"given 400mg cipro and 500 mg amox\",     \"started on doxy iv today\"   )) %>%     mutate(       abx_codes = ab_from_text(clinical_text),       abx_doses = ab_from_text(clinical_text, type = \"doses\"),       abx_admin = ab_from_text(clinical_text, type = \"admin\"),       abx_coll = ab_from_text(clinical_text, collapse = \"|\"),       abx_coll_names = ab_from_text(clinical_text,         collapse = \"|\",         translate_ab = \"name\"       ),       abx_coll_doses = ab_from_text(clinical_text,         type = \"doses\",         collapse = \"|\"       ),       abx_coll_admin = ab_from_text(clinical_text,         type = \"admin\",         collapse = \"|\"       )     ) } #> Loading required package: dplyr #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union #> # A tibble: 2 × 8 #>   clinical_text          abx_c…¹ abx_d…² abx_a…³ abx_c…⁴ abx_c…⁵ abx_c…⁶ abx_c…⁷ #>   <chr>                  <list>  <list>  <list>  <chr>   <chr>   <chr>   <chr>   #> 1 given 400mg cipro and… <ab>    <dbl>   <chr>   CIP|AMX Ciprof… 400|500 NA      #> 2 started on doxy iv to… <ab>    <dbl>   <chr>   DOX     Doxycy… NA      iv      #> # … with abbreviated variable names ¹​abx_codes, ²​abx_doses, ³​abx_admin, #> #   ⁴​abx_coll, ⁵​abx_coll_names, ⁶​abx_coll_doses, ⁷​abx_coll_admin # }"},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antibiotic — ab_property","title":"Get Properties of an Antibiotic — ab_property","text":"Use functions return specific property antibiotic antibiotics data set. input values evaluated internally .ab().","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antibiotic — ab_property","text":"","code":"ab_name(x, language = get_AMR_locale(), tolower = FALSE, ...)  ab_cid(x, ...)  ab_synonyms(x, ...)  ab_tradenames(x, ...)  ab_group(x, language = get_AMR_locale(), ...)  ab_atc(x, only_first = FALSE, ...)  ab_atc_group1(x, language = get_AMR_locale(), ...)  ab_atc_group2(x, language = get_AMR_locale(), ...)  ab_loinc(x, ...)  ab_ddd(x, administration = \"oral\", ...)  ab_ddd_units(x, administration = \"oral\", ...)  ab_info(x, language = get_AMR_locale(), ...)  ab_url(x, open = FALSE, ...)  ab_property(x, property = \"name\", language = get_AMR_locale(), ...)  set_ab_names(   data,   ...,   property = \"name\",   language = get_AMR_locale(),   snake_case = NULL )"},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antibiotic — ab_property","text":"x (vector ) text can coerced valid antibiotic drug code .ab() language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. lead e.g. \"polymyxin B\" \"polymyxin b\". ... case set_ab_names() data data.frame: columns select (supports tidy selection column1:column4), otherwise arguments passed .ab() only_first logical indicate whether first ATC code must returned, giving preference J0-codes (.e., antimicrobial drug group) administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antibiotics data set: vector_or(colnames(antibiotics), sort = FALSE). data data.frame columns need renamed, character vector column names snake_case logical indicate whether names -called snake case: lower case spaces/slashes replaced underscore (_)","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antibiotic — ab_property","text":"integer case ab_cid() named list case ab_info() multiple ab_atc()/ab_synonyms()/ab_tradenames() double case ab_ddd() data.frame case set_ab_names() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antibiotic — ab_property","text":"output translated possible. function ab_url() return direct URL official website. warning returned required ATC code available. function set_ab_names() special column renaming function data.frames. renames columns names resemble antimicrobial drugs. always makes sure new column names unique. property = \"atc\" set, preference given ATC codes J-group.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antibiotic — ab_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antibiotic — ab_property","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antibiotic — ab_property","text":"","code":"# all properties: ab_name(\"AMX\") #> [1] \"Amoxicillin\" ab_atc(\"AMX\") #> [1] \"J01CA04\" ab_cid(\"AMX\") #> [1] 33613 ab_synonyms(\"AMX\") #>  [1] \"actimoxi\"            \"amoclen\"             \"amolin\"              #>  [4] \"amopen\"              \"amopenixin\"          \"amoxibiotic\"         #>  [7] \"amoxicaps\"           \"amoxicilina\"         \"amoxicillin\"         #> [10] \"amoxicillin hydrate\" \"amoxicilline\"        \"amoxicillinum\"       #> [13] \"amoxiden\"            \"amoxil\"              \"amoxivet\"            #> [16] \"amoxy\"               \"amoxycillin\"         \"amoxyke\"             #> [19] \"anemolin\"            \"aspenil\"             \"atoksilin\"           #> [22] \"biomox\"              \"bristamox\"           \"cemoxin\"             #> [25] \"clamoxyl\"            \"damoxy\"              \"delacillin\"          #> [28] \"demoksil\"            \"dispermox\"           \"efpenix\"             #> [31] \"flemoxin\"            \"hiconcil\"            \"histocillin\"         #> [34] \"hydroxyampicillin\"   \"ibiamox\"             \"imacillin\"           #> [37] \"lamoxy\"              \"largopen\"            \"metafarma capsules\"  #> [40] \"metifarma capsules\"  \"moksilin\"            \"moxacin\"             #> [43] \"moxatag\"             \"ospamox\"             \"pamoxicillin\"        #> [46] \"piramox\"             \"promoxil\"            \"remoxil\"             #> [49] \"robamox\"             \"sawamox pm\"          \"tolodina\"            #> [52] \"topramoxin\"          \"unicillin\"           \"utimox\"              #> [55] \"vetramox\"            ab_tradenames(\"AMX\") #>  [1] \"actimoxi\"            \"amoclen\"             \"amolin\"              #>  [4] \"amopen\"              \"amopenixin\"          \"amoxibiotic\"         #>  [7] \"amoxicaps\"           \"amoxicilina\"         \"amoxicillin\"         #> [10] \"amoxicillin hydrate\" \"amoxicilline\"        \"amoxicillinum\"       #> [13] \"amoxiden\"            \"amoxil\"              \"amoxivet\"            #> [16] \"amoxy\"               \"amoxycillin\"         \"amoxyke\"             #> [19] \"anemolin\"            \"aspenil\"             \"atoksilin\"           #> [22] \"biomox\"              \"bristamox\"           \"cemoxin\"             #> [25] \"clamoxyl\"            \"damoxy\"              \"delacillin\"          #> [28] \"demoksil\"            \"dispermox\"           \"efpenix\"             #> [31] \"flemoxin\"            \"hiconcil\"            \"histocillin\"         #> [34] \"hydroxyampicillin\"   \"ibiamox\"             \"imacillin\"           #> [37] \"lamoxy\"              \"largopen\"            \"metafarma capsules\"  #> [40] \"metifarma capsules\"  \"moksilin\"            \"moxacin\"             #> [43] \"moxatag\"             \"ospamox\"             \"pamoxicillin\"        #> [46] \"piramox\"             \"promoxil\"            \"remoxil\"             #> [49] \"robamox\"             \"sawamox pm\"          \"tolodina\"            #> [52] \"topramoxin\"          \"unicillin\"           \"utimox\"              #> [55] \"vetramox\"            ab_group(\"AMX\") #> [1] \"Beta-lactams/penicillins\" ab_atc_group1(\"AMX\") #> [1] \"Beta-lactam antibacterials, penicillins\" ab_atc_group2(\"AMX\") #> [1] \"Penicillins with extended spectrum\" ab_url(\"AMX\") #>                                                           Amoxicillin  #> \"https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no\"   # smart lowercase tranformation ab_name(x = c(\"AMC\", \"PLB\")) #> [1] \"Amoxicillin/clavulanic acid\" \"Polymyxin B\"                 ab_name(x = c(\"AMC\", \"PLB\"), tolower = TRUE) #> [1] \"amoxicillin/clavulanic acid\" \"polymyxin B\"                  # defined daily doses (DDD) ab_ddd(\"AMX\", \"oral\") #> [1] 1.5 ab_ddd_units(\"AMX\", \"oral\") #> [1] \"g\" ab_ddd(\"AMX\", \"iv\") #> [1] 3 ab_ddd_units(\"AMX\", \"iv\") #> [1] \"g\"  ab_info(\"AMX\") # all properties as a list #> $ab #> [1] \"AMX\" #>  #> $cid #> [1] 33613 #>  #> $name #> [1] \"Amoxicillin\" #>  #> $group #> [1] \"Beta-lactams/penicillins\" #>  #> $atc #> [1] \"J01CA04\" #>  #> $atc_group1 #> [1] \"Beta-lactam antibacterials, penicillins\" #>  #> $atc_group2 #> [1] \"Penicillins with extended spectrum\" #>  #> $tradenames #>  [1] \"actimoxi\"            \"amoclen\"             \"amolin\"              #>  [4] \"amopen\"              \"amopenixin\"          \"amoxibiotic\"         #>  [7] \"amoxicaps\"           \"amoxicilina\"         \"amoxicillin\"         #> [10] \"amoxicillin hydrate\" \"amoxicilline\"        \"amoxicillinum\"       #> [13] \"amoxiden\"            \"amoxil\"              \"amoxivet\"            #> [16] \"amoxy\"               \"amoxycillin\"         \"amoxyke\"             #> [19] \"anemolin\"            \"aspenil\"             \"atoksilin\"           #> [22] \"biomox\"              \"bristamox\"           \"cemoxin\"             #> [25] \"clamoxyl\"            \"damoxy\"              \"delacillin\"          #> [28] \"demoksil\"            \"dispermox\"           \"efpenix\"             #> [31] \"flemoxin\"            \"hiconcil\"            \"histocillin\"         #> [34] \"hydroxyampicillin\"   \"ibiamox\"             \"imacillin\"           #> [37] \"lamoxy\"              \"largopen\"            \"metafarma capsules\"  #> [40] \"metifarma capsules\"  \"moksilin\"            \"moxacin\"             #> [43] \"moxatag\"             \"ospamox\"             \"pamoxicillin\"        #> [46] \"piramox\"             \"promoxil\"            \"remoxil\"             #> [49] \"robamox\"             \"sawamox pm\"          \"tolodina\"            #> [52] \"topramoxin\"          \"unicillin\"           \"utimox\"              #> [55] \"vetramox\"            #>  #> $loinc #> [1] \"16365-9\" \"25274-2\" \"3344-9\"  \"80133-2\" #>  #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 1.5 #>  #> $ddd$oral$units #> [1] \"g\" #>  #>  #> $ddd$iv #> $ddd$iv$amount #> [1] 3 #>  #> $ddd$iv$units #> [1] \"g\" #>  #>  #>   # all ab_* functions use as.ab() internally, so you can go from 'any' to 'any': ab_atc(\"AMP\") #> [1] \"J01CA01\" \"S01AA19\" ab_group(\"J01CA01\") #> [1] \"Beta-lactams/penicillins\" ab_loinc(\"ampicillin\") #> [1] \"21066-6\" \"3355-5\"  \"33562-0\" \"33919-2\" \"43883-8\" \"43884-6\" \"87604-5\" ab_name(\"21066-6\") #> [1] \"Ampicillin\" ab_name(6249) #> [1] \"Ampicillin\" ab_name(\"J01CA01\") #> [1] \"Ampicillin\"  # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\") #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\") #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") #> [1] \"J01DD04\"  # use set_ab_names() for renaming columns colnames(example_isolates) #>  [1] \"date\"    \"patient\" \"age\"     \"gender\"  \"ward\"    \"mo\"      \"PEN\"     #>  [8] \"OXA\"     \"FLC\"     \"AMX\"     \"AMC\"     \"AMP\"     \"TZP\"     \"CZO\"     #> [15] \"FEP\"     \"CXM\"     \"FOX\"     \"CTX\"     \"CAZ\"     \"CRO\"     \"GEN\"     #> [22] \"TOB\"     \"AMK\"     \"KAN\"     \"TMP\"     \"SXT\"     \"NIT\"     \"FOS\"     #> [29] \"LNZ\"     \"CIP\"     \"MFX\"     \"VAN\"     \"TEC\"     \"TCY\"     \"TGC\"     #> [36] \"DOX\"     \"ERY\"     \"CLI\"     \"AZM\"     \"IPM\"     \"MEM\"     \"MTR\"     #> [43] \"CHL\"     \"COL\"     \"MUP\"     \"RIF\"     colnames(set_ab_names(example_isolates)) #>  [1] \"date\"                          \"patient\"                       #>  [3] \"age\"                           \"gender\"                        #>  [5] \"ward\"                          \"mo\"                            #>  [7] \"benzylpenicillin\"              \"oxacillin\"                     #>  [9] \"flucloxacillin\"                \"amoxicillin\"                   #> [11] \"amoxicillin_clavulanic_acid\"   \"ampicillin\"                    #> [13] \"piperacillin_tazobactam\"       \"cefazolin\"                     #> [15] \"cefepime\"                      \"cefuroxime\"                    #> [17] \"cefoxitin\"                     \"cefotaxime\"                    #> [19] \"ceftazidime\"                   \"ceftriaxone\"                   #> [21] \"gentamicin\"                    \"tobramycin\"                    #> [23] \"amikacin\"                      \"kanamycin\"                     #> [25] \"trimethoprim\"                  \"trimethoprim_sulfamethoxazole\" #> [27] \"nitrofurantoin\"                \"fosfomycin\"                    #> [29] \"linezolid\"                     \"ciprofloxacin\"                 #> [31] \"moxifloxacin\"                  \"vancomycin\"                    #> [33] \"teicoplanin\"                   \"tetracycline\"                  #> [35] \"tigecycline\"                   \"doxycycline\"                   #> [37] \"erythromycin\"                  \"clindamycin\"                   #> [39] \"azithromycin\"                  \"imipenem\"                      #> [41] \"meropenem\"                     \"metronidazole\"                 #> [43] \"chloramphenicol\"               \"colistin\"                      #> [45] \"mupirocin\"                     \"rifampicin\"                    colnames(set_ab_names(example_isolates, NIT:VAN)) #>  [1] \"date\"           \"patient\"        \"age\"            \"gender\"         #>  [5] \"ward\"           \"mo\"             \"PEN\"            \"OXA\"            #>  [9] \"FLC\"            \"AMX\"            \"AMC\"            \"AMP\"            #> [13] \"TZP\"            \"CZO\"            \"FEP\"            \"CXM\"            #> [17] \"FOX\"            \"CTX\"            \"CAZ\"            \"CRO\"            #> [21] \"GEN\"            \"TOB\"            \"AMK\"            \"KAN\"            #> [25] \"TMP\"            \"SXT\"            \"nitrofurantoin\" \"fosfomycin\"     #> [29] \"linezolid\"      \"ciprofloxacin\"  \"moxifloxacin\"   \"vancomycin\"     #> [33] \"TEC\"            \"TCY\"            \"TGC\"            \"DOX\"            #> [37] \"ERY\"            \"CLI\"            \"AZM\"            \"IPM\"            #> [41] \"MEM\"            \"MTR\"            \"CHL\"            \"COL\"            #> [45] \"MUP\"            \"RIF\"            # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     set_ab_names() %>%     head()    # this does the same:   example_isolates %>%     rename_with(set_ab_names) %>%     head()    # set_ab_names() works with any AB property:   example_isolates %>%     set_ab_names(property = \"atc\") %>%     head()    example_isolates %>%     set_ab_names(where(is.sir)) %>%     colnames()    example_isolates %>%     set_ab_names(NIT:VAN) %>%     colnames() } #>  [1] \"date\"           \"patient\"        \"age\"            \"gender\"         #>  [5] \"ward\"           \"mo\"             \"PEN\"            \"OXA\"            #>  [9] \"FLC\"            \"AMX\"            \"AMC\"            \"AMP\"            #> [13] \"TZP\"            \"CZO\"            \"FEP\"            \"CXM\"            #> [17] \"FOX\"            \"CTX\"            \"CAZ\"            \"CRO\"            #> [21] \"GEN\"            \"TOB\"            \"AMK\"            \"KAN\"            #> [25] \"TMP\"            \"SXT\"            \"nitrofurantoin\" \"fosfomycin\"     #> [29] \"linezolid\"      \"ciprofloxacin\"  \"moxifloxacin\"   \"vancomycin\"     #> [33] \"TEC\"            \"TCY\"            \"TGC\"            \"DOX\"            #> [37] \"ERY\"            \"CLI\"            \"AZM\"            \"IPM\"            #> [41] \"MEM\"            \"MTR\"            \"CHL\"            \"COL\"            #> [45] \"MUP\"            \"RIF\"            # }"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Custom Antimicrobials — add_custom_antimicrobials","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"add_custom_antimicrobials() can add custom antimicrobial drug names codes.","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"","code":"add_custom_antimicrobials(x)  clear_custom_antimicrobials()"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"x data.frame resembling antibiotics data set, least containing columns \"ab\" \"name\"","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"Important: Due R works, add_custom_antimicrobials() function run every R session - added antimicrobials stored sessions thus lost R exited. two ways automate process: Method 1: Save antimicrobials local remote file (can even internet). use method: Create data set structure antibiotics data set (containing least columns \"ab\" \"name\") save saveRDS() location choice, e.g. \"~/my_custom_ab.rds\", remote location. Set file location AMR_custom_ab R option: options(AMR_custom_ab = \"~/my_custom_ab.rds\"). can even remote file location, https URL. Since options saved R sessions, best save option .Rprofile file loaded start-R. , open .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"), add text save file:   Upon package load, file loaded run add_custom_antimicrobials() function. Method 2: Save antimicrobial additions directly .Rprofile file. important downside requires load AMR package every start-. use method: Edit .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"). Add text like save file:   Use clear_custom_antimicrobials() clear previously added antimicrobials.","code":"# Add custom antibiotic drug codes: options(AMR_custom_ab = \"~/my_custom_ab.rds\") # Add custom antibiotic drug codes:  library(AMR)  add_custom_antimicrobials(    data.frame(ab = \"TESTAB\",               name = \"Test Antibiotic\",               group = \"Test Group\")  )"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"","code":"# \\donttest{  # returns NA and throws a warning (which is suppressed here): suppressWarnings(   as.ab(\"testab\") ) #> Class 'ab' #> [1] <NA>  # now add a custom entry - it will be considered by as.ab() and # all ab_*() functions add_custom_antimicrobials(   data.frame(     ab = \"TESTAB\",     name = \"Test Antibiotic\",     # you can add any property present in the     # 'antibiotics' data set, such as 'group':     group = \"Test Group\"   ) ) #> ℹ Added one record to the internal antibiotics data set.  # \"testab\" is now a new antibiotic: as.ab(\"testab\") #> Class 'ab' #> [1] TESTAB ab_name(\"testab\") #> [1] \"Test Antibiotic\" ab_group(\"testab\") #> [1] \"Test Group\"  ab_info(\"testab\") #> $ab #> [1] \"TESTAB\" #>  #> $cid #> [1] NA #>  #> $name #> [1] \"Test Antibiotic\" #>  #> $group #> [1] \"Test Group\" #>  #> $atc #> [1] NA #>  #> $atc_group1 #> [1] NA #>  #> $atc_group2 #> [1] NA #>  #> $tradenames #> [1] NA #>  #> $loinc #> [1] NA #>  #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] NA #>  #> $ddd$oral$units #> [1] NA #>  #>  #> $ddd$iv #> $ddd$iv$amount #> [1] NA #>  #> $ddd$iv$units #> [1] NA #>  #>  #>    # Add Co-fluampicil, which is one of the many J01CR50 codes, see # https://www.whocc.no/ddd/list_of_ddds_combined_products/ add_custom_antimicrobials(   data.frame(     ab = \"COFLU\",     name = \"Co-fluampicil\",     atc = \"J01CR50\",     group = \"Beta-lactams/penicillins\"   ) ) #> ℹ Added one record to the internal antibiotics data set. ab_atc(\"Co-fluampicil\") #> [1] \"J01CR50\" ab_name(\"J01CR50\") #> [1] \"Co-fluampicil\"  # even antibiotic selectors work x <- data.frame(   random_column = \"some value\",   coflu = as.sir(\"S\"),   ampicillin = as.sir(\"R\") ) x #>   random_column coflu ampicillin #> 1    some value     S          R x[, betalactams()] #> ℹ For betalactams() using columns 'coflu' (co-fluampicil) and #>   'ampicillin' #>   coflu ampicillin #> 1     S          R # }"},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Custom Microorganisms — add_custom_microorganisms","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"add_custom_microorganisms() can add custom microorganisms, non-taxonomic outcome laboratory analysis.","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"","code":"add_custom_microorganisms(x)  clear_custom_microorganisms()"},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"x data.frame resembling microorganisms data set, least containing column \"genus\" (case-insensitive)","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"function fill missing taxonomy , specific taxonomic columns missing, see Examples. Important: Due R works, add_custom_microorganisms() function run every R session - added microorganisms stored sessions thus lost R exited. two ways automate process: Method 1: Using option AMR_custom_mo, preferred method. use method: Create data set structure microorganisms data set (containing least column \"genus\") save saveRDS() location choice, e.g. \"~/my_custom_mo.rds\", remote location. Set file location AMR_custom_mo R option: options(AMR_custom_mo = \"~/my_custom_mo.rds\"). can even remote file location, https URL. Since options saved R sessions, best save option .Rprofile file loaded start-R. , open .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"), add text save file:   Upon package load, file loaded run add_custom_microorganisms() function. Method 2: Loading microorganism directly .Rprofile file. important downside requires AMR package installed else method fail. use method: Edit .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"). Add text like save file:   Use clear_custom_microorganisms() clear previously added antimicrobials.","code":"# Add custom microorganism codes: options(AMR_custom_mo = \"~/my_custom_mo.rds\") # Add custom antibiotic drug codes:  AMR::add_custom_microorganisms(    data.frame(genus = \"Enterobacter\",               species = \"asburiae/cloacae\")  )"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"","code":"# \\donttest{ # a combination of species is not formal taxonomy, so # this will result in only \"Enterobacter asburiae\": mo_name(\"Enterobacter asburiae/cloacae\") #> [1] \"Enterobacter asburiae\"  # now add a custom entry - it will be considered by as.mo() and # all mo_*() functions add_custom_microorganisms(   data.frame(     genus = \"Enterobacter\",     species = \"asburiae/cloacae\"   ) ) #> ℹ Added Enterobacter asburiae/cloacae to the internal microorganisms data #>   set.  # E. asburiae/cloacae is now a new microorganism: mo_name(\"Enterobacter asburiae/cloacae\") #> [1] \"Enterobacter asburiae/cloacae\"  # its code: as.mo(\"Enterobacter asburiae/cloacae\") #> Class 'mo' #> [1] CUSTOM1_ENTRBC_A_C  # all internal algorithms will work as well: mo_name(\"Ent asburia cloacae\") #> [1] \"Enterobacter asburiae/cloacae\"  # and even the taxonomy was added based on the genus! mo_family(\"E. asburiae/cloacae\") #> [1] \"Enterobacteriaceae\" mo_gramstain(\"Enterobacter asburiae/cloacae\") #> [1] \"Gram-negative\"  mo_info(\"Enterobacter asburiae/cloacae\") #> $mo #> [1] \"CUSTOM1_ENTRBC_A_C\" #>  #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Enterobacter\" #>  #> $species #> [1] \"asburiae/cloacae\" #>  #> $subspecies #> [1] \"\" #>  #> $status #> [1] \"accepted\" #>  #> $synonyms #> NULL #>  #> $gramstain #> [1] \"Gram-negative\" #>  #> $url #> [1] \"\" #>  #> $ref #> [1] \"Self-added, 2023\" #>  #> $snomed #> character(0) #>    # the function tries to be forgiving: add_custom_microorganisms(   data.frame(     GENUS = \"BACTEROIDES / PARABACTEROIDES SLASHLINE\",     SPECIES = \"SPECIES\"   ) ) #> ℹ Added Bacteroides/Parabacteroides to the internal microorganisms data #>   set. mo_name(\"BACTEROIDES / PARABACTEROIDES\") #> [1] \"Bacteroides/Parabacteroides\" mo_rank(\"BACTEROIDES / PARABACTEROIDES\") #> [1] \"genus\"  # taxonomy still works, although a slashline genus was given as input: mo_family(\"Bacteroides/Parabacteroides\") #> [1] \"Bacteroidaceae\"   # for groups and complexes, set them as species or subspecies: add_custom_microorganisms(   data.frame(     genus = \"Citrobacter\",     species = c(\"freundii\", \"braakii complex\"),     subspecies = c(\"complex\", \"\")   ) ) #> ℹ Added Citrobacter braakii complex and Citrobacter freundii complex to the #>   internal microorganisms data set. mo_name(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"Citrobacter freundii complex\" \"Citrobacter braakii complex\"  mo_species(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"freundii complex\" \"braakii complex\"  mo_gramstain(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"Gram-negative\" \"Gram-negative\" # }"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":null,"dir":"Reference","previous_headings":"","what":"Age in Years of Individuals — age","title":"Age in Years of Individuals — age","text":"Calculates age years based reference date, system date default.","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Age in Years of Individuals — age","text":"","code":"age(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Age in Years of Individuals — age","text":"x date(s), character (vectors) coerced .POSIXlt() reference reference date(s) (defaults today), character (vectors) coerced .POSIXlt() exact logical indicate whether age calculation exact, .e. decimals. divides number days year--date (YTD) x number days year reference (either 365 366). na.rm logical indicate whether missing values removed ... arguments passed .POSIXlt(), origin","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Age in Years of Individuals — age","text":"integer (decimals) exact = FALSE, double (decimals) otherwise","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Age in Years of Individuals — age","text":"Ages 0 returned NA warning. Ages 120 give warning. function vectorises x reference, meaning either can length 1 argument larger length.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Age in Years of Individuals — age","text":"","code":"# 10 random pre-Y2K birth dates df <- data.frame(birth_date = as.Date(\"2000-01-01\") - runif(10) * 25000)  # add ages df$age <- age(df$birth_date)  # add exact ages df$age_exact <- age(df$birth_date, exact = TRUE)  # add age at millenium switch df$age_at_y2k <- age(df$birth_date, \"2000-01-01\")  df #>    birth_date age age_exact age_at_y2k #> 1  1970-09-14  52  52.35890         29 #> 2  1938-05-10  84  84.70685         61 #> 3  1969-03-15  53  53.86027         30 #> 4  1980-10-27  42  42.24110         19 #> 5  1939-10-10  83  83.28767         60 #> 6  1980-01-05  43  43.04932         19 #> 7  1965-04-17  57  57.76986         34 #> 8  1967-12-21  55  55.09041         32 #> 9  1961-05-16  61  61.69041         38 #> 10 1964-12-10  58  58.12055         35"},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Ages into Age Groups — age_groups","title":"Split Ages into Age Groups — age_groups","text":"Split ages age groups defined split argument. allows easier demographic (antimicrobial resistance) analysis.","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Ages into Age Groups — age_groups","text":"","code":"age_groups(x, split_at = c(12, 25, 55, 75), na.rm = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Ages into Age Groups — age_groups","text":"x age, e.g. calculated age() split_at values split x , defaults age groups 0-11, 12-24, 25-54, 55-74 75+. See Details. na.rm logical indicate whether missing values removed","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Ages into Age Groups — age_groups","text":"Ordered factor","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split Ages into Age Groups — age_groups","text":"split ages, input split_at argument can : numeric vector. value e.g. c(10, 20) split x 0-9, 10-19 20+. value 50 split x 0-49 50+. default split young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) elderly (75+). character: \"children\" \"kids\", equivalent : c(0, 1, 2, 4, 6, 13, 18). split 0, 1, 2-3, 4-5, 6-12, 13-17 18+. \"elderly\" \"seniors\", equivalent : c(65, 75, 85). split 0-64, 65-74, 75-84, 85+. \"fives\", equivalent : 1:20 * 5. split 0-4, 5-9, ..., 95-99, 100+. \"tens\", equivalent : 1:10 * 10. split 0-9, 10-19, ..., 90-99, 100+.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split Ages into Age Groups — age_groups","text":"","code":"ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21)  # split into 0-49 and 50+ age_groups(ages, 50) #> [1] 0-49 0-49 0-49 50+  0-49 50+  50+  0-49 0-49 #> Levels: 0-49 < 50+  # split into 0-19, 20-49 and 50+ age_groups(ages, c(20, 50)) #> [1] 0-19  0-19  0-19  50+   20-49 50+   50+   20-49 20-49 #> Levels: 0-19 < 20-49 < 50+  # split into groups of ten years age_groups(ages, 1:10 * 10) #> [1] 0-9   0-9   10-19 50-59 30-39 70-79 100+  40-49 20-29 #> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+ age_groups(ages, split_at = \"tens\") #> [1] 0-9   0-9   10-19 50-59 30-39 70-79 100+  40-49 20-29 #> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+  # split into groups of five years age_groups(ages, 1:20 * 5) #> [1] 0-4   5-9   15-19 50-54 30-34 75-79 100+  40-44 20-24 #> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+ age_groups(ages, split_at = \"fives\") #> [1] 0-4   5-9   15-19 50-54 30-34 75-79 100+  40-44 20-24 #> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+  # split specifically for children age_groups(ages, c(1, 2, 4, 6, 13, 18)) #> [1] 2-3   6-12  13-17 18+   18+   18+   18+   18+   18+   #> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+ age_groups(ages, \"children\") #> [1] 2-3   6-12  13-17 18+   18+   18+   18+   18+   18+   #> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+  # \\donttest{ # resistance of ciprofloxacin per age group if (require(\"dplyr\") && require(\"ggplot2\")) {   example_isolates %>%     filter_first_isolate() %>%     filter(mo == as.mo(\"Escherichia coli\")) %>%     group_by(age_group = age_groups(age)) %>%     select(age_group, CIP) %>%     ggplot_sir(       x = \"age_group\",       minimum = 0,       x.title = \"Age Group\",       title = \"Ciprofloxacin resistance per age group\"     ) } #> Loading required package: ggplot2 #> Including isolates from ICU.  # }"},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Antibiotic Selectors — antibiotic_class_selectors","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"functions allow filtering rows selecting columns based antibiotic test results specific antibiotic class group, without need define columns antibiotic abbreviations. short, column name resembles antimicrobial drug, picked functions matches pharmaceutical class: \"cefazolin\", \"CZO\" \"J01DB04\" picked cephalosporins().","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"","code":"ab_class(ab_class, only_sir_columns = FALSE, only_treatable = TRUE, ...)  ab_selector(filter, only_sir_columns = FALSE, only_treatable = TRUE, ...)  aminoglycosides(only_sir_columns = FALSE, only_treatable = TRUE, ...)  aminopenicillins(only_sir_columns = FALSE, ...)  antifungals(only_sir_columns = FALSE, ...)  antimycobacterials(only_sir_columns = FALSE, ...)  betalactams(only_sir_columns = FALSE, only_treatable = TRUE, ...)  carbapenems(only_sir_columns = FALSE, only_treatable = TRUE, ...)  cephalosporins(only_sir_columns = FALSE, ...)  cephalosporins_1st(only_sir_columns = FALSE, ...)  cephalosporins_2nd(only_sir_columns = FALSE, ...)  cephalosporins_3rd(only_sir_columns = FALSE, ...)  cephalosporins_4th(only_sir_columns = FALSE, ...)  cephalosporins_5th(only_sir_columns = FALSE, ...)  fluoroquinolones(only_sir_columns = FALSE, ...)  glycopeptides(only_sir_columns = FALSE, ...)  lincosamides(only_sir_columns = FALSE, ...)  lipoglycopeptides(only_sir_columns = FALSE, ...)  macrolides(only_sir_columns = FALSE, ...)  oxazolidinones(only_sir_columns = FALSE, ...)  penicillins(only_sir_columns = FALSE, ...)  polymyxins(only_sir_columns = FALSE, only_treatable = TRUE, ...)  streptogramins(only_sir_columns = FALSE, ...)  quinolones(only_sir_columns = FALSE, ...)  tetracyclines(only_sir_columns = FALSE, ...)  trimethoprims(only_sir_columns = FALSE, ...)  ureidopenicillins(only_sir_columns = FALSE, ...)  administrable_per_os(only_sir_columns = FALSE, ...)  administrable_iv(only_sir_columns = FALSE, ...)  not_intrinsic_resistant(   only_sir_columns = FALSE,   col_mo = NULL,   version_expertrules = 3.3,   ... )"},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"ab_class antimicrobial class part , \"carba\" \"carbapenems\". columns group, atc_group1 atc_group2 antibiotics data set searched (case-insensitive) value. only_sir_columns logical indicate whether columns class sir must selected (defaults FALSE), see .sir() only_treatable logical indicate whether antimicrobial drugs excluded laboratory tests (defaults TRUE), gentamicin-high (GEH) imipenem/EDTA (IPE) ... ignored, place allow future extensions filter expression evaluated antibiotics data set, name %like% \"trim\" col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can either \"3.3\", \"3.2\" \"3.1\".","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"(internally) character vector column names, additional class \"ab_selector\"","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"functions can used data set calls selecting columns filtering rows. heavily inspired Tidyverse selection helpers everything(), also work base R dplyr verbs. Nonetheless, convenient use dplyr functions select(), filter() summarise(), see Examples. columns data functions called searched known antibiotic names, abbreviations, brand names, codes (ATC, EARS-Net, , etc.) according antibiotics data set. means selector aminoglycosides() pick column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc. ab_class() function can used filter/select manually defined antibiotic class. searches results antibiotics data set within columns group, atc_group1 atc_group2. ab_selector() function can used internally filter antibiotics data set results, see Examples. allows filtering (part ) certain name, /group name even minimum DDDs oral treatment. function yields highest flexibility, also least user-friendly, since requires hard-coded filter set. administrable_per_os() administrable_iv() functions also rely antibiotics data set - antibiotic columns matched DDD (defined daily dose) resp. oral IV treatment available antibiotics data set. not_intrinsic_resistant() function can used select antibiotic columns pose intrinsic resistance microorganisms data set. example, data set contains microorganism codes names E. coli K. pneumoniae contains column \"vancomycin\", column removed (rather, unselected) using function. currently applies 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021) determine intrinsic resistance, using eucast_rules() function internally. determination, function quite slow terms performance.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"full-list-of-supported-antibiotic-classes","dir":"Reference","previous_headings":"","what":"Full list of supported (antibiotic) classes","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"aminoglycosides() can select:  amikacin (AMK), amikacin/fosfomycin (AKF), amphotericin B-high (AMH), apramycin (APR), arbekacin (ARB), astromicin (AST), bekanamycin (BEK), dibekacin (DKB), framycetin (FRM), gentamicin (GEN), gentamicin-high (GEH), habekacin (HAB), hygromycin (HYG), isepamicin (ISE), kanamycin (KAN), kanamycin-high (KAH), kanamycin/cephalexin (KAC), micronomicin (MCR), neomycin (NEO), netilmicin (NET), pentisomicin (PIM), plazomicin (PLZ), propikacin (PKA), ribostamycin (RST), sisomicin (SIS), streptoduocin (STR), streptomycin (STR1), streptomycin-high (STH), tobramycin (TOB) tobramycin-high (TOH) aminopenicillins() can select:  amoxicillin (AMX) ampicillin (AMP) antifungals() can select:  amphotericin B (AMB), anidulafungin (ANI), butoconazole (), caspofungin (CAS), ciclopirox (CIX), clotrimazole (CTR), econazole (ECO), fluconazole (FLU), flucytosine (FCT), fosfluconazole (FFL), griseofulvin (GRI), hachimycin (HCH), ibrexafungerp (IBX), isavuconazole (ISV), isoconazole (ISO), itraconazole (ITR), ketoconazole (KET), manogepix (MGX), micafungin (MIF), miconazole (MCZ), nystatin (NYS), oteseconazole (OTE), pimaricin (PMR), posaconazole (POS), rezafungin (RZF), ribociclib (RBC), sulconazole (SUC), terbinafine (TRB), terconazole (TRC) voriconazole (VOR) antimycobacterials() can select:  4-aminosalicylic acid (AMA), calcium aminosalicylate (CLA), capreomycin (CAP), clofazimine (CLF), delamanid (DLM), enviomycin (ENV), ethambutol (ETH), ethambutol/isoniazid (ETI), ethionamide (ETI1), isoniazid (INH), isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), morinamide (MRN), p-aminosalicylic acid (PAS), pretomanid (PMD), protionamide (PTH), pyrazinamide (PZA), rifabutin (RIB), rifampicin (RIF), rifampicin/ethambutol/isoniazid (REI), rifampicin/isoniazid (RFI), rifampicin/pyrazinamide/ethambutol/isoniazid (RPEI), rifampicin/pyrazinamide/isoniazid (RPI), rifamycin (RFM), rifapentine (RFP), simvastatin/fenofibrate (SMF), sodium aminosalicylate (SDA), streptomycin/isoniazid (STI), terizidone (TRZ), thioacetazone (TAT), thioacetazone/isoniazid (THI1), tiocarlide (TCR) viomycin (VIO) betalactams() can select:  amoxicillin (AMX), amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin (AMP), ampicillin/sulbactam (SAM), apalcillin (APL), aspoxicillin (APX), avibactam (AVB), azidocillin (AZD), azlocillin (AZL), aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), biapenem (BIA), carbenicillin (CRB), carindacillin (CRN), cefacetrile (CAC), cefaclor (CEC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefamandole (MAN), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefepime (FEP), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening (FOX1), cefozopran (ZOP), cefpimizole (CFZ), cefpiramide (CPM), cefpirome (CPO), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefprozil (CPR), cefquinome (CEQ), cefroxadine (CRD), cefsulodin (CFS), cefsumide (CSU), ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftezole (CTL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/tazobactam (CZT), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB), cefuroxime (CXM), cefuroxime axetil (CXA), cephradine (CED), ciclacillin (CIC), clometocillin (CLM), cloxacillin (CLO), dicloxacillin (DIC), doripenem (DOR), epicillin (EPC), ertapenem (ETP), flucloxacillin (FLC), hetacillin (HET), imipenem (IPM), imipenem/EDTA (IPE), imipenem/relebactam (IMR), latamoxef (LTM), lenampicillin (LEN), loracarbef (LOR), mecillinam (MEC), meropenem (MEM), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), metampicillin (MTM), meticillin (MET), mezlocillin (MEZ), mezlocillin/sulbactam (MSU), nacubactam (NAC), nafcillin (NAF), oxacillin (OXA), panipenem (PAN), penamecillin (PNM), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), pheneticillin (PHE), phenoxymethylpenicillin (PHN), piperacillin (PIP), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), piridicillin (PRC), pivampicillin (PVM), pivmecillinam (PME), procaine benzylpenicillin (PRB), propicillin (PRP), razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), sarmoxicillin (SRX), sulbactam (SUL), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), tazobactam (TAZ), tebipenem (TBP), temocillin (TEM), ticarcillin (TIC) ticarcillin/clavulanic acid (TCC) carbapenems() can select:  biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA) tebipenem (TBP) cephalosporins() can select:  cefacetrile (CAC), cefaclor (CEC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefamandole (MAN), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefepime (FEP), cefepime/clavulanic acid (CPC), cefepime/tazobactam (FPT), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening (FOX1), cefozopran (ZOP), cefpimizole (CFZ), cefpiramide (CPM), cefpirome (CPO), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefprozil (CPR), cefquinome (CEQ), cefroxadine (CRD), cefsulodin (CFS), cefsumide (CSU), ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftezole (CTL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/tazobactam (CZT), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB), cefuroxime (CXM), cefuroxime axetil (CXA), cephradine (CED), latamoxef (LTM) loracarbef (LOR) cephalosporins_1st() can select:  cefacetrile (CAC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefroxadine (CRD), ceftezole (CTL) cephradine (CED) cephalosporins_2nd() can select:  cefaclor (CEC), cefamandole (MAN), cefmetazole (CMZ), cefonicid (CID), ceforanide (CND), cefotetan (CTT), cefotiam (CTF), cefoxitin (FOX), cefoxitin screening (FOX1), cefprozil (CPR), cefuroxime (CXM), cefuroxime axetil (CXA) loracarbef (LOR) cephalosporins_3rd() can select:  cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefetamet (CAT), cefetamet pivoxil (CPI), cefixime (CFM), cefmenoxime (CMX), cefodizime (DIZ), cefoperazone (CFP), cefoperazone/sulbactam (CSL), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotiam hexetil (CHE), cefovecin (FOV), cefpimizole (CFZ), cefpiramide (CPM), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefsulodin (CFS), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB) latamoxef (LTM) cephalosporins_4th() can select:  cefepime (FEP), cefepime/clavulanic acid (CPC), cefepime/tazobactam (FPT), cefetecol (CCL), cefoselis (CSE), cefozopran (ZOP), cefpirome (CPO) cefquinome (CEQ) cephalosporins_5th() can select:  ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftobiprole (BPR), ceftobiprole medocaril (CFM1) ceftolozane/tazobactam (CZT) fluoroquinolones() can select:  besifloxacin (BES), ciprofloxacin (CIP), clinafloxacin (CLX), danofloxacin (DAN), delafloxacin (DFX), difloxacin (DIF), enoxacin (ENX), enrofloxacin (ENR), finafloxacin (FIN), fleroxacin (FLE), garenoxacin (GRN), gatifloxacin (GAT), gemifloxacin (GEM), grepafloxacin (GRX), lascufloxacin (LSC), levofloxacin (LVX), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nifuroquine (NIF), norfloxacin (), ofloxacin (OFX), orbifloxacin (ORB), pazufloxacin (PAZ), pefloxacin (PEF), pradofloxacin (PRA), premafloxacin (PRX), prulifloxacin (PRU), rufloxacin (RFL), sarafloxacin (SAR), sitafloxacin (SIT), sparfloxacin (SPX), temafloxacin (TMX), tilbroquinol (TBQ), tioxacin (TXC), tosufloxacin (TFX) trovafloxacin (TVA) glycopeptides() can select:  avoparcin (AVO), dalbavancin (DAL), norvancomycin (NVA), oritavancin (ORI), ramoplanin (RAM), teicoplanin (TEC), teicoplanin-macromethod (TCM), telavancin (TLV), vancomycin (VAN) vancomycin-macromethod (VAM) lincosamides() can select:  acetylmidecamycin (ACM), acetylspiramycin (ASP), clindamycin (CLI), gamithromycin (GAM), kitasamycin (KIT), lincomycin (LIN), meleumycin (MEL), nafithromycin (ZWK), pirlimycin (PRL), primycin (PRM), solithromycin (SOL), tildipirosin (TIP), tilmicosin (TIL), tulathromycin (TUL), tylosin (TYL) tylvalosin (TYL1) lipoglycopeptides() can select:  dalbavancin (DAL), oritavancin (ORI) telavancin (TLV) macrolides() can select:  acetylmidecamycin (ACM), acetylspiramycin (ASP), azithromycin (AZM), clarithromycin (CLR), dirithromycin (DIR), erythromycin (ERY), flurithromycin (FLR1), gamithromycin (GAM), josamycin (JOS), kitasamycin (KIT), meleumycin (MEL), midecamycin (MID), miocamycin (MCM), nafithromycin (ZWK), oleandomycin (OLE), pirlimycin (PRL), primycin (PRM), rokitamycin (ROK), roxithromycin (RXT), solithromycin (SOL), spiramycin (SPI), telithromycin (TLT), tildipirosin (TIP), tilmicosin (TIL), troleandomycin (TRL), tulathromycin (TUL), tylosin (TYL) tylvalosin (TYL1) oxazolidinones() can select:  cadazolid (CDZ), cycloserine (CYC), linezolid (LNZ), tedizolid (TZD) thiacetazone (THA) penicillins() can select:  amoxicillin (AMX), amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin (AMP), ampicillin/sulbactam (SAM), apalcillin (APL), aspoxicillin (APX), avibactam (AVB), azidocillin (AZD), azlocillin (AZL), aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), carbenicillin (CRB), carindacillin (CRN), cefepime/nacubactam (FNC), ciclacillin (CIC), clometocillin (CLM), cloxacillin (CLO), dicloxacillin (DIC), epicillin (EPC), flucloxacillin (FLC), hetacillin (HET), lenampicillin (LEN), mecillinam (MEC), metampicillin (MTM), meticillin (MET), mezlocillin (MEZ), mezlocillin/sulbactam (MSU), nacubactam (NAC), nafcillin (NAF), oxacillin (OXA), penamecillin (PNM), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), pheneticillin (PHE), phenoxymethylpenicillin (PHN), piperacillin (PIP), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), piridicillin (PRC), pivampicillin (PVM), pivmecillinam (PME), procaine benzylpenicillin (PRB), propicillin (PRP), sarmoxicillin (SRX), sulbactam (SUL), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), tazobactam (TAZ), temocillin (TEM), ticarcillin (TIC) ticarcillin/clavulanic acid (TCC) polymyxins() can select:  colistin (COL), polymyxin B (PLB) polymyxin B/polysorbate 80 (POP) quinolones() can select:  besifloxacin (BES), cinoxacin (CIN), ciprofloxacin (CIP), clinafloxacin (CLX), danofloxacin (DAN), delafloxacin (DFX), difloxacin (DIF), enoxacin (ENX), enrofloxacin (ENR), finafloxacin (FIN), fleroxacin (FLE), flumequine (FLM), garenoxacin (GRN), gatifloxacin (GAT), gemifloxacin (GEM), grepafloxacin (GRX), lascufloxacin (LSC), levofloxacin (LVX), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nalidixic acid (NAL), nemonoxacin (NEM), nifuroquine (NIF), nitroxoline (NTR), norfloxacin (), ofloxacin (OFX), orbifloxacin (ORB), oxolinic acid (OXO), pazufloxacin (PAZ), pefloxacin (PEF), pipemidic acid (PPA), piromidic acid (PIR), pradofloxacin (PRA), premafloxacin (PRX), prulifloxacin (PRU), rosoxacin (ROS), rufloxacin (RFL), sarafloxacin (SAR), sitafloxacin (SIT), sparfloxacin (SPX), temafloxacin (TMX), tilbroquinol (TBQ), tioxacin (TXC), tosufloxacin (TFX) trovafloxacin (TVA) streptogramins() can select:  pristinamycin (PRI) quinupristin/dalfopristin (QDA) tetracyclines() can select:  cetocycline (CTO), chlortetracycline (CTE), clomocycline (CLM1), demeclocycline (DEM), doxycycline (DOX), eravacycline (ERV), lymecycline (LYM), metacycline (MTC), minocycline (MNO), omadacycline (OMC), oxytetracycline (OXY), penimepicycline (PNM1), rolitetracycline (RLT), sarecycline (SRC), tetracycline (TCY) tigecycline (TGC) trimethoprims() can select:  brodimoprim (BDP), sulfadiazine (SDI), sulfadiazine/tetroxoprim (SLT), sulfadiazine/trimethoprim (SLT1), sulfadimethoxine (SUD), sulfadimidine (SDM), sulfadimidine/trimethoprim (SLT2), sulfafurazole (SLF), sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO), sulfamerazine (SLF3), sulfamerazine/trimethoprim (SLT3), sulfamethizole (SLF4), sulfamethoxazole (SMX), sulfamethoxypyridazine (SLF5), sulfametomidine (SLF6), sulfametoxydiazine (SLF7), sulfametrole/trimethoprim (SLT4), sulfamoxole (SLF8), sulfamoxole/trimethoprim (SLT5), sulfanilamide (SLF9), sulfaperin (SLF10), sulfaphenazole (SLF11), sulfapyridine (SLF12), sulfathiazole (SUT), sulfathiourea (SLF13), trimethoprim (TMP) trimethoprim/sulfamethoxazole (SXT) ureidopenicillins() can select:  azlocillin (AZL), mezlocillin (MEZ), piperacillin (PIP) piperacillin/tazobactam (TZP)","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates. example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>  # base R ------------------------------------------------------------------  # select columns 'IPM' (imipenem) and 'MEM' (meropenem) example_isolates[, carbapenems()] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 2,000 × 2 #>    IPM   MEM   #>    <sir> <sir> #>  1 NA    NA    #>  2 NA    NA    #>  3 NA    NA    #>  4 NA    NA    #>  5 NA    NA    #>  6 NA    NA    #>  7 NA    NA    #>  8 NA    NA    #>  9 NA    NA    #> 10 NA    NA    #> # … with 1,990 more rows  # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB' example_isolates[, c(\"mo\", aminoglycosides())] #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 2,000 × 5 #>    mo           GEN   TOB   AMK   KAN   #>    <mo>         <sir> <sir> <sir> <sir> #>  1 B_ESCHR_COLI NA    NA    NA    NA    #>  2 B_ESCHR_COLI NA    NA    NA    NA    #>  3 B_STPHY_EPDR NA    NA    NA    NA    #>  4 B_STPHY_EPDR NA    NA    NA    NA    #>  5 B_STPHY_EPDR NA    NA    NA    NA    #>  6 B_STPHY_EPDR NA    NA    NA    NA    #>  7 B_STPHY_AURS NA    S     NA    NA    #>  8 B_STPHY_AURS NA    S     NA    NA    #>  9 B_STPHY_EPDR NA    NA    NA    NA    #> 10 B_STPHY_EPDR NA    NA    NA    NA    #> # … with 1,990 more rows  # select only antibiotic columns with DDDs for oral treatment example_isolates[, administrable_per_os()] #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #>   (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #>   'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #>   (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #>   (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #>   (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #>   (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #>   (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin) and 'RIF' #>   (rifampicin) #> # A tibble: 2,000 × 23 #>    OXA   FLC   AMX   AMC   AMP   CXM   KAN   TMP   NIT   FOS   LNZ   CIP   MFX   #>    <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>  1 NA    NA    NA    I     NA    I     NA    R     NA    NA    R     NA    NA    #>  2 NA    NA    NA    I     NA    I     NA    R     NA    NA    R     NA    NA    #>  3 NA    R     NA    NA    NA    R     NA    S     NA    NA    NA    NA    NA    #>  4 NA    R     NA    NA    NA    R     NA    S     NA    NA    NA    NA    NA    #>  5 NA    R     NA    NA    NA    R     NA    R     NA    NA    NA    NA    NA    #>  6 NA    R     NA    NA    NA    R     NA    R     NA    NA    NA    NA    NA    #>  7 NA    S     R     S     R     S     NA    R     NA    NA    NA    NA    NA    #>  8 NA    S     R     S     R     S     NA    R     NA    NA    NA    NA    NA    #>  9 NA    R     NA    NA    NA    R     NA    S     NA    NA    NA    S     NA    #> 10 NA    S     NA    NA    NA    S     NA    S     NA    NA    NA    S     NA    #> # … with 1,990 more rows, and 10 more variables: VAN <sir>, TCY <sir>, #> #   DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, RIF <sir>  # filter using any() or all() example_isolates[any(carbapenems() == \"R\"), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 55 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  2 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  3 2004-11-03 D65308     80 F      ICU      B_STNTR_MLTP R     NA    NA    R     #>  4 2005-04-21 452212     82 F      ICU      B_ENTRC      NA    NA    NA    NA    #>  5 2005-04-22 452212     82 F      ICU      B_ENTRC      NA    NA    NA    NA    #>  6 2005-04-22 452212     82 F      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  7 2007-02-21 8BBC46     61 F      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  8 2007-12-15 401043     72 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  9 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL R     NA    NA    NA    #> 10 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL R     NA    NA    NA    #> # … with 45 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> subset(example_isolates, any(carbapenems() == \"R\")) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 55 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  2 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  3 2004-11-03 D65308     80 F      ICU      B_STNTR_MLTP R     NA    NA    R     #>  4 2005-04-21 452212     82 F      ICU      B_ENTRC      NA    NA    NA    NA    #>  5 2005-04-22 452212     82 F      ICU      B_ENTRC      NA    NA    NA    NA    #>  6 2005-04-22 452212     82 F      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  7 2007-02-21 8BBC46     61 F      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  8 2007-12-15 401043     72 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  9 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL R     NA    NA    NA    #> 10 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL R     NA    NA    NA    #> # … with 45 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir>  # filter on any or all results in the carbapenem columns (i.e., IPM, MEM): example_isolates[any(carbapenems()), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Filtering any of columns 'IPM' and 'MEM' to contain value \"S\", \"I\" or \"R\" #> # A tibble: 962 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  4 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  5 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS R     NA    NA    R     #>  6 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS R     NA    NA    R     #>  7 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS R     NA    NA    R     #>  8 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN R     NA    NA    R     #>  9 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN R     NA    NA    R     #> 10 2002-03-08 4FC193     69 M      Clinical B_ESCHR_COLI R     NA    NA    R     #> # … with 952 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> example_isolates[all(carbapenems()), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Filtering all of columns 'IPM' and 'MEM' to contain value \"S\", \"I\" or \"R\" #> # A tibble: 756 × 46 #>    date       patient   age gender ward    mo            PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>   <mo>          <sir> <sir> <sir> <sir> #>  1 2002-04-14 F30196     73 M      Outpat… B_STRPT_GRPB  S     NA    S     S     #>  2 2003-04-08 114570     74 M      ICU     B_STRPT_PYGN  S     NA    S     S     #>  3 2003-04-08 114570     74 M      ICU     B_STRPT_GRPA  S     NA    S     S     #>  4 2003-04-08 114570     74 M      ICU     B_STRPT_GRPA  S     NA    S     S     #>  5 2003-08-14 F71508      0 F      Clinic… B_STRPT_GRPB  S     NA    S     S     #>  6 2003-10-16 650870     63 F      ICU     B_ESCHR_COLI  R     NA    NA    R     #>  7 2003-10-20 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R     #>  8 2003-10-20 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R     #>  9 2003-11-04 2FC253     87 F      ICU     B_ESCHR_COLI  R     NA    NA    NA    #> 10 2003-11-04 2FC253     87 F      ICU     B_ESCHR_COLI  R     NA    NA    NA    #> # … with 746 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir>  # filter with multiple antibiotic selectors using c() example_isolates[all(c(carbapenems(), aminoglycosides()) == \"R\"), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 26 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2004-11-03 D65308     80 F      ICU      B_STNTR_MLTP R     NA    NA    R     #>  2 2005-04-22 452212     82 F      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  3 2007-02-21 8BBC46     61 F      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  4 2007-12-15 401043     72 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  5 2008-12-06 501361     43 F      Clinical B_STNTR_MLTP R     NA    NA    R     #>  6 2011-05-09 207325     82 F      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  7 2012-03-12 582258     80 M      ICU      B_STPHY_CONS R     R     R     R     #>  8 2012-05-19 C25552     89 F      Outpati… B_STPHY_CONS R     R     R     R     #>  9 2012-07-17 F05015     83 M      ICU      B_STPHY_CONS R     R     R     R     #> 10 2012-07-20 404299     66 F      Clinical B_STPHY_CONS R     R     R     R     #> # … with 16 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir>  # filter + select in one go: get penicillins in carbapenems-resistant strains example_isolates[any(carbapenems() == \"R\"), penicillins()] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin) and 'TZP' #>   (piperacillin/tazobactam) #> # A tibble: 55 × 7 #>    PEN   OXA   FLC   AMX   AMC   AMP   TZP   #>    <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>  1 NA    NA    NA    NA    NA    NA    NA    #>  2 NA    NA    NA    NA    NA    NA    NA    #>  3 R     NA    NA    R     R     R     R     #>  4 NA    NA    NA    NA    NA    NA    R     #>  5 NA    NA    NA    NA    NA    NA    R     #>  6 NA    NA    NA    NA    NA    NA    R     #>  7 NA    NA    NA    NA    NA    NA    R     #>  8 NA    NA    NA    NA    NA    NA    R     #>  9 R     NA    NA    NA    S     NA    S     #> 10 R     NA    NA    NA    S     NA    S     #> # … with 45 more rows  # You can combine selectors with '&' to be more specific. For example, # penicillins() would select benzylpenicillin ('peni G') and # administrable_per_os() would select erythromycin. Yet, when combined these # drugs are both omitted since benzylpenicillin is not administrable per os # and erythromycin is not a penicillin: example_isolates[, penicillins() & administrable_per_os()] #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin) and 'TZP' #>   (piperacillin/tazobactam) #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #>   (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #>   'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #>   (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #>   (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #>   (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #>   (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #>   (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin) and 'RIF' #>   (rifampicin) #> # A tibble: 2,000 × 5 #>    OXA   FLC   AMX   AMC   AMP   #>    <sir> <sir> <sir> <sir> <sir> #>  1 NA    NA    NA    I     NA    #>  2 NA    NA    NA    I     NA    #>  3 NA    R     NA    NA    NA    #>  4 NA    R     NA    NA    NA    #>  5 NA    R     NA    NA    NA    #>  6 NA    R     NA    NA    NA    #>  7 NA    S     R     S     R     #>  8 NA    S     R     S     R     #>  9 NA    R     NA    NA    NA    #> 10 NA    S     NA    NA    NA    #> # … with 1,990 more rows  # ab_selector() applies a filter in the `antibiotics` data set and is thus very # flexible. For instance, to select antibiotic columns with an oral DDD of at # least 1 gram: example_isolates[, ab_selector(oral_ddd > 1 & oral_units == \"g\")] #> ℹ For ab_selector(oral_ddd > 1 & oral_units == \"g\") using columns 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'KAN' (kanamycin), 'FOS' #>   (fosfomycin), 'LNZ' (linezolid), 'VAN' (vancomycin), 'ERY' (erythromycin), #>   'CLI' (clindamycin), 'MTR' (metronidazole) and 'CHL' (chloramphenicol) #> # A tibble: 2,000 × 13 #>    OXA   FLC   AMX   AMC   AMP   KAN   FOS   LNZ   VAN   ERY   CLI   MTR   CHL   #>    <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>  1 NA    NA    NA    I     NA    NA    NA    R     R     R     R     NA    NA    #>  2 NA    NA    NA    I     NA    NA    NA    R     R     R     R     NA    NA    #>  3 NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA    #>  4 NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA    #>  5 NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA    #>  6 NA    R     NA    NA    NA    NA    NA    NA    S     R     R     NA    NA    #>  7 NA    S     R     S     R     NA    NA    NA    S     S     NA    NA    NA    #>  8 NA    S     R     S     R     NA    NA    NA    S     S     NA    NA    NA    #>  9 NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA    #> 10 NA    S     NA    NA    NA    NA    NA    NA    S     S     NA    NA    NA    #> # … with 1,990 more rows  # dplyr ------------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) {   # get AMR for all aminoglycosides e.g., per ward:   example_isolates %>%     group_by(ward) %>%     summarise(across(aminoglycosides(), resistance)) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> Warning: Introducing NA: only 23 results available for KAN in group: ward = #> \"Outpatient\" (minimum = 30). #> # A tibble: 3 × 5 #>   ward         GEN   TOB   AMK   KAN #>   <chr>      <dbl> <dbl> <dbl> <dbl> #> 1 Clinical   0.229 0.315 0.626     1 #> 2 ICU        0.290 0.400 0.662     1 #> 3 Outpatient 0.2   0.368 0.605    NA if (require(\"dplyr\")) {   # You can combine selectors with '&' to be more specific:   example_isolates %>%     select(penicillins() & administrable_per_os()) } #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin) and 'TZP' #>   (piperacillin/tazobactam) #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #>   (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #>   'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #>   (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #>   (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #>   (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #>   (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #>   (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin) and 'RIF' #>   (rifampicin) #> # A tibble: 2,000 × 5 #>    OXA   FLC   AMX   AMC   AMP   #>    <sir> <sir> <sir> <sir> <sir> #>  1 NA    NA    NA    I     NA    #>  2 NA    NA    NA    I     NA    #>  3 NA    R     NA    NA    NA    #>  4 NA    R     NA    NA    NA    #>  5 NA    R     NA    NA    NA    #>  6 NA    R     NA    NA    NA    #>  7 NA    S     R     S     R     #>  8 NA    S     R     S     R     #>  9 NA    R     NA    NA    NA    #> 10 NA    S     NA    NA    NA    #> # … with 1,990 more rows if (require(\"dplyr\")) {   # get AMR for only drugs that matter - no intrinsic resistance:   example_isolates %>%     filter(mo_genus() %in% c(\"Escherichia\", \"Klebsiella\")) %>%     group_by(ward) %>%     summarise(across(not_intrinsic_resistant(), resistance)) } #> ℹ Using column 'mo' as input for mo_genus() #> ℹ For not_intrinsic_resistant() removing columns 'PEN' #>   (benzylpenicillin), 'LNZ' (linezolid), 'VAN' (vancomycin), 'TEC' #>   (teicoplanin), 'ERY' (erythromycin), 'CLI' (clindamycin), 'AZM' #>   (azithromycin) and 'RIF' (rifampicin) #> Warning: Introducing NA: no results available for OXA in group: ward = \"Clinical\" #> (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: ward = \"Clinical\" #> (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: only 25 results available for AMX in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for AMC in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for AMP in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 24 results available for TZP in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 23 results available for CZO in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: only 10 results available for CZO in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 20 results available for FEP in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for CXM in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 22 results available for FOX in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for CTX in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for CAZ in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for CRO in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for GEN in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for TOB in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 15 results available for AMK in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: ward = \"Clinical\" #> (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: only 22 results available for TMP in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for SXT in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for NIT in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for FOS in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: only 2 results available for FOS in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for CIP in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 15 results available for MFX in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: only 4 results available for MFX in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 5 results available for TCY in group: ward = #> \"Clinical\" (minimum = 30). #> Warning: Introducing NA: no results available for TCY in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for TCY in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: only 13 results available for TGC in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: only 2 results available for TGC in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: ward = \"Clinical\" #> (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: only 24 results available for IPM in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for MEM in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MTR in group: ward = #> \"Clinical\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: ward = \"Clinical\" #> (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: only 9 results available for COL in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: ward = \"Clinical\" #> (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: ward = \"Outpatient\" #> (minimum = 30). #> # A tibble: 3 × 33 #>   ward    OXA   FLC    AMX    AMC    AMP     TZP     CZO     FEP     CXM     FOX #>   <chr> <dbl> <dbl>  <dbl>  <dbl>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl> #> 1 Clin…    NA    NA  0.606  0.121  0.606  0.0504  0.0656  0.0159  0.0622  0.0648 #> 2 ICU      NA    NA  0.535  0.172  0.535  0.119  NA       0.0722  0.0828  0.0992 #> 3 Outp…    NA    NA NA     NA     NA     NA      NA      NA      NA      NA      #> # … with 22 more variables: CTX <dbl>, CAZ <dbl>, CRO <dbl>, GEN <dbl>, #> #   TOB <dbl>, AMK <dbl>, KAN <dbl>, TMP <dbl>, SXT <dbl>, NIT <dbl>, #> #   FOS <dbl>, CIP <dbl>, MFX <dbl>, TCY <dbl>, TGC <dbl>, DOX <dbl>, #> #   IPM <dbl>, MEM <dbl>, MTR <dbl>, CHL <dbl>, COL <dbl>, MUP <dbl> if (require(\"dplyr\")) {   # get susceptibility for antibiotics whose name contains \"trim\":   example_isolates %>%     filter(first_isolate()) %>%     group_by(ward) %>%     summarise(across(ab_selector(name %like% \"trim\"), susceptibility)) } #> Including isolates from ICU. #> ℹ For ab_selector(name %like% \"trim\") using columns 'TMP' (trimethoprim) #>   and 'SXT' (trimethoprim/sulfamethoxazole) #> # A tibble: 3 × 3 #>   ward         TMP   SXT #>   <chr>      <dbl> <dbl> #> 1 Clinical   0.627 0.808 #> 2 ICU        0.549 0.778 #> 3 Outpatient 0.667 0.821 if (require(\"dplyr\")) {   # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):   example_isolates %>%     select(carbapenems()) } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 2,000 × 2 #>    IPM   MEM   #>    <sir> <sir> #>  1 NA    NA    #>  2 NA    NA    #>  3 NA    NA    #>  4 NA    NA    #>  5 NA    NA    #>  6 NA    NA    #>  7 NA    NA    #>  8 NA    NA    #>  9 NA    NA    #> 10 NA    NA    #> # … with 1,990 more rows if (require(\"dplyr\")) {   # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':   example_isolates %>%     select(mo, aminoglycosides()) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 2,000 × 5 #>    mo           GEN   TOB   AMK   KAN   #>    <mo>         <sir> <sir> <sir> <sir> #>  1 B_ESCHR_COLI NA    NA    NA    NA    #>  2 B_ESCHR_COLI NA    NA    NA    NA    #>  3 B_STPHY_EPDR NA    NA    NA    NA    #>  4 B_STPHY_EPDR NA    NA    NA    NA    #>  5 B_STPHY_EPDR NA    NA    NA    NA    #>  6 B_STPHY_EPDR NA    NA    NA    NA    #>  7 B_STPHY_AURS NA    S     NA    NA    #>  8 B_STPHY_AURS NA    S     NA    NA    #>  9 B_STPHY_EPDR NA    NA    NA    NA    #> 10 B_STPHY_EPDR NA    NA    NA    NA    #> # … with 1,990 more rows if (require(\"dplyr\")) {   # any() and all() work in dplyr's filter() too:   example_isolates %>%     filter(       any(aminoglycosides() == \"R\"),       all(cephalosporins_2nd() == \"R\")     ) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> ℹ For cephalosporins_2nd() using columns 'CXM' (cefuroxime) and 'FOX' #>   (cefoxitin) #> # A tibble: 112 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  2 2002-03-16 4FC193     69 M      Clinical B_PSDMN_AERG R     NA    NA    R     #>  3 2002-04-08 130252     78 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #>  4 2002-06-23 798871     82 M      Clinical B_ENTRC_FCLS NA    NA    NA    NA    #>  5 2002-06-23 798871     82 M      Clinical B_ENTRC_FCLS NA    NA    NA    NA    #>  6 2002-07-21 955940     82 F      Clinical B_PSDMN_AERG R     NA    NA    R     #>  7 2002-07-21 955940     82 F      Clinical B_PSDMN_AERG R     NA    NA    R     #>  8 2003-08-13 F35553     52 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #>  9 2003-09-05 F35553     52 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #> 10 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #> # … with 102 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # also works with c():   example_isolates %>%     filter(any(c(carbapenems(), aminoglycosides()) == \"R\")) } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 531 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  2 2002-03-16 4FC193     69 M      Clinical B_PSDMN_AERG R     NA    NA    R     #>  3 2002-03-17 B30560     78 M      Clinical B_STPHY_CONS R     NA    R     R     #>  4 2002-04-04 E61143     67 M      Clinical B_STRPT_SNGN S     NA    NA    S     #>  5 2002-04-08 130252     78 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #>  6 2002-04-14 F30196     73 M      Outpati… B_STRPT_GRPB S     NA    S     S     #>  7 2002-05-07 D91570     83 M      Clinical B_STPHY_CONS R     NA    R     R     #>  8 2002-05-07 D91570     83 M      Clinical B_STPHY_CONS R     NA    R     R     #>  9 2002-05-14 077552     86 F      Clinical B_STRPT_PNMN S     NA    NA    S     #> 10 2002-05-14 077552     86 F      Clinical B_STRPT_PNMN S     NA    NA    S     #> # … with 521 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # not setting any/all will automatically apply all():   example_isolates %>%     filter(aminoglycosides() == \"R\") } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> ℹ Assuming a filter on all 4 aminoglycosides. Wrap around all() or #>   any() to prevent this note. #> # A tibble: 427 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  2 2002-03-17 B30560     78 M      Clinical B_STPHY_CONS R     NA    R     R     #>  3 2002-04-04 E61143     67 M      Clinical B_STRPT_SNGN S     NA    NA    S     #>  4 2002-04-08 130252     78 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #>  5 2002-04-14 F30196     73 M      Outpati… B_STRPT_GRPB S     NA    S     S     #>  6 2002-05-07 D91570     83 M      Clinical B_STPHY_CONS R     NA    R     R     #>  7 2002-05-07 D91570     83 M      Clinical B_STPHY_CONS R     NA    R     R     #>  8 2002-05-14 077552     86 F      Clinical B_STRPT_PNMN S     NA    NA    S     #>  9 2002-05-14 077552     86 F      Clinical B_STRPT_PNMN S     NA    NA    S     #> 10 2002-05-16 D25302     65 F      ICU      B_STRPT_ANGN S     NA    NA    S     #> # … with 417 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # this will select columns 'mo' and all antimycobacterial drugs ('RIF'):   example_isolates %>%     select(mo, ab_class(\"mycobact\")) } #> ℹ For ab_class(\"mycobact\") using column 'RIF' (rifampicin) #> # A tibble: 2,000 × 2 #>    mo           RIF   #>    <mo>         <sir> #>  1 B_ESCHR_COLI R     #>  2 B_ESCHR_COLI R     #>  3 B_STPHY_EPDR NA    #>  4 B_STPHY_EPDR NA    #>  5 B_STPHY_EPDR NA    #>  6 B_STPHY_EPDR NA    #>  7 B_STPHY_AURS NA    #>  8 B_STPHY_AURS NA    #>  9 B_STPHY_EPDR NA    #> 10 B_STPHY_EPDR NA    #> # … with 1,990 more rows if (require(\"dplyr\")) {   # get bug/drug combinations for only glycopeptides in Gram-positives:   example_isolates %>%     filter(mo_is_gram_positive()) %>%     select(mo, glycopeptides()) %>%     bug_drug_combinations() %>%     format() } #> ℹ Using column 'mo' as input for mo_is_gram_positive() #> ℹ For glycopeptides() using columns 'VAN' (vancomycin) and 'TEC' #>   (teicoplanin) #> # A tibble: 2 × 8 #>   Group           Drug             CoNS  E. fa…¹ S. au…² S. ep…³ S. ho…⁴ S. pn…⁵ #>   <chr>           <chr>            <chr> <chr>   <chr>   <chr>   <chr>   <chr>   #> 1 \"Glycopeptides\" Teicoplanin (TE… \"\"    \"\"      \" 0.0%… \"64.1%… \" 6.8%… \"\"      #> 2 \"\"              Vancomycin (VAN… \" 0.… \" 0.0%… \" 0.0%… \" 0.0%… \" 0.0%… \" 0.0%… #> # … with abbreviated variable names ¹​`E. faecalis`, ²​`S. aureus`, #> #   ³​`S. epidermidis`, ⁴​`S. hominis`, ⁵​`S. pneumoniae` if (require(\"dplyr\")) {   data.frame(     some_column = \"some_value\",     J01CA01 = \"S\"   ) %>% # ATC code of ampicillin     select(penicillins()) # only the 'J01CA01' column will be selected } #> ℹ For penicillins() using column 'J01CA01' (ampicillin) #>   J01CA01 #> 1       S if (require(\"dplyr\")) {   # with recent versions of dplyr this is all equal:   x <- example_isolates[carbapenems() == \"R\", ]   y <- example_isolates %>% filter(carbapenems() == \"R\")   z <- example_isolates %>% filter(if_all(carbapenems(), ~ .x == \"R\"))   identical(x, y) && identical(y, z) } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Assuming a filter on all 2 carbapenems. Wrap around all() or any() to #>   prevent this note. #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Assuming a filter on all 2 carbapenems. Wrap around all() or any() to #>   prevent this note. #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> [1] TRUE # }"},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Sets with 603 Antimicrobial Drugs — antibiotics","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"Two data sets containing antibiotics/antimycotics antivirals. Use .ab() one ab_* functions retrieve values antibiotics data set. Three identifiers included data set: antibiotic ID (ab, primarily used package) defined WHONET/EARS-Net, ATC code (atc) defined , Compound ID (cid) found PubChem. properties data set derived one codes. Note drugs multiple ATC codes.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"","code":"antibiotics  antivirals"},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"object class tbl_df (inherits tbl, data.frame) 120 rows 11 columns.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"for-the-antibiotics-data-set-a-tibble-with-observations-and-variables-","dir":"Reference","previous_headings":"","what":"For the antibiotics data set: a tibble with 483 observations and 14 variables:","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"ab Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available cid Compound ID found PubChem name Official name used WHONET/EARS-Net group short concise group name, based WHONET WHOCC definitions atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC, like J01CR02 atc_group1 Official pharmacological subgroup (3rd level ATC code) defined WHOCC, like \"Macrolides, lincosamides streptogramins\" atc_group2 Official chemical subgroup (4th level ATC code) defined WHOCC, like \"Macrolides\" abbr List abbreviations used many countries, also antibiotic susceptibility testing (AST) synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment, currently available 174 drugs oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral (intravenous) treatment, currently available 146 drugs iv_units Units iv_ddd loinc LOINC codes (Logical Observation Identifiers Names Codes) associated name antimicrobial drug. Use ab_loinc() retrieve quickly, see ab_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"for-the-antivirals-data-set-a-tibble-with-observations-and-variables-","dir":"Reference","previous_headings":"","what":"For the antivirals data set: a tibble with 120 observations and 11 variables:","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"av Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name used WHONET/EARS-Net atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC cid Compound ID found PubChem atc_group Official pharmacological subgroup (3rd level ATC code) defined WHOCC synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral treatment iv_units Units iv_ddd loinc LOINC codes (Logical Observation Identifiers Names Codes) associated name antimicrobial drug.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology (WHOCC): https://www.whocc./atc_ddd_index/ Logical Observation Identifiers Names Codes (LOINC), Version 2.73 (8 August, 2022). Accessed https://loinc.org 30 October, 2022. European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"Properties based ATC code available ATC available. properties : atc_group1, atc_group2, oral_ddd, oral_units, iv_ddd iv_units. Synonyms (.e. trade names) derived PubChem Compound ID (column cid) consequently available CID available.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"Like data sets package, data sets publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"","code":"antibiotics #> # A tibble: 483 × 14 #>    ab        cid name        group atc   atc_g…¹ atc_g…² abbre…³ synon…⁴ oral_…⁵ #>    <ab>    <dbl> <chr>       <chr> <lis> <chr>   <chr>   <list>  <named>   <dbl> #>  1 AMA      4649 4-aminosal… Anti… <chr> Drugs … Aminos… <chr>   <chr>     12    #>  2 ACM   6450012 Acetylmide… Macr… <chr> NA      NA      <chr>   <chr>     NA    #>  3 ASP  49787020 Acetylspir… Macr… <chr> NA      NA      <chr>   <chr>     NA    #>  4 ALS      8954 Aldesulfon… Othe… <chr> Drugs … Drugs … <chr>   <chr>      0.33 #>  5 AMK     37768 Amikacin    Amin… <chr> Aminog… Other … <chr>   <chr>     NA    #>  6 AKF        NA Amikacin/f… Amin… <chr> NA      NA      <chr>   <chr>     NA    #>  7 AMX     33613 Amoxicillin Beta… <chr> Beta-l… Penici… <chr>   <chr>      1.5  #>  8 AMC  23665637 Amoxicilli… Beta… <chr> Beta-l… Combin… <chr>   <chr>      1.5  #>  9 AXS    465441 Amoxicilli… Beta… <chr> NA      NA      <chr>   <chr>     NA    #> 10 AMB   5280965 Amphoteric… Anti… <chr> Antimy… Antibi… <chr>   <chr>     40    #> # … with 473 more rows, 4 more variables: oral_units <chr>, iv_ddd <dbl>, #> #   iv_units <chr>, loinc <list>, and abbreviated variable names ¹​atc_group1, #> #   ²​atc_group2, ³​abbreviations, ⁴​synonyms, ⁵​oral_ddd antivirals #> # A tibble: 120 × 11 #>    av    name  atc      cid atc_g…¹ synon…² oral_…³ oral_…⁴ iv_ddd iv_un…⁵ loinc #>    <av>  <chr> <chr>  <dbl> <chr>   <list>    <dbl> <chr>    <dbl> <chr>   <lis> #>  1 ABA   Abac… J05A… 4.41e5 Nucleo… <chr>       0.6 g           NA NA      <chr> #>  2 ACI   Acic… J05A… 1.35e8 Nucleo… <chr>       4   g            4 g       <chr> #>  3 ADD   Adef… J05A… 6.09e4 Nucleo… <chr>      10   mg          NA NA      <chr> #>  4 AME   Amen… J05A… 1.14e7 Other … <chr>       0.4 g           NA NA      <chr> #>  5 AMP   Ampr… J05A… 6.50e4 Protea… <chr>       1.2 g           NA NA      <chr> #>  6 ASU   Asun… J05A… 1.61e7 Antivi… <chr>       0.2 g           NA NA      <chr> #>  7 ATA   Ataz… J05A… 1.48e5 Protea… <chr>       0.3 g           NA NA      <chr> #>  8 ATA+… Ataz… J05A… 8.66e7 Antivi… <chr>      NA   NA          NA NA      <chr> #>  9 ATA+… Ataz… J05A… 2.51e7 Antivi… <chr>       0.3 g           NA NA      <chr> #> 10 BAM   Balo… J05A… 1.24e8 Other … <chr>      40   mg          NA NA      <chr> #> # … with 110 more rows, and abbreviated variable names ¹​atc_group, ²​synonyms, #> #   ³​oral_ddd, ⁴​oral_units, ⁵​iv_units"},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to an Antibiotic ID — as.ab","title":"Transform Input to an Antibiotic ID — as.ab","text":"Use function determine antibiotic drug code one antibiotics. data set antibiotics searched abbreviations, official names synonyms (brand names).","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to an Antibiotic ID — as.ab","text":"","code":"as.ab(x, flag_multiple_results = TRUE, info = interactive(), ...)  is.ab(x)"},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to an Antibiotic ID — as.ab","text":"x character vector determine antibiotic ID flag_multiple_results logical indicate whether note printed console probably one antibiotic drug code name can retrieved single input value. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed internal functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to an Antibiotic ID — as.ab","text":"character  vector additional class ab","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to an Antibiotic ID — as.ab","text":"entries antibiotics data set three different identifiers: human readable EARS-Net code (column ab, used ECDC WHONET), ATC code (column atc, used ), CID code (column cid, Compound ID, used PubChem). data set contains 5,000 official brand names many different countries, found PubChem. drugs contain multiple ATC codes. properties searched user input. .ab() can correct different forms misspelling: Wrong spelling drug names (\"tobramicin\" \"gentamycin\"), corrects audible similarities f/ph, x/ks, c/z/s, t/th, etc. many vowels consonants Switching two characters (\"mreopenem\", often case clinical data, doctors typed fast) Digitalised paper records, leaving artefacts like 0/o/O (zero O's), B/8, n/r, etc. Use ab_* functions get properties based returned antibiotic ID, see Examples. Note: .ab() ab_* functions may use long regular expression match brand names antimicrobial drugs. may fail systems. can add manual codes considered .ab() ab_* functions, see add_custom_antimicrobials().","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to an Antibiotic ID — as.ab","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Transform Input to an Antibiotic ID — as.ab","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to an Antibiotic ID — as.ab","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to an Antibiotic ID — as.ab","text":"","code":"# these examples all return \"ERY\", the ID of erythromycin: as.ab(\"J01FA01\") #> Class 'ab' #> [1] ERY as.ab(\"J 01 FA 01\") #> Class 'ab' #> [1] ERY as.ab(\"Erythromycin\") #> Class 'ab' #> [1] ERY as.ab(\"eryt\") #> Class 'ab' #> [1] ERY as.ab(\"   eryt 123\") #> Class 'ab' #> [1] ERY as.ab(\"ERYT\") #> Class 'ab' #> [1] ERY as.ab(\"ERY\") #> Class 'ab' #> [1] ERY as.ab(\"eritromicine\") # spelled wrong, yet works #> Class 'ab' #> [1] ERY as.ab(\"Erythrocin\") # trade name #> Class 'ab' #> [1] ERY as.ab(\"Romycin\") # trade name #> Class 'ab' #> [1] ERY  # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\") # small spelling error #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\") # or a bit more severe #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") # and even this works #> [1] \"J01DD04\"  # use ab_* functions to get a specific properties (see ?ab_property); # they use as.ab() internally: ab_name(\"J01FA01\") #> [1] \"Erythromycin\" ab_name(\"eryt\") #> [1] \"Erythromycin\"  # \\donttest{ if (require(\"dplyr\")) {   # you can quickly rename 'sir' columns using set_ab_names() with dplyr:   example_isolates %>%     set_ab_names(where(is.sir), property = \"atc\") } #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           J01CE01 J01CF04 J01CF05 #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir>   <sir>   <sir>   #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R       NA      NA      #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R       NA      NA      #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R       NA      S       #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R       NA      S       #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R       NA      S       #> # … with 1,990 more rows, and 37 more variables: J01CA04 <sir>, J01CR02 <sir>, #> #   J01CA01 <sir>, J01CR05 <sir>, J01DB04 <sir>, J01DE01 <sir>, J01DC02 <sir>, #> #   J01DC01 <sir>, J01DD01 <sir>, J01DD02 <sir>, J01DD04 <sir>, J01GB03 <sir>, #> #   J01GB01 <sir>, J01GB06 <sir>, J01GB04 <sir>, J01EA01 <sir>, J01EE01 <sir>, #> #   J01XE01 <sir>, J01XX01 <sir>, J01XX08 <sir>, J01MA02 <sir>, J01MA14 <sir>, #> #   J01XA01 <sir>, J01XA02 <sir>, J01AA07 <sir>, J01AA12 <sir>, J01AA02 <sir>, #> #   J01FA01 <sir>, J01FF01 <sir>, J01FA10 <sir>, J01DH51 <sir>, … # }"},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to an Antiviral Drug ID — as.av","title":"Transform Input to an Antiviral Drug ID — as.av","text":"Use function determine antiviral drug code one antiviral drugs. data set antivirals searched abbreviations, official names synonyms (brand names).","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to an Antiviral Drug ID — as.av","text":"","code":"as.av(x, flag_multiple_results = TRUE, info = interactive(), ...)  is.av(x)"},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to an Antiviral Drug ID — as.av","text":"x character vector determine antiviral drug ID flag_multiple_results logical indicate whether note printed console probably one antiviral drug code name can retrieved single input value. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed internal functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to an Antiviral Drug ID — as.av","text":"character  vector additional class ab","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to an Antiviral Drug ID — as.av","text":"entries antivirals data set three different identifiers: human readable EARS-Net code (column ab, used ECDC WHONET), ATC code (column atc, used ), CID code (column cid, Compound ID, used PubChem). data set contains 5,000 official brand names many different countries, found PubChem. drugs contain multiple ATC codes. properties searched user input. .av() can correct different forms misspelling: Wrong spelling drug names (\"acyclovir\"), corrects audible similarities f/ph, x/ks, c/z/s, t/th, etc. many vowels consonants Switching two characters (\"aycclovir\", often case clinical data, doctors typed fast) Digitalised paper records, leaving artefacts like 0/o/O (zero O's), B/8, n/r, etc. Use av_* functions get properties based returned antiviral drug ID, see Examples. Note: .av() av_* functions may use long regular expression match brand names antimicrobial drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to an Antiviral Drug ID — as.av","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Transform Input to an Antiviral Drug ID — as.av","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to an Antiviral Drug ID — as.av","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to an Antiviral Drug ID — as.av","text":"","code":"# these examples all return \"ACI\", the ID of aciclovir: as.av(\"J05AB01\") #> Class 'av' #> [1] ACI as.av(\"J 05 AB 01\") #> Class 'av' #> [1] ACI as.av(\"Aciclovir\") #> Class 'av' #> [1] ACI as.av(\"aciclo\") #> Class 'av' #> [1] ACI as.av(\"   aciclo 123\") #> Class 'av' #> [1] ACI as.av(\"ACICL\") #> Class 'av' #> [1] ACI as.av(\"ACI\") #> Class 'av' #> [1] ACI as.av(\"Virorax\") # trade name #> Class 'av' #> [1] ACI as.av(\"Zovirax\") # trade name #> Class 'av' #> [1] ACI  as.av(\"acyklofir\") # severe spelling error, yet works #> Class 'av' #> [1] ACI  # use av_* functions to get a specific properties (see ?av_property); # they use as.av() internally: av_name(\"J05AB01\") #> [1] \"Aciclovir\" av_name(\"acicl\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Disk Diffusion Diameters — as.disk","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"transforms vector new class disk, disk diffusion growth zone size (around antibiotic disk) millimetres 6 50.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"as.disk(x, na.rm = FALSE)  NA_disk_  is.disk(x)"},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"object class disk (inherits integer) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"x vector na.rm logical indicating whether missing values removed","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"integer additional class disk","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"Interpret disk values SIR values .sir(). supports guidelines EUCAST CLSI. Disk diffusion growth zone sizes must 6 50 millimetres. Values higher 50 lower 100 maximised 50. others input values outside 6-50 range return NA. NA_disk_ missing value new disk class.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"# transform existing disk zones to the `disk` class (using base R) df <- data.frame(   microorganism = \"Escherichia coli\",   AMP = 20,   CIP = 14,   GEN = 18,   TOB = 16 ) df[, 2:5] <- lapply(df[, 2:5], as.disk) str(df) #> 'data.frame':\t1 obs. of  5 variables: #>  $ microorganism: chr \"Escherichia coli\" #>  $ AMP          : 'disk' int 20 #>  $ CIP          : 'disk' int 14 #>  $ GEN          : 'disk' int 18 #>  $ TOB          : 'disk' int 16  # \\donttest{ # transforming is easier with dplyr: if (require(\"dplyr\")) {   df %>% mutate(across(AMP:TOB, as.disk)) } #>      microorganism AMP CIP GEN TOB #> 1 Escherichia coli  20  14  18  16 # }  # interpret disk values, see ?as.sir as.sir(   x = as.disk(18),   mo = \"Strep pneu\", # `mo` will be coerced with as.mo()   ab = \"ampicillin\", # and `ab` with as.ab()   guideline = \"EUCAST\" ) #> => Interpreting disk diffusion zones of 'ampicillin' (AMP) according to #>    EUCAST 2022... #>  OK. #> Class 'sir' #> [1] R  # interpret whole data set, pretend to be all from urinary tract infections: as.sir(df, uti = TRUE) #> => Interpreting disk diffusion zones of column 'AMP' (ampicillin) according #>    to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'CIP' (ciprofloxacin) #>    according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'GEN' (gentamicin) according #>    to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'TOB' (tobramycin) according #>    to EUCAST 2022... #>  OK. #>      microorganism AMP CIP GEN TOB #> 1 Escherichia coli   S   R   S   S"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"transforms vectors new class mic, treats input decimal numbers, maintaining operators (\">=\") allowing valid MIC values known field (medical) microbiology.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"as.mic(x, na.rm = FALSE)  NA_mic_  is.mic(x)  # S3 method for mic droplevels(x, as.mic = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"x character numeric vector na.rm logical indicating whether missing values removed .mic logical indicate whether mic class kept, defaults FALSE ... arguments passed methods","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"Ordered factor additional class mic, mathematical operations acts decimal numbers. Bare mind outcome mathematical operation MICs return numeric value.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"interpret MIC values SIR values, use .sir() MIC values. supports guidelines EUCAST (2013-2022) CLSI (2013-2022). class MIC values quite special data type: formally ordered factor valid MIC values factor levels (make sure valid MIC values retained), mathematical operation acts decimal numbers:   makes possible maintain operators often come MIC values, \">=\" \"<=\", even filtering using numeric values data analysis, e.g.:   following generic functions implemented MIC class: !, !=, %%, %/%, &, *, +, -, /, <, <=, ==, >, >=, ^, |, abs(), acos(), acosh(), (), (), asin(), asinh(), atan(), atanh(), ceiling(), cos(), cosh(), cospi(), cummax(), cummin(), cumprod(), cumsum(), digamma(), exp(), expm1(), floor(), gamma(), lgamma(), log(), log1p(), log2(), log10(), max(), mean(), min(), prod(), range(), round(), sign(), signif(), sin(), sinh(), sinpi(), sqrt(), sum(), tan(), tanh(), tanpi(), trigamma() trunc(). functions stats package also implemented: median(), quantile(), mad(), IQR(), fivenum(). Also, boxplot.stats() supported. Since sd() var() non-generic functions, extended. Use mad() alternative, use e.g. sd(.numeric(x)) x vector MIC values. Using .double() .numeric() MIC values remove operators return numeric vector. use .integer() MIC values R convention factors, return index factor levels (often useless regular users). Use droplevels() drop unused levels. default, return plain factor. Use droplevels(..., .mic = TRUE) maintain mic class. NA_mic_ missing value new mic class, analogous e.g. base R's NA_character_.","code":"x <- random_mic(10) x #> Class 'mic' #>  [1] 16     1      8      8      64     >=128  0.0625 32     32     16  is.factor(x) #> [1] TRUE  x[1] * 2 #> [1] 32  median(x) #> [1] 26 x[x > 4] #> Class 'mic' #> [1] 16    8     8     64    >=128 32    32    16  df <- data.frame(x, hospital = \"A\") subset(df, x > 4) # or with dplyr: df %>% filter(x > 4) #>        x hospital #> 1     16        A #> 5     64        A #> 6  >=128        A #> 8     32        A #> 9     32        A #> 10    16        A"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"mic_data <- as.mic(c(\">=32\", \"1.0\", \"1\", \"1.00\", 8, \"<=0.128\", \"8\", \"16\", \"16\")) mic_data #> Class 'mic' #> [1] >=32    1       1       1       8       <=0.128 8       16      16      is.mic(mic_data) #> [1] TRUE  # this can also coerce combined MIC/SIR values: as.mic(\"<=0.002; S\") #> Class 'mic' #> [1] <=0.002  # mathematical processing treats MICs as numeric values fivenum(mic_data) #> [1]  0.128  1.000  8.000 16.000 32.000 quantile(mic_data) #>     0%    25%    50%    75%   100%  #>  0.128  1.000  8.000 16.000 32.000  all(mic_data < 512) #> [1] TRUE  # interpret MIC values as.sir(   x = as.mic(2),   mo = as.mo(\"Streptococcus pneumoniae\"),   ab = \"AMX\",   guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMX' (amoxicillin) according to EUCAST #>    2022... #>  Note: #>    • Multiple breakpoints available for amoxicillin (AMX) in Streptococcus #>      pneumoniae - assuming body site 'Meningitis'. #> Class 'sir' #> [1] R as.sir(   x = as.mic(c(0.01, 2, 4, 8)),   mo = as.mo(\"Streptococcus pneumoniae\"),   ab = \"AMX\",   guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMX' (amoxicillin) according to EUCAST #>    2022... #>  Note: #>    • Multiple breakpoints available for amoxicillin (AMX) in Streptococcus #>      pneumoniae - assuming body site 'Meningitis'. #> Class 'sir' #> [1] S R R R  # plot MIC values, see ?plot plot(mic_data)  plot(mic_data, mo = \"E. coli\", ab = \"cipro\")   if (require(\"ggplot2\")) {   autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\") }  if (require(\"ggplot2\")) {   autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"nl\") # Dutch }  if (require(\"ggplot2\")) {   autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"uk\") # Ukrainian }"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to a Microorganism Code — as.mo","title":"Transform Input to a Microorganism Code — as.mo","text":"Use function determine valid microorganism code (mo). Determination done using intelligent rules complete taxonomic kingdoms Animalia, Archaea, Bacteria Protozoa, microbial species kingdom Fungi (see Source). input can almost anything: full name (like \"Staphylococcus aureus\"), abbreviated name (\"S. aureus\"), abbreviation known field (\"MRSA\"), just genus. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to a Microorganism Code — as.mo","text":"","code":"as.mo(   x,   Becker = FALSE,   Lancefield = FALSE,   minimum_matching_score = NULL,   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   reference_df = get_mo_source(),   ignore_pattern = getOption(\"AMR_ignore_pattern\", NULL),   remove_from_input = mo_cleaning_regex(),   language = get_AMR_locale(),   info = interactive(),   ... )  is.mo(x)  mo_uncertainties()  mo_renamed()  mo_failures()  mo_reset_session()  mo_cleaning_regex()"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to a Microorganism Code — as.mo","text":"x character vector data.frame one two columns Becker logical indicate whether staphylococci categorised coagulase-negative staphylococci (\"CoNS\") coagulase-positive staphylococci (\"CoPS\") instead species, according Karsten Becker et al. (see Source). excludes Staphylococcus aureus default, use Becker = \"\" also categorise S. aureus \"CoPS\". Lancefield logical indicate whether beta-haemolytic Streptococcus categorised Lancefield groups instead species, according Rebecca C. Lancefield (see Source). streptococci categorised first group, e.g. Streptococcus dysgalactiae group C, although officially also categorised groups G L. excludes enterococci default (group D), use Lancefield = \"\" also categorise enterococci group D. minimum_matching_score numeric value set lower limit MO matching score. left blank, determined automatically based character length x, taxonomic kingdom human pathogenicity. keep_synonyms logical indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE, return note old taxonomic names processed. default can set options(AMR_keep_synonyms = TRUE) options(AMR_keep_synonyms = FALSE). reference_df data.frame used extra reference translating x valid mo. See set_mo_source() get_mo_source() automate usage codes (e.g. used analysis organisation). ignore_pattern regular expression (case-insensitive) matches x must return NA. can convenient exclude known non-relevant input can also set option AMR_ignore_pattern, e.g. options(AMR_ignore_pattern = \"(reported|contaminated flora)\"). remove_from_input regular expression (case-insensitive) clean input x. Everything matched x removed. default, outcome mo_cleaning_regex(), removes texts brackets texts \"species\" \"serovar\". language language translate text like \"growth\", defaults system language (see get_AMR_locale()) info logical indicate progress bar printed 25 items coerced, defaults TRUE interactive mode ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to a Microorganism Code — as.mo","text":"character  vector additional class mo","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to a Microorganism Code — as.mo","text":"microorganism (MO) code package (class: mo) human readable typically looks like examples:   Values coerced considered 'unknown' returned MO code UNKNOWN warning. Use mo_* functions get properties based returned code, see Examples. .mo() function uses novel matching score algorithm (see Matching Score Microorganisms ) match input available microbial taxonomy package. lead effect e.g. \"E. coli\" (microorganism highly prevalent humans) return microbial ID Escherichia coli Entamoeba coli (microorganism less prevalent humans), although latter alphabetically come first. algorithm uses data List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF) (see microorganisms).","code":"Code               Full name   ---------------    --------------------------------------   B_KLBSL            Klebsiella   B_KLBSL_PNMN       Klebsiella pneumoniae   B_KLBSL_PNMN_RHNS  Klebsiella pneumoniae rhinoscleromatis   |   |    |    |   |   |    |    |   |   |    |    \\---> subspecies, a 3-5 letter acronym   |   |    \\----> species, a 3-6 letter acronym   |   \\----> genus, a 4-8 letter acronym   \\----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria),                             F (Fungi), PL (Plantae), P (Protozoa)"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"coping-with-uncertain-results","dir":"Reference","previous_headings":"","what":"Coping with Uncertain Results","title":"Transform Input to a Microorganism Code — as.mo","text":"Results non-exact taxonomic input based matching score. lowest allowed score can set minimum_matching_score argument. default determined based character length input, taxonomic kingdom human pathogenicity taxonomic outcome. values matched uncertainty, message shown suggest user evaluate results mo_uncertainties(), returns data.frame specifications. increase quality matching, remove_from_input argument can used clean input (.e., x). must regular expression matches parts input removed input matched available microbial taxonomy. matched Perl-compatible case-insensitive. default value remove_from_input outcome helper function mo_cleaning_regex(). three helper functions can run using .mo() function: Use mo_uncertainties() get data.frame prints pretty format taxonomic names guessed. output contains matching score matches (see Matching Score Microorganisms ). Use mo_failures() get character vector values coerced valid value. Use mo_renamed() get data.frame values coerced based old, previously accepted taxonomic names.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"microbial-prevalence-of-pathogens-in-humans","dir":"Reference","previous_headings":"","what":"Microbial Prevalence of Pathogens in Humans","title":"Transform Input to a Microorganism Code — as.mo","text":"coercion rules consider prevalence microorganisms humans, available prevalence column microorganisms data set. grouping human pathogenic prevalence explained section Matching Score Microorganisms .","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to a Microorganism Code — as.mo","text":"Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 Becker K et al. (2014). Coagulase-Negative Staphylococci. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. (2019). Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. (2020). Emergence coagulase-negative staphylococci Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 Lancefield RC (1933). serological differentiation human groups hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571 Berends MS et al. (2022). Trends Occurrence Phenotypic Resistance Coagulase-Negative Staphylococci (CoNS) Found Human Blood Northern Netherlands 2013 2019 Microorganisms 10(9), 1801; doi:10.3390/microorganisms10091801 Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Transform Input to a Microorganism Code — as.mo","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated :  : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.0 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, genus present established list also prevalence = 1.0 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.5 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Contracaecum, Cordylobia, Cryptococcus, Curvularia, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Giardia, Haloarcula, Halobacterium, Halococcus, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hymenolepis, Hypomyces, Hysterothylacium, Leishmania, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Mortierella, Mucor, Mycocentrospora, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Pediculus, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga Wuchereria; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.159\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to a Microorganism Code — as.mo","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to a Microorganism Code — as.mo","text":"","code":"# \\donttest{ # These examples all return \"B_STPHY_AURS\", the ID of S. aureus: as.mo(c(   \"sau\", # WHONET code   \"stau\",   \"STAU\",   \"staaur\",   \"S. aureus\",   \"S aureus\",   \"Sthafilokkockus aureus\", # handles incorrect spelling   \"Staphylococcus aureus (MRSA)\",   \"MRSA\", # Methicillin Resistant S. aureus   \"VISA\", # Vancomycin Intermediate S. aureus   \"VRSA\", # Vancomycin Resistant S. aureus   115329001 # SNOMED CT code )) #> Class 'mo' #>  [1] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #>  [6] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [11] B_STPHY_AURS B_STPHY_AURS  # Dyslexia is no problem - these all work: as.mo(c(   \"Ureaplasma urealyticum\",   \"Ureaplasma urealyticus\",   \"Ureaplasmium urealytica\",   \"Ureaplazma urealitycium\" )) #> Class 'mo' #> [1] B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY  as.mo(\"Streptococcus group A\") #> Class 'mo' #> [1] B_STRPT_GRPA  as.mo(\"S. epidermidis\") # will remain species: B_STPHY_EPDR #> Class 'mo' #> [1] B_STPHY_EPDR as.mo(\"S. epidermidis\", Becker = TRUE) # will not remain species: B_STPHY_CONS #> Class 'mo' #> [1] B_STPHY_CONS  as.mo(\"S. pyogenes\") # will remain species: B_STRPT_PYGN #> Class 'mo' #> [1] B_STRPT_PYGN as.mo(\"S. pyogenes\", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA #> Class 'mo' #> [1] B_STRPT_GRPA  # All mo_* functions use as.mo() internally too (see ?mo_property): mo_genus(\"E. coli\") #> [1] \"Escherichia\" mo_gramstain(\"ESCO\") #> [1] \"Gram-negative\" mo_is_intrinsic_resistant(\"ESCCOL\", ab = \"vanco\") #> ℹ Determining intrinsic resistance based on 'EUCAST Expert Rules' and #>   'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021). This note #>   will be shown once per session. #> [1] TRUE # }"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Interpret minimum inhibitory concentration (MIC) values disk diffusion diameters according EUCAST CLSI, clean existing SIR values. transforms input new class sir, ordered factor levels S < < R.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"as.sir(x, ...)  NA_sir_  is.sir(x)  is_sir_eligible(x, threshold = 0.05)  # S3 method for mic as.sir(   x,   mo = NULL,   ab = deparse(substitute(x)),   guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   uti = NULL,   conserve_capped_values = FALSE,   add_intrinsic_resistance = FALSE,   reference_data = AMR::clinical_breakpoints,   ... )  # S3 method for disk as.sir(   x,   mo = NULL,   ab = deparse(substitute(x)),   guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   uti = NULL,   add_intrinsic_resistance = FALSE,   reference_data = AMR::clinical_breakpoints,   ... )  # S3 method for data.frame as.sir(   x,   ...,   col_mo = NULL,   guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   uti = NULL,   conserve_capped_values = FALSE,   add_intrinsic_resistance = FALSE,   reference_data = AMR::clinical_breakpoints )  sir_interpretation_history(clean = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"object class sir (inherits ordered, factor) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpretations minimum inhibitory concentration (MIC) values disk diffusion diameters: M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 2013-2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. M100 Performance Standard Antimicrobial Susceptibility Testing, 2013-2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/. Breakpoint tables interpretation MICs zone diameters, 2013-2022, European Committee Antimicrobial Susceptibility Testing (EUCAST). https://www.eucast.org/clinical_breakpoints.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"x vector values (class mic: MIC values mg/L, class disk: disk diffusion radius millimetres) ... using data.frame: names columns apply .sir() (supports tidy selection column1:column4). Otherwise: arguments passed methods. threshold maximum fraction invalid antimicrobial interpretations x, see Examples mo (vector ) text can coerced valid microorganism codes .mo(), can left empty determine automatically ab (vector ) text can coerced valid antimicrobial drug code .ab() guideline defaults EUCAST 2022 (latest implemented EUCAST guideline clinical_breakpoints data set), can set option AMR_guideline. Supports EUCAST (2013-2022) CLSI (2013-2022), see Details. uti (Urinary Tract Infection) vector logicals (TRUE FALSE) specify whether UTI specific interpretation guideline chosen. using .sir() data.frame, can also column containing logicals left blank, data set searched column 'specimen', rows within column containing 'urin' ('urine', 'urina') regarded isolates UTI. See Examples. conserve_capped_values logical indicate MIC values starting \">\" (\">=\") must always return \"R\" , MIC values starting \"<\" (\"<=\") must always return \"S\" add_intrinsic_resistance (useful using EUCAST guideline) logical indicate whether intrinsic antibiotic resistance must also considered applicable bug-drug combinations, meaning e.g. ampicillin always return \"R\" Klebsiella species. Determination based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). reference_data data.frame used interpretation, defaults clinical_breakpoints data set. Changing argument allows using interpretation guidelines. argument must contain data set equal structure clinical_breakpoints data set (column names column types). Please note guideline argument ignored reference_data manually set. col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). clean logical indicate whether previously stored results forgotten returning 'logbook' results","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Ordered factor new class sir","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"NA_sir_ missing value new sir class, analogous e.g. base R's NA_character_.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"how-it-works","dir":"Reference","previous_headings":"","what":"How it Works","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":".sir() function works four ways: cleaning raw / untransformed data. data cleaned contain values S, R try best determine intelligence. example, mixed values SIR interpretations MIC values \"<0.25; S\" coerced \"S\". Combined interpretations multiple test methods (seen laboratory records) \"S; S\" coerced \"S\", value like \"S; \" return NA warning input unclear. interpreting minimum inhibitory concentration (MIC) values according EUCAST CLSI. must clean MIC values first using .mic(), also gives columns new data class mic. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either:   Operators like \"<=\" stripped interpretation. using conserve_capped_values = TRUE, MIC value e.g. \">2\" always return \"R\", even breakpoint according chosen guideline \">=4\". prevent capped values raw laboratory data treated conservatively. default behaviour (conserve_capped_values = FALSE) considers \">2\" lower \">=4\" might case return \"S\" \"\". interpreting disk diffusion diameters according EUCAST CLSI. must clean disk zones first using .disk(), also gives columns new data class disk. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either:   interpreting complete data set, automatic determination MIC values, disk diffusion diameters, microorganism names codes, antimicrobial test results. done simply running .sir(your_data). points 2, 3 4: Use sir_interpretation_history() retrieve data.frame (tibble tibble package installed) results last .sir() call.","code":"your_data %>% mutate_if(is.mic, as.sir) your_data %>% mutate(across(where(is.mic), as.sir)) your_data %>% mutate_if(is.disk, as.sir) your_data %>% mutate(across(where(is.disk), as.sir))"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"supported-guidelines","dir":"Reference","previous_headings":"","what":"Supported Guidelines","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpreting MIC values well disk diffusion diameters, currently implemented guidelines EUCAST (2013-2022) CLSI (2013-2022). Thus, guideline argument must set e.g., \"EUCAST 2022\" \"CLSI 2022\". simply using \"EUCAST\" (default) \"CLSI\" input, latest included version guideline automatically selected. can set data set using reference_data argument. guideline argument ignored. can set default guideline AMR_guideline option (e.g. .Rprofile file), :","code":"options(AMR_guideline = \"CLSI\")   options(AMR_guideline = \"CLSI 2018\")   options(AMR_guideline = \"EUCAST 2020\")   # or to reset:   options(AMR_guideline = NULL)"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"after-interpretation","dir":"Reference","previous_headings":"","what":"After Interpretation","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"using .sir(), can use eucast_rules() defined EUCAST (1) apply inferred susceptibility resistance based results antimicrobials (2) apply intrinsic resistance based taxonomic properties microorganism.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"machine-readable-interpretation-guidelines","dir":"Reference","previous_headings":"","what":"Machine-Readable Interpretation Guidelines","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"repository package contains machine-readable version guidelines. CSV file consisting 18,308 rows 11 columns. file machine-readable, since contains one row every unique combination test method (MIC disk diffusion), antimicrobial drug microorganism. allows easy implementation rules laboratory information systems (LIS). Note contains interpretation guidelines humans - interpretation guidelines CLSI animals removed.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"function .sir() detects input contains class sir. input data.frame, iterates columns returns logical vector. function is_sir_eligible() returns TRUE columns contains 5% invalid antimicrobial interpretations (S //R), FALSE otherwise. threshold 5% can set threshold argument. input data.frame, iterates columns returns logical vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> summary(example_isolates) # see all SIR results at a glance #>       date              patient               age           gender          #>  Min.   :2002-01-02   Length:2000        Min.   : 0.00   Length:2000        #>  1st Qu.:2005-07-31   Class :character   1st Qu.:63.00   Class :character   #>  Median :2009-07-31   Mode  :character   Median :74.00   Mode  :character   #>  Mean   :2009-11-20                      Mean   :70.69                      #>  3rd Qu.:2014-05-30                      3rd Qu.:82.00                      #>  Max.   :2017-12-28                      Max.   :97.00                      #>      ward                mo                  PEN                 #>  Length:2000        Class :mo             Class:sir              #>  Class :character   <NA>  :0              %R   :73.7% (n=1201)   #>  Mode  :character   Unique:90             %SI  :26.3% (n=428)    #>                     #1    :B_ESCHR_COLI   - %S :25.6% (n=417)    #>                     #2    :B_STPHY_CONS   - %I : 0.7% (n=11)     #>                     #3    :B_STPHY_AURS                          #>     OXA                   FLC                   AMX                #>  Class:sir             Class:sir             Class:sir             #>  %R   :31.2% (n=114)   %R   :29.5% (n=278)   %R   :59.6% (n=804)   #>  %SI  :68.8% (n=251)   %SI  :70.5% (n=665)   %SI  :40.4% (n=546)   #>  - %S :68.8% (n=251)   - %S :70.5% (n=665)   - %S :40.2% (n=543)   #>  - %I : 0.0% (n=0)     - %I : 0.0% (n=0)     - %I : 0.2% (n=3)     #>                                                                    #>     AMC                    AMP                   TZP                #>  Class:sir              Class:sir             Class:sir             #>  %R   :23.7% (n=446)    %R   :59.6% (n=804)   %R   :12.6% (n=126)   #>  %SI  :76.3% (n=1433)   %SI  :40.4% (n=546)   %SI  :87.4% (n=875)   #>  - %S :71.4% (n=1342)   - %S :40.2% (n=543)   - %S :86.1% (n=862)   #>  - %I : 4.8% (n=91)     - %I : 0.2% (n=3)     - %I : 1.3% (n=13)    #>                                                                     #>     CZO                   FEP                   CXM                 #>  Class:sir             Class:sir             Class:sir              #>  %R   :44.6% (n=199)   %R   :14.2% (n=103)   %R   :26.3% (n=470)    #>  %SI  :55.4% (n=247)   %SI  :85.8% (n=621)   %SI  :73.7% (n=1319)   #>  - %S :54.9% (n=245)   - %S :85.6% (n=620)   - %S :72.5% (n=1297)   #>  - %I : 0.4% (n=2)     - %I : 0.1% (n=1)     - %I : 1.2% (n=22)     #>                                                                     #>     FOX                   CTX                   CAZ                 #>  Class:sir             Class:sir             Class:sir              #>  %R   :27.4% (n=224)   %R   :15.5% (n=146)   %R   :66.5% (n=1204)   #>  %SI  :72.6% (n=594)   %SI  :84.5% (n=797)   %SI  :33.5% (n=607)    #>  - %S :71.6% (n=586)   - %S :84.4% (n=796)   - %S :33.5% (n=607)    #>  - %I : 1.0% (n=8)     - %I : 0.1% (n=1)     - %I : 0.0% (n=0)      #>                                                                     #>     CRO                   GEN                    TOB                #>  Class:sir             Class:sir              Class:sir             #>  %R   :15.5% (n=146)   %R   :24.6% (n=456)    %R   :34.4% (n=465)   #>  %SI  :84.5% (n=797)   %SI  :75.4% (n=1399)   %SI  :65.6% (n=886)   #>  - %S :84.4% (n=796)   - %S :74.0% (n=1372)   - %S :65.1% (n=879)   #>  - %I : 0.1% (n=1)     - %I : 1.5% (n=27)     - %I : 0.5% (n=7)     #>                                                                     #>     AMK                   KAN                    TMP                #>  Class:sir             Class:sir              Class:sir             #>  %R   :63.7% (n=441)   %R   :100.0% (n=471)   %R   :38.1% (n=571)   #>  %SI  :36.3% (n=251)   %SI  : 0.0% (n=0)      %SI  :61.9% (n=928)   #>  - %S :36.3% (n=251)   - %S : 0.0% (n=0)      - %S :61.2% (n=918)   #>  - %I : 0.0% (n=0)     - %I : 0.0% (n=0)      - %I : 0.7% (n=10)    #>                                                                     #>     SXT                    NIT                   FOS                #>  Class:sir              Class:sir             Class:sir             #>  %R   :20.5% (n=361)    %R   :17.1% (n=127)   %R   :42.2% (n=148)   #>  %SI  :79.5% (n=1398)   %SI  :82.9% (n=616)   %SI  :57.8% (n=203)   #>  - %S :79.1% (n=1392)   - %S :76.0% (n=565)   - %S :57.8% (n=203)   #>  - %I : 0.3% (n=6)      - %I : 6.9% (n=51)    - %I : 0.0% (n=0)     #>                                                                     #>     LNZ                   CIP                    MFX                #>  Class:sir             Class:sir              Class:sir             #>  %R   :69.3% (n=709)   %R   :16.2% (n=228)    %R   :33.6% (n=71)    #>  %SI  :30.7% (n=314)   %SI  :83.8% (n=1181)   %SI  :66.4% (n=140)   #>  - %S :30.7% (n=314)   - %S :78.9% (n=1112)   - %S :64.5% (n=136)   #>  - %I : 0.0% (n=0)     - %I : 4.9% (n=69)     - %I : 1.9% (n=4)     #>                                                                     #>     VAN                    TEC                   TCY                #>  Class:sir              Class:sir             Class:sir             #>  %R   :38.3% (n=712)    %R   :75.7% (n=739)   %R   :29.8% (n=357)   #>  %SI  :61.7% (n=1149)   %SI  :24.3% (n=237)   %SI  :70.3% (n=843)   #>  - %S :61.7% (n=1149)   - %S :24.3% (n=237)   - %S :68.3% (n=820)   #>  - %I : 0.0% (n=0)      - %I : 0.0% (n=0)     - %I : 1.9% (n=23)    #>                                                                     #>     TGC                   DOX                   ERY                 #>  Class:sir             Class:sir             Class:sir              #>  %R   :12.7% (n=101)   %R   :27.7% (n=315)   %R   :57.2% (n=1084)   #>  %SI  :87.3% (n=697)   %SI  :72.3% (n=821)   %SI  :42.8% (n=810)    #>  - %S :87.3% (n=697)   - %S :71.7% (n=814)   - %S :42.3% (n=801)    #>  - %I : 0.0% (n=0)     - %I : 0.6% (n=7)     - %I : 0.5% (n=9)      #>                                                                     #>     CLI                   AZM                    IPM                #>  Class:sir             Class:sir              Class:sir             #>  %R   :61.2% (n=930)   %R   :57.2% (n=1084)   %R   : 6.2% (n=55)    #>  %SI  :38.8% (n=590)   %SI  :42.8% (n=810)    %SI  :93.8% (n=834)   #>  - %S :38.6% (n=586)   - %S :42.3% (n=801)    - %S :92.7% (n=824)   #>  - %I : 0.3% (n=4)     - %I : 0.5% (n=9)      - %I : 1.1% (n=10)    #>                                                                     #>     MEM                   MTR                  CHL                #>  Class:sir             Class:sir            Class:sir             #>  %R   : 5.9% (n=49)    %R   :14.7% (n=5)    %R   :21.4% (n=33)    #>  %SI  :94.1% (n=780)   %SI  :85.3% (n=29)   %SI  :78.6% (n=121)   #>  - %S :94.1% (n=780)   - %S :85.3% (n=29)   - %S :78.6% (n=121)   #>  - %I : 0.0% (n=0)     - %I : 0.0% (n=0)    - %I : 0.0% (n=0)     #>                                                                   #>     COL                    MUP                   RIF                #>  Class:sir              Class:sir             Class:sir             #>  %R   :81.2% (n=1331)   %R   : 5.9% (n=16)    %R   :69.6% (n=698)   #>  %SI  :18.8% (n=309)    %SI  :94.1% (n=254)   %SI  :30.4% (n=305)   #>  - %S :18.8% (n=309)    - %S :93.0% (n=251)   - %S :30.2% (n=303)   #>  - %I : 0.0% (n=0)      - %I : 1.1% (n=3)     - %I : 0.2% (n=2)     #>                                                                      # For INTERPRETING disk diffusion and MIC values -----------------------  # a whole data set, even with combined MIC values and disk zones df <- data.frame(   microorganism = \"Escherichia coli\",   AMP = as.mic(8),   CIP = as.mic(0.256),   GEN = as.disk(18),   TOB = as.disk(16),   ERY = \"R\" ) as.sir(df) #> => Interpreting MIC values of column 'AMP' (ampicillin) according to EUCAST #>    2022... #>  OK. #> => Interpreting MIC values of column 'CIP' (ciprofloxacin) according to #>    EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'GEN' (gentamicin) according #>    to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of column 'TOB' (tobramycin) according #>    to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Assigning class 'sir' to already clean column 'ERY' (erythromycin)... #>  OK. #>      microorganism AMP CIP GEN TOB ERY #> 1 Escherichia coli   S   I   S   S   R  # return a 'logbook' about the results: sir_interpretation_history() #> # A tibble: 50 × 12 #>    datetime            index ab_input ab_guid…¹ mo_in…² mo_guideline     guide…³ #>    <dttm>              <int> <chr>    <ab>      <chr>   <mo>             <chr>   #>  1 2023-01-23 14:05:17     1 TOB      TOB       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  2 2023-01-23 14:05:17     1 GEN      GEN       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  3 2023-01-23 14:05:17     1 CIP      CIP       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  4 2023-01-23 14:05:17     1 AMP      AMP       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  5 2023-01-23 14:05:12     1 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  6 2023-01-23 14:05:12     2 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  7 2023-01-23 14:05:12     3 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  8 2023-01-23 14:05:12     4 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  9 2023-01-23 14:05:12     5 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 10 2023-01-23 14:05:12     6 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> # … with 40 more rows, 5 more variables: ref_table <chr>, method <chr>, #> #   input <dbl>, outcome <sir>, breakpoint_S_R <chr>, and abbreviated variable #> #   names ¹​ab_guideline, ²​mo_input, ³​guideline  # for single values as.sir(   x = as.mic(2),   mo = as.mo(\"S. pneumoniae\"),   ab = \"AMP\",   guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #>    2022... #>  Note: #>    • Multiple breakpoints available for ampicillin (AMP) in Streptococcus #>      pneumoniae - assuming body site 'Non-meningitis'. #> Class 'sir' #> [1] R  as.sir(   x = as.disk(18),   mo = \"Strep pneu\", # `mo` will be coerced with as.mo()   ab = \"ampicillin\", # and `ab` with as.ab()   guideline = \"EUCAST\" ) #> => Interpreting disk diffusion zones of 'ampicillin' (AMP) according to #>    EUCAST 2022... #>  OK. #> Class 'sir' #> [1] R  # \\donttest{ # the dplyr way if (require(\"dplyr\")) {   df %>% mutate_if(is.mic, as.sir)   df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)   df %>% mutate(across(where(is.mic), as.sir))   df %>% mutate_at(vars(AMP:TOB), as.sir)   df %>% mutate(across(AMP:TOB, as.sir))    df %>%     mutate_at(vars(AMP:TOB), as.sir, mo = .$microorganism)    # to include information about urinary tract infections (UTI)   data.frame(     mo = \"E. coli\",     NIT = c(\"<= 2\", 32),     from_the_bladder = c(TRUE, FALSE)   ) %>%     as.sir(uti = \"from_the_bladder\")    data.frame(     mo = \"E. coli\",     NIT = c(\"<= 2\", 32),     specimen = c(\"urine\", \"blood\")   ) %>%     as.sir() # automatically determines urine isolates    df %>%     mutate_at(vars(AMP:TOB), as.sir, mo = \"E. coli\", uti = TRUE) } #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #>    2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST #>    2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to #>    EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to #>    EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to #>    EUCAST 2022... #> Warning: in as.sir(): interpretation of nitrofurantoin (NIT) is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms, #> thus assuming uti = TRUE. See ?as.sir. #>  * WARNING * #> ℹ Assuming value \"urine\" in column 'specimen' reflects a urinary tract #>   infection. #>   Use as.sir(uti = FALSE) to prevent this. #> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to #>    EUCAST 2022... #> Warning: in as.sir(): interpretation of nitrofurantoin (NIT) is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms, #> thus assuming uti = TRUE. See ?as.sir. #>  * WARNING * #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #>    2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST #>    2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to #>    EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to #>    EUCAST 2022... #>  OK. #>      microorganism AMP CIP GEN TOB ERY #> 1 Escherichia coli   S   I   S   S   R  # For CLEANING existing SIR values ------------------------------------  as.sir(c(\"S\", \"I\", \"R\", \"A\", \"B\", \"C\")) #> Warning: in as.sir(): 3 results in column '24' truncated (50%) that were invalid #> antimicrobial interpretations: \"A\", \"B\" and \"C\" #> Class 'sir' #> [1] S    I    R    <NA> <NA> <NA> as.sir(\"<= 0.002; S\") # will return \"S\" #> Class 'sir' #> [1] S sir_data <- as.sir(c(rep(\"S\", 474), rep(\"I\", 36), rep(\"R\", 370))) is.sir(sir_data) #> [1] TRUE plot(sir_data) # for percentages  barplot(sir_data) # for frequencies   # the dplyr way if (require(\"dplyr\")) {   example_isolates %>%     mutate_at(vars(PEN:RIF), as.sir)   # same:   example_isolates %>%     as.sir(PEN:RIF)    # fastest way to transform all columns with already valid AMR results to class `sir`:   example_isolates %>%     mutate_if(is_sir_eligible, as.sir)    # since dplyr 1.0.0, this can also be:   # example_isolates %>%   #   mutate(across(where(is_sir_eligible), as.sir)) } #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> # }"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":null,"dir":"Reference","previous_headings":"","what":"Get ATC Properties from WHOCC Website — atc_online_property","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Gets data WHOCC website determine properties Anatomical Therapeutic Chemical (ATC) (e.g. antibiotic), name, defined daily dose (DDD) standard unit.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"atc_online_property(   atc_code,   property,   administration = \"O\",   url = \"https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no\",   url_vet = \"https://www.whocc.no/atcvet/atcvet_index/?code=%s&showdescription=no\" )  atc_online_groups(atc_code, ...)  atc_online_ddd(atc_code, ...)  atc_online_ddd_units(atc_code, ...)"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"https://www.whocc./atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"atc_code character (vector) ATC code(s) antibiotics, coerced .ab() ab_atc() internally valid ATC code property property ATC code. Valid values \"ATC\", \"Name\", \"DDD\", \"U\" (\"unit\"), \"Adm.R\", \"Note\" groups. last option, hierarchical groups ATC code returned, see Examples. administration type administration using property = \"Adm.R\", see Details url url website WHOCC. sign %s can used placeholder ATC codes. url_vet url website WHOCC veterinary medicine. sign %s can used placeholder ATC_vet codes (start \"Q\"). ... arguments pass atc_property","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Options argument administration: \"Implant\" = Implant \"Inhal\" = Inhalation \"Instill\" = Instillation \"N\" = nasal \"O\" = oral \"P\" = parenteral \"R\" = rectal \"SL\" = sublingual/buccal \"TD\" = transdermal \"V\" = vaginal Abbreviations return values using property = \"U\" (unit): \"g\" = gram \"mg\" = milligram \"mcg\" = microgram \"U\" = unit \"TU\" = thousand units \"MU\" = million units \"mmol\" = millimole \"ml\" = millilitre (e.g. eyedrops) N.B. function requires internet connection works following packages installed: curl, rvest, xml2.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"# \\donttest{ if (requireNamespace(\"curl\") && requireNamespace(\"rvest\") && requireNamespace(\"xml2\")) {   # oral DDD (Defined Daily Dose) of amoxicillin   atc_online_property(\"J01CA04\", \"DDD\", \"O\")   atc_online_ddd(ab_atc(\"amox\"))    # parenteral DDD (Defined Daily Dose) of amoxicillin   atc_online_property(\"J01CA04\", \"DDD\", \"P\")    atc_online_property(\"J01CA04\", property = \"groups\") # search hierarchical groups of amoxicillin } #> Loading required namespace: rvest #> [1] \"ANTIINFECTIVES FOR SYSTEMIC USE\"         #> [2] \"ANTIBACTERIALS FOR SYSTEMIC USE\"         #> [3] \"BETA-LACTAM ANTIBACTERIALS, PENICILLINS\" #> [4] \"Penicillins with extended spectrum\"      # }"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Use function e.g. clinical texts health care records. returns list antiviral drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text(   text,   type = c(\"drug\", \"dose\", \"administration\"),   collapse = NULL,   translate_av = FALSE,   thorough_search = NULL,   info = interactive(),   ... )"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_av type = \"drug\": column name antivirals data set translate antibiotic abbreviations , using av_property(). Defaults FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed .av()","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"list,  character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"function also internally used .av(), although searches first drug name throw note drug names returned. Note: .av() function may use long regular expression match brand names antiviral drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"default, function search antiviral drug names. text elements searched official names, ATC codes brand names. uses .av() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(avx = av_from_text(clinical_text)) returned AV codes can transformed official names, groups, etc. av_* functions av_name() av_group(), using translate_av argument. using collapse, function return character:df %>% mutate(avx = av_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text(\"28/03/2020 valaciclovir po tid\") #> [[1]] #> Class 'av' #> [1] VALA #>  av_from_text(\"28/03/2020 valaciclovir po tid\", type = \"admin\") #> [[1]] #> [1] \"oral\" #>"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antiviral Drug — av_property","title":"Get Properties of an Antiviral Drug — av_property","text":"Use functions return specific property antiviral drug antivirals data set. input values evaluated internally .av().","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"av_name(x, language = get_AMR_locale(), tolower = FALSE, ...)  av_cid(x, ...)  av_synonyms(x, ...)  av_tradenames(x, ...)  av_group(x, language = get_AMR_locale(), ...)  av_atc(x, ...)  av_loinc(x, ...)  av_ddd(x, administration = \"oral\", ...)  av_ddd_units(x, administration = \"oral\", ...)  av_info(x, language = get_AMR_locale(), ...)  av_url(x, open = FALSE, ...)  av_property(x, property = \"name\", language = get_AMR_locale(), ...)"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antiviral Drug — av_property","text":"x (vector ) text can coerced valid antiviral drug code .av() language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. ... arguments passed .av() administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antivirals data set: vector_or(colnames(antivirals), sort = FALSE).","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antiviral Drug — av_property","text":"integer case av_cid() named list case av_info() multiple av_atc()/av_synonyms()/av_tradenames() double case av_ddd() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antiviral Drug — av_property","text":"output translated possible. function av_url() return direct URL official website. warning returned required ATC code available.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antiviral Drug — av_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antiviral Drug — av_property","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"# all properties: av_name(\"ACI\") #> [1] \"Aciclovir\" av_atc(\"ACI\") #> [1] \"J05AB01\" av_cid(\"ACI\") #> [1] 135398513 av_synonyms(\"ACI\") #>  [1] \"acicloftal\"        \"aciclovier\"        \"aciclovirum\"       #>  [4] \"activir\"           \"acyclofoam\"        \"acycloguanosine\"   #>  [7] \"acyclovir\"         \"acyclovir lauriad\" \"avaclyr\"           #> [10] \"cargosil\"          \"cyclovir\"          \"genvir\"            #> [13] \"gerpevir\"          \"hascovir\"          \"maynar\"            #> [16] \"novirus\"           \"poviral\"           \"sitavig\"           #> [19] \"sitavir\"           \"vipral\"            \"viropump\"          #> [22] \"virorax\"           \"zovirax\"           \"zyclir\"            av_tradenames(\"ACI\") #>  [1] \"acicloftal\"        \"aciclovier\"        \"aciclovirum\"       #>  [4] \"activir\"           \"acyclofoam\"        \"acycloguanosine\"   #>  [7] \"acyclovir\"         \"acyclovir lauriad\" \"avaclyr\"           #> [10] \"cargosil\"          \"cyclovir\"          \"genvir\"            #> [13] \"gerpevir\"          \"hascovir\"          \"maynar\"            #> [16] \"novirus\"           \"poviral\"           \"sitavig\"           #> [19] \"sitavir\"           \"vipral\"            \"viropump\"          #> [22] \"virorax\"           \"zovirax\"           \"zyclir\"            av_group(\"ACI\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_url(\"ACI\") #>                                                             Aciclovir  #> \"https://www.whocc.no/atc_ddd_index/?code=J05AB01&showdescription=no\"   # smart lowercase tranformation av_name(x = c(\"ACI\", \"VALA\")) #> [1] \"Aciclovir\"    \"Valaciclovir\" av_name(x = c(\"ACI\", \"VALA\"), tolower = TRUE) #> [1] \"aciclovir\"    \"valaciclovir\"  # defined daily doses (DDD) av_ddd(\"ACI\", \"oral\") #> [1] 4 av_ddd_units(\"ACI\", \"oral\") #> [1] \"g\" av_ddd(\"ACI\", \"iv\") #> [1] 4 av_ddd_units(\"ACI\", \"iv\") #> [1] \"g\"  av_info(\"ACI\") # all properties as a list #> $av #> [1] \"ACI\" #>  #> $cid #> [1] 135398513 #>  #> $name #> [1] \"Aciclovir\" #>  #> $group #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" #>  #> $atc #> [1] \"J05AB01\" #>  #> $tradenames #>  [1] \"acicloftal\"        \"aciclovier\"        \"aciclovirum\"       #>  [4] \"activir\"           \"acyclofoam\"        \"acycloguanosine\"   #>  [7] \"acyclovir\"         \"acyclovir lauriad\" \"avaclyr\"           #> [10] \"cargosil\"          \"cyclovir\"          \"genvir\"            #> [13] \"gerpevir\"          \"hascovir\"          \"maynar\"            #> [16] \"novirus\"           \"poviral\"           \"sitavig\"           #> [19] \"sitavir\"           \"vipral\"            \"viropump\"          #> [22] \"virorax\"           \"zovirax\"           \"zyclir\"            #>  #> $loinc #> [1] \"\" #>  #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 4 #>  #> $ddd$oral$units #> [1] \"g\" #>  #>  #> $ddd$iv #> $ddd$iv$amount #> [1] 4 #>  #> $ddd$iv$units #> [1] \"g\" #>  #>  #>   # all av_* functions use as.av() internally, so you can go from 'any' to 'any': av_atc(\"ACI\") #> [1] \"J05AB01\" av_group(\"J05AB01\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_loinc(\"abacavir\") #> [1] \"29113-8\" \"78772-1\" \"78773-9\" \"79134-3\" \"80118-3\" av_name(\"29113-8\") #> [1] \"Abacavir\" av_name(135398513) #> [1] \"Aciclovir\" av_name(\"J05AB01\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Availability of Columns — availability","title":"Check Availability of Columns — availability","text":"Easy check data availability columns data set. makes easy get idea antimicrobial combinations can used calculation e.g. susceptibility() resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Availability of Columns — availability","text":"","code":"availability(tbl, width = NULL)"},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Availability of Columns — availability","text":"tbl data.frame list width number characters present visual availability, defaults filling width console","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Availability of Columns — availability","text":"data.frame column names tbl row names","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check Availability of Columns — availability","text":"function returns data.frame columns \"resistant\" \"visual_resistance\". values columns calculated resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check Availability of Columns — availability","text":"","code":"availability(example_isolates) #>         count available     visual_availabilty resistant      visual_resistance #> date     2000    100.0% |####################|                                  #> patient  2000    100.0% |####################|                                  #> age      2000    100.0% |####################|                                  #> gender   2000    100.0% |####################|                                  #> ward     2000    100.0% |####################|                                  #> mo       2000    100.0% |####################|                                  #> PEN      1629     81.5% |################----|     73.7% |##############------| #> OXA       365     18.3% |###-----------------|     31.2% |######--------------| #> FLC       943     47.2% |#########-----------|     29.5% |#####---------------| #> AMX      1350     67.5% |#############-------|     59.6% |###########---------| #> AMC      1879     94.0% |##################--|     23.7% |####----------------| #> AMP      1350     67.5% |#############-------|     59.6% |###########---------| #> TZP      1001     50.0% |##########----------|     12.6% |##------------------| #> CZO       446     22.3% |####----------------|     44.6% |########------------| #> FEP       724     36.2% |#######-------------|     14.2% |##------------------| #> CXM      1789     89.5% |#################---|     26.3% |#####---------------| #> FOX       818     40.9% |########------------|     27.4% |#####---------------| #> CTX       943     47.2% |#########-----------|     15.5% |###-----------------| #> CAZ      1811     90.6% |##################--|     66.5% |#############-------| #> CRO       943     47.2% |#########-----------|     15.5% |###-----------------| #> GEN      1855     92.8% |##################--|     24.6% |####----------------| #> TOB      1351     67.6% |#############-------|     34.4% |######--------------| #> AMK       692     34.6% |######--------------|     63.7% |############--------| #> KAN       471     23.6% |####----------------|    100.0% |####################| #> TMP      1499     75.0% |###############-----|     38.1% |#######-------------| #> SXT      1759     88.0% |#################---|     20.5% |####----------------| #> NIT       743     37.2% |#######-------------|     17.1% |###-----------------| #> FOS       351     17.6% |###-----------------|     42.2% |########------------| #> LNZ      1023     51.2% |##########----------|     69.3% |#############-------| #> CIP      1409     70.5% |#############-------|     16.2% |###-----------------| #> MFX       211     10.6% |##------------------|     33.6% |######--------------| #> VAN      1861     93.1% |##################--|     38.3% |#######-------------| #> TEC       976     48.8% |#########-----------|     75.7% |###############-----| #> TCY      1200     60.0% |###########---------|     29.8% |#####---------------| #> TGC       798     39.9% |########------------|     12.7% |##------------------| #> DOX      1136     56.8% |###########---------|     27.7% |#####---------------| #> ERY      1894     94.7% |##################--|     57.2% |###########---------| #> CLI      1520     76.0% |###############-----|     61.2% |############--------| #> AZM      1894     94.7% |##################--|     57.2% |###########---------| #> IPM       889     44.5% |########------------|      6.2% |#-------------------| #> MEM       829     41.5% |########------------|      5.9% |#-------------------| #> MTR        34      1.7% |--------------------|     14.7% |##------------------| #> CHL       154      7.7% |#-------------------|     21.4% |####----------------| #> COL      1640     82.0% |################----|     81.2% |################----| #> MUP       270     13.5% |##------------------|      5.9% |#-------------------| #> RIF      1003     50.2% |##########----------|     69.6% |#############-------| # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     filter(mo == as.mo(\"Escherichia coli\")) %>%     select_if(is.sir) %>%     availability() } #>     count available       visual_availabilty resistant        visual_resistance #> PEN   467    100.0% |######################|    100.0% |######################| #> OXA     0      0.0% |----------------------|                                    #> FLC     0      0.0% |----------------------|                                    #> AMX   392     83.9% |##################----|     50.0% |###########-----------| #> AMC   467    100.0% |######################|     13.1% |##--------------------| #> AMP   392     83.9% |##################----|     50.0% |###########-----------| #> TZP   416     89.1% |###################---|      5.5% |#---------------------| #> CZO    82     17.6% |###-------------------|      2.4% |----------------------| #> FEP   317     67.9% |##############--------|      2.8% |----------------------| #> CXM   465     99.6% |######################|      5.4% |#---------------------| #> FOX   377     80.7% |#################-----|      6.9% |#---------------------| #> CTX   459     98.3% |#####################-|      2.4% |----------------------| #> CAZ   460     98.5% |#####################-|      2.4% |----------------------| #> CRO   459     98.3% |#####################-|      2.4% |----------------------| #> GEN   460     98.5% |#####################-|      2.0% |----------------------| #> TOB   462     98.9% |#####################-|      2.6% |----------------------| #> AMK   171     36.6% |########--------------|      0.0% |----------------------| #> KAN     0      0.0% |----------------------|                                    #> TMP   396     84.8% |##################----|     39.1% |########--------------| #> SXT   465     99.6% |######################|     31.6% |######----------------| #> NIT   458     98.1% |#####################-|      2.8% |----------------------| #> FOS    61     13.1% |##--------------------|      0.0% |----------------------| #> LNZ   467    100.0% |######################|    100.0% |######################| #> CIP   456     97.6% |#####################-|     12.5% |##--------------------| #> MFX    57     12.2% |##--------------------|    100.0% |######################| #> VAN   467    100.0% |######################|    100.0% |######################| #> TEC   467    100.0% |######################|    100.0% |######################| #> TCY     3      0.6% |----------------------|     66.7% |##############--------| #> TGC    68     14.6% |###-------------------|      0.0% |----------------------| #> DOX     0      0.0% |----------------------|                                    #> ERY   467    100.0% |######################|    100.0% |######################| #> CLI   467    100.0% |######################|    100.0% |######################| #> AZM   467    100.0% |######################|    100.0% |######################| #> IPM   422     90.4% |###################---|      0.0% |----------------------| #> MEM   418     89.5% |###################---|      0.0% |----------------------| #> MTR     2      0.4% |----------------------|      0.0% |----------------------| #> CHL     0      0.0% |----------------------|                                    #> COL   240     51.4% |###########-----------|      0.0% |----------------------| #> MUP     0      0.0% |----------------------|                                    #> RIF   467    100.0% |######################|    100.0% |######################| # }"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Bug-Drug Combinations — bug_drug_combinations","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"Determine antimicrobial resistance (AMR) bug-drug combinations data set least 30 (default) isolates available per species. Use format() result prettify publishable/printable format, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...)  # S3 method for bug_drug_combinations format(   x,   translate_ab = \"name (ab, atc)\",   language = get_AMR_locale(),   minimum = 30,   combine_SI = TRUE,   add_ab_group = TRUE,   remove_intrinsic_resistant = FALSE,   decimal.mark = getOption(\"OutDec\"),   big.mark = ifelse(decimal.mark == \",\", \".\", \",\"),   ... )"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"x data set antibiotic columns, amox, AMX AMC col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). FUN function call mo column transform microorganism codes, defaults mo_shortname() ... arguments passed FUN translate_ab character length 1 containing column names antibiotics data set language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether values S summed, resistance based R, defaults TRUE add_ab_group logical indicate group antimicrobials must included first column remove_intrinsic_resistant logical indicate rows columns 100% resistance tested antimicrobials must removed table decimal.mark character used indicate numeric     decimal point. big.mark character; empty used mark every     big.interval decimals (hence big)     decimal point.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function bug_drug_combinations() returns data.frame columns \"mo\", \"ab\", \"S\", \"\", \"R\" \"total\".","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function format() calculates resistance per bug-drug combination. Use combine_SI = TRUE (default) test R vs. S+combine_SI = FALSE test R+vs. S.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"# \\donttest{ x <- bug_drug_combinations(example_isolates) head(x) #> # A tibble: 6 × 6 #>   mo                ab        S     I     R total #>   <chr>             <chr> <int> <int> <int> <int> #> 1 (unknown species) PEN      14     0     1    15 #> 2 (unknown species) OXA       0     0     1     1 #> 3 (unknown species) FLC       0     0     0     0 #> 4 (unknown species) AMX      15     0     1    16 #> 5 (unknown species) AMC      15     0     0    15 #> 6 (unknown species) AMP      15     0     1    16 #> Use 'format()' on this result to get a publishable/printable format. format(x, translate_ab = \"name (atc)\") #> # A tibble: 39 × 12 #>    Group     Drug  CoNS  E. co…¹ E. fa…² K. pn…³ P. ae…⁴ P. mi…⁵ S. au…⁶ S. ep…⁷ #>    <chr>     <chr> <chr> <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   #>  1 \"Aminogl… Amik… \"100… \"  0.0… \"100.0… \"\"      \"\"      \"\"      \"\"      \"100.0… #>  2 \"\"        Gent… \" 13… \"  2.0… \"100.0… \" 10.3… \"  0.0… \"  5.9… \"  0.9… \" 21.5… #>  3 \"\"        Kana… \"100… \"\"      \"100.0… \"\"      \"100.0… \"\"      \"\"      \"100.0… #>  4 \"\"        Tobr… \" 78… \"  2.6… \"100.0… \" 10.3… \"  0.0… \"  5.9… \"  2.3… \" 49.4… #>  5 \"Ampheni… Chlo… \"\"    \"\"      \"\"      \"\"      \"100.0… \"\"      \"  0.0… \"  3.1… #>  6 \"Antimyc… Rifa… \"\"    \"100.0… \"\"      \"100.0… \"100.0… \"100.0… \"  0.0… \"  2.7… #>  7 \"Beta-la… Amox… \" 93… \" 50.0… \"\"      \"100.0… \"100.0… \"\"      \" 93.9… \" 98.9… #>  8 \"\"        Amox… \" 42… \" 13.1… \"\"      \" 10.3… \"100.0… \"  2.8… \"  0.4… \" 54.5… #>  9 \"\"        Ampi… \" 93… \" 50.0… \"\"      \"100.0… \"100.0… \"\"      \" 93.9… \" 98.9… #> 10 \"\"        Benz… \" 77… \"100.0… \"\"      \"100.0… \"100.0… \"100.0… \" 80.9… \" 89.4… #> # … with 29 more rows, 2 more variables: `S. hominis` <chr>, #> #   `S. pneumoniae` <chr>, and abbreviated variable names ¹​`E. coli`, #> #   ²​`E. faecalis`, ³​`K. pneumoniae`, ⁴​`P. aeruginosa`, ⁵​`P. mirabilis`, #> #   ⁶​`S. aureus`, ⁷​`S. epidermidis`  # Use FUN to change to transformation of microorganism codes bug_drug_combinations(example_isolates,   FUN = mo_gramstain ) #> # A tibble: 80 × 6 #>    mo            ab        S     I     R total #>  * <chr>         <chr> <int> <int> <int> <int> #>  1 Gram-negative PEN       8     0   717   725 #>  2 Gram-negative OXA       6     0     0     6 #>  3 Gram-negative FLC       6     0     0     6 #>  4 Gram-negative AMX     226     0   405   631 #>  5 Gram-negative AMC     463    89   174   726 #>  6 Gram-negative AMP     226     0   405   631 #>  7 Gram-negative TZP     554    11    76   641 #>  8 Gram-negative CZO      94     2   110   206 #>  9 Gram-negative FEP     470     1    14   485 #> 10 Gram-negative CXM     539    22   142   703 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format.  bug_drug_combinations(example_isolates,   FUN = function(x) {     ifelse(x == as.mo(\"Escherichia coli\"),       \"E. coli\",       \"Others\"     )   } ) #> # A tibble: 80 × 6 #>    mo      ab        S     I     R total #>  * <chr>   <chr> <int> <int> <int> <int> #>  1 E. coli PEN       0     0   467   467 #>  2 E. coli OXA       0     0     0     0 #>  3 E. coli FLC       0     0     0     0 #>  4 E. coli AMX     196     0   196   392 #>  5 E. coli AMC     332    74    61   467 #>  6 E. coli AMP     196     0   196   392 #>  7 E. coli TZP     388     5    23   416 #>  8 E. coli CZO      79     1     2    82 #>  9 E. coli FEP     308     0     9   317 #> 10 E. coli CXM     425    15    25   465 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format. # }"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Data set containing clinical breakpoints interpret MIC disk diffusion SIR values, according international guidelines. Currently implemented guidelines EUCAST (2013-2022) CLSI (2013-2022). Use .sir() transform MICs disks measurements SIR values.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"tibble 18,308 observations 11 variables: guideline Name guideline method Either \"DISK\" \"MIC\" site Body site, e.g. \"Oral\" \"Respiratory\" mo Microbial ID, see .mo() rank_index Taxonomic rank index mo 1 (subspecies/infraspecies) 5 (unknown microorganism) ab Antibiotic ID, see .ab() ref_tbl Info guideline rule can found disk_dose Dose used disk diffusion method breakpoint_S Lowest MIC value highest number millimetres leads \"S\" breakpoint_R Highest MIC value lowest number millimetres leads \"R\" uti logical value (TRUE/FALSE) indicate whether rule applies urinary tract infection (UTI)","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository. allow machine reading EUCAST CLSI guidelines, almost impossible MS Excel PDF files distributed EUCAST CLSI.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints #> # A tibble: 18,308 × 11 #>    guideline method site  mo               rank_…¹ ab   ref_tbl  disk_…² break…³ #>    <chr>     <chr>  <chr> <mo>               <dbl> <ab> <chr>    <chr>     <dbl> #>  1 EUCAST 2… MIC    NA    F_ASPRG_MGTS           2 AMB  Aspergi… NA            1 #>  2 EUCAST 2… MIC    NA    F_ASPRG_NIGR           2 AMB  Aspergi… NA            1 #>  3 EUCAST 2… MIC    NA    F_CANDD_ALBC           2 AMB  Candida  NA            1 #>  4 EUCAST 2… MIC    NA    F_CANDD_DBLN           2 AMB  Candida  NA            1 #>  5 EUCAST 2… MIC    NA    F_CANDD_GLBR           2 AMB  Candida  NA            1 #>  6 EUCAST 2… MIC    NA    F_CANDD_KRUS           2 AMB  Candida  NA            1 #>  7 EUCAST 2… MIC    NA    F_CANDD_PRPS           2 AMB  Candida  NA            1 #>  8 EUCAST 2… MIC    NA    F_CANDD_TRPC           2 AMB  Candida  NA            1 #>  9 EUCAST 2… MIC    NA    F_CRYPT_NFRM           2 AMB  Candida  NA            1 #> 10 EUCAST 2… DISK   NA    B_[ORD]_ENTRBCTR       5 AMC  Enterob… 20ug/1…      19 #> # … with 18,298 more rows, 2 more variables: breakpoint_R <dbl>, uti <lgl>, and #> #   abbreviated variable names ¹​rank_index, ²​disk_dose, ³​breakpoint_S"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Available Isolates — count","title":"Count Available Isolates — count","text":"functions can used count resistant/susceptible microbial isolates. functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. count_resistant() used count resistant isolates, count_susceptible() used count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Available Isolates — count","text":"","code":"count_resistant(..., only_all_tested = FALSE)  count_susceptible(..., only_all_tested = FALSE)  count_R(..., only_all_tested = FALSE)  count_IR(..., only_all_tested = FALSE)  count_I(..., only_all_tested = FALSE)  count_SI(..., only_all_tested = FALSE)  count_S(..., only_all_tested = FALSE)  count_all(..., only_all_tested = FALSE)  n_sir(..., only_all_tested = FALSE)  count_df(   data,   translate_ab = \"name\",   language = get_AMR_locale(),   combine_SI = TRUE )"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Available Isolates — count","text":"... one vectors (columns) antibiotic interpretations. transformed internally .sir() needed. only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antibiotics, see section Combination Therapy data data.frame containing columns class sir (see .sir()) translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant), defaults TRUE","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Available Isolates — count","text":"integer","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Count Available Isolates — count","text":"functions meant count isolates. Use resistance()/susceptibility() functions calculate microbial resistance/susceptibility. function count_resistant() equal function count_R(). function count_susceptible() equal function count_SI(). function n_sir() alias count_all(). can used count available isolates, .e. input antibiotics available result (S, R). use equal n_distinct(). function equal count_susceptible(...) + count_resistant(...). function count_df() takes variable data sir class (created .sir()) counts number S's, 's R's. also supports grouped variables. function sir_df() works exactly like count_df(), adds percentage S, R.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Count Available Isolates — count","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Count Available Isolates — count","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antibiotics, Drug Drug B, susceptibility() works calculate %SI:   Please note , combination therapies, only_all_tested = TRUE applies :   , combination therapies, only_all_tested = FALSE applies :   Using only_all_tested impact using one antibiotic input.","code":"--------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   include as  include as   include as  include as                     numerator   denominator  numerator   denominator --------  --------  ----------  -----------  ----------  -----------  S or I    S or I       X            X            X            X    R       S or I       X            X            X            X   <NA>     S or I       X            X            -            -  S or I      R          X            X            X            X    R         R          -            X            -            X   <NA>       R          -            -            -            -  S or I     <NA>        X            X            -            -    R        <NA>        -            -            -            -   <NA>      <NA>        -            -            -            - -------------------------------------------------------------------- count_S()    +   count_I()    +   count_R()    = count_all()   proportion_S() + proportion_I() + proportion_R() = 1 count_S()    +   count_I()    +   count_R()    >= count_all()   proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Available Isolates — count","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info.  # base R ------------------------------------------------------------ count_resistant(example_isolates$AMX) # counts \"R\" #> [1] 804 count_susceptible(example_isolates$AMX) # counts \"S\" and \"I\" #> [1] 546 count_all(example_isolates$AMX) # counts \"S\", \"I\" and \"R\" #> [1] 1350  # be more specific count_S(example_isolates$AMX) #> Using count_S() is discouraged; use count_susceptible() instead to also #> consider \"I\" being susceptible. This note will be shown once for this #> session. #> [1] 543 count_SI(example_isolates$AMX) #> [1] 546 count_I(example_isolates$AMX) #> [1] 3 count_IR(example_isolates$AMX) #> Using count_IR() is discouraged; use count_resistant() instead to not #> consider \"I\" being resistant. This note will be shown once for this #> session. #> [1] 807 count_R(example_isolates$AMX) #> [1] 804  # Count all available isolates count_all(example_isolates$AMX) #> [1] 1350 n_sir(example_isolates$AMX) #> [1] 1350  # n_sir() is an alias of count_all(). # Since it counts all available isolates, you can # calculate back to count e.g. susceptible isolates. # These results are the same: count_susceptible(example_isolates$AMX) #> [1] 546 susceptibility(example_isolates$AMX) * n_sir(example_isolates$AMX) #> [1] 546  # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     group_by(ward) %>%     summarise(       R = count_R(CIP),       I = count_I(CIP),       S = count_S(CIP),       n1 = count_all(CIP), # the actual total; sum of all three       n2 = n_sir(CIP), # same - analogous to n_distinct       total = n()     ) # NOT the number of tested isolates!    # Number of available isolates for a whole antibiotic class   # (i.e., in this data set columns GEN, TOB, AMK, KAN)   example_isolates %>%     group_by(ward) %>%     summarise(across(aminoglycosides(), n_sir))    # Count co-resistance between amoxicillin/clav acid and gentamicin,   # so we can see that combination therapy does a lot more than mono therapy.   # Please mind that `susceptibility()` calculates percentages right away instead.   example_isolates %>% count_susceptible(AMC) # 1433   example_isolates %>% count_all(AMC) # 1879    example_isolates %>% count_susceptible(GEN) # 1399   example_isolates %>% count_all(GEN) # 1855    example_isolates %>% count_susceptible(AMC, GEN) # 1764   example_isolates %>% count_all(AMC, GEN) # 1936    # Get number of S+I vs. R immediately of selected columns   example_isolates %>%     select(AMX, CIP) %>%     count_df(translate = FALSE)    # It also supports grouping variables   example_isolates %>%     select(ward, AMX, CIP) %>%     group_by(ward) %>%     count_df(translate = FALSE) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 12 × 4 #>    ward       antibiotic interpretation value #>  * <chr>      <chr>      <ord>          <int> #>  1 Clinical   AMX        SI               357 #>  2 Clinical   AMX        R                487 #>  3 Clinical   CIP        SI               741 #>  4 Clinical   CIP        R                128 #>  5 ICU        AMX        SI               158 #>  6 ICU        AMX        R                270 #>  7 ICU        CIP        SI               362 #>  8 ICU        CIP        R                 85 #>  9 Outpatient AMX        SI                31 #> 10 Outpatient AMX        R                 47 #> 11 Outpatient CIP        SI                78 #> 12 Outpatient CIP        R                 15 # }"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Define Custom EUCAST Rules — custom_eucast_rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"Define custom EUCAST rules organisation specific analysis use output function eucast_rules().","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"custom_eucast_rules(...)"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"... rules formula notation, see Examples","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"list containing custom rules","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"organisations adoption EUCAST rules. function can used define custom EUCAST rules used eucast_rules() function.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"basics","dir":"Reference","previous_headings":"","what":"Basics","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde:   two custom EUCAST rules: TZP (piperacillin/tazobactam) \"S\", aminopenicillins (ampicillin amoxicillin) must made \"S\", TZP \"R\", aminopenicillins must made \"R\". rules can also printed console, immediately clear work:   rules (part tilde, example TZP == \"S\" TZP == \"R\") must evaluable data set: able run filter data set without errors. means example column TZP must exist. create sample data set test rules set:","code":"x <- custom_eucast_rules(TZP == \"S\" ~ aminopenicillins == \"S\",                          TZP == \"R\" ~ aminopenicillins == \"R\") x #> A set of custom EUCAST rules: #> #>   1. If TZP is \"S\" then set to  S : #>      amoxicillin (AMX), ampicillin (AMP) #> #>   2. If TZP is \"R\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) df <- data.frame(mo = c(\"Escherichia coli\", \"Klebsiella pneumoniae\"),                  TZP = as.sir(\"R\"),                  ampi = as.sir(\"S\"),                  cipro = as.sir(\"S\")) df #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    S     S #> 2 Klebsiella pneumoniae   R    S     S  eucast_rules(df, rules = \"custom\", custom_rules = x, info = FALSE) #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    R     S #> 2 Klebsiella pneumoniae   R    R     S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"using-taxonomic-properties-in-rules","dir":"Reference","previous_headings":"","what":"Using taxonomic properties in rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"one exception columns used rules: column names microorganisms data set can also used, exist data set. column names : \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\" \"snomed\". Thus, next example work well, despite fact df data set contain column genus:","code":"y <- custom_eucast_rules(TZP == \"S\" & genus == \"Klebsiella\" ~ aminopenicillins == \"S\",                          TZP == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\")  eucast_rules(df, rules = \"custom\", custom_rules = y, info = FALSE) #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    S     S #> 2 Klebsiella pneumoniae   R    R     S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"usage-of-antibiotic-group-names","dir":"Reference","previous_headings":"","what":"Usage of antibiotic group names","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"possible define antibiotic groups instead single antibiotics rule consequence, part tilde. examples, antibiotic group aminopenicillins used include ampicillin amoxicillin. following groups allowed (case-insensitive). Within parentheses drugs matched running rule. \"aminoglycosides\"(amikacin, amikacin/fosfomycin, amphotericin B-high, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin tobramycin-high) \"aminopenicillins\"(amoxicillin ampicillin) \"antifungals\"(amphotericin B, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, flucytosine, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, oteseconazole, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole voriconazole) \"antimycobacterials\"(4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, morinamide, p-aminosalicylic acid, pretomanid, protionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, simvastatin/fenofibrate, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone, thioacetazone/isoniazid, tiocarlide viomycin) \"betalactams\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, biapenem, carbenicillin, carindacillin, cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, ciclacillin, clometocillin, cloxacillin, dicloxacillin, doripenem, epicillin, ertapenem, flucloxacillin, hetacillin, imipenem, imipenem/EDTA, imipenem/relebactam, latamoxef, lenampicillin, loracarbef, mecillinam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, tebipenem, temocillin, ticarcillin ticarcillin/clavulanic acid) \"carbapenems\"(biapenem, doripenem, ertapenem, imipenem, imipenem/EDTA, imipenem/relebactam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, panipenem, razupenem, ritipenem, ritipenem acoxil tebipenem) \"cephalosporins\"(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef loracarbef) \"cephalosporins_1st\"(cefacetrile, cefadroxil, cefalexin, cefaloridine, cefalotin, cefapirin, cefatrizine, cefazedone, cefazolin, cefroxadine, ceftezole cephradine) \"cephalosporins_2nd\"(cefaclor, cefamandole, cefmetazole, cefonicid, ceforanide, cefotetan, cefotiam, cefoxitin, cefoxitin screening, cefprozil, cefuroxime, cefuroxime axetil loracarbef) \"cephalosporins_3rd\"(cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefetamet, cefetamet pivoxil, cefixime, cefmenoxime, cefodizime, cefoperazone, cefoperazone/sulbactam, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotiam hexetil, cefovecin, cefpimizole, cefpiramide, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefsulodin, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftriaxone, ceftriaxone/beta-lactamase inhibitor latamoxef) \"cephalosporins_4th\"(cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetecol, cefoselis, cefozopran, cefpirome cefquinome) \"cephalosporins_5th\"(ceftaroline, ceftaroline/avibactam, ceftobiprole, ceftobiprole medocaril ceftolozane/tazobactam) \"cephalosporins_except_caz\"(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef loracarbef) \"fluoroquinolones\"(besifloxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nifuroquine, norfloxacin, ofloxacin, orbifloxacin, pazufloxacin, pefloxacin, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin trovafloxacin) \"glycopeptides\"(avoparcin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin vancomycin-macromethod) \"glycopeptides_except_lipo\"(avoparcin, norvancomycin, ramoplanin, teicoplanin, teicoplanin-macromethod, vancomycin vancomycin-macromethod) \"lincosamides\"(acetylmidecamycin, acetylspiramycin, clindamycin, gamithromycin, kitasamycin, lincomycin, meleumycin, nafithromycin, pirlimycin, primycin, solithromycin, tildipirosin, tilmicosin, tulathromycin, tylosin tylvalosin) \"lipoglycopeptides\"(dalbavancin, oritavancin telavancin) \"macrolides\"(acetylmidecamycin, acetylspiramycin, azithromycin, clarithromycin, dirithromycin, erythromycin, flurithromycin, gamithromycin, josamycin, kitasamycin, meleumycin, midecamycin, miocamycin, nafithromycin, oleandomycin, pirlimycin, primycin, rokitamycin, roxithromycin, solithromycin, spiramycin, telithromycin, tildipirosin, tilmicosin, troleandomycin, tulathromycin, tylosin tylvalosin) \"oxazolidinones\"(cadazolid, cycloserine, linezolid, tedizolid thiacetazone) \"penicillins\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, carbenicillin, carindacillin, cefepime/nacubactam, ciclacillin, clometocillin, cloxacillin, dicloxacillin, epicillin, flucloxacillin, hetacillin, lenampicillin, mecillinam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, temocillin, ticarcillin ticarcillin/clavulanic acid) \"polymyxins\"(colistin, polymyxin B polymyxin B/polysorbate 80) \"quinolones\"(besifloxacin, cinoxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, flumequine, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nalidixic acid, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, ofloxacin, orbifloxacin, oxolinic acid, pazufloxacin, pefloxacin, pipemidic acid, piromidic acid, pradofloxacin, premafloxacin, prulifloxacin, rosoxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin trovafloxacin) \"streptogramins\"(pristinamycin quinupristin/dalfopristin) \"tetracyclines\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline tigecycline) \"tetracyclines_except_tgc\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline tetracycline) \"trimethoprims\"(brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim trimethoprim/sulfamethoxazole) \"ureidopenicillins\"(azlocillin, mezlocillin, piperacillin piperacillin/tazobactam)","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"x <- custom_eucast_rules(   AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\",   AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\" ) x #> A set of custom EUCAST rules: #>  #>   1. If AMC is \"R\" and genus is \"Klebsiella\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   2. If AMC is \"I\" and genus is \"Klebsiella\" then set to  I : #>      amoxicillin (AMX), ampicillin (AMP)  # run the custom rule set (verbose = TRUE will return a logbook instead of the data set): eucast_rules(example_isolates,   rules = \"custom\",   custom_rules = x,   info = FALSE,   verbose = TRUE ) #> # A tibble: 8 × 9 #>     row col   mo_fullname           old   new   rule     rule_…¹ rule_…² rule_…³ #>   <int> <chr> <chr>                 <ord> <chr> <chr>    <chr>   <chr>   <chr>   #> 1    33 AMP   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 2    33 AMX   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 3    34 AMP   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 4    34 AMX   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 5   531 AMP   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 6   531 AMX   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 7  1485 AMP   Klebsiella oxytoca    R     I     \"report… Custom… Custom… Object… #> 8  1485 AMX   Klebsiella oxytoca    R     I     \"report… Custom… Custom… Object… #> # … with abbreviated variable names ¹​rule_group, ²​rule_name, ³​rule_source  # combine rule sets x2 <- c(   x,   custom_eucast_rules(TZP == \"R\" ~ carbapenems == \"R\") ) x2 #> A set of custom EUCAST rules: #>  #>   1. If AMC is \"R\" and genus is \"Klebsiella\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   2. If AMC is \"I\" and genus is \"Klebsiella\" then set to  I : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   3. If TZP is \"R\" then set to  R : #>      biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), #>      imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), #>      meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), #>      razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), tebipenem (TBP)"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"EUCAST breakpoints used package based dosages data set. can retrieved eucast_dosage().","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"tibble 336 observations 9 variables: ab Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name antimicrobial drug used WHONET/EARS-Net type Type dosage, either \"high_dosage\", \"standard_dosage\" \"uncomplicated_uti\" dose Dose, \"2 g\" \"25 mg/kg\" dose_times Number times dose must administered administration Route administration, either \"im\", \"iv\" \"oral\" notes Additional dosage notes original_txt Original text PDF file EUCAST eucast_version Version number EUCAST Clinical Breakpoints guideline dosages apply","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"data set based 'EUCAST Clinical Breakpoint Tables' v12.0 (2022) 'EUCAST Clinical Breakpoint Tables' v11.0 (2021).","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage #> # A tibble: 336 × 9 #>    ab   name                   type  dose  dose_…¹ admin…² notes origi…³ eucas…⁴ #>    <ab> <chr>                  <chr> <chr>   <int> <chr>   <chr> <chr>     <dbl> #>  1 AMK  Amikacin               stan… 25-3…       1 iv      \"\"    25-30 …      12 #>  2 AMX  Amoxicillin            high… 2 g         6 iv      \"\"    2 g x …      12 #>  3 AMX  Amoxicillin            stan… 1 g         3 iv      \"\"    1 g x …      12 #>  4 AMX  Amoxicillin            high… 0.75…       3 oral    \"\"    0.75-1…      12 #>  5 AMX  Amoxicillin            stan… 0.5 g       3 oral    \"\"    0.5 g …      12 #>  6 AMX  Amoxicillin            unco… 0.5 g       3 oral    \"\"    0.5 g …      12 #>  7 AMC  Amoxicillin/clavulani… high… 2 g …       3 iv      \"\"    (2 g a…      12 #>  8 AMC  Amoxicillin/clavulani… stan… 1 g …       3 iv      \"\"    (1 g a…      12 #>  9 AMC  Amoxicillin/clavulani… high… 0.87…       3 oral    \"\"    (0.875…      12 #> 10 AMC  Amoxicillin/clavulani… stan… 0.5 …       3 oral    \"\"    (0.5 g…      12 #> # … with 326 more rows, and abbreviated variable names ¹​dose_times, #> #   ²​administration, ³​original_txt, ⁴​eucast_version"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply EUCAST Rules — eucast_rules","title":"Apply EUCAST Rules — eucast_rules","text":"Apply rules clinical breakpoints intrinsic resistance defined European Committee Antimicrobial Susceptibility Testing (EUCAST, https://www.eucast.org), see Source. Use eucast_dosage() get data.frame advised dosages certain bug-drug combination, based dosage data set. improve interpretation antibiogram EUCAST rules applied, non-EUCAST rules can applied default, see Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"eucast_rules(   x,   col_mo = NULL,   info = interactive(),   rules = getOption(\"AMR_eucastrules\", default = c(\"breakpoints\", \"expert\")),   verbose = FALSE,   version_breakpoints = 12,   version_expertrules = 3.3,   ampc_cephalosporin_resistance = NA,   only_sir_columns = FALSE,   custom_rules = NULL,   ... )  eucast_dosage(ab, administration = \"iv\", version_breakpoints = 12)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Apply EUCAST Rules — eucast_rules","text":"EUCAST Expert Rules. Version 2.0, 2012. Leclercq et al. EUCAST expert rules antimicrobial susceptibility testing. Clin Microbiol Infect. 2013;19(2):141-60; doi:10.1111/j.1469-0691.2011.03703.x EUCAST Expert Rules, Intrinsic Resistance Exceptional Phenotypes Tables. Version 3.1, 2016. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.2, 2020. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.3, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 9.0, 2019. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 10.0, 2020. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 11.0, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 12.0, 2022. (link)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply EUCAST Rules — eucast_rules","text":"x data set antibiotic columns, amox, AMX AMC col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console, defaults print interactive sessions rules character vector specifies rules applied. Must one \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). default value can set another value, e.g. using options(AMR_eucastrules = \"\"). using \"custom\", sure fill argument custom_rules . Custom rules can created custom_eucast_rules(). verbose logical turn Verbose mode (default ). Verbose mode, function apply rules data, instead returns data set logbook form extensive info rows columns effected way. Using Verbose mode takes lot time. version_breakpoints version number use EUCAST Clinical Breakpoints guideline. Can either \"12.0\", \"11.0\" \"10.0\". version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can either \"3.3\", \"3.2\" \"3.1\". ampc_cephalosporin_resistance character value applied cefotaxime, ceftriaxone ceftazidime AmpC de-repressed cephalosporin-resistant mutants, defaults NA. Currently works version_expertrules 3.2 higher; version 'EUCAST Expert Rules Enterobacterales' state results cefotaxime, ceftriaxone ceftazidime reported note, results suppressed (emptied) three drugs. value NA (default) argument remove results three drugs, e.g. value \"R\" make results drugs resistant. Use NULL FALSE alter results three drugs AmpC de-repressed cephalosporin-resistant mutants. Using TRUE equal using \"R\".  EUCAST Expert Rules v3.2, rule applies : Citrobacter braakii, Citrobacter freundii, Citrobacter gillenii, Citrobacter murliniae, Citrobacter rodenticum, Citrobacter sedlakii, Citrobacter werkmanii, Citrobacter youngae, Enterobacter, Hafnia alvei, Klebsiella aerogenes, Morganella morganii, Providencia Serratia. only_sir_columns logical indicate whether antibiotic columns must detected transformed class sir (see .sir()) beforehand (defaults FALSE) custom_rules custom rules apply, created custom_eucast_rules() ... column name antibiotic, see section Antibiotics ab (vector ) text can coerced valid antibiotic drug code .ab() administration route administration, either \"im\", \"iv\" \"oral\"","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply EUCAST Rules — eucast_rules","text":"input x, possibly edited values antibiotics. , verbose = TRUE, data.frame original new values affected bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Apply EUCAST Rules — eucast_rules","text":"Note: function translate MIC values SIR values. Use .sir() . Note: ampicillin (AMP, J01CA01) available amoxicillin (AMX, J01CA04) , latter used rules dependency ampicillin. drugs interchangeable comes expression antimicrobial resistance. file containing EUCAST rules located : https://github.com/msberends/AMR/blob/main/data-raw/eucast_rules.tsv.  Note: Old taxonomic names replaced current taxonomy applicable. example, Ochrobactrum anthropi renamed Brucella anthropi 2020; original EUCAST rules v3.1 v3.2 yet contain new taxonomic name. AMR package contains full microbial taxonomy updated 11 December, 2022, see microorganisms.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"custom-rules","dir":"Reference","previous_headings":"","what":"Custom Rules","title":"Apply EUCAST Rules — eucast_rules","text":"Custom rules can created using custom_eucast_rules(), e.g.:","code":"x <- custom_eucast_rules(AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\",                          AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\")  eucast_rules(example_isolates, rules = \"custom\", custom_rules = x)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"-other-rules","dir":"Reference","previous_headings":"","what":"'Other' Rules","title":"Apply EUCAST Rules — eucast_rules","text":"processing, two non-EUCAST rules drug combinations can applied improve efficacy EUCAST rules, reliability data (analysis). rules : drug enzyme inhibitor set S drug without enzyme inhibitor S drug without enzyme inhibitor set R drug enzyme inhibitor R Important examples include amoxicillin amoxicillin/clavulanic acid, trimethoprim trimethoprim/sulfamethoxazole. Needless say, rules work, drugs must available data set. Since rules officially approved EUCAST, applied default. use rules, include \"\" rules argument, use eucast_rules(..., rules = \"\"). can also set option AMR_eucastrules, .e. run options(AMR_eucastrules = \"\").","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"antibiotics","dir":"Reference","previous_headings":"","what":"Antibiotics","title":"Apply EUCAST Rules — eucast_rules","text":"define antibiotics column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antibiotics eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, S01AE08), amoxicillin (AMX, J01MA02), amoxicillin/clavulanic acid (AMC, J01MA23), ampicillin (AMP, J01MA04), ampicillin/sulbactam (SAM, J01MA08), arbekacin (ARB, J01MA19), aspoxicillin (APX, J01MA16), azidocillin (AZD, J01MA15), azithromycin (AZM, J01MA11), azlocillin (AZL, J01MA25), aztreonam (ATM, J01MA12), bacampicillin (BAM, J01MA24), bekanamycin (BEK, J01MA07), benzathine benzylpenicillin (BNB, J01MA14), benzathine phenoxymethylpenicillin (BNP, D10AF05), benzylpenicillin (PEN, J01MA06), besifloxacin (BES, J01MA01), biapenem (BIA, J01MA18), carbenicillin (CRB, J01MA03), carindacillin (CRN, J01MA17), cefacetrile (CAC, J01MA10), cefaclor (CEC, J01MA21), cefadroxil (CFR, J01MA09), cefalexin (LEX, J01MA05), cefaloridine (RID, P01AA05), cefalotin (CEP, J01MA22), cefamandole (MAN, J01MA13), cefapirin (HAP, J01CA01), cefatrizine (CTZ, J01CA04), cefazedone (CZD, J01CA12), cefazolin (CZO, J01CR05), cefcapene (CCP, J01CA13), cefdinir (CDR, J01AA02), cefditoren (DIT, J01FA10), cefepime (FEP, J01FA09), cefetamet (CAT, J01CR02), cefixime (CFM, J01AA08), cefmenoxime (CMX, J01FA06), cefmetazole (CMZ, J01CF04), cefodizime (DIZ, J01CF05), cefonicid (CID, J01CR01), cefoperazone (CFP, J01CA19), cefoperazone/sulbactam (CSL, J01CE04), ceforanide (CND, J01CA09), cefotaxime (CTX, J01DF01), cefotaxime/clavulanic acid (CTC, J01CA06), cefotetan (CTT, J01CE08), cefotiam (CTF, J01CE10), cefoxitin (FOX, J01CE01), cefozopran (ZOP, J01CA03), cefpiramide (CPM, J01CA05), cefpirome (CPO, J01CE07), cefpodoxime (CPD, J01CF02), cefprozil (CPR, J01CF01), cefroxadine (CRD, J01CA07), cefsulodin (CFS, J01CA18), ceftaroline (CPT, J01CA11), ceftazidime (CAZ, J01CA14), ceftazidime/clavulanic acid (CCV, J01CF03), cefteram (CEM, J01CA10), ceftezole (CTL, J01CF06), ceftibuten (CTB, J01CE06), ceftizoxime (CZX, J01CE05), ceftobiprole medocaril (CFM1, J01CE02), ceftolozane/tazobactam (CZT, J01CA02), ceftriaxone (CRO, J01CA08), ceftriaxone/beta-lactamase inhibitor (CEB, J01CE09), cefuroxime (CXM, J01CE03), cephradine (CED, J01CG01), chloramphenicol (CHL, J01CA16), ciprofloxacin (CIP, J01CR04), clarithromycin (CLR, J01CA15), clindamycin (CLI, J01CG02), clometocillin (CLM, J01CA17), cloxacillin (CLO, J01CR03), colistin (COL, J01DB10), cycloserine (CYC, J01DC04), dalbavancin (DAL, J01DB05), daptomycin (DAP, J01DB01), delafloxacin (DFX, J01DB02), dibekacin (DKB, J01DB03), dicloxacillin (DIC, J01DC03), dirithromycin (DIR, J01DB08), doripenem (DOR, J01DB07), doxycycline (DOX, J01DB06), enoxacin (ENX, J01DB04), epicillin (EPC, J01DD17), ertapenem (ETP, J01DD15), erythromycin (ERY, J01DD16), fleroxacin (FLE, J01DE01), flucloxacillin (FLC, J01DD10), flurithromycin (FLR1, J01DD08), fosfomycin (FOS, J01DD05), framycetin (FRM, J01DC09), fusidic acid (FUS, J01DD09), garenoxacin (GRN, J01DC06), gatifloxacin (GAT, J01DD12), gemifloxacin (GEM, J01DD62), gentamicin (GEN, J01DC11), grepafloxacin (GRX, J01DD01), hetacillin (HET, J01DD51), imipenem (IPM, J01DC05), imipenem/relebactam (IMR, J01DC07), isepamicin (ISE, J01DC01), josamycin (JOS, J01DE03), kanamycin (KAN, J01DD11), lascufloxacin (LSC, J01DE02), latamoxef (LTM, J01DD13), levofloxacin (LVX, J01DC10), levonadifloxacin (LND, J01DB11), lincomycin (LIN, J01DD03), linezolid (LNZ, J01DI02), lomefloxacin (LOM, J01DD02), loracarbef (LOR, J01DD52), mecillinam (MEC, J01DD18), meropenem (MEM, J01DB12), meropenem/vaborbactam (MEV, J01DD14), metampicillin (MTM, J01DD07), meticillin (MET, J01DI01), mezlocillin (MEZ, J01DI54), micronomicin (MCR, J01DD04), midecamycin (MID, J01DD63), minocycline (MNO, J01DC02), miocamycin (MCM, J01DB09), moxifloxacin (MFX, J01DD06), nadifloxacin (NAD, J01DC08), nafcillin (NAF, J01DH05), nalidixic acid (NAL, J01DH04), neomycin (NEO, J01DH03), netilmicin (NET, J01DH51), nitrofurantoin (NIT, J01DH56), norfloxacin (, J01DH02), ofloxacin (OFX, J01DH52), oleandomycin (OLE, J01DH55), oritavancin (ORI, J01DH06), oxacillin (OXA, J01XA02), panipenem (PAN, J01XA01), pazufloxacin (PAZ, J01XC01), pefloxacin (PEF, J01FA13), penamecillin (PNM, J01FA01), pheneticillin (PHE, J01FA14), phenoxymethylpenicillin (PHN, J01FA07), piperacillin (PIP, J01FA03), piperacillin/tazobactam (TZP, J01FA11), pivampicillin (PVM, J01FA05), pivmecillinam (PME, J01FA12), plazomicin (PLZ, J01FA16), polymyxin B (PLB, J01FA02), pristinamycin (PRI, J01FA15), procaine benzylpenicillin (PRB, J01FA08), propicillin (PRP, J01FF02), prulifloxacin (PRU, J01FG01), quinupristin/dalfopristin (QDA, J01FG02), ribostamycin (RST, J04AB02), rifampicin (RIF, J01XX09), rokitamycin (ROK, J01XX08), roxithromycin (RXT, J01AA07), rufloxacin (RFL, J01XB01), sisomicin (SIS, J01XB02), sitafloxacin (SIT, J01XE01), solithromycin (SOL, J01AA12), sparfloxacin (SPX, J01EA01), spiramycin (SPI, J01XX01), streptoduocin (STR, J01BA01), streptomycin (STR1, J01GB06), sulbactam (SUL, J01GB12), sulbenicillin (SBC, J01GB13), sulfadiazine (SDI, J01GB09), sulfadiazine/trimethoprim (SLT1, D09AA01), sulfadimethoxine (SUD, J01GB03), sulfadimidine (SDM, J01GB11), sulfadimidine/trimethoprim (SLT2, J01GB04), sulfafurazole (SLF, S01AA22), sulfaisodimidine (SLF1, J01GB05), sulfalene (SLF2, J01GB07), sulfamazone (SZO, J01GB14), sulfamerazine (SLF3, J01GB10), sulfamerazine/trimethoprim (SLT3, J01GB08), sulfamethizole (SLF4, J01GA02), sulfamethoxazole (SMX, J01GA01), sulfamethoxypyridazine (SLF5, J01GB01), sulfametomidine (SLF6, J01EE01), sulfametoxydiazine (SLF7, J01MB02), sulfametrole/trimethoprim (SLT4, J01FF01), sulfamoxole (SLF8, J01XA04), sulfamoxole/trimethoprim (SLT5, J01XA05), sulfanilamide (SLF9, J01XA03), sulfaperin (SLF10, J04AB01), sulfaphenazole (SLF11, J01XX11), sulfapyridine (SLF12, J01EC02), sulfathiazole (SUT, J01ED01), sulfathiourea (SLF13, J01EB03), sultamicillin (SLT6, J01EB05), talampicillin (TAL, J01EB01), tazobactam (TAZ, J01ED02), tebipenem (TBP, J01ED09), tedizolid (TZD, J01ED07), teicoplanin (TEC, J01EB02), telavancin (TLV, J01EC01), telithromycin (TLT, J01ED05), temafloxacin (TMX, J01ED03), temocillin (TEM, J01ED04), tetracycline (TCY, J01EC03), ticarcillin (TIC, J01EB06), ticarcillin/clavulanic acid (TCC, J01ED06), tigecycline (TGC, J01ED08), tilbroquinol (TBQ, J01EB04), tobramycin (TOB, J01EB07), tosufloxacin (TFX, J01EB08), trimethoprim (TMP, J01EE02), trimethoprim/sulfamethoxazole (SXT, J01EE05), troleandomycin (TRL, J01EE07), trovafloxacin (TVA, J01EE03), vancomycin (VAN, J01EE04)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Apply EUCAST Rules — eucast_rules","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"# \\donttest{ a <- data.frame(   mo = c(     \"Staphylococcus aureus\",     \"Enterococcus faecalis\",     \"Escherichia coli\",     \"Klebsiella pneumoniae\",     \"Pseudomonas aeruginosa\"   ),   VAN = \"-\", # Vancomycin   AMX = \"-\", # Amoxicillin   COL = \"-\", # Colistin   CAZ = \"-\", # Ceftazidime   CXM = \"-\", # Cefuroxime   PEN = \"S\", # Benzylpenicillin   FOX = \"S\", # Cefoxitin   stringsAsFactors = FALSE )  head(a) #>                       mo VAN AMX COL CAZ CXM PEN FOX #> 1  Staphylococcus aureus   -   -   -   -   -   S   S #> 2  Enterococcus faecalis   -   -   -   -   -   S   S #> 3       Escherichia coli   -   -   -   -   -   S   S #> 4  Klebsiella pneumoniae   -   -   -   -   -   S   S #> 5 Pseudomonas aeruginosa   -   -   -   -   -   S   S   # apply EUCAST rules: some results wil be changed b <- eucast_rules(a) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #>   - a %>% as.sir(CXM:AMX) #>   - a %>% mutate_if(is_sir_eligible, as.sir) #>   - a %>% mutate(across(where(is_sir_eligible), as.sir))  head(b) #>                       mo VAN AMX COL CAZ CXM PEN FOX #> 1  Staphylococcus aureus   -   S   R   S   S   S   S #> 2  Enterococcus faecalis   -   -   R   R   R   S   R #> 3       Escherichia coli   R   -   -   -   -   R   S #> 4  Klebsiella pneumoniae   R   R   -   -   -   R   S #> 5 Pseudomonas aeruginosa   R   R   -   -   R   R   R   # do not apply EUCAST rules, but rather get a data.frame # containing all details about the transformations: c <- eucast_rules(a, verbose = TRUE) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #>   - a %>% as.sir(CXM:AMX) #>   - a %>% mutate_if(is_sir_eligible, as.sir) #>   - a %>% mutate(across(where(is_sir_eligible), as.sir)) head(c) #>   row col           mo_fullname old new rule   rule_group #> 1   1 AMX Staphylococcus aureus   -   S       Breakpoints #> 2   1 CXM Staphylococcus aureus   -   S       Breakpoints #> 3   1 CAZ Staphylococcus aureus   R   S      Expert Rules #> 4   1 CAZ Staphylococcus aureus   -   R      Expert Rules #> 5   1 COL Staphylococcus aureus   -   R      Expert Rules #> 6   2 CAZ Enterococcus faecalis   -   R      Expert Rules #>                                                 rule_name #> 1                                          Staphylococcus #> 2                                          Staphylococcus #> 3                          Expert Rules on Staphylococcus #> 4 Table 4: Intrinsic resistance in gram-positive bacteria #> 5 Table 4: Intrinsic resistance in gram-positive bacteria #> 6 Table 4: Intrinsic resistance in gram-positive bacteria #>                                                                                 rule_source #> 1                                           'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 2                                           'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 3 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 4 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 5 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 6 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 # }  # Dosage guidelines:  eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\") #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #>   Tables' v12.0 (2022). This note will be shown once per session. #> # A tibble: 3 × 4 #>   ab   name          standard_dosage  high_dosage  #>   <ab> <chr>         <chr>            <chr>        #> 1 TOB  Tobramycin    6-7 mg/kg x 1 iv NA           #> 2 GEN  Gentamicin    6-7 mg/kg x 1 iv NA           #> 3 CIP  Ciprofloxacin 0.4 g x 2 iv     0.4 g x 3 iv  eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\", version_breakpoints = 10) #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #>   Tables' v10.0 (2020). This note will be shown once per session. #> # A tibble: 3 × 4 #>   ab   name          standard_dosage  high_dosage  #>   <ab> <chr>         <chr>            <chr>        #> 1 TOB  Tobramycin    6-7 mg/kg x 1 iv NA           #> 2 GEN  Gentamicin    6-7 mg/kg x 1 iv NA           #> 3 CIP  Ciprofloxacin 0.4 g x 2 iv     0.4 g x 3 iv"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 2,000 Example Isolates — example_isolates","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"data set containing 2,000 microbial isolates full antibiograms. data set contains randomised fictitious data, reflects reality can used practise AMR data analysis. examples, please read tutorial website.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"","code":"example_isolates"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"tibble 2,000 observations 46 variables: date Date receipt laboratory patient ID patient age Age patient gender Gender patient, either \"F\" \"M\" ward Ward type patient admitted, either \"Clinical\", \"ICU\" \"Outpatient\" mo ID microorganism created .mo(), see also microorganisms data set PEN:RIF 40 different antibiotics class sir (see .sir()); column names occur antibiotics data set can translated set_ab_names() ab_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>"},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Unclean Data — example_isolates_unclean","title":"Data Set with Unclean Data — example_isolates_unclean","text":"data set containing 3,000 microbial isolates cleaned consequently ready AMR data analysis. data set can used practice.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Unclean Data — example_isolates_unclean","text":"","code":"example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Unclean Data — example_isolates_unclean","text":"tibble 3,000 observations 8 variables: patient_id ID patient date date receipt laboratory hospital ID hospital, C bacteria info microorganism can transformed .mo(), see also microorganisms AMX:GEN 4 different antibiotics transformed .sir()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Unclean Data — example_isolates_unclean","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Unclean Data — example_isolates_unclean","text":"","code":"example_isolates_unclean #> # A tibble: 3,000 × 8 #>    patient_id hospital date       bacteria      AMX   AMC   CIP   GEN   #>    <chr>      <chr>    <date>     <chr>         <chr> <chr> <chr> <chr> #>  1 J3         A        2012-11-21 E. coli       R     I     S     S     #>  2 R7         A        2018-04-03 K. pneumoniae R     I     S     S     #>  3 P3         A        2014-09-19 E. coli       R     S     S     S     #>  4 P10        A        2015-12-10 E. coli       S     I     S     S     #>  5 B7         A        2015-03-02 E. coli       S     S     S     S     #>  6 W3         A        2018-03-31 S. aureus     R     S     R     S     #>  7 J8         A        2016-06-14 E. coli       R     S     S     S     #>  8 M3         A        2015-10-25 E. coli       R     S     S     S     #>  9 J3         A        2019-06-19 E. coli       S     S     S     S     #> 10 G6         A        2015-04-27 S. aureus     S     S     S     S     #> # … with 2,990 more rows"},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine First Isolates — first_isolate","title":"Determine First Isolates — first_isolate","text":"Determine first isolates microorganisms every patient per episode (needed) per specimen type. functions support four methods summarised Hindler et al. 2007 (doi:10.1086/511864 ). determine patient episodes necessarily based microorganisms, use is_new_episode() also supports grouping dplyr package.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine First Isolates — first_isolate","text":"","code":"first_isolate(   x = NULL,   col_date = NULL,   col_patient_id = NULL,   col_mo = NULL,   col_testcode = NULL,   col_specimen = NULL,   col_icu = NULL,   col_keyantimicrobials = NULL,   episode_days = 365,   testcodes_exclude = NULL,   icu_exclude = FALSE,   specimen_group = NULL,   type = \"points\",   method = c(\"phenotype-based\", \"episode-based\", \"patient-based\", \"isolate-based\"),   ignore_I = TRUE,   points_threshold = 2,   info = interactive(),   include_unknown = FALSE,   include_untested_sir = TRUE,   ... )  filter_first_isolate(   x = NULL,   col_date = NULL,   col_patient_id = NULL,   col_mo = NULL,   episode_days = 365,   method = c(\"phenotype-based\", \"episode-based\", \"patient-based\", \"isolate-based\"),   ... )"},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine First Isolates — first_isolate","text":"Methodology function strictly based : M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. Hindler JF Stelling J (2007). Analysis Presentation Cumulative Antibiograms: New Consensus Guideline Clinical Laboratory Standards Institute. Clinical Infectious Diseases, 44(6), 867-873. doi:10.1086/511864","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine First Isolates — first_isolate","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_date column name result date (date received lab), defaults first column date class col_patient_id column name unique IDs patients, defaults first column starts 'patient' 'patid' (case insensitive) col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). col_testcode column name test codes. Use col_testcode = NULL exclude certain test codes (test codes screening). case testcodes_exclude ignored. col_specimen column name specimen type group col_icu column name logicals (TRUE/FALSE) whether ward department Intensive Care Unit (ICU). can also logical vector length rows x. col_keyantimicrobials (useful method = \"phenotype-based\") column name key antimicrobials determine first isolates, see key_antimicrobials(). Defaults first column starts 'key' followed 'ab' 'antibiotics' 'antimicrobials' (case insensitive). Use col_keyantimicrobials = FALSE prevent . Can also output key_antimicrobials(). episode_days episode days genus/species combination determined 'first isolate' . default 365 days based guideline CLSI, see Source. testcodes_exclude character vector test codes excluded (case-insensitive) icu_exclude logical indicate whether ICU isolates excluded (rows value TRUE column set col_icu) specimen_group value column set col_specimen filter type type determine weighed isolates; can \"keyantimicrobials\" \"points\", see Details method method apply, either \"phenotype-based\", \"episode-based\", \"patient-based\" \"isolate-based\" (can abbreviated), see Details. default \"phenotype-based\" antimicrobial test results present data, \"episode-based\" otherwise. ignore_I logical indicate whether antibiotic interpretations \"\" ignored type = \"keyantimicrobials\", see Details points_threshold minimum number points require differences antibiogram lead inclusion isolate type = \"points\", see Details info logical indicate info printed, defaults TRUE interactive mode include_unknown logical indicate whether 'unknown' microorganisms included , .e. microbial code \"UNKNOWN\", defaults FALSE. WHONET users, means records organism code \"con\" (contamination) excluded default. Isolates microbial ID NA always excluded first isolate. include_untested_sir logical indicate whether also rows without antibiotic results still eligible becoming first isolate. Use include_untested_sir = FALSE always return FALSE rows. checks data set columns class sir consequently requires transforming columns antibiotic results using .sir() first. ... arguments passed first_isolate() using filter_first_isolate(), otherwise arguments passed key_antimicrobials() (universal, gram_negative, gram_positive)","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine First Isolates — first_isolate","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine First Isolates — first_isolate","text":"conduct epidemiological analyses antimicrobial resistance data, -called first isolates included prevent overestimation underestimation antimicrobial resistance. Different methods can used , see . functions context-aware. means x argument can left blank used inside data.frame call, see Examples. first_isolate() function wrapper around is_new_episode() function, efficient data sets containing microorganism codes names. isolates microbial ID NA excluded first isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"different-methods","dir":"Reference","previous_headings":"","what":"Different methods","title":"Determine First Isolates — first_isolate","text":"According Hindler et al. (2007, doi:10.1086/511864 ), different methods (algorithms) select first isolates increasing reliability: isolate-based, patient-based, episode-based phenotype-based. methods select combination taxonomic genus species (subspecies). mentioned methods covered first_isolate() function:","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"isolate-based","dir":"Reference","previous_headings":"","what":"Isolate-based","title":"Determine First Isolates — first_isolate","text":"method require selection, isolates included. , however, respect arguments set first_isolate() function. example, default setting include_unknown (FALSE) omit selection rows without microbial ID.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"patient-based","dir":"Reference","previous_headings":"","what":"Patient-based","title":"Determine First Isolates — first_isolate","text":"include every genus-species combination per patient , set episode_days Inf. Although often inappropriate, method makes sure duplicate isolates selected patient. large longitudinal data set, mean isolates excluded found years initial isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"episode-based","dir":"Reference","previous_headings":"","what":"Episode-based","title":"Determine First Isolates — first_isolate","text":"include every genus-species combination per patient episode , set episode_days sensible number days. Depending type analysis, 14, 30, 60 365. Short episodes common analysing specific hospital ward data, long episodes common analysing regional national data. common method correct duplicate isolates. Patients categorised episodes based ID dates (e.g., date specimen receipt laboratory result). common method, take account antimicrobial test results. means e.g. methicillin-resistant Staphylococcus aureus (MRSA) isolate differentiated wildtype Staphylococcus aureus isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"phenotype-based","dir":"Reference","previous_headings":"","what":"Phenotype-based","title":"Determine First Isolates — first_isolate","text":"reliable method, since also weighs antibiogram (antimicrobial test results) yielding -called 'first weighted isolates'. two different methods weigh antibiogram: Using type = \"points\" argument points_threshold (default) method weighs antimicrobial drugs available data set. difference S R (vice versa) counts 0.5 points, difference S R (vice versa) counts 1 point. sum points exceeds points_threshold, defaults 2, isolate selected first weighted isolate. antimicrobials internally selected using all_antimicrobials() function. output function need passed first_isolate() function. Using type = \"keyantimicrobials\" argument ignore_I method weighs specific antimicrobial drugs, called key antimicrobials. difference S R (vice versa) key antimicrobials select isolate first weighted isolate. ignore_I = FALSE, also differences S R (vice versa) lead . Key antimicrobials internally selected using key_antimicrobials() function, can also added manually variable data set col_keyantimicrobials argument. Another option pass output key_antimicrobials() function directly col_keyantimicrobials argument. default method phenotype-based (using type = \"points\") episode-based (using episode_days = 365). makes sure every genus-species combination selected per patient per year, taking account antimicrobial test results. antimicrobial test results available data set, episode-based method applied default.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine First Isolates — first_isolate","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  example_isolates[first_isolate(), ] #> Including isolates from ICU. #> # A tibble: 1,379 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  4 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #>  5 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR R     NA    S     NA    #>  6 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  7 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND R     NA    NA    R     #>  8 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  9 2002-02-03 481442     76 M      ICU      B_STPHY_CONS R     NA    S     NA    #> 10 2002-02-05 023456     50 M      Clinical B_STPHY_HMNS S     NA    S     NA    #> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> # \\donttest{ # get all first Gram-negatives example_isolates[which(first_isolate(info = FALSE) & mo_is_gram_negative()), ] #> Including isolates from ICU. #> ℹ Using column 'mo' as input for mo_is_gram_negative() #> # A tibble: 437 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND R     NA    NA    R     #>  4 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  5 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS R     NA    NA    R     #>  6 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN R     NA    NA    R     #>  7 2002-03-08 4FC193     69 M      Clinical B_ESCHR_COLI R     NA    NA    R     #>  8 2002-03-16 4FC193     69 M      Clinical B_PSDMN_AERG R     NA    NA    R     #>  9 2002-04-01 496896     46 F      ICU      B_ESCHR_COLI R     NA    NA    NA    #> 10 2002-04-23 EE2510     69 F      ICU      B_ESCHR_COLI R     NA    NA    NA    #> # … with 427 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir>  if (require(\"dplyr\")) {   # filter on first isolates using dplyr:   example_isolates %>%     filter(first_isolate()) } #> Including isolates from ICU. #> # A tibble: 1,379 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  4 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #>  5 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR R     NA    S     NA    #>  6 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  7 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND R     NA    NA    R     #>  8 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  9 2002-02-03 481442     76 M      ICU      B_STPHY_CONS R     NA    S     NA    #> 10 2002-02-05 023456     50 M      Clinical B_STPHY_HMNS S     NA    S     NA    #> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # short-hand version:   example_isolates %>%     filter_first_isolate(info = FALSE) } #> Including isolates from ICU. #> # A tibble: 1,379 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  4 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #>  5 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR R     NA    S     NA    #>  6 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  7 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND R     NA    NA    R     #>  8 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  9 2002-02-03 481442     76 M      ICU      B_STPHY_CONS R     NA    S     NA    #> 10 2002-02-05 023456     50 M      Clinical B_STPHY_HMNS S     NA    S     NA    #> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # flag the first isolates per group:   example_isolates %>%     group_by(ward) %>%     mutate(first = first_isolate()) %>%     select(ward, date, patient, mo, first) } #> Including isolates from ICU. #> Including isolates from ICU. #> Including isolates from ICU. #> # A tibble: 2,000 × 5 #> # Groups:   ward [3] #>    ward     date       patient mo           first #>    <chr>    <date>     <chr>   <mo>         <lgl> #>  1 Clinical 2002-01-02 A77334  B_ESCHR_COLI TRUE  #>  2 Clinical 2002-01-03 A77334  B_ESCHR_COLI FALSE #>  3 ICU      2002-01-07 067927  B_STPHY_EPDR TRUE  #>  4 ICU      2002-01-07 067927  B_STPHY_EPDR FALSE #>  5 ICU      2002-01-13 067927  B_STPHY_EPDR FALSE #>  6 ICU      2002-01-13 067927  B_STPHY_EPDR FALSE #>  7 Clinical 2002-01-14 462729  B_STPHY_AURS TRUE  #>  8 Clinical 2002-01-14 462729  B_STPHY_AURS FALSE #>  9 ICU      2002-01-16 067927  B_STPHY_EPDR FALSE #> 10 ICU      2002-01-17 858515  B_STPHY_EPDR TRUE  #> # … with 1,990 more rows # }"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":null,"dir":"Reference","previous_headings":"","what":"G-test for Count Data — g.test","title":"G-test for Count Data — g.test","text":"g.test() performs chi-squared contingency table tests goodness--fit tests, just like chisq.test() reliable (1). G-test can used see whether number observations category fits theoretical expectation (called G-test goodness--fit), see whether proportions one variable different different values variable (called G-test independence).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"G-test for Count Data — g.test","text":"","code":"g.test(x, y = NULL, p = rep(1/length(x), length(x)), rescale.p = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"G-test for Count Data — g.test","text":"code function identical chisq.test(), except : calculation statistic changed \\(2 * sum(x * log(x / E))\\) Yates' continuity correction removed apply G-test possibility simulate p values simulate.p.value removed","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"G-test for Count Data — g.test","text":"x numeric vector matrix. x y can also     factors. y numeric vector; ignored x matrix.      x factor, y factor length. p vector probabilities length x.     error given entry p negative. rescale.p logical scalar; TRUE p rescaled     (necessary) sum 1.  rescale.p FALSE,     p sum 1, error given.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"G-test for Count Data — g.test","text":"list class \"htest\" containing following   components: statistic value chi-squared test statistic. parameter degrees freedom approximate     chi-squared distribution test statistic, NA     p-value computed Monte Carlo simulation. p.value p-value test. method character string indicating type test     performed, whether Monte Carlo simulation continuity     correction used. data.name character string giving name(s) data. observed observed counts. expected expected counts null hypothesis. residuals Pearson residuals,     (observed - expected) / sqrt(expected). stdres standardized residuals,     (observed - expected) / sqrt(V), V residual cell variance (Agresti, 2007,     section 2.4.5 case x matrix, n * p * (1 - p) otherwise).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"G-test for Count Data — g.test","text":"x matrix one row column, x vector y given, goodness--fit test performed (x treated one-dimensional contingency table). entries x must non-negative integers. case, hypothesis tested whether population probabilities equal p, equal p given. x matrix least two rows columns, taken two-dimensional contingency table: entries x must non-negative integers.  Otherwise, x y must vectors factors length; cases missing values removed, objects coerced factors, contingency table computed .  Pearson's chi-squared test performed null hypothesis joint distribution cell counts 2-dimensional contingency table product row column marginals. p-value computed asymptotic chi-squared distribution test statistic. contingency table case simulation done random sampling set contingency tables given marginals, works marginals strictly positive. Note usual sampling situation assumed chi-squared test (G-test) rather Fisher's exact test. goodness--fit case simulation done random sampling discrete distribution specified p, sample size n = sum(x). simulation done R may slow.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-goodness-of-fit-likelihood-ratio-test-","dir":"Reference","previous_headings":"","what":"G-test Of Goodness-of-Fit (Likelihood Ratio Test)","title":"G-test for Count Data — g.test","text":"Use G-test goodness--fit one nominal variable two values (male female, red, pink white flowers). compare observed counts numbers observations category expected counts, calculate using kind theoretical expectation (1:1 sex ratio 1:2:1 ratio genetic cross). expected number observations category small, G-test may give inaccurate results, use exact test instead (fisher.test()). G-test goodness--fit alternative chi-square test goodness--fit (chisq.test()); tests advantages disadvantages, results two tests usually similar.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-independence","dir":"Reference","previous_headings":"","what":"G-test of Independence","title":"G-test for Count Data — g.test","text":"Use G-test independence two nominal variables, two possible values. want know whether proportions one variable different among values variable. also possible G-test independence two nominal variables. example, Jackson et al. (2013) also data children 3, analysis old vs. young, thigh vs. arm, reaction vs. reaction, analyzed together. Fisher's exact test (fisher.test()) exact test, G-test still approximation. 2x2 table, Fisher's Exact test may slower still run seconds, even sum observations multiple millions. G-test independence alternative chi-square test independence (chisq.test()), give approximately results.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"how-the-test-works","dir":"Reference","previous_headings":"","what":"How the Test Works","title":"G-test for Count Data — g.test","text":"Unlike exact test goodness--fit (fisher.test()), G-test directly calculate probability obtaining observed results something extreme. Instead, like almost statistical tests, G-test intermediate step; uses data calculate test statistic measures far observed data null expectation. use mathematical relationship, case chi-square distribution, estimate probability obtaining value test statistic. G-test uses log ratio two likelihoods test statistic, also called likelihood ratio test log-likelihood ratio test. formula calculate G-statistic : \\(G = 2 * sum(x * log(x / E))\\) E expected values. Since chi-square distributed, p value can calculated R :   df degrees freedom. two categories want find ones significantly different null expectation, can use method testing category vs. sum categories, Bonferroni correction. use G-tests category, course.","code":"p <- stats::pchisq(G, df, lower.tail = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"G-test for Count Data — g.test","text":"McDonald, J.H. 2014. Handbook Biological Statistics (3rd ed.). Sparky House Publishing, Baltimore, Maryland. http://www.biostathandbook.com/gtestgof.html.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"G-test for Count Data — g.test","text":"","code":"# = EXAMPLE 1 = # Shivrain et al. (2006) crossed clearfield rice (which are resistant # to the herbicide imazethapyr) with red rice (which are susceptible to # imazethapyr). They then crossed the hybrid offspring and examined the # F2 generation, where they found 772 resistant plants, 1611 moderately # resistant plants, and 737 susceptible plants. If resistance is controlled # by a single gene with two co-dominant alleles, you would expect a 1:2:1 # ratio.  x <- c(772, 1611, 737) g.test(x, p = c(1, 2, 1) / 4) #>  #> \tG-test of goodness-of-fit (likelihood ratio test) #>  #> data:  x #> X-squared = 4.1471, p-value = 0.1257 #>   # There is no significant difference from a 1:2:1 ratio. # Meaning: resistance controlled by a single gene with two co-dominant # alleles, is plausible.   # = EXAMPLE 2 = # Red crossbills (Loxia curvirostra) have the tip of the upper bill either # right or left of the lower bill, which helps them extract seeds from pine # cones. Some have hypothesized that frequency-dependent selection would # keep the number of right and left-billed birds at a 1:1 ratio. Groth (1992) # observed 1752 right-billed and 1895 left-billed crossbills.  x <- c(1752, 1895) g.test(x) #>  #> \tG-test of goodness-of-fit (likelihood ratio test) #>  #> data:  x #> X-squared = 5.6085, p-value = 0.01787 #>   # There is a significant difference from a 1:1 ratio. # Meaning: there are significantly more left-billed birds."},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine (New) Episodes for Patients — get_episode","title":"Determine (New) Episodes for Patients — get_episode","text":"functions determine items vector can considered (start ) new episode, based argument episode_days. can used determine clinical episodes epidemiological analysis. get_episode() function returns index number episode per group, is_new_episode() function returns values TRUE/FALSE indicate whether item vector start new episode.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine (New) Episodes for Patients — get_episode","text":"","code":"get_episode(x, episode_days, ...)  is_new_episode(x, episode_days, ...)"},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine (New) Episodes for Patients — get_episode","text":"x vector dates (class Date POSIXt), sorted internally determine episodes episode_days required episode length days, can also less day Inf, see Details ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine (New) Episodes for Patients — get_episode","text":"get_episode(): double vector is_new_episode(): logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine (New) Episodes for Patients — get_episode","text":"Dates first sorted old new. oldest date mark start first episode. date, next date marked least episode_days days later start first episode. second marked date , next date marked least episode_days days later start second episode start third episode, . vector returned, original order restored. first_isolate() function wrapper around is_new_episode() function, efficient data sets containing microorganism codes names allows different isolate selection methods. dplyr package required functions work, functions support variable grouping work conveniently inside dplyr verbs filter(), mutate() summarise().","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine (New) Episodes for Patients — get_episode","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates df <- example_isolates[sample(seq_len(2000), size = 200), ]  get_episode(df$date, episode_days = 60) # indices #>   [1]  9 29 29 17 30 21  4 56 31 35 18 50 62  7 28 28 57 37 53 46 15 23 14 39 42 #>  [26] 46 20 62 29 54 18 54  1 61  3 12  6  8  9 43 24  1 61 37 11 42 28 36 12  8 #>  [51] 26 61 55 60  4 19 30  7 20  9 13 57  9 59 47  8 17  1 64  8  2  4 60  7 34 #>  [76] 61 44 20 61 55  7  2 32 33 18  6 18 11  7 10 18  5  3 44 23 54 31 18  4 19 #> [101] 25 17  7 41 43 17 55 21 30 61 11 16 14 57 52 41 40 23 43 60 37 26 12 12  4 #> [126] 10  6 40 11  2 45 30 51 47 29 25 58 10 61 19  8 39  1 24 25 58 10 18 34 20 #> [151] 63 43 57 51 60 41 36 64  4 61 55 22 19 27 53 64 51 15 55 23 22 36 45 57 48 #> [176] 40 44 49 57 38 40 29 32  2 11 50 38 53 10 33  8  9 54  4  6 58 57  3 14 60 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE #>   [1] FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE #>  [13] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE #>  [25] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [37] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE #>  [49] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE #>  [61]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE #>  [73] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE #>  [85] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE #>  [97] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE #> [109] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE #> [121] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE #> [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE #> [145]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE #> [157] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE #> [169]  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE #> [181]  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE #> [193]  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  # filter on results from the third 60-day episode only, using base R df[which(get_episode(df$date, 60) == 3), ] #> # A tibble: 3 × 46 #>   date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>   <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #> 1 2002-08-28 390178     57 M      Clinical B_STRPT_SLVR S     NA    NA    S     #> 2 2002-08-14 785317     51 F      ICU      B_ESCHR_COLI R     NA    NA    NA    #> 3 2002-07-24 F35553     51 M      ICU      B_STPHY_AURS R     NA    S     R     #> # … with 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, #> #   FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, #> #   GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, #> #   NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, #> #   TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, #> #   AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, #> #   MUP <sir>, RIF <sir>  # the functions also work for less than a day, e.g. to include one per hour: get_episode(   c(     Sys.time(),     Sys.time() + 60 * 60   ),   episode_days = 1 / 24 ) #> [1] 1 2  # \\donttest{ if (require(\"dplyr\")) {   # is_new_episode() can also be used in dplyr verbs to determine patient   # episodes based on any (combination of) grouping variables:   df %>%     mutate(condition = sample(       x = c(\"A\", \"B\", \"C\"),       size = 200,       replace = TRUE     )) %>%     group_by(condition) %>%     mutate(new_episode = is_new_episode(date, 365)) %>%     select(patient, date, condition, new_episode) } #> # A tibble: 200 × 4 #> # Groups:   condition [3] #>    patient date       condition new_episode #>    <chr>   <date>     <chr>     <lgl>       #>  1 822116  2004-02-24 C         FALSE       #>  2 144549  2009-09-01 C         FALSE       #>  3 982666  2009-09-06 A         FALSE       #>  4 5DF436  2006-01-04 B         TRUE        #>  5 023456  2009-11-02 A         FALSE       #>  6 725063  2007-06-07 A         FALSE       #>  7 FCC668  2002-10-14 B         FALSE       #>  8 D28985  2015-12-21 C         FALSE       #>  9 B26404  2010-01-02 A         FALSE       #> 10 F83217  2010-12-21 B         TRUE        #> # … with 190 more rows if (require(\"dplyr\")) {   df %>%     group_by(ward, patient) %>%     transmute(date,       patient,       new_index = get_episode(date, 60),       new_logical = is_new_episode(date, 60)     ) } #> # A tibble: 200 × 5 #> # Groups:   ward, patient [183] #>    ward       date       patient new_index new_logical #>    <chr>      <date>     <chr>       <dbl> <lgl>       #>  1 ICU        2004-02-24 822116          1 TRUE        #>  2 Clinical   2009-09-01 144549          1 TRUE        #>  3 Clinical   2009-09-06 982666          1 TRUE        #>  4 ICU        2006-01-04 5DF436          1 TRUE        #>  5 Clinical   2009-11-02 023456          1 TRUE        #>  6 ICU        2007-06-07 725063          1 TRUE        #>  7 ICU        2002-10-14 FCC668          1 TRUE        #>  8 Outpatient 2015-12-21 D28985          1 TRUE        #>  9 Clinical   2010-01-02 B26404          1 TRUE        #> 10 ICU        2010-12-21 F83217          1 TRUE        #> # … with 190 more rows if (require(\"dplyr\")) {   df %>%     group_by(ward) %>%     summarise(       n_patients = n_distinct(patient),       n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),       n_episodes_60 = sum(is_new_episode(date, episode_days = 60)),       n_episodes_30 = sum(is_new_episode(date, episode_days = 30))     ) } #> # A tibble: 3 × 5 #>   ward       n_patients n_episodes_365 n_episodes_60 n_episodes_30 #>   <chr>           <int>          <int>         <int>         <int> #> 1 Clinical          118             14            51            70 #> 2 ICU                59             13            37            48 #> 3 Outpatient          6              5             6             6 if (require(\"dplyr\")) {   # grouping on patients and microorganisms leads to the same   # results as first_isolate() when using 'episode-based':   x <- df %>%     filter_first_isolate(       include_unknown = TRUE,       method = \"episode-based\"     )    y <- df %>%     group_by(patient, mo) %>%     filter(is_new_episode(date, 365)) %>%     ungroup()    identical(x, y) } #> Including isolates from ICU. #> [1] FALSE if (require(\"dplyr\")) {   # but is_new_episode() has a lot more flexibility than first_isolate(),   # since you can now group on anything that seems relevant:   df %>%     group_by(patient, mo, ward) %>%     mutate(flag_episode = is_new_episode(date, 365)) %>%     select(group_vars(.), flag_episode) } #> # A tibble: 200 × 4 #> # Groups:   patient, mo, ward [192] #>    patient mo            ward       flag_episode #>    <chr>   <mo>          <chr>      <lgl>        #>  1 822116  B_STPHY_AURS  ICU        TRUE         #>  2 144549  B_ENTRBC_CLOC Clinical   TRUE         #>  3 982666  B_STRPT_DYSG  Clinical   TRUE         #>  4 5DF436  B_STPHY_AURS  ICU        TRUE         #>  5 023456  B_KLBSL_PNMN  Clinical   TRUE         #>  6 725063  B_STPHY_CONS  ICU        TRUE         #>  7 FCC668  B_ACNTB       ICU        TRUE         #>  8 D28985  B_ESCHR_COLI  Outpatient TRUE         #>  9 B26404  B_STPHY_CONS  Clinical   TRUE         #> 10 F83217  B_STPHY_CONS  ICU        TRUE         #> # … with 190 more rows # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA Biplot with ggplot2 — ggplot_pca","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"Produces ggplot2 variant -called biplot PCA (principal component analysis), flexible appealing base R biplot() function.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"ggplot_pca(   x,   choices = 1:2,   scale = 1,   pc.biplot = TRUE,   labels = NULL,   labels_textsize = 3,   labels_text_placement = 1.5,   groups = NULL,   ellipse = TRUE,   ellipse_prob = 0.68,   ellipse_size = 0.5,   ellipse_alpha = 0.5,   points_size = 2,   points_alpha = 0.25,   arrows = TRUE,   arrows_colour = \"darkblue\",   arrows_size = 0.5,   arrows_textsize = 3,   arrows_textangled = TRUE,   arrows_alpha = 0.75,   base_textsize = 10,   ... )"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"ggplot_pca() function based ggbiplot() function ggbiplot package Vince Vu, found GitHub: https://github.com/vqv/ggbiplot (retrieved: 2 March 2020, latest commit: 7325e88; 12 February 2015). per GPL-2 licence demands documentation code changes, changes made based source code : Rewritten code remove dependency packages plyr, scales grid Parametrised options, like arrow ellipse settings Hardened input possibilities defining exact type user input every argument Added total amount explained variance caption plot Cleaned syntax based lintr package, fixed grammatical errors added integrity checks Updated documentation","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"x object returned pca(), prcomp() princomp() choices length 2 vector specifying components plot. default     biplot strict sense. scale variables scaled lambda ^ scale     observations scaled lambda ^ (1-scale)     lambda singular values computed     princomp. Normally 0 <= scale <= 1, warning     issued specified scale outside range. pc.biplot true, use Gabriel (1971) refers \"principal component     biplot\", lambda = 1 observations scaled sqrt(n)     variables scaled sqrt(n).  inner products     variables approximate covariances distances observations     approximate Mahalanobis distance. labels optional vector labels observations. set, labels placed respective points. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). labels_textsize size text used labels labels_text_placement adjustment factor placement variable names (>=1 means away arrow head) groups optional vector groups labels, length labels. set, points labels coloured according groups. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). ellipse logical indicate whether normal data ellipse drawn group (set groups) ellipse_prob statistical size ellipse normal probability ellipse_size size ellipse line ellipse_alpha alpha (transparency) ellipse line points_size size points points_alpha alpha (transparency) points arrows logical indicate whether arrows drawn arrows_colour colour arrow text arrows_size size (thickness) arrow lines arrows_textsize size text end arrows arrows_textangled logical whether text end arrows angled arrows_alpha alpha (transparency) arrows text base_textsize text size plot elements except labels arrows ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"colours labels points can changed adding another scale layer colour, scale_colour_viridis_d() scale_colour_brewer().","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # \\donttest{ if (require(\"dplyr\")) {   # calculate the resistance per group first   resistance_data <- example_isolates %>%     group_by(       order = mo_order(mo), # group on anything, like order       genus = mo_genus(mo)     ) %>% #   and genus as we do here;     filter(n() >= 30) %>% # filter on only 30 results per group     summarise_if(is.sir, resistance) # then get resistance of all drugs    # now conduct PCA for certain antimicrobial drugs   pca_result <- resistance_data %>%     pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)    summary(pca_result)    # old base R plotting method:   biplot(pca_result)    # new ggplot2 plotting method using this package:   if (require(\"ggplot2\")) {     ggplot_pca(pca_result)      # still extendible with any ggplot2 function     ggplot_pca(pca_result) +       scale_colour_viridis_d() +       labs(title = \"Title here\")   } } #> Warning: Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 13 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 15 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 13 results available for FLC in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for AMX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for AMP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for TZP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 12 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 5 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for FEP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 23 results available for FEP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 29 results available for FOX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 6 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 17 results available for AMK in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 6 results available for NIT in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 17 results available for NIT in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 8 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 7 results available for LNZ in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 5 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 23 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 7 results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 6 results available for TEC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 3 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 18 results available for TGC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 7 results available for TGC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for DOX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for IPM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for MEM in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for MEM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Caryophanales\", genus = \"Staphylococcus\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 9 results available for COL in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", \"TMP\" #>   and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\"  #>    # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":null,"dir":"Reference","previous_headings":"","what":"AMR Plots with ggplot2 — ggplot_sir","title":"AMR Plots with ggplot2 — ggplot_sir","text":"Use functions create bar plots AMR data analysis. functions rely ggplot2 functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AMR Plots with ggplot2 — ggplot_sir","text":"","code":"ggplot_sir(   data,   position = NULL,   x = \"antibiotic\",   fill = \"interpretation\",   facet = NULL,   breaks = seq(0, 1, 0.1),   limits = NULL,   translate_ab = \"name\",   combine_SI = TRUE,   minimum = 30,   language = get_AMR_locale(),   nrow = NULL,   colours = c(S = \"#3CAEA3\", SI = \"#3CAEA3\", I = \"#F6D55C\", IR = \"#ED553B\", R =     \"#ED553B\"),   datalabels = TRUE,   datalabels.size = 2.5,   datalabels.colour = \"grey15\",   title = NULL,   subtitle = NULL,   caption = NULL,   x.title = \"Antimicrobial\",   y.title = \"Proportion\",   ... )  geom_sir(   position = NULL,   x = c(\"antibiotic\", \"interpretation\"),   fill = \"interpretation\",   translate_ab = \"name\",   minimum = 30,   language = get_AMR_locale(),   combine_SI = TRUE,   ... )  facet_sir(facet = c(\"interpretation\", \"antibiotic\"), nrow = NULL)  scale_y_percent(breaks = seq(0, 1, 0.1), limits = NULL)  scale_sir_colours(..., aesthetics = \"fill\")  theme_sir()  labels_sir_count(   position = NULL,   x = \"antibiotic\",   translate_ab = \"name\",   minimum = 30,   language = get_AMR_locale(),   combine_SI = TRUE,   datalabels.size = 3,   datalabels.colour = \"grey15\" )"},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AMR Plots with ggplot2 — ggplot_sir","text":"data data.frame column(s) class sir (see .sir()) position position adjustment bars, either \"fill\", \"stack\" \"dodge\" x variable show x axis, either \"antibiotic\" (default) \"interpretation\" grouping variable fill variable categorise using plots legend, either \"antibiotic\" (default) \"interpretation\" grouping variable facet variable split plots , either \"interpretation\" (default) \"antibiotic\" grouping variable breaks numeric vector positions limits numeric vector length two providing limits scale, use NA refer existing minimum maximum translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant), defaults TRUE minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. nrow (using facet) number rows colours named vactor colour used filling. default colours colour-blind friendly. datalabels show datalabels using labels_sir_count() datalabels.size size datalabels datalabels.colour colour datalabels title text show title plot subtitle text show subtitle plot caption text show caption plot x.title text show x axis description y.title text show y axis description ... arguments passed geom_sir() , case scale_sir_colours(), named values set colours. default colours colour-blind friendly, maintaining convention e.g. 'susceptible' green 'resistant' red. See Examples. aesthetics aesthetics apply colours , defaults \"fill\" can also (combination ) \"alpha\", \"colour\", \"fill\", \"linetype\", \"shape\" \"size\"","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AMR Plots with ggplot2 — ggplot_sir","text":"default, names antibiotics shown plots using ab_name(). can set translate_ab argument. See count_df().","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"the-functions","dir":"Reference","previous_headings":"","what":"The Functions","title":"AMR Plots with ggplot2 — ggplot_sir","text":"geom_sir() take variable data sir class (created .sir()) using sir_df() plot bars percentage S, , R. default behaviour bars stacked different antibiotics x axis. facet_sir() creates 2d plots (default based S//R) using ggplot2::facet_wrap(). scale_y_percent() transforms y axis 0 100% range using ggplot2::scale_y_continuous(). scale_sir_colours() sets colours bars (green S, yellow , red R). multilingual support. default colours colour-blind friendly, maintaining convention e.g. 'susceptible' green 'resistant' red. theme_sir() [ggplot2 theme][ggplot2::theme() minimal distraction. labels_sir_count() print datalabels bars percentage amount isolates using ggplot2::geom_text(). ggplot_sir() wrapper around functions uses data first input. makes possible use function pipe (%>%). See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AMR Plots with ggplot2 — ggplot_sir","text":"","code":"# \\donttest{ if (require(\"ggplot2\") && require(\"dplyr\")) {   # get antimicrobial results for drugs against a UTI:   ggplot(example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)) +     geom_sir() }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # prettify the plot using some additional functions:   df <- example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)   ggplot(df) +     geom_sir() +     scale_y_percent() +     scale_sir_colours() +     labels_sir_count() +     theme_sir() }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # or better yet, simplify this using the wrapper function - a single command:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir() }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # get only proportions and no counts:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir(datalabels = FALSE) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # add other ggplot2 arguments as you like:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir(       width = 0.5,       colour = \"black\",       size = 1,       linetype = 2,       alpha = 0.25     ) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # you can alter the colours with colour names:   example_isolates %>%     select(AMX) %>%     ggplot_sir(colours = c(SI = \"yellow\")) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # but you can also use the built-in colour-blind friendly colours for   # your plots, where \"S\" is green, \"I\" is yellow and \"R\" is red:   data.frame(     x = c(\"Value1\", \"Value2\", \"Value3\"),     y = c(1, 2, 3),     z = c(\"Value4\", \"Value5\", \"Value6\")   ) %>%     ggplot() +     geom_col(aes(x = x, y = y, fill = z)) +     scale_sir_colours(Value4 = \"S\", Value5 = \"I\", Value6 = \"R\") }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # resistance of ciprofloxacine per age group   example_isolates %>%     mutate(first_isolate = first_isolate()) %>%     filter(       first_isolate == TRUE,       mo == as.mo(\"Escherichia coli\")     ) %>%     # age_groups() is also a function in this AMR package:     group_by(age_group = age_groups(age)) %>%     select(age_group, CIP) %>%     ggplot_sir(x = \"age_group\") } #> Including isolates from ICU. #> Warning: Removed 6 rows containing missing values (`position_stack()`). #> Warning: Removed 6 rows containing missing values (`position_stack()`).  if (require(\"ggplot2\") && require(\"dplyr\")) {   # a shorter version which also adjusts data label colours:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir(colours = FALSE) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # it also supports groups (don't forget to use the group var on `x` or `facet`):   example_isolates %>%     filter(mo_is_gram_negative(), ward != \"Outpatient\") %>%     # select only UTI-specific drugs     select(ward, AMX, NIT, FOS, TMP, CIP) %>%     group_by(ward) %>%     ggplot_sir(       x = \"ward\",       facet = \"antibiotic\",       nrow = 1,       title = \"AMR of Anti-UTI Drugs Per Ward\",       x.title = \"Ward\",       datalabels = FALSE     ) } #> ℹ Using column 'mo' as input for mo_is_gram_negative()  # }"},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":null,"dir":"Reference","previous_headings":"","what":"Guess Antibiotic Column — guess_ab_col","title":"Guess Antibiotic Column — guess_ab_col","text":"tries find column name data set based information antibiotics data set. Also supports WHONET abbreviations.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Guess Antibiotic Column — guess_ab_col","text":"","code":"guess_ab_col(   x = NULL,   search_string = NULL,   verbose = FALSE,   only_sir_columns = FALSE )"},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Guess Antibiotic Column — guess_ab_col","text":"x data.frame search_string text search x , checked .ab() value column x verbose logical indicate whether additional info printed only_sir_columns logical indicate whether antibiotic columns must detected transformed class sir (see .sir()) beforehand (defaults FALSE)","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Guess Antibiotic Column — guess_ab_col","text":"column name x, NULL result found.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Guess Antibiotic Column — guess_ab_col","text":"can look antibiotic (trade) name abbreviation search x antibiotics data set column containing name code antibiotic.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Guess Antibiotic Column — guess_ab_col","text":"","code":"df <- data.frame(   amox = \"S\",   tetr = \"R\" )  guess_ab_col(df, \"amoxicillin\") #> [1] \"amox\" guess_ab_col(df, \"J01AA07\") # ATC code of tetracycline #> [1] \"tetr\"  guess_ab_col(df, \"J01AA07\", verbose = TRUE) #> Auto-guessing columns suitable for analysis #> ... #>  OK. #> ℹ Using column 'amox' as input for AMX (amoxicillin). #> ℹ Using column 'tetr' as input for TCY (tetracycline). #> ℹ Using column 'tetr' as input for J01AA07 (tetracycline). #> [1] \"tetr\" # NOTE: Using column 'tetr' as input for J01AA07 (tetracycline).  # WHONET codes df <- data.frame(   AMP_ND10 = \"R\",   AMC_ED20 = \"S\" ) guess_ab_col(df, \"ampicillin\") #> [1] \"AMP_ND10\" guess_ab_col(df, \"J01CR02\") #> [1] \"AMC_ED20\" guess_ab_col(df, as.ab(\"augmentin\")) #> [1] \"AMC_ED20\""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"Data set containing defined intrinsic resistance EUCAST bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"intrinsic_resistant"},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"tibble 134,634 observations 2 variables: mo Microorganism ID ab Antibiotic ID","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"data set based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository. allow machine reading EUCAST CLSI guidelines, almost impossible MS Excel PDF files distributed EUCAST CLSI.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"intrinsic_resistant #> # A tibble: 134,634 × 2 #>    mo      ab    #>    <mo>    <chr> #>  1 B_GRAMP ATM   #>  2 B_GRAMP COL   #>  3 B_GRAMP NAL   #>  4 B_GRAMP PLB   #>  5 B_GRAMP TEM   #>  6 B_ABTRP ATM   #>  7 B_ABTRP COL   #>  8 B_ABTRP NAL   #>  9 B_ABTRP PLB   #> 10 B_ABTRP TEM   #> # … with 134,624 more rows"},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":null,"dir":"Reference","previous_headings":"","what":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"According binomial nomenclature, lowest four taxonomic levels (family, genus, species, subspecies) printed italics. function finds taxonomic names within strings makes italic.","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"","code":"italicise_taxonomy(string, type = c(\"markdown\", \"ansi\"))  italicize_taxonomy(string, type = c(\"markdown\", \"ansi\"))"},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"string character (vector) type type conversion taxonomic names, either \"markdown\" \"ansi\", see Details","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"function finds taxonomic names makes italic based microorganisms data set. taxonomic names can italicised using markdown (default) adding * taxonomic names, using ANSI colours adding \\033[3m \\033[23m taxonomic names. multiple ANSI colours available, conversion occur. function also supports abbreviation genus followed species, \"E. coli\" \"K. pneumoniae ozaenae\".","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"","code":"italicise_taxonomy(\"An overview of Staphylococcus aureus isolates\") #> [1] \"An overview of *Staphylococcus aureus* isolates\" italicise_taxonomy(\"An overview of S. aureus isolates\") #> [1] \"An overview of *S. aureus* isolates\"  cat(italicise_taxonomy(\"An overview of S. aureus isolates\", type = \"ansi\")) #> An overview of S. aureus isolates"},{"path":"https://msberends.github.io/AMR/reference/join.html","id":null,"dir":"Reference","previous_headings":"","what":"Join microorganisms to a Data Set — join","title":"Join microorganisms to a Data Set — join","text":"Join data set microorganisms easily existing data set character vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join microorganisms to a Data Set — join","text":"","code":"inner_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  left_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  right_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  full_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  semi_join_microorganisms(x, by = NULL, ...)  anti_join_microorganisms(x, by = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join microorganisms to a Data Set — join","text":"x existing data set join, character vector. case character vector, resulting data.frame contain column 'x' values. variable join - left empty search column class mo (created .mo()) \"mo\" column name exists x, otherwise column name x values exist microorganisms$mo (= \"bacteria_id\"), another column microorganisms (named, like = c(\"bacteria_id\" = \"fullname\")) suffix non-joined duplicate variables x y, suffixes added output disambiguate . character vector length 2. ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join microorganisms to a Data Set — join","text":"data.frame","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Join microorganisms to a Data Set — join","text":"Note: opposed join() functions dplyr, character vectors supported default existing columns get suffix \"2\" newly joined columns get suffix. dplyr package installed, join functions used. Otherwise, much slower merge() interaction() functions base R used.","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Join microorganisms to a Data Set — join","text":"","code":"left_join_microorganisms(as.mo(\"K. pneumoniae\")) #> # A tibble: 1 × 22 #>   mo           fullname   status kingdom phylum class order family genus species #>   <mo>         <chr>      <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> <chr>   #> 1 B_KLBSL_PNMN Klebsiell… accep… Bacter… Pseud… Gamm… Ente… Enter… Kleb… pneumo… #> # … with 12 more variables: subspecies <chr>, rank <chr>, ref <chr>, #> #   source <chr>, lpsn <chr>, lpsn_parent <chr>, lpsn_renamed_to <chr>, #> #   gbif <chr>, gbif_parent <chr>, gbif_renamed_to <chr>, prevalence <dbl>, #> #   snomed <list> left_join_microorganisms(\"B_KLBSL_PNMN\") #> # A tibble: 1 × 22 #>   mo           fullname   status kingdom phylum class order family genus species #>   <mo>         <chr>      <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> <chr>   #> 1 B_KLBSL_PNMN Klebsiell… accep… Bacter… Pseud… Gamm… Ente… Enter… Kleb… pneumo… #> # … with 12 more variables: subspecies <chr>, rank <chr>, ref <chr>, #> #   source <chr>, lpsn <chr>, lpsn_parent <chr>, lpsn_renamed_to <chr>, #> #   gbif <chr>, gbif_parent <chr>, gbif_renamed_to <chr>, prevalence <dbl>, #> #   snomed <list>  df <- data.frame(   date = seq(     from = as.Date(\"2018-01-01\"),     to = as.Date(\"2018-01-07\"),     by = 1   ),   bacteria = as.mo(c(     \"S. aureus\", \"MRSA\", \"MSSA\", \"STAAUR\",     \"E. coli\", \"E. coli\", \"E. coli\"   )),   stringsAsFactors = FALSE ) colnames(df) #> [1] \"date\"     \"bacteria\"  df_joined <- left_join_microorganisms(df, \"bacteria\") colnames(df_joined) #>  [1] \"date\"            \"bacteria\"        \"fullname\"        \"status\"          #>  [5] \"kingdom\"         \"phylum\"          \"class\"           \"order\"           #>  [9] \"family\"          \"genus\"           \"species\"         \"subspecies\"      #> [13] \"rank\"            \"ref\"             \"source\"          \"lpsn\"            #> [17] \"lpsn_parent\"     \"lpsn_renamed_to\" \"gbif\"            \"gbif_parent\"     #> [21] \"gbif_renamed_to\" \"prevalence\"      \"snomed\"           # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     left_join_microorganisms() %>%     colnames() } #> Joining, by = \"mo\" #>  [1] \"date\"            \"patient\"         \"age\"             \"gender\"          #>  [5] \"ward\"            \"mo\"              \"PEN\"             \"OXA\"             #>  [9] \"FLC\"             \"AMX\"             \"AMC\"             \"AMP\"             #> [13] \"TZP\"             \"CZO\"             \"FEP\"             \"CXM\"             #> [17] \"FOX\"             \"CTX\"             \"CAZ\"             \"CRO\"             #> [21] \"GEN\"             \"TOB\"             \"AMK\"             \"KAN\"             #> [25] \"TMP\"             \"SXT\"             \"NIT\"             \"FOS\"             #> [29] \"LNZ\"             \"CIP\"             \"MFX\"             \"VAN\"             #> [33] \"TEC\"             \"TCY\"             \"TGC\"             \"DOX\"             #> [37] \"ERY\"             \"CLI\"             \"AZM\"             \"IPM\"             #> [41] \"MEM\"             \"MTR\"             \"CHL\"             \"COL\"             #> [45] \"MUP\"             \"RIF\"             \"fullname\"        \"status\"          #> [49] \"kingdom\"         \"phylum\"          \"class\"           \"order\"           #> [53] \"family\"          \"genus\"           \"species\"         \"subspecies\"      #> [57] \"rank\"            \"ref\"             \"source\"          \"lpsn\"            #> [61] \"lpsn_parent\"     \"lpsn_renamed_to\" \"gbif\"            \"gbif_parent\"     #> [65] \"gbif_renamed_to\" \"prevalence\"      \"snomed\"          # }"},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"functions can used determine first weighted isolates considering phenotype isolate selection (see first_isolate()). Using phenotype-based method determine first isolates reliable methods disregard phenotypes.","code":""},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"","code":"key_antimicrobials(   x = NULL,   col_mo = NULL,   universal = c(\"ampicillin\", \"amoxicillin/clavulanic acid\", \"cefuroxime\",     \"piperacillin/tazobactam\", \"ciprofloxacin\", \"trimethoprim/sulfamethoxazole\"),   gram_negative = c(\"gentamicin\", \"tobramycin\", \"colistin\", \"cefotaxime\", \"ceftazidime\",     \"meropenem\"),   gram_positive = c(\"vancomycin\", \"teicoplanin\", \"tetracycline\", \"erythromycin\",     \"oxacillin\", \"rifampin\"),   antifungal = c(\"anidulafungin\", \"caspofungin\", \"fluconazole\", \"miconazole\", \"nystatin\",     \"voriconazole\"),   only_sir_columns = FALSE,   ... )  all_antimicrobials(x = NULL, only_sir_columns = FALSE, ...)  antimicrobials_equal(   y,   z,   type = c(\"points\", \"keyantimicrobials\"),   ignore_I = TRUE,   points_threshold = 2,   ... )"},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"x data.frame antibiotics columns, like AMX amox. Can left blank determine automatically col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). universal names broad-spectrum antimicrobial drugs, case-insensitive. Set NULL ignore. See Details default antimicrobial drugs gram_negative names antibiotic drugs Gram-positives, case-insensitive. Set NULL ignore. See Details default antibiotic drugs gram_positive names antibiotic drugs Gram-negatives, case-insensitive. Set NULL ignore. See Details default antibiotic drugs antifungal names antifungal drugs fungi, case-insensitive. Set NULL ignore. See Details default antifungal drugs only_sir_columns logical indicate whether columns must included transformed class sir (see .sir()) beforehand (defaults FALSE) ... ignored, place allow future extensions y, z character vectors compare type type determine weighed isolates; can \"keyantimicrobials\" \"points\", see Details ignore_I logical indicate whether antibiotic interpretations \"\" ignored type = \"keyantimicrobials\", see Details points_threshold minimum number points require differences antibiogram lead inclusion isolate type = \"points\", see Details","code":""},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"key_antimicrobials() all_antimicrobials() functions context-aware. means x argument can left blank used inside data.frame call, see Examples. function key_antimicrobials() returns character vector 12 antimicrobial results every isolate. function all_antimicrobials() returns character vector antimicrobial drug results every isolate. vectors can compared using antimicrobials_equal(), check two isolates generally antibiogram. Missing invalid values replaced dot (\".\") key_antimicrobials() ignored antimicrobials_equal(). Please see first_isolate() function important functions enable 'phenotype-based' method determination first isolates. default antimicrobial drugs used rows (set universal) : Ampicillin Amoxicillin/clavulanic acid Cefuroxime Ciprofloxacin Piperacillin/tazobactam Trimethoprim/sulfamethoxazole default antimicrobial drugs used Gram-negative bacteria (set gram_negative) : Cefotaxime Ceftazidime Colistin Gentamicin Meropenem Tobramycin default antimicrobial drugs used Gram-positive bacteria (set gram_positive) : Erythromycin Oxacillin Rifampin Teicoplanin Tetracycline Vancomycin default antimicrobial drugs used fungi (set antifungal) : Anidulafungin Caspofungin Fluconazole Miconazole Nystatin Voriconazole","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # output of the `key_antimicrobials()` function could be like this: strainA <- \"SSSRR.S.R..S\" strainB <- \"SSSIRSSSRSSS\"  # those strings can be compared with: antimicrobials_equal(strainA, strainB, type = \"keyantimicrobials\") #> [1] TRUE # TRUE, because I is ignored (as well as missing values)  antimicrobials_equal(strainA, strainB, type = \"keyantimicrobials\", ignore_I = FALSE) #> [1] FALSE # FALSE, because I is not ignored and so the 4th [character] differs  # \\donttest{ if (require(\"dplyr\")) {   # set key antibiotics to a new variable   my_patients <- example_isolates %>%     mutate(keyab = key_antimicrobials(antifungal = NULL)) %>% # no need to define `x`     mutate(       # now calculate first isolates       first_regular = first_isolate(col_keyantimicrobials = FALSE),       # and first WEIGHTED isolates       first_weighted = first_isolate(col_keyantimicrobials = \"keyab\")     )    # Check the difference in this data set, 'weighted' results in more isolates:   sum(my_patients$first_regular, na.rm = TRUE)   sum(my_patients$first_weighted, na.rm = TRUE) } #> Including isolates from ICU. #> Including isolates from ICU. #> [1] 1395 # }"},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":null,"dir":"Reference","previous_headings":"","what":"Kurtosis of the Sample — kurtosis","title":"Kurtosis of the Sample — kurtosis","text":"Kurtosis measure \"tailedness\" probability distribution real-valued random variable. normal distribution kurtosis 3 excess kurtosis 0.","code":""},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kurtosis of the Sample — kurtosis","text":"","code":"kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for default kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for matrix kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for data.frame kurtosis(x, na.rm = FALSE, excess = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kurtosis of the Sample — kurtosis","text":"x vector values, matrix data.frame na.rm logical indicate whether NA values stripped computation proceeds excess logical indicate whether excess kurtosis returned, defined kurtosis minus 3.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kurtosis of the Sample — kurtosis","text":"","code":"kurtosis(rnorm(10000)) #> [1] 2.950875 kurtosis(rnorm(10000), excess = TRUE) #> [1] 0.0330772"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":null,"dir":"Reference","previous_headings":"","what":"Vectorised Pattern Matching with Keyboard Shortcut — like","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Convenient wrapper around grepl() match pattern: x %like% pattern. always returns logical vector always case-insensitive (use x %like_case% pattern case-sensitive matching). Also, pattern can long x compare items index vectors, can length iterate cases.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"like(x, pattern, ignore.case = TRUE)  x %like% pattern  x %unlike% pattern  x %like_case% pattern  x %unlike_case% pattern"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Idea like function data.table package, although altered explained Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"x character vector matches sought, object can coerced .character() character vector. pattern character vector containing regular expressions (character string fixed = TRUE) matched given character vector. Coerced .character() character string possible. ignore.case FALSE, pattern matching case sensitive TRUE, case ignored matching.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"like() %like%/%unlike% functions: case-insensitive (use %like_case%/%unlike_case% case-sensitive matching) Support multiple patterns Check pattern valid regular expression sets fixed = TRUE , greatly improve speed (vectorised pattern) Always use compatibility Perl unless fixed = TRUE, greatly improve speed Using RStudio? %like%/%unlike% functions can also directly inserted code Addins menu can keyboard shortcut like Shift+Ctrl+L Shift+Cmd+L (see menu Tools > Modify Keyboard Shortcuts...). keep pressing shortcut, inserted text iterated %like% -> %unlike% -> %like_case% -> %unlike_case%.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"a <- \"This is a test\" b <- \"TEST\" a %like% b #> [1] TRUE b %like% a #> [1] FALSE  # also supports multiple patterns a <- c(\"Test case\", \"Something different\", \"Yet another thing\") b <- c(\"case\", \"diff\", \"yet\") a %like% b #> [1] TRUE TRUE TRUE a %unlike% b #> [1] FALSE FALSE FALSE  a[1] %like% b #> [1]  TRUE FALSE FALSE a %like% b[1] #> [1]  TRUE FALSE FALSE  # \\donttest{ # get isolates whose name start with 'Entero' (case-insensitive) example_isolates[which(mo_name() %like% \"^entero\"), ] #> ℹ Using column 'mo' as input for mo_name() #> # A tibble: 106 × 46 #>    date       patient   age gender ward    mo            PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>   <mo>          <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinic… B_ENTRC_FACM  NA    NA    NA    NA    #>  2 2002-04-08 130252     78 M      ICU     B_ENTRC_FCLS  NA    NA    NA    NA    #>  3 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS  NA    NA    NA    NA    #>  4 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS  NA    NA    NA    NA    #>  5 2003-04-20 6BC362     62 M      ICU     B_ENTRC       NA    NA    NA    NA    #>  6 2003-04-21 6BC362     62 M      ICU     B_ENTRC       NA    NA    NA    NA    #>  7 2003-08-13 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R     #>  8 2003-08-13 F35553     52 M      ICU     B_ENTRC_FCLS  NA    NA    NA    NA    #>  9 2003-09-05 F35553     52 M      ICU     B_ENTRC       NA    NA    NA    NA    #> 10 2003-09-05 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R     #> # … with 96 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir>  if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_name() %like% \"^ent\") } #> ℹ Using column 'mo' as input for mo_name() #> # A tibble: 106 × 46 #>    date       patient   age gender ward    mo            PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>   <mo>          <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinic… B_ENTRC_FACM  NA    NA    NA    NA    #>  2 2002-04-08 130252     78 M      ICU     B_ENTRC_FCLS  NA    NA    NA    NA    #>  3 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS  NA    NA    NA    NA    #>  4 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS  NA    NA    NA    NA    #>  5 2003-04-20 6BC362     62 M      ICU     B_ENTRC       NA    NA    NA    NA    #>  6 2003-04-21 6BC362     62 M      ICU     B_ENTRC       NA    NA    NA    NA    #>  7 2003-08-13 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R     #>  8 2003-08-13 F35553     52 M      ICU     B_ENTRC_FCLS  NA    NA    NA    NA    #>  9 2003-09-05 F35553     52 M      ICU     B_ENTRC       NA    NA    NA    NA    #> 10 2003-09-05 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R     #> # … with 96 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> # }"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Determine isolates multidrug-resistant organisms (MDRO) according international, national custom guidelines.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"mdro(   x = NULL,   guideline = \"CMI2012\",   col_mo = NULL,   info = interactive(),   pct_required_classes = 0.5,   combine_SI = TRUE,   verbose = FALSE,   only_sir_columns = FALSE,   ... )  custom_mdro_guideline(..., as_factor = TRUE)  brmo(x = NULL, only_sir_columns = FALSE, ...)  mrgn(x = NULL, only_sir_columns = FALSE, ...)  mdr_tb(x = NULL, only_sir_columns = FALSE, ...)  mdr_cmi2012(x = NULL, only_sir_columns = FALSE, ...)  eucast_exceptional_phenotypes(x = NULL, only_sir_columns = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"See supported guidelines list publications used function.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"x data.frame antibiotics columns, like AMX amox. Can left blank automatic determination. guideline specific guideline follow, see sections Supported international / national guidelines Using Custom Guidelines . left empty, publication Magiorakos et al. (see ) followed. col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console, defaults print interactive sessions pct_required_classes minimal required percentage antimicrobial classes must available per isolate, rounded . example, default guideline, 17 antimicrobial classes must available S. aureus. Setting pct_required_classes argument 0.5 (default) means every S. aureus isolate least 8 different classes must available. lower number available classes return NA isolate. combine_SI logical indicate whether values S must merged one, resistance considered isolates R, . default behaviour mdro() function, follows redefinition EUCAST interpretation (increased exposure) 2019, see section 'Interpretation S, R' . using combine_SI = FALSE, resistance considered isolates R . verbose logical turn Verbose mode (default ). Verbose mode, function return MDRO results, instead returns data set logbook form extensive info isolates MDRO-positive, . only_sir_columns logical indicate whether antibiotic columns must detected transformed class sir (see .sir()) beforehand (defaults FALSE) ... case custom_mdro_guideline(): set rules, see section Using Custom Guidelines . Otherwise: column name antibiotic, see section Antibiotics . as_factor logical indicate whether returned value ordered factor (TRUE, default), otherwise character vector","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"CMI 2012 paper - function mdr_cmi2012() mdro(): Ordered factor levels Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant (XDR) < Pandrug-resistant (PDR) TB guideline - function mdr_tb() mdro(..., guideline = \"TB\"): Ordered factor levels Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant < Extensively drug-resistant German guideline - function mrgn() mdro(..., guideline = \"MRGN\"): Ordered factor levels Negative < 3MRGN < 4MRGN Everything else, except custom guidelines: Ordered factor levels Negative < Positive, unconfirmed < Positive. value \"Positive, unconfirmed\" means , according guideline, entirely sure isolate multi-drug resistant confirmed additional (e.g. molecular) tests","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"functions context-aware. means x argument can left blank used inside data.frame call, see Examples. pct_required_classes argument, values 1 divided 100. support fractions (0.75 3/4) percentages (75). Note: Every test involves Enterobacteriaceae family, internally performed using newly named order Enterobacterales, since Enterobacteriaceae family taxonomically reclassified Adeolu et al. 2016. , Enterobacteriaceae family Enterobacteriales () order. species old Enterobacteriaceae family still new Enterobacterales (without ) order, divided multiple families. way tests performed now mdro() function makes sure results 2016 2016 identical.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"supported-international-national-guidelines","dir":"Reference","previous_headings":"","what":"Supported International / National Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. \"Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.\" Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.3\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.3 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.2\" European international guideline - EUCAST Expert Rules Version 3.2 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 \"Intrinsic Resistance Exceptional Phenotypes Tables\" (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization \"Companion handbook guidelines programmatic management drug-resistant tuberculosis\" (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7; doi:10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu \"WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)\" (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"using-custom-guidelines","dir":"Reference","previous_headings":"","what":"Using Custom Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde:   row/isolate matches first rule, value first ~ (case 'Elderly Type ') set MDRO value. Otherwise, second rule tried . number rules unlimited. can print rules set console overview. Colours help reading console supports colours.   outcome function can used guideline argument mdro() function:   Rules can also combined custom rules using c():   rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline(CIP == \"R\" & age > 60 ~ \"Elderly Type A\",                                 ERY == \"R\" & age > 60 ~ \"Elderly Type B\") custom #> A set of custom MDRO rules: #>   1. CIP is \"R\" and age is higher than 60 -> Elderly Type A #>   2. ERY is \"R\" and age is higher than 60 -> Elderly Type B #>   3. Otherwise -> Negative #> #> Unmatched rows will return NA. x <- mdro(example_isolates,           guideline = custom) table(x) #>       Negative Elderly Type A Elderly Type B #>           1070            198            732 x <- mdro(example_isolates,           guideline = c(custom,                         custom_mdro_guideline(ERY == \"R\" & age > 50 ~ \"Elderly Type C\"))) table(x) #>       Negative Elderly Type A Elderly Type B Elderly Type C #>            961            198            732            109"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"antibiotics","dir":"Reference","previous_headings":"","what":"Antibiotics","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"define antibiotics column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antibiotics eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, S01AE08), amoxicillin (AMX, J01MA02), amoxicillin/clavulanic acid (AMC, J01MA23), ampicillin (AMP, J01MA04), ampicillin/sulbactam (SAM, J01MA08), arbekacin (ARB, J01MA19), aspoxicillin (APX, J01MA16), azidocillin (AZD, J01MA15), azithromycin (AZM, J01MA11), azlocillin (AZL, J01MA25), aztreonam (ATM, J01MA12), bacampicillin (BAM, J01MA24), bekanamycin (BEK, J01MA07), benzathine benzylpenicillin (BNB, J01MA14), benzathine phenoxymethylpenicillin (BNP, D10AF05), benzylpenicillin (PEN, J01MA06), besifloxacin (BES, J01MA01), biapenem (BIA, J01MA18), carbenicillin (CRB, J01MA03), carindacillin (CRN, J01MA17), cefacetrile (CAC, J01MA10), cefaclor (CEC, J01MA21), cefadroxil (CFR, J01MA09), cefalexin (LEX, J01MA05), cefaloridine (RID, P01AA05), cefalotin (CEP, J01MA22), cefamandole (MAN, J01MA13), cefapirin (HAP, J01CA01), cefatrizine (CTZ, J01CA04), cefazedone (CZD, J01CA12), cefazolin (CZO, J01CR05), cefcapene (CCP, J01CA13), cefdinir (CDR, J01AA02), cefditoren (DIT, J01FA10), cefepime (FEP, J01FA09), cefetamet (CAT, J01CR02), cefixime (CFM, J01AA08), cefmenoxime (CMX, J01FA06), cefmetazole (CMZ, J01CF04), cefodizime (DIZ, J01CF05), cefonicid (CID, J01CR01), cefoperazone (CFP, J01CA19), cefoperazone/sulbactam (CSL, J01CE04), ceforanide (CND, J01CA09), cefotaxime (CTX, J01DF01), cefotaxime/clavulanic acid (CTC, J01CA06), cefotetan (CTT, J01CE08), cefotiam (CTF, J01CE10), cefoxitin (FOX, J01CE01), cefozopran (ZOP, J01CA03), cefpiramide (CPM, J01CA05), cefpirome (CPO, J01CE07), cefpodoxime (CPD, J01CF02), cefprozil (CPR, J01CF01), cefroxadine (CRD, J01CA07), cefsulodin (CFS, J01CA18), ceftaroline (CPT, J01CA11), ceftazidime (CAZ, J01CA14), ceftazidime/clavulanic acid (CCV, J01CF03), cefteram (CEM, J01CA10), ceftezole (CTL, J01CF06), ceftibuten (CTB, J01CE06), ceftizoxime (CZX, J01CE05), ceftobiprole medocaril (CFM1, J01CE02), ceftolozane/tazobactam (CZT, J01CA02), ceftriaxone (CRO, J01CA08), ceftriaxone/beta-lactamase inhibitor (CEB, J01CE09), cefuroxime (CXM, J01CE03), cephradine (CED, J01CG01), chloramphenicol (CHL, J01CA16), ciprofloxacin (CIP, J01CR04), clarithromycin (CLR, J01CA15), clindamycin (CLI, J01CG02), clometocillin (CLM, J01CA17), cloxacillin (CLO, J01CR03), colistin (COL, J01DB10), cycloserine (CYC, J01DC04), dalbavancin (DAL, J01DB05), daptomycin (DAP, J01DB01), delafloxacin (DFX, J01DB02), dibekacin (DKB, J01DB03), dicloxacillin (DIC, J01DC03), dirithromycin (DIR, J01DB08), doripenem (DOR, J01DB07), doxycycline (DOX, J01DB06), enoxacin (ENX, J01DB04), epicillin (EPC, J01DD17), ertapenem (ETP, J01DD15), erythromycin (ERY, J01DD16), fleroxacin (FLE, J01DE01), flucloxacillin (FLC, J01DD10), flurithromycin (FLR1, J01DD08), fosfomycin (FOS, J01DD05), framycetin (FRM, J01DC09), fusidic acid (FUS, J01DD09), garenoxacin (GRN, J01DC06), gatifloxacin (GAT, J01DD12), gemifloxacin (GEM, J01DD62), gentamicin (GEN, J01DC11), grepafloxacin (GRX, J01DD01), hetacillin (HET, J01DD51), imipenem (IPM, J01DC05), imipenem/relebactam (IMR, J01DC07), isepamicin (ISE, J01DC01), josamycin (JOS, J01DE03), kanamycin (KAN, J01DD11), lascufloxacin (LSC, J01DE02), latamoxef (LTM, J01DD13), levofloxacin (LVX, J01DC10), levonadifloxacin (LND, J01DB11), lincomycin (LIN, J01DD03), linezolid (LNZ, J01DI02), lomefloxacin (LOM, J01DD02), loracarbef (LOR, J01DD52), mecillinam (MEC, J01DD18), meropenem (MEM, J01DB12), meropenem/vaborbactam (MEV, J01DD14), metampicillin (MTM, J01DD07), meticillin (MET, J01DI01), mezlocillin (MEZ, J01DI54), micronomicin (MCR, J01DD04), midecamycin (MID, J01DD63), minocycline (MNO, J01DC02), miocamycin (MCM, J01DB09), moxifloxacin (MFX, J01DD06), nadifloxacin (NAD, J01DC08), nafcillin (NAF, J01DH05), nalidixic acid (NAL, J01DH04), neomycin (NEO, J01DH03), netilmicin (NET, J01DH51), nitrofurantoin (NIT, J01DH56), norfloxacin (, J01DH02), ofloxacin (OFX, J01DH52), oleandomycin (OLE, J01DH55), oritavancin (ORI, J01DH06), oxacillin (OXA, J01XA02), panipenem (PAN, J01XA01), pazufloxacin (PAZ, J01XC01), pefloxacin (PEF, J01FA13), penamecillin (PNM, J01FA01), pheneticillin (PHE, J01FA14), phenoxymethylpenicillin (PHN, J01FA07), piperacillin (PIP, J01FA03), piperacillin/tazobactam (TZP, J01FA11), pivampicillin (PVM, J01FA05), pivmecillinam (PME, J01FA12), plazomicin (PLZ, J01FA16), polymyxin B (PLB, J01FA02), pristinamycin (PRI, J01FA15), procaine benzylpenicillin (PRB, J01FA08), propicillin (PRP, J01FF02), prulifloxacin (PRU, J01FG01), quinupristin/dalfopristin (QDA, J01FG02), ribostamycin (RST, J04AB02), rifampicin (RIF, J01XX09), rokitamycin (ROK, J01XX08), roxithromycin (RXT, J01AA07), rufloxacin (RFL, J01XB01), sisomicin (SIS, J01XB02), sitafloxacin (SIT, J01XE01), solithromycin (SOL, J01AA12), sparfloxacin (SPX, J01EA01), spiramycin (SPI, J01XX01), streptoduocin (STR, J01BA01), streptomycin (STR1, J01GB06), sulbactam (SUL, J01GB12), sulbenicillin (SBC, J01GB13), sulfadiazine (SDI, J01GB09), sulfadiazine/trimethoprim (SLT1, D09AA01), sulfadimethoxine (SUD, J01GB03), sulfadimidine (SDM, J01GB11), sulfadimidine/trimethoprim (SLT2, J01GB04), sulfafurazole (SLF, S01AA22), sulfaisodimidine (SLF1, J01GB05), sulfalene (SLF2, J01GB07), sulfamazone (SZO, J01GB14), sulfamerazine (SLF3, J01GB10), sulfamerazine/trimethoprim (SLT3, J01GB08), sulfamethizole (SLF4, J01GA02), sulfamethoxazole (SMX, J01GA01), sulfamethoxypyridazine (SLF5, J01GB01), sulfametomidine (SLF6, J01EE01), sulfametoxydiazine (SLF7, J01MB02), sulfametrole/trimethoprim (SLT4, J01FF01), sulfamoxole (SLF8, J01XA04), sulfamoxole/trimethoprim (SLT5, J01XA05), sulfanilamide (SLF9, J01XA03), sulfaperin (SLF10, J04AB01), sulfaphenazole (SLF11, J01XX11), sulfapyridine (SLF12, J01EC02), sulfathiazole (SUT, J01ED01), sulfathiourea (SLF13, J01EB03), sultamicillin (SLT6, J01EB05), talampicillin (TAL, J01EB01), tazobactam (TAZ, J01ED02), tebipenem (TBP, J01ED09), tedizolid (TZD, J01ED07), teicoplanin (TEC, J01EB02), telavancin (TLV, J01EC01), telithromycin (TLT, J01ED05), temafloxacin (TMX, J01ED03), temocillin (TEM, J01ED04), tetracycline (TCY, J01EC03), ticarcillin (TIC, J01EB06), ticarcillin/clavulanic acid (TCC, J01ED06), tigecycline (TGC, J01ED08), tilbroquinol (TBQ, J01EB04), tobramycin (TOB, J01EB07), tosufloxacin (TFX, J01EB08), trimethoprim (TMP, J01EE02), trimethoprim/sulfamethoxazole (SXT, J01EE05), troleandomycin (TRL, J01EE07), trovafloxacin (TVA, J01EE03), vancomycin (VAN, J01EE04)","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"out <- mdro(example_isolates, guideline = \"EUCAST\") #>  (16 isolates had no test results) str(out) #>  Ord.factor w/ 3 levels \"Negative\"<\"Positive, unconfirmed\"<..: 1 1 1 1 1 1 1 1 1 1 ... table(out) #> out #>              Negative Positive, unconfirmed              Positive  #>                  1978                     0                     6   out <- mdro(example_isolates,   guideline = custom_mdro_guideline(     AMX == \"R\" ~ \"Custom MDRO 1\",     VAN == \"R\" ~ \"Custom MDRO 2\"   ) ) table(out) #> out #>      Negative Custom MDRO 1 Custom MDRO 2  #>           870           804           326   # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     mdro() %>%     table()    # no need to define `x` when used inside dplyr verbs:   example_isolates %>%     mutate(MDRO = mdro()) %>%     pull(MDRO) %>%     table() } #>  (16 isolates had no test results) #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #>  (16 isolates had no test results) #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> . #>                         Negative       Multi-drug-resistant (MDR)  #>                             1601                              128  #> Extensively drug-resistant (XDR)          Pandrug-resistant (PDR)  #>                                0                                0  # }"},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the Mean AMR Distance — mean_amr_distance","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"Calculates normalised mean antimicrobial resistance multiple observations, help identify similar isolates without comparing antibiograms hand.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"","code":"mean_amr_distance(x, ...)  # S3 method for sir mean_amr_distance(x, ..., combine_SI = TRUE)  # S3 method for data.frame mean_amr_distance(x, ..., combine_SI = TRUE)  amr_distance_from_row(amr_distance, row)"},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"x vector class sir, mic disk, data.frame containing columns classes ... variables select (supports tidyselect language column1:column4 (.mic), can thus also antibiotic selectors combine_SI logical indicate whether values S must merged one, input consists S+vs. R (susceptible vs. resistant), defaults TRUE amr_distance outcome mean_amr_distance() row index, row number","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"mean AMR distance effectively Z-score; normalised numeric value compare AMR test results can help identify similar isolates, without comparing antibiograms hand. MIC values (see .mic()) transformed log2() first; distance thus calculated (log2(x) - mean(log2(x))) / sd(log2(x)). SIR values (see .sir()) transformed using \"S\" = 1, \"\" = 2, \"R\" = 3. combine_SI TRUE (default), \"\" considered 1. data sets, mean AMR distance calculated per column, mean per row returned, see Examples. Use amr_distance_from_row() subtract distances distance one row, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"interpretation","dir":"Reference","previous_headings":"","what":"Interpretation","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"Isolates distances less 0.01 difference considered similar. Differences lower 0.025 considered suspicious.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"","code":"sir <- random_sir(10) sir #> Class 'sir' #>  [1] S I S R R R S R R R mean_amr_distance(sir) #>  [1] -1.1618950 -1.1618950 -1.1618950  0.7745967  0.7745967  0.7745967 #>  [7] -1.1618950  0.7745967  0.7745967  0.7745967  mic <- random_mic(10) mic #> Class 'mic' #>  [1] 0.125   0.0625  0.25    64      0.0625  32      <=0.001 0.01    8       #> [10] >=128   mean_amr_distance(mic) #>  [1] -0.3704641 -0.5436561 -0.1972720  1.1882645 -0.5436561  1.0150725 #>  [7] -1.5768826 -1.0015510  0.6686883  1.3614566 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) #>  [1] -0.3704641 -0.5436561 -0.1972720  1.1882645 -0.5436561  1.0150725 #>  [7] -1.5768826 -1.0015510  0.6686883  1.3614566  disk <- random_disk(10) disk #> Class 'disk' #>  [1] 37 48 42 45 41 36 45 11  9 26 mean_amr_distance(disk) #>  [1]  0.2132007  0.9949367  0.5685352  0.7817360  0.4974683  0.1421338 #>  [7]  0.7817360 -1.6345388 -1.7766726 -0.5685352  y <- data.frame(   id = LETTERS[1:10],   amox = random_sir(10, ab = \"amox\", mo = \"Escherichia coli\"),   cipr = random_disk(10, ab = \"cipr\", mo = \"Escherichia coli\"),   gent = random_mic(10, ab = \"gent\", mo = \"Escherichia coli\"),   tobr = random_mic(10, ab = \"tobr\", mo = \"Escherichia coli\") ) y #>    id amox cipr gent tobr #> 1   A    S   26  >=8  <=1 #> 2   B    S   17    2  <=1 #> 3   C    S   22    4    4 #> 4   D    I   21    4  <=1 #> 5   E    S   18  >=8  <=1 #> 6   F    R   26  <=1  <=1 #> 7   G    S   17    2  <=1 #> 8   H    I   31  <=1    4 #> 9   I    R   21    2    4 #> 10  J    R   20  >=8  <=1 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\" and #>   \"tobr\" #>  [1]  0.21379008 -0.70858534  0.29772954 -0.27497563 -0.22745796  0.09238219 #>  [7] -0.70858534  0.36816222  0.54713702  0.40040322 y$amr_distance <- mean_amr_distance(y, where(is.mic)) #> ℹ Calculating mean AMR distance based on columns \"gent\" and \"tobr\" y[order(y$amr_distance), ] #>    id amox cipr gent tobr amr_distance #> 6   F    R   26  <=1  <=1  -0.99208370 #> 2   B    S   17    2  <=1  -0.56611233 #> 7   G    S   17    2  <=1  -0.56611233 #> 4   D    I   21    4  <=1  -0.14014095 #> 8   H    I   31  <=1    4   0.04301464 #> 1   A    S   26  >=8  <=1   0.28583042 #> 5   E    S   18  >=8  <=1   0.28583042 #> 10  J    R   20  >=8  <=1   0.28583042 #> 9   I    R   21    2    4   0.46898601 #> 3   C    S   22    4    4   0.89495739  if (require(\"dplyr\")) {   y %>%     mutate(       amr_distance = mean_amr_distance(y),       check_id_C = amr_distance_from_row(amr_distance, id == \"C\")     ) %>%     arrange(check_id_C) } #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\" and #>   \"tobr\" #>    id amox cipr gent tobr amr_distance check_id_C #> 1   C    S   22    4    4   0.29772954 0.00000000 #> 2   H    I   31  <=1    4   0.36816222 0.07043268 #> 3   A    S   26  >=8  <=1   0.21379008 0.08393946 #> 4   J    R   20  >=8  <=1   0.40040322 0.10267368 #> 5   F    R   26  <=1  <=1   0.09238219 0.20534735 #> 6   I    R   21    2    4   0.54713702 0.24940748 #> 7   E    S   18  >=8  <=1  -0.22745796 0.52518750 #> 8   D    I   21    4  <=1  -0.27497563 0.57270518 #> 9   B    S   17    2  <=1  -0.70858534 1.00631489 #> 10  G    S   17    2  <=1  -0.70858534 1.00631489 if (require(\"dplyr\")) {   # support for groups   example_isolates %>%     filter(mo_genus() == \"Enterococcus\" & mo_species() != \"\") %>%     select(mo, TCY, carbapenems()) %>%     group_by(mo) %>%     mutate(dist = mean_amr_distance(.)) %>%     arrange(mo, dist) } #> ℹ Using column 'mo' as input for mo_genus() #> ℹ Using column 'mo' as input for mo_species() #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Calculating mean AMR distance based on columns \"TCY\", \"IPM\" and \"MEM\" #> # A tibble: 63 × 5 #> # Groups:   mo [4] #>    mo           TCY   IPM   MEM     dist #>    <mo>         <sir> <sir> <sir>  <dbl> #>  1 B_ENTRC_AVIM S     S     NA     0     #>  2 B_ENTRC_AVIM S     S     NA     0     #>  3 B_ENTRC_CSSL NA    S     NA    NA     #>  4 B_ENTRC_FACM S     S     NA    -2.66  #>  5 B_ENTRC_FACM S     R     R     -0.423 #>  6 B_ENTRC_FACM S     R     R     -0.423 #>  7 B_ENTRC_FACM NA    R     R      0.224 #>  8 B_ENTRC_FACM NA    R     R      0.224 #>  9 B_ENTRC_FACM NA    R     R      0.224 #> 10 B_ENTRC_FACM NA    R     R      0.224 #> # … with 53 more rows"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 5,910 Common Microorganism Codes — microorganisms.codes","title":"Data Set with 5,910 Common Microorganism Codes — microorganisms.codes","text":"data set containing commonly used codes microorganisms, laboratory systems WHONET. Define set_mo_source(). searched using .mo() consequently mo_* functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 5,910 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 5,910 Common Microorganism Codes — microorganisms.codes","text":"tibble 5,910 observations 2 variables: code Commonly used code microorganism mo ID microorganism microorganisms data set","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 5,910 Common Microorganism Codes — microorganisms.codes","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 5,910 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes #> # A tibble: 5,910 × 2 #>    code    mo        #>    <chr>   <mo>      #>  1 _FAM_A- B_GRAMN   #>  2 _FAM_A+ B_GRAMP   #>  3 _FAM_AC B_GRAMP   #>  4 _FAM_AE B_GRAMN   #>  5 _FAM_AN B_GRAMN   #>  6 _FAM_AO B_GRAMP   #>  7 _FAM_AP   UNKNOWN #>  8 _FAM_AS F_FUNGUS  #>  9 _FAM_AT   UNKNOWN #> 10 _FAM_AV   UNKNOWN #> # … with 5,900 more rows"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 52,141 Microorganisms — microorganisms","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"data set containing full microbial taxonomy (last updated: 11 December, 2022) five kingdoms List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF). data set backbone AMR package. MO codes can looked using .mo().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"","code":"microorganisms"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"tibble 52,141 observations 22 variables: mo ID microorganism used package fullname Full name, like \"Escherichia coli\". taxonomic ranks genus, species subspecies, 'pasted' text genus, species, subspecies. taxonomic ranks higher genus, name taxon. status  Status taxon, either \"accepted\" \"synonym\" kingdom, phylum, class, order, family, genus, species, subspecies Taxonomic rank microorganism rank Text taxonomic rank microorganism, \"species\" \"genus\" ref Author(s) year related scientific publication. contains first surname year latest authors, e.g. \"Wallis et al. 2006 emend. Smith Jones 2018\" becomes \"Smith et al., 2018\". field directly retrieved source specified column source. Moreover, accents removed comply CRAN allows ASCII characters, e.g. \"Váňová\" becomes \"Vanova\". lpsn Identifier ('Record number') List Prokaryotic names Standing Nomenclature (LPSN). first/highest LPSN identifier keep one identifier per row. example, Acetobacter ascendens LPSN Record number 7864 11011. first available microorganisms data set. lpsn_parent LPSN identifier parent taxon lpsn_renamed_to LPSN identifier currently valid taxon gbif Identifier ('taxonID') Global Biodiversity Information Facility (GBIF) gbif_parent GBIF identifier parent taxon gbif_renamed_to GBIF identifier currently valid taxon source Either \"GBIF\", \"LPSN\" \"manually added\" (see Source) prevalence Prevalence microorganism according Bartlett et al. (2022, doi:10.1099/mic.0.001269 ), see mo_matching_score() full explanation snomed Systematized Nomenclature Medicine (SNOMED) code microorganism, version 1 July, 2021 (see Source). Use mo_snomed() retrieve quickly, see mo_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Grimont et al. (2007). Antigenic Formulae Salmonella Serovars, 9th Edition. Collaborating Centre Reference Research Salmonella (WHOCC-SALM). Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"Please note entries based List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF) (see ). Since sources incorporate entries based (recent) publications International Journal Systematic Evolutionary Microbiology (IJSEM), can happen year publication sometimes later one might expect. example, Staphylococcus pettenkoferi described first time Diagnostic Microbiology Infectious Disease 2002 (doi:10.1016/s0732-8893(02)00399-1 ), 2007 publication IJSEM followed (doi:10.1099/ijs.0.64381-0 ). Consequently, AMR package returns 2007 mo_year(\"S. pettenkoferi\").","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"included-taxa","dir":"Reference","previous_headings":"","what":"Included Taxa","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"Included taxonomic data : ~36,000 (sub)species kingdoms Archaea Bacteria ~7,900 (sub)species kingdom Fungi. kingdom Fungi large taxon almost 300,000 different (sub)species, microbial (rather macroscopic, like mushrooms). , fungi fit scope package. relevant fungi covered (species Aspergillus, Candida, Cryptococcus, Histoplasma, Pneumocystis, Saccharomyces Trichophyton). ~5,100 (sub)species kingdom Protozoa ~1,400 (sub)species ~40 relevant genera kingdom Animalia (Strongyloides Taenia) ~9,800 previously accepted names included (sub)species (taxonomically renamed) complete taxonomic tree included (sub)species: kingdom subspecies identifier parent taxons year first author related scientific publication","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"manual-additions","dir":"Reference","previous_headings":"","what":"Manual additions","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"convenience, entries added manually: ~1,500 entries city-like serovars Salmonellae 11 entries Streptococcus (beta-haemolytic: groups , B, C, D, F, G, H, K unspecified; : viridans, milleri) 2 entries Staphylococcus (coagulase-negative (CoNS) coagulase-positive (CoPS)) 1 entry Blastocystis (B.  hominis), although officially exist (Noel et al. 2005, PMID 15634993) 1 entry Moraxella (M. catarrhalis), formally named Branhamella catarrhalis (Catlin, 1970) though change never accepted within field clinical microbiology 6 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast, unknown fungus, unknown anaerobic bacteria) syntax used transform original data cleansed R format, can found : https://github.com/msberends/AMR/blob/main/data-raw/reproduction_of_microorganisms.R.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"about-the-records-from-lpsn-see-source-","dir":"Reference","previous_headings":"","what":"About the Records from LPSN (see Source)","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"LPSN main source bacteriological taxonomy AMR package. List Prokaryotic names Standing Nomenclature (LPSN) provides comprehensive information nomenclature prokaryotes. LPSN free use service founded Jean P. Euzeby 1997 later maintained Aidan C. Parte.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"","code":"microorganisms #> # A tibble: 52,141 × 22 #>    mo               fullname      status kingdom phylum class order family genus #>    <mo>             <chr>         <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> #>  1 B_ANAER          (unknown ana… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  2 F_FUNGUS         (unknown fun… accep… Fungi   (unkn… \"(un… \"(un… \"(unk… \"(un… #>  3 B_GRAMN          (unknown Gra… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  4 B_GRAMP          (unknown Gra… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  5   UNKNOWN        (unknown nam… accep… (unkno… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  6 F_YEAST          (unknown yea… accep… Fungi   (unkn… \"(un… \"(un… \"(unk… \"(un… #>  7 B_[FAM]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… \"Abd… \"Abd… \"Abdi… \"\"    #>  8 B_[ORD]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… \"Abd… \"Abd… \"\"     \"\"    #>  9 B_[CLS]_ADTBCTRA Abditibacter… accep… Bacter… Abdit… \"Abd… \"\"    \"\"     \"\"    #> 10 B_[PHL]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… \"\"    \"\"    \"\"     \"\"    #> # … with 52,131 more rows, and 13 more variables: species <chr>, #> #   subspecies <chr>, rank <chr>, ref <chr>, source <chr>, lpsn <chr>, #> #   lpsn_parent <chr>, lpsn_renamed_to <chr>, gbif <chr>, gbif_parent <chr>, #> #   gbif_renamed_to <chr>, prevalence <dbl>, snomed <list>"},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the Matching Score for Microorganisms — mo_matching_score","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"algorithm used .mo() mo_* functions determine probable match taxonomic records based user input.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"","code":"mo_matching_score(x, n)"},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"x user input value(s) n full taxonomic name, exists microorganisms$fullname","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"algorithm originally described : Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 . Later, work Bartlett et al. bacterial pathogens infecting humans (2022, doi:10.1099/mic.0.001269 ) incorporated.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated :  : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.0 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, genus present established list also prevalence = 1.0 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.5 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Contracaecum, Cordylobia, Cryptococcus, Curvularia, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Giardia, Haloarcula, Halobacterium, Halococcus, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hymenolepis, Hypomyces, Hysterothylacium, Leishmania, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Mortierella, Mucor, Mycocentrospora, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Pediculus, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga Wuchereria; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.159\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"Dr. Matthijs Berends, 2018","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"","code":"mo_reset_session() #> ℹ Reset 17 previously matched input values.  as.mo(\"E. coli\") #> Class 'mo' #> [1] B_ESCHR_COLI mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans. See ?mo_matching_score. #>  #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #>              Based on input \"E coli\" #> Also matched: Enterobacter cowanii (0.600), Eubacterium combesii #>               (0.600), Eggerthia catenaformis (0.591), Eubacterium callanderi #>               (0.591), Enterocloster citroniae (0.587), Eubacterium cylindroides #>               (0.583), Enterococcus casseliflavus (0.577), Enterobacter cloacae #>               cloacae (0.571), Ehrlichia canis (0.567), Enterobacter cloacae #>               dissolvens (0.565), Eikenella corrodens (0.553), Erwinia cancerogena #>               (0.553), Enterobacter cloacae (0.550), Enterococcus cecorum (0.550), #>               Ehrlichia chaffeensis (0.548), Enterobacter cancerogena (0.542), #>               Enterobacter cancerogenus (0.540), Ezakiella coagulans (0.484), #>               Effusibacillus consociatus (0.462), Enterococcus crotali (0.433), #>               Erwinia coffeiphila (0.404), Eubacterium coprostanoligenes (0.402), #>               Enterococcus columbae (0.397), Enterococcus camelliae (0.394) and #>               Eubacterium cellulosolvens (0.385) [showing first 25]  mo_matching_score(   x = \"E. coli\",   n = c(\"Escherichia coli\", \"Entamoeba coli\") ) #> [1] 0.6875000 0.1587302"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of a Microorganism — mo_property","title":"Get Properties of a Microorganism — mo_property","text":"Use functions return specific property microorganism based latest accepted taxonomy. input values evaluated internally .mo(), makes possible use microbial abbreviations, codes names input. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of a Microorganism — mo_property","text":"","code":"mo_name(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_fullname(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_shortname(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_subspecies(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_species(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_genus(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_family(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_order(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_class(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_phylum(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_kingdom(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_domain(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_type(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_status(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_pathogenicity(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_gramstain(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_is_gram_negative(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_is_gram_positive(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_is_yeast(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_is_intrinsic_resistant(   x,   ab,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_snomed(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_ref(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_authors(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_year(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_lpsn(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_gbif(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_rank(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_taxonomy(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_synonyms(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_current(x, language = get_AMR_locale(), ...)  mo_info(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_url(   x,   open = FALSE,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_property(   x,   property = \"fullname\",   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of a Microorganism — mo_property","text":"x character (vector) can coerced valid microorganism code .mo(). Can left blank auto-guessing column containing microorganism codes used data set, see Examples. language language translate text like \"growth\", defaults system language (see get_AMR_locale()) keep_synonyms logical indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE, return note old taxonomic names processed. default can set options(AMR_keep_synonyms = TRUE) options(AMR_keep_synonyms = FALSE). ... arguments passed .mo(), 'minimum_matching_score', 'ignore_pattern', 'remove_from_input' ab (vector ) text can coerced valid antibiotic drug code .ab() open browse URL using browseURL() property one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\" \"snomed\", must \"shortname\"","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of a Microorganism — mo_property","text":"integer case mo_year() ordered factor case mo_pathogenicity() list case mo_taxonomy(), mo_synonyms() mo_info() named character case mo_url() numeric case mo_snomed() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of a Microorganism — mo_property","text":"functions , default, keep old taxonomic properties, synonyms automatically replaced current taxonomy. Take example Enterobacter aerogenes, initially named 1960 renamed Klebsiella aerogenes 2017: mo_genus(\"Enterobacter aerogenes\") return \"Klebsiella\" (note renaming) mo_genus(\"Enterobacter aerogenes\", keep_synonyms = TRUE) return \"Enterobacter\" (-per-session warning name outdated) mo_ref(\"Enterobacter aerogenes\") return \"Tindall et al., 2017\" (note) mo_ref(\"Enterobacter aerogenes\", keep_synonyms = TRUE) return \"Hormaeche et al., 1960\" (warning) short name (mo_shortname()) returns first character genus full species, \"E. coli\", species subspecies. Exceptions abbreviations staphylococci (\"CoNS\", Coagulase-Negative Staphylococci) beta-haemolytic streptococci (\"GBS\", Group B Streptococci). Please bear mind e.g. E. coli mean Escherichia coli (kingdom Bacteria) well Entamoeba coli (kingdom Protozoa). Returning full name done using .mo() internally, giving priority bacteria human pathogens, .e. \"E. coli\" considered Escherichia coli. result, mo_fullname(mo_shortname(\"Entamoeba coli\")) returns \"Escherichia coli\". Since top-level taxonomy sometimes referred 'kingdom' sometimes 'domain', functions mo_kingdom() mo_domain() return exact results. Determination human pathogenicity (mo_pathogenicity()) strongly based Bartlett et al. (2022, doi:10.1099/mic.0.001269 ). function returns factor levels Pathogenic, Potentially pathogenic, Non-pathogenic, Unknown. Determination Gram stain (mo_gramstain()) based taxonomic kingdom phylum. Originally, Cavalier-Smith defined -called subkingdoms Negibacteria Posibacteria (2002, PMID 11837318), considered phyla Posibacteria: Actinobacteria, Chloroflexi, Firmicutes, Tenericutes. phyla later renamed Actinomycetota, Chloroflexota, Bacillota, Mycoplasmatota (2021, PMID 34694987). Bacteria phyla considered Gram-positive AMR package, except members class Negativicutes (within phylum Bacillota) Gram-negative. bacteria considered Gram-negative. Species outside kingdom Bacteria return value NA. Functions mo_is_gram_negative() mo_is_gram_positive() always return TRUE FALSE (NA input NA MO code UNKNOWN), thus always return FALSE species outside taxonomic kingdom Bacteria. Determination yeasts (mo_is_yeast()) based taxonomic kingdom class. Budding yeasts fungi phylum Ascomycota, class Saccharomycetes (also called Hemiascomycetes). True yeasts aggregated underlying order Saccharomycetales. Thus, microorganisms member taxonomic class Saccharomycetes, function return TRUE. returns FALSE otherwise (NA input NA MO code UNKNOWN). Determination intrinsic resistance (mo_is_intrinsic_resistant()) based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). mo_is_intrinsic_resistant() function can vectorised argument x (input microorganisms) ab (input antibiotics). function mo_url() return direct URL online database entry, also shows scientific reference concerned species. SNOMED codes (mo_snomed()) version 1 July, 2021. See Source microorganisms data set info. Old taxonomic names (-called 'synonyms') can retrieved mo_synonyms(), current taxonomic name can retrieved mo_current(). functions return full names. output translated possible.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Get Properties of a Microorganism — mo_property","text":"function uses .mo() internally, uses advanced algorithm translate arbitrary user input valid taxonomy using -called matching score. can read public algorithm MO matching score page.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of a Microorganism — mo_property","text":"Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 Becker K et al. (2014). Coagulase-Negative Staphylococci. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. (2019). Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. (2020). Emergence coagulase-negative staphylococci Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 Lancefield RC (1933). serological differentiation human groups hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571 Berends MS et al. (2022). Trends Occurrence Phenotypic Resistance Coagulase-Negative Staphylococci (CoNS) Found Human Blood Northern Netherlands 2013 2019 Microorganisms 10(9), 1801; doi:10.3390/microorganisms10091801 Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of a Microorganism — mo_property","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of a Microorganism — mo_property","text":"","code":"# taxonomic tree -----------------------------------------------------------  mo_kingdom(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_phylum(\"Klebsiella pneumoniae\") #> [1] \"Pseudomonadota\" mo_class(\"Klebsiella pneumoniae\") #> [1] \"Gammaproteobacteria\" mo_order(\"Klebsiella pneumoniae\") #> [1] \"Enterobacterales\" mo_family(\"Klebsiella pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"Klebsiella pneumoniae\") #> [1] \"pneumoniae\" mo_subspecies(\"Klebsiella pneumoniae\") #> [1] \"\"   # full names and short names -----------------------------------------------  mo_name(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella pneumoniae\" mo_fullname(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella pneumoniae\" mo_shortname(\"Klebsiella pneumoniae\") #> [1] \"K. pneumoniae\"   # other properties ---------------------------------------------------------  mo_pathogenicity(\"Klebsiella pneumoniae\") #> [1] Pathogenic #> Levels: Pathogenic < Potentially pathogenic < Non-pathogenic < Unknown mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\" mo_snomed(\"Klebsiella pneumoniae\") #> [1] \"1098101000112102\" \"1098201000112108\" \"409801009\"        \"446870005\"        #> [5] \"56415008\"         \"713926009\"        \"714315002\"        mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_rank(\"Klebsiella pneumoniae\") #> [1] \"species\" mo_url(\"Klebsiella pneumoniae\") #>                                Klebsiella pneumoniae  #> \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\"  mo_is_yeast(c(\"Candida\", \"Trichophyton\", \"Klebsiella\")) #> [1]  TRUE FALSE FALSE   # scientific reference -----------------------------------------------------  mo_ref(\"Klebsiella pneumoniae\") #> [1] \"Trevisan, 1887\" mo_authors(\"Klebsiella pneumoniae\") #> [1] \"Trevisan\" mo_year(\"Klebsiella pneumoniae\") #> [1] 1887 mo_lpsn(\"Klebsiella pneumoniae\") #> [1] \"777151\" mo_gbif(\"Klebsiella pneumoniae\") #> [1] \"3221874\" mo_synonyms(\"Klebsiella pneumoniae\") #> NULL   # abbreviations known in the field -----------------------------------------  mo_genus(\"MRSA\") #> [1] \"Staphylococcus\" mo_species(\"MRSA\") #> [1] \"aureus\" mo_shortname(\"VISA\") #> [1] \"S. aureus\" mo_gramstain(\"VISA\") #> [1] \"Gram-positive\"  mo_genus(\"EHEC\") #> [1] \"Escherichia\" mo_species(\"EHEC\") #> [1] \"coli\"   # known subspecies ---------------------------------------------------------  mo_fullname(\"K. pneu rh\") #> [1] \"Klebsiella pneumoniae rhinoscleromatis\" mo_shortname(\"K. pneu rh\") #> [1] \"K. pneumoniae\"  # \\donttest{ # Becker classification, see ?as.mo ----------------------------------------  mo_fullname(\"Staph epidermidis\") #> [1] \"Staphylococcus epidermidis\" mo_fullname(\"Staph epidermidis\", Becker = TRUE) #> [1] \"Coagulase-negative Staphylococcus (CoNS)\" mo_shortname(\"Staph epidermidis\") #> [1] \"S. epidermidis\" mo_shortname(\"Staph epidermidis\", Becker = TRUE) #> [1] \"CoNS\"   # Lancefield classification, see ?as.mo ------------------------------------  mo_fullname(\"Strep agalactiae\") #> [1] \"Streptococcus agalactiae\" mo_fullname(\"Strep agalactiae\", Lancefield = TRUE) #> [1] \"Streptococcus Group B\" mo_shortname(\"Strep agalactiae\") #> [1] \"S. agalactiae\" mo_shortname(\"Strep agalactiae\", Lancefield = TRUE) #> [1] \"GBS\"   # language support  --------------------------------------------------------  mo_gramstain(\"Klebsiella pneumoniae\", language = \"de\") # German #> [1] \"Gramnegativ\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"nl\") # Dutch #> [1] \"Gram-negatief\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"es\") # Spanish #> [1] \"Gram negativo\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"el\") # Greek #> [1] \"Αρνητικό κατά Gram\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"uk\") # Ukrainian #> [1] \"Грамнегативні\"  # mo_type is equal to mo_kingdom, but mo_kingdom will remain untranslated mo_kingdom(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_kingdom(\"Klebsiella pneumoniae\", language = \"zh\") # Chinese, no effect #> [1] \"Bacteria\" mo_type(\"Klebsiella pneumoniae\", language = \"zh\") # Chinese, translated #> [1] \"细菌\"  mo_fullname(\"S. pyogenes\", Lancefield = TRUE, language = \"de\") #> [1] \"Streptococcus Gruppe A\" mo_fullname(\"S. pyogenes\", Lancefield = TRUE, language = \"uk\") #> [1] \"Streptococcus Група A\"   # other --------------------------------------------------------------------  # gram stains and intrinsic resistance can be used as a filter in dplyr verbs if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_is_gram_positive()) %>%     count(mo_genus(), sort = TRUE) } #> ℹ Using column 'mo' as input for mo_is_gram_positive() #> ℹ Using column 'mo' as input for mo_genus() #> # A tibble: 18 × 2 #>    `mo_genus()`        n #>    <chr>           <int> #>  1 Staphylococcus    840 #>  2 Streptococcus     275 #>  3 Enterococcus       83 #>  4 Corynebacterium    17 #>  5 Micrococcus         6 #>  6 Gemella             3 #>  7 Aerococcus          2 #>  8 Cutibacterium       1 #>  9 Dermabacter         1 #> 10 Fusibacter          1 #> 11 Globicatella        1 #> 12 Granulicatella      1 #> 13 Lactobacillus       1 #> 14 Leuconostoc         1 #> 15 Listeria            1 #> 16 Paenibacillus       1 #> 17 Rothia              1 #> 18 Schaalia            1 if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_is_intrinsic_resistant(ab = \"vanco\")) %>%     count(mo_genus(), sort = TRUE) } #> ℹ Using column 'mo' as input for mo_is_intrinsic_resistant() #> ℹ Using column 'mo' as input for mo_genus() #> # A tibble: 20 × 2 #>    `mo_genus()`         n #>    <chr>            <int> #>  1 Escherichia        467 #>  2 Klebsiella          77 #>  3 Proteus             39 #>  4 Pseudomonas         30 #>  5 Serratia            25 #>  6 Enterobacter        23 #>  7 Citrobacter         11 #>  8 Haemophilus          8 #>  9 Acinetobacter        6 #> 10 Morganella           6 #> 11 Pantoea              4 #> 12 Salmonella           3 #> 13 Neisseria            2 #> 14 Stenotrophomonas     2 #> 15 Campylobacter        1 #> 16 Enterococcus         1 #> 17 Hafnia               1 #> 18 Lactobacillus        1 #> 19 Leuconostoc          1 #> 20 Pseudescherichia     1  # get a list with the complete taxonomy (from kingdom to subspecies) mo_taxonomy(\"Klebsiella pneumoniae\") #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Klebsiella\" #>  #> $species #> [1] \"pneumoniae\" #>  #> $subspecies #> [1] \"\" #>   # get a list with the taxonomy, the authors, Gram-stain, # SNOMED codes, and URL to the online database mo_info(\"Klebsiella pneumoniae\") #> $mo #> [1] \"B_KLBSL_PNMN\" #>  #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Klebsiella\" #>  #> $species #> [1] \"pneumoniae\" #>  #> $subspecies #> [1] \"\" #>  #> $status #> [1] \"accepted\" #>  #> $synonyms #> NULL #>  #> $gramstain #> [1] \"Gram-negative\" #>  #> $url #> [1] \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\" #>  #> $ref #> [1] \"Trevisan, 1887\" #>  #> $snomed #> [1] \"1098101000112102\" \"1098201000112108\" \"409801009\"        \"446870005\"        #> [5] \"56415008\"         \"713926009\"        \"714315002\"        #>  # }"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":null,"dir":"Reference","previous_headings":"","what":"User-Defined Reference Data Set for Microorganisms — mo_source","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"functions can used predefine reference used .mo() consequently mo_* functions (mo_genus() mo_gramstain()). fastest way organisation (analysis) specific codes picked translated package, since bother setting .","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"","code":"set_mo_source(   path,   destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\") )  get_mo_source(destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\"))"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"path location reference file, can text file (comma-, tab- pipe-separated) Excel file (see Details). Can also \"\", NULL FALSE delete reference file. destination destination compressed data file, default user's home directory.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"reference file can text file separated commas (CSV) tabs pipes, Excel file (either 'xls' 'xlsx' format) R object file (extension '.rds'). use Excel file, need readxl package installed. set_mo_source() check file validity: must data.frame, must column named \"mo\" contains values microorganisms$mo microorganisms$fullname must reference column defined values. tests pass, set_mo_source() read file R ask export \"~/mo_source.rds\". CRAN policy disallows packages write file system, although 'exceptions may allowed interactive sessions package obtains confirmation user'. reason, function works interactive sessions user can specifically confirm allow file created. destination file can set destination argument defaults user's home directory. can also set R option, using options(AMR_mo_source = \"/location/file.rds\"). created compressed data file \"mo_source.rds\" used default MO determination (function .mo() consequently mo_* functions like mo_genus() mo_gramstain()). location timestamp original file saved attribute compressed data file. function get_mo_source() return data set reading \"mo_source.rds\" readRDS(). original file changed (checking location timestamp original file), call set_mo_source() update data file automatically used interactive session. Reading Excel file (.xlsx) one row size 8-9 kB. compressed file created set_mo_source() size 0.1 kB can read get_mo_source() couple microseconds (millionths second).","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"how-to-setup","dir":"Reference","previous_headings":"","what":"How to Setup","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"Imagine data sheet Excel file. first column contains organisation specific codes, second column contains valid taxonomic names:   save \"home//ourcodes.xlsx\". Now set source:   now created file \"~/mo_source.rds\" contents Excel file. first column foreign values 'mo' column kept creating RDS file. now can use functions:   edit Excel file , say, adding row 4 like :   ...new usage MO function package update data file:   delete reference data file, just use \"\", NULL FALSE input set_mo_source():   original file (previous case Excel file) moved deleted, mo_source.rds file removed upon next use .mo() mo_* function.","code":"|         A          |            B          | --|--------------------|-----------------------| 1 | Organisation XYZ   | mo                    | 2 | lab_mo_ecoli       | Escherichia coli      | 3 | lab_mo_kpneumoniae | Klebsiella pneumoniae | 4 |                    |                       | set_mo_source(\"home/me/ourcodes.xlsx\") #> NOTE: Created mo_source file '/Users/me/mo_source.rds' (0.3 kB) from #>       '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns #>       \"Organisation XYZ\" and \"mo\" as.mo(\"lab_mo_ecoli\") #> Class 'mo' #> [1] B_ESCHR_COLI  mo_genus(\"lab_mo_kpneumoniae\") #> [1] \"Klebsiella\"  # other input values still work too as.mo(c(\"Escherichia coli\", \"E. coli\", \"lab_mo_ecoli\")) #> NOTE: Translation to one microorganism was guessed with uncertainty. #>       Use mo_uncertainties() to review it. #> Class 'mo' #> [1] B_ESCHR_COLI B_ESCHR_COLI B_ESCHR_COLI |         A          |            B          | --|--------------------|-----------------------| 1 | Organisation XYZ   | mo                    | 2 | lab_mo_ecoli       | Escherichia coli      | 3 | lab_mo_kpneumoniae | Klebsiella pneumoniae | 4 | lab_Staph_aureus   | Staphylococcus aureus | 5 |                    |                       | as.mo(\"lab_mo_ecoli\") #> NOTE: Updated mo_source file '/Users/me/mo_source.rds' (0.3 kB) from #>       '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns #>        \"Organisation XYZ\" and \"mo\" #> Class 'mo' #> [1] B_ESCHR_COLI  mo_genus(\"lab_Staph_aureus\") #> [1] \"Staphylococcus\" set_mo_source(NULL) #> Removed mo_source file '/Users/me/mo_source.rds'"},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Component Analysis (for AMR) — pca","title":"Principal Component Analysis (for AMR) — pca","text":"Performs principal component analysis (PCA) based data set automatic determination afterwards plotting groups labels, automatic filtering suitable (.e. non-empty numeric) variables.","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Component Analysis (for AMR) — pca","text":"","code":"pca(   x,   ...,   retx = TRUE,   center = TRUE,   scale. = TRUE,   tol = NULL,   rank. = NULL )"},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Component Analysis (for AMR) — pca","text":"x data.frame containing numeric columns ... columns x selected PCA, can unquoted since supports quasiquotation. retx logical value indicating whether rotated variables     returned. center logical value indicating whether variables     shifted zero centered. Alternately, vector     length equal number columns x can supplied.     value passed scale. scale. logical value indicating whether variables     scaled unit variance analysis takes     place.  default FALSE consistency S,     general scaling advisable.  Alternatively, vector length     equal number columns x can supplied.      value passed scale. tol value indicating magnitude components     omitted. (Components omitted     standard deviations less equal tol times     standard deviation first component.)  default null     setting, components omitted (unless rank. specified     less min(dim(x)).).  settings tol     tol = 0 tol = sqrt(.Machine$double.eps),     omit essentially constant components. rank. optionally, number specifying maximal rank, .e.,     maximal number principal components used.  Can set     alternative addition tol, useful notably     desired rank considerably smaller dimensions matrix.","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Component Analysis (for AMR) — pca","text":"object classes pca prcomp","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Principal Component Analysis (for AMR) — pca","text":"pca() function takes data.frame input performs actual PCA R function prcomp(). result pca() function prcomp object, additional attribute non_numeric_cols vector column names columns contain numeric values. probably groups labels, used ggplot_pca().","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Component Analysis (for AMR) — pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # \\donttest{ if (require(\"dplyr\")) {   # calculate the resistance per group first   resistance_data <- example_isolates %>%     group_by(       order = mo_order(mo), # group on anything, like order       genus = mo_genus(mo)     ) %>% #   and genus as we do here;     filter(n() >= 30) %>% # filter on only 30 results per group     summarise_if(is.sir, resistance) # then get resistance of all drugs    # now conduct PCA for certain antimicrobial drugs   pca_result <- resistance_data %>%     pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)    pca_result   summary(pca_result)    # old base R plotting method:   biplot(pca_result)   # new ggplot2 plotting method using this package:   if (require(\"ggplot2\")) {     ggplot_pca(pca_result)      ggplot_pca(pca_result) +       scale_colour_viridis_d() +       labs(title = \"Title here\")   } } #> Warning: Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 13 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 15 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 13 results available for FLC in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for AMX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for AMP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for TZP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 12 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 5 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for FEP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 23 results available for FEP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 29 results available for FOX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 6 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 17 results available for AMK in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 6 results available for NIT in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 17 results available for NIT in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 8 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 7 results available for LNZ in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 5 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 23 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 7 results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 6 results available for TEC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 3 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 18 results available for TGC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 7 results available for TGC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for DOX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for IPM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for MEM in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for MEM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Caryophanales\", genus = \"Staphylococcus\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 9 results available for COL in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", \"TMP\" #>   and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\"  #>    # }"},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting for Classes sir, mic and disk — plot","title":"Plotting for Classes sir, mic and disk — plot","text":"Functions plot classes sir, mic disk, support base R ggplot2.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting for Classes sir, mic and disk — plot","text":"","code":"# S3 method for mic plot(   x,   mo = NULL,   ab = NULL,   guideline = \"EUCAST\",   main = deparse(substitute(x)),   ylab = \"Frequency\",   xlab = \"Minimum Inhibitory Concentration (mg/L)\",   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for mic autoplot(   object,   mo = NULL,   ab = NULL,   guideline = \"EUCAST\",   title = deparse(substitute(object)),   ylab = \"Frequency\",   xlab = \"Minimum Inhibitory Concentration (mg/L)\",   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for mic fortify(object, ...)  # S3 method for disk plot(   x,   main = deparse(substitute(x)),   ylab = \"Frequency\",   xlab = \"Disk diffusion diameter (mm)\",   mo = NULL,   ab = NULL,   guideline = \"EUCAST\",   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for disk autoplot(   object,   mo = NULL,   ab = NULL,   title = deparse(substitute(object)),   ylab = \"Frequency\",   xlab = \"Disk diffusion diameter (mm)\",   guideline = \"EUCAST\",   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for disk fortify(object, ...)  # S3 method for sir plot(   x,   ylab = \"Percentage\",   xlab = \"Antimicrobial Interpretation\",   main = deparse(substitute(x)),   language = get_AMR_locale(),   ... )  # S3 method for sir autoplot(   object,   title = deparse(substitute(object)),   xlab = \"Antimicrobial Interpretation\",   ylab = \"Frequency\",   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   ... )  # S3 method for sir fortify(object, ...)"},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting for Classes sir, mic and disk — plot","text":"x, object values created .mic(), .disk() .sir() (random_* variants, random_mic()) mo (vector ) text can coerced valid microorganism code .mo() ab (vector ) text can coerced valid antimicrobial drug code .ab() guideline interpretation guideline use, defaults latest included EUCAST guideline, see Details main, title title plot xlab, ylab axis title colours_SIR colours use filling bars, must vector three values (order S, R). default colours colour-blind friendly. language language used translate 'Susceptible', 'Increased exposure'/'Intermediate' 'Resistant', defaults system language (see get_AMR_locale()) can overwritten setting option AMR_locale, e.g. options(AMR_locale = \"de\"), see translate. Use language = NULL language = \"\" prevent translation. expand logical indicate whether range x axis expanded lowest highest value. MIC values, intermediate values factors 2 starting highest MIC value. disk diameters, whole diameter range filled. ... arguments passed methods","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting for Classes sir, mic and disk — plot","text":"autoplot() functions return ggplot model extendible ggplot2 function. fortify() functions return data.frame extension usage ggplot2::ggplot() function.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plotting for Classes sir, mic and disk — plot","text":"interpretation \"\" named \"Increased exposure\" EUCAST guidelines since 2019, named \"Intermediate\" cases. interpreting MIC values well disk diffusion diameters, supported guidelines used input guideline argument : \"EUCAST 2022\", \"EUCAST 2021\", \"EUCAST 2020\", \"EUCAST 2019\", \"EUCAST 2018\", \"EUCAST 2017\", \"EUCAST 2016\", \"EUCAST 2015\", \"EUCAST 2014\", \"EUCAST 2013\", \"CLSI 2022\", \"CLSI 2021\", \"CLSI 2020\", \"CLSI 2019\", \"CLSI 2018\", \"CLSI 2017\", \"CLSI 2016\", \"CLSI 2015\", \"CLSI 2014\" \"CLSI 2013\". Simply using \"CLSI\" \"EUCAST\" input automatically select latest version guideline.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting for Classes sir, mic and disk — plot","text":"","code":"some_mic_values <- random_mic(size = 100) some_disk_values <- random_disk(size = 100, mo = \"Escherichia coli\", ab = \"cipro\") some_sir_values <- random_sir(50, prob_SIR = c(0.55, 0.05, 0.30))  plot(some_mic_values)  plot(some_disk_values)  plot(some_sir_values)   # when providing the microorganism and antibiotic, colours will show interpretations: plot(some_mic_values, mo = \"S. aureus\", ab = \"ampicillin\")  plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\")  plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\", language = \"uk\")   # \\donttest{ if (require(\"ggplot2\")) {   autoplot(some_mic_values) }  if (require(\"ggplot2\")) {   autoplot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\") }  if (require(\"ggplot2\")) {   autoplot(some_sir_values) }  # }"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Microbial Resistance — proportion","title":"Calculate Microbial Resistance — proportion","text":"functions can used calculate (co-)resistance susceptibility microbial isolates (.e. percentage S, SI, , IR R). functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. resistance() used calculate resistance, susceptibility() used calculate susceptibility.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Microbial Resistance — proportion","text":"","code":"resistance(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  susceptibility(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  sir_confidence_interval(   ...,   ab_result = \"R\",   minimum = 30,   as_percent = FALSE,   only_all_tested = FALSE,   confidence_level = 0.95,   side = \"both\" )  proportion_R(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_IR(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_I(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_SI(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_S(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_df(   data,   translate_ab = \"name\",   language = get_AMR_locale(),   minimum = 30,   as_percent = FALSE,   combine_SI = TRUE,   confidence_level = 0.95 )  sir_df(   data,   translate_ab = \"name\",   language = get_AMR_locale(),   minimum = 30,   as_percent = FALSE,   combine_SI = TRUE,   confidence_level = 0.95 )"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Calculate Microbial Resistance — proportion","text":"M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Microbial Resistance — proportion","text":"... one vectors (columns) antibiotic interpretations. transformed internally .sir() needed. Use multiple columns calculate (lack ) co-resistance: probability one two drugs resistant susceptible result. See Examples. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. as_percent logical indicate whether output must returned hundred fold % sign (character). value 0.123456 returned \"12.3%\". only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antibiotics, see section Combination Therapy ab_result antibiotic results test , must one values \"S\", \"\", \"R\" confidence_level confidence level returned confidence interval. calculation, number S SI isolates, R isolates compared total number available isolates R, S, using binom.test(), .e., Clopper-Pearson method. side side confidence interval return. Defaults \"\" length 2 vector, can also (abbreviated ) \"min\"/\"left\"/\"lower\"/\"less\" \"max\"/\"right\"/\"higher\"/\"greater\". data data.frame containing columns class sir (see .sir()) translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant), defaults TRUE","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Microbial Resistance — proportion","text":"double , as_percent = TRUE, character.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Microbial Resistance — proportion","text":"function resistance() equal function proportion_R(). function susceptibility() equal function proportion_SI(). Use sir_confidence_interval() calculate confidence interval, relies binom.test(), .e., Clopper-Pearson method. function returns vector length 2 default antimicrobial resistance. Change side argument \"left\"/\"min\" \"right\"/\"max\" return single value, change ab_result argument e.g. c(\"S\", \"\") test antimicrobial susceptibility, see Examples. Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine data set. functions meant count isolates, calculate proportion resistance/susceptibility. Use count() functions count isolates. function susceptibility() essentially equal count_susceptible() / count_all(). Low counts can influence outcome - proportion functions may camouflage , since return proportion (albeit dependent minimum argument). function proportion_df() takes variable data sir class (created .sir()) calculates proportions S, , R. also supports grouped variables. function sir_df() works exactly like proportion_df(), adds number isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Calculate Microbial Resistance — proportion","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antibiotics, Drug Drug B, susceptibility() works calculate %SI:   Please note , combination therapies, only_all_tested = TRUE applies :   , combination therapies, only_all_tested = FALSE applies :   Using only_all_tested impact using one antibiotic input.","code":"--------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   include as  include as   include as  include as                     numerator   denominator  numerator   denominator --------  --------  ----------  -----------  ----------  -----------  S or I    S or I       X            X            X            X    R       S or I       X            X            X            X   <NA>     S or I       X            X            -            -  S or I      R          X            X            X            X    R         R          -            X            -            X   <NA>       R          -            -            -            -  S or I     <NA>        X            X            -            -    R        <NA>        -            -            -            -   <NA>      <NA>        -            -            -            - -------------------------------------------------------------------- count_S()    +   count_I()    +   count_R()    = count_all()   proportion_S() + proportion_I() + proportion_R() = 1 count_S()    +   count_I()    +   count_R()    >= count_all()   proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Calculate Microbial Resistance — proportion","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Microbial Resistance — proportion","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info.  # base R ------------------------------------------------------------ # determines %R resistance(example_isolates$AMX) #> [1] 0.5955556 sir_confidence_interval(example_isolates$AMX) #> [1] 0.5688204 0.6218738 sir_confidence_interval(example_isolates$AMX,   confidence_level = 0.975 ) #> [1] 0.5650148 0.6255670  # determines %S+I: susceptibility(example_isolates$AMX) #> [1] 0.4044444 sir_confidence_interval(example_isolates$AMX,   ab_result = c(\"S\", \"I\") ) #> [1] 0.3781262 0.4311796  # be more specific proportion_S(example_isolates$AMX) #> [1] 0.4022222 proportion_SI(example_isolates$AMX) #> [1] 0.4044444 proportion_I(example_isolates$AMX) #> [1] 0.002222222 proportion_IR(example_isolates$AMX) #> [1] 0.5977778 proportion_R(example_isolates$AMX) #> [1] 0.5955556  # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     group_by(ward) %>%     summarise(       r = resistance(CIP),       n = n_sir(CIP)     ) # n_sir works like n_distinct in dplyr, see ?n_sir } #> # A tibble: 3 × 3 #>   ward           r     n #>   <chr>      <dbl> <int> #> 1 Clinical   0.147   869 #> 2 ICU        0.190   447 #> 3 Outpatient 0.161    93 if (require(\"dplyr\")) {   example_isolates %>%     group_by(ward) %>%     summarise(       cipro_R = resistance(CIP),       ci_min = sir_confidence_interval(CIP, side = \"min\"),       ci_max = sir_confidence_interval(CIP, side = \"max\"),     ) } #> # A tibble: 3 × 4 #>   ward       cipro_R ci_min ci_max #>   <chr>        <dbl>  <dbl>  <dbl> #> 1 Clinical     0.147 0.124   0.173 #> 2 ICU          0.190 0.155   0.230 #> 3 Outpatient   0.161 0.0932  0.252 if (require(\"dplyr\")) {   # scoped dplyr verbs with antibiotic selectors   # (you could also use across() of course)   example_isolates %>%     group_by(ward) %>%     summarise_at(       c(aminoglycosides(), carbapenems()),       resistance     ) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> Warning: Introducing NA: only 23 results available for KAN in group: ward = #> \"Outpatient\" (minimum = 30). #> # A tibble: 3 × 7 #>   ward         GEN   TOB   AMK   KAN    IPM    MEM #>   <chr>      <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl> #> 1 Clinical   0.229 0.315 0.626     1 0.0498 0.0458 #> 2 ICU        0.290 0.400 0.662     1 0.0862 0.0894 #> 3 Outpatient 0.2   0.368 0.605    NA 0.0541 0.0541 if (require(\"dplyr\")) {   example_isolates %>%     group_by(ward) %>%     summarise(       R = resistance(CIP, as_percent = TRUE),       SI = susceptibility(CIP, as_percent = TRUE),       n1 = count_all(CIP), # the actual total; sum of all three       n2 = n_sir(CIP), # same - analogous to n_distinct       total = n()     ) # NOT the number of tested isolates!    # Calculate co-resistance between amoxicillin/clav acid and gentamicin,   # so we can see that combination therapy does a lot more than mono therapy:   example_isolates %>% susceptibility(AMC) # %SI = 76.3%   example_isolates %>% count_all(AMC) #   n = 1879    example_isolates %>% susceptibility(GEN) # %SI = 75.4%   example_isolates %>% count_all(GEN) #   n = 1855    example_isolates %>% susceptibility(AMC, GEN) # %SI = 94.1%   example_isolates %>% count_all(AMC, GEN) #   n = 1939     # See Details on how `only_all_tested` works. Example:   example_isolates %>%     summarise(       numerator = count_susceptible(AMC, GEN),       denominator = count_all(AMC, GEN),       proportion = susceptibility(AMC, GEN)     )    example_isolates %>%     summarise(       numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE),       denominator = count_all(AMC, GEN, only_all_tested = TRUE),       proportion = susceptibility(AMC, GEN, only_all_tested = TRUE)     )     example_isolates %>%     group_by(ward) %>%     summarise(       cipro_p = susceptibility(CIP, as_percent = TRUE),       cipro_n = count_all(CIP),       genta_p = susceptibility(GEN, as_percent = TRUE),       genta_n = count_all(GEN),       combination_p = susceptibility(CIP, GEN, as_percent = TRUE),       combination_n = count_all(CIP, GEN)     )    # Get proportions S/I/R immediately of all sir columns   example_isolates %>%     select(AMX, CIP) %>%     proportion_df(translate = FALSE)    # It also supports grouping variables   # (use sir_df to also include the count)   example_isolates %>%     select(ward, AMX, CIP) %>%     group_by(ward) %>%     sir_df(translate = FALSE) } #> # A tibble: 12 × 7 #>    ward       antibiotic interpretation value ci_min ci_max isolates #>  * <chr>      <chr>      <ord>          <dbl>  <dbl>  <dbl>    <int> #>  1 Clinical   AMX        SI             0.423 0.389   0.457      357 #>  2 Clinical   AMX        R              0.577 0.543   0.611      487 #>  3 Clinical   CIP        SI             0.853 0.827   0.876      741 #>  4 Clinical   CIP        R              0.147 0.124   0.173      128 #>  5 ICU        AMX        SI             0.369 0.323   0.417      158 #>  6 ICU        AMX        R              0.631 0.583   0.677      270 #>  7 ICU        CIP        SI             0.810 0.770   0.845      362 #>  8 ICU        CIP        R              0.190 0.155   0.230       85 #>  9 Outpatient AMX        SI             0.397 0.288   0.515       31 #> 10 Outpatient AMX        R              0.603 0.485   0.712       47 #> 11 Outpatient CIP        SI             0.839 0.748   0.907       78 #> 12 Outpatient CIP        R              0.161 0.0932  0.252       15 # }"},{"path":"https://msberends.github.io/AMR/reference/random.html","id":null,"dir":"Reference","previous_headings":"","what":"Random MIC Values/Disk Zones/SIR Generation — random","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"functions can used generating random MIC values disk diffusion diameters, AMR data analysis practice. providing microorganism antimicrobial drug, generated results reflect reality much possible.","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"","code":"random_mic(size = NULL, mo = NULL, ab = NULL, ...)  random_disk(size = NULL, mo = NULL, ab = NULL, ...)  random_sir(size = NULL, prob_SIR = c(0.33, 0.33, 0.33), ...)"},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"size desired size returned vector. used data.frame call dplyr verb, get current (group) size left blank. mo character can coerced valid microorganism code .mo() ab character can coerced valid antimicrobial drug code .ab() ... ignored, place allow future extensions prob_SIR vector length 3: probabilities \"S\" (1st value), \"\" (2nd value) \"R\" (3rd value)","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"class mic random_mic() (see .mic()) class disk random_disk() (see .disk())","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"base R function sample() used generating values. Generated values based EUCAST 2022 guideline implemented clinical_breakpoints data set. create specific generated values per bug drug, set mo /ab argument.","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"","code":"random_mic(25) #> Class 'mic' #>  [1] 4       >=128   0.0625  0.005   1       <=0.002 0.005   64      <=0.002 #> [10] 0.5     64      1       0.125   0.01    4       0.01    0.005   64      #> [19] 64      64      64      16      64      0.125   2       random_disk(25) #> Class 'disk' #>  [1] 47 13 36 12 41 13 11 37 13 47 39 16 10 46 22 36 16 46 16 44 47 12 40 18 24 random_sir(25) #> Class 'sir' #>  [1] I S I S S S S S R I R I I S R R S I S R R S I R I  # \\donttest{ # make the random generation more realistic by setting a bug and/or drug: random_mic(25, \"Klebsiella pneumoniae\") # range 0.0625-64 #> Class 'mic' #>  [1] 2       0.025   1       2       64      0.25    16      4       128     #> [10] 32      0.0625  4       0.0625  2       1       <=0.005 0.01    0.125   #> [19] 16      4       0.025   4       4       32      16      random_mic(25, \"Klebsiella pneumoniae\", \"meropenem\") # range 0.0625-16 #> Class 'mic' #>  [1] 2  2  8  16 8  8  16 4  4  2  4  8  16 4  8  8  1  16 16 2  1  2  4  1  16 random_mic(25, \"Streptococcus pneumoniae\", \"meropenem\") # range 0.0625-4 #> Class 'mic' #>  [1] >=4     1       2       <=0.125 0.5     2       1       0.5     0.25    #> [10] <=0.125 2       0.25    <=0.125 <=0.125 2       2       <=0.125 >=4     #> [19] 2       0.5     <=0.125 0.5     0.5     1       2        random_disk(25, \"Klebsiella pneumoniae\") # range 8-50 #> Class 'disk' #>  [1] 27 43 17 42 21 27 42 19 21 17 26 40 14  9 34 16 49 30 41 31 40 43 46 45  8 random_disk(25, \"Klebsiella pneumoniae\", \"ampicillin\") # range 11-17 #> Class 'disk' #>  [1] 15 11 13 11 11 12 17 11 12 12 17 17 16 14 14 16 14 15 12 17 15 15 16 12 15 random_disk(25, \"Streptococcus pneumoniae\", \"ampicillin\") # range 12-27 #> Class 'disk' #>  [1] 26 20 19 18 19 25 22 23 19 18 15 25 19 18 22 24 16 22 26 22 21 21 23 23 23 # }"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict Antimicrobial Resistance — resistance_predict","title":"Predict Antimicrobial Resistance — resistance_predict","text":"Create prediction model predict antimicrobial resistance next years statistical solid ground. Standard errors (SE) returned columns se_min se_max. See Examples real live example.","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict Antimicrobial Resistance — resistance_predict","text":"","code":"resistance_predict(   x,   col_ab,   col_date = NULL,   year_min = NULL,   year_max = NULL,   year_every = 1,   minimum = 30,   model = NULL,   I_as_S = TRUE,   preserve_measurements = TRUE,   info = interactive(),   ... )  sir_predict(   x,   col_ab,   col_date = NULL,   year_min = NULL,   year_max = NULL,   year_every = 1,   minimum = 30,   model = NULL,   I_as_S = TRUE,   preserve_measurements = TRUE,   info = interactive(),   ... )  # S3 method for resistance_predict plot(x, main = paste(\"Resistance Prediction of\", x_name), ...)  ggplot_sir_predict(   x,   main = paste(\"Resistance Prediction of\", x_name),   ribbon = TRUE,   ... )  # S3 method for resistance_predict autoplot(   object,   main = paste(\"Resistance Prediction of\", x_name),   ribbon = TRUE,   ... )"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict Antimicrobial Resistance — resistance_predict","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_ab column name x containing antimicrobial interpretations (\"R\", \"\" \"S\") col_date column name date, used calculate years column consist years already, defaults first column date class year_min lowest year use prediction model, dafaults lowest year col_date year_max highest year use prediction model, defaults 10 years today year_every unit sequence lowest year found data year_max minimum minimal amount available isolates per year include. Years containing less observations estimated model. model statistical model choice. generalised linear regression model binomial distribution (.e. using glm(..., family = binomial), assuming period zero resistance followed period increasing resistance leading slowly resistance. See Details valid options. I_as_S logical indicate whether values \"\" treated \"S\" (otherwise treated \"R\"). default, TRUE, follows redefinition EUCAST interpretation (increased exposure) 2019, see section Interpretation S, R . preserve_measurements logical indicate whether predictions years actually available data overwritten original data. standard errors years NA. info logical indicate whether textual analysis printed name summary() statistical model. ... arguments passed functions main title plot ribbon logical indicate whether ribbon shown (default) error bars object model data plotted","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict Antimicrobial Resistance — resistance_predict","text":"data.frame extra class resistance_predict columns: year value, estimated preserve_measurements = FALSE, combination observed estimated otherwise se_min, lower bound standard error minimum 0 (standard error never go 0%) se_max upper bound standard error maximum 1 (standard error never go 100%) observations, total number available observations year, .e. \\(S + + R\\) observed, original observed resistant percentages estimated, estimated resistant percentages, calculated model Furthermore, model available attribute: attributes(x)$model, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Predict Antimicrobial Resistance — resistance_predict","text":"Valid options statistical model (argument model) : \"binomial\" \"binom\" \"logit\": generalised linear regression model binomial distribution \"loglin\" \"poisson\": generalised log-linear regression model poisson distribution \"lin\" \"linear\": linear regression model","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Predict Antimicrobial Resistance — resistance_predict","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict Antimicrobial Resistance — resistance_predict","text":"","code":"x <- resistance_predict(example_isolates,   col_ab = \"AMX\",   year_min = 2010,   model = \"binomial\" ) #> ℹ Using column 'date' as input for col_date. plot(x)  # \\donttest{ if (require(\"ggplot2\")) {   ggplot_sir_predict(x) }   # using dplyr: if (require(\"dplyr\")) {   x <- example_isolates %>%     filter_first_isolate() %>%     filter(mo_genus(mo) == \"Staphylococcus\") %>%     resistance_predict(\"PEN\", model = \"binomial\")   print(plot(x))    # get the model from the object   mymodel <- attributes(x)$model   summary(mymodel) } #> Including isolates from ICU.  #> NULL #>  #> Call: #> glm(formula = df_matrix ~ year, family = binomial) #>  #> Deviance Residuals:  #>     Min       1Q   Median       3Q      Max   #> -1.4150  -0.1855   0.0000   0.4096   0.9277   #>  #> Coefficients: #>             Estimate Std. Error z value Pr(>|z|) #> (Intercept) 47.02915   72.98739   0.644    0.519 #> year        -0.02280    0.03637  -0.627    0.531 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance: 5.9620  on 11  degrees of freedom #> Residual deviance: 5.5701  on 10  degrees of freedom #> AIC: 50.533 #>  #> Number of Fisher Scoring iterations: 4 #>   # create nice plots with ggplot2 yourself if (require(\"dplyr\") && require(\"ggplot2\")) {   data <- example_isolates %>%     filter(mo == as.mo(\"E. coli\")) %>%     resistance_predict(       col_ab = \"AMX\",       col_date = \"date\",       model = \"binomial\",       info = FALSE,       minimum = 15     )   head(data)   autoplot(data) }  # }"},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":null,"dir":"Reference","previous_headings":"","what":"Skewness of the Sample — skewness","title":"Skewness of the Sample — skewness","text":"Skewness measure asymmetry probability distribution real-valued random variable mean. negative ('left-skewed'): left tail longer; mass distribution concentrated right histogram. positive ('right-skewed'): right tail longer; mass distribution concentrated left histogram. normal distribution skewness 0.","code":""},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Skewness of the Sample — skewness","text":"","code":"skewness(x, na.rm = FALSE)  # S3 method for default skewness(x, na.rm = FALSE)  # S3 method for matrix skewness(x, na.rm = FALSE)  # S3 method for data.frame skewness(x, na.rm = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Skewness of the Sample — skewness","text":"x vector values, matrix data.frame na.rm logical value indicating whether NA values stripped computation proceeds","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Skewness of the Sample — skewness","text":"","code":"skewness(runif(1000)) #> [1] 0.08484826"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate Strings from the AMR Package — translate","title":"Translate Strings from the AMR Package — translate","text":"language-dependent output AMR functions, like mo_name(), mo_gramstain(), mo_type() ab_name().","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate Strings from the AMR Package — translate","text":"","code":"get_AMR_locale()  set_AMR_locale(language)  reset_AMR_locale()  translate_AMR(x, language = get_AMR_locale())"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate Strings from the AMR Package — translate","text":"language language choose. Use one supported language names ISO-639-1 codes: English (en), Chinese (zh), Danish (da), Dutch (nl), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Polish (pl), Portuguese (pt), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr) Ukrainian (uk). x text translate","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate Strings from the AMR Package — translate","text":"currently 16 supported languages English (en), Chinese (zh), Danish (da), Dutch (nl), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Polish (pl), Portuguese (pt), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr) Ukrainian (uk). languages translations available antimicrobial drugs colloquial microorganism names. permanently silence -per-session language note non-English operating system, can set option AMR_locale .Rprofile file like :   save file. Please read adding updating language Wiki.","code":"# Open .Rprofile file utils::file.edit(\"~/.Rprofile\")  # Then add e.g. Italian support to that file using: options(AMR_locale = \"Italian\")"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"changing-the-default-language","dir":"Reference","previous_headings":"","what":"Changing the Default Language","title":"Translate Strings from the AMR Package — translate","text":"system language used default (returned Sys.getenv(\"LANG\") , LANG set, Sys.getlocale(\"LC_COLLATE\")), language supported. language used can overwritten two ways checked order: Setting R option AMR_locale, either using e.g. set_AMR_locale(\"German\") running e.g. options(AMR_locale = \"German\"). Note setting R option works session. Save command options(AMR_locale = \"(language)\") .Rprofile file apply every session. Run utils::file.edit(\"~/.Rprofile\") edit .Rprofile file. Setting system variable LANGUAGE LANG, e.g. adding LANGUAGE=\"de_DE.utf8\" .Renviron file home directory. Thus, R option AMR_locale set, system variables LANGUAGE LANG ignored.","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate Strings from the AMR Package — translate","text":"","code":"# Current settings (based on system language) ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacin\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Coagulase-negative Staphylococcus (CoNS)\"  # setting another language set_AMR_locale(\"Spanish\") #> ℹ Using Spanish (Español) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacina\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Staphylococcus coagulasa negativo (SCN)\"  # setting yet another language set_AMR_locale(\"Greek\") #> ℹ Using Greek (Ελληνικά) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Σιπροφλοξασίνη\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Σταφυλόκοκκος με αρνητική πηκτικότητα (CoNS)\"  # setting yet another language set_AMR_locale(\"Ukrainian\") #> ℹ Using Ukrainian (Українська) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ципрофлоксацин\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Коагулазонегативний стафілокок (КНС)\"  # set_AMR_locale() understands endonyms, English exonyms, and ISO-639-1: set_AMR_locale(\"Deutsch\") #> ℹ Using German (Deutsch) for the AMR package for this session. set_AMR_locale(\"German\") #> ℹ Using German (Deutsch) for the AMR package for this session. set_AMR_locale(\"de\") #> ℹ Using German (Deutsch) for the AMR package for this session. ab_name(\"amox/clav\") #> Warning: More than one result was found for item 1: amoxicillin and clavulansäure #> [1] \"Amoxicillin/Clavulansäure\"  # reset to system default reset_AMR_locale() #> ℹ Using the English language (English) for the AMR package for this #>   session. ab_name(\"amox/clav\") #> [1] \"Amoxicillin/clavulanic acid\""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-1829098","dir":"Changelog","previous_headings":"","what":"AMR 1.8.2.9098","title":"AMR 1.8.2.9098","text":"(beta version eventually become v2.0! ’re happy reach new major milestone soon!) new major release AMR package, great new additions also breaking changes current users. listed . TL;DR functions arguments ‘rsi’ replaced ‘sir’, interpretation MIC values (now .sir() instead .rsi()) - old functions still work now Microbiological taxonomy (microorganisms data set) updated 2022 now based LPSN GBIF Much increased algorithms translate user input valid taxonomy, e.g. using recent scientific work per-species human pathogenicity Clinical breakpoints added EUCAST 2022 CLSI 2022 20 new antibiotics added updated DDDs ATC codes Extended support antiviral agents (antivirals data set), many new functions Now available 16 languages Many new interesting functions, sir_confidence_interval() mean_amr_distance(), add_custom_microorganisms() add custom microorganisms package Many small bug fixes","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/news/index.html","id":"sir-vs-rsi-1-8-2-9098","dir":"Changelog","previous_headings":"New","what":"SIR vs. RSI","title":"AMR 1.8.2.9098","text":"milestone version, replaced mentions RSI SIR, comply actually commonly used field clinical microbiology comes tri-form regarding AMR. existing functions .rsi(), rsi_df() ggplot_rsi() still work, replacements .sir(), sir_df(), ggplot_sir() now current functions AMR data analysis. warning thrown session remind users . data set rsi_translation now called clinical_breakpoints better reflect content. ‘RSI functions’ removed future version, late 2023 / early 2024.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"interpretation-of-mic-and-disk-diffusion-values-1-8-2-9098","dir":"Changelog","previous_headings":"New","what":"Interpretation of MIC and disk diffusion values","title":"AMR 1.8.2.9098","text":"clinical breakpoints intrinsic resistance EUCAST 2022 CLSI 2022 added .sir(). EUCAST 2022 (v12.0) now new default guideline MIC disks diffusion interpretations, eucast_rules() apply EUCAST Expert Rules. default guideline (EUCAST) can now changed new AMR_guideline option, : options(AMR_guideline = \"CLSI 2020\"). Interpretation guidelines older 10 years removed, oldest now included guidelines EUCAST CLSI 2013.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"supported-languages-1-8-2-9098","dir":"Changelog","previous_headings":"New","what":"Supported languages","title":"AMR 1.8.2.9098","text":"added support following languages: Chinese, Greek, Japanese, Polish, Turkish Ukrainian. antibiotic names now available languages, AMR package automatically determine supported language based user system language. grateful valuable input colleagues countries. AMR package now available 16 languages according download stats used almost countries world!","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"microbiological-taxonomy-1-8-2-9098","dir":"Changelog","previous_headings":"New","what":"Microbiological taxonomy","title":"AMR 1.8.2.9098","text":"microorganisms data set longer relies Catalogue Life, List Prokaryotic names Standing Nomenclature (LPSN) supplemented ‘backbone taxonomy’ Global Biodiversity Information Facility (GBIF). structure data set changed include separate LPSN GBIF identifiers. Almost previous MO codes retained. contains 1,400 taxonomic names 2022. previously relied experience categorise species pathogenic groups, happy encounter recent work Bartlett et al. (2022, DOI 10.1099/mic.0.001269) extensively studied medical-scientific literature categorise bacterial species groups. See mo_matching_score() work incorporated prevalence column microorganisms data set. Using results, .mo() mo_*() functions now much better capable converting user input valid taxonomic records. new function add_custom_microorganisms() allows users add custom microorganisms AMR package. also made following changes regarding included taxonomy microorganisms functions: Updated full microbiological taxonomy according latest daily LPSN data set (December 2022) latest yearly GBIF taxonomy backbone (November 2022) Support 1,516 city-like serovars Salmonella, Salmonella Goldcoast. Formally, serovars belonging S. enterica species, reported name genus city. reason, serovars subspecies column microorganisms data set “enterica” species column, full name contain species name (enterica). new argument keep_synonyms allows correct updated taxonomy, favour now deleted argument allow_uncertain increased tremendously speed returns generally consequent results Sequential coercion now extremely fast results stored package environment, although coercion unknown values must run per session. Previous results can reset/removed new mo_reset_session() function. Support microorganism codes ASIan Antimicrobial Resistance Surveillance Network (ASIARS-Net) MO matching score algorithm (mo_matching_score()) now counts deletions substitutions 2 instead 1, impacts outcome .mo() mo_*() function CRAN allows packages around 5 MB maximum, packages exempted package one Chromista relevant comes antimicrobial resistance, thus lacking primary scope package Chromista almost never clinically relevant, thus lacking secondary scope package microorganisms.old data set removed, previously accepted names now included microorganisms data set. new column status contains \"accepted\" currently accepted names \"synonym\" taxonomic synonyms; currently invalid names. previously accepted names now microorganisms ID - available - LPSN, GBIF SNOMED CT identifier.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"antibiotic-agents-and-selectors-1-8-2-9098","dir":"Changelog","previous_headings":"New","what":"Antibiotic agents and selectors","title":"AMR 1.8.2.9098","text":"new function add_custom_antimicrobials() allows users add custom antimicrobial codes names AMR package. antibiotics data set greatly updated: following 20 antibiotics added (also includes new J01RA ATC group): azithromycin/fluconazole/secnidazole (AFC), cefepime/amikacin (CFA), cefixime/ornidazole (CEO), ceftriaxone/beta-lactamase inhibitor (CEB), ciprofloxacin/metronidazole (CIM), ciprofloxacin/ornidazole (CIO), ciprofloxacin/tinidazole (CIT), furazidin (FUR), isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), lascufloxacin (LSC), levofloxacin/ornidazole (LEO), nemonoxacin (NEM), norfloxacin/metronidazole (NME), norfloxacin/tinidazole (NTI), ofloxacin/ornidazole (OOR), oteseconazole (OTE), rifampicin/ethambutol/isoniazid (REI), sarecycline (SRC), tetracycline/oleandomycin (TOL), thioacetazone (TAT) Added missing ATC codes Updated DDDs PubChem Compound IDs Updated antibiotic name spelling, now used WHOCC (cephalexin -> cefalexin, phenethicillin -> pheneticillin) Antibiotic code “CEI” ceftolozane/tazobactam replaced “CZT” comply EARS-Net WHONET 2022. old code still work cases using .ab() ab_*() functions. Support antimicrobial interpretation anaerobic bacteria, adding ‘placeholder’ code B_ANAER microorganisms data set adding breakpoints anaerobics clinical_breakpoints data set, used .sir() interpretion MIC disk diffusion values Also, added support using antibiotic selectors scoped dplyr verbs (without using vars()), : ... %>% summarise_at(aminoglycosides(), resistance), please see resistance() examples.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"antiviral-agents-1-8-2-9098","dir":"Changelog","previous_headings":"New","what":"Antiviral agents","title":"AMR 1.8.2.9098","text":"now added extensive support antiviral agents! first time, AMR package extensive support antiviral drugs work names, codes data way. antivirals data set extended 18 new drugs (also new J05AJ ATC group) now also contains antiviral identifiers LOINC codes new data type av (antivirals) added, functionally similar ab antibiotics Functions .av(), av_name(), av_atc(), av_synonyms(), av_from_text() added siblings ab_*() equivalents","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-new-functions-1-8-2-9098","dir":"Changelog","previous_headings":"New","what":"Other new functions","title":"AMR 1.8.2.9098","text":"Function sir_confidence_interval() add confidence intervals AMR calculation. now also included sir_df() proportion_df(). Function mean_amr_distance() calculate mean AMR distance. mean AMR distance normalised numeric value compare AMR test results can help identify similar isolates, without comparing antibiograms hand. Function sir_interpretation_history() view history previous runs .sir() (previously .rsi()). returns ‘logbook’ selected guideline, reference table specific interpretation row data set .sir() run. Function mo_current() get currently valid taxonomic name microorganism Function add_custom_antimicrobials() add custom antimicrobial codes names AMR package","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changes-1-8-2-9098","dir":"Changelog","previous_headings":"","what":"Changes","title":"AMR 1.8.2.9098","text":"Argument combine_IR removed package (affecting functions count_df(), proportion_df(), sir_df() plotting functions), since replaced combine_SI three years ago Using units ab_ddd(..., units = \"...\") deprecated time now supported anymore. Use ab_ddd_units() instead. Support data.frame-enhancing R packages, specifically: data.table::data.table, janitor::tabyl, tibble::tibble, tsibble::tsibble. AMR package functions data set output (sir_df() bug_drug_combinations()), now return data type input. data sets package now tibble, instead base R data.frames. Older R versions still supported, even support tibbles. data sets now also continually exported Apache Feather Apache Parquet formats. can find info article website. Fixed certain EUCAST breakpoints MIC values Allow NA values (e.g. .sir(.disk(NA), ...)) Fix bug-drug combinations multiple breakpoints different body sites Interpretation MIC disk zones now informative availability breakpoints robust Removed .integer() method MIC values, since MIC integer values running table() MIC values consequently failed able retrieve level position (’s normally .integer() factors work) Fixed determination Gram stains (mo_gramstain()), since taxonomic phyla Actinobacteria, Chloroflexi, Firmicutes, Tenericutes renamed respectively Actinomycetota, Chloroflexota, Bacillota, Mycoplasmatota 2021 droplevels() MIC now return common factor default lose mic class. Use droplevels(..., .mic = TRUE) keep mic class. Small fix using ab_from_text() Fixes reading text files using set_mo_source(), now also allows source file contain valid taxonomic names instead valid microorganism ID package Fixed bug mdro() using similar column names Magiorakos guideline Using random_*() function (random_mic()) now possible directly calling package without loading first: AMR::random_mic(10) Extended support vctrs package, used internally tidyverse. allows change values class mic, disk, sir, mo ab tibbles, use antibiotic selectors selecting/filtering, e.g. df[carbapenems() == \"R\", ] Fix using info = FALSE mdro() interpretation guidelines using .sir() amoxicillin, rules ampicillin used amoxicillin rules available Fix using ab_atc() non-existing ATC codes Black white message texts now reversed colour using RStudio dark theme mo_snomed() now returns class character, numeric anymore (make long SNOMED codes readable) Fix using .ab() NA values Updated support WHONET 2022 microorganism codes Antimicrobial interpretation ‘SDD’ (susceptible dose-dependent, coined CLSI) interpreted ‘’ comply EUCAST’s ‘’ .sir() Fix mo_shortname() case higher taxonomic ranks (order, class, phylum) Cleaning columns .sir(), .mic(), .disk() now show column name warning invalid results","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-8-2-9098","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.8.2.9098","text":"Added Peter Dutey-Magni, Dmytro Mykhailenko, Anton Mymrikov, Andrew Norgan, Jonas Salm contributors, thank valuable input New website make use new Bootstrap 5 pkgdown 2.0. website now contains results examples automatically regenerated every change repository, using GitHub Actions R Rmd files project now styled using styler package Set scalar conditional expressions (&& ||) possible comply upcoming R 4.3 enormous lot code cleaning, fixing small bugs way changelog contains changes AMR v2.0 later. prior versions, please see archive.","code":""}]
+[{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial agents, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"How to conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables.","code":"library(dplyr) library(ggplot2) library(AMR) library(cleaner)  # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\", \"cleaner\"))"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"creation-of-data","dir":"Articles","previous_headings":"","what":"Creation of data","title":"How to conduct AMR data analysis","text":"create fake example data use analysis. AMR data analysis, need least: patient ID, name code microorganism, date antimicrobial results (antibiogram). also include specimen type (e.g. filter blood urine), ward type (e.g. filter ICUs). additional columns (like hospital name, patients gender even [well-defined] clinical properties) can comparative analysis, tutorial demonstrate .","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"patients","dir":"Articles","previous_headings":"Creation of data","what":"Patients","title":"How to conduct AMR data analysis","text":"start patients, need unique list patients. LETTERS object available R - ’s vector 26 characters: Z. patients object just created now vector length 260, values (patient IDs) varying A1 Z10. Now also set gender patients, putting ID gender table: first 135 patient IDs now male, 125 female.","code":"patients <- unlist(lapply(LETTERS, paste0, 1:10)) patients_table <- data.frame(   patient_id = patients,   gender = c(     rep(\"M\", 135),     rep(\"F\", 125)   ) )"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"dates","dir":"Articles","previous_headings":"Creation of data","what":"Dates","title":"How to conduct AMR data analysis","text":"Let’s pretend data consists blood cultures isolates 1 January 2010 1 January 2018. dates object now contains days date range.","code":"dates <- seq(as.Date(\"2010-01-01\"), as.Date(\"2018-01-01\"), by = \"day\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"microorganisms","dir":"Articles","previous_headings":"Creation of data > Dates","what":"Microorganisms","title":"How to conduct AMR data analysis","text":"tutorial, uses four different microorganisms: Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, Klebsiella pneumoniae:","code":"bacteria <- c(   \"Escherichia coli\", \"Staphylococcus aureus\",   \"Streptococcus pneumoniae\", \"Klebsiella pneumoniae\" )"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"put-everything-together","dir":"Articles","previous_headings":"Creation of data","what":"Put everything together","title":"How to conduct AMR data analysis","text":"Using sample() function, can randomly select items objects defined earlier. let fake data reflect reality bit, also approximately define probabilities bacteria antibiotic results, using random_sir() function. Using left_join() function dplyr package, can ‘map’ gender patient ID using patients_table object created earlier: resulting data set contains 20,000 blood culture isolates. head() function can preview first 6 rows data set: Now, let’s start cleaning analysis!","code":"sample_size <- 20000 data <- data.frame(   date = sample(dates, size = sample_size, replace = TRUE),   patient_id = sample(patients, size = sample_size, replace = TRUE),   hospital = sample(     c(       \"Hospital A\",       \"Hospital B\",       \"Hospital C\",       \"Hospital D\"     ),     size = sample_size, replace = TRUE,     prob = c(0.30, 0.35, 0.15, 0.20)   ),   bacteria = sample(bacteria,     size = sample_size, replace = TRUE,     prob = c(0.50, 0.25, 0.15, 0.10)   ),   AMX = random_sir(sample_size, prob_sir = c(0.35, 0.60, 0.05)),   AMC = random_sir(sample_size, prob_sir = c(0.15, 0.75, 0.10)),   CIP = random_sir(sample_size, prob_sir = c(0.20, 0.80, 0.00)),   GEN = random_sir(sample_size, prob_sir = c(0.08, 0.92, 0.00)) ) data <- data %>% left_join(patients_table) head(data)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"cleaning-the-data","dir":"Articles","previous_headings":"","what":"Cleaning the data","title":"How to conduct AMR data analysis","text":"also created package dedicated data cleaning checking, called cleaner package. freq() function can used create frequency tables. example, gender variable: Frequency table Class: character Length: 20,000 Available: 20,000 (100%, NA: 0 = 0%) Unique: 2 Shortest: 1 Longest: 1 , can draw least two conclusions immediately. data scientists perspective, data looks clean: values M F. researchers perspective: slightly men. Nothing didn’t already know. data already quite clean, still need transform variables. bacteria column now consists text, want add variables based microbial IDs later . , transform column valid IDs. mutate() function dplyr package makes really easy: also want transform antibiotics, real life data don’t know really clean. .sir() function ensures reliability reproducibility kind variables. is_sir_eligible() can check columns probably columns SIR test results. Using mutate() across(), can apply transformation formal <rsi> class: Finally, apply EUCAST rules antimicrobial results. Europe, medical microbiological laboratories already apply rules. package features latest insights intrinsic resistance exceptional phenotypes. Moreover, eucast_rules() function can also apply additional rules, like forcing ampicillin = R amoxicillin/clavulanic acid = R. amoxicillin (column AMX) amoxicillin/clavulanic acid (column AMC) data generated randomly, rows undoubtedly contain AMX = S AMC = R, technically impossible. eucast_rules() fixes :","code":"data %>% freq(gender) data <- data %>%   mutate(bacteria = as.mo(bacteria)) is_sir_eligible(data) # [1] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE colnames(data)[is_sir_eligible(data)] # [1] \"AMX\" \"AMC\" \"CIP\" \"GEN\"  data <- data %>%   mutate(across(where(is_sir_eligible), as.sir)) data <- eucast_rules(data, col_mo = \"bacteria\", rules = \"all\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"adding-new-variables","dir":"Articles","previous_headings":"","what":"Adding new variables","title":"How to conduct AMR data analysis","text":"Now microbial ID, can add taxonomic properties:","code":"data <- data %>%   mutate(     gramstain = mo_gramstain(bacteria),     genus = mo_genus(bacteria),     species = mo_species(bacteria)   )"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Adding new variables","what":"First isolates","title":"How to conduct AMR data analysis","text":"also need know isolates can actually use analysis. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. method also takes account antimicrobial susceptibility test results using all_microbials(). Read methods first_isolate() page. outcome function can easily added data: 61.6% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 12,316 isolates analysis. Now data looks like: Time analysis!","code":"data <- data %>%   mutate(first = first_isolate(info = TRUE)) # Determining first isolates using an episode length of 365 days # ℹ Using column 'bacteria' as input for col_mo. # ℹ Using column 'date' as input for col_date. # ℹ Using column 'patient_id' as input for col_patient_id. # Basing inclusion on all antimicrobial results, using a points threshold of # 2 # Including isolates from ICU. # => Found 12,316 'phenotype-based' first isolates (61.6% of total where a #    microbial ID was available) data_1st <- data %>%   filter(first == TRUE) data_1st <- data %>%   filter_first_isolate() # Including isolates from ICU. head(data_1st)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"analysing-the-data","dir":"Articles","previous_headings":"","what":"Analysing the data","title":"How to conduct AMR data analysis","text":"might want start getting idea data distributed. ’s important start, also decides continue analysis. Although package contains convenient function make frequency tables, exploratory data analysis (EDA) primary scope package. Use package like DataExplorer , read free online book Exploratory Data Analysis R Roger D. Peng.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"dispersion-of-species","dir":"Articles","previous_headings":"Analysing the data","what":"Dispersion of species","title":"How to conduct AMR data analysis","text":"just get idea species distributed, create frequency table freq() function. created genus species column earlier based microbial ID. paste(), can concatenate together. freq() function can used like base R language intended: can used like dplyr way, easier readable: Frequency table Class: character Length: 12,316 Available: 12,316 (100%, NA: 0 = 0%) Unique: 4 Shortest: 16 Longest: 24","code":"freq(paste(data_1st$genus, data_1st$species)) data_1st %>% freq(genus, species)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"overview-of-different-bugdrug-combinations","dir":"Articles","previous_headings":"Analysing the data","what":"Overview of different bug/drug combinations","title":"How to conduct AMR data analysis","text":"Using tidyverse selections, can also select filter columns based antibiotic class : want get quick glance number isolates different bug/drug combinations, can use bug_drug_combinations() function: give crude numbers data. calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"data_1st %>%   filter(any(aminoglycosides() == \"R\")) # ℹ For aminoglycosides() using column 'GEN' (gentamicin) data_1st %>%   bug_drug_combinations() %>%   head() # show first 6 rows data_1st %>%   select(bacteria, aminoglycosides()) %>%   bug_drug_combinations() # ℹ For aminoglycosides() using column 'GEN' (gentamicin)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"How to conduct AMR data analysis","text":"functions resistance() susceptibility() can used calculate antimicrobial resistance susceptibility. specific analyses, functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() proportion_R() can used determine proportion specific antimicrobial outcome. functions contain minimum argument, denoting minimum required number test results returning value. functions otherwise return NA. default minimum = 30, following CLSI M39-A4 guideline applying microbial epidemiology. per EUCAST guideline 2019, calculate resistance proportion R (proportion_R(), equal resistance()) susceptibility proportion S (proportion_SI(), equal susceptibility()). functions can used : can used conjunction group_by() summarise(), dplyr package: course convenient know number isolates responsible percentages. purpose n_sir() can used, works exactly like n_distinct() dplyr package. counts isolates available every group (.e. values S, R): functions can also used get proportion multiple antibiotics, calculate empiric susceptibility combination therapies easily: curious resistance within certain antibiotic classes, use antibiotic class selector penicillins(), automatically include columns AMX AMC data: make transition next part, let’s see differences previously calculated combination therapies plotted:","code":"data_1st %>% resistance(AMX) # [1] 0.5802209 data_1st %>%   group_by(hospital) %>%   summarise(amoxicillin = resistance(AMX)) data_1st %>%   group_by(hospital) %>%   summarise(     amoxicillin = resistance(AMX),     available = n_sir(AMX)   ) data_1st %>%   group_by(genus) %>%   summarise(     amoxiclav = susceptibility(AMC),     gentamicin = susceptibility(GEN),     amoxiclav_genta = susceptibility(AMC, GEN)   ) data_1st %>%   # group by hospital   group_by(hospital) %>%   #                / -> select all penicillins in the data for calculation   #                |              / -> use resistance() for all peni's per hospital   #                |              |           / -> print as percentages   summarise(across(penicillins(), resistance, as_percent = TRUE)) %>%   # format the antibiotic column names, using so-called snake case,   # so 'Amoxicillin/clavulanic acid' becomes 'amoxicillin_clavulanic_acid'   rename_with(set_ab_names, penicillins()) data_1st %>%   group_by(genus) %>%   summarise(     \"1. Amoxi/clav\" = susceptibility(AMC),     \"2. Gentamicin\" = susceptibility(GEN),     \"3. Amoxi/clav + genta\" = susceptibility(AMC, GEN)   ) %>%   # pivot_longer() from the tidyr package \"lengthens\" data:   tidyr::pivot_longer(-genus, names_to = \"antibiotic\") %>%   ggplot(aes(     x = genus,     y = value,     fill = antibiotic   )) +   geom_col(position = \"dodge2\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plots","dir":"Articles","previous_headings":"Analysing the data","what":"Plots","title":"How to conduct AMR data analysis","text":"show results plots, R users nowadays use ggplot2 package. package lets create plots layers. can read website. quick example look like syntaxes: AMR package contains functions extend ggplot2 package, example geom_sir(). automatically transforms data count_df() proportion_df() show results stacked bars. simplest shortest example:  Omit translate_ab = FALSE antibiotic codes (AMX, AMC, CIP, GEN) translated official names (amoxicillin, amoxicillin/clavulanic acid, ciprofloxacin, gentamicin). group e.g. genus column add additional functions package, can create :  simplify , also created ggplot_sir() function, combines almost functions:","code":"ggplot(   data = a_data_set,   mapping = aes(     x = year,     y = value   ) ) +   geom_col() +   labs(     title = \"A title\",     subtitle = \"A subtitle\",     x = \"My X axis\",     y = \"My Y axis\"   )  # or as short as: ggplot(a_data_set) +   geom_bar(aes(year)) ggplot(data_1st) +   geom_sir(translate_ab = FALSE) # group the data on `genus` ggplot(data_1st %>% group_by(genus)) +   # create bars with genus on x axis   # it looks for variables with class `sir`,   # of which we have 4 (earlier created with `as.sir`)   geom_sir(x = \"genus\") +   # split plots on antibiotic   facet_sir(facet = \"antibiotic\") +   # set colours to the SIR interpretations (colour-blind friendly)   scale_sir_colours() +   # show percentages on y axis   scale_y_percent(breaks = 0:4 * 25) +   # turn 90 degrees, to make it bars instead of columns   coord_flip() +   # add labels   labs(     title = \"Resistance per genus and antibiotic\",     subtitle = \"(this is fake data)\"   ) +   # and print genus in italic to follow our convention   # (is now y axis because we turned the plot)   theme(axis.text.y = element_text(face = \"italic\")) data_1st %>%   group_by(genus) %>%   ggplot_sir(     x = \"genus\",     facet = \"antibiotic\",     breaks = 0:4 * 25,     datalabels = FALSE   ) +   coord_flip()"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plotting-mic-and-disk-diffusion-values","dir":"Articles","previous_headings":"Analysing the data > Plots","what":"Plotting MIC and disk diffusion values","title":"How to conduct AMR data analysis","text":"AMR package also extends plot() ggplot2::autoplot() functions plotting minimum inhibitory concentrations (MIC, created .mic()) disk diffusion diameters (created .disk()). random_mic() random_disk() functions, can generate sampled values new data types (S3 classes) <mic> <disk>:   also specific, generating MICs likely found E. coli ciprofloxacin: plot() autoplot() function, can define microorganism antimicrobial agent way. add interpretation values according chosen guidelines (defaults latest EUCAST guideline). Default colours colour-blind friendly, maintaining convention e.g. ‘susceptible’ green ‘resistant’ red:   disk diffusion values, much difference plotting:  using ggplot2 package, now choosing latest implemented CLSI guideline (notice EUCAST-specific term “Susceptible, incr. exp.” changed “Intermediate”):","code":"mic_values <- random_mic(size = 100) mic_values # Class 'mic' #   [1] <=0.001 0.025   128     0.125   0.0625  8       0.025   32      >=256   #  [10] 4       32      0.005   4       2       32      0.5     0.0625  128     #  [19] 0.01    16      1       0.01    64      0.0625  8       >=256   0.005   #  [28] 128     <=0.001 8       1       0.025   1       64      128     0.0625  #  [37] 2       0.125   4       4       4       16      0.0625  16      0.0625  #  [46] 0.5     0.5     <=0.001 64      8       128     1       64      2       #  [55] 1       >=256   <=0.001 0.002   0.5     4       4       32      <=0.001 #  [64] 16      0.002   128     128     4       0.002   8       8       0.0625  #  [73] 0.002   0.0625  8       16      0.125   0.005   8       >=256   8       #  [82] 0.01    0.025   0.005   64      1       0.025   0.002   16      32      #  [91] 0.005   32      0.002   <=0.001 0.25    4       <=0.001 0.25    16      # [100] 0.025 # base R: plot(mic_values) # ggplot2: autoplot(mic_values) mic_values <- random_mic(size = 100, mo = \"E. coli\", ab = \"cipro\") # base R: plot(mic_values, mo = \"E. coli\", ab = \"cipro\") # ggplot2: autoplot(mic_values, mo = \"E. coli\", ab = \"cipro\") disk_values <- random_disk(size = 100, mo = \"E. coli\", ab = \"cipro\") disk_values # Class 'disk' #   [1] 29 26 20 20 26 31 29 17 28 21 27 17 30 23 21 19 23 23 20 23 19 27 23 20 23 #  [26] 25 22 25 28 31 19 31 20 31 19 19 28 31 20 29 23 25 28 27 18 24 26 21 30 23 #  [51] 22 31 27 26 27 26 23 22 17 19 18 24 31 25 29 21 29 19 27 20 31 23 23 25 18 #  [76] 31 18 27 31 20 21 23 25 22 31 26 28 21 30 17 31 29 19 25 24 17 19 19 21 21 # base R: plot(disk_values, mo = \"E. coli\", ab = \"cipro\") autoplot(   disk_values,   mo = \"E. coli\",   ab = \"cipro\",   guideline = \"CLSI\" )"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"independence-test","dir":"Articles","previous_headings":"Analysing the data","what":"Independence test","title":"How to conduct AMR data analysis","text":"next example uses example_isolates data set. data set included package contains 2,000 microbial isolates full antibiograms. reflects reality can used practise AMR data analysis. compare resistance amoxicillin/clavulanic acid (column FOS) ICU clinical wards. input fisher.test() can retrieved transformation like : can apply test now : can seen, p value practically zero (0.0000002263247), means amoxicillin/clavulanic acid resistance found isolates patients ICUs clinical wards really different.","code":"# use package 'tidyr' to pivot data: library(tidyr)  check_FOS <- example_isolates %>%   filter(ward %in% c(\"ICU\", \"Clinical\")) %>% # filter on only these wards   select(ward, AMC) %>% # select the wards and amoxi/clav   group_by(ward) %>% # group on the wards   count_df(combine_SI = TRUE) %>% # count all isolates per group (ward)   pivot_wider(     names_from = ward, # transform output so \"ICU\" and \"Clinical\" are columns     values_from = value   ) %>%   select(ICU, Clinical) %>% # and only select these columns   as.matrix() # transform to a good old matrix for fisher.test()  check_FOS #      ICU Clinical # [1,] 396      942 # [2,] 184      240 # do Fisher's Exact Test fisher.test(check_FOS) #  #   Fisher's Exact Test for Count Data #  # data:  check_FOS # p-value = 2.263e-07 # alternative hypothesis: true odds ratio is not equal to 1 # 95 percent confidence interval: #  0.435261 0.691614 # sample estimates: # odds ratio  #  0.5485079"},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to apply EUCAST rules","text":"EUCAST rules? European Committee Antimicrobial Susceptibility Testing (EUCAST) states website: EUCAST expert rules tabulated collection expert knowledge intrinsic resistances, exceptional resistance phenotypes interpretive rules may applied antimicrobial susceptibility testing order reduce errors make appropriate recommendations reporting particular resistances. Europe, lot medical microbiological laboratories already apply rules (Brown et al., 2015). package features latest insights intrinsic resistance unusual phenotypes (v3.3, 2021). Moreover, eucast_rules() function use purpose can also apply additional rules, like forcing ampicillin = R isolates amoxicillin/clavulanic acid = R.","code":""},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to apply EUCAST rules","text":"rules can used discard impossible bug-drug combinations data. example, Klebsiella produces beta-lactamase prevents ampicillin (amoxicillin) working . words, practically every strain Klebsiella resistant ampicillin. Sometimes, laboratory data can still contain strains ampicillin susceptible ampicillin. antibiogram available identification available, antibiogram re-interpreted based identification (namely, Klebsiella). EUCAST expert rules solve , can applied using eucast_rules(): convenient function mo_is_intrinsic_resistant() uses guideline, allows check one specific microorganisms antibiotics: EUCAST rules can used correction, can also used filling known resistance susceptibility based results antimicrobials drugs. process called interpretive reading, basically form imputation, part eucast_rules() function well:","code":"oops <- data.frame(   mo = c(     \"Klebsiella\",     \"Escherichia\"   ),   ampicillin = \"S\" ) oops #            mo ampicillin # 1  Klebsiella          S # 2 Escherichia          S  eucast_rules(oops, info = FALSE) #            mo ampicillin # 1  Klebsiella          R # 2 Escherichia          S mo_is_intrinsic_resistant(   c(\"Klebsiella\", \"Escherichia\"),   \"ampicillin\" ) # [1]  TRUE FALSE  mo_is_intrinsic_resistant(   \"Klebsiella\",   c(\"ampicillin\", \"kanamycin\") ) # [1]  TRUE FALSE data <- data.frame(   mo = c(     \"Staphylococcus aureus\",     \"Enterococcus faecalis\",     \"Escherichia coli\",     \"Klebsiella pneumoniae\",     \"Pseudomonas aeruginosa\"   ),   VAN = \"-\", # Vancomycin   AMX = \"-\", # Amoxicillin   COL = \"-\", # Colistin   CAZ = \"-\", # Ceftazidime   CXM = \"-\", # Cefuroxime   PEN = \"S\", # Benzylenicillin   FOX = \"S\", # Cefoxitin   stringsAsFactors = FALSE ) data eucast_rules(data)"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"type-of-input","dir":"Articles","previous_headings":"","what":"Type of input","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function takes data set input, regular data.frame. tries automatically determine right columns info isolates, name species columns results antimicrobial agents. See help page info set right settings data command ?mdro. WHONET data (data), settings automatically set correctly.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"guidelines","dir":"Articles","previous_headings":"","what":"Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function support multiple guidelines. can select guideline guideline parameter. Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. “Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.” Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.2\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance Unusual Phenotypes” (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance Exceptional Phenotypes Tables” (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization “Companion handbook guidelines programmatic management drug-resistant tuberculosis” (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"custom-guidelines","dir":"Articles","previous_headings":"Guidelines","what":"Custom Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"can also use custom guideline. Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() dplyr package, recognise input method set rules. Rules must set using R considers ‘formula notation’: row/isolate matches first rule, value first ~ (case ‘Elderly Type ’) set MDRO value. Otherwise, second rule tried . maximum number rules unlimited. can print rules set console overview. Colours help reading console supports colours. outcome function can used guideline argument mdro() function: rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline(   CIP == \"R\" & age > 60 ~ \"Elderly Type A\",   ERY == \"R\" & age > 60 ~ \"Elderly Type B\" ) custom # A set of custom MDRO rules: #   1. If CIP is \"R\" and age is higher than 60 then: Elderly Type A #   2. If ERY is \"R\" and age is higher than 60 then: Elderly Type B #   3. Otherwise: Negative #  # Unmatched rows will return NA. # Results will be of class 'factor', with ordered levels: Negative < Elderly Type A < Elderly Type B x <- mdro(example_isolates, guideline = custom) table(x) # x #       Negative Elderly Type A Elderly Type B  #           1070            198            732"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function always returns ordered factor predefined guidelines. example, output default guideline Magiorakos et al. returns factor levels ‘Negative’, ‘MDR’, ‘XDR’ ‘PDR’ order. next example uses example_isolates data set. data set included package contains full antibiograms 2,000 microbial isolates. reflects reality can used practise AMR data analysis. test MDR/XDR/PDR guideline data set, get: (16 isolates test results) Frequency table Class: factor > ordered (numeric) Length: 2,000 Levels: 4: Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant … Available: 1,729 (86.45%, NA: 271 = 13.55%) Unique: 2 another example, create data set determine multi-drug resistant TB: column names automatically verified valid drug names codes, worked exactly way: data set now looks like : can now add interpretation MDR-TB data set. can use: shortcut mdr_tb(): Create frequency table results: Frequency table Class: factor > ordered (numeric) Length: 5,000 Levels: 5: Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant <… Available: 5,000 (100%, NA: 0 = 0%) Unique: 5","code":"library(dplyr) # to support pipes: %>% library(cleaner) # to create frequency tables example_isolates %>%   mdro() %>%   freq() # show frequency table of the result # Warning: in mdro(): NA introduced for isolates where the available percentage of # antimicrobial classes was below 50% (set with pct_required_classes) # random_sir() is a helper function to generate # a random vector with values S, I and R my_TB_data <- data.frame(   rifampicin = random_sir(5000),   isoniazid = random_sir(5000),   gatifloxacin = random_sir(5000),   ethambutol = random_sir(5000),   pyrazinamide = random_sir(5000),   moxifloxacin = random_sir(5000),   kanamycin = random_sir(5000) ) my_TB_data <- data.frame(   RIF = random_sir(5000),   INH = random_sir(5000),   GAT = random_sir(5000),   ETH = random_sir(5000),   PZA = random_sir(5000),   MFX = random_sir(5000),   KAN = random_sir(5000) ) head(my_TB_data) #   rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin # 1          I         R            R          R            S            R # 2          R         I            R          S            R            S # 3          R         R            R          I            I            R # 4          I         S            R          R            S            I # 5          S         R            R          S            R            R # 6          S         I            R          I            S            R #   kanamycin # 1         I # 2         S # 3         R # 4         R # 5         S # 6         I mdro(my_TB_data, guideline = \"TB\") my_TB_data$mdr <- mdr_tb(my_TB_data) # ℹ No column found as input for col_mo, assuming all rows contain #   Mycobacterium tuberculosis. freq(my_TB_data$mdr)"},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"transforming","dir":"Articles","previous_headings":"","what":"Transforming","title":"How to conduct principal component analysis (PCA) for AMR","text":"PCA, need transform AMR data first. example_isolates data set package looks like: Now transform data set resistance percentages per taxonomic order genus:","code":"library(AMR) library(dplyr) glimpse(example_isolates) # Rows: 2,000 # Columns: 46 # $ date    <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2… # $ patient <chr> \"A77334\", \"A77334\", \"067927\", \"067927\", \"067927\", \"067927\", \"4… # $ age     <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50… # $ gender  <chr> \"F\", \"F\", \"F\", \"F\", \"F\", \"F\", \"M\", \"M\", \"F\", \"F\", \"M\", \"M\", \"M… # $ ward    <chr> \"Clinical\", \"Clinical\", \"ICU\", \"ICU\", \"ICU\", \"ICU\", \"Clinical\"… # $ mo      <mo> \"B_ESCHR_COLI\", \"B_ESCHR_COLI\", \"B_STPHY_EPDR\", \"B_STPHY_EPDR\",… # $ PEN     <sir> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,… # $ OXA     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ FLC     <sir> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R… # $ AMX     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… # $ AMC     <sir> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N… # $ AMP     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… # $ TZP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ CZO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… # $ FEP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ CXM     <sir> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S… # $ FOX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… # $ CTX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… # $ CAZ     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, … # $ CRO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… # $ GEN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ TOB     <sir> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA… # $ AMK     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ KAN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ TMP     <sir> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, … # $ SXT     <sir> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N… # $ NIT     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,… # $ FOS     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ LNZ     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… # $ CIP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S… # $ MFX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ VAN     <sir> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, … # $ TEC     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… # $ TCY     <sir> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, … # $ TGC     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… # $ DOX     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… # $ ERY     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… # $ CLI     <sir> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA… # $ AZM     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… # $ IPM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… # $ MEM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ MTR     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ CHL     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ COL     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, … # $ MUP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ RIF     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… resistance_data <- example_isolates %>%   group_by(     order = mo_order(mo), # group on anything, like order     genus = mo_genus(mo)   ) %>% #  and genus as we do here   summarise_if(is.sir, resistance) %>% # then get resistance of all drugs   select(     order, genus, AMC, CXM, CTX,     CAZ, GEN, TOB, TMP, SXT   ) # and select only relevant columns  head(resistance_data) # # A tibble: 6 × 10 # # Groups:   order [5] #   order             genus          AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT #   <chr>             <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 (unknown order)   (unknown ge…    NA    NA    NA    NA    NA    NA    NA    NA # 2 Actinomycetales   Schaalia        NA    NA    NA    NA    NA    NA    NA    NA # 3 Bacteroidales     Bacteroides     NA    NA    NA    NA    NA    NA    NA    NA # 4 Campylobacterales Campylobact…    NA    NA    NA    NA    NA    NA    NA    NA # 5 Caryophanales     Gemella         NA    NA    NA    NA    NA    NA    NA    NA # 6 Caryophanales     Listeria        NA    NA    NA    NA    NA    NA    NA    NA"},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"perform-principal-component-analysis","dir":"Articles","previous_headings":"","what":"Perform principal component analysis","title":"How to conduct principal component analysis (PCA) for AMR","text":"new pca() function automatically filter rows contain numeric values selected variables, now need : result can reviewed good old summary() function: Good news. first two components explain total 93.3% variance (see PC1 PC2 values Proportion Variance. can create -called biplot base R biplot() function, see antimicrobial resistance per drug explain difference per microorganism.","code":"pca_result <- pca(resistance_data) # ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", \"TMP\" #   and \"TOB\". Total observations available: 7. summary(pca_result) # Groups (n=4, named as 'order'): # [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\" # Importance of components: #                           PC1    PC2    PC3     PC4     PC5     PC6       PC7 # Standard deviation     2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 9.577e-17 # Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00 # Cumulative Proportion  0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00 # Groups (n=4, named as 'order'): # [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\""},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"plotting-the-results","dir":"Articles","previous_headings":"","what":"Plotting the results","title":"How to conduct principal component analysis (PCA) for AMR","text":"can’t see explanation points. Perhaps works better new ggplot_pca() function, automatically adds right labels even groups:  can also print ellipse per group, edit appearance:","code":"biplot(pca_result) ggplot_pca(pca_result) ggplot_pca(pca_result, ellipse = TRUE) +   ggplot2::labs(title = \"An AMR/PCA biplot!\")"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"spss-sas-stata","dir":"Articles","previous_headings":"","what":"SPSS / SAS / Stata","title":"How to import data from SPSS / SAS / Stata","text":"SPSS (Statistical Package Social Sciences) probably well-known software package statistical analysis. SPSS easier learn R, SPSS click menu run parts analysis. user-friendliness, taught universities particularly useful students new statistics. experience, guess pretty much (bio)medical students know time graduate. SAS Stata comparable statistical packages popular big industries.","code":""},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"compared-to-r","dir":"Articles","previous_headings":"","what":"Compared to R","title":"How to import data from SPSS / SAS / Stata","text":"said, SPSS easier learn R. SPSS, SAS Stata come major downsides comparing R: R highly modular. official R network (CRAN) features 16,000 packages time writing, AMR package one . packages peer-reviewed publication. Aside official channel, also developers choose submit CRAN, rather keep public repository, like GitHub. may even lot 14,000 packages . Bottom line , can really extend ask somebody . Take example AMR package. Among things, adds reliable reference data R help data cleaning analysis. SPSS, SAS Stata never know valid MIC value Gram stain E. coli . species Klebiella resistant amoxicillin Floxapen® trade name flucloxacillin. facts properties often needed clean existing data, inconvenient software package without reliable reference data. See demonstration. R extremely flexible. write syntax , can anything want. flexibility transforming, arranging, grouping summarising data, drawing plots, endless - SPSS, SAS Stata bound algorithms format styles. may bit flexible, can probably never create specific publication-ready plot without using (paid) software. sometimes write syntaxes SPSS run complete analysis ‘automate’ work, lot less time R. notice writing syntaxes R lot nifty clever SPSS. Still, working statistical package, knowledge (statistically) willing accomplish. R can easily automated. last years, R Markdown really made interesting development. R Markdown, can easily produce reports, whether format Word, PowerPoint, website, PDF document just raw data Excel. even allows use reference file containing layout style (e.g. fonts colours) organisation. use lot generate weekly monthly reports automatically. Just write code enjoy automatically updated reports interval like. even professional environment, create Shiny apps: live manipulation data using custom made website. webdesign knowledge needed (JavaScript, CSS, HTML) almost zero. R huge community. Many R users just ask questions websites like StackOverflow.com, largest online community programmers. time writing, 478,396 R-related questions already asked platform (covers questions answers programming language). experience, questions answered within couple minutes. R understands data type, including SPSS/SAS/Stata. ’s vice versa ’m afraid. can import data source R. example SPSS, SAS Stata (link), Minitab, Epi Info EpiData (link), Excel (link), flat files like CSV, TXT TSV (link), directly databases datawarehouses anywhere world (link). can even scrape websites download tables live internet (link) get results API call transform data one command (link). best part - can export R data formats well. can import SPSS file, analysis neatly R export resulting tables Excel files sharing. R completely free open-source. strings attached. created maintained volunteers believe (data) science open publicly available everybody. SPSS, SAS Stata quite expensive. IBM SPSS Staticstics comes subscriptions nowadays, varying USD 1,300 USD 8,500 per user per year. SAS Analytics Pro costs around USD 10,000 per computer. Stata also business model subscription fees, varying USD 600 USD 2,800 per computer per year, lower prices come limitation number variables can work . still offer benefits R. working midsized small company, can save tens thousands dollars using R instead e.g. SPSS - gaining even functions flexibility. R enthousiasts can much PR want (like ), nobody officially associated affiliated R. really free. R (nowadays) preferred analysis software academic papers. present, R among world powerful statistical languages, generally popular science (Bollmann et al., 2017). reasons, number references R analysis method academic papers rising continuously even surpassed SPSS academic use (Muenchen, 2014). believe thing SPSS , always great user interface easy learn use. Back developed , little competition, let alone R. R didn’t even professional user interface last decade (called RStudio, see ). people used R nineties 2010 almost completely incomparable R used now. language restyled completely volunteers dedicated professionals field data science. SPSS great nothing else compete. now 2023, don’t see reason SPSS better use R. demonstrate first point:","code":"# not all values are valid MIC values: as.mic(0.125) # Class 'mic' # [1] 0.125 as.mic(\"testvalue\") # Class 'mic' # [1] <NA>  # the Gram stain is available for all bacteria: mo_gramstain(\"E. coli\") # [1] \"Gram-negative\"  # Klebsiella is intrinsic resistant to amoxicillin, according to EUCAST: klebsiella_test <- data.frame(   mo = \"klebsiella\",   amox = \"S\",   stringsAsFactors = FALSE ) klebsiella_test # (our original data) #           mo amox # 1 klebsiella    S eucast_rules(klebsiella_test, info = FALSE) # (the edited data by EUCAST rules) #           mo amox # 1 klebsiella    R  # hundreds of trade names can be translated to a name, trade name or an ATC code: ab_name(\"floxapen\") # [1] \"Flucloxacillin\" ab_tradenames(\"floxapen\") #  [1] \"culpen\"               \"floxacillin\"          \"floxacillin sodium\"   #  [4] \"floxapen\"             \"floxapen sodium salt\" \"fluclox\"              #  [7] \"flucloxacilina\"       \"flucloxacillin\"       \"flucloxacilline\"      # [10] \"flucloxacillinum\"     \"fluorochloroxacillin\" \"staphylex\" ab_atc(\"floxapen\") # [1] \"J01CF05\""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"rstudio","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata","what":"RStudio","title":"How to import data from SPSS / SAS / Stata","text":"work R, probably best option use RStudio. open-source free desktop environment allows run R code, also supports project management, version management, package management convenient import menus work data sources. can also install RStudio Server private corporate server, brings nothing less complete RStudio software website (home work). import data file, just click Import Dataset Environment tab:  additional packages needed, RStudio ask installed beforehand. window opens, can define options (parameters) used import ’re ready go:  want named variables imported factors resembles SPSS , use as_factor(). difference :","code":"SPSS_data # # A tibble: 4,203 x 4 #     v001 sex       status    statusage #    <dbl> <dbl+lbl> <dbl+lbl>     <dbl> #  1 10002 1         1              76.6 #  2 10004 0         1              59.1 #  3 10005 1         1              54.5 #  4 10006 1         1              54.1 #  5 10007 1         1              57.7 #  6 10008 1         1              62.8 #  7 10010 0         1              63.7 #  8 10011 1         1              73.1 #  9 10017 1         1              56.7 # 10 10018 0         1              66.6 # # ... with 4,193 more rows  as_factor(SPSS_data) # # A tibble: 4,203 x 4 #     v001 sex    status statusage #    <dbl> <fct>  <fct>      <dbl> #  1 10002 Male   alive       76.6 #  2 10004 Female alive       59.1 #  3 10005 Male   alive       54.5 #  4 10006 Male   alive       54.1 #  5 10007 Male   alive       57.7 #  6 10008 Male   alive       62.8 #  7 10010 Female alive       63.7 #  8 10011 Male   alive       73.1 #  9 10017 Male   alive       56.7 # 10 10018 Female alive       66.6 # # ... with 4,193 more rows"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"base-r","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata","what":"Base R","title":"How to import data from SPSS / SAS / Stata","text":"import data SPSS, SAS Stata, can use great haven package : can now import files follows:","code":"# download and install the latest version: install.packages(\"haven\") # load the package you just installed: library(haven)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"spss","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"SPSS","title":"How to import data from SPSS / SAS / Stata","text":"read files SPSS R: forget as_factor(), mentioned . export R objects SPSS file format:","code":"# read any SPSS file based on file extension (best way): read_spss(file = \"path/to/file\")  # read .sav or .zsav file: read_sav(file = \"path/to/file\")  # read .por file: read_por(file = \"path/to/file\") # save as .sav file: write_sav(data = yourdata, path = \"path/to/file\")  # save as compressed .zsav file: write_sav(data = yourdata, path = \"path/to/file\", compress = TRUE)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"sas","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"SAS","title":"How to import data from SPSS / SAS / Stata","text":"read files SAS R: export R objects SAS file format:","code":"# read .sas7bdat + .sas7bcat files: read_sas(data_file = \"path/to/file\", catalog_file = NULL)  # read SAS transport files (version 5 and version 8): read_xpt(file = \"path/to/file\") # save as regular SAS file: write_sas(data = yourdata, path = \"path/to/file\")  # the SAS transport format is an open format # (required for submission of the data to the FDA) write_xpt(data = yourdata, path = \"path/to/file\", version = 8)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"stata","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"Stata","title":"How to import data from SPSS / SAS / Stata","text":"read files Stata R: export R objects Stata file format:","code":"# read .dta file: read_stata(file = \"/path/to/file\")  # works exactly the same: read_dta(file = \"/path/to/file\") # save as .dta file, Stata version 14: # (supports Stata v8 until v15 at the time of writing) write_dta(data = yourdata, path = \"/path/to/file\", version = 14)"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"import-of-data","dir":"Articles","previous_headings":"","what":"Import of data","title":"How to work with WHONET data","text":"tutorial assumes already imported WHONET data e.g. readxl package. RStudio, can done using menu button ‘Import Dataset’ tab ‘Environment’. Choose option ‘Excel’ select exported file. Make sure date fields imported correctly. example syntax look like : package comes example data set WHONET. use analysis.","code":"library(readxl) data <- read_excel(path = \"path/to/your/file.xlsx\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to work with WHONET data","text":"First, load relevant packages yet . use tidyverse analyses. . don’t know yet, suggest read website: https://www.tidyverse.org/. transform variables simplify automate analysis: Microorganisms transformed microorganism codes (called mo) using Catalogue Life reference data set, contains ~70,000 microorganisms taxonomic kingdoms Bacteria, Fungi Protozoa. tranformation .mo(). function also recognises almost WHONET abbreviations microorganisms. Antimicrobial results interpretations clean valid. words, contain values \"S\", \"\" \"R\". exactly .sir() function . errors warnings, values transformed succesfully. also created package dedicated data cleaning checking, called cleaner package. freq() function can used create frequency tables. let’s check data, couple frequency tables: Frequency table Class: character Length: 500 Available: 500 (100%, NA: 0 = 0%) Unique: 38 Shortest: 11 Longest: 40 (omitted 28 entries, n = 57 [11.4%]) Frequency table Class: factor > ordered > sir (numeric) Length: 500 Levels: 3: S < < R Available: 481 (96.2%, NA: 19 = 3.8%) Unique: 3 Drug: Amoxicillin/clavulanic acid (AMC, J01CR02) Drug group: Beta-lactams/penicillins %SI: 78.59%","code":"library(dplyr) # part of tidyverse library(ggplot2) # part of tidyverse library(AMR) # this package library(cleaner) # to create frequency tables # transform variables data <- WHONET %>%   # get microbial ID based on given organism   mutate(mo = as.mo(Organism)) %>%   # transform everything from \"AMP_ND10\" to \"CIP_EE\" to the new `sir` class   mutate_at(vars(AMP_ND10:CIP_EE), as.sir) # our newly created `mo` variable, put in the mo_name() function data %>% freq(mo_name(mo), nmax = 10) # our transformed antibiotic columns # amoxicillin/clavulanic acid (J01CR02) as an example data %>% freq(AMC_ND2)"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"a-first-glimpse-at-results","dir":"Articles","previous_headings":"","what":"A first glimpse at results","title":"How to work with WHONET data","text":"easy ggplot already give lot information, using included ggplot_sir() function:","code":"data %>%   group_by(Country) %>%   select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%   ggplot_sir(translate_ab = \"ab\", facet = \"Country\", datalabels = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-full-microbial-taxonomy","dir":"Articles","previous_headings":"","what":"microorganisms: Full Microbial Taxonomy","title":"Data sets for download / own use","text":"data set 52,141 rows 22 columns, containing following column names:mo, fullname, status, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, source, lpsn, lpsn_parent, lpsn_renamed_to, gbif, gbif_parent, gbif_renamed_to, prevalence snomed. data set R available microorganisms, load AMR package. last updated 20 December 2022 15:14:04 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (1.2 MB) Download tab-separated text file (11.3 MB) Download Microsoft Excel workbook (5 MB) Download Apache Feather file (5.4 MB) Download Apache Parquet file (2.6 MB) Download SAS data file (50.9 MB) Download IBM SPSS Statistics data file (16.9 MB) Download Stata DTA file (47.1 MB) NOTE: exported files SAS, SPSS Stata contain first 50 SNOMED codes per record, file size otherwise exceed 100 MB; file size limit GitHub. Advice? Use R instead. tab-separated text file Microsoft Excel workbook contain SNOMED codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source","dir":"Articles","previous_headings":"microorganisms: Full Microbial Taxonomy","what":"Source","title":"Data sets for download / own use","text":"data set contains full microbial taxonomy five kingdoms List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF): Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name ‘Microoganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content","dir":"Articles","previous_headings":"microorganisms: Full Microbial Taxonomy","what":"Example content","title":"Data sets for download / own use","text":"Included (sub)species per taxonomic kingdom: Example rows filtering genus Escherichia:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antibiotics-antibiotic-antifungal-drugs","dir":"Articles","previous_headings":"","what":"antibiotics: Antibiotic (+Antifungal) Drugs","title":"Data sets for download / own use","text":"data set 483 rows 14 columns, containing following column names:ab, cid, name, group, atc, atc_group1, atc_group2, abbreviations, synonyms, oral_ddd, oral_units, iv_ddd, iv_units loinc. data set R available antibiotics, load AMR package. last updated 30 October 2022 20:05:46 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (39 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (66 kB) Download Apache Feather file (0.1 MB) Download Apache Parquet file (97 kB) Download SAS data file (1.9 MB) Download IBM SPSS Statistics data file (0.3 MB) Download Stata DTA file (0.4 MB) tab-separated text file Microsoft Excel workbook, SAS, SPSS Stata files contain ATC codes, common abbreviations, trade names LOINC codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-1","dir":"Articles","previous_headings":"antibiotics: Antibiotic (+Antifungal) Drugs","what":"Source","title":"Data sets for download / own use","text":"data set contains EARS-Net ATC codes gathered WHONET, compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine WHONET software 2019 LOINC (Logical Observation Identifiers Names Codes)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antivirals-antiviral-drugs","dir":"Articles","previous_headings":"","what":"antivirals: Antiviral Drugs","title":"Data sets for download / own use","text":"data set 120 rows 11 columns, containing following column names:av, name, atc, cid, atc_group, synonyms, oral_ddd, oral_units, iv_ddd, iv_units loinc. data set R available antivirals, load AMR package. last updated 13 November 2022 07:46:10 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (5 kB) Download tab-separated text file (16 kB) Download Microsoft Excel workbook (16 kB) Download Apache Feather file (15 kB) Download Apache Parquet file (13 kB) Download SAS data file (84 kB) Download IBM SPSS Statistics data file (30 kB) Download Stata DTA file (73 kB) tab-separated text file Microsoft Excel workbook, SAS, SPSS Stata files contain trade names LOINC codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-2","dir":"Articles","previous_headings":"antivirals: Antiviral Drugs","what":"Source","title":"Data sets for download / own use","text":"data set contains ATC codes gathered compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine LOINC (Logical Observation Identifiers Names Codes)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"clinical_breakpoints-interpretation-from-mic-values-disk-diameters-to-sir","dir":"Articles","previous_headings":"","what":"clinical_breakpoints: Interpretation from MIC values & disk diameters to SIR","title":"Data sets for download / own use","text":"data set 18,308 rows 11 columns, containing following column names:guideline, method, site, mo, rank_index, ab, ref_tbl, disk_dose, breakpoint_S, breakpoint_R uti. data set R available clinical_breakpoints, load AMR package. last updated 21 January 2023 22:47:20 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (42 kB) Download tab-separated text file (1.9 MB) Download Microsoft Excel workbook (0.8 MB) Download Apache Feather file (0.7 MB) Download Apache Parquet file (87 kB) Download SAS data file (3.6 MB) Download IBM SPSS Statistics data file (2.3 MB) Download Stata DTA file (3.4 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-3","dir":"Articles","previous_headings":"clinical_breakpoints: Interpretation from MIC values & disk diameters to SIR","what":"Source","title":"Data sets for download / own use","text":"data set contains interpretation rules MIC values disk diffusion diameters. Included guidelines CLSI (2013-2022) EUCAST (2013-2022).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"intrinsic_resistant-intrinsic-bacterial-resistance","dir":"Articles","previous_headings":"","what":"intrinsic_resistant: Intrinsic Bacterial Resistance","title":"Data sets for download / own use","text":"data set 134,634 rows 2 columns, containing following column names:mo ab. data set R available intrinsic_resistant, load AMR package. last updated 16 December 2022 15:10:43 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (78 kB) Download tab-separated text file (5.1 MB) Download Microsoft Excel workbook (1.3 MB) Download Apache Feather file (1.2 MB) Download Apache Parquet file (0.2 MB) Download SAS data file (9.8 MB) Download IBM SPSS Statistics data file (7.4 MB) Download Stata DTA file (9.5 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-4","dir":"Articles","previous_headings":"intrinsic_resistant: Intrinsic Bacterial Resistance","what":"Source","title":"Data sets for download / own use","text":"data set contains defined intrinsic resistance EUCAST bug-drug combinations, based ‘EUCAST Expert Rules’ ‘EUCAST Intrinsic Resistance Unusual Phenotypes’ v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content-4","dir":"Articles","previous_headings":"intrinsic_resistant: Intrinsic Bacterial Resistance","what":"Example content","title":"Data sets for download / own use","text":"Example rows filtering Enterobacter cloacae:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"dosage-dosage-guidelines-from-eucast","dir":"Articles","previous_headings":"","what":"dosage: Dosage Guidelines from EUCAST","title":"Data sets for download / own use","text":"data set 336 rows 9 columns, containing following column names:ab, name, type, dose, dose_times, administration, notes, original_txt eucast_version. data set R available dosage, load AMR package. last updated 14 November 2022 14:20:39 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (3 kB) Download tab-separated text file (29 kB) Download Microsoft Excel workbook (19 kB) Download Apache Feather file (16 kB) Download Apache Parquet file (8 kB) Download SAS data file (92 kB) Download IBM SPSS Statistics data file (43 kB) Download Stata DTA file (82 kB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-5","dir":"Articles","previous_headings":"dosage: Dosage Guidelines from EUCAST","what":"Source","title":"Data sets for download / own use","text":"EUCAST breakpoints used package based dosages data set. Currently included dosages data set meant : ‘EUCAST Clinical Breakpoint Tables’ v11.0 (2021) ‘EUCAST Clinical Breakpoint Tables’ v12.0 (2022).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example_isolates-example-data-for-practice","dir":"Articles","previous_headings":"","what":"example_isolates: Example Data for Practice","title":"Data sets for download / own use","text":"data set 2,000 rows 46 columns, containing following column names:date, patient, age, gender, ward, mo, PEN, OXA, FLC, AMX, AMC, AMP, TZP, CZO, FEP, CXM, FOX, CTX, CAZ, CRO, GEN, TOB, AMK, KAN, TMP, SXT, NIT, FOS, LNZ, CIP, MFX, VAN, TEC, TCY, TGC, DOX, ERY, CLI, AZM, IPM, MEM, MTR, CHL, COL, MUP RIF. data set R available example_isolates, load AMR package. last updated 21 January 2023 22:47:20 UTC. Find info structure data set .","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-6","dir":"Articles","previous_headings":"example_isolates: Example Data for Practice","what":"Source","title":"Data sets for download / own use","text":"data set contains randomised fictitious data, reflects reality can used practise AMR data analysis.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example_isolates_unclean-example-data-for-practice","dir":"Articles","previous_headings":"","what":"example_isolates_unclean: Example Data for Practice","title":"Data sets for download / own use","text":"data set 3,000 rows 8 columns, containing following column names:patient_id, hospital, date, bacteria, AMX, AMC, CIP GEN. data set R available example_isolates_unclean, load AMR package. last updated 27 August 2022 18:49:37 UTC. Find info structure data set .","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-7","dir":"Articles","previous_headings":"example_isolates_unclean: Example Data for Practice","what":"Source","title":"Data sets for download / own use","text":"data set contains randomised fictitious data, reflects reality can used practise AMR data analysis.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"needed-r-packages","dir":"Articles","previous_headings":"","what":"Needed R packages","title":"How to predict antimicrobial resistance","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. AMR package depends packages even extends use functions.","code":"library(dplyr) library(ggplot2) library(AMR)  # (if not yet installed, install with:) # install.packages(c(\"tidyverse\", \"AMR\"))"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"prediction-analysis","dir":"Articles","previous_headings":"","what":"Prediction analysis","title":"How to predict antimicrobial resistance","text":"package contains function resistance_predict(), takes input functions AMR data analysis. Based date column, calculates cases per year uses regression model predict antimicrobial resistance. basically easy : function look date column col_date set. running commands, summary regression model printed unless using resistance_predict(..., info = FALSE). text printed summary - actual result (output) function data.frame containing year: number observations, actual observed resistance, estimated resistance standard error estimation: function plot available base R, can extended packages depend output based type input. extended function cope resistance predictions:  fastest way plot result. automatically adds right axes, error bars, titles, number available observations type model. also support ggplot2 package custom function ggplot_sir_predict() create appealing plots:","code":"# resistance prediction of piperacillin/tazobactam (TZP): resistance_predict(tbl = example_isolates, col_date = \"date\", col_ab = \"TZP\", model = \"binomial\")  # or: example_isolates %>%   resistance_predict(     col_ab = \"TZP\",     model = \"binomial\"   )  # to bind it to object 'predict_TZP' for example: predict_TZP <- example_isolates %>%   resistance_predict(     col_ab = \"TZP\",     model = \"binomial\"   ) predict_TZP # # A tibble: 32 × 7 #     year  value se_min se_max observations observed estimated #  * <dbl>  <dbl>  <dbl>  <dbl>        <int>    <dbl>     <dbl> #  1  2002 0.2        NA     NA           15   0.2       0.0562 #  2  2003 0.0625     NA     NA           32   0.0625    0.0616 #  3  2004 0.0854     NA     NA           82   0.0854    0.0676 #  4  2005 0.05       NA     NA           60   0.05      0.0741 #  5  2006 0.0508     NA     NA           59   0.0508    0.0812 #  6  2007 0.121      NA     NA           66   0.121     0.0889 #  7  2008 0.0417     NA     NA           72   0.0417    0.0972 #  8  2009 0.0164     NA     NA           61   0.0164    0.106  #  9  2010 0.0566     NA     NA           53   0.0566    0.116  # 10  2011 0.183      NA     NA           93   0.183     0.127  # # … with 22 more rows plot(predict_TZP) ggplot_sir_predict(predict_TZP) # choose for error bars instead of a ribbon ggplot_sir_predict(predict_TZP, ribbon = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"choosing-the-right-model","dir":"Articles","previous_headings":"Prediction analysis","what":"Choosing the right model","title":"How to predict antimicrobial resistance","text":"Resistance easily predicted; look vancomycin resistance Gram-positive bacteria, spread (.e. standard error) enormous:  Vancomycin resistance 100% ten years, might remain low. can define model model parameter. model chosen generalised linear regression model using binomial distribution, assuming period zero resistance followed period increasing resistance leading slowly resistance. Valid values : vancomycin resistance Gram-positive bacteria, linear model might appropriate:  model also available object, attribute:","code":"example_isolates %>%   filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>%   resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"binomial\") %>%   ggplot_sir_predict() example_isolates %>%   filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>%   resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"linear\") %>%   ggplot_sir_predict() model <- attributes(predict_TZP)$model  summary(model)$family #  # Family: binomial  # Link function: logit  summary(model)$coefficients #                  Estimate  Std. Error   z value     Pr(>|z|) # (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05 # year           0.09883005  0.02295317  4.305725 1.664395e-05"},{"path":"https://msberends.github.io/AMR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Matthijs S. Berends. Author, maintainer. Christian F. Luz. Author, contributor. Dennis Souverein. Author, contributor. Erwin E. . Hassing. Author, contributor. Casper J. Albers. Thesis advisor. Peter Dutey-Magni. Contributor. Judith M. Fonville. Contributor. Alex W. Friedrich. Thesis advisor. Corinna Glasner. Thesis advisor. Eric H. L. C. M. Hazenberg. Contributor. Gwen Knight. Contributor. Annick Lenglet. Contributor. Bart C. Meijer. Contributor. Dmytro Mykhailenko. Contributor. Anton Mymrikov. Contributor. Andrew P. Norgan. Contributor. Sofia Ny. Contributor. Jonas Salm. Contributor. Rogier P. Schade. Contributor. Bhanu N. M. Sinha. Thesis advisor. Anthony Underwood. Contributor.","code":""},{"path":"https://msberends.github.io/AMR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). “AMR: R Package Working Antimicrobial Resistance Data.” Journal Statistical Software, 104(3), 1–31. doi:10.18637/jss.v104.i03.","code":"@Article{,   title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},   author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},   journal = {Journal of Statistical Software},   year = {2022},   volume = {104},   number = {3},   pages = {1--31},   doi = {10.18637/jss.v104.i03}, }"},{"path":"https://msberends.github.io/AMR/index.html","id":"the-amr-package-for-r-","dir":"","previous_headings":"","what":"Antimicrobial Resistance Data Analysis","title":"Antimicrobial Resistance Data Analysis","text":"Provides full microbiological taxonomy data antimicrobial drugs Applies recent CLSI EUCAST clinical breakpoints MICs disk zones Corrects duplicate isolates, calculates predicts AMR per antibiotic class Integrates WHONET, ATC, EARS-Net, PubChem, LOINC SNOMED CT Works Windows, macOS Linux versions R since R-3.0 completely dependency-free, highly suitable places limited resources https://msberends.github.io/AMR https://doi.org/10.18637/jss.v104.i03","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"Antimicrobial Resistance Data Analysis","text":"AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. Many different researchers around globe continually helping us make successful durable project! work published Journal Statistical Software (Volume 104(3); DOI 10.18637/jss.v104.i03) formed basis two PhD theses (DOI 10.33612/diss.177417131 DOI 10.33612/diss.192486375). installing package, R knows ~52,000 distinct microbial species (updated December 2022) ~600 antibiotic, antimycotic antiviral drugs name code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC SNOMED CT), knows valid SIR MIC values. integral breakpoint guidelines CLSI EUCAST included last 10 years. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"used-in-175-countries-translated-to-16-languages","dir":"","previous_headings":"Introduction","what":"Used in 175 countries, translated to 16 languages","title":"Antimicrobial Resistance Data Analysis","text":"Since first public release early 2018, R package used almost countries world. Click map enlarge see country names. help contributors corners world, AMR package available  English,  Chinese,  Danish,  Dutch,  French,  German,  Greek,  Italian,  Japanese,  Polish,  Portuguese,  Russian,  Spanish,  Swedish,  Turkish,  Ukrainian. Antimicrobial drug (group) names colloquial microorganism names provided languages.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"filtering-and-selecting-data","dir":"","previous_headings":"Practical examples","what":"Filtering and selecting data","title":"Antimicrobial Resistance Data Analysis","text":"defined row filter Gram-negative bacteria intrinsic resistance cefotaxime (mo_is_gram_negative() mo_is_intrinsic_resistant()) column selection two antibiotic groups (aminoglycosides() carbapenems()), reference data microorganisms antibiotics AMR package make sure get meant: base R equivalent : base R snippet work version R since April 2013 (R-3.0).","code":"# AMR works great with dplyr, but it's not required or neccesary library(AMR) library(dplyr)  example_isolates %>%   mutate(bacteria = mo_fullname()) %>%   filter(mo_is_gram_negative(),          mo_is_intrinsic_resistant(ab = \"cefotax\")) %>%   select(bacteria,          aminoglycosides(),          carbapenems()) example_isolates$bacteria <- mo_fullname(example_isolates$mo) example_isolates[which(mo_is_gram_negative() &                          mo_is_intrinsic_resistant(ab = \"cefotax\")),                  c(\"bacteria\", aminoglycosides(), carbapenems())]"},{"path":"https://msberends.github.io/AMR/index.html","id":"calculating-resistance-per-group","dir":"","previous_headings":"Practical examples","what":"Calculating resistance per group","title":"Antimicrobial Resistance Data Analysis","text":"","code":"library(AMR) library(dplyr)  out <- example_isolates %>%   # group by ward:   group_by(ward) %>%   # calculate AMR using resistance(), over all aminoglycosides   # and polymyxins:   summarise(across(c(aminoglycosides(), polymyxins()),             resistance)) out # transform the antibiotic columns to names: out %>% set_ab_names() # transform the antibiotic column to ATC codes: out %>% set_ab_names(property = \"atc\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"what-else-can-you-do-with-this-package","dir":"","previous_headings":"","what":"What else can you do with this package?","title":"Antimicrobial Resistance Data Analysis","text":"package intended comprehensive toolbox integrated AMR data analysis. package can used : Reference taxonomy microorganisms, since package contains microbial (sub)species List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF) (manual) Interpreting raw MIC disk diffusion values, based CLSI EUCAST guideline last 10 years (manual) Retrieving antimicrobial drug names, doses forms administration clinical health care records (manual) Determining first isolates used AMR data analysis (manual) Calculating antimicrobial resistance (tutorial) Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) (tutorial) Calculating (empirical) susceptibility mono therapy combination therapies (tutorial) Predicting future antimicrobial resistance using regression models (tutorial) Getting properties microorganism (like Gram stain, species, genus family) (manual) Getting properties antibiotic (like name, code EARS-Net/ATC/LOINC/PubChem, defined daily dose trade name) (manual) Plotting antimicrobial resistance (tutorial) Applying EUCAST expert rules (manual) Getting SNOMED codes microorganism, getting properties microorganism based SNOMED code (manual) Getting LOINC codes antibiotic, getting properties antibiotic based LOINC code (manual) Machine reading EUCAST CLSI guidelines 2011-2021 translate MIC values disk diffusion diameters SIR (link) Principal component analysis AMR (tutorial)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-official-version","dir":"","previous_headings":"Get this package","what":"Latest official version","title":"Antimicrobial Resistance Data Analysis","text":"package available official R network (CRAN). Install package R CRAN using command: downloaded installed automatically. RStudio, click menu Tools > Install Packages… type “AMR” press Install. Note: functions website may available latest release. use functions data sets mentioned website, install latest development version.","code":"install.packages(\"AMR\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-development-version","dir":"","previous_headings":"Get this package","what":"Latest development version","title":"Antimicrobial Resistance Data Analysis","text":"Please read Developer Guideline . latest unpublished development version can installed GitHub two ways: Manually, using: Automatically, using rOpenSci R-universe platform, adding R-universe address list repositories (‘repos’): , can install update AMR package like official release (e.g., using install.packages(\"AMR\") RStudio via Tools > Check Package Updates…).","code":"install.packages(\"remotes\") # if you haven't already remotes::install_github(\"msberends/AMR\") options(repos = c(getOption(\"repos\"),                   msberends = \"https://msberends.r-universe.dev\"))"},{"path":"https://msberends.github.io/AMR/index.html","id":"get-started","dir":"","previous_headings":"","what":"Get started","title":"Antimicrobial Resistance Data Analysis","text":"find conduct AMR data analysis, please continue reading get started click link ‘’ menu.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"partners","dir":"","previous_headings":"","what":"Partners","title":"Antimicrobial Resistance Data Analysis","text":"development package part , related , made possible following non-profit organisations initiatives:","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"copyright","dir":"","previous_headings":"","what":"Copyright","title":"Antimicrobial Resistance Data Analysis","text":"R package free, open-source software licensed GNU General Public License v2.0 (GPL-2). nutshell, means package: May used commercial purposes May used private purposes May used patent purposes May modified, although: Modifications must released license distributing package Changes made code must documented May distributed, although: Source code must made available package distributed copy license copyright notice must included package. Comes LIMITATION liability Comes warranty","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions — AMR-deprecated","title":"Deprecated Functions — AMR-deprecated","text":"functions -called 'Deprecated'. removed future release. Using functions give warning name function replaced (one).","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated Functions — AMR-deprecated","text":"","code":"NA_rsi_  as.rsi(x, ...)  facet_rsi(...)  geom_rsi(...)  ggplot_rsi(...)  ggplot_rsi_predict(...)  is.rsi(x, ...)  is.rsi.eligible(...)  labels_rsi_count(...)  n_rsi(...)  random_rsi(...)  rsi_df(...)  rsi_predict(...)  scale_rsi_colours(...)  theme_rsi(...)"},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Deprecated Functions — AMR-deprecated","text":"object class rsi (inherits ordered, factor) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":null,"dir":"Reference","previous_headings":"","what":"Options for the AMR package — AMR-options","title":"Options for the AMR package — AMR-options","text":"overview package-specific options() can set AMR package.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"options","dir":"Reference","previous_headings":"","what":"Options","title":"Options for the AMR package — AMR-options","text":"AMR_custom_ab  Allows use custom antimicrobial drugs package. explained add_custom_antimicrobials(). AMR_custom_mo  Allows use custom microorganisms package. explained add_custom_microorganisms(). AMR_eucastrules  Used setting default types rules eucast_rules() function, must one : \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). AMR_guideline  Used setting default guideline interpreting MIC values disk diffusion diameters .sir(). Can guideline name (e.g., \"CLSI\") name year (e.g. \"CLSI 2019\"). default \"EUCAST 2022\". Supported guideline currently EUCAST (2013-2022) CLSI (2013-2022). AMR_ignore_pattern  regular expression define input must ignored .mo() mo_* functions. AMR_include_PKPD  logical use .sir(), indicate PK/PD clinical breakpoints must applied last resort, defaults TRUE. AMR_keep_synonyms  logical use .mo() mo_* functions, indicate old, previously valid taxonomic names must preserved corrected currently accepted names. AMR_locale  language use AMR package, can one supported language names ISO-639-1 codes: English (en), Chinese (zh), Danish (da), Dutch (nl), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Polish (pl), Portuguese (pt), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr) Ukrainian (uk). AMR_mo_source  file location manual code list used .mo() mo_* functions. explained set_mo_source().","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"saving-settings-between-sessions","dir":"Reference","previous_headings":"","what":"Saving Settings Between Sessions","title":"Options for the AMR package — AMR-options","text":"Settings R saved globally thus lost R exited. can save options .Rprofile file, user-specific file. can edit using:   file, can set options options(AMR_locale = \"pt\") Portuguese language support antibiotics.","code":"utils::file.edit(\"~/.Rprofile\")"},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"share-options-within-team","dir":"Reference","previous_headings":"","what":"Share Options Within Team","title":"Options for the AMR package — AMR-options","text":"global approach, e.g. within data team, save options file remote file location, shared network drive. work way: Save plain text file e.g. \"X:/team_folder/R_options.R\" fill preferred settings. user, open .Rprofile file using utils::file.edit(\"~/.Rprofile\") put :   Reload R/RStudio check settings getOption(), e.g. getOption(\"AMR_locale\") set value. Now team settings configured one place, can maintained .","code":"source(\"X:/team_folder/R_options.R\")"},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":null,"dir":"Reference","previous_headings":"","what":"The AMR Package — AMR","title":"The AMR Package — AMR","text":"Welcome AMR package. AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. Many different researchers around globe continually helping us make successful durable project! work published Journal Statistical Software (Volume 104(3); DOI 10.18637/jss.v104.i03) formed basis two PhD theses (DOI 10.33612/diss.177417131 DOI 10.33612/diss.192486375). installing package, R knows ~52,000 (updated December 2022) ~600 antibiotic, antimycotic antiviral drugs name code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC SNOMED CT), knows valid SIR MIC values. integral breakpoint guidelines CLSI EUCAST included last 10 years. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen. AMR package available English, Chinese, Danish, Dutch, French, German, Greek, Italian, Japanese, Polish, Portuguese, Russian, Spanish, Swedish, Turkish Ukrainian. Antimicrobial drug (group) names colloquial microorganism names provided languages.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"The AMR Package — AMR","text":"cite AMR publications use: Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). \"AMR: R Package Working Antimicrobial Resistance Data.\" Journal Statistical Software, 104(3), 1-31. doi:10.18637/jss.v104.i03 . BibTeX entry LaTeX users :","code":"@Article{,   title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},   author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},   journal = {Journal of Statistical Software},   year = {2022},   volume = {104},   number = {3},   pages = {1--31},   doi = {10.18637/jss.v104.i03}, }"},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"The AMR Package — AMR","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The AMR Package — AMR","text":"Maintainer: Matthijs S. Berends m.berends@certe.nl (ORCID) Authors: Christian F. Luz (ORCID) [contributor] Dennis Souverein (ORCID) [contributor] Erwin E. . Hassing [contributor] contributors: Casper J. Albers (ORCID) [thesis advisor] Peter Dutey-Magni (ORCID) [contributor] Judith M. Fonville [contributor] Alex W. Friedrich (ORCID) [thesis advisor] Corinna Glasner (ORCID) [thesis advisor] Eric H. L. C. M. Hazenberg [contributor] Gwen Knight (ORCID) [contributor] Annick Lenglet (ORCID) [contributor] Bart C. Meijer [contributor] Dmytro Mykhailenko [contributor] Anton Mymrikov [contributor] Andrew P. Norgan (ORCID) [contributor] Sofia Ny (ORCID) [contributor] Jonas Salm [contributor] Rogier P. Schade [contributor] Bhanu N. M. Sinha (ORCID) [thesis advisor] Anthony Underwood (ORCID) [contributor]","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":null,"dir":"Reference","previous_headings":"","what":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"antimicrobial drugs official names, ATC codes, ATC groups defined daily dose (DDD) included package, using Collaborating Centre Drug Statistics Methodology.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"","code":"as.ab(\"meropenem\") #> Class 'ab' #> [1] MEM ab_name(\"J01DH02\") #> [1] \"Meropenem\"  ab_tradenames(\"flucloxacillin\") #>  [1] \"culpen\"               \"floxacillin\"          \"floxacillin sodium\"   #>  [4] \"floxapen\"             \"floxapen sodium salt\" \"fluclox\"              #>  [7] \"flucloxacilina\"       \"flucloxacillin\"       \"flucloxacilline\"      #> [10] \"flucloxacillinum\"     \"fluorochloroxacillin\" \"staphylex\""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 500 Isolates - WHONET Example — WHONET","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"example data set exact structure export file WHONET. files can used package, example data set shows. antibiotic results example_isolates data set. patient names created using online surname generators place practice purposes.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET"},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"tibble 500 observations 53 variables: Identification number ID sample Specimen number ID specimen Organism Name microorganism. analysis, transform valid microbial class, using .mo(). Country Country origin Laboratory Name laboratory Last name Fictitious last name patient First name Fictitious initial patient Sex Fictitious gender patient Age Fictitious age patient Age category Age group, can also looked using age_groups() Date admissionDate hospital admission Specimen dateDate specimen received laboratory Specimen type Specimen type group Specimen type (Numeric) Translation \"Specimen type\" Reason Reason request Differential Diagnosis Isolate number ID isolate Organism type Type microorganism, can also looked using mo_type() Serotype Serotype microorganism Beta-lactamase Microorganism produces beta-lactamase? ESBL Microorganism produces extended spectrum beta-lactamase? Carbapenemase Microorganism produces carbapenemase? MRSA screening test Microorganism possible MRSA? Inducible clindamycin resistance Clindamycin can induced? Comment comments Date data entryDate data entered WHONET AMP_ND10:CIP_EE 0 different antibiotics. can lookup abbreviations antibiotics data set, use e.g. ab_name(\"AMP\") get official name immediately. analysis, transform valid antibiotic class, using .sir().","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> Warning: * Transform your old 'rsi' class to the new 'sir' class with as.sir() #> using e.g.: #>   your_data %>% mutate_if(is.rsi, as.sir) #> # A tibble: 500 × 53 #>    Identif…¹ Speci…² Organ…³ Country Labor…⁴ Last …⁵ First…⁶ Sex     Age Age c…⁷ #>    <chr>       <int> <chr>   <chr>   <chr>   <chr>   <chr>   <chr> <dbl> <chr>   #>  1 fe41d7ba…    1748 SPN     Belgium Nation… Abel    B.      F        68 55-74   #>  2 91f175ec…    1767 eco     The Ne… Nation… Delacr… F.      M        89 75+     #>  3 cc401505…    1343 eco     The Ne… Nation… Steens… F.      M        85 75+     #>  4 e864b692…    1894 MAP     Denmark Nation… Beyers… L.      M        62 55-74   #>  5 3d051fe3…    1739 PVU     Belgium Nation… Hummel  W.      M        86 75+     #>  6 c80762a0…    1846 103     The Ne… Nation… Eikenb… J.      F        53 25-54   #>  7 8022d372…    1628 103     Denmark Nation… Leclerc S.      F        77 75+     #>  8 f3dc5f55…    1493 eco     The Ne… Nation… Delacr… W.      M        53 25-54   #>  9 15add38f…    1847 eco     France  Nation… Van La… S.      F        63 55-74   #> 10 fd41248d…    1458 eco     Germany Nation… Moulin  O.      F        75 75+     #> # … with 490 more rows, 43 more variables: `Date of admission` <date>, #> #   `Specimen date` <date>, `Specimen type` <chr>, #> #   `Specimen type (Numeric)` <dbl>, Reason <chr>, `Isolate number` <int>, #> #   `Organism type` <chr>, Serotype <chr>, `Beta-lactamase` <lgl>, ESBL <lgl>, #> #   Carbapenemase <lgl>, `MRSA screening test` <lgl>, #> #   `Inducible clindamycin resistance` <lgl>, Comment <chr>, #> #   `Date of data entry` <date>, AMP_ND10 <rsi[!]>, AMC_ED20 <rsi[!]>, …"},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"Use function e.g. clinical texts health care records. returns list antimicrobial drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"","code":"ab_from_text(   text,   type = c(\"drug\", \"dose\", \"administration\"),   collapse = NULL,   translate_ab = FALSE,   thorough_search = NULL,   info = interactive(),   ... )"},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_ab type = \"drug\": column name antibiotics data set translate antibiotic abbreviations , using ab_property(). Defaults FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed .ab()","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"list,  character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"function also internally used .ab(), although searches first drug name throw note drug names returned. Note: .ab() function may use long regular expression match brand names antimicrobial drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"default, function search antimicrobial drug names. text elements searched official names, ATC codes brand names. uses .ab() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(abx = ab_from_text(clinical_text)) returned AB codes can transformed official names, groups, etc. ab_* functions ab_name() ab_group(), using translate_ab argument. using collapse, function return character:df %>% mutate(abx = ab_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"","code":"# mind the bad spelling of amoxicillin in this line, # straight from a true health care record: ab_from_text(\"28/03/2020 regular amoxicilliin 500mg po tid\") #> [[1]] #> Class 'ab' #> [1] AMX #>   ab_from_text(\"500 mg amoxi po and 400mg cipro iv\") #> [[1]] #> Class 'ab' #> [1] AMX CIP #>  ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", type = \"dose\") #> [[1]] #> [1] 500 400 #>  ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", type = \"admin\") #> [[1]] #> [1] \"oral\" \"iv\"   #>   ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", collapse = \", \") #> [1] \"AMX, CIP\" # \\donttest{ # if you want to know which antibiotic groups were administered, do e.g.: abx <- ab_from_text(\"500 mg amoxi po and 400mg cipro iv\") ab_group(abx[[1]]) #> [1] \"Beta-lactams/penicillins\" \"Quinolones\"                if (require(\"dplyr\")) {   tibble(clinical_text = c(     \"given 400mg cipro and 500 mg amox\",     \"started on doxy iv today\"   )) %>%     mutate(       abx_codes = ab_from_text(clinical_text),       abx_doses = ab_from_text(clinical_text, type = \"doses\"),       abx_admin = ab_from_text(clinical_text, type = \"admin\"),       abx_coll = ab_from_text(clinical_text, collapse = \"|\"),       abx_coll_names = ab_from_text(clinical_text,         collapse = \"|\",         translate_ab = \"name\"       ),       abx_coll_doses = ab_from_text(clinical_text,         type = \"doses\",         collapse = \"|\"       ),       abx_coll_admin = ab_from_text(clinical_text,         type = \"admin\",         collapse = \"|\"       )     ) } #> Loading required package: dplyr #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union #> # A tibble: 2 × 8 #>   clinical_text          abx_c…¹ abx_d…² abx_a…³ abx_c…⁴ abx_c…⁵ abx_c…⁶ abx_c…⁷ #>   <chr>                  <list>  <list>  <list>  <chr>   <chr>   <chr>   <chr>   #> 1 given 400mg cipro and… <ab>    <dbl>   <chr>   CIP|AMX Ciprof… 400|500 NA      #> 2 started on doxy iv to… <ab>    <dbl>   <chr>   DOX     Doxycy… NA      iv      #> # … with abbreviated variable names ¹​abx_codes, ²​abx_doses, ³​abx_admin, #> #   ⁴​abx_coll, ⁵​abx_coll_names, ⁶​abx_coll_doses, ⁷​abx_coll_admin # }"},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antibiotic — ab_property","title":"Get Properties of an Antibiotic — ab_property","text":"Use functions return specific property antibiotic antibiotics data set. input values evaluated internally .ab().","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antibiotic — ab_property","text":"","code":"ab_name(x, language = get_AMR_locale(), tolower = FALSE, ...)  ab_cid(x, ...)  ab_synonyms(x, ...)  ab_tradenames(x, ...)  ab_group(x, language = get_AMR_locale(), ...)  ab_atc(x, only_first = FALSE, ...)  ab_atc_group1(x, language = get_AMR_locale(), ...)  ab_atc_group2(x, language = get_AMR_locale(), ...)  ab_loinc(x, ...)  ab_ddd(x, administration = \"oral\", ...)  ab_ddd_units(x, administration = \"oral\", ...)  ab_info(x, language = get_AMR_locale(), ...)  ab_url(x, open = FALSE, ...)  ab_property(x, property = \"name\", language = get_AMR_locale(), ...)  set_ab_names(   data,   ...,   property = \"name\",   language = get_AMR_locale(),   snake_case = NULL )"},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antibiotic — ab_property","text":"x (vector ) text can coerced valid antibiotic drug code .ab() language language returned text, defaults system language (see get_AMR_locale()) can also set option AMR_locale. Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. lead e.g. \"polymyxin B\" \"polymyxin b\". ... case set_ab_names() data data.frame: columns select (supports tidy selection column1:column4), otherwise arguments passed .ab() only_first logical indicate whether first ATC code must returned, giving preference J0-codes (.e., antimicrobial drug group) administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antibiotics data set: vector_or(colnames(antibiotics), sort = FALSE). data data.frame columns need renamed, character vector column names snake_case logical indicate whether names -called snake case: lower case spaces/slashes replaced underscore (_)","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antibiotic — ab_property","text":"integer case ab_cid() named list case ab_info() multiple ab_atc()/ab_synonyms()/ab_tradenames() double case ab_ddd() data.frame case set_ab_names() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antibiotic — ab_property","text":"output translated possible. function ab_url() return direct URL official website. warning returned required ATC code available. function set_ab_names() special column renaming function data.frames. renames columns names resemble antimicrobial drugs. always makes sure new column names unique. property = \"atc\" set, preference given ATC codes J-group.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antibiotic — ab_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antibiotic — ab_property","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antibiotic — ab_property","text":"","code":"# all properties: ab_name(\"AMX\") #> [1] \"Amoxicillin\" ab_atc(\"AMX\") #> [1] \"J01CA04\" ab_cid(\"AMX\") #> [1] 33613 ab_synonyms(\"AMX\") #>  [1] \"actimoxi\"            \"amoclen\"             \"amolin\"              #>  [4] \"amopen\"              \"amopenixin\"          \"amoxibiotic\"         #>  [7] \"amoxicaps\"           \"amoxicilina\"         \"amoxicillin\"         #> [10] \"amoxicillin hydrate\" \"amoxicilline\"        \"amoxicillinum\"       #> [13] \"amoxiden\"            \"amoxil\"              \"amoxivet\"            #> [16] \"amoxy\"               \"amoxycillin\"         \"amoxyke\"             #> [19] \"anemolin\"            \"aspenil\"             \"atoksilin\"           #> [22] \"biomox\"              \"bristamox\"           \"cemoxin\"             #> [25] \"clamoxyl\"            \"damoxy\"              \"delacillin\"          #> [28] \"demoksil\"            \"dispermox\"           \"efpenix\"             #> [31] \"flemoxin\"            \"hiconcil\"            \"histocillin\"         #> [34] \"hydroxyampicillin\"   \"ibiamox\"             \"imacillin\"           #> [37] \"lamoxy\"              \"largopen\"            \"metafarma capsules\"  #> [40] \"metifarma capsules\"  \"moksilin\"            \"moxacin\"             #> [43] \"moxatag\"             \"ospamox\"             \"pamoxicillin\"        #> [46] \"piramox\"             \"promoxil\"            \"remoxil\"             #> [49] \"robamox\"             \"sawamox pm\"          \"tolodina\"            #> [52] \"topramoxin\"          \"unicillin\"           \"utimox\"              #> [55] \"vetramox\"            ab_tradenames(\"AMX\") #>  [1] \"actimoxi\"            \"amoclen\"             \"amolin\"              #>  [4] \"amopen\"              \"amopenixin\"          \"amoxibiotic\"         #>  [7] \"amoxicaps\"           \"amoxicilina\"         \"amoxicillin\"         #> [10] \"amoxicillin hydrate\" \"amoxicilline\"        \"amoxicillinum\"       #> [13] \"amoxiden\"            \"amoxil\"              \"amoxivet\"            #> [16] \"amoxy\"               \"amoxycillin\"         \"amoxyke\"             #> [19] \"anemolin\"            \"aspenil\"             \"atoksilin\"           #> [22] \"biomox\"              \"bristamox\"           \"cemoxin\"             #> [25] \"clamoxyl\"            \"damoxy\"              \"delacillin\"          #> [28] \"demoksil\"            \"dispermox\"           \"efpenix\"             #> [31] \"flemoxin\"            \"hiconcil\"            \"histocillin\"         #> [34] \"hydroxyampicillin\"   \"ibiamox\"             \"imacillin\"           #> [37] \"lamoxy\"              \"largopen\"            \"metafarma capsules\"  #> [40] \"metifarma capsules\"  \"moksilin\"            \"moxacin\"             #> [43] \"moxatag\"             \"ospamox\"             \"pamoxicillin\"        #> [46] \"piramox\"             \"promoxil\"            \"remoxil\"             #> [49] \"robamox\"             \"sawamox pm\"          \"tolodina\"            #> [52] \"topramoxin\"          \"unicillin\"           \"utimox\"              #> [55] \"vetramox\"            ab_group(\"AMX\") #> [1] \"Beta-lactams/penicillins\" ab_atc_group1(\"AMX\") #> [1] \"Beta-lactam antibacterials, penicillins\" ab_atc_group2(\"AMX\") #> [1] \"Penicillins with extended spectrum\" ab_url(\"AMX\") #>                                                           Amoxicillin  #> \"https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no\"   # smart lowercase tranformation ab_name(x = c(\"AMC\", \"PLB\")) #> [1] \"Amoxicillin/clavulanic acid\" \"Polymyxin B\"                 ab_name(x = c(\"AMC\", \"PLB\"), tolower = TRUE) #> [1] \"amoxicillin/clavulanic acid\" \"polymyxin B\"                  # defined daily doses (DDD) ab_ddd(\"AMX\", \"oral\") #> [1] 1.5 ab_ddd_units(\"AMX\", \"oral\") #> [1] \"g\" ab_ddd(\"AMX\", \"iv\") #> [1] 3 ab_ddd_units(\"AMX\", \"iv\") #> [1] \"g\"  ab_info(\"AMX\") # all properties as a list #> $ab #> [1] \"AMX\" #>  #> $cid #> [1] 33613 #>  #> $name #> [1] \"Amoxicillin\" #>  #> $group #> [1] \"Beta-lactams/penicillins\" #>  #> $atc #> [1] \"J01CA04\" #>  #> $atc_group1 #> [1] \"Beta-lactam antibacterials, penicillins\" #>  #> $atc_group2 #> [1] \"Penicillins with extended spectrum\" #>  #> $tradenames #>  [1] \"actimoxi\"            \"amoclen\"             \"amolin\"              #>  [4] \"amopen\"              \"amopenixin\"          \"amoxibiotic\"         #>  [7] \"amoxicaps\"           \"amoxicilina\"         \"amoxicillin\"         #> [10] \"amoxicillin hydrate\" \"amoxicilline\"        \"amoxicillinum\"       #> [13] \"amoxiden\"            \"amoxil\"              \"amoxivet\"            #> [16] \"amoxy\"               \"amoxycillin\"         \"amoxyke\"             #> [19] \"anemolin\"            \"aspenil\"             \"atoksilin\"           #> [22] \"biomox\"              \"bristamox\"           \"cemoxin\"             #> [25] \"clamoxyl\"            \"damoxy\"              \"delacillin\"          #> [28] \"demoksil\"            \"dispermox\"           \"efpenix\"             #> [31] \"flemoxin\"            \"hiconcil\"            \"histocillin\"         #> [34] \"hydroxyampicillin\"   \"ibiamox\"             \"imacillin\"           #> [37] \"lamoxy\"              \"largopen\"            \"metafarma capsules\"  #> [40] \"metifarma capsules\"  \"moksilin\"            \"moxacin\"             #> [43] \"moxatag\"             \"ospamox\"             \"pamoxicillin\"        #> [46] \"piramox\"             \"promoxil\"            \"remoxil\"             #> [49] \"robamox\"             \"sawamox pm\"          \"tolodina\"            #> [52] \"topramoxin\"          \"unicillin\"           \"utimox\"              #> [55] \"vetramox\"            #>  #> $loinc #> [1] \"16365-9\" \"25274-2\" \"3344-9\"  \"80133-2\" #>  #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 1.5 #>  #> $ddd$oral$units #> [1] \"g\" #>  #>  #> $ddd$iv #> $ddd$iv$amount #> [1] 3 #>  #> $ddd$iv$units #> [1] \"g\" #>  #>  #>   # all ab_* functions use as.ab() internally, so you can go from 'any' to 'any': ab_atc(\"AMP\") #> [1] \"J01CA01\" \"S01AA19\" ab_group(\"J01CA01\") #> [1] \"Beta-lactams/penicillins\" ab_loinc(\"ampicillin\") #> [1] \"21066-6\" \"3355-5\"  \"33562-0\" \"33919-2\" \"43883-8\" \"43884-6\" \"87604-5\" ab_name(\"21066-6\") #> [1] \"Ampicillin\" ab_name(6249) #> [1] \"Ampicillin\" ab_name(\"J01CA01\") #> [1] \"Ampicillin\"  # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\") #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\") #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") #> [1] \"J01DD04\"  # use set_ab_names() for renaming columns colnames(example_isolates) #>  [1] \"date\"    \"patient\" \"age\"     \"gender\"  \"ward\"    \"mo\"      \"PEN\"     #>  [8] \"OXA\"     \"FLC\"     \"AMX\"     \"AMC\"     \"AMP\"     \"TZP\"     \"CZO\"     #> [15] \"FEP\"     \"CXM\"     \"FOX\"     \"CTX\"     \"CAZ\"     \"CRO\"     \"GEN\"     #> [22] \"TOB\"     \"AMK\"     \"KAN\"     \"TMP\"     \"SXT\"     \"NIT\"     \"FOS\"     #> [29] \"LNZ\"     \"CIP\"     \"MFX\"     \"VAN\"     \"TEC\"     \"TCY\"     \"TGC\"     #> [36] \"DOX\"     \"ERY\"     \"CLI\"     \"AZM\"     \"IPM\"     \"MEM\"     \"MTR\"     #> [43] \"CHL\"     \"COL\"     \"MUP\"     \"RIF\"     colnames(set_ab_names(example_isolates)) #>  [1] \"date\"                          \"patient\"                       #>  [3] \"age\"                           \"gender\"                        #>  [5] \"ward\"                          \"mo\"                            #>  [7] \"benzylpenicillin\"              \"oxacillin\"                     #>  [9] \"flucloxacillin\"                \"amoxicillin\"                   #> [11] \"amoxicillin_clavulanic_acid\"   \"ampicillin\"                    #> [13] \"piperacillin_tazobactam\"       \"cefazolin\"                     #> [15] \"cefepime\"                      \"cefuroxime\"                    #> [17] \"cefoxitin\"                     \"cefotaxime\"                    #> [19] \"ceftazidime\"                   \"ceftriaxone\"                   #> [21] \"gentamicin\"                    \"tobramycin\"                    #> [23] \"amikacin\"                      \"kanamycin\"                     #> [25] \"trimethoprim\"                  \"trimethoprim_sulfamethoxazole\" #> [27] \"nitrofurantoin\"                \"fosfomycin\"                    #> [29] \"linezolid\"                     \"ciprofloxacin\"                 #> [31] \"moxifloxacin\"                  \"vancomycin\"                    #> [33] \"teicoplanin\"                   \"tetracycline\"                  #> [35] \"tigecycline\"                   \"doxycycline\"                   #> [37] \"erythromycin\"                  \"clindamycin\"                   #> [39] \"azithromycin\"                  \"imipenem\"                      #> [41] \"meropenem\"                     \"metronidazole\"                 #> [43] \"chloramphenicol\"               \"colistin\"                      #> [45] \"mupirocin\"                     \"rifampicin\"                    colnames(set_ab_names(example_isolates, NIT:VAN)) #>  [1] \"date\"           \"patient\"        \"age\"            \"gender\"         #>  [5] \"ward\"           \"mo\"             \"PEN\"            \"OXA\"            #>  [9] \"FLC\"            \"AMX\"            \"AMC\"            \"AMP\"            #> [13] \"TZP\"            \"CZO\"            \"FEP\"            \"CXM\"            #> [17] \"FOX\"            \"CTX\"            \"CAZ\"            \"CRO\"            #> [21] \"GEN\"            \"TOB\"            \"AMK\"            \"KAN\"            #> [25] \"TMP\"            \"SXT\"            \"nitrofurantoin\" \"fosfomycin\"     #> [29] \"linezolid\"      \"ciprofloxacin\"  \"moxifloxacin\"   \"vancomycin\"     #> [33] \"TEC\"            \"TCY\"            \"TGC\"            \"DOX\"            #> [37] \"ERY\"            \"CLI\"            \"AZM\"            \"IPM\"            #> [41] \"MEM\"            \"MTR\"            \"CHL\"            \"COL\"            #> [45] \"MUP\"            \"RIF\"            # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     set_ab_names() %>%     head()    # this does the same:   example_isolates %>%     rename_with(set_ab_names) %>%     head()    # set_ab_names() works with any AB property:   example_isolates %>%     set_ab_names(property = \"atc\") %>%     head()    example_isolates %>%     set_ab_names(where(is.sir)) %>%     colnames()    example_isolates %>%     set_ab_names(NIT:VAN) %>%     colnames() } #>  [1] \"date\"           \"patient\"        \"age\"            \"gender\"         #>  [5] \"ward\"           \"mo\"             \"PEN\"            \"OXA\"            #>  [9] \"FLC\"            \"AMX\"            \"AMC\"            \"AMP\"            #> [13] \"TZP\"            \"CZO\"            \"FEP\"            \"CXM\"            #> [17] \"FOX\"            \"CTX\"            \"CAZ\"            \"CRO\"            #> [21] \"GEN\"            \"TOB\"            \"AMK\"            \"KAN\"            #> [25] \"TMP\"            \"SXT\"            \"nitrofurantoin\" \"fosfomycin\"     #> [29] \"linezolid\"      \"ciprofloxacin\"  \"moxifloxacin\"   \"vancomycin\"     #> [33] \"TEC\"            \"TCY\"            \"TGC\"            \"DOX\"            #> [37] \"ERY\"            \"CLI\"            \"AZM\"            \"IPM\"            #> [41] \"MEM\"            \"MTR\"            \"CHL\"            \"COL\"            #> [45] \"MUP\"            \"RIF\"            # }"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Custom Antimicrobials — add_custom_antimicrobials","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"add_custom_antimicrobials() can add custom antimicrobial drug names codes.","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"","code":"add_custom_antimicrobials(x)  clear_custom_antimicrobials()"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"x data.frame resembling antibiotics data set, least containing columns \"ab\" \"name\"","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"Important: Due R works, add_custom_antimicrobials() function run every R session - added antimicrobials stored sessions thus lost R exited. two ways automate process: Method 1: Using option AMR_custom_ab, preferred method. use method: Create data set structure antibiotics data set (containing least columns \"ab\" \"name\") save saveRDS() location choice, e.g. \"~/my_custom_ab.rds\", remote location. Set file location option AMR_custom_ab: options(AMR_custom_ab = \"~/my_custom_ab.rds\"). can even remote file location, https URL. Since options saved R sessions, best save option .Rprofile file loaded start-R. , open .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"), add text save file:   Upon package load, file loaded run add_custom_antimicrobials() function. Method 2: Loading antimicrobial additions directly .Rprofile file. important downside requires AMR package installed else method fail. use method: Edit .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"). Add text like save file:   Use clear_custom_antimicrobials() clear previously added antimicrobials.","code":"# Add custom antimicrobial codes: options(AMR_custom_ab = \"~/my_custom_ab.rds\") # Add custom antibiotic drug codes:  AMR::add_custom_antimicrobials(    data.frame(ab = \"TESTAB\",               name = \"Test Antibiotic\",               group = \"Test Group\")  )"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"","code":"# \\donttest{  # returns NA and throws a warning (which is suppressed here): suppressWarnings(   as.ab(\"testab\") ) #> Class 'ab' #> [1] <NA>  # now add a custom entry - it will be considered by as.ab() and # all ab_*() functions add_custom_antimicrobials(   data.frame(     ab = \"TESTAB\",     name = \"Test Antibiotic\",     # you can add any property present in the     # 'antibiotics' data set, such as 'group':     group = \"Test Group\"   ) ) #> ℹ Added one record to the internal antibiotics data set.  # \"testab\" is now a new antibiotic: as.ab(\"testab\") #> Class 'ab' #> [1] TESTAB ab_name(\"testab\") #> [1] \"Test Antibiotic\" ab_group(\"testab\") #> [1] \"Test Group\"  ab_info(\"testab\") #> $ab #> [1] \"TESTAB\" #>  #> $cid #> [1] NA #>  #> $name #> [1] \"Test Antibiotic\" #>  #> $group #> [1] \"Test Group\" #>  #> $atc #> [1] NA #>  #> $atc_group1 #> [1] NA #>  #> $atc_group2 #> [1] NA #>  #> $tradenames #> [1] NA #>  #> $loinc #> [1] NA #>  #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] NA #>  #> $ddd$oral$units #> [1] NA #>  #>  #> $ddd$iv #> $ddd$iv$amount #> [1] NA #>  #> $ddd$iv$units #> [1] NA #>  #>  #>    # Add Co-fluampicil, which is one of the many J01CR50 codes, see # https://www.whocc.no/ddd/list_of_ddds_combined_products/ add_custom_antimicrobials(   data.frame(     ab = \"COFLU\",     name = \"Co-fluampicil\",     atc = \"J01CR50\",     group = \"Beta-lactams/penicillins\"   ) ) #> ℹ Added one record to the internal antibiotics data set. ab_atc(\"Co-fluampicil\") #> [1] \"J01CR50\" ab_name(\"J01CR50\") #> [1] \"Co-fluampicil\"  # even antibiotic selectors work x <- data.frame(   random_column = \"some value\",   coflu = as.sir(\"S\"),   ampicillin = as.sir(\"R\") ) x #>   random_column coflu ampicillin #> 1    some value     S          R x[, betalactams()] #> ℹ For betalactams() using columns 'coflu' (co-fluampicil) and #>   'ampicillin' #>   coflu ampicillin #> 1     S          R # }"},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Custom Microorganisms — add_custom_microorganisms","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"add_custom_microorganisms() can add custom microorganisms, non-taxonomic outcome laboratory analysis.","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"","code":"add_custom_microorganisms(x)  clear_custom_microorganisms()"},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"x data.frame resembling microorganisms data set, least containing column \"genus\" (case-insensitive)","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"function fill missing taxonomy , specific taxonomic columns missing, see Examples. Important: Due R works, add_custom_microorganisms() function run every R session - added microorganisms stored sessions thus lost R exited. two ways automate process: Method 1: Using option AMR_custom_mo, preferred method. use method: Create data set structure microorganisms data set (containing least column \"genus\") save saveRDS() location choice, e.g. \"~/my_custom_mo.rds\", remote location. Set file location option AMR_custom_mo: options(AMR_custom_mo = \"~/my_custom_mo.rds\"). can even remote file location, https URL. Since options saved R sessions, best save option .Rprofile file loaded start-R. , open .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"), add text save file:   Upon package load, file loaded run add_custom_microorganisms() function. Method 2: Loading microorganism directly .Rprofile file. important downside requires AMR package installed else method fail. use method: Edit .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"). Add text like save file:   Use clear_custom_microorganisms() clear previously added antimicrobials.","code":"# Add custom microorganism codes: options(AMR_custom_mo = \"~/my_custom_mo.rds\") # Add custom antibiotic drug codes:  AMR::add_custom_microorganisms(    data.frame(genus = \"Enterobacter\",               species = \"asburiae/cloacae\")  )"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"","code":"# \\donttest{ # a combination of species is not formal taxonomy, so # this will result in only \"Enterobacter asburiae\": mo_name(\"Enterobacter asburiae/cloacae\") #> [1] \"Enterobacter asburiae\"  # now add a custom entry - it will be considered by as.mo() and # all mo_*() functions add_custom_microorganisms(   data.frame(     genus = \"Enterobacter\",     species = \"asburiae/cloacae\"   ) ) #> ℹ Added Enterobacter asburiae/cloacae to the internal microorganisms data #>   set.  # E. asburiae/cloacae is now a new microorganism: mo_name(\"Enterobacter asburiae/cloacae\") #> [1] \"Enterobacter asburiae/cloacae\"  # its code: as.mo(\"Enterobacter asburiae/cloacae\") #> Class 'mo' #> [1] CUSTOM1_ENTRBC_A_C  # all internal algorithms will work as well: mo_name(\"Ent asburia cloacae\") #> [1] \"Enterobacter asburiae/cloacae\"  # and even the taxonomy was added based on the genus! mo_family(\"E. asburiae/cloacae\") #> [1] \"Enterobacteriaceae\" mo_gramstain(\"Enterobacter asburiae/cloacae\") #> [1] \"Gram-negative\"  mo_info(\"Enterobacter asburiae/cloacae\") #> $mo #> [1] \"CUSTOM1_ENTRBC_A_C\" #>  #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Enterobacter\" #>  #> $species #> [1] \"asburiae/cloacae\" #>  #> $subspecies #> [1] \"\" #>  #> $status #> [1] \"accepted\" #>  #> $synonyms #> NULL #>  #> $gramstain #> [1] \"Gram-negative\" #>  #> $url #> [1] \"\" #>  #> $ref #> [1] \"Self-added, 2023\" #>  #> $snomed #> character(0) #>    # the function tries to be forgiving: add_custom_microorganisms(   data.frame(     GENUS = \"BACTEROIDES / PARABACTEROIDES SLASHLINE\",     SPECIES = \"SPECIES\"   ) ) #> ℹ Added Bacteroides/Parabacteroides to the internal microorganisms data #>   set. mo_name(\"BACTEROIDES / PARABACTEROIDES\") #> [1] \"Bacteroides/Parabacteroides\" mo_rank(\"BACTEROIDES / PARABACTEROIDES\") #> [1] \"genus\"  # taxonomy still works, although a slashline genus was given as input: mo_family(\"Bacteroides/Parabacteroides\") #> [1] \"Bacteroidaceae\"   # for groups and complexes, set them as species or subspecies: add_custom_microorganisms(   data.frame(     genus = \"Citrobacter\",     species = c(\"freundii\", \"braakii complex\"),     subspecies = c(\"complex\", \"\")   ) ) #> ℹ Added Citrobacter braakii complex and Citrobacter freundii complex to the #>   internal microorganisms data set. mo_name(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"Citrobacter freundii complex\" \"Citrobacter braakii complex\"  mo_species(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"freundii complex\" \"braakii complex\"  mo_gramstain(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"Gram-negative\" \"Gram-negative\" # }"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":null,"dir":"Reference","previous_headings":"","what":"Age in Years of Individuals — age","title":"Age in Years of Individuals — age","text":"Calculates age years based reference date, system date default.","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Age in Years of Individuals — age","text":"","code":"age(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Age in Years of Individuals — age","text":"x date(s), character (vectors) coerced .POSIXlt() reference reference date(s) (defaults today), character (vectors) coerced .POSIXlt() exact logical indicate whether age calculation exact, .e. decimals. divides number days year--date (YTD) x number days year reference (either 365 366). na.rm logical indicate whether missing values removed ... arguments passed .POSIXlt(), origin","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Age in Years of Individuals — age","text":"integer (decimals) exact = FALSE, double (decimals) otherwise","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Age in Years of Individuals — age","text":"Ages 0 returned NA warning. Ages 120 give warning. function vectorises x reference, meaning either can length 1 argument larger length.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Age in Years of Individuals — age","text":"","code":"# 10 random pre-Y2K birth dates df <- data.frame(birth_date = as.Date(\"2000-01-01\") - runif(10) * 25000)  # add ages df$age <- age(df$birth_date)  # add exact ages df$age_exact <- age(df$birth_date, exact = TRUE)  # add age at millenium switch df$age_at_y2k <- age(df$birth_date, \"2000-01-01\")  df #>    birth_date age age_exact age_at_y2k #> 1  1954-05-23  68  68.67123         45 #> 2  1996-11-02  26  26.22466          3 #> 3  1991-01-29  31  31.98356          8 #> 4  1931-12-19  91  91.09589         68 #> 5  1946-04-30  76  76.73425         53 #> 6  1990-08-24  32  32.41644          9 #> 7  1982-08-27  40  40.40822         17 #> 8  1987-02-17  35  35.93151         12 #> 9  1948-11-15  74  74.18904         51 #> 10 1976-01-28  46  46.98630         23"},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Ages into Age Groups — age_groups","title":"Split Ages into Age Groups — age_groups","text":"Split ages age groups defined split argument. allows easier demographic (antimicrobial resistance) analysis.","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Ages into Age Groups — age_groups","text":"","code":"age_groups(x, split_at = c(12, 25, 55, 75), na.rm = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Ages into Age Groups — age_groups","text":"x age, e.g. calculated age() split_at values split x , defaults age groups 0-11, 12-24, 25-54, 55-74 75+. See Details. na.rm logical indicate whether missing values removed","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Ages into Age Groups — age_groups","text":"Ordered factor","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split Ages into Age Groups — age_groups","text":"split ages, input split_at argument can : numeric vector. value e.g. c(10, 20) split x 0-9, 10-19 20+. value 50 split x 0-49 50+. default split young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) elderly (75+). character: \"children\" \"kids\", equivalent : c(0, 1, 2, 4, 6, 13, 18). split 0, 1, 2-3, 4-5, 6-12, 13-17 18+. \"elderly\" \"seniors\", equivalent : c(65, 75, 85). split 0-64, 65-74, 75-84, 85+. \"fives\", equivalent : 1:20 * 5. split 0-4, 5-9, ..., 95-99, 100+. \"tens\", equivalent : 1:10 * 10. split 0-9, 10-19, ..., 90-99, 100+.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split Ages into Age Groups — age_groups","text":"","code":"ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21)  # split into 0-49 and 50+ age_groups(ages, 50) #> [1] 0-49 0-49 0-49 50+  0-49 50+  50+  0-49 0-49 #> Levels: 0-49 < 50+  # split into 0-19, 20-49 and 50+ age_groups(ages, c(20, 50)) #> [1] 0-19  0-19  0-19  50+   20-49 50+   50+   20-49 20-49 #> Levels: 0-19 < 20-49 < 50+  # split into groups of ten years age_groups(ages, 1:10 * 10) #> [1] 0-9   0-9   10-19 50-59 30-39 70-79 100+  40-49 20-29 #> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+ age_groups(ages, split_at = \"tens\") #> [1] 0-9   0-9   10-19 50-59 30-39 70-79 100+  40-49 20-29 #> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+  # split into groups of five years age_groups(ages, 1:20 * 5) #> [1] 0-4   5-9   15-19 50-54 30-34 75-79 100+  40-44 20-24 #> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+ age_groups(ages, split_at = \"fives\") #> [1] 0-4   5-9   15-19 50-54 30-34 75-79 100+  40-44 20-24 #> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+  # split specifically for children age_groups(ages, c(1, 2, 4, 6, 13, 18)) #> [1] 2-3   6-12  13-17 18+   18+   18+   18+   18+   18+   #> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+ age_groups(ages, \"children\") #> [1] 2-3   6-12  13-17 18+   18+   18+   18+   18+   18+   #> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+  # \\donttest{ # resistance of ciprofloxacin per age group if (require(\"dplyr\") && require(\"ggplot2\")) {   example_isolates %>%     filter_first_isolate() %>%     filter(mo == as.mo(\"Escherichia coli\")) %>%     group_by(age_group = age_groups(age)) %>%     select(age_group, CIP) %>%     ggplot_sir(       x = \"age_group\",       minimum = 0,       x.title = \"Age Group\",       title = \"Ciprofloxacin resistance per age group\"     ) } #> Loading required package: ggplot2 #> Including isolates from ICU.  # }"},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Antibiotic Selectors — antibiotic_class_selectors","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"functions allow filtering rows selecting columns based antibiotic test results specific antibiotic class group, without need define columns antibiotic abbreviations. short, column name resembles antimicrobial drug, picked functions matches pharmaceutical class: \"cefazolin\", \"CZO\" \"J01DB04\" picked cephalosporins().","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"","code":"ab_class(ab_class, only_sir_columns = FALSE, only_treatable = TRUE, ...)  ab_selector(filter, only_sir_columns = FALSE, only_treatable = TRUE, ...)  aminoglycosides(only_sir_columns = FALSE, only_treatable = TRUE, ...)  aminopenicillins(only_sir_columns = FALSE, ...)  antifungals(only_sir_columns = FALSE, ...)  antimycobacterials(only_sir_columns = FALSE, ...)  betalactams(only_sir_columns = FALSE, only_treatable = TRUE, ...)  carbapenems(only_sir_columns = FALSE, only_treatable = TRUE, ...)  cephalosporins(only_sir_columns = FALSE, ...)  cephalosporins_1st(only_sir_columns = FALSE, ...)  cephalosporins_2nd(only_sir_columns = FALSE, ...)  cephalosporins_3rd(only_sir_columns = FALSE, ...)  cephalosporins_4th(only_sir_columns = FALSE, ...)  cephalosporins_5th(only_sir_columns = FALSE, ...)  fluoroquinolones(only_sir_columns = FALSE, ...)  glycopeptides(only_sir_columns = FALSE, ...)  lincosamides(only_sir_columns = FALSE, ...)  lipoglycopeptides(only_sir_columns = FALSE, ...)  macrolides(only_sir_columns = FALSE, ...)  oxazolidinones(only_sir_columns = FALSE, ...)  penicillins(only_sir_columns = FALSE, ...)  polymyxins(only_sir_columns = FALSE, only_treatable = TRUE, ...)  streptogramins(only_sir_columns = FALSE, ...)  quinolones(only_sir_columns = FALSE, ...)  tetracyclines(only_sir_columns = FALSE, ...)  trimethoprims(only_sir_columns = FALSE, ...)  ureidopenicillins(only_sir_columns = FALSE, ...)  administrable_per_os(only_sir_columns = FALSE, ...)  administrable_iv(only_sir_columns = FALSE, ...)  not_intrinsic_resistant(   only_sir_columns = FALSE,   col_mo = NULL,   version_expertrules = 3.3,   ... )"},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"ab_class antimicrobial class part , \"carba\" \"carbapenems\". columns group, atc_group1 atc_group2 antibiotics data set searched (case-insensitive) value. only_sir_columns logical indicate whether columns class sir must selected (defaults FALSE), see .sir() only_treatable logical indicate whether antimicrobial drugs excluded laboratory tests (defaults TRUE), gentamicin-high (GEH) imipenem/EDTA (IPE) ... ignored, place allow future extensions filter expression evaluated antibiotics data set, name %like% \"trim\" col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can either \"3.3\", \"3.2\" \"3.1\".","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"(internally) character vector column names, additional class \"ab_selector\"","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"functions can used data set calls selecting columns filtering rows. heavily inspired Tidyverse selection helpers everything(), also work base R dplyr verbs. Nonetheless, convenient use dplyr functions select(), filter() summarise(), see Examples. columns data functions called searched known antibiotic names, abbreviations, brand names, codes (ATC, EARS-Net, , etc.) according antibiotics data set. means selector aminoglycosides() pick column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc. ab_class() function can used filter/select manually defined antibiotic class. searches results antibiotics data set within columns group, atc_group1 atc_group2. ab_selector() function can used internally filter antibiotics data set results, see Examples. allows filtering (part ) certain name, /group name even minimum DDDs oral treatment. function yields highest flexibility, also least user-friendly, since requires hard-coded filter set. administrable_per_os() administrable_iv() functions also rely antibiotics data set - antibiotic columns matched DDD (defined daily dose) resp. oral IV treatment available antibiotics data set. not_intrinsic_resistant() function can used select antibiotic columns pose intrinsic resistance microorganisms data set. example, data set contains microorganism codes names E. coli K. pneumoniae contains column \"vancomycin\", column removed (rather, unselected) using function. currently applies 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021) determine intrinsic resistance, using eucast_rules() function internally. determination, function quite slow terms performance.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"full-list-of-supported-antibiotic-classes","dir":"Reference","previous_headings":"","what":"Full list of supported (antibiotic) classes","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"aminoglycosides() can select:  amikacin (AMK), amikacin/fosfomycin (AKF), amphotericin B-high (AMH), apramycin (APR), arbekacin (ARB), astromicin (AST), bekanamycin (BEK), dibekacin (DKB), framycetin (FRM), gentamicin (GEN), gentamicin-high (GEH), habekacin (HAB), hygromycin (HYG), isepamicin (ISE), kanamycin (KAN), kanamycin-high (KAH), kanamycin/cephalexin (KAC), micronomicin (MCR), neomycin (NEO), netilmicin (NET), pentisomicin (PIM), plazomicin (PLZ), propikacin (PKA), ribostamycin (RST), sisomicin (SIS), streptoduocin (STR), streptomycin (STR1), streptomycin-high (STH), tobramycin (TOB) tobramycin-high (TOH) aminopenicillins() can select:  amoxicillin (AMX) ampicillin (AMP) antifungals() can select:  amphotericin B (AMB), anidulafungin (ANI), butoconazole (), caspofungin (CAS), ciclopirox (CIX), clotrimazole (CTR), econazole (ECO), fluconazole (FLU), flucytosine (FCT), fosfluconazole (FFL), griseofulvin (GRI), hachimycin (HCH), ibrexafungerp (IBX), isavuconazole (ISV), isoconazole (ISO), itraconazole (ITR), ketoconazole (KET), manogepix (MGX), micafungin (MIF), miconazole (MCZ), nystatin (NYS), oteseconazole (OTE), pimaricin (PMR), posaconazole (POS), rezafungin (RZF), ribociclib (RBC), sulconazole (SUC), terbinafine (TRB), terconazole (TRC) voriconazole (VOR) antimycobacterials() can select:  4-aminosalicylic acid (AMA), calcium aminosalicylate (CLA), capreomycin (CAP), clofazimine (CLF), delamanid (DLM), enviomycin (ENV), ethambutol (ETH), ethambutol/isoniazid (ETI), ethionamide (ETI1), isoniazid (INH), isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), morinamide (MRN), p-aminosalicylic acid (PAS), pretomanid (PMD), protionamide (PTH), pyrazinamide (PZA), rifabutin (RIB), rifampicin (RIF), rifampicin/ethambutol/isoniazid (REI), rifampicin/isoniazid (RFI), rifampicin/pyrazinamide/ethambutol/isoniazid (RPEI), rifampicin/pyrazinamide/isoniazid (RPI), rifamycin (RFM), rifapentine (RFP), simvastatin/fenofibrate (SMF), sodium aminosalicylate (SDA), streptomycin/isoniazid (STI), terizidone (TRZ), thioacetazone (TAT), thioacetazone/isoniazid (THI1), tiocarlide (TCR) viomycin (VIO) betalactams() can select:  amoxicillin (AMX), amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin (AMP), ampicillin/sulbactam (SAM), apalcillin (APL), aspoxicillin (APX), avibactam (AVB), azidocillin (AZD), azlocillin (AZL), aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), biapenem (BIA), carbenicillin (CRB), carindacillin (CRN), cefacetrile (CAC), cefaclor (CEC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefamandole (MAN), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefepime (FEP), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening (FOX1), cefozopran (ZOP), cefpimizole (CFZ), cefpiramide (CPM), cefpirome (CPO), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefprozil (CPR), cefquinome (CEQ), cefroxadine (CRD), cefsulodin (CFS), cefsumide (CSU), ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftezole (CTL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/tazobactam (CZT), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB), cefuroxime (CXM), cefuroxime axetil (CXA), cephradine (CED), ciclacillin (CIC), clometocillin (CLM), cloxacillin (CLO), dicloxacillin (DIC), doripenem (DOR), epicillin (EPC), ertapenem (ETP), flucloxacillin (FLC), hetacillin (HET), imipenem (IPM), imipenem/EDTA (IPE), imipenem/relebactam (IMR), latamoxef (LTM), lenampicillin (LEN), loracarbef (LOR), mecillinam (MEC), meropenem (MEM), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), metampicillin (MTM), meticillin (MET), mezlocillin (MEZ), mezlocillin/sulbactam (MSU), nacubactam (NAC), nafcillin (NAF), oxacillin (OXA), panipenem (PAN), penamecillin (PNM), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), pheneticillin (PHE), phenoxymethylpenicillin (PHN), piperacillin (PIP), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), piridicillin (PRC), pivampicillin (PVM), pivmecillinam (PME), procaine benzylpenicillin (PRB), propicillin (PRP), razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), sarmoxicillin (SRX), sulbactam (SUL), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), tazobactam (TAZ), tebipenem (TBP), temocillin (TEM), ticarcillin (TIC) ticarcillin/clavulanic acid (TCC) carbapenems() can select:  biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA) tebipenem (TBP) cephalosporins() can select:  cefacetrile (CAC), cefaclor (CEC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefamandole (MAN), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefepime (FEP), cefepime/clavulanic acid (CPC), cefepime/tazobactam (FPT), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening (FOX1), cefozopran (ZOP), cefpimizole (CFZ), cefpiramide (CPM), cefpirome (CPO), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefprozil (CPR), cefquinome (CEQ), cefroxadine (CRD), cefsulodin (CFS), cefsumide (CSU), ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftezole (CTL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/tazobactam (CZT), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB), cefuroxime (CXM), cefuroxime axetil (CXA), cephradine (CED), latamoxef (LTM) loracarbef (LOR) cephalosporins_1st() can select:  cefacetrile (CAC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefroxadine (CRD), ceftezole (CTL) cephradine (CED) cephalosporins_2nd() can select:  cefaclor (CEC), cefamandole (MAN), cefmetazole (CMZ), cefonicid (CID), ceforanide (CND), cefotetan (CTT), cefotiam (CTF), cefoxitin (FOX), cefoxitin screening (FOX1), cefprozil (CPR), cefuroxime (CXM), cefuroxime axetil (CXA) loracarbef (LOR) cephalosporins_3rd() can select:  cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefetamet (CAT), cefetamet pivoxil (CPI), cefixime (CFM), cefmenoxime (CMX), cefodizime (DIZ), cefoperazone (CFP), cefoperazone/sulbactam (CSL), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotiam hexetil (CHE), cefovecin (FOV), cefpimizole (CFZ), cefpiramide (CPM), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefsulodin (CFS), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB) latamoxef (LTM) cephalosporins_4th() can select:  cefepime (FEP), cefepime/clavulanic acid (CPC), cefepime/tazobactam (FPT), cefetecol (CCL), cefoselis (CSE), cefozopran (ZOP), cefpirome (CPO) cefquinome (CEQ) cephalosporins_5th() can select:  ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftobiprole (BPR), ceftobiprole medocaril (CFM1) ceftolozane/tazobactam (CZT) fluoroquinolones() can select:  besifloxacin (BES), ciprofloxacin (CIP), clinafloxacin (CLX), danofloxacin (DAN), delafloxacin (DFX), difloxacin (DIF), enoxacin (ENX), enrofloxacin (ENR), finafloxacin (FIN), fleroxacin (FLE), garenoxacin (GRN), gatifloxacin (GAT), gemifloxacin (GEM), grepafloxacin (GRX), lascufloxacin (LSC), levofloxacin (LVX), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nifuroquine (NIF), norfloxacin (), ofloxacin (OFX), orbifloxacin (ORB), pazufloxacin (PAZ), pefloxacin (PEF), pradofloxacin (PRA), premafloxacin (PRX), prulifloxacin (PRU), rufloxacin (RFL), sarafloxacin (SAR), sitafloxacin (SIT), sparfloxacin (SPX), temafloxacin (TMX), tilbroquinol (TBQ), tioxacin (TXC), tosufloxacin (TFX) trovafloxacin (TVA) glycopeptides() can select:  avoparcin (AVO), dalbavancin (DAL), norvancomycin (NVA), oritavancin (ORI), ramoplanin (RAM), teicoplanin (TEC), teicoplanin-macromethod (TCM), telavancin (TLV), vancomycin (VAN) vancomycin-macromethod (VAM) lincosamides() can select:  acetylmidecamycin (ACM), acetylspiramycin (ASP), clindamycin (CLI), gamithromycin (GAM), kitasamycin (KIT), lincomycin (LIN), meleumycin (MEL), nafithromycin (ZWK), pirlimycin (PRL), primycin (PRM), solithromycin (SOL), tildipirosin (TIP), tilmicosin (TIL), tulathromycin (TUL), tylosin (TYL) tylvalosin (TYL1) lipoglycopeptides() can select:  dalbavancin (DAL), oritavancin (ORI) telavancin (TLV) macrolides() can select:  acetylmidecamycin (ACM), acetylspiramycin (ASP), azithromycin (AZM), clarithromycin (CLR), dirithromycin (DIR), erythromycin (ERY), flurithromycin (FLR1), gamithromycin (GAM), josamycin (JOS), kitasamycin (KIT), meleumycin (MEL), midecamycin (MID), miocamycin (MCM), nafithromycin (ZWK), oleandomycin (OLE), pirlimycin (PRL), primycin (PRM), rokitamycin (ROK), roxithromycin (RXT), solithromycin (SOL), spiramycin (SPI), telithromycin (TLT), tildipirosin (TIP), tilmicosin (TIL), troleandomycin (TRL), tulathromycin (TUL), tylosin (TYL) tylvalosin (TYL1) oxazolidinones() can select:  cadazolid (CDZ), cycloserine (CYC), linezolid (LNZ), tedizolid (TZD) thiacetazone (THA) penicillins() can select:  amoxicillin (AMX), amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin (AMP), ampicillin/sulbactam (SAM), apalcillin (APL), aspoxicillin (APX), avibactam (AVB), azidocillin (AZD), azlocillin (AZL), aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), carbenicillin (CRB), carindacillin (CRN), cefepime/nacubactam (FNC), ciclacillin (CIC), clometocillin (CLM), cloxacillin (CLO), dicloxacillin (DIC), epicillin (EPC), flucloxacillin (FLC), hetacillin (HET), lenampicillin (LEN), mecillinam (MEC), metampicillin (MTM), meticillin (MET), mezlocillin (MEZ), mezlocillin/sulbactam (MSU), nacubactam (NAC), nafcillin (NAF), oxacillin (OXA), penamecillin (PNM), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), pheneticillin (PHE), phenoxymethylpenicillin (PHN), piperacillin (PIP), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), piridicillin (PRC), pivampicillin (PVM), pivmecillinam (PME), procaine benzylpenicillin (PRB), propicillin (PRP), sarmoxicillin (SRX), sulbactam (SUL), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), tazobactam (TAZ), temocillin (TEM), ticarcillin (TIC) ticarcillin/clavulanic acid (TCC) polymyxins() can select:  colistin (COL), polymyxin B (PLB) polymyxin B/polysorbate 80 (POP) quinolones() can select:  besifloxacin (BES), cinoxacin (CIN), ciprofloxacin (CIP), clinafloxacin (CLX), danofloxacin (DAN), delafloxacin (DFX), difloxacin (DIF), enoxacin (ENX), enrofloxacin (ENR), finafloxacin (FIN), fleroxacin (FLE), flumequine (FLM), garenoxacin (GRN), gatifloxacin (GAT), gemifloxacin (GEM), grepafloxacin (GRX), lascufloxacin (LSC), levofloxacin (LVX), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nalidixic acid (NAL), nemonoxacin (NEM), nifuroquine (NIF), nitroxoline (NTR), norfloxacin (), ofloxacin (OFX), orbifloxacin (ORB), oxolinic acid (OXO), pazufloxacin (PAZ), pefloxacin (PEF), pipemidic acid (PPA), piromidic acid (PIR), pradofloxacin (PRA), premafloxacin (PRX), prulifloxacin (PRU), rosoxacin (ROS), rufloxacin (RFL), sarafloxacin (SAR), sitafloxacin (SIT), sparfloxacin (SPX), temafloxacin (TMX), tilbroquinol (TBQ), tioxacin (TXC), tosufloxacin (TFX) trovafloxacin (TVA) streptogramins() can select:  pristinamycin (PRI) quinupristin/dalfopristin (QDA) tetracyclines() can select:  cetocycline (CTO), chlortetracycline (CTE), clomocycline (CLM1), demeclocycline (DEM), doxycycline (DOX), eravacycline (ERV), lymecycline (LYM), metacycline (MTC), minocycline (MNO), omadacycline (OMC), oxytetracycline (OXY), penimepicycline (PNM1), rolitetracycline (RLT), sarecycline (SRC), tetracycline (TCY) tigecycline (TGC) trimethoprims() can select:  brodimoprim (BDP), sulfadiazine (SDI), sulfadiazine/tetroxoprim (SLT), sulfadiazine/trimethoprim (SLT1), sulfadimethoxine (SUD), sulfadimidine (SDM), sulfadimidine/trimethoprim (SLT2), sulfafurazole (SLF), sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO), sulfamerazine (SLF3), sulfamerazine/trimethoprim (SLT3), sulfamethizole (SLF4), sulfamethoxazole (SMX), sulfamethoxypyridazine (SLF5), sulfametomidine (SLF6), sulfametoxydiazine (SLF7), sulfametrole/trimethoprim (SLT4), sulfamoxole (SLF8), sulfamoxole/trimethoprim (SLT5), sulfanilamide (SLF9), sulfaperin (SLF10), sulfaphenazole (SLF11), sulfapyridine (SLF12), sulfathiazole (SUT), sulfathiourea (SLF13), trimethoprim (TMP) trimethoprim/sulfamethoxazole (SXT) ureidopenicillins() can select:  azlocillin (AZL), mezlocillin (MEZ), piperacillin (PIP) piperacillin/tazobactam (TZP)","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates. example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>  # base R ------------------------------------------------------------------  # select columns 'IPM' (imipenem) and 'MEM' (meropenem) example_isolates[, carbapenems()] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 2,000 × 2 #>    IPM   MEM   #>    <sir> <sir> #>  1 NA    NA    #>  2 NA    NA    #>  3 NA    NA    #>  4 NA    NA    #>  5 NA    NA    #>  6 NA    NA    #>  7 NA    NA    #>  8 NA    NA    #>  9 NA    NA    #> 10 NA    NA    #> # … with 1,990 more rows  # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB' example_isolates[, c(\"mo\", aminoglycosides())] #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 2,000 × 5 #>    mo           GEN   TOB   AMK   KAN   #>    <mo>         <sir> <sir> <sir> <sir> #>  1 B_ESCHR_COLI NA    NA    NA    NA    #>  2 B_ESCHR_COLI NA    NA    NA    NA    #>  3 B_STPHY_EPDR NA    NA    NA    NA    #>  4 B_STPHY_EPDR NA    NA    NA    NA    #>  5 B_STPHY_EPDR NA    NA    NA    NA    #>  6 B_STPHY_EPDR NA    NA    NA    NA    #>  7 B_STPHY_AURS NA    S     NA    NA    #>  8 B_STPHY_AURS NA    S     NA    NA    #>  9 B_STPHY_EPDR NA    NA    NA    NA    #> 10 B_STPHY_EPDR NA    NA    NA    NA    #> # … with 1,990 more rows  # select only antibiotic columns with DDDs for oral treatment example_isolates[, administrable_per_os()] #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #>   (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #>   'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #>   (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #>   (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #>   (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #>   (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #>   (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin) and 'RIF' #>   (rifampicin) #> # A tibble: 2,000 × 23 #>    OXA   FLC   AMX   AMC   AMP   CXM   KAN   TMP   NIT   FOS   LNZ   CIP   MFX   #>    <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>  1 NA    NA    NA    I     NA    I     NA    R     NA    NA    R     NA    NA    #>  2 NA    NA    NA    I     NA    I     NA    R     NA    NA    R     NA    NA    #>  3 NA    R     NA    NA    NA    R     NA    S     NA    NA    NA    NA    NA    #>  4 NA    R     NA    NA    NA    R     NA    S     NA    NA    NA    NA    NA    #>  5 NA    R     NA    NA    NA    R     NA    R     NA    NA    NA    NA    NA    #>  6 NA    R     NA    NA    NA    R     NA    R     NA    NA    NA    NA    NA    #>  7 NA    S     R     S     R     S     NA    R     NA    NA    NA    NA    NA    #>  8 NA    S     R     S     R     S     NA    R     NA    NA    NA    NA    NA    #>  9 NA    R     NA    NA    NA    R     NA    S     NA    NA    NA    S     NA    #> 10 NA    S     NA    NA    NA    S     NA    S     NA    NA    NA    S     NA    #> # … with 1,990 more rows, and 10 more variables: VAN <sir>, TCY <sir>, #> #   DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, RIF <sir>  # filter using any() or all() example_isolates[any(carbapenems() == \"R\"), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 55 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  2 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  3 2004-11-03 D65308     80 F      ICU      B_STNTR_MLTP R     NA    NA    R     #>  4 2005-04-21 452212     82 F      ICU      B_ENTRC      NA    NA    NA    NA    #>  5 2005-04-22 452212     82 F      ICU      B_ENTRC      NA    NA    NA    NA    #>  6 2005-04-22 452212     82 F      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  7 2007-02-21 8BBC46     61 F      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  8 2007-12-15 401043     72 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  9 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL R     NA    NA    NA    #> 10 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL R     NA    NA    NA    #> # … with 45 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> subset(example_isolates, any(carbapenems() == \"R\")) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 55 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  2 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  3 2004-11-03 D65308     80 F      ICU      B_STNTR_MLTP R     NA    NA    R     #>  4 2005-04-21 452212     82 F      ICU      B_ENTRC      NA    NA    NA    NA    #>  5 2005-04-22 452212     82 F      ICU      B_ENTRC      NA    NA    NA    NA    #>  6 2005-04-22 452212     82 F      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  7 2007-02-21 8BBC46     61 F      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  8 2007-12-15 401043     72 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  9 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL R     NA    NA    NA    #> 10 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL R     NA    NA    NA    #> # … with 45 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir>  # filter on any or all results in the carbapenem columns (i.e., IPM, MEM): example_isolates[any(carbapenems()), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Filtering any of columns 'IPM' and 'MEM' to contain value \"S\", \"I\" or \"R\" #> # A tibble: 962 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  4 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  5 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS R     NA    NA    R     #>  6 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS R     NA    NA    R     #>  7 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS R     NA    NA    R     #>  8 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN R     NA    NA    R     #>  9 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN R     NA    NA    R     #> 10 2002-03-08 4FC193     69 M      Clinical B_ESCHR_COLI R     NA    NA    R     #> # … with 952 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> example_isolates[all(carbapenems()), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Filtering all of columns 'IPM' and 'MEM' to contain value \"S\", \"I\" or \"R\" #> # A tibble: 756 × 46 #>    date       patient   age gender ward    mo            PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>   <mo>          <sir> <sir> <sir> <sir> #>  1 2002-04-14 F30196     73 M      Outpat… B_STRPT_GRPB  S     NA    S     S     #>  2 2003-04-08 114570     74 M      ICU     B_STRPT_PYGN  S     NA    S     S     #>  3 2003-04-08 114570     74 M      ICU     B_STRPT_GRPA  S     NA    S     S     #>  4 2003-04-08 114570     74 M      ICU     B_STRPT_GRPA  S     NA    S     S     #>  5 2003-08-14 F71508      0 F      Clinic… B_STRPT_GRPB  S     NA    S     S     #>  6 2003-10-16 650870     63 F      ICU     B_ESCHR_COLI  R     NA    NA    R     #>  7 2003-10-20 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R     #>  8 2003-10-20 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R     #>  9 2003-11-04 2FC253     87 F      ICU     B_ESCHR_COLI  R     NA    NA    NA    #> 10 2003-11-04 2FC253     87 F      ICU     B_ESCHR_COLI  R     NA    NA    NA    #> # … with 746 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir>  # filter with multiple antibiotic selectors using c() example_isolates[all(c(carbapenems(), aminoglycosides()) == \"R\"), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 26 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2004-11-03 D65308     80 F      ICU      B_STNTR_MLTP R     NA    NA    R     #>  2 2005-04-22 452212     82 F      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  3 2007-02-21 8BBC46     61 F      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  4 2007-12-15 401043     72 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  5 2008-12-06 501361     43 F      Clinical B_STNTR_MLTP R     NA    NA    R     #>  6 2011-05-09 207325     82 F      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  7 2012-03-12 582258     80 M      ICU      B_STPHY_CONS R     R     R     R     #>  8 2012-05-19 C25552     89 F      Outpati… B_STPHY_CONS R     R     R     R     #>  9 2012-07-17 F05015     83 M      ICU      B_STPHY_CONS R     R     R     R     #> 10 2012-07-20 404299     66 F      Clinical B_STPHY_CONS R     R     R     R     #> # … with 16 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir>  # filter + select in one go: get penicillins in carbapenems-resistant strains example_isolates[any(carbapenems() == \"R\"), penicillins()] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin) and 'TZP' #>   (piperacillin/tazobactam) #> # A tibble: 55 × 7 #>    PEN   OXA   FLC   AMX   AMC   AMP   TZP   #>    <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>  1 NA    NA    NA    NA    NA    NA    NA    #>  2 NA    NA    NA    NA    NA    NA    NA    #>  3 R     NA    NA    R     R     R     R     #>  4 NA    NA    NA    NA    NA    NA    R     #>  5 NA    NA    NA    NA    NA    NA    R     #>  6 NA    NA    NA    NA    NA    NA    R     #>  7 NA    NA    NA    NA    NA    NA    R     #>  8 NA    NA    NA    NA    NA    NA    R     #>  9 R     NA    NA    NA    S     NA    S     #> 10 R     NA    NA    NA    S     NA    S     #> # … with 45 more rows  # You can combine selectors with '&' to be more specific. For example, # penicillins() would select benzylpenicillin ('peni G') and # administrable_per_os() would select erythromycin. Yet, when combined these # drugs are both omitted since benzylpenicillin is not administrable per os # and erythromycin is not a penicillin: example_isolates[, penicillins() & administrable_per_os()] #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin) and 'TZP' #>   (piperacillin/tazobactam) #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #>   (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #>   'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #>   (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #>   (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #>   (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #>   (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #>   (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin) and 'RIF' #>   (rifampicin) #> # A tibble: 2,000 × 5 #>    OXA   FLC   AMX   AMC   AMP   #>    <sir> <sir> <sir> <sir> <sir> #>  1 NA    NA    NA    I     NA    #>  2 NA    NA    NA    I     NA    #>  3 NA    R     NA    NA    NA    #>  4 NA    R     NA    NA    NA    #>  5 NA    R     NA    NA    NA    #>  6 NA    R     NA    NA    NA    #>  7 NA    S     R     S     R     #>  8 NA    S     R     S     R     #>  9 NA    R     NA    NA    NA    #> 10 NA    S     NA    NA    NA    #> # … with 1,990 more rows  # ab_selector() applies a filter in the `antibiotics` data set and is thus very # flexible. For instance, to select antibiotic columns with an oral DDD of at # least 1 gram: example_isolates[, ab_selector(oral_ddd > 1 & oral_units == \"g\")] #> ℹ For ab_selector(oral_ddd > 1 & oral_units == \"g\") using columns 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'KAN' (kanamycin), 'FOS' #>   (fosfomycin), 'LNZ' (linezolid), 'VAN' (vancomycin), 'ERY' (erythromycin), #>   'CLI' (clindamycin), 'MTR' (metronidazole) and 'CHL' (chloramphenicol) #> # A tibble: 2,000 × 13 #>    OXA   FLC   AMX   AMC   AMP   KAN   FOS   LNZ   VAN   ERY   CLI   MTR   CHL   #>    <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>  1 NA    NA    NA    I     NA    NA    NA    R     R     R     R     NA    NA    #>  2 NA    NA    NA    I     NA    NA    NA    R     R     R     R     NA    NA    #>  3 NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA    #>  4 NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA    #>  5 NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA    #>  6 NA    R     NA    NA    NA    NA    NA    NA    S     R     R     NA    NA    #>  7 NA    S     R     S     R     NA    NA    NA    S     S     NA    NA    NA    #>  8 NA    S     R     S     R     NA    NA    NA    S     S     NA    NA    NA    #>  9 NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA    #> 10 NA    S     NA    NA    NA    NA    NA    NA    S     S     NA    NA    NA    #> # … with 1,990 more rows  # dplyr ------------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) {   # get AMR for all aminoglycosides e.g., per ward:   example_isolates %>%     group_by(ward) %>%     summarise(across(aminoglycosides(), resistance)) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> Warning: Introducing NA: only 23 results available for KAN in group: ward = #> \"Outpatient\" (minimum = 30). #> # A tibble: 3 × 5 #>   ward         GEN   TOB   AMK   KAN #>   <chr>      <dbl> <dbl> <dbl> <dbl> #> 1 Clinical   0.229 0.315 0.626     1 #> 2 ICU        0.290 0.400 0.662     1 #> 3 Outpatient 0.2   0.368 0.605    NA if (require(\"dplyr\")) {   # You can combine selectors with '&' to be more specific:   example_isolates %>%     select(penicillins() & administrable_per_os()) } #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin) and 'TZP' #>   (piperacillin/tazobactam) #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #>   (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #>   'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #>   (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #>   (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #>   (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #>   (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #>   (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin) and 'RIF' #>   (rifampicin) #> # A tibble: 2,000 × 5 #>    OXA   FLC   AMX   AMC   AMP   #>    <sir> <sir> <sir> <sir> <sir> #>  1 NA    NA    NA    I     NA    #>  2 NA    NA    NA    I     NA    #>  3 NA    R     NA    NA    NA    #>  4 NA    R     NA    NA    NA    #>  5 NA    R     NA    NA    NA    #>  6 NA    R     NA    NA    NA    #>  7 NA    S     R     S     R     #>  8 NA    S     R     S     R     #>  9 NA    R     NA    NA    NA    #> 10 NA    S     NA    NA    NA    #> # … with 1,990 more rows if (require(\"dplyr\")) {   # get AMR for only drugs that matter - no intrinsic resistance:   example_isolates %>%     filter(mo_genus() %in% c(\"Escherichia\", \"Klebsiella\")) %>%     group_by(ward) %>%     summarise(across(not_intrinsic_resistant(), resistance)) } #> ℹ Using column 'mo' as input for mo_genus() #> ℹ For not_intrinsic_resistant() removing columns 'PEN' #>   (benzylpenicillin), 'LNZ' (linezolid), 'VAN' (vancomycin), 'TEC' #>   (teicoplanin), 'ERY' (erythromycin), 'CLI' (clindamycin), 'AZM' #>   (azithromycin) and 'RIF' (rifampicin) #> Warning: Introducing NA: no results available for OXA in group: ward = \"Clinical\" #> (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: ward = \"Clinical\" #> (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: only 25 results available for AMX in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for AMC in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for AMP in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 24 results available for TZP in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 23 results available for CZO in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: only 10 results available for CZO in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 20 results available for FEP in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for CXM in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 22 results available for FOX in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for CTX in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for CAZ in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for CRO in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for GEN in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for TOB in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 15 results available for AMK in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: ward = \"Clinical\" #> (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: only 22 results available for TMP in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for SXT in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for NIT in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for FOS in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: only 2 results available for FOS in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for CIP in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 15 results available for MFX in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: only 4 results available for MFX in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 5 results available for TCY in group: ward = #> \"Clinical\" (minimum = 30). #> Warning: Introducing NA: no results available for TCY in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for TCY in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: only 13 results available for TGC in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: only 2 results available for TGC in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: ward = \"Clinical\" #> (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: only 24 results available for IPM in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for MEM in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MTR in group: ward = #> \"Clinical\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: ward = \"Clinical\" #> (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: ward = \"Outpatient\" #> (minimum = 30). #> Warning: Introducing NA: only 9 results available for COL in group: ward = #> \"Outpatient\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: ward = \"Clinical\" #> (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: ward = \"ICU\" #> (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: ward = \"Outpatient\" #> (minimum = 30). #> # A tibble: 3 × 33 #>   ward    OXA   FLC    AMX    AMC    AMP     TZP     CZO     FEP     CXM     FOX #>   <chr> <dbl> <dbl>  <dbl>  <dbl>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl> #> 1 Clin…    NA    NA  0.606  0.121  0.606  0.0504  0.0656  0.0159  0.0622  0.0648 #> 2 ICU      NA    NA  0.535  0.172  0.535  0.119  NA       0.0722  0.0828  0.0992 #> 3 Outp…    NA    NA NA     NA     NA     NA      NA      NA      NA      NA      #> # … with 22 more variables: CTX <dbl>, CAZ <dbl>, CRO <dbl>, GEN <dbl>, #> #   TOB <dbl>, AMK <dbl>, KAN <dbl>, TMP <dbl>, SXT <dbl>, NIT <dbl>, #> #   FOS <dbl>, CIP <dbl>, MFX <dbl>, TCY <dbl>, TGC <dbl>, DOX <dbl>, #> #   IPM <dbl>, MEM <dbl>, MTR <dbl>, CHL <dbl>, COL <dbl>, MUP <dbl> if (require(\"dplyr\")) {   # get susceptibility for antibiotics whose name contains \"trim\":   example_isolates %>%     filter(first_isolate()) %>%     group_by(ward) %>%     summarise(across(ab_selector(name %like% \"trim\"), susceptibility)) } #> Including isolates from ICU. #> ℹ For ab_selector(name %like% \"trim\") using columns 'TMP' (trimethoprim) #>   and 'SXT' (trimethoprim/sulfamethoxazole) #> # A tibble: 3 × 3 #>   ward         TMP   SXT #>   <chr>      <dbl> <dbl> #> 1 Clinical   0.627 0.808 #> 2 ICU        0.549 0.778 #> 3 Outpatient 0.667 0.821 if (require(\"dplyr\")) {   # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):   example_isolates %>%     select(carbapenems()) } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 2,000 × 2 #>    IPM   MEM   #>    <sir> <sir> #>  1 NA    NA    #>  2 NA    NA    #>  3 NA    NA    #>  4 NA    NA    #>  5 NA    NA    #>  6 NA    NA    #>  7 NA    NA    #>  8 NA    NA    #>  9 NA    NA    #> 10 NA    NA    #> # … with 1,990 more rows if (require(\"dplyr\")) {   # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':   example_isolates %>%     select(mo, aminoglycosides()) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 2,000 × 5 #>    mo           GEN   TOB   AMK   KAN   #>    <mo>         <sir> <sir> <sir> <sir> #>  1 B_ESCHR_COLI NA    NA    NA    NA    #>  2 B_ESCHR_COLI NA    NA    NA    NA    #>  3 B_STPHY_EPDR NA    NA    NA    NA    #>  4 B_STPHY_EPDR NA    NA    NA    NA    #>  5 B_STPHY_EPDR NA    NA    NA    NA    #>  6 B_STPHY_EPDR NA    NA    NA    NA    #>  7 B_STPHY_AURS NA    S     NA    NA    #>  8 B_STPHY_AURS NA    S     NA    NA    #>  9 B_STPHY_EPDR NA    NA    NA    NA    #> 10 B_STPHY_EPDR NA    NA    NA    NA    #> # … with 1,990 more rows if (require(\"dplyr\")) {   # any() and all() work in dplyr's filter() too:   example_isolates %>%     filter(       any(aminoglycosides() == \"R\"),       all(cephalosporins_2nd() == \"R\")     ) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> ℹ For cephalosporins_2nd() using columns 'CXM' (cefuroxime) and 'FOX' #>   (cefoxitin) #> # A tibble: 112 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  2 2002-03-16 4FC193     69 M      Clinical B_PSDMN_AERG R     NA    NA    R     #>  3 2002-04-08 130252     78 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #>  4 2002-06-23 798871     82 M      Clinical B_ENTRC_FCLS NA    NA    NA    NA    #>  5 2002-06-23 798871     82 M      Clinical B_ENTRC_FCLS NA    NA    NA    NA    #>  6 2002-07-21 955940     82 F      Clinical B_PSDMN_AERG R     NA    NA    R     #>  7 2002-07-21 955940     82 F      Clinical B_PSDMN_AERG R     NA    NA    R     #>  8 2003-08-13 F35553     52 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #>  9 2003-09-05 F35553     52 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #> 10 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #> # … with 102 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # also works with c():   example_isolates %>%     filter(any(c(carbapenems(), aminoglycosides()) == \"R\")) } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 531 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  2 2002-03-16 4FC193     69 M      Clinical B_PSDMN_AERG R     NA    NA    R     #>  3 2002-03-17 B30560     78 M      Clinical B_STPHY_CONS R     NA    R     R     #>  4 2002-04-04 E61143     67 M      Clinical B_STRPT_SNGN S     NA    NA    S     #>  5 2002-04-08 130252     78 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #>  6 2002-04-14 F30196     73 M      Outpati… B_STRPT_GRPB S     NA    S     S     #>  7 2002-05-07 D91570     83 M      Clinical B_STPHY_CONS R     NA    R     R     #>  8 2002-05-07 D91570     83 M      Clinical B_STPHY_CONS R     NA    R     R     #>  9 2002-05-14 077552     86 F      Clinical B_STRPT_PNMN S     NA    NA    S     #> 10 2002-05-14 077552     86 F      Clinical B_STRPT_PNMN S     NA    NA    S     #> # … with 521 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # not setting any/all will automatically apply all():   example_isolates %>%     filter(aminoglycosides() == \"R\") } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> ℹ Assuming a filter on all 4 aminoglycosides. Wrap around all() or #>   any() to prevent this note. #> # A tibble: 427 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  2 2002-03-17 B30560     78 M      Clinical B_STPHY_CONS R     NA    R     R     #>  3 2002-04-04 E61143     67 M      Clinical B_STRPT_SNGN S     NA    NA    S     #>  4 2002-04-08 130252     78 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #>  5 2002-04-14 F30196     73 M      Outpati… B_STRPT_GRPB S     NA    S     S     #>  6 2002-05-07 D91570     83 M      Clinical B_STPHY_CONS R     NA    R     R     #>  7 2002-05-07 D91570     83 M      Clinical B_STPHY_CONS R     NA    R     R     #>  8 2002-05-14 077552     86 F      Clinical B_STRPT_PNMN S     NA    NA    S     #>  9 2002-05-14 077552     86 F      Clinical B_STRPT_PNMN S     NA    NA    S     #> 10 2002-05-16 D25302     65 F      ICU      B_STRPT_ANGN S     NA    NA    S     #> # … with 417 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # this will select columns 'mo' and all antimycobacterial drugs ('RIF'):   example_isolates %>%     select(mo, ab_class(\"mycobact\")) } #> ℹ For ab_class(\"mycobact\") using column 'RIF' (rifampicin) #> # A tibble: 2,000 × 2 #>    mo           RIF   #>    <mo>         <sir> #>  1 B_ESCHR_COLI R     #>  2 B_ESCHR_COLI R     #>  3 B_STPHY_EPDR NA    #>  4 B_STPHY_EPDR NA    #>  5 B_STPHY_EPDR NA    #>  6 B_STPHY_EPDR NA    #>  7 B_STPHY_AURS NA    #>  8 B_STPHY_AURS NA    #>  9 B_STPHY_EPDR NA    #> 10 B_STPHY_EPDR NA    #> # … with 1,990 more rows if (require(\"dplyr\")) {   # get bug/drug combinations for only glycopeptides in Gram-positives:   example_isolates %>%     filter(mo_is_gram_positive()) %>%     select(mo, glycopeptides()) %>%     bug_drug_combinations() %>%     format() } #> ℹ Using column 'mo' as input for mo_is_gram_positive() #> ℹ For glycopeptides() using columns 'VAN' (vancomycin) and 'TEC' #>   (teicoplanin) #> # A tibble: 2 × 8 #>   Group           Drug             CoNS  E. fa…¹ S. au…² S. ep…³ S. ho…⁴ S. pn…⁵ #>   <chr>           <chr>            <chr> <chr>   <chr>   <chr>   <chr>   <chr>   #> 1 \"Glycopeptides\" Teicoplanin (TE… \"\"    \"\"      \" 0.0%… \"64.1%… \" 6.8%… \"\"      #> 2 \"\"              Vancomycin (VAN… \" 0.… \" 0.0%… \" 0.0%… \" 0.0%… \" 0.0%… \" 0.0%… #> # … with abbreviated variable names ¹​`E. faecalis`, ²​`S. aureus`, #> #   ³​`S. epidermidis`, ⁴​`S. hominis`, ⁵​`S. pneumoniae` if (require(\"dplyr\")) {   data.frame(     some_column = \"some_value\",     J01CA01 = \"S\"   ) %>% # ATC code of ampicillin     select(penicillins()) # only the 'J01CA01' column will be selected } #> ℹ For penicillins() using column 'J01CA01' (ampicillin) #>   J01CA01 #> 1       S if (require(\"dplyr\")) {   # with recent versions of dplyr this is all equal:   x <- example_isolates[carbapenems() == \"R\", ]   y <- example_isolates %>% filter(carbapenems() == \"R\")   z <- example_isolates %>% filter(if_all(carbapenems(), ~ .x == \"R\"))   identical(x, y) && identical(y, z) } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Assuming a filter on all 2 carbapenems. Wrap around all() or any() to #>   prevent this note. #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Assuming a filter on all 2 carbapenems. Wrap around all() or any() to #>   prevent this note. #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> [1] TRUE # }"},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Sets with 603 Antimicrobial Drugs — antibiotics","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"Two data sets containing antibiotics/antimycotics antivirals. Use .ab() one ab_* functions retrieve values antibiotics data set. Three identifiers included data set: antibiotic ID (ab, primarily used package) defined WHONET/EARS-Net, ATC code (atc) defined , Compound ID (cid) found PubChem. properties data set derived one codes. Note drugs multiple ATC codes.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"","code":"antibiotics  antivirals"},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"object class tbl_df (inherits tbl, data.frame) 120 rows 11 columns.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"for-the-antibiotics-data-set-a-tibble-with-observations-and-variables-","dir":"Reference","previous_headings":"","what":"For the antibiotics data set: a tibble with 483 observations and 14 variables:","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"ab Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available cid Compound ID found PubChem name Official name used WHONET/EARS-Net group short concise group name, based WHONET WHOCC definitions atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC, like J01CR02 atc_group1 Official pharmacological subgroup (3rd level ATC code) defined WHOCC, like \"Macrolides, lincosamides streptogramins\" atc_group2 Official chemical subgroup (4th level ATC code) defined WHOCC, like \"Macrolides\" abbr List abbreviations used many countries, also antibiotic susceptibility testing (AST) synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment, currently available 174 drugs oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral (intravenous) treatment, currently available 146 drugs iv_units Units iv_ddd loinc LOINC codes (Logical Observation Identifiers Names Codes) associated name antimicrobial drug. Use ab_loinc() retrieve quickly, see ab_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"for-the-antivirals-data-set-a-tibble-with-observations-and-variables-","dir":"Reference","previous_headings":"","what":"For the antivirals data set: a tibble with 120 observations and 11 variables:","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"av Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name used WHONET/EARS-Net atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC cid Compound ID found PubChem atc_group Official pharmacological subgroup (3rd level ATC code) defined WHOCC synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral treatment iv_units Units iv_ddd loinc LOINC codes (Logical Observation Identifiers Names Codes) associated name antimicrobial drug.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology (WHOCC): https://www.whocc./atc_ddd_index/ Logical Observation Identifiers Names Codes (LOINC), Version 2.73 (8 August, 2022). Accessed https://loinc.org 30 October, 2022. European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"Properties based ATC code available ATC available. properties : atc_group1, atc_group2, oral_ddd, oral_units, iv_ddd iv_units. Synonyms (.e. trade names) derived PubChem Compound ID (column cid) consequently available CID available.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"Like data sets package, data sets publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"","code":"antibiotics #> # A tibble: 483 × 14 #>    ab        cid name        group atc   atc_g…¹ atc_g…² abbre…³ synon…⁴ oral_…⁵ #>    <ab>    <dbl> <chr>       <chr> <lis> <chr>   <chr>   <list>  <named>   <dbl> #>  1 AMA      4649 4-aminosal… Anti… <chr> Drugs … Aminos… <chr>   <chr>     12    #>  2 ACM   6450012 Acetylmide… Macr… <chr> NA      NA      <chr>   <chr>     NA    #>  3 ASP  49787020 Acetylspir… Macr… <chr> NA      NA      <chr>   <chr>     NA    #>  4 ALS      8954 Aldesulfon… Othe… <chr> Drugs … Drugs … <chr>   <chr>      0.33 #>  5 AMK     37768 Amikacin    Amin… <chr> Aminog… Other … <chr>   <chr>     NA    #>  6 AKF        NA Amikacin/f… Amin… <chr> NA      NA      <chr>   <chr>     NA    #>  7 AMX     33613 Amoxicillin Beta… <chr> Beta-l… Penici… <chr>   <chr>      1.5  #>  8 AMC  23665637 Amoxicilli… Beta… <chr> Beta-l… Combin… <chr>   <chr>      1.5  #>  9 AXS    465441 Amoxicilli… Beta… <chr> NA      NA      <chr>   <chr>     NA    #> 10 AMB   5280965 Amphoteric… Anti… <chr> Antimy… Antibi… <chr>   <chr>     40    #> # … with 473 more rows, 4 more variables: oral_units <chr>, iv_ddd <dbl>, #> #   iv_units <chr>, loinc <list>, and abbreviated variable names ¹​atc_group1, #> #   ²​atc_group2, ³​abbreviations, ⁴​synonyms, ⁵​oral_ddd antivirals #> # A tibble: 120 × 11 #>    av    name  atc      cid atc_g…¹ synon…² oral_…³ oral_…⁴ iv_ddd iv_un…⁵ loinc #>    <av>  <chr> <chr>  <dbl> <chr>   <list>    <dbl> <chr>    <dbl> <chr>   <lis> #>  1 ABA   Abac… J05A… 4.41e5 Nucleo… <chr>       0.6 g           NA NA      <chr> #>  2 ACI   Acic… J05A… 1.35e8 Nucleo… <chr>       4   g            4 g       <chr> #>  3 ADD   Adef… J05A… 6.09e4 Nucleo… <chr>      10   mg          NA NA      <chr> #>  4 AME   Amen… J05A… 1.14e7 Other … <chr>       0.4 g           NA NA      <chr> #>  5 AMP   Ampr… J05A… 6.50e4 Protea… <chr>       1.2 g           NA NA      <chr> #>  6 ASU   Asun… J05A… 1.61e7 Antivi… <chr>       0.2 g           NA NA      <chr> #>  7 ATA   Ataz… J05A… 1.48e5 Protea… <chr>       0.3 g           NA NA      <chr> #>  8 ATA+… Ataz… J05A… 8.66e7 Antivi… <chr>      NA   NA          NA NA      <chr> #>  9 ATA+… Ataz… J05A… 2.51e7 Antivi… <chr>       0.3 g           NA NA      <chr> #> 10 BAM   Balo… J05A… 1.24e8 Other … <chr>      40   mg          NA NA      <chr> #> # … with 110 more rows, and abbreviated variable names ¹​atc_group, ²​synonyms, #> #   ³​oral_ddd, ⁴​oral_units, ⁵​iv_units"},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to an Antibiotic ID — as.ab","title":"Transform Input to an Antibiotic ID — as.ab","text":"Use function determine antibiotic drug code one antibiotics. data set antibiotics searched abbreviations, official names synonyms (brand names).","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to an Antibiotic ID — as.ab","text":"","code":"as.ab(x, flag_multiple_results = TRUE, info = interactive(), ...)  is.ab(x)"},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to an Antibiotic ID — as.ab","text":"x character vector determine antibiotic ID flag_multiple_results logical indicate whether note printed console probably one antibiotic drug code name can retrieved single input value. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed internal functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to an Antibiotic ID — as.ab","text":"character  vector additional class ab","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to an Antibiotic ID — as.ab","text":"entries antibiotics data set three different identifiers: human readable EARS-Net code (column ab, used ECDC WHONET), ATC code (column atc, used ), CID code (column cid, Compound ID, used PubChem). data set contains 5,000 official brand names many different countries, found PubChem. drugs contain multiple ATC codes. properties searched user input. .ab() can correct different forms misspelling: Wrong spelling drug names (\"tobramicin\" \"gentamycin\"), corrects audible similarities f/ph, x/ks, c/z/s, t/th, etc. many vowels consonants Switching two characters (\"mreopenem\", often case clinical data, doctors typed fast) Digitalised paper records, leaving artefacts like 0/o/O (zero O's), B/8, n/r, etc. Use ab_* functions get properties based returned antibiotic ID, see Examples. Note: .ab() ab_* functions may use long regular expression match brand names antimicrobial drugs. may fail systems. can add manual codes considered .ab() ab_* functions, see add_custom_antimicrobials().","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to an Antibiotic ID — as.ab","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Transform Input to an Antibiotic ID — as.ab","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to an Antibiotic ID — as.ab","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to an Antibiotic ID — as.ab","text":"","code":"# these examples all return \"ERY\", the ID of erythromycin: as.ab(\"J01FA01\") #> Class 'ab' #> [1] ERY as.ab(\"J 01 FA 01\") #> Class 'ab' #> [1] ERY as.ab(\"Erythromycin\") #> Class 'ab' #> [1] ERY as.ab(\"eryt\") #> Class 'ab' #> [1] ERY as.ab(\"   eryt 123\") #> Class 'ab' #> [1] ERY as.ab(\"ERYT\") #> Class 'ab' #> [1] ERY as.ab(\"ERY\") #> Class 'ab' #> [1] ERY as.ab(\"eritromicine\") # spelled wrong, yet works #> Class 'ab' #> [1] ERY as.ab(\"Erythrocin\") # trade name #> Class 'ab' #> [1] ERY as.ab(\"Romycin\") # trade name #> Class 'ab' #> [1] ERY  # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\") # small spelling error #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\") # or a bit more severe #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") # and even this works #> [1] \"J01DD04\"  # use ab_* functions to get a specific properties (see ?ab_property); # they use as.ab() internally: ab_name(\"J01FA01\") #> [1] \"Erythromycin\" ab_name(\"eryt\") #> [1] \"Erythromycin\"  # \\donttest{ if (require(\"dplyr\")) {   # you can quickly rename 'sir' columns using set_ab_names() with dplyr:   example_isolates %>%     set_ab_names(where(is.sir), property = \"atc\") } #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           J01CE01 J01CF04 J01CF05 #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir>   <sir>   <sir>   #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R       NA      NA      #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R       NA      NA      #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R       NA      S       #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R       NA      S       #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R       NA      S       #> # … with 1,990 more rows, and 37 more variables: J01CA04 <sir>, J01CR02 <sir>, #> #   J01CA01 <sir>, J01CR05 <sir>, J01DB04 <sir>, J01DE01 <sir>, J01DC02 <sir>, #> #   J01DC01 <sir>, J01DD01 <sir>, J01DD02 <sir>, J01DD04 <sir>, J01GB03 <sir>, #> #   J01GB01 <sir>, J01GB06 <sir>, J01GB04 <sir>, J01EA01 <sir>, J01EE01 <sir>, #> #   J01XE01 <sir>, J01XX01 <sir>, J01XX08 <sir>, J01MA02 <sir>, J01MA14 <sir>, #> #   J01XA01 <sir>, J01XA02 <sir>, J01AA07 <sir>, J01AA12 <sir>, J01AA02 <sir>, #> #   J01FA01 <sir>, J01FF01 <sir>, J01FA10 <sir>, J01DH51 <sir>, … # }"},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to an Antiviral Drug ID — as.av","title":"Transform Input to an Antiviral Drug ID — as.av","text":"Use function determine antiviral drug code one antiviral drugs. data set antivirals searched abbreviations, official names synonyms (brand names).","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to an Antiviral Drug ID — as.av","text":"","code":"as.av(x, flag_multiple_results = TRUE, info = interactive(), ...)  is.av(x)"},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to an Antiviral Drug ID — as.av","text":"x character vector determine antiviral drug ID flag_multiple_results logical indicate whether note printed console probably one antiviral drug code name can retrieved single input value. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed internal functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to an Antiviral Drug ID — as.av","text":"character  vector additional class ab","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to an Antiviral Drug ID — as.av","text":"entries antivirals data set three different identifiers: human readable EARS-Net code (column ab, used ECDC WHONET), ATC code (column atc, used ), CID code (column cid, Compound ID, used PubChem). data set contains 5,000 official brand names many different countries, found PubChem. drugs contain multiple ATC codes. properties searched user input. .av() can correct different forms misspelling: Wrong spelling drug names (\"acyclovir\"), corrects audible similarities f/ph, x/ks, c/z/s, t/th, etc. many vowels consonants Switching two characters (\"aycclovir\", often case clinical data, doctors typed fast) Digitalised paper records, leaving artefacts like 0/o/O (zero O's), B/8, n/r, etc. Use av_* functions get properties based returned antiviral drug ID, see Examples. Note: .av() av_* functions may use long regular expression match brand names antimicrobial drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to an Antiviral Drug ID — as.av","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Transform Input to an Antiviral Drug ID — as.av","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to an Antiviral Drug ID — as.av","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to an Antiviral Drug ID — as.av","text":"","code":"# these examples all return \"ACI\", the ID of aciclovir: as.av(\"J05AB01\") #> Class 'av' #> [1] ACI as.av(\"J 05 AB 01\") #> Class 'av' #> [1] ACI as.av(\"Aciclovir\") #> Class 'av' #> [1] ACI as.av(\"aciclo\") #> Class 'av' #> [1] ACI as.av(\"   aciclo 123\") #> Class 'av' #> [1] ACI as.av(\"ACICL\") #> Class 'av' #> [1] ACI as.av(\"ACI\") #> Class 'av' #> [1] ACI as.av(\"Virorax\") # trade name #> Class 'av' #> [1] ACI as.av(\"Zovirax\") # trade name #> Class 'av' #> [1] ACI  as.av(\"acyklofir\") # severe spelling error, yet works #> Class 'av' #> [1] ACI  # use av_* functions to get a specific properties (see ?av_property); # they use as.av() internally: av_name(\"J05AB01\") #> [1] \"Aciclovir\" av_name(\"acicl\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Disk Diffusion Diameters — as.disk","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"transforms vector new class disk, disk diffusion growth zone size (around antibiotic disk) millimetres 6 50.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"as.disk(x, na.rm = FALSE)  NA_disk_  is.disk(x)"},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"object class disk (inherits integer) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"x vector na.rm logical indicating whether missing values removed","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"integer additional class disk","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"Interpret disk values SIR values .sir(). supports guidelines EUCAST CLSI. Disk diffusion growth zone sizes must 6 50 millimetres. Values higher 50 lower 100 maximised 50. others input values outside 6-50 range return NA. NA_disk_ missing value new disk class.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"# transform existing disk zones to the `disk` class (using base R) df <- data.frame(   microorganism = \"Escherichia coli\",   AMP = 20,   CIP = 14,   GEN = 18,   TOB = 16 ) df[, 2:5] <- lapply(df[, 2:5], as.disk) str(df) #> 'data.frame':\t1 obs. of  5 variables: #>  $ microorganism: chr \"Escherichia coli\" #>  $ AMP          : 'disk' int 20 #>  $ CIP          : 'disk' int 14 #>  $ GEN          : 'disk' int 18 #>  $ TOB          : 'disk' int 16  # \\donttest{ # transforming is easier with dplyr: if (require(\"dplyr\")) {   df %>% mutate(across(AMP:TOB, as.disk)) } #>      microorganism AMP CIP GEN TOB #> 1 Escherichia coli  20  14  18  16 # }  # interpret disk values, see ?as.sir as.sir(   x = as.disk(18),   mo = \"Strep pneu\", # `mo` will be coerced with as.mo()   ab = \"ampicillin\", # and `ab` with as.ab()   guideline = \"EUCAST\" ) #> => Interpreting disk diffusion zones of 'ampicillin' (AMP) according to #>    EUCAST 2022... #>  OK. #> Class 'sir' #> [1] R  # interpret whole data set, pretend to be all from urinary tract infections: as.sir(df, uti = TRUE) #> => Interpreting disk diffusion zones of column 'AMP' (ampicillin) according #>    to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'CIP' (ciprofloxacin) #>    according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'GEN' (gentamicin) according #>    to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'TOB' (tobramycin) according #>    to EUCAST 2022... #>  OK. #>      microorganism AMP CIP GEN TOB #> 1 Escherichia coli   S   R   S   S"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"transforms vectors new class mic, treats input decimal numbers, maintaining operators (\">=\") allowing valid MIC values known field (medical) microbiology.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"as.mic(x, na.rm = FALSE)  NA_mic_  is.mic(x)  # S3 method for mic droplevels(x, as.mic = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"x character numeric vector na.rm logical indicating whether missing values removed .mic logical indicate whether mic class kept, defaults FALSE ... arguments passed methods","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"Ordered factor additional class mic, mathematical operations acts decimal numbers. Bare mind outcome mathematical operation MICs return numeric value.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"interpret MIC values SIR values, use .sir() MIC values. supports guidelines EUCAST (2013-2022) CLSI (2013-2022). class MIC values quite special data type: formally ordered factor valid MIC values factor levels (make sure valid MIC values retained), mathematical operation acts decimal numbers:   makes possible maintain operators often come MIC values, \">=\" \"<=\", even filtering using numeric values data analysis, e.g.:   following generic functions implemented MIC class: !, !=, %%, %/%, &, *, +, -, /, <, <=, ==, >, >=, ^, |, abs(), acos(), acosh(), (), (), asin(), asinh(), atan(), atanh(), ceiling(), cos(), cosh(), cospi(), cummax(), cummin(), cumprod(), cumsum(), digamma(), exp(), expm1(), floor(), gamma(), lgamma(), log(), log1p(), log2(), log10(), max(), mean(), min(), prod(), range(), round(), sign(), signif(), sin(), sinh(), sinpi(), sqrt(), sum(), tan(), tanh(), tanpi(), trigamma() trunc(). functions stats package also implemented: median(), quantile(), mad(), IQR(), fivenum(). Also, boxplot.stats() supported. Since sd() var() non-generic functions, extended. Use mad() alternative, use e.g. sd(.numeric(x)) x vector MIC values. Using .double() .numeric() MIC values remove operators return numeric vector. use .integer() MIC values R convention factors, return index factor levels (often useless regular users). Use droplevels() drop unused levels. default, return plain factor. Use droplevels(..., .mic = TRUE) maintain mic class. NA_mic_ missing value new mic class, analogous e.g. base R's NA_character_.","code":"x <- random_mic(10) x #> Class 'mic' #>  [1] 16     1      8      8      64     >=128  0.0625 32     32     16  is.factor(x) #> [1] TRUE  x[1] * 2 #> [1] 32  median(x) #> [1] 26 x[x > 4] #> Class 'mic' #> [1] 16    8     8     64    >=128 32    32    16  df <- data.frame(x, hospital = \"A\") subset(df, x > 4) # or with dplyr: df %>% filter(x > 4) #>        x hospital #> 1     16        A #> 5     64        A #> 6  >=128        A #> 8     32        A #> 9     32        A #> 10    16        A"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"mic_data <- as.mic(c(\">=32\", \"1.0\", \"1\", \"1.00\", 8, \"<=0.128\", \"8\", \"16\", \"16\")) mic_data #> Class 'mic' #> [1] >=32    1       1       1       8       <=0.128 8       16      16      is.mic(mic_data) #> [1] TRUE  # this can also coerce combined MIC/SIR values: as.mic(\"<=0.002; S\") #> Class 'mic' #> [1] <=0.002  # mathematical processing treats MICs as numeric values fivenum(mic_data) #> [1]  0.128  1.000  8.000 16.000 32.000 quantile(mic_data) #>     0%    25%    50%    75%   100%  #>  0.128  1.000  8.000 16.000 32.000  all(mic_data < 512) #> [1] TRUE  # interpret MIC values as.sir(   x = as.mic(2),   mo = as.mo(\"Streptococcus pneumoniae\"),   ab = \"AMX\",   guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMX' (amoxicillin) according to EUCAST #>    2022... #>  Note: #>    • Multiple breakpoints available for amoxicillin (AMX) in Streptococcus #>      pneumoniae - assuming body site 'Meningitis'. #> Class 'sir' #> [1] R as.sir(   x = as.mic(c(0.01, 2, 4, 8)),   mo = as.mo(\"Streptococcus pneumoniae\"),   ab = \"AMX\",   guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMX' (amoxicillin) according to EUCAST #>    2022... #>  Note: #>    • Multiple breakpoints available for amoxicillin (AMX) in Streptococcus #>      pneumoniae - assuming body site 'Meningitis'. #> Class 'sir' #> [1] S R R R  # plot MIC values, see ?plot plot(mic_data)  plot(mic_data, mo = \"E. coli\", ab = \"cipro\")   if (require(\"ggplot2\")) {   autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\") }  if (require(\"ggplot2\")) {   autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"nl\") # Dutch }  if (require(\"ggplot2\")) {   autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"uk\") # Ukrainian }"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to a Microorganism Code — as.mo","title":"Transform Input to a Microorganism Code — as.mo","text":"Use function determine valid microorganism code (mo). Determination done using intelligent rules complete taxonomic kingdoms Animalia, Archaea, Bacteria Protozoa, microbial species kingdom Fungi (see Source). input can almost anything: full name (like \"Staphylococcus aureus\"), abbreviated name (\"S. aureus\"), abbreviation known field (\"MRSA\"), just genus. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to a Microorganism Code — as.mo","text":"","code":"as.mo(   x,   Becker = FALSE,   Lancefield = FALSE,   minimum_matching_score = NULL,   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   reference_df = get_mo_source(),   ignore_pattern = getOption(\"AMR_ignore_pattern\", NULL),   remove_from_input = mo_cleaning_regex(),   language = get_AMR_locale(),   info = interactive(),   ... )  is.mo(x)  mo_uncertainties()  mo_renamed()  mo_failures()  mo_reset_session()  mo_cleaning_regex()"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to a Microorganism Code — as.mo","text":"x character vector data.frame one two columns Becker logical indicate whether staphylococci categorised coagulase-negative staphylococci (\"CoNS\") coagulase-positive staphylococci (\"CoPS\") instead species, according Karsten Becker et al. (see Source). excludes Staphylococcus aureus default, use Becker = \"\" also categorise S. aureus \"CoPS\". Lancefield logical indicate whether beta-haemolytic Streptococcus categorised Lancefield groups instead species, according Rebecca C. Lancefield (see Source). streptococci categorised first group, e.g. Streptococcus dysgalactiae group C, although officially also categorised groups G L. excludes enterococci default (group D), use Lancefield = \"\" also categorise enterococci group D. minimum_matching_score numeric value set lower limit MO matching score. left blank, determined automatically based character length x, taxonomic kingdom human pathogenicity. keep_synonyms logical indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE, return note old taxonomic names processed. default can set option AMR_keep_synonyms, .e. options(AMR_keep_synonyms = TRUE) options(AMR_keep_synonyms = FALSE). reference_df data.frame used extra reference translating x valid mo. See set_mo_source() get_mo_source() automate usage codes (e.g. used analysis organisation). ignore_pattern regular expression (case-insensitive) matches x must return NA. can convenient exclude known non-relevant input can also set option AMR_ignore_pattern, e.g. options(AMR_ignore_pattern = \"(reported|contaminated flora)\"). remove_from_input regular expression (case-insensitive) clean input x. Everything matched x removed. default, outcome mo_cleaning_regex(), removes texts brackets texts \"species\" \"serovar\". language language translate text like \"growth\", defaults system language (see get_AMR_locale()) info logical indicate progress bar printed 25 items coerced, defaults TRUE interactive mode ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to a Microorganism Code — as.mo","text":"character  vector additional class mo","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to a Microorganism Code — as.mo","text":"microorganism (MO) code package (class: mo) human readable typically looks like examples:   Values coerced considered 'unknown' returned MO code UNKNOWN warning. Use mo_* functions get properties based returned code, see Examples. .mo() function uses novel matching score algorithm (see Matching Score Microorganisms ) match input available microbial taxonomy package. lead effect e.g. \"E. coli\" (microorganism highly prevalent humans) return microbial ID Escherichia coli Entamoeba coli (microorganism less prevalent humans), although latter alphabetically come first. algorithm uses data List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF) (see microorganisms).","code":"Code               Full name   ---------------    --------------------------------------   B_KLBSL            Klebsiella   B_KLBSL_PNMN       Klebsiella pneumoniae   B_KLBSL_PNMN_RHNS  Klebsiella pneumoniae rhinoscleromatis   |   |    |    |   |   |    |    |   |   |    |    \\---> subspecies, a 3-5 letter acronym   |   |    \\----> species, a 3-6 letter acronym   |   \\----> genus, a 4-8 letter acronym   \\----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria),                             F (Fungi), PL (Plantae), P (Protozoa)"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"coping-with-uncertain-results","dir":"Reference","previous_headings":"","what":"Coping with Uncertain Results","title":"Transform Input to a Microorganism Code — as.mo","text":"Results non-exact taxonomic input based matching score. lowest allowed score can set minimum_matching_score argument. default determined based character length input, taxonomic kingdom human pathogenicity taxonomic outcome. values matched uncertainty, message shown suggest user evaluate results mo_uncertainties(), returns data.frame specifications. increase quality matching, remove_from_input argument can used clean input (.e., x). must regular expression matches parts input removed input matched available microbial taxonomy. matched Perl-compatible case-insensitive. default value remove_from_input outcome helper function mo_cleaning_regex(). three helper functions can run using .mo() function: Use mo_uncertainties() get data.frame prints pretty format taxonomic names guessed. output contains matching score matches (see Matching Score Microorganisms ). Use mo_failures() get character vector values coerced valid value. Use mo_renamed() get data.frame values coerced based old, previously accepted taxonomic names.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"microbial-prevalence-of-pathogens-in-humans","dir":"Reference","previous_headings":"","what":"Microbial Prevalence of Pathogens in Humans","title":"Transform Input to a Microorganism Code — as.mo","text":"coercion rules consider prevalence microorganisms humans, available prevalence column microorganisms data set. grouping human pathogenic prevalence explained section Matching Score Microorganisms .","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to a Microorganism Code — as.mo","text":"Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 Becker K et al. (2014). Coagulase-Negative Staphylococci. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. (2019). Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. (2020). Emergence coagulase-negative staphylococci Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 Lancefield RC (1933). serological differentiation human groups hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571 Berends MS et al. (2022). Trends Occurrence Phenotypic Resistance Coagulase-Negative Staphylococci (CoNS) Found Human Blood Northern Netherlands 2013 2019 Microorganisms 10(9), 1801; doi:10.3390/microorganisms10091801 Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Transform Input to a Microorganism Code — as.mo","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated :  : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.0 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, genus present established list also prevalence = 1.0 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.5 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Contracaecum, Cordylobia, Cryptococcus, Curvularia, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Giardia, Haloarcula, Halobacterium, Halococcus, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hymenolepis, Hypomyces, Hysterothylacium, Leishmania, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Mortierella, Mucor, Mycocentrospora, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Pediculus, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga Wuchereria; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.159\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to a Microorganism Code — as.mo","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to a Microorganism Code — as.mo","text":"","code":"# \\donttest{ # These examples all return \"B_STPHY_AURS\", the ID of S. aureus: as.mo(c(   \"sau\", # WHONET code   \"stau\",   \"STAU\",   \"staaur\",   \"S. aureus\",   \"S aureus\",   \"Sthafilokkockus aureus\", # handles incorrect spelling   \"Staphylococcus aureus (MRSA)\",   \"MRSA\", # Methicillin Resistant S. aureus   \"VISA\", # Vancomycin Intermediate S. aureus   \"VRSA\", # Vancomycin Resistant S. aureus   115329001 # SNOMED CT code )) #> Class 'mo' #>  [1] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #>  [6] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [11] B_STPHY_AURS B_STPHY_AURS  # Dyslexia is no problem - these all work: as.mo(c(   \"Ureaplasma urealyticum\",   \"Ureaplasma urealyticus\",   \"Ureaplasmium urealytica\",   \"Ureaplazma urealitycium\" )) #> Class 'mo' #> [1] B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY  as.mo(\"Streptococcus group A\") #> Class 'mo' #> [1] B_STRPT_GRPA  as.mo(\"S. epidermidis\") # will remain species: B_STPHY_EPDR #> Class 'mo' #> [1] B_STPHY_EPDR as.mo(\"S. epidermidis\", Becker = TRUE) # will not remain species: B_STPHY_CONS #> Class 'mo' #> [1] B_STPHY_CONS  as.mo(\"S. pyogenes\") # will remain species: B_STRPT_PYGN #> Class 'mo' #> [1] B_STRPT_PYGN as.mo(\"S. pyogenes\", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA #> Class 'mo' #> [1] B_STRPT_GRPA  # All mo_* functions use as.mo() internally too (see ?mo_property): mo_genus(\"E. coli\") #> [1] \"Escherichia\" mo_gramstain(\"ESCO\") #> [1] \"Gram-negative\" mo_is_intrinsic_resistant(\"ESCCOL\", ab = \"vanco\") #> ℹ Determining intrinsic resistance based on 'EUCAST Expert Rules' and #>   'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021). This note #>   will be shown once per session. #> [1] TRUE # }"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Interpret minimum inhibitory concentration (MIC) values disk diffusion diameters according EUCAST CLSI, clean existing SIR values. transforms input new class sir, ordered factor levels S < < R.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"as.sir(x, ...)  NA_sir_  is.sir(x)  is_sir_eligible(x, threshold = 0.05)  # S3 method for mic as.sir(   x,   mo = NULL,   ab = deparse(substitute(x)),   guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   uti = NULL,   conserve_capped_values = FALSE,   add_intrinsic_resistance = FALSE,   reference_data = AMR::clinical_breakpoints,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   ... )  # S3 method for disk as.sir(   x,   mo = NULL,   ab = deparse(substitute(x)),   guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   uti = NULL,   add_intrinsic_resistance = FALSE,   reference_data = AMR::clinical_breakpoints,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   ... )  # S3 method for data.frame as.sir(   x,   ...,   col_mo = NULL,   guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   uti = NULL,   conserve_capped_values = FALSE,   add_intrinsic_resistance = FALSE,   reference_data = AMR::clinical_breakpoints,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE) )  sir_interpretation_history(clean = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"object class sir (inherits ordered, factor) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpretations minimum inhibitory concentration (MIC) values disk diffusion diameters: M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 2013-2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. M100 Performance Standard Antimicrobial Susceptibility Testing, 2013-2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/. Breakpoint tables interpretation MICs zone diameters, 2013-2022, European Committee Antimicrobial Susceptibility Testing (EUCAST). https://www.eucast.org/clinical_breakpoints.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"x vector values (class mic: MIC values mg/L, class disk: disk diffusion radius millimetres) ... using data.frame: names columns apply .sir() (supports tidy selection column1:column4). Otherwise: arguments passed methods. threshold maximum fraction invalid antimicrobial interpretations x, see Examples mo (vector ) text can coerced valid microorganism codes .mo(), can left empty determine automatically ab (vector ) text can coerced valid antimicrobial drug code .ab() guideline defaults EUCAST 2022 (latest implemented EUCAST guideline clinical_breakpoints data set), can set option AMR_guideline. Supports EUCAST (2013-2022) CLSI (2013-2022), see Details. uti (Urinary Tract Infection) vector logicals (TRUE FALSE) specify whether UTI specific interpretation guideline chosen. using .sir() data.frame, can also column containing logicals left blank, data set searched column 'specimen', rows within column containing 'urin' ('urine', 'urina') regarded isolates UTI. See Examples. conserve_capped_values logical indicate MIC values starting \">\" (\">=\") must always return \"R\" , MIC values starting \"<\" (\"<=\") must always return \"S\" add_intrinsic_resistance (useful using EUCAST guideline) logical indicate whether intrinsic antibiotic resistance must also considered applicable bug-drug combinations, meaning e.g. ampicillin always return \"R\" Klebsiella species. Determination based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). reference_data data.frame used interpretation, defaults clinical_breakpoints data set. Changing argument allows using interpretation guidelines. argument must contain data set equal structure clinical_breakpoints data set (column names column types). Please note guideline argument ignored reference_data manually set. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort, defaults TRUE. Can also set option AMR_include_PKPD. col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). clean logical indicate whether previously stored results forgotten returning 'logbook' results","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Ordered factor new class sir","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"NA_sir_ missing value new sir class, analogous e.g. base R's NA_character_.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"how-it-works","dir":"Reference","previous_headings":"","what":"How it Works","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":".sir() function works four ways: cleaning raw / untransformed data. data cleaned contain values S, R try best determine intelligence. example, mixed values SIR interpretations MIC values \"<0.25; S\" coerced \"S\". Combined interpretations multiple test methods (seen laboratory records) \"S; S\" coerced \"S\", value like \"S; \" return NA warning input unclear. interpreting minimum inhibitory concentration (MIC) values according EUCAST CLSI. must clean MIC values first using .mic(), also gives columns new data class mic. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either:   Operators like \"<=\" stripped interpretation. using conserve_capped_values = TRUE, MIC value e.g. \">2\" always return \"R\", even breakpoint according chosen guideline \">=4\". prevent capped values raw laboratory data treated conservatively. default behaviour (conserve_capped_values = FALSE) considers \">2\" lower \">=4\" might case return \"S\" \"\". interpreting disk diffusion diameters according EUCAST CLSI. must clean disk zones first using .disk(), also gives columns new data class disk. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either:   interpreting complete data set, automatic determination MIC values, disk diffusion diameters, microorganism names codes, antimicrobial test results. done simply running .sir(your_data). points 2, 3 4: Use sir_interpretation_history() retrieve data.frame (tibble tibble package installed) results last .sir() call.","code":"your_data %>% mutate_if(is.mic, as.sir) your_data %>% mutate(across(where(is.mic), as.sir)) your_data %>% mutate_if(is.disk, as.sir) your_data %>% mutate(across(where(is.disk), as.sir))"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"supported-guidelines","dir":"Reference","previous_headings":"","what":"Supported Guidelines","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpreting MIC values well disk diffusion diameters, currently implemented guidelines EUCAST (2013-2022) CLSI (2013-2022). Thus, guideline argument must set e.g., \"EUCAST 2022\" \"CLSI 2022\". simply using \"EUCAST\" (default) \"CLSI\" input, latest included version guideline automatically selected. can set data set using reference_data argument. guideline argument ignored. can set default guideline option AMR_guideline (e.g. .Rprofile file), :","code":"options(AMR_guideline = \"CLSI\")   options(AMR_guideline = \"CLSI 2018\")   options(AMR_guideline = \"EUCAST 2020\")   # or to reset:   options(AMR_guideline = NULL)"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"after-interpretation","dir":"Reference","previous_headings":"","what":"After Interpretation","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"using .sir(), can use eucast_rules() defined EUCAST (1) apply inferred susceptibility resistance based results antimicrobials (2) apply intrinsic resistance based taxonomic properties microorganism.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"machine-readable-interpretation-guidelines","dir":"Reference","previous_headings":"","what":"Machine-Readable Interpretation Guidelines","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"repository package contains machine-readable version guidelines. CSV file consisting 18,308 rows 11 columns. file machine-readable, since contains one row every unique combination test method (MIC disk diffusion), antimicrobial drug microorganism. allows easy implementation rules laboratory information systems (LIS). Note contains interpretation guidelines humans - interpretation guidelines CLSI animals removed.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"function .sir() detects input contains class sir. input data.frame, iterates columns returns logical vector. function is_sir_eligible() returns TRUE columns contains 5% invalid antimicrobial interpretations (S //R), FALSE otherwise. threshold 5% can set threshold argument. input data.frame, iterates columns returns logical vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> summary(example_isolates) # see all SIR results at a glance #>       date              patient               age           gender          #>  Min.   :2002-01-02   Length:2000        Min.   : 0.00   Length:2000        #>  1st Qu.:2005-07-31   Class :character   1st Qu.:63.00   Class :character   #>  Median :2009-07-31   Mode  :character   Median :74.00   Mode  :character   #>  Mean   :2009-11-20                      Mean   :70.69                      #>  3rd Qu.:2014-05-30                      3rd Qu.:82.00                      #>  Max.   :2017-12-28                      Max.   :97.00                      #>      ward                mo                  PEN                 #>  Length:2000        Class :mo             Class:sir              #>  Class :character   <NA>  :0              %R   :73.7% (n=1201)   #>  Mode  :character   Unique:90             %SI  :26.3% (n=428)    #>                     #1    :B_ESCHR_COLI   - %S :25.6% (n=417)    #>                     #2    :B_STPHY_CONS   - %I : 0.7% (n=11)     #>                     #3    :B_STPHY_AURS                          #>     OXA                   FLC                   AMX                #>  Class:sir             Class:sir             Class:sir             #>  %R   :31.2% (n=114)   %R   :29.5% (n=278)   %R   :59.6% (n=804)   #>  %SI  :68.8% (n=251)   %SI  :70.5% (n=665)   %SI  :40.4% (n=546)   #>  - %S :68.8% (n=251)   - %S :70.5% (n=665)   - %S :40.2% (n=543)   #>  - %I : 0.0% (n=0)     - %I : 0.0% (n=0)     - %I : 0.2% (n=3)     #>                                                                    #>     AMC                    AMP                   TZP                #>  Class:sir              Class:sir             Class:sir             #>  %R   :23.7% (n=446)    %R   :59.6% (n=804)   %R   :12.6% (n=126)   #>  %SI  :76.3% (n=1433)   %SI  :40.4% (n=546)   %SI  :87.4% (n=875)   #>  - %S :71.4% (n=1342)   - %S :40.2% (n=543)   - %S :86.1% (n=862)   #>  - %I : 4.8% (n=91)     - %I : 0.2% (n=3)     - %I : 1.3% (n=13)    #>                                                                     #>     CZO                   FEP                   CXM                 #>  Class:sir             Class:sir             Class:sir              #>  %R   :44.6% (n=199)   %R   :14.2% (n=103)   %R   :26.3% (n=470)    #>  %SI  :55.4% (n=247)   %SI  :85.8% (n=621)   %SI  :73.7% (n=1319)   #>  - %S :54.9% (n=245)   - %S :85.6% (n=620)   - %S :72.5% (n=1297)   #>  - %I : 0.4% (n=2)     - %I : 0.1% (n=1)     - %I : 1.2% (n=22)     #>                                                                     #>     FOX                   CTX                   CAZ                 #>  Class:sir             Class:sir             Class:sir              #>  %R   :27.4% (n=224)   %R   :15.5% (n=146)   %R   :66.5% (n=1204)   #>  %SI  :72.6% (n=594)   %SI  :84.5% (n=797)   %SI  :33.5% (n=607)    #>  - %S :71.6% (n=586)   - %S :84.4% (n=796)   - %S :33.5% (n=607)    #>  - %I : 1.0% (n=8)     - %I : 0.1% (n=1)     - %I : 0.0% (n=0)      #>                                                                     #>     CRO                   GEN                    TOB                #>  Class:sir             Class:sir              Class:sir             #>  %R   :15.5% (n=146)   %R   :24.6% (n=456)    %R   :34.4% (n=465)   #>  %SI  :84.5% (n=797)   %SI  :75.4% (n=1399)   %SI  :65.6% (n=886)   #>  - %S :84.4% (n=796)   - %S :74.0% (n=1372)   - %S :65.1% (n=879)   #>  - %I : 0.1% (n=1)     - %I : 1.5% (n=27)     - %I : 0.5% (n=7)     #>                                                                     #>     AMK                   KAN                    TMP                #>  Class:sir             Class:sir              Class:sir             #>  %R   :63.7% (n=441)   %R   :100.0% (n=471)   %R   :38.1% (n=571)   #>  %SI  :36.3% (n=251)   %SI  : 0.0% (n=0)      %SI  :61.9% (n=928)   #>  - %S :36.3% (n=251)   - %S : 0.0% (n=0)      - %S :61.2% (n=918)   #>  - %I : 0.0% (n=0)     - %I : 0.0% (n=0)      - %I : 0.7% (n=10)    #>                                                                     #>     SXT                    NIT                   FOS                #>  Class:sir              Class:sir             Class:sir             #>  %R   :20.5% (n=361)    %R   :17.1% (n=127)   %R   :42.2% (n=148)   #>  %SI  :79.5% (n=1398)   %SI  :82.9% (n=616)   %SI  :57.8% (n=203)   #>  - %S :79.1% (n=1392)   - %S :76.0% (n=565)   - %S :57.8% (n=203)   #>  - %I : 0.3% (n=6)      - %I : 6.9% (n=51)    - %I : 0.0% (n=0)     #>                                                                     #>     LNZ                   CIP                    MFX                #>  Class:sir             Class:sir              Class:sir             #>  %R   :69.3% (n=709)   %R   :16.2% (n=228)    %R   :33.6% (n=71)    #>  %SI  :30.7% (n=314)   %SI  :83.8% (n=1181)   %SI  :66.4% (n=140)   #>  - %S :30.7% (n=314)   - %S :78.9% (n=1112)   - %S :64.5% (n=136)   #>  - %I : 0.0% (n=0)     - %I : 4.9% (n=69)     - %I : 1.9% (n=4)     #>                                                                     #>     VAN                    TEC                   TCY                #>  Class:sir              Class:sir             Class:sir             #>  %R   :38.3% (n=712)    %R   :75.7% (n=739)   %R   :29.8% (n=357)   #>  %SI  :61.7% (n=1149)   %SI  :24.3% (n=237)   %SI  :70.3% (n=843)   #>  - %S :61.7% (n=1149)   - %S :24.3% (n=237)   - %S :68.3% (n=820)   #>  - %I : 0.0% (n=0)      - %I : 0.0% (n=0)     - %I : 1.9% (n=23)    #>                                                                     #>     TGC                   DOX                   ERY                 #>  Class:sir             Class:sir             Class:sir              #>  %R   :12.7% (n=101)   %R   :27.7% (n=315)   %R   :57.2% (n=1084)   #>  %SI  :87.3% (n=697)   %SI  :72.3% (n=821)   %SI  :42.8% (n=810)    #>  - %S :87.3% (n=697)   - %S :71.7% (n=814)   - %S :42.3% (n=801)    #>  - %I : 0.0% (n=0)     - %I : 0.6% (n=7)     - %I : 0.5% (n=9)      #>                                                                     #>     CLI                   AZM                    IPM                #>  Class:sir             Class:sir              Class:sir             #>  %R   :61.2% (n=930)   %R   :57.2% (n=1084)   %R   : 6.2% (n=55)    #>  %SI  :38.8% (n=590)   %SI  :42.8% (n=810)    %SI  :93.8% (n=834)   #>  - %S :38.6% (n=586)   - %S :42.3% (n=801)    - %S :92.7% (n=824)   #>  - %I : 0.3% (n=4)     - %I : 0.5% (n=9)      - %I : 1.1% (n=10)    #>                                                                     #>     MEM                   MTR                  CHL                #>  Class:sir             Class:sir            Class:sir             #>  %R   : 5.9% (n=49)    %R   :14.7% (n=5)    %R   :21.4% (n=33)    #>  %SI  :94.1% (n=780)   %SI  :85.3% (n=29)   %SI  :78.6% (n=121)   #>  - %S :94.1% (n=780)   - %S :85.3% (n=29)   - %S :78.6% (n=121)   #>  - %I : 0.0% (n=0)     - %I : 0.0% (n=0)    - %I : 0.0% (n=0)     #>                                                                   #>     COL                    MUP                   RIF                #>  Class:sir              Class:sir             Class:sir             #>  %R   :81.2% (n=1331)   %R   : 5.9% (n=16)    %R   :69.6% (n=698)   #>  %SI  :18.8% (n=309)    %SI  :94.1% (n=254)   %SI  :30.4% (n=305)   #>  - %S :18.8% (n=309)    - %S :93.0% (n=251)   - %S :30.2% (n=303)   #>  - %I : 0.0% (n=0)      - %I : 1.1% (n=3)     - %I : 0.2% (n=2)     #>                                                                      # For INTERPRETING disk diffusion and MIC values -----------------------  # a whole data set, even with combined MIC values and disk zones df <- data.frame(   microorganism = \"Escherichia coli\",   AMP = as.mic(8),   CIP = as.mic(0.256),   GEN = as.disk(18),   TOB = as.disk(16),   ERY = \"R\" ) as.sir(df) #> => Interpreting MIC values of column 'AMP' (ampicillin) according to EUCAST #>    2022... #>  OK. #> => Interpreting MIC values of column 'CIP' (ciprofloxacin) according to #>    EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'GEN' (gentamicin) according #>    to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of column 'TOB' (tobramycin) according #>    to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Assigning class 'sir' to already clean column 'ERY' (erythromycin)... #>  OK. #>      microorganism AMP CIP GEN TOB ERY #> 1 Escherichia coli   S   I   S   S   R  # return a 'logbook' about the results: sir_interpretation_history() #> # A tibble: 50 × 12 #>    datetime            index ab_input ab_guid…¹ mo_in…² mo_guideline     guide…³ #>    <dttm>              <int> <chr>    <ab>      <chr>   <mo>             <chr>   #>  1 2023-01-23 19:11:56     1 TOB      TOB       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  2 2023-01-23 19:11:56     1 GEN      GEN       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  3 2023-01-23 19:11:56     1 CIP      CIP       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  4 2023-01-23 19:11:55     1 AMP      AMP       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  5 2023-01-23 19:11:50     1 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  6 2023-01-23 19:11:50     2 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  7 2023-01-23 19:11:50     3 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  8 2023-01-23 19:11:50     4 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  9 2023-01-23 19:11:50     5 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 10 2023-01-23 19:11:50     6 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> # … with 40 more rows, 5 more variables: ref_table <chr>, method <chr>, #> #   input <dbl>, outcome <sir>, breakpoint_S_R <chr>, and abbreviated variable #> #   names ¹​ab_guideline, ²​mo_input, ³​guideline  # for single values as.sir(   x = as.mic(2),   mo = as.mo(\"S. pneumoniae\"),   ab = \"AMP\",   guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #>    2022... #>  Note: #>    • Multiple breakpoints available for ampicillin (AMP) in Streptococcus #>      pneumoniae - assuming body site 'Non-meningitis'. #> Class 'sir' #> [1] R  as.sir(   x = as.disk(18),   mo = \"Strep pneu\", # `mo` will be coerced with as.mo()   ab = \"ampicillin\", # and `ab` with as.ab()   guideline = \"EUCAST\" ) #> => Interpreting disk diffusion zones of 'ampicillin' (AMP) according to #>    EUCAST 2022... #>  OK. #> Class 'sir' #> [1] R  # \\donttest{ # the dplyr way if (require(\"dplyr\")) {   df %>% mutate_if(is.mic, as.sir)   df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)   df %>% mutate(across(where(is.mic), as.sir))   df %>% mutate_at(vars(AMP:TOB), as.sir)   df %>% mutate(across(AMP:TOB, as.sir))    df %>%     mutate_at(vars(AMP:TOB), as.sir, mo = .$microorganism)    # to include information about urinary tract infections (UTI)   data.frame(     mo = \"E. coli\",     NIT = c(\"<= 2\", 32),     from_the_bladder = c(TRUE, FALSE)   ) %>%     as.sir(uti = \"from_the_bladder\")    data.frame(     mo = \"E. coli\",     NIT = c(\"<= 2\", 32),     specimen = c(\"urine\", \"blood\")   ) %>%     as.sir() # automatically determines urine isolates    df %>%     mutate_at(vars(AMP:TOB), as.sir, mo = \"E. coli\", uti = TRUE) } #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #>    2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST #>    2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to #>    EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to #>    EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to #>    EUCAST 2022... #> Warning: in as.sir(): interpretation of nitrofurantoin (NIT) is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms, #> thus assuming uti = TRUE. See ?as.sir. #>  * WARNING * #> ℹ Assuming value \"urine\" in column 'specimen' reflects a urinary tract #>   infection. #>   Use as.sir(uti = FALSE) to prevent this. #> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to #>    EUCAST 2022... #> Warning: in as.sir(): interpretation of nitrofurantoin (NIT) is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms, #> thus assuming uti = TRUE. See ?as.sir. #>  * WARNING * #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #>    2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST #>    2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to #>    EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to #>    EUCAST 2022... #>  OK. #>      microorganism AMP CIP GEN TOB ERY #> 1 Escherichia coli   S   I   S   S   R  # For CLEANING existing SIR values ------------------------------------  as.sir(c(\"S\", \"I\", \"R\", \"A\", \"B\", \"C\")) #> Warning: in as.sir(): 3 results in column '24' truncated (50%) that were invalid #> antimicrobial interpretations: \"A\", \"B\" and \"C\" #> Class 'sir' #> [1] S    I    R    <NA> <NA> <NA> as.sir(\"<= 0.002; S\") # will return \"S\" #> Class 'sir' #> [1] S sir_data <- as.sir(c(rep(\"S\", 474), rep(\"I\", 36), rep(\"R\", 370))) is.sir(sir_data) #> [1] TRUE plot(sir_data) # for percentages  barplot(sir_data) # for frequencies   # the dplyr way if (require(\"dplyr\")) {   example_isolates %>%     mutate_at(vars(PEN:RIF), as.sir)   # same:   example_isolates %>%     as.sir(PEN:RIF)    # fastest way to transform all columns with already valid AMR results to class `sir`:   example_isolates %>%     mutate_if(is_sir_eligible, as.sir)    # since dplyr 1.0.0, this can also be:   # example_isolates %>%   #   mutate(across(where(is_sir_eligible), as.sir)) } #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> # }"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":null,"dir":"Reference","previous_headings":"","what":"Get ATC Properties from WHOCC Website — atc_online_property","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Gets data WHOCC website determine properties Anatomical Therapeutic Chemical (ATC) (e.g. antibiotic), name, defined daily dose (DDD) standard unit.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"atc_online_property(   atc_code,   property,   administration = \"O\",   url = \"https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no\",   url_vet = \"https://www.whocc.no/atcvet/atcvet_index/?code=%s&showdescription=no\" )  atc_online_groups(atc_code, ...)  atc_online_ddd(atc_code, ...)  atc_online_ddd_units(atc_code, ...)"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"https://www.whocc./atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"atc_code character (vector) ATC code(s) antibiotics, coerced .ab() ab_atc() internally valid ATC code property property ATC code. Valid values \"ATC\", \"Name\", \"DDD\", \"U\" (\"unit\"), \"Adm.R\", \"Note\" groups. last option, hierarchical groups ATC code returned, see Examples. administration type administration using property = \"Adm.R\", see Details url url website WHOCC. sign %s can used placeholder ATC codes. url_vet url website WHOCC veterinary medicine. sign %s can used placeholder ATC_vet codes (start \"Q\"). ... arguments pass atc_property","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Options argument administration: \"Implant\" = Implant \"Inhal\" = Inhalation \"Instill\" = Instillation \"N\" = nasal \"O\" = oral \"P\" = parenteral \"R\" = rectal \"SL\" = sublingual/buccal \"TD\" = transdermal \"V\" = vaginal Abbreviations return values using property = \"U\" (unit): \"g\" = gram \"mg\" = milligram \"mcg\" = microgram \"U\" = unit \"TU\" = thousand units \"MU\" = million units \"mmol\" = millimole \"ml\" = millilitre (e.g. eyedrops) N.B. function requires internet connection works following packages installed: curl, rvest, xml2.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"# \\donttest{ if (requireNamespace(\"curl\") && requireNamespace(\"rvest\") && requireNamespace(\"xml2\")) {   # oral DDD (Defined Daily Dose) of amoxicillin   atc_online_property(\"J01CA04\", \"DDD\", \"O\")   atc_online_ddd(ab_atc(\"amox\"))    # parenteral DDD (Defined Daily Dose) of amoxicillin   atc_online_property(\"J01CA04\", \"DDD\", \"P\")    atc_online_property(\"J01CA04\", property = \"groups\") # search hierarchical groups of amoxicillin } #> Loading required namespace: rvest #> [1] \"ANTIINFECTIVES FOR SYSTEMIC USE\"         #> [2] \"ANTIBACTERIALS FOR SYSTEMIC USE\"         #> [3] \"BETA-LACTAM ANTIBACTERIALS, PENICILLINS\" #> [4] \"Penicillins with extended spectrum\"      # }"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Use function e.g. clinical texts health care records. returns list antiviral drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text(   text,   type = c(\"drug\", \"dose\", \"administration\"),   collapse = NULL,   translate_av = FALSE,   thorough_search = NULL,   info = interactive(),   ... )"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_av type = \"drug\": column name antivirals data set translate antibiotic abbreviations , using av_property(). Defaults FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed .av()","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"list,  character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"function also internally used .av(), although searches first drug name throw note drug names returned. Note: .av() function may use long regular expression match brand names antiviral drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"default, function search antiviral drug names. text elements searched official names, ATC codes brand names. uses .av() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(avx = av_from_text(clinical_text)) returned AV codes can transformed official names, groups, etc. av_* functions av_name() av_group(), using translate_av argument. using collapse, function return character:df %>% mutate(avx = av_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text(\"28/03/2020 valaciclovir po tid\") #> [[1]] #> Class 'av' #> [1] VALA #>  av_from_text(\"28/03/2020 valaciclovir po tid\", type = \"admin\") #> [[1]] #> [1] \"oral\" #>"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antiviral Drug — av_property","title":"Get Properties of an Antiviral Drug — av_property","text":"Use functions return specific property antiviral drug antivirals data set. input values evaluated internally .av().","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"av_name(x, language = get_AMR_locale(), tolower = FALSE, ...)  av_cid(x, ...)  av_synonyms(x, ...)  av_tradenames(x, ...)  av_group(x, language = get_AMR_locale(), ...)  av_atc(x, ...)  av_loinc(x, ...)  av_ddd(x, administration = \"oral\", ...)  av_ddd_units(x, administration = \"oral\", ...)  av_info(x, language = get_AMR_locale(), ...)  av_url(x, open = FALSE, ...)  av_property(x, property = \"name\", language = get_AMR_locale(), ...)"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antiviral Drug — av_property","text":"x (vector ) text can coerced valid antiviral drug code .av() language language returned text, defaults system language (see get_AMR_locale()) can also set option AMR_locale. Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. ... arguments passed .av() administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antivirals data set: vector_or(colnames(antivirals), sort = FALSE).","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antiviral Drug — av_property","text":"integer case av_cid() named list case av_info() multiple av_atc()/av_synonyms()/av_tradenames() double case av_ddd() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antiviral Drug — av_property","text":"output translated possible. function av_url() return direct URL official website. warning returned required ATC code available.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antiviral Drug — av_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antiviral Drug — av_property","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"# all properties: av_name(\"ACI\") #> [1] \"Aciclovir\" av_atc(\"ACI\") #> [1] \"J05AB01\" av_cid(\"ACI\") #> [1] 135398513 av_synonyms(\"ACI\") #>  [1] \"acicloftal\"        \"aciclovier\"        \"aciclovirum\"       #>  [4] \"activir\"           \"acyclofoam\"        \"acycloguanosine\"   #>  [7] \"acyclovir\"         \"acyclovir lauriad\" \"avaclyr\"           #> [10] \"cargosil\"          \"cyclovir\"          \"genvir\"            #> [13] \"gerpevir\"          \"hascovir\"          \"maynar\"            #> [16] \"novirus\"           \"poviral\"           \"sitavig\"           #> [19] \"sitavir\"           \"vipral\"            \"viropump\"          #> [22] \"virorax\"           \"zovirax\"           \"zyclir\"            av_tradenames(\"ACI\") #>  [1] \"acicloftal\"        \"aciclovier\"        \"aciclovirum\"       #>  [4] \"activir\"           \"acyclofoam\"        \"acycloguanosine\"   #>  [7] \"acyclovir\"         \"acyclovir lauriad\" \"avaclyr\"           #> [10] \"cargosil\"          \"cyclovir\"          \"genvir\"            #> [13] \"gerpevir\"          \"hascovir\"          \"maynar\"            #> [16] \"novirus\"           \"poviral\"           \"sitavig\"           #> [19] \"sitavir\"           \"vipral\"            \"viropump\"          #> [22] \"virorax\"           \"zovirax\"           \"zyclir\"            av_group(\"ACI\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_url(\"ACI\") #>                                                             Aciclovir  #> \"https://www.whocc.no/atc_ddd_index/?code=J05AB01&showdescription=no\"   # smart lowercase tranformation av_name(x = c(\"ACI\", \"VALA\")) #> [1] \"Aciclovir\"    \"Valaciclovir\" av_name(x = c(\"ACI\", \"VALA\"), tolower = TRUE) #> [1] \"aciclovir\"    \"valaciclovir\"  # defined daily doses (DDD) av_ddd(\"ACI\", \"oral\") #> [1] 4 av_ddd_units(\"ACI\", \"oral\") #> [1] \"g\" av_ddd(\"ACI\", \"iv\") #> [1] 4 av_ddd_units(\"ACI\", \"iv\") #> [1] \"g\"  av_info(\"ACI\") # all properties as a list #> $av #> [1] \"ACI\" #>  #> $cid #> [1] 135398513 #>  #> $name #> [1] \"Aciclovir\" #>  #> $group #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" #>  #> $atc #> [1] \"J05AB01\" #>  #> $tradenames #>  [1] \"acicloftal\"        \"aciclovier\"        \"aciclovirum\"       #>  [4] \"activir\"           \"acyclofoam\"        \"acycloguanosine\"   #>  [7] \"acyclovir\"         \"acyclovir lauriad\" \"avaclyr\"           #> [10] \"cargosil\"          \"cyclovir\"          \"genvir\"            #> [13] \"gerpevir\"          \"hascovir\"          \"maynar\"            #> [16] \"novirus\"           \"poviral\"           \"sitavig\"           #> [19] \"sitavir\"           \"vipral\"            \"viropump\"          #> [22] \"virorax\"           \"zovirax\"           \"zyclir\"            #>  #> $loinc #> [1] \"\" #>  #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 4 #>  #> $ddd$oral$units #> [1] \"g\" #>  #>  #> $ddd$iv #> $ddd$iv$amount #> [1] 4 #>  #> $ddd$iv$units #> [1] \"g\" #>  #>  #>   # all av_* functions use as.av() internally, so you can go from 'any' to 'any': av_atc(\"ACI\") #> [1] \"J05AB01\" av_group(\"J05AB01\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_loinc(\"abacavir\") #> [1] \"29113-8\" \"78772-1\" \"78773-9\" \"79134-3\" \"80118-3\" av_name(\"29113-8\") #> [1] \"Abacavir\" av_name(135398513) #> [1] \"Aciclovir\" av_name(\"J05AB01\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Availability of Columns — availability","title":"Check Availability of Columns — availability","text":"Easy check data availability columns data set. makes easy get idea antimicrobial combinations can used calculation e.g. susceptibility() resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Availability of Columns — availability","text":"","code":"availability(tbl, width = NULL)"},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Availability of Columns — availability","text":"tbl data.frame list width number characters present visual availability, defaults filling width console","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Availability of Columns — availability","text":"data.frame column names tbl row names","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check Availability of Columns — availability","text":"function returns data.frame columns \"resistant\" \"visual_resistance\". values columns calculated resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check Availability of Columns — availability","text":"","code":"availability(example_isolates) #>         count available     visual_availabilty resistant      visual_resistance #> date     2000    100.0% |####################|                                  #> patient  2000    100.0% |####################|                                  #> age      2000    100.0% |####################|                                  #> gender   2000    100.0% |####################|                                  #> ward     2000    100.0% |####################|                                  #> mo       2000    100.0% |####################|                                  #> PEN      1629     81.5% |################----|     73.7% |##############------| #> OXA       365     18.3% |###-----------------|     31.2% |######--------------| #> FLC       943     47.2% |#########-----------|     29.5% |#####---------------| #> AMX      1350     67.5% |#############-------|     59.6% |###########---------| #> AMC      1879     94.0% |##################--|     23.7% |####----------------| #> AMP      1350     67.5% |#############-------|     59.6% |###########---------| #> TZP      1001     50.0% |##########----------|     12.6% |##------------------| #> CZO       446     22.3% |####----------------|     44.6% |########------------| #> FEP       724     36.2% |#######-------------|     14.2% |##------------------| #> CXM      1789     89.5% |#################---|     26.3% |#####---------------| #> FOX       818     40.9% |########------------|     27.4% |#####---------------| #> CTX       943     47.2% |#########-----------|     15.5% |###-----------------| #> CAZ      1811     90.6% |##################--|     66.5% |#############-------| #> CRO       943     47.2% |#########-----------|     15.5% |###-----------------| #> GEN      1855     92.8% |##################--|     24.6% |####----------------| #> TOB      1351     67.6% |#############-------|     34.4% |######--------------| #> AMK       692     34.6% |######--------------|     63.7% |############--------| #> KAN       471     23.6% |####----------------|    100.0% |####################| #> TMP      1499     75.0% |###############-----|     38.1% |#######-------------| #> SXT      1759     88.0% |#################---|     20.5% |####----------------| #> NIT       743     37.2% |#######-------------|     17.1% |###-----------------| #> FOS       351     17.6% |###-----------------|     42.2% |########------------| #> LNZ      1023     51.2% |##########----------|     69.3% |#############-------| #> CIP      1409     70.5% |#############-------|     16.2% |###-----------------| #> MFX       211     10.6% |##------------------|     33.6% |######--------------| #> VAN      1861     93.1% |##################--|     38.3% |#######-------------| #> TEC       976     48.8% |#########-----------|     75.7% |###############-----| #> TCY      1200     60.0% |###########---------|     29.8% |#####---------------| #> TGC       798     39.9% |########------------|     12.7% |##------------------| #> DOX      1136     56.8% |###########---------|     27.7% |#####---------------| #> ERY      1894     94.7% |##################--|     57.2% |###########---------| #> CLI      1520     76.0% |###############-----|     61.2% |############--------| #> AZM      1894     94.7% |##################--|     57.2% |###########---------| #> IPM       889     44.5% |########------------|      6.2% |#-------------------| #> MEM       829     41.5% |########------------|      5.9% |#-------------------| #> MTR        34      1.7% |--------------------|     14.7% |##------------------| #> CHL       154      7.7% |#-------------------|     21.4% |####----------------| #> COL      1640     82.0% |################----|     81.2% |################----| #> MUP       270     13.5% |##------------------|      5.9% |#-------------------| #> RIF      1003     50.2% |##########----------|     69.6% |#############-------| # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     filter(mo == as.mo(\"Escherichia coli\")) %>%     select_if(is.sir) %>%     availability() } #>     count available       visual_availabilty resistant        visual_resistance #> PEN   467    100.0% |######################|    100.0% |######################| #> OXA     0      0.0% |----------------------|                                    #> FLC     0      0.0% |----------------------|                                    #> AMX   392     83.9% |##################----|     50.0% |###########-----------| #> AMC   467    100.0% |######################|     13.1% |##--------------------| #> AMP   392     83.9% |##################----|     50.0% |###########-----------| #> TZP   416     89.1% |###################---|      5.5% |#---------------------| #> CZO    82     17.6% |###-------------------|      2.4% |----------------------| #> FEP   317     67.9% |##############--------|      2.8% |----------------------| #> CXM   465     99.6% |######################|      5.4% |#---------------------| #> FOX   377     80.7% |#################-----|      6.9% |#---------------------| #> CTX   459     98.3% |#####################-|      2.4% |----------------------| #> CAZ   460     98.5% |#####################-|      2.4% |----------------------| #> CRO   459     98.3% |#####################-|      2.4% |----------------------| #> GEN   460     98.5% |#####################-|      2.0% |----------------------| #> TOB   462     98.9% |#####################-|      2.6% |----------------------| #> AMK   171     36.6% |########--------------|      0.0% |----------------------| #> KAN     0      0.0% |----------------------|                                    #> TMP   396     84.8% |##################----|     39.1% |########--------------| #> SXT   465     99.6% |######################|     31.6% |######----------------| #> NIT   458     98.1% |#####################-|      2.8% |----------------------| #> FOS    61     13.1% |##--------------------|      0.0% |----------------------| #> LNZ   467    100.0% |######################|    100.0% |######################| #> CIP   456     97.6% |#####################-|     12.5% |##--------------------| #> MFX    57     12.2% |##--------------------|    100.0% |######################| #> VAN   467    100.0% |######################|    100.0% |######################| #> TEC   467    100.0% |######################|    100.0% |######################| #> TCY     3      0.6% |----------------------|     66.7% |##############--------| #> TGC    68     14.6% |###-------------------|      0.0% |----------------------| #> DOX     0      0.0% |----------------------|                                    #> ERY   467    100.0% |######################|    100.0% |######################| #> CLI   467    100.0% |######################|    100.0% |######################| #> AZM   467    100.0% |######################|    100.0% |######################| #> IPM   422     90.4% |###################---|      0.0% |----------------------| #> MEM   418     89.5% |###################---|      0.0% |----------------------| #> MTR     2      0.4% |----------------------|      0.0% |----------------------| #> CHL     0      0.0% |----------------------|                                    #> COL   240     51.4% |###########-----------|      0.0% |----------------------| #> MUP     0      0.0% |----------------------|                                    #> RIF   467    100.0% |######################|    100.0% |######################| # }"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Bug-Drug Combinations — bug_drug_combinations","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"Determine antimicrobial resistance (AMR) bug-drug combinations data set least 30 (default) isolates available per species. Use format() result prettify publishable/printable format, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...)  # S3 method for bug_drug_combinations format(   x,   translate_ab = \"name (ab, atc)\",   language = get_AMR_locale(),   minimum = 30,   combine_SI = TRUE,   add_ab_group = TRUE,   remove_intrinsic_resistant = FALSE,   decimal.mark = getOption(\"OutDec\"),   big.mark = ifelse(decimal.mark == \",\", \".\", \",\"),   ... )"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"x data set antibiotic columns, amox, AMX AMC col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). FUN function call mo column transform microorganism codes, defaults mo_shortname() ... arguments passed FUN translate_ab character length 1 containing column names antibiotics data set language language returned text, defaults system language (see get_AMR_locale()) can also set option AMR_locale. Use language = NULL language = \"\" prevent translation. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether values S summed, resistance based R, defaults TRUE add_ab_group logical indicate group antimicrobials must included first column remove_intrinsic_resistant logical indicate rows columns 100% resistance tested antimicrobials must removed table decimal.mark character used indicate numeric     decimal point. big.mark character; empty used mark every     big.interval decimals (hence big)     decimal point.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function bug_drug_combinations() returns data.frame columns \"mo\", \"ab\", \"S\", \"\", \"R\" \"total\".","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function format() calculates resistance per bug-drug combination. Use combine_SI = TRUE (default) test R vs. S+combine_SI = FALSE test R+vs. S.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"# \\donttest{ x <- bug_drug_combinations(example_isolates) head(x) #> # A tibble: 6 × 6 #>   mo                ab        S     I     R total #>   <chr>             <chr> <int> <int> <int> <int> #> 1 (unknown species) PEN      14     0     1    15 #> 2 (unknown species) OXA       0     0     1     1 #> 3 (unknown species) FLC       0     0     0     0 #> 4 (unknown species) AMX      15     0     1    16 #> 5 (unknown species) AMC      15     0     0    15 #> 6 (unknown species) AMP      15     0     1    16 #> Use 'format()' on this result to get a publishable/printable format. format(x, translate_ab = \"name (atc)\") #> # A tibble: 39 × 12 #>    Group     Drug  CoNS  E. co…¹ E. fa…² K. pn…³ P. ae…⁴ P. mi…⁵ S. au…⁶ S. ep…⁷ #>    <chr>     <chr> <chr> <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   #>  1 \"Aminogl… Amik… \"100… \"  0.0… \"100.0… \"\"      \"\"      \"\"      \"\"      \"100.0… #>  2 \"\"        Gent… \" 13… \"  2.0… \"100.0… \" 10.3… \"  0.0… \"  5.9… \"  0.9… \" 21.5… #>  3 \"\"        Kana… \"100… \"\"      \"100.0… \"\"      \"100.0… \"\"      \"\"      \"100.0… #>  4 \"\"        Tobr… \" 78… \"  2.6… \"100.0… \" 10.3… \"  0.0… \"  5.9… \"  2.3… \" 49.4… #>  5 \"Ampheni… Chlo… \"\"    \"\"      \"\"      \"\"      \"100.0… \"\"      \"  0.0… \"  3.1… #>  6 \"Antimyc… Rifa… \"\"    \"100.0… \"\"      \"100.0… \"100.0… \"100.0… \"  0.0… \"  2.7… #>  7 \"Beta-la… Amox… \" 93… \" 50.0… \"\"      \"100.0… \"100.0… \"\"      \" 93.9… \" 98.9… #>  8 \"\"        Amox… \" 42… \" 13.1… \"\"      \" 10.3… \"100.0… \"  2.8… \"  0.4… \" 54.5… #>  9 \"\"        Ampi… \" 93… \" 50.0… \"\"      \"100.0… \"100.0… \"\"      \" 93.9… \" 98.9… #> 10 \"\"        Benz… \" 77… \"100.0… \"\"      \"100.0… \"100.0… \"100.0… \" 80.9… \" 89.4… #> # … with 29 more rows, 2 more variables: `S. hominis` <chr>, #> #   `S. pneumoniae` <chr>, and abbreviated variable names ¹​`E. coli`, #> #   ²​`E. faecalis`, ³​`K. pneumoniae`, ⁴​`P. aeruginosa`, ⁵​`P. mirabilis`, #> #   ⁶​`S. aureus`, ⁷​`S. epidermidis`  # Use FUN to change to transformation of microorganism codes bug_drug_combinations(example_isolates,   FUN = mo_gramstain ) #> # A tibble: 80 × 6 #>    mo            ab        S     I     R total #>  * <chr>         <chr> <int> <int> <int> <int> #>  1 Gram-negative PEN       8     0   717   725 #>  2 Gram-negative OXA       6     0     0     6 #>  3 Gram-negative FLC       6     0     0     6 #>  4 Gram-negative AMX     226     0   405   631 #>  5 Gram-negative AMC     463    89   174   726 #>  6 Gram-negative AMP     226     0   405   631 #>  7 Gram-negative TZP     554    11    76   641 #>  8 Gram-negative CZO      94     2   110   206 #>  9 Gram-negative FEP     470     1    14   485 #> 10 Gram-negative CXM     539    22   142   703 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format.  bug_drug_combinations(example_isolates,   FUN = function(x) {     ifelse(x == as.mo(\"Escherichia coli\"),       \"E. coli\",       \"Others\"     )   } ) #> # A tibble: 80 × 6 #>    mo      ab        S     I     R total #>  * <chr>   <chr> <int> <int> <int> <int> #>  1 E. coli PEN       0     0   467   467 #>  2 E. coli OXA       0     0     0     0 #>  3 E. coli FLC       0     0     0     0 #>  4 E. coli AMX     196     0   196   392 #>  5 E. coli AMC     332    74    61   467 #>  6 E. coli AMP     196     0   196   392 #>  7 E. coli TZP     388     5    23   416 #>  8 E. coli CZO      79     1     2    82 #>  9 E. coli FEP     308     0     9   317 #> 10 E. coli CXM     425    15    25   465 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format. # }"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Data set containing clinical breakpoints interpret MIC disk diffusion SIR values, according international guidelines. Currently implemented guidelines EUCAST (2013-2022) CLSI (2013-2022). Use .sir() transform MICs disks measurements SIR values.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"tibble 18,308 observations 11 variables: guideline Name guideline method Either \"DISK\" \"MIC\" site Body site, e.g. \"Oral\" \"Respiratory\" mo Microbial ID, see .mo() rank_index Taxonomic rank index mo 1 (subspecies/infraspecies) 5 (unknown microorganism) ab Antibiotic ID, see .ab() ref_tbl Info guideline rule can found disk_dose Dose used disk diffusion method breakpoint_S Lowest MIC value highest number millimetres leads \"S\" breakpoint_R Highest MIC value lowest number millimetres leads \"R\" uti logical value (TRUE/FALSE) indicate whether rule applies urinary tract infection (UTI)","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository. allow machine reading EUCAST CLSI guidelines, almost impossible MS Excel PDF files distributed EUCAST CLSI.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints #> # A tibble: 18,308 × 11 #>    guideline method site  mo               rank_…¹ ab   ref_tbl  disk_…² break…³ #>    <chr>     <chr>  <chr> <mo>               <dbl> <ab> <chr>    <chr>     <dbl> #>  1 EUCAST 2… MIC    NA    F_ASPRG_MGTS           2 AMB  Aspergi… NA            1 #>  2 EUCAST 2… MIC    NA    F_ASPRG_NIGR           2 AMB  Aspergi… NA            1 #>  3 EUCAST 2… MIC    NA    F_CANDD_ALBC           2 AMB  Candida  NA            1 #>  4 EUCAST 2… MIC    NA    F_CANDD_DBLN           2 AMB  Candida  NA            1 #>  5 EUCAST 2… MIC    NA    F_CANDD_GLBR           2 AMB  Candida  NA            1 #>  6 EUCAST 2… MIC    NA    F_CANDD_KRUS           2 AMB  Candida  NA            1 #>  7 EUCAST 2… MIC    NA    F_CANDD_PRPS           2 AMB  Candida  NA            1 #>  8 EUCAST 2… MIC    NA    F_CANDD_TRPC           2 AMB  Candida  NA            1 #>  9 EUCAST 2… MIC    NA    F_CRYPT_NFRM           2 AMB  Candida  NA            1 #> 10 EUCAST 2… DISK   NA    B_[ORD]_ENTRBCTR       5 AMC  Enterob… 20ug/1…      19 #> # … with 18,298 more rows, 2 more variables: breakpoint_R <dbl>, uti <lgl>, and #> #   abbreviated variable names ¹​rank_index, ²​disk_dose, ³​breakpoint_S"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Available Isolates — count","title":"Count Available Isolates — count","text":"functions can used count resistant/susceptible microbial isolates. functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. count_resistant() used count resistant isolates, count_susceptible() used count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Available Isolates — count","text":"","code":"count_resistant(..., only_all_tested = FALSE)  count_susceptible(..., only_all_tested = FALSE)  count_R(..., only_all_tested = FALSE)  count_IR(..., only_all_tested = FALSE)  count_I(..., only_all_tested = FALSE)  count_SI(..., only_all_tested = FALSE)  count_S(..., only_all_tested = FALSE)  count_all(..., only_all_tested = FALSE)  n_sir(..., only_all_tested = FALSE)  count_df(   data,   translate_ab = \"name\",   language = get_AMR_locale(),   combine_SI = TRUE )"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Available Isolates — count","text":"... one vectors (columns) antibiotic interpretations. transformed internally .sir() needed. only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antibiotics, see section Combination Therapy data data.frame containing columns class sir (see .sir()) translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() language language returned text, defaults system language (see get_AMR_locale()) can also set option AMR_locale. Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant), defaults TRUE","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Available Isolates — count","text":"integer","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Count Available Isolates — count","text":"functions meant count isolates. Use resistance()/susceptibility() functions calculate microbial resistance/susceptibility. function count_resistant() equal function count_R(). function count_susceptible() equal function count_SI(). function n_sir() alias count_all(). can used count available isolates, .e. input antibiotics available result (S, R). use equal n_distinct(). function equal count_susceptible(...) + count_resistant(...). function count_df() takes variable data sir class (created .sir()) counts number S's, 's R's. also supports grouped variables. function sir_df() works exactly like count_df(), adds percentage S, R.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Count Available Isolates — count","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Count Available Isolates — count","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antibiotics, Drug Drug B, susceptibility() works calculate %SI:   Please note , combination therapies, only_all_tested = TRUE applies :   , combination therapies, only_all_tested = FALSE applies :   Using only_all_tested impact using one antibiotic input.","code":"--------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   include as  include as   include as  include as                     numerator   denominator  numerator   denominator --------  --------  ----------  -----------  ----------  -----------  S or I    S or I       X            X            X            X    R       S or I       X            X            X            X   <NA>     S or I       X            X            -            -  S or I      R          X            X            X            X    R         R          -            X            -            X   <NA>       R          -            -            -            -  S or I     <NA>        X            X            -            -    R        <NA>        -            -            -            -   <NA>      <NA>        -            -            -            - -------------------------------------------------------------------- count_S()    +   count_I()    +   count_R()    = count_all()   proportion_S() + proportion_I() + proportion_R() = 1 count_S()    +   count_I()    +   count_R()    >= count_all()   proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Available Isolates — count","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info.  # base R ------------------------------------------------------------ count_resistant(example_isolates$AMX) # counts \"R\" #> [1] 804 count_susceptible(example_isolates$AMX) # counts \"S\" and \"I\" #> [1] 546 count_all(example_isolates$AMX) # counts \"S\", \"I\" and \"R\" #> [1] 1350  # be more specific count_S(example_isolates$AMX) #> Using count_S() is discouraged; use count_susceptible() instead to also #> consider \"I\" being susceptible. This note will be shown once for this #> session. #> [1] 543 count_SI(example_isolates$AMX) #> [1] 546 count_I(example_isolates$AMX) #> [1] 3 count_IR(example_isolates$AMX) #> Using count_IR() is discouraged; use count_resistant() instead to not #> consider \"I\" being resistant. This note will be shown once for this #> session. #> [1] 807 count_R(example_isolates$AMX) #> [1] 804  # Count all available isolates count_all(example_isolates$AMX) #> [1] 1350 n_sir(example_isolates$AMX) #> [1] 1350  # n_sir() is an alias of count_all(). # Since it counts all available isolates, you can # calculate back to count e.g. susceptible isolates. # These results are the same: count_susceptible(example_isolates$AMX) #> [1] 546 susceptibility(example_isolates$AMX) * n_sir(example_isolates$AMX) #> [1] 546  # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     group_by(ward) %>%     summarise(       R = count_R(CIP),       I = count_I(CIP),       S = count_S(CIP),       n1 = count_all(CIP), # the actual total; sum of all three       n2 = n_sir(CIP), # same - analogous to n_distinct       total = n()     ) # NOT the number of tested isolates!    # Number of available isolates for a whole antibiotic class   # (i.e., in this data set columns GEN, TOB, AMK, KAN)   example_isolates %>%     group_by(ward) %>%     summarise(across(aminoglycosides(), n_sir))    # Count co-resistance between amoxicillin/clav acid and gentamicin,   # so we can see that combination therapy does a lot more than mono therapy.   # Please mind that `susceptibility()` calculates percentages right away instead.   example_isolates %>% count_susceptible(AMC) # 1433   example_isolates %>% count_all(AMC) # 1879    example_isolates %>% count_susceptible(GEN) # 1399   example_isolates %>% count_all(GEN) # 1855    example_isolates %>% count_susceptible(AMC, GEN) # 1764   example_isolates %>% count_all(AMC, GEN) # 1936    # Get number of S+I vs. R immediately of selected columns   example_isolates %>%     select(AMX, CIP) %>%     count_df(translate = FALSE)    # It also supports grouping variables   example_isolates %>%     select(ward, AMX, CIP) %>%     group_by(ward) %>%     count_df(translate = FALSE) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> # A tibble: 12 × 4 #>    ward       antibiotic interpretation value #>  * <chr>      <chr>      <ord>          <int> #>  1 Clinical   AMX        SI               357 #>  2 Clinical   AMX        R                487 #>  3 Clinical   CIP        SI               741 #>  4 Clinical   CIP        R                128 #>  5 ICU        AMX        SI               158 #>  6 ICU        AMX        R                270 #>  7 ICU        CIP        SI               362 #>  8 ICU        CIP        R                 85 #>  9 Outpatient AMX        SI                31 #> 10 Outpatient AMX        R                 47 #> 11 Outpatient CIP        SI                78 #> 12 Outpatient CIP        R                 15 # }"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Define Custom EUCAST Rules — custom_eucast_rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"Define custom EUCAST rules organisation specific analysis use output function eucast_rules().","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"custom_eucast_rules(...)"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"... rules formula notation, see Examples","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"list containing custom rules","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"organisations adoption EUCAST rules. function can used define custom EUCAST rules used eucast_rules() function.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"basics","dir":"Reference","previous_headings":"","what":"Basics","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde:   two custom EUCAST rules: TZP (piperacillin/tazobactam) \"S\", aminopenicillins (ampicillin amoxicillin) must made \"S\", TZP \"R\", aminopenicillins must made \"R\". rules can also printed console, immediately clear work:   rules (part tilde, example TZP == \"S\" TZP == \"R\") must evaluable data set: able run filter data set without errors. means example column TZP must exist. create sample data set test rules set:","code":"x <- custom_eucast_rules(TZP == \"S\" ~ aminopenicillins == \"S\",                          TZP == \"R\" ~ aminopenicillins == \"R\") x #> A set of custom EUCAST rules: #> #>   1. If TZP is \"S\" then set to  S : #>      amoxicillin (AMX), ampicillin (AMP) #> #>   2. If TZP is \"R\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) df <- data.frame(mo = c(\"Escherichia coli\", \"Klebsiella pneumoniae\"),                  TZP = as.sir(\"R\"),                  ampi = as.sir(\"S\"),                  cipro = as.sir(\"S\")) df #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    S     S #> 2 Klebsiella pneumoniae   R    S     S  eucast_rules(df, rules = \"custom\", custom_rules = x, info = FALSE) #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    R     S #> 2 Klebsiella pneumoniae   R    R     S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"using-taxonomic-properties-in-rules","dir":"Reference","previous_headings":"","what":"Using taxonomic properties in rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"one exception columns used rules: column names microorganisms data set can also used, exist data set. column names : \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\" \"snomed\". Thus, next example work well, despite fact df data set contain column genus:","code":"y <- custom_eucast_rules(TZP == \"S\" & genus == \"Klebsiella\" ~ aminopenicillins == \"S\",                          TZP == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\")  eucast_rules(df, rules = \"custom\", custom_rules = y, info = FALSE) #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    S     S #> 2 Klebsiella pneumoniae   R    R     S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"usage-of-antibiotic-group-names","dir":"Reference","previous_headings":"","what":"Usage of antibiotic group names","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"possible define antibiotic groups instead single antibiotics rule consequence, part tilde. examples, antibiotic group aminopenicillins used include ampicillin amoxicillin. following groups allowed (case-insensitive). Within parentheses drugs matched running rule. \"aminoglycosides\"(amikacin, amikacin/fosfomycin, amphotericin B-high, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin tobramycin-high) \"aminopenicillins\"(amoxicillin ampicillin) \"antifungals\"(amphotericin B, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, flucytosine, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, oteseconazole, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole voriconazole) \"antimycobacterials\"(4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, morinamide, p-aminosalicylic acid, pretomanid, protionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, simvastatin/fenofibrate, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone, thioacetazone/isoniazid, tiocarlide viomycin) \"betalactams\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, biapenem, carbenicillin, carindacillin, cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, ciclacillin, clometocillin, cloxacillin, dicloxacillin, doripenem, epicillin, ertapenem, flucloxacillin, hetacillin, imipenem, imipenem/EDTA, imipenem/relebactam, latamoxef, lenampicillin, loracarbef, mecillinam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, tebipenem, temocillin, ticarcillin ticarcillin/clavulanic acid) \"carbapenems\"(biapenem, doripenem, ertapenem, imipenem, imipenem/EDTA, imipenem/relebactam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, panipenem, razupenem, ritipenem, ritipenem acoxil tebipenem) \"cephalosporins\"(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef loracarbef) \"cephalosporins_1st\"(cefacetrile, cefadroxil, cefalexin, cefaloridine, cefalotin, cefapirin, cefatrizine, cefazedone, cefazolin, cefroxadine, ceftezole cephradine) \"cephalosporins_2nd\"(cefaclor, cefamandole, cefmetazole, cefonicid, ceforanide, cefotetan, cefotiam, cefoxitin, cefoxitin screening, cefprozil, cefuroxime, cefuroxime axetil loracarbef) \"cephalosporins_3rd\"(cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefetamet, cefetamet pivoxil, cefixime, cefmenoxime, cefodizime, cefoperazone, cefoperazone/sulbactam, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotiam hexetil, cefovecin, cefpimizole, cefpiramide, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefsulodin, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftriaxone, ceftriaxone/beta-lactamase inhibitor latamoxef) \"cephalosporins_4th\"(cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetecol, cefoselis, cefozopran, cefpirome cefquinome) \"cephalosporins_5th\"(ceftaroline, ceftaroline/avibactam, ceftobiprole, ceftobiprole medocaril ceftolozane/tazobactam) \"cephalosporins_except_caz\"(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef loracarbef) \"fluoroquinolones\"(besifloxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nifuroquine, norfloxacin, ofloxacin, orbifloxacin, pazufloxacin, pefloxacin, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin trovafloxacin) \"glycopeptides\"(avoparcin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin vancomycin-macromethod) \"glycopeptides_except_lipo\"(avoparcin, norvancomycin, ramoplanin, teicoplanin, teicoplanin-macromethod, vancomycin vancomycin-macromethod) \"lincosamides\"(acetylmidecamycin, acetylspiramycin, clindamycin, gamithromycin, kitasamycin, lincomycin, meleumycin, nafithromycin, pirlimycin, primycin, solithromycin, tildipirosin, tilmicosin, tulathromycin, tylosin tylvalosin) \"lipoglycopeptides\"(dalbavancin, oritavancin telavancin) \"macrolides\"(acetylmidecamycin, acetylspiramycin, azithromycin, clarithromycin, dirithromycin, erythromycin, flurithromycin, gamithromycin, josamycin, kitasamycin, meleumycin, midecamycin, miocamycin, nafithromycin, oleandomycin, pirlimycin, primycin, rokitamycin, roxithromycin, solithromycin, spiramycin, telithromycin, tildipirosin, tilmicosin, troleandomycin, tulathromycin, tylosin tylvalosin) \"oxazolidinones\"(cadazolid, cycloserine, linezolid, tedizolid thiacetazone) \"penicillins\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, carbenicillin, carindacillin, cefepime/nacubactam, ciclacillin, clometocillin, cloxacillin, dicloxacillin, epicillin, flucloxacillin, hetacillin, lenampicillin, mecillinam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, temocillin, ticarcillin ticarcillin/clavulanic acid) \"polymyxins\"(colistin, polymyxin B polymyxin B/polysorbate 80) \"quinolones\"(besifloxacin, cinoxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, flumequine, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nalidixic acid, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, ofloxacin, orbifloxacin, oxolinic acid, pazufloxacin, pefloxacin, pipemidic acid, piromidic acid, pradofloxacin, premafloxacin, prulifloxacin, rosoxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin trovafloxacin) \"streptogramins\"(pristinamycin quinupristin/dalfopristin) \"tetracyclines\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline tigecycline) \"tetracyclines_except_tgc\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline tetracycline) \"trimethoprims\"(brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim trimethoprim/sulfamethoxazole) \"ureidopenicillins\"(azlocillin, mezlocillin, piperacillin piperacillin/tazobactam)","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"x <- custom_eucast_rules(   AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\",   AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\" ) x #> A set of custom EUCAST rules: #>  #>   1. If AMC is \"R\" and genus is \"Klebsiella\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   2. If AMC is \"I\" and genus is \"Klebsiella\" then set to  I : #>      amoxicillin (AMX), ampicillin (AMP)  # run the custom rule set (verbose = TRUE will return a logbook instead of the data set): eucast_rules(example_isolates,   rules = \"custom\",   custom_rules = x,   info = FALSE,   verbose = TRUE ) #> # A tibble: 8 × 9 #>     row col   mo_fullname           old   new   rule     rule_…¹ rule_…² rule_…³ #>   <int> <chr> <chr>                 <ord> <chr> <chr>    <chr>   <chr>   <chr>   #> 1    33 AMP   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 2    33 AMX   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 3    34 AMP   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 4    34 AMX   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 5   531 AMP   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 6   531 AMX   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 7  1485 AMP   Klebsiella oxytoca    R     I     \"report… Custom… Custom… Object… #> 8  1485 AMX   Klebsiella oxytoca    R     I     \"report… Custom… Custom… Object… #> # … with abbreviated variable names ¹​rule_group, ²​rule_name, ³​rule_source  # combine rule sets x2 <- c(   x,   custom_eucast_rules(TZP == \"R\" ~ carbapenems == \"R\") ) x2 #> A set of custom EUCAST rules: #>  #>   1. If AMC is \"R\" and genus is \"Klebsiella\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   2. If AMC is \"I\" and genus is \"Klebsiella\" then set to  I : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   3. If TZP is \"R\" then set to  R : #>      biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), #>      imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), #>      meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), #>      razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), tebipenem (TBP)"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"EUCAST breakpoints used package based dosages data set. can retrieved eucast_dosage().","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"tibble 336 observations 9 variables: ab Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name antimicrobial drug used WHONET/EARS-Net type Type dosage, either \"high_dosage\", \"standard_dosage\" \"uncomplicated_uti\" dose Dose, \"2 g\" \"25 mg/kg\" dose_times Number times dose must administered administration Route administration, either \"im\", \"iv\" \"oral\" notes Additional dosage notes original_txt Original text PDF file EUCAST eucast_version Version number EUCAST Clinical Breakpoints guideline dosages apply","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"data set based 'EUCAST Clinical Breakpoint Tables' v12.0 (2022) 'EUCAST Clinical Breakpoint Tables' v11.0 (2021).","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage #> # A tibble: 336 × 9 #>    ab   name                   type  dose  dose_…¹ admin…² notes origi…³ eucas…⁴ #>    <ab> <chr>                  <chr> <chr>   <int> <chr>   <chr> <chr>     <dbl> #>  1 AMK  Amikacin               stan… 25-3…       1 iv      \"\"    25-30 …      12 #>  2 AMX  Amoxicillin            high… 2 g         6 iv      \"\"    2 g x …      12 #>  3 AMX  Amoxicillin            stan… 1 g         3 iv      \"\"    1 g x …      12 #>  4 AMX  Amoxicillin            high… 0.75…       3 oral    \"\"    0.75-1…      12 #>  5 AMX  Amoxicillin            stan… 0.5 g       3 oral    \"\"    0.5 g …      12 #>  6 AMX  Amoxicillin            unco… 0.5 g       3 oral    \"\"    0.5 g …      12 #>  7 AMC  Amoxicillin/clavulani… high… 2 g …       3 iv      \"\"    (2 g a…      12 #>  8 AMC  Amoxicillin/clavulani… stan… 1 g …       3 iv      \"\"    (1 g a…      12 #>  9 AMC  Amoxicillin/clavulani… high… 0.87…       3 oral    \"\"    (0.875…      12 #> 10 AMC  Amoxicillin/clavulani… stan… 0.5 …       3 oral    \"\"    (0.5 g…      12 #> # … with 326 more rows, and abbreviated variable names ¹​dose_times, #> #   ²​administration, ³​original_txt, ⁴​eucast_version"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply EUCAST Rules — eucast_rules","title":"Apply EUCAST Rules — eucast_rules","text":"Apply rules clinical breakpoints intrinsic resistance defined European Committee Antimicrobial Susceptibility Testing (EUCAST, https://www.eucast.org), see Source. Use eucast_dosage() get data.frame advised dosages certain bug-drug combination, based dosage data set. improve interpretation antibiogram EUCAST rules applied, non-EUCAST rules can applied default, see Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"eucast_rules(   x,   col_mo = NULL,   info = interactive(),   rules = getOption(\"AMR_eucastrules\", default = c(\"breakpoints\", \"expert\")),   verbose = FALSE,   version_breakpoints = 12,   version_expertrules = 3.3,   ampc_cephalosporin_resistance = NA,   only_sir_columns = FALSE,   custom_rules = NULL,   ... )  eucast_dosage(ab, administration = \"iv\", version_breakpoints = 12)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Apply EUCAST Rules — eucast_rules","text":"EUCAST Expert Rules. Version 2.0, 2012. Leclercq et al. EUCAST expert rules antimicrobial susceptibility testing. Clin Microbiol Infect. 2013;19(2):141-60; doi:10.1111/j.1469-0691.2011.03703.x EUCAST Expert Rules, Intrinsic Resistance Exceptional Phenotypes Tables. Version 3.1, 2016. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.2, 2020. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.3, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 9.0, 2019. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 10.0, 2020. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 11.0, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 12.0, 2022. (link)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply EUCAST Rules — eucast_rules","text":"x data set antibiotic columns, amox, AMX AMC col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console, defaults print interactive sessions rules character vector specifies rules applied. Must one \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). default value can set another value using option AMR_eucastrules: options(AMR_eucastrules = \"\"). using \"custom\", sure fill argument custom_rules . Custom rules can created custom_eucast_rules(). verbose logical turn Verbose mode (default ). Verbose mode, function apply rules data, instead returns data set logbook form extensive info rows columns effected way. Using Verbose mode takes lot time. version_breakpoints version number use EUCAST Clinical Breakpoints guideline. Can either \"12.0\", \"11.0\" \"10.0\". version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can either \"3.3\", \"3.2\" \"3.1\". ampc_cephalosporin_resistance character value applied cefotaxime, ceftriaxone ceftazidime AmpC de-repressed cephalosporin-resistant mutants, defaults NA. Currently works version_expertrules 3.2 higher; version 'EUCAST Expert Rules Enterobacterales' state results cefotaxime, ceftriaxone ceftazidime reported note, results suppressed (emptied) three drugs. value NA (default) argument remove results three drugs, e.g. value \"R\" make results drugs resistant. Use NULL FALSE alter results three drugs AmpC de-repressed cephalosporin-resistant mutants. Using TRUE equal using \"R\".  EUCAST Expert Rules v3.2, rule applies : Citrobacter braakii, Citrobacter freundii, Citrobacter gillenii, Citrobacter murliniae, Citrobacter rodenticum, Citrobacter sedlakii, Citrobacter werkmanii, Citrobacter youngae, Enterobacter, Hafnia alvei, Klebsiella aerogenes, Morganella morganii, Providencia Serratia. only_sir_columns logical indicate whether antibiotic columns must detected transformed class sir (see .sir()) beforehand (defaults FALSE) custom_rules custom rules apply, created custom_eucast_rules() ... column name antibiotic, see section Antibiotics ab (vector ) text can coerced valid antibiotic drug code .ab() administration route administration, either \"im\", \"iv\" \"oral\"","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply EUCAST Rules — eucast_rules","text":"input x, possibly edited values antibiotics. , verbose = TRUE, data.frame original new values affected bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Apply EUCAST Rules — eucast_rules","text":"Note: function translate MIC values SIR values. Use .sir() . Note: ampicillin (AMP, J01CA01) available amoxicillin (AMX, J01CA04) , latter used rules dependency ampicillin. drugs interchangeable comes expression antimicrobial resistance. file containing EUCAST rules located : https://github.com/msberends/AMR/blob/main/data-raw/eucast_rules.tsv.  Note: Old taxonomic names replaced current taxonomy applicable. example, Ochrobactrum anthropi renamed Brucella anthropi 2020; original EUCAST rules v3.1 v3.2 yet contain new taxonomic name. AMR package contains full microbial taxonomy updated 11 December, 2022, see microorganisms.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"custom-rules","dir":"Reference","previous_headings":"","what":"Custom Rules","title":"Apply EUCAST Rules — eucast_rules","text":"Custom rules can created using custom_eucast_rules(), e.g.:","code":"x <- custom_eucast_rules(AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\",                          AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\")  eucast_rules(example_isolates, rules = \"custom\", custom_rules = x)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"-other-rules","dir":"Reference","previous_headings":"","what":"'Other' Rules","title":"Apply EUCAST Rules — eucast_rules","text":"processing, two non-EUCAST rules drug combinations can applied improve efficacy EUCAST rules, reliability data (analysis). rules : drug enzyme inhibitor set S drug without enzyme inhibitor S drug without enzyme inhibitor set R drug enzyme inhibitor R Important examples include amoxicillin amoxicillin/clavulanic acid, trimethoprim trimethoprim/sulfamethoxazole. Needless say, rules work, drugs must available data set. Since rules officially approved EUCAST, applied default. use rules, include \"\" rules argument, use eucast_rules(..., rules = \"\"). can also set option AMR_eucastrules, .e. run options(AMR_eucastrules = \"\").","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"antibiotics","dir":"Reference","previous_headings":"","what":"Antibiotics","title":"Apply EUCAST Rules — eucast_rules","text":"define antibiotics column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antibiotics eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, S01AE08), amoxicillin (AMX, J01MA02), amoxicillin/clavulanic acid (AMC, J01MA23), ampicillin (AMP, J01MA04), ampicillin/sulbactam (SAM, J01MA08), arbekacin (ARB, J01MA19), aspoxicillin (APX, J01MA16), azidocillin (AZD, J01MA15), azithromycin (AZM, J01MA11), azlocillin (AZL, J01MA25), aztreonam (ATM, J01MA12), bacampicillin (BAM, J01MA24), bekanamycin (BEK, J01MA07), benzathine benzylpenicillin (BNB, J01MA14), benzathine phenoxymethylpenicillin (BNP, D10AF05), benzylpenicillin (PEN, J01MA06), besifloxacin (BES, J01MA01), biapenem (BIA, J01MA18), carbenicillin (CRB, J01MA03), carindacillin (CRN, J01MA17), cefacetrile (CAC, J01MA10), cefaclor (CEC, J01MA21), cefadroxil (CFR, J01MA09), cefalexin (LEX, J01MA05), cefaloridine (RID, P01AA05), cefalotin (CEP, J01MA22), cefamandole (MAN, J01MA13), cefapirin (HAP, J01CA01), cefatrizine (CTZ, J01CA04), cefazedone (CZD, J01CA12), cefazolin (CZO, J01CR05), cefcapene (CCP, J01CA13), cefdinir (CDR, J01AA02), cefditoren (DIT, J01FA10), cefepime (FEP, J01FA09), cefetamet (CAT, J01CR02), cefixime (CFM, J01AA08), cefmenoxime (CMX, J01FA06), cefmetazole (CMZ, J01CF04), cefodizime (DIZ, J01CF05), cefonicid (CID, J01CR01), cefoperazone (CFP, J01CA19), cefoperazone/sulbactam (CSL, J01CE04), ceforanide (CND, J01CA09), cefotaxime (CTX, J01DF01), cefotaxime/clavulanic acid (CTC, J01CA06), cefotetan (CTT, J01CE08), cefotiam (CTF, J01CE10), cefoxitin (FOX, J01CE01), cefozopran (ZOP, J01CA03), cefpiramide (CPM, J01CA05), cefpirome (CPO, J01CE07), cefpodoxime (CPD, J01CF02), cefprozil (CPR, J01CF01), cefroxadine (CRD, J01CA07), cefsulodin (CFS, J01CA18), ceftaroline (CPT, J01CA11), ceftazidime (CAZ, J01CA14), ceftazidime/clavulanic acid (CCV, J01CF03), cefteram (CEM, J01CA10), ceftezole (CTL, J01CF06), ceftibuten (CTB, J01CE06), ceftizoxime (CZX, J01CE05), ceftobiprole medocaril (CFM1, J01CE02), ceftolozane/tazobactam (CZT, J01CA02), ceftriaxone (CRO, J01CA08), ceftriaxone/beta-lactamase inhibitor (CEB, J01CE09), cefuroxime (CXM, J01CE03), cephradine (CED, J01CG01), chloramphenicol (CHL, J01CA16), ciprofloxacin (CIP, J01CR04), clarithromycin (CLR, J01CA15), clindamycin (CLI, J01CG02), clometocillin (CLM, J01CA17), cloxacillin (CLO, J01CR03), colistin (COL, J01DB10), cycloserine (CYC, J01DC04), dalbavancin (DAL, J01DB05), daptomycin (DAP, J01DB01), delafloxacin (DFX, J01DB02), dibekacin (DKB, J01DB03), dicloxacillin (DIC, J01DC03), dirithromycin (DIR, J01DB08), doripenem (DOR, J01DB07), doxycycline (DOX, J01DB06), enoxacin (ENX, J01DB04), epicillin (EPC, J01DD17), ertapenem (ETP, J01DD15), erythromycin (ERY, J01DD16), fleroxacin (FLE, J01DE01), flucloxacillin (FLC, J01DD10), flurithromycin (FLR1, J01DD08), fosfomycin (FOS, J01DD05), framycetin (FRM, J01DC09), fusidic acid (FUS, J01DD09), garenoxacin (GRN, J01DC06), gatifloxacin (GAT, J01DD12), gemifloxacin (GEM, J01DD62), gentamicin (GEN, J01DC11), grepafloxacin (GRX, J01DD01), hetacillin (HET, J01DD51), imipenem (IPM, J01DC05), imipenem/relebactam (IMR, J01DC07), isepamicin (ISE, J01DC01), josamycin (JOS, J01DE03), kanamycin (KAN, J01DD11), lascufloxacin (LSC, J01DE02), latamoxef (LTM, J01DD13), levofloxacin (LVX, J01DC10), levonadifloxacin (LND, J01DB11), lincomycin (LIN, J01DD03), linezolid (LNZ, J01DI02), lomefloxacin (LOM, J01DD02), loracarbef (LOR, J01DD52), mecillinam (MEC, J01DD18), meropenem (MEM, J01DB12), meropenem/vaborbactam (MEV, J01DD14), metampicillin (MTM, J01DD07), meticillin (MET, J01DI01), mezlocillin (MEZ, J01DI54), micronomicin (MCR, J01DD04), midecamycin (MID, J01DD63), minocycline (MNO, J01DC02), miocamycin (MCM, J01DB09), moxifloxacin (MFX, J01DD06), nadifloxacin (NAD, J01DC08), nafcillin (NAF, J01DH05), nalidixic acid (NAL, J01DH04), neomycin (NEO, J01DH03), netilmicin (NET, J01DH51), nitrofurantoin (NIT, J01DH56), norfloxacin (, J01DH02), ofloxacin (OFX, J01DH52), oleandomycin (OLE, J01DH55), oritavancin (ORI, J01DH06), oxacillin (OXA, J01XA02), panipenem (PAN, J01XA01), pazufloxacin (PAZ, J01XC01), pefloxacin (PEF, J01FA13), penamecillin (PNM, J01FA01), pheneticillin (PHE, J01FA14), phenoxymethylpenicillin (PHN, J01FA07), piperacillin (PIP, J01FA03), piperacillin/tazobactam (TZP, J01FA11), pivampicillin (PVM, J01FA05), pivmecillinam (PME, J01FA12), plazomicin (PLZ, J01FA16), polymyxin B (PLB, J01FA02), pristinamycin (PRI, J01FA15), procaine benzylpenicillin (PRB, J01FA08), propicillin (PRP, J01FF02), prulifloxacin (PRU, J01FG01), quinupristin/dalfopristin (QDA, J01FG02), ribostamycin (RST, J04AB02), rifampicin (RIF, J01XX09), rokitamycin (ROK, J01XX08), roxithromycin (RXT, J01AA07), rufloxacin (RFL, J01XB01), sisomicin (SIS, J01XB02), sitafloxacin (SIT, J01XE01), solithromycin (SOL, J01AA12), sparfloxacin (SPX, J01EA01), spiramycin (SPI, J01XX01), streptoduocin (STR, J01BA01), streptomycin (STR1, J01GB06), sulbactam (SUL, J01GB12), sulbenicillin (SBC, J01GB13), sulfadiazine (SDI, J01GB09), sulfadiazine/trimethoprim (SLT1, D09AA01), sulfadimethoxine (SUD, J01GB03), sulfadimidine (SDM, J01GB11), sulfadimidine/trimethoprim (SLT2, J01GB04), sulfafurazole (SLF, S01AA22), sulfaisodimidine (SLF1, J01GB05), sulfalene (SLF2, J01GB07), sulfamazone (SZO, J01GB14), sulfamerazine (SLF3, J01GB10), sulfamerazine/trimethoprim (SLT3, J01GB08), sulfamethizole (SLF4, J01GA02), sulfamethoxazole (SMX, J01GA01), sulfamethoxypyridazine (SLF5, J01GB01), sulfametomidine (SLF6, J01EE01), sulfametoxydiazine (SLF7, J01MB02), sulfametrole/trimethoprim (SLT4, J01FF01), sulfamoxole (SLF8, J01XA04), sulfamoxole/trimethoprim (SLT5, J01XA05), sulfanilamide (SLF9, J01XA03), sulfaperin (SLF10, J04AB01), sulfaphenazole (SLF11, J01XX11), sulfapyridine (SLF12, J01EC02), sulfathiazole (SUT, J01ED01), sulfathiourea (SLF13, J01EB03), sultamicillin (SLT6, J01EB05), talampicillin (TAL, J01EB01), tazobactam (TAZ, J01ED02), tebipenem (TBP, J01ED09), tedizolid (TZD, J01ED07), teicoplanin (TEC, J01EB02), telavancin (TLV, J01EC01), telithromycin (TLT, J01ED05), temafloxacin (TMX, J01ED03), temocillin (TEM, J01ED04), tetracycline (TCY, J01EC03), ticarcillin (TIC, J01EB06), ticarcillin/clavulanic acid (TCC, J01ED06), tigecycline (TGC, J01ED08), tilbroquinol (TBQ, J01EB04), tobramycin (TOB, J01EB07), tosufloxacin (TFX, J01EB08), trimethoprim (TMP, J01EE02), trimethoprim/sulfamethoxazole (SXT, J01EE05), troleandomycin (TRL, J01EE07), trovafloxacin (TVA, J01EE03), vancomycin (VAN, J01EE04)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Apply EUCAST Rules — eucast_rules","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"# \\donttest{ a <- data.frame(   mo = c(     \"Staphylococcus aureus\",     \"Enterococcus faecalis\",     \"Escherichia coli\",     \"Klebsiella pneumoniae\",     \"Pseudomonas aeruginosa\"   ),   VAN = \"-\", # Vancomycin   AMX = \"-\", # Amoxicillin   COL = \"-\", # Colistin   CAZ = \"-\", # Ceftazidime   CXM = \"-\", # Cefuroxime   PEN = \"S\", # Benzylpenicillin   FOX = \"S\", # Cefoxitin   stringsAsFactors = FALSE )  head(a) #>                       mo VAN AMX COL CAZ CXM PEN FOX #> 1  Staphylococcus aureus   -   -   -   -   -   S   S #> 2  Enterococcus faecalis   -   -   -   -   -   S   S #> 3       Escherichia coli   -   -   -   -   -   S   S #> 4  Klebsiella pneumoniae   -   -   -   -   -   S   S #> 5 Pseudomonas aeruginosa   -   -   -   -   -   S   S   # apply EUCAST rules: some results wil be changed b <- eucast_rules(a) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #>   - a %>% as.sir(CXM:AMX) #>   - a %>% mutate_if(is_sir_eligible, as.sir) #>   - a %>% mutate(across(where(is_sir_eligible), as.sir))  head(b) #>                       mo VAN AMX COL CAZ CXM PEN FOX #> 1  Staphylococcus aureus   -   S   R   S   S   S   S #> 2  Enterococcus faecalis   -   -   R   R   R   S   R #> 3       Escherichia coli   R   -   -   -   -   R   S #> 4  Klebsiella pneumoniae   R   R   -   -   -   R   S #> 5 Pseudomonas aeruginosa   R   R   -   -   R   R   R   # do not apply EUCAST rules, but rather get a data.frame # containing all details about the transformations: c <- eucast_rules(a, verbose = TRUE) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #>   - a %>% as.sir(CXM:AMX) #>   - a %>% mutate_if(is_sir_eligible, as.sir) #>   - a %>% mutate(across(where(is_sir_eligible), as.sir)) head(c) #>   row col           mo_fullname old new rule   rule_group #> 1   1 AMX Staphylococcus aureus   -   S       Breakpoints #> 2   1 CXM Staphylococcus aureus   -   S       Breakpoints #> 3   1 CAZ Staphylococcus aureus   R   S      Expert Rules #> 4   1 CAZ Staphylococcus aureus   -   R      Expert Rules #> 5   1 COL Staphylococcus aureus   -   R      Expert Rules #> 6   2 CAZ Enterococcus faecalis   -   R      Expert Rules #>                                                 rule_name #> 1                                          Staphylococcus #> 2                                          Staphylococcus #> 3                          Expert Rules on Staphylococcus #> 4 Table 4: Intrinsic resistance in gram-positive bacteria #> 5 Table 4: Intrinsic resistance in gram-positive bacteria #> 6 Table 4: Intrinsic resistance in gram-positive bacteria #>                                                                                 rule_source #> 1                                           'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 2                                           'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 3 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 4 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 5 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 6 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 # }  # Dosage guidelines:  eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\") #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #>   Tables' v12.0 (2022). This note will be shown once per session. #> # A tibble: 3 × 4 #>   ab   name          standard_dosage  high_dosage  #>   <ab> <chr>         <chr>            <chr>        #> 1 TOB  Tobramycin    6-7 mg/kg x 1 iv NA           #> 2 GEN  Gentamicin    6-7 mg/kg x 1 iv NA           #> 3 CIP  Ciprofloxacin 0.4 g x 2 iv     0.4 g x 3 iv  eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\", version_breakpoints = 10) #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #>   Tables' v10.0 (2020). This note will be shown once per session. #> # A tibble: 3 × 4 #>   ab   name          standard_dosage  high_dosage  #>   <ab> <chr>         <chr>            <chr>        #> 1 TOB  Tobramycin    6-7 mg/kg x 1 iv NA           #> 2 GEN  Gentamicin    6-7 mg/kg x 1 iv NA           #> 3 CIP  Ciprofloxacin 0.4 g x 2 iv     0.4 g x 3 iv"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 2,000 Example Isolates — example_isolates","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"data set containing 2,000 microbial isolates full antibiograms. data set contains randomised fictitious data, reflects reality can used practise AMR data analysis. examples, please read tutorial website.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"","code":"example_isolates"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"tibble 2,000 observations 46 variables: date Date receipt laboratory patient ID patient age Age patient gender Gender patient, either \"F\" \"M\" ward Ward type patient admitted, either \"Clinical\", \"ICU\" \"Outpatient\" mo ID microorganism created .mo(), see also microorganisms data set PEN:RIF 40 different antibiotics class sir (see .sir()); column names occur antibiotics data set can translated set_ab_names() ab_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>"},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Unclean Data — example_isolates_unclean","title":"Data Set with Unclean Data — example_isolates_unclean","text":"data set containing 3,000 microbial isolates cleaned consequently ready AMR data analysis. data set can used practice.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Unclean Data — example_isolates_unclean","text":"","code":"example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Unclean Data — example_isolates_unclean","text":"tibble 3,000 observations 8 variables: patient_id ID patient date date receipt laboratory hospital ID hospital, C bacteria info microorganism can transformed .mo(), see also microorganisms AMX:GEN 4 different antibiotics transformed .sir()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Unclean Data — example_isolates_unclean","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Unclean Data — example_isolates_unclean","text":"","code":"example_isolates_unclean #> # A tibble: 3,000 × 8 #>    patient_id hospital date       bacteria      AMX   AMC   CIP   GEN   #>    <chr>      <chr>    <date>     <chr>         <chr> <chr> <chr> <chr> #>  1 J3         A        2012-11-21 E. coli       R     I     S     S     #>  2 R7         A        2018-04-03 K. pneumoniae R     I     S     S     #>  3 P3         A        2014-09-19 E. coli       R     S     S     S     #>  4 P10        A        2015-12-10 E. coli       S     I     S     S     #>  5 B7         A        2015-03-02 E. coli       S     S     S     S     #>  6 W3         A        2018-03-31 S. aureus     R     S     R     S     #>  7 J8         A        2016-06-14 E. coli       R     S     S     S     #>  8 M3         A        2015-10-25 E. coli       R     S     S     S     #>  9 J3         A        2019-06-19 E. coli       S     S     S     S     #> 10 G6         A        2015-04-27 S. aureus     S     S     S     S     #> # … with 2,990 more rows"},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine First Isolates — first_isolate","title":"Determine First Isolates — first_isolate","text":"Determine first isolates microorganisms every patient per episode (needed) per specimen type. functions support four methods summarised Hindler et al. 2007 (doi:10.1086/511864 ). determine patient episodes necessarily based microorganisms, use is_new_episode() also supports grouping dplyr package.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine First Isolates — first_isolate","text":"","code":"first_isolate(   x = NULL,   col_date = NULL,   col_patient_id = NULL,   col_mo = NULL,   col_testcode = NULL,   col_specimen = NULL,   col_icu = NULL,   col_keyantimicrobials = NULL,   episode_days = 365,   testcodes_exclude = NULL,   icu_exclude = FALSE,   specimen_group = NULL,   type = \"points\",   method = c(\"phenotype-based\", \"episode-based\", \"patient-based\", \"isolate-based\"),   ignore_I = TRUE,   points_threshold = 2,   info = interactive(),   include_unknown = FALSE,   include_untested_sir = TRUE,   ... )  filter_first_isolate(   x = NULL,   col_date = NULL,   col_patient_id = NULL,   col_mo = NULL,   episode_days = 365,   method = c(\"phenotype-based\", \"episode-based\", \"patient-based\", \"isolate-based\"),   ... )"},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine First Isolates — first_isolate","text":"Methodology function strictly based : M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. Hindler JF Stelling J (2007). Analysis Presentation Cumulative Antibiograms: New Consensus Guideline Clinical Laboratory Standards Institute. Clinical Infectious Diseases, 44(6), 867-873. doi:10.1086/511864","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine First Isolates — first_isolate","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_date column name result date (date received lab), defaults first column date class col_patient_id column name unique IDs patients, defaults first column starts 'patient' 'patid' (case insensitive) col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). col_testcode column name test codes. Use col_testcode = NULL exclude certain test codes (test codes screening). case testcodes_exclude ignored. col_specimen column name specimen type group col_icu column name logicals (TRUE/FALSE) whether ward department Intensive Care Unit (ICU). can also logical vector length rows x. col_keyantimicrobials (useful method = \"phenotype-based\") column name key antimicrobials determine first isolates, see key_antimicrobials(). Defaults first column starts 'key' followed 'ab' 'antibiotics' 'antimicrobials' (case insensitive). Use col_keyantimicrobials = FALSE prevent . Can also output key_antimicrobials(). episode_days episode days genus/species combination determined 'first isolate' . default 365 days based guideline CLSI, see Source. testcodes_exclude character vector test codes excluded (case-insensitive) icu_exclude logical indicate whether ICU isolates excluded (rows value TRUE column set col_icu) specimen_group value column set col_specimen filter type type determine weighed isolates; can \"keyantimicrobials\" \"points\", see Details method method apply, either \"phenotype-based\", \"episode-based\", \"patient-based\" \"isolate-based\" (can abbreviated), see Details. default \"phenotype-based\" antimicrobial test results present data, \"episode-based\" otherwise. ignore_I logical indicate whether antibiotic interpretations \"\" ignored type = \"keyantimicrobials\", see Details points_threshold minimum number points require differences antibiogram lead inclusion isolate type = \"points\", see Details info logical indicate info printed, defaults TRUE interactive mode include_unknown logical indicate whether 'unknown' microorganisms included , .e. microbial code \"UNKNOWN\", defaults FALSE. WHONET users, means records organism code \"con\" (contamination) excluded default. Isolates microbial ID NA always excluded first isolate. include_untested_sir logical indicate whether also rows without antibiotic results still eligible becoming first isolate. Use include_untested_sir = FALSE always return FALSE rows. checks data set columns class sir consequently requires transforming columns antibiotic results using .sir() first. ... arguments passed first_isolate() using filter_first_isolate(), otherwise arguments passed key_antimicrobials() (universal, gram_negative, gram_positive)","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine First Isolates — first_isolate","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine First Isolates — first_isolate","text":"conduct epidemiological analyses antimicrobial resistance data, -called first isolates included prevent overestimation underestimation antimicrobial resistance. Different methods can used , see . functions context-aware. means x argument can left blank used inside data.frame call, see Examples. first_isolate() function wrapper around is_new_episode() function, efficient data sets containing microorganism codes names. isolates microbial ID NA excluded first isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"different-methods","dir":"Reference","previous_headings":"","what":"Different methods","title":"Determine First Isolates — first_isolate","text":"According Hindler et al. (2007, doi:10.1086/511864 ), different methods (algorithms) select first isolates increasing reliability: isolate-based, patient-based, episode-based phenotype-based. methods select combination taxonomic genus species (subspecies). mentioned methods covered first_isolate() function:","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"isolate-based","dir":"Reference","previous_headings":"","what":"Isolate-based","title":"Determine First Isolates — first_isolate","text":"method require selection, isolates included. , however, respect arguments set first_isolate() function. example, default setting include_unknown (FALSE) omit selection rows without microbial ID.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"patient-based","dir":"Reference","previous_headings":"","what":"Patient-based","title":"Determine First Isolates — first_isolate","text":"include every genus-species combination per patient , set episode_days Inf. Although often inappropriate, method makes sure duplicate isolates selected patient. large longitudinal data set, mean isolates excluded found years initial isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"episode-based","dir":"Reference","previous_headings":"","what":"Episode-based","title":"Determine First Isolates — first_isolate","text":"include every genus-species combination per patient episode , set episode_days sensible number days. Depending type analysis, 14, 30, 60 365. Short episodes common analysing specific hospital ward data, long episodes common analysing regional national data. common method correct duplicate isolates. Patients categorised episodes based ID dates (e.g., date specimen receipt laboratory result). common method, take account antimicrobial test results. means e.g. methicillin-resistant Staphylococcus aureus (MRSA) isolate differentiated wildtype Staphylococcus aureus isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"phenotype-based","dir":"Reference","previous_headings":"","what":"Phenotype-based","title":"Determine First Isolates — first_isolate","text":"reliable method, since also weighs antibiogram (antimicrobial test results) yielding -called 'first weighted isolates'. two different methods weigh antibiogram: Using type = \"points\" argument points_threshold (default) method weighs antimicrobial drugs available data set. difference S R (vice versa) counts 0.5 points, difference S R (vice versa) counts 1 point. sum points exceeds points_threshold, defaults 2, isolate selected first weighted isolate. antimicrobials internally selected using all_antimicrobials() function. output function need passed first_isolate() function. Using type = \"keyantimicrobials\" argument ignore_I method weighs specific antimicrobial drugs, called key antimicrobials. difference S R (vice versa) key antimicrobials select isolate first weighted isolate. ignore_I = FALSE, also differences S R (vice versa) lead . Key antimicrobials internally selected using key_antimicrobials() function, can also added manually variable data set col_keyantimicrobials argument. Another option pass output key_antimicrobials() function directly col_keyantimicrobials argument. default method phenotype-based (using type = \"points\") episode-based (using episode_days = 365). makes sure every genus-species combination selected per patient per year, taking account antimicrobial test results. antimicrobial test results available data set, episode-based method applied default.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine First Isolates — first_isolate","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  example_isolates[first_isolate(), ] #> Including isolates from ICU. #> # A tibble: 1,379 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  4 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #>  5 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR R     NA    S     NA    #>  6 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  7 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND R     NA    NA    R     #>  8 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  9 2002-02-03 481442     76 M      ICU      B_STPHY_CONS R     NA    S     NA    #> 10 2002-02-05 023456     50 M      Clinical B_STPHY_HMNS S     NA    S     NA    #> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> # \\donttest{ # get all first Gram-negatives example_isolates[which(first_isolate(info = FALSE) & mo_is_gram_negative()), ] #> Including isolates from ICU. #> ℹ Using column 'mo' as input for mo_is_gram_negative() #> # A tibble: 437 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND R     NA    NA    R     #>  4 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  5 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS R     NA    NA    R     #>  6 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN R     NA    NA    R     #>  7 2002-03-08 4FC193     69 M      Clinical B_ESCHR_COLI R     NA    NA    R     #>  8 2002-03-16 4FC193     69 M      Clinical B_PSDMN_AERG R     NA    NA    R     #>  9 2002-04-01 496896     46 F      ICU      B_ESCHR_COLI R     NA    NA    NA    #> 10 2002-04-23 EE2510     69 F      ICU      B_ESCHR_COLI R     NA    NA    NA    #> # … with 427 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir>  if (require(\"dplyr\")) {   # filter on first isolates using dplyr:   example_isolates %>%     filter(first_isolate()) } #> Including isolates from ICU. #> # A tibble: 1,379 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  4 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #>  5 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR R     NA    S     NA    #>  6 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  7 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND R     NA    NA    R     #>  8 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  9 2002-02-03 481442     76 M      ICU      B_STPHY_CONS R     NA    S     NA    #> 10 2002-02-05 023456     50 M      Clinical B_STPHY_HMNS S     NA    S     NA    #> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # short-hand version:   example_isolates %>%     filter_first_isolate(info = FALSE) } #> Including isolates from ICU. #> # A tibble: 1,379 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  4 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #>  5 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR R     NA    S     NA    #>  6 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  7 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND R     NA    NA    R     #>  8 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  9 2002-02-03 481442     76 M      ICU      B_STPHY_CONS R     NA    S     NA    #> 10 2002-02-05 023456     50 M      Clinical B_STPHY_HMNS S     NA    S     NA    #> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # flag the first isolates per group:   example_isolates %>%     group_by(ward) %>%     mutate(first = first_isolate()) %>%     select(ward, date, patient, mo, first) } #> Including isolates from ICU. #> Including isolates from ICU. #> Including isolates from ICU. #> # A tibble: 2,000 × 5 #> # Groups:   ward [3] #>    ward     date       patient mo           first #>    <chr>    <date>     <chr>   <mo>         <lgl> #>  1 Clinical 2002-01-02 A77334  B_ESCHR_COLI TRUE  #>  2 Clinical 2002-01-03 A77334  B_ESCHR_COLI FALSE #>  3 ICU      2002-01-07 067927  B_STPHY_EPDR TRUE  #>  4 ICU      2002-01-07 067927  B_STPHY_EPDR FALSE #>  5 ICU      2002-01-13 067927  B_STPHY_EPDR FALSE #>  6 ICU      2002-01-13 067927  B_STPHY_EPDR FALSE #>  7 Clinical 2002-01-14 462729  B_STPHY_AURS TRUE  #>  8 Clinical 2002-01-14 462729  B_STPHY_AURS FALSE #>  9 ICU      2002-01-16 067927  B_STPHY_EPDR FALSE #> 10 ICU      2002-01-17 858515  B_STPHY_EPDR TRUE  #> # … with 1,990 more rows # }"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":null,"dir":"Reference","previous_headings":"","what":"G-test for Count Data — g.test","title":"G-test for Count Data — g.test","text":"g.test() performs chi-squared contingency table tests goodness--fit tests, just like chisq.test() reliable (1). G-test can used see whether number observations category fits theoretical expectation (called G-test goodness--fit), see whether proportions one variable different different values variable (called G-test independence).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"G-test for Count Data — g.test","text":"","code":"g.test(x, y = NULL, p = rep(1/length(x), length(x)), rescale.p = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"G-test for Count Data — g.test","text":"code function identical chisq.test(), except : calculation statistic changed \\(2 * sum(x * log(x / E))\\) Yates' continuity correction removed apply G-test possibility simulate p values simulate.p.value removed","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"G-test for Count Data — g.test","text":"x numeric vector matrix. x y can also     factors. y numeric vector; ignored x matrix.      x factor, y factor length. p vector probabilities length x.     error given entry p negative. rescale.p logical scalar; TRUE p rescaled     (necessary) sum 1.  rescale.p FALSE,     p sum 1, error given.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"G-test for Count Data — g.test","text":"list class \"htest\" containing following   components: statistic value chi-squared test statistic. parameter degrees freedom approximate     chi-squared distribution test statistic, NA     p-value computed Monte Carlo simulation. p.value p-value test. method character string indicating type test     performed, whether Monte Carlo simulation continuity     correction used. data.name character string giving name(s) data. observed observed counts. expected expected counts null hypothesis. residuals Pearson residuals,     (observed - expected) / sqrt(expected). stdres standardized residuals,     (observed - expected) / sqrt(V), V residual cell variance (Agresti, 2007,     section 2.4.5 case x matrix, n * p * (1 - p) otherwise).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"G-test for Count Data — g.test","text":"x matrix one row column, x vector y given, goodness--fit test performed (x treated one-dimensional contingency table). entries x must non-negative integers. case, hypothesis tested whether population probabilities equal p, equal p given. x matrix least two rows columns, taken two-dimensional contingency table: entries x must non-negative integers.  Otherwise, x y must vectors factors length; cases missing values removed, objects coerced factors, contingency table computed .  Pearson's chi-squared test performed null hypothesis joint distribution cell counts 2-dimensional contingency table product row column marginals. p-value computed asymptotic chi-squared distribution test statistic. contingency table case simulation done random sampling set contingency tables given marginals, works marginals strictly positive. Note usual sampling situation assumed chi-squared test (G-test) rather Fisher's exact test. goodness--fit case simulation done random sampling discrete distribution specified p, sample size n = sum(x). simulation done R may slow.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-goodness-of-fit-likelihood-ratio-test-","dir":"Reference","previous_headings":"","what":"G-test Of Goodness-of-Fit (Likelihood Ratio Test)","title":"G-test for Count Data — g.test","text":"Use G-test goodness--fit one nominal variable two values (male female, red, pink white flowers). compare observed counts numbers observations category expected counts, calculate using kind theoretical expectation (1:1 sex ratio 1:2:1 ratio genetic cross). expected number observations category small, G-test may give inaccurate results, use exact test instead (fisher.test()). G-test goodness--fit alternative chi-square test goodness--fit (chisq.test()); tests advantages disadvantages, results two tests usually similar.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-independence","dir":"Reference","previous_headings":"","what":"G-test of Independence","title":"G-test for Count Data — g.test","text":"Use G-test independence two nominal variables, two possible values. want know whether proportions one variable different among values variable. also possible G-test independence two nominal variables. example, Jackson et al. (2013) also data children 3, analysis old vs. young, thigh vs. arm, reaction vs. reaction, analyzed together. Fisher's exact test (fisher.test()) exact test, G-test still approximation. 2x2 table, Fisher's Exact test may slower still run seconds, even sum observations multiple millions. G-test independence alternative chi-square test independence (chisq.test()), give approximately results.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"how-the-test-works","dir":"Reference","previous_headings":"","what":"How the Test Works","title":"G-test for Count Data — g.test","text":"Unlike exact test goodness--fit (fisher.test()), G-test directly calculate probability obtaining observed results something extreme. Instead, like almost statistical tests, G-test intermediate step; uses data calculate test statistic measures far observed data null expectation. use mathematical relationship, case chi-square distribution, estimate probability obtaining value test statistic. G-test uses log ratio two likelihoods test statistic, also called likelihood ratio test log-likelihood ratio test. formula calculate G-statistic : \\(G = 2 * sum(x * log(x / E))\\) E expected values. Since chi-square distributed, p value can calculated R :   df degrees freedom. two categories want find ones significantly different null expectation, can use method testing category vs. sum categories, Bonferroni correction. use G-tests category, course.","code":"p <- stats::pchisq(G, df, lower.tail = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"G-test for Count Data — g.test","text":"McDonald, J.H. 2014. Handbook Biological Statistics (3rd ed.). Sparky House Publishing, Baltimore, Maryland. http://www.biostathandbook.com/gtestgof.html.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"G-test for Count Data — g.test","text":"","code":"# = EXAMPLE 1 = # Shivrain et al. (2006) crossed clearfield rice (which are resistant # to the herbicide imazethapyr) with red rice (which are susceptible to # imazethapyr). They then crossed the hybrid offspring and examined the # F2 generation, where they found 772 resistant plants, 1611 moderately # resistant plants, and 737 susceptible plants. If resistance is controlled # by a single gene with two co-dominant alleles, you would expect a 1:2:1 # ratio.  x <- c(772, 1611, 737) g.test(x, p = c(1, 2, 1) / 4) #>  #> \tG-test of goodness-of-fit (likelihood ratio test) #>  #> data:  x #> X-squared = 4.1471, p-value = 0.1257 #>   # There is no significant difference from a 1:2:1 ratio. # Meaning: resistance controlled by a single gene with two co-dominant # alleles, is plausible.   # = EXAMPLE 2 = # Red crossbills (Loxia curvirostra) have the tip of the upper bill either # right or left of the lower bill, which helps them extract seeds from pine # cones. Some have hypothesized that frequency-dependent selection would # keep the number of right and left-billed birds at a 1:1 ratio. Groth (1992) # observed 1752 right-billed and 1895 left-billed crossbills.  x <- c(1752, 1895) g.test(x) #>  #> \tG-test of goodness-of-fit (likelihood ratio test) #>  #> data:  x #> X-squared = 5.6085, p-value = 0.01787 #>   # There is a significant difference from a 1:1 ratio. # Meaning: there are significantly more left-billed birds."},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine (New) Episodes for Patients — get_episode","title":"Determine (New) Episodes for Patients — get_episode","text":"functions determine items vector can considered (start ) new episode, based argument episode_days. can used determine clinical episodes epidemiological analysis. get_episode() function returns index number episode per group, is_new_episode() function returns values TRUE/FALSE indicate whether item vector start new episode.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine (New) Episodes for Patients — get_episode","text":"","code":"get_episode(x, episode_days, ...)  is_new_episode(x, episode_days, ...)"},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine (New) Episodes for Patients — get_episode","text":"x vector dates (class Date POSIXt), sorted internally determine episodes episode_days required episode length days, can also less day Inf, see Details ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine (New) Episodes for Patients — get_episode","text":"get_episode(): double vector is_new_episode(): logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine (New) Episodes for Patients — get_episode","text":"Dates first sorted old new. oldest date mark start first episode. date, next date marked least episode_days days later start first episode. second marked date , next date marked least episode_days days later start second episode start third episode, . vector returned, original order restored. first_isolate() function wrapper around is_new_episode() function, efficient data sets containing microorganism codes names allows different isolate selection methods. dplyr package required functions work, functions support variable grouping work conveniently inside dplyr verbs filter(), mutate() summarise().","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine (New) Episodes for Patients — get_episode","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates df <- example_isolates[sample(seq_len(2000), size = 200), ]  get_episode(df$date, episode_days = 60) # indices #>   [1] 31 31 27 14 48 21 11 37 16  9 51 51 33 47 10 36 53 38 14  7 21 64 38  5 54 #>  [26] 11 34  5 38  9 65 17 33 31 40 13  7  4 60 38 31  8 11  6 53 24 16 61 44 18 #>  [51] 10 36 11 50  2 63 15  1 66 35 32  2 59  5 36 14 32 35 21 44 18  8 32 43  9 #>  [76] 60 37  6 30 13 59 38 14 65 53 56 26 20 59 62 40 46 62 10 62 49 60 33 35 27 #> [101] 17 22  8 64 29 20 63 39 19 20 46  6 60  7 29  8 14 37  9 34 26 37 66 65 28 #> [126] 60  7 54 57 57 52 23 20 28 60  2 14 19 39 55 54  4 10 48 40 25 23 53 23 25 #> [151] 29  2 34  1  4 20  1 60 58 27 47 28 39 63 48 14  8 21  2 45 30 12 65 22 42 #> [176] 16 41 63 50 63  3  7 57 65 29 13  5 63 53 62 53 17 40 27 19 65  9  9 38 31 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE #>   [1] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE #>  [13] FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE #>  [25]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE #>  [49]  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE #>  [61]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE #>  [73] FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE #>  [85] FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE #>  [97]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [109] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE #> [121] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE #> [133] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE #> [145]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE #> [157] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE #> [169] FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE #> [181]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE #> [193] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  # filter on results from the third 60-day episode only, using base R df[which(get_episode(df$date, 60) == 3), ] #> # A tibble: 1 × 46 #>   date       patient   age gender ward  mo      PEN   OXA   FLC   AMX   AMC   #>   <date>     <chr>   <dbl> <chr>  <chr> <mo>    <sir> <sir> <sir> <sir> <sir> #> 1 2002-06-18 012595     30 M      ICU   B_CRYNB I     NA    NA    NA    NA    #> # … with 35 more variables: AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>  # the functions also work for less than a day, e.g. to include one per hour: get_episode(   c(     Sys.time(),     Sys.time() + 60 * 60   ),   episode_days = 1 / 24 ) #> [1] 1 2  # \\donttest{ if (require(\"dplyr\")) {   # is_new_episode() can also be used in dplyr verbs to determine patient   # episodes based on any (combination of) grouping variables:   df %>%     mutate(condition = sample(       x = c(\"A\", \"B\", \"C\"),       size = 200,       replace = TRUE     )) %>%     group_by(condition) %>%     mutate(new_episode = is_new_episode(date, 365)) %>%     select(patient, date, condition, new_episode) } #> # A tibble: 200 × 4 #> # Groups:   condition [3] #>    patient date       condition new_episode #>    <chr>   <date>     <chr>     <lgl>       #>  1 0DBB93  2009-05-08 B         FALSE       #>  2 889500  2009-05-08 C         TRUE        #>  3 A42180  2008-04-21 A         FALSE       #>  4 C56827  2004-12-05 A         FALSE       #>  5 672020  2013-04-01 C         FALSE       #>  6 E19253  2006-08-23 C         TRUE        #>  7 28F906  2004-04-05 C         FALSE       #>  8 119392  2010-11-01 B         FALSE       #>  9 105248  2005-06-16 A         FALSE       #> 10 000090  2003-10-08 A         FALSE       #> # … with 190 more rows if (require(\"dplyr\")) {   df %>%     group_by(ward, patient) %>%     transmute(date,       patient,       new_index = get_episode(date, 60),       new_logical = is_new_episode(date, 60)     ) } #> # A tibble: 200 × 5 #> # Groups:   ward, patient [175] #>    ward     date       patient new_index new_logical #>    <chr>    <date>     <chr>       <dbl> <lgl>       #>  1 ICU      2009-05-08 0DBB93          1 TRUE        #>  2 Clinical 2009-05-08 889500          1 TRUE        #>  3 Clinical 2008-04-21 A42180          1 TRUE        #>  4 Clinical 2004-12-05 C56827          1 TRUE        #>  5 Clinical 2013-04-01 672020          1 TRUE        #>  6 Clinical 2006-08-23 E19253          1 TRUE        #>  7 ICU      2004-04-05 28F906          1 TRUE        #>  8 Clinical 2010-11-01 119392          1 TRUE        #>  9 Clinical 2005-06-16 105248          1 TRUE        #> 10 ICU      2003-10-08 000090          1 TRUE        #> # … with 190 more rows if (require(\"dplyr\")) {   df %>%     group_by(ward) %>%     summarise(       n_patients = n_distinct(patient),       n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),       n_episodes_60 = sum(is_new_episode(date, episode_days = 60)),       n_episodes_30 = sum(is_new_episode(date, episode_days = 30))     ) } #> # A tibble: 3 × 5 #>   ward       n_patients n_episodes_365 n_episodes_60 n_episodes_30 #>   <chr>           <int>          <int>         <int>         <int> #> 1 Clinical          113             14            52            70 #> 2 ICU                53             13            32            38 #> 3 Outpatient          9              6             8             9 if (require(\"dplyr\")) {   # grouping on patients and microorganisms leads to the same   # results as first_isolate() when using 'episode-based':   x <- df %>%     filter_first_isolate(       include_unknown = TRUE,       method = \"episode-based\"     )    y <- df %>%     group_by(patient, mo) %>%     filter(is_new_episode(date, 365)) %>%     ungroup()    identical(x, y) } #> Including isolates from ICU. #> [1] FALSE if (require(\"dplyr\")) {   # but is_new_episode() has a lot more flexibility than first_isolate(),   # since you can now group on anything that seems relevant:   df %>%     group_by(patient, mo, ward) %>%     mutate(flag_episode = is_new_episode(date, 365)) %>%     select(group_vars(.), flag_episode) } #> # A tibble: 200 × 4 #> # Groups:   patient, mo, ward [184] #>    patient mo           ward     flag_episode #>    <chr>   <mo>         <chr>    <lgl>        #>  1 0DBB93  B_ESCHR_COLI ICU      TRUE         #>  2 889500  B_ESCHR_COLI Clinical TRUE         #>  3 A42180  B_ENTRC_AVIM Clinical TRUE         #>  4 C56827  B_ESCHR_COLI Clinical TRUE         #>  5 672020  B_STPHY_EPDR Clinical TRUE         #>  6 E19253  B_STPHY_CONS Clinical TRUE         #>  7 28F906  B_STPHY_CONS ICU      TRUE         #>  8 119392  B_STPHY_CONS Clinical TRUE         #>  9 105248  B_KLBSL_PNMN Clinical TRUE         #> 10 000090  B_STPHY_EPDR ICU      TRUE         #> # … with 190 more rows # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA Biplot with ggplot2 — ggplot_pca","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"Produces ggplot2 variant -called biplot PCA (principal component analysis), flexible appealing base R biplot() function.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"ggplot_pca(   x,   choices = 1:2,   scale = 1,   pc.biplot = TRUE,   labels = NULL,   labels_textsize = 3,   labels_text_placement = 1.5,   groups = NULL,   ellipse = TRUE,   ellipse_prob = 0.68,   ellipse_size = 0.5,   ellipse_alpha = 0.5,   points_size = 2,   points_alpha = 0.25,   arrows = TRUE,   arrows_colour = \"darkblue\",   arrows_size = 0.5,   arrows_textsize = 3,   arrows_textangled = TRUE,   arrows_alpha = 0.75,   base_textsize = 10,   ... )"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"ggplot_pca() function based ggbiplot() function ggbiplot package Vince Vu, found GitHub: https://github.com/vqv/ggbiplot (retrieved: 2 March 2020, latest commit: 7325e88; 12 February 2015). per GPL-2 licence demands documentation code changes, changes made based source code : Rewritten code remove dependency packages plyr, scales grid Parametrised options, like arrow ellipse settings Hardened input possibilities defining exact type user input every argument Added total amount explained variance caption plot Cleaned syntax based lintr package, fixed grammatical errors added integrity checks Updated documentation","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"x object returned pca(), prcomp() princomp() choices length 2 vector specifying components plot. default     biplot strict sense. scale variables scaled lambda ^ scale     observations scaled lambda ^ (1-scale)     lambda singular values computed     princomp. Normally 0 <= scale <= 1, warning     issued specified scale outside range. pc.biplot true, use Gabriel (1971) refers \"principal component     biplot\", lambda = 1 observations scaled sqrt(n)     variables scaled sqrt(n).  inner products     variables approximate covariances distances observations     approximate Mahalanobis distance. labels optional vector labels observations. set, labels placed respective points. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). labels_textsize size text used labels labels_text_placement adjustment factor placement variable names (>=1 means away arrow head) groups optional vector groups labels, length labels. set, points labels coloured according groups. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). ellipse logical indicate whether normal data ellipse drawn group (set groups) ellipse_prob statistical size ellipse normal probability ellipse_size size ellipse line ellipse_alpha alpha (transparency) ellipse line points_size size points points_alpha alpha (transparency) points arrows logical indicate whether arrows drawn arrows_colour colour arrow text arrows_size size (thickness) arrow lines arrows_textsize size text end arrows arrows_textangled logical whether text end arrows angled arrows_alpha alpha (transparency) arrows text base_textsize text size plot elements except labels arrows ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"colours labels points can changed adding another scale layer colour, scale_colour_viridis_d() scale_colour_brewer().","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # \\donttest{ if (require(\"dplyr\")) {   # calculate the resistance per group first   resistance_data <- example_isolates %>%     group_by(       order = mo_order(mo), # group on anything, like order       genus = mo_genus(mo)     ) %>% #   and genus as we do here;     filter(n() >= 30) %>% # filter on only 30 results per group     summarise_if(is.sir, resistance) # then get resistance of all drugs    # now conduct PCA for certain antimicrobial drugs   pca_result <- resistance_data %>%     pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)    summary(pca_result)    # old base R plotting method:   biplot(pca_result)    # new ggplot2 plotting method using this package:   if (require(\"ggplot2\")) {     ggplot_pca(pca_result)      # still extendible with any ggplot2 function     ggplot_pca(pca_result) +       scale_colour_viridis_d() +       labs(title = \"Title here\")   } } #> Warning: Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 13 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 15 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 13 results available for FLC in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for AMX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for AMP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for TZP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 12 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 5 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for FEP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 23 results available for FEP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 29 results available for FOX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 6 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 17 results available for AMK in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 6 results available for NIT in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 17 results available for NIT in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 8 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 7 results available for LNZ in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 5 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 23 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 7 results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 6 results available for TEC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 3 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 18 results available for TGC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 7 results available for TGC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for DOX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for IPM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for MEM in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for MEM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Caryophanales\", genus = \"Staphylococcus\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 9 results available for COL in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", \"TMP\" #>   and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\"  #>    # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":null,"dir":"Reference","previous_headings":"","what":"AMR Plots with ggplot2 — ggplot_sir","title":"AMR Plots with ggplot2 — ggplot_sir","text":"Use functions create bar plots AMR data analysis. functions rely ggplot2 functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AMR Plots with ggplot2 — ggplot_sir","text":"","code":"ggplot_sir(   data,   position = NULL,   x = \"antibiotic\",   fill = \"interpretation\",   facet = NULL,   breaks = seq(0, 1, 0.1),   limits = NULL,   translate_ab = \"name\",   combine_SI = TRUE,   minimum = 30,   language = get_AMR_locale(),   nrow = NULL,   colours = c(S = \"#3CAEA3\", SI = \"#3CAEA3\", I = \"#F6D55C\", IR = \"#ED553B\", R =     \"#ED553B\"),   datalabels = TRUE,   datalabels.size = 2.5,   datalabels.colour = \"grey15\",   title = NULL,   subtitle = NULL,   caption = NULL,   x.title = \"Antimicrobial\",   y.title = \"Proportion\",   ... )  geom_sir(   position = NULL,   x = c(\"antibiotic\", \"interpretation\"),   fill = \"interpretation\",   translate_ab = \"name\",   minimum = 30,   language = get_AMR_locale(),   combine_SI = TRUE,   ... )  facet_sir(facet = c(\"interpretation\", \"antibiotic\"), nrow = NULL)  scale_y_percent(breaks = seq(0, 1, 0.1), limits = NULL)  scale_sir_colours(..., aesthetics = \"fill\")  theme_sir()  labels_sir_count(   position = NULL,   x = \"antibiotic\",   translate_ab = \"name\",   minimum = 30,   language = get_AMR_locale(),   combine_SI = TRUE,   datalabels.size = 3,   datalabels.colour = \"grey15\" )"},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AMR Plots with ggplot2 — ggplot_sir","text":"data data.frame column(s) class sir (see .sir()) position position adjustment bars, either \"fill\", \"stack\" \"dodge\" x variable show x axis, either \"antibiotic\" (default) \"interpretation\" grouping variable fill variable categorise using plots legend, either \"antibiotic\" (default) \"interpretation\" grouping variable facet variable split plots , either \"interpretation\" (default) \"antibiotic\" grouping variable breaks numeric vector positions limits numeric vector length two providing limits scale, use NA refer existing minimum maximum translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant), defaults TRUE minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. language language returned text, defaults system language (see get_AMR_locale()) can also set option AMR_locale. Use language = NULL language = \"\" prevent translation. nrow (using facet) number rows colours named vactor colour used filling. default colours colour-blind friendly. datalabels show datalabels using labels_sir_count() datalabels.size size datalabels datalabels.colour colour datalabels title text show title plot subtitle text show subtitle plot caption text show caption plot x.title text show x axis description y.title text show y axis description ... arguments passed geom_sir() , case scale_sir_colours(), named values set colours. default colours colour-blind friendly, maintaining convention e.g. 'susceptible' green 'resistant' red. See Examples. aesthetics aesthetics apply colours , defaults \"fill\" can also (combination ) \"alpha\", \"colour\", \"fill\", \"linetype\", \"shape\" \"size\"","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AMR Plots with ggplot2 — ggplot_sir","text":"default, names antibiotics shown plots using ab_name(). can set translate_ab argument. See count_df().","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"the-functions","dir":"Reference","previous_headings":"","what":"The Functions","title":"AMR Plots with ggplot2 — ggplot_sir","text":"geom_sir() take variable data sir class (created .sir()) using sir_df() plot bars percentage S, , R. default behaviour bars stacked different antibiotics x axis. facet_sir() creates 2d plots (default based S//R) using ggplot2::facet_wrap(). scale_y_percent() transforms y axis 0 100% range using ggplot2::scale_y_continuous(). scale_sir_colours() sets colours bars (green S, yellow , red R). multilingual support. default colours colour-blind friendly, maintaining convention e.g. 'susceptible' green 'resistant' red. theme_sir() [ggplot2 theme][ggplot2::theme() minimal distraction. labels_sir_count() print datalabels bars percentage amount isolates using ggplot2::geom_text(). ggplot_sir() wrapper around functions uses data first input. makes possible use function pipe (%>%). See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AMR Plots with ggplot2 — ggplot_sir","text":"","code":"# \\donttest{ if (require(\"ggplot2\") && require(\"dplyr\")) {   # get antimicrobial results for drugs against a UTI:   ggplot(example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)) +     geom_sir() }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # prettify the plot using some additional functions:   df <- example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)   ggplot(df) +     geom_sir() +     scale_y_percent() +     scale_sir_colours() +     labels_sir_count() +     theme_sir() }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # or better yet, simplify this using the wrapper function - a single command:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir() }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # get only proportions and no counts:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir(datalabels = FALSE) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # add other ggplot2 arguments as you like:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir(       width = 0.5,       colour = \"black\",       size = 1,       linetype = 2,       alpha = 0.25     ) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # you can alter the colours with colour names:   example_isolates %>%     select(AMX) %>%     ggplot_sir(colours = c(SI = \"yellow\")) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # but you can also use the built-in colour-blind friendly colours for   # your plots, where \"S\" is green, \"I\" is yellow and \"R\" is red:   data.frame(     x = c(\"Value1\", \"Value2\", \"Value3\"),     y = c(1, 2, 3),     z = c(\"Value4\", \"Value5\", \"Value6\")   ) %>%     ggplot() +     geom_col(aes(x = x, y = y, fill = z)) +     scale_sir_colours(Value4 = \"S\", Value5 = \"I\", Value6 = \"R\") }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # resistance of ciprofloxacine per age group   example_isolates %>%     mutate(first_isolate = first_isolate()) %>%     filter(       first_isolate == TRUE,       mo == as.mo(\"Escherichia coli\")     ) %>%     # age_groups() is also a function in this AMR package:     group_by(age_group = age_groups(age)) %>%     select(age_group, CIP) %>%     ggplot_sir(x = \"age_group\") } #> Including isolates from ICU. #> Warning: Removed 6 rows containing missing values (`position_stack()`). #> Warning: Removed 6 rows containing missing values (`position_stack()`).  if (require(\"ggplot2\") && require(\"dplyr\")) {   # a shorter version which also adjusts data label colours:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir(colours = FALSE) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # it also supports groups (don't forget to use the group var on `x` or `facet`):   example_isolates %>%     filter(mo_is_gram_negative(), ward != \"Outpatient\") %>%     # select only UTI-specific drugs     select(ward, AMX, NIT, FOS, TMP, CIP) %>%     group_by(ward) %>%     ggplot_sir(       x = \"ward\",       facet = \"antibiotic\",       nrow = 1,       title = \"AMR of Anti-UTI Drugs Per Ward\",       x.title = \"Ward\",       datalabels = FALSE     ) } #> ℹ Using column 'mo' as input for mo_is_gram_negative()  # }"},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":null,"dir":"Reference","previous_headings":"","what":"Guess Antibiotic Column — guess_ab_col","title":"Guess Antibiotic Column — guess_ab_col","text":"tries find column name data set based information antibiotics data set. Also supports WHONET abbreviations.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Guess Antibiotic Column — guess_ab_col","text":"","code":"guess_ab_col(   x = NULL,   search_string = NULL,   verbose = FALSE,   only_sir_columns = FALSE )"},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Guess Antibiotic Column — guess_ab_col","text":"x data.frame search_string text search x , checked .ab() value column x verbose logical indicate whether additional info printed only_sir_columns logical indicate whether antibiotic columns must detected transformed class sir (see .sir()) beforehand (defaults FALSE)","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Guess Antibiotic Column — guess_ab_col","text":"column name x, NULL result found.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Guess Antibiotic Column — guess_ab_col","text":"can look antibiotic (trade) name abbreviation search x antibiotics data set column containing name code antibiotic.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Guess Antibiotic Column — guess_ab_col","text":"","code":"df <- data.frame(   amox = \"S\",   tetr = \"R\" )  guess_ab_col(df, \"amoxicillin\") #> [1] \"amox\" guess_ab_col(df, \"J01AA07\") # ATC code of tetracycline #> [1] \"tetr\"  guess_ab_col(df, \"J01AA07\", verbose = TRUE) #> Auto-guessing columns suitable for analysis #> ... #>  OK. #> ℹ Using column 'amox' as input for AMX (amoxicillin). #> ℹ Using column 'tetr' as input for TCY (tetracycline). #> ℹ Using column 'tetr' as input for J01AA07 (tetracycline). #> [1] \"tetr\" # NOTE: Using column 'tetr' as input for J01AA07 (tetracycline).  # WHONET codes df <- data.frame(   AMP_ND10 = \"R\",   AMC_ED20 = \"S\" ) guess_ab_col(df, \"ampicillin\") #> [1] \"AMP_ND10\" guess_ab_col(df, \"J01CR02\") #> [1] \"AMC_ED20\" guess_ab_col(df, as.ab(\"augmentin\")) #> [1] \"AMC_ED20\""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"Data set containing defined intrinsic resistance EUCAST bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"intrinsic_resistant"},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"tibble 134,634 observations 2 variables: mo Microorganism ID ab Antibiotic ID","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"data set based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository. allow machine reading EUCAST CLSI guidelines, almost impossible MS Excel PDF files distributed EUCAST CLSI.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"intrinsic_resistant #> # A tibble: 134,634 × 2 #>    mo      ab    #>    <mo>    <chr> #>  1 B_GRAMP ATM   #>  2 B_GRAMP COL   #>  3 B_GRAMP NAL   #>  4 B_GRAMP PLB   #>  5 B_GRAMP TEM   #>  6 B_ABTRP ATM   #>  7 B_ABTRP COL   #>  8 B_ABTRP NAL   #>  9 B_ABTRP PLB   #> 10 B_ABTRP TEM   #> # … with 134,624 more rows"},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":null,"dir":"Reference","previous_headings":"","what":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"According binomial nomenclature, lowest four taxonomic levels (family, genus, species, subspecies) printed italics. function finds taxonomic names within strings makes italic.","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"","code":"italicise_taxonomy(string, type = c(\"markdown\", \"ansi\"))  italicize_taxonomy(string, type = c(\"markdown\", \"ansi\"))"},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"string character (vector) type type conversion taxonomic names, either \"markdown\" \"ansi\", see Details","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"function finds taxonomic names makes italic based microorganisms data set. taxonomic names can italicised using markdown (default) adding * taxonomic names, using ANSI colours adding \\033[3m \\033[23m taxonomic names. multiple ANSI colours available, conversion occur. function also supports abbreviation genus followed species, \"E. coli\" \"K. pneumoniae ozaenae\".","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"","code":"italicise_taxonomy(\"An overview of Staphylococcus aureus isolates\") #> [1] \"An overview of *Staphylococcus aureus* isolates\" italicise_taxonomy(\"An overview of S. aureus isolates\") #> [1] \"An overview of *S. aureus* isolates\"  cat(italicise_taxonomy(\"An overview of S. aureus isolates\", type = \"ansi\")) #> An overview of S. aureus isolates"},{"path":"https://msberends.github.io/AMR/reference/join.html","id":null,"dir":"Reference","previous_headings":"","what":"Join microorganisms to a Data Set — join","title":"Join microorganisms to a Data Set — join","text":"Join data set microorganisms easily existing data set character vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join microorganisms to a Data Set — join","text":"","code":"inner_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  left_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  right_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  full_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  semi_join_microorganisms(x, by = NULL, ...)  anti_join_microorganisms(x, by = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join microorganisms to a Data Set — join","text":"x existing data set join, character vector. case character vector, resulting data.frame contain column 'x' values. variable join - left empty search column class mo (created .mo()) \"mo\" column name exists x, otherwise column name x values exist microorganisms$mo (= \"bacteria_id\"), another column microorganisms (named, like = c(\"bacteria_id\" = \"fullname\")) suffix non-joined duplicate variables x y, suffixes added output disambiguate . character vector length 2. ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join microorganisms to a Data Set — join","text":"data.frame","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Join microorganisms to a Data Set — join","text":"Note: opposed join() functions dplyr, character vectors supported default existing columns get suffix \"2\" newly joined columns get suffix. dplyr package installed, join functions used. Otherwise, much slower merge() interaction() functions base R used.","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Join microorganisms to a Data Set — join","text":"","code":"left_join_microorganisms(as.mo(\"K. pneumoniae\")) #> # A tibble: 1 × 22 #>   mo           fullname   status kingdom phylum class order family genus species #>   <mo>         <chr>      <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> <chr>   #> 1 B_KLBSL_PNMN Klebsiell… accep… Bacter… Pseud… Gamm… Ente… Enter… Kleb… pneumo… #> # … with 12 more variables: subspecies <chr>, rank <chr>, ref <chr>, #> #   source <chr>, lpsn <chr>, lpsn_parent <chr>, lpsn_renamed_to <chr>, #> #   gbif <chr>, gbif_parent <chr>, gbif_renamed_to <chr>, prevalence <dbl>, #> #   snomed <list> left_join_microorganisms(\"B_KLBSL_PNMN\") #> # A tibble: 1 × 22 #>   mo           fullname   status kingdom phylum class order family genus species #>   <mo>         <chr>      <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> <chr>   #> 1 B_KLBSL_PNMN Klebsiell… accep… Bacter… Pseud… Gamm… Ente… Enter… Kleb… pneumo… #> # … with 12 more variables: subspecies <chr>, rank <chr>, ref <chr>, #> #   source <chr>, lpsn <chr>, lpsn_parent <chr>, lpsn_renamed_to <chr>, #> #   gbif <chr>, gbif_parent <chr>, gbif_renamed_to <chr>, prevalence <dbl>, #> #   snomed <list>  df <- data.frame(   date = seq(     from = as.Date(\"2018-01-01\"),     to = as.Date(\"2018-01-07\"),     by = 1   ),   bacteria = as.mo(c(     \"S. aureus\", \"MRSA\", \"MSSA\", \"STAAUR\",     \"E. coli\", \"E. coli\", \"E. coli\"   )),   stringsAsFactors = FALSE ) colnames(df) #> [1] \"date\"     \"bacteria\"  df_joined <- left_join_microorganisms(df, \"bacteria\") colnames(df_joined) #>  [1] \"date\"            \"bacteria\"        \"fullname\"        \"status\"          #>  [5] \"kingdom\"         \"phylum\"          \"class\"           \"order\"           #>  [9] \"family\"          \"genus\"           \"species\"         \"subspecies\"      #> [13] \"rank\"            \"ref\"             \"source\"          \"lpsn\"            #> [17] \"lpsn_parent\"     \"lpsn_renamed_to\" \"gbif\"            \"gbif_parent\"     #> [21] \"gbif_renamed_to\" \"prevalence\"      \"snomed\"           # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     left_join_microorganisms() %>%     colnames() } #> Joining, by = \"mo\" #>  [1] \"date\"            \"patient\"         \"age\"             \"gender\"          #>  [5] \"ward\"            \"mo\"              \"PEN\"             \"OXA\"             #>  [9] \"FLC\"             \"AMX\"             \"AMC\"             \"AMP\"             #> [13] \"TZP\"             \"CZO\"             \"FEP\"             \"CXM\"             #> [17] \"FOX\"             \"CTX\"             \"CAZ\"             \"CRO\"             #> [21] \"GEN\"             \"TOB\"             \"AMK\"             \"KAN\"             #> [25] \"TMP\"             \"SXT\"             \"NIT\"             \"FOS\"             #> [29] \"LNZ\"             \"CIP\"             \"MFX\"             \"VAN\"             #> [33] \"TEC\"             \"TCY\"             \"TGC\"             \"DOX\"             #> [37] \"ERY\"             \"CLI\"             \"AZM\"             \"IPM\"             #> [41] \"MEM\"             \"MTR\"             \"CHL\"             \"COL\"             #> [45] \"MUP\"             \"RIF\"             \"fullname\"        \"status\"          #> [49] \"kingdom\"         \"phylum\"          \"class\"           \"order\"           #> [53] \"family\"          \"genus\"           \"species\"         \"subspecies\"      #> [57] \"rank\"            \"ref\"             \"source\"          \"lpsn\"            #> [61] \"lpsn_parent\"     \"lpsn_renamed_to\" \"gbif\"            \"gbif_parent\"     #> [65] \"gbif_renamed_to\" \"prevalence\"      \"snomed\"          # }"},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"functions can used determine first weighted isolates considering phenotype isolate selection (see first_isolate()). Using phenotype-based method determine first isolates reliable methods disregard phenotypes.","code":""},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"","code":"key_antimicrobials(   x = NULL,   col_mo = NULL,   universal = c(\"ampicillin\", \"amoxicillin/clavulanic acid\", \"cefuroxime\",     \"piperacillin/tazobactam\", \"ciprofloxacin\", \"trimethoprim/sulfamethoxazole\"),   gram_negative = c(\"gentamicin\", \"tobramycin\", \"colistin\", \"cefotaxime\", \"ceftazidime\",     \"meropenem\"),   gram_positive = c(\"vancomycin\", \"teicoplanin\", \"tetracycline\", \"erythromycin\",     \"oxacillin\", \"rifampin\"),   antifungal = c(\"anidulafungin\", \"caspofungin\", \"fluconazole\", \"miconazole\", \"nystatin\",     \"voriconazole\"),   only_sir_columns = FALSE,   ... )  all_antimicrobials(x = NULL, only_sir_columns = FALSE, ...)  antimicrobials_equal(   y,   z,   type = c(\"points\", \"keyantimicrobials\"),   ignore_I = TRUE,   points_threshold = 2,   ... )"},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"x data.frame antibiotics columns, like AMX amox. Can left blank determine automatically col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). universal names broad-spectrum antimicrobial drugs, case-insensitive. Set NULL ignore. See Details default antimicrobial drugs gram_negative names antibiotic drugs Gram-positives, case-insensitive. Set NULL ignore. See Details default antibiotic drugs gram_positive names antibiotic drugs Gram-negatives, case-insensitive. Set NULL ignore. See Details default antibiotic drugs antifungal names antifungal drugs fungi, case-insensitive. Set NULL ignore. See Details default antifungal drugs only_sir_columns logical indicate whether columns must included transformed class sir (see .sir()) beforehand (defaults FALSE) ... ignored, place allow future extensions y, z character vectors compare type type determine weighed isolates; can \"keyantimicrobials\" \"points\", see Details ignore_I logical indicate whether antibiotic interpretations \"\" ignored type = \"keyantimicrobials\", see Details points_threshold minimum number points require differences antibiogram lead inclusion isolate type = \"points\", see Details","code":""},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"key_antimicrobials() all_antimicrobials() functions context-aware. means x argument can left blank used inside data.frame call, see Examples. function key_antimicrobials() returns character vector 12 antimicrobial results every isolate. function all_antimicrobials() returns character vector antimicrobial drug results every isolate. vectors can compared using antimicrobials_equal(), check two isolates generally antibiogram. Missing invalid values replaced dot (\".\") key_antimicrobials() ignored antimicrobials_equal(). Please see first_isolate() function important functions enable 'phenotype-based' method determination first isolates. default antimicrobial drugs used rows (set universal) : Ampicillin Amoxicillin/clavulanic acid Cefuroxime Ciprofloxacin Piperacillin/tazobactam Trimethoprim/sulfamethoxazole default antimicrobial drugs used Gram-negative bacteria (set gram_negative) : Cefotaxime Ceftazidime Colistin Gentamicin Meropenem Tobramycin default antimicrobial drugs used Gram-positive bacteria (set gram_positive) : Erythromycin Oxacillin Rifampin Teicoplanin Tetracycline Vancomycin default antimicrobial drugs used fungi (set antifungal) : Anidulafungin Caspofungin Fluconazole Miconazole Nystatin Voriconazole","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # output of the `key_antimicrobials()` function could be like this: strainA <- \"SSSRR.S.R..S\" strainB <- \"SSSIRSSSRSSS\"  # those strings can be compared with: antimicrobials_equal(strainA, strainB, type = \"keyantimicrobials\") #> [1] TRUE # TRUE, because I is ignored (as well as missing values)  antimicrobials_equal(strainA, strainB, type = \"keyantimicrobials\", ignore_I = FALSE) #> [1] FALSE # FALSE, because I is not ignored and so the 4th [character] differs  # \\donttest{ if (require(\"dplyr\")) {   # set key antibiotics to a new variable   my_patients <- example_isolates %>%     mutate(keyab = key_antimicrobials(antifungal = NULL)) %>% # no need to define `x`     mutate(       # now calculate first isolates       first_regular = first_isolate(col_keyantimicrobials = FALSE),       # and first WEIGHTED isolates       first_weighted = first_isolate(col_keyantimicrobials = \"keyab\")     )    # Check the difference in this data set, 'weighted' results in more isolates:   sum(my_patients$first_regular, na.rm = TRUE)   sum(my_patients$first_weighted, na.rm = TRUE) } #> Including isolates from ICU. #> Including isolates from ICU. #> [1] 1395 # }"},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":null,"dir":"Reference","previous_headings":"","what":"Kurtosis of the Sample — kurtosis","title":"Kurtosis of the Sample — kurtosis","text":"Kurtosis measure \"tailedness\" probability distribution real-valued random variable. normal distribution kurtosis 3 excess kurtosis 0.","code":""},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kurtosis of the Sample — kurtosis","text":"","code":"kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for default kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for matrix kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for data.frame kurtosis(x, na.rm = FALSE, excess = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kurtosis of the Sample — kurtosis","text":"x vector values, matrix data.frame na.rm logical indicate whether NA values stripped computation proceeds excess logical indicate whether excess kurtosis returned, defined kurtosis minus 3.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kurtosis of the Sample — kurtosis","text":"","code":"kurtosis(rnorm(10000)) #> [1] 2.977128 kurtosis(rnorm(10000), excess = TRUE) #> [1] 0.04861302"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":null,"dir":"Reference","previous_headings":"","what":"Vectorised Pattern Matching with Keyboard Shortcut — like","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Convenient wrapper around grepl() match pattern: x %like% pattern. always returns logical vector always case-insensitive (use x %like_case% pattern case-sensitive matching). Also, pattern can long x compare items index vectors, can length iterate cases.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"like(x, pattern, ignore.case = TRUE)  x %like% pattern  x %unlike% pattern  x %like_case% pattern  x %unlike_case% pattern"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Idea like function data.table package, although altered explained Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"x character vector matches sought, object can coerced .character() character vector. pattern character vector containing regular expressions (character string fixed = TRUE) matched given character vector. Coerced .character() character string possible. ignore.case FALSE, pattern matching case sensitive TRUE, case ignored matching.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"like() %like%/%unlike% functions: case-insensitive (use %like_case%/%unlike_case% case-sensitive matching) Support multiple patterns Check pattern valid regular expression sets fixed = TRUE , greatly improve speed (vectorised pattern) Always use compatibility Perl unless fixed = TRUE, greatly improve speed Using RStudio? %like%/%unlike% functions can also directly inserted code Addins menu can keyboard shortcut like Shift+Ctrl+L Shift+Cmd+L (see menu Tools > Modify Keyboard Shortcuts...). keep pressing shortcut, inserted text iterated %like% -> %unlike% -> %like_case% -> %unlike_case%.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"a <- \"This is a test\" b <- \"TEST\" a %like% b #> [1] TRUE b %like% a #> [1] FALSE  # also supports multiple patterns a <- c(\"Test case\", \"Something different\", \"Yet another thing\") b <- c(\"case\", \"diff\", \"yet\") a %like% b #> [1] TRUE TRUE TRUE a %unlike% b #> [1] FALSE FALSE FALSE  a[1] %like% b #> [1]  TRUE FALSE FALSE a %like% b[1] #> [1]  TRUE FALSE FALSE  # \\donttest{ # get isolates whose name start with 'Entero' (case-insensitive) example_isolates[which(mo_name() %like% \"^entero\"), ] #> ℹ Using column 'mo' as input for mo_name() #> # A tibble: 106 × 46 #>    date       patient   age gender ward    mo            PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>   <mo>          <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinic… B_ENTRC_FACM  NA    NA    NA    NA    #>  2 2002-04-08 130252     78 M      ICU     B_ENTRC_FCLS  NA    NA    NA    NA    #>  3 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS  NA    NA    NA    NA    #>  4 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS  NA    NA    NA    NA    #>  5 2003-04-20 6BC362     62 M      ICU     B_ENTRC       NA    NA    NA    NA    #>  6 2003-04-21 6BC362     62 M      ICU     B_ENTRC       NA    NA    NA    NA    #>  7 2003-08-13 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R     #>  8 2003-08-13 F35553     52 M      ICU     B_ENTRC_FCLS  NA    NA    NA    NA    #>  9 2003-09-05 F35553     52 M      ICU     B_ENTRC       NA    NA    NA    NA    #> 10 2003-09-05 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R     #> # … with 96 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir>  if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_name() %like% \"^ent\") } #> ℹ Using column 'mo' as input for mo_name() #> # A tibble: 106 × 46 #>    date       patient   age gender ward    mo            PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>   <mo>          <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinic… B_ENTRC_FACM  NA    NA    NA    NA    #>  2 2002-04-08 130252     78 M      ICU     B_ENTRC_FCLS  NA    NA    NA    NA    #>  3 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS  NA    NA    NA    NA    #>  4 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS  NA    NA    NA    NA    #>  5 2003-04-20 6BC362     62 M      ICU     B_ENTRC       NA    NA    NA    NA    #>  6 2003-04-21 6BC362     62 M      ICU     B_ENTRC       NA    NA    NA    NA    #>  7 2003-08-13 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R     #>  8 2003-08-13 F35553     52 M      ICU     B_ENTRC_FCLS  NA    NA    NA    NA    #>  9 2003-09-05 F35553     52 M      ICU     B_ENTRC       NA    NA    NA    NA    #> 10 2003-09-05 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R     #> # … with 96 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> # }"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Determine isolates multidrug-resistant organisms (MDRO) according international, national custom guidelines.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"mdro(   x = NULL,   guideline = \"CMI2012\",   col_mo = NULL,   info = interactive(),   pct_required_classes = 0.5,   combine_SI = TRUE,   verbose = FALSE,   only_sir_columns = FALSE,   ... )  custom_mdro_guideline(..., as_factor = TRUE)  brmo(x = NULL, only_sir_columns = FALSE, ...)  mrgn(x = NULL, only_sir_columns = FALSE, ...)  mdr_tb(x = NULL, only_sir_columns = FALSE, ...)  mdr_cmi2012(x = NULL, only_sir_columns = FALSE, ...)  eucast_exceptional_phenotypes(x = NULL, only_sir_columns = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"See supported guidelines list publications used function.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"x data.frame antibiotics columns, like AMX amox. Can left blank automatic determination. guideline specific guideline follow, see sections Supported international / national guidelines Using Custom Guidelines . left empty, publication Magiorakos et al. (see ) followed. col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console, defaults print interactive sessions pct_required_classes minimal required percentage antimicrobial classes must available per isolate, rounded . example, default guideline, 17 antimicrobial classes must available S. aureus. Setting pct_required_classes argument 0.5 (default) means every S. aureus isolate least 8 different classes must available. lower number available classes return NA isolate. combine_SI logical indicate whether values S must merged one, resistance considered isolates R, . default behaviour mdro() function, follows redefinition EUCAST interpretation (increased exposure) 2019, see section 'Interpretation S, R' . using combine_SI = FALSE, resistance considered isolates R . verbose logical turn Verbose mode (default ). Verbose mode, function return MDRO results, instead returns data set logbook form extensive info isolates MDRO-positive, . only_sir_columns logical indicate whether antibiotic columns must detected transformed class sir (see .sir()) beforehand (defaults FALSE) ... case custom_mdro_guideline(): set rules, see section Using Custom Guidelines . Otherwise: column name antibiotic, see section Antibiotics . as_factor logical indicate whether returned value ordered factor (TRUE, default), otherwise character vector","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"CMI 2012 paper - function mdr_cmi2012() mdro(): Ordered factor levels Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant (XDR) < Pandrug-resistant (PDR) TB guideline - function mdr_tb() mdro(..., guideline = \"TB\"): Ordered factor levels Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant < Extensively drug-resistant German guideline - function mrgn() mdro(..., guideline = \"MRGN\"): Ordered factor levels Negative < 3MRGN < 4MRGN Everything else, except custom guidelines: Ordered factor levels Negative < Positive, unconfirmed < Positive. value \"Positive, unconfirmed\" means , according guideline, entirely sure isolate multi-drug resistant confirmed additional (e.g. molecular) tests","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"functions context-aware. means x argument can left blank used inside data.frame call, see Examples. pct_required_classes argument, values 1 divided 100. support fractions (0.75 3/4) percentages (75). Note: Every test involves Enterobacteriaceae family, internally performed using newly named order Enterobacterales, since Enterobacteriaceae family taxonomically reclassified Adeolu et al. 2016. , Enterobacteriaceae family Enterobacteriales () order. species old Enterobacteriaceae family still new Enterobacterales (without ) order, divided multiple families. way tests performed now mdro() function makes sure results 2016 2016 identical.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"supported-international-national-guidelines","dir":"Reference","previous_headings":"","what":"Supported International / National Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. \"Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.\" Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.3\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.3 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.2\" European international guideline - EUCAST Expert Rules Version 3.2 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 \"Intrinsic Resistance Exceptional Phenotypes Tables\" (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization \"Companion handbook guidelines programmatic management drug-resistant tuberculosis\" (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7; doi:10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu \"WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)\" (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"using-custom-guidelines","dir":"Reference","previous_headings":"","what":"Using Custom Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde:   row/isolate matches first rule, value first ~ (case 'Elderly Type ') set MDRO value. Otherwise, second rule tried . number rules unlimited. can print rules set console overview. Colours help reading console supports colours.   outcome function can used guideline argument mdro() function:   Rules can also combined custom rules using c():   rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline(CIP == \"R\" & age > 60 ~ \"Elderly Type A\",                                 ERY == \"R\" & age > 60 ~ \"Elderly Type B\") custom #> A set of custom MDRO rules: #>   1. CIP is \"R\" and age is higher than 60 -> Elderly Type A #>   2. ERY is \"R\" and age is higher than 60 -> Elderly Type B #>   3. Otherwise -> Negative #> #> Unmatched rows will return NA. x <- mdro(example_isolates,           guideline = custom) table(x) #>       Negative Elderly Type A Elderly Type B #>           1070            198            732 x <- mdro(example_isolates,           guideline = c(custom,                         custom_mdro_guideline(ERY == \"R\" & age > 50 ~ \"Elderly Type C\"))) table(x) #>       Negative Elderly Type A Elderly Type B Elderly Type C #>            961            198            732            109"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"antibiotics","dir":"Reference","previous_headings":"","what":"Antibiotics","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"define antibiotics column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antibiotics eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, S01AE08), amoxicillin (AMX, J01MA02), amoxicillin/clavulanic acid (AMC, J01MA23), ampicillin (AMP, J01MA04), ampicillin/sulbactam (SAM, J01MA08), arbekacin (ARB, J01MA19), aspoxicillin (APX, J01MA16), azidocillin (AZD, J01MA15), azithromycin (AZM, J01MA11), azlocillin (AZL, J01MA25), aztreonam (ATM, J01MA12), bacampicillin (BAM, J01MA24), bekanamycin (BEK, J01MA07), benzathine benzylpenicillin (BNB, J01MA14), benzathine phenoxymethylpenicillin (BNP, D10AF05), benzylpenicillin (PEN, J01MA06), besifloxacin (BES, J01MA01), biapenem (BIA, J01MA18), carbenicillin (CRB, J01MA03), carindacillin (CRN, J01MA17), cefacetrile (CAC, J01MA10), cefaclor (CEC, J01MA21), cefadroxil (CFR, J01MA09), cefalexin (LEX, J01MA05), cefaloridine (RID, P01AA05), cefalotin (CEP, J01MA22), cefamandole (MAN, J01MA13), cefapirin (HAP, J01CA01), cefatrizine (CTZ, J01CA04), cefazedone (CZD, J01CA12), cefazolin (CZO, J01CR05), cefcapene (CCP, J01CA13), cefdinir (CDR, J01AA02), cefditoren (DIT, J01FA10), cefepime (FEP, J01FA09), cefetamet (CAT, J01CR02), cefixime (CFM, J01AA08), cefmenoxime (CMX, J01FA06), cefmetazole (CMZ, J01CF04), cefodizime (DIZ, J01CF05), cefonicid (CID, J01CR01), cefoperazone (CFP, J01CA19), cefoperazone/sulbactam (CSL, J01CE04), ceforanide (CND, J01CA09), cefotaxime (CTX, J01DF01), cefotaxime/clavulanic acid (CTC, J01CA06), cefotetan (CTT, J01CE08), cefotiam (CTF, J01CE10), cefoxitin (FOX, J01CE01), cefozopran (ZOP, J01CA03), cefpiramide (CPM, J01CA05), cefpirome (CPO, J01CE07), cefpodoxime (CPD, J01CF02), cefprozil (CPR, J01CF01), cefroxadine (CRD, J01CA07), cefsulodin (CFS, J01CA18), ceftaroline (CPT, J01CA11), ceftazidime (CAZ, J01CA14), ceftazidime/clavulanic acid (CCV, J01CF03), cefteram (CEM, J01CA10), ceftezole (CTL, J01CF06), ceftibuten (CTB, J01CE06), ceftizoxime (CZX, J01CE05), ceftobiprole medocaril (CFM1, J01CE02), ceftolozane/tazobactam (CZT, J01CA02), ceftriaxone (CRO, J01CA08), ceftriaxone/beta-lactamase inhibitor (CEB, J01CE09), cefuroxime (CXM, J01CE03), cephradine (CED, J01CG01), chloramphenicol (CHL, J01CA16), ciprofloxacin (CIP, J01CR04), clarithromycin (CLR, J01CA15), clindamycin (CLI, J01CG02), clometocillin (CLM, J01CA17), cloxacillin (CLO, J01CR03), colistin (COL, J01DB10), cycloserine (CYC, J01DC04), dalbavancin (DAL, J01DB05), daptomycin (DAP, J01DB01), delafloxacin (DFX, J01DB02), dibekacin (DKB, J01DB03), dicloxacillin (DIC, J01DC03), dirithromycin (DIR, J01DB08), doripenem (DOR, J01DB07), doxycycline (DOX, J01DB06), enoxacin (ENX, J01DB04), epicillin (EPC, J01DD17), ertapenem (ETP, J01DD15), erythromycin (ERY, J01DD16), fleroxacin (FLE, J01DE01), flucloxacillin (FLC, J01DD10), flurithromycin (FLR1, J01DD08), fosfomycin (FOS, J01DD05), framycetin (FRM, J01DC09), fusidic acid (FUS, J01DD09), garenoxacin (GRN, J01DC06), gatifloxacin (GAT, J01DD12), gemifloxacin (GEM, J01DD62), gentamicin (GEN, J01DC11), grepafloxacin (GRX, J01DD01), hetacillin (HET, J01DD51), imipenem (IPM, J01DC05), imipenem/relebactam (IMR, J01DC07), isepamicin (ISE, J01DC01), josamycin (JOS, J01DE03), kanamycin (KAN, J01DD11), lascufloxacin (LSC, J01DE02), latamoxef (LTM, J01DD13), levofloxacin (LVX, J01DC10), levonadifloxacin (LND, J01DB11), lincomycin (LIN, J01DD03), linezolid (LNZ, J01DI02), lomefloxacin (LOM, J01DD02), loracarbef (LOR, J01DD52), mecillinam (MEC, J01DD18), meropenem (MEM, J01DB12), meropenem/vaborbactam (MEV, J01DD14), metampicillin (MTM, J01DD07), meticillin (MET, J01DI01), mezlocillin (MEZ, J01DI54), micronomicin (MCR, J01DD04), midecamycin (MID, J01DD63), minocycline (MNO, J01DC02), miocamycin (MCM, J01DB09), moxifloxacin (MFX, J01DD06), nadifloxacin (NAD, J01DC08), nafcillin (NAF, J01DH05), nalidixic acid (NAL, J01DH04), neomycin (NEO, J01DH03), netilmicin (NET, J01DH51), nitrofurantoin (NIT, J01DH56), norfloxacin (, J01DH02), ofloxacin (OFX, J01DH52), oleandomycin (OLE, J01DH55), oritavancin (ORI, J01DH06), oxacillin (OXA, J01XA02), panipenem (PAN, J01XA01), pazufloxacin (PAZ, J01XC01), pefloxacin (PEF, J01FA13), penamecillin (PNM, J01FA01), pheneticillin (PHE, J01FA14), phenoxymethylpenicillin (PHN, J01FA07), piperacillin (PIP, J01FA03), piperacillin/tazobactam (TZP, J01FA11), pivampicillin (PVM, J01FA05), pivmecillinam (PME, J01FA12), plazomicin (PLZ, J01FA16), polymyxin B (PLB, J01FA02), pristinamycin (PRI, J01FA15), procaine benzylpenicillin (PRB, J01FA08), propicillin (PRP, J01FF02), prulifloxacin (PRU, J01FG01), quinupristin/dalfopristin (QDA, J01FG02), ribostamycin (RST, J04AB02), rifampicin (RIF, J01XX09), rokitamycin (ROK, J01XX08), roxithromycin (RXT, J01AA07), rufloxacin (RFL, J01XB01), sisomicin (SIS, J01XB02), sitafloxacin (SIT, J01XE01), solithromycin (SOL, J01AA12), sparfloxacin (SPX, J01EA01), spiramycin (SPI, J01XX01), streptoduocin (STR, J01BA01), streptomycin (STR1, J01GB06), sulbactam (SUL, J01GB12), sulbenicillin (SBC, J01GB13), sulfadiazine (SDI, J01GB09), sulfadiazine/trimethoprim (SLT1, D09AA01), sulfadimethoxine (SUD, J01GB03), sulfadimidine (SDM, J01GB11), sulfadimidine/trimethoprim (SLT2, J01GB04), sulfafurazole (SLF, S01AA22), sulfaisodimidine (SLF1, J01GB05), sulfalene (SLF2, J01GB07), sulfamazone (SZO, J01GB14), sulfamerazine (SLF3, J01GB10), sulfamerazine/trimethoprim (SLT3, J01GB08), sulfamethizole (SLF4, J01GA02), sulfamethoxazole (SMX, J01GA01), sulfamethoxypyridazine (SLF5, J01GB01), sulfametomidine (SLF6, J01EE01), sulfametoxydiazine (SLF7, J01MB02), sulfametrole/trimethoprim (SLT4, J01FF01), sulfamoxole (SLF8, J01XA04), sulfamoxole/trimethoprim (SLT5, J01XA05), sulfanilamide (SLF9, J01XA03), sulfaperin (SLF10, J04AB01), sulfaphenazole (SLF11, J01XX11), sulfapyridine (SLF12, J01EC02), sulfathiazole (SUT, J01ED01), sulfathiourea (SLF13, J01EB03), sultamicillin (SLT6, J01EB05), talampicillin (TAL, J01EB01), tazobactam (TAZ, J01ED02), tebipenem (TBP, J01ED09), tedizolid (TZD, J01ED07), teicoplanin (TEC, J01EB02), telavancin (TLV, J01EC01), telithromycin (TLT, J01ED05), temafloxacin (TMX, J01ED03), temocillin (TEM, J01ED04), tetracycline (TCY, J01EC03), ticarcillin (TIC, J01EB06), ticarcillin/clavulanic acid (TCC, J01ED06), tigecycline (TGC, J01ED08), tilbroquinol (TBQ, J01EB04), tobramycin (TOB, J01EB07), tosufloxacin (TFX, J01EB08), trimethoprim (TMP, J01EE02), trimethoprim/sulfamethoxazole (SXT, J01EE05), troleandomycin (TRL, J01EE07), trovafloxacin (TVA, J01EE03), vancomycin (VAN, J01EE04)","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"out <- mdro(example_isolates, guideline = \"EUCAST\") #>  (16 isolates had no test results) str(out) #>  Ord.factor w/ 3 levels \"Negative\"<\"Positive, unconfirmed\"<..: 1 1 1 1 1 1 1 1 1 1 ... table(out) #> out #>              Negative Positive, unconfirmed              Positive  #>                  1978                     0                     6   out <- mdro(example_isolates,   guideline = custom_mdro_guideline(     AMX == \"R\" ~ \"Custom MDRO 1\",     VAN == \"R\" ~ \"Custom MDRO 2\"   ) ) table(out) #> out #>      Negative Custom MDRO 1 Custom MDRO 2  #>           870           804           326   # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     mdro() %>%     table()    # no need to define `x` when used inside dplyr verbs:   example_isolates %>%     mutate(MDRO = mdro()) %>%     pull(MDRO) %>%     table() } #>  (16 isolates had no test results) #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #>  (16 isolates had no test results) #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> . #>                         Negative       Multi-drug-resistant (MDR)  #>                             1601                              128  #> Extensively drug-resistant (XDR)          Pandrug-resistant (PDR)  #>                                0                                0  # }"},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the Mean AMR Distance — mean_amr_distance","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"Calculates normalised mean antimicrobial resistance multiple observations, help identify similar isolates without comparing antibiograms hand.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"","code":"mean_amr_distance(x, ...)  # S3 method for sir mean_amr_distance(x, ..., combine_SI = TRUE)  # S3 method for data.frame mean_amr_distance(x, ..., combine_SI = TRUE)  amr_distance_from_row(amr_distance, row)"},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"x vector class sir, mic disk, data.frame containing columns classes ... variables select (supports tidyselect language column1:column4 (.mic), can thus also antibiotic selectors combine_SI logical indicate whether values S must merged one, input consists S+vs. R (susceptible vs. resistant), defaults TRUE amr_distance outcome mean_amr_distance() row index, row number","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"mean AMR distance effectively Z-score; normalised numeric value compare AMR test results can help identify similar isolates, without comparing antibiograms hand. MIC values (see .mic()) transformed log2() first; distance thus calculated (log2(x) - mean(log2(x))) / sd(log2(x)). SIR values (see .sir()) transformed using \"S\" = 1, \"\" = 2, \"R\" = 3. combine_SI TRUE (default), \"\" considered 1. data sets, mean AMR distance calculated per column, mean per row returned, see Examples. Use amr_distance_from_row() subtract distances distance one row, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"interpretation","dir":"Reference","previous_headings":"","what":"Interpretation","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"Isolates distances less 0.01 difference considered similar. Differences lower 0.025 considered suspicious.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"","code":"sir <- random_sir(10) sir #> Class 'sir' #>  [1] R R R I S S R I R S mean_amr_distance(sir) #>  [1]  0.9486833  0.9486833  0.9486833 -0.9486833 -0.9486833 -0.9486833 #>  [7]  0.9486833 -0.9486833  0.9486833 -0.9486833  mic <- random_mic(10) mic #> Class 'mic' #>  [1] 1      8      8      0.25   32     0.25   <=0.01 >=256  128    0.0625 mean_amr_distance(mic) #>  [1] -0.2344009  0.3848258  0.3848258 -0.6472187  0.7976436 -0.6472187 #>  [7] -1.6057520  1.4168703  1.2104614 -1.0600365 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) #>  [1] -0.2344009  0.3848258  0.3848258 -0.6472187  0.7976436 -0.6472187 #>  [7] -1.6057520  1.4168703  1.2104614 -1.0600365  disk <- random_disk(10) disk #> Class 'disk' #>  [1] 35 32 25 11 24 50 37 25 45 42 mean_amr_distance(disk) #>  [1]  0.20559684 -0.05139921 -0.65105665 -1.85037152 -0.73672199  1.49057706 #>  [7]  0.37692753 -0.65105665  1.06225032  0.80525427  y <- data.frame(   id = LETTERS[1:10],   amox = random_sir(10, ab = \"amox\", mo = \"Escherichia coli\"),   cipr = random_disk(10, ab = \"cipr\", mo = \"Escherichia coli\"),   gent = random_mic(10, ab = \"gent\", mo = \"Escherichia coli\"),   tobr = random_mic(10, ab = \"tobr\", mo = \"Escherichia coli\") ) y #>    id amox cipr gent tobr #> 1   A    S   23    4 >=16 #> 2   B    R   18    8  <=1 #> 3   C    I   23   16 >=16 #> 4   D    R   27    4 >=16 #> 5   E    R   27    8 >=16 #> 6   F    S   29    4    8 #> 7   G    R   23    8    4 #> 8   H    R   26    1  <=1 #> 9   I    I   26    2    2 #> 10  J    R   31    2  <=1 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\" and #>   \"tobr\" #>  [1] -0.21933074 -0.41717151  0.19830380  0.53628817  0.74510544  0.05006051 #>  [7]  0.19790396 -0.50063133 -0.63808424  0.04755593 y$amr_distance <- mean_amr_distance(y, where(is.mic)) #> ℹ Calculating mean AMR distance based on columns \"gent\" and \"tobr\" y[order(y$amr_distance), ] #>    id amox cipr gent tobr amr_distance #> 8   H    R   26    1  <=1   -1.4835846 #> 10  J    R   31    2  <=1   -1.0659501 #> 9   I    I   26    2    2   -0.7902446 #> 2   B    R   18    8  <=1   -0.2306810 #> 6   F    S   29    4    8    0.1788009 #> 7   G    R   23    8    4    0.3207300 #> 1   A    S   23    4 >=16    0.4545064 #> 4   D    R   27    4 >=16    0.4545064 #> 5   E    R   27    8 >=16    0.8721410 #> 3   C    I   23   16 >=16    1.2897755  if (require(\"dplyr\")) {   y %>%     mutate(       amr_distance = mean_amr_distance(y),       check_id_C = amr_distance_from_row(amr_distance, id == \"C\")     ) %>%     arrange(check_id_C) } #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\" and #>   \"tobr\" #>    id amox cipr gent tobr amr_distance   check_id_C #> 1   C    I   23   16 >=16   0.19830380 0.0000000000 #> 2   G    R   23    8    4   0.19790396 0.0003998324 #> 3   F    S   29    4    8   0.05006051 0.1482432883 #> 4   J    R   31    2  <=1   0.04755593 0.1507478663 #> 5   D    R   27    4 >=16   0.53628817 0.3379843756 #> 6   A    S   23    4 >=16  -0.21933074 0.4176345348 #> 7   E    R   27    8 >=16   0.74510544 0.5468016429 #> 8   B    R   18    8  <=1  -0.41717151 0.6154753058 #> 9   H    R   26    1  <=1  -0.50063133 0.6989351238 #> 10  I    I   26    2    2  -0.63808424 0.8363880331 if (require(\"dplyr\")) {   # support for groups   example_isolates %>%     filter(mo_genus() == \"Enterococcus\" & mo_species() != \"\") %>%     select(mo, TCY, carbapenems()) %>%     group_by(mo) %>%     mutate(dist = mean_amr_distance(.)) %>%     arrange(mo, dist) } #> ℹ Using column 'mo' as input for mo_genus() #> ℹ Using column 'mo' as input for mo_species() #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Calculating mean AMR distance based on columns \"TCY\", \"IPM\" and \"MEM\" #> # A tibble: 63 × 5 #> # Groups:   mo [4] #>    mo           TCY   IPM   MEM     dist #>    <mo>         <sir> <sir> <sir>  <dbl> #>  1 B_ENTRC_AVIM S     S     NA     0     #>  2 B_ENTRC_AVIM S     S     NA     0     #>  3 B_ENTRC_CSSL NA    S     NA    NA     #>  4 B_ENTRC_FACM S     S     NA    -2.66  #>  5 B_ENTRC_FACM S     R     R     -0.423 #>  6 B_ENTRC_FACM S     R     R     -0.423 #>  7 B_ENTRC_FACM NA    R     R      0.224 #>  8 B_ENTRC_FACM NA    R     R      0.224 #>  9 B_ENTRC_FACM NA    R     R      0.224 #> 10 B_ENTRC_FACM NA    R     R      0.224 #> # … with 53 more rows"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 5,910 Common Microorganism Codes — microorganisms.codes","title":"Data Set with 5,910 Common Microorganism Codes — microorganisms.codes","text":"data set containing commonly used codes microorganisms, laboratory systems WHONET. Define set_mo_source(). searched using .mo() consequently mo_* functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 5,910 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 5,910 Common Microorganism Codes — microorganisms.codes","text":"tibble 5,910 observations 2 variables: code Commonly used code microorganism mo ID microorganism microorganisms data set","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 5,910 Common Microorganism Codes — microorganisms.codes","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 5,910 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes #> # A tibble: 5,910 × 2 #>    code    mo        #>    <chr>   <mo>      #>  1 _FAM_A- B_GRAMN   #>  2 _FAM_A+ B_GRAMP   #>  3 _FAM_AC B_GRAMP   #>  4 _FAM_AE B_GRAMN   #>  5 _FAM_AN B_GRAMN   #>  6 _FAM_AO B_GRAMP   #>  7 _FAM_AP   UNKNOWN #>  8 _FAM_AS F_FUNGUS  #>  9 _FAM_AT   UNKNOWN #> 10 _FAM_AV   UNKNOWN #> # … with 5,900 more rows"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 52,141 Microorganisms — microorganisms","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"data set containing full microbial taxonomy (last updated: 11 December, 2022) five kingdoms List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF). data set backbone AMR package. MO codes can looked using .mo().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"","code":"microorganisms"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"tibble 52,141 observations 22 variables: mo ID microorganism used package fullname Full name, like \"Escherichia coli\". taxonomic ranks genus, species subspecies, 'pasted' text genus, species, subspecies. taxonomic ranks higher genus, name taxon. status  Status taxon, either \"accepted\" \"synonym\" kingdom, phylum, class, order, family, genus, species, subspecies Taxonomic rank microorganism rank Text taxonomic rank microorganism, \"species\" \"genus\" ref Author(s) year related scientific publication. contains first surname year latest authors, e.g. \"Wallis et al. 2006 emend. Smith Jones 2018\" becomes \"Smith et al., 2018\". field directly retrieved source specified column source. Moreover, accents removed comply CRAN allows ASCII characters, e.g. \"Váňová\" becomes \"Vanova\". lpsn Identifier ('Record number') List Prokaryotic names Standing Nomenclature (LPSN). first/highest LPSN identifier keep one identifier per row. example, Acetobacter ascendens LPSN Record number 7864 11011. first available microorganisms data set. lpsn_parent LPSN identifier parent taxon lpsn_renamed_to LPSN identifier currently valid taxon gbif Identifier ('taxonID') Global Biodiversity Information Facility (GBIF) gbif_parent GBIF identifier parent taxon gbif_renamed_to GBIF identifier currently valid taxon source Either \"GBIF\", \"LPSN\" \"manually added\" (see Source) prevalence Prevalence microorganism according Bartlett et al. (2022, doi:10.1099/mic.0.001269 ), see mo_matching_score() full explanation snomed Systematized Nomenclature Medicine (SNOMED) code microorganism, version 1 July, 2021 (see Source). Use mo_snomed() retrieve quickly, see mo_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Grimont et al. (2007). Antigenic Formulae Salmonella Serovars, 9th Edition. Collaborating Centre Reference Research Salmonella (WHOCC-SALM). Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"Please note entries based List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF) (see ). Since sources incorporate entries based (recent) publications International Journal Systematic Evolutionary Microbiology (IJSEM), can happen year publication sometimes later one might expect. example, Staphylococcus pettenkoferi described first time Diagnostic Microbiology Infectious Disease 2002 (doi:10.1016/s0732-8893(02)00399-1 ), 2007 publication IJSEM followed (doi:10.1099/ijs.0.64381-0 ). Consequently, AMR package returns 2007 mo_year(\"S. pettenkoferi\").","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"included-taxa","dir":"Reference","previous_headings":"","what":"Included Taxa","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"Included taxonomic data : ~36,000 (sub)species kingdoms Archaea Bacteria ~7,900 (sub)species kingdom Fungi. kingdom Fungi large taxon almost 300,000 different (sub)species, microbial (rather macroscopic, like mushrooms). , fungi fit scope package. relevant fungi covered (species Aspergillus, Candida, Cryptococcus, Histoplasma, Pneumocystis, Saccharomyces Trichophyton). ~5,100 (sub)species kingdom Protozoa ~1,400 (sub)species ~40 relevant genera kingdom Animalia (Strongyloides Taenia) ~9,800 previously accepted names included (sub)species (taxonomically renamed) complete taxonomic tree included (sub)species: kingdom subspecies identifier parent taxons year first author related scientific publication","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"manual-additions","dir":"Reference","previous_headings":"","what":"Manual additions","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"convenience, entries added manually: ~1,500 entries city-like serovars Salmonellae 11 entries Streptococcus (beta-haemolytic: groups , B, C, D, F, G, H, K unspecified; : viridans, milleri) 2 entries Staphylococcus (coagulase-negative (CoNS) coagulase-positive (CoPS)) 1 entry Blastocystis (B.  hominis), although officially exist (Noel et al. 2005, PMID 15634993) 1 entry Moraxella (M. catarrhalis), formally named Branhamella catarrhalis (Catlin, 1970) though change never accepted within field clinical microbiology 6 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast, unknown fungus, unknown anaerobic bacteria) syntax used transform original data cleansed R format, can found : https://github.com/msberends/AMR/blob/main/data-raw/reproduction_of_microorganisms.R.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"about-the-records-from-lpsn-see-source-","dir":"Reference","previous_headings":"","what":"About the Records from LPSN (see Source)","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"LPSN main source bacteriological taxonomy AMR package. List Prokaryotic names Standing Nomenclature (LPSN) provides comprehensive information nomenclature prokaryotes. LPSN free use service founded Jean P. Euzeby 1997 later maintained Aidan C. Parte.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 52,141 Microorganisms — microorganisms","text":"","code":"microorganisms #> # A tibble: 52,141 × 22 #>    mo               fullname      status kingdom phylum class order family genus #>    <mo>             <chr>         <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> #>  1 B_ANAER          (unknown ana… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  2 F_FUNGUS         (unknown fun… accep… Fungi   (unkn… \"(un… \"(un… \"(unk… \"(un… #>  3 B_GRAMN          (unknown Gra… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  4 B_GRAMP          (unknown Gra… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  5   UNKNOWN        (unknown nam… accep… (unkno… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  6 F_YEAST          (unknown yea… accep… Fungi   (unkn… \"(un… \"(un… \"(unk… \"(un… #>  7 B_[FAM]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… \"Abd… \"Abd… \"Abdi… \"\"    #>  8 B_[ORD]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… \"Abd… \"Abd… \"\"     \"\"    #>  9 B_[CLS]_ADTBCTRA Abditibacter… accep… Bacter… Abdit… \"Abd… \"\"    \"\"     \"\"    #> 10 B_[PHL]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… \"\"    \"\"    \"\"     \"\"    #> # … with 52,131 more rows, and 13 more variables: species <chr>, #> #   subspecies <chr>, rank <chr>, ref <chr>, source <chr>, lpsn <chr>, #> #   lpsn_parent <chr>, lpsn_renamed_to <chr>, gbif <chr>, gbif_parent <chr>, #> #   gbif_renamed_to <chr>, prevalence <dbl>, snomed <list>"},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the Matching Score for Microorganisms — mo_matching_score","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"algorithm used .mo() mo_* functions determine probable match taxonomic records based user input.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"","code":"mo_matching_score(x, n)"},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"x user input value(s) n full taxonomic name, exists microorganisms$fullname","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"algorithm originally described : Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 . Later, work Bartlett et al. bacterial pathogens infecting humans (2022, doi:10.1099/mic.0.001269 ) incorporated.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated :  : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.0 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, genus present established list also prevalence = 1.0 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.5 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Contracaecum, Cordylobia, Cryptococcus, Curvularia, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Giardia, Haloarcula, Halobacterium, Halococcus, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hymenolepis, Hypomyces, Hysterothylacium, Leishmania, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Mortierella, Mucor, Mycocentrospora, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Pediculus, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga Wuchereria; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.159\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"Dr. Matthijs Berends, 2018","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"","code":"mo_reset_session() #> ℹ Reset 17 previously matched input values.  as.mo(\"E. coli\") #> Class 'mo' #> [1] B_ESCHR_COLI mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans. See ?mo_matching_score. #>  #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #>              Based on input \"E coli\" #> Also matched: Enterobacter cowanii (0.600), Eubacterium combesii #>               (0.600), Eggerthia catenaformis (0.591), Eubacterium callanderi #>               (0.591), Enterocloster citroniae (0.587), Eubacterium cylindroides #>               (0.583), Enterococcus casseliflavus (0.577), Enterobacter cloacae #>               cloacae (0.571), Ehrlichia canis (0.567), Enterobacter cloacae #>               dissolvens (0.565), Eikenella corrodens (0.553), Erwinia cancerogena #>               (0.553), Enterobacter cloacae (0.550), Enterococcus cecorum (0.550), #>               Ehrlichia chaffeensis (0.548), Enterobacter cancerogena (0.542), #>               Enterobacter cancerogenus (0.540), Ezakiella coagulans (0.484), #>               Effusibacillus consociatus (0.462), Enterococcus crotali (0.433), #>               Erwinia coffeiphila (0.404), Eubacterium coprostanoligenes (0.402), #>               Enterococcus columbae (0.397), Enterococcus camelliae (0.394) and #>               Eubacterium cellulosolvens (0.385) [showing first 25]  mo_matching_score(   x = \"E. coli\",   n = c(\"Escherichia coli\", \"Entamoeba coli\") ) #> [1] 0.6875000 0.1587302"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of a Microorganism — mo_property","title":"Get Properties of a Microorganism — mo_property","text":"Use functions return specific property microorganism based latest accepted taxonomy. input values evaluated internally .mo(), makes possible use microbial abbreviations, codes names input. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of a Microorganism — mo_property","text":"","code":"mo_name(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_fullname(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_shortname(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_subspecies(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_species(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_genus(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_family(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_order(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_class(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_phylum(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_kingdom(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_domain(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_type(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_status(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_pathogenicity(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_gramstain(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_is_gram_negative(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_is_gram_positive(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_is_yeast(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_is_intrinsic_resistant(   x,   ab,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_snomed(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_ref(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_authors(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_year(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_lpsn(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_gbif(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_rank(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_taxonomy(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_synonyms(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_current(x, language = get_AMR_locale(), ...)  mo_info(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_url(   x,   open = FALSE,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_property(   x,   property = \"fullname\",   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of a Microorganism — mo_property","text":"x character (vector) can coerced valid microorganism code .mo(). Can left blank auto-guessing column containing microorganism codes used data set, see Examples. language language translate text like \"growth\", defaults system language (see get_AMR_locale()) keep_synonyms logical indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE, return note old taxonomic names processed. default can set option AMR_keep_synonyms, .e. options(AMR_keep_synonyms = TRUE) options(AMR_keep_synonyms = FALSE). ... arguments passed .mo(), 'minimum_matching_score', 'ignore_pattern', 'remove_from_input' ab (vector ) text can coerced valid antibiotic drug code .ab() open browse URL using browseURL() property one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\" \"snomed\", must \"shortname\"","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of a Microorganism — mo_property","text":"integer case mo_year() ordered factor case mo_pathogenicity() list case mo_taxonomy(), mo_synonyms() mo_info() named character case mo_url() numeric case mo_snomed() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of a Microorganism — mo_property","text":"functions , default, keep old taxonomic properties, synonyms automatically replaced current taxonomy. Take example Enterobacter aerogenes, initially named 1960 renamed Klebsiella aerogenes 2017: mo_genus(\"Enterobacter aerogenes\") return \"Klebsiella\" (note renaming) mo_genus(\"Enterobacter aerogenes\", keep_synonyms = TRUE) return \"Enterobacter\" (-per-session warning name outdated) mo_ref(\"Enterobacter aerogenes\") return \"Tindall et al., 2017\" (note) mo_ref(\"Enterobacter aerogenes\", keep_synonyms = TRUE) return \"Hormaeche et al., 1960\" (warning) short name (mo_shortname()) returns first character genus full species, \"E. coli\", species subspecies. Exceptions abbreviations staphylococci (\"CoNS\", Coagulase-Negative Staphylococci) beta-haemolytic streptococci (\"GBS\", Group B Streptococci). Please bear mind e.g. E. coli mean Escherichia coli (kingdom Bacteria) well Entamoeba coli (kingdom Protozoa). Returning full name done using .mo() internally, giving priority bacteria human pathogens, .e. \"E. coli\" considered Escherichia coli. result, mo_fullname(mo_shortname(\"Entamoeba coli\")) returns \"Escherichia coli\". Since top-level taxonomy sometimes referred 'kingdom' sometimes 'domain', functions mo_kingdom() mo_domain() return exact results. Determination human pathogenicity (mo_pathogenicity()) strongly based Bartlett et al. (2022, doi:10.1099/mic.0.001269 ). function returns factor levels Pathogenic, Potentially pathogenic, Non-pathogenic, Unknown. Determination Gram stain (mo_gramstain()) based taxonomic kingdom phylum. Originally, Cavalier-Smith defined -called subkingdoms Negibacteria Posibacteria (2002, PMID 11837318), considered phyla Posibacteria: Actinobacteria, Chloroflexi, Firmicutes, Tenericutes. phyla later renamed Actinomycetota, Chloroflexota, Bacillota, Mycoplasmatota (2021, PMID 34694987). Bacteria phyla considered Gram-positive AMR package, except members class Negativicutes (within phylum Bacillota) Gram-negative. bacteria considered Gram-negative. Species outside kingdom Bacteria return value NA. Functions mo_is_gram_negative() mo_is_gram_positive() always return TRUE FALSE (NA input NA MO code UNKNOWN), thus always return FALSE species outside taxonomic kingdom Bacteria. Determination yeasts (mo_is_yeast()) based taxonomic kingdom class. Budding yeasts fungi phylum Ascomycota, class Saccharomycetes (also called Hemiascomycetes). True yeasts aggregated underlying order Saccharomycetales. Thus, microorganisms member taxonomic class Saccharomycetes, function return TRUE. returns FALSE otherwise (NA input NA MO code UNKNOWN). Determination intrinsic resistance (mo_is_intrinsic_resistant()) based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). mo_is_intrinsic_resistant() function can vectorised argument x (input microorganisms) ab (input antibiotics). function mo_url() return direct URL online database entry, also shows scientific reference concerned species. SNOMED codes (mo_snomed()) version 1 July, 2021. See Source microorganisms data set info. Old taxonomic names (-called 'synonyms') can retrieved mo_synonyms(), current taxonomic name can retrieved mo_current(). functions return full names. output translated possible.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Get Properties of a Microorganism — mo_property","text":"function uses .mo() internally, uses advanced algorithm translate arbitrary user input valid taxonomy using -called matching score. can read public algorithm MO matching score page.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of a Microorganism — mo_property","text":"Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 Becker K et al. (2014). Coagulase-Negative Staphylococci. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. (2019). Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. (2020). Emergence coagulase-negative staphylococci Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 Lancefield RC (1933). serological differentiation human groups hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571 Berends MS et al. (2022). Trends Occurrence Phenotypic Resistance Coagulase-Negative Staphylococci (CoNS) Found Human Blood Northern Netherlands 2013 2019 Microorganisms 10(9), 1801; doi:10.3390/microorganisms10091801 Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of a Microorganism — mo_property","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of a Microorganism — mo_property","text":"","code":"# taxonomic tree -----------------------------------------------------------  mo_kingdom(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_phylum(\"Klebsiella pneumoniae\") #> [1] \"Pseudomonadota\" mo_class(\"Klebsiella pneumoniae\") #> [1] \"Gammaproteobacteria\" mo_order(\"Klebsiella pneumoniae\") #> [1] \"Enterobacterales\" mo_family(\"Klebsiella pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"Klebsiella pneumoniae\") #> [1] \"pneumoniae\" mo_subspecies(\"Klebsiella pneumoniae\") #> [1] \"\"   # full names and short names -----------------------------------------------  mo_name(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella pneumoniae\" mo_fullname(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella pneumoniae\" mo_shortname(\"Klebsiella pneumoniae\") #> [1] \"K. pneumoniae\"   # other properties ---------------------------------------------------------  mo_pathogenicity(\"Klebsiella pneumoniae\") #> [1] Pathogenic #> Levels: Pathogenic < Potentially pathogenic < Non-pathogenic < Unknown mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\" mo_snomed(\"Klebsiella pneumoniae\") #> [1] \"1098101000112102\" \"1098201000112108\" \"409801009\"        \"446870005\"        #> [5] \"56415008\"         \"713926009\"        \"714315002\"        mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_rank(\"Klebsiella pneumoniae\") #> [1] \"species\" mo_url(\"Klebsiella pneumoniae\") #>                                Klebsiella pneumoniae  #> \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\"  mo_is_yeast(c(\"Candida\", \"Trichophyton\", \"Klebsiella\")) #> [1]  TRUE FALSE FALSE   # scientific reference -----------------------------------------------------  mo_ref(\"Klebsiella pneumoniae\") #> [1] \"Trevisan, 1887\" mo_authors(\"Klebsiella pneumoniae\") #> [1] \"Trevisan\" mo_year(\"Klebsiella pneumoniae\") #> [1] 1887 mo_lpsn(\"Klebsiella pneumoniae\") #> [1] \"777151\" mo_gbif(\"Klebsiella pneumoniae\") #> [1] \"3221874\" mo_synonyms(\"Klebsiella pneumoniae\") #> NULL   # abbreviations known in the field -----------------------------------------  mo_genus(\"MRSA\") #> [1] \"Staphylococcus\" mo_species(\"MRSA\") #> [1] \"aureus\" mo_shortname(\"VISA\") #> [1] \"S. aureus\" mo_gramstain(\"VISA\") #> [1] \"Gram-positive\"  mo_genus(\"EHEC\") #> [1] \"Escherichia\" mo_species(\"EHEC\") #> [1] \"coli\"   # known subspecies ---------------------------------------------------------  mo_fullname(\"K. pneu rh\") #> [1] \"Klebsiella pneumoniae rhinoscleromatis\" mo_shortname(\"K. pneu rh\") #> [1] \"K. pneumoniae\"  # \\donttest{ # Becker classification, see ?as.mo ----------------------------------------  mo_fullname(\"Staph epidermidis\") #> [1] \"Staphylococcus epidermidis\" mo_fullname(\"Staph epidermidis\", Becker = TRUE) #> [1] \"Coagulase-negative Staphylococcus (CoNS)\" mo_shortname(\"Staph epidermidis\") #> [1] \"S. epidermidis\" mo_shortname(\"Staph epidermidis\", Becker = TRUE) #> [1] \"CoNS\"   # Lancefield classification, see ?as.mo ------------------------------------  mo_fullname(\"Strep agalactiae\") #> [1] \"Streptococcus agalactiae\" mo_fullname(\"Strep agalactiae\", Lancefield = TRUE) #> [1] \"Streptococcus Group B\" mo_shortname(\"Strep agalactiae\") #> [1] \"S. agalactiae\" mo_shortname(\"Strep agalactiae\", Lancefield = TRUE) #> [1] \"GBS\"   # language support  --------------------------------------------------------  mo_gramstain(\"Klebsiella pneumoniae\", language = \"de\") # German #> [1] \"Gramnegativ\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"nl\") # Dutch #> [1] \"Gram-negatief\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"es\") # Spanish #> [1] \"Gram negativo\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"el\") # Greek #> [1] \"Αρνητικό κατά Gram\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"uk\") # Ukrainian #> [1] \"Грамнегативні\"  # mo_type is equal to mo_kingdom, but mo_kingdom will remain untranslated mo_kingdom(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_kingdom(\"Klebsiella pneumoniae\", language = \"zh\") # Chinese, no effect #> [1] \"Bacteria\" mo_type(\"Klebsiella pneumoniae\", language = \"zh\") # Chinese, translated #> [1] \"细菌\"  mo_fullname(\"S. pyogenes\", Lancefield = TRUE, language = \"de\") #> [1] \"Streptococcus Gruppe A\" mo_fullname(\"S. pyogenes\", Lancefield = TRUE, language = \"uk\") #> [1] \"Streptococcus Група A\"   # other --------------------------------------------------------------------  # gram stains and intrinsic resistance can be used as a filter in dplyr verbs if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_is_gram_positive()) %>%     count(mo_genus(), sort = TRUE) } #> ℹ Using column 'mo' as input for mo_is_gram_positive() #> ℹ Using column 'mo' as input for mo_genus() #> # A tibble: 18 × 2 #>    `mo_genus()`        n #>    <chr>           <int> #>  1 Staphylococcus    840 #>  2 Streptococcus     275 #>  3 Enterococcus       83 #>  4 Corynebacterium    17 #>  5 Micrococcus         6 #>  6 Gemella             3 #>  7 Aerococcus          2 #>  8 Cutibacterium       1 #>  9 Dermabacter         1 #> 10 Fusibacter          1 #> 11 Globicatella        1 #> 12 Granulicatella      1 #> 13 Lactobacillus       1 #> 14 Leuconostoc         1 #> 15 Listeria            1 #> 16 Paenibacillus       1 #> 17 Rothia              1 #> 18 Schaalia            1 if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_is_intrinsic_resistant(ab = \"vanco\")) %>%     count(mo_genus(), sort = TRUE) } #> ℹ Using column 'mo' as input for mo_is_intrinsic_resistant() #> ℹ Using column 'mo' as input for mo_genus() #> # A tibble: 20 × 2 #>    `mo_genus()`         n #>    <chr>            <int> #>  1 Escherichia        467 #>  2 Klebsiella          77 #>  3 Proteus             39 #>  4 Pseudomonas         30 #>  5 Serratia            25 #>  6 Enterobacter        23 #>  7 Citrobacter         11 #>  8 Haemophilus          8 #>  9 Acinetobacter        6 #> 10 Morganella           6 #> 11 Pantoea              4 #> 12 Salmonella           3 #> 13 Neisseria            2 #> 14 Stenotrophomonas     2 #> 15 Campylobacter        1 #> 16 Enterococcus         1 #> 17 Hafnia               1 #> 18 Lactobacillus        1 #> 19 Leuconostoc          1 #> 20 Pseudescherichia     1  # get a list with the complete taxonomy (from kingdom to subspecies) mo_taxonomy(\"Klebsiella pneumoniae\") #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Klebsiella\" #>  #> $species #> [1] \"pneumoniae\" #>  #> $subspecies #> [1] \"\" #>   # get a list with the taxonomy, the authors, Gram-stain, # SNOMED codes, and URL to the online database mo_info(\"Klebsiella pneumoniae\") #> $mo #> [1] \"B_KLBSL_PNMN\" #>  #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Klebsiella\" #>  #> $species #> [1] \"pneumoniae\" #>  #> $subspecies #> [1] \"\" #>  #> $status #> [1] \"accepted\" #>  #> $synonyms #> NULL #>  #> $gramstain #> [1] \"Gram-negative\" #>  #> $url #> [1] \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\" #>  #> $ref #> [1] \"Trevisan, 1887\" #>  #> $snomed #> [1] \"1098101000112102\" \"1098201000112108\" \"409801009\"        \"446870005\"        #> [5] \"56415008\"         \"713926009\"        \"714315002\"        #>  # }"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":null,"dir":"Reference","previous_headings":"","what":"User-Defined Reference Data Set for Microorganisms — mo_source","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"functions can used predefine reference used .mo() consequently mo_* functions (mo_genus() mo_gramstain()). fastest way organisation (analysis) specific codes picked translated package, since bother setting .","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"","code":"set_mo_source(   path,   destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\") )  get_mo_source(destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\"))"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"path location reference file, can text file (comma-, tab- pipe-separated) Excel file (see Details). Can also \"\", NULL FALSE delete reference file. destination destination compressed data file, default user's home directory.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"reference file can text file separated commas (CSV) tabs pipes, Excel file (either 'xls' 'xlsx' format) R object file (extension '.rds'). use Excel file, need readxl package installed. set_mo_source() check file validity: must data.frame, must column named \"mo\" contains values microorganisms$mo microorganisms$fullname must reference column defined values. tests pass, set_mo_source() read file R ask export \"~/mo_source.rds\". CRAN policy disallows packages write file system, although 'exceptions may allowed interactive sessions package obtains confirmation user'. reason, function works interactive sessions user can specifically confirm allow file created. destination file can set destination argument defaults user's home directory. can also set option AMR_mo_source, e.g. options(AMR_mo_source = \"/location/file.rds\"). created compressed data file \"mo_source.rds\" used default MO determination (function .mo() consequently mo_* functions like mo_genus() mo_gramstain()). location timestamp original file saved attribute compressed data file. function get_mo_source() return data set reading \"mo_source.rds\" readRDS(). original file changed (checking location timestamp original file), call set_mo_source() update data file automatically used interactive session. Reading Excel file (.xlsx) one row size 8-9 kB. compressed file created set_mo_source() size 0.1 kB can read get_mo_source() couple microseconds (millionths second).","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"how-to-setup","dir":"Reference","previous_headings":"","what":"How to Setup","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"Imagine data sheet Excel file. first column contains organisation specific codes, second column contains valid taxonomic names:   save \"home//ourcodes.xlsx\". Now set source:   now created file \"~/mo_source.rds\" contents Excel file. first column foreign values 'mo' column kept creating RDS file. now can use functions:   edit Excel file , say, adding row 4 like :   ...new usage MO function package update data file:   delete reference data file, just use \"\", NULL FALSE input set_mo_source():   original file (previous case Excel file) moved deleted, mo_source.rds file removed upon next use .mo() mo_* function.","code":"|         A          |            B          | --|--------------------|-----------------------| 1 | Organisation XYZ   | mo                    | 2 | lab_mo_ecoli       | Escherichia coli      | 3 | lab_mo_kpneumoniae | Klebsiella pneumoniae | 4 |                    |                       | set_mo_source(\"home/me/ourcodes.xlsx\") #> NOTE: Created mo_source file '/Users/me/mo_source.rds' (0.3 kB) from #>       '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns #>       \"Organisation XYZ\" and \"mo\" as.mo(\"lab_mo_ecoli\") #> Class 'mo' #> [1] B_ESCHR_COLI  mo_genus(\"lab_mo_kpneumoniae\") #> [1] \"Klebsiella\"  # other input values still work too as.mo(c(\"Escherichia coli\", \"E. coli\", \"lab_mo_ecoli\")) #> NOTE: Translation to one microorganism was guessed with uncertainty. #>       Use mo_uncertainties() to review it. #> Class 'mo' #> [1] B_ESCHR_COLI B_ESCHR_COLI B_ESCHR_COLI |         A          |            B          | --|--------------------|-----------------------| 1 | Organisation XYZ   | mo                    | 2 | lab_mo_ecoli       | Escherichia coli      | 3 | lab_mo_kpneumoniae | Klebsiella pneumoniae | 4 | lab_Staph_aureus   | Staphylococcus aureus | 5 |                    |                       | as.mo(\"lab_mo_ecoli\") #> NOTE: Updated mo_source file '/Users/me/mo_source.rds' (0.3 kB) from #>       '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns #>        \"Organisation XYZ\" and \"mo\" #> Class 'mo' #> [1] B_ESCHR_COLI  mo_genus(\"lab_Staph_aureus\") #> [1] \"Staphylococcus\" set_mo_source(NULL) #> Removed mo_source file '/Users/me/mo_source.rds'"},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Component Analysis (for AMR) — pca","title":"Principal Component Analysis (for AMR) — pca","text":"Performs principal component analysis (PCA) based data set automatic determination afterwards plotting groups labels, automatic filtering suitable (.e. non-empty numeric) variables.","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Component Analysis (for AMR) — pca","text":"","code":"pca(   x,   ...,   retx = TRUE,   center = TRUE,   scale. = TRUE,   tol = NULL,   rank. = NULL )"},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Component Analysis (for AMR) — pca","text":"x data.frame containing numeric columns ... columns x selected PCA, can unquoted since supports quasiquotation. retx logical value indicating whether rotated variables     returned. center logical value indicating whether variables     shifted zero centered. Alternately, vector     length equal number columns x can supplied.     value passed scale. scale. logical value indicating whether variables     scaled unit variance analysis takes     place.  default FALSE consistency S,     general scaling advisable.  Alternatively, vector length     equal number columns x can supplied.      value passed scale. tol value indicating magnitude components     omitted. (Components omitted     standard deviations less equal tol times     standard deviation first component.)  default null     setting, components omitted (unless rank. specified     less min(dim(x)).).  settings tol     tol = 0 tol = sqrt(.Machine$double.eps),     omit essentially constant components. rank. optionally, number specifying maximal rank, .e.,     maximal number principal components used.  Can set     alternative addition tol, useful notably     desired rank considerably smaller dimensions matrix.","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Component Analysis (for AMR) — pca","text":"object classes pca prcomp","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Principal Component Analysis (for AMR) — pca","text":"pca() function takes data.frame input performs actual PCA R function prcomp(). result pca() function prcomp object, additional attribute non_numeric_cols vector column names columns contain numeric values. probably groups labels, used ggplot_pca().","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Component Analysis (for AMR) — pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # \\donttest{ if (require(\"dplyr\")) {   # calculate the resistance per group first   resistance_data <- example_isolates %>%     group_by(       order = mo_order(mo), # group on anything, like order       genus = mo_genus(mo)     ) %>% #   and genus as we do here;     filter(n() >= 30) %>% # filter on only 30 results per group     summarise_if(is.sir, resistance) # then get resistance of all drugs    # now conduct PCA for certain antimicrobial drugs   pca_result <- resistance_data %>%     pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)    pca_result   summary(pca_result)    # old base R plotting method:   biplot(pca_result)   # new ggplot2 plotting method using this package:   if (require(\"ggplot2\")) {     ggplot_pca(pca_result)      ggplot_pca(pca_result) +       scale_colour_viridis_d() +       labs(title = \"Title here\")   } } #> Warning: Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 13 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 15 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 13 results available for FLC in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for AMX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for AMP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for TZP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 12 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 5 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for FEP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 23 results available for FEP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 29 results available for FOX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 6 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 17 results available for AMK in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: only 6 results available for NIT in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 17 results available for NIT in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 8 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 7 results available for LNZ in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 5 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 23 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 7 results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 6 results available for TEC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 3 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 18 results available for TGC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 7 results available for TGC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for DOX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 27 results available for IPM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: only 25 results available for MEM in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: only 26 results available for MEM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Caryophanales\", genus = \"Staphylococcus\" (minimum = 30). #> Warning: Introducing NA: only 2 results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: only 9 results available for COL in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (minimum = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (minimum = 30). #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", \"TMP\" #>   and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\"  #>    # }"},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting for Classes sir, mic and disk — plot","title":"Plotting for Classes sir, mic and disk — plot","text":"Functions plot classes sir, mic disk, support base R ggplot2.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting for Classes sir, mic and disk — plot","text":"","code":"# S3 method for mic plot(   x,   mo = NULL,   ab = NULL,   guideline = \"EUCAST\",   main = deparse(substitute(x)),   ylab = \"Frequency\",   xlab = \"Minimum Inhibitory Concentration (mg/L)\",   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for mic autoplot(   object,   mo = NULL,   ab = NULL,   guideline = \"EUCAST\",   title = deparse(substitute(object)),   ylab = \"Frequency\",   xlab = \"Minimum Inhibitory Concentration (mg/L)\",   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for mic fortify(object, ...)  # S3 method for disk plot(   x,   main = deparse(substitute(x)),   ylab = \"Frequency\",   xlab = \"Disk diffusion diameter (mm)\",   mo = NULL,   ab = NULL,   guideline = \"EUCAST\",   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for disk autoplot(   object,   mo = NULL,   ab = NULL,   title = deparse(substitute(object)),   ylab = \"Frequency\",   xlab = \"Disk diffusion diameter (mm)\",   guideline = \"EUCAST\",   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for disk fortify(object, ...)  # S3 method for sir plot(   x,   ylab = \"Percentage\",   xlab = \"Antimicrobial Interpretation\",   main = deparse(substitute(x)),   language = get_AMR_locale(),   ... )  # S3 method for sir autoplot(   object,   title = deparse(substitute(object)),   xlab = \"Antimicrobial Interpretation\",   ylab = \"Frequency\",   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   ... )  # S3 method for sir fortify(object, ...)"},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting for Classes sir, mic and disk — plot","text":"x, object values created .mic(), .disk() .sir() (random_* variants, random_mic()) mo (vector ) text can coerced valid microorganism code .mo() ab (vector ) text can coerced valid antimicrobial drug code .ab() guideline interpretation guideline use, defaults latest included EUCAST guideline, see Details main, title title plot xlab, ylab axis title colours_SIR colours use filling bars, must vector three values (order S, R). default colours colour-blind friendly. language language used translate 'Susceptible', 'Increased exposure'/'Intermediate' 'Resistant', defaults system language (see get_AMR_locale()) can overwritten setting option AMR_locale, e.g. options(AMR_locale = \"de\"), see translate. Use language = NULL language = \"\" prevent translation. expand logical indicate whether range x axis expanded lowest highest value. MIC values, intermediate values factors 2 starting highest MIC value. disk diameters, whole diameter range filled. ... arguments passed methods","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting for Classes sir, mic and disk — plot","text":"autoplot() functions return ggplot model extendible ggplot2 function. fortify() functions return data.frame extension usage ggplot2::ggplot() function.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plotting for Classes sir, mic and disk — plot","text":"interpretation \"\" named \"Increased exposure\" EUCAST guidelines since 2019, named \"Intermediate\" cases. interpreting MIC values well disk diffusion diameters, supported guidelines used input guideline argument : \"EUCAST 2022\", \"EUCAST 2021\", \"EUCAST 2020\", \"EUCAST 2019\", \"EUCAST 2018\", \"EUCAST 2017\", \"EUCAST 2016\", \"EUCAST 2015\", \"EUCAST 2014\", \"EUCAST 2013\", \"CLSI 2022\", \"CLSI 2021\", \"CLSI 2020\", \"CLSI 2019\", \"CLSI 2018\", \"CLSI 2017\", \"CLSI 2016\", \"CLSI 2015\", \"CLSI 2014\" \"CLSI 2013\". Simply using \"CLSI\" \"EUCAST\" input automatically select latest version guideline.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting for Classes sir, mic and disk — plot","text":"","code":"some_mic_values <- random_mic(size = 100) some_disk_values <- random_disk(size = 100, mo = \"Escherichia coli\", ab = \"cipro\") some_sir_values <- random_sir(50, prob_SIR = c(0.55, 0.05, 0.30))  plot(some_mic_values)  plot(some_disk_values)  plot(some_sir_values)   # when providing the microorganism and antibiotic, colours will show interpretations: plot(some_mic_values, mo = \"S. aureus\", ab = \"ampicillin\")  plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\")  plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\", language = \"uk\")   # \\donttest{ if (require(\"ggplot2\")) {   autoplot(some_mic_values) }  if (require(\"ggplot2\")) {   autoplot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\") }  if (require(\"ggplot2\")) {   autoplot(some_sir_values) }  # }"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Microbial Resistance — proportion","title":"Calculate Microbial Resistance — proportion","text":"functions can used calculate (co-)resistance susceptibility microbial isolates (.e. percentage S, SI, , IR R). functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. resistance() used calculate resistance, susceptibility() used calculate susceptibility.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Microbial Resistance — proportion","text":"","code":"resistance(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  susceptibility(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  sir_confidence_interval(   ...,   ab_result = \"R\",   minimum = 30,   as_percent = FALSE,   only_all_tested = FALSE,   confidence_level = 0.95,   side = \"both\" )  proportion_R(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_IR(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_I(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_SI(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_S(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_df(   data,   translate_ab = \"name\",   language = get_AMR_locale(),   minimum = 30,   as_percent = FALSE,   combine_SI = TRUE,   confidence_level = 0.95 )  sir_df(   data,   translate_ab = \"name\",   language = get_AMR_locale(),   minimum = 30,   as_percent = FALSE,   combine_SI = TRUE,   confidence_level = 0.95 )"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Calculate Microbial Resistance — proportion","text":"M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Microbial Resistance — proportion","text":"... one vectors (columns) antibiotic interpretations. transformed internally .sir() needed. Use multiple columns calculate (lack ) co-resistance: probability one two drugs resistant susceptible result. See Examples. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. as_percent logical indicate whether output must returned hundred fold % sign (character). value 0.123456 returned \"12.3%\". only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antibiotics, see section Combination Therapy ab_result antibiotic results test , must one values \"S\", \"\", \"R\" confidence_level confidence level returned confidence interval. calculation, number S SI isolates, R isolates compared total number available isolates R, S, using binom.test(), .e., Clopper-Pearson method. side side confidence interval return. Defaults \"\" length 2 vector, can also (abbreviated ) \"min\"/\"left\"/\"lower\"/\"less\" \"max\"/\"right\"/\"higher\"/\"greater\". data data.frame containing columns class sir (see .sir()) translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() language language returned text, defaults system language (see get_AMR_locale()) can also set option AMR_locale. Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant), defaults TRUE","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Microbial Resistance — proportion","text":"double , as_percent = TRUE, character.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Microbial Resistance — proportion","text":"function resistance() equal function proportion_R(). function susceptibility() equal function proportion_SI(). Use sir_confidence_interval() calculate confidence interval, relies binom.test(), .e., Clopper-Pearson method. function returns vector length 2 default antimicrobial resistance. Change side argument \"left\"/\"min\" \"right\"/\"max\" return single value, change ab_result argument e.g. c(\"S\", \"\") test antimicrobial susceptibility, see Examples. Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine data set. functions meant count isolates, calculate proportion resistance/susceptibility. Use count() functions count isolates. function susceptibility() essentially equal count_susceptible() / count_all(). Low counts can influence outcome - proportion functions may camouflage , since return proportion (albeit dependent minimum argument). function proportion_df() takes variable data sir class (created .sir()) calculates proportions S, , R. also supports grouped variables. function sir_df() works exactly like proportion_df(), adds number isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Calculate Microbial Resistance — proportion","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antibiotics, Drug Drug B, susceptibility() works calculate %SI:   Please note , combination therapies, only_all_tested = TRUE applies :   , combination therapies, only_all_tested = FALSE applies :   Using only_all_tested impact using one antibiotic input.","code":"--------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   include as  include as   include as  include as                     numerator   denominator  numerator   denominator --------  --------  ----------  -----------  ----------  -----------  S or I    S or I       X            X            X            X    R       S or I       X            X            X            X   <NA>     S or I       X            X            -            -  S or I      R          X            X            X            X    R         R          -            X            -            X   <NA>       R          -            -            -            -  S or I     <NA>        X            X            -            -    R        <NA>        -            -            -            -   <NA>      <NA>        -            -            -            - -------------------------------------------------------------------- count_S()    +   count_I()    +   count_R()    = count_all()   proportion_S() + proportion_I() + proportion_R() = 1 count_S()    +   count_I()    +   count_R()    >= count_all()   proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Calculate Microbial Resistance — proportion","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Microbial Resistance — proportion","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info.  # base R ------------------------------------------------------------ # determines %R resistance(example_isolates$AMX) #> [1] 0.5955556 sir_confidence_interval(example_isolates$AMX) #> [1] 0.5688204 0.6218738 sir_confidence_interval(example_isolates$AMX,   confidence_level = 0.975 ) #> [1] 0.5650148 0.6255670  # determines %S+I: susceptibility(example_isolates$AMX) #> [1] 0.4044444 sir_confidence_interval(example_isolates$AMX,   ab_result = c(\"S\", \"I\") ) #> [1] 0.3781262 0.4311796  # be more specific proportion_S(example_isolates$AMX) #> [1] 0.4022222 proportion_SI(example_isolates$AMX) #> [1] 0.4044444 proportion_I(example_isolates$AMX) #> [1] 0.002222222 proportion_IR(example_isolates$AMX) #> [1] 0.5977778 proportion_R(example_isolates$AMX) #> [1] 0.5955556  # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     group_by(ward) %>%     summarise(       r = resistance(CIP),       n = n_sir(CIP)     ) # n_sir works like n_distinct in dplyr, see ?n_sir } #> # A tibble: 3 × 3 #>   ward           r     n #>   <chr>      <dbl> <int> #> 1 Clinical   0.147   869 #> 2 ICU        0.190   447 #> 3 Outpatient 0.161    93 if (require(\"dplyr\")) {   example_isolates %>%     group_by(ward) %>%     summarise(       cipro_R = resistance(CIP),       ci_min = sir_confidence_interval(CIP, side = \"min\"),       ci_max = sir_confidence_interval(CIP, side = \"max\"),     ) } #> # A tibble: 3 × 4 #>   ward       cipro_R ci_min ci_max #>   <chr>        <dbl>  <dbl>  <dbl> #> 1 Clinical     0.147 0.124   0.173 #> 2 ICU          0.190 0.155   0.230 #> 3 Outpatient   0.161 0.0932  0.252 if (require(\"dplyr\")) {   # scoped dplyr verbs with antibiotic selectors   # (you could also use across() of course)   example_isolates %>%     group_by(ward) %>%     summarise_at(       c(aminoglycosides(), carbapenems()),       resistance     ) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> Warning: Introducing NA: only 23 results available for KAN in group: ward = #> \"Outpatient\" (minimum = 30). #> # A tibble: 3 × 7 #>   ward         GEN   TOB   AMK   KAN    IPM    MEM #>   <chr>      <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl> #> 1 Clinical   0.229 0.315 0.626     1 0.0498 0.0458 #> 2 ICU        0.290 0.400 0.662     1 0.0862 0.0894 #> 3 Outpatient 0.2   0.368 0.605    NA 0.0541 0.0541 if (require(\"dplyr\")) {   example_isolates %>%     group_by(ward) %>%     summarise(       R = resistance(CIP, as_percent = TRUE),       SI = susceptibility(CIP, as_percent = TRUE),       n1 = count_all(CIP), # the actual total; sum of all three       n2 = n_sir(CIP), # same - analogous to n_distinct       total = n()     ) # NOT the number of tested isolates!    # Calculate co-resistance between amoxicillin/clav acid and gentamicin,   # so we can see that combination therapy does a lot more than mono therapy:   example_isolates %>% susceptibility(AMC) # %SI = 76.3%   example_isolates %>% count_all(AMC) #   n = 1879    example_isolates %>% susceptibility(GEN) # %SI = 75.4%   example_isolates %>% count_all(GEN) #   n = 1855    example_isolates %>% susceptibility(AMC, GEN) # %SI = 94.1%   example_isolates %>% count_all(AMC, GEN) #   n = 1939     # See Details on how `only_all_tested` works. Example:   example_isolates %>%     summarise(       numerator = count_susceptible(AMC, GEN),       denominator = count_all(AMC, GEN),       proportion = susceptibility(AMC, GEN)     )    example_isolates %>%     summarise(       numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE),       denominator = count_all(AMC, GEN, only_all_tested = TRUE),       proportion = susceptibility(AMC, GEN, only_all_tested = TRUE)     )     example_isolates %>%     group_by(ward) %>%     summarise(       cipro_p = susceptibility(CIP, as_percent = TRUE),       cipro_n = count_all(CIP),       genta_p = susceptibility(GEN, as_percent = TRUE),       genta_n = count_all(GEN),       combination_p = susceptibility(CIP, GEN, as_percent = TRUE),       combination_n = count_all(CIP, GEN)     )    # Get proportions S/I/R immediately of all sir columns   example_isolates %>%     select(AMX, CIP) %>%     proportion_df(translate = FALSE)    # It also supports grouping variables   # (use sir_df to also include the count)   example_isolates %>%     select(ward, AMX, CIP) %>%     group_by(ward) %>%     sir_df(translate = FALSE) } #> # A tibble: 12 × 7 #>    ward       antibiotic interpretation value ci_min ci_max isolates #>  * <chr>      <chr>      <ord>          <dbl>  <dbl>  <dbl>    <int> #>  1 Clinical   AMX        SI             0.423 0.389   0.457      357 #>  2 Clinical   AMX        R              0.577 0.543   0.611      487 #>  3 Clinical   CIP        SI             0.853 0.827   0.876      741 #>  4 Clinical   CIP        R              0.147 0.124   0.173      128 #>  5 ICU        AMX        SI             0.369 0.323   0.417      158 #>  6 ICU        AMX        R              0.631 0.583   0.677      270 #>  7 ICU        CIP        SI             0.810 0.770   0.845      362 #>  8 ICU        CIP        R              0.190 0.155   0.230       85 #>  9 Outpatient AMX        SI             0.397 0.288   0.515       31 #> 10 Outpatient AMX        R              0.603 0.485   0.712       47 #> 11 Outpatient CIP        SI             0.839 0.748   0.907       78 #> 12 Outpatient CIP        R              0.161 0.0932  0.252       15 # }"},{"path":"https://msberends.github.io/AMR/reference/random.html","id":null,"dir":"Reference","previous_headings":"","what":"Random MIC Values/Disk Zones/SIR Generation — random","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"functions can used generating random MIC values disk diffusion diameters, AMR data analysis practice. providing microorganism antimicrobial drug, generated results reflect reality much possible.","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"","code":"random_mic(size = NULL, mo = NULL, ab = NULL, ...)  random_disk(size = NULL, mo = NULL, ab = NULL, ...)  random_sir(size = NULL, prob_SIR = c(0.33, 0.33, 0.33), ...)"},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"size desired size returned vector. used data.frame call dplyr verb, get current (group) size left blank. mo character can coerced valid microorganism code .mo() ab character can coerced valid antimicrobial drug code .ab() ... ignored, place allow future extensions prob_SIR vector length 3: probabilities \"S\" (1st value), \"\" (2nd value) \"R\" (3rd value)","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"class mic random_mic() (see .mic()) class disk random_disk() (see .disk())","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"base R function sample() used generating values. Generated values based EUCAST 2022 guideline implemented clinical_breakpoints data set. create specific generated values per bug drug, set mo /ab argument.","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"","code":"random_mic(25) #> Class 'mic' #>  [1] 64      0.01    64      0.005   0.125   <=0.001 0.25    128     0.025   #> [10] 0.005   1       64      0.0625  1       0.005   8       >=256   128     #> [19] 0.025   >=256   0.5     4       16      2       4       random_disk(25) #> Class 'disk' #>  [1] 27 23 46 49  8 14 18 19 42 37 46 37 26 31 39 50 23 50 44 45 46 37 41 34 14 random_sir(25) #> Class 'sir' #>  [1] R R R I R S S I S R I I I S S S R R R S I S S I I  # \\donttest{ # make the random generation more realistic by setting a bug and/or drug: random_mic(25, \"Klebsiella pneumoniae\") # range 0.0625-64 #> Class 'mic' #>  [1] <=0.001 0.25    4       1       >=128   0.0625  0.01    0.002   0.025   #> [10] 0.0625  0.0625  0.25    0.125   0.25    0.01    0.025   0.125   0.0625  #> [19] <=0.001 0.002   2       32      64      32      4       random_mic(25, \"Klebsiella pneumoniae\", \"meropenem\") # range 0.0625-16 #> Class 'mic' #>  [1] 2    16   4    2    16   32   8    8    16   >=64 >=64 4    8    16   <=1  #> [16] <=1  >=64 >=64 >=64 >=64 32   2    4    >=64 2    random_mic(25, \"Streptococcus pneumoniae\", \"meropenem\") # range 0.0625-4 #> Class 'mic' #>  [1] 8      1      0.125  1      1      0.125  0.025  0.0625 0.125  8      #> [11] 0.5    0.25   0.0625 0.0625 0.125  4      0.0625 8      0.25   0.0625 #> [21] 0.125  1      8      0.25   1       random_disk(25, \"Klebsiella pneumoniae\") # range 8-50 #> Class 'disk' #>  [1] 43 49 26 44 44 15 26 44 43 22 42 10 37 42 48 47 13 31 48 35 32 49 34 37 44 random_disk(25, \"Klebsiella pneumoniae\", \"ampicillin\") # range 11-17 #> Class 'disk' #>  [1] 16 17 16 12 17 14 13 11 15 15 12 13 16 14 11 11 15 17 16 17 17 14 13 11 11 random_disk(25, \"Streptococcus pneumoniae\", \"ampicillin\") # range 12-27 #> Class 'disk' #>  [1] 16 21 25 21 19 15 26 27 27 26 15 27 18 19 17 25 25 26 18 24 17 21 18 16 17 # }"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict Antimicrobial Resistance — resistance_predict","title":"Predict Antimicrobial Resistance — resistance_predict","text":"Create prediction model predict antimicrobial resistance next years statistical solid ground. Standard errors (SE) returned columns se_min se_max. See Examples real live example.","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict Antimicrobial Resistance — resistance_predict","text":"","code":"resistance_predict(   x,   col_ab,   col_date = NULL,   year_min = NULL,   year_max = NULL,   year_every = 1,   minimum = 30,   model = NULL,   I_as_S = TRUE,   preserve_measurements = TRUE,   info = interactive(),   ... )  sir_predict(   x,   col_ab,   col_date = NULL,   year_min = NULL,   year_max = NULL,   year_every = 1,   minimum = 30,   model = NULL,   I_as_S = TRUE,   preserve_measurements = TRUE,   info = interactive(),   ... )  # S3 method for resistance_predict plot(x, main = paste(\"Resistance Prediction of\", x_name), ...)  ggplot_sir_predict(   x,   main = paste(\"Resistance Prediction of\", x_name),   ribbon = TRUE,   ... )  # S3 method for resistance_predict autoplot(   object,   main = paste(\"Resistance Prediction of\", x_name),   ribbon = TRUE,   ... )"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict Antimicrobial Resistance — resistance_predict","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_ab column name x containing antimicrobial interpretations (\"R\", \"\" \"S\") col_date column name date, used calculate years column consist years already, defaults first column date class year_min lowest year use prediction model, dafaults lowest year col_date year_max highest year use prediction model, defaults 10 years today year_every unit sequence lowest year found data year_max minimum minimal amount available isolates per year include. Years containing less observations estimated model. model statistical model choice. generalised linear regression model binomial distribution (.e. using glm(..., family = binomial), assuming period zero resistance followed period increasing resistance leading slowly resistance. See Details valid options. I_as_S logical indicate whether values \"\" treated \"S\" (otherwise treated \"R\"). default, TRUE, follows redefinition EUCAST interpretation (increased exposure) 2019, see section Interpretation S, R . preserve_measurements logical indicate whether predictions years actually available data overwritten original data. standard errors years NA. info logical indicate whether textual analysis printed name summary() statistical model. ... arguments passed functions main title plot ribbon logical indicate whether ribbon shown (default) error bars object model data plotted","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict Antimicrobial Resistance — resistance_predict","text":"data.frame extra class resistance_predict columns: year value, estimated preserve_measurements = FALSE, combination observed estimated otherwise se_min, lower bound standard error minimum 0 (standard error never go 0%) se_max upper bound standard error maximum 1 (standard error never go 100%) observations, total number available observations year, .e. \\(S + + R\\) observed, original observed resistant percentages estimated, estimated resistant percentages, calculated model Furthermore, model available attribute: attributes(x)$model, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Predict Antimicrobial Resistance — resistance_predict","text":"Valid options statistical model (argument model) : \"binomial\" \"binom\" \"logit\": generalised linear regression model binomial distribution \"loglin\" \"poisson\": generalised log-linear regression model poisson distribution \"lin\" \"linear\": linear regression model","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Predict Antimicrobial Resistance — resistance_predict","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict Antimicrobial Resistance — resistance_predict","text":"","code":"x <- resistance_predict(example_isolates,   col_ab = \"AMX\",   year_min = 2010,   model = \"binomial\" ) #> ℹ Using column 'date' as input for col_date. plot(x)  # \\donttest{ if (require(\"ggplot2\")) {   ggplot_sir_predict(x) }   # using dplyr: if (require(\"dplyr\")) {   x <- example_isolates %>%     filter_first_isolate() %>%     filter(mo_genus(mo) == \"Staphylococcus\") %>%     resistance_predict(\"PEN\", model = \"binomial\")   print(plot(x))    # get the model from the object   mymodel <- attributes(x)$model   summary(mymodel) } #> Including isolates from ICU.  #> NULL #>  #> Call: #> glm(formula = df_matrix ~ year, family = binomial) #>  #> Deviance Residuals:  #>     Min       1Q   Median       3Q      Max   #> -1.4150  -0.1855   0.0000   0.4096   0.9277   #>  #> Coefficients: #>             Estimate Std. Error z value Pr(>|z|) #> (Intercept) 47.02915   72.98739   0.644    0.519 #> year        -0.02280    0.03637  -0.627    0.531 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance: 5.9620  on 11  degrees of freedom #> Residual deviance: 5.5701  on 10  degrees of freedom #> AIC: 50.533 #>  #> Number of Fisher Scoring iterations: 4 #>   # create nice plots with ggplot2 yourself if (require(\"dplyr\") && require(\"ggplot2\")) {   data <- example_isolates %>%     filter(mo == as.mo(\"E. coli\")) %>%     resistance_predict(       col_ab = \"AMX\",       col_date = \"date\",       model = \"binomial\",       info = FALSE,       minimum = 15     )   head(data)   autoplot(data) }  # }"},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":null,"dir":"Reference","previous_headings":"","what":"Skewness of the Sample — skewness","title":"Skewness of the Sample — skewness","text":"Skewness measure asymmetry probability distribution real-valued random variable mean. negative ('left-skewed'): left tail longer; mass distribution concentrated right histogram. positive ('right-skewed'): right tail longer; mass distribution concentrated left histogram. normal distribution skewness 0.","code":""},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Skewness of the Sample — skewness","text":"","code":"skewness(x, na.rm = FALSE)  # S3 method for default skewness(x, na.rm = FALSE)  # S3 method for matrix skewness(x, na.rm = FALSE)  # S3 method for data.frame skewness(x, na.rm = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Skewness of the Sample — skewness","text":"x vector values, matrix data.frame na.rm logical value indicating whether NA values stripped computation proceeds","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Skewness of the Sample — skewness","text":"","code":"skewness(runif(1000)) #> [1] 0.06738265"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate Strings from the AMR Package — translate","title":"Translate Strings from the AMR Package — translate","text":"language-dependent output AMR functions, mo_name(), mo_gramstain(), mo_type() ab_name().","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate Strings from the AMR Package — translate","text":"","code":"get_AMR_locale()  set_AMR_locale(language)  reset_AMR_locale()  translate_AMR(x, language = get_AMR_locale())"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate Strings from the AMR Package — translate","text":"language language choose. Use one supported language names ISO-639-1 codes: English (en), Chinese (zh), Danish (da), Dutch (nl), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Polish (pl), Portuguese (pt), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr) Ukrainian (uk). x text translate","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate Strings from the AMR Package — translate","text":"currently 16 supported languages English (en), Chinese (zh), Danish (da), Dutch (nl), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Polish (pl), Portuguese (pt), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr) Ukrainian (uk). languages translations available antimicrobial drugs colloquial microorganism names. permanently silence -per-session language note non-English operating system, can set option AMR_locale .Rprofile file like :   save file. Please read adding updating language Wiki.","code":"# Open .Rprofile file utils::file.edit(\"~/.Rprofile\")  # Then add e.g. Italian support to that file using: options(AMR_locale = \"Italian\")"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"changing-the-default-language","dir":"Reference","previous_headings":"","what":"Changing the Default Language","title":"Translate Strings from the AMR Package — translate","text":"system language used default (returned Sys.getenv(\"LANG\") , LANG set, Sys.getlocale(\"LC_COLLATE\")), language supported. language used can overwritten two ways checked order: Setting option AMR_locale, either using e.g. set_AMR_locale(\"German\") running e.g. options(AMR_locale = \"German\"). Note setting R option works session. Save command options(AMR_locale = \"(language)\") .Rprofile file apply every session. Run utils::file.edit(\"~/.Rprofile\") edit .Rprofile file. Setting system variable LANGUAGE LANG, e.g. adding LANGUAGE=\"de_DE.utf8\" .Renviron file home directory. Thus, option AMR_locale set, system variables LANGUAGE LANG ignored.","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate Strings from the AMR Package — translate","text":"","code":"# Current settings (based on system language) ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacin\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Coagulase-negative Staphylococcus (CoNS)\"  # setting another language set_AMR_locale(\"Spanish\") #> ℹ Using Spanish (Español) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacina\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Staphylococcus coagulasa negativo (SCN)\"  # setting yet another language set_AMR_locale(\"Greek\") #> ℹ Using Greek (Ελληνικά) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Σιπροφλοξασίνη\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Σταφυλόκοκκος με αρνητική πηκτικότητα (CoNS)\"  # setting yet another language set_AMR_locale(\"Ukrainian\") #> ℹ Using Ukrainian (Українська) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ципрофлоксацин\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Коагулазонегативний стафілокок (КНС)\"  # set_AMR_locale() understands endonyms, English exonyms, and ISO-639-1: set_AMR_locale(\"Deutsch\") #> ℹ Using German (Deutsch) for the AMR package for this session. set_AMR_locale(\"German\") #> ℹ Using German (Deutsch) for the AMR package for this session. set_AMR_locale(\"de\") #> ℹ Using German (Deutsch) for the AMR package for this session. ab_name(\"amox/clav\") #> Warning: More than one result was found for item 1: amoxicillin and clavulansäure #> [1] \"Amoxicillin/Clavulansäure\"  # reset to system default reset_AMR_locale() #> ℹ Using the English language (English) for the AMR package for this #>   session. ab_name(\"amox/clav\") #> [1] \"Amoxicillin/clavulanic acid\""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-1829099","dir":"Changelog","previous_headings":"","what":"AMR 1.8.2.9099","title":"AMR 1.8.2.9099","text":"(beta version eventually become v2.0! ’re happy reach new major milestone soon!) new major release AMR package, great new additions also breaking changes current users. listed . TL;DR functions arguments ‘rsi’ replaced ‘sir’, interpretation MIC values (now .sir() instead .rsi()) - old functions still work now Microbiological taxonomy (microorganisms data set) updated 2022 now based LPSN GBIF Much increased algorithms translate user input valid taxonomy, e.g. using recent scientific work per-species human pathogenicity Clinical breakpoints added EUCAST 2022 CLSI 2022 20 new antibiotics added updated DDDs ATC codes Extended support antiviral agents (antivirals data set), many new functions Now available 16 languages Many new interesting functions, sir_confidence_interval() mean_amr_distance(), add_custom_microorganisms() add custom microorganisms package Many small bug fixes","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/news/index.html","id":"sir-vs-rsi-1-8-2-9099","dir":"Changelog","previous_headings":"New","what":"SIR vs. RSI","title":"AMR 1.8.2.9099","text":"milestone version, replaced mentions RSI SIR, comply actually commonly used field clinical microbiology comes tri-form regarding AMR. existing functions .rsi(), rsi_df() ggplot_rsi() still work, replacements .sir(), sir_df(), ggplot_sir() now current functions AMR data analysis. warning thrown session remind users . data set rsi_translation now called clinical_breakpoints better reflect content. ‘RSI functions’ removed future version, late 2023 / early 2024.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"interpretation-of-mic-and-disk-diffusion-values-1-8-2-9099","dir":"Changelog","previous_headings":"New","what":"Interpretation of MIC and disk diffusion values","title":"AMR 1.8.2.9099","text":"clinical breakpoints intrinsic resistance EUCAST 2022 CLSI 2022 added .sir(). EUCAST 2022 (v12.0) now new default guideline MIC disks diffusion interpretations, eucast_rules() apply EUCAST Expert Rules. default guideline (EUCAST) can now changed new AMR_guideline option, : options(AMR_guideline = \"CLSI 2020\"). Interpretation guidelines older 10 years removed, oldest now included guidelines EUCAST CLSI 2013.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"supported-languages-1-8-2-9099","dir":"Changelog","previous_headings":"New","what":"Supported languages","title":"AMR 1.8.2.9099","text":"added support following languages: Chinese, Greek, Japanese, Polish, Turkish Ukrainian. antibiotic names now available languages, AMR package automatically determine supported language based user system language. grateful valuable input colleagues countries. AMR package now available 16 languages according download stats used almost countries world!","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"microbiological-taxonomy-1-8-2-9099","dir":"Changelog","previous_headings":"New","what":"Microbiological taxonomy","title":"AMR 1.8.2.9099","text":"microorganisms data set longer relies Catalogue Life, List Prokaryotic names Standing Nomenclature (LPSN) supplemented ‘backbone taxonomy’ Global Biodiversity Information Facility (GBIF). structure data set changed include separate LPSN GBIF identifiers. Almost previous MO codes retained. contains 1,400 taxonomic names 2022. previously relied experience categorise species pathogenic groups, happy encounter recent work Bartlett et al. (2022, DOI 10.1099/mic.0.001269) extensively studied medical-scientific literature categorise bacterial species groups. See mo_matching_score() work incorporated prevalence column microorganisms data set. Using results, .mo() mo_*() functions now much better capable converting user input valid taxonomic records. new function add_custom_microorganisms() allows users add custom microorganisms AMR package. also made following changes regarding included taxonomy microorganisms functions: Updated full microbiological taxonomy according latest daily LPSN data set (December 2022) latest yearly GBIF taxonomy backbone (November 2022) Support 1,516 city-like serovars Salmonella, Salmonella Goldcoast. Formally, serovars belonging S. enterica species, reported name genus city. reason, serovars subspecies column microorganisms data set “enterica” species column, full name contain species name (enterica). new argument keep_synonyms allows correct updated taxonomy, favour now deleted argument allow_uncertain increased tremendously speed returns generally consequent results Sequential coercion now extremely fast results stored package environment, although coercion unknown values must run per session. Previous results can reset/removed new mo_reset_session() function. Support microorganism codes ASIan Antimicrobial Resistance Surveillance Network (ASIARS-Net) MO matching score algorithm (mo_matching_score()) now counts deletions substitutions 2 instead 1, impacts outcome .mo() mo_*() function CRAN allows packages around 5 MB maximum, packages exempted package one Chromista relevant comes antimicrobial resistance, thus lacking primary scope package Chromista almost never clinically relevant, thus lacking secondary scope package microorganisms.old data set removed, previously accepted names now included microorganisms data set. new column status contains \"accepted\" currently accepted names \"synonym\" taxonomic synonyms; currently invalid names. previously accepted names now microorganisms ID - available - LPSN, GBIF SNOMED CT identifier.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"antibiotic-agents-and-selectors-1-8-2-9099","dir":"Changelog","previous_headings":"New","what":"Antibiotic agents and selectors","title":"AMR 1.8.2.9099","text":"new function add_custom_antimicrobials() allows users add custom antimicrobial codes names AMR package. antibiotics data set greatly updated: following 20 antibiotics added (also includes new J01RA ATC group): azithromycin/fluconazole/secnidazole (AFC), cefepime/amikacin (CFA), cefixime/ornidazole (CEO), ceftriaxone/beta-lactamase inhibitor (CEB), ciprofloxacin/metronidazole (CIM), ciprofloxacin/ornidazole (CIO), ciprofloxacin/tinidazole (CIT), furazidin (FUR), isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), lascufloxacin (LSC), levofloxacin/ornidazole (LEO), nemonoxacin (NEM), norfloxacin/metronidazole (NME), norfloxacin/tinidazole (NTI), ofloxacin/ornidazole (OOR), oteseconazole (OTE), rifampicin/ethambutol/isoniazid (REI), sarecycline (SRC), tetracycline/oleandomycin (TOL), thioacetazone (TAT) Added missing ATC codes Updated DDDs PubChem Compound IDs Updated antibiotic name spelling, now used WHOCC (cephalexin -> cefalexin, phenethicillin -> pheneticillin) Antibiotic code “CEI” ceftolozane/tazobactam replaced “CZT” comply EARS-Net WHONET 2022. old code still work cases using .ab() ab_*() functions. Support antimicrobial interpretation anaerobic bacteria, adding ‘placeholder’ code B_ANAER microorganisms data set adding breakpoints anaerobics clinical_breakpoints data set, used .sir() interpretion MIC disk diffusion values Also, added support using antibiotic selectors scoped dplyr verbs (without using vars()), : ... %>% summarise_at(aminoglycosides(), resistance), please see resistance() examples.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"antiviral-agents-1-8-2-9099","dir":"Changelog","previous_headings":"New","what":"Antiviral agents","title":"AMR 1.8.2.9099","text":"now added extensive support antiviral agents! first time, AMR package extensive support antiviral drugs work names, codes data way. antivirals data set extended 18 new drugs (also new J05AJ ATC group) now also contains antiviral identifiers LOINC codes new data type av (antivirals) added, functionally similar ab antibiotics Functions .av(), av_name(), av_atc(), av_synonyms(), av_from_text() added siblings ab_*() equivalents","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-new-functions-1-8-2-9099","dir":"Changelog","previous_headings":"New","what":"Other new functions","title":"AMR 1.8.2.9099","text":"Function sir_confidence_interval() add confidence intervals AMR calculation. now also included sir_df() proportion_df(). Function mean_amr_distance() calculate mean AMR distance. mean AMR distance normalised numeric value compare AMR test results can help identify similar isolates, without comparing antibiograms hand. Function sir_interpretation_history() view history previous runs .sir() (previously .rsi()). returns ‘logbook’ selected guideline, reference table specific interpretation row data set .sir() run. Function mo_current() get currently valid taxonomic name microorganism Function add_custom_antimicrobials() add custom antimicrobial codes names AMR package","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changes-1-8-2-9099","dir":"Changelog","previous_headings":"","what":"Changes","title":"AMR 1.8.2.9099","text":"Argument combine_IR removed package (affecting functions count_df(), proportion_df(), sir_df() plotting functions), since replaced combine_SI three years ago Using units ab_ddd(..., units = \"...\") deprecated time now supported anymore. Use ab_ddd_units() instead. Support data.frame-enhancing R packages, specifically: data.table::data.table, janitor::tabyl, tibble::tibble, tsibble::tsibble. AMR package functions data set output (sir_df() bug_drug_combinations()), now return data type input. data sets package now tibble, instead base R data.frames. Older R versions still supported, even support tibbles. data sets now also continually exported Apache Feather Apache Parquet formats. can find info article website. Fixed certain EUCAST breakpoints MIC values Allow NA values (e.g. .sir(.disk(NA), ...)) Fix bug-drug combinations multiple breakpoints different body sites Interpretation MIC disk zones now informative availability breakpoints robust Removed .integer() method MIC values, since MIC integer values running table() MIC values consequently failed able retrieve level position (’s normally .integer() factors work) Fixed determination Gram stains (mo_gramstain()), since taxonomic phyla Actinobacteria, Chloroflexi, Firmicutes, Tenericutes renamed respectively Actinomycetota, Chloroflexota, Bacillota, Mycoplasmatota 2021 droplevels() MIC now return common factor default lose mic class. Use droplevels(..., .mic = TRUE) keep mic class. Small fix using ab_from_text() Fixes reading text files using set_mo_source(), now also allows source file contain valid taxonomic names instead valid microorganism ID package Fixed bug mdro() using similar column names Magiorakos guideline Using random_*() function (random_mic()) now possible directly calling package without loading first: AMR::random_mic(10) Extended support vctrs package, used internally tidyverse. allows change values class mic, disk, sir, mo ab tibbles, use antibiotic selectors selecting/filtering, e.g. df[carbapenems() == \"R\", ] Fix using info = FALSE mdro() interpretation guidelines using .sir() amoxicillin, rules ampicillin used amoxicillin rules available Fix using ab_atc() non-existing ATC codes Black white message texts now reversed colour using RStudio dark theme mo_snomed() now returns class character, numeric anymore (make long SNOMED codes readable) Fix using .ab() NA values Updated support WHONET 2022 microorganism codes Antimicrobial interpretation ‘SDD’ (susceptible dose-dependent, coined CLSI) interpreted ‘’ comply EUCAST’s ‘’ .sir() Fix mo_shortname() case higher taxonomic ranks (order, class, phylum) Cleaning columns .sir(), .mic(), .disk() now show column name warning invalid results","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-8-2-9099","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.8.2.9099","text":"Added Peter Dutey-Magni, Dmytro Mykhailenko, Anton Mymrikov, Andrew Norgan, Jonas Salm contributors, thank valuable input New website make use new Bootstrap 5 pkgdown 2.0. website now contains results examples automatically regenerated every change repository, using GitHub Actions R Rmd files project now styled using styler package Set scalar conditional expressions (&& ||) possible comply upcoming R 4.3 enormous lot code cleaning, fixing small bugs way changelog contains changes AMR v2.0 later. prior versions, please see archive.","code":""}]