From 0484d7ab4ddaafdd94173af5d76e45f0488fc3fc Mon Sep 17 00:00:00 2001
From: github-actions <41898282+github-actions[bot]@users.noreply.github.com>
Date: Sun, 26 Feb 2023 20:34:01 +0000
Subject: [PATCH] Built site for AMR: 1.8.2.9147@4416394

---
 404.html                                      |   14 +-
 LICENSE-text.html                             |   14 +-
 articles/AMR.html                             | 2016 +++++++----------
 .../AMR_files/figure-html/disk_plots-1.png    |  Bin 39067 -> 0 bytes
 .../figure-html/disk_plots_mo_ab-1.png        |  Bin 54464 -> 0 bytes
 .../AMR_files/figure-html/mic_plots-1.png     |  Bin 27193 -> 0 bytes
 .../AMR_files/figure-html/mic_plots-2.png     |  Bin 37293 -> 0 bytes
 .../figure-html/mic_plots_mo_ab-1.png         |  Bin 39352 -> 0 bytes
 .../figure-html/mic_plots_mo_ab-2.png         |  Bin 54198 -> 0 bytes
 articles/AMR_files/figure-html/plot 1-1.png   |  Bin 41864 -> 0 bytes
 articles/AMR_files/figure-html/plot 3-1.png   |  Bin 28422 -> 0 bytes
 articles/AMR_files/figure-html/plot 4-1.png   |  Bin 78250 -> 0 bytes
 articles/AMR_files/figure-html/plot 5-1.png   |  Bin 49863 -> 0 bytes
 .../figure-html/unnamed-chunk-13-1.png        |  Bin 0 -> 42740 bytes
 articles/EUCAST.html                          |   14 +-
 articles/MDR.html                             |   70 +-
 articles/PCA.html                             |   14 +-
 articles/SPSS.html                            |   18 +-
 articles/WHONET.html                          |   14 +-
 articles/datasets.html                        |   18 +-
 articles/index.html                           |   14 +-
 articles/resistance_predict.html              |   14 +-
 articles/welcome_to_AMR.html                  |   14 +-
 authors.html                                  |   14 +-
 index.html                                    |   16 +-
 news/index.html                               |   38 +-
 pkgdown.yml                                   |    2 +-
 reference/AMR-deprecated.html                 |   14 +-
 reference/AMR-options.html                    |   14 +-
 reference/AMR.html                            |   14 +-
 reference/Rplot005.png                        |  Bin 13241 -> 13182 bytes
 reference/Rplot006.png                        |  Bin 13251 -> 13199 bytes
 reference/Rplot007.png                        |  Bin 12741 -> 13063 bytes
 reference/Rplot008.png                        |  Bin 18897 -> 18932 bytes
 reference/Rplot009.png                        |  Bin 8344 -> 8638 bytes
 reference/WHOCC.html                          |   14 +-
 reference/WHONET.html                         |   14 +-
 reference/ab_from_text.html                   |   14 +-
 reference/ab_property.html                    |   14 +-
 reference/add_custom_antimicrobials.html      |   14 +-
 reference/add_custom_microorganisms.html      |   14 +-
 reference/age.html                            |   34 +-
 reference/age_groups.html                     |   14 +-
 reference/antibiogram.html                    |   14 +-
 reference/antibiotic_class_selectors.html     |   14 +-
 reference/antibiotics.html                    |   14 +-
 reference/as.ab.html                          |   14 +-
 reference/as.av.html                          |   14 +-
 reference/as.disk.html                        |   14 +-
 reference/as.mic.html                         |   14 +-
 reference/as.mo.html                          |   14 +-
 reference/as.sir.html                         |   34 +-
 reference/atc_online.html                     |   14 +-
 reference/av_from_text.html                   |   14 +-
 reference/av_property.html                    |   14 +-
 reference/availability.html                   |   14 +-
 reference/bug_drug_combinations.html          |   14 +-
 reference/clinical_breakpoints.html           |   14 +-
 reference/count.html                          |   14 +-
 reference/custom_eucast_rules.html            |   14 +-
 reference/dosage.html                         |   14 +-
 reference/eucast_rules.html                   |   14 +-
 reference/example_isolates.html               |   14 +-
 reference/example_isolates_unclean.html       |   14 +-
 reference/first_isolate.html                  |   14 +-
 reference/g.test.html                         |   14 +-
 reference/get_episode.html                    |  124 +-
 reference/ggplot_pca.html                     |   14 +-
 reference/ggplot_sir.html                     |   14 +-
 reference/guess_ab_col.html                   |   14 +-
 reference/index.html                          |   14 +-
 reference/intrinsic_resistant.html            |   14 +-
 reference/italicise_taxonomy.html             |   14 +-
 reference/join.html                           |   14 +-
 reference/key_antimicrobials.html             |   14 +-
 reference/kurtosis.html                       |   18 +-
 reference/like.html                           |   14 +-
 reference/mdro.html                           |   14 +-
 reference/mean_amr_distance.html              |   84 +-
 reference/microorganisms.codes.html           |   14 +-
 reference/microorganisms.html                 |   14 +-
 reference/mo_matching_score.html              |   29 +-
 reference/mo_property.html                    |   14 +-
 reference/mo_source.html                      |   14 +-
 reference/pca.html                            |   14 +-
 reference/plot-1.png                          |  Bin 26290 -> 26657 bytes
 reference/plot-2.png                          |  Bin 26518 -> 26495 bytes
 reference/plot-3.png                          |  Bin 28001 -> 27943 bytes
 reference/plot-4.png                          |  Bin 39308 -> 39519 bytes
 reference/plot-5.png                          |  Bin 39096 -> 39090 bytes
 reference/plot-6.png                          |  Bin 38254 -> 38235 bytes
 reference/plot-7.png                          |  Bin 36975 -> 37582 bytes
 reference/plot-8.png                          |  Bin 56274 -> 56465 bytes
 reference/plot-9.png                          |  Bin 25989 -> 26378 bytes
 reference/plot.html                           |   14 +-
 reference/proportion.html                     |   14 +-
 reference/random.html                         |   47 +-
 reference/resistance_predict.html             |   14 +-
 reference/skewness.html                       |   16 +-
 reference/translate.html                      |   14 +-
 search.json                                   |    2 +-
 101 files changed, 1551 insertions(+), 1855 deletions(-)
 delete mode 100644 articles/AMR_files/figure-html/disk_plots-1.png
 delete mode 100644 articles/AMR_files/figure-html/disk_plots_mo_ab-1.png
 delete mode 100644 articles/AMR_files/figure-html/mic_plots-1.png
 delete mode 100644 articles/AMR_files/figure-html/mic_plots-2.png
 delete mode 100644 articles/AMR_files/figure-html/mic_plots_mo_ab-1.png
 delete mode 100644 articles/AMR_files/figure-html/mic_plots_mo_ab-2.png
 delete mode 100644 articles/AMR_files/figure-html/plot 1-1.png
 delete mode 100644 articles/AMR_files/figure-html/plot 3-1.png
 delete mode 100644 articles/AMR_files/figure-html/plot 4-1.png
 delete mode 100644 articles/AMR_files/figure-html/plot 5-1.png
 create mode 100644 articles/AMR_files/figure-html/unnamed-chunk-13-1.png

diff --git a/404.html b/404.html
index 8dc7e520..9cfc0b9f 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -59,11 +59,6 @@
     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>
        
@@ -82,7 +77,12 @@
     <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
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="https://msberends.github.io/AMR/reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="https://msberends.github.io/AMR/articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/LICENSE-text.html b/LICENSE-text.html
index e45d9d08..bb4437a6 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/articles/AMR.html b/articles/AMR.html
index 685b4cb1..9d35fa9f 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -61,11 +61,6 @@
     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>
        
@@ -84,7 +79,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
@@ -187,7 +187,7 @@
 website update since they are based on randomly created values and the
 page was written in <a href="https://rmarkdown.rstudio.com/" class="external-link">R
 Markdown</a>. However, the methodology remains unchanged. This page was
-generated on 24 February 2023.</p>
+generated on 26 February 2023.</p>
 <div class="section level2">
 <h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
 </h2>
@@ -195,7 +195,7 @@ generated on 24 February 2023.</p>
 knowledge from different scientific fields, which makes it hard to do
 right. At least, it requires:</p>
 <ul>
-<li>Good questions (always start with those!)</li>
+<li>Good questions (always start with those!) and reliable data</li>
 <li>A thorough understanding of (clinical) epidemiology, to understand
 the clinical and epidemiological relevance and possible bias of
 results</li>
@@ -243,21 +243,21 @@ make the structure of your data generally look like this:</p>
 </tr></thead>
 <tbody>
 <tr class="odd">
-<td align="center">2023-02-24</td>
+<td align="center">2023-02-26</td>
 <td align="center">abcd</td>
 <td align="center">Escherichia coli</td>
 <td align="center">S</td>
 <td align="center">S</td>
 </tr>
 <tr class="even">
-<td align="center">2023-02-24</td>
+<td align="center">2023-02-26</td>
 <td align="center">abcd</td>
 <td align="center">Escherichia coli</td>
 <td align="center">S</td>
 <td align="center">R</td>
 </tr>
 <tr class="odd">
-<td align="center">2023-02-24</td>
+<td align="center">2023-02-26</td>
 <td align="center">efgh</td>
 <td align="center">Escherichia coli</td>
 <td align="center">R</td>
@@ -279,309 +279,185 @@ for cleaning data and creating frequency tables.</p>
 <code class="sourceCode R"><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://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</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://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</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="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/cleaner/" class="external-link">cleaner</a></span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># (if not yet installed, install with:)</span></span>
-<span><span class="co"># install.packages(c("dplyr", "ggplot2", "AMR", "cleaner"))</span></span></code></pre></div>
-</div>
-</div>
-<div class="section level2">
-<h2 id="creation-of-data">Creation of data<a class="anchor" aria-label="anchor" href="#creation-of-data"></a>
-</h2>
-<p>We will create some fake example data to use for analysis. For AMR
-data analysis, we need at least: a patient ID, name or code of a
-microorganism, a date and antimicrobial results (an antibiogram). It
-could also include a specimen type (e.g. to filter on blood or urine),
-the ward type (e.g. to filter on ICUs).</p>
-<p>With additional columns (like a hospital name, the patients gender of
-even [well-defined] clinical properties) you can do a comparative
-analysis, as this tutorial will demonstrate too.</p>
-<div class="section level3">
-<h3 id="patients">Patients<a class="anchor" aria-label="anchor" href="#patients"></a>
-</h3>
-<p>To start with patients, we need a unique list of patients.</p>
+<span><span class="co"># install.packages(c("dplyr", "ggplot2", "AMR"))</span></span></code></pre></div>
+<p>The <code>AMR</code> package contains a data set
+<code>example_isolates_unclean</code>, which might look data that users
+have extracted from their laboratory systems:</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">patients</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/unlist.html" class="external-link">unlist</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">LETTERS</span>, <span class="va">paste0</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<p>The <code>LETTERS</code> object is available in R - it’s a vector
-with 26 characters: <code>A</code> to <code>Z</code>. The
-<code>patients</code> object we just created is now a vector of length
-260, with values (patient IDs) varying from <code>A1</code> to
-<code>Z10</code>. Now we we also set the gender of our patients, by
-putting the ID and the gender in a table:</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">patients_table</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>  patient_id <span class="op">=</span> <span class="va">patients</span>,</span>
-<span>  gender <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"M"</span>, <span class="fl">135</span><span class="op">)</span>,</span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"F"</span>, <span class="fl">125</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-<p>The first 135 patient IDs are now male, the other 125 are female.</p>
-</div>
-<div class="section level3">
-<h3 id="dates">Dates<a class="anchor" aria-label="anchor" href="#dates"></a>
-</h3>
-<p>Let’s pretend that our data consists of blood cultures isolates from
-between 1 January 2010 and 1 January 2018.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">dates</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/as.Date.html" class="external-link">as.Date</a></span><span class="op">(</span><span class="st">"2010-01-01"</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/as.Date.html" class="external-link">as.Date</a></span><span class="op">(</span><span class="st">"2018-01-01"</span><span class="op">)</span>, by <span class="op">=</span> <span class="st">"day"</span><span class="op">)</span></span></code></pre></div>
-<p>This <code>dates</code> object now contains all days in our date
-range.</p>
-<div class="section level5">
-<h5 id="microorganisms">Microorganisms<a class="anchor" aria-label="anchor" href="#microorganisms"></a>
-</h5>
-<p>For this tutorial, we will uses four different microorganisms:
-<em>Escherichia coli</em>, <em>Staphylococcus aureus</em>,
-<em>Streptococcus pneumoniae</em>, and <em>Klebsiella
-pneumoniae</em>:</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">bacteria</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
-<span>  <span class="st">"Escherichia coli"</span>, <span class="st">"Staphylococcus aureus"</span>,</span>
-<span>  <span class="st">"Streptococcus pneumoniae"</span>, <span class="st">"Klebsiella pneumoniae"</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-</div>
-</div>
-<div class="section level3">
-<h3 id="put-everything-together">Put everything together<a class="anchor" aria-label="anchor" href="#put-everything-together"></a>
-</h3>
-<p>Using the <code><a href="https://rdrr.io/r/base/sample.html" class="external-link">sample()</a></code> function, we can randomly select
-items from all objects we defined earlier. To let our fake data reflect
-reality a bit, we will also approximately define the probabilities of
-bacteria and the antibiotic results, using the <code><a href="../reference/random.html">random_sir()</a></code>
-function.</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sample_size</span> <span class="op">&lt;-</span> <span class="fl">20000</span></span>
-<span><span class="va">data</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>  date <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="va">dates</span>, size <span class="op">=</span> <span class="va">sample_size</span>, replace <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
-<span>  patient_id <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="va">patients</span>, size <span class="op">=</span> <span class="va">sample_size</span>, replace <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
-<span>  hospital <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>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
-<span>      <span class="st">"Hospital A"</span>,</span>
-<span>      <span class="st">"Hospital B"</span>,</span>
-<span>      <span class="st">"Hospital C"</span>,</span>
-<span>      <span class="st">"Hospital D"</span></span>
-<span>    <span class="op">)</span>,</span>
-<span>    size <span class="op">=</span> <span class="va">sample_size</span>, replace <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>    prob <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.30</span>, <span class="fl">0.35</span>, <span class="fl">0.15</span>, <span class="fl">0.20</span><span class="op">)</span></span>
-<span>  <span class="op">)</span>,</span>
-<span>  bacteria <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="va">bacteria</span>,</span>
-<span>    size <span class="op">=</span> <span class="va">sample_size</span>, replace <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>    prob <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.50</span>, <span class="fl">0.25</span>, <span class="fl">0.15</span>, <span class="fl">0.10</span><span class="op">)</span></span>
-<span>  <span class="op">)</span>,</span>
-<span>  AMX <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="va">sample_size</span>, prob_sir <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.35</span>, <span class="fl">0.60</span>, <span class="fl">0.05</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  AMC <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="va">sample_size</span>, prob_sir <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.15</span>, <span class="fl">0.75</span>, <span class="fl">0.10</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  CIP <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="va">sample_size</span>, prob_sir <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.20</span>, <span class="fl">0.80</span>, <span class="fl">0.00</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  GEN <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="va">sample_size</span>, prob_sir <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.08</span>, <span class="fl">0.92</span>, <span class="fl">0.00</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-<p>Using the <code><a href="https://dplyr.tidyverse.org/reference/mutate-joins.html" class="external-link">left_join()</a></code> function from the
-<code>dplyr</code> package, we can ‘map’ the gender to the patient ID
-using the <code>patients_table</code> object we created earlier:</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">data</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 class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate-joins.html" class="external-link">left_join</a></span><span class="op">(</span><span class="va">patients_table</span><span class="op">)</span></span></code></pre></div>
-<p>The resulting data set contains 20 000 blood culture isolates. With
-the <code><a href="https://rdrr.io/r/utils/head.html" class="external-link">head()</a></code> function we can preview the first 6 rows of this
-data set:</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">data</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<colgroup>
-<col width="13%">
-<col width="13%">
-<col width="13%">
-<col width="28%">
-<col width="5%">
-<col width="5%">
-<col width="5%">
-<col width="5%">
-<col width="8%">
-</colgroup>
-<thead><tr class="header">
-<th align="center">date</th>
-<th align="center">patient_id</th>
-<th align="center">hospital</th>
-<th align="center">bacteria</th>
-<th align="center">AMX</th>
-<th align="center">AMC</th>
-<th align="center">CIP</th>
-<th align="center">GEN</th>
-<th align="center">gender</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="center">2015-09-16</td>
-<td align="center">Z5</td>
-<td align="center">Hospital A</td>
-<td align="center">Escherichia 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>
-</tr>
-<tr class="even">
-<td align="center">2017-09-08</td>
-<td align="center">V1</td>
-<td align="center">Hospital B</td>
-<td align="center">Streptococcus pneumoniae</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>
-</tr>
-<tr class="odd">
-<td align="center">2016-08-05</td>
-<td align="center">B1</td>
-<td align="center">Hospital B</td>
-<td align="center">Streptococcus pneumoniae</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">2010-08-24</td>
-<td align="center">E10</td>
-<td align="center">Hospital C</td>
-<td align="center">Escherichia coli</td>
-<td align="center">S</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">2011-06-19</td>
-<td align="center">R10</td>
-<td align="center">Hospital D</td>
-<td align="center">Staphylococcus aureus</td>
-<td align="center">R</td>
-<td align="center">S</td>
-<td align="center">S</td>
-<td align="center">I</td>
-<td align="center">F</td>
-</tr>
-<tr class="even">
-<td align="center">2013-07-14</td>
-<td align="center">L4</td>
-<td align="center">Hospital D</td>
-<td align="center">Staphylococcus aureus</td>
-<td align="center">I</td>
-<td align="center">S</td>
-<td align="center">I</td>
-<td align="center">R</td>
-<td align="center">M</td>
-</tr>
-</tbody>
-</table>
-<p>Now, let’s start the cleaning and the analysis!</p>
-</div>
-</div>
-<div class="section level2">
-<h2 id="cleaning-the-data">Cleaning the data<a class="anchor" aria-label="anchor" href="#cleaning-the-data"></a>
-</h2>
-<p>We also created a package dedicated to data cleaning and checking,
-called the <code>cleaner</code> package. It <code><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq()</a></code> function
-can be used to create frequency tables.</p>
-<p>For example, for the <code>gender</code> variable:</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><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 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">gender</span><span class="op">)</span></span></code></pre></div>
-<p><strong>Frequency table</strong></p>
-<p>Class: character<br>
-Length: 20,000<br>
-Available: 20,000 (100%, NA: 0 = 0%)<br>
-Unique: 2</p>
-<p>Shortest: 1<br>
-Longest: 1</p>
-<table class="table">
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">Item</th>
-<th align="right">Count</th>
-<th align="right">Percent</th>
-<th align="right">Cum. Count</th>
-<th align="right">Cum. Percent</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">1</td>
-<td align="left">M</td>
-<td align="right">10,355</td>
-<td align="right">51.78%</td>
-<td align="right">10,355</td>
-<td align="right">51.78%</td>
-</tr>
-<tr class="even">
-<td align="left">2</td>
-<td align="left">F</td>
-<td align="right">9,645</td>
-<td align="right">48.23%</td>
-<td align="right">20,000</td>
-<td align="right">100.00%</td>
-</tr>
-</tbody>
-</table>
-<p>So, we can draw at least two conclusions immediately. From a data
-scientists perspective, the data looks clean: only values <code>M</code>
-and <code>F</code>. From a researchers perspective: there are slightly
-more men. Nothing we didn’t already know.</p>
-<p>The data is already quite clean, but we still need to transform some
-variables. The <code>bacteria</code> column now consists of text, and we
-want to add more variables based on microbial IDs later on. So, we will
-transform this column to valid IDs. The <code><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate()</a></code> function
-of the <code>dplyr</code> package makes this really easy:</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">data</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="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>bacteria <span class="op">=</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">bacteria</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<p>We also want to transform the antibiotics, because in real life data
-we don’t know if they are really clean. The <code><a href="../reference/as.sir.html">as.sir()</a></code>
-function ensures reliability and reproducibility in these kind of
-variables. The <code><a href="../reference/as.sir.html">is_sir_eligible()</a></code> can check which columns
-are probably columns with SIR test results. Using <code><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate()</a></code>
-and <code><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across()</a></code>, we can apply the transformation to the formal
-<code>&lt;rsi&gt;</code> class:</p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../reference/as.sir.html">is_sir_eligible</a></span><span class="op">(</span><span class="va">data</span><span class="op">)</span></span>
-<span><span class="co">#&gt; [1] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">colnames</a></span><span class="op">(</span><span class="va">data</span><span class="op">)</span><span class="op">[</span><span class="fu"><a href="../reference/as.sir.html">is_sir_eligible</a></span><span class="op">(</span><span class="va">data</span><span class="op">)</span><span class="op">]</span></span>
-<span><span class="co">#&gt; [1] "AMX" "AMC" "CIP" "GEN"</span></span>
+<code class="sourceCode R"><span><span class="va">example_isolates_unclean</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 3,000 × 8</span></span></span>
+<span><span class="co">#&gt;    patient_id hospital date       bacteria      AMX   AMC   CIP   GEN  </span></span>
+<span><span class="co">#&gt;    <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;date&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;chr&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></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> J3         A        2012-11-21 E. coli       R     I     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> R7         A        2018-04-03 K. pneumoniae R     I     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> P3         A        2014-09-19 E. coli       R     S     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> P10        A        2015-12-10 E. coli       S     I     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> B7         A        2015-03-02 E. coli       S     S     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> W3         A        2018-03-31 S. aureus     R     S     R     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> J8         A        2016-06-14 E. coli       R     S     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> M3         A        2015-10-25 E. coli       R     S     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> J3         A        2019-06-19 E. coli       S     S     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> G6         A        2015-04-27 S. aureus     S     S     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># … with 2,990 more rows</span></span></span>
 <span></span>
-<span><span class="va">data</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="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is_sir_eligible</span><span class="op">)</span>, <span class="va">as.sir</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<p>Finally, we will apply <a href="https://www.eucast.org/expert_rules_and_intrinsic_resistance/" class="external-link">EUCAST
-rules</a> on our antimicrobial results. In Europe, most medical
-microbiological laboratories already apply these rules. Our package
-features their latest insights on intrinsic resistance and exceptional
-phenotypes. Moreover, the <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function can also
-apply additional rules, like forcing
-<help title="ATC: J01CA01">ampicillin</help> = R when
-<help title="ATC: J01CR02">amoxicillin/clavulanic acid</help> = R.</p>
-<p>Because the amoxicillin (column <code>AMX</code>) and
-amoxicillin/clavulanic acid (column <code>AMC</code>) in our data were
-generated randomly, some rows will undoubtedly contain AMX = S and AMC =
-R, which is technically impossible. The <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code>
-fixes this:</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">data</span>, col_mo <span class="op">=</span> <span class="st">"bacteria"</span>, rules <span class="op">=</span> <span class="st">"all"</span><span class="op">)</span></span></code></pre></div>
+<span><span class="co"># we will use 'our_data' as the data set name for this tutorial</span></span>
+<span><span class="va">our_data</span> <span class="op">&lt;-</span> <span class="va">example_isolates_unclean</span></span></code></pre></div>
+<p>For AMR data analysis, we would like the microorganism column to
+contain valid, up-to-date taxonomy, and the antibiotic columns to be
+cleaned as SIR values as well.</p>
+</div>
+<div class="section level3">
+<h3 id="taxonomy-of-microorganisms">Taxonomy of microorganisms<a class="anchor" aria-label="anchor" href="#taxonomy-of-microorganisms"></a>
+</h3>
+<p>With <code><a href="../reference/as.mo.html">as.mo()</a></code>, users can transform arbitrary
+microorganism names or codes to current taxonomy. The <code>AMR</code>
+package contains up-to-date taxonomic data. To be specific, currently
+included data were retrieved on 11 Dec 2022.</p>
+<p>The codes of the AMR packages that come from <code><a href="../reference/as.mo.html">as.mo()</a></code> are
+short, but still human readable. More importantly, <code><a href="../reference/as.mo.html">as.mo()</a></code>
+supports all kinds of input:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"Klebsiella pneumoniae"</span><span class="op">)</span></span>
+<span><span class="co">#&gt; Class 'mo'</span></span>
+<span><span class="co">#&gt; [1] B_KLBSL_PNMN</span></span>
+<span><span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"K. pneumoniae"</span><span class="op">)</span></span>
+<span><span class="co">#&gt; Class 'mo'</span></span>
+<span><span class="co">#&gt; [1] B_KLBSL_PNMN</span></span>
+<span><span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"KLEPNE"</span><span class="op">)</span></span>
+<span><span class="co">#&gt; Class 'mo'</span></span>
+<span><span class="co">#&gt; [1] B_KLBSL_PNMN</span></span>
+<span><span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"KLPN"</span><span class="op">)</span></span>
+<span><span class="co">#&gt; Class 'mo'</span></span>
+<span><span class="co">#&gt; [1] B_KLBSL_PNMN</span></span></code></pre></div>
+<p>The first character in above codes denote their taxonomic kingdom,
+such as Bacteria (B), Fungi (F), and Protozoa (P).</p>
+<p>The <code>AMR</code> package also contain functions to directly
+retrieve taxonomic properties, such as the name, genus, species, family,
+order, and even Gram-stain. They all start with <code>mo_</code> and
+they use <code><a href="../reference/as.mo.html">as.mo()</a></code> internally, so that still any arbitrary
+user input can be used:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/mo_property.html">mo_family</a></span><span class="op">(</span><span class="st">"K. pneumoniae"</span><span class="op">)</span></span>
+<span><span class="co">#&gt; [1] "Enterobacteriaceae"</span></span>
+<span><span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="st">"K. pneumoniae"</span><span class="op">)</span></span>
+<span><span class="co">#&gt; [1] "Klebsiella"</span></span>
+<span><span class="fu"><a href="../reference/mo_property.html">mo_species</a></span><span class="op">(</span><span class="st">"K. pneumoniae"</span><span class="op">)</span></span>
+<span><span class="co">#&gt; [1] "pneumoniae"</span></span>
+<span></span>
+<span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="st">"Klebsiella pneumoniae"</span><span class="op">)</span></span>
+<span><span class="co">#&gt; [1] "Gram-negative"</span></span>
+<span></span>
+<span><span class="fu"><a href="../reference/mo_property.html">mo_ref</a></span><span class="op">(</span><span class="st">"K. pneumoniae"</span><span class="op">)</span></span>
+<span><span class="co">#&gt; [1] "Trevisan, 1887"</span></span>
+<span></span>
+<span><span class="fu"><a href="../reference/mo_property.html">mo_snomed</a></span><span class="op">(</span><span class="st">"K. pneumoniae"</span><span class="op">)</span></span>
+<span><span class="co">#&gt; [[1]]</span></span>
+<span><span class="co">#&gt; [1] "1098101000112102" "446870005"        "1098201000112108" "409801009"       </span></span>
+<span><span class="co">#&gt; [5] "56415008"         "714315002"        "713926009"</span></span></code></pre></div>
+<p>Now we can thus clean our data:</p>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">our_data</span><span class="op">$</span><span class="va">bacteria</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">our_data</span><span class="op">$</span><span class="va">bacteria</span>, info <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span><span class="co">#&gt; ℹ Microorganism translation was uncertain for four microorganisms. Run</span></span>
+<span><span class="co">#&gt;   mo_uncertainties() to review these uncertainties, or use</span></span>
+<span><span class="co">#&gt;   add_custom_microorganisms() to add custom entries.</span></span></code></pre></div>
+<p>Apparently, there was some uncertainty about the translation to
+taxonomic codes. Let’s check this:</p>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/as.mo.html">mo_uncertainties</a></span><span class="op">(</span><span class="op">)</span></span>
+<span><span class="co">#&gt; Matching scores are based on the resemblance between the input and the full</span></span>
+<span><span class="co">#&gt; taxonomic name, and the pathogenicity in humans. See ?mo_matching_score.</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; --------------------------------------------------------------------------------</span></span>
+<span><span class="co">#&gt; "E. coli" -&gt; Escherichia coli (B_ESCHR_COLI, 0.688)</span></span>
+<span><span class="co">#&gt;              Based on input "E coli"</span></span>
+<span><span class="co">#&gt; Also matched: Enterobacter cowanii (0.600), Eubacterium combesii</span></span>
+<span><span class="co">#&gt;               (0.600), Eggerthia catenaformis (0.591), Eubacterium callanderi</span></span>
+<span><span class="co">#&gt;               (0.591), Enterocloster citroniae (0.587), Eubacterium cylindroides</span></span>
+<span><span class="co">#&gt;               (0.583), Enterococcus casseliflavus (0.577), Enterobacter cloacae</span></span>
+<span><span class="co">#&gt;               cloacae (0.571), Ehrlichia canis (0.567), and Enterobacter cloacae</span></span>
+<span><span class="co">#&gt;               dissolvens (0.565)</span></span>
+<span><span class="co">#&gt; --------------------------------------------------------------------------------</span></span>
+<span><span class="co">#&gt; "K. pneumoniae" -&gt; Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786)</span></span>
+<span><span class="co">#&gt;                    Based on input "K pneumoniae"</span></span>
+<span><span class="co">#&gt; Also matched: Klebsiella pneumoniae ozaenae (0.707), Klebsiella</span></span>
+<span><span class="co">#&gt;               pneumoniae pneumoniae (0.688), Klebsiella pneumoniae rhinoscleromatis</span></span>
+<span><span class="co">#&gt;               (0.658), Klebsiella pasteurii (0.500), Klebsiella planticola (0.500),</span></span>
+<span><span class="co">#&gt;               Kingella potus (0.400), Kosakonia pseudosacchari (0.361), Kaistella</span></span>
+<span><span class="co">#&gt;               palustris (0.333), Kocuria palustris (0.333), and Kocuria pelophila</span></span>
+<span><span class="co">#&gt;               (0.333)</span></span>
+<span><span class="co">#&gt; --------------------------------------------------------------------------------</span></span>
+<span><span class="co">#&gt; "S. aureus" -&gt; Staphylococcus aureus (B_STPHY_AURS, 0.690)</span></span>
+<span><span class="co">#&gt;                Based on input "S aureus"</span></span>
+<span><span class="co">#&gt; Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus</span></span>
+<span><span class="co">#&gt;               argenteus (0.625), Staphylococcus aureus anaerobius (0.625),</span></span>
+<span><span class="co">#&gt;               Streptomyces argenteolus (0.483), Streptomyces aureus (0.474),</span></span>
+<span><span class="co">#&gt;               Streptomyces azureus (0.467), Streptomyces aureorectus (0.444),</span></span>
+<span><span class="co">#&gt;               Streptomyces auratus (0.433), Streptomyces aurantiogriseus (0.429), and</span></span>
+<span><span class="co">#&gt;               Streptomyces aureocirculatus (0.429)</span></span>
+<span><span class="co">#&gt; --------------------------------------------------------------------------------</span></span>
+<span><span class="co">#&gt; "S. pneumoniae" -&gt; Streptococcus pneumoniae (B_STRPT_PNMN, 0.750)</span></span>
+<span><span class="co">#&gt;                    Based on input "S pneumoniae"</span></span>
+<span><span class="co">#&gt; Also matched: Streptococcus pseudopneumoniae (0.700), Serratia</span></span>
+<span><span class="co">#&gt;               proteamaculans quinovora (0.545), Streptococcus pseudoporcinus (0.536),</span></span>
+<span><span class="co">#&gt;               Staphylococcus pseudintermedius (0.532), Serratia proteamaculans</span></span>
+<span><span class="co">#&gt;               proteamaculans (0.526), Salmonella Portanigra (0.524), Sphingomonas</span></span>
+<span><span class="co">#&gt;               paucimobilis (0.520), Streptococcus pluranimalium (0.519),</span></span>
+<span><span class="co">#&gt;               Streptococcus constellatus pharyngis (0.514), and Salmonella Pakistan</span></span>
+<span><span class="co">#&gt;               (0.500)</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Only the first 10 other matches of each record are shown. Run</span></span>
+<span><span class="co">#&gt; print(mo_uncertainties(), n = ...) to view more entries, or save</span></span>
+<span><span class="co">#&gt; mo_uncertainties() to an object.</span></span></code></pre></div>
+<p>That’s all good.</p>
+</div>
+<div class="section level3">
+<h3 id="antibiotic-results">Antibiotic results<a class="anchor" aria-label="anchor" href="#antibiotic-results"></a>
+</h3>
+<p>The column with antibiotic test results must also be cleaned. The
+<code>AMR</code> package comes with three new data types to work with
+such test results: <code>mic</code> for minimal inhibitory
+concentrations (MIC), <code>disk</code> for disk diffusion diameters,
+and <code>sir</code> for SIR data that have been interpreted already.
+This package can also determine SIR values based on MIC or disk
+diffusion values, read more about that on the <code><a href="../reference/as.sir.html">as.sir()</a></code>
+page.</p>
+<p>For now, we will just clean the SIR columns in our data using
+dplyr:</p>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># method 1, be explicit about the columns:</span></span>
+<span><span class="va">our_data</span> <span class="op">&lt;-</span> <span class="va">our_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="https://dplyr.tidyverse.org/reference/mutate_all.html" class="external-link">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html" class="external-link">vars</a></span><span class="op">(</span><span class="va">AMX</span><span class="op">:</span><span class="va">GEN</span><span class="op">)</span>, <span class="va">as.sir</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># method 2, let the AMR package determine the eligible columns</span></span>
+<span><span class="va">our_data</span> <span class="op">&lt;-</span> <span class="va">our_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="https://dplyr.tidyverse.org/reference/mutate_all.html" class="external-link">mutate_if</a></span><span class="op">(</span><span class="va">is_sir_eligible</span>, <span class="va">as.sir</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># result:</span></span>
+<span><span class="va">our_data</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 3,000 × 8</span></span></span>
+<span><span class="co">#&gt;    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN  </span></span>
+<span><span class="co">#&gt;    <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;date&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>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> J3         A        2012-11-21 B_ESCHR_COLI R     I     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> R7         A        2018-04-03 B_KLBSL_PNMN R     I     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> P3         A        2014-09-19 B_ESCHR_COLI R     S     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> P10        A        2015-12-10 B_ESCHR_COLI S     I     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> B7         A        2015-03-02 B_ESCHR_COLI S     S     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> W3         A        2018-03-31 B_STPHY_AURS R     S     R     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> J8         A        2016-06-14 B_ESCHR_COLI R     S     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> M3         A        2015-10-25 B_ESCHR_COLI R     S     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> J3         A        2019-06-19 B_ESCHR_COLI S     S     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> G6         A        2015-04-27 B_STPHY_AURS S     S     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># … with 2,990 more rows</span></span></span></code></pre></div>
+<p>This is basically it for the cleaning, time to start the data
+inclusion.</p>
 </div>
-<div class="section level2">
-<h2 id="adding-new-variables">Adding new variables<a class="anchor" aria-label="anchor" href="#adding-new-variables"></a>
-</h2>
-<p>Now that we have the microbial ID, we can add some taxonomic
-properties:</p>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">data</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="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span></span>
-<span>    gramstain <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">bacteria</span><span class="op">)</span>,</span>
-<span>    genus <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="va">bacteria</span><span class="op">)</span>,</span>
-<span>    species <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_species</a></span><span class="op">(</span><span class="va">bacteria</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span></code></pre></div>
 <div class="section level3">
 <h3 id="first-isolates">First isolates<a class="anchor" aria-label="anchor" href="#first-isolates"></a>
 </h3>
-<p>We also need to know which isolates we can <em>actually</em> use for
-analysis.</p>
+<p>We need to know which isolates we can <em>actually</em> use for
+analysis without repetition bias.</p>
 <p>To conduct an analysis of antimicrobial resistance, you must <a href="https:/pubmed.ncbi.nlm.nih.gov/17304462/">only include the first
 isolate of every patient per episode</a> (Hindler <em>et al.</em>, Clin
 Infect Dis. 2007). If you would not do this, you could easily get an
@@ -614,13 +490,11 @@ different methods as defined by <a href="https://academic.oup.com/cid/article/44
 <em>et al.</em> in 2007</a>: phenotype-based, episode-based,
 patient-based, isolate-based. The right method depends on your goals and
 analysis, but the default phenotype-based method is in any case the
-method to properly correct for most duplicate isolates. This method also
-takes into account the antimicrobial susceptibility test results using
-<code>all_microbials()</code>. Read more about the methods on the
-<code><a href="../reference/first_isolate.html">first_isolate()</a></code> page.</p>
+method to properly correct for most duplicate isolates. Read more about
+the methods on the <code><a href="../reference/first_isolate.html">first_isolate()</a></code> page.</p>
 <p>The outcome of the function can easily be added to our data:</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">data</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>
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">our_data</span> <span class="op">&lt;-</span> <span class="va">our_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="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>first <span class="op">=</span> <span class="fu"><a href="../reference/first_isolate.html">first_isolate</a></span><span class="op">(</span>info <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="co">#&gt; Determining first isolates using an episode length of 365 days</span></span>
 <span><span class="co">#&gt; ℹ Using column 'bacteria' as input for col_mo.</span></span>
@@ -629,496 +503,698 @@ takes into account the antimicrobial susceptibility test results using
 <span><span class="co">#&gt; Basing inclusion on all antimicrobial results, using a points threshold of</span></span>
 <span><span class="co">#&gt; 2</span></span>
 <span><span class="co">#&gt; Including isolates from ICU.</span></span>
-<span><span class="co">#&gt; =&gt; Found 12,372 'phenotype-based' first isolates (61.9% of total where a</span></span>
-<span><span class="co">#&gt;    microbial ID was available)</span></span></code></pre></div>
-<p>So only 61.9% 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
+<span><span class="co">#&gt; =&gt; Found 2,626 'phenotype-based' first isolates (87.6% within scope and</span></span>
+<span><span class="co">#&gt;    87.5% of total where a microbial ID was available)</span></span></code></pre></div>
+<p>So only 88% 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">
-<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>
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">our_data_1st</span> <span class="op">&lt;-</span> <span class="va">our_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="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="va">first</span> <span class="op">==</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
 <p>For future use, the above two syntaxes can be shortened:</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
-<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>
+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">our_data_1st</span> <span class="op">&lt;-</span> <span class="va">our_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></code></pre></div>
-<p>So we end up with 12 372 isolates for analysis. Now our data looks
+<p>So we end up with 2 626 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>
-<table class="table">
-<colgroup>
-<col width="2%">
-<col width="9%">
-<col width="9%">
-<col width="9%">
-<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="9%">
-<col width="5%">
-</colgroup>
-<thead><tr class="header">
-<th align="left"></th>
-<th align="center">date</th>
-<th align="center">patient_id</th>
-<th align="center">hospital</th>
-<th align="center">bacteria</th>
-<th align="center">AMX</th>
-<th align="center">AMC</th>
-<th align="center">CIP</th>
-<th align="center">GEN</th>
-<th align="center">gender</th>
-<th align="center">gramstain</th>
-<th align="center">genus</th>
-<th align="center">species</th>
-<th align="center">first</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">1</td>
-<td align="center">2015-09-16</td>
-<td align="center">Z5</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="left">2</td>
-<td align="center">2017-09-08</td>
-<td align="center">V1</td>
-<td align="center">Hospital B</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">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">3</td>
-<td align="center">2016-08-05</td>
-<td align="center">B1</td>
-<td align="center">Hospital B</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="left">4</td>
-<td align="center">2010-08-24</td>
-<td align="center">E10</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">I</td>
-<td align="center">S</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="left">7</td>
-<td align="center">2011-06-03</td>
-<td align="center">D1</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="left">8</td>
-<td align="center">2016-04-15</td>
-<td align="center">T10</td>
-<td align="center">Hospital B</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">F</td>
-<td align="center">Gram-positive</td>
-<td align="center">Streptococcus</td>
-<td align="center">pneumoniae</td>
-<td align="center">TRUE</td>
-</tr>
-</tbody>
-</table>
-<p>Time for the analysis!</p>
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">our_data_1st</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,626 × 9</span></span></span>
+<span><span class="co">#&gt;    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first</span></span>
+<span><span class="co">#&gt;    <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;date&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;lgl&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> J3         A        2012-11-21 B_ESCHR_COLI R     I     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> R7         A        2018-04-03 B_KLBSL_PNMN R     I     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> P10        A        2015-12-10 B_ESCHR_COLI S     I     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> B7         A        2015-03-02 B_ESCHR_COLI S     S     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> W3         A        2018-03-31 B_STPHY_AURS R     S     R     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> J8         A        2016-06-14 B_ESCHR_COLI R     S     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> M3         A        2015-10-25 B_ESCHR_COLI R     S     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> J3         A        2019-06-19 B_ESCHR_COLI S     S     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> G6         A        2015-04-27 B_STPHY_AURS S     S     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> P4         A        2011-06-21 B_ESCHR_COLI S     S     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># … with 2,616 more rows</span></span></span></code></pre></div>
+<p>Time for the analysis.</p>
 </div>
 </div>
 <div class="section level2">
 <h2 id="analysing-the-data">Analysing the data<a class="anchor" aria-label="anchor" href="#analysing-the-data"></a>
 </h2>
-<p>You might want to start by getting an idea of how the data is
-distributed. It’s an important start, because it also decides how you
-will continue your analysis. Although this package contains a convenient
-function to make frequency tables, exploratory data analysis (EDA) is
-not the primary scope of this package. Use a package like <a href="https://cran.r-project.org/package=DataExplorer" class="external-link"><code>DataExplorer</code></a>
-for that, or read the free online book <a href="https://bookdown.org/rdpeng/exdata/" class="external-link">Exploratory Data Analysis
-with R</a> by Roger D. Peng.</p>
+<p>The base R <code><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary()</a></code> function gives a good first
+impression, as it comes with support for the new <code>mo</code> and
+<code>sir</code> classes that we now have in our data set:</p>
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">our_data_1st</span><span class="op">)</span></span>
+<span><span class="co">#&gt;   patient_id          hospital              date           </span></span>
+<span><span class="co">#&gt;  Length:2626        Length:2626        Min.   :2011-01-01  </span></span>
+<span><span class="co">#&gt;  Class :character   Class :character   1st Qu.:2013-04-14  </span></span>
+<span><span class="co">#&gt;  Mode  :character   Mode  :character   Median :2015-06-05  </span></span>
+<span><span class="co">#&gt;                                        Mean   :2015-06-15  </span></span>
+<span><span class="co">#&gt;                                        3rd Qu.:2017-08-23  </span></span>
+<span><span class="co">#&gt;                                        Max.   :2020-01-01  </span></span>
+<span><span class="co">#&gt;    bacteria               AMX                    AMC                </span></span>
+<span><span class="co">#&gt;  Class :mo             Class:sir              Class:sir             </span></span>
+<span><span class="co">#&gt;  &lt;NA&gt;  :0              %R   :43.2% (n=1134)   %R   :36.1% (n=947)   </span></span>
+<span><span class="co">#&gt;  Unique:4              %SI  :56.8% (n=1492)   %SI  :63.9% (n=1679)  </span></span>
+<span><span class="co">#&gt;  #1    :B_ESCHR_COLI   - %S :41.1% (n=1080)   - %S :52.7% (n=1383)  </span></span>
+<span><span class="co">#&gt;  #2    :B_STPHY_AURS   - %I :15.7% (n=412)    - %I :11.3% (n=296)   </span></span>
+<span><span class="co">#&gt;  #3    :B_STRPT_PNMN                                                </span></span>
+<span><span class="co">#&gt;     CIP                    GEN                  first        </span></span>
+<span><span class="co">#&gt;  Class:sir              Class:sir              Mode:logical  </span></span>
+<span><span class="co">#&gt;  %R   :42.0% (n=1102)   %R   :37.0% (n=971)    TRUE:2626     </span></span>
+<span><span class="co">#&gt;  %SI  :58.0% (n=1524)   %SI  :63.0% (n=1655)                 </span></span>
+<span><span class="co">#&gt;  - %S :51.9% (n=1362)   - %S :59.9% (n=1574)                 </span></span>
+<span><span class="co">#&gt;  - %I : 6.2% (n=162)    - %I : 3.1% (n=81)                   </span></span>
+<span><span class="co">#&gt; </span></span>
+<span></span>
+<span><span class="fu"><a href="https://pillar.r-lib.org/reference/glimpse.html" class="external-link">glimpse</a></span><span class="op">(</span><span class="va">our_data_1st</span><span class="op">)</span></span>
+<span><span class="co">#&gt; Rows: 2,626</span></span>
+<span><span class="co">#&gt; Columns: 9</span></span>
+<span><span class="co">#&gt; $ patient_id <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "J3", "R7", "P10", "B7", "W3", "J8", "M3", "J3", "G6", "P4"…</span></span>
+<span><span class="co">#&gt; $ hospital   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",…</span></span>
+<span><span class="co">#&gt; $ date       <span style="color: #949494; font-style: italic;">&lt;date&gt;</span> 2012-11-21, 2018-04-03, 2015-12-10, 2015-03-02, 2018-03-31…</span></span>
+<span><span class="co">#&gt; $ bacteria   <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> "B_ESCHR_COLI", "B_KLBSL_PNMN", "B_ESCHR_COLI", "B_ESCHR_COL…</span></span>
+<span><span class="co">#&gt; $ AMX        <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R, R, S, S, R, R, R, S, S, S, S, R, S, S, R, R, R, R, I, S,…</span></span>
+<span><span class="co">#&gt; $ AMC        <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> I, I, I, S, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, R,…</span></span>
+<span><span class="co">#&gt; $ CIP        <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S, S,…</span></span>
+<span><span class="co">#&gt; $ GEN        <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,…</span></span>
+<span><span class="co">#&gt; $ first      <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…</span></span>
+<span></span>
+<span><span class="co"># number of unique values per column:</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">our_data_1st</span>, <span class="va">n_distinct</span><span class="op">)</span></span>
+<span><span class="co">#&gt; patient_id   hospital       date   bacteria        AMX        AMC        CIP </span></span>
+<span><span class="co">#&gt;        260          3       1808          4          3          3          3 </span></span>
+<span><span class="co">#&gt;        GEN      first </span></span>
+<span><span class="co">#&gt;          3          1</span></span></code></pre></div>
 <div class="section level3">
-<h3 id="dispersion-of-species">Dispersion of species<a class="anchor" aria-label="anchor" href="#dispersion-of-species"></a>
+<h3 id="availability-of-species">Availability of species<a class="anchor" aria-label="anchor" href="#availability-of-species"></a>
 </h3>
 <p>To just get an idea how the species are distributed, create a
-frequency table with our <code><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq()</a></code> function. We created the
-<code>genus</code> and <code>species</code> column earlier based on the
-microbial ID. With <code><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste()</a></code>, we can concatenate them
-together.</p>
-<p>The <code><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq()</a></code> function can be used like the base R language
-was intended:</p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><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="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="va">data_1st</span><span class="op">$</span><span class="va">genus</span>, <span class="va">data_1st</span><span class="op">$</span><span class="va">species</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<p>Or can be used like the <code>dplyr</code> way, which is easier
-readable:</p>
-<div class="sourceCode" id="cb19"><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="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: 12,372<br>
-Available: 12,372 (100%, NA: 0 = 0%)<br>
-Unique: 4</p>
-<p>Shortest: 16<br>
-Longest: 24</p>
-<table class="table">
-<colgroup>
-<col width="4%">
-<col width="36%">
-<col width="9%">
-<col width="12%">
-<col width="16%">
-<col width="19%">
-</colgroup>
-<thead><tr class="header">
-<th align="left"></th>
-<th align="left">Item</th>
-<th align="right">Count</th>
-<th align="right">Percent</th>
-<th align="right">Cum. Count</th>
-<th align="right">Cum. Percent</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">1</td>
-<td align="left">Escherichia coli</td>
-<td align="right">5,982</td>
-<td align="right">48.35%</td>
-<td align="right">5,982</td>
-<td align="right">48.35%</td>
-</tr>
-<tr class="even">
-<td align="left">2</td>
-<td align="left">Staphylococcus aureus</td>
-<td align="right">3,218</td>
-<td align="right">26.01%</td>
-<td align="right">9,200</td>
-<td align="right">74.36%</td>
-</tr>
-<tr class="odd">
-<td align="left">3</td>
-<td align="left">Streptococcus pneumoniae</td>
-<td align="right">1,867</td>
-<td align="right">15.09%</td>
-<td align="right">11,067</td>
-<td align="right">89.45%</td>
-</tr>
-<tr class="even">
-<td align="left">4</td>
-<td align="left">Klebsiella pneumoniae</td>
-<td align="right">1,305</td>
-<td align="right">10.55%</td>
-<td align="right">12,372</td>
-<td align="right">100.00%</td>
-</tr>
-</tbody>
-</table>
+frequency table with <code><a href="../reference/count.html">count()</a></code> based on the name of the
+microorganisms:</p>
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">our_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/count.html">count</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">bacteria</span><span class="op">)</span>, sort <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 4 × 2</span></span></span>
+<span><span class="co">#&gt;   `mo_name(bacteria)`          n</span></span>
+<span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                    <span style="color: #949494; font-style: italic;">&lt;int&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Escherichia coli          <span style="text-decoration: underline;">1</span>518</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Staphylococcus aureus      730</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> Streptococcus pneumoniae   426</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span> Klebsiella pneumoniae      326</span></span>
+<span></span>
+<span><span class="va">our_data_1st</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/count.html">count</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">bacteria</span><span class="op">)</span>, sort <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 4 × 2</span></span></span>
+<span><span class="co">#&gt;   `mo_name(bacteria)`          n</span></span>
+<span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                    <span style="color: #949494; font-style: italic;">&lt;int&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Escherichia coli          <span style="text-decoration: underline;">1</span>250</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Staphylococcus aureus      661</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> Streptococcus pneumoniae   399</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span> Klebsiella pneumoniae      316</span></span></code></pre></div>
 </div>
 <div class="section level3">
-<h3 id="overview-of-different-bugdrug-combinations">Overview of different bug/drug combinations<a class="anchor" aria-label="anchor" href="#overview-of-different-bugdrug-combinations"></a>
+<h3 id="select-and-filter-with-antibiotic-selectors">Select and filter with antibiotic selectors<a class="anchor" aria-label="anchor" href="#select-and-filter-with-antibiotic-selectors"></a>
 </h3>
-<p>Using <a href="https://tidyselect.r-lib.org/reference/language.html" class="external-link">tidyverse
-selections</a>, you can also select or filter columns based on the
-antibiotic class they are in:</p>
-<div class="sourceCode" id="cb20"><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>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/any.html" class="external-link">any</a></span><span class="op">(</span><span class="fu"><a href="../reference/antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="st">"R"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">#&gt; ℹ For aminoglycosides() using column 'GEN' (gentamicin)</span></span></code></pre>
+<p>Using so-called antibiotic class selectors, you can select or filter
+columns based on the antibiotic class that your antibiotic results are
+in:</p>
+<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">our_data_1st</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="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">date</span>, <span class="fu"><a href="../reference/antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="co">#&gt; ℹ For aminoglycosides() using column 'GEN' (gentamicin)</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,626 × 2</span></span></span>
+<span><span class="co">#&gt;    date       GEN  </span></span>
+<span><span class="co">#&gt;    <span style="color: #949494; font-style: italic;">&lt;date&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> 2012-11-21 S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> 2018-04-03 S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> 2015-12-10 S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> 2015-03-02 S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> 2018-03-31 S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> 2016-06-14 S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> 2015-10-25 S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> 2019-06-19 S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> 2015-04-27 S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> 2011-06-21 S    </span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># … with 2,616 more rows</span></span></span>
+<span></span>
+<span><span class="va">our_data_1st</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="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">bacteria</span>, <span class="fu"><a href="../reference/antibiotic_class_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="co">#&gt; ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC'</span></span>
+<span><span class="co">#&gt;   (amoxicillin/clavulanic acid)</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,626 × 3</span></span></span>
+<span><span class="co">#&gt;    bacteria     AMX   AMC  </span></span>
+<span><span class="co">#&gt;    <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></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> B_ESCHR_COLI R     I    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> B_KLBSL_PNMN R     I    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> B_ESCHR_COLI S     I    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> B_ESCHR_COLI S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> B_STPHY_AURS R     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> B_ESCHR_COLI R     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> B_ESCHR_COLI R     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> B_ESCHR_COLI S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> B_STPHY_AURS S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> B_ESCHR_COLI S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># … with 2,616 more rows</span></span></span>
+<span></span>
+<span><span class="va">our_data_1st</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="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">bacteria</span>, <span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,626 × 5</span></span></span>
+<span><span class="co">#&gt;    bacteria     AMX   AMC   CIP   GEN  </span></span>
+<span><span class="co">#&gt;    <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>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> B_ESCHR_COLI R     I     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> B_KLBSL_PNMN R     I     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> B_ESCHR_COLI S     I     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> B_ESCHR_COLI S     S     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> B_STPHY_AURS R     S     R     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> B_ESCHR_COLI R     S     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> B_ESCHR_COLI R     S     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> B_ESCHR_COLI S     S     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> B_STPHY_AURS S     S     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> B_ESCHR_COLI S     S     S     S    </span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># … with 2,616 more rows</span></span></span>
+<span></span>
+<span><span class="co"># filtering using AB selectors is also possible:</span></span>
+<span><span class="va">our_data_1st</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="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/any.html" class="external-link">any</a></span><span class="op">(</span><span class="fu"><a href="../reference/antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="st">"R"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="co">#&gt; ℹ For aminoglycosides() using column 'GEN' (gentamicin)</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 971 × 9</span></span></span>
+<span><span class="co">#&gt;    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first</span></span>
+<span><span class="co">#&gt;    <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;date&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;lgl&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> J5         A        2017-12-25 B_STRPT_PNMN R     S     S     R     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> X1         A        2017-07-04 B_STPHY_AURS R     S     S     R     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> B3         A        2016-07-24 B_ESCHR_COLI S     S     S     R     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> V7         A        2012-04-03 B_ESCHR_COLI S     S     S     R     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> C9         A        2017-03-23 B_ESCHR_COLI S     S     S     R     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> R1         A        2018-06-10 B_STPHY_AURS S     S     S     R     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> S2         A        2013-07-19 B_STRPT_PNMN S     S     S     R     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> P5         A        2019-03-09 B_STPHY_AURS S     S     S     R     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> Q8         A        2019-08-10 B_STPHY_AURS S     S     S     R     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> K5         A        2013-03-15 B_STRPT_PNMN S     S     S     R     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># … with 961 more rows</span></span></span>
+<span></span>
+<span><span class="va">our_data_1st</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="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/all.html" class="external-link">all</a></span><span class="op">(</span><span class="fu"><a href="../reference/antibiotic_class_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="st">"R"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="co">#&gt; ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC'</span></span>
+<span><span class="co">#&gt;   (amoxicillin/clavulanic acid)</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 471 × 9</span></span></span>
+<span><span class="co">#&gt;    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first</span></span>
+<span><span class="co">#&gt;    <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;date&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;lgl&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> M7         A        2013-07-22 B_STRPT_PNMN R     R     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> R10        A        2013-12-20 B_STPHY_AURS R     R     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> R7         A        2015-10-25 B_STPHY_AURS R     R     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> R8         A        2019-10-25 B_STPHY_AURS R     R     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> I7         A        2015-08-19 B_ESCHR_COLI R     R     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> N3         A        2014-12-29 B_STRPT_PNMN R     R     R     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> Q2         A        2019-09-22 B_ESCHR_COLI R     R     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> X7         A        2011-03-20 B_ESCHR_COLI R     R     S     R     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> C5         A        2015-08-30 B_KLBSL_PNMN R     R     S     R     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> W9         A        2013-10-02 B_ESCHR_COLI R     R     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># … with 461 more rows</span></span></span>
+<span></span>
+<span><span class="co"># even works in base R (since R 3.0):</span></span>
+<span><span class="va">our_data_1st</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/all.html" class="external-link">all</a></span><span class="op">(</span><span class="fu"><a href="../reference/antibiotic_class_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="st">"R"</span><span class="op">)</span>, <span class="op">]</span></span>
+<span><span class="co">#&gt; ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC'</span></span>
+<span><span class="co">#&gt;   (amoxicillin/clavulanic acid)</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 471 × 9</span></span></span>
+<span><span class="co">#&gt;    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first</span></span>
+<span><span class="co">#&gt;    <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;date&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;lgl&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> M7         A        2013-07-22 B_STRPT_PNMN R     R     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> R10        A        2013-12-20 B_STPHY_AURS R     R     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> R7         A        2015-10-25 B_STPHY_AURS R     R     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> R8         A        2019-10-25 B_STPHY_AURS R     R     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> I7         A        2015-08-19 B_ESCHR_COLI R     R     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> N3         A        2014-12-29 B_STRPT_PNMN R     R     R     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> Q2         A        2019-09-22 B_ESCHR_COLI R     R     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> X7         A        2011-03-20 B_ESCHR_COLI R     R     S     R     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> C5         A        2015-08-30 B_KLBSL_PNMN R     R     S     R     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> W9         A        2013-10-02 B_ESCHR_COLI R     R     S     S     TRUE </span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># … with 461 more rows</span></span></span></code></pre></div>
+</div>
+<div class="section level3">
+<h3 id="generate-antibiograms">Generate antibiograms<a class="anchor" aria-label="anchor" href="#generate-antibiograms"></a>
+</h3>
+<p>This package comes with <code><a href="../reference/antibiogram.html">antibiogram()</a></code>, a function that
+automatically generates traditional, combined, syndromic, and even
+weighted-incidence syndromic combination antibiograms (WISCA). For R
+Markdown (such as this page) it automatically prints in the right table
+format.</p>
+<p>Below are some suggestions for how to generate the different
+antibiograms:</p>
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># traditional:</span></span>
+<span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">our_data_1st</span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th align="left">Pathogen (N min-max)</th>
+<th align="right">AMC</th>
+<th align="right">AMX</th>
+<th align="right">CIP</th>
+<th align="right">GEN</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">
+<em>E. coli</em> (1250-1250)</td>
+<td align="right">64</td>
+<td align="right">58</td>
+<td align="right">58</td>
+<td align="right">63</td>
+</tr>
+<tr class="even">
+<td align="left">
+<em>K. pneumoniae</em> (316-316)</td>
+<td align="right">63</td>
+<td align="right">53</td>
+<td align="right">59</td>
+<td align="right">60</td>
+</tr>
+<tr class="odd">
+<td align="left">
+<em>S. aureus</em> (661-661)</td>
+<td align="right">64</td>
+<td align="right">57</td>
+<td align="right">57</td>
+<td align="right">63</td>
+</tr>
+<tr class="even">
+<td align="left">
+<em>S. pneumoniae</em> (399-399)</td>
+<td align="right">64</td>
+<td align="right">56</td>
+<td align="right">60</td>
+<td align="right">66</td>
+</tr>
+</tbody>
+</table>
+<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">our_data_1st</span>,</span>
+<span>            ab_transform <span class="op">=</span> <span class="st">"name"</span><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <colgroup>
-<col width="9%">
-<col width="9%">
-<col width="9%">
-<col width="11%">
-<col width="3%">
-<col width="3%">
-<col width="3%">
-<col width="3%">
-<col width="6%">
-<col width="11%">
-<col width="11%">
-<col width="9%">
-<col width="5%">
+<col width="28%">
+<col width="13%">
+<col width="30%">
+<col width="15%">
+<col width="12%">
 </colgroup>
 <thead><tr class="header">
-<th align="center">date</th>
-<th align="center">patient_id</th>
-<th align="center">hospital</th>
-<th align="center">bacteria</th>
-<th align="center">AMX</th>
-<th align="center">AMC</th>
-<th align="center">CIP</th>
-<th align="center">GEN</th>
-<th align="center">gender</th>
-<th align="center">gramstain</th>
-<th align="center">genus</th>
-<th align="center">species</th>
-<th align="center">first</th>
+<th align="left">Pathogen (N min-max)</th>
+<th align="right">Amoxicillin</th>
+<th align="right">Amoxicillin/clavulanic acid</th>
+<th align="right">Ciprofloxacin</th>
+<th align="right">Gentamicin</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
-<td align="center">2015-09-16</td>
-<td align="center">Z5</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>
+<td align="left">
+<em>E. coli</em> (1250-1250)</td>
+<td align="right">58</td>
+<td align="right">64</td>
+<td align="right">58</td>
+<td align="right">63</td>
 </tr>
 <tr class="even">
-<td align="center">2017-09-08</td>
-<td align="center">V1</td>
-<td align="center">Hospital B</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">Gram-positive</td>
-<td align="center">Streptococcus</td>
-<td align="center">pneumoniae</td>
-<td align="center">TRUE</td>
+<td align="left">
+<em>K. pneumoniae</em> (316-316)</td>
+<td align="right">53</td>
+<td align="right">63</td>
+<td align="right">59</td>
+<td align="right">60</td>
 </tr>
 <tr class="odd">
-<td align="center">2016-08-05</td>
-<td align="center">B1</td>
-<td align="center">Hospital B</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>
+<td align="left">
+<em>S. aureus</em> (661-661)</td>
+<td align="right">57</td>
+<td align="right">64</td>
+<td align="right">57</td>
+<td align="right">63</td>
 </tr>
 <tr class="even">
-<td align="center">2011-06-03</td>
-<td align="center">D1</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="odd">
-<td align="center">2016-04-15</td>
-<td align="center">T10</td>
-<td align="center">Hospital B</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">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="even">
-<td align="center">2014-03-09</td>
-<td align="center">X8</td>
-<td align="center">Hospital A</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">F</td>
-<td align="center">Gram-negative</td>
-<td align="center">Escherichia</td>
-<td align="center">coli</td>
-<td align="center">TRUE</td>
+<td align="left">
+<em>S. pneumoniae</em> (399-399)</td>
+<td align="right">56</td>
+<td align="right">64</td>
+<td align="right">60</td>
+<td align="right">66</td>
 </tr>
 </tbody>
 </table>
-<p>If you want to get a quick glance of the number of isolates in
-different bug/drug combinations, you can use the
-<code><a href="../reference/bug_drug_combinations.html">bug_drug_combinations()</a></code> function:</p>
+<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">our_data_1st</span>,</span>
+<span>            ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span>
+<span>            language <span class="op">=</span> <span class="st">"es"</span><span class="op">)</span> <span class="co"># support for 20 languages</span></span></code></pre></div>
+<table class="table">
+<colgroup>
+<col width="27%">
+<col width="12%">
+<col width="31%">
+<col width="15%">
+<col width="12%">
+</colgroup>
+<thead><tr class="header">
+<th align="left">Patógeno (N min-max)</th>
+<th align="right">Amoxicilina</th>
+<th align="right">Amoxicilina/ácido clavulánico</th>
+<th align="right">Ciprofloxacina</th>
+<th align="right">Gentamicina</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">
+<em>E. coli</em> (1250-1250)</td>
+<td align="right">58</td>
+<td align="right">64</td>
+<td align="right">58</td>
+<td align="right">63</td>
+</tr>
+<tr class="even">
+<td align="left">
+<em>K. pneumoniae</em> (316-316)</td>
+<td align="right">53</td>
+<td align="right">63</td>
+<td align="right">59</td>
+<td align="right">60</td>
+</tr>
+<tr class="odd">
+<td align="left">
+<em>S. aureus</em> (661-661)</td>
+<td align="right">57</td>
+<td align="right">64</td>
+<td align="right">57</td>
+<td align="right">63</td>
+</tr>
+<tr class="even">
+<td align="left">
+<em>S. pneumoniae</em> (399-399)</td>
+<td align="right">56</td>
+<td align="right">64</td>
+<td align="right">60</td>
+<td align="right">66</td>
+</tr>
+</tbody>
+</table>
+<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># combined:</span></span>
+<span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">our_data_1st</span>,</span>
+<span>            antibiotics <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC+CIP"</span>, <span class="st">"AMC+GEN"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th align="left">Pathogen (N min-max)</th>
+<th align="right">AMC</th>
+<th align="right">AMC + CIP</th>
+<th align="right">AMC + GEN</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">
+<em>E. coli</em> (1250-1250)</td>
+<td align="right">64</td>
+<td align="right">76</td>
+<td align="right">75</td>
+</tr>
+<tr class="even">
+<td align="left">
+<em>K. pneumoniae</em> (316-316)</td>
+<td align="right">63</td>
+<td align="right">78</td>
+<td align="right">74</td>
+</tr>
+<tr class="odd">
+<td align="left">
+<em>S. aureus</em> (661-661)</td>
+<td align="right">64</td>
+<td align="right">77</td>
+<td align="right">75</td>
+</tr>
+<tr class="even">
+<td align="left">
+<em>S. pneumoniae</em> (399-399)</td>
+<td align="right">64</td>
+<td align="right">77</td>
+<td align="right">76</td>
+</tr>
+</tbody>
+</table>
+<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># for a syndromic antibiogram, we must fake some clinical conditions:</span></span>
+<span><span class="va">our_data_1st</span><span class="op">$</span><span class="va">condition</span> <span class="op">&lt;-</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/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Cardial"</span>, <span class="st">"Respiratory"</span>, <span class="st">"Rheumatic"</span><span class="op">)</span>,</span>
+<span>                                 size <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">our_data_1st</span><span class="op">)</span>,</span>
+<span>                                 replace <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># syndromic:</span></span>
+<span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">our_data_1st</span>,</span>
+<span>            syndromic_group <span class="op">=</span> <span class="st">"condition"</span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th align="left">Syndromic Group</th>
+<th align="left">Pathogen (N min-max)</th>
+<th align="right">AMC</th>
+<th align="right">AMX</th>
+<th align="right">CIP</th>
+<th align="right">GEN</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">Cardial</td>
+<td align="left">
+<em>E. coli</em> (431-431)</td>
+<td align="right">63</td>
+<td align="right">54</td>
+<td align="right">57</td>
+<td align="right">63</td>
+</tr>
+<tr class="even">
+<td align="left">Respiratory</td>
+<td align="left">
+<em>E. coli</em> (396-396)</td>
+<td align="right">64</td>
+<td align="right">61</td>
+<td align="right">58</td>
+<td align="right">63</td>
+</tr>
+<tr class="odd">
+<td align="left">Rheumatic</td>
+<td align="left">
+<em>E. coli</em> (423-423)</td>
+<td align="right">65</td>
+<td align="right">60</td>
+<td align="right">58</td>
+<td align="right">63</td>
+</tr>
+<tr class="even">
+<td align="left">Cardial</td>
+<td align="left">
+<em>K. pneumoniae</em> (105-105)</td>
+<td align="right">63</td>
+<td align="right">54</td>
+<td align="right">59</td>
+<td align="right">61</td>
+</tr>
+<tr class="odd">
+<td align="left">Respiratory</td>
+<td align="left">
+<em>K. pneumoniae</em> (110-110)</td>
+<td align="right">60</td>
+<td align="right">54</td>
+<td align="right">55</td>
+<td align="right">63</td>
+</tr>
+<tr class="even">
+<td align="left">Rheumatic</td>
+<td align="left">
+<em>K. pneumoniae</em> (101-101)</td>
+<td align="right">66</td>
+<td align="right">50</td>
+<td align="right">62</td>
+<td align="right">56</td>
+</tr>
+<tr class="odd">
+<td align="left">Cardial</td>
+<td align="left">
+<em>S. aureus</em> (225-225)</td>
+<td align="right">66</td>
+<td align="right">57</td>
+<td align="right">53</td>
+<td align="right">61</td>
+</tr>
+<tr class="even">
+<td align="left">Respiratory</td>
+<td align="left">
+<em>S. aureus</em> (217-217)</td>
+<td align="right">60</td>
+<td align="right">55</td>
+<td align="right">61</td>
+<td align="right">64</td>
+</tr>
+<tr class="odd">
+<td align="left">Rheumatic</td>
+<td align="left">
+<em>S. aureus</em> (219-219)</td>
+<td align="right">67</td>
+<td align="right">58</td>
+<td align="right">58</td>
+<td align="right">64</td>
+</tr>
+<tr class="even">
+<td align="left">Cardial</td>
+<td align="left">
+<em>S. pneumoniae</em> (152-152)</td>
+<td align="right">67</td>
+<td align="right">57</td>
+<td align="right">64</td>
+<td align="right">63</td>
+</tr>
+<tr class="odd">
+<td align="left">Respiratory</td>
+<td align="left">
+<em>S. pneumoniae</em> (122-122)</td>
+<td align="right">67</td>
+<td align="right">57</td>
+<td align="right">64</td>
+<td align="right">67</td>
+</tr>
+<tr class="even">
+<td align="left">Rheumatic</td>
+<td align="left">
+<em>S. pneumoniae</em> (125-125)</td>
+<td align="right">57</td>
+<td align="right">53</td>
+<td align="right">50</td>
+<td align="right">67</td>
+</tr>
+</tbody>
+</table>
+<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">our_data_1st</span>,</span>
+<span>            <span class="co"># you can use AB selectors here as well:</span></span>
+<span>            antibiotics <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="../reference/antibiotic_class_selectors.html">penicillins</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>            syndromic_group <span class="op">=</span> <span class="st">"condition"</span>,</span>
+<span>            mo_transform <span class="op">=</span> <span class="st">"gramstain"</span><span class="op">)</span></span>
+<span><span class="co">#&gt; ℹ For penicillins() using columns 'AMX' (amoxicillin) and 'AMC'</span></span>
+<span><span class="co">#&gt;   (amoxicillin/clavulanic acid)</span></span>
+<span><span class="co">#&gt; ℹ For aminoglycosides() using column 'GEN' (gentamicin)</span></span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th align="left">Syndromic Group</th>
+<th align="left">Pathogen (N min-max)</th>
+<th align="right">AMC</th>
+<th align="right">AMX</th>
+<th align="right">GEN</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">Cardial</td>
+<td align="left">Gram-negative (536-536)</td>
+<td align="right">63</td>
+<td align="right">54</td>
+<td align="right">62</td>
+</tr>
+<tr class="even">
+<td align="left">Respiratory</td>
+<td align="left">Gram-negative (506-506)</td>
+<td align="right">63</td>
+<td align="right">59</td>
+<td align="right">63</td>
+</tr>
+<tr class="odd">
+<td align="left">Rheumatic</td>
+<td align="left">Gram-negative (524-524)</td>
+<td align="right">65</td>
+<td align="right">58</td>
+<td align="right">62</td>
+</tr>
+<tr class="even">
+<td align="left">Cardial</td>
+<td align="left">Gram-positive (377-377)</td>
+<td align="right">67</td>
+<td align="right">57</td>
+<td align="right">62</td>
+</tr>
+<tr class="odd">
+<td align="left">Respiratory</td>
+<td align="left">Gram-positive (339-339)</td>
+<td align="right">63</td>
+<td align="right">56</td>
+<td align="right">65</td>
+</tr>
+<tr class="even">
+<td align="left">Rheumatic</td>
+<td align="left">Gram-positive (344-344)</td>
+<td align="right">63</td>
+<td align="right">56</td>
+<td align="right">65</td>
+</tr>
+</tbody>
+</table>
+<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># WISCA: </span></span>
+<span><span class="co"># (we lack some details, but it could contain a filter on e.g. &gt;65 year-old males)</span></span>
+<span><span class="va">wisca</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">our_data_1st</span>,</span>
+<span>                     antibiotics <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC+CIP"</span>, <span class="st">"AMC+GEN"</span><span class="op">)</span>,</span>
+<span>                     syndromic_group <span class="op">=</span> <span class="st">"condition"</span>,</span>
+<span>                     mo_transform <span class="op">=</span> <span class="st">"gramstain"</span><span class="op">)</span></span>
+<span><span class="va">wisca</span></span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th align="left">Syndromic Group</th>
+<th align="left">Pathogen (N min-max)</th>
+<th align="right">AMC</th>
+<th align="right">AMC + CIP</th>
+<th align="right">AMC + GEN</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">Cardial</td>
+<td align="left">Gram-negative (536-536)</td>
+<td align="right">63</td>
+<td align="right">75</td>
+<td align="right">75</td>
+</tr>
+<tr class="even">
+<td align="left">Respiratory</td>
+<td align="left">Gram-negative (506-506)</td>
+<td align="right">63</td>
+<td align="right">77</td>
+<td align="right">75</td>
+</tr>
+<tr class="odd">
+<td align="left">Rheumatic</td>
+<td align="left">Gram-negative (524-524)</td>
+<td align="right">65</td>
+<td align="right">76</td>
+<td align="right">75</td>
+</tr>
+<tr class="even">
+<td align="left">Cardial</td>
+<td align="left">Gram-positive (377-377)</td>
+<td align="right">67</td>
+<td align="right">78</td>
+<td align="right">75</td>
+</tr>
+<tr class="odd">
+<td align="left">Respiratory</td>
+<td align="left">Gram-positive (339-339)</td>
+<td align="right">63</td>
+<td align="right">77</td>
+<td align="right">76</td>
+</tr>
+<tr class="even">
+<td align="left">Rheumatic</td>
+<td align="left">Gram-positive (344-344)</td>
+<td align="right">63</td>
+<td align="right">77</td>
+<td align="right">76</td>
+</tr>
+</tbody>
+</table>
+<p>Antibiograms can be plotted using <code><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot()</a></code> from the
+<code>ggplot2</code> packages, since this package provides an extension
+to that function:</p>
 <div class="sourceCode" id="cb22"><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>
-<span>  <span class="fu"><a href="../reference/bug_drug_combinations.html">bug_drug_combinations</a></span><span class="op">(</span><span class="op">)</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="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="op">)</span> <span class="co"># show first 6 rows</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="center">mo</th>
-<th align="center">ab</th>
-<th align="center">S</th>
-<th align="center">I</th>
-<th align="center">R</th>
-<th align="center">total</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="center">E. coli</td>
-<td align="center">AMC</td>
-<td align="center">2826</td>
-<td align="center">1184</td>
-<td align="center">1972</td>
-<td align="center">5982</td>
-</tr>
-<tr class="even">
-<td align="center">E. coli</td>
-<td align="center">AMX</td>
-<td align="center">1517</td>
-<td align="center">1305</td>
-<td align="center">3160</td>
-<td align="center">5982</td>
-</tr>
-<tr class="odd">
-<td align="center">E. coli</td>
-<td align="center">CIP</td>
-<td align="center">2041</td>
-<td align="center">1879</td>
-<td align="center">2062</td>
-<td align="center">5982</td>
-</tr>
-<tr class="even">
-<td align="center">E. coli</td>
-<td align="center">GEN</td>
-<td align="center">2065</td>
-<td align="center">1813</td>
-<td align="center">2104</td>
-<td align="center">5982</td>
-</tr>
-<tr class="odd">
-<td align="center">K. pneumoniae</td>
-<td align="center">AMC</td>
-<td align="center">587</td>
-<td align="center">263</td>
-<td align="center">455</td>
-<td align="center">1305</td>
-</tr>
-<tr class="even">
-<td align="center">K. pneumoniae</td>
-<td align="center">AMX</td>
-<td align="center">0</td>
-<td align="center">0</td>
-<td align="center">1305</td>
-<td align="center">1305</td>
-</tr>
-</tbody>
-</table>
-<div class="sourceCode" id="cb23"><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>
-<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="va">bacteria</span>, <span class="fu"><a href="../reference/antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</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/bug_drug_combinations.html">bug_drug_combinations</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">#&gt; ℹ For aminoglycosides() using column 'GEN' (gentamicin)</span></span></code></pre>
-<table class="table">
-<thead><tr class="header">
-<th align="center">mo</th>
-<th align="center">ab</th>
-<th align="center">S</th>
-<th align="center">I</th>
-<th align="center">R</th>
-<th align="center">total</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="center">E. coli</td>
-<td align="center">GEN</td>
-<td align="center">2065</td>
-<td align="center">1813</td>
-<td align="center">2104</td>
-<td align="center">5982</td>
-</tr>
-<tr class="even">
-<td align="center">K. pneumoniae</td>
-<td align="center">GEN</td>
-<td align="center">440</td>
-<td align="center">426</td>
-<td align="center">439</td>
-<td align="center">1305</td>
-</tr>
-<tr class="odd">
-<td align="center">S. aureus</td>
-<td align="center">GEN</td>
-<td align="center">1124</td>
-<td align="center">1044</td>
-<td align="center">1050</td>
-<td align="center">3218</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">1867</td>
-<td align="center">1867</td>
-</tr>
-</tbody>
-</table>
-<p>This will only give you the crude numbers in the data. To calculate
-antimicrobial resistance in a more sensible way, also by correcting for
-too few results, we use the <code><a href="../reference/proportion.html">resistance()</a></code> and
+<code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">wisca</span><span class="op">)</span></span></code></pre></div>
+<p><img src="AMR_files/figure-html/unnamed-chunk-13-1.png" width="720"></p>
+<p>To calculate antimicrobial resistance in a more sensible way, also by
+correcting for too few results, we use the <code><a href="../reference/proportion.html">resistance()</a></code> and
 <code><a href="../reference/proportion.html">susceptibility()</a></code> functions.</p>
 </div>
 <div class="section level3">
@@ -1142,397 +1218,23 @@ proportion of R (<code><a href="../reference/proportion.html">proportion_R()</a>
 I (<code><a href="../reference/proportion.html">proportion_SI()</a></code>, equal to
 <code><a href="../reference/proportion.html">susceptibility()</a></code>). These functions can be used on their
 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">#&gt; [1] 0.5813935</span></span></code></pre></div>
+<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">our_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">#&gt; [1] 0.4318355</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">
-<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>
+<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">our_data_1st</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="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">hospital</span><span class="op">)</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="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span>amoxicillin <span class="op">=</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 class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="center">hospital</th>
-<th align="center">amoxicillin</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="center">Hospital A</td>
-<td align="center">0.5902375</td>
-</tr>
-<tr class="even">
-<td align="center">Hospital B</td>
-<td align="center">0.5636153</td>
-</tr>
-<tr class="odd">
-<td align="center">Hospital C</td>
-<td align="center">0.5920537</td>
-</tr>
-<tr class="even">
-<td align="center">Hospital D</td>
-<td align="center">0.5911290</td>
-</tr>
-</tbody>
-</table>
-<p>Of course it would be very convenient to know the number of isolates
-responsible for the percentages. For that purpose the
-<code><a href="../reference/count.html">n_sir()</a></code> can be used, which works exactly like
-<code><a href="https://dplyr.tidyverse.org/reference/n_distinct.html" class="external-link">n_distinct()</a></code> from the <code>dplyr</code> package. It counts
-all isolates available for every group (i.e. values S, I or R):</p>
-<div class="sourceCode" id="cb27"><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>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">hospital</span><span class="op">)</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="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span></span>
-<span>    amoxicillin <span class="op">=</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>    available <span class="op">=</span> <span class="fu"><a href="../reference/count.html">n_sir</a></span><span class="op">(</span><span class="va">AMX</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="center">hospital</th>
-<th align="center">amoxicillin</th>
-<th align="center">available</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="center">Hospital A</td>
-<td align="center">0.5902375</td>
-<td align="center">3790</td>
-</tr>
-<tr class="even">
-<td align="center">Hospital B</td>
-<td align="center">0.5636153</td>
-<td align="center">4315</td>
-</tr>
-<tr class="odd">
-<td align="center">Hospital C</td>
-<td align="center">0.5920537</td>
-<td align="center">1787</td>
-</tr>
-<tr class="even">
-<td align="center">Hospital D</td>
-<td align="center">0.5911290</td>
-<td align="center">2480</td>
-</tr>
-</tbody>
-</table>
-<p>These functions can also be used to get the proportion of multiple
-antibiotics, to calculate empiric susceptibility of combination
-therapies very easily:</p>
-<div class="sourceCode" id="cb28"><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>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">genus</span><span class="op">)</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="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span></span>
-<span>    amoxiclav <span class="op">=</span> <span class="fu"><a href="../reference/proportion.html">susceptibility</a></span><span class="op">(</span><span class="va">AMC</span><span class="op">)</span>,</span>
-<span>    gentamicin <span class="op">=</span> <span class="fu"><a href="../reference/proportion.html">susceptibility</a></span><span class="op">(</span><span class="va">GEN</span><span class="op">)</span>,</span>
-<span>    amoxiclav_genta <span class="op">=</span> <span class="fu"><a href="../reference/proportion.html">susceptibility</a></span><span class="op">(</span><span class="va">AMC</span>, <span class="va">GEN</span><span class="op">)</span></span>
-<span>  <span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="center">genus</th>
-<th align="center">amoxiclav</th>
-<th align="center">gentamicin</th>
-<th align="center">amoxiclav_genta</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="center">Escherichia</td>
-<td align="center">0.6703444</td>
-<td align="center">0.6482782</td>
-<td align="center">0.8836510</td>
-</tr>
-<tr class="even">
-<td align="center">Klebsiella</td>
-<td align="center">0.6513410</td>
-<td align="center">0.6636015</td>
-<td align="center">0.8919540</td>
-</tr>
-<tr class="odd">
-<td align="center">Staphylococcus</td>
-<td align="center">0.6615911</td>
-<td align="center">0.6737104</td>
-<td align="center">0.8899938</td>
-</tr>
-<tr class="even">
-<td align="center">Streptococcus</td>
-<td align="center">0.4654526</td>
-<td align="center">0.0000000</td>
-<td align="center">0.4654526</td>
-</tr>
-</tbody>
-</table>
-<p>Or if you are curious for the resistance within certain antibiotic
-classes, use a antibiotic class selector such as
-<code><a href="../reference/antibiotic_class_selectors.html">penicillins()</a></code>, which automatically will include the columns
-<code>AMX</code> and <code>AMC</code> of our data:</p>
-<div class="sourceCode" id="cb29"><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>
-<span>  <span class="co"># group by hospital</span></span>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">hospital</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
-<span>  <span class="co">#                / -&gt; select all penicillins in the data for calculation</span></span>
-<span>  <span class="co">#                |              / -&gt; use resistance() for all peni's per hospital</span></span>
-<span>  <span class="co">#                |              |           / -&gt; print as percentages</span></span>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="../reference/antibiotic_class_selectors.html">penicillins</a></span><span class="op">(</span><span class="op">)</span>, <span class="va">resistance</span>, as_percent <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
-<span>  <span class="co"># format the antibiotic column names, using so-called snake case,</span></span>
-<span>  <span class="co"># so 'Amoxicillin/clavulanic acid' becomes 'amoxicillin_clavulanic_acid'</span></span>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/rename.html" class="external-link">rename_with</a></span><span class="op">(</span><span class="va">set_ab_names</span>, <span class="fu"><a href="../reference/antibiotic_class_selectors.html">penicillins</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
-<thead><tr class="header">
-<th align="left">hospital</th>
-<th align="right">amoxicillin</th>
-<th align="right">amoxicillin_clavulanic_acid</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">Hospital A</td>
-<td align="right">59.0%</td>
-<td align="right">38.0%</td>
-</tr>
-<tr class="even">
-<td align="left">Hospital B</td>
-<td align="right">56.4%</td>
-<td align="right">35.0%</td>
-</tr>
-<tr class="odd">
-<td align="left">Hospital C</td>
-<td align="right">59.2%</td>
-<td align="right">37.3%</td>
-</tr>
-<tr class="even">
-<td align="left">Hospital D</td>
-<td align="right">59.1%</td>
-<td align="right">36.1%</td>
-</tr>
-</tbody>
-</table>
-<p>To make a transition to the next part, let’s see how differences in
-the previously calculated combination therapies could be plotted:</p>
-<div class="sourceCode" id="cb30"><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>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">genus</span><span class="op">)</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="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span></span>
-<span>    <span class="st">"1. Amoxi/clav"</span> <span class="op">=</span> <span class="fu"><a href="../reference/proportion.html">susceptibility</a></span><span class="op">(</span><span class="va">AMC</span><span class="op">)</span>,</span>
-<span>    <span class="st">"2. Gentamicin"</span> <span class="op">=</span> <span class="fu"><a href="../reference/proportion.html">susceptibility</a></span><span class="op">(</span><span class="va">GEN</span><span class="op">)</span>,</span>
-<span>    <span class="st">"3. Amoxi/clav + genta"</span> <span class="op">=</span> <span class="fu"><a href="../reference/proportion.html">susceptibility</a></span><span class="op">(</span><span class="va">AMC</span>, <span class="va">GEN</span><span class="op">)</span></span>
-<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
-<span>  <span class="co"># pivot_longer() from the tidyr package "lengthens" data:</span></span>
-<span>  <span class="fu">tidyr</span><span class="fu">::</span><span class="fu"><a href="https://tidyr.tidyverse.org/reference/pivot_longer.html" class="external-link">pivot_longer</a></span><span class="op">(</span><span class="op">-</span><span class="va">genus</span>, names_to <span class="op">=</span> <span class="st">"antibiotic"</span><span class="op">)</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="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span></span>
-<span>    x <span class="op">=</span> <span class="va">genus</span>,</span>
-<span>    y <span class="op">=</span> <span class="va">value</span>,</span>
-<span>    fill <span class="op">=</span> <span class="va">antibiotic</span></span>
-<span>  <span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_bar.html" class="external-link">geom_col</a></span><span class="op">(</span>position <span class="op">=</span> <span class="st">"dodge2"</span><span class="op">)</span></span></code></pre></div>
-<p><img src="AMR_files/figure-html/plot%201-1.png" width="720"></p>
-</div>
-<div class="section level3">
-<h3 id="plots">Plots<a class="anchor" aria-label="anchor" href="#plots"></a>
-</h3>
-<p>To show results in plots, most R users would nowadays use the
-<code>ggplot2</code> package. This package lets you create plots in
-layers. You can read more about it <a href="https://ggplot2.tidyverse.org/" class="external-link">on their website</a>. A quick
-example would look like these syntaxes:</p>
-<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span></span>
-<span>  data <span class="op">=</span> <span class="va">a_data_set</span>,</span>
-<span>  mapping <span class="op">=</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span></span>
-<span>    x <span class="op">=</span> <span class="va">year</span>,</span>
-<span>    y <span class="op">=</span> <span class="va">value</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_bar.html" class="external-link">geom_col</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span></span>
-<span>    title <span class="op">=</span> <span class="st">"A title"</span>,</span>
-<span>    subtitle <span class="op">=</span> <span class="st">"A subtitle"</span>,</span>
-<span>    x <span class="op">=</span> <span class="st">"My X axis"</span>,</span>
-<span>    y <span class="op">=</span> <span class="st">"My Y axis"</span></span>
-<span>  <span class="op">)</span></span>
-<span></span>
-<span><span class="co"># or as short as:</span></span>
-<span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="va">a_data_set</span><span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_bar.html" class="external-link">geom_bar</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span><span class="va">year</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<p>The <code>AMR</code> package contains functions to extend this
-<code>ggplot2</code> package, for example <code><a href="../reference/ggplot_sir.html">geom_sir()</a></code>. It
-automatically transforms data with <code><a href="../reference/count.html">count_df()</a></code> or
-<code><a href="../reference/proportion.html">proportion_df()</a></code> and show results in stacked bars. Its
-simplest and shortest example:</p>
-<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="va">data_1st</span><span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="fu"><a href="../reference/ggplot_sir.html">geom_sir</a></span><span class="op">(</span>translate_ab <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
-<p><img src="AMR_files/figure-html/plot%203-1.png" width="720"></p>
-<p>Omit the <code>translate_ab = FALSE</code> to have the antibiotic
-codes (AMX, AMC, CIP, GEN) translated to official WHO names
-(amoxicillin, amoxicillin/clavulanic acid, ciprofloxacin,
-gentamicin).</p>
-<p>If we group on e.g. the <code>genus</code> column and add some
-additional functions from our package, we can create this:</p>
-<div class="sourceCode" id="cb33"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># group the data on `genus`</span></span>
-<span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</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://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">genus</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="co"># create bars with genus on x axis</span></span>
-<span>  <span class="co"># it looks for variables with class `sir`,</span></span>
-<span>  <span class="co"># of which we have 4 (earlier created with `as.sir`)</span></span>
-<span>  <span class="fu"><a href="../reference/ggplot_sir.html">geom_sir</a></span><span class="op">(</span>x <span class="op">=</span> <span class="st">"genus"</span><span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="co"># split plots on antibiotic</span></span>
-<span>  <span class="fu"><a href="../reference/ggplot_sir.html">facet_sir</a></span><span class="op">(</span>facet <span class="op">=</span> <span class="st">"antibiotic"</span><span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="co"># set colours to the SIR interpretations (colour-blind friendly)</span></span>
-<span>  <span class="fu"><a href="../reference/ggplot_sir.html">scale_sir_colours</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="co"># show percentages on y axis</span></span>
-<span>  <span class="fu"><a href="../reference/ggplot_sir.html">scale_y_percent</a></span><span class="op">(</span>breaks <span class="op">=</span> <span class="fl">0</span><span class="op">:</span><span class="fl">4</span> <span class="op">*</span> <span class="fl">25</span><span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="co"># turn 90 degrees, to make it bars instead of columns</span></span>
-<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/coord_flip.html" class="external-link">coord_flip</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="co"># add labels</span></span>
-<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span></span>
-<span>    title <span class="op">=</span> <span class="st">"Resistance per genus and antibiotic"</span>,</span>
-<span>    subtitle <span class="op">=</span> <span class="st">"(this is fake data)"</span></span>
-<span>  <span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="co"># and print genus in italic to follow our convention</span></span>
-<span>  <span class="co"># (is now y axis because we turned the plot)</span></span>
-<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/theme.html" class="external-link">theme</a></span><span class="op">(</span>axis.text.y <span class="op">=</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/element.html" class="external-link">element_text</a></span><span class="op">(</span>face <span class="op">=</span> <span class="st">"italic"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<p><img src="AMR_files/figure-html/plot%204-1.png" width="720"></p>
-<p>To simplify this, we also created the <code><a href="../reference/ggplot_sir.html">ggplot_sir()</a></code>
-function, which combines almost all above functions:</p>
-<div class="sourceCode" id="cb34"><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>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">genus</span><span class="op">)</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/ggplot_sir.html">ggplot_sir</a></span><span class="op">(</span></span>
-<span>    x <span class="op">=</span> <span class="st">"genus"</span>,</span>
-<span>    facet <span class="op">=</span> <span class="st">"antibiotic"</span>,</span>
-<span>    breaks <span class="op">=</span> <span class="fl">0</span><span class="op">:</span><span class="fl">4</span> <span class="op">*</span> <span class="fl">25</span>,</span>
-<span>    datalabels <span class="op">=</span> <span class="cn">FALSE</span></span>
-<span>  <span class="op">)</span> <span class="op">+</span></span>
-<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/coord_flip.html" class="external-link">coord_flip</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<p><img src="AMR_files/figure-html/plot%205-1.png" width="720"></p>
-<div class="section level4">
-<h4 id="plotting-mic-and-disk-diffusion-values">Plotting MIC and disk diffusion values<a class="anchor" aria-label="anchor" href="#plotting-mic-and-disk-diffusion-values"></a>
-</h4>
-<p>The AMR package also extends the <code><a href="../reference/plot.html">plot()</a></code> and
-<code><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">ggplot2::autoplot()</a></code> functions for plotting minimum
-inhibitory concentrations (MIC, created with <code><a href="../reference/as.mic.html">as.mic()</a></code>) and
-disk diffusion diameters (created with <code><a href="../reference/as.disk.html">as.disk()</a></code>).</p>
-<p>With the <code><a href="../reference/random.html">random_mic()</a></code> and <code><a href="../reference/random.html">random_disk()</a></code>
-functions, we can generate sampled values for the new data types (S3
-classes) <code>&lt;mic&gt;</code> and <code>&lt;disk&gt;</code>:</p>
-<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r">
-<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">#&gt; Class 'mic'</span></span>
-<span><span class="co">#&gt;   [1] 16     128    0.01   0.002  4      16     0.01   0.01   128    0.5   </span></span>
-<span><span class="co">#&gt;  [11] 1      4      0.01   16     64     0.25   16     4      0.0625 0.002 </span></span>
-<span><span class="co">#&gt;  [21] 4      0.5    0.5    0.001  0.125  32     64     0.005  0.025  0.0625</span></span>
-<span><span class="co">#&gt;  [31] 0.125  32     0.25   0.25   0.5    0.001  32     0.01   8      256   </span></span>
-<span><span class="co">#&gt;  [41] 0.5    64     256    0.025  1      0.025  2      32     8      0.01  </span></span>
-<span><span class="co">#&gt;  [51] 0.025  0.01   0.001  0.0625 1      1      16     0.001  64     0.125 </span></span>
-<span><span class="co">#&gt;  [61] 0.0625 0.025  8      128    4      4      0.025  16     8      1     </span></span>
-<span><span class="co">#&gt;  [71] 4      2      0.5    0.5    1      0.002  128    256    0.025  0.005 </span></span>
-<span><span class="co">#&gt;  [81] 64     2      0.25   64     0.002  0.005  1      256    64     0.001 </span></span>
-<span><span class="co">#&gt;  [91] 8      64     0.25   4      1      32     16     2      32     8</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>
-<p><img src="AMR_files/figure-html/mic_plots-1.png" width="720"></p>
-<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># ggplot2:</span></span>
-<span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">mic_values</span><span class="op">)</span></span></code></pre></div>
-<p><img src="AMR_files/figure-html/mic_plots-2.png" width="720"></p>
-<p>But we could also be more specific, by generating MICs that are
-likely to be found in <em>E. coli</em> for ciprofloxacin:</p>
-<div class="sourceCode" id="cb38"><pre class="downlit sourceCode r">
-<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>, 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>
-<p>For the <code><a href="../reference/plot.html">plot()</a></code> and <code><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot()</a></code> function, we
-can define the microorganism and an antimicrobial agent the same way.
-This will add the interpretation of those values according to a chosen
-guidelines (defaults to the latest EUCAST guideline).</p>
-<p>Default colours are colour-blind friendly, while maintaining the
-convention that e.g. ‘susceptible’ should be green and ‘resistant’
-should be red:</p>
-<div class="sourceCode" id="cb39"><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>, 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>
-<p><img src="AMR_files/figure-html/mic_plots_mo_ab-1.png" width="720"></p>
-<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># ggplot2:</span></span>
-<span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">mic_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>
-<p><img src="AMR_files/figure-html/mic_plots_mo_ab-2.png" width="720"></p>
-<p>For disk diffusion values, there is not much of a difference in
-plotting:</p>
-<div class="sourceCode" id="cb41"><pre class="downlit sourceCode r">
-<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">#&gt; Class 'disk'</span></span>
-<span><span class="co">#&gt;   [1] 23 30 22 17 17 18 24 30 24 17 19 28 19 31 28 17 28 18 20 31 27 25 22 22 18</span></span>
-<span><span class="co">#&gt;  [26] 30 29 25 26 25 20 18 26 26 28 21 22 25 19 27 22 31 19 18 25 30 24 28 30 19</span></span>
-<span><span class="co">#&gt;  [51] 22 18 26 19 20 20 31 26 30 22 17 17 27 23 19 30 27 28 25 28 20 23 28 27 19</span></span>
-<span><span class="co">#&gt;  [76] 25 30 25 27 28 24 24 21 22 22 31 21 21 27 17 19 29 29 29 26 28 21 28 29 31</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>
-<p><img src="AMR_files/figure-html/disk_plots-1.png" width="720"></p>
-<p>And when using the <code>ggplot2</code> package, but now choosing the
-latest implemented CLSI guideline (notice that the EUCAST-specific term
-“Susceptible, incr. exp.” has changed to “Intermediate”):</p>
-<div class="sourceCode" id="cb43"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span></span>
-<span>  <span class="va">disk_values</span>,</span>
-<span>  mo <span class="op">=</span> <span class="st">"E. coli"</span>,</span>
-<span>  ab <span class="op">=</span> <span class="st">"cipro"</span>,</span>
-<span>  guideline <span class="op">=</span> <span class="st">"CLSI"</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-<p><img src="AMR_files/figure-html/disk_plots_mo_ab-1.png" width="720"></p>
-</div>
-</div>
-<div class="section level3">
-<h3 id="independence-test">Independence test<a class="anchor" aria-label="anchor" href="#independence-test"></a>
-</h3>
-<p>The next example uses the <code>example_isolates</code> data set.
-This is a data set included with this package and contains 2,000
-microbial isolates with their full antibiograms. It reflects reality and
-can be used to practise AMR data analysis.</p>
-<p>We will compare the resistance to amoxicillin/clavulanic acid (column
-<code>AMC</code>) between an ICU and other clinical wards. The input for
-the <code><a href="https://rdrr.io/r/stats/fisher.test.html" class="external-link">fisher.test()</a></code> can be retrieved with a transformation
-like this:</p>
-<div class="sourceCode" id="cb44"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># use package 'tidyr' to pivot data:</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://tidyr.tidyverse.org" class="external-link">tidyr</a></span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">check_AMC</span> <span class="op">&lt;-</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>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="va">ward</span> <span class="op"><a href="https://rdrr.io/r/base/match.html" class="external-link">%in%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"ICU"</span>, <span class="st">"Clinical"</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># filter on only these wards</span></span>
-<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="va">ward</span>, <span class="va">AMC</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># select the wards and amoxi/clav</span></span>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">ward</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># group on the wards</span></span>
-<span>  <span class="fu"><a href="../reference/count.html">count_df</a></span><span class="op">(</span>combine_SI <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># count all isolates per group (ward)</span></span>
-<span>  <span class="fu"><a href="https://tidyr.tidyverse.org/reference/pivot_wider.html" class="external-link">pivot_wider</a></span><span class="op">(</span></span>
-<span>    names_from <span class="op">=</span> <span class="va">ward</span>, <span class="co"># transform output so "ICU" and "Clinical" are columns</span></span>
-<span>    values_from <span class="op">=</span> <span class="va">value</span></span>
-<span>  <span class="op">)</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="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">ICU</span>, <span class="va">Clinical</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># and only select these columns</span></span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">as.matrix</a></span><span class="op">(</span><span class="op">)</span> <span class="co"># transform to a good old matrix for fisher.test()</span></span>
-<span></span>
-<span><span class="va">check_AMC</span></span>
-<span><span class="co">#&gt;      ICU Clinical</span></span>
-<span><span class="co">#&gt; [1,] 396      942</span></span>
-<span><span class="co">#&gt; [2,] 184      240</span></span></code></pre></div>
-<p>We can apply the test now with:</p>
-<div class="sourceCode" id="cb45"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># do Fisher's Exact Test</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/stats/fisher.test.html" class="external-link">fisher.test</a></span><span class="op">(</span><span class="va">check_AMC</span><span class="op">)</span></span>
-<span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt;  Fisher's Exact Test for Count Data</span></span>
-<span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; data:  check_AMC</span></span>
-<span><span class="co">#&gt; p-value = 2.263e-07</span></span>
-<span><span class="co">#&gt; alternative hypothesis: true odds ratio is not equal to 1</span></span>
-<span><span class="co">#&gt; 95 percent confidence interval:</span></span>
-<span><span class="co">#&gt;  0.435261 0.691614</span></span>
-<span><span class="co">#&gt; sample estimates:</span></span>
-<span><span class="co">#&gt; odds ratio </span></span>
-<span><span class="co">#&gt;  0.5485079</span></span></code></pre></div>
-<p>As can be seen, the p value is practically zero (0.0000002263247),
-which means that the amoxicillin/clavulanic acid resistance found in
-isolates between patients in ICUs and other clinical wards are really
-different.</p>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span>amoxicillin <span class="op">=</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 class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 3 × 2</span></span></span>
+<span><span class="co">#&gt;   hospital amoxicillin</span></span>
+<span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> A              0.343</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> B              0.569</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> C              0.375</span></span></code></pre></div>
 <hr>
-<p><em>Author: Dr. Matthijs Berends</em></p>
+<p><em>Author: Dr. Matthijs Berends, 26th Feb 2023</em></p>
 </div>
 </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/articles/AMR_files/figure-html/disk_plots-1.png b/articles/AMR_files/figure-html/disk_plots-1.png
deleted file mode 100644
index 8e0463d520f330b53188ba7d28225e5b84f567fe..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 39067
zcmeFZcT`hd_bwVMDhetnuOLO~T>+^=Kt%{$q*tXQ0#YM{W<^0MLBP-g8tD*vFE*qT
zsR2S!TIi7$N(i|t_<rY{-@W6GJH{FJk9+Podkh73_Fj9fx#oPHXU?^DUK{9XoI1gN
z0s?`Yf@$6|gg}m@Lm-DfA3F-pe0x5Y4SpQ8)7H2Jeu88BW^E|=ar}{{xeo-w8BYIq
zAbNVm5dz_Xz;3A-Jx*C1g}-MtZuqfMnZ@w((g`8h;aj)VVTDg4#Exb2{4iE?>}>aw
zJbIF?^xk=v!fc^OY*#*fusS4iTPN)D)AVCcrScrqr8>G)^~a%lc>iuAW4NiQezf#B
zZf(8yW<Si>+9z<UKgWk}zdQf?zyEE4|EmRJ3K4wqHFb45KCNpW0~R~Y5A=<{Rx>~#
z*<V+EM(NiZx!i~K2TirZTZviuJVs??BmNWw1QI49CW$}wp9YQe4yPaZ_YZS52>ZXk
z#+>^fPJg-l9Gq@>*--64|2S-f7Xt1#*m%z87w21u2|*@lS@6ysg0b)iUlL9#9e~}3
zS@Uhg`o6<dNV%WX9=`0Fis9y`^2{m0BJF8o$jBdiPhRj{i&1D~&{h;9k69VAz!0!u
ze%EIm0X9N&%CJ)Yr4}_rJD~x&qdm`#D37T=YO1|s)#Hq`iBo{iO|XMuf<yIF-z|ak
za~qF?MRIJ!sG8BfHkmLHzsL2-eB-L9uryQ3h>fyxTk0~B$QynA1#+h5qDBs;7I{&-
zCMmGb&nUuiWEL4(uk)yP`Ruk5?heXcQm0sVE{zh?e)XMJjWWzyvU(~RWoOBsQ;*q}
zaQ6?a&)*_xi<J1iJ+$8eh=N}XW=Tu@XZa0*>=`-8lHf0dc7(fVG{iTf?pcgnKwtc8
zl*EQ|>W#4&o%A7Lnmf~+^l?=U{(^U9`X`oFS;ELCf)D}y{NstjWzzaRAG!Px^%d>c
zjbn6Vv0lK^Ctj-`1GAI+vIaS$nTxX9nUT7CQl`ry(jQ)gscp|GbU5L7@Jp%{+4j<0
zgIIrXbUV3GD>s(U69GGAP1gN0dKpe>v*L~aO=G<Ti{rPRmJZcLxhm8IVsbR`^1y=C
zIVVHK4gy@O4tIR|bIwI_?!&L-37714@(PliXk<O(i4Q{YxM{!=+us;68J<F=KX6W@
zvS|ih{-wC25`&Vud?yWK16#FOETL+{@~*EIBwj!f)D3x?aDHYpN!z6(8mGDWq!S+p
zymrVLY1Cr>RYJ0dg=^&s_~;F{Sn=wO#Zh$ABE0`&&?m#DW6>LpB02FBYRwf=PkuV|
z9(W9c55I_Kay4y#qobRdgSn54>MByc#uq$|ln|NgV;w&GJltx2GYP6>Yxr8bA<lX=
zMQrZyCs`qW{T_<Mw4^ptu0>e>rC?4wj`gCS<9SQpw#|eToPGl)Kq7$4D@Tw(HwFEO
zVZpa3r*9%t4&4jT;(AU->iz5%|5e4;G^wp#LS}7I+Ddu2?iDlqxa^yIaqY6ioVMfb
zfuk9z8ntzq4~|Pgg0+#dJI7UNi5kH+K33dwNWl-vS=z$UZWket^c!|3n81{_k))!7
zO<NV-TM5iHmiDG7n?32d#Q>`@G+YwH+w#oag=xqyipKfAt7FqYIUMcHtdllTGP+b6
zsMAFph(=#0bN*w%+4{~gql3e$qEU2CE;?w<s)7hpl(=tMxbHM6m-fT6db<5^bxM$^
zYpTYWBQk`!PQA;i<{`JZYo5-ZHFH$=h+vD3=-Kb%CA$2<m!)Z59}H2|>Gij9PVK1@
z1T1{eL})YMJ^DnZ0uqzTMIkd6aY?E%q%-oB`wwm2M%JByHC8Py%oK^6{x|Dgdpo64
z|9O^mRez<0#6RlF*0q-1ykvaYH3jqSt~^sExvIV*XZQAoV$NJoVYbe_o@LfV-^jc9
zqWXq{u$z3!l5>nG4wfb<wZ{w7vijaeJ)K<B3<^h@i};$6%d8j0mFFW|HYeeM)ziV_
z8HEpRC&84RI}7gv5h5ao)u5PSVxaLdYLCrZ*-EvItb6XkY}*e@yB!%!`xsOvsHw!K
z>pLXJ^LzZJbx-x%4;53bI=IzZ%r-JD<~Y(nzS`@~O~Y#h(ZxF^_mMRsB9#i|{5~PM
z`~lTo?8esJh4gsz{)><{$hW>sZ4xNgD<hf0nlth+Y<qy3I`np%(k~0Hk=xm}SzBJq
z{5h4{HF)K=uDJE24pvUY_5*7VK8jA=)34JxncpfR{OLabj`846Fp7ABY;<s^VN0W}
z!711m0a&w^qG8Wt7)?())DyOJ=z(@9Oj4vWR8lggtnEIf0Xh65M5UjfbQpD~&qE@S
z^7Q37^9?=<vu0^sX9&l{UG<tDe=$Am&2)Qk$j}ktDO+OfxOUCjp5k%X1F_O{0zZ-~
z7KI;l>$0k>el<E=s@Y@8HLaXC5+%<$(NjpVqp;O1l4kEXrFHCfc{B$->lkU-9;Oa{
z;|f?_&6aFYNatS3&PviysZ>a3yuO<9`;Mu>)4KmWHN{L|^WMN1*R};>3V>l*axfot
z%5bk;W%%yhp0kP6JE+*)>JvBcaMxa0)SaJt6qB~vdP?<FWE&#3(SmFH=}A@XWyan5
z$ynm!Xma(DbpAP8iGZ{}EphH4`}1((wJ<$oMH-8_C|tfnT*!5^^8g0Mry4%noZ_Bd
ziZOqTbFE>7?IdF~;*)vSZ^JwlA7c4kleE)nr-GlQXEZl#DwF-z2HDEgB?$U4YrAdN
z96VUJR>IoJU$jEI8!eE_<RPZo^#3uSw(F;gNta=`mJSBah<b}QF$1{afaQ63e8;f?
zBeqz#$&%n4+>V^~PF%;udcDGT=I^htjd+~BI9oEhu$E9qJOcgUh~PxcX|}bCB`yU>
zsjUXv)0nj9#pXCr3j@(?eDMkovF8biZFcX<`A3C(zTJDJon*D|<zTF;1J}h#b27Vi
zbr_|kuW`lHPg1-U6xf4H(jWTR11=&_Nx!6`WmDhZM_9BWLZ%3D7vZwc9S=_B1PR=|
zD}=4Vywct?gb8Yky!j*^)2Gps#J6!&`AD;uFqdbpBvx3SZ_Y5fF4FN}vma~Y`Mg#g
z#~xFGE1vp|Up__sjWl{6x)%ARSFfd}kDgt+VAbvqQH;a0S4W%ZW+{hj4Nu>}oq|Ov
zb9j(lqN&rePZhd(g%g}ZM<<IVXCc03J@YaIIo+OG2@3K^`c0`X6dmjrqk}((_I*`^
zFY_08KsLK9Oi{~e-BBbY9<_#dx0#B_G!ociKc1eij;ui}atllIYXk%;zL4SI50149
zkZTn)d7s-VGTuFY26m`dla_{&o>H!Vm6(kw<yT&o4h=ZAN*4=4a{F>7H;Tkpp0Ius
z>C5PN`v5I%<NT3Haa4|U#6dBiiaO$FE^%r1n*rwhy#kV(tpO7Dg0zG_o1QFTOz^Fq
zZ4ugMqO(*a)1DHnW(5e7VbuNLGbj$maIK_CUfK=N%*f14*DYDPgVGNk(HqFXwvL;%
zH=Jr-pF;|0&KHa_2){Af*7<TKq0h>x;kT%rJHpoKOZi)=k5FD@NSt}PM+)Wul0D1&
zv&yjjcAmD%f}cmk2E%U*pS$Zs=?Jv-Fp90sijlzI8836AXRpFHfXk0T`AE36J1nt9
zr?^;c#==a@$Gt*Vlai}#-J1F9QIDp;9~FUw(8!K!Rn{|3T}G%ozXi1(yuT4J)bMY#
z{_XAm<Nv#i(=~KZ`OIx5^SNY;04NO-uV+)p@t-@hQ;w<rD~li{Pa!h@IsZTN(_n5I
zHZR~>2Ee%jHq7H%gURDtT0(lJU<_f(rQ_c}zu>nP18gxV?)Nx2zI0%^i+f4BGtNki
zh?86?S58o2<9b{E`}@UybjLTTr3E&F0Mtd#pYA!Y^4ENxep7)qLv3a@)6>2+SiT!3
zh4+%E6NBVE;j;T`VoR=EuDc<4z6Wk@YjMIev6#Z*b!~?(d>{<1U86rU$oSCpn-;Z>
zzv0KH!L%JI;sOlD$nPGjz)jMsxssvm@j$cYMo(g~y@h})26NjyUn#B*+tf3l)1yMS
zgDSsw5eCht9#?l;vrWH+f*Wi|o9-Ln(KTANPecDZ4)^>i^v8{)*pISW0z$0ueNy4>
zf22$^gBcDR*$6aq_*#lwUf5kE7D<jyLS+&{s@nH{Yf<5tENvxyy$sGsJ6C=95V{qv
z`+?!%9IqBvc7X)Tl%B~ZvEuq~)}Gq>JrqV6Y4rYv0ta9DiqOYcOuR$@FEXn2#we<y
z)=#XH%GG>?-q-9~fkJ!SKh%+W$g;!^q4Jmxmzt0K_3YZca+he_;%2Tx+AEVPMu?~g
zWzECsh?y$RoRY%d<KCmEnU6%+diHydoEHp;sKnleOgnnlsa1ZuT!KbF`U;FsyCW8S
zD3NLcQS84{?XPwuU7A{0+@5%0jw8NIv5EXP`V&FIHNE55SlwV=0>&RHktlNsrjUTO
zn?9v=wLb0!Dt)V3N^OW;iufT3^Ud)#+VYE4R`=KYO;9unY4Lp(AAdoeJHAYNM!-78
z&w1EGIHYrVx`dlPC<B;G%r-9P+<VQF&-vzs*0#JIf0~c++A$mTpHiiDTM4e2*C#E!
zeC#6H`-8vQLsKE=L36Ui!fwz7Rr7&aVwQ4`*@0Q#%-o{GXTq3vYG<m<%o!arpS;rA
z>Eo@ZzMMP*o7d}4EMDz8m3>{%&*9+A9T=PZB2;}v)idMUo^UZ$-yvgJXN~I|Aru{5
zaR3s7wCbz{0Oh|~G{Kv_C$Uyln0Vxbz&*n=X1t7YQ)TCleMQ@oN_~b;cyij2xM#}F
zO=snDde-Ext%X1wN6#WcWx`5I_-9E;ZgwPE%(~hlaT!VWf{n|PxnkG-N`9E|Se`?-
zea!l{$6-f;x4b!~@1Wbd3P%ZRKFwF^t@Cl1dD~zUzX@Z2s79R$I|||1Ji%|xO4-(e
zIjXd;SQL3!JhWv%HRpLIn&)TBlU0nC*Zhaq2I76=bX4tXG3Lizqu-%$+s>9laM*(h
zgXP!7-UR_Fv=5_fQ4ffL7=%{VSgHNAWFjTBqQGa{@GH5f<Kx_>c2d@soj$4kjfA#i
z#IsDKM}h*JtloH3p-iwJSC)yP#iFnS7F+WYsBX{c9Qb1J6Wp%5<N3vs^S1~>(o%aT
zsu{}oq;prjVWbu@hYH=9t6P_beJ)jb6mJemMdxn5Td(UN{z>j=>Dz$~S7R{*2bFJ2
z$N^a3Z9_!#b!Op)_tvLiiLf2Mgi!5#ak8&qz&m4`=Hz^FaeF_#I8;c6S&t!#huFZi
zq_3{n?BXi8R$DDJkbrh56vVnUhlHH{v}$g4!twU6+f%e1Tt#|=o7jfK?LDoYWGVb=
za&aiLZ>#-A?0tUjVC(K(E`)vxOKs8wzFSEmAhE@YBOrp|_p@xJS9Ok3YV+5dD39o#
zsmkwDHWAI|TynM^AM|D5sXo5t@J7mck)6a8qIfxRG$zK`LrgcVj_vFlO-0^}kp`~0
zg6(vElCb=4pD60yeWRXsCQ6q<kV@}&jht`+*R{qe<>;As*lM(%Cc4d8@zye<Z9yWU
zNWz7zv0%B~653dY>oiOZdUg(N_<1h=G0T^vr1VM*-g-4Zc1@`1I&72Al{juS?AlMU
zl{(;tNUOZqQ=SiLUg%HwraXMFzV6_vO^@=ABFt5;Ie4qi#^m>ff8v7A`;XeYX7Ph`
zQCR-zPGPatKcWkwgo==2chPf=Id=H(11}e>dN6s{Gk?W&z+?J}!%JCxT?_eUNe2!L
za2kzG+PkK0-r2{>U(CPGqG((09YrZdPvcE^u+JuVe=-?gH(QA(pFB3W7Q!#xcEyXY
zUw2;$y<xPUUgVV~eu}4^4zJ+kAjtJkSJfT-8M06yA+0DSGG|91Q(2jVIHwH8c~B6$
zcC!lucKYF(fpQ5Xzr@LZT-<QhQoI=jFDltvI?#c-H`0uty%(fD&VMalWkO!?VvApY
zrHyz>7PLf+-0rO{p3$c!>hNGII;u~#2PF_S*=J%%U1kkuQQakPS)o~o_BUEkLPA4Q
z!CO&>gN*|LrMD{+zmw6w492Sl8v>+y-}b#JFRj3=Ie0w2RZeU{AnNa+)Ek*%*EF?6
z-}bsRtzI8<a4#;zKRZXOXKWt{Dmsr{lRhgyYJXC6uXZy|C)7^-`+z}@SrY~Sou?Lm
zRX}O)7XC-i7?RIg;?PgEV7ytr$_%!`{Y3;?t}@FkbEqdjPC+m<VY_ZuC>pYUHKJ0X
zI5pLsQg?73C(8AhId?gIb(vecRHbuuKXnK_ZK67n=Wskuli$o-O#1EZ55p0XM!xT~
z$NapR#0E@up;gLO!?J2Cs;-yajM4~S{X7oL>MXctJpNo%p!3<)b#dI|qgg#rpxJt~
zZfxP>bP`s}S_D03r@+uMEWC$exY~9F)#$o;ChHRy*5r7AbBppUDbrL8CyU}>$Z>IZ
zo3!^<k!1c(rYYa2<P<OUPOJER?ktu`4CtejY1fl|_2ewuD39xhkve`&p_PWHgdY>c
ze>@H9QuQJmr-k>PtKyyq4_xi?X-QmO(U1BB51jbgHBTzwaJIpIUl8ks_!^8~>siK|
zOvFhKJVkiOHJ;$Fzu)c|N)_;V*5x+*?$!{KD&BZ^4lT+_-Za?Fe4W@`><ecwgBt|6
ziR@jFr*Z-KJ~OkAnfd7knQLB|6Kbe!Cl>d+yU-?&XohiNBJG}~kF7);s&}B(y?8TC
z>d7RJg9S-air0-_`trsF)V)obaD&TT#mf=Uh9H-(4fYOCz4KUidAlmx4+pV`zplp{
zT!|(K_KuQ$%mn_-k}cw6DjgStNvsRGnOmnK_oO}|?%o%duQYL5_vpLSnK@WX-Ne*j
zhz=e-DVH{15S)IDu^gHkFk<C!%uIZgi%(mwf4(#wTxa7BYtn1N-N}`M?q0NaM+x7_
zzYfe6-+K7gCuQQ;hDF$w(Mb|3!Zy_|u$2PmOZ^<2`*gRkIPD^;K$$IulLNthnK&tw
zP@UoPs)K0iK&Y(5SUMICl^%3)&OG7Jz&+pI;Yodb)X}^#G-prG!4O^(u+*rOL;ePV
zVBXNRVYTy-NhY!Inv~lIEmhvub5E9xyS__jW$vq+xkDB~oe4IaiHtj9PGUtVU^e5A
z-^RwGoW6;x3ZY_Al`V%k<Xkq-M9S6nId(!R-pCTF*-S~j&I->xk-Nw0M2UCaXMGxs
znzSdewH!99vr%GMwfGW?b37liz(;~u>7x{~oLi|g&sY|Q8}z%>5U}~Ph)Ohm(&4;s
zKL6UQK(9sPvj}tI{717L_v4Nha@xy=yMexTpR}z;O0(KFfzR3H2(Kwgo6od9v1`J8
z(?j7r^Xj7Ug;_n&A8`&I{yGWpUvP~@>iBU&!J714gqHvA>-;0oxVKNs-<YnBvs7|$
z<{Z9~x`$@#gX@~D%so9zEJ(!Q9Cpz2-l2->QgW>x@fUQh?;2{Ls(-=T%0eSBvKK0@
zv2064p4fx+G$Xs-`OxaGkv(+Vr+%;tr~Ole!+`i5-gKUvVSuDZ(Cf3R%i>Y>cvCWW
z@J14)cNpK|NMlM|8t`Rti|eZwAlo!c8_z$@#W$S(J?YJ*?pkf;G_^h|^(Ak|o2&T!
zKYz+^zbnw`F@cU8j5G}7<s7|zKlALO$U9W81+qc!qh32NvMX_03>$|cryB`C`kO8V
zqX#LpOmp1p;>~DlayTn9hQOx0=t!x~?8shampYI&AAuHK@LXsbPBorUf(<)=qR8Gy
z1aM<bE%~Ga{o-_fZC*r$Ih|_4J-g=jz1Fp?A9vLvU?h`NY^Kk*`>tqI5jw6gwW1gz
zXmVPre;YUYwB08EUWmvz%K!^EdGAs-De0qw#}Caeyzgran>{p3aonpI^8)Gd4#m}4
z1M`c=$vTOMNqcX}!+adhck0%8Ic;~6eNIJ7SvHTwy<OJNqFa!%ZM1L@!sAF!7hCE*
zA41_iy64{1UnloR*3;%o#@!FcUrWmKLe>;eUw^zwJ@H}e<*@Q&zvvMS2H9BDT??tm
zkJl29%T5Wa*m*tI9vinOslDCndTHa$^<wVY*O8t!@|ridpN3;*m5YG%*Ih{|A8|Cl
zbmIbz4+E7YFSE+~#$mVKF+yuFt~@OF#)Mo;Zw7~pY-jh!=-eBy!Cq5-7Myz^i)=o3
z_K?wj9M6fA{scvuuLyTXkLBm*pdoGzL(DxcShW51>*T#sjiewL)eb7DHRzFw{%*gn
z7$V1LPwF((A%$Pls{iCHvNj|WO|AP;p`1-(M;L6u6`%@UHV;E>142pi=Axyq@~4!a
zDq7}u<AWVl@M)`DLk(TzQo_jeC78yK5St0)5B*WK>GJWIdzWrNRDaPkz!(I9zy363
z`>Nw6WgEvu$%p(*bWLzQ@<wk%v-j3Y?ETkkw4Hd@`unIeN!R$TCFd3g$<b5SBRZ2N
zw_q@PyJ>SqMhhnW4pw_{2mgdNXSL6(PDfTF%7`)ybFbRooTWig=@}bCV`(v6?4xtn
zhOfk)i=n75f7~WCjj?F>IZ1g|`}O|T2w-@Y?;BZ0JobH6#Rgk({T~EW-Dzq^3YDxh
z;6f?t_!?#y7&*GFaC-rc6UCvVKK6~@4Qg-`4hcz#>R#9y4LXDHB%(y52aGPYo>zS6
zNEr(0<$Xm;8XgW#g;Z{jEp@HmiT2|ytJ9dAY3=9@eTm_ANb_9@6uB*R0KR}&JDe!P
zAQEB|a;#{XXH{#3qr5Q-#S961cN!2zx?PopK$uSvCRAt}zJ4hpI#a9pR0LGf-SqCt
z5lW`~;^u0=BK2N1j$8RUSw(wAqhA^rY_qi^R^g`8*l$yZnP(mnr9Ag9yFGtzdAj)1
z2vIwYpGDL4Qs|fW5zE<wh!SCe9>>mwWcjcC?5%SZ_cG(Y*^xXEm^pT!MMXz*NxL%h
zrDi_yf=Y5Wkz?_jD}m)rHD}i9zcs{Q7+9H?YdZCpzvHfUy{<>kmNdS=z>3jWy+P+x
z?kv|EyJDZ^<;Y|G(#B4ElH`!G4tN+xo+LU?naI(#UOV${kfMiD>0W?A7GB42#P!7r
z+4k@kqoJ!>{}jb^94sTY9A2(%IlOe&D|9jVc}mv(YGtyDP1pD(R9`C0ru{%MJnM+U
zz66Qav;{nH2X&^jzKl}WRPJ~%6ZfNMUAKjX8ziSOIqFx&*Q?GH2Z(AuO7A%MY@p%$
znHrNZCU3a2m~Js<FCuz9S8$EnC(k2GDk^HpC%Jh;wFw6^*dRx_kMradx+DBh>E=dG
z4bHVU?Fqb6A4hVVEATNF3TnGe(09{U4-)q7OZA4D5o-!Nhzfj{^hMA)c%mV@_FWvW
z?&*fee(5WnLCu=%{48a8AGfx%K^T)fi@kBM7W~Z>pMVz=&1}ek5!x-79RF0cWWt_R
zG`WsLyI1%=<!Z%_M{HSiLBX~Z+R+m>Sw<w`l#7E}59XODW^u6Y$F(^tVG}uVSM<5#
z?Y#!xHKlX?%XgueuehuJCDRJ#E910bsmRwB`a&oDvT=$~z2;fJU5^v?0sg`&3>9&|
zi+%Mo?^KLVneB;7efgDCymSdQ2Te2DO(}zmsG@@LVz1m0@-Hf6OZ)A`1qStIHZj-^
z&9(W8^&?W$A;F3ual>q)Hh<$l+;QQMWfLK)>&;b1k9X>h9<ydX6L_QDz=*a0_Dr4m
zuCmdDaC^tc%6AUabC_&C#g<(`sidiEh6t6J6s@Awqo~g>?xOOh>mc_<o^%y;*oD}*
z@8xNa=>TOuKdK>ck28Ml-34)nryX}ts`aVocD(|Fb0713cE>F0!iE(UFHyNKK~k}g
zDZJik%maHRxS`<EI-G`)z~jlOR=(}CY?2g_3Mx<H>Wv7rWR|epqMXc#jozB0<%WQb
zw#VGa438vka^r!vezffF-I*V6H+*V(ly3`f6TQ=JatVjLalcNk4nFUZzn4*}!7d8n
zG+rH#=eW2Ebi+=4R6rt9zxOw>)(J`=4`{tTr&AFUt%Nbi^6LrJxCRP*`+EUSdhilh
zj+_beNHS=|6DMTLb)-?->7!9O&gbWuK8yxLjH?#RA?X_pAO@{qHV;L`UvM`Qk#3&X
zkm7YtuA0)mW;SHt)b{$56s^_vtZ3!WOSX1n4mGVEH>+<8{8McvpxQUWt1ke`4hyH6
z+g?Awy;xf8iuz=pGm6tr>u?!CNuk@%qt>K_Oe)UZBPZo%eCn8Oe<4K-{n%mWp6G&`
zyR&!fz-)q+csgtR_QS>KUAC4t4Hw0IR4UrvSXlGSdGIrPbMi}Dx3!rK<vBhL6MV3F
zC;Pr7M+#STVh73Cs8&)aA^Ta!gp0QQhWqPszc(GPmcEzV&5^rm?HBDk?NygkHm!+T
zz}|BDQbIh3zBv{s@@47eU8x+dkE6xN%r-HM+2u7#K->tM(1zj<BPgTbz}>Sf*LwyJ
z{cN>)&D*e4({gw*u_YzTUFyqNhE$|>=H*cm`@+lar>yOu1L{UIaDqj4$)^sMOZx5C
zm5aswPl`I6ud2IM8{MKOz;VC@n}~<s(M2^W5Zl*D(Dv)o-F?7behl5q62;V<HxKp~
zi$5DHsyRTypxmwN<r_Yur+L)5TdYb?f#2ve^O+m818Hga=CCtt2`X4TM+&~rI@3Q<
zRRx@;y8T{AntG#xjdoaS<7Ee{(Cc34K19XH?l0efg`vd6>&NNW|Nr_hxbWN?Y%N&+
z(CwmW*lbn#x7=&Q-jrc58*U|c&{!h`)Pq0Y)$Dmv?XiytkZc)Q?aPq2-f@Ww`!PVb
zkBH!3tASs`@_FcsmHS7_iT}k;^EfN|&OR~^3E5lIP7up+9UsfmM>jtI_Pg~p_UY57
zuXU4U>pzCl_5>x(iqb1ghYBn!xl9ZUu6Rwi#UBYFu-#H>({7I9lw=J57DuX@jJoMF
zdLD8v+!89<M5+454BhSd9;~*Wkr2)uvh}$uRf)OK6gvi8U0Xv-3JGmF_h#yn<_8K0
z0q_y8=8OeA7MtM8Ihcxv-^ep5@Hhz?ZI0$1mum6$`E<GBxN)?pt>`w1VJ?z3m}_Wz
z64E+F1O@#z{9v)Yr43@KTrTn5yS}!ve6Ur*duMT|6ulM)@4>(*!IY)S`e>!)szhd$
ztzVqvU`I#C0Pyek_v@cNeJUR)v_`ZLJR5^slt^_xdz_#%6!*%}iou-SJ0EW=`~0}z
zd|me5=de<z&ZLbxwCvIDsMU!96X<Zc8+;m!@li)hEN?h7hwMZ1(~d_!U9LP;q_oj@
zS3i)L3h#P-`{RYtMH~(%Sxp{x<F4NvL$GBg<MYt*6V#V?IiAhll=B#QKVEF#%wdvm
z*4^EpeD<}GWv%~`nj5}Q*<~Q#JPQgMgXWu;&d5UjjMQ`1pV^Eb2fe@FIL@XPf<H_*
z%{1@X*Uv4=ANaK=U2|jVD$7^&o8MK0FOOI<$^QzQ@LdbsC`XZ>tv8)x3%&}$grE7T
zdQLicXZ@!C;@}-9cp`4S+@<?NVF<yiUA)rjGA#P~YKw2B45y;+))n@*Ohq=ya-J&i
zkt(lm^LQ-HM~3~>Xb7+>b+uhGiuZ;dl)CQMnRLxk20>ozZJ_N@-aK7Gl}$zqoqcVl
zmSa#zKxWg;0vZ`R1ICsc>k73rG&FSn@#X^OZrP>ykOAG(!7kBU$`aPacIwTK<gHdK
z8>vg&jBFBhRTzxJJs7O@Gdgr{dlwcbV2#+B%c7Ou|MoJE`p(w4oB806636xvriE5R
zmtHlx%DE5s1<m*6bW+Q{<zYL^xG|E2RkM)?c$D9S9nH;Vw3O>sP9<-~SGo`y`>FNY
z4GU>9u00`nrdXBrn43)782jcZ!bs2b^mP0X&AQTKwB^^AXQ3(+uXvZQ@BaBlGdD3w
zp#AyATp7CFtwB_rZrqvAv#r|OSs-1w{`ilpv~yR`7f*z=yGFnknc#_jx9(6l&+zNv
z{QJpuV&U81X-;L=;^m-+Pq1}4<Wd`{xtoo^UKZma6n9S;`?UvsOQzO-{rY68xJ8*z
z^`EbdrZ$g%zYwLCof^ySf!{DzG?8ay8!*5h*Wc=D{4-EnzLa&@HlTiIIDJQLX96m*
zBM5nAe^|yqeVX{f=%21NYTI|<p0vxbn=elW5cKyz6ncU%UVZ@E*`ts)eYE9#XMb|%
z8RwZ?r%&EMfhF5koqtZnA=Q3kgVQFR?Ar}C{dwCYH|ZtkPaQiy=X$f`;xPt7M*iE=
ziISUyqYzCIRzn*#zj3U=(NH{aG_?)jXkeR27(^z_yBq|~JaqTZ%}V+$bDt9TQd|ST
zai=o4KhHGUe|cp4ql*lC4X^>h*&-3`9|R_M1F<phH=sgUl$L@*X+yxLqm*XTpS>Il
zAf_&jv_;&yoWbEyl`;8*f$Onufkk=RiSTcdeTCMP*A?yr8)+~g-in7qCCw?G9+9z_
zy94j9+LM_9nj}hC59=a5>Q;W#E=Eeqx(z&h9$!&aB~Jnkxga{OHlKVjSoE9PpKFY$
zNl(8w0bCqTIKpQ2^IOi_^OO&wqNasnvv};daPit&&}faH25lP`%C*(24?Q=$hUt|}
z&t=R-cVd{ug@uo68_KYEib_c6QAnL&THny0PwYyyY<8uA@#I=I(qc6oDx1+%0FU}m
zYT4dOGk56i$scb?g(d~VrALk&VX-W8@oH~kvmF@~|1+6|uH1Y^2kD_wXIv?VjLRPb
zI&7a}mw7x3z(!fvORdOv=Zg_rwpRJF@0J2LO4BbEYt-nO5+&I0_SI&*=aV+0zWnd!
z(x5lKp5Wfnw;XZ}O0On$iMuxbLyj8Z0QUXn`vOLmcFMbK2^H>GFQtTsR)X<rB@ME@
zAwO@}>ew!71Z*}zVou(D1Q9Z25zvuWMOu`(tmxjqf8V@hU2g`5wsme#5bxy6(TL{K
zuInK$k0SO`6nu6XsboTP6nJzRdS|!7{&U!Y;7lFVsx8^Qsj2CRcA~_Rtljrl9>5ao
ziQ~bWqrBv`)30yca3@sMdu*>xTQ6=`c#b#VK%kS&!Q6I7vPq`xjv+`?bZ}#@9p7An
zv5AQQ;zGBJ4C~w5OSC;4{Kw}fhbRr(Gs$fHTx)Idq68>qv2-a*FIDkgSBe6@(hcu{
zuw!@Yjt3si9kkJJI$j1M|L8yihz{B9#Zp|?Lww=ramMpvRXr9Rf~ge;jIB>o^P4=m
z0~d>%3rj<|<RATlc>-rDdnCUY`0dIaFyZ<YpOL*GjltP`sUbUaRGU!hMkI?YTfSNG
zhe-@9PEo?Ds;Coh8>-yh0KCStygol)Y<Fh?%T13abh!_)i`KDJ<2Cu?yeH_oI`Hjz
zsLxCXM}G58?;OKj^K_Ban!D4RK@OMW)f;-Y8;Z3zlZrwMO|86|%;{YAkRr90rnR{n
z#i`^^c18!2vzj8=XxoGK(e-viC63X9Yc?U<)7IxDtOjd~iXKf0qJv5Z7te<jcIOJ1
z+a|k0T{P&kY<&BJ6^P-Olf3B%-sPE>I&t#A+=t7|lRU;ICMN2#&>=MeXq8}(cnlz&
zg2wsWyo;u(6~0Tueg%Bz&YkmH*JATkddb8Y`2C&m-Mu_;5dmAb+Qz^pX%p04XkB~M
zW2|m_L@@2~#LJzH1zJha_G%%OvI+f`wjYYM;BN^Kg`!*v-c#<PCi(KA_(JPL0E+{c
ziRe(3t%*oUuF>o}A5Z0!NspEkR!u~FaFJ2`_4yEUia2a@#0U3?G7(9R?*^P)wE?D*
z3y?xtyWGRUM`?Z!ao5*6u5~8M*%k#;$iaFl+f(iJ!*am$wHNyHDxFrRT5rM^2fg&@
zoCBV=H(%qoP$(fTUf{F0O++tcxvLr{!l215p4`Lrh)rE_%Ze01m?v=X?eYix?07&V
zrOP!7g*QPkkmQjP$3iGSzdU2$wm)*c22R?W2PXGMm22YV#)EuX<4tY0?*SPUT25Rc
z;0W~_1D0$9*>~*QtGp&(E)5n>rhth|k@lMSz5wI{Hl9%FISwS6N~PJy6VH|zdUKS{
zsij0egb^uV?I5YBZvXxQOZXCSwnBhV3)S5&zMnzo{Otvre{Jpp&tJ;{!UanKa1+fX
z<MPvMfD9iGuKXRRuM$weM<vnrNSr|SiTf!f@L=QG_{?oN9H5%ODN-H;oU4{RYEXXF
z?xbq<4!EB=2J?(bQE1Lia6D+ObLID=s>F44_YqpPYXzV)@%JjhJJ1LCLe3SDN&V2B
zUMH9cFd|(=&3n+koG9V6tax}l`hv7mHeWpmQ<~4jYr#E_r98_@4~LeROX|Q-tKuLS
z)Bez(Wd>%S10dFzFbn{ohd3-N)qmJWZsgH7W|IamA`LLuifV8#I&0XY(ecHN3@KO}
z4hQEvcNJI<ceb*>z*5c|%pg{AA-{B)0D^-{R76D4Zf|$n4@jlD{+u^~8}kJgHNJCT
zu>uabreLhvhj^3C+P6D<dyR+&c$8Kg-3R+bcK|(TvW`ITEKr#mehULjNgqCZfW`y)
znS9m$M;I8B%hsRYB0@rg!=JPhH@O05GvJ`z68>KQ;d!0faYB`s{oeEAEQK)`Mv1Cl
z#UP*pEB6ZM%Hwu47d1ZEQh-`#Eq`%L0cbc50L7LM-D_t*M4GJ6c2CY6V!r-p7sL(o
zKi3`@B?BEqB?>qjP}9*hqlf)eO87@f98mmoYc;hmGa&kz#}TCZLkd51x;&{n8}Rzw
z)pqzgX`J?&4G4=yeTC_F{DP^zJy<$g{Be;<l6&;)b1{&vV-G58p4I7zBpdzJ7Ir%m
zZ9mx#We@*z;8eO5n3uwMzH$(e_9Aly8k37^<MaJ_p4`fT>oi4OQcTMp!_L<ijKR_C
znpvFD(7mlEK=+Bb5Gs~*DBL0bbaA7w_3Ifl`u4{R;k%hKl@)TXJq~ev;?^}~<>M7j
zAM<@#&!1251Ny)RmL%(L;Zx~L?;&dz$1!3WVp#+RR6xH;frYU$Li~tGmM_Iz61AQ|
z_gw3SplvEnd2{4iGZ$j95BLVnxz7RygQentr0Dn<rTAcXoVK_5QOZ55NxxApaDDb!
zTU*<z+dzKx{jblD3y)5<W6+fgATi+}r$2L~Q8#IDtWABUR*xvQ&RZw985za1z%(D^
zpj|UA7gBh<HiWuUf7a-adE4$Tc>(W91%i=w36^LRIQI(VBC8Q}MsXjl*+`+o2`{$B
zC*ZA#Z_fH(rPMVW2{-Mg*g=dr_R%&~34UJ75UA<6z2^Lz;Y{2TW<~e%9cGZp(9n>j
zjGN(UQlO{3bQIG71XwQ`xVgNf!1*G0{h^mvF;+!#)V~8I?HmfT06ckhdwW|$$iGyQ
z{jEhb?geHmB>>Q~n^2A>(gT1^K%9W?aKoeDU&nxsaOnle0;g<}8mEAuu(oUrp<2(C
zDYcZO)KQjePjLtR`NBn)sYGDpYvbi^rl;J{eh<~v)lZGA(gE{rFB!Oi#?1@^bHIdz
zV*7AnT>ymqjvgwfgJ;v#o|%BB%&NmA3(ZS2cgkFQlk}2g<_0AjwkD=ZN=p8KJPy0m
zn}suWPW2lY)*&opjZ^0HuokeM`{A6jO8wP$r00lBf#k*QR7=igkf(vJ(i$QnB8D@2
zGJTJj!wrbMmOa#l6(8L}&fxX#CtQ}NiJ)i2rOsU`L$2K))SKz<WqJSp?;kiQEy%ZI
zvVHoRMfay(ywfmq51Z0Q$uVnnQse<6caSO=6y^d^ECy5ZU(~G0^|7%T3*~YJiEcM<
zl)T*nFtM%tRLo5kQBl!$D5-9(5GE^_6dZd0eg-z(t!8P9x;egy?BGVkYGQamf)xq8
zmF{Hr*rH&yvxu?t;ui1dqQlgGuE%jWQ#YBMEali13b01~BbbUmH^4M`HY^m@GQrA6
z)e_=eex|E6Z!8fuTS3P8C&d;Bi}aqJ9*Y4l{)@?Okm~gG&l|B#20~MAv0RY3jndY`
z-QmO60PFew`Sk^g1F*kMkHW^a)z!;LU!bry+RGlc+BQ}Otj*LQ%RpMH6m{MQ34?m#
zF)$XqU;q%Q71qdz$jF6H!-c+w|BG>fa>stxSCLao9govfbgkI%0pOgOCH3~E1hzY(
z%hPrt3uImXw{7ccBgXz4WPsdeHWBbHiB9i3A?=SB3Mzk8y7tbz-yaQPxtbXB`&)P@
zDZw9jKP>=L0lYuT=M2MW6o_HZ#EuDJG!RwVa|yt=n%!-IkdX$^hzJ=jbA77<QZp(T
z;AA0CkiB=lg)@`Z0b%OWfVdKT2RBauvHuXe4|)7^lA+t}(D0KNR*gaNqH+Lh8=82{
z?vp9j#<>%u3aT~DOguhn>!k?~$r+=y@NwU*-{JabCAbF-puE4HE11WNG@m*jq8^?m
zlSLo_W`XDN71%b8ZM=Sa3q@W_00SydAT$7G=zoX#`t@sPNV_Dq=9wb*xff`+uM(1y
zgI875QpN$R;4c8P_zg5d0T}wwhXmfa`sbIt;s~<ct?UEi2dwTV?sv11qd(b5g(VwJ
z5D~W9s)vM3fiAWdfdNl=Mp9@dkB8DiHvuyk!vaB0D!Hyj*}&W4<1lnY-YA7+p#3j;
zeLtg)ECZ4LW~L|4y4HUx#iz?$I%Idt95@<c3b22*80!gjVrMcQP)@aLCykgTMwkq&
z4FJ=^6pI0}xqcG_V*{ZUPC5l(XH$<diKOk|8_D`v!E@ej9AWo%%(Z1H2C!V)#=Y|}
zkdoDQ;@sYf?JiZ0pGa8+6Yq(2aLgJenFo_Us7FR=`N^0WP<H{#Pweouy}2OGlz>SP
zx7?d8gZ748iK}5b;-?~V0*b#CP}WS^7MGCdQu_Uz#pCs@%SY(CZMmdff`cE%W*fBf
z2iQ3vYV6diV$Q>-^KPR%YDznH&g)IJc@KlHhPAz*=b?Ud!z5y++Y`Cp!xW-bc4n23
zWpwHUBBRyBV{WDc)oX?XLbd!#+)j^c^UsW%iBr?lOT%>mYYp^_JXWp0pDy=aym-OE
zHwOw@*~tYZ5Bjq2h`|6;_*{&=V`XJ!jRdtVPU7_-;M!zi5s?#%RlTkpVZV0Fg=>Rn
zL2~*PbhpV1*#`oCbmsFzaC<CY{c4*igVHqTRlD!5a|6Tyl6Amzyf!^h>1vMxN-U%#
zEpxSvHM;k^HUj?UdXgG1YI+_3GJ~^L&jsg{tAqy0?2w=OqaGz2^@*Y(g|8pBt?vRj
zUZ~qxz*l+<<PUvt?ai#;2EZQdHJcIM1+Z4*n|-u0`2@C3%|<XD)L#lr>FR&+O~Ye&
zd;t)8)nK77H(N4<@WHY{cy%N2@ydzu4IA%PekiP`ac=|L1v0k=N;-Lo;7RJs>7^ij
zfmbkjYj1vG;Py?xGz3$+)-6qImIi^hzyTM2iiY)oC(7mn{Thl=@NrUhlhN?<@)`sA
z;k*re)Gw786su^(iTK7)S{wt@#X(9r-o~vCuq*A2mPLsJtP}j2S_4$l%M62TdNYk8
z#~U5X0faci0+-71JMvVu5r9q8$aN3^Xy0C4IX_UdvTxv{%a!4x``;ANUOz4{V*>Yj
z;`7~A)ulaF4Xs0IR<_tmr~GweAfoc88kvJZ2_hNr#!ATowy~iX%=O^sc|kr`AfNhw
zfP!QRh`w(C@rG|9Uovy6jHWf(Hin=zdwhWCs01a$a$#}tQ!pp?Cm_?YDFQL4qPMX#
zw@C(Uo%-zPsX#5{k0?%rJK$HnRKV$+c{rOIU)SHD%j0t$(zxs0Y0aKcPqx>$PJl9=
z?`EWA!=MbP?_G}7oSjaz^%w@|rvzUbHZ}t-pgpU8zvNyFcskEO?OPJ)T`Ru#<>~P6
zM~}VbFQ(BmOSx5qjv8P@%tQsj!_AX3%TG6Jn5&VvN&k}ustYL=^?@5p(oP+ZKL=XO
zmEu%5Y(bJ-PRG)2&|Af>0*^-g>v8*ykNFs*cERf;$_B|6Psx@0@A+a1aL-g`NXt>M
zmnio=5aJ$*81>{zw{9o=HAzE51A`d<19W8u?6%Ya1hIdzyq8`3rJFCeYytwNT4L`~
z$V-S_kfSR#fGp=Ny$Ik(rh<Gn10W=~Ane=AQ<MQu<&fd49wSvl9C9A{)HYG<$vseo
z!)??5^J;Sw-?1|X5>sVurgLEv#7yk~yx-VVc@QL07|}wC_yS-9svW2cCZ0ccj<XvS
z5}bijkzqX%f6Zg$aVvmIQWafckAQ4QnqDY!2OX<?b5X&&QYu--wWJDE9z`2Lo-!F%
ze$~2l_`Gj$n5_wIYlorH!7AZemVNHid1vXlo7&pi-$2=Nv7WA~K)o4=0Z{ppeK=S|
z0g}Da@-T=#t|WR?)5?H5A1bU}u0ddHmlY9dvkJtNwLUQ2TbtikcgYC4#1jN%VD~?u
zQioOo_1Z=~v<embdYcI#KYWo!1}S6eqp!zt^ipJ~0!Vzhf$a5x6HLv_s#XB*HV{<m
zV_B`BvsoUJs5EA``}q-4O-QucWmHLt)DRm)I~={H1jJFz<|uspEpi>$a(oEDl%1RH
zey*_IqYWqj*Ia=Jh=XfQjZ&{Cr7&RB54h7A<S7?k-^wjWn_;ij1;v6+e@vXpU+)kN
zZy0>pFbH(mAxOy+h>SrKy@qOg>vHTL0D)40R$`Kp`iB^pc7N!PC41Dk&eO|X?j7&1
z2Gi*oGcKF*c;a;<ka>HyZ0F8V=Vp>URVeGd`k^BYG>{cS?}3t9ImoP}3V@DzQmBd6
zQlgiY_C~6G8vnU!cPkp4=Bx&G9sm{dZ8~;?Tw-VPYV$>DI-89xT?_#rilY~%mg!3X
zQ>)YM%!Cq0?KlG=)&j6tk^7BFX>q~Qvwq{*WHeOeQ~wug0^s|~&NxARE_#K3Z<=0_
zcnE~VsgK<*^6YQ-h)R>7DNbdO#8!hUj?*|Df?M0$MS&Wd>lv#@gep39yhnlj)nYi>
zA@g#4#es8QfbI}bUir>4o){nRD08qHWXb%%6XC=9j}CX^oO;6Pz}qO}Hn6w~@`{{M
zFhFL@QYYO75L4IasSqP0qnr!~k8I#DwxgheW_$6`=L1czx4{SPfH<-&CF}ltRiV?}
zO0ul`Fcy@5ZUXt#*Z{Pj?E(Ps^I7Q{Af=^qdng$101<pZS^<SNl^{@|=v-S{i{!ZW
zpsWtDxr_#igIaoaZ42n}E|5=q-j2Gs-Azxbdg&#e@4$uF@i}*$mw=(yx<R6wsnof_
zZUp>r*<m*=8ptRMBr8)moC~OQj-CQJ%Uvs$p?6*TEDx4}#tVRIwis^+M%Pb7h?1+$
z05@ygc*elI*9^kBuo}3f20atb{f<-Sz{3pK<vi+qzP~yH4+T}U0%>u2HM@}s$Y|r5
z?Yf;|9f}%b@YP^1#pxqnDi9tHk?+S2LAt;aAb8itXEf^JATYE>zD@nk&likrHY0$z
zI$qk<x_}auJ6MN$0QDTJ(^i5nU!0Bj4Ke6;9LDv8JLyNB7xyryFIIqJ-6;-G`Nvgz
z=oeapT778949dAse)`>Af%=sn9G=7)<zMW&8Wpt6Pm*0+g+4}J$u~28BEb4$E)xLE
z7#2W%_`|T8nIyNKB<NLRdgV`r{@`%{m9D$ROz9$R81yG*b-E@WI0K63wSYe;YK4S@
z7}4a=-X69?5E1%Yc-|98Z3%Nay@%{}YjB@3qf=&jJ2zP4;|F;l|J_WT0O8q^1HYnq
z;zANsSf@XgmC2L^-5r+C|Ic24?N#0#@{*E5Vy^-u#Bt5jV#f}m>i8uE1@WG#B{gvH
zy6VWb+0Ya0At5%%p5`vObUi~zl3vQceK~f`dz6WXWoRckq2V8hf%%n#|NUibu^Y#X
z?!^b3xv{@c8p9%X<CS;QF_*vVrVv#V`lh14hr<!Dkp1`gfB&>PSl$hTK6gUyh`#4I
ze*~GNU30gm0@Aj>4vhItAvI!Y!0S0}OiWCskA9*r0?`3>4J06ySMlx#!g9fTL55eg
zdOnA>=L^q8gthN467yXJ5#2}ybgMB_!&$uwprR{y7XVa?pAsZJmk09J=nb+=%}_q9
zB#Qn*RuZHQys`{-lIE0^l~wTdtK18n#uuU%X5J-R6Z~BW`syOW!Z~pZ6%bY98xZ!K
z_biAbzt5l`>rr8UySnxBS4&s!P|&!Q+{Y1CtVMx4^xSmK1&F~kFr-KkV4U-BZ)btN
z(4Qg_{hCG4=G@!cer8$Ej3p(J^yi3cC(IFY?w&jGB7_?na-D`d80HNgmHq2RhC%;!
zBz;qXYQQTkL-R5n$8MKGf}uy-2S)%jNY-hZIm93&cO;^c-Z(hIR{w8ayZ_&(_wDmv
zzKS3gy>L|Z66_jZ|2x(M*4VO}J&pY8K7$hbSAy>-06Se)*^s`^!Iu(rz3cnJyHttH
z)C$z-F-5kOj4A$m)iL^2ei4|f$fT_I4Z(}oZuGFjOksh>Z{GYHMt>MSU;H`E%jfz5
zBR9nrG$t~KAFPebL}qFR-U-_O5(LoH9^{xqLNtQk+JUlZ0mH)-bqw86xQs%~kK)a`
zLGn$0YjNqFAt}|Ko93QZ|9!N>D~K<7Np$Wuq0uw(!VzVyp35*oIq@BP_aK?XugtC;
zTIx=_hO}_Xs{Z$m(<i|@lJJ}*q9Fh1C1Iq~1xMs^-1;kb!mpEr{r9xMb>`|2MT%Yt
zH3<IFag=EIA8Q!NA5JeNT_NDyq{SBG6214gtbjMszk)4Q!JjJ7-!3FL((3k7@0<Vo
ztQceZ4R;B+a3`~YOBHAux?YJnab0ZT`reXn`18XzcvJ_!HvIE-9?9sycHqU;SFfs`
z@JzgT6~;61Rfy?C)S<6o6ICZ8T5>N(oO~)TCH0i&De}x0)xqPZ4?Q`3wr6g6`=f||
zx4#_Gw-J}4NDNZJ8{@0<<^xDRpoXg07}&MeFfD}G@8(JVFfJ1A{5+ensp40a<}NJM
z%$DBEuy|hZf#VWXVlt|qkcTyQj<z)MEa*ysj$79|Am{c+a&)MC@j%5B?L5zdy}-R)
zrv64F?pzy-`j11M$0Z-07bE5B6&Ci2J=#l~pJ7<vt(e#T$gsalNS^)R?Aw)@`2(F%
zomCUJpUdT>RdlH!^1@9#-#?Vvf4<tqFU4MarhWzU7y2C+>ippw?fp6Z+`8A<=pTPR
z?XT*Yk28ki#hy1f`r+r_%schF_OEnCEpFZN)7xm6r*$<J%9ZW=nVHCeb??Q(y@0*J
zlZ95JIj;U4Xjxi~{ZpI$rcSV^mY#1k4yJuq_LTO5($C#%I+ku8X)3D}8b~y?tU6lh
zQo*=m&$7M`hbHeKN#$E>+x91L1!-p1@^}8;qJBJG4y{y9+nuTu_u1d=6!z_ms;5fm
zT}~zJXbUsFT{H7$G4RzO^rJ%Hz~5IlZyp1CpuzFa*9$)<t>F_PDu2(*9yzf7nA?@6
zduadTic__&|6a#5`*x$@0qG98YJ>F|{Z_~u0Wev?{qDpKyh-9k$HsPV%)XtAj>|u_
zualGP%My1xM!zuZE&a+R#*I1c<o&_idv-j%SqYJ`xARGNzxkpshks56Kh9{Nl)R+h
z0U31hxFJ8rmP<;)C+hzsZ5<EXKYda#Z0w&<`TN*<7V>^e|NJ;Ze^(U(IeF<XOWr&7
ze;(nDzk85ApFa4PJ71pu`zGs^=YPKtCG67SpZ{w%F$e--kk#Kv&LxRiOfM;&;o*^H
z6TBt)wD#(!`CUimFBO%gL!pF<kCLWn3o`JI&t#r3C-FVRhCRz>-Sl_rz0v@LU$Y}~
z@vU*w`iDa|iwf!+YH%gtW*?eOCC&El7!U+^bQP-fJ**t;t_k$(pI>8Hf7-doWQ>RY
zuJRW{AWyBo7){)KF^b6bNQ5L!*WB#i&{NJxZGMOjWt6OW3}*}ZU6M<XM4Sg6jyO&C
zdgV}^-_`vrdHVW&%(CU7bu@hC+Y)TrW<&Jmn^I`W;?-Z{^-Iyq<K=~3yCuzoR~k1p
zxMz!@G<Z7~5F>-PN1p%n^#5fi?*A(uh^?5+%QI3+P~H#I&w&6;288>c<#J=WEUedf
z-26?V3sr2a4Xur=ZK&#gn{O|$&?Dv7?2-i;LTHK0C0?A`5H!M}@us<BhN%Iclk9UT
zo!pW+Tz8f@rTFRBe!jH`LN<Eo*KeQxP;6HczHuz{B$|1>Xr4_ViIINAy?+4YnC93N
z{n_R1pUmUVQ@Ikl)YPw=$DITX)%J^YYW7;p_b{RM--yn-rqs1BNAEcT072ZN&{WXY
zqb24%RJZ<uyh>9+Q>?k!nr`RUx%#!{>dvU~t?(BO&ih1K^c5hC@}rCpE5D(yYone1
zH%v$U_cm5KJrp0OD>J$=FYb4y$QxWX>Xi|)sWt|sgb_nS-`hmu$5&$V=v{@Fd@gik
zE;==GFJo^)ZLD!UjA*N<gKa9uLnmGjka9Xbg5;YR&MOgZ^^?;9WhD1XnnG=s%*Z_2
zMYTh=6i;!)>9E(z6@mT)X}4ihs@4WNQTbT@ho!CLueS--oKP%WhNA1*{Umf&WiaVk
z!Klyr$AY2wyh(xO>a{${1CV=>ig`6XAV87!c@)90qZMfsxpwA?ywbC(-_(FfDERq>
znGNCoZHBtg@<VCd4?DjMG>~RBEK9RY8~bFGjM7FI#TJ<Le=<VCbQhy=+f91%?!2Kz
zp4AeMn`;BdOKpo5uR>lbQgifY*=^zBj?u;UCH7jHcbc<yM@pK*;^{!{zQml~SiVC&
zG}gFz0Ef%nBVGzrzLycw+#OvZQ7brK8mm-!0AhAu$$#lVbK%ljBcGE0&L!1*3xr~y
ztZ~8<>@R`mlhl<PRF3`&j5ts8dKVj7^fsOd|Ff2=pK2TW!c_kn58c|Ij~$#{*~{Gd
zUZ;|=6WU+huh37)i%VK^3NiE>3hJ!x^w`}zwbmS$%xUh`AAn}elXGubr{4(CaG~t#
zd|zF3_diXmC2((zJN%|9_utIZ&ebrIbo0NNV5_2oBYD*D{*`Voiw*_a*JUZz?}R_A
z3t1qK$B?G`^)F`P96P(7gnawHwog(zU4R4Eu>E-8XZUOtTkA(Lez+W)JIeVrZhm$f
zlKshfcv*t*{*Kq8)x$qINME-nAE|Wzj{ddZDYJm6zrXy?a8%}rbX${Dh5P%o50S0T
zIXFqmAG1j{rJDJ@eZ8|!hD9*ldi(9+{<S>DT)YvS>yPsOa|aLqALYGwR8!f%H;m(q
zjHAHJc&#)G!k`od0j0$<ilHhPLPtbEAb@}gLVz&CsN>L_fb^mwEdfFc2q9pF2`vbO
z7D7~dLWm?JF$qbYLzv&abN{&OS$D1XdCPszA6YABpS{oC-~BD0Z#n0bfA=8bs#U3I
z=yj+{^|VJqdE9w;&(Cx@$0s>l$0&%7P{<G{cEy_*{E_H_S%An~C9&|a?VX155srs2
z4u0V<P*6cILajS9{~I#&?pa>8Fx08G)?`&6xBAyFrcOPKPvlK8Mi_mJxgBh8VjQAz
z=8W`Nn+-ntgPwdKSY6ub@BAZp0*b&@2a-`a;C)w?2+)n}>QqzPxW!mb=M&?XO5Vj^
z<$Yqz_y4+hQ3ulL1uT=!SfQk+sVQ%ale7f8qmTd-$5_gy{YGhJA`oSQ;&9(dF6l34
zO^+pY1UJ(9ePM`sE^Y6$n9As&L{j!sQua(+l$uwxt#qZz>$zWEyR>Z(TC3q9>0~7L
z55#l~trHC)omXg2+Cq6blmH46YXdREOdI<WqB{Wl1>D);CPN|422NnK6tED97aP6%
zpm#%g`V<PL0$OtV=aLN9Abm^J;M-{Vj+_{;eJCoNg0*mm^I0K2cY=A#j228rP3eNo
zUPh<}<<7>K3a}SdUoDbP5ng?G)?#L1KI1@HIkMQwzV%=eEX4wQkGt*ONE_&!fUaD(
zc21v`cN>2WG{`#Q0(o8Oz#X2p2fmhLO9j{yU_65Th<sm?^grbW$Z?>;%h6L5<;5b(
z%slTNC1va`O#m4y9P9M)wtjCZA5m}O+LpC3S8~CalV>-)!j+si#;%qkp2f;r-5RRW
zKYVGv2gGV$tSIf^x$o+iPw?e;xd6cz&P#rQdkLQ2n@rm&-^S)gz$`rVNA}`o+U)i)
zJe^L-`Ay;Y2EWbTi({MDQd_S0hCSPKP~5h-d^1?$UYP;hx!YjGH}{sD;DE{n=0pYh
zSI@K+=#8Mv-fyx}brLXqr&nc3Lprm#{Yq!#KgU6}UjbFO$qv*C%NY;$7I((~v$3S6
zxc>7xoYIJKQEuQLpbTR>J?^s3P%5<Z<qVXxyACJ{Bv-CLi_V~=F5K=|3J8M4QlVp@
z4{fN*#JJaIHtDg&g_Tr0=}oJNd%p$e+ak&i?2b~~XDthd=LqrgOJIjwwhcmZAQ_l&
zZ>!F>F9$;niaTTCc*&F;onkqal6Ie%`#~*5S{C<uJ@N*8Xq{(x=kt|(3crp~<-q;r
zT@@EMmj7hzQmh}G3;;DP-&_2rLm)#WfPlUn({Q(HT{80s+lIshxf=TN4=UV#a&^nv
zvy@%t5r~+Yf!r4hufngi{^3}ecyr={-h}&h0QI{ifTq@D^oBgkV*pe>e(T6rw@f=V
zs}k{(XU^d&Tvc$>X6JBeb)}3KMcr#-^muUqx#4Nzj4x=y&U$SrUj`_Zq}?s03|EkJ
z+_8OCDaWe!e(Rz{bFpx*)KM61p>W(*q2FV<W*T%AM|K<ux}fetV?5uLZPj((eC~yR
zjupT()zsArAM7qif^-Al<s_`U4#-eYcz{<YcWv_g*3`)@2{gtYiV|jKERvSel`6OU
zFE16CCrQg0X4nGFkGxGlpQL}I=g8?|X9>hc!&-h--93B!R#7$ur)Md%=cQ0Auql{?
zT?v}<Eiruv@QtQ-CEbTcY=b@96eZYs7xh<>o%ckFyEcb_ApveQ^Qe@;pA~QJ<c$sX
z^r{rtkH|4lk^>B2W59>S{-+aVJ9so5#-?)7Hiali1{i{Q9{ZY({fOL!OmYapD6}yi
zV4%>ks%Z!f@%rxx7!kA?+@4t6t_5-ggjgZ+;-xb_nebyj|IC%&zwRb*e-%u+)n7~~
zZpG}qKz{c6t~}1Sa+Rp?B?jQe4#v+3@`mxlfNtotyt=vO=5K9o%TND@&r@#x>&)4f
zS`BAfX<Hb4PRNA1yG55WY`LPXl#X+aoA><MMRBm=njv<Da33F2<ztQ`V|*-DH|0<7
zeU{B_FT#Z5{JYP>*Kev(5csS1#B+H6*MG}%13+i>e#D6qOO*6JBaTAB(jL5+=P>qj
zMVBaf|4Dbah_}whKvgOSJm0`^zLlD%{wd=R3`a7s{OZ-V4E>RqqJL@ytG$dE&Ck;v
zZ@gki=Mx@043taM^>6HVpQQu7<l47LHPEX~_pthRU^a#ZBNhNNMeg9){JW^10QLw_
zX<1vRsmNSrc=MG`_&;?;gjXfIGo7hHywVx|&+|C1N)*1W=QZ~NKy*0o?LCu!G2=~u
zE!G)1^E?uTfEiY)M?iTr3UZGR(i#5#=fCOt6|gzj;%RAgVZ>Epi|7vg_3L~yNRF#v
zgslHNx|d|~x9H6c_^N#86GPmJJMsEQe5hvK{4`6Y80+IkYne3u`1it+bhj>IXaI07
za3xn;$2>Ce4cTK^;v-pH-;6Lf=XxiL5-UX$!kI6D3+FY5mkWb}tmJY6+gke7;>8@-
zlF20Yrg(if@w-C)k!U<)B*dmsLB{P>-?Z%s<<?vCOB+k$Zu4F-UNa;V@jOqA5gSIx
zqXtR5yb#4T^@4j9HcanbYi=gp5EQ^GRf)^rGZC&RFRRbrv+0;PZ=RTP1WVjCAon&-
z01#%e1={U681%>YNXk@e8@*kc`KB7_DmxjU7=g!``+vkgFrz9-s;joiBgy+-{h&6Z
zuTijyQm&sDQC#*Ech;HLyMK-^VEo32-N6Pmng_hzFXsVmGr-V2z&D=|e_<dfr5Nl0
z0KO>%oqYe@H2<g4#Y3a<g7cC|hK<Ty8Jf{u@C`*JuajRyiEuo5aD(zMzXhZX=d~az
zsZPYlc{OrVWufp_KQ~tAdm4=7sNLvq(Hj9J<obZ9QUwup*JeQBILzJV_HwO;FR{KY
zke6P*;|;X`)5Q1#^BGb44&IpuOYoKR5}u9a5Ar)~a|>J~;Nqg-2>5WIjcxmlhul=o
ztHAE`k>#m*u1drFmwRynzc`kAB`z^5x~4k;({0+XxThr#WflCZPj#`*HG0$Xq3qcd
z$}mO+@GBIq+X<a0DtMEKaVf=XdfDf6ggammb`e~7yAtNg-?47^0*J~9@by%KCJ5>D
z&MTnjHT%!X0>oPv#Sql(M<x;2j#wYxDiP;UoW8W{&a~EWP5+U6b>Yb}Ri~+pIVxrO
zRuL;cdKgue7&&o4LBX?!+(JZ&R>!{{d^yUUp(N*6z5S7<w`HxlVOb$AvW0D?8AX}r
z>+~bypjIt$4vj_V6rU++r_>k|qyB8HW)H*6(d5H{^~*d?$imk|4M>7#V-XvsZ-Mbo
zpSap7ORF$MW-IX+8O3xgh#!Hs0ui?<bWK9wnKp^O-6e4>z%coBe0in9s|Uq92)VoK
zOjww<SIHWKkO+7$t{Kt$V)<7x{}nwkQb<gox+4^bUsR?Z+7;ZWZgJJD2_Ii>XLg3{
zG4*Y#n|}<T0o7AGs@N0DHb3rRRTdLJud8&YBCq&XO5>mT2ntbFpK%!gltQf4j1js3
zt92#F_wH(g4G>d%fu+r_)kbBi>LXK?qWfxf`5g+?&Dg2+z@n)6F@uehBpvJ4<saP8
z0kX>VqCJcl!>~W<`6kp=<UQj8^^(=!7_-7qLV?7MR)HZvaTrJM7xNwzy8`u(S9z<Z
z2t~P+y=>DrPU?tke8T$<=QE_H`q7H=W^ZxH_;TQZl2An8GVaul98|sgK1osk>qR?E
z3YL70Zrq88f#-y<FYS~YiQCUN)c(mDR{RHbiU|gZm;H1f(~F@O-pYpX(18{?zAi;g
zpG9MRnu^iuTgV_E8esycncFyyhXHy`s4m>{37LNnQ&I%<;Ez%8*lt&lXHi2JbD$=%
zK{U+ZoGg_KpnYhb*`fA2QB7dClULo_Gp3U{1+-hAV=4U@i)$57GhD4DO4c6ulzXOs
zple$Bqa1(Mz1z^-Z4;{pLL=Q98#4?;5Xl{^5MSOeLb-an-kWzUY0on`LioHp-JLI=
znF7q=zl`jaw{8Degi+tcGtX*KT9KpRm%nYG=5lkyGoJ@W<{#v-+Uw{4<(d6ng*Xd(
zKA}8k0R^>w7I|XE08ZgU%&J#>X`z1oxPdYG@&JUf`>ZS&?!gmt7f_y7O`^BwMy&~L
z;k=>1b8G$>U);byES1e|H7JqL76L~!UKd{f$lX1FU2&0IfS>1OP#*r%5GLM&_p~S8
z!6TK%mh*5m?zbh9e>O|g?};=412(aP*Ycmx^vVui0D#Fk_eA0Uyxylh?_=CRly%}$
zYs5c|SA=&Zo0!gAslU=0@Xzyxo|PzETi1cwRuA6Ly#?HVg5bw7<UVF5R^=Lf^i>9B
z9Fo9{@7LvySwkg&UX*V{-B*YN-qFw~cX$qi*nfTj3q`rIYNmaNos&=+rp?87e>}Tt
z#lzeDpVmE}b*tkduZ@u^4DG%aQw&m`FJwr$p>isZpWk5D?=-8zJ!#m9)<L9lL!c86
z!?5m0{Jf(?eo6Ba-I%k!e!jeU;fxr&3yXENz@%Ud2gWvMbT94OCwp0;drqSwKMUj?
zuD`QS_*!n%6t2x60B(!B49^G*I`11R6+!llPF1!IwM);Y&&m6bJC`dzaSBFoUx{n|
zSl*bN0cIm+%+Yewk+C}kAosEG=FHfW@g1K}FnuB3Cvd)l%i8zkonb!!HpC*Ib=^8v
zx~@dwVX#nwN@V{W#dw~VhB4Opm$LyNkc0}p;$Q4qjVnbh_nAI`X1?{$ZBYRb5tQXV
z1K0GdWdA4PAq_^@)*pxo4_a5y_e9G7-20Bz^9f5o@J}cH`nR*PF&kg6%9z@jwwXPQ
z4Euk2V_`dZx{B>Bf)isj*waMjzwaj?DfAat=qU9utGK6$w11xWZM`B3fj_>t@Bjen
zOvb|<p?^owJ^upyH6g{YA7GXkNQLGNRxN{o73j6|pEI8Q6QV(_h?0fV^k;Dqyd}nO
z_e>%%1~op@(t_O`ts?yP1vw1?EVuuxuMe#;?$ztiG3)F7M5?AGlvvjmfe-o*FAQ+8
z2-t3@SxvEf!pgzWjuE}P)<n^JA@CSKaKKIyw(%rac#Yid)&7Bd*5wmt$OCcH@!`G+
zsNwkQkpkh^jLzaWFcrE-0(T>{cHFwjDk6=^c1ZvpHF2FhN|9AscPyX#6c*C+uUdfd
z(G}-}?v0B9?OA30V)>kcHsTU*Yr>pUbi`g{f%;Ps5Gec#77h?y=bFVl@10_MOfawN
zVTVfZsxc+TVTv27y`l*7MqTmt2RyU)>5Sk|FR_>c-!{;|NXHg8EFT&lO)PC=Z3N?)
z*D}0-VEis*AbfP=zcCi6hy3<>bOGRY*O?=H*r%t?o0e6$Mug@&#cR6JYD5d$<F-)W
zY=_8%&1Q^o|7J4k#&n!?uPDVSvAKg2*x}p19Ri9jP(`c{R5?v}ct^-uQsQVj29-Ps
z@z)<5D@6}A%}ZTsI}#GdnO1Bu%n-$T(9@axa`eu53VbCRR~s*3anHzd$emA|I#$x=
z1c>Wj0ZT<rYMyQ6#lMJ~{+Sp)sF#G`o7EX)w7%mtLn_-!)uCSxGWm-IzPQYEnl<QX
z-1^A{?;p;-vHkLkBE7wU_8HTw;s0j+JrfjlQ9-FLz?-W|UY@1W11J1pxrT`!rjCDg
zE2-3!A|s*hKApCv4$HUq^m715jv0>VIQ-KwgY4iDwg2gd#ZW>Qc<h^dH%)1?*&MA<
z!;4sDUIwtAgF{RvfM<ZiG_UHbPtXA*s|eJY$b(Xf8CDdV1s;_9;o<zI`XB)^p?OUm
z`DiT0HKuSg8aIBa-11ksg;J2xu30?6ng7f0l6xIa{~n0B{M8~mRQ6uoFz@zeaHILr
zk?l`VEfO;|%WDey&*jEwd|vLGg>J9fRD!S5g$z6S?5<1x`<be7c-9!beR5;IwSN7t
z3Xxe{`B-&_(@m4CgPxf{$0MI7Z=Ui{dnLc+%kOO(wu(y9KX2yLoBuqr9o^~2(Uo5{
z9`)~6{g-C<zdz0YZARpG?_Q0hH=<uc7TOcv8!5`?>1McPEkw*>jK}IH$w_Cr```Us
z^}(5<xPKcnNIZL+7Q)`bMu~oklW&nc_w3ICU7EQS&pC)=<vx+eIA4DMF9`6h#S^oE
z6-GPUC;H7j(Tt?~$mR>=U9%P9+IL`>r!4NzbV3YataBV7Q{6Gsv;>6LnNZnAhL;{L
zrGVtQ*1C7ww{#PNd4&uc%-(AtuLLA~#pr=Y#lkg}q8AHcfbefnvw9=<k#jAh#^)Nb
zye$lHt`;yskQ~E^CKDur7eN`=9+%+X=sn0T*+?fSC~RagR2PaU4j7rUWMrLr1gq#8
zWEfeCzq_!0^4@~!jwgnBm2#iI?pgWi;T$~GTXF|}3GUc<R%F$m{^E4_!)Q6_>>U+|
zYU~=vh~`~w3^3&Z*1|CK;Km)2s<uDL33B537O;Mmb~DT=o>+Q~qt_>|jYfsc@6Q_2
zA7F7<4Hs68#?^#|wunS|z#+-O{$=X{zH6sD?TLuWc+OCw3<N04!)doqeLV=gBZd)9
z5ttDC4s8tcvK${evGud&GS<fx$ScjGgXEJsedDV|3U4Ct(58@<&;A|S_@9%p{ok_r
z|CQP6Wgtlh1D^d~nL4HEHg#JH@BW&okhJPp+mQ&E98v&<ilOyt0Is#^@hzu9S3M~L
zrzx)KF2PoXuDc2FijA}|8Iu^e;p)8%JPM?NeXe>FHiCqnt`!i?jRe<;X(h}`e0L~(
zJ<?_U>h<U^Bf^|DL(TR0ewk>CLeh~V8=cJl&i3xmVJIrVlJ<hc;j|CtI(mR##2o@z
zwb;n|Jaeo7`Gv)WpWjSG{?AL*|KGat|Ele{G1j1Xo#yCOY3MWgZyR!_;CoMfP?fpH
zK)EhJ#TXPOvCTYI-O3u85Gj?nySW1;GTbZ%k)q-{R@jLkF!Crt7`D&QruYL@BW7KK
zYn@TK0zhT?5P#~_-stvn&!J^C_6tVK-eT0VHeWzrjo~zWyicIme^QQffLHDT2W-o)
z1FI`ksj6Zk0Kf(#V0+9_4RK(=gK87zKR88IVvTKDzY&B!GAZnX#U4iYwY};$`;Ssu
zAmrNOYY9+zEUzwvy|`uUx=rJw_&=_!KmWTbq#y~<%n@#tGJ)SH3tQ~P3P!wn&93S2
zvI>z-8(DS6O#wwVXa1werkg-IFS%z@jS7L!1^&W5QgClDBdqn%8VIuBYN4|T=LHg;
zfI#QucH#OxAgxMm900;y-B4&K;_GgI4B(je*H?cpikZ*QwQPyI!CQI=usd)Z?@3$f
z5|5luSU!N8fLc8hH43>puH?eoaxx`-1aiy7n5P|+K<2~K>O~xPUFZY>mG~NO^u3<>
zePFskWd|>q+0N%MJTWok5)d(Xz)wz=eU1uN({#g;nA5*9JH2Q%=0W+98@&`v?*nT`
zc~r7vezP*D?RYBs@;*au=ju*w64Het7-06MKO-+-EnGp7BfI1?g((1rM~umF8e!7&
zX{qvhitBS{Hc`y~cPnE49}%<vvcTvq-~+rY|Lj6BJOxDVpldHa0ty&7l6GqYiZLBq
zdn(e2z~0!8>nXdwYS`0xMV+emoAqQjcfktO?l&yoM>{{oQ=xF-&k20TO&sYKiP(%c
z{zowX{{Y_lZyKT`8GX9wK`(*=e=RGvIQrZ7zwSJA`0HPPJDaO|uKe4>r;@*iZU5se
z_Q~&(?`jU?cBXx0^t;NhNq^8PliCj_51uzpK6q;Iu-~b(T3T9P@4Rq1X%;;@KaCV+
zKo+c+g@u?&2tFpQgv=d@Mie#jJwjz6lv)V0nrNzUR%hoXH3qkD9o!(U!kyQDf1aS|
z#AfB!|GO^X_qBa>Q=jxFINi3vg7j1ibUErGnzw&c9#gn}c-xbT2z^pfrsW9|S-X{%
z)FjKF)dx>Mp4|mjj`6YBSYT52zNF&X2U<d|7HH^%Bn+8BcZ%{u>l}@Z?%S+!-IHYU
zq{3gHghjXOOB72C9XK`9<RZ0_SqHF>CQklQhdPCwHyyjVnd{EHbyaY2;jAe*-7!`@
z9USGwUZ|aBJApX`x{W1fmWN3x<~Vto#LM4yo{eE&0vCH&p<H|P7We81<I$2<5&8g=
zNJkwa*&&yJyo00#T{!3fmus0xKZQ1A2DaJv&)EsEa$0WpM=it495f@Q)7(!k2@2)`
zz!fT(<`N4(AH&7WO(;G!Va)ulI*EFXO3yukix7}nLS<)2?D8U%+td>QSEN5{Vv?h2
zUZ{x@R0MjbRkbblV7n&SA4H;_cMJ>vu4CX8ez8+P&AYZfLW+u-8j|ip7vwz_;g*i>
zdduiw%U}rHl{fiXt{`Eh;3oOIZ<uJd<~}4{aeYYK5JP6XWe{@GQQ@dXoYbGs@-8wS
z6$mmkv4)nijK=1mh!lKY!~=uQ_~it0+{coeVeF1Qp~iG&-^h34;$mJmD%)G^`jacv
z=M2BYSCrZLSM8DXTlJ>gFtl5dqypDhdn5w}{AGRqWmUY>SDFG15SaMTZ-mWLMUa_p
z##Coq*Z_(~Z*eOSHYeU*x89o4uW(18tkoJAfsE;iIxvIjB&E`isM2VeQ*CoVtwB_x
zck5wNEI2%lUp9mCbGsd6VjpvYl#{u>_+2+6<w!Y^m-7x2JZVvU#MNPVnm5(^7UMU`
zEa6h@2$<e>@#}AcJ9B4k28h=K{7pg#Z9-@V!W|0Fiz)45xI*K()C;=|s;>IO9~|$1
zU)?x=1f}dF466^x<@-Q=){1HwM(j&z6en+CQOd@EswS=~*0)!Wf(@1`&7;Fc+%8tT
z?Zz6X1t6q{NWnywg|@Ko>)0anY~M8BBGdBjukDxWkCQThR9B0iRllOi!mF$9v;u3N
z{MpbW&pR)n3lJ`kTVveOJ1$*D-9#6_ujRzHfJjiA%c!5xyDT<Va1PD2x!h?;>fiWB
zu$<Gp@4#{p!aOfRwS+z2E(x?+FWLa}hL*n#5i6C7Co+d>UF$Ov4w71fHLx^YR>*FD
zI0PL%Wa&z9zJWI%W|)Q9-~Jx^QTgZ@i_&+Pe)ipz*(<h$5c4xI{W9;G8`a7cP1vh9
zc%|<Bq;#fkeMlS`htEnBGrYDn1&_FGQL;3h>&F+*y67fvof$8foq_mtj@q&|gjd1M
z&8EDsXL8?Z=^b>YP2bFOt&&R4=|@J#<qqEU@S0CYBN}hBwKS*r1e}(TswG25MZad3
zd!@JNkm|wVxw8gfjXbRH;q@df_EK$|d{8~KVp+9cv-PW)E9eJKgirF5eVt6LSjQfv
zuuJ>zT0ffo23%_<IVQI$sog)SHP)L2crPHe&m5f_s4#PMHx&J$<?GaXWYHaLJtU63
zu5BXs-sB_w5BqVu71~&<P@UwDo;pPe%bYq&B4$ouMB;vQJn@`0D0{ZlfAnJ>*yr?o
z$MA@2bqE1nm_HkYo;pGTgZbJ*544j`tS7ofyY<d`2>Iu|^^--jI%rEH5-qbZ_a@4g
zfJCClc*TZ);I+DswkFu7N-v;21&P4+#exSebLO?!4pHxZ7i#AHh#f{xH44_b;OV?s
zT&CBFr7iYXq4kVsMFSqxdPZB(fb-sNAXH_Un4=ZLoJ+W2>nRTJL4HoqUEmouA+%@(
z2#eE)Nl>&GPDI-dHYhwn4_-X%3JCM#BO@P6!7Ik+Mecc*2p)E^hSqu{33}=<2?FlO
zn!T`LQ2fEQYO)jM&sc8y(Vavl0&kxnsP)IgfM3u|P8E9^Qk`ATR<8R|Bl~8;Uo$ob
z=6U`~9ERY_oNIcyXINLr8(m%Xhkim0Th3d(0nl9A?-W*MAxMjy4A!uY<yFkDokC?S
zJ8>$1;8>fG<}PsJ{q|Hiz<<CROfFg8vT*fo9(HGb8n?DSU#w{-M0b8$PE+=2zR=O_
zl)KP$=Lc+e`lE$7bVd{66phj5U4%ADGSy?N*dzA2yhl`Lx6-Er2p-KH$eenQF5tIh
zwRbdzeb}{k5_pUMb}sO_KG<=a;hJX9=kVg8GwMejJE^ZD?JrR4+w79p8QrMhnS<rm
zvm9kzGfPSwqU>8C(KO}#pRA1m9)<yv!w<&}zN=5;+zYiL9R=4?Kb?dJ<g-2~Lo^gI
ztN^v7@B_!U4wklHefJ$A2dE~6|A-A(X^ElK0wmiY9G*Q*30gPe(+Iy)8u+v=uCFhk
z<4=>KT?}|<bV(-YcvD*OTeioXY+jkeK2NR3fU*po@*JX)vwB6NXtkoKB}kDh4$X8*
z{HZ9^*wyB!&<9T+LtFkt!aILzBIJy58%!K^@X<dNnd_5OUAFC6$>L0|^aiS))-EfB
z`!#D&b+>XXO+6K1*3@2Pgdu$<nwNJ^B~(yn{{CWhAY97a;IPAd?z0qsq)U%*F%cY&
z9x6BGSR9_?$QEF|5kFWfR#24rfze%4D2FJ-<FRLe_5T4IryxrAhQx1c3xQUI+o7(a
zyVp4Mzr>YVE%ux*TDV)Qxot+h(W}pRRKg#W&Gj1zXoN4RzM~z;z4acW9TqQb#dq4Q
z9?HBQ%V_3*q4K*PQcg~Y{E&n#!2<0w6}IBv>qE_vGjB1{Aqie<Ozly#6!*oV3pYP?
z#@y4;FTOviiCjIDU{IFak{NVuNorMg;l<8N4<R7$650Gk)p~|eXYA0s`mhq#2%Fe;
zOFNCymGholvC~=alfoUBQea#?wUHn<*V4BkzCHGBX-z@TcgENJVK4yg9f`oD{fLFI
z2od`P@pw!E@U@Z|D3LvV33Ur1T2Zj8>dpLu$F>{xd_vt#--X?UNywkF|Fj(<sz|-s
z9edKTdfJ=Cee^@GTluR$O8Vv_y@z!Ai(%dt;e*Rr-nzcunGQ|k@%aNjnQlrAwngJ$
ze+oR2`#i-Tt4K<GQD(XD>P5YI)ntUf_LPyzS)G1=JY(x1nCoU$ZyOV9+Zj!1itX@d
zP81^?{5K}&G3>TP*y~_D!=t5u#TT$6F%=hj`CAmVK+v$xgQSn{kel~ApkZcbq`VL@
z-Ltm09cuv%I(1p5Lg70#dF8l^uHk7BtaqNzr}Qnr9r;GdbtP}W(#kmLhgGa;O#a(H
z<jP^0aJo5}!Vl1&JEH>Z)rwXGD-2{o%JHaqzwR+Fu&3s~4bdmDA!M0nGr!gKVgV~;
zLd<gTFJn(zx{6jVW|HqF2yVEGFX-*tI#}n`j5>LjRej9JpYFgRP4gYlQz2(3t-y)c
zIB7@iF_Q{JMsA1HdGO}wyJx2xCZ*>~P6;ZTea8-x1+G+(MO4~6G}G;BlR7paJ%QSd
z=++uzhRN*E?3PCh?fZ0GJM(5|vkX6wLy4ETp?P%Uu+Sf|1%cdz$6B}*j8SxL4Fdn2
zgkN4i6ceFwJV7vDvMO5XrD@)HwJ+)ZTvWG~86!;QkB;cCGic;T>&6T@_Bc2=Iz<)1
zrYT?k(u#3xeg9e?*(oln_UW_s4x@LR6FqRH#)PyQG_GY*_^P7kt#G_#bz5vAoZ69c
z+lHn6?^L9oZl>wx>;+kqK!U^=2)d-?i4XVIh#O6dloCg+D0_Pok~&;l{K&|2{h5~7
zC9scpjO0`?l^X1B@(@CE`Vxg^-+>mjj*hn8-7R7P%u5lkZ1O=Qz@&I~$1%wvg#(Kr
z>SdiZ9AZR@a9~KQ-u%$qg&074Bf*K~Wuk$h)AeytcyjB6x%dV_91uFCtbCbpIo+Pa
zf@ut^@W6u6)MkSzkLE6B<wHOQ9sEuH!c-p_yHwrQ7g3ywgk{*aHYOy*h()V^>iMby
zK#$;T@ZQIX)1WTx=nFlY@gp`)Y4Kc!1guf?PCaX}BDlIZyWg$-g@8cRWxo?V_nJ(&
z+LJm$C(VCg7zJ~^+EXR`$<y^7_zv;?t2gk+5(K8eS^&m@|4ZOfG6CjUl<4ME=qH2y
z;b~fi<N?htCV0T!T@$uHbr+Ud-e)>7&R*6M49x4{@LFb<IU_KH7xN1%tb$?OnB8DH
z-L+~NcXrYkbEie^Q7s}8Pv&*w=DqeEbxp25uhU%P27gSq%$pt8$doSdLF7;Ik+6_L
zH;JtwtEG{9@LhxQ*^C3&@~wla896b|%n6_$u6oGIbS|2;zB8SY(rB45WJ-S5I~hSg
z0j_qfVi%`sU?EF{meu{8z=CQ4!5jo^l81NUTNJ*BduBt7v>#H)0nDNdMJuT}=?B)e
z*ubtdO6joPB~{<k%!L$<#=U`DH>q}V5WdL0%&Oi`%A+;yO}HJD1?Y|MipHC7P#}ri
zn`nR4HNZXvXb<{RtCv&L4q#^*&RmYfSqEps72-zRii&4j!7FH^7mXDy<YFx|`#f)i
zUvo3=!AkD%r~SY(utU}-=jJ0EM9LAGX!f&Pz=l*y_)0TfAr#RH>2AHpy$+;m$trdz
zhTR5g`Ys_Rsf@x0$Lo87^dg4N7jth+xmi_BvN>7lQYB{t8y~x<eys{^A?)!=svdu5
z7Fdyq>-#X!Tj~;i<EkwD&l%o>3Cr}Bn_nEfts!L*<|&_^g_K;(yI7xj6=1Z6m>bko
z$X;Q8rfEi)e!zV;n9A{X%KN5y@lqPa1FaGmeYUg&u)He_t42c-0n={S_lfQlG~!0Y
z*mg!%32dB4=d2Ew(O7ow7Wm@#t;MrHu%IZ+q_wYpXs6YFkSlgPrsTPIjWa!Aiu?t#
zOo(}}>A?rgk#ne6E3q5K`dvR0sKAZKlYh}N(|Cl4L91L_Dmj602#Od&!0*`F@K_Ve
z@!pSyV$VFyndd|H=Qz00-QDdtXqZNK!BF)ZS#wAU;gnv*%9?j{Vb8$$i~0jM-7V_F
zx-lu^<dk4FDyxC_<J?8>6a+xFXE{=_?gBqCJ6Iv^0E<xxQ~w-n{^Vo?zy~`Tc+o8V
z{g&U#hK{jf-TT}Jy_>KBtQIH0GBF7)lv>v^c8B+{d<_v}fN63UuP7XU%F6r_ie1m1
zLk}utPP?&}l!oZlwy2N$;DvjK7u75~j?E;iKYm~4Jz<{AR0_6E2A;7=tT4HZN`tZ9
z8cgnGgKwDpWrVmvO@~11OB9DKn-{Llts`s#?gUS{sTcI08&XsT6ZEzY4#S{4^S@H=
z?rH+xfEcR2f8Gz1saYaYMFY-IR>*!aE3+%)v1nyjn(DjOfdm2O30lLRCO?-v4Ok{@
ztxh#{`ZR{&nCHH8AH!Bf-r!{eR5Q8USs7zF9e$uFSp^zkSz2n;)}Z4_?V{goQbtMM
ziDQAqG}xj>FPjt*!5h2+-<W4keF;8TlS;!hNNe9jwE@zXh?!(x?}5Ee%-~rEqsq!=
za_<MXVChvoab6GR?)VM4(Fl`*vu_d@(b5{5pF&3g6w^B%5pl#7VDXZ7LF<PFG{s=f
z{!ZHj!8vV~ioPdZ{d!qb?2jm~$76QdeW4m@yRd?5;`Nh14gzJt`*whTK6mYI@*35m
zzB|>YzjYA4@S$tsK!qp#$|Y#}U!%PyzIpG1ObVS@D4*oD&Z}jpmEQyUomBzG$TQzy
z4CXj_NH3jL42g|ceo<}eZ~(wpaZ}who~P4;b1xD^Y)GsM8)(4)fc3{p3V?4`h1MDL
zDBQ^bOst3%_AX?%Xr=MAuYMFgAy->2sG5O8(m^90fH)mJbyo3O3((l2Q?Zo!0YLM+
z<DD`K^a~%=6g6Hf3L6I|s|1k-1x0S_O1I@agpEq(_TyHNjc^t@Q}H?NaSgklka2Zd
zuW2{-Tz1(mDx$jl2eEq|rJh)Mj2jGmc2+6uZ3txBlc4vt>GrK>CZ8!)Ht)waV&c<P
z(fq$)VbGMiuWkOGX%_$qRvJZWULzgIT^lU%ls)}+gC&*#mU!Om_=)FmG<(VDDPRml
zc_Agojqj$t1~`@ZX{-O(9ex`_t-36Jf2D<}qB$3plTI2n>_ugf%;~5rbG)jobe3bW
zbD21BUY2roD6v}Pe%}#Bt<QH|4)SyDcJ&Q!E1m_}KkWneA9h{1d6J&d_&bGfw_rGM
zV`^U$%dN<Dj@NZ4R&w<*e|dXPVRkK*V#K3T(<uOM4T|jWm<48B8sB9YO(#Ay6<z4v
zQ0DGoMQ?pw=D3x67$zA2CgSh6r8{wQwZ=$+57+=qaNw-MBv(3|fX_4j5=o^v9gPj_
z*;`uLXmTm0XVg&|haSUUqt|+yc?KD+pW6{Dz0#APrW%`gm=xA<=G#aKO!AsoJiFle
zQ%k7#TTIDC_S<H4Y;afN<fL`;uuJ&!=nTNjH5e8wVDVW53f<Z>=dznq)TMdUS)k(T
z`VSH3#}U<zcAN*gOjXUCsJ!({$ce{M(<nD|bR*u|D(ZDq#nTi7UT4mkpJqwVnQfj>
z8<{AYwQ(8UUo&IEHlr4FA#b~jSl(|#0M+7+#{z;I!`t7Pnji=kJlI3ra0*9_CLK_w
z8UolO!FwvFHLw?6(%kG1mxYN<WA)B2=mmpZRqVL?h!%6b3x_L%EZ0)No!$6tvz}z>
z0sh_SdDMsFOWLm9#j?OEmK&VnxNwfz&Hu8zZf!BLAYg(!ia2x8)BPCr4a`ohkF&oN
z0Y%YBf8udi1e^;aRws<lL{P1&9q&jqyvh@iP`MYl`TMsocx=NL406=#4fy5+nZNS6
zk~RI2?O?s;h->yOx+#0UyJ%}z7lWv)K4EttTe6;I_%!7i7C|9@)N*p<yv$1PK0K+b
zEzwABHvv3nFB2_Vugi1=a|Rk4IQa*qiMq5Q7qy%M9dC8dPU&)}%eEc^jfw{2=04Vw
zFhEC&fzWDC+U%aIp7rKB0oh!kQqOvXd$p8uzZqaKKGh899>6B(3|qArUZ7hAFBm5R
z!VI?9FuE4T4d2SPZ><dnU^SHNYmWV+uT@ay<%pzf$#mx$h?&mTKXn$*8iS{^hjd$S
zU4j=XM%YDlXDKrzE(R0k+H;^eP5ahVHoMWwH(Q%0%_`8fGM{Vs<l<t0T#rY`XbF2y
z5vZnvQ@+~5iY(qvCx<9Pmf^KA$Gl&n5onYHXCR9!YYB9=+tW|i7A|D%_Y(9363n#&
zz)55s;Z3wGZ}yF@F$!#OIqF7_vqFVV?AabY5fG#LBpuD_cymmEGhJ*kt6)#}ZFqoy
zAt>)4NaZ=E|6!4&|3k}32}7Z`f~~1nrQ$fD=4DQHdQa|1a-(}Uk`FevEljuL^l8Zo
zXOWQUlJP^Vu*`NX(gMavSu56cW|Dkqj1_nlPs?`m4h36hy*sUGPpt>n+CS`P1$pgN
z$UTk>^adUz{TP3Wl))2HcY_UVqXN;5=y)U2gG{f}H})fu83y8GNF-<h4{FNpH?QS^
zhfg^Q967J7QPd8$G1{`lX_9uKCl4RoRB&-5(DM^x)OnjmCP$?R*;i-ajl7O_$@F?W
zd(jwhIECTpy;?$v7S1Wk7X8M!<_Cqohu%Q&O`+~K|7LVuh<L^W9G*RU2waTj99^ep
z#_N-g_}>oQ!`F_rdDCQOCwmq2q!Y65CB0}C7e_U;kDA!YG{X6eiesbDiv10T6;3#9
z<bQ^5mulhcI1#$Zh4y4trW%DR1?l46tz@|ugo0Px$|Or6;@#ld%pu>Y%i_M;<2(9l
z9vp4;ix86o-Typ;i0ldSMz06Q(}7%TQhmH7DUKc48;0_<%iD67mi0@2kd_efIMHg(
znYXI~GGWaRXta(fi1Zc-mtdo-KD%bQpO2M@Rlwo}$fT+=xcAd7tbTnX9@Zt8F|M_P
zBFen_jmQgqv5^I^<_C&>FANk;>|M9nqmcWS&(j~vRI}$4X_Yu~+O@iMguG+@9a-?F
z0*BIH*JYtHZPL*TLmF4Z*z6|;FK@Kj^t4)ni$T4A=p`9v7SgEFWOvt%XCwANthS2w
z99fQ`!-}>`WIHbtK%`%^O4QK{yv+4W=$MMW0fJG$Bi=CBCv*1wgxaa9G^4GeFIo!%
zM)W}vSs6@!+o6>K=G1D@*%V<7IPnbWPvR+uz#4waumeUkdJ=R^l2~C=#Q)ITNeP&$
z7D;xgs<FwgOJdr~s5dWg!9(pdt$<NRQD%EnP9myD=@k8Ol>J!PqwWrv#+WCDPS+*b
zId6Mv;KV7*!uz%Oo*cXnyf-H-0&VfUJw|3g+M5}VYhmYsrN|y7M!tR2(q00xyzec@
z@F*fkJJtj3kN9imLFT#-+NrGCCjJ~+_Gg21<)_+$DEkNpPQOoU5HEc}V{7_CK2fCE
zH!2u#WY?J8xgx!vSl=Fs203zsKE;Z0aE)$iLoDpgXlwCYsD&Ym@o)mBqLW&;Q84vr
zRq;tC=V+ooJ)(T)S1ULDPpomba^h^8l|?(_ypYrm2gN5&w);n@``U?uq$Y==+}YP#
z;Kb7bFQ1W~_;sExI&n}IsR`c?L?C21rF7xiG0BrHi(sGO=k5eBnt4>{mTZ)GTtdAS
z#X~e5pVJOY=EP4vYD>FuAgrqS<hN&*S6j2rwV!M54tGMEsr(g<YF7Rrn|x$$>33PU
z^jKKEtvmV>dIm8r%U9yl?zXokbIg|itrxYJIre@lXQ{$^VC>XoQ12K*V-nK9Qxj5^
z<mJwB+WUb=2AUJ|*e!e3>R#2wooyuf7j9W31VjhU%z?u#KL3^98rhRN?Z+ePk#=Af
zuqPC(mO}GSss2nn*Q58be6Bs}tPk8G4%MSKrX4#SakkjO0jp2RS3ZlYczd^E?A9Cp
zI1zuYcyC1?Bhmhi`BdUrA<{YftB^QLx?Nt8Z^2E4wlt2}Nd5ZR9@v*ep;zcxdr@l)
zUIw@)WDdRvPMEAaUcjz0G`+l0oqr9OuVYWt9#t`@&MDt7*-zE%jfYcfwdlFl*yX&M
zwrrzZK+;vfoUT-Bg#-_tAfiGq_+%A6Yfg!=rK|+J|9cNRca+e8I@EOX)|bj-C7PPf
z-B3udqLK#Uq<m#NGrB#R2jZ=BXIH_AKNXk_TAqPw-eScZsZVGXh4T7+)H5csgS=N_
z6F8wEu3<}MKyEZD`91Tw9;rUtXdD4UD;_;lag4cpD`!#{v2e8230>MC%m<wa_&(uC
zf7e0TQ!Ri^vS^QQ#`Fj))47V<vSi(mHaoPX!CY4XGA_XP$ee`p@EA=jp2cHrxevDl
zARto5`u5Xh4qg_?L91q0Wm{+F|3w`PkuVuFRptcH=K4_wa|Ov{h>X6J88~=-{7;IM
ztx>vu^RvPl@EKNwV}8vyQ*Viwctf!Jwi`M~bo@`AidVGvpNhZvm|VxpVa^@d)}IAK
z0{by`YQIJX0XnB<FXI!kXWv04cQkU%+o_6#0Y}*{<;00LudcLhnmuP5L+l)udq;m^
zmu`EGW%7hD;`Tt+NLcV#X5o+y>(2@Ex);OD$n!iwbOYD?r7GfRtj%Mosl{`5tW>(X
zB^C~m^4)xLU&+oMpi}_gP*p=CDn#~Nd{MvzRpRqls;o4$_2p1VRPy%<Nu~);Cs$g&
z?mqYc!@}9S<xF~@7mf-Wr@6-HDFafBM~@7OVRe^JeGb=A%v_JR3o!Vj)@PW-82e!6
zvMn{Hp?Frdk>%#9YPUj?*PWc)T@X6Zqp=8Hd?9KYmb=h==ipq)Y1Ngf+nps&?#?%L
zWEP|{3U{GGc2>9>ENzPG7^dt1Qq@j&?hC|{H@YWlnP;kMdu8vk+*iZgdgnhZsW^YK
zzyx<RbFU$YYnL*)iVAxAK*_<&Cd$@3WBdGNSoFF}gm}BInH$x@ugEl9L$Ef@jnQef
z5vgdWmuj4j>weU<S6g^JC4fvY@EI<$1cI9dhk{kgcU}&AL4LGGcgho5{H23ssj7f2
z{%UV2Ys9cT`40FcF?c}jvw){1bGFjtPZj~j;10LLmnm5vcobz8Z(NYPUSWC}WS=vq
z=}5rESPjP%qLdxP_@bPK%vn_m-$C`cVp17CXmmI5{t8E{Cy~P3*!2Z1U)xqa5vpuw
zTl*0b(q+_MGL|(F=b%QX#`qV}p%r~sEG+|Rh^YQ7yEmCezehfKWgS8Oe2yG2G#}cQ
zS=!JV|8mUu?&#8e6)3d@@b@xxw|XRdxrSwM$vHRqVZr)G_o%GQ#YXj{sWajCnP3Bl
z(VgKx5=6GYp)}FW#S%gS>GvbfjDoB=xT%!X6iOxEWS$ILzwn%-w76}lZJk`PexX92
zClskBxbV#S#}LIm)5X#++m^Om58e+{`<AmtDg0Ld`N(r<O-BF@b^d8|n)_(l{|(Y5
z+w#l!qjtv8uR1cLT-r<(OEPB{L&SDUf-{|S=6wWH-{GQ>P!Cjn1g53~5~fm6A3yP^
zQ|FE1oBZ4W&$g@Rn4_(%Z^dh#TiZsp5IiL3!=t9;Pwfd|v)Vjtnm8jl_%Z6$??NRP
zl&@NlFip-8eEzz2n3+P|pdL^`ZSR=?#74sJBEV?6W8Tvkan>(0W&zQc7A2Vg=u$O$
zL5{v;nuB@~jvmvQusTP)A8h`D#y|Mw@GYi^dU-I6xupW%Q56`JAGVa6<-S<D)0$lB
z-kI*5gSvGW{pUz>r9+^kp-Q7&QxKx5;P&0NCtH$&+al4sFblHL^Tp7kLN7E_JND<E
z)YpWY(P3m8Eg^4<*f-axPMASlkm-^NhF1>WvD(FceqTJ*^o~Oqc@Bzw`+KaTD1YU7
zqEa}`jN)om*-R~VH?MWam(d-fil+HOutsszk>*mD>Ei_FZO}1i>7T|p#uo`2Hrl%6
z@2hRh<k&!P$*4EFImAWA#LC`*>OBf46y?}#4JgunmKx)N^;0gZ-XC8AtGy?xJ|qSp
z;xD>(mRh7>urz4WvlfPe<XBWpDDgE;Sd#iiLi?mwlGkE#>)Im$Ip+sOYx6R0GSO<1
zsm4j^)bDff06;rK<3PB}Q3YA3A+JF!`6H}IKe4C}CjlF<!stg>^yR2=0k*a`!jrfn
z>JM~Hd3Q}^yu=J^tMJltqc+ICw+dW*#R`R(bl=rdqN#PJu_b}Hv*gTONKYDN8u&WT
zBdrJrWb$AHLo)Sv7NiVDdi7K<$HKvQN#=#26$cJh|FH!lw2j8ZvPx9ZK*SJuWr8gD
zZA05BR&$d@LE|eB`eP_l`uevyI1557AvgyPJ7KIdOx&3*EDqt$X;M%em6v9@e`PjA
zT%AYxN?|?;c){b7P(tSV4YUfRQyhnuoeWGgv*nC$OzRGs%4=2$`l3`mWXFA^#a^^R
zT|u{06rEdyx>9kDQLvIbKu?DqbMRmQf~jWCu7Ja*b!Z-FkgYrD?jr-=;YnXtDv)Q~
zP9rxWdA2<;33&8)1ht7e^buC8AD^=`v1lcC_KbekL>Z{2**7zg>A`jsR=j(dAeHi_
z$(hFM7Os2eVwS8Zd=>JOb>NLaA>!r#n0FY#d9<S)j@GHD-YaG>*Z?a&gc&?Ifw2Nh
zj|C{K1%tyM8yQEu!8?cF`LYVDomp22PCU|_tZR3>y+*JO(vF2{cV3fT0Cx;hJHpY?
zF4>at+>@<3b6rTy-gt7NV^1o%v$7ba7jR#EDU}bDE|4rT*8`K++Y%kj;<XBxmo@W@
zep=fx;g#+W?NOksqAg1&**V=mVeN*)?U)nm6@4ec(<PjYq_i=hlt#2dw&hgq>0wSz
zfThn{h!z2{k8{cj8yUPF+@G_1z{Od!s=CID;{G<-t;cj+&}aKagC6z}63epa2NvIy
zJ=;NKH5#vDHfo)oa8|RnRPnOh|5U_uiyn2yE`dCooBtehY;<(Gb;VHvRCt_rr9vf=
zu!#vEgH|;}1ZAq6FUd7}X;nRtqZ50Tx9*d`QT?|HrHpEhu>KHFj6KGxGeP4r198HO
zVCn74>XUaJG6PpnTayGfKQZyT$zY4%q4W?P=#nP=pyVL1!j{l0qg^xmDg0j1tXasi
zwAK~`%S$Q24Dmf>aZWwRxGY3VaZT^Bk;FD9QCjqjs<|F&#4Vk%cuWR0k)y{^R>&I%
zY?D*)6%_@ykU+q*${c@e^b=uqaX}TVk!e)GF5KR`9@0}ifC_!Y2dKk0*jHM30qp3+
z5fB#y4td*8lMb%6>YCja4<wxuyBLsU72A9A?%Nr~Ym0@tTLVkf&|^(L!&<E;%G8VB
zA7r_>WM{Z*HdY%r5cCraNHYhXBX6NWp-<au)$N0$oopK?SiTONy0wT>wtZe?2d%3$
zQfy=$mP0S=bxt?bh3;I|c$EF=9ED#XD*q~Jw;%9Iw_C%z!5!IAM>=z~Vz2d>69A`5
z=A2E>9f?B=%JhbBS7c4#WkbE<vp}H3-B%<fHjt;z6kGyy6G7B9%|+@6yX_JC*WGnP
zcYq86w$1L=-FlA=ZoMOWq2*hAP!=o#>GBdndXiS2W=*_{0PnACyV$dH(XU*XuT3&@
z88wowYT+)&Iw&7)JvC<n7(fSsPp;4-&Yuw6Qr7ACP{wr*w2gSX3}ielNPu^S>sv^Y
zI4W0Ef0M|{5(t4f5?`h4?W(_SXf9o!)bAW_JINkbuIMvB-Reo25$>NxOzL%p8ZJww
za%KmNF>emX-e4~TIoGbsEAF0Tc<Vw{sw;N>HcYQ+XkG70-f;SXBkA7f&oJd`4YQfH
zzQ(cVZx?@k@oC>i!;?R06NTSM3ipo%_-usvMGpo52X*7vG51P08LNo)itd8v*^uoY
zlHE^=_iwaZ*9z4&vpKax#7K8&{be?;r~jJ#?i+3YKRl8G_^2%x&!n2Q^}n9oSU>op
M<IlC{umAeL05l7E(f|Me

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
deleted file mode 100644
index 65bcabf50fde946e7591784f9ec173d869e833c0..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 54464
zcmeEucU0Bgk|k!v91sJ+07?)L5EUedih5B|vSbwj$w-bD%!&wl6;O#PIZKqRq9Pzc
zvP4nfl97zW*~jqub+7I}dR9-*nl;UO@B94lhVwh8PSvhmyXwvvxns*2H!(6WFf6AW
zKcc|EuppR$VczS-3-O!0>rD~(*TM@Yjvc|j@$>U=rYrun#NxP`6$1n7E%N_4UTp(<
z3=CTsC`Y7~?EQNh9jw<WH_Z;5E)Pk1xnSGf;M>AtA9rv)pS!J|;p*FKD;PbVZ+X6W
zVln@#ucc4q*&8gb$j`a5dfz&x`K$Gg9o&%m#%;^pxpS{<P&;&J)VjHuT{N<DM>c2Y
z^X*wW^kLDePK~r2o6Pb6=Zp!JM}i*~|5_IVgWK-Ap4M~z{zuZ<IeY&6`StqwWq<z2
zpt0)Uygz?7k!D!_=Z^^s{tv!*c%x5HkU)5aVWWqjWc@uhi8`YslP^N`-Cdtw+)Bxg
z)kv$$$xizGgj(`@HExA*vLQ?NGc&DUztHr%WqOS5%%Ls|o?W|^Me>i^g#`)!epzth
z_hpy<tly1qaq!<KPu=1c7qUanpG()nL$IqlRwJb7&tJI-ZFeJwN`7juGHm+&Cx&xN
z=l=8O<aK;}u-$R=i-Ap*vMRTrpnO3=L05fR{EpLsF%Ng1_KA%D{Nh+qr*&bcwb7SE
zy*c$6jb^dh+3G8$rV`?ECX6^aIl1@lRl&QA8Z%O6CTenx3LkOTr5O8q2r_+<p(x19
zKUlbY)5R*g&oa!NKU(sb`f4h4W+zmmXFDsxeGMD=wKC3jm-y`~H9wG6AA8DgZ)azC
zkV?KMtMZGZ%Q<)Kh;kTe-y~)ov3}Rt;H<1H_da)KT@PiyicTdnGqW<A#H6I`>Lpcu
zjhQ1oNu^Q8yx5|YqkX=8|8CP6>f06Q)b}}2uSiKrX`Q8x!Ni8&#_;;f*24Sil^+Wp
z70CK=HPCsGl56_>#S0O;K24kcCar;if$Gvmv$W^WpUbAW{&=%U#IT%$QcmMMF{OR`
z2R+DD#g|{p`s?c}_l*K1oKh#o`gOYKS{c@#-I;hfDBRrK*r?Fbk)))g5xUf@T6cBq
zLyi=aFACAB@hT%-Rfp@S5!G>8(WAZfCup1YnZ;x~jonPvc67|Z@$p95f5ID?n3?HS
zAKEMpM|-|F4u3x8{q*Ukgxq)Uj=11Ix+=m?mzmxg^xvqvj&-`Z)Wy=$GRt$34z@x$
z#Yj0`a;~WJc*4w}Z|>RB2U5#7?J0YEW9hVH4d+`MkKeZOwIOd~+4<cjwFz!5NlGkx
z#(L{j&2xV0*QPr4)Cyv)LapZ#o|jitR6Unj%3Ir0745s%tYO3G4>cM&^XO)m$x*|x
zzQ(PwV*~l@8bx-EHmZ^M%l1!CUPxbCxL|ou=(itf=FK@UhZ&{|Z4Xjxd)2ip-Yk&L
zGoZ+xI#t!?s}t8&<U46<73L-Q+c5pL76<cQ>T5`UDyVzYCRaIHMLkLX9v-dLHq+bB
zFM6cACfc+<l@@Z0&4I(Do=u9Hg}>yWR7ELQ++wFj9KN+8GC264Sz3MFOiSJ^@(>ck
zWrt;tALpt`we4$&Ji(uSWOB6kAxER@?DWK7kwDQN&Le93Syo6){m84HHmnF+pq0Pj
z#lZJ`jj?0*HZdi*RD_)xd9#T9((KH1VNub;`d$?nvnRV$oXp+zkyxNg{rS9`owYA@
z+iD*Q)61S6&gjeN#&S6+_#uzs$s!6P+LqpEre|PajFoB|NZ7%`^1ax{zR;hDfB60A
z7Nh4GJsG)n?NXYa7>PZXNH%V2sI<*uzif(4W@E-s$pNyCM-vVX;a^{kf4dud&>mK0
z^!d3=`WR=~I+h|IK1Ccy?71OtRqX-BTfLTFU(XRSsyt3>wHCAKs^otE?%lX)u1&X!
z7WHQvEd;;g;^Ql(*YfM+Xs)<*nDv0|@K5_Y6+ua^Q{UMgCpyEYQ@vW(v3%H|g`=J5
z%k1Z%^wy<Rl!u;}9__Www5c~u>r5(@e8{n!RjiV(S>N)HN9k)>*1>o-+cAYnGXMSi
z&KF*igBvAJydPS9+HWskOoj4AJw3G|UrkF&?Zn7%$$|a|jWjcradGpeEa5ehC(3Uy
zYhPN>vS7gi^=wC**Q4-2sXpwkQ<rmFEXk#t9;sQh48}$8=}l`+?aLjh)_CSa{eZO<
zF$U>gxG+3YqqAsP>WddIMw*=Z)f69ndMubnU%*`YPVl!YbW38MW4*w>WZzWB3$KL%
zj$7cnTqo<B-=<CzM^t2<J8fHbx5E2|;8ERt&noyUPNqdYe*4T<bY5_7X%$Tyy}o4a
zRBBOaX-#FMV(jF#h07U>Mx#|?qwWgioTWYT@d=xnno1bnY{pI<f0jbN<>s*TF=ywj
zktx}7A3hD)l$S&Qa6?`U8FR%;KW2A^`5jUZhwnOHpF_7^rTf9-GyBYsMfcdHwEgy)
zD(LI$r%y`Q_B=Mr9#P32E<ZtY4^OJic1nwT_065JMpGs5dxk~Jp@99D-x_3wp71Sf
zv!u}K(#+#CY`Pcr9*~mKxx1dP{IQ^J<xAaTRk0eXFJHd26bsN!gXdB29{1xFB`eeC
z8|$j#Z5@eAZ{K9>KraahWlQV+yVUe63>p3&g4$QK@@_6atlkgvT&c0V*l%CWbD6u3
z9zW)y;QNAzE7d|EiIRVDlSZRi_h*k8wH12v@bECEHwQ{Msawt4XIOqD)2_dI0kh_+
z)QqBSTenKvHC(=BGdol&HTI?NJ+n@-Pmuk$J8mwUbob~Lu2YWJ){<}<tu&6$4y%e*
z<&}`of^{G~4re&<aB)SvZmKoFlB$V$U>y<gk?-^Kc_k&a@sQQU!kW3$qxG>PFneEx
z>AHV$fCm}0<ibzOtm-*r$$3FnH&6W-E|3kpp9`$Nt1&aJW<j5jkkB5RE+w132DKF9
znrdbpXZiL`73JmDgRKP#)tV|R#ecY^Ou5dExrVZxK7Bf^<fz?#7D<5_ItgLbE@O?V
zhIy@tmYI#!Uz!yz9}j%^@P)rWOIuqTw}8NDJOx>vz>|6nTB&F-r<3PjN4R-$a8c**
z(%QE6_M6Fptj`QgOk#*Pzx3uRdGN|-hi?s6sp=ShyuY<tX0@=ea8<mH)TyU?xG%Zt
zP=AI_jP~+I?muTzmmG=cv34}$uuYq{Di1d|MPXp$EA0KKV}licpHqMKzH=XQ4NBQ0
zW~YX#r#)E@7`42;A!hN_O)fx;)3=B-t)5TU!^Rk1=J8mB$<^XamYxglEvx!;m;H8I
z^o4UfEZyDR%Y$ZycXrQUS%=j7fhKIdIVkR)o+`K-mC>pK#g7FKr>CboVi7U<?9M;l
z1RLzx$;oM0;xAe~yzI-DFGfvS>8Ymm`o_I8M~)pc?x}tG%Nwf|eb|5-(3Bx{;oWUF
zmtud>3U#(Um+q^?X+~}~dnL`RX>n!E+O<rqtXe!}63*imGInp`&+vEA`#-g=k(ZY@
zs*ZVL<60A^r9H<nB8^$werw3nyvzTHfG~hLz4d8nKDjd!-S?~25-;A-Y8zj-cI^v9
zle4PY4xuAS_crfas3jmKrVc|6yt$H{KXUw2FEG}v<TzGw`W5Q;d;4P2pQM@Ahb1TP
zp(x;iv#y@iQHfR8<C7YAw^q88t>oj!vX{E~g&MZpSd#nQyGeZpdU^}A46rWgdh5>e
z_v)h!`NGe#@;Ch6)Dr6aGN#?<UNac~#9dhC7-n?-{IfuZ_KlfEgOPegKGl|vj*iE>
zkI2Xv!U`S7TW)bu_<c_xGFGKEQ;kpDyW;OU^V6t4H8$ki?yQR?^n!;wHB1jJc_dN#
z$wn+dI_<#aZ|?>lZN5*p8kinyUS!(ua5GKn@Xci}$+cV3MyLD6zhdMn{fEEY41cNV
zIz1Alot?qKy>sU=1lo~|2)~`W9!EV{BG>MeyRKLN^XE?>8O`Gc^!E04BLwm4>7@c$
zyIXs)&Q8Rn&&@NV`??JR#B3OUdh}a3e!H_XrftdE9bE8Dis5o8)b^iO+@u1xM$(no
za(1aDzVlhR%yF#Gluv4+<ApZ!D)B~-jl#s}7xX3}@ETP`d2@7je17o@3avaM%OYld
z+)HBgY-CVS<X`hN5SZJ+XdxExR7lU=#<e;|&8Q|WV6SQ2MJ<zrgoHD|X<e0(51Sn5
z0SRWaKj}fLmX?Xc!SZQmorO(A!?HP;7BxM2@<jdRg&XQ=X0aX~%P0z(n*MdkhKlW`
z5<{PawTGrgyQ)qk8I-Ol^}HToP#Wm&$uD`4nO`%4;D{E}G_yvfOxxaImj=h~*lK`|
z+!#UK4;^3MT&rHR=*Ep3b?H{&eL0h6z@3dZv$+DDGTz}TRK{xTBQdSc_wCz5y|ph7
z&s(&jveB-YRUrsg^i;|<M?~^2vw#ZV!brQHxe)-0HgoPSF0M01Mp5mhLGf%7jvZH*
z@|R8a=k_Nx3p@O^`ku9Vhte50O$mLtR2D2fG5+1_u+eOTRjFaatIO3cY4yEUb+;F>
zJD+*D;}lKFd50O6pT$;MS~~Xh#D1lv>v#PSo0oP3o;LZydq#?atgL&@led9kasg5B
zCN&vSrgh2p50_iKq6a!T2c~CT`uzO<TwULyP;zCQrQfgKe)JMRP+g8oCWj%`Y*ZQX
zpfvSOpz!Hyi&l<qo(MGi_U?`&0C()<6{_AoliJ`FtYT-aF5ZIeIfnAz-}o=Chpogd
zE;u5B%LNxe`&pt?l74YwyJB2KZSC3VA9*V?ezyA^HquGbKZVnL_4dtI1acAM>RsJ4
z3Fq^MKWsKviYQIoAZmFrGDtK)4h~*4WQ29at(@smjg(8Dw_U3D)pn!+5r<v4aG_As
zU^fu=%83R}4vtIMa%3ZyK2GD9R3alIkt>;h>|VBc@5#-3O}Ivej<f3izR3HXl<rv1
zF}T*zaY<sbCn;mB$w>t9jPGp1LA*tkm{E#xI60`%dh;4sA#?Rzvc{7^t~2SLY!Xp7
zmNIvB$LCffh+F^oDvhWXO?kK@W3<k=D$QI6sE(o>b)DYHw)>nLQl7BQ=2`MI0N$0I
z049!Ik>PL4UAOq^P}94rqQl{yx(!|&y~m??-2c&|kpA3R?GwKIRk<@G0wkF`=E)+j
zpzsSa!1o)zcAFEWlbgeuxrO3>=gwzjJaO=ZMlgHz9Orc8E#dggXe)Y2#=wWoHr=sl
zJjfg2$o0A=vxZ745GS8)KjulW`~;qDq~CQ`13N`NO3zm-3|BD1X{@iSDLYfdD_!7$
z*k2TEdya9%@4aJi<9mGdZ{q%M64rl8e1Ai_-`VZ?B$r8B85tSn@oEI3<Nbu-z>nMR
zE&py3d>8rh9qZM<U&P>+w9??eohbMZ_zKl+i&n7QujqVK(OHRmvt7$Gw!ti|z1S~Y
z(q$sTtTCgSI+=lBC|uq)!|#70zqG$NHm!*#ydn~RCzJ8WGPBJ`1F*j;)6QIDVkLdU
z7p?Ao`GRp^feQNfmJLY=2*~G+T0cH$>zlXu{ZkQR^6DHHr;WPXw8#OtZHYRP;g(d4
z7mgrNGkWjNq@HOTyMkpuxp7E_kC5nS_Nry!^v_aP6<m=>n|p#lCLRZ?415HSW;>3Q
z0~H=J>bFcciR<XGNM)5m#-obg7iMXzt3QSN);lcImIllU)}!mGaoini?zRgaFGruj
zDpO6iYwB9>Fq)R&+4vBs#K&<Li>}Xu2hZeR94%<G>|~!EKX$xZsdZY2m-hs4nGeT3
z7Ey)x>{0dfF8+b<UQ%7iOe&DA59h6v;@Y=Q6_=`#z-g_^@0pC6bFvA*)v|B70SBKL
zx*qN+h&n-;of*p=fs5^wn*HfD+TR@C(_=z1){Kd+=&D>-*G7xH<*?u-={jI3;2Dkr
z$y@<y4nz2gL_SD3lfLY+rr>zrmMC8T$(m<}7m;L(>>Qyk{LC`ATL*}}t(vp&{AJ{1
zR;3bS8z|D|D{kd^Y~ELL(ZIlI<fZE@HTsmStnBsCcT{9`OAY;H&ER~ztP66N8q^OY
z;f#e?cXIAat7q*j&-Mr6k21wQd%z#ASzcZq<8n6B*0gr=&#6D4k0Yr`t|X@p5VDD)
z-%<8Vy8h<(X(3_Z3ZtRd^Hx5JZl_TlM|(C7b{8FEcWF)XvZxfY?kKx&6rq+BrUp9U
zxC>J4hCQZrd(OEc6C2nV$=figI+%6@Xvd?djnPn?Jk*he1_y7w0SohsMgk@c83iiA
z6Z$hQT{`l1slZt&^U8?Nq65n1rBbsE>e|Td>rzcMx=g~aUY+~?!-v4ma%xRU>UV?h
zAMWXCha<>`mdJS|1NxsW^3}3co6pOg3r|?*XmZ2KqbF`;jH`BB#rhK-WCT(51nq+-
z>*fU0S4{$W&%go@I8+jFAg?aI5g?wVJ*Kq@(yCfE-p#haqR5<YC@3n<8{G$>g#Z9<
zq2HsDDUOQ7IHa}pVrW=cm!El#e7tscVfHEVe4`gubTCdJyR6EvF^+c)L#`G}yeQBC
z@IC%lSX^8+Gd(HdG-{&d)SI$8X8#~{YTy_}0h9x|TtPZ1_dMIn#A{+T)63N*9fu=4
z1f|V2Qcdn?zM)QmCwl24@apT;#VQI8f=5ROS(*n=8n68nLU{;2p$Z$N<=7cYtyfMR
zD<Xid#p>iq4{g_Jo8IPJ9b^(@4e6SNg@xs(rKUqft=cc)qzT%L4aDPsgj8S3%%2<<
zW|6K*){57u>bSIxOyR*#gE298EfpW~V3Mqf*U@RFPR0%n4YeW5AOCTF`R2W!j1FLz
zKw#9XtG*f|XqX%;N9UDAY+tYK#NGr8-F>ZAW3W7}-c-!C=aP$QGU8M_=#Z_knWpu7
zhd>|HX`XMZ#4c<vXmB1Xrq-`pzg}oTzG$3!iqTP8Bmi^BiOBm`<}KPp3A633d%z!B
ze!-(hStth}XsJ^(aMFiP>rIsQiqldf_52O}zAcYOpTixfu!B9|Q)O?7;#Arx=f4{X
zVp{4LGRa93-+%zZ>D}X4%gk&5Cd(x?vp;7tCI?`}$VscEj}Ek)RA+`HNwa=F9^#yo
zoN%n0O?R~+5DF~>F3J#Tr4dll@$S9mO`3L14&gPW@PS*xE4blo!Q)P7n&XP9_|b&@
z2){&d-SfNr3KuRsCm!aM|NhVYUrx?Ud?AUkV;47x?VuK^xs18y&5{z+!xhB0cq3;G
z3E>#&_<Yzrd&EaBP`uMB0DR2Z`pI9ki&gulmsAx?-qHn&iWEzmvYnVVZ|24K*PCVX
zotwple5u?#+*xr#f;!kr;nRF|2IPS}5a%O*|CsS{dtx>j_@orewnCwRCxl3Yg(wj`
zDZ{K40=N`_bRnbV=H}s2*O`j`n#gOcQaOP`m6b{oBbAC7ARCz2+2es*HF0mVaG4Ip
z)vVaKaU*Pq#xc_KMR=D>SZJtWc?hNJOHz1=)T}cua}?ZES3`PIpv$OQXh_I>@%oYt
zF9@|886K$cPt-29+p*_melm61F{s;hsCXYO1h-aWIz}z&s9m#58tvgjI0C+r{^sWX
z{wx86clG2;50Hq(74c`1%`~b_cy@Ey#x>=Yob&*@K!3ULK-U=uiUO6I1O7*X>?%IM
z8=(|%4P@@Xd**qg8XG@wosIplp|>$pbruYzHQpM9)Cz8w93Fq}l%)i+w>kvSvff8B
z4*8)+Y;9BZi=T!b=zDb-XebBta#fat6+jVx$1r2LYkrz>O`IhA%8{QVdN5h=68Aaq
zY{ll87P&eqN?;8U379DgAag1a852g$eGzuMo)`qa?DOr(>r{z3d!AL1Y%mxx@t|I9
z$4<_mBHJpaJ}R~#mTrbjrLArU%+yVI%hi^#{-$8*Ycay+k^q*BW6XsztuRrO6j9{v
zPsD7ZnwS1miq~!gt+{gRh%hWjp?jZML#;8ZZ;>7w7-To*l?Y`64(l7h;;$-ye+!Y&
zL-1L^9IXeA9)kK4!<{ObfUBkbRtlEu)%u&VPiTGRmy(hSnI;xXaDj_iUPYF}(8)uG
z4wcOSGPWgEAI$fnTTua2lg_=Fn>)RegX1yGcz$dRKY($9k;7zf8gHd<QJc_<V_qvd
z9Y{`9Drwu_WC|a^WIerL>3aKEx`Tcz__dQ}W^tX>v3|`XNTfDcyAoKg2w&`GvkY`&
zzf3yJFvb@6Qv`-C1PH)im49dLPT3TlRFfyo89aF#ZYXp=;#NE)wjPy{4Ly;IDLBM;
zJA<4r1f1{b2LRoZTR+C4qod2*HPn5&U}N&VO(Kl7#IZ0*bl0|7s&#|BmVBP_s0z$^
zY_Dlk))xam_#21prbXp2i)d376`xp}FP}@t2fp9q_--whzF{j)=M3fYLTgdby@aNx
zxzo310U(#!FDEw}R@*l=@afaZ6thMGxR=zHwiY~e3DmNk*yuov-R2p$D}p5f8-#<J
zQ-9;IO!r2ii??k)hrYSC$hbMjCA1psgCF1^QrTE9shL*|%&#<Zso4ly>wcW<*w^f=
z2xeYcPEIa8Rers&LCG@vPwlO(5x^+m;1aXf>xk21OhIeknAjw08KR9qbsuc`N>{Ft
zB+e5)9pxtks>YgK*{!E{oO){CI41ru6`5BC2$B@*PKDg=X4`txIk^jVBahJnB_Mm^
z#L~0*Br~7rj-Pby!PQ9hA#4cHYBy+cZBt-FC8Qk+y{DUV*LdrB9Eub{!RXRG+VIzF
zD^<q7Su<^9rVzfFFv@BM8zzNKYInz;efgHI8~D8*Za~57mXQ7x=fQ%Vv~@q`T6nii
z4_bfz{P{Nl0FPdQps`I;z5__vfN#>NgqRA2me*2-gq}Si7d+7$^|+7wuPgYnPA41J
z$SDlSaE|pi>-6=`#d#&jhphe4%v<WPh}Usc=}uzc9KHe+1cY5CtRFsja2^|7P+GbV
zw^oQxJvpInxa0HoOHxagENKUGvJU}=%f=^O@I#D7T1oUY;2eoF_h8|OSNdQR!@%QL
zf0qldxTvo`(QpfaZ6hU2Czndi-&VV^tR-safdlnoe#w`=f7l@<)$C|8{-dRO0*GKh
z%>e!@9i;H5hIFf9oc$?Wh|8IcA9`*5Vq$hB!ChK4+SI5`@6yhyHIPCwHBuKuot}8|
z=+V09TofFvz+)yk|Fjf7_u7pyXbvA!(AL?4ok3^w<v=F=oSEz|=JeIPkfH`>+D!i_
zH*evx1f#`mpDw!s6va|Ei5OkXnVB$t7#L^<&k*Axclz``9oOlUPSk<8xww9)y8_f`
z>sI3?khMH~^288Zbva#g$M)@cG1}R6!*JNG?d^`SQo0|O#rGaySuxZV9sd+rC<>e>
z;SNzB5rp?lts^W4JqHK<!KflwdfpJq9FIW;s87=Hp~Ni~8*c`|7saOQffU&;Ym{3|
ztS$>tN#)d)x=F_@p?mTP>h}co!G*R5%}#}r4@PvV9%ov&?lJDd6GY{+F5+O3BqtZk
z$mHuGP*rCL3JU6jYS<wnQnikWX(QYs!1E_qZ0ueDp5dQ$y`nid#I{YsuHvPED~3-k
zH~?UIxq4vg=TP<sdoYyxL+!;o4jh>Ds96(*+w}yr^Y#L9cwB0|x4%epvSCHA3qMXL
zs<}Ds;9F%_pMB2Qf~U7wV08yuR~_`NfW^kT+OvTVO&C@SWT<Pt@sN>`@gxvMtSvIY
z#kC~Z&#SIZw=#(C#rL|T!UFVb6V9t-;+|I+WLjum-|atQFSinS{fw^1z|+8QsGj`L
zFyPYU_e}>)Ft2Y2nW2$sKX@~~8r#A(2M+|uJ@oNuPYS|mRvFpNPLKIU`?w?SB^p`m
zXW4<Dj$+kUqj<gA^myiCUnyYaFc?<#1Rj@nh`MZ}&^z^97&6&8gG_i{^~LO?TmEVR
zdO@-&b`8KpyWeW&T(vw6H}`S^8IWmYe*dfQE4l}8Zx>{LRHaOU&QGn>^)~dlTBW|;
zWnx&6b`rRtD1|DM-v+J*qz5vjk%?ZzA79_7Xy_>NJ@PO|$;%~9=Ye2-s;OT}@4*VY
z+5zguxU#Wlx7Y?91$L!_25(-*CSIF0J2Of3DzK<ogZxCOG1D$h@;d5wLto+|B0fgt
z*DNXvuTM67ST%!OF*TMya{|TdmtE}OFVj<rMBugd$v5(Hpfsy36%-VX7;S(LZeF?*
zH8gvz-R1+|-cu(x3hR4-@a!2I0HNRA-B_b#7hsmrxg)3-^*-VadBrlu6Tm#mmbq|w
zS=3a-s0jlQ$}0?1E|?w9mx_rL1yqhe#+7_%<mj<u-2l_~hnw$SaVSJ(spm@)CnZ)r
zrTp+p38iPfH^{l3Y0R*3kyHtnV<qVYZC!cgoE9H%?@;isXUDYoeGxh4HFed*$MYyh
zJ?e_aMpqA_n57aXrcM2+XFTx~Ax0G&%{`Q3FWJni8aF(tJT5ge;8ksDVqy}Rj|4rQ
z1gA@%58<p8kzO>LR!6IzMs<+ptbNiPRe<V2B-Pe{P>ULF(MEIl&#68kKuS*@i=rfI
zA1I7^{(W$Erg=8WrzTb-d@28{*XH$LAe+@CVWE-vCq9e;V<wf3@Cxa7c6L@<1{<>R
z<V}W3A$NZY0271s(P?^$=n=rTTj#UdMWC46Rr~S<6%k^wGWSN^J(jJ<2|s)J@?}yT
zZf>4e+L^Rw&6=yDz1Cu}ZD335yAq>=0DQacyt((|Fx?98pmHTXD=6kLcoAnQZg25A
zKmOG>M&mD5a*Y*@mPLGXCP}?K#ktvKY=Md2<Hwg4yd`Och?n*LTKUol`4=$^-vHI*
z;X6@p5J9Q({-Fq@NvBYqR6$g>!IqGM6FhxJBXzQu=AJiHLAM%d2!eFN*D0_waleF#
zs20qZle=T8#g^;zM@6AU9i6C{nHO)ZgK0FPRs?Fzp=@_XM8xr5=~X1)jU@f}${jm*
z=5NYHGOC7R+gN?Il^or1q{~4<<?Pu@#Wp!0y{aPQ4-?4(A4-{|=Jiq~RF=uuv$eu&
z3>*gUo*oplP_^%JoBaAEr}gliHNS41qhlkCpmMvH_;Zk~T%w|1y6vh!3xVXLg`iX(
z@@Z1(^W}n~qNG$Q@U+3~t19EP>eVHuQwpt|U8X&+i|NnUQ_rkpXYWtb(zDN9*6Pk1
zS4N6~FiLM;)h;kcDnXJisL5SQQA0hLKfQHL5c{^e;$B;0c(JGA4vF8seM{CWTCZjO
zS%zBglymF4<*7NPWuj%bG?-$GN~lq)@%$8Ndj29D<EoAG%i3Bux*hqpOP-SP<L%OU
zl5DK3rU4=y4$mShL@t$WIVTl(UW@C>Ns2u;1#U~a-q)LTgp?n^0u4`mA*3WQ_rl)U
zF9t!%C`9bJ@NRK&kdzKWnWDCKV9myiK<TfY29Kw4d#f#F_#2egbI!(0B7NLRnImx9
zE(5UFmvK<s{26sa%g^%#2+ex#;f##-ej>&`r8z7sZoygns%Qf;k>Y=bZEqBSSp+l!
zj=d>0&I>O%jrP2h<QlJUo>@P*!G2JDZRyZ1Wnk7X7x;qM;`+WM<%{VfkV4k*$k*4P
z5#d1T%$akn#(||@5d7#>zVsV=*&SLR1mh%UP#{ze6Qs?pQU8Y0^BDxvDujwtk9SMM
zjsw7(sdRZvtZ7SU(3i=7KeX-6_M;w)>zah;$AQ_c0lMn$t`~adp|)S?=2+1grC4?4
z8XebcHiy>Lu(Q)@YHD?;FV_rNpv2{PFTD{spc258)bfCTWGR#-6O!_<v+fvu9fS8l
zeZaxhmIYsCZ7cN*q;OsgJo``_92D<1f#M~!#7JW8AyLJ>kM?rv)U5O!q?$JJKdT-B
zIqcLJB;`siv(Yc{?;NrDIk@}7*&>5)JqDSnYfYj&&cF#4s;{uUX~u=r?&k8%Vc?aa
zTsTF@YlI1Jl-m*DBaoXtJzy7CBNli*oW5v-UdzU0!oAI@_c_T8wCks!ct?pN;lT8+
zL!~+5xm#Pj3??h2BBD;}u8UEjIF3}Qk_xJxzWz;39f!Fc?w&=+7Gv5DDsK0#eRcUo
zit|LWSfK7V<4s=Ybk}>(tv6kfG>KW(@7uPTi7AxGN(jEUQOPW7OAI@ir~zvtXAc%8
z0=W%XE<b)0?eAAlR|L&)P)Nn6#>qnfh1gV|=~Kn);xc+Vj?qS(db{(u$<94cO{cwW
zet^0`BWgx8_t~u@{;DG<k=z}8oex<Oh}`WI>D&*j$5F6K=`oi3QsHoO)0G?_ttVYy
zuv{h%-|L(x;FSMTYjoRY#-;~+|MqPdbOI?9k;J{IjaJ{AT2dAuY~a~U6`yTsPBB(3
zn~sb@MQ`JeU2SKi7IXb{cf!%-vF$)HN>wY<wh9T=Be1ZrELonwnXLi>rABno<+tfI
zKZ-6CKNUf`b8ckWS7)_^b5}SbQ3(rW<BfdZV)3!f@`m+M`-0wK;U%i^+I*4Yc3ib;
zKLUF-k}t{g49Ai~sGgo2>u$Nl?xQp{DS_l-8ilFqFQtLG(VWt0N;%y7dBl*F58h2-
zF<GIh>w&yQgz~<^#lEDCciPU`Z;#<OE2A3+b=NbNCh5&PRin7ENr1O2F0(Ju=fsC9
zIdF7F6=C<lGKIne7~#U}IkNYcJ-Zh2ZD7>Dy@0utzP-%(drJgw!}Rg@?9@ljd12y;
zZsaTy*irrT*d$V@o&yn%cR+y4(KOeIN2|l@Gi;($G@FFiarO&;mu=rMvJ)kJ1kQ&_
zEJn4L_gcT|F8K5*x%~(sm_Qnx3vh6%IJJ*xg(N2l`InfO$Uz|#a$nYv98v0krck!F
zPOee=fOL7Wp=*=<_j|zx(E-Kc^pI>Y`X&<Fvk=rLCYxu?8o{c*AM)ru(|%?ol;e2+
zmn4JN(;q*5szkiuWYSR@vJN!3t70uKdo>Kc$U8DS{+F*{R530v4n=MRq9FznDN;@%
zV!0@#$P1?Ry|x<6tg(vWp7@nYyGvBZEBl4---PXc3W*z&{p3@H(2LH~#Xs3DeLD0x
z;rwA^(b`)E52(2@5y&@;)3frOBB;|Nglj)Z%3Cl^+n(Chdp1Z0&=-jWxr{z(whfmH
zNX+TAA^<|XJx^YB|0H*dgW#ug%^_r~iHBzbX}GBStsGLTGMgGLgBu)gdYZB|*j<L@
zZ=}wG8@U8oiX=NP4^KJjcrlZ2_{EryA3Ai^CaeR-4vtGU*Ay_)xLEpt^Y{--^P!Vf
zx*nhT(i)zlbSuiTf=?~+2;3J)jQDlT%;8yC;xO?d`qcYCX~hEnf*clc`!j2Iocu9q
z56J~|eF7AO!-rufpL|Q}{ZcaIF?!~(f(~*dQpk%h>#+W?;Z%@hV}=emD&zEveby9G
zQ7y`vo|<wtrZ%2y2{f>vcW-m%)Csjq*ZdXWO`)|Y{e_5w=xuFk1~s<8xtF8@dtJ1G
z<9;PG;9vzJ?LAwOg^f4dENUx9Q6U=K8HFe3xUPeewdT{krbc`MsF9l!08SW6n!D6H
zvFT41r30pbBatK5?x}G>{a{%9u>a?kaDiW^gxtJIJ!i0x*R;L_iA7ZiWi2a;=0qJx
zZgg=ktNSkj)M?3|AvH*P2xDoS-l{Rzwi>*+xC7NV7<R$bwG>C?c4`?92Qq;D0dm_5
zjte0l*GK(WA3IObijgXU)(Un>lbor+6BTeQ+Y3Bc4^&6T<C1GkhlGTTH#C7Dh}}!`
zsOa2-vwa|H(*<GgV;E8rBx-Qjo1;%bA2Dy#3g|z0GF`uTQ+#g@sFe11ch-K`po<s@
zIgidKLW1Q(_4SD`Qlo@r|HCh*sRA91h#-xx?{Xj_jlCej1{_TaXq?r$pt?^++6)a1
z#pZ*xGbL&b2|7q{77547lYV=PIXF1%tJDWI9RNCmvg(#?+Vc?!U*5WP>&{LGh&z;%
zWR>tx2JCM<gA&E*AY27$p8(M}Bxt=tVJ_)pey9|$O3ZHBNx+OxsKyqfB1y7dJR?l*
zA&qunVx-%^lvGf)yZak6Yr22hLml(!!S-V$WBIUdHZU|a^htc~Ox-e;{bi1UvOP$X
z)$QP4<`@U;F{C}X2)I#|8xFb8`|go8<ZB~!?8stqlKSL%V7lfUPmo~ybmb@QaE%w3
zsq3#xiLU(q8~Pzzaj1e<H1yz}$rii(?fR2_=7B%Iv(H`v<T6X~6SdMW6C4bE{+$*W
z*f%mU10l*|KuP09l%~Ik|9Cx5yS_G2@5F1tFF+hm@^>kXHCTB)xQK*^CE0X;vjECJ
zu~atuuU9OqnoznJLV?B)#`7fa)=IyI2F>rbnfC<W=O5MA5BrjE{#qvyTiJsvk?2X;
zpnDRc0uf2@aYD#Q1nhJ^Y#2gKTg^tqq_&I@EvOu9(slPrB6&r(L}KgM+qjIk*{+=0
z_ALvH*=4nvNqty}e}38d1YqNbj~{o8tTf_Eb6;3Sc*l(8xAM-zAFoOXI&&=t+|kV}
zbK-@Ysc%;wy!9dwbt5&emB@1Gdy$z-M;GIuK&_L^pfR<gbJ#J?`9}VS4Z`Q&UcWy^
zH(4HhYx(AOo1PEv-p$V*ftpB3Dv*8G{LJ_7-#@9WtW<D$bUpoz87PHaDShEVR3nK%
zZ*L}xpvmD*U5+Q<$bXs&U;KDqC_6PIJlwZS7m>aCJLeVsErGp5gM-egk-JE2M6q$l
zrF$TtcH?Fw_059SmCpYd<nD1-Fihz+O9x5UfA-{AIa^s-6^ZLVBQ^S}(*?KOu3Yb)
zE*Bp5URQ1`gM3bvT`yLewh`imNEb#`hDP{#q5cYRXx(YKgu*~taU&U$iQ>fUIE((~
z&gls!_FvH3;pCgu3*mv3QMz5Yl8r@Sl4u%6Yjug_=SJ!Q$Kgxapa`k#@hY(-kJEgX
z&LokIB)nQ?Z0hoUmI<tQ1VUhuUG>S)fk>Ebdh3*Fm9H-Ao(dHILZ5)gO~_f({NmV3
zKrly%8F=R?R8*;H?C{YjvNd&gm(74>Jc_jqI=oD<k}2>{Q5bl%7!-}Pc}|X{1ky{g
z9_h5Pe5o1v1GYVc{?qAt2`*CydGpe`V+Gc`17*WrM*#drsl;~KY3X{b7tr1nIX*sU
z0KIy(Ov7AgU|xc8dWJw>YJLii@eE<Nb7boXci#X02T#$41<@%W+a`*1X1>{N2I8)O
zd1SYb#(un^!7IeCFwk9cYG30zi$=hHE>6z({YoO7y5$k_+k+J6-x!>_`5e-z>h~)p
z1ip57Y4!U)%TlqI^HX9YL@vrw>dzz#w>_HXesZDE7jzPlu}BMq{X`vu2r>*};w(n3
zrp7OKAIckRwA1-GNrL9rPutv9Jj6*FL3#~F;j#Xw$In0*b|WWH!I=X!qhj)G>*1T1
zU|fX|qajz=u-kfxjFQr$nK9Q{Rq&0UZ?E32luLC%fYfqm^Cm*QN6?#BFC7FIN>~fz
zEuX=a#DTIZYO&ygWHaD^T{Q$J&yWQdKlQCNYm0?)y52muF~j0BB5^E3o3(P3(&umQ
z??S{(P@{%f`upx}SCFUHCF<QJwG<S3c=1Vc;fg2|R7S_=M1ho2CZ*nwlY3Bb?A`!z
zmlw((<P%WL!gVoxdwmI!nIa1>61?E80ag^bt5W-jbMX>Ql&Z5KjV8|sq*@7Ug3CDt
zrjO_Vk9ur)hNKj<xt0-*xs<A_tDADMU`?z}t~L^*Gq_pgYfy{B(hNbws%P21#9qo9
zHPT-1sTF^Vdap8YJx3?R3Ip~0J9Zomavr>sYTg`&s)7P^Px62vaLQ!2Q7GudUR0sb
z)@nzk)1Dp*(8vsN3I6$nmza<U505MaW7S9?ZNTCdbXpT_CUn-mu*etyC@ydb;J5%K
z3qR!NFN)#4kGrlk<y?wvmE`QNugrrgRszQ*V%BgL%P+gSU@2<$TcLR`H(&nd(BzrG
z<}>f!z3Twy`-p?=%GTJmcg9ZWpi&d+Iy>3?6fp!%86h~(Xgn|xYeJ6%a2y7g*adRC
z2IZ7J)G2#W7SDv5^Wjh`b?39YR+1zdMebkkxpPMzjK;`V?yMHboQ&_D2exVHrx!mF
zK1Q={xxpL-hp{?tAwdgL@*jjx9S$7>KbxPyP!98~0(x7lI-g;`FA1m_8(N?Z&f<T!
zxb}UoZ8Z2s+0&<MP+qDGKOJ=3msDq8uPh-lo;DTqgcuk4L7m0@w(6_qOQR~D(D3kR
zpj_!(3i13~5=m>!!5iW;<KHd!vp9k?ww@XvIPQDR>NQN5pMsPsjcTX(0<UGDZz8QQ
zXSRYgu9msX>v%<L%VX?6(Rh)BVTSHHv$MR7ymIkX(O_4D$MgO)(Y)?o@K1W7Hyi(@
z-^gO&e<17mt7iJ|YD4-jU4}eo`GP67bj`TqzGOET7^t^UTW7*grx3$ekF9R}x4=Ul
z$(WyuD*)j)2L+mh!cU((EMy)q!->N|$<olCg7Wj`7WZxONQ>R?-Sg(qyLd4IlA^av
zGTM-z;k~~^ovY(^4C3Q)efcN~Al*<C--ve!fZsxG{vk(OYb)2zojb|9P_9KSmXiW7
zgQDwge4Q#Zf69>xQ}ik*UM_mv!LNM%{dZAN|57-5^yu~IJ6Vq(Zy=l*4J1oA0-wyQ
z=O0fTKMrqym4h3SA`ri;<G2PvdOgD{Deq^UEarMZ#A%B1b3w0dH>i#W{5!uPYkH0w
zBTIu=!W&^7E9@T|b`7HU?`QB|`k$4r{odAp+nn_uXx;wj#l)|WpMUOayUX){|JN^h
zUU*z}o~FP1ES%GLJ!UQ304)UYHNCM<IAhxJ@)9HU?VTQGYZ(~)vw!!|y}ni1U~;N1
zo3C8Y|1xWL<xb*j&gZQo#hPC~{}<W_|4Z-he^cw@zq@9lg3UdK!2f{%O?*ekEH&(_
z#eN=}SOZyao>_@cNZ+=drL?tJ?2l=K8s&pbQ|^JOrsI$~S6f<J@6ml;9?$7<XY4U@
z&G;H0tE$d;&JT|lt0<=bP*?agG<H3=$~|C4{o7Bcb%rk;UOwR8x{c@iOSQo?3VQxR
zqxsg1=f>|Toi*f{oZT1bYCP|Cx*J0V3n~WNv<@9-S&`o#RxOMs!?5yMLCYQXGjBlm
zGQW}KSy?LBGWKC}JZC7W`YhN%=P7OWtF5&X4iOEYKVG%a{;sEIdrIq`ET2d1)?#he
zV%xQDa_*%!IGaW*gvy3ayWDekk4jFF!80n4uNvRuJv}SwTg2442Hx_UK$GQu+n%!_
zCw>q#h1gF6%^Upb)2GF%>l{$oh*D39`lYl7jZHieyr=#RnVqKtAtaH%&g-59y-@ec
zvBIeX)G#zmp!rCh7J||TVyO^uir_BrmK~AfDIEXeCYciT_4Lpnw}rd{bY1Mh0XQ=M
zIegrrqG~j3R)~j($70pB4nRz#eTReesDQC?Cl2)f{c{W4@gLCDP_Oe&#9NJ+ccRTn
zc5ICB&IkkO6^Z?x?{zH0|28(j9gvGVa{MQ#M)LMCr-|J7Ru=|_5+-l@r0JhK<(9<w
zf3OSp-!;Ym{$}9+S;l`))Aj!k`#c+z?c`<tyl_@(CNPBJmwK>B=DPcqrCdtVyS7|)
zyQ*_WRc6(@&HUYri*FZRz53QsZCzGu;<heE-lkQnAKZ0UHRIW`Pu}c;w~RcO$|}at
zv5QpKAI2tz^Rho(JT&Pj$>y?OU%%w%?uREI+MWK>oBp4Sd*2>jZuzSP_^*}I%gZsJ
z6RFq(>694h_#ho5bFa9SNKTrNIuGe~T9#*#@n>cML&W+hC58Z?J9HRb0^R?fO$RD$
z(sPZP^g;A>dKbNaZT`m(C1`jv2E(^SfxaP$L<VG{m0(9V-h2k>KsWlEYtZe0v|!hA
zaB-aqe=u2aVYJZUD=XQ=Bbazqj?>tjdIYCCPrIV7;#1V70L{~It*%$W?q$UR2UJl_
zTcJvbQ}Uy%<Gmz!7HJcO1|~NOnr5G~?3*`7BR8Ya_L|hLs;>L}LWW?mD4{vSNS8P%
zAdehBn*Ku8-8JzmPyEs(<7H<8l%I$O8&^hbrA&YkJ!FQ&l8sUh9?-L11s&x%=!3c6
z4MODX6~??59n054DFA5A8nZ(PrBNo0_Q&g71MRc5Az51#eP`Lt^YeWjJl9FFX`b->
zrmmpy+4xrecka=w$T-hzpT}XH=R_XdFfNdn(b;E~=BN9dRzo|!#&G2N365Ot<$1Sj
z>)u9oOghAI9vjeJ&ah1t&sn<v`!Y&7REEJ&oN@^YzO0=95<^u_wwztsnhRNO1?cU~
zmeU17SH%F)H+=dY0dlG7Hp}+Cd#gLwY+DiU_pQNfUw-Qb?RZt~_?k|kZSJLXtBmWn
z*P`-ly=3+KHp|O=qBrxuN2bm(z&8j&*?2j_Be=Gq%b|aRSN__U@v3>dx25h4|B)Ua
za(uP{?DSXbB@C-nly|trX{5!0;eSE&{Lr0OAR;0&*iI=2^%{kQSA0L?ox2j-Zn*Xf
zi0x_F8(y^d!0DpB?(EaIEA+l{_+$=AKXK@k^IdH#SR8(w|LQN3dR_3MaDEwWk6FY1
zMpSK~k*h$jvbIS;DZK*HhLKOgrT!L5GWa^uIdfAp$eSsB`&LbU)gR6Du}Ne|-4-$>
zcir6?J(WHc72S=HI`~*kO=u;nb%sX*@BT-tFUM~WDrKJPat^6#pP#q;>UQ;cSKLrC
zW{Hc~|6+^cAiFJIScD><<^!wcLyp;R;xo~cSJ*#xK=Z*6^mtcw?v|94=rgXE9ysN9
zJj-YQvkuj^rHqWohUoZaUa>Xe<xSDbHFOhcY3X;_>^xaG`AD4Ft?0{aL6frXt)(vc
z-~6l@qWpN64w4>k(lH@9J*?;gmEHuzWl$(b-{sFZP72ECs+grr=w^7HDS;_8+G=#j
zN8&~%C7baAk6$o9vaR>ti?w7~xnDuaZ|`1iY-?MUzV?#n{JB?dJDEWnvxpw-kzD8h
z)iV9+9BU5k6V6u-ra1SaU7~2uHhSKp^BdmJy>vDC1v}T$c^!}*&Ar65GI)Eh`nUOB
z`HH*T+8?vHc&=MmMz9o9h>QYX%en`-le4qu=2Yz29=CTVuE7GSF_;j~bEz@bYn{Ff
zO8b;~+<ZJ2CZJzM0jg8km`O+iAjr{3TVbd6=Wd?qRq~s2k(#SR+Ppa;kw&gi{hQ(|
z>!d6RrlVm8tAXD*iPqf98Gmz$h{34Jbu7@ADC2sP4lrm{D1;Ls(<g8joP?GghKE|-
zbm>o*EyKAW>@%81NmDVujTVR+GMfX;<E?a_KdXrdBo2>8d%lRx{vcTqGzHJWw3De5
zWH1E?e3q>5L=HoKUQ_Xaw&|Lv;>&|=uL%A9Yled<s-|X#{?1ofEby=9g#1q``p;8S
z{%1G-6T5MjEBsg0a9Ui{xUHm-Wt2LUm`&rT*u(TNh<$WsNLRq8xnfVx^xO!^rn;2F
z2ZUDMNI9LiyLG^xgL38IK~PEOdv$1L(-mq_%)LvUav9tnt|Y0$L6LLodb|(Vov580
zq0hYRS>e>LApOZ<#V}*A*w4PQIW9Kmf-3Z`H!i>DNBJzLI)`EZ#@!1@MT7kOU!D^5
z|2AL+9J3I@WEG2y>-U%CE#`L;54;^GqNy1tHB<6!=;u0C*4}d>?-wMl<gMu3#<B`3
zncBrpX6s!7e`Ij;Bg}vEq|D6mZMDmA{}BxVLhGQI*3mPcYfizpVHN{HC$Ad&zFGn&
zu*8ILxKfkns>!LYrjoH0Rk!_(&wd^=(vIKvEiPV;%aQL6m_$H=gs0!WO*_c>e!YF%
z*=&7|Eh6f>s`deUfBrYCA+R`esQ9BO^Mk4Ml?+h_&=~$>a{IoK-OIL*x76$@#^U`M
zn>|R#5!-QPaTKTjwt;lDJ$u{{ch2nDa_8NUvewUOYJrEpmHe>7n#J?iEuPOpFw|hm
zyFI7EPgoq;vg-auY5LnX%K`1!bu!J-GUX{8G@~WqSj$lv*6EH-cT$vVx}%%>y<S<Z
zjtrXgR34t5%b>Ifng<KCS%sfi3~I5cTS81M4mtXB<K@2K9?|l9f@W!D1>H%ng^Uck
z9Qo*P1}3Lhk2Rb;d2(6K>OXOjkdu@L4mp1!qrQIOo^1w?lr=RqEkZ7!b!Bicihlgf
zbE1>nO2IIZBRAGGWr@p_B<*wta8NpTv_$07_ksDx^67LsuW*tWl{xBFZEfw>Zj|$n
zA#kox9ljM4V>F&Jht<XGXk9-wzJ@n%_r<6B@2XUOp()Rg=qNkC-STSX?U@EUkK>a*
z>lkWd#i-Q3x9$3z|H<|LcS$&zM->Z{40T+&zo#Yt811y=&Zj@*fHiIu6>WiEbeMUs
zP<%aM3@+Bqed*({^}REKJ=2ca+79Au6a|#XAR~NRms`pjs=a&@94lVdb)BK^Xq<qr
ztY7ubl+%g|Di3D&y{(&^jequD^w;dPozGNlsdVcl=T!wh93vV0x$R+Jf4NQk<DWU#
ze?3d=|B<LzG_M0PjA%bnSRz0E*LST9MlCBEBO8p-x1q9+sKCk3{yS&c4$@m&FQIEd
ztT(x$GTX_{1+&r^=g(EK`wRDOTlg#@;sT$VZf_1gjntE<I4h!rB!V_0NNhfK_5J=Z
z28PvzgA9(C+93wHag)oKmQ6>QG%XpOMnp9X%FgH4_%tL%Dt3Rf#oN2_;k4EfN1OLd
zs+Yjqk;!U1PCgC>S4t@-<0R1E$+>f<@yyiEhU8U$zA1`JV{XTDD2PH($K%<){V-A4
zWLS4@qYyPB2E;_80@Deou8NX=<6r#8@b=><L&h0YhS3llSt>bsk__sBd^QH3go?{C
z)OeK86p1cN0T-ya1}CRmc>XT*+{1(gWin7{oB<E0iZK+Vt;=}|H8(OXjZ{`4(y^*I
z^k;?p@7$1Z+j~k8*=)`7eGS2=My|`;dLA!rwM@5$M?@>Oi|omhm8ko_)!O#wU9ab!
zUm8quZVZ@^gRDXcsRZe`r6@pYGmOlt1GUi!%z=CK<VoZ$Hpk;MIO;w2Kh8ljzF$+1
zwEU2tT^EcQSQpP9M#dZk-3j>`l)#hi`n4$vUThL%xCN?;#^{hojJwbA(^-;?3{!t)
zaXtavl0!kZK1+Uo5JQ6WBi4I;=)sN8nTR6dS@h#djoYL0j(+55$bO=rV9z)Go>;`c
zls_Kw6;GkRmt=3ExEloTY3upam3m&H5^Ic(l#HIlBH*6FO1aOyAUHxrelPj!I~1Ap
zo`zNEJ@U){{^0xuIo~-4_s%babVb_20cz@u?zl|SS;(03tC321{qOY^a_`$W7P2h(
z#q;NjgRd+;@4(QA^p-pfIt79tzvii`Mz^fFyz=kYN8ENZG-_X2@6Z3@q~l(bn4^~m
z@J+#sabu8GT*=-2d+|zJho}>8I@c72*j&(>duhwtS21Sye{B4-CtJSko~ur(eb<U4
zgGv)k|32`}l|{!6qM1+wVI0-JDyX;QUQI*Ad#!$UxZ<>m@81h$al38KFdFZ!6+tf{
z(K1Trb6qX!&l>Wq`c;f2-@|gjfngd3yvP@7`G&P-&EQeK4nl7b%l|j#d|icQLj$$Y
z7n9d)-u8keS*;13O8mch37Cng&#$`Q6>q*N-<9*{A>1DNVG-Qn{%bT#ii@!bR0ZF(
zUB5m*`tNu}hW+mb88VQny=jBgXRTQ-{`l1rL{iMZUI8Tf3w<GIw13;X^VHLIn&iy?
z^>esDaKQsb0c{w9=x)f<y$zp+njL_8EgM}tG!C!)7bv=G{F==`miKxqd;&Jn5z4l!
zvr=e@AdUp$m~tqje@%vh)`PT7kj~f!Gblr%(98di?_^rPczz}1TWC;?tsDWmCi7&7
zPr$ni=|hp|N6Tn`YP<*Ej~?aIoTGxL6iL%7Tu1c5EoWNlc*^%3>PB~OmUcWOpEdXE
zhcN;N^o*gQ;d?f7%s#|lphL5;(L7>rv^&><O}!9uKt%aGvoGTWjWqi<T34LfnucKl
z`8IflI{81tI?kL25fes46>3TP-e*Y<CV8?0c73}>$K$jz(ZI)_4S5bNgmktT1lXup
zWS`$a=KDZ!69UOlC!|CCc3Momo1dZo3&VwGU)8r_qEW^vwNXBEKjj;3+k=-9R))|N
zcZ-*}#Nc$gNE;#Cjj8G%3qE*be3}Xcnk<y?s_@2lV}(yCf(xvHWW}aLlzs^#&BQEQ
zZ{vdET8=>KVF+Pi1{u3y>Fx2AH1<@;9XN%Kaf9?@(yFQETl`Hn&e6g^1^uTG+5vJU
zJB^t^;a9NdQ~)NmpwcXF5%(vd+ER|DzOztNfE;|fERQRdUqnP1^XCv<s=z~HFeI}k
zGXOR0J?i%q6{kqk%{QMx%iD=jK-Ey+*c5R3K1z=(9KGkRkzpNK!Rc@Pc5vr?07}A8
zk`a3mka|X7uIBa9-{yY%5}dX#2jNmVt6k6*Mhn3!)-81_dVz6;v=H#9kBoB*hv764
zirPjT51S2Pro+eX5i&B2vl?iG2tOdph$Nk<uU_pZO|C>0h<?oa`=-$DD3d8~C1TyX
z;7t)3m56Kw%KSrpN$3Ds&Mv75y$E1@1<XH|c<$WU8K;jf?j`TDbn{m%XA?h5+&$tS
z8EXP2Chnai%=aNS1p)Ebyu~-VSlS4232A&q&c%AYY2rMVyS6RR{qfrYx#@<WPaP7<
z40P$Q+q_xhf(xedagg6I<vch?J~~e8KrA}XCn5YDqI&mE9<f?6IZee#tfD=|YrO2b
zt51<WFUg6H?X3GP4w4>;EQh2(OwCGxGW*DuO^fG~rlk(VpK7Q+$uto%FAL^diRRF7
zv>iWs)fr^?bd!W*GR%mD%v~eh0K_1mdzBdfdJSscE7cgnVAOlVN-!6<pNN-;rVFi;
ztdh=WF~?8g)(Y0)c-QIZN?psI+}t1G9hdiie#vW|yHnwb?rQG8lVRO-odHy6A-wTf
ziIpRWmvJ22n4O2I2a}hNy<<C#)*KFsj`M)bld+dk{eu&&4^JmUKaD4mg~^kS7??L%
z874SjeniGQCC)!OHJ<pIXiSe#Mw=yY5E|<g5$!+R+pKyaunR;OCJzNFpeC8@IAVmM
z5xlDwa{+(|<cvQb92_Krtw^6mj17WoMpsmHGWhBRbLZ&k=^1|T*hmW@LliLdXoXol
z)Vxo^BGGF|p^=V<iapn)R5dTlJn@ounBZH=>v^Lh%7h^xFfa}?j?{>T3%v~@m;#4`
zJb~*Y=lfeD;I=po@?nYs9woAS?V2@YpdY0iK`$Qet}D4huHydI!(<{<2RL>fMJc+!
zl_y6hfYmN5Q<V`d?Hd)xv(RotM!Jf#c`eI31ChRZs!1e~ecicp=S{D$kPw-3C`%*t
z1<0qOAgBU|$L4j6Oh-hEH?&&WUnQYX0g8HrM*bIMk|}}(h-yS~;+i3<jDd&MhD8yd
zeyI8>^lm?g=+OWV_$bMs(<B5A<HjAQ9lf_n`9jd!Em=aC9D6+X*;$)i{`wjWy<?L^
z&qll!fuFFGN(iXd&M5g9K6OOvJ>i<W0_>VT6YW2k!D(o6pQ8JN3_OFYJo`i7HyS>O
z39?l@6a~=Jhs~_;Mq?Cs$>d@VGVk@Nq)Ucb*1%CR0|-r%L`UygU%Ju2e`}=koMdGe
zUsVi+;pludw3m4VY^5x129r&QcwMi~0K<b}E}IV6#yCsrMAmCs6^qb_&%R+|07x9V
zumEqRaqwXvDVZb{6)7ImQxm1zl8pFq3KL^7tzunj%d8Bkr+}T$K%b>GZet{Re6f-8
z($ZI8B`?6A3iOElzVKgL2aESLAE#l<tr~iv8;mEDFK~Ox0k%jbDI0S!CJ^CLVB(lk
zajCOBGy__>6OFm>S2U%*$c-@_nOP(<qFJJwOz8r`B50C~%s|sDkBBwjve!!wmnU+u
z=t-D0y!v`-i<k2L{t<K!9ZFT)f2!amYAs}74D^8%IP2vzb6)ePj2c(Hw0PP!$NK2e
zn`GPrtEeT%_*oFK=pO@fVrpaezNbFzG<isl&UAWU1uoPkP5OX6QOw@kAo#ZWj|$CV
zu&AKmf%P6mp1Tktb&+{*n5r2}hE%|X)ws=>mkg7@@C+T?htQkJH+tD6onO68QAzF}
zjMB>dH53<f+f+&WrR2=GE=2*j6%%avi4}$WTgqBYk}gLYw4u{NV=oCjK6b5YZR5Gm
z{bRQ@RZ-)*-@R$CNd(UKi;#Ju_p~~(MF$26Jnp&i1`Nr=teeEMt4zYvt?0616E%-5
zMt%clx2T=KJEEeZ=yr`Sn?bMdXENUtH<ENHp=6?ny|~lMSTCh*VW`^tj78uRbm8Sh
zBnk<lJQ_@DkS-B~B7x3sGEESI)hL9Mh28gO7C$d~Zn2^>t;z8OIu78;^Pxmjf|fV3
zoAFxXt5>fW)7U+!v104xlAKp`pi67MFCMcuVZoa)Ln8!oTFC5V?(EVNLBC^T#8XE4
zt|s)p-Kghsw91Z&iBU)+BV9tu6!)ENOgq2fP1~6<N<D7dl6o(UIKzn%dojihdWu;b
zQ`B5G>Uhi7y<|QKhatkHUfcmrP8k}H#67bJhbRcWuT})qKG0DA60e;9))3GRv(nzQ
zz0I`0c^DP~r^&%8aR<M1Y<+Od8jXZq#Q76A1>NlRShnA*^v}X{^|=%>{zs{0FB#UX
z$)9zl`Q@(}MCC*c-9jy_z-ndX<b0GSvfjXZ6QOTmqfU!>g-p_$x6;pt=%qD?4;o>%
z$p5&1^~Ldex1po(S*0PberX^dh0JsTL3cNnmnB{ENu(%GxjB~#>(X#F$p!g_;Q?Vt
z5~OJuSQKj}8G<f;SuwH8lVlJVBI3dt$o9w;QwJo$o!Xq$eUa4vmX>g8Jw@3f_@qeN
zQJKi$-0jMygy+*3PkB(tGa>E)ZK~2EzOkm5`v>*?V{)5~TCFl0-R}m|Z--p4I<NLK
zCH$fC=iJ+wjeWNd?6@+IpW8A#E%6jW`yW=ZJF=9XG_AdI=e6g#RuBuaf>9JM<xLuv
z-lwb%gZCK!n0}LK+U5Iz{kE=&O}h!5cm4c^t)+)esB(`{6n!w-@My-~+%jvZ?a}cG
z=hbpp-fA2EZ9lD=U%Kbi9KG00TFG9#xPpE;<qOlKSd3B*8H0<xCezV=q>rIn!5CxI
zh;n=zg^ch!oFlc1#Z)tpAtTYH89m4Z6D(Z85<$AO5r{Gnd`S&w&b)=!{cUmZZ1U*n
zBhixpMl@q8Y*|$%kURAp%I@PllETsuFV0{XTNlackra;<^2T={=~RRk2o^}D&t$_}
zXY{7lU)`RHFgT3C#85(CCOTI#ZAQ!Xi{2Y!3=Z)mql_?mFbcgf@SKW~DR6A^`<m=u
zkRhZ<{ZBhMH~_|K?`C#JgUpE@tG;x47t;J{AdTl_e6(KRY-ZwgE46X-uNL6ug<l>|
zwH4Cd&d<l#y{)YWhvBl+8)YIvZ*sD_Joq&t5i2CqPssdUjBP^;O&R7W{A#I%i$Rg!
z^kXU}Y+QR7;j0fqMlXuH`D#Jq>7z(J<hLHWeYHQ0a^*+(QJL9Qt8%37PHpY_RGz#1
z>zHG-9z(BX`Zlo$_trZ9Rd?vT`MZ@^b!i<P9k&^S??!#xoD{`huD$Q4nO(|M-YewS
z2i~QRZ~g9;c-Xl&ndboS%T)0=TL0}RD+boC5p}J2(+A_3xn%<-zKSugB5MkOf^Uj5
zYq&P6_QrAF+!~;j*>q>0QU~|SRSswa<)NsfArja|@q!D;xInbBuU+Vl;iFH{W%F@_
zWVIZfi0ptH-n~vp<sDH)O);$40d4oIcalUFj=QdKAs4a)Idn=a{z6(bWLbWZRgklj
zPGi&pRlBG+T5O&<^`#%fz+=Cek$6|qHL!JZ62R_IoD)7&ME}!<Z0G?vtI1q+48n7{
zlRt~G!W~57fW|73%ioSb2avAGPuijh&vAHE`3u!6-(9z54^nA3wMFwfyd)>MOx9jx
zo=Fj2wYUS#Y&B@FTcgR0xw#Nebdu(M^k$7<l8oX1V(%@0vTob<!P~^{L=Xc-K|l~h
zkg)M4l~hs%L0Y=QPC#!;8l^!%Nog@aK)M7Zlx|6B_P9{r|M%_8?(EFY&g|}d%=<p`
zJaEVFcm1yGjN?4c<HYv48oBZ_qr-@y_k$yT9D6eacUA=?eeFWo@;z6;w|D32*YujA
zT5a1=o)y)>R$n8$AZX9IIF(m>muybgsn0Lix;fM}E~B`M`^sBz-u%t0FDs>*ghEwa
z=4Y-k;+zT`mvAUIb&>rC??IwT@HP;EMPmCBHTFu#VLx+xEnwF93~_awySw`})1!gs
zuTPCl*byOm#Al;`1PJV>+mMz-Nby9|9R<@vNlnDpKpUkLwmlJQDu{%{NhJZILuSyb
z(!=@=**sb61L2eTz$m@%DE6r&8Uv`m!X6UQYP2)T!+Bnt+IIgX2^fKAR6(q36s@jg
zI};QH!UB;*1QdM*%Zs0@_$V7?%9A__0z4sD>+=uRGAZEVc&~m~44Kl@N_*gHp`b>S
zb5w<(F&6N}mx14fhw_-JSjz_nFB!uJzi8vXeLWHKimLXbRlu+y3#*%|c4D&!!e#OW
z<z;b;+>g$UAY8qzF7g7W`OEs%_@-*`@K24I_deOP$+Yf_chl>G70L3CjGHRBeRw#{
zRhtL+&U-)jp!}>taA#^*5vRFF>hG=IzniwS-}ck?t2&q4#02WaHRLSYr$beag-V;Z
zqiElB1)4DtmD7qAzrv*5FFxxx+kVxnrum@vxz?w-Yyy3`XnSL|v-}kmg4i}k<gvD<
zrhAWbRKu$^?9YyyK0*ag^<-am0h6g(WKXXCrO1IbjLS^V7Tcy)?2FpSDEo8JNhj*}
z&~L_wFPBHJ+1Yg6-WeKOZ*nuczu}$0*UkKK$}{)rE22hfoMn-v?fZBbkRbYnH0=Yd
ze);m&{hBf&1P<AHOL@2P^6Wvu=7hB#ZE<SBc4#v!>a)4*o$UPN$??dntIxf?4?6A&
zV)Hy?lIG5B-TB=`S3M;QAljjc`ybGOfo=sRk_FeO*~To{yoC>pA%b&ELg~m1vnZ4r
z9n2Avo07gB0jpm@xR^FG{%5QA20>9An%;CmLtpmNW2%9a$c!1$A4AkF`b+ZT;9Z|*
zGiM$Pza-5o-AB*P$#{5qZ5_I;j#dbN>vr$mQ>+=syB@vU(|KC^7!vV=pA;WQwRlxX
zRIHlW%$A*9w{xT8UHiV*H_m@j{Q20?W8_`Q)dyA*vQ@_I8IWG|UeBX_rEG`Z_{{7>
z+kj&~zUfh!mz2hyc>i1{!p|b3LyOrz@FuwR7jC7uazz@ddvUT>ntmFHyU)jPL36gB
zleJtoI<{tm!*8!ogAqQl8AD=3V4h&Wh~(J)Ki2a|5-2z4`TG;{*MUF2Gj6%IHwf7n
zq&&WG52%-^H==(O(R4Y0F>}a=K_ANF+kQ5-=x^{J<mbcz!xUx2HS_)vcwTqL@aw7j
zUy%0`<uBv|DRspxuH7%igwo;p*r-H2L`;<^I*SVk?6zTUTEF$r2R;9RldgzXpF-aE
zLuVu^D$#{fi8%5~z!Eum&+s?stw~JF=LW9e3ps*FB4Po-#~R)m6)<kX(bG#nZ*tO^
zH>aQ6{pT~+J+AMo2HonALB-)!IBbfZ$;;2M!dgNlSGhsc+d5NCXrk4Pv~9<dh!jcz
z3XcqkRE$3g32Ws3f|c*3njGgcEA};SlQ-P?n8d+A?Q8(VBms+_ft3YDEr%2Vn%FTo
zCH((E#N}4Xb2xc2<NDxRBOxv&<@FEcu2Q(PuoJu;@Y<w-UeZDkAKwpF{&U?Fr#t#9
zgCr|RmPoX?baB`Fsy9!9t`kGF%*zSv9}&UIe_+SoH$*Wimp7t$51N0n=mVWto>GLO
zpN6Z@ekuWz@i??assBFwuxV{sCpx&dt_nove+`OWr#KY5RH4VW_gw}|>G6VpsPM7(
zL+eY?@=j)y?4uH$0g{K$tIx*(ZM-%Ge0;H&7pHnGQF2n<@aG#?ck{n=so3XE&B$v3
znac&p_6%Ik60bmnN6+>Ku0#w$=am9w4RiiK?}%9Jb+z-6#a02;rA}_{z;*$Y-9i48
zpL6}=VKL62FOqbQQ+<5@;N25T;0nFNy3Dq>S#Vdc!v3Hu6+w=4lV3m}O=Sv<DkYV_
zRU%e!s{-Ax8b;iDM)^=OL=iw&8<IT_vynaW$3E~^$}u2f%4q{a@*fD7^|O1=uW+fY
z7x#z7#e9uO`v<ZUdLp85FEcYSXSgB;%c)m;!+;;@|K~0?$;RFp_UCvdF&%J!!vdal
z7hN15ykF6YjOeB{au=5GvtVOM-<&`}AmKlpBd60SaG~imnDn!hGg^Ls39ffAqX#57
zvHeFXWu>V!^wd?MXYSwqqxJ=OTY!T@ia@f=U8t)NC`!$`Z8PvNx)dnGdO$4VvVMUx
z)Tk1^^RfS{rk&v^>~Qi7L63P4A%@*w!x)7eT?cLIpR1XU*9^n|RV8Rk9~b-24)}x}
za1U7ZD?ELOo*Ax4l|*oo1l*4ZU|4wkd*RK8?0|2HSOUpdXK|!-O9F(5P6<L>B7FpT
zP*s#1iu?b)V@*7-69-^H((Mk+f|;S=-(U7P8OYxJyIUzyeE)^&bSUt)2Gqn*+}hkY
zmpwNVcdzQ->-<R30l*2Hh)DK_sg3_At|zLlOl)F6(o1r`9Yf&&)}ui1dP`jOW|g&n
z-hJlriCsa^3nqWrS50JxhHNqs11r4E!zqNZiUVrEx^9_VMAHQWMFr>_0A?Kp6sA;X
zD9;BOHCPgOm+((%2($8gMp-exC4M%l+&bQF@d&`+f<#pi%1EChN#THyR+Ef6;y~fQ
zdIf7Q(2z<pbZGT$LiY;{RNtd{bk%9uFbN1a`6oj5oJ0$4{d{uu9`fHf3Ru=HyDP{9
z%i+mX5oH#@kiPr}>LIU)6~p|NM~!yFlB4|%%3(SJ@<cTnKNp@h4b(s{iMTm{D#<ES
z(15rY?r-5kR4xinYc^~CyhLGVoY5=A!WvP_QLh<=EQ!G7zaOwJ(Q5D-DZL@Q=Ux@8
zGDSZRZ0o@Dk+e~A^PUZdRMX_bbVylE%4@@OhSfp)i28C4N_h^osi*#2htr;1^o~>0
zriTjI5*<-;+D9cl#;^BN35@rrIzd9k2ID+N(hdqAPGqfmVDk>WEx}@9%aDmhRG<*F
z3^k))9cYQ<d|QIY#{-0sz}LX!v;*yvciMFBCS|DWQGGC5<%oW(0DIt3L1`l7(@|KA
zU~&LRIRH$+%A^`Kf`%=rpTY-@x;jpuMqBp<uwIBb5`@?%@8(79NdgN)53E!YkK)Q8
zyL5a8dtJfMu-|w}ppl^zkA%RrJZSlB&VEVCH7Grd;OV-56ISUs=*JLfmfD$7LbPG;
zc>GxriYUqr{}udxc@)Q)psPB-OY5>7vk|OoPz~-4FDilj55_?}ZyHz|tBh81Pncd(
zTtFW(d$M{N|GgOp$G0<&rJVJA!jRSkhX>e1eVfIut^%w^4jDin8HCC!FBAPns4P9a
z*0W-Y?K@m5KhUB}Zx4#K7yCy862c9dLvSDfJ-!4EZk~h$<qa4y(tFF1-W$JRMlo>=
z97;@Oxkx7P2q$0txRmHPLl7HEGMQo{@|{+uCs46f0{@bI>>El%Jf`gu&|r{-G*>l3
z4Y5SUFR{a$q+DE`iu3cAa~z@jOT5~XgU7d@ng&4(wkoOrfZ?Hf0@Y+x#p><cXUGf)
zq~R!Jl5F}s4I5RMQPAq}I|GCn*L}1OKmDGd?TRpKRt3W1IR=D7j>QlIYpEO{07yG<
zU_lTbnQu!5Opaj+s7r5;PbPB^U=In-1>TU350rDY-x`pO0v|`FO#vt+84DysIjE!-
zM+B@1piqY|B$pH)&jU3xYB{X3IC_g;C7~vM2}`7a)Q~7@k>VHHcc3L$0lZgbPb+)$
zk>X>gA%al`yL^X_>npmX5wa2y6oLQ}#HsJ2eZYsDK}s674yx%UZ~nU4<_PTE^E5mp
zwklLD{Pt4G>?}6binp=Zvo<KcnhLbJ%W7`B52S;nMLqX-7(OGLApVmuhgbABM^_IN
z<Ixix35L585i3P~42}qS8qA+A6h3TbCpyqU848eLuciU9Ed-GuK*#Qm5;F0Hkn2&L
zQNDjQ0>KZd_d($hdpDHa6mFReUcm*}2?xT-hU5QwSVK}-*_RX^8rtQ|*Z2%k8omvE
z<cBcT1_f2ZPbN{!p!6qW==+F~pq;o7w4+{O$5^ClOu^ViwSiR-@7;<Wv<CkF)`{l3
zBB+;6g3rwZi%e!?<=6vI*8S(6PoxrRJCQbM5lb@7-L8E;p2aB(nY);M#?AgN!p!|{
zzyXQ~XpN`<px(u1?yDD@dX5J3`|4SrAWA?i8OAM$m{osr;W7b4Qw0R*EOiVvjgbjS
z$}&?JXU9wh3qzKqqrDYCmzBfbLhbtwvxk88??;!+@f6{?fv*~-OuZ999Rg79W1`!@
zz}f`84#-s0besG&lqPH$CsnR@7!V>na~Hf#teIpUM3t{bx&omEeivtK4PPQf=<M&V
z@ny435|vJB`8>i+6o5C5$$u+;i|x+R<@#&((!Gw5&dMw(4*d#yfQsqb%X(#zoCSKw
zb?Kp)NT~MX<KtJ@(d0%8L0syUE*V`cd+eCQ##3H}p#(fa0%Z4P_un%AiDR4Z`|flN
zm+9No?}(;+u^KZxKGp6P7J6$tK@ww)<Q<~RlO1Wd%7Zs$>1C7KV)d$v<<!g+s}dl$
zISy*5ivx$;w#QyIkWvi6Gkx<l$=yS~;~)bfB6OEKd~kHfSU0p0qb2?o?7PC`y}!Kc
zO9%fKL@jsx-V_|$$H(#dipOjwp{gl^9me)u>iVf)kE3q%-J3!|a98V5l7CnV5v&$N
zOf~}&!NDdfFNogpHJ(?u0q^FNz2KPP!)81Gm?k+LC!SPE{tel$RzdpaZv`^$SEpi^
zYx59>vm{0FD5E}co(2<~`Ecz~SNXEKl}4LVks1(>9cq|hHTct{h6uh=%SjBRn|^O^
z9NSM#0qL*Se*+$d7L*5`F_VJ6hi(U28{C;m6~lu)e**-OXd&o6l&SfR(4IDeqK+=4
ze6B_puGkzi10e}dW&&$qpR>N5CAtPUNCm3WKe*QLPJPS<()4UfA$0i6zeX>HV4xhd
z5K=pj^8YZicb^jn!=o8kP<Me$qSXWe>WfrS(OYD8+voN;nB_7=EYXm{lta~Ug>>0<
zxw1NF<WxeE4WVW<OgpL8gZ%=_2Z7OG4nzPE0UtU_aY|xPB@sBLI82oH(QrZ1=H0(z
z#5x}R8+Uo^9AbxkRE(~PkURb#u<f;(q{0C|VM{P>ux|jT(ojgohGF{NP9{cTun-*5
z`qkSGpMdQ{hrypB95L)fj0r9&e0KY8`NT2rBidv<WgW!8ApEfVPu7BLFbc+rC?XW7
ziX)J#f(8;P7$YKDT|A?bVyH^QN>JZd`eGklZoS9pZ#T2cbBtz5C>9VWA`b)<smuj~
zi0~!w^yPGS>adaOEEcI;UQsm^5+TU;{}iX2kKVsZ#2(~Oi6zs7{7sAbq4Q7rIFXly
zqZ3}2l<_ce<<1<f92btJ1BhB*mKGXyFNPcvavgBUhyq(RqWR#~*47nO@u(e<=Z+K!
zqXkHU4#E!b*!>v!$J3f6-zB(Rt1OEBFl6aS0kJlOnGCD+Ou2mVdS*{(&pm2+Skh7S
zlP=T<bf@QIGxzTa@D2S+GwqVQt|jOGRlAYHzAY}j&F01#tCf!zYbPr-7dr~5p5?k{
z`O<^cLc2Z+<1a~oLUU<+%MV@QACDgo_XRS0&GDfPC;hdJYax$V;PJ1^VM~T4>}W1o
zZ;J~HP2cV5z?7Z?tlgbL0nmBoeA1*P|CrD8x!~nO0Fb>VdJV5{cP59%?wA_&5)-mJ
z41mb!+=C}_ECOMlS{77P@~Ydlo;X_^wY8<<Nng50nxS6qfX{{1O(m8>p|{TY*XDL{
zKSq(;1%=>U>mn*w`}Q6-F0`>X51Pz$^fOV@=5>j_*qydR(LU->_Gsz}<|oGKyy+El
z9y;|%2JZQ-J-Pwq?*g8g$CDEl;o=83I%clt)}Vpa!QvG?)WWe9lR_}&<pyFUQY9$N
zoI?x+5Q(qr&RXHpP%ky5E*mGei|$pci;)XdT2oosX#a8-IeRje1v5kmG#u?XH;V}R
z>y%GeB+8u9khsX%k-MPkIqHtsk{*G9ai{`A{zP~ji1$UvwXOPCiU973GmW%`NJ!*g
z{5z1$NfvTjfUR}V{&ryppJ65FGm7o@I9Acf*K~kMdOpovDy0AiiFiX67&jRaN);U#
z8*iGM<=ZAKE;_ex1TK@fdMttmXvib5Wb04;;d||RMW&0{G~c2l;;tlakeEWl5l~n%
zVv~$jDcZd7dC?H1Zs2zhfbvyRZ?Z7VpjqYNJv;=Qh7nLk2;8fL32um*f<P4UlN;Xk
z$M6N|qFPjk@F{hSYt?CW`%w?c%?ntz7Us<%&gDEdiU{_I*xiz|I02AyQ>7(Z4{Nns
z|2>dStjK|FU~_U%yK3EN=-BS9z0|A8s%7<mPm^sXhT3x4TAUp?Q2V-5h{DC2|DJn4
zDgI#x7roIi5b>YNm`&z?C%c&;guPt|KLMT@EY$&X^}kX3`+VHD4cu`~8|FPoV)a+0
z_Q6Y;6|$!+o^$;4aQ*-8gAihxRmm$-X~>94ZJA81L50*c5n5Q#n6)H%!+enDXIgv_
zBIC?mKuzR<FaH-#+_Q~M``-9rJ3yeO3YaF*+okb<Wmyy;bi7rV#Ag_2v+LvG{}A0g
zG;O5J)9%J|z02OVP6wVfG?;Ir9bh=Zqy%CorH6iSbHL_xWM&<3O}Za{rW&6(W0LRK
zJtMVsK{NApab$*!%k9aSV9+n*pbkbV`a~3nW>#=8oam?$gQnW>XWs<T5Clq8pe@ei
z_;$UeQLOc#ypq8T7+FhZcp=w6=FLC4KZj?ovp$mkl>J7`7IP8W`P@RnX`@N^q6e2l
z23{5Z_|&u1$=)v`ZOd#;B>lUzS80g7S+2h*<m<cjWxXfdNI}7+%rm(?CHFI%p6)61
zZ1PW9^|g@hOUBPhnVRi!B5WF?w8M(bGisW(k2E&Cn^_PkuS{oiPQJOg;q{BBb9s%K
zqWSVQT^}5yqK&$WSD(6IxODdcKG6%CUOc^e?D1BuJr7rPA7F5Gc^a=J7{6)xVeZMO
z_h*(2S_t}G+{AeD*x?UBE%bC}*WY#8+q`(so&)Ef;YZvuoKt>)$$Ny_8T&oWQa-$A
z_;PejRApn*@06_Cq#i}-zPhP`v^dKv*4)3;Q_{P$#x&pb$k!eobzk~=`;9Gpg0ttd
zCh8y1tDbA{w{CuE{jrhrhS~PgV^w0TDRZ{L(cd^iPrFL!{H}H8SoBRobzx^?W8>>f
zZ}%)(v_pi(c=P>&(M#-Pi)zSA-jB_AT_&&ZRlRoF5ngw{`-g>{x=B6=C54%0Iz<yS
z^J(iVj;10NAI9F>PZb_~Bps4eb+UGTi@TlNtz)FFuC6v!SJR$cZ&=@+AtWR;@xXEX
z0dqch4?e*z0#OfX^RJCaI3?9HQH^2X$Lv5?Xc>lJ-7$9d;JEIpIET?+eJE@sJ^T~c
z-f%Kr8$>Et9U%5d(7pzO2{=127XVI8xC)1>z}yqHkyk(J|5#3^e2wpmI`I+FA5%CX
z)aoNDY{U&p!jJm7iNHDj#y`Pm>8_TYC+2=V5Mju&|917k>Lv~kvEbtDiL-Q=9@RDY
zhx6&Qbo+10WJ>l-->*Iw7Z*mGUO>6Isekwy^w;q<h#1y1?WCulbYzP%W_6jJo&8ad
zv~2sp-wS_+3&$YCZ$IkmO~ssxTwet688uq{p~jxL^VF7NkE5`vDGM2YGXESA+qNAa
zQ*N59{%=<xX=EcSaQwI%Ge)mX4Ws&Qs8V(Sqk|EWn4lD~pNcsSDH)ShaXbI(_ok<(
zgOpRlOogC?2lDgw9`!k!rAwFI??}ZPhf*A;Qk4BnT2QH~{qbZAiCIrPXY~+U!{7Rh
z0qnlLVVOOu17fzeQ(GJv85wKAG9DgF$reiaZXzC4)g4h7qY%vxV`PeQc9=|LubI6@
z<jVlH-)S|WnVCY+oos#Kc?9Cxi76=^gz^$zkXv#lHs*P*kdqY6O-ev#ypkTK^~>?5
ziDLGJort^IR;rJG_?10ZGB+V&-KVFnd}dxi_e}-n@Av>H;(?l2?L(bAckXQ8w(Z-`
zxa@3^?5T!<p%^23wm9n|y&(*L5k7tT^xmDytR6$PB@RcyA2I?sALbuxbcnaIBCHN$
z35NY2?l&=TmOD@JXVwm|)<QP=Hlu}cAcDUHco?l|x}9$#I(stuRRP(iPX63HJnoo4
zcASpU?1GEn&=;R!dt94%l9D9;7b=sF@Oj81&Tb!d$Y^fNXrfy&TcEqeg}?npFNmnv
zal?)-T?abfR#MKg9m1W1K*`xOp34^x`$y&&On}4bgBfUfm($F2FbmWK6x<G{MQaYk
z&m*wk4mM3S{*Jes7*c_(_CXIZ-j4d|I_*#yB7|I+-Z&a-*SeR9N;`yrwKC5`ER?*=
z8bf%7J~GYTlt}8=Up-3x^>CFgFNEe5Pxl1P;t9+Ki%bS*be`obI7!FIW2bIwYdeOL
z#aB2{0M;c>)Te(+cynLRP@D(FTiTIroTvF=j_>i&akrD3SKs-^1&|UrNF726tVZyI
zb%mhgZ%fA%*`95#^s~bvb4LiJcg?Y8Cw$aCOkuSW9((KBMk=JK-(S9bX$KNqG4|wS
zZ`M?QSATyo(@Yst8@X&UMPdDLjR4)q7)J+i8Yx^J#MJzl5gOPhgOO#4=g#hV{P5vt
zOw-MD6Fu3pJ>+J@EG;cjXmPW|&P_#{t}7gOdJR8wUtga!$dd{BwecBqGXo`l{HEm%
z4gM%H3nQfYEh?xbs;74}rX}+|E~cidr)S5&=lSE%Ac$JeL>s50x5!L|h@_&T;?*8+
zVS5`Ff$oU!D2H55HMx%BwEVZUfM}(d_R~z07l<@8d%t?TCe`!MWg1|K1IUJK$s5?i
z)~;UNhBI4;;3@0#O!jme=P(E)+<}vo;qeH8$TEDaICq?G#Dzn9I1jHjFjnj7P`fdB
zhiUqX@Z3a+k#Q3WG@f8)a3eOn&XG0#^N`BBn`fkt`sYoc7*>L{P5mqY5@0o%Pnrs5
ztO*998VtT;jEt#$I<>ykH&7<H)|WJSypQc-5t*RUG%<zvc;-{6|8Y@=!W}2WmHqa#
zq&AL(@$I8JOjZQ;kEB_I-G|Wsa4N=hn0CH=(5@{qb=k(&RyI;@2)3vlLh<Jv9UWaj
zRD+_Y;gh_XW;&5CxXAz{={y*!d}4BH3NJ3Nsqq3cXcu{8DK-T(|I5W}YJ-pEqN{!!
zwga8;V@F1;Li7YS&M>>@%!jR6hXLKC3_lQ=-o0qiqK_p#uoO~hFlxA^HVlb~x?G>$
zG}G^gqNz1lsxtV7`g&imsd}h1U@Icgig7ywEXkvVsYew*|6@uG#Q7VakRqMa2Wa)F
zVzkVf<N6Pn>MJVTj2wo2Cosz)fO)Trr+utfM(aK*4LPRigPNoSUfdZ6HlGC^w+C1m
zEO`v35-`cKtRCE$!jRD+G>|_xsXm8!)<VRmC4JVw{_AJX_rhalF!{1uM^8^YQcg@%
zRFv3PJ1lImVR~P35xu1rzwfD$m?e``6YS9rFjxzR1Ab>DCMT~EiG!IuI5!zL=fj-(
z^D&#Q@T`H6t#Z~#DCc#wNl4)Hw6(R-i2roDO?~z{?ddUHTTxxyZlg0fARkkFXQuZP
zBi8HFkA26SkBy(qKLCCwmt9tqNvdCef4?8RosN-F2^PbvCTlE7*?yW>VP=h$L6(X4
ziOY|aW90`bsHYa)S7hX1N^3<)r|##h<kk&jFo$JY<Dg2hi%lDwx;JxhK#8>*mfo>)
zicci`XnpuI*_(Fw3zas%K-jxS8>7huIh`^{yU;Ke;AUOH@4}Z|+JtL*`?+?ZZY^Fz
zV}{utkdjL?<<%FMv;xIWS>>?NaJJ-v!9{BY-+b1P=5KRPOiF4qnr$~4N~_7_nEO39
z+e9P+c{xok6<V=}d(Q8($<hZy<z87lfE*Yd)EXKZs5H<=s_lC5@j~4*zAj(-ySlb}
z(fj7Sqbr5O(p1Co??81H!@s489Rbao4|92oVJQN@_b`ceBo|+L!`|MWN=r^oHYlD-
zinBKux66i4vWAe!5y#o-=@yY$4Jk}pGf-nOs1N{+IK5)D|Hf*^@sCV2W)HFJ$KE;g
zH-)Y2GH^JV$y|#tei#vbJCK}`?3w!PHqP0foI|&_W`J^8Q0T?k6zh|x#~(cLEl=1J
zoAprO#8+(-RFbvPl2&i0=F8?)7~GgG)RC+uj%{AzeS`%=#H2aEivK}st?t3D!UCLD
zIV<CdFC3k(UeU)SBpk`l4St+*eb{ss`X&s}_ML#VLm(Pd*Z|wM9F>+$*I|T*sqeDE
zHjoq@BD1B)V@S-#k+a-eoq~R{)~M)MF(V@*hruW6t#BFF)(DR`tC-~Ps|Bz%{AI7J
zzG6d5l3x+wCrxyUiugdNVj9QW6g3E8J54M2Pe;eOHJon#9Z$cJmHoxQY2PL8#tgvZ
zdt{z8yatHr=CoG-0XyxbbaZ;f>DN9#X3k$P%J5dUr_P&R4P(?<sD@BQ;fe{yJ&7_H
zpFvN;>*=3cv%g>l<HRah+>L?bXHI*%F{99>M{5O%t`NwIACSDY>JJZ7!GiN<RNH?%
z&OBr5vAWIkY33D06FAUz2+kJ_IQ&j$fHyjx^c2Bn$HToYUjP~Tg1IVy0?}l|@C-sC
zNg4wNgdJDzUsLm@>(yP1rIhCY$(*ceOMDh_Gg$0rPc9XI^*-XpjLQ7{3BSmc4mg-O
zNjh-IyTndQBB?4~D2X@NSY+Yk<~Xa*FJ{S#1eK{x6e#YeI!qL*_m6Okh02iAzmM7o
zzf~jXpBHAnY5j?ssH@nmsjW0|n!aG&RGnU2!%v1vY*K5$mZs<)X@zb6cFj9*x|L;E
zdTvB|VrIZm#7@o}(F^e>M@j^bP)$B8XVFhchEWa+{zPEFn%<7ExpW{;EaW);I=fmi
zR>d1k!gg6WtfPAM+v{bOkC^-!@oVLR)nTD#4f_u~)#1<EX{%2jjW*QSCvy3+xVi=T
zzTZ4eYn7gDmCnVi^_U;T%L|X^tkuFUwk}j|q;*w<hs&{s%CTODd@nyX46*Vk9cn*m
zn?XobaJ+HQr5CRBuN_TQ0i<GTg7yqeAQls)4}=qo(k!(2=Vr3!elzDoHh2(mXLwQ)
zFPy)!UyDs+hP5{)Q;<*?Gg7$QO!yH(A@UQQ{lPT58{y8@yz*k7<nUxIo-hiW%yGOA
zc9F?^vbnkW7ZZ6Fy{s_Mfn+zNB8bDJtVu3*y^8f;n8x4Jol~J0q4I`;vF#8zc!ET?
zr^3b}@xDx?yR_qEg*E)93Cb<4`#7zs1#KqaZg?UWV#VtXNn|rO)-+qcr7#Uv-ULOL
z4##QNx#wH6e%(9hRp<?7q1v!d4LNQFpx+{P$t?20&7!a71~KdzgU&`@;Y~adRIOUI
zDnuz}O$i(mYSJlxj^***H;IXfnvm_!C(-tIg!1z-)7xZIIr0?gEBJ%xmM%@UIcBY>
zSo3Qnzp}F^j<w)CJ3ITMovYbA#12+<i*EcK+1}Hc&9SUhKRFGm<<h<4=X7;+s7XA=
zHZpbk^@lvfHg?Y#@2Qe8N(!)zr2gKw(V*UMnUa#yxq*CU`nZ7ualY&ZedfU|!P0u?
z#^UyN<+YMBZ3oG2vJl6&!}smrnjv=yKs2eSEdz1&Yj5Y7!t7~rT5sj&vE1nUch(3T
zKsQ7IsQbx!{`i?l5SdDtt__^2wWygO+e*dLO<(ZU6f(u{-GVVOG5kHR6HKZvYdZ^M
zDsVc?^r#z|r;=<T*~ipS+Mr15JLjoyt1KW1-sfYaYHK^0B2*uOiE$%|)tzNgG6##s
z%fhOU9;r;>D5*~Md=wND!(O2~qIufVGIuO<C{_pC)mtW*rRTUn{LA7|iN!FQ;>yke
zBSNxL<`Kn9Bq{Ln%H`aXaW3?r6zDi}TwQruFFedqJ~%oe8wSy5s^-?M;#Eg~tikAj
z$b-^MnJh6eMf{rr@YF2=%Xxp!in%Q<#gpL8)2(1DvhL3v8yoWx@*WzA7f?LHWoBtv
z(@Tw6sUm4{MC<F9{v#GU%$1JZ9}1PxNl#dqy`d@T(4{xK$VLaD{Py%`_36*=jGB-P
zfNSVL_VbS?3qNR9_yzZK)lJ{+RX$P~V#JCd=3rH4nWn%72hqwpttgG@<t4GYH*Q2|
zNweD9o5yNh>l_#;x#AqKinr_DN`!(Hioq;xV*84Iztw8-aE}gsG`u?{I9OwxM<p{e
zb7S{MxyTg0wsTv{C^IuNyy|@HGc%IbgRVd!<ivT$;!aDBODY`g7Z;!t75=TYItUxC
z$T;7$#Ip7f(pB9t&5!rBrj_hk?!D6;)!dUgS4DKX6OylwoPW7p<hOiuRbTN6#9}LB
z?+?mxc$wB-_i7{FloNg_qxU-Jj+!=3^a~>mzXX#mAX;`rXL*q2hp{zjO0zvHe2c=6
zM@E(fYkl`nh_0%umvFnkz(Z``9bOw5dItT1E5<i(9<EXn+D@-q5TI|M&K;^`fPeY+
zW_MNjCu^hH>CUoSw{ETPh}R1ldVfN8X(@8Bc3Y{b(hIU#9jn%?skST8$#r6`edpM4
zxS-3<hu29;*LA>mw0l&!-ixPaD<47!URxQR@)CPQxsp@&&al_luU#+_+d*QzS->2^
zNYovDQK_;2?x`H3Fr4)}lam~MDTG1B${Z3re{he8mns|`eAyF4?>JNBSldKqTSRj0
zQxBK%wA?yO%onBd)}Jbh)2Y8rWd}aJ^Pg|8-f5gvT*O(&RX0***M1~$d)yy{=0=e5
z${>-c+7cI?$^HMpm7Mu8TolTIPcK}Y$RMx0=s#?a{oY(lO0fsG#R-sadl3;PWn{Lb
zK2*t2GP3L}MR{9_={&>c@baX@kiH=!zO)rrw(grRmRym8J;u6~U8kfU{-Z1rOopum
zi&HF&*_}Da=TV&UsefuxCw9+^8c-;kPW{g>urU9#9)I+@|8H#vr5&p8t?23L3s9JO
z@%hQt4%8@1P@6BWtMftDahRYYpci?;_@48c?|LfTv|pYwlUicWAu?I!dhWX;Tu2*o
zzCy+9$to4JXw)Ko*l~f8?A;Tl-f{~@Fb8nB@pl#<3387>gdaJ~^&2-1WCN2JzQ<{|
zd#%vOg)JN^FXE6h0qJq(m?yekk@1!5aB|2y-uQf@v>+FW?(6F7;or%nfp;WA!a8t{
zX%H@KeZ&%Q!z_|~CB>~VWJOR)Sa9%O6pV~$nuxSk&kMLyt_<n<P;9PTxibEx1CdNz
z&L2jcgo9f*LuqiNLNrOIO(0OAiK`=NaYa>CRcHMCr|!?BZa*=HptlIV!ym}7SSXFT
z7L4HGx32bw5P*L=$~5N|IL(X!<*)hi>FE9zs75H}I3*S!ht}8U)o97}Pfn8)ODZ1a
zQHqo+=;xi}O1x@cU6~p%?_Oo~=H07Jw@S&G(yA(tVtS#juHLWr1!|70mk1OVGDu2x
zt7?;b(5+Is_zmujg}u+2L*rZf))9ZyU>C7%*SXP`xJt0TleIf8At9lC*8JuTv8BE@
z>5c;hb`N)LbgQnW@^5MkYp|$iPY8P|xGPx3Ao*h5IXGEw`4;mdnF5zObobu};tM|;
z|1<x8`B~d3nEcpqjEmH)e%gTBnArC6vb$KQPyH9u)vI}boY-<h*Uh4G<~!r$P+MVw
zSywrO#kZ^V47=o5Jw|@lxVZQZ7MY2<i|rG#aZ#GN(JtJTf4*q?yQqdbH{c7gb3N2h
z&PCF5rZX#b-^lcQO)R$g*jeFYRj<?DAxu9-d#*bg;+?Lk!c7a%&(G3i7TQh~p`7D8
zxTurt1HJ|`S(@|75$swMw1Tyx@Lx<~PrH88Kyiq@PmCkn=mlquH{3`d%0@X#(q<Di
zRHn9nC+`znOtrAVY|q`YFy6gF+jaDWbQE?>amBM#y<ELrWg>HOYvmPof79~#`kr(C
z?dC@&6SXvP%`tD45_T@|-xe+8WuJfEhMQJmzqgC-edqq`l+08B3<T_Cn)o6*ANm5a
zDInKFz-3-0)3024t?ILF>bg?uZ?Ao}h)KVG<;sH||5d2Zxd{$`ji^xaxp`9o{!YT#
zp%wF*JH&!~w=*!ju5~M)9F??QtbokZ1_8mx`(eHL)nof)@89<sN=g6^UryX$Z$GDe
zP5AaW!^6jpy-4Csem-<Rsq3|=(8e4f`0-)qr0b?8xlMY!r%@yI%DGi$%YE#dU%29>
zOdIopO#8k4M%9!wnaD~8uh(d-EUuDRMFJOF;7iJ6XQ1P^rZ@k@M#p|zts5gV=MZlx
z&wT&Q0PlC;9lg_|P#xiHFR{>Rpug6%R!|UhZu0&)lj;?VOCJS(od1tXS^P(puK9RI
zDFH|CM9-HvUrg_pq-5eQ?1p`tz7pht6cwfro)2Nr;L)vWR#zcR^4!eALJJx(t6y&-
zpB2O};qzqO8f3dj{)+I3l}!Tx?f?`%06u7>#`XCxEYc}oTuMU0EI^MPs3(2}<-#9K
z4IV^k-a@t(YXmxj2n0;PEhS}^BH8)E>5J0+^7LeYc!#^JR6a2B@=lrg&+};v;r~m(
z+xX;vA6wl0pI=~+{#UH^KkGsE%>RqmBk^dOlj=on?GB3~zHj9;v797{GXw-cYA`EM
z!@VA6-mJ1yG%0xRBJ)`7JvWg0+Byfw)zxud>af{=ySlpC9ggdbN;0Rl3yk?+4-yh;
z-+EW<NR}@08{JG6uc+XW$mQ;ZZ9YOeU3$e=)s>Z%mHNb!OUuf*x-DcP6?R;ZY(XUK
z6X!doHbv{uH85zjr^W=}9mER((=ht2-J<n26ZZNyQ;;=pN9Wb27&erqIvk^CU{LTr
zDLR@hhSw%BN>1#ELt<o7(JFKEw%YmYCtq5NFLGaChI&d^WoQ0ma<YB|F^ZV5Q?3xq
zVh<8}I_LI%PO)p`D{7o<=wm=LZtu~GsH=4!$T2W5K<>7v$9H&T7CZ^=Hstc@V3-*R
zf0CwZtqIa|77rVVi8JNhcv??SuP7ifg-`oHQ(4)?1eT&S&aUq6s9HCzRhLq*M6o$J
zT6UsKeY`0)EGR`^OK;cfGRk$$$_Mw*jb2Ds=fm2gRk}%IM-z1AWdrE6uUrYzk{0W!
z4xP9kO$9t#UDHifSC<`wX9$c*<@Dy}sdSI?M~_dKlI{*;$H+9{y}=>*M#$at^z`?(
zt=21lnx{EBI@(rCFQhZjSM()Ddq^2NdrPN82dD{4DP)SA7^BdCvHXHl`bT!MK0~JW
zQcK##=A7a2qAOrH!wWQh3Y=0{waQr_rta>PTZ}`$tqUT8O)9J3e=2_(*fej(BNqKt
z$h-GfrT)UQ|M>;60ROWd^L&^84{e8s)#tWexQkZt4p70rKy<_#M6T<AUHO09-DvXN
zI4@L%gD*{R-n@q$?poN1B8wKPHYgl@{Efy5W^vC$`DkbG!>qDnU=pl;J+Etrm?cW~
zCgLk1rJx4l0jZg&pv;hp$&jWb`#_bW1U(#n;+&a@Wq;J(c<xa*pJ)PuZaY%7XtmKO
zKjYl(UA?`ig%u2AAQh=T05Y9G@!zwp(2FlkZvL{UMu>a>dI9{jq%os|rS4&us;a8>
z#~qE&m{ZY3->o#?7kA=_56=R~UqvUNI?tSsZ0>lsAOa*aS}UNVC}mahX_O+}a+SL`
zp&CV{f!eXR&BVIW8tlF}=6pcQ@m1G_hQA&m)%>C^#C78qRdR3@*Xt7`1z<rBCZ)Iv
z7`!P?l2{A?+JJZRqJduF1^*uJWx)39U{Yl`_}Y5HR5-%VN+-WbvZqGMMG<=niyal3
z{Cp$p%qXYU!f$n1Av`=f_m3Ti2vVq)w63%3{&aXCfQed-UQd(#<)W&V${pdVv}s68
zuJaO9JTf4T+#&ROIYUj->AJc)-La6%^MQKNLtWt3)LHc}f<6aj!<eb4)gIyDku&{e
zhUM=dCGDa%kd}q&<tx&vt9dk-OHn$n?y3;=PbNM_=u_4Ee_Q~EL~pfVrqexP?eh)?
zB}^?P=K%c|-1~ZZNG?}a_WU5PO?*}i`;LFPT{3bz<;HUNDVk12*gp2XBH!d5h;`|m
z5OP-F@T!p4n^Wo*n=265%Ci8jug0!n)krd5+twTewbaye(muTBeFe;Q!=$_DjU5yI
zp`;BnnFOM|p{EyJ>xLw5K7F&WsP3gEI9x1iXi$YW8egK0g{^h%0K}JTsTt($5mlAX
zRh|Ley`g+*2X0YDb2(DOx!3`%m3%_TVa`YMXkc>kMVCF|CQ#pT5vQnLPU7w|$~qsE
zIMwT3r^}F^I-MU<r=MrySw57JDH_Og>{&s<{)_`!wxUWF^%|Abs>adC*vqONx9Q_5
zh1f$QgV$bSD~T1GM5=qS#Y3xDEHc=%Qm5*c2Q_KJLF9sNpuc9~S~Cy1DaFckzg#vC
zSzTY=$wV>rs5^Sy$|Ywm1khxx>muBuPV2e7Y+qR+7E|HoS@wIM^zX5d>~cGV50;XW
z*6ueWd^Bq(lSVlsatcB{1+$}VD|L3<zaRf<dDETn#N_M0=?8N3=q3}wvL~|W18W<-
z`{@Ij6vsxxj3Oo$eijNh3yrxx!Z{Ssd%4C;UDrdVps_(VMXxa@Cns#?=fWFOZd$G>
zIvtr^f~CRRL4;yDoIG|U5@k(3Bis6VU8#Y-hT^qG{;E6Xx+6kEua8IEMVVscH(&F%
z-kg`3Z_c@f6}>iMuYG5rKN)`&Zl0^@sF8uMcb4r;_c$j4u#cklXcF4(A+{)MQ$($s
z=lzk8gap&^^Ui$^&aNp+Jq82c#}!3!nEd^B)Lf2~OHMOlmk3Ss=nKi3s@80IPV;q8
z;71k+ls2vAS!0ZSB)?7Pr4W>FfBbHe%;tznR~q1>(wC8~@`LN#>9KvMsWN(*F*=}C
z%q)z#Iz@Y_i!@VLj4E#b;G?f=6@3zC*Fbb8XKiNj%=u)7XkK0Q_@o-Wy*BX^Ss`(8
zoaQ205i&8gZq+<tEWTs=V0P@4Ywxc-a_mJTec;|Sni=m+_40@k`#U=(^=9L>v?2v3
zE!&s)EDM}U{+*d!opY1oRu!^5NE=uQGpT4?Fb$A2T8c`B@=SgkNSwntYaQOSk8Eb!
z-+xgH#^=ecFO#E1R@`^k%`Gep2VaI={qb{gd3ibaG<|1Z-{{6S`r8X^8}z@QJG!KF
zY~K!ZGqY86rO0Qyx^%Mk{tSptPq#hh)Zf#i_th(4qlB_@{mmPd6&12`r8gGV2|6Wn
z*1mJr-+7`Z-_hTsD?3*g&Am6iZr!|5LQk(}i@FwX>&mR<S`|49t8}Y+_U}kpKSg8j
z6APR=+P0Nabgf*4{c81VXOSFxAjNuXN->5wk~x0?9m9o`w0T?VGk>cn=Gkdtl4RjI
zf9=7apQ$tr7yd9q^Z7`MCz`tP1hQ#T$Do>kwc-4t5j+o4nWfT1Cfr0K^$g}es^|uc
zJk<o!7^oz+RVy16VUntl9Y+BH#z0{D(%gK75OD+vljdgV{CnJ*LQ<DqjUPNS8#w-n
z#Uy9R)>Z<W;EkD|N1!FtN-9csqUK%p4GuR}5V#02Bo%#TrXN@HwGt`}DItILKh75i
zUf3|9f3=yUmhGGG0fD>PdV(b%r0*ecI#>p*OKj;?=hYEURQLeZo6oPW{QUnxi1<(@
zNh#)N+C$YMRFf+@Hzey0&y@o~c`vqq4e%sc=@E@!$(aa+XdRuw29;MVC8OU{#N!x+
zw!=YCMb2^W-%gc2SMK(FL{l84!9Fn|Z(w<FRP?G#Pc$qGV(}K;X-yor60H?8xl6)@
zM<bUiR$7s|GT}Nf=My-RFf~`lMhp_mZ8u=sBu=vfRj&|z5RJ``=?6mGqzV9`J_j%b
z>B{J`HlFW~H?z&N2B|~wUYUo8Al)4{-3&}O-A5|3v{uyt8%4q>Am265ynrMC3sv}c
zYDt^x+_>uw*lmAAAl9I})0jmj@}<iGmEybfu3Xtg%HT9oo(6kSj~=gVfKjvtozIJz
zf3#9I;pUGI<YVzMb1%?0R7j<vgS3UjB&FnP3A@Dvq0xRSVKDZBc0g_oA&+8cN(vu1
zNFBe9C40|vD^MUUWX@mC;`a<8*1Md|yp4c>e9=d{k4jqnGTPXUbZ(xd=>L3nbbi;b
z*!^ec1+DwrZ18{bi%{JpB?GhdPXn{EvZg_>l%f#~793Z6qVsQP^b*YZJdNOrguFxU
zQwU6)%Rtg-t?w7Dl>lpwtf@N6_+0}7(g@VZ^XN38H^dr=dvQq8P{SmQ#~b7W38Cz5
z-cHneQA@XO91f_#T0n&KI^o*U=0id>t;~PR^skRyeSIKb0Aht!Y5%|^3WtPs$BcmR
zD|n5S<gG~AtVha3-I&o>SO6kJ1NkKg{siNuF+&kg9i6PTWWk}pQiNt<8Z#*zR-iaZ
zUWxuy#Iax<6z0$|eaBVDV(~Em=O#@Py$04fE7i!uGD0wTIa&WQO%p8nVjOpRmGu<g
ze`U5arc9pm7Uaa$|HI1FtE93eZw%loz*;LN9JcS+EQF^Eg(Nq^SRlY(QQqoRt6EVe
zua7nNGbuR8kN&T`=++N?`qD=orwN!xxOIeU5D#|3{0f3M5<toZg!{=D@Ohj*F2D{G
zV@n{H07{+-I7=O$HcEemB9%8liwD|VSqgD#{y^0^L7yoZhLnUBhID#!X`E9VqItEN
z$Jwe90$ZO_E@YmcfgX1REgJ>!N~YkCw1SL4kmipG+e9?F@-C&sJ<>fSwB5B&Zx4Op
z@c*^yih!{1AXnpD4f$9??*ly$bJuwZWp+hW#x{JcK}v%sQUbH+!fa4phQRf)$|&Th
zv@+NWx>U(;lt;<hUCy$zhRB&e;T7NADD?#>?B!nx+tiQOyX6ZyF!oq*Z_kU<VDyzp
ze%oV};VKpOR*yS5K%#n(?R{*79JtNS9JgcAu@J+9u8ww}W;ktY3x~>F9TMAHP?R2O
z=gYQEuQnzQ1MIEF+6_I1+O{<P-a>3!UxfYd+64#f2W(BDW#^LgHH}_e8iDn&B?z*}
z47wsJ2r1kThge6a*4%;TEog;=gZ!;qkQ9Fi_oa&ADJLk;D5FS(%53K{Aa(?5h7|yc
zI7YV^yq?)sIL3R?Ih3X4t-a(K4SO{*YT02Vn5oK6A2e8w?CBv@*7G|C=6X=1A#_M&
zVN}}uQchN<<c3z;Y||5Q7;+12tq7bQQDDkO$135rXhbCh<ZA3d6(|+r%BJW(sZyDL
z*45RjE#IEM9np+;QO`^Bm&hILxnzSyjLrYnB+63p&D;%t<91PBU?HELdmuUAqp*!&
z$s~IsI|j_@z2pW`Cf*_R`i>NiH=I*FOVDvt(a{4e8SHTrL2-`n#beA=bR2aHlH=Xo
zT>vSCKtke0`~$&VNkaCTCk_*0C8_QglC<x3VE*`aacRPl)rKY=!Lo}*=B9hI=Vnnz
zv&KV+$L&q`YKs&SCU2jN=yrf?^H4XFqum<8U+=&A@gG6`2NsG5P`i;j#pMhuW5PVo
znCUZO8XSi@O?$?0u{cOmq>}*}0O)UFoG+E3$E6KMtr!hmKF~9cTTo({=bXf(QJb|t
z3XAf3#xEjwGK1_!vOBH2#MOPkKMMzy1Z;Ie#tP)L`>}HV+`=gk#$@ev7YXw0&?dxA
z(q}?h8?fs*Zf>=-MK5D=kZ=HutUm7OzGQ8j8_WXbOx3K3BBL-6o(LUj=&@qP9*|>D
zFZoK!L7)`XOk8#@!UMaEUEd(Jzh&I~kdXINjT%k;fnYa--n9JrhZ#pnf6bci2mW44
z+LfH!-A!{dO|ClpgoqnWnl2r1?eu{k=x>PH40)zxgwDSRjnll{BcO&Upx^C6CNw{C
zQwt{R8UtQq^5y(1(3>6w$)AyjT@Z+m%K~5NHOHklL20m3x1(aRaSp=gU$+Ctyc>k8
zhh2n*KmUy!Ahv%>M8!psv=nsDf0EFsoKOM#_vFbzm6jwXpDz}PEG?to9_b${iH?hl
zvni*t$dfCvhamOJs5i3~AuQ-8i^=MeG>TF~VYUjM`Stww)i;hM3&<-m)pzF+-#h>5
zgc82vf~tmoGz+=pUsJ!_q=jppR`eJhnxf_NI5d1uwkbc<Qd3mbO-*o7O&Jxg)7Plc
zHP6f2Qgqm1$%_b!m?mZ}!>B4<!*@UKGkf!h?lN%284L&rV9viqxAR!HpszzgD!6}X
zRaNI*1&7YlTH*b^kgoV^Ht?3S2ndc*hp?|RVv{y^fpd61-q`mN`mMH9pz-3@^GCt{
z58|Vv^~TLj(f6@gMaN!0hN%da*8BAgL*a0DXXh6n7rxAwp;h*-rux9~a<>wPu>|78
z2tmA%VL?{#{+?u!E%n{`g!il2GV#>XW3OVKu67DBjt$lCLSP(|Y|3A2)Wl@E9&P!?
z>z$#o(kcHur6B~3HR>Sdu@HX>4j#R#R0E)K2J^~`R2n(#mDWdB%fUnbBDV)`CPm9=
zUc!m)3gM|<H)g_+>z0uf(swY-=m=P81d3qoo2#d3tJdlU>Q&HXlE5rzMUS4|%nb#N
zu6)hDj!OYG@86$*Eb}AgeCT@e#hUY$kBzkweoH(WrP2Au4}bq!RA)8U<&>GX-;|`u
z(x<k0&PPNSYSQR+{@#|7w2%3;QZ!4EfJH;y)Qux9s&4tmlPNEr`YlSMHdwVdQ)#l&
zLgwW=@5vVu*E0>Z+mh>Es-CXsvEc6vx+4Qj0`AyxrpB=dF2;*A)x7z*M$atLoD1YG
z=6uqh<1>6nIr$KE$hT=ajDxr8W7v~w(g}iYih?Z_3gA#O*S5?V4HO6aqzxT&a$K~*
z^WBM)p;cQ_WY(3Hrv=MJ(J?At(DQnkR25Zm_PPLfUg+o8>LoGN<pQV8yJI`89?O>%
zNc7y_Tyt9~x_XJqg)^IOE`R<kW8#h*-$~lDzJ1JgA|0)M&n!&`?6WO58bW4d^$PB^
ztL}#3)(wXk6t!7*I(x8l8P`}TnX291)eI`trFhNdJoKxf|Fc{~MC4#}*M;^^XF_Fm
z^YU)Sgxa6GXzAQ3&KS_vf<E(4fY_S{23#LLT(*jyXBnoE8K*luihO~Ivq4c&D=6z2
z7@k5VWf6KCKcP=K=)!?gs9b)^%iG~U+8y=U-qoyQR?nVG=jn{12A7V$y`t%?>(S(_
z==9(04<CMCPZzXfcc{!Gb%%*1lyw_7wzSqAQ}aD)@CJ>CSk+Zva>tlUyu8qiczGoR
zHo8sHwUncwM?`T#yXDK*uU9Qtupr4rQ9<Dv8u}@-P2QH~^78U<JDd~>f-3bMhHU$(
zqfT>{m8;9_*F{A|-98u0Raa}dWb|_|SNy*H{q)k}#nErxtb^dl{awp`t^&!s{wJF7
zH#^Got@-LqwXqS9W8Dn(3`Tk0@HZjOe#b4PGSp>cZqsNRdZCi?{Q2{7Bq*eJJSIB0
za_hzhiDFCung*+U9D44bG*~@WKmqC#wC4xW{d(o=3$8Vh36*~qCYm2hGNUNVrL)Rj
zQGCwz=>04`jV|SN>r>f&cfNdfb`1k&zCC>Wc!Tyjo}-}Sa*g-RLEPeI|9AcrC6PE`
zO!(`pj@d!EGC9)q&a7+0;*(ChmL1^WI435yV)01|MNU!ictVuL%PL(i^nbd0c<dGy
z-g))vRrB(QDqTvL<CfP=IyG~TBBHrib~-!%+a&bPVbyxUAt8$)u&_m4J+}D!3qN6d
zdU6+cmv1aZe<oc`x$#%$2Z4c1kU6}Ibh{Z5(EIixU#!M03^e)DX5x3ucp)6f&6|Cp
zSEVG(M7*=jd$z!r?^wko%mC~B8LuA1@!5f)B#SAUT3UNJIZIxSdfjFpc-~*<n8du_
zIdo1*U4mY`sfsoi>>N7K-EY5g&mPO%?WNA8DC$!x^(`v4L7`j&0K$!`sLlAm2k6~@
z1+kk8tRCMoZTTQxuiV<+eho4Guiw8dEzB9OVWF%Lvfu2h<CXB?iLB;A^CI)UeY;Tz
zYpY@3ym$d5)t6u@f+7YpYfO(1F0J6zkr}Rz*s<;Knc%XAnE8_YJazDfg#M#P>A4j?
z!w~gjgwX54p!Osa$j2U1&tiZw@3#~qCIB)jhV350rW&slqPZX@c=ztzT^t<6IZ0*=
z;&l&S?mNF}#ge6~wmCso<?4sK%fA#Aoi#RoD{k~f+fc$RZc&WF6DUPp?fv%lCl?bF
z)2f|<+tBB`Q9FywY*-VSy-6QuD{nS>AD7Od8AVf#pZ))~(BHG=-!s#(+jrCE%|D<a
z@T{=SFp2so6?+-<<5iUNl9FdHUD}qBk+I7yABXlAAm1Or?C*+&en0@>r=py9@xZ2W
z(0RBZD|+|-{p!bgNFhEm=9-IGo1MMA>k0sWa+{DEegqtOo2rNmpZME%@4nq|fEElz
zS718!TWu2aPUm~`+p4Z5Lr_R)5TWx=ApB>*GvZLsdWwTgmDA|Dyul5cTQ6U|T8M!t
zx0Dm<PWRT1L34y{&4r$72lKO@pF$AT|D3`2jypQgFJ3^|%Bf~nd)Xm5d81`-w$cW-
z-17My^Ku|cqj;_?x74z#!w|dWRj$*KpdH>2(W9JzqHv+5;A`W-pJyq&J)KLIE~S^K
zfJOy*WRN2H3?<P9i_+$kaNFCU5(L2&m5xge4#Jcm>{gdpPt{K0QV_c6Ad8X*sR<*K
z_6M;BP~iR8NpD>fr%p$KoYP4kG4U_uRy<3SKYdbd(HR(~<!f3&9p^2iJQ?^+7Nh9+
zlu@r|n`iEs6iqzyjol0tGk3?KoVbna+G0vG`lipo?f0r1KRRDFhOw^?_lmkSgR)`~
z6{>NWe&fvb%S5s2j?ShP_pGvEe$~{}#-iUW`WEc}PI<1X`~}bb*=XLrPySP}_g_qk
z;KV3ue1B<l@=WEWce5;-+`i|6Pbw&Ep}ZTFz9m^!#K2v3u!hmJDyghjGPvvtL_;V}
z7^oqTdepOG?!?B;o9{ty)kY)(naq<NSGLz@v_lY;;sgnz`jihF687)xFZO)#PERmY
zfK$;yJ!#*5SdL=J6yeBOW5qr4J0KfMp?ImBdhheX%GNd*Vic6w?A<@^xQktn8HxS0
zv+q;GWL)*9PYqLXV-6z5qgl}hb7SL<|5oj3d2A5yFoNqR#cZ!h?>8=(G4k&Hmgg<B
z(?*%&R8p^v+zBvzF#7D-Ixk<$cZF2UPO;DpMD{4s+xkQiozIe$TdqJ{j#x)B?;%0B
z^ou4pywV~Hw6!mFSY5xq4~BkekXG*V=r>1q_J93%BPZ8=O4^~?$1+l#t6BEy?6>&q
zPjY<wJ4ROAJ7xD(zELAockk!VpMO#wMH@8hh)bC1(f2I9%;fIowusVM8R_<}IGXK^
z;&J2f+2|xL<Br+rO#>{k-z|ARSI%9u>`SlC^O)Ll=8W@d_`T=kswux9#&kS&fAPjm
zo0w;N^}!fF<~W;itOrTp4-f@8NlA+0%qE5L@t@FHbhD$F(Rk}%d_4D|%khiRmw!uQ
z|3?5MDnpBk@Lbk!-~I?;^QTv9c|JZ^`v8B#-XcahDvFhILPzHy6wxlIsygGh>0kF>
zPR{+vSi51PrE@rYhrEKqeW;~0gU9x>XGw&2s^X<f520YYY%7O~ldJ1m$U}t)EJ=6k
zR_bY#R_(Ii{DyreCdXe&*5AHj<w`vR_fVP0XTH8qz#Cdi`2|hFAHW!%hs?^nNpc3g
z>&T6|#Dhe~8eqvem2mkO1r110TuL)LNU5v~dh;@&qbFB&GL9UaDe_Ooso3OMCi9dm
zw&tw)lYD-E=iE|vdNfqsgLKNEQH%oHteIlAo2Q@OQ^?2c_=+J(3yGYJlaJ~r^up|W
z{dPqE5!Kq7A7hs<T)cRdXAa%JOCg8CQKf^u3c1`|^yjQ@kG^o=uy!$nm+wjRWNtQY
zkoT6*GGWYtiY95-HQy;>{BtpuZIv(!GxK85LM^=;stlYT2sJ*Sr&qpq&2_A5U9E{4
z%5tM?o*mq@0!e1c^`8sQTgI4AU}VT9wErZCt1{+zT46#jrb9Fy_I8Bj1qjt8BQE(Z
z*jXjhW;3oT$+NzcPq*@b7yYHgnx0&LtVJ$X;qO|Ejf-?k*(<&ud+|<V&g0=1`PAZp
zae96Yz0=(yhTZ9R5fv<qn8^2Fvuqr_ykjHUGfDWw&(HrYNpCA+4>|-{=!MzM&AkEQ
za3?|S*BtevC2?5;rXPfWG}NpZ$I-%hb8goD?t=%xj&rjQot@1iZ&6M!K6yAbE9z~H
zl%^&#8o6A)ewBbz{Ryjt4ZjfzcSJ(Q%YRVUKv3VFN%zZZ=SMLm244;d#UTy~2@h|9
z%Goiy@xz#7cj5BoN07hxi7ym2Gvk38YM+ewGQlsZaY;#soF9Of4><2OgiRO(%$7l^
z>q4a5vNdbYVXD}=9Xp=jHqyT~d+)AJ#?G={4DcrpXZQ^|Z&b4Ewj&U<!p(5$e>?0o
zS>Ap!Im7V#+qo5&wr;fS&tB-6M|KohMMHZ<+fT5Nac9oN-aQiZ=;6Pg^BYta`TYBj
zhYy)^7TnnLpC8&{y8rf42B-c1`P&}qP0IgMZ#*x9o4>8X@j!(m53(?QK9-p5^1|}s
z#pkNS$<@s{cMy0ELY!%vprN5)q7wU2qsFIjlZhDLV^<6j2Q@Z-cy#DAmci9Qr0kSL
zibbDNdVp6<h(2BH2Q`B`-@$beXf@&AFXh+S8+EJ*4q%s8;d+$qPp<P*Wh8eQAH2Uf
zma=|5f0O@l%XQnf-T3uFg+mk@yDU_ED|QQJeQ&`AJcmA=uLa8M79+T{Rj9erxcd70
zQ!xssV-$kZ((0OxO@HoQwh%%5<KEt0fxMAnyoKY`>C?uLHWKJcREoLB>*eX*_g?;6
zumO*jcCp_{Y&c3Yl+BR}viPa&oq{G_UI^7KsO-isp#S{o0#d|*oAp$$#=HzsI5x<K
z_^a-lzvPX@<Oxycm>6t-SqwA^p>2ABa<cwOU0qg4l`g~qVP<A-hG=bZY_c)}B(I#D
z4mt>Q<~27Td49Znb!l`}_tN2eZbGQ^Y^8TziUYzsdG?CNb041-6e~!v?D+0=;|zW@
zh33C0TE8J}GrFNa!@m6jm;Cg8x9t*JH*EN+aOZ3?H_J|<4{U`~HPhLcc^qL^cZf2w
zFiH^o8VREa7+__^^L4<pc1?HQHeO*2&&r*i7+yd@f03z)fu9l7%9pKJaq8m5^$_K}
z1z{<Hbg}vGr&~QN%Zi6ngm_}tvfEY_x=9gHQKw++kv=#<?C1lEdlG>(8laRd$KK}l
zcVH|4#R+5DA?1qOO+Zk97gL&z9H(|*C2FqF1wQ)n2Bwr=DP{{~&=IXgBt*BE?cBL@
z9BIu03dD$}Pu+j`@XPP$bLh(0Mm=*<?%@~7Uv9XJs#hZKR#CXUS0OZ6wrbTG=sZ62
z@p(KnWHOEU23sm14S<8a#3eSW9@*GJ3U+GU4f>f|-ehRhkih!q_VS8~PxOS*4Ei}g
zN#__zQYr6l{#ZzYa+Vr=>%)h=w-%LR;oqXE?i%3!hfkhtWU44qvMmJ>1$!4q!gzYz
z_>NAcE-x(bUAPOxW(yh{8-WQf!Qh!+s1;tt_I+nL5KuK*^ySOCp2JWdyTL;rJ+yxD
z0z`vnuU*@NDM63WP`hjQ?gc1;9zHIuF*pv|_2)cCcrnTe=-Z5cyTgajd@vQWLZZzN
z9X+Zbn2ggkI5^q84d*9S#RVBLy`)B85_dRb=cY}Ynn~=76+ytgZrwU3Obv$y$~YY+
z>PiUnLGbZVctpf93MM$-b$8!{p!-8j%~sstN9?H!YHEhuoZ{zekB-R8$z6d`-FYdg
zbI{pcziHEABr$mPHAJIGdn_{(&aM9335b4ts%g$;4WmF1xE2pF>j<fT&+CuM7cbtz
z=!p$NHX}}_UY>w20#I`VFVA~z3fekwCw46QBV=^jPy$-Ea^**CEHdQwPD!f63^5fr
zV4w2vkM4~9aE!f5v5{7g5ZkqH-;(v~*Z&$FJ!@t4?s7+*5qA4eFG+|y#=l!5qjp3)
z`8hm-aRRl0`!w0Ptv{iNqdQ{Ql*LY2M_Gnlr6m&g?%gH3fCmpA1j$5pha5a~Xj26P
zMh+DZ0$5rB2EsLZjT8)i5GcXbM_5fGSXtV047n|rB-FKos&tElMeLy)wiQCUR}&q8
zX52<xh?HRo1<7~DmBUPUh;Vb=&bLzeW-bx6N4pFdr&fMw70FU|WdH(S>RN9S)2&B$
zd)G30=W{QhR+*oXnUQf9LP+#N59sR8C@6TAWlT>`-^H13(3JSP+q}M%GtaN533D}(
z#C+)82IM;Th8iZQY^g887Fdi;a;IzIqDA*`7d}rb;5wH8cyTj#b#>iJqT!CN?2;F0
zFa<@iZS;?8u#N8mcB!^@z+;vSNZ(C_?<gmhEngnV7Zd!yHFxFzQ0{H`u|yneoH8o3
zDSP$^O_41mN5VnMh)A*(a*!=+%P9#Rs-a|v>I|}#EhH2nk{BNqO4gL3_j=|%=ZE({
zc>Pd5#yn=``F`*Fy082GURP22gT8q3G%PoP#IQ<3G9`qAjCnVClE`${<ORV;W7rE5
zR7MvIA!McgK@o0}y8&)JS(ItVXu#336k&s}1W_C~Y7>U~#8`VzK|5M3F?b0S{j9XK
zv&bQNw(^W=-w!#JcDh*KV%juy<3hV(P~Npr=p1h)TVP~ulOlCMK2>6?E0ND&DBWdL
z$8~E7N$JJI`Q$1yd;-T;w*u7A8*wmW3ZfEX0DXp#Yfa9ogTkPww6w2AHVhz0Ucx-L
zgZ4+s@i&QZHRw*?@2zw?Ei)_Y98~msLi#fnj=w7AAQT5Y+xPW6a~R>EV(D|dR5Y=*
zxA*K0AfrOw5R1?d744-#Z;Q1EddOk6pT#=-1HqVxY-%zk3$RlY{b%eLv388zC;$Lk
z@4@6O@Kayc$1p~IzKFE6%2L?&?b}yF{=PKt_ySb!i=oC!XlBwtfYJ*JEc&mj?${xX
zm{<>e3~9h1$Nk1eMloem^y8E6kn>_{#k_p<=267KunAPl-fN-jo!#}`E7`Wk{5@b0
z=3ZmJ&f_HJIj~0vH%OGAtfiTKeNbH<TQvKbOi_nhMF3_{IYvfCo_jH^8C^~7?Tt7V
zAVh4DldAy+Qo37oZbM&OH8cQGF7QEG-PqkdSK=r5nLz-84{$Jz%-H!Er`7Ka`~b{M
zz^0?QyAJ{LvzQ1G^s+l6s2HF_MF12cCk*0#vVxjhTI$fx<sr6(&TfICuJi5Wn>W`d
zuhteKNd6D&k%c)qyq?1$Cg-JoO*_}z+FCfb%n)%DY27MIPtM>Z)WLL%62Ox7F`W$2
z{t(0}FL0@HtZa*tvDY(oMC3u`LKJ_UW5s*Fdp1qTY}{y6*&f7(R{fD~o1Mf&_ZiYY
ztyqK22LeDW`#UBxx^#|vIh?@>>%_*k>>a*-Rf0-2e&4_N453LPo@T$bq(6Go0zCh&
z1bldo+8FCF78-o<qXo`Kc8YMX)79;xN#T0vYd?DrVpct)&1KJtSD}St+kM|mixV;M
z{FQiS$mrt7ZC}+1xwJAX$we(>kytFB=n&^e;m#J?@b5@!H)(v~LpMNFP;9Z+Sx|+0
zq!>hi1#kV1&Qake@$jW@9?_%t?0lv(a3_rk8E$Uw<?zOn9hZVaAR$7M+PaN<|I*!b
zC1C8O97G-}oEC#QM3kM%tbKdubU?LK`DxnmnmECXab5;9PPekriLDwXQEJ|yVF4i0
z`w7|E*};$Q-|x8j9;GdPa(0%R0A6l-SMF4CQV<Bu7_3E9bhIC2sTfCw$8d72skxbh
zs01qtWz4fRfuK0P_B>j0|II-#y{ahH0LuVUfE)xqMBiDD1tf1ExfLGHVBbq+XK+Pj
z$I|0IllExQK8K(jL51>M*W7VWpSrp_E`)6&5>{3(GsDrcM9S9)wupEVg6%nl-oB*8
zDoM#iP;*?=LWif+)<5NN5l#Y%UeY+`B0Ii>COij$Xy(@Wgl?0XiDHg)UT{|V3p-?L
z2~EPoF9LF_kF+TZ6JdY+(a@ulHYkH&=l=&AfoFawDjdTk(~AI{XdyQ<K}F^co&9hI
zhQ9o~!cz5!GD=Uq5l2Z7i5bpc11I_^kb2&Q<)!qr?gAom;{01^p<g<RcQZ6R>=uwN
zcu~KoD|*)L2ae2KwLpDX&oPFzk?C|=8w4KB1!;xwsxB*zd(eliqkREU3ge@IWI{-O
zv53jO<#VApQVf7&@lF%f5xsGickMxF@6$(VY83Q?Ij?_tEN?CL`y0(rK*<}{TL#C+
z6$UD+s)jy&QtEaty`@K7zH%j_phC_jbvtAg8RI#7OMmdVe*1pwWzVzTk<kx41-VYA
zvdFl%hMw?1fr@IzGD3f^_YodhSy>i#Mm^Mhi{S-`uxF&VQbHmg{>>z#Y3y--tP<z6
zSc_n?GOtZ}CV;w+pMMSi2em>ASwjTqPX>l%H;Q|hp%=oFgS^!<SAYq^=g5_p9#mw#
zMc`Japeu#GTBO@k<DDIIrN`eXP(@385Y&3Zj)7g1BIOIGffsJ&lsrO2T%2})4GwM*
zLE6Ke()|Y*U5FsEGcOLj9ejxQ_8Nk@jB%QZ%|UgKG!qgOOq^vWF<E#ivKB$-I;NTg
z2X7<mPEZ(tP_DRdrsn2I>$TWoWcLA)DdU+N&eF3M5m}|=Q=jIS>zRshCv~*Y)G98I
z3%nB$=pA<KHII1SB7UbRmj3$5(^Lf|$uO<64}<QPOmquPyf)AG4{CY-N?t~WTIoC5
zv77yRZU-R95Y!VVik)ZJMMdK<nP40Wb8?KFDtfS7#yC9J;%owZZG~|2cRM;t0?ed9
z8iR|#ut+rQS?KKmCM(AM5t(KonA|-r(@kpF2meG#Cfms$6$$$LmD4T#QDOcr?Z-~G
zwzQMgB~A?9(e`VVAPuWV=X#En1b*W@?N57+binFsGcYhf8d-+q<p(YZNyL-sxIBv3
zDRLn_{b-QRm+t)i3R6p2`lsu4GXm5-w89J(dNLLqD!7xkL-|(xZPD2GAP(R(QOz*+
z7Gf-qvi5%S8p`GIJMNXN5*8NTxOsC@#7}5^$#x1$Y$YB(d^kBdNp*3NBDr2ziuc*_
zE(+ELW@b<VzI=435D0yJY007mwGYMe)XY1W_|+;_*)7bbY3BJpFu*oYegs<T=$B)j
za@+3vU|F+}#E3|6O|G*aUI5hWM2RyrG_-F#DK74B?&KxikD$;`wv<+@{FWo%<1#xA
zjW5dP?wms%2RE1F&sY`kk*~j(i!8~T4g=t!IBP)IXlMi+Rcw3Tb1<R-+0Ihg{rV11
zkG3PWs5GCz{%UA+Gy#1c7TWnMBA)sP#_l?UL_rX6mO771P%LhvQ4;<mn9*`EajCo?
zeJHi7#v<Hk6g1NeY@8I<BAxk(l_9h8;bYLsI0?3nHO&uCm@B>RrZpFR3?l^He#@k|
zfQ~@#qxg>E>FKGd8nC(`-1qJ^fje7Q2-N=*=Eq+*wPlT%*Svju(CPNl*QsHvyOwqI
zpGi?r9qLHXT!#p^{`4TZ+Pd~Ix=^xk;%Q$Yav+M0jDAae<NIgn(SY{$*FIxeiGm%0
z36+e3!Y|;VSj2l6Cz2%)P@k!V?}SAebnW?Z1#|%bmK9-ZmWVu@G=5theTk`X$d{6l
z!tV&vm|lZ|ajWa&NFME`WdsIyS>;SD(R!2)4RiMx42Ivr?9m^Pu&c#SEZ=SDVs>Eo
zq9k|+C;-f4p+e`iR;PxKc5JG|guLmVJzPX^NXQUqui`ubxo!DAA5`X5LuWmq#~Sa=
z`%6?LRPR|x%7*s#S9h<T(5Xe#s{gL4s(Mu~L=9;n8kK$0d2bC#l01XgIQkZv+v24p
zB(@Cf#kV^2xA+)G07^LtbV}+DxtAw`J|Z-W0=%_K*{C=!mMj)+Td*ilktq)#0HYHz
z1W1#Yw*?|$o0(c)Nt3m$K7AUQPxSzPz#_bWzqX$a2XH&dE+CI^6B^##0^n4&x3?=h
zmy@i-<jhPR4vv$QYe$C-V@%uiF(E)v<EFM+yaZ^aV|wL6GtILjhfnv^L^7Or>g(&T
z!}q{0bzLgjW`k%lF0-E5y$cBonQh&r=E0GX*vZKgWaAb7Wkv|Z<E?uWy_XR%STeP+
z5dRsD&K2rLBHe-q+Z6pI@_Z6&3g;f^=#Q*SUAN4HNmiY3uI8n-`pe;C#ETRg2Dcmo
z@(3)9B$j_k%fsG1rlzKaDg`KSV~UHnLerfpdnw@_|MKNjkmjoJ<pe3ov@+n2<pg<5
zc=7l^ssgXDem<W!BKA;`f9_51&#2;AX-c++^RcghUO$#hZ6J50jxf0^^Msg=N(0Fh
zHQI9WCX&v>-3t${kk}OSZQhH24_qMGNEMZ$fk0t?LBR$Xg2!Pd>IUjxT%;^~vAkjK
zax)F%YSLtDielLb>3i}HvvhT9!Cy3NQWi)^N+KOLn{(IZRNE27$o_tGeK*|Pdnm%m
z<+t*^$}*FZ6bz~)auvtnUCIEwV=SS-a>VoyV$WB2+<Fz79Zx~Q)g59n?~?m_#_mM_
z+~;7G(844{w5@Y9m}5b!Q^u+T*?Y3*2}ug7nJK5yp$0CY8@L6X;ftxMu?R~;=$4aI
ziDk&?&`=hN%qPg1_n`irETPH?c9IODXNA^IV&3>LOsmWMNBCi-;JKfI4cv(2EDA;d
zpa(hddR|@=D-3Ah9&DvQ_CFL;shs45XQL3-V|_!zAxuZ2C^(DJm;FJPGvFo$W!_lO
zvUqIg4eQ74?ZWUEr~^APiMoO8hsw^JsAo>k&Ke;@wPooiL40Y0ygWZ}Al8TBv15vu
zzfwSHzy;KlzyI5whJ8MDpdR3R)YQ~O#p9@UNm5WuEE*ps3tNA=uyAP|#msH+@fq}+
zpCI_+5RdP5^y^u3f_!{6I6!h1YjOw;qpRCb-L26Jog0e<dG3Z10n~&#7_c=qcg67^
zB&(!}JO`T?FEoKU36cm4{5<fcnfc!!c!BTMNii}Uu3vA4nS=ODQKYt&WT(eJ<r-om
z3oFFf%#80J;?v^*o6!A;z90LWYu7OqdsR0`KC&-UR?Cj0cJRwU$Ck?8Y!c#b?7zR6
zgJ}Yk0x^hzxN%d@4TW_o%&j`%SH4CsSOosh=+VVIV6dNcvX+9Nf-kETxNG<m90a5e
zhA_R*4*v86Y|!u?)*@V>=}cm@rgPi=ms|el>FjH4<P)Nbk$Z>zb#dc1!jk=S_CJ5J
ov%tR(nnjVp?BB2d?<cY%R*x7NpZD6-K+YhW?Y1(yyUR7|KMZ9B9{>OV

diff --git a/articles/AMR_files/figure-html/mic_plots-1.png b/articles/AMR_files/figure-html/mic_plots-1.png
deleted file mode 100644
index baa275c1370a58ba2d93a0ca629e2d86a81508c3..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 27193
zcmeFZc~nzbw+9%t6crJxEX4wW@*_ey03ygdspSxvRE8ik0wQAs43Qy(a>6PZs)Ed;
zBa%P@LI@;;h$vK)Nic*lQGtMg5G96?gd}|v>U+<(x_hnur`PKB`nzi}oqO)t`|h*%
zZ}0OvC;Wca^_1FX&CMVXNDXrO*YhCI`brRJ-SdqbfR>J%%%{MQ4HunG{R;d9F41u^
z3iz?<%IOOUAdq&3;%iOL^p`LY=w}e**P|ZSO6Mn%T0_JAc@j5+GOO>7r62nH#~*%P
zv->kh<?Y4?CpQ%Rdh`T7{dw5VE6+c~+dlXRx_;%yZALnu_p6j{oBCI-X4uXTuh;yv
z_Ttao$6jnZcH5QZ;Pnb;Mvml7P=;_5Hay`RAwc@hr;q;*ABo^GrL|QRo=VmJ^X>m_
z3p~F+i^n<*Sq6|_KUlVIa|MA?%O<c!fY_mWvh~F*epFcqJhco2TDH!pC9P_csK3Tc
z`CINb$90Nd4sH5(!<FsFK%gcv&lkQ11bY74_rU$S@5vZb>?sdKZ8lc+Se(mwMThRp
z6!mg~>He7Q*c=a7jf}^!%Y`Dtp!hbjxQWw5uf<Mc`B<VS9xKC$-|b`^A^6jeU`wFH
z=xY<inzs9-Sa=ra8Ap|#qSa=E$1;78@b!Y&$66l3rwfN{Vm-72kk69c1v&Hsn#__O
z>sAu7gV!P-{VWTFCCZ%W;TUzOZuB)4%OPdu@w&7lig&0}llsGHnq5)hjGPj|2>E^U
zQ*pMG-I^%C?G0J-LiuPDeiO@*%fv$o9US`HyThoJ6YyNMUgXa>#wdOUeY-MVue>#A
z1!eYKDPL!xGf*Pya>s_4dk?Y>7EZ!6zOo4KkNBOJE?V~qo~@r`wzm7g+$kwXDN)+S
zuw_ulL!JI@KDvLWq|Lb4sw)(VPJu<nUAcC(<IGIfcj3g69t8Kc=PRwB`0BN&Ky=TB
z66mm}^u>m`VR>_CS&RBmiH0rjuEn85=<)v6p=EhgmP}>4Bd@kMYPlFU<G6M<tX+5*
z)fO~9#`p>0Eg(CUBSzwa;Z<-pUi@lZX&^`qG=Dnw*hbLvf1R@U4#{e6m*FFZIUl%c
zW7g3x5gx+P)CjG`Rt`hISQ2E2nLDb+lN=^iyZT(=o>1|7d(q<Z@E;$~%84B~&SU!I
z)+;OZYPv(?!H^1HFKTi+e;qJ-hFJYC5XgBZn?7OjR{rR=T{MhDn-NX8EV1tBKGm^r
zZw*O09)*8qUn9oBJMFKvi-^z)@52iYnqD)^Hj*oRYF@Mz?|ON7GUe^&kG>ubt*-td
zqo(bp>X@|4?F1@5)>HfK=d{w%Z6y3OTrK#kj}G<yimIl}&+kh)FLs%~=t6&})wB+9
zO;Eaa#M_;_p}S$2*r9Eu+VpMDYKd9T!|2FMQuU~fqixNP<TNMF*!<AeE~p0Hon`E0
z5xa3WqK9K!D=xZSyYmV2`a{kq)kxnaqF`YswJF7DS3(~rWo-CkZU=^8TqrMvcV@S&
z?{XhX!u7<q#~th7(FmQbBm%2s!jRduQlwfw(|Sal7Qf`fNTA$ip%CMafr@Fj3_Yk?
z*%z@fLMUugI3BB|vry&WClnhkHrI+a8sjhDRcq%T?(%Z;t?Jw58WUG`H};xPTU8u5
zP)ADK5`M5cs#|??{`!7uvujp~TBUB=R7*Y=h9s{KS^6sQeS9$4PS<;e00PZlZh8*_
z1r>F97U1G!o=%;uWa|_~ypvxf-~W7RWd$82#P<4!uRs|^AqRD1<19r|)n14AcF~L(
z6T{6p*pfJUy05<c&8c$U%+69&jG$%jB$M!d1&He<8^kM>H4^d(!SL-o5eNjeT6-A;
z$`@s<pK&0CcJqua<)XbI=E;LE1iMG-o?SDni2V{d?kUP!KZ8olO6Y35rpunma(5*x
zsceM~&&!x9k=-EiQ_j8hjDVKjVLN))PCxRX<_xp7lGlJWwzw%ldDz_#_PwVy#R>&I
z-%3oOuLnXpri@jmyXcdDi*`5mY14dXe8{aU4BvR}v>Mifm`nA!A~RR}Fty^co|<x$
zrQRr$bsuT%VtFHdk6%k}xa}yqd0UIuBlMlRt#z&3B=aZ7Km?62<I(36zT)HC?=caa
zm0&zS2R+$m6P9Rud0~7n{jQe%uilLxH9_zHoE}UBfp%Wl8EG`O5d1cOgYTDynFo|y
zER(vvk9r}nR(7SGpgYR@GO6*^xA710<_yoA%gg=mC+Iv+hk<RC0ad}qlX~pz4o3MC
zO|M|x$b4zJWz7NV5+VD<kdLQy(R?ZRd8-~a!&5$9cTC>wlC1(79{R2y1Uk4#-^<9a
zJxjUPp5%~%<BkB6R{fzf@ksQ!c*c6^&e{jK4|b>%9h}knCaaOnZ;pNT(b)pa3t?Cy
zF2E%_VXx}T$!4i`h+4+?p!a7#=mODl@YzHYcKD95QA^@oL+r%_+2aE+LfkDa4~q%{
zj}SC}*GG1={h@E_@ec0%-*6l&dV7MQ#LncFDh7s+g;FZTqxxMeLa^#iAnc9;K=a&n
z@zY*LS{=9L*;SZeP8BD8InSsFxPpAk`J|aPB(L4esPLP36nx}Muo|xi*VKi)=;{~G
zy~D|L=_bqzUrI-XB#V@TW3X0E8NEZx>r5bV&%LzE9p!_%-r@6hjFzpSD}O6;Q(dl~
zpLFbcl*QBD@fSYklaB#gGzIVY>T}?t!#PSgtrS2lR+6EEuk;c;wxj8LK6XMQ>!&d$
zXJ9=ewk34D%tB<VVbR)4I7q6WEL`%ch#1bFJVD=%8Pr;01$<2rln^V8K<S2i(l>yD
zszh0P8M0@43jNu4I5V9G!jJdj0|=d>NengM5WCutr9b^>FC&0DHCmSMr!^+oSt@4^
zF+)VNw_^;=qu!qRoIwzk$h4tS&YE|1)IP%U2DGnW50FNu6w^9yW{h1r*qnH=BXZcl
ziveTxb&$2Le^P<!Mr}k8M`6a6sL|_uW8d&L{K1x`7}eguTJb|TTYJ!VXQ@NY9x(M|
z$Mp^@tXf9pbgF!|MGcHv^x9rbjG>h1cxkihVrU}z!<}D~`~-F}EBe1z8ClBjs{uX*
zAY}~CMeRU@&_R|+D+FM!foPr7UZ_`)nY4nQ5tpoc%KOc!JW8)?uM@lc^#?VJ?y=KI
zRQ+$F?C6ajH7IQ<UR~W{9eSPitE!<SW?1{mZ>ePq&)ON}x076_^11s?ofPUG%Qhd$
z(k6!oq+kJ3?fy!t-1y+N?3pH>6qyDma!#sYpWq7-P&k+Ky%WbOiZ-VJ%Jk!}UAI&s
zJdFbuQ^!owapGHQL&VGL`Lgnsb`d)#&^JD29wh$E$t|hj;js*(=B9(`O!~I*obRvV
zH5_%jzf_wy{Ywjo)F9QZAW-51irQF2&BX&*H@<vS`)Wl^9@{V@5ph~wY~@baK<2OV
z8-%Ll=lWtQjs$x{)VL4|6WpN7Ez|3Lr_s07HFn1ASqC_svfy#|53qY|{Wy72zmw8L
z5`cHq^0w1`8K3x#L(INwhJn~vcWK84v3-NMzR7-NrR#Rd*0h+iG~=fW=lqkm^#O9C
z;aHBg$|0GZns4~Q(E>?OD=8Q?m*aZK0`&a#e1M|3I#i!yJ`YRnxn0R0!@y!T7UjUa
z&#f$R)YN><O3B5C<%4+hn}6MnUef<f!-F;Qxmed@f?)lS)Evy4gnAlB1b%M$<HUYE
zY*<5-fsq_Z&C15xpO<<H9&}PnSX3{>Q`}%3Yf)Q^SpoQZl+33?^5y>Reo-ym2HS@>
z<%MWA9cr1LZ+7WW`Pm}&8nWw1H2%viPAP|?M&<S5>`jO6DoaDfO#NS8!;SvRGIQ&i
zlvAZ|`rd;;eqp<tIBS)ihl><04_wa20J!+i^=}K*5wdaF_{3V$Hf2i_F&888a(k+C
z);}uCJ_D+b?ZqzjX4}zO9^tVrMan+A&y)|hIVQ=jjr&#TVTs1Gh*w|GoH|3Srs8&b
z4ELO&dLHk{obWna>>p<fSB+8xJt(Nex-D2W>%fai6kf;I?um7H;=&5&UW>i_Y}sht
zfT(VSU~zSNoA{Ao>gohVA9omS>tD808;kb)aEB9<z`!lTg_```0hEMToQfOtuwqzH
z#`OZCv01>J{&nKq>jNdc)=->HuOQ{sQ%~j0XGSwHs?D1%tAcH^FxxT4JcM+X^Pr^*
zD8h^iquO{~7_A(S$>g>caeZft=tCUc1(V^Ws~c<&tXC3K1vfdxSfkKi1F_}MGBW2_
zQ?-`)IEp>%sd4y$u}2?gPOiNk>M4BMmO?*~Ip0ICs3mPoKR$AT>q}_;<ui-?o4rIx
ziLO~|@jeVG&Dy5Ew0y&`Mi=xRGkY{kZaSW5nTF`Lh{#AfiIt0;<5phY;eEfG)7H<C
zJ7**P2J(mp)+ZJcmNYN<6*R%tzrfvw;rL7Hr3_Ak7t2m1aP+n`y2AjSk#mTF)NJ~f
zRrt*o5XgIe7&GYRKp~G$8)0$Nl@uP5H#gtVhZ~u@=+fm?mc%Za5DyUp;D#z(pm_nw
zO9>PeceI9Jb!4A2h$+JRx}R+2qCQw(x!ox<S0*UhJwt}Ta(Tz;#NJT@&x$dgm@Z6d
z^{OC<)84;#8mmjdhUb_uvNYGu*7~)pfv)7iU+=5BDwnWO^swEHA=y^!KS0Is@uemr
zH$X^YVBT0>o+(2xcdRk1tB_3a(zKJN?o%%>-!e`OPFaec7F!4H=o}s_m}Xit+Qu{)
zJQ8a%%wZoh1Sid2huf%_^B~oYl~ux-1n!I%u{b4_&pFYs#J@zfCzIMW`K7j6s9V%s
zSVu+A^nHq+>U4uk+z_nLOFmSmvZ#XHuIXj!P#-)g4GCwUaHNU(Yv%<#I{gyg?<^(Q
z38VB^OUI@3a^^IB0*fFTf2MvK7ueC)VeV=!EUXx)AyCq+y$SYpAK14QQb1$*JNkVm
z?dYHA4){1(=52b9r*UmsGR?NYY<`_|^shN3bnPkwdd(cCqpES|{2Rm<1)?KjyNBXH
zdX6V6KpRS-iX|LRwF8e8%L!1E-I}jM<e<cZ{NM9o?aHP)?#OlJK<SW;mCBaFo1oRG
z`%lCFMtdYDyVdA;l+|$n4n*M+Xi#!8^_=va;1#eW<}S7=odLS$dVj6pIY%2{A-bRp
zpBO7;=fUqC@tp)rxMu}c_JouGU*4~(1jJn>pya5DYH@?Kx6Bh@DAEM?I2(NZe^fm5
z{KxP5edi46>Rtw3IXHVe1UHh7!uMjCUK0<k<FTsWDLU=C3rap>^fZl1N`p%{ZbEWe
z1_zJDd${3UCk8l*9-xzTz;em&6}MJaExymuRI}K(0;9tL9^`4cnT?E)94UCs!TS2m
zOkldqdsTK4OQp$z@7ZfR;d0J>I!*zu>Oc}Q3LKab(@iyHh}W)h=Q>yhybJI>buQyo
z$-mmnNyaj?Vk0Tx^jn|<@IKBtZq2JO-I!(JOc>VayY#q?mBbuX%#3FrzKyWwYTEm8
zfhne-chtD1d~j&Js>A5EV>zEX_4-tHw+3R`A{5|VbDLMo10*v2MG~%ST&%zyYqYlM
z_rRyIM9#lXCVC03a{|5mH`bkZi*07~yYe60e&qw22d$muhQkECoD8hIA=y#zhAv=>
zSEc|A_O#7;8sMjGKj0^<mI*f|teG737u+<ky`Sr5pF}6@ZVb2Q7a`fE71aVcZp<Iv
zWy7pbVM&;|05?T!oi?N_=HU;bx;k1XJabaUCrL5^p33prI@(Ynowpa!-0~e}p3h63
zKhzOCjlKF%tCp01V=09yOhK+p>Ke0Hpu`_p?}$WWFI0aWY1$W45xemTar72_?o{zy
z%?Li_RI&Q5ky;pXE;f0v1V_rekvL!96^oJ63@XOCFqGtX%!qZv+0EV^wXtxls|~l&
zvmmZN+h(EJvj7xr8eSTA68P#=V7YM;&B@DuEol2NMDi;)GzE(Q)|+*R#SW}nP2;~5
z?vM`OSgCFo80q~zvcpqD>4kNfAaQ?~s;c64kneV2Wd&S^Hd*W`WfmzsU={D^-}Mis
zjA4|$K;G+puPAzbLk|9**uwub{I4<4Fom<$dzM3|(Q%wXx(3u2SW^KA#r9r9q0D2_
zD#fSaM+NPibqz}@7&0EG<d4&PzISxvJ>|eIbtU9~wP_W<;q+nLhnmywa_IECe7Mrf
zwywF}{b{1!Ix4Xh0ijlT3YoldsuypJH=YA^u<(H^#DKN~1Hsb9G8;6$wj33hCgh-~
z);N+Ue2z2Bxsbp>QLH0gYAT)dqybPSAbJT7O_hD?W=8QC9e*TzVO+7McPO(#$j@Pv
zQVZ_@*lke}71&tWC+s1h(rA8EOGaDY8mJOf>(&FVZgUYwi8@*VD~hUU2*sgFqT~ht
zRq!a*XwWsf)opP@_SNEXO=Na?R3Ir7XP3a;uKo<ybIn_zn#KR^)ed-t?F&EP4NiIC
z7hcOHU(XVW;t5U>P{o#zAc7e>!OO3!&?t5|nNJ6hGAMfO>?cYaa?RHZba}(c3L;Qg
zM(K`x*68zLL)}r4_c-Qkb2U_Z&L<9lmcWwgNEx#Y<;Df8yMicxmtn_nq&u5d&aS;2
zr1Aq`dSk3X&uCvwz&T_X)!sOkrS43`FbP38-)=&nh1YLdpO-mAOvkChxqbBG81<|}
z8X%CPrv>S4mbizt(xZTwUOVA<`O1_5o|Jj~MNxS9h@P(KZ{-bp$$Yu6b*7fIC7q@y
z>hd`u^ejzTO23xY3$}5eAnAgjk)x$~jALW5Qh-e&W2*Y>WP=f}{Rf$x*1y=|fzy1z
zEGtzLWCUW7mLYRRX#`yRfN`=6-JQ^FV)HR7O@NiCzj)ru*1tJ8=8Ke%PmWncSA1FK
zXf`Ain2&pW9;6r1-;ft|XbhaJIaoDE^TqpGzctF$N?im{{eCIubj|4KLhZ2D)MQl0
z(#~*C>gcwUEb5-jwx^^zakQYLp}xQifiYtJ@j~f5TkjcKHudTrsqsrY0e>+r^6kwg
z?540L`53Z!^rux2ti7;?LXhD|u7Z~we#tH&Obz=0R$L-f-p$x}ppwXZrDma->IHA$
zlwb$3z#hRRh68<@b%i^HEPiojKR{dpEbzv)v-A;P<TZdq1g?2Y1y>7PK|d&nRN&HA
zP)@-0pN9X<_8>$3L4n~=cqBX-eh~oB-vEMTamSF-Me6ne#mw{qUDw0HdvX-zk%J3p
zd%Q#GX1govo8B+ya+dUZiMg#!;<1tXZk`7|mixJd<3oRg(J-q)E4^oUsB?2BFOWhJ
zdkBU(#d<yD?c-q!-cuP^gKoRFMsWqb4~p{kKaAiSU{$hwmF2<n<<5Zm)H2QpeFh8l
zK$O0>N-Tbe)#$ZLJKn+UQ^%z}Og;_l&gCT_%YC&8p6itb#O^!wFFp)ns=NXLkmYd<
z>EYNes6p0OKbszZY)Ck_&r4WiG>-2(L09*f=>>uk*_H31T-aSn?<8&d&_Z{|1XlM(
z4_kjEhcjF;<wtA+Ch9OkL;AFyo$&sG3VYPS-@mnz6-Lj+IF#76!8#&8)y6wd&f6h>
zVJ^o*JLXsrBg_(xv=P)a{()N~KS3aV-~nkUs6^9dOKT0%Vw{}V5%7xIe`Os7lhU2d
z#|B~p&Ld-|G`Ai>6h`4SevwQ4B)(n7VfU-NsAh|$7TQT|)w{hYgb(^9HZ=Lz=kV{-
zvDr}`JS>jNId*dCD<|B`AIKJUP5Yn^({Ra`1Z1_~Pun$Po1h&!U2YZH_}X$WO7*B|
zu-zElg*`fV>&r|Zp>G%U-mNxo<l|uayLRgbAA}Q+^|}c=vLkld>%!mNrv^pym=ynb
z+FruSlS0tEvjrYUjbh-kZ5RLcWW@I`&5sO*75IC>XLb|-hCf)pV{DIKfYe=}N7n;b
zzGz0=>v{2ohDj$n9%_uUr>uv^XlM{%>a&8i%WuNFg4HCBu)G8I2X}RtVk<DcS`lrA
zaqptuIn4a~EWo)_f?`JNB?VP**SM{jJi(2{8m&v;K@#=Am<bh<aRvC>FSn~N{e8+l
zo(`NL0L|^1)4!=Dn7*AZddD$XA}1c}ko8wYdaEHXP0m}jPEf*d6z1khf3Bn$8BrXs
zKs8WjJ>BA_%>yy+Hu8R--vG~*KB6Z1F%2bn`^jnS2i7nHA1_<>licK}I(6!rFqr#z
zn-q`L9gpz@A<qat-$qsnSyw;z&{43BrEaz59(JLLTrav6T}{jDq%KDmRyJ4V#q*Ar
zzI5qcji&SNz%HG$Ae}>@pTYo%e|d!t(4AVq4n{zr|9NSYpr6x8Pq~CtuMkl7H_uoz
zU{(xS*FIHcz*%~S;M19&6Hk((JNC1s>lTO})bo^jQZ&t07rWpomCS!ZYI&X2{X>V|
zc|&H5PmoI3qcH2b)t7DR^j`Dzh^Ci~{Co$7odmE~0n(#TChwY^Zb?+&TqVZ0RwLVz
zVWZ`B2a3G*GD#`&q=gqqI)Y2R(TX8jM3;TJ%^6r%n80h_NmwTp{ZoSvKdmReZ+;X^
z;Pc6go7wB&tnb+hGl7EEcW$nfSBnK4xb|@5%!vS#kBTA$*mS;MzjoFp%S^cg4YGL|
ztmJ5cH9=WK&5r&IXigEq_-R0Edz^!hf7FnS!g=-=N2yNGD_}&JuN~0%*d^1ZCAqUk
z+RN$Dqbtj)=YM1o)^`c}2!Rd1$KlJOSW%CBl=6l*0FUu(8M4p%oye;=?#Wex#^!Ip
zB8Kcyjo6FhCla`(Co#%NN{<Pu0f{vexNj^>VtIZNjvif!F3-&mDU8vwbgpC<;-#O&
z>3_5jwJb#d?Sm~#WB+3uK?1j8<Ylu#4^Zr!H(Y)Fbs_8fQT7a3J!e5{7vG=WF{4Ah
z`mL_ivv%->FIa4qr$OyoVKLnp3M;h{W^xAqOJrTj%a(GVGS7*$rDa;fq&uDJQ2=Fm
zOaxKm<bKkXJF69IomwVgEr~P0vB79?5wc{u@JRHVderht2EFW3h|;9PM?1GvVvVYG
ze9F$*kx5sw>@|9EWdU}NNY|#6G!h7~-uU$prUcv7mpzHuerbmKZuI`@(F~hzk2POg
zRkvF!?e908Nm;LiQ;^5M3y6RHyMS=#u0c{bKrx1SXkoH&V({MTlzC~{q|0y|5xrW)
zGGLX3uRERzY-#s-z~-NKgewh}_-DPBBdb+h+tO80n?^_u7feU;PWP>ju;(H$TW=G%
zL}<8IL8h67@`(&ZZnBKRN&mdrr)+>G0XDZNk3wC3WVIShH?wv)qT+Fs@NXNa@i^ql
z=>+9`U)iX2AYC@uvQ(tkqm29Kd!hkRWpsHdA<$dfW}sC$^6m$Kb79k-iSKeqbTVcv
z)AAn`3Mce>_|HcCx_We250c#gQYD-7Lamp-?!Vo2ha-!qIR0Yb6`u8V_v&!R0BbAX
z3?v)$K2V7_AFx&4nBNAxfkw{1gXX58rLy_I*}hD``DMLN)9S>%9CNx!1HY?J&=Loq
zs*!$(<oycl#RFH}+7Oj~15ea>ByWS5SuLKtAPJyjPvTY`e}`s*YWAF2O=!7#g|YX5
ze40KJ&YkffpRD=kK41_gz3#e5v!M%Liwi9C_9q2(htb``xmr-7ZuL&3jHcg!=$4PR
zfkYE@JB(XQ(0CMkRH*&!UEPW(hbN;A?Qy^_D9P!6bW_U&?2&|%E=X})|HBIpT9zzw
zJXY8FW6yz51=3fb&2y`4o%_mVAE@bfKoGv0pw4>P{E$65ll^UH-<eSZE`qzbcQV$T
zk@n_W8Pr%DQs|PIGVCXz|5rdMV*6Rk63?Tn&n}>GtvHiY#TNg=r~>}CcDB!xY91B%
zFc}ILUvDF0RlImbHo}*jH{XunFL=Wp>k>6`0<nD_UA=;&;{pQbF_(Pd0}Cn-d1<Y*
z4-ijVDvhxVes<vt<M5@q1jf>OVDLaBj1c~97pzgO4z%pNG#M__M7E7?v#kiQOEy<l
zRO#B6gun9B8pW?h;;weI^=BNJ=SSmbvLp_p#EUB@um_dv$DoEMn=5CKwc@WkZTnom
zUgzAo;JX@PyU%wr|8X)5u#`Z0A{LKkmdVrKC)1}T{_^gl-O2^bp=#j0VbzIQhx*7T
z3q#1FYm%n7mb)taCdYU2l#JqYFyQ<wrsW>2;||5k1!dH&?;2`p6u;B&8o!J~1_V-9
z5#f2$5x~G9G4r=?pMtWy?HWm=^W!imPp3MYJIklTfvh7>6i8xhPq#fn9(V(@OpDNJ
zB9mMu#~T-|^}5@KhCL@p*75XPnX?X<1gQUa(Rv0RRkfr$e<AC{uQ7K)UJH@*9;pAz
zzwJGcN%Rw-n!j<Jube^!Z>WmNiLPW3$ct-V9iLzVdxu3D(C>j@G9C++19(!E>@0ZC
zvCz6cS~h!+^AlYUJL92_UvQU>?Y}$vXa=hOQ&O+#$?}{pQsPMcU}}nh4Ny^I&x7eH
z7%e>MPe1|Ev{C_odNtPQVyJFy?9gX*L^`&(%cCy<{{8l5gy8jFhFgt{Vx8y&P-uAE
zLJQ|#!`9f*>yrbVI5q4KZN%hL@VlI6JLsm5?_y4Q8e;<9)}fd6fO@nFez}#+IPwm;
zyfFcJykq}tU*;i=L2CkMD%Wk{=5|+a#h%Szh3smeY_T9}`Rr+sT8Hqf7PKsJwAcGP
zWUtlCyI$4O^mPf5+-4rkBF1~oFX<a_f4J6?h6p(N?*`?~3IcJ)Uf5#P{QkP8y|*8m
zM6W-!JMLGD7MhDbJ}R=nFCH>eo)Rl6lI{=BO94)Mm3v5ew;g9?bh%HkoqI}nhk6$E
zyj{}L+zl;zu9$V8CQ2`0hz)HqUhL*njkX+MB1$uCr}iNGgsFX)H_P%7GVjm@+zZ!X
zYRkD_Do_7;>tOJMZ9$*WN1q+~`9>%@?AJp<e?^aM*s|5B->9zhX4r)lkm|1J8zvPS
zU(A};1vq;LL7JSmt^IEEck63FuSw7k9Hx!rlwkf{=A|d(@ZL|Hg(TL@r*PN4QhPYY
z4G!<YW8G>;w3wf7r!F;Aa7kK+63t`B3lUVwgg+vrCy_gPWQSybWert~rM~X&o(h~<
zIhT{{aVdPd7vzTIxF@{WWN>vN>qrW~$NrUroDW&Ya9DnSqv~U!^ub-D*ARoUcW{eY
z$KS1xjF019qJ3q8i&tYIi+_Mm>03Ft{46aqbH5tRty7-70=<H3JF*7$JCGUzGMU0b
z=W_WXQ|jPqTGe-)%u(*RI!5@b^q2TfQ>^DsSR_7fS^ij-n`+b3Y9Hvd*j7WGQD&#Y
zV?ZbMh`he}8jT6k&otEO_;~aL#Pv)(r1-9Bq8+eT30&Bru;)qF(1SigfCAH<3__q0
z1}*Mrynagz+Rb2OIIK$9ZRn!={Y2N^px{r==yhn@!!(mx7Vq@-=Qu=xzSVx(VD5_l
zPS<lvxrff3S>F$P!|53Chv?)@VU8h-t~NmI3#Te_ZJ$oIDTk}^sRp(KaFE4vFzifx
zScY^L6#976eP!O&KVJ<J2Shw!?0T6Eh8nck_l&GQ<b419ny>1rgw?EmZ@3@6T1phU
z{!>d}{vmSD-uh>MYyTPlo1gbUE^Q_dN>SD0t8ctMZ*wK}UTDJorhIps_e!*^{Mmh#
zBtCnh+O)C)&E7Jdp)bm*WudHS2DyIsLd|K$`c_6XJzqF;0IQVx{KtJjbzn=o50SMY
z<XA{~@f54zse}zyF|az~vJDxJMu0_e_g6JPwBd|Pd;Jkw5o8InIAPYlc>V*w)G60i
zpJtIeUo<raS=#>&Ahm$2(-C<2YRK04yud%F^4I7zV4)Tf*XP1_a#qt^NTmlzT2i}6
z%(ym=@SQf;@l8AODz-J`P8T;iFX!9gNJx{8V0Jw7p(oStsj*T@=5K%{89vje)V?$a
z#^h1|N4tveG}S5oIVfkuf>vEC(KY&3wGK$y&;ee-R0c$+;&Byy_})|;#ad-44FN~X
z@n={4ch|ZSG4!Xuv%%_ai&C@ud>O?4uTTWq6kS>Z7}5@H3s$|s1AODJsVB#ft5y4!
zdz&kB*_mweUQreJ)KVI85AApg@o{OzR5Nq63N8HC<0tP66N1qaa8hV-(tU*Cxo{|Y
z5N)g9^V8cG$|^8@pU2{sS*r#}+c4SAlokLO`*7}*7(AA|TI&{GJhFDSX~gU|e$(qW
zt8%;VmH(6F(C=*4i31?z=qc!6_a<ZslP8R&TOFk32Z%CE3|FVzW1qI8ZXx^QjjpX{
zg&!cU+`NiEr*dyely2{samKYhPGS~*nAkBB<@FThgFcWlO{{7;y+>(t_Xi#zyIl4b
zwlrDjP&TE3p0Ak757TKdrA4Jocc5*b{+IP0ubs^ZwRf~PrG0@IX}^!Fh|H__A2Zb8
zJFTHVa^(G_QEZ;>xo_vxq;|3BuFRbLVlp;5j|cts)`!|f7i@G$&RVHboginx<V3=2
zEx?h6yhJa)oeBA4Px~uxS&8`1&E&?{fd{^I0MpvVxZ}19>uVPszV$unUtS_Z$WrV?
z;KG*Kl)Xt1Z1!pl2K@oZL6uLzDT`^4w!Eo3VVu}Ai;ZAbgmfV%*DvrJ6|bUO_4#~D
zmLW};>sKDy=T+9Xw`CtK<1niJg|WU!cfCY8Y0r24@{=RaIo=sfB&H!GC*V4HqW4x1
zX3^9zn7nyOi4N%|XAh^lqJ7b=mNetNNk}xd;Az6~;`gFEVAOERmemQl<o-Ajg`T>{
zjL9+MAA_AX?Q3W$SBe-?#78;gQt~wSt}M|V{buv32c&O0w?Dm2pT-VIy7c%D<tR1k
zkJ;&o-yc=}vGbQ5j)DFAZPu-BuU}mAwaRCDY-i+l#ohExUi;JSYdzCK@0%;76b1n^
zjdEF}1&ClD4=VY?bU5AvR0cKxi~P{*a#I~>_`M?@bx7sl`lqYYm-Q%3iNXb4^c2cq
zByY+FV#LXlSlvIX_}~NxH0uq32MuCw{6&%lSzwWsQtGnrmHvCVP;oxok>~zYTiN@`
zuRw41F|P8DLwmipJIWK;RSV0G?f-z*380B;%-7iMXOzD4ywa{U2|yHjNB{Zf>I?4k
zI*@)T$rru*&rn}p)4dzHD_&?7{gFZd(d@@0$|cdE@4xGRnBa&;?6s;FknwwyPNG$P
zsggmkJmzgk#aoF821&D{9mx|bk7%6jzAf|o<$WTg_sxsXRuG*biF6M|($`ztG1H{7
z*UExs|JY`=V=*Y%OeP1bG%W;Hh#CrsU{Mg-E_cNR+dG%LJ8$YQ2y;j*<!VeQBX^h6
z+F5b->BY}O(#hQUl2Ex>xETvQH4BDyuiVl??WOI5w4tZ&LS#>&;k}TEUW_s;?D7Sk
zDj(iYHKNJS93CUf1z=-N-b|W|2o5)T+gs4^dCRA+ALsQH26T)wI_AxK2ta4xM_$G0
zMQE)AMLy=sb6#8t5G|XzBQjqcg=D)FsUKVzk$mb<?;JS?**ZtszMkv4I0AOyk@ODf
zFS#x@gMG;hr;kGQMb2n{e*{pZSH{$?`RYIToz~r$-IcxP(^@-anGf}Rr|;%3OZs+B
zP_Nu->W#|dQ8+qkJT9c%tGP$=W32j_mQ!eB_{8s<?aQYi{)n}mFHvU}pW$rIwdW4B
z4gr4A+3B-xmaj@LGHn>m&a7YOGhI@!Ecs}fmya=>x$Pxdz%IP~%=|0#)XucJIv@F-
zo(ugB`}=pkyFvz!AXrHLa~1o#xwe`SIyA5hmc}pnJ!$ejHxUy5^7~U$e;(iZiDVp=
zxHJ+zGvH+G(%QQQlx`BbIa`c#GQD+T;Jf(-r*|!T`5Hg_ad;8Z7)0@>15Nih??G(?
z<h;)Xz@A9Vt<f9%x7vN}Zw0oq=Sf;oOTc*~m{IzMomR5t$%OH^F1VuD;+L>&H5>uQ
z%q#=;e8T}`nxn5TQm-WHx5TN%^n|7@OhqyJl}kZKICc}-KyLHtp#a#0n$1%`6aVE0
zc)*|SPl&_g`%7LZA$(U<Zzzrzh_<Cd+fL<gtz<a_`AV8zvo1JQDT5?(te?6$>MN{v
z(IhH*F19wl+NwwyW0)%&t|Dii6y4T?EKoX|vVoLJ>TFUzgZaUcfp9`gG$Q9p!N&7>
zDDS5(iW}+szw6JP?(jz_dj*|kuhJluoB#O;i|0Qp%cuXGJapXge_Q1Ib4JnQpRJhx
z3;_cDf87=r-5J_Jms1F@$86@5%!#9JZ@Nyc9(pAPU13n<67PkEf=6I?%yF*IY7t84
zRiM#ZQ3IIewm1$}5u_nlV7%L61K4fu>1x>pc+9EEPY`C~3zmg}=qy^;S*Y|a_hZ8s
zSVLgc1WC>Vuoj5q3~*8+P@JGrT+!-X&8wXp`!BHSi>O!L_YHKflNJGG+g^zMpHux{
z`8zECo>};DEiFaIpa(G=(z?0lu%_1r1}$m7TU+QSrJ@0OlVAh=k)o*!kVMA9d(cAO
zCC$HnxWC+y>_FUkSy{U73IWhmXLvs<?N)LFL<Xke%rp+Bo<x`A+Tw*oBZ{@lF98GJ
z-x^cCY-c8j)Mi6`%}_fl4}-+!PTh)N-)ys@={_3UCcXy_*B|k&teqSvT`!fcjZubR
zh%?Bah1)aNr=JvuMh-f+$Hk*d2Aq4|dTx|<fMEyU4IKr5Mf=w1>iG9mC+LU^*w&=y
zUETUt`)CHq!a($tV7)}ii<~V3#4mYlmu&sg46K+=FTP?*3#y9ljua|oZ9iIgfo0)4
zeLN#|z89S1Vk(aZN|!Kpurh5tbp<qG_)be|KYp<=02W80Fp^T}A>gc|xHmEnh0KeI
zc3XT7cJNPG4gZrNfIoPv7&$Fw1|)T9j<%T7q`51fic^%@99j!JcPTgZ=~-Xsqn2HJ
z6!Y+W*z_MF^*=xX`d^HAt3aU+IXrkk?o^<ybU*LkLCK4$`@XG0{SW`&dILKh$9w;R
zE9zIc4X6;<&=(tZubfbR`V2Irw#MwnH4ik)t*8Od8<e!T{iaM?=_dLB93`OV146Wf
z```Yx$Ih6x>DE|l!iOcrDCs78z^J9&6#~ftNUUE-ip^p1KD#sr+9`DO!TzBH^vX#z
zZ^t)r-&QCBPHur`;EX&^vN^3wl>qnP&G__vr!3Dp{QzR*!pKjxi{F<Cl$ki_<#+u8
zB+)a?F`S|h2-;<`<|<O7@%1SLU0GJ9pEFgtievyqC6sxgl47^HQE3_=aFfmKed-J~
zc*>B|zrJsk1piMO1%dv5fE2^;;3|JRqwUc1MWG>Re@N`<cz5*3R*5pl*69Nov;?x4
z4$g6kKZcgz&I-v8SGUD#aG={WaxzcpXq&%x%o&=@Z8fEpZKy$2Fu8Pyk;~#ou<l*c
zhYA-<-xRcU_H7|#u?<}D_u>b>03bvpEx^(z)r^)AGOidSebdi>@6KWvSXIO2&%h}3
z6ce)KYPyx^5}(2BQ=_nSi>CHQ^rn?3Ttr(yc_xZSo@;KZoU;Mb-uMawbJ+)HiC`-m
z+R}LPnXC~ek6JNROA+IvrP9Zct=K_-rHsQsfY{+Z)1YWMI+XEe%)IO3Z7^y?JfS)?
ztR#8TF+dM(#(w!L&9UT;!(3%tWtO@4BZ9P>5!lob%`vg)Fn4Ha9&GFctV?29k|grl
zn5^y|3sz~at9A8UY|Y5R1UVzEJB7(FFHPoD??^WFf%?V0IxFb2;k8!p;+MpdB4)cv
z<6rL^c^2k$i<vpJx4`ePhqMp5kRTc&ws}@tJWQB?Gt@j$44l7veQjT_QF#xBDr^%{
zthkMX`@ddl#I-KIzR!^_cR7^*VL}zLO8|k`00Aa?f<!n5J2?leAh?0m({}?`F17*Z
z{=GOovwkQke`NT<zs~~1GdKZEfMUH@R8+K?@>6ZFFph_kOA^GNs-QscEo2mV_QBmJ
z7v)VMUD@|_`J;@s#QjUK0)O*yYPW(E=iks>sXPxm89vq&bKHR~>O-ZOM;ERnY|fZq
zy4eZdr7q@H)VwQs+al&*GcYLH-U8=WGH$+a>?e}y)n9>ZpAAkCyw)0YuB!LpA__jZ
zKc=U6qf>Uo@^8Q3(g6=tMej~~dqM*~p7&sb)fvgrf!)jMDW4rW6Ai5JZe<nVmZYw1
zZQzqxg}PSrzsU>7@~5hhoYGt~6&coQSvQ2m;|t3mX@yg`xvRc?o>2chERKYUN1VK5
zPHRDO{0gT!ePdgu&F8Lm^H8Gc0>;9dUnc&1#BwYz2~CtNaZSgFFx{gCC!ernh7p}B
zcN8Zk5(7_u8Xp&rn%tHC9C1A>xFgHN{BIfeBX|t4Fy6wjo}k)Yd%Xlpkl(1CY6X*F
z%^z;7DaIELRxH`PEc?X)GQi87)&<j!F#K|(a4k>La!$^a+M}po8r%l4(8{$QA1eA+
zF3S4(vZM>_HlN3<m>P?^Joh)4HJ2xcq9+;sLx;@4Em6fF%FU6lELwK1ohecSxe_O)
zw)cTs63{e<yo*dv$ObEGho5QM`-Nfm0CPD=9YAA+Q?W%j;cchfE%7(Ss*t7H@q%+C
z6RAAT2|Wh3YK?R|$uszA{`hF%>#JNAt~ZF)IO;&tJ^+lcVS*ZSx&c+P;{J;5pMPmw
zVr5B7%aa^FWAQktOxSR$c(O1e{%El@#R1{kARGKxY{r|B&KF!tso2>Nu>_OibQ{Zs
zFoZL|&D=27wE;CJJUTdJ)@3p{^<qmRdh|m?c3{;jLd05j+L8-j{<?A3Rr!W3A6g;;
z`~X%mFO<NFVwFy1diheJk5(%`=Nh|#(&|GRyDN&O%8xh23BiFdLcLzk1>iF|z!efz
z+;+*XHyg5e>jYH-LA&R!_!i@L`R7jMoAKj$1?;(B)z2(`H5&|Uv@@qw&B2u{aylg5
zuUOT-2lt`r01cK!npBRJNd;=kq~g1(TejQsP6KG6lGXM`c?TP^IXgDm)fCUyL+>t{
zGJ<S%F%A6c0+HGE>^Ry7S<GYhX_tNdQQ7x@<F~?}Q@}~CXq0pO33Lpk_w*tMoZ&kB
z%!z;)0}EXi#~{q&sT#125AnFyTVmV*y^!G@`@1>HK(&j=;;LfjqYCtsy2H`qCN#W(
zsE^Xr>l7b@=0bX17iD0#Swq;i&eqoRk{->;vYH$d<CuG6e|{;lB?Mq|W24Yc&#t7{
zH4p00>-W%1B9?@#)A9ajtz3ZlZUpx^KXZeYX`Szxlof&1ofe;f!=H@g1e9G06h8sy
zI5+h4mlcOJ1}v#OG_UUZV@c&XDe-FYjaMT(V&zAAdgI3n{4o>*K}3i-kjv%=Ejf|h
zQ3r`0C4Jn%&W~fz+q09Fg<nljXDZA`iQEX;Z}VM+1=@VHQc-7lVSza(=I>?aavwY$
zB5WE@mR1>Fu|7<D^~qs@jBfn}F3b7lF*{EF7sO~+rL+%E64rQfO?=cPH;kM=)<2N0
zsl#cPv?Fl3r$=sd4ZJZLVVbG*`)4I(P3K1^6yL~+*u@XtdUz%lKR@UY5|G$U-dw4o
z%?l-UwKf_x?U^)9illfMj7SetaW+2F5t~aF(E8pwm&OB!AZ{w|bItOf)-~E7wp+p<
zF72w#0M04tbG_fnK;Dd{H!mz+G@w_`pnD?P&LghGN`L3(c3vHMJeHD*Hr^|`3wCpk
z7lMhO2;}^AZ*lM=>?8TUq@JbrPHBc8HO|~HME24wa0VHRFB{uCG7uG|-vbS=xHmO$
zB1Wg>SV?E=d||;jY-krRG#OA8_g|{g*<g()7rGqInC^IGw!t+#G>sDLZ0gd-HFa1>
zF-ZD#=Yl+ZFj{x=X=j}L?S<wAC^*ThcqV8eY19<h6IqExBWmG$cliJ&G?v`JIS(jJ
zxFDVB=6y%aou1LOe(cFZo7vFfe3zaMO?TJG9Ix)a`rpbUq23FRik_J0zGN0T3|F|A
z3diCzlSD|#`N#!}jGw;ImU0_e>xyx3Ye)+>7%BX;r8KtW*3s8z@bn%?+B;0jMFBI9
zFe=0wMzF7zrOaC#wn_-bYdqZbP*CSk_?^G*fl*!19P`P+Qbyckx3v~vQ3Q73MpyL6
zL%#yZ)=IJ_U-M1vTejH--9a;U$1=ey1&Tl0S6F~rC^(-&wVJ(VFlfbo3~y2Usp6A)
zcNF}G2|NBA0ys_o#N>+gLH^v;FCc+iEO4s)4=E;?zzZ~vv|H!|OY(V|Q64FoLvLh3
z=dUK!Tt+zt5_;EOkBcLA2dXVSFstY`3P%(lc-Cu`)}22wO>#!#%BwWr9AV#3*rb!_
zGcDcRXaOOmEs*QVyU4c%&1xo^gDXB0g;SH1MHD1HAOl&=9ra2Qf&}5d(@vX(VY_?4
zNuF~u)aw^BnG1BChk{0j!XN!9@?qUW+t!#3E&v-UDk@LqMP8ztCccRUbB7_M<nqXa
zj6(HG#ht0UQW{^gjQg43PvREdi)0<1=Fp`1%gfIED8XLHLJ|9T2?IV}FbZse_5_GD
za(CAsn{a^yjFRLdK4qGE83tBQ;c_17(}n|<w(zdYc3gG+6HuN*Mwcvo&Bn^2L}6rJ
z->yp8iMu-FeC$L)z<<jpE|E%0+1<AeprhRqHoTVrYq8?H1I~X$X!3(se6TBjt<eqX
zHb9`7i@&%&3Ga5&Do{ju1-;FyYr<GRDFMIo5=^{0TyQQa675e}{0Z0+3zc_MWx~^o
z>1G;B<WoEZs(30~Z$1Q?*5WA(Mjs$^Hon1Fot%q99oj^)_G)whoo>+6zSP@U<Ru+3
zgGOi<gyqEh>nClU#dWrJ#z!@Z#!tZW$uVw=-OeY^TDXMRKLrAymC@S^R;fA5C@erO
z6u2i)t-8PUl_T>}@bl596OLAofMLELd4skybHNbV5osWvp`R3g3h9TXx1uNy1ck(}
zX+$d;%s_M%9TV}Qrtit7Y4v@>fg9YTGKcuGpi;QPeL#PTEV@RzD~kI2JZrngV>9#Z
zUc-x~KqRaQzJ6RYW@~bt2_i+3{)RhXmL>Z65_%;BTNb`l4eQo=yu^J|O<?U#Y|Z_&
zn?FDrzn$54)u#tg&(@*GT579G{b(0Hx~^*MQIfWiP*WOEoHT7fk7QD1BthZKlM#b}
z!aQLZ3}WiP>ESs*Cy6X*HyjD*^xV<8y}dDtx^JtPUa93XO_=}D$(Z~GUIg~Ha|1x?
zVb9!P_u$fdvjljID?gyls^{oO(_FtHjQj=IZP%3%e`WY<4W>?*D5$%4_JXXWkn_Tf
zj%>u?vXB6u0!7|&#U|CdUbg`!A@D0DkXTNgvH!KmV)crE=3`}ggMR5;3ZyCffqO_0
z{={^{fb*L=bitENv{Y<}S!CwhWR`9w7MHVws}36Q+mtwLT8g`DcgZl^GB0517e*Mm
zFMUCoW3ccM^ptyTqEXK^RFIWR2(wSiH^%{}CjNFh#Eyc7P%lKa=F+tI1D$bNk-of1
z(}>76FS1$glxoD#9I$O^Q5$aCX33v}hoCjUEy!ZVN9W?o=~=VjH+~dY=rj8%XD^{l
z&^8p3_%$tUZ%^s4@1U)N??87E%ic7>oc8;AN$=vBoK<z>ER`O)_<J{RT*u5&`YOS8
zfe<hF%P8gE++o_9tI&{ukQV<~YKneK3w@|^jf=*Uh;C@|d|;D~<n29m%S_Sey>VFf
zML=OqrNTbZb~$~IcG05<2MnHEz|W!QDaR-gTS~X~zHprG-@%D>UKDvrbFo5Pjlv`M
zs0x3t3ZGV|F-I{eM@t2kn;!%5%v0}tYpD%j>7i}m=MQnyhWD#4ZfL%ygVoBfq4zu^
z17JEp=X(H}WsB4<Tubbr4E1}(0Fdf5@+UirV*F{s!LSE<9ig_oeDo*&mqVB@uKH2k
zP9-;EE(6+ucTGnTR3(o^`w;^*jHa!Ufm`8?ukn!hFPq_ry-5LcGHle<%iSy2-bx*u
zpQYRq8^iL{ou0wI=+t}DOni8f8*!$wii%>(ub`er$x*EB-#@u>TmI;E;a870AN+5h
z91|2)jO`ba(eVX<)ilQLP3fXb-;w3Nf!+FEtH7gzyU9m5fNB8Fl-!HCOv&zRp8H6!
zj#NL*&-(-xg%&rY7mz0ig66xvRFv$gwQqZe@3irjv-0a+qU?xX&C*LV#Mi_}qDa{p
zz^vRMY2YtRy`2XDutA~@xpsa5Qhxex3Oo#q%0s23i*q9K<iX!20B&;k81yQ&$uWiC
zJXlDKaak+^yDdz}OTg4zl1gD`tBrpq;;>an!04ms<8$!T&I?dH-6+O6As90q`-WaN
zB|kDYIA6nFR|P3iN%m$f1WtQ(3OZO3o<QVI%7Xkd=M;(BvDkvVRC+?_cYonFSfhz!
zAKkqQC|0W(ksB`o&U#i2CUCR(P-;~~_Y83CW3E;VbCZhR0v=WqMjd%|%^>#(`*GH!
zdE;=hPDLCY@G#Y!-WOJO&;-fEUqVtTF)Yn4fLRqgkeby>S;+mdsdh;^S2{Gg&|kHn
zb76U9-XhiF!maZmjZC4+WYD-WPSLIAd>bNcdZ?niSJdD|dE?H0UjOykDS}b%ZbyR&
zMLm{SZ&WhiXA69c40~9n;aU8|gc`F`=OtyIK>bjbXtXwQRW~4QXN{S^yOi4L2G|Jl
z`JwYt+~oQ4QW2bFb)attbq}q|SKtsxL4+yP`n|TREI%($9TrKqL)}WgM}M`;Wl~ld
zm3u{rzL&}$lohdPNIM$El*(`Q-?{Kzc&=@&TZut<nmrbgd1d6tP}sg)9mj&Sa*`%*
zAjaN=rj@%pm!12mw#dP9xZigucfjxTg9+xK<ZIk+=QoAAg}1_e+7fS(ZX9Ayccofk
zM_~Cn-_(;uk*!`;G*J;Mc0<e48L)Ti5DN||&r{^KOF(XW4-OCgFemuy;ljLT?NT$!
zy|@KGqazss3u!AENm_&5Dl7L7rlysjeK5G?(Or|`PU+DC{b@zXSy?6h-H$rfH6Y*q
z$|0JLV%#b4e?}=fi2QOVQOgF{Rn-#%8s1ASC++5;W9*}fy5@cEQ-oXlsc|N1A+pgE
zf`VkJA!022_a&fUGcj&q9kgT8zUCxFJY)i?B^2|;r%ZoP(-Nqrh3W}J^~8a}_IH*G
zRHvP;;qQ2fkssg>sONItHZG`>4nq_{3$n}UtyO=vlf}$$Z}YKnw)9LM**jM9X|OY%
z_;AB~IOB|+n|}_X+PuJK*qhU2*mJ^ApF8&|*PeY>{cuK$+IB}RzOR;kx)DLCiFg-(
z;H08nqj^jctX-a1T}gH>#+Rud9>Tx5=9Z_Z#rqH1^5a^@_7&B%)K}4d>AGAjRPirt
z3^^fLmtJl+=m+|MdLr6Ck!==eBe@ICP=DK7_rZe}RuByvT{ruvn{T#t(*Hq^S_JOm
zN~u}JMU)<m9%0XA=Ccnx;yLsChK4+sfH__)EU^FEOfk9R>t+Rq0e6hlI&7Z{0a2bs
zsF#`>Bt@fzV5<W??{?b^rW(p7A>{>AmA%Op&Ba|(t$|V#83SVE%3tyOYsx+1i9zwr
znefr9$jm1Il0@cSQ+VZnuK$M?aL(pJ62VqcuM23)Bj`&u!+%YEz0SooBH{JULDCC&
z$;~3L+@Oa<eKLk~ek?_gm$F6D>1@eb$2YfeHXzV(aU^@)Gi?a&%(J*VT@I;lClTPJ
zkDCP+!q^h8!4Xf+#iFka=F)=ogRr6DonpestHa@k<>P6D_wkIo$Xr`Jnhm<NXlfK}
zq2KZ|tt+<_J@wm9v4ON%mydHn1Y`i7mjF<_SMe)sBb$NyY7tB4Y|yPW0?7}&dImV@
zcyY{J$6pHm1$*@*dgKH#L+#6Qj?tH8gW9vGQ~2Hy?5Fwtz?+DpU?~W#_Qj^geBtl^
zyk7UCP4(>jl#}h#uH<zMU+rUB#&$qtKlfOgrs;oy*NxiayM!%S0A<|3#B-aC{Yfx3
zu}IH{YAn!6am1FfypP*}ew^BV6!fHBSIrw%0j7F3=z;VWr@#f_&B)Q;B6`tjad|px
z4kVpN+bq52%|6r9E*T5=ub3L&%ni(&x<Of#-|1c#ba42mb%$uO`$GOpaEkg_`KH>%
zQ!0L)`DWqQw5@)n4q@(wJxd#YV!#?4?0!2@etwby(J9ssW!__3q7ryAfC=g{3lDr@
z=?#<IRD&>?2T+G;VcJWNolGHVRH&>3Og>;ZqeiL7*lX1^0Q}3G^nF9JKiGOc(^YDp
zhZ|Dh@_juQ25&gGuSs|kwmCb__tN1aclE2&{Rd0ECTp0y4GK2nr)bpbjQk)OzxD92
z+}7^VWZTtGy+tS;oyJ+%V^&Dt96=yZZs5*2OLXsz?G>f6j~C|cW+sn6JbVpAvFq{6
zdqbcCM@4$9C;~|!WU&C!>y}_r$49ps((J@v6Aa&^{)$FCY56>y1!*go+5w3r|Efj(
zbIMaexA~&)=C&Hp>?B{gy|rP~r@UgYK_kcEO7~Ehw^U|jL@_+5u>l`8cE*d@mhZ!M
zF798PfEdl&$-2RK*LcYhqXayYhqx5{l(c;8z70Axw5TZ@ZTmCLH-!sxGi7|K8i;<V
z0^s;`=R!L~mQt*akDZqy@nCtCr#yYls2kdbpkq2~aVUS0-tFKv10D%ci!pr6=C=&R
zAbMpAcxp7m3rhx_Pc1$+OPI6&<%a*2FVDfKLiVw&0-^Y-YjKS9!zbXN)cLuVQAT^+
zSj-q3ES#&AXP{Sp?z#1$scMAPX4CU(F+XMNka(0Uv}ZI8#XMVDvb^IKDidA!^y2R1
zVtMls8V@X*0W<ctSoh3LSrxHncC$EhdK;n<3&T<2#W8iYS3%MTUMU$nE~^9o3Fy?Q
zymI%wsrp*UfwJ|>#Q|j{gDZokmq8P}W01S+$Z8OoxvG3mU~0;%KcXyG4qNOM4R3xk
zR(?x<Rvu6xD{DQ*N~`vS{U7CB`CpP*+ecf>C(CJ^@~OsbksMshtz2oDG|a>_HF3|}
zQnb;GTu3J~n`totH5Wv4%iU7I9a{}tC|pPpc}x)#kr6^f6rS7O_w)V}@BQn2KIe1J
zb)D-h-}C)m=V=?6z^I}4f!HJM8c|O{?K32|5SH`(j%`!37Ao(Y`%*^t<+gbfNGx)e
z@#n?Uux_iKkideY?+}CTtSP9;(CDO3D@T^DG761UEp6+yyV=ugbc=ir-NrOv`MB2e
znukdpkO4K_^k~!6@hUu1<I{%|n2F%c|7R-w+-ma5GT!3PuxbbT-tUL+fk{*ZYr15d
zJ|dS7>tssbC6}wO<V8)_Sp2yd+j0jr3=$j_hl7>tKc^b%oFZp=D_Tz_19vkvuTG{-
zVq^fXysbaPs?s_=+-krl7={O}#zzi*-#4Qx>ixxUwpZ=Sq-W$HB55>jUCs^7^*2vD
zAOX7m4AlPaN7uJ%mOu%LDe;8By4G(Nai)${Wo;PysaGla*Sc@#Bmq&1M>Fo7VI@mV
zskGo|#zHztG9Q>d5}3&)K3w&2|DP^{)3;>DlewyM7b6F82U_q^Ln*Av*`(ZUC2C_a
ztgSmr0FX&rzGq%sbXs-Jopr4q{jKClBvFQQNa(l&?~IQ@d&%9&$jDir;tuF}5IWOq
z-N|%G%24@!zdtq{`rD@ifClJ^(N2Jl`^F(&dCz$Ff?b{DSzBYt=(ZAGOrCyXN<rg?
z!E$xG#r%vZ>XwYW#M2n3qhrnFet_9)A}(b{&nmx7=FGg0Jd?8{^3BT*<9SZpl-B?#
zX_#+T7pyw>GwaM_q~Tt>wBil`ec4+d8Uu^J)RF3cIkv+5oE$l{A0f~%3*@=%X%G+U
zQ3}Is9xaPsoxGO3U?wmoPI`kA;|l(RtWJ~BeG`PtDoSt5e0Pg--k6_Ue`|G%&)a{$
z--s`%|HE-xODY;WQOTUVAHb5r#lbV;yht8}Jl<+EaQOggK@tx9I?IU86db(q3*UNy
zA7sfA6p@e0uoiTtBC=a>Olr57H`A&Iy-t)joIw{4#S+}&3hRibI+tx<bX_2{`*-UP
zUQ;0U#<Lh&j3Rp|HVpAZ?b=jS$fDJG5PRoGsoz(jWkd5P#ZL)K78`oFJ_JA?h4WDt
zGK%7r&-|asQa?s*`NRvCrN&w5x3zdmpPuQnvmJomzhG@cRyZwv44q%h&QN|bAs!%Z
zn!D7{*K~hJ3A5dJ_zMX$r8>9YJ0dEiV0+Tw2wG~8m#>j}sChZl*|s_5%5m(BaeaGU
z*f-7UJ&n?eHpyPXLw2>N5{;fG(Oq<|?c`Rpq<4?u@KvPcALd$oIG&tB*^T%RTyJ#;
zSHYv>n-6U#3zJPRReev|DYv;YaLe`)015g9KrH+O=eEfOTFx<8;&Mg0P4h?q#hTTT
z0=KJ_NC{Q4psSGm*p#Htacb%%6h^I_-nZ7}2x1qnzTl{307g*K0dg&{gM3Y&9Tgic
zvTKyd^&IBEO62S5@o}|_$1`#h3A=MoU}fK&!mynUaOLEen&fvsr#9-G0$`;3meHWD
zQDo@{QjUi9DQF|zOfzDHozFFG@U%(06I-j;E+1y&W}|y^R14no(F-8-`Pof5TvdFz
zdJ%7ajjqD-r*%JEZRPA6N7Vi3I~hGHDwMYnG0Q&u3tMOpiB%da55bCnBzxw*Q=0AU
zozqkG%UJ<mg+$dk%#VONI8LW8tIVTh#Vdb~a<YU&qk=t>=0H#vymm2;Gx=f{FnOFV
z4giRLUV!G6uF`Wd|Drg{69c%``vtFsj+1B7)>};m#;zL5!fHDi=d?0?W~E*$@@PzI
z6gzPK4HU_V$nh?4FTEe>n}=bY(PnuuQj&wf-eBu4@5yJgE+dyB)YR8W>3CQGr(OBc
zL4wkky-8bN5qru0>Kg_U!>xS?o!7YfM=NDx$?+1)j9v;zRYNO{=y3#fBJ-(t7#BTI
z)Hl>WhBwmbCM8Jr;m!L2562_8m~gh#BdYLM$w?yg*(&vEmX0L(>t9`j4s1##+!sn0
zc?=nGy?wb#`*+i$^RK*Q|Lii+PCEyX5M3T>l4%0#gYa+bYkmcvN-JY4Wtlg?EPw)S
z<XgL7#^R%4gmxy0w%%>vH=g917fi}9A=ML1uE$#g9CuB_rinYt04M>hq)#gerbL>O
z+|`klzfi*mDbTmrxeNrtZM+6YK7alA@B5KW&!2mR%$(EvZCCnp$DW9#EjxBRIDP)y
z^+yP|U$#{|y^{Uld1<M8<dOQ#=jz^HM~=8%ueY##+Q2eqv4Lbq6HCS6Hsby+^Z4*E
zT?fKU*x*7DZ!%t3)YdHS;3)=&!iKvnsb_UqqHH})3og_3Hrh}y^MU}UzO`vA3!%di
zLH@+jEZ%dp6xUL1ws=83Wng*+2NPh-4A$M6e~rmCz1)|5eJ?xGJj!e+A;mEYy(;+V
zMZ42wL(Rla<YWq!;kB#HB5l*Yr|7s5p@&YILRd{a>dQrat&XHa2<OS&H!o(|94Y(j
zdaP^PjWz>`1EKkiK=qg-XkS{_*Gat-mfQ=4y}eakyHVLJQpfzyb%m0UxCh7sjkM|0
zb$|551nLGx%NbQ@{+us)paG*NbMXv#ueoMgPg63MVi)Ua&5jn6BW1=;Sieaez3*-8
ze#*hnyB*~d2U6|CwkMa@UTQ^2PJCbTYY~nn7Q`+#HAH@hKLRyFmWX#jUXwq^l^`6$
zwj5?I?X8!5Bg3f3wJb>Q_g8w_RWp{l9Oe25$AzY1fA5fdriN3sI+&rBtPmJbwBWTg
zGU`jrEAa%S5R0{=K;VV_alc89`z)W+1ttBIwl&CfFMIM%u=BwMr$U})=rdnzuql6^
zQGNP8UyrCAArGt$@W<|>E_3r9cX{z(x^#Hhr-{?Ry8*<FHv%}!B|W8|J;(UO!eQ3j
zRlHEv>598GSPQ4v+f%E|Tyzm6tuFYe$|q0GU*;_h!hsCWHu{Tip@0nG*MKFG%8PBM
zHZ|p?oD^t2C%B4em1}aJnC{Den<%qRCjR4f3@5f3wIv%!yWXb&8MJ!Dwu+1Ph*shG
z&pwN;$#Nw)GByNiOnq1=BIZLAeizVdM16Tp-PgoVc&#?;Z|ZL9c2rBnWsuV}hrU<t
z#nE?xfNq9x@@KX{2-8FEj&{Ne9h@wmvvR90`0^#V-R<RJ1m9a%+@J+2w(W4nSZOL!
zt@;c`P!1E#Uy+;Kc5PToc4qwZgGN{~e_6M#$~xH{uPyqlGoQ+K+Yx+&7Z$EyM0R%=
zNb%RAFg{ACJ#P6f!#k8GSRJw}GvJuP%ffD-72kGuHb~b?43Mnu&uhUaVO~CLE`sTv
zSh_vBvuei~sFhglcg>26A)<k?{D2Ux9Wq;_MfG$#uw|nX$~Pl5EBxNoJ82Q}7js8u
zFAA|}JGUG{E6J;W`P8^j8r%O?-yT1c9Og|`=$S^+C`^ja_S$p>A>i$@O2me_nU9n`
zZML?tzTw9SwKUH0vc*63pw>aY|DF_wciwTN%~8uYH!MDjw@v7&y-+-u#Q3Z|_@V=f
z3|P@?D-9`JZ9OD7-~0Jp6@_6pNtpQnM)H>RQhn`HQ*+JDP(vH0AE25VH5D5c_M%~T
zZu}|Hs1Wjk4#<a|?CAzX&>ED85B#&l#3A`L@7|$VlSX~-2@4iP`!Gf%TUp*K(Z$E8
z%vDvgc~E>~<o0Ue%r~1x70{-Qk=ob>w$(qfDyc)6wRg?BVU&?HZE&Ipm*fk<Hsc+*
z39cHk7vn~8%w=$5__z|@ZENfFQJR1f-wp^<>JoRJ@3s!b%~((h%%%{SiM*N7HUzn&
z3^qB8Gj*myq3w^?AAciwClv@5G(Ol{hKfcSu+SKAV$%4-tw#!P+4YW~UUu5pI<cio
zxiu-C?c7t7gt&f!1<y)8FSdTxyVSD2^k%`~=QTdIv-_=0W`wv>N_A+crv<(QyyK-S
zt~Eo?$mS+E?=>y5r1~<ZHH-M*VU4I$`@G5(Rh0<Pa)AJsHuhU8*Y{SVE1U(fCORpO
zQpuG&uKs#n{cnJU!1H2RY`t+ElL{!kB8UE1fK&UD&|={cO6Pw_F*Tz~k1@0b_i?0?
zD)cb8>7VB<Dp)i`<*l?*C%Z@wn!BhKF7Z^0o%GCU?2kj+P_w-00E(CjZaYlf0?|f0
zVw;ebbUIvQ4iR#DF?PYx_`=(EQDpr@U&-3IUMyVw_7lgn*osHT?4eY;zARgv-*$u=
zdxE<T(Kc#~P*e_jr#jQN-19D<u~e4TnW)_)&|lk;uqjOtLh(#HZeqMFenkP0!9?wP
zgoW0@L*t#_(66#6bu{g|`rXaInVVA}vc4$mU_1#@U~o8N;IgxOC!gh9SgX+SSr1C9
z*W--ggdZ0G5D?hb2&W;?t3d5zs(jhlb$dFiVlumOuw|F0z9(NM-pR6WtWh#{BgOMI
zin^Yj_nz!XGPBr+_y?KW-E!m;*+b+UGv59ElC%qkwJ`25)%Z-tu;t!e1AJ_XUNeU$
zz`>v>^IEt*wYjQJe!jUARj06ql+Cxd#M)Am8C%0pETKtnQUSl0l*m@)7jQG(9besI
zE0bjJ)%m`>r3D5G?kqxWUkq`-8^M%Zp-1FfKzKjN$9VC_Vkzix9MT05;E~gK<xHVd
zdy!V|h<>I`-mL@#vD+|w*k6kJ>cJ3n#+{_bdeI5TgZ<$(0fg`C&<4d5`&^Q;9}#dO
z6b*kdD!rgX((Fll1gIHSSN$-t7i?j}dZ@~^2TFNP%Y#F|x~RLS+;r^91IyUpXkh_v
zmt=aUMc+#K6=}KOww2#A69Nk`dqR@4x8C~a{!3ooj@J5G57nExMV>F?Qk{2lVW|7j
zUzh51U~PDXXbUsm0-Vun$Uv4agu9!#HyEH#aR3hk!dfkzdHWM=2iE_FwX!ho5ytHA
zWaU1VS)TxcRZG2-XQSr9U=9mqQ|gl9A-e+?M4I-jm!?Q%KGDUtnB$wnVaCfrM)@EM
z(6OwnigKGa3-HMsM&MS70;l^W5O1bhpvB4tC>pXu$_Cuc&AskNm)D56&f3^Q5gDxm
z7a2oN13PcOch7boT#I%-z^ba6sqL<h<h;gRYG`!HzahA&GfLD*Xi@}WC<ksQODi8a
zpj|L|<>eK?fqfICcWO~j*O{7~e91#d#@L+<MQ;SWJq|!N23=uG@fV#-7H-orUKRrP
zyUx&6y#Hvt9k}tIGIx<JMW32lzQ8ZV^^9|2-4Lo4HsxUL_UOTlDZ$o5WDuCk2zL~U
zi4bi_w;eUGoOMTYY-8T`e;oIYfWugYW6=g+Z^;GhnkP-#Q;LhH_6=3?Zl?h7-0YMi
z>cd(vEra0MQJ&ao4Nlkv4E-K;A!IDS9dr<~iLwZ@K9LeMfb_|=$r(7(8(>?TB>1|U
zYzXVF99J}t{?M|eo$j<mewsF9PV5P4?mQ@vP1RYFLobC_vGZVwoq<K)UVrm!tr=VP
z|ATn~sJ_X`&IH-RosNpx-jujprL$#y6)3(-&7HCR@gOSC<wbmmcs$eVOGCCfgCuB+
z`r@$BCU<1TW&$+dYHq1N!D`!=D|BaTr(a=l^93w3K<=eDS%K%3&(yin5*IH)ta(D0
z<OW-e<k5}EfhdphTfUF(Y;gHqgXpgN?^Wlq-0xa~rb+Hc+a`uDeX6=9N<@x}iX;~C
zHRkR|t0!FJUKg-7_?1mhVJdTFu;R`^M-`)zDA20gsCnDLjn-&CwgI67G34HN={<i5
z7xbV-Gi;aw0{)U>6nchIi|srHcKeZ00Vw?c=R3OsJsz{ZhY6efk=g`y&h__(GeLL%
E3+Ur($p8QV

diff --git a/articles/AMR_files/figure-html/mic_plots-2.png b/articles/AMR_files/figure-html/mic_plots-2.png
deleted file mode 100644
index 48a082aa3d9a642fda9a44511fb78d4a75a7f034..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 37293
zcmeFa2UwI_vM$`p-eLe070I9|pr9ZiIT;XCKt!UF6eLKL92;;OK%$CBrU@dFMRHb2
zl0kCTh9(D_oVm4HXJ(%>H_ZM2Irp4<pJATaK=bw2>sz6!-nZT=pWV1FN4b|_F9w65
zyn03ECI<6+3<g8?arZ8GrTp>0JNULsUtUfIzQfn*r6Ldbw&%f>TecVsvlsgRjz9v@
z5Q902xhiu()hTkS-}#lcVVLxf#N=gmd9MRU`QkkD-#<G_Prl>XwF9q2m}D+TUAUoq
zA@GBD%vtHneqv4{WPkV^c>c*>vE{&1_R|++j<Q@P-?j7L!{783j-DvdaBQCqRN(4w
zZtuUkHk~}<CN;VsnwM<Z?><qIXW753l{XM^>hRttr15!Dzdqx*gY=`H(oPf74=)d#
zCqq9xe~e*3-#t(K){MSmP8{<@Pl-9YXU9489dmVe4C%dBE@CLr@8=Kx1Giw|EM2MO
zm9?<2XzS>xio5JvtJ9eO#vk42&b(?!5k^+l%F1fkroh|#<l()B=|*l}tI*BgJ4f#L
zyH5!`M5|+`3{L}>64M<^Qu+w!TFhStcIod2W|bgnJ0kPz<8j*?qu$<Q<KxdFEDKLv
zmmpSjIgdug$HpFW=(q0_Sse}<YLR1Qejq9z!sBn-D9X7s=&@~LX&I2Dl32AqxV@$M
z{?3Kr`PE~SD|p&6%~XSyxXb0~9^D@dtv~%vThUiwYp}L3uB@R!tCD!#|5lb!Weq2e
zNx0umrTE9^edW3XBP(Czq;96J`f#UrrH(d6OBSv4!BCQh-B#VCUZ1{oa5U2PI+NRv
zPki^kzA-U1^^sie6Syw&{W1DrI|m2LY57xJhxb+%nwpu#$HyCd8O_egS#)iDa7L%_
zT%-7Gaj1atb%ii~b_NCp{pEhgKqdFNpiGmNICy6Ir4MtfZhfw^CDuLn6Dw11WyK{V
zB;=o&Dw-`Nz6f+X;l5JNS~V0X&GO{p-LAUdVP5a_zCLv&D7SX1J8LMtXvW9e+l$p*
z<gG&3K`YnZUj5ZM0y}P=!0RDey4qkR%MtWUn}-&+)|QA*PD$qy-G-0A2Z+yjkBp4G
zoOj!r#|8TGmWr^kS(a@s9pjbB*RS+h`Qatav^?NC8#8b5s*Vs>Sm>fTPt`f=zFZ{o
z<0IK|HhfM7)<R;jO{LsrAXzO%-;3G7+uPe*h!K`lSx;}!DtcgGAb+XHY{*Zb(VK=_
zouaV#?c292<4PV3a?JK^G4qs({`;O>yq6VG+Iio~D)8ykrx!wcJ32b7`l%LQyUi4g
z;zJDro~_T8c{HyY)rV5=qvi1{fgv@oZmo5eRiS4OGmfUWEA>w=S?XE&NG^3Y)fw&I
zu3S+vm*4u`*Qbeap?mWWQ8v8R;F`L&btO$pZ!hC4cGi`g5_g=9#p&Bak-0C&<x(4A
zuElj1lQk&C-L4yUq~7AQ##eZEJ>XdSnjr7BSd2Dk<v6Ut7R`5CnF@d50lI2t$G+#^
zZ7ZH)oM!q8tO>yyR9SO#tNpX?v;xMBO#E&ET|d~%rPikoISx1**EqvX55XEQzb`jN
zN27~r=NPr!+IXUztE(#$421aY(SBL@8Mg7*)Rf=g)>0lZS)(LbWY$=Ey?doCQHmz7
z-$Cn~pkQU7$HtImiHrTh(h@qP66;f0#LPf!s>n9J(s$?~IZM|BL&zDeBVH^nCS~iT
z3kkfH27!`Cn3<VhG>yjR<q6-`(i(ck8SQPBUcfQ1+URJxxXpY;O-(J*q33>OZ-}1z
z>hmTrPJC7a#rSEJ>!OCutAh(~g)0R+wGVl$_YTHoWeLEZU07JC*vDIZAaB6MtQdA0
zPviPCB{w#B(yZSz?%AEzE0v%U8;ij|KYixR89tA#4X3Tu(fGtfPIxbzk4hNrdeov@
z*Iln*CRxLQV&|5D0TycNLmUd;xQ$`XOt;m!v6-0wHz`&)g<@i2_=3@B-^;#7Drrl9
zJTWmhSJ>7^>rF>X@hLNSjp`G1=CK1MV{yK0K{dtt129Rfd=@>~?#t8Jt=EZPjt9!f
z%hz#_@PdshT`u9ZEL|P(_Fft0l%@~Sap?B6r*L%g&MPLIAtp+1o?;XFEmZ#X>1Xg4
z!_W+eVafS=fz0_vaXA@ew6b$^Wfc__YgS=jNLQmprlxLvtLPidtwGh*ffig<O|9}1
zIjh9CAx6Ud>Pnu+dS3oWxEUK8eo}ASB@&DnS)*3tF_FDbE;89QU$7p;E?<_Zklvn`
zMjK1nrW+nvYUR`Zs>Mz{R%&X_$OoT2hs<X`)bV7RcYpBiYR+|?&gqQF*$ETyapb|S
zh21^Y)yzYm_wk;lcR~l*dV^TKTr|5jnZbJ2Ex`NBJ$|CUm0P^idKIHpU-BA@`^tCy
z6{gH%Z_XI6Fi{gHG|CE{axEPlm!FsavGI*u`s8(w)jB<%?v5<urhrRSqW9n&>6g=e
z1#7$Po5x$S@b&v^sbm(94Wj;vwu;K3ygr+{m`@Hu5&kI|rG+9AH5;q*<i%b1wZ%!_
z)(qGFN!ff;wDEK8e{KBz&atJX<+l`~B}507a`Py~JO*z(Xb2Z<X>HA_T-3oXwpWgW
zk62)IkBe-CDaJ22maX@{oVT~PUlb?X$R7$|e6c;)*QZT^*GSi<*U`~A{-N*1LJ)4_
zjR>}i-uQ5JBa6bNOPAaxRB&7aTSKhdD?ZLfP0>1$TU*Oz(!;+WAUQkR-7#{_JII;N
z4ohIYzKM**JbEf|X_;VLFk(}1hMOmD@WwIDD7&vmc#YqbfaT!6&a0h&R@aSKxns0w
zC_p?8juRUjmBf#au#K6YYCC9XX!N;ErddxUC04-(U=p8tf2pm#{W9#Dva-Zzm*kbm
zn?vj&t>AKfTIHCmE8gRlm9`g@D#xo?T&HLPmzgbx+f<bNbsYNzM;an%(I&K??6A0-
zgJ$pjLyXIBl%uI_r9)&&QDTCIqMP7$-OH_tw-z&4bG9X4RaI5F(`pxgf6BNka<+GH
z%fWgrHLH=qF6_z0V<b;f@$AshkMjM2aB|PifI-#FF;%m3bd=)>nOyGFfDNEt9Da@k
zd>wuPY;1f|(y8$9@Hp_r;Jf0|)6-Wvg*CUw<fL!A4YVprZ~73xR7YD+rsc`1sHn)8
z=U*Xo8{?R*u#;K_U*BA9`TF%B%-_vE>~g=B{gNblh+d8z3%eO6%tz9qJj{ySOrGpH
z#NpSo%@WXrow2PPw{oA4$Z&FNPgbiW#-=1E?<&UJ(L)Z{ZLJlT{^hK*Eu1KP*dR6I
z&bg(fB@=KSjg5`SXwJ;c;PnRYNaC7kal*}X&D7N3mNdZ}ae$XBM5cZnd-a3EbWi@&
zhdO-orTzZXxxGB@iz#{uuCZuje&RdFEGp{CXG&>tDZi~x=jjpk{pwzy@lB8qS#pK5
zzU^$i_9p1MPhnx<%Q_eQGfw=*d=u}0)XKL<2H!tFA=V2t$(Wg$A<Ha<3%nS6W24tB
zqeOOvhH~)65KmwgEe&j6>w3QGia&ne#}-moIY+1)8i}w9KsH@jO^t?{da@(U*JCxr
z;~8?w&ZG(R+YiH%;RqUcZ21&IUQBr1U^E1ubb6q4W3JA(6;>8@ibnBfSd*Jkv*G4S
zwKUIlR>wYTYX~)}T?gi)Lq%<kz;j#{iojlex87&lWHBvI*ZhEILSv$S*q=X4VJfql
zrLq_Nn^V~jw|tMmwT`@}YPN8g=>?1`sO6Zb=SQ0Y0|El_zx}b#C(r}M3?aHM_ciN~
z{khZ2;W_Y>$z?3Ia(s1uG$ixo!$oHDHDt_u4Ft9!5P1fMQC6nHk6vKyNhaB?VkhgL
zyK;^KpJUcp6)tF&n?2<Y<7n&bq_x^VsxV*8>f!d~=W(4`Uz5EA))slE%t7~cnpxMe
zSTETeQWRVTdr4?mZ+lVCx3vs>5FP@>GX{bg=df!FBHuj8*!0Uqsu`=@wm$85zY%FH
z^3Y~5%NAD1YoiQe)|S_=w7x==qZ2vf`n~$*T6DioZMF0k5x;QDIEn^Lik5nx`q+A*
zdfIKliEUPRR?kd1ObI3IaO~O>?^(A|af3=98pG7F;6|~@)So75Y+}Nvj;AQqY0yh*
zA@*2E%d+4SbFIPAyg_%@jrFqa^&_gcdtS(~+=`Tf;Al<f{13uN6OL7Q&~5&Co!flG
zpf=M*!^Meq>o#RMN>RIN*}4$kKwj*)d`5m*N(y)BT6+f7<kT#z#tKaBt>R6v-7cEj
zjA(Qg5f~T7mo@kBPZ=#w4(Jxx7#73j0pPfJA)qLu@hbN2I&fWTbD`CGbFs5pS&o@T
zQc^Ph=FoR9R+jFmrwul+i;>^g_gsfSo;$bKvglZdHEfR~iQ7ZcTgR_UF19%>bsHbY
zHGwPS3GoH1bs9zNB2(|nWj6$wVB1xWmu(Pu5fI=;{sBMvg<wM*iw~^wlHT|(LaaFC
zl~b?4ai|PTmam0vuSH(vb@A!LA=)-qH>RyTe9<W`?(gr<L~-b1h6l6lNd2=g0plu<
z%}x)>{J!}nY3aAuqZr_m&HEgBEr`oy+a>ww5KN|O<?+lEPNsJ`4q^?advd6$ArxhW
z5ORA&m;$B;(;GQqL;G>8$0DyE4+~L7GKAjV5Mhi)yIMxN&J-A?n?G7xTZ7R4kdHBs
z$F!Lqx0C~T6ACkWFxE0tIi0%1kg^SSHZ~r>4*38d<Quo)+;JS?)ny8lnERrxT3TaS
zG<}41y{p<McDCPfklD(qobg(tN6Tf}O0XcS$3n#6iSoC%)`#G6T>)ch7CBf%+SH!1
zu054#Zr{+U<EW$U*l%ARz6|brR=80Rjr|p#V$7mBQq&folo;&;<}|M_fnsbpNXfkt
zo0F5X$rC3Mnw*@R4^N_>QL@BfGk~_Ibq#xnjG|(LQKx7qcX~csfYjR9h0*)w=6>WX
zPDc@hxy!rkNk)Z2bOe;VHY1hX@P|+|T8iHSGq-}<Ud0`o>@%4guCa!-T4dD!4Gm}4
z5DcfyOmDph;K|kjn9Oa)PF+`v)4IhX2r+@bLqT+xeW#8A1ULT3c@qO1i;pL#$XThN
z1Edd;jvp9?BeUX!S1Ll>CoJ#Ze<r;-t^|RMV@0rrE<Hs1RubDA^T>(7aPJfkG0%GM
z$BtdfY`rd?;Iu~>;tt`y2bUxknlD+e5<;q@90%x;xqfztvntk$g#m^7LtO@e{s28p
z%*=XI4Xq$FBBtnVaz%S=t)lZozuaf5v;h%8Aj|;FlwvW&Nq(ElgVI#sy3wxI$5%XK
z56OY;RzWG;cOXS0BjCwC+Nv{oeFv8ZJv>nO-O}8A0If3K!pc>vK1$rNEsq4=t4bAO
zZ=8Hpv>VMBri%5_1JV!wQ%qmJghEx0pzevXyt2({g8M1-o{uO0jt}5}0{&%&jdVvk
zkN*xV|L5}u$#ps(H5b0h^9C0^zH0_J7>+Rnsy)5EbvoB8O<`970t9!|)6-LnUNAB7
zhArvs4Rh}cgB|hVQo&-esv1^ZCAoQda#A>6+sna8>FGSCrlxFcaq;oTU0hx3w3Q8$
z)6)FmCO`^+TaS*8=GRaSgKHrE;9+BfT?wu;xFD`pRY!+;adDBFx~0FL8BB&@`a6EW
zF_$6ilaYag1*Vt_KH*eEgRFspfs70w78zq><1p@Ig)jvUh-=_OA%FvHQLC*?Fa<`z
zyV;TcbCpzdY27!WB_bOeK&3iuMZ(u=Miy-G_m*nL=**sKGZT}O9VG?7HEZ2DZEq?(
zbM$zXdVhQxJSyFJv&64(UmtSL0`106SNCA|<6T@_+{Rwt08pF}RaNUXW0I1Vb|zQF
z$|?h^x~ILpz0#p}biOC)vAK|zIVaBfQE%6EcocY{bhaounq^w`;^1=BX6rYhT$RVA
zb$8%Xe0F3zaHY+<Dv1Yobai!E>G5bXYYvvm!!1kY_dtZP##4SnMMZ!kkWwi6M;1H9
zClu=bW5m!ufm`}dxP^>;!9KeCu=7|T3d!GG>NObun$XhGaRi|<_?0(61n=E-KQXFS
zhjd^*3f<NIy}3Y}r%__Q-f6CebG^R|;HGA&n{%ICt0EC-3NiE+el^n3eLqd7v16gh
zeJKlF8=slUhY*R=5aS>nEMTT(e<31*Hh_HcW(Dv>e4SU!?$BplZ4<H&P5jw({}jF$
zS{`jWYHEN0rc)4hBijXgkV7&2Fzf0UIsHJ%B_L1$vcHQoL_rnC{7{u5M(5<=xRMe{
zHa0|r?HCOcG*g$60rVMgTL)c(=}opibl#XhtjlA2^EjJ6FkWPh^WbKWv9T>dn5UdV
z`usaug6UPBQs8-q_Is>c(sug(#JW**v|>_sx$qrqCy1i+39q&A5CjDDJM^ZB9F{wU
z!can8NQj#oP-n+=Tx?*l$tl(C6W3+MgJ0L&^J#@AS3Y^TR#TBM(R&?E-KJ$$O<f-6
zuGbBgYw7vP$=9@koAaVRbf-FS#lof)NsGGHdc8b8>S^NaRt?|kkNb(=ThQ;<Tvz#=
z!4wz>22T>Ihbg8A*t=n$K318sTA6+DSGXH#LCIUXqF;}bmPSy+eOVYd5bzPS2t7R)
z_eI>zb?ME8mw~*uKHQTrMGP6{ogFMLSd84U0)ylFgA^!+f~lXNS<VCIAxKGzJ}oT`
zv3dNeuw9QE_ZQkDuJBcnpU+vdj#Pvd>U63?g&`Cg8yh?6NN>0t-dW6n9Hvdkj)lxS
zpZ;+A@rk@nNA|J^L{kRfBM3ZXLUs9jW2{ORJMxEL%Io)J-zP@Rx^beldzYRbfTnqU
zrf3M(=WZV?n02>dNab)%kUWnuTxz}Y-7Cbc7$UsOt5*?Xta$2s&cV?!q#(RjO-oB_
z<J2e3+hy^J;*?6@rHGt@U?kifR4>ADC(@n*YtyuOt@(O+>vEzQgmgf3X@R*7a$txC
zyCPA9Xdf^{wc0Ho!CDUi|9RSxp62dolw*3AK@ga62qCDcfgd_fvaDZWs*=EJbHjsc
zz}jZ^syvn&crac>(WE(Ew6Qtsu^hJS0d~`8{o%FvS$+wF#ZWi1r{Cy=ci4e})wZc&
zr=})Ccq5J?NW1}z*n0SdoUEgxqqZ|47?D9DKpx8TOe$KrsJOvFI=Q+s(Vilg{-Z)@
z<`ob`sd{Cl_3f(Zr&Qj8GkkV2HYREppYz->{`v<Ps{a1#wH7evl>tUtTD_sGXX!0~
zHaf`?3A~b;`9~E?_(0^8sTrw+?9Y*RG&kSqleTwoko{%-Vbb{Setrx*N{zXd70C-B
zcG9;1f=?gHVPy4nE>=R^O5;iq(qLpz`|1>R=J$R6*U|*on0hyH*YuRIkJ_)LXZ78_
zecPuxyeBF9Y7hqv*%%O*K9m&Pt93;!9X&m_Gly;n3k%blnORxo4O|1x>hzKh%vx1_
z$i0S!2Ca?0spc#{N`TRJGBWj{eDQiKeVqW>!4zl~J7qrNMqAml0t`V3Q7ib<b@IU#
z%>?&&a`f`qXg>({Q4HlxNqbDY$YI)Zz7GOl>dd=-`Dyfdq$tfZ)*mjKXiLfjL?R<o
zff!Sd%}G7qp>ziahpOqkK^6^d>&r4SOs-RzhAD^8jqhmjS@K+@qb6=|4Q~4)Oil!l
zTTNTG0-T5F@bgPP1{Z#Y7LBmmIhCTB5I6*GQy9hGBm)U1>#btM5um6vn1E=?gk2v}
zq=0VjY4I7}bes*2rvB1YH=pa$luZc9-o0036gD_dy77%$|I}Q^e}X^yk4f76>p}i&
z><dyD{Ho?&t<Gnox_kTvA7*U8@`5Lu>=hbtMJ`TGJa_~vEWv9<JALkb<_AeNQys$1
zr0MTiVIlwE#dGQtr-i4_Nd#|%RdW~^O8N(j)mdoW=iISyqzkYk>9$5$aN8DewArm^
z6~p)w`q*D(1M`b`|AM-AyRDq-wBnMJO+QvgW0$%$3v37{Aw-6JOUAp53|>e2OW|kl
zUM!HOJA}x)q@<)iL)BuS29X7Fq$e{4&P<NQ`3=1K7%@Bx<E`Xw=2~N^6dG9UW~j*1
z6nM*+n1u?X>Y7W>yRYj!5T0lvliX+cN(YQ5N=^}Y3^c*|)#_ZlYVchXn+;#83xz!<
zBv*J>0p;f)!(<b}ce+5^(ig|=Ij|FaE`jH6myl(}FVp~`HCO^JmIr|ei{d>DjEu)4
z`p?=Zfx81*lV9W9```B+sQ@shH^a~Mxsq0o2|k-l$^qO~Ra)MFSS^j(TSN!s0xbj|
z6a$dq9Uup9Z$t==kDo&50_=KznxnnFMf;}~Cw1U-rasyO-3gv3jGLiLph@Bot*+Bu
zG^e(sjsC|2C2T%c!+m7I?FQEoxR&U6f@%(z9iE(?_6N!ZfXM9xmR|o{NN@S{N3#hn
z>Hvj*d6pbvLtNz{B?;vj{@Qeyy*TMsAWVHYQ&nwPn=<=3P+klO(i#3;sz4E^ru_};
zFr;9M)4FE>O~hnnW%aRB^j2XCZ=Cc?DwNX#)IvOZT|8J_Pfw2`MqFH+d+A$+S0Bft
zw8YH0W5Nsog}9bV2A3(8u2E6coq>^(jcM~}WKL%FO#ltG!%j&_sWLZc+AZi)hl++q
z=SdBf0)SZD4z7(xS(O+uT$6@*l=;o^$)wo~H?D$4Hz~cDgIu!g8HsRbJkbb-TA414
zEmq_zn7s*eM{%mCs0eF)<DyW+nOzV+Th=qRs%cm``%SSuM&|vWl%4)RZRkGSeD?k?
zT7dtsl>7hbhx*rppbh!I)=@kIPVjjxN@61hAF=|K=w~4R1IhMFoE_Mlajee1-&(7~
zf^PRQUwCHc*u~fbh<DP4B-Q4O9)Ly?^wWd?S`YaP%i1sIyN;u)F*JsK3GYdKo^6Am
zGO!hTE1zlcX;xS>V7leta7qCEgusMN$WNfDh=3*p!b9O^>Ce2q<#|iaAs~r25EK+d
z`NwLA+_lr^NH@<tH8jEY7~LJ>H|#>o=4VoB86!KGzb6Lh(9^fGPqMM02M8%3X-F_t
z4F{MYb$1ixdt@p=13-rq6_e>jJSZQCgbm<KI0u$Xmn$J-A~yU%7oehMq22wO>cN#N
z;Q-D4c=n(g#F7=%(H+WGxgVzRRzwGoEKpH??XNoW8ZC6-k2Mxz5zNAa7c6s-5yH+c
z>nH(Oq**WJ&I2Im-WpseRhMQZZkW~WW5G%5J8<v)qH5A)1{0UF>r4@=qk@!>U&5kl
zjlCGh;Jwt;#A{L=ZI0dd-b?8gloZc?{<xOgsyR_)-r$byGp97`Ob2_9gdmVn!@CZy
z>k>1;`sc%34T18)FFsI8EI@i|<wD3JkguYD28t|--R437z*WJy=yC)B7(EA!rA^3<
zn_#R9<loU9;=Jw$Wbh8wl#~=kf+<($<2{F}DqPDlGc)_x{q|5&QUb=ff!+<QR?X(6
zf|3=yFM%gk9~L*HPkM1_DZoZBI3+jNL1C&I?!jGn{o(gr!KKkc$qS{vKkh$5Fk*bj
zpVA;<_G??4>de8a$C+)_bp#tg2G;Y>nqktY85?!CU`1$WJ+{|}$XHj-pA#0ALs>@K
zCP~EZlGF&mK`J4iY?w}maug#-?~r;sCmGyzKk%3bjH8{6V9Dm)At_#6zF%4!`P1KN
zwZADC(<PGN1!n@dWTS>~8b~!?Bxb^6Bh3m*)2nId(4Yt(@=^K2ACMFSaUAke11&9l
zHcvQni4^bQJVZrB8K%FrX@>+((+ZvZq+>Q(^8LHH3Zf#ZbFa$D$<^Rm78e%$(2Rhe
z)6W}+&;(B}128ss_TU;-{L4Cm`5-$a&S{#CCJ#IGIO*?T>`+PC3^FKE1nA1;ARP^~
zLz&Q*(RrE@tj2`Wteln<j@8a}4N^AL+#2ajmB23wMKEVa&8=>nR97k3`{Z-Xbhc3g
zLvQQ$IG}j?^BNO;@}hphrScjYA)Dv-VApvJk^(y&MamikEq9KUO4Eo=yo=uzIf`<F
zDd~k=(e7*GfCAa8HJ$q>b!m+wtz~Prb6gfD(zqQCTTsNzlavg>2%>|!2q==hPyTRV
zKnH~A8B<bHqAz}m>bel<TX<LR<_b#V=&b<#A&(8KE#W;mHFa8d)^iI53b;vduYVLA
zgcGy&d-L@4v|+l*o#37%E-&!sHru50@oN?ujC^<4f$*cUqlV*B8t22>U6&U5s;|ZH
zGwd?s#D*wB^zvJCdGo-4Zl=L5fS=2gMPs&9sjzGH1`}SEMb4xY6gd0h#4X)>n;kbI
z0AW*zC=C*g?Y@y{!mkVf;$bj=M~|}!={)~ISc_)I<F!aO=RV*`F*ZD8a{|1*AvmL0
zSphPOk(#<_IyZuQj)de+2&L~F1IfUN@Q8?^()Af%(3!jyalZrEB1pq>yD!AEUY1!X
zI)Civ9sN>TJs~4E^v6CPZxtm;K_urv${{_zCOPS~mM+-mbvKyNwI6d0fwcpvMWRw5
zcSbQkl5n7W2OiQ1{;yxZ?gDmSAJjN}kYD+mL3P{#;2IcVCfj;GlzM$tbQs;t)1o7v
zf$XQYuJ)gzsr#3M|E~eh{+}C#vd+xTsyK6pDZ)~if{{Xp4pq;6rw`rcfRSuMJ{^Fc
zz~cthkWA~#<rmn_);p87TAg`OT+Z0nT7wQr!ww%jdVYCXZLS<iU0sa?Fdo6h9;xRZ
z<pg0@IpBs&lT=Nb_Y*U38jG6yVrc=4hVmQMz8@`_EnSZ*DiXgdXiyH~5d+Ae0WEl`
z_8xykj(eUVXuH7Wy#c%co|6v%VO(jc`^V~Q{bgsuC7kC%oo2xVg%myJi~T7jsnx0S
zL}7&!@STW0bpkqy6;3qkk4(_H_#^ia8+%t_3Rjy3H&Vz}0b_c(^VII%7!V;gFM@v%
z^Z{NF0*MQ~Nrkuj4)EErW`T=6oRVa&@>by#eZ_c@IX^IkJ~M$<=YZt12|3H*mmsCf
z6Vi(xU6bkBIoX-Px>SrZjY9w_6*L>Vejskw0n(H<A<8#xZEf%Ihk=x}1qi$u8!mMO
z!aR!*>)7Nu5FUkhYK1?1kaQExy$a-`(R8nz3Yg1GgRd<u)IhzWBvd=39?IM%rlwN6
zHa`L?qy|G4SPM_Pf|N5K48C@B2-U(<z}L%l&g#<<5Rqw2xk~_m6AuBu0ZKUC5ua-*
zZ3e(2_V$X8Exl?QmCFQ3+B8aTkqF>ZIrXg#&E6*$dTRh&!V~c#=a#7>3dsTWDOw50
z012qC@$lve>V>L}9MSp?>+@7H<54~(I7OqJUK7aPaYYlAYwTZZfo-%dgz!W^&noH<
z#a+G8_IriKXJ*p7mfZ&*)oJsL`aU`cdyIHAJuxv+zjIVmG@ps`Q5xrT_EqpgH4Y!7
zyG{U*oLVon3cXol|Doo_WYW#RhjP6)uoBh75h5I{(daoLY1Q-Q<vI^UvXyXq%W6CJ
zg-v!vo{*HT#Z?<@=j;f>_9H)JnlU$y`~wo^f4;f(bHYG0BKg&q#GT%uU?7sXQ(|z2
z8@qa{Cf`?I(r4bGpeJGqK?8~8wN)vg8x56obwG)4h&!&3gcukMSD!a|b7GXkZO~VP
z^abQxG+s<LS0JBEMT*)A?I&f|21yS0My#xaXSA@DE)XnIAbuLG06p3PYHE~e2K6O7
zDMz%uQcV&;k9&|(I_ICj7{u7(a&v_S)(4iW0ID2ll9*R;12L075Ix@B`4h=0q^y$U
zQrB^J(DD_UkR0wuk>C@aGiD?8Vb%lAqm_U#4kH04QXv9mk7OE1YaPU)=mU;d7^TVE
z5=e`3*5@U8Gb6+~)u41c;!DdLfHVWbr9}Tw;F>@MT?L|TTI8ESwpU>VLea75Y38&K
zq&Hu=*9P&`TO}Ne-fFFeK4j;JZ-q;eoVG!VafF=N?gp?&6~)f;+Sv!t^>-*37ZC|k
z@seF04^lthD2JY;!Qc1n2R-Q*qpVE#%{38-dE*lj*m=q}=302D(YrjU&zkO7=*ck~
zf)us?cxz%c#K9da-A(RCsHs7_prdWqa+!$t<*fqgX?#wOVChm$29Vz%)glS9AAJAZ
z!bRf1J*i)w^K2ZQCxW_M3TY-xK$`$rBT;pb8d6guWyMgm>&!E?6!pqcspW!D31=G_
znRDmPRX~P{5h=^9MfJAVI#pipK?6I#v;EYOLWr*O*CtYifK3k+H0wA#n~{+b2n3YC
zb+Pd`C^@-HrwXaH@C%prOTLOD@#UC<XcIzALj>Ju;UE@hcE?B%m=dFxi#dThdzuqj
zIuCj{>a?FTumD@B6moe!t+>^CWBfk7m5Wmlf@x)^DtWB69#=bphJ%SWuOJ&kk{UGH
zY9?C+5QkZ`e<<DFoDIC8M!Li=mwJB;&TXy+l%vN>G)b>jg-%`Jy|F@)(wvw1LjZVu
zlmco6Aaq=amvo6iwKr}@2`G1fY4n4AD6+LO<Q2J{h1)c@-b%!|yS%9*?fDo5G|=(4
z5k9R*Uio|;=Y)S&InHQRZ2fPpSRIM*ZAFSeuSgG2PxB!8<%2(8frOo!k<sVw!#$Kj
z&&G;uf_(S>pRaHyR?-Tekq}?N3xw5sHj&bUcgz`KS|RIJIS!`j*k}{MrgFR{h;!Rx
zX-4|WOkpYOikTJQ*4^ircd&CD;?E3!rv8lDzml~@HB9-cPwnp8i5E}ad^dggjH12C
zz7_V%3{hgoFK;NnlKE{PT@>|itH+e@e{{HJ`kMv);kXlm!=0Oub(>{1b_jglH1M)_
zd1j!0h}bzgGhfg&Dwwf8H8Z-|*HoG}Kp;(G^9lR?F-oAcnwvxLb$kg_LrrycNJTrL
zLq)no@#Zqd1td-cg2#%NN2uL8#KRwoH&F4#pKme<k{cPII%Q<~+~ymIP1_qy%3Q$#
z`O_fFp+yvijEtR~T}9F0=8Q`SDfe<ds7=yy2FOlWip-7q%d|R&xz$s>Ge9Lt;zj^G
zGDQek_@GF=BTY+VV8&N(Lm$P~i#(*flqbi1rOZ~?-xXE<j6=s+SFgy(nD^$MfrN8p
z{j{)o*8$M}IQT%#4t$xsEF9c{{em<`)u2&dN)?UWp9ca>Qk?)&geCL;y6%MIdDah3
zL_A7V>x2Fx8n$LvfvxeYYY9lKrN6bYN+^gU?X6Jfm%Ez1z@s9vgozX9W^<g1G_>>k
zt_b~3`U#U2@$buOUYJvj>1Wa*IS}yd?1&JVqdjna1eB5TY6nT9pnP@0(|w^uZU~gr
z22*_n0+lmAKXBIs%lX;<B4JcQQ2FT*d0iQjG~r*VTz>}J%Y~J+W}ZjXmN9GSB#64{
zW}7I3W|++S^B&H&^|PeAXXh7s?D^gaHfe6IL3r@A8_C&(CcwR=*T&)r1jD-5?XJtD
zYZVTm9Rg-!&PEECvHWWo9B?Vlc_fTL*j!)k{PU$CyxSiVQZ^qo+xF*|M?rUpjK7Qw
zlFq@rtIb{_-Ty3QcBiYCxZ{i;vhg$;+SCwY1fmomF>re&Fw?X>IaJ)y5+pXs9v&cr
z?I=1ydS7_V%#H<+w#I?#$%6%8tuIgEyOA>9;A@LKBunVY@oxPv6Y1ImFTPQYfvhWL
zpy;Q+Wk`0#ATeX%Uti$=wOW5Iaf+K!dB9Zj?c1HHff8{b-eqKf<a$T#MLN_m&tCrC
zoN)7$WdY$eQuykxFHJYya{K}YVH|B085wQ&RRV-o&lN}`@T_?GrUZhYOu+L<j>p;W
zaOcj^^uh^HEQx^L-vA0}PF*8G+L%u->vv!~ZW#xcj|!KJD_VmslGW;cu|Ef9Vr~w~
zfr8?GJ{4&Oz<wV_)fzG~NVH0Xq7Y$_IA#NLA=l?#^OBul__KEFW4$tL31p<5SAp=W
zDV|?_zO}VAHwS5mo3l2gX@Vm{-VAwNM`vg7ngoJ2k7LC&r~CM~be*C<TZ1)XH7hSK
zBswHB%9vSL(rrnqLCiV%ezGw*TDisAr;l)x*M|BS+`IRtxEMIcKc9GaP&#VqrW;=K
zf99x=p<VFd&sL-}T}Ml|6lg+@{w>Hbo#DmVcij5xaZhr^lz?kPr5fJeNVL2((=YBG
zb6nvQ3g~}TJs=+r&a%De*DSr6*<lAc4fBCwQIM)kbss|U1TT~vAW2EDd4HiGPvKac
zv0_1{^Zdx@X2KKFTx6#&9TvxS77frKsbM6<CYi3wGb>Q8u#&py)wJjaN{dQhPgP!#
zRt;>~Ce_#_FjKTj5=0qPbrIH^s#7H74g%(C@O=@<eqC9{O6Vn2pMxm2Am*P|%vj!r
zeBl`4y%;U?kENGwtvP|D9}gDA53B@|Cl^BjT>xm)UU>!HOghXcWbWP4(gNjjM|`ga
z{xmn2zhc&&0~x8A|KRRv?fm!DUfe~n&kSH+T7#yv0#aB#%?2d1TklZw_M1sA895cn
zY@V@lmM>mRv$AjyB|UgnqaS6Auw@?|6!9Rr5pYrsKh5o(jwCoxpw@}_s$JKq=dt<9
z3?Zu_s6FsQN={J5NfI_mX6naAyQJsV*O1b~PcD9cx9y(1V*%((ZTogLVy#9g|7%`7
zchz+#s7V?ELd|+)t+QmtUU0UqzjMf@K-<pVUa-hd;n22HnznHk^FZRwq_zrE&|)-n
z?RS*00ss^?YA1!WdLWpF=siQ$b+{BptE<7EotGzVR-m?xgI^nXVhvT-rN>n+OW~TP
zR6vgLDCNEm@U3Uo^KmQ1{k8Tt`MS4j%!v6lRZi(O+{ueIg3gzL?i_KBSE5Oia0px3
z2HI>v;`#R<d;@lT!JTDrJTjarx%KVr&e?8ub!noiG_)<|W_6c7gtSlZT(iZkYg15C
zR_bjL^jH?9wm_>Y-jSh)D{$}4wSXtAtjPxnFpx&~9q5B<2{x^9jq1&VgM(8C`4tm6
zf3&TwuM0@;VatCvHaQt_BybZJ-6-Vj8SpT9Q=Bmy`?(8?iUe34fdr;Ze+wB(d&8ys
zT*BD;b3lr=;+>1zGoJbDR4Fcb9DL^oOjk+|=fm&te4&R1WpL9FhzG5%@K>db&CXhl
zcfP!&z1;COxSGc+qBh64A2+_WIk;W&b{a8}NN!B3761h@!MdDMSYh><V|4&CN$3BB
zR+kPkm{DI#O;YQ4%Os*9pfU<+v6~-h@I}#W0}ol~H&pf)VO3htZJ7Ny5IbdH4RSW&
zA_~vZ1kKY`zy~)^@!$S@DlzilD9*z@s}6JY#Tl*K3Xq8mpg$mKe5ka?7Ii1cnz0BL
zC48dEc^l|9e4O|pq~!Ffb@-Bc?>^lhf8;}Crw<x-bb7n;tn|R665b8kL+uq5;c1}a
z2lQ8!WD7{Sp#5CGNJd5`IGduDc+w;rh{|NF_=9ifnM5AMJcJJbA3AF9E_fRPnN{X-
z_4KTs#Ir^bpFVw}V`LP^i#ax`QBYACMYodgXIa=-(1LH~-PP_uIj=T`zIgHCHI{{r
zg~ifg&lxa%C+6LNZZ$>Q^v211OZOi<c>k$AI~|U0y%de_<))!B!S4C|%sN;fo?D&j
zS^iS(Z&kU}rdw4@FPXiir=$B)Ojf9tF&%#Q!Gp85d#MaUd>%gBVNlE<f#VxcwPdoK
zFqVYlf@)O)plYNFX?Ryggv;m=W(YLZ6CQ9fn1WD2$h_+;AuvGk$a$qmseAl9_^JYt
zq=tL6oDD+OREh9xuO-WLD%2<_DDEwlU)HXKlR9Ex1*RK~_WSqmudx9W<KxcinEvD)
zZ!v`zF*=Mp>?}C;#n5Y{DXAXzy?g;n`CCd(N(X1>FwM@f9yCMO6UR8>_Zk%Us7|(i
zf6Gr}*2>9v`@_Af&a3izYhRVgo4rcU{Qx!h58Dpry~6vML=J!y4zf@lYl$8{pm}C#
z&m>u+Q0-Q-v1fT!gP_7w85q1b`@w~l1T13s!dcEMZFmz@rE0UShpOuBA!6XgLsgnz
zX=!N}q|uaFN#@S8huPUCYS$V}%q?Aapw>f%>AXfBM^E%MlRlv>ea7*8=bWJpa4(t0
zP0{&Ks6#5X*>=bOj0jt`z^{Q?D}Jq`0Yv0gfchr?5*vSA5RK{W>A?dua6*{6r{`<^
zZkw-HIV)i&N8cMHeFW1jxB*Ii`Bdt+TY%yWl`dog1aumg*JA)vwaD{xs2nK}mQ;Ie
zR4)#&lit~SBV6<++6i6!A99UtoASF+tz7lkm`RBvBZSP!>rx~$d?gWLBoyWW#k4q(
z_A?H>6OP=d4WSlZ<dcwK0>i|;T?!(B*E~85AZ5(88e}<s{5ZirG1BH57@p3bBsM1e
z9o!7^btSOF0|XjHj<c}=Q$Y=m04oY*e<y>l5uqaP1gb&qDzrC4GOtX-x+BL(rQ;td
z07VqS$p>t?)|G3ag^Cb}AIbL1xUOgI52_>J4S8K}AyFBrG#POi2n&En^(5fF37wx&
zv0}6OEc1X(-5ElXuoM6lX->E3leng81Tm<op)zONFxnVRoF_ea{RNG(wf4C=sH^4d
z-(u%=hRp{RiK&i1{oJ{$uqaT5kyxxfJu`X<R>gT0>T}Zm{O4!3BD1}DR$r%KgWk}c
z0gmm@e}y0;I0TiJC>3ske|4!SBgKYyTwoQkNZ}a*#dh20P$JCOavh}h6}XKW+$)b#
zu&tyGw62h0_%|&81_7-#N-L*J#SlFEd3%HN&v0o^!9!(%9-QdGXUyUb6$o3CGL9%e
zcUmj=G$}MKp0=>^6^5#$zlKU-8(KSYiBOG=xB&wwb|gt8+1P|EdJe-5sojV`1*oKA
zI-te=_E@u?0d(8^pO7s^YwgrvrW;hyk7~zIic@?h|2WWlH~&r012%|M$pEL>%lg~L
zhotDGvuw*=MyB}N@4ViqhL+E`F;dlw6syG4K<s>Iae28K7#DpAQT;`JMrvn{*8Gas
zo1crvkdu=eEYI{qkjBQQ4^{U5BBx15Lh;pUPoCgw)^%s{Dhxw(j==hOG!|Zx0*<x1
zF-jcj#pmjH1DBSb-oulwKk@p+G&8`1_7sh3RBJJw3-;f-T}=auV39@<<hn}Azd+aP
zdsJgtjbbTGQf1?cRz65**wYi>_KvjDvk=7{CXnvg{2O2*R;2$1V*!P>Q1$DJ3f9ry
zg=j+>zXY|Er+D}M?3Q2@0C5A##@E($YTGJ~L^XLWEH8(IusRRF_;X+k<VjIo9i++6
z@Q&IP9JiDv%9A@Hp!BEc9M#P^7o?<2I&V(@`0*erg>=qiZUG!)g#$+*=x1t*-l$S#
zLMNC2S+ll3g~1D`hGx#@jEbp3v4IPrHu$f*1z7~ZWOH+<7Ib_Gg}lzIP|TV9w~awI
z20;5Kz}JSF)J(_O0U{am-Ms;6T6tBdT$`F26r$QF75wLbetm6CAL0UPYGhkMX=?2T
z{9pi7Xbc!ZBxwzmN|I)^{tXer(zCp0NZ>5SJ}0qjyl7xC9i`?-6jpzcV>JZcpN!L_
z-O@*qB~{PRjfx{B*D&F4z<{%U0M`y-2qzvw@0va%;46spxNV6>z)Pxd$l$B_#MK#3
zse6FlQ9M(Ds-1!OP?dn556Y*H46O_Wl29iU1qaRjAid+9GMESy0P&qWcNowfRLy4o
ztP??1IiH{Go5YHcF1)P@7ozesXe}^N@-uz;HmtB|M+r1un6pVzP5w8y86t850lU8R
zZy>hxLhODpClHDM?d7X)zvXYOO~Rfoo^SN!jCP6}@FIQS9fo)7AqRk~6F>#cm4h1%
zgQPO1eMe4v2SV19DCe;tXARQ<b^eQ&P<-|`Pz*~AhzyK>(^~&iN=E(ir<8)5CA>VO
z$q%KUkotv$WlrRitwD+ZyJeoA)WcKQWMEM<(*OE`!e6RAR0_!ofnwZv@(A`_(=BN+
zvD>0IT>GtZNyI|T6U9?BJcsuZFl$#()y<@Q!dF!)lCk`F&S39Pn(2S7p82~jBylCW
z;%mNTE50UXt`5;qz>u6S{Za`#p|Z&JivdX1!jwR8a4Jn&=4WG&vjU-i{PeXa-rA8b
zAYiLTvPu|%`d2&03T#I^Qhtudh)(4JX|DgLB~+v<{>mBr@3ckvM{^1$Rt`H_l>zQJ
z>}kE3Q%bNY*roop=l1GG_=*Qq^8m*duBj+AN<ujji3>?_Q1X+dWMMIoBKhVvF5CjY
zeZR0xwAZ<>og<{qa4AWxY2N5v2PfA;lF*u}I7Db;Dkvlw5%iQ&qZ?|k$}zG?j#2_h
zu*&W!wHF=fhF-Cx>YThhjp>Me#nu3z)Yc)dzIhVigk9=w;qdNA_%Un}M1zc)ie}<V
zT?X+*MeE*^AKaxG8=}O8=QBH7TU)=G-tL5=LjLsy;PKeDpvE&E#q`KaBXWw#TF1k~
z17S4CN`;NK4wSeuYAa4c=vgzRl2S_DM}d$Zmg|Tw3HmrVsF!pc*7g%{NKyo1swZ*D
z!TM6g%24ucoSzOCppX1|iUdeCK@iJh5YmHa!!Yc`^t1(v$IHsfMk2FroMc+GE@oqT
zGAeKq1flYpH49(~QkEJ%-suB$*Di%!E*kWo?#&Z^AJ@?aDK}7l)N4!g^MCzldb_t2
zyRPr#wDR*Qrw7ZV1)j714EHBLJ|qikQkGt=QPPL1Aj<T2f}~l%Z-4vr2o0VRVF)F1
zDkc*c3@Y);Qd(c0MS#`}U_+Ktx&Sx0^>#BwD^ov4X6CdWPJ?3WAb%ZMIXRf%Nv_~)
zYU}5M&l|t(E^@Ro!dUoeWO{6G)l1PAsj4y-LHsS0AKqdA<J+fj+s3563JzFE-J{WE
zH<MIztauy{(yg?7GM^*_RZR2vid9uUF|lyx>w9>(AV4+3QSfs|D&<&9wHtwi7Y1_`
zy<sRl?l$h-S%)M*DUEa)LRCCD(6=LO^p%uaK1YUPjuf*c7f$A^G|7tme`^%<kDiFX
zD^L6%M#g{X3iKawF8?)(f2jM@f9XYyE9w5dtrZEgAuF&=zh83U26TgwekSR<L~7*(
zMQ%hd$a)Zg$;$3#Coz5X3<A)D0Eh;+Z)B`ajk6G;(ICzgwVgn9<oQsv3MK2pu@{kV
z@a#6M`VCe8p=_R&OKSuk%BTZ?BozYbls+8xT#Mc#8hTiJ3Fzy9C=8TqjL_jh@BQc}
zY%t;(4ca)jDFaqtM2|z-k`2n?LEZ#J1xWD?H>L--EFk3qm4slrBDBwt-dHbeuyd?t
zV{3NXT}B4d7F}f?9zj1t#n4qFMU147kh<>7#Du9nJGvf|Rqhu?=E5)TFa?EyLD$7j
zf&44kavg?>vc1m>wia5IGFI~ow9`i{j6VRVfZTQ1WN(jAgX=Lh#STur7}4uE)6V|>
zsMWVxuIs7JdqR=)d_*)si3W%%u)6?F!Me%gOj}!8UUVk{Q@Kl{wGc8@0_($XeSF-c
zaNV4(vudi7hiFI+=ANH0SQ!VfGLXM-l(1Sz`rMTulwM<|l7@WK<Y20ZfSjx>ej+8~
zCl&1Ubs7c0UGMK+8d+N|9+ebi?fpr%ZEkLcP3lDYf!0xNefV-7*s<a<^@YVn=a#e!
zpeh&<W&f5068@U0p!4xaeUhaVRI*1Z0%%BZmw9=4hz5ZH*Gu{P`JDs9+KwK9bBEn!
zS6A0|vy%oP)-cTbW@g}tKHW2r0GbAf_4?|NO!>>eJCBGG6gNmr2;&fQI?3f2ZE%l*
ze?jGGC@OZU79XH4TXrq$oO(%G4x=aZo9bFgsy@))Mhs<#&`P700fL+TsPEECd7c_=
z4>0eeYGgxJzo}04R)`iFXCd>hH1jR#T}L8tOejj&wQCoDQ$$j+t7t2Uwo;inn@Y#P
zP&?HM*a3Ok_BBW(w;{(2FBsu=k&`2r%Ro<GGu8U><9T!m_z3U;I4<x#DY}>Fi~P8c
z_*U@<hMN10W(r$=;ET?bM|miLSx+H{BsxS|wRU$U#P!fMq5pOf9;mEVQ~{+dpKwzf
zl*vRW=#xgg0ZO?wu$~ehe-ng<b~jcdYY^p2XNb|2i6jYy+EELqBQ7k+H{W_qwFpQm
zm{8w-pV-*gdfVDc%SzICwWrkm{QSmLKxuLj{MKGHNZ3%6{yzGdq1~LFztgm@$yDAm
zphfOeCx~%KjjpK82-q|M&yGYNQ}p^@{)-jUPE)aGs_~2bW9Y_;8zfz-5a@;MzMv8<
zT9xyU8uC>g^j9_zfen=IY~6m+Vujz4Rr~}|7+wXprius*qK`%~n-uYx(rz3nZVta!
z)we^E&rLVi{FXNKc={=w_T&iXb388%;Nw?ACdk7aMB3SrT$D~nNk)GzQv16*>fwAl
z7Gx4`CQS&xS?kb8pX#|_q=3QxqM3j&C5>byjb4D9#I|74F8_TWJW|;EpB&8o%W3}K
z?==4j!4&el3UZ_z&E1|FWS3vp5f}hEq;w?a3eD0p<K2=KfmAojh*Y8hfpQoYkPZr*
zL4Hy%m6KQWMg*c(#!(du@S^<d!{OP;s1K0b2iW44#2)&g_P0Q3X0fiR-u2U9>>SQX
z>PReCBsllCZz`jFazn4nkHEUodA0SCPtxg7EoO?N_)-n%Dj5q}I`cb%Y161FAke<Y
z(<i}GqS(AKQZ#H-Rejqi9vOHkkeHL)L2qwFcOsC^$fJSd<X_JQqmF(-xtev=(?>`!
zW@@S}kpUDCswGbk(gi@3ZU!l;hv9~qPvI3~eE@qJNtOB$x%_lY4W@2Jz#SoczZm<V
zt(xfb;BYi#+r#si|9db+k?=_>2;6)!7A*ID7`nymKnZXbb`|#J<>hGJ0K@PCJYixo
zhvy){lfaUP4<DjIQn5&cb|*qM5Iu|F*|X2P6CtH9jD9(H?p1dp@DzgR7YT_-u&7})
zP=7HYsG6i|3ZG+UXV=!1uq())_zRhYn@{QcX`tRz_jWZ`@z6l}vCgghtl5+>BOs(f
zxVF8@$t&X816~A4uHgO(Nl0ik0;NWj=$sy<#%Z25idI=uRZdgdHAK^x*yHE#FN!n0
zkw!uzI%Z-xE&!>j3PKLCp9P&Q0H5SssDv292s7=L44F&&v|D6<U32TPtV<*V`OoQR
z`d>Pt{U;pGf8F9g3BI-rOzphsG!kZvXB=FS-GIKReh7%5h!2%<6@RC+g8os1Q*)%v
z3_&98`Pr__<6CPP+hU-9t1Sac!qt4RR5I7R`ygs-84vWwGn${Mg-XD5ZvY(Z>Vyzo
z21s>T!{<~f3ip%5BO7&P8}c9<I7=!m1v0mFik}p_z=zPBhl<x}#E|aa`nDgB(hiUm
zLJ;K8b&+FGx4-TIy&Q&sp1mLe?I{{~XwdNPR7LRZu!9g*%c?{WD#PZZmnDp`5N#K4
zPFn?fdqbzJve+EbXVl9ApmK-d0Gk4>VUa@dxaB;ONwcw`0uI4c5*qUCHU%WLz3j0K
zsh+a+qB6!+w=M79NZ>X>Oo}?dQT}Q{l4}Z-+2RMQ!BX#ckL%^WwzdK{4ZI{ggTSK+
z&{Igv0G(RD83`3=f}K!#YdYY-0m*V-&^jki(o5?lFP@r=jIvlrazOg%_ICRU`{`+c
z5%xQ^$f7qT{X#F02;pCUWgp!jyQUgJ0tis;iwq|TAS7PGZ5eC`o=H)v)Xzi^!W0_^
z*iB<Ph;KjyY}3zlDWbZ%fq8>=LHD9nz#}4SjhxqQ9F=hJlx3B~A5dN=qsrQ)=(dB2
zkuingydLyA2wNAOEuJ?`Pk{<-Sy@@r0r$5Planbf7nc_o5$GV}HdTZ{nZ{mzETz~6
zj5hr+Gzy$(wNi7P5(V42ShK1MeLKK!LE9hfTAC@=Mhd(qz=HQuo9a2EiTauEl*-hm
z-F)a~;~&`1NkO_sJP{z6deCBxneOEr%xxT(_9Zk-328NjrEuYv*r`R6;cE#u$KP!i
zK0$4=h>jl1j;MubKHi5rAirU2bG^=yce}T2Q_Bd0ZIWEB(A#X+Gh{J?1f&Wt6qCWO
zVE|fM4jZ!RS3ri8sIai8ftD>wz<bkekICi)l9fP{S%0KDu-$|^27aimiU5Qx<2Oje
z0wUr7j|LhFXf1LXHXrh8MUk)Dp+kxL6r}i%8hu&Uum>(uWy`w@K#PI7d1?(%kl^L#
zd^<mW^bEXWj2bz4$e0620G}42t!xJ2z-f=Y&|u2s+H7OfJy;`x(q)SM``2HoKZQOk
z#)|z$$-c*7|8NgVo&(~$v}2?jQeu$ek*iqg?}BWTV){FK`l7ZfK(HX(AQatCY^5u0
z@gtfVKxhKoq(WzAo$6bjFbMMzz-QA36;#-~I3(`M0Ng?wTDAU8kX*5%mdpH|;M-mI
z1`Bn>`sUwEy7xo1vLcXiLM8XoBceA^sz5~a!!wl~%;C1tqv0!fL4JNb{zQ4j>I_ti
zeLQ;!s@+#Jp$XMQL9x%J9d?<TZHW|M3<#YP*jgdt>Q&qa#SM|wuC7oiwYw04T(UBe
zp(|1qARZ^Iy-I_6>_{Bot=;MZAi;jEw+#2plz)nJA%1=e5qEN0IE-FH=ja{@IYq+&
zBK>@W6>6L(T8|QfumK+Jm-^L}sC<HCltR-0#Y92wmwDC+TKu^|d4@;h8m#q7M_wrn
zHFXB`4`IBGx(#AJnx6wo!`mC>ondlOMPlat*0*!juOJV_j1+?qJD{FQ(7kq#Vmc`^
zbY~=j&-T2Wp9l4KLWmv<x=1lXs1n+RMCZ7F`r~(EOQ3u9)%Kr8h)00SGr(IDFq#|D
zSI^H18pRMFv2GkCiR8fq8z6}ZDDaNEiylJ*!Ym38p#cG9Q;Edr&?i7QOP+ME&>W*S
zrk~=1Y^wi1ozwqIM;dGzKtnh1=odm|f_v-KK~o_ip(8+afeFaYaV|Sn7wh#<O3iVl
z^)gS0*-iE|3w_+e$q1?Ts-bit2<XP!hb3lA<CSu3(?mr_Ri@!I3`Hh4#Ll0(7?=#)
z^URHmg_FH@B`mX(lZfn!U(?#LK;ejb-9nsK{No9)U$0eJNjD)o6?(%;qFe-$1tMKt
z15cu16bSG#4XWYz<fED*U~$knN0Px@%YFuGY9yH~9=ptIYI8Yl99kXwLc$xG)*#s_
z+5<sgKS`=~)b10~ZwT8X|7(IS=hj=jX5N?2$HoRU04wNL?xR<smyQF7;Bdg8?c>Qm
z3jH|u3l_w6EP%-6?ABD%)@ks9sC<GwMEmQo&+S%_TQkf;5iuNh*jm<&*Ak`vq6L5s
z-$L-#Y$!N(TJIf%M%74)p$&E)EQz4Mzdtkr<^Gu!w{BGwMjx{X@)psh>0VmYSprHQ
zQ_#ea8X=ZJzeKR3&}DJ&qyW<1B0>qeQ8zl49)iTI85%pPVF1~N05oPVCRS;JP6tf@
z=nPLvdw4*%3IERG5kd6XSk#Uc_342-f+t?K1__^VLEM4_+5axP@N4`0VaEblaL|sl
zba&J9I@idD>s+tv$uy9If-}_04|GjJV8Itb%X}KxDQG-K>b5o_nR?tA+D;p6Z*9U*
zUWOE(5EB<i)_M&!T7*_Vpd~^@6p(I2ul9f@7wLG}*p8gmJb=m&&;vpvBOg7G0mB{Q
zXiHAIk2<<1qHThrE+h~IZ7$Rl4E@gQbOBusDVjhOJ+QH%ZpplE-}gmm-SA~#V)Dan
zP8q{ZwtI`_e{j_1czheQyBc!%B=3_)yAQ{nzs03a{dni+9p7n`G-Yo5cHzk*&!b+Y
z(o2tAsE+!2ddBSD8MoU6t4vKDFcouUN4HFEz|<Knt+9)(!>gMu!UjDY_o2-y$M~|X
zPDy&Uz<hmF(XvDs{7)t6CVLy`WeMKdHTU?BojF5^xpS88MxLck@bRx4$9{vbO&D`t
zQ<IM2oJow0OwS?&UFGIN$3;Zw{27JS#hDt!>?aP-43_Q3nDk_yvJ;D{rM}+^F!Xs)
z5E;fhFi_6K<Q;mc(wFu{aPaRKZ(m<f@jpf{fttd4vf~Wqe3Y2|jo6F7!zaG=^P_l^
zm`H{(1tnK)Fn0>mi#ZXDqRd~ve7+HU|N4u}o846BAujv%^@8o+U5YvPmp@TcpJJ1l
z#?(gC*DJvOrGg@yeGnTw%Fh=;`b*RWikgWj^;%K1Irro9Mn)XhaZ>&e=?;OgP}b6t
z=is43MCg0$vLR7SbbDpm8Dt?KAYeWGg_=pi=>X^T$dAxO^9D@ii4!McKzw1+_U=V>
z)8(0Y<zyYD-2M_*;gQCu$?Se;UK4Y-{3(kxr*ZZxxrD$oR@Eu@dZSsS2i|d>X@8ss
zPY*_c8a4^=(z}w?(|q2%Ie`ZFPCYHWf77{#yrv=kpy(@Gw|8OtM2n;3JQ^M`U2BUg
z8~M!G<`y-_c@0Ol>jUWE?=;6;IB@YO7uQ}Kb}6jq*!@2a?1Z~kgY?52{!xizoSfv)
zu(}*_2u~q>jfV{V4RIOV4|Uez8PVV}zK8}9Yy59FRaC4O#+0>7TxgCRJN8Z^g9TJ;
zUQnQL(4}NxCYO-2JtwQUVz!;oA)0GHxYd@Iw8}#BW42VD^YH;wa%J5&2?=*MR*3I4
zPba*ttgO5Xo%wd}+c$r|DB7~fW7{46fr*5+L00U7axt_#w3H^ds(5n`79b)j${(UK
zasMQUTA)_z4>>DOGIk}*d$UfS`e@1C``TGX4Hw<qihY|t+uyE>e_8!n;2PmmYc7FF
z+Gg^qnc$AYQK6f&+grTQI9SDNIs?}G*f9_H^wW9!Uk1q~*o=)6V~u!*e>kvg3huW#
zsph*Cy3a~^(!LgV5=%w*<?(?~5o@*pR%t4Xw~x<f&}XX!T*Gn%?HQ`_FNZ25rgQ<j
zu^V>NJ?&n~NtY~HhigQGrCrd!m4ID$@T#NSMF#zm^-Mn|ODyRPB=7N<DQgp}Udk&~
zY*>Exu-Wmx@CS_T@{Eo`=-C}de1C@+mI)^2(fjvjVdD=$OW05F8D|}5wBdqJ#?!Uk
zLU7iuUcG7pRUQn2rjNlmqnTtGt!Csq)t;W%J7Fhwf}K6~k{1<PBIrs=efaR>KmOPW
zu_^|-;mbnhWJkJ=FVvs~_eyj#2$|D?qr0D{|N4@zv+6|GO)lMpo~0_{&{fI%nH-p-
z?Cig%sHa`La%J}xG0@{1#Ef!)srK&MXT3O~X5RHawoPbY;!$5i{L95*m`HV=;Tvp$
zTcPBluTn2_d(=D;P9lmCtX@fwQ`i+b&cc1GTjJ&RLphJIsaHHbcslfa2k7aaLD$<j
z<#^fMd-hy#a1a84sVDSJ+zG<^DsXE)FzqSK>2bqL7osCoR^w5Rld<Mlla5q+bSyw#
z8roo_UFt>;|IS7Qw9`22zTpH)HVUz*>pkiJe&W-X-<|hd(D}nnA2$7GlXE`sZ>j{}
zejOBa++|_xn4fI?6(yzRTSWtY<3GNaqrZa?uP$vR*sn%wnv16OAsPAO7L&u!bMerV
zCr>Ob7)_kP7bKo#@JE?>%S!H$BlDUs^8(Hozp8!o(n4I^eYh>?h#4LVir{8@<6BO`
z85`+vk2;mIf9_o1wGNf517cFIYM(Hb*2f1QnLq0FQgk6t`(zDmhRQ?PM$tP*i(Hq}
z$GWo;LA_j>??jA)kBS2*YU-7+r%8Id7=7|!Db^t*kX2fj&R-Vl3td>{-by*H9ZhIV
z+E8?NFC7~wDT2%JAY4QP-VDhzWousJ)r&G9zdiW2&9&|Pc#+egi>wDOUcq@TLhWJO
z+?Ov`#t%J*1j_mwodo@t@x_F3X#4$Q$r?6mNFKM9^G%;4^Y3=C54GcoLw@RJY_L->
zKc=#pTo!{oJZfXVzWVZn$&{dM=KQ=|&+J95*Lksy;`{LLWb6hl`}1R$d~{yi8rXB}
zc|^oM4D8hCe*4?G=6f*tj<eBC_&K?2Q7Wqua0=R>vH=6$W2htdVTXrv3P+bm_h_o_
z&P*$niIv^2N`1+irJjd{Jp*$_=GoQ~a!>L-#4Mvh4#7TG1Lj_H_O71n5A*m?Ot^aL
zc6~g<D?bh2>Og_pse6w724UM?3<46`1Bu)lr@XGIu+iGQ3zkbGT%htuyUfM9b6j)&
zH54V#Ku0V(0V9gf<>l>Eaw>LW%;CaTR2UJDt>S)rAUk&>XO{${7QPm(vije8`|^J(
z_jmmV8A_p2NttRFAsUoSkw~e8M3FJd7@0CgQA;JtR3T*88Dd4ojO{EUbLO#R$W$^F
z!nq#p&-Z-KA8?LW{jhh-TF-h9_kCU0J#7iD@I_V!K>*tK<Eb0}JhjxopQrBq#S&k1
z-$7x%1X*qbv3KuYHo@VX?zTzvuRd&{lk}ud$ktW$<VgngsWNC6|MZX1OPz_3v@K(o
z|Lu*;!tK)1+^54Oj=WHNR+njUmg*Jsk|vvcq{5}?@Quz?!QIVD+S(get-^FEGCdJ@
z;bBnFt?eoSB}RK<aL{oI*r?ogWle|QKa-u)ThQC}h*hewGOl@opF*|&%AUtR_G4=6
z9`*jg!AnT)KYr>|2-Eh&+#^0rpOI<e0=u@yb;7g}C>5mYeRVaBd@Q^@znB;s9!o<;
zt6Fm+veJ-6;9*o$z>bq)CDsSybf^Y?{{D_B4-|%r3pCb`7DtMt7|^HMp6!*h<e%2x
z>OFH40y*JTRG(h18_CGXxF@1otV=^%Enk)Pmx^oU*&|18Ix;sR`#i2{+J@0S%KH0_
zh)0iZk`A(`h-c->l`a_J5Ysa9!Gkxg!GyOd#<akaPySmo?%lTi``0Vf^S7gp2Kmv7
z77@RHd!V9k4J3khuWJwA$H17ps`D>9hkQCK;)<J!53ud97oc#vHk$DEjh|y%5ZDyI
z+rEa5p$UxbQFL@*U!Q5#(fXInd>$MqFkOkdlK`-Sje3{&aE+svJCA<8v$8rz@uyKg
z%|R{Q>!1H&^yii1R%96(uEL~%UgXX`meBW3p-&~<<*Oz8s!+F;@kOVT;0+qDUnAO?
z(aw|@eEg}SXej->s9533ppR*qdn+^yCQQ@Mk0WFD6H-#KivUyNlTxj#N@bOg9WyEQ
zTWgMLB$)f>ABS+K?gOomGaU{&k8)ufeK4dVWLEZK!oNpF`i3ldRD7LQ-9)l0`NFUD
zam;Q%zdcAy6zVMY;sa<XMN6VrSG%MoteRKfy?fWJ?29E|-wED@)Wy&$*s7X1ZJ4SC
zsi_wiA3DF6V_vOfVPSzZ>@5af3r<?eS|`TrB*qZ$bS_SR6dde>u|l7qGE_ZZuL=Xs
z`nv7n=uZ;eB_upY?@=)?b#A|f$DQFuOZ`t|rq{7}Mg)D=U`YDjOJuV9`f?_;=^mPC
zEfUI|8w!a0bG07|3ar3Dln3Pfgg@kxb$K=UX^f>*wr&1R&|4z~#0l1J+C=&Mj&cd|
zM(&M;H!VB6TK+v7Jn~WG+4x7A|8n~Ix(u_E1t@K9$Up?xaj1b&N=m9BlmU(Ri$0#m
zxsa`hlW!_+$b0ziv1_9}Z=bp}Wif=cfyp+Kfr%TJ9io8%h$=820PyLK0@wW;ckBA%
z_8X%ti*&+@ii+fxAlLd6eAz!(hIG@4bxMeS^Q7%`oyToyOghU9F@afq>dkfd)tBef
z=}FfZCFlMMiIw~0z1K7@o>W^tF3;Rn`WBrYT0SloOVTrh(L&|`=G3sYRDAdVqj9IA
zNyM%%Gezr^{R+1zpck3zVklAb!7~}5DGqNn`{u8%!bzxk)4XEy>}<uB%qJ=bgf>$*
z2J&5*j;Dnuo==aR%GJxc>YOuH_nd+F1d#YJZ7#E1Pin>1!Ft2to=sQ@9oI%Tti<b$
zSz)Z2(UO5#3K?$GmZoNA#%Q2eMOi;PJ9|FUd@W&w!}kaF3&CuzW@13hX>-9urO5l*
z+H#zZa{=1<1+HrS)LTA28Y$YlF;Hjo8g7XakhE7<YI$FDtgv6&@nYHNs}-(L>>0TK
z|L#e$RuVmtd+p1yBx>>b1d=5TH>ZyLG5iWO!C9B!l?7p_190|TJZQT7gB5E>eCJU3
z;q0!b^55UO<Qj=|tc$PCkj&gU=K$C0hD#%BgjFY;+34n7rzmqj&jB7P^DYRm+b8PZ
z*R5nsI(hffZuPCwjx59;k6(7=sF~K<5o$5pviq9K0SZavN_7L%V<(gy5_IRYgHWx_
zK(uCAZSv(fe7`!{Wh7FV<Ex`IIWvNR05&W6>~^!;t0HOTjA25ap7>&S&g}5AalHA_
z-Tb_xW2Qt-0}T3DT5xzcycY6sJsL>6ur|>(W9$hC3EDybbwNj$(K0Gh3XUn0&$j;0
zVb_(tL6nm_3$*<Xf*H%F+AZoKArTRJ)(b%)?*)wH19hohh69Rh36o#NT^}AD({0og
zJ1(cnV|BC4`rzZabRP3pi>U<}2Gs+j$<ibHMoteTO4vr8Wem!&Opu*2>`uf(E6XNr
z^7_`{G0S`4;hY$o!o0WI3!i%P6-0b|e28w;<Hr=-C*G3i=y9R0<>&TuU8iV-@F!|p
z1z{NPV1me5=W<RzS9|g_H}Kz-v~_;|$6RH({63T-nA%(ld(BE9AIGO_1{&QOl060w
z6lQ5|qj-Ojwai=ee8wsjQWYl;2Y$dx!zz#8NyzCXV-N&2<Au3tXRnur9}Z%7d(cui
z?rMo}dJUu7?1UM`haC;L*Ax5)4~BiDJ0vg|Dg2N{Al~&kiW)4*!qsBgRD46PiA;mv
zS+<kY?ppkI%U|($Ag^4^$Dvo3%Gq9Cczx`R=ptDB6A6oTfXDR(t`7N<$!#!*ZjcU0
z2$=%(uD$DKh@e$DbQm#^!%wK_nQ3Re&fmXFM1&R2y&PHK_qs%d<>mQM14v8Mh$D!=
z!*fEGjftO)%SJzvJF3OompL}{V|z|i=K1w!4$uA-8yh<g!O9qDwxJ*k%mgOA97vBY
zrghD(zCQJ<yrC}z&m@InZQjSp4;*1ju&n2EI8v0*CuQVn%wt|x>FpD4Vqy}XbPE`j
z0+OJrqct!%_yx*DX@=Pcg1|T$jooTqpN7B&LFU;oQRX&y?S^^jq6$+at#29!8}0>w
zVPR)iAtewSz3w^j9hR}O(}woLG`{(RS>16=syzwobt8`N@P;8=jTRjl`JynJ(9*;J
ztWNIk$S^~$`FU?&-xa4K4pv){TnFobU~%Y;ob#d$I;XAKOW=JoZ91B-&IhbejS@2~
z@`rtA5OVu*?I2$r>hjcKLMR^!H>{5-lr_72*`&mq&N(OjUSME($gZ=Tv(k5)CldS8
z`~5Xt$1`ot1oGK71~*&M3xzB4OG)Lmwxn23fA1-7y!$lr@NukK<?|QilyF$P4%ev`
z+4s+tcF$Rf6)2~+?DC48!rB>C8*6ECqHZA7`B3tQs;biWkG4bA=(l@Dvf_%03Of0(
zJ95}3`XVcPItyH1SKSoydFxHGjbX3KOH2J@;p63tCm%Rimn_X4JkS%zq}Y?tW%Zgt
zT~U!9`Aj!afNV_qF3<Ucy}iA45S$!GTkF4^+1Q^caz#(YVRPGJS8%N){q8dg8oZI!
z3tCZ=oIE@a0J@CuYsXKYreUBFiCMs&;^3vgm>9SvufA+$4FGg+Oi$1%P|X>u`SIm}
zTZ75z#zYMU6iAg|EJz@H8OaqAFXrt-g~DD(yj@kW*Y$b+JXfuZs6Jl4qMF-VRR`*$
zfg0CIO|@Kf3FfArT~xj6pdjo85Q6D<_7KOmkS7uRj`V);hmZ~EmQWD-fN2ePBG2Eo
z?}vQ)0GNHAJmEtB87@+Vnfcxmh_dDF;^OS6kWt31eH0SnOO`2AQHLKtUQCf~^cC^&
z3|RB)de6WJe1ZCl0{U<Wr2qbKzLkoBPWSK6@wHpM5%T-bkFp){KVC{_{-1x@woP*d
z^Z(i}9yi`3Zs2P`r5I32_RDbDu+N>s#uKzyjM;CGnzS!u^`?5J@4Wk4$5rYEUb?!_
zAP_Zh`eW+98vp@-Yq1C`1bo{+bF6*DbdCHHm04iZs<vsV@<<GEve4_09B7WlYCqOJ
z!$KEau_CylZ*2Tr*QO2Wsn79|B5%YymZ+VQvt~{Aa%TEfPe;kRYF2(m&k(t89sUoM
zmH&WI?=i3Am#}PPVe+BV(9j^^q=Ea`0g|e}uc*+Fsj{!;8*c65`tZ(56_VI7e=dBG
zFU0X4F!@}ALboH-mP;_`4LyBD0a+FwnNT724*b-A&6a~M`!8<Wwu*z?jad8Ixq<e%
zNhk^VnO#twRc%i82Tw2A9*k&WqlvpZ7|GT3_4OIOe<<J=5Kx8=KfajcupLrlUS1v^
z<Sl}1Bz%I?T^}lZ?jMXGcoY$l;@sLFB;S}WU;5Q*|K%$0HtRBEI^GjLmz>BG5DP&v
z5SQt#>AuglUh{hCX7+~Vmun(d5?qi!L^s|aJ%86v=#_6G4}mEH+FYxd$Ok|;2Q9=_
zDJpI)J{4Yld1zQ*#5tg}tZZ)dL6P`R<;7~9tbf9%I>E5UQRuT>My4ZSy87~d40l?E
zb%C}gw3R26`R0w^m#<v;f|6rK!hegTTEBV7W72~ssz0Y6ZlQi}%Eqn*;s#vIvuA|E
zQAD7wu>J7bwX>22bWl2{yzZ>tOb@exS%H8V<wI|9P3}*Ho~zk>La93p9ILdn)WoMY
zP9?Amw(!xjXKU`$=<m~R6%@##*$f@VVeZh)vP>{3RazWGu^NSDa^(KT-4FyBvfgQQ
zLc*gXe3;fKsz1C=p}%$O)*h!}EusX5jA71)b=x+3zU6DNcm~epQDo$50&p_zqtjq@
z1T(>Pz>*V=>A8!wlGY6tfjV?Svk-pp>z4~D#6Xu)hA>A8xqvzN<4d7zhq*6w|5-*J
z3ItM~L+%llXV^9-A}Sg(;K959vVUd0P_0S&C&=t{1loG5Jn}?4SysaCTc^}-Oq04d
z>f`74d;RhwS(A{R@P_+fd;0XLb4x`{O&PMD83`}}yQeno-7m+%X%fQtq#cjz-srE~
zK$M`;b`MaJgn1qIc`Yd(A#gXaXQ$M>w8Oya#X+w{W}Fgb*mP3a#B8^)@OOLRm_d4Z
zdA)vL!@(#mFaLtVSjlhQg(G{S@I@}R8(x@H-MM22PXEazhOA3JH`*?GjcFunkDfSr
z@~FN2K7b7d>>q3xJ^Y)UG~<KU3()qCQd{D{d>VSfB`h?D2ck%a*f9fxjX4pfXqLVX
z*ANeU?6H_WMs@J<i->#}a!CK_1#l;eo?5>6%fD?K{R8d;5OV>Uy<uE{0>(6>cAkkM
zEyy1x*OOplJ=T@K30|3Aed^pS*}4;X;bJx&5D!*wJyiGE-gdBROI9QhT1W`z6yiNj
z!;MEE@ndUmqURAez--7&|8dCPhnIdkko&<dJ5IoIa`5p5lJp7zlY8=lXNFsw=3a+}
zhTcXbV4IZGX2=`IPMunf7m|l<HMax{lELj<nv!0Whs!MZZnXOFG}>!%GuS@it@cbh
zZ$kvtz}=UlHT@DZ`gXbESjT84sD5ou-BR?cmv!hs77$0>$g~T_us+Ln>MdQ_H!!eG
zNQec6e%CFQzTLZf_XzwZVTAI}Kj$O_>myvA@%l9*L8pN93>ifh0W9`5!VkS@+PW^I
zT9Mv6Q7<QrLy?TRp0MeB<CM}khe<aN!}z4^;~OV@nAuf7i0i-k3vwj?g29B~zI~d%
z7w5mTMF-~3<5q>f^K=?&Q0CmYag~7YA^Sc*;Gdj)1DK^!604U@4!ww09l6k99}n%C
z-rHp;;qj>0Pp8qejMV3kwyT6~&WT*2>So#h=v5|78f88i<m(qeVP|*k^hgU&w#EGZ
ztDU(f9Z#z-@4Z~b^`gV~&YgSMrpNZK_?@BL>;+d$6ZGYvjU3v({he`yB8!H)I)76K
z7V%ly$a@4Qg~q<a5KbgM2r78CKX3ZVG~WH*`}gnnS&?+yIcMox{`bnxZm66vFtn&(
zL8no*g_Q!O&=2(6RWOcBSz9|y$AmL>^oAnKShMzpJ8V3{gdcLvUyLM8w+BZ@i<6X&
zGOv2-6sN1JYb=Ckd{YeBdzLK;TSBKW^{p$vF(bpEymPPToFkpW*J(H7gAry~XkB*G
zM~4j9gzmZ4t3;Hbz<yLLr{>z%ugRy#R%I84Yo@a89USVBgM=ln>~2AIZyo9mC?7D{
z#vj5H2aM%W$kPv_qiwGjg0sFgm#7RV=UL>=ms~0?dKt=o_!n{X&gyHqvZy12h>`0&
z7C!2kXMz*i@ZjCny-!blMK=RYP0dPc+aJ9&XDWBLL3L}2j<`ipBQBq`jTKfvv+x1e
z-q<S7j*dY9DLT&Tq~TGoQ8x10Oy`)_Zv#5g54SAU2Y#p2n~OTklQsz$SJcVL@<fsB
z-`1h$yuP821YdiO?e~wf*R#&+rJ>cbI8l-s(LXz!rNVL(OY{YywClMw5blj6xOu+C
z#Un(<KrpT5`qVHNWr>zDL`+N!fy1fpXo&G(Dvi8!f`Bc-g(5LPXCez@aD=pGdC$4b
zOws-O&$4{(5{bDPn{uKUm*>&br@P?h_B=h+3rRZ`+JA#IB*pA`kIpWgQ>Xk%N<|8<
zh|O#h7e52a<`)ygKcxB^e1?@Ufm2^rudNTV23P#W#v}cgWlP%bEZo7nn)7FeSD71@
zeiOc)<{dV_bndk_m^=-vA>OmA*vk;iRslqZGk>nOYk@_e6#9RH6M`w<M7vDz*tFho
zX{Tfy$A^nK5H#08Zhjm#GVKO8rav%w-`}5;3|Qql3<i*4qh|6}68yqCX`@rgMG*8@
ziCCMCY_(|0c666{XrijBN*?r(%BPWY5U?($XM=j}3fL;OZDL}=mu9jSdVMc`%>>$C
zFFKA0IWbNFTO`<E>NySJMH(*c?F!SNIUn-k>rm)95H3GAC6#x!{9AJmZp3Zq{?MDU
zavg_)LIm&Aw!saEKyDUnN5g}xpBS{Q;_R;&;`!S$J^LcL=vkAPN+qqY^PnXP4|-w*
z*LUNHJL{@drO*tOaPHD`BlYQQ+zVRs6}uyJ6gryyU#E26^-J{lOhzk1t!9{FZgY^&
z%AqhstqTNzAUF_t2nN%nGcPm{PctmOD%m_W<!Dg|Q{16n{>&3{;5}?^yUi7pxYN2@
zQN`T!t-qWJjqld-oKQNV<p!ayK^h=HIO}2(-Ji{4is@hUYD(Tf^ulR6ckB`p;=uiS
zmD!C`*wr4$p%{7ST5J{FW|0m$8Y~k&$RynlNK9s7Da_*r8n-yS>4C&|eB>K2$~Wn3
zA6cxiLwnSAl|bImb{F)Om$i)hYlr>LoJDdXSL%LjPfqklBwdMm$Fi?L@b!zG-0L<`
z>FeJ_2|v^j3(rM%CSAHdQoG^u>kFBdO_f7N@iKc?X<+^#t=*o6<*W2~@M;t3GhQyK
zTv_<L&3Yp$v>hx6H-z&+>0)WTwrmQ60|Tly3n<$TX+jr8MmDv#N0gLI%a@5Yyz#@L
z0lo9*{or*Oh@jx$4Q<~s<#inO*|cvNAS}<pK?v`66n1wr!*j`6>G?0g${W7m2z@~!
zQA*b|!r<P3u=E6uw3y6Joms2V?%zvu8QKP}{TqhLG<V#-bBCO3*6<yzCA>_(*FJ)k
zB_(U34h<_PhQ=*Jju_JwC1F#h!spqS*#&+}Cc3G$BtbWXszXNrL}la$9AGE00GxPa
z_MqR`X;uUnMm4r%FCTS8PY+|3?n$Bq1s}J_$>C{oPnRz3dpZvHLr-*j{?v!oD05K~
z&eFDMb|>)(rb5m3$<T*Om0=>&gZFUP{kKZ3BVJj2+}So3s(e4IKN;HW&^3UIi`_Mw
zsB%z5Tl24r6EO%okqw&0vezT32$Aj$XO5HHm9x8CPa=vxRXo7C70XLoCRQ@KndUc<
zSks3{0_-&5jmy0qgdVsCgUvNei$OA--*ie70}t|?0S&&|JssOJiU@o6XE*6*Y{;u(
zRR6uEu;KRsXCNxi3nFIKo6$)wP&LqNVOpnc-k}fHxZw*7rnG*G(VpWBCOB(O($QC`
z289>fmq7Rl0-9<$64meTCLt!%-$*;PS;5SVf}In_#>Nd_UcM9!Y=4!YRnylPZF33`
z`i|NQhj#4~c-&wU<f;JhF#x~BM<WpBd;2yZ9#Gq+`8}Ut7w^dmN?lBqClyF0j8FB4
z36H~71M_?s^}>MqctEhGx>^~53!N{bC&(3rMLHgc4kv$BG~3k^XdtreZ1D|GrDMmW
z^#uVm5oaR2#q{4C(r?jw$uQbNHT+qz=;wEQemRrDR7G8#qs)IZ1A*rDY=jY1UE&_o
zDOsM2!uO|7M{Gx59n{^miMktZ5Z1EGt#X@QM*u|~vMHA~8mL(FTDG5>?moFR@<BIA
zKR^3p|B)j{AfPc2k#g?ZGmBgEu6?;P1)Y4(EX|fDk=`2wf-)jUV#Tpyp!o))TI`L1
zbsNGD2`N@%UFtRGU@sr@x_?pCOR(ShVi8%f3FSY2{4gui3K}bkt1wjrW}KLuOo&GK
znUj;V#B?7R*an%RXKO0wCZ1&UTP|QW5F?p+@XGkzD?}|L<-K^J95+mg&R#sdGx*QS
zB}f<+%(UJ(zHZBwkxZVBx7I2z`C_p{9sRmk&547{IUiC+g%jq8$RBbU=RbJ0lO@Jn
znm!@deMgM=On=Ig&dn>Dbw|@CF3aksY)I|r7COw|=byqSMBYxMBoN*paT`<GAd%1!
zb4bZi4HeGYJQQtBjqNOS&*{}k59_n6>HA%Ln<JxL!}60xDz|gOA`r^1(>r>p9vXUS
z8PB@Q+KgOI6uFG0%N;IAr$U!oL5!o_oGW)w2OuaN=aK>@Jyc5md0JAZ8oHMX*h@!1
zk?}`A-Q>+-hUuP{E;bXBxauKn6j?_=ycZvKZ)8RG$r|DwFJHFKhA7Z4gutl$&VyDn
zHRLEe#0;8dk(Eja&4uSViFJ~e(NZ2)9iknbx<uU@*U8Hl#w-M`>}ad`?mp3XA}5E6
z^pPnS7;O$^N$u-H<)I=@S!E{a#~oxS6v`KnueXr&>=5TgQ2o5<#-Uk2`I;-&Ea244
z^p18%X)23#6ofVXq%T3s`T@HOIOJOE<=mzo@<p=3)5f>VBMa!4X|$43yyvn2V^2<@
zCA_Pus!WQ5t*oj0M5L~y;Z3hvT;=dy)UtnbHz<HtPFOB`A-qs9(o@}de)<Q(4y_h4
zI1RsMI&A3(#Q4MvJuige&Yzr2)P2#PZeUDSW>O@r9LxRIZjUd=8aTRrDabGvxKd*h
zj!=1rjt2rTXWa@gLxD=`KiwVKJcx^%>;6-WFv6+hLiVo7rAuo!Y`6_*Wx_XgVQ_9*
z*<bhNZk@K~hk|^!geqqK_Cp_I=Qr?bSDwwVH?vo)X7W+_*yZw%v%v^LD6{|nmy88k
zpxwwR{2&)PhWWigys%R(&Mj*dIk(ttAG`3zaaTh_!>AZcQ7J;H2|XX)+<YRJEm?2x
zd=v=i_|P4Dy=?2Z0X#BW(`2r89<oTjBXIOiE>H0OV8Js?q>laxwDiegPib#?TTLMy
zhy`_MibN<&o<zQL>c@z|HY~eV`Vo=TzA9}HIE3@o#F%@JBIaaoHILZVknj(<bFu}F
zL16v*wC41D>p}bM8ZYZwSn_!HjfZ-xxkzw&FCgF;`q6p!^2Jyj0UKV9cK<?Xe;gRK
zLmv!1#C5V9{1|Rlx=P1q5p%LBQ@@ZN@T21J-T#=YJ|$CA({!q>in4MNPmrNSUCd31
z;vqyy0`QyL;0P4zOU!h2xykgCq0{>;bl~Y;VXKBLWnQ-}>jUt<<b5V$AQqT)OMKw%
za1BJ$pP?zsSu_z=aqE13lv(urD}}i`puo`0l!|XGAYMjKC{G?_=h{;;_9W7xDLHg%
z!y+!402qK<Auqtwr0P8U%dTC!z$OWA@RiTV%wq*>_Q%K+&WP8|kcqI;I9&`_P+x$N
zFmCmaiZ!YkZ0!ssFjaL_LwVpKMQ-a2iu`z6GI{_>Q)^hb7UJ%Szz(Pf5Z%7O&E|S7
z6-`~o_d%xFXiqU8Fp{W)ownHZ;Z`291rRUUrwmeFn56>CO<V|LfQ9AAZkbPafCk)#
z@Cbz;JFO`d)L=as{^W@tZ0^Z&Z<@(l*ep^KMm9Xncnea8KtGrXjP+)%WU?khp2WSy
zmTW%7^=ma|%a$Fo<|#6Z2Q$g(gxvObqTC#ra|$m$3ZN4W9Q{AI8b6Q({TYZ=TT0j7
zbn0upkdRObIHVt>1X98A_5Fl@3@hs8L4qP`$6XqW-WompI}NST)|1h=XKKua2JDa?
zplh5MfhO`CaG~LQ+_49W;MxdGa{x48<gbYiK}CcU?=U4KrUKDvyjjnd&fC$c7nL7N
z>~PHn9V&PvyRUTuaKVIfc`X8@^|{wB!BZ-}_;^ApStE|g|JZh9a9HN--re^y9!*d;
zY~g?ic>v}V>W4V(9P;nLD35U~z22~8i^yqDdIGtrM?E}b5p785#FvRd!!k6}VJr*e
z3&bt|7jc4~4ym?aE1n~gueoLgBcs61olGDE$Bc~yjs^0LeO$gH8D*Y}zg;crE{c$6
z1e}Nv3NOzyEWV-HvjwX|cELe2BJ-9s9x|;ZX6iKWnJC#^YjpLfiOw`!Bk`uA7vyKo
z%d111EdC(rvSV9pN%@~`Y6N>vpjvbwZ}JM_N@YVjA5Q3#`W`a*w~A3A93tn$5AuU+
zK_D(}*jz+~=`GNfqBpkFm?d&T(#+v6B#A*>kwPSG@Y<=GDMU3|zXQkqoI6aq8^EG`
z51Kg%Cjxlxdm#fiA&m48I|K#EzFx@CnKqwCo|6gKFKr-PCql+)Gys7g>1T$rXzxa0
z=PN@VgHCjLBCxnJj%WN4t&!78S=bw6IAOIH5|$0|E72G1uj=Op_C`I@gU+>4a$Spd
zRl4kE3n7m4Gd4q)gUwE}qXlk1>$048TmyC3two+P0M`^ijCV$R`1kE231K;;@CST1
ze$gR(U`aWlhLh;eeP9&?^nY*{(T13vAKw=3m$}Gv0086nMQvJm@M|-p7s%8L$WhMH
zLYuKE=w0;*sv?*C4Ou{IWO(|+>^gp}qeH}@94{LODX+X&@f3(Y7AA+d_c!mWLV*ZL
zWo2b!9N=81k&R@!2OR@b43HjH>9zq?1)38H_B}Ac{XIfJRZsViscneA9oaUC#(~Fc
zNiXLIX$r+32(i^AofX6@n9;iUaoP&;Bzy`GmoyNIAKn!?G-_@JXdp>eY+;bEX#C%Q
zn*i|LljkGt1vJY;j$b+8(|mB=$kl;81Lt{%z}q7T!N(qOC#{Rqwn`DjQZ_ue4O5w9
zFOhJYLb-&*v|kY1>(MS&3x)DPieDQNSjmLQt%p6h@ShDKGN5(h*$c>Wq2u&a|006Z
z%RnF(xFa<Mv%G}HhkWyMo-GGcga-lY;58Xx)&Q>K5TUcAC@Ed7tgH;Wddy{R%A%A;
zOWOL#9qulkotI4ILlrs`VK>%(fDAGbWWE+4)-K-rFk|Qj;SI9kfsthf5oE4AJp=Vm
z(8m5Fx}`DWL$FH-`e~2Z`MqZ#C4bxxI?m$s1bHF2Qj$V0ES9c6b38CrQrlxRz1(9T
zn!JN5DUwOEI#jVWzR;L1EQd|}{N5`=1Zd5@7B|(j05M|nn4Z#&D!0`T`8bgofCP?;
zu-$rlAXJe$AM%@SbI>cYbmm`Y2SZW7UW4s36G*3%fx+8mGmk}OAjmLDG))FMheQeT
zf=UiQ^7B*uOj05In33dn9B=k`E}vS|t)^6YA=ggeSisi1Nd5hcc)YsIR-y#aZy$tu
z!txAoP{_Oi+Q9ScH2fcuCra3MZv{1|8*)sh;l{L=$}D2Q!rHa2pU}^|@6&z_f@`nI
z3It@HJ9Axy9L9})i)q83_7kvwOa<hGvB>{CjTkb+6mdCzARu9kc%UWas1jW=&L|lz
z#C(+c<Z~h*i`#)8;b_WH6GNdE&8;GxD=u~uTd1F3;;G16km3LH!}(U^e=TtK-=Fb+
d5UTs_C1-zcw}IiS_hc>331u~<G)1FZ{{^LWe0Bf;

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
deleted file mode 100644
index 7a61e3a0e76f8e3ed2475c1e63293fb52847dc0a..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 39352
zcmeFZcTkgC)Gr*t?op1Sa+Id@jsi-PVxffII|QYPNN<J^g6$w6Ei@?slmJqrR4EB!
zqlBWNccsMu0U;2C5V$*>^M3Qr_sx9s-goByaqrw`29kN|-fOS*Tfg<&&rY<7k<Rg>
z=Z``lkmFF@TV@c*fjkI={r%yCV9&=V6R*L?!8`gox4<XZHg7hfz{e3kUE4qigg1)y
zw=ZFK#SH?v0D<1RZt);{aXj=Lr{zxTim{K+53>U)B5VS$@)WGEYwq87=93e|P5Ni$
z*2hO~Z(|+b{unWF)+&4buI#`WNjA2a(=C>No<2*733@kitbUW}?{08dKKW{W+{OZY
zRVZ|>I>W%ohoG#i!?xFy|NHO%THya{f!S|DP^}Yn33^qgL&thJ!3lJ<<H&K=iBoN9
zDbsJpJ-PKb{ykv%&jHp!3xoZ6`&eJVcJ4al{6F6yi6{Tl?iPV3V7J&pSZWA6>p1pr
zhywUHbuuxi2}=}SPqfe?u|aXrFjG=U`ZY)4<LIDA*i3Xp+FAydEG*smpmf9HrkODm
zBg89A(S=$Hd!yG<u?fO>K~L`^b&|bo=zGw6G8DgzX>|v)o6vP3s;FljHV(ZbNoxw1
z@=U{G3cgODgYvNIXce0}ZS*7bfbNzVDG2II%+7K>AY7APKNp{>yt`gbwHGc=UrWaF
zyLB@J7M9D!`S&aUftVaLOtP5qW3-LHKI?IY)SuCvKV?3*4?PE;IijC=L0tAB-7uz?
z@Zbb`Eh4-2)})BuJ+$+1@oQP~v^e^Rg~*h9`Zl*KTlO~@%c)jAM5DD?#3S#_MYAtt
z849~htBDY$LMa&k1MRE*$lF`klc0~l`1p2&KTLWZA$NVjq8nZ(H2<%eJDOx?`IO3p
z<Vng&?}sm2!4I`PmoO6QQTPl?7aD$FD1Cu4uz&O<LNdlYq{01NAL9-ioL%mrd(n2N
z64qE0!kbpDH>VsYnhL45{ys`<c3egBio(*cqSxlv8>oqW%0`>$Ap4kE&PV`dXHLcb
z31H*Bp>T#KY+-*dN=&~0<DguxiqNZAY;Ur!-Hn{&Ak^O-M(OsAI}(8t_-eGpsN3XF
ziv7%zW=64C2g4&O61#$~fL=zqWypG|JisbDkdWeO9Kfa*Q#ViopjU~?yuujtt*sHc
zz#+bDS2bgy4E2*_-7Unyj&)*D0G}I1xEPV$#O_#&lJ>08Asm;>4;<Am5fmRRkrgX$
z=;WF75q!px92AiqvMHYOP;OVE7)K<?3h`bP*O?1fNIue_x@jZw7uEF{k$zbYcZ`wk
zL2Pmw(QPnuSNW6WkPt}Tjex929|=ft%AXnUZF39~r6&~F8ig^Q1wH5^)!r+*u)%y}
zyAjpI((HCNeO#pYt-<@t&NVa>=uNjmySBB{Jm|I8`lC&AwcTlqx0rb}*#asm{<cNb
z7On0tj`GZIiZE)qEM?@_Zq#DxIF}jJK_g`M7}k!FFQdi^ax71H4%)PyH%4YG;9S@|
z{~77XfRB<8yoHD^_W~l}+HI&K@9G*&7uBT0Kp(#!)&+0U68W`QP>DS>ja|7ZUtibj
z-O?x{Ly2|O9^86apwqa${p!6W1ZkgrRfIcor%}hziDu6~jTXh?i}d_eb3XgWA7FLd
z<>(1ufCu&3B$+7cGJGaO*0bbeNRfB#gX=$B$EMyc--k+0%}kyv`lESUXyf~gr_AVX
zj~l~$)whq{!^b$WUL5}*1}U>Fl1}A^ZgmMxUQ4IiqK%#nyQ5D2xo`<;O?=*Y2m%=*
z|H*Qw$aHXhI%4J=rIHz%a4lNK?l0qt&bB+E3p48H>#E15E1B9+-kSlrQJc!BOu_ce
zV~6Z3X^5hZ9!O9QworyK5tpneMBz$DVt8x2{Gbdh)?2QHnJ(-oDlgsfa7z9h1oHZO
zrG6vJhGJD$+dHEcKS{n=tj)_D{16av;Kw{-5n=A0a?f@(o=CTnYia+fbpX+Tbu9Z$
zlh>c)sY~mFiaZE=W}sQ$(D?!JjWWiQqNx_~OCI9b<1%TE-Z>}Q+<RI9-Pdw^w7Ght
z?dM&?@=;iR?HPbxbNc)1P7Z?S&YqpD1xWWk&f28ctKm?c_;I}{lyO~svaqM=k-pqA
z4BE+Wcu;X}`JxPEOxtv{Qpiz;R?rd-?a^(}6p6IcahI9Ser^A?{x7Ke=@9sIu%WAG
zk4tDnz5jVjX5lG@+y%P|>~-{9$Mdf^v}**gs$AS2q>e4F4Uw<hsQK!qx{FXjklYx@
z<&KR)-DA*vdr#7z%WjsIZy3ON8tFZ<{?lRg#2@J?9VbRZ^zX@T6e~?BU!U9j<CFej
z5S&iGE`pvwd^$#g&O{FZH+XZ(ODo{)-JasB_Ydja^HEbD7hbl3qfGq@#<&!uhyl}!
z8bZ;PO%>4wLsAqaXshmdK}v!UZ6h(`(Vm}OfB-CwjB&-Z5@;4zb<+M8R{LJLd}?8b
zUxqQRZLB|sVn(UaC#8y`JMT_OWdw4X4BLnn7Pb<2pwh`=#Dz>H`=n_j`rMVtcS2oB
zYoQrQq;=Ovr1bVqWxSHng!%L$@G>#Q!<xYIc>>}o)tzpW57F_mEww@}O+<RBoA!0p
zxMz;io-cJW4s~=cFV1K~IiPkI<-KpM-3_g~7I92}WVUkIFCly!T~=f=r-XSYz4)`}
z4R&3wH{f|{5C(fzj^^9wSn92x=Ljq#{AkbQ5}7~v7(0TTFV@k56p`J`<U~XCMBtE<
z7D*`m+C~Q=Gi8L2p}sOSIP8);NeCT|KxdO}Dum?AwJOK7A90wi#_m`q1;LJbKU>cl
z<4Tk&mSuUaT>QT`R}^~cK<`gEcN~Go9*o)6$d=}O!6!!H%J}XBMSOB&yp$Q8d}clo
zl7PfG)LJL61!M;>6?)weX$Fa7JCzs2iZNJU;VVWja|UOc{M_SfWG7xlp)D-kN6?1o
zgN;}a_0f9aNuS(7Pzt@^XN@>8B8`x84$6&Sj0yG>%fLUN=No3)*W2Vj<yW+eSL2#>
z9HYl#u61;$<aIuRM_2paVVj-p*grKMzia{Bv>LgVK@l7HQamVmDYt_bbmLKL)=#Qe
zLW2ygtxi|O&%rPW{vl9^SExAMz(iezshzHpV>z=OkM6TK6U;E}4XNJ;J*3+(mais?
z-~l7b+Ds}eK0$&sNwIwEsh=U$LgSgvj4|y?tzp+%R$uvUAQow=5Z;t(k#@QvdM$~l
zjsqu)_$}+?sX9SaFr#+DR|U9%Q#d&9L7l~|+ZhxEJr{|%pr#j^nTBYB-kT<Sp>n-f
zJ1giVJpp9H4fK|9(vAz%qa6`;Qt>vF3=LCx+9X_{J4ZZFC+-Sb)WOU46@0vY9V2`^
zxzB>+0i9v!l9JAOnueDOcf};nU03e$Se)U2mJ1o`U!YE-Wh~kcX%rEqRDSDZ2e}BX
zCsj+vf#+1D1wFwIi_gHs<b(enZ5pFIfVoF3%$^ftwYSeTeUC6j{$Vd8-|R1)q3IzC
zMI{}Pvrau{x?O`cb}PJVW2(}%-HViK(N%1hIrYxg%sRtmrxeSFw$qbH$zc3egUX7Z
z`YjcAT_U%wGj`0HG%VL+Y6;cQ57QypY&Kq+<Up(FmI%sE2IyqadT+QB<}_TpTTf-=
z(WIb(LQKA*u~OH<OUh#nHRw$??Ay*2%cKk^mwapiU{`_ThN;yPxW{{>jx|pIl}*<!
zuNrg?0!d-N%}<;{`GK#C@|AjffX&(;856zd`>gL51oy(3_4U8EopDOTcB6Ar6kwzf
zD7Ua-T2LI8>9`t~W+7B;KbQ5hF%SC?O+YJ82t<u477R3y%qMbsrJx4_B6jZhs+O%4
z{5Vp2Tr}N`xpGaUmoTB8n%=##HJ(=4rmi8Xyr@6>yS1(9#66|OM#E^ldbZO>Y#9}K
z>h4E2N$NR1PctMgy8l{9SC7aG9l$&(T*PeLaWo-iEwA$}S*zf<#0u>!f*a$ExYv=5
z^AMI!skWR84;P|Ly`aA<7NST%VfL-n79>Atu$)b(3VgDbWEJ15&N77Nfq-||T;ZqF
zV@oM1yvZAIFIe5Fd!@NfCfhDKuzTf_YO_@3^3vZu`N%9YJ?|6)6EUe`fAQKDB2NaL
zcgh@2;lWLMi^nd~0uw(HToZN=OENE|Ge4&M_QGcAs~Q{T@lM)%_5=;*b|VbSqVh>m
zr!`EnBspi|tvAw5;g61BLmSoji`395xthw{!dGZw`SDmw8Td`Kid)`N(CB!d9xbue
ze~-sVBv!<PJ8wIY#L@KRa|LG;&lwJylA6mE@OM_z>zoU`HMHUxhpB23^h+lrm#jLd
zx+%!xyH+E|R}zcb*3-;8y#3Jyx(ta;6DO6{g;4vBS3hZ-&xg}HR!}|#xY^3d6Pltb
z5{j9yeLsUHy<WKypRXO)w@Y?re6gW~=+2AEKT;$%SSIzEky6oLzKFNd<18m4_xGx{
zryq&QmE}w+7YdXK0F{OO*4T+TmAJDfBv!XO6Z_gaV2<vmkv1fs3|Y!{nd*3mev(&+
zMmm#a|Hz}S(mJEeRWF_7J{;YzJx3;<xvmONc&28-@PRZm)nA{h-wO3q{B$!g<Eo4d
z_3xBKB=teETW&56DMsXDZokqwuL{9*TAaD6!S*$xc`(~KwMNtZ{IfS$Pd-Y1v)?rp
zqw(mi5sBsS+>SsqLlI}bw4kJqJH4lmtj(z|<~mbRBJ9CMSsON9yN|`Ik3%A#qzp76
zAP{Hc1aY}X^G7ps7s6~Wpx8v*9;#LPxfIv3MJP=)<XV=L{~8!+*e$cLHw~cIOdF;l
zBh*)3mW`G%M8m}-&9@_P_1z7-MOfqVR!1S;?`T4OFh5#B(aD9F=pAD)`>pHtuHgW4
zTu$(V%dmN+!$tW6(Yc@<p|zb&-xN!xc1ZgmCLOGhHQ8DfcD$oTuru?InSCH_qu4$&
zXg5dh4{evSvTz>fNMbZ$zQIfN8?;0&yuC^4d{N!n)=6#>C|N`Z_Xmn?xRyE~a(b$2
z$RA9l9TS3l+xrkXmOFCwsG-`PS?<fDkL5HBQM#mST4xbkx7>ql1<ZN+(t}Dn>tmhP
zydrNRP9cpX^|zur!)xdttT#HIM@T}|$`}X4=X_<VoYV0aMnp1_R;=6A@`N)?Q?2#3
zOx!P4QROrq^Oxbd9xdD-??G=ChbcmF9oIOT+n*bhpDV%~(rdUmr#yDi6OT<7s#$HL
zy%vZawnPWjV~eeJ%B}2Q^<t(>t)RTVO|=TO^Vvf3%o`7}!gANU+0{mJpSi|=8!(%z
z*~Is!<+@df;=Dw+wm<33)W4Y}K7W5)e0@QqcRLLCwm<RQrX+N^-GR7li`ocmciBj~
z{+S=?II!(A-r60F$!`l@d#OM1S#D4}P2l<ijd{AD?^}aJ75yZ29+qR8v;<kqh_+M8
zZY^Jdy}^I)4VJsER`fJxN0bcL&!_o3Z<lY?OBP;!y8kA+J4U=NT_+5oezzTQG~Qx9
z)!@w~DT-kf(nDM>*K^UhIO>xpZFfd3VXohQRpVT;dJ;M4%@`M9<idtCX7UxvZc^y!
zHb*;SOv=AqQ8!8aK(^0Yn81)Z=UU=gynaqi0AIliVakpYbsInmHEdUg^G$p=jc~K!
zi`(}H_>q7V9qz>}Yt0G$%GSS96i9+hpN!&&><7FqO=JN(E=ej@iJ}*H^hEQ7*L^N~
zHza3Gd0mZnssOCyRw-Bi-`ox)p;Dp3?}?p=#Bs8=s%|u9FvBX7pgusgp5QE3z!jS8
zL1iA#JBzu;lZ6UIsF024p;bFcIX3EHtw(1j?=DAb22J?7YM=WOk8Suo0uz$|pfo8#
zhGZzqQn#gfMnbY{d|wg$;Ro`}%N4ENgIsM$?_X07fqbgc`I4|tZr`UbR_Ew<jHQK;
zuCs{NS*0zl8nRPH(pUNi`x@6dW4w%r@v9!n=?v50>!nQ}UA4!a3ac%V_?nr*C2BtT
zl<^^2(T=f5qP6AR*g>}(;tu+6m6^uY4r?)ayV`gGau6!RlmZe(PxQTMy^Qxb<iz4P
z>eGRpWS)6$Z)%pP{J5@LDaKOR@dE9sjtwHADWO79s_LipG35kob+1yKl~nt5+~8S8
zn3_Y<prgqg@epFA!9tr&6!HAJmJ>pQxF$-!alb}5&J2r+J6?$ZmY+@t#;4M2f#u~P
z)ImgC*OJT5`5I|h5^atynFclW93W;VPrK#dEK+Vk8j=OP<Gno^Un8CNty`Zt(!{W=
zgTXm!N%wUt!&%;_seJp85FtAKQv9t)sR!hG?|v#NX}h7-+A!lNOMZXHQcL}NNU-Oq
zjTT$M+eK5j&#}S!19`pWbx8vIKF-PQD-s@<^vSou?{{N+ZN$G8c;?6&(=u9rM%ht_
z46)VM$_dl}V@Fe@Do<6nO%Di(ET67F#2gQ*TfLvYId$QrVgAVPl<Xej$AKNmJGHq!
z-nmj6#q2&cB!?x_<ye_@?V`Y~$^vY{V5#)VjKu1Qo~zw?dx_Qu$pX|MyKQOxB-r7g
z*kKR;>1$Ep?R>irB=NnnGOvc2c;@GfL20^*zT%#Zs>wBH7{qgk7PUIr=2c-lcdEt~
zJ*$%|$f>vRQ%4Y_p+TT|r+(x%%36z{u(FCxT~g;?piBC?Y2~mN4W5KRJa3=43xVk6
z@}AZURqwuaO+bF~ei=0;Tr7kE|D_hN6Y1T9thaDCvKI}t*%)_aYmLyYn}E4$59qny
zn%9?WS=tq=ylXCZBW62%^Vtg|#`RG^>Flm?_A>g&?=N=u$F0#C8g&Up`hC-R2H(F?
zYgI&iT^1sC_b+sq&K!$Xm&-?R-j-FWGjd<^8M@fzzM#GpjFzOF0YX4Gn(@I~zKk)X
z*`Rjjvq%O-ID0<MXv4pgj#hWV)e7B77a)s$lhrU&{!$`m{%Q!GeHJB|cI25Jq6fcF
z1G!z<Iyuk)OLh)xXIw!Wd{;M(APf#Q^JEm@xe|HZ#yQex{-@i*U-sUZ+qCDa@kK%F
zhz?cct*_ldv+Ye9eJL4}TNH(kN*F4dAVMlu9d7!D!`x!r2HK%EkQyUN9*3q+NIz`I
z)@B<mwB5is&{gA3r3gICtW)c7l9qpEtcUWK(^$Y<kdIn>DA)3{My~h%y90-X<>hEs
zGn}Wjsmga&tkN46H$5hwn}<lGHQblA5<2{%?KB&IJN)$Axg)jHw-$B;G9a;o-qr?A
zs!FQ`ogaL?XL%zRMC8e92KA`T@1F>@{3(H7ozNrp8{f$5`!f#kmbPu*!oP1gW2eY)
zK>8F+@|hPh-P-Xn4hcq!L2qLbA?EFTIBWa+scug4({2&UCV6e}Az|9Kck;%H_Cn!I
z!pi9kS=BE@)nARh4ZDKwjmQZ1yS^*&8fp0N9!;iiYA?s^s`xWr2_``bd&$=TKt75w
z;5x19ix)CXr=S96cNR{!heJaeJMYeSphwy|4>ao+GC75==vS3f+R|(-e#xQ<i9zw%
zjAicuyqa_}Hv;?M{(E&y1G9r0ufYieiUQrACE$u7W6Kzs9oxn*{M=6UM!)}XbFW*Y
z+W4mirC?mmQYpMpFB`V5Eqm*Cb+(S_$ia^1*#u*e2bSjDf@fXW3G`=`mi{A|N9*3d
zN*UADsWW{My}xnQZLT^ETMzB?L^zP1%Js}1m+R#_-M&9>L>0U2Ile2|9%fX#)a*CR
zZz7b)hyQtd<8^DQn|7)-`iRo3L_o#jZaJ5yaYzRL7tdVGQsWD%FtW+XJE`+$f0x|D
z$sM_UU3xLuhY<7663}<OBwFv1{j&(I?z;j%$S<h$%}v}*KHPNZokJHnF}q{Z?-oCw
zm=j%9zr8yp*ns-AfS<F;kaZCvdo`Gl76ztokyT|9t4&B<a=n7XFf+&GJKVizQqC!d
zKf(^n#m;?+@ONiO&-)7}$Yt4qvD*+e2LPDQ*u1sC2Dxzi9}7-ut1T^NcJMRci>0^6
zcjqf7z4xc8WtRG6H&B^x^KY8Z>fmc?Jbukb)F+%M6O}Kjc0H@(CB}Kb&CK=F6h;P)
zs#{{7XJU?w&t5y)>-D6*9NIVSCf>)JmNE6}Vdr65mD`-1y0v@6AnFrVqq`yWC{IJX
zm21b-TnADg`p6|;boJ+#1i{m&zI&P?m$)5qYHByvEZB{xRlT1NzSi3UshD)#{biR_
z#?y^VVt75L#msN&rn`pWD||7}wCMozXoA8pI?O(yIbql*H+RToO(jC{SUKb1kwW9A
zYI^v$9ul-Mg=#)r+{n@(!Z66>cc}7iz!aMoN*3O{tYj-%^{i}0?U9Q4;<rJ(YtEq0
zD&H_ceg5s}*z7)Nt4#t|BNkfV)4ZyUy4Z39OcYF>1Ug~f8Z~h(bN#ot8=blNAORTy
z`MDrOtY+6PLST!F+S=X>ZKBLmeel;aPG+VGkBd>Rc`?WVe4pKW{I|=nNmg+Bhex&f
zcjRpspLTXzwo4thAuVc71W?twuFHLp%m1`1$f;1BJKA`MG@P%B*XI6G$sW+$hEzc5
zFQ|uDc&O0QMqw)3Ld(vHm?Z>NjqUATmPoMKC~AN10F$82#;c8}i&b69OBuTpTFjHr
z9HDgt#&?RXC!EPxwu{N;FWNH`%yvUs&AgyBM?z@gMwH<zJ{=V0M=Qe6l3H&RIU2tF
ztsDB1j}iM7%@>y?EN{GPi;hUyEQlS%-4WvStD96b4uA%}79h{+fP^84JH7dez9B^0
z2TA;sr9Pw_AMDugcOrew_%kZO=Vja764x<on{iRTrIl+7;m8RFKHPJhUuI)7KgxDP
z{tO_r4W#mo>lw;&MQxp`+i9KQUyr+;6?*QL@MqvLfvahZ@OF7OHu1O=yGw;-ssjxP
zil1*)F2a#6I`2jVmB-I^1_fY)cOU9xo)|zbI#5E8Tq35qT{)%KChAx0)++WaMCZD!
zp|p5RH_RsKd&TJXj1kBT!voBA83s4>yoVa$s(UUnBD?Si)NS7v<pJcO9t+B5Lhz*N
zoZ9=i@${Z=6SU(+45z@%V1QW0Yg@vR%-F=N>AsVa_N0P==^p&4EvXAt5N}k*dn6(w
z4=KP`xwM=@Q;-U^-Ef)N=0h8eQ)X&-?~JQTw;f?Ikk^`F?azN6BR+pYtxzLNS4ePP
zLJsToN{Z~#2IxK~+>GNT5Dz<h=BO81UdkQ&V}x->=jRCAo;a>%mB|K0o<2vhygB!{
z-m%X79B(<&hxU%Iq)mCHbb@by9)~W`@$N}IlKIqa4iO*nq()6hzC(I&#0lzNy;S&m
zE1NBqON+`cA<v9LR+JL2<vw;<6<W;>Q<K%Q$~@2Kx%~@PniBz?TM+VmV(_&hQ-GWw
z)45G(ww<LXv=3S)1;SGWTDcN8>v!AHKGE&8ywsiW1bVn@fhTtr-rNFN2UWRHQV!%C
zHx6ii&Bg*bLK4>Wz68@vITo{*ox^>ph?_~pXCBE3qBK(AhEHqAeH_ucvww|h&njCc
z4WcYFKHs9k#QyMq-J`By)cP}c+h?`>TH~1v_R;oBlH*m-6khM9R8*<KrFWGzY8T|0
ztyMAE$yP=O#PA!L2L@_ePu=x;{5ov$^M3n|CL>^Ig`POQT+!ZK!?=z?v4>#<t8Fi}
zoJaw(PXRjjs*wF$Y~`CyE){zA@6<E(IUfRY{uakH^m^A(FPHv-gPs0IEqFE{$1m{F
z-{)h^Mo%{95vvIy&UbzifpOVf7Yu~Ho=dUq>?!R+1-EAzK~z%1I1-Q8M9p4v(-zFQ
z5NIP}!rygbM`^rJ{M2DTZgGTucCGrkWWu%mc~xgTQM%o<7VXJb<VAwz1X)muv&nz;
zes=iV*mR_ew?=0{6KE*Wvm@@OtiqU&5zTFM{fm>n4`pobf^7Tr`aONcgsazNX`kbm
zO1ABM`KNc`_3tkxoL2tg&c_(bIi?u0`?XszjyLjz<c`;@NYpl5?qLMZWup9dZxD9X
zDbS>kUa0q+*Ps^GTF&{pSN&jJ67}u&Va>sW;>iWLf0oWxiOYYy3t(6H5X7cWYo9cG
zFyq^jlomm=d=brhu>jO}``xtJ<I(55ZG_}o=gD${9t-s|`U*C-_U;|g!gBnpT=uhb
zsxz@&+{PXma4~=of?VzUpPdhzfr>&f_wm<Dv)|C2hGa(IR+*L~v?kZ~=U*@vX-)T2
zl{MJQDyfjFL5t&YZrU)J6y@D4*Rx)Y76{`6Gd{Mjaj7<%;!?D+Te~wF#_nsg$#P_u
zsLt2@2hdCA+ZP7k%JmkMRdzTDa_+xoi-^B*T)U=)LS8|`9f99Hy7ShKC>3A(sl>Om
ze;`Rtu-1iWgq;`)s^_EVO+DcBBwm&qguU#1<c5=wi)vz)?yr;0o`SCZmCq~c`Kt3q
z%u_jBkNpfmvv}~b$d-fBTa@tS0YSqch{}X?$fwY1y4*g-XF0NGqrscdXPr~%Xk9w~
z%g%?p*B{O5&jp`MgL{u2v%H$Nsnr%M*PJut!~0P$<A988e~w_~bo(vH$im#ZlZCCj
zfA^*wlW{Zy2JhQwOEwyhsBXm9Xe60ZS*C})(-V322Sm4vEQj>&BKaIn%rkbY7#=AY
z-<w0!++<Nwxn<o90=<@XG*aZwnmp^e_o9FZ<heG10J>4gwY#6RUeS44>}{{cp2H_9
z>?z5R$W!TOH1;ryb<O{=o5{)jEU9<w>Bk?D*sBHOthq<v*0)dZXM4!6-*|E;bWikt
za8y^Nx|(~>LJ*1P(&)pQ`JY_>eSd&;#Tm|hpk~_JG`06;7Wclp?*HFxx5+)bIv*rF
zB!pfX%OzW}SiSqUl~1+Wu`%TFy}s8gJ&TKr*0j~t92~592aiz=C466MTvosH@$cix
zZ{e9&y;T%sWtX2?mRijgj1ny7b+$&TJp<vF{^CJxjEC}{#N(mKB8rC|x2(8)dG5LB
zQLm{r*WW{59HuQgUsBq6ogd#I6~d47+??y5Ea**7`y5d%ReOZNS4S9F@^JBm!{M@h
zIN0uzI&v$!!MWv8iIW@S8$aV)Jo<yzd5A>eBFP65iM|4w`ehJ?VwOLB`qYvnz3{>%
zKI59#K$*2!yQ<IFyT=5^QX`*ew%>=WDE^4w-9M38SG_xYdJDS@C9Ny&bK=!bADu0;
zuUmQ(yfDCfHE^y|J{Pf6&p%k<h|mejJSnK%6f|J#q1IpLI~nNFoxOQa!dt&6;+@xU
z<!ryk&Tp5ZA0MMQxK)CFn$-n;{`}b%{9TWtUN53X2S{?YJ?S;pV5V?rY`;M%@fa--
zcYskpEZ~}Vq7U&saK2p$$8Xt+uV(t)6GVCrr|QDy|FbQG1y3J6kg4DqFgjA>#bI1m
zcCWvLsR9}?dO~?7@sTSnY`w;3(8JW!G!-<y=J)r<9CqQqe?)OjAi)TX`?vDOW_N7l
zYtZv~g+KQU2EE!vr2Snvb`{dLUt5!%@uEFhv~H+A(4)&lEG27+%2;k*PQ0h;(@3q=
z7pAPV^5^n2;=Bec(#sv{D-AFGCAB>|xkK-HdW`FYl$6w>wT;b?@8_5^T=CqBOIz|i
z*JzudnYidK#9W3)h40tAmW?X+E|qCuc3ttd93!S5*U_ho&enO=qin7tO>kQL1RfDu
z5X2b%@ZrNf^Fjkpj2?H>*6@R$`b3#6v-dU)L7qD$mZf%#6%FcKR~ENXw$j_%5>#C*
z&U?IJ-Q$PfI~;Pf(v7&8uam%M2cC82ho6$)pkwIx3Q@LMDc+C1*Y#Ob+a+0Iz$oMb
z=U@AdFXqo<f*xg_*!I$c9~&qi++Of8tY7k<{=!@B-jyk6SAF+#jbYZ+(fkeOua6v-
zd)|Xqxbx{*V@3U3W^Vq5B5=50Q?as6!7HD+Z~>CLBVITp^$WLe6Y6W;b&t*$m#1r7
zN9J^xx;>$j$c-|rhkhe(B)>@fwd<{+ud~c1L|dgvYR1yK;y7!KEU_)psuyftXQRO7
zX;sPab!l}xvSZU2vx*^%r<f^b_`$MLQ!ROzq3iSgHy<B7Zs))DO=mK&&s0YZ=HQUc
z9CAsh37d)*ol}}MF4CY^@d`g2Q~+;AJ&!y{H+|@AVhgVqAAeL|@7b>w{`*&zu;;Ju
zE&QLBvaZ1ckkF(1**;r4!DlNiI{4*W+l-Z!l@G9n%!RqN4(*bj*I`1OiNs?i0R@XG
zKK07QyICNBMPdl2HQ|3A*4=C=OYv>doM$)brFxClc-57Mt<QT2QNVaBwMjdwFyH2a
z<aNOkWT2GSHC}^rJHMwBFzgRCSKQJaYXw43w{PDzL4Ww+XLbGhbv>kokB`qgfIYcU
zAAh{R$)VOKk<mmMfEAk+TyAmp+s;-FEK#Py8biD*@W1rMPLC{b8jGDiIu_Jx=zT(M
z?dw8`Me(Y`rN6?<L6Gz)_)cs$T)g==4?cLos#STV&@ju2_KDN@TB*-?!^Z5)OjCAx
z`o@iX?KmH9B|m4bIu426Lc`KwJYpk|L+N|ukKy5AS?dZ1_}c9#-m5b?h$T--Ncgu{
zYiXyZcL8-i<K3l}r33lkEZjhe<-)hUC?7iTSv#Hw?gtRC*|i<rjSix9DNb_ZAx2=}
zmYy5swoS=L4`7~KbRRM~`+32G)6$7SvS64NPpuV1%S>6G+#=oLa&V}*XX&&&fj6!i
zS-%HHJ@*>08-uw`>)Bk17yafX&_EE@Pn*dNB0)X+i_OUo0ivWY43r~s`CGF(H}Tu6
zMAd@;bo)yz5hK*CnjRQ!t_v5anr7f2dgdVvSuQOpDY;lHi`g0K2@zs!85L;I!wg03
zoaC5pU?2uQa%hY-nHL$c-y5u8niv^%$Fh)^C+O?-P=}yzT8{V$c{7R&n%VppMB-@S
zcZkWWFY&yHXF>poe~xZ%Z%+*|TX{pr!WjO|5lkaE03y)?kFO7+o{X>k{N}Q=I9$aD
z|J8D+HSVv=cbr~rlFBuBPI$E1D}DAxf$?Mw+~01*H0p{fiB~m*=S>iW#GWSOIO6~9
zmDV)C+v(j~%Nn#!c|<d<Tb11<hG<_O;FbmAx5oCJ-|edUKr-P^e3kDcK~2=)#jlN@
z-UkjGfa|A8QNqcX(2xiav&}ops9nZ`#XwhCr}b{tWghr!s&!5y06Gn_z*x1XdH%)(
zVi_%Bn7Nj5+2xBN-PklXHg-d4&QFQwaMZ_dD38{@AN9Wt<bp*d`f2-8v{tr;T~q1^
z3jGllB{oP;f!GI@DFlV216-oUIpM!+2AvSZZj7qeFr(Ic)XH~jjY6)zKfH=PaO|uD
zwc5m^a`4s~Ev(<t8e?x^VIh1EHj>+jR%-^nnRx}dwbm?USN-$%biC?PNr8U4Iv-}E
zLlSqt+3&{(zRk_etU@EqmcbgqqT(Aejvq<g-lUZRbgX$O6iginerr8q!Ek9FNm^V_
zzlU0+XBrhYxo#07KzxTS<2PiY%YA{HlsiRi&RTJb8ZD|K=!@L;wfE}<uNq1q4JvoJ
z*fq6U4&Q|c-<#{qh|)C!=#ZvFT_zO}=f1>o_atLE)PH~fAYobZd}#vDs0W7gjoqY?
zRpXxol3w173Px9lZ?0Z#ukOKZnXdpmRfN^vw`F8!XFmd2bMkG#Y{#+C<%x0M(OPdZ
zYI8RI@xc>3zg;mZ!B@4(=M?Umh%!F<r^`6*s0GjW5vf`P0+T+fk0Wf&>&y@0cD;gf
zSTU}4`@@rF^32TQyRG?Be%tE1--H`OmzU~*8_{y%a~WCp2Ff;xAW^8SQF_Tzb+v@u
z9R`<miG}CF&u{rrOJns_yI_DA&A&bzQ(UZ@j3^CR9P-!f$x-vI(h+tF?zbd<{NXnV
zFexZaXdJc6{P>FNy=`L%mA5^+$aUW62|t-#On+*<s$I+%=_$c{eG6vyjX{d$6m_x!
zqgXTDn!sNfNbcSN429{0T<`ZPwZ;(&U%!4m@|4Tzfz$mQi;5$oAp4^HLA(#fvC^sc
zXf1~SL=<C(KI*Gj6!=GR&g40H_iNTQ_xjg0m}};(``8a=JNxeb%ul$d!C0&s)F(CX
ztm7IN263>N5UB_!(KApLq&F*|V`T|LZ#;T(>h#T0B`mKx1a@7~$FE{XSX9cQq8T`d
zVtY!u0gBZlZxAFCKLKxZtOp@=Y;ih_QjlKZHT34kBUUu}%@-FrQTl6LxBhBo1VF@O
ze}^2`jNYOY<(^2wemtSt8T_Nzw#q&3=U6>0n3yiRs}C?fPTah34I}3k^Y?M?!ltGs
zZ|Z2>?(c6p{LD8<2Hk$V<2MnuQ>lfHiHRvGcWk^mNPTQsIB)#9%(g01k95u>d!@rr
zaeOTwj|a&95)70t1>8RU8%5E3Bt;DjRjCl=Rjf?d{AVs$HXpv3mA!MrSKEDG)qzO3
zq7*RGb`iY>LYg@}lWe>*K4FayTmSmK<#Bp$*nDx_74MOtMJ`zQw@?rj%LC>1q1N~9
zAGdt3P!60M8v`_j4KXU5?oe3?0HTgB?B`3DtM`Ze+Mb;h%skGkvev_K`JrZ>4Z5<b
zs`vb6n%=n2whF(dEm~J2>-FkDy2xmP-dX|U2Fwn4LO;NvdK~D*Ma1X<E{8YnfMg~(
z1+)tb&h_M?%*0NIweuC^c3e3rEi3Cwe5DopH??_phrr{aLaqbkq;WEw*44;ZqtH6N
zaCkbZ2oo|gWp1QAI|!iA*G7q_8wADV>tizSoa5}rSuS8Ghol03M%=md*P;wld1}4q
zq4Oc$ps$Yr^V^l*+6qD&p|Xus&G0*BlmtB1%t?SbdrY?Jl3Iv8i*Equ{4pQcqqfn)
zenRPc@hmZ&Uk$aheUCa)GdgFXU5PY!<;@-$^W@}Lh+f~;`a+hj8OWlF^##SS=7?_~
z;P}D-v{tAty_*)XDz{tjIjIdopuf;KH=>(WYRtt*RwxaW{=r!J@bp;nd2g}~v-q_&
zZo(aESLq7vy+zX4oJo#4!^P#nO2yMdZ(TUIM0bF<sRwN=jl;XL6mw59_X)5}dlXRY
zU$P)foj*J|avVbnS{mCN|H+YG`xqcp!qRx7aSFC>BCOP0z*{dZH<%Hu9$6`r6mivb
zwp`Y*mOu5UKJ#^B*+v1&|MwStpV%blHrt_Zyfo>a?=RhTxUHP(C13}#^5m-IZ>a)Q
z1Y%<ee;e?<-4rZQrg5>`C0=>11O#or??^Re(He$a55zU-)6PpTps8L|%K2B13H9~$
zsyV<fQObtPe@bF@lrSOKFlGUXAF<NdbZdr8=Bo1=T+)XE!jJZ9PQB014q*rYo-Sqz
z0<$9>+mR~S%5=W!{64au{oo%D97zknBfBsPo@PuIeckA{hGFa+9C&y@CSF%V(3|GQ
z0(%^}vJ}If02lxa%TZ;$XbRv-t|F+5c#m<(ESi>DmAivew_h9Qs>gy#Wi9xM*T5KW
z)=ri$!BYUL!v~)!yG6FbfOA^V)gTue0m`F8+!B!EIWek{dt5iE-V)%mojqxFD4-#D
zfex!vF;e>RgkODghEp0wXoCM|duK#k&G~;~BBIZ6fI+(IJyI4tl9QFieFBhCz~8;d
z-56ANOD2eda+lV40$&zCIkfM!X_b5Di*IxQk{Y+$(ZU!Sji!hM3B&k{DuL9bO>A)8
z8UeA3ss)9jDLRArU}t;NGU|_W0VACmpfn+82lDL#`iYwdfKLuO9kJG0TrJqYAe*`~
z6gxSq#n{8q)iy!LW>7AMWGVXkyaPk5M^|UOOOu5;5v5U;$Uh*9v7my;y9M~;X<ZUP
zqGUT|JV>TmEMg9*!gK59-&3)>#Ey=mP!e#wp;2+<TKE%?rahVFnR?9PyPzXK6eq(2
zgBf(HsP{zEX5JA_k!>bgU*;TZEC$aj`h+jwHzq~}RG4Yf_9gnH9mevq()rKr%gwul
zTvv64CpTU;fpU%W=+l6h*x0Ia`%7WBPuIbD7y3)dDrQC3A0Jc|D$dKx(|$$O`};o}
zy+2|A3SZDqrs@c9nQ6?+EFe9doN^Y7$JigfciJ~(Q4xI?IE|Qra4vUq_p*3Vwj42H
z3{a`|XoDXnnS*z2(+ya4$U6DTUw<6@sKbw3s|DGpX#hYJ1=PF4Tn>IM!7EN&|1Yx+
zAN=DlzZ*Cwd=7K5s%L9=YXKK^=FAz(w+Z5N?;P0M+Ub`eZ)ET6SBN}iS!9F(++aB1
zJAjRQg?8G&tFH0~_n9V>J2QGcHdkk*F{CobMtV<feT~ofh(Dp$U;tD=i@z`40VtZo
zF9XDfDki9`BRqj^aYmU6#oZpc$dF$wX#YXZMX84jy}9i@8Nr~ogIvrj3GFYno)m#{
zU%l_gn{=`ipt5S2%kPb9&)x}L1y~tS6l!8d3F`}k)@fKkYo&|RU}pc|#;1gc0V_TR
z$#u&#n$VZfAZZ6<xvR7!aq_ZyMD`>gK-S!s?|eE=pUjQeqHOvSHs`ECste|+_mu4N
zsxG7D<B+?|jaCI9Q}hBYAZ&=aF*$h$v`^z*L6<9Pu|GbjqS=)JUfpETMFyTe;<YOZ
z1>K?39v|Y^0oBaaQ9x?yNBR6g4ZRUqWRO8p%$Pn1S&U<0u?1lK>AIu@jhz<*+X<|Q
zmVmZ6fAw!k17kXnh#ean^DdSmq3<#2cV7UDNcV_Of<#Bx$8Ljq`|qud<>nAj$I62-
z*!ta9Y%a^OpX~>nHn*NB?_qQWs<QPnKV6@+F^NLmf0f_9cnv^_fmb&uMv5BZ<uc^l
zI)0%0-uX}GX47?;u2r8F;%5gpAswfhw`_~UHwvQH@=pPp6x>>J7kmuff>w+6)9g)5
z5`#%l(eblKpRR&Ba=l|V9j1N*4fvqaeUO-Q@<W+TMiS>Wwnp|4y~43^#~z*YU@AHm
zPjau+px=w)$1UGtOdF(<2VjKN4oPXDVt~i|yNt=*o!3Ah;0Ep8b^O+g*#`7qRcV+m
zEI3Z!fpi=L8mOK{Ydmi{lmvY5gkjBdKu``f11`xa#9}k?ft=krYK;{_S3F6sw@cQs
zW+*^V)AUL9zWCsG<`b&RO|<70^NTD}3>+m6y*8boVU><8#Sy}wR#sMb!MzzUP-O6)
z68C6Jyck<mSsCIB<fHdqKvH;NF(Lp3SyOd79TqVxd8ryWViOCAd7vr_WmWE4%cT~@
zW)FqRVa*f43V;}eOz#;}W6RcFYhEA2@<D}+hH;B(f&$1<$t1Pr@y5_*XQ0v<ax2{Z
z#l%9BCxD<uCa6q36D(W=WS0(vq0m%oCk<S3%oiMi9xa;l+XQ{pXY9eRsvh;xPtuMe
z3>RF8>(n}RfS=*$)U;EXmv_OMMR9n^D-pnRIcx~waP2LH<}DTp1TWn&UYBcZzjkwh
z8B%IjCzr7f%LVC9TUA0#U~4aH6O%XA{A)lnxq2VyZnKIL4_roj)EQ+1z>#;dCGN5m
zMV3a$Vq3RCDNrg31-r5qEqaP_BSwtSz#py%X3z`lQ6K{s>5(LHO{;Z4nNyIIkT|M4
zSA^qL33_+;$A_nC<MT_sMLos9VHQ?ErW+i8bNgeiAo{GVlS4fyG>Vo%_d_oHDYi6R
zMaH44K^?Gs{*v?iK(qo-brt~PBez%;)Np=Hnv^XXH65o|s|Iq1Q7v%<@cvN8kYP7t
zD9PMd?MgLZ)E+%AF6aM^DFW8<0=j0u@4Fv=heYuNv}ufuoCkh9QtOSF1d!yU0dzdT
zCB6eWpllL!8WFNQfq}6UE5-4k-h^-P%qw2CH_#IRoN|o#-8obLp^=eU-=@=leu<jH
z1|d)#B`NdI2YW>Fo7M~3>8ud4{i)9-Wn%&`IiBO-<LOB_8KY9nmkcwj7dgcm;_AwT
zUP2+Kw{Z!11C)<<*I5h!q}oQ8judf@Q9;!rko61gYVIunlq!k`q(Z5se8~crY>{x~
z4C+Zt@Ms;;oWQK4C8W%+m*TwpOZwwM2^%2*<+%3iJ)|zEnBGdM{Wpv1OU!;=kOg%Y
z!2mtijX`us*Nu_BMBwb2Baq!+U)~(W1P|D9h=%Q;XRTo;AhQPoSqdZ|SIdc5UP-Ha
ztud*dsj2Dd<c3ReSz!3MuJ}%be?bfDtFX$lxI-L#O|24CEiJ7Upg+=WK&_tu*vY4w
zY|RTM*Mfe2J;Y*U7ein`40Q&%UsRU_9tvi1k(2>ek<<+bS`2CexWh%oF~5)O;NQ)l
z9KYQiW-fxcK8OR;eB%wlhdQ$qaT|d9Q0$hk3_8$s!M<Idf)$d!tf6C_k7Kzp;5FMU
z@*KQ6(~gSkVhM4`znB}Kj`Z*zsV<%em~iDMh@yqvVUOJP&=R5(PiH$+6tgxDQcwgP
z(8}FUEN<CK08nAb50nt(Hw{<{7I?`{DXU_v1qE|Hx1!H>z=b~#A7Xnr1z44g6L6LB
zibK?!<4)U3{F+jYsBm&Gj3h(MLCKo}`QcV-D~SFGi&wJJ$B@PNA#2-B<uCxP*FbWP
zaq5#gSDZaLT;*Yn0>X=L8WyYqT+9KAD%TK$l|>kFiWBcxzR=UeN;IJ(o<-72lg+zM
zwChD-7;Io!bct=%T!r(ccV5<FDYP-t70>j+yz7rK?bwY{Tq+in^2Uq(rGwFc!Q7*<
z!cGkcG=n<JEJZS(x7Yy%9L2{>T-FH%Zx>E4W63`rQnr;?+OHoUeYw@cfAh_PWbA7S
z>M|=NgIXslLi*zRyFZ;>6Mh}iuy#Pm5^Z5?W@4sGGJ>LX?|gRny#&e!h6IQzMc;|+
zfgXHiWhIy^X!bR4M-Zj!40wR1M@|ydp`X77Fzp*d2ERsvomaT)Hw$FiZvoxe$~qu{
z)Kciw(FH)L##pco6m-W4pjr5WNe%*zC9ty<t+Mj>%@FE{WdX?F$>*=$f6=G6Mj6P>
z&4mF$PGgd#Cv9Y_1kXnU%BU`;p@DJ%xtFZ|>pi<~o%d)+sXb&1XoE=jr)MVvfmHI?
z1+co18NgH*4zNA+i#vbCiXT3c6zJQ`q#GMwTlqBULPKt^wC=(I$~K38e|X5q0q7Z~
zEJFGMgK;zgG|A1eA!u{;WfWiLxhxM*?c@T6OD+XjsA_>Fst>=peY1CJYHE?yDL`L#
zL0UEntiaRl2FV+sB4CF6{`Dgl=KuMOhYV1beu88-SZ-B5(qM#9L8^mty4@&{Gh}HD
zPW-(x9R)5>^T64>ooWLUKm#YDKDPKNW%nUWLHbT!+JxMR<53P6V`=|4{r&yz1QWrW
zio*jSdkd?Lbd}~BTEG|e%~`CIL|W8O2Si#6{RouS@w}=&2DK9g(Ams3tZTO~C{L^a
zwEJ>Ay`c=8AZ)Xwv|>b3X#~K@aloy&$RNIOjf<7UrNxn&g)47+5F!<v&3&L&9|ZLp
zd=kw6j4O;+g34)iA#xwPm1tH^0>KJx1fq44rBKzgv<PB3C;%riJaRUJ)^>un_GGaI
zRUm^xC1!<5un3@38lT|1_MjWFj34v@38)KrRSYPpo3VgC^a5#DdHT70x31?_(}V4K
z;=a5lF;2lh=$H3Fb@b<@DLl|}HBtnaGRATUBL$0<rps?;oH`Lz<yw(^l>DBZqbaZ}
z^NNm?!#h7`5GQCpD7E1B$G=0%obV0(VBBOtF^9pGK3QHkf8}1#C@A(upYy4O{RDAl
zl@fx0ywk7CEs*Wv*&?YqhOc~XZC)ekTLS^WbDA~T!=cxx_Wit}w#aT#%{PAbwn*uR
zn%}2MKKtpso%<Xsjgr@5vBT)4w9sF+Xgg?wImsGr1y!*Q_l!I`1(XrMhQYLnqZv)q
zCI)xrKi&oSLSr3z1G29baFX0l8b2X)H_BSoT7ei;-lO}Piln61#%t4jb!loXq`jkW
zxBBXq3q(^(6Vl+iUkHDJ;s`zNNV=cARvNsFC18)Bow$0qB_j1@@j<gEb$C`j14x_*
zU77lv%9IEN#3<d}!-LD{#ibv|n>+SLu8LtI(g>%83tqn#dvkxdG9Lk0070QB5Mq{E
zyzA+`)(V#EL~cIS`@)WviQYnBusT9WAqq+^P+g~IgC{YAv*9h4Q+Qm6GcpZJKwIfI
zs3#vksP_SAzmrL5?Je6mmO!yubqylcV@CC$=$o1a1O|S5Y4Y<C$>&gFyur4ySH8wU
zh{gWr*sdma;c=ez4M(~BJRRLFC(;Qje#m)fWalwfv}~<To7!w7qV(@_ZHU`h+7XY5
zOBtIv!W3KR<6beZCkDXmOX)#~^KLJBUbpQnYX%EB%)sJwG1KRf-A(4N_$JfcZ8;iJ
zj{Q5a>~><`&rVgj`IQC*;#OCI!@s-50uO6?IIZ2AXi*@>L=bj#tEB!t(k90CHA7K)
ze&wWRTED>j%9;Z4-``w_d@caNz?Zev_$0*@KL-SqZ5z%0dq^|mCPaniWbWGVJOoWh
z3wnTdQ%F}ZH$yBeJEEQF1;PK`Jn`ai!FVL{JwG~3Kl2WA+=?UsB?{vuJZn3ro@BYP
z5r@J08Vh5Q@8JZj`(?Kr5l@do?tiA&ArQ*pln{3M#iBNYtfcN6Pd=l!pkc)HvoAZ3
zMSZkIcZ*X)_MQ@X)J6ZopTFN#b3a5LGWSc#<`Ck&KyiX<LFdMWJ>Ov)1b+YVPjm9J
zo%aV(+LAH`H<h3|x+=Fw*U=K~2E%XdNM_JjVWj7{D0j8?f%ibSW^Lc)wKel^T|N{<
z84{)-7OeOb68m&^p1<fx?lU2&p=%i?e5}(JX@Dg*P0q9@@0vL{bjo6Wyap*jKa9Ru
z|EhX)pY!BR$oc5MF3N9I=St`|bdcp(F9<m2Dgb%$A((zyuo<Ch4qpjf8j}WM36I4H
z74l~JA|&qC^TcXXige!H1+n5(Y>j#fSda>qodc!c!V+yaSoSKjUb$eLC*|S~Kbzi&
z@mGMnTT6m?8d5wNsV(4cuc&cNhBI=4Vh(3r2O&b&?-kERr0O@G6T5liw&w`WUa#>S
zgbrT#B<4z(CWNNgNty_-J=FA9;>iI`VqIfWtG2sMvwrgNw&T}=g3SJLTy@rCIoD97
zvS>~ZsF}^FT-kjoN^D<+OP^v<x+;#O9rRhfFh^1q`l_hS7ZE6ZxtJ|N@lQ8nz(`U<
z{#if=zYRUg#ijrGbHVu86yYClZd|0jMfapb^w>+M)3RTpBNBs(Szd3aAErsdp|{e_
z8d*&N8*5!%`g5AF+TvRc9nu4+%jdv<y)p06g`o^M9R1}M6K_Xq2eHPH5|o6UEJw1t
z70!Hz|MNx$#ANB@&(5ag)3!>#tTW(w8fiyVa7k;G*vCS6-vVW0cLjx>e?u`cAnt<4
z9ENxO_fhC559ze7iXrLn!ZJbbp=Zv&>rKIW>3!Xusv`3%WIoCVv`^yNv-OPw=pAA4
z#FrueK2THpB#6<+`Fy^H7np|VoLWAP6a};WFaJH9a&v#4yljf=)*GysD{E0mHLu{U
zKmYwT3Qc8Jgbiq^bwe*1+mM9O6E!;oD-yeU?SH&)1D4wc%`yL6f2VzFGe#IANIAB!
zoFzOip0oD?4=~v5{+7<pEbjH+c|x?Mv_5@O_`-6d&;jIK)-PqSHdh{i6%3Ji|IyQ{
zTzb1b6Br%Cuz}zml;XH;8*qgSnwmE*i2S8p`-j)iX&$9VNB8^f(>`tb*X>6~PweoV
zIdrS#(kuT&<qP?XF-m_3Ug*4V_^^Oj%b!P_Z9W>=+_;gpG|6w7A(Oc@M_F2kD9I^;
z;R5IT>XsUomT77Vt@*hPO-oBoj+fl`Rslt>UyRI|(2(xi813Opw+Jm$hku{mYi3!3
zH<9oAg<l!-I^^})Ltn{~{obW8rSH*uM+Y84!opNb(vQ!tKXvC2+|Sx+1CRf{chLD1
z+dr`^a*?%=AH>~%VaI5i8ie~pf@O2x3x28i+-4X`Q+Ds#r$_Q^V-Xg0=f-GZj~RQT
zD5(wUvb$Ch=2H`_iG!6yG;Mv4bYh+S{wSwr)!ETf+QId$4|#j5dfvy-{=C6D2Kk?9
z&3~f<|4T1<>pjf=<|2<3W&&8pM-DY9m&e8G)0+?bzm|PIR+9VI7;WG`TW1?yg(2D>
zT5He>o}M>n5BtY%2EcCHv)flb4EHCO$a2i@t(q<q<G8ptfd7?Y`v2)=&V+4$g7u&s
z{Q|3HkOe424L@13gWb|wubhwH?BQkYRXTk5#^B(`(opN%@*EpS-}-;N%|Hi&=NT-`
zu}2cZkik9o7UT}?!OUNV5IkYYjyWtdxMa<ooG13<tQ4J=U$SlmDZCKr1Y3U1XXjWH
z7QUYGbUD5v0>9<D9@@`da&r)&$vP*}bpKbPzkje%aQ^(-yk%qfu5anCB@!PN5=?3^
z+Ux(NNJv<Hhy@CU8NP45)<Z9Ee8o|m<KSq*46KU!i|=i>{9aQQx%REizk9dk+ZCcU
z`8I4*W-`|fU)F#PyB?esjM+<{&iqI6tb5|;)%pcXTuO3_Wk%e(j4gMoy94*kzsR|7
z?H)sKU8g^;bX0M4-yZJ0xp+8F@o(OKL@9rHg`-;SR-c*Z-t&H+*#DKg$2tqM5BZ_;
z(B2J(xc9Ansc{+pXBqWk+&@2}6A8U<Plp_OC|@E?<lDQ{8Gj_NXd{%!=NyT!Oz`TA
z-$coyG{QhYeg6*ZpqQzacP@m?*>7GVnWlCa^1fxyFM@*SzUPRZSULpZap1F3b05C>
zW_9OP*l1-G7`7LzC~UPxS$Xq*T`&a(4m`BR207srW#PCHUq^k181o*kdUHp8C&qds
z&N#eeZdYv|#2L==&h%1ij``s9O$Pls6B7A&=%gaSbg+>~A@V&w6Zmam(xHmALPwZW
z)Aw5FJ@U=tll7g~Swjb*YeVoWO*NQZ+!K3x?cTeS&KVVN7x!!d8Oq)dUTp`P!41~$
zMS$QJ%HF4jpFXrf%ilYLW!a*Qw;}2ZdzXICyR9hAIuDZkhCK|4fq84Z=PQc;>mS#@
z$M%zgfnl!Ve61bBBAGHhNqaQo{lV{t<0*Kzm+H4|Z|+@OrnqlyZKB2`x3XG%Z^u9e
zfI_lNU#QK<>Af8b2Eg%;am~x8UD><$`!L#{A1KHgl|Uz$a^B<*_OQLv42&Rnb*C$D
z2I&v+dpks?5C22L{yRXm2N1U%nlJy4^4>fwsr~I6)vVn8EZx;EE2r9_W;s-rrsmM3
zkyFmIRHo)!nvy7BqqQ|HG$%wYD-E1YO%bq3LsLveQ&bvM6h#U}1VqjP_ujw#oO9mu
zp67k9=Q`)SXZ_LDwfL^@c;ENua}Ta%>8X04NGjJ=vcCXl#&8YZFdN_V1tA%u<&TYk
zebjiNkF8gnjCHN`>&{+3DWy1N`(+P>UFlje4S8#Zq^-3kcHNRr?cZDcf7tW?@7$=@
zOOP?T<$_H%o{&$$f21p^bSorrp{Kc@nRhzWZ$08ZWAdrS5`~bdN?diR|Jwl8P@Z=*
z@Uo>$e;WZyhb;VB)=Y6X@LK@nsf8m^2*``&7el5+10>w6FUA-!$<ypoY}n|cwHmY)
zlWnfBZz6?_h=PbekouQM+Q3#yy1l{NeiP=`;3Wg+7a?SHUvD&SbkaULpPXgBcHaaV
zve-kF1zvh(HU8J$pZrU_|NC1lS>giIiP|48dK0K9wFOm`tGjWmHPl^m-Yflt@A(_1
z<E$LnYvnJqzbUP5^PoZb%eC^|Qo?x78BhoK6HMDB)w_=DiJ>f6xKyRIzWybm=jG=G
zjZc~`X|Nloj*G}d(aH}=k2C>3p^Q^8GQ0B~ijDD3c#s%xmd-wMerbgg^j=xrHt$fj
zd>M#m3)=XHCE}H2Zx3jUEv^tZ>j}r2mJ3dE#ewpslAo&Xtt%R!Bw*1Vq{HGR6>4(c
znjXo08FZQq4DRP7ijJLT-UzxrFT^oill89XOMXlGDzW+gv1hn_d`2x+0cU0qwp04U
zC*X$|H*Q%FzDQi%be@~WJVkwIn=f_a&ix`g&++pdA;Ns?2(YW%DJGH6ldjrmzbqs4
zv%fj13Cvw!dQ(-f<sGV$_xZ_uvHGRRqvI5H1g8<U&~jWl#)dFjjr<qdOsT&&foKXR
z*Glfn%m8MCDR8d+6}IJQwSd|>lS7(V5?TKfm{lQu(94uC1VdBuK#R2zbNyttk(vIR
z+#YnV3oan<cDKAh@HU>3UPfP(HaVC&BrAHr+(>0$>1F;sGrz2B#k<r6`jcYR#Iqi2
zx%`R0IFJe<fd7ig!y33(zL-7wnttRnp$^uzBvt*Y8tdC{8_Ej&;Q?QVZsy&7S{SzQ
zD?UJ8v|=rdlAL7c+prJ-okmJvy>Fmx?v!%kSy3f3Qcf)0Z<&3eSAR_m<RY=`s5sR&
zug)6`oOuz9wltK1n^syDO6Dq4cGpR1*D!|S5$qwZYl3q%(B`F8Oj9Zl**A8jlr7gc
zg}#Z;8ee=Zno*aUFQZ#Ja+A1-%4MS96EhgA5fbOyc5jZ&Hb%nhVAkAkOvCB@12Dd4
z><0-z0hedgGy%O3*12jT18c+PjMQQ2SpCp?6+vof2TW1gXUmc4YB|`aT~q>2+^2hP
zGz0IBbP>gHZ&FDPmP1WPNP}rV<gPI99+P(Xyxw1S{vI^|jgf;->eC4X0XOLS9Jo{6
zVKHNl9qH$H7SkObJa2s!5j)gO^oyO6UBDl;S+VXaEtxi#eQRN!-0RHURF|Rc7RX|+
z$;1q1Bo$(;;7-A*yNr~<#EGna{?L7<o&tAW$BsX-`@UjPgKNVJ|FZx~iLdqh=#gN<
zm7~_-AUKP<WDAW-^;bXm$?}xOg8zU*gQdHVq=nSe_#SzzQ2(o5+!<y%)^X+pCEcKJ
zz^rI0YkK&$K#>a350j!{68F&Fq-9?^LjNZ|PYv_)Ir9(EWaD?MWbcj2`h5urKYo{N
zLCf=lf?c<=jcKf<C1R}n-15)|n<!rvj+#3sXqOc!YIIAf$hqj^Rg<L30w+SXh{kHx
zuH61PEcZV^o8jIh;`E9z%7kwk@LZ5?uuQl^ahz3DdBOflg9`uVRw1ISX|#FyW^4XK
zY4D)sU)#1=(JNrH&C8GAuh?s;F%#SbY=css^e~e&Rbp4SDe3ni>y&te^N$f<ioB%V
zdddRRNzm`p7zHY_VjnN;lp=SM{Y6(9IOjN=Y%vU-Iu2+4vvt5$CQEpko^~5ZSO!Uz
z{@j{W*e_ohSoFu~x7d#UYxG?TTo3uqH`fMmL<!0!@|NIz>40BaWoP4Vc~*@$bIj9A
zqihsEvrFcjm-U)%cA2_qWt0~N-ztnGo)wQ2d2M6IQ{MuJ5&<BJjOQIlMM3?{F3%7T
z*ETn0gM?p!TY#oP8Vr+`-!mVEHM7YITouWPj6*_5o&>P*fZei`GJjnf7Z)iuZ;R{c
zO!HsREz21o@-yJyYxXIH#Em_b*^zX!r&LBj!CB@FSUM4!1$6q?f&`Crzs04RW3~H7
zX%m$jyCSz*A>n5wQ)#lXTzsk?GohC)fuJ$AFok&K)1wY`Ihm}`K9gn$wt)D9A4@gF
z1wd;Tny8g-QU0$M@|lMJ-l`#4zgigqf;u%vam0}Hu>dUki_pwg{4h_cv&()(N;Yq=
zlWPhJUQH*!zUmL8M=iKc3VE!rb17JS7h7tI&TZbl-O9ty-~730oq)hZjg&QBTprP(
zI=a(Uk4ig{*>`SiqiTwUr}>p-)0(FKC4mPGzGsF1X&Ji75GGN#x};JdkNISCS^7UK
z&7>7K^!LBKmjl%8-EAHXWs%NmL_l^f=UrJ5oTy^C$pjBdrNa<KxTafr5B=`L?jwk5
zSd37^LOO%0dw|u2<CqP~7RU-gCqVWGuxdA#SE!rHJ7C*?Dsb^wLu`jUcV$?)9e|vQ
zwlWLtR$^k8yHx<O4u8DdT~r_)Bm5j7u#N_O9vhhJBsW^t8Ue~#sK9l)30nd2>-Yav
z1gSY&c%W2U`h?17U>tL*24!08uXdnR>M6^p^p(mqF-x!>9>!&(J{Y4a<CV4_>yN?x
z53pm$mgdM*A<n>qU7_z4P$>UzJ=$U=z8w1@vI0`lL@y#q<-cX^LC&9GzlgMEC0W4E
zYPk2_#MSPYyZOFzf<2(tt)QsogHv{I06f03GTS^6g~^kMv5aD<rHeTXa35;oTn$S7
zUjmw82`n&5k@+@M@S3TAGeO<*l}316iF>-Qgj3mofb%)mSl~R82t3r229|-J-M+=D
zwo!C7)}dA{N1l0)`WA~+mkc;iYPxi%Etazx87K8Oc*aQDsY+ywiMN7G(m39lngUq<
zkuN_^NZ8ld_iW$(06O<E2ic%{RpN_mq2lGarZHzVj_iprpQ%c-;6DvafBgr)J)U8Z
z0I>DNtqmp<rIE0)^AhhQ`s3pZA8t}z#hem%rSe%PjJsb9LR5PDuraP;S!d>LXIIjS
zt;pD>W~Jdnc-h3>(LY)TBR3<?afe>7Ab-`#-4tJwib02o_dodG2xZ7X?)!LAb7)7)
zpVq1d(E6+(vz+u?B;o|%o|$%bDUD+G;YujeXtxrjJgH_RZP^c)P!}_~F`k=JslNAH
zII`}n_{TqOn2mm)V`+u=cX**^Dck7MR_Wopw*Z1tpuiQz@gv*(XYp4bFOMGkb7HIN
zH!$b?6u5!H!S6q0V^r7A_I1e{5o2<1N*|r+1RkAct$)y9l+g0UTebcyGQ3+CJ>r8q
zF~Q9u=t-R$&*x4ymvv{~na5?b>)GQ(AKgCownHm%@qOo9WkjvF1<QbC1*E%D<)8Qe
zxGjL<inm&@L*(aQP&2mn|57B~OQVNQ!B$A3z<}Bd_|QCeC9K7(WD@@Y(^0ts;|7oD
z*2PT#B*9v=!YB8?aFV?WSR@{H)_J7Hv{K+3`|eGiu^TUZq38{c@Jt}O9#fW=UN`sn
zx^z+U_aEXzD#N$xTSk2yx`HojbfoXFD0CTVXbSxzjflyVB|S7N!c~+ah^BbK3uZ8G
zdZuPVg{on!fTd_G@jOQOEWX`LooVd5ozfm%RMe~>EC?DSFM7%R5yUtSDIPSTpt)rX
za}$zjgg?EaX<kO?SFqb$(mZLyIdk<(P%mySvsW$gYfNX?E8@_<vA^6#240mga2(#=
z^IY1yXRyRE#h<004{?#+D60kbhh9H)u!xc%Kks=MK<i-LFu-R4`sM;=1K|;%o@)I2
zJO9Iw@~->=55-C7Vv^LCmB|3iPA#-@$LBC%i@&t93_#7qC&SDf1wWWZ)BtQTV*pVC
zqZgUvwAM3Uw&rg~3@52ivxcbP4)q(4Vo!0eFCCE_`;Q0!;3_}2aY?SCNbW5r-GhQ9
z9=Wu&H37lRxS3F3`ikya@6K>DmY9Z@!ETOHpGigr7vu%7A}3K|ERIQ};&B-&G1dpa
z6RHOlC(GqvBTonQCn<`Pb?S<hk$qWuYO!-@y@ajqA0K7=_5>D+5(jqyFN2k-^65A#
z2eaXhF^8G&n7gPQ*+-NT7XDa(XA2AR{mfr%sQ)h7k*wasvk*#S40E@kMEo3*5ibEW
zAQ%~V|41*-YN_M6sQa0Hb@lLzrn$d}a8@G#P&QY5S;8!%_pO|M=7V@b;NR*{1GUnq
zIjhn5r{ljZa!__2vz5uyi;y}4GIxA}WJG?{eaaRj7&u0{!TYZRr)60&)O07-2RrQ!
zNNM9`5r@gY8RT`iO=-4a#&25MF6<y+@CiijQ3m{qTWlfIR?<KtOf0UID#hojtVZ<0
zH)aD)#TAKvXCi$jr=?V_yTr)MKR{2vlIROBA389}j{1$^K~t(~d$D`1^vRqdK#AYp
z6ogwdJW4vJX>Qr-=SUqfPo)g1O7>dO$owdPD^+E?q<!|IfnWyPyVTL^pTUe6Wu}?#
z$|93uwEze2wgPwizxgV_lQ3Aln=lsHOncVyQKfru*YrO5d9y?-$#Kafkpb|cd#fER
z9VoC<yX-jSCYKPQ@p;ARI{3FU3NS&-mxGGqW*(%Mmqigk*?qc)85Zr%tDLQ?nt(D^
zHtO?9K=P1Yn6sjEKBK`y3E;%&I!Z-arGJm^U4$=Jo@yiCDaA~HJOZ$Lo+`u^<6_Wj
zI`9gGm2pm75!KSw##QX8X7W!9d7fH5Cn&+~>%UBKo2xslP=QSBUdh#!N!*`w-9GUv
zB_5!M;_iQ%T*Ix*4&GPdK4m8UpLW_mG3P5We>`#VIQ+xk&1u9r)ZuuWSW-N^kZCY*
zjc&e`*{~t+_Rm>gk!t{=>Ii&!_Hl+}!>S}31{vy-5v1dMTp<EQo-ZCoe#6LC0$LY*
zo;jAW8*}KDG$m4X0oV?MN<@iih3Rv_Wo{{xOWlI!qv;S)I`cEX&cFj{DpAV|ToMk@
zF!LvaB(eNjAaVy>?h+2cVOCJ%JG-KZ4qQtdi;Bl0U&Og;dq9~!>CvySO+K-M65d+d
ziP*pGt5N5wN!(hllm)p<xJD4ZAj*qS(ZpREI-G~mNMx7~#~h0l^H<_o;lJ-z-`g-5
zKn<Z1@-PYnykCWfTV;5?!ot`siev&Z=$;R1-5xY2Lg2FCC-VbyGSrq^$DE#%Mg?TT
zFSxAkPKi6DkWaGsNta6Vx%a6SH>BgsV5j1&3cbx=Hrdn~KCUz&A^P6rq1}?Zrj;0;
zQZANcA9z&;9$uTQGz#4i#`Y#$^tY^nvA+C?x=$Y3Tvi<j*PgqunE2DbpHw6G){OA4
zP5y0#aDh|*Rn%~Z_B!$7(wqub#ckk4#pk!?9`25X1I9Q9jej5+yd-Yz2R2Uqtl!H!
z!sXD%%U}P-SKw^YeM<|^`qi}xEvnDb0q~NpTPjoL%1j=MGm=J-cq1ahzq$0kib!FP
ziQ$$Bsut%~Sf#s!RZc5#`vE7Y>b4-@pKa-lXEpF0P#W)Lxp6*_uakZwAeyeXl?h${
z59)yUejF|a9xqX54s7`6{rAU3c&d{^A@8M}ppOYw2>Ub2g#G}GIY&k24z#(l%N+*a
z4n<Jk;&=wWW;nsn-$D)0IxHlNVbax5)`he1QLC<+u%`x6JBIjGZyH=lmY4rrK+6Xi
zK|8a6I=8h#t)U}N_|ouNg=tAsR4lb+#h7`@?v|Ux?!y*VI+PVS!Q+j?h@W(?Sv-R+
zEv<l;QEtR$u4sT6GB@zGv+Fj|x;CZ&!<D*;mwyG^L=C5i+=1*F&PX6Nc&$j9YmhOz
z3+V29AoK<67~NWP-mmt6O}#1ylf5E*uYm<(SErx{qm0ZP0-vIlfXFv+mE`~r-KIh*
z(|XjjV{oqxkjS_=UcW3sF=Bykrc@}wza;y}cMt#N22e0a*jY#SwwOE-UftviS2$Nt
z=u7%3k=3L?U{a*$$Hakul6(@*->g!c5O+)}#H#avgt#eHjJm4%U_3_fSAy-N_p4r~
z{_8H}0J{#p%x|TlaX2i`L!)qjPQEGMDQ(b#0xSgbmFY#}fd=;m3`sI?nIm^okN&9~
z%Y{y6Md|8&D}J*51%QWj9)n9;eFiOPCoNaUsgL~BmJ43ds?CFiGJ;dA#4le8R$ba9
z)xMTvk$oFg@!qzl|Macm?z5Yvj8A3(^H<*CPs)~a>;K}02kk4>iJBFtw4zs)^=e;4
z+HN#x419V=|4f&S(&Yj-Xn3qO*N<9)GxmUd4(#k_Y6E}7F>(zNk-#0G8e$tN+KQxe
zmuFhZO)9J{ZyX95ol0=$=8yOyFK`p)1l`OC>J&ictYb<1X-BM0!@^mqc|511`nuWz
zQ8AKCaTC|Uz5uZWqIu#d)zOt?aY<j&XA*e^U>r!m-&1tAaijPwE>#DepHB<I7dm47
zeMg9b7yP`2!$2^Txf@Kx35uB(a}<)Zd4u5Ft(cWFd>~qfVjoHTqmy-vH5`J7j7?^8
z_zZ@z0$Low&AWY=S5e_R`SVjyTU0a7>#Sd`U#~%4c&K!2xiKKQV1DT_;<(vc1|a<6
z{gsYrk~k5dq!1SNb{&2}%Kbe}(t2`6BP8}>LKp+(FYywl>aK|+c&|d6H{;|b;cPP?
zoDDc+l3qQ}1B`Lx;^SpOB#{)JO`(6bt$bf+Jj%YeFk*<4M6_ci@dtniy$A9m&{MIC
z&7(atUvH+=&rHl!gvB*1VC6I-I)QNDQ%Pu>fM(sW_(v|bg!eek;>eHB%sG4_lI3T?
zj_jQ6uLw>OUT0cvV8+JP%D@INbV>g$THklw>P=uA7qR2!MDRHNQVU@o)^c1lD=Luk
z)pKf+1Pw!htKr_@znF+)-kuYXI_b#DkwSLjO}@w@fm<pol~5qlS1{^>bL^bnou?N3
z^Aqz16M_vpOzTW^kL4$J+TcoDq4^{rXXmMA&W^dleIA=DwHoIvP2^xO8cyiJ4h#N*
zoV(4g#Hzddd7C%ELSip*eG;blKZ2BuS3DW7IIGHZo=1tRxC%X8VniG91@e~43q1@@
z5W|Xe{zsx@Q8I4_WeEri*OAAj&zmB-kC-WVFJ3-#91tleF0IVMlkf}{QHxm#Qtxw=
zAuQ_`kQOFfSbqn|8GnV){}ajazv34EC6BD+mXFrMyd^9oXc_TVRtn%rTeOmb^dkL6
zu3n}T%k!kt?w%@>S9i^sTNXN<<S%Z7*~Py8XXf1u>!`%!)~%SDq%DlUQW$?{=>GdD
zjQ^4r(!9vz5Oo7qj~a}v)P=4TKfnXz=R*gb&0h&l$6nzYQ2BF$CFXL6iqzztqf6Ao
zlPlm)$0v9-)Cp3U6zpc`oQO=F#ws)D(ng<UR_FHOV<curF2RfUk$DX7YgQ>>_*IzK
zz}l;(q#~pBJ0QK^s<$I+lMfqLAg+9!Goe1DmgCqj(n<^PKSYMu0QoMdM(ocA=r_-^
z)RLHdSP4=J@i8X~THn;!;>4bW(62)$?$}8!`u>eT4l)t|pQhx)mQ`oP9DkU3#N*}1
zf9ussLiog1CIDeT42^Xh{@Nb~zxtT?_WJs|^a^UYHt#M_M{wXj)DiRwPsgI;;o)5V
zNSR6EW8wt^LWOr}WlT~GE5))50Z2jb+P|d$21AYj1QkIooDJ$U!?Do=$?o*w6P52r
z`jj{1Mn?F%`qfIz2hUa8U0Lz(9OL{vkWuC|Ti{{#T&`b#^DB((d^wIc`*=VS?6leN
zYHd^=$@&N$=FFu6*<#>I+5#lPdVvu5&v-b2*-8C`FN;6LCC9_#@Rl0!w%nQ9l*%1P
zc1xPJTm~#3s}ynh>)cdl8s*xB*pEv?TRPSEF6J^*nUAO&w$3&pxTqkTYkj(9{uH&9
z0H4qf$it38%%i`~J*36}(J_rO1m5e_61wxvWbz?}VuN!17Zx7o7ZB_%R6iVR`oIzC
zSj*9C=LP(XAhT?Ui(lAR0h9v4$1Lg>T6&F?VE#-3l<Ark4Gf6O({No_BH%y3Uk+AG
zqCGV7&aGh2*O?@LBprxUyk@vJloA_EpP9aBnphI1<lXL4c4gy;FEhbX`=<z$D8$FP
zY80#>y^-3Vk<I<<B&kB+%J|hvs-OQ)-M9x73ngv%DodqE(8#Bgp1%EWGy+ndU#V~a
zch3JCcYs;~cLPZhn)`}rgH`$uN$3zRaxJ$>yaaCp(xbrD@(1VtEfqYHA|(KnLZ_P%
zUl0CSli{&ylDcpMh!p4nQB2@6`t9$-F#`<9O}SUlpYk`p<5)oL$)Mb%)NJh60TkHc
zQ&+$t#djspn23%fqU2GTjLWN~3N9^C5cxX!y-eo~10lPMbi~RJE$y3toNWqq16u9v
zPcy1Xx%p{gGo}rizaR1PFwGg$m!mc}uze*f1K6hSUbAYL1Gknshi!AWbcJ8dJ;5rD
z!M1If<&XTkT4*Kr>hDQ-F3r8-fI$DgY~!IkkzZ`O#Lf>y)hh^W|DCENAVytAng#cn
zFXnhih<T|g^j=K)`xFL{Q>Z9#g-1$q3IM%01;kmh>+nwm<IH~Qn$A^IJY%yb->Wpc
zq@+e->P)(D4k7iMo0w(HH_Wb%9V@Kh5RixUbu-5S!60N=q$i;m<`g@&i6P~W+m6c+
z5`mC&rMgE%rMe%ysVQn%%4?H0*tIn7s0U*Ak<J7{lRNhgP(zd~edfrozzIb=#qyYK
zdFE9>d>WYB+oAdy*+nCug3Z0M(gDI*@ymojwNVqXPxqGeAU{7)eJ$0#7SMkQDE;5>
z^a7AkK6m1kAGfr1>D(0_UwBwjd;?tWITAQNBQ4zd%YE4f6mtAjpweA|>My}Vz~}ng
z-<LFAJQ>?n<gaa{P*kbfCk@YdI;kI*Zv212GJl90po~v7ezBMq5|5B=qjh}e`g^i(
zcHUiGT$R+if7_Ogr?j?v_uN=t-W_^bMrHMuyqzapFKLC`T6_Ij()tZAllH9YIhV42
zSB}-rC+mJaeq8Icx<Pz%GYvU|tq|=!?13?L;~^}<FtLs!Lv=h0Hxkx`;US!nld;+j
zS_vm#y0v=6Lf(I{qMk(?H?7$HL&sO`knCZS_30Jc*?re90-E$f&8eInQZB8olL^=+
zOYpVK8AEFE-BF#OU`_rA$i~sBZ40<!c12|N*{*`cBecoNsG|qS_GV%;9kK$-7d58r
z&6IW_nMn5c1K_T0SH!!(qo^_N&pgSfGCsF12G2!47^|bQ6f%~sw(ptzo^e#49EcGX
zJQyqg*|gFq>16Pn`h(Hiat7dBc|nv5s*8S_GXZKi!y!84+B{3WvoIuW7-o;Ejkko9
zTWXTs<l1NKIbT2qQ^Sa>XD$p)OM#(GZ(Y~KNU^v0$K7Z&xFBmR-L%2cp3?+U*}x6k
zL2-!oJEOtyL)5x3%CiTg8dbDmQ+FpmqHY<puHAAsd7s&|<7rMwh~+xMCo7GiQ7y^1
zMdu}LBHKAYmw5-**f>9DT>}r<f3~nBcV%A7h4R@LQv*+bX&FV5(P=AVHkvBfwb>%b
zko-NPxgkbqoY8#lIy~?F@6YL<ePNXnbxZ1(v{Rr8?mxdad>xd2n)9iP6|5ES&>SQg
zvL)}q$MDf$EZ4`L|5*f9&l&sJ#bP|SyDe4KDh{X!&3h3lvI)AjR0p0#k2Sx)DmAI+
zOn_gY?sdz(hW*vrWOGy^5O0%oD5Kj}QEyhYA<>Zh0>g#J^v$tAP<zgNqa+^$1mji%
zOQhLxver;zL)zc}A|1xPKu5{~Wd1Ya^~<_Tk`85d>{_Y(`yZ0g|8^(ufK{}3zY0Xj
z2LtKprOS-U&ytf*(sS7K0K5c7*%-X)!t1`WkWAs+`_op2_Y6_K`4<_hm`zYX`V91n
zq@?8BZ7}c?<!aWcYawS=!PqyLQu%Su{PXW1y8qiV_)n<X|5PV4p?JBAyUCBiijFmg
z<XA8fJ(dST>n*HAl=U}tcQia4n*}4Z$h%Sbhn~!kK=B?ZB@o{gC3l~vC|&<CKr5$T
zgO?AH^|W=xCotnCVEr?kJ`g+g;U`IhmL2=$=cB_xz;{Y`LDx8ZEGYZxvSPF^szEzG
ztT}R}$%7BRY09AZ#8{zJqx8XPcJXRyXQETO=xP{oVa}Mbi?t=x+?1?gJQ^T*B*_E}
zi1s3gQWj}x9QfP3Eh<0bqGN^Sv(_Mn7;>6Z<G>Gg<B8o-oAaimhtYCDq6=WE0GF}U
z02;9OYP$|Hu;cVFk))%+D_n4grtC;_VR?U{Ww4)ts<VM~YVCv6IQjbPc-Luqer-k6
zh8GPX>9(9yU4WaRq06J;vax!d*TZ<@h9&03tjMYh6*N3_DgwcKlk)EOfm$*4{6J+y
z*P~#J-qywfw%?WU9@^4GvPE8pMeIZmEwIi{SZ<p=Ix$swbYEp+uX6UNK8~f5od|SM
zurP(}{AzapaB&H;ixL>WXqk^W5f}cl&lGowe!c$5!+Kx`q9CZ<rqgCF&wF#+M0(%y
zOqjX-WbkbB7t?I9eYnv8$i$KyvEaXWKMVinUp}p$CI(P5gTo~8FV!D@>A3rT4&@6S
zIXp1)Tgv+C+%a3*ON?RAAd*`!LxNE@<ppeD*>a+Qn<?OEa^S!isw0cP1#>vsOHtjf
zFVjGaPgWo$ur}pgu-BXRs4>to`MP9ov3YyDK|L+C@JSwT7No=~qaP+SCQ7H@fr6p^
z^un=YF@1;ctAUA@n%9ZHpr=$1Ror{~{Ym<I9svX)N3Y$giWz&Q`SLAnC}Rv~8#+A!
zPV;j1DJ$HU;rsGweVSu*A8P1=$QOlL^tnURGPw$ls74SAE^aT`7XfNKu@ySp+CRtk
z>N;2Nr&ImQ&G6@jrO#MtE$*CmnZ^S_D2mpgd4PE`h=0*-AVaywOme_7{r!)@3(vEU
zSQwHq&&$52fi2vq)dqpCD^k;ba7|luK5A??TrA{)kPpWSUGyEU1-8Ff!aF<ES{jkf
zGmdHn3Q~sTG7_sAXQfo4$^l_SUY@r;K&kP~9(&j^yJFyQ>{f6=_SleJ_O;RMvD@It
zrX_u;LHOAEn=0!FDA1oF1eqKlvoePF1`;R3P`)|Qr@CV@5}W6skbUM-swbWC|7!nu
zh9zsoA4fs88xN7GV2j)_6L4hRH&!Ib#E9&u%@;ZNK^!<NPy(RhWB0-8Jt0w>NSPPu
zzjfGXl|iXxbJG?dtWg;m`47iFIuJarGA(wK)3qC4-);M&nA$LtW4@OR1~0pyvNcWi
z$&-TG9G$Ya(F(G*)Y`xE_`S8hI1thGhr!l}hr)=QQ!c^)8P5csZH+N`;_mo+9kX7y
zaGp~DvozaW2XeYC{@ZV{ky`x4)v*<MvB(i2CTDEv02jFP<3U@BbTIhmoO+{-;pjgE
z?>;Zoe$0vZow(^h{fMqG#yzq6bjoPdTiv`N4Y?NY1S>82F``L|*%!}?_}-Z@d`E5T
zd=yUO*NEE#ggEAJT>al=_0qB`@8?7q%IdZD(`y3i?=7Br_c^Yol~J~M;UTEVYmGfC
zUG`jr8YX(LH?$4vj>2o2fXHc}HqH2W#ZeQB@HI5}B^2q5$=yaD6k2m|<VD!-ao6T#
z&BoHouFW=mUK+oS#0u<vLwv?#*-a37fWAHFbLNrB)|Rhe#7=T0D4zQO_U;oibYGB0
z{#XFGpm380)GpeyMC7G-@%`t8Pu3reHbU-gqv;O`^0z>Ad!F!U`%|7Tw8`wRW;;g1
zPziM-57X$5QOP|1`a%`npj%_q)0v|K)g!wze9uz(ts4WU<(_0rghQ#}&j#Soc2&v<
zr_dq#t%=Yi?T9zA{z6=iAucZO$b&5b(^1q5^+xuInis<Hm-Yp{SMKPZN2mlHU@dQx
zv2Kb-K{{W@^cM77KsLpVe=%9M7JX~lO&-A2h|h1Ju>|+9#C_J8G_3qdA6Beo`Q8R&
z^8Ja)?}zFWRP|hFh1+lHez$T@Ew6M-9YkcbO2p!(Q2=ma>o;|mVM7+$sx);2Uakq(
zwgJVTL?P{>ub@0MO&&Li^NdWX48<wGSRPKt<Gb2~70M7xQ}TWrhqv%4*B~Sb`a9iW
zBgUwuW3fV>bA>)(J<U8V>jjIFT7S2iTc>znAF;Ij#K%?Prxx16%{F!KC;5R9x`D(J
zTt+h>#qPYq-00i1;*6!<o&+eISJ5wyzQIBIlv`c^R%QCqTl{7f)5q9nDwbrALc2GJ
z6Pxrq1MzL24%UfsKW#lwPwd|XY*k(CU9Y6_56d|b!5%cVq{DR&qw~IP&RFtpPkdRY
zfLd}ob5QVY-SiR*WZ>;8?^1DT`ID@e6jQfMUdQBz(gTd09k%4T`>^6fC+n8$*J;dU
zg4#+{i`23XV%jaW<AYHoN0W$HKi~{S$v7_3K6-GyD32PKUK|)IG%VJ;5Do{{*tJO_
zDOh(+U_w<RADb-Ko<jo^tE2?<$XJ-w;i5kljo%KukRi#G;pMQsrz-(_FykYA!2H#u
zezGI}yj}x36Ou!sz6eI5(5ZWTDBvTZgBfGWW+GpCVEy6|<}$!5pr_k&*J{|AZ2sB<
zLS}P_rifM|Z?jDYbt1TRzdc1$r?sE9w>3N>PpV1XK_CAx$a<~XUG^k|*CrFb+4u|~
z|NCz8kUB&ovaO4L!9lk2_dv}eOTS0_>+6JZ@!TA=eVrSt0snAp#Lb*^<oVK5`I95#
zTMQS%2J3sKmxq|k+HviD9w_yf&7e69cQ>HH<vD84$Z}o>>X+RQ1mU0GuRNI=As^8S
z=<s$xhY#1zq|1I@tDgNDa)X{9o%hAmAs{9WMmsQdi6y`?+RDA~1{KU}F0JCTKT1Iq
z$7l+u?P+tX3Wn77Jhg_fN51XtiJ%$U!3bdU4RAt7Th9IUh{6Q}BxVq~XOm6GfcwX^
zp0Vp?+kB9?Z9vB4e<$2|UVyfS%ZZ9nmic3?<qMY(g`M-oe){F=hSQg_jfUV(+(oG7
zpveifn==la>)6?p8i8``$eBu^3RcLAT}B)wk7Nw*98Up0ipSTP-nbSGRyTiZbhkL}
z<(AswL$N#ewbtJOwCZW2WXGF)s5z`nkdL;j^4xmHmbWDx<`u?>oH7$KnsvvIUz%Rj
zTqI0IbeL=Jp<B}F8^gM1t=s$djI<HWQ|PZX3J$L6cqtzlF$j#VAZ%~mS4>Yaz_k|?
zl5KVX1J(fp>Iwigj?lwxt?jrA>~ndoTgu?Ecgk}Ql7hDAh9TFt<e6z{ZiM?+6s8h?
zC&!=?kzoY%sBO=*_=NTzq^IKOgqP!HZ+E;|^p9T#8=f_4{EOLmS7Y?7N=nTme;#CZ
zpW*CpUzSbN3yL(pmBJ(5&+JJ#3K%CJ%HH=g2fy#z7%(0B=tA6YKi`*uib7~Lytb;E
z)fTBxFVui$<B9gAYsd#<?Okvi?fA$u(Sk5AVmJAxVf`FUr+1xhs7c^yGe}v#_I6hE
zbg0Ny$E?dsw|K-4Jo<3#Cx#Z~z{vs0Yh9bsCAyYZ3aBX!J<|yPx@AJ9OSH`(dMt*@
zVyn3LtCrsZtmgK2pZj0#J%FE)Zo2oQ95tIn+3C{zq60EMAJafkTMiQ>j1fKj6s*mE
zX2;WPL=vasvt91IjJe=8v9u0&3yB+_;aF|n5y@Mt6LS@+F*kI`@*v}|TS9vHtMYh>
ztW9D+64wcxC^7MjjpG1D05;ji>)q+S2kK+XkAJL}z=Dql?$_FkUm+jcf4MZ&(+?cC
zUN{t0c!o2uJ^z<w&-SSs-zUgE?yCe3RF7tlVyx#wY)}f=8twGKJ5G7wydR)KY&+@q
zPWBfi`|Vz4VCR@E;dKL|VeGJ8>-^Vl)uqvGMr)Ffc<V`yVNU(SAJygMtelA562R>m
zN*e^<HJI2XYp*f{;BD1?)kkA}py0sE&Wq}-Sk}&xl7SM+bJe8v95c7Z6hrY#bNiPG
zu8I!U1rH~nKk&}_i31UHoU*pt7cKiWfv>FZ0iQhP3X-UJH+BXRI3I?vc3WuP75MH`
zD1eU1xeSTiDMh>wSNZwqZgu%Nszq?vx0HCWRc&|#0MegZ(;R`n+J2I%@u)f6nEcu$
zIdL!9uSe*NtX0i_3HZbcyO{}RU@?xQ(0eTPjGH#!^DUmtfYkHGPU%$k>Nz`Ln27VA
zKTV0>qLzFh8XAVd_>LMkf8JBo%mT<GF&<=@IeLArD>LOL@mb|)^7>L+)$$4ne0nMU
z3Do+gl6}Fv+*tc9CGcTVP;TC5fQGy?a&}N&`VQhnHX78yR!_Ye$7~mMuqxdGA%u5v
zj_iFT%M8fVpLgVdFE~Fy_!>V+uhgg|;DYuw&-yh`UY97Q`z&8LZT{_2UERi7mF-Kg
z^pp(Y`WKF=5e6M;gQwagNQw?@18m)=tpxfVuS3<Ack}|bmpHtRYsei_(1|&Ud@@e#
z8_hTf<?>oH6HcLQo`ttz8}NpKt7kwRht|T~XTgn6er7x`!$nBU{F(MW3<a${!UP@p
z=8Ru$aL)ki%7ZEWkpXtB{w=`A$L9cz>M9`#xKOhoJfQiPqDdQSFGVBQhe@zc#+&;%
zQ=vw5p}lW4&$jV%hY1+q;<33i*vaXh*&`Bu?`(Zy*q98~DGpCR(<swr0=CE)Lq7BM
z<qT~Vy!u5l2X7P&(3tjPKjku1v&Y&qd+*XS#?-+27(6e7rhnz79ZnzxUqQcl5WtcE
z7PtlSzZz%%FI>;dC`S|=+%@zqq6QXM^6oVe#P&W%DE@}KZ*-j%j7MmNgZRmr5y(iq
z-ykZ@Av&UcPWV=jw<f7QtsZdcTm&a!>uTU&eaD)ZKicSc_D><|=+%?_6Q&&%UZ!Tn
zMne~yO&5qraD>rDh40Mp;?Dc?1vE$9su9*VYp{bo2MY?;;>V!~;4Vi_0SJiarQ7km
z$ei-;!gKu=QGfxWfEppeqIBBK>6EyqiPnIVoXQlZd~Wm49lNbl&F%jH1$M_!a1qr>
z93RtB7ca=BBR}8e2u%aKa-Y>2h8Ekm;sV%;wwI!y)X@ZGPj=~IXmRILP<0av5q+H%
z8QXU8NAOV9cn^q_$1$yi_5XzL4=Zf=j))9Kr8w6M=d*-8&og>bay819Nu}@^dn^nQ
znFf>b+)4UnRn?ZI)?4F|Ib#p)<5r&os~3!Yv*YE)*|qPPDlzKWeJ(Ru)1<z38Ea(L
zQ!^Xf?*=CJS&6c>`5j$?eY+X5RhtsG$yIfYe&P!!K{gI&mv>PGH0|&JX!|dfn@c7K
znujiBuuj-jlG>hr{N(5O;->&)nTNVK`iNsLe$d6jYw;^}iVe<h32&g>QUm*c@)Fa*
z#K8-Yp_dQGNLTwI*<)5GSj|;)yjvipOQk>t=bQr0@l&&7-Lk^wqYL>CgS=;{c?B2$
zr8qgX*X44VHyZdbnR$1X&-j;+!(Gg!s7+?5d=%Njz>650s8JSBNV`m*FDOZ<8_qTw
zF(NR&7QlIWFb(0jgI}Syl^+m&GIG%<8rCHz_u9TmjYc!CSmu&9cPBVcU{&44v-A3S
za}`W6fQLOG#xvjbGaX?6f-(8l`IrJDcE*^ai~iAiWAY#vVN7<CV=fXw0DjkkD;zj=
zAY|5<Lx3QThGq4(MuItau}kLe;Xcm~&wjb~sd~Te_M?vBn#JG4&Z1Rxz#Rji#-h1J
z!;GWa<W5HT+}$oQkMZScsQC7Ao@PAlL6b3R*qq#9%4LIdrx)dP4!}OH7O5-@iIPD`
z6z4m2v`b_I$UJt*UaQm+9O-2q)ML1RE&n8p(%T4D#1vN?%S*Ii+#jh>(T;Zp{JnS*
z1btAA+~z#dPYBk+fBAL*jL;iDeqR|XxEgt;jBfUwvemCIu1jE%V-y(%#6uo-Es!+H
z$}alr8??vg77ptucc?6=eiV+XH5B4HRWj_`zJo^5qjQGLHz~Lp$L3=w$Qh0?n3ys4
zM<$QFxkvah{w#GsXy+Nl!w)QHZY$F>m4$n|(bp-I0*R5&)=arfv{wAN*o3}4EsVH!
zUvzZ0VaO`j@NJY}bDSA!iK3clHA-5V{h*gm>;xE($rFOP$zqa#93bFjZiM6g+#*qp
z4H|BbG_sN+!+>1|uYx_S?mii=+3R=f$Aiq(?@Em{%xus$sW0&Uqb5b8Xf1x*I(4Cs
z4!G?6+V@}a$$haG6_<zk)OGciI_n<K1|Y7!X`9ENfIw!C2AID+#7)%S8wY*1^y2`y
z`6%4ZoPsGzbJT8pzo|Z^pZEIZ6`ut5gJ@9)!(~%m3NvQlsq!x0lXga1(lfAyIO8-O
z{J(_nG){wmDB-?yGH=ZS6;gQiYOWT+i5#`#GX2TsZZ7W;-QNmG2DBLU6ef*gMt>;_
z-Vpz>D|6#fv`+CGRM4hwjWgS)4wGg1j(Sy{JiG4Eah>8Ub+hwg#I@5jd#$2uf?jW)
zmMr0}=cV84QGg6<bS;j>K;z$}Ms#JoYl?3#Z3b(Ihc=3)>9b0yrHnf|;K$||8vVoM
z#c*efn-#N}q(ilk<nehTLf_ei!gz843e~bb8JB&2-K3w+?q}NN44O_b?NB2o<+PRK
zriME3=5Dmkl`F+-xQ$_`)$>m-)V!edfHBTge(8Zg+Nk!Z(SQck;!>k&V8Qq-3%sej
zp|qWjY!Rh6Sg}tVbP-=A34kzd#8XRIOs0bHs?wXansE*l>b>S`>h7(+3T-?>zUks{
zf8;dy+OUqiwqqCMGMb^y{|QQl4jny2raaA_AF^W?37bHU_F(MHX>i)e<4WXPl+XX!
zUW^1rp<HlA&=TLi*+N14{T(`{`p==RT)Tr1hBZLyMPP(xplPZge#)`N{PJn|&3YjR
zq~r*0-f(8WW#wMs?f^l;8MlRo)~M05!5-XH+l%%A+bN9ZY9W&-SpFS|A1O}vH59w>
z{9F*JOOz8#eg-yepYwChk^0`k`wnp4nR{<dH@-?`<uAbZ+YZMObjiKW1(hl#`K)fZ
zdb$Ir7EE-e3Yn6<91ec-dw_H=srQCx={mFtiz~YcVS!fzrgrHRGmM>|kH1P#L_}T9
z2KF@e#?lX2p79mTZGlbSa)>1Bac4(n((+>_@+K;OM6On$$82ZRURk*R9gQ(n)Yud3
z%uj{Tx%^DIx5s9)r#1Ez9C<qP7*M6S?zlskB>#zeyRf0v^UW)|qgljDD-8>7Ut^9B
zgct^($@9twaY)yp!ms2%?t=_owEJhiwshu3@lG`^-=!cL%Gyci?FL~NnV^_GO3vB>
zquS5$p=gA}^q4JBd0(!m9D29TO$|c+!jHknjeS`pb|f848SM(8%h$eR9f~XY*m!hr
z$dTz&zqG-%bPsBw1-h8NTsQdliSVeHLLm7nZg58AY`YkYQd+1p1FsT%$J8JV$CkA=
zb#wb{z9aSkX{1H*TFoLLAaRrak<0KnTC-O?#uVFY7PYB;Ir?mGLi>%OrN<jt5l@Xp
zAA(Sps=Xv-w?O3O7LmPnQFbGsLPoOYQM!jV`jD@#o7BD^GN<J>DB6y=QhrLi2m&x^
z?twt)v(z}%M@3WgS$Vte<fqwZw`X;O&%9~$nW)q*gFaB<a(_;mFqs4Mb0$$U{l!`0
zYj%FceZ~FqnwdhX%i5z79T@G3-`t(i$~rXbM@5o=6MU59f6>l}>t%XRz#lX`#y8^K
zi>jDKcIPxj_7705teaGE4@|}oKAl|Ew5-U<SwmgdvOWldRFrWi2en^!Lmky0|BYDz
zdpc~kW3~<IY@KqSlN$=}JGHqaWNv07FG`EgU@jCXi2E%3%G2|lAFuO?@Y%XuA>y68
zbC>$IaSn*hEMwbkf6=r)_V@F?sxyw98_b3DbRFbQvW?bE+Z>jgtf&(cTDSaUSD@CX
z!eP3xjh_fW73Gb1?$sRz>MjF&Ml^OAbWBq6DpFh~W*pXPL&72mHu&fFXd(GN<p$Ax
zq9gY2$7&!Yg5(#-LQZwZsJ2DZ9M1|xQgaz-IG7lkcMMVHEf(si8&^iTPd9j7z{|`(
zOj>&mcP*^WpCZej>7pw=1E~QYpU9MZ<5W@6ucYD~S{>0(6Wu=$NCK8+=63B<J+0nu
zGq9}w0U=X$LXIj}r*H|H|F$>5+`taplR8%&DtdY#kOJlq)(JUZL3CD`csWUE+=D;D
ze55m1)fI}Qvqob2+`<I>eB(GJ+bC?Rt;;4z?W<$$r&y?id*P13gEX&-@+>&D$GkUa
z_`MzNT6#aQ$cp=!ESNirWXq}ZMG6fn+SAdbYfgEo<Uo=`>?#5Jgzbj<kMM6Br{K>M
zO*7v22(|b0QVC~S3}?hE-z*q9-RJN1z5<c8O{Xa{OXj->E>{>P5ed#2py^gMJEKn%
z*Fy=G_oz{yMCjHyT2_c)z&adbMm&ug!%+nX6@Nr|mLspcY4de1PgnW0Sf?YXu_SND
zaLvXACHLui<Ex-x6Z2)OAgCLig;vr%On6>Mi4SNfDB;I!?mKKJ9{!MAl0A9!kS4$1
zPw_=o#g%&>j7E#=LcSU4#HbWL;^Z%%M+r+lhK-U$X1KQXd2&tb>|3R0`tf$y^Te5h
zyn8n8(~yFqk+Cn7WhWr#&M=#Ps~<F&DKPPxPq&b<JGN>^Qu)&an9$s1(|J-~qlj=Q
zCiGx<V1}0M^wZYXZs*QRPtphM2a^ZfrX6STl?B=aBtj6PcA9gWxlr18o)HqTDS|jr
z>z$403e9^3#O_H?bGLcFj7=w-$~MgyUdc0>3X7qH!t<PZQ_hNIs-WZuxw8^6+Q-Mw
zE8a!uk$X+2#k*5529GYF*c**6H7g$)ci5bTkFB#{=}gp?+N>Fkf78}ie7i(V$F%GA
z6lNLe4J)bE$Q`T3aFv1<UhEEKSM(RwBneQ|1PdXx*B})=8;}Vz6FZ(kMdt-NxfGP$
z2nID?2^QS|rQ5}8XyhLJ7H<Qi!R-`8KHQvqYxvjK0m)gR5K+D(Ot!@*-2nB}SZR5{
ztm7ax*uwYaSiNj1P$yCK$QM0&>0NLMl(`T#G}V>mN>PZ<Sl2E-?V|ShN<-J{xopL`
zq)_-!O8AC@v%;x@dBoYKKt&5bWRB2vy0Gw>OF;f8A8!`8_a-RbGghpi+~)v)SS<`b
zpbYnY&#0|8CV=KU4HJ%Eb%gci44cV2R#yL!chq6pzwc)r2nyzOWj<vUWNKWQ{`Ahc
zYiui(;g(JgNo?>YMT8NyvGG$ZI{V=?Wd7;{`7{F*2CPo^68BEw4>y=jArT5Jxo#l$
z-s0YiOo}<LXc2+o`#P12-{sT*Y;t_9@c0}8@lj+`l3HMQ<{PtlJMG10|BR2lUXZKv
zmH=HcB*cHs7%Seq5VAwVKI&-z?@bA^n=izGG$3cqfMgGa%7O+S*d61+;?H<E#&Bpr
zDZw2e6%51{ZOrX+0gz3>Ox@qQe65vYn8?f#qM1Nd*4R_Ur^e8D_?7o=wYi)VC!I$%
zEG?cd*&p;;C3rVq&qsFl*ERM9bL?P#axy!1N@hRDS4}+dMETiB%BP6K2`JxwFG~|L
zD>;*Py736j>BMZXd9x{5@6K?;Zt`@m(1QP5_2_Ygw{qO_sLpp%asN89$J11l>ICVT
z?`1u(zI}9;9YG}miBg94J38bdi3vM|3WlQQp10u>UoH;c(@}<C=D($@fM*|#rmywG
zEhH}BC>P@071d)c?|ZmUM}52)vy*KX-xXyXfbPwk09GkC_5QoLAq=$WmvJ=ehADbd
zvd%*mfE9oMhg6B^qUU_wO~Dt6GOoEY*ickrNGqcA`&cNvU*oi_rwmXgQJ>eDdCLpB
z-0qR4-<4_PGSN3HXan(V4enhzea3kPU`8>Ge#NqYe{lwtTw<)&)|DCWW!b|=4zN21
z`?awGv8{Fb^QCLB-Pe`u!Mgc*{%<bYoO$yjV(R{B2bA_jjzX|K&S7*ZzHa%BZa*Jf
z0KE1i0Tl%TUT24YwlvvG_=RT8Sioc~!P>+4$c*9Vm0fO$7Gc042UA%}TJaHQ-qf0{
zO57cIYYJ%Oj%wRb%4i0O3AIGSarusHsJaWq1L@hW^oyf??!Z#H*QjcDZ?jSmkgkSf
zxW%Z%J%N#|+2F-4Ng>ZLs1Q@tr6p))9x%C2D(`3admFS0Pot2mGK~Q*@r!xu{p>Tj
zHRHu#b<<;Tm8niQag#1`e~UG$aHgy<`)9ETC`nl+IX+i?7p~PfrOI8hcB8xn@<szX
zhT$ivv)BTjL-#BO%OTY~HKIE|(#YQS3h5U2d8*F1LU1Is@epC-BwA<gOvO;WU2z)W
zm~x2sRVmk)T*T6rPNS59m!_L@qd!rcTHjwqEe$_t;>Bh)a35Lf2fB`cL_ipDVt!L?
zaeV@c0n16Buj4;9VeViY(An*!J+cgo7bFRW5-qgvjf{XT4l|6jH191u)?rTR-_&m(
zd>7|1XOVL_(s4&mYti^1RnVc_&8PrdY~H#h!ycaH!2EzE8mH>e7A$_I<<7(aeA)6v
zBt6*5uK_o!@lwq8U<Xv%8X#KrC~D612&@1Rd}BfO(gWb7`YkUjPICgL{6L`(6r2^m
zS=MH%f4YTpM*SuZwrkYZj;F2~0^ZSmSAa;qnzfvA!7r0@qNPN;RG6#Ndm+55_w~B`
zQK(Q!C45>>XI&HT{ytB>L%VAG&93!c5+h|WepO&NQ5KAB|4rSo^WyG6Amp)oyym%5
zjzq~R+VMTB$JJ5fQJOtBo*EGC>``*m2E6?}q4qs7b#6Q}MIK`E<fEHT??7KAe^ho{
zeVCu%hcYagX-k{`7Cd}#hQBlzDq^|RE4ADJ1GQ<jizFQv%uz#4lgoZ*qNn|I<Ud1C
zZ<p<a(Xz)fGv)Y}j=5=HfR~Xb5O)TSQY}?Dp8;r!re{7Kpt&I4ZOh)-d?ZkvmhkMO
z2RyO_>G3Qz8t&b`X2AdGF~5|v+S8S1IMsJ(cu?T{)B&)1;X%FkCO}CI=csSaSd{a`
z2lGak*=OL2?ap+pd7+4yJ+>X3W}o|wA=0^5Bd24T3jdv~@$B8F4(q;{oq_&eRi1rh
zFHL~NSvBPe&YQx5LHtjk2769{u0gd)hw$9iO2=FezLLv@=R#BD{&AZ>(3*UDZa5x2
zBQ=IiG4I$8xkkZ{M7Hg|R1#{8?Xn=3;A~-AfEt}w)pkv91Pu(B8%`LwAQrvqdrgs+
z*#~1c0uDyY5I#V$ph;$@jvPt<3_tvuV+Y(T6Sf!q7WhKQsaU!wd$<zBe~lH%*L6!u
zpsaTLWBhx*FJ=i>m1WsH=kYJ6WC;&e2K3Uhj?G=OG<uuS+wr5-fd)ybB?9`Dr1#Bz
zO4HY3FdC9Xtu-V#byLC`4d#IQ2iW6T#z=@<dt!48ovvFA@v|uzXh?o39i-)W#8XhA
zB^nqg)5vUfS(*H^MC%#b=#w4r?PFCS<f9(Scjt*RhjzyBedmdM(oO=U2_6!BYi<vz
zu%{s=K>AhKs#b(P0c1zWN2AJQCmS9{KkRT*TLbr8i>%n-qdx3loNQ`3{`(^|c(y-U
z8nK-?xJQ;?NoLVzerr+HvbSP`&6JfTwQQe_n5*ZXf&EpK)mQh^t0m<H#+oK(nuCdc
u?Q29pZKCCK5LQ~>1~mTv%~iBctn}gu^SIkil?ReVgHJh}tT=w|#{ULsiPO#i

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
deleted file mode 100644
index 53a77b31a350c82ac29fa882dffc7d745171a443..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 54198
zcmeFZ2T)aOvn>kd>^2~%1OupmfFwb(sK^2Ylq^X^KtO^d$*@&q3kb3#sst4!=Oo!k
zMv$Bl$ysuSH>Tm-d#c{6d*4mxuUm(5ZxPm7bI$MUuY2_9(fv?f_Tu&}2ewd9P;4hj
zp1(ptu|AxFV(q(48}O5l_uHfJ*M{rT7tiBwe9fQBalv1k%_Y?=DJbaNiT@~lddBrA
zC=OAO&YxAZeLmdg@ch#BO40ai`Dq7J_M;XLE&QZN{AZq9FbCe-q)8RBm&82%kExY#
z<iUb17pV8Xe)h6e+>O!iAD;73kJWW8_H*}&IJmgDl<Ov@W;SJJ#Wm^XhWZpa3)Pn|
zB#bS@jkP<R4qMg?GxOst-1PINC@5UH_R$Ue{+@!uf6wNde}2IxDYo|a_qWcjIq>`2
zYg>0${du|iV^`u?iLb`H>v(>BNAYeSV>spS?=ucjZvXvl()$0OAN@eWi{@}-4Rd5o
zbYlJjOLeEBlo#i(50ky1bcBxLNM(-&v#6*x`G(g+a~}Uay4!yK)>UB__y5-o-2DFO
zAy;8B&8q~>90>v1-?!$!?!V{OcGc!N+&L2>i$5mKX(Y0uynOilWZ%$Ci<!zRiAQ@G
z7!obNoVhsLYUz_<+BTROMki>~|L}OI{7`+oQo4~+U4o{%indewK6Z^0nJsi&-n))U
zsNQy*u2az|Dll$f&zq{29&E`>{KDV0?Q>yajLk^1QA<YrXjUJ6MVP4eMQ_^8IR)R}
z&i6ix4~o3>OgT<h@XqKhIzg9}CD)Nw)6Dur?Zo4uw&J*~;-dq+enCM|p6uC@_crdV
z|KPUO|H%`DX6a-a-MF{UWg}BkQ_s;q2@W2xlzD42-1x#E)J~!;=T3uRWXPMjIopss
z?RME+-hxwgD%#QE;fIFv@%hT}S6^?Ww-qyK$xy0z${XJx9;+O0HCe%DT<XW%obQz5
z#i=v9_16|W(e^uhbT4O!;GOygZ5K9@l&ow$@!8TGd5ViOBU^3Dqb|RiTAHk6Wnqz;
zpPxS<U=`JF*B9bC(vo2kt5+5fZB!RS+0uHa!|5rHA(_!|p!)S;>LaZME(Hs-V~I+!
z%Ii(~e8a*LS(IY#*PYhraGDKiPB%VIGHQLBY%|)rzy5Y~baae*W&$I#fWXz@;9w6=
z&q>2Bi!2`F`jf&_Rp*)uTwJPFcJJ9U-RaH{BNrxew#ALRu#o1$#nmyv7jbbXKNl5k
z9O2{UuIbC#K5Zjn{>?R^V8uROE%n&VoDJI#+#LV@IeYHA=e>-@<=Kw9EK7rtrqts%
z1%1>~^#>jtG~E=-_S;sS%Uu%=1+Z(zOje4D-keY)7kbmh8rHli>9H7yiA~t@)G;6;
z-MAq}B~eRnU!MmRYi*Lw$D}8fHip=J{FFV~{Md{)o!|$7(N{hqDH&!tBN<0Z3`kG8
z4ZJsOqxp+#{v_2gSFCDmW{0z2dGXP{oU#0xV1v;8#BZESs$O2$%BGz!bnV$k_nkxE
zJvnv=&i^?nJ2&@l`Tl+*gQBu>0M;+kdZ?b6l~vj_Q~k`DGgtHM(+`}!bu&G0aWse5
zs$ZT>E#>^fhYz1}>OR;(C%_n3#uQlgI?rKtFhJcjxj9iI>xywh0-Lp0j?KuGNZAla
z=h+`0cN$|yOTT;xXc-A8>!}{^Dw-ZjEEvR^sRXiXN;zq{(C%=jxBW&r)R)<A&)Usd
zNb_hvcj1EF+>g$)E!wu7ZuYCov*`}Araj%=#UnPc;o)L93SR@YoudQT)UTSjZ`=P8
z4;J`Ms<bHIX|eXjxs9=m(lAh7rv<C!`Mxlg6DJ<Gj412tzfn%m7~metQqMG{EQ|8m
z&n=;*rbdo{r48KMDN=<u`vnFb&un4KY>6KpzD37xE~f1??LS(u>deH**yxh{YeS<>
zop`_NY&#Qw#E1L+pC0a^qM}lY7}0iFel1`<IJcDGyu8qyqUUK;AEy?P$0>pJsegIF
zlaX|%^|t#P*^og#Z}YNqOUq*}4RWH(jM1n5&T?M14{@4l($O+$YDqQlIlybgNU{(J
z5pZ65d;R+LH5naghEJ}h-8@W+QHb=ua^=dB_w+<hS&un?ZL;pgf<<8*PSd<;<-FCE
zMRG)BnDbRRq4E128NMgq>v&yu-$6GulG!0eX3rVsVpGpJd94H6%hzuEN@Tu!52^aw
z`+LjvO(KiKsW3uzlACTzuH9sK@|vFy`>polw*D<Iweswrp3)J2cfW5SGAyCRG^e8P
zPMG7w!;bD$#tRoN3^W;pr8|dm8^}g~mzI^yAXi?|A)hA0Mg@lAQsbJk?d<Fhm)(^K
z6>4gx=QUD%mps~<mC9bQG!b565S{d3>Y!P!&hpa2P@?-m`z*qh_&a((eE795%!QsO
znU0Pw9)6&KMUD$QTHwj1f8&N~OJAsi>Hdqqf{SaZ>pH9T<KOP>e4r6kne+U@l`CYz
zoraI(J-O??lR^3H*|QJ39A?|@#E*=&<t+Mz{S$GK!Kt>Hp3hX{olRC&mI(K|joWDC
zkM$*9O^@;aM~+0gY-(y6Jf$8WGWTt*@!|`2`V=FBjeFqZUz~HlDryEdBl+x0h@g6l
zS?;(*@%q1hMLhGt!<3q5(+q2l-8eY5o0d+%YSWo>v!B!w;NzIK%1$s-S@eb633wZp
zQlE3j%-8`=M8JM3s>7x%fbB_H51A2rAd5q|z3GmrX+r+eg!CiYGgk<Y-j;1mb?_i-
zWX-^2W0qy4wM@8ui7(-w)mnrdX0|Q9by=AVtDng*Y4I4IW#{9v=#~sYI6A<s??nWL
zsi~>m62F~u>3yu$t}k(lV+-$ay$o(zhC-UKiGx^f=d2Jxzx(&sg{`L+U7k`X7+D!x
zU8!uQ7q+*m5_!b=luOS89w`C;zWL$)=Bw#O{#O(f`~w3cmzEsE+%t1?bFXMT60VO|
zH!!&~+NuU;AtfWD=;)aHy{ikiZ!Da<XJ~b0wjjQ1Wv<KT$g-Bz*9!|1WgPxNK_{Zk
zeM_+`imP))qKdC1w%5g}DIg9-D#xi_j%k|xb@=%0eBn*SEA-h-7OVM_)5Oz_>Pkmi
zJKxT>JH(IR3G$0qYHJl^MbbWY{>Y&S&m<z^oi=L~H<@G9fh{8$`p(jBn<wDk_4M?Z
zj~~AXKUZ2-mP+6L&^oilgsv}fLmDGC;1bNHez+;ME>?vjjDu8NUCm=XsAxS{OTq=K
zaYBZ3<bU0fq9kMZuIh&+remM`Snf)*XMMWrb-N)!vpHGUy>i7zWTCh@fAWhUp8jA`
zBD{oBrSR<YY0DySx^&y|>j)Ybi+mUYvG;J0oQ3v0X6?CNi6<Oi46+9nQBx%+CugrL
z45gcQiud&O8F%D6!4dc({xG_KJWP_3lWX8@(0E09a;%Ad=db%=*|EE76Eh1-slnIx
zd-mk|?!)o9NN@LPVLm>yJtgB2Ct(uPlZY{QXLxlBw;4OUbEUkReBCV}cS0(}t~Z$2
zF)J%8--yw_*KU+@*zD|<8l@c`Tta{O@?|)+;OWVkkrpR%U#AEH*+J#<uV23!RfYd;
zKQkOXP!q{2D5%bnH$_fg&0id?N!Bg;F!;;tD2|Kb#-tG!;zsbgi>G$4<_?J!Oq8-J
zoAg#Z<6~uFx<IZCKf5;UMw}?CN<v!3MIU-Piw(E0UVXZQK}4fE$?5!s3(V~7Ulz_h
z5AJQrG~3zq#<K5Aea7sxzk5&+jqs0mYqPt_(s3Qj2s=FoeuLK|?(29yaG8G-GhR%s
z6sdu29;LZ-+2Hx@2hC}QN28OzBuDm6c9%SfKi$dbmuJu?5c1`5UkLy0>!c6c`w=ZO
zGV-XMw|#Q3xpU~=hV3cDUB$PywvyMYY-w-sfBN+5Xy3-4D=&8Av8#kCwx{#S`Qhr<
zo-<<|4DbK(I3R4FmM(1C%HPx5YcyCJT|Z`;J@DqSldblvJIp&>2w)HyY9F)CcSAjF
z?We1mP!s!cz9v$x0dYforYS{FI`)1k{7!A8T-f8+f<jpTNOuP3x5Z<YmX^~vNbbCg
zf>?K~M!K^IT<!+}zF)>5>kQ)zh>AMlv@j4^U&+kOOnBs}a_-8Tv!iXwC0nCUotZCU
zaOr;{CvrJV#0i*Io}F{1|LeK0U)?wcK6_Fdmj`hwugOGUj{)i`7Fn1cGB7Cs@HueC
zB4TE=jTP~dR9act0MBE!IFcEQ=&I+ueeH&ANf~@*?QsCwM!+WZ*4;2)AmPD|f&$Zm
z<(Z@fFAtCEyX&{cC99>}csSUQnAo7@a4F}`n6j2uXx(WJ4!OnIu?D<=fZc>$+K=VU
zF3l{qT2@8_8siON0Vma70^VM&Oy`ss7#LVU0I7>nVtxJkHHrNF`}f*x>!I&Ee?c7z
zSt%lAkfp!$l;<n8w&M%9FiA23XQ<-@NqD~d$u93Wc@2%{8+Ov)s?b;&b6GVWap-^H
z{$cQyL1m~>dv4Zgv-XoDM8UhqWP<)mTE_WFv*?jBX-av$*LPL!$+1gTWo((Mqz)=p
z<tU&#$`-lj{Ff1Ow2Pf9g(hVH93@6tGF6<N^9g6-IA^|{K_s`9vocgzJ=LIcN9@S)
zFvsr-eZTW8|AXA&mroLq96<&T6)Wlfy+L@kRiXQiY-EkWuFuy1oAT#-5AvF|3*Bg;
z*0MdDm^bqjaKVGl;8e|<OEu{YS_jiEiHqN@Ptc5ge{Z9Eo7uC|CTT9K&eJ0;e8ajA
zfScgxsOFt+-HLNwneTgi+n$k$$*_dXB*G-|%Y5YLIoBTeT>tjAu1RCkS;T8k{feir
z{aICvmKUZ=%gYs~2kWfAoau{#X)H7pth{DTcq^_H<JVCZcfoTXB1I_BN?Zr=q^hLv
zPkD_8fljJZ^vb4?G#TTu7J!MBQuQwpVNXTlwi}MdvARU<4sU6B85u7TgdpN9+YiNO
zra8`Aj5Mc9!y&7{hgj7qsYGiRI1|oFUtd3Y0k5qg)`CEMPX+HJ5NqQE<OrN!fK{=2
zzn(e@k2_c&{|sjFX0R@H<8Umn`odI=oOOTIVK%K?)i9TpETFbmfNU%xB3hT8ovwQw
zs1*ZX7_d0>Yegui&LMd|FYqUQ7V9~;dB^Wxx&C)$%o16RK!-11zLZ+N33ndBvHsWF
z#e)9d^KH))x?T^h{`zx`QuqD;hw|_Lc51IlLWI018>xUluq_WO{*2o>ZBIpA@)xEC
zUSXN9HVlt(>K1xQx;tFqSJDzZ9t&_>GGG-0cjCL_^{0fKQ30gWu%fbnl`;#jVejc1
z-TOOmHs{*3n>n8e*`**^MFt#NeSh=w6y-!MHn0IW{UtPe2F$S}u16%rR1P7Q)+~+{
zB(|6pXi{sK|BbAUNVwBY+pZD-0?{S1yj-TB=zy*tQpTmFB@6HDB^uq@R0BCpr|G(d
z;nYgMr%#PEQw!Y7dOWO(kGqx5HJ<^3s^U1lM%bJ7%<h3IPt{!8<cZ12=}MOs?&=qQ
z|8N#&Kfu{+vG7hg@M_{8;>MpKOWW6^(#$C>EnV}Y^P@km27_-ld*<ckwL33tC%sUA
zP36-3WV};{%c_&=IDlQbfc~d{cIjxV7>ZP`onP!&HT}$G)n9d;ZfLxWqo5yY++{qT
zb+ch4GfZ-Ns6hqZ)EfEjK$nkbJ%h_q=aWxXSsF|SR`)Q#ftn;=x6aZaAhdpY&<n(j
z7#MwwW=;kGm^Cu&0i<eQ;Z!}An|f?=2n4d7=9Cy-qjdfH%a!>`7bEz49j6U`N*a20
zPK3kq+{ZT6cBjLz^Mjj?4`HkFrrVB;&W(5VdJB%8B8!W_5!o$`7hOVvCC32&ozct)
zT-7f%3}!>yjaMSH2zdAi-2~|cSH2C7mqJ{FM_;E+&QZE-pH~-st|nK62=Y~T`bkPj
zd0219lW7LeY6s}vxD$8$=-9Ys#w|79<=1qn1cs61g1g_*cuZJ0@BCPwpWogv=iV<N
z>cgrv)zxvjqj|IKk4MVBPqv1Ecvzr4NJnRql9KWvdxP$lq9nv^9iKLY&6{-Uh;lE-
zMaRG5Zj-ur3xf#vSZ9W;K`qNdPt}S3jKwDnWS(ai7yDoNlrklx<cm}m_#WW7c};^|
zMYW5-`hI~d;^#*ilg|-w)h{%ZUfBW3utAuUGC^ku+QdySN<2ECw9vv%0BK_f*tk*6
zn@4a(j?LXYJ<O+1XXKs}j8-aa__9-UNq$)OYR0W-1nPq?nX`Zr>zebw-Q9YGhE!4)
zuijAKprYpO+m;;aCH~0Fu<)d?uqH?T+)Ks*-;4CjYOYk0YEGex+$PODBy!DwBH||k
z+dj6d5y-Sfy6q+=CTibYItbo|E$*%CX+uN9!N%lQ078*2OI-|<EqVq9v38TazZen1
z1LH_)T2}D(>{d~mw){+`5`_~-_|g2i?^~yF<UjST!wQrhVB$a0kMt1<w11_G@<lJ2
z7h%o|Bt&A1oHqPH)?bfwix-TGrv1R{U0(bt&htIgq$}VDYJm#~62rs0mub7<XAi3S
z85rvgug&v~h&J78c6<XWNy|XwEZ-z?<u<3p%mjUh{q&%}VFyAju(C27jui~r&AJ@c
zNmswvL_>I-0K?q(jq`R|;N~{(IVMRCwI5`LbzdS+0XMi=FA+em+Y+%w%XwaJq&=@i
z>PC(RyWWi({UA?jo}aPgvHfxF^XJcMMS`^6;ax(vzn_mC0km0@p`LW@tm?QuZe0DS
zgjdz~&z=M+p{K83+G9~NV#9j$=s6sf3A;;LSUNmC<)Xu2bYxt&B|_zN1z*PrK0XyB
z-Wzw&Ry{s^E-r(w%}U|jZe~3y6*X;bE333nrx`U;HB7iH&=9K|r8@jv>(M)G<N!9y
z+EWiPy#kTwgMiIsZ$&Q{y5ut-JR&u7Y$V88GtI_pGfFBeGYkE8(?%v>L-v#2AUlJP
zRRmvV#u$u$ToCZv;>np3XyC|^Gn~RT!Y3-)!5<=fw4^*#Sn#Gg($!AC_nST??54GG
z33UhzYtFfosFrONJw1s{JQw=v@+%4DMZ`omDwW&03xl!BtAaMeCni_!ok0d0t(BK;
zy#lY+W|jkAy9Y;)G5GCU{wGhL4raD#-f7dD=0yV4(#105jC_V8b_76D+DaBr`fi5|
z^`pnIlAArH{y*s$LnOOMDbA`E5&2>EbiKNhbEZKhz441MTz_8Jw5#xu^1;fJ;V@~#
z?qc7CmW~yDwolx*zkdq%JUTyQfb`3LIJu}cPK}$O@p7r0iJyFbc)T{lM4jo_u^7OZ
z!S=iyEth3$_~1Cn#d!71t9=VVB}^pC-g4%Yx0gxOv?oo4$Z3{+vS4FN2doAQ51MUj
zUPX#}<8yzOTXPGNn>093B0~?<v<a?Ua~?L7L!_&5YBEhtO;3d#G6=S^V0pyM<I$tK
z{RYopuqNhS0h=Auq3$ES(A<PWMei`oN${;6K0ZHvE$oz;jg4*CcI0tDz#A{GZKNwc
z^unbPm2h_euOwmnsVd@=8XHyN8dO3AtgE>zg<f@ccZYM=cyV4WaLyw>(t5b@GOtms
zJls0={x$rbDuV8`!xF&nVG{C?Cv}woLDmFh$MF^zNvpB?xSemEWBU^Gi4#=>y@mrh
zcy}wiJkod2fKg}fZE#b{?gm~8tQknt*Y54@9mLU7LLxy#TY?q`OqN_+T`XAL+H7`#
z=9PT%0bOGK@|I^>h+VWE|4bc=r6)ie&I*tH)XkCBEbeYQk5>_0-+hCFql38(2r}_2
z@qpl=HL?Bnh%M&=jyzuB%h$}d8qX1J?VCN1C=mj;#z=zwv(+b4T~c!i4J+o(2-L86
zNZczzXY11!OW4ni!Wm!;U6#h(8KdC{faL8|Z-<;DlgU%H3UX?q4hhre;cwHYMeKkx
ztdJ%xn#_LK$}!iL!J3dV8i}>3w-iF+zdK|Pj5X=Px+fixX_RX>sHQ4{Kznj!mimjh
z#lh3JlA7Tei)2Csc)z{m14`u+U0t$NT}C*4RXqOaAm7ZH!XArYcY6C1{o;b0oSYZ*
zKP-a)*I}zxke9#awb^BZ6{%}H=(Nexx=}3a_POV_`WlEDvBhidfT7aSR0ok-0h?Q+
z<yiy_0cbB?yyDFus>S%_d#(*qF=JE~Xp&Yz-=yM6ACEWd;T$QSBnYbL1&b_=pFl%c
zj(FM*5wQL`2s!&F=G?YYFVOZ04_UUpy?snju*GVmnfKecCpE_oJK+w;$w$#3rmOgo
z7}1(jvW_){Bg^DP_RR+(gr&OZ>#K{NX{&>=iE9d#b`_p@t{N#5yu0352Zfdwp&m(o
zRvJPgD|5QW0k^}8)Xnm!ek`?j4VevpaB^NAFXu6-jk;W%LFKf;3LEGkXx0C?BVX8)
z@7S?pR{_S~YOoK@VFUaPZ_)ccr!?{6#9d{4DbV@2p}^$9PKMFR{rhARwesRJ_V3@H
zQK)xO6j+m!>{T;xgHs%vrASlm>P-PQmx!=ebSm;=6S8Akb#0fY8$f4Qxh~e!)DQ_H
zku#iK3<lclXi`7^r8o!n8vpst8hF%(?R>4_O{oSOO;q+t9jpvLdoOOi$A%rWQ7}BJ
zGZ{EiaP>&f0z?-2U+v~F&$Jo2s(Je})ma1a0(fPjo&yLXW##=nh*MXhs^J!h_Jp_J
zw@~ff+eq&z+Rkf?d|5LlpQ!T0&KRBOdu}P~+rg%ptq7hl)EVaZxQNfBxwg+>EI19;
zP}38roq+ntdZ87<9oZj$=f|vIHQy5m`h@8wEkPQ<_J{9uj1{b!nt)4Eqt>+Y1%EPJ
zgGH5qM>tWv@@Z%?>V)oI*|?IDlEb=w5uo=IYq#e6>uWF$pkP(?h_C%-1=o%n#g7jY
zIrZ}BBz*^tEmaf4m!S0%<3$Y8)uaOXr)^<<+y-CLGV+Q_6%zTmxfNnn5_i}-B9_6h
zm`Q{;O-J&~^(gU(YO4ODbh>`)IRrvUmN)~a!u<y8M#f566Vo!JxwSe!VebeZ8u8)t
z@jGMfn%^f0{#ozgI;(ZG0AtPhsEvU^Qy=B@q7zi!;eHO$Pyxgc!lQ+yWjYA-p_vMB
zklPRNoVY1kDY}}wd-v|Cc89Tn{;#jFLtKS(OObuZ5gG)?a&?!NfcRN>d6n1$rGeH6
zrfy+o#CT+NwqVu7U;ZXYu5?%5K2fCx4a-ZupC8fgxY*dB_0(1fS=-dg;+Xo@b=Cvb
zpO1g#Mj8)WF(XI(E?GJ24z%TD0+YrPIV29#)hHGK1DJ^tcz8ytfOnhxR9n)G17W?U
z%Z?Si4JXc=X&Ov~iy{a|u=>_Dm-yU-W&aieK^lXgN*tb{^e4z92y6w`8x<|wd08L_
z$8?s|2M5Xz=G3Pr``-P%_9^U_F&a^N0d57vRLtGGYlfaGC@4(BykoHu7xvw3$+nJ7
zR}Er>B}wC^()Tf1W@$(vTY(7^?tgEVUWcIX3Q(`n#|PUFh&tyYf$z4+B5Ix8-8Xnm
zTUE(^zP_DL{+gOWQDG1X9GQ{#W|bJsEuU~fn$q_6+4(CA#;`O=GU{wQL9#|linenS
zBnzC7eWx3BT#y$hLAvJO+MMpHdEvk-^L;I40E@AS`7yW(_#3sZK#<BfV?=mYZUJAo
zIC7NsHC-SIsC8GpPQsM;9G9(KSzc6<mp=sHjo>IeQG8T+5uD}1^4&5AnI)zXscf-M
zI83Xl{>!B5&(zwqGq<UHL&EwBX@pmoCdpe0ro4Q7GG}-VtIre6F{2?o;Mqkx96BfO
zgS<w5&Immg^B<~BQ9mTsf^p#Qjr8)MR61<n00S9ATvp}}Q?t&jv`SvOl+Md8xG=s%
z7wFjW3breD{=&`PX=W1`YW;{G!2<<PskL%#x$^DvKZsoAxSd<+sP@QJITQ6to($Qy
zjWKC|v;ZLjY9`btRlj9EY8g3w?!mU?<~(?l3(bCYNABxU`Eceli4#nf7pF)MGr_{1
zsyBzTv>bRHxFK&8IksBgK~ZPN<jTvf4c;{aJmCGDTj+#rtBmg8PN)R~QshCpux@bw
zsDp$=Tm*To=Tz#=uSa6T#I`E0Cx?WXfL}AC6Gd(Lx^@xa^}0aU=z-E4%S3Q;GDug?
zv$ytLhTp9}qKpCr4@x|gl`u4y4Q!Xq2_|eYRw;I$#o3c`izxc073X{KNW8et<}Kp1
zKtp<?Tjb3oke}9N@$DVuP$TMQd*>HNOuoJQ`)t{6+rmuHNzOhT13?6!P<<_!D{U^h
zWk|R!QM=&Ca^vN@AH6FreGpPNu;u+2%^AD8=z|nJEfL4N!)$W^faNn-!BbQOIKC|&
z_mlMhW}c<-27Xq8YzU$nPH(&=Aw2w@N20^*=;1|8Af~z%qO{7!p0iL=TAK15aG^-r
z$`soe9>=*>U=4gs{0QhP3C0INLF+ZVP8>T{Xz)IlNjhNNumMc8epuWEem<@x2Qe(X
zJ<nm$gqn)#HBeO3(_kLGQoqLy^1Jou<Wlu3c#u+%+_>p+oDE}nE+V;~#wqb<Y47j9
z+2$~oM_$RK3Ce73)j$bTnp_L=x6Nf0wMA`yqHc^7XQ2%Q7#)#Ok@qSOxTXa7`s!4~
z`Q4~6+Bp-YU63E67=0yzV@z$xWqDd9`MWJZSNsI;Sni}ZSQ&+3<8kMFl{nR1KQhLT
zU3#k8vZAl;jZ!GGHiG5stqe;H3JP-VivxLQ&G&b~=P9BJW$b_gyZ~y1_q6Us<`t|T
zW~rue^GN3hjyy^_Y@oK`O7m^IiIU^-Bf#<fpqIBb&R}OUGG<3wRBjX)IId17y37mb
zN9gtn$MPtiS?4027dbgOSr)(UF%Be=(kZ@8(tx1AW4HjhMZ8I6pV*;2$7HG=Y(MCz
zUoTw^I1mlPQ^{<#@I)dW4yxtqoiW85g*0uJmBLp+A%pQvLx$)&W->;;)>(eYMj)i#
z26U^*?WQ_8VA$zd_uYizBkF{t*hSY_mm>sQ<4c2JPGhy&vAS}{Sg?`pd4uL1%_`>|
zwEWjh9Hc1Lk%NM6GTlc6R*dkrQcF4-+WFC4eKpzzsU5*3efo4B+6>V+##*4N*CkJm
z$C`EI6YE69z`zk%V<^0WI<GW~OlX*DW!<7Z6fKv(3)FnReSJ;W`Ox#EsD)0O6Qu8-
z6q<X^gWOCVG&yZtFGFMz0UX-&T>CYF4E;s>0oFSX47|9Zr>E9+XC%F0@TDjFmd5&=
zH<J(b{=(UfzuKpsaqBWf9}uCK318RMC2DPX^{|BNb&?|E)1kq^hD<xaLA*vPyCHAG
z*i0w~lhDsSoc{C{*6?B@gQGmb_TVm@+GD}zndMCfFsk+3^mvulMSk)`H^c=6`NIW=
zK=lTsJALn985^ITrQdv~d}(}zT$~g4siZieauQ|pSg$f@JC;^V5|!hh;w&DIPV)2H
zoFeP0IG42o^g_f#R^_-VPmcU3ID-?MoL`Mn0VP8mMzfp?e#D(XOeeT$EG8>EyX-+e
z)7p-enM~J_ydw7JDM#B$eb=-b{ZF$CE)qE>qg&iOQTXzs-d(lpTL{I&PbV+`tUBKv
z_3`%3(BHlxCYU8!a^&kCyW>M2NX5356QCt_H08e?{^vNfCk&21{;nJ&7{Z8%h{wyK
zljYn*ksVg|X8qQEH<31$!d(QH^o48^x_!DHL{a=m^+^jw+R)X;4ZEqSkCV<)e-iu(
zTEUZXBA21ld4K1!UP)_~rDyGy+1Vdj-DS@{cFJ2=q_jIuUP4wN2ZTll9~5zfd2cn!
z!^D3)Ghdi)(2nQWf(q{Hec=u5xd!oel~<+>kmlCUJS?dr_#pd=w{51sYL-Rz1cAiC
zQE4A<Cu#tKF<VZ%eX9v7)%Ol3zRy{HxV2F!f|Go=Xpj?xH~_9RktDvo-9_jC2Ej{S
z&~LZ*yFpoA?d>^x<RJq6)~0sBT^a?>PGL?!=VPY9=7r@uaN6#ujtxPikXD@B{;=5G
zK+>V$_!86KBMU;S08nuw&&=}AT+4xsNs{IH`+Pv4q=f|3&r#U75XQ|AL@*Z@SDJ3o
zj<AMuB^Aw{$%UvBTefT=kj70oBPmHqER98RWj<s)s;N(xE2cz!?`WD;(rvf-q-P*X
zgwYG<)(EW0H3*5v2N%cHPyF>orpdf{ufceMx!+WVb<X{Rl~50KPELZ<6sPA#K{KqS
zNBV<9p}JkUbH<R+3n{((#vm5zZKC#PcbW@?9N@a~&&txI-0O&ln<%Xj$|GPL9<aSY
z(1s8uh!oi#7hG;T&9GzayfV_%yBX7degq}qd*#S$(mx^mU0-rI#snGHjGMZAr&gje
z^b-HfLh35=`B{oZp*^XKm9!>1tt-WDQr}om)sWGL!Fld|vq*BB_iWG{Yu7HL^1I{g
zN|#$X4oFW1cWEm3RlPW;L)W$Zya6Yt*f~d}Qkc7wDO%^_1ImKusQKpAjlw`UzGeY`
zS|L*y#J@7X3|VA}2TY$ZLj`*f<;LFf;8!4i48acRaPSPNCEZ58c-X*WtTFjx^A1JR
zn7HpLuNVeg5?j8X+(C#I8>6r5Ez0gf`L^C9l|8TwypbI;9l5Gy&{PF03$cuDxsL>c
zAPZ7&SurXNEf$Q{DT;5h`84@)r>N?~8**INt0PE&`fBf-+e|Z+TF&6O5@E(>tUVsQ
zMV1D8R-38uu@10mYi7EZ!@A&L+;c-c216Z2Hw!%DY~@%Zl=%qRWnTGemnPC6fa(hh
zZ)7e5*b9JKicVTOCoAh+_a$fBKeO>8pOfvUjYw|OQh9qcR!(ZXl_%GNK0dN2l*~`N
zBTFM8N#EO^{`i+{b*K^|KFrETsr_gldB$g^#W<kuTX;e-4s37z+MPm&qJJz4>y_?Z
zhVGz#*kEin=J;1O|G0G*4U2kwL|2kB&eeJH783df@|H3u+lD-UP!Hgprqv%DY7BDw
zE*zUCBt6Y@tFfwiMs{W+w#}9t)IK~Xv<gPvF<)lW%=(boZ6nMIZ|0WRSRN8;ndX_?
z4J>!3rmTpP1ZvxgpqevoHC>5wd1kwNnpk@)f6?|zoumVUdb?EAW{*-iM|WizM=>&q
zVWTJnAG>-#CIU|jBvP?l+}vf;OUDDe_`kln^ek?K{6*f+(V)=I=*b2+>Q|5?BH$P`
zavsc%>mtd?Wf~nJR8-9n7V%DIAh{B7oJ;D+-$^GBL6A$;MBxO9xe}ts75EA&I=T_=
zGFzCtj%Fp;s@)d)Dtf(|PiC@*5>l-tZ1Iw51j~knrkuSVvtA?SgO3)vND;IBUyt@%
zM?^-dbDseOlD|<fdkg->{Wx<Cs+-r-XET2Sefebj6=eb19m~j~h?Dp9v`I{jtl%Z3
z7{%l-BQ`nl7cLIfMiX{SwfIy@kiGrkcX@74gNmnmOe)L%isN2p;ZMij7oOZZcHZ31
z`F5|Sy%GF~j{l4uc&zvlv8}TK(|vYPm2bpaP47e-^Em__V;*owjRW?T2cD{dVoWi|
z#>Av8oA<``x%2JXB2GXplr40k&aK(^2H@3pEQqjszAa5**g1AV<Xjsak%#^aF4g%?
z4#ql>Fu9m>?mKikcIU#cIn8ytzZO`9Ba8+YIp3y&Jn8Vc2dbj(SFPBCz{D88=|{OU
z?fPdAR}%)i?p?;G8*tdA9luO?6w$mIO30A~9dE%;dlf`0Qr0wJaInvWFa@A`>RI;>
zSspv1V~eSG#<T!<*}tuT_G>f$xSbq<QV=1$q-;^PY3FYKc!|7_irVg_U@oSck5qNN
zU>f?%jwl<cwS+bgkf*+0WT0AH0+FQb)FxjdJk}jjaA8h}`?Mx|Ab~X|S_@WJav<5r
zIZ{wrQj)CSpamA=@Zz}XnAzXiKe0QqvG|A&2TE3?PD#3TIe?!as=(we)P^S?R(qGZ
zQES~a;n2+Hxd!>+qAUaej}_;b>K}4{%qz;odoOtJ<kf$H3bPvTL|(ygnmPf=>@slA
zVY5l1VDnQ%jJv*nUsIpWURF7Y-1QNttt*{v8#-IF9s|EPon81z;kS$B`V>IsAyucM
zMkt{O6LMOdi3Tul&bBu8weRWdhq^3%8A<Fyt=-H_qplCuK#*bBD>iOnyw~aYd5Eo~
z>!`7@^7`A3=&!CU7Y<k*AW4uPdV8OS2YB0rESbmyntOVBg4b@`5mYI%SZ;-kUy{p5
zXyP9^ZKBGxK$4Z6ame3(@U*H4<9=X0k)<E!ESKhP>w0mnU%y@-iQgO0aLUivHM7@^
z7>u>&9Zs}F-YwGS)Z?#G4&Lk$XCY$xBnllt1fRI5d;NMj(p8VCngmV3qa_*+Bk7ct
zs3Dq76Qv#XE<3z0?lkD>@up0xfh)!x3Ub0q-Hk{Uj9m~O%S-)P<NI1iY@WeBgW)O4
z7#SJoJ`LI><Psc(hzZ1^*Pu)`Li!oE0`RMkda4Q1NR0@e@A)?3!hJ_Cdh?LW)eJ(Y
z(lphcfv-x|3jEs<Y?%Z(8lWgxz&fTW(<}`9@To$YCjf=;H=r;ybVFhlB4tI2##*y#
z`sQtsg_Z-Z8)~oza=!WD6hJ0l7XcSqQ{dt<WH-@>+NAX`0M^|?0mEv>qb2a(u`Xr=
zLtEJq2z}_HhEG`!7wC}F@RU4!t>*W9Z#d(e9a*+5+S|{&J8BvF7ti5DN!`6H^@Mjl
z{so>@SEh4u5M+=3%F_HB=%L;q6;RIE&#ix-_NWUy3FN>cE**Up&kW!{<8_fz-LZtA
zWZ}6kP}bR^8wi%4=~K;HZx_)aO#~58oPm_6ex4{Q?hdK!!Y)l^t-!Qfmf*XNp>{%}
zFc&IGB5`sQETzD5GMe+o3lE-MWQ(n|tn6SQ=`YQV>u3iW2rVx!8>8}PhditTTU3GE
zRK~`JsFW85eQ3B1zC<n4G-RMIHn6hsR#OU7oF$O~)pMe{vPrO49%~%W37*uKC`d;y
z<Ur&Z&kw#CER~wB@ED$}7n#<~eCup(!-z+0Tw<l}aAR^kukA`bp@8z0SR?QOvF198
z;!Z0y)L&kuZSDtTyD#MavGbI}%<$%BxwjAV!KAo5nGh^ahe#<4rRKizTkW|`&7wD}
z4i)sh#et4H4ZesiWfhFi7CTcyhNv+wEp|}19JumBerV}aS9$=eN@Ie%?S6$U=)%JI
z%JsvwM<Li6hUQ0-A@qi6)J1?zPg+5TnOnv#5)u+s#-lZnKE5}yZAK0xRe)yjn9gM4
z<xQ7lK%$&7<FNbbd)rL7qw$tEFTE~DsS>)Q$DB~&wC19mP!FDO$+DPpO9W5XbuVV}
zwH-665XW_h<QDM>3g$HG?(^;`3siTq&8OJ*Nsy9_Id0Yll|Qw&<}W%{K)`9m&7OxM
zDgM0PDZO#K_E#5o4$W@dNx#iO2(}&HMHJ-1oIXFgNZv1GYu?zG!AjW%Uw&P53?N7L
z9YMXDf}W?YpRRm<re@}C>B55SFke-{C9JxuWca#^_calBrAVZ&Am=_BF+hh$RAJy?
zY@@#jUjMWyZ)u{8a#kf)`4YDCgc6;&84NNsy<h7P2u>m~F@=bQ>rABPI5NLz=|FaJ
zMB$@@#uMEo&OQu$rq8XV9Z`FUR~is5^kHB~dK0B+TQ&>0-fcN#eh6o|wmg`7nSGy4
z%#Q@81Gm1ikGU7LJi!CT;WqfY=OL_riL%$A7x$MFDY$aG0<xWRGo8#EyDAjcyaJ09
zgUZo6-A9~6b{+AdLFEv@S@J&8);G7i3JGG^8cf+nFT(Qz$!}^3jC8_TTFd@G<uU=c
z03m7sR2hEI63i}CNJ9!7nLJxF1N1c_J`@OoAl+eq3z>=E^XIflijNFrpnLAlJTK2*
zX?6lgF>{~DlvXY$ZD(#Kh=R5CpThWWzf|fy;)lx_!jI~(rKvVoPU1OLU5daUb*HN8
zo`8-pvwj_xo!W8V@skN4Lk&SuFp0FYcMA2rPe9$}@=u#tFHYzT+N^3W_3Tqoo3ZRJ
z-eo-Aoim!vI&6&Xqqs%gxsE@VR>ZMUCQPJl8!dm;a|pCy+J&f?*5uk*a^qld8`sCV
zX5J65+BbxKsemF(1_ALAjulacsx;~CoY|8=<ZfOEcuUiuQ-!B{^7!#J-0Tf4IP=Gu
zVF5kc`pJb|HaSkE9E4vvyVwB-dz+uOew$3CNhLiINC52Lz`Y(jeE2<aaY(+ootJE5
zhl?OK&=29L+IG_i{*N2lXyld*3V=6V=KL~9<S$`AD{wr^QET_0RoJgDW}v6%e>0#1
zg{rkP!2tpKZ)Ae6XWtn!1^cTHB2WgWt{jh1jwXbq089R_!2ZNzl@qZYG7plB3=dEg
zH{uJgbim1RL5DObs3geyxo${S<>!^L#a&Hjxp5L)1SfQ_QOKFnNs^MMcDR3ps<m|+
zgGj3*+C|PL_MyIpKtp$@b=QPY`v{ov11TmEMJOI2j|hf%YoO}Iz1spIZ%}mT&1f;@
z#>JA`Yd$kG@A4*ld1gXl7X*j(3@&~@D9i(VrneAJ^ilp&jj$?uDAaZEri{nr(gvi{
zOGT1gKxW&UM?dYa>J%Yl?%~Vy#J+|I)J=`FG}OkbRGz<h@mWL!W6f)6>Pc|t&!8R&
zm}{1ruv=+1PE<?gP)mtu>~rjT#J7~+?t@dGzq;bUjo8I~?HvV}bJiz*em7pedUXl<
zK||CI2H@`OaG=D$NX$m&>WjHgZtAlv0O45vjIXj4;O!VQbIGSqpMua)6HKU~oyRDW
zS=j{>#!wwe6LI-C??s@_QfWU<$~T){RL`U05`V!B^m;5FQ-c2p#k*)IICSf;-zU<v
z$|GJB9!2Pkz=@T*bg2%t!8!;<CEW>;hU$s;P^*+`0^P@1@Nwk+5WoM5Qr48nL5z%I
z;JzZE?Z5D|6YAYYq6_M;UFc;Zf9$LxFf9((c>&sQ5k%RDR1Gx=yZgsooj*Iow23A!
zvGXXg*IfN8GtF~oO=JxC4P)4y5%xVHe_>G4od`gC&u;Hm*u?v%Bg^l&{(osJd%x|!
zbejeJk9Ugwx0P!D!%b-a^B;YgshaY+5p+Wl=dc<mN{a7rKU#el;B(#Pev59K9P6q6
zuafS!t0lpPptius41x?eeV6y9o{~=UD4YRuL~gFo>&VE9{73vUVW>*cSLQga=YHty
z*F8eJt5g9<8quAJ1R=SKtgP()qvCcR-rhvR)v=$_T!2dAb?O8`xM$B%l7L0`Kgf@M
zzRuHA0Vy352_+5?W8UWHkNd#uLqE<^twTZa?cPty^yiKL@9p;cpI_+z^F4}+53MNl
zAlA5ql0tKm&OJiiL?>*oNa!v=!_+~ztZDv!z0NeJ!53OWj7s!+5MkG4c}P1O(H|{5
zrJXE4R~MxQizuGW{cz>mY59|i$5x?|LEDxFDt#)*jv$ukcYWARC|*!AM@h4O45;oU
z8hUO*nMY{)LHnBI{8`2*8J0_H1VC`&JR*36SV5(Gf|;2Fwg;8B8t}5y)zTaUtA)C?
z57EU(eMSU^hx8VmTJ?zAiEt-SsVM_6)Iz13*x|f*i6FUvv5%Tt|GsDQiD(|)a8!EU
zK%RFVZIPFeh;*Ct??v$gnJOXcB@!w|xJ5M-|1NlICtdp+d4T|`B*4KeV2sdJKnR6V
z++cP8_yXmF699m@9Dh!%td##TN;)Z|Y)qs=8VI%KhPexGLd8LjzztHJIeqx>Srsk2
z*NlcR<I@)jVvG3l|H%BrbtYlR@j6{!S{e_u_fljCL;7duz<^7UfkuoK?*Dozg?!d?
zuFd^eSrUuPsiLBy#bH5EB#ea4l#=>H97-yThJ9Z`PD^b5{RaPwuhs6K3c{fzdN#>F
z>*vKQ*g4XwG}mGm>1tn354!u-%P1zQCW|8~&*pFdd7#R{qC6a#(%--8Buak#SiakH
zt)eNOzn*}lP^288*?#2x;@B;m(<3ySk92;jbbb5w?IwwPk}+3aZwaG}JTzA63ZISM
ziSu`74a6KB9eo8=DJkBSyskbTj@TLMvSLRVAh4A(@=wn+C>R12(|FwZ#$49rw2iuM
z?#E(){iqwu|K&#p+*|kkU3f6^0i=iRf|2~8s|xBU;LCBXiQFf$cIia1%4lZU5jwYc
z5BB|9c1mX!9nX9b60FuyRCV02*N5}iPe*qC^-4o7rv%aVfs*M(V%Jf#9B=tD8FoO9
z?xu@I!s;@2kP8JO(V;6crU_iy4(!!B;a-fb7CVWUBOk5Qye^GXf-o;UULC}WM2n_K
z=9I%^YjX&hygR7?l?9Sp$>6or+e8_|v@LtStq**Wea$8|=wdf--b{|b+cz%XrSSLp
z*qLOS^`)##0VxLI<YF(sdN94d)A#7bps$u558?8liehAjVgcMK;b(~+QCOZIBV-x<
zh^)K3qwbcJ`mMUueZS%7@Bdn6#pl~moCVY+{T2B|LRUQxr-8O&`~CDXVh-d=LMn4L
z(=;B5{l<`Vl(N`J)c}7~N%@ng8ZeOz!sr}r#Iex;T1%srIc-ZDv+sLxGKR@=9vA5l
zT8%utvf+8N?TVamdnI3G0Yl+q*M#%7k2V&~QBasq#9E~@=02&!>8jTC5#E>LYz_($
z+GxfBBp1#taq5|}<S9s#Q*W*P?E+k-Vy-ZSqumFY0wIP^zxh>MPGtUus%HaQ_M$-q
zk|RL(Qa6Q#gv3LwPa-#GnrUa8C1N(d#F&%}cUOgWPa<Wl1K?fm6J6cRIzN0Hs$QaL
zmnh*Nzd-NTaS~4RV?uL=)gv_a#BtUP{Pi(7iBhklDf2+{#G^lyJOx)Wgw$bYqYV^7
z0w}jQIiq`eyD)$mA=%@x?3JlNI%VJj7%~Ozf1uO1-=8E;QhK^BjZfDLv9xM=1&~t1
ztYA5V;Jt~WW3lg1qw0u<D3HDZ<FeOSg7^3Po)5wmW`j(>3I%8MVl)a^qi?%*v?0^i
z`-|$&i?UfRZT|yC#6FAv2lTuDaot2Ky<*FrA2=*O=K}n`!~a!n_5bx{{_~^%uK+bG
zVR?c(eO{Kfh&ZbV3|Q^nv{~n`HCyz>4(oKR+qHk6nugf-2QPOFx*lEQ@s?7=Ys;3d
zy9<XtxOT0%_wDW`Qy=e5FBwy|2^=~^=@tIhx;2~cN01_OIz(;J#5yD!Q_bio+TcIL
zVsxf2u;v2CoftW|apKFBY11E2PZ4nF-zGt*-lh1rSrMDp{5ewpp)Y<|b&c9QA-U<+
zd-fNFr?!2}q}{#yLt}`pxVZD`Vvr7v&`KZwvx;Mh4F`w0<R95?=g*{aTC&@I!mZ+1
z=q_WQ$B+MdXqii+1<x3zxhXtW?bfYZNzzfisKx>3Pz`ha#i*mBv(#~>=<}yf>vGnu
zyL{tD#F3*%?`dwh%gDpy*Wk)+JE9}7Z}`Zyr~mx3=?s5S+1_2dUi|aVKi2{;>;Bm2
zK=H2WABya!C_)G|GS$*_c6M&rxigRv>@$HPvW_T5;uh+n6)ygqIf3TGLn`+N_fb*x
z<a{4M1G;0lKvCm}%^BJqKjr_<&d!fn#jUxvor5p-XG9GyM97Y-m?}iiV!u!GrvWRL
zu*yxJ@4LCpi}wK<4%9{~oK1-8J>fXIj^f)baSF|fIf{{js;UmTO0e*SqqGWBu>tfx
z?nSG#ic=m9P}!~Nx0LzXN1?U$i|~a=o$tKid*|m{i(l^prOEP*mg7yoB}F2Pq}DO_
z&hO?Xihoh7fElj|92+lYHA^Z>^6F%NT%mc0B+zqv$gha5+apf5__6SPV<H)+sFhuZ
zzN=a>h0I%3U-qcIXJ30vGDbzarp0=z&3@tq*EX|f{yrK1ickA5auombHvU6T!lvyu
zl=>6)d8MVL)nEnFZg)vQ2SK!^pberHo}f&41~d<{wbar!13)Wc#7V`;p9T&aH;TVX
zC1!@<H%Km6liRz-6>Z}hhWuMb63iKhGk`Bw7b7JKlhmf4&2<(fOavJO8T0XqAkL%V
zrwN(!c6y=4ms@@xm2a(=W7qV{h6td*B%ANTAY^;$+i7v}pnV5_eH=x(So5EpE@>U*
zzs(rhoo=*c2e;!)lL67dNsJU>fhLqhA`$(?L}T~IQ+GA#e}6_T&AKXs#yp2ivw0L>
z3ABpJS3DBIK{J=dXTvrQSw$`=14H1a9Nj{2V#K(KT6^Qvmw&r}peCIBC!@b);N)Y_
za+*{o#>*;j+yznKFV-`HZf<VtbJhjj?|VDJDZ*s;ainyI&8}0I5CT4&De`1qICF@L
z>&mWOyUxY|c_~s+POVb!-yaQ1CI*bY$HRxdIy9vf6?I^cry)onTBPmKj0;U_ghdO|
zHx*@N<=~dA5}h8J2k)u+`%clXk}Wjm?aeB`TnnUpqR|}nklo|3BY)udO1Pt!+-<GG
zSIV5*?pfy2=I-3))iAW(23f107{&4`)D^EkWh2j99*}dnLiAtgm(#p{^Jb7};R2@+
zJc1x{;o`->?0BSCxraB&cRssRIMS*(^>IU=*zMyJasy$4D+rO-$P1l2(&LJh+WWXd
zyQJX%Z+TrD`95uLCnNp74{L6;bInar9%$y!ut8`PrXY+6I4GtK&O00Z{?cu|<F<Y%
z=_&=M%l-<LOeADt;Kdb*$d%G`E8mbBx;AT+&<?3s*^~(QQxEUH_VUSbxeZGT-z>6D
z#%!po=&=}I`L^xw;lufce4rFkQiIK32DLtECFSlb!Ge-P*Lr|RC)8XKHC;~vix%l!
zP+G@cQBX*I%Zm+LLF1kj*_&SYK)2LyLkZVE<T|5(iX0;|xc*}Q-#-?s5Th7zN9!RW
ziY7;Zwq2j~2jfKT-xSV-4!R-qTi2i<rEx1%2R*8oK|$zb$q_JZG;WHh>ivu&$g3V*
zi8U4Pw-T@h=d!pmGH%OAf=HzvG`r&v1LokMv{5!|me>%xQ)EsIRiLxMCYeTcQXmgf
z!i|34P*eyG85_woGv~v!R$^vj2F4!{GonxoBzUOmnwlOffx4*6N7D^!B%BteE+gOF
z_Ax2V=Fj^t;r%1#{ODjWm7;6G3sJ_7YxJ-F{d?C^-5viD$n8JF#eZnH_?JiZuvgc2
z_j_NLhNf~g>Z!7fJ?>QZp0dBE`fz&U`>~RPyXl6x2%DDvizqtO@@QplOXO};QjL}D
zz2%pu!ge`lEpaBEw*=k&&3gdH{c9vG|MhK%-_v}Tb?oHHlSF!O_;A6h(<l8@PMf}<
zmp(42y!x!}7jH|r7D%y2srO7m|LU&VTMu;Bt<1ID+NX5y+NP~rpYSodS{^znV>(oN
zmN&KO7ME?9f>(U8C2bgy)65e&O`iSqT}|;vd!gtb-u>747cX8Qp*g2T)2U#x8`0~>
z8PlV#al9;(v?|}cAG5Z+yemI~SqiL2YgaAb9W<)p+&Xl28agJ6A0m-5C|{$-(WqBT
zqm{_l$OM9K4W$*YppBQ$))l<Q<DY*XGa@Isg5I0637hJe85uuO7o&dx6Ta4Ey-bzP
z;KsY&oktObvOr|--o2`d+hf!*!|9z*0UMu+#!gj-8wLht2a5v_Sjb&f-WsEgj^~Lv
zFL|#vQx!!;Ke+oYD}j3&mn(Md*|TSOHwDFszmZ;LwK?ikYhPHk9~-x&$*~M-80r}p
zy;sz=*=F;p%X*<*Z$CuOM3q}Ue{U%k|J^31XL^JAj<;<@UNdehG%+C(Nh5+mFY-A*
z+W(M7|2s{kW^F$*t<+~k5s&z)6??O>3hhl*==UOqc%(l2L*&#R!f&A1CWN|#7z$0o
zMqvFXY=!ji{4r|&vS4~KVjzi4jwTr@d86skHg%%Ij9|~en?7r!`*j(LZzuKaDTE=T
z#Rwd6=vH5VP&@XsVnnnbpyE1!w*M+9a4JUk{eFviz~=Oqmvh<@(7$t)m~cqcL5OgK
za`F|7v1+&N+D>R=(Dqq7HBe)X(H-*%K^wjmbR{_I^@MG)*&l%1yfkG$86)LhKYw!O
zY(nwXOGk%Jd;*5UQ$}s(+DVFh{yyOYSNjk1tfNQeIQn`>Uz!(l4%p=6Za=U-2Qywy
z>X9fkYtev`h~X;(1g<0c7A}Q}Xo7@I&oJvqKxYwBWP(`EMjKTv+RS6iD2*PuFf1vp
zE}UU&)O*J$?V$C-{Np~tXNYaTb~5C+ZcFdAK%M=85yMATPcIwi80<2u+4{PKW8Ke7
zDw!nq;Gi5hb<LF+va-B5YYa6aF?A5kEN~AoN)g2zd4d{7z0KPHPd1z+DZdg#i(z6A
ziJn0H2y?RXry-fj!uk?*6M*G=&-VR64BM4QDAO8-K#M;kX3RuEeeu~;vJumQ-0uIJ
zKu2_bq53n8E==V+4}b3p#U4d<iV%?3sE%A1A0HoVPLIXFoFgPmLabTETrZ-8d=;8L
zD6pqK?U9ot3(@;vWWj5Y#1Yx0f9x*kQ#D0%*LZ)6S%DH{?6{0IO4|fx{8@k*z@As5
zQJ4n<j=+9Ml8+oY!UG%^ubTX!;R~h;8R`Z9xuLE1uYSF$um1*h`Y4P%l0wNbe;U4m
z(A16QFPfse>}~652-1et+uz=PMfC?VQJlO1lZ2=g401{%v_j2>K==oQY?D5G_^@OY
z+3!s6Q&fB4XGS6R!!}O~Vqh>U%r-f~a6V#>uG>>AV{Y9-z!<U*qM0VU|D`8OUA^||
zf=X)XuT?hiMgFlq=f2co*A1@kg6PGt8}m;gcq4{Bf-hH!j?c2}d#d>B=<~PG+VmRs
zaEQY63cMt5GPzd}MiXAhDeQ{opUiC^Pvxlnd~EqL+KM|yP3>*(**|hL@EV_`*iPa9
z*L9nF_bdM_OUYf<a3(^|pGmuGmris3@1MIq{W!%MK0DTtSX?Z5K|;a^ZAMh1qQLAO
zE9hI|&G|FERO~Ts7loz|nT}}gQhB?IZu=f}XOtT&*ndr?LRFs;J_5r+lFhOp-k=)o
zfWok20uzU>+no7xy+gS96-db(%K$-R%~y$5qX4Kv9a78w@EYqL4p2@Zb#XQmium5U
z{o{#g)!*+HtI)TnXiL&L*D--ij-|Du$6`K!5H$Y#Tfji0S+>a6ua(}eh6+14mIZ3r
zM>qBSS&6%!rYHo^51A>1O5&DT7oribj1Zgr`)AcA1}yN?k|WUJB}b_5a<$B&7ui*l
z#E{gG8Ih0?12|9p`!`*bkQl@Ow25U0X#0pMK>Ikf;#QaXM4NN%Qi(xQ7;<2YN__q@
zF%$r4&@;_nbA8^am2{2&936z4k8atzWeYKii0SxoR@9vk8Htf-#2gzoTp#<wb{k1o
z9aEsjODHT$ks)VilukXW#@Gx6_+AXYQqNi>d;<P~Q2V()6N+$rk8uH%vzp4!#H_cZ
zzx;|~#Q+)vYcVC0g9KZDg7J>^T(d5{!3I1Kj<HJp+7NzLqET3MW$qOoM&o)53S71E
z>U>ZNP^%Z1b%;PkB}Yj57!A>7Moj52%h92+hYryi5=|pCMPw6$BNAd$oL*T$X@mkT
z)o7nZmQ-71N7q`zJyEi6v?(?FAqs{dHOHf3v(p>+P;~4o>=d4QtfcpA+n!saVFxp@
z!lA8>L0{kJ47)rnjF?g(I?6D?l$bMu#0^VPjDe@<K7qWze-sKjxBGrxHc%fWz^@JB
zB3tKJ`eSCcQy8RlZ|N6jyP8%7wOzNBKsurw>b}K8(=pI<37~V+$@0Rl!1i34m|=-2
zi;_^qB9Z=_f!<L=q|hlXBap%<0SuU_-@F+`G>joE#z2;c4k(%jXskADb6y-FMi16O
zmRc|rW7H9q3tQrX#|VfD5LH5`SRVIrsnrYAd;^J0G`d@UAK7oudC@&Us`gjWQoG)Q
zm0=_SJhAMf*m%9a>IFHXh~D93cvnnyW>J@Kn`vO1DTiek#;cT=W*TG82~!3LSz;gp
z?&&cx^$ZC)Q7}b|dJ=l_FY7;p614{7bx_V`y>R~g3Dmb?=ZQqSKrg50(vN$EUi)7`
zS&rXDp(}xyYJmpVuUtON0@RZ@R6Nk?VCV%aN}4!21h3#ZQUIQ(X~c#Y;zh2FAnoax
zL#pqDCdbb+vj+M4E(_Zg^$OQ)TG#PA<BgJlZ9+{QyD}9i%FM-;@@WRixg;6t;j04&
zH%nZ+7z^@(7&QSg?XpNhjFSpaU((MBujGayw&Z+N7Y~RyzJ=V18^n?fkR0)8grXAD
zHv%@D7pC`tW-;-!N%PH_;ihKDxDhdtwoaoXJPK-VCex+9X?tF@N~W8%C}ZM)5|VPf
z1f0zqC~-BQg|CO%NX6%wv-hHS8IAw!QJM7j^?iZHU}BO8amG;(pWoeeyLU`$&a#(A
zk;mrmsUe>jul0Xz`i=2jjndGtPpXE9Sz&MnLv1C=_+JEET!6!cTXv9J9V0FDJ~LYq
zDU8csJB_h$>aZ@Nd`ut;05m%xdSdWJN4_R5dy-SBgqX#FvT7N28@dcgY66P7F8><(
zHU*_cKu{zGB{qR-3V>vX=+cIY03QMd!5GSA4AN79Qk)pptRD|CWerBfSfNK2uYiPM
z5CfI`8Hv`a1!x7Ch~XFh4MR$S?3#=u%$0fEFf_fafq_<(k9-T37s|YnDVB(MLww0U
z!W2*rl9ZGbi?sua(E633Y$Re%8s<XPr5P$bq8GkOwERJ2Hl!j7ErdU#6Iv>q8}yYs
z{$_bDaUF^ag33e0y+QNf!^e+x(Ar}joh1gFOpg#HWN5BQ<kPnr!y(DO1epoy4<O8p
zFdvVYL?yb^xieoEP=iP3!#dC~0BM(q2`o?*Zu=+?#Q}jl5MfzBMdCh`gVT?p8*6@|
zhZyLLSDZh8e>8iD9r{i=GCtz|^+oCj-<0wUb;FkfisJB~I%*W?A0cK^TnajM76OR+
z3)Gqs@JY&ZzkG%KGccW!WP;@D6@j0s!{*i|YKxMpAtg+|_I}MH8cuP1z6uKKP#9~N
ziejkVi->n?Zl*5mP{dv^MYq~L^IX74NJf&1rnNuH<zo$;n0bI5{(96$=p_m|S7x(t
zn~Wsr>rSSIKz;6a;24^duaML9D-NKZGq0p@sC>BL=M9r6LNMS3^>A$f#|EMu5`Mr4
zn$2eDhTMqeH+-cxxvVZ~ldfoJXk;x~k2EW3JC3_ydbdBB{1U;FgtrDV66QyY+D6ze
z6Uv7^mb~{z?Kb@>x+wT-ghN9J!VmD9hhrXsFwym-_vM@>jVtwoh?Nt$r3>f`VYA_W
z_fpjD>>O}?*{w+Pr(|w7clTJ>7dZm7*MeqX!SB1?Wn^;?v5J@<BO59-px%JN*VdTy
zikg-FonL4}{uN}Zq${ebLE4KK;VCe9Yw+tUcjCNbG7zD;B_e?7+s+Me+)=fxx?5XB
zanN5I$KUGs8w6`U`~c`rMnnKf0uwVr1=kOIZ@9mC4@P63>sUc=Kma2#Gsk)MGR+rz
zq0lX6N&PlCWv$AHrd*lue#X!X1(ZL#rC*@`X#KaHNT$wM_Rf9igWrVYSr!5tuAE;C
zv+DtTeDQ!4rZ_1L&S7E^aaq7+V!EM(!~>|cu3RtpFjKabJ$^9fWRF`gF+dXY)OlT2
z9G5YAlhAjg^^njl5E=%|Qc!@1FLB4f?4f1`H%rL^%zn-8d~k3{WK;$mCkS6=YjvHZ
zgBTh;FcbSqJGF4l4|kv{;Oh}}XF%&sVQf<-y=JA+-JVL9<mdc%awn*q{-XsLn9_C!
z0=cW<YklCpVC~_Ivq@z??L_e&w$2#xW8C*in<u$qdxhEg_%dsqFum?b^l<b+me{!b
z?H_-zQSaS*f+y|9a2kq(TUze$(+)ywOUSV>r>`IFxfu4?#RDxdF>l}}dr2Z=qR|V{
z)%i;2x;E@KshSwl`?|^+OKb#e@^BhyeExuJT=B<0mTga1cCR{nZVO9X@{KXm5ka;2
z#IN6mzp{-A{c^ng7ZI|~4K*Y-04^ia#Y&}*gD?Gm*n7{Ys<y3La3e=BV-`ihfTExX
zDj-NQ;syzlGbjR*M4|*YV2*%pk|aUN89{P1A`&GEh^R;wP@?4O6ZF>o+V@($_v%Mg
zYgL!#x%V90VXe959HEas`WW+F+|eyYVAS1IBIU|mD-}v(yZHoMC72{xh%Stl$!PC{
z)Tw3um{H->a96dI;;+)KyF7^xI0;z}JrfEr&^+eG5VfU;L)wN&u8y~I%&=bELmxsb
z5EK9oZ;xarnxmXC5XHEA|Nc<I-ds~*E-tRnT}NXZEgefYEH31TY`LD?sdv^~FtDL)
z&VbaM2V2fdd8D=wFssv-E6n6fZ-STmD0CGHO|j@Th(mdR9*%z0(+~{Wl@|PRgmGbL
zT;nMA>Mm+_Z+g>nL}k>dj`g1*0g2E@xBdAxd9h({5{nX|czqO12?7<Oj6^NCRbJJl
zFj~wN;%z&A^A47KP{*CvIJ^&zKjGu*O^^`^5Vna7uU30P_Su{EA~d}dITNGupcrp&
z?|ns`L<|1*ifxxII-tJ_ZBHWG%Gm`%_s(?JuWLHVIz~k4#GEQ<<2Tz)aYZVD3mMLD
zi#=sMCn&7Irh|N*9u7D<w5AtS!m7o4b3@U&9gQe;5wN8*tT6RfR#rsg|IR9|8tjog
zNPHQN4C2tr%*u`zn8lg4kiiHbBgKQV7!BGu-Xs_(ix8DSHqcm~R-C~0iXuYhL{}qU
z+(IxA#yS$p9)!Atuh)NO0K)pyg2}eMB8~&$e=@UTz9lzq-i$?rg>=pI4%-j0$)B$R
z&?^A@6cczMn@}F}TlQ|<dW1;V<2d_Q2b`>eW<XSgj9)D70T%-6mEGp6#YrW?=}0g&
z&1YWjI)VUW2T+a5N(IDDBwAV{v00@vW2@2qIg^4D^V?SfAx>)GoaKxDK**ga4-j4@
zF7e!*UgC(L5=oRvkung$QL<#fsSrX;Lo(GAe0o6kNBxy}H%ho@gXpOUsv%>5ads?C
z3eg|H0R+!=^Vex_4lIjAO3@8PQbka=p)-8iv|zBKq8`UF{7_%!QR1$!T|qCaK}fp?
z4YG(Nd91GGfjmHx6MS|RDECirTknlhyP)h<sW&~a&H@hhwfDN4XF4IXNi=oXojSLW
zs0&XAHiASKL+`eEo=52eYDSj+219-YlgCLQdLgTAEnGPXY;mYan1sx~X5|Fejvd?(
za|Q+QG^Ayb9iTdstD7XjLXDV6xV_$h;rFYbOit+&XAlmP3}nr!RbkJcKbMw|)@yPQ
zWe0(==J`lpQ?7?_sM(zhRvoawMQ$D%BPH_V^%W*-`fqPIV^YDhn*H0%^W(SV+f0UT
zChd-2P-}9kL@@j5>EggU-W?zM3u;lNU#oJx+gmv)KQg0dZ6*Jctd^ak#{S3GJjreo
zbkP3%z=>hwMa3;F9?6NDm3b@&PC8ELu~qWF4dOC1Z!$l9JX-E^<hu8V8fKD8)i`;1
z6F<s+DL2whQiB<9bew#&!(5&1hxOz@UE8(Q1}tiL<gzCHWRL(#IYi&K2Leq8BFdhU
z!%Z4&A@~sk;qG%z6CF<xmzbh=2W42rJST=+d&Z7~)k$vU@7^iEgArjeGKGOina;`-
zLS+)+odVd5S%1TVW+MAd#!!$JP`!m(D^M^Z@)L-{@&n5lB$KZPH0uL@yDJ;rXDb3^
z<zRtl#+R$+v(CaT^=_F_H95v4RwG(!q$NeO32jJZ3c?UT0B+9LLuM=tn>ULtp361=
zd4cAH`0LlNyYYUCyNwhV!Cx5UFR-fv>v$>3zFlgPNm?1@KX%N$-gFc8D|%QTHAbFc
zZOrv|{KgO@c8Vnw+K;C8UbB}$kyHRq$%BkbV5JgKbDjO!{WoNV*xL0jrHkG>AO($y
z4-bhvKISkeOJ)3drQ7C|-EJdJd1yiRXLp^nf{RFaoqmMSP$4_ONhMMX=g+5|`}XJc
zlzSTV5s_dyqIe1;XLn<QNZ28!&*AI>C6UN7#P9se@l`0>?a$~0&5Qh>S(pxjj=ZGR
zD;1s8;9>{&Bx$6+y=(pVV-M?(uc?H8bVZx*B5D;;=z==u5mY8o$pj3$eWU<@)8!Jh
z3w^-;IB!p#edm|mtIsS}Q9PxcT=G-q{Q7lu43ziAfU3}QGUkhxXO(uJiW~4=ty<F<
zQSui^35wvo9UEtyqdkjKuH6UB1`!>t1f}nM!&iLLdqdyJGP7`qoBsiJzU*!tb}~$D
zxT<ra@cnxP5{CQn7T~0`5?DQkP0nysUU75J%$V&J9~u(CTcU(+fF~$={hH`|(m6Al
zdUIZ8#m5{(QI=LhT7dq5D;LwPjdyWgGDwd5c`rlG(s8LX;t{tEMMIQqgngR%m$SmF
zl}rH+W`3Hwr_0|Ad4a!ZlJTxr!4VJQ+Xyf=|6%iXHWc^&c|W9;xr2*q!dERpQ|0S4
z(N?`&g6SSY{}s$(VG|@p2KoUsLLqYs>5KN>b&#kyKx&k#2%`gjyYYWTwmhIVUKJsO
zCJB#CO2CPRV6G7Jjg4U8J|O0~E1UHUExvA+nhX9s*MrXf;>^!Emu?c79Z6sdaIFU<
zQ#jCb&w7o^hjWoLU@H~gRs7dRI7>xU78LqAteODqKWP3l+;$;|!e?Vc^g>0*<Dg>t
z05-l1q)n8ec?)r&S~PXZq9G|s5F$cEVXOz?;IPgM8WCSbE{%N@S@U;icFZf8cb-ac
z5ctKdQ)qIk0hONA1PIj&QE@huV?i)GW9mvwKE!vW<3}AtT{aPYf^HJ+r_Ugdes0ku
zNJCqA0KWlK^<OHyTgdLEp^M?};IOnwGu?9z9Alsg-E<k=(}=8wtK}t3fIzrxa{l&u
zA<?5`_mskjpS%lD8AQ&|Lu6F(ngoDl*zX!}i1dhpzh#J;wY4iH)%^aQ?oruqpq8k;
z3nG0$S-N00M-wd3>`DR{bg7~jfC#54!uiN1{H27LhCKkEPw=Nx-Z(~na8fbL#NbZv
z!-o$EB+S_bE=Xcs<DX{|8o6(35n2|cN_y?p#($F`89P*4L+}J(^Q7rDSRLeS2sKHA
zya^6>HuAUFRM9_gkA4V~)q?s3AZ2L|j{6i5kj7`H6f~HpD~_U1_!%dlWf$%Y42{ug
z??-9r9P5cS+{Z26W?5a>4<5#sjjH}dA%hY2Uw=tttiQQs{=lPw-}T%K64T#=L({+h
z`iD|u4{VmiL8x%~Hvt)e_)sW1uYT77D|vq*BT(mV3wlj?yG9>m9RT1+4+rj$)!eu5
zS~t-(gq7QCr-hUr>b&>3RVQ*%OeM<<GMd(`Teq%~|Dn`AkR@IyK-)#wd|G0Aj`gcO
z@l3UQ(BJc=!>zJX(~j+yji$P~dZDN5l@;6T7v5WRB<-HNyU&UZJ)_gQQkfna>MXHp
zU}<b*5N8fruvn>W@7Rk5&>EG8=UtvGfpL*FTU`69^%v<b0gIpp1t1mkChWr)(&ger
zv=#wbwm4zm17hWokkBKh61Fs}K?SMI5Nw<2ho{GhS|Bt_`H3hFp^cy!_<(*P1<dYv
z>4KK5rwG*>Q4}DhC9=m2`-Cvk0eeQ1V6MK0iLeHmnLj`!Lj{5&ZsWXhW%|NR-7gwm
z49O?qqz#!B01oRy3S0}3Eh6h_f^R)M!KdT!#@fN5;fmac<oecM%cp1S0dCwx`Ia;@
zKo>sqbvlTUkTY=<eSEfO>8nbTtCLAq$ntB5%>Km!_@~qG+|Ys6HJ^eDC2{`TI3B=X
zkW5l#$Z$>Ebvd5<+rQw|{p#iEP8aPLG`1;`WrHqZ7*qhx6P+#0{ULIap_iwk`F`^T
zF=rn*-jT-JpL7qvd^K=k?e26qY;R{fhtS_W_Pt++ofyT1$}g|F8jt`X!TAi6eu?k3
zCk{?Hh(2};VRTp;E#|OP^6=p+OU7K6Imlj?xbvk|;L9gf|9ZE9i%*oxQ0x={Dw76;
z5m&pU^CO-U8Bmptw(kQFfpkl}3NK5fERmVlLP3|HFv#5rD1+z@ChNIj$y%~dRcdOC
zlBW+(O(z0p2F8pOBpl_WW2BA<O((@(yVWjvUtt^#`@GON&}FNx^ZA|==1!2w7K#}_
zxe)w4U*zL!&XRVmq)idrC&?n93HM?T^O2J8`foGLH(R3yc9ShEBqa2ugR);DerqN)
zh32_T9<{M~b4)a?2pR%yOBa6GdjFHx)@9LRkUQ{Z`(oqv)%uRcHM3hUlPh*#k?Xqt
zW&FFBJXAopl=<(EQXIYJfNBF#!|j27rZO}kro;sid_y(P2hlnLZm@kXHyt^8v^MlG
zBZ0SpQZ9|&q5K0cUt+(|_s}{QsegDo`*rqacW!MNKn}RVd&45VaJXXac9k0d{sTF8
zZQ1gqv3suIlva&GE`TImN5cpY4gevXUC)Zx7k6d?bfMBw2UOX8dF0kCguHiDTo>(R
z$QtU=m@93L-VrBUC}bBmV^)G58Ou_i7|ibS`waBR!%77+0Fq)2(3Pc%QakA>gopuV
zOQl4V8bC;a8S}MHpkapm9UKIy{dqf|(T8z2%xLlfl8316JosQlkQl%mMty0XZEOJI
z3R`40OMrrI-0!zV7czR_P<Yd4(1AkU`SA3JSny<*kb=+pd+)h>F6|acN7PzLpls+c
zlb#b2M-@OGcu~``@1i>!Taf#c$P_`{JA5=?fXK<jEBAZQz>3cUE<dAKPz;_}3|3>8
zsHisS+>R-@wWi~Q-9jgdj_v0T00p8u1<*?S`>`ksiYndWH{^G~^CYEb&l+&aUM~Q`
zq{&_0b=(V-Y@XT^xTS2{uBVVs;lXgX{#Ha-+o(i2BqCo!=flPVh#&F##T|RsZl6<&
z8BLg7p&ddS@ki<v38fA6PN_f*Bp%?c%s-dl`%pZeZO?<G0jZ3j4v)Hr6O=k?pcm6J
z|Jn^?K0D-sqTz8(Auff9Jy1`E40Y2Gz@s>nasi<z#iXq(f0GMuPBL904~_WM7XFpA
z%pk9*CLrNi(elaW#jfQWuibsi`@m)Dw9m8X_`B^{x`=*xGr!C$!ypli22eu59ey_q
zJx6Bn(-G+!{)3^(1(@VQG~S({6zlW&F%*YX+IH>X*`J6mo7sRBd&Q(YNO-$T0px2s
z@yy6{xd4bKeH-PQ_!NENFiYq?xoykivr3n|szmOwjp~GWNXERPq-1cawmYSSH|~$m
zmF!I+koth(3Ln^VK5CUv%jMQle%Ol#ocN!UfL`~EnriQ#!mNWj)ELQ}LU=#o7_gjJ
zKO~v;V71GfIzB+=)Br6<HAfSo4S+q{KpceZ9-7$1U?8`*#~?IGM~7?#G7(T2xsfX1
zLjX_`?J)$a<OzaR;;qp`V6AiSmT$sPH@pG`2~sZDP9>-YY3HUY0(fm3cB(}WI5ldo
z>5x-ieh6yYBBA^P8G5e%{A-PJpw>fx86u{FsGhV@qDOic71ej^nCg`(?V>wvf!~oj
z?fZA{9^mLAOLAjij2$|pY@`jhfpV5IRIYH6i0Dtg1$HlyS3Lkn)@)a=6ER{kl6AWI
zD!ZgB)K2o-;9(A&w<9VrKqyG=K;JHdo8Q|t6nq9E%TM8xh+r15%te~+v)etiT?-`{
zPN;pxNcSSXQ>7Edw{hC3|7*ZsD<a@M#&Z;6suf1&m;o>*m<sS;r2kG}?5<{G>uHhz
z=tg83ZO65`GMeoJ(eG}ELcq(HM4=LVg`=*&UvXS6f($uP&LSKG;0$DR4+?4PZ$5#z
zB`#E&RB{L-1QT8MnaN5^S3&W%7EBDh0-X_L6|m<XbDH2I-i+*BA`$0Q{Z?{fkEe#g
znRG0+MpEv;U9@rTM4oc-AQ?B*1GVsnoH*f$AE$kUS?&`0udZ{i0O?r&l2s?8<YSe9
zcC@|BdEf5x*do^<2bk2_aQ(yOhx)xW!u&f<@FCf828`@V#OcsioriRf)UWzK0zS);
zrX51k$R=1ix*yC2Y8BK1?<Jr;H%VrrR+6q5;h4~$pDSLe1K^CP%aP(HMhj>W!6`_k
zX-ACDMU!~eoK*ZWPVvy+(gKxCiXcT3^s-XvkXKD2s)HB^LbN#15&9fwmb=zI5>-4I
z#jHWWb;EG~C+UgV-P~!GEuWJN$Ve2=qA=2Zi_kEl&jxD<#5Pe|!|2d{qtcxMr!dSS
zoYMqf`@UrV%-f$vBW88#k1QY}5|Ow!sG2B<ys1;e-tC6UfY5daUgcV=UT7GTiY{E-
zY(%c(K83$TMKQb7<(i#bz?&gTBS3SbRG*1eYu84ikV5#w`C~W93^42f?$LfIT1Xs^
z8ro?_JB_rwmc-;{iCHH=U{W}F@-{Dgq&tM1$q);OEBo}!B?ukqL=Ax+j;2@cv}*WS
zYHUq?KM;dhl27AQ6R;h}A{-SP89J%J0a`OdP^1!@^gk(eIR5XJI{2VZbvdSIags|#
zS=pZS{CypVPXoD(udy=ZH_#4oY+tW)`PUmKaHwp5emN`~bnw~BU9Mztwm<)-6TfEV
z%BM49Au}J~<YOmQ=HGgCx~-t`HR@hiDCnFh`!vp_+&g8zP7xYQY>nL4UtI7Lwrs#F
zNxcO9-cR7g;iQx>D#D9QUuC<Hc^KJHthMeoDwza>nkZaxIBK6=@DP5sA|+W+2O_Z?
zEN^AT41(e9RKoIVp{-`Rc%!9oue_tsQ!=5;zX^4}G(>GO?XrTYC_=mha+pvvY^W2V
z4S%sSr&%|q_5InmnKy6VH2wMI1*y=6R}U(yajJKOYal0rII+UlJ7%d3Fb;7z5EJcF
zb2oHw9aK(G5=Fwqou=e)+3~A#C{bo5!e*#S<8Dw|CXJdcMSgw@QRg|fcpYyIT>dbE
z2?84~Ub~w#mJSn%ZUkTmRPsf_TNqEeuGT$1ffL&aZpY7w%>6LyP~nZNvADr-PFYzw
zqAAJ4rEJ4J{kT#`1#kaq%aiT*d$2QyP_G-G7K2bX{Dbert;hPoUPgrJg1p)MFA$Y>
z0-lqoh+~|GujAkWSBQ=uQ*6+yNQ!?duqJrrlk<;=vRh>({aF?G3_Rv75N1F+L24(Y
z91ope4N|QIyKNZo4V8}YoRdn1>a}Pn0ngn|<)96n_Bm2dux~`x+v|YFPedLfyW!t2
zTt6b#!wiy#0QNY$hK9D1-g%tmn1Y$9{`|l_zB^`DKZJ3^oIx?{!9L+MLBUwXv;)%1
zzMMMfxKP;>^9Yf9d4~4EkOW&1H#uo(y7KsBI?j(1IOmLgNr=Pf;qaj?*<@Z9eRi}K
zR3ek?7g=_uW6~g*Wcx*Akr<@YBIJ^iVcMus{RV5-ukX^*b1*bjiIQCKyk+2#z}gq8
z{)2_mD(lsd++nlzfKf*35fE>TM!>;?j%k3`BV|kVHter(*Z<2Vjy^#ODc@UaU$*TM
z6@NexL6XlR`qPbsEx>v(;k7WM7dbnDkBERQH5=qX6;vM17PVkTFg={0mUv)Zwoc-o
z$ciEbG?e2R0BmZ4fto~2l}2+CWk*yYPxK6ioImaiUS@}ZrBrH^<)cjVhJ7uyA1{o~
z81$Eiedq3(WN?j*%Lre%@SiQ_Onl~tB{}~Yo6q=eYx(*$tjA7EN*=ZveYf@^>n7IQ
z@1GVKu0H<Ho8%>@YQFGZJvonI{+s0D9Zu>m=?@~jhExkA&N?mV;?xPwZVoOV465!>
zj_sTHY+0L^mC+4F-HiRN6HP7FK;M!M2)u&U=RV-&oF!8Jq~yX==9vcoN)*+7e-ttR
zfMhk+cOqXcoA^AM-<RLi*tie0s33=Lcc}Cd-W>CYHnsbb1>Lc6btalVru8{HdImH_
z5fwGBtLdp2d*WQ>Hx()bPZ4`SP_RA0l4e#rJTz=Kg%9(CD;0+G)WfLh`<%rm*etyD
zCcmwt`rc}S(oUCE9<4!We0<}*xd+xMg@-6dhY%7ics#2sGLX&*%}D5oLg;5Vq@6UD
ziFAgVqs83h{P~kH%F;2)@A2?#YEDZTf&tNhY}*FmaAMUoTINgOuKh_Zas45CbLj|0
zAaEm4^#l6<T`6Q-*|UcTD%wDV&9KQLqgU5BOS>F<YCqUL_o03Wpk-Xzq8!Nc<)7C$
zN-=`R2Cxn2WZNSYcMT5@H(bc+wZ=@Kgnis(#9^ti_@Mu8-Q0uyE~7cA*h7~jFgoI<
zA-YQZ_ZhyIR=Stl2zMG}mXs%m6>-G%1Ox;OV1b0F1HdSVSWty@Nz$VY-=GR4Z(v~1
z-P2S3@#7=%;$F6Mo9c1|X|z4uKGKa%P4$i*1DahWF3Ezy!ND{a51OM6He7KK{xP|5
zp<Lc&z{{uI`7<<|1{x|{1Q7`JF7t-y@5I||(IU-Nvzj<VVPL3<U^tHq8q_^-<(cxd
z-RjAkQ8|+z!A^*6e<=RNjd1{N@g0FJlSUpQ-O-qbAh>ktQWJCYQbg^8Us+raLi*1P
z{Ep^U`SDvz*?bWjaFVeSx)Y7{k=ne*m=pM3Eq}nLG(dm{Mytmv59Z4eB#nn&aw59`
zJksz~K*#Vox=ff8XpS8?!m00%*K~Jx5A<5P9wJvGC(;#eo?0fj%df<-*Gw;v#pUO<
zEQm*@zFDgkUlXa!-3GH;4pIbtfBC^}2%?<HXWnOhh@nj1<5SDuLxO71HzU_DE$IWZ
z#P1g<>A4m=w@iG#&n_n7?(Qy(8r~oh1aWw;&)g*tT@uHriPA%#e|eav6z^z$p%<ap
zRkovR(5XH$De2AK)9DaWJ_(=Wl$TbAeSfkxKe4yBmq&Jc%D0zylZLV?XuQrpo=Kr*
zIQ|I=OmU*RJ+!a_AMV;|OZ9v3JT4I-mNoD13d`0R>*(k>5`J7=3)hT;)ML%ui@l5Y
zb;@qcJ05wzB`#J_XnN|`<d<Tpy8O*OH_W&;1w>|l4*3|Om(Cego}W;7w^N>@;n%RR
zZE5T}Z*{NK{+#_RdxP{8jt@mOpSTdPc;u0~O5k{1OO(k6_xUQvT+Nz)*u>Ph$3K1X
zttNOn|J$OcdMX|w{60rLqGM&ddS?8qf+W@|ub7HSDI1B|TGOHZmkSV7JrPoqRED#r
zmVNS*zG{qe%J~mZy&E5&8q3<d%vLzB>RIhN`oQh`to%=ROd(1Y!%jE{s$%v=n^Jz2
zjDq$6Giw~RHe-nijnU&}>O!a@8n6QsE`_A|HYruB3+!ikbAP@Jf--j&vYxmNI`X|s
zeQ4ITB|eEnyEO5^ljfr!%)%R;q-S8vQ3LjbH8O^Foqi`Z6+L!kZl53wA#OmppR@Na
zQ<^n~TNoB?x>OoY^<v6iee-qzb0v}rX=i6=RK$GoJy8?W2swU0^lO)oG^PQ`H8wV8
zb2Pz-R3rQDnT6V!BCNB5U;%f733i$2Gv@wsxBOw(1lm<cOw{A|7}=6srJa(TNIsJg
zkH*mOmi>A1>D3oW?F>WUhR>QVsg2Jjjn9F$Rk}t^`@<%2u9AWn=btZ_ZQk#SI)^>K
z9$^|z#g^;j{sdV;>ocEjm6+bffvSWJZel1_HbSu&B1^sq(>kKVJh(HD2p*6H?XW^;
zshQFsB|9~BKe3df$9`k>w^VnJnyxI2BRQryEmOR(s^yk0gMp`l4)T-asS1VW9y;;t
zOu@K8d|!O5APk%9WSHZZE5lM{u2ELdAhVax`xV@O+1Tgjm=6wBK88ziQOS(*K~WM#
z^KB+&<w(Vt`1*Cu2ItpvPn;`b>_qGxIWYqBm|oWg^t7X|iL8s+ABrnuIF<#br;+9?
z8y2Fr)32mCgG`y%^qY;#3n?*<l&SZ^K?wSi>)O`s(X656U@&pr9C4RV(v+Pvs=dIQ
zGQZwT<CQ?i8GF+#%$4jC=SZPJh1RZR`sXc)AUwxQX(iL-w1e01X{z7TWhg9djw9^G
zpg2ewcVk@kjvRANs<E;0=ihII+VlEpmM*{U>3zo#?)S*2g7l`k)r7xZUzGxb6P1@I
zMzx01=-6ZFD3dV9RMvNkpW?ov0>hjI=8g6Dhf?iP5>qDhbHG~s!8tT-+Gzc5i@!L{
z2_}E;Jxnpu-AX2``zsBH<YL+lep3V&2(W~DwXItf1RYeea6|ww3!C0a`vS7lEL8NY
zu~`;}AnesYTAe8?QWPXqsX|c*Ju<KJ+}O!ZC{6QWsf{s*&l`?Q1Y@7I>E{rDRj6uN
z_@K72I?V~sOMiJ>oYu^V)oh~73VAQ*b*eR<4|7j}xQt;`uzj~@Es3hHX{)K#OZrhH
zB|80P#TE3|EY_2&4{>^&Zj-!AR&;a3+sG@tjQV-idl`m+5)43JFPtn)%By9EhKfw_
z8E%K;2PRXyatBhb@P?^%k3M<?@rGDcb&5`mvNd*9yG_W1_N<{d;JMt!qepm*UYuE|
zBq{1yDxhqLIc*9GCmVARqHMJPc!t=6`fPJEGqe1kH$aH<0APLU5#N*Bvm2PwX?#Q5
zdPJ<>QU22J%aw<j+i;+@dTT8Jw|wbqA`WWBpUp$T=9z)ldPjBwXEB8I2T?Q=At6VW
zcAwQ)uzh39MEabhgnnl<Hez$XOUA?qXb{<r4=Z@gpMN;foG~-tS5T8Z$B1V-i4CuH
zAuD2b!`IPrl(~m9CwcaY{Dv2=|MwSx|DG~oj|n*{7JeWKw$8OzvhKu_mvY}wOUp@3
z9h^~p9uRYoSRqT#O^0xN$Wl;d_k2HUUCkNJ`y<}+NS%@oI1uyif5~B2bGkXhbuh3c
z-fqm2)}z&&(K7gW^uJsr{Nui|vN9@N{_?j~j)3O;lA>?zI*8EEpMXD9c)GyvC~+m)
z&5`t%Jb=B#Y&bIS&x;d~a0yT?YqZlu<mKf#`@>wegX#2JTIm8oja2lWFUO_o(`svL
zeb5siz`XHdA-88RiAc=NwY!pHVs;_Q(m}j4+9p&mCX4gpIi<?&^8^060YOm}-q<l-
z7jbiXs4gR40B#vmn2qaVLO?Qzc=n8+>}YrHxZoGsZ1J*Kb56&OLoE$rX>Y+Sc(6)d
z)GC7F1cmsq#`^raseWR1g?}W!y1R2exhe(1{20XKlQv+7T5ZofJUpIfWF!sM*VXxw
zfq7%t6OTv)0<lsDkwb^nOtiRt24FaZfjjECU~uJVR=!=#6N!Xq3!Maq{A(Zd6PMOS
zBAcMfi4E6?4%a9NBZ^YZDLpN<$%k;VIVQg_-{U40O~111$B$B&gsvZmv&gNg=6u2`
z<iTIzkFA}Z;ft&Dvw#@UISJcnkZidkdrPP4z?3q<rte-Mlix+$uj=}3pwQ>xz8mbD
zECw;?LjoMF65RO17T_3JpnDDA{vy)S1W1JvhvwE+8GJj1)z)RaI>ZQQaMsV4i<P*2
z@a;TFF|b$r&c41)BF3>%*O^hy;&lbT->^49K#2?^b;QUKYqbBwD<#kpo)mn-zsD$Z
zH{k!6pDznF#-1Goz3m5r>;@<s<@B2&BE)t1;b9p9?P);fwBP(=vSt!V9`*As!cFg>
zPPgezN67cKjFn&7K_#xg*5jh?vr<F^4dMx5YXxTGGAZ$ahj?(y)(e`e>&}eVcOZ4X
ztc&^%yI()n5ulwP%(5uBRwkO7nkiW8?<D#L)#0N-U~L&ga}&~+i9VzuMgViEba)!4
z2*)H)&)WM8o0^&i<MgJuImRn*5wiyZ|8_vcWpEEcctG-Im-Fm_X^|WT#ENp1mV?P@
zA^8Ko%$nF1C{vAbTg1jJWr`cuy!0D$s0F}IPq3Tii5z`NEE(Le1T(vXK2iPs=p=}a
zG#INbmzwR=bb;a^=*&*G%hQBAM=|#?&Yo?=L`SK=7f7SuVM~cpDT!QdCF&X){27-W
z+zfwSByB-hYoE|hG*;!8?~S>N{8)l6Y0uFE%(iK?ycC+TafkGvX2HObdm&?`#sjrU
zE_M;(Ax_;f+USJOLMOb<hvVzokYx1z642oH9K_tUN6_@Pi?I8Ih$#ya$2L&1H3G#a
zPPE{24bwH1GAYRdYw4vkvs?hx5fVmF2Szdq@+xSPol<CL-{|n=3Xkl=U#2A1qkqRh
zGg?S}t>1?H@!F(zDE^fp`XIWsyERfcNuUd(V}pO95)$^nJV?S>)ik%O6^yHp4CFq-
zZY1WCuzs)wKH$r>V*u7Wl2!@GsSQZi4yB<M60R6yd+RH@4{b+->V2fQ&x|L{G&c7G
zB@cnS<i#t@kssxR5Of4{dg<tSgGXl9FtFhGUC3;WZQTy$nT(p68rpE^7}3W~wT4>Z
zInd#oOH`HEmZ)NHdmj9DyRQk@P_*4BIYT{ZM68#Z0w_SGIUPp~NdybYthtK0!wmH-
zU?k+}pRngulH!sCc56Q08G>Gh{Q%XFZi+cp2Y&Vj`Nvy2!aVoaTj4furGb{2sTLCp
zi!v6(Vtw{x_zC?xNwQ{l-+=?+%G?n!nD|yRUR2si7LphHY%eWg!H4?Qovt|0<@`k(
zz4oMk+=@)JxuT22Xf7kEA={~#hcX~gXAgC=cD9z^Cj`+Bh%Y`MB-C+zmE1d$^>At!
z*4SH`U3(GR?%c~P{_{#-Q~ML*`PCZ$g?Vw}K<M<Oa0OjFGJ3h7%~}{fKp6AkNG=LY
zaLcVt_8&JOahn>U*6ndiOutD6=5%#;zlW52TBDiUR8zr}7X5pX#I)5YjfZ#ZZ=9H{
zinO&K)GPs9@>89HO2&M!<U#EHu5Ce$eBP_~%`ZYw{E%~YWq_;HI?Xn|^(3rW7uVAp
zRLawS(53ChwA{h3*L_@W<ckDaq-)T><B<0pjiAMydDGdNS|RQnvA8{N!1B1{WFaGq
zZN{PQ=rF{%4?Jl<3><s3S^@|_T1-r3t?rkLvZf97abiCU8HF*(Jrxa_Mq%!P-7a#t
zRVp1*GtayCW;B|{vG?ooZ_P<dvkNx6{(^+9<a873Lo!@VEv^0Np$W39)9@E_wx0%=
z+<^hw8m&Z}q);Ipu?r{l=+Rf2egp-n7i+NuKb=^;Q;$W3i4C*{oRBm;uEkUL?Bds#
z;`Le<OUd@7hsVXm9m;eWZn?X-D{tVc6ESs<0mN*CYW)~|lK-W=9vzYhH;H8oVEB)-
zj?JD4rwHir4K`S0Gy|0L`;Pyx0TnR!ZAT5};C*VH$_sAUP$z`F4MtKRN&Hb1I5SEk
zNK7j5yY{psnngNpSzwy%ge8U~Qt<WU)(uoEA47?9FuOjFmq010V6L`HNsNEcjFQlK
z=7Gp^DC@JSnL0mx6g3LHuB(n>Q*fxRS~0zKT9(12s0Ea#7<p4*5-1uC=x`!9lzCdM
zDw*T$taEA4gfyL?1|bulI;uW>3c|^lDZ_2$NZnB5_IaL`rc%pczOz)DG{{wY5&(y@
z7@UZYQ6nf1mw-}3=j3|qa8^V_L^bCJ2?jVFU$K3zjItY!xQVYlMBRruo#LY{_-LWr
zuS>_?Nr}^u83>WUz8V&GF>j`R;W(KX>%~a`CJ!9`+;)MauKeX^=(&*GRH<iTy9!(X
zvD!yu_LyA2-Y-Fn<3mCRa!2DeMI@BGDU`c~#0#iJ`6A2DFKT-X_sUs}o!V4y9hZ3K
z&GCh(w{{01Qg>|b(Zy69fLYh*yE$%XE9;>+DorQ0L(BZx8}42b$)6imiqgjO?9p!O
zj!zI|Ej1ON=Y;yOMigOYx;-(6sv(sQcT-S7!y&)i%GPp{4u2J*-fPRwQ+N6pB`<pe
z0awr^xkE#|ae_P|lV#n+w!UEID1`(MLA&}UW_k*ydC2_9X>_64dPeu*IKwyi;2#01
zb?1{?BeCiELuqHde`ByxZEH`~*_>djx9hX8bF%lI2Nh^<I~+^7U;a>pZf)H<v$&;9
z`{ziOLt!9_a#17YL-OUOejtV=5Z!*)>B-A86D|E#{@C2Rs3|BUBvog}CVc%L0clY%
zhRWQAACu3l$CyEovgvhob$x=Ut6fBQc&0hhr9WWce*^HGlDfNP^gz0t@Ztny?XrQ5
zO-WBL<n7zHeZq-cvH{+>IAb&$k*G|O*ALNAN`p~g+hV=nNAjnKlahK$vNXxfq}nYC
z)zZ|gf(gWX**W?<J3FUmgS)XezwEIE$Rr1iT!&vG!)a&#8Hf@ej#1&gnxX&VDsceh
zmInjsbB%)Qlq{Aa$%gl~Vt4%hFbHedKpSmV;`Q5)V2nfw#=&-~fYX9Aa@+PaPicdJ
z<d2ka76HSq0|G{Xf@Q+rACT570$Cs~aZ*vz8^_4V%OsY+l-s(g;hdLj6}I-s$Ot0L
zzK$WdmK}Nsrc&X-A`%mM!4S5S6aDpi62N2mE%Biznz=z@npzfok9@d$lA{C#1?^cT
z`eOpq#PXlOw8wT`9}1al-Q;Nc>t)*BccDU7Lu>u_xg^Cle)>IHG<c1xp4Nx?j&tyo
zb&{W}O9MSfWFmbY&r=E7xuW`xO8zG0+V&q4eWkA!cdP@xWRmePu>Gpo4%OPo{NYg(
zO<RM*Yd*AS3QcHp+ii<GpM20Rsvs%zYNwh{nB1wh2isyzmBz<iBM)?V5)>=eJe9`X
zp{<%aeZrHyna2P8MWSNNIWVkZM5ix`O!p`;>c}P4=n3wLaF;SfeN&*z60P#W7}-fG
zmTiv=JYuLJN%oi~@*ROkNd-LTMsQVb#53w!oFWqDl*)?F@M^{Y9ZL`{XuS)~la8PR
zT;d`tH+r0bfoV>7Ff_!cq)i(;z!ApW1_k-L+?l_~5SZ(2HMMtK-LAAQAPw$(6b}?R
zNNhU>rm1eV3y+Vq+@ap*B=C{iWTz}U!4$1JCAkhSr6h0R2G|KFZu;30C=SQW`9uf(
zi+poJp>9Y1Xd&*rK9{Px9jP&2)(O^0b)2?JT3#N_-E$NyL*5vbIr8t4(17`be(zV`
z^=l1Lmc!4{Yr784{>#qSBJOT32xiPw<{Ar*mWs@;1SK$Dg~&7%-i*|i6Kr${jqCJL
zj5$!bof7Y7D#%Q<u_Ohg4T>VZ^3*T2T~Aqka<=(zC?`96xIabt#9>Bd`MKZ3DaLsF
z`uYMhaC1qXrZvw}UJIT6Fs~)I{JEgjN9MW*|5tv|&7AGFRMy7ErfeK}q(INTD=LWS
zhVxJB^%wLj5HAv$jeTe-t#+lhvSq`D4YA@(HML{Q9HTo+ghOjSD<}voxgHrA8Ls_C
zyt39m{oDH|A+`1OQQB?%p<&u1a{9NU<KkqFE|u6euTY=!MYl1V#q-l;<159NY(l)H
z($9wUbiT?f$@3;ARGX~PTi=gQPq6GFXdDOQ`?k-nXI^|!1+VAx^d<{glfBXG<9aeO
zGSb6&EzH~z3qo%_z*iqob6ldn6LU;}beoQ^f2J5y6Tg=dy@tV7!Wm7|W_4<Sq{_z6
z>Zid2=2<>ZPhYFNl<_N&h*;BqhH@j)XB1K#etnDeWZ1N6(|nbqu4a~&Nlp{>8N41z
zTW)!vlpmp}c0A}~_CXhBAzQrN$jYb4O))1a^d{umJpx=#d_r@aS=~BH&0R`%$k>U1
z+Gm#LAGv+*()1bY{NTdZqcLTvR&9}+^l^&V^`R~?H=6!Qt5eMS(SzH_v{DLX(HtC0
z30?lD!}UMEAn@&f*5m(~?J&%!Q>br+(qad4O`X<#5Y;F{&Hj`88e+mJ@h$&U&M3zy
z!xLuJ8Is>nIyW&4#Rb_nnm9H!eHN^o`(^xSDZ>=Eot^GV@)9@A2kEnDvciHt0)RL8
zn1|vJCl|aUjsjTF%sW(5Nix{>;OMeq0EpuVzS7d6Hh-8T`0N}o?CU|?PmnQMu+v8x
zPFp48OecWuUG>8*9DF*Jm5OR=9jwU=Jte}0{QBqdf*571Whb*X`n_crfjyvedorx)
zaN81w$S3iAFAJrI4c9$eZp}b}KM<|U?WllQ*rS_fvdDE(3UN*S$7z&1i_yDP*=m4T
z@;wQ+18OyUZ>caPp~^vWNR_npf|t|}*YYhVL1DXfKTC0mCflO;o6~}qfB%Fj86KAV
zdh|GrMyqWq2-_#xyLb`zZ5JWy@9(_3oeNl$H?ZwXDs<}9L*OlA18Z^vr9fic)DU>R
z<Ib1~Q0C5mJWv1Uudd+`fFF-Z^5xHW<_P&VI01xhpAhX3h|ZT_Bp6&nML4&wGGcbY
z<JaMNk#=j9J~I=OVkG~Mz*ubC#xZshq&puJGX)XJYgkACZZ%vc`m>OogH8Oh1MmV<
zl@!a2TMrTw6Jtl+%gf8J#-7=X#33T_Q*BRMQ<J;%25F=R@Bs&zmuv#UHQw95kuMww
zAeRm?$pV)piVJ&w{D>cCt*ot`7+tdD0!^4Xo_h3}UNHmT?**aGXOMChqa)ZFC?u&2
zXc|!R5c?Wo{d<2s!}pRrK_IS#t>Q^oSlG73Yn4Z}x*_7Q1}6Iq>e420XQwNUm-wz$
zUpi$0(ly)!E@)Bg&FMNn8_6m9b`x$aV+%Tcm12x^`|I8$a*AKyjq!Fev7$`gA|hwk
zc0PHMnwr{eQy4(-BO@AvWP(F~qfC->a}PZ{l3^d&x`hfm-1;Jr3~enQSbSsl3?$pn
z>(q6CaOHmR)j5lKbAIeAtR=6d#B=;pNkzj~+vU-mmNIDPWL4#61Ia3b8s|W<yvsi7
zz+0hwel3f%T1lT-1Snb8YQ@Xl1p_|;tzZDqP%UpN>SE^r7)`0qChUddvXtPqK@mL?
zwW9N##IfXR$frKyHyA*vNSt|-tpb{}LlleCB4@AANro~dkLOwpl*S(zwRNb>?C)7u
zmd?#+`Co|#{%?d3|9K191^+ui*Z)6P?5RuW;*XUWCzWeNgXstuep86h93dch5F`X-
z1x_7Mk|)6_<i|Z<6=4`@2xC>?^;`Vsbc<}bx9cVYKhW@S?J{wN;;NUiPYMJC1tUqY
znV>DFc6hk507kz``BlYj3Lopj&<Ty%$Tl`^zZ)$YfoPmlOLWp`%cY&*b|iAUUFh!K
znY>|K>nPd-iYOg!Pr~+gxRq~TYPFhG1e6lR*#0STg{~jVC0Vv0ST%_qV68l@#^uJo
zb=-Ox#ckOApSTb*NkpJ04i?3T#-9wpH14wy@K8k(7L&*^CUJC|rk<|R1k3h&>-HwX
zE~*OEVpiGQjDXLdbrl4LGV+5MyS3Iu#e_O_x}r5$J|gk1Uix!hLC??2s~Q^v-l(uM
zO855u(t7uV7{$nt6=>4aNzJE(s{T>x{!iFACwuyzsQLfhsChPyB$yP*h_WDUu;21?
zE}?)CBzW1uFK*r?ZC7|_1q$@@wz#?6!0&e=9I?;FPk$gl7}T=8puh6{={;U)C8cVL
z>THgiJk2-1NCLg_d>Ca&MS@D|*4kj-jeYQR@kT3;D5;1NNLU;7Ek>Ok#3C0u9fd=b
z01!vONmNS92>XDfw1}V}0&os3BnTLZe{BaL`QKtEDpFzKY>zpG+aDRIIwGR`O2S-U
zPEtOfNSiIIyAAtLL`wFGf|i)VSuEIOUfsG6#0E?HcTjb@GqTAQQ0k0%k39Mgvf3%S
zd3u8pzD<mm;Jzr@0fH7783w4;hW??RU(Y}t0T61piCae_MmbhPZ%I6s1m!c;S|hs&
z?w%;1We4hpT>=Gd?MUpCke{F5Z6K)=-2dR#U1C2Pb8oOoBR{8GubGgPat@k*(*qxw
zjrcd%&Ms!PGRssPV&ErpF>r`3oe<QDXKs{=k@6rt9EBth;Nq6fkN^=tZfH>vi8Tq-
z)uY}!vtlk^Z*XXJk29@?N;0u;fULu_K2038Hp`otIZB18g%#C1vUF3Mbyjz8%ZCp^
zBUihh%s`-LYsC2HenuZ_o%S{AxR9(Y;WF5?wRQD}n3k-VjSwTg#Vw>TaWL4@M@HH#
zTk2MK<_%nP0e>R&_ZQb+GtM%NIU>y2OH4xpzF6JZX8&=QWrLA91LE)4^;1EdfdVWQ
zX}9uHLrps^!vX^0E)0gOY6%iV;Qyp?W`?TBK0KrN`Ml>mM6=%SYj4O$oO!jsJFXr<
z<y6A+5AR}|o?mv5u1bIIX4-jacqyX}17)=w0<bW7FWU;nX0P({nTZDlkq?Uw{YXAa
zpeJ??Gmry8*%()Ei#-wUU9+@VR$Wi8V3I3ltl(kCQPu5mYr|B*O)r}Yu<BWkjyvnI
z2h-Ej84{z_OMF#lwpj19oH;k<ca_H@15w?k#%)fGhf>1xvN>ujpECqA!jn{g;D64w
zF?xFH@UG(9Tcy{p)^%Lg+1;(#>im4mlT?<DOn0FK+NNvonCzCFq?>v&l)dtsS$Z04
z?`-;X>DP@4!y|gv))dK6SWn&T<ErPrzcK4_%?s3bavBX)FZ0DGCF!+xcHm~VIsf=D
zcsu-0?A@c#b<+?VzWM7bG``%Nz9p{;JXdW|=(@eSqJ@AKvB$Ds-2zXTW$Nb@pCu3<
zh_ETF62DA+H6E9kST3y3sHUgaLR)s;!a^3$kD_+W)%3!JzGb0c!kUWQHjV~ql8@E@
z*nwhD;Ji4|O*3gK)gTUfBFZEeXYH4M??06qE>?wWT9PGDRubc#i@5_o4@J7fEhYy`
zpSHuTe87>3RLr=j5GA^)!mgKcj!H(aau%Z#yCo@YHu#NCLMmkYdW94Ufo-Uxl%dR#
zz1Lv`<>Bu*e6pw{p=II$%)ye25#*C8J<RL2Oh!SIuN_hw?|@kbg86L-3KEZ)IH^*B
zOu?u;E4x}aE=8GIL|BrExdZQs$s@LeRM=-t-X?qXW#G-sn!FX{lV?qy51N`gyp`mN
zcSQvuUn9S##Ml3SDMEh=q=1`I=k?8}adA8dXE-|d)v+ag5|D|gqNu)<bQpe8ES~)J
z8H@Qxpkjf&o}*XT1dH7An*g=v>Qa`7%sbDNKv*YZV6R{f3b;&!D8_OTOiV(kC*ZT!
zER{JQGzaqQ43x{K2!c=lPM$D$(Fd?mLrC#RhBu3oYK^R|10rthUqJFC^c&=z0h|q}
zC)l7-e+l^n!eSyUF*+x@Z{8urV#?e)UQO7)m(g(`pK@96U{|;oeo4{S`J*}gKfsRv
z8<_m}7sp~0W0aY)7jzb*95;x4x1E~aY~O?dv!-r9_5n<`p`Tig4$kLRc(UfgwZ{s;
zdo3c-A1A5Ryl)#I2gwP%0@7W_NliyDmU(tpB%?Mcrbv{D&JH~sBn+6SSLCJ65EvxH
zQqE!kIYICq42ieN@U2_|yPTw>GPbq>V_lhNZ|U!KUjK_^{|gTP*WdgLA7`;;FwbnS
z2nittC&H9N8;Ku*7eJaRV_@F+09DeYsd8{pNp*eTmc-;1a&hEUI8;U`>s4sAjMx3h
z?n#W(X+fjgEOEnK0741^=%%&_2~l$0Wg1xXw4Z=8gaAlbD5b%NgpwS~%=Tfx!bYdg
zCvn>An@4t^NEr1*Ibm$DqXQs~+3X90g!d;TB#=7PET=qeZIi>rnBnO&Y%F`yK1-Ol
zJ~7rQU$|sxkM$>J3X>4S0mg5Hpx9s>5)2F2H$egr@JG&}KZKA4t7>X$_D(^hbTi8M
zFS%)A&~M0s2Pc%Cb0`{ukuL4YK?hC+vQxdfT7+WEc-BzH)AZu(k-r>0Xprc3O}k>@
ziOQTcDV#(GUXZ3u4ZQO2)wsWP#1jl!E>zW~xkn4EH;FxSXAaTq=ScrmsrdTWD?xUr
zaVI(b^Xm1j;uOBjg`r)S(0Wsf4kbeF2=f#dDcWdriZI|wFhOW9_+0=Yq9Io5@^g`i
zP(-(p_qAmrI|;_I4?SNswY3p&_=FKoND-xv#SYc$I$ID%S%Uo(2#lJaN76Hfy9Cys
zKW^crIYfK4ROmbS-iJY?AB>Twaf4wm9}pN={o%ty!jZ(8Q3nv1I&!yB0^C;ebwH<t
z2<d{vIoh@#j+<E+v$60V|3U8R^!Kst&)jd(^B=Eb-C$}*o&%SSWOYAbhed&WOW3tG
zFBfu!BThh#k3brzuaeRp7%<of8G7TJ_|G0I7wL&7BwX~0fO9B^76woP$#)ZQkxEDJ
znu~i_LU(sv%Zwau!>)G%@>%Z)<(;!wDF2r`VGj`e4R1sO2$}3aVg;JRLRK}UE;!!H
zVTmfRO`V2u8)y)vYqiWDqE%)zHrPTmbM-UVk8h7kJ?!v%EyylC`*fmftvD#GLAhpJ
zRB~urrur^{n2@j3>Ga=Y$>weTr0~tqFGT(P^QMq(k^z7%l{6iW)nknbW-T)X7z=hU
zyK#mx{rK<+eTARWk>?Z>GdDlv+|^yn=@@@6Gu$LV)oG9H;LAK-6W@r)cKX+VcGU|v
zJbeO8>UHk%7-lNpjby!W`>9*;zE`DVQ!gy@m-6xKZ}DgNoYe8vktyG}TzF6{WNg`>
z|Nah6E-psO_-MZ;nywma(}inkqQSD|zaK0v`e^rQ4adgT*eBe&61U&&|CKF&?3i*I
zKZDLSlQhksgI4qK*b5;F@C<?~=OFajXmsHMFFHsWC}@=x)Y!qzy@+yg^0!q+{bFw&
z=j0T#&CdITD}uM#E#8vsI^#kq1S!lyEyq`R^p~xmaO+XtjRy5rYL_qGkGmP`FtvGW
z#p@pN6)R5Hz7+^HW6jg`gez$UTjmX9tHYN%LCbQ*x^=gH{`|RUbPUQz$q@0$X)<le
z7yFF8x6ietxR`<R&;0rR&645JpQX4V%4+!Jt35m~B9MzA!tyb&*)rdzoJ)K2A?~Z4
z=d|X^l`H3)()U1!s}eHHJ6A8tX|&xwd%>6sdN}L@g@P8=+qjBt!ous()^t@f!S`%B
zq`~ZmW3oM?Qj6DfSbt^B@yPjh%|KfqbF28lx5osoCDhZPtvzt+lw>{S$bxf5MrR>X
zK!)0Q*O-0I6&@eyy$7M_dp&5EuFl=Hef#z`)4m}gEMJm@f+AU`V`|PHsO+*8?6uva
zmbby32}~*q1=J_DwI$Pta;kbUUlR%ot#}NW7w`)9lhH@oFiq^zgPu>qnwRiE+^Sx`
zLEj}raumD7(am4w(FyU%BG`v>O0K3a9Yg|yer;j)cxINh4Hv9(iw9SKN_ncmrkM-Z
zUGe%KTLItTV0Tn8zkq^5|NM{Og-@*LFg>oUyn5cedFHWPf`V(oDwpf>QjMFhKNe^6
z)>3@fDNok&L|Zf(%vvFaT!h|!^02^^_iCG*oaBRgz_Al2w({{UUvy(0B#eKfPnBF@
zkgyFCjN5B;>X-~$WnR-Z_;$YeW|BoqmJKtW-|?1w8b8{ue@{$=WoEKMPx38d)MVO(
z{q<Y7wr$<|@~oce``J7CYL3lhV@EdATH#i5NBciI&E3&k+!2~^#W}ZcXV@8h7(<m$
zVvzW@ojczpOhM7`!1pD^$Cd6IYc*x(<fZnkIRfgz{NE5>TCr)9J2sO49=7%CHNIM!
zrftAoNg<rzxBsCY9v;4#ho{7ZtxMQz?xH!b>n2HIjW98OP1hq&PZmh9Tg^<<Fqf9y
ztUmD#l84gt(_U|@8@}IXG5*GybBhPpsXhE|J;z3ar`;8g$>v|-D0tzBy6k$(nkTAp
zr=#RHd45cO#n&T(uQK<4`SJ)Q!cyBJcwt+q(~afo&(E)b+QCiya~6)tM^`EZUb!mp
z+^a^r$0ri`t)BOxZC^TLVyMetAYaUBh(ZBvh@<LM&VXs-+@kH?XfSG}LKbbuB^?QD
zUzL#kNfXD89sBMQ#GoT9hFJW3ed4BN?4q}!TY45vdlYEVD4;(UXL8xvdYWK+sq6yC
zL$XjZ?RtD0(%2DiETo)od>&I~AsnasN+Ji(?8LcqoS!cb%HA@z6*Q|`f(U;VIJK<`
zV%87MPHjwRa=7z|(1CXJsKwP*S-78Iw6yHsS7i2W?i!ci8m*6$YvjG8J9~|O8)uK0
z_1=8CniBTYix)3gMQnFMdHE`sEz2S0_ZEVZd=UOz!Y;}R-5n`YQ|`NW@9wTM{CK_c
zGlx>(awaAwX|{Pf1uo6!oS_m)aRbG?s=cV^zLWb*gZu5<g~%F$_ObC`(sFkg7oA)t
z+M634z~`Y%rfoUnh&^oho-Hn>waeW4?X9qbZ~M3|k!uwEbB$iwf39&ys2SzPkt@iu
z&y+sm|CnyU25jFQZT3MTc8ehV_7Va*d>A_&B<fi8eIFAJ78>bpHA|!#R|q!vLFber
z2{qmut?2gu4Jj-N1)@98diX^Yla|jTCkH-i1v|S3=s``pW_}nM8d5D2#q6qAXT5#B
z-VUnt9F%0UdS0(#4l$?Ilu8VUHAy%fYBXoPJif3xGm+DxJ)G4u{(^r;Y{2*IzEvw$
zJe+{lf92rlxMlb5C7}49u(sY8DHnVfau{2;Z=Vkaqaf+Hapr(ETa3HIW;g>*b>;a3
z6gS917$A9q%+O(|5f#f`gG`be+UCEaL(EDu@20Cggm$kcC-Y%BUbVa04-O0@Lx@5f
zO{AqeWMHsPQmHRcFrrY<ReSL2wQClK4qw<@|LCCRC^(3hU<z7M0V_hMeMH)kG|fF^
zb7G)9qon`RMZ%BExr{F>GZlSIeYXOxtox|P?=P&YR3tpKNAKLe{cdpA_2n;{gEx=n
zT<T|sR+9&2LFYmPGbQ6vd+GX9fr4e<pFxEYeN`No1+~TlovAF)9fPIaFZ?Qd-!P=F
z)_~(VUqM8xq-ui={>lNFN<<5g5Poznr?y?GYEVN1+|X^=&3nk^j$zw5)o2e7<KHHD
zAJPO%t}!j8yL33b$A#_W=6(s;=<t?dC$S}asc$VsFa_m9nweVHC68wvR`z5&turoS
zF3NO@{b4pwh2`%}SgkrTkAeV+8M30c@7%GXIzzx&J>$YAblQ7Bh7&0VGo*i{ps!Sk
zZ6sacaPhhBE2ny254Ocimb`||dqAgO1*QWh9vU6J0M~U4ySp+>iUr#~Qu;^Z9<tn*
zE?(qA6ZI|dznO^T_2E%dfQ$m9co?9&ySBxx<~#g&*0)!mezVRrS9OGMG^p6zF-&%o
z@FBRCTEDQQh00<do#)JO(fUdlmOnq7ixU}S|Nh6ZV=4OcFmdzm&$$=>5dEw_KgCxU
zo`+-l^FyD%>c4!<j!-<;KR+keo|44o{qys1_anC*f8Vxss|Mfy<O_v$jEr_DbFaZ4
zIVeT6HEkKC%E)-V{oR;m=95_Dt8Qheb?m#N>3f??P%T%8QFef2R&z60UpTP%&)Xgz
zH?g^zx8OSm9B?JIo1xj@^;w(Eg;zKCd^OFz<q?|;44*+t{PKC432QYI>inB8X&qAb
zwc1FebsBwtu^t-O(SmPM(SkTl)tHRDe3I><S{+?ob~xeN$flk`>O&!}_UGuR6pX4a
zZCK?p<WVwRXYzA$Gz?UH4yH$i2z(XTE+n)LEu+^|<KBQk*U{IP`5FQzp^7od=o;^`
z^vL&sBFTK{3S#$i5c^<k?}tT<);v2eAo?bMZkFIA=$BlA&6el@U(+We;J1azRwOtl
zN3rc7&j%fy<^op^MByj&^*5tze>`3%XDy2BM-Lxn=xZtPM5~@YM1nf&lQ<AC@Ij;)
zvw<>mngrxj?m%x}Ud{i|C0<*RO~g||*}J8=`Js-L6+d?GvINz8ByBSE5FQtSxVI>!
z5UF`+eb1&o9z535cB&=Kn>KG9#S-A2#UZ@G2R-MSSi@=S(CV&m|D!i2G*+%$`He0#
zo@jb2R$T4vqSllFq^6!Irp|-@v7k?Vm+d0~v$fAP_W%RtvFVS_j_a#vhEf|j^b|gb
z0Sb-26;M=CVxWL*dl(9YL7JEFAXltjeI0K6OvPg{2Xxm2X-dpq&`a!Uff@i?#9u2Y
zxWPJT?4AHTAwQnYU4Nf*tMI2>N3tmm=@pPjIOsUQ3Avjm^*#7=T3!8iiQnE<P;w;^
zUVi#$@HEyC(!Z5G!x(P94RV=X<jLU-GEfkXU03Y-lp!V^u@YWm9tG65d`(w}!#G6H
zR|vgh8jZdQ3Z!!>ZqS*~*q!6>>nx=Zc69m1jk}#&ZbuufIHC{v*DA#g7=@Eb1|jUx
zy#Dj&MPf+cMDYhEZ^6QaFCY{33ry`VV6;O?IW3Tb;h{0lu3Zo7E<c7^Z=iGQ2B$oB
z_<Ux{k_`t=`U)~FVWm(qZM#;231_U)xEcFDb{WoI><jXMDTRRA9|i`RrJQnc5r+d|
zqOb~>ddm)PuvB*dwQx}pdJV!`q3Xj((ay173OSmia3x=RdhX%kkf}DA7nqwAX=|Z}
z<Ipskcfw$z$o_|nTQbqcHapnf*7g+_41Fw7hBQrg1Sq+f#E^NZ|8avjI`OHd6^+?+
zsCD3wRvpaD=`a5l|4TqBQJudHmh$AOQ?F4wpNBUh23QG`zSi#ve|;Sc=o7`5N~oxH
zSrWn0<g5-D$I`mshYzoGRYz?@vV({ankGlh&3F6m)$_o7fP0>AYBFI|_2K_WW^tKD
z`v9Sy1zR8~BonR7J=E9S6gfy#yYU}$G4035304_6Je+Ncw~LCnfWR6uv_p2(0&y1_
zvCoWTrdIgxKbw`Dyu7%$n04Q|1)#sKs7cgeL)g>VoFnARtHYgUD2KC$j>+qgrVB;T
z`g3^r`1$iYk>lROG?~+NMam5a(*>sQL)!ifHZyMe7!EA<^gnufde6-pR$_m>Mod>)
zE|PFcqBl#<;jFgC02Wn}q(SgsKl(y=be*p+KWN*z9Mc*ck!Wq%yqN+n!wcXTym^lS
zI30;l{EE0aL$d`uP8<eGgyJ2fI&p102(}l%M^W4$UsYViL{3iH=G)P4-&Itsqx?hp
zh0JF0mg`uu1j=TpOBf7@17%)LIfl(}WBCy@*sI5G<Kim%PUF?f-|Zs|b)Orp5Mg=X
zp_gTS{0AgD$MYsVLqbA+16|^-^@vG_xjdhqfC){f6&1H$U;Yi7{AE#5(K(`1aSwYp
z(7YeXsL%WL-TR_&Waqg#fH)}jv_E~HM;N+<wR$fZ7S;RPHL&qu_|aJyGovOlF6uX0
zn|_$`kZ7FtRsw-hqC%8h)G8Rf=MEB<mX;5Lg3PP4N=r+<A2+>~@Dv}9UjEd4BiM+6
z2}%pL9N6H#+LFwNRIdv{55ZwmO9(&v#D<9)hwPU!X!rsEV=ab4tG0dtMt;C?1|hd*
zo~yQY(9WGZAGGnS2zvD1k=Sy5CogY$OfbfgJoNKZ`)Z{c8IrEeB`S~@@7SAG{|{xw
z%9Ur+;t%OMBjSAx^xrH!7?Mq}H9OH%vxRaSxuF(qBW%b@%4-~To{gy^B1WD-z!p&O
z02c<!btvc%&s@947(VPG8Mj0s$MCz=tMQw@MusA1fuXFij}%gcxq|{>Sa|sBuj!Dz
zxdF`ve=&>{(T!JsHc#lnCt}nTl$Fn=Xv@hhgSO1ZK2I^sZwVI8PrUB~6?Ow?%Nuz@
zVFj5SdHu!AJzDoMgrQNibR!%A#8S6n$dD-w@{I?C%|7x4T0tzn$|(kWBU!y3>W$$J
z5R43}O_xo}avU_GM3}{DZZL2@I1wbgZ5lo0H!;@BBxZ$k0^AdH&sF;iY&ztIplI;&
zN6motSkOMBJ<~s|09e@ee{MGH>32G--S7<pJ<OCGq4zd#%siLRL!d*V?$UHGU*DSu
z##4s_Q>@#hDTSDiW05a!XxsN`Jtg$R`HN!%h8j&I+K5g06wJW2wVq#qaU8mNRV~nB
zEb%V5`1PvoZXuzwEDugoD65uHj~!N4Tkv&qeWhfVc@-YF_V@PoMR~e=pKB(-M}z~%
zy`gLb!5I4tgII_~Zhi(Q{R=0JyavkT$1&ndf!n9bJQW9wL2@@l#6M-*$zWXRlUmc`
z5C=aG<sRjFoI{@HT=h%Ns|_`)j+DCDpSv8ap=NmYO-YFi$Vw+n<1#bVCo!0J>+aq0
zr9UK`n}{e&L@U%6;?k=3KnUyo5Fj{>tq>L4&;%sp9+tE#6FvyORFwlKF61}57hWSV
zzM-3xq~HR{j(KiC0Up26pke#2r5uA{PY_?Lgh{pAhj&4lO3}`nt6tcK(|0dMB1I-5
zF}Q;*b_cmFJmPUG_0GHE;uXxxHZI3Ji$p%gam>o7qA@Zuz5!hI8e&Q}RO5USnaSZd
zx;<|>k$vptZ(u7Ld8=n-_3ZY&d#ga9ipaEXyKDOx+Hk}P9E40~Cx)six1d?fI7P>K
z+@v?F=Z=-(Jt!5#JYK`2s=lYcyZ$jF^Rkmgd3kvy+Lr`2xF57>zmJfPx0=grf2e!N
z-bkZ&hisUhTYXXw7?Z>CQ1;?ykX63+NLq67me)XI<}3W_8H7c^Ekl4Myg>L6LxN(=
zF;!LHVy6qAb9ec$K|!p!*W0qBqC);tKCS6o7FcdRjy5@f#=P3#B)NPv(#<t)v|4Wu
z)%kEqtHJB<<62n>)lESSxv&qnF+4)14YPcDO@SY!uJz(?ThF!n(vOd4Qts;}tpj&4
z<zRjrM+>@CcBfAuO_DK0Gqh34eJgqg&blhcy<PYy-)>ZOg}7Cu9Ib<3cpMQGDEcF4
zXqbA0TGn<3Z<oy%NBeYAF`G8Hvo_9;vKfOwwl^812zm6nOK4$}eTY%wy0?(}f375Q
z9(5XxuU<kecZBtAW!11oRP`M^^M)Wd<V<N4X}|OQZ~PZ_LJKb@5pAU<PqInS9?Qe~
z;_$IYd}roSHba(nJiwAlU5HBmHi{&4_suOTf)A}jEg`hn0D@%1oKPrS@{et$wXhL{
z9Cu@|S2h`SL7Iqnlt34CB?5x^lu7jJEE_FWRY~hF->9ed_C5FlyDfIBJkH1v!e-t!
zHH{G#!k-=sBZ)ug@N+XI6oH*l!OKL2oymxrOd9SZ^Za-=55bfJdK32en$*Rw$I+%x
zpq_B0c;C&z**vJHuIwzWoz+rP9f_87?U_@FHti=TA?Hg1ItoP;s!XKE)v)Q>m@C44
zXyWXm=%ZP3!IC8hw4eg=4bF;*5*`($^@*?Ar(%~u`s<KA-qPE_@nfNUOf~j=HLURs
z+cXZ4Uzh#Gi^)2pJ`-sju;0j#87NEE@B0V&sgTAk#hBMPB^G@dPvRpYyo27<*Q;%?
zBnx}&*trczm#zaLBG4Oh>x0(c7f`++Tt)H;IP(Og)X$mas3qyH^<-PAU#@PJ0sUSh
z&-Dc_NLxdhuhP!d1OReNvFvVBRY6temz`m}y4L<707-rqP#URNmH~<Fo_eTb<&6Zq
zNij{+l)f7*PHX(;l+DPrBsqrE>E=;TN4;|BQlGc5D*W;GNaNB+414$P4eZ{X1XQJ}
zH#ax;UkkW?6!NPKi*CF;tJBt;X?3)_iKGCkafk?crbyg_={q;2Bd$tEa1Wj-#0nTX
zp7KZ*n5KL|psb!{y&Z~aAE4Fwdwg8lQz=4`+OrC?R1n7;ObdA{%AaU%hrQjEo|Kd{
zyPieAbBQ4H+KH;C#~1$|8`Id$=&dU?8~+1~Q7t)iZOe<tK0a;qk=YHq0ipH+*gv@Z
ze<(1!%PGk~!zmHP^!Gb_&CfVg8+icwqew<o3x86Dbag5F{!-olqql2~rgB@uAG#nS
z(VZ31O{plCS}uvot+bEil1oVxxwJ(KX>F1&w1}m!HYw%0NQpw&U6xo;5m}@~t4(s*
zDGXa`pJ&?VoWEzBKgXZOXc)fv&2P^6KJW9s^LfOXIKeNDE>Jkk7Oc;|^Pd=q+JytX
z&~EiB&@xmFD(uRBFE}{TEXtfqybs)~RCSzAf%iGgCuW$s_k?hgT-ALS_*c7jHqB^F
zL*b@-nH(<BS?mnvSvx;yOj$M@^0mjNFBo2GEk<lFPWbDi_kbQu_UuU*x$(*yk?LB@
zQG^ISy?U*NrwfUJK9da`gSpsWB-Og@Q9Ju@R$yl+=XYAZeg27~#&L{;`yWKUEi&ZM
z;$bY{Q1Dnk;n?}^s1c06ZHW&73+n60ywtequ!8@17e^96piJy~wfMw`I$|!blJh4H
z?N(@Op77K38mnzS__VokldZ1mq4QkYYDQR?2RdOXCYW{hc>!XU1ck%?ER#uX37eQN
za!f+^8~en({8gptIy-pIlHFhqHE5kmk4v8j@rzkE&GOXtJz;ZWuap)i$>^C6y-^YM
zp1s@dSRG+Vb-{~^Ykg(4hOsftL?HK?e8rFJ2vI<FA^A-{><c?giUMMfLjAb>uJhhY
z!)YEqQav~m>N=J4vfzbuu@)4K*8ckxb;oE&te28&c~IR^hG^lFv51y;9HLcDSypfU
z3y4x|<@eMF2MpihGQWXcH8v*LJt}Fr6K12|_EdU6W4(^6NYBu)bLg9%_e+!VQxLH{
z*JP7*Y@*n+v@rWs|7cTtBe5aJ(1CShp_sNcyLoHT8!MJSl+?8P5?Ao>qM}*>KUcqg
zywa&+NW2Dicn62?Z@-an@H&k1E}dxHjz!bd{~c+~`^&n7AIT%jb|-F++EFHcets#!
zx?rA8Lw!+-&l<pLk%HLBl`njoq>zb`BYmaA`vwO+2e(O!idd&X0?gyjQ&Q>%Hl0D8
ziH;V~C7M$)>6ZODi;{abJYVk%yFx+3qQMQI`1{7-V6(=L;}y4pqhoan8fUD5^ictH
zL;J>n$2hI)yaLBA8<qJBc_3*i3g``2%$CV3D2SgveR?IkUYj<uuyul6k&(V&AE0<f
zeK@7ILS!>X&;Olo@c=dm*LATB?Q(9*C(E@V|5(WDcZ!LLsmQXy;e0*sEO?^J^smo#
zJR|hpb*KA&#p5l@aHrcU(esSFr-I)H22?oXpy-uC$1xO%G|8gcxx=i0F0v^pI5wlN
zw|B*{l^C4SlFdQSd<D4RXl1ntJ>%_#Mvmy(4kApfHY^f;0a&KSeG*M~5Ecb6XNGP0
z0Ve>RwSMVd14I>BVPE?3#@H4dRsa>#Y*UwhgNX28c{Pj0+GrIH`ba-?B&YeC_qwq6
zZLT~YgT<{Ty7*tsrmE>Z`uS6DFD)Zu26}~ghnCJDvxD-9I7NK=CUIeZIlpf-G(XK)
zyoQhiNm{g?yeEGl(Npb=Koi0KrsX8V+WQzZngyya_2DNvghFAbOWtvC3ahyO69|wV
z(2+(E5VuXePY)Sd5(;(phZf6UD1l3cRNqb=xREGBXgCTU0NqgerI=9gb?Vg5r;9eR
zdK=Qb0&mf&N?bl|8dNb1oeJK`k@DimmIxIOVung8?4W}at(Z7ugTS3tGz^!DrPze4
z+0w`w11-OUaX?0kq?}?x<B;g-T{Swjxe@ZnhdZ5s|FJsUF{xlUx8~b(SF5+syE$yH
zo$V6jpjr=`S&m_ty=AGiYJ5fp3-*~R$YBfCH-CkmMZ8}~f2%C`mYutI?*J|w3Hfzb
z4A9;rKEqWA*S35pST+#n5r&%(5>10>Ya=1?CmBiA_{790NZBvlxbgKTUR2{iHrZ}6
zG$z1SZG&O%v+>I9W@hfF0sS=DKN`w0KCJ>v$wx><*0kPu(_ai8n<5p`-}?g$pzcdq
z)^?0<n_@2~W)a?UmtDU7+Ddc{QkFs)IRtPt_1Uge{5&?c9iO8ZsS_PFdT(LX!?ZA~
z9(8qfgsm<Vv6<D?#){I@VGSBh5TM)e*q1MlP}~_PbmrPy@$E6ZnFzu$rkTvC$oL3U
zMST+M(k0#$!MwuyDihk=TQhA<a_!XA)w7<<bO9-40-&)OZHJ-YRf*P3YH5S*ByPV)
zf$-eIjqB=iM|;UuuD!&{Q$L_VHw#qCFBus$Md|3EbkOpozK6nMS^<A<qay>lwVH|Y
zxDTv@lN2RMjZsa|1E^IbT=wRfnwn<a+)cToek3U?8;xBM<VW5GW6DJKeO232y3nm!
zt3y-_gbCIqOHgQFVDJ&fkctwBweVX2WuypC#Q6C5t~(JVPpg0VM{KOjsv|?INck4b
zHL^c_Iaq*{4}~%qncx}Ps!tnZiq0ZYrf%smtRd*F@aC*zOQ{1a#T4aQBdj-sU!z0$
zI!*7S21(qG5We{M9UR>jZY{bu0gBq-{`IsdIWY|MEE)V^)*Fyz$USPnjO@>yz^}M;
z`%h81_7k6~ilISZ-JaC21klE}y+jIC5w2ZE0qM6)5kfqme?*sr8F*TxdBveiz&drK
z6!B1u_8@`53PcnR$*#dcm)vix1slY1-fP}%t=vMg8`Sy|o{l~}6&@b&<%0#myIO-}
zZLWnxx|)Wq?Ba8w(x}5A4|Cq%m;=6w;K!1P^4up?2Ra9lWC^}^UsY8p_?{Hf+o*ix
zv4wN8Bq6Hva;IKe8{EYb`bl}zZb;RDpsn8C80324&>R`ZfwKqeHY6l*4Hhdw>rG8f
z>tS#V3sXr~S68^HO3Yj1z(m9}C=o#8RS|^uIav5%74nXnHu3qhK&#yk=oZJ7rAUww
zC@lT<;)NVak~J{JIs?RmHYeAdpp1fC=Q1sb6H8qS`cq6~BjD5tg*2r5CLj>t+<g(n
zt||I&Yzzp{)WKl^ZsB<1-q`$S4Gm8ri*2nn|ASQT^CXC>Td}y1PnCoR-<2!#BO@c_
z=gy5nRS??p+ft=mT|wg}u-Va=Fc0)TdPBu|<UW?^XJoVQae$bhVo3Pmeh;!F2^ona
z3XQ+hF=i$<0g41<Z6RsuMxPU*N%`OUasxf%%F4=0ia%p0Z{Ma}fHLuIuNNwlXje0@
zj`$AGD3|f^@ujGrlVkwCXHqm8?Z|^)!Nl6-cpv~!mc}`OY+zam3Wh8P-xT<e$d<ZY
zb)fSCSe$c5IoGeNfjHcj-AvpWKEjx@qQcB9_!*y{sJpBZGYnbop&k`MFPP1ynAxc6
zJOTxzsaz2#nt?qlMnO<rQc^Php)ojI2z{Fd2kTb-nDEKwo=3BXnVoXr3{rY=90fEo
z&2A=N2|foWLPE|x+a)F@c6H+s1?HVJC;RE>TQ~{v#VjEa&(yA!P16ki`0_W6IxmW(
z7*K$J1$Qm&x&Vw{E$`e^1xGtSD{C4GZ^>0v#xUmfD>1h-UzlDEV@@7o=-W=^I-H}V
z6iOl-Kr2&T+*EQBgd*V_A-zmmODL1@a5~(0-FklD!BSJ`?u26oWY)zghpKRcqVoie
z+6PF@CeQ7HP)`l&?yDy|_qzG7P-Ea;m?De-Y6;SXIdlk|Fh2tyX2K58p3h(~x{>(B
zACnDrCjJ_DadQ+|kP2klu6)~0Bd@Z%*5x+7!?Wd@_66==QgSXJNG(ArvUkX@jSE9h
zq?6Y=5?@d+Ax|GLTMkp1FnE93O#GVyA%;0hO3`@L_wXY58B?0G=u>mH9RB_W05fQw
zOQ9hJq>gvnZPoO!Ap_rzQdz`P@8(-^6&04`z1TGx`gr=kes<2|{Fjmz9{cyt|Eq@f
h@8`|=Ump0^%s;p7S!6T1?q|{(Yhk|2tkBdw@}GU{Fa`hs

diff --git a/articles/AMR_files/figure-html/plot 1-1.png b/articles/AMR_files/figure-html/plot 1-1.png
deleted file mode 100644
index 2cc035679ae6c12ea97b91f03a865b1b3e8ef9a0..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 41864
zcmeFZ2UJvRn<ZREt_pGy5hW@hB2hp<i3Y+gvPg~sB1n{+DXxhON>p-`oFr2Om89fc
zWF$$>nIg<{P<y6lrswPKzkAJ^UO(%);6l}@bKdiWz4x=<<0UUEL3EnpGzx_xLjNVI
zfI^*!MWK#;`r{=0rt0NT3Vb`MCnX^Y-{EWfJ{AMtPCffe%^HQG_CWp}37A+mK%p+9
z(4u!09U|ujo!@90MDZ=@*te`TK4#Qquq1lNLqR8gS?|V)_&>cwRf~K*Ph2k>N!L8D
zA~sN@`B5#*)n3grTS`*R;A&Rv?A2EpBnmm27dWvgM+naxf7l@N<@q0{j&qZ81--8*
z$23NSzI*@8EPA1NZSo<HlHpF|s$iw{j@?dwa!gRFB6G;$iMn%!X4)S){LxqLsL|mM
zfq&jPhWzm6C5i(1?#^($75R>0xZ;jnF6#2BBR7%nDD)q(hwuIC9*PM0|2yaY;UU@-
z7LQ(}e0A@NWRgO8L+ssgs}fJo6h-C->|wK-M825!(u^N_;}&g%7Grro_L@HCdhhAU
z6xQ${RdGg>#1|vr&q@EqkT}h%L^Hpzup3K=qIidLikw<p4EX%{vs2jJV_%=-SuTAh
zkf?4YC*>$#Q;1@DzMP$z$;K!fd69&~)z!5Phf|sKezZF~GvmFnvEk`CH9hThFHZjO
zcMA*tp`mdBZ_3NdE4HrCz4m_r*X~>UClzve{~-(TAFzi1uN;DHogIbxE5YIhC1uf%
z87=W9X=9GM@n*R1u$JEqU}bN}3gwnu-Jcjjud^pSk$u-jyBJlTIRt5fTH-=t=^{O`
zMPpqM2XD)jjmW3Rmeai498suGU&AM-E}~H9oNXSO?9O@lJB2;q?kM#^-XT6g_Y%2R
z|A(9PMeL8i!>1@4s|^m^o|QMGyuW{qZ0zB+jZO$B2=~%+caHIJt2TCGUxr^|<!LEA
z3cvNO{)aIDhpqFHW+Z*@>l2-uC+?(qQ<A!%)3@JuQcH-Zy&}1gV!Fa2nBO?*?S{~F
z`EG?>W!{UrqbMxBJIytIetx~tYOi(n7biQ~+s_4ElgqNdL_*RgEWdc#I33<l(ACyP
zLQPG*&Q4E9$5^3={JOkch*8cw+x}AL=R|bSjMhCR=OCfu*C5b`7Q#C|6VPfdo-_Kh
zMlW~hDysGS@z$~G@82J-ZEoVQgcNregS@h`vLLxaB!6ba>ouY$bn)p!2A$F+n*Ny@
zMO|1z<HV$-Bnp1wvuA5R-O+a$YabpyN8(5F8utZ`WIEV)5g)zQ*VjiE(;^`%iosy2
zj$eN9!i?lCoD)yS@Gz}L5pQk9wesfX=C4mY#4QI(ZB3%m;~qfL8$BgqL*3w7JU1qq
ztLpnJcYBuOF6%KJHw(UO8hh&4lz8uEr*f{U={tOde2OUdiPj{AR9#rRg2F=OWC8+$
z1bL>;-0)$Rknq{1QhBE8Pwx7yPukkr9td?)QBnCqg_-m=I&NlWw$6UU{p937jEjGL
z75jfB1OH(;{{JuJKeW63*Imy1POSU+Let)JEGk*P*u@F~w@vJDP2+p+F|9=>m9jDF
zMNdukA{8^T)2~$XjoN6~@cWyUBEl<g1Lkb{wd@DU8baA9ZOfCS!fGjpmR^^qok41J
zY?~hzwKL*D>+i!+ajSm9YQ1oM7FV~kK0Edy!rZ%oqf!vc%SKw0LTbK51GU@M2zEd@
zRnZRXmZZ>|?t~lyb@uoev-=QK7FO*t<PedTwU=^@+Tx8&Onm$(`DzWqv@QgQ>^rb(
z6tZ66&@QVpD=aLmO;bu2kF0#!@!{6yfHi&4x9Z5-*5aBA&(E-X+E?NSoeP8x4^HsW
zlIP?ayf|KwxyI{S^hLuMSG)Y8A_*v;sSlHD+moc|!y0PBc}(;=KFH{IPtJ6u)6<R0
z?T^brQ4A>7<Q2%W<3SGf$=okOF<&9Inuu@UPZG|#cTJ>>(kan@yWq6!{nTQRW%Yb!
z$9?!x+KYTg)<W4d^d3FBOh@-YCW3d4tB%@UQ&e=ox<yF8eUd&v@EkxHPREt;cfzha
z@A6L}@4KPx8^IaNXE8)YLh|r~w3jA3-yD|*mDOLOqJqoSr0_mJz99fXhLM)y0IVK_
zmCqOUkA`ZPdkydY;A<Nkq?Vv@e9WqvbJ?GNCS@>-lGCQp7K1tPT$M^{rzyB^{p~Rq
z(Q2^~^sv-RMVDHzPR5C@a%Md3uER{Wos#tP&h5_IuJdjLsEy9zQ?bs@&h(?3S5}`v
zbEBivd-m*xWKz55-Sz0H*;${MgWX9I5>AVuvO)HPt+4==WInUL^AWse-ZA@Y$(+s`
zbH(F<QsUGi{B-e}>;Zi(WGb2@iw<=H8#zwlVp394($Rt?Lk6OvqWC-IEGe1deb6f;
zBqY}NJ`DZ4+GDK@N{i_{9bJ58rdhRtHqEWDhKaq?v5OV>1y9d++=jB!k+&?XCm5yQ
z{fV5R{=z^BvSNT&cXoD0!zy<TT;@%5d^~8Jn6ipTy;aK`mW6Q$azM3u!m$V2x$>_I
z)c#E)o2r;5YnX@U+g-QCX_1qVEMgAu5?bbPJnTSsZ||jRQ8o&Vk+->p*Hh(?z#1)?
zjP`+8^Y81<P|FjK#I?1JL>Q*o3DPo$)XN6oohY0f{3F652h#vWvmW+aX~u_p3VE9B
z5+#~h)?)W1lcYuG@L`z*;Z`M7HG2AU&g~6P^18-K6jINJNG3It5nT_<<Me0G`>2tm
zaNWD6wr4XDu0HL)#n#;Cvwl_s$L@?#@I7t4OF(^|grr4ibHKA<?&AHDq$GpNW;eGV
z?+r(L4P*RVglQ$HZ99dFxXt?V-wD{8XJ}!0X&`4G9GlW)Cm+TSZHh+@_xDq3KpmM>
zzbFFue*N0p8a+-YC#M<Bwl@`@rA2SHSlCD<1x`LDeQIKozEAN4zcK#{pP2TC26)z9
z3cp<N_uqf_d^FGi{vcjjS{lE15MaODF2$jQAyTn8b}Wjd?PAqZ=a)jWe!YfJgcSJM
z*xRef*#OXg`i{dbB^wIB;Mwb^?pRauB^%CNG+E!CjG<ljZGmzjE?Vt-iF+eGyWbqk
zMn`vnU9(u{uAOMLt_Sr65)zJztx>mvLw3|^gxuA^L9M;+^`gqX`J9FU=f!dolI8BA
z3fma`^$<zR4jDc%(dz5Tksn*;=W=wbUVC0lqeUX(k>Dl1BQ{V%UY#L_4_wFTR5tAH
z@9!6H|A>I<@sd=wXVQ(vW$8211)t>^l}i4zx5wYOnqSj1Dt<#9w`DXYaCiz-NMKQu
zELUunb8x+v&d@IBSI$r+dGX@@SX=qhmovGKzXie(IPDh(bnDbBR+|OJ);i<@q}WRy
zcM7|$CD6jz#f$maX~@Bsrgam~@_3Nkf{kVMe1B0L5_w<FaQJ$9>igejJS();Dl{aE
zoV@$7&&4Hw=vKGB`UCh?yXwcs=nU0dI{4(=j@{{w<T~<-rB}K*wy=hS1Lh%mou-oH
z<ZCoeQ*oX>dAfqkA(8{ns)#JBi1>Vq8zG?emPc!_W{2J2`yKAAqf?%qy?KUp1vqU=
zq$cv1bXP-fxX5kT?E6sm_ElNo?eV=*qwkpgNsM@P>z_2#)Y!^WM|*pFR-Fn#$Pm)a
zA!iHIo}4R%7Q-|TlV$x9{P~%Gw8YR#P+5vX)pN}{td+434B%9fhQx9XoSdE0@B6n7
zKsZvtnv;+`U^*Y4oSY1KF%2YPcz_EEO=n{q0oj=KHM3NJ+ka_jXh1pOq@#n}kI&A|
zhL>)4<!x_;IbZ9-`i9@u6~~m>ZxkMeo~^AIR6<e0J;_YKt0nPQ(rIRVV}x??q-3=|
zc>(i&31=+{`&dh{*n}j5DXI@m5qus2fueIRRL4<OmsIqBlJVAC|A8d@zb;Jqw+j;g
zch^FFMhjAkpm3<YaBEmjN2x<tLtULNtU*jvG)~?VRF1p~oro9;jN_DqaW~Wss;@{G
z%!PfH<aFD`9%KN|vX-74Sq?)k!(B-CkF!XMq5_|tyor9igOA?}RYI6`{lk5xlRwJy
z4w*IT&V7<QLE>?LvQ@g}4=Hs@KYip}E`_|!{k8t@1N@5`&<M~dUx7)XZOj<oixyl)
zB{1F+O?EmufL_536XEOBB$i{d904x18vJlbzq({4RBhDy9_486d?h3g&qM#dlS5_F
zewC&vSm`HI#83{2Pl1lK@c=*1CpLSZxDzsHT4U`klMF`dP%1ckx%S^LJRPpEexkyq
z9TP^i;RNW&cj!s)9P=e*o5hLMvGH}or(L?`rcD_A?58k+5+CX0L{{T)6&VV%7IDiW
zFIu_|uT<;Rf2YiO74&rev;1`XDHCKo^Q7<w_gV(`*VX$5tC`DD<mQ>k%^7SwAYF`P
zIz0bRza<2^fB)tDCsYBiP$%+FJzpnR-?mK7YSk`=c&}&0et_7sv9%=<$Sx=-sO{=h
zadyV%;OPY3rQYXR4{CZM;Udk@F{ItUKf?;--|eMqWD_rLGd(IQs_m^=|HOvBzb18B
zYD{-`ceuzRm&^vmFz6SYjlv5YH*xrVqwM8vmsKIg(o%}4i@}LjCBv23Hg<O1x#v*d
z@6drB0=4-53HA_8_WE`9_}9?f#?@bA_zk$4$jW|zcyQ^7&bXv4(+7X9_l-IDD@j(o
zlp*08sye-<ydQan>ip7@lCG+M#4h-l7M5sZ;9&DH_V1-13N6A_J$~)%$H9jSqfRKV
z<?F}qx2|TD6u!Sw;8vbw8Pd?S_Kn!xbtFYMvZun;negmch>tXB3yaRJftFI{3qEZ{
z1`Ro@6w_|0ugv-jYeMI;@bMNmNJ)ZBbmXIgC6ju%zUb41gjQ5cr92H-FL1?gT%^*a
z<?APii;j-I_;t?d<Y=MUw&y!V{PUT|0Pg_`DR&6>gP8HY+1_<JDJ=eG<7!q>kA{cw
zjA1@*>+iptJsF*Nh6>FzOncik>-d{8uvjdG;O5$zXi~eh&S%a*gwE{nu%n2QEoI-I
zKmzxXCdtpP@t-2${@<;b{|O23|3S|FFR~T{?~<dPm{+!4##gshi_8bvR~IV}_Gfkt
zqa7L5MAq9=Y>!|;6@}31>;>+rK%_|PIR18!T_;sBUY-eBRnPpcWYWhL+xe`RT(0hZ
z;+y_ZRFF_?owGZhCh#FbCeK2|*YHq}azEC%Hr-)zuCqHWro0O|G}mzg<hB3vJmF>(
z<jhtP24A$bJ1IhI;oRS^S08*G6h0CUS~rJC>*m|JPU|y+XP2KH>}{%WL~b^6w}^^X
zbGHbRN`<m|RS}B_0{0x9N(k_;Jiu25uxS($_Se?>QPRh>)V-CUQ7fPSN*|C>h#j-|
z7I;-4bF=fIoYEvnpXLFD+lVUBg4thP?!#5^@(T!zWm>qJXgW@W<U-<;;Cdt6cujl$
z)Y%;+A1fa7_W_O5p@v191Qya*)3!^gt)t^%(flF|CT6oMg@u>?CayNo!S^Am#C>9+
zn?rjG_I0?5je;3hf<#?HWbzW53gt%J-hMM@mBx84*I_QVZZzB^%WKSEfZe=uZ{yv~
zr|mfn`y;Pb%`3KlIJ7tq<#lk)c4zw7&UV)zDu)R!%a5;O*$<nam*7YJvmZy9vxG>}
zZ<Gs!NM5bjZnCHas#Do952A-0q9rWXaJX^0E&J%qoj<xLBQjxin;j_5)=F4qS;ssm
z43_x$qPqE_VVW5Hz~m@_$kts%UcqL>Sm9MN)yZhLJ`n)^wMg!daoy;3*yuGJ>(;RF
zYjOF(jSw<1L&Hh){$m@_+=p|b*OtH?tI^k@@+HWz_-6ZxJgKc+E96t!(bvRsRjC@z
zZGKf!(JDZG4HFnn!{$ijn4774aH|c&mbQsOUb?l~$UU|*n?0N?yitRz++V*npWk73
zD(3ILpAjl!IP&Gy@)+&Gt!THMX_yRUN84gyiHawqoky!l<$OKkba@xxmJJqes$kYt
zIG?sB7B7^}0#{^%DHMpx)yIeh|IEhh&C=5$s*dGkl>I{fP`dVd8Vss(-S&27%w}=f
z0TA#V!yo`{I%<TmYf%6Qs#wpoP%@#sc+s=K1XnxPn@6w5ba}t0PPh<fHtf1NxIAxx
z!R{O+g=teDN(azy7=+QduC*bTkhUtaJ3?I7ovD$2kleRVM9Ev7EV4H}bC4Y4<|MfF
z{Y4kPRF$>%M^nUDv%sQnUNgT*b`fQd^<hrMrXrbJV60TO=(^|wvKGQQfbD#J%+=P_
zMOH`SY>LoBf06wyPtUDdzKUE4GvS@71VkviH5L1UF6f&mh$>WYF3Wi_dl{tzAQj!3
z+vzp6>zU5Ag7m7Z6WL!jHrsX)9$E=4s)nl}w{Yr+pract>^Jv8&M~`huc30cVFr=Q
z^dO&>>s2bap!eqI=U30_9PDf@2Vkh+CYXy75@A*b7v1c`>}s9x2&bM|`)JUhH&Gw5
z{DH4L)k$a4s^k`2vHt$<cCKE{=_-pm_kNZRkvC0G#<*j)r9ldFn$5&&xlOkxEvKoJ
z8}~S3)+8{BM0vCh-~Y~Ku_AVz)2B_3p5FCP*U%W#7~WA{Kj^opbPedAN8~4va7aij
zN4>7qetC7qLyH|0K$VJ>5aS$|^)8h~OIkdly}4~w(E>k@0C{X{U{W|NU*wU^&*i=@
zbg;X+>}TOZFDo#g|Bh2%+{VU6R5&Jk-xc2D?->_s`|R1X&O+JS6TuHATi$bDqtlD2
zQyZcM{cs~?HQ@G-cQb34Jsf7<5)0yiMS4ff?)Wtct-`##JPP@2s6b&pg#JlkINLTc
zaxumrbT@g9^Wi)V>O}Yc!s0&r{!aFRQ$QHU`O|>gBqBv1j33}EEGz;%JwYw4bM+6w
zmHpsJXl{9u$7%he(P4MFZVX8mnj-w}YKuy;y|1-U_-*SSiXNAz7Z>Gt=W3uZrg!8$
zX_9Ow$eD~hf{b);hAg^F3OJJqFM5d!H`vJ;r;F`fuPvmJg;9(w-^$+6rk_KTn|^?-
zsQXJ`Bvk>IQx_yBd<ibX4?-Mj3NURLIIEUe8APR1Q_2agcc!d1OjrqS^yq^Is9)<V
zWNr_Iy=(efw1ZwzL#4~^tiQj%(1M{wxeMz8h>{FyBA=pvVj7vTSLE8BP^00-Y=ZK!
zH6<ZJ(UB|{0Mjg({R%{Jf(*0Som87kL%WS1KG;uL#bs)!L=AB&uSYwqr|W1%fpoAx
z`5<f%0G{oZAE*rhPP}XT<_8V4J0P}{c3f(^GmkJ5l=}ye0@6AJY7J<bJLE*DRWvQd
z*d)C@Jx`G`Dk6gA%@1an)retVq#gA*f|At?;@Hdq7W;0rLFV0!E0C<9@YxSoN<j8G
z!Mt&lV?UmMe|?+7mf;%emlt3%M#NM@U~X}vsGy+cy<s%W{g6tYo?YQo*>`Ls;BX3W
zM3L<>!IIuVyEi2<mW5sL2D!-dB87m%>W`*n=u9k6n!*bnsz&vVe+x?Q(AjAZ8?7PN
zq3YNOfHYUl)e|%AFJz;8&Hj3;^*(XUavd%0=AgZfsA#0i$~R8eowah~&5sT2>9Q>`
zA|hF3EA^nSNr@89w;;GccrA{&CPYOWPAoGs(-JE3Vtccy#lb|Fj&@YV_vdFu@6+zc
z?`~oa?AL`e6`9*W;}bd9ZW)6Pow`8&XztfyLY$5T#q2&76*V0yyN#?3NZ_8p9|5+1
zkbZv-sSW3SYKo`geT<b=3=It%qlE<M=*-L4yWa^qS|R1^kfi8CalsF4h>>fZfAQkQ
z@@HbV8z2MzDq&^QG8W!Ye#J#a*~5HHvditV``z&U!!$^*N;}YT7<skqtx^_WDv~T0
zLnE^FnK*4VyL_V$>8|YazUv#bc0IZRQJ?o;(C%LLzcuO!pV`%SmVYYt*2qgzq#^hA
zH=W#g{`0OdL(@oq9aZ%d@{WMU;quUSvwe3?T3j60zB~|kuoX~O=;>(Mw23W;WV;wt
zZ<Ah=^~En>Irg^#4jf|o4i@YyHOh0w{(|&zIlvDG5W7y@r)THx?uYfm2qSVHEV*+z
z`whE8;Ctuc^wg9mez8&n`P1rTO8|r-)SUpw)gLuT4IQiXTSm(J4_j&oyJe`m{OqMu
z@o*jVwXC<A9&=faydtmN+Z?7P*L-%D%IfQtw+$1cY!;4EyHz5)_XVN3TCTN8EL!8C
zpCN&82t!7FIQ*ehAS()=SG~KvI+m`pXDo;mL0JB0opuVQ2Wo~<WD$H8{w7%^j6)#y
z)&^gH2vSB3eAD~<{Dfr<nM(C*BFgx@JnKsQ(br+A#pkDrTOn59CW1)8+|f^}rGhCy
z2GCFYOyU=h9hmeu9^j|5>+H3Q3kpwkwb^nX$dy@-a$*OD_}+O{mKdzuF!7c1pi%T~
ziONs<;99sl^LeUxqBeX(P3~A-i_d3LSqi(M{(by&2UEO-v4%Xg(B2PPF=xighWSb-
zUfKhz#VipEs_dV}1FimCe4m9DSthZNe24ICszsYb{LMXE?7;rL+-pNytsb-T4_v7_
z932MiwwB0~)Ys(=5$^$oilT@iZ9k<1b^EQl*u*Xv3b3)Uk#OEL{TpOFLajNo!@>SB
z_me*kVIJrT|Dw3^ucs46srE;%oA!RZs+^^H$$r??eitIi=RLP!Ep})<3+5z4Bqf^W
z+5OnrEUaGdM}q|RJe%ouhbqb;`MhuhYojOzi_gT;f;?ZmHR_X~$*y9dQR(KIp;IYr
zUON4ucz2~C0Y)L&eHk!TU=FdvO0$_9jx&sG{%!L%xp|#upHD2q>0#rHe;z?`%$RUW
zDP37CRnePNPLQ)%7(ZtO83d&OQEXvIC=QW^NWU=q+DP3qwOh1TnfZ0vT`*b#OpB<<
zBqRua(fT3x?na!`#tMuXyYhJs9Z8+_@TwvhR~|cd3|b5fBy_`W1Ly;|679V3wD0G!
zj12~SkCa2|z=Vg(KoV>T@T&1Q_u*AA>+4AtHpk8v7Egp~jPg}(%fJu;Aq|(#TwwJ?
zL}gGVwEpRW7~L1<=lwi~2}-Mw4E6K`O9NOd=wE-KcZANH-hlu<806K+VRky+2|3$o
zT7A0;1u>{gkv(jzL1%x(bDmYR_{NjQce!=_ftsnR;P7AuAy7pHm$M2G#za%bwV<eo
z7|ZeT1=bDC9c_@;OF~QfEx!m?Sjpc2g^^a5cCq2z9>GVTB2!-r=dGM2>W0c&sHc#3
z@0T^)>@{(L30d_Gd~s!~<{}>SkIB`h&398X^^0zIYhQ(7l&9zBGW4(JV4z`_OZAvk
zMXjBD77N2j8>nTTo`r>4BJPVzE5|?_DyuuP_VN_zXh+PxO=FY*AIR4*MHSW2(b)y^
z;G69_Z?cSZ8f0weL(U<q{4hzu3z(2wwcXH8cTp{g1XZp?|HCsO^Jbi?i*&|J5l!N=
ze~b(dxwTow!qv11H&!t5^e!v#Mo`;{Bq|!4`91M=S65e$H+bnfU`jAGKR@3lER7m`
z+-n2?Q`FhnjM&V(L^Ig9Pv3Q7b^Ght)iM*xAIp5vexN-)w!pXb^-;{v%WUY~7Zq&^
zur|`H8U5S#IRA|sf4+D@FqGWoA}nHDh-&@%-)D82Fsoa`2Y5PL+YJnqSlgFm=H+qA
z@=qteyKVi%W#(awHxi*hqR1g=Gtn}TQ=f$U`Y7K&5KiawmK3JtoQ`!0JCdUVhjeYd
zd!OU$&(Rq;a3rebZwM+ICdo$dvi)d|qD1;Vf+3PoPOK*NP^7z*RQ%K><m6h0LSv+#
z%yt`nSbXABqqpXlb3j~ef!nNu;Z}UI0nW7z3ZW;ufFnf~>@I6-Wr`ib)_Z$x(vz`7
zgoM?FB+>R0Rwamj2gs1{iQV2Q7abiFQRx)dyS7|#?LtERCVhsp-yhxVIF;RBXy&tW
zqXj#3&jQiz;&59dI+&9u`{=``m{PK{Ih>qMgF%U6dS)ig?a9-`7Me~K;0){I>BrCw
zGnjz67Y9m<Tz2V#vz|=R%W;R7ezwfsiv&isx9RAjG5gyjbqYhpminUA?*c+xdUQQ%
z`U*_O0H;VRKje+@(+?1`tKJ6UM?_}x<n$}XohhE1qV@h)RQBcbPUt2nJneCPz;qM$
z=8ps+#=vzoR$e8PP`f_c=qD<i1?K$eS6_*^=l;s=(QLs&H(j07%F;AXLjh+JfBMeV
zC3bJu3FEh4F|0Xuh1{nGRLPW>u*%8{J~bblD-9gnzMc8B;2UA@y|Az_BZk5NVmOO!
zEZ-cWB)LC!H6RJ-n4*bM@Xxw2V>WQS6(+wh9lQvm{F<E|Jbvs)goVGSC#mc$-Nqf)
zg_6m%D=RSGqKIciimki&4Rd6)wOPLC`u5vnz4e(cAU57JZ_aa+E!|^P`^ey3qX&6p
zpKKX!U;IB#>XQ4NotzGJfNL=>R>?l6%!ez4kU1B*wu7#yD3x_1r{z?fhyPO~{$P~w
z(USY5O?9i-0sH@XIEU2qOI10)3<-QtPCfUU!+BW1A|d$WXA!0PTB`)}#lvCzMxTj3
z@UvWinGVY}<oZ7v-&@vSPp<xUEk<ze6-uH5+FnB#M*xx%$f{BDntCE*#Lm=*V<`##
z-rK8_b&&HU?{(StgVTPl$+5dKN2dVC0wg0(Q~4SVEUJB<Q7)Pa2)A~m{g9(`S@ZJ~
zXAtmE)f~ZZYa9uL-g0L;8JGX-0BRpmrEmoSFwESFsN9ZFt#sRCU$xr;7*Ek6j5ie(
z)pVLpYHR0OkgY$ot{O?A+lP?zf~o8|`$1crc^E<mIb5fufDYDPGOUJ7e;9lmS;{+p
z1`lsKb*mXFRZSwNkJ+umi1G@!s314O7KEp+>-C4tCiZNtpnC;41x9NrEnr}ZU@<^n
zBmm7Rm+v_viEXR?(;<7q&DRm-+QfrXOb%ny;2nM6A^<A8QZfdJiJ$Fl<YQ&B;fj!|
zlCGTTz1kuof<R-0cCSWncINvVG=z!)-h*K~vCx{l3_dIQp_~v8NO$Y}M)ZyV-4JaH
zz`9z{e(Z%>7s4rl#j-qp*YUR!%E{dC8<$R$&BecH5jogZ4MX(2)-^;-QZ0Tq!d`@=
zMVCCZqm>4ZfO~O&l8_v%$sL?m(Xv%)+R07^Fgv)=w!2j);tzks>?C8<4?km<a0*uB
zBmZD`ccIGcSwccz%R|Qt7<6k9Ix`GX*BJCu&kz=s?y}biOxA{5ehb(ieMD33%0z>~
zUCj5H^lX2XvQaWiC<|TK^B1}GY3{&OS0x#|CyN3Ux=jAiQD@dbIjk#1+_>%^>}OJw
zYl$&iRa;9QB2?MVgR_W*k|%ovK(CXi=<fXRzM0GJ7;ONMP=tat&8B$3VsJxqLvSxW
z`|4a?OD!PM(X!QMN=t?#xBm2ln*-fiC3*AlUw&aRoO>HZm97~L$X$+YMv}aLc;D`E
zUhl2x#Gp}N(ZIFAqvX=LJ_MSIINTNxEFD(9T^l2f*%Fn8;?5`&e%|K-n>XlBN@1>$
zIAcCKyLwcBy4t|%fglM;7~ejyGkHarmtIHiaWxn>sR*cT<RhJCQkSz2cCta0vR8B4
zUYP*ApbyCaW@x+jl@Mi!a)bC)k{eCrY9CkZZS;eCz?ZxEqTN1Xp#mYtv=LGoF_+f*
zURpN9Y#0j6zZkrJ{`B(V!T#d1mz+y{Y0L-UL9_>3bz{f}+eLx8i3Aurh|!P#aRU;^
z5PZ$ICnJTjJbv8xabyi4)iY~yZYx29>=;l>RFYfxXESoy5e?U!AG_YI0cf&LLS&~6
z5j%^@CfGPFN2|Ah&(KivnNwMo>*Zbq-3v%;>8-h(Dm}QcWe{$wLHR;-y?}%OVgBwc
zEy{yckpt3f$0n0ekP`hs!eT|7HrGV9(Pi^y7Csv0B?di(U;`bCT68f)uoDu=Tc9hE
zLkl?+E|KU%d{b-d>+|`|j+0U3kdi)#($d9UY7e&Z<)O_AKA8WHAt6(?QIMV4+#kgG
zK8!<$2IvpexI8Ir%-)>gVF^R>-xm=h5N82W-_o;6nf-xYKiC{;*xQadaE!F7zn<%|
zy)xqlw=Q|K&m`LqfMZiGAaAX1z*fE;vI1;{=7SWKTQwZwrg#>eZAs``Ght_Ps@@T_
zA|e{%EfcLa6v6j`TV)#qy988&ZTAnBaYDzAeZAW%iEe0Y4_3MvAeN-T7!si#&C=9Z
zl@*fd$X>*1Pb!nyl)tjnbYDF_>%H2O_sT36j2%_wRNpIlGtx0J5pu8MUkpqCS%o&d
zs23eDJ8E1SuvjwrGJAVz|9s?EnKS;0i40BQJW_$@$;grf7K*DnCg7IB*QYvdmt`?@
z`_f3&RkCqWdI|qvhH_xB1`8rCThIJ1af?q+Pk-#6udlCf_avcbKnpo<D0-Zw?#$HS
zqZY7}w!>=OzI{8Zt?SrH!e;{E+qrc#9bj)$EHc+}g#h}fo>|}0g3HpvcIN2bpYO@N
zPb|1}=dCMYRV&!($Y^M?Ko`1q{rdGRTd;vBDkubwj*YPhp6<D{u(3F-bL7Yov2K|d
zVXZvFme3l#w!o`W!SCN+#|~zdl?f&#CK`QtdFpCm#a=n2x^ay+nWU6dP?vGYpt;JE
zCm)8K7lmHEer*huLu?_aV=O>~9bI$JJ~<<UHB+Zjdt_w97+}$>1%Wh?0D{)IxVS7n
ze_vlhpc=O7We%2aEgS;_1J^)SKL502c>%kq?9hJi=b|Z^Rqn3%*v<9CwYJI^Jng70
zaQXh}@4&>DFOL_^v!15BY4irz%?I@Y<8L<zUwL@ULeU0jpkQpAM!{>E0(Jg6Kfn5G
zT_y$wQP4@`Uz{Wgj*iZP#Xo<0$cyw^{-e)F(d`@a{av7Aoj88{t0^y5Tq&aINv0N7
z8PsO2x2`7L8L3ct(18nOtFjY6ei$zfl~eGUN7B*J1;2ZzIzrQ|<FfoJ@p(>JPGh6A
zOysQxfx(fH8ApztWTK<H2OoXohCEIOQ~p6VDh=vfndamr9^>Ghwdn+Y+Zm?Im+!z!
z;Dv)&dwak0F?<%<xqG9dqvxrpGODXZ4Gay*H4dPr{1q4tB9oQJ$P%r#qqxoD&|sJP
z7~lemn?~|)QT*)evY@|&&mK&+#ALH;l{zGx|G9u$t)SPkr9P_81r?gDa(`!52?#0}
z(nEUd9Oim*YeG+)Jej6n=O35|bE;aiss21eIdr?6$e!Zkk~}k0zygZ!PY>UfrqsZ5
z4-exY*1`&SJq28Z!o!C=&%WPL$trN$a{{+Tu#?V!+Q-LUiKD9tx34{&>nljt)7NKl
z8Spm3Wkn@Ex4T|R{^y_HDJ&Pv_oO(=8PHB!BOc|dSN2oNHbSbw+!Y)io(AT>lxnku
z$^$o7P|PW~9(xwm|GXCWfS!3fpnoz7%g@?x5US(|f+<TOfRvhg-mb)Y+@k6=(O_1#
z7PgRK8uVzfP=RBMV6w$lTiY!Rs0HR*Ps$d}f?kyc{e+2=^TE@nPbq|)^36*oLKPNX
zIV-0Fna)&5mW?`3L81P5kZ;(9e!A%LASt9rHt4HNyu8Y~y1E}#S>eAdYg$2v)_J$)
zLb~Rh-szrP{j9>PU_y<mn-N_f>Ec|5ejgIbu9Y@0ph<n(O7viF5rY`yX1{-aQUC2*
z8>FP|d;!iW?+_%RP+MP{qXa_UzKtujolS$uLrwSQ!k|VVgy(a;E0(%f3v8dUb3AoL
zXWnS<=(vgw)3Q_Zn#@p24^4btxnO_o^5xjS-1mC)140}!+UEvJ9@=3C=TS{Mm_4OH
zMYtj#{*6UMZ1?u^Y6H7*fnf^`L>89uul!!q!10~MMIN(0MHmV0K<ltw8Asz*$XJyB
z3akyFU3^RNU{6u<Awc|i(4@pIEiFsI@Y<P(CVlntrDARBA$UW5r*FQH0SkKi^l2-<
zLfd_0co%qDT+(8oIJbSq>UBf&<-RDWJAdt%onK$PdhHsLsFr&e2(1BGJJkz@?#_T$
zFKA2(7IAx4oQE<MF6j92`HL3?PnsyA1RdLXNaCtS<h8Z6Q}N_9xfRA;%;><Xk!OCM
z8{<r7iX1j4ehxp~>n6s>i=8N?to-uXb{{(Ao87y2FR81$%+VTMxvpwAnb#s5#86<|
zmDW>gJ=r`B{WDOpl`#Qic1c-Ty8_DV98mJWQJ4m;yL8l>#XGZdxt8)O8pvg4tK}ew
zKt2r9pxG)1&<NRVPlRC^>4FnMIS+I6g_#zb*+_&AO>mZ~b$P7Lb~dvpls}wm-|y!Z
zFcgZW2=WB25X2~>nP57&cT0l8qQ6k4E`T;WxDRxdv*;5+2T`{y<9l|XlHjI(Krz3d
zHu>yUXSR-r!Q;nFSFYU6R4-@;i4tip(CO5%_KWQH)E>mN-JrnpKp!cZjY^2eWvC?_
zBRCt3FJ6cgu$NW%JnpvuZrHp=eNhRCbSP&0PHQP=X@xVyd@i5^0o7Qe3*wyUe;R5~
zcf5eUu)_V`+S)oj&u2TMh;E0iF_LpNtY`pQirLzbxj<7Yx9+iAhUy9s`VB*>7S^s0
zh5#%KA~sWR_-&>xr+1}oZ8`9m4-|i`0QA-lV9FNEoF(cCFJHZyh8_{hU_0F|1_g^l
zg&L*}(G1hjM{KdeTwDsk=bkg90*F#CcPg;5vSJ`xGZuz(1{AN5b%A=muONuQk6J((
zYd3FIa0V?WhXWbKTZUcr+@vcopw|F8=)bw*|NM0QQwISRuc@k6Q+<Ozj5KL74jBvz
z)!W<a?d@%~SILdt2IsUvuBv7O3?S1gT=5rraZmC})Sm?70O4m?RPHGUb|y#!a1~(y
zkK{w-U1eaHN==St=E>Oz22L{z9UVy=GD=DfQ&(we-*jAV2bkqkKEEJfxphkwrpqi`
z+q%#Jd7id2?5)NebjIF&p;;a!wF-2XnVDJAkAjEUG6L$acxq~@dd{Q2SyB$l!Ri{~
z$UZq>inN#hLOK{fUB`)fH<OG2{t99sx_HCAWJsPF8Y--AS}SGy;xE4n%0V0LC^Ira
z&g~T~AQi?Q!mvKwApxb!09dc9vzWN}3^b0NjeZNQ@__Q|aE53!S{-YbIpi=#WHwl;
z#2X<)&2Q7%8h5{|$U^6R(-Yi=a(cmEQiS$Fq_WY1YG7-qI7dp_m9E0EzA%WbUZoXr
z)0FV1YAd!JlO!6DVGIJRXf{mAvctIz{lRNWN)0ans`n69_$CBt7dWvS@g^5v^P2S~
z0t9~wzHR<z-`(RA5^SLiz%oZGzzD6L?2gB)SE(x5+F8%ssy#^z7LFbKSVr+fA>Q8J
zzM56kKhJMB_f9s7e?lFxb<+(OBGt%U;0jCFHyic_oPlEaoQH=LBmcF~C(|2$L<yR(
zJ69Qx=4!Ms-D(|~<ys<uQqSx$HX1GPf`GUiYBNiIOSKm%4?t}z3k$CI!EfI_Fx?)h
z@{k%_jItkU1LkAhX8TT>aS+tvQXs4K8Jst6<TN@pKFRB+X>C45bf!N?_XR@;g#vju
z?55ZoKG@SKDYUg0MxXxUk5+(nl7xN@cq%oR;*}I0W8gU6+FxYBfwQ|N7u^ZU%?D^f
zsgRWFr8cQYj|yrdMnrexYMUSV(uUpy=a+P*dl{pa5>S1z8-$9Kz<l2HnkvKe#8vC`
zcO_vw^PV?Lmb{)`Jiq-y2ArV^fQrf~i_u^0QzQ^3e#{~wF-P8~Y`E+cX};E-0vb}p
zFb!S36FQHJ+E3>rfYlc+L(&<RW3^fR5M3);SAHG>g$h`4L}_ic5b~N2OjSw#{%!R5
zDryIMZdU)=uOfnSpQ)NWVlz_F0(~@(nB`}iK%pE0>ymr(`{yV680qQnLsjj9?9pmC
zGyE$rQM<X0g6?O}o(=6X&RlXs+Rec}UP$YoPE5jO=y}y+u4ypzcfj<^fNQ(#=e|-{
zDGvvi<7+Ti0aaIWu-2591#%0gY3&!zYnJxu(;d06-)FJJ328$tvYV4kd}w5b>)1NB
z;_y>mK*@(p4AXFoh00d2CrJC>@yV7#xl4)v`s1#Y(L1U*-HNQEsNJJ^`#+CZ`RL+n
zconMBx{W?|b8(o%qiW=vIs=-Mlaof|P-a27bJyK66v)<;hz>}<9#Qj$VsXjq>;^cn
zRof98xP=A<>OyKh431Yjf3Czc3j7!q8{Re{yMd{rykImxW7)cb{TPb<6x*%e%dTw+
zA4g2hUu}eJ_C&t!c(L=loA>0zU7P*+3lvI`JOBIdpB+y8+H?$enB|wv7ybsv5sGE0
zH!gtSsBD~vmdPAP@#{VAMUHIlcDJbd${h2Mw1%xkvtY`Y7y{4f;nz~#WC&^pel;^t
zk{1{hRBq|;>o1>Z-Rqq12vD7^sjI_&nS^9Ff}tbVdpChDZ!(sfc5|vwThQ$gg7%rP
zDFcA^vdy7-gS?-1*#Wf&s-_Xt&A`Os;#&yGp}6tLoy9aX5w`0X0fOGy$1jorQ05zQ
zxDU8c33{^N{}3sHG<P`EK33a0Lx&T4J~?dC8vD1l>$;Lw@w2<&Vp3FA4oyCkC6LeC
z@~k^H#ZkH2ZY#jZSUb=A>Ka8EBsc?_oL#e3!=ij*+6s9Y3blL48(g-npaS2)X**}a
zazQ~qCbYX$)dxAB)cGbTC;_m$5STBrD3F>4IMxK3Pkm!!MlgRp3xM?tK4P%NbjkKQ
z*#%l!4(PRGbpa}=`kzH0`1hxiV>FAZzI@RvBC2{e$WC<!SY5v@9u&dV-|OQx4<$TJ
zqbLhDINU7<&4S-*+!MB0#CCQnvZ;L(EgG=$frS3ByX%tOl>;X$CMK3@F<hZu;Zl<R
zIzRs=vf%e$6FzwSI1zND6Ka{t*<BAvpsXr=2=xXoNs~SpRQDVMIYCW9F=OXwW0OB?
zCoAheYd7*}2d3&P|8yozHwdA$OChO@^h=<Nz4LZ}E?EF#!yXg$BJ(KZ6S5;QyIadn
zu&X>IEG+r&V+6)<RrSryp@ZgOO;0+YBjh`-KCzweQv!jX>e%_ejTxbLmw~4U2pc=P
zzNty+*|Qu5q&gz#g<%>*tgN;+fDiWbCR~oD!hw6gy#Rv5Kf&OV35>A2>qA(4*s>oa
zyms1L$jr&PF$l8Y4tO|{z(t~b<VduS4|r$JAk;T-4OV`}Utodh<IoMlt}HE$D1KXn
z5ac^=ngjlFnA_c-0RWjEc(^j7&Z|v)SaX3FM(9!)5NZ~I9%zl5x2V{zwZ#G)iar!`
zAYziHATt=gnHQ0Nvtkda8fkSua*P3uXx6z)y-__qs<zOOtJh(2eQ;B+`VST2X|OEG
zIqh#x+Cq?@+kD!WpUQf{hl`urcCLrhkLtDp5yee;BcqgkaP*k~>k%u2#*T%TK>A%p
z!`%p~(X-rxDP+Z0=-f&%E-PNIiD|RIn!<APrXsw#6<S?^^X7t8*E#?(<E^EUG<Y#E
z+RkJ-+WFqRx`KJwgt8uh*{!nO1!iIbNqCh8n^DmOJBd2JzNNQ?ttSYlM&cjw+0jk1
zw&XH9nT^Ci(viDy_vY$*%-R7`)GRuH#h!s#IS!Nqz){4wmALyJPKWX%tWEG2^CGM{
zF6BX$e%&5We_(*jXo}lb<5#I0MxGh1O|=;hm4W?u&lZ*ijKEJ<#=m*{`?Ck$C;Krs
zQRcL6jHn;sLe7OiMnfR+AhD<5INgvRK64L?Cv%&gSg+qLRL>Oq!5fs=mLQQ1kSLtb
zA{xQJk&#qMA3n@v77Zyt2@ZMlM&4M9ML9!0LEP`A#gO10Xt?~&8yPSjPKDcqh$<2R
zG+kXPI+@^UjL90a=*veV{SEXh`l34aRExYpa~v$sI+)JTprmxerP_>qIe}0>pc_i2
zrlz@Qa;e!PuAI0F`*mYSY&10^XE%`YQtAq0zA2CpJrbYOv_j*?0mjILp%^+)PfxEC
zfSBB&)(mY<f#K>^bf9_h$VpH<oac(0E1fL5Gu|6_XE>tkkfnh6LK6IY3U`ekvtKr6
zhlSYTfeV_Lm`DZg4)8$JtIh#*ZXVN~8^Ad3fw%;jgd45#xrzqvpWhsLn;s3KO(xY@
zkG}k;^k@WV!Oydk9GGpXw`1Vilt&gyT|GQQIXyiK_6x~@t`iRAML|J9GQ$Kyih}!z
zFR)dEqW+MOkR%d%@xKC{Hs(^#QuC((-9iosCkEhNGj0JA!rSF<`A`Ttla0AvAlT|4
zBfv=`Z-#Oa9DE*}KqK+$<Dr{<UyvdR`cWs)G-PozRdeINmQKcCw86^3!TVqYf;%KK
zk~Ob)?sv_We-3||1sNrn0fA&O5&`8F!@_8*6D8!cFsnec3@AsLAhV_1_c(hR(1-3t
za3~=VcpZ>E%e89~KzKNoZ|&hXaqtFPxL03q5S}0)FabHr>HFUV3~0C}wvXl9o@^{{
zsQ;P3^GQhs$h;PP@MKej%IH2wQ0@6oI~3EE*)F|~TUvTnzEaNuV{l0s870W&0@GeD
zF$swVkh5OCL{v9p=vuIhw&P#lT4~k&>m;T+mW+4^uo{-U{Szlnl(?PdCcKYSd+2!n
zv?5u+W@Z4bsTV(slQ^h8+y)0#DzDcRfwVTeTb4+T;TyCGk_f<T%&DOjfOSd^e0XMI
zFxdjuQl-Zcu)nIoyvPJ9`sq`rES)2xg`6b=hcE|w=UUcbdmL74sNC5eeS+=2VAJhk
zwWsB8>inrSOw}GeiiP{mgm!}Mj*l;j=bA-sZUc>FR$+^A+eu|eg(l+d=f{Q<o|y14
z@ew(jeZ18my7RHZV5zMww14t5zvTuk2w2cJ-G%qJ^pWlZjr(I=3(18GjOe679i{<|
z-tC~67@fxvFs70Ug#D)9d2<R?S)p=-GRzNGR#sbUfeG0>OhQZyIQu2%+<z|eWR*s0
zq0Z6>s*YAP_hDLYpDO`2W{!i7r4a}r_sXhk%G7I=FiYrL1nvUH<w&&fBXe{1I4%Sq
zpze@C)bc#~gu2Se2rb&&2~1)fX^R$Wup5vO-B)65=+%Edy4B)fe-HGS>xD9qM2XK!
z8)ocH0k>+ZD9ozZC-|_8f?R|HBbwCUhx}5>(hY+GRI1S+D0t}%sX%$i`mzPfY|S1B
zIV2IRA#vKoH+(*V?3e6UF-8~z0(_S9LWxAWO7=!yq$Yr*1cs*|US`=*C0)O6Wa}R<
zBQ4F!btx7b0|DW*a5nh9q@*{D4m04n6bL>5Qeq<`I`!;Ua9Eg1>UwbPZN0urqQ^pN
z#*XAe%Z8~UkOmVi@Fc-}Fl-IPO^2PI&$L0MxPKY32DX=PH?Mx;xWjJ1m=<WJ88Gid
z2LjFX^AVN>T6`5v>$3E>#LP+}RPDb4wcWN2)~DL;hv~SLn9$ur^Skck6Q{G(Z@>Bk
zBW_pCQ@ehE(Lh<jGM*Q-oe741mJF&wIzT@VfXRe(4woOr{2q!h*uxVi8p39wo3|3s
zx_s|iQe!r%%yC%OL$?D>%?EA4hhG-XkO2)Y_qNSp%wq0N{kcn*uA>jDWB)B22&1K1
zBGx~oTgdI+BH}N)9qypb_fJP?RgPgsmr+h&w?x!>INwx2q{X3&puCkkZz@A;K6#cZ
zWqf>`3%~L6q`tS`s+xpptOP)cb_rTnMZHHe%}{PZcsNf>t21IfY*|N5!KwF>VH!k%
zZeWVY2Z7vNTKKK0sSEJZO>jyU)p`2*D&D*IoZ&i<U@tE(F?A<RK(nAY$-{3!MQ7I3
zN2?c_UQhkLuaT)-)SqvHlMO_mHfSadA+|}$$tF-!kozU5+>+!CRrCx;+2zAhubizt
zPr+oI*K4yj^+4u5*Q*QEOSf9=->KXkTZTE27}Wb#P?^@DyOwI_2j<&MD->7^Ya<v7
zxIPCoQl|vu*)Q-_85S5Ak6dlw8bEj^03Dzo&bRFE;eF;U@_HW^s7n#*HmtyG=P9_8
zxgwgJ_SSJ-8EV{6QK9ldU4#$>tw|aYj%4@-69HM4^>89L96C6QfPe--0VPq<mnH{q
z-u#KY5G;XfQ#wN;0Kx-LpVUxiy@06sdbvh>JL_3yAgJ5)n-yIL$_b?`u-6pod+FTA
z24qc}-gDRKuRDB!4G#%d?!RV&gO@w4E5JP@tdhTT$33g$2Za!RE|2iyt2)Q4?(D3r
ztkUr5T}8ug=`omnwNs}~Wqp_gmwg+EWC#%`Flzg<{^XCJE76kIKc97@cQBx8WNGU*
zQB~`kEB8TFh=X@)AfW;u1(r<M5grFQJ&m+~yTzeQm?431)-%ruPNq0$eu&luB{vM%
zzJo(CG~@)x0+!pi)!;DLx*2om{&4S+NfF@ulIL4raL}E=uZ0HD=m98F(u_f%I49qI
zaq?+Pv=D``OL5)I^6{W=i6CE@KxuY@$u`LRuE56-IE9GrFrUkU0mW9Sv?BNGD#}Gx
zgJ(bj%bkl5EXJl;EYBN}`ITQ$Q(0L#BwVfQYrSGm#KDH`rI1?X1LKJ`+Ht$VcQHSV
zmQ#sBG~N(r7B4ss(zxm8PTY2U&4^L#Cm`Fd4dGc`-;_0EUXMI-(4YAxVAqh#Yaqh>
z;SgKYKo)lU)3D9fmlovRd!1GV&xxdRCk_P6+XBb`pj8~FEYft=9t<~BnYsOfuPpmh
zITj965!_+uZc0RPLBow%<e|)_@c0XFm$BN`+MVKDyqo{xB2gCx^GE8qw~OH;EbHF&
zQY#DrQIv?r2kuKDPO>`u0#1RCS9Xfyth{`EZPtBc<m9rs4(yx_q!RrNoQeX>l(@DR
zY`St3{Y*H}ckkXkO-#ILdubvxA|f4F&)z)#lKRH1xK7x^56&hvPu0dIA477VKR-Te
zh58=9HO_ab>P9Cxg3DopIwF;th%5e#5!9Xaks#C#2p5P7hYWv6NfUJ3wrj|L(|S}{
zO8ZeGP^O`s&4PXI0VsxuCP~K~1dVGrfji{T69|WMX_)o<=uab*c2z3W2t+Y{>kmdE
zoiHavhI2D8khypNez_L%2sY#^_8dfXGn6o>uYi7I0p5_Sk%P7j%FlG12aN(`Pr0Sj
z&lf{u`@W1SHEfCAfh|1`O}AkaQ#`=BDKKmVr+PPz{z`t-5)G&;P~NQvL2xXG<{Jw_
zFrXQSo!=Mi&fEyt7fRtx<4&e^7(c@7AzE1Q*XMjE{sMo65guXH(wgpX%?5LW#-dhw
z>-UGeDf?vrg*RPxjQgek-%;W%96IKdD666q_TU4Pb@4yfZt4p@tIVitP0w<nK5_hh
zf92-^C6s*0FFC}07~;Hw{RnFJ^e?SsiKQX%MaZsxEGRJ#eVn$PZZ4D^DmQv7w!cr#
zea7pz$dCF?_G^0`oLTne-+CD8U(#Og{t}kpX<ere_eCJ9;I8uf>`<t(pEhaa*Z=6y
zcX$j7)R8%p2|}E@(n7v``GNpVTNr?yA!qM|bZ1HOSIc|yO)nQIj(`S2teVp<3kz$5
zA_0R44qGgg8#UGo<vTO!Ay&eQTA;uI9Ot(jeVHv)^7!|abq#>QAPq{^Q;0DLp0JO3
zdC2$*TJh7bZ?2RAq(zveQ^83HTHyAeEx~^?@Q{Yhne8-C>De2gmqH(~1-7qPzb$V&
zF*&IcQFr4L?!+f<!Ya@;p_Z_Vlcgnb1>5ZrRi%s@yEHuJDk;D4BoCuSpi{;u!+5YG
zo0~WpE&~07i?SyBM9Zng)w)fGl9F0REJg^&5VEB?W;^M+zfsB^(WF4;s7+R}p)_5x
z5YTceuNUNB&E5H(gSUtL5p-%qGX-Ho9TFS#4}OQGSRkw)K*m-t8gfd9l$t5PZNWsw
zW~TEx^gLMsySdgf$JI69h%8J@Us)#c^iCiMkU{o{`%!F{p8ur(M{K$m^rsWY6{9>l
zbpLX!V2?isZHbAQxqiMh_U6IPWuh}@tex3dON5zfJsO`_d$pXh$bI#ixl*BJN&~Dq
zr&&fQ@ESL$FI`Ig^WpMlcn&M=`LX+OhTh(31DgZ8v6p+9*L^Esy3ZaIfs5*j5nfZO
zovgsP&K(@I?9~eyMt@68BQ1B-QB+h6ITd>*eQwxVa?Fqx4~<{Zh|DSbTgzth@STI6
zeK3|>(0G!o+8Hl)26mwtBJ2l|b08W9!6dAmstc4bFcJK6cVSA}4%iP7OVR+aYv0yU
zT7gHGgp@QPL$C3#B=7`~=Ecf^+b{?MqMfCp<EjMvz;s--&ayw~LbF<;t*otqs*k}P
z@U@<NylhJn0-H{zT|)7R*Em`#_m!f7$7g&R#nD}Bd2?QS`^2_q)02nF%63W79X9)A
zM_J&KL!jtJp3}Cs!<Gl8FK2?&KyC1N>+Rb|0*w{j?g|G2Y0Stt6XMi-C%r3s=RHgX
zzb^c+weNQ7Cg|Fd{p{?5j=_Hpq}{s^jC@u`Jy8s+9d>s*W?W58O^MO{EZi4AZ`Ns^
z?`L>5QWy@<anLteG|aoUDL_|NN&PHee2d4pvlX606boQr8iuOcx9cFip!w5*sSy+%
zG)x(hb-D>ZD;%2wo5BoiIlw_crGfWBCW>DP_P?nBBLj(xT_*e{noT|b4MUW$t2#nv
zU}r)cv;Z-Lg~8M-9*j+?)nlNgB8Cc!vgOy%@4-XW8F!z6h?-v!@GB;_?K@0{5cfqE
zJa<I~R3CJ1>@6W7EkvY2v_YV@)3Ej8tzv5s3|*QK84c9Q!S0WiW8!E95NigqhKnp>
zhFn%<%m#|vY$IWMu2JPdY_Ij=qiUwIS?@<N<lckoz7AM_3bwj*fU_wMd}|81xw$nP
z-<q416%~VFYI~?#>8v884osXhh*ApWhgC)Bs^u*z$gYpQf&v5d92|1UmB8_m!H|&x
zV{b$r<N*ncO{=tX9W*U`>EHo8*(V0<z3E^co!VL+Gww`zizs=Bn*~;wxzKfF(!$QJ
z@Z7m`2-(2t9QCCY(FSLU%3D_i!+>(Eg7*XBLFP!4rIuH_9$xaUu7i9lIEB%aS0m&!
zzh9Z4$WF4#-5#a(_06KotRf=A^95fUR+BNz7hF`8H%z!LiwtKUooXN2r1DQ&4ft69
zs6gGoj{Dx1;gby8qj{~5^R|^xX7D>}Yro9xt^__U$B+#gi@fa)0n2YO_@VPt&$g6M
zQNW=LkRHCULy!Zr4X&7?vmOU7z072S*N&#O4j*adk8}D8DkpHT#OI$H!5h_c8Hy)(
zr9>g&jR#8$zEo8e*v={=dJV*sB{n9Hcmag;HF$DP=GV$x6;P?b9cT;^T`6olAOjFF
zH#dCpD1rmQINuHy$`r&8(UZHnvI3+d8%WoAwF<cTaJL`8tJ*+A$)ezKbB?*hZ<7KO
zJ}}%c0z8(0VP>&O6nK4LS936EIS}XAFNUcuLhnJ+xR%$Op$ywt)y#|f*EcH<5C;+a
z$6Zjt!UgQJkUIy~@^jg`x`0>M?IJ;-hOI~%u<x)4GD8t$ow%<%VD7P99MVGk7hp_+
z4FkMIb#4<OD$pQ6p~~T!U3vxbSt@iFm@L0B=><u$0PGwH3|(GchJZ4+rt+wY1D6JZ
zdBj2&`-|idr4k-`lL9lZG<c&}w?IrKP~mQ{nRp+cfyXDc0V-O9O*x0Qr45!{O|Zy_
z!S`)v&VXbNMZe+4NXtc`<WoVX0<$5(`dsfrkW&$<9voifR$1gaE)N2cfhPJy`8W(f
zucHyJ-!OprH>h98BB0wr02_Gi9U!#v4xVKY56V3_IV%FYjFsHnD(vR_k`UJwg4021
z@}ASb_!_ucCP+s@@I<n#@7jS+nj=$zGr*i>woQUIvvvJ8sFnyLsKgxT(fcB%7hrA;
z(D7ja17ipqqL)%=bQ=uK>j#Ulplzt<>b+`jZ`af{`Csf@LEJ|6g%7sN|7SCov%w`n
zha6Dz#wq3(2|XY*(%?qxjaca-!Y7PlJAuyu&6YtVdMzweoa>PAAhC6pSWgZt9KeQh
z<DMK{1X;lPHphr$*Z7c21tR;6=`fD1@T`X0RA*J*xq{>kKj2zmcLzDHkR0!z%=;TA
ziR6Nj1;ENr<5v2E1aV{{v<@5dutzoI@L<!(*RMMZ&a?}cuE2cx4q5?bHSaM>oJOse
zAtYp=egx`X1Q?HF$u5JfZ;23`5O4CJ1wVv=2k^hY{_;fd>q#OCCMcxHEw(BHs%ZoU
z(!kT|H2D_8L$)&DaVmh%0rK?kb&cVsdd9~eBW03=goKOmKFm!@!K%drv7zO*Ju$)G
zlJXz{(dJ=tE&*0OcnQ34W~dyCD9`8<^tl)gm?{BckVGV%`hn>e#KLbFgto_nl#Jum
zlR@)+3dZx(goK^YjS+3QME#FJm(?a@VC99629pR#-U^@*AQxgg)p{2Wm1V+bGbLXR
z=Ycquz+pT{lYlwcN3t3o&UX4INhhMZ)w+Jd1G_4at^X9asz=*W4_ib@erC>6`0!@;
z9^8(Q;NZjo&BaEV18^NkF<-RQbojCa8(Fkrny3Qu7ihi@XE<yB7maY>bp^N}L9_);
zQ7jZu+XE3VOjou}Wse)&JuAP~{iP~mpUlOcT(|&=B)EZaM)F#vHg6e_P6?}&G(>J6
z%Bn_}dU=X%4mO~MFu;(hYj)42zrf^LYOGfMB7{$|cqCTi04%cTKpM9#T@Gc3WnPOR
z*3|E}r_6&fVAzqIBddV<6pXg~puJqVa>cyJQn&>aPuHSZXmd}C8W1ZEx~5`$>18`j
zOa1S0@%L-$`9>o<5BGr~g7Bz=5O&za;aFfkSlTFDb0`&p2!XDtLRa0;{Ja9<vXqc4
znsoEF#6EO@*7+Sk*HVG(noRs^1>8$^ILf|MD20KicaCGZU3YB2QX&dE5ZEz0rrHv0
z8`z41tvCkq)IsJj4gvn2*xK4kHw&I#N_6Fk$mkr*fg55qg?@ed%o!LH!IR@Gqwt`Z
zn{Qedl3BiPl7%v3UBFfXsDxRu0q~3FB*!i(DJgww?79bb9m*{ke+#I)SHG>ldL>X9
zL)iKhDs5mQY-udcgvaC-N5|kd7ww&WA*I^qS5i`#&?iU*bTWKg1zAXcF)DXjh(twr
zDgdkkKZ2#<9XOcN2|4-0?hR&8X~3goEAUwt_{mkW<8Ld8z0R<t65UlZJ1uGa>Mb%4
zMAREeK|{Dq1~g3QtgyoW8xNcoXx`Tg7r=cPl4vD!LzM=Rkzkr~6GT0ja!G&*DC^X@
zsD#lo0sb5rfImPV?uJ)q!lQ#rL$g(L?*-m;+ub^i(=Y1Rf@OGEY*>U{C)<l3hv8$O
z+ZmU^60nF3u**qFu@rQ#Xkx$6ADl@yN)H{z2G`|r{r2N*<$fVOnx#`Ylu+jerauO<
zmk!X;YsCGv4_$I!$^kI{xAwj}D#|<Bc5F#BL{qF$v0({_1q7sH4C;V@QpEv@N{0cZ
z3euuR6APG8KtPHbx<I4~2pEkb3@ylj)KTfZ3>|^D59+<|-nZ_(Z{79Y`{yMqu4M=e
zGvD|7PT6Olz1;`vB2UlI<W{%-b*kiWMP49Cqz1>_o()Z-33rsQK5lq>Ru4!hhPI)C
zx{|fy+P{O9i)k396_tcbg0^Sys&my>Cc#fOR6<u`Bhz8`s>HAUUM8t_7I9W}(<{fR
zF62edAaYBV@ia<yy*7oC*O`5$-gyS)QZ79qw{Eqi2Ru>6j4R=gao}5RJj_3Q=uTg^
zKH8}!v3YYoul9(8GAFIi?VGQ^Ryekoi3)#PdxkK#XZPL`Dg#*HWXc+FFJcV353s$C
zgF|wB{Q);TIFzkX=bo0IJwk|q2!dt$_xa8ih_;;HfuR~Ix(}8iX7>a?)EsBuJY@xJ
z7Z2#<FCwdVpHe|{T7@~?(GJUj%c7-oJ(j%jc%&X;+lM9j)7g)3UN~MMmJj0k{%SNd
z{K#y0cdOpc^;(m7!hSnd+5I{)R^@+5iN37&bJXp?>K6?<{6h0~VXXl(w}NR+P<L%T
zk^ee_oe&9M$f2SDr;k8GV0_d~qhN@_fmTE~0utt$*}2<e^pBD-#dt7;vW<IJEy)2}
z5pOo4F5kHoPcl+O(nCpLa1wryolXEZzY`Cs9DZo2IM^W=kQHs@wXlJMa6~3C{dF<E
z^n?lxPY?_3Mw~TwWSSOyO)S`B@%<y~H*DBgz|Q$O(4_Ex`iOB4P>$GupI4LTorfga
zdo&E45rXCx^z?kMi3H$1JgNdFpxQcmcas-ZfZ{|TbHI!o&H~6Eh01s%6SM%gpG!_Z
zKszvmg+nF>K_?n7^6>isG!3!%gBt6ypPvCXqpx&jqsO7(O1(+^M%5!nZqXQqIqxU4
zH=RboGz#Q6fzX#_yas(Mb&Q+}Wb=e$IW<zpLoql2gezrMc+(-RjcCClR-hphgb;6h
zz}-Cy>GqJV!=Iepy#i=R?y@LZ#q6oQq`Q$a1;%JJ4W#Y{K<|0XBT+dZwuNYK_PYwm
z?plf8wQG7O)^7^~SL3}$z=^xKKb=RtX$Xr`gd3@*rZ#Y-7`5+B-M(?hDnX5opM}{+
zm?j3@T?mM4-rsH7v=0s5_2bKtYmp2wmf~Sj75C!B0kBua6cp;Zr<R0{cB5pjL`)ZB
zcHmP5)hZ8F=x`R)07CM02yfLc3RgA(WM=>@3q^vNH0Vk}P8glHA>d;6b9zY5WgO{I
z>P+8{G)u(uqI&cP%qH}aamovBY?iVti*Vre{Vy~@n&qMA!M%$_>5NS5<HIn9_M|&H
zcP{TN(ZMt7T|y)zBr4+c#88;vTe#b@4<h6`{#;;o+V?XM&BHKz-Gwm~khz;SCMLu=
zOUxNQ>It<PYO;VCgVhH5M#3+@d`5*Pr!Hf1If(E35FWa^Y=^pQ4Uk?Fs}7?`LI8>a
zojxmfRo?~l8Amv~%nAucdJn=soJxNDZ?AGkRZ<cSb-}tIbOp=<px^|agHXt#K?7cl
z>PNp&M`)99i14#z>(={4xR~}yxCeM-3YQq2Wq}fcGItO{xciaw4+FGE_lY8DGw$y}
z%E;Q;JZtgtub<X8T|iLR$2<X~{TQ@K0q#?Hayrqbd@JW<8N1=H541%TsBMI>BIx3N
z(bUlffL0Co6~vlsnUoUROU)FNj)Br*TlaU|1go-$JwSA|er2n8ZQr`JNa}<!XW!N3
zhZ83~#1#~bD7)b-6K5K?0&{pH$hF+1vc9d&lG(F4beGUxgn~op{7?eLqaWsiyM`a6
zo(wH7hS~SQ;;%nBB#lW2$eJCM2_bjy-Ze6rE1MV5U(sl|LTJC4XTsbhOrzqoQ&`^`
z1}!)%;Df$a{;I<y;r7(|y2O=&m#f5;((X8XK5%DAm10uuQ~Ryy3Y$yIn*8cYR~fA~
zHk35+?$sUW%4+zsFRy;VSX=q#sL^{ba@wVv>&pwevqBcV)3~(#ol)kzu`(@j1F=_s
zEM`sRH7*rr`u6Qn%{|RcYvm=l0i%&T^K@cCU3R>7X}h|t=O-^JBaT04jvkQYIrZ*U
zig_VpC&a$*w=%moc4VsI(B#OaIOW+dbUsPvmWSUJ8*wcw8%r^hyKy+SbucpJ-e#t7
zuur=-IIE)Al+gY&d_>>hTP-(vqjtW;91r7tkwG4=S=LA5o~H%=9ucuNV}p4-QSQNW
zurhS`gX8VQmq`<~I=tP)?|Eq8vlE989lD;x{pk{Qpx>f<A%Fn1w9%|pd9FD2HiU|x
z=6#SZ_HdV8qs)r;W$a@=$T_Q+r&RMgm*qN+_8n(8JThE*C9=Y0%fpX~q70RdwOP*b
z0|J4uxAE-WaUh*T*o^DI!*B>a-0$b+&y}%#6@g6?b<tb9U)oAfBo|r#YVm=}=VM)r
zX*$M<!4hVd(M8ue`q8?FT4JH{j@ciWcq(c7(VDBf8**4$3jG%n^QPQ^GZ9G%EoV4N
z#fU{pqdtF^9((c`>0r@6ykj`-l<mi}gz}#L%a{L~*fLQDXM2#+CjL<|>p0^XcA~R&
zBI~Mz>Rfpb<Jk<O8M6ue&({VW`>XYo3w!=8%>E;$A<LJybxMd9?GQ2vWH6R}B*u@<
zd%EK@$5|1ao%7ety!^kTC;p#=$p3li_gDD;#W>=hYyE%!TC4gbecpEwt2J#<3-Zey
z?+^o1u?>X8m*^E@eOJpD+De;L^wU%sy;aBKpXL<4cyS2$6%RWx0F2_%_H67StOWcq
z!$@IeESaE6k3IF_Ne3HR5B35mSb}#PKZknDrW&{`fDa{4fVIJ>CeNKaSLpc&@DMuh
zKVA*c(h>Bzf*?mCNxdE55~r1RK(4E8)3r<AoZ4Mz{E<5R?yzqB40V`zV|Zz*XDHKe
z*>V8v*fK)zKuFTWDnL0{`CQL~Pj@U{wk(=tk*GpJMT~k1<dkS6ZA*}RvBFacgfkw5
zk3arud<UVpmmtw~|C;@g<n8?#FYePp!0_8cile}~)5DgGE@}uMJ*d4f0bL6)6PQ&y
zcsI>T=9sl(1u6-dIu?Iu%p^23e7SPvC1(91Of=q+zE09XalFX)q-}<egkFel#hX)Y
zAnV?sy^H8q<6xIP+_%;nHNCLBJ(?ZkR%M`b`M#fj2zB00yiGnzjxAR)76E&+v&uDF
z11<GNCaz>!ehKSgm6NKw`UjSlin9URT?NA0zyz|pBr<NyQqu-hCDM*W)A!14k+8y+
z*tLTT=g<G{bT(kyBWpYdfAg9ev!D!$k`_mxGJI-q^NNdyTij4T1M^ceTC;rlk;od4
z<$5szg+DIe_iI(2f}g#!c6r*4poa?^^!*#=h@Rh`cv=q^+f<@1r{`Vg_~={NH}<{#
z&{SKP(c3>YWOPF<w{&Xs_G{->V|+1gebR#`RSi3?iVBYC`pb!`|Jj+?anQ8azG@BQ
z&}n>J%$zxctQTFrl50!2Tu9-H?Tp!$1dOEk*3P}0T@Fnzv@?hfYLuZBrVxwD%^Inh
zn4l1zku}^Jpgn@+k1Vtr>PE!S0WbE!Y~I<xr>0v<t5kLGq8z4uMGV=#nUa!FqG~<u
zv%;Z0JC#hOL)1Mq&s-7Tr90;DzK;+3+Tk<vt46i?{Ao+nREtlDP96KE#Uk_$3Q25Z
zoI*XLyyd{gQpVxQ3zsf&n_8R$!&^<>?6t!D$bVw-U?wQVdIR~P0|z(hg|-z>pik5@
zo89(AbYkRo`Dk^1F1_f{pQnEPExcDN@2A;}ZTr&ZM<nRS|6X4E<Kl%PT`m(F=Pd@E
zjcP&^?9@4RTwG~)D%%)C{z2>}5x)uZx|_$B6Ap(8Vu*M%fJPuZ7X?SIGk>RjI7upc
zy6o9)K0fEN-*(OE_AMT+T^^}Ern3i)><UTqwh;T=!{D1<6k4GT{oI<=@6xw|nS)Zg
z93~3UnB)`J9!Qc7o&|LU-{Z?Wz$^O|0*>pA)_M59w&0(WU*-FClJ^^>H&bh8#w1&g
zEI!gcG^+Ne<6Q@nPZx$LUwgXs4BLM&%W?NRD_f&K1OJ$1r&MJteM4k=#Zvvg+c9-j
zKRsQ<Z~Ov>Mz78Fy4s=f)?7oLWw5^l<jV{b{3qo4@<&GFBxcL4UHh14cSkg*XSFYb
zIVA{O4&=QuuK(tB-M+ETcOxjAdUQjyensP<rXB0af#llu*w@RVudJTzI<<S3P36`}
z%p&x#AO`Z&t1CTjwDyX5#y%R`VlhkoiwG3_$If=AeO+Z?X7-(=q~LBx-zOWy)Q{7Y
z7Sm|4=nI|UfvsA_2Yv@l*ev*p$(7p*d&{S7dP|-*iFSJY<ee?w-nDY=C%fkEn6)C{
z;11D3iSKuu`*z(N1C?IYXZ@n<&v7FZ-ppz}eQ2A|IipqQeRudQU;EjfrL#6%P+kAk
zx-SjqK4WZ}yLav9?OSsyCO2f%UT|{F67!4;=_ze>8&C+9<4(xywJJt;u%a7MJexo7
z*mUjMwI#hbCObOHhYt+qlwwSfeCJy0!z9oC6dXI-m>P#I(NZ?nH-wTr_SBoAgTbDW
zhHCAqhsU8UXDyDozs9$$cGvio<CSN&cdu@&N?OAiymPM~7ctqv%#7bDWM$p4_bYGd
z9vejwA7NPsQ}wckcx7RMnaat=7lU{G(pf-V<91aHUu{*Y9u;dE_WD(bq1YIDaM>%b
ziEDpWVl`1ZyWC9&BTNU5DG?vzueJ1zVX?0m5z_&*PdkBzK|{Z<s|XXT7(DjTnOvZv
z_0nc`+ix58r`Nq871@LmN$;t5B37<juB6+3tE2^+0PI-yazczIo40#2lOK4|iT7#v
zy=rcmo}OM1WKB4xNMEdjCfP1*5}Xn#fYC-ptaG4Rq!Dy7G7%~yk3YF^8?$ZWua&kN
zXQggjY~Q)*;>?p{ub;EYN0URE3G`}k-M~PlT4J~d7$yfXJo?U^b?C^go$ogzy~v}B
z=0eILrMj{BjQ|>+f%qfChai*ODy1Z&ObK9FZUA^vtBb#aYGNQJAI+&RHPty?dDBzX
z5K!RO+s?dw;aG$E*yC7i8-wcK$iZ+WMWpc}jFM$SlyGQCBsO7NZAHb+=g*(-+O^Ae
zcoc*|M{K2UWKKb?qY3-I?3773QHVhO(Q3>)R<{sqIFhh^kq&fgmFEY6(&iXnJCmCF
z%wNXOZWF1-R+uBm%S}MwiojkNKzCqX_waNOl)J$Ee5A400{Yw1_NQJcc<Nv%TSTfb
zMIkcV!JnoHOyGT1#x@M{NNnr|IF`Gn`xZ@KkBf^-ey?lR7VB=z2o)iXP^FQ~(q_l-
z38PDavS8&oV%6Kee-zpu!%+Lp+mamsz{CPnwjgQt(dzfxN@pZH^gdcOIDP=}wtO^D
z{QjRW(5d<7dd&30KK~qt8SdGCavTObduF$N3$>6JDEhOrujYwKMOM^H=^nz;y@bbT
zpMEs2vQqzHa1Y?e*ZSdCf`{r49h8ZSjkR(N-=YRg>vDQk{U<`pum8kW$mj7qJ12OU
z6q_9y@(~wa^C;O~aS>b%RwzA+D!(=yE2sytm7_miKRzC17ZWv+>UtH1p?ylPilxIw
z#3qDOEAoB&!6nghe<58}@y>Hv7+cvg`pKCZogJTJx*f2N6*io$5nB;ha)EJkFQfj7
zuAZK=>wIqmJzliZ_05W1W4-1HipxCv+ha$S{>~pa*3<L%8ktTTG8#;4jg}w&HMgQQ
zcqnZ$PMoc{$g_Lw@7~J>dV0JK`~xr$$k|-|sy8e9avn@8{*ap8cI*U_V>NVATm%2U
zi-acB-Lqaa22tKw%phGZVROzl|3d}_f&Nk~rDe`5<2be^W9PD%Cr_T#Pk*4bIMT$}
zc)VPSD308V(qWBJQMt}iEf8!@zGKZbVcj>>3Hd$Q;H_&kYI%$1La8Ne$JZCXXgB$L
zX?7J-ZTTy;JXZZ5Su5k(mHwk7tkd{eqETjYzR@g2)WU<E8T}iY@C)1z_Pu(wTf(#b
zUN`=${7|iA*o0|gQu!xBZF>Su=ae-LUUhxHgfJ-gVMxB<gB)hWm;Zc$;^#ltW2PSX
z=QzAaN&l1MFqxI@vz}bJusL&*)Q?->r^Utc$N-5Yld2n*6@%>z?GL|K`Ww{E+2#ZG
zO0=NZ-b+$LLqpt;#wKo%L3IGzsU@2~X@_un=<lJWdSrCNP|>Q4zxet{j=OUSB!hQJ
zNj0OQD+Y2Lj!sP$(qDeb*eh*;*r^UQx_kAUa#VRUdNE}$hvFFH%koB5zg@Y~5im?E
zw3CVu<#iAgQC77=i%lJG0F#735=LLb`j_|Ugi3dtzgiH)Q#no0_a9yQ^3z4aw?XBW
zV3I<;f$7Iz(q~?VnjAVX)JQNnAenV1ln6-ci#dQ9q29myMl9QGW<vG(Qbk2Ys$3})
z-EWGxf&3?$+;I23!0Q>wwyOGhPX6esH;$c+s13$C!bT>L>+7g*rT4x`JZf)HiJ!iS
ziaR4S3?h*0u)bs=&(c1wajo8Xk1DUC!W5J21kFx<p7+o3?Z>c6XFZe|bBzDRGpBpQ
z!@oXxH{HlI+4<vRA0f*|*~G*iDSnd63pBBkG479-`Yk&8cShJHcwq{y)PKY7Ihj9-
zMsRb&V7Bv6$waZW&DrAG&V|)y8u$aFIx$KvSJxm#`Bk@QD8~z}mP=hJR&BIjscL8w
zrlG~x7r4qZUM<aOSxwdpoNGV*z>@YnS&nyqM@F<ZxgqgB+`*d-^2YT-I%OdnUBiwp
zS3Cb{^zo{CWa7)QHSO&7s^@~d{IUnKr<%N$B*siUGAoPGA9)tlVfv1heJ8?hpz<p{
zbbA^b_D4QgG?d-Z?Rr3CWodu*#XL#*3g7#uRO>CueD<t3lw<a#oV6vt!}Z?&O4G+a
zg%`iPChx1Mv!=s!^5PfjdSa7y2ZmlNIVVnBT9=*XcGbJ{MJ}&*-xuBCYI*Y#yeGC5
z^nK;ePF*>)W=BN++_I1kX1JnDwbF`LmvR@a+GVNo$zVh`+tRYF=W9mW8+W+%CGnqY
zSP463N?p6=ohKK4&mf=8b-xnVUL0ZF_Q#;|*kBJDgwZNv<sk3+p&Rwa`!ZHmD9Z}&
z)f=n!>gTi`i*0aBqHlR<&iC%@`P%)0;=WE-)p*&#^Rf6d^Zh*UBf@7!%<y-=zCUwh
zrta;YR2~cJ+4hrgOUflD*Le(;vv?<pC;oaKzZN1(H7yF@A!{V>T;9`GnwoBwV{EA7
z?Ck6{oD!bKY6U~G=C&sPFp7cXUa4KXn(%=2wp@-Fp;H7bgd<`I2{p{-tBRNG>6O96
zH9%|!pf_iO*L&s(t(3msEP16=co%&)_n85sEclpX0Gi$I8zR8+Y#g@&IrWldCQtON
zo{sHGzn>vygLbG8OVNL~4C1JpVW+b@@qF&_mtyVkWL(sAXqNpdO=jNtw{Xr2$XmH`
z<&b=gj|^&`X)Qjl>iO$WA_VTmw-%xKItbTg4(ta%w!;OpK3#R_w~x`|amb#e`4RDP
zaS|YdiWGowmwt=5f^{ynY$oUYLb;gro#G!vLS@b$`$lDR0l0yocylB*j84;b1?MeP
zm<qsUd0AF=9fC(@*cxDMJ&U%T8Z)%Gj#*AQwaHs4SUnCg)Qb)LqAu;!n<r>hb9h!D
zl@-rhW^^69X?8o=poeF|Y&02wZ3|3Mx|4_OI+ItM30Vf~85mfgBUk5CR8cwPUvKo)
zpcj>;U}ADaaHC@5<2&hwti&LeI}wfHY*cP;Ziv{q274^Mipzwsay>G4Mlh4dCe|`6
zHIg#tA5Wo{o16sf9`!$`{xm6f+#2g)f#2ywnSvB!#$S7!RmsxTs9gUl;v*M;J_bY4
zFe!U>R+7WKUByPTzVL5dU>T!ept83)b9Qj$!`a{ZB((qk+JfrhKx(@#`!gN;Z_XW^
zo!Qw!?mjXu?Km-ZadXEPPA<&KkMh*Q$(7~*9ZX>qQ8Am#i1_pymA%_jpu|g(oJ<aw
zU`-@2DPu4<hY$QcRI+6E2Brh-X_`s5bLPufQ3)qYAEpB~5nFubENr6#AU`$QD8TCc
zcg%s;=NCo6lPg4QHlsmE=+Bv6M`hiI^^?UPO~(J@FWiNZtAAGZXAzx1PMRPnq{b0i
zAt3_z=IqQH0NYFeXHF3&8L^n++|Qg!tb!JR=y*c}vYIyNN-8NNj9`vy4oH;A0jMv}
z<0V?mW~6Jr=S8Wk^DrZe55|A;-tL;XE@P;Y6R!i7U2L6mu_n<9-P0r$)sP85A-4jL
zxqk<l8jVjD2^;kf*TSZ!wF#_{Qbd+8a9Y`d@xuDb#KiMqk(qvBE;UwTk3Ur^>rjAx
z%_*|V5GeqaD-?u@^n+q2rejR=w%(meuZ-R9oP@jz28uHHg2f;{hGI|B6ll{589Sq4
zzV|elZ$hB5aVa+xNRu0=BZLf@;*qOA<W?){xiDps&IN~YbFH4h)&1s4YZzw)U#q+n
zlaiK>dhp<Myg*7yN-rW<+&MM32PDI?$Ta`#1Y_e*71ae^&_zBP>V<Nm6<*K>IB-AV
z7XUFzsUBQE?58%&UEg7E6FROvVhFPx*f%9L<D)io^+T@F3<_&O5JBWLDMPwcK+>@(
zZYZ<dyE^sT9dCKwO4PUGcNI(~Z)oTmn!~U^HrvuY4IfVN(rT9@u7R_ho<LR7AL4?$
zcsyR_>vNw=fWX(YZN3~VHO!!<;f67cB!O#C8Leh#)N8X?AyH9Lh@P7;EH4CU3pEt4
zzWMv`JNB~~x?zmqW5u6dDzOnBh{ztgDHn4eo3I;U;^X%c98@D%Qml}7v2_j9{cOUu
zHv_Sn+#K4gl1#Qee0@#`^DwicsKX$>5eAKx?VuQ#$mq}a^vj*kIOI0l^2~YsYPd${
z6_8O9*+#@BC6$6ACP;M65yRA@1g1DgjeB6$@&cO{zhQ597m^MaWZ(h%4sv64f&Q{|
z?OE_t96>B<!qQ$Lb)ryuZMt>Rw*Y)-zFjDIj~?WHr7-YfgBvJ_67GX-t6=dn2H%%p
zH1%m&MNy3|xuE)Hwnv2wfKuB^nIQvSOb0>G-+Pj+e}YvTfA7JAo!H|jviA~%kX9@-
z5lSK7M8N97P&p|Cc~*AmYQ>jS!eYu4y<+#NAIS=<3-zd3QvB_y2eGgvXu)*jMK7$+
z2}^_ksy%kJAL=JP-`0BTL7G>3!jS9DT;1E)Uld^u6L35`F=6I#LRdQW-N7k{<qw*h
zL}>ypC0=eaJBc6FN~0R*Gv40!e1)sPv$&2W7$qz-mX@Ljpn^0jmo#UAJ!VLPc}9LM
zEVgp>8P!fGU)?5L(+zd|OVplto+M%WT>v)TluO>#o~|)lR*h(Mj&F&HsQ*Ce^t)0f
zhjwvz>E2^plNJc{9wtYy7D$r!n!hac>=l^nYVx}d<{gw9_KG*O@Ed6!tS_lFDVTgz
z?inY^wN=mSQ1@KGkV6Oyv9z`>guHGL*`1~2W0`o2j!z~82f^j4vK{^@0!1V&qfV21
zOK||5T}PauR*<Mkhk@5oe2ff^NuE38nraR*Gu;*P|M68EekC3N|70SlN&pcq7WRff
z_y9s4;pDRTHIh)~6AxeuXE;KAEeNX<OKZbYl`G9RW~AlFL6<n(_V81{`4ZS{1|%I_
z-0B-fHEmpcVj(%YA^7jYH5w5FWIT%Oa`vxP4kl%tOz?80&)Q<EJ4o5vi-i8k4oloG
z(6Q)1O+#Mx-KmnYz{@XJ*#aNm0{<hOycg9@{4<R?3#3=ec(RV=wFvfD43Efk6rIjV
zPnV`=0CVgh9OwJR+YK#>gZ&ZIR>3!qot_<g@#01DuQP=3wz;1&oVZB3n?9GvNH)J1
zI~CF)$4bWT*QFO)CEXrRmrl1AjON*|I-6J5_$ov%(=+AXWU^aBLJnVa<cx_OzuR)s
zzphlQT0fI}^JUWb(eT+mzF(?5-L!P;k?UmP1C+ghq=@M_$0jCf?%lf=)n~BDWQPIE
z<IosZ=)y2lmO{J4MZyUtW2F_4XGQcHz>*-Yqb%F&Ar(muShsE=b!!R?&|_BUdpsu-
z1|p7dENF(AUJ<Y~X;uqd;ED;*#}c>)4qFN48Ua|Lw*Wac6QXDWGS`7<pm`#u{?bWZ
zxQF|I;ol}R5LAy{eV3Bd>}^=&6wz{3vg8Fp_ITDKv$Et+E*C=p#1TiLg>EQ}W^zdn
zy0dJ&O$lphr3Cv^AqlBkU3!A0lOFZ{lGwI}ghxqVyXmHmW&yZ!IwmIJu!@qI9+{rv
z8(~$U8hVMSg|jz**L&-*(`4wK?MtHF){x#EKE-c%EOYlx41?1qKq;`EuG~z%2hZ6o
zgzNmQvnptE@|xUPqi{@gAh}Qi<?VPLW1Jdqe5~I{2Ya=!mj!11xar5i`1eP%*t)v+
z%zjiU14ydKG-H|wJ<83>Q;o_JFl8zZ5Nq|&w_`P@W{9`emUMJ99?>$Gc|_}U&-P}d
z51ru$<<<s`i)DrzBquloez)JGQ-@LZ6|dfyvAF&2m2HJkYpHqXifm>tJq`J%5Yxue
zmHAg!+`x?mNEwNp2uxQG(pjg9$ZQHeN~C|v<Um?s2jpxmk=SqJ{UH#ULf)_hGVZr9
zZB0GOUw3I02Lxu>4g0Phz~sr=#>NpHbaVJrdpO7Rir8j!C4tZ`pDskH>;MxpGZYZi
z72w4r0NQ%A?Ojs{^MKm`8e0)6*#ftq9ml`Veb>C*q9W$-YOgs)?QC}G;r*-q(GP6F
zp|EwkMsJ3WCYARb5r{NcT&mhXVzn^b6~CH7N$p}KSH0<1Fhiuv7cg}gDRPll-}y1B
zRZJW8qKoCu7RCH9b8jucjU0mtRk&%RR;uz)tX4Dm%Hf;WG*RfLfa?;Nl-Mx3G$H4t
zlffge&}u$ECMACe@rJ4{0!NZjIf_@h;-iT$DHSXX4Z-%~Zz#dU3Z4tf(MkPAR~lUe
zIIUcI>-c*sB|4<}iew|%))a?YglM)R-$ma*IKM$CJ<{IgcN4p~gb|kU=||h6d+idY
z)OuR|ZR6|A3VBuIiy2NAkl2&PqAw>*o^90H6!J6)x?;|j^^jCNz&Qx|%l!x1)A`e*
zYZ8<i6fV}S<e!4+UnAlvJm*fLW~l8*?!{u_4H5DW2<i5E`%QS;p#L)ehDwKS2y-)~
zeK)9;R=tyajk5q9F0YVEp5A05P#W7bZ*uY|c)sovLOy;M<X;_j(wj6^HK}n~GxlP?
z10Kort0P$uK(E87FcS{+g6-1-+hKvHGtjC$oq0W7etg{79I38ExC-5$!;|@&MaIs)
zs)NN)GcxM|&MK#}J>8XcaK02)=a3nD_3=xq({CwZk&d!7lhS*J!pFd9&nnFb<@x77
zkh!8ZRGi*m@BFOai#2Y?68-o*7?^bFF#eFb_&UNs9?W+-q0VQf$b@6;%_8qx;1zGB
z=^z}cl3_k(js#^tq~RsFbfaa_{Lr*|m)f-PGXlM@?1h?wlVToXGu#oyN=BBgW6Tt`
z@6xO7WMy3A2h;6}!S}gEA;IvEANNpTMPwB5=1=8;k8Q?dntAp(hI8?>1d($WrA#_z
zshLT(8U{mt=@(bFp+l!?Xe%-c#ITQ2;By(jb@<nDd36)fN(wq)1j(b}bfZ*ARXx&_
z8?Ms;-bV}R$WuApg&x5y!JB20SJKsMuUm3@(zExh3Hg7K4FF0oCv|12M+6XtYDJq;
z0%}xkLZ+ePL#o>1i`~FrzYYM;EoH}p+XBN(w*Sk9^e}t*crm||_M22kihu8(IhOo@
z+}ePDJ(UI%iy5i=VO&vJE#n5vfJ?@PGuamJ=>}DP1<yk^lOH1&&vRO}mH^up{vrtt
zPDvZAt*zzsTm@PI>XF))>@JrFcumUjhs1Icx`W`dL$mB2g>JU8Vm{ug0Oa<ZX1jj6
zfYp0XJLyd?M~(`42?NS(x^ft5sF#nO+_$l#SI9Hgfue<Lt{`+~o{U9)bEqv*^&kLL
z*^B_qB`YS)HH)&}4s{kyP3>!5RRuAcTPUj(u=h}a3G@vrshCIY{;F)ySj4x)&oepL
zm&lOS5@_TGmmUw9st@}2mua8sV6iznhu?9zaWZ|dK3&&AImwjOyiF%Dw;`GlAw{J#
z3{{m(#tIEk$VWG!ezu{_!8$NmGT&TW>Ckvh#vEB<g`&F?C!N21S>%|xCLjJ14%na!
z$O&^$I-uvys!cxA{}#f?k{D!-L8dMY7ItEQ_cU-u5Ms+;y}AW<oU_d}Crw1>sUeeT
zDOB^+o^bukLMKmE>P?<RGG;e?nIgMu`Hmk|xF<tp&2{fi`IZM}RUu{%@RGcw!pAG3
zlR<5J1WAQz07vqVJPdNYLhJe2Pbf0O63!I+Z(}f=`wh{8YJn9yPy7Hdqw3AjE)%Qz
zRi+TZ4whn0c`d9R>@B%nTF8#SJ$zY?pFU#tgTd;2_w(%TkPz$6NqvJ=y;s_@Kh`i#
zKd>g%P{S0jnDcGTAp~*s;)-j)9B_n%Mk{29JrGtMAiR@NB=B{8w1H{%{G>+tz@P72
z^CpZ{Yie~PGt$K246=qJ5)S(Y$H%iirS?%=#+6w!y>@S@p0RPLy+`9pH<;+&K>gjU
zZZfVNtt{fAH*INC^c=;R0*K7a@{T1nt7Xkoqsu2*CLLZO$HuCvkN=X@F(l^d&0m_`
zm8|YkcRHWCAf~agG2!f+m`Z4{TsuB5M=vK;z|ZHfi%~g*nF`)4D3lHNuhp0BW_OOI
zCx}^7&780;J0br;!wJ^7`F^Ko{kgy7vQTYv*#6l?QH9+Gi1xN{lwW8qJm*yZ+h^i_
zla0z#Cpo4KkKByqHrcSipt>cJiIRo9-JaCG|JZ~lmJgR64e@?n9HNEeKnOkZ!gpc@
ziA5;xYGS`kjUUga!!K@kC|CrLnF+@fQKzF!&n#iJWQ|VyRKj-Wyq4=*svQ)XTf|n@
zj?eeRZ8uKKbKQr-L?*HUF4(uI5h?f-#>Scn(PN)DfSuvWT@oG=7az}sp4V;D#%uY(
z{*Fpw83_(GV$r!cl&G(_F^{g1e3+QHjUuCbYNCeo@a{@hDo-Y(a=Uz-m@l;)2|Q{!
z=pr2aYBDZq_&WwwvS5vm6Z)6*Xh4nYzc5n!J8CCkr5oJTYyaboYxYgbV+C59m@_>&
z<nnAYwWDHVzat|j2Sipgpm)aFd{n0cv09#@%mI*bWXWbi{j=Xhu(1AuBKS(XhwC6A
z=oFZmX;tfASD8+Uh{+L4e7GJd-iUiC3Bu8>g<?rXaK}AjIk7lCDrythJtVv=0JDv_
zGF)Io4R_44<GkqzjrmQF`UQAqVm?dPnd(uI0+{6Hr54pz&qS&%p0P*hcT(G83Vk-z
zo6Y71rC2$V18<NEQ$>FfS@^t2oO=DZIDn77pCdvSkCii!7=Mfcy>WZQ`GsnnqFXyR
zKp&FGO6j9Pla*J$m5SS#ItBOY02Bz3x}x~k45w%&TA&jlN{?G@ZA0b1mLU$M8Q@}`
z;WVA)PaMO>Sb%9`KMeL%#fQ?OC<S~{TmB#1DD7i;9X8Y(K3QHiXuZtX>F_pm3oflQ
zokd5-HU6`$#~$q_ZP41=uF;_`H75=hL!z_MFx}P=>zaw=_LQ(~<jx|`m>d2X-Qm+?
z;VBNyg=7GVH6^bQfhX=`PqK6fLLVyyst{)28kSI94FJXSO1sJElVuWUa=y2TI0A4-
z7J!X_Tv4%>eneAwa**8iu=CxgAJ~0)itMJF(bkkg+B5H2s+PywZ&oOKd2pw>^sIC6
ztc&VnG(vVV0b-*y66nfq1G{1=D7g(Z)j<M|HcaVIrCse-K%GfN7x(?Gu!5$dEKF@2
zaJM%zeNl2HR|+Dxdwxgo9Ay+}P;yxZ`KV9_FluwWlF$7R75sJhmtX*0=k!v;FxB#~
z(nNKsoix-9PLIJqI)H4(S@)yk{Z1;@rw<k<3~@_dRW^FcdS+}GGWeKbG(X&f?~#hD
zg2Z&me3*(TH;ma*sKaKZZ^hiH5OY8Ws?|{eG`xkhr=H$ed0_6%4ZgeAV;}4+dd4z`
zr@=twT7KE!+g2`b2R1_U?>96HVc<1W+vdR&LV&`<{ISn0Izj=2o5Ci-JKC<PA}C!f
zGl9+PBZr|(IGN3~{g6^CLQfV*9y&#Y<)euHtl0f0gmI5f7Qc{SKxksN&;tbW<7C3c
zGULC=`HF*kq)dY981r$!$u<WR#D(5mAi|GLiW~0OVq<TMn)1NQXm2K(a0!pYmF*T4
z9i(%bf?^bnjv0<zAvKZME#Hl%&o#bL*l&g8COTUXBfY4AZFBLwxt6)*nIE31?t5z{
zEqih&dW3`(9(JWud-LbKWlb0*vdKGK0n2h$fIz_M4FYc8giC2R#&OeW{*dAd6M7Ns
zxWMKluvEI$Bv*l8S!!{~uqqRl?ne_6{`c-C;NTMZO$<gusuIZKt%!Jq=qYROm@HfT
z1`t^(%`vENCXero3WmM3;+@{v45xsd5wjREG1{>=$&ZyuH{96~zFaE5y&L|N;d9x_
z*0xAIqYh>{0Zod%Zdz1C0c-?I>pJA1e4bDMBBvyovt6H?jD9bp2Nt*PW%^&2V=*M6
zi%B-`RG?>NL&iMfN4xK5gyU4!)m`|KaOMAdJSU&;f%j7~Aaf@g++JP2bN!l$SNZf%
zMgr+$4j__jL1{#upXph-)Ybx*q7En-9!zFfdYh>Vw|n(p8G~a~RZlW}k}YwUbx>?T
z%waPVMiRHtpj#k<ynyF{1Tio%(=~*Za&~7{OR&w+O5UT@Y+2Mi^fNa!<zjj5LqkJ^
zLQ-Qx4AN5II1(rR`i0=?=?U-Y-Z}^F43#yTjq5Y`1IF~Ya3e>-@1;;ds6~jP7Z%~0
z0$6qhlvHr8Fi=v$>snb_TDp2-<Isj0%?cimJv>UTUGLzkUPOnA8YI}b7(?(O_1OUO
zBYsHSn~&Eds5Glu0i60X!rt%tXCQ3?5Ag`a^N!*XJX)D1^~D!ou<iJ0OK;K>heT4}
z)eqeoQ^-Y7Q{x3AwbsXT78T7|q)>K;lp)<07^uu){0x&**^Zcd=~&Agf-Z(RRt+(f
ztk<t{1>|Re|6*MzwRgg2qS+Q68Ee;EP*WYQ^tsWXljZ?`@D6#fK;5Jf489pJo%1u!
z*%q?ma|GzHi8(&f8%}4DE<WCFGN2$r3*jX9BkVFr5hyJ?5qGVX9M3p32FOIOmI6#0
zbGv9%0R$RR)4R&O?GBTJ?>7UI*qtc2pHfl?6xfksd4(PTp?RM#HH9)PlSAACs;n}G
zVl7{mN2&{&ZlJgrMjfgEMyMgiOmyASZIB%^Q5>gWUwMf>w7XRf)5K@-3E=hO-X!T5
zGB1YETzX#ca@z@0!E!*ZOO-R61vfGY2SV$)4FE@-?KpT4DS*caZ<7Vy?TGu?44BZ$
z^<X=OR)n&<AzIc9kk28VNUFHS{rFJBAQo)C%%g9lS68{AB(J6+V~7%JhO4XPrD@zb
z{Vseu<vI}%FE=f)WJGXjgSt7c1zd$Y#qqB3@$mw1DjwE5^Qx*^0j#NUj9}YKH_wPq
z(b=@|VOe+m!(KE_wOc(JDsT#KQ6tUd;F_hf`02|5B;odQM9@)hzrPyKf6scKPZ(Jg
zVvbHtnM`+jr^)xCm6ZhiBORSLSgtPKC=02lf)x^`2XJaD2JDefR-hO8@Eq=4DEqrv
zH2}8A;vu{4xE7idrJCG?JyjF9znaNAW4r;DhSH67g9T<XV*Qhvc??F(Vu&@AHA{ct
z1wn|~o<~S<_ynFcw{xu+M!3lYY(Rg$nU66Uoh2NSjH~nOZPB{26I~&I(_IN~sK+A2
zn#&fj<B86pr8H)$gz}{o3$_K24-1>KaQ9mIu0Qrt_@Tf+2>0g+81_YM0b4DCiwLDz
zY=VJIOX}U(uZL^8tV1roO{j(;xdV-=@VkJEPH_-*#i^eqxGbeaPK*f3m|qy)vBpbf
z`QkZfbD&?t<wJD+iGJ(!(=}6NEvXo(JZSDE_!2<$(`l^w<gr(l`)bVTN|;v!zulfP
zIU8&184eD9;1m^O_m8HDlIJ(4i9CBTH^{qbvI7TJhUEH;?eL)4jZMIK&M;d*l4^D%
z=0Dk^dS*?9q0LKQR;7xg${QGgxKB-s2T4kwT?HrD4i7k2vd5+Y_Hv03ui{G55yp0H
zWp{#hS!r|VPL%-psP6DU|2g(#S#DWR78GW0QT4~<0DWvi66>uw!A-@bNPFiApPe*0
zE%3nua|a`(tNMW{n*!zUUTCNdE98L@Jd4~INCihsfK=`CJq0tXv9nN!9IGz%s&t5S
z)X~uib887Y7$mU~ZG3e{Rtd!I1M(KZ*x935%%p&VCUC9X=6)2x#F5IIsP7%{o+_O#
zz_9Ao&#aV4OdvL(iez$hG-WrU*kse)5kUlgL9<9A9MKm;I*(umxKMGh>=9m}1PTwq
zbXGTBJ%)HrftCWAxPZ*5AB#ievd#T{ymysDSa8IFZ9(-=fHOEypyid!8(WBXh&QH0
zxLjO3Yue5Ot3hK_MtO|y4x7BoV9f4f{;)^)Ww<8C$fN%m!S1ze@0WMPE|+1V_pUZ?
zl2rmHMq*=K_(s#xITyAp(xnQ5gY;&ktA;QbQi3WwJA({*nh~rUqJnYCM2nY{VF-){
z%>e+I1y*_4B`H`sXvHILnb|=P1iNH{x+c1wwq4CVE|_P*CVQck<2ax*34EF6g}inw
zubm>S9NeUg+7gNoggM0v@Jj}F(NnPlOKU|m4kWCM*z+_+012Z7OCE<3vpV9it-KcB
zW1A^=24T26(H=>|Fw4n`CV;oky#191zz8ygAX8wQfgNEsU;#8t7NKbYKNnL5#M7Pv
zbZEDTQ2~?}h5spExsDjBt;f*!1$j-@O#Nwy^GFqy1^g#7QA|o=tq47nD2A^TJq0kw
zTn~YK$0?vIEd<lQsYMA?#*0(yt{?gY^$vAy__-vJ0=o5n9!(oCh(~-ien!_bgA(?N
zDp}^j-LHLm#K2ti*|r(+Z~^Emw`Ybtga;xNA@kwiM;Ktz_Qp(x87$k&<dDTUEhESn
zKsQvyKw|Evj6=5|g+=@pR%jABPa2Ij45w%4MTp|pioXA~H7Je+SiSl@99P|a-As<A
zN|mA{wNq$HwZdReN#Ov6#L0znZ4s+*K4#{r2(3=8v3Rk?Dj!J#{|y4ee@$`tufOB%
a^sHy>{Ii}1XDA8`rmE%-iQoTt?tcL0#Or_n

diff --git a/articles/AMR_files/figure-html/plot 3-1.png b/articles/AMR_files/figure-html/plot 3-1.png
deleted file mode 100644
index 5bfbac2aa2beb1111310b7bed25f1420daec3a38..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 28422
zcmeFa2UL^kx;7j{VH_zl+X7N8C{|GE9UB-CumRFhq)1hIXu;8uu^<{0qy|Mqq)81O
ztbj-_p$3&+0|W>mko@-(X3l@sIqRJB|LfcPTjzY=-pf5AhU9sla^Kf=-Piqi_pE^~
z|IZ>nV=x$g?5PvyF_?9U7z}siPdxCQ54S0=;WwTudb%gzclaOUcnKbUTYvqOsV4>_
z9*X|Y6+Sp)hrw*aU{4&q=odH98}Ka7`!ai_;j5csr_`ghk8ZtqF8u4KQ%5DZ9@{?o
zl2vzW+bc8IX|LxUX19k^|CZF;XZZ28f>BrPZ+oSG?SHPL{r2?UD;L%~$UjRts=X)j
z__1xVYdt!8qj9WLTi0Lp^?c^OyR+1a{Pez>96bnMA=eooNEI)?a7|t$j59{<`$pXw
zT%0fONOOsxFG6;#b>w{G-Y<!qzu9vv1U(=o@u&ag7yME0V;IstGCr=CVeCwo7uHc#
zRgLM^EU@wOi2pEcl3{!y^&+9L{&Jx|%iAi8+~O$TrM~md{nN>W$2)Zk0z@hgZPOOq
z=-i%;b$53!NqKkZT<XOf=2VF^c8STPeU)p-!|7<RFPSVbf79k95~gFEeyM3{6tnJu
zsFKW1od>4~>1O%+=Kag+<kpzEPIP2%RdBjUxcced=F#zSXW9c{nKx~3F#LJ9cPVpb
z;P3e4|Lb%24}6CgxxE`5lXa3FCnO~F;Mprh{SVbVY7dgG&X2XJ3_LnM+u0E8lh)AJ
z(h`BA4Gp%NyVuPQ*2-OLepx!7;a-<rB@z4Z;lnFcYtXDs{Bg%9q?}f-Mk{IvnmS1$
zkI!dl(tmuq?>SX8>~=l#<ozvnO)pLsO%;#N{w^E1eW&h27e+Q`zSZ7b&R?qw|9hx9
z_Bzh9UL{)2!NC6OvnB#96BbU+{_CkStA=YS@sZRZYCzFYjPq=md*DvyB*|LB@7v9<
z2^uAXe-5G=2BA6Ec$`*a7<{Bk+}C2E%Q@?TghuhM;WGI0LRy>0T$8TD(%dN81N)wy
zUZ4a}zs4x+7Z(>deQR?xQpU2Y*_N~aeXf4krybSkDDN@fW+IJ!r60TSf~gxW5g%co
zx)*x{&svTY-+;#dHfdvGXd_IL_jvm*>_V?^U)YrA;`dN#tQ%~}fBmBA)(eL4iVoXw
zFE1|_FJjO_r!;o9yN^|1_viRWRJ%29PWFCzx<6uBbCdfSUteFsOA<*b-z;NmENQ4i
z=nZ*1TE%lHWEe*;>5Dh1SZcea{<n@JF#JxtD9bJl{=|^e$<51I<6?JGhVkwyJ`){q
z=oRaE_B_v}(Y4ycB{WpwB!uO1g$SWxlgUw1TU9&;LYV4oQKo+xWtrJ7#yl3Lvy;7)
z#a8`>qbwAV_*D(e<&C9-*w!Tu?;^)6*~HU?$4?9DZyz*D-Mv%bp!%tUqh%@6!kb#?
zOnov#z~$pRtlf#u!%M+W<(KYI!Y2*cRHYKaymd$C%mY8~^Lv-?zh`~Iu0-e9p4FMe
zv!u2@F0A*x7|-0*rqQKwpMTMgr*Yq!xnB~PuCt!bD(+_>Y`B@ERam!Ob$%$mWr@9i
zK82m$n;Q~uV(0QEpx4spfet)zS9<aAZ!dEKnhuZEYQ?k?52*4SdOPOUhb{iXI$u;i
z%a$th-6GB{e95jy=)$OGa&HR;j=IGA!W;brDMR&{k0H!-)7;%POW&54O%A~Zm%W%_
zOq+Da2UIev!X=4tOt`4-p%0hdJh(=evXO3gl)s0!dVSUPKo4dz|H`L(^82{)f=xf$
zj(fX{t#O@$!1S`hY{7YHgcYnMc1$$Gs!TO#ydaA>y8r3vTxX06f1fSiWqL2U;>Adf
zG*Rq&*w8M$NGa?>AD$g4&OG+?^e$ep>y;t&EQ_KeWo2cz5^Q6gTklYK_q?g7z>mZi
z9NT5rAjp?toR&OgcKb5=y~1iim87Yh2QgI-Z_8Zr*PLL<x+b~?DsFwhIMd(K*7j5i
zH<4YXSllp4>PwJP`SHh2GoJ%!8&^Iw2$49Je7>eN^+JSHADczD_F~5<ID4$jlclkg
z<sl`~#3l5?5b=1-ik4gP=&Lx7FMCy}r8FzAKG?(yaGrVsX?4+RzHXkW_w8yU?S{Ta
zk5Z`(mqW!P%jlGn<bOG(Z6b}%<jlH~zLlw=zVUf~!oXAswSI5PGOPD$gUWE6SkPRP
zsF-;cnVeOW_jvw%RhVcLtwBpm&Y}KmPm!mb?^F-}6jAxc70sEuhAv(P=AM5(gUh9L
zBUe}6uN?0!-{t=HFm86h!l%<NTFKpN%YjReQi#E-5xe!j+?7svw)es-mpOhO^BaHu
z<eTPcpQ;b<;poNI2Dr$oiqaj(U89o;JQH~$TO$VJ*=Al3PPf}1W3NA1h2bH$;DW}Y
z&w4$WS-Ehb?gIK&!JGacb4I&`#BHOxqPI3J8Z{jbxv6ns#mhg9oz~mZp`CA52N9ut
z>Kf)lh{E4J$^HWq#orGJ{Bw|hFHH5{Y9=9TahWBZPknhlH7`I!^z~I{jNrtB(|D`v
z@yD4)`!<tzOsp*+QRi>Acgf&i_0<n?PZX{lx%{G}M6F~ZN3SF$`+0eCs#e<+wz59$
zeCkztudl4yDO>$9y)Ob>d~WY=ugB(3<Xc&r>5rWZ#qb<<>nVJhHto%t`{N~P<d@2A
zpC5|Hp7#At?Nz!^Hg#XCQDn{Bp=`I_VhHE2I45txrMO#TeaBq|kubo5Z$^Zlrq_+U
z*oirg8;W*le(kkc+}11UyFz=TPMS%UfvaU_)@YJbzSkykF5$0T`OYnU)(fhA77&na
zs~)=2<wGuOhe+&p$~W^5AZ0}z@KRh_>*$yZ`G2zpe%CnqnnIzpURW@rZb^Kev9z?*
zOolh0z9Fx3bWAEQFQ04wVl^C_W;S1NS=)Khg#)0pe_bPw!Yk(FPmP@4H*MGEMqix0
zjp0YX|MLa^oQMDZ`=P0GYW)+0hW``FP^W5QZEX#Rx&X^e@E=QM(6rbzez#aHe6W2(
zg2u>=T>H8&Y1S)abumKdi7OY}I@XPRx-T@`<Jq8JX5~&PaBO;Uz$|YM+0xgteEN6U
zs{_@KW4-ImGT<G>uUB%(7H2d$^5FuLnmW=M=q)f4KLItc%6BpXV)59&ETP3f?O@9*
z{o3lsJ9Dqr5G=00TZj90llMr#!o=5ShixMoRIYsd!y;fZpPN$QHQF3Fa$NE>fTQi9
zVn@COJ0#%j4OB;8*7>V*-dru`az4*oKG`bPEF&^0TvN3r#jxS^If#fCbLf-+hxT;S
z(UFnr-_~u&f%l3G!_n%L08;_9PEJd6u3UN3c6t6H=dkl#>$unilq@Z)TotQWY)>0$
zN{Y?WPJpSY{Tv4{hT!Kdhd#4%179qr_djGE4{&`!bUPq&ok8?oxf?$|M#gPKKW0-X
ztmv$*HQ#yxOVC*8*a^fzwATTc2$rzCK&o)9c}BMI)F%w&wHW9npWo`$M`X>VFrx5`
zsAW2y?fV&E@GSw=J?^+#^Vwo@nOpUrWa-3*-P#DI)=+oSK70B4N@M*40)TGu@U_KW
zfnhK{$p$}`QW<(?d#8telUKsgP1dqlUN1_ft|53K_e_Gi%5c0dC2?F=8k=D(>J=Rh
zOj1@?0ynU`K3H_LTfvm_S8gEn`s_!!VfJe0_K<gBl3JRnTguy`j3WHu&o?J?YGtq{
z-^WWGPBfeQ{A6PT0b5^Zb9ueeg`_4&`lgV}S31^?KnRwqftBa&8D_jIvdVG?>N?+U
z9w8Mf=25-m{uA#Z|GAOnL2H(sfZD{{2k;pX`XMvai~IIG#xI8As{zi&W(5xJk3MYs
z`H`*W%3KRKh1}Oyu~OD_Ie*N-$T9Z^QAi}y(~;zqAG4{v-Ez3mD$`S#1(b7NcW1-g
zd~pMxwliA7&LY-xb&|l`=T@iup@nvxXlgLM1WUMJsNvnXf<9iTR}uECm~YRGX!&dB
zHJ6&g?ce>j4)^1(G}>E&{+aKN`R1GTvVzr$%IbK1j(yUSEv$b&DH3+a`PVxtWEDPq
zcfWUeuL6%vsDjIyxqruYE|n={1qTByrY-h|r=+Fr&kE=rFIZFufyEtteh+Rq-n7(j
zCXuT+DQTw`v$KJx6hHXoX`T05l2Clu=Fa@yh>-Y8Nln!)ziY`}%u(EU?;(cIjxjs<
z$+}3n+)7fDYQe9T_;kO?tE}_DhGg=~AM8<GbLbhIqTOX_B;Z47ZBC3`D<Sdk-!e?E
zK=L;vs51s4L`$0)jK~9~1gTC?CrD1|XfI+3sTWe*jH(R?CgrnFu{_pRoO5HND-X{G
z6erewfDQ!P)r;8eUZH{Y`1q@?%Zzfq*+CR42m>EC8ut94sSiI7l{h`C<-Tn#IuwT<
zz(QE2UOeJG)+$v)uVAkT^;X!|#aQ51=CK5&3Pzo|dVN;#eA*nHK5Qo&V7k!hKs=~v
zm=j#|X!yk<Z0ACwVNMe7pujuDOS$8j-JofCz%h52>M0tXoYW^AFfTX&^yLg5k4Li_
zmYE;eqOUag<bpvBOt8|3u-4)`L4p*-BvB8|{l@9l$x&7@p_0LVx0G}(N_<=qf61*^
z-H-Jcc^*2HEE_bsTgw2AK1BT8TinL?S64)TO~A7lAP?9N)yKycd4lZ1{&;sI?XF?q
zm87cn@p;%`@C1T>tj+t42SE7H&$(9X2l9YL(0p6b(pVZChe~e2uMf7$-BDi+ybtYx
zPf10q<2+;lsC|u9lHIW7Ky{0uRy;Oh_aIE>*C%@~6pZO}vse1q8Z}G1)Qd|>LVNJb
zi!+SRO68B&ZP+rf(CyxETu|dlzBAdLT1KxQx1tw|;g@G{lg`$$r;~F&d9F^Kl@UmH
zNxcy^;~5r3IYk523omkS;C(XUDO1-f4bmPTHl;AKPF4Zm-aSV=>&0htT_c{Hk^po}
zxKB%=P2C8-Ws{~VV_<FP(RA1+H9q>+Fze00_dDd;3{yx;6<r!({1?;OCJMUUfYu%2
z<4gOKg>?k~6LO)Q^iSydA3^XF4kgkEhfHR(gI!hMG=M1Wa`R#RLy$bAuyCTA9ci~a
zc7$2#Ty4GW)363(Q;T*K-xxdE4BJ331I>EN3{_EVnL4gB5TaBNumyuL|NJ*G&ZPe%
zaDMz`0_Fqxf}ZYxiLZF~)U~&}_C59T$z;!go_T(p>W)U+mMJz@;A4mmUq|%%#!W4>
zs}A*eYr~QhF13s)OP#@H-fj(~aN0Q7uNUA9^}ntSjJVL(I);HP{6I`42dG!&AHVMG
z(pc9q`jl0A7Q1s)R(0*6+nJe}^80?znm?U9!npY^O!)ANMe^KOm(%(E@VD>-Ixm}x
zf>zE5A`$S=VUK}oFCRB+cAV{J(A_rKq!<ILs+B2uBL1=|dMVcq2%!?loG;B5%!Cr3
zm+6E{SS~yJmt`FfxuYn8RNmg-D}4LTfa=+M0dwc>WZI>iPXNdw00j6L+Hymv1zcd0
z0&3puT#KQQcoHFhtBKcIn>`vD8mTER+ZMR@<dH!Zx{ueF?Czt69(POvz@%8_yvpG+
z>>=23;>n_f?K4Mtk?vl>P!z~6gv48oHf(t6Vomh5ak!X8zTkWC{o8_r<z#x9T3%Gv
zaOL$O{h$8cwU+-@y1;*4QVO};|5#T7Yhq$jTwI)U{rh_#!_fGU@S=C?IRuzb6jbP-
zpdcvH>{qP^7)yTe5U_!DQ?`%&q<?~vLyznHVN%i<>^QVJZFFfCUV(10N4n<KnRHii
zPSq;pvh9zLcYoX7{RH0D^fwMv__viVVMTzMkB4Hb4q6mWpQQJBHfYo_B|(6cssSh;
z1I37;5N?4}AjJ!4E`AGS(TIkIh7I>0TIXEF!oWQmkMb(GtBoYyiWv@^YWNy0#~ar_
z)|TekW@IubPW978U;z*@C$;KoEj9Jrs1m=WSsqr$1`zTEUhfI-kPK2t7Ou{jrx!o-
z;}a;Lo;iKNOgo?=NTT)l{=3i+yp8K<hdIrUG=#ItzH%~nxnB}qS6>rLnR^AAuEgln
zR6Q53*dxK<>03pNp;%svBer^<#m8)?7!v*?&jW|#h&A6sB@+O$UIj^ok)*Yh6u2^O
z-EeR7ejW`UXDy%ZPa=O#eFL6W_<YJ;UZjY%G)|Mo&q|9<<iZjiuqZqvWO4oIR4J`q
z=wzEvLWNaU3IOQq<{8G_BZ~-RvE~ff@&J}8-xdHu%AzG>%}BBjUq~^i^Q~a^!dwWd
zvVnrR2h(Cz068mlIP?~Kb5ChG)W^l$_`Fqg0^jXQ8iX`m>!3-C?tz+==tpwK)&OTo
zYNUDtDB)YRf|qrM0Tj}QSxjmJLP{S5wbYe*t{?a5^(G5Vl8X8T<`2_*RO&V8J%Nl`
zYZy#hn#u4TQMYP-rJhIO_~m{E@SS*|fv!ozj-RUB)cn?Q^cS_{{h5V7PI6Hh##vVJ
zP!f|_Sy?%_mD;Q)TL;p(^(I^E5m?4^ug*lzz8GN~6SDMby1(W+fh(N3M6O`HSX>U=
zZ<6^Hh$5j%5+B%jL&<x>Sc_s@JXz4~0c-D?t0cJmjaO?CUU>|*H*Q$1FPe&HGmj^@
zo8u?k_*XY>d~=G{%IHM#cqU`Yw;~p553S?#bMy0qQ5DN}R`YL6c447d6%*@LoQnHu
z6)^b}NF&tP6x_}89M6e_edfUffX#n*DO_{OQ4^5mZY<(%@Mt^2H5M*`By~QE3^Y5Y
z$D3@a!dmQ)kOE{UeB^``5aF#F{$81-vtJHam#feFYrruS)LNd_p%WFHjiGewQL`&b
zUXohuJ?;Ud&3<;E2HURNh+y+^P2b~TW%=R0dc<L=XjN~g;KdJ;L$K(U5O7-p*U{b>
zrFo#W9Z3o|$O*>Y7j-Yaam$uu-*dCae>|gTdA2qduf<-b<pNDcXce9p;J3bkCSzb|
z1y%oYo!Z_UZ7A9$Su(kUBagkkFg7;!1#P7>v5E)#KZgNr#X2}RptHJMg<sb7tZ>dO
z8ai2JoYofRCOFVSWO)(L_QwH?J`%JvH<8<*IXW?M2FN`yb`H4yEb9Ihl?bFzapR6U
zH4DEoUP>>c)&ai&SPu*~Az-S8S9AL2Q2!)+x+rD(4w^?C944X^W7H5I1BDG=a|NF?
z3F?q$gQcZq5mX;(u;DbAQ{9OkKmFweC~?;b6L|`SZupgLTQvlAO<T8e#*i39By4*H
zA;4DAx945p*af1Kn>C0L2;5uE5~5z3WxUGGmCN#POPl>5$vPyt)EG)gx7Hc>vOh~~
z_J-SN)l|XmKj#N(IL4?98b%=vCj=<&9F)Z~YBNMqva)3RpsEugWzCw6;<ZRpHkDLE
z>~6Pdg~6<iJD*>@%J6_7>zkk=P6F-E*etK$=a8HS2zoNtSSxUpgy$@7g;|cx6r6_A
zMwRA5wiJ|IXCq$NqDmKfD}t1+WRTY@gZSd66=1&QOKn+-w)9Uz*6JfTQd^bV{#D#P
z@gyN&a8g?Uiq2A<FBKm1A||RtR?>vpK2(37LBvpE1E4qpMdLH8>B8gd;VSuX%L13q
zkl~;zHA=QA?U`oKQX*XvB3<2N3AH?+F^E^Rq_w|S%uthYRi=34MO@%)4Pufly}i9+
zSwM8rhKfiR{0I@{;8sstiGYdhp#@#d*~dre;&S$9<j%@k6kcys$mR^ys~~{GQyK+I
zpoX1D4;N9`hdrOxR^SvjtXqjh-T8Uf+3Z`Nnxe`k>|zzdO-g@kzD2RlFh&(~_327(
z-1IwM-d^9g*?Jlj{dp*pXENT5={G?M764I(gs*J^3~*?{(^*b%=+SY3YcjFfa6o~L
z*(Whw)53d=r+?;HG<;6S=Gc4ee{wGb)SK9R_A7OYevko=Xi0n%?cV;(j`YRSvd-4|
zSO02;&o;u2?d)MO8t@4^N!c}c#X+Oo*fB`ITjy91ikZ|3Eo=^(#dM!hp7bvTtW;D$
zL&t$J7&QzzqaF?}JS)VE*nVJv?)R%Y#bCB{J(;A6mB~j;EqvY3)YQ}t&#v~>oZs>D
zt&Fx<DWoBI1^@+4PLWGY%Pe+kWD3bxbo2zf@qa&fZfr(iUR@S=ahh?)X+q$*dEb}O
z6k@{Njneku(nvK<6D@UG1p4USS$GS_I=_pJGRsIgR1RHbDo|}tKUiUI!Oc${d2=uw
zcL4hJ_E;5rjV3Z5!s-B()+1PWHJywG^{lCg|7v}l3KBAOsN+zS4Aq=_KGYZLA5roy
zZ+6D5p0>Q4|B7hNjk5*)rEhS*HRE(GBu~gUH9x|XD9>Wu%W;)KT8tV)#VA>o{s(de
zeqSfURwe1!YmnvK-S9K_t-k^}))-+~FEvNSbXSRao~hSlPW1Tp1(}lEENKg%4<fe(
zKmf)Mp$g@zQoVoUl?=R&2s?nv0+k^oEbR<eKjPc#Z9=&E<;mV?<r_bY!&=@{m8dk(
z^x*IhEucwa(1soAVid3hi2ErC{`;aVJg@?=%eMCk-|%Dy@p`^nCnQe_*J3SN_<jF<
zbaeDu{WvUlO#p?KfWCtzz)`*mMhJ6t%EBvfg1`(WmSwURj+(B_A%1M0XqKVK7Mf(4
z9lVD8I0YZmDDpB{%Rr+yD3U=UJpv2R6F@D483}IYi{uz>mglY6-kNMigAyH8XJsmu
zXG`If=@A0w)2!=|UEyezGJqB{)nN#`YIwRYK1z$3t0j7!2%G_xYRxr_QP5OJL6cFf
zt4X*oQ@qDeWBwxTX_ahiTU%S83{OrGyZn~Q*z2TONR1^$Vl}1?@lQb;0VP!yWD1lt
zW4)%tZ=%I{8^P?PlkH|Q%voP#Y{mlz-oL?=-HHK+$wTEIfAG5nOg+tSIG8*g2(@8<
zWZo#H#Z$k6&xKSYMx;V=s7DgYOgKI;iB1LDf%cLW1WeM^QezED9WKG4AtBy+`G-&u
zh;d5(ndg#p22m~hmY}AxV+f_^MySW&Qxkhgy48Ikk_Z+Wg3+a%QRgnOwBa}L$E%hj
zn+;;Yi{DXv@C39toSzlsN^8r$6@A3+bb{ZHyUUAJTJ>|IEhvzHbL8sWNE7UeE3CzE
zwldmi9$<w*8uP(;cgSLBG6JTGq>)I?!Jb}T1N`oO$5{*RY()eIP-={P*m)1_C{KX3
zV9-hU8n32=;0hd}c!)+~o?j0~YXtZp^^IIQq!24lKgghqpMtT=luo}#ZXp$VkEdt#
z_E33ZXn(ktrZuyRJUb9!C{VH(C-qe41Dhx(mcHs+m`wQ0!x|^E<2?;K^4OcX9LrYO
zB`@1`s{PHsmvfXoXNyKnb6$)HS5~s<-fVXPzyAHT4`=ZBUH9FC64AFcJ-*M0F*A2|
z|0U_fCgbkhf~c?N?)O2IkdCL70bwB3$z5^i-~1ny)8f0YJW_COzuK8&SLb^dJrn8E
zTNFt!dFQe*y}$==NiJ{0)0FTLfUL?!%7+6%vZ{rr1>*#Ol$OpS{g_qD*zcQD%aEY~
zp$0gaf<;td05s6N4`V&LuB8mV{UEUa@GkV$9iw89p+rlCHx=aXaMrFEx1>6K9!4{$
z1;^$>hFjoka3;u;+yMprt%bu56Zy>~5~;AMEj;Vsz}r4l*{wwJgWrapaePE<ZUN=Q
zqJx}tkKvO%DA*Wf<$ui+DjA*J<+BVGRI_wr12}b?_d-L!I7USBjv(Y%6okkNY5&tN
zI&?fMRsdjDL`K3Y*LQ86#jH$!5L6=RJ`^i5t;#efWq!(C9grmq2plneJ(+Dz6_So2
z$;zPi$98X&HgNgZ9|3VJr>JbNo|8IzbBnIH_r<gn4g=GU%~RJVLgA-w63-;zMLpQ}
zWy=eTRx*9d(%SxfW_|bwLzTfN4^Wz&HNnrDm}X!3v~*K3XKmF%QV*ds*FhS)`^>XP
zFk5;4s+{M_!`5=oL1dbZrv^%6a{?K&XJX{{npoU833xY#iHHQFqe`>j9YM5@QSYnB
z)Zf5)o{nJeNaazm;Kac=lqFfyp}8)dIVf;-e`5({&Z{G00_SkoD-HtCj@`7r5$o}J
ztHZ#{2!k&vn)9hBcXf?uLLhc3Umlco0A?bqz^U5aDPpl$f7YR0+ME<PB8Gh8>nmKU
zSQJNw0kM7E6=gxFk_b3}PA62A5n2QAkcBvB&381!j=SkWK9&ilESHj$!+EE3s%m|l
z=?83L_lP7Ck*Q~8p<5P$Kwp6{TD*|>#)Dd=JK57hQ6N+u@A9T+7cG;7;On>gdCPxl
z4%>eH(}Hp$WYv@KM`RXP5IodN<22Wq8I(^pIIJ#L2`ra&Fk=vyc=wO~bsP{9^8NWB
z*V4CJf(CdzJA@8)KXov+nHIR1(hA7s3;CmCV@~rIIm_ah1lvt*s?auK!Mxb(8taEF
zYX^I|fgJ~QX})Oo%l?+m&e>7UOB;8)25fu}oK3EeNuke1l1g`>mGYRakxprH%ya&8
zoiBB-@gjishR&lNffF9XgXfZb40zGIglutg4ly8bPDKy^7?S%A&YS05?B<@Mk%P*=
z-x2Ln=9I%QW~q~0{*zt#b*|2TogY`h#s+ITL*L#1f~W&$!POFA4VR}wwZ5jNB*a9O
z&m|+Ojq<`n-_o6)Ltks~D`dRuT%lm#H*R?FFGePo`C$%U>tA|~SSlKpS@pdPij=iZ
z03xy*?XFmo7G4w$Nw_D+u67oL^234}bB#PCt=a0Gfc$ljxKm1{YVfRK{16E8JvK!6
zd;~AgH{3edHjB}4YJH9VxNrj<Dg%xA*2u5VQ&Oa9Lv^(oBudO+ZInavODqmHWsGgO
zh9ZKALFEWBKuEYXB?nI0Qh?$VdyevMGYry~$NTlt14xe;sEP1wIw>?L$etDipd7yn
zSP<<uvGg{2(^epRyVBszAeX<VQCb^EQUC?R+++Izt5Rj47gIbHMW(<&w;VKkGM-r?
zW4-uZ=m;oyb*c_YO{bu==RtH;0Vx%O@o|H?in|9?1YQ9YM`Z;#xiNHaA0JM#TrVB>
zXrqDh^RV2j4rF*_ID*@4T_OLVGmo}AQcI3NjW+T@8E`@nN_GNOO^Nq0gSv7g_=fFk
z2)l~D{kCBiX7DOJH>%WaiD!c;?<-b1KF_jlJq#iwzGa2pN0d@o8c9<6qv5T_HCzZP
z=9ve5(f66mC%-aILtGUl-&hh1l@bkP3J?_iDwN<%VKXoE(AW;tsicAQaVERMUWrt4
z^T67}HZB*>^C>;vbiR^jt<-N4_jhmk@wg!F-d&?w6P1K}ud+XFaX-K7(z_P3quKBN
zMm6}!vGjRQRm<-o*AMW2Zh0cQLHKVTA3jUP)=hn-3I?a&owy@4A)Ol?q|Oqt&n(KM
zJ7yN)>hsBB<>c|csGWIw4M+g1Y?KG8Bvk`KNSsstn7k<8X?|K(j&YjZXmc_a#mkD3
zg~-TAN>WsmthMG<&el#md?sF10h{A9*<}H=o-v<=w;aCv?UTrEZTGIcz1<{tL&BM7
zhn_ds89*$Ss)3}c^w&@3<>wE^IA=oPxVB<h@1go^HN0{=Vv3&qVM;@g3SZN1ANncy
z_VvVc>k3V*N5|#R<f5XY20MhjIO1BdVfP}SM}p+ah%OG!cuRYGBvM>}^i|JPNfM#r
z?)3_GARv>{zV50ATO%%i-~k}Pn&2MGsu)hVc^>Q8Z0WVJQqR%J30~}D%{!Ksnwkpw
zuO!hSWW@nLF&t3qu*6-3BJ2eNm0jqFAm<~3%w($o*G#YlOVuDNv#49)cIgDaEAi5H
zue*k?FsgTkLnP!MIDjR<klct<<ff*_1EixQ*(BX?1A>>gcXCRKLl&{eB+IHd00%L|
z6smMc7dsPuWLK1RFiz)z@Hv-PQ2*T^Q<&H{kIs+-=vxw6kt>%!ylZF+SS#ENtQgVW
z9MM!)z4_>zSd33rM#jNw`%1Eyn4jmpAw6#f+1Z6RxAJUDSC<*9jA`}8IBj0yX4!No
z6rzhjRBDECL2-2mDPc&2>GA1yMM1=)87n>bP;IIj0HG1}+}7U~7?z(w!O8_Za5D~k
z$SO`1hmik)X=y)P2>KmeS(}uXhv<Dx-nEuySPM`kGR#SwcLv#MP<?s&gK6RQ%R_as
zq&|BT8bjj61E=5N`bGPmDV5KJF(5|9&5x6Zd#S;`ZWSvF$W{T<l5uN82UTRqc}l1}
z8~;77HVF90Xj!R}S~q-<m6xTnvva*V?JLUGNXclXP!bR@17ZE7{hN#XN;KZ%fDl4t
z?V9ZG=sO*FlxrgXN5h8v%+I*(H@RCMimRPZd(unRTW%A5I}EjebIS_7!2<Jby}cSE
znk{6f{9cK(RW~p96y7L;*zW<8W52RAk9K>!N!gguyu|(O_QaBRNEkUfzuOKz3rm0j
z%$0mCaP20jOWxrfJJr8_<L;|ndDoCjBnKjn<(fI|`qtLgC%<nHS_lY$$VA<%#dH6l
zc|j{$SV-eD!@r+al`CYO{pC(VU93`y*<xDaySW6xJ1<~Tx8U`uQwvD~XAQdxTt&Tz
zqp>kDYe#ry?wORnpwG5vn2#&3Ki;N^H$!DowMU+%E=b!Vqyq};^W3XN`%07E7t}j$
zrIgsMiI@<Iav$#S-?HBg%YyL(am!zpI_3K%@yQ*PQ9W~Wb80vSMYbTBTyQE&)j%dw
zP3#T^-Pqqj_<$S&8I2uX-QBdN@L+jFK4A!?TI)QQj<?&Ddc7_n8Y@*Z(V2?`8UL^^
zzqB9XDn6e&0Q()-9Wdsgrb&2`T1aRFDG0+|A+El_Ps@G<mRNJ^3TIRzeG8M_98~NO
z?`<pqnj)X1raD+F;1@hyZ3$&Q(}hy1A|3B_KC^<g<R4!9OWPp;O7`_}(Xbamc-enn
zNOnr&CL7peEF`oPkj;VScMq!9$ri#fJp!rC0+33H1skkFgX7k0e+eixTLzzb3v$`y
z<RphuN~z3z+~^6rKw#tynyp5nT)py6*x<6Xr&)o!sQ%=VBVd_9g9BDujiLj}>_w2!
za$xfE^72{(EbhF{skoFIA+F{#(IjXNJqG?n(qY(pflDpK`eg<!*Kg(rBt^fdZfS$K
zXHc(7Z(xE3IX5>~>i8LATC!f0Jw)aeH(!__Fep<(IAI*Kl6r9;N=KgH$QYJo&&tx5
zSogOa{PN=D{h!b9R2Uu5hW(5@8Awt@I2C3L7fz*nps+)>#s?wJwy>~(77lrxp_UK<
zvsKa{xuz$+pr8OTas7aR0BDkVj4)?>d_2hMfe%3`i1ThYRRI(S8s|bcXJ#<GzwO}i
zhoA}BQ-@mgm`=LU|Je@o958uLN%T2<ey|n&4UvR=ILNgHNOxiU&mYa<(&F{XFs=o9
z*)2*;fwT-XDmitO6uX3^_wb+z+cI62mH%jR(desK4{$^9&#ls_)|wFB5yW9(#ju3Y
zmXu*|E7TF$3?kJd<}ReNVH%Mg$O1=_Qj$*C7vu!tFjnY{&<<b4tnqw)Ar!{PK@%(7
zpj(5xI;<zMIe^mp6=GmZQ`6zbezf+Ov(nrWiI5Q~0Swqd<DSv+c*p7AY5RPAe1?|B
zvm{4ua9;L0f=jTG4WCF3zya7OivkZnbugYA`Dy%R!OY~9ShY%y40zPFNd*N9xRqaZ
z#hL<e<Lwz`QN98#pbt?0_2?47%|jiW>?YBzf<K5-8Ut`}e#@druH6gWmt%MWSF6Kg
zS1a#RC0Cjx=dG+Nf`h1or1E)qKWaI1ic%}6xoATjj4;&1{p$}$eI;d4lF%AMsU0Tb
zgQ<g@-8&d*KHD$JRt6KwL|HJbA-BdV)AK&!l4zm~QaGg+?H>vN?%h~)I+rK=SgueE
zx#!YboKIg8LLMa4ccQU6b%##GEiY(Q8(m<RQ*29LzOqkwZZ{%4jp>EVQD0xL>CR+U
zu$jvg>WY<2*<3Sh)nxh-Vpzyx784uG>(6<_JAWJw&9ncyJ!4*iy8|seYDl?CB%ADe
z3eZwo$U5*wd!}V9s1l{KPqQX`acDjK_Hx2TGR1ZxpZkV=hxy4hb9&dDoW6iWKc0OJ
zy+j1sMxfzLuQ}qhPisJ&#`)UUY`ndr3oJLuy>IFdd-S4(lFPldM?9F{&{l1Z%s5CQ
z2k>>s@+?0Xc6D`yUG63kIDNG52YM}r?=t*sfWN;7)R+bl^+VM(*}(4`aAT<{JpLSC
z9)`~YUZlmE2~SB*-i4h71!)LmquSf+Hy&~A$_u0h^wmOW<@JwPeamC;>MWRIr0alX
z3W+Y%d8D0tfPnWhKVJdrOXtv8b;?(5jOFkfn@2Fo@lthlbt;p2Ewj*|q6U&|J=z$6
z=JM$a2+E|yd7S<XT@wv}(HU)7!A4j3I3GCn`cw#|6Om-l&eVW*oJFehflF_<aq^1`
zpg!XAfHblUmVW1VuZ9jTz11UC8GK${n>t~X<!_`3kB4e@sD?7jqE~3aSg-^zD=7<E
z`F&@Jt<tdH7Mw$ZkFIu<(6XSPh=Kl7I%bshrb?JIm`xX>FlDaY1)R1K%^-h3D~0Od
zO`I3z9O97Q7R*C3jnj%in5I$a<w^$+9z+!o7WhST=?nDIVU3byCC)qX7F<qNng&DC
zRn%lQ_9iXoTFWa-w^*n_0a}SdD)lv2*Mk}yr5M9J_ZZU)un9C>6~sJ{P*Lj&=D$<O
z=Cs^NTrmZjarO#rb&b}*8nFiqSyFnD2XIaEsBA!cE*P{2!Nr)EF^@6MeskP|wkneL
zraUa+sp0fLMxpD+ecAe0sI&lIRn@OPtMrX)iP^`7Q;F~>aq*+H2MBQXpy_d!?M0s_
z+zcKn1k!{o+3-LE0Hmvt01b_C(pt;k1zUQ0EJD|B9phO(JAfh|LSzZSOQT4Oh1q$v
z$XV!hK>*5taG?{BZ>En#$A$c?U0AoW`F2$F&7$4rGvW?jI|shD{h@PczAJ<CXnWlu
zt_i4)Bw7T{e8MfyRC#{dY%+kNElAYnRqMCD;aUF4ab_zTC{L(wIV~PbfWm5uSw=ff
z<j#%?9w=@7zgBhm*Jy^Z9$>Pc@`w@Wu;y77<EU}kL9V^U3NW2Dn@ubpOQHUpc<C#z
z|HhFdzF~Y31BF?(CcI2ZNudWepfn7zqyp8uQ-}TdF0UGO<U=6+mW~u7jTpjO`Um-G
zb?IOfhC{e}5FmvbBK(5-kpp;8aV@bF4k<-J5%5`=?$tm3`a}{<kWW!bY4~;0!!M{f
zEOz7Luc(HO60E90)!Efznl`rxlt#D6TE$5*?pG|{hc_nUcHG&q+94~_iYoDlP>qwb
zs9{ZSwN{M`jfsHrSqvgu1v~p)5MS6Jx$ib%wM^n@;~v;A>Iq2Q4qyxdr(+rd#~-X#
z75JxqPfC=9$O&Ad4Q7{cd0qm|E~Z22Pe5ShQ3SzXCEv_d>&)5`kF|f#27-xPvkYVE
zuQt`Wl7RGYb-wi%#8lpNW)=*^F=IB4Y4Gq}V=q-tiv?aPOMx+s#Bv6$eSG9vux&xu
zQj~xA3}GKZ?D!w}Ni=5p+h&8CQxz&+P;4=3tJ0HlqJolAN`8Jmd@E?R>sUF_Xx~#f
zDE{GGbI(&LoG5^4eD<IFNdBR{%l{kvG=IRlhTJ?1TE+vgLP17*k(``N*;)Arj7~=Y
zZNKOiHtw|YTB+1?h7eYs1&Q&+IIoX8gp5yg+R)Gt=TEJ#&%vL{K$RKOSkt%-xI|NH
ztNaK>UO;q+DAd|L{r$uJ%nAp;{E?-bQHp>Le=v(N{PT8Np`XbRQ3AF4%-6^tI=ksT
zPg7@SG-O-<@a+3gl<^P89EY7y3(4fZn=P~b-Fzwc@g^|IM45e;z4gY(TJflcE2Xq(
z(b*s5%BFel5)!oj{a`XEPLPg4QZl0+4&nK>dhRj*2(#ONWZL^5&^JT=#isf%FTg(=
zeg0=RJn^N`F}o0cLTL?^al=HF?2!yxYfd2`f(amrnO9C^FAp=Ivpy0P4Lb{O*rMh&
zC}Io%VXZq{CDuX>0ydI>ishtnYv@V0g3=9j-U0<t%q`Oo>qd;yj0ZOo7f--t2UKt5
zH3D-Y4V9{h%${K+wrW^eqe2~69)v6_pbZX{n>o$>Ah9_BTMYU<xhm`Sx+NK>P0pxu
z-kbN{ou3;KkSxW~&Sw~VvKSn_7=iHwXp;+E$R*bO>1(rtHwh@M2TG0rPI_+rmvg$}
zPk~&@D=0um%YH%iX>DX6O23r7qi?C5G@F87N?FMqhC$kJ8p=O@;CS|wx5>AvB)S(3
zMglD!0QY6XNg>N8P(n?HCO}YsP)j}#g%;3*z&$=Sqbdl<BoKUW4X9WHR21|?6(9oO
z3;7BXEvM@ri>f~kBwbY{1%+02E9cQ<Q!l@9gc#PGtQR4b>(CI~Gzn0$Om~~Cb$MWA
zfg1S!DU+bN<0{)ZE&bOMou6UK071I+m1|&+Tz-FBGYa(23j`^s^Y?+WG3=>Ntt(3A
z5Izyh4{_Z7peJDo9|Y9W8zi~U5OzLP-hrJ3<yfG@$0Wn-=>TIy16oRgqnQ7>+&N3=
z2HS%vI14LCM5s8afZ|4Hacz=^<bVP(1w0w|GAquzKBFG~9-;tiJ<D8@C^g~wvi0zl
z6EiD4d%2&N-w~9wLgCzsee-5Bs5H>EQRBYT)7t8%k5E)c(wovnYAQr{3*6vSEI|;z
z_@Tto2LMu86d8yX%=A#b8WlPFk^Q6MYI;VS6IzK168K>bq3)1{D@(IxW@eCR_^!e9
zLrrUMaoFXjlPAf7rFre`?T}go6VF<MP%2YStmtXbsjQWQngvP<T@2ZBE~|%k);EH1
zaw><}Vl%P)>Wx`OdAgD*l*foG6ArnsuyMW=9BMlt*5?0zmLwZ;Ig8Dw!w)wb@CUG-
zmtL0`DTkdkXBDi1zXIY5D#Z`I<CR341@K~Aa8$gaYuEiu$g6eH4hhkyso}Wf(pk{K
zkT(t*f6DvjQJrZnNph0DfE2*m$b$iN?VmyFGB48&*$z$joAFj7JwJYOd`$wy%ziO4
z$9)vQB}#<k&Q8;1g`nYv)0Hez5Od)uJz5i2ZjC9mfDu-~v6nK<wb!qxE&b(0D8Fby
zgRT}xI~Lw;MjQ(nLUxkOj;aMl!+B{adcbxZgjSPku;TcLCW2guT%jE0u_|#xc?t6f
zrbFwrmaGePVqbCaw`5dE`B?D%Bx$?$@gUc}^0GzeVy*npC9unh{3$m&+GxvvmDNYr
zq+oL(P6St!;Q^`>t`ZxbE*}d7iJ?ReI4c-_sCMj2Rp^XMb-um)q1H?g&2*(r3+%3p
zj0}3{G9n<FQBa~iNf>}zE_%o3sbWO-H$m1m+Hiy9Bgrv8P#Ov9T3YA_wRc{7hWc$h
zD7fKW*m7nswR~g&XrLWb;;{r|$;@iEOlw1y;2x+rsC>V@an={SBHVu?lEFtP`iaT>
z^*DN?5N@LnYoR{{I3Yl6uw`YUGL)rd12J|E?6P$r!=V}$%-lRKa}dboPNY!{Mp>8F
z&dYurMheq~S7&Na1y|!?LxTq0qMS9KH4LSPzQMq1P{yvgyEh<1m{bj_>;p~ou$ELr
zpIn*PdPwWoGj;ay+o&r^4z2@19}VgxWpDuuOo;Q3)5>8-gNq!hg_OZ1RB`~%gdt%7
zF&PwI=_M<u&kuDL1+Yt2D>bO7R6$2uA9yRB5Y8zgf75O`3o<BJP6^{#6`GJ>u%O@p
zh<k)~Eb;)u)2`$g#GgxRTUxpKLCCGo6eM5dw|iAqrh&SNpbm&c8Pv;$d>-|x<LSu3
z1!5ypeIw_IY}&5FXCTc0T8HdG9|{O0g7@qyXneB8!QhyLFm1koi=64NLS_@FgtcpC
zaS-}9?bfryA)G3*&Gg^@`k?;}5d5DX!CAe(!(C7RXUycElnioKUO_>7zynJc^}?h-
zDVYbDo;LOB4F9p3?!dPDYVuL`Nm5hmz_WF)Q=mQe^`i6A+sm3F$29T_3byn$(2e2#
z9{Nw(x29#1jzS08QTmU@-d;;YuDjvKxyn$u_VpF&6LMZn91e(-wY13X=`$CU)GVd!
z#PDl}xc!2%_t@AQx^PKHvA6S|>4#&GZgO?$Q!PyJwb|2^W2YCR8>Ml~^x1DqU67?_
z$aCO0L=-VAH#c`NxSSLd6LSQf3^=wt25*SU;Qzc^;C}#D{3k#BFZto&bPD&Aw~+1+
znC3`9y~SP8t>+m^kq6yzdiBcv59F1U$aZDPoZ2-;I9)_WI}2_YLn16No`5X#A`d&`
zOp9nqXwYEv3~NS#k7SBG9~r|rBsPbfUthS63Y1U~15ve(F-#;<1Bca+P)GxfT>*Jg
za{TAV5QbxV|I6o0i0squ1M?Xm?;7OYMRFz7t<Ze}P_D?edG{NPD+LlYSO|OF#}6v}
znUKi<ul}8Xqjb{MU5@~*8G#k)q}IQxbbzO06<xnTeKkUA<L)yfp3z8M#(Kal0FzOF
zJ-|2Z#zS2H!c-0b9}SZ6ga&CTtOT?>t>jS7bLzktv7hd=1%%v0#)5xqcu48nqkeW=
zygC}l=THN&kLr)yFauCYUvZPDVUe4g&qG){l?F9T=!m-KX6x6o0I|oa3n~uxsN>M;
z0P<6>^R;Vx%Zbz)hn<OWO-Z^!eGOb=$(Ps6K_Q<Y1!Qi*H#)xWB1l;p{t@#Y?P`zY
z+MMz^-5eF;$kkNbJ=yx<U8vZT{#S;Xbm2o+!<@uTS-9tB*~H`V`FRDK`pD8m^}XWb
z_zPa5+H3*Mxo^Nd&PkW<zAGD>XeFTL(UZtL1J?X;B$a{LMNo8M`8e)hoC!Uxc@7Se
zI2BlmmYY9}D)1***BG0jKDEbjVC2^YPvJGJ%cE8B!)H6#%-J|#W*GU`umtF`0Rl4n
z_1S@OAaa0ls5AwCWr6bq3JMB?&{)zl83zkBM)&Cug~_1zxkEFVqC3T6_-@GGkTDlA
zXfPc?%`^@C7+gTDMm6^QfIotepGG@TsSR^i13i)iDP*5&hBPvK*Kv~?US_SI3Oq>o
zzhL=><Yn+WNnxSenhaY&VBiceHPnt#LGtzTdI|SVkj6ta@@Yky&iOG)*@xD4G(0~%
zDHaFytz{Jj`NVkoctV0i)+paI9p--5#9)!3&X)-5oA-fJy12m()ug0Upjbn%_1CN8
z(E01z9RCYk|8KS|_6V5gfM22J8F1#`pzht90L9C%L@h1o(TG-bHG`%l_6sHj1PtW7
zLVj{COKNSAEC;dPOh51Z)_MmHF55!@T+=-kT+y8CKtK$YMGaSOwQVNW@km{fBQm~*
zWY7mGzJ@Bi^4zKCQ|A&sl$Mlq3tAkzr3zOD2H<aB2NFLo!Mn!>3?N%I1N{idQBlvu
z&+mvTzC>vv5C-_Gr236bdmxFTUZM5?zC<qhui?Lx+kjUeHOu9~Z6bLjk^}rGx~~D6
zXyW4FnSxlq*o^*l5FwHj?o15br>iaZNQ{gB_^q9Z&u3KDh=>q(Chq<8Yh$JMwb!1j
ze#Aq5|CjWpUj)*eK{-A>3u3YgXale5!t#&ei6TJk#HG1T!^!Sv_L7zsydBjSJWoTZ
z_cSn^vMP*72!_A38jhRC(r5};f(;YmP&b%&!cTj?BS~#(e!Qi*nE*?dLg~k>Lq>O0
z^&||S`z(<>4JYsB0<t?IenXH#Mt0<eY{l)sbUeLq9qqLLKdJXAh~;VYUn;*rM#=RU
z{32BYSGXYOzN+EcC)4+>mv_L{zUJmu54I9>>Wz>!DE2~?34%5BG+pg@dl`l<?1-9{
zQ1+5SeGbhKR)_CmY)t-=x|P>)O054$!+tQ2s7N*FT{L(0f;*M~`WyZWI97d2meGk&
zqn65HIiAl8#HEWBwe3#%sMap3=yHmHe9yI#>={XVNgy=RAuR+<FX#$YBo3mx{J@vF
z9h_`B$bk;0w=D3sb{U-pa_11L*`bpG1@dGF77gA=g@CpjAojHqb?YahgK$AX-*Mk|
zN-|C%$Csviw<%;n^rpxH59K`rSU`)V%kLiLfnb6>`9)xLKz=Kip2EZMmWUZ6hbIV+
zP+(7PU@qiS7Y4zXL8n1;Q+XdV_#3xmHL{!x2a&U^iC_u23a1y9))r=)xGLS(H#TmA
ztyT{Gd|=>%`!_lLbCB?T1JDhRltD>ww4wEY4-1lELQ8`^RJu!Cva~>wascnIY_eI#
zl*bCX1OweC2pzrQoJ&PPKHRG0)+3>zX>)yI7!{M@`ojoxNelz}5UY@WI!UX2i5Nc^
za*;Llql54^CRo$o)3mLbbM+0ss+ofx1l&5J+pXjvYDi(hC7fMCRfj^RK#U0-)w4eB
z>r0m*`-1rp-~zZ~&~2!k?%>}x$ei-@q{-CIlc=oBzM%5OUZxfJxKzf|bCFF}=%7hj
z+k&4O=B?TqdHFve5y-<RkkJT==`_^V6ih&KwK)A<7S{)u30)q9<PYdSTe28}!_sTl
z$%0~KUo~Nu9zhDczHXG6hrURr-Kywv3GhHWOgCyIL&Zr2vPEup(&3wy{E;imuI>Tm
zsP2sVL29F98&H|l(v6Koa3r1}`w{9YDf7FnjXzycjvPTaT5Mk&sHS<y9T3c%<d~hn
z(2sifQ1cZJXN%BS(7;%sf-o4eb1mq6LmiErvNgI}1@&>FYoyK)HOm^jCrhbQ86a7M
z<HiB@BhoL*HH0x8+$h3d;Yd##WJ?dBtBv63(ooGie2R%2-cWrW3EH__;_vUD13TkP
zoN{)bQUw;w+TnAV{ivgqHI~KNhjbjdz{P1e5`gE1p?9JIU5knCsRHe1%u4gLa2L;q
z-~@H{&3Sj$kUPX0C;{ZcMOl>Y0=M26xGY5qi&C7Bc=o!9vXz;oVp>xjWNpx@Q70-&
zmQppS=6(}*=+H-WONuYP{Kvih9;3-oL#Sy;=b;$#$$epyDP_DTalLpkwtC51kBv?z
z<*c&(Qk4?rx9Yz+^4u)nIN8bboZKrNQT>PqSEN6$zl+a1oNX)r7+Wg;tL(LxsUo7%
zLdop};TFO>n;YlPm2SS0F7)xI-`>9M)>@od*6y$q@+~U2mQ$lmcqR-7)ZbK5pB|i0
zx$`{JvSi=x-8xW|Xoa}DSwdp!2qix7$Yt<f6JA2C{&i_-X(t$}9DiQ#n8^J)J-rns
zNNMYx=g*!Ady11ROMGx%ct5{FP`X}ZRj|yU65@821)gGaBO@bdWQ{x!`5Et`9B%+p
ztHJp?+1%{xY`5`==H&YjHYUz7F?nG(tW4kzF$Fv;(8$EZ8Em;j4Df)R+`M^njFNi-
zG`#g+reX@4yBf$KGh+!qZ`jZ)Q?V}bns*fqZWi4vBt#z2K+Ol6Hg4>XTHxDy<OHOP
zM{m;7ELpeUrflnNT>M@bxCi)Y%V;a0_szn>uaDm1*{iI434DHyV1CuOHxofzK5@Rz
z-;2bA1_>!FV0{l98w`J6U$%qtTsFBTWOv^0H-bxEzLc7u>^6bkisYP}9It`uU+o+m
z((r6n4EQ*zZeR-AyBbW|gLndm!G<$rwE;W=&V%i(&tM|bjngQgjrtQE*I+86PHA(C
zYfti3cWpJibNN|CtAu7?N0Kl^P$T+OZ@w>;;x8BXrM7k+tPRJ}Xo@zFqy~qFV{cfv
zUgt?<biG%ui2rn@39eX`#hT^XhpBAzhZ~+G>R#|P=Cucx2SXnj;U)CfK6=12)d)8t
zNMQjg(Z+D&hDVPcMe{1PS3{%hsW~@h<n`kCFOI|a?%nfRn!C1c-MVXyiARSRA3vVJ
zHc$1IXe?EyUlvA>cUn)+d#Xo;7U<(s@bZ<zwN0Bgog(lEN=$f5VhYK|pK-SS^r;L8
zNw0Hq&co`1mKmI!pb_9N&a2&THqP)LZ$Bume7$n{5^Aq8%L`82Ia}oy43{-4*nZ}V
zk~L&uS8|CfXTX`)1TB_{KYm;x=JR7h%%l=;`pC%1T_DKYeR<+9ClCN1Q(b)uR(>4x
zVu?>8YCh>wqm?(ghl(@;7OI`{o#1jgH)=+2;Y;9WR(TR3(p_JaeUshZ7lHM=?jBd-
zjf3-n8L=N@G=qwfTdZv6vnatSatPvDsr5Lo>aDIBu`OGMW@A1Rs-X|?S$83{G0Ipc
zEZ^#~&%O5j{qKaAu$}#zc49E<$JdBBR>c{lz=7F`<!2p|=Q|FD+ecD>5p*R!ft4_p
zhhO9+H?P3!H*ZcuwWbBkkP5ijtEL&oBsvc-uO0#BwQQ0Hqtw-`?0)9PjqLMqKP??>
zu}$z9r4PAbu#CVA{YzK{t^H7WOasr-xxIV$9;o12)SmP!$I)KZI$P`_Sb`3n3T$$C
z_UWTX-_CV|9V+qd+e3Et_KL-(hcOu6-`2`EK8)84GJ_;M2E$r{7blkbyt38NxwCKo
zevNyua%WV;1jMz1Er`KOsZxM|u=*5o1s;!OR~YZY^f~JFFZ&pzT!7|Z!iVpTQ0_>D
za!6`vsRf+;VsbV{J~KHXizJ<0;NF)BEiL;M6(a}azU_pa7jcp1;+s6BoW8Q@H??vR
zqH&rLDDRO{D`2mg#sVjHT9@a;jgjvIOWK}Pd!8QvZbYAug9qwIy~aASZPB$b5r?To
z!xC5p=P$u<!|}a8{y2N_;>8I1NnKq%6cmh%joa-Njk&r?3gqkENqBZ}ysGzU0QD_^
zqKqspGXS8SKo7QCKjtMwuPoa&ud`shQqf#&QRVB2Qlrq%7Y=8xZfjF!-dv#=>?-J1
ze<54Gc<lGzf7dU75{ltrZV|_nef#!#jWlkjRXE*viopo{hW?F!AX~{cPh~NC20AkL
zL^o}E3Ee9r5N`I#$h1R7R$m$<`5ZK>p(SJBu(W^1S;AIq1MS`}`_des-kL*v>(;K_
zvuoE$!rW}FY}$(#@6(=PM1C-S_7W=#pc_L}*oBqEFW=ZtdbR!jrx%c)P7}b3zdO7=
zoCj065uK5JYHH?$))Yg@GO*vtkb{_z=%ldlL^MB>akzvnrYa#*)Is<TAtGWI765xA
zls$T^tW8Z#WwUt_pBE_BJHz^~fYtY@)Glf1{X2HNKY(Tit^;@jcotxOXIkN(W4d$M
z5xmfZmyqrE%F1RvHE(T%Ca@%{GXGI{Xw|^Q>&{BNn8u@zLU_7TAxmHhaHGj~lAO@-
zW5=NFMHt&GO$_{TZUTCmpWV&qGBru~<L=!is9;KA8#I~e6#wyqE6^LEpk#1EH6G2O
zHG9cqWj3lpo}93*v9Lh7e%C0t|L@lmwXzl$7j+`YP_{BTapJasgnGwrb|FufXhN2D
z)vrQA4iAT6FFJ!+a^HaiPkP;yva-7;;Y)d??QU3zy}60L6CF)I=D9JdyW3&3&*3JN
zF|f@OUV=T3R5KnyAfPxuNhveOVzI%NhHhY36ggxQ0$|^9H%VlpxOfH(jvVaF+epc8
zyTPhc>T`r@V@{`wKh)+#GNcb3ItvHZtUR#DIKwPCFHe3Q56@DvhPJ~y+U5~uXuNtk
zGUDjk@%AXJZ+7>f@*M&3H*l|b;Q>6v#;4#{EGS2D?aEBxR3~(QY!(-HWl2C1iQmD+
zpLaYuI-205`1AUoe|DN(Ld`rs^Yga>wD($=F!ki#gg?Z0JVM&g_}H=EG}+6Zc6N57
za31#U-Fpshmfs{Qng)SBOiYEq9}W@dhjLbCreYMu4+g#oMZm7-$3vZ<lBDW4od}#J
z&#`F-k;FT4#;<&hcqpU=<r^<x52vK3AAmQ8jbI27?!xu!Z+Qg7UVr}(>I^_0rb;_v
z#z~9+d+mY-w5u-Fxd->R6k<Z^Yp>_57Jz}TRSI6>=KQl3@bxz#snt$L_&WILnt$-O
z6UF!J+4HE*KBCV4f}5M$t=YIZvE1eNzi$Z6NXyS_^Ffw@>tV>Lr@dbVH~TXBtOoqI
z+d1AKWx}Uq!Y3eN&>)5Ou&eE&W%&<%wt0>v`~W6UIx!!03^tm!?Y`(7t`=KQLI21|
zq8y+R_^!Z-lPB{p5ejyV&+3@Fa<1+pUs}EG2wwH?C%{VtE*HwsTpHO4?J8#wgW0ro
zYwEXeHv9m0zz}=Z-rhb!Pkmru;F;*KW#RRs1h4P!*CoGv=~(?}`_BRb#ugTFX!C*3
zQAwm9SSIXFk;7MR<J>;}@vD7Lm&xhV_e`zMp0!D6sH>ZQU4g^l%nDqhdH94A8ynB|
z!bv!-uipkeF};*weaqqc_!dCkiJ%1TmzTc?Y^&uZHX;vNy@H^pRdc+8G`UY!7Pdl(
ze87SWzwjY#$m!>(l(Kn<NEcDEV3eUc<TvYdG#jeFfBAAc2yWH6mUlo)%bLMpKuT-@
z68`k@<LA@U(>hsYLH(t0qs>{kktoAg-_0%4!_#vf?)TXX5f37~^DiliItN2PY!Cf#
zj%wlU?VU8Q_z4mRoWxdOsaDiLW8+A`Yjf{6$c_MH%8J7HwCS3N350?s_8e$MtD3d3
z@v8?9`0TD;MRBdG$TKAv?uL*r?CWL)bje`5NbW?q={5u2#D7@Us`NEvFDEu@#UCUL
zBQvu%jg5`F`_WEu)U-!3Q0wETPm_UK^(EC!K{ljpV_2A&nnq7mu!8{^F>>zecfGxQ
z{MLY{B@ocFfq|tmR;3*is&Hjw<2FX1T1jsvuY{j=iO-~yj|Rmby7QhqdD6O#fsDe5
z-VjqUnE1~S{quaOR-3nMc~e^26X$Jy{`?C#h;Z})Xde;f6_ju}LP1YEJ$=0loB_qb
zE5-20uxUZHef91ZKo1$~^7P}O0&OMK%#tYr;A0c(AiD(SSX$PAm+E*K+^5u=9-L`a
zW(N7%yujsfx6!$C&nG5aK7IUn>J3()fv8nsIs;wscqa&WLrersfB+bKd*{Qa7{sf>
z29(NOhFdJoVX=1^6zdf&->EoYR(okHkQ3)XgRWh+GBGkref|1WsDOAgv_kegNI+X-
zddqs~T5jCi;i3ri&k(iRw~^ef0iHsp1o{EcCQL-(Gz7n5kQ=11Akoh58G<A?2LOKG
zjvdGDT;8*7oA!+xH<rNA|8<-K!h{Z$bm>5t1oN1I!9%z@=5Vg1Vyr$~^g((kq4B1p
z;}W*{CO7ZJOP5BxCx9&1t5aan&!0cvnxrdedSDBrXUFMp@48BSv$n`s07zj(kotm4
zj*3~+@$fIx49&pxN^{ULvR6^Dec1}S^Mau9!syDC7tJj#rorf$9Jx4gjTH{S9mI%(
z*e<|U5#%hGU(~;qWCu}RKB5zRCN#^P8i#-odA_1euHoj+x2xSk3j&;Gl3n}d%N*oI
zOM<*xkFtKOVn)+A--kx$tTOxEk<gMfVWdHM^ZIooc-9=S&2X<6_d(0z)~tE7HK+MF
zQ<`c~<Z%+F3!aSdA}MLJnAm~XI{R|CQtel)!rgW_ilpq_25}<=>YAEXgek|2!J%is
z7rJLrBlOS=^hu5yO~7RbpVgsPD!beH`0?X)YrRqA)aMCGbt?gMVJCd2Zwz%Cek%%T
zcZ1)x^%&4#m;z0Y{()t^Zc%mio~9DOH5XWkiQBN}VZT&)FCByP)EjAjU<<#9{HYW}
zVmr{5(tAOJUpCVUed3*cfAsg)?K%Z;W~E@_6XIt5kers1IV~|1TOJ6|Bb(r?O&QI0
zZglmI%#faRN-bdeyE>ZyJjeAIS0D{q+u;OCC|-U7p&u-|Rrc&T1v|F|8q<v6^gstZ
z03elk1Na4gz<`XNE;W<0XTKHne80X2A`@YgxcKD%)7`cHvK7W*Ult@Yf{_s<lw~Gm
zlVCF>r6`%|Tq9X)){UCgHlt&ROX4<6P8dPNFj=~~Y+9EZ%_fV8)`nQeuvA$(#f&VP
zY;@15|6)x)G(8{QbKdj*p5OEQF7F$+1?{n~14FpV<_He80O7vPz0eiOFthE-kD)va
zV)Uq-^cR|4n2wMjEE#i7dR<rmLN`!Z*Af$(JZvJz&J1w!f6-rwXB)LHvi%u<{&ZGW
zmO{OhEeu!yhuhboG5o>fZHNH*hHpwr%JNLv`23Obt`a(3tEeE;|DW<8BaMnqedG_d
zOiHvDeY^Ynqsh&7D%}_ajYgBJ5xn{1v4fg^u=KZ~(?X$8d{0H&FNm=}w@Rf@LQG9f
ziLO#(b9Zm=`3(ulyhe_G12R|BL`i}rJ@vX&g9@fP3RLbL<j@4xAi+jOnFa)MV1q%(
z)d)<*mYX}p=jM)dHA_mLR8@(wT+JLVi`j3MzDWf?`Hnc6M5aWQTE2}xl^TVlniQ&~
ztGUMFsZ&rdkcmmEa|$}r*en){Jz0q8h+GX`@}j$Y6HiN|kGH|_oBaj7&_=F|;^JZ`
zSazx3_>O+sQ<mWvgziA{YE-M=jY1|IGmaa0j;vG~dcMCa$(btBAK!AD;`Th0xhsQC
zr$>Z`M~8=3oSdlpkiz0}yI|GOXzpOXLy4ob;Na6%RxYH8%J2--2`}!zBEIzmF((?H
znwk<K6w-Pr3oOZP!pqviq8$K&S&Af}^AQ_b66uf$n#C$n9H9D#HN;<$_oc?h*5~Xj
z^PXj{NaIv0mD!q3s)sZb`IZ*Ir`iSP4`=waEDlywnAAA<$Mbl+)f|#UAn9O${P%zu
zIXCrYNlD4WtuGf2vC9nqUHpPa<9&Dx>gWoe%2D;KuXD+hlc>}19zqoK2|2PtMU^Kb
zQ+7Bw^1Aappd6v8T6BkK_w0aOh@(X?nF-0s?aBkvW_JTD(ecq9V*=Iypagm~G&Q}f
ztFu_l@NuP3(%|i}VG~hiX8(C@8{HLRM2kBysF_pX^4ZFm2`@2s%5r0UU4sIwXkZ&x
zl9Y`iOl?B~dlsFy*0#2Su<XR1c)VP9*c3{gNdJdf!-B2HNC?PiQ6Gy9e`>$_9clFN
z38Ib;XQ);lVnJ2a)%6SMad=&%z{Q%{L<|UwcT6qCy&Dau`Yk#i_u&D3jOE8QPPm{u
mL4u#*Q^YaMrs4O0@%LZzgt8v`(XC7a^JLJYLV9V*CI0}ZQU0|6

diff --git a/articles/AMR_files/figure-html/plot 4-1.png b/articles/AMR_files/figure-html/plot 4-1.png
deleted file mode 100644
index ce760d9a0b9a7fd42208814422d72ef01a1f827a..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 78250
zcmd43cT`nd_a%5SE2z(ah@w0d3?QIH$z}zNB*{n?P;!vWGk~I^T#}NJ93<zcA|L{S
zM9B{kkStkp&y9Y+uByMfMpt(g1C^oPd(YW>?X~8bbFTCF+La3%H|*L#p-?tTTs$jH
zp{x#}P*%QOw+4Sx^q@T&f2`4zx^NbM;%nwit`q)PZ*o!5oI=^-N`BEi?;h2mQ2wS!
zoINFbH(;>M+Fn*}acOkAg2hvEEzOoK3OQ-2G%>V~LS&Oe{!VcFYBI|1w$a3r{@nHH
zfs~xrfA9Eko-6e59{Mfe>-UE|*x_e!F|eYwgL&Spkdb;xmukl&aaWZ2xs6C<fZgZb
zfNXQ49#7$3A-bYttCp{cLUAq*=-l+@XUa7p4`*`m<m>CbRlL7{qP*R~5<>Im=ZwE;
zHvaiBY4!SBe|~i4xOj5qpPz4^TCwZTk2h(zR{!~tqWt22_N^mo9&I`B-72@+DmVRh
zQ;Lb}N=lNVt!<9rNN$@Eqsx}z7m`nJy1x>&oj6v0yDcaEV+)s&q-F&^uygB{Vcdb<
z*!Qws{I?Z?5`~#I$;C*YyP@uo-kfQiDCRI5Z8P3uc7o~L&Ir*wwM3D<&sjVkP@JXC
zHsn38US(b}aaSbcnX!b3uJ4DJxb^oBc|!h?uIL?qrM}ZVS4Ug>@R1`DQa(q&h%b(A
z#>Gir_UEs6-y{5fkI?V?m88L)&)@DSP<^&rpdsT<%lkc&kGZu!{xu$z)!{gIC##5t
zqH^!Vj)(5Yv2IDs>u>$L^-Ge*8Ag{*>+u<TNePKBk(d1~iZ%-T@*Zi=&*kRfDcXLg
z^YY7M?*{cUjoNM-bMf%hilhbve_KI$9~x5n`n8zuj;JZAesZ~9EG!}aNIj^1HQqIB
za$@_oZDBh&lx}@_eQuYSeb!8KXOU~VS@);?PyhOTu@M`wk&&ZarE{#Y+qQ0fzw^%y
zOA6s8E`9m(rN1u5bLMVaj-|%aKjFc;2QP6<$QzR!Uo7)HbiduGF+tU~FT%Gj?80Wb
zc!lf7j~|a!FDw`?Tyo@4&Nv!A8SWq!+z_uAb|~oX={wD7*B@@6-%d{-@yVM#PN&=_
zXZ*A9WV_vne(&c%n}o&_njhAUzdl{=!;u;xn%HQVaon<Bc6{b@ps;_HULl`xyU0zt
z-Tb$!&8tMCt^^9jsAQYDJ$X`Fe?$GvzB4Yj-rZX*DJ4Z~FmF5YLn9;GazH+HF+$4M
z;A@gbta4_8rlzKBoIFRHO>f|AkH7Kv@tJ$EbDEDIKep|9CQz5CF0Sf0)7_Jmu3vlp
zxyaa~p3i~1CmglA#kR+{marW-aE3>>qPoqx<BcDmA=|-&=Qr-=uS29xj8};>MDK~4
z^-<1y_3Bm6R-tFR_@q@-0$PnS8xqykLln5Ie*SwZ|L1#JgZfzSZ{NPjW}E3u4m4if
zcIZmoD;_--iN3x*$2r@if#_yI&WCNi8;gDp$QT$H6qvFcIy84@+avmMl~djyW_o^h
z7Lm1HG3;uUVphRoSX$|(b^UN@#bCT27j2Cr&IqnK4l5C@mT&9!`0@2(H+oCN(vS2;
zwc3AHt{rRT575%>Hr+;{gnT$g@lqPepAOnr_ClN2sP#nj9U<%C+eWp?TBY{ohgew|
z@3ds_WtI)dhn?T3g&mn`m~JsHWW1qfqf;=c(^fFsAEz?Fh`>_BS{5Wn<X2Qwgk+Wv
zh<>c7;1!!2<Ow_fY}bqtGXsNOMWC=i$MM0Mh$q>VJ{-zVwMyL?v3hh0(|nC8S^;7X
zLLE7&Od@xye|$~Ws&<&ZD`L~2Gby&+?MZ3>bcd4w+l>UKWA4hv`SlmEy|IpQb5^E#
zcGJU^<{dMguFM<w_|*~{cZ%9tyv<VO^gWI+Il#wPKVlPH#n51ytQkB#WAE54BP~7l
ztZRN|+_B|*ajXreEURh3=!|^{dt%<!B(bPkeR7rG7mn?;diqL3LD)#1>B&7o-DW}c
zHFg!Bxvhs)4hRVebrfvcEAsW?l`ETCUh3%mUdixIIvk_4G~PFA`OWsNSU>0P!lgyK
z`glbd!^VWl%;E5G7R0Vfxi42$YQ^Jihw9=Ld4@U)#eap+hDNuPSH>%*^4_Zco0XNd
zTbW-_P;p^)veuQ^ai_%syL|jLoWIF|#KJX4Gc}6VSoYT)iRRgMBS9sGJt6C~VMF|S
zA@@9m{LS>|PoKVrtQXx?`h4wC0j(0cbfY#UMn*=O@}{=ThqMf_jtjFk!@}qHefHy{
zJF{}_rn+*jLZ+mE#sRsG0*A?trA2`sIYWCHgr{uSADXuxc|2ZO9eRe(pf1{3$6hRW
zc5)!fy4~i`4~6?W(=R28oq{W$)sO2OE_uorhId|VSuzt9>==tZr!P|+`$xg16{Y6z
zW7LOZI1isjo(zSYx<7W*4oAUq@avbO>Y=AsUw*}X&UG`hysmB}K7s1#nQl92z-QP{
z(VBqWwOLHFXw5Q3PV`{$J|c6zHu7@qyZdXsJv{aWR~-tjimI!V-6Ldi5wTgT^;|Ua
z>({R<rZba<vQ;=4UG^0n<GuUy=J*UfyZlS!jyoL%qE!mJ1dQUZiZ96G-Q|>&{P3Yj
zu$E)rB{&pQ&T=TF9V_3oM@aV2m4J1z&!3A<y+q=P(yjE<$2k(Rn~GPQ4idGqDBWZI
zQU|#>mQ~7URXo@6<JVPlEJp2yW1@m;5(^h&aal9=VmA3|f#ObTxz-~MWL*Xlayc7w
z2EV#)(5j2RK6Y0mRxLkg$5Dlw?sDhPyJQ%5BrGn>`7giimILR%E8e(1G1R0NO#ZRI
zJ}wIHtEBfiz@w^{XFAg`F+aokz&?kmCOsU&#EQ~4%9#zD#pgr3yta><CnHM6gJrLZ
zjGnzVxuZBsA-_ubO>}Q|tC!b|?$ltDAGeO%OxC$4J7%X^j2EVgEC(85@QWJmao~ar
zH{HHu!;0|t&;7kiU*A*|RCm!Vqp~_K?Tot~=CXC`*7@e8$wqZPlYdX9R^Wo`Q}wPE
zI(?$=eyR9-zmwhiWeq9kxL&fJsHv@eHq?^2dtq+6YBaSe@0Yz;n8VzZZj;qe^Xhq<
zoW=n@qx?zv)cF_t&X{rB$hEqYTG8-E`OLuJxqCDE@!msimW`^qs&O|GH`(T*R4}sN
zNT`^O`bLhr$lau-k|$gRFZG%R#XWtrTkS^jT^a`}Q|Fy&R#v3s+fIH;4ZvC{rQOo-
zd2=oF-ss#&VQ)v7NPmhJ^M?9jt@<oeEs_gte3*r;;?-wxkqr7BobH)=RY5*WmxM({
z+YZY_K5Z%?LEGvCs4)6=<>v3d`tBOdnO}E*LO++MDrKS|IPxHtrBVQ=?0kfzcSQ;g
zE)OE=zMDX`<4!i=l8R%4yEE_YeR5j$UQz4}x7QR8H}|~H{I7EL#jQWh1x>%5Eu3QD
zQ1UVq6fUj!)OC<TIVo{|#jeA}DThK>89h8a95bI3#_KJ;4#v-V`NCF%KjX$LUh&M7
zP^r{!nQB_767Jkhg$g}WwukS{Xx`GXTfMMix0`wI=T~<~t*bI?@?7Q3eq+-wwOuY6
zgH5T2qQfhJFy=p@7RS7GNH!$PvEF-r>q@cUs#^{5uM`)vdxOfamwYbJc`ooZ=gcJb
z+yBmo;^J=M&kTn)j5oWw8K}fJ_YO&(R2CfhdZcx{GDWAnf*WulJE6;8702p&#%;y&
zZPlUctSab=3QXBQtxBUurWi_8NPeHVP~^I~w<Qq=$*;R5<Bq=}<Ntl@C0m)^vVQ0q
z{nf@Pk9crcTA23Z(R1H_;J|piUUQm$&%&W#duhZ%W#-V!mpes&zFpbV1qjxCxHjGJ
z#`t3HNP%)R-|hR?>xSs%?d|h`Uk*qJnSFnRSR8Nb>5B@+I!H>tQA(fRd+5-iUZr8r
zt76ATrhj}X8adP(nqtVz_7n&8GgC+238qcYn8n-e=ZAAgdJfCTI_ws*xOL6awI5M;
zY(#uvqSnpbeUpM1H@EcCe6RTUo6P8v;!h6kn^<+)*H7DfI*8kkl^q&abzxy+ljO?#
zdAi_ubIjjb2kDm__yy!EeM5f@rdCzVS+`mG#>&O{jPNHG%*NU{4K=4D(cBKzT<7gw
zdKoaC`V??nMpN@Oa>a|0)$S;ty6q!Pse09udg`A1nNK)Gr(4Z?7D}0&RB(zB*Ij8y
z0LlftJ1p(~36F|0#QC+IA1$qqkvSyyMoI4a_3LK2ZH}3uc2h4~R;3;^YG{uB=S1KY
zxdfFaE}zzT>`~s*zxOxyC>#H6HFW0TmRptMgIIg5c5%n~ENK~;1vReNg2^9W4*bYD
zGYUBLYT7uf(<LNhy>5QZ>s<`&(>B7}U2URRDg>Ii8XBUm2HOvK56)e>IQ8t6?EG|7
zs{)_8%l^4mi@Bl9wdt2V4_=<%%Z&5<c8KA@pIbh<zm<k^co)G}z}E9Y<rNjO($ds}
zSA*hFTqUV$Hr@0KqovHYh>!930o*$8i1kpj+?Fj2k<|DPmQ$;&rc&o{W^J5M*#!IZ
zktNf$%eJ=Jk5@`wxgw9%M45`X9(G=W`pK6ok!IzZTmQcOr@uP%ev{EDdP}?P<8)_6
z3YQA@I!s&#=zoxQ=U8{SH#>u@YSWxg3*##nja%bAl|5Z+Htt#_XRJ2Z?7ATgi0hPh
z?%hjQ15SjZGQYQ1k##`QJb!J}<^3_bq1NnL`+$7)e`FKYL_4_8@o~xzja#kn&6A<_
z_zy1KUS}nqH$PT>pN(NdZs{eq#kt!?z;yR@cDewPGK`x#>Z$9Q3IzhmhGvT+t6oKW
zu_=4uUeavVdJ5$l)pQN7$zu~CbH~;L`}dQBpFdF}C3*4UvEX1{ed-P_wE#m=#T$lI
zR1c3(REdc2@WUJ&m-Y4aKYjdo%(%&S{$tQhK<D!G#o@Nxfe(+j`H4B?*iQC$Gfel@
zMolixjl@a$a>mNWP<tv~Z5uoBAUFRB2|U{$p{}Q|++J1QX`&!qRb1xIt@CMn7H95s
z=)DaYS2wwyG0JSQ4_LVL;q{>M)XsRb(K)7V+qTtN%@j*}b!pa%8Aq8^3bR@kthW+>
z>gJXbzA!krmnTMhFqI)yvw*I+c<!vdy?u3ye2jE)UNDs^9Wq7x{!gB~uws{UIIZ|&
zX8zkRXYN*qoXS3E=l1Q1nXIfAsVf``NhkL{zp!@mUReO6;rwYe-~x#noSgbXC#Qv8
z@uefCLpn|QcA099Gdkm@=qJ)lI!^<ZsrOa|`!)`ESM_?DcHT?oraOZalODftO=Z3y
zSlIe=N{-u;@!qPna-WaU6>7^)eU~&HQcmJ@7ylYu-s>B{-q*D?{@Q0rj(!}1DgpT+
z*HAVIE&00mmt1E|N5!^C?w`T>_AJadY|W(piFkJ$^4i_Tc^YaagLN@7wm2K+UZy9Q
z91Yoq5ji8})=8Oz3~M%g=(8XQtG_8VD)Q2+*FYNiqb0k02A+$}Mt=?vl=&Fs8kPTC
zeDQ0ZIPl~fm27E&_Pb#Q4e>EqStkSoKQpP{+`LCfTcRZtJ+LFg)n=s4(0;a04u=&0
zR5r!777tA5s{5vAO>rn}axEH(ntV5GeBt@t%FK4E2&c43S@*plZ}sitS0wz6Q@C#Y
zHJFnR{FcG%PUY1-SLrsxy&YSQbIZ$o=-|OM>48<f<H|W*pV$+RUYlF3@dqv+pxOAA
z^rnsKPHpxgXw_7zL<XAE<7}KJ`|HnbRrdM#@uKMHhi$W{lA2<f0GFEeYDs#hbeVJY
zf*trfQqNuu78|lYc;os2Itm-7PhLkJ#<B;@T~jN-5eoUF<Fv#s8~sAd#`)XV%xh`J
z9qoApw5(099P2J0=a8u>Deg3Rx%9&Gp0M5Vx!I<N8l@sPYH$qaI$xiAa<6&0o#Az%
z^6N6Bng`yB>nXox@uT`(p165WMMy^WulEr%Z^Z9R{s;1u0pkATvG{R~r;nU2!26!V
z=4h<^rh|ddRwVW*?5y<T;~4?aI662~TwMHW`u>8x$rR77`!kAT57uwH7u(3`G}qEm
zF^8y{7LGeATdcvH^OedfWr3{ZtV35+omwSkDCYR9)79MTh`R%AF+n+VT_w-T1r{qR
zE{QJ9kIytHZ!Kk=>lK?T{xHUkcCMwFYjF9tgom?=@|Kzn6T9uKvP-+x8;H}Lli1A@
zbF=jIy(9x1vbX=Nteo>abafM3(^9K>)vIacOryYo)yzXoJnYZ?K^3qvlBWN4SlB{C
z9uuE|tUK2t!OBkZw)4MCCx==Degp?0g<rcWIw|YkTU%Spr(5wdq~Ka)WTXKAXHVC?
z`S6DRsf3(<w(-T1XS?sa?Xxq}2T4Oy?l@j4;B)B4>H0hy^TL;jxx+$eg6N|;g{Sth
zPYg72-DEP(pY-9s{q?3;!AQZJnb(m72U>Y>4waVDRRHJclD>|dc5Y&H2}KzYiT-{|
zg2@-D_|<f<{g^}{?Gd%x2oFaF^UBNDtIV6c(`I@SofhIg`EaoJot8HF_+lT;bkT*q
zHfM4CQ<nE!`kni^)jGxYjBcnsvC<Kejzepersry}ild)Pttf5s{L<7Us4Kr$0ECMq
z@5+-gguh^<(n*}hhTL!G6clWF^z<ojX5cZ6)9&1J(<1@_SE3Xn?PtbH&_CZ{Ie2gv
z`hDH(i3y9Yf8X6NY0Wl&hNQcj`V{m)cQh9VM`ip#MvD=T&rwB9CPA|gKktgTXzX+W
zoW&8>I;vjKdaF9*A4Ps4q2`O1FIQgg04-3--C;leY-k9aOT|$c;4jj#pU2l*(;xxk
zpzLQXS|1*5DFI~P2wG*Qi-xwg_D;abV=i=z-)?XYWLS^1s|*a`JSpnpl{8u(fs**l
z{lyo59jG!xZkT%VuFql@SHZ8>9xq@1XzW0{s|A9pWTLP38ECbaU4DV}bJtF3Gsnd%
z17AG_?Xv60kt4Y9^XTqI33G6N&Bify0qu`3L<Z91j91$j(|)_ZS?uS4VX6C`XGpQH
zMoiJ|B!ji#>3L!-7R=RX`=mB&Rce)bUZM4HYtt_r;%5kVb0)M)My;l$d$Qe&isOT<
zPha2K>t7|l)XL?;f2VopKx3l7wD;4ebXq9V=drP;n>XLrX?)4(;?O1kLd!$ryHiEp
z30?Ny@|!IJbVUkDH~&%3M_+xMU@I^#yK&KIGA>@a#7k(bLNl~ipUs_;%z<W`e`EA>
z1fy^8bLOWj+9_ZZkbUR&?RXC#AD^&_9Ls_Et@ZEP$D~fWnkgsUJe4r&N_Eb7osV2i
z2!snZU>eSzKfeor{(P!#<$OjN=n_BvJG(4e(848s377CpI`{xgEs8eNSXTGsx#mv}
z(SN5ruczh8yP2hu5YlXnVpp*Kl<=&)?D|{5ReIQiLqA@sql^`n{<GDoSV!evTklBD
zKthOCz%h+`9bHcpMMoaIX8A0DMXmboXp{*UwI-i=O;N&e9=f@bH-DGzFIp!`b7#_?
z@iuqkF}kkDhl1AijmzZQT6UpU*)-_&zSKz`TZS52lUJ>ER#{jWaq=zirdo9NRxvyN
z(s1X-BFO`!fhJ(E$Uoz{sUBtMW!Fq}^kMKwmjycP&96;*ajC1RJ1wMlT>XAV!_p2d
zgWqwj^X|Q%2*JeS8@<zlnt8U0Qo4aR*2K?v4s1rF@JL?R$B!RFzNO1N-z%D?SGAY$
z!qt|F*_-L*mlhY&@3iob>n+*z)yMfF0KQxQlhe9As^H<LPLo^6KLq`bbIgGx>~mV|
zb*d=E($jD?$k&u~BmO>G2R(ZBtRDNui_moLcC!sCPry>4kP*V+<#DYsj=7oM-~p^j
zQrZIbgzjp=9rO<f_0fK}Yvbi$G0|wt?6q_|LsWCEVl<20Y<!Nm9Z&tUSmz!HQz*qU
z=tY8@7Ht-~n3rC#JYy1+o^H2GeEs_M{3k+Bqpo}bH_Hd$kE0_gKL7JR+Pm$Hj4`P7
zeSU@s*QbZu>htZUoz><2dG%j|GwlC0+NG3b5(<7dJl(LdA32=xeMK%C>oe{s6LK+7
z*joPV+0MvgaV-N_7k4JX*D*3tZ2R_|5?`7(FEGrPkG^)wU%<GbCPHcwB$@$=l#u<e
zBsSUTkg1`TIJA&CUGBoN=%r6j)*IkM8o-={?-9285tmp{15zR#eFp2HLvh#(!mcW1
z+>QZFCDxIgYR1eK@3L`s|Bo+W*kHmNq}k7y1)eZH4fZg?D64bLa_c5k^6u~z2U_p)
z?iw(NF&CH}WC`j<rxOB*%g)a)k57}umRs}&S}&(!h6CjV?fPnxHCZ@927sbUg^u~8
zFX%4!Wd{+1)=<uuQ?)MF+IZv6V<$~bO@a46XBNB;>P=yPJpQb<uTF|9KYT5-{WU^;
zW;36^a3K!+*8e5Ub<O6z@oaLjU(Gbi|JzL_SaNc>O__Gb(ds;#@iZ`0dr<3=lamdg
zJ>*#Qy#^;0C;vw2RQ-+Z{~=eyO_5?B2(N>J5DE%VDOvL&i$3z|Db}kVwNDv&EL!el
zO5~VijN!DuXWByO_>iod>mkToyD1iY6}c>L=2xV@(FUz$Eynqq3lU`w|5bEsqJpRD
zhp)P$hlNIyd*R|_qJ}#WqA1q_lCpMEC?7Q@etaP?JprXu)v{jB?fG+ZPOExrfqc^)
z7i>k*x}u%)%&tVMxSg4qxg>g7<0&R$0Odsy8uJMNwpFSHSeISLH6J|Nvi$Fo`+fe!
z=R6$41>EZX<n8w8-=${q$rLAh8Bl$K_7h(&*y2~ZMV~b3+<`59L6#m)Y>Y#&#32jx
zH@xwmogPWVDnAfbAum07y!#kk(K_otf&|66#+CI1`3Lg#|D)ve`vd<+H7o1vrGOLH
zjWU&?23!msAgcyWAKy5g8{`^MOe=FTK(hY$DycL_AjpFUtH{xli<9T;Li)`;dZ+Z$
zbYfN?q6@DwDqNT{an;N=E?khun<B)0HFkDxT1!6MIRr8pCk?vg1`x5<HX?iTe|>wB
z?bDQP9`(u#5JB(lEM3uy(k_qEt|J!vkJ_nr{xv+6y8GBoXWQxFHxRY@K%MKVK!EVc
z(CVJM(fXB7x9Q=SVzRXBrag5)S7b|+P>(#)X~_qk)L8yN!aAT)1`2pB>CM)y6v=yt
zRq7sy1by(jotsvkTGy-B4fVj2MRfjW(b$arxn_Rya?0v0%Tn6_C?(s4$wpQQBnOL^
zVP!Em2^yF0y5=q%<<a}hJK}^^h8z@dI7K_RxP^gHje`ayDJfa=t@sJ}!zvTkWli$r
z$pD&-US>-(RZb9Q>T)a%>!HZBcK*Ftao}pEafce#>(I<o6~VwO&-Mt*Q+e%w{nQ+-
zf|P-;75ng7O`^W|Rb3UE(S8|r>TlA;v4HX5tMCZP%I)@1W|6R)8vFwMS;X{P==uk$
zr(`lxjv5F&YPeBbPPbAIau)azgPQQiGb$h_DxSpaw4HyxcTdtAu=vmRiYj4;Z(O?c
z)K9=T0q0N=Tec~yjl=?f%I6gYFH5_=U`+@^3dZFE*cONv0VFrB-Fx>W0R9a#Gqbl=
z8)^$wkE2`R(rxK*m=0voheRe=(mpWh*AgITex8<rv##s8cmk->7_1ga?VuJd@n7|E
z^n3#8Nk#HM-fG?9;ApBX0o5=~zt*ChBQqL3`=3I<cl+zHH|mA$)DtN?T|(>QZWyAQ
zKo)g_^cGd%FxL<Izk)BLb-V8ThH=H@F^4#n>=YDTHVMM3+;3i1{G5}HLnhiRI`Lv(
zSv2B0NC$;P^WvxJsfpUF{w?$Py=Q#wCZ=PAp6aziRxIHvoTuu|fnnz>H)={@Ege7r
zaubfr*dANRRlT#ju^{mH?UoFV=;SJ3A3z<@bNAUgA}{am0=H?CVK+MG>yJuNe3Zv_
z;#N`bc=ur*r$ze%{QUekk2=gw9G8}sKE2Dg73|hByPO&b<hBcB#(Cs^I*F1>fsO#f
zJr5ZR@7c@8-x3f2F6Uuk1QYSoHh=YQ+2x3Fqv)Mn8Sxc6ctj#YEqV~EEe||9ZZFEp
zdXCgiU0vOemb+a-5fyzOJq|ovpM1^j3|*)adL!s$Dj__fKrMUif1ZN6AxY)cuVEo!
ziKaQR)$52%UDQ=(kSwd>jTh%<a)xsT5YwmKS#w*AaxWw1KaWoW@WvQ-6o7Pjz4BM(
zf4l(fvcl4xC5h3^cN!A{S&l$-#6qSEaxY)=2G9i8Bm)s20JT&W2qj0W%&;lm8atZ{
z9CBw`QMbVubqoROR9em**D8rdZJtGxS<X)A6HP*7@16EMMYP$TEGVs)AV_V_-h9EG
z=?gY(zk~rOVYFHyDE(!<<M=+p$0vu+uLmWuaw_@Q;L}hE&x1X`bXm8ftqIl|gxHo1
z0OY_6<g(|4$;p_Q7+oePxwSx3b;(*6Bp9})fHvBABnW7HvahyOr9lI9<StOD?q-PE
z9JF*S5~$scXx`!h8q2yyUuun^*NVZ*s07&<%ue*}E2g$VbLEAuC&9q^UI4Utppc~`
z3MI~MbDm8qHsuXbNKk4wD%{X0Njw%zAJ>ry6!~geaqY4z;aWU>-%JC=tpc_6_F|e-
zQ;N=vPe-zrtD+;?51Mja_4n_Ftaf89%F<iEgk7jK1C}aJzZAlH%xa|lPQerr5L#UC
zeKcP0{`%3oV{}Bzu;JXZWdr)DGyu?>s@WXxOs57F!yv8nq#Uu~xS+Yy#k5LvdMEYi
z270!g*s~2grGVr%N}NY4w4pT$4W{4d;;*jf**>HRm{WaeN~b%2dL~|>$;b-LlP;ZB
z$<t((-_rZwTgK{>M<AUYA7AYDRX0)=Cb>IS{*B*&p|7gdtLBV5>=IZE^N*H|@rtz~
zbJJMUqjz6CyvC%t?je8=x<poqCk$Mh4E#aC$C-AOkfp=Hj6}Hi*zji}bAevpFG2FT
zT!H<JbMpK$UIllVdXmBI60!_-fkh&--8PN@bVEVk@(7p!Tve8eH*h=3uS;(ODK8kv
zu&MLtyJgI9_%pbX2-$|Q-8B!jy;XCpNA~`l#95WhdGVc24n$q#l|VL$T_;RJ@O}|a
zOLHCMcwGrNvBfrNHLg4BE3?x)VN_r#aEz6Sa)*KsV@PES`es(ui|xAF+N<IXJKsMv
zKtl|y5sl<dy2+exZtP=6oqqk)Fc<9wMW6s6h^%<r80px}&5c{D55|V<Avhn-3=f2!
zajlpG9yflQLWFw@3yT6vB|*o~q?|Sg8<NtGm4cbb@iXaryv`aee?~luf?CSfduoZ9
zCnq;Eowx=rq#AI1vg}asEd))Ep*Ncxv9R!hH4>?MpK;ps(Y|uPPVFE$Cc5XP2Qpg%
zdj~d){R**hLNceRy5%#!<niz5-S4A!@3}|(UDk@GyC~18Uhc-~qXQ-<AQq=O>%}UI
zI=<U*BX>CwJlIW=mn^Il6>~Uq8R=anCvL|HOF?m;0F)<Q1-KeO|5jtuy?uNlNh-h+
z&mz{O9O$s9rWYkDZi-CS$@DC==i9M!aB$q5xwm*AGz4fY#y(`>i~A_8i|P=-pgdI;
zHMD$SISSvh!C)fYpHj}rGs2)f^W@vdZnM7FxCH5{AW=Sz_p1W&|8Wp=So(tOhOc9{
zVxGN&dB$+ng$ox3!k&xO;KMA3TLmGGA|3r!X(3^U4D5lAKt5h!U*G#(jnF`3fDeMY
zQ%7z9SaY|~^~fix<-eFyjgX`tUqtCz;R6Mmv@r>ZO?-|s$#HGyuFa2l&3~p_MpJ(W
zEXUoHr9F59#+nSp$8WxU?z8BJW+1>^^@=B|uEYe?72^I1bponTKSUnjmUO^juBh5R
z>mK812B|@WeM5pOdsJ-<$<@F_LMTEBI=HG}^l~(|Tm1$Zsw;ASX{aGX1nP~oCXAMk
zq-x&CZQ|m_(HalQT((AqL=%`11Mbm(U<TEMuVV~lvS(qi?2SotmT8!Lf(oMvqs!p>
z3&NZw@=Tg9zGpU=h0qR>glb$cqx*4E-EJtIG&(ArzC<V%2S)1Iy1D7xg$ssnooT4y
zI3T2Z+m}i22AUV$usn65m_hv(8jfrqxQa-=4VqLyOxw#3+IFM_Z)^wA`QKe$GAT^d
zw7a9<?91h2Wy_Wc!@lojo*BjT@hDia$j8LJLV7c_<o=KU1$}j=Ek_RK1fKF1(}TB6
zADtRgcl(BwY(SQ%NYCF`RE-Opc(;~0TBFEipX$zIx#Y@8(g7I8lD&blj)N#8_=zZO
zc^wd_p6xt-TG)D67EYF#yvBigXb+@k$)9RATrKIQXEW37TRDe~szIXC3<@2Zv`yD4
z-9#elCU@(nQQGcQ&BnP5t05%`X>ILrgT{nC6IRP7W;@?jWKVveG6`wOKUadq_9Weg
zM~C2@zSHk>!1I9-2k^u9E>YXGq0ydh$oA4GZnB6v^QL=0e%vA+j)8_Zp380ofi?>n
zME8)@K&b0pDhp|DQIw=X1Tk%L1>*<U7mw7!#=|3X;QZ6WCc?yo=D0YcS6otJ09lF@
zTX$yh;Tw^NyxAXN&nrp^)kz#2_$Wc9P53@{_H6X&hZ_if4zlTCgxMkb?Qz_#qCl(3
z{cu{n?Er*rY(%HN0*sTcTe%A~mIRdDvh+>trCeTBZmJ_K4C?m#Bz8ezQ{lW(-LjmQ
zuho(Bp#WfpY|2`eSFQ3lpzf#9faU(eqj4Zzq+s4+@auIrlqQtN{uUqw2F!|=aW-|=
z86T&G0d?JPJObcv$ZqCfy`!!A8xs@J<HcNh#f`%&jrX&d_;J0E8xS?qhf_5cC2)J}
zo14&8D_`+kSXSsSk&8yiId>U%|02E0tb?%4SUA6N`%wv9v)yFloWHpN^SGTuw-4I{
zmGdb5i(p*H54eIFkz(s0<Ky3&_h_Ev^pzqx6~!YIEahnk`Ztgtzgq~&MP1pmaTo7J
zpqhHL>T+;yNbf%5gHnZslE6{UCQba;uL{|1Mr8SxW*ZC1rNjC1k(z-RdIQ)RhiwqD
z8hq&o`UQPJA$Oyybs*tXV65pkcAE2FoQM)H>oz+A(jwiUUK;s|*Kz*t0urw6Wc>|V
z`m3mRJypSp06PN^803LJ==bb-1LD^Z1T|SkWXlx|8vj`ce?CJWmOeD%b+AZn`xC_v
z?ia1I(BYu4wu1tyc(P@<Jzv>rwl)|Pg7ZBzXT(ziP!XAJ-Wv;S*|`bYAlA_N#L8WV
zQEJ$uqJnHCsY}IO6fY6@DjGloBDtqKOHs-Hqg+!lfsDBRgk`@MD1`lJ+_C@BptSHt
zCuvkj7D9Q(sf|Fp<0`yd6)9uIjMZ;hL$E%!18*`}Bmh~~oAXjOd;{f6ST&1EzQ!Mf
z%CP?3*sp@6ayba`ff@h8IraPNf9$>g{Pq7kC+Yvnw|+#5e|-1oUG1Icvx<P%<8jFM
z@82*LW%S#=R1BWvS_<MD0qG)4M~R@UzZK#oX!5T!Sk0n6sGl123=C1b_zbq+YJmf;
z5B8HC7gkYDmTld#B~-|=|H1N$q)h(YI^}<vq4s~tGMpnwoG>`p)5s4WKA5=5Xb0K$
zA3<HbeCTRWT6)3Bq3zncDeD>{gbJk^K8MLyby-!768#k?ju#x~>3SI@_jAQD%*97`
zu+vAXuV25OydTQG#u;sja~?4YZ)b$BOTRX<Y+&-!z@z~LkIqdoAMc9n1!8C!psl-d
zDuc%Prpl-BT<swdgGmviX{JJ?J^ZUG!t-HlR2tJ0HyLp^nWOS-mkQzU0=a?Ti?7ox
za|D#b+#q56@wQ>$CqMOB5I%J8^Fyr~G?WdGXBKMsP~wYAO9#+1ZMHrPR_>)6*>`8D
z3k{5>XI$vM055yJco7opI2%)~ciDT@(p=$^^6i0CA%2Q)kd%$1uiJFM?OrA)c*=lj
z6s%iKT~QD2429g392m_{o1&#i^1xl|yx$`zC;<ktk5F{cFpwk;nv@qQ$$7mS@v)#i
z@)NdBAP%%DvAIiVZR8{+-3U%DTo^JYV3?&3yb#gpeYn&U$VmmH-^H!{2sw(Xx8S1j
z1ITxovP5?Rathcu4k{0EIRso;Vy3MO7FS<doGv8N8&Q!33rvCm-_V*!qx=Gy$D-g%
z<Rqze;+4o@>V(|RE}_1B8NHfz$1U&?Lbemp;PS%Z+KNNE(Pd6uC8M6SqTbtKzP6@j
z0`|2&+(|8RRb9xbl_Up4B;b^t1BRAzaP%Du_AKHnTpL^EHsgSIqlObYGrM4~Wa0QX
z6n6dEgD_Mo?G`Yq1CEeQP!U`JH`Kt9+4>qG*z@%u3=H7J<w;_#^grZV?rqoxUyf&x
z<E*~rNV_W3NUv~ODP9ZwOii=st5L}W-5qrB(#sI1#qnU&;QTuaFE&hzDK2EX?pa{J
zcywm695IK69o$aPCT6jCSVN`Ta;<NmT;CtwOnIwB{P5?#p|SD;q54dqJsMIqXexC8
zI`m)uSAs-d^)iITPoDL~mY`26H?Ax|gRWm6E9ZB@^ffV?*bQf209Da5KyHkzEF65X
z0GNXPpDQZrz%-=ml<z3)ZA?^81k)6E>i!y%SGVukbElvIv^LT7$t%GU)xpOw&WG~?
zm&p;hMh{$YpOCt;N-43rcf2pqy8R0phxs6JaQeg*jG90Qb4M*%*@0)e&z8W+PNERa
zdbgzLJjuA-^crBL|FiH&8p{p}YgL3;p_ENkSZ7ej%6h%xc`+Od*e5_DRgKKtn-Jz&
zg8pM(=ZV$=!K)94dCt<}93MOfg^gihVf6(Lc4Y71?~;KAXa2?xY~q-0et&J$^N#VE
zXHE@5?_D&$?{?Cd^`MtJ)p00pX(}nqY4u~)YlSs6HN0T13TZ=!o71Iot%jsw-$=;r
z@e7&79Ka5c)F(Y&LK<Rur9OrXYy^btFxwYJD0ZXlZXf5Cr2+M&nmyL7p;)>jVq$7&
zkO-m02hxTJ<*;6ot;g|?1SC03Y~6aF>%hYk)|<#hf}gGIu;OXh24ZMOrj|o=vPuvl
zg!s<UH5d$jP2L6HVD_`3&T1IKqKSj({e$%cAtz*g+aOT`uoppK12!LK4ZUiuaRSIe
z^0_^FR1MQv45%6+aDc-g^vnB)wB{ew>Pnt7ClY3kG*2^Q-6%F`X5Eso`dooLFJI`m
zK=}HGoiq>Dj@z|Mj?V{Csju-V1G58(Ri-Y<-U|r;07Pg4HFOCD-?O$)7D)quiWsNB
zX-X_R`qgy$ItmNXQ@s&1@A>ZMeC5iOW&0>F5ZS^9Ti9RTn>z5MzEmS_y7K)*)hC5^
z`|-=H2M-eO2{E8sbbH^Bq++@u5=@C9>cXk%^^}`?I8U#mB(<TdPB-tpg6_m$f+9hb
z6_!5u7s{uPzTRz=b&7yAAf5xRMWOh-jm%;ySE6KKF_8l+#Rs~NtU0tdbn3f!Z@mD&
z5sUVtALqhxGEl@u8bxEd;fY1in`Nuun`lo^;6)LiK*QDmpJOfjoSaucX{E5}fB)!#
z9hbaxsSXy=D6~f;22LeDd=(H74>IeODM=5Y2MGGdd%@*OG+%Enb+PE#K{HrT^`V=`
z!a<5Gj<y99Mgn>`iJYIso8Csa&WIXEI5>9XSjMsRVH2k9+WQ?la9;b-JF>~gANDP0
z#eI^d3<P#!{*)g;yQX?P3LN?jrEIf^j;3)%(qu~HfS`8!9!yz1bYTbk^CGK3|J6gm
z-pK*DWwc&IB10;TgQnsiynu9Ph-f=Cy_Ry5gG}1AIKW0h4fi+7DH}N&1B4L`GG7Q#
z8sWSx$sI^cwA35`8BL-m%f`JTCmO>~f{tR5aGD==?HETs@nW%rfldycU$W{eZk>u?
z^`)Ohn+4U3zr21(6tF#_wz7$NV{Yf&nT|-nMIC@Kh80t+x$yd2q$Z$03u(AO=bPtv
zFED+oyI6vW;=872I?sD*#sPdEm6OFbmHm^WfI;2Gnx!}A0t3str4h|666mJNM`sBM
z+qbl+xb@0EvmV8tx=gN*`2RDjr*l*>cmc;D7A=LKTB1zU6_0?SO_Yz1iA0nExH}6A
zB}?}_wb$eXaT5*<Wp*^6Bu)Z!_bOgR{F^(74Lp7Mq_L|)8kPufhg5G>`O_?!>1R?^
z%q#hbHVKB^dtiVN+^AwvaLP(j*U;^J(e>o6qvPC=fW(sT#Ulx0yO!dE=X$KH7@cdL
z&4?z|h!pC*k!U5J70WbMyV-o}5jC(t7s+S}(kO_Wd9@B?=Y*EY!6up7sH=^*#XjrM
z52(^VGCLe)5bJUnt0@0garM;PK1DmG48xA27nkCnvJRwnfOQfn2c;;FPWY&WGR3(X
zu$=N1j@Y4^nekq^WX*G^C+wvIu$_Cbu(Gfqi5;l29h)sPZ>Z88pPjj_)<TFFDr#o7
zNK1u~9r8X;cEPY&_WV{R(zga#c6Y-xK;1rZ&hb5K-RSCTv&Re9%KP<BBe{L*9ghJ{
z5L8Re=>SudOssPbsQ=BWde4!ZD`>}cR3C9B($?T2q~NY69!(2OB`jO*FH+ZRV!9>q
z^xff0;w*;$O&k-d#Y}WgP%6tekH%Sw9t0NUHc`AK7#g~B?*@Jx1WY63Gd6@?LS=@P
znS}sk>ev;{d7Lb=RWrYkL+fA$*jA%NRn+^b<3O|0nR@Tfez2M>jrkUSzgzjqk$91q
z9VR?*UXe)!RdMwMU!hvy5CafquQ4;iPq@-(Of+0TCq#0q_D&ZuP)ZW3#RvGWz5eR%
zs8HGOka}z(mD?vi^<Ao1NA`MNE(&F^h>@JQ@Q8>$sNMX`dg9x9zNKoiyb|x`+<^Yk
zg9QTXRU4lssD&&sup$YnNnO5NkDPyGSy-u0P!(Rl9rgG&-_%h}8^E!q)fc9$JJd5h
z@7*xd);q2bLNit|H4;kvi!Mv{xxh&=(Xo#Q2yg>{Fjo8b$_CC?U*X)7;O-~{3r%Pm
zSo{<dmYhF2o;Ou{+yi*Nf5g>0zFmN}R-9d{Y<AEXN=y}Z_M5p)%R^@r=Q}B!vofi=
zmpRl6R4`=naWdoPyA={OC`%ZJDBJ&-V<4VT>OzhDo}Qk34zm_GJx@IYHFnyL7Bg5b
z&f5?LKlVmq5Sjn@_3Q5CAQ8nv4843b1?o6IH#Nk5%bF5$5n;uALEpc_lS=hMvBGKY
zna%ND_^i};V*mbW=3(9Z=l{qe;{)n6qLz4jd1<SO9M62BF3GU98uY(UxJuVmlqYC~
z_ua&w&@UuW>*sVemXm{_I9NyJva&KkwT?{VU@Pzy(GVwBpTQjBO@HKA2`Yq_1ZTtT
zB{1EI-^X#$_xK=><1>D!3q)-#*iFbX*!N9LCqlqcq3Hm;mnZD;e1>Y?-DEiK*7E7s
ze3)Oc`?p_Fl{)d=^J-7lVR^N0U0r;b>%+3xqTy#7e}X}pdfWmZVnBLkn-PVTVcc#}
za$tTDX-P82<alPQLemIfha|P#VOn(o!xu<lRL=PE1-NSmfFjnh^}wrlhB^UypL9M{
zGj%bRa-ht^61E_|EWt@gFnpeG$2rVidhkBhQK9%h0ZOLnkRJiD$fO_SY1|af6P6?t
zt_t4hMYOJ^yAZd^@WL=R!}Q7$1G#0!hZzDg0Rp7O$ZWGx_E2dxLdg`m`!j^Rcy2hC
zt8{>PKS14GE$k(X2kLt{STFAzYNN`V;Wc+5N;fa&^oY&cc5$ZnzFQY?WF6T~bV>|r
zclR}NgYVf;phdZPpl4dj&M{crsQ~L|iM#4mEQxhg#|brP&&wt-6grD|=>QqNX-6To
zA1D*5bpRx%hJz|$XJccl8bip}gVYKo4)(Hvqa<|RWVZKbp1*s2vPe>X*#9A^oFd~D
zJhKba9p30yK>N()lQ}k3r-eyBL37<}mNb<3<%6uO{bpk`<n`hYAHFLW093#*(MZ^p
z4=!!UAMKdgBuYF%b}&F<B$c#FL<R=IHTI+p{Q{PY8t$uRTb5^X#fq?9z*u1@BzTG3
zsp3uxAwIM37xN)7<e<rt1DOb*LX7xWk{Xz+IP#@MDcjeK&qW9q2BX@Oc_;{xD;E#E
zi*>~OYW_@*zwKahsVoK}pnb!pzs&v-`!C4!we;4|qB~ZvorCWx$sWCs*usPiO1uKm
z1yN@4DHGF$OR5C0qvSjbX->~!mQOM3IC`6u8;mi+z7@LH=KB^ht_F-iHXAr93OMZ~
z0((R3UW#)%6D)C^n^rhm0#Jlu&d<b^hH_E?Ue<>JET7DNRPPHEcWVFk$e9YfaBi2l
zW8T3l0nu>BKLAO@|K!o5V_d`QC=cI}%!y{&9yN;y2UneDWwOqI3}Rhp_<M|aY!b)9
zRTzeVYb#)B|HFj*Z<s?qcQ$J!<>niZ_T|0fiqgI<Lq4@OolCOl+<fX7ufRcOCKM=n
z^(sh$hdb!$f%5jnSmX)7)<O01@YvFGaRZl-kP?pJ`Ix3gl$>P>NHUbLL+8#(Q0>Lk
z@%J#i<*6BGojI}{p1{vE#Je$MxMj=gB_07z&d*QF+)kuu9@#ho{|?w}?HIu+?S(+k
zpdikoWAB-+cb+|a7K`ZZrgc?+ZnsyVxf@qz3rEp0fDg<Zhw9`D$S($ZPCBflY<RMY
z*I5Jsei*|lH4nvKzLC7`DuqyiCqt^3xg5<apf6Rjlpz(QD5J}Dwt|=7K%}U+I9w$+
zH@Bqyisn8Gzmdx@l;UqbgE{!N&2P(=Ej@Ogetrf`1wP(+Z0pzyO)i`}cLX6twLqL~
zU2+5y#wI3KAj@(aO=0DSoBF?cPvUc7e3Pt9J~Y5A5TT=Peu63D+t+Oj42Q6W!Bsa5
zrqU5$TZ5fHFIu~ocEQ*Y@>PR4qn5w^KcxxxzuyDGdFbs-s**>VI{HR#x&y79+ug|K
z_sJ?LDPgP9eu%&Bwov=UtcgbBW!RCtXszqi@QI0uV;8<9>V3OX91*Zt-@opwf&@HQ
zo?+Vu4fVf#L0&eyE7G|s;u@XnnV93psTQH)#kbYjAL0Mm@Nj?sSV4z4`evT(H8~fd
z^&c<5F8YWcdowRyxsg?rd@iLl?3JAro2aO&TA~0hO7iU42dzfdeJ7^Qo-6ClNviU8
zDKy81(t}H^tE)3{{T5a0aX;4f+pUW&;TJBHk1s8KzAov;#Q#Cgi~BX_e4q@QyXf)b
zXnQc<;I5gGNPpJWV&Y+86E`=)r*nR((7g4Ikd&|L-r-x6zt^J6SkY`f{Pf^LieRe-
zMdOQcrKTWt>S5aDsYFfI1=>^=9S1qijSffdj9iv?lC)K~&#Cr}-qaSs-CeZDo#V3|
z_X(eG)cVCn4zt@zyKDHHYSUO9+&#nN>vZ*hNKHAt@pW1B$|y8f;Y*<2N4RP}`aj4A
z_9yoKH*?WjIyZ%6k){rUVGN2M@jI#|-XPKp8EYjZoMNgj<n2$%ZD^M#QAh`XI<KRh
zd;gbBr`N=<KeYRHW|j~xcoPW4$H=4m{q7Llqe9@RVK{${`PUy9Wc`#pl%SgX9S;#;
zW>%NoryLnxy7~7qgxuXn<KTgEb`>TiS-d%6Ysk#*rIJWsV)Q>sxmIt0M_*~RfLXan
zMH~OOYti*^Q{~YA?o9$Kfd<LQ4Kkwsw-1j6eL^0==LA8KNHi*$gt%gf0jHT!YT!!U
zEB+?|e0#Xcf*+c(2Md}g_VCH)&w)vZ#t$lm$_q<OWuS1Qy#u^XyMS$27R0B9MZ{vq
zhL@J+Dp+{RwDs7956{J(G~S=$b-_V4IePWpd$LB!=~-vvGB+eRq^%y1pO3jS*&w}&
zme*eS>~-10(I4N`Q{4WhQ8Zd7rNDCX?*Z*sJiXHwhF|Hvy!4=g&XX>&mzhIQFJZv`
zbJ=m8@gTzlDZ<baQ!<B2)-@j;V%hpsUM_R}x(Df{7Jj;l)BVT|2{{Q)w;tO@=8r9Z
zRG%UOAqc@Jp!X0ZLfPfziR8m0X3&!H2Jk)%hH8a}4<Gi!q)#lb7!&5rc4RJ3@d^$m
zRG}f5Hn0ld^?VZw7j1R)^aO<N%@P#`cGnOL21F+%T|<E<$QRbnI<!3@84P0zy|pF0
zm)q`kJwrmPff;!BpdrScl?eQW&S&_eI`mX;y4|$V8`WI-mQ3SDI6g@Gxe?BX8>*Jx
z=%PMewD`R_X}4BYPclKbrh0jKS*Q)U?-gaM<z1Okzm$!8rS`8Vg+f@yd-+aN$~mWB
zMa(OjRm{p*7o8d#8u}kHs8wUUXUET%_t$PFB&;nQx)@^PD(f^^HMj(pRNF5yHa6DF
z(=!Zh^snimw%i+4BI8fN`t3mXF_Skijg<Wb#3&YoIZ0GpTzzI{hB#O;#`h)UXC1H_
zIxDa)I&Y`?`wbj0PBROVBmLHw{W>~2#orZrTY3P<xG&4BTtl~xal0|(?MVGt=!P^c
z9OX6Ou&^U}&O?Y-qJt5>KrhJVJpS{oB=tXsgY|FcgeVwiso`kzmC^Oj2v$85!Eg}a
zZHeidxKu#<la~F5X+%WC;?3#JPQUISyzED0eB^i0e@`G}P(MFaRW-tlh<-vAuAl4t
z{+BVAULCwNujc^VJUiF1lmK~rt_8BCoV>gwHR$fQ^#qubi7(V_Svk1~*rEGihO5IU
z5m6(vRT9Qy)njQ(V0m;T>IAH&(|qJf0br-3+ineksbuHkx=J)r5Iu}0SKwX>Adhk&
zo`S;pZZ2ZU1PFZ<854Hlc|Tf{Yp}^(a<BK|hn7rb0mUEv&aiv#?I|i&Xit(cjwm9(
z7UN1}+mKrhFY7HAN}n?~qSd;0+K{=H7bB@({uS#mWfa_acEb_}4FgqU=N7w7v{Atq
z8)ztRA2u)rNzsIet@vR};+i1o*!cLmqRry*-@j`=W8^vC)YJs^IheQtGx#pcKyvaz
zy|%Trz5jf)GWn<k#A2svi``-{lSn%{78vF99R%UdaZ??P$^?{m1Mrcypbl5A_yKRj
z+>)rxn6@e=a~FgE^uB~(M(aV`D|~l<{)<JV5+ej`R0A--)Br9s{CrS{S$Gg^QiU^>
z#%YjBoUeh05^q3EM-mSq44a#GW(T4pLSiLM{BdD<nP&TC;tuaCaQ6+)A3_{r^#0to
zy-CJ6gw$~CNO_;dY$nF(NFWkN{;ok8D%$iYSmp)4FA@R+zJm?f%x|-W=L%IRU(iEa
zw0E4PE$$lS&Awx1HfD3VbV^l{{fv_d-G|%<Ya%zU`b!k{pi?VD{5}a>K16v?zGGm8
zpC`%un6PPt+m}7Y=<Fz591|b5UHuUyz7P|HVJfh%Dep20VmLW-2UA`IZ$&@Wxb*T`
zXKKq(gZkna12h*=?T<fTSPDI}R_?})xX8$o!Zv}27l}8>asF3`6~<;koKv@3?Cq6y
zl8|@|;{z)!WKcw+-jyO$q^M-JV<{sxmTdve0hOil70;nt%`X&~<Br@3&*|f1dB_x_
zqM&d%X4o&N(mKD*Xj*X3vYVMgx%GG_je`%)B>SO57x18#Yo|{AL)uA9MqVV*hMD&?
zn{i!a<YPC7Z^jrI>Be*>ebIwij9T~OM?J9&_~`VZ5JEkZ{#ZH_ITkH0WDGAOH5^B-
zci$Q9j_B8^XBy07oRC1N;mflWp`FU5H?A&R&sVn}eF&6K;**d8+Dpko*xZ+qG57AR
zfR7_0GLrw-ODIWF)Y++_-D$@2zeY*G;Q<HM`mmLgrXAF-^%Zqf1i^-#`HuVufrjYB
z)VUR$x;{F^IjjDC4NdF-e=}QvBVhT5AgMe2C3gSrFEu}6a!S#MKCMLNgu|%Fn!mTM
z3ps<yC0Q6(hzIp@ppYDKJ%f}%Y=LQx14(@3-_e4fjRI_$`^Yd3OAOfeeav0E<=Fd#
zgL&b_UAf}4`qH!dB&Q`uiFaXERS}D@8cIrc?X%R^j|tDY{#8sl<F*GeLknr=D(uDP
z;8{GZ#A9OevHEMHF+7g<r`oJq?vTo5W^PVIRiZYc_-s3PiFoC9FEkrth%eow^Zkpt
zCr_SO;yjp(`IbEJqh-ocxSQ``GrH;DR@oaj1adyVn=v`T)I0wsGF47aZnb^+QKn~?
zJJ+bt?Vr@x<ZRwGdKa_dTRjSH^S20Yu36z+^4H1WFdQg$VPRz=l;Nos_on7n)Lt_m
z$!Rn4_VtZ}V*RXuacuRw?Ck7bzUdI-P?4fzr#}bY@{8m*YCSB`|24VGrk<;wWB$;Q
zoBIk<;YX8_ZhP+{R$xBcK3y=a8nwk!i!b7tG0ppH2Rts<*5Nd=F4&ot&kkDU5_yrx
z90)8K<Qi}o92}}Svan*+!gIA>Xqx4BrTB~1-uIhGT|<LptUr5>SS_#Rzi(+Uq5C2<
z)cBvbc!UqH?bH7L{)6>ByFCmQ<v%7q?d|T4iHtl?{1xloVNYgHKhsP)DutV%uDGym
z?sLqMFlNpV$$m8@0v%RZ+}*8(kgybeBGS^*&NIlQG~FiR4*?jM7C3^{+55ay+;7kC
z%ehsyE~M(ZhDNB9*H17zj7f>{@t36kmHkU^e6<g}G{zGrKyk#d=mTW*$^{#rO_&%-
znj74uZrjHyQN!gp`Xt0bx@%I(GVE=HQ<aWG1}Qz5NPzV$8c*ID=@|CE_j+++e@FLe
z5-%tUtbFtToKjSLMdT`p8c<%2pB;9&bOw*@7ZqIy`0Z1oP%7zZ()xGgDW~1?Ak>4K
z+lHlen&l}L@H6K_ar|u{Zd9{-14(#Cb-TdYp8%DoKj#nl_q<~`=1K-(C2EL52Y>|D
zYHqq7P=m})<=dt^*{%&<TIg8vmKD$&fo=dpDoc!!#W>-!aX1Od1^Rfs(#cVk{zUZ_
zGW=uQA+~gD08CrobEo+*gtj+SeAm6UheO&RkaI}bLKq|T-E6Pygl#@48Dw@1wX$ST
z;j(_0e}Wd|5H?QEDRpqii%Nto!c%WpS3CWi<S=K&p_Zqxdi83WPIAtO<VGMD4h`f-
z+DXIT8%0r~>v5J>rQ{p62@z`OnV1o(7i?@w)l;zK5fulexCZP#oZz?l{Q>g()#IxL
zaxxZgwYlKoS(hMa)?-R<@;xor1N&CYa_#y#4k?dC0)rFbx}O7L&=agmhuzDMsICX=
zxg=l-sKFfE1Prb}{#m<sU4Il-0OkavYE5C(UXs1w$qL64zFxl@7tBqdwTX{RW8^a+
zx_lHVl6vBEif;?BP8SJ)1VX*PBcK)DYhql0Axhvb6Usnuu#+0f(#GLs3nzu5v~>=H
z6ndpOj}&&p)c~CNL;Yt$Y^-E@dOBS5GN9~vVL5GcT3R4i;FM<K@Vmgo0tF&H%PJ}!
zDNo^rS)9NpJPH64TrZ%bfpw}qZ-^&6+)y!nh+4)CS3Eqju{k+PV0VZ_?Fip9%E+Oc
zTTYdyOu!1lj+ubMU&UO!R)Y#CuRjn#1T5pA#E?-k&}tiODoXkjjiD8i*?gqK2&g{n
zcvu{6zY!2D9&wQRZpIy^NMO2p2>F0*c&m@Rmx-TGeD=%p2|QqiH|y)a4@v33133t?
z!6W0|prZq&6w18t<AN<9D|vcBcTZ10b~tCULAl#PXkiEnY#1zAKnk%bDbgUJK#KRb
zWF``l5Mw8Q-+C}RgVtbQFO9f5d?iBCiV4p*F+aWNbW)0E=3g~duXDQerj%z=v!mNz
zeP&tm+ufPcGDK7Lt~U&e5~+Va9Agck28v1<#y8H+u2k|HJ@lM?p`|F9b7j`y6A5{e
z=Uq83t10*2cG37%*hWj~JQjbERqoL%Ds^j{Svkit1h*SZR`9*Qlbw^(r*YufMRnrb
zzdNQZn$hXBzlOQG=IESudccKWlr>p@ajQF?sn>rM8y)@VqSGeVurfn7FCU-TsP7wY
zK+KlpNHkJO-M@c<hNF@_w?ICM!)Ez9!av(fmQ=ILNpF2cvmIa=zBadu>J(0+3YtA@
zaikAQU`33dvj1s<ot+LiyRU>!;W^Pp%e3P6;d=Y<EM?DH5a0VGPL1;|yLT}ub#%+g
z(tqGwk=$y|^XJvK<hN?7Qr>66Rifbg=U`G!?fkDtBtF`(;y=c=ld^yP*CP^N-=qBJ
z5s81%uUaNU@nYxbzyIqIiDy^S{D<}Yn{C~H8Rf2@qWpO_ALW0;Tfa{_TJC1Kg|Z*|
zxdP~?{SJ62O7!`f<WFV@;pm8^Y&;N+JXHn9QcG3xD<FhUYQ2NYT~zg*H?|ePoQ*Ps
zI9Bl88lu(2D0q21uUT7HSCTM{t*wSQ(1^}tPS&H?H_XOU!<(C%b*&Tm&cRv7#?CG$
zBeQKl`aN+T;n^ik!d9LwU5~$xlysW>a_a4k!zj9DBOi|F`YTbJe7vJyzdnt3+|nl?
z50MY1SCu@@ixLuWqrVQ%>97e`6EBuDcL=PFOi|J2tM9ERJ|e4JRqD4MH6FS%-QC@|
zklP4Pkbg#Mz0mnEl#=$VQP4$$m_;)9hq_Hq$Pvilc!%E~ek^*A0*Xp2i3jH%4%Xw>
zMR`(WZ!2*uK2tO>h~2tvn`QIuCSy&OBkZEJOC^KaLo=(xE3coobvartt#3Z3c}3Fi
z^R!WNroPPd=2|byS&I)+In}on6}b%zw9_ZvHW>MevP)658|pr~-502w;=iNq<y#&b
z>t=bR)AWS)^y=chS8p3Wrk?e`Og`A)tMdHXH8|sv&>W9E&n$MmhXn=8M-8u4D-)^O
z%;fKnHAD|bFOb<Nc=;}8x{mIrplj3e^+J}h#T|wj6Be-Lv!4#Vd9+D6(???`cb=MJ
zad9%|q1zc1mzqTODss#h(G=-=wlQqa{JB|(=0lY3)D`AGXW%cc|EZYWzpr66<yN~}
zTx+vUkJqP9pOoe|e3$T!z~sU;8(Q^lC91R`+j1x=C@JmgGI;MRt-7x~1(}_3K$=Bj
zy-!5wh0T01ckbBcun@=Kqx*K1o_=hJr3b=d*%~4ql<LadF^FX;o4BjZ>uJZql-}2u
z5*w>sSN9>uD@kGf`dG_suVW?(@lucd*lNQn4;9Gi=tPL@y&7M>{z*~iHEA1Z#Eyxa
zH~YTO;h}3bHra^Rr|cW9O6yRuvFN&YAS``wO}TBH5~k*faM>Zs52iCu-3cAk9nTUs
ztgV3(ceI?kW$m=zA=Yz|`Wf?xq|oLx-X^Xg_K3*RoL!W4rzsmlu6G28#gy{T<FhH8
z(fn#Mw2Q|JCGHFq((~+>xpHZyoh@_1JbAlQ;qH!^v<dU$a@L%$xC-}0=#=CHL?4bx
zXb7_z&{AFFyrX|;dNKHE3RS^(pG54^7h5T(MGuya(l4VXoH|r#Juz0TAKuBFt10)6
zt^QMJDgWnWn*Xi&@!aWn+DAU$zG-4=QjK(UQzIQ*wY)^IpB(8>CzTv?M<}@Wn@OLK
z+EEKx8qi_K{ZG56@W?C!Jd0qqrDHJ?>{piP!QW@X`PUX}nlv3nr4;SQz&m@>5<$a(
z##2hweMX)_1?uudG#Ru(py26RT(V^yV8{r$#pye6Z}qzZt%IzrTg3hpqg&K0+K^tr
zD#&|Bkhlnf#NhhiX3x0TSmIho{vxw;r|w8eO0q~`SkgmdCqvqBNdV>(sGw?-XdNri
zU8p2!{i%76xJ>C!62}DC5h}*SNVgkugcq>19*=azbH2jfO`)0bWWgGU_ooLv$CD%B
z^4PqWsj+l<aF;^)OZ^+0^CDaitP<c)#1m)yVX{^3{{H>rz$Daev_ScI%<JNUIJ$a1
zJReHJvetqL4{juO89+oiboB6=V+joKxJS5`WbcPXq4WNKz<(-q;K33HBnaneU}%U3
zUJ8NgyU<+dR7gDiXmurkPigL8+~W-yaDl=`hTF~r{a@U@2{hMh+cx|ol_8}Pg)&4T
zLrS4RLS>AQA|)hKg+yi|Lz#+5Qic$f217KMG8K^!Ntr8S88gOvTpIR%?zP_aeD7NC
z`quY7*1p%;`%eG=Z@8}WI?v-c&f}Z}_#{cH(ntvfT(Wh4B=aK?RltR$&4%<JWHmy+
z=u9+S2OJz6ZmoVv?#QjbVEzZFB^jBRKA(O<aBm{bBh<E~3vY<M>`AqnS|SQXg8*v2
zjDlQ9o8yWD*n8^prZasPuH-l~YX^x+KNHzLQL~X&7GMtO(?4JV#Ja+gU-s9kvB||F
z{Sov*T}5Tf0|yl7;TBOejomXJA^Vugn;CiF(FL16rta0hP|gXc5{3ac&7bQBE<IeW
z2IyZn8jYv44U`CKH9}kiA`YE;3#o|rL=*0T#7iRgtOcAq=YuRd!3fDrOWO;B6@)P)
z_^<jL*2&1oBsZhz`uSPhV;^Mgs2=NEB(~ajv)0~6EecWhK6iI_@A{)id`a)W_x*EF
zK|sw&e1<*y-o1NA5Y}0H2@t~w@E2b_s?4eY(g`#IIxCh9f=v+5;d=4D=R7_XkqEd<
z*U8C=rZM2+#SFYp<Xw9S%8OsFI-2+E;mE179H2P^@Qz;@z4VX8@}>suZk9U%sV0&-
zg0T~a2G}&?l6QNDhzbe4g%IEYQ~>MNty4vgf%Jg56A)DE<j3rNM57MO;^#>a{zx~_
z%f4;kJbJ!U;mEZQnE+7dYKXFiA(yoz?W^Fo(TbIqY7s?r68sj>YD^4-c=U3&9zJ|H
z6@u6<eBFUlr%uUHCzt!;#S?umX(FDLf<AeUZYD5-&><xCI=ECx0A%!L<GGib`i@*B
zsh5E5=E6cL4#0wHC?g{y>957c?t;^BJwE1VpGbK%k)jgS1`zdk;vVCwC~!5Pq)f6P
zt{cSA_{z0wD+o?e0Yw@=so^a9k6@8QlN-iQcFNIaY#ZPqw;^5?1ZaltB27qbK}w@7
z=|1AiT%Wo>&wJ?snqgpr4+!lO9v)8WkZ=en(N-u3Ac54UjM_azKKF3}NH=)70iM>+
zJqc=;^c}(Xz=7D&eL&SCN4cT#ZYB>M16);mPM=Oo(ulba>i`{q>g3P!pO=s9Q6X(j
zG`x1OlOfteQs_^w&yY2wa^TJxOFcyZCGsxl3s@!Jq<@bDcHpELLkv3LqC)mkSMKCD
zCi*b|Un!u(x}@^PgRm7BvoJ#MSV~lbwW)`sh!7Ns(X<=sLxBAlO5YXfv$#%FD7MV}
z!)sH%6*40J!^+6h)9}vqD234DZ*GdBL9qEIQc)=LdxE!qGk>U&BIY`S4YK0&44hfN
zEb1;SX64B~X#0K6qsz#(`kyZkPS5+~Ad8UtGvJ_K`T7OnPuH!kX7>zO`aso27P9|Z
zgd_jq&)1V!sTa$zv{AtiBlK9INr(UySeAtU0m-Hm>br;=)*eq)(I~)R*~4^L9ANC4
z9R+^iuC%>}dmA<$y=lq~?^{%=#DDv!bI`;|NJ^SoxZW}_*yp|QeK7)rL?twLdcx}G
zSm}-*)>$H|#MX(wD{s0dPrlB@Vf|q(t$4hDv@?-5PK*96<6i=a>d4~|q6%qrQoh4k
ze0ax4=3ijWe??|*Zn^&5-R{|^uRnJ;whtu3|EI&fJndL|L*-o!ii(<3xSo$K<NQ!k
z!}<k)^(j#;{uosn9xa5si0-5<FJHZS3x5<HO-;gB0h^btCVFD@0+7aaFpdi%Cl7>r
zL<RW1crk%^Lb^9C;nPDxKB|wOldh{zLW$2=b^IUbHiq7HNH=Ds`^4@BIQ9fFKi+Eh
zC6GS_CUQF`U5BJRplwVODfj3l7%&le!;)>PCPMPe>vVRWj}MK!`7HpPO}9**$i199
zqA&{;gLj2{-A@^hB?$arKR^?K&s*rXx`RH2z`PTq^JzxV)p!IA?)M==6GH${$Igb!
zZC_=VkC)MM=E|<aF{XN7ZP=QF*u^MMCQ+?7My<mpdTehJDNnY$GTW|hEwk_YqXnRq
zbF*can}m7T9b7j7DF}ND1r*VggRvr1qPo;4)_zArC0B<|eX_SnXMy|IM{kZ~e<x^D
zW$q~Q86ii>J`jxp%4oeg<lX10Z5_Q`U+)gn@vUb%V*w4NWMt@&kcVi*J>5SaZfpdg
zX8zH&^-IIo*2boTO5d3}dNjsc4^`Q~YKFJb=T56U*KY_o<=ay~XOpg;tm!i%vXGNq
z$E&#e@uL9&e_?$AAxkxRxOT37)prkw6IO+Hl%1V@vWMwF@{_7_j->%7c{Y<EEG*1d
zeH9rvdg8)*l2cMHD8V6&W<K@xMViRKKDNsJCr=W(1iw^qJmCGARVedqIptzfp<6Ah
ztgOQ^We-_a?9Vv)12GlXrFsC)?MF36-ULbIaDyl@E*ULP6H(PpQrl;5Z=cU?-b@RI
zVr-90Oa&N991EeaFevrx!)DZi_T%N^2hGc(&CJYZWyXl9#IZXE2#m0}cs=yBoY4~n
zXJ7lW@rVctUP|!v8LND3RWGJ&Kco?JxE-i*&ZWLehuOsvG<Qbpll@0HHt=tfF2n5-
zG56C}UruyfN__tM70m}tgow>N(Xauk;^en1zd-|Y2C+M@`jF@dysKDtI0nZLNc-r$
zW-qI(;d~#O@b#w4Uckfqu5Uhh1tx9WKc7N}PnZ}q=g`>Ao7f${`N#fBu*&ahYt3Ir
zO3-NAjD2Ca&;9Ubh1Aoh$Qqfyjm}LVj@1yrUAcb!E<&6Yevesp+90@Vz>(wQDZL1Q
zo&s}&*WAB<|5V0dliz@|#(x6Nm$<hn*~bW;ML#8)JZpfRUb%5YG^DhBCg*qFDcZ1W
zsT?~S+r%CjL#NN-G?UoP{B!qy*2JzLK2-dhvOSGZj=94XK$Y?4h8I#=x}i9kR@FWR
zG%cPp9LnFto;S8DU$!}e3pwcmJz(C?5NG7~hTEV=0l50tu{^YA2`Iy6jZx;LqDg7*
zr)OFG7RY9qA0K{v;u>j{#EZNGLE&^?u8^Q>z>gaNv*4wWxcJrK@BL46)6zDOb0RbW
zW+_wAlQ1ipaaoWVEAZ=JtO_mKMdiQ-i-^B*+U(o4%f}<(5pKY3BBRM;#trtkZlMV{
z6*im8ni?*DM)2c!ceC%KqWWV%kPCOLJ*uuA1lvkgGL&HYBsodVC&^WZc7Lgc82diz
zN&a_1Pn&?2e#@Oa1=R2pPX3)tt0$6tanSGa{(9KJKtvf@2f3GWGDvT5nnBNa8%#zr
zsE-MSF5j`>E+$XXZWPUL(e&I2aD0r1&L|Ml;nE$``@kPS$5yYfXgh1wH$;pG{+z+G
zb;LB}XFXc*fWj$#e?AlpDxc%m>G(7l{Pmc}B4io}D|NWVzL*U#Z<wa(Bi-r9ckjl=
z^6)$t6BT_&Y`q{TEc<+V?x#I}k_lFJJ2~t34|hWRUxQz0=xhg0S>5u&1*ro$h(Saq
zfZ4`5*>GYr2&{K`2aWdb4JLhEM6C?dlF=;m{}b0V!<mT#%UD_I)L%V>+(NmUbk<<)
zT0)e(vlNZOvk-P{ANsx`Sf$0!xS{R}m4Bnqd)FGtBJp!tP^gLSmzSmP^%C=$ITDc2
zbmBAK&RMjLB_r<-%KZ_JkN*|rZola^4e5ls#YW3oK?E2L9I<t8+<&Bdx{+1_@*L1B
zHQ&wgAwp`bOS0@Qd^`75z?$C(Vui8+T@}*niGoG$<ft>p>YcS;6BfP_6?F%85jq5)
zMuoWSbQB{FbhIxKB8hPGXfqRntl~u>nE;SC`RO~^t2hgikqdIjoK7GHYfv<dvsc{B
zqvA~U$a+9&`3^}BdAFnzCCdYDRc_c^_2_+?(w3_=i%R-c*mRtJ!f_7=;iu39I6F!@
zyNNyNSVDTG(a>gfOM7eWj^oM88sSEtg2!bJnwsc|0kshmDF|pu!wk)k;Ur9l41abX
z_<+{tX*BM2<5gd7?9;{W+vGZCa~S;>%#~rTaYQ@-+<lfgfk~+aRs|07n&f@AhQnf}
z6hXcXc`QHeaA^k|b_hp|;()Z;VPBxPo0O<OTjuYm0@XC<3l50cAN>u5jF}r&y?`HK
z{Gn4%cA@{i$|PhvTNnrI($QimiBqkM*nlGaR_o69(Z&vXP|tZe7qW?rfsQQa6NNL1
z2|HpF&D%F+wr~#tSFzdm%5|K1|83Jd@$tZ(Ww?UZJV?>h*YA!AU1tER7U9h2Pi-qM
z`VY@<X&d_CGNZ9uPx)TC9Mh#OLqaPQ9_T15AN$6By@GB1%Y{^wu-F&RQ}_R3(yuKK
zLTq#A?%feE04#qh{p{E_W5(bw#3}#`b-c%0<ESLrv6eYRNJ1J>;hHbz_y-ggJ4IKu
zF9TKx%5m(A1AVdRx=`B<&qO0hC+?=cMg|Z6AkT7qp22qroLxQl#>N!n(nPF7%>QSt
zcGx9;7xkXz_`P`KzDN2^&<kk*_x<NBrE`BhVbAnO^t`|R^?w+8bn*N_b}H7X=SG8m
z-t@E*>Z9_sdk3#BjX+V~(B}Y-kYI<fc6xdI?&N|;_wI?5ygUEq^XXS<B2PYjJ9w2(
zS!LMS^k?t%noM<7)i8dioTlt2MCpt73vB!#C^_g<_?1_M+-~@2)v5#{3rRK3bn=HN
z1+GL#i>%(RZP}o)k=IoE#>B|zp0YlN^7etEr_m3Q{t08Ti5+2wP3bRC5+}|tP%;FP
z7eL%#RtW@;`V?d|e{n_5@#Vg#X|LBdwX5v+YS=dIkdTyAwtfLL15%?=nEipDd#Vfp
zM$3U1@0SnB-C%Nf0VU~4amd~JAn@#r{Z&;R1A~uk&5;l>2t#rRzq4R_A*1rLvcWtF
z*fksryvtw+Xn?@k78dtZ2hu;BJ*BmE&-Klr{56uM#RWMLk>LV&lXmN>E~@EskV#AH
zo<6L-`tFKPR_dS4Yj$t?{5eg3;`(tvh2Y`gr`&BV-sgPz8<x&~vA+JDsHmvlPaofp
zJX~8;mQ_td1Ighf{u{Dx`biCGCVvoZcJ@-d8`I;*Cw6f8Uv;TKp>^eoWU9Br%Ig(h
z?Sku4L5V`8aZftCe#yp-q?JFjfBmP24ISq1R6>tE?q8HdQ?xdz<@dU#{tvkr{-USg
ztFP}rEO2)?@XVLMjuAk!4-f8NHn@ggN&WD-=PL%B@0h;tv)@rvz}TUCa{njBQBzYw
zHXJF}9-T>Af%mch(7OG)B~P#PzwaA#z9W$G&9$xLNV>3aLU%W~lU05SP2DOD`k6uY
z0{cw#Blc#m+kfP<uDZH90jz5V?|eMoc_#eXx9<*2U-qhL#@|K@nAYbAiztcvg4@Kd
z^ZIqYy(WAXZ~M@G)A%Jzx|{Bll#}(L@}~7{Sj)(r@MmqbI`1TpSf|2ju)ZhN#iQzl
zlhxg0*OGMJx;_|}dDPIc$?8~wnaXCDdv|F(dB9+C?0?qB6ux6LG9h9noS|8DI>LQb
zsLqZp0e(Y5z^Ov~>{4cQMOIaoo_&z{Z~S^7`I`aZ`BcgS?Pd1aRv%7zWu;s6bO9wQ
zH}gS%d%Ek+d*$>g(|dYEe~$0Dm{%X0vN0x9Vz}(!Ra$CiTkCg`-!Cb41>gU-xwHS*
z5N-C$)zw<+@T-J4@`^<ivuqNmU^2#vv|^O5a#1^#we{!>3~!TxzL8VR*q)%+_-)WB
zdPNh@FO@RdtH;}>{(7>W5&R%$T3*OtdIsP*C~}WwHG2QxMjt4#DaH|P4`UC48*OgP
z+i`2Xx;8Qf&|;y8ZhTr{7Ez>K5FK~OJ62bl2}05c85k%v<Nx>;UuuFc4egW4)i}_+
zevmYSH0}f7g3#|SA-#8okU1$Yf2gCeN=UgH0%OzVyNf1HDnTSMTg5Nls+(-JB*^Ob
z*_0tkSH*rAZPBx0tDffLvo#ATroeg;_fwR{2fX{>+{H61St_ywig&bVa)AKoJ)ClZ
z<}q>il_l>l`}H8Is#)X=6=Cx!OiJeK{6vETz$ydtuhtQ}7+`SskT=Lbo&h|_FLX@q
zFt7{cyp~90h+2CK>S~BA^ic+3jEAxJNr!JAg_OHdiQq&a^Nt9ryv4TdJTw_Z)29c_
zp25;gFM>EEk<S6oKt6{!KVXDcHxV2HGWOmrGxH-W4{pDWL_>{qZ&$*!W_`jVD^tA)
zl0l<0mkg;877@9EC{Hwa(89prOt|~jFLO3v4(IUj_vz_%8EQU+W)&7BS4bn!vSrJN
zJP5BD{^;+_AYBSgfTCgUi3b1$U_u>>TNXJw2{@GqyNDi>^n^nz?lm#&(*~ghk>7!W
z%pV{EJd%D8X99vVk!LkHI7mA|lrd235P)|(`m{*(B6t0lHW=Z1_dVPRf31TuoOq@a
zQPql>yFg1dI5b2&);iI_w)XrlQ#`qVJ(SyJX&CeHr=W@>psqWd^vl`@I(N(r?bzfr
z*ztYAR!FpPu>dT&9wy&3)r;Eh@)(~*n$3wk4n~MqDvsA+R}5n)gdQZjiPNyfm~Cr!
zoA{FG-koxrsAq{Hl$?<`aoNNSlJR&n32v5_o*sTl^XxOU-s>YbC8Bcx??eMhx}0z!
zL~R7OJeWpu+1$e(g?N_0i_uouBT{G7<xh^{ME`$;Pjmjf@jL2OtfU#<6%WKo>yuzB
zNwOjCB6kI-*?DiDTR=A%WJl9@AXPtcYRdJ~nHqH>5X#R*Z*AAxJ>2hLP&o{vu6c8_
zbLY|vbir)C1|npInKuwnKgIjZv~AxFnAZY))^wK$@nuscACK*vr;Z#0w{j1iimQ{|
z4JnTw(XU%;xx1szfB0tize9arQWG$uBZ?vKN&YUpKjP~q|7{01c=wzB!*o>sSzyJ`
zoGk7_wzVe99=%nNdMK^LpWnGpWE^eW&0dPxNNJ!gKsrv*@p$GcK}@Qk^@&p$WDSyO
z;!3ym>knqpJ6|21ja<!}+65$zXm36q-3s{%k+%Xnhd78KvgAb;Wd!{Y?F%ztrNf2X
z)0(8lMP1dkDAtYUL1W)S`dH3(PK(1HvRyVkAtkiuzYfaBA)pfHx0gSYEH+CYrpIZ2
z<I|~-gH+#u<j>VQ&pv&d`Lc{MP@=1%pM|9+caoyQo$@?T$Ij3kT|^32RLu8L)^`*6
z*eb`Cb9T;`YpUYWE;HbRkca&ZBhiFQC)I1NB*aQx$(1EeC4MKaM?|bqrqLkIYxTqi
zfB?Y6oE#4o?BuiEZwlG-C)th$9{c$<or>eftLq~33W(+v8cr~j^Hvi7W!|3O#iu?a
zZD)X-y?rK#F;V4i%$WqRol2Td;hlc~hTEj{>exCt20Va&uGpQghvYOT&f0LOKvS*h
z*~3%}_@(+%AK^!dTJn|$?5sZ}AruKqNgV*tSC7tZ!jM6pMXcSp_3KB$9;nWW!XHg=
zK3x-Z@@n^&2kt;#Ik)R3Kb9qLiZphg&a3Gfp2;l?kBZ{JlcS{Bk)BTP3M)h>X;q7r
zmtCvFvqFxYhYhjC6ennGZ2O(B%et}fymOnAj8vS@4b&C7$NY!qwzTc0+!hN83y12k
z$4`9O==$x+S7IY>38d^P$Osr+#9rz{WB>r<#rCcdB{IK|l+@_+<`!&F$adju?l<Lp
z2?rTG`T!FExt(h4`|kvvwB5GPJ`qF^h~IY7U3qWX_r7v+UnmhDlLlF$%RnR{4qEGQ
z-T<vZ{}oP0i1_XRQaiKI%&+jRxg0>9sT=3EV2NEe)gxkfGvN@Wy2X_SwUpTIsR}tq
zzHzSO=?#3<i>Utk4!)M_VUCz(F?|xpUOk@cI^}9>_aRPDj2n)*G)I@4i61<8`0%HW
zC|4$y%|D99V9f(nODF2>ll|%m6~}>e+#oX`36ue>1CANfZ!2Y?o4(5WT=1y$h~LvS
zagSB2z2-9Uzw?>-7seQ~<rsi=T)8glRHyh~1-P66&*!J~V_*1JA+G2D04YN}&VW*r
z%;C-*jw5lOq?k)^h|PICnN;~6;Gk1|2UD??rR6YsrRVjDBOn5A6M6ROKDxNw%shTc
zZ?yX5-E&RAbBP~y`F%+s41ubvh62i2T5I?REv$AD{pEZ{QIcjz256cMN--^#{(}^1
ze`Ye^Pvd6oclXg}^YYeoU%wL%)R?BRG>CJpGHGsy;oyX)45yWql|87cSsR-y>qQM_
zy1EfJ-G1oJ2%3hJ-a=Rfznu&<BJnfva1O+Sw;E_G78VqwU!G%t!Ue=MLFLhd%g7kz
zR{FFfRF&_@OanRB3fEr=4;N6L0Q?q*g|2K*GX)AO1sk9yudjFF*CzFhxF{3b9u1He
z8CXKT&y*|Xzz|Pp9Ag9|CL=xZv{v;mZ>EO^f6;#^JMYEC?Ha|~Mmul~>3FV^NPJp6
z4@7d`j1wRj{an{4P$M+8*rHN|1b5ui>1LwG{C?a<$ibh;f3Rdp%LY6Uw{&vho6%M?
zjMI2O^r^t;tX}H>NfXagTPnxD@b86gU7z($X{${q+KqE&IX`rdBiFiG61;L<ideoF
zG7&rsOY7ynh<r2z0Ghyon}Wd91^k>Gt-zd!Yc+9!1RF_j{`HV&)#uYdD)$n22=0`|
zZs;!uX*UrvGqy-QN>Uj;{S4+({~a{s%_ceQxQ+Pt5f4P+;nv#bs!bNX`pVVI4j9BW
z|NM5gtfJx_1PHYlo<PPk5P!wAv^3CAJjy|0atQV0CxhR&xcZ}<fzZE1jA@TgX^z{4
z>`Fj?n*4uaM6vd~mHUyVdRX4G0c4O|JJ?;aX&v;Ao?~@+k9FUO7_#(isFF|oBVB~3
zZ2H7z&N&H`BAwm;-&KSEw=19D^>vP$nZPHTba3*=^mI}8sv;plUV#5L2JZ38`hXwW
zh0^S(ekA-59?n7tOCF<HNIBG04>BzTcnAzpP_tntUb(`ryg_~Asc)Isir11LEPG*`
z)%e*)`dGFWKCw1?$<mRUBR|`_N0%*nvmMSvL@Vfcqvkavx(z4nO%mHa6Wt)B4$b55
z?|tCV+Y#kRbH_5u(d9<_nJu43oZG+H2j?i5IsJpH^d1(w+_U4m?ta@xBG%>ZGVMz0
z`*9Z^Z)*_zLIB8Zv*dxB+g3pE_VxA4OQw2v#6*LDx!7e|tWFW@2)yc&iXvpwvSqW9
z$!CY}-xE>R0tx^rWz*nkCw)iisGW3&ipGOU-i2OCz6k<b&Wz^A35$xJ9N>267Z4z}
z-U#@X1Lsb{=?%y&e;3rmqSJ-veoF)Y0x7}}zNK0||7hiF1XW8%^eXF-Rji*cMu|PK
zw}MRYLH)Y3Tpz8JuU*55aYslz1Q#iBV)GOI%us%(LynOPd1NL2Z>Y=|9*I$MLlL8W
ziz~+44AVr`LE8s(NBQDiyUT4ou2DTt0&Ncne*NhYU1OqAKl$l>RM&WPNcGItm<V^V
zh_>%%?LMr>Y6;+0Z`t?h$+0O9k8rJxCWrUxus&J0>1W~D>wV4HL#9*BIrY^81|M7p
zo-v$Md1awKb#&MJpOWvcNxO)N287kiXnnG?D9g!<u+?+8r}`WJFx5Y@-_a5&DT{Vy
zlF<JBH)~7|O*dYZ8#p67a7Ika^+w3&nV7L-v$QVX3%gYhl;e4urgchd#t()bi-K~Q
z;;5~jU-2uzfFBq7pClaqKMMgwLhFTOwY%<+_4eDP=sQdY(XDZ19pitB>zLf9N?!k0
z37)QDYqS}E&+q4`&qv#NoTU33)Q6&1H6q6oJh1fjA6ebq2A?;700{+@JVqnSDxG$x
z{q`7$P2}@D<@@8?r*+E2NfD$@O!4E$d4C0T%MUEM2a-ILv{zvo{eOJ1FuvFvC3|QB
z(um^hP+wx>9EwrIE$`W?Jz;|n{C)$Un~&Yi%lBqAebXL4zHJq`Xf?w<=Aw6yh2`N{
z-itZj#?KY+9Z+XI6gco<Q;EaK9lLAF&40827m~VizN{%Z;$MHzi$k@y<j9g+UUMIM
zeiF2lljKuZ)cKIM6{Ds5T=lZ@#nO9ezt<0qZNp#%nV4%0g$)?6C<EsRfn`gpHh1D6
zr#w-IM?HVQL+>9PT!JFlrl(FcAt50a9gOBp&t>qZzR>9PxbaEx^3uTJXDbTbi^vQM
z8yZ&R=H_<3yUUIa@7T;Y@QR|LkX6%MQ4v605rk8lck^a8U?c_5IQm1&#2K^^QvuP%
znh#v#QfR0bw0^NjNyuk#adDj~!$dcBsZ$^G!Qb=J)1qQK@9$5KfMo>W7Z-bX?-N+|
z9qii{RE$4fwcYtV1R*@a!i*u@JACz1eY3KpF#f^^Es=g2>PulyL4Nk7<kMvYy?Xgl
z?)dRFR4jC9Yx>sF)1rj;$A_nm0H}}#%SEmjwwAHb3$<;|)DMEPuX|hS^&2;WV7=D{
z*&Nc;bloLjpzgNiqKi!q^=Wn=V1+Rh(*0=piHD!g775{S6%1(OGBPsK?`dyqW8vZo
z1o+Y7F*C`{%IH()^eNkRj&L~Rfd@x+X=+}o%O#f2du0|aT9mq>*y(0T8|8`ei8Kjq
z>9KBlegVW~8=7E99Ts_h4d1Vy_$46>=A1*rUD}i!u4hD+;lyi*WN@$N;LU3Ump_na
z^LqRGE?>VMP*kMIwbfw(9ynV$lb0u`TOJXS1u%Lm00>Z(puALi%NF@WEnZpKRisZ)
zCH{_%FoyloP}2~hwE^5wfEmrcTvw-KW@cobnzZw<ATj=vl9)tE*N&xuneD4V{du8Z
z^hIUmLbRS7eC8kuD~d-@9|R(q`X~<9is&m6NR7eM7oi!r<;$0(@Pv0)tT4{BZlX<A
zL&4Vkx#IvWcKaT{P5Uq!{Cz{i&J>+CG?_+9S+5d1^4vc=TiVgdY1u^6pEtX>fp1Oh
zNi{XK1)k62_#hBG`p^J4mjuwlRC^4lm`}Mv>}hl63~T*XX=!3Cwj9bm-SJD8SkC+S
z6x7r#C0%*W-S3;4R-q@bn2hPp3^{HrOGTp9Rd?{+t5*zY?Rf!~eE{QbZ$yTy-0Dl-
z2wIPe9vT&9Jcf<WGL-Ifp@t1a9bzRAyGhxJii9Pr^v9CxlAoKn(w}6%snO)(0I2s?
z@3y93Rhp@)D@15xXubA>?r^!992$+l*R6EUqR9sYs)<MK-KfqB=Rlc-`;^T}4f$D?
zG0mSpKO4OlTNg-6OGi%ecs?@WU3)0hWbxvLSAF+Z(l$sxF*vo<O=$5xcQv`jNc6b`
zlt<lmAF-%SzFfIQy;>3`<m+fsH6U1rc?1K`cZTLEWjzzgr9+UscC9onY?A#hFml}Q
z>a}YZu#FcVIdWt<8uyw^*0D0ec6*M-&22luz%Q>sYd%Fmw7MbEwbD>*CR6C!kR<CK
zBWMVg9sB}sy7y3)7WARLTo#U0W~V<X9*cY(%>BdvqIXVC&ihif{ijadZ<yR7Ev>{h
zpIQ@pL1}}7tA_mChw15Iy+g73sSzF0ii)^}<rUe5J9q9(hQFBdBtkJK`1KO>=1ZVD
z7#kmtgSV2i-??np@eQ?>n`r0E+4sn}SiU~)S|k~b=bUvrF_ET9J@-hi>k2QP7Sx~1
zF=C>i-756kc+B&6{GXWx?l+5k?dnR|Z*6<?cAWH}%lM!G#4di&z>t^)Awt{citz&}
zT8UMU;LiN(r$HFYP07Jw^UWO|3#nu{w7r_dl3*j730s2f^_G^S(p$IQf*7K0te7K8
z@ho1f`ki(>3~IsJ1Dh1Ip36)XVJ{Y$nrJx8anhjqsv(Eb7Aq;uJa@NSlNM$8EM<cO
zAHP}%E(`+NV*-eNZk-|K`Fc0PQsV-ds`&9+XJ>MFtr#P;NXo$qI@KvmW6yReRzo_s
z6E!M-X=wT9<2#C@5xQ{PD7V?1J{?LPE7csPSwPe~J%z_MTsu~{b;-Vl61og#Wfc|L
zOIJNNeJ#9bAhrjP-`gx}!)<O8tWwrZMbHY8&cxoP98Ro!wyh!XB%~<F6?Hq?@9GF+
zdMiC?;2R5DRbk7CW8myhJPB~>@H-lSJ%<hqB|H09M4g6XL=8v!quI?su`FBW12JR%
zyLZb#KPUppYW?<Yp^J;l%VQhpy*v?AS8v_g*je)dBBM??X%_V1kjCu^bu@64#%=Q-
zq_11(nH;E@q7yn|<g?PDyxLZ2Q-tK=#Z>Y;*8}n;3|>5X+$Fq*{z}4AR>lgF?F%Qz
z##p<gq2$I4BQ|nIV86!!Fn(E4u`K5G+`*f@G-{mO`!<$jJQ|a<2g|s}Dkn)+8vqV1
z1#Ce(naqGhT4H+xtA#p>4sdEVl=ARUaQgD~_V$)mECa502@f5ISNg5Be`2-PIXyAX
z_!Vcfaj=B0)Ls)-@afY^&mVMWvB#(kzY;;E6DLGKI1hbs&O&^K_zFRE?>%jWjW_Le
zbadi+=1hwVHDkJye7F^6QvHsWcuING6HJsQ%q(u0U<}Gp$g8QrHHY|re=nafbEM~E
z-Y9;HohB>ux}f#L)?@<zka1ITuZBh`)J$G@_W2DBdh3KfKt)02<78XBVujGO;+|C9
z)j~oHh)x*3{9Jc?l+oG+#aE-E7<t6cO@Ar!gW#Q(!klp0xVX5NuV1rf35BbGnQjBn
zoo(L}(X@pr>e4>#Z$!nWZ#cV@uc`Us;HD^qV1bg$kUPURSuJw?PJ_|ogHbq!XMd6t
z+Ll*s##z(~Njl{Tj-|GfWdTe;R>@9}nGDQe23QGz^G6_yaz5X<apMsfom2^W1iM8$
zcI+?!5I{ztHvM#l>UYGWqNdz;jgsrsm<06`VQyj~9`*_J%*@5G^kTl*czx-xMd%=i
zAgSz-N2iP3aP;1gv{*tM`t2SZ+pvP1e#TgsaHQD@N1Bm3RU}QbKRVUxX-=-jnwsNO
zxG6dC^{J^ZMQ3rYDMDe*f+H9@D;_+(;Nak7!#r7H>rRBiYDKHVUag0WTXEN}R!B#S
z5oXUpYLDSAOQE3eMAK-UqCYYNlFFo~Z^Z5$DOt91B{OACV)$k}LA2$@LjO#rpu!Y~
z8I5TL*g#lD{Uam0AW}pdDzj}2)Rx%0wdOahBV6pNpSNw(FtMfb;Q|u`sXQCR--YnZ
z)`Sb)*yQ9b=mL{9<7lxgsQga4^S|#9+2I8>7P`H+RCO*wEFhn%t9n}G?AfyhYRdUn
z*(;S!Z;KDTbLjZIY5SuAnT0&U@3!go?R0Qj^xXZYerUT8D`V=oxEk!u#TEwb*AWvF
zQ(~`OKHTW89K1)-w6UMvVyxx;UMs(yo6AOOzVi%?a~^r_$op!bx3_o9lXbDOmx<ip
z1Ag)4UF&Jo|6Z0NTLB$*`JW#>x}{bB{MoU^YX8IkqK+jAw~Rit_y<|?LkDS{dHtKr
zxc+$xI+pZaRH7C`qL#&5XV?`MCzzZ$ld-Euxcr{|4fT|)>?3vp;W?Rec>YES3rXcY
z6&v(?Gw*Nw{@_fc=ieMttFWdfj6p$I*R^6rDX;XOP3rIel5Y$zVa4axPP#nHO>sKH
zs#94`KYskU^y*lmvDzABDv08g9MXmGTS8m#pNLghlvaN62kdVzpjZFEdgq>e92NW1
zE;aOZK>>|rCWml%*@rmR3u<c+N9Ry-sk)85RuxE>X@$e@6g6{h-MaOCW8)mgJyw3k
z5{yr{+}~O9NlCF=W`6k4%6h>-3_YIWo4zyk_72`G6=MFn`f9n6nb`r$iB%Po(bYEA
z39gH}d9%caU6!Z{27R|XF}~g0$fD`%dnX;mSEoc3b@X4I4{f@>^l}8+RA0${eERHJ
z00XB`h?OW#9~&E+d?e(lD^)J<Pu8S4{e+7`-D3RN{9+}-xEBx4_U)f=>3VQ@qM>lL
zwa@FoO!QBTSXudL@#EK*3F}aoMibR7oKi`j5SaG5g^5b6jB`JJ{76ygM+(iU8|1Fn
zLB<j4_T+0@n->ZcWp@KB+ta7L?%rJoOj5Tk%nQ9F#vA&OU<pi@b3rkUq@8cmrg!-2
zYkq!vn%K4FQk+s>9ov8;`USb3M3r!!uvf9VcU3E_MDeUJjwH=NaF?-}ncu}_>u6t2
zR`0%3uRq-$Aqld_3w0C4vth#$$PjBkxQ}%2htI((iiMLi0K_GU#s_npH^F|v8)e@u
z7=A9_=D40n`d$<kDp<{+!oYz!AC=Ek^a(SwweGuXA~#vFp#@`Wjs!WHkW#$QvgXEb
zV?^>i4GoPmu=oXsuoINZf}1h9V<sjh@S1XlaTFyXA<;iL_yR}if`~kqQ8D+afjzyA
zS&L9rn7@sm3xe|^99`%URYKn#zA_|QX74`GHuuGg7dC?($_CjsG=Pu`L1OL0&=<(5
z;gJ<coUV(+w*}~1?|krN${61Tb`#Y+Ei?^Y@^PLpivsoThx@|O{HY~(ms_7cQ_R11
z!mZfb!_CnK-ypZ`gz#SX8bW*vPWi|k9<nHNFtK@QYM=Eg!&Kd#Z5gJW<rSpjLydqD
zwZ3%|80Mq6XAeo_=oc&~0$W!kp2l4KK=<w=xN`(zcHmt#2ZzT6g9QZzzseJgXlp}X
z;TT}_p#FXvSemI>_a@aHt{vL+b<5X|j^%34oANxkA?7c|MZAU8K7l5_D=;P19f~yc
zO@3$T3KruaM4ojMNC3&5l9C$;-y>vl3owP@yF`LxpZ=WEf9`Pu@%7lk@O%nNhAOzB
zzVfwf$hKXL%-TNmku`Lw>wyF4wuoPbO7(6SHSefR9?s4l{+J<E4Gl=WPWGc3y*0x2
z_*BZ911jv|M-;&d1@v9rO!{>84vwp<Brf50?We&a(ry{JIGK5f^`B_H@0z4pC0F*7
zCr0>m2;#g^%4?;NG#SY};sJWC2~YV=8*|7IOh|RpbE-L)c_&N8G;vtFdmnsG3tB~J
zFG<n8Mn$@qn|l$WAgXwrusCqzkk(V42w}8KFJm|R;e3Uj<01xxDx~Tvu+VLD9n-WP
z$0Q$lv8yJ??b^nVo0+X98$v&Wx^$Ip<;LS`=j!jrJ`Hfr{c^;NA}<pB-N4v@3A)%X
zYOK$59bZCj8reQg2eXAO)?%t#fYt+Eb}@r|u&_llZ>~!%0@8q#RZ}=T8*}im<kp_z
zmUWdzkzz3%`Q^Dost<$t-?$ux5P{m)54mt5BF%<t3lL(>-`!aT?@jTDt!O)|%#C_=
zqS;{cF?63%gJ1M61O*iX@PYuB*)~lJMdjGoSPO6qtXkN_fBX1k?y6Cwfy?og+qP{R
z4z`#|f&-i}IP|u0VOGYmi3vVoVHK`tV=yi&MClQrlcn?3s@q;ykX8CjCky8Gh5#-q
zL`{r~!9R$O{X;_v=H`M>xKKRbl~1{;rPfChryv7a`~@(|K^P9=zx1?}c@>3nycDK$
zR0s3wH00j2Z)!f)pRzTf{hR%jCE)qTBi=Y?5p!HMts`&|R2mTsSHTF&xzxjBua?{j
z1LoAw3Mb-4p3iMVgVd|1AO~qdo+*5W3-M%e%o+DEoFH%!=b4^CwYWRP^xi4e8xM4l
zTdaqT8kLVW7|XX$vB+7LETN?ca)Bj25)t<57(FB7lCV$E>}+)Y*yh})a_-+RLsFxO
zqZ;E$DZ$#vmnF!rbhw%wfwS1y+U|UE`g4&7vOVAo=Mhch<K*PzJhQC<4;8fMqH}Fc
zu>|~oZ#BBPx)yzgylA<o)$BzHp&jfICT9)z)Gq>xqd-1q0&g7+UJH4|cSeVWtyaD<
zQB+zQP>w`hQopXac$b=HbGB_^XXN|hkq_Zs|31jm=1ECO-D<oM9ZivQNNJ=)1E;5o
zV0fAQcPkMYeN>QPZONFF@U{<CQ2&n~B`D?lVY$Nb>vF7(+~A<!rv1(bCwiQw;6;N6
zvWU5DX~$0-H$erodg1lU*RC<(=*jtfu_trgT-kr}GE*(16151g#8A)EPd+9L`p7iD
zII`5l{?Nv*Cm_AULf<@2df2YcUT<}P@t;k<SWgo*Vew~JM9Q(8m!DbpT07kb-NvhL
zHNLal^sPB>5k;#1hLM9ku9!Y9{Wv`=Y$;r3&I1W(L&-^Y(%pB)+X1z1e6~s(R`+@a
z!51zFJ#?LxxLywM;d10fPGm;Ms;=Ll8WuLOHx_L+dFE(+;($e-RN~Jml?_dtjm0kb
zzYI?-Z45lQezUGPReS&NAp_ECkyt2Uj$C<n7M{0)L0us4iG`{+tasMS2nfs-H_WA@
zrlyA8EXO3LZQHlEc6T!(MM=HwD}MMXzg|vB2lt(0Xyn*ke%#dbLZNjA4z4N<g~lzx
zH0nBK#OyrHgt(<odAp)t+dKVkgyeXhn@9?r!2)2U4ul8k*^3XYN=XULoQPp4nrL3S
z=pV(&rW>rp5p_XcB@I}ZZD_EkCk3$aW7%TmLR13SkaY`hDIq|FpnGBt1*>sVGaQtO
z_bRQ~SPTrJcty)q(L=t_aw$8Uup(2!GZh5WP?%oWSPhi#nUaQ6LuhEzRayc>^|urt
z_BuLd7ngL1oJwqMLcxHtoD-#gG5VtJtqkS|#8_;rrFsX<+Q9bY2M@#%U(7!Hmn{S`
zVJm|o2wiWR4%Q)LV6l}ToRRguVC5E8NUlVJ*JskrpWlvdi=B|h**}J-jq%59+qM~|
zsE*3zLT~D?^hA5yvoJY132gHqx^<3?u$_s<Ek|#6VMD-IB#{R~q^w@7VzGsW8z9k2
z?@r&9xH@R>pQ=D7aL|MI8T=hl#&C${fj_pDLCFq%n%tQ)l2C3HkD%m&Y?&ZQY@+%)
zM?-750B^m3oQNcGk17Fr5MbB;;ze5OOivy2&6_uO-Pv<zXw&B{K!<i2til=~z!44_
z$|;Y{@kj3pgda~F%<R$<@hPMAkx1GC%0W{X-~}N6SyMt5IeeHFAQsS3c-u$Osdl{H
z%<7-yn{UJAfB+`_409TaRD1hUv;>e-3$ilepVzRwzx(|5?X}no{L?#tsRF6?#oj>C
zihuA*2zm;PtQD?Ta;V=EB;G_y%t5CK5IsSI!-tEvz`X4O3UJCPjEOYu@4Bx#dq2SX
z)G&FK>PHhP9UUE@ffWez1Tz6+fgAEe%1Z0uWY+z2Q%Q3(_Y9gxi)J*gZ)Qf0%hEnL
zJX{2bzqpT#U`og<OWzE5?@!h68rayq^t~xM@+mqO6@)r42t+V?o6{2fO~K&)GiWiJ
z1GH~m7N#bx!r+%=GTdrJ**-VP;48Lzzy%XGG&0!qeb|J|aeuWl6OIsFTQ5b0j?*%L
zn|zVFT|0B)kXbn083O|@xC;7UuFX*-6K%Kp|11SXfn5j{16V7@Xbv*}kRb%d0W)|H
zWt~z|Qp(4QUXL*C=;ERvc9l>Yc+s7oo=LHZ9|=VEU<BjP(2{j1;h`Wg$Z=i<_jh@S
zDS57zxAyb|0G4Y*nvC-~Ji%VYmRvW|_*Nh)=u4RoD*{^V2)sO7u}EagMB=6S=(?Q;
z^X2l-2cMi4K(q^il#mwU*ToMMFvGeZjo_<gWL9A1ZF;uwkia<qd9*Ad`CK%M6t%ra
z5Bkj1#@&I-<1#BNDwuJrg@>$j1ndFx{rb`Ib#!)?XQA^Z=T6|MH<<rmIybn`;K7mY
zVQZh@Om~FC*#M}Es0{9-`cuA8VsaoID8VHMBz}vJe&8!quZL_1qN3$6-zwMu09&Z`
z32Rc5*AcsMCl?nJ=%@~2oP1o_*R>;7S=ma{bjUDoFJ7|53;4P%n{E&qCT?_Wq%fqr
z-P&W46UbxYLcc#N!r%X%;^5lYMclF?V)N<f%ugQ5bKg!K#fOsG;Ygl)5Ii$^k%B=W
zCG9>Xjbl1-;RXNK#LjD=Q67ZJx;+Zx@Q0j_lImf4yXN#`#Wi6UngbDu56N(pJ-`5*
zwwzZxu5Z#a1dLPcq7D1}b$eFlpra`k2?*s0szeXJXpP0!W)_0`CVx$1NN+LK+b1yd
zQf1dego|VyVU$|>TUpCWKYcpft2+K9$p6G;HNozUlh)=nNz6#+=TX4G&ZCDvI~CuF
ze^&&j9tmgOpHIh<Fr&WpJq!-6v;rFX^%Jgl;J(2*1WT#~fh}=R)g$d>=#DMyOP8(|
z`#PgL49hdP6&1j5E#u~UD}X=QT&q@z{qtrO6^{vou8mDkOB=pF1495B3Wa60e#{I*
z7mh}il?(rT(c;B=7djX^ky2eKkFtG6wZN8v8Y~$v7oVZ$K|!hIr|@^R93LMWlVhQy
z_sVaTdMV7WAqTMSh(Yez?>Dobqp^;XgT-h@+3lpHi{<#Y&@Ehl5>-CFv?Z@@v#ydf
zXMwJnff_^0whf#N2^@!o`~n%N{c(x9Z7Y~5GH0N+m1Q3<TyKq_7R$D2r6owZwYBvW
zh7;^TnTG9TUj|qirpMxA5I0@AeEA$$z@y+GY|fsocX16@@xep81y$G>BA2M!md!9h
zVaBYqJvut9$Z@@q)02D|iMt%AAX1unPu|4rndpWOmylpu2Hky7;IjW=yn!LJb|Xu^
zi#AK5)&(Tl?00&hN0TrvqEEFyA(gb0j)t5H9y79HhPi>L!r;Fji_>iEjmNZhvPonO
z+v`tn>b_}PNY6i1np^AeNIX-$S2ZAJ)1;Q&sOa6%i~biw@a*&R<uG4!84~|&hdvIN
z2N#{3?cW<m9?Butbg&BE!I$tbh0g=`Z9~BJ)K6ur|CH;-Nx)9DWIe=CmQ>|kb9d%L
zf7K5_oo?>jID;pr=@%|60o+O|S@1%gwU6MNiZjj_HrmCf_dZ^;efvrjp|yH3(b0h*
z)!MN&k*pUImDy-e91lIRr`<g-cO>kawts$L?#9sx%g7JDlBMlALI-z_?jkk*mY(;g
z)Yw<7xB%Et*UW9Y{;|ZDjJbBB>6w!{9F+xixZ{((swdeetJ_Rdb8A1kl@I3d#T)Y<
z%tv3m0D7b4C?1LhiT8QZ$A4w3%a3~pzkmmRs3@qY<2gKhCJrQt{L!Pa50NACf}cV*
z_X3yd1GN|9xpU`UKth`N14HYAS(YysggfINgRAHurkgj95#?re3J_{#)M2bhW=sc$
z9_<H!@&XIo2h}qp1l95&ngD<JKor`FPiosf2LIiWZysM>ui#z3UXv)sl}84TEDL^8
ze}Lt+m4a8k%W5CQX7Y!faLxmepPGK7ujCAlquI|zP~(MnG5w-N?1)7^xG=Wt2TxAM
zNoK@V+bk5Inbc}5$;~)sY$&|lYB+IMLzYPMIzD09&=4Bt4t2%lD^~^qJXyVE3&#V)
zyt;9rMVCfvhrp8sH+#&i1fBfru^`^>Ow@`49k^WPG(XNffH;~t&h{b(*(?B)u(Op^
zRF=5Dt#A6H1sHweJR(fx*VJemfL(0){MiQywfRaz7CI5VG&+F8ooFN<h`z!sY?!-^
zj5i|e8HOFh2oZoCsaf*!cEF}>4)q>QoNsTd_4cYwrdPO_a%evgKYE0d*3mkFg@c2U
zA{;#+xq{Otq<p@%w|l?YF~bFvW;g6^TY7s9I^8NxG9|__hx^(IGQ7VUv7lE~mF-7j
zb^2~Y{VTuLMw`iHI!OwN46<xabSIFQ*|Y)uoeIco-vX7UEtTNo>tgWo1e#8HHa%(u
z2u_YD+LxR&>g|Y&RP4|ieBT|!$G7_Iz}m^KK?w9yTN5TZU7bMU`rx-$Xb=|ID)v3x
z^jf1Y)pR|62^|fN{yEeDhpNwL$(b*YeX6T`RSIRUu;C)tKoxCdXnE*jHQW1O|Ce=o
zA}DMgizBL_)}%a9^7e?<0Ar!@(b!yUKa1x{r6{{&jkRs2UY3=~YiiCG$B47a94;1N
z9?_c>OrBoz9xY3IeNut?uD%bG@Zk16hfIZcHr~VdTq<c}mgp7V&DckA8AlEEH#}n2
zTZHwab>(rsgQDW&U<UK}4QfLt&jM9so@R_g4>0A4$sD$a!B^8zU<laiLAk&to`;7=
z&L9iW8SqgP95tb(M?QSaabcHh;w(V|vO;7Oz`qgPA(taQlN1i)x>^8`%v(FsnzJD&
zrM~{-X1e|+Rx%fvh5~^h+|R)En6v!9T%RQ;-X|CKy6V0Uw2h#gw;;1Cdx$Me&5Ryb
z<gS>X4N0o)wqktnEtn=9Oh3*H`Y%o^g=}qPJ+5eQp*%6{k3dmao|0Q`a(zDBh@^>@
znue3n3eic_8sv7?uAPrqNzcGg0MUa4K%tI~j?ODz|9z<#DKZamA0^l0eVO-!RD{{7
zLxRjFlO>}SEb<YEABu~vcz^Z7&G?MDk5}`h+5gbu#-#Uw-D`gezC=O9AL#2J_;%`b
z+50y-LmuAub?+84c#s?qy4Q9fNb+FF(H|dzl~jPd3fMFhCW;6+E$!`dDF2X<QqWT8
zfiK>|9_nn)+u@87?n~)H?Yr*?8i?a87%?A8>VwuYNck|hZZ71lK7M}lk(&c{uo)jT
zy{DhaNT^jf3K89iWnZbwe(5hq4DDvmBUV60P?}oQ%p<(&!Qj56vB*X?6$MUV=C7L7
zh`P+bcGlzDf+x@Q`TL)^fpdn~3k9w`#)ublF(k*WDTb<WG@9s`kr$fjtp|=y3}}G^
z+5eDn=v|H`(}yUDC?{)8w1)X3^KFCJS`qqPAXUD2O{fC)#it87(e2Df@UZ<{Mz|sq
z)MoR_k!|am-9T}I%4-1*ZZ)VBNmNo&B1)9ZvVkMZUVrR4bcL*{NT{FTiHM#C4$)Ev
zZP(X-1H>!5p@lyZ1(>ktwaKEt0?LsyXWnCYckHcx!h{s7L%}8k!-v4SZXSC8FJaUm
zX?3!=&t+Ll_YK&dJv*P`=jUH85(ZF$=ev4P+(6Nw3y@$hZ*K|c*c}}mDGJr-e9%?|
zU?EkEex@)yoq`ZtTTzoOiB^8Knw}OK=wLvCg>T+0f=k4P$qVPO;{&)CO12xa&HKK;
zb@{X02y^V>APHz73UEFF8^KInyL61UTP<qWC~sk|kl4zlB8=OakK1{XTf2E#qUghw
zpZDE;2X<NX@{GUA)>ko}xvt}<<I2!@-WoGAwHA_wfC$k;#=x7}vK$n3wgD-lqu`?H
ztCCnfxtm`?Vlkx&&|ZF}9s29Zr8|#)W9IpO{Rfitcy!w7bCY9`k>`7bC8)oPXI2RW
zeVH=^>w_vNm|Za+V2cKC<Q+)R`~w3O(e)lT+L|N0O`*Pi$w~T_9EmRg`Q}hKOxY$1
z!?2C2CH_Vveq%%Z!H43RE3xCX$&SDLnCX?qBheo>GYbVT-Obs~Ha2!>>w<V=qs%~S
zKsb*u8UjWzb07>r=DP(G!d^&t`(M0_H4iikYzPZCcL-?E&MQyObS<G;kk*OyJ0fK(
z1#|w;xR`N3A@;b|l8Wnm%m)q}pyXh+a=9`t;nmE!CwSeF+g%(kH;>J6TIx-)#Hb0<
ze-F1<aM&<~>=lzhmBbfFk|+w|e!;<k_oos}{ze1k!r9Z91g{VGXQRPMaWE9uPH{e3
z-u@+@VP#71C0F`|+BlC~F&!3(@iM^N4>uL^3fp0?;*6)Eh#;!rCNweFKeSYn)7O=^
zO0|T;(XB7>^z8RwK)3-to6`C7#*gzOzEe;&9iL{nvfhj5D5@=KyDtlHCiuWkZ%3%>
z(Y;DaEX|r6QI0}8n%31|S!q4y^&Cy&A-U_RQ(`#POwUA&zBu~qa>PGb1;b=@=mocw
zzN3bg&;A}zo8U5w<CFC&x*3k;$oZEg1B+Hz6UHkk$-32{mnHArUepf=phW(K4co0I
zJeWkfe-K^4IzOs&eqs=e>ZcW(n}u$tfydX%m6RNWPW-Z~8xzL!U4QiI0kfhd-tfvM
z58#Be7-d>*l+NmP>zJ_FF?^SU$}cUQ4+)1zzvJ@*A1*q(C0dfBZYSE#*r|NDBr=JV
zJ7`b+v*nQ!d=UG+M|6Lmb82fkX6_oL0;_$<2|~wlq&qzP7O$|&DG1+i+V_0=($#eV
zg0*}AkuB3v*DmeAB#NBo<kI3|A_$CJZ$wMxzHW2*u>d8WcT7yoSV?BaBwDQ2qb;3s
z@)!yX(gv)mk{VM%Y7XEmyF&oznYaS#oLhPGwM(Mv#hyd8vqxYDj`!sf#&i_H4FnI|
zkA6FNLh8$_va%ODrUw=c%vfgXPkCP3@pcrDGx15<GI{+^SU%eG`{w2#88%AI%}AcK
zywc$kj>ivJXlm6A{krLF%XbGE3oCY>yK$q*=f;hd1_lQARxZ4}OK&^P4fKLIxK>tH
zI{uu<B<n97q@ORuDZG4qv=F`ktVP_~UIq7#TN><0j{xfBL$U^cI|=Dv7a$()6J2W&
zo=Juw53+3lPbK`CZS#<ax!6lcbu_b19Zt1JA&ufYAEz!M_K+9rJUFrv{XTn8rd8p*
zp?x6mJ={pk;P*MS9L3~r8=LiH)w}*^F!TA~hin+dwvV&$DI|>a^z>EFDn35e<oKXT
zs-TkG+Q&GZ+)xGiZJMl(|8}VQJ2a+>kITCmI^j*??-7k`jikGuqW%`c`xpx8Bro@+
zU7hf>%zUb8O3qI}g~sqhYMYoU|5KhSD(a*u-}H0<C90Jo>9oVc!(#!a&ve`TgC@LG
zS@7oLJ_DNf4Mr#fkwHjoxlI30B^)jix`SpV7uSMt71NAB$eer_myu){V^{JE3U+p{
z|JNs#q5KU2FW0T`SNXMgA48FZ_`iOK?_Xe@FcDQfLZ2P3i3NK?5lgNGD;HBoxIyaq
z2L#kSYX%O4lLIC-kBx&P%a*+IYx&E;$G2S$4W)&IrKqIDGSy?ab1_l2;{DT5q@qRI
zW)=RbqQXZj<-C5X*ki{OIBC=BL$Znej@i)#2&063qxaHmaR(`wrK9xsxh4AY&_g%7
z4x}gaK}`1!KV!p*x*Hc5tCeyoBElC;=276>08r%UkF8`=mTP`?b`jG05}b6RwvK<Q
z{P+!khh4IrhSngY<Ns`FYvVhWo|Ux+8R}a&QUaEHcX6MV-dZQx3GAc(fq??#NX7Qh
z2Sc3VgW+$r@BY4waOJ4Wbm+xN4tHIXMxYE2OxcteC(smRnQ>5OX&v|nnMiu_^eHWc
zkN`Tb!f5l$HOkX>V1juy`#E$#YhwOFj3U2K9e_?qx_k;-L)AmK(YRqGiR}{@GHP+(
z{QLGOBnPvCT<R!DS;+X-Sk~VU0<2(yDgkdR{q%W7HYGWPfkOrn7`^wezdD^Kq@C1)
zxY=2k%1N~V@*{;fq@ZLik&p_ES2sAcY{RRiRmi57Ts6#f6&h*%dy#@M0P|Zx$~h9!
zy0CBnINvRt=9K4->9IL@T3EP`PG8Z{#cIC^4Qloj@^@vozwd#sx)50@TBGvwrboo5
zFgFyX(BhcDX_(KV+D+x+4c~<a+Ebh2O=>6#^Ta~{&Zpe{XR*;jlE(nhb1sFr85qVI
zy8peZTRu!ZULXa6@8U|r+kxygh6b73<;PE-c*9-=elrBGn4-WZtc|uP9(u3LLVRSJ
z@DqcDKJrF&xqz1eDJ<E8m8X-2BUKvX=(jEil|w`-`V10!-EMi)@D2uZkpE!x24)aP
zBDdHhD=}*eOg3+8Z^v0>)kk*Wy|UusxhV2BST&;=<+<~SN*j4|Dr}GkVr12u4@Q^h
zpg<QE8!FI9oA>X1#8-|%llg07T)nEQG~th8wf#~=RMbTbuLv-aTeNE1azc<WhL^2J
zHnxz7DQx1>-+eOd+uM7BDB3UycfEdPeLXu+Y;B2MyAsp~>4?#2f-L{v^_xS?Q6ZM!
zt)xW1H(L4}91g+K;Q%gZY~-Y&p#fe>kL69jb?d_Oa&^2;|8SiCA{uML;sCCgzkkS!
zK_N#6r-8*6AQ2Q{S@CwJ^#J$ZD^fhRxENmz$vXTLVs(URJkyi^2}j{HY^ril3!+zj
zE{^F<V8c_>)!Q-gtrQ!Vh60)IowI$<T$~`ke}tJuLi;LEt>PrHL0F+J|05do-03|p
z4sj`h{_isN_rG{;SBd8;0Kbv0->MUtKrx%xBvk;Cy-*I(Uj+TXaa=iEC(t(xwxXwS
zD&%8VEM{S$Fl;Kz6=Zs`>A#2of0aep8i+cqSAq4P>VIWMl0MsUr0V~IBNY{O`4C9}
zTD6@<R$gR!O)V+Qby|5o5QG23km-gYye>A~vu97(kBZ952x*Q#w~Y14Ht7`*SyEUy
z2ZtX;g{uh2ww^E-3?}7j10WM}fV`APt!n%2qYPqCD<-VTA%_JsMr&wjh{^EefUKYF
zxeDWg1%Pb}z+pjZylb7}shdy>fjghm+S&?C#*2r3i)q~h!$soa584)0GOBdD@$(5=
zV6WJ;zL-ls8r%pWhd{t!7SG1#82{T@o3eB^4M>V0Q3W3@a6jbIQ)?4LWktm+d+=0|
z*^?POl#_xb5YR&N@hJ_p$;nAog7NC*qgUfMM4dW9iWBrH*kBSXiqGX38zCxu;Qqb+
zyHj+!d(C=~g%frI0=*CBGH%LU)pz)t_VC4renBWYm|?yc2Uunu(A_rRY|`*4z#u>`
zczYdv|IldOT6ghNV3ZYqEY=!5*}D#lWKij*=PC%QBRyqd1Ps3VRK^q{M$u>ZN-L3T
zQ$7L7<ohRfYpGb6RTAt0+GPf8U}#g&T7t+yi-VD*(I~x7eD)9c1_}+?&n65FBTX3j
zZ{P0g>H3B$tsRGGmhpK+l@A@?tzCvY&jG2p1%`(pS3LAc8J7`34;l@ofCu*v4=Vyi
zzQ?s`@#T?scJ_r2orCVJ+<h*(BlLEM|A~l>2R~@$%tJcKfcP_pZdM-@>)Fqq^{n(-
zy=@y8aHIK@4rE@q`Y-}<hgRYB7cVNr9}ta8#^dtOr*-b?G9#qyL2)*`#`FN#T#-jA
z1catou;?MXxY6gk=7VU3N*h@LLiY{x#C~J}xA6`M38{0qrbc-B*~%6yBe38L=mgPQ
zU8}XLJ2%Kp;Ie(~*&#VMsYQ@SDvskZJJ{M5;Mu&z%#Upz(_BqWO$VQz5xSz1cyJ9a
z7}<V4^1C8?)LxQK)$)7*9HDoIL%<v=5R>56j!&(@6`{=Qz9%KQsts)+{T;8Ca&2>D
z5kK-gA4?KJ?Hp1;!u}AY6o}Z7x^RzXTolpb5QH8Lm1LC-ct7KzC;v^Fa&Ua?WTAtR
z8Y@_#0y5!d$g~US`g$NXw&p=dNZNq(qI+Ljx&<h;&(l*$wN7LGMx@Xp8kPvL!*{|y
z5*aegTrNX0jie+V=YHE};QJ_WNy|(2>CcSFgXf`uCqfn+lzYIb2bFLjc_sQP$at01
z)D$6fYx(-s4>I+5Ku`LnK|xm6X55dNni}CSr)Od+1Rh+Kyl*vOouRXxdt@tLe3}x_
z(Sr{uYCTMHZ*-)~`ixJy6V3wd0w4_(Kmb>zXs;#d959&rXCX2k<;Z^muuJ_5_b$L}
zWvfZ_toLJ7@tpN}Z_2pqu|SGPnLyFO#cw4h=L!ALgG!(X*KdPCMWjGMDO-#jknH`O
zQnA;O6<WjLy=yOs;Miz*t*Mx_Ueb(XpM~+|uRk%!*$?oO5prO{0|B<le4Hm!XNO!I
zV3W}WO@DtSK5VO9#>RXUc`j0u(!M1MwSnwTZAA?C;0|YX$k9<6*@Xn!qHiopA0Nd$
zJAN-OuQQKn<WOqD=lBA$GpmzOEo~t52Jl5*K|!S+2D6<kXN)(QitMrL?giH<ub@D{
zA0o>GPr;Iv0%aK-8^}f_V}in6=XpW|W&$clzlTIRbjx$?aOXmeDM381m)_P-EkVbB
zZ2ET`O1AwiJITU>S9d_2pP5O!zU8-4RC%*1?$ZPgKx03FZa|H{u1Z|ZC!CJ7pm^E=
zw&=UIMDWlPSdol?%9KTe21XVWK>rbDtw~xYOlsTP%|v_^A3eU~vd4WoyGaps17tM2
zfy~xgW@h)HyPQ<xn103t;t)H71N*=22MG8UBy0z>ZFqtCJ+M1r;&%Oto4}+pUwPGN
zQx3nN>d;Hg2Gd70W+ab-lP5d8)t9+XkDKL~Vj7V@O4CkPZ8adaYk})iq|D03yqtSC
z$p#>lCmF>};t8@NX+%mARY}_eN@r|j=TT-xMtOvhe)yr>lJ{ZM*H|=v?%Y<O3dsi_
z@c~Tr17Jz}^7?|&=9y_JsCgx<o3_1%LBq$};0cf^?nDBTJN|`J4HlUR$2?{1%YbEI
zo*<EDqRr<5a?LpWkMe}OMmsh;S)8O`zA=I0<Qy1`cmQi{{Rgma2|Y!=5&bKm{{2g9
zNc6^EnUA22MoXsY>1i7W2lUwOmY1i6IO2F+*b3~d-FWvBu+c$AEtjIhhk^tq-1)eJ
zx0n+dkQ;%#jeu1&6sRxM3|a{$z*!doRylX|Dm(J!xquLf-Ua9h$Q6h@R=^HC7^M&4
zxST_b%9;mJ{;cNbrz3qLkTUO;-@F5_HwgQ3mYV)0X@D|mwrvR=3q?4=pkQkK{yhYF
zNWk~C1d)Kw!~_FH-@_wX+3mW(gvyUF#R%Sy!Cu{2Zgbf)He5?*>R6hW=CE9`_(lBc
zoApq$BE%Kr@e}L{S`#^tGQe_(eyTtU?UTn`aO%+Hx2#{T<pj6heqdS`x!br_t|X;5
z7#ySV!OsWb`BSM~$$_kX436Vxd{dRw)r-+fMneG@-9<9vlUVYlI62oJ&8J9?fGb9S
zN<`gs)IqiJqk31izy+iq{;Eb$EJVuKts$aC()Iz=&>I*m84-K-V;Azbu6uU2w&%d)
zK*Mq#<Y+65VItAHn*9^sfD(nR{q;<k8DJmWL@PWb#D<-|)&oxf_pLCM2gw@A3IR<M
zO$Jo4OFS+@bnhhjGxdwAsvQiOi~;W2*x3~V8_o3HdIqXDj2A?~fvzh?{2i_D5A{5q
zJj$6iN_ok+>AHiEM)X=*Gy^i;1W1iE^rM)ULV_QWuLKsqeY+S-xEO7@mm(wm;X{fx
zi1d7En(~ofLD6w~!Hd3iP&3JS-Q`qi^5j+gA{24%IEqiQknnCH1Cq3?^?zl?7r$i9
z#*Is7vA4I*SS+`t-ZsKpnsVBqj&!i@*>?oc5%<oV(*oa_iN|W@k^&B_9(KQ{IL*8o
z9;xb?;c-g9%NZ>!##klfLC*>ij&ENKYt*HbQ=vx(h)@Q~5wH;xmqM+MultsJD=zln
zb%oaLQPkmJP0H(oe5x`}$}jmT_&D^WO=BLw@@T8IP#eiLX&&%B*lztKPIg@$ag8#&
z0RUo_%0cQ{KY3Hmt`Z2BPjIQ$Z1CX|UMDg^`W(VnMTsx0si_gw4~c8t1i1<j;q8Pb
z*|cfdCd=9?p`kBf<s48<Q?X~C?+F6bXN+U2O(P~i<>D4de*LjS$)1#JI<^;t_<H^C
z_&!@iX$X_~WMz+7Ejda#4tHLzo5A=Gs%1v@l4=TABj<W6_r1{`27CGV%5HU88|FBh
zJ`9Yr^%;5wJ+|lbaR=nN))|$Mpm{fnjAwnUmA$*()(L>SXXQ2qj;NyCIPvt6M>>YD
z+(Z5S%LpgopR9@ZM#-horSbyjnwPczzQGDy_q-jKWTd4#4?j?jdiLxY=Qamy8yX6!
ziQtLk-OBrJ<1yhBCo)?~4!BlO9nlk>_>l3+{F>BKRuJ^mQJcWZ`1O*<l#Se@(D|(E
z<m^m4miL5x7zzL4tB}|F6Gz`GPSaB|l(~Afng62=5fg__?Ty(VIl;uSxjZ^s6z1<S
z{74GCh~QKB%GA=%97s1_E?vDM^x8EGn-@2b=u_**w2c+G-!q08)NQjkE@E_oQK<Vl
z9tY_XpPqqhTLC%fM(6aP8uP(fzcWs=g+%QrDeGWlDuw8ag_a6hGh%eqpfSWB`K53Z
zlK87hc|k!z;Iw?-iXW#bR8~_roPgr%7Anl@d3uUpe(2S`$i-t!HYcFeatT^=TCV9s
zD^bh0Zv=n71#H8-IX7plMhN8&GwNn}1eQx+{Zaof=gNARjNYE%;A_#>udkb?tE*jm
z2|}0o6y$8A5FCz}WshSKLDsBn)UwAcUt`$GQ=T}&7bD@7M;?J;XmO(-tb#|Nd{|0M
zvML=EF4(BYuSOR!U_Ft}4Af9B2!ev6bT#CWC|X`XKKOdeUO>uJN6_~ZRt)Bd*;YoR
zuF;VMK5@41L*mB&#ol`WRkf|@q6^Fc6(b@BbfKbvsAMH51{MfNkR(Aw6eLL$$tFZ4
zE(8=s6c8jMQOSrXf|7$|1d${nl9c$q0q%WHpQ`TL-LLPhy7it_r)r-qdokCXV~+9v
z-~R=h0AU0;+`w7cv$5S5EobnF?fGz~zX^Er3`#vxDB|{+COF8+EjxVp@QJA>sc_m(
zHma5Dyd8%d#6ZDcFGGFn>)GAgyM!WS-Zqz{Z~Eq9>_OxTb13wW0y_6Zz@C5^&vjDN
zViZUe>cB6rqmj_Vud&;4sA8$iS+n>YquMI{oh49-vGefAO>_(5@nEn_I1{%q^jnQx
zLh|lf?lNp*zFw7>vQVx}qOuG<nRH$@Qj%Y4aQ7BT$@32yE~qaVm)^QHB&2-WK6dMm
z(a~EuIXS;vk4rv1Nf;zx4SC45<pN?LibB>5OT4n^TUPHVMO)Ns)RfMT$GU(5bYzzk
za52LF)#Hmo--PZA1*9!X=z_2poP;ulYyr89Viq{zZK2SP98#WmV1)7xgovA{#Th{v
zEmFq3_YKzJWlAnOJ?2%b=A>wVHva=}6q(#%J(BS|{wFJzU1dFm$O%&7L&)ul<-9_o
zIZPQKGXsTKEnN5+hTU}z1?vL{?IySOE8!q^z1!^k2K~wK+wPv8JD4P*Tp9X-a_j6_
z9GUExV&NR^DD}LJc@X-aPc3p7hyr7g(A0cXb6pIg0CRJ5e5+f{dZ?Vv;^|Oah`fUc
z3kwTZq(SYlg@<Pu82|53IB<CA48pfLXDaG4+l7B#QGj+qI|GDc9zq2+jN?8O3c0pV
zkI)QZHbMpksAbWqUq$DD=92mH_r^Rtx)s>fLI2^=N+-W%@y)|MCvUqIl3{`*RtEFx
z)i=OOxagtqC>RPEQq1{!5f^HlRr66^*3=1{|G+XJ868O~YH}`<k3GKGF)V2kob=bu
z)8jfxp4|FD*o#Q7i_?q93x}z;XtM~@2wn0TjF(BtQeZ-mc$nf{njEtx`fw3z*<i7a
zAVu1>vk4fp;Pf=dq?zd1?TUv074Sx&?p%qGPF9#NxR9|vS}E}Kz4H<QYUZ!gad?{K
z1o9r_6#4jZj}ddQ82zZ{J<yY!g*F4EwB0Bt76ZVe5P^NO(|F(3;Xk`gp#xshQ@hp>
z>>p<ZA;6-3cK_uniFSqHSEQOn-QtS3kf><Mc7mJ6ylIms3Xm-*hVhk&Q2;dgTdbd>
zUYZa6B6@8i{DFk=sG1rJu%w%SfI`5T16+KF3^))fpLE{5zbQiyb;d0`@OB`rSO+d%
zUduss)U_(~x&}G{nV-X7ffN<9u&@VEd;&Cp>T*}k`IK^aO{8KZujSVhlw6ECcgxw`
zYatJN?OIm=gH}k}WUGY^SC`D_EWRuRZbhsGz{_R^3RZyGl>!R+>zlv!K{S958R~0T
zw_q8Y#JO`+c$q3daGPw>uoeuZ?OqwEl8$0CE<<n)k|5_{g$X%N9RwQZfkWfoZq;R|
z^9||}IWR5fDmn0^pM}syQ8Sx=DP-2nv}MM(z7`s~4JFc?&6_uq%3F_3rl7cZ30A^u
zl;1?>32)p(E8e{P(3cc*(~*Oj+2{AUYhSfoj=&z2drI>cUP|!GZ<hZoFE4MaG$wT`
zia!Pl>3>klQ7A-qLw;8O{*h-YCr-G(Uv+h5-8E?9+fl!e8!$VHV)na)85}7;)yLmr
z(^<41dst!-zCi0b&D5RHkU^|=GFY6UT7GxTM!7uArq&;mJaB0k7+mgW2bxnBc!K7y
zc*T7oRH?~`$29Jx!_>r;+acTj2INB80PBhH@hNKx@I{+D{dTr{<lHjRGKI=ZdgTTJ
zu$E8j^*84~jOm~D%K089Jaj_gWXXwT9b8Y=ZCvc$9d7M5C+FHuopN@iyOr^aLril_
zx~xPpA2kd`@7umO#yEe@wZ*H}{O-|YIO=_k?QP_0;d^?g>yu@_H|B_^pJOuIl}wY&
zA2ip}RDIR&@O?aJ$dwxJ@N){DF;qHk2I5m)C(K`Ik_-Z3noDXm1yc~teV#8UL$=E=
zOLg@+!RG9ckdWpV*44FhR?WK22-W^}5QERRw>UpPKXEn%4{bQB-%%ciHJqiIpS7{A
z8K#Wg*&VlVY62p1$M(J!q&?WZvpe{>wG>;7r=s=jCrPwjL4yqgS0o0u^s6ErS=;Hi
zKCJ?pJ^24O3aPqTo~oMD1^dU{zXo9;sfSP;;WiVH1>+EJ#{G+gI?Q)QRFC|R&VGl^
z0}8RsluIMO2ZN_$u&uhy6C%In3A1tP^cx3w>cN%+pkWP8c1v0XcD1%stR?0O%b$=)
z0pjFH|1xY>jZTgjtzp+L14^5Mr%ViM12LTde;)3P9JF*~3Xt!+$MzAXi}Rf~9k4M@
zE}lF4l~y(rXHi-On#M1q<L#PEB}ZE_rCX7%DT(s5hE(Ty3pPv9*K(;n0rYo-Z9l>R
zvH*{=2UiiT8jf5#RSO2Q<vf3GH)Vw$u8{*4Uq;|Y%;bw9V+<#!^>DK(&;q-k?{`?^
zHWu@`dpn|_u;!Bf6P{PH9>*DS%h`nYo%^|?O!MqY37M4rPjg1I`bo5wDgqv1X&H~$
zTACutpDNJ4^v|7p(=#;tRdR)+;Q(ru{X5)gI9i8BhgYtTkm?P~Qy&<+(&0<$2hJb}
zk6(rUWNKfMdw?E{IN{OghJkrbrAWtu-$ptteB0DFmFRw4`Ip6;8#kOnQ%#^KVujR<
zEsEh{`L922#s;6Y9fwb;`*Rd0ei{5tEdGf*9wLl`t@4=!3ln2(1KccE{}mcr_PSc@
zOqpp~H1>fpNZ8}h{~KbGL&E0U)i##sC$~~oo<N;fC01>f(FDg;`o(p747!8;MS|o&
zPKiX`N%J#4b*PtM0Zt`u_pbuOk>*4XgTb1RYK0XB(sXKZ;>xcYgTEbMrh5A_r|elI
z7sAuNdL&=wo)N6c!kgpOQ>iu?qQ9J75cXT2^7WDstEd26$K)dR@3IyODmlZAm4kO<
z*E^4OxANH|Dj)EciryX(fqfwLP5*XgolX{4cCUA<`V|AYeux~Gc5gut-W~W*BdI4-
z#*m&KZ#m)QTosp+n<JTQjP7ZySZ68(T#z2(WsB0z@7@tMx32|D4YuSR89aIoV?1Py
zL2<RJt>9K=H+%^1TwbrBFnZZ{Ashj#UO%>7GNHY)Tf<1&`*LbRopD}^>{czKt3P5m
zk{bH(fB524!Vj7q9TYD8d}(gK&eqB7l^xcHS}!YIeyb(}ueg<8UqyWH->wjsa^%W)
zIDjr17K!+N>jw7&U6#F9f_xWFPn|SEECwr3a|EeG<xF(!8Q@va`NV%(ddckKlE{L|
zBFvSy(1-!==?THEvY&qk2QM;4TnTZIv;xG(t(LH*xuca$@~=o>>dRcNE_f}*7sL--
z^S>F_{^t{y5PN1sFHLVS6FgVwy*8(dM`Qc>zt$Y(>_544t@=k8@%?{<5x<%H*Sex?
z*-mc*nJcLwE^(FJh0<ksd?~-GErR4C5NLp<(WWKsi)<Qr!qH#_XC4>c4PQw_0>G6H
z&nxlE<oiFXX1}Zw5`^1K-uBmrxUYH__#*09LY;EA1&w~9@}3W+Rzu$+@kG3;Yw0>X
z`!{B3(G}gwKb@Pla|pL<$(>MJ)A2J?Ky@mRb$$5yev802C!`gh<+F!INniXNza%P1
z?)SG5=jmq}t}XBj4lL}wRA`n~J7ucza#w(i!0A5C$Rn?6u&@=hHsaGi_)Y}Z3g5Pf
zRrjy{sk06_2~DQ2vJXgKY~g6Grbw{BiH)gVM<K4(#?<5TdP>PzK{GXOz0ncdfbuOe
zicM+nk7X{t9g;g~*gaYhUq9Y9m1oN-dT5S;{Ie+ktnHB<)7c-Kr+qZ090#Vx<t1ct
z#Iv2J@w1Y2IS<CuV~&e8FK|yfN!=dRWPMq)@^iK_O}a<)<a`D`m!uWkGdneTj(h(p
z`FYVlyx~s#y*BV1k1k}GdF0+J+--u+<(#$3EdF)Ozj$N+N|*D0trC|TXRLa;Lr?eH
z1~5EX_SXj`8z2plCt!E8{-eG5ylq;HD=hz72?phOvIv*TET)`XzLx#Zc24;>YvLba
zzyF!b{73fne`2})Pkgl0oy!atqCwU%1W%2`{w0H}2J=TCB_u_Sm88m}LJcThBVdYY
zBZ<i&75dQ7kdSgSA9of;7&Ct^ENAReQuBX?^QjBn`SJ!;uy>FXORUdX1&r?_s*zd1
z22ceKHJ}n7PgztY5B;%mr99GF_(Xm$oIk*WKLM6{Aaa)RTURXALt-njXI`^<^>Y-`
zx_UA)G8`nYbl=iY^<Ga`e{EQbl0Pm<OBk#+A|O!%Dmc7W+Bgjp)~?2`M-1svhpY7e
zLc1?T$9GsYsG;}tSuI?e$e-44hbda`FcRNNgVkEq`eAV~D2L&|j-!}^Mouzd(kmOC
zD`f=@(R|Uvbf$g=5WizUi@S>xldZZH$<>909T{!3^oo1gcE?+<zdl8V6azp<tx(27
zr7fqbW$lL%BKE=87a#B&`6ID%2>Qrqw7YijZ$U2T=190#K{-+Z4KSZoSJ|=J&1cm8
z5jO<HViy`=Ik?7y#8-xLz|YsWvUO<>n#>*yH)_!hpTgY+7eLG6JqK)BPoZdj{Z^p!
zRmnMb!$1<7_QfV61!Gn_uM`ZbNYtG8^zTxBIPN8klX4|i?Zsi7<`p;{s?x2EfQZVg
zYF~MV4}N#TYW6-Q8_X@U#?a|F9iLTd(1@l{Kk$5EYPxej?1#eYb5V$Uz`qz{lUm{%
z?7g>TtS0T6xP6xPBr)NKa~P||Lu4vIkaV1YB#2EE77i-$o+QTJvuUij>sFv>w`N29
z*aZ?<6?a-=2tGH+e3AimBS}w15H3q%)@(d5EBv=M0Rc$(QPn?cgo(XIx|Lz!NKem9
zUceX#AAA@G=<DkjbZA}>`HGq!*N0xrgJm_z)<Pg5);)UzcX5T1yy!Dbf4uu}zLR`Z
zj0PgHBoy((v7;|%Z-9i11V!sOm_S$+aHPX@!?r%l8-0EZD!Lwg8^|DhNQnk>?SuyS
zi_g2>L~L`@ff?{)iiR@j#-n$MTpu3^D<NZxRm|Zffnw0Y6rWG(#>J6@fFVq5dP(dO
z`nA<;oddQAp5UaB*jYZ*d`*|~nTkGqAi=e0BEtlmZ6!&fCpx!#bh^c{VVRV?Ux)>b
zz-4z=$m}5=_Ew*7u9J=miWNnhhIQHnW^DusgY_OC!Zf%S7U-w&;Adi_P-TP5gEFih
zLjZ02K7O+BcsehBxR1Y)rbFGX)33MYSMDJ?a+5N>9Wl{UJ#^?QG`S>&m@IM-(|`VU
zfY#j`e2ukhH5OD=Rn2X9R<#3V4N9FhCyJYZq{Hbs<+?1#svhSX3pA)JEND5;Na5S@
zIG^&!W8srt7E!xo(o-WioFtS<%gNQaF>fO_%V1aS#v*7_tK>t?MMYYeySuxcnUWx1
zCQzQ&^(J$<uzTRnrVqT`z;E96#@i9iYBfwIVnAFXiG<4*Jz0(!=fo8d0n-sMd)-+c
zACG9SA*?m>24h%U2J5|&=T0;`@PktjMW=<_JyLB*KK2HmH4gH<UL@+>#894X<zi<n
z$T8eT#GkGs84c|F(|qalDwspywdOV@w~lE2=~YP=O6@jB&juS1@F|M|wBrTU5Fney
zE6uz<Smt8MS8cr9sL;RnS^a}CfN_>@CQ*tj04v=3eSwQXyX2lDcN_Y|+RZ|awhjJ9
ztqA@QL>~4JzKN@|rsy$H%Dh1w@HQJ0IQm3(2ZircdxR^MIQn(_scM(=*J^Hyv^h3l
z-=7shl1>Dhtb8{)Pnd5+7FzR*t19aNhnT;SUvwFUAepjZKk8t8P7L_dG4<Me&PpT*
z@MRFvI8G@kt!mt4z+RDMf0-m@7(dcwA$e-ln@wJokvTB^1ZuB@LKI1S-|ft5VVgy(
zz#6~afC^~nmiWXW`VjPQt|?5nM_;k-KNe*Y&(7I)q60?-Eyb%llF=*eUAu6s<?Lxq
z;bz|dMBW?=0%?Qth@mWpnlKq+4o9Txl68i8Wi%$ihIsjPgLkf7vxXKWZoKppd?=9{
zX+NA)D&~u9H^i*O0eo^xTax_q`GH<zTp~S;<fbr7e#wmqQIf2wjC98Qh90(P)%GKg
z)~tRW@ja#)gq9OdIXWOVs{q*Cwn<zY50s^n4=bGquF0UwR@;+64jLVE`8BC#;GfuG
z{9#?Ib=eRiGjenyh0R_=$DA12V-;`xp4QOEzWD^-QN9b#3{sl@`UM$tsS7Ll@@t+M
za1|%AP98iTc)seqBSN$St6+5N(b8{Lk>=6^H{UOl0u;Wr-HLOuRB-;01W$~QlHpbY
z5KJrF@FMy=vhXEMH!pOalVs652;GD<9Vg$~0fDRPDfH|CCN)GoOHnooIZ5)vtnRep
zub8IF$Z1IGysIurX{o4ekF;59Lq?MvG)zIfgm@m46^!pMO(&=EUcO~)X_(9Dj&GNL
z90P6jP6TU{zy|2<u>XXRq&wULBqe&;eg_AlD=lZz<jo`uKbD9I$gYW8Cq${z9}8W@
z!id)xnTc*iFHk#$w(tdyaVLdi^0&sUiGlL5fbZk;tj)=F#;CGFu2IJLhQHM*VLVL2
z;E~ANn=pAbmrt=@3l8(>%zH8iON<f<B)2RqDJ?bQy>al=nWqPlpF1MQhs4*K7#}L$
z;wkr@fQ##<d{-x~Xo&=*w=2>{H{T`KF}?NLlI(8NHyxTD>sP6{B$#gQOGiFAG#=E+
z3+Bb?@XW>@6xth?AqRNtq^|DMW`{vp;-`X^y8`@%lCIG|WyB+oE(XW>NX`nY*Dkk4
z0R`Z{u*2f$Ql>de<}PF9d!A>htg0#)m743WS)91lX-zt!NpK%n_nti>wa`gqxOPo<
z??HckPTW()F5|Xb$BQ3oT>{(5m35^xVG?aO3R#>senM0<E3e=lDo?xsPe^hs=7qRF
z*m?8nzWsGpA=x4D&fUA$lWvgoV{b=9W77|xKJ;kU3bhezVtIp_R^><OL=G0D;COZY
zs|x4W-<6C=0tA%?M9}_|SmfuiXZ7l>70!0*!52Y|B|)k%@r*$O#`{vV>{^Jay^mSN
zBeF#B9HgVB=Uo5!(}HY+^e_lo9*^v0%3qk832;)V&wTc>a_jC;G*Xr{1IU#JV6BRJ
z?b3V_1?n{Mve|J|s|mXkJ<KTX)iL5(zkYKULndfTK|<ys&6acP56)%$Duo(~V<t}s
z1Sbx3zm1*4cGjSDA@%a$r)Kt6q+JGt)j!SxCr*(@yB$_vhhJt^1NS9lnOUL5zs3E;
z7I!<=1k(>xvllL<4rAav3?M)oz(Y}CVHxtDSoVEKh7g^a_2c!DF_dZ7Mb}I^<sfe*
z86GQr&@|2L?GhQDq}&BKpZ9JI`k*TO79{9!aZb#+g#*A4lTx6xs#3Oix5CL`jhgTz
zwnpewHg?y}-V#bO%aDtT2_*|0{4Bh`OwFdKG6RLo&wd&KDbyNJEZ70nIHb<8xZ;_k
zWLTjY!^@kI`rWu`OcV7U*?n;{s#_0RLLh&(um!$nJ85<R$oV0}mwm90!}tS?UI^SH
z80AFkY01eUqImH5)W95b{$x&;5ezQic+P*$9-d?*qY`^u&x?1PqDQA5X{cI0j^3+>
zS~eV96eB>h>3yjUhet$zwO%LjppXY`N^SiDYQ!*xBfTj8<3|rd`e*m`!36{!^)&r?
zLz1Z@(B71|UuGT<z$h6t=AM_WS}0}k(f}E%20-kwe*ZB%qF`LX%17Lq0kcO7q&Zdq
z_E8ZOC31OeJfH&DJD{|)5_g=`yd=7ZsRsB;_GF(~FBHQ5rJYKE1#wvUJ$@XAkq1e?
zJPDm=;nr*Z`rW9oRFQK`FeegYfNh){B#0R1$@sQU^hfR-v@2?FECrOEwE*gqyaj9g
z_Ko6Bnx<)|Nvb5`a$-;bB{e&}bJaCKy^6^C2&5Mx?@K>0oUpe0G1o|kuT8I(al)&T
z>ic_n0%S<QCO{X&e}zEtFcT_@5N~3TO_C}=0ZT_tvZM+VhQBIc4j^EZlz!OcKu*Td
zBtvp-Q0#9Okeobm<=|sC$K29;4ShAWe$XjqhdgInE^&4&-Z7)&pgCW&{*#-Q(`PbI
z1RQwz^_Ws!--vf->wX0-E$&LbP(@%n%JejiQ~f-5iUueYw>MYn{PxvbUmU7ej6nF<
zuRd*x!Q%WoDvCE>g0ojmN^p=6dVG#=x_w#LEeR#_`~K169csY3H7vh8*`z+{tRTF}
z<xTRXVd-bzn>Y3ED|?+{HwF9rG)27N%ATE8A{(;84}(vXz@e&yh{B>%3gN4irg!gp
zH;1x?o@^*z$l~w-cQ4f9;KPsMKv41NloFF67dZ;HU2hU;y1T^h&-;?=yh37!3;6fa
zV_)Yj`%@-75oX4}#G5XosFrj7&riDTVC<R;;`w}9h~OVw)Fnw;F>{GTCAM(h=05?&
zKr#N?fY1L#2mbG1N&gcc&5NR<O>3@87c`Cc0R}32`aO8Sne#E+!psA-Z03~qw=#GY
zz+Y6>&|oD%B1GoMZcyy&&wN%~NMaR`)8hsBvIbotL1-6Q#$L(N#;bTNTUOTZTX>!O
zsUTrw&|pc^=(WAF_l;shLJlM7VSwiu4XFuuvGt1!=@H?_keH?*9EY`sA|&*!zn>pM
z;<oRy#>{qCUH845Z!eL`i6n?ni$+__7f8i~s=d0R<rvH{ia%eHqLbI_amTcmqc2{1
z)L%Fjwh|l`!~lE&DyME7e?G4=#Hr+aAmrIqx3GV7$A=VBQa!eBD>$DN316Olij^Jv
zjTP*fE+*Aoi#K6-!Z{1tJuEg2D1OZKu(GmkP8VOmX%b=~e~RF8T$e3)UHmK8w44pu
zQrY3JuWq4ye8-R55ra+d=@;j80WG^|Z1c*IY3_dR&#cJ}`o%?b>Uw^D`bXx@Yk?*H
z*4ayMosPO#SNq_zRZ&ghla~bv4Mxs7HW90zojIVrH0^Y1GT(bMDei--`S$HQXDKp0
zDVS09dXB+1{!;=0GKm^f0h%VU8t+H?DIyBw8&X3%awb#tgH-KxCZ=w@s+kZAzE{Pl
zUCj69yf1fe%OUe6(@$1OoW1zFl;zKR;F7fRpD@b*^}0%)(6?oHQ<Yb=IQD?UNj$ei
zjbDqu#I52X*{&)oZ!_*~m*EuWc^Esg{|!$5w?MyTcn)kYPDEe2hTQzv5M?^hlU0A=
z-@<)+*V*l=>X$HI8hvr@L)mIhPC7NXbjLP^xRjn`zTeT&`pG}4zfU5e#MifDV&pQ%
z`T?FD+YgEbt2k*<z20rB<O8tjE5x=8g!vn`-^~LyVH@phcv@5>{d;msNVSR&V*@-f
zTr1WcPH1b-jG^ZMG2gY{uHT!J(W#R-{&0HQx2MXZmk+U+vs?gWAhE@X=cu_UUu9`N
zhJpK{qT98kD_LBll$1KU1l2BDJ7prmlNWU;YjWt&{l{Y=TblJL28Z4Tah;k@n_k(!
z@30!ZCtFtT#{K=5oHfQA2ck!^g~pcoSDY@$cyxbfbmZ4pt0j~j&E5y=D+@Ra>5PUB
z`%g`5qh0lyOTbJi>4zgX-1G4HC1Myl{Q2{L6MFc+aql9)<O_>$pZ0=kEFJ7P@_-Cz
zEBO%SczU?I@;Et&(W>K^eEpVmK%jS5!NdY5{-#?+y`xBpHo(a}M#g#oA#RB7n7z9$
zZ+tE>B<rhdB%Hab?L0Z0;8;*lu#1PMhV;dl+8d(Y*VV(YkO#3h{{A(Z9L`vMDHtgs
zn~3l93O1pLhQ93%v&o8-vYm_85f7Eqr00dDO}Jc86xj1~^S?wz?LwKTLz19S6n^@I
z)}N*b56h+b1~us$EI{<8Rd57oR($JZ<1;v!6!hnOk~m9yPDw|cJU-C3xC0nY*6NzW
zusKr4=uQc<>Yk0xU}4h~Q6Zew?tSnHoh{Wc*M(t`quih6bDQHSW0$<x?;~`u|DNyf
zX>kCn7}Y-xZ5jGZ`=XvmeK{Te(6j<Y2S;^k_os}@@hb7!vFHT|1c{ax3@lZcgP`P7
z>HpS^+AtE$3fwF*QSXHU43Q_6mFO<2K>>%gmK^G&*tG0fr~?f@P6(2~P0VaaKqml?
z?zbDvl;sd2e;;Lx(a$gMq2=|{2DIWf>IjZO;QkAq$hRlS=+*WmV}OxgzdlT!JNj<l
zckK#*Gx<QFMVc6&(Dez>59~nkK?a%Knsw`Tb8s9D(6qRX#-JLlYQl?;SK{`s*9#0M
z3GW!U;HXb%*5lUBRD*`SmdtUPHXRiD=7h#wmSD7K_GU0z(<TiNS}K8+M1mU?bZY;=
zY;EDP-|r-Y)tQ<&U@xKO@MsJ$3}kQUnaTnSDld2%9LBEhh~Q-hj-fjQSUU`+a&LtA
zq@miSUj^%Q3i)I)1_F4o=TqBZIw6V?STU{9;x0%D>ukQE3YMWyIOTlL9FzkTLp}Fx
zmHfe{$OENPb4_uNFz`Zmg4P9@*K9V8ckV2n#BWqSe?A=Fw^#H6VUoSAuBz$+etZgy
zk1Ve|kX(i_?7byAA5Rh&Sq+AWWGW;?y=ZL87HoDPM(Uiqckkxu6sg<`2`j>nd(e%&
zd14DG5)e`#QA_Jg)%_pa^>6|*ykShT=D=J_L5m{#`-?C6;jDLmX0IC8ZY+KRP;@uV
z4lG~3ORc$;-#ZQ>ES6g<U6~-_2%%4gLk}3THTDg74ZziPBmtmc!=Fp4!}L4_;JoP6
zuXfVk<A<xzdbF~j?#1R#tI+cj4F?aS*L8#GNqlKwNRVA`C5;k!gTcywhH$FZKae1e
z0K~)BH&1L4%Fh7H91^ce5(POiG2u<A=Us{iuLk_08s6fuApG2F3J{;{mj5|EiR8~f
z<lKJ}hIlG}?Uo_CtaLz%aQGD?Q}w`o`geFTe;lS&0DR*67aX-!s5tSzg`}pORrePl
zotg;1@WUhMUp2{Y0bd=U;IMx=04Vx>=U_J;CuSPzY6eWRFL*NFqBVqztU)kzq?YsR
zy<LhNMlw1rdjDPlRJr`uRq2dRqcP7#sr9Bo%f2r~w*k4Nz;K=rOFBC=`52OWdQ-;m
z;d#MG^;>7*jy8jE7=j&s-X?u6`TOi*>kHEIK_dLrb2b@Gq(2!ZiKzKy>5L3R!1VGN
zy8zn}C=N}fvs!Rxt}yC#7Ml;?1FB=xGxIWGV-=*f;<7TpTaVM3Xj`X|X=^UEa{?>2
zo50V|Ru6sHUs~90HGh<h*Rf<t5^E#ro309Q07qGB|0t|}`EJYzpj<l4=oFdCe~U_B
zbWG)5HhsSI92cA3?s=d`G@NSElC722Y-jXlGCpgB^)tWA%fyCNJstA`6(I1+YN@}t
zG`O@jOoY`=)Hb|m*X|G-E{Zob|NPy{i-cr^*_H(%=1)Ob<%ujO0Mx^xMg-N4&-{5O
z?$uXTTFQQ}7H9)RM?UGLcZWY5`ql1rD>?r8?iK0UuoL$9p0T0ZL;4B76b>HAF|GwH
zu1_&fkycc5y!ryiRb<+6D^x8HC&pe6Lx)47XT!qFyAK^Hk|zDAlcm!W@^9?qnZhyS
zB1z0RaJq22<3`OF54kskXL0E?n&qh<x5TBQf(o<*8$H5=OL31BP3)Oy%=yhT2x@Ir
zBxCKv83Z+`sCW3-gk*~WfJE0$MXa7Jb}LQqRDf0!0lXf-z)`3+D**Qmf4;T3hpaQ$
z$)fv;0NCU@H08J2*iu19O^r}K#D0HS6|0q@0(rU$`nXFvPt2?c`SmS~XOGf{LN`zD
zY}*hh(bA)oUz*F+u)*}@(`IwftG%iEu+#2aGGsP|#vJitKN@F_a_oaZ3+FcYS6wbq
zTDL_r%Pxg#2tIsX>$vjO2ievahMl0N$D7HDaR3x=TKzAT_&b3YzePS3hHEJIFhm?~
zK98M;J~enqLJLIN3J8H?N$e|x5LKYpBB-#T(k}nuy?bF;tj8P8SdOX5557&@o5Uy)
zsy4tw;l8J@uJF)AU)>{T*`$+r=`%`+sq8QDWw*KOOP?L|VsLZ4vE<1dbTK5q7{nET
z!z8vU5ZN_A2|+lrYWEOyZ4_IvGMF{JCI#*#S-;xuw%C4GTK9d)2MN<IoUHx+tHmCU
z5R8>--Cgml+5BqFhKLds|BKh_2s95vHN3-0*xG=CU~1MvC_4a;hd1rNRHD>>{EWRM
zyW<w`_=ca`5}pvr!Zy#l%956k0LXwOu`6=K+-o)K0?*TF8)XQ66>+v304Eqhj7rW_
zFi!X|cs=9QFU6DI6_RjsWiJVM^xQ1&R6qKR-N)|f90|#gHI1Jel}?>{cIQr4a`YvU
zAY?~+#D&guXM9zR%P6eHiChC(OjYkX2P2b{`F6catTT=fr7fYb(i0elB1x764yRak
z_*gklT7R07dTNvl+<6F39C>=Oh=dNn)HM_>BH7Y-mm+QutzC3_y2uytu3SbNSKYZ6
zgoBItWXVU+51&sB43EUbX_u&|=vnbMW{XQKEi8t>2{XhdLeMKx#2~d*5p>EW4}=|h
zgnLiBT!L(Y{ttLJb$Te0U;9$tJmz;nQ8RA|)4XZLzV?@fmD_1im;F%})WSW`3SrDM
zkARD-3Iz2?;(iCD6P#)7>+F29`)%3EM@lWve%NKvFWMii1;7K`5cc_N_72%&M8rWO
zIp+jV135|u1UR8_Yt(Ke?3Gy4WQVN0&uOHt@+SV;S`3MiNC+5cp<y0#*D6iHGoq)?
z*jQsr>uK7&P$~_9QCPSHQzxS!j0<|QCc7W)uLu$Q2P)jt(=-1af0*#h-Y8ZF6eRBQ
zG~Qs);m~PsbNaER+9{+Fx!Q1wy@(6NiQrS(xumz@ufyX==LrfFGi;AK9P0D~ZhqUn
zXHUTD`l6>im}KL)!@c3J!@)cGRWQeAb@RThyh<u`K>4c$QZq9(OGQT?QM)1OTVA)$
zpcwk495-u|ru0(+54q1q-?+<@E~RJ^+&(?oGVMo2O)h%2!OgV<n4vta0_b4xM&dHN
z5m9fLTi4uNr_F*n(jjKG<bEqY@@f;IEq!X0cI_!`?YIk%uN<S#R1;$1x-5AudSUZm
z>qY*!_*2;#;k2e|)SP*xKHD(^Y{F~%Jut+@q9W&053%;3Y9TsRck<pG-EZJtU%xvR
zlrElN@VRAbB<+lRE>bK>NNW0E$${$)1dZJ{GK(8)r~i!`tJnT3ZtTutz*LSwyj}k&
zXxvoV%p`f|1$}*hoivEl%Rvga!kSnh**!M5Wc@;yUX0@Xs05xzce1pkN6c>MxzqG`
z%Wgbl2+Hih5tMwZWnF!W9=1-~_7KU<{_z5v-Y(QzbPgF1AEsUg;Z0}k6Tw`0#f0en
z@JNKxD1r&W1fB#GgM}~u1Dsj<ll31)O7f9K<#RBkswT7TX4`g;`DTJn6Tj@%H3Q^1
z3954nj3UVC56t9z(#%F&uz2w2&!JwOX$_Fc(@Cq;s)iVFjP81>Rdq<B_G^uO#*Hr(
z=;RnQLSuk3N&3@oZ2Hfm<FiGK$FT)aZ;srIi$+hO_(kl^)iE+Xz=2xCyA=J%(ab)<
zpd@@nOh9gkA-$%_d3sWJ{J<<2eRJ3of}e8i;>qwsCMWLj<I$?|mYHz_>C*^$pJo<p
zn$ef)?YJEI!k9;eL9}$s(H53!UlWX;#{niH(+Cf3J_y^%AEAfTBb1<kfC$3-)W~tl
z!YC*TPcagZ6^;|^kavgr%$!f748Hp*{59`V(8OiHKCc2<LLSxmk#IxT8@I?B0=IEe
z8sh@8{zy**C|VO^zaSK~*Y19;)^aIJ<TAn0h)?YS<67JH^E^<}BruOsP*kMzWDO~5
z;g065T|p*AIJj}v@8sZk)|`%AU^@-NJ<}fpziZO6pAj4$Tc1fnie!`JF^w0F_-*8d
zVg4M04(5Hg)kOpV5P}Wlq^uRwBDuUvC%;_~uOu5V(h;;E$Mgd>O~jj!o_8T6FD^~q
z!jdd2>=?tZm+a}1cOLVFA@dWs)xk6Ag!shg<>;~-T5qvO%>RvAISZQFr%3yT%aH<h
za15*orzU$asNVo~LF|sXf4!I=61K*=-`c+WUr=KiCQKGE(Fg&?PKcVoqhD5C{c4Hc
z9_Y<;w%q_7jXc?^wnb0x0VI6Ml;v`w^7Q10PMg+zjr%I4IpIA+Dl_xK0WvSFRo)4c
zrPMDMqd*|{Q+I0m(cA|3n?mi5<#6^_xU#QVzm>~Zpl{Lo>w8F*pzL`esqXOhItB?n
zjw8#vlBzMBqSBzhyK|ZeEuktR-oNKeJH5UzyGSS>cd=_+dK3$*R!$ld&)B-%;f6CT
z8?;dfMBl=MSzevdpP#*Cy3NJ&DZ!oZWV)T<f(%#!$%+?#^n-Y0*Y)()IhJEd)qj4L
zvXa5-gb8>j^uUx0ist4oNCIy!TwI}`^WDmGcHy6&dvS!aQ4*`pc*Jtq`h7>BNiZ21
zWSsrW+(@+>EB^f4ivL3lvHy)2&p&<i5#aKaOE$d?=^l??eOv4o5Kv5byOmT7T|g@l
zfm;;;V^M$98K(rZ13xYxxFe0`LnWBxD|$G=tEpH}z(gIyg3&3;*`GKsn-UWzs;5@{
zAgRt60J1Ojw?j650B=O5wi#i`!VCv5Ut!?~`ULm*mF;vDo;33N@tH-PgFx}Nn+vgl
zgd%sp^Kn>MeVQbcuJ>B{y)2K)w%cTY&84iZy@_C+GYl84mGPEa2T~1p+^U|aH3lFA
z2vT1inE8Y7E63t3OODMF%17cv&%MoHp*&vzBIA7hSB<O|hwL|6vKbASeW<9Yz|hR9
z(flC6wpCPAet6U|XY4Pj=F>uv$W<)uCinmb-PzRld>`>eFdI`=P%uB%9<xN`&GV3B
zi47fvz@OFm`4d;RYFIvU&W^$b14idN5M(hietpB0g9F^#k7?RuxN4SZgBmqMPZpMK
zv&lfJY<MUwZtZlQ6(IFTCH0+U?IrE|KKgHL07zfdVbQHd-%TzG!i#I0mr;vS&a=3Z
zsm<lGn87t(#|uEy0J#sKL(L04^XFUA>4-RX5L4%NsBqCudS9NNLVa)idGm=mQYZe~
zU?;U8gF?BGhVezVn0j2|&wY5_WRY-&t74Jt+P&v4W)=RrYKe6h#U&3aY${k&-A?_u
z59JZ61j4`F_+kT{n$r@@TN_y0>j!8lAhF%zm%{*0qypDN*=)V_^r<Wn2S!b!?22ta
zRaK<<`N<%I>y~78l`@C*cgz;fzU>uENgBF=4+MBIgG)z?N)M<+Cub@XLdT=+wBd0)
zLMlyPpL^O`E;0TX%0l+X_7xogO{M{1cxV81Af?#AFGllE-*jMQM-=P3%WVlu^Pf+S
zD%Sjr=O0u{pFmDBLE6J#)O|Vdn0@@_Mry=?zZth<KMUDK6UU~pLAoU6_HNGRKK;IS
zfG4zk28@n(nfF~@CTCCh#eSLSP95J_G+IdI)U(a;8W_(}l22}w4W7DO9^9toN{!4c
zDdnF0eVop?`S_04j-0;tX17k<Ny>`t%u~UCfF8=DoB!+4!9O!@|Bq8y|7kb*XHxe6
z_*D-HhsDI40OnAQ$v-%dS!t0mF;$YwMZ-`Qk~yi*yY@wO(*%Ns`wcm`G_+8w)#9g7
z+HOkG12J=}==K-3F|#{4Dq7Cwc~5>5<r?5OP#=7nOqVz0fcN?l;&Z&D>0>?7*b#R;
z>=O@GKoN)rp|H<}u;F&`qkzCX9Ih)E)^hK9=l%wj2QUy<-6MUPKAU{i4yw%l76}<i
zisQ062nnf%C>eCe33Y16W81?3AV6L$N3k%O(7=Z1o3>wXMNp|y4H0jFf$+t5vGN$F
zQ#qkjzu0+a=>D6w7k`c;4OYf3%R)kzA?N90(<Ck#6h#PS@BszaX|h)*8p*}Oo-Kc&
z)C$p&aDwW2=T60ukAuylXYaOub0b_SV{D0JVyg&Lh)Tm39E167EO-rv;pkOHXqsSG
zy0zzw_3WnIVLAC95^Z}nCLunGrU-!kl$KU3io;&QmIa(hxCTwIfk2_PY{Lb0e^fi+
z&<8?yDl1~~#SMT%EHL2iqTTVzIZl(W&kIlg49KYhV#|N|yC$B;(PvXqUNlKBKXA+b
z1Mqz9U|s8<n{T<rMeI0+lk?ZDHf~6bB?5<Ao9v`ePN79B2N7VaYYYRZvjJ2>W;vHp
z+goksJbv17tUH3e;qIL~B>gE1?Fzx}zF3Lmd#+zt;3yVMKy#cJ-h;8A1}zsI@+Q>I
zRvSQ)RZ8*z?YDeU!>{-kMk~|$BzU4@-GDW6G?sjmus-?_$bOROWvxDq0DE7vc5M$l
z5Nc2wDjf!ZME)e(&MoV7n;}sXG?X-cM5EC$_<N`YJs&G*CxWg)Ak|`gf%1p_ZVwuL
zFg~I{)+Hi-(A9`qsLohmqnLx*o~$7{@!Fom^Cj$-*PiRUWG3INk|z{0Le3<|5#a8R
zK?hVJ&%65AaYP;X|4z=x5euzshgaZFS0RHHjYOnYh7Ax)Iv63~DI^0qvqok4g*+#`
zO$0uC6)zi`ll_ibG2-%{1fb_T|Ctx%)_Gz{z5wHomXdVC89C?g(IA1g$qJ0y&|^7Q
zlpr8)?>;gB!T`WDbKsclBcZ+LbO1~d6y6AA9zqJtXKc)6yg}zf8KY%huqH1D@w+px
z9ePHT5OJ8eEND>Y!jyte5N6CdTUl=9KZPQ!$MeAVh0)?Ga)axAUvUl}w>z=bWcf?n
zp!`$}!I@ZiC1AekHQZ=fY6zws^PDbFtS57(N2hz5cF;&S3!HB2ckxx=-|l+H;LTzz
za5_^);+o|v&6ZytF_j&o;~PY4u$f?;?6Ur3ED%|=%1@6$NO1l9(i|a2oT|DlV<zAc
zgjldq$Rajw&Qw?I+i(B~Jy12mw-M_b80L-wS9$22_>=;kMl~2Rn*Bl%m5~JEg4d`D
zBrZ}L?&1yr*IBkGcn(g$HL99Vi8~C^5ArA?D}F%e=S@jxzxtrYdYmShGNr~Pao@*h
zz#}a(E^gUKNt{<p|MHTxAi{?5uQ>bheA3ar>&s@KTw9)bl;k61zY~t(-29bMLMt;-
z?D$-}mEvPvp>`d9s>#>|CZ|So2z|y`<LY6@yFHFT7ZbT;h<PpeN}J7N3<K$}FI3L=
z{5=C@enKlmTfE5WIOYo=!<{A&7EwE*gL;mxi>cX5g0fl7WpChc@OwN}@&L)ijm#D>
zS3vE!)+Wy2<C#BG!nWI3LMO2UVfH0M2cb3!Zv}a*Q1eDt!D3_*nC`?|JrYSrKfJDb
z3D=gLlRJbwV3@m&E0^g;+d3^Pg*WB|OC)4Qv6m)ffCi2H&%r@{><ffY4+sLXIAT+@
zW@&8i7`h4$T4&H@?W&&m{=J3<8OjYgV;Dyn|IqY#J0GTPTCguLe>1tsYe!fXP3cjI
zyD_D6StVaVfD5w>23CKwGqd6s$sO0%e}-yVxyMaq_i{=nPx0BcyX1mvdzl`5sag$f
z3;<yT;k7Q78yJR!pMXWJXmY>HCSw*EBXg}R;M}CQuEL4t)-GFF1txd?+Uh0AR|`u^
z#oGmK!CnP?hbnv7gC>U_6UgVc<?|BswiRQCIO0x`-WykmwoO@0?KzmyN~9+bIeHiD
z{!DK>)betV4@NHGdATJl{+v=TzVPj2Ejj_R!D&_?jXemQglL5s6rdFXa)3v+S$uq+
zkNaO-0BulKE@H;|#<IaUb$-hSl~bn%PH|Es?gF!}8j}?m78=T$OG#F~P;ALbnak_b
z&9<V8b`U-Zt*PKco)jOiE2YwMeiiJ&EYZiUeU4~=yjDU&dU|>}|Ge{E0(^tNFzWEb
z76l%*QvX?7+%_C&CoyD&mt-v9Q}iXR#+@#hq|z4atWNmzq6dVLQHr9;FL7w$$!r4H
zY+emJz$OK>tw3}N4(rVB&Qc4*G&@}+<lA8L<!Zbc++!Fr?WyD=;~_lI>H}hB^FtA%
z&E7C4c0caT<+uFv4i5kb+XyElV`oFd$j5-6!0^EKHL$_!0*fm=|2S@@c`SSseYwAx
zz2Tvva~QTzoGmjzVFhS(n{6`x!V1IoYp|`Hy%M2^%o4$K7_&Sxz&%biBTVRnwh8dO
z4$+zHQ5|+F*j^SD0);z!gXO=^<<!Kkb;tGteAh&WtCaH8i^PKELG1@e2uQWxCfnAl
zXYAeo3HvJi)&W`O%v|ur*_*(<Utjy0`zm`l*6e83l+tEjsRocyM@q&TTBgGBrD-V)
z6X>GiK;zs0vaf1Jhcq!g^8Mh!JG+|Nf5k)T8L=u+O5&t|!a3_X+n+BCDPelPN}naZ
z*m~|J)z+e&alB(M-7l{`>U}&&VZkkiP3Ds2%mp3$7PqftVAf|o$>X+k#bGHa7vm-^
z>uI+pNng<>KkZ*mk8j<QtB$Pf@2=btG1gbaNujKWSDPb}_j}TbOf?j{JSG^iHG)29
zkH?@uq%@cuJb!4K&KY*1b@As=ug!c0-UMw%F#|A3o$2k#7THlo!7UD$d|Km$uel-@
zIdc9w5UeFrnK<Ri9kIidqQ`L)Hf-!Wt}%mLlLr`Dpn`TQXel$QD6^pvY-W?=P`QW+
z?@~!9x)XrbMqr<dghH0-?Vnoj&8n4Ui+B>Yzi8--vsB8YKz{*e3r(UUfIx~k%Nb$>
zNx!)26Tr@U@7g0RGj&<W=oDUWkpSo<Fv}<J@I7!cVcv^Z=a5L}BTI-SI=$QR4TfwF
ziVVD2HQXsatxiNOcz4<HJIKJ0>}*7bV9^N79tNj-FqJmI%Ze6E5p-HPvES#&y8&G6
zNYDn>F$b|e$dsU#f)1DYZccV>*o#_edWqP$vFOjjZ5l5xJw@iAJ?gAOc-yM=1GK`y
z5yjV)rL$%+u+J{y74l2(HUu&*0yY5D?^qZixW#huR3-0GJrX8H8R+wH=2EaIk2AP+
zb*|s=x2>nriYri9Zc_{{?ufF~ar+A1+;Sd|HE{qe{7#3*{rVB>R<)q@Q)NMaWw5sM
z-g@0F%fecIA74MG^c42bBR!TvY?@o<ZP}_ehjsP}C})<eEc^41l+)fWe-0DMr)x+E
znf;TymilBCd!tZNx4`mz_D{mRMSn^*iVL^Y-&$7VLyZ47e)MN|8w%y@X)hb~K)y3i
z@W`<vC+9Dc!Gp(S4#Tt1jnbdsI?-{d%ITIZ*tk27uM$gZ;HCiqx?@58jA^Pb^+EUp
z-hTd^mUz7Tzn!|hllF<Vn1L`hqtqsFKz|DU2L1#Fw^sTC62g3Fy<uO-#ychzT0F9>
zyeF}R0+=sfwu1|$EfCN}=Gr)%{O;YO(xPHw?imIuc<ww1mM;dAAfc5n<1jw{xRY9B
zFMDzFid<C7j^5h#t?Lz|uN>SoLpodeHS2s}I2g*fc(oaxnu225<2(H==O`=J;OTkv
z(3|CIhk7EwY<xL=#vktt=JP0tj~%#|fRX9~aX!RM9v2S$fin%PXf%h%@i!=trJ`gl
zwinqGh55)`Po5o|rb-kV&EmoZ89Kp=9#H!EFUxs4+*<bj&fvN9{0{noZJ{H#=KiV(
z7c(q5r&Sppj6If!eEKfECZG_0Gpsz{IV$pQ+lJUTnHi?&sLnD(t8;>RgQ}5ySu9}-
z5hL*^{N|6mOQC&BNnaLiAHVQemEHG|K7AHf%u7GdwT*w3NEG_Y)?Fu)B^TZOe5uf}
z(VR}*Uy^fwzW05#Ykxcl{;Ny>(y;>+%8QT5T)(+xv@7v2$aBI!oJbm(7V6}eec;OM
zz}BA`&e_{Js-C$dIWJhEpWkdr1O42XUjGKu;(HF_Eo+)PCr8_D*BTtpOe@f>N@eFX
zmzUwDWvjS9Q@dw%<N#Ok9UCFtfxvM0;PHH;z@P7~wAjijdCs-<qKOBuV=EV2G@!lT
zef#)ZU3KZivzgNl+V6R(mG0BOkB?p1G+f|(n$0t~vm(skI3s0Px^cfFg?ZrU=N!Ji
zVl0+95^N7fhu0M~C-T(m-bkSOu?<*$;3z!JUiR45?5=!@$$L8+pDLc+^`Ae5Ja0=N
zn~A|KhAnTKQsYmq<FP+buge!T<o0erY+6e3cj@$w!>6UG&t^9l51XBolgGDCJpK#C
zd9&sJ-&H6SW3sC}hip+3qE#c6I?U2Yl_PLmxfU9{rvOxXp9N?uB4^{FxU=5(^j;ne
zH7A3Kx)UwU%~PsEeeUPjX1803gIgIkUdO-Y$Z2VbNyJPxRX8vmoGv0Xf<nde{RQ;n
z=sk*gmp+S#xb<)U<4p1&lb<BE7VfCoHaDH=c|k_874RGjVWVjg<xUt%W_WcZ^em1D
z@^Sz3nz*S_pXd>d@$4wlf;v17@)-vWN?SXtIg!VK7ehD~<W{vt?!dP73udo&MZA>6
zyC;w}AETr7NL#zv2=Iq-T?o8Sq&5f`pTt}Oorw_XE75lNfHQQmf6XhDUSqfcI)no<
zd%Tz9>Oe5uNx1o6?h~6WIysp^{d0u!WDK1kxr@<giHMg6AG{TgwKt}kJQN%2+U5uY
zPH-+&FnGkq6#>>Cr?BuVx!>TXog`HP%$|VQ5)~*8`saWXhyx1-#%d9o2&t*6uTx_K
zXoas?f$BICcA*CNB=q_bk1XyGN*HhwujugElhJzxzVCei3FykC>EHlqSjFh86AA|q
zgu|)p)~x9!KnF^h7$S?rUbp-^{1-q%inUBJMUf39YdAbWiI|6-M#yRNy|Oy|Pb*`H
z-k1jJ8sXzrB^y>+l?ZoM-7fDPhL5xQ7&JqYsr{c`ND_S<;h>R$Ht0%WT7=Tr(q9W<
z5I@>aZ#{XD?l2J)uZf3?lJyU4xGPB)Lxcx_ztEBqumg=iN*qH?6xlL8HA;Ot`=wTl
zk}m~lLyC^n80CJyT#qYYM4mc%@(E<wgg?>)O*SZB7=*xnFB$^nD8OxbKmGLy!IfNH
zHAn(YpzIOC8OmkQwNoJchXvwJ>g^u<A242N6+}BL!}ID0<n)oa;0P$IiFO=%^~Y;@
zG}X+`jy%1!i7<%xjX&{fISyBge7>;?*f;4AM5iYP0b9lo;eQu_gXXp4V7V@+|47t6
zm5AyvaIb~hY9Xy+eo>HJPb7OoLwXQX0rN>{r9kA-!sAT`?*=0l!V!e2At6Y45n(pa
z&*#7uKYq8LUD8|D`nY)R1>oYVoT2;%;_2S`#YEYI$hd`dBbc7ThQ_Xo9i`mdG+5z9
z<4&ai{`yd|$Qdp;Gxq{oR?x}ze~HoqW&zi&ZU~&03U9xpCq@h8GgOrNSq)YWM8h*m
zeKc~_x37T9Lng>r-Ma|Ej(wcyeo$2R05q(^@c!`1va4a4)Fya_<#h;nuHAWgDDb06
zQ;BQa!e7<#PNyzB1icq)I1a@<t@PlaJNAJ3GmPl~%bb4~>k#mUutd>>ZD?#+2^fYs
zazXBkg1r7^$;74wv)j;(uJC0k{C!vm4_aAiqfa`8Do~Qeya+$McP|2peaIpKJ$L@*
z1#x4s@?g*O8ZV*Bg83=AvTQgqDyjlsg*$R&am!by!o`jE_XrB6HFF4H@{<NRh(_TE
zY3@4qI1~Eqkk0h5&Tvhi#Vew2Cd~@4*}VHR8<$AC`+n9FVw=^!7uJTyHq@PD`{?K6
zQ-1CNh>XX~F4+<fo<5tjU#}RpD6+Y3t7qRL2i<=w+XkNC(sirMrM5CKJyB;{_Er7{
zoMVoB8+>9Ih%ZYaSD%}n59zcDp;%je!;TyU#K-_Vu^E8y;lpU)V>KB|-@vdD#2mmO
z){W-x_{W7A1;1vr-PzsKG((oQ_Kk;OHiR2v*dF=8hnnBJoZ93yF(U4L&upi;qtz=y
z20dtYIyLF3kf#;s(gb#e4Xj@(Or!86TC*hxFQM$4+U)D+qIQ%~#1(SRXvm%ER<9TB
zNdEN;FqbyLs9|jN7bt@k1dQZmaXM@(q^y>25QsTiIsmvy=AzImRyBHCSH})m6F+Qi
ze;fDB|6)9&*gl3ysMMwDXlTA1^ICF`XzebYKAnckVr+Jvb`%AlR#t5K_KwG&TC?HE
z*M+ribbVY&Q4z>;%@<3)<BqDfb0m}k$3&O&g@FKzs+Bo%_apH`aj1nqjo;6aW-QbE
zJkc?KMeE>i;<VsarH={ql=M1E;9-2F)kHrno16MIUm7$8B<jQ#^JJVLC>h>y;v7Ms
z96O9D;F>dWW~CkQa(WoH&+NM4@kysiDB+1p9L0Dq_p0Y<Fq0fM)p>bIS88h}Cu%|>
zvjY#IrR(SVs}|UB9ST2gn0e>fY3OCkElWisfV;`w=RJfyk5J;Uu4e|Q#5@ZKep6R<
zJ#OiRN9M<_kE8y_zVNW2@tC^*8Xr3f+i9-5;<ZP5?}x8{AsmyPL%Dm5%|}CqRidrE
zaf8npP_Vx8`rl+&!8svtoBgWwEkaaP5k(7t5l9F*GzbF+EKNd}(tfbJT3eTz^6m6U
zE^DPn!8%({@LgPummk!T>1)bRrCZggU*L~!d~o5hdjDYRsRJACNGkq_>VPXJkpmJM
zE)JR>bADnnq72**dAhvivxoK;cJGcm<oAO}zWId+Q$Arehm~z;MB=riVpj!=Y#k^i
zGnD?}?`d2z;2`<ue_kkdLE?;PFHSnX@!w(2J33&1UK?Wp>Izd`b4{P|&Sj`Duoz_M
zFiKF0D8X?@7}fNuuIi6}6)Eft8zmWO72!1^V)dvQ2qzmoFZUPecBpo<unvKidVLwN
zJxmX|a^(s=j2MKF{aaZ{X<Ouoyc~uQGlUYrkjt(-&P`m!RG_M;xHVGXs?$(#mM6l1
z=w#?%2!C31L`%O9+GUa12wArvJv?phH12VAziP1~$V#zrB-QkSq&fJpXuN3^M)6r|
z-N83JMz|`buR8o&Q$v0K$YUik&oXtfe8z!9Aoz-)%guFNE0+?!zbPoVt)+;`sM=tb
z`4sBf(B_|59Z;AqRrj@pts}@+u(XLLo&!&t+krx?|7qFU)8T6B{Su;L+HlK#GeVsK
znFb2hIEWnhKc$A6m93L1x(v-mo*wJ&THIQ$W@Je8fU6u^sp_iR1*U8bbl<Qy6o6bJ
zwz>09UERjCsEdVJ?Xp^wR@~DU2jVw7wr2pie2#Zu*Up_~VB;t&D~ExR6y)&py$Fs*
z{<Ck~L<Dp9E|nyLSCkB)1S3JXOb}XHYiud76I6v2=h9cqAt)C>VuJ_(kEh4O2(yNn
zg0Qv@C>@yq5l=6|?Z<Dif;t36CYigz^wJwO2^%ED!nW>!9${<A>R1w5ywR~TH`Vxp
zp1D*7n8&dY8yq2kD!}o0Q2h*ujn=os;(H*xVu8>ULj|||p^>%S)-{jT+VT{X`$E2t
zZdDKyM*0=p3>@jWGXrk$_V@0&m!+?joG<Z+v@B5#`o!0SIkL3F;%4J?2nVSEWb|%=
zASB!2AGur1VfWTy_#mY9?XJ36-0W$?;-=Sqc}n4Jt|h_Hea6_9Em$YT;I^>l|Fh`F
zzk}Hbg>FPzXbn5R>=i9}7>Ieywx{MZ<XuAPu-U1L2@mKPbZkHyI}Bb2K$lMYX_uwo
zNp--mVAg4jfDjI1I!Rswqdj|e4+?Dvy(pZ|3r!W37H+uxgZ;<OL8A2qvLM9fS>M9I
z`Q6~w*$ruxk(qHj-uDg{@qIM7wTj>+IL~i9u*c8znhTZQ=6=_6MK0sZYV}v!>bHc{
zv>)6dFt5atO?cy)mYnw#>aP4dqx-oj>=@c@9r-M<fB$~l*KF_YLOI~I2y_LUVRWs!
z#n3_H5TZhoBve#>V-F_A{`!$1uGc@k!M$S#Y>0@$FiS;ADHPL1a*b3D4$P=eE1HCG
zDl{8G8Ka2vkKhb5N(e9=fDoVUPubRM5{X0IjY9>K(|dq@2JAu$FaX1E$ENHxe!~Q^
zi63_(@wYFh_wB%1#r?wEfZWsKq9S^wx_zv2NM`NQ{F8t^@cE~~37>&DPTU_2oSj03
z0M-FI<#`}(q}Un0g)c_yeTEq&g#OzQ$A1moyO2DGvO;~?@h{n`*iC$OS?E+)VrX1W
zR`d5;(YKSE+fSEeQROCB6ozo4C4g^uUT}MssG`ZkuwQ1yK90T%`}reNH{Sk{QP(?M
zzt5C$P}RuGLzxZG7A8Xrr`Y-@*W9;Xn!m7@-*X-ukd}(c-FQLeDCti_ALEUWlD)kz
zK)M3G$aOt&=K59M3r${N6)<bBqrAvVU`%4Cu<66dk(5}}Za9QhqA=Us;kM!uILU@k
zPhb?J0$<<c{Jc_#J}@MqMq*$<3Xf1WW=5i-YMJ;dNDl+zN>FeG&xH&`Rxo2DS&>q!
z9gP^0Bf=qtcBtc0aL~~z;J0iI#_w_At#cdUkTmwiSDs2E%vP}1*OT82cLXDB==3nc
zH9(_Q#LRvMJ0T%*W2~A2>so_b*Mrg0$^)2&a}SOVJ6J1)urX6^wDm$$I`ilM1>Wka
z4_p%go)1aUNyb2Q5R<t<G{@B?V1fyeS0N-!EWBIlKet}MQv=3NpE*&`Jdrzq3Dy|W
z&NyhG7WV)zxVP=F1f(0dTkJGalAuQpWm7muuyJS}>0u~(cVogxz9_yS@ag~}w3jhW
zJAi-Hif|f$o_^FwFmx@1UYhPwA!Ofh!(GrKBGKlyjnJ<LASqx{z(FG{SJ1rgZDVvf
z`A>*+YxKGQ!flz|(lAVCAX+`7c0B{P%({p(qDy?LUEB%}Z#O4rq}&yNxMT!K#DxHZ
zN!M+F&J3<tF=<ZtJJ9&miH;AIg5hP$vlW;)456sL2r4hAv%dIDa%xq|9gxN0s*D1A
z!@&{K(ZSl4HP#tAg&vIJgZhR}RldIBI7dUol{yyAsU5EhJ2d^IB2O4pfaj}KDDYOc
z81x3PA-Zs<%FuueW4!ttMB)oU*8XHZb-ubh@I_gD``Oswl~0*oc)sGZ*&Fb7``3!p
zbst|Pr@&joao&?Y;QCKMA2mY70jtlKCV-=ew;VA;A*5$%8g0>cuxfBN=iqD(!(9gJ
z=M(`IhzkW~C)h1xODwrGk_ne2kXQHc!(`5ydjSDPXt}LZ`>|dz>3u=?)kG42TQK~X
zMZ=#!Rb7^9{-^o{#-a>k9cR8N<8Zm4tWlH>qa0>#A`L8p+@qj+cZ1LrnKW5AYNMyI
zf31eSX3VhC)@1e?d>MhS6`u+;>r+Gc15$CZui`1m>`QX@#%5+V65WnpaIhKRf)<p6
z$tNn4!PwT5eSEO4sI)YVOq;y*K-oSAwaRJC`WBl;$Hk?hgNp@uyZVYJNDN@fw-6jj
z*WUi@DJ*1u$1y7-gej^3q5|oJtCBAkD%)OIJS`c?gHTqve*k*U1o9ccJ5sx@bVq+R
z?CUjBkKG{Yf3)SuYrGS&6B7=-iN!ogfZlt*U1&^h1pgui=-+9gdxN)}fg^_SDo#$>
z!*GuixMFPd?(kOkr$-DV51!%iF!}Io;PxxNpCNk#TrI!7XjG-tFEf&u^X?@$zsJFQ
z#2Z-&l2?aK295&&Ghz5(ELs|LeORiCHzk3}PE*AGcD(`oD8i}+>F7q4nYurk(x(`T
zbzNJ+WAN};!HjDcWDKXU^%3SaPT2LXD7#i8*HD?P1!_0?NVB0`t8Rtr=cIN=`x|!m
z?oKgXN*j`;azRN*w7)w+BAH*j*EG-!hQGwZ3?=8njm{GTFdzLWmYSl+&iwWyp5!Lp
zfVT_2R{VOc$$F*yzD&OGh}!qv6q8$X`7SNR(ldkG0JS<p;L*GGax8JKNSnzt7r<mP
zib1|r=)a?IKA$@cA2<QC<`{_mP$eJXn>u0v_ZR7?K3`}(j>Tw?6=}zAlw9e!iU;Fk
z^pUj1*>8Ogw<K%+eSbr6f92hucIonXaDR56_m<g3?KmT=kSo!PU}vL;q2H}KeI9i^
zJq&pW^V2W|k%`KRd{1=9*T<=VGo%3(5VK_Kw%a?7@r@j@{32q)6x&_HI^>t7aR4v!
zc1<!kKYH{g?&)TW0y%1#Aa`y33-5(`JKIm}*^FsyGsy^GuJ_(~U#5MP-cGywGI}yX
zdRNWoZmIEcneVB4)hq*IB-YGr7T8TWXuzD7m>xji%RwUw4^z_f0OLg5+or}5Fk_0k
zt$Jomu=M2lNGGRk|N4GlVEvg_#5Nnmk_^~IE^dMz16z0mY|X~zoApTQq@0k^X{qsK
zzn#6P7O3B4S_QZ=<zLnaxo;RxvEib=W|6)jKwXTNFISUQ{9iATz{OLJ`xJw77rYD%
z3~aT#gJ7zYKu`!p2qlmr7Ak1(0f3*Jr#hXb>Eyg2=r2evs-eB_pN^zB0vF?;5r=D5
zZVd5N$ZV8w4Po02^>PSobmhq+#5}3zUPQb`__KI3qj%m%T5g15|AUI<74@bA@5P1f
zZ=GT{F1gL?iv91l3--U+h4yJxiP{-91~=;xg9VTzGCH5H{uCU!={Hk444^km9mEir
znD1EwHhPYSN`(HPSUiCl5-4kUqk3bKN-Ch6<Dj7xt$`Xt&=CZyi%9q&fCWNS^ql&5
zEBS`e&PD;B0U@ym?8~eU@I*|rhE<3N7<C&;V^lL55FQd{8KKXib0j$KVb9R`z=)K;
zzEV4+9rvg~)-~#cqjJ52MbIST?^mZ8o8bJ}SI7SK`X%n$C@(z8Yhx*5<UDU~&64O+
zpBB%}S4NF6$c8$Jkio1y)zhsaiBKG}8JQ^2Ll1a$ufd$o4rB&doXf;pX~xy<J}>*>
z*xTX&x``1ckt`Dz9_Z+e6TU$K-)oG)W+hZ&RyZOJP)woCBnD;Jc2+BTW?$4W(rLN$
z77r!42L1K+8Pn|0e|z<harMjo<?1<vT=dicNl+sb5)$(E0kxrDl)uQTA^%MZd^(~j
zBosINBaKc(Eto#`fNK<x|M?_p2!5vtTO#|0r4q9NZQ=!&4ESdi7lIwb;ZoLwvMV<G
z>=S}RFJEwVc+bP@*%u$b)z&#cu9ELO?k@4^!^Vi2Ls^d<i=a>S?7PWazak#*;{r=V
zW9$yH|GYbZ4y8>8+MMle_Qoq2BBmL(=fhK-gegkMb8t}bQy!Nc2YNkxS7&1D7fD?(
zIsxsR<5!p~#v2dcQ=&%0bMMi-S*D2Pok)#@jbh!qGS!c@WxYo4Sm<33-#S3!Ob>&O
z{JgU*7WQ@vjRR{}>MrFtyrGTR(r^NoweiEV7o3k?<O(sa=*7(aT|!JuOsgyv{s&rS
z58yjPBLswR+k3|$pBu*JO-}s0<yWshq|cSR!w5K+g9b>-K<kHjPvP<pq=<l&EjvMe
z;#vKM>r#ugAC#>gi_&McbKE9MN|L>Ji!pSGqY`U@Z)3Pwm4DwXGqQQ%W?@Iw`sbU4
zBLv4Etpd!co^SD>EMvFcF(W@`jV<mS?g6e?wg=#ItKSOxwH=3#2fG8akfQI~w@;(R
zMMw{)6cX)lVs*LiFDaX|{NM_o<sUE@%3D?BYMN<9C6{OKxJ`JbVnVuREQSwqc$<&)
z|7>P+e^_|2PfhB^-g^s8+;+^KqhvpbyY!Z^eW66mHU=iPcD%pb`Mk+QM+?L+&n)Hc
z-MiL+L?DAFTx4t0T^7lZXhs1l0_{Ypa0KV#aDR&q1POF~^}IgR5p?SC_of4{x$nqc
zUH*Z@%3IePMs#CJak0GKh?pV3_Kd|s&j*zQW=magLieRIAt!ssqf3W<hs#x;zIJrn
z#y|5Fi1ja(aDa^yx!S|~{@!`t%$X^BW_I4wvxkHK`Wl}cehQl}VxQuP%XqURs>oW<
zdf}D3;h@;19bfFfzlvx!tAf4eQ*i%#{ZE^g!~Y`$2Wc-ZLdn$B^v4_ia!{tH@h<yP
ztF@n&eG>5_;!?tVnfQ$jj}@5#p~IsvKk+*F(+xu#R_^0E)oX6o3O4SgqI83&LnX{7
zchKzmQfkrI)*D=TE87cGp6)=SxJ6UOf#-kB`Ra2!`@r{?k|<T8Egk+oePi^nR`67=
zzDhPZV#LEV`pHEv66M@R42!8Ww8gb;1$W7K3BLb97Db!@aw?Cm(*nMD6rD)XJx&Xe
zpuoL)$F5%vZUMs_3b`CZ5On_9lrcjJtG*oVC3xJ)z_bVzTQrF_G2YF^rHsM>G~{Pw
zR*21>*dN2y#VT*!y5}IP$!7Zxoyy-jFqUdVN*hZq^~58j_9YT_azoYM@%}%}ooi53
zWf;eE!e*S5)N<5NG}*kvq~cxCQBzNhCW^b<LXBAq4Hs-k1{EB@%Lur*AS5Yh0ge!t
z5CkF=B87<(BSmKsM9>id1rpK#v)^+dzt@-A;R7(Rd-m)(@B2Le+q1(BY&+FecL^)+
z2W0976!pBgH$NQBUBElr`%|)yP5h_c`DNDI`-|@W#^h$m?EgkVGv;*}NMGP##}_oL
zo%xO&QJIBw?mWyPdRM?2qyhXeFU5D@KbHKAZy;G7;s!8BJgIYAJ{Mmf$`x7)C)V%s
z3!ovgSns;!cks&MCmtfMrnNYe5SS{-DUwGKtKUE3F?`0oodrA%c#D-SG{`y*S=@_j
zs@<KOPIEst&%O9ZB;#8M+Hsa>9tOBn(GZm$9?ik`ZS&Z6fAAW0;8A{~W$kA*K7lCJ
zpe$0*^1o66b0DcAyxljK1bqj25ZXg!5p(R=#x}ntz3rhg;z?BCQV7W8AX0y|mp+Nd
zFmE{bBVsQoXT*~$D~h=BglonRH7t8CAgMW9A1)!&lCK9S@T&JLjwfnzzAA${pU<C$
z7+I$tuB>2Kwi_@;j0O+v*^QNX1f4KJVnXY*(>8jNW$9QlqxU$Nq4fyO9EtO4&f&p4
z*T)OhZg+Dy6o}{jgxwjM2V^F0&uN`ixyVLboYj!z&8?M{l{efCMad`wQmMaEys^5Y
zx?IO>&1=NdX%x{7XX`5_+%m=1Ft%Mzzp`Gdjg_Ev))Cfb{sTl*nplY})ksHy%fiWi
zxv0YWg<a>5lbW7netQxHou}bA9DuMjrX;ozcgs~xQ&Oq;&^vxbo{UhSI}2;6f%*(-
z3}JCYQ!i+-QiYM-;1wQ!A1oox`HOY5$c2}Wj7<Z9X7~%<U9?fMVv6G3(=$QyfYBro
zTgy3)e(<As4*Wq$*l(mUwb-5mIH_+G%WQg}HG+25K1Y)S$&uRY!=$Z7=@+FDvb&@!
zE!mtuE}(vZlB;xs=;bMhb6;BO{+6n1t6@)WB3?31!#PN+^FJc`Wu*!R<}B3A9G4r&
zUL{Xm{FFM1-DC0bQ^8JpN3QnNIXh##zkHzDp5N~dd3XY~V?XlO8%Jy{X!hn?yx?or
z?r{3)S;FzT3mU;=cnyoO1Q|3_atT}|n|S>NnD-`3&Zf?`dkdN$m)jUEGT+!?nR#Pc
zT*dY7Q>9seRZ;Wg`ZPvvZ4jAA*2_B4;T3C<0i?hUX%v}Gvg~(tpDVlAeLb^zaS#dv
z+xHyu_IGF8wQTQs<}*rc%R_5a1z)ii@_~!_sHUj1$`KGQN(1lZ5!47nqlK(J>9yw9
z!5I$;tes8d`7>(E1*F&`LSbTr-W91kgJdihUa?8cn?dNuHs4K@YoSI)GY^Z%oc~<%
zi?vp~+CG=%eRoHp#A6$8L*6LD?@Qpe(p}pYq-*n|NR{VKGT-eYss0+=IfMfVePNnN
zUAo%{Z3OYeC1s!yLt^nT1((mXTGfJgw!Pyh<Rq_AHbGS&Wz`OvbmA0=n2x4|fQCUz
z6-EAdMfgKC+2~PuS9I-;U7=m1%Wn`;XWHfHwF{;^eahId)Vv72tHjY*rI%r$Ws8o{
zsB!}c$kM|yEacEf>YaIY;-jJ`o=UE6YA`HIiAAS~d$^JUjS{QV-of#A!oka3x!z}H
z061tj8*nOoT~5>1;I8>dk3;=>p9DWweaoQ93Wml+X>^h0x{!CZE!uyesnsOe?2@O_
zr_1*DErhWFc*+Uv+9o{QmPuE+sP?(6P|vMTgUQiuki!*5Sk--Ppd}b$-MF*Be#`e3
zO0MIk!Q&h}ps~=X-|?Lc%q_$guL;dFT)PK#p~!QS%(G8r5=bLan(s*XPZ|}l4EgcD
zG(y+JNigJOfbK~^LP;Xmq7aZU@aT^aAEI!8brUd%3e?P}=h7jc$~T8pS*baUHeZT}
z2e;>Tpg&ForTC@EPwIH@GbgL-gYs(9MLYysy5;7KIyhU|b3=P1EP&6!{@XFDi#e;f
zaI&LvljU7|4N=voImfy65B4tdZ@xCssF3a`6*>PQkOwh+M_HVpieNaW3&SeW3aj@(
z1_)3qfB{`C{<bxo86-aib%8CmMdYheb^M&s_GP%;dO9DOm@Njpm<?;iWKX*%f$ahe
zy}zg<Oo|Me$5lz9l3hA|hB#Wr{ynf`e8<1oVR@bxYp;C!zt`*jIc~r*y<uqS<3u@J
N<=wR_GL~%)`3qHmRxJPk

diff --git a/articles/AMR_files/figure-html/plot 5-1.png b/articles/AMR_files/figure-html/plot 5-1.png
deleted file mode 100644
index 9b01ac21ed152a785a02cb1d38b87ee21c3ac638..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 49863
zcmeFZcT`hfyDl0;0TsjwNEHd7h*arSQ6YdJO?pv^0#ZYV5U_xSBB6JYAksy83n&88
zAxH@w5n~9w1qhHk`HA2D#=U#oJ<dJn{&DZwYbc3ft*kZYe9QAZ?>l{YS4)-Q80#?z
z1i}DQyQvF-9C-_YP&d%gfKM8qjeh_yG!HaXZ-Q5FY^l^B!OKxsHDfpg!V$Xvmnw2*
z*%ku10D;}SuJ4mFH|8H;=2^E$A$fOd^6R(Vc_bHosqEd0v9~9`7MN*X|6FRUcPvct
z?p>)**Sm0SQ$J=tuAjuQOX2ZHZpnw5AGuy$&20pGZ;v)%i+y$Jl<Vu?zb@Vox4*3W
zDA|ioyhO2HN$m3*Ev%WHC~VtLk<(Yi+!3KfEV=hS|L2&*1TWP;&-wqa9}YZ;nkS&q
zt>O2-e;9CwG;}ns+b+I$w6sn<p`abg`QAaS<*ub=y*xPU8aSSRXM$Lr?KP2tKoU}!
zu9|rCG&D4n%qUr;blTOM_l0~pBVLkYp{<&yQ!oP&adbuGsE6CTAz*ybBsE$2<W7t|
zir4Okr;F^GJcnOGZY%rK=(EQ&&qEr9?6z;wLPYMIJNBGJbW!?gd6jSvcCRGl2CCC;
z%;SSjrz}Jy)eYfm>!@GS<r>Q2HMED)ieGDW7;GN0YwSK=GQ|S1vi#9sO$BMl|JgAI
zN2N)Y<Uu(wu2Ww!9mdW=teU5{#WZea_C`ZnS8F#+NQWShFSj5JkPp=%At9N({TkKH
ztF;EGpT6Tt{pD964Vo?Mwi~Wp2qSFyon%P`3!()CqFkBqgc@?!^ZH3M0a%q;;rtdp
zeIiYhPmb5T_EGP<_H@75oY8Od@q)%;4@b(K<ai5O?Mm>_(=Q`s@j`JPF^RjwtF=hN
z{QPbXw1050UI+WIGyj%umkeoscVlERsf7zEZ<!Mh9%x%P$D;}^#G7*+d#0;O?#J>b
zgSO(<&(|s=yr*AM2r1-Rbo(dVoK(o5^xVhBjmV}4$QXU*@Y@qhYkk@-Y$oxVe6-MA
zzU0m{Y4?d%8}~vq@{LC!+8R2;yF0X6TjVrcO4wWep7285^LENeNp$PaD%2hfYYJ81
zCWJhe^nZ=E5+KtA86FypU$!)D&K1?^$6&5WN;YqAZ;$g6UgwG++HOV^qf}1F{0yqD
zt}fxNv&0@{-Kis8$B%Z_)zwWz>$4AUlocW7KQ}#ad!4vjSybfWo8`B@zP>0@k}iW+
z92*(&uY-1`D<Q96V?{K}kebHF$B~7u^jDx|Fj<M@&T-Suw5*&|io?#FZl`r(2c|_f
z<oR)Cp=J3+2iQZk;i(iyc-QW0Tz+y6EyRv~A&3T|Z?F{~dK`+N>@SRkML;T4IKA6&
zr>Rhoj}T-dw#C&|Wq@Mk?ey%DNW^V}d43N*)DkC0C#DJ$tYAmQYa9j(&DpMPS|vu+
z)$-FlxzknE&db4_qlG;!Uc(pP?xe*Rt$M;~WN?s#gzlPQ!&_}uhVI+e8VPQwz}D07
zSJ^mE(65^lxTRm-{Ba4E%sY08a1*u)<q#l@UZ4v!u(X``Siu@CGTWF5<p7?AOhzxN
z`-P&bP!Vx!o#0_Q1?JcwH=5RMzYNB`lHkk99gN3dFctPo-o0|S3zNT!E@TuIE-1n(
z9*G3WSDH<q6|cD(rS>@H?D^sHfN|u{6w^3OK1PnLpRuS1O%Il9cp@h{F2>S)*lCax
zXB#wYIz#Z?+>@vx`bz|Q<;)iA6YEmdpj{jCizE~lb>fY_iyeiB?g<4uxkUx}4xzdR
zc{_Yh4V8$neRN$ic{dB~)K%5JU^{1ziV!K0dRT&cbTKMnI>o;pdgS^N0;c5Eg{agQ
zm{0GtQ-Br4uDw8e1M8m-VK%AtSq?^3w;^|YGVUhCgI{;AT3jU%S?P}t5M9z~jvagc
zB@vYt`@IhM(Nky+2D6vWTQ?%(J@fufa-c%mnVz1WkLhsdOd6zbH8<01-V)qnXmaOm
zg>FZYl05lt|Exu6#m<h1Rr$rMxW|%lopzaa8<MaQUUL!T-p-LCS0#vu4tZ_eHp#*i
z-D+;x6?^SXryWu%;QqNtw>orm2u2EAmiu{;P|#I%=Jd<2RXpVsN*iUj;oFVZ%)*L2
z9bKr>!1lPS-4WIk(GtCBq53o17Bi&5XAxN@WQ)>Az1M-8^0sy;f&eo5Sds_=*|S8a
zOWkaYAYoX&*Wine<cn@q46n4#2V;9>BW4c_WGTuz4#W!nL`@U1#L^7Q4%P3YMi$7K
zZ=S%+0}Oq~_(O!Xp{JQ+<0~St4-LUN!2U6gUn?`)rvI@e-M}%?6`zp);+q%UC@)y?
zu`P&9Y{D{#<ttP8@{EkBH&NEJM559?3*@$s-;p2_++dOLnYwcQT%FDty-JKvtIel+
z>{wNQbzlUy?l?(XV6F6OqNa22i$&FoQJXE`F@eKoGgvC_&*F?l5nEP;c>8WD`|WPM
zDvaUu!+2P(k6^!_3*+vXCiHatJ(eFJt|{p#THc9v<bsG8k5YjMn*<z9O3g1C?I`g;
zt3@|wEp??G$)5RjS6gBI`|)#e6f=~3ca8tgS3dH4mNW4$r`l8D8oZb3o|vlQ2IJc$
zEH~UbfD@>;Gv4;ANwkHo`=ycgqFTh6@Ma~@SoJL7Nbim-lY9GNTFL!>j{a;ER6Lo|
zR&f|I4bs^#Sn;D4maMF-acW6yhd<Xxvg;LCKYsi;ELNqn&_=9^7bLfsdM`)USFb7}
zSWZ83m5twedGp=!%+;!H_1Q%bt#SH($1?ET24KjmyAr+DIRlu`J?Gn*z2(R9sA2a*
zh>#%BrIR-x%KeA0ySi=7Fo3AAeJW1@=H6w_mtzpirL4D{Szve1qwDhNmurz`75=1!
z3#{Sl{^Qr<V$i|C!3u$wuMEz4cD2e>w5aOwM5(<u3dpG=N`f$5qZ=-IO-(mPe^~aj
z+x=W0XNB%7v6c~z5$cc+M+${e@9|}~k|x)A>7JDE+EaE(6ocpY^%R?VBBU>lgDCY8
zzVJ;{GLNn5?cGbGOs|^?uhd5>UV<HG5;S`EIfqBFNQX1UkRf`$D&J&~lv`WR=exh0
z{Oz6syVlW9E^L;-cPgnNWeprXkf2<nPd2UWFcmJn4G<wyrYuz$QxcOk`I0Sy<0Lh7
z<vEoqvFhEcOzOHP5Az`1R$n#!NLnFUz%1|ZMHi(`V*Gh8GrgW)jxvHYe04?mwmz;b
zmxjfwIUskITUO5vXm7E2&kwI(hp8svyrFeoT`4l;SUk5E?(qO@+cC?{w8DApvE3L-
z(e-ymXje3}dv(v>cfHe}{RLdbyU?&Ur(MQdc}T&~*kIf;T0YL9u0krF$S&Qs{c=y9
z7V_crp+^v9gW9Dp#}|98PA<L7uoSx;2$RjP-Fo<Cp+%<RIxs@HBCMZyO=<0(t+y3#
zbSpA;O$$G&`l{(cCK`oa*q>9yU<BpE&L=GUGf2?U$?X+v;KQ9V+s{vqyoPofK36u(
z?Gly6j|;(~)P8<?EJ^Or(^iPp^G%8#to<bcgB5XF&oM(Ub4C`Kl|Ir*g)dx$G1`>i
z9tu2G^cdX9iFHWCJukZ1-LG3f8nlVXdPN?;0DFGpzU}k=H24B4=Q}M#lXE{N3+y8t
z{Yz&BI&1i0ZFVKyu;ELD(j&*2p2^}}p{J`<bgk?LLA;3+GAaF_g*u_I*5f;K(&{<-
zA+wN~&tgx)&5O$xuypsXv2l=vM@qUCYno(87O*1X;v&U6eAPPN&MO{050W3M^`|GW
z=AIR!3aiggfM5K;HZm&77src1Aa02BVr+#V{d|o_n+X}ZDb6^sXD04e?Mz$&=Cve1
z$fDh-E72F&b<Z!xJ@$HvHb)h5valHCe4dD>WMRuq%O4ZGyA&=HhCuiVw6?a^M1f#s
znX1X>^2WnG%dLN*$|QGvFR7zg-*Ezj*LLChG?1(~x6=R=Wk;z&F<0&L?XN>1+!sOa
z*ECj9QK>^$X!yAj2MUr~h6iLw2C!GRGtVuu=f!2G2l^$WOF#k|0FZS71UC4u4qV}9
zxu3_<@O9bXxWK^q6ke!;O+O*~`)&M!$?W|6*agCBR}~yQlrU#ix;g+{LfxxcaWT<r
z_At}vR+~4Pd{5P&pTN}hKPYCwX`F`);oT5h$;R$ZTP~=;t)10wm!i^7tSu~uKX;;z
z*?Ad2Pva6b`J4ba^o6l?&d~~JlSE#<OGGqvFlkiZ(zRb0ts;e$7bluugmqOduC1>#
zK%-&e07Ir`ehy`gXg}*;_{yQKo)0eAKkU7AKaA`9<XD$C140|PY94$+6K0w6O@<1h
z&j_r<s;y)0$x6HMyweB>F<h}NW?65FRB(^W$2yo`)jHTd)__CX)^=V6c;DkvPQz<*
z_DjJs*H3zA=n68KZhzy6q!0T54^Q+=#!>Q$ijeI-8+G=5?<W}O={w@rU@%5?0kvT}
zJMTo?Wq|iGC}Kw;;v9qV@yV@&aa*f+;hxFS2C49}G5tul)nA>aymb~&sa7Om)Tn`E
zoD2eiFnr`X8?7UQ#~241<5S4nm*55(C@{sc4C91aOl`WNcnt<Xol7uMWLdhyhS}AA
zALN}4qullJF1wNmco)Z&9F?cR8*miHSdeMrcM5sS$KimSCEN!Ye5=(4+jjjL1Tqxr
zN(){9kJRs~dd;h_F;~*<oZ~5caPt3`U*6<-QbTg_Xu0~;VG|NW^Wo}o>IGBp35T);
z>V~veuTFHh5Xa4kieEgiNsFBM=2^br7YSX8b<6y+qGX$b5y;)ouZvzOlB-Q?;QQBf
zoGIA<#!It%x<sZRLjGMrg@>P19LH#b6mA*(j`9d7Ak#&&(G`CBQ1fX30qHYVp_GU7
z$rXe5Lw$QS$FkLj?-CUu9r6|hY=0-VL+fzQ!TX@QCzYuW-ru+m*w>$f?g%i~KgWf$
z|9$d*JsYRMDwX{p?-5hr@d-G0*lS)V5J)fC-zhfg$TzRq5SDO0bugiJrHGXizx4$?
zgVFq*bJ3`AK|p2##N`@0Fq|)MrpXfXOHMbol$MwKVHO!TG4&Zt3ff;aXV3k)ykV}a
z`$*5#t5?_Uyh@;nI9@>J_9HdAxL8!z(9rwt$-~(RxT~4;k+Jo5PZGah`Eyy2@uT$Q
zCylGM-pPq6aLZO3F)0{0eXwVinRoZy&XI#>OL&413X&|LZWl(1kBfr4WUsHSjq2@9
zzXZ=hUjXkK8}99O(~(V0i*BFslEn|Odyl%|<(C?$x#B!B;0w~Qf<(<<6g4a%JAE$~
zphq1T*Bg&g^gJriOduBX$!mT%^!9?k&zO&VTGxy_vwF1=HVPHEm{Nnu%$z;}lN`4Q
z<uI(n`^G05tLr{R^QXYQzfLc+4G#}nu+Il<Vw46u_CCva{~K{GtEiYz`Z0RPhwDs^
zQq;U&&i6y&{<pEr;)xwfh9639=q97}!23Ft_T=Pbwg<&SwliB_7{TH^v||GxH>a*{
z7n1?sCk5b`oY|<lES&;ZK-t)(uON^MKFv2Tp5nhO_PDoajj8wookjU*$BFHE7o>kE
ze1Qo+DkmXb@1c*1yK1`8>?03|xFc#@$i5K$9Mu(h^CIJW-JC18?YZsjXlGWpBw<o8
z*>d@f!{X#Xf!OU}s8o~%cwGM!cyh;_J4`i(Z1%uz@TYrKCqT^K)WZcWF*|^<royGv
zWp%LfuuyebDY_??5>ir9nzs9+O`LDXUwqeQwk2P6f1O!1v$sAba!L*6X*Y0zuycV<
zvb?mET~z(*pP^Z$S9Fh2jrH~}w*%XYOibmrR~3jgxk_lU+psb>AIo%da*|Vxuy=8x
zz+_V+ez{k@OWbwuOp@S_D1Mz~w^0dSsGJ|Epgi>U_SQiqB_~fuso7*g#Y<+gbP3~y
zg@qi<<{mnwMMaC@T~#9;a~coRe1DC3PasHVGQZC=Jel0Ag5wpTu-)BVRX}CD#7zn0
zK-uWlQJFN0KEhfsP;WF)DR8dHmg$P}va<Dbx90P--HGDa2H2L)#|S;WzIPdW?b93W
zGwy5>EGy$D0d$u8m0(>izw(X8nY1u=T%ntMSCHvAUrpr8@97;I(Nifkny80%=>oCp
zl!5;KGM-c||E++)U^JZf97*@xyM=`XQUY#Dk5GW>b{f&2M>XW1KED$x0_nSZzkZi-
z?Bo5$=lz95UyJUV@%2Jlelfeo5QgKtW%@Pore4kp+uhFt%GwW=sDGTuWBT2McO0V$
zIwnckCA3SKxz7{x%*sD{5U?Y;W@j3%l>nbb%~eiEm=5&ym0)E^#^Q3SSQ%7I`^-=B
zUL@2!uGMBwI-m!|tC63bt=d&pr&R&Q67S{dStocj?B>OX>ay49p6JQS$yp{@`}OA<
z9Dzlqcn%l`X59S-NW>}>fzdU`o9R7#h@Wm`Qus*@j7IbGn)494me-QH@e{(Af25ED
zR8?ETRoWM;@M(Bn#23;o;DC#@zN=a>YbE!myW%`PqAAI(HbSxJ{yam*f`!<%B0XHo
z%~@?ba;s}sKMZi5>WsX+E5a~fE4(mcm}S~yb8+$_>Yiqq66B0{i!XVwnidj!ZbNC(
z6*iIYHFkkck>CCXRcT_~xQyYt#Vil}CWImE>JX)Y2mR1tf5N=FU0CMzMxuYEj0nAi
zuqOQDfY6+>GKp_CVoJY%|CW|a?_LxpEOyMv!<6FJEMSq_7z!y`kTUdb$ru>SVl3bt
z16!E-nw?l-B*WQb&mXoCC&bjpvgKrDIk~DtqFZ;2pHi(wb#`_xFJA+|54(bKE+`#e
zt<A7n=V%0x2JX9K@pQ0t&idjyf61;5pp4PbTbaG4`(8cp{ypD9+TlMw)NF5AOTBG7
zP+&SJOo&<;^hQh|)6sHJgf{jEEGFP_%#2D|GrdUX<I=@eE>&Y8SfsiMdt?i#Z<2bm
z`DcoTx~^h+(I&1OL?NPY8uThlaqe~0#i(xsZ#G01wxdS_zkmhyzn5=R45fS0ee+_J
zWp}ca@GtUSUssij9NyX4#U=gw{CS{uW_+$0$SdG~mei0R#rT97%ApUE{M==X6EXAp
z$rQu+>SCjOmv`1Fa7NneJx22uoE$^DO3+lx`RazpKk7cN--l(6#ZbQuIkDCElzCB2
z(C=-MghF>Rwloo97lKd>;>;;7UM@^a<AnL8E|TFOfEFH&yA=K@pak0Vp!nx(e;)tN
z>o;hru;PVqR7^MCJF;ZJ4zOzuL3{GISP&U95?f_Rb1w|)Q2K&Qj8UaL&k&mEy=N65
zD&7-h%F-Dp*?9%Qog-)F=jV}c*0^C@7FF&u+|O@JJ%Yt2<N7g+Qnq4=>GPtGgtg@6
zOVw8cv~jRJ7sgCM5y!*M?yKe0be3qNA<tp^?A`JKgc=r`rQxlf>nF@#gh6POIUXtr
zH@q~rtjP)KYYayEEfx3e%{1G)70`&3%th<DhRtBe3;u+z%9|H;Sp3F@haIpJzYeEe
z5+_GNfl_pw4>wE%%U|B2LIlgP%*qhb7s=SEQ(Ixt`S>aH*2Sn--scl>Su^qVxgPX@
zT4EJw4DSDl$z*V}3v2&lVRZ_d-vT!qx%NCkARPl-ZEoe$<!<o<nALHQJj$`3%daSO
z`$wa@K)$pwH8rh=IyyT?v^*%5(x}b~c(|Z2LwopyD1MYrT+R#k$@Vx9L~DF6l0Cb|
zxCvKKg--VNFKMnXxi}6Mj$LfU64p}2YP`7?Qshi^0pXO_&AAOdy+wF+?#j-_#)c}d
z{npBi%dsbKGi$eJmp2^jyruCNZi2}KNosMZc)Dh6Y^+LOK%%>&5IfnpoMv9>I#tIL
zF(m}EjQ2=fBvYd>F(6b4z^)h;%IE6~q_TzIUNo!o^PI5R5RKZ7#s!X5d+uTul`a#E
zCP>{tE29)U^yhwQi@joLF340QSrV<92A?1md3TNJ3B)ce&dmj6?CcH~fxJOI1Fsqk
z)dh8c{ezsVfM-jX(argO#LjEgLUKfq@LQ8_b#&=DW|1NcOXn*8Fqu+Q24KTG4_O8N
z`1TnImJ8Ve=gctyTRLg@F=6|07P51PjhHY3ZEG~<43l9{gu0>2!o$M@9UX}paQ^Uq
zN8jCJFz-mL3V;;*!Ey0+p<0%1O9F0})mDi3pm;MWBUD7o?$5d+CX~#v3g;x}`zE8k
zmChXgd^t5Wwf>R|vL1crwhRr-Pn9ovtDl8vsl#7@NmXWNY5ZJe-aWS6xX5m@@0_hL
zh0szbdCUOf8R2Jt1+o`?1;BWbR0Lu^mCcg;W0ilPni1lEEJaIUt}C3)0YLo3hXAzk
za7Ir2O7TxlER)3taubfgf@$z)oa5Jm9~4(Uve^`b$$8CfK1oEVW08OWzB+RK<oQL6
zoSYn2L0+CIm?9%=H}ARz>zg%Nm}RWT4Zdh!*O)1A&Crw_zEIG`(!EOoN>Jfx#Dh%H
zYBQ{ng}o?V1IXwMd?^9GFbtUL^VlyBVY9z~Z>GYXF=%adzmI1A$?%233v^n9+1c6D
zHbp*Nq}a$qu|!XlR$M72V1W!Zw*+7`22EDo&;w-9+Kwx3jfVkac#JjR!q0f#t7%$O
z;(yjzWNZ%Zt=xTWx~hbg<qJno!%!tPYXS!R2>J^C<<Sg;E<VTf8fw-*=R=m-E&y@i
z&++UD<nPBPUp*>dYZwAZk>;z}*un4ZF6<W}{;%gA1Jcu<L-`Wj+rM?hjQ{+L|F=5%
zU70gPSpv6Owg5@!GJDJXRS9#b4+Rq*s1E&j<deD94-<y443?L3r;*x0_Lon8Fd}ju
z%;f*n3*GAj;@n?RpEaNW>Fa;R&slbyYg1-W!752Wy@s)7dYqE$$#q}0c$W+_6yUtK
z?^C9VI}O6~Dg1lU>A@nl&o69$-6>y4{Wsn`n*mCrnufG4$SLOQR{Vp&!(wltIbkh>
z)sZ5;SwC#~%#Hu~BBsS1V)v7VaRZ{F$f}H?v;j86ghXZaYuVwiEkUfj_EoJv$G;oi
z|5o<#Z{9LQ5H<PR3-JHcb1s??X*O;VJp8nT;ztN6rhR~3-WwvXB9ILofhBgGNoN|z
znGfciKCzdobdg*H?=pX2*Uo!2u5?U>^cF4A^gy%(5X<`Sl}*K8JLY0#DIi&f?O7b?
z8VSC3b7$EBr8)%DPQvl?)*0)jqPOh4LJ|=Qr(eng5dDxhq};{HX+2<qYYBl=Deya3
z<sd|B82wvQ6O-A_IXjqGi3ZTmcbc1<)%N)|P{Q7V04Q2wDnK6|TmRSkxs#*%#C+07
z&f?MVg)4vz^T9xfWHs4+hqjFQ*I5!`Jk*cG<v#ALNKNA~Sy?lsX=!QxZAkfE(r$2u
zXIGUe0Ix!<+i*C1l?uR%$K9*rE_VEnme>^@*-0&qR;~JjMBOx17OHetgflW5UN!n0
z1q#||^aKd%i@$$Ys-VKLX{!i|8H@rYTt%h5@ldh0p1xX8Q1E#P^RD+d93^QZmMAW8
zZUsSs!jvRRwDXNie!X`~iTCSX6_P56LDRsrrSS_dTBfB+%gXAchovV-_#~W?G`=g=
zBMe=O=JHP?l>taW&aU1kT;Dcw^c{i|6?h1*!Kl-(d^hIKxr5CC@5eJ{0QP1mHk$ji
zVm2KrhffnPVE`HW`}lZZ`fbYi--KU!Ugk(ByigU@F{g)F>>eB0075qG6}{=TYv)u2
z>7Gorp95e6Xb=zV9;(S20Y;oHjdxC#av5*-klp<>A3B?eyS^2Gxr4ofiio{th&^hz
zv8ykzP7N#kQdxyroZutWpvHAL;iYI7MIc#Buw(863B*gh#vDu7S5z|D9rDmaf#iA8
zqIJp%n65yqUnJZ)I?-H+K*!LPQTMf@uyw{g8F2Qf*ygMM&d#6W@poZ(fWq>A{rVM>
z^M^^)(bO--VnVT#q)SYPKWj|n&9ui0N8HXtUgm0yLd|yMU&n4;Kl%8vy}itX7~o0!
zM}wlv?eTxHbZr^vc$9k9J&o<+L<J*5L((Ht(~+*Jk&2263N%$q6<%f5+Ywl`?<Osm
z>c?fgb>umbm0*pP0M(_Yrf$7JR~WSx6`+VLM5|LR>WUNp?|Arw4to@*TUVVed1D^A
z-^C!9-p%D`M@)q?qjhugWMyT`=|}ctVa(Eap{O<7)N{ZGu^n2`9*y6oxh|(_aV50R
zR6_ymU-pp?bOb<_a{gh8+fs@Q+=Le~+A$y{A#s&8u*F+8bzB$}?!Q}o^H&jMs#!>|
zj=%v9A6W@-tMHoqqDrxmV8YxVOraeDUl4<(B_eV`)xs34R)mM_JkU~(pt@r9M>Sr)
zl6*Ym`kqE*D|dUe{7%2n(GJ$)YQyjy+RKnWwfhUTN7y7ESAH7yOog*0CnYT;e@j>=
z4i5UKc{`W+Z*_Iy$2W|)4d*{WMEH{Z8ywN&CKbWPR;#U77e`!M%fkEf?d_<u)BI)`
z`ty%kZ6`)il|tnfDyQRsDwgljg&64Qit>nn&bZ5LVJJMXc)(bcPB4I?+)Hr6HyNp4
z9!G)wpJ}9w4h-Bwt!c`pVnpKG>$*D>-?dSdroit21R2R_y7p#~d<T^l;}MPqcc7u6
z(E<$JKaH#WcW_)>oVFZ(FFmpD7PdO$oZ~>gZwG0Y%!?39)w1jx7}&t-U$om0g{{ah
zYFKlmL&4OLG+s&L6(>L-o$gt2=Sp8fP#93-axUYwXLxrX4^@^(_!w?dKx;$1Iu`J_
z(0e5H?ZE?+-L;X%&q8zb1;%>%`(xuR{IqR&5x&Q=lrGvg>658N0*kAwit;=eA-4^E
z`fF)mmib;l07yX}J<`)vb?g4xrwgagP+bc$OiHey{4gN};qH?@uPWV}PfEi(#RKZ!
z;V$le>;%lg$%#QMv4atqR759!m8`KXCoZQAaB7_)HWR@6-J3)UkKS9wK&VPvB%5Ov
zU6w&Y0HM)LsKWHAo=vT1Ooq0%hljkP`+WeqOK7Nn^8j^kn&;kx(#3$dwTjEPQ{byt
z=I{bAxYy!%DyzvGt!%v7HX7{-=w|oaJj>GyMglMpgG}s!*r@V(RxU(Xs^r(oW*tpY
z^k2z??#U$Iez~eb(6oH6;Le>pp7-aE|8dg<C%ajW9^n$_vFborWK2DVNZunu0Uz^6
zHSZSH?Mn@yJ{~F|J-GPj_VEi8={9$mpFZ}7k31%whU<;gPn=G<FbtJAFY4j}l8Lvr
zc6Uivl_T#MFX&=&Hm)k_ju^$9#+&J)R<?FHrz)W5UN>2EuR`rVzc64xoqc(;Jft+z
zZ7}`k#+d;e4p;J^=@swwlL36EU+(yg*ZPW1kkUcp2k6#V^*Kb*2>?XR+@jx(R^il*
zJ689;)m6?*p~m+Cl>R-^PD#CRthxck<E}jCgk-O1S5+El2-tQYtt>SZcB^f-I1C;M
zDjq5?sE_lwQ9y3!kfLN*1}rT<52fTLNmv&TC8X=pipX`mg7~39ZmjY1^MevRLHq_5
zR?{pl#Pk}`(-8rjrA3B>W3~)%a|8mS1qy~*BzN1dLADvJV*)ex?RIQh=~bJFsp(T-
z^WC!ed;0-)5$GXEVS))P5|9hpW@Eb&s0{u@Tq)vb8z}Sjbo@+VJO75Hu3Ky*e-j&V
za-V{p8CN|0(x})d=C<!dGLEWiek=bQ$Wlsb+i%c}MP^kiZ*F9`mZIA`=Nu+T$-oM`
zH&{xUHR6TF<a<S@-LSJGjhBvh91A|TH7X|(?wj>sZ)`D;6kWEUf{GI|PwJcGAIhBt
z{H@+-J_+(ss!%^+WicZNS~p8**?!+%#@f&!W}Q9B(vBdFdH3`Xfxhqt4ZJ^kPcqwu
z*KWfL)`nk=2cor7-`H5Ho=kvNmhL}g=!zmxeZ@`jFc%sX|Bzs%&*tJvQt`=6fltJq
z1$ZI{Bo8iL`<ICbhYy2wq9y-WK^L3t?!E@WTD+q=;}*Xl`r#NkL2bA7@dP+51AQH`
zKnIk0Wrg|PQmE1nF-gL)`pnC(xEWi$w1)@0DWSmb=YOd|c+V^3-764?2~FHjha5GJ
zai5+8xuL*=b~c^Y9<&7*rUY&iER8Ch&5@usATA=}8gTOP#E7XSz%JQW?&U9yjEr!_
zqQMyIK^IBH%4}YC;LoRE@(V$JHW05FU9D|GZG)z_q~!>y5;-vFHy#Cx<Ye!;q2)24
z(}Lnb(nC8#`=u$LZXvVErSYk0P#J)2SEJhjXnV7k1(X3pm<dpj?gKS?^wBjxJP2)M
zzLGyuFepD0NeBuI*0PO327*4CNQ1n4=k67nZOuL?_9;tDm0vO2!XWG53&8e0kU&H|
zjuK4OS{wxZ8o?`7Kc{c}ar=f#`;(Q1=Xh*qDN+&&P}hkG67>Y>Dr@+!j=2iEjeD@<
zc1GR+uu6I;=9f3O116*<NVGu1$jw*>&0lt~BQV3J>E%?+qNz}PJMHz4y)q=~*z5q7
zTNP-^u_u#J?n=RUU(gx&>>r_mqSFbVOd&gL&AjZY$_qU*@86HZRo%fF&nN{t0za=o
zOh}232oc0l$GSk7IzO@1#!CkOw;moEQD!9QELu{uFkUAohTGTQY|4de&^r_V)Uh`)
zF5r=5V0g9a!Y>5B>_kuBG}2BFSunnR9<_FLzg0$beD%bik<U5W*+4iVTU_8>b)6k1
zsqp$YYfsS1PpRB$(2QqaH{Akxu4-x;s0z!^SM<+Oaz4M6*#|vTwjgKU1FdB)b`@&F
zeD=y%KN#M_!^13NrIAXt*t#Wx0|Q@BhaunS|Is1-$TN<2%g$WL*;%Tw-0xzc4BTu1
z{=cD{Q~IsLIcBYtrhCE~giBf)8YV9fkDWqnWkf{8gbe<d1_<%A5<Z+2GWZ2BoiU&+
zF@BKR5PZ?b`~(|@KucQaI;{F{C3`O0ND$M~p(sdDWerF_cWtwwRanC$rPU^P8*)Gc
ztR!)(FC7!!9LP+(Aj@mc#tysa>FJFD_1q$64KzqJ_I>Au$=a{aYaTrm_JfCUob);t
z8Q>F{;|0-V)S8t+J54W_E%0apd2;vlU(%^Kk-CL5CHqbM!#a;>WpjGrn;HJ0ui`xT
zpz%Ra>uJgC{oRTB^=L`U?9Wm_61?c%K70dmV_2es{NvtPVEZje5(g4B#5y4guB@_a
z=Vc9wI==oBXAiD&cq;cc`$Iv2lX6jy+3-y0hNuf@8s0yG&Iil=w+8Y*q6qCvO^~|=
z+TW|Gtg4S)T#%Vwq!V$HvDYi`r%P_#H4xiVIy`w!V)8-vs`$y=&+uXM|D`I)L(}b_
z-u<A`0^!TNG~#PCQm3$QT8leV1x1kwga6d~o%0rJn{H|F=6P}@JU<o{%802UKR2lW
zipI+U(39PN=F;y%fa4&Vw{On==_1hkdXY<<(6+C7c&(}*IbMmQ=cfC0_`e$n{@?77
zSbPfd*CTs-oOHh|P(dPq!VYM(r^I$H+N3VRATNph$44PcQlQB{s8u6Nw>ts%1T=V4
zj|>lc85$we62~su>lGUX_=8A0u#%eqFEp7TCnP@KQp|NI%KV=DOYz_<Vop_eTFp#K
z-Lp3rh^_w8fq@btJv}{<6M#nixH4kyX(M*t@Lz_ic|y;%L$QmrOKh83MS$~KI=?TS
z|MOK19YB>WQFC<6BQ;Pn2IygrpauZ;6gMm|G5FwVd2qjtJBNEpZh|OTnw^ao!jB$<
zRSfp`?>6Qd<f)%2fNvAB>jgL?k@{F#D92U%8Z=kDdREbXvqVO3b5<WgO(?$RF1863
z5wqrxA6@6*;P6)aG&Ld#%HLlf6t{Ogu0l7a!n+f3G~KJt4lXYBE}I*mOCbU0GLUZ!
ze^BPfQ3S|1H7Kv)KttI?hgQ$zL_BTh+^<wQ@4%v(ni}bT#KDu6oCjk)v=y*7Nqs_k
zG8=VIty(d<H2^Sar!P~hpKRO5St47=P{bYV2ybU>>0Rs=z%`#i1a7*dLh8n^I-#6V
z9*=!T|0OsGcTU#^Y^^l)K3WsD^QxkGIx8#g_{Jmf<Y6`-Cq=O6)cl}6l-)zD1Z75Y
z3cMl#cOG>j?wU6CGN_&Z>P)T>1?>Jk5G(&VtR$yT{5Edr;CvI~+GXAkO87<5IlZH!
z?4X#&M%#{C0ieZYpBujxs=hP~Fw1THsFU4BmUy@P>O##ThWzqWV|<da%gMtn-KlaU
zZrJSNqLS?Cm!-)#^$AiRNZnfhG{&}ymuQ3TT_kaKb~s8ayDmVnUKv}3+VC@6KI@<Q
zVjVPVfqf5+p!mB>@EB%eVNs*G?i!Ac{JFXX=iMdS9CI1^I3<rC)zSN1@X2Kd-mh^8
zvZT%es=eC)!%;YH5Yn$nNH`^Y3j!pZ3pBe3<$Cc{EDrgtq#vtth(8h(FWR~c^?sJ&
zLTHQhqEt|J7aRuYpIkuB=44ywbtXaHa~=APo9aiMJ^Z=6mFdO5m&K8yrM2>*YdjBA
zJ>x7do&5x$jXWq4$&AYke#haqHb35C+`>#i2{7288tbvQ#Qz|<=#{ZyP|6PE1qK6B
z>B}jk5NYe*sXC0N2h}B7axQwF0TP6JUkS?nNjOUPcwI}2!Qe{@643>{Q+f7e)wSr0
zbhqxE(s@;Vv9js7?l5fTwDEv}**a}aH8IuzV=*vWJ$~<Ndo}ULlh>7>jINgU=_*J0
zQQf+HOvwADP%HXd>WyS#Kp?W?C_OhUx;0TV8j}{;BH#5hrF$zt5uYNn)lrKVPw!SG
zzFB9o(xv)+^2?6jciV0=ahkWh{ibwO4@UiN*rWXLiukLd(}VX=&`7<P=hc#$=#{$<
zaX*W1VL_g~u^D+IqsL5;sop|`v*i#J=6);VE{lbN{7nrOu%6iY(1=gux;(=o4E$#s
zD1fy9vc&A#^P}mt>3-GQP$<4W5!zall$6x9FNExQf=<GULd*I<-Sn^TjRuc`Qf*(7
zl=G;Md)V^ThWq*<Q*SFOnBSiFA?UBfLt!ZJ$4-vAr$kvpqn$%re);QbAj>#IFay24
zq7T<+`_IhYF|(z3;AAb{u}nWIN;GZ3%`jO}043xI!jw4<IvW9!^m)d8tHVr)L<Air
zlFY}R`>W7RoZwEW0fk2Ltl8jE<60j#4M3?G^qX@y+bd@xv#YAM3c%fN!VAce(;2cq
zFT-@l=9hY!*}(1@i)2qs&D^K<IM$7=^{0%Ao1^(eck_wp^h0}!!~kBxc>!~Mj^`?g
zj`e=?pC*Lm=9!IuRrP7f;mj?EPp|}~@q&pVHLy)=HZxb0mX4mZ-q9UCv%MPq?E#N6
z&~F20+F})=1@{`<%IhF+L76j$5edjt^?4sQ*V*M3u6i<Pf8}nVwGK-cJbUalkeIcJ
zU?T0EodY$(mfxQI;(ddT3prQZ!f8|=h~PR&1vX|p)#QdCenzP}x8)Z0bIt~MDtwBC
z<}BQ(lJ@~8Dvtj4gRlo~PEjlW@|1CyITBIN4NEHiCGSCb@03*#cdZ0!s{JnU2k1ma
zO2bepLH<GAo*o_n58jcRS%j&99r&m++)|=|c9jwDpKZ47aq}v?(g^&LPdCr&y3HT2
zxlDjEzg=Elex}dUH1($2E6>z7EYnx0z+@_Z++PIj{TmmRRaI5fJVu@VS#v1NS{$O#
zZsy*~8wsxCJT`jXddCB7Jn6C8<9_x!NHw@y(7bvrx`o4gR34k|<mu(rWnAq+2nPKC
z9l!*a9+C0xRq6Xe%q@cA{Z<sOFrX<)Fe#+Fw0Xu|=^9W59^%!Alg}2QZ~z5joaQ9I
zNakmKqU?3#;RNxAU7-;&zY<bIRxN<}oDywzeE+8Ck2RK#i9i&N7XZ#(eDcfv2B!vo
zFu(Uge_UdEo!maNa;o9|$uBErLW6pj&bB?j!3~szjprwwv$1kjwlQOa)D@uaoQy@Z
z!BC_0f)JtoayjK*mcqs6N+B33G;d{seur6^Zc4?okob)L#EG=y^bjsku=jl+z35&Q
z;7K<Xs_b3htFiAFP}s<E;2GZmag{0qb97?#Z~z*NOR!w*RMT*oql>+h)0o^dSJ@@;
zN!VMi%JZAYmR<oD9ntX`D+PVPH|VCquFc=2oAPcLVG=SkGQtG-`btktPEN8t2+KYc
zuR~~(SGGOfSQ51Ry6Hy|pL>Ab2ja7Q(~5;)DD1=w(KK3l)Gq|D>%J(cbeO(Bsp4RU
z>Rj#2?3sK&toC<Z5QHT~9eO;3XzCA@s^(nx_}+GWPvXn@@WN0u&Z+<TMA)XWrRUuf
zwO-wemI#APBDejoV%pL_Uj)q&qE|i`Dr)u%<i9@#i~hf8PLf#7sbILHA2WJA-#~Mi
zX1-kwFI1^(QfBTgH_;tyf@Ji@_6T14`u?`z^*^@uKlKyscQm{(2tCJMwz6XzUikEy
z9{XyG9L?TBypp}MhXT~#=Z%G4eE#N^Hk)~*pWiy;9(fXa^f;?IBW$Fi1X=3fT2Ct}
zDN)Ut<ZE(cA*t$0;GXgyTj`Cmi&ArkWfCuRo|Rj7uHt*<dK3I6=c4X7gnj(C-*-LK
z&vDMFu-k(w*p2zBWX?M7XRePji5ZF6QZbgt&H8`)sU`WMZW}5e4Dg!V`JlK1`|E#B
z_2?iR>6)|r_s9Fw@;^8yxAZcvylwtHH<!{kGD5ydcfS)v3++c9h-1%hAj6iW_6roA
z1-{??G>v1f{+Vk6j$T<i69_cq2Vwq^@qEvgCIG%rbV|zo&Tc(=&w{#>`<dXZZo8<h
zLHVDBLJQE0z1Lx7apDxdQ)Sk5N6)kTGAQysj<h|99wmuXziECGi6dLDt`z!w>Ro?x
zaG{_3=`S=xo`dG?z$*W)da}aX3OBH4Fl}OVbSIc)`n1)=$uCbuoAXf6tzUqQ1Rni5
zy9PG(w6IK1JUgjHWpQYYN=Y5{GjBgldxbr?0TRrSP5;TelZ*|6AhP<5ugtWkpA%F9
z-KSm-Aei$n8G;Nx-4F@3)nNh%S@C;?vQFz?gZ6ht!v@X0$8s8KZqS8NwS&0JjP;9@
zIb~HBX`lazS=gdhLnvNO6o!hn4l8==UcV#R?6YtNAeB0RkQpO%4kiRTM^$&n6zuid
z162spR~98Y_QR#kEO=^{tMToI!sgW;pI@vAAC?R`xyRrmA;LUFEuCd1L~&UD(X^_1
z*=`@iDK05B<OOl$9FDE`9P=DRINJP}j*we80_vU$4Tiyn?+JJ+m(CgY`gzdj({su8
zS__MJ&E7Uq_!xHz^9|`kk7g1rgTiD_O?t5R8QFqT-mcg)SBtRa@CG>cljWIc?rPb;
zy#P0)kzm)1p|_WpbNsaD!IGV^WdutGcJP2rQnxumc$$4WfE+zQ{p_S71S;Ch$jHF3
zi?8-1N`lnShd?0wsk0&4>kadBbE6p*6(k3N>)0Zoh9UQOg?(FhrN%t;h5#?Cg~G-M
zMn~mM5{syvk8!haZ%#)})Pmw!Gbq2rH?b7abJu2q9Gbb0Xt}Jwd|a(ETbNasm_}t}
zWLSda=iFzXojUNnuW$9q$uC=?&BAqrJ1*5fQ5r&9jW66IB1Ff}-;kaII1Ln~{1~-c
zM4g&qxu5l!n3y;l6+dW-1{FLQ5{bl^2_ad1{K0PG%MITAZ@Np&Jo^o@Lr+@hwqvHZ
zHXBK{sae+?%gPr@ks!7a>=AHp|MBg0V!cji{-B^q>7y|lugaCSD~#V}+_^fcFET4G
zPo9!mcWIS1g(by&PW5)mUepdR46%D#IGDDHSzp|Xj~44H_wIdnE7Q{^`r2!jAm=c%
zZ5BhijB{3x9{qOQm%OR>pYY_}LtVLAV1Kmt#aN4KiUsJ#qW$PT(>B#P)_`vo$gZ!a
zNW@zPZZ#U@6`BIbrFw&oa>`o%Y~`T~pj3HuDAP=+-fn)lOo>Y?pi2f%50OfB{c`j<
zXbZyvXkkg6&T8BR-t_L@aCq2F8!sBZFR4~+FRqPF3<By^2zHT6wXjTRg;PDUatm`a
z4kxR(DHr;7)!rVwj_nIE@g5a#-dvEi8Kc$|fN<PMFpR90=U*<UKsR6Mwjayv_3Y`m
z_Hnha{Xv+iw)9c%XDw9JJy=0}&E$WC0Cz`1+sukoG}PA+G>ZG~!^nE)@!G8c;6|i~
zILYQp=`nnMZ+YbO@Tp)E$PGF+?2Ls0G2)0C>gTH)86vfQJ}mJfA|h|lBVem`iX7jW
zfDnSr`{fj`Vf#)tDQ9_^=1rza5rNOHe*XScNx|`2pDg|)7_3(l#DjXSnpl{5qFxjW
zaGKOUN}UAg&Q2(vygph5Pd@(q3rJHgp(ne{%Nz{WeU7hExmS%+N9T7L)z`5edLbH;
z>)EJe=)xh~xKDcW<oJw*6Vj|%`}b)VK(o$o#WpsKniUwQjlsTVAS25azOI+c7>tAZ
zxcBi4LR7C+bD{*i0^XM12lgQB;-LVEP~86Q_@UA4GR0%ryPhs3$`gaSb{@`&q0k7N
zDbtAlL>XSSB#kcauI28h(hh5gA6o^0?#aOwRN0(h|7Q&&Jw5xO0Fma+)*BfgKNtz_
zUAr^PAR;YYDfcl)s~<<#Gdh}LS><lq4JC2pX4Dd--|GPXtmkd^0T5J<`&p#Y#*j5*
zzGXdz@dX6H`0MXa2A$xxA=hMmkGdlKLA4wOz4O|yEHc`l$dBDt?kX(l^N*gpqxXg#
zVB#9T6y)@!mVdk!4txX}XNf@S($XAMke~sqQY;8CKTNg3g{#(9R+Aj-0@kG~v%Z?s
zF}vzP!08$DP0D659>1gMdU|`KXw&4zJP(k79jm{xOCvz{IPPcdp&=oli`HvCQ+aMb
z!T^D)2=bu+H--Bd7sQfQ@$RLw+rh%1=o$)%P}<v60H<r&J2-3yGAP8SpT1E^w<)%i
zn57Xf8QVTXsa?ZrENT-a9)jKI<dBnEhr#yL<@dvp0oxJw?EgkTx-HzA&S`rAYC{0}
zOD%{PMJHws@JLC-en$Hf9-CS7NElF;Qr_Nq!7@#bhTiwV)_WY12WG3?dDc{rNeYy{
z+0R+mGHuc=MNJ`SXlbeTgCS$K?jq<3^#$xm6%czTy8FID@un8$MJLl(O}1X;#ORq@
zfV?C4^yyQ)q=W>5b@LHm;i|wo_3(<%mS$L{TN|9$FPZ+CucWK&PclW_l~-9JvoU8`
z*x3VA!j|<X#U^#EAeHB&j}O%Yo5+>z16u_qSZ)j*-&CEfu-^vJL+Mg{dyz3H1(Niy
zU_f7%DX8g^06jmkL+^9?H?1=b=1}cBaIIJF41IEhHESn``kcTIgIK108+i01menWs
z_a7i%3^$h^Ah+@>_lYoQ=ULUl+J>Xt=%cbzaTDh7*~LK5Kj5$-WX#YE;gd<!(N-;}
zowVdh=><4&7-Rl%WkhNYJLvrfA+r0-r%lvvA6Pu&e~JZfU5!3zad$e<T86g~WS2j4
zG4jIXP%FP)qM+~<AhrDY(4Hxm7qaWjUafLqU!~{D4!@;bSv67nYj7O?V*eH0c^4=8
zmj40c<Xx+YqcLt;sk0XjZt3tgpSnlx5dIKS<|X1^v<WL1V+)a`3ZxM{91@flG?+bd
zkNS8Ja~9I_h&<{(?%>8~C(Ssj-_{=vr&H+IY2=TsQFk_cy4LC3fFo?2!Cw1&mhq)_
z7~Mpi(8TAV;2G86dMf85&KY7hp$DV3XKE9JAZ}LII>$KO|2jdT>!8-^{uprZd{{|n
zJ7jH~u+p0ANlg%wx;T#@tQ3g}TK=64hE_3%f_#pZrwe{cc~!`IZuJv=`+)=mbyE36
zuS}6NU(hdGkRq%!@nE~O;~8y?Fn{mOhO&ihuXt1;`>VOo?M2jb+1=g{Oq62%wZ@f$
z1%Qpbc~8GuEoh#0c=U~8{n^bkOQ)^QxUzi=%5HhKcOzux@7$Mj8`Hd9gji6o8Gy3Q
z3F^SxSr6B)8xSLDo&Qhc@i-i)*tYE~P?u&2k!SQdTN(9`Cim~{O7<sp`KCal%c$g?
zzkZ8i6$F#`_Y}WZe+h<tK=z^ChFjr<FYHq0_saCzPqH)X&wIDA5c#KL3izY`jFXj*
z_L?}N5$CWmv|uS$#=hCAuf-}a?qgOLDE4>F?fo|x8;te+S}|pxJri5s6XpHt_%h-k
zYc=N)zaxqV4n`@C^Ce_joGxFr_;fz8a8)|deRg*k_p9Gsbk7k*{QH0--2Q@pr{zBw
z_%)1PaG-$oUc_3l#|VxvAxHi2P{kqbLxT{cimh066cd^p2=rayzWO{aq}=UP_l5_P
zHzpOWj=P>&^0i8Li?Fppcpus0a^>lv-D#J$Pd^?&Q+eFy(B{!t->KVj<tA-;>y>=|
zoYbxtVvAD-Az7s<HGS^P;(nv0aYTu6$A?2g_U3`d<WI=c?9ln}f1H*kfBtTP?0X>F
z9D5#PG-H2BV|d&g_xqTd_$^Cp0OMCcZbQ+p#9HU~Np38UcZikg)tut;Yn_+pv4do*
zf9T-l6}3!akCjX_=c>1lX@q*v#yzXZ-1#DPE+jQO1HFOMaRR3Q_MU>tq$`(zXb`n(
zK2m3Ulg?Gvwd>yO$ca&!xofw#`t8%>-#Zly2YPI8N-UWOP<7|T7!uv?Y{>7)#5T;S
zk>5ww9}c8tZxxTs`DZA!R;NBjJWMkLt{EwuW$(;lP06X<vn!3Ko_M!1e_fMHL7!$c
z(17~yirg8H`|!0QP`a%gsa}CdS6^DxXt2<K5_?tOkB+gWWnAX=9+zfC<e-MvkTbA>
z+mn6*Zk2JRwdO%2{aDhr+bmf{Kq0J~W<!fKVKQ9u;jk2SY!E?LfNGd-$0uxCtF$(U
zlUZ{|UI>j{-Og$-6X1A69XsCmI!Fn<m7nlHMr6+se?%UksYG)^jfkSQpx==k8({2U
z<Dm#zAjR7v8f@hecdZoy)n}fl|MBdR>X(P#@+`d+-JrC1kjJbaBQ2|Uza0HO<8MFg
zc)rGPKDqB2X>~3GnoA@<FNiTn-B`6o``$N5QkqcboQ{?1pMKJs+Oj;S&ik7!=P*25
zx67v}m56mR6yIL%m?Bthwu!EE|5%r@{`tYfoJ(=iK>y+#@<c7F?YNC?{{}HJja&JT
z^PqDQ)F6Iu)Eiu{kyku4A7|^7YhQa?rD32b%*k&K;neJ_dla^PyL|k5sqFltb<T~(
zYn>Gps1~CcTQ{>e<`7mZr=&qqJ;8_hgIa9bT&o(*I0{R%64JPKZ&m;}wKiI;_O`Gq
z-N!I>sWnTTtzSRL4M!W&sgi@@b&lTs>vm(2IcrHAoz1!UnmZX`v&u7_)9Bao3fkX|
zsK$sNG@;U`>TTARQ$rbOAH+vQ+tsdok}rJeKqDKpxU}{R(I0)3GyeUE%d45NEZLwv
z<TN^uGjBV{4ST1BSj1<%p{#h-JgsAse_rx!blY8yY-&pMeU5|S-`;GL9-+xg)8^%@
zK=!ZRA&8wXX6H2Z^>MG=vi`YFhQ;{z-7BAn79zbBQ~&bl%viXgMO=%-DW`@Ik17cs
z{_`DetJoO@W#1H!{_EteFop9|9<_T8C_vPvL@f#vl!w#9hE)G7dQ$7{<JEQrvObm8
z+mG&W!Lh31-SkNj=O}DNs;g>luSP2Ff$uJp2yWZ)aMC|Sd>w>;@5`utn<a?rdjiE~
zmTH<dy=C~}wZ7%;JXqYRMcSM&Zmd*rDYXIlAX^Ss*YwS(G^A+?F3H8U&iOChD_@Kj
z;(9Qr{M%<{%)E@EAL;1G1fhN3ZQ0bPP^hY5s7-p`j@LUH5P9xJA!kWdUZF&8w4lE-
zBV$%E`7m}NNr)o#H&>t&1uJ6b8yk(}<6kmxMaA-bSX1Y0w|w93De)_P-7VakGc<Hz
zlgFAi-J8lnw#mKxS6nV0DXCR$cu#@LB_Z7D9tiy<IrJX8QAg3ckXRR$J&FJL?%x?6
zzLS+XlT>dL6qVv1Tl<^Bf3XmmAud|!>3Y`V#vFq;yQZxP?v`n|n#y70^D!k7V-4Yi
z)grO&CDzUcDJ<Ucyqp#0PW#HO?F+6amj)Y@#J4Lh7Buh&ks!i{{r;6ylU|1SL+Z~;
z$1GcWxn!SyA<tA9(k60RPT$FeW<{Cy<Exx?EJyjVO-fKVq{bk2m1c5#Z80y{QZpm0
zLVSHX>6`D4(p#yq4TL~6)rm-QE#s1cfC8o^8SgAM=qR6!ZXcv(&Yoc$oKY6AHlUo3
zos!WiDh><Dv!(wnEVg%?#N5!**V3J1_MNWwMcdnP$d%ywJL$*|A+iT+Skkl@Y?^bJ
z4H;j^_U^P#fbddhZYiT4VW=viZHazRQ)PGIh+@LPvUTR;`cp<7-<zGgepK?gbCQFl
zsN$(ws7PVornT)AFrSC`;C0P#@z@~$Q$}%$w}%hcMyN*;&gX!vNA~>{7CkK^fC%%r
z(GREY`D35MX|ci2^=~ssC#pj{R0UF%Rf%-Ncd|(HI{LSHq+hk&WvOdGG`M^??2hah
ziaw+@V6N|XJpH<D2fi-IlA3bVjE>&!ZZj~kclc#@5Kxs431*3HtcC0QDN*>Be%_mv
z!4W0W+H+Fcy$|uf-#T$<P)DX{;z(M8P1;CIZ!;4vX~w3{6FI~$)^QLRbS3wWZk}E8
z6j1Qbg5_n=uC3%>;Xc4lNr?CG_nLo!l-Jk)TnT>{2czS6P@RNaNRQJywAV23Fe>IR
zJks~4Vy1EnYf*n&aK9}2;L}dEP{?i=M*wFa{h{M*`l^3#Kwm<=BoYf*9cyrF5Yi8s
zpldLjWN4VTPihHDW!2I7ds@fJ34!1lwxBpStOWJodwcEl5Wj=pq3kN#fpWx{s65P!
zwlB(c3_rLftmKQe{*$&lXVj9>_UZZm@i{}ZO@g72c^v;6$@lHQ{vG)7VNHGC71Bl=
z+Mr$-Rv+HTML_R-0fJgIv8aBpv!TX1?0E2LR~^8evApqSE5E<30a`V}-@Mt+GA*jl
zN4M)yY_zlY$QRW88iOpUXaPx68d$t!R}SbHX95(a8s>I+Nmsz~mpRvs%C}%^MGfdb
z{LST54-`oFgD@8^e&Qf#ShF-JTH3bFjDs^q*H1J!N*Vx)y4B!xMs3?0u(#3KbK};y
z*}1un^^FZqHQUpKdKwFwkKq3<8~{Cxkt0)Y<NP0%kGho1NZVDL49}X394ngm-tV{Y
znbh3pR6Sz9loXylP+lEZo*TDY>xBxj#Zhn55>}MTQ7#aS>;o>?nI0T2apdED713s&
z`(ewMtW>S2mWqKW<qsI@YHpvf@u#0Mpw7ABUbWK>{v)k<nv}ySps9N~BFZ;?UzKQR
z5^Y8nf95g;>ZIT^SO2hOjfRU>QdZ1XI$_JBfC(!J?tew*Jm@rp>j!R$&hj*~<o5aR
zeiFwW3_9+)dlfqi-h@F3h$j-6b3}!>3~-_Fp$dp4wZ-Q?y!-#b-FpW#m3IB20Yr3A
zVa8EF5XLcxsE9P_#E}^>AfTWiH7ZSr5Tw@x#)1wAV`x&NqkxEj(jh>w!9=Q5sR0Qf
z(mNp}xoe~IzV|!l+<U(7{&l~@KMHJi_I{rAtnyoH{g!QUpLXF0F-4(HsjZ{OqN~*+
zfKza;zFdeQss7rhq-{{GHyL^To@ds}3q9Cp3E;JdJe1Mcd{Lw_;8!l6#+I@lHTc9)
zP4zEATswOqNM-QX@s%djj|f1J{KLtQx86%e+l9%Pmv^B6`63B3cVO0M3p?|1n2Set
z69l<xwX4Oj;+SDBqwM-{5(;WG3fp{jRL*U=EV>#*M(2A#Qp|nm^{w*G&dy8#UAA~S
zL|rFB>4;~g4g{Y5hj&L_W;y_fKznX7wp2gxYqRbdE>R%gYIkRzU2xacE`T;}!{3&>
z7HC6bZE*d?z<?O1{k5ydJpH2PKEHbP%1*8Vr?Ap=t(@Upf4#xS+&xFG4Os@YByW*w
zb&s3l*Ro#WOVo;sI*N-f6i<#7$DUV!(8!^NyvRCNGn2h3i6G=5xVdfQrE9V}pNPxF
zYSkR5ZPwYkFJw0Y)qRGv_JH792zd#Ic1O+#<q7V9L~w-V)UsGj$9^JlN%&Hhq(|9M
zb~*-;QKeeb2vHNAu~hHPnM0n=&yn~W){22-zwt<~@<%vK^&s=&)_`yD)YR0fcK=kn
zTKg_itTZa#01(PwYu^jy<3<kzP2(#;^err*@YpIrkFyvsURhUg?#OOl#>FE<PXu`B
zg4FivuglpZ14#-iz1K9OyDvR+UqW*u!nzdAV>MT*)d!Z_AEj;1?=CygYthwX5lF)(
zKRZ$vcKf}$y&^wXEMRLqY9)MsYu~2Sld>liTz}7_PFD!D5+0EJ04Mj5da21z5jwj0
zyBb36d%2sU4pERK-5lU1l^<VIYLRarYM1tmW><#foDco@o$iU=mX@<STM~UG>}lio
zJ2;!|3y<6qO%MRG6non`<w|?=v-MRfHq)iuUQ&f=?n{$H!Ncy&K1FS=n)ZczI)WGh
zv{yORJWhYCEf2C$F?Y+>-qNIf8$z=07fkZ$PVR;{TyOT&pz5H{Azcc^!0GCa8_lgu
zjs&D|>mIv9%2_?rH0HT=sG8@mOQxw;qVoBVD?&N)<*@CN$1C^Omf@o2I7`{f%a02S
zy<Hw|FIzzJ6JF1KlxlhMKYk~&`@36paJ*v@kJ_!3`1BRtuOv#Oye-ckK?JfNs0(g{
z3+}r)1O5M>?#@A?K<)g$1<!4#YmVF;W>Y0GDVbTmQZ;w{iOG~|Kx2Ug-ln*pK`6K7
z;ckkOGzFw-R~O64r()qKw$+S1<4$kRLsbi=3fS)GOS~M|>L+{ih3VR2^oJ72s0+1b
znT8(LK6Y%@_@~m#Z*H%k_N5iCy#YL45m#La6p^2F!wGO&X-6O(K4Hb1`h`H4u|k1f
zvf7D{I*Up-a(Z6WbxX7fIh}UnO96j$<sUuZGmS4t1j`ie!;fUWb;Fqh4%^NCy*K@v
zdcSPti16j(_MTboV-E!Mn#K6-sdTo=DbAfI#;RU`h#kY9nsk{+es)vT4|PY$L&7~6
z(5g#oK8ZuVfHen$OurU)Fo5cnO`Q><14yqRN$4tIbVohM0fki*dJT*K42y$5dMNz(
zT?|-@NE3~rn<Z<~9g{DCmt*BD>s_4-oOt%ahgLw#yrbrppmEAO^4*nI=pp{$q{g;Y
zU(h7bfVb<sao^BVeUoEzsKSb5K;xGYq+?0`=IoA#>{cVis>{!P1|05L{`>8^JA1y+
zxyajDxNV#fzxEtBBUYVm?m3kVwDE-mhZ77wsWTa`?nsOtzMCb<9;zih0nIzaW=UWI
zZQxz@)<~Yp#8vpX@@Z7iXe4Q<rl^Aw5gA}ViO^m9{MgQWtjSIf>g;V?TwI8F%;Vkx
zgsGPwC)flX7cw%3`ynsa{EQp??vGuZ8iFa*<?Ig*3Yv~msxCTvP%5rTbUUap<!*1>
z1agXkuDZMH%GYk0ZH`)yeiQtB&aK?vxt?>}CfcDS`LvB)Jx>&@<)Z}e88}b|x`=&q
z%I;*>vW;8MBc|=O59>Ll@pN*9=t;2?QFCD*O6tT{PE!U-iYuXm!+*y<+bxv~NWDn_
z0My2iWr4JhFI_gVuZqZ7dj%CCrhML|*SfvjoQ0Hxp%&#x)_nM~Fc@O}_F2I`&9PJ!
zD63HigfOx+VPGOL?PXJF0<smE2O$|I_-Fmd(Py#(GcU)@|Gfzx&iDSa3ANgYqH;bH
zti9d7(0W4+AX-YGg@HP5F7b!HSuH@Q5yAYG9z|38ZtAyYSrQcT5$;<d575XA!H<_C
z_KxzEi@r8j*-i0o(iEtf(chAvZuZV4AE9{3uM@{?4Jo*IB%S^vkps|<rrqt_jHNXv
ze33V01X<o}gvVslN3n~6@Xk8cc)bCKw#WbWAd(tDUe*zLNvPFA9`(5DUx>Wqx>XUr
z$_w9TgK#naEEI*$ui+TMm0Ak*M;yM7o1}DmHtmJexF3Zj|5#V=GTxez&p(iirem*h
z^e;v!nS3@LTz*nmsA75WN3QzK*_C}aRiMHsD~pU<%jHq|u0*NFBz8_D=E8znY%J)Z
z+JI9P>t?xN1&t}z(`nsaw8n(t2MPt(a^w~e?PIlPX5+vf>qoRt=3oCvfkYVh=UKf$
zh!Mi%!tr6eP1V~{)oS%yH-R?0a`5Kt*09XX%)^PmsUdO#8Z34|JtuD4J}JKS>%eA1
zlRg~N)YPQ8?`AycjC=&bA6jAqEga`Wx$RL!DSLn5`A`#pmMO=Ry2T}8kahEhN**tX
zS?585L9~;40#qsOZ3XwJ8Yx#Q1kTz_)%-r7)3Mj^pYE#<<AmEl^7R3sqKa6YJ<{iL
zot80P<!lZ)0wuA4!?P_z5i-Q70N1LzlI{O?c_iCgEnMa>j%C;K8O-*e=}<ynKwG~_
z78~CC;Xm)KxpjygKKlKHeY{@n!lP|axZnmkRbB}^N5;OJz?M1^EPRU}2>ITms6)1(
zOQlJj4&PDu(*r);m21kgCm~ri(EX+;UJ*!BJUmH2^HLX%bl^oGWH_{!w9=EFLJIVO
z=<A6#4DH;(AFK6FwF<)F7#IS_&Fk=Ny!f!?Xj5AKY_SnJt6He<BjmZQ!A|5X^_eb#
zY3V-yPTX|!mVF@}!2L0T7#64evj^8TXA2jbvrL$`w@a=G-#RJr)3=<g<;=fznTnl)
zyYFtEerrJ$sO5O%{MRN=BdU6TN2^5_Vo|Zle?ZtxN_gKQ85?>!`Qfg^7Tfkh^%((j
zue(-dk8SD`4tWGRjE49*YMs@v@j`qq2e4kRA^$04;GwuXk`k#zRK4lB1W!_eQjAop
zz62~HF<lb@&f!w=iUa9O;(PW4)i%Ea(e<r49F{NO&o5B3AJRDbTcB^-z26_Hv2)nP
zT*|S_9U~q>1X)@1<QB@N2Y*M1%)Fe;zy$B*(h_od&U0upR{z#*2}x&dbUS$a2A6U=
z&No%f0PBYJhVP!4P*U-2gEi8lzXm(1xA&7)`-it)cX~Xb#HUZw7|OMZ_WQ}{=X&XH
zQ79R$`|f`g%RO5T8H}tkuGu|Eg#5`&cxc_nF};T}A1`YidYZH!d_}3jh4c1zY`m?L
zOa6r`pp6@~!>D1TP4>Ua5=+HqPaTTAJFvaz9wgfx&YaJkvOysJ@EBB#$xl=JU3>nG
zcR#b!_Vid=pLx0|-Fw_YeNe3?@LqPZ^Rpu^wIN0om0tX=`G$$4t<V2{vEys-Om<sX
z^I<s+rtB}#-Mp?7dU|I9U_kp@M+A1BW_wnIT=*B1{;WT5d|rKf?k(qd(4N6h>K|==
zqL5Wz^)H0NZ0h)vdj0Kr%ztVDL<F>t7yO*83vmC+llr@r8aAyXzXln73zDDF@L~1g
ziF_$fx1|N|nf1Cfwz<<t<s!Tp?n%8RyjXqPh?+zK9D8I7vi-i-*rBntOv#QbH;X^S
zH||?QQG4!}d>?L3QnyR5Ytv0NP>INLsn@~$gU+ChjU`t1#oM=N3n*^p?pc-aJmM)l
zJGMP1%{dib!IIyfpUtn1$``*+;+NWCD<ZX}&uGME=eM}GH(f3~C5*?%<=4=(<5k`6
zIIUu0emihGAK~HrUpiaLj@yys;-}d{78z5fZ9D#zzId|6`lTX7FFtVaxcZBu$@{>!
z-0rFVLdMgzqe4(eWi>=_R%dWIs{UJc>pX^fLATT)`v4jw>5Vih0TpZ}v40e%Ecl<3
zs9X=%ij)8PJg#ohS<3W5>q9Rq<+7u$weVNo$r|&eN(r<iKMdORFhUn)8?W~*GI#Gy
z7YI2X8s)S<9=kPTYqkr@`s;4DHeA<>|9)EnDqmm+pC4aP*hE-lSo{gxW01wVe@SoH
zzC+=o6Je&tUW}5-oX6b{0eTU<z)n-HU88F_K{D>!3aJ<?@D0a?k1n$09iCIR{e#Dl
zRfRi#2qUWhdFD#hw@Y2M&l{oEXa~x_CC<r3dkg9czWwt5u?o&jLLf*vxjX1jYJL+`
zwtf9nwG+BQCuFwTZqnJhC{Se+_^*W9BTz&_gQPHX<p%R_vaPD`1eWP=`VKvk<mypR
z^<${F+v@M58%2RAKxWlA`+Sh^;|b-2s+9pY3ra(TYbL_e46p?cp#@sIu_Jl6%Fx<r
za*@LOzqS|eO+dRVf~?UncGbcf>Tg11vAwr#gM-<4fE6;7`&#wjg!>I<Vad-YzkFHS
zem1WsOSAUk3~RGn36IO3=>cY6$#_lSQ@b3twOz~@FEPf+V9hegfeWL1!7^~c^rBy2
z;+syc@T=bzJxXwMtN4^2JU#AuP0c0hNYB~c%h*=Gef1(8ru^Fcxf(AYq8krE`E@TG
zkNU71wMJqNAvRF;EZBBFR~}CwkZhvnLIxY4H(>WMjY<Vdv!almghO9G<hs*Ob(|wV
z$?gfBsak0s@k%U+_mg@EDmqa}Cd9K%^*sIwn$o;dE1o|+KCW58H)7QKAbNesKSu6t
z{Jp%oP1`u1ZkDdSODNH_sy;~I+ik|$ZAVr$YM@|fRklYNn;ZD@Am6K`p|Q$l&(KIJ
zqRRupGkZ^NVIRO2pl{9w+{n?E2dAY)lph+U8IKOF>Qt704{3UNBcSJ#8MHV=TP{CP
z8!!rm(KFH^v#;b(z+QaJ<<g}~D*%ur!djV2T3cP+u`i01gT}hWm22ySjPEp^th9tu
z!N&6tJ2XH3A<RRdLZ!iy+^+G+_9*V8?CN?k^feRHHS<W7up5+39wss>jnOFJDCT+@
zkX0oGFYStdBwgLt*5<u=|L9oxLX$Q-EPe6%_@d=Ga!Ke!bTC0?7AaGNPuTJ5!*_j~
zZMRnn%1OsPk$~R%Kp{EoJgJ5PvBM~|L0mSYc}ci?r9nM;K-2yho%u~KGNE6jq_mWF
zxHdq`^K8@{@8lL2=y-{I>Sl0i(i@oJF!AqG1)xd{7?{K!w%P;(HgIF#%^eVtMu@)t
z4V(%bbs~={o%5`VUL&ZZySyOk8Z(B*+P8Of_`2v(cyfd04%=TTeJFru;NrFnnb!yz
zX0)n5TlnbRtn2UL;ZeQos=J$;)-3EssAE7c(i&m}R9JUw=rxC*hPbZxioPxfS`o%@
zw{A&9jJv@g;pv%e>pvWNbA@vdOU?%}LOX5xg!dJA)Nl20=d15%hy>p;YCTTiuCpS5
z&QLP^UWS>gOzl<e16M%02IQfLDE2*bAp#W>Axhj7Qck6^{X1OcdJ~FZEsoX(X#Qzb
z_>@SSV?g6>(Gcc9uU>SB$OmXiY7%`tUL))fMKd8YDbTJ%B6ki8*3>yf8HEpszAp6#
zuiP~2mo*o0JpV6nG|1@+pgyb458Qb!Kt!eT_1z1<3RUl9wAD6<9lki;XwH6lpv!f$
zU;Ws^;VV&fB1ml*f7vlgGYtsO(*9QI{nPnUDTE@CVX@DaUHShWTOdFsgXPWHs2K~9
zXeD9=_+XC#f69OdEg8}uPTDCl8nC86V?j$zJ7cA*tBZp_$rs?lNy7oP8ByCd0{V|4
z!<S);toDX%h7gM*JNwu7ztIlVt|r3=tt>7sO8p}1$&G_=#Q3}Ty>l#@1x;YZvS4jy
zpg+9-La}@0M4=Jr7}ofc*f%@)s^go-a2F264`?>YlhN)10NlMqDb?mH-5T_UtAwva
zb<H&_JJl843&6wo5^xWfdmr6jyJNM62p(^%-#qK;YS-J*B}-}MDYj@<D&Kr@u(s?f
zY@Pvls$B8mjt=d2;+P81Nsq%V7bcsFo89!?BQm-tOw%I$ZdV7w`TupY_*0_)@roU-
zr?Isx-5p0VmCN7Rov$vh7A|v0jX=Bh7K{FLb9O?D8LB$_hxqVNhFLaHN~qluwcp$)
zEX9DxO0!1~8dCTppxbEFEc?d_DwXF1RQZhf_hJuGJSfCER(k#rB&~JCR<+`B<F?qf
zcX%M5d~yDe;GoU8UT}h8#@R#hC!y!5%)QOAp8q|Noe=oBo1QZWG>)IZRUk1HpIsAu
zEn#P8_c)DlIDCJ^0<aty*DQqbcI>PCD>rv4?#tVSv1SI8539wCRN#PeuL(}0Ep*=*
zk{yXrbC0p)Gnu9B6IY@lWU*dXgYr_YL@8#qcG4X3tCo9$e+a~q_Y8Z6iM}SB>V42W
zMu5%dbi_b=fJ>A>6)>|Ws2-DyMO(G@_EO=|4H3Nn$3sj2it%whE|kz6;u&|g7R!P#
z)wya5c{X~cr0Sj8Y%SNJXVE2iQPd&i96X8}9-p%Fq=v*pP+?ov58m|r0%JFtGR)+$
z3zb^~E&v<M<>55P7kd+&sW)G#Gfz)H;ES=549$D;!EtXGaj&P0r{|jgjsU$Em%i?c
z5CrERf*jM)^uK^`X#4q~L)(4*{dpc&uU<W8J(^|ED|ge=rzdhXJKYvQLCqw?l!a{2
zngkJ`_}zH|tXdEZ6R8p_+jf-#v^y%q1CkC>h7(FdA~%cUmY@|-=X(d+O_Qw*2FVJf
zuXS<t4-i0{bZ#o#WB@GDk|($3ENo;x+(r1K)X9Kg-!)L_JHy9NJbRF8|7P8l#j~uy
z>9dwV9|y*43{g_8zsBr@x|l8>U*G=IBeo(yu3{^8a&^lIsP%gkOE)67i&Ec|O$F?X
zA1E=^y%N>@`8xDvkE-(pzZFL)RPEMgfg*p$TL9^{UF$2G%Y<{uUjr+Ni?OId_mDNN
zXg;Ht#B_`{BrWv7y4Zw0LN4|3_b17eZGIpWQUFRzd%SNq5R&vm3lu{@^kl%&#lRhf
z;1alA18Xp6Hdr^lfZ=CkD(~xXmb$G^c77JYM@DxS?+9e{c62N~r$qFVcpTC-=u-p(
z(DJ*Wu=S}ZYCzo`N*;gwLsbZKxid=dwS+Z}W673$m^8x-yh09`>I9G|bBCz0I`PO8
z{$#YA$|=cmNJGG>@$uR+q;S5rf@sn)>U?O*!?$X;z_#t%@2zk3$<3RBtqNeihqc{~
zC5t-v?DoX=EZ^&m1`!{M)_=3Ge|_d4Jz8+fKHK}vV-a23{T;u9BlM=(K=_XHK*E`l
z(C2yeK;Els{S()v=6fvJR@qA*;I}Eb2<2{*lQ{JPT6*f0QB^v2T*+DcZCXaH*F1G+
zM;GNNmd>dw4=^H;N(20p7^oK%h^GhN?ut><IVBA~2G2^Sy_Hj;zvZTA!D_i%qtl32
z`KQ4_^Sz}Q25|H`73qMjZ)pW1tiNUG;q@;cv_8$S5F>5X*u=^DU~vGetdr-Quv5Z_
z{AIZGe9zpQkA~XPClA(Xd@(5Fw2l1j=knx(o{e))Li<O(Xq!aw;Y(Y9HfDwSw>wC*
zJQOY)7p0fHA*1i@tKX+#pK=s)`;`kec^4TiAw98L<;c#EtqC8#7R-kTmkNK;zMJ=B
zzuVrsrKPoXKJ~+K_;?XGj4^AMJ;-hfhb5$>Y-9CiHdm9I$0+L7(m+N<6h@4@cFh7v
zQ=z_Alhf(CJmS^3{t7<eO!lWN!eTmSh`#pJ=IWg~GpfE-lCM?i-W!dW=rf0OqxbaI
zjbG6|f^8KCKU0L%MwNe~Ba7V*7Jeys@YF*vEj;hqZVU|?QyKRUFR<42&i~%#A1zLF
zIJ56&4hmkvcbyJ3gRN7vXH^yEhYwSlNjo%tzB#)LiHuw4Ypd*Oh-y0bK0x*aWY|u?
zQJjGjHZ9BX@~4C@(*>IMI#KFdvu1LZKBBROM*jXQ_s$<^#S`#&8+(W2iF|<OR`1UV
zx_7y3XWIkaT)eAHR^&gjz^U@Fin)GL7JKh|=$ukM^r}?_o^S+Arc=i+Z7@$O-xoeZ
z^Cx(TH|wm0n3OZ4Sn|3f_)dy{&8xJwfXzQSC^tT8YNAT9J?j7cSunZ>z&v}?ILmm!
zj}9@qqnY6hry2@0$gV`v+ZA&VPbXSXPYM!!a6;EHoN?2Y2+?T4bRIL-l$iKE=J7ox
zu96+`D&mQ39UQzNk{;VU76WKPg>zBRJ8=?9NBZRI3Gkgl^eBw|jp~zdU2rkboe_Yy
zy8nxK0{MxR?2hf*eNiK#uj}A#Vvx_ChiM8k$5LS>1r>l#D56%J=MklIlwaDox5{)H
zxSJME_vUVRe(;(i4Py%~O)2U#gG$R3PrLA=gHEmz%_e8{*&24&z_jc^0{O!1Ga4G5
zl497_ZAI6IYpLNk!h^#c!X`k;_N6qBIp|ShI2?`*^cPHTsy+_GHips-lxmlhYI|eR
z<?@u~#06G}=1otP7l7foW}n~V{_~S_{ovilpyZyAxRxXmFCj!vW(@S3hUbA}f1>F?
zi}mr?u@5?hhwxzg0rl*tai>m|^ippxj89Kjf46#m%g_^0J#9Jl-~^5F3n<mb042EG
zJS!vw8V}dPavY*CXigFVoa!P?EctPm{owu@fj47niS_+5Cs*GO4qnQ|&=Sy%XIB^`
zo}E&yn0eAc?h<Txh3!#-v_~ECYLX7?dp%WWWnl6L%YKOqjw8VETMp5D><0S!`eI8I
z=k6Owj~`B~FBPB|k(pMs&)Kg^!_W%*M;BOJU^Q!+t+np?c49uliNTz4YG|Sfa>FO<
zzKchzod)sFJ%32yj*_&|h?wFE%IT2IDRmSM0H#b(t*`HnN}T+7{!sjPfQp8tWG@zQ
z?V{$~!IQhC%A>bmzYujDb0~IbAZ40l5H+WQB{x80bj5XLmeaXTM1O<t6%+=h<T0UQ
zJd^u9D@1GiTb=>fy+>~*papgn<%#l?p54+SVk_aGlMNrJf4-@s5HVsO84Q}(Y|Qxs
z>3dMpca^x1%SeU}>D!^l647cVVD=QiEjR&5dXj+uSy!Xue3syoZPr1oruN>>PgZO>
zY=M8&oX?<3P3-xe%oP1CIPe<t55^nP(%f_*esBpF5ujzY-c~bL^YhJ}8uodKSSlZ@
zrd0NChX^}H1qhk*&VycqB2hBGuAaROk+u8q4lb{ox-eUjw`RY}@QrqqKk9r$ROma|
zjI&$xU;Su(O5J^@&b1`eUxb7DM&+*Md&{<E#0iTf)*F>8W)#Jhs4B?+`PyAn{JDbx
zI__i3ZP7cDnW8<)ar-n*i2qP`*AeSdMLNCNVe%u{*Xw8YLZGy+R!?v)V=ZmJm0@*{
zHM^+Iq5+rNUasbG5vWW&kK{>)PFl(O8qwGq-xc~xP}>C`^^G1G5gXzC-A8!3VsrnA
z<{yDyNjt)ZTg*1M<kR^`(i?*eYVNwr3w&-gy12pC73S1beGJw2Y?Sb}G;ZZ5q@&*B
zu{Fm7`46hBan+3+DpX~c$mC#&3YZWP4UM0_&c1lTI5B#1RDdx}W;URC512c#7K^#s
z32E=Hy}HijTWHd2n7gIH{#zfpO_<GxDe`0*sq$y~8*-Ff;3YBxJAYem>78=ngPK*#
zg&Rgw&B*m%sU&LLjaI_y*ks#uraJkW>HOlBhnj|wTOaa*cU1Y-4MlGdSJpGK#u$v8
ze7hIVj~oc&vIV7q?*oNOpb0h7Xa(PXyunaoA~l5;G4>i*uOobMP&}gYbx{T{1uK_|
z_#F1;TO`tA9c^%0SFEhxqt?ok*uU$P8Vn<(w_!F0+88MvIXZjcM$W^br$<JN1$bI?
z#<Hgl@>!k}Wp-q8`TA@$ma9SE8EZ?<X#7#LX=x$yo!BAdg1+OvV~j3zmt1!$a|%n`
zM^|CD@2o#dJ2}V+t~;~ry1}n-=`vBB?n9;-dYMszFDHGrL~$OnRxFqP`Ry{A^#+Ye
zU<!s#-*WnN(c@&mz%L6zV+;zV+-O0hbih<D@a=k?L*0uF9t+QjwH!3>Fr8;Iua2$m
zW%!lBo}ktwSigJ*QnJT$6zqV{wuy}uEX@nW61C|VSr#dFeT7@{#=ZaLrWVCU5yH@1
zg+O$(w6lrvd>Wc!=;cIwk)(;dPzxz1=1PO$0>Sgwx*tyPx_^;@$v&39@+b~Zw<5o<
zx&G-Q|K}UJ8d$5>LC6nKM;weKPVm@2bZ)R%UP{964czke7ky^-e4#I5$$diyJ6Ekm
zg2Q&ce_UG0@!(AJX>e0jH7^V(EsGR0LOnyYT^}~}U7jqMS)|6;uZ@20>GgG4&Ykp^
zLA?<7mnq{0J54?d<xC4)BqnHudC0Mf(fOyn7>`pO7*{X;oEt}2qzVnLs%RHoarS(;
zIG@=0XLp<(R{Fz?UQo7O7Oq?RpyKq#y}IJH)6*RdXlzshC1agJw;+>D3jDU>Hy-`P
z8HEw)mgc`jUXSsTm|5iKX9|9%4=Pk{kRy9aS-(I0kQB`2<jV$*{m{8_%q~eFKa|np
zadnL@?nGWX%*hQ79)6TT-M=9pD5E_mXue!djN)bEjYUbw&gc_ZQ;e-VMvo*{`fQy*
zKKcZ%J$tQL({(u<-Z3F3W81?Obl?ec44P$6?vhKgf4zqMt!GZf#A-$`*|vX1?@~D;
z$vmT0lleI`iOjr!9iT9Uu@$AqW_~mJxUu=D((jRti9z2G3ekD+KkJIVU0+Awym-R5
zI4b-6tKLxV`r`lfucat!5+_Q@>$Wmo8^`Wp#M<{K@=fN2u+>RShtP{M$RmZ>yfT~S
z3pKh;UN<CT4Cg=FxPE=jwWo=EKG^vhG%bx8i0u(dr5>eAlGn{ezTGS)==&24d3qbU
z@(8_Zht^63nqV~FjV>0#Z+yC^<jeDXizmo+&K`uZC84z5B*&@s;lv{!zKUF5?Or@8
zl!B%uF$J*}(w^%^So;*FF19+&5l4RIVEaw$Ilkmu<i#>auqN7rAC0jC512CK^*W{H
zZ?9VV5w$kOdbO)@#&A9nd(=po`lIp2UcXo`j=s)S!)jsmc6}r17P@#175ZwO9gNn*
z(3h|SiRCHVk9OSKzH$A<+TD^TFe>zC=;qV&<t&F3^5p3Pb^1iC&QDm)&dp!1Nso3C
zj+TtA-0%I%f~jnvJDH9?Oa>`%i4BpZ&m3@-u}MKz6m>+su0m*$h&wZHkVHMSmPoc$
zC9S6?QGXfY`W{D1*#D)ralf+dHqTH&Kt+*=4!lC9qFHGT{ZBn<?pP0#`6jgKY2~uF
zUv&U1Wjo(ucy%l@KX(0Bas--{g6?N|yU2AXm2>{uxR=I`CFQVz`~xf>GB4fHY1G4F
z^(D(YFKk1%YM5;Adp}_;D?uT_Fv&uXk)^MnAK%x5+%}zA%(1R?O=icLrCZ|r|75nK
z)#J?a&J?&iJ7;<i-4Y{~<El3PLQ2Hro`Qyl_o-8-(qoyC#hsYbARlkA(-wH$!p^nC
zDL*zXeVr+nujknWKBkRv+ax~O*#Hw{e`n<D5FgKVts(x;_axN^9(S%Kz=#Yzg0u6e
z2Xx%J=35nJ>gwo()OnP$SFI-kBa-wUj1&@<yuMBif;aRQj;_91Y)!Mp8_SP0Yo#@%
z#5!KNa)mcO5xZW${&g}`-QZ2(hxlCo2c*@x$7zM&hvJ}+qU*N>8NE!`<WP2VyxD-3
zo*uCYve|#o>2ygW`Ce&+S#Sxda`7XNC74O4J9=leu`pP#S_zl+j}T%L4g>~~h2+Zj
z(S@8{TzHeOg%#J|zQW5~^asqr$IENJ97{Y*=fs9&#?9ZJo0}6Rw?Cd*3LEo{vB+6^
zZ9Q?C9DIOvQhwxj2xZly@7=Q_m32LKO^z?_WE{*$O=Ip$qLwp-taCeY*ZM1#=5N?j
z1@mA2xh5=uQC9X^v7WGut;}m1UvJiL?~ZdkLLW~v8N;o};9zp0u8@!r?idZXT4f~P
z)nV;lWSy%{fO`~h@^ukWoPA?brl4UcPbEL;Tq9??T1Yph`cdu3MIstSq~OR`u-5u@
zuw3h<BzCE=q5N39nRHq?Cqszr$XQ!jk=(PVCyl8{);UCfkCu}Qbf)%KdpSBXe~WOv
z{Gv0cE;~E>C7NI||AJK>^SqeLxPSlt3R+%x+!wB~G?d5X6jvXkhmNszay!?a!D{0T
zPMw-2Jv{}E`)nAxE5U3aj;VyTkY&xGUHqy2F?m4bkX~0%d-98-^ZGjeL(`*pR#&#v
zSzbG|YAQBkBG_^gdBp=NbQNm<bO!V^R?b{@LqxM?>&#6me^5xs`hNHl>#%M<Q{dw0
z=x9V1X}8Xol9sLxx^``{_Qn{pz!YHh5IP_y=i-(t(_P};>)>(uF7A0Tu{v&k7AaC|
z)ApZE#gd^yXM1t)lu>NE^~7x;C?%ymc<>P_C&so-4pgy;8F8f@*1ScmX;3SBV5NE`
zbm*9G{<t4C<{~gS%8?ys2C76nN_%baxh`tPDm~*q1HP(NP`^2F*~`o8XRM(-O<h%W
zvGe82msKt>Whf1qn4(Fa>9EF>LBkoPkHK57jD@FEY_$EGT??UammYJU2&?vjrQHRO
zYSw71O>So))O=7Q?Hu+kiK!5k4CNJ1w>~SbcJ%XGCdeH)upAW~T>=FQ)9K7fw2HyO
z>!%Zeykw`Lyimk&-Q+F?r^hUD)9EChtlVO215DW++pF!j-ecC~UTjSXdc8A!cKlWY
z3ywwzbij72Bvd4(l%%r5;=71W+4cN2xt~&E)<<vybCs5qf;csNZW~NPT%#rw9PsS*
zel6I7{Q@IHW|yFTPb(*Ge%^Y*JXS{y3oq2B+Q^QreAqS~GF^ksO7pYsFUEtg81#8u
z23;Tq4^BL|^;&7Ksp0%^zsJg4Zz+Mtm2QKE=xHBv4x(Sw+uGW2l%%A<Q)G<Z`F^wa
zJyX4<ByN~K<!Z9CJ6Z8S;03b(UgLbA?V=?=u`^^1!=Y54!Vo|FCKf73pHVknXv3zL
zL9t;Sn$^O$kWOZrV7V1-<2o2DRAdzJ7S*s^=Ecs7r#bJUa8YLOJ;@DCP1V;y*&M<G
zKdf_Xh%S91=Fzk;`I=TI7v-^iWR|4KZ{*CFn)alid1dXsAfJcHM-GOiCnc5R<elij
z-NEx!K)rvdya)bcU(&7y+qXTbSvhRDYip^X0r}cMWqB_MiHe>c_XV7*UfIC2xl0mQ
zy;SC1Y@SE*=$M!l7bCKR68j`>{w><I*jh~8*n45Uw|%|0dVG95zllQneEAE}3C_TR
zkj$iy7Vf<*DwKyv@m}K_u#jS7T|pC%no6y6Ij_DbN(EGQR(KG(-{0tZ_Z*Z>qy$YI
zE^5RoLqQhO)lC8^EPhjEy<v%?)#X0zYQZp}n{^<C`HWTSuWGzdgcd^wx{%Gq&dp;)
zyhBL2HrtNjb0gTPA=Ag|VwqpiS%5mE#g$9W_Wxz}ULLEgrnZ!(p9ULOp4s6hOYaxa
z@@+=Dmi98feAr`PN4h^Ob`5B|sW70{$GWsP)Ro%L{RrF!n^Q7bhVmmahVq1N=(DsG
zGcWCM<9vXo*NKD*2AUVd+=mi2kL@n|E?HW#g4@fp$8#Dp@_Ic=#X{(FOeQlcA|gVD
z+Ftd1EuX|oYj7VDMAbL#rOl2rXmfGw_1+&6)9gM4)ws88l{q1E4QfVb20$dqeOaKR
zLD!Z#jsk7vPbloKznsq$Owj04Z^p+udV0>rK_Sqj{jYdTsPNcyA{xX^UB)RF4?E<Q
zg;tjeDd_j0Y#6H)p|pwvSZ8PF=~PEk5k#&8V+f_azRu1EG#k1?bC;Gg);}N5f=+(U
zHL{Cr;Ib7^fQ`Yf3dsdt&RvQ(ZhJoEqb0L;A8RmsQ%+~-%dIBOJu&@WM8)^~a%IU<
z*V*zK7E__->ghmXo48DkL0i#?obA|z%0vQYNy)U}u@<VnBQ0-j)n$6+J%(ny{(N&n
z;!ZE&XdAC?r^Np2ykRK8HJu?LkaP0-CDXxMphqxRKo}^6e|Jt7LWNr}l2*+$#@sLW
zTw7qT9Mm(*FYernZS^RnTz|08Od+j)(ASOdqjJ_|l8QFrk38XCsht5f^qKWHZ6dza
z9d5cSr;Vyts%ALLpmY85?5oz~7wVz|DXxg2(y&kSv^+roVEZ}POciVia4Cddhk42b
zas?1nKLY(HbU>59ecu;3N)Ro>DP)9zO?d&W@stp7QS7OI`WnfLp;?9K`~fKAJW<-q
z=@nM2oRvgozI^^8g;32J$cW2AkNaq)<?OidxZeX;0DMEvP=#qJM~9jnVrK7DbTfrG
z@?qH;U-d9<-V$ryui97~ygV6L*OaC$2d2xS!y1=Ro-!P|0MH!w(Ug>-{EP`Wp7t%O
zjn!xp==kb1H0)fb9TYS+!%*6QE||aMr|5j|Ry(d?HBzxUfY7E2e&LHu*YxtBr7t9{
zl|JvRW*C=9ZD?qyEa+S-yMbHCbR9TrSbi0>_&lQ52;2qV*6DsKMUQ7)aYz(dtsTQR
z+52n2+K#nbGrVqI?m7_YnblT4U30^@Z9N}N3aNQCZuZ`$uCA_GV((r%B~4Au_;OSr
z^XzZxh~>4&@eL$jD1EV<Y?8IW(T(5nl&V5^9Q7avyb$hsZq<h9qF~jkCJG~t85%Ku
zxgu`<4rox*y&+e?u<tAGtoMFU_1wkTnRta~EG|~KVXs5lR9(>JpGEck<pOQUiGI}n
zIs&7bgBz{Gr8DnG1n$7j3&UZ0T<XcSSo)|iCj2&BR{bnhWrmgIZCZx2_MSLI&(n^v
zTf0MC`C!B#esQo*B#W(3MNk)`Jm};8p<Nl;YyCnm)>A!gJRwI=l^ZwjUEJB25l6z#
zSE2hGrsOKs=xxYT%@59MVwWeo7m}?fjL0Ec5V?b=6Wp%u)KeYG?s)U&jlW{Xx&>K8
zt~&+7jphTa1#}E(OiO9!wRGL!Wz~#0vE~>G$k@;H!W}y(F_~Q^S%pCAu`GwrB^b_w
zQ!<D#lFuI~B{1j_-%(;6LFt-j#8G8gH_(<%U0e_Ok$%K<?cBMuOR9d4a5nA19K8D{
zbd1sbD^?fLpRB%3L<I8_eMD$QMMY)daB-)LTp($*+9FfMU;pneInAY#AN7zps>Z!>
ztH0o)m$#~=#Tz0x^g*w7we;<=g$d}T?(psI3GjRNATkkV6Y+J9P+0mmvd@;C%ifju
zBqb$##e!k}Dty)ddHCUmC;xftx7nz=`@Y5q59Pj1NL{@7Ki%aUt8CE8ucK8@n0}j|
z^8f3bY+ma}y?EjQv2e{?R(r!+l1ht%TGFJyNR@?kQeH>18!90yyywd@bt5!J7Uw_p
z^*O$EW^-7^!bb%H6-Nt3nQV($Z%v#xkYSGzOJ7}gyi}p>o24Mb@}jXnXVg_qR8l*`
zm(xIQYxdKK#@x@iqm%xUM^xI>``3%jwW#oXGBxumEyz9F*H(!Bs8n$eOBc+*mrlxr
zzl;>djXG&KkDLf|fg6h0`A@`BK2K_>y4{B_ox|+lqedRHPlnoJCqE;%Y5RBJ|9{wK
z{uf?n+afi5=ZF7WbXQwfN9mhbO3v)I#B)}`^hmc&6mRVHvP~zS4>bD9XXxa2R_vo^
zV?7RL*d{slHZ+_WLt;EHicO{Ljp9z5i6xmM8Am3X)sVk?v3{giLF0cL0}o(`P}r24
z>AI|DT=*ELyKw+hPeXT66-1@Mpt#BC;?A+ghK4?Oa5<)cNT>+>l$noHelj?`?u@88
z;D;N}`T3<&xq^O9$J&TB-cX*@30`6<8dMA06h~p=M!#JY>m+fa%X;P*Z+6%~g*aS$
zk5&Zq_K~BQkTY$n>}AT~j7ncnQ>h%8e2B7^Ay${qwV1HP=f*Vj;eDKPmlV_o0)`><
z>C^PSG$lYdr~xS&Elo{UADR}=<YM*A4Flh-uC5AVzg+G)Y}^KMtADJHDqW0Rc^#gA
z%pEZrmvE3=?UL>OFz(Z&bN*4Lt8@yJY&|hU=q;IQG?=UCd)(nx^`cXY%uL5tgONje
zhwj1fh%Olq`!?0X_*~jKLrrbEKKYC+>jb=UWUfuAJ@Gl>O`NQaiHhp0>RjtU=TG=(
z@fJjVkIPCJc?ChN-o>edE+7A9@DjutX4Hr?X77E$If3_U1fQzvNCdAB0qJVCx?6d?
zI%n7&4=bOjYFs#fdboGW58J9Y*uu@#*bGj4cpXCgZzVrcX+4pIosM(7K(0$P8z_NT
z!3e@e($#ftP;sZWiLvoL*+6e{gsfE(^L}wBPg_}eJ{>(Gx?pkKc%cM6b9!EQ!CN|&
zIg?lR=?|K==iuA7Z?_kBF2tt3M0Q_lG~2^OIiM9Q#~D_rl){q1mgD3%e45wjmBv2l
zo-FV+)HF2%r~dx?Z;pr>0min9oc3~;A2G&C$Ch`E)y1avPx)aG=O|eEn~6zcSIT_x
ze$Wsajl9YV#u74Q8op3}UBA#g#Kqpm=dPTYZvaa;UVBe+CisChB3_=aZ4xX`#Kgpq
zAYNwv#riX#89p)rE%UV4UK8+`rH>y!-V=&ufai^giH_cJ#>Awl-5OH><L2x7PO?N2
z%$g+j>{(;NWdk2ReCPxVYXV_#zTx}?IK(b;n&)fS02SdY`lt6J(+cjQyTK{E(L1%=
zX+2?O+!p1hmFB-!bqrv#3x<K8x2yNfb|bTDecjx?TrTc}`=_hG;ue4dLXbMfEykvU
zAlj1?WiDQY=Z&+TP?QZEe$~vo1RPoZ4^mT8Pm|5w&uj+=!57@enIs6Vv!I;&n#p`H
z+zkkGmgPh$KLz{vEIt85!bnAIzWO@&^2H*7f9qFY@3>$Yk2s4=@Is%1I89&a`d1Vv
zOfGWERZL1t<MiIWdsi1eFBbwesvHPx!}o?#NULYTqc|^$!}!!$`?>yn1z&8K{+>Oy
z*H?%uHFHjk_r3k@Fe`vYZ3ka9F(01fDc8+|CPG?$lLHPTrJ%j4a;bJgdimBkJ>#hG
zi%T~I^!+FNMUG&)^n&_h{d8tJvJ{*ettYD6Ofj6kjOSbXmXFk<w6LKtowJ$<eLybp
zX7={@P&abc0XmcEn4B3%B8!2?+dM__gyw?|U<=BK^NQ@1g`Y7nv%>&Bbfa-l%pN)o
zVN#o0E-hVOs6{^Gc|Yvpn25&W-Avcc1FS1#OIb~guqODSGzp2J$HMe2ks=fkBYBcJ
zKJ+R%a)t5{R4eJYv3DRDBMvw00C`v`{UN)U>qiDu3R9Iawh<xyhk#s<QTM+H#>;bF
z^Zs+eIE&l$wkx)hEB=LBicX0x#)J_itd)H7H`aT!&V#oT%k|2h=(NV&f819x#ja{-
zNVPV!nzy$o71|%R*+cj?N6O`U5AIwCjl?`B+~!v7ar)=5+rxbYeZTL=9*68Mgk3Wd
zGBTu(?d|PTWSys-Yf)$!69~X90Yq33&J9NJpy;S59l-Nov8q2nBmgSk*u-QYwOm1&
zxO~i<rVJrH>xKx(xuVn2uxcKhq}w~RLRr5Id6Q+XDVZ5+wi7aN36Ad?)fqYkyc)!K
zp-F!-KY{WiE}d?E**nF(f-GVw(B$TH^vFEtz77Y}ZenyyMk&Cq;j4cc`C-bBk%|WW
zJC=*1agnw#?Y^81OE(x$Xqz9#-3OCv<_6G~jkGFw+AP`fpXl1LsX;})K>n*<6udHR
z>g<uvc%x~HVwJy<G9~B4rfR9G`k0M+{8`FS7f~S|$GYWW3J8lpdgnLYQ#_H95oakE
z=uZZfkVl!B7!iuW#QhWK?;r9fLrh}}Yf5GgV)Cn9$VI(Vgr%$Z9_P{io)az!WiQ*M
z9Cz2s?VYMF>h~aBgBBVHUqUkC=0yNolQJG89Rvlw0x>AAq@CkD%|M-Ko=WRuC!s~a
zYm&m;rm=H#0FR}VwEJ2Dcs9s0`3RaNUM|h2mCU_+ogCuKFjh2~mo%~yyYy&zAf)XA
zHesDDRadG_8huQ={)WE7{b<$+NK;*r`+*laQeu3IuYh)aVH%E!EIl+i)2e&i$3rVQ
z5Xh!z7WgWX{K!;BTq2IdSz`16ZS#3@GTOM!Ht~&63m^a)Gv5#ZPN7CoV~V;zl0Kg|
zdtVMpIG|;DsuvT>?7-?V#(nZM-Ya(<z{-!9U_CTe9jX0#<(<i9?_cq;)Ue7bD&Ax0
z;Gh>sOcfA9?azbwfPT4p8lPLLIASofs~iBG<^zEiWETod{qu)PFQO+|3?#f;wXp}n
zxkoZdKATJfUhmw!%T|W&P@2k!EI?td_>}}VVA=*x<R*>NaoiCc_ZkgYD>7X@3=5c5
zIv({bhW(dhROE+T6=HPZCI8h=F0>FEmeuwniuHc~14<xA2W%fde?vD^B%^cfDLN@R
zIp`IL5Um3G`REN1&49pWq08$Pjs#O6K4-88Z@@mU-EFwlMq$t*W?vr4sN9bQ2&c;N
zt5LJgU6N%7>w=#Uji5oz8V#(mX3YBMU+Neb%nS>o?7H<U6vu$=9biRd$sordX!?Fa
z$V@PUJq4C%3dZ*?aV2-|UC?XWFlv?7VATHiaKe|K$R^#DzHydoaol{d^~4+K+9u5d
zo)9p#&yXaKMS79{Qfbl{R@*o`VBdBDLKXZgpi^V_*W5~b6R-6YWh1Cm2etruPbpye
zh8MDMfs@T4CJ>Rkf{ALz=PF(+w$3`x9sFKC>zZ~CtGE-Oc2YdE1Eh!Q(wy!;2@HFH
zt2<uEU2;mIY7VHI>amz4T~B+6BjNiNVenoPnWoxlO{KBS=Y>rb699g7!%?5<fp+xi
zZvY-??w#0-d-7-`R=QKQQN=Rfo@0ujri%!gl$ey1WZ7vg<3Ee<P;HbnloxAkYO)87
zzC0tF4W>+P3P5e(^B6gIi@ULcKO-v$V4*fRBIbAw*av2MrfY?98-m5tttV(5)}-U~
z;stft^=j)0hO8@K<^Awm(0=!#qC$8cIUu<Nta2LD2`b;zj~koI$;oAb7m$$=x8g{w
zk9hMjiHU*lFD!6;01S1B?Ng`Uf=m!AD<fn(tdAaVRtXf0v}lpc2RJ^o6efK^hRXp!
z(8D7+v(uVzdcGCimJx^3EOUmX&W@WeL&svd$LOWYm@ar*Lc29@eKdvOq3HJ&e+g_(
zA@P<GND7~v*#x>Nk(rIP55AK)@u9f$E*ShY4$dVuHdbAh#YW3#RDQmS^GB9K;{#aP
zAn;V(GvX*Y#U4j9){V$Bvg6K2e;P<;j>BtWKvCprdkz42V6Q^E@GeV5OjQIBkQt#k
z@Y5M?#nzY$cwI%hf9xax0BF;_U{TJE5_SZL1H9DT7H4LkTCOYLVkA#FJ^vg60ad8M
zvv0k(G)5q<yhJ1J75i4xjE@%5C`$sAC=-pN_f4!&V0mVGOJ|rH;EdY<c;{J$^8QAU
z;mvF4n^*;8oC=QMFhJ^gnh9#V%}7eaAVcr4(uw8G6V+&HY~|&EfK@*1QA`=2vPn3|
z$7$2qDl5Ln)5;}Hs`cpE2x24Dynz{vu7BR!b@5N+48S6pW3|#uT$r8K{s>Hru*~Yw
zd*S9JYH{aC$<#}mrc&80o;ezvNZ#Ds7>-D9UZRRb%gA>7j=eOJLGt?J*KL|ecE<L7
zGkPc!UOtKTiIq5|kw+UVSgerEVl3GuPH%*@g-2Gx^ge(s^tcMHtOC};aoTWWj8Isw
z6yKsUe9ix9Js}4fEf}~Fl1KQO(e3-)sa#04+Xf%nHak1360Nf_L+ByKmM=64ApGSK
ziyot3Zf5B6)JBdpLD2HVMugU~^S>qB@n4IO`7U8Ys~Fo+a<@GWch#*(C7d;`?wFAd
z%C+&<m*@7QIO-|OE1$+RgxgK<2+Hwa`3kd_hq|PTS}~!FUKzM)*vJIdl2pK+u(TZj
zyPf*LTA;tmzj|;#W!zt;YVo6g702ERco3!qH0IWu_lo(Frt!FCm2?NQ+gbV5_1Z^i
zj~Jn-`sNcfNmRnw(vTvC0;;|%hEcZiCgXw(V@6No7vK6kbj)Xe$*S|4-_LY-AQBa}
zv$@J+wP_^dg5C4iO5-LXso#o5WN3UF#RLDx0bc(F3E1+`^aSMQdjTlVsIzu~QD(mZ
zL;#3J$&uf15e4U~2F!9ZY&VX>YIrzNz2+05(Th8SR)&U#w4?6b>kqlJ(EPMGJ&}nA
z?H7~$;K6F=diE=`_hzBfLipUObuH^wdl7TIeAkBLjBf2uc*z>{pFKMUA7XzV&uK$b
zB&DUfEC@g*2>sIur?T4Qp{5AA`l6JLrxeMsO*KDFgrW#n5m&voQ;=VUjWq6Rsk;xp
zRp~}lZ8V>X7DMs~*p1vEjlEF=WO9c8^YB|p`U^n<YiZi-eI|NLL_~yF3pK6U)D|rm
z=UN*B;UUMFoFy0NNtPssTmodb`Do?rYf~wYfzO|>2LCTp`T}(u>1l8V6`w+!;PpR%
z43jl;u%<c1#E{++_ZX{Z_A#B{7I-iqKho+}Of2qP`@4Y?Tn^ehL6T582LpVjQ#Cs`
z5T(HYXW{7K8VE7cJV@+b9NE#FtPUZXOUyl}w+OL^Tn-1yX8q(?k?0u10%bZKo8IkF
znlB|G5%}S9=75Kro7)k3O03Q`;=q1<F3%sl4k)Fe#VkN#!(^|x_^Ma#(yb)nXA%Dc
z0&sRpf8eKC@gQ9{@Ff!C&6;wG(~}?<Y9SjKB0u7eXmt>dqR{7fWM^vT8esjyttWP2
zEfUS_k3(1^7n%JMNzeFEL&t5)D=x!A;*iX9D#9=ThPE09w}Cp5(AL=WMI`wXiqp8d
zrr2ry|DGw2M*{+nb_LHvSzDW33N|$Ol!1W_;0gYwUVh~L_7_5;s*b)DGLj#IOhBj?
z66nLe;ThGTSw7T$OIg+uMCOID>%&*R!@;}`)595kGvYzoPb@=L@J4n&XoMG;uDuLi
zp$eJUwv|#jUm){c8InDbV5V0DQbV%(J5?m~vpxnwDoRy*RVr!N7sHcji#4*_J$R?0
z)W$caFF>Exdnu`xvpOoFoxbu#hBd)-IA@rI@YPIg<iPib>FM>!V!^@j7qfZT?}3r0
z$3Tt4MpA^$i#<+@SiE3LoR3XGlDEwb4LbYjGBP@}xB@@$Fe#Y97C8t&Qkaog`*=qZ
zN;ef<SXf9%eemFcA(@p^OiO1jm7dBs$bq^LZn#YI3JXRmeHb$_G0}s(*o2iM%FZw_
zXf*GlP+6n6xV?N>CLz<bqF)}%T`}v*Ew28V{yU5z>h2DXH-jW0wHOJ9*B%A8J_`m!
zJb^~n9$3=UJs7P21D~qY*e2L@v-izVnsC;Tm6GYY8SFgn=wG!vW1n=m+4ZS5a^J8^
zugXJKJ{M}mb>A-LM**C~9lQbFgAs&uN^}mubLy%}N=h9Nr1rTLrvqKfMwS`p-)5{H
z>M_=oCcK7u=`gwUGz~=OMwfl?RDP#5NrC=GrF6RiR1L&B9-}u)8*Nl$Y7A_B7$X2)
zB`<LJp1L057Q|u)G?bMKt^mUMH1B|3P4>b}1i=+D=x1>%6zVN`OF2zn@Zg@H&7qut
zQ4FcSHrN>>K%wd^a;?=qsy0Fi0`IJ0U_BDa^bFg_yhp-x7`)4&H%ys_YLu376_7~B
zB&WlAaoxt#Sj3HA?oOzB2>UUXSPd1BSG2~p8LREk4_A?qM+I_nUEwrugr$Bx+P`vG
z7s@9NuS|vhMutGE0%f4Q+D}-w89n+e{5L9lL`IymGO8(K^*$MLQ@-*Oe5J;S5`(YO
zCY^-_Ay8MLpg6J?EKkz>_+;~p$pB56Bc%$#;q|;lg;Xkze8$+gzmiu2kB>=VT4B>e
zVJ3WkU{KK7E3ExRe5g~KDjv2iBIx79Iq3LUhhT?eBu`Mxs9ay0$mexJiZhGcj&@BR
zt&q}3ao)=5&1oBTOoU5QN7n7*1WiPJOQ`t_UC&R43Jcp19ooh2Hz4awxtjJSmQTG(
zXdf<tV`A2|Nn?+%vtI>6_r-eddaWfb^lKEEyI<sTdD-W`BC7!q09>>l%SGVET?X2v
zooiflQhK_5L%PWdlD3YSl&paLq5N&D!C`UOx-ylM5akUJ07G_1CZbZ5Nt1Wf&*{|K
z#~9`=o!clY_#J^te2FQ4CZmz)(~-z<M>N*pEQoFrqIMXFA-PI!?y|-~_>&Xu458KU
zXok`J7br%n_JU$3IXS3M(9){70?;`IGQUPCnVM5)7eGQ}A$$!QTu(&;ysLjxM*gV<
z5CT<WG-^uo;*~+l_89n~NOxA>c-Y!TFlB``!|Q&cFr@JL2IEH90+??smUg{X0#ia;
z&NNPuw!_=CVl)w(6*e4?TGLQHe%voqeq;#C)ukU!&eZ&5AqD3r6H2r`qk|3QQ{YLa
zzXo7{TV|WRCpk`kVLsA7!}uqR@~!~9q!2ru!h8)e#CjW_gNz+M4Jzgsnh&|gSgsxv
z9Vvdfo^Rj;bv6pv%-G6na3pFu#SkMPLD^gTdSuZ&e+AiVhgc#Vti1uc{19CR0Io{C
z1<S26O%RCXOG7zTu>43VIYfg#fM%0Jj?*b}fsPP^&5N3^Om-J$LGTnnoV+GKvJ;zL
z45UWRkO}GM2q3vxfD9(lGtGS$8rqVmcZVioQz31x7o^$@`dRnTe~gyK@T6mObI4-j
z;vD^679&2u;IG#s!_hA)b^PjC(?4xH(&O!Kw>C72(c`QVS6<!CMpLJC{KogM^&2Sa
z0;-XpLyu(l3weHG^)_iMVpz43(j7B;7YF`BfHC~tRph-<|Gg5N|0REK!?XI&<ZH!0
zL&DVTkmV9p;1Vm@y*S_a6MIYOIec{s5+0<4JYKchzWA!zk3BI5F8X;lf5QLQ@E~}l
zw;9s0HFt;9Wcsw&H3U)>R7Gn<w|l*@tJU+eqV+-o^_nh^n>)T1i$n){y3@&ujg_BU
zS40*el*HX=E41w6R>`pakYaEdOzNP9nav0XDNR@E8?K$POX>-_NMjGKo+JNLNv@VM
z)@c!V9j=cyElsIl&9`?xhTLpje)g-*ajYNQ_7cl=iMp{`aLIqWNN|<cOI^{>e5N%s
z%xKe18%ODXx@B~%=I2shyA!cGPy5ot#M7SS81Qc|poaZr`nG;p(3Ux;HGA-n-?u!C
zk;J`-e6dz@m7J7J|8vVk*r_{?zYqR?Q0%_VCoj{JnZcRVChiBdM-mmm&d+ixT!Qpw
zx^i%K=AmEOXWHrG<}735U2uqg{^e^q0{mBW>knV+(?P?X{_|^{0sQ#a-#4kkKgicl
zKcK(XBf$Uf{{%G+U%&2b+WPfGLR~%j^w+o?e)IE(*3YOG@uV}(7ROWiFGr0}jgMOm
zd=8MRlPKvN=<n?fzSg4kdx?Bh=tk&*LIo)N(Bd0sl0|vZIc=UBr8LTbK@=A4@=tCS
z`#oEiJT30<`i|`Bl-E7~3=#eT-T&op)2IW_;^%Y}?Xx^Hf9&#YJk$5>*DVdFPpCu7
zVXp(23q^IUZ)2rVcYYArcP&iQdo<u)^jFbAq5i7f)nbnDYOMeI^PClaN*OD($05DA
zldHTtlJf!@{Y<bWMU}%ER4Y8jF-p_C^*-5)O#D9bUEY`J>4A%v)oZV(s>p_#wVoqO
z-}hO1eM>kWFuxGvf)JRDK!p~6;>A|XX|0p3$Fa17KYqus0>5*{us%Ug+!H$bvfO)$
z#-*?_O0`G+Y#*<fb|UuelG!Bgn{sH1T2&R=9H2vVJF;8*jdgI*AHyTk_^D!TzsXg4
zP0fkw*VW%%;m+ZQSw%nqRaFlBql%q93$lBU%Dd*KDUKg$UO4{5EL*=ejuiPMxAwsd
zR?EUH<%znXR@*`)6&OOs61y8cqK9o2#~HF!My(!)4#bm?>25Oj6e@uo>W#IHP3h?h
z!&dF}dA6a}Vd)q~tVx#Q!NM5WmuUc(3Cgv$E>W7N%*^t+f~UKM_&OgO$1nG{wt8hL
zjcVbif@Q0k7%N+AA0R_DjyozRNd(fqwnCvU{Z+<a!B@~d=~s9C{lM)v*3>7ZrT!<#
zQA(8f9}7F}F9L^H2C#$#ABepRUAv*^G<nyqU1cCsejrm1uaFnNz=ky<Y!~^^^hX2i
ztw()#WLny3^!urWe)6<>tyD1p&O*yQctselH{g+5)hO=mp_Rrx2|aH(*=*p&Qj?A$
z9K7wC2yAw^m6a6&?&X!={;%$?G#<*f?W?=es{8H^xe?V2g|b!3ny6f(QZcp~$|xa8
zV~fzP>}Il57-cMxk!8eKrl&HKvQF8ubtlFcOESnX<~hc_Jn!@E`S8B)hiAUb@5jtF
z*LhyYah%8hSpEkPz@C)SWuEc|bSxYT6Nv{08S0!fQFZiG+RfAuXV1n|FHc@F`m{5q
zGfG<>_GnDp0KJz~`{qt~`1FL~*g8b1uK%(dhVHX?vXTkJBH<`uNSs&R412=r77i_O
zAKQ*^cyzyhxBlv#@xParPrz((>fQ9AlQNo4bCI<dF3pa-P;$)|y0%}Rb7d8)8fqD?
z_oqoHBbd@r|9u(weS}~ErC&+@$B#DaO?HoPK%FRSM*a2Wdb7oJ?Jx6rYWTw^h(P_=
z0&Sdok)fd>HH#oecHaC=AXhw!9armCDx=Q2BaSqWmO+*kA~={SgeoWUa{!1OOIt5Y
z<_WSMbI!zb<T$ZaJQ6*OLD36J3)l~Io&^V26`U%+Y|a+GPtpt!*H$!eCPXOim%`av
z&V=gJ^t8!drwU$2mdB|!p$)fjzA$Vv47Wu+s!zL^FHB#olAcdfB<kO|uu8((^s(Pm
z4`^aNn%w23Br2$WHFbl6pwnSSNGLP834bLd%Gz6^+1(wf^YmSP{$jb&z`19%KG~Ed
z_`2N)D*d_&%i+>bUoV#%K{7z}J+R-farTUc%&Z0Uj{8|A?GR3ZudJl7VZ+wo@&~li
z?%s1nc2Zsnq?`nl9DbQ_y2`S(%8KYx*dE04IcOK#!>oylih48G7^_xH4p$*vO&t1=
z<pGI>_;x@Ta;=}j?w9PVaV;yVQJ-H&?Acvg_J<L?CU<*@?@x6PbhC#?^q50Dp8W9O
zU|=5Sifm2acyB3<hE`x*>Dn++z)bc8W)aJ=7h29l7;URdTFqgoJ=3%x(pv973oaI=
zJ+pmGEA3+Bg{#OV*^qZ9v;cVp9W*-_TRH8I#>SfM0x0DM>}^{|HyMp;?v_wqU7R*s
z5RAS0R{iIQuew&f#W3T(?-exw+MKtLx(7Pk$W@*YxUL9Se2?4GshPm%KIbi!Kt7Fs
zw+yfpC`HHoRPQ;TulHhYnxNL;H$};7-0&s*^=;kV!fTXbbSeSzAeEuu{x3&3Cp{TM
zdm3EY{Jf_ljnN1+T2B&753O&oMKGv!v-u!Q?@=$dFKKr+`eb9fIsV5X&ynAm$x8D0
zYM5A~X#~|8aU@rCtxt*@)@*SQa0iw@ciHaOl`H=wM@Hh^Hx>SSo6}ERh@3qQ4F_49
z5U5cPGCeU^sK6MygQ&h<bb4L#qhFrBX;3VGs$=(4vXe`n>_66@5vNX00ZZshsE0i|
z*)~F#ETT=xAQ7y-cpO%j9t+hPgp5yz%7q6yveafn!%<*L8vO$TOn&bbyuqB%PwlZ{
z%MU3o^pi<QXmwv&%3nXQry+rv><2U2@F4J!#0x5<2UYMC!s1XTFHeWz>l#tGE!^Um
zzSWlWF28OLihJMsXh+_$ttlN~_z2h3lLEAvT_~;ru^N~|96>0Pps{7XCDyYQT2!tx
zldaL^R9=r#W&fn7wRBh_#i95d?zw9%(XtK1_%i(3E<?l0w-c3Q^XDdih*&x|?%Ks`
z7j{>4`=nsPHcsB+(Isc!v+O-hevPczDQGegEea%}@O4Ww-cxy_hqFJhPI~HL1eKJ=
z*o)AEjz&OkObXB!+x!A{KqYjty+&#Ch7WSFECb6vopWvZ32kjfWVY}?cNXUR%p48a
zf08T&`+==TR)N*wHyo|qTiBYm$^GK`*@YOqgv7(8FFv1-Dx;X+ZlgM{gpthC_MVOL
zzL6AJbhJg#F}5_ZXP7>U%S@Q!^Sj)lI!>}pEa@yw(GBeZw=_;_Q*W=xCja{M<fL%Z
zgTtfvsF@s!fz8#y?&lOqREQnB8TQAT7kaQI<48XrObV=0s<hQfmAh2BGw>1R19%5J
zjy(@{{L_8VjgVt3zsCJihSh5yw_LPuB(ze{JNkfBUtQJglYV7``?pD)yv#2Bn5~K+
zP2MND1Q7gv1V_O)p}a}ZbU{z^;GU*R!wk|C{mN@i`2{ujv;GqH|3C0|{tsQL|JF-C
z$!uOEvApHZK}m_z=h`16PrgaH;O~~vw{PNk>wICewt4+KV->jp4~h5<wsYFZ%t6+5
zm2)ndyY!)7`@ii~w=WAeBRK^~N!!7zvP$Cnc#iBxtSsr<HpV(v11^_kB|_Y^z1J}~
zf#!dY!b0J`&`0b0hx`@Xu2U*dilg*Va)xIO9}JFEbd8I<rx%9ZiK;<)?Phj0efkuH
z2lApz<s)n^ALBV9nP(GngGUcYpr(sn{qnWllBDFlF2q)y<s(JpvsSDZCiCcRiPY0k
zx7ER?Def44OzhdhC8MfvL>Ne!3>j^Hu9v*)@Xq&vo{9d4155{o(g{n2b`|L?r!QS)
zJwrxTmVAd@rko*V0yb`B&_E)?C0a`2<(soQ4}N_93|)W1r?ja+qyo`5XUoEA@2tSu
zn_e;p8FDAil^(KTV+8?1s|0ka3RmE(A2Qlbhq96QxDsgG_fwgBr@(SJ9W_IuO<hJJ
z(e()>*_4{rPo9MGt}WoZ1>?%Sj2a^?F*ZRCp3ak&rUFevlF=0VE9;Rt`Sw313^UY1
zb_u>4j_I8(JsWi!_$ERu*-9wfcJBGA%Nltrb^ITmt=wg4kX4aR@QW+IF~90i9A4As
zV%PG?lx(!%f+4K-uVrNd<oWOxjQSN~A||M5qVguLEm{sgWix&YZ-u<b{i-Ps=lm7o
zmq?lIbeGeomB;9#H;L6=`Bn7d;t6bD7&J8i3rFbeNPv6QCtXE3{C6VtUX2S&g;ZO<
zN_Iz-DydecaUv$pmrU~yHQXq2r|A=Gzn%iD({2Z+UTS9lAmiH+{+~}!<_@qrA9_C-
z&!ek6I9xhYvvHKin>EYS-+=%9d8@G|7oPn+AiZlaf3fOc%XH)n)z(S#oy$}R!{~-+
z`NwG!CFbO4)wib<U8yUJ6hkD_*RME#`XP*AjgrIfNT9%cPZu~CGk{FMs?opA&t!{x
z2E|N~<zQ3PA-Z33rjG0^bYzbLx5*e)Jq#Z0TgTvg+5;aG)a_zEKHmGm7QPlj<%Q+~
zxY7g4EKR7_4}$mGaQC7M?(RVF@r*X+<?ZdAZ*yozQl222haP0a!7?QkKu%?zLUX26
z*f`OnAJ%mVH_fB+(t0~P4d75=cx~=nB4<#q)AKM>u|B=EJZN3-rgcKu+**a;hUo0H
z<pb{bjPpE-2!2iF5#!6!ZNZ6jcY$UZ$-Ezr`VD}jzrwGMWy{tRp3QbNVs20eH>by<
z;;4$P96l7rb{22@`rPe4bl{k%x<{e_H6(7GG8MHPvhO?cr16z&6!$*GRi|A4RY;Tb
zuH}Di@k2#|-^Ayxz@eus!;!IY<5gP+%g$6T%8`a$75q4f=rFq+N6i{1Mki!r)g7u#
z+1i<5UtIG;k|?Pl16Q4h&^y=&fHaymrQ3YVhyXCk2vtwwe}mpHXR1wu<Lp>WpCNBg
zgJn!#*F#KZ!_-*tZW*C$5uz}wEWWYLeR~>Cq7_}DW_-X5#CV*Q!>^J4OMQ3J?$x!B
zen`a+GL9Yj-cGn^c-wrZSby7ZG+?9jBzA3DwC3=y%w(akCj4E<##k14Tz0KIP7i{p
zX2>{1x2-I6=B@KIOIn@UJ36>D;F3dg!_zn+Fa5exk26hvbuI2tN7YVLwi(*vS07@_
z+f+t;?gm5%O|gQUUZskru=wPr!nHa*D1in>j9!6Z#(D4cFHwcdR{Xt%-Agz_D?-98
z_|8jIt`6_jOLKgZ4BDu1!IY!Ml)vT}e{EUT3VhuKq^MOYVYZS++cl;+x|dD`3Me_x
zk(1A8?ku10#AN_3VeFysI*L|O(zVsy3&dC-QAxn0j_hf;8Pj)xpuow30T7LmrP%r{
z37C-m?3GB;6nH7QrykgAOkJZ*eRb987@%L&0R8$6FJmFd2gTeRZ(l%^3}_1b{lV~`
z3sJL7)adBmK%i2x#giYrGcQLKGXGY(haG{0Nmqs$N}#D>8e_y^S|73zE8XqwgQsc&
ze>#?DF&ex~`DzA9IQZ4Xc1M!DVq<}T^a9X8II7C+!i<etzlFX}U|^s#g310M_RKfL
zvSce^aDf%T4L=A1&uNothxK~M&Go)9eOEDTERpJqo3>1NCuTD{E+Bv$Jf{Q$w(;o#
zxVy2mwYuIX<+V`N-13R&l}-m<$+r^WJFEv|<KF6s2pDQTZqGHC_I*qLRxXdtU>?VQ
zzBj+hEDqmTKK}8Se#7m}Zf92NU)<m=;ZY;q^m6B!s4FKxmnD;CyBuc^hK<MewItNK
zPH8dcY}nzDXquiA&m4V5O9H#%1U&S3NN&zRV;fH6sTTyiQ<{EjzQyMHW=Dt~SJnA-
zud?Z<R1n2x#NLykinM-WC>0nAWz#S$U^`K1cH5=X`Y93(M`rJO%3`Ly@L!f72P{F+
zf+a}wV5JfIwq=Zp_j{?dZ<kh&#K(vwJGG906Wgr5s0TMnz}NjU|K-m3(7SWRCUL$D
z9$gQ^hG6-0!g@VsmyzQ+;D6pi4E2uMTblLqQ4*4Qq7Cjxjhjd1IZ-5PImZj_>n3{Z
z1J809ml6!7&cx_}%{ic-YP_F)ooJiyP&`nRKB>hV+VZyDJ#oBMW8}Qf<N6lTj93M=
zu=-Oe@>Xl27o<BUz3<N7R%*ICAk3WYkEkXF?+fQ#AtzDnH3n?+R1+`|xp648AKn=)
zB12neY7_GLFtOJ4OHAJ!fECqfZ88BjO>`WZ2;+%-KtYU;<!cCKsjzF$#Jff-Y7K1&
zV;G)Sj4BlTOXChCgNRm(Mf7mXf{2<`am2>+K$FjF5K;AY@O+zFTCiHos}4l*fMOo_
zC(cBj2X)Y&CESj$(<aV$0gmj%_<_N4U?yW*Kdl1zOWuHEh~{TSSRx$tubTZ}c8G<A
zJ_Os23}W&hK&+tdHz3S9z~&Mg<byRUF#7uXF&5b$$fI!iF!4y`P~5mU*p&&aiZY0I
zB0rr+m$ssF8K-Y3%xW<oIj0%tnd+xrB>5&%I5OKEO9~wwnC%~b{@dQ&R3`Xs=-ret
zn<Y|F9W3NPbu>6Evz<+DyO#ecyJpL;yHjYi$zXs@+wgU@X&j@`Juwig8f0}EH5xcz
zLrE$1=QI;b0BtMUB~oEBZU5cAJ<f^z1RFJ)2TUC}2_?x_<0+|-<1&fKHTrOT_amH3
z)ZPx!_$-9mtRD|<qVkN51vfcY5TuvZ8u}tX|42rNY~+Z*=yR+2W*_1pqiLzp6m3Cv
z5@=Ja;gq-vb3Q)g*(Pes7_)qOwsECIES%)!OP4OS`#=rwGnyq#3zo$E&!hd1mCjdl
z8AxtFnD#FF$uzMK^9=s+$75UtooQL|R4Vn=lLL`Go>6-(*{Q^p#EnV59Z-zb%b%Py
za&+$mAFPZL34xODP*FJ7-iT`x-l0vcVkWPF8VfTbgw~9nL8$Je&4~MAALQQUg8h%b
z<(kM6Z~-Jr1{%Qf8DtC5NsIYRmzk1qSY_=g2s|ZX44#N?tO^?TloKE7iva`XqK<qu
zQHm=3HWN0bFQ}5yoXO!=jP1G+m3mIjyPh4RqWid<-nw9OaudPk_?Wf$r6mp)U%pY-
zV#-?6_g7T<e;poZ>85URw`U)TmJ)-HyN=JmTbGbXr}pHzd?yU6t8$M^lb&Q_Fgw*=
z$|+O7jC;BJD#Hi%eOw5M*J&KA`O0Z;(?BE<eH^<HWwed^a#_tDoX}gEG^4juU)(8r
zI(H$eu0<Hha@qeWC(x-Zn^KIDMvZLe9+d|6yX0gQ|FaW*Y8xl4x5SD$f6i+y#O!sG
z?20a+y^?%0C?$-GX_T{*QY*zjJ+W}PYlRn^)oZz=-{iz!5x-6g0?83}1}Bkx>^q}@
zB!^w+gtFMm#_@#tMgmHxgiJsEq5Qfab{iu*V)|e;4x-kYh~PG5ZC>E2I1lD1Zw;|J
z>&xqP0TzO_h+EE&V~p`7(fG|HMsGCnmQHx3?}1K8D(469Vy<;vum#<MeUs>v$d^Zy
z*l%L~;Qf%0INWZ&u1OoY!l+xzygDTvCTt=0P+T^b?Hm{66N@6zYocc=`I9#&8M$v9
z=><RKexw~|k79?OqGRrrOgE|d#a0_1Kfx)Vr<2<@4d__}W$jlCxQh~JDmF8>?}0b1
z)_Xx{?V0y0&-S3WI}j6MNkIfB;UN3=JSTjnQw80f^#%LNM_Q>V;1;D9d8p2XS{{!c
zl>5zy+HX~JA@zx0Ek14Io=3W+OV_B!c#MR+b*_G?8C>jirL1p|H``aD9g#eZ9WTRa
zx+|FsP$+AEkd-vKH%WIFI`H8Ja&!Hc@-HCSIf0%l)VUoxXAc%oX~Z3QMdTx)57wxK
zHNVzCVsqxajNgs$9!bBe%%s#AY`KenGLk^Cn(YtOKUzLcy(XnQplR36GMOct>^}Mo
zt}1?MoylA=Sm{)L$j7vx+UWHvHK%(9w(ge+u`6ej?M@85RV&nx%cinJuoX4TMxl=A
zH&caQ-N$l3R?aX}?6e(l4c8yG7mDlZJYFsA)j4Vz<(4SoX<*HSBjWZnaQ2P~w!@zk
zF>SVkGloeN6=M_`+Z>F`Fy#i=tAS4Fj1|2$w|s0oy^~)>&DctZl^<(pCH9y9y0G@;
z6fvj=%{b$4J#HFcI#>P9P4dTBSo~M)HoJ#Skdf$F**~hJn#lMu=53?>)akA=Z0{QD
zWNniZXJ@b0>WEa~+xuM0#F?qn1G$Nx6|89L#3R=PM-m!`_jK9{4D0sf()13WVV=r)
zz7al~Bfp>p%bz%&_~9cGDq~H0HL9k+k+AQo{8H?vrhCaB@<`OutxHEGBo-@)c>&Ha
zwVl=(YMfCr5Z~zI1r1fc5VqA}zEiEg=XlYn5&vIUIe`^%7~{{3=|c)E^BT|g(&O6V
zP}3igpli>K&|KrN8MOq)0rUOrX<@%H(#d){@nj`$ZM-j?QCTGAV>6K=3O&EZ@1wyv
z_@45Kh3whSw{?ZnBy_&*_-;97n4%ijU%KGiBr3!=g-%BcH4$gh&Nk<yIQ~ZDk&vus
zA@F<S69uz_Z+nw`@82MMw(0)cwIoqhR_wbJDd{n<;h6#xBq%AuZnWCECNDJa$|-gs
zamRiBjfiRd#>5h*6OSV7R=>!b3wiiD3UA0r;-@p_45Y^nrB36Gyw^>B{I?4y_z|ZP
z`FoLo+~m96aLOP`q!KcvLe;)4gD4f}dpiPY`$f7S$oM3CKbEoPK(dcb-%R1Dt?o^|
zhfdYx(Nu$-2ZL%>!>YwEUACP~W$w(-K}m7>gz^SBZWjLjpClLm{NVr3TUM!P+2);v
V7MIsmc8GrwIdIgRy8rZze*#So0#*P3

diff --git a/articles/AMR_files/figure-html/unnamed-chunk-13-1.png b/articles/AMR_files/figure-html/unnamed-chunk-13-1.png
new file mode 100644
index 0000000000000000000000000000000000000000..d3f1586fe743e793461091a93a129602c2fd2bc3
GIT binary patch
literal 42740
zcmeFZ1yq##`!71`77JwCG+P}|P(o4~MZqDZr3RI5=?+C@tCT}GO1E?~DoRLqgGjeD
z0}OMX7q`Fvx$CaG&RzGOePgY|VsFNQdEak5@%en7=lj08FE2%LoaQ(Rg`&XRyQ73c
z9gat#4t+dI2JciqA5MiIWRGQ}?!Zs@B1n{b!jEH5?rGSfQ1m{?e+L34mJLv-zfhPv
zw^W>>=ZBnwwe4g0_Z;olBmK~KnBOuTRlLK;cz%?e{M0%9Kfl}>f24TCI{cOt<2m>H
z<4(~85#hz7+(*tCJYbmkK;`FkK+=GsTJ*20zJDJem*Ek=UDqA6n4LYs7-88yvluA9
z?T{px(<2~|qFlU~)lylUzPMR6=ZY^@$x_QWfQ-@WRQLV4gUBlsRn#A?`!CMkMo}Oy
z#4r9UZ_t?OeC$rt%rYdbOcb&oLQ&m1b46ZOqWY(2$I{|rbGmBA@zkTJ)DVlJzT|Eu
zl)3dEtqG|qDOcWH;K;V8MtwYLYGx*5YrFbkYHoJ6ogL+MLlQ;78F=yL{@|}ZN6{cJ
zylx+j-=E>X^Tr*$tP`lB>-6;WrJKX9wmmwI6S=hv4LBwlu1BAr&%<kh>3DB0*Nu5!
zMzN}H1EqKcw;yMmcb98d#~4XBl~d&D*944W?9~`trr%MB9`VY>XuYeVuqSuiJ%W0D
z$%|}z&~3Geu-$`?U0huB2;k5u<8d5zHd?G$ZFXCY8flagU3nXA^NocitbsQ@_>wAX
zhze^6g=k7f#vE_lp^v=@S@%gV1z!s79YWnbC4LBlTk<6Bly1*v1fiwqtnbL*9DQz%
zCvKRSm|zMuo4$oyTV3l=n3~PVX*LMc360=3GTfN&<ISc)QL*)d!J8jHz!NCN;GDSh
zd<BNkP_?r-VC9mg@+}b)l-DA<qaWwKQ6Hor8WD0$z5BgN(VtUso*r2;sE21wuKChO
z+3wGa?{1oAmYSK)m+tpUY2)=*;xg^2TNU8;m@6^CU*BAqD(H}J@DW{mCx`7G*;^Qy
zBuvG%*!Sp!!9^z{&9dt&H;1DxV4W_GY}N4REmiF#!)1E#d-jtpadAEjE>~qjuh;Vp
zm<PFU4)G)N&7aFIx9nU|&wTLqT22Mm=Af;DS`Iem(8-&BG4B2J$!}mUM`P6Bw*t14
z<K0?TO|X9}p~V$UDKQS38@+k~P1s_8e}8S~iI7sb)XKMOIr_yz4h=B$=;F!+3tH`(
z4h69ovz$skO5va4q1QEV3&kU%`9>|x*0uCuug+XKDX8G+=$Ku;ST^{inu5>~w-+4a
z(9hz-=;7oqveCP{5{7StS;pBm3taJ`cX)s&@EHh+?QM+_zFZM)b2M4l4wH*>T`Wbc
z2}e$MC@&%5vJ|7cou;Oy4RO7P(z$-!n^H8q#dB{};K$#WZMrp#{Ob6gX%09~#dHh~
z3eOYz2L@K>^IOi}KjnMf;>Bpr0IWp&W<OZ2^PJX5B&GYbgfkeF@c3?*DKFTvRW^Qa
zTX=gqAx$mAxj}0fmk=N-=tF07kA{Y9SgcAZO@D8fP&$(wwNgGpWFHzDinyUCc~bc2
zM>^;6w-K<AUe4WOFRhxF#a{8DGN<IMT$I5MdVfqGpwKG4`{umC@{g|=+?c<RWqxRv
zki+*T{*mYt>?IFB7N7}>j`*lj+o_GH?zXnKQI=&a{6lui@?ylbXt$MmTJ628;^9d6
z+rLCaMBF<TOUC>MVVO&(-@oB<UKx*enN6FSo}P}{Yn7m!c<00Lt*2_Ekk0A*)2Ir|
z)v1;iSdt#A-D<F6bb#>6y}iYyJ@ypvAus2QF4L;b$|Zy5XaORF=k`p>zB9bOmR<Ma
z0ycceVIXT{ZDV6&w_0ph0`E2Z>Pl?g83p0Dv{n^2c9!s-tRd3)g;q1=;?=Ub!ab^h
zxjk&@9#vpgR+hllcW-Xby<KEI`;6k;$*v1wJXV6@62Wl8_57(n%**vjqRuL4J#w1c
zXwco`B5mi8`mOqh97kfxm#TyqcY14WJ|DY69$s8tE)=&jIJHuDIbh9o_vsOax<i!0
zfiO0<3))X^X=-WhR1T+z5oeHF5hrIv9I)=|U8Q^5b4Q*Rd;5)5q}7t{(4tUI76*-c
zzlkh=IfLom_)eQ9)UEsE(_sO)tS!vj=vy!gONg|4hhby*TMkb`L!awXWquRiK;pZ1
z7c<mznyV?rqHSA5_%G<XS)UYHYn}g4eNuv*G%&K~ATPZ91=*(v&PTTqqw{9m{R`rv
zPSx%N2ZW_-M;P6ni0&;1AorM=pKny~-0skJTmDM;re1}MBhDLnZ(Qo9LR@=gc(aN>
zCDz51Ss^w$J3D*OV`qUdtB#NK+$r{KfY=&!><sf-l+of~DFiBchhgWb-6awc&u!M7
z$-~QQus+*Wx==J=1Mg3O$JtFb$3zts6;%<c_SSQzU>};~g%*N6_oh6X{e>38^(_=8
zI6Ri$9Fp(sE@T-XG+BArT9z*qMO)WU6NpQmadCTVDb;1w#F5>mk$vmm=r=neLeMTW
zRb7OT<V(wM`4KaoY<U^=Iwgx!7}>)}{B8rj(MW8kugYM=rh$zx$&Zg%a$j=oa~yF;
z#(7|1?5z|-eP^1o?PBTF#(MlN<b`k!_ZgY}TOw;b73*p_B1|c|^^n1Rt!*B{E+NHp
zI|W&C6Jz6h5LeE#X$FH4=8ibff16|=ov}cn9#->ZsUG&FRuCiF;oerNAVI!C!zd;s
zO*!S{&1C@yEvp;FcoE1za;4hx5W%;W$83LoIx;n<OLD=DPl=HTWMQ7;Z>1&~_eL4H
ztbcq7mQI2xvd8U?U+HLV4TL~#RO3r6FcodPjm0M=F<pssv)TK>v3IE<jH4xvv^yn-
zEq1J0jUhA|iFP>V4Gs=QU-rLwY00Ih9H%eNkqDmTwpl*X1gXQO?e2vMHY;bD$f&5D
z?w#p`fMz4n)d)K~yYa~=tHmg*dK^<J#0n+$u+^a@4}PJg3fpfJHt>x^XKkm^7qpqS
zwneKo(U~U=b1FBaZEbBWVa6UJ-&}@Z4KcV)Jc&q@pXIlwJ-{hgaPH+4g_V=UUJHpJ
zMhtcDRFs-%<ZhA09jN`8Boj9IlGgGbPTQ{SZXaZj9e)CZZL=y8aVbTceF|dux78N0
z5&jV*v!LsdaW+N^<XdU+g2_!bMi6>yMftu<EmQP$j(NTZ1Z5tMqA9XjSXjW|FhR@|
zglNagIh{j$6b=OA9AyO&`ylzp4kPZHXfqD}p+FcAY09e>nc1-G61x6~*w&|8x+Dl?
za=41EA8)F&vw9v%n4{}I9bt?{6XnT+_A{T0i;GP==1Njhl0mDp>s;B+V);%}SmuHv
z&jYJ`ddETQpxV^I-UVUp2^)c023K?3IAm^GP9kJe46b5lXlZeC3o%?tpQ6t{cZWuZ
zL-Ca<5FP8#W<HCW`G#^MW0WFm_i^L&OHNYQ&9WYfaH}6*UJ;`3geWwUj|VoLtT-kR
zXALr=XX!_wKE9-ScBsgvGK|Y|T7G>lV4c5Hud-;wV~3(@l99AkJ80J-M`$7Kwp`aN
zxT>S0LrfN1ZRDnn_Utb(@fH<@K#s0oo9Q?bGwk}KVi7a)gkrtFQpC4=X?=BTEZ_=;
zy=v>n?f{-RKo(Z{Wb|_t-c^CwheqlKW_Y8h7y)6lHrtbyQ*zd@xkx}S1>}I>u&UZH
zNY95)oYx2AJv7W1Rpy7HqVGRmoxk^!rZO@zG7r$ecv?#AxAr6%ZoL{`bPmHRKhNUY
z-p*1SV*BpZ@6cx6eP9D(q!kVl4vEi5DjwdQ<TrxBM+v*tO|}9RczMa|WtR5RP@5~q
z7+s@9@lf~XCNguk@ym>T-?^e9BVR^ukoHDM4HIFyRi6m_cAq23-Cr3m)py^&q9f7d
zSVL{3|LNJ`Lk#%EvN^IfQHI4lT@S(pntDz0>73T4z%{kZdpHWL;DGCCk?^;@%~6lR
z{2~PHO+CP^Cg7sY!7R$SifsnLnZzKn#{9H_qh4+AvN$V>K9^XJz05R&(K^qjrEC))
zR=N>Z&<JMY9(SwcKkYUVqP{KSu->IU_2UgkGm-(Su%k^)O-S6Z1@yW}?<3{SheC0l
zJ-}l=;Zkes23aHDWqp<+mWN3q?n9PYYEUr*F7_~84`*=d)g%sg7Qin?%<|FUFJHcd
zSOPe;DmPe4c}ZvPO)KqRqz*BnGJaT`R%isE|4X;#vMcZA(kC3)L!<>@_$x6>ZdZ`(
zjf8#E!pMy5GS>Qxu(oVSx2I4E5|BA9UO+^-=qZjZvopc1(ON$K@Wk1@va+RzGA%_e
zDm1@ER0v5gU}Y`iaFvD%=T#e+=RTsO^xUehfUGc*TE-u?KQBG;YBfuMV$<ca>)UfV
z9BYLxwY+<^p1ZYlHgy*xgq>|hC~R>QaZcxEkR^ic<@-MJP>+LBeJN(xN62!Ny?h?0
z|6PuvXFZNYi1QZ3&MoZvJ62rAY|G!gz^)w*fyQvDa+9IJvFAUY*MD5XuYM4~RL`3-
z$KA>CF1<Z~bvMW7HZ8;WtCsz|<*h6&n=>`?ZGc)#BKIY2>yoa8NHeKxTYs&|`hK(x
z43F$GVt5eJA;ra@345D({HGf)_|=ytlKuR=b=__lHAjEOmaA=yc#_cd$mW6%8YfJ-
zB0vyLs8{f4cU(=eX9LH?Llz{9akMSAwNl9ZwD=f*BUwHslD~3BPNTxfI@WWKu>0cz
z{vOvB#XFqbh$PDQBw)+ZahwbpJ^PPPElUh;zH}<q#KL01)eYjYOy0)L-T?_0oeZ_C
zS*<1SlY+(xrqdQu!aMW%v^tx=bJvrD#(zNsvl@wM5L<qwFuuFm((?JmiJhUJ2!J?o
zfqj0iEsq6pxGR`_zy@C8e#HpMJCq(jy|n@HMCG2>N(Oi^zC_wv3PLjyr=@In7K)7k
z6S#K>2?^yx%s}uwQGQw%!r8=kZ%TQ{q@wF-3I=WO?*izZnVG3a(47JRHHoT8h$T9X
zL%f(3<bqMcJoSM7!u1z)mLT2qqjO>(=2-1pqLkL7G2$qtXB~-%C-FCgvdQEXO|7yO
z58`s%7t8=`En<6+Y`-<?a|MIopecwo(h3R+>0PF=?i+n>000z{l<E&0p$nvU=zV8Z
zbuGK(690&sEfzph)tEi}>3I%p^lGyZM}5YDr5dVNztRl3KDqH<n3XgIcUCL6F<RR_
z!qgNP?#OqhBG>s`sj8nZILLQ+HwJB6fCh*HNSCq!pQQ}nn^Eu-1nl_fLs)r|5CFn_
z^S->@ty-~WG196SC85H~eNKzGNBYX)aU&(p!vzSF*m$tA{0zTEExltSK)#K}7$L`x
z52B*VV@8OJj{EE}z+)|8cUnqJ8o&iE8a$tNXf=J)8Bj&bFXo9~X74H9k4OnwAFM?;
zaswt@M|QB5mFwLHI)Ky$ti70@+a8<}4MV~R9#YgwTY!h%$2I<nQHn$U0YEm**}>8-
z&h-K;^qF@S4u5-d?+TK6#KC;(`y0$gR%18E0VOpcFq*U#K&sOV5D5jpOU}w-%QtNL
z1{pS{7*AYq*E?tV%`6&-bp*c^7GY`EwM#ew8Z{w7|BU?2i$V(p9p^ojN?D|nl=jD3
zx#Vs&*%TE9X3EhbyebPsEDr#RAtdr9;2iyqK`Gd=8b2ho$0AV&xiA9L5Z1TkHYK$B
zl+rDJ$z~8i1WuJrea#4@VXyePZ<Gl1>~I`QFfA-=n;Y!z4wax3;ep_%fUCmVUao|M
zCDMPKc1mQ)S>fc(?_T@7l(e^l#NT;HM}$@%(O?l^xvfa(wH<wNVG=<t2szvZ%qIsH
zga8gYK$8N1BDW8YVv!-*_CE4408-}+rn&n01V<vQ62M<A|C>Mlt{9_VqO!Rf^vU<j
zve<r%VO8}Rc>gd61P5Wlcn}CURF?q>wTzGYFg7Eb?-4eBFu`mp#!-G>qcO9xV(i|^
z{200beBdDxI2xn)t?pRrlXqSb3q>Ku>m|4bjy?q!kk`}G1LmPPXM;#MWK3RUy|>x4
zimr!YT})sRI~}>>o12>lJxN4?F<)*R+LN^DDfS(wjp#jWFK)yiD7ueOL;iy_jX;pX
z5t134fv8)(P6v&>*g6sat7i%u2b@<22rmrik^(|kHXvZq**4zjgDWeuhM4Lw!)HDQ
z4nBW!w)EIVl&^^8?W3qQn=`k)p86j6zpstG&+R^JE(aC`F;#PptB4iAnR2o$250Bs
zfT&`;*?(t!g0UiK8&V#VOlW5rUC}{ACX<no!Rhzq8O$s$W?SFY3bQG#z^wA}@tI!m
z&6}K@l#S$J!L$z#$Es$ib(LJIDN{-dMluwpeP}4gpdl3AD<TWJq9Cs~)~`&23xf{V
zU2+Lk<lrKH2<D8sfAjzk@)Pyq&wt|$<4pSlm$NjpCK3Lc<FTFrobikX@Tj5z%kuf+
z5f6dc)C4X7Ulh%Fh(-`!_z_703+SgYlJ^=Sn;5pl#@Lh!OPlShd-q8-DyX5Zt`Nk?
zH9kDNg|9{A5@B$HQUFgj0HwxL6Z~oH_G%eDg>|<+Uuf^>=-34EAQB3A`1<N(Q-eBw
zyQ`p};B48+{h8~14L=^AQ#PA^{lSO75Ldo{-}*Wau-U}iym4isf!k`hLff{97ttLM
z5z26TZH73hll%B?v*^zJ+S=MU;MA65SE2($Lqo4)D{krn!_9m6>6zQkz(_M#nodID
z{#vJU=$VpFBO(IEW-`JE0R{5P%F19DMSX@*h`84W!^ELMTKeeaoCC59K&Ra<U81V!
z?kHtSVIgml5=%#lLJOjE1UF1TgcorJr<b&k*`LU4SNO4bkTwv>3*84g+7b6=#4XSo
zr8X0AK_q|G!#Ft~R#}hLOymG>4MIaKocg<q9Jv5zs9yxCOoi2eMBG%s2@}^kyhW#g
ze#q|Ub|Qmrk-0{9rbZCD?~xBZSxhm1MHMWEWFi*%uvf07tlt45gl-;$!9q^ib(@jV
z{YDD}AI6%AtrSGW9R|opu(KIfSN*Y|-eGXEQG9F@HqNaEE#WsVV^%tq6)Zly1g8f|
zNG=GLldQr@;aJ#9K4)b$5Z5*-Qfmw-XxP#IOBIby|6yOgB>~eW*OOjspI{Tgo_4gs
zbV}FSWo$tiTaV)N*xicu*q%Ymti81rKeh=Q8dO2UMTw+_`hKrcr8IJCl@X^gKLIeI
zBQboyM-U~Jwo=qL6CBJO0%IY&ch<c$m6ce%P7BaR3fUiJD5t6LnTU>@D(vd+?(V7b
zZfouBo#t*yjRH^(>X%n8_a9O@2%m4oegg>f?b+cIWWJ<mH1Hn`4$S0YSh?l*!Hy2@
zgCYB3ysw2``jIt6lz@~mQtvQe5r*V;WD`4jd&9dj)TcHEtXdFqi|jh#6QxIBebAN7
zrk>ss2ktfhLn7h!uKZae-Yz;2Sg1K`>~a7TC6<@FmSlW2yb21B7=ubim)v3!S#{e^
z4DO3jtfC@&*baMcmI^CiAl_`_xjca|{vCfRow=eDz%uPvAF8YsohZ0V%?5AK8A*<m
zO&vsK@)}lA3keGDqjb#5dX8u1$b`jMZ9t3b@M<iCvq@l84M}0xNbVNld@GlwiErSi
zaX#7}uCA`Q!XrWNUXI+u_2nBGf!gdXKL^kY*cZP?Qm^?xF)^<w#dYb)wfl*nUnGMF
z1tKM2s|<Sq^TR+XdL7TTygqj=+T1^Hc&zb>YDQNDh_sirDQMe4b$}epq%pAkuq#~^
zLTaw%&^rg#kk<|e5+1LN*SlYw1|{@|&x|6UMSp>280y1UYHo=q2mq+>w~qIr(OL+U
zIEleE#);uaZk22jIDmKtR-Q&M7`hKca>-1?Uxsep_(yBy*iEZxuHIpjIbBNuuo)hH
zfT)nNc%~~hM?5`o<MG}n`{y<HV^$HF9<x`hsJjcUkpCjxY<m&lj%ye$+sQ_cTlRq7
zZ`9HSp+VSc_Cmy4+iD7av~%GFUgTydT0X;r(<U9s>l6XYgceUx1yO?j$P*e+a|vBJ
z9w9R9rD)7A!H@)ElP<&(L=Mrm?@|THO%_)Ho`489NID>Hj^H__fi@tbk#yG$JE>=o
zAtr)=>ylYba|30LcAkvzKH0SlP>&Bj)EvLs6fM9#0`R+|zrU=0z4X}VwALo`^O%?z
z%iDFS58x&Ea2m2V-Id2i^9&jwg9gh*a2a^>UuBS1R}YK&(?8GN>UQt&@FMf`vz9|q
ze>ylimLI#A@EBgQYvg^AJ1P|s_2)tI&IfY`CELgAgKTG#!X^PEAv`)1VebIs`#_z`
z>%qECfEL;qHN2L5acTrKcpKOi1JDG4c(~@-&9oyK1F3Zk6Pg_@+v}N-nSSgxm0kB5
zZl5_Ivc2Y&e16T#uwdqZj+Tpf52Z*vwS|kg@(Fky%Fv2B<QD;m0{KvH-v5PK5>Iy-
zeDopt%nYVui7~_e9BtCd1@3lkd(Er{D7JMVwY86RX=)vUOGbs+j}tPTYs))WyvR^j
zga2ogvXI+*Y3_8#<uprV#8Tx>@QQQ<0~$bs9nj@YEOr;^RqbvE`1Y<KGkneLMONfO
znX@P1QMmh5Gi*5$o0gere+>Cu_2Z+z665iUFgo~(|MQ?R@)LFZ5jni@B6TY-Q0E$i
zD9vhN6$*{+gvFpKlJ<zN7>LR;=X0(GOq-p3q-3UWvNtWE8)}~kcaZYp&yl?qN5+;Q
zv)CMa>YSNzi;)n|z8PgA!_K;G6Jg<fD2-~tvP1YFn-`f^*5ChG5!e?1DVH0rWwa|U
zz&z~j)Y^OVH&jRyi($%HYEt^86T$fwV~M9U&ZD$0>|>eEy3#iu;SJcb+-`%4WcsM$
z1_@Vfr8zECs>I9h$Ta@D3H@h=HC|+?dU%g)CcI83{4G{lxim6pLrW1p1~XQK<x16i
zqaM6E9T#b)Q)6aoLKjh<gJkvfuYb}Z*F^nS6_xxGVnyTwm7eBJgHp_$WxjqZq)H=<
zCdG*(_s{%~f!C8Ww;$f&5hj^She&5NILUP#JJZ2!KcE?gT=s)10JY1H2Pwp4W<m@K
z*~fV8EA2^kbRH^M4G*tvnoph3_%KO0rG?dU)B=$UhrXtsEvul=@;967#&h@f`!=7>
z!d0T6getDXDz;k7bKN}+)~P5o)NY|l9q(i5GBF&zI{1~B)z~T4bFQV`{0lpLfbT7e
z#;dGzO&5>puwsO|Y)+cdx1vM)hS94D?&9UQVdE-5DKY@7$M@}kkiEXrTpfiJWVeq$
zk0HM<vQw4tST46NWY<fmn6ZxmfSz?{)pIcPC6m@(tF}moQR3=H2~!<1lrQW4KH6t3
zM~Efw?&j}LQk}Js{Qycbru_K6i~h^F^<M$Qe~u5$s{HN+_&3n@zcOkB!fPP~n9j22
zS<-1LvpeOvxosO9tz`o)GToh>!8Pl{NBsQOmsKCIhaIW#iHe#_*icI2`4NX#QttSh
zJ?AO~1;wGwfon~?>4sZ10gQ2>FV&0f2hz6*3oe5t))AcVQo0Su)BbKbOxxZ1b9SdV
zEv<fo12Wm%aXwApKaoAIjF~&4eK?8@hU_rDriZmPO{%P6Qg+UFspq2;)8zYkyy=TI
zHD~q8ub2(+Sghx!9jJ-kHh$-OalO5S>Wy>~Zb3I*IxW~V>rL-b7>vn-CHMOrqlAY`
zqc)|Y^0=V4q`f&4<GN5<wyJa$)+FnX9vN{#g~`duJXvSN`T4qQr<gQcaU-1t2A<pk
z?0(ty_@#ykn;|z+Pt41gc0FY;{Zv{Oo`KqVVC3TZ>DUWx>^WgY(+?M(xeQxP*tol4
zaU{!*oAU=dpQBuYD|c5K5C{n+4sL^Q!Dx)Sx_T&wE_Up7P4+Pnku;ylsYqV4_T1sA
z4;dL5l3nCbP~P-f3f$PR#?*8r$3IF{%ecHp>?t#pn;oTt!n!`ei`b1#FnwtSJkf-`
zvS0f_?t7MczQSw9RB++gu!gU-4=ZaUM$^?YG>Xjoj;dF`1h`_<o`}KVpxT|OS(sjP
z&QCE!nz<XM(g|#`dA%y(nti*sYf~ieA^)tHm@`2Ioye_KSe*$IZ;5-fJwq`rTh7H9
z8D`?;58<z1Z=C=&l2uHp<yguoF{%e$2OdxF_mw1j1K`$Y-9z!iowW*C5K2&}PWpe9
zbp9)D@IUjSf7fVUfOeawtRcZ=f@V5jCDZtd(G=TK5Gx-*D+r3JnkLR!&_b*<uiL;p
zxQkKXa)`-5R~eABhkwjG-X{$IUat<ym+4wJPhz>!94l7KR*$Qy6x<WpHO`u2@sIu>
z{@lZ8l-M<p9xMnXa5H)gto+JLAE}J~4`IW9vg~gh@&EQ4eueB_Z`EgKrJYBWk$j|*
zj2DG0YX-^Z<KN{In`7-=%IOG9p%`#5C|-a)0wAOpjB+7o&C-l0iqlbsBqv>)%XqIR
z$^q~U3Uzt0Ii)|Z%(j6tp3+qh!Fp#UQBCKkiWd};Qu?_sHJBP3*Cnnb?z_8M7MAx?
zgB%WT92q$yrz~aej1u=0>59#qH(><ere3DBfE7BZBb9VHgY|L`yD=MYO6fb3FQG?Z
z5HC=XY|XB*H?J*-n3)0+*&bXwvr3>&=>+qj%0KUjkFI1CDMak%jl-Vyu$X}Z0zbXj
z`~hVnyZHaT#EB%ByNHfqGv>#?FBF~Ns0i$-T(n2JDG>eK2uL`sO%<?}eYg(_(dtSA
zhY@HI0=qw}5#|C4gQE7n$l|rlH+gUxR5YZBPXK+scWD_BmV2~q<+7nRh)7I+euvbF
z%e9Ox7eFir0cQ<5bMylC8@ixYv;xG6myM0>#Xi0Rm*j*CA`L^(A94=_OHmLJr@RiG
zyk}-+R#sk~Z`}4CkvLlkNMjLFd;&>wzkCLL9Engahd#Du^{SN+L|y_7z1SAi$AA@@
zeHhc3a)Xy_O@^Z~7!m(5D@e^78k>y!`}>txL)sy`0xyIz>N4Yz1)>`?0Hr`9NzUGE
z&I-)zCN!Kt^F{<POAv%|lAvfE>$+GEtH;@&ad>~T!$2()fhrKX9_de-=}56!a|1>V
zT*?AB{``zQ6{kC-V8R@JZw_4_<2WqvTaR!D%9^c~Q92Q-g+qf3xAkFC=YJ#r{39)u
zf&#>sAdTG2E2}n~`p|ZWIu>JOWQ5ZPnauget1Db^d#Ep8rWW&X`;YF5h*OQ_`t>+;
zeSt{_q{1UQd%o15@G6L`tK2d}E{EbQ@{K^bKu*JSzpN`Bw2?tn^C}ah1#i7u%XTiS
zcoI5-8lbAuZ#6v85O&3xSD!&vOG^uvn4ixbB5hC?zYX)k+d%aKq$H$qA=c@KWKIY3
zoH559ujD=G9f+*)o7;SlrjoA6TipSxZ9UgD+<Kq!!Z|Rg1BF)EE`3&hU@hJ5sV%Fa
zh98K4cT|u}n>b=i;q5zxX9BlQog;z>6bh(j&cY%qphX5(P*h|%SQ4ulb_4o;00_Og
zrJItJ^s|SG1$HbgJ72C}P`&3v<R~=bQHKyUo`3T9i%L?WE;TrpnU==lXWt)7YmTRI
z$cbWW+Xp)>3#Bj+06CEew$iuIDk#ha9T`<5y>46JL{TYWd>z^ap;7J;G}3HN9AexO
zXO;~Q<TdLahklnjq&EXGd#JrB?A4OC7~NML?Ck;4{NN0>r{w{2mEd~<OY=(Grk=@L
zJ_<^n&@WT)C}-e_>VSGm{!evkYVZe>)Q+ISPlu*y&T-4${i8J|Non?-Ku>S)&F^>e
zvGNbdD7nm38adCMDzNA0OJ=y_|4E@c(oCi4rynhUwk}FXN@LRVNb6U;yaD&;qPC;g
zT{Sh^Rkt#HePQu}yD8!uEO;{wouH0!WWGg^V6lN8;JNH1o~Dv#I3B8&3Q}C~Tmko&
z?_%jaso{@UFL^DR0%F?<8q0td3e8#-lup89zFV;S{j-S+<ec4|810RImqUrMv9U-?
zm;lsu{^@Oj-I)YGbJT5_0>>*Dsi-`%JN&0B=Ul9Iw@l7a#Pqy0(*xo(`9DL)%J!be
zlIw`jwCzcu`45P=s8RCdQCV5pHv7|I!zc@=N&KU4?esrlqoJkc@6}p?;82H%%(qgI
zR*|o-&(UJvp1QAD$hy8#Z3#k{OZNbCm#N5Ff{qYf!}d<T5_On<!;%Xk(ww^{>5v+l
z92?sy;rR|)p$c+}M<B=`>j+}-fu+*s=1{bjM^6w$xO3Ok{HM8S%gbhesCU9V5A0Y-
zh&frBAaSVK-bFkIt0NCcEE~~Yuc@6KQSeZ9UCttEpB-U0Hnz%%B0~!BN;*bSne%jg
zOMaK^L2~Vl>zaL@c+%e=ko4uPN|m(EPj5}Kz>*U4hCSwH(tJD$;5T#lsj~6`CCU^h
z<j;h@$SWr)9f>O{Txj-R-_Tk*SE^O^sh@|QUJj#arrgSzrCrK3l)(8%?Uz?)PIyvy
zx#Uk2%kwE&{T+?|)ffu`;?hoeettfO*Dz?GLFj$$ZA^#*)N~Ber(yoVA8E84u)-7L
zI{;@Qat}qb*l8L;p*dzR=$MsPr$2mSZgz2z;m=E|8SB<*PY}*~<l*{CY=#Q!q3%ko
zJkOB<tD&KZP6<?uuiLa{y2|V&B*b~o7jJL1UDj}|ke%sjxu>pP>DJ%VU1cb@Jm6x>
zewopa@iGl8aq_1gs5*QT97xR1z>=MV_PdWnfp^S><UNRsYCnvji|F=XbUbO?(j{f|
z_oof5r1of+n_p??drL~ciHO*@Qt1EO7@Q=Z;OQqTGMT-&p=$=PnMD<#Lg9VNsYP07
z=}F?)TBy7@x(x#U&=R2%y5R=-8K&OfeM1(r2?a_89GzY3t;<Rrh>3oDzz@nGS^&Bt
zacHP&(AsT7{Ek3J@S3UNX+@y#0x57|aFDpj!*ePtMfeAwNZ`gn;-qei+|NBV={L@a
zx5SDFone-(gOYa$q@ed!gOf-D9&AD>G$93{p)t1K%XtLC3iNSIAR4r)x=!H+ZVb&)
zxu(<%+M=DKqY)GgTA+tV%IXC&G}LTwsF-*!Cy<OOt&wfDX5rPw{W^1i=kC~n7Q*)<
zV&O>D^5fWmdA?jTyacULpma7p!=_7BeLYGPl+kGWZjBdR`#Kb7w{IFs5Q8Oc_e>$`
zG6nx&pwME;Wu!2iEE|C%ZVYfBtxgWm9C~qT=zky#o^-Ljy+24Yen{(R6Em|0Kmu`N
z0b(A#t4J510MtE@*2oL@0oK7fck4l2W4{LYF;wDTZIu64y6^wjMUh?nFLCaFt;he#
z_1G_3TO`Xxsco65$Y6{{bGW$no4VGEFuwp3x8-2Tft~zZc~M2^7`wcOoi{hWXMZQ`
z0g}>XA6N}V#hGRQ0#-Z^@qV=G36Ec<sj&KOxn`Fa@`jw!d3@vB#EO?aBi_Z$FP*vy
zDO|Hr$hng|(=uLuCGUW00QYg9Ar;oB4f*RoY;T0eGI1ng9R}ciWlhLh8p@PXp@lG}
zn-4v^q4r~8VG+TXWifvKL7IM39b49Yq%M^R38_?je;ED!g5O1Y+Le%D<mOs`!IR1&
zBip&(%UqWPC|HvNM`k#Rx>Es{85>i57-6nZ0Z8!)D%DX%$vqwnsLMI>Kk{<*CccHd
zz)PB}&zAM&0vMg4eqo{X94eNu6iOyCc@uF3NJyOc=6_1ZP`Oa__A>~mcA5XXG(HJw
z+zU%2^_6eljF{UajkQGd#fUU&PSNEby-+R?hre{mZX)pW3-sC7SA9&oCf;Y<{x<p^
z&Ghv};<bwtfz=^@o>Dvc?v8r*#qR#XatC?5*hX=Oe@x$4W3~n3=75Ezd*c+*8Lt~h
zXf%&=Z*tYON>P6Gs>i)QrQVe7v}euOlbc*R#kBmdrA?U^NLt<G$EL^ip@E7yefKI0
z3+C0UJq4-f&&L<m@9k)x{pfzn#^!A)$31Cj9Cx*ujYwCq82>H{6BAR%x}6{!&XRcD
zu3yBDGKHgeft6Jt7CNN$%P!d8e0`gSdm(mmax=Q=eCGR~*RcI0+90=nBd;OsAEFi}
zn=$;BWtM7YZCjD89i(E*!K1+{JXM4e4`SK;fjv?T)Aj3(OL{1AMccWNnBDrs#4Wy=
zsaMN2ewS<?!HK3!{z~TNOSrm%KJ$p|lnxCZLynttC*$xJ4n;R#vhJ`d9^j5~RV3W)
z=&;Z4^JP7L%(#A2jPz__<I-C%2f-MIY^}Dfb_r>j=9?6rjX&k&C{z?N;wj0=tV~}?
zIri8ytxBH;FZ}T!>+WRLU%b4k8{|3l6lQINyc*L&my+1KV>$G~MEs37`tuQg6f_ws
zo1Ut?ux>Xrzaf6^h3@jMH3!Y?le14v{)%l8$??P}w+at+Bu+HHkvu5K#B}!(<dhfh
zukJ4Xs<og{SD)Scr3r(7?mqkDS62q)ACCXuydD2wu082ZqhUUfT>LqQt1o8f`|`P>
zn&A8Ot5+=ZPkvrGb-#BXd!SI?yPwSW-f&;3ztUgfobRzUdW7l9l?;_Mr84N^=}g3&
zDKzPj%+f4WDm3lV=cGC=q#c*5nUx3MedaIdrQg{I#=t*b(7%*=_3BmshezH;76Zv5
zt{c;UG2s{KDrq<xUem--Hm!7-Fm}_{_@};-lY_(3zgP3D?XhFWW(SIM_{@6)O-2Z-
zQ}%ni&I^O3$^fQ<&}_O@+Qgkrj%iLsav0bgoC|4<7O)jw6m#D$kdG71f|C3-K|xKx
z;GIxbet*lGjD?f)gIB_fE9hTEMAYkCAd7{@ZMSO!7&+$e-><uL>C(8J;0Yn}usF{>
z4M2a&+S=Ow0Ro<T6%3xc*3{%=6!e0f0c{sk!UKL;#eHA42OmC$xSb>??}WRrHE`gs
zf+3#tk#H#2f$<C4&jyPU*OcLNDxW@mdO^p#H_xtJ62HAZSI!g{=Q7K(FjTH?ZEX#U
zW^<Dc)}_pikhifkk^?2>H0@HG7gW5$kH~)6n)nSqv+nCX8+*hZRlJ9LF_Fys)}u#{
zj-NQu0gdJFu$3<7?Eb!L7%iUymrqFqDNBjCy<YgJnoKoYTR?fh%L^64qAUrTwqg*o
z+#2*L%w*(LDVds@-twW~2>*qlQ1@l~%=uyjY$b7>(B3LrKNbB%FeY6&#UJcu5qjF_
z=($bW(Gg9CFJ8Qusqv$=+&M{i^RN8A>-l||(B2`3TLTg>^YwzRN{pcW+0&=rzoej@
z?NZn6f(@}fJJO@;nFSNbfHr4Vjl4vcwdr?onCzQn@zhnU!i^YFcTJ!c@8JviD74WT
z2rdF}jpQ-GX%?BYy!rYb+7U9qU?RmltA31)*@{d;9|DiZu2UDJ8(0m~p6nRW?di(`
zbSI1T#>U2YT{p~mt%kMFzqxBa*WCqej|xm^w7$S3^f>6~)qMG)-^V8)paDG~N~Ara
zYNd<);lqd1Y$m>yfpD@0XCBHPH@_JwcQo7CSimL1&CNC!hZurdVyXNj?H30%hRW<4
z3OI6lZdB@Eb0(m|u03YB8Ee*&><8;ryK*u1hnUB139M7fY*$9Z<J?qe1d9}M%+A)u
z$4=Zl{EGV55l__D;5KN*{}LY`FTDPUlZpq{n1Y7q9xb1FEA&aKRJl3lEI>pZb{eD5
z$Wrs87vk$Ekxx-p%jhbs^dN4_)_(~Pr~3Nki}7q{T7R)sTu<U7Q`2<B5MbTQMevqG
zf>~O{^1$`G7l+F8MQVL-z!I@(<lPAn-A>ht@_l9JQ~hpw+PD%<C|O$Ia5&I?n4vO&
zfu>4dC{tt)SGpPrC3nnp!X$Z~d>1d~6LOeu^`{qFgBB!Xur#g+SqLMF&<zrT!~HPb
z1jud*eI3YbPcn*TN{6r-2pOt%XX|8u0hAkFO?l(zr)qAV2@W3LmLPTQ#tp^uZ<q%5
zEH{v4FA@XkP<VY5W0|ApQof6ASW~(v@F>4ezfjX4zc0^^1ihqPKS!&F`XPu(fyCGb
zVnBge5BuG_cPEUsJs<zaYYcd3x>6M{W7?ZzgzTZMMR<J`PH~jt`am^%6l#Irp1r1S
zvxXcr$Xfs<ZQ7dItPH%T&}w8@FHar^GFcD{A-e79p(C9MQk2E|77pjHKL}(oVX3z`
zzk>9wu{?6-&ge=Pb>B9I&Xzv^QfO%g$MNN}9886lC#~GvT+^Yl!k*PqaPBh4rPu{0
z4QG(YI^kTRY`u}L^-Vg>+{Z7__4-_CVA>wkSFZFZt`G~?LLllHNsAiUE%fv9S`I#d
z3mrZm#Q$Fb4AC~S7c>h^Ss>Iw6S;f#Y2SD6-a%juMw@^oMBtpLE1>}gmk7~!7GZ~Z
zBhaF>bexj12acH9ug|LX$hLbQp-gvJ=*K035^6fvofY9fl&j|h8+DC^t*z?er-NU=
zeytyUf<(3>lng3w&a;7SB-1rLljpaeReAC9rRhw2QuZDM9*|!yVA^5M8PH?)alsAr
zfve?1?65e{E17E45}QptbmU}X5l;<7%1rRG892S6{`m2Gx|2Q`e@MFgAi>#7^#BHu
z;NajyP*!Kb;j(W~(%p<V?MlZ;XRO9lP3v1il;~fO@rHmm)A~+AT3$Z1dNi=fumd#4
zLi;(j^|>An1wHL5x4yy{tp8`mymEh>XLeGKY@DbLa}WlGZ{rSIQ}WJzXQNbl=0TPk
z1vOWq_>X$P9|-nFc|Fc_-CT5m7C>|oQzq7RftS~!Uj-rva+VfOU#7!(qY**JMeR@`
z(u9XgEGfB}b8HBSe&r4eICLd6_4L&k@<rMI_;PH0VL%=FI)gtwye}^9MZ;&FYE?z3
z>9U0M65;Q@{Plv)0DLe@(wFL;Qj&E2L(Z$5ocAec`8u<8$~(*K=gh}S`Y6SA<H7vO
zwi~7)&polo(J5C$!sNFw4vvJjR0!-{**aQq9IzXFj-4e-whq=UpN@lvM+I_Q4>T5W
zy!m>``&0b)?+@1(hqRCk8q7!QHd5u@pJ(s}iB7N=r8VZAsc)hAYJ9du`LpPi_X!C)
z1JIq_4$bE2WE3=4Z{B<WDF>DiNg+Nb=?flzeJvc)d=v7cnUK3f74*ih`eiWZrARrP
zIAv&0&qUIe(G`61-RtE^`q^#s@FqiK9d|aD)V;|l&`C}Hr1c)%6W5s){wwE$M|4AU
z=qj}}DxLx@zfiyav<QgP=}Jkj`wPudK#p>r2LQ5o9~wcKFx-ZXKYI<~m;kQ*e5hGZ
zwoZ|?9PIKrR+XP6OF#^4nF1&ogr|!*05C(e3PdBbZ;ImI?DeVFZ-=9lVHR}M5EJEj
zO*?;<Ab&MbWC2T4J)rgOe!k1^UVzdQuy!ESlo)d0q1|7@U%-9aE`&|1d#uWr`w0mS
zA}D}d5>V@4KijG3<Wz#5se5}_!+)wCF1-fog^aWed5WjWs2Q~xXBfMVU_;Ri@7qd|
zJY1K3@UA19yFp#xMeGF%3UYEg_il8lJK;Xn?<EnJi{xp84GIYij9(Zio`V%b{A18C
zcd<8DqR_0T8}h60#xm4fWTd0mTqeGqD|Y9LY6=OD<uZ6J%dA{<V<`GhyoSr?ok_uB
zY9liv^=I+mImb_)Y~~BKgR^9TJ?_-C?obHGo3DMGTY`(capQ(YDBDbLZUX$~Oou0Q
z$yO9`Q<0a!)R~e(*y^)<r}!M^9|Ul}biX+EK6rU~*@If*+qR(eUU_I0mvL+S^-X@0
z_AiA*FuWb1=<9ExWV-KHG7lbvFIMeX%7t^@f+LrTw{JfKa`+C;E4FtfYZY6*e)Fcm
z&U~sl<{S+TD+UsN#6T4ZPR*17-#|NS?=}UkQI`)VUh!XWC?8mbm9()?&%uf`h?0|e
zXK3G&2Xy*%uF7{TxhJ(*+U1}Q4i^+uS#(YD{qr988Ez{k1W{@Dhs6#y$vj(wb1c~l
z;9!&6ThZxIkvH3cV4?wWvM388*?`{9@1F!{u4K$zB`j%fOS{NfS@}vPKp0}efcO-*
z%b*aNdv<nue`2VTBmjqq55n01N4CF=iwATjsJM>9es&%6fTK-=r(<%ew$l>}Ui3?c
zUXK{dUHv>`H<|8kSyNx%xDfp<T)0_Uer&ZTQ@@_byFPW3+Q`mxwTZtkw7o<_URPNc
zTRZ3*6%*|t5Ex|ky=5+#_IC;hyi9ErW~luKXTqcEml)}5s~@aJL@_2O8j^ZC+~FMS
zE^T5Wbkvp?wL}X98EB<+q*eP+?&uHtatA`^|CITFQKk<4>C-=gsCJq3*cKT*ciXJS
z0NNmaqSXIB+*yb72Hwq?jhp#dz6>V`GKj$IB}lj$i;g$x1f8K?nr7G(DKOii``cju
zk}~IuG81B9sn?5wPEZ8g;}ZFYog{DwPPm8LgA?{nIyalbSk<%V`>}cYm{1FaSmBv<
zZJTR)8b0&q`DHPfHt4WGV<3=k_K|CP);n2(wurY8X%Oa@3}9I3XW%goR8msf<qPGn
zstBdNpj=VP=OP)#q1&v|G8h#lGdOsinYpGgCRh00qmsgrzJE5!ATg{hFcB0J7YndY
zG^QQo^|Q3J8Ia5{+j?(rd*(m5jwEXI3<UWVO_|=&C)41)n|&paQxRZ^MgVjB#=<~l
zkvpuOPGN=*CF5c~tlibC_uRMF-lD-p6_zSi{YPp{!H(v(bt;?`;ec_m2Z`v2_nUgf
zkz?RT!>c_2v3jG|2f!4ZI}I7z8g}2*p%?QQ921Rdn%t;rw!ER>dHFXW_boK<Q?ja9
ze(5mZ`>wb5K~iF3iD9u|j649@X+Y%4#g;>B0L{z%UB5p)QnArjW^V?^&@FI@@-c!l
z5H*3{&9)`@cq%Y^`_`|(VHq<x#Vai<8yu`wz1^FuXF5_<3D+L&uuhSWneAU}edi4)
zv@>9g($cOXpg?B;EP7x&)f^J6#_!r1e@oQwlWsfyUSJ}4t`a1t4}bzE(>vzv1nmK-
zARH|~WFy?Ht$vV;ihO%KNI{=3iU|WbRrSU3bI93}IV5l2yZ6L3fSHMD1`a$&@>xWx
zWot*Y<nMatWE%i=(B@5%@}o%tbXv!oF<_XF{M9}tZ<Au2V6B3vX9|Uz?4CZ$;yHkU
z$i4w@;=s&8+yWwJ<2+e)_aM#w&c{;?lso3YnJx{8m-3Lr)$ZN<8=M)U2K@hXb#>SK
zyC<@A@x%6wFs^gd)H8(&kXE68TL$OPAi|1?<b3oDzII)?s0*w{usY=quo5y`FcQ^Q
zr<s8Lp8fOB_`14#3lNeQ`U{l-0t3Q`;<M<)dwTZZ<2<%aLfCcs*Igv<+%bmpB-unj
zT56CeT52{^Kq{Aj-eNnVEBxlQuWxXlxkHRD>=4vo9cLhfw)8wefCm5omG^g!VNCM7
zj2gq=N&3;S2n%aVCMZHehW`URflk~GC@#m(_2e{|^O*rP$^OFuOPJLp`{R#k==rvZ
zkphl$?(}H{3Vi%14$MFSgdmlr;mWS~Ti$eEk*J=OoNW5h>j1Dy+sYhT9^>!nm6lu)
z1y0L9b}Rl6z4gzMq6E&<(`UUnalvrF^83f9mLvuNn?(HWmAZ$V(@<jXfMG8c%IWJT
zBH+HY^?saxrQM8Dv0zPXxGXcAAd*F_Mxl127!Ep^z&=8#T-$l@_U+r((5a^R{!7O*
z4*-gtaJ0QEON&45%l8a30ua_xX&0w<zy?_la&H5l7I<wo-<uoBZRFoWWRebU1>|54
zXA7rN#XJ%D2Kl=0%f!q)3zW1AItO?jencTSI!QVN*JiBp<jDshn#dlp>6EDe3rDOl
zBU)SyF^OC~DU8X)2`=_owfnI51wf!IhRRd{*T%!I&(YCk0-czHdG>trJ_2j{W7S%Y
zISBC^IL^Zga2#@fXuZA?)Sm_IrsF+{bGl`AGdMDGs_(10CvM|Ch<1b(gGx9phWI9c
z@hd^~iGad@&dNs$*d~L9!S?1WZYlTr`8RL`gkGo8MHS9f_I!9$efHeBBtUWzvdqA1
zBqUyvx51+v;t}u;I2>Wsl+9dz;6)D~#v`H&Jb6F`&IN{keXIzBaoLzpgN>g90Inh~
zF77`DF6sgq&Hnqx10ryM3=w8znS)>{(vheQ&FWDATEj;=HeW@@o!ehi)Sev-3es~L
zs5g_QrwZm1Atar*+^H4YdpkA;Wc^3_QdA7!_p*@ZKfgTWOqK9CISWyxhYyncx`r$@
z&?K3TWI5Z6m}q4I`VMoxshdUf&D`L(weBHT6>lSR3cM&`(<zFFe3^#u#gH#`Sru3*
z7SA587}&cUY-MMLI!BHL5W1iYQCg6afGn9Ir{??{Mt+MKsJJK**P?lc^4k&;U9c9P
zPaYA!_*+jwxz!lvL+qDxf!-E$QkQQtrv4<#Q@-&ZxFTNVN6jq>i1+E!r<Ny3@Q>s_
z6As?*{hjGPatwRySj4d(JrX~C@gH+T#oeIsGwsVu>dw+ifp&)XAF2=BuS@;sw_a{h
zK!(9aw0a+*JT(E@z3rH}KF?|6_ge-)q40nGz7*=`iPQh61OLL?Z@fPLRyGf}SQcM!
zI9h7RNu|%j6?gjTe~ED1AZ1}Nm>2Yt*?@3B@;T=~g|d12kNPQYZ7-r&GvdKEf7RxT
z;v5IAUIivr$Mk#HUxSx_I3IS~UHW|TvXoMSE;!}cTW6d8Q5aEMhyNj5K)G}S|5<g^
z|3hz|)dF2ov&=3H_<aHZ0ai5n4#4*|cxF->Ff>W|l>848cB^nQwH{(A)D$8%>}T5V
z8s=*N3m2trVRV1*bnNQ<FNZ*Ref|LP4S3`ohw`BX0)&8BsDfyIc=!|!biYG9P&HkJ
zCGqMIh%TVt2+;Y<?qq^Mu9l%tyDAlT75m#_y&g{i1@3`xtY199Yu?LAFXE!Y!omXT
zptPJ^7YBa#J<u%WT7UWukRH@}M-SdT&;5^}c|Q{lM)619(0hqCffQ5*-A02ON65%n
zFI~EwAnDf*9#sH!`OinWdx|WFG<z?iA~+BIN|C6qF9D2ve0qop1Lu?KgWak$AXWe=
z9o>lTGVKD)!29^i(XKRQDabRYPMx9=v`alnFF0P90~#GH&^1v}Ec6kzO}8be!b2)R
z9<=Do`<g{69(Iud9V-xG4=~BK98aPQzvW;ZS9j(t?-Z}$U-<u;_ZU2(svBYk8iPD4
zC3c%i%%cMCJGC(gQeC0b@?$n^#RKqGWjJ}3JO&^W77yZ2vSNZ{>4Hp-L<~p<YFU~y
z00y$%fn@eT)K~{tgdkAt*tc}7Emn_g<;`zTyUGVR?g}Pdt2i5!sgM*=)6P^xik#gg
ztY#lR0Mg+)91X00oNEk^*ATS+@w{gY!6OjtukrDzfm?w3hD22~aCKRLfpF`>HFz3N
zonbx;2qnNHJK%(1x<<a?Iq61m>~9Vvo@+f`cM78f6nu5C7Qk0mv6ZfB>it^>4jh0;
z48n{$0Iqd7tt^c@&#pbazJ-7|NHgk?B@H;Kv|DOxYD@tsfZEodZxjFtOrI;_EEQEM
zOven&;>CIHK=t4B;J!H;jlPHb7Rojkwx?BKd=Y~v@jZO7$JR2!DKN+=^b9;aO9hSu
zaV=PZ2oJz}GT|O6Fev<K#qS*I^*G_qtJAndIH@q65tkqpVEXy_G1J};?>>M249Ly^
zzH=X-F(5T;x|jq05fNoPbLKKegWb0mG{@VosOvJ^*luqj`TKa{&bP)0Zufr1Uyc*V
z&c&tF&R7_~`U3z;J3J;M&pb%AxzU`@)lF1{Wzl5AwhZ`-5o7!jf**1nrueDGh%tME
zrvHo%<!REM_$slW?<4SkWhW=6Y=1G>F6caX4@nY13ZP;tLS{7JiU6nv`e7w=c!x79
z{vrmH)lf8uTh1aIa44)rwW8`Zb=@`TC%?uP-<zsaUX-qu#fotOwa&(U7nYa_gGdzc
zq^Ed<R-IoDT2b#$009=4h!9om06ZGtLg`{p+>Jwh_38(=L5C@GSx;B^XS!Z^DsnPJ
zbj7W~xo%@%x*A0aKz9(?aoZ7eFf<e?TJ?5zckMkvU;+852DF|~6H=~TZ2`z<+M9DN
z;ZNtsuN}|<;uI(LPxSFblB=XY9Sf!mo{Gi;C=O&Pg>0pKFjI(D5Q5$U+scGmNQu8i
z+xr*j88GcGFy)CSH_;g2O=@;_1z1nwx^S&(lJq%*{HbNA!dY%o3PiGRGj`ubpBRH#
z0;jQv-Cj3p6nD6~e})Fp%BJ9fVV!V(QlE#0*Ql8eN$n6Me+fkhZUspy`|rPxR)Ttv
zJq8atNdT#lg`FLdq8+*v+10b7L18Nt4q2}=h9{Aw!;<^c3A~5ONu4pzH&8@DFlSXx
z_JxPU1kH&O>A3^X{Z21e9RR^2!WkGQG<n{g?aNO=fD~YNxFHgZ0d91`&gQ^Bi^rg~
z{yh?p6crV@X#5aGiHwpV6J&EgsvDPdt&w^L#13$PP=uq_>2tBLvP$Pvth|OmmzbKG
zDjbs`rbm`{`hKAI*V<Y$m?&_oFpwA!b$NZhPZ<gf>jU9{BVFL3AW%s<cm8}b@^F#9
zJ<$HQVMo@XM~W4u4kfd8cvMdYkT)&npn5Co{mx%h{P&RS8cKkbx`2k*y2*lG2%9>?
zI%dtC(lN7)WR=NOQhfYhApfiB)%rITSZ)0rr4e;2sU3rWK3mvQQ4_5iCoT{6v0x{-
z4vu{f_srY7tJ2jm$AaH#I1?U@Hlivcg+nSNccrAv;Nva;m<@Qg9c~vv0)Q4z{ro;O
z#-UuKVFUbG9qc0Gq(7Qoz~+vWKV2Jy=xjhtELX4ArQG;6Xw=6z)hezkI1HV!y|Iu{
z<-TJIJOn#H1e!NHSemc3B9H()VHO^^Bjs^>*%(5Upw0N-4AApuKERAdBjFX2+w4$z
zvFP5GUY16_Bs^C{1+D?vm<3eQhN=JlPN!ST9hV#|K<g%A2Vjf86pA>n=*Nh-s=-s~
zf(BL*nFZ=Xt7rV9zI?gU6u~V8ToPIAD2p9ne^6-30ELGJ1O!+Zh^H!WOdOF6fkeTD
zyMb`HpTGQ%TkS?nTsu%{J0;)a0O?b>=#`&e7u2uVSh6IWAX3@E%4LB1fw<Qm=o4lA
z9v})3j@81UOj{%(k5>CMJml2x>z=Wj1yU4*1}&JsV-M&M)Gpj{?S`uR9no;Nys(@<
z*1#Wi1`z48*AV<HdtrI3wtN9}w@6{<ytxrwx8=JYpREbdXIALAgcX5iQAEQlA_ADV
zAFH9jD1kwW6qx|_M4N!GioQDa&ouVtS3<m+OKglFqu265Cv+%U<NA3}tAslCAbXb<
zQPK+r$2~z~0A{!>6!vtMSnEMU;dQB6(9kt-Do{KGsu5tps+x{w$%_4T?iHj_*8>EB
zEwJot3o8S55Y(^kbMIO{sKRA00Ow^98@-2vQb~Mp@L6zn_S#zQatFC~MvS4t?c2A}
zn6$LCynzMmnJy?fbb#dvFJ4nk4~DWs)L6;NH<lD(j!mbzV<%1o<(g|0Pvu>Uu*rba
zL8^UGk13B{rO5@QL^gLStjjpKBSmCe3dr1b{+M!t=j83z%#H^S9pct0Zf$+P91ANb
z>e<o<yS-@=u=j{7f=jn}E*4)<c@C+D-l`Gr!E+>?f6Q5`0wI;KWij=(dbF;?dT&eH
z>BkoFnYjn!SAHNNED|q!&=Zow*n!V~o<AQt<8nU4H8L>p&k(jKqAtZ`_)zxym4r*6
zxHCb=#PH&(6)zCfh+*4Xx9*OKT=?_P@2hd1MIWK_AdU*c<2(Ks*Pl#`9(%hG<qXR#
zLuFo^m1O{<lDzn|ulNvZ3tM%%_2R{g(iqHJG!$eHla-Np6y)XApk9Wafrp5!^h(vx
zu6B0r4y7}Qd1xbnE>$rBrch7EXjGV-Waj<sSw&I3)8*MGH!Ft2`xhLbO71#%;p|xz
zPW<0z-|9Lf7R>3YcnGXM?|qZ}-fi&<mS@Oq@$O7v31Q0a)NvxI7Tc?+pY_-9iMu;Q
zH9I{HQ#TsdQ!ex+mM>DS*2e1IB@A+pbevWC%lZD<#S^ISEv&l7rs3Hs9fenQVUU2R
zCyPk%j0Za;@C@oP%1<%cGFMkuuhP8%KnXfpIs|2?pbDthlKqibNj_sAFOoYz3O6$1
zammp>4g4COK+!Z-_4DUX;TGIC3ZnzQqt^hGi(}7t{-krQO7A}#UeNpIvzx)ls@)LR
z9$i_Nf&b87X~u(?o$Y*VL5^12>!G)g<TVssTZU#yZ&(6%o?Y7T;MRANd)+Q_aPu(r
zS;Cwek5g3&i>6q@o6C!LPbl0Px}YDzf%5u+%mr{)>FHLGsS!{+w2`A-+FrPj#b8^L
zXa&J(m97B*z&S?79BOXE@on0PwZ8VLmblG+W_LAP;MJj?p*@9W>Y#^pjfp~K6~Rud
zMHQSiwGBTu72PgFv9mi08}#1hP-t{?^o&ctc~Wpofa=o^&;npH5vj04ZhQ0fm#isp
zoM4*{dl}NTzqNGw?QH`@1O!ytvviw3&Dd@4PU%ATHtTWEyC%g0_6y0j3ewW7iN-b@
zkYfz18N~n)od^zI#dERB!pn|xL8Lbd!BBrts3nZqj$*`bKfApv`q~O{ONUiP&(|JZ
zF1&BLi$fuD>Kv;e#3pCHzWo>!y1VMx+M7B)4&~BY+5C8EX=$ZdxwvZIGYXal2%2Bx
zM)MXvM=yDHIcI$6GcU9GS{%1@KSTBIJEzxOrcHLbfn9e}wO*x+80F|vhMt`ycxlOM
zYe(Y_?UKq3;n5enqGYJEyo7JT2=IU;7mD^{6vNUBIjDeugr0$FTbyBYGz$xxVoYwg
ztt2Ga>;>Nwrza6f42`)g<=<GqLDcGpQNzZ_;CP_qul`x6(!UFmZ$-Cb)sxX@`!cn&
z|L>6aI>?P^aSyJZG5Dg(R8>VK7y%~moDwOhU6z}vjY5I5V&kp#=qKRuQ&;FPCfw&O
zJ@lg$qxQ5V5%s7qy69*hJQ-DZ1F{AdqXhi}EA+qe#z(g5hgWGRQFoQ9{#$$B85L!^
zb&1`rwz(ApK~dWh%pg&aWUF9-h>D^FMMQGWl5Op)trE0QkgSqKvV<ZQDlLde4n;=E
zQA8|qnEe#mcV>O_t(kA;&ab&F*SdWd#ar+Dyw7vOKKq<)x)8m4O7LM-z!JApQ5>{s
zoCD<Rv=_lAO4ioqF0q^;oS@SVXAQx#kRyC0`8=ts9iLjU=mu)V2^ilfhXU_I)PTCv
z<v>*cy64~Fw*M&Gx?g)*a4OvIw2o_JQa;CL0l%TX@sA%rO1X|FVZ~x#u`@z_JUy#n
zrNtaSHSO`y!Iez25wA|HC=CA^SguiX(;c8O;^I`dDF+fP!z$Av;9f6~0wc04#6wZQ
z@h7({%)65-d+Gh|3wF55YV6$$)Ei~ED$KAWRe4dK6zyk%;h=!mZrIwP1q73Y`Mf=#
zf>h%I!X*#9|NOHY!!gbstC&Xsw&(?%<v4mnbxk?<!bOWFvx<LE1FwaodGH=F4nr;R
zI5p3ZSdlnF%3<J9dxRAGCge~3VwsoFPBmP5&~u|PpZYoeA2YchKO1)XVN(4JhS$Vj
z$QD;LM+zK^s)%u2lCHZqnGuf0ax1s6@o;CpyZZpTbez7+62ASD=*jAi>~CZNd?a*j
z-SVFC@v8)+B#sG@H$u6yC^I3vQvoQ6B2(njU8G5y{KzlZaqp+ESfsBQT!t_?L&jrt
zK*PK#;}r6wTG+oh&;r&TnV*G?aQTN1V{0}l`ta2th1deS04oERtv?C3&O(-FiY%|M
zAuW~?W7*0euQ4zGJ6f{#mpp;T7(1v>t!&D)O>-JCSivPE5Bs2wcTkh4Bj+~J>93h#
z^@L)Kfv&SF(Tv9aIVlhK&j31+ZVoVZ3)LB=zMOO?gs2oM(BJo}f8U$dF*qGW5miB!
zjGfudpVGYQ&HN&cv%*A_<Ya|}gycScyCjFj)(Itf|6OO5$U}nG9)tgNHmgc8RHc#`
z5uJ8YB7=@nA%Rl%&nYp${U?@uzZ<J%BrO7^heyI9*n6{F!n0>P@C+vqdsvwBs{~RI
zyrcU`g+MXHNtDUWX=BGTaUg;D$($QBH2KcLdl{yP`-PV!a4B&5=P2fx01kYPepz6B
zT_$ACTtA@Svq0Lh&XI)q(AScijn74)8RrnjI)?+uoSkZ&dyY76LU!x7=X)8y_2q^T
z;ESptq1+%L%znLs9`tUV0_>P-RPig&d`*;*nJ_S^co7(LcTNg4AJh#&x(G*I`5d4D
z^^+&>3I6u|^)>zaMeIkZZ2)-zSoGozl0pR<3F4Qo+hv5a0=^vTG+Drd31x}AOOmP+
zJQ;i)U)T8xs{HTV0A9c>YTSmihsci!#32q_^{RGp+_Ro}cHd<^y=ATgsN=si6Yocr
zbD^xbg^H}Qe01!|C)XKZyAUD}md*KVg(6UKTSBK`9{zTpnFef%O*0?tfRt?)oB4ya
zi$QT6!P-AInS@4!sp%f`F#p|7R#iS4CnN~p$HIgqQ`8m!cSK!u?%m#UZGI&)w!31I
zEgp^hJFqd!VK5LgSSVCjTi+Ih@V|NgvSV`E@0uS4%r3;l=fJX3Lv|#p|86b8wqYZO
zyOZXxC7<qDAa`01i6w#hks*n(Yj_A8m+nq=MbRr3%MXiprtS#`Ls|Y{$cYd+kBcZ~
z6H*c)5>QMiF-TrW^;Q(T6|^N-qMZ8>vwTmXk~VH~b^d!^3IHPCNhQ?1-FfyYAPF5R
z;Cpi8I~M@%<_s)45dWu$cGS>iK}*oiY)j5<wjh+68aRCtR{|yj?nTU9G%_`P6CG?`
zpkhcI`$4Cu1d4wq(?<Uvw{FEEt)B!;l~WLQc14fpOem;#I8EyK4gf1@`o-(1#@nUE
zk-V+mrtl1};>GzrcVI}d0QNoQTYdE0$C@3na~e?MqQn5ovJgvWCh82;wjk>u>>5-D
z&tT*bNi8pA*;Sxq*Txbn+c(<LaRpR2)R3f$cP$6c>|VWDEx^y-v=y!btxJqxn=pDx
zkx)J0@T!w^4+D)nNs4E%f5gR-sPw0w*ytGc38@Bv=JE`#YZ9Zs)FSR?%_ldH$H5=u
z0hZUtOhm7gyH>Pnn7WeE9g^hlREv5B(hK3MHSPyvl2l3wWeo;_q{#rDi3tQSVSiLX
z!9Ynnnt2*CMPu;RBGF^23TOjG#ByZeq=1L4t4hyPqvqO)AxBu$=NEIcT3km|2&E7#
zxw9kA)8oU$kpNYDh9F}X$R1ewnzlE0N&b!;u(>~jyG&BgYtVBd56n<9u9#Sh+C-g1
z5PVSdQ$a=VbeOCg7;ws9R(DE`BK}{&Cw(4F&j#$jLayzEbOjbC<?%9NO98)-x)19w
zPSqZ`U|IxTPiEAZ0J2_mb&}b@7c4*EX^g=s)MU73!reNLeToOnwBg<J;BVWp1Z}k2
zsH8@#6|j;TC}9~2TNO#FjV-xaMMXt8S9g4Tm^kPU0|TEENCCihf*#p1FhaY4ufg4%
z1sM<r07Y}VuRW(oc(v%pE4@8rqk&CghcFz#G@hv!nX)g1@H4>ldS4~!%_F7+dYHr6
z{dkLH^(ZkYJZfgzM3#oh3C7m+N#&u<=Qwsd2wv5guirFj+LDurlL(i{PX$RTIF<oq
z;S6?RQ$Y*EEqj-G+%tp->HwI;gH#Bg3<YsiRb}DLwx}lipyYB|lUDBwCIx{r0lx)e
z=Y&NwS7!PmNe1~a)w*2)`|t#kWHoFgl;NULn<iXTg2&(wfFU{bBE`1fWGKW%%6p^=
zkMev4A)n%mn!-u^ZS1!Qg6sGdBc!!}t-qic1CFqm;^gEM1BwQUk2<kPi0;;I+_(o-
z)6bu+2;PeaNGB9Y*9jY@b#JFIaS=$R0p6X2eZtT4bBUpZt4YefddGn{Y34eokpvpo
zz@{Pn%%nI#F-HB;Ns5VM{T<^K*`9~TCOFFT-~O?5>CZn~h3Nbbh;VZ2*FR=TZco`^
zcCFR+^1P`}2MIxd;Ku>S2`Wo)ZMYev@I9DWnCUHpEr9=1O`aam$`vUGfT1+7CHb9H
zCiCft4%|^T*iJp@_~{IucHP>w9Sw%!K{cQexZxMHO2(!v4HC_P3(?=7m{*HFkSH>s
zPGEkp4#$>4u){i@pE3i$ntc8%hCspd5PU2_Bdr&nm>;$T$k9dQvwTyoJx^{?sBkCt
zGX59$ZsF(WXYM}{BwnH?><GpUEdmUR-I9`$%$XAb!rN7s)WKgIQ;H1_-q33wH3i#%
z#L}Qa2L_F~rwbwKYrV}HZ!AEVg~y<JP=czkk8w^_Xw`JfR_QlBO~vPakeWHHmhE7k
zXpZ_Kg^JU_YKQ^|B;_(12aeka=eIi8z1_WHcSQENN*I-Ik@@`c$AE^YvMZRs;h=R%
zuzj0MeenJbqX_bL;U%LB`hn?_Tpf>@n*Pf!++dHrZ6CpFCp7km)E8?qAD)#}Rl4i(
zPn=x4+wdDkUdDP<xq0F*9R4!XAiQ`g4ZMXRYY&HHIkeN$D&JXMarGi^6~J_iPM$kT
z0%d2L-ev>(?yru))M8M;a?`1E9N=e*6f;np)^3%bw-p?-qu|(Nn5=CCg<HyA30Qm;
zf{|!YceD2f3(VZ_hOw|%HlXhsk$A&zR%fTV`Ve}~S$Ja^>l?7k17kzK00PS`I1i>l
z;uJvRJyZ);l<hm<@y%nE%7O$E1zF+D@#CIs2J=K`0a%yYsi=>QGyrlJX6Ftv%w%QS
zo68l1O|fA^bgs)-R%jS-Rm!P<Jh=u;O$N0j>S+MnYdJaFN4>7+e=G+9m|b$ee3m-@
zEKR3{{nCX3j+1tSRKj6@euhetarlSkhG?cS=3o0Xudf^VdH~+1F&V$EfLO?b;NUpK
z=sggoI1T=T7<hUhziVLFVM-xaiqgJlfxoH@w|C#9uURi}BDfrY3l4T1sfWW4ByhK(
zsDr>KlW%wU*A5%!UmJW035L{9f@14L*o@Gb`*sY3F$Ak5BpVZiG6|OzW&yd%xQ<&9
zf+P7dl+1U45D%Lai*p8`P8~TWiBVy<&ke4hf5Z>!B#Hnkj}4w;mQt@hRmn+O51c}$
zR^cyzIf~kJ9NTojN}dol5nqt`c*(i+tO^pGUH$x+5(9VZ<lSx1NVoK-l!VG?VKn76
zutJA4|2#>;5?bOcf@eW0WM&d`(n*X&iNVQr;_Jo)w=+kM{DI^u5@!+3QZ=*)Xh>A_
z|8V=Wk85rN%n7lkc%{Gk7E!XB6W|5<P#=VI19*PN+ndldQ$mpEvVWSE;?vP^4ZT1`
z60W3Dm`};*xeAo>I((BsfY`FryenCN5NBm-@0{?7#Ti|?Vg-x3r*gAM!jC`xDDx>y
z($>JQ5J|<m_^_bJbIRvmT)B;2iF?K#uC6(lr@oC!f@9nDsf%G^2}<x_RC8t|Z0-R?
z2263MPn=G^Lw0!xVvvA<fc_Lb3S(H(NsL}5Jly6C>%*ja2%J#xGJE&}+IUJY2^oPh
zK#M?p|JEc;Nl7^Yyo(Do67INRx;o(~ZSu#(5W^WGYavV0KXFdS?JSbZDpY*rn4TuE
zZm0>F086Ju;IuLI_AFkxv77|vs60}Yf+lh<zgbGcgwqYj^>s}C`})g;q9JfPNS_L7
zh0cD3(Sm^(r?ENfBHa_tB7$&!3zP)=`vK;oi~`fAj`asA`mX6rW=>yKv=7C&`q`;T
zRE$}W-KjCvST*ib))llEd>?RR%h5C(3HwXP-EbtI&yLN%EcxG!!ImNn5A1F>ce@)i
zS|v$TfXI=F85j}>OaWbk&9eh#anN!C4_H5i!_zLD#<Al$VD%VN3>}cPzr%-6C_#Z4
z;Kpf`Kt*+5|KwwxGhDz6*%KeTMsqr$G$UF0anbU757N(H!d7;3ZXqQ({KZ;W2b_`O
zE5i8HGq5uW{`?ifKl)D7;NYR~hCB#i@VZ}qL5s<Ny6P0%I$3-0_%%p8I?sI~ogF~S
z*MS#<`HPg+?lq{qpyMZV{c%@7@*~XxHa^z{<{$pr;@*_UVNPA#%r+NnB*LpB98_q*
z5aPH$7(1YJ5z{I&H$6gvYOpBz&dI@XR+H+-*26cSVVlF%;srfGk`RHYw+D3JiyPLs
zV#l!X-i^SY+NG5qhdd1#&vU{ifZBkZt9vQFP%GW6Ld!BY5_vDTm%`bY(?@Z#)L~Ci
z$hzM-jI}14jP%8zc35mdFE&X-0WPcIr^Sm2!I_ki2s;`G{=&tJV?kjjG+9!iN=gz6
z3oZjzxt$6K$OIiIs*^`P2_0p3Npo<#c?a{DiisX(jp%uLz1KSM6x@TDUcvGb40dgF
zM>)hEZQ|XTQPwb3D6IRnkM`ajcd|hXhndf+pMNgLLyEy3`Ap^J^7m@eEy&b?EA<6N
zq6l^_BD_79J{(a@9gsMP%!xu868L?0G7GoOeJ}BId{8X}Kw@w^+JDiZS9;$emuqtC
zf=Z%*r4hyi3Ao{PwClF6)B0Y-=9u1zopBV?W$az{W&eZoqt1g5^Y4=5bM$32cRV?~
znD8!<T)61oQj6RFozsx<rbGXg%rT@eMGp6mtleYq9Xa?H_3!=qIO$sfNB*Oc+Ea$R
z@hDaI*8Ob;+l==--qGf}jD43Uj|X`DzDsX8;rYDpwOjA-Gl$LXHQz#~9cz{^I;?w(
zV~hXyRXTY*vSCG5;2BX`R%iakU+0#umHEC9cVepYf<))?wt|l1{oZxQA%$7L%{y$y
z>%LXu{zZ@Vf8X2q`22qYQ@ri9bCzL%eok5reS8F8KAY2q1KkE!i34`kyed+81IdyL
z3f5n`@>%TfLa+Kf-J@|Em=%O(0w)75iU82{v!qnne&12jX+>4yiaX+e|B0XM_=g`Q
zLr#!?8&{I-4UHc(3h8JX%A0fFs`&F-x9<ykau2tcM??Xt$x07)Jb7~XmI-FB(ISBC
zuT?j?mx`VeTm=zEOw!>)vVRkmgnLkLrt~7lMpUJdE`wYLMe1;rHE=kQn;$|_{no*g
z00{qc(0~>bpa0RS=J}`rWD~F=MBXLJjoVax4s3p&{g?kBxePK6(n10pBOx6JgLFBa
zfmj{+ioR-6vmBL5KtbgjLx;KlpX8heF*8W01+caVz?5jX{WkTxE@;$BNSBOR;078B
z;kUtdo&_D&q&6;wFlj<Km!6Z?{f^eHJvxV>wxS4Z=$aJ>GMLCM#DJZs1RvO6wI9I}
zMpKldO|U6c4mDkp(u2Ne0$Kwu7tlC6V6wGw&;fiWpSuE4Os$JZ8$(^cQ0WnqaAm0`
zHM=nL-Eo%=BL^%NyO~LQ<}9ErBbfI|C~DYYGQNJF3$k{S^ETRkOV$Y!hy?f*6%`lD
z-dNDHbKSYy%bqVvMG%RZ#=gb_n7~bVfTTkZNopeSwX4#63vYcV{>U%tP5BegGNc~W
zZa{d-H&<;=!0vmC4ky6tcwJ8@y`-HK7ENRmhKoxBicuzZIoE}bYCkv=Yf<(=O?wx1
zBY`Z|aB{l2U)-kPS^mcoPQvOuSJHofo1zj8T7p}FW+}-ye=UcB>##aN`kDycoQ65u
zt9?>lnf{Y>1kaveJ28+_h8#-Ny6s5F0qfT_45yJUU}p(GP2umg?K477L<7C_3%7;H
z+fBT4@^;~Xc7T2buz`ey)@^?vsY%Ik*l|GnTbLt{w4XK$;jDhpiu@=(B9BD+TJ!QC
z(wB%Dn+lu~&_GfW2RU^MwIS0sT5Af$6v8qfBkGZgRzE+x8}0@n!47ba^KZ_`jee3j
zmhhr<^XKU3?m*rpOO`5itk+)m+mZS|*X_Ih!_w=I|4hqj)7Q;Sip<!xratYfz|yRx
zPwx)w%Ko92XT#bDKWJ#5S?mAvj>sf=rPYGZiueBXRzE>zW@?+kF=5|g%1r5T)lh(W
zLe1U5V=_Z7S$Q+PRo%g%WSlaRfFd$r{pQ$Eyrr8dTPOtT27FmvtZvz{Obm|iz!+{f
zNP;C!LnTHLK{_3ydF<@%JF&Q>d@cj`k+O74Kv5;ho&gE4;+@gKG@>2_b(Bqg#ikcW
z0*N*tl}5zB^8K_nth7`<HVqd0O0N<SI6}!^V)+%IGKVo>Wa4}mQDB73jBuZQ6@(3l
z*>^Yw38udCv|E_>&Q@x{9~oP+r?WgnWaFGt456FNe8WTiI)|Ozpe?mZ_`rcjMEtL}
zuaC#No{U$c(l9H<A+@>wDG^i2pCwbQ5MD@AZ7|mbldH^LsDO_A;aNHz4TqHa=cbo^
z3REPTAFzkckOB8M6M_mj*-kGhU(+$<BTG=w8Ztv&z@wVlUX(f4rYKBL;rP<xWWt@@
zAWo%7>%bbdd?;|~pTn{>(<s2%hQwKhwzcstdi|wqTX07TuC4%3s5DG8TD>`s410rw
z#+GI4_q+v2e{)h8c8O91oF<*#%bNk#kFWG5PmyHIn8mWxZN1=7uYZ`y$H&F>7WIy>
z{FMjUY7!@8CpuPJ_Rtw7X;N*iic)tEP>B*J#nkdKecyP?cxx8#mv|37hC+RH=e~41
zNlz6PR9hfcq7R00G0}Bv0$*MO{ky{qqPRzGTXaKZUN`mSO5>TAX`1l{VUWc1m|=S8
z6Uj&-H=+&7(~9PnERk@~eBQ0F$dRbbBf=@+nvS<$IxD+R6zg<_Z=NxdI(QKJ0!%2i
z@Nr88<y=gRPE1UE>%a`TpS$l*gUj<v{4&hm-d-O#3AMH*)e_um-G2AsyE?%XqI;l_
z)--9j#qzlf?8R8Rgc)x$sG3w783hNH@MWxt{}bNT{6&cJFJ8$ObgsBdqmWs2cjLke
z*0A5;OJ_3RDhJ{*MDyU*>mG&kj%xX2DqSArlIYHv7^6xi?G(Lb-gHOTzAYkWiKzSG
zNx&DeidPQM%G^g&g0vlwO7X_&iF!-*J);zfF|~b(z)y9tNc#Nbm84Kyhzz=iR9jBB
zOz+T;<y&X?+yG$7a#XUbh{_33EGTcsWHK^e&IU1dH-h%&OZ8+`sWfsz*s6I~2Gb&j
zz)9PPGQbMccB4HQ=1%>B;rK#Cf994&M+W-9gCVxV7SdDMH}!f0Lx^gFTldKt1kUh&
zZpvd$kov{nW5S;(_DS017iz})k#{tKi2Bo-O`PB4WyQZ#`ZGkyeUD&wcf{(<nB>>G
zKx3d2cF%(-E)cnzZ^srV7Zbw1T;Gth1+00#rDvE<%<PtjM7z<k+5Q|EHVBJ>dHiG~
zAo9qP0K?GwByo#)$YpjC9UIg$ZBRCig8TvIyF%1#qB>gk;ax$-Ro`L;^z*1Rwt5go
ziD_zSRkgG*YM9NknAmR9U>r@<5G6sh7r{Bfw~$$MWin%I{n#h+!Nbs4!8y6MI_}`I
zNeE<_)vaLRcG-$9{&~~-val2e@NUoqAaey*O7%Q;?K!;p9WhMXe57q|bdo0{%9TV=
zbtJf3S^)C|7xP`cv@fgYDH%hD1kCi<ltQCFQ9mR@q*Bm-irH4%$^ICrr8C6H8ng_1
zJDS<lZ^-$ukUh;0vl6cWh>oc-!H!VPz1aHPoDNHiFS2o;tP1Ngw{+1WS~gfT>2Arg
zBID=~KlTtP*Dev>nzQML`>#q6yU0;cx$d0yeeB!IcgwUgtUmtvdk@haDcLu6=UmR^
zyNCK1Q=fu}6y?O%Xt~@s4sjv~*&x*$_Etc4__nT32t*)>*o=a<h4LU+kVOWG_POXz
zfw0HbzY4M3BM>b;I0WT2MORnbsgKY*>ou^XjFWBP>k~f$m8=60j+$e<>E7=iJkPb4
zUp;TMIqrO)BKuvm_}A~_h`vum{VvCH^!o(*@7m1&;Sxu_J@K{SWM;5tsZ&kgutkA-
zjFi`!KpKa$Cyd3qR)xsu>~V3P$lP3+c-}!@rAXlu@!TPh#?)=EudLiSbhE}J?W1k7
zMuB+*<6LZwwMj`2{M&L<7cH&E18D|dd1Shu$CnJRSC2Up=-dP|RaNoD&gGy~VEa|}
z=wLg0l_oct%BCGJLPkg18jT(R&a7rO3WU^@TASfobh=N`Rljn$vAT4oiI>(nUCii-
z5jWl|Rhc1Xt5Wr+z4#4-grkZH#)08{%C)smL*iL@ia)9^#&>g94e~w??sB3(q5vg2
z?aIsgB-Sg)cvxU$X{lPwHQ>7jm%Zs_sH|-3dI{TgMcCb$hf0A@u3)5>vVAy1Lb*b&
zG6SM1g~6Brw<63ot+rP5@Lv^`l{9bNc5&;&6<$QCAe_T_Bd>@OBn`Ol(jOsEF~8`r
zr|<eJZ|~)6Tb1-6X%Y3JMYi|3xa#A^mKIqv>7eazwY925KkFTe_hS^q$3MH@cimu#
zFeVS)D#@&^?RsR3S&1t=W_X{erwk?*=tXPBi<@5%IxWLI(rO(ASu@UYmtJdf8wk|A
z97V2ex`x+<c_TUF;+mmXe^t9{8Mz<0*~3|{QEG0>^8E%cP>|7WmlP5bDX}`wVzKlm
zzC`n6l^Wl;VSLj^8b-3W-l%DEGO%6G#=~PSNs?|*@_@oQryT5@+wz&2R~i~Bns4|g
z>{|3quf{;~l4XEy8YfE6WQ|s<nNuBj@)mi$CSuN=dppCKpp=_e*=+Z|dp{K&a|XAq
z!yxin-4HtKr+0Mj4tcL&otV^Jt?f_o<-h7U01fXiohzn$?c@D=y|lDWA%LQ`DzvMc
z(nK;`oSko$WVW>QQu${k{sA0LgE4*kW`eDfhezPY2iF-4hID2{2J7orPc>5oLn|w*
zx?ru`ED@^hPmg?1K7SOvfJ`sN&WbRYc(K;VqGXsQ%Rc5Rir+@D{fBe$FF0ENzQn)O
zQvG{5{|n1$`|9T_-?;#PJAtS6IlSe^RHcWHkdUtf;iv{m`i!{q$-iPys~V9**YlR-
zCz@DvlTE;n?V)8Db_HY~B1cC;v5lCp1S6?e&IXOH73o&~R8D5?o{(Wx>w0*Asxbl&
zCf5qgnJ6X_Z|8gH3!-cr%<Lrxv&Rx62vh-9hHArqOQH$6vZCTt+&n&ya^RB3&}Bj~
z?E#VgC6i`B5DW10^ZUwTbwZ}vm>pq|2_XQISvkEbmOSaxsFkL`wJu+|vJ2@?Dan)I
zK%@b;Pjm>#1%OR1N3^b?z$3jfmkos($@+HjloJgsA{$S_JwW3p(lk{fPg4~Y70LH|
zjNkHrs4s#@1QYpwc}UdMVry(r7VH8wP6eZh5$`xO+cSgFp^hcdfQl$lrAq$9ZdH<3
z3lv@O)sF<i$7$95tT<qUHwyb$&J0$(<#y@`$<7D5#iVwa=!}Xxh0G=X=*I@gj;}m;
zoFjMEK171yJwgN}TUuHuMR-rLZC?bPij?u;W(Tb&>*qhbk35Lh3y7XkOn)RZH&^g!
zVG0k8M0-IwhdyEyr60u!L7+Iq77?juBGXZHje{o_bg3^^s2VUoZTysXdwN=0WJ7)C
ztJYl)-@#+sk$A8^!rT&*g=<F#YxcM$s>kL}w!qo9zVQ;Zh>tHB5pL;`q{lG(ZzHx8
zWo2OMYeaaYdCy#9V~}2+fN87a!Gi*$42!SU`)NvAjecRGhv3Jf8E}F|#tDH2dkRwi
zxZlD{FaFs-JLnntzRCzz)3Ns2JwsmGO)3l$x&!r_6rLM51op^8VGuLhr7uE~blGE<
z={;C{L~LQuB8I9V_vkq3Pw(ix=S1oMtslRuHc3-izk~$uBTy%``KyrZA~H%h++-^K
z+1O}A;<!3bIIg;v?K{({2!W%_t4H}`HbJ6DDsHebHt{r7<bf^XjNXQ4LnUh#ft;b+
zUfUvj!izgnDYEjjq(phGpTVC8Amn(H<<_p#cKizJHjP`ZebUSf5Kt8&{b}cO-gw$s
z-i|-jmcdnY>D;+V;*$}D(B3&uT`{p7JCJT*hiK@Qm!WURBeX2=jNd#R8%eh|-Tgc~
zd|QoILA=KG_aw}+O`s|Tu5RU}pYuoye6?53yeTa`eP!&?pzVKc>UE2~trU1XL`v*J
zNpkE0{*bO`3F-OhDUyXUs{zg~86rM{b+W9pot$F2?`hQDlH~p&XSP$C?E<+pd|ee8
z=GdC-vqdzMFe;X$0AAxc_J(o^*(<*utCh<(wlzFDkT*B$)H>B7(WVD|YNWBOT2Z&`
zGg;YH6oIxVI$h$1UXrR(&(P3j@z5JLE*Re&vNuW7R8Rb5+#{vcUt%>o(qP@+Y}Ay_
z8hDeO?$9J|=HlVuLc8s;OELFiSznDQ;*q|W@_`>aUL%CUdNY}O^3<p#FZ!KTdhJth
z48O5=@p3X4k=6G_hsUz4>}_GhzMfv=z^unmx@WOQivk%142uWVjg1G*r2APrshl~d
z<KwZkS!ZI!N^W^#cj%@Wr39M^gDPlNx?;Iq1h;Hkyql%jfZ)dGX_}lH%}x6=8M-WO
z+vyYf<}!5MMi&<?U(rHiLb5LUz@>d%5A2yY^abT+{O<NIys&WlGCo{qPo@RCWdv!X
zl`qq_5vc_<_@H&q&tx{|rpud{n>P=LWdRNI2U_MeUWZqNd2Pe-gRjL+q4t!L(}|!g
zGS4NyEv4niA%igm^UWIvj-~um&P#W5QHf;+MfD8Tlf5X9vg=A-`s}Py;MQv=uoan-
zLgi~^5}EzmJhg0H%a0SxW4ZdGo3nXz`d)l~Y;-g#+vQTRYSW2E9(f;82_>V!er2X#
znE2MO_*FX2WUu9bf8=)DfR|9HT0!LAa4~dv)n%TK)pu^MRX^k5;V~JQx_*;!lSx{w
zXue|3eUrdJF@39~oeVSHn3a}(nsyz5Q6l&*h~h@?yS9jH1E<{1#zp<FyGep4+4K0u
z3z+DV&@^d~<tRnKma>W@=Fm`v!OXyBBp9#Qo6)yCV%cNnsxYEmNT&H}<-~qQBVN^P
z_dzGj%lF_JmL&BPS_c>o7G^itY(V}P>N<I5Kg)GAC9H%9Gm~_9^Tb=lbS_x|)1fXG
z)Vl<z2~cL#^}*c{kz0&FhA6X_IeTK=ONyBB4?XrDM~Dn5WZUG3_%D5z>&0%Q=*J<g
zi_WoNpqI;=xAYMmXbda%MXxm(qD+MKl<nDfap`oxxZ|@rCCnz?JOVUk2lY$d+*BS(
zp(Q4oh)WFsb7heS*%vnOe_ePypfJiv4QVU*TG1tI0~kPz@o|0~s^JNl=MtF`E&?A_
zn(LjBOG6HlK?B)T3ia9&ATXiWMxCf$mLj|Taw_B$dy+s>co6&!yhSJh<+7P<JvO3T
zh*mq^@?GTO*ETUDGQX9zIPY?>-PN`KiwyGLaoK+%F8ezc`%e?{e=q01WI2sC4J{fl
za==WAw#H}7#UdwNcYEyzMsr5IK>(r-!I~mcP|?gUNJ&eRWK<#9HyzjBXk<*yePX&N
z9Nt$=yw9cahIxB+U<J2vQ&twR3Je`4`9D&6aXT-4KuEf7VK1CXyz~{R^+RvJXrKFv
zQ-OM=+lxT=6<%bS9mAv#)+GLg5NJ}bDky5-BM>+i*&lMWG~cKtB87N?S3I=jbQRD6
zgYJ-nJoML^duK@QOM-_~xPQ%fZzQzQ;o*99!B9l@mSh5~7|(hP!H&M`2n_7ZtH-<*
zd9F^ctFEjRHfxHKZP;kLPeZH9PE4_Wl~C``j|<O3y?Mgx>u8kZyRL}DL_t<^oxsB7
z_~9*k^{P}ff7P-@ix)%Nwz1q^oxcAXw`m&gA-^D=TQ(t%m%gG=R;b+XO53?gmUHgM
zM322`nt>m;l^t^f-s)#$FgT^2+u8d|Qet9Ygrw|_G5D|O=;`6+isZp5J-{DIIy#&=
zBJpX~7s?x%bz{$IlT%ZOc5+)DioV)i(-0W-d!llhW>xjmnVFe<lM3^h)5dYYl2)5k
zct<Q5ojoR2GWuNHTIO7=SfRi9Hl}DiwV^QjP&k}>kW&2gu#CRaNU#xykor&MdS)np
zy`fiBR{6p=S8}<zx%IXhg^4Oxw6?a&nMp5R+*q}IfnB^hUURT|qQoo30XdJiEpVr&
zzxs$;%*?1&;@6eqz&U>ZJ*sEo!-o$)x7SK9K&atp@-Vuwv8u|?V98?u5ibl>CTu<E
zV%HLw{d%W2q{4y5l4vqIS|E)zN_F&Lhjq>_E{a@bU3DV>E4)JPrfEh=SePGWjg3WH
zo|j>6Jk)ipU0mp-3cS0Q`%F+JMaLB}tKBMIde?Z!yJJIIZf+sQejLttC~mWL@;`ds
zx*hg_?Ptm#{GVfe|28fE64UZ8L|^~?M*jIm2oiniDbf0|L8D5yfZPO?NRW^y2U6Vc
zKsXCU)vcvuK7;4^p@&7Z;lPM;fCalX>wxk^2N5npOy-X)R}e$h+v|Q~M?760*KG7i
z$JipGt9|3f4HA{vV&Gj@W9zH~<7=d#KB;YxLf^7|8JBW`*aD{IqCb^;IDqLDr5@Xf
zQLH7Hy&8xC?Wl$fpjadc;uWMrQ!uK8=n}OzpJ9Jh)Rt=}PA4))Q1snRwLyRIau-)L
zIyu$EUVKO)I!Fbn=H!DMB}yD2X%c(`gM%tGA``w%wtKsv6P4D1fsni@4(!7&Dzd<i
zMl^}2D2G9-MXJq~)QKrYXv-2Z7-T~LMh5Zi&DU;!z}+c*v3n~`0|-E&>ged0q&4v0
z=nJyQ+UXBPo<DsEfyy6kFAnJr#eFx@s_&3w^h8MDyEqNr>G;zz1Uroeu$&@p;*!>|
zv9&ifEo}t`3O`Rvjc6$s^8=OPeuCFzOKq(<f&WqFMS@mSv(YvI0Dx=h3J1F@NyK?-
z#;@E=KYM|A&LEH_r8Glj6{5z(3r+1_jJi42#@(KJcLoVDb*92tMQy`-+?9r9SX}QW
zuEJW+QqWZ3AX2JDfuskB#pYV?j;pEMvyF(R#N_v7-tV8j9rd?6%(@gYRxo(h)ZF}z
zW-5fZLwH%a?f1RE*B^N4JAFh8`?X)BNA;R`zu0`Ka<#kwsSCYG|FiJ{Ygq+yOqKOf
zAQZnNg_zZwal2iQ)~2#z)O`K<G+hsRoowz5h%rl5ZAqTG-OC$1zFF&{ux*}hyHoka
zqR?zlwHpe$pW>AAyD!fiUFItm%bQiYMsu`5U@tFgrY3tMO(sixW?}V_WJhKSAamKW
zW8SW>4u~18ZFufAC$AGQwjm}JY1YmNVe`o(MxIpx)B~D)3uBIkSJgHInsoM#4oSyL
zc4^bz2RG1?-`+P#Gxu>sx?AVuP*uHfuUfR_$qny~!fHhe-{~B0)T_J7H`x@CxTJE<
zsm#pk!pWG3mOZwg>TPe;EVOJGd?Z~-zZ|TY%DR|UHJe{BcrCN&gjzw&p>QD=lXlaJ
z_a3@^Wjqr$cQ~o~%;uY{8U2HKJ8JxH&%G%uH5a*=<~9}*6ur;LGS-`Vf#apsL(~M8
z>#W_tN(r0Uvw5nnP}pO3nv}n|h9%Jr$yQfpmu*vuuJ^LVTOiNEUSI=@tlp>sQ67#*
zzle_&KP%zp;yPv;Ym4oEt*9hAltO?=TE~-Hd<`A8P%QF|n^`j5C3}VFN=Xj?#i{6O
zW@RM=K1`iM>Vpc+xo$Xmx5NHgztc#lhpJP=#l%$?^!Stl(a>0)c$NI!^5w^;b?zVM
zZ=4kQ;+&hXkBfj8rh+5))6$yzYB=Zo4~o)JO~(8l0gb81DqwE=OZQ$@6hg!66Tt(R
zJHpo;>=lo_;pxc9!5`d}aqPTq|DD&0o89bu<-*-Sh4rO?Q9Jf1jg5*v4p{OyWt6vV
zbTo~*dZsFDR<zBQsA*G*z<QVd`_%r>6E|brL_}jKM6>3mObc|I(6=B<MQVD>)3}js
zB?2FEOqQLH9{vkfhX~4Rp!<Jva*{^D5}YTxIf&$lNccN6+e?-1Wm9R4R$BZvZb%mL
zyC@F%E5AJ&FJ85~3?wU-O7+KCn`|Q2mmgUzpJ@2MN-h!V4qN=n{(RLh@|IV?tN1Q&
z<Kw6{f1iP-^R&g2{~x)uRBy*09Vn>N)g!4d$mTxyfB+gEm`D>w(MY;<sPsU)r?tiE
zgV$HNe)v%XBRrtXK2kO}UDn>b(SgJNf`f)dE~}POgfTZE=xA~Y)=JegHm*+}8bKMb
zW})7|hP?5k`=B3BDZ&uP5R}%Og0=p-!elc2>A8dER2tY;Y*oL)%%cN81G&oqZ1Uqg
zUjuxAP2KAkB!zRz`y?Y(7`Ax5QXfYL%nnBysgaM$>0L$x8X#-M%KCca(*69i;3}-l
ztpx}a;HUycSgzRp=}2sz=;?#_H!AlAkvWvw8XHaVSuz;qi4(K3@^Z@pp-<Y^*-rU?
z14SSu2#)P&W0SFTDuyN?X6i5x&~I*f)2}l(iGtv+`1p&Lxp{AGv^i!_78*hU<K#j?
zrkId*3A)6d?K^<yE6fxd`1yHFUwQDz<oolIph1j0-H^M3T8X-9Y5am~5pci#?agcj
zHV*#H{vX&I^znaQ5{Lif;3}d6nJ2<*R9czaC);`eGU8Ly1h<(0qIZw|K4m8>d@R0X
zvYch0XQfHNJ4#XObV+Lmq#jxPVXakFzG$`WVh=FaFMfe|1K5=Sp&XImr5kuOQpoUu
ztpypL=7YsqGgHvnlVIcUu4ZlzmA1p+6rsa94Ct6tnM=!vk^`jh;)Rh**g|}e1O~+@
zrMw`0_kerJCv3_OE!M|OAylB>{aNfSdP{*#^n%g(%J1*ieXunE1s#LG^kKw8t4<sQ
z+^tefaPU*3-nUDwWhS`Wg{v2KsqgIjfkWXiN68;*h+MoZUAG|#q<#q*aupbIW@d|L
zACFh(b)F@{U|<^>0{+}4I|iifFq%>;R;1H+5@MK5#nBDeU{*Ks<^a^a!yQBuewx!K
z+pr)s>lnglXw}GhTJLPE^0%LIjBrbvW$3gXUM2GYN|ie#n(fq90xZQr*~~SzN~KGk
zTW<Q}DqC{VOP4O)$U|<akA^T(W`WwKU<d9}k7#+JE^hHTN2TP6mw8U2_{C9l|CFp&
z+<1R<bX2!2mf5eByD9K)yYm;30{v&p-p8M=UdZ9;#}T-s`}f6sZ%NsJu<<7F0w3~P
z=Ikf$6)ARl#sKDU+JI<oi0^cIv-{%yHe1G2^N_{oCY8*A7sVt(6`*=cc0CHVdcB0e
zfT$Y;BA(cz!;mHnwS<heJFXv>>C_F>uQq$jCDC&JYbSUz@4?LqxMF^)6}FxbTP85V
zDOk%Qb32%w@3Bc*-dcDCX4YKoAL~T7(|bbllkV3j<C#pNgCI3MO3wqqOWQZmZO{~O
zq6G?7qHS5VVTZ%d#+<1{R!sbdK!{ysy8X?lj*QT0PwV!WWwpX|9#welbgLCc{K0G&
z^QAz7v0rhM{TYPLBX@PCFUR`HeEfrx9HBF7%`vjB6DVmJNQlWrzwMxYf^m9cAB@2h
zgwi665e*0pBTvUfXvTMOmt9pr6Gw)&^_z7>XjQSkcvGXdbOFIZZ7Zn)oi}+cjNVSt
zlKYD%4fmhj^$^`x+c9v!l{hs@5oA6yXF}>vbgAog*CIv`srD!GZjvMKMN-gU9|<G|
z2Lt~|u?{ouM(+E#QZ`($J7w_xQF0N>yez=VgLTdWp>^Ax-B3QvE$M$UG4QsK<$YO^
zPtX34Mwed}$F<vsu(vB=Hp!af3<k(~%$AGg5==N=(;akZwiUT0H@(#dv3yb?oOugz
ztoDg67c?~$LT7R0NePjb_K%VUi#I0Kn>*Oqbv#UT?Nm=&rI+)m``Wgd2rDh7M9T;Z
znSu&U8Y>Yr(`#(prJk9|ijv6P2`f<7CJb|T4rSKXdxMq(nPqMkEuU{nM5xD_3opO)
zrM|+af72^(*@C|7Ht+WWRpz{fecDN?w5Ty$#I&zy#JJ!^DMRX;)NQeVoLXt#;oefa
ziW*FP%}G|GOhYx(%D0%vQXD?owL~CTs9|5y!h(!s{@-*8rY61nA8C29h>JSROp``s
z2-egTBu;|Elz7Z`dl;Zc4(_So8vD$6iLIV|y6<Yk5iTEvQyVyvWPU0?zN5eN*!i&%
z4@iyfva&!*6R%lF0B6Kc-GQbF^&jHyLP?QuxUi|4IyMFPb`X<O?ksMfzdAi+zeU{b
za(~R(Dx}f~A+mk)NmY|c9LztH?a(aJ%k*MUngcP_ga&YYdwUa4!S(<2Zn)5yR7Ns4
zWM)J^w`WLkqxBJ52XE_mf%=SRB7|<!65Y%R6Q*^#OX9Exu}*<kid*Z*Bs^8x02VU*
z(~m)hp1hXQQ<X5`umjdThCYcd&HV*AE694Cdn!m&QBO$qMySj-2FibcV5~Cg#E7}F
zP&|LCYp&O3t*z=_kTG1n&@@2HD|#QtC6}Kc2MAd(Cb}p^s2XIyvab$h#Tn|OE=wW-
zcZ{pIsL5I>*>R6j<R#H^;49tIeIy8l<a!JokjNg$3OWxZ2)DKIhNfKY1-!+KxVf!3
zp(oyw@-`@Z2Z|cP!Wh$?f`TnKRrb?)EHCI_WOzFCJih7<4_bOm88EeCYs4okO<xZg
z#0hb5EILm;a_G!8*u`Ln24gA>n|F73&+3u_wi_r=+YqO3Ar^|mGFTzivf|p6L6yCX
zi4sfxqNK}dSi}h56Ys$6>vV-vDdIK^PJY7zIJFzwu^6}is7h5%h7bWrT(A)a5DyY`
zAsyKy^#u%<`M`>8^%Vu<*{w4c`!YLdx*j@?+a3{;YYFBlN(~JExbnajl18LEAJmi4
zF->bI_b9{FF&RVWBI+K5O4vRsqGf9uf7;d*%)}tq(#a8BTMw=7Z$>q<#=P#qxtz9j
znbTj2ZP5?hoj5m{IO1t2t(VO#`s+pbCg#DXo6J2CRTVaAD#IEcX<Kf@{|6xlkOUC)
z;um0;u5xj04p>Zxn^jCE{cOMMps7v}%z6`A046GY+#gU5JLa{3uGlR}4P_YcdL@T@
z4QCznvHW!0`##@hE-tPbhXNZ$s8d%!CKv;^W-lP3knE33v3a>*v1h*+UrD^Mt?8$x
zOls|?Ruv5o*|>xK=C(Xys?TdoS|&;z`Kbhr5W5m`e9yrK>)dS(6$R%n2szA~10Rvc
z0ly^V%}!E^!0i0ez+^bowf8T5=A0WEhWR@|xs6|?7H_gUNlRm1oyy-ldk><UJ7K~L
z*ZVgjKk=o2hJ6W<pNOu-i;4k<)cvwukMtC#FMUBVt;<uS^lj_xU~3nNWgILG_jLCk
z$&8&TcptyqJmtQvDOkx=+B8Uu%8rg><pHWe!nGsqkO3{qn`z8rD|+5oC7bbPv_jh<
zwcS}UHQXyX>(am@E$)UWVt`+BmwRS|$>56C%FUB=_Zd%g#yDSGG5<&xLj|0OUy~&!
zCxe`__+5Y0Uf|tFh2rp#=L-qXzqsVgFHG^AeK6EBQq$zDc{>x+M^HwDhG+Rbd-2oC
z%F45!hGzFszX;BK5!A8|ITm<>Z822UHi-IVvPA8F&*HfB>uPzCPqocQC+o}H9T+o%
zmX@EZ;#qTUn0=s11D0eKJ7kqJOP{&5$)QetuUgERFO0EVL8{H0b>}(&&@>K=OifOT
z@37-8<?<H^n0&GKeY_1wAVimxQiM3w4{Zouws&tHB-Ku|EOf#umhPoD3AdCyIkb`K
z8@7L+p)gBd>ycLHvt=B0d_Nc!G(!gUA;l@5w<W3GvL_nU0C0D=Q{6$jhjkdkRG25f
z**4cN*3!cXAWjO(dXwe1Y<eU8yO1OEWZhOk;hgM-*14Hhr+%$gDZ9M~6bw9z1CP6L
z@3oDMTexifa#m+tO6j}p{@OH5;1&Ybez*)UNEu$(0lWO@<TEj6Zk9S^!GRXIPCfX@
z4jVLlV+6zXEgnX~l4+q@*v>9}0(QDbAzq$7KF!SO5}OE{n+S7l=WTsUB9*7i^)7`6
zTkaD#P9Ud~$&xfUQZ~!=Zd!OfcYE@FEL+<9_+-#_bGBhmIx@!J2zDVvs2eD=2m_P5
zGZ!wJe{<gF*ULuG)8n4fI0ZsA7(nrJ?yf{17DRs;(G5lx-u*>oTs~e!)&BAN7G5jh
zE*pR2D9Om0=nC?U%QZe?XxP0zY;J?c@P+^>dlNJ{#E{(RutffjC$MI4-TH*6-h`2<
znS<$#Hl&LOo{yv(<fwN1xy}16LUB1=%h$rIx&C?Mr((|7faTOp@IZ7P_~AA0MJG@(
zsmB6%LtBEZ)2n3{@8LBPOtGnLvNmM6=Itoq13O{wx0bVnW!Zayj~7d<RoukGi{Lx`
zXym89X?CKr_5)A({D1t{$-MuMa_{juNCuF5db!1;|J1;4Px9aAf1Z~iTED<aNS$M3
TnIn66EcNJ#BMHA<y8hn)0XMwu

literal 0
HcmV?d00001

diff --git a/articles/EUCAST.html b/articles/EUCAST.html
index 4e37aa7e..3e82be48 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -61,11 +61,6 @@
     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>
        
@@ -84,7 +79,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/articles/MDR.html b/articles/MDR.html
index 8469f31a..95419422 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -61,11 +61,6 @@
     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>
        
@@ -84,7 +79,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
@@ -385,18 +385,18 @@ 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">#&gt;   rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span></span>
-<span><span class="co">#&gt; 1          R         R            I          S            S            R</span></span>
-<span><span class="co">#&gt; 2          I         R            S          R            S            R</span></span>
-<span><span class="co">#&gt; 3          S         R            I          S            S            R</span></span>
-<span><span class="co">#&gt; 4          R         R            I          S            R            S</span></span>
-<span><span class="co">#&gt; 5          R         R            I          I            S            I</span></span>
-<span><span class="co">#&gt; 6          I         R            I          R            S            R</span></span>
+<span><span class="co">#&gt; 1          S         S            I          S            I            S</span></span>
+<span><span class="co">#&gt; 2          I         S            S          I            R            I</span></span>
+<span><span class="co">#&gt; 3          S         S            S          S            I            S</span></span>
+<span><span class="co">#&gt; 4          I         I            S          R            I            I</span></span>
+<span><span class="co">#&gt; 5          S         I            S          R            R            S</span></span>
+<span><span class="co">#&gt; 6          S         S            R          R            R            R</span></span>
 <span><span class="co">#&gt;   kanamycin</span></span>
-<span><span class="co">#&gt; 1         R</span></span>
-<span><span class="co">#&gt; 2         R</span></span>
-<span><span class="co">#&gt; 3         S</span></span>
+<span><span class="co">#&gt; 1         S</span></span>
+<span><span class="co">#&gt; 2         I</span></span>
+<span><span class="co">#&gt; 3         I</span></span>
 <span><span class="co">#&gt; 4         S</span></span>
-<span><span class="co">#&gt; 5         S</span></span>
+<span><span class="co">#&gt; 5         R</span></span>
 <span><span class="co">#&gt; 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>
@@ -438,40 +438,40 @@ Unique: 5</p>
 <tr class="odd">
 <td align="left">1</td>
 <td align="left">Mono-resistant</td>
-<td align="right">3270</td>
-<td align="right">65.40%</td>
-<td align="right">3270</td>
-<td align="right">65.40%</td>
+<td align="right">3246</td>
+<td align="right">64.92%</td>
+<td align="right">3246</td>
+<td align="right">64.92%</td>
 </tr>
 <tr class="even">
 <td align="left">2</td>
 <td align="left">Negative</td>
-<td align="right">967</td>
-<td align="right">19.34%</td>
-<td align="right">4237</td>
-<td align="right">84.74%</td>
+<td align="right">939</td>
+<td align="right">18.78%</td>
+<td align="right">4185</td>
+<td align="right">83.70%</td>
 </tr>
 <tr class="odd">
 <td align="left">3</td>
 <td align="left">Multi-drug-resistant</td>
-<td align="right">437</td>
-<td align="right">8.74%</td>
-<td align="right">4674</td>
-<td align="right">93.48%</td>
+<td align="right">467</td>
+<td align="right">9.34%</td>
+<td align="right">4652</td>
+<td align="right">93.04%</td>
 </tr>
 <tr class="even">
 <td align="left">4</td>
 <td align="left">Poly-resistant</td>
-<td align="right">227</td>
-<td align="right">4.54%</td>
-<td align="right">4901</td>
-<td align="right">98.02%</td>
+<td align="right">243</td>
+<td align="right">4.86%</td>
+<td align="right">4895</td>
+<td align="right">97.90%</td>
 </tr>
 <tr class="odd">
 <td align="left">5</td>
 <td align="left">Extensively drug-resistant</td>
-<td align="right">99</td>
-<td align="right">1.98%</td>
+<td align="right">105</td>
+<td align="right">2.10%</td>
 <td align="right">5000</td>
 <td align="right">100.00%</td>
 </tr>
diff --git a/articles/PCA.html b/articles/PCA.html
index 00cb901e..a7c0e494 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -61,11 +61,6 @@
     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>
        
@@ -84,7 +79,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/articles/SPSS.html b/articles/SPSS.html
index d5854875..6a24c95f 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -61,11 +61,6 @@
     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>
        
@@ -84,7 +79,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
@@ -178,7 +178,7 @@
                         <h4 data-toc-skip class="author">Dr. Matthijs
 Berends</h4>
             
-            <h4 data-toc-skip class="date">24 February 2023</h4>
+            <h4 data-toc-skip class="date">26 February 2023</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/vignettes/SPSS.Rmd" class="external-link"><code>vignettes/SPSS.Rmd</code></a></small>
       <div class="d-none name"><code>SPSS.Rmd</code></div>
@@ -257,7 +257,7 @@ data using a custom made website. The webdesign knowledge needed
 <li>
 <p><strong>R has a huge community.</strong></p>
 <p>Many R users just ask questions on websites like <a href="https://stackoverflow.com" class="external-link">StackOverflow.com</a>, the largest
-online community for programmers. At the time of writing, <a href="https://stackoverflow.com/questions/tagged/r?sort=votes" class="external-link">481 631
+online community for programmers. At the time of writing, <a href="https://stackoverflow.com/questions/tagged/r?sort=votes" class="external-link">482 447
 R-related questions</a> have already been asked on this platform (that
 covers questions and answers for any programming language). In my own
 experience, most questions are answered within a couple of
diff --git a/articles/WHONET.html b/articles/WHONET.html
index 05175db3..8a4ede0a 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -61,11 +61,6 @@
     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>
        
@@ -84,7 +79,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/articles/datasets.html b/articles/datasets.html
index d91f8648..ddc8487c 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -61,11 +61,6 @@
     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>
        
@@ -84,7 +79,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
@@ -176,7 +176,7 @@
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.svg" class="logo" alt=""><h1>Data sets for download / own use</h1>
             
-            <h4 data-toc-skip class="date">24 February 2023</h4>
+            <h4 data-toc-skip class="date">26 February 2023</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/vignettes/datasets.Rmd" class="external-link"><code>vignettes/datasets.Rmd</code></a></small>
       <div class="d-none name"><code>datasets.Rmd</code></div>
@@ -206,7 +206,7 @@ column names:<br><em>mo</em>, <em>fullname</em>, <em>status</em>, <em>kingdom</e
 <em>gbif_renamed_to</em>, <em>prevalence</em>, and <em>snomed</em>.</p>
 <p>This data set is in R available as <code>microorganisms</code>, after
 you load the <code>AMR</code> package.</p>
-<p>It was last updated on 6 February 2023 10:57:22 UTC. Find more info
+<p>It was last updated on 26 February 2023 20:26:58 UTC. Find more info
 about the structure of this data set <a href="https://msberends.github.io/AMR/reference/microorganisms.html">here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
diff --git a/articles/index.html b/articles/index.html
index 8b9456e6..48d9c5e6 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/articles/resistance_predict.html b/articles/resistance_predict.html
index 520328c5..a036c429 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -61,11 +61,6 @@
     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>
        
@@ -84,7 +79,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/articles/welcome_to_AMR.html b/articles/welcome_to_AMR.html
index 02ec10c4..825c0814 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -61,11 +61,6 @@
     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>
        
@@ -84,7 +79,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/authors.html b/authors.html
index 86099ef7..3b4598dd 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/index.html b/index.html
index d58262ec..569401ed 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -65,11 +65,6 @@
     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>
        
@@ -88,7 +83,12 @@
     <a class="dropdown-item" href="articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="articles/PCA.html">
       <span class="fa fa-compress"></span>
@@ -347,7 +347,7 @@
 <h4 id="generating-antibiograms">Generating antibiograms<a class="anchor" aria-label="anchor" href="#generating-antibiograms"></a>
 </h4>
 <p>The <code>AMR</code> package supports generating traditional, combined, syndromic, and even weighted-incidence syndromic combination antibiograms (WISCA).</p>
-<p>If used inside R Markdown or Quarto, the table will be printed in the right output format automatically (such as markdown, LaTeX, HTML, etc.) when using <code><a href="https://rdrr.io/r/base/print.html" class="external-link">print()</a></code> on an antibiogram object.</p>
+<p>If used inside R Markdown or Quarto, the table will be printed in the right output format automatically (such as markdown, LaTeX, HTML, etc.).</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">example_isolates</span>,</span>
 <span>            antibiotics <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="reference/antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="reference/antibiotic_class_selectors.html">carbapenems</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
diff --git a/news/index.html b/news/index.html
index 11b4badb..1c287eda 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
@@ -142,7 +142,7 @@
     </div>
 
     <div class="section level2">
-<h2 class="pkg-version" data-toc-text="1.8.2.9146" id="amr-1829146">AMR 1.8.2.9146<a class="anchor" aria-label="anchor" href="#amr-1829146"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.8.2.9147" id="amr-1829147">AMR 1.8.2.9147<a class="anchor" aria-label="anchor" href="#amr-1829147"></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>
@@ -156,32 +156,32 @@
 <li>Now available in 20 languages</li>
 <li>Many small bug fixes</li>
 </ul><div class="section level3">
-<h3 id="new-1-8-2-9146">New<a class="anchor" aria-label="anchor" href="#new-1-8-2-9146"></a></h3>
+<h3 id="new-1-8-2-9147">New<a class="anchor" aria-label="anchor" href="#new-1-8-2-9147"></a></h3>
 <div class="section level4">
-<h4 id="sir-vs-rsi-1-8-2-9146">SIR vs. RSI<a class="anchor" aria-label="anchor" href="#sir-vs-rsi-1-8-2-9146"></a></h4>
+<h4 id="sir-vs-rsi-1-8-2-9147">SIR vs. RSI<a class="anchor" aria-label="anchor" href="#sir-vs-rsi-1-8-2-9147"></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="new-antibiogram-function-1-8-2-9146">New antibiogram function<a class="anchor" aria-label="anchor" href="#new-antibiogram-function-1-8-2-9146"></a></h4>
+<h4 id="new-antibiogram-function-1-8-2-9147">New antibiogram function<a class="anchor" aria-label="anchor" href="#new-antibiogram-function-1-8-2-9147"></a></h4>
 <p>With the new <code><a href="../reference/antibiogram.html">antibiogram()</a></code> function, users can now generate traditional, combined, syndromic, and even weighted-incidence syndromic combination antibiograms (WISCA). With this, we follow the logic in the previously described work of Klinker <em>et al.</em> (2021, DOI <a href="https://doi.org/10.1177/20499361211011373" class="external-link">10.1177/20499361211011373</a>) and Barbieri <em>et al.</em> (2021, DOI <a href="https://doi.org/10.1186/s13756-021-00939-2" class="external-link">10.1186/s13756-021-00939-2</a>).</p>
 <p>The help page for <code><a href="../reference/antibiogram.html">antibiogram()</a></code> extensively elaborates on use cases, and <code><a href="../reference/antibiogram.html">antibiogram()</a></code> also supports printing in R Markdown and Quarto, with support for 20 languages.</p>
 <p>Furthermore, different plotting methods were implemented to allow for graphical visualisations as well.</p>
 </div>
 <div class="section level4">
-<h4 id="interpretation-of-mic-and-disk-diffusion-values-1-8-2-9146">Interpretation of MIC and disk diffusion values<a class="anchor" aria-label="anchor" href="#interpretation-of-mic-and-disk-diffusion-values-1-8-2-9146"></a></h4>
+<h4 id="interpretation-of-mic-and-disk-diffusion-values-1-8-2-9147">Interpretation of MIC and disk diffusion values<a class="anchor" aria-label="anchor" href="#interpretation-of-mic-and-disk-diffusion-values-1-8-2-9147"></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>With the new arguments <code>include_PKPD</code> (default: <code>TRUE</code>) and <code>include_screening</code> (default: <code>FALSE</code>), users can now specify whether breakpoints for screening and from the PK/PD table should be included when interpreting MICs and disks diffusion values. These options can be set globally, which can be read in <a href="https://msberends.github.io/AMR/reference/AMR-options.html">our new manual</a>.</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-1-8-2-9146">Supported languages<a class="anchor" aria-label="anchor" href="#supported-languages-1-8-2-9146"></a></h4>
+<h4 id="supported-languages-1-8-2-9147">Supported languages<a class="anchor" aria-label="anchor" href="#supported-languages-1-8-2-9147"></a></h4>
 <p>We added support for the following ten languages: Chinese (simplified), Czech, Finnish, Greek, Japanese, Norwegian (bokmål), Polish, Romanian, 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’s 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 20 languages in total, and according to download stats used in almost all countries in the world!</p>
 </div>
 <div class="section level4">
-<h4 id="microbiological-taxonomy-1-8-2-9146">Microbiological taxonomy<a class="anchor" aria-label="anchor" href="#microbiological-taxonomy-1-8-2-9146"></a></h4>
+<h4 id="microbiological-taxonomy-1-8-2-9147">Microbiological taxonomy<a class="anchor" aria-label="anchor" href="#microbiological-taxonomy-1-8-2-9147"></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>
@@ -205,7 +205,7 @@
 <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-1-8-2-9146">Antibiotic agents and selectors<a class="anchor" aria-label="anchor" href="#antibiotic-agents-and-selectors-1-8-2-9146"></a></h4>
+<h4 id="antibiotic-agents-and-selectors-1-8-2-9147">Antibiotic agents and selectors<a class="anchor" aria-label="anchor" href="#antibiotic-agents-and-selectors-1-8-2-9147"></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>
@@ -217,14 +217,14 @@
 </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-1-8-2-9146">Antiviral agents<a class="anchor" aria-label="anchor" href="#antiviral-agents-1-8-2-9146"></a></h4>
+<h4 id="antiviral-agents-1-8-2-9147">Antiviral agents<a class="anchor" aria-label="anchor" href="#antiviral-agents-1-8-2-9147"></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-1-8-2-9146">Other new functions<a class="anchor" aria-label="anchor" href="#other-new-functions-1-8-2-9146"></a></h4>
+<h4 id="other-new-functions-1-8-2-9147">Other new functions<a class="anchor" aria-label="anchor" href="#other-new-functions-1-8-2-9147"></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>
@@ -233,7 +233,7 @@
 </ul></div>
 </div>
 <div class="section level3">
-<h3 id="changes-1-8-2-9146">Changes<a class="anchor" aria-label="anchor" href="#changes-1-8-2-9146"></a></h3>
+<h3 id="changes-1-8-2-9147">Changes<a class="anchor" aria-label="anchor" href="#changes-1-8-2-9147"></a></h3>
 <ul><li>
 <code><a href="../reference/get_episode.html">get_episode()</a></code> (and its wrapper <code><a href="../reference/get_episode.html">is_new_episode()</a></code>):
 <ul><li>Gained an argument <code>new_after_days</code> to determine episodes based on epidemic periods</li>
@@ -282,7 +282,7 @@
 <code>mo_synonyns()</code> now contains the scientific reference as names</li>
 </ul></div>
 <div class="section level3">
-<h3 id="other-1-8-2-9146">Other<a class="anchor" aria-label="anchor" href="#other-1-8-2-9146"></a></h3>
+<h3 id="other-1-8-2-9147">Other<a class="anchor" aria-label="anchor" href="#other-1-8-2-9147"></a></h3>
 <ul><li>Added Peter Dutey-Magni, Dmytro Mykhailenko, Anton Mymrikov, Andrew Norgan, Jonas Salm, and Anita Williams 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 cc05ab74..3690946c 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: 2023-02-24T18:58Z
+last_built: 2023-02-26T20:30Z
 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 e78bdae4..a9bc0425 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/AMR-options.html b/reference/AMR-options.html
index 27da17d8..9f127732 100644
--- a/reference/AMR-options.html
+++ b/reference/AMR-options.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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/AMR.html b/reference/AMR.html
index 211ae728..24887701 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -40,11 +40,6 @@ 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>
        
@@ -63,7 +58,12 @@ The AMR package is available in English, Chinese, Danish, Dutch, French, German,
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/Rplot005.png b/reference/Rplot005.png
index 3643123ee68d85fd1da9e850f1dd9799fe98ba44..c7b0048a100119ec7b2a0622088d4d46de9addce 100644
GIT binary patch
literal 13182
zcmch;2UJtf+b)_S9Rx*sSBjv3NR_G}V5mwFq$AP^1nE6!6qTk(k*-wfAiaf%3M##K
z2qG<%1ZjZ~a<;$oKll6Yxp$p){^zdyWhL2bWoGZ0dEe)G+e}{GHPU6G=cWgNKuoYZ
zw@pAGF!t|<mKyjaVicVMT&N%F>)r;g!0Sk>H43=UA?`fz1A!RM|NVf6=ORx)AVCo9
z_ARsEtgX3_Wa|JjWoOkZTgtR$aM(m|>h}E}lTxn*$g9oFPp@7)S!1TUtx*5ulfc~z
zb%l4q_inzrdhxP^ApIZm69|37&0E#ZKI@u?0Szao#Mx^>!@_UVMVr&pSC_J~(r3eV
zoAH^mzf%U<6%U^X!;=0#znA@_25D9Kc@FN#ZO&wSm6au4Nzp6Qm1w~gFuWdh9=mDl
zudly=-wbR0zgnh&7BjP4gBB3%+0IO1XiFj_2*Qv*(}y|BHNs1)N?i_GfhdCWuNQ@6
zRm1jTFwchk@emGBCa4jrA8mnjsvX^db-Ii>V|X3tueGuE+=GTQTY}oCvT~PprWEGb
z|L{KI2DM$N50Je(_IYpNMNx#36S01XkeNqP)IQ{iuE&&NQZUqfb<B&+@H0lSOs&G)
zfl*y`&r}y|L+pfMRBKwq*Y;q>i{XpBR5oWB<BE>XzJ~Ch!9`2vcgsPY9>U4?a&uM=
zrVd}$BIzv+h=_&j3Rh1j{qh_YwdLWrXya4vsXQaPk9LvoWzTf4>_^qCpK>#0PQF|f
z-1Uu!=Ex&GLrG)4TdF<HbF9bU+$Jj@@S8GZosW+aaZ*q30TV*yHm}q;rs;l(<lwpg
zeb;tv_aY4UL<YtDYYcP0y!Bggbi?4JxWY-1+vHr0<2nd^Nx{oeZnL|_F@C7}OFjwu
zaCcHzVNPwRx`*B}A(*H2>vN6U-Qd|Fzt=}iJkiBY6pr&~cVq2D6`__6UH_<CIf1`=
z*eSzF(#Q^8o>VT(GxddM>e41ZnubfnG8xK+_SQ}r>R2>?ozWEZ;>$e9o}^IXPWj&y
z_E|Vlr2DR4Wg}O-JndTDVTWhhk7Sl~2h_2kkl*jG)>5f9<gW^jtfR0emk0sVsl_Z7
zx=|fmWHm_z)wzCMx^`R;?Z6k4X+|l8`jLmk-qH5v?nSn2aCuJo%W7l1ODFm!QZ2d_
z`P7|qEA!`0RMGfVO5@k(&XK|0c%M6ydsAZEc2v9>u69!wtX!>h2W}OK3@t#Tx0D~u
z<fB%9vto3S(mG%8ai?c5dU>eFvuy8Q;BWEWS(V$pIjotB?*|q+#D&4FyFl$l!img~
zoVe%sTayLp4epziJ(u~ISJx<%uG0l{EW9^?tf72B@y-qouLM?<h>WFEBd#L*l<%!t
zVN=&AF?k9()bokYBa#~ivSR4$1$6@R$a@^wr!|gFvPJ8?yL0KQ+7il8OSK2kMK`P?
zYJNaMbAYCErE3|L?*^8-c(U!!+8KST;=-yhOHY0SSafK7iH9-k&RRggBdQO@)C612
zD*UBK3%7Gh0FjzraqPeuqTZ7fujL_+3g%eIbE3y#0baA7HIyLHtKBKtZls|#{2SCj
z<yN%T^&;{ABw8=3#p25v)GMqBPbx&iS&E~i{J4a^>*$Oa(?l|P8If{UkDHRJANbJ`
z_#Hb|vr*N-MHmG%f4$$`(XJn;T%v!$YhVKOL~cTuCZhf3V&*H?!SN;d2T3`htA~-O
zyHT$Y@5iJWyf`$g=g)3xPj-Y|TfA9g@IUzV*XoSA^LtC1KM&WHv*;w%4R5>!ek;hq
zBRcU)JajZ;d$&o@)}Wmln&H#U2hZx}hMZbFqB@O>Kh<{#5suFH*0z2utgg+2nS6fE
zYP8<ONaTTcT;aPjV~iZ{cB;z`C`|^pq4#r)W*($-N7t(COzy7F;(b-}li{wJ7L^Ql
zZD2|=x@udw#X6m$+vn;iKTHIsBa5>=`SewIh}xTxY>OAt7-)5!ii-!bd@gNuTrfK)
zrCdWxWZfEAY8P0{V;;C&zmlO~&$9~FlFj9W;a*vF`e+1+oP2eHVi~k^bWa{}rJU@(
zamT!tQa`Zk#NXHd0F2#|xv3FR{tAa=!34RcyPVmIGM$Yfga*|mn4dGc{D-E~T}<dC
zlE`t72TI#PO32hCMj(p%kd&7zwHok_kZmnu$^EF#l4}CJvhd19&<sS(IlU_MauiLu
z$uw0HTW}8INsM!yZLi>5KCd28a`g+Uhh+Goi&-RGi5yJ`o2aGZ&;Uf0M&Fi;@c69r
z-oC3%ws?W_9dA63@!Fe%4_lvtJ3f%PQjLnXY&u(AM^(}ZLMkzc>2I2wI!zuXMO8Xm
zauCw_%8z@&W+Man?gtmcEeAxhY%apXumVGmiv|Kt1Q$JS#pW%yrZqe*pogoRzU#@K
zUV+d&D$%gcbfT*|Qk|C4121}+R&Z@;5#x8F9v&_glQ!?50~kB$Zbcd^X-yR8IeOB4
z$VO|Lv;{FP9`;Om1Tzp9+-5uIIz*l$Op>kX#CC(T3C@ezydx_f(9Y1i-<v;+lxXRm
zGva;Ice68wbhq;xBQ;Ct!x6g7<e2mQJgUe+*>?tOTV6UdMJp1Y4v~I_QH%)o4>4@a
zNC{15iHP*pM3&A1b=~%n{1)o-3eiCY8jF{I?YZ;?<u5)(&bZB>(BMS|t>aCD+w$eY
zXJAZ(Rkvnp?VcePrj8hdXq7W2p)Q+5JT+>t?QWSEH#8=##yL(+^XdhoY0jfTs=CBH
zd*fikU-IX|dY5aCzUG##4-stCu;Ck=Lr&^ClgEUKNkThq@$8BE;^Sw&Z@+hlH)SAP
zKJ7Vk99}H9c0U{*pXtw1u;5|IsfB%<s0Q?%p+);NVOERw2^)&vCe(Dxc8CC{bG_*y
zx+geH>N9zSFZrEu+K6F8MZ6U6j3LT(awX-&kF{L7mNIDi(*pl^+WrN&^BFa_JLQ`L
zBSHwyL47B8AcSkNr(2p%?MTaQqq#Wh;O%xZMJ&VO*y<Y%G*@V|+s1GW%q-?5A1oev
z@*L$SGxD;5<{Q7M@g#5SHLLO7Rb}f;8t|&lh+<b4+@R^FSzA$^NL_Zz`Oe+U$jafg
zmtI8?mit0BeU}%M?`t*rFB)`?$1u5DH|9X3M9xqK@(<|m83y-D6>aGm#kxlsZ5Yz^
z<iMwG;5TRd3hX*T_r=?9PJV5Hepnq5P*U&l&lL%uJfu3C4>yG$&$d-*Czgq3m4!TT
zfpR@Ir0{iyprpmZmGE?`x7L0{WHs2$8gxc}?!B^A@a^g4-NTwj7WH1{Sf&}74CA5;
z2y?kAecE=M;q{^^ZleBP6qDn~b(G<DuBdA9dKua47K#5z0mbhYdaOB}ps^B*lD^Hu
z8k&*lqN;A4DUXuoG)EcYWb>D2m5(*%Bdx9wsNq8Iw)Dp?c19a%<u@dX@_tSB;R9Hf
zT_t+vmu08BZsR2{oS|`t%vNLI@1*Ve)7LB~?zKqMd<6xl`itkTnSZs7ZaXPKDhrng
zUW%rn6?kIIdcdVgGHhu)ui)GdRp*Kq&?Cjx7z>|Sm#x$IxgS(R5$KmCdt=gvnkwkj
z7O6EkuZr&d{*_5VbC0nu&}3CA+F1zEM3Rw%UkrTi8`>A)jfcpJbl{1bli@Q4YmOG@
z65{5RG9qQC&vJSQZVLD$Oe<ZpdcIe8`%w35k#xZp(WxOe=V=s6<i)okDet}DX9dIQ
zEMaT$6WSv=x@#Z0XA*gglzu7svHD+vFDj)DYS|bnc?<GQHjmfLxJALfeMUumlSfIO
z*QZ-0so%&?waI+n?J~-)fjhm~>9?=Cs1K`j_hZ6uJ`)#qZ+E875Z<Tvev<mX3{r9O
zrOG)4t9J@bbcX}C%wb9F_sz}K(!a*}*?LzLmwpDN3fIG~vbJ$P6^7AWLyExkkl?E@
z69}UNEa|)hDCjeTblWvTvZ>KgZum&KWlf$-Zp6V2w2gep$)^gMxt)Mq^@z|f(KP(Q
zk5&i|YF+kcf)*kY4fBe&3-Sk`6LibNGYL=najDkcT(VK8C#O?g+2MbF_{8z6g~G!p
z712BKUm{5}3-dJ9h{ev9bC+~x&klA(QJxj&gyP^D<aJ}p)ZpOY@Zeyz$MN1elgd`z
z$g4dkKSO)BidvUG+~Klx)KRrqPVle!%By9Mdm8z({D)yaaa^*Z_|R*Pov0Dl{;V4}
zW;V_a4iB3I5I)lg?Eiji%&*`!`2I%YeCS4j7PFfFt@YH)jlKJqQ}mq4*XO6Fr%R;Q
z)f~w-x%+|>p0k<2DqKL&w4A7I?OZOkIw?Qmu0Z+6NPPRD_q*%PKV{b)B_$=z@g0=Y
zt}oA-JoT7oJjyC^BDU%$D#J)yjT?;%ktEM5^f7Lgw7)SC`s<~bdKA#O{dfl6NcL%)
zvl-zq*|={FuUDRknJKG6Pg8nj2UOQ;y7^l>2!-~P?t=?yw&9<(yLnYzviGs}(o*G-
ztJy)`O*>sYyOlJ+;A5a>yF+_2&LXonUDkD}*>Cl<W4R}7!XQh@cK}%7-j0cssm+Oe
ze<OcIqDj5A(W<TgqPEuI@mJ-I&wnOd$J)pTGl~Y;%KHNAj_Dh>Dkqv0t5|>b`CP|f
z(ffUAUK*h{SGKC}J@vG<o>~0`3_?oez46#6+|u0K{K`$rDcQQLtSr;h!@~r$)SYNA
z=eN=;DZ85Hc`O~d6U$1?$ayW`3zOo>Vx9Yl4!8}NcZA}c{|J6;bkv}%qN4p0n(ngS
z>^)t+fNA^FKWVHU9v<5n3h?QV-(oo?#z#kI%}q^Z;bj#K%J{jUg^GVL*W|loH21hi
zvSSRuOD>t%tufCnxLczgmDCz7^z>N4K4g0moZfipK)^OluW&lMPIM;pb?QcdkDHqt
zx4g&btdQ1oxkN5mm#yre`6J?6WAz)q-#H~5`G;ooDK4C-RBwA`@<;voA%-6r97Jdt
z8ur;v*!cC0S8s>PMjY=ww+)<HzT(ss*Nng*!KTt3Cx5oylRd-9`x8Fr7Lt{G=G)@0
z7bo^Kc)PILYxq@oPNiNdJ*op99sWy9edndLi>FHMbFtj;$KU_1gsN$~Wurk)Be6NP
z<8A~ucZP1R|17e1=Ji}q*_NBy_XBP%yq#EbrW)4B0}ih!EiKji7Snz(?HOinVS#_6
zS6C|D{5wgXw732i^0B1kM@zHl%pS8U0rO(2)-^Fh(Py5Ef&J>o8J`Yv|BaomtW_Dx
z0XPL+Uf-{m-ijKw<P{Z(%6pENN3JW#cDECM#X|d)H_DrLd};u6<$Aiim$U#%Q`Eq>
z?H-(N2X&Bod3@Sx<~k^3d~8NWM(N>{YwqrV3cjreV8wi{tNQ(BXKUu+$L-s?0ug&{
z6Lw+n6)}yYA5PJuKu_}T*Fstu!)mJ^=eR8o>#3UXXp#<^(8(~<akP@@S{!CGHJ(W$
z5RUk>IaM1Jyz`BtV-BFNy!UKV&wvKy#HZVS>94J6UCjwo&%$T=pqp)1_SSuy$NN+V
z`eL?RtS3H6cJHl^@XQA<#q+uJzT3&}HEe742;S{etm4*)Jn79(P{v^au6UDvUa2|}
zR*Ps@miE75>iVdXX3B2qQljePNFRH#<ZSRP3}ETpPk>Slq=v@*acc^>e!{MGTe0J)
z?}bmqhP8bYATE3A;O*vBYJByVv$e7_-xbJCiE0Wf>&3b1j|te%F~ue&xbgAvjIlBc
z3wy+8D%nKUKi`)ECmmzv4i};*9l3t}H>3+^e((mZq`RVRY;DPlF`s{Zi}gI5bV`(U
z>C33M*4Njc1AxL0&Y23Bu%+ajzSckN+Q8vVcDf}xyf$uGRZj3Z{QCNmlCI?YE6U2s
zs<bPPYnN>8-Rw+XW7=7C*y@aKn6Qr``x&e5d@cHGU-NhR<^e(1drsU3^x7|g?h+tP
zcAp;o>5*mzNaFTmpW8Jv^80I{zUGyh(0v?E6Cyd&k_x|(=brnM9ew2S7qdBha&Cjt
zMF#1A$o`j?t#al9XW${a`+@fx_62w4a{wEhQpx(W%WyDzwAeUzl(6_^RobqN_(fWJ
zojEl>Q9X2CG}G%(f5hRO;(3SZI(L=akd<`lM-6Y_#{iN@a}ftqt7sAZ)U4Uz;Xlo0
z5&<?Hkz}hFCZ6%VQB&i&zmqkN1WamI$KJ3$bY$yMLKr|nnyU{!J-QouQUH6~z@-wn
z*>lCBQZ;0_0LD6!IkM!_R9Z?<ud1r@iPyM)^e><W&H8qWz7;c`|5R95hy%vnsC~Tr
z^E3H)-!o!4#iGF-AVDZLnnnlkf)8oSNz>}2&03yKpd)@hlC(vtu&nv@#dOu#+6T9>
zVeJZFZs!>T$9P|+V%2tGn%#6j3}2vSI$pkCAp#w8;rLc2+_tBI7gS$gFWEf(!~Dx)
zj6me*GY&iAm$J$h91(y@6fq>#C*+m5-(UJmTh)E+573<A#9&G<cpilO8DqZgW9lkx
zDIZQG1k5)9<T8$o0zkO^rlR7<TQSOFUaXXI<hNIB^06G!{3kn!QQ;5JA;rZSizwEW
z1WuWULod!<Dl)UMpo9_$ix%MU<tz46wM9iMmFNi4yq&#$hfC{L1Br0?XRmJz@8j>C
zfJ5<lJ)al`n8bn5lbvpjF~BZv04mG~(wJno_Noy*o$SnM&T(GGjpB@XcPDAm8-Fx*
zpg#@n3odL#_R4hkMgR^tXnUe+K{^P)zJP%5*K^F9t$1{-!n9ld;l{>>PcGn4bC{Tz
zVjbru0%x0idyJJ9QO(vq?faR4Z_Uh@Ur6(8pL+Dx@D$MMWURuBS1nJ!(o&qtG2mLc
zqv9!h#TxN(6RGgyy(XJo5GMdm<Fx{>o;Umxe=Ugr+MwqrHPPDVu(zUcc?1??&S4+E
z(_>*DvCl`ti@^9ErHWb1q?dAM-tYKRfsc9}l1U-y-e=-b@X}t{+u0YK@l<{I^+nfw
zozX```<BO@+;6cmA7e8A20>IDHiY%R=B}|YYv3O@KCCZI{%RXEXWoz>$Z)T68E+JB
z^v4SDnJM3uy_hXc(!2l(mq|#57jA#$uNE#*tfc{BFQ11X{iI|N)H12y3@0W@Smb?U
zHr)nXl~zA#R21LL{ZG7S;^P}WOm!z|lt*6F7AXw-W+qPKm~C|WG#~fV{Dm-V`L4Kz
zR>ql8Ry50_R-krpe$9=wHuJp*Oii!wL^SX+;ZDv@Z8S%ZS?u4(m5AZo(=Mq(h%O1f
z&}e&z>CgM|7i1q=E{u41diG1YWJ*?){J;;`2RO;hB%ATiErW`eqFWi>Z69K^ZglZ6
zFkZ3JHW3?}m`I>}V?LPPW>sNxl@C8%*3R;uf13+pbSUA$NrA&@o>3trS^@&I%-F}w
z@7mgCF&U4g{NbU7*_w!hH}OF9*{)X@9&-?cNh+`_OzPa4R$ZzTw`NNcPPtXMhg4C%
zew6&lObBL@Zl(vy9r;XD=`ndG408tV2IN-tKg$gR1h?W~>gxa3$oBs(;{Vk3qhSm3
z-8qaeCI@rP;T%B@A$?8kbBi!cqD5~Vj0d+t_GrARMIolq$_~`mgqnwe7BtYZz_`iS
zmMwGQi#5XfV03$IlZ9c0CgN(?HyF~5uD--BKzmNWv==z%bO!I6vQ?kEc7t0CW|DYM
z92`zZ2=(^_k3%XjUJg=(xvvs$g-gyzI13;c5CQ%?w0j~b!Y88!Q^RfNQjxvV*1XhT
zQ4id&J`RJGz4ZPzdZysvA?ZRhId!e*=RIvD;-`1v+2(H~M376Vx{Z}e7nH7aH=V;=
zvUT^rliNb^Qhsktks7Z{2)bXn)zMTF0=d?12^WRmUTD#MK6W@&Q`BPX5IB`rZ%AIL
z^f(D+Fo>n*tG?IK5~g#oRolY31?_z>rsTRfUF7@C!&uwbKbI+He%N)8T011$w4%a1
zUO)A;*!{<F#H*nkGlLXP%oH#4tS!dBBhh<`SC<&!Tg+5hRuUOb$&L*aoTQTOJ;jbw
z`YWXGho!tX4(NNYv3Xc?4!ZGbgzj`dbN8sruj(+K2^V%eo`0Ox-nVv3I~{!O;6qek
zAVaKP75?3lhAiUuDT#eKz#H!M+uk)oWdfXIlJ{45y4!bvB6`xR5|dC&@!KehW6dEQ
zEGhXTpe<tvj?SL;)0{92cTYlmwz5xlJL8i%fGjREJ?3CNnk8d#{lBn-6`mp7;EU_1
zy=s0Z$xJlmJiy0&iGLST|IfVYe~msS<Rzf0>xdy?a-%iCeRSg@Fq0v3U}9ko3e^nN
z5TaZ)NC_Mq&(aUfhBIXtil!ne38BD|^s{Gp>3OMbXu_M3fn6Y#WlqJjL<jQE&4$uw
zsQC6q`+1`0xtX_SHv*vwQjaqeS(Pnc0hq+owz0Kp#ISIJqRtfEvjj0ysb@INcA*B>
z;-ANJ-PPFAw@%nJEvNPgh8vA^)*C*J9m^^BIpq5Nb*nDl!TOtT9zHv}vQ}y66WkCc
z|C>jx*vDiLnn#I?=<H8X!ZHt0$Ve`#hF$zxYM(DEqNja$1$tb0BWz|!o1r&s-imMv
zJoKA~kqK2vX@xr<IsFTXqpW?r-mnLFq(@E0h715^X+f|4Ebh4|o_8$d4grMMd2Lvd
znFOE_eWY#lB8r@{`*4Y0uizpqN$;ZxL<xTDuX!{(oAO5%uyCPl|6xx56W9L@#rzvx
zKNf)I`IPu3ueL-9Bcu_ccQeiI0a(%muq2L=Mp9$JSPIyl+8ZPa;t;jS0F1YVxLVIk
zOn0;hCLTkt$ys$KhuQ_4#y;2|iIKU{6>)~R8fqZ{GdTz7DiM^bOR0KwTKV!+0m8#R
z+24UY0!%P%oqvfkfc0_{2$3IagAK%5VyUnQ>@6$^`vx-efVwTqY4$9pdjE=PQo*l}
z$q|@Ua2T{U+Avxz8V{|&R3UfYUNHok%98mB*E{bG__jix=KEGSSKR@eM`<#s-x>Cg
zK<QIH(OCiJ`P=`Ias4->_)piHXplM^r&W=%5tV|}NSiQB&jt<R2emPtuqmDSDDB8+
z8f_6B1=0ma=qpJB6^k@!q!=_G=>v&@_8>Enwkp9k7I4}uCSHbeP$Yj}S?QVGBw_J;
z^?Y&0U|D1pSVM5{c?aYwbtR|><QZ$GFH2w~gkZ}sA5bj(e0?Ncsyg2@2=4^nFVPz1
zQRzu7wJp(5*Kx+Rg(}Of`f@Atu{y6KDb#>=i31-MLb6&BakiQjA%gIcJ7L5+I&_>L
ztv}x+))G&RzKdOo<e2+(<1A;xX(0^PkE8av7Cez_XItlF<YMRBM~cnK9TI?AW+(fY
z{$az=U|geLV%RZ#nDY)o2=Qv4mmAPvj;2z^-8aG|UVrwHxzti(6w#H0(7|UC%V4`C
zVe_nT@NqYUf5D_c*+knzH>to|XhC!hj+Ay`Q~^)c_%8#&g39j=Fok<k)@hQA+2!m^
z60S%KSHljKx4vv<<1}q~446Z{KU4%h)B>f;80E|T!SyOEX<Q3!ILHA>1T9eIT*^wp
z8Df#cX@Er<NR>ufKv|J*Fgh5BgZGVBKpaur!+|l!cw?k)+IBUGi<AN2u@kfhMF|k)
z2(qf*0IX}L0GnvACLMgn^|6b9(|&`$y!ODkOcUrRY7eqMYjDwqHQ1rc5SUv`c;@@6
z_%4BW&a*Cs;=qdLh3r8-<_!|SG@bvz|5m<4hVOxVfc?#;8!a6)gT)1Gz}5-FZw4$-
zl|~jvo8~)Sp(`!|Jn;r_r@gKcXhXWrmTg@d8)w1+tiTw6i!MuO8>nQ>$I!h85N*(R
z4ooB1{?)0ENEXkr8+QEsgfQTv)9|i$SfgWTX&m>dPMNNXWeHgQGe2+}^<*wWyz>p9
zu(0LJdcYoyls!nmaNBV7ki9uU!od`~wMKD@HqG;G1Rahv0Jjx*OE0Kn7g6&*#|JW4
zQHO9koKh}gkOMP;tb$lTEI`HDUx4+=06H#q;X3f^40sZ|)rv{Xr-(sjz*q7}x!_ZV
z*}FGYptNasdtH7XU$_jTnnn(~ix)N5ktMX%Zlgi{E0WkV*dFc5C1(dYN7g-$_u>kb
z&k!t@LI;AQX?^UNT3gAxN%TXmUxZALeXirM(_m;u?2z43Qenh+ZwRHRt5CngH15Fc
z#vdxgN)vbIRE^!`TYL+`yuM@+tToDT&VuEDWOSxdygIu(&sw1y{AIjM4~DxL0Ed%P
zi*xE6DKh!7!}G&wE`O#Aa>qCdWz3Vq!EN*(W9NLp{9tv9C(5UE;nCIUAp>rcKQOH*
zL3GKP-DF{LnIlNkEQjqoU|Woq9Eq7;69uF{G8KVY9=uJ8u*t0W2m-O5|9ckTuOn7E
zf)Io0TkQz8+NIh6fG*i%A{m>YlN_A%n}$jndr}z8BmkrU6Er{JvV(<#j@P7dv_74#
zgnm+i)Xxd1I(4wP)8q!mRo@~(7}l<Z2A!%1p_a~qPw)MU3-BlE=Yn~G&4xem*>t*M
zc+=-VB#0XVI6{N5?7Wez2w`XG*OdTBVydelJ55cbN3aWpAHF{kTYD{B(*1)2Lvt=N
za8C~5A?GEs_)|Mc*j){P=LQlQ-NDiiWg=We=#<3%i#V6uT2CKI99x@C{|_R<Q9V#d
z9mE_ngsBjz1j2ZJ>4kfPtaKA=Wd1=8qGB~K(Vq(k+^!4o&J#(d)40VABR&}Z4=i?^
z^<wHx^ai@z`LhMJqu-OZ5CSA83P{ca>xx;yPzTd#0)9gjuoVKgmnf<K!8L0j4qyjc
zr!=zz{xjYy^Zmj=6Xos=dVnU1>uV1gK{Uy2^gvU3I0^YyE)Kzulfd3P;Yt=RiTN=G
zoqvXq#MYp8HM;SMlDGJ3A5wrQ1G;%Oj%FSbDDhbps5Bci@DofizbDBZuCp|gt=|K@
zxJeXUlq^DxZLT98ar@vFRo$+PgJuHYi+loW)Js&>W(Mybj^WI9q%xA8X!lqs%-t_P
z5U&5hjv=MAzdAS&MMX|CVAQG914Wt@Eu^V3#`C30hT)(3*m-OM`^9INCGJ06I9l9g
zU~(vtlW-Rx1a}6+q@AloC*Rjsl5`Pr(QWi9r6r{n@_X)m2YafAVO_KtusxNzH4T<<
zm`4@hTwSoEYZMyUuIw}6wQYb+HM0Uj5ZB*ahgW9(uflMS^hbP%VMhDEu;qgQDI9<I
ze~qIoq#?8cCr5-!E_l3^5`4^e_pLKg2Xo;)6;bEG6+rzcAcX1wozM{ty9lz#?&y_I
z7Jy}=GY29HaO9ad8Y$?w&KGMWNqMOM17`h3S3Kl&cnmVO3vateA>lmA6o3G|udl*$
zwbwr4LiKw~xE}5X@JI`=Jxv$H7{UQ%4*HNCli%&S`{4Zt{jm}Y&cQ~P!{Tj~M{gh2
zAJ*f^PM^t%ZSsszOA~fLV(zj?H9At*01Zz0h{)~`lHgkw4YrjwRI@cVG~d90vu&3j
zmsOM*u~o>x)Qk$LuAx}k&%UGfN<&Bjq10^=YQ(ha2^X-gn9{ukFpyha;`*B^|NkD_
z{r9Lugo;3meT#X&c3O#fhC##F08y3z!5M}58Uyh>;X2|nLEnKNp@#4`c?LwO^z|?f
zmS+0NbHQ&1StA8FVYJu<=pm>BR;mP0ufhZpe3fv2kD~gX?VUhtM_BY0FE}gHX%>NH
z2JH0tQKorp)CTq0*;30bc}e@BDj>NU05fxx8JeRr&=JNZT!WPEdNQd(Tqmp~H5HdC
z@0sIaQ&(UOm#jzzol0J#50-AB7z(nH?P+?iR%b70H+m&Np9j?6gx$3@Rq>k0wW*ge
zH`jLe>DNk>>qqZfFloCR%;}VtdSFxSk7r*yhOtdI{;YCezg`qYN{W9qY{_N*QyuFA
z75lbroIsA^MHq$rb)Iz+g)}GXgr~eq+u)3!mOy1e1PE-g;s5n)`0smI65>b_xE1uY
zHg2Y|t?*w|odN4d^F#xC+}FD_tPWlQ+4~3J#MO%v5K#cj^`8G7y#b`3+9cb})k*W6
zYwe|KFEx+k0=>I5n@nbGHGSVO4BB4CR6u$&&!Hr793_d6KrkU#31dcewc?K7dR?p?
zTsSt~7U<P1A!YKTRQyjWwLKB@_BobgO^>EN=E;A20>DH=d-AaLa!W3xpQiFmB;DwR
z$^P0Q$;>6BqpkG4B#AGW#K;2o+5rLUx`Enf#u}|C5pTh(je1iwC-Ct(JOe`LTI|mf
zAdb)bnCy@VwgGZA=bKtwo`4E|1C#`3?8aF?_!}ijJk_HI_VGfQ{tJ_zHK5M(Pi4x?
zU`_;y|5paCunxIc43!V*nqZskx5J#f(FJGdT{F11e7(Sa%Z|xhzW73IpLt(j2jzXu
z^5=&Q;xLIEJvuB56NF6N_gXc7f1|s}80<M-OHNvQTdKb2ITLsM$D8NuRlP8LrB#oJ
zH2TIYD7E0Hxj2{+0nhQBxG>p81BkQ_>6l&mF|L7sGS^otmKF1KLt@=IMcCO?oP;d)
zzlQ#p=?XX^tN-dO|9?({?q>RfWiBC?{>l0;V%@QJ4n|J`0L$SIB=_^sV5&9dSw@31
zMUVgtql4z-LZBu5Z`#i_$rY^PL|npTV-}E!sjZiR<hKwoC&Glt1mE5Koo7lACB^P0
zA<SHMIRkRJPqHU4ig*;%`e)LMU0z`fdS&=9)1)M1^<<-Cmo#Rk$evFwGAk`=es=iq
z>G)~b&MVwQsV{By@mmc!a>Z5)N25+7y5<%`R&fUwa*YR6sb8rjvyZ2<Qi9rLC{G{O
zF%M>Wz#BcChJMw12C8o!YKJATU$ru2F4tSIuWbm~PLm4uu#1Q=G?kh5@$xG1^f36A
z=D5F*lWt-A+u5zrsG$fLV?q(F#&U-(pS)sTMuO-&Jxy)f-5_zaA7TY2F62j;m=$JZ
zF1%#6YIXD7?dU(gKa`VSB%;3&&Ab73_Xzb_F`q!&n_JJ=#2uI#8b(-USpCXPyuz-&
zJ1{zIacWxU>}J>eSo<W>({J}ADto^It?e20?I#-ZX^&iL<}t+6NDlpM-GPpR-*Xup
zwi?OUQ7%KIK{doSX^f((Ex<frMUcjwk5T}u>bS8gD4#px4)DDoDnh#(ke#aOm=9Au
zl3`iXr(JaBGr01<cY<_Ojm+W}=k4muweW8FVC<8*#k=sHYqUhJMGrFyDDbL}JJrm&
z;1_pSwJ--YzI^REDT4N7mdZpjLnqZnF62Pc)t3tA##Nb5CcQr|oiRUj-9P5g5AD2m
z7QdP>zQ0X~yq}&;l{NQSP9F2n;q7qRh{#mxA{SOpb1M_d5dC>svP?@JIP&@Yru;R2
zH|k?o1B>sT$tX>nh5)Bwlh>LHin;jSvZQR4W~$@)>aZjIE0!NN?u8%gg=qxUG0rWL
zZPr@sO7N~CSVm<#t}ZC$s+Oc}N;9JtozFaOvbrqV^ydRJ4mG;XRGISE6CX_;m1%6R
zV!+O&g=N>%9M3PR4QBri<C7qhvr_GzZy!hY*H;D=ANs|Vs^r|>Sl>Xgp<C}%v6f6$
zvpWo&@mP;j$Mj=13mDe(5^xJI2+@09Qxdo~n;wE7P#KfG)-bCbV4_>46%?$|o%TTm
zo!k1QrILPdsIeyU_d=FA-6zQ}Wtf+!zFR!dSR~C)8w*#nb7&d+*UmOU#ICHW(-As>
zcjDOZkJ-myN2y><md&%}qT!hPc?qc>RN8WDCAES=G&nw01_2`eya`Sl7Xrvv3e2V;
z0_j96pwIz|Wb8Gp%R8xrj6*c^T3$>2w4j*1Pi3)6yW5=UrxMC!MMndgqvbWL%u_DO
z|3^t)J%hIM19Q%{(%jIdIyBTK*Z-XDHD=<~#rN?~k%^#0VXJ%IY3CB&t3Ex*oN9^H
z#2JGbkh!3k)RM~nwO-FbJ<BQyL!q9#>-bfhp26(-ffRHlM+(?RIUiYpj1sZE!@&z-
z6f75@0h7Q{ck1#sMbGCfNsp*~QM4!5V>pm4Yo|JxX4F$p37BpVg4;3o5nDAYK_0vK
znB5YoIi9Z<rLk)L9QY1O3$SJT72Js*G8w8kM-1<h<6xP;kRq{gJx;Lv<O<T7uH#a<
z9GNztaq#poJ@-`?#~*HylKf+^C}@CHL0p}V*J6cR#pL_-O$Gz8O%hqQ+A~Cd^R7@2
zD1{uk?Z(yeh18rM&_28{@b?ttzyD5IV{^F6Awbf`?%wEP0`Z?2htNRC$gq5pZjf)}
za<b1<=o6>tr#*62D$z_ma?<;tF&Kwn0QTH_nU+2w)J!DTh$;=^#{2B){f{4>AK+7n
zC+ak+)O+zB<f{Zte~RLU%z$QU3FjY4>l=pBpTj+oJ<+<cv))n`;02&QH45d#l==r?
z>~0D;FcQ?Tde~Q!a!b+AoXf@4JK)h#;F7gd1r&?EbBK+_>OCV^Ur+>EaQTz;2a9Qw
zCk?eBTuLtq%lb(z?H!O-7e<~tv`vdc<lw5U+gNkW7X}4e`YRiC@F)C6YrgO|PB~gY
zakvLLL+eO+Hc8w&DY_j{H#n}JxFA4XE*u_mM}<jAJy64<JzZ_+7KRV=%59c7*<^tn
zxcwy!k_I;4X!Zgf>h{!}C-Pmpm|tN*n;A@@yGq?3i#stiTfLwuec?TSlY&*hMYNXF
zmJKX;J|q8VYv%LbPKX9gTZ+>IpEH5@hwZlM{0`R_8S;e#!locfYA2XA3J2{6nK*)@
z+H00aOVNG#?!#c!j6u^W;Q>{9K`*OH>Of&sC&%@!VlAAq1AemUic`QjH3fc*ef)IM
z)6k_|P%yvJGeUHSh!BfTSL)XN&eK&iW~q)jr&7w=N?z!K{SVGlWx}po4m3IlDMm-S
z<w@5*pq1w?Kz(X27Y@aWJCH1`s|MAvDjBB%4%~wp-H$t4s{CAWTR-<e_F!~QvFN&%
z_mlfU`_voM+VqxG<zOqi_IyRBB{7zRk!@&OTK-%-t=jL_JOw*QWNLB3{b`?Hy=}S1
zy8A`(!e@R+=QX%OH9!e*hJG^D${rh{4mPQQ%B^1R6&O}j4gnkIn|6EG=IktR7BKSW
zTHieV<}KRtntQf=sQJTZ7&@-qI~qRE444t;7LR(I{muMbH*9m)IYt_sb$<4M?g!O#
zZETmFfRvsN%C=;(+}$y(nP64Fxx2MA`(kh=F3h)GqOT%9bVNaT&tS<ek1V?9Ttl&V
z8<Febng);3O!SfeP^_9T$P?d_g?}<h*6CC~iO-*r=p0&%$Jo<WIVwCe>bPvY1;T&w
zW^R5RI^lZ!S-6(jcJ4K}eA^8~5Fcj((nQvSne02092^U6%0ex<%^EV^;A+#Z>a7+V
zak-&Ljm7VAJ4ZM}>*T^Bcyy!61AW(2I25Qjb9U~|#Sp?*ryH{dXi$b*WI9qHi6!T)
zw2Oho*pGD;<-siqZ9DccH$(5`UDYu|;9$8?DIH0Ja%b|4iiXq4I4UAV9>u(>(}`}a
z6Tyhl>6!@K3EZWnnJfs)x2}yO?VTFTM0OfR`vm6A*B#P**lYe+WEhP<9~m{vmHnVW
z9VN|tC2v}tZgcp@QDL9R)i0~GYP%9xr#cPGb;Ie<re_BBFT(O#de&u8Pa6u(0^^^}
zxr7=C?|f5}#+ty{FBeOYeIJzPP6JVNHK<Kx3AJDe9h%yt$ugWg3=&zqSyrih$-`4*
zaqV}e@&joUC=0}UYw{{7W{qM)s@WCypJInhu!K(S($Tyi`*5tJ1zr=ny?Q?M*Lr^J
z2wGR^r%J>Hf87y|Uv!=9&mKqg!<lDwC(R^*B50uMvI^_CT+)q~FaLd;hZ{fD=vOMQ
VM0S~K|NR>cOxx&orKZD^{{|svFlPV&

literal 13241
zcmch82T+sIwr&bY7Zngi1d*m7MUdVE3x=u`kq#og1f=(%5d;Jjg-Dko2uJ{>mk>cg
zr7OK9h=fjpv_MGmemrOHyt#Alopa{ReGDO!|KHjF+G~C5+uz!I6Mx%CmxGm$6$Aot
zz;0bP0fE5y!x!@r;7a5WCJlHya$jHfI`9ts>|JY)2HseFZ&~_-Kx`)tU*I3pQ3oK<
zIS}l+wpmE_+H`2Pbzm!f!@{Ll-nKZP<VpIYb04@RZ%dRrA9psrYB_)2?qsVC_=S1*
z`SX83H?*xKc#bhLa39mYeezD#E)(m8+vz7i=~(LXPW&+1&G<%4A+q?3iDdL#`Sz`+
zB{xM^Kwf3xcetjw$~QHfzfm6;mMZf9{423w;2=f1<u&$runHddp#rUmwkfL+DE;k`
zDt*-iH4KU@u)ax%a>Y&BM%px*S!<Yl8y2-i6s1mxFF{0jy5OkgCCwvKkV^?Zqu{v@
z#p9eIr`#X}@F?VrtKlZI<I6?Pn9aha8Au=aCR7Pbgno<>emGRk>G*M34u95xPqu)o
z`wp88^Cbp5roO&SvQ3sxV(8Hi93Z==6HP~N1<F#D3fu+1@<lmcIha_a)ecf?v3z(@
zyotl(qZ4tYDKw)|0tdzC$}0N3dtxZ2`A^HO)P&o{<mYh6k>eO&A9foE`?KOb#pg%X
z!F-6`!mcY&Cns-en_H6tRt#IU7{zx_hwO_}SG+_UlP_=_y{Qn1JjN$k5R|a-^IcMN
zfogqH?+X1(F~#$W*F*8#$E#i2jJfgayj&Pi;r(s)gxFzld4Z!EA;jRUZ^V_Tppl3V
zC@4P7ZOp={LpnZ|Dx~=S^+NO5!j04+Mkf}*&*cayGU;UA%?X`3uIky*n;lVS&ZM*8
zvz$6k-sp(pKU1g3x3jlMLyW24>?&^7;M-|EpDsMpd9-?VchC<Myv6@OYn>0jF-HG_
zgm*d(&Lt<W6Zxj?@4d+(fB4c?!_A84a_aa1Z5aFeP>C}Zs@o4X5yMD<>1f1Bom>2d
z%t$GxpP17Wdfp)Jv472;$tr!<pIu)!+Ox22Becb6k}%rivdnT;ib3ey!{NOL$5Yl8
zRlWwv2`SSVB68~HFLurk;@~%<jT1QfYc?<IRPGzLZ;~{<kMOGG=_v3}B7%29U+DFL
z?i$x6R~Zvk+c8%W(}+m~)Qzs4)wda4H^NV^{~YTa9nwYQ6!zMGpAuVwc+A=(iMqei
zvj{w@?#h0%V3nb`XZTZ6_9}Y>H(Bq>LHZ)`TzfI$CG+ge)s6f!ywOG<IYp#)_SRQ7
z&!=UB-{-EI<2{j_bZzW2H!>P&n0Y$uGp*l?&|1l`NjF}ldpD1DmRHleKGSfeTq*s#
z{0}I{eUIle(YdSK#g}cMHiFb>`=QP`Z1Z4M%HU4MCTN!J^E;!OyuE^-T8dr~BUf@l
zF>0GFw?th*<>H<q{2c1p0~AZx;@;J<sfKJ{ZYeAy5|@)K#(!!^&)o=8t?qHgfz?|B
z^9g$yX=kCE$*iG(*ko?Sq@;#(h1MnKKK80WQp-k9JX*JOcyN648`*LzxO|Z~U+pK;
zMZ@J*@uPwY*5V4<;*M!H*D)UL$t(sU`msOJ?BS8Bun%JG#&@FFFP-COw7y0@*LPq;
z2uGef!(*t$MdqkFy%wIkGH256()$)WiKOP%A8Y>=e}0l2Hq=MEbEfNf)UTf}?gIw6
z<D+B3Sw3yEe}PLxCD#eS*}U^v92<AVko!(ZXhoFygXUw8w#6KR+iBBKW~KyZNy6Ux
z_s#QvIj6{I(>J_Sm0Pu-k#DAC*e>soinb&2#1E4FrDoq`=DOKCprkc}v?lU2%3s&1
zg&A0^>64et&^vKpx_NA95N{xN@ZBpgi3#TRi)T^b=)$$&w@`Nq%fNof>$;CdY4V8C
z`k9yRYLY*#@LNXeJ6@{0;ZuDVTqhd|X+#J&>V#3x;8n_kyObl>?yU*>gVNkOocn@x
zmz)t()amBD^Vz>13eCE$_H2!B+Uiqe7%#Tp5I+gs{Yg`w3FngDCtWx5#JIE7`kj=$
zLWzG-gnlP{u;Z`WZmg~d!r1%@{cN0mr&mj`k>}KpFI_X*<Y^C;S0C5B+C+Da1x@dq
zJq97ys}!GxQE=R&Tz16dk!tQ)<oRfk?4T3v#JK3wwU-g3W@3Xo6365^$xSx9k{9Vt
z>Gju`EwQ4ql(e^$U~Bebp<W#PSx%Vc{DZSq-`$z;NAT|LECT6+L6;vJq&&PZTc6E&
zOq!^4kx%OtX?9DuooH(;ZgYT#*Vsysq}xjwM<LV?gd7d(P3(}{)ODSdn%uhXU)1Cs
z*L$kTb^a{GRCA(Pd)%rT@~8AkC-HXkmmeafJY1!!cxMyzQm!jd&u6*>x5sU(w|^6r
zt7D`cmvSZ8bnPBPpR7@~!l=2a-6|Wan@edFDf1Q}jq`6Wc=)T&D&!eY-ne)!+q+Dm
zJd(NXGA(#D>L4KR?8Ta7ZDpaatw{NRNL9YN*kcnJk|JNiyN*S*WtYc}#?TWxHT20+
zmys_ErmiYybV(v**x-Z=Mf)?yMjqeq<B*1(Rek0A>#5C=6Pi<TC!}3u5d&OVu(2Zb
zSH3fNFf#X(nGW35bV5;~<m)uFPl-B<bw>)^RR@_M1<VL?!GfYl*tOg+*PDSHB3=;U
zt?2eUpEG(h;Ws*%z)C;rSR$eAys<sw8Baa#X)~u<;rj<4awMb|UgnEZn>DcOMy$E1
z?QYoScr|khV(NTcCbV2Rx`zq3+c!0ylNj+A+j*%++C5KH#*aBk$M=fUY<i!?F06og
zt-78HZ2Gm2#I<$C5{;DS3Bk-L$!A$Qvxc7(kn%5`rJEbn9H+lGJg%Dw+Ns%_K#;Nx
zwOeB~9MXQOA!Qf8t|8C4o!`RCWqH68W8sF%P!sulrWfPzbg^J#QQTG=6UKN9^;#_A
zDNaxM+K-pjR`t+gt}wkP%eF=ow#!!=2C!BW(U(uLWgAaE0n07;+ubDp*^tyRrd^!~
zThGX&J_uW<Clk^d?{0vxyv&r#7RSS|!pznjc25Pgp7~0<s1EMD8FS)e&T`_t(Va=P
z0b(4D?{V9&t@b-}94*IFdC}>jcL^FvI&jG??{Pk#<t})48(Dsu$EwSN1tpz*V{Bjc
zSzX}DW&C{|68*eK<tYW^!s_`6^zRzv&O*%$vTC)lxOenOjfU>BC_6A}d8Fa{IcAMK
z8upG#G3ESh2S$dZGNCx^($`CQxYfYd-k6DUR(cMM9VeEpOFq#ax9Ni|MCMM3yRUrd
zJ-^D^gZW`}Q~l|OXu)ue*^UGMYRAbNo8QupNGB4uZFMTc0!FS_L!#qpm7-o&u~pxw
z^2miN{<V220~lQlPd-y-2Ie&Y>OIz3w!xdoT*5WWl>T8Csadw0301JjyEWO?H|brI
z{cenQg;EUuk)>7!$^-6hLpzSh$FEr{oVZw8H_ee(`Hhv=78Q|(tr<&71=Nw`2)Wh~
z!4r?PoR0eH40RS`!v{$=KM9`Db`*+|`0iS2f*fl@7D<S#?3gE1gDVP$I+_w99P-=_
zZco@hi<A?(mG_2v2Oa&CO1~Pj6bvy+>hy*!vM8NscVNjE=fxfD*1pRnYvK)BH<}Vu
zNGnW80T-8#9t63_UESS<6A<)^s=d05fexeN$<L-QWJZ}z96jYBvMLBqo*0v{itVYn
zzN_0=EK|HjcB+fdeH?u(>io;dv^O^1N6$sD+|yo6+Kn&PNF2D9QZK0V$oI3~j@V*t
z&6Bwh9Zo-<*@hSc2Y^?7im0MIOk6uWqbKTawHPbocKs}Ce@`-KXqMNJ@9a{J=a!eS
z*z5f<%z}rw$`}a2R;1JhL`p4uxoZALm1<J?VmTx=S2)K=P-n8%LKJ58O7E?U$mO{R
zl=Vuf$7O^l%=<()gG}c+m<fd40hW5g0i>V$3Ix5ETEuV)YfI{p?`K!1k>|9`-7<@d
zizhU8r#9c|4Htdyt`!LA8jU3MWF%46>TyEj9Sc1fKjD?FJF4g7Gn-x}YjWC%zcPla
zv8tA=RJKK}K4N6sU*HR#^6P3<bm@7mu;GX!cPeS_QgC#d5#mPT$?elB4}TkisFYp%
zfOZz~De})e+E!jtQj!$#l639M9w)~eM-*J#9E<qLYrT@K;=i90@+&?sd}~mbxcL2p
zM4Qgar|K>N%EVZ;6C0Z8rI?{==*W9ZQ~%a=kd%~EDfFl^e&DpcON7tB8*Sp0ZwH6z
zQPssaPv)%)4nB+)2B_w2c87R+dX`{{c9Glukt2^ID*fPg;;-_+>^2cQ8N3mWJp-EK
z9eGier37HiwDB7EpSfSzJ>A`5t(5iVGaQ1N5w<R4C#}6Ues{~XMGT*Tw+vRT)lJ%k
z?#-jLrYr5+Mx2sW2uqTcEf3>>n;Z0BYlJM!YpeRP`-$dznp1XROK)i<xnavRPOSry
z?H3v9zE)icdFIA~jV~GOq*Wtuu(EeU0gl-t^rIUInU7Yq_4V~Ru3|>1qiwY9#o(Dx
z*TT?E0=56oe7C2#ac;|`Hq+UyTPmoF(lkC^tCSJl#)*oekJ8HYNBPafgZyoWJxPBL
zT*zEfo?)IQRWQSGd_@~G+Mra)^}Qc1hs9$!dU4Z1J&4uG1w{dy(TNF7Uf15t;pm}v
zcZi?k-kOx&MbM~}`jMfbE2ASL#b7a`?1m7)>shY-GPo`Kz3&!c9H(R*_NyE_qC!?G
z>V4Qr6P}gZ(+^%6O24nDXstz~*9l~?H>-Ye0%41==)G?HWHfMnZNwTr9r7(zG_ba|
zwtjeIWTxrMN$c!w;Icwbw#rApb|j0q7vP759U_jtpheqAsb}~a<@GJ^w~xcuH<(^j
zu1eI^oi%)9*A}eg=bDk#OHYljUw5076cy2vz<3upPRkXAu8viE`ZUg`>jIFx{F~Il
zNjGrG>{Xmxog~eUD$3Iu=8`8rR#$Jh9$I1R_l3v0J#I8BL*7+!y{hA={aLl3)jjAD
z_SaG0L{VPQHT${pO3c3ShGG2XLO)J6e80hF#YK8!TOQYx?x|9cX*}Weu~&@Pve{?<
zZ8#{1L*Aw5l$>Kbk_&`xS}v24>vzJ;%iN80a@u_Dl*0c!^2~>BqrkHjOpSI<i_<H}
zKOdQ<U=<Y=rDhUo$(-Q;sDdj?OUD&*j7B4Nn{llT7>p*zZENc+e}6;t5)ngLe*u`Y
z+zA+?<viQ|BywjaoG1?<@O5{waUN#)6O-Tp{?$cOj6Lw3TzOZcHiNA3x!h>l9=IeY
zXrhH&KbD@JE>l}mQ*?;7-agfp_Zod_#1?V8@U>d++{m4Awvh^Ji;oMr;kl6`maeuB
zyUqq5oTpTOml+Lv{(=~&wOZ9dXgm1R{c&g9HBT6^wN#R)>Gbuv(2AVK!n<iJGRc$8
zcOfS@IgF6uT9>01yuOqnV1eEy<c6<j>@SyBCWq7Z7TSh?{7B7|b!-=P&5PWr|5dqh
znElgHlr_Pg;Pt7-$p^-Hr%~|fOQflY0Uvar)ab)QjBM17+MU^*^6!sS3+IAk_y~PD
z;p3m<xm?eNE#@=zWGZ;BjDK{ci^xZ!=VgK<O<f;U0-m0`c2wnoHr!tPRTg*(HVFhQ
z0m!IYN?rYYx;2eDkFv)s2e(nCzxwxU>~{0o1`H;Gj5W50@P2cht(ns|(My6+<k{*J
zEgD(qF%u^(LNy2N?(JP+TkB!otFn+Szw#V-!o#Tszvk6uA|_gE?wP#Z*P53a>qDl-
zk=4b;gvXqZMfKB%)|yt(B|QMvN`N3}a)}v)91FRB=-+U{HR+#ch(Ak=RcZHl(*J>L
zs6np!9`2>MF)bLN1P=h%xJKIkO386GQ7@M^>mASV?O_Y8?Zv_rpN9aws&nI3b39uu
zKe`z9?o7hzpF9sXQlby?WH97kh|bQ=bb$NNOL6R{e`Lc2%I{W`T@)L5SmD{2E}Xej
zZ8K~AF18$i>^JSlN2m`cYTR+(kS3V<arcs(^k6f%>%0}`2m3kLOEKGJ)qI;NhH2RL
z_V&_o;hUH4eR%MV$=*&*@KW5_ps|C&Lfy8|!}voQfYh$n+8#2^`{pA&x)r`!-DMrV
z-nfvXkCN6vzkNHezZ9_lHHx}ldlaDAX{Qv8pzcH&jPmxNu3!$iolS<l=g@~~7Ivqr
z0+xPgXKa`j8;pA#kT+z|_o{z2Tm(M$c^Rd&&6<>wG~sS;Zb@2qp8$r+{b^lS!bNN@
zmYSCM%m6?O117W;i}&{~H`upDReW^qpDBC$Ry1&TIylA4!-EeZ80DQ8@#h=I&X`k5
zy-(9pacfp)rlxYB9eTSsKR<uFaUqxTyWmFReovlOTR)pjOVwP1-`rj9uq1y&dpChE
zQ3C|4(T7q@S8Z)=^)W$zhDm$<(X@fhel2<&$~I{H4&d&6{O0?CKd(%7!k(`v*tLZC
z368SN-v>b@f4-mb@6;zc<BHpweCMAY7uMYBq<%AwdPvN0&298V*A*M(?02zAH{jnL
z&es5r*lEATwHn_Kw8;MyjNBUByqV1381nn;i=Q4I9#Qi5KR@jTVfKeB*Y8?ehg;(?
zk#G+;w|lJs2S)gEJMT0=In{j!f!Rdq>uQ(Adczz+{m)n7JRdKwmf8F`0f`2|Rbj!s
zugheo#Bf4bI#2nJ@*vHF+1(FtQ+Sy3jp`Nc{81{vt9ijdws_%yDbPOYjM91+lVAjC
z8X%F;UmQwPr0Lk$*sNm1tU7Bar@0dVDX0OYIIj!X+agsz^>Cdu+fm}Wvpa7MznLOH
z&D)skvaeby&Z4cpGBs*!JPgyuD49kf3i4D+jc#T5?GeDTZUb-f24DbS<pK1FDg-PJ
zNIujMYmNng;C0y-k4^iVx+wdoz1}?Ps%y%n8uh)2zI3Z*pW#*iwoSVo?@z6qXnxIz
z5YwHljqP)zp6U;Of30nG&$+!3HaYwp^OV==Hm8z5p{LWoPh~-#Q)6p@*sYR>vI{Gv
zf8n)n+4QcTzFS=wlz69fo@f+d#Cj;cPFTjRf%TVDE9z&$oNJ#{4&{tN(?zyjb^s~1
zZ1|rg*jAmqV`~hPBP?}-TixREU#+(+m8{nUf~AHP*1~uJ-FDHuTl=24DH;DxGfm=p
zYLVW%^gJO$Fdwr$^TTNon91pYn)q?Ufz%>_w^mVHYTO}JSh7XTf}~mEVRxnC%kC&)
z^;2Y*Tt8@xJ@}`@-K3(vi3-QDfffCU`w2`Vm#iXOV3V)(-Zb;$1Gq_(=Zl+p8)W($
zgb}l%<+n^!yfg_<uhma1%7>@@1`Vbwc!ut8!Nb|(%S>wd8K+-lC1y)=)DRlq7G@Pm
zq?f_8%dXU$85&Q1Ey*pi3d7Ql-b)=SH`C`F!-4fv!_quh>m}KqH5c4Bzm;kR9{vGc
zU;O%ePSWIw2#m)>3LJ5hX%1d`izijY{8lYPl?bdg0etDd)UK%_qF7M6$VKoGpcRWO
z;9fn%f8X?t^b+L$O^7a}8Qc-`1v`g5i&bM85rvt2GuP(~v2aiv5NtU5n%#hDAJh+i
znJQ_RTJ)nlU4Rsb-NBx*><|R|fN4h}-v77(OEr@QtQKHw$#R`~<klhr)3{m7KvEd=
z7V%bii{OYU9aIKl_soL@PlQxnuRrO)F~Hiw+5_#=e=i41{r<*k2V4(A>DI?wwbx;m
zS$UwQ@Y}vvRj1M_wdk|CKB!A7#mCm~n6Fi?pKuIQ^B7tLCTgAp&hEa0g(tpVpIJ=r
zeu2dC0wTV_W1MN~Mb=B{tEojMWq_^Uu6oC8L`s-H6@jJQmeRmqyb7IV(?(|jCh93X
znM`Bsw7pX;Ap$Eycr1a9R~!yDcuI9ri&#t3L7Ih+H|oubSYfFWMOF}?II#Qjulx4j
z2*W>`2EurceFXc;R-KXM58)-t`)UnjJJ_a*q}kMNA?g|J7=u8Wh<*@TOkoU)Wflm3
z${k=hD>23=bWY(*u(eoi>;j@3!IkLddWjUTJ|_kuR@i4a%kzy@fA+>Ow3z%-6fNae
zWC)V>%c6r_Pz}8w%F;nIM?*A%=w}_;7U^2xdJ`KEC!=P%>(otb0{Y}VBVo!&-iO_H
zLatF>4!CrantC@w|K3)(R2aTD%@cDwVMnC&mB;?JTG(RpT&3Go#Lv`KJ1@&x)ml6}
zMXT0jwm?IF(C)37K8Su&`6$^ufPM>~GDxQ)781M`#{UK_vfF&A$}m=GRo=rFHj();
zf@in&<2$P$nOi^N*m2G!`?JkN6Z?d^k=C|nPvmJcYnq#7qKJMljnyD!y$x)TK*OC;
zJ|ABo0+T=i=z5Ecd?U=v6x*AU)GYHN0k~QSTz&TBFK37bG<y?(B}ImRDLMY_0RES~
z-Zlicu=&bWxd+gX;g90Y-bj}o+8PH~H5X$KNy1KJ=^$~47E}zpAa0Q<0()yArP2Kj
zD<1O}t8kS|=$Qp51?rDT1fmMZlho%lSr7bMCD(HKZf;k$)#aMeO%xi6({>4!cK#1b
zCroz-WCIpY4-K#1E!#1<_@jJ5rsWtpxO8G@zg$ppwZ+W4_vl*>nKYw%v|YoZ4Sw6C
zcAN_cPYOf2F1_*@4V_FwGsg0a_q8DyLp#@)Y641|*5L!4k!Ox(t8Nr_=nvzoQ+n;L
zk2$}Y5&1IOnG;MHDKZgt_xV(qQW3tgyE~H%wlOy;-HM;<)AHOjsom|%6R_G?e;iF^
zJmRZ1d;(h!b%iK_bwT~0Jf?N9CY$=b2Ws62-pyxcm|k5%G-Y)*WfCfsE?6?Xj?K&@
zq$(8qyrqj3UNi@da-M!A4Z9{$VP@z_H@t~@-?*>+*h+uo9;b;E1<?=L0ogGsf`gzK
zXbq(8NS_h<3M^Hx65!fyW?hZ0HD>nY&b>8Q>QEst+=Y)<fSsnpD2y{m!AvUe|KH{R
zzel0}21?%C2IYa+D{h|^2c0o@vjd)kz7668*)h;q`I)c0s!m?Q>SMJK*J3cv*(ecM
zyd*c)G$ttq4#{Im2R6Udf=CT$z9ZR3`N3DfZBGel5*+}~>Y3Rx&HcpsW9bmBb6c@(
zte3zTP6O!3t9R)xcouvcq7uw>Tqnff3ZN1OJ3nDFLOYW{v<8~9o=MbpN35NRskJ}9
zLT$xcH<{@vpwewwk6THX&az6_G1E|G#j84S;@R<B*tgiTgCtV~6QZ^I_9!cUXHoG8
z`QeKc%C&&*`CAU;eUun%Z+K|fr*`ePeAkYkTCvDlE4}@aUGAUFUi}}h%k=UMzu}l>
zpcrO@BbpE!rb~<`!MxD$v_?%tJ)&DeRuJIW;@g~LKl}++`2F>FOt$YI3gCg6X!s)c
zn`nt&8&aZW!nOrLBCzF?tlp1iSjl=LRfREY-z}1X+3;Gv011wm{t!FOsGcNr3HBrG
z;t*7zBTngZJ;bT|pG`oR3=#!Lj+Zq$zx7NMXmg>4Wj_M1jaLWOVl7JvkUXhqOMo4`
zo<Kr)WLZuzdI39#1oMImz{J(01I~s7L4rX050bL()&ab-%*e_}3I_OCOFWwbd@b!(
zK8PkZ%hy`|S7Nl@%rAViVD=uEJHj$_z!5C6PkasWF_ucO)EWU`02n#GHv441tn?Co
zphcVxxaG!|vgabDsEai$RzPnsU2qGJuaK{8K(xgh%zGXHxNN{i95v=lOjit7fukgI
zN%1QY*h>Cwko^s1(27irEzeRY{?$WXz-Pu57ZAE9!dgH}i*WDI?iV7Z!56r(N)Y;4
zDzN6OwB?B9a7M!bX;eAT|6B~Zd;DnoGMhTvCGl)Q;Mo<2*6@v?d@r`&!G>ce(=O<x
z7Ucp^6}(9^914Ebkj!1hLb|y{KaSlPY*>kDcAMk`lBnLoO_0Q_LsasoDEmsuR<j3Y
z8!9id0UJV<U=#$$WRwsK48n<f8Eo8td6gHr!ho6x#ikU}#i5fR!$GPHhz^drdtD96
zjJn<9Jhab!A?>R0TYFC2+qKqIh+<()+ueICEvD@|H8c|}VGw6x3GR_}442T!2ldSd
z;@$9{PmFGymyNAr3FE1fRzyGXJ$CPMkmwMqTNfeTZ1tuFb2Di%cwAR$690Pk+CF+E
zWI0*NuBo_R(jPUg0{`@#9)HBUZni3_e^t@3Bh|KpG8o-r-5fBUdHk)1vFK$?6nQ{b
zY3xs-r`|CB2d|5Qk_?>Bv~0&L8^DGJI^8$uz3cbeT}mvvHgqLlX-CuDT^35DIMjlB
zysu+W3MCvfyJG&fMqqixb46y4uyZYV26H8JXDgBD*K=B)@BU+tplQF)%hr|qLtk4s
zmy6c4qDMJw1VJD!!^5)x#7TWr+N!_Z+dJ|Bl#O0N>VCXvcn&@AyGkWs$a08Inpr-`
zj%6!;8V(j<z?j9V(pVxgsxm_B-NyW|*2r_1I`++1A{Q?|fi(Q!wSCR9YQ#*9!e%at
z0Yc*EcqBHhDtU);D98N(VBg^mZpBaP9g1ZD$q1^luY47+Ece)B->Aj2h?r_XJuMgM
zXyB7#&c-a<?}%cxF;F%EvLNsdkcqQcHp>n>Fy<cSA<*nB&>fCNh(GuoDdkU^FwBH_
z+l_1MGFBKnk9-ODGFDa%?PnbVP@8i10s;r^<m$6+7OFbd&m#ddfdy>I7giuDAtlG#
zgDoVY9~}jf1@L!^)~u^;3RL`h$K7LS8$i&HT+0VxSjRZYY_sYEpnhn{tLA^Et$OOf
z+!J5(^B2|vZvum>1*}Ozc0(aGC!}c+r+lwvcmmZxmt+9<)`ND1Fd2q@NI#@IrXQH6
zfk)N+BNIqmMR$;SqOG=vGGloO=t2^kj(r5UE>*8+Koh?kL;G8U%oG9Hc=UjOjC~9w
zqkRF8(cBXZ)W>cIxQEXsYt-j&r4Fh6X2s`Y?>i*2j-Cf5o)?~6Tg{fc#PaJx0P~2Q
zwS5N0e{`+{^ru}h(6I=Icy^pBqdHRSF>qUUFAS@849%#X>q}c8Q#YL7IT@|e*APlE
zwupG_3Re8PtTC{b{j!|t;FPRF5S?hb3*NclcNW;TrXQbWw@JCN3m4&k(HebxErS2`
zE7z%H?3DCK$?v48H^zBRdgBgFwcBa)Qc{BabFy#XYt^;23c`BBQJx9u#l?Z6;Bvj;
z;-Xw(3lPzELt<j`@v;5W{h;uZdt*GmaW+)VDj$tylMg|(o|})QwcNI<_DvK*K7I7J
zx5Si5A(%LQ$Qzt}IaQ9O6M11j^@&;=tL<9QkNSsQ7G~~%jYVvNLr%{w>z@up{=9#3
zZg??WKcNb4IX%?y32Jrpv}qHH)b6@aJhw~PEw|qMLe<;l+SNMPu23*mZ?1Z<23xiS
zWML)L=qasD+;gfLt0ZX+%nbA9K=Gs(<JZ<{*ah%i@oOAdyqA<FvaLKV^cU;@x%51R
z{ok1W&B8(b#)sAG_k5t@I&T54Rf&Z&8$hiPaI+d<^ZWZdD+sw8OT`jD#O6WNAy+I~
z{&_Xz5=q$MJW0dB@Jb6~!$+<)eunI9*QrjdeUr)5V$`<MaFIvd+IJ^^cUV0-+;w<D
zI7Uf-*yTs(R%M&=jb81yUj8%Rhf9<?oZfnwmI=!JPEiP~71kfN)A!#j3)D{+V7??{
zp0#`yaGnLko~aWmk4Qu?H4XGZ2j~3v&JT>G{n{TIGT5JVt;f|!ru5DOL6F8Q)dnjS
zFfsZjWI~C!g*mPxJKt)>^EcN+B86~aVSRT`VI`_3q9TK*XbHq53G?ue(d3_d`lw6O
zz!po6nG`sCgTLyGV>EI6YMzlM=E(v4kvSHD=1zMQ;AHFmi17_tF-_Xi$-Jl<oo=FG
zO!X+IlCK6@lul#@c(-9hJu8drighu0=262ZvBz|_B=C0~vXGv@G<T`&GD4m(*Z&+S
z?p{sq0qW>~D1QG9<@T>NH8TZt^bYvQjon~|KBgVLvU5OW-vW^}#q#6ZVt8X7BkaMO
zG37BGsS*aMMGxQUHzB?{V}r0ZI>uyM2W}w*k<o`K3T5U82mmXfd=9~HFVgp}N>-f<
zibkEER5_(A-`{W?D+tVHK%D^i@gUs63BR^TQ;G@LiWYRZ`(o`tsA>M8h-^zaqh6|+
z-tY%bvNg84nquli7i7u4GY$~4L?uw?wy_<Y>u;Eh3FLWyBem!QP}BPg#a04DxODVz
zwIIdm|BE30hg*lau~fua#3z-`HS=+kzXr14y%D=Ho{0Jw`4631W*dL&xTYeurw1T1
zH;G{YWWZDmDuFH;)qFfI)Z;&X^j@8S0x)1Xpuh+R$K9k?f{j3E5SkG)5+9@p-uWu;
z<LPjhcjcu>X%T>QLE%-3E&sGJ)D?<?v_U))y%xS57Nb}a8U?WSl3BMV9=<V4#ef!|
z{_k>CMPU;09xQbWh{Vlj6oG+zpR52Yn>|<@5Cq8F-M=DF;!Wn?1%i(M#RY=@TwgZn
zg9Ot8zyMfGf`Q{+|6KAo1l2#*JQBQIgYkIvxw<2{D+YIfX8Hi8xbc%$yTj@>PYeJw
zQW7(~GL8Y(Qtn8B*?pa3`~b1i84idQ!<gBevRkP|a;52!*q^GWq9bk`s#BfA(tFY2
zK<Yo1NYa7r*u(wQ|4)gAI0V6I7Jio1BkW8kp()T{5K-hFP%Mc8RWsf;X8dY2P_oF$
zo?V2hGa2apM-4a%o`k|5a6~gSFp|%Zs`U<4a^qp$3z_Ma_tCzBImHBZC7f1bAbX3i
zKpaB-yFb_pgzQ(K)fG`+JVzBkh8dKVA4_|#2P;}^`@<verEVO4o*<l@N#?NHTum*`
zG?DH&Q1G1e)^Inf$LuY|x%$|=EU4vPQ&OnkVNCBl(yB$B557CKGx2CwZ&359e^c!=
zI_Zo4$om<Z|8DxKid92MAhb%(KYBZp5K52ibJ{$^=P{aAK&|z(-hXeMl`PXy)~0c}
zt@v~IfKQH>A@Gn&c!cM<=h8PQy^<E&j58@5R{I3_y4AssMzob}!Vc`Eo|01AX~7zo
zjZ{K(PLR*H+`>S892}(iuHJdNsovE#Xui3$sfl1xlV?|t#yH#RA8b~;4|35B+O#M*
zQR`R&k7VYrUF)nY-h-7Ie@gm$YB5h_jB^I1G$3rlQ;Fs>yVBO^X$W%}_+d4fxvg3r
zf&#UjTV?8ts44*#{qn$Cx8D8LQC9d#B>a}UgW*;^OM@(*3_vk2^qLU-M<9_bLM`tf
zyNmv7r%szn1R<V<lkO^=mA!rxt1o4eM)}UC7!I<170RMPlje0b1>Q_ky6-kM9VW*b
zlI;pTZoi9S9_^xu&OX{s0f(D^XgD8O!)`H1i@cr0Ye;lE-PA#89FPl$+x6F72@4L<
zDsMM}8W9~btsGcnn-1m!U})zMh;saGiVOC<gRgV<@@*;Ki?ZzRz+#ZztMvCv386`;
zw+*PSP$y;-$24%Ls>aW``_jAD&aWY>CVM{;?yiP$=V2yh`p-&T1G?vJMq8{<ELoJF
zBwNJBW3?o2>svU>blh&wsuxpSeTXOPogBU)kI4BD%d^R6o@le(MTL;Po_H*ktvbKf
z3E>P7e_|Ks5v}F=AU(esleby1GQC8d%Fj#|9}L@9b$mjUmRq^5s`#F9Oe)4owNPM8
zAVgtFr|DhczLH49IStF|1XUTF+!XW*IQmGZMbkT@<;B3oqr?TWTtL^ZfAGCgb4JyD
z#gUZutdC{dz|dKgW2;o$gdHyX20vL@e#Sj}W>ZC_GcXR`^JG~ogA|i-<Y?)GJ2_@<
zr_V8blEzB3GkGa2|MjZMs9XSQ*Z=Zguk!W^_=cWko`SqLXnJlm!KIu{a@^F%cVSzu
zOG>QDIdG5)i`bS5bheF|mYMUnZOelFeRM0eC1*izJP}k$b2D)uYSXjSY|qn8-IT`W
z_bZ}KAk=7mO!-Uc0yYBFUWC7<T8nfC|LMBqcVakF_=0gh)unE|;Pw7btPWlrtIbnZ
zF0Z=DS94;d+{3g-&Wu7)!N~=b?E2SMu82B=Q2m|Qli0RY=_0nH3`dTQq)=m8W3<Gq
z?)bc9>EDPC=qE{;MV}=(zcCJozJ@nw|D-TnROqZ6Uu7)E7y%*M<74zr-b3CWDFkA}
zYZVLp4Mqjpn*X}cP8|F(=q*LExBQuXEyUEUT&G($wd#)&Sm{`l?mh4Q<I6tzRHD4r
z*(4c)f}V-)Hq-!;E9PaA4d%C;<x?~1ky~Z#5m}{B<*S^W^;d6S?gkB0qt^cc+`_=t
z30!~b|9BYYzq>o^rYQR^F@?%XvJdU#0Ih>ZKol?)p;kbN3-pUhGjkgcdnAm2N?fV5
zMmX|hqQVfFMQqPiN$08*n_90i*SCHw4rM(xqpcRWa_5@V8cuR!FOntX)Z&@p{Tuj<
zMH+F$Dfi1I(~I0zb$XeWv3nyB6o@meVLCz@iZRa*EoG8Ln6K@FnxWg+dw3nZ37%5z
zE(S@IxMvy>Weuf6ehgA8n1#~&HX6`<g{i5T#Qg~E@2)aBl{IhKy%+wR_q{aRjS>kA
zk7f+&;ZuBm%vZR8vSBxQ8LN*xmSHf7>alPGP7*yDnA^XV8+w#+9auvH4nNVlt2Z+C
z3=@~#@+(jRZCd>Iw8?*-`<?JLTaChmeGD&njL3>9cANS<)-aCcEtn-jaZr<$MlVqC
zjqYj)87eDRUNMd|Y@suxgIsR}(xl7$`SN*L2S7Hbr55zD+SNhQqU!M8!GoP;hkbHO
z9>lKy<NHxib3pYB+JI9wjr8PBY7=l^Kl?Psev2-T95`h}t`fM#{2?CeGI%8eByer9
z^qI75zbkWAx+h|dIiEEDek0>~6YH3;;`)z!w%1dVm`I}qbxUt%$m*cnBgx_sc(@TW
z@=mpLb4(SzQHI34_PC_HJN?cz<}c%x9roa;<GudpNMf>&s|0J32Po>YK(&RBE|@(7
zN&!!HPGlNV5Jb0q3xstHEi|1=^FTGY_|R06IevxFBUKYG<`!sCxiS0Z7F053_^M=n
z@D(M+s1uG_Yq{9T(242i1J^K89&?QN1;%B!w^XsdRmzh9GkE6(o#zK~9u#vt6IQ6&
zT^e%thPN%7!Ph|}SCmvCPspnQE`e-?_2xl!6tqor3xpHia)E<_L>WfN@6awfL`npH
z@qOrn-1_^)ObrOq!Mwa!S>wlTbpMvV+oYT@&jV0-^TaDoVtE9o@{<AieJP<gzq0OE
zibWt!PINU2<=3CyJqR<;wAbBsVHe&qn6rCB729&IrdzxS&+~9adE4uxz<KgZ)MEzD
zBz5N!+sCN2$Vl>;8}(_(!j4f$v`|ZIBexp$7o-;2@+B_(>zAQs&7YiN8Ig<2vFr|k
zx=@7i=Im_<S<SI+`Z6-oZ=<*TWdUWJ!ZmjilJ}Lb&JZQ}B(Mf&tSsRfksl_1a^ao3
zCqBt&<VS|{B&dM};J=7bu3TaSC7mrarK+@5%Ms_JF{`qtHxf{4WP}@x@u8;-u%U}K
zu>L%`B~2Hh_oiVSj`W2*<}3%^2T8cps>%=Pq}i7ADx$R0#VXF(Tg%k1-kP(ZECYCe
z{s=s8CeMA+xPgV#Yc!O#AuaR@!#!NX;wvz_+m$nz;an~r5$cxUG08NR|8leYweG!G
znd4!Ei}<-3t$Xg$-WpT)i)A|3?o6j`F}7=4o5TS_W->nB?hc;)I;PL8%kG+WjeL3_
zYp@0yv+|+X@)~n1kt=Mlag`(6Xlyt5;_TIOw5q6wC*$nmZ%;K#86+s1f!|>45~XXA
z9zm&QyBIWn5^QlSY@Cgm=_%Eomw@sE30+?}9`-AvFi~DwgnvFqg|(@0DRJY1W1`Mm
z&4oI~brG+!L$Rrr@I8lR<!-7<eEh#pd&?s<EhA!m#Aj@G=HY+fz-|~_N9#B|`aiZ6
BbmIU3

diff --git a/reference/Rplot006.png b/reference/Rplot006.png
index d0c1765df27d2966eb6540185c4ffc67663ca398..1f50ac08ef4dc563bdbf72166f7c4ce239f92283 100644
GIT binary patch
literal 13199
zcmch82UJs0mu?CzAc7#hL=;3iC`~C*5HKL1RHX$(q(hKi6BI$ZQl$%01f)psL{w1e
z(tD8JLk~6Y;{VUQ^~%h=nYY%k2q8B&_nx!&x4-@EeNI9jXsOXsvr~gWAX?<TyE-5c
z7=QMlq5zKg_cz4@PZUoy)b0Y$z|VnlJqCD!q3=C*1%YT7&K}_369LB{&?OM^?j1d^
z)U^rER1>Slw$1LnINJO-^h$39=?hv^E@e_H(UYCie|_na`JLN$@mX;zOn2|z38WDu
zyZ`ZWrpXc?6_w68rP~6D4;Yk`sFC-|>dSpeUR^cTA-45NX*cSRyqnFoN0VH;<hSLl
z7dkxCb?33X$e91{-#J(I9x{1$4iAjvYKK<Uz`%lgqI#j4VC`}?&6|F!f!n5T8XAc8
z)w+p)eP<MSM^C4o-xQnuhq$KGIMIr`Xdd8J&pa$|ncNr{g3ZBk;@QpJS|y_yq(e{7
z$?_+!E8x$Wi{L5DB?yAz5*l3fjM9Yx=Q=0?pr0l~4?cps$mhZ3*o_6RfGW-E@4_$d
zLjw~(R27pKP~Rs_*o+=s4<=n^5768pCNH`$mNIzJ5@DrAfqOi{V^{Gt!=>9<7M@S<
z8+UB2|9bwGmnk!yz|(w@^**uXCVLG%>qX6N<@dDjC?r8EYTdTr!O+UxM4P&J+$fH)
z=)zI@hA`a};PB|UHc$0yaj4zlIUdObm9>jAh7UPB@47oN`xZ5S%k|}D_b}j3xHG?f
z2o_U~4u_$z4?p`*sC~5yoa-%hESJw?mc7bOvTI#=+C_<5=`EeH{3;p|Ji)|a(6J@z
zQF||@&YY`-a`U-4RI%}U-rK#NVL|ERWSe1;&lXFdCJAYW0Lj(g!=<@4!-*f-pq3?{
z4IJu>KUlC_lz?waSB26xhqQoAY}lSEZV2M@hfkig$gKTb^bS#pZl+gAPs~drHhgcc
zn5Drxe+#yvK!0wpAD+AH&N1b-+&K>tqEe`n!CvIyJ%5F)MfJ1oROAFk^spY2AdL#2
zK&4teGTy1addPu^d|cripy`}@YLc0BiR^CGtqhSrwXYKH<TAp(95my8R&;Aih=!d8
zh#mTxIC#~iMZy}FJz?z@5^KR14{jaU33_;Y0#oI)1{KQKUtR61Pbd|mH&?@7HHY5{
z=&72k$;g*#Khbk|G#}!5yoqkf?LM^eViM=+wztxCNV7e=lS=PYW1yzo<1uKE)fOBV
zjLWtm#w~7`x8%;xP)$$X-pu4+LFFb<#xU16$km%<$K1dUj5U&{-X;>@=k7-bV**Q&
zHyqc7Pj;S7`l=HSoxiO+4}O=P44Z&6){_pj<Pk&Hf9$y8+k&`{r?BpF4;R$&)x9Ua
zS<>kc#n-`(_m8Z*{DZ{kKiujKI1KN?4ws7D$Xx<})$#nTdKbvRdGMYryBAsJ{x&Rg
z0czno!_e}u0z^W}Zn%lG1l}o%n^(J(aMuxPqU9UyOob-P9Mb=xqohrXcTg&ERxWeC
z+^q*V(T;D`urZk44MVBQym`ykP!%H+dTMr&W4~nhqzJaX8LL%sa(ibV)M7pK4A;cg
zckzEtHbsOlHSD<nfoJ;Kuw4tK%b{Bm4b>fK!Q=o6^o>$(F5mRRKm0+R$>G?2*pWgW
zeaoMu%VV-P?G`j1a;PzCZ3<++vwD+HD+{Yhp*IoJInMo)XtS}IYeT0=wcW_U)1Sf1
zY2)tyc1?+DkOP}Wb>z9v!+9+s|J~d~{M_Zge3PxUjrPsIJ}Z}1%P`odUZQ<QLOvNY
zi$of=!wQ8!t~%m);}X%{_<M!v;!V>^{%O5Jhc!CNsMX-1RRKGJb;EwWFY?qzX}!1G
zGM)sX`_VV``(&iO)9)RX$V!3p-2T)WGX^Z>3$62W?v5u_(J15<R$b*>OD#I-!;Zl=
ze&$9PGfnsB69u$(-Q-Jr-CMLH1J227wQZ+qgNwrziZ37Y&day-jxE{VKvjoEp{NU}
zSn?l%j+3IAj~Y3qFJI`sTRERBJw#w`>7Drcrk`JuK0x5wdi?bvG=vKikyV{ceQxcK
ztMirAg;B1$^0oWbZyIACFb#mG6}&YPY*l}6a?bjUYKs-{rN%Zm_Mh6}9)7MOQH*O4
zlhmmeI8ZE=5^vDyUgOSG3yjuAEUYYzt?3mRS4_2uN)lK?UsB)J{S<pyoONJ{mAC0G
z{T^0d@T(1JIxNz5BlO%%Ut>_R4N*Uc?)yG=^K+FJkI5q5rG?3^04s#8hAn(a*ItbL
zhPjR|j~MzfV9?Oz{H$TUw}KQvI3Gm!YsYH)^d1oxfq>)pKOg**B8}vV&29%33R~R@
z>~Xb&c<`A7F7<wFol@yry-O@jS0+k?RdiyT84}cvV?z6=3|WonJFR_als`L8G5dDC
zdg$niZ7l20DC!gmWWm-}puRy`N^~oqRnSeR&SF}*+c4Y}tYh@2bYgeAPu}f`$feMC
zVNOL#)g&GGcuM;W^}_a}cv3e~i-I5H7<+4)8M_pM30DN0`HzWhD?ws38}sj^4LRs1
zDZd{l)MT4oLeY7#THG*YnpUG*|Db^F3f071Sqt1{&s6KZHymfGOf<ldXC{`3T5QiG
z1hrEPGALH;N7@|-tyxsjqRrh9jrkJB#$TzLuezV(g8F^^xE3tDZK|>;!Ua$1uHbmG
z9czDDu9?vdYI)eUB*wU*@|6-AYuSy8Bz(VoVy|^~SL_NIl9pgDNqPxW_}*$mEb1i)
zDuE*T{CJu_#5xXbF4=G9=J|_TKB|%qxo|4>k_zQ@$~@3}n>eL!Ws70uS?nueL>VlZ
zUOw=_p?Lh>w$EQSr)e1sQG4>$^YZcCg=nfonTYz-<CGVLu;!-;P4{QCaE6vM&L0}9
z({4HzOJ5Q$Z+{qVMfWJj1B8~~@^#*vymRc9U<`vVYQP#xGlO~>0!yRqnJqGgD1uH<
zB0>Jo#4h;o(e<7NR#2?5Fx+c4tE#%R6V|NMCbStrUK};5S;+4|ZKhPS)ieCUHN*du
zl(L7F6|_8@$(r6giCv*@s>l}OeKR?GDlx6mz8e%yy{h3$hiEhz;w2kGz!b`NDkJxK
z{O%;F)sRiyReF7%-(k=%iT<)SkRiwR@WZa`R4JAVj0umQm53Xspf0a=n=*)m?9@oX
z40G7I?GcU_))4NnY5zDggQa%$sve0?%P>R4iyqgGz2;9mCd=rn(<GVHy$O&uLsf&x
zJz|zY#@xY(9%I5tv@NDtBzwl$yhQWezC+)<9dtDDt<N>+qnru}i}DE#6)g<?v~oSX
z6h8fO35ClH+MhQH>hVq~9Vt)l={ph5+^@2hYzi(tAa+LVl~<Ba=Ur`9BG!Hn>@7_S
zGIKdzz9k2VzTo3Drc1<^m~4|WDk#6fueY8^vp1m}hKCp3Z&ZR2k<0R81q$1*36a>4
z4@Xi#oxeaQfnp3kLraCwB3`l)<zC4YdY|`8OX0S1r>V#wF&%q`gbgTLclY2k+G!e9
zCX3j^4jV<8u=TbvZF}<5=so(5ssm5(^zA&0hhvnCP2cVelYAi4cK7syte#&h<;wJI
zN~M2o%n*?4rrVM`xl>^u<FA3tbt~Dm4(ieNWt5e+voytju<@$0b*%(Xx3_1!?dN~$
zw61~jJH0=;znhD>LA<)<{DMn$m~KzjVrpvrvu)Sqpt&1CbRU;(MsEhC|GB87eW~&1
zfyu3P>qa3DM0t^y!pkg=KCRbw$PK6yuOCs2cW3-}3j0j^`^cV^mDS?~SBcD!q=3jB
ztCLesg@6=#bYxIxwMdW%Odq!tguRqJwF6d;41$ImWl8Tv1!pj%oL(FiYk1%=wz=S-
z{wM&$mwGwYTR+pm@jR~|_>t;j=#WZZx|XcY&B=iA<)AB3<&FG((=~jVAvLM!hC2;I
zx2S7wu<u{AnA&-I%j|^<l?B3u)*}4dla`H_@U({-#hN|F&-y(u4&#$SR=bdC4P>#c
zEA7VWD`DPUC0e(0@19su=zbR5ytYU~qkS34RSPpJwBxfVw?TP0{U*Qn?`dYeR3$^t
zCH>LS(ev+X!ZS5u4ucQ$_4WH=y{1Q7Vs`|kSaC7g9-&q*SS;W03V<m}d66)2EFV%G
z3l>J|K+c)(UK48~{&{a1b@Q2t-|+7o4SSCft3>740%XC(k-=!$q4;0FhoW8>ysF)w
z^fk<ouxuqb%y>+CFVxPoMWa~ZdlMT0p3R7pCGBcxM+TzV>6p05-<5iEJFK;}Rr^`T
zyWW}s17nd$ZG`{fPA1~8H+s)+^>dpO^qsgxO8|c=x}}_w6O9X*kkU?bS;|Rts+jN?
z>m;?KPOm>7uW^A#aL714^lX|;ac&wRaZC|g1D57`86~Bp^oyz|eGs=u$J-?s;&g~i
z!@+zCT&pg(_@3AiCASxT@5y{#et!OPS-a?%Y`nVvvRiLmU0uIzUfQ0jrlzK`pO??c
zy&N6=qt)_m!(8o*rYZcFm)4}y{pM!BmLS+RI|qmE$Y8uWO8fosiuz4foO*wB_szNN
zZpYEmQ(Ryrm*PFeV;k>XD5mM8wk_RjI&7y@Pf&mN3nl#1%_rYp&kvW{VXs8V{&|Hu
z{=+hMKVAMHv||XkBT2FIePv~3K~YiBlP*k#r0wr94#$dKm6w!s>6rN>YqS1|lIkxM
z9KsvOX_^Np)ReUE-s(0u3UNw&j2!CI-?Tz?9FMyXnO-3s4JDG$!+RM={Wad&3c6BG
z<2AY-!xkammn=4SEw^TWT0ZMeZ=6rP>DpBL^k+<RdDkubHW=eodFtAscy+UX%&Z1x
z6k1S_JHHw$!$FActKwvBPP=Y9b2^~!$TgX9!@7%8+^+xQ$jIo3Q~erq2XL{Ij%oX%
z%E2(1NiWOo+}zyr@T;*`HQ8mXe#KQ@+L0VYVNPyTjoKAD5BB$u$2AQM4o<#H4>%jn
zpP9di0H6d1p|?9_N*?R#y6OtX7bHtqs#^LaPk#*|IUMdRDQ|1~&c0P!uSZX}^oi9i
zW~~4}dCykX<PT<JU(S>$U`~&3VNdp_FjK+7!Bdgk3WvvPaDO+O`RzZI+0JK!u30Ir
z*XkJZuwLL%INlp`hPxkcwrx<MfAS?dxSbv>ICC3M&QPA`^x7p%f5k|<FZcIuE`TGr
z<o2@X+7k^Il1pT!{K_#6>n3Zt3vv^C-$EIiy{2EWI8mc_det_54-b1;X=-+ACYn6|
zsXCINUChoMK%BOal9G}c7#h;%mT?-NPAZADPx`hXqDWUe&);XqQ9N~h-ah19M?+E7
z-l%52UcuLj&D{^5KY#Y#S?qNUKF>8;)^%&;Q|fiAUmup1%8G<)2V-s6SQ?9;7W2PK
z(9Iu;Pj3YoME8!48UaB*kl<*dsYwgqD8mQgT>6Egy)BCCNQrcMl6Pi!w<-U9E^;(5
zWW^&OoSuteQ76lW99pTen}KXMo*ioo#^3U5+#U37%FkbVosf|5;rE)ban+~wZ;aXv
z!tv@{O?#vE-5?u#`$8=sP8QNUC%;t=8m-LjRMf9kjXCD)EfbKjS0|pY6jeLDa~?JM
zI%DwZ(Zlj?#iL`MvyTIDuRtb#28u}x>Et2Yf2S8VWahh860LZ+7+bC&W2$jBgjh{!
zZ$YD|frHWYU-H=`#1h?iuZ^WHxed;PL1X90bUDxU>U7`rT6miO-bn19?(lSDU7M!s
zq~_XDP3^xZshadz=|t?0yP_IT_Qup2iGRXV&i>2G*2(|cEjtr2<SLTkAO+A4H;qny
z|Md1kH)3zCdTJ8A^gBoDyKpSA!KzESYJa8RbqSmj?nf90ILL8+y?#T{Wq-nxv|M4^
zH+WAw(b4p0W%})f!FY!iq>p!;rRkC_K%lHCPIXIVH(lo=x}?{gF-K!>I%TGgiyUUS
z{Rm$WIf$qGqxK~Npp{SMUB3ng2McOl7Y0o|C)|BAfJtpK)rq)HjedGuZonb&<lA-Z
z^71k!T5c{*R}pi#l&8Hi)l6#M&r>)id=1ezG@LqKL6Hu}&c?`bklQIEzqWxP9T5Ol
z_9J@$AcC5()2AJMIy%&}=VfVWX?I3sfKw!{jk(TLLNX4wVv5#^JCqga6n1)UX)EmZ
zgO!8N)mnU|m1%Gq{PcM2lSysaA;4hr@)J;>-2uZk&u2}~D^u3LC~kgZJW@Q_=^Kk=
zmH5MmZR7TJShI{)*b5}ChnuwX8`4^*d(RsH9M)tDVA)wlTKcs9Xe8e1H`n!{Xn=QC
zGNR?TemK%867g_tMIefqMNJ;j05s&V0&{cvYgq5Wf57`8z<E`N%Y~SCr<X!gZn-ZH
zPMz*mqlEia-Q9PamFIyeHgCLtc|KVdAd~$W_{XFKF_f8t<7kEH8Tr+2PM2GEn{6-U
zWlUanMND%0uDZC|+pk#2jHADfOdU<`U!6|@im3>14$uCFYK`phs<V_+3*-+^2Rpk0
zA0V5A03j?UCb{vwYGTCu+EW1g0nUl09<~0xSt@FI0IJ4-gllVKV>1ERz)1^(cdlE?
z)pqk}Dytl8#*hrKB2~kG@`nH-jQJVMzp)FTS#q^uyK7?t{gt*p$G7-kwqW{CxLEpU
zfR?`|3O~L+J7S$->ZqT9^aL;n5bKPQp^3})Lx3Ze0h%j8RXL2jA*W&PzvHr^?xzs=
zXE5Gm1s3NFKv14aBh=2pWbG@g4IxohS2vS8H=moZOe@VMhj2Yaoo*$1W_XO+qH5Ob
zR!;pvKhJVJV0_u9M{Cujnj0nl-!7<ioDNnu?H(L$G?N<q`ZV8(Pvd|D<2k5!wAz=M
zZiw=CA4i;SM$cT&i*6c#Uq|C{2P`PR%^U!OQu2<!vyn4q05^(7!V#z8^wQSAor+IT
z0S9wJdH0jb-0---Av&&GJ0}1oua{l6t~Vdk&Anfnd1IS?_bHt%Krl|gBU#>itGX}6
z97A{s=L`TG*_kBmIQru46}8i62U~N>!F2Fb%o%XC%XSs{HIsMDK5xetO?Yq5r;&@h
zE%vAY)a&R8ke)LTvs0F5w=Z0;nTe`ai(qwecXf50QDHIN`OpZknpV`@Rz7<df^^&|
zR)c>*`jnq<JNhiek$W=WXbq8mmWwj2V$6!=)++k8#+>RM?RrU^=0w7@5KHs1-B=-l
z*sGQ_V<385^9(rHne?@9r7hmj>|i#r`FO4*HQ;aw;bdd;=s7-+a<b}gD(`aV8}fn7
zJvQ{o;%;WFzi}qmTEZo+UAOK^Jp|ylmVL&IhRC6q_H1NqE6QgnN4rfXZN2Ge9pw*X
zLZ_11D)4!E0<JMnEA7y{&TusG%-B6keR)$cZd|4VjDzv|;>`sG%e^hJ@#)}CihmjO
zrpupi8K#(%U2C}a?Ar}~(`dc?LtNbxP~uBK|NVON2;@tj@V`q!WDG;kP!}|SlCC9&
zw{oTbX767GnqPfOatXqVTn<l;5xpIgty2*9vQ&$g^yQ)ZoZ#J<?B^fj`re3AvQwd`
z$|iV`Ix^QPLPs=vVzSM0bh<*hD8@6akBxDNUwYwZJ>YzqWyi!T<8Y!`l*>!(O5jvn
z5xc;%+1CMWfLus5_$94d<Y?$enC3T(FVjTM_3EBS-?~_n^LlBWV*RIt(`E6jr%cGS
z{+<W*6YeY<6-yWg{p9Ltni65WeV#Un4hKr`d7sXJlhH8&RjX8YMaBNgs-rF_)F_|M
zuhZ$Wc+<}p&wxv(6h<!V3eL$@EPw&&9H&lQjhbciM2-}^;mtoMm_uo@_+!@cm(D9*
zq?L{U*v~F-Q}<tA0s1-P2}mO*4g`G^lT8K)ZC*a$w*Oa^4VcvY-#+!DW*zq3TO1EA
z3P)uQCR{{wh|7Jc<3$SAsxN^tU@s<CkO@sHxe0AQSPVQ9E2J5d{kt%Zn^1`x#L2OJ
zg_GxkqM;m#_1BOwbs9N4ST!(wmUZ7k(hRWu2OS8pU{N||FS!6Kg%!$jiQ(O42d2IN
zE>Ww#Sju2SJ_(rzS@Rnt@ggVQ>+FH^u;E!_m)^s9f)MLZ<pyTc^Nf^A>V)hBP7>pD
z8LAU2k{1osJ8v7Wy7-5VX{*TSM+vrkNz9ogJ!Z<+fyg=`-B!Y2IM3%19I)}Jc-4@>
zp%Yi2BLb~^)R)&#?=Xjbr71EK$d+Sw!}iM2zyu;IGr&R9Jp{K}MlO_c)P0b>G<)%W
zB*j{#Dyhy`ZMP6*7Pbd|hWRb&d-`j>JH9|6{@a(`uOTN@0R%g~14MOYm4TFq(Q)^@
zO1P(him#4<{u7Y3>ov5-g}w={iL#qP=m3{I+Wf-&0JrWU4UbF1Y+_Bjy&F^S`W_VQ
z3EuH{@5K9hjy;)uU-#SXg>%4>a??@u(9w5_T{)Kzy}Nl1*Po%0W}xA%^o3jXuT&;V
zrf!9B%i8p(*C))9#z*_VZBO02F$7N6$r7}Jqj_YmngXjFIeG#Lc*dX2q|*MQAYP=p
z9^e$Qe~pY~eL;cofVbA;d{%`@G1=EY0<LtXjzHuJ>)(sfy9zibkC_#b^RKS}j|u#r
zh1<UmssE+M`}a@%i@pKuNg!9?1so;rB`zZ{fouhc&{-e=8rTTTrL?6NiI>IcWX};<
zcvAV0%TmA_$HwNqN>tEe>Kcl7pjAo}3cnApJqE#5za-XOacYGI&!Y8dS2b-(Q|(wa
z_2KmFUz^_Moeb3n*D_z`+{+;&JTf;}-E>eg2OW!v$NzzNv6i|-x!5x(eGp^++=UCy
zN1-r!{`u?;Td4Asg%SsDoq|1H{iwrCWoNd=DHgT(g2q>34gGb*_@VO@+}HF|8t(uX
z6~Xo5c6(=(vA!RUP689d92PcuMN*WhjuMWtcPaLaU4B`&#R-c$r%7$RJP=CJ7)Z^g
z*3mcayETs!qr0Z>TO^c{C>$_XsUtOHucr>mxHFvU?Y=|gGWT=C$e2H2P(jA@n+oz_
zS-@kELdHGAuE@xG{<mQOC#nKov)%s7i+_0#IHY^d{NFeHf5J!q(W&oaAYPRCyXF&%
zn_!$Ht~ziuI}Grk954wu)PzzJ;t81|YXY0VA}G6%#gfPv^%5PQ1Z(gZG#p!q%?T1W
zqmU<WfTg1D%GHBWof1yU_*(s9y_jr<k8vPwm~iMihT+8Y!igg!m}R7E8y9l3b&WQ=
zPA`oY$!7o%qY#=M4HduOb%A(YoOYO|rum15B83^i)2`;<3gQKjI<|Vh6!Cq5X@S+)
zN&skZa1joU-QYJ&27<*92$pD=nnIf+&AHIl%7jLsZD02=hYT$m{@0-E4+K))1F-vV
zGU%DjXg(#mU5Me0*8o5@fV0P;0!u(AF(PU)+3WfO;B(@fvgGfmgvkRa9cc9U9>)RH
znJK*ld18dM#$E|z#KMB;xuGWKjvHP}?@?;COE|gXC-ud2b+ip+Y<fD91EGhB1&#gu
z8G2HfhNm3n7Wt!kQxA|}&gyeDQgxBwD8VgnPnBwi^I+9aKDIGFWpzA-v?F_lEb*)F
zVv|VjKPhR<ylg=frML29r0*<jaTEHo!-0$y7q$d0p*WW{(=@!VZlb0fIu^^DpEXB=
zm%@f*9T|g4Kq*jds9Zu#R24%tKT=1fodq`pnu2nJld+$1EP*2J4-$EijYduWI20Lo
z_<CNJEgTZi(<ueOeg(M9t@l!OB>8FiiGW@-GNyuh%kf3PB@Yq=uhNpcxLscf`0Wtu
z2R^>LdI}AKmL7s#|K(W9d$A6$2G*~JG1)QKc9c0!&zGO2n;trsig{KiaSh;N)Z}Q9
z(|f@HpbW2Jb*=INW>ma7rHChq8Z!Y5C~*KGHautILF%A%Z5VghgTlv4Xd0m7(2k|`
zEFjv}wsx`A<Yttr3;|)!$;26nN^HwO1Q@mtL7S+F@;pJ{b+TNDC)n8C)fBj#9nkMi
zfjV2A^Q9cFIr{aMH{baO3(ga)_VnD5ouo+&Z_x|Cl=s>c+>`;-4Il@oFu8}Q@;^QZ
z@w!kdC7z5H!AE09i0zMnbIX8pyLp4wj&K^isIy5jEOyLM@ga3W^?uD!6=L0=?9+iy
zIVaAGT5<uXQpquiA{^l?gZfgnAm=Y1Uwp<v$OwW%Ys<cs0I~8!aPE1KH8fFmzZ)XW
z$LT_gzC@t-PENRO-pM(mTBIHrqnABEg_to`cg0%qIqAX*<JvDYK_Yl|u!+!4HmMz<
zvNSPJ$Q<iF8-Api`5N;3N4VmS40MpaQF{#+D3W7SV%z)Ykf|I|llzlLeHR2`ymGb|
zz;^QwJRwU#Kgthm($#(;AR3(+QUXPQiovJhJx7W(HQ7HrxWk-AIF*PB_Q+-FfyzqS
z75Gu*G<1+nH)fBySw0qHdWxXsv%Sy6H+AQc3^?|BFc(F{TJWuOXcVLvbV|ladradM
z*JFS3Sf(MD7a95!@DAIsgyk?-5bFDK6n#2)m8>6p5*XhLXdE+s6exPS?+9ybi!l9I
zFWKh;2ilj7T1}cgG7MU<u#D$!oSD|b$QXk=P27Z=xRW5=pi>G5ng-g{ffFkLv-JRG
z=Wt#v2j%vlZK{PVWK6cc-lDuYmM$v=P*-oCK)j^P>ACjFn}-zU?#1-W0L#%*Hl&B)
zB2>)=l{X2J8Hx&kRCQNs0yPB_X!N5&nnA9ZW-5or!GJnj9@t6?-ce~`3cHj_XQH`y
zf%HDb9)R2s5LNCKs3?NBx$JZnmHUJ#zz>b4z^LiIt)oB<_l7U}2UHsX3K{-J47(pb
zfx)6d1_*Pfe;yOT7M7iZ5@qZ3FNiXpNxEx5<Yp5BCQW!mkTGFzM6e;&@@A~G_MO*@
zfKj|~05|Z?xCoWjoT~)pU_C2G3j-FPlb>I_48%%I5v9EFp)sW&lUIY^-#I>|!F$4{
z$Icy#p}igp0pq3n!Hk2pn*kcv|9+-Fpknhm{%YAvQ`!%&+LD#ZOkZ22#L~r{vET0k
zK&zyDKC*;SzE}e}P<BA7)U*CYb`C{NkZzXp6Q1sPT}Mk0axRaKKv**@$E$E-rk}Kz
zVTV%EgJ};Y4C-9>$IQRPSarr~45Vj!0GZQ84yxV8-AuKrGwYi8*4K6SNlU)Ij&tSU
z)r`k$FLa|v9MJjf$J5<r@0>vz6CZ$NCwEDu=8w*?XP$lFP`JZP(PDxolf_h7zkMW|
z6#Dxe-xA|8P!oPgtVmejIObMC9H@y|R=N%$VmUs9-H5N|1k7p(aC`Mu#1Yi#Rs!$t
z_4wx(-4ugM0RJt)&U)h|mjAY3|Buq~f1(U{{~PM{x5?knWfv=Y={ehjSSuns&KY}=
zFi~~}5*c9diqym#gtmz3DF`>Y(J!;N0C#@Wfp~G@7jPapa@;Q)FY1~`#<lw(tqut%
zNdnrCMsK5fo*n8`QdneEqPJFAQBj<FGQCv-+EyRP&7M3f3K;QE%{9z#5iIddq7|;F
zonwdd5cYb#w8Nml6FU#Dtv&xCL49BTw4)(!&vAr38AWNfFd%*U%pe}Ts~abZ4~Bj&
z<YD=4wu(?vcr;4xdguc!`Ii(Pdm@R(j;8L$KiOmZSM-cP%2d7W*nC_rYzV+(^~Y}@
zt(5RQ9$*1JUmZaQ6i#M8|4TK~f6WDg0$4RjJ@n;=wlT;8sQ#+{8^Dw~fGIf%R|)QT
zCUXc{L7assg+}YV0tn=<OSW{)g$dG`{KsJNFnXLPv>JlBUmy!es3PEV8s-LW7||YT
zKUUuc<+p3Abg2oJ<IA{4Q;`P@{j`tQLhJ6llm?V#r$)k_vsncZjc?Ma%>QNBS286b
z1*J4Gl6y}I)2SFrP}k2h_~cb)`z4FJva$m3k2cN*f3mo_kF8n^;1VzY;jw;3y^x4S
zyTF+Db-5njs+y0NlJ)fCvl~bD$ES7%H%L8?Vp{txEA;hgjYumYbX=cxKl>LRC$nHW
zB5rgWf8dRbf0(lhRb(U@t!^@XJ_-TlCHylIcCZMLXqUvg!TZ{XGZt9`6n<*-v=DOH
z-A4(CgZ`EOkMj0E>#Ey)<Y<gJhxxq(m->Y2i~kJOT-+19Al@I}AF)Xn_YaBa_?!H$
zC`Bu{8$^p6z|G+VZX5HDK8~fIABiAWG<(I)m(S1-mV@f^Ik@r=p1{U*F8~1cByVDv
zK*2a^OduxE)Sgg}!K)1t<p;vz<>9+Xog7v<beaT)y=tP0&mO)0HQN7o#o|mapZx=s
z{5ViROzDQ_ot+w5q9TD2@AE$QhevwkKfK}-8l%TX;n-lYU8klcJhCuyoR@C;?XnDg
zg7KNnW3@N-$z>_MVt?i90vhOJoVhPd7SUEigKfP&corCiaUcqtpEsNy|FicX04&li
z-}vt=A@axwnMjsPNF9M)fG#D@T0Md}z}C6fsZ{;5Y$(I0a&q9K1=jt?_?)hb`l8B5
zL+kyI^j+@@j2&m^%&kW!>-4FQ<nrdb0Y(<144}$3_?$L`P2il_0~vgJax{_EROHiw
zOo=#cT?nBQMEY*{m&I(KZtg7uwXJ@!I?NpVxA*ea(eL6!*<%*g-u$p%HXrUliQ}ce
z$p~-i)DNrGW6JX5{Bg|!;!W8p*1#&J5y0X3<gMBQ8h}QON9K?L0>MA*pG%wn9o1L=
zRnyxY_wc}`F>|s~5J1DU@6yZxofvNdl>E)02I_O5V)8CXF~u@A5xe;_{wh*uNnf3g
zXk>1WPm7q2hUjA30wdecB*I@Fx=6Sa<pNio3IB{h8%4P&2O8gc%mUId(t&TFr)W0Z
z=W0iUZS0&t3Au2Y>dH=q-2)lLRg~_8Uf5pB*r=V6P6M2yVrzpY(ID|T_k6L#a_Me%
zPSNg!0(Y`RU0v9qPDOJ?P9?nbnzYU404cd~H5XVu_@Glk*YkUD)MBXh#Va90hsLQ1
zK4aBw=1(>tMI^%PbD34wP`B4}CuL<#SAkJyJM{r6nab}A76&uk9Yvy>@@`(*RaKQu
zN5`JsoMaX8StkkF3HwmyHVP4+{dqa&2E-c1FfPk~^rM}<t+$M#&D@JuBHfq;O5i4)
z;mk51QnAdy`0;5+#_6Az1*4-0Iu%oc<0ct)THg`y#4gNyZPoXZ)AFa=J!K9fzlk%0
z2?=x=q%OL{_)jM}?T27^9^A09QiRz2Q>Ht`eVa>2V+)c$m^>IkDi8DIA;`4^;CCo}
znMx_pd}tRXcYxOm2S5m3KV>_pfhr~uvY<M1*3>A75xo?C=Qcu#3S(k!^#8U~H9LLD
z`nImykygT{=3^)RxT3m0vne58a3uO-)m=k#2X76b(MNc0rL=ue%J){LL%9A6zg^`T
z#Xa{Rnn46?)u`)Fn4S%{=!Y4f2cayQVv=+U7XDo_XMaU^%r;;v<-b<ta02AH`Cw*<
zy*uWnX^bOxIymY|e-pPjoK<JCjJg0E069ll5V(eAHox8K_Wd|I=E41wDauxQQI0y%
z>94kK-t8TQ@4Ra`hcl0Jn2Lt?ANn1t8)wjTDr-N=2=^~BTt56n<`>@L9K42I`1L@;
z(5maJc*`$iE)I_i==(Rrm$Q~|3paw9wh_RNfaT~Tu2RD!yQP8-@0!%o^E<o;r*!d6
zig`H;nR&(CwUa9fw*I0Y>j=C?D*TL-1O=5tg<fWk1c=A|_X%AeDXS<Z)$87f3hI&A
zgXprY+YKHr%q54|;&kwL%-f%{3?N^fo=rZgTUpyA!?(rPl<9p>_2<*ssZ@PfS?5=K
z+=ZadJN6J7tX#W^p|#$HCW(x<RwmlS*3^<yMi~5^BMv+#ToAj}2@Se541T0V#1`Fs
zHJ{1<se5ETGw)OP_l6<gmO~RsU)+Q}%AA^j{Qb)9w<yjK2A3WJCqi5qimo+hK!gG_
z_)YJz%oURC-Y3u-Ej+AEaiuw-GlEfqOv(N7O;p@<GQAgJE3Q@EivlaI+TJCJ5#aI0
z0IK6#OPA}+MbY%r?<2*)9FTo}qlZo`^il7RnYm=jcQ1bLpQ7l12EguL(=|=&O(NY9
zn)cVX4ND49M`gGI4l&9QI%zc1p+N7m(K>+SGu(JgOU1<B5Kf5<r5tGIEG8>YW(zjV
z%XGG;gXI$PD9CbFn>{G+!ujHS-dFR}vMq`~1<i%<y!fW)%#VVeOsWB{Sx7m%y6-Q*
zmVX|!9N0Ma7Qtk%TPEM25>m<eB|~=w=6sri+kh=xFi1WPs?&9oN4Q4Y_(_mH@G=#^
z&E*LYi`YMI_&3*w9^I4JE)^1gS-P=*$9!xn;n4$$ZBTrpB1>{edZ{9dPl@5-AyAI}
zSGIWTzX-)CPV}r_H20FJ5JECJTjP1m1qi%o)(7!6Dg}~KIQ!ly9S)k~PaLE1*<pil
zSq|3QR1+3BT^tz6oD)_~iFT5yG$rq#ICR%Nis`rHJb!Gsx@nGARiZm^#(8{pDN&v2
zlZFPsef=nV$b_Nm5Y>6_3CBQ|z*?NP?S|u={Y<Kj$^2UCBma=QYocUbS&g0~LGf#K
zDB1>yNrHs4_OpI3<9;o_WH*i#u-JEQ7CVG)lMxSzl!-tKh||5hLqyy@ohz3?yQtFg
zL-?QOuL+um2=6kN`%!B`(d4DN2}AMOik6qqjN(_NSIP0LN|%bYi3CojNZ+(5aKlZs
za)L);peYRgqc#;LN$;c7N7Y#TWb7lwXjZdJ33Q)f-u@Drj%W7w%x|!_SO5APTepAk
zo|S~8PlKdo@|_?tauP)KDO+OU$^8SnSI49fHEds?j}j|9w%4O-PM1!Y0>Fws&X;Zb
z%^r2BL4r^CW5rjwU|DY2o9!#=IwhWLZ!Y`YL#YwzLkM7mjO8dCG8M7T!hy5x1}jtd
z>(JaMA6&c3p&vAQW8buydmye#fZ*jYen|;Wq8X0WrX+m9MSp0Uf}Ef21DBIqFfJcH
zHj_ggC2b8PZ;(nU#50bM)_`UBcI?E5Bg??0o9WH(w8gfcBx+~cf|}S<RF`8qoTqfO
zT?BcE23PzZkgl^Urj{Zl*RAk3KDWVpn{2mpB5e10{XU9Cw_-$nB26aAlRu2DidMa~
z)ml9TI?1hRFh9yiZ{}*FFPvAm2Weg2k-VXx^hmW<I&<E>dDv+C1a83ZCtxd-b#k8s
zO=X@ST}30KmL{)o^1oS<pccz`lFOz<WZ-d60)I;XMEeMG%t=&|bO4v?x4LDJPlhU(
zy)3<1&OLlp+D}T!nOGR4S+E;K|8q#!ST1>;tL5}Zam8EPr>!SRY};jYgxgUIRiI{`
zRo^rvBIM1|C8d&eKPA>yCcA%(WS+4p&Ta0;?huv2S~vaPiQ+rUy5=62>bph~?RoEP
zt7iL9lR)>1Lv<LDG)XsnPxke4|Dp?1W}ds38^%$IHG7|OevC{3@=oRG!eBk4lHyqI
zoCOYZw{HN<a$(Psc}x}PH(+>on%Jo*L0oJc`_w$xOPpj0wJ;xX9=)+F?+xuee;j?2
zpFBfh=7TdY87-0sEcmgYB35_Y1vbY8BSlaf#JP79Uwhw|UfSDw5csx&fFx~@2>j%&
zL7;;9t|Sds8_SQ44@w_Zu5f<g1Z{;Lsr5~8OkkQ{S1m9~+crV{?#bHihjR9bk^%dn
zzsm{vOmN7JsEYPsn;E6!o0VjL2(Ni>!C6)HbeVW~x4b2M(v;;!0wgM#4-u=H#I5p6
zKBw(zUY!UAADrP_(!2!zS{bI5soZZ%vz!erXTkE4H$U;mYNlaXwjGzM?2m#6lz$%7
zEhLua#+>}HN9xc$1F?9VN2@|ggyQvpV&R_7+X7f#x&nAL9xd{pqA9_bB+A2;f~hpI
SxskK))gV>0?iMSVzxW@Zo;T9~

literal 13251
zcmcJ02UL??w`K~xBO*wPNRw`%cTvDll`bt<DAJJ*0fHhbh$sq(H0c7;rI*AYp!D8L
z5Tz3Wp@f=w`TlS2x^3P2&)gYS%F3IabN1Qg+0WVU;pJ^Z9Y#7XIuHoN2)}jH7z6_2
zPM<VXz?IOU<^<r4>b{=NP2e5)+rQBe4!qHN-MZ@o0@0s2eS!z3!;V0p3n2K->!yLJ
z>(fCWcpRE)HXEFTuhE|WK}QR>WGd3IWWRag9ve&61$)b9Jhxe{YiVfUK0!)#&aqOm
zGks=J08`vFcR)NrU9whq^GV`NV&t1QkrIN}lGDS;frV)w^J<c}eRuTny?#5g(`Lg)
z<ifln^zqODWxOE#fBZ1(Ezl4rIJ=D<_m^F37&8yZ7&UPz<}3KM5-+7;Tr&(l&ayV&
z^|lyY$E%g*4_T=ix5^6IqH>}q1ePI!3GJRqE30zgJP<K!QWkSgac~W)LLmWmgjzte
zALj+VFN1I2$E7;g2Sf};^ka4~Iv57~`{_!%7u?KkDY_tGlwZc5?SH23p<IAmi`ZZf
z3~M&1`6Kbv1{#r+U0nh;7Rn<V%B+VfwuGN!L@Na&{kCh_oxygLVGLjHWQEdnR<o0W
z-Nxdw?$s>nTlZ|?dmg_POvrpo;73z%-Xvia>1siisvj2)HK=OYoFQ^A(g&%&aJ#Nb
z_xnm>lrUvW-c*(vB(K>EauO#~CRz6^bYzdI`DGHd*Wb@`+~Ka#@$=vdDMo+K3lZe<
zH|I#ajwc>KrEkV{(jriA%Y&)3TDVXS{blQ0D*0dqVJ>xnMZd?3P+XPsn0NU-j+-s`
zXhw0Tpri&HLHKl;)ZEj+^JV-QQ@;lFb)4OJxx@Tq*7nNpjf0x`<=pIK)&|DPMx4iz
zvf7~!O0Ou%O-uHd5j#J`1#(ZeMa|w=21!*hJkAIZBABj)DbA(rJlt+<$!gDt;p>$g
zFhtJ$LRdvp5=QU0Jciaz{jtSAejEJCQ)$PcGU87p-}I`+N0u|sSt0l4$G&cS$E$N6
zPpccHl(yl+-#b|fA6UbVW6^E;N^)pNd6V$^A$=CeMMPS-_<UV%;`I_Xns56exSL7~
z`8*uj;b`vT<6yVI>NEqIrj-Di&N%@k1}UOn^sp&@3%~OCtymAp98vwG%xHrP+pK{~
zL#3cXmyfQeh<d48grpxXkCja^Z;<BiMH*zp_7l!t&ivu}$HXJ;?&(a=`e#E?-Sw7}
z&`5`^yY>S1#xh9V?Esd0Cd5(NxtW{$ncN(RygKUm#D>O(dYhc+YpCJTCR!dN5&=e`
z9~%|^x(t5JHEry8&2c6~jBrrej`y7DmYaDs4P$E{CrhcI`>y`jF~L2L<U5*;*r~tm
zL{CUrI#IpDZ3N!XX_3pEzqY7`<#_Sd)>-{f%c*LttoU)>a>o|F<L`Bi=qqpqDrD{v
zuVgMl@A=H%$KLr0B5NsZ_{z*-KKY9g1z2LTtHDzF+3oV23Qzi0#T;le^QpY#GDmAz
z_vPO%iUCfnV%sgUGoY#Gh+uVSG#n|-MyF_pOTUUebXq%+i`*x9b4-L?I0ymFRl54v
zqf;Ms*v{D6N*TAF$E<WFvKVUBoh6~X&bsl=Jyi&)@7j;Nz<u68ow4@3Wk!!`Z^GsS
ztYh~xOi1K$x(Us_P5or|RL&~mv(K^FwE3j>#ywYmB!HV~rwnLHI4*^oZ0h-L2M+p1
z3P<kGq_M0RoniV~U()5zPqDg#>4`jUI6T92w$tqI)H=l*<D{V4CR6Tc-hLbU)knpn
zBBvKadq<a#=yeWqdZEhx#Ja=2{n6@<M-4+h^{d24lj5x^bq0kBYP-rB=TD7@J0^_8
zOU`~;Y-!=qtk^p(!=~+p<v)oON@1-9><V@Cz0*+yJ)>!F^|{ZBC0}h<6sM~g=bRUj
z>lt)Y+JQ#Z3sc6CCi%be6=VnY8}YKV>az7TfvG)<${7qc+##tKj|3NNu>F(ck9-g{
z2|Wlp<H#7(F6tBg#o6PTG_SWWoU*Lw4Xm95x#yPfnYXNjWLU#QFX0o!M!i(h!e3>T
z*0XXBbT7{6D)d%~Y(Jc=3o|+~)D$a(Vnep}g7VH^U-Wsyy{ypmIl{|u%m2=yD@dnx
z;zZd?dd^9VKh+cIKGddx4f^GczL;v1>Z)pQv(1%}KhLwQjpc|QrcqA|a(erSo{^e#
zmN2YRT4aaiD-(0L#H(&oPRpzMHWOKqbQ{KqjwfW2q%NzCjQotG=T$MR)ij{1g#^ZU
z@vQ|#&fGc@fy(y=#cH~@t{!RktnwMOZ%}XCB7N$zliOg9WbPsn!C@RJ8Jffl&jVw%
z8?_zACG)hZ`R4ZWSoRNmu`WmMGTl9q%&X&(u<RcbJS6dZLEoj514ZWOZV<EgyFBh)
zs#&*w&|7z*&|R)9ApL+?RmV<Hx%~Wf3;>sYg}bS{{HgDb;>PAly-yP#SYyd^`%85(
zOJfz4qH{-!v!D>|7Au;Fw5BZM#sG#n<RVhGiz59+ThlFtJx*-g=cCtzc~~iHvqYnb
z{e-5wMj-4x(fU{X0qVXgE;F**R}>q^H>INkN@Ghop;CgZnrX&mC6|ddq!Wry=rpAr
zRIv-~#WYEbZWoZw8yv%17D`w*YaC`N%-`feb9L02FoP9NC2ErPok@eB1!b)zNnVbW
z-Y=^Uh$hY_L<2;ZOJw%zN`&xCY!ov@xIG<BH5a3`JDU1p1))n5AB_9vd_v6{9Npbd
zu+Ewj)da-~A^FiwG46Zw??!)h8y?#Clp)=jgpLsABFMS@5K|qtx$vZp!IRsXeNG6G
z<}VcuH=}n@XnoSfq~2$k#K>aixbIOL`lOdVFcv9K&H%+$dCF1Nu%8kk$!%#7rQh3T
zb#*+I15Pbr>6nyl*9k<#ItX`BX>aCN(^Jy2L=B5tUlL33y&E){L8|v)qxx92QbyWC
z^aRaem)*r}UB0Y&=p#ubDZw|PO-T3aOYGsvJr=*t)7!9Dr3~L1JKOld>rZC6(4rdo
zh%C<w_BQR=M#M$6?-`Zokp|+M-e~y0JG_WZN?fJkU>}Qn%@^ihR|3F8I7_hOxGVRD
z9_h)|Brn?Lmys;I+GAh#ukznakDd^%S-)PsD8nk(-XkDUIc0aRSq`l+meHVK#We2i
z%2uK)D=`u_qNx<Nat3>Nso_gSQi5BykEQIHk}!(Q#Sa_dca3U?=6tbaPfp$jaJryd
zP#ub(ReYtFNGt#bgbi$lS!*oLV(gw@-q_ER9HR1-5ZE!MDieFehIMR6>PS7gfPL;C
zj*E$8&I^Y;KQoh(O`AzPcdyckH#7fGRC8mU*iQ<^F3ih6p6DGt7n$+#=<3|!Icn{e
zJhtH0&7##c3Ki|8x^<1^r)auva(F*03pwQ)UcR3o-4+!aYbS3(MS`0y#$`?T#L>)h
z1}fAejK;n&Tn3HB^fmO@@qT{B@{n0;&`ozK!ZBYuf1Ttb%-Vdb*ub)x`L~KXB4sC1
zno|@jytCYo^x&C}tH~H<I*NJC(p9q`pf;zGpLu7JnyvZ!^)Yg=%ACtBQS4R4vt#Lf
z$_Q<m@5MCgCiUm<nRxn7RQ*Kf3R+Z;24CUduABkAj-9Y%CSB%A_dJaj($<!fVDXv9
z@_P=_YT?4XhryNT^GLQ<`EN`@Yt$jBWgDJxV=_*x*sb$eYExU8bvI2TZ@Ns8H`Bk7
zQfYgYGwcl+lje=-!`c3ouj5>P;-MbGNRsr0?Wu|suKCUjH<ot%{7z!(Ui7teHEZuy
zgoVH6Lu=90I${?l<*<p7XT7-iFT7Zg55e5NHiyZo{u~>cH7C-Zr=I+pUi$r{|B+$}
zYvL?|Hvh+OJF`oO6eukMlz1li*KyV(BLSWQ9#3@-11xL%{>-~^uc_$18#dYsGZ$uD
zd1<FuV?(0LJcF;MNq<xLP?k}fUZ(X+0K4%NmS&=tY0zi=XUIR?ZAvZDVMk?751t9&
z>Bh=43BjG~2EI%m9{-^-sdL%9v_!9KbP=9bM{6Zs^sMZzubIEc0OhU!iZYv~8(Rb{
zn_Nxq0>2Nq;tL-ryk{qt@bTk!dinS1IYtHTphQ8xTM}%IoKFSew3ksra9tEw0&Wap
zu!qN=u?H1d1kF9;Fjrsr@Sv^rx#Zo7oUl>b09BcSk2zLvJ&#}b*cM0@P2A}#ys|eF
zvhi^+&wxA8b4sdV?W)reX_Nj}OEWQT1GyO&el+h6A}(~V%7-11*z$}r!nAyRi0y#9
z>rC)!ankqv3?;vnH?Sidbw>DFdAkSYhs*b{9C7L=Bs8xV1`OLCQt@leO|q-s+@17P
z-;fU9?|k7s-`1q=F;)9abwAj%akC9o8oKU^=#q+?`BYUUTXk}<O2#!3dm8X_&tWr5
z3n`vq3d<ks1sc|1fw+jtj^vDyk&z*XB-?7ZQrgM)<*eg_9lea;m4edI(NU*gEtRV}
zN@<4c4I9mfbU--{YDO-Z)d2M2^dB~1{ST8<Q>i^QB|`_nkYR*H;Wv<ZR#;%q&)K7G
zM+0sKp0U2=r@x;vCk?9-7AMm~*Iq7`{$SF}A1v6L4G&+RYk6M!g0Tpy@au&@I(fV|
zta|<T&vzL=LSucEjyDDwCMG9MlJ)Z(d%`nh9t~J?1FL)Xl#20Vz(TT<-PmAh&0G|>
z)T+T8ec#yFhh$mTkswezZ@^Sn&gyarsY2Q*3}3%m!b(2w>_n^kJ~s-#KQ)z~4v*!Q
z$NvVzHnZ2tNC#Jfwtio!bnLbY*<Bl-wQLOZKECUeRy7z&%f|N%yQ(W$DsubSFPCeI
zqWEU+%4hY%@7)%Mn+4X4uVxL?zy3tko8}E3`W$R87sc`@J~nUmubvG%+#k$0n!OM}
zTI$bELIV!4{#sq_e{eY&SVgU<N#3idzQHm3r;LWPfYZ@;5QuP--I2aNuN(dcKi`SO
zy^KqdbGIU@9j^I+Fx~RAjsBsb#|F#3O0w<~Ul-P7TA~;m0#}9#ZbTb|{C=y0ud)C3
zWKgP+fL-O{=JxCwoebLgDMH<f2$^XNItJ~|hLev!e*74w>@xJ3$QQcZ&tKOYGaBDy
zkf9ce^JrL?E4Q4qO?$pZnDl6H|Lxt*Tf>GCO0ucg3|dU9(Q*rlgDH67%uXjIwQ+m$
zu!r+u#>yX{>)ggG>NSZwz%_;!qqd)?*N{$==?dPz>^N+^zBz()V4>S3-QC^jlG0N2
za=f0@xWep~sA)?Jv%1RH=H`>UwzjsP^KG%pDtmr(lJ7{3t&5p`N58EeWHU~*A$Q(H
z)YZt2+|8FwGYt4B*BmAzFJH<j>oT;)e<SK^aRa_y(j-qmNJiPV*)O**_>AOy#c0CR
zPR`Kd!tMHjNlwg)>}m8An2Npgv<VGWU0+|%2vqm1or~&hd(C@zgaSi7zMLx7e27=3
z!#$ywB2D&&pAehCVVf}sHyQ6=->LHqQg+pseGdpbqX=>xkR<{W6BDmHsw;h$2f{>B
zy|50aU;2&Qb>h+xephfB*0AFx9X6dfzT?OK6Vm~+$0NQ<p`;x6yRh(1$r?9Pw+YRb
z`9x`i9eShr@WfLoO%1tPToXoP-|=2Fdp2aF=`AdbG`Bfw<I^o2r*^Q!MpTuz);kp&
zG>{J!vwH2<I)U#|c}P|tXKoGLSbnXH=iCG``t`?aPCf8mAYzz%fEW1YqE>IQv@D{I
zJgd3xn6J9^Lm&VVe!R1o7J{$42Kc=CtuzoX3!Y6oBR@|UF=(WIT&3A5f?xy$7LQJ~
zZVV#64h6K6v#NLq4^`|GRP(GOzS%ggzxr#n{Tnq*#c6LkAfvgRFGPRpL7Zyy<HfXq
zl7uv=*?MDV)rgW}@%@o^Yg?b&{`Q94q+4ZfrBg3og6GsM`DE{#wOe^>%6^+F3HLO-
zE9?ipX>Y~^G-4Ytow&QZJCv&Eb6^{?UUirXD74A0^sd3veW5Gal>w%P6xFb`okq8(
zA-8^JD6W4U#PeLX$DsG}RgVZQ&xePHr;j=jCtKFkFeG8MHBVAbWwn(9!6WC`^??~|
zTwtn<0amNJQiM2OnFr!?nU+ns%eEQ0zp|gsUCK`So|J@i4{+j(4qD25J`%c8kk7=a
z5=hwLcK_O61(<oNIyW~Le`)}gN$AHb586m2T^5=Cm8xSR>4Epr`59rshXAmJGZ4le
z#1;DhD6Ymb8+yli|7_u0*R|fM;J#1S<zu;ISBe)F7QzG68!GjKHSq`DKo}j*zt!QZ
z+X2QtYI!M=PU@zlKPRIx=O;8946(Jgpeq)+o#vNYa|Jc*DkCT7I0A$Ta|KM$TYe^R
zUT*eaq{i;ju=c5KZY+EqG;G-T^pxqN0<i48U$D=0iIUdl0f3M2_4BWldtNeU0C+h8
zxP3dZ?r?K=%BMGFa&nTn0YIzX6MO(#xqR&Fv#7<c$=dAKN-o;SvD1Qc2*lb<0Z>(T
z2rbOa?rv^2<}Ndx?hQr6+Z{l)+rpPL;DaZz&*<X}^9P?hT}{3L0MEqU>EM+xQi^-^
zIGb%+DRP~O^YSNNdmtr{D^;U*4G*_|{|rGN?@k&ttW|V2i~^yGA5V9^^30`nDJv#V
z(yoz2XpP}uKAD_7sc!3#c$dzCQ1<|Gx!=OZyOjz+#vP#S%1YVR`x1`!iHFyNR&|l8
z5nCe(nya*?p0>;GDl~d8T|DgOpVGFgUdbvcA?E#IFLw9i_E7S-!*8pT_j@<gc7{yU
z1J<iXPYyxxLVAfq(w1fR1SaLRvd`kBq2F0`x|Ei4-l_SJkN3!*$9#d}r{D#oPf8t-
z%Kn8;ZLiMHpKOp%HdgBfb!JzX&jX;@7$a4`G;n|L99O#Rqk$anUUl*jM_U{W%@Nt1
z3?$(sDhUy`KMDZZ-K$<W%xV<F`K=kiAR%{?D{U**fl6UBn-KIOxZ?T4tI75H!tXw+
z1Z~w+q}cZ=%)e@322>jlm<d`uVinLnav861c>0`?d$;sCU+_}<esIP+VT0hBsr`;o
z{8XJ!FNE{z!ynj#gM+S9TuK3qy)kw=d&i_F`nVVfSVYzR*60O#02inMAlh4lbpBzJ
zO$ElcP7OcVCz~M<v-N<=YK<*S$`T)e0_9WS5N}(eywOOkum*snt%9tGDZAHVr4}5d
z?bvo+opd?Crt^u`4p2F)-zuv#gdH7>qQkcI5C;QEM#}47VREvvl4=AD)N|Uag)wR8
z_g}!V@<qJqPU-B+Y4&9Og$n>7y9j`W$5iOvG<tufsPyFGvK8HF9VO{WH*GJI@&;9R
z$L7W3RJMM&S53)bBB=Rldt>EiNcPnh^<v!WVMm!J`G&`#1Ae|1o|YEo*hW*a9p~kr
zV!fadvA^*13IO#nr9eTcJRM{&zC@?ctO!$oA7t?qpnx53EkPkHiT?>JEGtZ-K9b{$
z6oRkx<@#g^{nbN3_&J|a(XV1I2K69#yjX!b6ILnW7q!k#kvV_7pxKS$)F@xYzMCY?
zHygt6_+X6!gD#IJiGn+#RgVqVZsSfa&d*Yul<HRfMQ0DPn*9~A#}OOnIBS%=cnL*?
zW&#C8{w`c{+w$!0S6V|Qi3=H4{c^c<OOn(IymUv+ua@ytc+txq7cV#7XNRxQa=%I@
z{V^Dwajl8wx$x2B(VzWy=#49ah48A!XMIKG-z(ZuIZJzW-js*SN|+@*F?H}LsBf-*
zYZMAE-bSf1TgHFYElA)iTLkOHn~PSR^-veTijOZe<A)ow59u=34!!-^ZkkI6k1xzI
zhmhW%na}^1PnPjHX3_teL+i!A2SI_W6r7lNK_LvlxJAKKW&ep`?Lq%m^b`Lw<i0lO
zIY=4Y60wgl!4zY@P^`ua7W{I_r9M)$rz+#}V)Q!a#oospNXwaF^9ZoB9iV<GxEegu
zbN=MgWy(OFv78Alc>ItF@Dr6YfoM!EbJk|O;LiD~GZ+i)8dl$-_mB}t$%{5X#%V|w
zM5YOptj~=&_lY-<?E_<Knxz9EjItW1PNkB1<xvDZrsZ$^9J4-jCor9hrpX1^Bv^=p
zd1@5u{n{yTlay_-ApC}71E7ehB$b!ia|l}cL48BEwc2x*9%*3V-c=RMwePusR(yPp
zZeao+fmS2*?dEZz*$YAV@NIEbTxWzZmg5Oh9v*-2-iod!gZ+E}e7Nw9U;*9fnv2fN
z#}(+Z1H;3MDF~uMZASm{2{35U+5amu|8pLZ5wz9l;=-I@3^87~P~3G~uZHy#KX|;U
zchvxNh9{71?3^WJjk1f933>vjaRT&zWwi_@Q;kKiU_oe536(GiK_LZgHrG~Og;;ZN
z?mfb_B#2kGzoM!x@=7U%l$zBVzzZX*P`&20-J=ob**z+ChIK)4l#XqDy^!6PcrtD6
z+bu}or80Pgw<}by!P?%4s~^3YGp)m<==W~;FkifXrZKU)zF{^kU{)Dc!(;h!SFj*{
zg^EGJZOPx-cA)WFF#CAZ2fJ#Oop19-$4w6SMqBZIyQ)*K^C@uEN(CcnygYrwK?LLE
z^+wL=Eyc((&pKh=#vVeRsy0)}Eo)B+yEfIzh2ai7CHx?APB|>ko7AH$XP#Be4wJl(
zAIcAzcCrH;vy2hInDx)sL~QmSPe;6jpA~`|8x<sg>~4)w*9*^K$`s4J_XLgK3c^)C
zn?n>_|JucxGB$an)B&B>i2o<a^Ury8$QZOilM4F6p{fR|Whb5Mdt-VB2>3`VTHFJa
zYlI_?3CE2YMIoT`5kB$a2Jtx^MG5JD?qZlQnmAp1TK-o_5F@ZBXfdI`2amC!do@WD
z=$|XTp7v3@WF*i}pkP>R*j`T=)^m5}v1aas$wr@y?~<s@(1vNjuCeIV&c!ZO4w6pp
z#88JTkKEdtY3Ouyf{S#*&#%%M${P&!j=2+S^e#du9Z*d6AxtMcNhjPo;+{h_k%w?S
z!ut@k>|r)~$jCK}euNgUo^=k)<vLn<cWr^y|GcwV{4bJZkJ^q&jxqZq{L}Vyn@v1y
zqnQ#~o0nspT<Y%K^N8EDu<34iu>JDkp8A$G1I`eb$rEfySwbxgkp>|kc#2&rymV!n
z7Dk-1%^hqifeKDph^ntPD!x<BblJeq&z;>>zp2Az^;MoRcuL{n?YxVO1r^4A>qW)a
z>hE{UGI0&rmA4(d0l)*vRYp$`E@LWVBS1g#k`TC%p9#Wn<3Gc?%z>;xQ@nN^^{rn4
zt@>Bi)8aJG3EQH8;K($WyRHWy@s<D3m-x3p`Oo?Kzk}b~;$Wming4*+t$0((Q+NcI
zLc%GV8Mck8d4P&#UQBTra~b#0{$d#gNwSRhjc&yy;Iih80m|=R!Mz|INIR5`hF^uM
zmWK4`y!#q+s+IlJM@*AAB2SQg1js>3za2jv6N_29m%CTp-5J;2#ii`Zz53Y`oQ<lo
z6Cny7qF#Ew+)y5~1tLD$W1k1YfY=`pEGVmwIxvt|UmfzZuny4InmAngvwTF{B9DK%
z7+a7xG$6dBIYSv1@5>6HqOqbXP8K!1+$g;}O*h(aI=3qbFMQMr36#WTE}bM|#4+bE
zgBU~h5&@I{{T0at*MJew5WjqGjDD<;4nVY_eD+D|Mh=3duR5>6A&Zwb^C)nWB*Q`i
zbqF4BS;`Pd&7V}~l7h#ZYnB`IF@MKrFgI$9oei~%GZ%yph<&mIsk1haL35xlCzw>e
z;#)vH)BprIeyg0&v{=OFL|@vu!E?e?2{`!E5VQB$14)yocjI#sZi}I~2|8B+jcbj;
zsU@c{Uwl7$9eT2HT6YJ;@+?8*&sKnUz7~pB(FRLmweX|R87e&3anJWOY5iJ*F_Yj@
zumFv^mLkswCS=Lqt62pjXpvC>3<rSZApv0PJuh28{1G5-4f0%I2;m;fS?qW#_-OdH
z1Zo5F`p5a*1m{{oxD{=y-1Q6^6{aJKK)%1w-S}C(Abe#IxGSk?&uxEp&I$vAR1%WP
z0og~VvT&NoJycQ_I;vC<&fb^M$*LQlLstO2=b`)_>@kB+c&3?3?(+hU(kixjO$fm}
zh#X99rscUw676CjAR=MVeZOZ2llYwPLmhu;da0yh@fnnE+6uq8a2hzj4tiV!W&$X&
zHp!4&I_JEtjh4M-OABMnfkr6hB>96EA=gERZVc7oADQ^8m%+mJbPjfVhD_>42Ftf5
zH^^5f_j$Z5BfQDrqv8fiU2)gnAE99t{Rgf*SCF^J?78_;?kCv9u+pZgrRs4Q@~G;m
z_zw_>&EoVdK&*4$fh!|70c)6H=Ncq5K^?eOx-lwkGgJY9l!=zxumW=Ffva2N9lcyG
z8`9Qxb#<#=n`xnHLcS%)g7`RcR>)>)PCeopswjf2vlN;^X=CiD%&b*|Wa2+cuX_KY
zt?i(VzPX{huh+vQStaBofm!j_`vQaV>wX!N9^HTB?c_XeYT1Ft7W4wpHC}gG)fUap
z``xG+FLcjlLz|bAi9RA}YL)Jr@dIpyS<$?aer%UToyP8A#`r$Z-^E?R(c$7S{TTHK
z9onXmm=K&S(CE}J=Oc7u3>8~EM2^JFjUov$_D-K*mqE@iJ%I2~1hR%9V~Tppuzgum
z)qyf?51@iWi)SY(LZNf-Kat-0BSf}e3Bd<+FO5LesB1I4qWfke)=@R6rjTX{0LZHE
zgBuw*jTAh+CGDlsMF6<ddauOHiG6vpvF8g=;$qzn+P{;PG@UN01Ek4~a*$ojYgpA-
zJi*n>8e~NLIrt7pJ&v>VxFTBg?H!svU4^{>Y5O9vA)umWn6Js?G|^aGdg7(##ha;v
z{8x$E@2!YkMBR&wxwyT$oCO5!YP9>4unYcVB#siBw?qv7dJ)K9a4UoqQ+~+I!h~62
z?pp4T1_GJ#H$6_1BgpRTv~gr71rWd<9UK_R_cOiLmvVqf&u>u@%2M0uv+k`2<s|^8
z0NzNWI2KhrsMQKJC4og<jZTZAxM$)MDoY1slv#W!wTjHaBr}PQ5V-SKjsfzs{E-`U
zI3vuQ*+0Oc8N_F~BL>Xf`hJ2MFkSFJTF}wQXC30Bpoc84v0)(6h4IG#GJF9z6`Hj{
zJ<;pJ*lWx#M&Eu_FdAUQ`2Z;o1n!b&Kr*P)U-21HNkN)#Yb#qr=ILKeG6fc;?1%;l
zj4X|AywH{V@W?aV-CHTtXm=BHvV$ltEw!#RUa!f&C4INz7nl9r+==%?0RSm#Ux%ph
zh8#i4T$6ikb?3T=VXTJ5#v9w?O(T51bCLZmT;jvH@FvoTYfO?K1p)WXAj!_REq}Do
z^w7eFyrR?FsIYh4Ui%-z8G}5Tb7(DWqRDt)D8VY(z483?7MyigvM6cdJ^`~0ejv6d
zt+LlzqhvemS_Nj5q+YMIYn?!^z74QW5AybCop?7?11N=n@^DY1uQ^RwL~NMX{9Ts_
zaILmuX%8h}J9XuBe{%%@pn~3I##17d@!;R>D*oRT%m1)f8G!~Gp6&pL*tp`QE<8Se
zJHv@&g(*eJ5Yh^Px?s#)m;h3vBV8jrPn{D1$3fi9N`cD2XDSYElp>hehv1N(gt*eF
zJXST8*-&hBued2yrqX_CGdnjnOPSRK1*E4KDGfH?AE_9*feH{Cb{uH?G7@MPT~C|f
z=Cj-0Y2@)#M0`ANNa5j+CO^g<0kL7b`eOI-XiEA$=sAl*gJLd#-KbL;QdC3oAnFwO
zRc+-xt=`_n3!mzgTX%|zxXGlM$uFa6iwi#Z7|(9>31Xwv07i#9>vwn@6ep?P6pTiO
zoT<l;B>N~5_vPhULFUhfnU~Pha!IM`6~^n`wImLob5fSIR8(d4r=)gO$X*0{GP40b
zt|uMIJhOP>fWhF;COpyix#94H@>8Z-LV@K2*_^t}bY01O@J>}2@>6h;X{c>pm<Bnz
z%EAIMDl+P{(`ZF+Ps1O4-b<kDE*97u4+*$jK~*l`9s#yf>sUwrpKU1r$F6q~1-NJk
z=xJ8_BzO_jAY}FiDB`*1kU$;+6(Iwl7Zf-pdnSS&4wpmsPOw1abkp=(1Y?Pl)HEW6
z+6(fdHmE$nCvQx>ff{H9>=eu~T}vlC042Pz8`yqzavY{mg{hT|2x+kXBnVFcg5yZX
z{w%?6o7A8oTtCbF!Ol@R;IwFfqXKF){%SMqf-`85vb&_{vEv&klS1p)fJQ~;!I%vw
z2Rml~eOXrYsoCa%-J{K`IdS-kPw)*OA%+(JJ2v?51N?trI}J%n0?6Ll-Yea^!K~)&
zKe!~p5jZ=RfC<5fD$c_`2WtT(=TlS;{R<n4SI~6uM^q82GvZx@KF7q}6qbekSCr?9
zZ^i;f(+7;^fl10fl*E){gfPRHO<QLtUi&1-yc(tchGQroVhV`7iws2*v)j+(LNIX=
ztEdB@^jrIOF0$AM?&-Z3EO_kV03#$LAETu)iWu%8!!+P$H;XAI^)Nw{ZE4Ih7Ukyv
zEo}hCt6}}tTd&CcnIPQkwjO{d17a=zd;{ZuV6%UefM7ik?4vIj(9YPC7Wa4Cz~xpw
z>c>C&1|4orPWI%}wKonWoR&FJfVU?>XQ>Co$9sx2tRuWbAe|N!p+KZ$nL{{nPFiiY
z#*W-D?Hc3=G%BC~D1yPair1hgW-mAY|MzqLIdlBgXrRHd*o)XJT!sbG)v|v`um=dM
znGMj|QK#+#nL|s!J&+Q}0azM5|L*TiDIY-=BZrw(Y=Kecf#RSkrVXc?6I~z_ZsCpr
z1HEWVdJv8qg3L#O&>Qz{K@uK%(i5A_i?i!JKE&kN`Yc+@_|V3S?~;8wd`iPFe`+qN
zI<sE-<KfEWKqCkD(AKXfLVgLU&lM$$)2h?II!w%t6Eh!WuNZ%|$T}8sUq6m`R(>63
zyVHAD&+}lE8fKmqimKKR$+zVua(tDTU5E*=a68|fhHj)KDI5j{=}oSYRgQlu8W`+|
zi}i=Oy4KG|kLV-$5>-#SQtRn@Z{+Vi49PoATWt1OSkcHkvtAS0_(h#?$E^<=;L#j<
z5)yFo2q2*%H^c@<b`6oYJC7*cdqWRjMN4M0)2q8cPHa7cTch{#$iJO7^0~MsfH@pn
zquaKleUFIVKO3FWyOA>UF!q$^)wy~}-(fX1RiT8bn%*Tu`EHWLI<rKFnxVs0B3X8S
zq3)Vzb5rg`={Fvwp1u3(MMw@18aRs36IPawssj1~973i^K+nQTl@N-R&td30b8HJq
z<&I4P>hCQfTMfRyJ7(X%{#SSD|JAYAromPXSKmYb^v&WRk9LdV`mM7m&#z<1EG61I
z7WQs_dg7CoI6lp^Jy1$}TfzZs_{sg98?|b?Rgv@~>a=U--M>0aUHGK3iNUvHI1Hp^
znVar~O3D58x?{J&xLR4bHi`KulvhA~4wN0zoa*sYp1Bu<5E*J#yUfgKGhLxuNFBz&
z09`??N66V%r7m~u!-O(Z575-F_-*8TRk6<pKd&U-HB}^fDV9Ch(G$0>+sGzuS5>Pe
zeSAJsrj~SQS310Nmsa8BfM?Wtgj3ONy?{SGEw`~9)*Rga{9gL9F)Mim7^lZk2}Jcm
z_WhN(L&>s$1efKKsi4}_GNx_e))VHcW;G{=#mt(L-e)tbO^-sQ3QD#Gt>7YOW(ZAM
zyQ*(rGU`E<g>LIv$Ur7Rp2D`b<P!?RC9%{4vxVk&M%h0;SB?<EJjIv~OBKfG9>aoL
z4tO%2&(x~zMeWtQXcTxpfL>}=EIRi)<+(+K08Pr%5vm1ffN5Px$t~9AD;eodHlCz5
zn=dD9G_VinaVVE_!&<EiU`>`a8VLa;W+cm)*vCDxh$Qw|k1Re06a*}sqKZqef?cL0
zDJ*N&J6qtyBbifNBlIH1Uz(dv8KKVM^5Gl<2CXY-I_C?KBXOUnyX$gKsMjN*s1l0i
z+r?S+pNCYoqjnT$Rkjm%>S`KC#oIvmyR+0`$-b=9m#GzAc=fe$pkh&nY{j3;h_pJl
z({C<zyESPk?l__9Fuf79W`#xK^;_!BB8{ak28zVntE)blK}!a!KE>!!@eUAPF&o7g
zu^2Cv!@UcoIx_=9fc&ZP24Ay|dM--Lm-Z{S)L0%fV(w_5mo}|11(8o(UEl>lB&B;9
z59!9ZNWxwSS$c^ZiW}Khh>V!-8^xu)O@Eq7xV$gTA;0STx>COGN?!2A>fR}e?rx#M
zri9ZHl(9jPRgR!cyi8he@6<IeL=X8AJa>hflUc=REJuM|A*Vw&(YQxtjY?l&?J$&O
ztfXEj(X>ZJ!@aaOcOg^$OK<MXDi8>I|77C)JFzODP2<V@Rhg1=1iG$k+&xJdNMi|6
zq2-~<Ia7SADvvKv_Q3klgNT@rPqsjd1d6JR7^cZoJ{w4<aN%c`sn0G&&w6IH2}a^m
zICGUWys2#tLyl49qnOfI;wSTtsmXb0%c!~f-~!6qYoNs#W1N)z&9|e|Oo8AJ(4)xL
zGv?)TJuln-%=Ek*4!AXhhu|cXg7<*4z1fF;08MgGB<?;mjIGgL|DOM|x?GK5yyji7
z>%(5e9!-W(fs!CT5sxCt(6b1&LlDQGAuos*GJT?i!oSPJQ?WCDNCsp9QeF(Qg<Cl=
zP<TMvzDKDt^W32M&PW4G7s&Q5?N>zAfJ%A?J$TEw=b&|Ou0;rI8Z8-WmKIIPt)TR`
z<?*sOJ1mls0?!|5<8LE(fjMrXNoBFFx363x>SkQhZ~aQ8IlXV{5pzLp>@MmS<yM&_
z@*FEp=F?l2w)Wp&(>(WOATZ!0eY+@bIyLA=FePVN{><Y<qk>1E*HFVQVm&~xY^ic+
zVFROQ)V}j0F}**NcMmc0S%-h9yhbW9xHDBUKrd$1FEjRR=I&JJXsMR0gFBT9A9sxs
zKY>h(OgEw;e8Vtjw~K(Ow|=6=<I8CMo~o4EemS;jb&=c+x1|4|vw6JxEV0)C!`FXY
zjv0dVl3y4}5g#X=D$#r<MSCT2%5&D()0>W;G;#rXo2<g6_NojngLlBG-;Yzzc`&%3
zr#KjvPRMMQ;36+C$<hT;Te40&`c=l-R6f{qVuf78>*FRdM=HIcue@$su@MY3jjoLf
zL^_x{v$>S-b{1ESk?$E`lj8NyuP+|Ja6?Nc@+dtz{XMBUg$SFtJHh+!uXWpFlV}Pi
z8SHl)&)Z)inBaW+%l0uNii4qafy%pqlGueVZTrbFf-FtbTq%oOf!rC*97`kjH0Syn
z+CzbcLG2P&#l8H+uFhr*lrnvi>L|Kw!;fx`UZdnGO;bIQ<{qa-;qhZvz#t!VNX7$c
z0;33_?XTpgWgC0nAcM!xdK!c(I_|3d(XV9BINB11hfdtap8Q-3!#HBEo95q)Gl@(e
znd?9Hqq+BH8fNLnw(Vsv%&TL$ah+x#nUeS%k`bG?N^Ns%EbgucH_+fBNAw-KT;u`y
zK6INBj|4sOVzI;vFI|tk-F=g&hB`V#@gw&G1<h~#70OjZ<T+_f4UP4c1F+8TueQ$n
zARhWg_~O&W$LsL1-)YwW2xXRPB!h+(?JjC~vWr@cDjj4+qgwTRX^bKb+y*_=8Jy;x
z4ljLGy~rx}sF@L|q2RLj5;7<Ss^;fuRlXKH&0RmH%^v`fJ|mZa)>QZ*v8osi<J2}a
ziDo&|+Q`z>lWFUQPPNiIhV25nCBsz7f20~0U^J>Y@fdv~r`Oqi^?ATRt$E`-wXtO}
z*W-tDe((0V-Zidb65@GiRtWXJq)ODYl#eI>aD^L7JA#t@nY@A_rQ&9$K;QS4aq<dH
se!41nEzJvPew^M2{U;xVPsq$6Syjbcq%OwO|HXl88Qv_>w14uy0EoPG+yDRo

diff --git a/reference/Rplot007.png b/reference/Rplot007.png
index 5554bf06877cef97a5a69f46852a5b20af66aea3..0d8e1efeb118837a2f47645bdbddc1f013504dd6 100644
GIT binary patch
literal 13063
zcmdsecT`jRx@`~@w~FYt(7^&CA|e9PgNmD?QZ~J#Mx=K_387d~X$k^L6;Y6yO_v%K
zAp%kZWK)AuA_<`f3<$h$?Q`xs_rCkj8|S`z-Wb=R16T<wYyH;uo8O%Cn?5qs*XG_S
zuoHzsaqH?_GD4v^Mv;Fzw!@K-p85p%vi-&t?Mv_#{;)M`!r;p<UOJY(DAcZBk$)WB
z!=XP>sKY4TOBYN6Q<=lTe$QBGr1=gtU){4+`(}*JAG+7_o1}>K!P|}2{brSW^TK_q
zD;`&r51Y1E*gE&V!j`wp``4Bp+$Xc+ZMbPbq5o6cZPJl$m(E|ZJrVHX{NebUXOpNK
zUth@nA{YPSOvlf|we~cx&tKF!R#w)~N$fAu`(tIBcA!uPo?(Kwpirp~cU(iEP8|+c
z&CNB=p$`X8%hkD2=LNR@u`A~OT%=akH)aS*$)D|J@KVmI%;_mp5`~KKR^!}`dcVb$
z<L4@az$hqfH*$8)J+7DhT0MAK%yU;nL-l-<U~j&;OqyS}ZuLZ>^?^s1;wtTZn#Ge9
zJ=2dHq!*NwoJ|WD2^y#gAZ#q<XgGg*xQ{+io<CDFTW>bjctciw?mee~lGn?;yu78-
zjP<LThHr<1{BIUi$Xt+U5>X5MquQUDN8Q|5c}yDPCxtKyEB%_qlgg@#GBYoTi;KHB
zIdy({A$M@c)&o1X%Hw}{N!z?TgQ<IZOq)&!TX$XXQ4%scyw~v5p;p}5%>hSL_>jdf
zqYV)}uHCP7aZ_)SOziCm8b81C;1=5y<0}o)R7|<Kxd$rTQ_$;6#>}BAV{`iu?835>
zd|reIPA67l;~kw&m%sjTE4Q$UNy<6*?rJQf%zf~j`oia9t8)!J@r<88X&MxX4E!q=
zgW|fmv97ZCMNZ-R$9w*?fv3d-^_w_mImLxJP;OOvD|&gBN?7dBu+P9R=r(ziqWH58
zN}0W_s5loioKL9Fb0%j?^6jSAugw_y`j&qD`iEJOO)i%x{><1~d+x}75BAIKIwpAp
z&50WSY+-0`?|Jg%$*Z=u!}}_QRs25doXs>m5j1?8lPgM$+}U|8O7_;rd%sMwDE0;;
z^&xBiV=SEBsY6%n0$@#(CDH?rDyNH)dwP0Y3P$RJQ#v#@)y8amg<JVB@EG093axtc
zGGO5xg(MOLJ!`wvHhzpXw^DyJDSjC!cQrn}Hksm?9JWCV(^uxgMDd?<d){A-h4nW!
zj(>XdRhN_5padDY(DoL&(e7@eLc2OEihWq!asAY{%D$6g8B@J|eR*YNCLgYyslq3n
zwiVOm;t{!ON2v>58Se-s+8;c4&?l8J@XPkQkF~PmUazmMT#0LGZH<>1+`ff_*<YH|
zEBD3Ehd;kirvAsp57!JG92}T2<i03=IX{wcz-PWJ=3u2OrN~x5!R48l^KdmbS;_m=
zNhv9)oF%4bnDt<Vduz~c$r@(7a|Q>>$B}8=+3<jy9B+V@)I}au{RWHRIy+Jab!v3t
zfDzo*<H$&HT`U$G_qwH}WlyxJsi`=Dl5coIj7ZJM*k008?rN?eFMr~|&fKyx`5Y`4
zx4`STxL-q-y0nqPTVmL6T8YGJfAzA+(vIz0n<WxH+~sPeElx!%j&x#T)e|ccJ81CS
zeHVOMR{~0h(NoKotz5@Y;nNmXv>~X_L>c?^-S^HPe)-_R_Qo-9Qxg*>c)rQ%!4*fJ
zK1@zYQE=@$W$WMXP?&y@_UhHEi{>`o_dV*_x8S05;`RuuybfEZgkjeM9QI?VUJcxN
z1VWOmmV$!9>Jr(Y)M`_a$c~<=U-K9aSsAYW_E<_RgdOSuWe96FvB)m-q@vrB6{Ij}
zzwAH$JvS%aNKM3CNvLnSzcltaCi~x3Q9OKYWtNbgeLPx7*$B4lYNJeOCcuF<nwn(r
z86I<<mr_BxdT?IA><FBgOg7L6F~PK_C>q$W4N&}=_#NNAetikq{;afFytYWJgs8NG
zeAot_TS&=J^TF=l;S^<V{b#xP&K%CiN&qmi7io+rxpu=?5p}p?pSf4RMatj$!k3Vk
zNT=r$`b^Dj6N=m06sZN$))mOP9Wz2IPS?(TG^?zv3>g?i5F#*Y(13#j^?qOA;vPV9
z{U4}35e*B0YLc77LA10q4;$3`;@e3tIZ%I$-V58l8x_vI^$ZGiXzzOz%HlkV6Lsx&
zg6Hp}zV@i_{l8Vq;7<mkw2wX=4^X9j^cKQ02Nc*5oI{3YX4w{}vYVQ;%;~BzS$TOU
zL*^TKi+Yk&w0R~bCZNWPSfeal@0%-0WAza&EtkzV4{HSlRUwZyIoT$c%8#34<MZCW
z^(v2e{CKB=qN3=E`n`th_ZzO8)7S9mgrp?K``esw!kD6nYiFJ><Ub4gMF8&R?@a(M
zJ{*NW`xd@`^59_tNA0x_Tl4@jc{G0h5hcq$1??m!t`apu3?r(E#HNRB1hHnR_STgi
zZRXA>l!~x@{mz5Dysfmgl2j<|Gkk8?wWer6aoyYJ&kxrGbX3)kDK!sxcn3#aWL=9=
zE()FtRPzyv5IO551W%sC!Zb5j)#n~i7}7dC9y447iqzB(bmGq5hWXG_!g@SV94yvx
z1HwT;%+h;Kp6?o)iy8_pJtx}I)oXfR?w8PiCKk;nd$DrZ-#Vgc4p;ZM@do?3$K^+J
zK8q8+I45fHfTpc&S~`xNTad^KRI}_LLU~YZeD(BwpQ`jV!0s%+_Tf%JV%r;7(6{_@
zw{+q*#xyoGMZ<o2kC5Jt-q{8HXRaVo&uh)%^1ZQnJUaEW`t)#(dE_DK7F?7Y&EC}X
zjp)x&uIASHK$}qF=EI4FWv}bxY&`)Lzqdui4cz*KOjsAaH(vp_^nIM!nk17X_S?~Y
z!b)C;X{#|~Zvno9trV>R+2GP$+}!-$z=8oDy^8REmXstV5MAAOm%=cGE#WhhSy5r;
z+E>uLh6ix+Nsol%@O$kHK*5d#5$F?fzU=Q=v1R_uf!bJEr_RHyDY8Tk77+E|qf5{P
zo~^KT?9r}n{WXH{=ANM;{LsqYJrM`@MA+ueF0xJR?9zRCf>&m+6Zr1{zNf3k<HjeT
z&d!D}E{n`T<*}9rJzW>Zo2^Q2KGIiZmf;h&qh6Am8Nu&~Ys0e(rh8D~`q9+C*u_6x
zg8#(|{+Ipwd-Lxc>LRh1uUt_rOMktmFhx3}LjuZ|<W2K_L>iki+<dBY2`MA9)>S+j
zu)J|>zF<<$?awp1-R)_r*tyXU1x-7)ZgKJOXm{y{?wk*M%2GOIbdf!-6qCPpp=I3y
zsRxWoVay|8RsXj|MW^#wKSPN}pX!*krUNxYC>Nc<P*v-=YPCGNSmj%3WMcAc5q(AI
ziXDNHpsXB2{<>D?7F1`qZOd%{9OxIRvC-~>6%N=hI5iPAzP_L%0!Tw%NeKeu%54(X
zF=J%|=~FYUJ4N0%@!h|enM&~$(r#>Q{6~ov4R@);WFe;tew&|9u3H&NSz#aG<z)c*
zOBAbH9&p923<oTtn^uUMA?r-)hPysPDJIKFo<0z&GsHh$EjpBJ(CYgw!Q8GZS;2Jy
z-SkY-7{L7bou$3eTDJVhg!a&CJA_ETi~agL^kXUC1vY+RZM|qj2JYkEA>!B6KI>|~
z+&+SR#tQpqgT~Lh?P`lxDeVz~Y9lVe+wXpO);j;I{C_}j4i13zZT|<X{|`7A#621<
zs7RO4U_Z*s&&Pi6%)}?czBHbn$Tx%~oFQ(84hPTHK`U-Gw*<(3Q@d@huh3e-x%+k5
z*P5Hnzv>zRhr;$*I!@S7$L4k7fCQ<coBadp?381UVWOm1Q0HnQ2JPzd3JO(cMW^za
zQ!LzHkl{0<D9c;9Tt22GoM&%hGT!88CA{B*$wj0RzJ2?q-=%qaDEZ1AcvU6GSGZwz
zrN)YAs4g}`^0n;XeuR<SyW^b^k-2=8qCK)j=}@FIwLYYJp+%}U$3S#_l|nQN3JO|S
z#xnYJ<F@bSdnt=)8n@9X9vedMjg~En?&=a5N;P;@Va-)dVYCHpKsV`CZU4lXX+xV>
zoM8GXU%YX;YUF~0RI%vJuDgSzZdyiIQO!)XR6h3CLAeyJUfXKFE+7r5<Wv=${%n7#
zw(sPZX6Gn3+}pgoZau}}XJnh5SSo#y9rFU^zlQ(3ydmzHwZa@UpRHR-pX|sWAY!05
z&oueW^@|J3sx$92<3K5yn42g0hLF4wswj-cE7=>PFIrl@xKaFEKu{1CO(_P@9k9H<
zek6g-hl%rjbm?eO)V@6tzB84zwKiQ&J>~09l-NFL<|hvLIH09hH01@b4pBh2;B4;>
ztG~mZ62~d|eShNO>bhvBArec&U;Tkc15+HYwS!(KrLJ?*f~V^3+qZ@wlaz$ooz*Kl
zB7jbs(<{{z?|it{0v&4Z73}#s9ILx0*O>K1uES~Rdl&m8((2<WG%Bw>zW8+gk8k+I
ztx!haR>J@5#NuBD%m4fb%^k#@k0oVwcXy9ZAWxRjS7=#CTV%sCEfH{pU8OSd%-AP6
zrGpkvVI+*bTYUFvY+k4<>|6=6>e!<0mqsY>vs#Duey*~IPC)Xu8Rm)eNjJw}vINP_
z<)V0WfPV9<S4V-g<{{61_5O6`>(`$WR)v)ng-KJLI1ouW6Q#XAr-9;ZW&w9{ERTT>
zFE7yM67_qQR<0+tUb(H6!2*7Gf`(~euqMm!6+`GmA(Aup>gyUAsX&w<=D^PTaY?Y7
z^EWqUH`~{gfRsq2`5Eamo+Ttm9*V5;*9J6x{P?jID<c}IiAl1L{kww;ZEbDJEAYyz
zuD7mhN4y=MFlrH)UVp+&yH*4m^<w5;^_!i`^^$_<3<F>}&((6w3$5M&Ypsp_9at}v
zbZXx`u5M>U1N>Dk<c)0fu|ivWh+dfwtnHP3I>W&$a{);7e;?ZNbN<O^{5A-M^19f}
z_a0Gsced^7`SVYUw`}CtXTJBKg5WZ{f$isnv6I5Opb76%kO4d_kKzyl!MGB(xq|dA
z)|lv~1;sA7@c7l|F9WC{m4=I)unk;RG%cF=3au;kiR(;Haa~0!zH}sTjN3MtSzcrS
z%%{C2X{re%_eFq_u8Ac3B9OpuK?23S_U%wNj~R2C8LHa$LJWmU(`!P_bY(?A?~)->
zsdOAv-RdgNTw*)wykp|L_;(J@?LzzNMe%dD#Xx5E)LC+%I-4DKkIHqZ$>lRpsPi6Y
zxBo$%)%ez`1e82~{}$Bfr6;oCGVeyWtj}(4crGkt>!tX0{l;5`2ERw8CQ9@pZtepY
zcJhUz3s@vRLfSHQOK~I66E3!K17(xKpuntX9;gehJm>c3%RsfF2+>drw0)B)eAb}>
zJ>!gy957!F-8c=7)0wmNT(#a~TG0Xumw`1vSGlsmi9%^hjLD5k8fQB-$B6^=b;_LN
zM7{sz2pqi2#X|>OjLV$d0!NNvP#Q~}nm7<fu|I6Vyz=rpTfbF3WYrImutxcW*KQsj
z{sCqv6bOG#r2{(;x`@E18kVdEzXx4@|M@NyN+K$Xqt>DI5W@Tc#C7c`1beV&w!!ix
z$%YeZW>nVM9bq1<4UqQ1wiyRt$!(iZc6GRN9E+Y%>lXEt!oY@r|L5n=&l&jh$9!I4
zp`Kk)Ztm|`Y&j!-3+iQQ>krBA+M*#Qy}iAUFCDF3rRSI9**<lf8*BHI1>t;VO^)x^
zS6Sr82c=Amjk7Y=ryapDNm^Z*%_ts)@33pkN*Xo}KH?mI2FD7$_sUb9@rMlS`cuP3
zz-&T7)Mxi|qB>*XMjSiR$2(x3M)JzE^A$;%7xir8wRvfpZzk+L2+CVr7i<yA(iAzK
zQu*^977d<BZf&hv4i=BLU&s)I0zg{xBB+O=HVN4zjJrl)-3Z1*19T&0GX8m_wK^{^
zZ$A76yUm|%_7Ef4r5O@ZC&jW}?(FP*=_EfeqtfMM60t|PWtD-8jEn^D#Kg)9XX2}u
zvzKI~tNRF%50Q-}8ztF>Cte4trT6;#2uZNbSBB7{cRsw7VhNL)=^Nsh0FY|zQVOHD
z%$eK+4P5WV*@=e^JVHt@jrEn^^r}d6^PjyG*8>~KjLTn5#4{jHcwa23ROPnS0<KQj
zO2!&|liTM(6nV6f_~HwgeJdfN`D-6$S48F3G9BD2mo-lvGKN;j{E)h2%HxqnzCV3^
zq&CQ^#@~H}u{jr^(NjfBACz2fSY{4l2i+?t>O#RTKzt${pUu$b!u8IDdQ+nTi6Uk7
z+CNJjV=rI6Y&25gGjX{sL&F|EyV&9?JOPh`J&2|9TE;%xgWYE3;U2Cd_Nu9==`VDH
zHC84hNt3)yjEv%Z*P$hwPacz0=B@nr=GfW?+M*<=wF=Q%JI{#ayVf1y(X!1|??TYf
zu)8$<clO`4<`^0pDu9!Xt>RDI<caG>liT^;YewwR8<8d5kOfsJZC9%_Q4)V#4|Zw1
zm@arKjxCPODAec`s8cAnLf-ZT)(^|lTUfjnYzoxb<9XJe;hZS1SFO+g0Iy0ij9c<U
zp~0~&sLp=Ci?FrIFftf1O9-OHAr8y7yC2MmT3b;lAJ{0K@4jvaH6YF2M+f2i7U~e;
z#@do&=D+(h`t)l2nV0QGV5O=9W@lDkqfp<c&!Ra)SEq7Vg`Oc-$T<c{V@f#W#}+BV
z0T&Obbt`_(A7C_5D%=Olt~YGw$1K%Q*Y$t<?YF;gni`_x_{y4s*XVguGqXcC&~OiX
z5jgq3*vk~X<(Xl|-!EhP90+P~s0rf^nB8FvUR1am0Q?v!*4*9QeZfahFB*vOzioV;
z>*}lDv+|0H%*1}}>NU47oM=sAtgldIdWPVh%Fb?|v)@?YEwm`UD0t4zP+p)nD;Ax1
z{;=WODu7*Q2mCLqV3Q}2#=53*GK%EdmF~`MLmf7NUjyxv51i5tTAbk`2Ws>lU~t${
zmZ<aU(iGJ6&aGRTVSq6D{U)C0A2?=&=G<uW42}O8G}aWoEoC>L+2dy%YL4xx;~j|c
zy7L@A3Z;L5A5qb$8J#r|FW___hhUR$q#D}U**$57%d&j4dlc|&s>j$ML5%gi>loYj
z<IHf)a0dKxnneo=yx`zW8rWUG-2a!cw}0>_!tqgG$DOy&zwA}&2SW?i!Y6e|B&h(F
zm_Z?U1}Mjpj*+|%0I1}t`cGRRo#d&GSPuJU{zUy8XuYVeuJutR{r0u5K+k)?O&YX4
zO<6w^`ZF*M$`3^G7iV<8OO@!5iYQPSK&w2YebRYLBEXA<udgCLvG?xYZktOj0Yiz=
z!sju|Me+tq>rrd&0X5i{JMUj?ZUv^D;0>0lb##0*3^W>V6sOKQZ9@Mv0H&Z^7#~e!
zrx3fI9MSCQ?0m8^V8jUdYW6wcrp8}tHhD1=sdU?Z9%yfUFE8A+*)^KD_Mvd`h|nIX
z13SAkx8MJM#2bC!TWb3<$E~8F5gw=#W7c6wCg*9pJO3q*?w@D4hySHS{;=m?P1@Al
z+;yPL+3KCw_4*b7CdmouaF_xVfC)+m#5Q(!FWe)f^n}n2_@Ppd=k*iPPDn_|D=2Wo
zt~(^z9@iCsv4cm|MB+FLfP|fXM=TdCJODfB%gAu1)QW^?)+kx#X&|s#F+!5f>mozn
z<^$tc!!z(STw$MCc3f%kfWByl)KA<H;|Skwpn{i?3IGpIY(?|%UN48JOFUn_{``xV
z7Z-OSUymHvS!G0Y;0YyC6J){Yfe(EH(~=q^DuZ>XAfmcj1vJ(4g}58V79gqwz%oJn
z4?vp4v9eH~5VD@4?&^YmNqs#!C`9bnX4-)`C^u6WP66gm0sU$VUI(Y2OC<1o+?u+)
zy!<;ODqs8%ug8j^wKbz9bjS9))wJ|ru_}tDlF;ctL%pQNr!qt415-sxI{9%<E-s++
zj8?h)f_9C9>wJF~uM1dnJGbA}9!d|a;Cpn*N}c|YG{#Q;xNi~Bn9TE2`KtKJU@s{P
zG2G_|gQPgw2O=f!mDAELG2QGg&>uR<Tm6A)E)>}rgw~nLXr?jMM`O{eI?xsSfPR?T
z+7^N+q2+_AlBPeB&cP#!wvi$J^r>$FK+2sD{@+gde;>*J|LfhL1@GeOM#ek9Xh@J1
zA_0kXS1_v=cOh%yL}GKUsVl{(#!Bo55LkWW{_ICTx5vt1Z}iQqC$xrn+$n6u;)To9
zhy>I!yI!ELO;s<WgGRrd{{5~<_ad$iOs>M^AUIbIw#5H|$giY0YjvPncUYr+?9LJ{
z5bzgM$sQRzX_@d10%I`n^ioRwsSJ|ttd_ihDM)=7<4CdI5LRDCEd8opHbi4j`tv;1
z`P939ElPKJZ!F1{zk!L#EoF7`w>txy`i6f9MbRVkso3PRua(gNmsBm+|4e`EA64^&
zg0ib{djaYwJ0~4?x#{`ejIw5HR2b-yflAMe^xzrgZFeuMvj|~-n{T9nSr#L#Di6&^
zIVP(ZL!y^<pk1-SnwoblsOUQ=)UPQ>7C=Bi;J}`U2|%6AA*0W-Lq$}}Ca;=$Ys@?K
z&5KbVLea}jN+S$%Ls#tAR?0PLd!OY%HSOB1>K;Q%y50PashL&2wBrN~TB>hPO~5Q(
z@0jzCxv%uT!W4iVY`;U&L|r|20hJ!ZV!AL{G|NDTfOGnA-!U5|@J{hyrIjJy-bznu
zZLIfzb51Te$tNWxz2ATjmoa_)%X3*el^8ZcQd(ak_ZC_!!n~%Y8!%62`3$sQWXv{;
z5yPE0dD7<%ViMov0mvkS-M@&|`i2LKx@gJJ$f!Fn0*Jgg@=l~juORIhD7_dQn6VB)
zXGp0(OH8bwe+5d?w#Ekn+!(HQ9DKs5%z}bb{dZyN@$^}1(AD&jf?=2?*#%7;v<;d_
zu!83STC$fOBPM$rCh~?Sp8cD9#-tK8M9F%&1qD}uoJU7T7xe@PX#)taev6c`$(6yz
zh#=YoTw}%uutlyUohF6wAiblo5c{Tn$pNbWlAfMkQP1pd^}jh}?*=pxE}D`OyCwpz
z2K<#`!KK4lu%que%;{IODVk^aOwRDBeOs-q+d5nq902EYZh0mN=j?5k$QYvg6R9BD
zWin(u4XQa%N*)sm+#sfnY+?M%KJ^P2XrE>|{+}8hfl;#APdK%*en-qi8tdEqJ7mmz
z+P3=LnS2?AbLU#Tg+LNQpE6X32Dfbs+y)RJ@OHp2ry!z<{Iwwh?iVHnrk0jdmqVaE
zg3WU=^GUw?y^BZK>!DzpL;nDA2Qx=lgQX|Y@FSc6#*H;b_A1pP;0K!;8TpKJPbw9M
zZHD?T_dC|8G%+iw9_f&ApwMTBYba47>I3DgO?<vc@L=1nL+{MOz=pat{bzAeuVT}4
zYmcf%wYBNeqF!n-CW2ohwAPgsEjyW=;&B#23GhRu`{1q9U|>+_f|xG_mZe|5HAaGq
z)$f?`BJy<gXXMn2`S$BAwLiW^!bwUTBH8T$^<!2cm?$>PpRXrMTTfc8x1}hiM9S3v
ziM(CTck=2YNn@j{d=-fO8S2`|icP*}l<ek`!RAf75LRz(-?+FFq-W;ZK_gW#b#ERM
zi3RR9HRRiYb&rDeKxQNM#L%K1gQYXLB_~)L`{hnzSXc<t)!z3_C=AEJ+-p~+3H|LA
zf<1ApIs&LD?5Oi0t4rQ*LP4^^{M^zpZQ^psRKAG5%9NNn9kd<{GHON|V2kzE1bCX$
zwX=Q)^%ughruw|8H+hacpL%wiXSI=i+#XSM!G*iqbIl2MMLn@vS)f6Y@QH1%Oh|vj
z=!d(#rH&o7y{CR4?Kj*2@z@$=h2OU(N|Ql{FTrM%9Ny0|X)UYunCgC$e9k?2vMnWt
zPIm!XCI#NH$uaL?SUMAE?hs6I{`&F0Wry4_)1Bby;&QotexX&SWAL1_hll>)TG!GO
zm*6sHX4c>>z=zdlgV0xS9tfqJU{-i4kGx5;OZG4D8$6sM;xla5|3$%e*oLH%Ts?Ku
zw^pdoZ}0@Q4{XUnPvUU->aIhlIuYj?=v$7usP9wf!Af-r6*}Awkf%O)r95!{hAx{b
z`eJgRD<rN}`P4+cr;5*r2n*fJ(5^m<*x#1LH-78OI64Se@<KJB=uzX1Tl3!<Zy~O&
zG<l&KA_FPGGgUdgOdj>6C-ABzMMuH^)FC<K$K1j~_ke!C%AD$Ypn9iltL0}0#WLMz
z`}^7)g3G2ev#^4Kf^7;E8w@Nz{*#S$OoD|!te>TO%GJTcwmi8vRn4laCXc2Rs~9N?
zPF0wq%ZIhtFi>?H!7-$L!E+d|tB`G)hPmu1i$C6f8uMO+l}zbCubuKNAM{weO1q>D
z6T!t#`wgChyCXqpC2f3`r{JgK-P*rq^(*$LSF-<<C_;0dYx)k`tPkQu1SoCBbZJLl
z0E}A4nSh4P+J_#65m$Q&V!EKBI)BWMO(?}Q#R%ua$4oz5%}sO;vW2J;3{x`9Dph0R
zjiXI4kV=5q0f_I48n@~}vMtf6>!_`F<3Zfl`@AJ|#UzJ;<#hL9n+ok#5$J?fdwL;L
zlaEBhN=izOO%C`Md<vx42BZPuZE}xy7JSXf%vw7_wjuZGi4PY06sAP`4W7&}a?iqB
zR{6XE;g+;g{R@l_f!@T4fr$vS<jMi}%8+c|Z%zje9xPw2>%&jSUiEDwp_?a#(8(e3
z%%GImkZ-H}hoZH_b&+v8x4DtgE6@rRs{!E6UNOo~+6_{;C26mbG}*uE(?m?4Q%$<8
z7NLGM4>bRTl77wN)qvnU1~^&eUSBV&!(IWQ0FIowS-FqxaSGO_Uqm|ip;i`jd;4}Q
zruaGdMLAN0TBFiiZI?s6uM8H8iIZv|0SAqx9H#RHYwg()S^YdylgULM)m7z+X4-U-
zUC2EudyR(cD^DB<QcJJxa|fr^1ZL;GpC0XJ!7Ox=MJ{%Kqbi}|0(%jOB`r_)$M1fE
zXU6*ZVtq5p(t@>D^iI><(aVqWIi}|W5rUzYa!!wP`|i$`1S!^lD?!||V`=w0u>Eng
zw1AsYU3sSY@?N9wkI{3ivL~>r0W&?|K7^VmSQUZYz67;FuU%|6c6N3)LzW<$1=irt
z(9L}T6~e>EnNv<YxyWOMXj}FQHH@e^cpL_98$a3AKNnidU3&APFyejvRlYf(occl>
z$(q{Bn!&z$=86e6%Z{h9?yjDK!i5P^=JW@G9)@6(ni1^M@C3PxbM6DLGc_Yz*b5V@
zGXX@zm7#@1-7}$m>f(c-ALN;ej1AOHAywDH*w9fluUyv6s|vVD9mkDKVloa)Yg#~u
zt^WQh(yDH2+7!Rn%FHm7XEr#=5ScTB$X&^68S35)jj+%G2P-??JrPJfk2B>Q+k1$c
zYeXcCPz|nZX<nDl^nX?>IW`q_%GovO`6J=2GDJ1yl|+?lJ+WL+ZvDZ7NwTT~nt9AF
z#3aZEH)X=6@;^_-BuR1gtD~PZAQjTWV7W|wc_B%<T**Da$UL@|Syz>vpK|%!TJ16H
zVEj~Ia|FPgJ+fUKS`(cCFI0ff$vd<>tL;mA4Us)MvX{r3<I7>@n&v;?9F&{lGMJ+R
zHL^I_h9Dn|!k0d9iGF`(tlx~R)?bLw@ik^T#inBrKfOPc2MPAXOxbH99`(_GFXulS
zRfKW%6cU4W)`YH-GelT~O}`%ataxJgqxQQft-kFLRf927dG!oLH-A68x6|6QPFxpc
za0{Jst;DuyFY<=!>T3U_$V*3^Jze_^Dq3}YCd|DC&Nh9&&uiBS*)hF@XV095-g#@U
zyPjhEmknkzSJ~)#|Jf19KG0f3@%EJ-X$U3Ior-PD*!)QgT8}#@B)H?+O=*E>I`pDm
zl4w|J?M%DsY!4WmaY^6~Kk1bjaICR8GCua=H|@3d1AS~Cu+Cj!ihyJ)!FqWKVNFw*
zxQ@uOqUcVQnuDyx5IlgPfyQcA?1CBLO=BdlD`e#2x<w-~q?lgsg(S>V&Mkb(YzW>U
z!Aiq8B-aKLJ%eLfQ92_d3oo?c?nO6$XtQ9J3&%QP+VR}K3P2_gHk*L5&+7_L>R%%e
zb=*=X%!`unT6-fK<()cTnl0vnZ?KW-o_6(Yo28N2x=!~lncA1=kk#*yPwNE4Gd`^d
zyaT#98;=Gu1}N`7QfmntwH_ije$Z3OaLsaicYJT;wKpM#uKSHeE4sj7sCf`VU=Q{P
z<392Jh`4xU7{@5I`s3Pd$P68U>CnQ0Pg|<89NRR68T}%UhNK?9eR_0KTG|xu1a@>B
z19+Au!f;Gr@4ywEVTViG7McuZx)UL&)ZLk>iP-vJUoZ$nI~evkQ#`BERJ-l1kypXu
zva(HL&M!mhIgNjQN+;*U+TrIL%{uy`WpQwU1M9=?Fwuw5Q9;agA#Ihp^)96gkOHFu
zf0TpRED}UTQiY|?<l}$wKM1_?VWK3Zr4{A_hu}N0Qh?(9k&Rz~e6-DdZHO-)I9&-X
z9yyfaS?>&U1U6xFRR;MP(89r^hItMXB@6z{(kzwOn+hT*Uc=E^!qgs|Be#I|swKIA
z8JqplT5XW@yM<exc0T&_^S)MoOcaHoXBTtnXf`OITL8R(aW8T*LI+?dUnvmw;m$VX
zz~tmDxxl&6@oJ=L1burX5%aw;JG%+LHb~X)52(OYFqH+OlYtXLKBcdW-uPT*r3;)Y
zS7;QDK)?IJ5?5yR7(>ps_1(IaM}*0Gh42`>LC%-fu2&O!Lp9(vu>#)s^qEg6Q`i%h
zI~%<9TM$M)4(BC_1b_LlzHu|!0L}jNb7O^QVCNBZssh_<YW@OZ9ND$pB#AA2mJBM9
z=<MWlM#`+<3=+jt1}KS=J&r+D){;-k$Qan^SK44OnEWThFm|e1>b?>!+rQ209M*`{
zdp4ZhrkoV|>uuL4S&j86gSH3Uf-x5Dd}Mn2JGf7fgU5oDMPgH6(tT4}xA5b4NOdVf
zu1pp<Gtk`qJMaH(L9RYObHst#AJbn!OHLM8Y!KZz<4_0J(y4Pcl&HZrhyxnc_SXvB
zt5`=vqrC;1wP|)mkU()Qs+vq5cXy{4xY1&kDCeByws{4UtihPC`uadHjsn?9Gv7Ke
zw~&g6Pm<d<2bta-qxuh$UOp{FL^cS6Zf?b)&Y2QvYQ&Y=@fXfu8k6aSNTnDu-J#dw
zRN_zNUTR)jg}yc|)m7tBhF+rRj>|0ZAJP=}pfzA_SulNdf+bf0(P6yNn1sMDxXE9B
zh`0vB6Yl4WUv6E6?jJgLSM+UFl`^}7FCL$Nc#ui)_W%XncPv)z74$MGp*@TS9t}6_
zshh^f1~&#EF)9Et43PUqBZ)sWdgpINDSxwYnu5nq2Krh`$AI!xnR*>xVs(hb1t<jO
z4x&dV*06(u6xyOQtv#*TU|A$TJ!ILEA5(@Q?WvplxJ$b>?0F}s&HERR=pwt71q#;%
zt{$Klcy%|MmI0zO!)F+ZGA}n*7dV*h0GB3&|5g!@b0yT7+2iM7YPGqeWh_>s$VonV
z@p+s)WX<XzPBZYw^@e?wNVc6&x10<tJcXR|#2V@r^e;@oV3_p^hFq$%wQ~6^z=V@Y
zw*D{B?D6>2KHVrHY+NS99h>;nVIi9bVF;5yKSpw7T`&G*e~8ij=1RNo`{GDcleYt~
zZvjP*RNTr;$QS%$m4gt0$<pRoAEvISA^%C>O<e;K8nwc;_b9+c5T}JURYovGGC#xD
z1HHKbwyDaP5|A(>5LK%zMw0&vBvQj`7ns?uv55zb>kOSo;OxzZcyz|k(6Dw4#Ok0e
zAiLCTVYc}=`rgFKTDixt3KDh#8x-a*FeNfqwa#bx>##02hI4TGfpX1<9-pNZt@15$
zmRv}Ex?%J}7V`kAFliA7kyS7>OPXRu2_ovazpCO$0R-A@jdS$XZ=`t)S3_b+Y!m*2
zL)~J#>MA76Qx<<HRm_w4!pEM;Y6T2qlbRsz4>_ej)Ae%#SiR;9B<2scbwpn*X$(nC
zB4K}Rk)TV!=+YrQ5M5yRI78k!F7cFQ+@f57(BO`1qe_rkcne|F{G#g1VLuu~W2VeG
z_5Bn_0<&hS8K+_G5U&9@T}-lJU*r)A`z!l9st%KM_-`QpF{ra4s!EW%>QQ{i5e0vA
Nf78EIbn)7q{{_ERe8B(!

literal 12741
zcmdUVc{tSl-|wiTyM@w*7%f^ADLXSNWl0fPvX>!c-<M(N&ad08M3H1qWH+)iwl@2|
zPIfgIOk-?g4B@=Lp8Gu4InQ~{b*}5Vdj2>rnr4jee7>L0dwDJIpj(=%yS58#N1;%=
z)YY!)pimqm$j`Q|@Cu=;G6Ej9T4<<Vg-7^eE0_7h!yis+hOQ{o4qoJkqhrwT7YcP4
zrGE9Yu2(F5(EF9{SX|9)YR{02#~+6S-bwEcLWiH))XBF?z3CMF@Y^QAz1rcjQQ9XO
zF9r7pCvDa0lAFGvyG2G|k5ddzG9Xx65^Z^l<Byt`hqvV^8+ejkXmLu4dXo+r8JQWh
zp8i2!C92Z|$(_L<__KQGskSUWTrdiCN!#0A3x#@w+rzOF^@wweldzQQ_>Br?k8x2d
z2TCmf<#AbSTh*PjC*p^=9>q60`fMV!cf8lI*Wy66eR1aDgImesoM{xV%F6LyrDde0
zwG}+<EU+aLN8P_W-I*V6?i~3P;6C77JNefkhg*^YH_g@u8GZ(#igSO9ktUAn>Fe7x
zygJ=i;Zy9l(m!l7P~tpA&PkXi(TLm>xsaswrO@@ZH0>l^1A|omr9l5%r}j;`#Iu9D
z*+(Y6eoImf)vxkfbz7kqi=soV1zCk;vrf;dl|k-CBO@bSef^ZzuMafGDHwdZdkemC
zwISWNwJ;I6Lrb$1CRJ%&&Rb59Ta<5SgL7;aODhGp6;<~<0)=%M{X6AmOp0{^LBF}V
zdAZrqFUi21AmzQ<6m_w?qvM@@f6=6t|8ls;&u@uxuHzy(q16^7){qagO_^u1Em1jI
z-aTWAMl1B5zn^VhW!7J0-?_>f&f&o>9WHGS_5Xz<EVQfQ*H>AEo(!`%=Nf+a1Di<K
zX6vk$)+#giZat%7hqxhcst|p!%zzr}8iHM(ND8KC#>#4E8szk>t*(SOn)%LNYmS!E
z7#kady(-%mbafwhVB!5{fkD?+41a>BCNtJ)MahQLr(`O05p^jfm=kU#g+%HTly{p*
zqj3vk?L?w(oqA>BG4he)Sbaf30fa*oEZmzt78y=<9IhN(^2JhQHC=j}Wrpq0eoNm+
zy~1u78yg4a{<V|e0=xceKG?O{{VXoHbmHB!rWnV{-o%7ssv(wwCnLP)$D;z#_*33&
zQFHcF!(s>126uA;Q*S;z;g`smV6epfrZ*Df;aXUAGY4DSi(xm;9%!^#x0Jt+dNg2I
z%4MRhomgl~o{F|5wm&&&mZmUWa?D%HaeZwS;_H^Tx3|k`U!ReB$EN_U@W!n>`Cj~z
z+r2G~L^3jCnD?C)7dJS2AY265yD(iny$IiL(tJB@$V5}2>3b>Rjd$Yc{l!|aZ5dfv
zKV4#5h$TO^58`Rr7iu1?EYkZ*T~jp`=#uIk%S@X4_a_H4Qc_ePbcTP8%M`-7$JV~c
z>sN{j;YS_z>@nlS+;8H;z~O`?dk%T4o=ot~^I!~RlSuMUt{f>WEKDyb&<%5&Xgi{=
zbMxj+GhBCfcRh9f^uFz<cW>L5^5<D5jjVO<+_~*fuE34eJ?!7BDSO$<%4&J$3lH;W
ze(Ti1s--82=+MvKKb*Mn=B?*2!2lxu^)Q1hnp6IcOW60k{kf{)95s=!@_{w>pdCw#
zgzH%g-GjR1k@ahQpvXQ(Kieo`=fCcrPlG(Dra0>v7}!pArIRL8V)i{(*&lRO(tpj@
z)OWU8XnH^O9c*Bv89tp@wT3S&C{PQ77|HTqXZd@sR^$cc=2R>iNy*4G@+Iv$Bs~G)
ze_Oxb+;h;~=)>JV51hU8Rzti(x8q<dRpscO?yjyn2FtcJ9uuwP?^h##J5{qA7m7*o
zQY1RbX2m#=Ce!5Z_x-br`kTDI{=LV>*0vLp63RhJ!rDSAp7LB~l;!Un=v)1C|MtKy
z0+J72K-9f!Xt;jPFfZ8{hn=fCw%id}ncS(RWQuHAf7Pln0V>GrTFU9G8vN&s3e=KR
zo_DC9j7x@C$u=p~f!J)a>3`Un^058gHTl(rPAR96OX}u|g5_?LIyWJ=_V3>xebG8Z
ztm@ZXusgB~_JbrnxVHR!(?$2`QmRKuW#(#Ux5Q@DXbo<la>q6lD&%vxCkl1Psfo);
zRuDc~oy{ofcc8gaHlgra(T}&HK0d~+q0ggG(z`#x`!1n2qwaES6k2`kwLi%O4phkg
zN$gDe1c%^rmGA_5?2a6Arr+xF@haJmyCNbYTm)6Kv$Kg^EPO>qW@cw4VZ|g}ocSD`
zk&&_QS+xo66LmhlsHlj{x^??DW$K^>#QSN1=NZ}A%@NClL@o%9sVN%+pEM#-Uxas(
ziTT%;dr!XHGniUg_0!YSoBa6aW@1+)iL4Ac)ex#Pe&hiPb@uQh4%CN7C|LWZO-Om)
zcK0{E_z{}NB}eDcx;;`-QdWfyhj-snL`PC&O^L=vMj5^N)}Eet6rO(*U!9JmK=)s}
z4sACp&dbXik>XhJRot=*=}xed2ma$u{#RbSJGSyj<N9@%p&H8UXoY=|FE#=4%ogXR
zh&G^UOtVIqezP($F&M83Gxq8lnyh@D&kZN<EhL^pHh$F~CQ;K(xx~}^ynSzLph@kv
zEt`nlo-3^Qt_n2oO36i0j-RrcE&U<_b=LiM{5Z>CXZxKwWSR|O@)`QaY>6$74<}OU
zFNn9`aafGCrCSJb6+7nWFe=X-ubH4@)GF3x)iA|eRZ8w%Mt_XlIs1BEItbMQmpbl2
zG{)`A4D?bQ%JfkiXd4vxLY-G{sNj-WKV0W77Ubhm!S+?#;_HYN=EcvmUyOM0I1Ih!
z#VvH9J23bf*Z&ZK|CN3FUjq35t3Tg&_R+DIe>;@XrJG~rZa2O@C#q|RLM<mh=A7|i
zRHXi~_r%0xYJ&dZ-EA##3On=!P>-;+=4n?D!0=pVC=K3YKuz$kTw|=SX(<PC-!$`|
zd>?r|OcTqtG=&=I9eUf?xSw8{LV>tpsi~_6ZG;kj;&ChW_Tk-5*<7Ku;RZapcbX#D
zUKK;J)E~26bA4h5gDAI*jHa#=@O2@r)bL>jgNcEr3wd9vQ<civ9a?)^O#BWiG9u#P
zr^lRO$;HEd#vv59lC^|&c0xGq$(6)dnvWtHsyemMzNn$0VP9zNe(pfibOsZzXK1L-
zV>*$JfzDoXK^S+V#r1TJ^Qn#Icw2<m`Msa{1X4Z9%VW``wuAUI=&T~V+L8jz-u-va
zzj5*Yv`O{|Z_tCuj`yUsWtzfSB}(u`5%u;5x`Et64W$l0;!Uy5)|tu4S3@Wz(pxc>
zsRadH-DpUp<N+Ti8v29832Nz*Z)JH=<26OJ=c??&B05+iYycx1EJ1h|c<1c64G)H0
zv~SxM9poNWkM2QE1{x%3DkBrno<Uj%3bi1|xnl&n*M3p`eHns)tOXdGe}7&2eYNga
z(aGK1f%~7Ul$o?rW}ydWmX^w8k~YxihqBJiPoXm3%)OJ<$SgO@QABI?P?aH8HvM_Y
zGnMP@qb#l&z;3BzbN`AFckz;uO>sWcrJOlCN*?y+S>dU1p4-hlYl8WsWt}ZP(Kgiy
z^IZP-Zt~lbaYTIa5js>ATA_c`qv5#ITLM0DQ!KwQSdG;AdYU_x!5doZs=NN=z_u-T
z0*_ynxmgam8!a+6ploZKd8l*pgVLb9@b&B0=j4Uq79ko$UC)f$*Jq>Q84U?;&VWy5
z!^mCR7Bjvwp2hbmlZLIX@!Z7)UrD?*=W_Odt5vL;NXNZcJAm&E&VppWc6^0d#H&}M
zia91;it%Q{0c4sUye&KMYtSoO9*5@CRk`myY}j|#X)=v@=AZT_cl>cRy+14lv$X2R
zxrxKTXNK+j^y<-y%0Pf1PO?9*6RL*AtA>YhK9CbyrLlo;e}C7T>5oUD-m-cNZ6l|v
z*4MInm|luuBDfoow4UznhtmV4gVDxC9@7Kq2$aiocb!c1Utc4QN86H2LZSbTHaQCl
zDSCYvabI2gQ5|NCfy2n^q5StY>Hjnt4&zXVa8IRp0PvjV-|Pk8-Kg9v*T~rTnUpZR
z_6NN7<*!``LV0!i_Ja8OoOoX@PBnL&6Ll%!wZx-<PjA=89oJMGE10kSW&(sKGBSxB
zUFo-hWIFD*^@BWDH?7<T$P(}{t>%#9D-l!wRVIEc!oaOt&!7}52c`Oqe~PHE-_pH7
z=UO4sXqbjut&rz~x)(>nlJi@!#%4JNI}VDSZ4lu=P1s)8Is?FlxwgU(-M!82&h)HT
z*TBHfsjn#1MB@eQ7L3biUD9V(1#2i=L{C?@L`D=YUlPpWTf=W{S$OtdXUfgx?>zo#
zH8m<iSl^PB{LQGhp$$vB-45@5A)$f}Jz1Ky+DZp>KRI2-=!x?jN{7n(xrG;c#t9tC
zpK@rpZ5<lLR6(a^xQP8wxglh(S%bS0dvx#380mBNODE7Iv2*9T-(C&~YwYC2dwL@v
zgTG&+^m%e@G3_*SF8~C*I}dzP4ka?%7l~Yl8>;YB?qG2XS3+wfnf;>GqqHfVz;Kws
zR}i7c7Y7abVn_RxC0YVa#={sM4ik!zZdCW0-XW+c@=&=Y?fWF*b1*AE^YOrzZxd;<
zA&WnIlN!a!Za_op7WbRo0f_K;P8EAR<~U(?(nw~|?Y4{edA=r6y2-0ouiW^Q4h79j
z{VMl7;;k4TOO1H;H7J_qM}R|G_{1e_SxBs<)Gy<%9}*A<^Z#fiI*fLC+3Nhj*4C9E
z<}kQ8hY|M0nujWIm#Qt?($V>Gef6sBc;A<y3ZFvD`mn6px<%hiBC#h{&Q(OcRcXB(
zVZ4t+XR{p%8cZgWSiyZNVKs{3d`h1ugpzC`jzbCi|C(I}j#S`GQ2}N%2;}JD`e#0A
zyEj9AEA|NdXH>0M4*AaCycs7C)O~KO@i@}GxVQz?#CQU~^m;}F^z`&VaZAd}(}5ng
z*d7B><nHb+*D89Dy*N{=w5H9dTycOVY2eQ^5Rh}R)a*y_^?_%l9)eKWfU>w^xg9Eq
zN8o6vyg^q@_><nhKTh?_1aSUb#dPHhz_av}l$!4OPjvJzzbeQN*Xet#=|N(w<LWX~
zqs0n<NZ*Hh%AacaF5V9Ye)FS`Jp#~ir4u>_K>XU+&bW>8#16EJWRX|>s%k=ohsPcP
zd=`MZaK9xs{pB&$sbyMDt8)ViC8z+A55k3{&!Qb+>3g6MT2zaL9+_`dG6(SDch7no
z>XPs)iAUB=ZxZ37(<>`0@62<cM&7)bC4A)KL3h=kOfbnRMWLLU>i9l_o{{rf+&rF{
zNdc9}14$gXE<y~amKL`ao2CDO`j~qZhYGxsXwkeG1f=Ww!(6@-ZHX35obXJg1~o)-
z&#ZZVD7CMzZ#3`wm%uXYX~;AZWE!*EfCe&+X&}|#lQBeJPKMN^!Q%T0ZKb?sMn<i0
z!M4I?yWj&va3~o`$p=9F>l+VA+ug$9l4#+ZP;YY@m(EOZp}j6WfByWbN)p1`fa{Jm
zaQ(d*<&;&=_wh?04|%FPvk(an3kl53$IV1PDSi~H_GvJOqSwsbYf0@%uZ#Wf2%u1-
zVmK6|uTYfj3h4j#?Y-ik;R8??wtfIUxAt?MEos|xc9)R6?#25}P6P|6R#v8dy!t+6
z9&{Z;!zg1{WD8%;qMZnY3X9w~BK6LK*OIj1jP_e?yqSOV5~x3nk~}5K6zF{#{Cgul
za-u#8@$W4n62->)U+_l9#O%NxfkMg&?^Rl#R|4@vY;}Z7>4gXq$e>k3V|+ZcPjI0w
znMX)GLYO#65$D)L!=l8JD*;<~9R<oZkrM2vJT+WZ1q~&tyd18ui|_nLraI_!WUF$n
za?5&N0^QcU^Jj1V7!wa!$6GP;c}I2hA%Io-H7G2z>oy1+()I%-+BJNuK&NA%jX#x=
zfg{Tj8@2|ZCTY`k3aI@)92R12?*%MZxjRk&`5_risD<@bu;HEr5JDH+;U=asb9xwl
zECtR@e~#u2mU;gB(U*JBjdr0GWxad(zL589Lft~~Z!Sqpyu89<XqgRtxECOB=#Xi`
zK3J1!&B1%fvDw(!=>i>u%1~g_eWqe%y6oTAy!J(S?QM6afi0!x`O|6({K%hN`*v=9
zCMC0Q1X#0C{{5T!w5<V`tAXPg+`IpSwX(tDJ@K`{w@xJk%)&s=mE%5s{CM;QtLvVr
zk^77Br_P<zQ&v`%u@1a~XAOEX+0+Ej(s)1|=lOI&`_gx>4HeFzgeVZpI%!1^$<0#a
zwA@_n)Ofs2?sxz;qp(m<ykbG;6YX_m<lc{R4<0^r9+L$)d+^+&n3x#T$;6_f*Qn`x
zJ=w;dUF07JK0o5vjK7Mq_T^q`QDAg|n33(JXqLr>9J~QJIJ~~bl7g&bZ{Vg;B^0U+
z!!P`p`^lB+XfBB5SFc!)a>}MjqpdO0_GkJmQ7Bpc<eejkUdEm`sGN&<cXKP`;<Xwo
z1jr+W4lI(ON1zWNThjUm6#Q#Z2&~p?K%t?aXv&T4Q}2M@8Lo8Fcd^%cx)mg0>{%-m
zsDTyV=1~ympZ^~$@^Xzh&goezRb%bvtd|M@7aG%p@8lt*mC;?I!bQka8f?}`kMoR|
z;vCzTh|pi(P~{b#q|?b?vDbh7{Ohaz=WKNH0L$89)wkGHIoCm>dLprS==Jgs_6s5p
zFl*Mj)tzFGM`RlPyk=3qT7!3)owp(yzW@EwY~Q(+RpO#a7R4kOJ@D%ZW!$-r>Op7X
z=lfc+XQp;w)TW1D^HSZ+Clc>al|#3WILXHT>U9=0sQJ6vLa3%*1^PTiR?ac<Tn<?j
z3K8@DFCV8B*^*vYVj%&W(9qcEIwZy$G+N+S)Hi$r2)!syBt%&PU-ufwo}r;(s3LbS
ztiaozs?QT<QgsfB0cbApuffzoiZ8UktbQGmlD-%B?-f@XSR5=V1<&cOKR0uc5c-h@
zeN8n)us&lN6nLq-A5l;T?R)c%XWC!a(AXbZo4*{d0cz9o@^aSH;#LKZpKYM00&$@+
zGv2*BN~1V|XrFFg<u@2NB!L5IRNi;VDU#+a=#+Kr(T))&*_?dHv0QtG1bC@ajvAW;
zIR%<;+6YraL!<ESr^k=Ug;va|?qx{QTkx(_E%Io6_ztt;oMm9qj@ib=#1VJv=IHl8
zzW0@j-QDruL1%umUI8)!4_XmHh6q^&N%zT_9RP84KcI-&+1*d~i!Lm5Y60gngzXa5
zeF&vfu<Fn?5SpMYDV#0){?{Q9^;%*7^0jHknpmmg?rkL!oIq{TGBVDj+W+$NpEDO5
znEnKzIOU@lKZ<oJ3se&UsARDKG6CR$GaK4nG2;li->istgW2m;y{SzsE$xk1ru#f_
zaL_+{faVedI%@zI&wWD3>xhVSVCcZ+2BU4|t4q#-w2+>WaUD9!hNgH;LHKFA@(E75
z1Fa`}xn9ffUDX<`N*^@OKpGer$JIA)-8td(C8U<}B=$-msW<i#osCfXi#15}^Sai%
zV|y>uh}d>2;~iLs>zQc5T5X_a^ia7;aZHK3=E3~HYtIfm93;7O=B!sR2wGsNNY@?-
zu!!%hp18bx)L7^A15_v)W0a|%-2nn}a&wG~EwGDvnpo*5uR0<FCMpMpH4gT10Ca<z
zE@w}fXOAN4S}F6HT)VQdaf~SQ_;H{2feS}p{tbl0w2aFxSbcG1kquSmZQ##+`CP@D
zO2lJ%s()VA>GSCWJ>i^j(SxCKZ%9!@1a=3XWq*bw6hX~To0dkywR+$fx9f33;d{>F
z`27U?H%H`>z1By2aCS(mi~adVWXh~sSg?dCkp{i4^QMfuwGEX2I|&HA1~uPg2Be4&
zpr@j0xi`e=-zV^%T4r$|Qk1UlFRM*{o+-O`Zq?J)K2qy!l0IC?Q|$A%N)oZlaab9|
zFstCJJofhXAUu!ad7$|so(_lyqReN`#@83xfT}^nFW%DEABV^I&W%`g91duux?1fl
z4h8m4zl{+QOVwFGN!Og(xBa+yt%Za^P0-b&V58VTzkTnwf?dAC<M5=sAEV|{f$;rX
zr$&uX2Ps8Yc}!`x)B<Bq>;RT~)~j5LdH6J4*yJ~h%fI=Ee}~@ve_zt&%bUH%8X}^l
zY+s{&eoFA;wi_bq=li=H<&MKxdT+j}7M7h1O%L%)OK-ggpy|5(z{do?<#Yi7fl(%2
z>S2cn;n!@f5Yh4BLv0O>XW&+Wv^AGt{8a9*e&$L8p99&`c4slwJXk`u1>+bzWR`#?
zDg9M-E1=N7u9q?^$lSdEo#D88YJwl>fSAb}6Tcoq>xaPYpG2#4e9OgkSNZ!xU`_mZ
zCNAbhNN7z315_gnam1iF9PkZxrqq=71q?H^7M%dJ*nazPk5ib^Xug)W%wpu^<y|nq
zgF;`AKA{<#w&$42KHp&Qfb~{aSOyOtri&t?iX~6@iJKjDFOTiOs=Nyf3*$AwpE`Y7
zeet~aoaJ)?sf-bvNK4?|^Ap#3pfU>KWoN(c-FWAv!y|Z}UAuP4PbP<N$Ey7B%p7KT
zz7Kr*^eHzc)}`Szr!WqR8G^|XAq)e8)(M{%$qyu>T`-}i+rkTdSkv7*#LM3{ipA`E
zp^rlCF+^qo%5I1n5W60x#`SzZsBW<-_s)&PTc@bYKu&=)b^>GGAu;&sjHu|CYzR{D
zHpE_V#|bNor8?$QhcN$e)E>x~+mL5Dj6|?70bhH*uUxps19fyG`4~G8WelX_vOZ;Z
z_rjwu)p8g|zzi4UF4Gy_pr6-)Dm%Te1XBY}U(d|UuWRlxW(x*@1Y+8Q5ZPDdUlpRX
zw!}^5+Qb1G8+(<rtaBIYQjT7mWGt3IO3_L%s|!Qao$`%2f!}8&;esWq1<xONHVJ1Y
z=Xfx*)_rN_UUyFq>3pVmK~YfyK$zs@uMx}DQ^4L&^X(+*RPiD0vW_|r++DD?9cVMi
zmryq|($k&aSC0CDHV@le*NwiC_+rzak8G&{6j{WUB@#W}S6=1mbrMtsqZWFw%f^lX
zf`U!(SlFKu`K`<|M-Ci9Qz6Q0V+V;)>~fkd6IiW11n7tn0qk*5RGNQOKQqm&a;0is
zgmfWK9l|y3k{P%g@Acyk@|6GbWNJ!c;-|vp8oqgm+YUwOOT_MB@Nd~QGL_$vi$p2;
zS3b0{amii-{9p%ML2=_Et^RAS`Mzuy@|5EssoNL)Pmk<MiJD&vsY+51MQ+(^ug@7b
z0f}L3TtvZ>=)K0K!b*S^yME$4SfW47hg+LWSgS8z`tgi2yI#2sY|4=`Fo+%UFhO=-
zDbC<z#}O8LiO=_Je<Er3?ffob>_EhF5E{9?>xiYL63mw<PMFjnc7@hbZ0zm34XxwG
zHh%LLk1#k@084t{oTS_)ZW&A|gd{;dMn*-T{;zd>0X2_$olxrx^9v@7dtGVT0v9a*
z7G>gc-@I#2t6VJ1ftWs{iJXr4>Xd<Ly0|((+V($A#=b?ss*ciuXF#`+5em3m<;1;(
ziFUAxZ+)^kuqp?(<;P4!)qSVRHPa9wDg`tiv-k~n-BT6QEl-ckzLR?;|9*4Fu~5Z#
zfas*8rO%*4sk+B<?Enk`>+H%h%wOobt!0I2bt8M44fXWAj-?!rG|~A$0I{cIaVQS0
zLl14ijFve{+xI04ugwG^e+5EE=k&@V<z@mFL%%b*cv%`VN~+3>vL%s9L%QQgla{VN
zlSH?bv^F4tsm2ba)rHH?4vP1{Y>E3Yf%P6E?E3QIuqXE9ocqkkWmuKXcvF;(jZK*D
z66NJXA{!lU(sJ)K3m4w%-A~8qv_-mEIYIR2<C#-g=GTf~q9otx`xT082!zXp1m8Jz
zeOiHe(S!D*^EC5y(ibM8>o^OZqzR<gcnH=GfVFiW)D=@=%=EHuBT(FC1picO@yfH}
z?FUU3Raek++5LTeHpKF|>#n19FO$am{Ib51MD?@kY=$lSxF+#x$NO_Tnxzi*21jTO
z+9mfJJn8Zl-mfFVD*;MHbSbF3JIwm|a3DA04v3rC0b}w8H@yi0+2liC9E*_RdenT@
zYe4bW%$NI&p^AE|C$i|MaihO+Va;*_aky~0xAEV&A5$0rF@EbS!!&T}dLXRb#-psK
z6o%QFbITy)jhY__@>g1Cw;osjKYNpEw^pI%iq$cd;5Hb~Q7{32%r)bsjUCXR=x71Z
zG~7xZNVLv#<|MatF`(}l$Y(=lOrw09Piv8b*h&(C2fCo8+5Z%AZvu{1Z7!U@6EsV#
zT(*%533vXtt*B$kT7rB}O1D(dC%WgqbTHqqlR?luW!sb0zQKNE5@93+Y?H82)(X^A
zvwt?=odSq0^5r>&yK9ein(T1CktY{e=4zLRafJVyD1AY6C72lzETn+eTwqcClzSa!
zE)J^;p)UZHJ}rDjtx%K7<c*xqZueHe1tY^j5IIl>J>(#GQ;p1kKY&JYJydC}1QH98
zi1e~e$o1|q8z&5xhtmmnHLZ0nux>@0dh}W$lN)mZ#`2-ZPs;&Xo`-RR0#|BF)=EoC
z;Iv?%o9j&jnZW^)fM@~<a~Zis=oW23C*x0nY1u}iL%)6iN+$^<rVW*Up*uJ3y~4@4
zLXeD1y}myIrO)8PrS0yD9nIa%xDM}WHaf<nrT>cHUW3iwfV+YugybgF4P>UNVxdE$
zo_29?GaAu%y(qi*aSP)yjy6y){yAENzJka(lr2Cnb`@0BQk?x)%8jUZ(2BFhqo$W{
z^n*eGVpNh}?|7LODa?^49f@oNAb@ZzlS6=fIdIM>SwpP!s$(VleoZcK|Icr^%ClaI
zw95R+BMFqYZj;XLo5v$5Q5vTGq(0R_WL>5{^g<-(;cmiP-4Aq(H^=Cio9F0d>Q6#o
zhV&3kjb|iyb>j98O5hhBWE&M;PmOhXD(yIA(nfjdKy(la*L7{BejCLP9r*EPAN|@L
zPz~i7<@552TjD0sGawG>8XBhaU%Zb9pz>==6j0{>we;@VvkqdXpP{T^4@7e~xfKT7
z)|uw(@1Kd0)ZlhJ^`-hkSHA}eLvvHlw{F!3(G&~9|0|k@RIOX%Uhz#OV4#G#I*Jby
zj;e!zT0f+q6dd)>o&Z$Y%7cz|9fZsPrS8r}>y|D`s1gxqda|I#a^sGZYqP@Z+Smjw
z2#1?;`XoY|9uHeEkT@PqbU4#1IPW{<alDiMes>2w=}MHeeY8H&BpnRgiLCsVj$r<a
z+l|b_RGtgGN3<;xOpX=U^<E&b8_mVk26b1k-EuE;fS@6o8py3}h&jJkOELFt?Xkg)
zw7>+;%So5U7eA3b@o%64ztH~z)Z@(m4P4~CqO%^6m~p8eXsSxf+Qq|b;W@B5r`WGG
z7lgK)&-3{F$H@usXJ{XOg1vw1JYSb>PQ?jz5a=7H?9cZS4R)T4b$ZM#B;PfLZ+{?3
zcc-t^dtlUw<(#P(al!H4<NO3>ra4&bQ75$G2k6DQ`;0tU%2fIe!V2O^KyI6|KeG@w
zt6fz%;?-Kw5=D5u<gIYt<M=;ODm{>Hh}5<^8|r^Utc+gN5AICC=-6g1ZcC=hiXB<>
zuPX!jb;XPu_e@U}54b#oVwVDwS?Yj5@UztinT!37tTZ3p!9kw5V(|q!?WHQZomI8I
z`ncOR2aX)Zt&q=@1*E|Ya}2r0z3uMksI}5c6jWWbdPh9QM-rqQ5N^f12`fHmaHkQ+
z1?Fv107*{<@L<)-hM)-`&HlNtl39PTBbmS&Oa)yD8P%-!*%CsT_}5;|iSr305=f7|
zJ@q1;vB#~tLq(9fDIo8brm3{*2Dfy23=c7)Cn*py%rF1}6+}9uZnwx$kzqXpGjFTF
zKt(VI!Db*r+q8u`V^rdFZ;tdpG6Q6(B<x3}ecLA?(R%<sA=6rr#GqLs&S`z<6wRhd
z7X!+q=|MBE#4~5kfW}e?qf?{ONc|TB`y3G3)PQU2&9zUjiTW9<ZKcESH6k-AE6w1J
z!RJAYR)8HH4#R#*)J}72jYzR>7#HihXINBR3|&%-QX5K!1S|Aq&vCPQo=YHxB*~ZI
z0dq?r{pc6pTR7_N>n9na4SnT2fX5qg^4$P3-*E0Y@CJm87-S?e%dDc04|n6*wI@Nf
zlo}ZPiGniRARB@!vg@^|2@#6P>h`WkNjU-a#~WOco8I1~Y?$s4MxOu7t!+Z|7y(>x
zP_@N<n35A3*VWd3`Ae=}K7CmpOpHj^miGxyb{vepbM`)VZ3%brLEGWI&s9Q=TKRBb
zx6Qdo3P=IbkuffIkP4x^Ij-^gO-)S=H$~j6{2QzRBolrN-p!@|pt)~5(tcH$MfbjJ
zori(fi&n$EA-$zkxbW6p0+t|>#Bo>f%~wmvLm_E~5W1m=mH`Lk0#eWbPEgittAur!
z{ocVa1le)cq~tt(F=#bb%RdgxxnrltevwEO*N*N%7MKq%!`^4rO%P}gVDurcMpc+#
z=}@)0I6AgiT-^fWcP{xbrT2=Q=y~+f%R-=RZ(w>6@QsZt;G?Mg7Wqa#+=Z=9E9OQJ
z4E2qZJ+}7t^xb)&eM*ALC{ts3@MCY6dbhy}N>hevvaG&^2#9z>Rq4VZ83#oh4DLpt
zc@20;=M7Apug$r@eSkhzZxkl~ul94r0~W~KE%1xICvL!*PNWS<+kLy6q8S&R$FQsY
zIWP}t(!{D{RlH(imqs=ptKR|+hmtx1q4-(mE2}Rc$^h651PdwjY}v#CYE@H&fmu{J
zasHp}2#~&w@^ts<2dkrWvClTVF{%AQVCmX2xk}o&$WGm?W#30w7bnL+?aXdT7iq}h
z)w#&3E*Na?A0yEd0yPvzqaI>HYdgR9Un@6r3=Gs0s#RZ$ohq}*&kkqAy3$Y1=GV`0
zUQWkusah?f=Tyx#h`J+QIZPoVgN(QRD2*^ry#Nl)eczD6ObG=R2xcqyRw1cDmu0|E
zXxMtNM1AV^yg~msDiG>~NpRB1(BfdUhzVA&Tgf@g+sFFX<mbH<1r$88VA5L0EOJSy
zo8HXl+GU?u0TG}BbE+*Fp-^L{N+=<XHl_>QTh6<T&$A2s*ZmBz=Ea%L;AX=7^h;kW
zM=j>py;^mANm@#)x~+=yP1>!(CwB6kzm#}OEld~M=~!c=Ev)`R%>*o_8<CnX9e&}`
ze>q@lI*ij8m=oOll>P!j%*;<%y<uSL9<fO8KRNkK{Id5%Vj$oa7xe8gpsPtxCoUr)
zFhyEk#v_md#;i>>bFMf*<o*%(1+D<{eH6eWm=i0?Dr!xw(iN**vOtEl<?O@x*3(nt
zJEjNAtb*I2CA}<n0DNQf;6dF~wMCHQflQdW^yZ!@-FA233{;0`MejT?bUScn*W#X%
zY2C;`0%B%Yt<oJk*FIN)jV0wWs>T^`8N8ir!+aH_Peby@`%KHhgsTWecAna50J0cx
zsCbV!W5)_TYP=5-N~a(Q#t-I3>k-ib8qNM~TU^lK7t`p)!#%&)^WnoMW4!htlWfQU
zGmIW&l$V=8!2>~f+QQL&fF~WD&L`)R+@d&t1As5m1r??O7@hVBTK?%K&ei+zqc2rm
zhQ#=GPL)#QZ!0SYY~>bAgArrjJw3>jf7C^5bm2`1_x(G!hOYya1`{9=ek7=~?fJ7-
zJ1>dD{^a;EMUjcxy(cvbFmYJNmRLDtNL|;|G>*Ioz7Y7{ouD|m!3ex9v=pSU=HXBI
z^%XocDzY<dVGXqB@A-I1ysNJ-V{XpH)Q>%e4B>Ys3Gb5afy$~?wcMQp<z9-g?1oty
p2_X8u<-!YZ46FX9uFB(euIgu>SQ0C?1QdL!U(>uwymI&P{{WaMj9vf$

diff --git a/reference/Rplot008.png b/reference/Rplot008.png
index 65ba321c1761ca55d8784616c3fe3cecf0eacc15..e30f5bcb497e881f6921ac9fb6eef6f050572bcc 100644
GIT binary patch
literal 18932
zcmc({1yoh<);_uc38f`PLO=zj5$RCU0z?`%At;>^k{gf~0Si!4KtZ>Jba$zAx0G~C
z_nqr^&bj~d-EqEq#~I&u#&xLF-ix)~HQza(`8>~Dq4!mkNY5~wL7`Bjw{OWmK%sEP
zk$*%4@Ez}-$|(3iU~*SU9zMdKJ-IR;_;A|cmaY>DMSK?dhtoBRIYgl@pl-|G)NqSm
z8g=)g-a3+5>u*_UF%%;@9T4}NQyU*=9arx@HP@3<H1`iSK3fFtcMSCv-ulvKm3hZj
z>a$5_w(pscC%aKst*W}LZiYP{xuA8Mg6J~q@1aLe&385q*hej+mjr&0>^YPtMiP>b
zzHcv=N!oklYE+ij#3@UJ!^XyDrH);~p`fHpcurp}FE4+V-&dcLkxZ`Ng+8cr(l)zz
z@{G+;{yhDQs}*0=(<kf5Wo$Mup&^{t2^_fO*<J+<YvHx=lYDn;Hqfe)mwy|pzl~BM
zXpZK2BXx?6O}u7}hM$u1)u}FAx8<P%(be(|3Zhf|mR%R$Mn}gk$|h4__L_}SzI~%e
zka*gd?Q^sk&|RsGcEnHgId;F7D0xm-SC?MOLx_w?JfLU{bAIf2yQs6$+w*We(&yeB
zA4B-$dJ~IMwqeD~n3!OeqdBF|U%xhswgm+djCd?&e*gKi_9d&_+Dh4~uyyw)wR7o_
z)g2<gq?5zh;;9G;im-+U+f{a>;%LVPn$ykCYA$$5v%vqT@vO3yXIYCKE=+g?B-O7k
z-R)ZG{&3U({(}c5zsLe*;rpEy)Hh~Ia!VtTD}Ia0`tqgb<Lx)!Od8Jg^!1t4Ji|X-
zBkZ!EamxQDH67j5*vYZT_Uc$<oPFgW+Mb>u(PQCCOOgzWrluynge&*{)?zSRzDakQ
zGAA!@Bb=VO&2VAs$jtVzeG{FLmtUrSQA&9^<7~VWSz`oKoMVQ1+P#~>WK0gbYYz{n
zSWc>+;S<LegjO}hT2@W6y&`)4{P}$yoe+*hH(YG&emlQOJyH42FHcdg-ESo}t+ChN
z3EMIpVq{R`kv@m)wG;es8;*~6s-}K$B;7kmbk5xgh?B0lEuyWp^C~1nkm4*{)ARUv
zMm0^%UK;}!mjcv<g@uKd$5ByHxV}UL{<1`@a9Uh_Sq9cwV@38!4kc(dhVa;C%tqW;
z$ZVo#+Y-62g@whfxv%Q!DwD7Xx5e6ab!3xYmShwc>!2uJ-Ks|myZ?EVgE`vIKcHdR
zxyx5^F^G)ms#d06RPI>n(Vv(q>W$fU5&I<X?ZL@6s?xUyk4K-5(a0RltHy=%W%-;O
zo2>q-m|2GVj&p-P=#^uZ;4(WUr7rt4jl%_s>LAkd4zmeQO(yF@-8MUwTD9GR$ypRH
zQ1Ba#;@l{6T}GjlA{Yf?XsD^*on%`wJbCiutl{nbBh1EE+3D$Nl<(4#&4xy1)*>el
zkFwii_x!qG@>IB#4wq51mX6K<_UQRRZ#E_nUiN}TMtbWM)8qZk`Q6b~3?b{O?d9QM
zJVG+#wegynedRdehj0?#TU!IO7K=B#?y8~DHYFJK_cK3#N}r`vGwYJZAFcB7Icz^U
zBu5!fHHOc1r;{VWUtTWR)7zVzoQz|bO^3R#sfmw*qu6R1xx2f=dPCUzr&;n0W##8n
z!*Y&fWn^$K3xC<>I{t0^<Mk!Cqpf}c+aV!t-P}{WF<eukDl$jTyI#^uLj}ZKS{c<+
zJ0o}sYAB&fAJ2(Ey2)-;nTB)b@i;}p;ZJ|L6Shlk-23TaS9E^DVW50;^T>e9Daw)s
zQoyw<k?$HB8gwc=3eMq{X0g+gu+fIGa&QouHbpde?~DfDy7<gN^o4Go*~G%vY!x@n
zk+WiAV#V7-)&k~jj2r%UdwYA`)@lg5F*3sNt{6j)E0~jmu{wSdAt9l0x~g4;uiIQI
z-9nPP<Nox*HuR0*=lho=8osn7N}Z3C{6oa-{+kl*xm+;wq>i70EcjJi95I}7ZmwWU
z>~${N%pB7ulZo2EkOmW?K-uEe%7e4pVxwr?K_xb}pZD&dcIG<qNJ&ZQg)Asw-($A(
zk|tp5ag3}q(*5Sr&XVV(mTih)>inY4@#yj6hwGCKh5KH|d$XMd*4iZJ1W!pPk!u^e
zU#i@xSbd*;QC&C31b=&J(Er8--(<O9lRv+IWd6!dTd6#-L%UB$AvuhJfk80Br|aWw
zH&|o-N8c%&=Q?W8%tWWY*$fK4pclaPyMYoJ^SXvv^Jn>(c^y`0X)uove$8x>k96aU
zb8o&$kD?16Aq#(9;iKp2Tac`qPL1WWv$4G%e@i}opYxQ0+UsM_x#VD#_W~4@YQvLP
zEFiz1R@u`=61yP3Z)ix!MPn2dCFSjE)Ma>^FU~!A`HMAIsH#7G`UETd&fU@Pra!?C
z<}Swuev)|1{(RwLfGjIq?#Etl-N9d{gC2gcAFDif5HDa#U}a?$vFE0u_=ZDOm@h<D
zzV$Y-G3zNq?=4M|^TH%>-eY@hPrqS~JW%0uryTZ&)qM85llbj_Ne0wC?CI&b<!<cY
zQANfm`qH6+&7eQ-DC46hAvtq>N3xu4<d3QKafm`&7bC60r?oZygaS`~f6w=3mUN?l
zI2;@toXLeBR}9AY_w-z#4ZGRDWmXlMxmdbaHaH*Q{$c(0O*=cgE^8J0kz&eJrAS3B
zE!xYfi8hAI3v)U3CzlSyx_lsUA~AL5%$c<}Nrx9FIPQP_`qgndN}G*~ix^3;wzip{
zmb)^`j<$!_A$-$zi78&?qcG!sgx|dOo*Zsv`zY|!*T-JBUh7n1so^Kl(9$wH44;V}
zDskfQ*jXV(Mf4Z`oIgUo#H@!IU0@`OJNX5>LaTSE!t-fd`5&9b%C7K<!;N;;;hDYJ
zq$XvN(YN1P_(`BNUT$%h{P4y=Cv`s8xk<8v-c00|8#{#is{jtG8e<B$q(-CTKXkB+
z<Adf&4b6O2B&ZpBSWMrQ(XzOrB%kBG07pm|4>x97b&Bkm?%cim>dhPf!94T0#q@jc
zpMA|LJp-GM^Xk>wt$w2<$Fh6ZAqBp7NG4|!fBqtjqQ9^2OiXj{Qp&0$lx?aUoqI&A
z0cn;ddw-@=oMzihAe)&SY%iNhD0kO?&91y!>aut_n%CfJ0EJ0^mVwFUTqm1^g#NuG
zX#)*y?XWnz(%UEs-k7^D&Wor(Jas~f%Tvj<4WLNDV(*YJir$Ap$r?Zbsffa9riD_L
zsI$o4DA_XBqCYDEsnoC+1s9{<z707|b(yGYj890JDhac1*>&UriA@21V}maaUVUGt
zzP@B6x%BP_xMYJa7tfs)W%{~<)hfOK3Mfd=Ig-2{Y_5#zTf|<53K>8ldk963PE~5_
zZShv0zJjZ3;V!@mn?6AjImyiqxfJXEtQ7qsyU9;6hJyV(hUGd)9zu3@ib0vHNoR_J
z39L=#iZ!H$nQa!I!<-HwWjHq6d!Ao_K0Y4AVp&)NLgd*dwX61{<_2<7=Z1^2;TYsC
z0yZPXIl4*82`+uQDQRh!AL>RjOVz((S9%?R;~S!wdb$}W<UZ%y^>=i#A<evH_ZUST
zZj4|KmQ98WZD;;KJqdw5kv^hA#CrAWRe7j=-G!D55CznbL%&eSh8Xj69%8E8Z5mAk
zCUy?qL=?=sYV{>}u9V!K4GRf)dA2%E#9qangH|@=#sy<(dK5*w^g#wciJaHobW93l
zZ??0c?-pr!^d3piDX>w#N=o8`+{T{L`B|sjEoaYI(r!dlO-t)?0L5vXc)Rng0lWQG
z$Ct877p~?!T{U$4<vx45j{j_<6@8E!9Izp;UE2EB3S9p{v14ip`Il{Ls7c5j87+Gn
zhJ#0t@5anmxoeV)#h!amnic0H;4pqeO;2yghNL@Pl@8*~c6(eLbLfVYFaRN!YA3#l
zE@tG(D<}lQwZ6Y*{yb9VScg7{6;f<{1Dz4~RW7bp530-e_*ovMt0wW@xpPNvRJ(h5
z$|UFA=d3JRxNUdE$-nG}?re44<%3g2j`Y2~oo>QG2dUtuNvPOXBe)|0&C`OjBda^a
ztb&9NX{MDaDY9sX8hijeg?6J?&D!GMe>+Z#vpZWf@+8b{qgAA#-^eGy`5^k#8q?De
z)?Z#5%B=EJRE=<7FK4hwd%1OCV(rWykCJ27@Sr%6HBYgLN8KVGhtJc{KF;?W@U)3o
z&05|)tUM#Vk7Eye*93sX%(4#mWn4dB2&B{G#mac1qr#3mV?I^i+uCTyyf(s1mf(`9
z0jW~Nrk##yzGPQ2O$(bzztDyfvPSBsBdh)K#Ds*=rJCb#kzf2Qb7PQ%0$gBEyH)%$
zQze?*-(Kc2Yft2HT^e}UcLb@f{&9>!^b;Fpqc3QvRVi6nDo9-5`ihY$cb`J#_?%$i
z_@kvR*Xnda8hr&xQZ>D=j~WuO_GRd@S8gP%5@q*up?S5td~f%+L-FEGyZ8R}H=CAs
z0x7V7)PS-!y7JqLopBgtB4TsTR?kQ5xo^yb_I}ll^M<0FI!FcE^UQh;AxntFO0nF%
zcu|3eHaw@tn^=6%l55p?Gs}~clV&)QP!h~=yfz`Sm6N0saPGZ*_IAhDb88{+>6mx_
zpN6@(N_#Blo!SFaY-};H(ip+Zb$`|QLQ2_jK#&N0zHeYa`eD6J%;$I`Dd%Aw0c0k?
z-K3osJvK=hhG|E4T%|@GryAd{&Kr2GH=JJl`ST~g^W6Pnbk@g$BAdZyVZ5b85S$Gm
zl<X)JBno8@si}npE9b>N(vp%Al&=nV(CgP{caJd0QNw6??zpw96Z!EPlzynJWQJEj
zc4I;zpEF6gLZn$~8Yvfz722Lp^jr}uKi*#iXg!c`NyS^S*=4r%U<(f=;ks0PDF$uw
zOPB-?5`lm7bS<<cA2{#GCMjQe{tR}~1$q+UuW>eQnwo+Ai^>)(X97Q9uL>#O^=u(#
zT`L<qB2!RMfcAwslJL@{OFCt)wz6daP)&E&Cfp9!o78l5L!lA5Ji6+P;IP!fLg9&u
zp=Zx<z4qtRTO;<KJ$sfvtgBn(ec<Z6GE&koby1w=*6DG;nj;WWND@_3Q^WbNUgLRu
zxOZD7;ShG$O(MVSs$+)i%KfWtk{c}_T@Ikz={NKw1>i^_Yuxahrg$zT?1QD=&zwg?
zGkiXK#EKD&p{mk*r>Hno<mKe>pg93}27lRJ-KwPt+uvD@gs{dNF0wDWrf?3nGBH#`
zl@h~lA+&R+Lh;G*fg`j<s2dL-vKo3WUk{K?4k(05&`|6+)iQjrv)Tl~O@Nx~`9ej@
zt@BbSWP4>a3^J$WgzKUDK<50{`E59K6QB@)lVN}UJb@y{1c2XOI7s6Nbpw*ZTGh!x
z7364WX{uFy_SFx!i>eeH9dq_h&!u@YFT?(6gr@D4vw<L_1>%|3I6^?D;fguNHMm}|
zs=+ComXUj{aUxUCh<R@aF<ejj{r2tKaftR)9lJE8m+nTtOXB}k<s&0~uypPB^t3MX
zdDP*4e-#00#Hn5Cbn_8LrcG)GouOB7MwSTqKUFdOS<}?*GJ`Z1fM*#QEnd$Q<&|Fj
zDb*KBH1k|<plhi1!-IZfGTy0;@?^*7gd@NLgLQ?f#U>+TL_*Jh!)trc+${W29Ryz-
zy|h>Hnn%Ut{$jR>s!N(Ueu7;b#Ak?rY2!On-6~9l`yD5!1+}X?VaG=YVdjbMiihKb
zGW?6)TfN#Qhr8>t{aXR#51A^Al%aRJE;0JPZKT90E`b5FQZfrQ_uhL!1t^l`hS~2#
z9a9FNT@;(G+H19!$6zX<kx2n~3F+0)WByW(RqxfjZNOLcVSJSqb{idX<e_Z|5^<~E
zjWQ$!kA9rp$hSP)Ud*ogwNM#rXQZyq9z@Q<3(Z@7#m+A-yRzlN0P@QXpBB>ROQCsn
z9I{LgRS2VfZ|XJ`E)u)to)2NEH{#gDbh<eLL$1>;w4q_^?On0^LlwjJ=g%K`=nLzY
zi^l9QzI@V&kD3l`i$<e2>zw@w$!U#zjtZcwm;~q~QdBnnjJW=16c^QynK-_In%YAQ
zg@E*mA0A=sA{Q4I5_Sl1f`tLBb9Kt~y;YWekqWnAx#;w}5EYlEqiLc@>FIkt(RP)4
zW(chzuc8vMlpODHSvfXfx;Z9Y3QxiLs_9>#-C^W}`chp?NQjDJlQ;66gctvyZ2mQ}
zzq!G3LAr5LhS(wyi-pyV_PHkhCXG(1=x(==d}^l|ow0rRu03&+kLnPare-qMsw-*J
z=8A&a2g}%0Co}pWb|W6AvQY|IQ9*Ow7~<zCqQd5OMZ=w@!e>L_(;S_pVo9^dzuNKq
zeA$oA7HGr~I&jm<!6JStj7?*uAkwi*x_yD-1~3Nf<Ytg_wzjq+BrQ(i;t;Vi4yr~r
z;br$P-r~{JoY>fdj`ndGEed*4fPzZTcqJsx2R+om2*yP8F9xPrMzp*iUJmMCyv{fB
zFlKC3Nm)6ZJ^4LsPzTZY&8K<#*RpmSxR;*%!DMmX30s|A_hz-Smc&<v<^~7vT}a3+
zT!Oz?C=z3&ANqe^vw!0a)jXak=Rfz2KiXelga2(gK0IqD4xQ70=BjD8szhRWzeB_~
zqS5Dz(C=fETDxvUL|Dk2TYU_}-)Odfeb4@l-~YGv?Z3Ri79(Y>pf<j-$6er3g1)cW
z`^1wA_8Pm3hGt;Xl;5ze&zY&~@dK(w!^;ElSDtsJRK-v^8xV4D&ORw=Ss(ml8Efj4
zhQ+2jrE#7qHw55ZX#E9y#-KkLyPfpZI+mdNz3H}FWfB$BU0ddASt97ub*#QxG6*T>
zjYz+N-uFJ|@9+Nao2G7xGAdp?n`6*C{iMLcuq#xiT3n6m_=813^q6bpjf-b*_Q?7`
zCl~Zk)YYbV)K1B&WYo@0lDVmIdgp-hOdwEmHvL^LOcM_DKsvZ)?oyF*Y^!$*HO&~Q
zy`>#%Kf-WCpi`o)H&6t|dl9Sw{3l|AMe1AeSeK0L?k@}~5zTj!c%-D`=kn%#B4hNF
ztrJ_C8bkTb<(qKF@cvKd`(Kvse-s?IN*Eu;KQKJAzG`r#zX{D7AjN!v{+>bIauB`v
zqz*~DSdu;7)n@f9VadeClhc6@3f07&pUw5siQdOuIuLwZ_AZSz#nN(?#E^x5uKYz^
z+RRTm(gD|xY||bw&G<%9<OKx<mWwa0E-h?!gv^~{zwX;WC-H&TgKf(LMf!bBaFj)K
z1y9F&)yJ=u<rA7EwCJw(@58VQ9$SIAQ|g>thPMW}_mg}qtzDL0(Bl<rR%T?@P9@nF
z7z6_W@Soo&{~}KRM~f{=TMdChF11OKrjqa+lB9%YbV$hD$KN_A`@vj%Izu;BXhzQ{
zcRP$%<D}m~5~cHq3Jn9lF>0k^$53*sN1a~8j<Irofy;Tms{vj0xS1B^3qhN%l7K?h
zE-SY9RbE%NR?}w<SeB+yfL&v|Z}J?sOzP_ME>YK*=I!_f>&GeB@b-2+!75jX+|RU4
zGfzqw&&4%QKf_|9t#ps#%)7cwg=zDARMn^(I>qul7#~l%mN9+(#Q9wJ&79lpfZD>w
z-r9HeNEAwXe~ulJaljUUdKuIQATx`Bti?e+goG)4ph=An9C-9FAui6{<!2kr0YrUi
zhS{7HvNvw4*Ig?lTJf7@n{#D1aS!-m8B1WKMJrnxK@eA<p;;hl&hvikTf)twU`U}>
z$yIKs#ZtR6RRuNf)%2o1>oOS>PpnVX*y^n$EVg!Wt9EK#^Z~I2$>crZ=qQuvn51;u
z)Wg~1w|8!I^Lu{Bk8bLJ-E@jgm(eGdo-8;AD65wD*Ek;TLHB<K1+B^Fj0_vJj<)#$
zV=={K)s`S9CucSEJ=_(zTG9s;cWetXa}@RyNIQ>6K2w&h4~u4#Mn&CUSnLj}qyfO)
z75@eM95~MA4eRAE${rByA&P=KF$oFbKp_SZl&LB)$QgAD0pFp+kVKWyW{JT_W3gC9
zcl>xL0zmr$b{{SPPMcccrh$P$W1(kr^S9#;jQ-K%vg>z|WcFucgJ-PWuTkvs>zW)C
zS#aGuahIzA7918ci`e-1+P`!`Qs!W4!qw?_$v~HMeR+a>eH}yopDQ2#+uP#5<M;me
zvfMxahH<+^GT0mKvv??Z7p5S*>r-19034%-0;xY0)CoW^#<UaGP0RL}@jwcTY!CjN
zHkg=~=Hq{FIit|!dDfzD;4zvvMohX)NZB7Y&9-{K%kU9SU!*S7uPqPOfN!JeSS&Ov
zSgRBFqBKiX#<Eg*CKHboS>~_y&_`AqC|cPI)Mu{$;*Sq8IxI3I0QwYa#Vf*VTqJeA
zlxJ|%%GQbHQ`O|<Se0hDC+uG;O1{l#%cc^rrB|H7n0>HhAgTK%S?Ej+`+K=iquFB0
zv~P8Xoso>4Hf(SOPDY-M5}3+i*h+yZoK2%pEuY=E(2+6)(0BM8t=iWDcNE_4s{d{7
zneSq2(UPJKCIWYHGKg@>vNHoHD9D!CJ49VM8fmg!xQB*l!Wy`@I0C+n^xa8X_s|*=
zPyV#As!^r2(|w1jdI%%B9?-u~JY_B)Kw-NrANRRnm-#<|S8n{ACJH4AsW;IvGK?)$
z*80Za;^Jb-mnkBejS`Dp*<1^mqX(pUyNe=hQnjpTAZw%J!^XL4d5#N-&;Aod^Pj`%
zp9$%I5wKkZ-zZWt8*j?XJKwo|zWV53rzu4t?7orFnAQ|4=!};>l8!@9#m>h^j*z2#
zn04Z%v8t-fMg@4sgS+ReOS4P?gYT}@k+0XW98(ni^56o|DvVs_@LOi167r&-yR6)-
zr-W^X0z6kM=urGXz?W~dNg!=)<lcA)pl+=dXRah@&L#KfQj2F|t=HyKBU3;KLitYA
z1<Pq_N**lb*$UobOPSK-q^?!^bU-P@mYF3`t}gP35SJ|CxymU^ykfavvVl?;E@;Yi
zvN+FJSzAZWSHrQXefGT0Z}({xc^vdINl1t?h^u{+!avHi_x`H8YX?;gMKPXtosBI;
z3oZtfz=%(e9T7eN+LEA@6gCa*qT|5}l$4!6c$cqOD0g27@J->I$xS;M(|{iOcr#sQ
zBBzO2PD?8~FZCj<*AG}PXshT!Q9DOK&P;+D0GgV&<oD~3wmZ-e-+puX6Ui9pj<_gE
zW^!@YB_iN&>Ed@Yd5R>K?S!nYr|Bw9t|VII2Vk-HN9>3caw?LZrwBzDXfl0`3dIU1
zUa<UeI5hpF<b%!}RKQ|1uTmpaNbdH~=IPu~mJZQ$UP`Br(Y@~XsqwK`aY1t|ws2Q8
zx6aCFosLN5C?SGyu`g3DN!q)k#OcpYsDI4`tt@otVpdsJxKf`ua2LqwB3kG-uao1W
z-KD&CexM|8vs-&wM$Q4xdlu13p@TZ5shI<!tQV+$w}%x|)!s%$O$42{yAi`{;Lt9$
zb3sC47|n9R&<*sl1JGhPs9Hn5rQ^ov6wcE}k4(0D-3#!N=eInP&CS{8TvNA_B5gie
zGI$LR61huSeAB(Fsrg7ZJ}<h(exU8caoz03&DNhIMv}Lj4T?t7Q#hk{y0x_*L!AYt
zPLZ4O$;W8uTwGm+kBg^G-->*!RJ!u~_4<$5$D^us->uceb^qZ7Xd4yW9(>JVZkR1%
z8h206L&pC0@YLvY8m_f^DpeGUq^_E$Xyh4)Px!BHoho~}+Gv@c5CoJoD?|8Y$@N>k
zT0v<OR|9T&FsA8Y1n+vT&JX5skTFY!EDsk!ZhQEv-2KX`1JHMMN-W+10G4ooyJ`Rt
znksQ3Lnj9pMG-20Usrdq_*+)ncJGA%ij&Nkn>TObL@<h(%tvO_n|wQ95Tz$c$;v%z
zRd08Q3sCHDwcIw6iy>Su_L`>VtC$#bcTB>rj-jnBPs*#7qsOp-XG1?E*Q|8EYV_?8
zBE5e7dUWG#z|Ws|dIolf?x@4ppEb|}ZgVKIp8i2>?=W3;QN{a{*O)4n=P4huc%6J|
zj63??p+M}>6^FWW6X=5J12NR^+1X*HUjpiCYJNz|T{Px%;{D>>HG+1T6R+A~Kj21c
zfq{$wW%;qquvTo49f$>>;Pnf$)UETH9s_nGVGtsMre5X1Mnpt}E-)|<0S1O%>k0_>
z8A0|oWKIK){_yx17qvOxP1qd6*Q9S>5eSq}T(gApoNrfGSAea0!Dxl2i1h9_9;zBt
z%2xZrrQss-qx~)atVNw-2UfUea8@s)q8<~JJ*0mp=5#qXH<#aaNe2+khmRjm<(akC
z&Zj3u+~qB)_o{X2H+cH6HsCZTH+MadEIDSa3{E7gfL!yo9e?bL>V6^}2TBH!l-KsW
zz~mr!@<|ZFzx>ZnF1O#WlvJ?lH$d*ow;y8$mQ&FF>3kRd-56d4Lqldnj|Ay~5_R9u
zFrv1$7CCce7i>{+F+H4u;Mg56V6hzsAIBu8r4dLxT{(p+hxF$L^q-oxb}-_=u&+8Q
z)U|F;lycjh2wwlw%!h}XX-f#B;nErh&a8DGfT!+3@gwi}j4pz;w^!8H;3cWRaRQ{9
zk%7ds4T9S@pnC%zgOH6Vo0#1?mdT@?F~W}Bo0-?zCG!Tie++q4z#V9y1A?$Fh}3MD
zl3nq`=g&kSS~QwQN(Jd~6BzVUsTehx2xJ&mFy!cv$PytcD~drTHHGtxvw;$K-|mEL
zOmozqJ03rV%onB}y%qM2WE6WQ?Y*Dhs{_3PC!!9h@?{&8(UxX`*9GC$iq{*@s~H(Z
z{(@WS8OE=xj{-895FrtrmxpM8%`!W{C#Jn1Bg3L?<b54+Z2)h`2rCPnwSekPBh22k
zPM#Sl+}RI{kTM5=3C*LF^4P2fr0lp{(2t8cLrlC56-dPMaU#|mjg?kWw3G>eZbWsW
zpx>}gQ`_Luw6Ygliu&j~0n?Tf7Vw7DPRxvn6ayI?$8)z=OH;EP&apLG6*x(dMpShQ
z9VTSC(ZwC*%dTYtKF40*Dj7p-q0ukELO~rVwXIWI2wirZuq{NvV&Crkexj7;BoqSc
zzDn5iW^t(pe?=1{);2Hy@_E$<mk(0Uf?vnQ5^vzAHVG_LPj8;N>gLj*`ZmAHt>`R5
zwB~e6BstiH)Kc@aeRn|dhFb9ugz_1s?~sckybrbuen7QUIThnP?^l=Hcf$GNZqA8Q
zg7YNQ?Q7<3_2A(V0fDd%-M1-WDKDQcuAt`dI5N>UoF&_(L^W|N>+&Lm4tO@sO3U&F
zh<?HRjt}|H%X+G(E7<S*u~vV`mfKn1_L7^Or&*WXBh^wBFC7L90?n$Xq2b&@h}8Ss
zy9Z}>VLK_RsZoL*24U8aI6)*s{qS(4Q;ZV!MNSs_EOKCbgCEi(A<k5CcpfU~7pS0f
zwY9P!&*LEZ1MWo!m;hiqRc1CS$~*srm1BZ@`tBPyJqaQXz7|S}=DKpFW~jhAjz?zy
z>h1K{d>pi%9-?AsXlMW>O9E0(9mICGmMJ@%_;e;@S>V|TI(B*90)a{ZNe<XXSs?~x
z3u}4;yVCi~z>&?BMgVDkr}3roy05Yeews#eW{>2#kEB5FwD<rU2X3Ldgr~m-|2pI+
zO$`m>@oK-*&6CLzRy~}EQQ`FBufHcl*n335b)w7p-9t7RH#Qu=_Q3^NYSx0KDJ6}Y
zXMg*trR5bbX^ci<$Y&npi~-Z!?soxuLiwEsXsE)7;Eg1L(__Yi|D5M?kjA;X()*wz
z+eijZIFOjGmY)QK9z5t-XJ}qR+Jo4C6%`c-5*O&41W>u(UALx1>?i0aKQ7H&$6F~~
z<h}Qfzvgh?s`~~8l3Z*-<lR1{tLw4IYp<D;fogCM$>>23-xWms3I;_5v423Af$HVe
znPHZDOb)U)XhV#U2f(h9?oJw@aqx=)pq5b;Ms(`MHHXG?<{z}P4MlfGoK!#WFhOuW
zf?BA$H>Cudx|MgU|I=MMZi7;4@X{0?td!+APAQ_ksBY2H(V2pz#mU80M=CPn3*HQ>
z7z5XH;CuMyG^>KDM#?~X5(WX$ZU@Un>tNT4D?mIT!7LbhzN$k(lrg9zIyokRVl!z+
zSnN=SH#%4PL1;g<?1i(T#!!hMv<M3A$H-8(cR(lE-}kT?t7NM1+_BTTrh8&g>Rbcz
z&RWH)H#wYn<Q~rNzphILzrtm)ufB05zcn=E`%Qd&{DE?Jr!M$jW*y?7Gi$Ce<ZFZa
z_Zj;fC<PYCp#J@f$ZGjP9z_x={dFs9lyQ9sCE|TS058a=^l&BO&IYQ*hc?`_bql>;
z1we3ha(8qTfc7kf6Z$Y%Uex%L62RO^&gDOb(iiKV!{)oGskym4Ob2EHv&>WgPzZ*6
zD|0aldjfX~k;0PifB#dNFF&MRZC4~Q!9qz{kO?ZjkVG%~AE_2?Smw&(Y!D2VC~%88
z06mY3^4Og7LsTMwH45(TC4(67)PX|V)T?b24#j)|s!CJWr@IsDZY|wKF-90MxKaKf
zL_rWjX+)s_S9hi=k+;N)QNdw&d>ifCBoEi>S`FW)Td~Askk|1$K&2~kt9-qThq|V-
zxUjIck#r(ydXnVz=X7pf9-`Jr9`8<_)+UtnFnZmaFH31D{VW<>hPN)helj&QG(`K9
zK2-kL)8!NS3Hd?_haQ^UPsVouZJpjgeCEu3Kn(NhPk**&2S3oawYHcneNR!x7bt$h
zYP_LTXf<&dMp1T5#<`mHEO!#$vq0Qcaj7!>ny|Vi4aDH*&!4|(2(PsoY&YV6VBH}M
z@o4)wfH2|!_ee=IPjC)Jv-^paySpk^sduuU%eh%JrM*}d)D`<9*h5c=u9~nfw3ZT@
z^V)2+3}*2@lqTyLE(@3=P2Z~dX!l;GcxvY$ne+LlyU~^U``2fd2hFU}gLb50yv3rX
z5(;WBlalQBFl4B`y*(a}UYGp)oB=vNDhe^rA8fjvJ<f2={8gDr0~=$uEunZRw>d(W
z1$-cKkUt<{ySD6GkaFxuEjJHU9ZvIsU9N%LXIBnYP0Ux0sv3(#%8Y9h@G4)NSAQ{q
z_#Y$n!t|;w>SVV`MiCS=_g`M1cY{%a9Z35*>c0QG@_cl7GS*#57@w3B3EqcyMR)Js
zRl9$G{1$So^jxSzbbN*{pn}r@aAMNXY?y2agF0!S-#Y+%ok(`kx34OUWMKH6iiRbg
z8>C6ZW(m3{Ye2QV&w;+1WMU!1dCE>l=u|#Tk>oEC(olV|3d2x^s+>Jnr4NYT14QMK
zk&!}ra>~xr3PgTud675XGPR6|;vdbD;~8fAr>0Qd976rdGbcx68;HPnEc4*Xkbqy(
zf7u^$K4r*jA2~1Esair|OpBii+OpjfiA|q_;V}?fO1OL5Qz-vdcimuvp02WB3%IqX
zZV1=+$Y2P?eBDZ;mgc&!{jV1wp_@u`vW0v;@)@_35?_ZNjf{{y&BsTKp5%|L7{`bF
zRUDb4&2F&v$X+~K(<(Cd0)+J`#7y-yr}IjS=ObqHtDe;_4L?zIzBghSC{GlK%)_60
z*~r^kf*9*I8QPe%B;`CuQTlY13@S5tIUe?6H7&*}rGQ>@T^<Uw5Q=q(7S#0oidb5~
zH?s7x<alNM@-o*rBp)L;Na29*%ns+2WNHDDcR)M-*Xmx$Oi96oyLV4#J6Prr2b_1s
z5D#l@607f;mBJYU0kN0shHETm&Osghxn}E;ITmfP?8=dvkK+aAO<6T+FbYw~1(673
z2ZQyk$xuN7U)*Kk?YE@}i51`B<wnGC2w3Djg33FVu(!Vqag?Es{!4Mf-L==btpD`?
zz-xJTcQJsADk@}%|L?tb%-grdZSi8F8!gua%v$KdO8RZ|3$HCoW_!R?r_}jN<{}f=
zG#3#pFesv$=r(y8Sfan|!;3$go8JWK<!=Ary<r3Z3`~#7Q20eg9Pm)@=qgnU?fzB^
z@WIV_DzeFcqg{ptnjQeZ$Hs<n+C+XpbV9Ow2u8mFFgVw`rkBEncPooblrCn0(l3}T
z@I$*sUfwqK!v_@N-qA_JUWHK4(9K(e^{hcxf{{7iedc|u)kSNTDwH=jtv>}W;!sq4
zLGK3)fz7Y-a|j}o4S%$V=A~O$T*N_vCz6-hZ5#(K0%UHfi3cwcF&T>mpAz4*8sop6
z1X^3$TpI68a2<l*t<D2*zU5&h1|~U3N8m*RQEzUvOk`xGC+Vy0kn|w_938ig@8O{r
z8WiEvbh(5toee;XXU6P4q^6}cfvQtHpBBemp*iSWrf$$x)dw!eb?`*tq5v}cLAz5l
z{~;vgbLU?Wm=8LjNX#bt#x~-L-Z%lxcHe9`&06N~ShpN;%Oa=#@W5`gG#QW?s>3D$
zf+0;gu4VWH{9SQdW1C&x;Ev&s5gA?4h~A`GOO2GaIz9p4+^Cs9!X0U@on?fLcJ`xJ
z-3Ki83>FJ6*7qkYz&?*6dzVdI_Y&zi079hB-I#jXJPALWH}3>Sh_m7&PD?ZCSxVhh
z&NG`yG9;AYg{48wi%t&m&MCJJ^gF;Fo(M*A371z=B;q*hayrQG`DfazV`deH>p5;x
z_BtM+nnCcyjaD})M6mnh%Bo<w>~JqOt!8?o#-XHRKhj+2NeOyw*DJ&-c3I4NU*vmF
zo0We0Tq)VE*M01gjEHmDsDmzjS(+uNeq9*xE&Vz0V7?0Ba*`tk4hIYr)WH$DTpK`q
zqO5l+-1H^%JQ(#VlBj<4%;0bb+~B0PDt{<SS+#3lL4xats-1ACB@bk|8}zW6C=hi%
zDdI(#Vroc`Y($H0?bwBIH%{FIBMy@xeiGnU-2!|^MML4oI`m~JoDOB9(c9e)Ywt)E
z;`a=;zyOShf{wQILDQykYHx*;^6mjke}UqgR|~H1*lN)=?i8&zf38?;{0Yi=$39!F
z|D>dTw^v3`jgbh&dMZHffJi7}G}N)dnklT%6-d@*pVWH+6w!^$tL;|$9HIr0zFSAm
zxmsRJJeI?>Q_qv|LyY8u=G+>0{u{gNzhD>t4Mq3?uhOk1LWQ0hnxO=bij)gjf^oag
z%K97&Ohh$up&_3Yc0aqHQGX%mhNVllrTT&^d2Zp96glb)`6afL=<&Z9a{rBA_dn9f
z{yEV8Kls)E?v$!?P|{C+BNGMuFwcN63@TBVU`t#Pqpb$-O&u5?a-8QMz=Qx4rrN)Y
zC_6cv3+rwl7CmZn0cYc(Aj1L?0m|C+Glvf{MZ^1cAo&u#D(QchGH~uJ>V~E!r=3VZ
z%D^uWrx=>Duo1lv0<ZaCKsNZ?*85<@j+3mlD$2BO`pJ7IlTVpcBX%NawwYUqtnl=F
z`+Lw1X76b(%d{5_X?u~y?@LfJ-HjkvgHCs?JL#Bu>}cgFbRbdRR=|ZxgEFr6#o2AO
z$KaueJS#pI4FXRG^jzV{@totQ2g^4sfO(h$ey?;iWFX_y|AXs$Y%V9XYa2;y5wG45
z75)vc05(-mhK>ByWf`$<7pBT3n~c#N8fnfWaa(-_wJvFa#rIKR^G5`=Km$5ygafRk
zBo1c$0uuMDgoNc=9qy9McX!g=jI?As7q^C(wGT0dS}gTc!_q$nguCA?NwkX-;u|I{
zJ5ml#SfG*|8_$8${`xU^flL5>%>lh#VmJLW3gru>1H5UCfi5nE?S90p$NGH!VA^67
zds5%UWqgU&f3%+p6158mZtUBpyo_Y2$+>6mcvmbtDx|7`Ygsm2qb@WUQr}ggh8xv>
zdVi^JS0}EM40w}vcXaqVJig=_<?X*io<=@?ukuG$LsUX3c*FW;J~L=)X{9teOwN<v
zS6BDH(*W4Os!R(pfE>ITTLq^RGLADKOiuzA2hNbkk1yTvFDN{TMmZ?=8#?SAqL4Z<
zycMEXmxDrB7i1WycG=bR`%Bj1->>h+Uj+w;bC)Bx@4@E0Y2{A{yp+vdssCLQ$A4ah
zLu?}Q&WE9=UpZEoBqv}wK?<1rU5mP$Vg~R#=tLraOe@@JgIM6}iE%Ix41Xs6gK6TK
za24~n6iy%xQNEwE>Ks0pl_$~|X7@iRdx=MPwqJ;pH;Fu`Iq-9<9&ilIN#ttEc~a&x
zV>h-2GJ3u*ZsB6Ls&0yxX5mS*yuEtRPC5#EQJQ7M?jX1Xi-m)RPdWLv&&pk#1hyOe
zChfESlN|z!P8MwdIbgSdPsD%=)8b*OB55MToX6unazUJSNYXcu0!B2#MM%t7?wS3)
z{O|`*L~O=|KBVWDWxZe}g7LEudTs%QB);K<!0I9{2b(DM85}GD2Xh-q-3=k?XG%l^
z;cwtIw>Sen<881cU8}1x6{bEd@qh~?J?b-JPtfOs{9ejA(S|EBCFMB`)QCA<mp<G(
z!YEn&WnJP&)tlt(o`^mn8_6)w$&4br+Te!*nO!tL^2XFa0=;%QtscMqeEm-M=^UMw
zKVzI!tN~L6dQUPwP4yJ}o<y|%&{GLtc1!e0x1ubd{g1g!CImX~lRrM-u#5}H%kztk
z&5zljIwHiY^vwfmiB0_H531_xN*^ReL~P&aU6?9xZiwdq1yA0&Wu00G8N~blyP*6V
z+ilzel@0zzgy^abBxVh8$b~EDyxow4%KH5IhOX{8L(JY|u-AfpQdBb*q4lO*u^nK+
z4*wf3@q$t8EO6NJKvuVIfmVh>fx`#{13XkfGd_nQM;Z8ztZSAP@2T1JKhUIkK1`a-
zS=f}ud1nfP#hMmqOtd78O+UfGIu%eCoqsO3vFcie*r3cCXIrM93})dAF`NITVu9)7
zKs50hhjqzl8<V+8FPjyQf~a%WF4*4XVEPOusr*3ndkB3pVs&lV0%Pkbloxb<jX-;o
zqe7yX<Tsl~zYy*u5x&irb1q3vxQ3gO%-lE=*%bkH_j;3gmdz2o;G66C#$Kh&Y-bCv
z3-=eb1?UanceWR?YU$d2jUGy-LRajwd3-pP<83sA{p5D!KrT+lrbkRU@;;Zz*tV@F
zqsDhqF~iKXQBO8x({y7iXtK?pXeA(|FAW^&GFxr;B}dmZm1*u+VQqVVc1qEsJ1D0~
zw+1kxM`I7v-o9|!9_1aS3{}Xf+`KX5sx@=6^ep0guhee82%(xiy=4=@$FcVACr>)r
zxd_W585J$RwP|JICXwU)Ipe^u6V@K7F@&Ag(j-~!p>a_>#p0tL``1|oxh$Qk(!Vw=
zvHm;o^Cw2)aq!zB40kbzbl<@KNy02iKepdj6k8Am4ihXkR|7m?z>OlMEtE9{Iw2Yw
znuv0l(JTWAn08@38#<6hLv-pwzzu%-e1qnx_3Fhfy~HVJ4NXM+fKdsXeq3MZ$x};9
z85Cn*e!Kb)QiZh8+P%WiA~q;CmSMO600!a=0UPl!%sK+!5Afj}Uc>W|5i}Uyz;rN}
z1vNaG^UT7+gW!Bkm0#_D2~wD_m06%oNQ__C=Ir<a3j}&k;1%_>@wJJ%)9FcI_v3t(
zYX1!F(yLj#fc+ZJeH-@WgUSC4PJQk$xv<G&44|7hNhwtgT$PW^&4*W@(PUSO_yC<|
z1lp^tu#gs17`vh)F7dwpemOO2w+h4I0?4N@`vw5rfQvRP8I}rwUY-Fl3j6gdCDAFM
z0B!o=Q-3gIHcjOia8aLu;U}<IFoeUT02vJU6<XsU)}NPe-r#8#oS;kKyP&aj3k?^*
zd<F>9?X!6X&HN-i{rwUiGVQIc;8aqPx=c~<PBg+BT<9i4`IZqq&~KW|btJRVlZZc9
zJvpj6;SjbN7{8TS;xzjlIP=C=dN4~I0Zn2MdjYi67kSIqWR<K=v)0pSqgjEbn7)+>
z(@tTJo1?`ZSP$ktHUWY#q6a3giop66&t3qNO5l2*1kEa<CsxcU4b1SF>;*IJNqo%S
zn`%l{-XL#H9*mt-vKK(B+yJjr;G}-?k-0OcULD#x`Lo3^Ra|-Sh@6e4j{gJa84%Hz
zZdJhA8pB~D;&C&xvJ^p@MwHMpEq;1=Ky01BXAKv?ysfiAldgHf<1#J4L<R;-$f*w(
z_%`mb2tFbL^$3<XGgASwRwwY%!v$b~hiL3bS7kwNjqgKxWKeHe4+R=+DEJfFU#Twx
zVnZm4&SW{lVdQjy9P>4-@EFXZ36+NdfHvs^n_;+O*g>@ri%ep+s?OX6d!EQtV7=;1
z@W<VDMB_VvWfKGwfcoIRM4`Yle?11a*Tb<&@60TkB8yJ8dkNw+pk(+*QK7zA1=c-8
zrlb(HG@8%I&Jyjpy>t`a51n@qnAk67=oeVgAYXtepvRz%^_btT_*BppUi1krwYvib
z5&jZd-KS~v3nr!aep4Z%9fgg39UaDPgV+1freA4MO9MH0MWe3g;hbPt!}hC|^$Onn
zOva}sAFN)c*s)(v(+%s0lSc)5o`+NgVxcUkyYpYt6VG;9<PZJ<sV+o^+w7~BuI_uC
ze2Z6qH*C{^O!xS!we3i;S_T-g>rDi5o=o3CW?GsHV7|$1;Y)fwxH(|3=xt0)n0b=7
z7I=u(e{yAq057kX6_sI_yFT<K%0}1P`ZTLNH!T&|2;w>U_=+?EXM_O>ABr>_JPiGv
zTS(kMf25CQ*UZ$nD^UQgcOc6pKl&G8MAqZkU76D8gejwtdeiEa^`s>?tL1go9<7M4
z+SP&*`9kol059aZ0FFPTB|8CwA08gk`WUUw=_yy0ExMM$P?x@eHMBB_;0WV2&mi!|
z!HQ?4mI+Df_sk3~s`7Z>`US&vpfgN<e}A<LggH<D9WA~=x8*)@4><b<=sxC3?Y{QG
zbox`TS&1>r&UX)bVQT1HR>ihn`ToM!LaXJ`GP*aHm94gdTOYLQ!s09S=XxHBWWw1;
zhOyweMxLY<#74x-xw+OdCNK79H$AO7Fc@|L`T&_(4yEEi9v2`F;-J1k^X$gND<$Td
zKhw>fAQm-~zkull-8=$_gBkw-izh#o)u@}(#~<`sXKI*QD<5eLFT?aJG6Q01`DOa$
z9j&jqxn~h+q&Je2(V%^C%LJKg29209R32=eep>y{6;uBq)d2aXdpz-8<KSF>*TIck
zy-ca&H@&dH<U%q8ON=L(Vu>LUgdFlH7>a@C2T1&hN&-191>_>6!Q!ntd}1^3t;V$#
zs0+v+$?*Mih<T(O2DV|4*SVID5AHK5JVihT7-Z0ad3-9^H7ojVX?-o&>b89fa+C4a
zLN7w1xvq>1F6jBd*vC4|>C?l-4t#s)KfUv|I~=apBGya{^}2VVFY|m#C#Rv?1U}lS
z^|}rWSu8siY)ZexYC&}0Yr1ha;-ok=(yx#C<cAey0%t@SiWdeqVURxb#~Y5g^kihd
z%_UYqHxu^!gg?1-_3s~VazL_c9*a@d;yQj93DJiSRN$vigiVn7C!(|sYrta=46Gm{
zOw^+8L%IO%1V%IPVKBb%FkxI{>vee1^@0bz_RGbttG~kGmB>#aI&K234WS2P%sQ~Y
z5myc|Z!rqyWi7V`CRv0^!4^evCXm~wN>mpKMTGKO==p-&C?20k@lkZqKdwuoK+owA
z@5f)4^(7*`kMTs_HvDg}dA!&Y9|n`1um0CrWv{aqKb~4nb$O?hz?eHCYh^9+li-0P
zL0J6h$_scp#n8DKfvI{IuSmjcW-S(-+R=KZFs{i@GWBlzrhFg2?<H|y#*-Jtfe%CE
zdxWEfD@#^3G&F3?u7{^t(C&;ngDAcW#6bX<6j&L_>Xu!nX5#D{LGllTU??a~dbE`g
zZD$m5iNpV7yv85ct_bv7>4ahXstQdRAzAr0Q`Fr@^aL<*i|kuukO@RBUXQK)ljTum
z3o3nT!Zn}xmV);^8lkqDR+-uN8Ps3$$|rN`oO(6X_3YU=;lSllUB{`u$=XO|bgwKO
z{`pLuP!oI?HA)4_Q?E?0jivVw19YtEtls?iUEbh%%~{-1a6T_>t&JSFwdIG@tm~^c
z@9)hV2mbhRo81fwhoMO9_ogLAKBGz@Oj|Pc>hrKLP|7!l8;x%H2=;qP@3wrZs$zlr
zer!9{`WQq+oqDd9g4<QKq{Y=2Z$GP>03(@&wRPS4R8wbvzxh&s<Xi~R=;$cqykH8G
zwk<QcWQw*#DeFO)`Er7qZVC^W06%b=_q9ld;o)HcNy!&5kx#@5xM6Lfmy3uMCiT@I
znY;7&W^A=G$^gX3$)SYl5HR>S|I{S%19BU<BwSEcVaV#-ti@Aa;4vXtfM~81$*dsw
zUTC@$b0iLE?i&cT<vu6OQc_Zg)&#&#;QIB)^_bl?93CDXMC1W6aBY9fno+`)1Zv;B
zvn<iWkxd!(j6^;VU;);gYrlvtd4dl_e44GRb&EM~E_dq3kM;TC0$LY{eab9_W5A)b
ze|%JCISga&pG!&x-Bw)7t|X|abJL~*sDB0IlOyV7nx*sa@01LTjQLBdm)`)Tq6h-P
zCylEL6%`d1n{zJ(!vjJ>C)Y(ODV0@K)xi@*N%`r)6>LU^P&gPjLPK)<(j&96R{?@D
zu&{6_cf<2n)b;h_ESY2Yj4s2Mv|+EtR!`cUIv1H489!E5N~>ojC$n}5g)1s5-cqCX
zSR2Q&j8&xLMSp;^WXQI)u=wC6sWR^zTj{kY3*Z1GtlO_2{fy$FMUj$_JVG;GNKm0d
z-5qtc&Oe`SZEv3r`kBsx@rxtf5np19EP|1qo=1}X=YRI}P-hxdC3<*zTH4sOY?_h!
z>gnmt0aJ5s<3&Wo18wb?b+l%0hVs{vlJ}P{Uq%4#tqu?ZLpVM0+O(3SkLTt%@o{lX
zJ;f6)7Y*AAEe|SN02Yf*d)toYH7vh{sY-gJd-?WjPI{8qV{?y@Y@@1s8z2EvLGBL@
z34x@Db3r#coDc;gs&QBBvviK+9$X$|_?E!1+&B+aM#Q+07DpPt;`>O2CmkqC(aR7=
z@N5={VmVb+a*(zkl@3^dp%JFA6dQ?Oh7i5Jcoe-aq<rl-v82x`Yv1`jt<$@6A{z(#
z-o1%~Ij%E*_BPDpWqg<;S!5L1ULoUoz0%iW>L&O{>wTc$G>!-<*TLxZt9zHj2`L|!
zFPmfyl+fk^bXq$&+J*NvdY4$WaFC%I8ymlYYWSjnzwwySt{U2xSJl9UIxh5Fadvez
zd!a^+k61pyCfc8Eq-;L`{2au+qPcncG-*GSO3=1<ch}iNi16_6Hn3{1FN!9pn8puk
zcbk1x%?2yoIH;!=*wAIJq~YP=FCP!OlDf{mzI72c8T<vvrvekPCjlp|;QHZ_^xp6J
z<7Wa?;qLlWaOMCS8F>`3|JCxf(d*-bEA>;3+(wmNHBJJJxl;saW9SHEiC_@x#full
zUS5pA4V~Rtn~;MY>@1GBz`(<!gCWSXACt~6ES!Yd!n9N=)>EKv`6ERXGBY@$5}sDb
z$;l1CFe^O6<*~S4fqD{mbFt5fG-!t83Tph26ku{o8&l@=1@>h$k3KwG=Nb4PhKbo)
zE;kDTcN_HA<1C<)1BK4l2a^TQ%6W_qh~g2BfhnWo<74;Ucae||Ch9|}h!cYaqR;a+
zRu8asc6R!~NN&y4I=4<lU!N8l>Unk^*^8XO8q#+Hjr3e7Y5Z1t(-$;I=+K*Hpo(#>
p!AJb+zyH*(`h`HF4$dKIC)X{G<JW(1k%u?kR#1`8k~Mzze*w47epCPe

literal 18897
zcmdsf1z44Tx8)&3kPZn&N(n_81OWj_`BS2F3P?B7tssqvC?Fx}p+Vx%C8<)<0@5NN
zQX*ZmU%&6poo{CDo#&hT+?i*_2L+BNe(x{#UVE*z-|)Mt3dH9woI{~d#EN%h@1amw
zGss_jJb1)!sPPqi!!uP<kcIE?XJ4i+0KO48-O+u7LJ?9Rf3XH8{f|&6CX}M=Z4J+q
zpOap`8pFquC!-A>YlW|apG$qods2pBV&T5d#Sw8{_sO~3<7%o3>0@hqTm5W}37biK
z!(7w;m_Yonvc1@AHk?J&6}cZsOkV|a-I4xq<74;Nn#Em(#z)D_0&$9Ktf33rzT&?E
zYq}0oe7^VATdZ||Ex>2S4GRm))yyjslaiHn6*Wasl9MYoqa4t8u3%S95V!IsHLShT
zHVTivD9jLQ^j6sG*F)dm{R%}>+~nkMJ5Uaftpt;t#0CQeh;aBNToQ(nhrhp=e0=}?
zz*{AAkH=wQs{FoZm1SkGJgz_)m0J*VSI=PMVq+;M@?x>FvX0a{i9{J6|Ip^(;E?Kk
zc4;$t`iO1z-8%A-G{(N<l<caqT3Xcd&*;NtLP=&*J*M7QH67bm*iTRg9L+OQjz_ke
zdj9&z?)?3=C5x~yjpWH@jQaiigAXNFe*AD+DXL=O<Rn~t<HJ4Wv)&-zB`A)Y>QHxq
zLVAX#Y46Nh^*l);`gPec#&&e>6K9EXBK5haGh6#)=boZa9+Pe~Umb+u>tK5&v^|n0
zXA+HtgO^@i{iXI2N$9M59eHFsO7ND}Ylq8l&1|fJ1^M~BCM~$vjT`BQhldk|9gU>l
zsk{-V%ead#CnvYp7jP1Ee7I}&V<>OLx0QFJTcGd3+Si)g+}!4+{;d8SHO8M`N^vd<
zJIJc5lb;+dN}8>V6pe^hP5X;A9{p0X8!J2W_U&7d_4+Md@83&ohoaU4S1N2rNfe&Z
zzn47SSL;p|F6rmr?9)jeFRr!{jA&_*rlO*n$;)~3Bp=;)F@*SH$nJCiBRstnU1Zd7
z;XYdV@$~vx)X`>4fb12D+amNGFngrp$G-=yCF4|gUOTnYh%9$p8??W|lF5cokY7+B
z=k8t!<FyhjDJ#2&A`=o48maj8%LPj+SeltRm@G>nhT3+du=M--WSzEE5}RphqR=WV
zSCQxJQ<}mZR9J0oZE&Ti>k?Dr{`cgU$5Fei^d$O&5fKr%DDr6eB5PP}D!6E+I>SwW
z9=(b?L>C0_6dKg<o7Z(7443$`e(z1Sos?KidDNDZ=AY!UzdPllcJ$Ly(srn+y3y}2
z;+D^jP8BRXF3N;fKF(}-=mrJ3x<z(94hb%{PLuzwwfZg9C`L(!7>R@2ZIbm5v6u02
zu}A8}P*#IzV$=*Q!$`%^k?+cBZSiF>F|pay?CJF?|7#k0dgoC1ctIl>dQ}tAFJ8O|
z_K5Ekb@_%5mx8LAuvg*a;IQg<Z$Ca5ZIb7uZTnJccAxY!A6g|jMDl2sluY`;`c#96
z(+moagx;!Wy4v}2M+{?3N(z1Tq=SL`m^sI_YoDt9WmKXy*(UDD%BsZea4o^*%mtk8
z24JBs-msG@F=}vH`po?8`}a&V8qdwmZNs|(6&Mk54ke|ksv0@|si)^D+B!Ec?{V=;
zk6uZ3Hg3Pwkly;nY^(LCBF1o&=4>PlPsc^a`o~xAqaSR}wSN{Y`sn|AsUJroja1BI
zt?k!Bmvi5(T`6@c)OOl52^~Lfn%@pxujtD4Y6I^2!?ilkwVK6yBadnf!em)SRzuR(
zE%>!m>|y5%seBT!fE}w<q-Xv#aAH-SgX)QO(udyO4wz2oX}?|O3(+hT;SaudzpnnC
zWJ@u1URc51{YG)!nrQc%TN4L!_sq>%89ZkmS2V@+z^lv?3`0U0yzxU!(u<2pnvNED
z^eYUVYm4U}pJiyCc^|}Q(VZyYbXC=Sq)^xMTdcyyhs!ZDwJXKt=!+qb?_7C;OsfGm
zy@W5VfMu_;zAap2qGW)C@}MkiaKGKjgm*o#spF7MjaDR>ahR;lfX~6fBMR(WY=txW
z=xD|G^huZIX^3xo#CiPCR;%g8Td>f9g@xCz8&rSRO6F<YV-fXOi+a>|iw34o+tBTN
z<LU9b{`!^sx{ba!-Pfn0rI-S52jQ8_wS{v{6^E=f9y%Df4QtZgbh+~5$B(3+r@;i2
z8v{x_vtJ+e$<?{97A`-QLMbJ1x4$!KCBw(%b^D=lv=J%^qu=>C_NBo`*jIv)yM9AD
z>VnWU6aU&$n_gsN+eF;irG5_K`x&9Xl>$WA_tuQfvre=TnOBZ|Hr}6AOO_xbS6`W9
zwqjvnAy7T^XqJ_g<>w6y4-32U;O}vsjfLe!AQlpy-&<2QLqMh#_k8E({1_tLhqVEM
zc3vWwzgG8Dh|+MmRqgiXWsQ_Xtx~X|lkFZI{9ew@&lg=gzIk3HMWnx$=^VKb8&}{{
zGS5}aD6i+ngFRmf-`%?D_FA_U)Q~Y*^RltR@v>S#OUlhhWZ|-RH8oim!rw2iiaO0m
zwVaRq>fn(z_?N{GSY=v4tLLi{Ug8YCJ7bny4xDk@sZJX+w}}`f#O;&Mk}?E$ElBgy
zR-m_$(3z;Msrg<|rFwErezDEHZgRw{d~kJ3>NpZMLrV5&%G5g(93+Fl3MwPR93L+A
z3qNHQyR?kNO(F(y+Wr0gJs4M#)48EfpZ-GD>cgi`E!m23cMT0w`@NUHPx>FOA{TY<
zD~{|HuE6!CQ~&Gwl|%u@E5_vKp1$+mw(&jM?BpfFSy)(5d6QLB<Nou@<2Sc_umk5Y
z0t>(0S0S_`dzn03wy+7pR*4+OeRX{J+Pb)YU6HF_^+pTfAotc>B+uOUWJlFR3tGIt
z{`!leL!9CBNAALYC%sxfhFsF}WQSUU81Y|sS_m<BIf7#^E6ni(>=Wka=WiTs_02=h
z#6lI2o+Y7|8Z9;~nt*+3DlKVaWAn~>kgd?LjvAI1IhTdH<%RokY$`3o+Q!jvxm48D
zb0HTUtXvw<HY6~?v(k*Ru0(V+%*DmU)JbTe+9e-@<Qw+E)=AR%%&vwsUGZF$m&Dwu
zaL$rYO;p;K#}B$0*6)nlX2K3O{rG|vp5?yw^(xHx)hF-M6K~ij<rWrF2~Yc$qnHF$
zD9t*e$Y-5&ey>i9=EvDxVh)CkLI#PECgbiMd}bsPwf`I|e@Z6(du>vGsg`@Z!um3j
zsmVz~i@e+0+fBb#JGZRw5*m)STc-KNyVgy4&J*|7JhJRBF_tVf?~1pGna3d&XBsIs
zOm>}uHJ~_v!@!8o930keI=7sYMh$6#s<t>15+o!o(p$fudjfiBE2^5h2IJlPsOE1v
z%uggEzBdz;`Dn5C&t>&z%8k@NC8UWs%it*mn@GLFdQkql&O0fXOmw?F7X${q(>OH=
z^4ek;m|WkRTkR8$O{uQ>_c51)$@;A{jvzNsuTE6aH2CaNGy3mk42Ke5ydh{)p0Vz`
zD3+O@f6YX%%3-ptKT9EQ2P+VUN-&5O<|Oj<n*>iVS%R^D4J44j>f@b>_K_m}qGecR
zGFSlRnm1(9FJHY1hwDTeqBBYw5B@?@3ao*2#`BHC<B>+|Bs6?iJREmwQ2w48aI&x1
z85tJ*oT3@l^sjv;YUwa3rU$p^P-K0Qr}ex<GQQix+L?ZbJ1iHX^|q+5>pZx%ESIGo
zn~=Z`k&O#-x=yuI#`Z&Ti*8!yy$uuQ;5+y;t}C!|O{d3>&hX+$nBOo$?rJI8kl4@K
zt-NiVCG{+C0#3YE+z)qF+X09a+TXz#LPnx4)lvRAj#2u(GFmb=@A;860bf(I=(!6V
zVV&RW)5b4fz7+h?3RB%?7ISN4>7yGUB-2kXuV!XuO3Sj4Z>FyMD!|1PFb5~{nT70q
zi&G0Wq0KBVRyA<>$eLMS|21FO;j74sqC4!N%<Aguc6%WM$zDMfbzR-7@KBs9{XGn&
zqb)3M3ontnmKNKuU%vnfk(+fsvoBLYMR%0wS2;)z3fgub`!|tgE~6Z5(s(#IDQOH{
zW67k$xb!Q-=madTbT<K<Y3Hi?$};7>!d2=x-I%>1%pSN7n|5<V-;sd%hcPd5uuM)q
zXUN1=4k8P24Dp?6qRO$a+E_M+(i%$~Fx|_o7~*`-nRCo-zn2G#Jl=Oo%-WAvC@Lr4
z=&7|&LIYNTHDME<y+H&M-gdm^(M0+F!Cl|29^sJ+*de+wjcRaU;EYv$b%}%l_9P2O
zLd>|DwQ95Xje!X7;;k`cs3VlefQA!z`vg^Hx4kbmFoms5)>S16*~Wiq^sCd{T`6gz
zhWGRO)F|Ye!~gID86fJ*jR!wCv6_E`7O!9jerXD5g16^3tkp5OF6$>n7t)_ud7&6K
zYJqkM^)!s^>FCP&Bs42=qU);fW;>1PK(->mpnUWN)=!^4$@=>@3UL!pL@CiUZyx7n
zC}WFEN=RDv(wEu{Un^LJj4b4R8(9sBqYWA7&PyI)oAaIQzl&~S9l;7m9IiJ_*BWYQ
z3~>a8k}^(l1jZQh#CZ*(Rm@P7Z<!VZW(4}gDc2izjXG$u9QSJSGC2s+V)AU}*yw#O
zO2rxb{#Esi3_H#L;OaQWHWb1iYTLMXe=F@YYI|k$GCc``y5aPV{QCGq2xIuMI7bXN
z`iek<5_ioNld0L(P(G{AOjb$p6+7R1lAYn&=1V&nS|M?xQ2Ld2ir%;8;XPfK2hQBQ
zc@u>i6jV1bAZnV9DblYpegEVPL7LY&=FfTe*@s3(SlHRm!un#N;AiB(0Vb1!4H^Ig
zKmhXw;58<yb0+g0(bYf8x-JYW4U?ekrW-FM7<uQpP4E)gP1eF3n=}Vv-KAp9=n0{c
zyWQk}48_ZZVU-)-^YdF)w%M*Tl7wdDUvo87tLr0VhCO$L_4Mger@F}vPjL=z?v5lO
zTZ(wwiAt^zl1mZnX}%PYzW}}}46LdyW`r_G1JrD>#31SMXL;X+)h+nRdbI1|RHgmI
z=Tfsyi`}>-S0>iL^0G3g-ARvm*zIz<x^#gD?CUj;7TJ#7e`U%=G6gpSn%x+;Et!Qk
zQPrzbOOx<C-X2xg(7<KJjps3b7Rl2{TU(s-Q|bN+<j8>cfLk{qP@5j@ZRQpf$cWH~
z^eqPUEy@U9P^mUmpKkQqge++axyL;uR}q1)faLG@j=Eifl9A6J0LLsmfC_x_1iSv^
zU<EK7LD$+?`B}(3r4H-UO^mR)Z=%wnphSwa0jBYCOVZ-%xrpG2O-GZe?4pnr$B9bf
zAUtAeRaKI|l$7}F#|4QliB_mH-oxOaCTrbVPmiZhnW-ZY<S2PK*0*sueaZke-iUDe
z#Y(S2+u|BLWB+~g0k4l=UAnju1uRJbGF_)`t@qm62x2@!MPz1AE(+U^2S;+(;+N`l
zd&k~4Fo=TGf|5E`TcYMMvh!>8JUuzygR~x-oSf`DjhYc|+J_S5jnA55;@a+^^U40A
z*}|t+P+P#iw0x$x;tXoz+et~5w0IC|>>8vlXPVvdn&$dJTHbK<t1C0$^rU6gJ#wp8
zOzr4+J%C19P(q{5X;vDE>rmjbOPn4(Dv&Q*bDXTzglpCCY%p?eB^K5R6}^AyiE35v
z&gw*au6kBsW1)UkuW0s7+mY+DyN7bH7VQ8G+5Pug8TGasPY!JP+<)poaZK45?Do@L
z=w)*9#FCSxXw0o0oh+GFp8Aa^5I7X$*a!?qkr*-G8AIc<yN2mFLh8g1U0sw(=<ulq
zA429}oeHa1of?<7nm*e<hUXR%j4MaHAd$-fOHkA&F>f5ASMOQs_C?VDFj{!Z%2}Uy
zv?M>D0v7fB*x1+{)cfbFCf)U0XyWGXVFsOypmY_Pj}mBma)z)_q8_4P^tkv4?8~-G
z9$#a5SHJWHx*1lH43CVAM5LYk&H*w(8_Drs0r-v3?9Cl3R`yN1PuMFqDGLKyg&;3s
zl7#l0j}|W6=B{00DK%}62sTd^#WmE@vhXA4n_53PKGfgp>gsBT+!S2AVhXtn@>b@@
z?WFt~_f@@8r`eX5ek`JssT&EG#dn?ysM{uqx?MZj>y%^>6VrFsRu!dWQ*JIZ?;2ou
z%p#h~_XoF&%&N(K++bzNyx&j2h~W7g7Cc^Z@>pD=2MEZgMINH~Zz2BYJ>0fV-J^=Y
z*LO7`TA2IxE};eMMQRP9#HEfDszv!Iwe@k)OQuy^%Fmb99L~=9ZO1LAp1x9>WoAJe
za#@lKYCbY)5q^s#^lf#j&W`Q{mz%YH@>%NeqhJrEOWLb_5mH?2XBA3A21N|bXzR?%
z${ShcZiWg>WFI{D(ZKa+XlaT5diZ_Q^RB-B%7-|4CbB@Xme|$bp5jo(J{!8Hc^=BB
z^XJc3+G0TwCc{DHa%JhsdnTgR4}0wfqDCb&wZ&rm7TgJhMkS-QPN9bWO^lkFn$LnQ
z;CQmc-Wk}ksLD3pjMV+I@NM2e{iTIu@BD=H(6}dlQdrm>?0<iLno<8+0%74K{&O#Q
ztmJ++OqC5y7WTI0a~-dWtSO(Yih%tk!v$O8%)v`IiMezVH-8Z`d~NJ^Pd(qt<B={b
z>&idh<@7&atN+kg{x<k0T1^b^GC4N(ed)LgtS@M2_IU0q!&z4Nk(+-mrV}Tj>x8Vs
zR-`nQJE5zT?>^OA-Cb?AZE*%tSHmhpr9r`h0QcqEH=BtylMWNBSg)r?BQl#;wx#h+
z>Qpo};aF$n?-+Y@uFJ6RL{siNtGbF~yitVAceS+>`pasIWk$E75*+_xB9juKcmMXW
zdw!FNBAn?$^my@GYrXOg*3>=9LQ2e>T7+tJ6D;%<ipM6jm9{ko+_WLHI@1Q++VYr`
z>L#p*UmYQ1I*AYs3ffmK1JM>P3)Q0CC~TC7OcDN*+G0!b6BTu;w_`Dnnx-A=3^U`c
zwo!P`H*sX}O^$DlRo>rD$#<)-9L&3r)4C^oBphd+Y9lacuD0MeFFV~A_Ke0K4=H*7
z>Gl5q`UN7?&9QfINhS7}*wo%cK^=8dGd?VA>E$;al;c!AolFSP`xmTA=_{fn^dd7F
z)+`W%o^|C(A0KSDHtbGCTE6kZg9Q72)HonoHC0r1OW1LW2n8@>2|%w!>Z6);j&rVt
z9{I_i-7ZPvPq<OZJ?>iQ-pI2#USVRdygw)i@#Ld_d;9wRIt@)Avz|r2&d;YCcKIRU
zsM_Ra7;2qVIiW$--p}{QM)J`wXNKvjl-jhgXY6t!vGSdJd-E|s$MOPJwH=4DmKe1(
zQN^<c<LVa8;Q)A)#m-n>PlefHa4a*rpS)7H&wh7{Fv(6$Qxl(=o?y%K^Udum$;*l6
z0QTEV`8sW0qnE!V-V{3Np(_t~^|Q4wAmBk=4`#l&$Lc0~=c&dQ%ceZyS+!(IYk0ef
zy+N#?*!69!+-YidBPt4^gn1sy%dz<J+=dZj<(BNq0YDs}Q2ka^0IukU2iz_J+oKS}
zVDCNKIxTf<XIOsdQy9TFik(#VaRJJbproV=vRUdA_ITJ>_{@Ok>_&h636)SNzcW<Q
z)C3yP!Z$$Zt^GtLRMt@PRN9^ix(zQG8Xg`iUfA}|SP=zMAjzPzwszdz2gT1YSYI!$
zp`jr{;R>kqZ;-0euxou+MxAPCXy{Ava7yQX#$BP2OXH4pJ&g3rSUrnp?@D&a^8am9
z#IdW%183*Zu&o83EvgA|b?N224yta_p-`u^CC>#(M1m(I|77_5zkZyYKCU$rbcNlx
zC3%t%k&kuFc>V)5B{rJu>Y^>0AdxK-{n~}@Okm`$d1RtAQ@q8|yjhyV5th{A-r&^`
ztdu7%eHt9YudXh3E@u1uMK;<ONbCJN>p1hfeubs5*3ZLFTG2-DZ+I@H5MKJxzr3&>
zx?|S6R#a(&YbZ)>;2JJ-i$#=ggO&B)%ijN?jBvZam{dodYR|VZDt=gyI6vQ_qJG1h
zymaCw3UX>hU+BBcFU4<HL@S66)?9|CiC;fcx{u+%RF39|7$`jCjPquQIa^yi0kJ!2
zxC!0XIr?lbu@>?ICa`<mR!)6p+nag7@Uw&rR2r-9GHx?1JDZ2RajQ;qC(qVHjRxNt
z66OY^h+&K=F&YJD-;rU@I*A~-04m7WT#U{v{=n8^Km5{!rVYWm9Ha+CldZu3xwMp~
zrh-koOlP>B)Nz~}=n~hd%MF!A-GEF`pH8vj@js;v5x{!BoOp2T4;(-u+PY2!Sa~Z!
zy{4Fn?EGs~av!b4Qs@QP4@ib2dt3F62h*n4J+j%<A3>_Z`LOq~O;81&nO}cO@Y2mU
z4oPeelI{ivPVP2K<A)I)a0h?z6%)uk^H8GWwNqJq+=O+K1>_{p-7hIS{QW7Ss+F8(
zl49ze(;ttMa#{E2{$1$xzqxo)pBC`LKX{#TAL3z3heoHiwHf6EFTj^dP4{cvv85k<
zq70LO?g8bgl5aIlJ;lKYRoZ;@l;^?YRp*@j8{7Lo0>S^y(fo}Z{wGc<lRW?ey#hug
zB)qD?`dZ~^3_KYMgTWLHGeXiBlpaS-RajHHJbJ|E^6k#iuY{&H1S%I8Bd@5a!20Q4
zhtv>=%DC4b;~%W=*l-?{r49u~zahQj%PcIStU~*&*O_r|ID8EQ9)kwO>$78rXcX1k
zBl<Bui@=DhY8JZ+7<<m15kFYUss?Uyb15q(6KVrgV1`Vnw5F!`>CslvfFesKr6$M%
zoverC{Fz+Ed=)Y@xHwp6edO4KVCcn$Vu<QiTg<MkO?3t|y-tjotSozElW*yHpL|aJ
z`jh;fOP78Ur(PdmcycmQ)i})I`zPz+Zwy}`Cs&VrOFRPw7f+V8GcY}!_EnQ1z&UDY
zRUQp9k(2l5aj!C48ICds2WE1NRD_&5=0Z6ca4?v*(MGAp#L3w;W<)_wo@zRPx51iV
z@C4{NdSLu;QHV?k9DwzJ`R>oLAOcFZY@%tPqEIM+apP5vY3hrDDnXeXPBjDWl)$(W
ztEN<SEx2HD({xku2qwvBYI4y1HbfF1|9EfIc(+qGf8LqqSUu+?^v!z;>QumOs87#L
zm{i?L=vvS@O%^}{@<v<Vs4>W=>FmkKx9KoTTK50`ZBGotV^O=Ff)7M%q)~%+hFRyg
zG|EtEW=ILxfCBtDcMHuUA|rt&Ac<nQC2Dd2%e=Y2m}b_U$oIl~<X~X!v6|$uV6WJ^
zIHCsU=bP^TT0k;+9QVb@b|>H#ze0VrG?YgP+7knA&~01)BTHojZy+*``XV3*;C9^Z
zg%9J-w2ihL`Ab$jAbS;ID)za-i03Qa?^`FhZa)`Sx%yN1x+;v&n5bhlbei$o=E=5Y
z6-<5U-FizFt%D1)B0b^8`Bb^m%?arEBq4m8US1FqD(o!)3czRX*<7<5#eTk-crD_^
z<}OzXzGzHm&;#DE1!KR*YD%PUS81M~xMKWVCg#psZU>F7U?xlU{Ff94d1U#?@o~wz
zUB2_V)N_F(tafXg3f0U@gfIf0z><RCS!z4Vo#Na<_oe0$yJ@L8!7%^7cmZ@;2-`He
zK7HmjcuJ<Yo2)UYW^RpB4TZ9Z=LQpSFL;B3e3rc_FH0`exh_A6kvyU0Y4}Z1X*c%7
zZDoYDz6H^tPyoDv0~OEeU9Nowf)t#g8x2n8Z!c@d18Tti)RmFQ_Ome!|EYBD#|Qd2
zk5R#7@beA*!T{`T?*@Pxj8ysgPzDRnA9CJp=yy+Mwr4VZW8leIbvx4EzCi0sw_MxL
z(ArwjiDUjhy2mrWL&-tDo~|w#phD!=1I<bkCmk{>E2$Aq$-5f&RP^C`w+8})_EYJ{
zN>m1Ay}WK-lj)5L00r<<7GB^1pM?6WSFbXFs~A}YAd1S8k8S~ODAB3StSyXu59E>0
zIcjNa3HDsXGF3p$FMX|fRIocMJNnQ9iU=ZFUKB!!`+Q&nX6?+GGl}9}{6@gT5Yh3s
z^*^H^PjvwHv2cqAh3Kj8?8f-(fle23`4%SBU|8qQarLUAj_`09=y5HfjQ;qjFYio(
zw!7kiu+Id>3OF-FM<i-(268s`)hqKebtbe>+T)d#m4P5{rJ<>rQC*FQj<PWfx15&W
zCg|~0uhp(_M_m*qfx!T0DydasWWPOoygTh!;cHNKLsion#}g=VS`jBEQ25}D3Y#=K
zqUeIOjs4Dp&J$)9Dit24stq`=JwxX7m?Qmuj;c=CLjs7M&Oo=BwT2L*0tW`}fzE%W
zFxh5IBh))GB7*nPcjc9=wyG&_LWJl7B=`=FAgUPziD*zvs6n>(Tuk-YfP<%lLDWjP
zTH2EOU*}LW2$hLQ!GPK2?ep`;t@f)Q+Nb101OhE=11u8hu$&MGM;OV;F9iS@_@%=7
zev4qqXe1#|J(=V|e+(kEpmI+$N5(AECasPic6m1#`R*?y08s@ecccu4EMsEAd;`cY
z_BWo`XluX@?DUN{R45Cyia<Cflb*>hD8{~f>YM=~41|DA)+F@X`WHrJ3&}R~8ZU1I
z!t6#sxWQ-{0}7=DcC?&?r$o0L&!OrfeC>XHMCwqx%!Y8PmVIxcC0LXzucKU7Mu<zA
zPDBye7Us)#-)rkTGY=2xsKFy*5HW)|i>tAf;;%pMoG&H0ZS35BUZ=rZ2-ai9y{^>n
z&>bb^<|YIhW(ctTh}MjQLYx(|p^Of{d&P%;Ip>XwoPT>vF#^nU5JbL9uzr}Aq?@pI
z9<YVWP9$M}$g;%BcYSqgC5}eF)zi|{{6j&Z)sk-z19h#Gcq8D%w>wR8x^~s2hSy`w
zpgUD;G)YTCBe;vZjz~>>Q?{iR!th1mlikSIhPAF(cTX3lU;;Dyf5C61@i!}@?`w{u
zfPh;mBK>gjM3!wV^)E=Muazz~!+IADlS1Aka9f|cG+F17Fc}?pPkt1~eNOl-Jm<Hu
z^)wa;x)%|?33$GulII?)<L4pT*uUz91VqHWIz+6{!X-Hyi579bz$`kJO+16lgk!+r
zMVJ)}72CaJLCEZ>nH<&m`X0?4A8mya1Quy?;*`cwMos>HE!rQRl08d}dK0#!!4w>*
z_luXzem_sF$3YFpy3bBEC$pmSmV6X#tkb!2mE;>4@7`@xdqlwMY9#Dj+9>anK%_b-
zZ<JF-&%-WhflWN3=Z7ExbI7p8hIMmrq&guiKA^>W<F})$z`ZIDW~N#tAN<<%bkyoN
zrZtTGGOx`LhwIX3EI3W}MU8M>J4oURI!nr?O!!n9VH>j<gp8e?`M_8(XmwDkttdJP
z7y9A_1xQR8g@r_*Hgte+y#b;<Mr#^ChEG=lj{?w<SVO1h7HSsX1Wm7Z_NIzmG{hN)
zI1joEC^oQ#_YQ1*Mu#dn8Q$d;H@~#;W)o67V~qpId_`?gQ290g8ILJ1OWW<}u%!wu
zEv*p9lI+Tnp=&VYjdmL2E8T}PL8QpwHMF%aQ-se%3G`ZF8nL5WOCbW%3RpyC+0(})
zl?kqlPJ_U^vA?J3#gaL47v72FlIWFGkb=clt|>Y<cyG4=nYght;mCgU#2g;p+h`^I
zV=rk2B?Aiu>n)ci;Rjou5B4H$$m8X~oXdbhYO!DZuL9jDQT0l39WSD_Efx^t?ElvY
zcQ1VOI;aew=Q@LqZ#I;x&P&vq@AmTs6%CCU%=6M@oiLc2y5xo8Faa~oSmOKN;&@dk
zVM21;h=VmIrYIooLGTq>DJ*vy(Y2ff(^MNkh=6;3Yf?a^fpTI0DUR_~eEf~tA>$m?
z)OQa*Qb371w^p~_0YW1xTVhBb4$*4?%Q+Agsy90>B8uGXuPz=1P0jNl(qo}K4_B);
zL6|_CHDInHnetk^R4}qOS+@!E^EXjleCLaqPNQ$l@M6g7zeL4siju3rQi2!SVa56U
z4qWX(GC;j>2qMD<sQabT{-QT{H*zh?cEsF%1cRMo1~dW*1jwuqe#onAU>)!$^adq8
zoUm?yTUAz;<WpZ?Cpd;)^y=5SbNO#2PYWikZF@U|5Cm}57s4tyGkEKbcn~ib(Zw4?
zAenZ;7D1sPqP7NN;k<L6m-huH92g?bf{~(S!BS<gEnl;M7qGOgHyCtW4MYD#RG42>
zy7dJcTiduD;jv%K;~yZmUQW-y=DoXy*Z~SgN+(b#1YmxmRD5gG1W64-4A{eZJ@u0=
zuZlfL=z6sQc9SAo5aOdrghu}!Y2PG_7V6@FXAr6}pS7B^!jrD>K_H+aS`VVnzl@L9
z>*`AuCiU|2LaacL%NY%jSPL0VoFT?-ZzqDYxcSF$0c6)R@Ft*^<DkHG)?cJA43H6L
z)X?Kn;DK?k_|Zmd^wjGdJqd2ziy<Ztz);u2Q1WmB)qHLQsFR~PnkK8VV}O=a?;ntD
zeeY=nUH-L`TeDdet8R}n2TPCkXGoxczwQ?s7#dFg#LMcp>d@By2VUlE#*|pY*zwKy
zn}^ovX4)K0)mVSIy)Dw4JD?+z;jwk8kzztI2N(5G%mL`GH!8WT{SMg4nwlC8I5AfH
z&B7jI4&~g7p<!N)IF9@GKWFYVzNgzZj=D3VbA9iCu<*<7t0KLIC-xhkZ_><7n}=@9
z>_n@Sd=3VZxX-EvP}|Fd1k2yo4m;C)1931r+|1tdbPc>2U38uYkz9L+8S!a)<fz_0
z&gSk$oK2&T65V4B!&*h@N}W$4x!*V56*_5BldRArM4k9fdGz=HA#-}XM%@a5R4v~#
z($C4HntT%?S)dX*>SzyW4J5y_+)d);$JMMmYm<=>^S~9eSB>eXP<8MbufhcEcNyQk
zckc<~@sa{65cUQEaPW}n8dgKVO>}R09K%`bzIv_hVkab_6L3&G$;@O~son<#<#VH-
z4^Z&^)`bvYmZlo&emdNl{Z^TCOe6vc7;Fsh91>o>WRjGejz!)})e@2sk3lsV9CC@`
zrW5bn{0Q(}%=HLQ+x>~!RKZguT&GlY@2dDy3kUF=KqdnrF&LuyZT!d6rzg8WITj5I
zLuL0SlSAro*=ma5JY}9ud2hIEP8eH&e)fuJulSbow{PDn<OAUIu1Vv|z6rpc+jx(r
zQGI_L8H!ah`rfGjBh<0~ZB8r?aQ%mzD7zWJv@^ChmJ%Iz^Ycf}Rp*$0$%w$Dqu+{h
zK><i^&TWq>F>aEWjbf7ydzteKX#dP^NLl2U<*yj1n?d5|xPD!HEj3dxn4ryc=k!M;
z7K_Dc-^i$17)L5%N}YNCJB<fQDkF}?D=&8hW)S!W`fZqeRw8IcV7YtRCvjMaIgnT6
z2=KxA^ef?BZ_G(kQ$<PPxJR(pbot#Zxs6wnuPYmBWu(%>C7zB?l4b~EsK_yh&1Q4x
zz8Y5UDgHa#(c-&QXJ`zZu(XN|!Xb}>y~b>@Hx)6VC|teIm6iwgocXOjNlEkKCZ9~j
zLvc_LPcMQAN&>-gjHi2@h`SM>z@M}%TJ(1iPcX<3aO4c6<Se-TTI$vtZB-aSscZxw
zFE4bDD=i#CXAaSH$moInv-o0<YkysAhRDwjIyNk-GsH^0Q5mr5&6hy(y(H>-C(#|u
z0L=?7vyF#qtze-mT!z$c>pGmR(ZybeItJMganXXQ5`YwAb3iNqSbg1EEg?WencKIq
zK*$9FBQ`ZPI_>mmQELLMM2WY2ZuCkV+JUd`T!_h)LXTR2O8~ZT<FD(Qu6T#a$`0`(
zaN&1XT+SQsb5Ocy8+n<x)DH{{WM{<N?LVYR=ec&RrTeu2;)Fq}Rn!B+J&CdUP6i($
z00*5wHq2~#IzbQzhtD~v)2iL<jhJyW`#HcN6%Gm34Bnk!C3OuDHtg^ya9%JZp>;<S
zztuf}Bp#cV76X8YRr$W=c_@#;Lg@)Ev!Z2KuyzDr=gOBY%+sU`t58bg|1lWeu%kUk
zNLUJfFY(PbN&=x5J%fV8kdIJ-;5j1&`>!N?G~!DQ(XP1!OQE4D2b&xmY!oA6ldv%%
ze6F0odxfI(1We&>P`Pj~XDoni4!{>!eE*KAooem{lvK|C`p7LM={P0+ZDC<H$+lRr
z`uVNE?5%U>&TT;6%mUnnBqq41lzxR*#G_)9tPNEbJxNayl^gXAKs6Y^sx-yqWM#L(
z7h-<g1g}8c+eH%rCtGPnr_%^j9#N?}f*Owx0P%`DXw9I+R|Ib(H7|HiP*Pc5e=xB_
zg~REh<f-+Lf2-0ixH$TfrkolEbq73}{}_EwANAhM85Wg6AmI$F2+%`U?(9FlEhl#d
zga+%Rk<<An?;jMnK0^TKE5Sg*bLT$Sc{rlBo46uRC5!V0GvOqwdn@83CS1J2@+k_E
z=;@pZ{tEp{PmY%#$xfImwRPVa6<OJ35Y4K^TwhEQixO-q=>W3+M<w=08Wv{+b{bDP
z54}|qo~EXNo{?vR)7+KU|2-SaAn9koHbFP&MtkGXeDZ95r^Q=uc+TC_eTwjjRXkq_
zj`KUl7i$f;tt5=c2e-_xH8P62asO6k#QCGvv8Ct>PnLm>N0KIEhPJ){r6r>7aW?jO
ztJL`f+$tGO-t?1lt*=0&J2H|`0X?lzx24)QW4}mJvqUMvQFJ>cS3^viKcvT_tzXAP
znw%1FOvPV_ZVAUH32hC`Zw=28Xyqj%44oy%cAvjO9m#xw<g&&5M4s~|p8!2=0E$i$
z56Bo$X63c7bY00#Hl@XDA&J(#PW{NYJ4KYn4@g~|f`Egq!{cW|S8TYiPCx1W{VQaX
z)9qnJy|t)|_@tmpvtWh@84BTK(?kR&RoM^D9C?VPK3{gXXc1Id@vq{PK&y_p{1`~-
z$(w&zr{e6=jY|1E-`7?6!tRLZKSPFKT5t(={%e`}K_AW4MM4#sq4~!Y=iYK>T9e^~
z(uYpnRM>YBbE>$$aFp1I!;Je<a8&fX*Xm;d?@KXju~$NPOB;XR9XBttDY$$8e%#Ir
zf1L4e%=#W;lb`|gX5+ovbGbNlM(u5iBX;hY0prsVMv+aNJ;#DT6~>$t#+XUhi97>O
zK4rX(t8Z^mw^E7;j}>pt1QOCU%d#^wJoyg@*#88t`%e?W|K<zm31@(d%6#{Z40NTA
zf|AA`8oG#@_`(e9D_Z3uLLWjcej7?|pledD!O3#{<a{Y66XXboDlwCBNKhygl==|-
zaZvawK~CuiQ)$IjZNPiJ-JJ{JVGPJ3OzRvYGKU;<@<p|j&i8&jPC{elKf+KY5Jt$%
zue;QwgOF|6(WyKr2ztiBu`(t4G6rbV6<h`Zc2J~yS1nbCS!}K<>^4KeJZ~v^NY}XA
zu+GfM$!RiG5^!vbk^-;Q#>DgqHHvK0Tiv!?MNXZJ2#>sgAbs3|3k8nL&rfb^h>|72
z!CiYm#+*Q?o{Rwq&O<BvW&U<*j~;MqoV3BYTN7(EsE6j}xBH)jiQMg4+`8R!jz!Pm
z^vtV?)es(9yWghoSOryNcxi)6&@Jv<NE-t0WfKLP{bPS%Cl#Lbg2qluBt7ajfid8w
z0oMe8(N(?8&KkzvdMg54Y<!C|m-6I>?mffX(6vl`s#%~l3&ZC`?8#t$(c980)WJdl
zp5Wl&DM{10Z+1vqO_tec1T<miAYAO@UoISIg1U8f=?pR(pbX_#I}PCY8hYJ)Ij%Qx
z9MNtHFATj<6x`?Dhx8-hw@WoY)&qtpOs;EIi0F%7iLSPG=pC<{KYsjde24`$DB%3^
z3sOB_p9O@Q^~q^&ffQ`uPhI3>5S^#sZ)_oc=xUQlnB<5|#`9G@aFo>7)4yW2>}V=0
zqgYVKK|N1MSiGZ>`Q7DuAP^N#k@g{{8Uqoaew=}J`&W5#_s4LTcY+cG;^oah=J$Vy
z$N#4Uk$>|X{NK}P{$WVkw!*m_=O9U-n7+HWI;Eq*YX>r+b9(&f%G0?m=0c82TgW9n
zi$NLIVPl1FtuKg6Fq+mn_;eQ@N^O5B)aYB;#?J4H)q^9sv7fH-CBS|O&ey)LQ_atH
z#D=)IHN4x01s0$F-0yd|z=V?4ceXvlZ)EXvqID7;{9-t2KN_)KZZsHf8Fk=y<3wEc
ziqhW|!|Ice+3ujL^dvas>~l2Gdm>dIaDxs6LwY<@_jwvXs9{fApGD~&N!-m+7a@NS
z?GJa|L^fT)KyNzBD=yvakOF$XY^zBdJ?-gUA|1(E`E2f^Lp!h(lEHC;H0EyA4W8zC
zGiv7zXNHj<X(vbiO_znjlM04$Xi>_8QKy};W;1AOQ6nX1CZNd4NVW-c*OMuaBSxi<
z{NG(|OjU(~8vdVxO&Y(`_BDw^4&^cS$%HVw?akpTO0T2Td^mmoIXUV7?H%kS7^n^9
z2GWppa<mT?Gu=P7f;k;j!mC%az}9#!ywaZ8Jp=>o&wsU>09}rdc|cc$kTO1lHiUF2
zD@Tt0a0O6`gMk}`k{+CCJAC+`mJ{SeTnqQ08Ulk`GZIrlRx|+^UkznV(LNX&&!T(}
zC#E~r{ddJsfnIw|rpBv(P1c<52EV9`Od>q+YGwLiV`6t8Vh=91r`G6zfk)enMC~+z
zn`5G3R&83-6^^OIm~={0W4ouiMm7cU<Ha}?0zQ}B(`mm?mDmm6tM{+QOCcpM2*F>y
zQ$zoJEHO_P2dn)|xs6O5CYWsb@cRlu`zu27aV+uz)dm2P%WL@UgJ+DjHPfr}2egXp
zs?OqUO=MLRQ%^k0ZsIvFXYJD6IGV?qX+gB|7fH4ucig5Vd($nKuM9_r$w9e7Q5AkT
z1CEPyV>g%LUqu@##H3%*Fe}h%><jg;*%zVB@1RlWvLANIyB6ZU6m{Wpgj(ZO#XrL$
zc9_QUPjKK`Z;Pb)6Ree#ln~?Y92lG1KE2}P10SXDivF}sg#qZ>h-M`8$2HgtJrJo&
zEWft4zy$vN!{Q)-{@W@l#F_oMt9oFak;aEwWCHVtcya77OL>-`|DmS`e8;a>^-!NH
z?e)QL*m8%G;`KSaAjoWBcPTgQ0u$S)8)!7(k8!Da4n^Q)g=b%i&!B*df(+)!f0sWt
zE-nbvtl(CY2@Jqjs6tg#emw7e85i?NP90LtXkn+9kr4w<Vz$#yff;BMgsg1>zIG6h
z?zGHWLT&%ox1~@f;Qob*8xL&Ql@Rgv#h%!|Va)sw9p1+*Db=EW0^o_*eA;JLN(<Q;
zT31%xpOT4q|K<P>(fo(@s)B+7u=mfpj5TGN+1qo0Tnh4}VHYnETq3wKCos?x2g(~f
z+pLZn1(U`?hA)NSA!r$zajAjA)o%1$Xe%(bv3$^G3*Cjc!9)vl?DTs{{h_6$jOJ>S
zRx$J(0#YenskDWxe;Xewt<SP9-h?){sL1WN^P9N1xXr|y+bL2*c5}EpAKBBw;Jl06
z<D>`=g9f?qyBy!{G@S0Iost8c>5)AGZ60i3alWZw(*SzDDM&NVhM@QEIW7Ogu*<Ny
zkuF^-Fnqga19@ED1}(;y@-VK;_xM2aQm_F)s1!vf&=IRAjnC)0bQfeSW(Av`L_QgK
zGCT@=`FW%wyo`}JSh~CkPHd39p*lAGoPL{yo<!_k%#!EdyX7k2<JTp|Qtv<q97n{H
z$2ez2@2vy1N-OG`dpT4JkWoJv8-QsZFFt?nDOlkZT-T>2-HZNoO-CawJ(aev(cw@}
zBjTewSGxZZv{xFymKiJ7G&GC`dN~Wkyh_`rttK#)S=uE=m9~)AT6vMSxd{v^9WW0t
zGLJ8Zj>9@4?0FVwlrYKAHy|m=2yQ#{svZy$`T27xk9tcDmGB<}`~sR}D7JM+w)1{L
z0Ag{JJ)#x_e-RbC21<d292&h4)Qz^rMgcc%rowssdK-`?`Uad1(3*g#rNA%*DU+bG
z^UsINZh-7y`s>?Aok}}h`QvfuLIi4o&-@eBb)$L_lPTl?!!JO!NM`xJ%};QcNY>{x
z3`o`|%#|cE)X@q17{@*jRy>R*=h~o}WILh>6gWaZ!APQ^&nl+U1`E-eoG0w7K<^;~
z#;DQ;^NsJhLw8c$X-@v|i(|$+4WtzhPt~*v-X2P-7sTzIdH3>-*LOPb`CU*+6Lx&V
zt1_rn)EY_}d4`Z0e^9U;+FO~SOQnE%bR3jhojUg-UX}Y$O-TQoZjzkWgI*)?t!{xi
zXbphwC}4xyA%^on?nl)4cIcntFMd^MRCY3+_sULB$rhT5tdr)R(io9J*L9_h91D3V
zxVAT;gCaxsShUi}-o8ku)KsqAHap*~i>oROI=Ch<&xXHtHkV?4{ygm*DJwrEjIAd9
zGI<)es>U^W8m`wxuuUSuJ(j=@O`u3X`8mCENxxv$(Zi6N=Ey%F(iXT2Qs|)R$R4+A
zNrig_=py$8P>e&)n=g>)J^8O7MnQus3Tdd|HEqKOauhmJ=AnlARIOF!{qexIYNT{f
zPmdGIg638CiTv%DfFm5FAt!k_M`IgtB!W;wQqr)KRHWy#^YcBhXnIHH<r-!qFa8XC
z?UdMdZ0vM??l5rjxXGj_<d{`7HdWAP3CO*ThIPL__f)VFAQ=yOGxoq#1N818_2{pC
zU!)fo(bPd5XP;-6XT}AwI~sJm6-8-5k#A^@zdAx^?hV`KY53VH9&4<ug5C(ZQbAEs
zv4i<lp%#<{h+vW#$3d?TfYKCtls!Q<2A1A=rui}TC{!9EzI(h5^_%kDDK`b?Df_a=
zd#k@o;Jp~OSw2ZC)Kc0FNZ*urV0r5J@kG{@MdT{B4*p{`iT$?Nv?IhZ<9G1;J;)(W
z?dP=-CA!j9o`w9nVJ#IrZtXIrb3H^ZlG^0khgbqjqaOor2~W050)S8kO4Iyk>zT&3
z9*b^K{D@lB)ZeS943G6Gq){f)_IRH0v{}vn7bP4oQ+Q{(0CY+*)SgmchQ?viEdr*B
zi8^xjZGuq?X4EJW-f?VA`w=5fo(<;VsiO^}RqqL-$4`mR1ILoOT$yq01T@V+@p{6&
z3wWL9WLJ1KoSmH`Nd(vm+@MQwu9Z}>tu36g6pTqx0X)ftT1Q<B(CtN7T>B$G>J7aI
z(29tA0J!;Ea9o|CXBH*p?=OLv6ySaV^w5DkKaHD{pHSGumG>&&U+EBNW^QEUkjcK(
zdkG+mFsFwo!EnH#so|V~YrkBgp!L1TL+aDae&h<AL{c`_BjbiYef)SEcvZwLjQ9-Z
zr`2;VuE2`i8@Bm)l9sQfv=hLz|L3}fPljF`r{!Y)UWKJNts~70s%QVPX+aIEXP?X7
zpw>|k1c3(|Gw(q=g^pT8lM{9SiNuqM<?qh(ZEn75xPv)rjw5REdZy4~%}X?2U3pvf
zwFydTC`*A9_+L+P2H1vY5if?Wu5JrI=rSrNSJ*B@->6#+S)rfkfDR9KU~SKW;Taf2
z2*m9$VbO1u5^mI`KHN@?chG;v&K`6MjmS_~J~MbJkqYLC208W%%q)w1fvn(n0jU*$
zMJwEgAY$wEbZ3pVB3vi}XHQb~`=GK-ep}@=jMf=z<=eFzSaR=K!iM6hsc?%PsMOwf
zp4W1oHKWwVe3pAJ&#9HEbK6iuRvq_tc4B_1A%}!s*iPw2tTJ0?dxL<hs5SpG2JO^G
zirv%w(W7@%PSt|{wd0lr(#tzPKQG_Cx3^d5AQ&67d~>Sa%j_rxyj@i1&znF$V}RXJ
z&2QmHJ>P@+l;N%mV5XXtyfLK=)Jcd}J@!?zQ_in}gvu|jh|rTjdILXkintd=YHDhS
z|8dx}Fef+@AbQ`%=bQH~huc@sa2vd{3)uc~9%&|aS@;WlmxGOY3eiPHMN2CyGS=2t
zgUMi`z{|n~CZf<*ohpaa1q`ZeMA+>JP8OglSP`Hr2Cr%}<H9{Jg5ahRh?;#N+9D-u
zvIe@u@tNO28mE`^r~mkH;3agx=>sb3?1;X&xjC;;QB{=%w=@uQ|JnTQTQ>P2L>R#v
zH!^c`@sZxII*)ayxi$jmZ^#*hpzS<(s$A;b2zLSiuEShB&guBTt<bn>s-njsdcRvP
z-W;Uo<VwDda}U=Xg?lXSsjJ_nq@+aK_oYeZgoNPD*G#OUT}4$&b)rB%m(9=D>%fvq
zLiYnE&CAK@ER%e#d=IX-A>j1Qy*w&6SJ&s)yW;6<4-UK|I_JSlu`<d7h8v)>G_J`(
zOAESm(m|M9edOTxCN#U3j4H{<$YfhzfUIxh;Bcd79`p<NwcV8O*WvN07q|E1ty{N-
z#>T8a4NpucC85;=0wk|SNXIZrvZ)L%fo9|Sy_;?xAPa^nrt@m5>fq862eiV(qOGqe
zzPPGgse>AtoJ@6g4umT}U3Z%(>UT&f#$aB73Bx`wh;PTk8HYRl^#vzCzlKc%M-D(|
z6&5`gek?7ezkh#wFj)v?V`rBQJ_n0#0bX9@ek=lS3i6XM)<S}UpQ{|f^S}{%=QHT)
zVH`d<y;^A!C(X^MGuT*WKBB2?a3c)C)nyech}`k$mwg7s1~t<DO=%Bx*%V)J&=Mt`
zSbBejNmki9__aW|I$4(x76xJj7L#s#^jXw}ix&kJdd?RF-1&4xAE)wu)})9b6WI)2
z_ZRWzob<)l(DDz~qmi;npt~+an?gsJm7^n~mn+j;T!q#?7+kL>76tk37uk~c($Ll&
z)AvX9dNk9x2Te)t&!hbV3QEQWZjJOB$ji$+Ef27Y`|J>f%VNV_GBgf{41m^lr91@~
zLHh{w5-u*TJ8W?sL1Fw;?tI!ml?ip>l<Xb*L&m#2-)JE8o$@RyC@2WY{vCR>W&xza
zGhRS;&j6GX@!7dPK0eOh0f!2B!^>3XW&zlug@1x$5G<PMMMXWoLRPl+owk2y`koxo
z$p){jt?3SPC{m=T=J56YF0d{i&KZS%7bt(vl94-1)rZ63`A}w8hgbJV@d^bTGWdc{
zE>P#X0$NVL*ZJ&n8`XQ7>`OsL;P*W}nbfl5Mr!1Zjg3N^ZiQ>Fq{SIJp>N~6IE~qL
zF^A?e-@&#WjI>$7)(Qy;0T(xyv)UW+$7I;5Ig|k5MFLLh7c6-Xch?o}-=`#D5VsN?
zr<9%nSIQTREe+h2;2kus>5fiJOhiJ_Z3dQ2be5*r_Q$d)Xla;jevAWBqMx51L;<gb
zDzOAG6d--7AhtpMP};!M;#zinexWM?tcLJJX?zGXSbK|$dH?`B<aEYN9|32%1oz0$
zjNbP=_<a>RNrV=?Xw7OiO^Kp<D3N>Jz^!;@H$A^!M~3I{wK{fG_iV?Pudr2U&7#hu
z(w<mE!Ql?P$ZHnGr-k|&NM{yRiv2JDEBv5?)~pcaj}zQ9Oi-rffI;req$sB<TP*$H
G$$tT+OU-)#

diff --git a/reference/Rplot009.png b/reference/Rplot009.png
index 17d40e60fc8a1d98d4bfcefa78cd4ab3e5c1a02c..8372ac2445c10027120bcccc42d0937da564340f 100644
GIT binary patch
literal 8638
zcmd^lc{tQ<-}euNDBUeogsze`R3bZxm~7dzR}_*Z`!=X6m4uMy!q~SY`%aeXl8~{R
zq_H#heP%G0=Q~}`dmr!pJoo$G{l3S2ypO~DhM94m=kGkf=jZ$^LT~7(A7J5RK@j8s
zN<&p2LFk5Q4`xPq1>I8@3r~!9G}Tq%8NN2I)&#-RK35G>cLZTQOncCE5dyam<P?HZ
zy<+HtohSIa8j`kmm+><F1iqTzerstIt`NU-$o9|kW(6-boH}zv&#~yA2r3)R%ru_;
zF00=Zu`S#3^4m?P+|p7jK~X^?u{R$-&U}|;FN_fM7B$GWXi?MJk9qQdyt1)9B2LxR
z_|E4VO}V!k@a?><xC{}kBjr1ZCGaJ=<Mttlhscm04AAck{eC2vj^5&y^M~bYdDiuV
ze)|z+IcC>mg)eUj#~#+5Wl-K>H9dIaxs~7a+1Wz~;u~@17J}>_HwwnTH%$){ue(`v
zQ_CPrt8-_YT2gWL@IHm51_n-P*Oa=oOz~+uC2CxBvn`oCn_r{Tu0X+N=_I}@F1~2#
z*=r>0Ivi{jwCx#wLgHF>qE~$2a+r1LO>5c2J_B8a%#qK}a*K=gmDWEVG&C{sZYhyc
z+KVQ5;-~xVl6y&b>jG%S=|}1Glh&<)p`9#fKflqFL?Q``iW;HOwS3cq{)<L@hxPal
zclr_&v(IY28OOTMeJ}b^=4e88I~{cc-jhvS=urGxmWTu!46pnNJzAnrp#vp$k*&5D
z7luFn?%bKC);}Pin=}`={+YMhrYWMTx?0wIHm{;O!KF^dpPGS+u`n`<rLJ}BS_;Ry
zxf5}ov9YlW%>v0D;o;#6OCz71|9pPPd3Ca_pRzGXsUt2_&x(1kP3d8-Xa00=S0IYj
zZ><<4dCv6YIM4qm>#w>NadMixK$yK4r-hr~k3k&^zh>#_=}E56)WlSqJ6C1)q7(Z3
zr!z6tp0j;rRn9~#mx5m@wLNGjruJt?$1q=&p>XelqP>e89U>s^LLUHqnOIpZuyM=w
z%#?qAc2EwLpPw)0Paq}-E$~%sZu${_9!oVZxu;Dg6KcF`($Y?2bh1nE+gn5>?{-D%
z121&6#nr1<!#oooemSwwt*fLkZJOxFJ$$$I&4h)O`$74oVCuX}P(p3M$GyiYDMLZK
zPbfiS;g^4+DFpmvbz&>`FqwSCe_$UX?d$8#BFhoxK&bYtCMNk`@?EiQOSr5nDJdD9
z5g*Ulc;|vzdgt6Wl{_zkdY6~iW`!@XuAdtialJV)F|o3}TOZnKZ<u3vJ$emF02RHt
zHXWmo$rY+v8nnAJ=cjwGuYCWBiy7%PS1Jfz2|WID7i5T~{uJkVLXC-$kx~DE()uui
zc9Og)J3IRe)zh1s`1<@rId3nYjkdCigR(VH1re>eCRZYP6wKO{c0ce<*HQ+%zeb($
z9B!a9GB$pfpRaS^zyV<)p(s9XSr3h9Lp{BR`m^uezlR^9W@cuNvOQx+DDz!P@JYgt
zOeCSn{ZNa&>b&R-n5^k+rELfD_L0yhVl{IYoxT@dA!6MlR8n8<z&K_u*Kf4#!yID?
z>G}TskJ_JG8-$3t-*FEFkd(2N$=oSiqFkBVM9YJ7r=y<F%+AI(+ZtL~<)dQwr$?Ug
z8efbvw6RH_{1%t)(4AE#Exhn@WyOu)(XH(_mHMn$+*LMUy^sgJ*69@#A%%Z`#b@aw
z6KQ=ca(20i7q5Mp@VZtp_IgfHksizoONffB&r%As72|Fi!>`@-DTEbzd#%B@w)FX+
zor|LjuLsGCUfJc%WjdaVw$C{^IWKw5-Hd(nW~NDLr)bcZ$Q`0$eKJGby3Qh0RfU0Z
zf5!<sY$|M3QM1yi*L@pM^waN4a9x!T#|+KP-pv-&Tj8zzoodwI&$s!#%hHNVJ7(o`
zW$a71!S2p>3BKvhh1#_)&Faw?B1gj-FFE#|Blykc$zVtwR}VTgMR3z5_$<aEEw`Yq
zx^cdWe8t2?09m`2<hba15JC2cxkLohAyeASu3U#hpUAg6t`7aIa$oi8M}m`nH|Ri+
z<HO9y5rkpSKVE!ET<ksy$B~o{>=Z_BLOVF}q)eLX>Dvm|BJtU}yk}!jl9w*sjC?FV
zL9G)O5izo~bW&hhxp|Hw#$Al+c>m!_TBm(Ny;R(<9!@ntH~;Djv+2POzLRVsM_HV)
zhWzPGQacib6QrYci>)#OL@Y^=aNqTPWF2*4^?lRSA^G+gqVJ2wJALV=B11mkMp?^S
zx!w<`pSK}=*yjqxb2^Iofv*szQ;BWsa^b{p^@-+IIn#CrN_f)Mm(bRYjg9Yeb4z&k
z`kvzmYnI6d$e7jh`(S_R$0P~+m=S3}=>Df(lyA}H=j9p6^WeiT555J2F0XWIe8C=n
z!R9H4n1vwG;zsTsB&CVvTmza)?p>K^C5?uO&rEfu1C+esGcqxmg)_$NgHKUf=UcD&
zAM8ECP^?8NmPH+-qj%I!A3}8{DfnNy{b|oeUBU8LlOrG_r>w_YJ_piF@8*PfkPxbR
zcZVu@`_q$#dm8t;9*MGUh#zH{F74KVwQE|EkK7Ky!s7GAzJ7gNw`(=C+leCBfh>M6
zx=JB<C+B}yxekXFN<Yj1mqaY93}|FGu^;~$6`i4o-kj=4z51w4CD<QhD9k=Jmi-Eb
z0H{Jf<_23XmXBM`>zhw>Qb9(B>WFJ)FX<8EI@TT6*%B*K=102kG*oAqt{&N!a^?5Z
z`@O>9$1kWj;`0H6jmSFy<-+3P7(Q+pH`P$p(}kuN#g2S0?vTJv)o7LWtZAR8vQ|kG
z$FXA%4+kHqB?}c2gOzsHZ5qcOY9}jNCP+DTJwJ4Y54F8XGB7ob%hFBGkB^U^o3;Zg
zBhfz5+#3D6BE3%4%dG7&Qp1~PTVBq-nUfK<-~C?le59ZO#qU`Q?khd}kCprmih0H_
zknJB);J<*v|IfevHcJ<Ol$~AZaOj?LdGQ#w2c}<xl8AdZ*M)48h(+}s`9KR~h(6do
z4U_@JzP1EJj`+ISmfqazRjbMl>*lh-*MK3rpj1no437ZaLtL3JdT@_)OtWC@^}XdE
zO2@8r5A4~1wzP-BOX^=~^2bEEM>L@j9S@F2M8N0s%KsBDf4dp%J@-A;M?U|A#6A1w
z=(D2E=0$fx?tS|sHH7t8i_EdV(14zGNrs3`pJ>H6E{`_8%gN~k<|c*$1nK+uJ5!m@
z@*TBwJ)n*vZ;j}Y4x11+!W0(ZKG7^?k^1iK+Z$?XA$$()Nec1jZ$Azg!mlrnm7%F#
z2e@SoqdkDvY*UAJ-|!vayrcmTr4Z+2AA><Lm7g0|YF|AXs`}UIVFN3xrl#hGTbAC}
z5Gorl(LmeWSQ+<APCqWe(O+J3n20qlu*&n17aO9K;3av}HYW1SD`rBDS-s*T0Q;6*
z{U$MBsjhx@pKHM=wGQBpbX<W2cKRjs0dZmBi%A(oVBAht$r24*mz{g!B^^C_jZCIn
zL<)Kc-a~mMIN97bYF4*{sA;jPLAH+5a;SoR$EiHCvg>EAz3c!+56}`@>oodjXuR~p
zG`?vdZ9pLB*PbS7w3O9;6x5pbknL#F{cRO(4Tv`<J`QAJ9BTkFfh4G4b&1nyRZ9g&
z3_cU}yl$h-<vxH*^(s!zvx(c=zAFP|Rv(DYr3S8bs+(1N<lMtyb70Y6x`2X`X0w6Y
z(2xmOY=3t<@{4>B?>6IP(CZ`5{6<3s3=Is@W!xvH@+t<q76>(Ew>~~@V3ctie||Vr
z^;WCqKZUB)Y39o6>K}eb$1g+#|9&L=+CV^y08wblE*WhMTB5t6e%pygo$as>pYlzw
zw2EUEsgD?sBf*1wcEME2VgbA<a+0m0a&T3i8}xxD69ba+Y9fHm=X>GpywMtmZhn+C
zHVO2ffPh`F_=lxWY?e$2A`8EK9&ydJ=I-ye{ziu&X00cN3j#^Gn?U|bFcmIC`32~C
zM@z+{2ofSj3@e1UtfxP*iub%0G8m`Uy%9Cf@*YB0)ZByaZcXf8f<vUtWk}J9P;C^a
zbxl`kyHq6=yGRc`r4Ztus{9ttEmtmgj}A$}f898_RPHojXk?Vx)uj)Mba$5yR9Va_
zvk&n6&DG@H_j>8qJn`}{!z?n9!H$lOl5XQ)7vlFK!B%JNf`hhat!HCRtQ<%sz_*5b
z#^v`axKDnYv;JhiBO=7b#Z}`aj36u)I0WC@>xn-J%?QirUeUDPQW(DajLoy@9K6CQ
z!|=hTiH)X5VR_rEF8eGEi}A7`!73Puh7!B>J`jEVqZ>mEOl%$-HxT4#HI~k*!a0$V
z?c^V|b65jCJw439y@+xuOxwOA{BN_e`2Br>%f4tD`(JaGn?h_)E`a*6y_jQKa?fF+
zCH6<*?W^8xjEM5bmJ`4Sm_S_)cJ<|%$6n986tokdb~<Vy{s0pEI98$oIySr*_rMdq
z+TDd9DXVUbK0uTED<tAxz7ex*2`fYpW@Q}WpM*izynU-7aqnA4oLy4Rba%G*28i~f
z#a=;2jNPF?OdK2>OxoxX<$@@f;hwj8*E98_GdyJTU|7Ioyjp*K8cR3$!PmW2?wQ*l
zH#i12aA}7v5TyDV?vt^xaruUghIRyx0_VCiOdX>P!&c4Ewhpw7PxKxXLivq^96K6*
zO%fKt`2-tGAq-z>voo?3H1b^g&Y;(T6X*F`H10TjbYkm51}IBQf679e)nI*4J?ty|
z#8o=v#*`Zmow!-4hV@2^$tCwmEnp#PVH^&<?~DkNTnMsP#4Z@5TEP6zDg*Nh=U8{0
zNcolLFT%oV9Hn5jjcb(bK$J-ath*W+8hSMSRXoWIgGDyazPvn<UWcaME8vp%asB(M
z^_SSqwp|`Fls<{G@*Zw@^!L@D?aS+Kc*IbGNp#<mQ9%&;R}u|J__c0mYipBNzJznJ
zM>f?22tuEhxMR25l9jBNeJq-7^-J!xb<s%C?axpDzKFXZtx0|6E)LKpOnbaiP~GV$
zjsL6wUzW`z|Ai=`uk7yDs-*Kz=}}fhS=o(oJyam61I(7UTKwIXs|eB|GQNMwyPj&<
zhLte@a0Wr!4~7#IegIn~DEyj9s1Fl|Rp#}NOuXOfYMlnkow*nRAkx~9_JqsgRPx?q
z79CEXp(S(p6JS0b;YSpi0;W2CgE`jXk3rX<k`>X_cctN;j1uU0z|g@dP%KdTG+pL$
zxxd228m|jQOXn!jP~*M0upk37&CXIrC5FnKi~H*g8KSU<HU3?J0POaxnJme-O90$E
z1~OJv4ZrYy5#5~e7ZAz>`f9j<RkU7I#kTTGcKkD(_Op5dJD%<@)9ROV_3v|e^ooQa
zuNFT9xnMN2;kJ#Zqoc(K?d=P>GZK7L3x6)T*W9%P8u?>&plT(Or#vamTym#=*Pr0f
z)e`6yxl?Cu+&fDa1@kZ%VL)#Et1qVtk;_t2!aR{8@Ey}!`hU_YQ5veP7;${Bi8Wz#
zb7tl~x4h4duw&xJmpFU#Eo&Z|okb7@W5KDtn}Mxai~S|)unk@<QaP0ZKa|4(k$Uyn
z_2@w~#ZnY3fM^;+0666@l!=b>xCLYpPGI!rS`kfJ%Ing&rEDs0^}V|C<!M}dcmqSg
zhskWLLc8O`D)4lwr_#dsP#_qt^T#~O!yptfT$M7fQpmSec@RhBW3Kd9f}EeIIAxc&
zGRXfkKCr$t!l@Wg`HK(Sx3_10P%DIS*wqm{{-5no(7>I@CIUXVP@CbwRFS$js_C6l
z1qauRI$--2)RN<AZl$i`hH7+AfwdBxN)B)s0S9YN3S>l5F0-yNS59yI93!}XK2SaK
zl<TntJIoEI64Sp=P5!^#DYPA+4T^J-iLxDxc?g13;p(f%WR<?s<nPvr!@hk-Qn)<v
z#A}0lD&cqnXW@}(dwT|hImb#e5E#WkmN=sC3dq*pefwtVI-KO&4fuxJ!;kD$aG}Ru
z&wQ=1hjL!GN{d!UKo@1b=KcU0`FN2ElJstn=VKyPnsI>}>;5Rn`PVuUPXbS2WIq3Y
z^`g?pfV@!&4QG-3V5e}cVntrJxw`N=Cs&^Ov{&<#Ni5rLB<XlCAN@-!oF=!S-kn_1
zae^4(6uarc+oz1&qDaij9N8)VJ{qV)l+NmZS0Q+AurN*)c=`tC=H_}X4POzp`fw9$
z*cz|-nHf+VuP7>(wT~72$@ZU~>;u>yYl_5^=LaM`rf#70g=6(?Y;4F*?W^?W1=+)I
zLXt*od6cdwPIgEvT~PP0p1sgDLFXLU(!ctF17nRQ87Yvz+eBxm^Z2ULOu<{T+H%>Z
z>0cPj<;N@NGW8MzH>VQ=>$MzWFJ?Aco<FprhuWxGc_Fte?eKh;x9ZDNu|JqMzKDe9
zjPupK8IRWDAF#sAjtbmWqRvUsw8GE^&b#zZBMXQH2jIcc8Cv}59<rjScLfDXBd?|^
z!S)c0(|VWsNSwny0n`&1yPlq9B*CvXkZ|B+dhvMk$gVMf46vz<@!lKm4gbxBm<|))
z1AaAp*R``2(;>C$7+yN>o~SeGV4=d1uq;h~%^x1>u6s_NEI9)#tAq1jVxK7w0+EGm
zLQ+b~EV6S9#tPmb=y{q;2?nmm`qFLE`~V&lMG>SJ&8za@3EhOdfv2~nrKP<j-y^s7
z*x;}-)k~jPJ-114tB>VL)7i;zUd#faD+2#-4q^>Mh*ReJOY+o(ZvHa=l)w5RduGAb
z>I1Hy*QCrN0`ZGHPX!Ny@50t<yNRIc>FUG33cNwc9%7@v4bw;IWRrRfbRB+_Ish@I
zg+L^YuLfmhWq$Kz`1ei9Ng&;5o?1_ifutSoBp5}1`ei(B+_ytt<TM}!lEIj!G347z
zc$j>}Vw!J82Fpu<F^nGL8tMSzNCHdz{?__(-x+qkh+x(~P3;mhy$5~OxuhNp%NZlt
zwjUqwjlX#J#hY`cX_{hHfBIn`tHH+{vJ<nCx5fp)&+T4qS1JI9>t^JhlxNjr5puMm
zvGnLvgMpRtWZyb8Ob?XhlH0h3rF)y?k800ck~HEc*uFaMo0O%Uki{-yd^6IZ(1)#3
z`2qO+G)l_n@a>NaG_x*TfWD&}CLOF1p(u^Fm6hM2|Ih7owDbPI*~Q8{tfPyvCdS4u
zFn-9!GbBAFLthA(>=dPb7o9yQ&JhN#U2!LitXH$`%4@u5+FdM!4zjTKo=r*z?%tgG
zw%>_>aZ!Ec!hD{kWx%CA4-zD`NTnU0?7Htfyg5Lfw=x1ViOqFG&C<P|nff_wLznTF
z=?R$<5~ZF)3tQh2?kmB1HzwN>vYB{%lxcwgh%~dG9HFXfnEEZX-x&{d{c*vj^lmE$
zsteMRT#yep1T!@F(txIp+C%->Z>&skUb27nC4xH_@}*g*C}Uudkp7vJNxyio6A0X~
zZ@f7Mh$8q6Yd}_QF%;1K_DjPb{UGLAgOI`zbUk>6`1y*V!5Nd#x~-K~nxO#fxRPU_
zC%`iOTDU%hd3Wz-E>sbDU<Y{l4SC+%g)&ti+U%<^G&atHe86lMf)=xL*&Ad~R7adY
zFH>TBCW{B+M^p#UX;i5pYk?m`fgeoD7+2*-Z}aoLFjjs!?zk#Y(lww8Dk?3b&5LbA
zHM6@H#v+xxh>6}-<Z@bC)@)mh0e~PE^Hq3E>qB@(;Bxse0v3lqb-uz<Hqndqn3%^d
zwSISCW?Mh!HP`uD!TyW26ti;`4!%K<Dq6rtnr*vi;N}d8hFGFb4M?0SANj%2I#!Nz
zt~rJ+=#^#x=Z0W9u{f>r_#zYpjljpe;7xmX^LQ?ZVRC6^#||n}e*wGzZ>EDh9&uV<
z=7;Ks68#sdpv8(Gb>#Aqb?T%Ud6_reVt~+xP9R^+M<+S*B3I5d2p*?NbMq><Ot@h!
z{;{X~PmDscQ(U2We_5?cH(9a26<prN;)sK;-2*U2_p49$HxIfj4%RL_<#M(s1%fT>
zR`JtKGr|3O3!@z<-i7i3=hneD&9)ZjI2;eX&8kRrUjV16ZlK!JG55sodXo~(U}=Xm
z!o<*U+_`?6ELD+NRZjmiYP$rANuW98^T?bMDnBbLtCw6>ai9t6vN&@0_{5Ke@`Zs?
zUbp#G3o63QnVq8dH>|tgTv5}}$R_qsRYMDl_m(wY4sG$0q6_#fnT8v0$Ld4NrniAg
zn3|fB{e~z5Tit;;nT?%LrP}zRzP@dX5@JC@`<$;;F0q_4Kag#g<a-U2nZfC(4)8Nx
z>txFn8s&M~?q+m55CT@)?mu|&ZO0v#Q(V0AWZ<<USNu@LnO0{v;ekZQ<m5S<JyYlC
zUp_8`d$5G^y`J6OFa8tG%C7WC#UH7ljSG??D5OuYTQgi1+5`VTpilc>fT>Qivin9g
zK}T&siTh2&nt({wf$;J{|3z)AwBCrTw12YF_Oz}Wj^^a~ZA@TD;Qm)NUW8EVx~8TP
z1ZlAGs@#c;<=oowi^<@W&>{lA-EE58mP!S=JQbCHmv{F<OBta`F^RjMsO){JL6Es!
ze)T2f(fJ(s13dCKL9Om?F9r>ucLkCI^glm6;JBQYXmE%nWNo2l@jjdk!eV01%ctK@
zei|v<*`d@sLP+@HTDl*p*QCt8GmV3?jJDpUQt*;^i*&qoP_2>R(*s;tfUv5NE53X8
zuInzw(yL{aw_syONLaXwi8mlcAjwAsxC96eFgCtz?94k|jd7I@`1D`KIvjZcnbLPk
vlwxW)1Z0k%`FyZgW2bnB)IU*yk9JvY&5Mq{aC+&Cz!yqQM>SvN*5m&Gw+sDD

literal 8344
zcmeHNXIPWzn*IPqrKkg<4t)e0D5!L)iV6_{MFi<8QlwWY($qo5QHXTupdeBs9RegM
zBLR_w9_b)p2%$p=oqaNA&tBIldv<^9kNt7H%oig0%KN_0dp~#a`E@-lZVrA91VOk_
z+LsIvgk_ld+RYCC64;50g*WzlI$D?D9saCpRtLk|9ye_hPXyuQVZK<}2ZA;bL=-_?
zx?t#=JU#Hp&5%Aom?!m2N!{n&oyU^zk#{e$`M0}HwXp~D`BK_H9XS#^-}ZZaTyL(m
z^mT#*?Y+zyYx;xOxNgq&CkCN+eio{E+2wa2U#!t0>E~>XUqz2S)43Q`yOF9MP^^DZ
zJ_$L#>LNc&=91|tN%F)|?kmhD4;-CO_S8d=bhDlLyWmaXNF9P47OnI9k?2_1_KaH~
zpMW4<A&3u4^X_9jd*+MPkTG039a){IqNJ7xvmmcV71`L4It{&@z5`fq?C=v#Lqo$%
zYDacnZmvY2g|+qDAEY$Q$EUk!I#z2@L7OxyNnvz0Chfd;Zfd{+s^#@r&Cj1dC#v`r
zFV7BPj4k~xk2ZvNoXk#sGqgG5Q&1j%?HGOHEqd~+49QTIzY86_Vcpf$m9mt7I_*g+
z^CG_A_gHv$-I9=!>W({Y)@wmg-(J<%O*voIRlK(5&v)=(E)HiI<wZ&Hkr3P~DY(~h
z>RXX%(Y?#vN(8hBW}w<9TUtS0p830kYY!(6-|y2$>QHV<-&->@3tfkBg~i3~Sq3M4
zhU-`=oO+6v=pFBLJ=Is6^!wZ1YFSuVq*Tv02n=|WE#h%OUt&%c=I4J6T%%@7MQT@u
z3Hvu*etUz_D2A?l_wJa_MC&gK9%I@S-n8x|%Enxnx>v=(!z5*ovEOvWd1ji9r+LMj
zdo-a)=dByK7)0gSObWl8Lrh<_4@M`;z=SM-CRm8`xWnkFHj>7LUJCtgnn``~u8cP?
zq&#S345`wYk}M!&`*NKWycLb>Z*3hGuex-kZRG5Py*dkWT*{6$V65>cGCnxiXKwhT
zJXz?xS0l>9+uNIJlNP*H6+JddqosIS{ptb>Nu@ESdPX@oILyx_T$5gJaSa-UnSQ>s
zEME2O*)yZQ^V6jr{n=?j{+~+DljZC3j0(PBadEKJvs+8q>RHlhK7)QUn~Wh>#xkaM
zIkB2nSbff=uWE{AX5z3{X&Ms38!w}O;Se9+gUvNMc5}H;&be3iv}vifoSa-#W?~{g
zA0OXIp68ja>UZi8l!3Fev%krM2M^Xpub;@=+FY01zp`!d^3sv3;yh8AB(?QxQJoko
z{L)SAh}-R3w{B6VQycRWxCG?$OTsQF40uHg2h3eiqLkW^gr^6pjcsgfs5AxV-pmIb
z?{YFTFTqLiRzXz{r+d4o^oq{5vx<Y8I}k5Ry;@HHudmPgPv&Hcqh@OvsaL}Jvo_Zm
zlKkPhg@t<D+}x9W6&P{q@W-c7nNd+uyuC)U{I@P#csw~dd4MA{AgRn|F3B&IOr@uK
zcbiC@I@K8_67>F7o)Og;zjkYFQ9wYzd;aqwN2vJo>r*9sT<rA8Jj^7s#;a+Z>`xsW
z9IjfqtggA(MSPr}I*z0-di<!KD72_9wQWiCI`wJf1FTr=)vQifQr(1eml*>-Y2W87
z6B83l?|fvn2&8uv5PwH&ZEtOQPnVG=Qy<Ns@E@sA*#5(hcb(q|F72_U*AMs%6@+pg
zav1p(ie2t?>}ckF{wrL;(7@o$oip#*cI~zQ_++OES<dFmfzuXP!yLn_;o;%fS=@R%
z+1rZ?>#xp|uF#x->=Tf`1{1=s9#q4@$w@Cs+q~wA-z<OfQChFv7-Lz`&~SyDUsgLi
z<zeFQFJ6?vR*LTZ5v{M5BSTcw<UMxn`rW%3h1f|63%~JLq12*>vb3f|wLolY(Aopt
z*Jm!kb6k^_mXK)oB&lX&f;VXr@N<8ar}t>Mf~^HvmC^t9_un~Sw@-fJl<u^Nx9FZ+
zb0VbYF?bPw8Irdb7QihaUd9g4ZRyw!#i#H%eyQ>ELz??kV@hc3$daJSPHrTGg&4wu
zoYDB(!>gP48;==JS&#+YNBI7eu#Hyc?#ju_o1)uB=`^zbystToy!)-F)=pLy*zmWE
zjm^+c1dR+0#VO`%<j(JZTs{){5-*>So&Jv(|GRtrZ$A#3g*ZAo^1b-%)gni>AO8|t
zP$(%aO`hs0Df6NlC{Z?g#Sug`&Xq;@%$*P;n18#pKqIXPLDY89?Bt|_kfNJLbG)>%
zI8WQmK=tOIUpD$6Y1EsoFA`dR7iC~$lXXltF;hb{{W<S(2MRHTUoD_gJm=lJE3mzy
zGVNt^KYjXCcsSd5&kk0J6DO{p`F7OGbnLOl@QbwSDzs>lhNvh-kijeBn;P$4h0!X1
z^X8(@fOkA@wZ%2JvhsG6sp|NYpJiS%b5{umZ@8n{g?#!mTmwc0a4k{VSxbG4p?T*(
z)z3wQ%8J8A{Z<O$YGnVH!u5+PFykbDuCWh4L^>~KyqXtRwUUVaQrYjR7F_X=NX?43
zY>&rp*o6Nqb0sZsIib7IB9Df_TaJ&9OGrv~{PvSrZ@}P4eOTAGZxeljd*a)>wikbQ
zrD?h-X9pYZ3&+>NO@7(G`viaZJF8=?=T1sUM0x7b>GbZmOTLlXmm;+@TRes=*|wl1
z9eD9s*0KAPb5HSQF|CLg@z}UH=Fm!PTMTq{b=RnN2$bFsDMbH(U7h03$t*s?c6Ae6
z#E{L7ab_8&FYZ1A6ye{8|7i}MbhF6<7%r%gD(IhYqK3Ld`zd~@Ys=G)s#nm%e!>J!
zArAJZ9z=Kb`3s`2R?QXcSd*_E>+0+r>s#CHHv2t!k-im8VttIj7ajJVVMIq8U%&p!
z-H*8An^Lx+#5irOwR**Go64<d+1Q{&S!aw7=GF?&^Z$Ew{x`)L!0C4Ra_GIH*S4h8
z0zOHjQfy&Ojd_V3sk=Q(XFPe)F&Yr}Hg4z2R0*kjvMaAFVATWGW@3?~9xo0^NvnIz
zW{L}_IuiNY!`Z=U@-Y@<qUcEw=FI-x@1ZpxJ$f`M^UIg3x{0!=#~K>E$MrgiDavvI
zD{iQE*v7YR-V{SYV>LW+V8J=U@Jq$5jIa|YPJ9r~(AvENw^Eff8tSA%il+GW>jw<`
zSvHL0*kRG*E&WkNw@+<~x~|5QQ2Yv{@T)(P{FmCaj-nPRB=T5ObYRla)T?XdQe(I~
zXI^h*>=Rm4L63S1MyPELd2rLat-bQbwl9lyHe1Rs{l1nnhZR}J67Qe$M8j;HOX&Qa
zPf`{*9H9Qv!Y`W7?5BIn_?0{|2mL5Qv9YmPm?Yq>@AOh6a_BuDM0g@@4iAb)YOnZj
zH`&1u+w5cDPBq(pmj@#?yRFleFX?zMDGxEcV<WZ2M`dbowd@-sVImHI`H3=i5is-1
z(|yNL5~ok!a(6ERVvq&hd#Qs2({77PVL=Ni8%g6<?lsVCiOD$$GIncaCjppLwnYA)
z*LN|j6xjmAQfuCQ?zz`zt(QOoOiZ-B9gy3HAhj2}5!b;QlQw(9=;Qj`(!r}bfeTH?
z2jmVRh~H_)kou7ixb=Zqhx%vSSbDdack}5Ad!XmNAMR@UwCqAOEGOr5ZxfZAdrMJ_
z!AHR1fE=~uVQ{%-E>&60VQh$~)y@)&)jxRk?c3g5UfW{eF_&K)q?UH*MT=W_G#%yG
z!D?E3A63D%2hqSa@ee;{+vPy(F5n2gSP=IpH5(d&7xfN;jFsf;UqBx|`$jha)WZ=J
zu$D<|4uUj#ny>|Itx|j(ent>QH4`>Lj?mtXb9+gPST9jno~QXNTiq47tbJ-LqJe(U
ze%F>O@B`zTptoj=aeHVC{B|u-eLFxmP0a#o2IzNt^(?N>q6EHBQOxeUGB?s&;hI+N
zI#g&Vkwp-GbnWAvZhaO6Nol>S`}AXa16pKAs08NL@qQq;FCjt*n&VAiU_i|}wh^SR
zsmY&R1)AID=4>z-=DgZb7@jIL->$B#tPE6%p-&4dqJfE&s|#MK7g++%Rl16YrZzlw
zKaj(pn5>kG!Ng}8;iHOka&jEASrJi^4YwP}C7%`+IIleizbgPV$js}VNQkAFV~C`L
zgse-yBKP4lqP0#Ftr-?-2S77RIfhVLlCsHG!L?m9{5oTHb|C}i&c}qUCTP>2TUbRg
zutzET$&)8iA{<CaK)hUC^fBE(oA1JpmLwoY*nkrVvS88eb3xj&=62ETYe3aJDa5Ws
zImc4>(T15spnNs4a&?+vd%FZ&Yci($DkLMd?dQLL<_N1FkQ0Dfl5`B&wNE(TS7p%F
zuQ@x51*w~F<{!?@&0Qh@6$TwPRjhEA@4$ig`e|yo0_j(OqJ|o2F`#yPOop_mXh}FZ
z<JT>gp5*n}T5Nyg#>2dPmSxj^Do88YN6StrL2Fcanm9~7%BrURe8Q4+B8s&?Zzb?g
zFbEmlS^8E|Sm5KGADy<`b474l!`rtfnl%VlvkXA{zzwMexB^bqr)i9|fbNYBMMB=9
z3gr(ax7iVf_1jv8o(=aLEMLb{@o3cNQTGD#s&?{61epkj8i8^xaP22Pko~g)UtDzq
z<mETv6bQc}v!#$}3uJLV@UOMFn6{k{dVav4RuGj5vd<K_kmrxD)VL8?4`Zlhf{>3r
zxzK{Tz5b{}SK&cx`h^{bk&x&ZI{Iuf=%?OlAJ;XHTeo7b>gZJe=c33A+cd{f9j(?A
ze|$}N3rrI}J^-lvuX!h_GR)z#PG5y<yr&*xC|Dhj+lwF~UMt%#f%*|u7O%y|#CQ=@
z0O~kQtk??Qzi+K{AC<jR7qV2Gp&9mY^*Zdp>U@?|1=m!|;7#w)iy2GBpx=D{`U^{Z
zP%ZmD;bh!YGObu$Yd50d8ui~6k;2{6|FVd-*U0##1_4+5_2p?;8`BzJ_v&Esey~q0
zyR*|l3_bKq%?9~m3~EOdhafBBjv*pJD|b~v`oVJk7p!d$?^16~R`N8wem&hyF?kD=
zi=JLLIfOPjiFJhW`^UbttMeHO^jRAfQhw;w2ROcT@MpFf0IDSd*c_G>+-xwANV*&V
zP^(;TAomIgQ~)fv2^F0L{262Rj0tcyT(AY7dJ~l>+EMI#1z&Gf_TU27u|elx&)jZ6
zW_wDqt0_QKjYpVQ;@bcJ{IGIx7+jXm_s+uKEQ1^)a%_CO<MLFGDb_0|*)zzQ2MG~w
zu0X=-?-p3rn#Nl^G|3wWGv&P7a7diARiU={8!&^`%TO@G`RPNFTsuq^YKw~vn2;j6
zzx9vjyk(vgJ#G;-^C+*Si5qYxDoTaqxL9?(UdR32Kp#-ZCc75Gb7<ZwB!5;fF}#R?
zr7}Zgh%F#LPG9L!DtU4N>rE?l8@Y&TuVoD8HB00BE2bO%!e|e1vGavrDIiRoRUwV~
z3Wb@Hc-7k(djZ>zs;SHKJipX1ZXrDUPss$R<ahdMQmFF*D<5+6@&fx@K4qQ9{j_l9
zNaWmKm~Y6Nwi5)JqHj)&;;tDXgPfRIbT|z6`iV~4Dh#Cua8Vk+QEdD5bp~2}>voi(
zscG)_@Ap~~<#fP}p=PH6Fth}yvRT&pdpC^B*bzIwCdh2|1466v&whe#fv|uB`0vC2
zGzS{3{vozIckYDVf-4~D91S!(<dH`4!wm*6BvAYWF{3eaDzu+k!#9GQcCaRpygiTA
z5tWoE0Tv?uhj2eqM*UJ}(F&qzE1Tc!T?3VPhoQ6ks?7AQ@kovLVPCB$dor(NvZy~Q
zh#@W}^HS<+!-mk|*H6T_D`8mJkUJsBzYqU<4${q7%`GhpdrIsasqI-FL~jDfk;VtG
z^ZvmzsrCn9GgfoQ*fss1gG7wBcxPv4%+|L>m7#K46W%++Zf+5TXd;@Iz}c8zEi3?+
z{VOoMkjA~=B@%Ew%4>U*LHi<!S#qvMQDl9Ve22Qj`sLrBd2>@=wF|t7(9teG?xO7|
zM~4r>Yy)+YrwpvpL<Hu2RTj(558+R&_t>Q|dnk5mjdlx0Qe1p4{P5X}Q88DuVy+!K
ziUKENdhc^0v;d}2$j`rG`}iyCW59>^fE)!{7xn@X`v=2gK`NNLvb5BY>C*#M0avnJ
zUtPy4Kx}vDMIgK!JA5_ek#xqyg3K_H2}1k=0sv(5ifmW3;AQ8=J|$8j!|X)||4(p2
zgSeqOjK!V@Y%&1{u8T~TJj@0DJ=1doGt>)gcHoh9{s+cBywyT~0mo>%kL({Ki~%<v
zq@q)aiXAIg@TuD$G$?7oIHu=FyYcF++UC+tXdJa*MT3(Urn=B52KzJ-m;?QU--|z;
zyZxoY$QCo<kOlckJNv30j7}h#`2>v!Wk$azA6L|Gg@4>^h5Tgrq&>4?4y#1|XL<p)
zuF<K39pGFSXkCoREhy*$^mkHa_b_yJE?|mhz}}sl2Pfu6>bu3pPp1x(c$FVcwXV#M
z(mT?&3{xp4=%<45SF`f9H1;Mb`xN39Q)cZ!+pG;g-6!k*{Sqy5fyb`S@B2#`Vr!o(
zz93vcVS!gNB)a&16Ax1(NE+q0h90uCSJfLl0$+={sn-S}fhXUgL$sXiR0B}(bZx}{
zJ&2#n;FPUR8sl?&drfKX3kO+<VqlNIy%OHeXKc<S$YyA^^sogL{ZtB-IVwNDD!QWI
z3v$Tf9}VFG-cY_eQSd{y)yDTj4ZJK9R4{7tE*;4L!<A0$AowTO)NeBbBSing-=mSz
zS2Ug`7=GomnQBPCW=Nzl&+}ieUcI^me%^Sc8s`1`_g&+>;c-JnF9@T@YYUdU-MdZf
znC5#*;^?<g>k(huMlZ9V{TB*<scV#Ut)<7gRF1HpHm{tk#PtXZ<dD?2H+~JuqC5{8
ze6HBze3a+8-jICXKGuP8=81hX8@))%1-R?M)25lLixfwuTr9HI%A<kLzBP{ywr_m(
z3%`O(a=CNgF%))@sLY2ZfMw6OHw>!IH03n-rc7d?`HZU#)~yV(2G!+n*8GZY>Bsa_
z`zKEok5i_5O0*z9x_RS9BwSS9xi`jgh*P>I9mwh=<Y^sRB7vDY(zS-bQOPX|L;XLD
zpQ~+eES}$7>X5tp=~tumP7gDvX(L5J2pYPkD~1Z+y?ZyR_K@Hi00NF)I~%AEn-n`$
z+`Kd!yj|VpZ#rY8DCpuN5>Z=_`I1JI)qJ!*Fz9S1j9HHGdqhAcc=k5(0+X3xMo2^@
zNT!SwKvf%&bv&SA+?wvU2ZAf&0*M)w#>|2wVy{|BNl09^iW@U3G^a*2Wj=yn!5mU4
z=5*^Ei*^CnoT<QXIwz4J({ceBFauP_v25@MU3KxRj8wukEVL{=Rl1?^jT<9yzFE0b
z!i&%M3S6q8QscG7`w1e!Q|#RQW8dd}lYUM*%Cn~e45NzsO;In`+uJ|ftv7deo^}^W
zyL4nks%ngf8Oy=8Fb8F!OiA{Hu*#_<SZKPwsG_BO#u|U6XIZ(!ISH*<Zk;-P7zq-q
zYZO4MF26P_u?_bmR)P5k3Fi-fe*Si{jssn{$_gJuZO*v<z6%?{?Y2nJLJNDtCes;m
znH{Wk0hQvc=Gq*0dghscOQT|Trk9(%>)@0NmilhVJIC+~Bvxe{hKmHE663ANW~1{E
zuD0bRBo$Tz--@g)P8{%n)<5$j$(2|secFb@%`Sojm6R-gh$FOpTf7QP8~Jr|o0{OG
zJ|~*mvSW-hUS^<tRq?*^kDdsYVTIHuS1jF#)Q`JF3{M@r2?da*7Fha-;Zk5$QP?$Y
zieo>_^>9m}NIp1~;s;r%p7g#QtUqs;rg`c;)>z+%i6h%!Qyk~lyqgnLfwcB61Jhqx
z8AUrn{N~`~)CrFQ4MMrd*xCEX3W!AuaO;`z)3zt^B!75$wSLcX#<;U`$&BsslA)f6
z)#rm+A)}W=A`iid?_Nk@wVUpr+YEoim>KvpTcWRKWo1E2S#(Z4Yu&&!z=XDLyoAJ*
z1}M}D3D#J0NA9gWFYxOn4sc|-E<(-d5|wTl8BMNI(#XcRg;*n+y_1t6coIyMMHA?)
zn)_DEMOsXS)0mfG`uyv&_#vtS*1L3HOL4*aH_VOlP2w_r>g(BEST>d`mcASF1n)lQ
ziZWAquZioRewhZ5P|%hbTIJ18VpmH2JBUrY*`NADYE}ku8K6^kSJDSl=xF|nEedjW
zM8V5PeyUyh{=&4}$t16D2P+G=pyG97bN5h7$f)Eq4xhdEt5nV0MbMz{^YgDAvI>|-
zk%IH%2-)fB8cFib?e+WA^MPi&Ddt1`ZMV4*v7BLY;(&wC03j&6c-p5NV`mqk)GD-M
z&4pkM&v)E&K@j8=^Zx?8%Wh$DW9LFb_ONoL>$op--oNs<iPWDd(H!2n+q1Ubto#F<
PhoCO&T`IhI=gEHnM(Z`O

diff --git a/reference/WHOCC.html b/reference/WHOCC.html
index d11f3719..f2decb28 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/WHONET.html b/reference/WHONET.html
index ed54b9d5..9edd929c 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html
index 484f0733..4ab9a22c 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/ab_property.html b/reference/ab_property.html
index 9634acf9..702ea6c4 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html
index 6a09bb7c..5f586ac7 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html
index d34339b8..0a15a7ae 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/age.html b/reference/age.html
index ef808362..e60835ed 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
@@ -205,16 +205,16 @@
 <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  1977-05-02  45  45.81644         22</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2  1988-09-17  34  34.43836         11</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3  1942-02-25  80  80.99726         57</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4  1991-02-14  32  32.02740          8</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5  1985-03-09  37  37.96438         14</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6  1980-07-27  42  42.58082         19</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7  1942-06-08  80  80.71507         57</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8  1954-12-30  68  68.15342         45</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9  1972-06-30  50  50.65479         27</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1935-07-18  87  87.60548         64</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1  1965-11-22  57  57.26301         34</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2  1939-05-10  83  83.80000         60</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3  1953-04-28  69  69.83288         46</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4  1995-12-18  27  27.19178          4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5  1936-12-23  86  86.17808         63</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6  1954-04-26  68  68.83836         45</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7  1982-12-19  40  40.18904         17</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8  1963-07-20  59  59.60548         36</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9  1981-07-23  41  41.59726         18</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1937-01-13  86  86.12055         62</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 94c328e6..a488b6c9 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/antibiogram.html b/reference/antibiogram.html
index ece60d2e..1d6c312d 100644
--- a/reference/antibiogram.html
+++ b/reference/antibiogram.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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/antibiotic_class_selectors.html b/reference/antibiotic_class_selectors.html
index 9cd955c3..8d9607d9 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/antibiotics.html b/reference/antibiotics.html
index edefc8a1..0703e8e4 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/as.ab.html b/reference/as.ab.html
index 52b456a1..a6dff866 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/as.av.html b/reference/as.av.html
index f4150397..ae678ec0 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/as.disk.html b/reference/as.disk.html
index b11aa7f2..baaca508 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/as.mic.html b/reference/as.mic.html
index d5d9ead9..640f97af 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/as.mo.html b/reference/as.mo.html
index ee69dda8..87ec3213 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/as.sir.html b/reference/as.sir.html
index b688bbdf..6abb8e01 100644
--- a/reference/as.sir.html
+++ b/reference/as.sir.html
@@ -12,7 +12,7 @@ All breakpoints used for interpretation are publicly available in the clinical_b
     
     <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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -34,11 +34,6 @@ All breakpoints used for interpretation are publicly available in the clinical_b
     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>
        
@@ -57,7 +52,12 @@ All breakpoints used for interpretation are publicly available in the clinical_b
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
@@ -534,16 +534,16 @@ 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 × 17</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> 2023-02-24 <span style="color: #949494;">18:59:29</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> 2023-02-24 <span style="color: #949494;">18:59:29</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> 2023-02-24 <span style="color: #949494;">18:59:28</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> 2023-02-24 <span style="color: #949494;">18:59:28</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> 2023-02-24 <span style="color: #949494;">18:59:23</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> 2023-02-24 <span style="color: #949494;">18:59:23</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> 2023-02-24 <span style="color: #949494;">18:59:23</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> 2023-02-24 <span style="color: #949494;">18:59:23</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> 2023-02-24 <span style="color: #949494;">18:59:23</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> 2023-02-24 <span style="color: #949494;">18:59:23</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> 2023-02-26 <span style="color: #949494;">20:31:28</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> 2023-02-26 <span style="color: #949494;">20:31:27</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> 2023-02-26 <span style="color: #949494;">20:31:27</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> 2023-02-26 <span style="color: #949494;">20:31:27</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> 2023-02-26 <span style="color: #949494;">20:31:22</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> 2023-02-26 <span style="color: #949494;">20:31:22</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> 2023-02-26 <span style="color: #949494;">20:31:22</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> 2023-02-26 <span style="color: #949494;">20:31:22</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> 2023-02-26 <span style="color: #949494;">20:31:22</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> 2023-02-26 <span style="color: #949494;">20:31:22</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, 10 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;, ab_considered &lt;lgl&gt;,</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   mo_considered &lt;lgl&gt;, breakpoint_S &lt;lgl&gt;, breakpoint_R &lt;lgl&gt;,</span></span>
diff --git a/reference/atc_online.html b/reference/atc_online.html
index df24a0bc..63f98eef 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/av_from_text.html b/reference/av_from_text.html
index fb79134b..d18e5d40 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/av_property.html b/reference/av_property.html
index ab734e68..172066ad 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/availability.html b/reference/availability.html
index 8d74a2eb..8374972e 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html
index a5bbcadc..622ce9f2 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html
index c00a3864..88ec9e5b 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/count.html b/reference/count.html
index 638f37a8..68337220 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -34,11 +34,6 @@ 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>
        
@@ -57,7 +52,12 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html
index 7f48fdf1..c167f747 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/dosage.html b/reference/dosage.html
index 48aa4e05..9df05df1 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html
index 3335b646..03b4e734 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -34,11 +34,6 @@ 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>
        
@@ -57,7 +52,12 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/example_isolates.html b/reference/example_isolates.html
index b0d066da..93210aea 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html
index 0b5c664d..ed49d264 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/first_isolate.html b/reference/first_isolate.html
index b625b8fb..39df58c0 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -34,11 +34,6 @@
     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>
        
@@ -57,7 +52,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/g.test.html b/reference/g.test.html
index 22548ce0..4b55335c 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/get_episode.html b/reference/get_episode.html
index 30369ea6..a3fa82cb 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
@@ -246,29 +246,27 @@
 <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">100</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">$</span><span class="va">date</span>, episode_days <span class="op">=</span> <span class="fl">60</span><span class="op">)</span> <span class="co"># indices</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [1] 11 38 25 37 31 42 44  9 13  8  9  7 48 47  3  6 46 41 11 16 43  9 41 29 44</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [26] 42 30 28 36  1 30 39 40 32 35  6  3 27 24  1  2 35 17 20  9 36 34 48 23 22</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [51]  7 21 12 43 21 17 50 31 48 26  5 26 14 15 10  2 35 36 47  8 46 16 27 13 45</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [76] 40 28 25 21  4 50 36 45 17 24  6 18  6  3  5 46 19  4 37 49 48 13 33 10 41</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [1] 13 31 23 24 16 19 43 25 28 28 46 31  8 41 30 34 11 18 39 14  4 22 27 10 49</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [26] 22 28 19 34 15  5 36 40  3 21 38  8 43 36 40 37 23  6 20 47  1 25 11 29 45</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [51]  5 45  6  1 32 40  7 45  2 46 35 34  6 41 41  2 12 26 48 17 15 20 24 12 31</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [76] 18 29 28 26 48 41  5 46 43 34  9  6 14  6 20 15 26  5 33 42 46 45  6 44 23</span>
 <span class="r-in"><span><span class="fu">is_new_episode</span><span class="op">(</span><span class="va">df</span><span class="op">$</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]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [13]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [25] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [37] FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [49]  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [61]  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [73] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [85] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [97] FALSE  TRUE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [13]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [25]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [37] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [49]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [61]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [85] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [97] FALSE FALSE  TRUE FALSE</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> 2002-10-22 718963     82 M      ICU       B_STPHY_EPDR R     NA    R     R    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> 2002-10-20 F35553     51 M      ICU       B_STPHY_AURS S     NA    S     NA   </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> 2002-09-08 B8CB09     60 F      Outpatie… B_STPHY_CONS S     NA    S     NA   </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  </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> 2002-07-30 218912     76 F      ICU   B_ESCHR_COLI R     NA    NA    NA   </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>
@@ -306,16 +304,16 @@
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Groups:   patient, condition [98]</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> 032343  2003-06-09 B         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 059414  2006-07-21 A         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 063988  2006-12-26 A         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 065133  2011-12-19 C         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 067927  2002-02-05 A         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 069276  2015-06-18 B         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 083080  2012-04-16 B         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 092034  2016-08-07 B         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 097186  2015-10-28 B         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 125492  2017-09-15 B         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 001213  2009-08-03 A         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 001213  2009-08-03 B         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 005088  2017-09-28 A         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 010257  2004-04-03 A         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 035268  2011-05-31 C         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 065187  2003-05-26 B         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 080086  2010-08-08 A         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 083080  2012-04-16 A         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 092034  2006-06-12 B         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 0E2483  2007-08-10 C         TRUE       </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># … with 90 more rows</span></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>
@@ -329,19 +327,19 @@
 <span class="r-in"><span>    <span class="fu"><a href="https://dplyr.tidyverse.org/reference/arrange.html" class="external-link">arrange</a></span><span class="op">(</span><span class="va">patient</span>, <span class="va">ward</span>, <span class="va">date</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: 100 × 5</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Groups:   ward, patient [92]</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Groups:   ward, patient [97]</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;int&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> Clinical 2003-06-09 032343          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> Clinical 2006-07-21 059414          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> Clinical 2006-12-26 063988          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> Clinical 2011-12-19 065133          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> ICU      2002-02-05 067927          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> ICU      2015-06-18 069276          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> Clinical 2012-04-16 083080          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> Clinical 2016-08-07 092034          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> Clinical 2015-10-28 097186          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> Clinical 2017-09-15 125492          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> Clinical 2009-08-03 001213          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> Clinical 2009-08-03 001213          1 FALSE      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> Clinical 2017-09-28 005088          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> Clinical 2004-04-03 010257          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> ICU      2011-05-31 035268          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> ICU      2003-05-26 065187          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> Clinical 2010-08-08 080086          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> Clinical 2012-04-16 083080          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> ICU      2006-06-12 092034          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> ICU      2007-08-10 0E2483          1 TRUE       </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># … with 90 more rows</span></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>
@@ -357,9 +355,9 @@
 <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           64             15            44            52</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ICU                21              9            20            20</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> Outpatient          7              6             7             7</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Clinical           65             13            38            48</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ICU                30             10            22            25</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> Outpatient          2              2             2             2</span>
 <span class="r-in"><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>
@@ -388,19 +386,19 @@
 <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: 100 × 4</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Groups:   patient, mo, ward [95]</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> 219024  B_ESCHR_COLI Clinical TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 960698  B_ESCHR_COLI Clinical TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 696587  B_BCTRD_FRGL Clinical TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 850429  B_STPHY_EPDR Clinical TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 8CBCF2  B_STPHY_CONS Clinical TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 559068  B_STPHY_CPTS Clinical TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> E52521  B_KLBSL_AERG Clinical TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 304508  B_STRPT_PNMN Clinical TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 990867  B_STPHY_AURS Clinical TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2F0932  B_STPHY_CONS Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Groups:   patient, mo, ward [98]</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> F32657  B_CRYNB       ICU        TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> A66134  B_STPHY_AURS  Clinical   TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 001213  B_PSDMN_AERG  Clinical   TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 126334  B_STPHY_CONS  Outpatient TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 34B622  B_KLBSL_OXYT  Clinical   TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 945BD5  B_ENTRBC_CLOC Clinical   TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 5C1947  B_ESCHR_COLI  Clinical   TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 284FFF  B_STPHY_EPDR  Clinical   TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 662978  B_ESCHR_COLI  Clinical   TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 874171  B_ESCHR_COLI  Clinical   TRUE        </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># … with 90 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 2a65371e..d1044b1e 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html
index 6ace8774..99972545 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html
index bf15caba..16d9755c 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/index.html b/reference/index.html
index caf6c06a..cd9b04e4 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html
index 693e2922..b4e338c5 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html
index f56b31c0..e3062308 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/join.html b/reference/join.html
index 24943809..9689c55e 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html
index e23c438d..0552ceba 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/kurtosis.html b/reference/kurtosis.html
index 07f52691..a3758d95 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
@@ -182,9 +182,9 @@
     <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] 2.980001</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 3.037125</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] -0.02531765</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.04694336</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 bbd14645..3997dff6 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/mdro.html b/reference/mdro.html
index 33a99eb9..b7d23ac7 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html
index baca5f0d..e0ac993f 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
@@ -201,30 +201,30 @@
     <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 R I S S R S I I S</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] S S I S R I I S S 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] -0.4743416  1.8973666 -0.4743416 -0.4743416 -0.4743416  1.8973666</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -0.4743416 -0.4743416 -0.4743416 -0.4743416</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -0.3162278 -0.3162278 -0.3162278 -0.3162278  2.8460499 -0.3162278</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -0.3162278 -0.3162278 -0.3162278 -0.3162278</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] 0.25  64    64    0.01  0.025 128   256   4     128   256  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 0.01  8     0.25  64    0.025 128   1     0.01  128   128  </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] -0.8840919  0.5294878  0.5294878 -1.7046495 -1.4710682  0.7061853</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7]  0.8828827 -0.1773020  0.7061853  0.8828827</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -1.3051841  0.3547377 -0.5058733  0.8711043 -1.0776509  1.0432265</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -0.1616289 -1.3051841  1.0432265  1.0432265</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] -0.8840919  0.5294878  0.5294878 -1.7046495 -1.4710682  0.7061853</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7]  0.8828827 -0.1773020  0.7061853  0.8828827</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -1.3051841  0.3547377 -0.5058733  0.8711043 -1.0776509  1.0432265</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -0.1616289 -1.3051841  1.0432265  1.0432265</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] 13 36 18 50 14 40 38 15 45 48</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 17 43 40 10 41 48 32 39 42 29</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] -1.2438749  0.2860247 -0.9112880  1.2172680 -1.1773575  0.5520942</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7]  0.4190595 -1.1108402  0.8846811  1.0842332</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -1.3988539  0.7280585  0.4826455 -1.9714842  0.5644498  1.1370801</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -0.1717891  0.4008412  0.6462542 -0.4172021</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>
@@ -234,22 +234,22 @@
 <span class="r-in"><span>  tobr <span class="op">=</span> <span class="fu"><a href="random.html">random_mic</a></span><span class="op">(</span><span class="fl">10</span>, ab <span class="op">=</span> <span class="st">"tobr"</span>, mo <span class="op">=</span> <span class="st">"Escherichia coli"</span><span class="op">)</span></span></span>
 <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    I   17  &lt;=1 &lt;=0.25</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   B    R   20  &lt;=1      1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3   C    R   28 &gt;=16      1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4   D    R   22 &gt;=16 &lt;=0.25</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5   E    I   24 &gt;=16    &gt;=4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6   F    R   30 &gt;=16    0.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7   G    R   17 &gt;=16    &gt;=4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8   H    I   23    2    &gt;=4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9   I    I   27    8    0.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10  J    R   19    8    0.5</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   17    2  &gt;=8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2   B    R   23    1  &gt;=8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3   C    I   18    8  &gt;=8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4   D    R   25    2    2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5   E    S   30    8  &gt;=8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6   F    S   25    1  &gt;=8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7   G    I   22  0.5  &gt;=8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8   H    R   18    1    1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9   I    S   19    4  &gt;=8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10  J    R   22  0.5    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",</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>   and "tobr"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -1.29633203 -0.33470083  0.68999803  0.04843526  0.30058957  0.64264079</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7]  0.40191868 -0.19451042 -0.15234167 -0.10569739</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -0.30902553  0.37446780  0.09125296  0.28702431  0.82880923  0.01327092</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -0.34052588 -0.50251321 -0.01669175 -0.42606885</span>
 <span class="r-in"><span><span class="va">y</span><span class="op">$</span><span class="va">amr_distance</span> <span class="op">&lt;-</span> <span class="fu">mean_amr_distance</span><span class="op">(</span><span class="va">y</span>, <span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.mic</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in .subset(x, j):</span> invalid subscript type 'list'</span>
 <span class="r-in"><span><span class="va">y</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/order.html" class="external-link">order</a></span><span class="op">(</span><span class="va">y</span><span class="op">$</span><span class="va">amr_distance</span><span class="op">)</span>, <span class="op">]</span></span></span>
@@ -265,17 +265,17 @@
 <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",</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>   and "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    R   28 &gt;=16      1   0.68999803 0.00000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   F    R   30 &gt;=16    0.5   0.64264079 0.04735723</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3   G    R   17 &gt;=16    &gt;=4   0.40191868 0.28807934</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4   E    I   24 &gt;=16    &gt;=4   0.30058957 0.38940845</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5   D    R   22 &gt;=16 &lt;=0.25   0.04843526 0.64156276</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6   J    R   19    8    0.5  -0.10569739 0.79569542</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7   I    I   27    8    0.5  -0.15234167 0.84233970</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8   H    I   23    2    &gt;=4  -0.19451042 0.88450845</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9   B    R   20  &lt;=1      1  -0.33470083 1.02469886</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10  A    I   17  &lt;=1 &lt;=0.25  -1.29633203 1.98633005</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   18    8  &gt;=8   0.09125296 0.00000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2   F    S   25    1  &gt;=8   0.01327092 0.07798204</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3   I    S   19    4  &gt;=8  -0.01669175 0.10794471</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4   D    R   25    2    2   0.28702431 0.19577135</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5   B    R   23    1  &gt;=8   0.37446780 0.28321484</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6   A    S   17    2  &gt;=8  -0.30902553 0.40027849</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7   G    I   22  0.5  &gt;=8  -0.34052588 0.43177884</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8   J    R   22  0.5    1  -0.42606885 0.51732181</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9   H    R   18    1    1  -0.50251321 0.59376617</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10  E    S   30    8  &gt;=8   0.82880923 0.73755627</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 5a808b29..34cfaebe 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/microorganisms.html b/reference/microorganisms.html
index 4264f554..ddbdebe5 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html
index 17021383..0b7ea5ce 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
@@ -221,15 +221,12 @@
 <span class="r-out co"><span class="r-pr">#&gt;</span>               (0.600), Eggerthia catenaformis (0.591), Eubacterium callanderi</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>               (0.591), Enterocloster citroniae (0.587), Eubacterium cylindroides</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>               (0.583), Enterococcus casseliflavus (0.577), Enterobacter cloacae</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               cloacae (0.571), Ehrlichia canis (0.567), Enterobacter cloacae</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               dissolvens (0.565), Eikenella corrodens (0.553), Erwinia cancerogena</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               (0.553), Enterobacter cloacae (0.550), Enterococcus cecorum (0.550),</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               Ehrlichia chaffeensis (0.548), Enterobacter cancerogena (0.542),</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               Enterobacter cancerogenus (0.540), Ezakiella coagulans (0.484),</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               Effusibacillus consociatus (0.462), Enterococcus crotali (0.433),</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               Erwinia coffeiphila (0.404), Eubacterium coprostanoligenes (0.402),</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               Enterococcus columbae (0.397), Enterococcus camelliae (0.394), and</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               Eubacterium cellulosolvens (0.385) [showing first 25]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>               cloacae (0.571), Ehrlichia canis (0.567), and Enterobacter cloacae</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>               dissolvens (0.565)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Only the first 10 other matches of each record are shown. Run</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> print(mo_uncertainties(), n = ...) to view more entries, or save</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mo_uncertainties() to an object.</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu">mo_matching_score</span><span class="op">(</span></span></span>
 <span class="r-in"><span>  x <span class="op">=</span> <span class="st">"E. coli"</span>,</span></span>
diff --git a/reference/mo_property.html b/reference/mo_property.html
index 320a1d3d..cd0549ce 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/mo_source.html b/reference/mo_source.html
index 3fdbc605..5268f933 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -34,11 +34,6 @@ 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>
        
@@ -57,7 +52,12 @@ This is the fastest way to have your organisation (or analysis) specific codes p
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/pca.html b/reference/pca.html
index 968b7dcf..d6ef0875 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/plot-1.png b/reference/plot-1.png
index 577f89dc87dd3026188e254d08dbc40dd43fc95e..6c99135eb91d0964fc0aa719e08d17ac04355931 100644
GIT binary patch
literal 26657
zcmeFZcT|&E+b<k@8$`!Pkv={|7zYFu>CG~N0RfQ`dKm<y3o(QQ6PVG#8Kr3GT|sF9
zLXSWaP!tRZC=fyoC?ygIp@m37l5;0MWzKouZ+&Z>wa)qD`<|>N2|N4V_jO<8cm1ww
zZ~XJRjfK>Hx&0szND6ZG=Nll<u4oWw$1i(#10!SU&z6C=-M?5_{0zJUug%LXvB2BD
z+gDv+Adun%@xN`!vnzfe&<`NU&leq{a~CI&#G6nLw!k((^;a<X#G$=K*Y>^M_2h?)
zksp8fVfVIsKX@LsQ@&t#Xm9}?eMsT@;X_q_fBNH3bvynrR*mb*j5~Mlecgq<dwmYd
z@`W|HaG9Pm!^><K)1iRN=ZxY#8@QvvqH-H3oJ)XKdPs!&&yW8@8t7&z;~qhWdp&R3
z-IX^}19=B3)_&Oz0!@LUuEM|U=R-$3KYumY2C`asdPPPeLtDq*=xyRp>8Wv&5pT%8
ze-7kSYy-8;HH{S>09I&Io!SLTr@W#DQn`+k_7l~Hfwdxa>L&F8PTGN8M%P1brNKQJ
zhEx+Znqj43)2NQDXc9+oz-K13#+c>zSw=Y`N->RwB*Su40B4KYM5X!X!aA8JsChVb
zsB)RGiC4$;;M?`-l*c#_@<V7r(Ihi_(lZpA)6aawk4WrkobVh-)Gz+T6~yAnI9@OF
zA$1ouryS~}h3g&r+^joR#e5mh<t57Ja}(w1DNtNerDo_?0XILspj#aW!-Z=+rB~Kx
zv3n|c6}x~Zj(V&u|J%a}`QfGdj^JD;^lioId@n(pVSan7lMKE(&%^*T_`I)T!NYk1
zQ*ai4gA|%(=ygMpoMea#b0wkEB1#A{diNi@g|@U7i(XoyaZUd1v8oju#xg=mbg{<8
zCTNxci@1E~<*`=7=`B&v-a{n<2l<#jc2)j$51&MxJRRSJ*sPDSv;@ZO2+m8#Do&v+
zgA|e25VjEmQ#TC;%o3+KM;nWNSC{_b(_^Lq|EzGN^7K_yd%~LNg<+wO)4XdH0!N94
z24=o$N{4yUMweP|U*|lKzpF_0oQRU59Uq8_wWi4{EY;w61;G`*ldR8<9dxho+=vNA
z)u(X}sf$aN3{k84`eZ>I0>LSZFPIO~{eHQ(dG3f))6itEKD!5(OE}8z=@EX@*i(5z
z?62)X&)P3a)oF0Ka^bjxZnI7A_p9^;qU+9!aG!z~lsy6x11fGzR>hC}&gZ+1qtX@<
z0s#%lOGOtn^%@ovdJaajO{bePbnY|P#zQ)qTIACehH1jx#1<S^4|AxMx{d8b)YIeF
z9RqAM{4D=t*TvU$a^#1YTbI8^+`fDPQOA6SUVtvL&j)N3WSte7H%@v;WYDV(olzew
zaDGAF(__H(!r9CF8!0=WE!w{uO5<Ug3?tKm6aB=J?2BgC74-Sm?Vz`pKk|3yFX3|e
zsetMO%|#~L*bI69^ONO1=1cip!9{ACT>41#Ktc@yD((KF>g*1BXJuq%;!dL53VnRB
z&lM2S`vK2kQc3@A@rhf`JrHUUZtT52W;WjCLEUdD8M^mR?$O(+Mk~!XDG@C(Q81><
z>4dxOGpIc}6>Q!gjy!hQ%U6T4DZ)LDqRJC*vZ$hR?I0u6xgkpiu`n&*f>QrtLr}pv
z1{^RY#yVK=>eJC2#r&la45>LPW(T1iCx3p+Q-sP<UUo7GG!I=HI%fKOq>q)a-P;rZ
z&ln;T-{JfdM9&<16}6|(i$Bp^!`oUM0efzJvuCNxi7vi{oamW$$E1e|e&kgLyVrmX
zfmP{gC2U>4>`lOnGGV<-%p~<(*j%m<k!d$Z%zD2zbcRGtfDNs}1!qGTwE7`sg<d!8
zRJYWtC1VSx);i0Av+k`U@ZRQUG*-ZR>q}~eo(s;NF{tBz$V_WWz;O(62gLY`TUEPg
z3o&G$#%bZ#uYjuc?(RaStH@m{OY3bZ9jTLI{Qku3%yh0=uK^<zc(Bo2n9}*_+~oIP
zVuuohF$%<A8Rv^xTiHXrd+pLaO}+E5s=B@G;90}i5OWcu?~7=U%Fvy!3Bik1VR{TA
zU_vjq7ag44L^lq7{5@bo?<1A7I2#b?(Rzn0zrYFIaANB#j+r4x?m{OHZ6I>nb4=t{
zbK^PA0h|6DtZe5&T#{Eihs$Y}<(ug;a?SE<L<eBK22sDGALzvd{u!I8?IcyV&CFnW
zI23Ats5`x?Gx0~eoPS5vOkE|$x3O~Lt1Mr+?loE6!OXBoRFGhA<CyW@AZp?;Ub}Br
zC8;3|u#Xs_bhFGJZ;N9s*+LC4q<GnTwqeeqHGAssQhK|2RR#=?(B7Jcoq=_)+vUQp
z)+@=JugGi0B42$T3s`Io=`~On2*T;XE9>ziU2BcOJ^I`sSjR-jBaF4)=jR5Up5te7
zB8`wE2?D*)y`p0DWCEET&f!D^M<<5Ut1G(J<ibj$Fzqqu$;sq8cbF7SwmlPwOR}Ec
z0uYzOH@AC&yjx--f)B(MD>|7xM+IMYjv3sK_OCqRdii!u_S&364uRNiIcL&}{FTZG
zSoC5~uFR2+0bcKG6rSTpmtyFS6?zRMW^!TClgHd+Nn|_0W`eI-ZQr!T`!9OI$Cj1$
zIGT+e>S`tNikRD^w$1)2)&1g{$kRBw*BR<O>pJ%Fe-Wmo-mr4SOWCt0Pu^dyYSw0X
z;cU3>EDFy_H0Ye`*-gCFA!My0h$=GCtQ{PkiLiWKUs2I0-&DVGWMNNV_AdAKU}1m!
zNrevA-M;GY4`c;nmYW@&P~l-Tv5OoP7VZaW+|$w-|6$&VS|>l$=8-%gPu4YMsl#||
zK4&%UJ>O73<itR0zu@Vy$XB$<@5H+Ph2Hnc+|B7Nk66cB-_OU}P2BD$(-L3>16w}`
zeW=o5#QNV2Lei!uYm#ajd88)Uf(B5V>XGH`pzfK|)V}lYO<P<qA6l7WmPrL8dtrTO
zrPwH&Nu$bV>0><x8-MELw&uN$IgNSgV@@}3M4r%}3<GY63GK^z(t?}Jk62VwZ56I2
zOxO8~hL+i>lR2<0sQgf~>79N=AG_)DTyC9Xl`&)Z8MSQTc(5J2=d4-qwm54^rvJDU
zFRwwW@i{i7<ydmQg0lHhmN9;}pmQ>0!?lGFE`+|8#`oq%EMoof)6E_pgNxTuLp?Re
zC$IDf1u==tgKo~<D57_Vupm71ZA*VbuPv0Ftv#nBH$?NG_?AzGel>{+oHTTc88Yx4
zn^^Yrq9ihM5hZZXfRBI}K=S)0#zAmF_=a_k(|*ic&!M}kxxOOYh*!I?Xb!*9?9qcd
zt<=v8&%NWo$>BqLlQ?e-I)!9vhO|LFFKwmECFMYQ2EJNU%2W=>UAsT0RTQz6>x<8Z
zEjGfE5?N;ci>+4b_=uRF+iUkMG$>ByEC|F=_XOZU!%LWFJmC<=w{W3vSoG@XhDmI%
zzS+nb?Uihnz;(?LSE9F*SZC}jr4we+7e4FF`#`CohABA0-^Sy6y{?<wA@)b5$~b29
z=ts@QkK`s-RMF|J-OBxkwuCo1l=$U4IDrrdz04ZX6<h+h0T7#?Ab^$&gVG{8@AM`J
z_UPRI5cB%$J)D~ZyR?e*>p}=M2Br-dM@$J8Wu>$QnpOEOf(!7<_^ewoL*NNVj~BcV
z$Yfe$OYd>fCs?lwdn45`zgylvm#YQqZDQmuc)&Xh9`qP64#TQAQw9tY{=`<(jTkTP
zW%=#PEjl}c2L!a3qNc>rCH%aLd?{N9t7=GReKzn7ProN0wzn~zCk&ICRvbEXrw@hU
zR#cstkBM%N+{LJjoIt+fnqzDbp3`d{3mbC=w9$v$Ise!pfOdk62J=7o#5S&Fn{>0D
zG8Ye5t>ni)4a{dLnJ)$Ho$T0)+ey5axbs0vIit6U9yIp%?eua{QwPt#r)`2}Kcr7z
z-*5v=rMRr*j`q*v2z=|z!smAVEBAV*I2UdM>3{GTKlEb{Z<IeP_3LDX{FTbL3*~Nl
z<A(yXSXE0<0sD$fZ96N>In3v<)#=4#M%A-EfHne)<8}Dvf2boba$dsMC%rOs`2LU7
zk$!OYoR-<y?sG&vC|~Rje**5H|Cm(h)6<<?+0VuTV5E1;wUS-0<zVJ8QO4u#W{xFk
z;xOc92q#=#U*sX>HJRVA@l!Ai$r)y%sAl+SVjYe>p8C*<JI@?<<Zammx9<C-hs)93
z+S3)3f5W7-Qd-7q^0s|LcvU}pglZyZ8mt>+)T&40xf}q6{C4>mP&Bpuq(onCSdh1E
z%+bB?Negp6uv!|A-x*uOyWW0dAHK@dEw8mVI8R%akCJ-Ds+9Ae%O77pI=?3WTN>f~
zT`JE+N_*kp&+O3XB{kiOGGq=joq9?Nzo{qxI#*hErv*cosOjbWbIqRWWSt7F828JM
zzfQ_y4o;5To~}3`M;;Y1<MGR}znt|GdbN%m+UTjJ=R8H1HT$;1gf+}{=3xBDDjVnN
zDe#NA_x8#)Z=_>;z{g|<v+-r^=3h8Zn0{249Jj+a2S~VG63zs;#()2sy3i2(5g_N)
zDvL@H8nF*Nl<_8@wrz|5kLAjLzs&!CM$<bFQ;*_29%=3rKeKm<5xE(`v7%;C_o=TN
zyhL5)lm&5bwp1A6n%jp`_lf7U)mt>`Rp-$$KIMHr)SQ6zu{wbt{6o5<l=$1~c9Z(e
zXDpJcAfIH}YBF}Du{ugT9;dkQP|8YUabJL$r{FGUnRz({E#pATVXieZc~aVN>1>4<
z_@vybQtR)1l6unH0_7cZc?6-O(O%v8jG9xN#M<OMKH32Y)jjBJ)`UE{L%i;F&gYHz
zTJ;Gq%L%>ke%z=>c0t!(;At3?CjPdqf285d_NX{%9Sy>D_#F-vouq8%q0jmHkV*?k
z(3#+-P#Y}DBXsoeN`v}(2&3|58_pEd%;LB-DC(hylwJU-_qT=P-3C;vV;*mGVUg`5
z`KszKm&|Vma&nny7*D~$q_BA?-JyPyeqF#Sd<61t9j|O&D13CBvPn(BJ(BU*tFTuy
zgk3S_@+)siXMKdge&Kuc<!&wJSonHl>sVc5H3p=AXuNPmWb8Q;6#@TRivO_6pExFh
z$--hj{?vz<46i-SkFsn3J_MwJUvt|kQ1cBlJG0c?9@WtAiceU;#%B?9?h{(L2ITOk
zEjJ3%c%F@-7nLKv<$AgmJBY2lx17sAUsg%j1u~*MafFhXjxBxYrMfS!8{KvJoC;0S
zdjDm4p(g5M;lhY{QbqFwD~#3?7FBnJR_mk8AMWIp#PUkAjSCEtDh%hJr!;d(Y|=bD
zv^c<cT9!p8@vA#FJLEx+U^l6@qIFxuu+M|aA;g&BLPZ_#i-nmhPUP}_+oiUaO;hKI
z$bM5YZ_BhZd4#cIiFRW0g#xY69#cD$(C0N()I-Z*v)|nW>3iqie!XvkOXhmqk>$_!
z2^agC*Kfog&73_(c=`6mo_t1r%u;?F!n!Il4W}Vs&r@PuHJZNznMZcW2Hcr>(w&n?
z=1;KcN0w_Ze^s9AK`c4MyeL~KVXuAlgmpA`I`AiT-4Ba><MsvMmgIT))5b;3l)-6v
z-SNAHOk34f)5_+K{nPI7NTE`!p3|=qH;Viom`Zaj$gfYXpxdMCeWXUE9W3<Tcc5>!
z_n0T;Du~kJ<!#5yQ4X9Hre#V5*@1+zD&Hf%w()c@aBVlP1sWb6{uDD&Hl5)Z;3VT@
zW@plBSb_SSdzL+Udren7)q9WjJwz}OXZQlOu%B-mnwjUu`Hh*uNPtDO8xjMO-Ic|&
zs_(y_nlMzZAh#Gc8dmWlIlmv}2`+M8GcQtE<wfOOM|_zl{mg)*8FQhn1>}_-vUaz5
z)9SqVVvsoF0WWWte|2g8OWWo@17ZdJyN&gKSRZ<mDmZ0alINWhV#BokdIgA<d}o0p
zXO_8(TDjURfjN~!=ff)bP?1xfmr0?aPtB}&E~xujTg!1Z2ir<huRO<zGQ;R6yCl}_
zwrFc<2y+wknDUog1;Ox(0F#h&^Z1+in(ADj`LK}7#8A_44;%~f45MB!^~!A>!^@ZK
z6dS3##Iy_4J^lEKO!Z}S8E={?OGQAF!}emhFX5bI>PhNAxmlSh@vV*xKZ$9YxTd_F
zkiGR2F}@Np3wYp@N18WxW;kk<nUXDAmCLFK^VE>USiBJV@k-mVmMiUqyn{PMjzGRx
zDLk+(PRFnm<-Mm@FLCnB$W)xllt;ycl*n83ggG3aQ!R+4QdK*pE0uIc=x!#@cn_HJ
zZtje5+aQI3GL_^#6-%F}(=r}!{s{A!K=iKIm&=6Oop$?F82?!K`ijhUw=cg_rNmzg
zaj1C!nB#Ps;q9?DN<WgvACua)0SZFjE%PZ}(#E;z-f!j>7-E7rzd`u6xDqIPv~V>4
z!<l>03G%P<=x2gA1ps_j^nG;HYu)TInoYGo=FuIWhoB=#b2|7kL;tiY3#cMuJYs@V
z)orqE`J@&SlHrM(|0FeuIka4xq5v>w0GfVRclY_&5Hj4v$RF!86Rx-lU<W{rDMh%d
zs0JLIVKj*pf4el%67%VRm$Oh|4x~CYfr)~KmtpF8P0aVq2r9%LjX~A2Mhmm)_5$8j
zUy~OnrUY|Wr?)NKS#X<d*6uTgee??%cAMl40ez_K-007Ln#`P#9wGkL*w*d2Q7Z=f
zD;e7kthMbit)5Oc!-!|gke7cq*`@{3$V;&`MV5(7O^z<V-qvkB)JV{R_60e!=h9hu
zuYe9N(D0w~_`U^4aFo9F)$&!JLxJb5E8^j{*ZZO~`k>_jP@4NcocI@DgtqjL7O_V9
zKO;!oOpb~(a%ef`A)y<$rw#+ysCT+qybRDHzWJwk69N=>@+8nJ242oLfIh*02L7+>
z<AFvq$ltKIIj8~03*pSZO_yL$h@Ss)m{v<U-$A*I?0|lX5<M3PnM;q<CB$zyY$q{?
zz09njY^)my4ps@tyc{M<U7^*E^N`uJ*(3p`eftToL(5xi8oBT1xkglJRvnu}ja{r`
zcjCNcD-FfA_72<^tv9(|yC0wTks(JdDp3+ez#ZTzOe35MuEa)B#A1Fu94v94^v7|C
zwTXyw0Yw(QWJbjxIn_*gd%k7`rVK?HB=eJQD2i9Ct`|!dZ_hdqu--K0z^Bb6a2D)Y
z+W+DK`ZquBS_D0?X+Su(K)=8qm2Er#rhF{hK(KA?7Vc2v51|r7hh9=<X!k6R1CfwU
z;o!!!n*Vrmm&_C4x@A1x>DJ;5NzHSG2V^O&dLq+vppYZm={?*hO#hUP6{*<6qqM{J
zEUUQ$_=p3)i|;~1ZTZ0G-uYq}@1}t*k|^#fv*VHGFFzx2y`FatkG<<Dhn{Z!<FLo|
zyvK)>y~mCKK>}^in<52sXaFlG@W>E;HM3CG*Sc@FX?QAgXigf3*XgYo8z{HONhhBH
z8s8czN6N|Em{vmjL8A~cuoz8l_VyaFiP^@=Li~~PB%`I5Hb$&^faZDi0&GoA^(f2r
z_9;-suYgJgXih1Fe~M})|GGc0FEqR@CWRTQ-O>EffuAO3uMC^Uv&LfV;Q`nKYP*5_
z+OABm=~Qa@a!z-VfLS1H>?Br9jnc?b$;kJ<$2>YP2K>dkF;dJ6>aK0WN#i#|*YfPq
z03CeBV=yiG>qt9J8F0VKt=8u)<!aU@7|I_yk~Cy>7088AP&0tA*T>HuRkCAkhV!lD
z!m>9;m<Oqk<ikQ5I~~gzIvOk2FCQnSX-hVq;zEI|bm)$PuY=P?<t4frt|Bs%EBAVA
zGGuQId+gAL=WuoED@M6;UTzEoKBjR;TI?i7`8v^rUN4>ZM06m0eNZT`$G2&=SX}#R
zPn~CZtV+Ch_!YpsTt%*V&L*|I*M`0PTkRF}1t;b4`w{?)J9%?Ad#I)KlX;MBCAp{>
z3Acm`nEO+v89MiymCNa#=!g?(lII%BjzdI5L6!4E#J2`5`2Pp@05$w?!JLj$8-I&g
zf4EZuQQk*=chzz$>F#+@!Cm<H)oF>k&$JI~huK!-JE5PU0^vE<FC|$9lqf@{KSf>Q
zB$lDg8~}%`Lj4Tix=NE=@^oc8-PR$wGO5f4?`Z;G{K_n&#yX0eC&Jz=NG2>g`|hdC
za1z}>$F@gGC5P;d;=Z)t@F-*pN2u&3PhwZ24*%ZgK(U+3EC+Ty4-_YPG@)*^uSx|t
zA}S8j-?&O99n^QLad<sTZG}pgCRJeyd0z%sTAc7@WM_dTyy0}Iq~`XX59PSN^s3Bv
z^JqQsB`@j@=M9s{l%gt@d$_qMh}UU5H^PkLe%R<87CbT#gt?I#Q3&{iHD0{W5Axm@
z>ev=TiXUn63U-EZ588LqohM!x5`mUeZkexMnW;lwy^delftGT42X490f^(UgILbSt
zB))cdPFMRp@e<shlh0I<T3ArhHP}c57-I@<%n@I%S!Ew?o$}AK002*W*&M~Gf=u31
zLnD4`fr?zv)hL&C4Mv6&8{dn^-1JG4MABIA@HTc8(;w)B`!A5IeU2?DF&q%Mq%tKa
z8r7j+5R&<-xszm31yj%S5Om+nVNj^*<-x$d*d<P3JZ)(7jaae^4-(Gz)=1UCMbP$&
z@xaN7d<PSpPZZM?9a)Zkjd3J6Bf^|xqVtt2F@emLlhn0xX$QRurD6I=pP2`#iieBn
z{+331oPk|C-o~M|9w{qzV)Kb=cl-q3Z4j$p$Qvl@#8>r}l1&@fK2mkhxbK57=A6&W
z!pa{HNtBb{u|Jyo@}*DXk~V)x_N9Eodn@e<>OA#H$_Ar6W`p&L^*a?^F0EH#d)EQZ
zk3Vx<a@8~7DiMjtVy1<LG%jgqDMnPVn5OnDudt1ZaVwmKW2`pvF1t~4iu6l+&vJ)z
z-DY!&SB8(`r+dKOwB0v~9sb2FARch~#pEM6Hjp(`3|dicMvSpHZQE{E#Bm!{66=mk
zpvp<E-u&#cjnkc4e#K^i&U>P-=_w+KJ<A`Mf#QtQ7rs@X0;EV7P$e*dw#7%qa++52
z-Z+k2%%8i5*oOA3K72p-x3Uc<;Rzm%dR9u?l}kG%)wg*>C81%Y$`igyQA2QWUTATS
zfs$9#=prGPHnRylX2J~9kwBb1ZS-~Otgz|jab@B11O8{3R%c<)PK~mf?eu%N2ezYL
z<@qfgYf8>}X97ib?dNtH_59BAxW}z^<awHo91IH1kkSvXP5g9<s^#RR6iBp25LzZG
z_ZUxYd!*^xPG3nvtiQ7`jr`~b+;C56r|CwG$Gj!f^^UCk#@9|WGL1N=^GI`tcYBAk
zeFL+lyu_w4g%&|~Ea`3G{DhWvU|ef}^0oRrSb{9HAFNB_o9S->caXMe5CtkS@=T~?
z+SP`T06kn-ZQn+LeP^=g?AGOyQDu38q@MO3Noe@FJ+F9F*|QTlaAMZxrQMhju-_(N
zPR=Q`t{Qeoz~bO--<~NFkUkd{^x3b=Hh#Us=t*p5{L~q4OpOY#mH5P`j_g-4iw^u%
zcrGEP9Q_`+tDbZy>mm;dEJrBYkk@rOD4uxx>2dWbfqP7NRa7*mM<-v8RJ`~rYM|N-
za3X?(bbdXp?r#!&yA6_4pE4mdPd-QAV@?DcvzzT%dWjC*zb=N$d^<){jZSFT&Bhgv
zCIiAJX9u-LKL3VEQTGA!%xArjE?*+Y!^i|)R#~Uk@iOs-I?%}T0o>~m^+>T&YauU>
z`SmDI32IWsW0av|ucM>>y~5B`<q3eYz(;3R@HUNW<p`$?s0iq-m@-HWyoSRI;>AG2
zhk?g-gG1na;CRBKSy{7Nd#w~dz9kkoe-NoDnX+)kzs(IE0rcetnSoRq^vfEDQe$6U
zP_8N0^sQV_^m%_$u9$>Jy}4;rZioVol~gM5B#N)L$vL`g!$)0`JaF8{MAFZaJOOh1
zxlK~iR6pNIl-$KCsBUP=q^$WOJQDuKus5c6E>$w~_LQvAlz`66qH^@2$^R&15<A=r
zKYgT{5ms(X+AMMK<H5VxQn<++)K(Ky4>+~rRT-6hN5Vcqsz9(S>ryc=_ygwWCmj-z
zy?6PZ{&<^Kwqbj6TvF)_-|^i=q~!<2-hO^o;)R*MfR?rGS>5JxN5lfg8Hh2%ryTwE
zNrwcrypsSMF{~TdXZkZGPw@UMkP6e8TB<Cw$|QwAcgdhX;4GEAc_zCY=NOI)GyFU(
z8D5-z{G<c<@n%JlYI3*(3D^9TM_?`lD=<t|sNWvDO<sM9gb<(k!Mjhup9f1WJL3l^
zTJmZ5lGvmBuTD#Tt2PIumq|P?aZph3e=XyHr>(}@<M=@03v>1GsTDm0(yfQ0ZeZ-o
zQ4*v<vWUXtj)T$3_<XMkKPhy$n@M;-8Om9GBHptCf%KmyHvQcGr<8U?N&AFW&%Ed}
z^FYeR0M0{2vQGrMEtYqJdS(ZAm@-|5!$F}{npqLOFQ`XUS;5efL<fnr+mg-zIx=BN
z-J0~AC^8I}Vh9Et_;eNOH)l~vIJzwS>cIa*^#j7itYnlR|5u;4v1bw3un=zUnr3d$
z<lTRJ`gZ!lwT9NFS7(!sKa;2+PEl;VV0+d*0Bk<lvuqRoa{rhm2mV)dl7}gS$j%HZ
z(7th6^7uxt0oYM58AW(?BBir}T7UWL`ZCrH1@0aZ%#{jRVu@3$KGotM?t2hWd;uO4
z+;7^5a)WmzkRLd3@pPRA_7f`9qFmxy-WFmXK)J`zK9VkS=*%tVyE0RJ71@RKl%(ey
zW6f!`sF2tkn5aEPs&BUEte2tHmIfH>8@pQ^W8EiSG>5z~ln5DhA2|L{ZdLA8S_KtE
z!C&o1lRq9>1oogpBEQ*w;m$kNM1BpErZ#q1YGGAru)4RZA&RyXTaV~igB^3(D_*8r
z5aQHk#LA%t5T*?G6b3*a*o=NF^rzes{uQA&)%};3?m#D!VI@1hrj&Pc4KPAXf~3~e
zXaaoS&7sBoI3rXgp=M$$AST0QSh>K(XYx4SKyr)cK)6hf>z^oY`(FIJccR9}YPe=u
zuN%XFmK_vZ9`5z65jy-z9G-)tVZ%{B!|Rx@sA;NvOe>0i6&_*%Jmt`t_DwF%ER1!L
z^_G<<i|$#ma;v*so}=4qy=kN1^Bu4<f6Os&beY}ehk$VMuQiX%Z{_c9d;th3Q&}hS
zEmwm|<1w|oiTEGZFWez9rOQPnC=^GVDoeGHaTzo;spFMB=`@DVtN=02nteh|0d^Yx
z0GJHw<CW^`$z}O3zx>^N{SA`_v`nf+$ADyWlBxyJn57#6H+T`Ve;2hbOS3}B)uaR!
z*nVv|8Q`p#lG*59d`KyO>Tri)lg}~srnvnN3h|=h4OiYAF`CN+YO?NlG%MI^fw<O-
zqpbN@ti{L;rypzD&0b>KQCsBwo9G18r|k<PA>n0JZYE?Nm4Y{O5nOK%uFfNsa{Bu7
zXMv0yHPt%|z*#YnjitYMa+_&|-NV&-#7|Nt8#T#5`#wZP8uj5`bEnBtjkq#wz@tN3
zSL9oPU*ZlY))X^^U)vn`m;<<97To)nIS+Cil!Nx(oupRZZ~t_lLK3jfmmVFA*2)Ts
zoqSzBjAB>X1ZGm)+D9Ff@nLnPzljYSH(U1w;JsxVcECDkHG^L^jl*#W2Pm$$Oi0Y*
ze#>X_)xeyX;?Zy~=cA8+4t+Yc&oKjPW`{2RGzWVm6tflJ?CbE~cf46}(8@<`Ar6%L
z8oeZ<{Na(D4(1o;B6I4>F-b6mgo{aMUqj=WNGRML&S2Wu^R>eE>LZ%h%EzE)u7WF*
z&G#iU+Xz6|p<U&rRkUz6c(VSD)7@bR;M`&bvwqjN8Y9FP2wwS>7fcr_+tJI}TXBpk
zBY){z!|Q0`(dGQn>aEwV?(QDf(G5P0KS_j?7X#R*Cmj+{^iDkJdPnjJ6q4}I9@~HE
z>nFlf8rWq~iRXtj7-6elCCga(0r&)`K2u55>Gt3A?%UYg?<K(nvI+uFh8vve6O+R{
zYtQ%cA`y8v8(Ny3;9O>}Jv!fbUb3;#W(dF~xuV&hlabOHUeQ}&O1d<u=BVb_7rYl0
zTLm1uDf&0C0)G36I)+nsWQXkygYHLg_3=C*vy%FRYE)ii{uiJ!sWZg%W3Iv}a=lF<
z{e*b$ubqGWTJWiC!)Pdi-cm9e@IQg-gR+gc0J-Wf+hF|__wvXtW2m8ilV8fwU#@=3
zGmCoxwFAV@764jAZcr=cuRzPNZ4eb2hDk9L>`lU%D+m6%7TSl?1mO=e+o{rxtF;Fn
zIGCN8hi%=Q+(q?D{wKcQe#i6}n)%FZq{hf!nox&|u6uZrns^rg_1SSu(81MrUEnRu
zoPR6k=<?7R74B}Jyvez?q3AH-Ut2kInL44y_?D6miZf@YT6vBWP|IX+Ymf5Wok%Zv
z!gyKI>u;P!>NspSTd4&nYNzLQx*>YFJ4E9FUAe}PcR&2`%<j_BD~W;iU!LBVdstXr
zzS&S(Xx4?Vym8qM6My5y-|%ulKR$eFiLo5Z{O#|D9shj%&e#;WYK&YRpHyBLr;Tvu
z^5^NKK2!d<<S2GaugOA1!-hdH8`jIMAPtp~SP_teS0k@ue+NTPHZ^BR{2~s1>+fxD
z4VmA6AT@tNNnA@ex4Gn&<de5$w%n_jyI2d=&8AcXBzHj|YO~DuO$k|eAu=ismCVV!
zI<bx3Zhs6KGj$xv&YMw2%|8HdGP1`cDmg=A<`#bXBlXEk>eSNg<yDa{)(SOGd!g}K
z!mQddnfsSDpC`0U&|3r)upT7x7ItB`vA-==hST4v+7x3QDv6lkL%=+`iU7o#hbX1a
zY@Jm?Se_JAXODi<`-#D$Q|L=41%qH(^5#pJ`aFMd@EiS=dR7Hu|CzQvufF7_u=+AD
zZ9Ok2=o`C;zYdM#9!pzq2-fhOlpB@Yr`iWtiPVgx8^4;~D0|*lR`xC3%U|-?Mi-Sn
zm-dR7q@PG`fu9txbsD}U?8~L<ZTvDz9#l1YobAxBjQi^~T&I4#Wu*7)#?!;ZPBo<Y
z$z+h0SZ!@Pfd}$Toq0=I-vE1AN49K362Qni*l-6S_dOU&{fqeFSmF-?<eg-zRTfwV
zJkp9~3_z-^SC`ghe2sJ{3&A1Z;yeC&KpesrdnAQewc4uUG_(2p`i>UJYCKr_S|mD=
zC9xHt#_yI@DR4-B{)`jE6FXDuxaywr{V>S`JIOZrM$h+!29}Pq)sUF*F+D#|y7j5G
z#q3}SZ+^NPzAKt*Tl>L^?Hn=L0KvH1n@E;j7c0$}GUSQ!aW<EXFzsjx#A6T%QOT@r
z-cAgF_L~F8+<@1$iyrppbFnjxr1e*js?xG?Wc_K152R`-6lY*aQNfO%rp$r8tX8#1
zt&*i6O!-&*<g|4`&`&VBLi!8|5_QtRjN%9(Kb{#rx|yFhLj>ckSHFUjtyfpUC29tz
zDI2F6Bm!qf00N7&DQ4XMaER<%tI5Qa+_V`xbzv-?E=r!Cz+A#YzdOZPf{;^Yo;J%%
z*v=hWz|vr3G}czlz?ecv*}BWj*<QFHJT;4SiFCwv$IOR9=xH}asZ(FE8Q`Yyu0Oue
zoP@D&9*&8nZQJb#1#OT>wHq5@$|*BmDeeU`@(|1itK`+?>``s2u#6dc+L*LuR$cU2
zzoZE*u)zsRH#USd+ho<1`kj@H^{NnDurPu71uM#%DU2Tl7z+q==K>(ogBQxV6{EQ`
zy`<10!!j%;0-FpCi#<gdz;0Qy!*3MdD&399*mCRIJE_JEGyB1BM&f4G7qc!sLvr!Y
zCu_O{%8-4_nwu>r{75GRAojg;BoO-&w#dFORcX)A_1%Mh_Sl^Ii9fR4(`Ftv%5OXU
z(hiFr3;S7W1H2hAmI%nvTv7$XUuoUlf<7s@=Lj4=f6Ic!r^201q95oGZbjm4R`=*+
zAl-XrU%3*sDEJ0otwl)WFJ1Lo)plF{*w*lt!#XGkmO+>RH2u^K2!N&XvRvdH=aZz-
zwfe}g&pv&#I%(^nL*s>%yeHr1(%mn+Jb0e)nN%KM>rGn1f*&ZMZs~?s6~z#y%W+j*
zRUR#k&$pIU+PcjnOTZ(sB0-%<$R|vs>j2GbL=|zh>p8*(@VU%4uG@*NPlPXc?24Mx
zYs{|HZi|$2)K{N>LRMMD9-O8w+0K$lr}{*V&2swjdYnc>q9H~5^En6qcMD!3o&tLP
zW2l~Qk0+-bI$NYHxHZ-jH)Oo>r|o+crIkM#b87s(wm!?OjWbLMEPp^viVf)?_E^8P
z`N93m`?quR@-T6T0<aOyZs-!P57(qC1{&{s!9h<NB7SR=e@nxTH)N3QurMfP%}6$!
zcds&SojWulsivP!XYXI`=PE<;$E`#+ourq^==Ze;%bvH^<eN+IYJw1W=h)EvqqOxu
z7c>@LTJqKwG$iQuMPDU4hntrgD2J_mEkGGpV}-8`yCnf=XcI?3(oQzPto`z`X39wG
zu`~x&Kqk-!yY0k@)%-?z@h_nF2Vvg=_D<?I$hu%riFg=aaJ3eZCJI+l=?fAD<gvb?
z^O+&}DM6SuGF_vh;e_OB`NUJ-N>9U}(<;%e<c?0s%<DxBJA>FxG3t^@nVJ8bWciH`
zJuTY#?XOV`|1%)g(EpY`8tW%Os!<nCl8PFfBx<hI{teL#AP*bso_g~vg_g1vfn^;@
zjc~!@VkEY%{&nB7O`r0~%_ywe$tF8?EaKWK7A&KRyko`s<}#LX2t%cmnc2%rli4#9
z5V{;*(&#>;`s&RbD`lo9ZOqJa_0g%K=w!(1eQ?J562B76{*xw2kjL*`_+?EgQ~3Ip
zO!516ep%|%B_xENHRBI49qL|tm-><DDUrgv^mM46_l~a(mb@7-v=SQ-fZd~p^uq?I
zA|0?M5T$~f;R8|%1L@!cI;kqRO~!Ojj*LgC1l6kfoxMDKtFdt+E_=~B5-owMO!#(U
zPm9}0LDq@QV=2b!v)zlzCy^mo4+zGHwWmy|K{*hlxpqt^qm!qY3m|k-Xv+zm%}8v_
z&vAbw&Q_ip5WK&qj?H}>=?Of)H)tDuZlV@Tj!f9<gVcIqON#FThi_-N{b7$0&~TjZ
zO-3<Y^^0yyl9krW79@<YXT>vCz=1^4z+(di({j4M?EzDYt#fLtuXkRNVSQ0-fAD<k
z>QCaHz?u7?@HJJbo0_UfRF}+HXUVmN+o4^9QlYz$UPEKdWhS`qYGj!8tX<u7l96UO
zY9MDctfM*iPCujx*ro8l{A=PT4>k&#r2kn0MEK~p>XBWiO6n(o#1Ut6MmcB=;|@tS
z()#=hR!w#DDtkj4OMKoTfglj<0)PXPRnMifrXpfejEyN}q_IBhtOK-Q2@wDv*2s;=
zJM`=2EZ4+fw{BnuVhCQ3QFE^(MxBcT1SdN{CIh9C0acJ8WH(#jFAjmbB2To95%JLe
z)|VaUqMlcPh3y<Y2}8-0aT^V5+r2yuPG-G8;EQtAvEv4mI@-kF#n*Xj?p@XmKTBx|
z{%PTJ>f0gl80gwZ$=e6V?cdsdiYLD{Qp89l`Q|?Z614xnr4PV>pd&37*xx;QW}_hz
zzVtzJ8+~ch9a~9Se_fp*HcH!F?JE-Ecfn%c|C&Lv?h%j{H23_c#^RZee~v@Q^+ICp
zB+dD_(Y?#(l1kJz2L`oK(IWa}&fs+ki4VE8oj3sZ$C4H_roS4>oqOhsRq%vm@u}{T
zEw2!LAl8kvDYl$`1f&+0H_Pf>FS#3SgUu+;KTOoJiHyXussCQ7$4CcYKRwu7s;B+X
z>}qybQ)6+YSQ_tkobS)7?Rid+$*O-KC(Hr}5s@Qgvf-}-0GCy(r%ImQTbKzv5fnfu
zEmBGR>PF==31jm<xOds6lOLoJ9G>VWNqma_4<%6*m-PL&!!lof-1e<^@Sgz*p#0y~
z$7p}lj(3-^=#<5Rn=tG`ihIh;DEQIoMoBznMgSg1csx@D!5C25Q^q5%(nXf5ufQeX
z%^O%!B0<tU{qo&5`aJ43Ru9}XpSc-ZP7t_Y*HVm4D0_anDIwp7V9Pe$m#~DBn^$-5
zK>%f-51Sp7WV1>Zr$#BU%l&cTQ#}z(IigO8&7E<9EIpoSOQn690$Xed!<Yim1|<K<
zITV6YgeBIl9FxS9wk_b1onU0x?8bo`<}2i%C<N@{JE;<P=S6{N4B|P^Eh2UaVl!uk
z{j(*Ha_4sds4*>Aj4#&AHd1ikZ_#?SAB?ZsVk7=VGIs)0Q9W(ltD>$V<KT~y(rGyE
zm>8^Y!?8P1^F61EB51yWh7T67A#MRK)2K^W#`ApTok>?Coh_?*NR`z_)(c2}%)1j0
zW*%QY7}#<;IEvLkn|pmg>l)TRWjy@qPyu9hw$hC-_oXN~-+Vw0w$#M%kBB%62}_;9
zo^WX`m^o&ljK0$tHes07sPhSHrAbLrG{VlzfWzgmoTHF@5}LoEaCiBYeW2TssHO3&
zN1Dw)LQL~!wq33jhG0dxb*3Z}%F*^()uwstu*j5<ky^i>LW5twmGR0Au4+<+<mb+?
z<8Pwj*nszT1;U@PUP@RF9b(!VI@YKFAG8COwZ|;qQ`R^seJv|J=<!;Lf{q26_a}sI
z3SL;i8aFJUdD+2L6!CgsW$V?c6E3lulrrF)jBS$f>btV~%2R2kq;Raq<yxSqH}6z{
zUDFMxk4Lv;-<Tf&DtGD2wdAuDinxF|X!zwRFn?c~so)t{S{}=h!QMsn^48P8Yu(PN
z1&@i7y#GM)Qj&7Zm%>N2Fh;MqFTg!W;mv_<mrw=t8ea>|rWq_48H=?5Mjt^;KXOb-
zFJETD`b-e>bEW#jj*x@7MW%v3z{<Cp46a>7n4Lm&E)B{|RpxtCc~}^vG()k|!J2FA
zkl%X$c@`i$yF2)`EF|*nQRvJ=_4v2;hSLd$xuPE^Iy4W&gfLC|jz^RyR^4L8K!tL;
z*G#l^c*M}>ECpc>3_vtnw^rRTo3lNHQe+WteFPHaiI{-%dmsg-dhI)WAV`}q6dlR1
zerAw3P!j%y4~YWKqQ?{%%nt+vtTVE-u?jkBXjC}1&U|L%7Md4}U8FYz(;9*AC#_`f
zpM5V2S>lTeul+rN<HKlPZMSB(Uxvrf-ACELQP@~53cMwXHEO7a=2avJg(!1u46t`?
zmU{^%hd^W5N$-km5o4tSk1iNNYOo~hi21TE`!d9a`GTE06TI-_!UlvqVijvlxi@4t
z1lB#d`DZE!u6QgZ*Tvq&x&_JG#?y2&tiii~2!C-o<xY&V^Q81S!{d!7DG~O!y3f1k
zpX(1z^-WovyzN$evA}b}m^0sv-HJ}QyVhUA1#hwwmIfZ2oL@SWJ=;t9qI{Q7e0O)&
zp6QKwH}D94r1;v~muTK{sycD*6`-}GciOgSUO!kS;R!qaluAy^Qgf<zhAC;h5ljdp
zpR$jDU=MV@BYfnoCt}B6Zq8kM%hl#}&L$3*{Cd{-)a?DH%o&^uzka`4O0LtX1w?S*
zw=K;btejOG<A(RfI+&1GoiGM~*o%aTtmKcDVhn0C%q;XVw7IrAa#X~}bp5u|>spgL
zdo|Sx-1uCh5axhwQA1t}i<YHno!q<$>yd!<QzrDr+~3{YJEPED`~MKH0=hQx4C0x2
z6RLV(ami-2Kb6uW$$Qw$!e^)5?B}Q?TkKd##fRv+)J+u7t|>~Te(Jlnx=Ej$sH~Tq
zbe630|D7M1Fuv+$C0N-qG6IWB$xWZ>!;V+@a)un|UO|>JXY{Z{RiF}EtP1I@s6i(T
zImK4gg$V+DGo!pJ>%2~b4Su3%scxpEqV?4&M<G$*O(!ft4SBT-lQgXdtq&O77}+;!
zq*Xi;Kvb&3|2bXVy#a~zOy_nNxrN%Hbx#_cogEj<f}yG-Su-sV;aSQwR$<U-+L<~8
zCfFJTh(f!Xc!l5=to!2`ZHQ2xvf`UM>s(OfA|lqmL2iq4&3n9RUaNnT-<_&QaKRI*
z&M6xfCcFjf<vsOCz*h>L115NV9cNf%S41mn=J@RCP@@}UaN_FoeO>N=Q327Uizogi
znrDvu64!WVba=tccmgI7Z@XH5eogiPB`<v?xi8i>a^`WZ!otA7?|oK(u5AbD#CUha
zwxa@=F}{I?bi?kyJq@;S6Vxm<a|$+T=3b#|2fF(=h==|5|9Ei7ykG_Oo~)YGUe#>R
z<G*adE}`PML#MW7Lp-M$_5C-TvcYXFnKQNE2hu-w*O;1kd?n5hK^H0;12*@3!K(pH
zx7n*XH%zbN6(B1gWGe|v=ah42U=ZQoDG*3l%FR~Krr=HQwp*c`PF3bm?3|k$x?u?e
zztJ@tYs+j9bwC<98$KsyS34f48klu+XE!c)RqXy7+xVi}oQE5_W8o}6TmN9otzazd
z#s*9JhNf+P1}I$T>Pzj}NRdi^Y$DK^EzmH_&?;WvHOe7_wQN@7z<RdGJ`dCcU`}_e
zjQ{4s#;@D_42DWd4iseM%76@))Ud=KDJG*~SNjT{i~Ls-Q>A~xAu%T>k*C6q>zvs2
z8?);btb5yv8mP)8E(D>r^5`NC7uf8jn2DM4JL{L<bg_KfTO-{M=~Y3vzG#!VPH?A>
z%;{Yea@tMUoLXDV?eskX@rn=JQtslhT)RX<Qga3IlJ5rtjKUhGf)MCrs%4-Bx-)nf
zg3+Rk%ok+ZhSZo?HQwAbj%&yGjHk{#NF$mV`EqPvUOT^Je{_n?jhf)uavMgVd$P=1
zd7GAs8B!Frr_q-~H)QdhcV)Tyi`iq(TfGSLf^g66#LnVYWk%8y(Tt_gEU4OJkX^g(
z)sh475~Rt{n6A252Tu#j4xhQ6u>)&t>{lx|fZc+3{dt$t5PCl7_vwDG*|j~VR8)75
z<jl+heb($6)YzyDb6_sGRCMOg#8EG@D<nL;^5{gp9sfH{PUrM(7*?9wADdr$PGH<r
zh@~5u3*TDuh}mP8>NXtup?KiN#zT4!BRq{5yGjpD`5g;|tin#Mt-0sT2p~%?O``SG
zqdEk&&Au|iQv8w!q%}BUtDyY3vZ*hpu|aFFq=$B0<nz6JL+hd&B(|l&>CS*TOM5U}
zDY-oW+!WfCtgIE*blUo*F$Id6&rxfdcuaJ|Zu!h^w4SH_7=&Pe^_VYOGiSQs>3YeX
zJoB@?!3-kk=yu}!hrfdnS69oyM%di!+O9b)3~Q`Iu{(97wpG15w#KbT+y2-uWx+-f
z7fd>#{UwRF>~EMxLzP>GsHB`yLM8f{X(&0KC!I1}Dwt)c&vTPDGgIu%=R9&(l>p2N
zw_3%WdXyLPx4M2Hh9?N=)3?(nj4A3d^C?(&+AnU|>kE)7_6u99&8pkCc%2($$WnIg
zkAt=wLhd41#-=9mS{STr{e&$fcDWZ0QS!JvvKCbl4xT1Q^&S9ee=a_yHhiG=?r-aF
z!5PIG#gRCjbitxymwR8-=Qxz9J4L%!A@+cJ*F<c;VsJG*aOshOL(c!?NPsu9a%KuW
zH)F60y5MklFU@Bx37l+&*g;RZRrfEa4zXOFO%)(nKXLoI6^K?z*88?686Lx5U9C<Z
zeRmt^2ZOSyo-KVP`P?a_(NF+I1MO)EgqSPrDz>wv0z+DT-l9&y*&jRO!p<lP+!fvG
zPFYLYv08B_aQ3WT{w;iVB_f;u_Y1UyM&5M&!cc58zK3jhHAN6~g7V;}S+lU8g%LX*
zT8%9Pg|Rh)Bx1Ec2V%|Gz|TvwYt<?{<Gov)-YJz?uZ&lnlX~`Zi~hPMruV#B^Z;so
zo3Y|taI$V483;gPTdK(kT*2+htaAi<`bKu6|Kzn;vkt{jcaM>nO6A6!(*Ir#rk1*2
zt4dMqo4Yr$tyd$t;0Gl{GtG6OQrfxg%q`2TM-ZhOu$%zv95GTUtC}1Pn0Rt3Lsd()
zi5+LWb`1ERJHSTTbm`OK*0r`qJvaaG*+zex`h;Zb8lVW+voG6)M@-e%0@eRATRPhJ
z{QZ*wKQHUp*bYUzI}x*IgB9%te<lr#%WP}*uLq#WgCHD0hi*_WW;3B$xK#eD)Xm)s
z)u$<^(rWwq?&9%h9-)haibzTx1A`I6>A!;utj+eGduO~Wx*%UjUc9zy1WCGT(0dH4
z@Ob7v*b6_yk8C!GyIJIXRThcK(J(WYNijww`t^?0m_G8KU&F3#zhITFse%hLN&)h$
zZAp&TeN(KEJdRG-QfKTm_Ks)f&5`$h&?%}E>a&2%vJ_m0KX?Pt>UuRR>Q>o%?hBz-
zHoxRr`1!!8x<~#seHHajJd>+3J0gbTEnY14r9loVV8wW4QPQg1k}@80mELpe-qrNg
zg7N5u(r(28<ur2UOy)i%BYY=MBRj3n3h?~XM#d*UxCz>t>OI74VC(1L%A9drf4B(Z
zJ2R=(%|%vbe5Yf_KF_@pS0xLioIqNo=#Ezo8vfXjPb~(A<5Icw^IVm=9Cgn7wa+K&
zh69}H>^ze-Gv^}ot;YsLCq89Z%;aT&*meDr$&AD&qV%*ahTgCyllvL~(EuRE0T2zK
zR^0WB^o-lDW-@vkf+ak^7Ky=uW3y*g>FBW(;t#X!Uz#AoA5sL7Zk+e)&|8(hVh{;%
z=$}sbu^D+%ke8~L*>J0n-nj8QuYOaGX?E?Ge13O2m~FfIhR#B$BCladQdDl9B`P1f
zk8N2n@*}>pB4-1T5MiD5Js2W1Xg6zh+GCyTwkb*}p$=z{w_t^f$2a?`o+n$H96+2^
zEOF>Z$g7Bql^52+Ncg<9?GAgF`x}7`NxQC6I#5RHoywR6&(hjNIkgwM^isC`Rr!VW
zrzk^mgo{EFR+xBploMyk6Zp;)_>)3e2RMOiI)ky9Z-^TiXzbme1W$6pAxKfs>LY)+
z;}><gijq)|d2RVwPNrX|!h_I}+!a8)wJ27MH~e8*!<Mrzxo2tE_y<yU;+=}V97Ay)
zh?6Cpp0ygWfVH1eIF@o(b2ia?&>f{NbH|M>MzBWz&ATxK#r|SzNN?E;iS`Hjc=4a8
zk|r(9ns9SBLBuG0!EA!&@1=N?BnT3Ly~<Npn$Me^ZX>&pBiC1?XtZLNMQ!!a=QLlZ
zh6n|lF+rWMufaA8g`+VV<_YNBw}hmgRRPQOLX1j`9}hpKEKMOCPU5)eNU|fyaApde
z+_UPag&Wh)U|nl})E_<o)sB9{R(4$Vc+6=nJ8uV&1i-}%#4a{eDSH1vm(L6u8o(d4
ziS(ac(Xw6hI!}0BagOA`c#$RA<>z17=Mega<~vj$q0rmR_A{kxDW?lDi(Si`_w|61
z;G`%(E?g1b6p$WE<7aK#R9ImB!{HU-*Sd(!jFQ-2Olx{}J1V5#wB@mQH?T&}MQ@in
zPKA3?r8Sb3ZqO<7eNQ%M_7#M(b5NBFwA@CX?-96k;XfrC#UJcj{aC2wdU?W8?IhBr
zD`p(Psk55s&5_hqqRDxR=;kJDEfYR8wkvd97H^?9h*s!*gmk$XI{W3TdWx|r<=MP)
zGQh@1z$Jd0HPNksdg*>jS+%!ENhu0XA{*xVgBu@5I?mauA??#!!@PQ#znXQT`2MpJ
z80kFy+<o<HmqvjzDOAmGGpD1PwL?~DHOq#sk|Q2+bnWIYIeD!Yxpw3OJEDEZSvRWz
z@h1Qqui+CV^!VfPWW{>LdP`)oa<zyi{Z;e}mT+)n5~<BHo#uRrZ%%GUp4Ad>IeT}Q
zZDcee8v1XP=VK{-&t&|TN7a)!^I4p4#8|2+HtcILAu=Y^-0LU9zzdaCsGJ{RK0nLe
zYvkK}aB`ykwT5W%s@!^gj-m^RLo~3MsaGJnqc6$^7PUQ-!%F%3xiV{io3idQRLt-v
z(l~F=T6bw2|J#&eJxOKuobVz*#<dp;GSc<6E>y_vh4b%VSrDa+nG+D3t4$g+ZV=P-
zTEj0Rie-Nx5+A(M`47ONtaIDv05*_0Gjz%;8c}Sl<po)_J#`>?DX1i?GTR?u>url)
zGg8ZLU`|l*FWgb+oYc*iv8gk`5L4_-J47?AEK!4-4lXHvkB_;;V|=)L15?Tm!vejx
zl~fw{wUa9(17M&3dHug=V4zNgvIO=jSToEr8N0!IwFFkKs2TSW;1UggO$;c?KXMGa
z)tZWqz1U)%Wi^(vXo<wcHqSPK?u-@#o4;-2{7bxTr4tZM>ssV*os%(drx9uffmel<
zq|k0xxfb)#LCP&EnR~>(yzWo(nez9#O!upzCc&jdPT1cbfPr1MR+~r^Rvo)|Eiwu#
z4V)yww)oY;P7cuCPJ35|(o5N}*4&x<p1ibB-RD<wZuHu<q!^d1iVW$#TDCyvfXvDN
z!xrdEyf-{e`TUUOdBj+V#u*ErU0!7`&2A3;TLQ%R-<V@gWo#9lbfb#c2D^Y8zx`~*
z$#?Q4tt~BAU~J^;J#uHbH#`IUZDo@0H8dhs$bF$Uk(e$_Yw2ub{e03qH*3sM=tYZJ
z^ht}vz!hgfA-6Cmc084eJ>yTlhczyw#DlI;+JP#W(i`}oBw-beQaQan9(fB}gqm*+
zt2=Sp%`m_tZN^9{H8v27;Pm5@kMC;B)<<qXW6fp@&<@xgo$o}CEqQ3)p_<cv_xo9D
zw`iyaJ2m+SrW5F7NR~>I;p}U-lyU8of3%#Og>4SeSw?D`lc$RI_#6wVhrKQa<AIY_
z29#vC5UU)(Xn0TFX~>4IL!VVlEO6YVEuIN@!q79+zDe{?^A^b~!{MA&jE3JJTi^bC
z8y1;W=rhE;){G29xrL?A4rtybaa$uoEF*vEF!DNw_Aox!4E(v!xSoJ9uXLL5!kTbR
zgnY?=$Y)}bO>oemjG!NZ4sxjr=RjLx%3W2#51nTSrN&D#=YaDJgO-zH{a$~YxtAuI
zJW0W&+|~1|DXPN~bt$$<w-_iU(H5)sT;qSVcJ2>J-Txogva6M=ZMjmj%`0A)*EE%^
zsfia7QBq54<~=1f#XBuOt8JDRpymZ|DO%pArlKOXQiDtyiWVT$6wND!C~{H0$L;$k
z?EHK<@OqusdA(n+*Yk1SG_>2&ffYIln}+~R8hdZr(GfVHbK)6d%BMOEIBBmT6HpzB
z3H^n+Oym%uuz3=Ibemt&%)8Y%KO070-h3KL&;+?ZcQkUUm9CHAlp$Dyv5*;cs!Bu`
zR|*+Fe}Oj15Z!Fm#`5gx@tH!=xzhMGJl-vH{hLJ{31<TcM2IXYCR(()9f)_x=8FMS
zuy<l;#!wgHi-u{<Sun5z>U*?aWMd6|CWV@VrvDD8!*p<suBZoAQ@?(Z!K*<t(={1-
z#3^au;YU4{<2AN{ayivjKKHVFEv0erdG$s|Ts-#F>Y8f8d-z<)dYl(Sw&n-Q|7m~b
zfw8QGf=p6<D2jBg8(c7HFbf91I9yy_Bz>8K1!x$kA;pFs2^yrM$e7`b^W;Y9kv1Ng
z8J-1`Uk(C@y4oPdsgB!XKu|pzL{sCL(yfCMwRHN|ZiN#sfskec`<DwscSl8@z7~k3
zy%t;dns<Vab>G2_Q;g`lZjW{vyr#8mF8#7baH&q{teet_UYhg@7HjQY2HH~GQIlt3
z(a4xbC?lL@Ylpp1bF;-+pH|?k^-Vxps^XcMkZ($-0nr5Z-E(MvYbaRx_GasWA@Rzv
z=-wYH?>$`>uG7Q_w<z*taJz8bzS#C$AT#<>4%&yIr8y}ni7qsq*%5*NEHw1;zXBf5
z)b-_{>fLOP3mA(!AoUmBoZhQEkFmK=IKS1wq##m#XW^?;nxbF9rH4-I!w1M9^$}q0
z67@yk%OzgyzK9~l+=1m_aI;I>zusuvx18vM(fDfxzywnmyYc<_06%YAbw0HAF8t_>
zrf7Ry!^(?@QFJWhaXGMgNgwIhc{EO)A1iuYZM<iB>@V%IPhs?Rbw2azjqB)GMWg)~
zg?Rk0gP<zLK#sEfPCH7NOR}l1$b;RoI+fYUe7$@*8aC{XkcyENPn*L&Kl*yHA@E;&
z!v-`ZmI-;=dc$Cz@`AA8uR(}>I|?aPBYkkTGiY*Wx^|$v|6BW~8T6Ct{BHTxz!_E&
z;R0yo0ic&`5Yu1tuEW>jxNOi0&uZcrFnR$>sSN~$wxlMiktp6DvBv|l`Djce&?IV>
zSme+{EUzawGa`zu0<L}ctf&3b{i)}m5`U{|jjs~Rh0lL&i*H(4FqHbVVw#6@aLQ!4
zJGoy%8x1E-&4S$X{Xoxaf!l>Tzx<f4CTf>P!f+bt^Pe>RB#l><Y9dyLEj_|KHPI)e
zeIAUM(s5uV!AX2LD7r#+NV_$gBsI){FmCK&&qPLkF<-J1-^s~EmQ7s&{nW_b)@9u=
zMIHHNjd+2sxKo8jRic6!N4_>Kduc~xuC7fuHkpd_vAh#>prdwJW%Q*m3Ui>x&JQDl
z7+n^Tb$6;V7ehW3p$qZ37xuUNMSx9Q4~(%;B|zKO1|v3~4fLG1b}i)Mln|^ah>;WF
zmk@3<S8+a?bZzdUb5q=L$r#{G8L{jBX6O!|oY`+dRWxl_O+r_EvA&KE7jS%>TBa&!
z2?ew%bA@>GS`aXn4s8#;DXleAg<1a6CF!53kkFPLz6JoMotR(Wm9_r10pmx+)vbhq
zkiEevXA;*vuDz`jDxzfC1MIb`Sv8<T1H890eVqGGC|y<O6V5U?6|I*)!uXDje+*w0
z|6EoF<}A=ZTz&=shCyN?iH*?+r3hwbZW|m%bjKR0xSmsu1r55<2e@qb_SpO<XyV%%
z(w4Kh4W^>!RcSFdBg`YSt?QY{?yG^tQqidfZ0*uUh2-P~9~$9h2i^&FXNPdC`xWPL
z${9jV>ywn(F8^k{W8Cn9gWyYO0}m4b`GB!6$7LsbP$xCg!GhO{U)80K2R}6W-?N>&
za}p4{e+$+Gz}t())Q395DD-6`l_+>q00%O=_AfZ|3~0lXVbW2U;Ea4E(84(5^Dk@E
z2COu#o+r3*6PPyGJn`P%JHPl(^t7spP=$|IS|EJaBfaV8fz;I*EC#3kz_$cdt=`^o
zI(SLCeK6)MOY6eEy$=nIDtquVRT0HKWoLuilN8R3&?mE7$+y#aZmr0GakR#HF7xTl
zMF`u6q582%G0$hN?o1zC?8J4U$x2|GTG|se)V~&=tYb0@PKY~Y#PwmuhSt%5v3rR2
z6!=YPC4}#e(6<AO&E1*d>leB0+U|G)bf4N-#0g=(57y;d;UTUO`$4z})uo4slKl1o
z`K0=e<z7c`f<QMJsLri}6Pr6)yxAq@vsX%Zj#v0=<l(-zo&pHtlk|#}ABHS_12V9z
z{ax;n0|&puO&Jw&r8*BVC@iz!eSvl`%Vhtnm3I(%<aY7hh{)w^>6VT3nYMzyFhb%-
zYcIV05UVh|<mx&lioOGsVr){s%dwv0>{;T=LYX8*8C{xr{5ha(4mXlZr~@mrF@FyF
zD_lPZP-$7X?6@C7fUaQFs9r{a$dM-Lyv&U*Y+wIZ^E*Dd05}0hOoVhh?-HzJVZa12
zxz8KvE&J1ch+QPf-JZe@i%z96bsk86gJh19t5dl#3L9@^OciS5x<_eV1<YGN>@2lv
zfwVa<tHK|DWlE~A7M=V~)QP?57!~G*n<@*JLxU-pZ#Vk&t)Zp6QX1D9x~K_ykwNww
z*-f3IrIJnfUhP_q;;|n|c#j^eO<krJiu<0M7Su6OlrRf<5Rh-*k#slM4|ujMsu#vr
zuu30nO3I53S|t`Us{A80wiW8HNUhtMj&T8?@fsO1{j_`rZynv+C;5CrY!MRr%St>5
zywBe!l~hRlk*Y>8d8T<f%*s~`z$g?dZLkLOWIEGoUT^&5^OgYQSlQzglo8eDTsv%H
zvHYbcx5S$>?kR9ixlwFL*D{-p&Hl;lG4>cbQle=-8(<(FCRAExVS9T^`Vtb;Lq1=E
z9bSeogd<sPX^6(IoR@VJ<8jFfg%8|A6KQ+|-u@MX5-Wv*?{}df*W+Wkq3?_D?v*_K
z*FKz0=fZd(aYWmMf?@Qs%%zwV;feY;&$<6IR!p@cYv*1m;!Uy_>;zwGHB>W?R`IXO
zD42&pRS;_J8uLeA4V;AQ#=zAd#siR3d+e-url(e;EVm7<=(bClH5+P6%6q`BeE#RN
zi*I5c-5ZC#yWI(A(R%*wn{#2C&+H<N@(l$|ykicIKppmtS9!B6G?h$<xA&&Go}^>Q
zrEc)fCMiAcW)cU1wM*cQPcIco46AbI&+l&$RU}WS5sU%9mTwwuSnC3^d<az{sOHPe
zKm!>9{dT!yo2VmVx+lZPb=K%re1IN1P5H3!NkJOnM)jmA7@`s{*OD=%D?fQ36Zl|k
z;z^;Eb7zYrbbEZs5PyXNTNNt<M%?!WPB1Y(d$#$vvh9rqEC5pHY}PC=!TwHo((U`m
zmrFGgsbcvs$8kO<`4IbOcEQny^*QXBY~GIHL^o$2?54{Ucp+f~u1CRa;^%jMvfGOM
zcJqzDZZM%L7T@vdNCgE>C=s=s5Eq6vR=iJLu^@qy@9zp`;jF%EFAE@<psQ7RZ~WSn
z$xH4_f!>%1%X>#ZdQfRxm{lqidnb4a6YIAM*AD2$%!Q059wssC2(!B~3SV)y7o1{|
zrvK)LxNZDt5hu=KvmT|68&l3=wW>8d&;ZjHs<%HYW&nXGkETNX|1N{biyNQ**dEWq
zU}#~9U5_W@a$|24U-K8g443c6ImS=axv#c`J=U*m1F+W^F2ni@Fhl&hEQIvhY?h?|
z03DAN#I{)4iN-y7G}lN861MHE;8KJu`zGt;iea#JkaG24hGQ+*8jY=Rjw&}!ijy0x
z79UPij_?KMxa_`~%wdSQpRuS_LwrDPTgz^O6LwCP&YR98P-*2@mpDD4T_kJrvZX^U
zn3IGJC<WfhCT#sj!0O5oSLS?#7f%4+GNd3LB~S4}Xcv~-=YxLQtk%^B(w(a1uJ#9N
zj$EOH#>aEI;mtoBL5F9keFwyjqVIt)zp>Y1(W=89l~zrQlTvNcgI0gW_tzdcb{#yi
zJYeW`pbHg<?W|GBM0!V1!d3U&QAHN_?xt_Z{&lKVBwlbFkYp<1*ozfYVexSj*8%=8
z(mJOZFQb)MrR2sMlg82a`z^H6Cfp*E=t<h0Vds(yC3#|>s72YuCz)Rlth8p2t{a9U
zfta6s<KE*WbfU43&R2{Y4n7ZE@S}nGt}nc71Wt6#LDRNh9hjM<ikBwlv*qn$zx`Fm
zlyPm-rz9*2Mz?TbgK9%ZkG*SJ?Lrt)$Mlb@I~Z<7TgSsg_e0CT%d0QiY=7^^JKZZ$
zW?{c%X&t)ex5qihg>GBJwL5zKG*)DkaX~&}f6c#Cd!>!4vX+788|F2O#45z?!kx-&
zxj~53PxklbJIVG#<*;qZB0tOz30P(D0P>p{82iI30fL3{p$_|ts~L+u#!lAscpT42
z=v74as5zY3SqK%6;>X2cEbm8;(zf;NU_#Lp3TOjLr`~|n`^g7~G(iT2z)5$!i7gN(
zfu9(Y1&}=8-F+R>hjZB<6=aQ1B%)2TB+G{Cly2g5VC=^e^uf&8JY2SOWa1<_WqsPv
z(MNpC5UJNTWXy5ddKv*~OT-E__f!DL%@VnAiL8Hrb2rZt6qs32HdTFs5q1vKwA^`B
z4(_lvlmU6U=ZI@4z#u$)Gy=%O7RHpnoEfq_h?ABc7KOHERvvWcnhCS7-pxdX(N%a5
zTebw*3_$jZkn~fZ#IMhqC+K!us@U8;*N@NGh)O^j2P|gQTb>C-|I@m>P#~TTGmT}w
zp0we7Q2x;awslYwdh-F(EHo-h^qbPF_U>Mt+MIj<HO~Ez@-DY<<)RkAIHcxg>P6lY
zKg2(HIJtyL%~~@PK9yw`jGzHbi{&gm0dlW{LeCGgIyjH={1#@mB1W;Ow`=qd&YBV;
ze`?<jRP!_c`~PZQ?*d4a@NYF=eit1Mi0#d0ouW2KNM{|7#!;vsZ0WoC5fQkmLwld8
z=x~vEGv+QZTKWE#w%W8H(mU5`!{d5zia(=ds@a$9VfV2PfBYHk-YY2}*>~TTyAf@^
z%O(R;7vUrDOsxio=HmGt0CXL$B)RU_eUTr`%T3BlrJgcUTl*v6h<TH5DX`0=xlYt)
zSp$cVOj&(B9x_bCgN_l2pFJ}-j|;t?#a;?`n@wstEI23TMoeX$8#vWb9=FmUr6*k?
zh;@x3zY<R$=GrhqK>1bSZidVt&`3EPxk9L)%DOHRt$O>DTJ*|UTh_QUX8<x-RNj(%
zb3YV7_Z@Dl&Xw++q=1lp&Cor^a=sU8E+l~r(q}s+y#0M;kCakSNj;L(di|phY?`X%
zsuvP~RZ8l&$Bb$%^tu(G-lYkfK1lUqWIvBy<Di2Wrf;Ij7N)Bu5%{1xao;QJ3`y}_
zeVT!u&|}0_*hYDv=MUjHv5V4_ZUUP&r6Q~a^|*_tDtkk++z{PX^c1Xak<E^w!N6?+
zS%JCfa-a&YmQ;=J14W}4y-jzjhnf|`hALcZ^C{Rigf%poY1rv2{^)Ccy{i~Vhx{^|
z=EeL#`4DSfNyqD}Css_6Z27s4@25jVxXIA6s;n?ZyII=;4K}?u7lOt3W$3?qkZk4T
zv@xqw(C9M#C+{t*NKXWjU5Vrn#}taE_$LWl13S-dzhWfkz<vBr=VxjwbliB!VRJCT
z+*Q@rQoRt0XfrSof#o&9RC%F~w|0*F^w_aRAAYh1n9H>Z4+85!!8MBId@5Y7Xyv#t
z3Yi~1i0pc)2q`70=mc>3_ci5Y{kJC@w@!~vkyoW7-zA5QMZ_~Cwmv`s(TJT{p(@-?
z^ru<AC+2dGvCeD&A38l~QLTZdmF1rQ_|ic~W2j*lxxygL#Hmu)(GW#5R6S%3R|QV*
z^Jio%-N!r8nBJ<3ou?M>zSWzC!Z>MBclld1(Cu}$Bu|&UIE25v`x!(Nl)wIF&Xm8=
zD$huGyY?Zi%<1hK-edhRjz<Q58*tbrK2;MD0ivlbKyCi}#G7a@PKe7E4q55IRulDm
z^K~`r)hEz!ED)vzh63L`)8VAn%kO(|%1p@dZ?>-RA0O+g#pmB{(MmTfRn3>K{KHxP
g$6xVZT+U7X*_kS{eU*d%ycF#1;r~O^naj8S4>g%xH~;_u

literal 26290
zcmeFYcUY5Iw>J!S$3Yx@9Hfs!gt38i>6RHV(v;p25s(t9p+gu^7^P`IK&k}=5+L-N
z5ELbbs$fD1Bp@Y{0D(Y=BqaIn#CgtnzH`ody??!byzlkox+JjfeXqUtT5GTMTWfD0
zT(dHh_(A3eF)=X-@Ri?f#Kd+-i;3;Lxo;0ZA>}?_1-|y&G&lPl_y#^(mpfp<*Z$Be
z&Jki_@~Ogq;#u=+K4N0Oh=G5<Xct|yG!;{HE6SZA7`>%@VEoFJUv6Bum0G*c<FC+*
z4o~cN{jt;Iy7)!Khrfin&z#yPb?B!<cB;?z#a{dq`68*l_u$<#KlG}~>>B_5*T8%W
z4XlaPeBRBk2#*Cj$I*@Tt>_{%7`=$GRbuUEZLiPZ6WRX|iS_Rv|7SGN6%gm&1j|@H
z+IQw*$TW4ASXEU*pDHj{<_#|5<>(leXq<~1+ArqhY<9Ney9mvuT?d4})xOw;7Ew-L
z{U>4amV}tt&(woL4oOOdNm61?C*qI|NJdq#i+e-++gk!Dgten$rLhywo5jb^ss%Ci
zDK=9+;nfYu(++XUvjTTkCe0^%O;VwU-H>ZUIEyT;2zD&K#=FegrnM3Be2XHGG(G`>
zutmaZJZyQ1tdi~uYCmm|&|=*j5**zrz{J|JdT8$nFohoNpCRQ&<*s3iDKNGG!)d0)
z5*`q!NNlx-Gw%-;9#RwdY11yxKf2npV}{!q*&WDa5>68SOo71!W2+G{J}e^bw0T98
z!C7Wgn>(WkSZkMRxYgxrVW9^|)w<Cc4M<7I%}Hy=sL6Ni)rkOs4C%Bo&N_eNDHMZp
zpoD$M&#NXkSrw}xGN*nowjV{2F@vomqyw^-J}ftnl#b-p*ivF^KCj~LW$@+rRWp+R
zNDIGD<R@<G?)p=9ElqP!kd-19HjzM@2;|>jeWlsRRxQqJ_IxXp=CS*~DQr4sxlvuz
z^25l5x=!>Lo`s|}520O_G$cdl$A?TWr6v#>B)zvC8|!{qv;?>n(ppoTU&*$SEtO~8
zo4xi>O$TEx%XSFfUMGY=)?&VeL)RtTye$lPdQ(B6(RD@ywUFA(%Pl;5dO|DNaJGGD
zs;ezd=I&(EpujV5!=xyQeS@`@Qo0!1e0X)Rec|P38?kioJOe^6YLQ0`{DB?a0Z7yF
zvB?14<%Rh;Q<37Fn;Mr9Wsj>`$MpZ8L>XT2_2-*)<A^5hco{~sRYIJMuav?{#M(05
z%}*poKo>XNlBkY3t3QnyNvC^^9YPF6Bui5jf}FyqucgYaDpVPN{C06Waxa>+HsQ7r
zcNUnj*4zlwlzh)mC;lJ2uPLkEA(t;iCDY0|oUAcQ?BF-ae09D<v!^FD?&(p}U>{Pi
zS;dfhd1u^^9=nO)EYVP$g{UE(ONLf=3hd>vgH1j4fT^l?p((I@3hu>DvNr-X(7IVn
z)MMi{2e~9^p4!1^6VkbtTgnoe;cAIfiuT2yYx{dBgU7fxSSG~E!2TrmW4c@5X>mp*
zT1Sfa-9|Qw4B4Hju#kN+#EAPeDxz@Ud}2U%Rk!mSe46ZAI7Z?P{wbkhVZ+499cpDn
zzyCO-z>QIn!g5zcTM3dSbo1LuWrFL37M*+N8#txvFPpPfysM#ueJ*i(<~n^*_S`wW
zq3--hW`s+j<U#A8$mrO$%~e{j`{7o3SNE4Q6i%LNe3vWhopjTHt827p1Gc53|2$Qd
z5Y@6a|A&XR_qHS|VkKXkfvSqA9@v8>u_oN+{?_im57&*)4SzEM1|<{L3*>{gIYsLw
z=c%#lZ$C)t?utz1l<QIDs)Cz(^r-!dus&dlLcC#!#7JTPc|Rq^QN?bnM61B{H#6uH
z`}xS@W5v{Ov@rUHbT^^UomwRun}AE_b6sIE*%|tQR_@+=*4B5BQ&G0FzB|dw(GXP*
z=4ZgjWAA+jzHy(XjAy3Kditd&)Xpe<XpkE{bhR2v9xX5;g?b!|!AVmM69@Ge1!05p
z)S?K6G<ADDov&8<NpLf+Wz{IdU&W-nX={45JEA6{LvPeMh20WaY-0FQu<o}Z82Bkr
zKe9*rphHW31Q}3-TJ?7cVj-$bKmw;{8S$U@i`{riHuM(QBGq*6DZ=cG%4S|p`Zd>4
z!ZIP7K_6PQs&M*!TZA-UIfK{wke#C@pJjcQ1bKSY?Ui(1X<@|R$-pIp=5cfL?fIg*
zfP9H>b<()t&cU{{=6>p()0zJH(jB?hP5g}UW)f;(Ot2of)L{ojN%I6l=jZL9Z;HGf
zrPuV=-L$?qk+vq>J_Gity}?iZQEcfBwq<Y)sndTf-lm&Vs)*{G2=#szQW!y=r^=D8
zJkmLBST&RKd*RwvN|WuVhMH46JDn#rsOMKr5iA%<SqJ7<8-{Qfq^XJAqA)4Q_|T$*
zo7Z5SlurNaNihFr51+1k4>r;nVM>u~<a1iJ4RN|bP7d=i61sNWJKcf2kFcx;H~*cZ
z(%pdKB0F`YSb)=?$p^B**rLZ+&yHvQI6F3qJbs8+Qfpf3Out7w<W@z^j>sHvC3~G+
z@5ILZN$~SqqAW3@*Gtk~$`I>{TLv^3DG_PSOMqe)cyeT{#3-c-Wd}PqJ0_{X{PsqZ
zGf=QQzV0QW^!C1aiaXDWby-4ueqJWm{3*YsHEPf)&xPt@@aew@bI{IDmpj`{Z?o*S
z=+2njpQyPo@OpBosZ)bGh8|DyEUbf-Z)ZkrA4VmV8J8Oj*!nHOobu2It^7ARZyJ2X
zM^}rmtzP?6>vG?W9bCV(P@sqo&TH~0G++r`B-PVSJjp)Pqgw(VY-5D<l9(K5b??UY
zlqYyu2`-y>bmqn=mY239GF)<bpSS`SCQHgosOx^No?9RZF;)btFs|$2Ya#=!CZdYN
zRvHQw-wE!pj9k?q{+Z6yZv-86VkA~aN*MM<+I2d?s8INLj=RK^xcH8TDA%f**Bf#_
z7_(iemk@&a(<NA)I@Tt89;Wh??H2FZosO;!TE7(9`3YUxE!n8)XR1)8U@$wDW!cFe
zlE(QB@T+G8wk7AN-_&%fpa~mgjQ4_t%>g~Xjv{^P*e8N>;jutx)POoAP&{e(k<}ey
z$gelLC34A==MJnMCqQM0bK76jBZ8wp%&r$=h$~TA0;L(81GWEr<i&>3u&K&U;?lKP
z;y~Ro%4h+P!S1K!lAQW{x7X5nCL`z4qN=5fODo8le*K!V#8LOYAxA8EHDvRGr-^GE
zQP108Y}LiHuZda|MYr_u8wbQ7|5FSz_>d0wwPoOLlXc&H#{PqKW?EeDG3>~;wnr4g
zdFVZlw4IS{RiO|OJ-_6VRT{B0<O<t*^b+ZL3{@nt70-feZ<b!e-09KF3g&Cbln>}_
zBJ^;<#?-MfzJCv&5xlG&oE7M_d7~lnOJGC;01LWy?2i^tDqgurh-zAsJ>s6w!<VeC
zV{AqAv{`rezdH@Z;|hi<N{EM62bmiZpIeU@VVjZ{U#j4U9btBa2NH_Kf_Rqg?2~3B
z`dNEuPo1r-Z-j9p=e6pVd!py%)woicx5Uh1BsW8vi&FO%93V<F%p)>+Y-z4Ff`7X>
z47ZUh&CLqf_gwAZEZadB!S8Hm{#?`G4&>m@?>TsEMAnzHj4HW4RmWLKd3sw41#T~I
z4`Ln3?hJ&ql&bT0Qi~eKm+|y?wUB}^GNoUSsv1!f)sr5P`7~lMnd(MQj2MiAUH%qf
zlV`{<)7>>4b$cK2H8*6_G{Ut>2xUT^cg7925lx$U5j`F`7zy27q$>%q=0Ddm{r9!|
zCBUL8Rkau6g1W<Zm%35g*Y7f_w~K-s-Y}wGl1K|~fd(FhX^E)aKwyuoOuL-gJ#IX{
z<Q!~fk2*FZBN#iRHPA59{lQ3LhIGv9Nm5kplj%@v)}-F3Yly2Ukj46Bt3rrBPF^;O
zNuIR~5yTDF)$Jr>2-uMN&O9A1&)1`?<DY=!z|9AH#HO#bV2@ubVAZkHWOiVkbstXr
zrE5nahh8qTkF4eVNo&b-|DYOG>(zGO=S)-R`I3%5jZ0Dd$mCg`O+(1N)?AYsg|-L$
zRQg+AN>JEYRMiT3dtSxY`Tpoatk50q?gQLGX}nK{h~CbFa@?=+u60Wk5a|ZX5d^kF
zOHC*GJ}2_U0c2@qjVmVM02Nz4GfQe9diZ#&A`)M<2e1k(i`Wlou?H$|CjeWZ-KrGV
zv{rbi#M2!+Bl#OWCLGFWHgSvh?zSWhdX`ff5C*#F(IA3VA|!Z27<ZCH0RD!&WYD)U
zR+9>eC3^{VTDNL<%%yI#yMhKzXt&LNRE>vcLTvVEY~QUVq|UAuUH|;2aqm;IuE5%l
zJH?oFn^g<yynLq~e@&2}aOY)+pV7`N3WBa>NTurD(^CxCmr0&jMaKtAzgKv}f8~`h
z-lw(efdDf@4aKC$`XzMEyyT&RzwHyW^_^T7u=7)DLvjbmyOz;32MNl$q+<(mFd+Y|
zfKNL?P>clEwx;htg9?aN<Gi79WfDzQ90YCvKws*?tc|_!C?su(FcaV$|MN$#a`TL^
zG>N>Qtduz)H8!v|xl>H;O{PnvxaftT3?TCWpBsMw`&2~v_mBU*1}cO~b$Ba*jB_;0
zI7i%1Xt84>`H*{SVmDgGY>g^rurJN-w%Dn;ME(RY>v(ejk*pLlG)ZKLi8)X*WCU1T
z-{htvE|c@4I8Zh<hK%p7J)GDX8_^VXuWrR*s-CmD&s)4JI6R&p?N{`P4HK}{I&!<2
z{eH#`JNF)xn_BJoI-2V)DJE8>A+9DH7R>tmxCaPcN$jpGv*KMf;he?tsI0h2?{Ocw
zT#A54VRVUOfgVTKu^uPeqr&ASoj3#}CE!!_o4A_ryX)cM|9c4&ElWFYTSMKT@l7r5
zwVbQaL}=&(sK}iSijtzZ%ay@yKy>HR_C#<xTR06g6cUS6b%}<GR^EGPH5kRHOl)cY
zvWM#ltLFu<@U)ABQeR(@W*%urxAc|JBoWx4$X4!qnr1dCu<a7-1<jfnu~W$4@IQj{
za1JIqtZ%l2ZUo(4MQ2;is%9`h-YB46&`ucA=_g~xBps~XqTh?=%T$?mM{6QIi(<Dw
zq&TC2JXOgFr?k$!eS_z-?XlX6f<9ZEqK$iu`n<?Q$V*)v(d@zi6HKffbVeE1S`q8U
znyXA;oAkE}873VSGBmJkQjEy=HVW=(70sdw0*G|WCKG7BcS7X95_z`Em{dLD#t?nG
zTTaxha0=T&ejnFu$887b(AN_ohW&N_$r)P|VA7%Qn>H(Fqz9`G6RI`I{R$w{5CcB{
zk6=4Yb^9hDl`kuowuGNOfH0AHr7wK@hR?EVq<BZy7C|04YNbG_jfJt)2<wOz-O2c6
zbjoz3C-f?GE9C;)gFuj!UXMed&a{V$Ui3S%8k{w19~JE6F!M#W+Wl4RJ7rv3g?@SL
z``b@MW<A|^Sb{`FA^IG$YBocdlLrtuM?X73$lTu=@!7-J<ixX1OB`otBVFxAt7{zG
zEA?x<z^T@DiZ#a9F#b$v!}D{(*+TCfSyjdDCYV_22>(nv`_pRuPm<M%O<}nk=OdY{
z{-1h;W=L|k?CLWuaws-r=HqH8!gMT_Zw8PI(Xm0Z{nH5mOd*(=9TO=uQd^usYx8gN
z8&0I_c(D7FGG=|lgm0WX3_OUr4BaTh_<5KKDbpWxddyZti*nlW-}YkQsw(-Yse<)y
zz+M*<v)e&l4h_zm{0GoT0`#lpoBa14){Jf$-HS*&7?Ix4^V=@rL#G75q}&To9+*o)
zpcEFOxeaq7Z+&z^rn#H`dcx7gK^U9Fx*nSWrVJ?z@bvSy7ZSwG<ir_YFj0tv*;n==
zx#Yrjq0`tX+htc(<UZku=?}BYfobgfw7n|JJC)L-Cii&@$zo<=;*8DcPk(v417(+x
z(Dg&Z@1j}%uMRK&`^YE^Ku-JfDuYMQ!w$7B%ppyLT|%)gufu>ZVcn+!=hRUqOrWz2
zbYSTFqR0R8v*=ur_o|ZB>Hos}3dDNh;2Py=A0-cy@t)N=Ad;skW$YD>@5qH-6IsC_
z=f7m?e-r-y$OX_xl%GM3e%(aF(j}qll8OP>##kX&JdDcp%k>j7jN_18N1)<&NvPAm
zhC#Q_Z|3Y54y?Khpu!tcF}GQTflIJZ)@Lv`pO!%wPu<W%wDWFpU(#@hw4ZHG2*=iH
z^iNMIm=Gprf(aq^QN@k0@{k{+B%uu2btQr*DpZ{V_D`{s5rnqZMo31KRvK%JvnOc?
zLYB1xwyxc@J6s|TiL^&Om6`oXqR&XkRIDREDQc~=#k_>db{GxX7Z2;uN5;BBud=>W
z8n;09!PE+Z6d_I}GsU*EWNqOKHynYeMP^$yDLO5e#>QXlwjd+u)}6)`Gp_?u+d~nI
z=d+^N(D4Xh9IQo1WXs?vaUW)}g_^{TE!~1~QJfk(x_P!E$$k2v?Xp^R*!K2i1(?sE
z(@4V5WcVM2QROL5-+OOgoOa1Zm8YL0zHmpz#zWuMACP`?UqW{yZma%)c#>sn2z1Ct
zN*yk^{Y{HV&2<L?gxSx40^G03>-sY)4=RMm56NUnE;l$(4oG4fB|03tWxBlEhd<1o
zeTzYMobCAQr)Bf516=`IP0j4AiL}jnT1=nn4xmGx21ssf$7=y|8)*%xa@dXIS)(+7
zjig+nzwG7_LIeV%l+MZ~Od(-%UR8gPT%oB3lg~-y&E~!upFU&e<E#xKU_z6{os8m;
z3TuCv-C^QY03dZX?#%&@j|P;jK_=xU(30;`dz!{3s_u_wIPqqV<3gf7%yu|nO}wiN
zteYr_-(Eq+91)}GT10sjPrm^ha#&6D`_qxLvXtm?pP=#11dj!=s^$<LF=V-M#L6{N
z+I5rWh>aZwyd`v3M0V4A?nve42$F7P)alDw0&@ixoyJW~CJ&=~=C;##<4(Ipc0z)M
zQMkfb-CvPXtM$eVDe0(KWfh#C!TlH@C>t_EdR7E$j4L?s0-?jViq=orUKDvj-un3R
z`;LlsTh@x|{?8~4==NJ$5e-SmvL8ZaRw|hI^~dQ<qDARq?7h%&8I?7*n1~FXpLYLX
zHD{Iv8<h5gb?c*1$J&fV9Ctr`(~)3oH`S{Ds@;O+NuUzHvI_~NcI=wq>&4eF*jRTK
zJmqPpaIDx4@`>{y^#kh<7Suk82seNN68I$9pO_YH1*hvfJVb<}|1S`{ccX_;g9>4W
zqO}s$9e#<+qCp5MK(VT-IV^%*V*Tef(@A~y*44(2nAZ039W|oZePQ-KYP3SZJ;5h{
z!gdI$G`2Nr)rXV?iXMK97H6~(P<DAW!H$?%+L%AV79ptcaGARKjxO?zB^U3#)q4@x
z%1)$LTS}_Vt`4b=2v!fiGu1v%ev2cLrlaRq0BkhJhC@xEku01o&Z@d(tTw*859w+j
zC*>sC?Oi|l8RTAVt>ARfQW4omMWnGlbbFIPM+mACpSN%8d=?>xT4r<H*-15rq3Rm)
zp$#j8w%B+84_gAKO-3YVIpJhor^Z*3*k4tj)^UsUc)=c+&e#y>llzV$lh@wegp5FI
zT#TN@UV-)>$C2HP5XV1aV!H=MR}Azbr)b@TCo;a9^>T3tO<Ahs4wZ~^LZ3O)ElsHF
zQ@y!M3gqY9M^4$Ey%Ordy6+?PX4C0bS6n5nh9C&o7f_z?ijzTeCaApL#G=|gFHdvf
zv^^4PHD=no-krgP*qdlF^GD@kgcfotE3HY48~-Ey>kPp=&;8xO^$D6muKT+yoel}E
zmx(<;1urE<Swnd#=T<utX48jS?63})*ZSxDin6rtbqn-k;@P{<eH-MI()Z+_dh}m;
z{mg)6oD=rfgYsk@ith}A-Dx&?E|F(lBl%lY_I<VZ=QL9w^6YKDPFO52C0{p#uo16k
z^%&^&2`o%dM}Qt)UHJj<p>jK_T~<X+^oP22q>95(6(4fMJ2426!11!j&BPLK$#!QW
zORJ5IhJ|>c7NW44wD%6xfD$8&Hgrz`M}buV95<%RFY67(Sm6|5+x2~8^`1O}=W<3!
z{A_*S4cUyNtBJ2?_5_TOiM5Syk+Q_d@v7uhSJ7c{*W;k26Ed5+{lo-QPm`Td$Q9$a
z$AF1|uw7QSgNzMy%I7|TK$=(1s40Y<5z>>CdY!}>17<QJJdz~gGUdACyI+QT4@^(F
z8ifuUqheR~3~?0+i`knDyS=6e(a`C?bRD|55Q44pvRTj<v?hX5)JVCGi7AT}vK7mW
z_CEV*)yQ@kOMVxF$`7DG%UnbcBJTr&xLP-v)IV6~`yiH0AtG|&Ift&e>dMVL-7vCa
z`GjAF-FL695z>3@xGyY=qCyb~i;n|D+iL7+&kxZingrv>BUE`K0{`umCJ~z@p$$Tc
z13bNtP~CkHycQ*bXf?H`(~qup1|WD5h36Sww#$y0|H7YJZo3@k7&XzQ*HW!;j`c0e
z3?cIN8;-|LPxFRopKP-{9gJ%9tqv;U{&^PwlF^x0so;$94WNWLl*&{JT?Z&5yeNNp
z8@0D5KsV>s)U2AV9R%0h?Ggf^3&*LL2F|OW=prcr=?mVtLNBu9fpf}n*nQeTi9Q`U
znHBy;=sLblG$iP4zIX_MPfH{?6M$0|)F*0Rw)be|ifaO7PDV5$^1gcB?fgwX?M?Rb
zJ=z?euhtm(^}EuP&z_nt-kiVJE*kh!Htu`m7HfX+Us2}g7sP4IC1?iiAt4OV%y`<#
z@5cS08QB#_qDd17EzzBSzm(kL_F{&L7;R*Qim+Ld<*~p$Kn12^VZ^s!<<sN|QpjW_
z_wSDM0I+gtNdUIG-KPmwRwpu=Q@4}3)?^|gfpYKrf%*Z!h2rKbH&abzjtE6f(gUn2
zNI)daT=5o}?HmqpPEV^QYdL=l(OMP&izJZkxVcIhB5>Cw2iTiYQKIQD@PKY#`3{TD
zbvpsN%W{M{xOc`nLU}*oM2Tzq5Z6?Ox;wkhD&MfvfmK3VCy<b8A)!SK#mW+;H?NSI
z2qQu{Lf{gtC!DLxN)wQdP5TOT#a*Tn%L147#32~Sl56g)N8XB63NAcCq?E`{mMpwg
zKXpxHR(lwam*|vJ$h0A(U{p7yL%aWBqBCo9ds4_Rsi*Kw-0+oIxd*|-6Ak%-cq=T<
z9zboKZ2P?cfLP`}rtQq;i)L2UAzFMyWi-k^SOLHtFWuPB!9<t!ElQTC*%=JQ(7w@5
z0!;+hVx5~AqVYd4uM6IH5bU_#nw-f91P@LtN1`m2xdE(4v_EXQE9zJOZiXBNV5N5X
zglF+5>OtFO&jL+iclksVDf|x+o1eU@w4)`V4y>IL4UQ;Jcc8=ZuF}}CCi7{d$#5-k
z>G9XEm766*zHx{afxlW*XRJ@?T$}kv6L_>{(7(uJ-((PzH3k&a`F_^j?el?Ct>H&8
zm>J(fv97^8c2ElKYM<&YPzb`K$*Q!_k%EzE3QDzh973o%MIIL`)CL^Rg|)4-xd*mR
zi^<+}m(5i^f0!2mO%_4Kzzw}%gb~9;Sqkt5qyBcC+3N(!Ov44Jse<J6H9z|y=g6Ow
z+Ml)GX)ux4A$DfeQf9|nrT309!4-+0ii}}4?qPAg<39@kqt&O0ja8}dB=^|^80_gp
z6!~~oIi@x?;PTh4M%pX+rw!4bgcIV7Wu&0Pw6w*6qUWzTvLribIkbmA_ZtDykFsM_
zZ)Tn&$|DLL7_wexl47i?^Z|6(-siqjzvObTZ@q+#J}7rS^iDTS3AiUxt6A&>-Rkq~
zBvJUAeKqs7hU~VX>KR{39C0!BCIPboMP=$O<U`w6+)!~-bba<@MBu(gafd;`^-EHu
zTcuLlP!%g9hc2x!=*jprc>rk`y#k;@1F|K<ZpeyBC3Gt<t2nCFbl$mAN{s|}#ZA%N
zHGA5LcHAf4M_a`KSJfPLD%aYD=9<Y!-l*yG*sSM$&EkKp?>m1+E%sBIe&}>peV>?^
z&h!tCt#5k;D{HB+Z>jQzt!rD3Y&6x#V6luaU^-;Po=n^6k@Q51Xh{s7IP}p0)6pIw
zTj<3ZwO#JWvg@;2&!u^1Z!Qu7IX&1PI-haAmGS0?{L_miv<E-{)%vHe$^bF-apmSt
zE~n`xb*rUv^Q<Ytr(Dk|Vd(R0gX~+z-Y9%J^?*!p!Db~bSEA3cNF6%pBTGEp`7Od&
zF(l6KdneQ2BcPt@@(G6V+!%TYYJpOU?tUCF{(w9(nmVnbb;fdSi^d}yk$FqPY2CxH
zZ|{r4x+Ju5ETAZSo0Qh%vtaTi_V>MZjwpksXb_BHY2I?Wm*fq`6}9`(31Lv?XMUh{
zbCVpWC?4JJo_A(TI9zP$CXg13JN?JVKtfuwi_@*T&7Yu!6D}i%AYaf~I{h@vRo3&}
zpGAS8>RFxdYG;!<@u^WYXXA5SjQM^y2!fXj2)fuw3{`3>*NPYDR@K<LW7<E@eN&OW
zG$pBfCT6YFyi%0T$36k^&~qH{#$trY*mMlLGyoe^=Hl@F4NWQgdz)ya$K}P$Zrf!Q
zdlf6A676Jm70s`lv()@OW>41wX)m#vL&@GeN$8Ntm3Qjvq?HijUT+#Ta=!d)&a}^3
zf<y11u47PMXUVhW9CW)rmNTo`CQYIvC<=84g=+`W8*ruL0+dekx8<rvT`46&!L_ce
z4nmZpRHl<C?swIk^X(F5wr4;|?2Q}(^44jIH!Dy?X-U!dUXk*$>&I4sjZu8P{AjHz
z<GKWW$tCc6t-mYnhv@QR&x8Qk8qwHVWIE=1he?d|`#&#Q3D;{%DEX-;X7umI)c2f^
z%vA6yQonTbyP$6li_{}_I-p7#%?|eVS_>mcAh`A_mYY0N*2-g=aG2Pbwot#dc)p3J
zQDy86Xen(JIrJaMqqgdVjB15~9j0C%6B{>se?at3$6JljX(K12pyAE{Xx0hhcX$z=
zn~>ckY1IU~@pqrC>Y2}8w3I~?HsW6iX4XyCPA*49j85Mk7iU-k9_;ZSgs0*R;9LP{
z9s8LhL>jnpVp-O|sI+P`_!*^yrUBgka<yKjcp7>CE0`+ZWYHIcd+rmK5dyjY03uJ#
zwTc{#p6kDi9Xb3RbvS_m*9A`Fx9(?*c_wsnZbC!L=%Q(s^nom@DD5vQ0K{}|1MiAN
z3_bSW?z#Pw5KvWEHPtu)M3x3L^v$e@O;rw%^mXmbvb2b2(hfcn<$Q;SC%(fKhtE!$
z(XeM;Da!+h($Wb*4{!p8LnIS)9itGzngl91J5e-e&|6{3w{t}BLz&+O2zmq+;#clF
z+7E<vnY-lDs>E{6*Z=g}v!6nPSSz$L8gOlB*=1(sy|HtGBWn;ykPPGz)Biwm08G&%
z_-AkOkOy=^2k&QhG6MFcz*=K_uCUJ4lvXK_g^j(9Ir==%;Y~uZ0|`7~DPin5b+&ZX
zE2_=Co6!x_0>_okU*m;$mv6SdL+NhAh8|Bh5ZHEt=A}zX_w|}$|9~z4-4lEtV(*!b
zeNOiPRWJ$z1U%)zIEEcJcz<*hBGA!ME~Y;l$XH43)+|e)AOcZ7uRi!iT&)8?g+L0s
zB~zN>jOQz^#hpvPnlgd1Y+W-&X?&bu3CQ!;N@-G_(q}+BXUiwJ08IX~ax*I3?!au^
zmF}Qqb>CIvdd?cHTPb~amrMzNqdaQDzr30fmKw_$DJ2JbawmN9#LXj0xs&Av<h9H`
zRqsCFD{AAOilNuI&(duyI}SUFS1Cm=X*4fYS|<dA#Eul^y6>sA3=U0}-T2GQ@TRav
zd6T@?0Ju^FJUAm1W#hbxo#(`K49>DCOQG`T9^mYFYEC_9gR)1=^$xAFJd(4BG`P6b
zI-BW=JtFtU%5d%7lSBzm&TyUwc0@JmX`kC`TPr;uA?G@rLdVkKB*>k9bzId3EbpL#
zVe8W28#6ua_zBn76D_27F)alF*sy<feX4a+sja*3PO4iw$`iF{{huZWl1w&mngfJA
zGnti&c4GYjF2cZy<A%O4O+^X}Y|j3@nVkYxS5QEei#q<<sEN}@djZt^du54X7zgM`
zr?FFKZTlZk{(m~$ZnSp)2aGAJp4Oh7U}mpd7!<e_3N8N+&<+4xx1~uMhX9cw&SOF|
zjHMjZs({Mg6!jf5-#E}y^d_z@nLt-2Njj?7so2LM)EqnuHbk3c`rMgcr;8(NkALJ1
z-$i|of%N~3fw!(}Mh_tppS9a}7ohVBmQ2DBLr;+2%F9`|#=o~_ETs*mjjCrB0w|b2
zJ9SYdy8e`=Sp0|YwUOK>A%G8;&<rc@o?2hnfO)anz+8U`U9a!4CK(8ZY_0w-BOm6s
zqXb?1atA5kX50nVS1`AiF#fNF6uHrS$3&E4RE=@lpX>ZsL<0L?%QDz41h0$sR-%L*
z0-P!U9=a;1i=u`NyFoGEOxDkgJMI7--~Z=@Lx+F7n{>hH?ysuqyKe2hexd8HyC0a|
z+{%6R4}W<Us<cmPYk&O@A5?dY9&p?5r2+y;f%kzwbQ;INRq&d?-RU7@pz;XG!rsww
zh|7y51R~0<OCu0;JLqVOb+K?kT`AWbli01joE8Vemh*E^(rG&OnytIY?mG9{cc5F4
zRh}(9hLOnU3L>Jcsk=Bi@D#mJC3>pn2`eH1@x7W7@e$Y&C7sqL2alsVF8lD1+TXju
zH!fG4?&7xfazd<D+shq^CeHsl0dR*9dmQia1eYAUs_<k^?}^q9v}mRyz*}))aK}v|
z+nhN7ijsa5X%5Go*b1J94OxamEx(^`jvb=?u&Rvj8&))9e*l?W3HO2<o$@y>oofd%
z%wZbMYA3cjWauKtsXhK#dK#mS4+E2JwkXw(Wm``+c6NZ7M3AIqxNQbYG%LXGM}VIM
zxTJXQFA(Z#_|_@^8q57l<P&&BmWRj(y6!b=<uJ0c(Ty!jxf-gB*EwO48BHQJTuTO2
z76)?vS{>rLHVFkMI@pTJB(aUpN2f8r;)!I=+uD1gi9WxqQIEESi(K-Y4|I3*P)!y6
zjfl&+-N2tF?#`6**M=g=kG-f7W9&Uem#jHyEqXacs8LTazq*B=`Src%rH`$^)L`ZZ
zP^oqJ1-K$;YaZkp(61;Qp7gMNM=gUf-_*eV_6Q_+Jog$zv1DeGIlpC9RT_&JMxcNh
zl;A<`^Mb<cNleSRHi~(SWcFM!#6i>&%Qs57mJbQBHVIYzjxo;IIwnOi3Ya4KZdd>U
zqfjSHWA04rd$<<wVoYz~&U=K<=rcv@(+@r?PF8uX7#(k&0e$L7^<}L+P=WGK30y0V
zdkW`&_L6vbI>C`6-gi;l<Khh=DJex;8$WUW;&jGB3P{T0TLH;|2rDR*{N82_x?#CH
zda*+bfAOR^18c=3rYlxVJx+YKUCTl0G#h14n)Kmzc_2wFzKq9R=UNz?+vxtFz%6$!
z2b6YsILJybmz{q#qX23PS5s+gUViuyaTc685Z|~cYdf*A*?fA3ZRBVp*QmvcV!oIo
zE_Tj-2YSHZS3Cu@)eQ;(Gt)tm)=Ub-A>fiq`Hvmf%S%yuCQfNE^Zf@0(vHC6Z+=)G
zy2|W1N$`;&7ti4#j&~i5ROWFbF>6`i5@O_u8xJ!r_CFj<OS4rr?W|mWC?+PGCKj^Z
ztP<l3k5b;cef7q=8JKT~N7}|N^|uSu@QEie0wR6zBqn;O^0=B4>N0#T-#Zf@+_6lI
zbSEz1O!#4{o!`EcwHRHNgM(5PVs8gW6q2+&P_Nx$=AUfMNW${d$sP-o<KXZyuhjYJ
zWXa$T)xgO2M;+(-HHR!3{dG!6AiPK1@{^aD`iSNBs=kxuMQ~8d)wa&VKp*?{)ugCc
zE6Z>$NX?r0f=np~>8UqRB4#u(uMUP-EQUdSEVtgYV^;60E!_$IDiIQW*?8@NmwZ&7
z0sq_V`WVi*`HhS=U6%6A)MNFoRHM6u?}z;=MUTvkXxAZ*-UC*8^Ev3Rf2aj?$b2kN
z$~^xoDrO*XqNb<aaO1h;K!$GQb0AZm%c32#eX=(7Xzo2Y(<<EIY8SiX<mK~A6E`s;
zcR6@?8iJFAPo8+C5&}2W#gBtmNDlW!k5+z~h74JPJqm}@*d*}5t4sojakbtrjU|Gm
z`ZqD&t7LWjpgkWOldgr_VnB)IX@geGG;m4DT=hxKw}MExnVFa<{vErC5hphUTy41J
zRef}^wHRz=#asg^T7(C|hniiKgwx$vSUj%ps(OW)1hTex`sjNF@~mlNnl~upiHrU@
z8?B4?gHLwMe{Vc#2A%zLB97p3z#+o<>Z9)%-#+EGkdxTg-^4wH-$|K5EBu@A-}O;(
z7UGy>6&(sUu`mL9E7j~Mh71!htMBM_um}8BOY>=)WLY>`9e)zyxJ%68@U)MdwKel=
z!Mfp#g>68w<KZ@Nm1g$`G7u|fe)DN@vD5okH8TB_w{G-G6|iE%;j`KCs1zU}C5<;}
zgELL|-zI_1MUZgw05^=NI~BiO7$zx*<!kRir_VkF30!PFaGU&<s*@&wBc#a3%sr0z
zn5^(QQkxjGzA#`8m@^-&>?}$n|9vt4w)X$R*(Zi0cju3lJYhkqzjvx{7b~Dgpm`uc
z{@fQ(8n`nX2nw5pzhw1gyN$Stm`GRvA<N^@++J`=!CWpzAfG*F4_3X(Y|W*KcG8_3
zF~%K3V>~^@5UY*n!qJ+WqF(7a<u~sZmJ8;VpWvt`wwiK0(5axUDNx>KN!(R9>;X>{
zGSV;pQWqm>3A@>tX45mnbGv$ty$xc3!=2#Po5P0suXIrfA`}1E^lJHsATg}Fv&l(3
z0a-R)GItMzvSv<#vcTcqaFr9|<9BxB8m(#j^%*Xg6}0>UIVM-`OcB7r0Ud{h%diB3
z8X0qn?s*Iztd3u|*<qu0g?SG&s<Oo+M?hjN;ZnpRs(ip;7K@~Ymj)jU>iAV#?<{`m
z*AGHW(625|UYP2vPb3^$^vu7-2EmFnlo>2=$<xSWw267RGkidMuF3VP8krvOAuV?5
zIR4KA2M&yTXBkb@^tA_(nt_~Xwco{4-%uOib|Gs2s!qr-IT?tH#kVLq$7QUp?oTn+
zUEUodXm0sDlL0Qt))<%5{F)hs_O>$KnrlYZsj8Gj->m73ZTfUgxSWNw2j~KJLobJ@
z?%9w(hX)&z<F<=HC`6r=@Nt*iAz-<Zr|4V&C>Dbip3a$LaB5eXQguVZ$4Lrr-Yz7(
ztWO-fhXP<6N@dG|vDRlZbY67s`2FQk0Ke9zjCz*NiGv3b<@zGFXd&CEQ*>-|k74tP
zHb;1A4fgLRV}NJrB$b|sxfGIs(`UA6;HR<;VN=>7GcVi)k}?plQLoXe;HN>NNZxfw
z^d;tg;yVudc>Qm<D*tbSP}%>3K3pei#EL^ZKv`FS5WRhq!61r4v{;gaWl~-kS_khP
zFt`qcWZ{-ck}wm}PT}>yI-`$9!@w(#=hFXmczn6g)lP~7F54@*5vFq{yGjH?fgP<T
zl;DEd{_ow-e-r+TK0fC*pT6)jd@1)<76?H3Idc=B(G$)=bs~(>m3{<h+1>}SEts;x
zt(Sk?ADcI)1U4?7qk|)LJPd@oeWtl9x}1Iw6k-|vHGdJ-*|Ply`~ck00H6BOPc+#f
z?^Ub5OYo=@ThV*=Mxm_2uQM0{2d$WIK!U?u(a!nn!b~Gz-|W*_u-v&A@JhigR1UA!
z8oo0Qrpw~P?!c*K8)SE8J_r?p?h($})qe!ozQ@5Hb}`%aKs*k)67B+rv~4{)kcJTm
ze!&;UE_&L;@U64u$QGiSFsVkk-x-cXO75Im_N11Vf<ZP~A1`}a5XaG{e~<n}z|Kx=
zsOLM<`{{(8Oc7D%+5P6zwc>8sw}!}^g&cA#y-usBTLK*E?B_AdnF6VaAcj8vtj%45
zn)Ksl2+E52B7r@n%U$URAwiuR=rtXxQto%I!{blllT$&w_n`9NsdnwXit(#j2x5N3
zkdba$S{gXK50rNh%$WtX7<`*fZh+E>m7lBk=Xu2XH$zCdAe9q(F;|9xnr54~NmAo3
zH4NF&i1(IBEz~b*YF6+el`X<?Fzh5AHsD-HR>ME(hAZoB=(Mz1HV2JsJZaaSft#!1
zn?^kfLDuHXtjq9Db$rA^g(XZGAMztOHVCf9sP9wN`}!>TwN1<G?Ku?_XP2qdK2C^>
zs#n54$~7&jsKl@zl>tQ#&OKyg)FTd}+BOFVZT${Ek^)M&23K2dr`PDMcI_+&Ubmar
z2nN?E!Z*HvS`6nNU9OK=U+AF|a;_Hgi$MZI&>{(5)F|L{{TLW_$<?WXYyKmOmdw8Z
z`A^`f)GEsm{Hnn<HS2oH^HhZb%ffaNYzbK8;N~(Zdk!emO#lYjhShXPEU6jNwlQp;
zJ$D(bU<6u3o!GJ%C2f59a&r(kae0;c^2-fJmGQvLiH(==_||#ax!qH|*OT%FHl9f>
zv5{@T@>>?PL~tfTZzwQu5VgNt1^>j+<LPOX+|SmF=_6)_f^5(zIka0M&aU$#bbJe3
zBlCD+1Ju$nefNMVh<@)q$V5&^X#4W;bU08nX?ea}$OstmQyEX}me5@?R)KIPf0Vp;
zV%|t^V|#t5^b%C2+>$bxmF<T0#$~!w%PdQaKT-UwtiwMRH7*u|2jw37zHC|)Sk=#R
z=qQSh)x<`YjlS295wY;SMN#A!+X}1Mp5T!@&%_v2>^5^D@H6^4gkuH{Zsl|6ErZ}f
zW6hT+PiyONN-5C86pjwQ8czI6YvN8z#svA57JK3Xq(JZa-jH?Ug1L8O&Qx}MoP3OY
z@f;V!vOb}gC?8{a(-v;<dRGO#Hh(U?fEh3W8Ik-LH9q_xVg6p?>E+Tg($rf{BK*^v
zS^xtSU2=at^Dw6*_@!LQt*jKfhQ!1MZK2ZAvt-WCyR0xzC&3#oDEtKB{0wd<-S=&a
z(BSN)RG*|=%+dZm`JZ<I7T&Z%IFaDUkmggi>cNG^zsw&*#e_z}mz9S5Z3{ck;O~2v
zbtGIJTBzwbeq!vErCN(Q+(66oNoA`R{txdm(rpMQ7aUB8hra%TljhAbO@l_dj+GRI
zzjd=<4pb#L-LD3BI^gM&0hRK?s3Ep<IhTo~+T7T51+N%-DMVdmvaTu^XeeTy2%jcF
z&;8zgM=ozJ?KGb~o3nqv6&UzG+z)eHLCs|rFCwnN<5XhegA-*(0hAFG@x71rjHYYb
z^Q)^HF5I^F$G?PJ#4N`7V2_eHj>XSoQ;+8eCzA_BxDTZM`(@YW*XhVO0m!Vp|6{aM
zZ!~7~gr1sD<Tp)td>cE;?6kHh=7_lbzw={ZG8kL}pL+>b1&3S0fnAygN`+QUwlgPH
z@ao`_{5eYitKTy7(Yuww@@{ON@GkZVG?DFYBn1>N2OP&=1Sg)wm%a+>ehRL!s6UJM
z2LtUBuahLbOp5FE((8RvDisWLzHecjiLd7ooL3%z)3?ur<n3*7dC3X78t&HH#{CR(
zJORM$Dv;t8rU7^}7#Us_5D0&1J2)<Q6kgML9?t@+S}^Z})U3ikAB)i=mIo5CYG=bd
z#GW}wT5SE|ELV@#neT=W`>Ib5rDlEbR>wa-@;Pm-iJ(9~<H{XB_Ik>J9_*N<poD$p
z<J$Yj@Nd?|zYM(xd6qouT)!~^>EY)mSjwfkZ!S#6cTkUa#od8NXvQj1a^{@<7Yq(&
zb6t8&jIjlCUB`uUmmi?y9j5LU3)uXaF8GsG@|pc+`1dyG!pRGxnSgSp0OhQsGMXnA
zcJ~c4;XlRv@Y_M6=|5NR$h*WLwZ5z;v&0zkHaoPkmu97!ZEPJ1KwTaAb4<|QA3yZf
z85_BO>zqT2U8vsdW2$|mfWxzLNRM@1#Xt8L&;Bx_=@L8@Q}SreBzxN)ek6PHBz^@G
zKgxWc^l2g>%QB~$*Wc1S5iqq(dzV8JluRH6)$pZZQp)`tlB{CM+}$&j+?T0y&Jw?o
z6YjS6cQj9=^VGQo=MQ$=4unV8G%s7*1Y4KniG^riX*HUU6u6dSv)EU|1!qk1)Lu94
zo?yKJp)8n4aBv~>X-6X^(lY!OJkM_{k-M6NmZOi1+-H~%9uSj{!huj%!b_V^53l<z
z!kprD=hJ!vJY2w0iM@M=s-75BL$(}<o8M&~0gUui64Fc{Xie2@UNu>*&(|27{DqM8
zMP1_aE969g<|b}CfA0L!i&wvl`-64-5_h1vw_-wl$HH2*@mZ&UYYfrL6@9%1T6>CT
zaq0|QY@zQw7rwsZg4vWlUNgI>nEzm_^=Ep+E%V&0o#&UNdMv?-E?};?ro)O>%O6QK
z(vT*ce_l23j%OhR`pCZ#=~<H#w)N=Fj)%IVLdRX`YsGnuPM+i~x7#vc>xZX}40+4v
zqtZ82?1!<>-o#$V_Ki@YS^&tg9$VD{ap+XjQ@ikot<jBVs&UFOw|Z}IrIfe0IY+kO
z^ofekKbXBX>7!cP#9I7VuZgFh%E~IO3nxwdOdDA+{+yWq&J?0Xsj6a(x}0tIf7vWe
z)6K%oEPxd0)dHX1{*Cpy-d@h2`mmY<aR}9>szvYV<!ICU0~NAZ88?d(sA#dHJJ9_v
z&Vm(kG>nI|!NxgrolmIPCmJso62bgHT2n`x_h~vqUWrFNMb9ewuQPYp6Ja0!b>;~8
zq~1I(%dy!lPucD9oGgY#Qf}*e)aVM|_MczF>e`<Sh+>0Uma0qPa|q}HAS}mJeXuja
zBk2cm58wBf;2p{_m*Eg_XO2eS0z3k~9R^Rf*>ejPk}J5`Qb2ue@y=#6d@?CK6Hx_&
z{9X)azVF~nG!3py{oV$F9ANoJC<d5V+<e)b=UG#Qh4qgX<qT^w;5kDdM&Rhv_>fl?
zk@eU6uL4GD4>^mQInx1wkFtr@7U7rEp#H<&*^{4L1unf!Z~2Rgq>dA9e!XSaOh&ns
z^=vn-_xc)b11Gyv4V8F<2D+daH;I@bQeEfc^bSUml$h_k=O<Oh4m89kZM+BNy|Q>6
z{!V5+k$)<t#!oN!f}SF>OxiCUd;jRxiuz)TwiDn;?~fq{ZX=DXI)awVxf4OiWWbvv
z&BMdg1hUzaH?K_4@=@Kn5OAW(lti@kivb;PSbr8cvs-m6XYO;(a?JXvp`pHGX!C>C
z`*&pO{|s!hzjA3GLai;ZI|l-|D}9wz&1iBa%UQ!*6n~P4ONDdblMS9I!$ptMxr~!X
z@~hZiZo7z+ZBP3r245(6qKZT>I2?i4H_Tf_xbFH|@W~;rC~|zln%y++u-Cud{VZtf
z_6Y$+`f=i9(wAXoZUNmOZ6XNRYw`IT=a=r5zY6Mm@0@794v#sos{E>a+g0P+H*R8Y
z{#5^K6`$44xIo&Lcog7vB8hU(s{x6X;mbO*5@*vxBn^8Ih)dkX5L_!J1F0E@V)aEH
z`CrI^X@;{+d^WQ3v#gko;FaRJq?{q#eKIB4Cia5+HGG6RzB{01y~@8~U`%n{Yj`%4
zD4!o5$h-;P`{NbO5OFe2dAwvT@pg<K+?`aZeEG)%tE#UzmJ(?Cefcfk3B>+k_N#X>
z;?Itrjw2)<_en2O<@$0+n!#Bw%uk`51G8Qz_ZBZS@B66zvU>CUa(z?jN?=3v!C||O
zSj!WXtg>`nr&M=%d^?=I`zd4VsBhhX9i)->N!wCB72<q_`F<n$m*jriB=-1XNZ%$1
zfOqtq*`VY260k!4oWYYj<*y6nujo9ypHo7O>ScQ0QOBnd^QmZgdc*BpVVDqWytSxP
zPpdb=7s4mcPN5HRqB*2+hWao{?nvm6^r@%<(ytdr)0-z|8~ccf?Wlof{VP*I+VvTG
z{>soKpQEP5z9LP&J>2CdHE^@g*<Wcnt0uoEY%B#=7BplJ-jsm{d+b2(Eyv0mz~yv6
z%L_SHAoE4*0CeQ%`TUKzS8B?7x9*M(((8=V{*IY_2e0hkaKMJDp3PGMpg>emb!2wj
zWe&?EEPbQHZ%YTo%6<Mns?5f(V<KnH{mC}DbRqFweB7&|m=eQVKa=_cbb9$;@A@mG
zjx>65OhDT`e>DOzG{J*IT7#<(Q##@NyL;$=@H|TRopCGqsHdae2@V6j>=V@86`{i~
zCGwPG%zA&Jnt`^~L5>?GK+~+xrZc^yaL!s|T&=}^$P$)9)X$yWlM@l^lTf7aM@w~;
z;N1L9-?Ih3wNyi%8$(tuFQ<b0BjU#4KqXQ#8Mj>m%CfF+c+O08axSGhzwH?I1tiRe
zju;;-Uo1I!`7Q{L_4&pg4JdyQaasVIXguYb4a7`OIVIls6IQa#&^vbvT={7_TSkdz
zs7PNA#&8E?#82&t_9p=cjK98eWT+``DV}VZ2hERmVJ)BjGS3Aks%5+X2Dw__)Z_CA
z<v=VwZ;DB5s^>pfAGU6oY;L~KX>5~hQdCw)E&GguO3ks<1p^xZbD%#~4gQeelRV#8
zeH!BXaw*H12pBhaKw7{J)P{`K>_lF!s+z~i%t9h@3*vO-_!S%nUFl5$9IL<8-A1+7
z_;I1+3{Wqa*c$IM&3M%keyN3}ij(-wFEBT&C5n0+3Ye_u>I64qEXAwfH^AGz{LJ-j
zBq)@-H*bz406F<0kyNvJMTo;g!0X*RmU8Dc%@hftF_41yIt|076N(LpjM>l$1vm?a
zO6d6<h%pYa!mi9(bV8$ZLqbM(OvsMYM<^j`3h-bhIeJ5YC^?GV`Ctq#c{JbP;gA;=
zOnRE)Sms$C;v1Spk+K_h&u76Zw63o-HUp_yPneo>0ZfysL!t!`M`|{~;imH}l=-?;
zfvb)0Q;$>Y_5Ff-8Vy8Wgw7X!zBWWIwPD5VY1UAmSs7M-)bCsL+d_DJGn{`QV>GjU
zQnwGqN({T8kBja2Cq|HmYt*dlUM$Ve3T(J^wB<^;H;2WEg?GG$c|{>}BvCY}T$}`+
zQMQ4*!sVXKCE1lAe|i>Z<+@z=pnmVC#D&WMXwVLXIPSd_6Y|Ni>iH7lNH$t|UUQ`@
zhjc7YHBsjkIz$pUd35HJ4HJv!BQMGVi1TYodscUhx>|Ns^^!cyUf5PP;mwA%fUr4p
zfzF9%jH4%>_&hZ$w<wAXg1eSaR<|_{{cN++i*|))H^gI$3kW-5nXJw4+axK|^-A0P
z8q3+ZHkM@7&y>vY>Pyw!&g(TnlqHb<(Fgf;=zf*)qM3G<tYZwH@BKOZ4FDCP0OIM#
z>WHA?QEznQ$9nduEhGP~O^VjEAPr2`yA?+rXe6ccK1o0oQ`c_-aRki#m=0S~JIjE)
zdTnQ<lwEpRZE(8H5x{+#^zW!xhWTi6`*O%Ub0NLc{?2fci~GFJW9r+#4ELOs%zsu>
zRa3p`<TpMs)N^jR*(IvOCFYF=UIUyI)ihs_0|x6%R9B7wDacIsbAsGpS`2VYz^5d$
zPlKG8;PYd)#(_P%qxYT_<b=x#e#vAlK4me?&5NXd@s+sOo?whm1X3%$8*X@RzWLDz
zJYK^m@?(R6O|UEJQo_V!>V#zWM~_@C6(o2vymLVuoCyw3%{f3`KFR3Qg>MJ;zI!>^
zVmshqDa<nG4ERPq6*-<@Ch2602j!%X;mSHZ-T;rwjz5DxahC9Sp8R;u{Nya%_#7|d
zYv)Om&&5yX;=smF>S1aDuX67^T^F^Y|KBMQ{|~I>oDm7WbcI>0r<Oh5V1CtL?D3qE
z_JToGLeq8S+O?q15pOTS1yAai)dE{iMfhVuqvdF9RA8H`SaEaZ@(*I?*q!i1u>do8
zA3UgJnuV*0=4FCZ4uY{wEkVxaKBE;G8q(Dhn=9vEX%yZ*@RAwyvj6@Dg@pR-v|?Hp
z0#^ZTaY2-;;Xd$X@Sqj54y0xoj<SfXxy5OZmaA$Slx0P%=)w(aTlgHE)59+;Pf{!7
z!5&UqF!|CtM^Fs(Dv6W#m$k6Z$#H?dKibDrj^^!kjNx^smQg;LM@+b!>M(`m;BOO}
zqvrn6OL}EP#s&6AIdKDC_LuE?klV|1$(SGP*jdlfgM17J!~I?_`T1H)WhFNeW0VpH
zAy(lRdn2(g=Gz+=^EXKS?xzGOy5{E3PvR2;<+sFwZYLh!`Ah<);oFYMHbmebido>5
z;BNW&LI1sF8_a}ACG8z4G12hkSUQa2`-e;Ooxyf}yrk?0K|P4ETrF^a_nR2;9sWp?
zKj#{Q;SFEXHsz$+6HU(hN_UiPT+@%e8(Zxt>r)?gdt{3JEk_-WysV1W#3$I^ZFRNj
z%nMmI>WqU6R9>-v>A#y1X$9cdvltf4oZ0X*efaf#5Nym9W(F^oc(P6-Q@XPa8=q?k
zdp8y2w^4##&TLQgvOlJ=)MAlkFnCC!_LfJ=?ffQ<>|k;MIk&q$e(XMDgRh%u3C`?`
zrkcM-rQt40(I->(mI;if%7qHqT*OMUJ=WZ&O6&YKnVeXO7~mv98|fi7c?vHzEE)b|
zL`N?@@x<)jXtRM0j5#O!ZhRS25C@9!hBw$K8|Z7|3$wQ;|B9_C@8_z-02}`*DLdW-
zpO(GUa8;oN%%p-lpUxG4sLETZpeTjwEhNX@MB)dao^l4mP|~Daa7O#Ls*bZi{?J@g
z4mr_H8V&GKE!(x*+A7?N!J+^MU*KTOpay%1C+ImN=~7d)FU$<imi!QR9B-73uFMlO
z=hXR{6c3w99($RXyg8+IVyiGmF}0apLeazv5M|i+$F@}P&!3E@$L)O+0AM5T9aH!e
z@$qOtLA=Q*DKc-VbdH@J|I2(d*4U@@p}<0#v_X~*xzx53zJ0y-_d8OXv^%C(T}^ae
zGOVfs4Mt<-EGB}H&B|N%$(vX;{Nl+Yo)B|AN=cE1^w=}@4GeCjVyF$lYR11N&7L<d
z;d3>EZnimZSxjo<89>7pj)c3Fgcli};6)6sJ!vPy`s4FD<Nm+q&h;;;bnW9>O-;Mh
zX?!YeQfmTC3p{IS9-0~r6wPyZtZ~rP)I19)IGHswnHGXtrc{C_Jd`IyC2ZPhVr6;2
z5&=uml&vUA2#D_G%!}tAcwRi~_5E3&`?@}BU8no|TZ>;ZxSc+yz34<I3P0IQ0f6*y
zHCFR#KZqTMIaRlx*N=^E@M2c$*>yX_{8&QPe2RpxTlAY{1j5V=J8}z)nc%*gH%Sa=
zzuAS2i)YoI2YeVuOlN!aRjcpGD>+-b;^O+LJv$e<ANSqA9NhP7VO<=O*=-asUC8yG
zE<Yk|fEk5>g~1TToL-}Ph;-=vA$seJ0_|6^jmMoOr;_vLy%62#SsS^*vdu9A=3|&!
zy#?j;eXO%MrZd<Fe?hN`b&7D-$?S(C1-^v`Pvp*rh!f4DJL{FJ*(t}0C6(KDi))vO
ziN+{;1=4oJu-8AUQQ-LF{P9f4dI&-z>%3txZJP&yE@kh?yPGg5@mG1#PX(rDGn82(
z=0DE3jLuFkUcr6G`n5aOwra%%<uN-#veunYXCl04iLeYpYmcR~tt+OOw$gzaERl8c
zvYSeGFM8sFAvW-AMt<A7vphtP&LaMBMNX%Czc2T~H1^tAhUcum?Sfe7M8zLtsD{o+
zKRSTFG}X&j*LLfqK|}{rkY;KyO4YTu@q+h&y^Rdd9Z5^?%lPdX4Sha4-5ud2Ok8GK
zisJ7=7|r<_k`wc7G2<GNUl5%i7?t*B`o`VCfu&dGLhKtRO)*Z`vtw<}x(Sq0IVUWR
z2|+*L(RwGhW$4sV39n2CMhzzTB)o$I>&7OKaiAW+@6ex#v|A5GwiwIlU6Hf<UP88X
zx%cP7J<l;ylMaI)UY$9v<M|*o`CgPU<|@$<i(V+2vK!Pfg;o;0alXW;s%=nP+BQml
zh&TK6^9sn;t|37Y95_m^uYg*=ZzC>k1?~p)SJfn@JBJ#&(fT9*NqK(7*h(eBX)>?4
z{f>y289|Jq?k+x`6&|qtDL%^uPmhzM;f8m={}2StR!I?K;d<V}Nz>?!l$B|%6*a=C
zV$08kB|R#ppiR}bSD{WO0-;dQpWtgI>8x7&AaBuBtRcY#{k}J_C#nM}xnX!YJo@H{
z3(vYOd2sR~bUqE}*-n{>)ML&x#`>VDJ)9`?kX9g`CD3c$y1SO?&Rr=)mbv%(Yh4#y
zIy+|Tth;}ZLcdr8bv#a!%YWiL(UQNGMnpt!I$p~~@|3fT%u5be-%S#kgMky#TLHTk
z*E2-~`Zs%uVVQ5?8`l^$8WNO#=ANfE=zS_$%gtfG>1dPvVRX#Jc>H18%UmhHY3*8K
z>-=T=ZihE<_lBvO0$E!wWGuiE1$<5BPM2+!$Axy%*D0H#dmc{SzB0GcAFNL3J-IWb
zMQ+*utIX)PfOz>gOiaN`vUWX{F#3DTNI7{j?^5Uem_u<EvV7gKl}%sas`e_%%wux4
zpXeg?7BneWY3{aYjOv-#G<r_(77paF79RXRM9b;<ClAMij8Q+NdR8|hZBt<eqYP8U
z?E_YwZwCH7X*v-8;QQ6^H`5?WsQ1DNO@Wzw5FgpSjNFz%KQo!9JPhbEN*<4F>Dpz&
zfSXNEPx9vd;Tt>18+Lmq3{o`{eZcdHHe|)}8BXvyLdKsjL5&B!*`t1g0yKH1RC^Ja
zctC8Tv(s5|kN#*Qd+eJ~tM{M<kBMJ<c9);3UeJ&@9Gl(9I6DS4VaaZM;{q6drnmac
zpw5Ne|0)52A9j+abt9%b?`H)LC$dwgT78BjGgw}H>*&1LZOX=@byOCk$s^Y?NE;)-
zyTB?FYc1yU;*ArpfH!tDNHcvk1@^pOeP;#}{e+ab2x`Kz)siE5roia#XnnwNvL66p
zBF0Xq8(Hs}sK@WXWFj$Ju6!G7;I&(W8(|^(3k=8GiT3fpBjsU6G3|zd=#{()VRfBO
z>HowNfsZRsDS$IdKf^}br|^*)vg{IL-0qT~jrO+fohuK4kjO8=+zFNPHztnIsz=yI
zPPGQ(B~$+7ULC``_$Z=TKB}NNkn45u&h$>QY!sNF<ddH-8;D=U(jdYC`?2`c_T16@
zSFt5DBL(cuV)7f}F5eyx&j6kHfY#JJ*gY!W^K%{tTgK{fi>^g_;+*{ntBR%Tjb&e>
zK1W~)00=q$dl&5O#3T2B<%b+4!DF#QrbMCDX}-tzKV06Hp?SHV?Z>4@EV5eU@@13#
zGNjU#^UbLFs2?}osH9e4N#|v)|K1W9z;)vzf*7~kpoJ~Dwqsncz^Rk3fS&v2==p`C
zI4awDz<uYnJSl}Ni*ThnLi@BLxT0O~2B5n6XgXj<byiI7HJQBFFmn5tp`<CltM)#A
zGkK<a5sv1%`)qNKeHsfy5%{b>tDbR10v!!3G9aU8J@0xK+O#F+1t16Gi;q*UjJgxo
zaKz-~<$3lKB7S)FX=-%S-!^aUa{i$tCUt1dW86xe%&F0Dgl5I$S-TfOKCh*5baIA#
z_|rL67ho<k0M_uX=oT>(h!6mS`C^FepX|tx<;Om__@Vk%D}*zSLWS#tf@&6&$X_br
z`a!bTinuZhdpL2*m@JD9agl_gjM5`lZkdWJuv29Yr$ggCq#XifS@JUPPsv@&-vWK&
zDS~^go!FO?!HxH0l8|hQIIeRfF^TQy8kSRcur#e;W$PcEv~7%{qIxpAui7R2hu+I0
zpm|%9h<?1W$T15|yh+VF#!6Dz1%@?`>2)D5|6?Og5N`J=wn}lYJrJ#aZNWhnQB*fS
z+VmyJxjT^DQPLdFM_yp6k&MjVpKS}}uV+d~E<+NTq3}**d}uDlHr~AHfb|8vl3IP_
z@KWVY@nSwlX;ha1h>c~OrRT=2R((3A1vhWMVU}C=yq|Uor#B2I*F&NiDv%=Ve)O4C
zF}AdByIxh6F?^_pZzpf4^BNR0v_~{lUrma&-bDihPs^-uh&{1d0U7{i;|0SGVc?f1
zD5{*ujmWQj%<B|j4&;U1B@Ob!wHnw=$JVk!jpt`ia4@q%!}YqAgk-_7zCx^+@@DMj
zzG~%Vw3>QA>K-Xkhtyzk>(!qQ-KdG5+3?)C#j|PH2Gnyr;}TJg6VHG!ew@UvKtur_
zIh!EYgbpWgc~hWTUAlp_xxcDbqG;EBz)wpdg4Btx2!OhyW-NK*+W&4?xq=&FS*wF_
zUh!4-$u;&cA++b5b4*)O<Y-)T=Gv>%hCwJw%J+{(@{~?YsM_d?_8TdWxr0eSt2<+B
zhQDq6qUM7|0df!~L!<~C_47>qo_3R34xf))QrC@@<L}71C|q}9u<}L7!BWu+v)7Ly
zjr1Z&eJ89hv@r7o_-F@I<Te<S@Ih_$tYhw!I4<e?(Ex6OFbL|}X{#QP<3!Vi_6^gc
zZpMAnAg_gbcB(X{KG>!jpSDZ1HKhoEb>f0Cnu%dWLhfs2NEm8Ev?rPY!C-SzA7R*z
zpA~8!>;@QYp29uQU$dRmqi_7wzUFg;8<qcHy?q=mrCErr|1hHoeG{U8>2;Q<S*Cr(
zv8FSxH{%%rBwION)+$e?&6(>Bgt|FN=G1)Bl!f?Jl}VxDj@YLs)q;lbw_g&&DCVCP
zm49zO#9_$Dp~D_CUWfA7xne9@%~<?>h4CswQ&2G^oF;?@k8o*NV@~@dXX|$W#yKYE
zAE#Uq*+i!|)nl5WYE-AB_<3`!8P&t7bs&3Y_67rd@@jW-DF7C#C4>#qQe>FXRF^>L
z5VexdiRqRA*<x+GqMx*rMg-`Js`{F=7yOc`R+daf%iCb3lrGb)6z$J)jz?mih`*5f
zz`bST@rV1e26Z@Q_x?64RL`tW8a7$D+R0INS_$-8N__>(M}X>%3OT9G{Uu5{za-EX
z6e<?rVNR~)$YIRp!{ro=Vk2W)_3D-7Y=<12r&*b*cj=2jNGGmcy)6qWQ2BF5m0LmG
zH$dIHTzLC@xA?eE?pWt9PI+9mKzmGr!;#YNm7HzCzSle{2<d~_fZN;ub8pHGsEpRH
zYL)u-yae#B4fl0nYLSeLSSOTxVrjA;;JvtfAK0bep8<JzJ8bhG0ppZAc$Z&%5iSer
zS$Q>*SoQ1LAHPBTwer^n0AqX_n+s31N>|I_Z@r|`Ao)3kH#(YFlPj340FHb(&(@sb
z@r)k_P?^s3w;oa*axVob{YuLI?4vWCOEM(7ffYj_b|=Cq3asF5f;>jsgyT45YS$v)
zcgxCvr+%O^+>&LHVZ7-ki#}tV7zh)YoDV(jGmCHF+V0XOkno_@0OX%XnnJG=R<s7?
zeSHbqU@-7k6V!o2lHWSP=yL^T;FF1YYx@R`j;$HoTQ8X31;^7E_yAXV0MNu#eu*{N
zzw;J6o|&JyP9+Wi-8}ETyH&N@I~Y0B$;p+O-asYvkT{v)V0HB_$xXjO?GAO)2EyPb
zw3sT)!CIh0*eIYw^y3L)3f~R$|L$HEzxqR$0o?V@dlkk4?epT2YwIjXQLl-Se1S^B
z@2*df!Y)k?Cs_3126ZyEc?*dG@#$ynk6#Zo9%=M8qzvjNkIy&42ExH?Tj4<B*a8gx
z?AhBm55%3hW!<UfmO9(ni@Lm%eTh%FX}H(fmI1+GK`279R%)2#+mnwPY(EY&Q$le5
zQXEjv#StI^lgYBcHe*F8XHwNj9Nj~n2~@3x*C_RIzRLNiVQG`5g5N+C?&>*el>INq
z49$z{q2*JjXT7d9-%s{|5!n{x1MjcXeXnV3;Ldh9pOeaMNb(wz;iqG9^U!0)YUUyE
zJN%p;hJ6(M;rdIN-__lX7nWAZT1B#FB#jToZs59hQ}k}$$ikJ9m-6NqH_kk_F*wS}
zu5$O%;(zi+hn9^^oVR$2ze+dj$klkF(C7&Ng{9yr->)o+h0GV<C&`bRu<qC~E);ve
z^|*IX_UGZ~22XtfwPS$MNL;us5CX&A*uSt>5Nvf`&~_GujY4fqTo-M<k^v-)Cl+fI
z(j)UQQqoeEFJke_5tIE-+$vWlXuLGgNtoy-1)*}h0q@KCcWxs>UoJJ;-j(L#P<ZMP
zxO(L|$!MqRHxn=qeYX`SlgV!=@9Dx36P7il*z(bEZ0{?&_Q=K65%_xZCn*du>xgmT
zc*qjGX7^y?II62*!~traH<lgNVFzKxyWpF@;u})V5cfk)9z}m!;zir`IIVJRafJ*b
zlnv$!tU;7AlgW;SQp;xxIO1A5r!5{(SF>P!S%u?HeZ7{f?oUDaIjtE{oc6Xxk7z4*
zm7*E&8ZiD~qr2wgo8C4HAib^Du5y^ki`v&s5?bEgwLCyUBt7{ADhf&RQNFM{7>0TC
zGm}Yka1fMAUvOF+>2f*G<P2|f&X<=`b)4O5aLB~q5$xPayUaRK$1u%)xX<udxx*9b
z_?gO(MiA4Twl*1jbW^DGrJ6xA@;gBVlo8D$?^%kG_7x^foO)i#YZ^Pj@#0KONUXa$
z1HUp8i!FfV=9Mo7buMStZ!V8YR<bg}1xcaYh*-{|mHSL*R6TLJpEcuT^%4vHXr}IP
ztjpaLD`n@S&h1>lTd5!Zl^7TBZ3(Kk>cNpT(8p#`M_r^GphjwDo|{B$Hx`{@BydWE
zGgLwY?`|6Qi}rGqNvgvUtYmW>duA;_c{+bC$rWL~?IJCg1Ye76-7i@{sk>}1pL|pn
zqumnD0cGBTFT79Ml+tDEGlH_^J)rc<%|04p^Z4xa;IVtBHfhL*wj&x6FkE1$OV{M5
zpC=ka6jdOh*Uo7H$@0=i74eiU_`BV}y8qYL1_5{V+~y7sec6Z&{P2IcS0~Lg8rh$S
TkKZhB-W2|`=h=pzE?)aD9_(^v

diff --git a/reference/plot-2.png b/reference/plot-2.png
index 5091dafb3fc7c67c92e516d7f6064238be675aee..b50113617860dfc71c7189d5155f14e7cb21a141 100644
GIT binary patch
literal 26495
zcmeFYcUY5Iw>KQSj)ROkih#&CL>L7`r9`?8BN}N+Nob*n^rm3wNwC2<v@!IqA}ygK
zAS4hJ1*s7slt6+=(?AF<2_%HPcjEJ$^PKmb_xk?&{&?T(xvvXI_TKm2Yp?QKYp=C=
za>M+p#1Aq*fIuJ#=(RsAL7-h>AkdCmdv^mZV`<NqfIqu$nOyw?_zOH6Mg$D-XWyM`
zcEKQ!T&(D4Thh$3I|%d(2>Qom>#&>!T6oUwPy|ajY^t~)8hz>SzxSW7F}g+k@?gm=
zwQBE&B9#{}UVq;GBAVc+_Sdg__wIZeHy-!mP(A)dbeH1ZLx1f0`RBctf8N&itL~$7
zcC%Y|ebA?4Dj193tPqF+^4!$Tq^%+CIlg{A!$9*Jk?$~JB_{0Ozy5#XKrCw*Cs04J
zO7t_%hpUu*qToCmmIZr2A+J!~XMr`3PM0vgwtZ~~G$yr4`~Ij~o=0ov%7aGwIcEvq
zNji`eEhYNoa%ErGHqi%I%0C+#J7qwisM0{Y5TK*#=5{BLi;@nmDID3sI7&&tSt*Sf
zTA^M>1R~LFGKHW>HmvPjjj~~vCkNArP3Co;7q%wg!W+#|7q;%J67!6hr2!6tJID~^
z?`#W7BPAG5?)@4}V4S2t;Bfp_U4$7MgKG$Up63e>t!(jLS?`Kru`w`=RrB)*DEo<O
zj+;s(&cQ~qY>|2g;b`03#G^3+Wx1Ypc4J891;_pH!1WUoFL{j4;d$#}oH_2S|LXu@
z<D~vd7sHHl#iS%uS9L|7DYFOI;%vkvqpx>9pF7bo@mV{-soC@J6#C^!)A2utVf^7R
z;;kCtqt5fSeeA~!t=RaDBNT_>(&hJ*YT2^oXSKM7a`nZvsVfv4NO<CT1pef?Ecnzs
z;YE7~Svw#vT~p|JvHL{@jg-?rS2?@ZXxS$J+falYAKy{2YJZ@kG`KG`P9kwRSe++?
z8%1Aa@^y(U_WhV)OrgZkt5KFPPrA|-!_GkVvENDy=&#?{O9}GmiI{vq0?|h}adK#v
z+NU5_XLgQiyzs9f$;VqB#uFr5qHjhi;b|~x>Nc^m#GvtwL1myD*LK7~pLve7CqFRb
zGJzJLr_D9?*}P=rg?zn@HMtWPM+|{DkW;hdR|K0m!TuCk&z#jp*{_7+lOGUxq0n|}
zy?iHe4^C2xNE!;hxQ$g7Tp?8^?2ktWIu%cZ5R0V*EOmgFRbK)=4ZVHH-u|drLS4Nf
zBC!G1(W6Z+i{=tzW=?2D<OzM&*EjfDb`#BWgD|7@wYLXbBqv_=g}2{JWejAOv6vDj
zT0K-lvd50*E6#-M(z)xs`Aosb#>%MqMXf1fy5{D6x4g>Ce(t3<?2cHfPiy3}h|qcA
z&D+I$h-1rnPHUP6Kz?tGJpf?zi_Ood7K~DmvcFTBC2H?jRBS~W*|O^;5`_;Xd@Tbg
zy*k9ChKZV!o79qI*`adcq9S?q6_SGqMGB?)PVihiIoi_Qby<xvN@E%v))81!-^QMf
z`8!m-4|hTjecIQ4t&VWBLvxcDpyejyT9c1$<itiG+pybXsSY&P+?EN58$TN_+53&_
zEKzF`=!B?MZkf2M=?H1?54jXE6~oSH=yu<KELfL5pgSX0#gHmV&Ww7?R;_CYHNw0c
z-E{H~L^nm|L8_g4x05A;bP_Z=cFtjGG&s@l1|qP%(WCdX{`z`$XCp_eER%nD{y65@
zr14E|-f>dHNxe)+0Xs#y4&i=b$R6cHr(!lIh8LutTV&qNJCBHLZ<I9HDw*^M-?O7x
zR`XH(Jj!BjbRoX?iW5P$eC~CO?2f*OX72bURk!d@@iMdJuEa{-mw5DqP64?A78)?s
zjV1cDuS=B;g!V`iNw4}EwR9hb66gbQE$w3Mged<M&b>Az9Eo3+v?lw8Tw8vmEy&X-
zAQPjx4`MR!#q!d%Q3tFW916%gW{+dWr0WD9a|~98eigJ!qqJ74`Q;Js;)CVA)@^zA
zhJ@yF`IWxf%u9j&!;8iY?fQ+$#RzrJj*y4ibv;ebhy6W{lSmsTFZ|l2gBe-ut50Ph
zp9SxcKI49^1Uc3>=4&xc3aw?Mk%msWuisY-QSBppWQH1ZXI{rddzYh~{g|V7SGUs%
zeHY2Z?EB3~<9y%VZ>!IwQCrCNWsLUJ`olz>_+gE-P_j0ccw;dpn3o{EG<2(y#s;W4
zxtV=r$bZhhbAYZ{P{hi+yV<Coh*|klQ%-m4_#T`C`5VNel|YwR)RJ8a7><nSAHE2x
z+BNif7bxT}NZ$zBoIM)z%ao*y$31%EWC34x12H?oSd-@W;SJ6gv))tUJQs9}T+2Iq
z!vyh6p;j?DkUX-rtc^0acN?qjb%K2%&&Jyr-B_1b=g@p|TO6t@T@wcDhy}b4IW1Be
z!bpvxD+t28b{%g%rp{Bcj^wIJ4~NXzzjV&57I+SggcN#IKID_yPZ#4%W*5d5?1y9L
zR}OYi`?za06?Xxvc6l7-x$5cImvx1&OBwE!=B{ZAAVIaS<nsoEvcnhALqF2PFR!5H
za;D}z*G6@V$@Y*xvbd<%WhZ~i;bu^{Ho;EAFhp=)IC=DvP>TEwFmF%4vkWStGS4oF
ztl8t@R5X>1n5dJ~=A2MZj2zxgu4~{?V+w8a-~xZ6b>ZWdHJQ9R81p_uYBt_*_D6cY
z(_EdO$8J_gOR*FxVF$?Jb0rScYQFqph&0;&JhAkIWpm+4KW*0pH;?iSyS9XfL&1|d
zsMWO4i-W}BdQ4wSQtwF1=)}}4N*=Hr2|d>N?lS2|ZW-KumZaY^p>d*g8-Utv$hfu=
zPd_OelCIl?Kr*~#NFMCNQThCkqqVthc8W5F^VHb+bYgI+<Ng%ylka+owj07bH<<ay
z*hU`R8<6q2E3NxM3qPl8MGZy`UA;fN+Dpl=uO>7Fwi8G5vbBe=1ao@%BOEDDzlqbN
z4mr}o_F3D&qD9*fY8`)lHa2t2eMn{gD)POreyL7m`(2EV=Rz~ZL?-QytuGXt>(iXy
zP5g``l(GM@ntSnP(l*c~O0i5p$XWuWR(7bB7GI06Juu42)b5by-CVBA(ea!hx=S6X
zL%2?<<U6fwNOsK4F7N|ozhbDikNsMI744eK*rh5A6<}}%ui81ja7flGmUaOvI7@i-
zX{?qCv&tvY&Qr*SoF57y6^DKT0N2!xqlN;3d^chlu`(W5zztfj-)EFk2*cm_<*|Cg
zr5v|F(tY~?ea{I#<RhjygNs_PkI6*Fu4YI@2U3V2!2y5(LiG=$kIn-YMGOvVsT*&I
z2<Xl|AO0+2JRT*P7bPf_=FgQ^6B~F5+I&m}4;M396-oGFPlFVbLmH_$sH<>KzguPQ
z&BYC?Vk}L(;oN^`m{S+9u=+wXQP>*qCaEEuqIGDgJgtI}1m%K`6+4JsHqxj_=JzB!
zcJ74&^7&w1kNv>fgy(T#ymp=H*sfl5?r_EcD<YUz4#|$Fg;1#Ry-tLAN^q1>iPk*Q
zfG`t7glFFzo|ExduBc-@?5*5*B`p{tJQ>{=%<CmgewlQXtPQxukqYLGdz+(tX}8Nm
zW+Qob0IPD@-vih`tiK}*wWDZiXhbWpej>1Ml>5~K^O7gs>#Q59l*LAcUCSnI`?QZz
z8Bm@h5k8cIvdF1MS+Li34PJ;5QPNm4S#--+_3;h%L&|`?(+7ShqtMsS@Oi{&yg`yb
zdM{=r2UVF>%Yp@S5}3{&exzXE%Da({M#o5Yy36|d96)$wMq(wE49s{_-=F;>&83~g
zEvsgZYfnWvvo~Yx?(Z>t5p~_@()w2bzZ>t%gk}Xh=%yG%)C4L(W_7A36_)bL(0;^E
z9ua_vNmigquKM(chJ*pxfEHxKV|d78kw;nn#+a8Kx`yaZf{cgx+j;C-h5#F(lZmsH
zy_>?`5%dg0a!5MVbPlk~hLevBUT<-4QhIS1zt<rUU%;z$|HG>+^;FpMKHE_c_)5PF
zn~*!4$-I~(;ntm6!>*`w=_x}$^ZZ_eybE*4h3Qi=KDNriFjA=Iql@sxzgQdn5|i8p
zjh(j#Is#`EgB_N~wKe4z-ST?TV2sGC%xgFE-sUXJ*Y?djr4-2g{xxCKaVu=cl$;ep
zt3bFF!yQOf>^R{-t8TiJyPDYiM;jz?i5;+Z-uFoiHIKXYqEdvEpZDH5cWI<+>a7Ra
zcFdvq)}!FfU?VS$Zga^nuErxTw@2n<N0#yi&-n(c*EJK=BrA*LSC0p@5=wkS)!8|A
z2%R}SLqeagbBc3@)xetD(2wIr`tA>>%nZ@~eJ#~H=mj|8_-&M$FNbs!@K($)3pE~~
z?DegOBtWfCZ()os-W!b<q(gR=k|*A3J55y4zezQmYp;<jS%eFQYW9ylvao6JT9*!d
zcU5-CA-C=<)4Y~mN01g;(meXMIv^^eRkS?9eK!L;0dsc;e<LQl+JA6$j4s`iZX-GJ
zZn?5-abR;s$;0k3cP#?|xI25dse$f*!@}D-288~(Q)f$CYEN!DutvP)D;I5OExb+F
z!G;0Hp`TWA?F2N}HB4k%N0n#!^QOH*M?|F#CU=TXUurUFS^BHJx*uQxeQ;CrPqO#A
ztuTal!w9ju86iu$JNcesCxN+VN4CQ4sS`|ok(CL+g)HlI=o6YE+ej9%zDlHGsk?v|
zu;Qo!$C&6Rv6@kjc#6p#b`tmL-z4UB@;)vt(=Du}G<KpZE4G?=9~8+*`t5~z&7BPs
zBj?n)Iui1sTi1-Ptw>79XK&3732lXEB0SsXTRhsR84Z4~?S{={TPCC+k1nvh_eRqP
zm-OvmVZ7&1{|5Ytk)LkmQJ4Bza2qn-X{_DoZMEMV5EWwjfAZ;TsvZ$mob~Zlr$Xl8
zvTs}Lv?33`ve0zg@9c~U1x`N3Z9nouZPff<@0)nza#uRp!HIH5Vw}XQkFhILf(h*f
z-t3(A=H-zWKzz6?#xy{~{^uv;g}&&77p>5K|6s>TB;(6Q_%_h-ZU5(U-Tyqzzgzzc
z2TC|91Q6`)FiaB1Njx{Sj{>(byn%9gv<!+Kt*{o{M)tZ()%4OW1(P*#z>6cNoowZf
zZ7l1K@qPfggf@jv2_aEOPNc(?5|#uzK+$r66E*u?PHmd9+ZiPX+nYj>V~hs}+qZ*e
zbz2xQ5Ta{n(?D(-qPsq<FF0@Dy_7StSSMt(4g_iq`Dv-<Agi)Z+PkdMZ*Q2IoVOmx
zMXoqE#Gc#CLZu_x>o0Q0g9+<j;?fa7GRQCc{E+o7(N<jc?Ien~q<kGfdElvf@qclH
z$iAsoa&S3kIU58FVU2K<v%Q57ZL9Tw(q|ev5*JvqXpf3yYX&ExuChBQ&ej-bM9UIG
zG$cCp<Y$@_w**aS>f4=U=)1C>PkBsf!y9(>IEngy@m&f?*Qc;xn4yI`+*-<2CFkKF
zANCi<u8l@<DrDae4c$|?Jr?@HJM0n$9)DOCeE?o+o*6~c9YuSvlO<|HC7Uqpmyh8;
zisoAg+fIK^2w?|Xk>v-@#c?5cOsT;{x?1BMml{DXWk9h*_;zsXs&%nik)vq5i_P9J
zDb}7MD!B~Z*yNY&=IA7dV5gM&OOF0ykAEFrI!mV&QAthjtI??T*P^#t&w@WAh?RLh
zn6NlEl;6KL3app~^%sZM$n#%R6UEw41{~bKG^25-*`pmz->mr(4#&kxVS5)Km0w%R
z3C6h{2+k=`5)8|T(?BDScM{`51K=vfcWN2E6f-UD_5|E2F54o{*;4T4H;R}q%D#YF
zkL)^Iv1s2Mva;!m5491(^ecIUnCwav?&ND4Jl7fs2|uD-kPnU8iC2;RFZPS_E}#&t
zZ7fb00?CbWKXQQX#cZM!K(za2C_8ZQc$l<kL{*JSe-&zr$oq>?P1!QzG{209LSAQM
zu2y`f`=-ZIK&1tYH2BjBy`%|=^gs$2uNbdmPRSWqBW^Skc-fu9y)kb^Q?(v6ZzUX0
zw-#Oq7kvdCKe|m#cKHJXqt%B!(P7@=6#1z7DDwc2irUwG_I9>@L&Qi;oXhZfZcH|G
zQ86FC=-lkOqXUg(uP_+<%f05p&5-$w)|&m>MxxJS;7&)<HHeE74ncCDOWU2aW~Uiv
z6q3Rhc(E$9T$v$#y8mK+bUHIw$av>2Ro!b|wm8zhojzvm+&B^x@-k9$GxW<Bcd6}F
z+qmAF6RGew`iGacw<iAb*<Ar$9pO0Db3H7I?MC4nAM<sHVsX)dT*8DP4blD<#T&?9
zB(%&E(h$1|gZrJ99+>Mbe;|jG^||AHz>gW1FppqSjpTVBcUiw$9p}@zi=Rx?5Fyf_
zY|bMkz31v=z2H%ImGSVo+~KJc+}G~u+d!rVmn!lctRoef5dW!gL&HxOl|yS^7bF=p
zO(hwkMWeR_%G`p<y+NDH9jw2&qy1OqQMLjObF4wR#n2{_vtTFuC0g$5>#4-CBSM&b
z<ieS#pCmycQrlRJa1`>~$tBDikp!yZm_@Q;4FVm%`QK2f><uBTI8iU}#tG5blAXk{
zNde-nm4@hBbbp3~C<_jtSr1^-tctS}Uygak-`p#1;{iI$U1XF&`SUn6zq>?%-HLdX
z4~HbiCXrUmfi#<)qJej&4+8d-T}*`_?2m{VS`S`rC4{jud@yaFaH0lK;zf~Ra7#J<
z>HmD^ZgSmwz9~`&<$pTZt{d>^yl7msLUeR!Br)t=^;!A5Dxwyb$96BotNss})c=-R
z{lB<@=@7E5>=7RB3F(vH8KAMjNZP-!n~&4Af!l?g99v-FxvB&}EF-7P{nmwvZfS@Q
zT1~_Vf3|b?*_|QVMJDJ#*>Uy6YT46{hq+%bQ-aeF?+!&h<8`@90qi5ZtF%tC4LNAC
zG)JMOBX%zA1dQv8U2^5sgmXi~T0LJh2}!iiDYyZ1-ST;7A+Ni_eex`6bHz!>8y=oU
z?f}XnUs7<{^tGbgpsuGv{JN(hf0VwG){r7(MGFCwOjw`qbo<f5dvBPb?x(P-DH?C6
zVezD7_=lQq+t(&%8>d}FZqu)pKR#zbTTTph43XX+y0tMU|6|zwee=(@i$q=a8xRBo
zPwQWYmj2!FKl6jt7vhM#Rp&YSSi~uiZW3TFxsJ5rh(FlT3>n|!!Pe}*84oE^I5I39
zhOwiCM0Sa76!xaYU8*WhuO?zeW(@N}$McgxQG^k4)hKb)f#$xl{*dv8GPA$*kZ4j{
zX}}vK$@?<zG%&m=`btdCwVBDq=O?TME7y=>TU0g$>{|(Dksq<O#h9cd!CAR}kpakN
z#vUc|#0^@NYhz3SZXRBlDKphC(XB6<6hz2~4qaZP9GRF4wTAfYjl$q{3L=7$mTYXP
z+v3Ncwww&YiIuM5F%6~(kz~;e(FikO6>gS~VHwqsy@`#<B-(hIwHOd5i+p06{1_uf
z9b#u3auRUHG(Vi6UXw$b`g;yz=vnq{3z8lFvuKto<psd92(8E>YQBS;@O`Wv$*a$H
zpu>q@rA#FfG<#G|Oi6Bs<VERwKdKhJ-~ty#2d5}^lR0^e){}g0+4AN`w;>%)O?i5~
ztE8Xpjxf{W1U)gDF6aQT7HPp|Cv!R}9S24+6r;RKWRHDEOXPf^|A-`<1eeo%Yinpp
zE9T9K0@Q!*Dv<G4G`E7nlCKdi80AgW@(k76oA}mzR=pE724UUo@v?eRXG6y~?ShD;
zs>=a2^<fwd3fcoXJGd%qL(Y699G;%nE^lsGJe|C?N3d+WOKdTCD8RDHN&^jV`H3ox
zH+<ZF+$tWg(bJB)U55$k;p6hlTkfueS+iM`RHdmXTm-w`T2i#cf^C|YEwLL<unC<1
z>yn_!im!%0aRc={;tnzZY06&Lc0*rn>m3sfjYjnRNM|l{7)2C^p%Juk+rl_*4sNLF
znIn*xUuwBE%@dD0a(g$66K=tlpg<`Uyaim|dFr(iT&?7UHLXeKc?6u@#3+<dM~U@m
zC(&f7w9_q5QWNt;RD%V9OvL^PRCeb7MM#iQ{;c27wDISZc=J_>)!Rq;u)vD5WoTj(
z@7k1DlIPk+)B`H*a;;Fr2q?09R}O%|0PoxPI|th8*Ef0jj5PxRWD?BqX0G0+Mo}Ng
z3#bSPC~7wF^1ccu{W{h(kU7NIsNn<jzW|3=;0Y<OemG=;C*c+cs~p=DWFOETE;=>4
z)-Z2U;PFvt(U53_+f#LKLzsS{PGM6xd$SY;$L9@JMYTD@8t{qsE~=tIpsP7d+cT3Z
z3>KbMR$;0CI$~mQi(p-eH@GSALzerO>=O-gnepe^zs@hubD%vt`878rm9Y$*6DF)1
z$~k_Xq8twhguhJyQZa;E?Tf5l?L`>H8O|!HAS`{48DJk<{B_7|oILKtO}XF?Ef5%K
z{HL$<XOpwO6NaOq9>~i``?emZG&c(*vwh=XYwFcw0FP|tlwfe@kb`3jQ<OQt(}4(W
zzwV$b+i=Uat{UCYWxilMq156f&HOQ*>>U#KStj^O@7EhtHOS6)DjfqjdHL`jS+R9j
zt>)M_7oVJ_nHg9iNW4lQtZaQ6xDXv`frJ?yTNj`<;>oKx=Tavt1o})o`=56KbUbr}
zUVCGOsFh8FePQX}<RlE^3XG4<zl^aX=AUo{Vj34u0vh?H1;uaTQ9?Vwbc?7jhY=Cq
z3gO<i0XJIrx4umPGF#UA&m!(2bB#C%NBvS>E@Mabu~ow&>hOpq(vMQBlyqLNkk`pj
zP)Gm{01I~@jh$vXb2)&|Z?9kPmlyQ+845Rn5VZB|1z0P;KSs91EHb$5(+`yuXSL(c
zOZwIQCgOgy!sv*J#zJ65Ilrvs-tNW8Ch}f4Q3vSw)Ak?2ge?({?5+cW^@}QM(%*xz
zXoW7@ZLD(R?>Urc2`XVhtgN7mdTu}k4Hq%;Qc%ihlwIH=D4L2)PwVu=@9@%kFc2tR
z&f|*BsY&!dNLDx{3o1~<zWXvlyk26Mn`-}^q*9Kv--`+5A}aTI=}}QfG{O~ljbp{+
z2E3RcnbpxsBt*?FhXL{e^(b4LiaeAn4%L1QINJly!Vhk{h)QO4Y7>8uc33g-iPqxN
zO>4zD5f@51)!AP|*R1*K3KTKbRm$;4mRh`Vs5Y}T!lE@l@G9>6Ss#8(`7?@Q%`b-Z
zvu;{3;s0fxt5(cCHlbse@^!5ZHQ1XQ|2i4^Ma073YC4?cluK-{hF6}jYWDCMOU6qk
z{gaZaz<1R^?z6CIV|s&Zc0vj!v_?1yxD0CSTBHekRbl8mBq+ygK5SrM8CsO!1EXjk
zwyH$8)^VA|tpFq*>MY)?mTOORgAazigCb4QrpygroLx;uLCRZ5ye}U#|H+NB{pXkn
ziiPP3C2M%5pU~7dwng0K_vyuUx_7=siI_j<nyS;i#O*HGd;j;_3aUO5bSu0pY`8RW
z><`kMO3`uA$Y=%f$TpT7&eH~Rv1bdo!XRpskO_VglV9O9Fd6&(7KGnhTlKI&YeD<q
zY7&fOFEC<{GFq?AiMQx-3y|z{D<%XE;6ajxmS_f$?=c_=Ofan@8BU6>S%ALVf&m3h
zzUKBF6*~a^!<g5p$$FcL27qP(Q3-$p_Y;K#G3c}&2ULKZ?K2Vb^1OJR3^!mMdE7+y
zD`6;d^FP4Vc3U7t5{7X^;3IsILw~x7(e=)sD20U8x1F|1rg;;uBO=+I3>J<Ycqb=N
zzBEtajSb!kr%#E+Tc|aNncz|^t^B#!0=XtLRtclIzYJd34J1zR=z=EtNq%4D2|K|?
zh?H2P7EImF_P0_K%eR77!gx7TiMXwoJQ6DWwL@Fk*K|0!mn_z6FQPONe!@FPp<k77
z>*#8JkM`m)K2olRnM=9P=qdG|uxgGB(i3gFDlS3u@LHULe&QdY**-#cyjulAur|0l
z5nxMR?*VcuQ}|+YCdLs7)K0~WQ~q#OAjJx?VhS_v_Jo?&Ios>M5;+km+*NC}JZi|q
zO`Mnourwe_3(%l!y^VKkamNW(gWGxUOF7MwgkMGLe*W{^kPI<ar=ZEXZGlpYQ#Py5
zzliMlMme9%iIg46(my1pPoWP`D_1?NnEPkr*)63Uh+koeSre2UEdps(CFdkL5ei1z
zgf=q>_T$2Q)*tCMY9;jAh2KGmJKcnPrl%R>j6T_m92jL)kr}C_Vs;128)o<_GL!L1
z<#&7TS}{##<B_A*d>@B0Uv(hIBmCe-zc;-M&<E?El2II7&~i?<DB2!>t0$V9McJT0
zt;P(HA6C89aDCo;_4A#Y%M^l-vhYp6er`5l$NC;$En*}lbI$j+OpdSlecpeyG{6mA
z8gUglxYrG3f{bJ2D#Qa~tYVm#HMPkte)KNRtzWP|y7+d#+Cpk&lshFg9kCy%8k7OP
zq3y@<hpvmtIF|3;lU#1$o9WBxu%z-lD&TK>@sK2IviXV<r9hEcY^_#m5c=UfnF$1f
zJ&bZ7{9Cr>r@9?^y=@Lo()nboq`8yGzbXR+XD&0jD9$JiIBYozTaR#u8d})EVXF07
zds8P>Rc1RC*I*MG=%#KeyoE#MKlK^)?!#}neQGggXSHmqrK*(~T=X_9JbBJ=FpmMX
zB0KBdN)So1^#>pd5#*I}+IXMpZq049TJwh#C^ZAeOBT<wH)nwokHx+B@pC3J0a=_$
zM!iHgJm&&sPFZG1xutu0V-HZuAd06spaE-*<d<^B4vazq5i~Wxctc*=hrDRu7M@(+
zCt+e0RRhF&vJ#WbO07rDT>rh1q#LLfo)^0nIsr_6BXlF==L4dd798Em-|%g$m~oQ9
z@l(bnZc6-zF_{f&r4<+I1V9nO{(vAas!E5ToE9Y-4hpHR_E<8X&{!xjThk4}3ATW(
zcRD#BY9ns5m(A89o!iRj^Dvg(rSEyoA7p%Yv$C6R<&FYcWXjBLNNHskQ|A(_yUn@p
zV`NM9>n7P4f8+pTHzie(ujaQGspe(7^5w8esrVQ%(hs;C=g}kESTx)O&4m;Tn2>>`
zD*nqQHoa49IW7b3_Ae$MTQMif%yS|PfYqca7UpubWB))$RRAmmwrt;I4wT{`n<i)5
z-NMktHCXsZ-e!+J^$~;B$Pr)_{E2}h^Iz#zK;^`q*kK{J%3yq9Y#{Ec*ALr}G;hg?
zcTaemv5i$oVd9G|{0XxsJ9218-ULtr;h`00Wvj*f1bippApjpTNnztUZ1NqaGU8+&
z(6V8Yxoh7MIGLmBHIBif@2*^|a7ECYCFU3GeZSvY+Zp#mSTVv86(-v!-dq(vzWERF
zC(zg*|4e|3_Ul2(JJ=pR|Btvq?0^F+5>L41od5lnh|p-n8=#~ndX#<2q=}L~Zm!FB
zDZ|#lwVUu7<`c@8@ps&D85a56NyQ{#CIf<xwV8nHKj*FghhOgomJ;nm8SiV284hz5
zU6BH{R#f$9hDll{Wxc9|(?Xwo4+6?^+$U}`H%mE!G?*BCTYs<*X^rf(<|mrq#aQUF
zOI7!?egqEn_8-|P+soYt!UE4z&}0AE@M9PW7tuesYUpJ7o#V>h6W69TuOSC_CyrSm
znt8Jfj@1|!PL}!CP$j43o<6Pjb(s!<_wGN5cYHBvMG3Snw#L-H(-Bb)f_(x!BiYLq
z?W(|m=a(5c$Q}E(DefWk0Uj>$>OUha``Lf6&+q;}?+^aBBv%45xeiD(8q4^mK~%%U
z))Ad53jBDCuTR^v-21nQPm!(HtG|n|^)Hbv2xL4)=rz2|Qf6R@lfB?7637)X0w@qb
z;j)vb#I%e&s@_gFEH}raF)N!1Kss5n{O&?B&I&)LxwF~OQokT#;y*Si3gG<M^^7ix
zuFQk6y0^54XUi6?n+tVXcpZ#K|Eb?uZ}`!SX(<0B<&^B#;L+)k2CbcK69|3Zml4N?
zldMT27coD*W=zl586VpZa>1i<yv~qe2^7gzpBOO;WQjp8^MH+z{BeS$?62HJz%?CS
zTC|3gR;aIJz)ythfuh@%^(l4fqU={BV0zICZvlq-7gPB_F!u>aKpNv7FYCXE_<gG9
zGs+aG_U{sb`?<fA43?kdqIuuC&$>-I@u|a{9!~=xqYDC^djT-v+g<Y#gN8Tg4zs$X
z6QxJnL!Y_QrqBDWQGtw{7Mwt(G_`A1R4-gOy4VHhoIC@p+G`83(+$L-IEp-408S9|
zpB(rhjGXHubV9Nv)*5}-#L_vnWB;l`yMTfTGa1K)alM%CQXHGm+~dJ3x5t$5hW`>2
zYld;*4LJJfPYkDWUM*t<s3~kn%)h3;Zo1+uqv8ysH8q93v=<98<S{_MjK?$pDB;N2
z+hcS_8UUteOBS`heUFY6<Lmxty9^Y?a4n4h40I1&oULJ8q2$VVto;xwMv4P%8R}t@
zIyagFR@N68j{q6xF={DuiXGwws&>Wg4J(%O+I!Ei;q$7vX+g(LtzZ19Rm*PhN4h$W
zZ`0swo6NnQ%v3U$3ywhcycEz?#OVZ3)oHgqL?y_$BlAC<K1@xn@%srtftq2W;aKac
zxOne1zf`+}RKi<-DOiiit`d`0bs32FB4s>2=V^p*{ly^J!0~4@`{zC}s*^e2D82t0
z(Kla#?|c<FFRTuoo2+p}HZPI*rrNg;?92S+rw8!|x9|P!r{90NyzL<H<;*X8ohE;6
z(`>zRQ2LS7<e3MRQkl~~Y?n&ed*RRMAAY+)e!BN|^pAgQYlF45=N7n06h5sELLlG?
z_$ERG#(+FPuAoO!a5f|yt&TU0ho6$X9&|Z@ZD4DMwl(tqj`TlgU0>Srao=c2UMO#*
zho{aY%ldqmN6|;?2i(D~6{%r?e|T%euyq=1<c5Qon_7>H4iP)xX_a!diMbN5{^57L
z&b*04x&t6Q&xG3<6hE&QkM;Y(>{OVrye4YBiNutxqN|?i&JQS2758QTZnck|WSu5|
z@oj`fg$m}`zrHhJXNN{&cPNwj37?x0SWj5jweRWJ_-Ca%S(3@bR}E2KST%5?e}2sv
zR_{zF{0u1)hQ-VL3;htNQwk;gUSQ_5EcER48cbo&)nv!)X*s)Q=`khjrrkrPn2wR3
zvrLy3p+P>_VHi4ey6FP9*XI`SZvONZl%qBB-H(|)@7x)Pb<CfZhMHensR5_^-<Z96
z`U!1JaU-x9DW2z8e|;K55P+3hJE3beJ43@>AXhYsu2+LVG5|M2g<zBB8N~cnh(C6}
zELA)$NFQL2nng`>15u;o7x65;UOoq$AW$(^Gim@%d&1KafV)M&lkoufDqp4|KYk-u
zJoZPCkPxXed<|<Qal;Oc)XxGg7&GCp)qU)}Sdt%`fR~^vOJI4kpfGG8cw-biCQ~La
z8Xo;u(V;5V0;R5!yV(xiI^9VJJ41s40>d+6A%o)7V)Wx-U<Frbec%$m9J)9dd9KOX
z<cTYm=$$4Z-q>u?>7QrEWNWf6rhlh#NTdfhjM!pwZdd^lo|nOS9+cC3(?rSsESF5t
zPJ}yZT&9WoFLfj&e@6Mi4krcrR;k7@h<<v8%O!mipF>R~2F1kR8@#xW4)N4<9aVdy
zyzfWROCM|M18QJO`-r1}FdiHnZuEBFh}>8{EhFlTHY+RNsfIrb4Z3&zEnf&-;SE|0
zF82An*nB;k4IA4AHSeQp-|s+1v5LQ)0(rfaNZwp4lRfWlczvZ59O*&(2?%sK;IOgK
z-?4_~E3d$nW-BRRNi*QSi<ZwHn5P$*#>oK_cTgZdbCyLA*A8-P_tw>}Jpcl=Dk{Ah
zYw^Wy!myB28-$d1vkl3cPT2Hf*@D0=3#?JHpZ~Lpv6t~*^o#E|fj+lGgFZo(+(`mX
zHqya9bn>y(?07KQEP@Y2DZjkY)z3ExS_P|_2Su>v4rBGeop8^Q3LS&NNR3HNXkO;@
zUongw(K&#h&}Nhrc+ld9n&<Rco%2rICOZhAI2j6NJ*C%C!%E>!*u%*=sn{jK^{$@}
z#2GG#OKM&zJ6V2F3Z4E|#w&`Qwb-28UhUXlMC?1KxQ{+Mnmv8wbU_#?W%?^P-+W~j
zyr-uMA?m$!)l_E>%K|*w@P^ee^jqyymgz{|SsmR@i&No2*kPp&Hkgz%{pwn)Sa)}r
zW_{MjKfj=y*6x3;gK4rN1?{`GvIypyu1sduiwVhyH2$+MeLr?wxzo)m;Db2l`f>Q8
zLKsPJHe#?gXPOB$NSO{$4-o6ZxsG<|AiZuGR`t}x3M-ELL9!RFh!?wb75W_=?xpM$
zS8x8^@W1oJKO4E@>mxom+&*jL(&v|S@<SOQ@e&=vG+pTecl0%#wBoSAYS(M@rb0j;
z<E6lj10utaI<+AW4e4z<`O4x!x=tP--k5p4=aW1j&r3fI%`=!c?rCjHCZ6gH^&CF{
zQVhL^P48(ssJ=1@_5}WlUqUN)62$87Y_akT?Ck&SEC@uc(}xCxcRS3UPYUlT8@q6%
z62<fl{#KZ-*o@lqY_(pg^YR|K6i@wmyV`(mYvO7yt0&VlFY|Spzsw=x;N5U6$-kr`
zl7n9KEinLr>_ggQ>F2mD+-6T&d}-K}?8IyA=Up!M7seZd2OPT%IMqqz;IL8f_-*ka
z;8_9qdwxmh`UCMn<KGScCw};hmC3e#I8dU(%Cb`yVL)`I`h(D>fb8i6C_3Y|c4vCB
zo?7Q~g~7XaSbU_ohU0QL`EzdGoZ^NtR!LF&1(%=;4!6U~gTn{osbVh%m?-O>o=&Io
zo=sFFHWVCgb^Nes>Sv0b?>Nl#X^GUi35%6g0D=N@rs2tspZH^kv72Vczl&=3lXo1N
zAs%APzucuql!rnYdHK`+&_t7!`%akymd2|(;v`<{r7;g$wAtr$<!%eJAj)p{3h2sb
zaOL%tGH{Y<jZ)`S@`5knzNTD`2eyx|c>~B_gR(m~We0MoD(IBk+%nm1_a5#H<S*LW
zZH+_+bwl%VZwGcsjGjoIZUS2{1paATzjd;-9OqPJDu7)_zYo5FRZl*yJ0D{@>JAOa
zztF4Ddg&06NeFk}=#rViKsqI`n;~5}#NksL<vm)Rk*-)1u}$Ox+pH9u1nB74GBgok
zZ{M}WXBp1do4(f^c1%X`IdW+i-WTjtJC_2`!%4J7Yj>(+k7F-fX|uAzJ|}lAV2MIJ
z*Xyk>R!{vb@K1!G!F#|(j=6-usHw8-^GC?xeZeb_)uLR7dO<qx5{T4LSNHJ9USwMG
zW&~C}f}Ec@?JiL-&RW`n0E+p>?i+s}92!fXo`f3oN1`LJ6G?7jWL~>=w4Sw^Ix}VL
z7;1razP9q7zA9Ci8Gc6;2t@Dt1@M1lSQ_hMJ8$?@BOF_s<t+|zs(u6fDR-u8g`8y9
zQ2`D$TWRjh7K66RU;ecLt%`SCu^nq^p%ypDN1%%-)7pm{#LmHXFF=ih`AT98qv_&|
zhcB=Q2vqbvL;Us3zVFH2M8M8P?O%WUcLTtN=>N_Ss@DAOkNY~5!fi#cUmRiB0J$Mm
z%T<RiYR?;+j?O^odDB^T5K(#l5`gedPp6+s42R!pW1p9mzaI1f@Burzp>&OTV-cIL
zEm?MSV{?*k`|d#IW=;1&`~04r^<e!C4u7X`;%Q-Fp<P2E(wX-uJ!r_z^Hk^aTCwN8
z^y?cFedk5}tfef#^y?%j9mm6{19|nQ4-qRdf_eBMu`dsPV>TpO1N3iH{{M1HB2>PV
zhFvhN!C=dw9L0^BSl?t7^7kmMH3z`omO^kSbR`-rX%Q5=&7G7r-3&Fzn68C#>gYNm
z9zRnDhZWDi5uI@qHM=KTxa$J26gb=%%YFAjY+1j|e$GuR1P7W28S|7v0o;5@Cnp?F
zo~A*!6cc4dQ~dPgtv3Md&XszkbDg!xo42t$tP<PaKoc2)@lSlg6Ch5#5E1%bA(@C{
z9~Iksu*HK06a)xrXEx$2jh9<=I){@Nx{@p!tH${P2zC=%xw)#05$1v+zQy8N@+J7S
zrFs@Lb;i(QWe*g<zK#~Kn#D?17b{QCRb1SOPE7vnKe0o(TP?GyBAr_*=G@4mG%@xw
zz<bux8j9lEbKiB{J20#?dfpX*6!>5#%+%%g9?zVPIo-*alRT~Kt7Z|zI~jW%Yji;S
z_K?3fjouu&Dqvv@0T&mP!>~2SP9^G|H8F`31_h0}r_bQ<71z6tQ!6W~yS_o4&>t`F
znW<_!;9KXv>N3%S+Msh=(!qq}1$b~>ck=O|<m_><L%qtDXJWcDI=q4iL|fr^u@jZm
zT?1I+R`iYoA0}hv={{q#?;Gdx2sL33!NWtgGdW<0K4-+O)_<&ih^nb3dd(5NF$2wf
z`z=!w7H*0q(J?|n@VkRoUs3DB@6yBP+{44Qp^4@}Hxq1Hu+tpi65qX$g$&rRPe?@z
zkKqWEcA+b2V2hIKu7_A6X09(+d*wqD?O}kfaC5cRlbi=`L}CLewlmJjIZ@M3uX+F@
z%Ip14EKuu|(;b7L*iC<|GqB@M=oYdgQBIrpwZOrDjQINVgZ)7Q_OMbX4I5S8euf3a
zcnXOZf911C7`%V>o?)j*fArErd2nY3cD$keOoPQrDzK-S5w|3NfMH)=#GUOz4kOaR
zT$7c48T=*^+i@JdKB@gVdPmIB#iscR!dWTAy_?u5gHD7`S&$<(xWR7}n?3$=0>zT*
zmc*>LIR^J?LRbC-voh}nXFPb1QPPMc3^+b$$P$#S7B!saUJD|`%x~UoW7kb)LxbKQ
zflvRXT#<ovDEgMIkk-r;U6q76%u+H7QV0wj9(JfL`+dpWNAeo*0ZKb2@Wjp_2qCIW
zY8}3p)0545>1a5=?t#tD!)$G+YI&Ze=*PCC-kwN8uNA1KPH%YJaqZf34H>20$y6gQ
zy*-+(p3cZK5~kiJ*9|)n{F8Hy;J29xiF-CYu*s48@Vze=wby9iVJ4t*B(snMh~$-%
zx8){GSDwYGwY+{m+f&#y_mPWrYz73h12U?84}}dJR^j)88>g`*FoWn(6<~HLrN%Ho
z@vi#S1bYQyYdvUti}F3|Km7A9fZKBHcrSh8RQR>P4RdIV#$|+(;)Z?ThP<V*wP{-#
z*zhU#S-{&MhSCQQW|5uQ$H1e))K8fuZl2iehLN=Jd+u0P(v2-e=mDQn)m4Trv;uDz
zauw_FO4NtM-ZVI(t=Rcowa(osH<<@q8Z-&&K00<G37x`&J_Ku@nHf{h?hHRI^hNra
zX~$|=k=C11h8azX*MbVts23_*@`HLS3%<1kNtJ=~h9*>+@@0#NNBtG06dTJtMhjDd
zz0@UI?V)-An-7?MJM6D$c+C>xKuDQ>WXf`AS#@#l(zXu$iFWxkt5+-8#IjG}*)|K)
zMlV&Rjo;fbZ)kbb>ZwiW+Na+O+8>AeQYz=lz#gQGX)T4JyZDsGY#3uC_x7@qr#L6S
zn`-KOCA-inIVy_AYRSHEkcYXA%zHg=)shzM)s=90F>Sj4wDWK&?=e_Dpz`~D7i;}<
zoq1)6Z{_ByAu|X56hR}%bkr5<loR9@ck>W|kWAE@*)0Yp5Y)8)obs5}EJa_t^U3MZ
zzYWj2x6pm97O6nyN<BHMh8xT^wHIH76;Jd3%&&8B0FgnqH})uL8+0<0$5lH^p#csX
z{NCg!k_Q3^2}{q9Ex!d<9t)<`Yf2X!alqzar(L0onbR<6z0!sgR!vb`p_7kw)I`@7
z7bUENJBPqr^Odh)z`B3OB2LeZ@RNzn+^9U&Q`%=bp{J`iO2G~Df&d3>ZF1Dv&PZ$&
zxU)`Sh@^hXKoS}h#M&56-ZTOdHQIrn<h<;%m%ZnSIrT+ugB)$`{r(FBoRr}i!l}l{
z%EiN$efVyxE%Mg+86;0OfeF<uLiGSpMFU^8Mu*mdrA%!Dv#EdfSDp%Q+v~q>jpfJR
zKxY-*J)l~+S*fqlDZq|j=ww4%Qm6Biqt0~}Cp$v60$$<19aFW>Z(;^}9pq8`+kxMM
zQ2y|21s9(ja_iM^D4*!ZoR8pcqZ1J6=PiVxlFEsxeTP|Qx><=gu{jx^E+2!j=wJ(S
z6sDD89EJTPt?hZOITEXPt7f)g@ztPEek66eT44}%9qSu2xbasHcVhq+Mb%OBeE#iN
zUATGQby@ye=0V2hy)N0Ma80J$R<Z10WX~gHeR;w${kxBJ(g97ZZuWK>oBR0WbSK^w
ziSetUdD#q{w|`}T%%1!BVhum`SO1H((&UiJ-nfsI=2=Pozp_iW9i{4>3X{wY3#9hn
ziw}7E>CN#|TBNa7^sB;zxGkE`pdD&+=%sG|i<>(0%1Lv>CEP8AL2i9dpy#Xdbrnm`
zR{(WKR_%P;@{PkEzRH=d0mnjKTbO0do+r5#Cel)8u3TRlO6d16TS|;LpzrZWN9xoY
z2PNaUq2hq~<MuDnNZIrDFAb2N=yl;Yc@*fX=$mHi?~!+~80ZQ%rSir28Q>HarOhO{
z2=BSevb?=z&AfDsDRGOMrwddPO*ec-ErA%O<&bqbM!PdXaUvC_A(t;mcD!lFjf9@a
zn0}VB?#HBXKb0kr%SF9FUXEZ%|2m;Pb8+zlW7*(h;ncm$LyP1mI~aNzzK4~Z7b(rn
zaMt;a<%9Pm{X-gL1^{V|w$y8u|Hn@0xhp!sBrwEG8v|pEsm;t!sX4(Xh)&%_$espI
zWXcG}A_!Jk5pJ<mf3U#k0oUALGwM23C)V5!zV!v1#G;Taxm(bH-09QME#e!L4^_q4
zf$*_7K-nN}urfeL#2j;Kqn4T_Nypp3IBu|b_ee@h2FJN-;^t^O&4Fs1-TtKija}sV
z=C<gXvE(R%CFiMI!#%Bb{a9S!pQQBZ=xy%!{NFGxM326N9krbK;6S+>oX4p_&d_gV
z{?ofsgXW6wEjIb2tp9i+VYuGT`;4(a1tLiuDP|X-c>=?bH!qj%!rRu_Deo?)A0i?X
z4izxiejnNBr}2mK<xVSfz`D?9Pc6UtN)3}^MC6Mm@+(3kZD_~*hYXMxsCD7$9DH&o
zUoCFDc(?6u{v;Q9%TnQ&d#l^E&Eo@LcEx>Qfkk-=X7QMRPT^hhb4i<_$fZBWzC~=G
z^sH@COJ|?1RrQ_QBdby4ZgM>qUASNY8TvF@ayZ}hXtO%?k9cZX;E7k&78Ee0T7sgj
zJk>r2OJ&21F{-y7KuYBcdNV$oF*@eHr3MYZT$6yrDLTg<eQwe0L=#cTjzHdemB}B3
zHv6u6EW(wN+0I9sEzG))PTtz}Gs)5p9gaoS`g}-1+_bBxC3mOJtjY#-6LBGF2JnK-
z37a$)?T$kcbARd#dUcL=VJ5K!NWFJmYeaKSg}cveJZul7Is%0DRsdjet7mMaSU+_J
zFN+I?wLc*br4a5ab;^S`U<ImKhH>kd{+Bw-cGp)TQ|E;0zWi%(6`cUK@CT7owV8a%
ze<d@Hf_d%+4s$(U#u_B~!z42AcPB@+G(-)68{}?;IAAAA^@JVuGMb+CiMB1&=auR@
zu6fKd6-<FeW+W_a!l|H7RI)J$ikmgbUH1zy>+m}0Mqe|ao$fr?JR`go<h}#h_vzwt
zxCeIZB~csgJ@uayY8&8%5FiSzmr*ay(uqA<XMuniWH&k^l#H_*Ef8EP5ui41=pXcr
zpYBYE>=vd>?@{;bR@uDXMMEbCx&1kSJ)TA(R4<warKfZ*1k(7l;F|C+Ywx>OpqgDm
zOZSaFi3WEny1r(!&%iwZZntHcphHG9Eb2|LLo;-iM;EdZ`+sdGK0#5lR@jtc@CKL8
zs<2oo*$0H##tCFI;JnhX`FF}im8QFW&x?!VgIX?jR;1j#6==Q^cN&7X10%m!HMa=i
zSQDympQ8_>4tbM5Sn$rXmV=*v=T*xt?Pvu4`5VptmFo=pTF|SF+-kDqqGT%~vlO57
z_gbFC0k{qahua!4bL;O;tuAy$OmtEY{xDBd^PQ^myfhl#px!B2+awQJuv$b(`!7}&
zmL>FW9R`e&(e9aXB~Pua^jMwEgx;~x{1e$hu}lV{9-n04alWtk=2Y#e8DCQO>Cj`&
zfd}V4%bhZ&Xov054>=X?*p&X;$Tix!o*JGnWYAgl#haC=i!=DQQpNE#QRcAZjJRi=
z!$ViTS-}$ko%>i>fs-w`!m@(g^><oBBeMnRR~D^ysHD_8RpCPkwA2-3X+@TXG343`
z)i0_VVZKtA68^l&XMFv5-i1SM?lhpNkVQFYZURj-4T^y2KBK($&nJGfYDl@j8tpRB
z3%E2&w&|ipTx!IbD9?|3ZF=<e{I)FZaZ|Uzti2?GH~R+6Ut3?pl%r9zLJ23isdsab
z2zN|I!r?t}X`kgZjgg0m%1OiZpBUeyPZ<Q3q(t88s{5AnV<H(pz(l)!TJW6*91`60
zQ2rGbyz<y7<2ZV8z!xg<tAL>eU<nn#l8gS$E{2VktT@@S(4&B4DsMC89QNqbQ*M(y
zvh-|{-_6}EmC8<q#8BoC@f776>K3W%^G!!*e2V^a4*^AYE989Vh3io<c7uIh$>)6v
zTEfqD8c0L{j_bmO*(YXNA-e%(x@l*2d>n6)dBtQYQKkSm?}0mMmWNcwBIf-}g6?&F
z!0oT$IPwZ5d>Jt}zpQeuj}{k?FX8$$g#sf+Zm3yH{aMTWi1)9TM}HJBjuf<eJ>?IY
zIc7^WJJURb?tt2X_7^8eEfkhD|3ZKDJi^m`{Fr`04PEoJ+?s+_3!^DzIvNZpcu?W^
z+^6QJ(#d&7B<yt4kBcdKXJ=+XlnJD&IVOctPlB`s{<-}4`bLw2#Mz26b(Vs}kgTRb
zo60SbPu4_C=y?U@r&$`~={|_&?$ZU&n!E<rRZeY$!4Cf@P_cZ_P%ey<gBsYD@*mt=
zZBZy_pX*IsQFfNwy4~ei$}h}u)3uy^U6u%QG++5Jn{9Z+jC4TNGG9m9&>*|eIX51F
z*^k91M8?mfm~owtR$E9^{Y?#f(_%FgQq~lZb4<?4&NCD{wL68DHSLzVVj)K;tnGiN
zeb423?5e<yX_}k<>2#xAWe+r!>8O7>ve^H0r*93F6I$$aRS@5$6N+!l_1Uu+eOE?z
z$CSu~81Y(i1@*TKOa?}Zb#F7_E(8tu*VoJ8w<|YOPgf2IRDORDDl_+S%Pu>rH2ZeO
z9>6Qi*hTtB1nyR<7rwOQEZp<`IZ$@)BcNhRPskqM;1D3*3(AjBKvwnwJ8FIk1#}E>
zwYFaVL>E%IrDr<n!RuQ&$7}*amNsQ(0-a7{-iZ8_H)1W{a<s3*Vca~q{x<V#VQA6T
zpXG4)P~^3Q703r_qL&o?XNyCCuPRih-ixQkZ<Lt^=Q}x$_SM=`jR)Ej$j|M}j163_
zjaDdy6^9?NnS06XGJB@@!~z*{UuUj^=R5k`?N#VeAYM3HXrb>NrDa5l$f`6#ELcfe
z&QXcdJjz4UPZ+xtAWVAe%iG0&b{!RYK^Uw<Id2|Mcd(mV)a(jnnwQ%QG<_ACxa+EU
z-PJW4O{<%G<Lv$_F@kNjY~q_jLKbdjD%9Q6QcNJ9i~A_;uVd`r5p}ev*&?*kZ(dF{
zp!hUkOLJ%DnVk$;BP2qFdzvpR!MvJXggT&KTvM)8_d2)8R3_g_d492?(VWfZhbZ{@
z!oJf?`y=XTv$H;P_<04pG`2KY1V-$0$!pGl7nZ0xts!_IczGRF>HQ3_KJ$13N4q&s
zpA_ebgZg1>0HSD|<#o9GM+J<13dyQ*9zyFcNk|Klg@={r^AQD`f$%QU?RWlW4u_Ye
zi{^9ti`+C4GAzbaINzqbICRQNy`rXf6mB2=uDpe}iy>7bF8Hh3nX$+}y2rAt4=-!K
zszK9s1|lnZM6p@;0oC90n@ZPJf=CfmfWh-t!#4p2e-JQq6u*(~H3P(YYivAdPMOZR
zj%vuNY6%B8y!EQaJQwg_X?3~fXA^MQ?N@}kss1^s8|J3W{OMXdf0nPhQ)GiD(=nkz
z`bLP!&=!m47SA*@u>uJ0786{aVKGp~`DT;qZxHyym@NG8vfbKFmP+6A+|!U)#_uaN
zo$EPvrE?`G-9tT?+8SgljaT1E?Oa#!Aw?VoNM%?J+W@4N2pCNwQX2tMO94_5xmB2h
zEnPK$+Ul3p^dY77=El>?<T974s~vP?0Xd{w>a0-Ad8Z`;3!r;EnX;pBkDA0)c^HHD
z`}Mhd3gq$>!f&g5<DzftKYpV0c@3P;^5`jchw-fi5mQ0f{H(WH9p~fLqjYIJO!k%d
zA<QoRryf?XM0O(C=nS&i_Zp(IEJfe<fq#8DGq_|P#VDGp{Zqj3CLKVQTOfvi8=k!~
z4<FX_78c@x<C0l%r_@3Nj1jT$2-%x__tq<PN?$7UME-PDSqMEuapM~}zmpobh|2O=
zPcaQTAaKM=6%n6mpYwGKo-1QMQ&S3$+`;tkp0wk83+1(L%t=_y(i}=xYoX{1c<6mu
zcV_nF3^>Whh2Za{VDRu>wa6Tc<W{}CBJ#~m8kaxlCm+weeHG>$n`XI~g|Zw%m!q|3
z0ywJ~zvWZTvp$&(0WpV><)S&pr|avWpe7=~4;n6q@xrohGqu;g?rRuJ)9QQff7<zp
zQ|YRF^0)!MGqoIw{?*;-gHQY{yXe-yy5W3KepAA_ian_*;TV9PP369;zq0YgqnK~E
z@?~6}tGd6EEhy77udox|pJ(VidsA;=GH||0*o2(dn{BM6v}><@98BtswWNdl@QjjZ
zboE#6MXyWEl&2PLCs>_Pin&<MmjRCNn1<$eY&2sHSk){GKhX_H&gLs^DUc_tUa$_L
z$v%3K+uC|O`apAOs=lwW!`w&J8qR!w+X)P9AwUzy6xLR7OUM2q1H_H}DLNL85w+B=
z|AXo*?N98JOXn(q>m()ArZ@kqyK`Y{;#&7ORx5RT;Z(P65rKLLXaGTi0p(gZM`VD2
z1|(v*Xdxh%5CH>{0MWXut%|@T+yW%E;eIVaK;#nLRxraQQtkv2k%R~c(IANsLeAhh
zUtvGbdCq)*dDgsZ&3gaq|Nh<?@j}RuPqih_lwSuNaB#zPy){7=5A;OGsR>BZ-(`V?
zva_yg(8-a%73;7+xZ#}Tn)CLQP*82!n(om$TDER%l_{1~Mw`1-HuxCGq{olzbL{2|
zb%~39yw0~m?B4Tv4+h8dFZ>xMdKATSYXjFQP+#Md-z(u|M_??SL^rRx+G;fqtI7lq
zVdYbQ#oXoISbd01?dW`+vJ!V3);>e&D|dmgM-^$&5m#8Qm@m>Nv9NXPq@@R<!}n;y
zAe^1NP?G1@>5x>-xHqk$ry}|K$l{E9kwaa94kr9!tOHZ!yJ|l049Px5bCNEEYwf3K
ziMExQWs_HC<B_3(&Zbg}WVkSX6G1DWByZb3vetuOdlayj7>Hz#l&#l1pbADKn2P4R
zmbA09m&|IzSsF4(^VNTLt-glkrdys`y1oFouq7n=x)*e0q;I|*akwNkXZ;a^mxX$x
zRxs*4dDc=UPhah3@v%~Ouzrb@y*5M)E&TRjXs!0M>-GP}X8LzL$T7Vn)A3?S&(UEX
z2d7;^$ca$~bjsNP;q?SIiu31O1;W}}AqVOzhBV{5A(0C|223sGW=sJg{%tFb1Im=a
z^BA^QY={@IadPUqbAGcMJt%K)Aw6R98=d@@&4KnXNikIpPBsa|b(n;Bu-^2fEnfyt
zCb-jmvP&uA^mV>aiUcW(JWE1ThF2OilrdNdxQJw=h0(NAVBSY$8~J5Y1o40j^DR2X
z>cdPfb}toJeJWwu#(D{jvLp-y%mEGG7otd3z6u3`2QL-aL<$#s<G1OAO`sOw+HS2d
zcR2jjTK6d+jwN{punHAss&$0eOYzpjsPA7&gmFcna(0eA`Q@5$*A7K3AR5D`p9ZTV
zVF!4nyo22WD4qt|ZPPJ3^lYb2tfXtRr0D7g{^BhF0Hyq>77sOyF;VkZk^EwF0<D3&
zE(7*>&53InX_ti=U(SxW(GO1H)2A#AP~BFT))7-a`-M6Tp3B*xNYGMc)Dog<DP?>e
zd#4<+Cp1fPCr8nP3?;Z+zL>EXb|yfW*mYm{m%uurDMQ8ljdjL<zEzvWt*>V|Q#=Bn
za4Hjj%{WRxcd`rO@e^EAsqnkyb>>NHcwd)2x9)v>ROi???6Y4yo#m#dKa(P;U=S#|
z2ZS&s`3T=?#&|Z$TK*vFc$Sv7G>mAC^%&Qf_q1KSvhx*2<(+cW7tHJ99x{C2gos?p
z)n5Hf#N9}*GPzDV`7es9Zz8*0OZ;Ix7@7Q^T1A!vi6LpBrye0(8z&=yUTd?I7_ggw
zMxH}MUx~uNqQMx!5<<@y+xft7GjZJl>fSZ2rU9(1@W^`a;&XYLH`fL|sns@O{Hn%1
zGkV;4VIyFwtIvW*qhx5(u9v3r3_jL=r^+{55fql(f70#+y3XZ@a}sVn)hl~9IE|hk
z%O2KHNK#d@kAlfS2+*)8iL#fieJ#gaQ3q8`up|$V+BUg|q7Q-lIHI`-@;q9%;EGQf
zYko{lfIJk9em|<35CX4+_uI(_3neNr^$@{jY#CgaGgf`i#9xP8I;{DpI~G~vLN;~J
z7<(&aQA}^rIvQw59gROP*1!F>2ZqVIflg6%Vsd@o%n6ffwOuT0I?za%a<ZPq8eX1`
zFsa$(F#K%dezHfBK-@^j5mSD1c24-rsP53pWDTZj7v&%~ZtBSn5xx}B7XWg~_Z-2G
z?f|U5do|+axAzM&m;i^Vamz}kx*~4opO9Qj%}8^p#ipPADH#l74mpxQ$u9_YT|<T#
z&?X*V{jhw+3&K=vA6{)gL^ODDyuY+62O<8W8;1qMZ`EW@H6!HuXJvIYm@MsVmYoy%
zrE*X>doTK(Kn5OcdbXxJbRk*#f%Q!@??=vsjbQeZJqOSFx(;hnV=@$tPR+|#Lrh~r
zP1xrXH_`okCICaTu#%9bCw|!DWp{qxZL-k};<}tQoSia?Xw^J*u9iJ!d|ml`f=UMS
za4gd8ZSs=t<CS?OPACE+W@@qh@(;no%WpYI&hdqz6y6r{2)Z`0vMTPU<dddd28Nvd
z3t_6x1LP1U%6#<$T6p!Z8;c~b0`sz^ji7AQllEwE8)=^~^jwCt8|$8^s+Rq>#b!=6
z1g(`1A93vob6i-Cevw<V;hWazzWGU&{3>|7K*7=_f=2fv($;ePQ9>hI%BY_b!7eJ+
z9dA|I9>|Labq}ZD+<o7>lqgijW!@>rDoYvlJlsAzJul#~Woa*s+CnBUJs{_1tU$0U
zYNpVFpld3d@ObY-@up9IxByLLx53V}DSvk(<<?W7q<de1CXVsQ#5KLfYQE{iYce^Y
zg_^`(n`aKUemo3QI0;){Jja&BUho7W44IOfKxgpy2wSfrhBH=NEO7ypf_WR-51t^U
zwAXWQ8VQUnK{&S{#+eF=f5vk6_E>GEh1AMRQNl3T8Mi=GZ%1ma*|iMy750;6e<(HS
zshx0Q!@Z&XxU}d5;?|jmqvtd?UVnpz1d6f#wf3sDvxMUK@-8?!Hv(X~F%3`9$l%x<
z8GqEs-MG^=mX12{OpVXsr8%z5{#ZK`l0&ifDlofbA_UhHN6e0x0g&?(tl*DTQuW}@
zT?ghyul>~WYZ!A^X1BAsaGbs&lVd>~4hqT2kev!<dplTY_MH?qCOQ0UpeM??89}xh
zZZL`2*Q3@uf+LQOHY&|%=gVs>+KME9y1@FL_$H1s6W<7ldBqvB%fSt%d|h)XE#Vf*
zD~Csm_#v1`q@`I}zkIEC#eL<4=al0e##Dv#pi^_>q>{qJA!*$Qq+2e<4YfY}Lg$ax
zG!A!?bocftWk4>^@iM+D7i>SyU!VwF^CWu!tEh?%afPL;jr4Yi!8l!VZa1;fV#1+p
z;e6&Z+eS*cdIg4yG7+wCon0OH#F!!4MW~2n(CWfB_Ge`-PrtmI;*ytCp6bDUV}%~2
z`a_I;2CJh8+<6wyQnPPh)NHj8TF!76yH;{xA1<X?J)XCBCff1vuu@4oO7KtKm&;`q
zt)KHuJ%jEGbMzI~`ShKYt!&kqy$z$E$Gp*7l5W|I@Lc$ZDKPrgdUHs*L-;5mu|}q1
z(T+%$Rx}`arot@3<ygtPFX24;x>*)%(0$D$v(8dGoW-SL_qtdt*_OagfC)}_HI=aX
z_z*$2K$SSx3n^;VxfjU<uzvR^bpoiZa57GM=Yk6qy`2r`jjL1mS&Ma#k5mI6{h}Gh
zWqpfsug2~+2Q0ZrFSINzRz?s436}}0pFW2<7KSl(ZnWYs-v!x57{Yu89vNDwQ8P-q
zbm3YRp&zO*yKh%LlP`K=v0T$>O8Dngr;V^Vdn>lnp@@Nawsc^j-Ss*B?!6Lz*pqBc
z<JyWs9{Ik4zh8?D?0lZZW%~l(?M{Gc{31PjaGdSsg>1Nfw!U9jGWod9K@dZJb*A_v
ze&NqL2XJHk5TTlb8}&v05$Z{$qcjk7=LCL4<2|x82<h%2$Nho!5DK*vNY@Ng*%=Z#
z*5Xft7tQjc#T990<vFAOje75*d5>7X@xd|(=Z2qwbVt+G53faGFV0{)_iE=3k<at~
zkq$tZ7N+--8UJRElr$V9E4nQ_CTBX88C3QogH&}&*9uELgLvvn7Vq$LI)>b<2zn9^
z>Y24_zFPQLc&nw#=UtVSPQA@A{JU%=ad0FmbolYFt5HO4`_~HwWosL8MRWRPDY3FB
z;le`U$M!8ZDmU5SW8N!fzVyjgb6kYLQ~z=j*7HZ@RG{I&buEJJ+3L9Vn+qi4IygU_
zhPf7L{~WQh&?(M=KlvrbaPabcS2Ocf;5<G$sVv3Hun2XR!jLREF<0VascPz-Z}kB*
z5EwaRUR}li5YMaqLdSMf|LjT#f(#|N^~kMc7_PAZL|Kw-q{*A5+~L;!#s!k7ts}he
zP2)fLcF15>+BwROk2eI8s+kK1#jlk@|8ni9+)AnOrg+<9owv6v;G=6zEP61R_YaQN
z^p}b;2^UDKMTks~)`6j$NUa4P3+8ztjTybw(d!%NYGR#wESuj;QFE&L-O@iYTBgXu
z0~3XNplSF|0_x3w1bo|DvtQ@fb{LkslqJLETK8YKU(}BvK}EGH{(KO_`T)0o1u)7&
zefa}+DOTGO6}MIyoYOpMx)S%ja_94Vz24*p0)vqwwC)`(_Wd@&BwWCmFT{wL^4PaB
z38cWePln*(eM;jlmo>p>XQ^A(GG%c*uZdHv*Rwv6BdHQ8oCnwY)^B$hM^n|9>V9h4
zazWGYg;hH~G)4}8rSsh#_!WL7fWhL6DI2;`5k7yVr!$5fD~z+JO~iG+t?@&5C<5am
zNm`T@U%ey}90k(TJ;pg$_x*zE9`hbo$C7tSgm2uHHjdHY%0?M?-NFru!)yXahutRw
zV9Uvh*f)P=);HqQWLajxRVtC1D3|3j242&vIv)%d#5Bz<wnT-3)+@Qy_U7S_h5osM
zBA9!<v<pVmo>K7i^@|{_Zhc7&Y^d^ArcUtp*``W_H-V*Bcv0T8{VSlp_ZB_%H`V%0
zK*Wv!+esChXWNHq5D}xm?T)R-v~ek_RVt$OG&{iy2q{3(7QQDM4Oa1LEO~YW1j&{5
zDh7vjFZ4x)UK@l5yVx^7S(n`NdgfX=!aD9!A#tq{QsDhaA=9-;<G&TXlF7w#O*`f`
z?{IXOnz(kAusWt6%d1{G7w8fbQLA&qX0v4mkDWDYhZ|_hdq-DuA#uHLg1ja@INe_e
zw%eFK`_ztT>6>sIFlHo}v<Sk<MpHa5jR9s6DENK)zG#{4t9a(UaekiWqsR~`rh!g6
z>gb=)FFYdB{u}kRS?b3)th?m~FGt4q(SO*nm$L}?cq{Y(Rv@RdIdkr=Zv@#$T#N+r
zy2Hs&ZXZSNc$93Ga*EJ#_<lpJ9Y!5=v48%Kk?!HuE1tinNw1SI1)=KGK<BHc%88@^
zP&<qlF;SW>c3FGGdD*?rQy|_9Ce(KNx!+J3pf}(K2j8C8+3mzw9>%!RQk=3~DF3|_
zp;|o3&Y9+jU?}xcl?xqwALye$+|s7FV5B(Vs5wIos2yq=sNfe4kNK=Lz}u966Gx%4
z<u<^l8az@p;LVaaA2Pk^RMR(#NI5TE?N}SKKjLFy6kI8*bDL0gd5&LB95TwKbg#cb
z(2kev#vB|h32}M=BuKv!Qt+PK2orKYr%LD#xt6-Go`UCa>S0%NfRS~Dr8bJa=AJR-
zXfC~jx%ECVhpU-27Ue2jouQPLqNdQc`n4A>Qe23Ec>Im>Oop^#PtA~a_JA}pB{niG
zr1nH7R@sW4*rJp8;;Rsyv7lyH*x-ybLHUvoNX|f~^R%4+qo4DMjfO(<QE%S0+cY=Y
z^P_+bl6*DI&z*|_B^n=Sca@d?*$5njYj(AHmQ*&)-DUi1nvdr{=9k^(nIkqBXzCr@
zE0u_bSS(b>OGUmgdhn5;v>uUy%}9XTLCEkDCQ`Bet&t_7x9<|^PRBKZ0h))tO@|1I
znOTbpVT#fV>wLy}CHJq39e%TX0dslsCH&uXW;dHMV&mY<9s*@RA#_Ota=8dbskJ5E
zf@76Zgf?G+-f%3xzekf9GSx;;MHAA+YE`qQDWkfQK!V!y<nzw1umwWk)&uMURLcS#
z>;68t5d1}b6~eV;dMZNq4Y^{pxqpcU{q~ko{oK1d?M=VF*2DO^{kivYz8U{CGLgUG
z4Z8ZCG^5gkauU2@A?eqE3-2t8xImha{Z)v4qy3AK-DzQ?z6wT~ww?odf>g6V?YY(j
zt64YfhCrMPo)8!A?e;D2J(Q+G*!#B5{m4rb=Aj(c^00d;vhYu>gA%1sc~>Mb1eQ=7
zyKDSGsw<D7=-UsgMVOi&2T^NjCrt;uX)S&8A9v|7<Nv3Z4n$<WjDDl2&~G8II?$%>
zvKRJ2pLyz&4d`ml^-!mo=H6<zM&>qcsea36)cI0|ZYjb#`S8h0UBrSj*51Dg3T!B!
z@uGZNFQh%q_O7bikeP6+UCJmt{eRfs(S}m5{;)Z|@84<{lD3&|#_8X4Jw3Jm&~K(r
U|M6|W=KX=wKL1tsk4r!OH|j?ME&u=k

literal 26518
zcmeFYcU+U%);}7%9tW7w8IkHZA`C+l5kg001S0}U3rGoy^b(38v;d<c=t$87=@1pA
zB=jnT0E&Wi6+#P1KuRQ(Kp+GNN$!(4=brPq@B9A#yMNsGp6Bz?g#GNj_S$Q&@?C51
zJiKOQF7^-Ue}F(BG05fLZ9t%X5g^c>TL<?8E#&m)E5OJ8TNdWO1E0XNWlD$xJ`M$6
zb_xT5<PwGdcBRg)dV)YdgCM_Ou#3oDq(tThAYJK#k-okwyAK`u{_QV{)gH3fZ=XLC
z|KRfDzU$XxU;O&Q*d=(c=bhm14@llQf9@aMXI@+%`(5sPDJOFU)i1lX_KqDX@Hw-%
z_m9UDsz|=>Q<|E(D~ZI(XE2(0F~;)jukgIE7TVhUvMo3diCTyjvj3mw|1Akje&of}
z!8<(iO>#cbNf!7!M8;U)&d#oQxuvi6lEADM)eZV{-*3wq70{k1X(!}43<OQnB|wWl
z=4DOam=aHp7j^`>TONwoCHyj+_RogKE@==brUmYZ0A5u7x=RC8d3334*$w52NM&r0
zq%cbY2h{7P*HJKbIl~A;7u3IBiwR*TW5OuJCi^<?vwWIeoHH@>ET7-L87p32q+hhn
zXYff6NrEyr$2=Iy%M8o4OT{lkDX+H^8Eg_9$}~)h3UV@fz0JXNN^0+v3hJV45f|9i
zjH8JmDOHOl%l4>f_OpbAFVzeLiD?%{dfkdI8!z|In7W}`H{~7E$*@p@!R;x{`mdS_
zr<zdEcK;b|k~3b#;Tkji#+?7Swm7iFUiOECk2eJ;4VMX%ddqIaxWm)2uTNfec?YxO
z>O?e0RSVvAY1Z_!8yWuM(rbkzGPq>*6)EKKa0Iaij|@1wygn^~$23HwXi}O_p2>zz
z%NSgn9;C~H2e8@#q;5}jIR)DHb*+`YuJ?RO?62gOR1|4YXUk3ey;N8~GId|)W|tJt
zWcd4^t+@H(OWwrRDXz)QH`(>uYc#Ft=uxN<0ux5OZPp6ba9pT{4U$)xS^(FT9r+W_
z59mDFd(a#@V3M>7qs%@3nB%q1qmfp{v=EYdBT&l@f-k#kAGf*c{c=^X9os)~Q)@Gz
z{k!p{@Wif0$&yu=^hLa>ScJssYp!#31X7fhPwWSdZCF3zHPvuBm#*;<X>IzLa9tp%
zezTf*bY;0hJum_lICpI~)rmf#>L{3&;!*1%txFM>8eEz;z?6F*rK<g~gwEmbQ~WZs
zB45)Zq7jR^YrX%7pna~KXn#T9a%}G$&=|XglEB(Ji%kt$m~HCqa4q|_3T5oE<F`=E
z0LSW^!~Dp4>91<samp1VrE_uh9Em<&zXYvOt3NuhT}E~-W-O_NE0Zp(J36($nTzMt
zZ{_@AU^Gcd8CkEc2mwu=Q9awf2jp(B^EGK{0N)>Q&_2~K^M3RVHdFd74ymfk=jfC|
zB400$KSwq5^2Me`URe1%Hn4it^64WwV*)u+L;olzfuT;)6g!dF8rXg#PbSh5aZhbH
zyf}aCdsfi<F_K4zhNo#yOSmK=rk%M^uC)>8&0c&m5Lr2*t*RVcS%9mcyksH=M)~!O
zdtuwC(wpJ)6nqY1l1^-xYSJqj+chsIJsd_=A5p*A3y6=I<}V9~Z}g4&(qZoyo`3sd
zZC~zn-tVRlnx4pj_o~!*r4viH(FvfL;)Jog136(kd`77hJzM)8XS*{;k*<%s**9^5
zGPB7O1OFAZIkQ<H{T36cb;BBaot&)b5jaGD8n`nj_|%vG!!|HZYEaEPm%H700z9G0
z+&<6T5IZ^TWil99ukR6-HM0DIElUgbXmg@5O(lyxRR=P!w>N7irIwdxe`3mbuWxlQ
z7Mx?8&?EiF1urbOQ}pg}lyh?Z@mdiL3);j*(MlY{YIZ;2q@3eR^dFClW8d{k{IT_1
z5lzc$Jxg-MZRiYU**(srXn@Z!mRXOS$(<g^x=!Zw&1=EcK38Oa@_v#^IZ=KhCuEr?
zcEW=86^XRVNo-yVIQ`LiqmFRds8icyf)X&dI2vS7l<ovx-_DlmWcu^bzdDD|^$>kg
zwl6!xBCySnWM{F6hG}i0+=wn9-Qc~M+R;pN97a5BF)mwkgP(v1^44oP={{aevkx>h
z2y$!VK9iznA+;(S2EK-^>7nF}3I<Gib@}#MDXmnh>1KWhi>gP@m+YnlS+#2Jq25@o
zBRB!v{25pCwrR;lC2~C4YiM0gD<y6!X&S3{%d)dix0BDHhHWQHtptVNBb4oLCbP5M
zHUPgPyWf9hH^|_9j(31b#r7T3$E#d1E&bM(sRHh!9knnQM2-}fYJ<9EMN`3N^p7F~
z+HYW-S39K0f)Fb#y?2zqs)w=~b0>%NxjB5XrYG5(UfUed+MxJjS`w$fCTQD$LvOOD
z^iUdp`~jb<Jx$@@Ff(?BnE!x$0IdB+^IA}U-BkgiV?!yha_LX6ihEq==`>psDQqJU
zQMO=;pgiyhr29wNizHWRCQsUt%Bb=B#OE+n=8~dw^mvkuPUD<`d4au*tcm}AX7kTm
zIrQ9V74LODKPzl<(1UYKdT^lW&$>)aJky^Z14~@+3hR%i?&iVtiv)(wc~y<2{>Yjr
zFC*i@Gxs8&27O%I4vPF(C6N^}5q->X<T@z>toojHmr)`8RxrTc8d)i=^W#7Tk^P$<
z{gb?hmMs}&y_1ywf0}#*^e51$GQYA99)73N1K(*MH}ZXb?&~+2HN!ilQymv_8Uy-U
zB6|#E1E`#Ocz;W3|7Z)Tb$X5`o8spv6G8PzchWl8d4^d)6I7oUTuPB@2i&$PBGt6m
zI{<3es^vjzkYA4}04w-ys7@}(kyiHicH)tC13?FW5un<0_H=Rp7O8q3d6iv_;V+hA
zESa@{A!PnCxepZnm%p^JpYcLLTTUa4%{oTFa~*nfmKvXixoUdTno|Q|+8$i1EraF#
zcy;%bJ}*X!XOSzx8XlY3=<3y19ee9Q%ZM$GJD|Hf8`fcri*jl|A}*r_skRN=h|ZF1
z4$4S&8#GvwJ^(6QOcQ8=K*deXQ(opUd!ve*ViRSr;}9Dc$o#<m)dO*MF{iwH$H4Hh
z`}YE&qq;ps4l#Vw)hs=3P}O;XbE^G^-RU7q^0)&~jUaa1f`G=jClk=>iQq!G&$YIV
zTeskU?ObdBEWR)|YPk{Db*y82d1T$Hsbg2XGM--`0dhYT=Zu22U#-po|M`#9ve>#P
zrXs3!^%?y{cGx0HvUrvnwt;g0gKzM@8QTul?kzP)>ud(?1os{O*c$!^pCg5Ttaf~D
zoYG5BqLV*>q4b7vDGV16%dBE*Vk%6GJp|3#>8ZHb$bLSywB_|poyF^NuN_kJ2fYas
z5&bP5I0N|2QhQ=y!x|tA5GdsuIPkSU*9^R|)h?DI$dKw>hVwfKss4_?+$@OYj&Js{
zoWM>w2-8C8i>XfRJmZ3as<7?e8-wduFImB&UR~Mvr9Qqf{A>};DQr7XCnpLICXN49
z=0?aNh22ZI7C6k=YIcxeQ6tP;vA&t_37=Xla$sv^-s|K=MK7iPc`!5KQDUf3Z_~pu
z?7;k5ydFF74yJBAt76`?5fECt`+fjJr5KMd%AApFeN!J<!;AIoZ@~s_nB%aNow_jO
z3n!Dr&N{Zt&v}l#iK(YGkLmqm9GX`E&6c-)#80Z-p@P!q@+sK1DmndP@ivt)a>Mll
zw~O(UOn^z%c8wG&e|YD6eytwY&A^*RoLiJ3(}0M?4s7mNS{1vw<FNE@WPKDc@XA9%
zFlTGna&tfV1z4(SDQSsGb0;6BC#_LPkEFf!xe0!l9hBMq7MnlF1WOi&6h+>YN&MV-
zbwe%GqZ}h`yj#%4IMAsQSeVf}#>r%Gq=%=8d?Yd_AqSpESnn2h1kl9;YkSyOr5VF4
z_T^NEKmM%BgH}q?(poHCJ1%gpBEO24Kw`qrE@x8DdT_6com^)x#4*d3Z}6}Q{z!a1
z5Lg)B0?VG(u|Hl(<U1P`Z@)-z@*Ej3o)}zNr7__f=2<6nJ$6n2>VQx6?%OgBXB`pK
zE`6$pr$tL&%#{hB^p3r13Peb;U1RI3&W&9YQ@U|heTmzd-uM$;Fd(q_7&Vj_gS~f(
zf><bx9%{n(QBP<+Sk*JsDpH-+Hq$@CDDRBk#%sm`u180YIcjlk0{(`H{M1=`TG}t#
zZLp`M13<>{r`p)N-cA{+`)?%G72u$$!6``XKh0_u*W&>+^+3n$Ypv@prm|R#PJ<0O
zn)`OeZ(DQV`Tl0WLpRtAxZ?cha_J_4ETTqFT)yq#y2tQ$6Zee#->ep%Ffs9_jqYMn
z8L$RN8djT2D~{a73~w&XKqc=1CB8W#25Nh93udZ&XDNv{qP@37Lq933x#}iy5HIeL
zWKV{nEZ8!5_1lNlE0#a47`VMNmmYS`t5aXHsu`;z{1gX6_2u%c;Q;<3<h8bGVoUjc
zZ&qV*Q(gg&qOOw-?rn`To;d6BwMnP4TX){6F05qB3++r?=;BiLRs?KDY?}myPLN0d
z_Gs-($=g_E+$5p!1M(M927Ps6Cuy#@R^vbU%z(|~8>QNI8B?w#ClXgjZ#hqlHqx2l
z_DS~`F}&eRSs(1)W^+O+8(t)#Go(GBQNNjdxT3T_C}Pl4YXB!t*+_gOd2YgPsoOFo
z+KoM#5Qh~fS;bmvObb1rERnGr1X7HBJ#}gbnQPb|bZ|Xru(Evmq1Xu~?{~y?(@9Pf
zLjx}XUv+_JC7~QlgSqx%T48X~N(fy7{HH=3d8KL|S+~`>J@Sbl)56TrE=`UcNXS7@
zN~Y*2Pt#x5_z9q|Wnv~6`};*Fa)-jP46@9{UM*?kZQeLz9|=|)aalKgsX1JdO&5DU
zS1R*}m&*7{9B#&S;JKKNf_og`cf%vw>sFqUkanDk<%u)xd?S*IbkJKQs`B+;U1}A9
zulKjqZ5Jjul`7c_t`VD~VyCGQ0ZtY3c@e-i{~sQMPx-<muk8m9!2^#i1H{%&3*ZAm
z|8r{hf1vfBSO1hi@kVJAX!vagG_^cM`ex}-yhgdaGq301^e#}_Z^>H?tftpZ+e-`-
zUVSep{(bYyaTC^bD|`V7jPF@xbO@Xy<>ceebVx{mbYsjbgrqPyFf`iy_`(!Pk96r&
z(moIfAAS#)XtFa!`Esnm5us**>^hyXj)M|$iZn2&GHSQmvDH5q%}+W2hZoPue$ufA
z6#tY!<1LSjd1Hx;v+2us539d679x!Ylv=AFg{-&zaH;LE@JnUk-e%z^$p6a!cN-W$
z`Qm&uZW=FR7mfGFtKd}dJe5+>`bMYrHk0Ajhz7<5Qe1IMK!25_L!1pSk_{#yEGmTQ
zJ;+gJH+6~qn4w21J7&mV?3(t*_24?8ak*JU;p_9mlN9x69Ib?3J2tgkgipQN5;T*<
zctv`&7A-QkBR>XZz>4Ly*gK{qVE@ak{5FjyiuF+}c5&AR!A?+hb7Ps^QWw?nSw&ck
z1BDyzVzMaJ+g&Ae>%h$STon=xid{OK;WBD6#$RTPD0T|yB|H-cikIFj;H*HPg^O*3
znAAK!ffu`mROc)^-NcZ@ds_xZPuv^^)B^-6)dMKz8Ibsm?XnA!-btdBFJ9bDo#lQm
z*+>)*D^2Y`E~?v%-rw8Y(*Va2IcGYv`6$=OOb<~R$KMK=0-ozfn?2LnG-$`Y+b%02
z;ut0*GFY;)hWC&N$@Ng=fu;XNi1SulwJ3Om5lE72(Ct{lH`pPuhOJH8yz4lTWQK=7
z0gzSlb#>G=wj_oQb0ZdQcM82Acp_$rl`y&Sm667n6Z>Pi0oq{X)0=1qRV4%q7v%1J
zbR{SiY$p-q=rFY{Q<mq%TR+y>SRQqo&G_`&H`?i^rRxU-xS$(GW#bL+TcA+u3abjf
z&y1*26|e2NpSiJpo1K>Mn6p75;t6&z8%mRY%S}k4goC6#vy6IftBG{&xNtVzw}&F2
z(+mbvNkaH%yy-8Ko<CR@#}<a9uj>Y}*y$oG1c3<I@fnwL9Vk9CnyKWS?}dHc<#%a3
ziU?&NPS(1|nI}m&WF(zACVa8zx0}iiOZ6Lg$r}zdWO|!RclLb|jZgqytaUIlA<+ZP
zdxXM|KSe%%qEU!QLGJthi#3+MA;fqm8RlP;7j`HHw0UZe+-qf^)&^LyPw#$_0yx@w
ztprtY97g2kjw9s@zOlwT6p_IslEyF_P&=$a@9KtUmuQloEzSibg0I%WiMU^cRN_yi
z#v`|=I;57tZk(_Il%fmlIw2T`YItfdQV-b>Ky5hIwo73S_Irgrf@eZ7b+g<ChO^R=
zzu9r-<_{ghW+0vbwzT^Bht8&;cU8iMcx|tTBCU_tJ`H#v;BmCw?e^`>|E31SI_|K$
zTJ8->YY6rG*Sr}#STf~1K;;5SmjY^f@FUQX(k9`y9AkNs@*G^>>2QPD86>{F<XhIE
z2&Z>&JDM>o(>v8r9=|%rShts~+D*GobSGT`hE$fw*vHw*5f~R8CjD{XvZjOS)@?n%
z<Qr@?A^)suX})=kdHPh+dq$oc0?VB!GKvO5Qu-^x=9fo=_0Jam$b@l#$imkd#;8ZC
zQGErUQQ?MABY$yaMnO+-M?1R(gk@=A49UQB9?vIzzY-hua&>f9J}a$g@vnG}-aw>P
z+`4&OE#oXeX7mNd+PJaEa8?<w?%}-CJFs(A(k1u}uE$-*>A+7whySiJ{9j7N|G#fQ
z)=%RxI3pn_5DGOCu<C_c#t)=a`_$5)>NlFvK!GB|0gE?wb+skp^u43IMIDFVi2P~f
zMRu=Py}$=SeTfP{TtIN{rRd?JCTCW;BoEv|y3J`J(Mlbl{2^~L+A$SGs<4;HtHn|z
z69@;Q@33o5kwk`Asws>bcD(JOH3_@airAM;5!FjOj8aCl(tt*B&`70AJC$L98?jDa
z9Te*^5)uUwG67d1Kl}%F?LKKqW;cyiyzD|O(|;8;Hlzt7f6sRVN4k8jG;F6?CUZXu
z2gJKt0+B@`$4?Mk2WM=N#Iz#u{rNGdn08B&c=*y#)q&K1MJvLB9am0fv32DcJx<HJ
zbe@mQixk0?N);h+5C6m2vd*OD4PQkfD)xPDRbeV|_9V0xLD%V3m7f;-!{BO5{@J&V
zm4m1db`rx(rQCx+te)DB29>tT*bS5fRWUBskI|sk#Tyk(-^cHpD423H>MU=19C*i-
z?JVcGd?Z{Okcg|RoQW@U{;=TkM9rHur?|^jC!&|8GU_YgaK}9nR~zIb4M!igyxNw)
ztKZtU{4`#2@^Xk3+g13N{KsHgl7)lo!M5G8l~F6WM~;8YbEV#tD^9w$aG)?fU#tUa
z{TWZ`1W+mR(9z!@Cc?(wwzO_j{Fnzlc<0#<7_LC5F`!U!Tq>s}FM7EVU+y_y-m=Ze
z-<eU$5Y&TYAsY^qDRJc2s#Me>&VTG!>$*kjP^Srhl||EXM=h3Z*TcE>EniexZjtI#
zDX%iz#v|!@P<~#504v$fBG&EM#k_~38Tbfz5>fn*j0W!{ZC=U*>_n{ZVZw_j-enIX
z9~P;TGOFFLJDs!~-asg8N_s`uX5gW&yFnJA-O7Md5Tx7Z1;0InSKl@HI2SSOMEF8u
z$WT`jfQE2cu#<C(Nj`v85E&)(hKhU>U%~LqLS$fb%kn&iKf=XS{o|W1V}wD+5f%Ko
z<wE=Cy%}~uQ1*Yl^%=kh?{3m&GA(cdI}~YV^&JC?Tc8y-HAhkE?L_f;(J|e}v1I`l
zhDYA{`I<?XpP-I(3O`_<TH-buOz$ZgkXj>42T?^)XYm#g7*mlBNE|=t-V!n>GtZg*
zg3(D&s;RgsM}?-koDeE$WddL;#>|Rkhdi6S8z|iN=KfBjny3)=R|XY0e~QL|?J9gS
z=S7P8^Q7w^JxZyO#-lr@ML`&(`bkIwR9XICV)y@o2EWpH&V3TaT=p9@vP}BvLu?qk
zo>9#($Jv^H!-DZr9r+>AHH>D)G^qy{tv)3@s{nz{xot)f7`~*RY{~Kt#8=yw*##uu
zQ+XmO#&;Z!Fu7**O24rwj@?v(Dzb0AfhyguVNg%yi}0oUh_rzZv2dG4+9RFrA92yN
zGMab*DtWcusH$oH16>4UMzz!RFdGG=8r&eB0M*DIM@}=O#CpKZxF+j5<ik1<7qTJs
z9wmP+;95)M?J31kR}o!9`~wc~V&m()BE}r~+EHPzwsG=uU9gF39*k0d@@rmr0b`!z
zNy@>6;f%;mqSdtNk1fY@GR1pGc@(`)3VyqX@s`p13M%rXl?f@-!nIs`6pZabsyeU~
z9mcK!P;H=0P|Jo928Aq9#|2T&)MSuGZ7J14d@yy;1DI7767*?<C)lzI4rEsdvB2(N
z1;@F#^H#<3H+UHd&%(!lBE{kJp{QU#E&<=Ev$v(9xTSPz&fkRm{r5G}tq!~oA3UhB
zGphit;ZK7&tQ+D7m6cvbC)9Os+Y|RiprC{ZH$A>-gqlhkXs`oLM^T8r+z@`_Nm+hT
z*P6M)r0c80GO?1vY%?vvsTnJMQx)*o2;>M3LwNT-`JZ<IfQUbxVh|*BC@m;%(+;c0
zpM}~JyxH-O2<sC0v6b@aGVyVwC*X5ip@QoBnu16i3e%2DX=gWgd3-m3<9o2Dgjp%b
zG1@xnGp31d#B3C%rAzR*XcPXl;_~h!UqH=HiGi=>Y}6;b`pFIIiaO|V<Iy)voKltH
zGdgm{nbU~ke>Y*ue(*Qe)cqy-rxm^>`!LC0zjmytra9mn<OHSs>ankxBklW54UgR!
z!ijVaH2E<}`yLX`ZpUoQlC}<Sa0R%WtK$RZ<x(Xga2<b&>bQ#uRP*(H1?$JQ^W2QW
zKJ&?pzEdPo#u5BZ#{XeL;?+7q%we9J5h?cT<!>ss_z&THd490w`>_IDQh0%mP^aS6
zj;l-(+wHhD&R;o=B^!C>IFYirLxFZ9PaKMJY<mgohc&cO6Ltp?-U5@G7mjxZKBsBg
zF2%`|h!Tl-*PDQB%50a&Kzo+$Qmgd8x7XP&VMi~vDp}RRwEmsW3G2EOu6cy>*ov|i
zAnZOe`?tAyzq4yg=oO{0Rr-PVJgm#|ZxCbs$Xa-bWIK_Z+RB61A9xs*MY$t5U2E7}
zLv=hKj&Nl&anABXOePL1T_wt!g`7z>+e4u+TO+}<C2u?0$I5{AH&e?@J5Kicx3wPb
z_$~s!VV_#I?2-pXh2QfqHW)(|F`{vJvvN^p0Z1e4VS+ESB=!zVx2oyWZEkB_$k%Ut
z98*)7%-b0IQ<Twx3S^?cWfGwJhyT2D@J}E)R6RUtRM6y#z%w@0)}#5M-6G<;tR%UW
z*g*CKqdsvI_IG{bnP!I1=0gTsF6#B)(*Rll<cJ#9MuN-iFcQrHTfUKcN2C!TML@^n
zd~m#a29O(H_7Leo<sVh)J`t04+~_0AqWoNy0_<F1;W+bz?^V%O&H+2j8!6eSJC%G$
z*w7{mC`g<>$=LU$riigj4`5IC0qK;Pw8;O&>jCo!>bEHnZk!$XxUY+dj2A$r3@F=M
z<8q+zihlW!08z7e==$a9=G$k7A7bdpVyY9WdV7IUb2w$~gc**0IXDy%Radlqja@=Y
z7qPo|4wyW(IAzS8n~Xn@awmo2Zq2qNX+XW@YMCg~QJkQTa!JIM!C<Dvxyq_(9Mn?2
z)tLU8ND`Fti#5JO`e**c>t^iiP=)iycjUZ@Zhz9DcZ5>2rzTSXoa)6^MU7q{Q(c!T
zme1BL8Q?iqDI(Jiud4F8>!$sA`tqG+JFbSeY*-INsW9K9=%&#$kdWV?JliIf@LI3s
z>D&UG4In3J)^))wVZe@y^_E==v|WPQnb^5%)-8nr$XFw9e50?xPDHp=O;|#S#qk=Y
zI%5D6E1FAU&y;MmOEiiy+Q|oJhNYVi5W?7MVl~i#a;wY$6JJIq*{;)SToPQW9j&16
zg(DUvw+;}j>NK*ohELlr5#+3{JKyPKJV{<lXfslRrOE)#(7i=?R0wuPoFp$Uk$NNz
zH+pbzzfFU{hS#`l3IhgpInee(o)kR2N}2i0XuvN3*spReS#BlYfmpe{y)T6bW#0u(
z$HXj-%0A>jpNf<9+uVw|bI&6M>jyLPF7+=O2OIY`)qnBE!{s7-4U!GzK`ZxLo^K}$
zGr$p@9SXZ-Uka~Gym=QfXQDZ32loi%*p?;tK|DMM=>T^|{Lt19_G$xSFN&mWOIA1e
zx>ajZ+V8(P%^ks;EPTj)&m9-v+bmJacfaRBy@8?t2}2EB7i=W6S$!Sn+-r+Fj3=H-
zVvCWCaL81ka&d+RvG>l8^rGj6^$X#pM(pY%0t1KN&}sgo<#JoJ4aroV_1MaYJ@jh0
z{-{gL>U-i=AH2M23g^Ge@gu~(C^z(qMD%F8gKJNHM4!Lgpl61w1HWwlv2zY>grb}K
zm)SR$@pxv?!IK>J)|(l2c}czl&#ygH49WZu<IRY3u+FW;+7nB+doOh6(*B6&sEeIY
znfS17ZsVrq@W<f@7(47#GUAG98v*`u6%=oo8#$MS8bP8)a6O~PG~%{;0A`(-PX{(C
z6OBr1`t|(}{j{W75k<n6OGNA%?oNisOuMepst;>4uWZLlx}Wr*++LCw+Z9MMz#V89
z{cg|%>t}N7<&E%cAY2;y(q2ZPV4La_7Sh8bB`5b%eZ*cHk+cf)TkpJ+7FtZaHn1#i
zp6$|!<+YK&^60R_agKaRdY)Gt`*8^!2|w5xSGz|mzar?h?|33EOgg<n7lj+Jlo^%<
zvbC*Ofz0njl@Rv>(m<qO0=dtDrJRQHWqRQ_%-BZF;FAG&=lh*^39pomoq)>OY{JS0
zUHUB*9&|9;1d6H$0M!zv1qz;c)}MDy$c}r4jK3IFBi|1e`M^Wg<f5Fx7!%yvjB-L0
zlq><K9Xg>a1Av<H<s4Zedmnw@k^RO1HCRb>{)(Ki<L)^{s>bjxA!-<hNSzHIndk&3
zqPGJRcG2~laY_T{_|68E{foY&#G;Kp=PQ#Ih2wB3V8P3{1ep?GA31_qQXY=%pmcQu
zHvB!qZK!hQy^@2CvT%H50T8ZR3V{Tqk;GITWrhIeonqJ1GzxPyj1+bN1&ldboNK;~
z<EfA!wgcXMlf*FgiK3LTK;ZZhc-n4C0dCcYy@uIPCXr>*<wiezA~o1EbH_LSWDK4n
ziF|RTO43g&4n}o*7QRx;qmaN&25_eS9D?+e^hiHgs~&9Es(DLJNDYX$nY)<UzzGx#
zJ04zhFQqZ=z+DQCZU=+88vI)qIEh4>#@FCr#m0?qG@<AA5|Fr9pU)yU6tAWq1(^om
z%}m$yW&$EI)z8s;<OV@R{*<Fbo54~y!;~ES4P3Z-F8-Y`j4J-K0;WBDL=0KBk#we?
zY<^titwrbn<&5Y(IGyyvk#7$CAL%|Kl8&|>eUqp=kM6(kgu)z*>YJu+ljHDIO`E?L
z)B%Jci~5Ki3=UQj`+9qsk%3E1{bx+C#GRHdFPDcA3!5P9kz`4oziH!L?jhsY?;zY#
zMh?{RTSOdpVp|SV!6bhA-}S%B1P8el0P^sg{=Gq7i)0+)X)off^tt_kW=^JGtH-|@
z_BX>^cvC}bCQjGCap*t)^-0IpAAZc;ol7NFWx!27q#w71(fIFsmp?+UC?fnc;=rje
z*8IOA&2Je#uba1L8S4y7mC|p5QcDKHcE4DUhUzUCyNfYM;y>vZB`K8)fC{)7eiq*8
z0fAI~o?4s}eG5MRKjve8r8%XPf^n_Sjy27Qu4&N(rBADNQ^5|MN#}-Dd&D1$EN1wx
zzqfFB$Wbez_0`RUB_q6&Wnk;Gs7vet5})x1$V$1Ea%(D>Y0;dccL5?9(1!9@IBTa5
zNK++(f)}-LS~$sbdx<~-L}<GG@`k8q_<aCX=$1#BBib2n6DGq8f;mwc_7cVX@}>*y
zINZ^Hp+~?9PvHj@mpbjC8XxK7T~%=S!GzX=sO8J-Gvz0W^CxuS2XhU#qSk)s*sc4S
z@_f61F=Q`U-RA~F(eHK|*G(}WQfB^QaB;1Uh?9U>F#x-a1#13+fakRPwo8dhla!KW
zM~dH!CR~>l0WyC9V8}b9OQmfEGP`?KBy~3kcxTmyT)Fw1Lii=j<OZ_*nB<GC{(#>C
zP-qgf69@Clm1?#wYiVfm`LhZXe*b{s<SwJ-5P}DqQ6SGQE^nO6?vpV5P=n<y21O0#
zs6T&^H~X|A#*>bX<Kub&frLK>1UDaIRcc)0-a1`0-kD@~c$!SB3}PKWM$`di>hd?I
z30;;!)r_<C@AHkK#Y<=<MfpuCv1UqncRu4Rgd{Snf~q&tOE!3bFEh4XI{fb-7&T?b
zMLI8fol$~nWJdA7Jn{SH5`O}K7|aEA`?K*Y;Hv6n84W`49rm*>%_7Ts0B7C%SGODY
zkT66AgCh2PIT>$KR*t7S5P_P)h=CpF>)O3pe|&VqXkN95Xr>Uy-85TX!a*I7Spf#P
zCi+a(6F^mzP~RrK3vB&s>H%C0o~vEj6TP&*)5V#0lO3NRMSLsrz44}J=DwpYELBj?
zM8=pSZXw$Jtl9GnEsTdqcXYq&IT%s21V4Dkq-JJK)U>GYq4BLQ(Wc`x{2kQ*O0%I9
z-L#}0RU}(y>(A+ssxTYxo$5slxLb6HM_Bv6ZX6%c2wo)OB(IK3l(}h!0*T3rV3e>H
z>{Q<5wn;-3%AQU9^YN4Ok2TN!<ND9;-(T4OMDs)O?z@vesY}j0oqF-$?A9+Xmsah6
zeBg3n^6uTA?;R|D@Jrai?;p5dytw~EZtizV^jws{kw|lmu0bLZNP8rdLtiuGEi#DE
zvKwstI0YX^^aIMsy>*I3KDuW;6@s)Iqc34uOsh~|_*Swq_@>A-pA{A!X|_;46)dr2
zB=A9dpk>o%IxL6BFf+)g9wi06#^?1SA*lWd^fP%v+Pjp2HdO7puRY8S_T0KIR`)GA
zIeG5sB=3GVtK#&Sa~N^$x1OSNL*H_)`ycJ@a)!ED8N<B5#(}62`*h~`HD?jcV;5}?
zQJo#qD^66U<cZaa_;J4pP$CvqHzOXfH}I-h^1t_u!>gWXS%!1BOOXrREN^MQZ$9>-
z&u?$IRoyJpL`SC7(^(1af1ld*>FkJ-UL`B0=7(nFRaWyAsY4>95HDi+0vfuJE@8=f
zuYeqVxmd4|Y8`4eiyW{HMVJPP_S5m8<>?5_%&b$MIY$T5l0WlBfeY=9G3;VPs9B_(
zD%5XXC$vYuC2{vZ-oA^pM%!YVt5}3nBTpmGI~*lIScM7_SE?J{g#Rr^#iPxTP!BX+
zQBSiAk9KLfkmYZCgn+!vqD3u;x}13#s7B(#Aa3K)i&wKUkQUvBgt>8%oPEv%660Z1
z+3CnKE}0Ec_wz9jjae5;?U$|%D$AzzuBhc)L5W@jML|sXhx^;?D=Yc7PC9qL6@+#J
zf5yJ9jtMqJh$8(*bAT)on$DzM%lk&|6R>GC%#JI3Yrx}25o9>O01Q&>@<0P?pArh9
z2p!;G87u1kTlST<7CdyMg}UZ}+3`RVA^n$G6e{cIRNrDFQID{SlD*6VbO3FV<BXXo
z<G4q(W*%u*i0nBhBjTV}-uBntda$>4i257Rrj`mhc$UBrZGuQi!RPD-44bx&F97#C
zkaryDx^5w>#}Oi>kpo}3%Yisq8nzJM)c4}os#ka3wKuunqp4|M6EXksdVxX&Et~Y`
ztk)qoh`W%b`1CpircNI+c`3{TeK^sDe(dS&+FHBqw*Yszv}~^iUfnX(?#f%Pz>-xs
zWIeRp@AqP7b1uh$ybEI0HLmxEIiiO;png@OO1leXvk~_sM!oCwV9?&f(;pN}be7Dn
zj%q`UF}Inuz!Hy!+6;@kIqjt@Y<1Mi>}tF9cruXo5E5(BRfB2P?9##*p6x;wjuoHe
z`F7XJST@<Vv+HioW~yatC^eF6W!}8qyp1-nBK%;xq5|Ouj3<NQ54}d9wTdVYlpU=?
zFLoO_fu7kPX<j0zcU34xTA*7kApx-lU2w%n1lnlO|3;v|pzFEvsT-XLZrb%`y3REy
zImHqUl%mu-Wy5-F$)B4j1SGu&#t;2WhG#zZz|1ToTchg(a|8`9*9J^XfRm{UUj*O9
zzYn>CmW`PFqj6eJa@v5RHDIVU;ugJR)BQB}NHeH5h;L#!kTRguXpe>}L|$joZ83(w
zbU}+o%k_;vN1vM#gXCw-bSE(UDfdq7Od75}w9XCJx`BNE6LjWNYV3M#&YQbYTRpJ+
zJrah&<9$Aq`6A#RoV5vRybl!rkT9zT&l+i-(;f`!_}!2@HqZ}l-RkoN?}^{{`CTq<
z2<d^AEb6_02*2`{la!_`yODECgdgL@(UW;86-te!-Iu8fk+IzhJDhBXdm<%RelnSt
zPlUMToc)W_qr9etQtWcZ$ewv0F48+W&1KnU6ZCD`{#M$Jf?l&0EwAXNuek~)=`&Ig
zYT69o3iZ2upTvt0?Dlc+o9J*;PY2cH@y99Tj2UZ8%+FoX=n17R^P=%zgwwgtNuAVp
zR=*lbQ?S0$cv42#)>ddH^@jWOhmnH(Sq0{0))NIK>qb!-tL;0P)7PU1I-K8_sarr;
zB0CBA@T170L2{?R*~!Q2fZT=8e;P!7<6rWq3mg}Fc61p#Gv+&Y_z%K*to+b<5dx_E
zs1Q*yEd{aif8Bpnrd$MckEQI@K<xOTISOOr=UrRVW^O`SfG<M9e(V01LfqgrSI#_d
zV&oc1)Lc1nTeKC=0M{bY7X2&V-0OcD{wI0(0j{j=qj<3@Gy8^$P>As*T6dAv%a3O?
z&^teOg`z7IB16!oiN(__g^_hpuGh93Gntnwskjw|E;_CEf<rJ+inK>Fl_Gc2zJ<g)
zX?vNs4NrD4*<0KV1?E*&x~`&-^m$A4!zHptSB}a+YsQSXjoGSv&Wt45Wt>a4#q1zt
zMUn8?{)g{2jDE#~&A74MhRr7|APl$1GfR*ZYu0nOtU(*IHGLfsF&Md!62EWdYd5O~
z?LxeewSYD>>;nEX>6%6_LRe`E-0>iH%L7GOFV|wlhD7S{`(5ObWhW>8@7T~Dh}+}a
z@NTiO{LGmI1#2cRFxw3?1C}md8x|W8Igi8UcN<U*I8~T^!46!Z5rT$UWMMc73a#1m
zijf*uLY>fMVncOWo<B0#VnZS*aM%(U5uK>onmf}75r<^Gm~v9?`bhVVf=~sdzg=x;
zjh>Ku_wRycNG*?k>mmD>*W?0TE9MzzClcaDa?cUIj@SG74Y{QPnl2%Ai->~AV`glL
zbd4)4=a62}Yx%CX8>r{iF*}bJmLaT31sKC9E;2jofJlOwdw{6Jb@ALPKUErMismZn
z5whll!$4~n0R~|us)F7Jh{Y?R0}Aw1n&=q8-7O#|e9y7AOOX6gXR48z5md3!84Wu@
zxFj6b<{}J@p=hLz-N<QWu(K$@pSdLZI1hyYKjZ)&VAH>W67&Bw{P*%0s`mxQTfO!~
zo4?A^*c)5Q35(t%{52t{?ILFKRmzBBq)R&@e<=8yFAElWM0-fTWp&*jO;(nn90hOf
zYoh&CJ$J~Bv3Z60=}+)Wg@_8^uunLjqhnCD=ur4y?)E?NNGO>9Mjn9Y09B{m_D1tC
z0hZO^t~yA$b+zH&2t&*2E|Qgwo;cNIiZ)d2%HyW-fFN{wD;Pa+dTiudLJ;5ocrvdv
zQ7O_EJ?)p?YRg=8hNxd<O(-yvpAdzUp6`8Qplku5X3y*yGi;1TpqZT5pqZ}<ObBZv
zWJ1^@{_>Aj-FWnl&Ft1Cz!%M!V`MW!WyP&R*Qf((L>e}2CQ`Wo2{lyjTDSq%6plEv
zrVv3!2B0PF^IHu8x3hqWQR^z-SxGzGL!`wSb)8u6Zhgt(2%gOR8J;bQym#*Jg>&=W
zocI`p*1X%y$B0D>7DEAEyt6FjQ7`K8Rre5e!>G1;hu|^HW>_*re(c!C&$Tj@pmIp)
zd3dCMx6edK=~a5L2U^Z!*nUJqVJrFE+WodCkf3aIX>3<~_233WK|_D$v1#aslZjUe
zYGq^s65hfgGEa9&*reFX^*};*Ry)0Yefcme_0}Dh3yMvGWSMf=+76M|(a;ljK3>pU
ztZcs#NLC$ffW!Jt1Um|2dAD1$@vcv19t#1nbA7J>nF4hQxm4c7z$rvtLep?n-2hCV
zkKZHyVJh(i)sO7;9@)yJJbj;|(Ad)P8(p#Sy=JE@CXifTH(c%%Lb8X>{-siWx%)?}
z0en^WQOpjB&t+myrRcsO)%X3i)^DKE(*rPShkoQ=8fu0%kV6bzAFJ}PamCLA)g1rO
zG0%+CU1YRjUB_<{7OXdU2FSS&rxwB`nwEg;#~*+a4SFUV(qixNG!7El{lr{83+Pcl
zUhh=tKT_@zHUA320z>3oJ)YX4cY0Ff3_^#lo7VxaL#FOa&UMJZuc9%SnDUO_PF!VG
z0!;nFWwyrQ19SF`z~WFX0v=PTO9^fGtm8N2mC%m*KyqlWW@oK%`09H}dV);QW%L2X
zt%ziM{%>7JB}{f!WP{sw?KzC3h=C^_KhkK?g|s}Lk=J(6Gwwn*1dIVm26?fCvo?Fc
zWb;BfVYx{mG62o7YSg>hwi9<GyM^Mb{>Hg4c4+G(IW1bL(W4#VMh->=ZknN8B4?jA
zvbt4@DCv;xj>n$NU}_}2j+E|%7(O#Ayvg(Q-O7vO8?xIE-EKuZ8A|>H#;7vK>->F3
z%3TBNy@z9zW_dL!Ks0^7PtxV%tuQYeO4wIt$cAh5Ql0Z}U2B(uuXjscM#P-z%2RLp
z`r=rQ7KV{;D#*G$z#f6M2EOTku2QpM*2x1pC=>rA{ZbMw&V!<5!kJVe?QWjw$g7w`
zPMu)1;Ud~X-M30lt7KX4PAuAgV{t5O5_k_*+zRijrd>u`Xb|tr9?raUog>?Hpdq#K
zpLYQix=wR9mJDtI69EZxe)y@(q^%R7U{qYr%n!s_!s#v6aYSC)OfQv1D@rQP{8J4Q
zt8n@P<<Hq6>&KR4wd(9=upb3kpR<ZRywT2eqv=j}0AM6`o&Q?lK;2L}@GC0?@Q^Cu
zR9BgEK#yvZV%HhkU$S{KAJfOr;`=Rm8oL`#H(EXl%(^sE82kBWNKk$#yZ0A8joG-e
zlrm-d$6wfw;FlxI$`>9efc;C<Y8cVV%q_$@3UvAI(|3d0IhWvbg~}C|9?iT^$QV&2
zq=kyBeFl|2eP;^@`D2>#r(d>W(M@CHuN0KLLZMszcTvjpYm^Ij4!nxdaaPDE%r!s1
zCqJE4SeHBVNQ$=)8I2xrU~E4%yCH%@ZH3{5F8MX+Or8yzEaB7{Xge2lqr?M=-ooaJ
zj)A}WKT*GwQ)r8c38gUkDxSZr3qCLmL%@YD>0v(I$rr4wSbwJVAaL@zGX+c9qDU5x
zOH>@gkgmTjFx)7d*!6?RFCH>b?NYG`3edcI3yDNSizX&T_~Pe>%F>0N(cR5WMGAV#
zv*N7IqTZ2cRJUvNfK{ji+L{x)oPWha1oMPs{`Wjy-GDa+g)t@-8m@*GDJUuFX?Mx}
z7S{@B7O8Y0AHqkx3vX8K@;7&B2(;|*d7rMpv0xP|a3G;%1u`ZVJ7WPcemuj2l*<#x
zjt-i|pl|!5u~%7tDsZeqZ=qe4>7xfQ%>|s8bhXosk!ZrpcXOTSi7E~u5Yi7}Z7DD<
zSyc)n5>A5tv`wIJpiHF0ELa=}6+P1&f_&x~*ilrr7Wz3m<vN)@bFQ|^nr!QqVQm#!
z;YLv^%!NW<?qGSQ-3#j@GGt8n)$L4X#!M?(_WBm(5JX+JhN@HdLjmk6YwqOuN(E%u
z<o7K|_B*}WG*;`z2L*|%p*aeG$vU9puCS&Qu#nL9uY^yyLXUHB6r+CNdE{4UgH%D}
zS6ii}RxpwX%d0(F5IJwVr?<{r(Mf(qH|W9kvQt4zpS-nPG<^Kioh0<k%QW$~&!RYr
zkyc*Z!{s_(FlqfC8uu_Ln3<|+?N&t5E54wuT)|rU#WMGFW6=4$ioO7>N#J|o5~YD9
z=Fn$qfhA()Ui1&Vi~(WoF~)OouP+eIQ=PoY;}1R!CKQM1brs2wbLQ$$;(2N4W@$T)
zQU^KNL0hgMa>2GCQoPTiwW;iQp9NOmmqUz{-zGuUG_#A0wcA{BUhQQ*_h4EYWT$*8
zv(?XRe>Vq~9!;&UT`i0YP1eV!!sL)jH_l!4M$N9GrP;<l54NfKV@pZ(KVc&oNjh=`
z^|y>I0~ZI+oOqm$amk-q+0Q9~U<~JwWkXejYD4Qo)F#avLwlK(^p8gqi^JTGG$U?_
zB-#Hwk^yb_p&*h0NRkL1umEld8Jn#Q746UTm#}6HKw`nOHfdx<AO)&;$&*|>Q4ugr
zR0}LPWZ7A~T^Q}!{p+Jsw(4rmS3=#pPhTKyXJ94*c3gcg4X%!vbUl5F7_er2R5<YJ
zowt8Q3)CwxCdhzaUi;+-2sMv&L9ndc8Dj2`%6X$KS9G{0JkE+?OMmW^qq}vbVtI)e
zmD(?n`Yif(4S|#i?ABFIU70U1YgwkXtqgZPKW!xIfMVmN?yVXdTC8$LYU?dmRL!=V
zPKig05x(FZKN?-50{kHUZbR~M`=iZBfBe+Z&%=6mS`o7vK!WJTQ$aAXK-kXDnz5Fi
zC@QpEtE)Q<sMZ%PYaZj9n!U{9%mLC->g)8$-yQ-Tp11H7?BnJ=4V=Kg3as@_J1&=7
z2QNe}+lEJ@;m~g5pqX^5&SLsC_MX)(i1=6A`t4*^qM83(ukIOt4XgptdMv}fJMYP7
zC-Mgim09)6^AiZtnbmpkBNc&zpY4*yaqwe+f-RWy0yYGr`=&d*XZK8x*c>_FdSzu;
z`)U%7>CK}u>0M`5$DW)G4S2Okw>@ILR-Qcayqr^lPk1=+3mcDTbogB74s``H`yP!}
zSamR4d&98O?&Y<-yQs^r_!gi0z#p#Px^vMpPam^xg16z{KUEu&ZDy2cY~#e7G7TzP
zIP@pBo6+LZ0?7(=GB9y$eWK=Uqra}=8P{BuZe^ag`Q2G&-Y3OdLtdzwXUQm>Vo8c`
zalfyWwsAvQX~WrVe|eys&O5Gl>n^zD1eR{s?V9l!?DC^!i+G<YHj3D^>hv;}9fi16
z`vJvxNsV6ng@ebeZfmSjiwZs0R=oE8XaaMJMW9iGejomHb#f}N8R&UBdssTCrxX{S
zZVW5fp*f_-RZrDl9hX$;$?3x)bJ7g96}ynOX5E2aFf<Ks7&rRWGUpKDOU+lnmJB<Z
zlTqWgPA!Gc^@}{yHUlghWsvBda5P+d{Kc47szA~bcNF_dq17ppZ&FjAdXAZGm^fEu
z?Fu%2G*MB0wBXXRtrDA>BiRVc>xByLr#zXwzg5{{E7<VWzWtB#%dGoUmKu8Ex%F5e
zgl|5(Wl~Ejma0K~V3EX8d7lo|1bBOHE##y%T6=EEX)|^lJ-0MX8+_`A9uB<l*Ed@`
zzE;SY4}zq8RTG$_@zTX%*=7$1*3c8iv+b)YGVo7}S)GDMilm1FYBk$&7zMaLUQ-KK
z66WK^9YRdkeBd$kHE+xqBPg7?OUuv!=fC+2ZcymlNEc{!af(LMeK)t#2t38vj_-kb
zjVd1LLCKntSH_Id$~gqsz?XOP7EfmhL%#~bfDiCQI)q!&%_8V1hjju6I{rgj6vRS5
z%Qfym$x-a3kvmCqnsb(mgTrY(P;Ax=ExMJU*)`qiGg5k7$GG7#tBJ}wW%K0>(@ZZh
z|LNK8Ewlo`#_S<cyKA6uqSPyY=%F>6$1}mWjJpBg&$gm$ZM;Zc(fHB0NYD$msVr)<
z504SK+zP6Es6!vx!e60C_`F_*NGq8sy=*Q~mxyr5ngPzZke|(x2M+(XfKu`||Fx9U
zR+>DLZ|vz!=~poMz=)l=g9&901c$|`c?KqJoFKd!ybd<L*Si*T)AD_B*|yi4V($9O
zG=l&rv)UCEqrVnnEGC0hQ_kS27fsxZAx7rjLuMYgr%x_%MxV}f&AYvDKZTfH`%=0r
zScq;V=L6yCQ<}wn4Tu!5wN93|`tK>ioawKuUA3>o9D5FPwFjF4+8p$S7)j-l0ykiD
zjn-mkpyN?*%Bf4?(C%3qSM8vrAoNV~*k)h2URRd#+C>DZcva8PQ=y}~$s369_*1#*
zXxXfpOHNDi4%79hokHsgo9<?B74FK6v8M@48A&jn#i`><!)iX|=xMjV^L`w;l%yk7
zpu@=7H@!G$TfODa&N<6HvBEfh+PHN>g?ld`S2wc(aQ~XPoJrTn=BlR-*8OKzBg0-z
z4BAk0`Yi>U&Bl~vuwq|JeZMU>>b>r27AKbd*Uek!m?(xKHHS`wQM?46fZl<^ipnGx
zz3mvNOZV7T?Yf%VTctz~|JF`t^y0`>jFMmOrlB7DRt@V-<#zL<s;EE*vLIvL<gbO^
zD`Q9fhhfBNxFkmP|H-p$tHk9V<x?K{!?!((+($Pj4Dk=|af58Us7IRHJHFN`wC2pr
zr2XDgPt_-*WuKm{X1pym%7_hW&u+Szx!GJ`v!SO9T#jDL7rfcU?r#$ANQpJ^*3hQv
z#n!0zm73XfW5iKr-x{?ds77$M0_7LqIM{J`!)CbUB=yDfs3Omy<3<hDRD`nJIuOnf
zu2(|Oqq(QIuDqI{4-%h9P3N1MU}qB6nQ4Z4vvVL44WVXL(d9>`WxN5u+r0HEhVo>8
zhTU)$`^o+VK$$MgRcVdiNy;vVA3N7*ZJ}IX4=0pUXVb1<b3GRjla7u#i$9dm*K2#X
zp-k{d4r1(5!hL&Zt>sjE2fgemOC>`*PT#Y9h-cGZC8r$b;$W_#t`zCA)`xvi2uqwR
zvCd19$6lI;Diw74{IES#&;r)5X>FS4_ZV)J?3%NY(HKScjHlR-Bp)d#P+3^C3XQ>5
zJ7Z#A*zlcBHQdq9)=d<@Iv*T+2A@~5W}|;vX}x>mRdt?!hY$0m+UZDB2Qoi@XrVan
zS#^Jxc7yFwo7|Zzz@7{Np5Hp}Q=!L@LvNAq5$+v&DRFPTMcDlmX>2FE8}^i?Pt1zE
zykhl5cHG1tqk+JDR|5=c*7v97P&4=smrRP&jFc$S@5vo{=d4kTkS{gA@)(K${#dq!
zzg&)9d$EoOpA8at3PWvJS+e9}1C+7II{iS!?BR8`*8z=^>wJf&EXkCQ9lqH^IeaY@
z^49G~WZ%!}rE<je@}Uz|?CtKT)Z*)WMJMtPOK%gugnZHZ(0s=%2Y_yx*mTJ9pvrIL
z$XE-q&p2Rh=kQ%062JSE%-+jX7<kUet#xfYG^D#)Vx@77zT+rx3;%j>DKDWOU~k?w
zRR(+ME7Y2WD1L-u#4?(}8W&reupvOy`V7FNXv<dj6LuwVYp*z~B7L)mpSZs*OyJG7
z$|)svem3xJfy-FGh4X}!ZN2AG<VuEAd4U`6+D^ChEF7vFH+)SkFC}DJ>w}?)QL-jB
z0m2+4{zi`S2G-WCySg>s9Ti<2g<MXS@5xEy%_eLVSYnJ5V~N#eMGdd>3X^28wkiwD
z<&9QsHaEpI!2ia$e&XCKYs&92b3~ZYJOB>ZB?n5@sD?})<3h)LYp*GQ2Tq>j-Q}%c
zLBV~VwK$!*l4N|tiLT_A2BZC86tU@L)u%vRPaPQFoOu3f>6~Y4DbaAHPl}%-!2Gs=
zwJq4Gu<sTuKPG3_Dt^(rJYrB_+39mc6I<)MIH>$|iAb0YO!lAHv^&~dV4OeJ7F+8p
zP5+2>sOOBPH)xkDehKsO<TV|lGF-B1@+Sk=WU~{E5w@#Y^jP*wSYp#~QF%ds^qL_D
zzi_-r@R<#O<F`JnebLHYjZe3O3;@(|EA2S%rVzi3VISA!m8mD=a^B_e3BQqb4Qlc-
z$4$EE)#FsKSM5g7z<8rHoL01$Udp2pI0wl&X@vm886t*lG_fyzmj)MpBgd@ntq0?P
zr7#aseRE9>Y=gu}_h&pYt9p2wfxZK2l&m`XM^)=jALsPWNdV4pTC0M`@NE`Hlvuk$
zg$Qv8I-+eQc+~?qmHVG(4u>P&@*`iTdA|QuGs@00Pv&!6f1{d{!P%*!LhBok8H8P#
z{{S_Z=lDm+SnBKgn|$EAbu8!#tX`FC6yC7{v)*25e56G<aXUN3+Qb#`>v9E<ju@N5
zI%`soOa^9WbZ@=hU%v0?<oRI%`P{8q{*lF=vji|WK;JmEc!<5c3&C@HwK(PxP!kx4
zHFag$g5|K0i%{!rM6o}L(UcGg&u@rDxK3}tn#POvJL+1txMST)79bwO+7^KJZeQ1j
zo{qeNZm%AXBxlWZVq$dsX?K`3yLP49xnW<6FwLs7_j><ulF1=>2OdD_bs0$ez@5bh
z#^Kz!qCfQE*=L#5D|uK8R)Le{KPFWDO}^7mij3Jq4m6cnoiBPM^dRTvGp#QUa_`*e
zPBvf0%zRKVsNUAEO$!}aB-~!|WcMf|Yq0Ns->OZC%m}I;zLO;XBow)AYim}toIwin
zu;^acJcWr-@*jAvl+;M6uMH6ov}ja_det9yIkc!$5n8Xf^&~CwBwjO{l=GM67eQ$c
zilOsG3(jNc+!A`8dLW#Yz>K>17Lvkn7O31#Iqo?da;0xu!Ua0AOROGc10ctH!)4BN
z&7Qn6o&M+Mg|Pv~Xx7OP2Yd5<0mmNo$sb^sH0ZN@KTZ>~`d-?8c8eL04a|A@%{i9L
zqoTZY_^@$87Zcd8pJz?KsAdmqGHvcQ%pW&8-HtVd`~F|uor_zN_ut2D>&fj|<!W1-
zQMo2yWRvBIJkr*(5S0QCnRzHfor+HJFb_b#x#c>Tb%UCsQuCDO6vaH!mSrGHp7Vs2
zkcol?3LzlBoBjTU=ee%udhS2qzP`uL_x`-!ug~`;3X6uoWMm24TItW`8;fS^JzDj&
z@iq74pm&8;)MSV;FHB~FC)RLdoTV`zi@2Ob`N&mJBZIisaN^#oJL03O{PO4WFHRiU
zR`Mr2e6@)&_(tN;rmta`U1iq|kfZFhK-bs2V|eE;f}2hsa?Ds&ZWz<*`AdoMUK;ed
zecimz#(XpJ;U$iP{g^0Q57H>=lc}kyf%QM*aTwoVM`P6Amzy>MPuni@RA3P(q4a9y
zE2WHax?^Gd`D=q<S)E-|<Zg^M-G5fU7leZ+ZFxv-=Tue5P;Z8^OH~_i9a5GiBiJ2h
z>qtA_KFSAl{t}IaSM7egHx_uC$;_j`iK=tXDbozpqZODY5il!Ha<nPLL#h@(#K`l9
zhcZv`D}FSs%6z2bG5UEMxDLb1h;tUW_8|eCU=!FK%7>+bdc<8Qtu#G#^Oo^-C6wh^
z$dXer@?yoNH#Zrl5s_A!Xn>1Lb2DuCp5-0V)<O-Xw!bd+1iEE!_h~QJ#TdoWctA(y
z9~eKL90Ghsuz{h!<z*2(GDB}AR<CLaBITD&=$0Q?9caCDV1U5tkbztNO~4ZO|BuM@
zztt1LufdoUD~%_1ZRO1v*M+A&JN4wjzWg!A(q>HI`BzCwC=#n!!8Fv3>f5*RMF<`Q
zC_X*;L=HtZ&aN*;X}cC{Duu9J6L?SqLWP*YWzV1}P@%FR#C>9Nod|l@CMGTNQ2Gp+
z&JAXX0ca%L$(r{iIGG?nH1k9yDjVo|tP%+WW!yu=)*LL8Q4OgwDwK3?eW<zuLrtYO
z2bso3PQepINvG)xJJGQSSW^K5=_R*&%xRJB$H)uBPoRy=#Gr_QFEAk`;x<5+4A6mP
zX7n|aupfg!Bgy1W@^o|2*0Y8q2YB^H5Bd{=yBIWdbWq$M;vxH3(6=rk#^(*tJ0HH@
zwg3Ab2VUrEekfC1gy0bU_2*X>WOUc0qHUWlC7cAEREU!!&jrY|gI#$lo`LZ;x+OHM
z)2<+b$6gy)dr-x)&_CY*<()_9JM)^6F`Z2h1?d-ovh;qM>%e8f{eHY?2@vPLQW^~-
zJ1HBJpOHHaVrh8(N1R85o!LllMV6;V!oU{dihaXTO_sGX%|pg?b0KRW8nWWz;g(xJ
zs=Tv_IOmJV7`@b-bb_$36B5La&P2ONI4wjhf9xx^uE@!nYG1qXD648_m&sRG;ty;J
zYpP>~{_Mmng%-dycpw1o<jiAR6I03s{hi@Ew}}m`3<r#j#i<l7U(v8@>Vu>a3O=Nt
zF7_LVJ!x88C|(vRR=n8z!8YG7lzt?d-r6MkusH>~LR%t$=~Bs4uI-&cCQQ=`wZNDM
zryTR#sl;M@a0+o999DDL{I858fSe(lED<06kU!C6$s+)nM|ghSj$Aa4Kwn<YtG1IS
zvX9LEIV_46sQszjp|Z0n!Va!qTS6^=g+&ObSr8E1$!+WY*VDb|>kHm^k6@9d(N0_O
zZ2$g6HbJ%cc6ahJm{xW-Dzu$eX~#T)g%0SPXCafU|3eLLq1gD5aETKP!)z*C;{jM_
zEEyFgLfk{Y$Y*^KO)fAZRO5tba<SuGqwneZes3RCGXM?sDl}7dL23E6hQ;r;<0h-r
zR_zbkX4&alGRs{{KH8|yXjai8%MA6Yt@rg`Fdr-W=sGIUxF(&MjZq$Ozd1&M*bCp<
z=gR7e1B5)O=I6gZT>|Crhd*8~dg0B_7Uv}+eK5_Wi?GS}t?tR)-^I`m>i1>W1+~Tf
zs^%at>^$+CY6^cm`2z5Xwq>oE@!Qdo@|lPP=esz4nS0IjktT)K5E9P9)iPEJUhG}C
z4PP5%LY=<hMMlTY(=IB>_L`T<C@kOm-ujs4)g}_;33RZcCJ#Dyr_UULX{L_%#RUOs
zRW&(a9R8F>JPIjw2-7Z-loAKkf)W$g^VhoR=$TZ>JJBu<_d?m34H-On@AInC0bjLp
zb+@D{EjIFHm*;qohdcMRGq^CmJ3J5l1^gg20~MdgE(a!ky}ueyyAHhQ4)8zmI=DdW
zLs8e2&J#S!w@0ryU_ye^dOrGCl#u%q9*If2$UiXAF`Y(cb<`{j^$|RN8n0IViz2Oy
z!7t$bW;d@SdR|94dsvV6$@LD>u0up$1tFUA(uTs}eoL&m-JvBD`EL|82|K&IV`;}7
z@>wU%2f72@OxLdPB7ExWXlHX2Bd(kg_eTF%p42dkie`X@$)zj1t^Q&%7PtG4)1JbK
zTVPE8I2F4%aZ*O2&p?SS5<EY9Eu0R9i%z@DU)4MExQ4HZ{H%m;E6#0%ct3YN2z%Mg
zur<hdOMLrVj0}85DeJ3ePul$XD(qnEtrvUMx8YgyO^jB{R5dg$0$@h0ZTVN5(9Fsf
z)p>gOhVE@s$AgI{SrcP{vvMcqXuw3vU<LZ5;#UUbz`lH2;v>AL?b0(SNZHExoQ-l^
zFyyhk7^=oK)BJNa$MC?YHPIh<lR<Yy!4}(Lym7`vS#&XaC{oJ>m4`H-s%qxU&q49i
zD2#@tIae{$hpLXjRtOf>W7+kx_|N<M@q<^64bwhP-#do<C@#262d>f<VQo#W@3?t#
z^j2^>qaHu_Skx9UZdmi?uVNi#b#;bYJ2<RW+}n+4N&K8ocl(*82k{l6enREP&!2Lj
z21e`0R}+9l+`Un|D6ndE^#i@fx<kne(ktUQB&_gLR2P-XR0MCynuruUQQm35!AnX8
zK69F8r#Sx}u3hmA9dRy@KEKs^MWWX-z$^7)0yF5`8g0FGXR)~4n=cnk_0D?q&Hu)q
z<Cf$soDXh-@v;v}Xh~VN*gP(cg9|2+Xp}=pF9R~_@>+%GtP7YH+;gLY6F;L$=LRBZ
z6T2mw!Xif7?|xu%|9MYHpeXk0WvU7c*D(T)FPK#m0kquBkO{b0wYS_|r?ofhS{RUY
z(nTE5hiVyUh+4Yc5a6~&*e@-SPXzfW3{VYqMROV>KUKCdf`7d_^E3xT?NnqkkSF{F
ziuM*42^RQ5eXm_KPpB+@{*()C?@;LcCS7+>C;+;~buMaEKVsdVd$KSx!%mC8aE`R_
zU`n)sth_Fb`y201r%zP&rk(+t&u)=PN4F-2piROej0aGb?`L5s^YeNLJ+r1wSI2;y
zBw4V%tx<jHm=(<lygY;(*c|uySg#;^3#0oxo)u(9RTndVJ5Ky@o`k#SR0_`X@5sk`
zQwH}SQ5=m9y?4k0eJ2*prrMPF1@_EVR`>_!i3RSoP#=Xw-a^)m*#+x<$ZYX)Ksp+^
z_x|h`Uq(7yp>lUiOn)M4wZqHr8gBURmGl>2ET#h|Q-E{6fjz_=g~@kz9Pdu>4l+S4
zd}{-w=|<>DMLnZG!e9fdes>pYfh}Y!Y7u1v(jy&ha=vSAJl6btTYN8vB)-JJX43)a
z^Wg9jvxuMZ38nUFUMO(YBaw!X_1vO!?}T|07qbJ}-x<H1Mo;+WpU@;)7H+h+Fm_(c
zUXylF_&G~8$}WFX%4i|G)n~cJauNB}DUNe`vYzpQwxR`v7-_7_yuv2V3NOms>oZ@T
zTG$e#Z|T-IR%D(ZW=NOTjx+P8W}vyUL!=CfrYweGdZ}<fh>dPd1jW`=kGlCTXdg5!
z5M<3TRM#%`nRiVL>?^e0odzjPTu?caRLc{`0ej8_Zuk!*50V~iV>j@^J8nsf|4v?s
zHt6|^J3HDYw!Y7>Ju>OrXD_S>r-LV9$>lSVY^PsprM(U7==R8?lhlL|G>TNB^2;f@
z%^Fn+A<aGC*RWPR3QNxeeJS~z?w+0mA;jeN0V8>x<r}ZCAKX@AgHz}jJ0ems7(tI!
zt>tbDhB%ZUzaeVORcPvb%zpj_;QF+a(}Se&T&9n0>i2}cq?;X0_3)&8$8R0LeNwR9
z$NadPS7PceZ|xCIx4t{;+SZDq@;bZdJijsV`+EVkCJ%j$3sAp@m1wH@VlMX1AcS6g
zOFV$tS77^%TwFI;|Na3~C}(f{j=1uOw(Ler0@Rh0(D-ENBq^qW+`px5uF_f09gtt(
z7AE8_derp2xWUXnyY!TwIAUpe!l9nLhywPE2f3qo;e<H*<S@R0pU)73k@!m(yJk0+
z#*3~bw}9-a=}zN60+>tgDHmNwL>u>Ols7FLUXoISlUr236Jq|$<oZz%D}A#7%adSj
zr@S+^OfqdFm-KAsFD3n^bs)Q*UGBB%Cc|ag;9bO|Mh8@Ut3KHc`NQp(pKn!H557wq
z*)3Udey#Fws$2p~=uW<vZ%*M~9w^2Kx`#iNwGz)R3?tRl5SI7^p=YyG<3uSX&}B+<
zU(gVYX^?+AXEiV<h<~<lm(w=aBJG-J1G&wP>Y`84k!L{!{j_le!Haf*NWW4;N>L#_
z$!;QIZM*cu#kWt}AcVL*Yqvy7kXxb^VHG`9YBCmJnV(X<MwDHR9FycpSi4VwMpmSK
z%k25w#dKP5;D%-su??PfTB51CJmb%VQ*1~B(x}(C$hR|<xIN?yQbx?mLOx^Y#e7ZA
zjj_U*)<s#{PoV%(onMPOc=7&pK)%q(m4<Bwk?4o%L3G^(fL5cEXMn_f@A?gIMa$NG
znv^iWMV+rEzTK8IY^{6?wE$tpD8Q_;5W2#&?vKYNzJW`?guZ_ZXQDDVuv@F^rq86)
z6TuyVByNmTr(7%jXd!{ra1bRQ(4{m6W6VkJgPB9#%+>Z5WG%U~w0NTrbXY=$9y(61
zp2(Ao5aL5#)+0jyyZ8Zbjys`E({A1H8hxUgULE2q4ilbJ17<s$GgYH!H<?)81aI}?
zlndN`>4PFq7B(tLdEB|%Vk>rfyY4;*rn$!0Jb#Tovp;6#N7^ZV&Wt;AEG^04xE62U
zYDK(y*=)7c;02gP>af)Y1aBgvKif*<!*BW2+H!+aF>_|wu244Ai7{__Z}2On!HK`A
zJg;-CoDjS@tF(}u^(psCYxk0|w9M2>XmIK6vBlk-a_BQrp12}!t<?E%&XoNk`6s8^
z(DXgU;(5dsRo;BJ1Cc5#u+m)ka5`ps60;Ft#J5%<wA+dzzY^p+91}+e$y6Ip^G!XI
z_ukR!N7c)#7QAU=*TGty*{b_`o3oyZOCrUjxvO+9cud2kY{bVbP_?FXTIWhO<&v8<
zQNo(M4#(XNX+Px1d@VW&0M5_lAkA>pk>kMA9+bG3zN2?pRYGr?wrV<{jkT`a75LiP
zwUMh<zS8!EODg^Gisd^_(Pr50a4H6v*cx^0vp)OkFLTT2=Nx)DqEeu4@??qfo>Y~m
z5Z0aUVPRO<)P|<|ygsaTKkWgA^5}`YBu|l5`ThGv&&h$;7NUFw+D(wwR@_=J8=zl?
zO9Db5b7QYm**TK?+v`RR<Nth1R#8Ig9}p{+KZyZLnj)_5=rXx~1{_ScfPKvN13UuW
zql}UvAW>07-&d#@M)v_??_`=~Y-Btz0_a#GrD^%~ix4ZGwuAxk4W6nJG%&Cnw8N7W
z_)EpseOD-Po<B%G`A}Lkdf1ykyBtp!D;@Yf2Z-snBs<#1D#0E^LetW7643?XT*&i~
zX;pbKS?Jw8G&$$3cp6K+u|eVgvAE_R0-3-mhgVh8N#6Vo7_nD7?oLGPI~zz9R#upV
zd4N>y!wa46JPiVMFyk0tRUsg!Cjo{MTen9K^69!`e4;KDXTEpcqPrs%R6O{)9y2LP
zE6Ay>CdTu-3RdK9Xa^oTqN~4L=vg?uwurEN-+;sPQtTzea;B9=cv#9Rr(u6uxrX0a
znL+UA>p{YHBrzE~y0W|!x8c0U2r_TuM*xj+f@jdrl_YwF+)N}iO+;`iBhJJRFAN9a
zUm5s}TRql?wNb!=ReK=7?T>}`qMDIJo7czi8^XC#;K&?vXP>ZKu%FThZS!njd=;VP
zAc;w<!V2i>;J^!V6;IYCYwc#a05jp9q&L9ADwXOa;e!=(Ml5|2uwAD(!G;8{<u}I`
z&G$J<{!<1A#q48_^-2b+X7U45(mk&aUb8-D#DFfDH#yXn{Stau!V%OQV;5~Uv?JR7
z;TywpvMZp-R4b=2KFC@ju~&qFhtvOqP9Fv}E}Gp{b@tw(#H=m}gL(WP_{i9T|I5!q
t1IcXrNn;B)>uIw$j{eil|G@^o_&P&+7c~s|rp@?L%yFL|8-57;_1|$<UfTcw

diff --git a/reference/plot-3.png b/reference/plot-3.png
index 89912e3ab9e0ff0762c62a053ead51938372ade1..ad088aa8722bb6947246c2da5527556e74024986 100644
GIT binary patch
literal 27943
zcmeFZcT`i^`!5{7Ix>uH)UkoUsD~g$dXcVUfkOz4fb=R&q)7=ys%1bFK~F+&76Q_x
z3j_kA!h|lp29y9&1EItalH8Lp^S$5Seeb=0yz716wcfRE)>6XR`|M}$XFvNXpXb@c
z{BEFg;3t8f5D3HpQ1_}40`Wr-0<rtnk9*-KqlvFp;2(Q$p>(doKj9DKN?io}ga59s
zg&zVT6wUp&D}H*_8G-l}0badq5|q9;9+K>FGgxNcu4JE-d8E{fx__OO_*qn9m!dUr
z_Q~TD58eW=txRHH%C(_hlR9tuDGE<Lxu>WD{yKmB)5ohOuYbJp=J899-~T1JN9O1K
zdoKUH=f3Dut%%t9-twoZn7PcG{r#+lE`=;%r55M#eoglnyJxD`|HYf!|M~C#9S*$v
z9;QWVKG;$-wg0Dx6A1fYbERdNlKr1WdS$gA7zVQ^g}9F`%;mU`;ZL#%zb*G?B)@3W
zKJFLUS3uA%?iVBuJpjMy5GN1_|0ZR3gn(!?s+1?)2QxD%Bv2UZG&{gh$Bv~_m|#N^
z<q2R`X*;1hiP_Zk)nBmWLk0&yc>~`k417Vz&vMF8W|CErrsr!8nTe^MC5-?RXkQm7
z9;hkE=`zL=BuL(XMG}P%WM3dnh}DGdT3t+~41)*n2-V@Yi@o)IFGD3_41H+y_}yp7
z*!hOrt!&UXk@6QVWGM$ILDl3UhfSd9oNYGUfcAc%D_U$=oKyua8`jtr>(;0cag;PL
zTrMg|$1OPv57u$u1x7m0UlS+A&jp{3rUyUUjkQ*2e6J!FY?|d5A<E-m(N<-VGG?$=
zjP%OW(JPD6BP_>uggTh9KFw2D>jB5H`PDq&(5V#x^r?ZcRfaz&q$9V>6znXBneJdU
zeA%^VeYC3?chz2-Y}Csc!0ox!52j2^IH-|EA&<{G8g_+qoN4K>oIB(X%|OJ!)IHb!
zb>?-EQsf7Y)U&S>*I=GU{=j_GuI<xjc-HYI)O$jfT{D-FUi&hKnuIL`Q}RZ;Di7X%
zRviY7G8fk>*Q*UbJOQW~v((9DfRFC;40&L!A=&V3sa4hmdG)EfUb)yk-bP1WWE9h1
zXl)tb0blhE&eXxB6VPOa0c{G$tW9GegBFSzRl<Wze>CxF$hzPsSGOUKH21_R-l9rL
z#e9~0QF_*SX8N}hA2+C@);d?ZDa4`m4TEJnJAiAMjJRkY+C@ELWdzx~Dy@WEeA|La
zm0x7OChejAF&7rkx;#M*mDL%v_BL)kv2T2FP?T(=USW6pL7*C`e~#_^5dea8RxJcJ
zS%(m@7TV<sunc}$aPQ1Yq`VLZ`;Up2->pW`3P1yNuvD!`iN1xP->b}IGU>HJCImfN
z7z_|!aMki{Tp(2Iuuu@;UCwd=7%obh$F<PtdKL;5SQ*h_-^QUC$07PPY$ime5A{7S
z4hwIE{<UNZ1Y5>8$dsr?_&}Wb9_j#Y7|(|sK2sj?Gy$vvEnI7*FK-P2V(X6!z_cjY
zne$pGtUqTr%$T;Z__fItSCt&%qoGdm_y%o!PPmQUgIuquQCCPqLiBL?S?7VTZZ;!^
zV#DP#mKcQ~*SLL_gO)GKT$UF?DzXfQ-_0|MHYJUqi-G=bL`Ss8XhrjU-)y>n=B0NM
z8w(t-w>1XXs9-ywqjR!In2Ih7aiQj6#3Mc=tska`*4H5uy%zXO^nC5mM8`W@r?He%
za}lb~Yd0R3EcKMkogzf^oa?;ox)L{EuDHk)foJ^kFeWCYKu~~#OI^*yhH7k(AR@fv
zaiy?Y(V!A^%ICr5R#+~v8kJYFPGH<3%+VfVfpfi{=)T=|w$|6abAH>;Y|xFuPKI56
zxO(7LiL_k5=Qqr4H1pY9!!M*?H<yl>jVB#Oiv@As>cl=o^0xF7yrl?4cXF-8qY_kE
z|G!!qid`1xa2T(JLJ;)F%m>=3b@ePHPKKFwV93MxF1%7$z<kf_$5butw{DzIshbL>
zol}}h&dKy^J)sZiB7%?I&de(Z6{M+#x}cdvFWH<9_hPvW%JWn$nDE5Nx#r7932(Nf
z4lvn#h~{<yuIi#aOI@R)XluAftu0WYVX#K%;5vQcN?NXl#02RN&}*TGPVN(1R7P2>
z`TlHIpvrk89j0aO$Ii9c@)-RxHj`&(`nBAqS+8~RPflT;`g@&;KLiHC<fPUtvTDGH
z8-8rMdiv<ujfF`zvEvaIU^pk$ULH)&Ea4M4?T-8JT4%A^NV~jf`MnG&8mp1l_P#D~
zZo*K0x!-0>r2AFa_<pii4CRD>O*305%+!Cb*Ud4<ix`a-S4gN6!i04#&>B)@3z@gB
z_bLRL9iYub*|+tzRJmJ?&h}N*<#)Lk>CztIM1R-Hq`U@H#A?L+s2R1DcXF}Y4jn`2
zuE{XJc)I*w`^h)L6Qjal`E{I0vq0F3GfZl|Yx_tpplt#rA{PiB2)J!ry?NV6S$2Lp
zM;bGp7<hYdsorzZC=k`bxXiJ7H%fK5);iKS9;b#;IF%+3Pv~T^A?Om<D+8t9FsA=m
zHJH)xqwc)xt}gQzv`;ODSnEW}Zt<AE9zu)H%PPPPvc<4-;e~tF?s5Xtu5QqB1j1ez
zHan4c3LD_dVZWf*hMyx1&v^$jF6#&82~sSH-;4&AR#%yKI%N_$$H#pCnvxk{zQ%?(
z$e#Rz-k%=f=n`>g6sh_;XMEVE+4coRK^%3_-|{D?()mOugIY0L9enRUbdI-0^zB9{
zPP>YyNeexvRN$2E^7&kaTB<YZ%2CWsodNaX8k9}7^NA4*>X-OGg&Z5$zwMuDI2q-o
zP^vRm2eJ!3Fl?|IIXA<-&=bAAthUF-#)$#RifJ2fBl-}I&$h}H#p&7z*+V>oi2)(3
zu}j4g4jK!(1`$DDOngI2MLnA;?RkiLF7ZljpUs30=hN#^>K7Gbm+u|3-x3ktmRR~H
zBjpSVCqIuVQk3PLpI4H`O2ctDtHshA-e8$dtH;o>lzN@!dTAf1aKWfrjnuF^R;joP
z;+=4F`6|O<&4%8+SF+~2*-@!gy>skj#*8=$@Sm@i%OKt@8FeZeo%y}RKgPQyRVxiM
zXIx!1k5O9E?iQ^A<-di!P^=?aR%^YflFo1>NID<)&aD;6Wxm;te>*qmezd&OW~<-R
z|Kh}i&p6V>?px37U(&0X5vL0l=~;Nj7zfdqoC5n2QfAfJ;pg5e33X!9FyAw!F&u@B
z_<)FC#|JxYV&}sTm9$O@$`zq$n}7V#`~EWZDSmMi$Xd7??g@1bt_NGy%7~WPB-O6f
z_6d#Qi`~oy(j%^`mH5;lorfP@zR=8DD)RXr8N@r>wpXiuZMcg1NWsiDOfF;CIXy{?
zakX{vV~P$wP@l%e?eWeaE}d)sG}G>HpjYD6zcKPy0<6*t{)Mv%<SL%9EOH>9K#g;3
z{MetBX1nI)$Tl|f;q^1y1E3ac=#e(&e&N^jj@)w@UrGQolPT(8KvoOp`i6xs&juWw
z@>Zx@Z;FOABuQT4<)bdbf?0aO`x*Ut5!qWQN>jutRMzGA12*KATeh#yh`R?2Y}84J
zrc_{DB4!JWp$Q#4FrUA~H|*rq5EW)8)h{XlK~98otnpsXSW8fv=&(s|gkx(M;ab7c
z4A=R}5gzos7ZWw{ZH=`V(s$wW^VpexD|b1CnAkr~tKh?D#-0GP%6Ff^mc<NnJmf(j
zOmXlk%^k;2b{Ic_Qf*2DB9htRIv9n!qn_XV2=l8+HuI?lE5-W0Q<1A-S{oc?f7D9c
z*PW41Dxee*i1v|WD0{@jm~%<*?8ec3@0$mq)3~zLk?d3h-%yC-I(9!+w}g2skEtu-
z>1F79BWtQ&$yuQ)JnSqf>p*w-D`nB(&xj@18yoVs3xC}SgeE33VaxC|6W+#4J_fC7
zsoL|Omy^J7N$1{NThD^<R7x;-(9oBz@zu%$e%S`H18GIU>qL!dpOxB5>l07IxpG5<
zLd$j10<$>>c2m)`?>HItd&AEuA7iy@zHi{r{mV<Amn(oS14f`j!|^p&5=`-^egAvd
zdf)DLHx`~I*I_iYNSWgNPnL`a(ydcy=hrI5QKIQtlnLyZ#$=_m$Fq7@y|0^*xRjbh
zhQW(lGdQOXQSo?hyBG3S-gyjdacW33PDw2ba^e8Y?Cl0B8a!x&g&uFYv{EU#=VE<u
z@NwSQ#5vhzTvoZvw19GR#w2Ax->XoVu}hU*0>uy1N*(_A^&&DSJ;^tMAWQPjX}>it
zK{^DbXHeD^>sE)}X`x^x`M>$t`{y(Z!R;pcDi;Tg*xoioF$4kvBzX_v=WFNw2lxJe
zGuHoU`u~^%Crk2t_!_SU89_-%i_eYbR-(h2BoHk2w%6dzr1qjD$^S!(+qp14?(gsR
z6u~i>{v#Dph9#76AQs~ReZX4xHk$J~$96~IP7xMbgns1ys$Yv7F!_P|&3^yC13UP*
z!Jo!Q&?K)V`S$`?Ob*E>IF$fQZW!hY7903_LVV39PjEZF`%8C4T3SbpaTl^t?;B60
z^Z{7{ZakQw@wIbI)E9LXQj(R93KjG<P!kKpQXIfeJzvXKDLd}lh$s(7Xl~%f>?6Ij
zInyi!h#*Ru1DD+kw0$109vqFr(cguS-RJdO?~!b(Lz2c5lxIaPMh9G;0<LBwV+l@c
z4tv!91)Ed&)BQ6_F?QlGSmlur0a221kG%Ue!8nv}*uusL5FH1oaXZ<6h9-rGQxelE
zc&V0d5(^}o2Zt?opXFf>$On6<--S%XvnJjwnMqEw(hlt~0XRaKs@B1WqT;Kn&03kt
zB3z&aAqxUQE!0fCVB`3zLltq9Cc#o(blesTk78pkUb6T(K%zXNTs`{eL0*4x(`V|D
z&Mriapv2_^2{dJ{V;O<LZ@<ZTutOfZhk82<4pD5uhJJkbIKq)P9x7c_;CPbby*{dq
zctlw84nLk|og#b))Y~%5<`8sPHxg(+tnK4TfVl(^6iuZHoL+!{lG_QyiOKg|;*ERY
zU`{GaxEEunPh+~dfbkiY+ST6nQrt%K0U&6u<qRl1xRN}oq#?|GCZ&Faus)>(Dgze7
zjph;0<<EVe-`mWcXQVUwLEx%+f|CSkH>mtfH8J2&cFm(jLqP%F1QIp*$*V(hBnL3g
zpezEa#}*d%aipBDRIXvSQII=UKmOu%E&+tZrk-zrURl8Nsd<1e{tVe+=s7KOAYT1v
zZey}Pte+a?eSKT?$7Va<P#v+>&na)@76iDBw_i*OJ)0F{{64*r0FpB)o?t_-TD3Sy
z(kRh;NSl-gN_z>;e#D(>H2$#vIQNCfTWtW{HK68?DfNnAQ#|{VC#Ft};Xfliyi)h!
zr+__DpSisd`mts%%5uz3=_U5Np5?<r^PIOvL~&6cjhLPD5gD_Z;%_+CIaJ!SEF?6a
z3#FU6`Q0jah>P7hHi{nw;7!rRhdL$&Xy@S~3a+sHp=wiGk;>;Ux#LHY#G2GY-6f~_
zYAe!;ae)PHe%J8Z6~Q`txUEwBM+hJ2AM}DfJqw)B_|On^T8QNI(yY*7ZtFWhcje&3
zwFKIk$|6Y2taYN$P2r~agevthw>47q@`J$dS~tWa&XW>_Mm#bp9})($3QrZN?f$NP
zeCMpne&I?+0mv@DNxnd$b6t`+FhJMWsdc0_-m7vrnPPXRd8FJ(t9tGdSMVZ9Wp~DQ
zN$sYK1&2c@s}hk+id|hrL@-o$XFk(dJi_72E1rB}^&bQ(N~3)5LNdtVT2vhtN*~6v
zAwb3>fjFvvn>#(kx}zs#CaS7U8V8qAv}ZuXb0?-;vyz@5q%UTny?lw=$G#_}N!>C~
zOtlEZ{S_hgn2R#OA0+M{tyFe(H)}+$BlOI>s{3-hwr{;5M#|USY2PPxCwQ}wy6m#j
z6Yq}QHsk8QN=&O1xlZ6D55I~$Jn~%rVIb@MZr<`hSF93ujQ<(F^$)>+tHB)=gE*@9
zfT#EDe*;%|XIB6G{~9Fwe{>K_7_CeG@eqm@tGM<@Be^K6!J5Z_llKjQ6tC~!qi;0u
z)^Gl#eG)GsqFW|MYTesBqV0T~dgy<(ej$_pzpdX8q_uL_f4?ZpUGVQ+71l~|s{Jx0
za-G3NyWyBs{$Wx$L5oC0ay6G2pej!~b|99g$s2$4)J<us``f5AW|xzA`s6>%-9P)>
zigbV4WdhYhi&!TcC`+_PjZ_H=pLFCgz47e1rn>j1GbkUxfTiC9-63Oa=$-7tJpMmT
z6cvb~jsbRFLgNH6lImRg7#fNeRh1;UiI<1+x|}R~2Ig4lWE_?7%&`jV)TJc?xneaX
zMeL7fn$<l7jmRIieE4|M)r~6ILrnz&%|_CboII`4E<ouvZhmG^NA%&vA9Z;Wc<GAN
zXW4KaV7Tli8-yH<jx^k)1FF)bjiNz!>}fUV8*huRp&kTo#}(ivaAl{jh`}e?aeAZl
z%2S>nko+TT-U5Hehla;j*%jgF-dex#_z3B)#7zh%&@O0>kZr}OqoL;;&nhHPCX5GY
z?DEy9->y`lgN+;=Av+;It`(8m#T~{FPsr05<+IeGO+Fj4A3LZET6WBSf2h@Lhoww|
zT3E^eu4Au^0WB94mb;W5f`lE1G-xb^vJcEmf%KPJQcWkGftkvUnAs@{@SuCbgeJxP
z!m$GEwq9#J?F%l1l8DQm?*JRpDMfP}R9TYqyAXz?W;5(L2UdLDcG{-Oa;I`n5UXDu
zXP{-3XV>pon5Q>78d70XsGR+g2ycD&``z*(5eM%P2rWa=_|Q{5p0PwdDzLO_vz3*?
zhCHdBmD+XjRr^ZiMPF!SP&}_R3>f+PY!Ru_`elyss|Eu0ggM~E`r)Z7l1uS(%na_H
zI^IC9EGNmA7v2kfX~gLkcJwrL3z>WVRY;kijCVi7Pxh>fWybDmW|+@o3Gr_$LR_H>
zM$p?9lTKo7OGv17!FcmJn;_lilkh%3fL56ZT|PokuuJqDbS5|eL0HOE(dOVMDJ~pu
z-j~l$wr#nu**sF(vl)Blle)(d0^sb|loG<|wHfhDtI2vrMFtzSPAy47i|LfG6sGFE
z@DB5ciG1SuLk9mMzPcG`IPcn1qVMZjFpjGGIQHlm=;dLvP|jWFT0F>>C%pgzP#~p5
zmyzb3N%;)2&-zhQyy*5yP0Wvbr_vc^_G+XX2jaLoRng=59;)5?$%VXxDzO9NWC!e|
zD5&*fID{c?Gs5!0k57y9*BY?j_AEDR1YM^*X%9bc*Y_%7qYc~AZ|#WW-(FfL+EPP>
zEMsO#Fn!;CEd|0dIUgBBcfT0rE+1kDGq;hjZn>85hq@?BJDj;DywPuA%hKAr|7ph+
zi17X<+pYE7Mn<6?EjnRPEZYOuPf~{nNK&zQ`MpE81q0J?7DSLHBDBTHHX=)sj(Kj>
zAsGv%j330o8i#v+3$rp<>mK~Li+EaxC}|eE?NYFv4T$APX|JvTDgfhFD<nJNt~SKK
zfyW)_a!KSHmY#0Af8$<6>;82+R9OhLpc5Jf^TGaO94ZAJV1x|pxULYSs#>mac47!W
zu9XnL0vf_qHp;&Q=Jzd`_Hv3%S#2YohWOA|@)`2RK|=#B$^-H1x2<fFnC0Re>FuzY
zPJK^pvKsEKSnq`tR=tODuS>E*5WO&#6&2OORd6SN;wMjgN|53e()vbTp+?P2N9y0i
zm(q(Xh-F1yqk7|7N<RHXUzmTkBU5x%`#yY0Fpr~tu1cNM)FxTXWsYU2#-09c3_BB(
z!V2r_IBOV{Hd!Ps<a_gjBNw#Z<`@`&UgO=3t7|x?X1izWLhsBq#Qly!urk{K2Mwy)
z#{d^h#@I1%**hcRa&*IJuHO;r8F9f9wAe5){$%P(oSsztJeOXEs+R#R9d|0vSR9<I
z$6A}a`Pp14Z8#%(dAQFf=!#FjMM~MD7Ls^dn0|FJ^DpTzSg;QvRn<vF;IaYZ?3Y*^
zUbZUPRo|{KmKc!f2u}{-zd>Fn-moUN@ab$FG?&uK91fX$EbQ4;T%q|kzO<nHZmuz`
z|3o}9IF`{3MC`DW9VT(PJ$C32-G`N9oj6Q?m!mVfxrbVrXLtmTV~p`j#Rooa<wz|~
z+~1s9*?TL0YpER>uWmzqfKz%>mxm0h6ABay2KP=xQVk^>H5?inxU&dL(JMniI*NC<
zZKOWGv-ZT+klxw030WJtafY-?e5~+Yo1U1EoAd08qN-{MkV$&VICD#n`N48_`L8jj
zRcUe5GwZ>7B6gsK-m4f)X#qH2$Gkm}Ik}YZ>vo3T=!aRM>B3K$lbWS%m0HPs5&YdC
zk)ND0#oFB*6pE+v@80Cvvg?xv#LH{vl~*jh3Y+Re>m|x#3OCW+YBjHW!n?FeZrDr&
z&K}(8$Apc>cjajOnFXY|2J9|&y}2cl>12%Ntd*}Q-K)-z4ZJ3@RKQPG?+Mxbwku&%
ztW&!^=JSOn^$huPdbY6R=`6|;cu>F0rgb97W$tN)kqf_>>vco1;AMoIu;rZVZf+E6
z{wP0wZ^T*Ark;=bL{PM;PNUMhxXMZ-OuDFo8n~BQl%TZ)CpR>tr^|y}^~yHg!MKhw
z%C|a8$Iw^4UN$bSTj9VoD(x7U2@PZJO^{vFF#+vkk9=hM_fV6Vw~2;$7HKWk#|Vv=
z3SLbHsBqh&)$xm)F5>jo_8=8JCt{XfupTn;uPN^CM7``CiLcJq_=)`v_AsT75ZZc=
zDui(CdR()*Ot(%C=`c5yf@bR+`t^^jvvH@=6xbCQc1udeocf!sWR~d~!OLjiQ^u*;
zwXlj3WRUi#4zs>I^eACzvu$j6XrB!>sIA>=O!=>*BTUn+^s2^_Tn0oXuIcyq+%tz!
zTyQ;HZn~7Z`RK5esP|hO(<1W>*kC6fXzBIJh8g8V{I;fcQxd!FSFk+~XczU>=WndB
z#ABNB@5paP23rR<vl4tu4h)zlw_&%Pr#GG+zetubQl_PqdS|in+wP5(|4k6ihb>G3
zoW6kN6!A30@yxQcdq>qrKin$Aw5s)iU~J~7E3rUgI^1Q&G%4)6ZENgbmop&wwheW#
z^KH6a!f@A2wfcIU*mUW3b~_@oWWM>1`UU&+8^KpvjekN5C7zXl{f3=dfarL|ts2x_
zDL_tpDkOn+1<oiw*JI?2n8FMH{mO&D3TgddT!;<q18oQ5y%vCoOxkz)H83-S_FYN8
z_JnCG)i5}=UIhL^9M@r<lbqg^G1@Bia~Zs(DNWj)P}?Cnoi(^ltl97z*C^I}qqgw<
z#lDlSCZ>SJ_@vw&N%z*Zjc<C4I4!2hQO2Os&s-(Njj)5ZrAU0(MMz#@Rb+JjkX|r_
zSqxAO$_91cJe<+Tx9exER_|V~M>?CjLW`O5<yD<e0PkV~dvI4V7I(4BBY~qqIy5@3
zV%myvU+gT*8~46dg5@)EIV9$cHfvMdKe#42-FE!qKq??pL||)&Z5EIR&17V`8rc5F
z{Io($xhFIM24AfXGp?Eg%#gu8dOfbtu#;ynqraW}VGXpcFY4j+^mWlf{qW-*r?%S)
z92M&BTaGKaXr+12|6ZNr%R0B05KtFr3I|;}R%wMD6UOrNi6Pbr*@1jqWtwYq?FClX
zkL=&@6Tc~c<1RZYdtwhYHV+^sQjX50f6Ewl3nMn46FLCfgXwcA_1;VimsjIgjFpAc
zPEC!1!b!uF;Z2a7P?HsZ;!x1DBIALE3<G*)_yqZmUzl+ErSS0SEvrL8tN_D;*o_Cm
zo}xjsYFf@mi4U=h&uPq*JN;-;i!>VkL$o|SvnIQzcs^L1pZ)>6m|SMkqF;ARoIbl|
z>=J6$qW-EYC|90IJUqBQ%tZiBmckjfn}<N~&#~mr7%~XJ>d7OahQ2To9JfaBZ{Vm4
zWf{y~wNVlA-q3ubQ@3QK9Ft}MJ9oqD#$YCHc+aZcfuJRWd}6h&9Jtbn@l~5Rk#?uk
zP^?CDL-2LRMc4%s`IW3G!6J{<i^KF}rT4+zlEJeXDK#;Vc;pBgEHR0Y;CnOSiE2XB
zwn>!2_PqkC%^Y?0JB$Lb2Ybe@JIq!z6J*9kHj*YY3Z$E?0z(x^$~MgfsFeV<Wj$Ki
z{>>jgh$pZ^_IGphlP6yxj}UH-0>L+1Eni`E9|Q&`>-pNaP+4M%vNMU|{DdyC8ina{
zj74h?e@MF{p+ml|c-e37w`!|aWQ<DHt@pCdj2NU!k%m+`vF>4Q1$HvS{TkGg4Onut
zoWb}}BbPBUmO{gw8YO0wJ-VVjrKvXE_N3Q1(f57564?fnHmErfr*F~V@Vgm)`@-?s
zCPD7PufiEk*Zo^3w{D$j+E-unmj{ipDA)4m-9_v$$1`X%nTsFUKTuMgiv670Cn^w0
z7S^TJnlfs{1w%_K2>6Y)7O+9K^AbE%GVDYmp7s&}2Gx|&=d<^>sVX$B2mXqCqy;y(
zx8?irz1x2}kz`|V|C4LddiAN3s1I34#_v!ylHuQ~R;J%zY^fcAqom?64^Pwyer3Y~
zYmVar4zFSS5~8VqaUy{~$ByL#6Q=@yR$P7|E?9(qa0Lo=9J>q?Sg}vs0Sc0T|0e>2
zBW+}h2~;_C@L%SMRj0U>CJ29*1GZao36vXdGyCpi4#bw8*3=*o#fJT0l(4O5)_4FB
zF~y7S?WOr`w^o|~V(&_Fh>8Z;h#vA{?1yI8Ymf>M><w{F^FkIAT^K5Oianj_<h<L_
zGs|fds8*64@bztjGXRP8c0f%CZ*qvK$}=Q?Xk+T2)1MxR@_x&4xQVO}U+%`z-b<0>
zxIF$`N)phIdIs8NQO4lVO$t?lwupv-g2-#v|1Wq!T49q@jW~(7u7G8sopm9b_F<<t
zinym75tTSOu0mP`GgB$6VAbu6iToZHI*?Y`I$=1B=k7ZqQcEdga$K5}0%m^J8GVQi
zCBPX+s7vWI59UCGjhrDZi7WWH#%&JXEP%#F)FN!4yc<8XntQ@7eThimuiQao=S&q9
zJ55^mk9k^^bfIC71_Op$^v<#YZ})^iLtpzVrQB`^seNeCpse)aJxk88Qm5o-ZH2uu
z(yDs311Tr|k#mF~Q-rfib283idA-FQay!2RxjDbe+e7`5Ky&uQEV)xn$Bo6cU;XDx
zAttT#PQO^C10P`y!uXblV!~v7fsw81B_eDR76hO0B6fR=+Y(L84!z*Is#?C<^$-($
zsKb9wjA$Y*qIrpEcC&R>@t2+D?;g@}R;Zi<s>O!$jRFJl#djuJ8ECLdI6PVEUxG&L
zng0%e=eVqpA&QLEv?ta&)hR2rssJKfBuyvydF#^FqHggfW`C&w3O!d|?wgAp8{6F4
z2I$opOufCM$<IhGdwFQNVs_ps0tk)?XHr-Re(DvKs%QPMH6r@7Sez&iUqGB}%#Uhe
zIztoa6g;C9C;`igi#lYW?JIg`zt2aNblrtZnv%J=MtD&cdLo3&kH()1!7^jW=GM7N
zaD&utWBMj$>fFCk{%5z2PuGL&kUfJALF5d|fMU5Rtt7YoE6aBG4rna<o3aI0nj)hV
zYj!{m#JRq{BL@r(<dwP_dd1r=jzN?vfUfw<Q64pDz~lkl9W+%@9Du`g93@tFlZQa`
zS;nFi7n3Nv!SF=NH@Ozx!*{oplpfN*!>h}LD0InLVk#va;0)hmJqC0{gYV%xqJR@(
z!xu<$a0H(*QP@v+0~>mNPv;Us_`kD))Qt!F;Aq6aW&|#tS}UpwgbQGds!@=4#e5DH
z2K76B{_3ouoV{%`_%|B-(wqw%d>qGvrkkJPL%h#2Y+1L=8ShmNs5mK;LPD`k@}@hA
zqx*Tzz+~7BXvuQgOl-JMCyNgv<lH19Nhk@7AU53yM*Dx;&BI&G@1*LjuwC$>3<Rq<
zAD1;~O5f09bl*S6Tci0`xYhLNJ3+p0ff4xazN%VviD{d?aXiSxKE_?5sV?8hR{auj
ze#Wg}bUsINI)v{(8ex%TtnAtnvKEhcQ{+{y$Ee*k#S<rU8a!_1O>xc@D0=;~YXQJ1
zplxA2H%4!^azer#czvX7;3KD<N!c_Ft^)>vV3!%`aal)aC>QVXuF7~}ECbFx4=l$d
z(1H(q;^#8;os+*_V`obkco6gr*85jrG+?J+CQiBr7NaeU14{rR`HnHh910oftS>au
zBEGuFBlC9kEpE83H%wStzs#`1++C6n)e?mxvi?Dzu*G?z0xsLXk{q!bn$GBPw9tsg
zb4nI;Izj#oJwLT{x%f1fIHJPsPE1t(9Kab+gVVEnmj}QcXj_A1Q>4smmTHuRZD+4I
z|9D+gC^r!aXApAd{y7;h9J(f*-GVHk!R!>7J)7>VvD*7Pis##xcHFdbsz&F@Id$G!
zqPNWzf?#$S!AS)Tc~WB7X2{g@uMQ3dAT1A&6g;^Q0jMJXjrA$sP6Mc2+pd_hby)4e
z@by1xFEzcX&JU8r7P{Zn=S}dAVyW9e`$fBU4_|sKV2Cce0-+NE)PZ0RNV_DRnFUt4
z^QMi69eR~w<MJgMR4(cB)SneSdGKwz_PsrFWf#p~^EfKd)3=|TWYSuK^0gBQYsjE1
z713sI?d3Xc@dmUpKn6u4;4He(tRf!anfGVQ%YCXn3ko_XGwu|O78%jE&a_<QkAti3
z{K=8aiGa*$yW7v?9`Z2f<0*9tX^%a!-0s;W)g~m=${bW}_EG&Ozc%~Pi|J!2oPItF
z&Q@_pfA`nei!F=V{!@AG>gySKb$-da;%<x!e7q^<<aH#_5!%VNrKAB_2EJc^o`R7o
zF1&;=cPei6p!r~|#u*8<Iu-pz#eL)2l#?xZvj>6bna)TpnL`GTykAfj_0sC(1JBig
zXLqv42i)#V6;n-Gtt$J&@J8&)s@i(V=>owg&ON7JIJKEF#K1TZ{BAmqrMw3Qt8Gir
zzTb3T^X%R6BG`mXyv5OP7pUhNu@~Br3T9%#cKBt#1E1vG{uiL7xoKwd@bqqSx2Z}3
z<%u3^9Bg<_scN?|s|sU8;a?9PIbdcwAGj<x5Rg!HLh0D#V~f9cs4V}LPqD7ASHbq%
zs@geNUoql=(VX^4&HSQ{0_<4#?^0Nocs)j<u%~wkj_$dGg+_J?Llm2ojG8)eXC(D#
z%c;H}*HfE9-0|W0@vF+tn{<_!c3#3XlD}GwC9070z?PrfWjY_y@isHRqP|{d^wZyo
z16V?i9C)}jom{N$#hh+Z@Gd|Zdtl{1)O614F?7&B@Qj{*4$Kjef>_L-KQawcshe*n
zBuUk^eI<fB>AJ{<b;~Gw^Dwc?8>|gA_UjgN!=egJH{I2PX*;Lt<(VnV*AA+xqNFEU
zqwN1NKX@Dt+FWdg`I-ejt!&#obhLpe$BJll!P7h`OTIJ(S9;{(yx)3$iZzVqojip7
z>Pc6)OoY@XQBvnA-5kWSg|tBq3hl3e?VG6FIj{B>X@(Ezk|{Q(4%as}RC>ZbROA6x
zNJc_cILBSMR8~M8X#wXK-S4UJj!J5koap^giqDPI6rt7?53JMZ-nc_K+Z}`P%Aq_t
zyw_ZF^@E|23o~4iG+XAcVj<YKgU8FeZ|-EFo0;mh<*$5}-EZ^skoo2ec5-R`Hmezs
zlynTRdVL1our_OMa$2p#Fyz0Y^aG(s2pKLfw4M*|q2@56%Ylrv;mWA20sMGKPuTDS
z9$^0hG>F@y%AHL2^CC$;zng<n#eTod6oUjg(kT#j9M*Bp2l6+>4$6@oMt^qSNeCT%
z%{?ekoup?cs;sK0<2cw^Tu~!Qn)&c!z@D8n;>$DK+6%bUIkAXl2%W}4D@AZAu@9Wo
zueVL3bicUD%cDwJu30>+7Eu=q_J^W#*K#~C1|kL*pN{w>(9(<&9lyc#aNhbf!=k|!
zq-w-tA0AWN+Iu1Ew3>xa2Ysck)hFJ5UuHz~i!ZGzsHlINhtO$1a$sjQ$fVmreozFe
z%<Q3~G#~7VRj=O;3BeP**IscuAh6l6<`>3NY!U{wLE0ve1TcaVmlwy7W7uJ(AeD{V
z%u5UAFZjGa@-i+`3b45{EW*hc_?ox&>{$<RfMzRNC5;D0iWCxtEd~P*DxBwzYQG9w
zO=i0uBU^WQU|8l*CO+f}o`8l%YuD{;#5r&CUUGXylKM;@1k&{wsZq_=PZ~zwgqHvl
z^F~CkNS;}U6fMp}4|o;gLW<6z>i@^AxSg>=kaik{ZaH0Kq-^@;q9{wOCxk_g%g4zi
z+g;=i-)Qt1_H?FF&?~};l=P@(o1fUlO{LNx?TIOaxdUn=r2Jfg6c`?aF0`E9_jE7{
z$8`F4(r~a(fB+|<08YA7E|7!_4t>bOO%dHWFv~jcJgcnQ*eV634Sho(Z_%2%E}uB}
z^TND!iI%_)aVcmTc#$YgQV^`=@eXke=AFfeDPF4P^G8TnU6F?{AfC@0Ay}y>9(>vx
z4W;+3Br}!&$;!jssj$Ew&y~TqPujo<u0w_?pF;fp?e_iGjH*&;js%xD!tvLCihNQn
zA6AbyfN_SjK3t_$^?!*kf<3rp!2kE-i(x08fWb;mfM6`HaJSmCUQ2}}4GxGhoY7fK
zETQNoS(9}Cz^5HIQVMqY!+c$iRwRiA2S90C-{^nnF^2y3yUmZoj#5K~5nT0$4%@7Q
zV~*_q3?HJ+T9YdKD@*FFFoyW;N8FbtIgZrM4^9QGA*&Hg{nLA2fV*z^I4NGdnl5_v
z=$nfUcOMIMJX`C3c>;*Je)QQDMXi{tP_kY6V}Uoxf+f$---to})ai36##;B62fL2F
zjyxTI`eJTG#Nv3hsme{8y8OV6<@v49Ojqst+D%J$Vx>DKW0_uP2?rg?{GRZxnBXGR
zTtuv_9`mf4y-Nig&%8SCefrh7*EI$$aVMJKb=5Y*(d~S_2IZHQEpghTM%?rn0-^A#
zXb-jJDUJVJMTpl8wk~SN4U&B@nmwZLsYQy17H_7Hnx;z%-PCAMAQd4+knW9hdA4iD
zwFh|)ZH|qVLQshTZL5}G2$cY;zL&<)+%oq+J~`$h89x7v_ZkgK>w23mheA{t2Acq=
z$OJDMpYn86&PhAP^BlUNDwh@H&99r^fe1^DfKgG5aONk{ST!>EYW_cN+vPNVG609M
zPqAa7B#~^(_`tlZY`sO{6v{OWFGfWSyet9_>iO0U=^o$~2qF-d+?9*|RDuilLJIEg
zxPd8U0{pf)6E;vfE+BFJy*CSk+84%CWU?PE@)B>}UJIh<Yw_4=9zBxUR3}IB?wfY+
z)d;4q1AKkc+J?S<sMYS`K~YA)NJ<m)ok@9+jMpn(Q1<eNmK+s@wPWWh)GKe(C;2~q
z8gbmo_umEu738s$(rb*d!y{pZ=e3LRJF%?GZ&v)|si!nzeD|{Ywc-6de<<=WypNiM
z^WK85#y4Mwn*7bh_D5Am2+UumZO&Ds$+T8`iSGoxDcz<I=zQ0Ls}GNOO1BPH?}Vg{
z`nM&Um^H6M>4!(gYtBbB{3k)fU#2Hu89_azwOH-Mype|&lkRAa|1tGwucvTTcE{zT
zPva^n>n0_1ahsjIaBp$ZDT1~>ZR9I69tgeyv6y}1G9)-lYP>omBF3=&_g2H1(&wMb
zeP^-ODU>l2aDuw2%9)lTdpz6d*jb+Wn<^mK)hp$rkUs$~QO&rjXZE*|67{_B`JKHE
z<SM2r0h_z3jlA+Atw1NpRkWsbnu~hFs92LQxLFvF-_-ZL8@8_A<?*+tYFU<t4=+Z0
z3ys%^&Q{?$`aDh|OkWm>3ItU3aRD{>qFN<<J_^svWb7!W#ywm<SD&Yj8)fm@rQX2D
zf$u4;tGZ8&_n?i-L<SIHmo#y>{O^lM#YKC(Vd<d|0HzqD8aRY1%lpLUNznO<gh<VT
zWp^_c>kP;iCv^fRkc_Tcjdt+Drz>>^y0otqYa;)O9<-yo{Ub2yP<!>=cgpoDON55$
z)#96BJPrNspv7<Zk(pVP63D~jRmlk;_&3PAs+;F5wO7DWP<<y)WFRLi+tT;Vf%tr4
zYAH_#O7_8$(44r%o6O5s%E}!)o%eG0-}sg>Tw&neWewGd!u1IaaG>Vv1EH{s^Rm+1
z#o9khfvwbAz)+9o2Zkd_%NtW6$1n%cWq4^$h(~P#AO-|mL07iY;hMOoQ0U#8J3*^U
zGaJ-Ol&~13p4Ti2+Ab|PIdz9=4jB%#_>$9`=tUaj%sYwH3N_9CwTt+LOI%8knWIR?
zjpJhHlEUpqaCc1SL!mbLaj<QAQI|7ockj;eiCiJDQ=e7_Q1yJLptY`Kdjb}QtGMsF
zw_8Ip*-4SxEdbx(Y$EDzL-xf$4k>y##(|$)1XdlxXuWXi62$}<`Uc#XjRNY>XNPY7
zZHV04clV;SVC~fWb<CH)^TQKA@ASK|HmBg59{xUT=Bf1e<G4FHYrl=#-BxPt|GPx>
z!f>~dc9&Eu@r9G?hXAu*rlTu<X{%7pJD-(!M>Q%O&NudaWs2!bo#oEXUV<x84d){;
zq?+l0%yC;BwckjpH^(~lE8z@=Q*)l<!Q63odb43s%XmUFY*}=4-{+(MM*Y}yL3AJM
z#tp4}*6-Z0BHeo6?TmU&@Ubtlk{C>@C*aL<(5g>^hkB0J&7Z58zV5;8))?!?JLm)s
z<X0y@Wb>5fYW2i<1lJW}?i(syKe)I>dc;#_`cGlO5s_&Xi|t;nxyCqm;Zs?n5SH>y
zRHx?i82QSGw{+`-n+i{D+MA9e`(5s=<qgD)7hJ3^x@e}B9~9~eS)sMs4Hk8&!3Wis
zRkBNVKdp5&I8O%EHE(X``u|<wSqCd}+YHJECO8YQ1AQrxwQ10{9<=XiivgN8m3S+~
zCH|Bz`|<1T6mTz3JPX6pEEgC0!Ei0oLugEsL=<IQhBTk3&5EjOJsnJC&BFK5Zk=}T
z*w?G^LQ9Io`s;NlJLn5{>4^1eRnjT92B0a+b+xukikb~{<;PdK*p(>nsQ*TRz*!TN
z^0sWX{=63|VMz0qz59T|PSBZr)(U$XM+vYX3g#6B${6|@=Xb1_-dogzH!$pG7Viud
zUhynysykCpS&BCnT|K_7NTOTT9vmM^zcWs$InMMb=c#@B{f7)`q%q%9QHHrAKkFay
zW(^JXtcsOh<*~iu*4QaR8br`09py`pxDEMXVbnh+Pm65mjDlFu*zY2&)9LeM;WP>H
ztx6#4Eiu5^6~$_=-cebyIohokT$c{2vCH0^gqZ=QCw!vMJOHAl^8zjRPPEAY!>$Qm
zGk!@c<WBff^uhM8*;9Xj!jRL138{q-$)G`Mf!~0h$|+^A802IxJp&KsEok~(f>UMT
zIU(h0JIwgjz1D+L4p->kYmM^MR~q$yMMjxksI8pamNMvp5oVoFbPnVc52!zBvCWe3
z2@i#;rQ9NB@~=<8pf`6XzI6}7@JhT@4!QJNy<y>R*6#f;EYj08&(wF!#-U~MLayJK
zC5LSVe}lrk6h6Nd)7inO)%-2)fz6tUdDT`osHvprN|$=6dge;4)@)YRr<hTd3%!G*
zPwyGJtF+(z`#1{K%GCo?W6x{VpVpSo6V{iT9Kt$9(m(N&KP1g9kgwI#D+OorU+hH2
zi5@54pi}yU65o%jUgs=ye7?reW~OtRU@ycjrv&wcrrErMpP8R%23O=$Sx;!s!a8Np
z59kHulVaCuCOqu9i?ff?T`?eUTnp}oj;kD-o_4v`B{qCBsk#u@i7~c3EghJ3_@ujw
zu2Vl^u#{2BSl0`+Qlwp&;A&vhtb5S<9tanJ@Ul9HcLxg<n?l^$FwveP%Fh}SDGeA#
zBw)6lw>Bna5Ot^w))6f~P4P}f&Y0d&!hU)U(3RH~og49hK7N2%i@DKoSM)MZJbS&<
z_fQ90JX8uA%yIFCzT}^_YC~>lMf||x&Kj}Mcrj+g(JKB{7?YTn=%x#W6uv9b`DJ>a
z=@8G7$WFI^%z#C6tPbBGZ9o&mFea?#dC+XK(Gf!L^llqoP>3iL60GG_6Wi<ifoxGy
z98j{X{EKCoAtY#ApF>}~cs%(ho`rd*HUr<lpd>-tUeF=28i0fWt3+$glj^V<(xido
z#no1jq_Jr0@wkVARrM&STC_%jv;lS&O=Y?dg}aXj=s?j*hk~8r^?a>u*l+l4UF(O{
zVL%tYV{!p($ow=#c^<{mzol=NMmYc~=WYSzW5*f={O&;vU`P8jN`x5Y6Qfg4j~a<i
zn0}4bVkv_w(kEsovMv5xx`1Jo>%`U8Jj;Xrf-|M+O$&u8VlLmm*M)s(Kq}?XB}X)K
z90Zds1}5<_a5YF$0DI8mi%3ikX92c5vQIa4X@lTFG>y-(f@lU|7ij1_mn+IX(SS%{
zTSPL2Zrt%L)C3~J5snG!?Dn*L*w{JJ(}e~0mhO%CnU5yrI#^1VSdh?&Tx&AhA)liU
zqmSAs+Gn5+DInCSM$c5_h}7h?)R}Iaf_KiJx|#>&cIo+M0o7ocXunfxjfqH_wp_hM
z-oBR4=WJd5ptgl7_|Asg=fQWU2-L!bFs<tJWWPIMULku8PmY>fQ&Y}0Yd$%Umzb4o
z=-aucq+D22nUsqqNcSgeNRgIdaX5+DO9fiiHJ@z7dz?Ik*bQ%ywkTuvc$}nBy(-ZK
z+68Mb{nGj#RCi~t<p=*?O7cc+aG&@GVKaLm&t;)gELbmVjqf;X%<+=QK5$aTxh!ie
z^!DMl-N>K}3tE3{=A`GHF}X1C-V}eN(x331M+D-7rc}MtQ2h1Vf##o=-))uV@RP%y
z+|$`m)iz7lF70Amd9oHA_w(AtP8kPr%0IOLc_roNJ|tz0LF5?+5eZ?UVLtJO3|dK&
z0hufu7a<3{Grk4!lbr~1VZNr``n$Thm#`3xcb&^H4Gw9~z=oTH5t!AW-aXXI*Xq>|
zzV*8j#vV-6&`P3gZ;eR`G^-bDDvaoNU%1BdGwLu2cRfBafp2NEiods%=KmltT`nx?
zH9PO<WBy1vUhU(4Go+QwouL^#hSgRcWL)XvvLZY@yX~tGlx1xl(j!<aV)t=1d+$cu
z9%?Yy*-9h9F7*kkxR9cq(QKpzX+``W_bmJwRm`&vUSAxpF$(u?P?IU~^Ry}Km}0Y1
ziD6l}G<|f>Lgef1rPrYg>n=TPUns5R{pR`byz>#xAFB=-Cd9HY=d)bAG9*;WRBswF
z{jQBym;BoNLvBvB##O<V)e9f{5^l8kLyLMeQf=tf{NYiAe>>Oxj-2*H`(pBm8D0iu
z6VJB<q0pM*i0fHvXx9}aDP;a$*0SI74|Bxjr9Webst?T7U6|JIEjlotq!2V~JRW~J
z=FbkWSYlqd>`!w;RQd3q==dqKlU=?@)rWz7ayDD_XNyBBVNlQUIadLn^pGY+_o8mI
z?mSfP%3H9_VDop~I6?@NoM(IGQ@&Z}fBf39F(o5DFJh)0U<2Fy7Vo5aDzOdnK+*C{
z+QbXju^*@95045|S)@)1B?6(BM^JC|R^n80P=*pCT9u#PBTiy@+if3_Y%9N@nFl$x
zsmk`3{j>%OtG(<z&ri-yl3fl=$KFw>28J)xTcpHGjKDiov3^lT(&|lM$?FntWe;Oy
zP)XZL$UTo6oKsVOs3ss2CWSEiG8^5mSyEo%eD7v>JHJkEUO!?Xq9^l0?Jw*6l+t}M
z<(IFcVWw%qw!Iz`tervOhb>-+@%U`bCr4(EN-upJ+%!;fr{4#w2IGmv4*OZBlRuHZ
zB3t)R#iIMr`ISK@=C!?JPv-W+@cEm#x$rpgt#8$P_z?yAT<d6GTC>hJO*N!dyCwb@
z;%md%GEuPNI|Y~XxBqrzeFk8jh$rTUZ`ajK`<>UmL$oHesb+th5)r5pKT4QYd>e5U
z2r`4&7N}46Gx=;NC^*qnTi#RY4WGlvRZm@7F`NZrvme3*P<I9veK$cGxpo@|sOST=
zY~`HPXDL9JA#LJQ&Fp|3N3lBep!08=LvkfyemA_zngj~G$K*71YqDE5kA3`MA(2;f
z?(b*}aWW_oeG^2Lexy;IKt;WGeo7ulqk{obeq%+q1Fx-q_iK)2{*mWW6Mlh|@@Xo~
zsN)?PlG$*p)+F7;F#eJ&vGFPl=uQqR%rP$Cc2}79+0MVNU7HX)ciGDZS0Z4(&t*vO
zxH8u&HJ4lESGq?uu-QU+iMx|U-jqCZW3ST7mM>x(4jOn1^<m2{`oK;Fdo^-%qJ%jV
z80#)ZGJHbIfZc5+Pu;Sbg<nm<G{;&KBu&Q3!S0<j_l_t6QQ#gA%;qNj$SkkG{+L!#
z14I}nMQD?%lhP+{;P{G4LoTs4YULb<67J00H5%7xQ!G4yJ>3i2)TdV84n-a-<EA}j
z+cpgnvMq-c=h1mba<S_oZmw&EV%gRAM_yXwne}4~7W};254U_U`?*E%Y4~!_%Lep7
z>Zw3%B^W>F_q3s9JpIssk^(o_w8t!FQ3Sz*B}6!xKG8~XT~2H}A6xgizX;mK*s!Mo
zl>=|-om&f}8t2Y0FRn43IyS!mU=N>eaap@?EW2gh`X1-hT{qJ}h0BP&vfPYL^34*o
zH}Y#Jl-3*SS7cQ*S(|ixP$Q^ai)>b{?WbGMG8TO?izQs>*P+iEiQcO2kLP?zmXjKi
zte;ho%KW&WjGyU=h&>>fc4)455GIiEq#?rZ<1D|z2VQ9Zt-09*C8VfYyZ#a|L?yh`
z4Avdz82J8X6Ye$cVg^N^6Bb$ekXf;8CaiZVq_r<}s3gB}9Xh$7qMRmG*S(pn_3Avw
z^MkUi9}f_Syj%q^>T0+96q%2=TcFda6d9u;Qiw6sCCcarT3lCX2j7?{{{Y|M3bXna
zU+MNm`xn@sDmyrZcxfNQ4Wv!)T)8J$n=$SqrQiHw?n2kfw>Zc9N;@!w{m7!8cfjlv
zbb81EZf=_|nLzt`CEahd6yu44{u>UK5`tk|v)TLG0(?B$YqwvU@Q2w)qHXvZO)g51
zO0qLJKL`EJ!c-sMg55Rqc@VVi!Tem9DuPr!-{0&>Om=du{?ZMp_t{m8F^aa#7uZA~
zVjS~xJCffvHRvWJRy{WR;sPBO=jQ;QSn%D!%kSoAAdQy`zuq2SPRY!gB~sN8E=hZ2
z&;<GZxCB9~Zwoi_TvpjuJAhTAJ{ERWP*}n%e8Z+;!sPg8Bi@}%*>aCEc=3(*R{H>%
zZ)!i<BQ@1YB^aSS<s{xF4Lo$j5@h-<3lRlN%OG2VRE-VI8T~z3$%UIucqcq22i0Ao
zA6$ZI$Ma71LX!?Q6u<CmWXfg-AhN9X<G-J80(3Mjr+D`+m!4DLatZ}DY7Uz`ZTZf&
zU5rsu{1D;NlK=*AiGzPvrRe>0izN?cB)mt63tJPV!vgQNq!)Hp3qM*QC2Z_MC^YTl
z4iLuw+<^J_yW%@hN2K^ae<lXMgCHSt{<*h<`{x=B?8<Vf{Kp`k^J<8kKSB5}cc^pD
z+`Y}h5P2Jjh)h8?p^nC*X)hC*8t<ps+_&su<=^ws4@*!YH6P!)2ST|(&D%d3cuhGV
z+=K=jhK%eCz4CY{c5?7e2?T=cL32C(2U&}UflDbU-)cYyIjjQT?2tC_b%Sh#ntAEB
zLb)RZ_cnfzoJe`DHCqhQUIQZ5{g1gLL@Ko%Ie?{?0$GLe(*#wLPLFm?8o&p@*E>dy
zJ7_y4D}M$6DJAijGcEg4N=DODD9&&Z9;I1yHcPH4NNj1{r0WX~-X`vFCtb`Rz}H7S
zuVokl!+v1KI5yp;ujUNN3;SchvA^$kprk%`*Ta9c=8ZH5&cqvuhN4EXFyckqVrpVF
z{BQ2$PzR*=$;%lgI73wB=_}eQV!>{!tfR=|hq**#j{!k-ft=T&<ma^<Yg|OtKaj@R
zrqTNU3DUuRymT#i_Id4pK{j4YE|FcI(wfg;W_v<9inH{0(kT@~eHetoO!Am-;_|U;
z!BjxoI)^p$dOM?P=Tc)1e5rAMYwP)1Ad2?Mxs$5?n;myjjcTqjQI*qjS2{&*;ItJ;
zm$(ljE4%iSNg)oYVm%QWl<jj*X^g2qc&=IDi6aCoJ?=Pj%NKGToR07=<&9$h6S!iK
zL|j-IsS`_ae<k9$6B-?>S0;tFwVW#0+Rq)<UPwL-LKdTZuRzZ(tXHb`zpu;N5gUa0
zwcK1dk*ha&pWCsqLg8WHmd3C3m3OrVoqy#%x>OAW<#y#7U+{ZgQ9j3w2N1G&W&fRe
z0L0WzZ2KP))+#{22HtIBfvvoG!&gMjK1|PfKH))$-2&xsu=p~i27m*29mp(Du(f?e
zjPxwe_F+f>T)fqoYQL8{Uwe^Md~DOlL=c`+g%eyOi%n0aoWR0p3=G_Z3HvowNUp}B
z(|c=B5S%yhfHb*RBMqTY4N~aClT}ED0mR~#M~Jlq38OV6NIrR)6C&tu0t+28K(uI$
zUA~WR7Yrqnxsc-x$Pj=F?ukm@s3QPe^OO&W==%DbvYGJ3IH`p{O(K%p#fIzuU<drd
zLci%X{uIt*lw^QE)Yiiscj_*+{&NFtt6-)nGCvMm&2PEJubxgFNv7m)sg-0*Vg}M=
zoW6fIzo?Z)Y1|vOY*rDh&pfjm(Rj->o4)Ux!-aLOb}#(F5ViJk{N*01&=WOp{f(?I
zg2tjt$Au%<&o3_D7#w{ev8p02c1%u4ksBqx0fOLL8t41<7ahoEgG8ysjNR~`u*%FN
z%M`|YztV<P(M$IhXCI^GRG>@OH_e1pRRe?osv4v<X!}!SIN73_6FQ#b&kN3EB&*~|
zcuc)(9Nk0!{<8qBTWYiVD{2d$Cy>Em`P1sl$r3R{+3NwQ%Ip^RvLFqX&BO2$N0@7Q
ziPNyG#;79e?X*U}U(A9Pb2t7%o{NR_wiay~s1|sWbzqKVyN1)6JtS4^6Td4=3b;?i
zSs@>fu6LhJ<b!FHFCMl?r;Ha}PD8=@$z2HhTbl;d6a5ixJ4--`<<jf^+61bF%d^6s
zgl~DCRsWBu?9>(G+3f25AK#&$RQGKJ0F2*PzlRyIg#F#}nE_xvo^a4xeMsDt-O{uq
z&?nRKHM<3tT7_kBWA)MTt&!hP_v*sxKWESyD(FS!*ZcnvCS<bi*Rl)q-`cy*u%^~6
z9gYg;TtV*9W1}851Qnzj1Zi>*H3U!)6{IRkHB{*YIAR9@6Ql?dr5aF*)BvHVzzJO-
zlmI~?)Ib8E!~~Me7rghGJ3nTgnfW`je?qeNx4*L1df)Y~74Ns*g6lZ#uHtUu%^m6|
zOWnf!>T8X7TX2lmwca&R8o7nWPQP&RVgi_C)o&PhPndY#0j53GF}Q2u^DS&=iRdx6
zf5%~lk)aQ}-#XwYW3@B;Uq_~W)mJ6i+8PjQeK&8m`&~>A&kt6u?<p}SbhK8%egjca
z$$c=LX2pB@kRKgtKGthN2?PfH3A$@MuX;eed9uo);&W332Ip`?+`R71<H;fbhkl_x
znA||fr33QXz947ySA>CGKjGa2n~UZ?p^vrVC+!}4=tyH_GCYseOZVA{PIetxT~3%A
z`=mdz3)C7{D{PHk^`dIp`ZTw+Q%G^R;FGPZVI)ONMzZIt(W$z82E7m6Mf{yNNOVa3
zb)&L0vuU>eB{hIbdCl+L&dfcS5dxZ>6=X3=HsBo|gxmh13a!h4c*5uBr|2<)i7VLA
z$j=5*#qLqX1BZc=5Csk~dTwof730xu3U6O$DdV6K0ivqezExRNW}az$SFWkDN(Tdt
zuoP5tOX-*eOTEZi>joG5<Xd)_i@Db=ZsdmVOKLxR$7{&d<6f{$@A3{0)i*T*d+NGv
zrmy>SP7Idg75Ad`Dqg9H0GJ6}N#iOv9Xw7^k)YAJ`+*?wxGkkSAlW0>0w`D%N#1j)
zk85wUc*}^OUG7niS1Zpk9Zj6Z?+oRcEjgw%b%!^Vn(*A>_D}cQs5I58*=^V!S?Ba*
z5j*FBr#?Ane#}N5z$+Km+%jj3XVv7b@AA5kMoNH-C4bTyDGuyYlmh2m!S=CfYs~=Q
z#wO`C2^R1^Rt%aOaURpH7;56+(_PcuhmFv_GNFP1Du`%nucAyl!%Ck5DC41ZyM9K7
zXWC<s<GF=-q!=2AxfaifXwThSf9l*ul({P3$optpZGzu34;Gbsp<sMRFw(!(8ZkC*
z0S}_4(JB$tp^|R;Q&!XBeIS>kcW?AET6xph3_?LSC?+>1d>e|uNZ#fMAdyH*_J=ru
zeBJp0^k~K{!Nl@4azfN8Vp{#qwgd(fJ{>4|jQjo3LsZ+rAF$3+5bE-|+EE7EtXY~~
zbUwoR%k(1l7YT?nN;4(}#y?Je>5dfmO0|9gv7+MuaD9#Q+;t4oDe2ar#+p+ytvQg>
z1Ff>~Uwk|`ntJ?s#=io+EB+<cuv-b{6)McP-YRY8S~e~b`-PUNA{9$JHix2BWw4lO
zizqR$p++(A5E17vSzI$cS8JJ^)oJNsze1~7yjB)cxs;bR{z$^Tw&dK>&em%04=i}d
zwQx};9`Be^882iE-pZv?i6K#+v<3iC;F>8+0-s!~P^XC7#RTbyDe7da;F6ll9t113
z8OrtWHv;81;zN13-==gEhFd);iJr0}9u<CPLUqLUPPJ2Z?tnOlm6v*ed}!G>3RF;~
z;$z|eHIZwF9j*P&Z~L_fo<j4PXEA4z+l`YnAO91_iOf72e&J{po?w%Aqt`;Y%?UL$
z{WvSaFIJcY-zJpT<&8}CZCuzCE8A@G@Hsy3czPl*Gm$Fl8RMGkcx6!MeK+BKbA`OY
zL<Q1E85FF<aJ6iuFB0@V0b_w0VvJ^WR;SMaTXh7mA<=V{$H?cu?Z<fHF0eG`6mFb?
z5$p=!*6sJlCeKy3yvN)r92xUT3o9<9+h^KEYAk-9pj1hHqagL)&CWcLm`5(H>m4I`
zVf3^~sLNyN4+MWtMSgI@gu^)P%5q9hT`YlLiL(mXjKP7Xlph3fl3J?Js5R8W2<ov|
z|8`lXbg6(%UhI;w@)1CF=`}vcinx#Vn2ns2-}#?@EhbtMzEDa+cK<@{T>+8Mg3QwQ
z2uvY%VX#D?yLU0NthpK->f6FY&GmhxoJrE14KX=4!v1CySS#JTaD?4Z-ce!rDo9Iq
z<HFeZvxD`Vl<hv<z`?$r)JLq#y%Fr+hhp|$?EwJ~3xw*<yrD$eufjeGPlb)aVC0&&
zsuz5)WqFl3RYeS!^X|c|P#^P(CnNDtoH=W?yz0%8-<-m3reRJIxa~s_RB$SwOfyz4
z)X)qb6C94&dAV~SGOR#B=G@wQYxeuOhafk0W+!-3v`5(?;^&oN+15X%{#xBVnZVHA
z2Z^?*fER*=87Oo>SQND<ApM;mm0k+9<*n7?UE{VvI+TU@;s8Sc<v2P-TBiJ&h{ouZ
zsWcqD@{=6I*;NQ2AkoJvkC9wzcHO(O0tj(4maec_<i%PEH&=ZEpX-FzBI=fYvZ(<9
z;7ZM|pCC5ULWI&%1t)h{xVE=InatBa`(MCF2Fjj2uY&Lo88ia9&6s~k9sVPVIRH|&
zM{0Ndl!^#fAfSJI5Ug!u2sM=!>mVl5ku#z|I854-WXuYi7d3`B4h2FQJ2r4agmw2K
zaJ|lY;|r$&nn7pN!O$5(3>G&-LQnx<Z8WPnL}EfiZG{2p7#5tz1KoQOj>B93(I81<
z?$A&F9r8Lny=GT5-j@mOHWR`odtM;>UtyCfa!tKoOP1>Y1A_tp?=f)-8v)ybO@Cl;
zvCljpHu6C*omwG~47fcAreCer;-UtQ_77wd?eWj%2>&Zo_fJ^)-++b`03Se*I{>1z
zVF1!GI4H*h!`byC=`x`G3zd8-g;tM{dqK;Awkls{OAFQMpuFDngV)3tFm52u8wAZk
zo_o47-nI%`a2+?&>$JGI7GW_5W(r)XK?7eMWCK7B+us5+BKdY@5^DIiVLiYX(_qf}
zGh|Ho8OYU6=?@`Vj4J?#`rpOq2ejJ~0@fyhm>p3tTzh-tRIzD4_QnsjzZ!fOX@a&;
zXobqtdMbf%tk1z2+r|08f4uV<WJG@yE7KuKaG1nY9M-uhUu)I*lL0YgfuIy8WVImC
zhYWDkcOxV3ONvK+L!)m$KnFH&aRCr_5`gciBJ87W<A4UcCOI428wn@*<i0vME=G3p
z>HC44)cT+yiazHHB>FI(i(?K-vxR0Q{JT4Nar4zs!#;mp;Xy;;W8NVH9K_VN8D!9w
z136SgedWD+AS>>y+57@ca5Qqt24{XLu3l%<pXUHDv+x^`JuB~&9Zv<fr}FMtupVR;
z&s6RHfpfN@%s=Q8KQCOv%BbIz2j|`DOKUtdy!^ls;n&n2fGXJ2Ku?BONI)x+HZT$D
z<Y})#7~vSX=C6$$O58%Eu$ILeNionMwZisq2^=7~doZIJROL~>_*aO`YRaEma4B&i
zod!pEcep<plSNPyY0+fKEy!h42te?&PW%8FeF2k=9fgx*t=<oA^g#tLXuY7;K&OFU
zla0NwwH<^*`v2>HI0J7x0>M)6DLq6o#sIV_f`~W!00;R%z9y*(tS2c28*zkG>HTqg
zs_lNLPNj-G=mVh}x5GI(iP?bcR3w}xGuv>&0~UMr7+Bx$QF0lyT70AtDr|v@$bpp6
zcg_*T#)*?Q5LX0;KSHbioy322_HR8s!qm&_E1*e-A?eVTxQX5vgNe|GorQP-pV`PQ
zy@+X0trr+!++YL;%n3Yo<rHlA2x$?!X^$B<g~x&=ckTQYpIl_Gt(9X+<C|e5e_I1p
z5c37+#emh-(IF;a9A4h)_WmT|d=S)_&9)q6Hu$K{JD&Ph4QT-dJdNCCAY$;plK~LA
z(sE5!U9j0}$N=c-5)JCTOgJO2Ou!@PpWw*)aSGx$h%xZG@8kuxNeQ#cpbM0Z{4N_7
zPvX^C5jB8Yz`ed?ZFWI}>BoWTu@Q5$OlnA_wG{mX0G`&u3(u=f5q|cmv<C<V)^kh^
zG9U_+0(V`8Hki}gWwz@wlPqcf9wGusMYZNo>`{@8et{TGtR9RcC)@mU^mf%V*V@?Q
zuMk1MwRWuvWVt$E0>LX%l*sW7r(Aurg1_yjT+4l#3t~(FFJ{ki___<AJJ_KyVbDv;
zvqoxWB-7+ILR^_9>goHPD}cee>yoscDS?PzgqZ=^0Ek5rkgQ>4{?r=UL3D>&4DgaQ
zmh}R;dfI3n^bsr+xld3OLGu&^H8AF{FwG6say=+*h^EQC{ja6X$D^FC_tz?4VF3^~
zkVJr9%VWpVKh;WzIp={nyPG|?!L-Nhd|IQL5#bJ*9PR;4e(t&&kY-|;k_%hY<uZ-k
z3|r>OD-KgEl_Blm9a0^D_^I+c|B=HEz=nGDC?LA<?RHqb{7*oz_jO<9^JcFlxnAKA
zngKgGc3_RiF3I9o0-ce{$>`<Orky2`fGbP~{E27&I2wD$0)F|_+MqsmR1sv?T$(`d
z)4>80u!?=(Fi?R_3i|?UVHYT$a(<<&80$vU(l0`Oh!R`=sY3%~0BY2`Hki*%(xq?i
zQ!Qx$_f{L>tCJi+(X-H1H%hI&hsqbXgAh=D?7>ff@U7G#OWgqNYFeN(Fv3Z)_}DA_
zfOxGM$rh3Mg0>FU<P4}7m~E?OHk{wTSak72E)T?G|9}10GZ2lhJOLyRPR!GZA_HL&
zsA0@}DB}X_O)}ShG5nNL@!nOJ&25cRrEi3R6~0uRl+cT8Ll=(tYFzv5-}LjM)?jNM
zVmtX>g`875C9<dx&7{;(cg#JTsjcX7+B3-)|58-s^k|Li_&12AF^JUM=ieaK5zGYc
z5sVDg<Lqwnck=1I(%k`tAr6MdUdDsP#)E@mkgI35K6)E17v5BG9+-<p!DN)XNHlEy
zA`2PFaR80b`~^@|Q`QLHfTDY%@~&*--@WwfD>{fV<jENc$iUBFe?`MGuO^LSXn}#D
z0hsgeBDdI8$Vv4SS^;zgQEwO3pg?{%#(@Gb)BQ)fEW*HAFA^v08+4K+)aUCQi5X;%
zl`$r#x!j$t@dmB7Xo}<r2Yo3+62TgPrjY$2HA(H*g|YmE5d{uWs0K5}C}BW$siyf;
zT#Y8LURc~2vli%W#^QmJAQlD6MphV6tzt4~c-T!pOstiDx9ka>Jn(wr*}~rlz%xQU
zR7mK{3L6cMtr6xxO9e1`3tP>uH+-?{7+2qb<OCzsG1!H|*1?q$+&lhEEk(k$g5eqF
z#R}W6fq^4F1A_)Cov=TYH^uGyUo+I4E-)iU(Pv&&ur#eHhGe3iF1dOnlQv<^UT(7G
z8^r61ZdBNjhHOdQe`xUaZRY4Dy`_3{?A~6}aZQTU2G}}kSO%6V&!2i(>!TuPm}6v!
z+!H2^snd+zvlE$+!k%;MK7?wUWOB>plauVEX7XxqMuS7i&7}J?l21H_YMRZl57Vs_
zieHXC>XS0)mXvgFxb}q+ZH?gF7}Qf}_t{qoNEGbmWTF91otAl$5Il48G1`Fi0Ka=p
ziRgIQd{3R%H@Bnsmi9vFa)Cc)o3sHg&!uFsRMT5Vi72Lomc4p9S&`>h^ZFS(uCm1d
z47ejFIG-)HV$eMKXm5hRA||rUu%{adKk^Q7^V|NE)v}krP*<!v+oXeD$-{<U9)Hpg
zEecl&z`cZ;R{|Z)t#1dCzw&Msq}`37j2CzZj6e;gzSa21vt-)nlE|JDG7a@+I20^o
zhCUNU-|JY6x7<_bz!yiE!j0=Z<OdZ9oryDjcg5Ges(MQ`Eo4xmF=W%FT~h6Z&%dJR
z`q}SGCBYCi^UWX`V>6r0a|){56WcBw7o2g@uE)_z@yR-=$QLJd4##3sQY2=5j4fx7
z61a;R*Y8!ueVf59Shf=2q-?7b4JDV{4t*=pB*k7<2++!@Q4MxtWf&0deFv4d$)DNv
z&{pU$G2X|xAUup|O=8cSjK~Di=KI!9Ugv7gO(*L;O2_J{k|J$4DJRTN>-#qb%m(uo
z@dM$G2PEz0XO=h$YoeV$qx_kmASXAlVI%J)kKo(Aj`05jBRE>Lw!eLqeEVKiJMN94
z3Wax+y!((Cc`y?R-z9&EZ3~-vsfU|t`Rc!TRU@e7N%s5j#5lp}a(J@hnj>X8{M?9(
zJeUF3mcG6XW6m4nF2h?FoRESu4JUbnWo89uo3EIRMIz61?44;)qfv6f?Cw_>Mx7a(
z^l#7k^kv(;cjv_YrPnhC%Ju%!s}Tj;TeXu7x(h#S-XV+a4fN^y3Z>caQyura!Yvl5
zS>=vshTs#PAq=egdF>)=6w_*NM1{rd$F=p;r8Ug*?mCA}aoOwToP<@Un6RdY%8_G?
zQrhNQ7vD>@FXCTWPP(tiK_?qh;9qVjUz{hFcY-a}y0yjPZfM?ZKUgm>*E`s2*u#Y9
z@PQX@W$QFt|AmUP%_}BA2lod$8PX^|%JXtMt`SomO@>nb#3^NTX2Jn0frH7|_yMcn
z%LT)M?Fz&cN5QZ$ceMCzcmVoYF2$_cpt_psw{8CJtFMa_ZnJQc^hi=$?k?r|25%C<
zvi0lhASGGd?3ORn`WIm7U(FvTOd*IVm7L5k>q7tzN2mF+SNUfgy`PO4d9A8k;r|)w
zkUSo(gor;e&mQ^R1HQY?d_u4It>K-Sbw$^Z-WxU%gL~1dc7WYvg)+?Gbd=6WEVsSW
z#(oKt71==R>b>9Th@Mxz;vE!hjaxy%?AfYCYoj88HD*geYntVW_iOWR@Subm@0B@)
zgoM+;pm@0~#7Uh`XaAlSaW8ntCbK`>Bb|8=m4JrWT;rnccR_wfWI82{6U~TKzfCEg
zTG(*9%=<>nkbcTyNNk|&u-by!qfR<Gj6jo6`hn$zOvfzROKU{7mVFW}52U-EJJtza
zcn+}CZXc$n9tvzoZ!Tv4KC^GiSh`>SmJ0b?a08aM2LTEaT?+1#KQ8mK_zu^6-y`NG
z+}x|I(_Iy=^!nPN?-VCU$7a{1i1nkAN9=uv4#?yHqoN#-pvL&UBbVxt*l`{l_BO~>
z^M-^<5y%}xg{2K9LWL|p8xsL1v8_;QtF`zbBl)QWrqRQ-QmL1p357?;^>P@dxXU^_
z8D2^Yg?F11SE`P;je8EwNIqrBKI|t}-@t(|SJHdP6>n^%u#s#R=7Kl!S_;C);@;%a
zd*j2Ll9A;S(lIgR`auty)k)!M1r>V9ikwXK6M{%c<Cg4`%XzSBkhj@rUDlXfc8R@?
zAIRBkxj%7JBPjk-@Zi#M#6Crvhv<y3-RSkQ;DS;D^8o4~GxHR@?SOK{Kkxd~0~n;W
z6$ywYNWDVN7#4$od-~9Mk0IC7amAadD<!ujDb`)7^s04~VbzJYPp&3%UrM@l_f0Y3
zcP?`mS9jk`$-lmb6OSM89ZQ`Lo+NOGqT=|2wf-^@Fk(1)bx7mS)T>?#$~H=D=l5WH
zHn{yrXgK3zuzK!07I9dz(~9ra$o%0<M-02tmiW1|a@Eyu+#uF4QvWaGr>qCY^Hg<G
zrUW<QtR>$fhKtnC=X3k4H9G|=R}OpF71%zU694FtdT=-oW@tQu*{oJE)b2cGlAc4B
z(%lI|OTMg6G51uiT5~)-#}<dYR#S=+7lCy0C>Z~~G!NCnkvkVyvsWIMnK^g#!cH@f
zw&fR11R7pS!fE6%T2@<(F`EG~{HV~sIB2U*E3<;_1Gy4hd#S#U;!P`>5whL+&xQmi
zrDoh_JGk_RC&#V*dSUuO$(|c>aop48`a(mu^;ihxb%F#UGHG6t&#`{;Jdbgye8UsB
zV)t}q=Hx8%aTb3?w4dXR=1-p#B+z98(_=+5<l$ZH{cDdD&PTp^yYZL$r0%;}7lT+g
zSp3cY8IT*`uUnU~L_-}c+4HMWs5W|czNwY0_Ui@-5W4gO(KB(1F=k)j2RNr1Wy2K~
z6?s$FC64}VerNL<!{Bw-_3sC3!oSRPdQ>(IwpjmCU#lD%(Ld8y>>%yp)~`*e6We(%
zR2HV5thg++)GbipPTxrjRTH^lc`5xM%T#fsoOqZhyU2V&>)AIcMZ3qPw?GA&W$IWq
z2I`|!FR;N><(s-Izhmm8+)A+aass^!(oWd3H=$1N+U+iP-ix>%CQqa$9uEz?y07@#
zCf%cw)SHRmA0h4O-S~^w0@C=G*Cuy5>V$nyyU2Q8Qx{yYj5Z4+l|IAsu@huOy~a5~
zpJzYr4HCPF_%i*`8u2L`vv9S4ME}a+)n@!a9q$aROw>n%Q(D#!Wi%Zv{se7dL8(YZ
zUM7gKAgh*LcctCZ9=PpYbgBh}m#ecrEY>amgpBU|<U0dY9$J+7N1T(XV1~-nMxBw*
z?#k5DCQCJ%7jIClNz+E%H{MY5F`I(Lu{W{5S;~kl`SjdW|K+2CKqNc$^BwIwd=b%@
z-?NOG-hE9w`xz#hZu0APBZ<Gkutp_6U;msXc2kGgw^aLpXcFu;<9c<PvGV7iH$pq=
z(9F)A4V=z!RVl}ng(PmLXYHqeeqP%U$D->iYDIhzv14y~<<xBI=-=PRic?iuEYid7
zl0}cKtQ!xy5E2L{=e)ZTLCW{?Ux3&=x9;YZH?p#CFhn~j^3dMxbA4E7v4FKf?aXFw
z2voiB&z7$4ix}_5f1OMhy{(!*;L9I-y}-6^+#tvKyJ1GXeTh+_>9o5s{_P`m-HGL)
z4<mDvMRihK(~@zR3^P{!ZP|R={-Lz~K<SPVNr!}~`sv>KX>o{i97GkOrcrO(!|P`(
ztZ-k_rlvU{2&uHgAJnp<>!~P)CemDjy7P&KSbI}{H4?`<+7-SgXvj4n)IaycrOQ&;
zNX!vLJdA+7p6sH(*F8L$cGGj)TY6ft>HtROqW@tMv0rhKj-V<Kl5ZWeOUZMmdwZVf
zDa3btvK+(ZIhx8`U}@JX{?)&JkaEFaY3=5$_3;&!ae;nR&b<dp_}fmq6hf2ul)IKL
zZdNI%o;>*)nRawnF1mAz3u}Z_I8XpcJ&)OvMnTjr{q#fKvQ43Ji(vfE{{Xht|Bb7s
ZOP+aKVa@$sv9s`n5hfOAi~exF_ivxJ=&1kz

literal 28001
zcmeFZcT`i`_Aeff?RXV9>Nz4s5#4|h1r!14cG*BciS(*;X;MOw5{`Jlf}lI06JnuA
z?;tgBL@<#iRZ0j-0BH$^5+Oj|PB`bj`~ALq$N2s6d*l7aZ@fK*LRQvZYt6aVY@az7
zkFFbB6ZnVdKM)9n0H|}>2!Yrhh(K(;{qqjEWhnmTJp5zFZM|!k;h*rca;XLj|JZd`
z$J`fzI26VEw<UJ`yCVW|1OZ;Ya5FG<mKvPsZX7DJ=0)5s-oE9*5tSQ99`C*Wi1JYM
z*Bh^1y?Q<>m27wQ=Gix=1HrQsw|n%X3%^89^w!=Fu$6q4xEFu?V$Ti0=HZ*hy^WWD
z{zt?S70HI@mpUYJ@7)O?3>x=RYjW-dV6{3*iKTZ6KW<>y1(6%QgKkp%<^A9P{x@;p
zBP|Q(KINOL()MEBcMHNK3eznJPi5h04K0PXv(;PZKdKOj+&`0Dck)-z@KtC2C*E&m
z_-*pMpXc?!zZ+0}2!x$$OqxI3oM*GI7SW(vu1vlKhAEL><JOFzI`v5}l#ewe0jz{U
zdbp6nh1I_8HqKn2#jgNk263d5rlPq|0bv={O2v)Yef4QJieQWms|2_vHGroNfd+=G
z=RjreVo(T_ARTlOvc|1hLReka?*PsGq6P;Dx(8ju#Dt>j`}=_^1#$u|<P7<#-rKAD
zr7^Z=$2$QMfes?sgXN%2oR4AofIeiav$7$PNS7pFnJEs1Kn+l(J|0$ec6?;b3c?z&
zi0Fp_Q1gn-ouPFh9!W+WdSF<q+}CqHO>W}xbk_FhU~4EV=G?L{#A9K9Hm%1M9gwdM
zX%slOQgI=^I?>n!2hLvm)P+tUVUI}Vz0^3m&F<Q(=w^&<h*|{J=E+p4J+$C<J81ol
z?fg%(*84l+@s~%Bk6iEOM&h^K?%icR+;K~pY>AsyxTrzQpW?1;33a+tA9)HK(V*XR
z?px$gelC_C!^)$+vX8<`Y=G*JPYlJMLLbZ$=n;AcxwU%?4Xn+&*$bd@dTY<2af{g5
z=*nOy<?8H0Ms$VYyJUcAL1&LH15vSepGrSns5jF;>V2JfsEhEO$~<`Oz6$cuhp>Lm
zho3{8jrLWouX4SK%kYM`ORwCZglc}cy}uUIA2Rc7CiGP%tv_-xr775Tm?XRfUW%x<
zU3`4g!T1kmxHAhr+5JD9pt&7ZS#piRHz==HUfWDY;#)?!p}&9EbUW`3g7*mn4eT#B
zE){g+y=lTI=|FkPeDJK$)eVa9gsW2jP%^9W&m&QL9tIG#<`R-e2`C@)9oU1PBDXPt
z5xuc`?v`+*n4|v=#Cglw>WMX1FzmEccD1zQ0fr|Srb?DII{f7Ddp)DRW~-bi=fILT
z#Flj6%hhsA%4!nnZNp5ho6fqIRFEf%DvZ-OM_$FZu!MBX70ZYnQUh-rJ|D0jW?#V_
z#(iObZcQu*y+DC}s_O%SzCNp$&r#);1KNi>nUVN`j3}vrV^re%K|nn#PtiKzNa%HF
zQs_}GXxXAL0WZ^|nhSF#+?a7~z<**uN#vAdMF8bBGd4S$rX<yfR(Usv0}nrg9-8CM
zxP33%GUtpNCQVpi6ayz?cB}VW;7Xhrxlv_A)Iia+Ibqq&2s&4}$A#pKa#s&%p6;I<
zUL>9`l3SYLdKOd};F^PMfwuP1{KHJ$65DEaPXSW<9Zgh{@&2AO>SRRUewze(L2_72
z&!Va{8K=1Fa|AQ9*Y@-a+it}`u7ghMZbGAaHoNOBlV=seul7he$+@|#;qEjOQLOJh
zGI|?6=Scy5iz4{Zx}%O)Z3~%#-xO2m-$Pe5yv}ou-MOvqeV%y1?UhzZX2<bZuKfGA
z%qI>SyGEQ<4$>})D;&wJ7N|RH+8w61L;gz1FFS=E<}%(r%-mn=CAx3y;{IA(Abg@X
zcpAq=pL+67Q>g*(NA|Ynip%a4wMvX~I#9j3R3p3YJSCtzuT?&w<|WZNnW7ZBEZRBw
zBUy`U(!EitG&h-N-PGTM_Z|@j7j})1+>T^sycrtJ_nerYQkvuDG$vLQRyDgrEq#lw
zYK@#`$<uOY|4FkQC9iQeA`h_5R^>7PZ9VW!$VoC885_mKecHgfaPZg0IaSBWPq(g)
zB&|N6`Xk+J<eC!hv~%yCC$jrexl`d<HH)g1q0<R*=bRhNz$EJ*5=xjTp{bgiW7YD_
zj;Ci7Ri;iUO=3wMRfmj7f(&lI{y7jL3ca!XCffZ3V;EtNV%7Cja3o+Jn7y}owwmOD
zX|V<kP^^br#uzFuxW~zF69m3-KWNR(IL!;{0@}e1N`m=QrB3IGVPc8`t6J`;q->3E
zZ)Sow!V7bE!Lv@B)r<(BUaWt6`g7vy=jAC^PIR(AP3*gc8>Dg4aTh*TX5|hv0}Vf4
zVH;2_%1gnGBB$9*=lS}u5B(+2L~3y%@=rPNrewCtl)|dr$7F|Bbx(&sXp>C0HQJ}0
zvRamH8GIHgc2K}~F1-4}NA*R~#c~gk>j9x{D}QW|Mqe=BCF9F~-+2f#?Q}-j(Te^4
zoK+munwrVgXT40Wo?NbFurHi9(%v1|o{SgL3QML#022f+Yn>n;M(U;9WD?4^HV@wV
zP#*sptWOy9<8DPfIl|wi02$SjOR<2^!UMqBxr{n4eB1v#^<u#iaqILBO|3A~wv}-f
z_r6$*#)eOw{Hn<K)(9{#plzi;lN0(`ihM(9uKlpWaxy^FH~;e`&FIL--WP!HnRDOO
zkn5X58-B%1j*AvTbmCLc%GUQBpl+kS2J45fA&s)h;ry+GY^{=!juCjWQ#GA#*Ixzh
z^GsqbCEON=DcqC%NE4(Pu*S3|*&s`gz*2Nu4O58Fjt;y+=@RrPr9NBTdD1QqBqa>`
z{F9)Yun9cvcy-7INq#CUv%&=B#JbptY5m;b^Fzn3JsdCII$a?8lIwH&8)tq7sknB)
zcaLtkN<Nu$TPg*`qTQL^aGRv{_xAL+ZJ;PL3Fg7;+(>|v)f#DyV`!e4!#u({^l!n;
z;KXagi!+yOcoQQKvds)+O$&R-?pvPLTw#H$dP-w#k{7Y$@<mdZWfylVyBgKE!$T0$
z8vbKBZ>p3Uu7z>SwK4DB(A;|44fh8uWu+;30@I4FHJRAx%8b^pbEfRqd&2PCFs1cK
zkJ}Zl=SkI11%)}91w&1<id$DTT+fHBE1T=ZPX@c5W@{r99GzfCBDZ%boA&daOGMuY
zr$cFHUNB;fxGwDAGl6r%<8K(8`L2^8$1+jiNJZIQ(aKJk;2I=3Yc-XIr*|ubjwCng
zJ(^X1!C2b@%i0^a!2q{*ZPoSux724PvyB%|wXh*rpe%Im3nR?HFcn8>>YJ29hpzi>
zu(T!(Box}8K&j8qsCzSROljiQFuM0E+iB6ijQRN9Tb0S^515YhxEu$lXKFmgq9I)J
z?)<)U%qMXSF03LNk{6r>MVJ*^FeD@2uq;4A?2IQ)E=QFlEwxLtlCeAp2pO{S0nVwf
zYPfsyReUqh_antGK`YP}%90*Xar7S=wjWbO#!8allYCP<!)eV_@LOr8{X%%5y{i1D
zXLu#`nblBvsiZP=HEm%DNoKxA@5A&i?;ddQEN%|9&Fhg8g&>Dosn^tQuEL*zLsA14
z-B>+JXzit}xn-UUR(`2qXmOu_;lb}ShnC)0zLHF8g^e}Lc-V)tpV%j&r&@qoZ>>@S
z+ucS*6EcCq>67ejOnu!mg^%d{!2ViTgP8c-R{=K^UWdj`JB=<6YJ3gs9qZWHqeK|B
zPN64nZ)Q*Ra6J#=M+rPPfWM{jE(pZ8K?q78v_NAGb&oDZOIeNSfrsNtzFSzfNe3A~
z+<?K~^*<DGZfC$=hKHvvt9Nfpusy}e{k7ULa*A+gSayYU(8y1NSUGaBNbTBXf^uzw
z2?ut_KBmDA=y`8XidwR^co$<5w0TxC)m>C!tc_y1fie0%oTFbYJ>i#)p`9`-t4CL{
zGUML!)#X+RPj0~bHroPj1nH!ZK713~F_J`oiIni)Q>se-o)qHq{Ub6ZhpL)Rbp_MX
zSi!f7;}?R-@QkO5JO^O|e)d}&GS)}pv+Chw`UY~>V=B5Y)hdN`eBn2wo|M%yI##;M
z^Y{MhH;T*a=zW#3`bkN_G<-`zp&r<0mCE|DoPjJ|GD+sfu`tZ%C08fyA@OAT3p|re
zK$2g6MDM%7O=W4WOl`AIrH2+9)t!<WFm-;J@`9R5|Bhr+yrfi5kQ<STL!<;c8q`Ym
zyzMQzAKh+PrJa^S??*coGZN`ED3w5uaP=Q6_O449=E58NAD;$#tG+t??VCi8yLoYI
z-0FHm1Ofp2_>FM;forg=z|V^eAdu(P{om*RLmWsfNOIkIURL8Mc{eW4)h_Q4ZZ?7b
z05DZjILu$=*3=}2iZ5ejkHWU|J{SFzpW=@w`*Q{gL6Qe|G^;6-pM!E<b|?9rrCod}
zdq!|shkoXLEVJJ4mcx(p6t=^G9Pc?L1>0Kq=`{Mgp&S;6qNho~PC)fIxd8Mu@G*yE
zp<%`MZ+ZQ~9k1RG2nw=*(74&|EJh?ysN>Uj`51rD_)~gkqiI2)7EN`Bsua0uia3md
zf@M?{$*#!K5dJ|sSb7R{Qolx+G#za9Uky1<7K1_*$kv)mEwQIZyoXX*N~s!i=Xnz$
zB1Pgysn6)kx+x1d_i5MCOHiq-ho@f2`nmfA#4a0N7ng@pqM6J$fSCF?5m1$u`eKQ>
z28}^hy+L(I@|g6#L%dE!?%=!u%{z`YV-#=yZIS^L=GSWZyYJ<%C;v#JdT4A8UjAU$
z@{>5$%eJV^F!<3n=Hi?!(pEct4i(30{MTmc4W!qhuI04$=k7Io#rQK&bl@4T#IXh?
zJ)FDNlc;KGcX{pM6=o`P2$Q5sC37dKxw7K~-0-;<+*RF`DAacT-j(6DF&#pKb=J3l
z^;$2&`nN~(oAd6*yO`O;h!|n*GS<euckq{V66-V{Ljy%LpKp{(E?qOF2+PTd^JcNj
zcs6>7#8x@i_a3wvMsi;?dh<vNKLCw-BpB*XRkz5jte-7SZ?Pd@1O7S}X1ze5$a{O7
zM*5f20d9dBym8Dhyf!8<km}&E)qec~C3^Iu=t2JBFj3kyGH!1TB>`+;KyBRY9u+sS
zuSDC!g}(r^B_J@%z5}=hz{lbePCU^eG-G1|FVHKe!_tPVo;&#ed74kcb?@^~DH<x@
zttzMI^W)dAhoah$w%z1z!EL<x2R+-$wwhfr&`)~AX}z)tM_IdZA!nVed0q_S(7syS
z_dI}^Tf5;2RZ0c9L0*`44;1Sweq2lt#d-|b&WKF@z>72}j_o<Xdm;R`Jyg?e?fFGD
zbWo$*h%2Uv;Yh7qo{%4yuNnJ?|4%1B@p>W3n)SL+t@WYjYqayqYHW$CD#p{5nLx}C
z&zpcBm#2nKcfb0#r^WeWKV^kUlUL%DTU5j*nl|y0^Oy1()x%=*@r5Ft44F9A?$w?S
z=^*>@rB8ahco-eRMSIuC_o16en&Jzpdq%AnDn(CrTt-gqDDO+VE4-b<7UETUD|D4_
zQdD}&s!EMULO*SuP1*a~m_I_xjl$eLa_OJqSlaz%N!fTWYd=1?*YFrVx?#>YDNMbR
zV#Aa<T&hLTdZm)Ru`_}@vu}*ga0`R`0c-V%bZxcqP+W*M8J5{3hgiT+*W8l*J3-kJ
zT8m9CX4oQ${eCZa3p!tu2kVmD1}c~qM^7R2vr(+rt%IcFWDg}w#~ZwM>0&3ZTevcA
zln|{4iKo#^L4VOs#seTr=nd%{*;jqsOJ%<(uMgt&XFNRvl+*DEhH9V?DY67G5c=2~
z)inW>f(B{+M|gcY{hj6w`sP4`8U8^+=-tLC@!G@Vp;5jhxFGe;D&%N(x9<EYMf4#5
zB!urJKqaRwa4o?ytH1F&H~hn4hy3feN6E3cJP*4(f|1krk-i2q-d70y&I!Cot&E}i
zFT77K>M41AssA@9*Z%^0{U2`FRO)p&4b`@gBX{j4Z6)M*Bp~z~L_jAoEiDZ)AE!f@
z994v+HW+dTODcXZ7DOez=XeQhs*#3m9tKH`!K|{ae0@++DtLl7!oNU!|A%S(C&c*Q
z*U?kWw}8@Qy)Jm?qc9uPp1(G9&-coILw+u9%^oTjh+bNQ&$f^2R(t*~D_lrHJI$y4
zWlvtrxtH)j@_(^(|1^Ed=9XbjkoYtD3!p~2N|o#g7U=kN{JJSb@glJD8+V0+^b$j2
zwHQ=;lx)8LDc_!&Rh;~!5H=~b5;A#^jt3?)u|6swgFtTuvNBp({1a@~S^*@@iDS}i
zT@{C>7G45gI;=(@V5vt!yE(AM!<W*3_pc2?9CR|fA8-j8lJ#geEXmGl69n4}%BoJ0
zm&bng|7p`vdm$O`l34#5a-YRxA$lVIfmiP#Fh<YEcycKdh)vP);}O_UA_W_@7Gp}G
zo_WkmVhyggg0=d&_zGXkmcdG8Q8UM=-cDyo-h>jREWy`PoznYiyDPC7A?wd7RE#M*
zRsN+p;95p4MrSBk?OMs)Rd@ajCyv202pdz}qRI#$+u466@J~L=2X9kh9quSdt|;{@
zEWigC_>6^b%*c*QDF5~=@5N37SdTBwNiOXKR#wATt%Vi%%DU)J^H;ZmRGoCze~)7&
zw21JTTeOx0D>xa)#mlQ5kv$UDNZYq>H_d9?MOdwk&_>OKWrDo3)*^Pa@L3H<fR)x-
zr{{PRA;QNm-A}lYnn*{vI<-w$t8p2<n@6Z92b@3ic&E%jC2acc{B8Q2G7lm06>ttr
zz)kGnj3K&jZx{+{6Nu)+<GJx7wPWmLKpXTifGo4bKi+PWuF8B_9M;!)jyG4)v{G^A
ziHJH)atkyJ(W8ObXeQ77Msy+`HIrAL0=9-QK5i;g#rAu*$tL~#0c!9$A<0x}k)3hk
zNlty0d=gCd?iwMhX^?5`)L>mm9;iZ+iNJt!*9HD-owRs7isB?Y=@0rCL2j~`-mzRb
zmT{<gbX83x1zq3#x1ZObu)eR>*U3oPT3vR1f1M>%t4Q9m|B>IB%~KTK2S;=HgHq&=
zXzg5f4Kk(7L)<bQC%NefmNmcGHKM8rZs<XZUD}`2Q|M(t)Y-#R4u7Qx@{2pSdG*#o
zFmIC=4Hqk(Z%Xf32iSbuy|9E*lO2daRA%cmpCX;3jY9`Y!TKcn_Nn&i_3t+~UByg;
z`<0=dDDwz**k9&9QEu1B@|_vcS{~!hU-~OBvADxKP}^g4&M3|u;e8Vxca3M$)c)G#
zIPyLLUh}{4V;u!-uAauxx42Gh{oPvZZ{x9!f(9aE*{DTX-do|neb{4j^?NyRP1<~@
z!K_ARK#KhIKdDbL-?05ZQJ?u@-EViwz+rHUOppt7NNqA8uWUe`>`!0`L9H>!O$X(-
z?5S-`PhR?izIX6&8hs2#k#He7Up6h*!B2-lPu-+P>H&-O1r`bP$o3UkT&w<x>zQ|a
zDIIHc*PVO|@6q2QTC0duyc;l8t0hkkg=UlKlS-is16H3w5DAD<`jzj%HRN>OywCnL
zCG8>)vKE2<m`<i*9III1J%5m*6KCT_LvLlQQ2@(pvjLclwa<~a`6tfow`AS<!{$ia
zE_8p*vS;t1IMzGS$D4}H*yTiH4XiOHkG6d7JTH!rCF?`nBnLf6#Zcu}(GoqV#waY4
zq%qE;$7~Bc&WH)J(E2=SPIf#O9>Yw_30l*I0@7O<q*G)RSl_40e$dL*RQM1H$m_Dm
zfQn!9ss*Ht@*!<+^5X(onR)xe+$a{KhEq*ycf|WMfp2MV!)N50(Kzu>NRn@BzXh^S
zCg?7u&gTwA(<fBdXsH??8nT*!o$|M5r5TMWxV0-X?Ie^h_YiCc!lhJY9FdU&K=5)8
z^+xs#=57YVer(S(M|+sP9GJa-e%3rkj+?4)nThF$Rp{HB#H~rEeNPZcOAl=i-^g+<
za~P^oo9j6Ia?U6Z1p=_!H<MUc4uzb0$E>Ovd*#ld<*A{l5g==KoK{P8U?|LmgSn=e
zoR;Y)_ojD91*(`%huNoYDRI}CFA$Xf@C~fEwQEF1-YzdTL#xrBq+3%dGaxDT=$`)g
z+Q-Ny)-!{eQ%JDdTdiPUzm0u;pKi&sZA^DcG>aM;J<y|Rw)EYABWr(v=M;QwU?g!{
zSTS+cL$Vm--hmo8(R6l0!F&f0Xj*^ljP@7IC1?yCW|#+pVIKOd7l4fPO3D`dISA4x
zh|LUvho7?oImtk<Oi)0_Qkt=1-%hn9+qQ~bZ~OhG89F5encP9Z)Niov?)g@f8IGRZ
zUfwRUP;Qb;(qm16X?O=3{_@6fXk{q0Q#wd*f~H73s9=L*+w~4dZ7&65Y#RD`O4WX$
z!jEfKr$N3zSg6}If<cyIm<6fWrOU@0li_t%%o%CXlY+f$Q!_#X7h2+q>v5|~_h>g=
z15g!&y;agwfV!}{NGt8)N-Fgy>ivX0_PVgY#w<3sY2n*xTetFQLY!rQml8&3**B<~
z&9goF)M~(%yr!L4I1?0lVM4~=85bhCdnoa#KW@}|z$T6n%!vWm7Jr(}oWVXy*eT>v
zBTbGy;UUqBCh27q&?YPt<5t5;<=NRiOcj=CQK0WkvKP1>{WTrvsq~A%zDdPirUcJD
z#xuW+BTMxq&c@hT=B?gp3SI3{2@+aHhm!Qa*2B^k{<6<`=$i4)L(HkgTT9PIt*e|M
zD<Xr#Dc2KczMz<yKJDBHke#D-p({-@JZZ@rm6&O4&E%Rsv|AnHB`P&9HHi1jlMw0h
zuhAXyHG-lgK8(_8yu|f-=5E&61~AYxy5{GOb=f&v#f!l(*`J$VB;9)QeF3k1S4AcW
zglJ%in_&u!&3^%~1bPYSOI-ZlwE&TY9f7mBlD)?J#kL&kTB)20j-49PCfCUpYS%V>
z7c9gZO1xa3JTX27t%2i9gg92FR;crdNw2BwU;~+Wt_~~VSX1%M*gU*;U3i!nF7-LR
z>eAqPB|by3-PX=Zw(VVaoh>Pi-lye}T)UG~b!MEDGJ~r<OGX~E;(7Mco|h=IRh*`)
zcRZFVMM6!M-?S7ZJu2%c?OI>4`P2}Yb+cK_u{-B!5;oSjpA2K(EQJn6cV+brx@6n}
zTbI3e{gUv3jaTbeq~dq(akV5gT3U-viXat@oQ<^fRTaor;*?F5f1PpbvOfCi(b$K`
zuQ{d4j_9)exZya)nS&&=?s{@?QM<G394GV#yd1<<)a?V~1aO1Bt>E1c*nkSSI_1-C
zLGVQ=>GxtY8^IjBcJE3+C?&c}II}ep?jQxW>m_-tj%^t;#-;Sf++A0{XTyBPYB~=M
z<IZed_<_&bvr(Tl=S{$3)96^^c49J1vey;daC~qu`L17v#y3uPhY&Am<NHxHAWP%a
z_aAuk55XCE?COrA&CGJ?kHJX<8k|%ysIpD91qT)vt|_gj)b5<I!qbri)>d4I+k+Y2
z!u6jj1)3IgcM%>1UZmuG&MleS(>?92JFV?f{kF@qr>)U5Z*ib5ixI9%mB?xhBY;Ut
zYAQAC-niv!e(hA~jG}LQs1ajK7QHXqR>!>g<s&KmoRm7MR=Ni~(nyqmE0?7^bWHe=
zWB<&VAConmuOB7m#7NxDXFF|lloK<VjbUkMSMJ*0LY@gxP})~3+dnlWonsS6PmYf7
z;rOj;Q^H&`+Sac{f4LT?70k}YlEcrng8}iY%Tt~WOFg>WV>tKxDeQ&#jhjs|ao^<*
zy$n@q4AzU3-!=b1mop|T;pDbQcfS2p$K5}}H^$!X4Av~@8rcyD&vw#mzhXN?{{$W`
z{V?_kS=zn~2BrSyl2vnJoTLkShDu3gzm*(kw=IK*Q|Yt1Ls?m^w`a>Uj1FT&!Uf`3
z)X-@f5eAKCbh#%Try}KLR+a!#JXDK<Dl^?1mIeUgRO*nD)FW6mTtoLwTBnrEejtD`
zx~z%4V|i%n2+T{pp*1~S=<c(j6XU(}oMn3?$V|c$hXj#|-jm87Es6B4AY2B36=&NO
zPG^R_1|GmXvO8IkUUkh6u472gi=n@}<FJECF{X;UE0eVy=A(3&Ic0TchT63>vy8Bx
zf9@}o^HNB8?`4L$p2ekt{<*2#?)DW4$DyS+xv>Mxw;yJt$0G`+TPJSiC<b<jyo5dK
zWf{3M&2e9!wGyA!84{IoZ+8k6lxcfBSmteyAzCpo#ZL!f1905aohiOsp&H8!T7Qfe
zDZ%8FO0)Hn_F67Q=mz1Fo{HVG5gHoKA(kT_O#wmX5Yc4XW^jdF*iD#H&q|ICr5`O&
z&r%yJWzW1cr-7`=xc-irZl`^0JB)ek2x<_X&F)R+zS<7Z#~lh-Oy%B&`m@%)tkvQL
zg5<urmZ~}mFVGBG8I+YdC^dz?OW^&_(fz;0u{v;8>gnr;z<T4LP-5CD)0pzQHEp=k
zXd^6A-n6p=M3!2S;^>#+7G}B#Usj%Ktuu7kGny`Y`w3{OnMa{iv?8()47&e1d+fub
zt2FDdV^hRLVw0xXx}E*!U|vMne1`^9X4VXJzMwZTkcuNt=zXHSZEg%)iRa|-vE0h{
z`W10a`vheB<&sE2y~pd#r+n`kQX^zBrZEbAvaqgJ{<@UAT(HQbUUx%h6nI85qzJ1j
zr@jvQ_T4P-<vgn(FDAGIT2IJ>MLlC5cHFjj*kV!;YC>EEha-B&HPf_j^U}dTNXf~K
zHIh;{O3}K|7*eG6Lgx~gmIQsEex52VMhBoMc~l9Ejh8MfI`Zqb=E(`X`4e#9*_{r_
zITau%aC}Kgb15M!<@kual&ZNiiqid)?}qn>^58Z=wGO>e>RvN^S?QK_Wiz8rojYQL
z)_xq^Md+<Ptu-uK>1<3%Sj^_}<j_LNUJksN^J8J^@0=@_7CK=(EBI;#W3k96v9YFP
z-D0BS;VkuB{`8@^3e%plAh(P*74*KA_1gL_M)k?Hce(c6)bsfa(SHBI+5$srBxNbq
zVzfNCuI`KpQ~#??cUt3+Q!IIH;9XQ-nHXF1N0Kfn!@ZN|5a{{%S7TpzlETAgPOP`j
zW#?)!LNQxUmuSHLk^79%VMk&xGmXf)&~9bq?g4Fub9yV}a_7Ei_HLwDY+E_!Dir0{
z3{rgLdoO=?rzDT;E=E&)el8WMZJ!=e^C+NHkZYWc3rO8R71CxN#TBP%rsr>ChID|e
zcpyq<;Mh1vkuCX7pR!+)1ZQZHupL&Zt3t<`gM`}CTYpJIC7s#>uMzEQUyc1Nm|Wa%
zh{>nsB<Wf+W0uMiwlOK}a3H{O5o4;Y<xo4FH+ri@revOWL4vus-ucYGCSy%583ntg
z`~IN_dsm+Kf5;05&V~8wvu8vOzz}eVOeQz9xO`&GodR7TDL4#!QzJO0t94;_(eR`P
z3#;0C53D|PT`Hcaf$_!OrzHcw%pGa0Q5%+DaAx%#@}juJmT>P>D;}DO35Y#s(p#9$
z+0uQSRFigQbk$aOeT=gk<5^7-?iX~CX;inhzF*1s;Q|q^i3f|bN5Qyyj{FPW0X)m7
z_2cSjI&E2x$ywxt7+NcNf2Z!a?GOYzDVlIyj1Qb2k2kMUCkr6mV(K-hDDu_v(^9{)
zr|?2?73uELOBE;OR4c5jXQt&Gj<tm1YE>N3Z%=`8x~vpe&u$8!dX!4T#)@{rm}I>Y
z7>0DT{vnipGV88AoS;yLLS!79m%TDiekr<+qD9d3R?HWNMtPx~^z;Ki+zu6>s{iLu
ze|?pzF8P6?fQgQh+m6ph|M=3E&f$EYMYjAID2``f$I`U;;l`06=MA;m{tOKYd<cii
zktsi0Tf1WQphNctHS>+IYi6&UM1jCtXFCko0i$W?m_H^$fpO<xOfq4eH{}!myA#Z_
zT0=phw<uRHNa&ltE~#vDrxZ-owdeIPH77nNiSM#G*f!|r{Jq|hQrwwm-L=jfQp^(P
z#agE-JE;><LFXx}$wK1A>q6+msC64+H7c$I)n^jZ28PL#-P9ZpOq3(VB8t^!<5&yz
zGLF>#nk+Fv0ab<ZOP`2RRY#;g)eOTa499Lkp36v11P3(cq8EwzHA-Y!#>p?E#XY?(
zdqy%;Wryms#08`5hg-hz42k)4<yD4JoAgRn`}h0!q_^rBKo~ZjUSYsbQy}8)#CMJ4
z4B#IyJnZXv^?-<m9Q#MxT%wWyI4t}Ihm?7`g(#m7WfAaKc9vsljUOrVO#lh!r1eXp
z-Y>H-2$xIV1sbII|I(0yfop7lpzAz5gh%#o`)Z@VN`YzA`9#v^`9N7hl5a!VTpWGm
zIS<J-Gii?FC3E(Ig8CrcL%f*g*7B>69_+uCLOjrxn@mpx5|kDC?!bPjykQ9yKZY<V
zT!5@p`BDGDLIRw*r%c>=qH4CHoVAu`2m{Ec$}FMXr9apS@4nnUxf~djyz)#4W>%z<
zYD54NprK0KBJ6*%hU3RJm>!c1EUE-EcmZB%QNFZtTe8?>>6CN;2`+8|mp`m&|IF4I
zcro5_VshiSqtjQ<e+`T^;iLW{ERo&`*y{R(Lcu5I3JiUMAQc(aJYR+fc$h$M{-4pZ
z#j7usC&o>ss@xuE(R4t@#tq0Yqm{Aldg*%^T$;EQ633r8wmJbMNL3wOz>Vh_WC5za
z@Un8-*;)K-)fmRLTC8{$cK<VsRJzfXUKxyD`<P;Zw0QcpDN<t_Y!lS$Iz{a_fpRk^
zPeYZx12wbnthY}0nM`Y`PaMn|uLX!q@rYFDlm8j*Q$x~}q05`ejQAu=BW=A(wPP4G
zvECO`8Kc;M%xSkPSXgT{{OSp^Ho?hOL#RVqH7xpjOv)y*b5fC}r?T?4K8tZ*m%4s$
zy3cf)ao9Jy`^-t$UM)DEClWpOe^CciYq};XfT7r9pLaduqxa#Fi*iXtX0#l9z-%_>
zV#b>7y=lQbyu91-_zF1P-E6QT1IB>h4-@Kh`dh%1Z`<BJy3B{%PFkX*0!c<lCJi0n
znXzU%C9T*G$D<hxw7~l=<^O<f=A95S3|I;8U+ifEs>+P8!B7WKb$X#cZqB;?R>r}K
z%kKhYmx6^v+$wd43YC8wePI5I=b|D+OA20k%b|R1vev@Wst15;GAsQ+O?vC7W_4EE
zN63nL{iH|f|ALL4$Fg-<F@DYrPgD|_Gw-@XY0EpO`H}0{M3Gyy`BP!sK^R-&4ewI5
zByuXLYTq8c`80I4g!1cqnO)17k{$`Ne`PUs(0XT2_H<<VQC-!6q4wH*IaD#6ww+Y_
zFUTp(5N#8YnU(#sL&~(nM%eREq0k!|X3Hkn9qsV>%(k)Vp&h-2UZ20mOWJGJcP&3S
z{a)RZSFFL43w^gok-hYE!0oC#=f7tV%>;LiRAC&MNq2r7TiDpqQhkp6M15T1>^XkY
zq4Q$5!Mx`id|`pd`&7ffiU{mh5}ob`NRz!xZG;A`qNi5t04($=A^D(#TsFyY6M}EZ
zsYu+$6z;M%xn;1lWO^p>U0D(!hSPYPMdOklnk7bS;${cu6U8D``1_DtAtm+u5dC}a
zSw(gH_W@P1oPanJwUR{C7?|gEup3XB*e9S3Ce3j^0YnOg>frHm6Sl{jm#WBAoh1L3
z#9)8xi)^mSed*1k+OF49|9&Oy^^zr&mmjU_@+dN0-8W$c7N(v>TjKJ6&0gof2Ld^5
zN9$y$+n9atqe_nw5*QZm48=Pc_&qiiqa$FuJ}VuNxq6?UtaABa5ErqM<947wC~Tq-
ztd$~1LA8?Po#=R_H+_GB`@CvH_$g~D+Qy6oDoqDdi4JJPltWOewX3fsG}p3scH7^H
zL|$=*&rRqlfn^AZ<DmKc>SJVYuwc4&1W)(g|A?=y%_<I*?qD8YHefXXY!nwYFdcGN
z<I736f0YMiGA+PHB87hcbTc2P%R6JJ?{j6MP`Kuqy_D#GmVqw%K7m3*mLVwUa<{eD
zj;p(}D(T^sgBSAD;zRc@P|2`zOMq%$vsPumk$C!7K>H(wKp1+U-enyTF!QW}?H8jo
z;D1b+&-ggk6OEk5x+`$aj*fY9_!rm>h&BlFpjgykoVo~@(3x`)eC#ov1cwBM*?7Z;
zbWg&6PL!k3yGGVejDHjp^_5y(ezv+53JE>GUa)&JKW+9UdC*}lSvTmjT1k)ke@`PQ
zDMheT0a8DbnOIUR`kt3U*P7+{`(=cD`MR05FnJ6wLc&m`G59j%D<&Es6^M<nw@#(M
zhXGqraegA$Y+MWPpiC|Yl?_x6#22<9+2nsFt_!l20&#RHa6^)8!$Z1X3dHb1>GQFf
z@h;~|75g&VXLd@h7(gjdh$eaNSd6dLrSCj0A@TxYsg6aJ)OV~{<1{i}y761Df4X@;
zA<n~^0*RqcP8DC<YR_+Wm20d)Um7dYsFNq}5{M1D`)-UU6%9GvFt%F|*?cQ#)e82d
z;5-<HhBvS6SK3UjAoiE3lHH-da|8db2Ba4Tk?RBvSgAm)p?M7^s(s~<tjDrmi8Y*D
z!M7~)h4bKz?p-5PC^d~vK31lwL2gkWca~nc`L3A1HicK3&@leY{s6|eQ0sCzvH)Sh
zR5~f?<_rBe-u(dt;)NtCzfm2=K>`xmXBMDg8Mw40%%&SI$MBlNv-Nz;E^Q8!_YasO
z5(Llt%Mf5_=ri~8x)vHKF?9VTf12Ej@Hr4Rp%m(uV4A{AO)0S+#}Dwn2{(S3y^Z-i
zjwPjusdHnRz(pF`c`rAibdx{e-Q}240_1$61q7P5%ws>-EGP^-UJYAgCe_@nI#P1t
z;o}=O4k+o~7ZtwP^Y)jgqE@f2?q`Q6-8qkQiT?EFQSy~n18Hyfj&*E*ef3dIh>~}s
zl3ddt;o$ijm!JN8|HB`_lk96tAvKv(X<Ag@>fn15OZU<i^f5#=j8<rMwezZCuQ6|t
zDOW9bjmYM(;`hc3<({zpY_v&K%0;-@&}2N~-twIJ_*=`H<E55=VeDuZ*+x!dPDt0!
zEt<tANLd*?io)A&Ns>uY199~2r2>D%&l2g=_{qgAXYt+cC!*3%W>(<oT>!^~Q;@gZ
z8<WkT5$N}DLO9H58GdpNZ_djb6t&OB(T9+a^2g_j_Quej(~Cj4SdN42Muw@87jVN4
zO=g~HbYoD?0CsfPQdNe!7t38w3TJ6V21f@nZlvN_BEF_36Gy*!Ns?b8JH{7`Rw+TC
zoF175C;De_Yd+1Evf24sKknlvm)u;v?jG%Ic+!pfoR&cU*1is-z4`pg#oqOf#5?J&
z5YNjvk1f<^`?6SlXB&erLb<ErwqEjjrQ7B%nYxalT;uNsU&-325pC9vfkAM31|Xk2
z8l(*MrVWkEQGi&{mvhRT(cgPdvn3>V*gdU~Ub$>ie^-90D%8Y8+CKpb0bZYKZ4SZl
zypp#CLs=ZvAX8&cXtXXMS!x!BcO-LVK6@JVhPpy^XsuEjshUf^^tB4LbWYIy7w%hw
zg4=3-_a_H*u7dM&UL@KJDm+<<ivP6jFRue{<y2&FvEn;Du-&SWZI^g{mR*JiD5uXB
zE11j*x*x#J)BhQZBzqH-h1FZwVFuXF6LWPH=t*>y=RQVHdws6aj-aK|jK|#5mu}lW
zd5@nu84snZHZHiI71C$*0kL{hGEu-lnvbS1G@H<+9e?eryjM#>m(x(_-2s;NKjNX`
z%9Tso23cru;~ZJ23;U2u161!geLAhjuji7jQyBN{GA%iCVGwBK)aEc$kqbYT7lw02
zD!Z|l>9?yNRY$Q0u<vr74(&PD;j`~YsjHSP^BlwGz0c&XH0RPm6)INkWJc>pb)A&Z
zqW++%Wf{9j|MU>!uUCtoiSnl?x6DXctTvvk&xAMDTy=8l&g+xHIIEkzb!eg*$CML0
zRf>H%Nb}k@@uhd;n8~9I0q0D0fu<Lqnm$%!)wodAb2zbjE7AADC>RZS$MmWEAwhow
z>M`o%Lf00|AS0qKEq-bVi4f(NgdDE`RxBva>B2YJDyaFx*1a`FjVoz3z1UmC^(YgL
zm)eV`vT7V`-Q>m}fCO_k*fYYc?>6zQn0d8(uraq4e|gL;m$T~8;i@^kf|glJ*wN!!
zBeYx{WX$c^v2+V9x@;?_LlK)MyE+>!5a-s0M=n~z61Om0iO!T?yGl75^}5>4I1C-J
zDv?K#ky&|{wyvE`;Vi0#y{zVzq=gF2H7z^^<lTPxXd!MJN7MXTtd6TyCBKX8;otzG
z9_!`aA)1c=&GACPH{JdcvzQBzo6*EV=xr!L<=jq0XzKHc-IGex<h2C2vc>LowQDso
z){|CRIQ8>Lg6UH7NsDqtS?z?S_UAQ;^uhM}s72L{XVagDjM=u@)WRvQe#E)HX(?mz
zN|)=T!;Lky))0LQ^2y^<>3hP}Pml+U3A-&GF7^0jMY-6k7ypr%e9xh}|MvBx6?0mj
zJ2<(k>l@z@DK?hJ0xN-)e1nxJ*R<N;n^1)MI0;<2^5^QpSAa|KzdAp1%<)%d)u}RA
zm=t0UbQ5+j7eXQ4t^U*WiT482w!OZ=OhCK9lL?CavGNUH*hR|E+8`5%{SM;~+f6f~
zmqRcR*6WuR3u@&FtXuRLDGj<wDye1;;ybdniD&+?(ur5K6_QvG|9Aj>mDrwg2(<Zx
zWE!ygw@#N+cXCwK$)RfF#}qeawjccZ!;hPChGM1uG1pDbal9x@S<aVtYb}Mk-$+Y1
zR9=>}gOmsJuETgKS#HmA#B}1N)mf5bIX4LT+E&=;YK&YgBlM~<_d$DTLPm7EmH%se
z2Ua*8Fw%z3Jbh=eQ?Pe;owaH@qhw@nZQZfV;B_Zsc2n`I*?T4(RsGfZN@>yupxRQk
z$-;c2fPJuZT*_UIbqN%-+as^fe(yj0bIT#iLn>3M_sk@C+3fJP*+}HEqY2P(=y?Jx
zEApmGo+I;X!b&cjT_UM=8j9nVwP1;<E_4q`Ue6gb)MuYw_H=giph=HZ-kJF*5SzWf
zHo@^n8Di;3#kE-B39Df9>R7Hs>FQGQO^G~eU5?jx^>44A77okEaw8U(_Qr6CIqv)j
zPHf;JRGA)f0DB3F%`dLAHm;gFi&%R2<w7@5FHQD9-(J5#=@F37W66St6M1O2aE5-(
zj}3r1GaPVUcVED1Db>Jr{DXOGqXXVG&XDlg=yKvc!9HfR_Zle+_=l<Q72}cMXy4Y-
z1nCv3!3q(lp`2@52re~R)V;99Jy1}{)azRJqQpyL+?Gsb!xVmhYle;HDP!f_sEm-q
z)f$e#^bypx`84z18vBTaeqv*PwO#Kgf^q@M=Q@VFpw70%&rIx`E*vae{$c7H*xOtq
zb{HKys#$tr{dLZ&p(u>gl|5rjE@c2*yweK7b+OStXSLAtx`Z;J;_5uZHjW*A*J|DN
z#Y`nX9iFGc$%-CCK~c`*$3J=Z(q4F+DJc%9r`>vDDNrIsz4A3Jm5$8~HJn{r%k{gq
zDwn&zq$M_paj(glwDLrR3ggyND|790m6?m5MkWi1@O1ZdV@IwGhL<Ibu6r7fxIGw~
z{=!N4dot~mGtgOZLux#CE~3KHq$RYpn!8KWBP{=fiil=Vcg`A7M9aAGh#@F4zWN@>
zGG%+AL~f-3#5vz#*aS21v4<Cim;SsH6*JI$mXO>_oViiC-!jI_4X%S+dD=dwl77;m
z&u@O@?5dsSk@>H`m)q@kgS`fFP6++ymi3#)8#ncMu2sXm3Mt2i?iU`{sUscp&Zgtn
zl+H2bO~xyq{^B)P1QgEV4cjS~?KDScu5FDWr7tXmpYE`Y{jrw)dwE~%`@9XS*y?iE
zLO?>FHQGMc53DRjFSpIYq^mv0UU+A3hjxYMe3`SReoUO*nzPmz&nqAZZyT-k6RuJ_
z>UeYR$P@FGp!GWYRM=-oQ5)CRWzjS_Gutb7v=s;6ot$*sx-3*G<4C^?%|^S5HkT`s
zTX1U^CzoxrT2(Zr-!R~rs@r^1qIN`2H{_)Pob(+n!>=b3gMQrE?&ka)t{M#QT{o>J
z#(8O^GoIvB6FWw4q><3QHNcp_k9&8kcfS!(>v&I4>zFR}y7t>U&6acAk_3~v;o1wK
zPxA81Y;|Axt(@L))n$cQ;TRFj+!(O{Z><+Eg`FJ#T$VDgf+k9~;%Z-HF^GmTm;1Kx
z;`Z=f0+BH}ervbsp~RB;&pzuN(p4x*k09uyqx)0g#-5E_`zF6S{EAmX$*NVZJfo*d
zp&vD>`R4JLd-hF5dGOVwXFHap0&4_ygFNFUXiqF$e4)_0l8z7NjVLa+?Eb8fTJbZf
zw}ZVGSOK46f-Ca2%#DtW^&kf`62CacL`4_Aw{bXeCamvpS)JzC)4mY<$sgTOJ?h8F
z3<IiZhW_@VXY{vz`<vv&#@5P1yF)eJeFcF)w<VXM41AM&_x2GJJ&IwD&`6$|Cmwm>
zccss1^m5mYS7FvB27k6U%kYBNqUsy_YeAnvziVoOgEmRuGo9Y*P5zMWnw4d-&7~q^
zGYJ=Lm{Vl|nCfF!c0M1Sn?}vYln97nG#_dGz)ycpd3sm8^ZsjG?a}cz68n9~6=&L=
z{a&Ts{K~&P&(v*RMpr*-A;~p3t2D@+bCFWEOGQjEer~EgmlM7>rp6T_p;k<o1%R_m
z(9J>1+4Tp0g`WtKJ#nLR$qx5bTuDM<`MwPKXz!B>uL$7slzZJWxblbJ=kRrRIlgm}
z*9*HilL2k#4oHuE^TT~4ogFi?L%)d{eRqe?M3;A$y4tP`-)%1Ltnbg#sHEu!A0yqm
zbZ2IJNlk`7EH;SbACmxW3Qtt~c;1D&0Uqu~YAcb`M4Eq^8R?53tU5wf#R%U9cy$-0
z-(TSWNct=ed(AG8wvJE#!SIS3!?g<w6u?lAWvLjQk=-w?YTtLENVOcUx3R5;Lt4nH
z&!`4|SK)Dr^vZklosB)GfU1_fh!+?RjCkioxP0Tbt09yND>7XI-HbQ>h#%xv@!9?6
z0i!{2bX&FY{`Bat{6OFC{-D(#^K`&j3a;BXMc0dd>id;f_Nvet0cm*lT|lICy~b(y
zZB8Gq`MSH0l@l42)v8&dPF4l$pF8QAIOqOd3$UL+95&J@n@NX*Fm>)PZ!wdT8+?;r
zG%E;>r1|eWv*j$tT{mnt1xfRZ%H|3GC@{1-Op+Q;m_1~nCRF<UA;5O6YD*~_hOaFY
zRZ)*ZH4W~k0M#@5YW0(lzq))S!|}4zEs_Z-i6!*Ad}Svb$2F9&v-E`nTl2PVQO+-v
zQT2rvyHXZ6T4$p-SERLm&XMv|dAM-FH#=h)CJdrK1}gPf&Y(e(_sNEdg#cu?rH%T`
z`OhJPy7M+vBXu6}1yoQj5%Wt!rr#|fAZhQyett09irj?W!c`rgmoGU>l275p3G{G4
zTi?eMT7avhZ&|@FFN|6vi7yeEQ)}is8zz+C0P{2m-wv9@4J(sJ2po3)wD@y};H<SJ
zGe23-K$n#Rz`itl9WX?(`m(RW8>L_?H|vgQI4B6;X2&~tkn)#5OmFPeKi%xgIS&a1
zRP@$rL=;-q)u}n+nYC)C#=XsY`O$3Ob1|UmtSs(%U8S2P`f7$;MxKva2l%mfR+rmi
z8+_TsCJVkaYRH-e3iW(29UIpS6-GdQc!LPXI7yPTx>4urwaK>#p}A7$h&SeHy6Nvy
zAHnrShroioY53NKmL_J|AnDqjs`lT&<>bpnXx@!W_?c<#$VdrU3xQ7c*7xp9t;|`j
z^ZWC3I|atC<c&G<<k6<NKK%3cj~Hg}9enq(&6iG@yJr(xZjRG*OKQG&)AA?*Zpio`
zUmxQz6N=xSb*mJ5Qt#YO!;?pOGuX+&`w7Rg*q5*>@a?cMj`N}!om|LFOa7WCiQ$%?
z;OcW;@;V|GcnP|;9F}3TyZyD@-mR}wjK`S**}<;HTAAzDc|F2|x=ik~ALrCM+U3vW
zOs3QoWtdH%G#>|j?l};wX6Zxy<1@b-Dia<_CYN<Vle6qlv>9JJj`YL^oB4!DQYU9w
z<OlrH^42F6Fbo7c_D}B)@6TlgTRe8utg(jdgDdX6tXhv0C*_~u8rHnzpP-apN#v94
zQyH!MYrM*tlsmR_iPIZYus~%+!TGjnb*%4a{}xFPYEe$`>VvO@vTmcV+Kzp{^On<4
z>*ltN$%fXvvud$Whm7jI^HhF7tlArz1>eBeD{d|22n}Fi%l#SH%Ac%{lz*yX={`Vu
zMwf%{z_EIH>5bAHUlVA5sZYTG7RBm6)m%KHA=S2j>K!!9&ZkI7$n#Ok@Qn(oAjf1w
za9+h+weV!Fx+-~0&C>F9VOHC(e2l87ef=f{FQEMagR3^p1pH$~1J8;jZ36$T&kuou
zD4*Bv!+-QBS5E>}(vGKV%RPgQ_SI&gmiF?A+0oM{Ui)N9+6#+^nk<)b0L^tuqqVsL
zAA}ROa4&7E;P1fcVMTJ3FV*#+R&vRaL|~-3I3r_D-u^MaAlv2rTR7&B4)TSf_I$>&
z0j3^n1Q60)xu^Yp8Q3XZ^{RBq*NjrGsU?Mk{UeF#wbVH3sq)euzJMbd5;rvZ50$IC
za5y6G-;RhU(xdZgm5Dk&mksNj9p}r}{Gj!$UzzD<4t#o@zD1fR`Dw_Isc^kF6tY$Y
z5NEP|aY>h}!^9yvXNp)>r$+ulxECvOJ!?dc93pSRw}S|T;$pfV?y0STd3bJrJpNPW
z$ECE><=^_UVx1kKWfqWNQpr6<ZU<^|I2-4ryu)rori<d()tj%q68>UlXH6ICY+8|S
z-B|8DiR6n`Grgb8lTtO2)c2DyhAP<{N>2Ns)iPgpn!jL@OAqcZ;ZpRwPz^Q{a(jky
zbJ*+TsB%wDb?VIgS1k@PHSW=nwsl6nsp-<;0nbGAKi|N>Z~P&N2{6T|<d$~T?WmkC
z<rd}X&yU=Tt6*I34YsejW$<)!Rb>v|(e~5njH0GF#_2(tOH4k6_N|m#qgg3$m65-Y
z!}4g3;a6jS{-qhNn@--E%jykXW=l76dd-)|a)!OmD)2VA=n;GaYGm9vs1tvn|K@n_
z)hqH+1Kmh3TRpy@HmnU*H;;KGdHz{UJaw!G1#xZcHD*3-2L%~;wysaF=%9s;3KBaY
z%DadOhXvu~(t#abRg9u5Lld8back~bA$K9K<?Mx~JpD+a5?BoTMK_D{Cx$Kd*Y07H
z0mE;2FT%#Ik$x5TnN{PJ)bee7qu5IF-dAfYy70DbTG4o=32@GTnSU&o)zaU|2h@wU
zMC0CQ)-N4buHfn?WlDmcrOg!#>p1P==FNkv>I^XG);PN0sd9`bM4QHwMvPK;Yivk`
zMhWs>Nf_P-M8j(Fxf%X%h=Z}4oaJ@kFCh^<F`Dt{5XeaM-;lgJ*52{Qfk8g>ZTAqX
zU(&-`uFlrtfBqaE4R@@RQq>^G9~i8@LODJHdxd<x9F9~w<+zQBEZw{+H9@%2$DeKD
zuRquFFO%`z9t6TycQfZ_XYki8i~niz{<`*{kj3|a5QzWXgWQ?P+iE8#j-#7)w`*@>
zbgfjG?)Knsv_g{z7;iCRquqOrPmb8Ly|rh*?*!`;9rSj4P~~Bu!PN5qHylV!rT3IA
z2>pIcO<TzQ<tZM;pL>8n*RNYZMwd@YR5{O6LLl7L_Q6bO0<bAOCTJcbzr%%O{J}qt
zc4!=YLfoK`%#4b|eVh8PXv%z3v;CW$b`%`>kIoIELG4&$O?@gJ#(4#qrA!W(mQ2q_
z|8=pYQvZ`ekc1BFOM69ff=rO%s!RZmJ_<MkK^V$$Ti!Ae((a~EM!WKdV>xPi1pEK)
zji{d*Kb?AJ<~+YE|0?HmI;`QMpxJJ;GZSFuOv4ug3&Cybe_cfF+=o8fIs)edXYp`S
z9=?E08#D$D418`)E^C1dt+;Qzfx<C*_XCy<3Md`AN$|}UH=IUk_4??e&6_Q)+jor&
z)S!*E+;phee2R8=*m2%F4I0l!33dO*cR1Rp9&BnnPQ5hEm~tn>PCVZSP=;^&45-(f
z@Dec!_^-Y}eyN}xuFASVU6-9=4A<6cn~neQFkZn9{)2ymCeaYSL9>)0$?-~}E9TB>
z+dbISAOh{b3e0?5kDAraIe~SD_$~lqhPbyD$Ht#EOy|0M*x;Cq$Hoe8Dx@1cMe1hZ
zKI01>rXjI)@BMj_60Qosl{+I^?|hjAhnIi&FRJoL3rF6&0cFHjni~9Vj2+HOp{(Lz
zzu3~k#m!QeRFk=Ra1e>Fx{a>Rr~S1db|WH<>R3i9!|E+D)$s^#tOod&Y&$VFQ{VSl
zS?M<sUXw!B0cU<q0s=9{ui`);648IF7m9c-F2L;L97y4_4UQ|bzWnJYe1-qX<>{vv
zyb%z-l%GeY)Kti6N)_JvVw<!t7~T(HwWEB73|8Q*KM#*OJ<fmY7xY>EcO;YXl-3h`
zlVb<ZN~kq79e?e7fUil5zVMiXy*W-KnDcXz4UzCYq#)s&<NKX{<Htos-_0C6SaOD}
zB7>E|1A{}KE{gG%(Xa!)D|v|9bK5f5!kw>B6YVoyc;EaBMgOb9e3JL6DGW5w;RQfJ
zlam+!skOv#C(COITK`3B34#9716>y_TQZ}+*1h--Z2!-q`v1OCev$MCz>Rk>g@jv`
z@hCSgV49_|Q(JQkP9gPe=Y>Db<;TeOIJj_`jk9`A?*`L|bm?Pdz6(I1u8%kL5myV}
zCo$TWgBp8#PHHM3c5ge3)Jv(pL~ks>nSs}LUX#`o$a;{w9xMKb2E2jj01GGs$ZA#l
z1!0FW(D5;*FqbY2eLSN9aypG;u;^r|d7Dq!+=3etO+Qzp1JB`9vJ0G6c{4aI5Bk_)
zTGwUv!uc?TMA&?`l7gL=E?d{tNk-uORkEk+&XPxvk9s(!Z0#VOla*6FLvhfL6g)0B
zQIG&9f?idu>dieRtO_?BhsjDj0TY+##mNHD3HyEEnjI^O>4Y#2{?lG@=1FD2o#7QW
zLj&AwE0yekJI*v?utM-$m>#sCzI0fz@3pJbX>z6dq^d8xvWJ7S^5x@A8%7YP;xLRb
zDLmsX?SHg)oncL7>pG5%I)lg=nWKUVC;>$Tq)WHV01*NL(nJ9TL`00#NDU(@s1)4^
zqCkKdnuZdZART2SlOQ0yhNuK0!6Xn$3~6`8Ip=xq`Emc;zxVE+DSNNA_gddt-}}Db
zyBirLxt`{S$Ud8ymW>1+>jD}#2QY??6rQR0-pF#J;?0aX_)^eNeEUq;blZcuJ_U<@
zn_+j%0;4su00R=BGho^EOAPI$#GK2#Si-&x0k{gyoan&3<UF+^=?1L*giisi$yMRD
z?uaYRLnpQ?AzQD<mCaPaInUYFI&+-D71yedF`8-~C~9=|qFt`2q@XM?R$5^483=EZ
z_)(F~KfILPJB*2Slialw?5JF)Ms_ZHM6OF>`$e({tC<Pjw8n45*H#PN{EaPZjUH?L
z4u}iw4b4Q_dfsS}j|%9wXu6evVLk<Q+wa;gsr^G0!G5Gqytr4^Gw(gW2`!xnLs4S{
zVd<7NYtJkvM{ddptKi&R!p6Rqvs8>%e|t*wWxfrRZItS-ok65<l$Xcxh?zY4rPE9i
z0_s=izfWZm(V^?vi8^-SNJ@OWlLMFQe0wuM4C!fxzWi4UqX9D8Vb$%e7Dd{30PBH~
zjIGdhG@2SBB&Rp_#o0wovj_rW0eei0xl+GG;zCC<ybYiGW`4Lhql4fk!38B3k}H8S
zJ3QKraeLQgOC##Sfyr^l3;q`1q9<p3M>xF9?_f!C{o8zUzT)93_FgYyE0yy=ZXe$S
z=5EpgK_PFq$#aZbKdTKZeC0ZW)hDbBX!`bIyq&06b88}kJHiwL2dl^Q5rnwy<o1v#
z8bMg8mm!ITrVcep`3z7FDnzcP;}(u&*gf;Ol^1c;+d;9n*{6QOcgw$63~YRj7YIY0
zuik-7dSx+UA>+yQ=Huh?1pgfi6X>36v=tYd4xQq(eHWFff;58o2fRSXUXj)G#YPk3
zf8h~iC$gg$?g+vA5PUwfC<mgl*D^k|i5_iqc#T+wRUB@~&sxk-_UUODy*0_!6<O31
zB&m9rL7=pOf4g4Kg2oV_Af7%1*ITTU{LMm8QU3t4y*Rvp<FOxe8KQ2^u+|C@bWgUs
zc_TX#&K<g?+XJ*t8APUaZ0oVHO2lfm51PnC4Zmf=VD2N`e|Ui%%#Id9_|OX&Ai6>X
zUoc2$3KRlCDkToor5bn#ME^~bJOz(&hLT<9FC_Rf+BdsEuO1@AdVz^E+6XpOn>u9)
zz}8E7qTz_<#GtfUiKY7Ts~kX_+j!rJ-YF{aXoE><1(-Da%fe2$?sl77G<gn^<3i?o
zA^%)Q9(Qd+<g&B=5S-75#1`3>Rs2wGPA0mMEZbZq-U#JvIu|Fid<bh%>dI)+R0|_H
zXNpDSJtSYfR?2&sC5zm1m=`0659v=-x5BPUwR1_O?LJw#X-}pHB3ck{s>YtcIiGO_
z|49<Wq1~(!(^fRJuqkbscR!X6_%h+~oP(i&j=C+)2#^VtM`$%CMY-;Cl6SX?+zKH2
zbGWZVcx;qp6rO^n{5QCNwv|k|JF;VcU3##YQQ-MI4Gjuod-@GG>@D@b0(>7NNXMk{
zJIAc$h&AD)$hU%m+B+Mc0uueQ_;AXqY|lD@Vy$#jsE#k#nij?mnA<j3paf2K-r?C|
zLuF{Cv~7P1zL(3~1RFmqO#-em0ZiWqVEO<6(`&BG>%?(V8RzY|rH=eeNQmwi$8I8?
zjQFBF&5xN#5_Nq+D4OQM&HZ!kIU|cYhC}nGEDS+lyHHjVPi22o7^Kbps@c&4uxqPi
z+gP!q8wX0Ws{ZgA@2`7)Zr^S+)1th;eg6obWBREnM7MIXDJFSis(d5J_&Gp5BnbcR
zSNX{1j#i@Cjp?_mV4w375J`2$n;#2Yu9+*I1O|Elh^U8~!pQ~hf^100Ol7?Cp$Hr8
zy%N}?;OrNkE^5fAoWo0E)$%){GYR+<!{@rMTYxB{=M-hz#}eGY8!+j9mY!4?=jX0L
zcb}Tchf?>x-D^(Dw<?}FiHJrs+m8e)-MtGZmE@`>PNXu}FHy~zfu0ukWl+{wrmjhO
zh5$cO4)V58ur*3b|LC)jk-rZVY7{y3-puZ+@hG0~U~}pyeCwR(U9xdbWo_j{`(w;f
zWl$LGXI-Zk8Esw<YrwzDr4c*bKj@4L7XXz`hz|(|0-1Mm(+6hOBN-zJo!dxMLo!W+
zAARUv9oFIqOZ#yAspk8Cn#;Y=dTs7GC7KJ-9An99zXhU6e=oaQG{s{(N*bYUBu8dj
zr83O<-hTt&4~mA6X_CP-pcnMRdfA1?L)H%5k_xfW%Lu4dHL%#uTkY%>BpB^aZRHU>
zRQ=w*<GVnGhQ)5$yKcU@BG`j2CE=`3<?84=qiBwNKRu3GocgUzNip^6ROeRRys)FW
zRPlm1UD#ug@jc9MzM;=733>Sdc@YwJABjrD(FwQ(#j3F&$t8qX6PRBDgPZ*5(=xE`
zY6Ei6mHjfVb>%B7@-E_KHm7FdI3S>yaLnL@ajq;hjREL2Y%y7e6DdTv1Jy2;-VepA
z&SloAl%<{UY<R@|4WP{(Pw6~>Od+_{aN3!0CVcZ2n39)6_?L=gKjto%`J}*xb;^8#
zC^T1^vgeXNdYO*AS#fRuWb)uGyni8>k-E!2Ps7y^R{i)(?sBP5?Zowlm)9kr-#cV?
zI?T+Lb7|`QB6y4Kb$=%)yBKbpZ5fvS&1nS|0VM5QM*>CH$KP95@x2c0Sb{a4-dnfh
zyK&r&C-hag$<wfXAm8%aF+*-ax{3RgrZ#4hq7SjgAmNQkeoE)>_=1#*^Rlqfjz%Ef
z_y*Ol<BY5tN`#d=VAn5$n)ya8vK5~Dq$2d?oZ0IQtWmuIK>?a>m@_U@w1zpn`Ms+g
z=78fJfT_xYEJfi~5VSR#g0|znHL3?XZCx~lgXG$WTn+j8A>sypV0VglWn0AgYqGE_
za}qee2lqG<HWckeVWeSlqf}wAz0}#ki>&n(Mak(84e)}h$=GnMflaWGFwY!fEUIqe
zdT8kGKdrODoq1S+oXH);XMEV9iI{X|EVj5ob=c{e2v9zXx~gsl^V3c=T!bYd7nryg
zA7HR|ug$*)Mf4qTm4)f{1c<24FMy*QxX*BJ#}c1Huc$cz5*)uCcwPO3xG^y_zZnwV
z8vN~BZm^hZdaI043=g^zx-!m2aF^hy5cPvFzsmJ==hn_ue0HtP7Fd!D2e<zbFk<M)
z9+-dEtU>mTY~VPl>cIRgyrF%Ti>gV;Ai%HhhMl;LwrMDNS02|0CuubO3gyBl9_yEM
zH9~m>^l6|Q5gL+qfkE>B$N%OIbRN}yX9nPb%X$D~wKz=118OC}7UUE0YUD8}UW3el
zgtm}Ykb*{M>Dqlb2~8mZGl=c}o5mfOJerR^aDn7Da#OH*&*)mN4?wmiE&!$+hv(#n
zOQ&WG?*Ht!vJu(Ld2I{AQ&Mknq9n97;UM`W_@eN4K%l`+lmjMa8J^M)@(|cKj1AH4
z^;Ey}B!P9#I75UL+D8dm5XxA(4Wgl}m4QOk8j#bmfbpOq72ksH=Jq;kx_aoK9%|m7
zmTp_zI7{#{M@!HlV|-bOz^xXrLk3efVV-w2m0(x=aXgxbN0e^v%?mJpgWJ3PW?!M)
z63-va1Hg93@+U&8@Ag92C?K1X(q}c?_#NL8+3VYBoTfH^4QJSPV7X8o7GHEk#i;Hu
zSV8@dzhbQa)8PJhe%rqNrOl215^=+{`~>)J^Q{|hVh5-^=%3p_E4$h?i++4UVCBJ`
z5Fo*6y#%X}-{rOWY5FBj$CMeb<A9c=O57Cu{We(Vd~^%$?Z}fY*J<|Qv5?@<glW;9
zO<-wwnzbQ9qoIo<4dx=ry~H;T2SL;xnluS~ODi`xKF}iE%35)?(S#kl2y*^9=MKq^
zsXn{#uh-wmz>)$&D8t+P&v4n>l_vyQdwOM$g-U;<cLvZga8kd91IR@<g>X<x^`$~M
zyOv(J{YO6qfQLR!-w^ReEd>o4rU%JdwOrI(QJJDDs6C20<1YRfyL7#4)A%{?q^=Y#
z2sj4iu|SN20n61zFFP(PQgXA6@gF}p1v#CID$wV@hjrIU;n%0nO5g>A8?>A50BPtn
ztHaT2Mpzwowf;6WunLgg`>lXIjll>@<=qi5Hat`M?8C2=wW^U7u1ICX7f3~Q;X7u!
zg7)s{EWP|-OVY%iH4)F<O{9hWV$=k#;V+}x*$w<>eDdihHMK;_j{>|YcGSp3HWrjG
zNX93gBp8w#OLu94IG3^lwRX4yP35uy@Zw35W527|_joF@<tP}ask{9Qq(jBHJ4RyJ
zY&QR3&s*6F1DmDquHWD|g+GK#0EG#jHy)TD$VXEIaDMv4-S1$XHVxW=8PzF=VuFDp
z)6_cf4x5cL?dn5>+dw4sre*M0RKYpkQC@L6NbaPI`2lf)znLm9sQ>@?AMZc}5RkS&
z@xi0NZLgl04e43Ok(#u~=I5_POpe{Cv+#JIDVB#V{`si_EJO3%PvF_5xj4b(^)B+M
zfA$=*Iex=CXv*w<;d$wQU8gdmCdaCJR}v0V#qmA6CPOYykNty{v$j(0OtatQHzeJU
z<uu1Zq9M^)%sI%r-|*lMTZ}U*7uFefHS@<pt|mNPTRFD4vKba4<zo%kzF6@@^TS1;
z@gF41D(7K^_miRNGS@`FNn)opft{DohvE^(c)RYk_1K@WnJCK$mS1e^3>ZAez&`Hk
zr^jyme@Z>!Q1;=Rg_Lg#=?Mi{AnUEg(f{aUu{4W-IyR45ji2+6R*$ibl7t}~?ZXqH
zawyp4%1#(;J5~CQfV^yamZC)m#|j9tSb3P3sgWS_qW<MB_e7PS8B|CZ3dx#<0Xyyp
z=*w7H`d)Q@7x3K({}n<3lTJ$O+pe@`ePD2>TtKhHuUx<b`(G6#_YXMQ!Aq4mR!E8|
zgJT}kwKOv$JSav)Z8(1d7a++t)5BPk1(1by;Geu>Q$q>tzKTgpJu>Z3Q}qygH)LT=
zmQ$*qpihR{hYN5643wkS(WTnH8eevv;Ev(mZ8buruV<2*yW-8Li(&L6v&A{1bLNNV
z&bMoo&@N`tr*Vt7#wrr`lUT{33~zgyS5L?RMTt_yT8w9iZ~kgraA+k*F?3A|sf^<?
z?0Lx;O9s2ATp*8n?}$2u(j$k|>DRn(u`F{k7i>2zotc?oe7%HdnmOJWJ*gO(p;f2<
zY>9qAPcFCdRq)`=5TFboN8NJoBJuk+kvQJ_(KpY4Qf^ZxGV|fxwS0^>#FGzR88}^q
zz=oi&B#(nff`y*@F7k7(<$Sm(PyD@jHG8+=ad8CV#`w~*AuBnQhYy?eTr~1rT&W&y
zw^-l}28Ol<)sOH1RP5&(o6xNClt*NDxJ5}gCRfYENq~n>t=71I*Y`_sP|goCbuKn@
za^}El;ENsNpwa!sGLI%lB&0Dlbo5eftDOB(;_*N@wQ!a=W;(wGp|k&ED^Z!dhq)qe
z|6Usf5UXBeg;sk!DOO~ZPkaH?L3QzRp-9C8Gxg~`&^~>-w1MEJhR_rbWf)hzuHV_3
zyumfx*&5pR?-mZdP=Ij&B{?05-7PBJ^n66+eRiWgWT6_ElYAFfOm_Bq*4pZKBJQCZ
zzTY_Bf_(Ld8MAx7cC~d14B*gV|19xRQ(yR-OK7O93?8w|wW{Z_W#&RxNUAPETy?13
zM5E~<cqhTKcIky7^I|xy;QdmVSe2aYy7Oe4+QP0_<*t{&q=epC5zmgrI`Z}}?b*Of
z2snOzwxDah&;RQ=r?!)7r*bc}c)(+>lu<Xk*kE*g>qJ;UlyndxMARdpz51`sd&b`T
zo+Dk%(f{?Y-|-Q;#j9QB=*bFfI8wR)TEfkjv!jFP?)ycRA&=4|7WE_9ht_;1=i_Xq
z5~PK;#_6^tf|Gpq>2+GDop+nc`+g@0|0sCmuU-+RP(P;Ly(TG3$!jsbcAKY#SXVmB
zyK}*0;pUg5X7gJ80~Via<R221P1|tLhds8O+1BIbneJ`&ZoJ9uj;C}2=2rLbE|csl
zY-4mBNiN$Mhv=4W)DD%i#L7F1>$sRx+5L?XZy5I7_Dg-Aj3R%DdWR{I>>KXcRmY8K
zLH3KC1s@7$M<>xX=|#QLw)>3!f^D||j`7AcgGsgkG`>1```+KZGnWz=j~ZgF3u%dV
zB3cDAe>_M6o^&H%qwm7AJ)$JH{%Z0)KVljC?kn?gGRBG?U%rr#$5VEZM7dFEN-pRS
z%ID@0Xr_&U5k4}OuVDI2nc5PK6{^scedsqyW_6Cz_N~5QK20Q3d`v2s9j}6D&l7IG
z4+~+L7j+NJ8`){(en8&i%^}*d8~FJH(KL_&Cm}!n6=|(XG$yeNO=IK0xYF%m%HC8b
zFT05nW#LG**HiGmI>JVm`wfiUy+ix&y+wPC*mT4oa|tR8HqIGU-b5gDOs2jK?0?SW
z*|S$QuCKFP%RJh~sd@?Pjn8gcJ~tav*gE_y@HX0=`1>4unzwh<3F1U$@UQlZiq>Bz
zl3ot{>)tt7y*97Vbz-*F_wv#j1Xi%?heho=TVapjPE{15Z;7-Hq)Vwm#L-k~{U)Go
zxzrLGvXdckpF_#Ni1Tur(X+9L1NHp;#504><$Mw0y+;J-(!HEU7aMJP*F&{8Op4jR
zdhz4sx$T7bUy?VHj}{+|<OvW~IC?Ik$`RqIjmkc^hN`(2futPuIUQGWG$#vJ{4op9
zBJ@Q^y&M?>I!?WEZJpy4@;e5jX^Oi@aN_IZqagm#<GNdgVp!6P1>T4;EQzHldsm%&
z$knP)o_iNgLwNapj?pYH%;bKIdvqz(;WYL*58!+$!xtC*3AV2j!qP)W+D!u_YXt6?
zQUjk#h{qm?>JJ^CHz9B@$z?7?-8xf?dila6RxT$Q{p8BB5)+>tJ-k?Z{*O>km3_sm
zc7vlC2%lu<N=CZ{nU@yB7j1%llQyhI5F<NEg%O%Xo$hrPc-@*2E@IN1PnY$hGtZ*?
zmyQ5r!1G=LK6Yp5dY$hXnuTAQzal5c#ORDl#Y-ys-dhm0k3EQC_6)@)Z0;-#1lp6r
z0>=9EjB!A@oVP>D@`!JQvN7vqF7u}d(R@3LvBfQB{P|x+N)1wpF8&^AI8m2+XZ)^a
z#6g;2;EpkEP0U{1$cE(ZtC*s4!E4fBn>(_RrH~qYJ|So#wrR^^o37J_<!0D(X@Otc
z4*T$IqkWP)4w$~KUam>6+k!?6>d}JwjVcH9&%E?H?&4VTbmfr(gJL!k80!}H#(B(1
zg3or0DVSMfApJohOY?rQmaC28sM<EhrGS9koYs3)4%{L~epPV$;Ym2MoO3jH)#&Pm
zkMWE4>Cw1_VwwRgiE@R1Oa^8M>@em^)rRt^rhwhhMIPug`>p2Lu7Mb&n^n{UMuNRt
z;@3yb-h(W81eYi`Dakq@;5jLlJjKb3^5rJKqzA{``9Ly=SMImX7#CY8F^nQ(8H~$0
zx`gAwShjiFWc!vPEBVlGQ_)e{W3c!4*CUr`s(j6M*{O!47MrnU6V_40^@54Qwr_gv
zvy4Cb#34gMm9Z8xQhZ>i*m*WS!VTjYPWN%63NjRFl?3A_wc!Du4%;N;0d(0nOaBP!
z(Qc2Zj%;y1GC^E|gOA8<t#5ov-|t2p+B)pbn$#{{#g@Rc1J@pBqd(tgWdWV>xq_-b
zv~=<RXcQSRGXFBTp%W-~WN5u{Y?Za;Rr9L!c7tpCLVc10i!9`^pL@XTDMD8){da;O
zZM<C#AX}3_3^#14MV7ZczMi{KbrmCk+t%+HQ!V%`1U7$?fQU4@r7pRgFa?NmChkr*
zSC}u0h%SAX5OW41)!&=}uU>Ef`9<K*bhRN7bM5jOE;FqnuZv$3-0;_+6+)~<P973b
zL{D&}rN@1u9<kr*63AIB@!UjBksx|OgVazg#D4l7VP$P2Sh+Sl_MVRr*P6}efoZIV
zSSK)9cxYQfEG4fIMGlz%fYhL&W09Yf(zq55f9N`WYKt7?NO{~7Dr<I)VWnQTE<_0*
z#;zkT%Z!TZ!XqcS4dr$C0U)7PdEK&aHfTsv!<))CDol5_by9w!zxC$OO8Wuz35(Bq
zN5=$qki5zDl^5lIntYk0-ncVf;GJc{-8K{EbK2V#dVnO(aYtKJ&+k>cHTBti_RoP)
zBTr-I%KK>Fd|(+rI1_j8r&d_?kJ4KjIMz>jd_#gxGoJCvazY|*I}FBQ^Qa7QC_Z$M
zQIK^}Q}&~10oNZF29o#)IZ)6ZxPwu$B?_p^mc-+aVgc1;A~(604k{H<XF1tpxAJ53
z`$9fX;$EE0&%dIk-SL=y5+ROwx5vYvvVEwW6>{wFb_|0VF#~4A0|3QWSNI(LEV1{(
zC!Djk;|@5+1VIX;O2hw%bD573&VdpF`kU+d#C0DmohLPMZ*<*hXzdUq%r1?e+vs<x
z>eo7N!BGct9(o@_tP^-XgQ|JIp$@Vz2;B5MJFuQ{*#lCL?iN&B+6UXs$sSlC0_v&@
zzxE)kO(#(2y|tra%&UEZtuA8CFgplCo=51X3zR0YbfSw`Z*@s1VNKV_bspO$)9I_e
zcqR8JPJ6JdFd}&3aVpye_RI3A;kh|Hfr0%|wtsjkTaBERU?jR0ZKD-(Y$zqGt~DZ?
zJ^78iKXJN_2*~?H^7m3{G9XF51B?M!B1>hy87#t(KLFex963Axe$SJk`rm3L>1Y2Z
cjwDmvvFOe%3+|A+w7-a79e=4h<#+qP01nQL$^ZZW

diff --git a/reference/plot-4.png b/reference/plot-4.png
index faf923b0da6cf2e7fcb1e17fadbbbfba67b8156b..9cb4aa5f9306ecb6e8a0cc5192bfa0efbf29542e 100644
GIT binary patch
literal 39519
zcmeFZXH-*L7%dtEMDz$MDo3PA?;I)81ms8yRRKY|1yH)uixlx#Q39bES`>ox4vExY
zrGtc?P*h6jp(PMP$X(HM#=Gyw8~47ycicTjNJw_}Uh6CKoAX<H=lK;2qoarU4nrW2
zqfldgD+q)=3<6=jb?6{?hmiJm4LlvZWoo1kp20@F+!zC%IPV$T2SOkMPni!E*zCGH
z1R@N9>g(BrWi3yHXYr8fZR?>IA}jg3UVUeYdlq-#)^To|UYV!cEK);pdxd3%Hu6Jh
zFE||PWF{RdQ!hV0Tz1PK{&23yn`gpr;)K6H6n-H>?EL5#9rNIh*&*d*H>J$w>I&j?
zl2M5ArnX1O2Hj7G+Pf{w@@r84_s{>e0I-0KpR!o0`bC!>kNirZeG3TW4X(2%gdKbm
z%X+;Wb@Ay)w##M8TIc}?B-PdeE&HF*)}BHX|9x#^#TNE&Oud1Nv>y5Q?bMU;rOdZy
z3+#S<Etd5xiwgvD1@Vt~FnrJ&2ZcL8y>2zPnByd%Na(H&&IoRM9qVsemGwzH+dXv%
zihB$H+jgI1>L9k@F3T|HYnv7mTL^;MnJk_wdV<P_OPFRzc|JAGP=G%%S(GR$N2$U+
zOm_=V?w*6hW6&t^?7P|-omr%#@W~_<J6sS{7j@MzyyPx%5QbM8p-aOP(t}D-8E~|5
zgdNTs?vhe_{u5co{~VO;Ho0Wf#lPLJMC$koUo^(c2GXO%HbcYBaR;D{)mNs>Wcs!}
z-r*g7P4vp+t#nQs{RbBtB<u|(p(R%Bp~<L8Y5q_AP;oApUD=9iY1DY9Y+8_mQ_fLw
zd5bH3p76s~g#}`DskLHyEmg20CB1gDl6hi~Vcf2G!0YaG&W?S7oOEhjZj(yV=6Xsy
z+kxc!$)>wKN-UqR+qF+c>WFyOqCSc(sSJq2)SM{X_57Egqa1ls$XTWh;n3l_rm0NC
zUGHBWxpYd|YX8y24mN=RaqU#Y{R)}-OE@isg{edva}OI&rP=Hovm;kL*zPEKPTB@6
zDb5&2v=hTerHcYjxU&`IA_r_(pP~~{x{~&=9V2L5(|~GU{`hk!>SzaBKsIh_xiUtR
zW%3z{tQ0e#zsD9f(aE3vrj+p>6{)D#VAiD8)mX-iBnaf%w|*1F?}<|LiK5D#N)t+_
zO*<{`$@zU~r<(Yt%y}RP!z1smw{4ynR1{Ng_-i8Y{;jxxIGg^yqA3Vz^yJcUniL7*
zFO(i&>K?b~g0DxtYB$q3rZ@rd*T<Q#3q9)#idEww^=$b4H*4}gS*)DXZhi)R$(+&u
zxE}~w5p&Lu@~<K-CNcb-Hnun4OMa+&&n`;8xHtYc-5_q)c`hj=0d+wsFFR07;GA;Y
zkmpnxyjW67R_YWzYnOA)<@MbCl+ai;SDasZHBHQz7HirN>P*3~HcfTBysM+<p<hmK
z=}WRP^~iV?>b#L)%=h|@i{8O7fAcG8NQl40WajL|Fvua0Rx)1A6mE5&L{1ekoIdUl
zRI<^hLRNY4oBTZ`XNl`4(m1pDMHZkix+vTsY*6y*UmF4?P5cO|^Ha%DU6GpB!SLaI
z4LH7w?fy7Lw>{s_z}CgW(<N>5P`guo-!)onTlgU>1Dyh^#fP6V0=RSVuq1efG~Yeh
z#2{36U5oXs(Dzgn&1rE?6?3nA9JV5tu5t&edAY+<aihU>zk~;MRH;=<M$f+JV3;y_
zQmzkunmK5Q%~e0WlvI}4m{imK<IuiN?aKQwqXyc=&dtMy<!PLwUudJE=K5B=PgUsJ
z6y1q@KNnF%^W0zBAH>a-FiRY7MUd9g<kq(%DQu4J2O3?9O&erC=;+;*-i~OHpAXLH
z+7~yu*bJ@4hYiZMUuv?84Rwpte8=~_=W}FD6XCx^tADlAXy{RUTT>^rLrWy^NOh){
zlAd%yP~)r){+sNdV+8S@2|>@(jVRCMq&cN@6>ldBa-kY4zdZdq&L*j^C?(?iy*eWe
z?TeiqPfi7t6Dz>y%he1z%WzR^{>x`5Yi%6+neTM#-=`jothvZM^_2v8{SYT8)tPhk
zP5YIo6{Qcb0&!=_K-ukcCaW=72kK!@vJ{ntN-~jR=7D2ribc^UX4@hOv*chwPr=Y|
zdL&FGnBPS<kv4dOL2fry%45L>jSXw);WyHH0vC;1y4$ZyPD!nQ^g)C)b}hvOg_T{w
zi7U_;+OHRl;gm2RZ^0WHE}aLuWs^aOdw~$Yq>*<SFpX0ax+i@6&ne%!_p*Brxg>t7
zOYV=LCZmS0f&@4xs-B%&yhiw{47$BLd-1bu<@?XGWGk481%nZu9dG1jC>yDr(SXwK
z7oFNjTLHu1f%#gy8(Q0nDK<1s+Dh#3mK1cg$fkxxJ9T7DPrJRl7H;dIS7)w7BJV8q
z_uPc0@cbjp+Jt!EpJwL>Aiwj>FL3WSzZSsq<~kKX^6n*x>yDpp3Sq+k7Uy|y?3UMd
zp<+QGD@*`s*Se^cK4MK5>RWP}Y45(hM7}p&_uVPHi=sjPv_z^DVVy1TN=L0L5O%-N
zb;jLwF;`pB{7}12w%K&!2s2#15=*C{(m?)am-w5oro%ib*N(eyG~2uQ%0nPhcbLWK
zk^#ofO0s@RC(Bq3k@Tdalnr^fqCcf5t*+~K=E&XF355!~6vle-ovLM4a+a+McVkoZ
zqg>J>%>bnCsPD;2w_a)xLYhA=<De){Q(QMZ^t-Bc)@0_XIwqcRuEAs=kitKrQ|yHo
z*r1l3-48Qg2r6M-wc+QgV)j;MEt>oawllCJP_%#ZRL1N^*JX5Hk&sKw(>x{H_(8eU
zt-4Q3PmR6Y=?@Z2@dYyi&x9lvd>GfdGWOUAFynI-W>~eZJm#5ZUowkqq1kEZ?yK(k
zny*R^pPhpXrXacoy6o>`W2US8N5mEbirdhNaAVh`u<ZCFZiX||@G$ldRk{_}PhW~(
zi;9ZE_ZO>u6UloV=_`JDG2)vVv;Kkve>h@EvkyPgG8dqYb2D*<`G&*ss6;#TWTqav
z4zPf%<KMvmQm^WJJj@SQ%7auS4?1{Q^wueOCdAX|lJ$Lckr_??oQjEqQ|<)8`o3w0
zt1D@dSMa{~<=Ff&;}uhv#j<lgG@x$1=vo?SfB7?*ub*0nau*C26j8>OC+nIt7|mr&
zltdYMH|BFX=+8B;&fJ9sIa|v~5=cG5%-VXT_t4q_z(P7g16*r6q}QfZ|GxLK4UKK5
zUs!IN(QflLm<>^QB13+h>}A{_+Pr9E9~$WssrS5qyCXU1_Prr!=V_>-Mw82B+#l&G
zd9&KD45MJa6_Gwm%T@Asg@D7KWD(z&Nhq)A70djLR32g?U5e;|CNTNjMq8X>>fT3v
zd1n{b*)_Zy?7_jVmu45u`Ux4_@U3TNu_HY^(7`H^3^`(JP)?nE|HrFv8|%4Ysyw{>
zaJkpD&Oo?{GkPfXOiy?->d`5>tB#$-n$~q(y3&UMv&NBGvZ%95jDlXY)<tyo&y5Iq
zsHJ4Pn(?NyOJ5S_--Hi4l_)N_PRd}nkp?2dKkW`P!N68rDd}don`+D_TZBQqQxff8
z)_`7y^O-c@@FNTN-<j^)MWUeO^-FQL!`s9s&I)?=Zb*@Y(^aT~o=+lER6mPO97cS%
zlsD*$%rS#BrJLQ54`JBwW^9&#LXXf{RBO#EbrTd93{Ow5?2fz9F-W3{ZFHq`1~m#y
z*~1T)MICW_Cp-H3jZ;M{28p%Ch{4px+9Czr-G4UQPcm~4Y=ybYZ!T-XUIhrvrim^L
zK{|JgCBDt%=W>W%R{DSuj=RPdc2xJ_r$p3q!CCPLH_=vEXpBX5w4dm`Y3)R}=qi|&
zHkw`F`%zPT;w`#Idvo*p;Wdx{jxQx?)(_6X6(p|2B90+UcDWsh_B&!AKJ5#Y?38<E
zgD?<tmRsoeu|vIxZ#TmvzA8G6JAeLp6kvF29eev?+mDZBn<WK#AEp(Q*sR9OYXU|`
z-N=dlXzm{7FYy~$&oTKcmz!c&cX++Rhv-N#XPKHy*11u)qZAjMpw8keTUW6!3T<$z
z@Pwq=Z$`WBN*Mpfp5_-bDNsJ~qQ|H)X;kDjT-B^q-shIITHy~mq0&@?Lox6{+qokt
z`htl;2qht>EZLeuyd@_k(#pyef&N<YVsFw6{VU;x)d;7+6pg=oAqeF#w4kqhoJ)_}
zTwLk7K+`pDxNVnN>R+ZfNBD*O>%$LZNvD+_5_sGU=LBT}<F_Obt(_tW$1+{`8T(M-
zUH!SyvmM<<;ngTtV=wu@-WWLNTq&HBUp`Soaq3Q9HAiKz(q`9iGN)LM$Y!Xzt;dg2
zc_j>`4_^P65x;D+c_*};jW&*IIE0768`}9dE5yi&gP{EG9fm4GGf;PLoIlHY-6h>K
zeizzsY0lrq8pj7!G>I_zpm5RFh16rlgpvR4jmze(j~#RGYk!NiHth&qP|b?`B|-j|
zlH-5-6#vtX9EG-UlhQUG`*PEUuO0kGBY(^g(LI(KFUl|B4B+ofcK^otLC=aeD=csW
zH=pcuSlNp-o+I7&INf<s^pX34)Gx{K7ve)9qZh3DTV(q4yq)l;hL@s}3qM(TPX%gI
zI+6zMai8F6#`tp!98hI(XW1gX6SKw(WUQovr)_6eO%Ko{LFwJ=yw_=uQ}@E-V_uq8
zD+Iguj_|<Kpm>Qu9hcOtrPmE%MG^~MP?CLHjRS51&X;<7Yyp++x8kU+M`zAPSae@P
z`j)my>NrQ$i$$bTTfZtRwcd9#`JD(YA653kMbr;AEr3f%;ykg{X>4wdad@}dQ+;Lb
z<{3Gj!aov{!u?+cjOmy>C!C}8t16VyTeM`KM?COZiOlJu<cXI-bGb0aolF9|ujVR|
zm{qtJT1x)}U0$N>+&Mv(HWNV#iPdej=Ov-Y3ZACo*;ZWc1&*ZC!#dkx{JhNZx#^d8
z8zY{fj+*SV-ZL<WRlDANlKv(#-Aq`;&ZE{Z2X$G@S;{jl%jmp(9S+^D?{01LX1;3H
z{cZQXC!K6E@oH|9XUR@z0?VYEQR>&jGOYxI{_V9k_^xrWsB*5FnDWMs8<bH+S>Fxl
z@@LRcbtDTY-QDo*+;tcq9B<4gu}~zo5i!;2ed7eJK{|y~-Bn~@*v`5ZrtadgRVyGD
zyeRqc#eUQr)XOVh5gvpk@47%a*quTe$Z5GFJ_6}0%t<63pK#O3l1r^OKQFSdQaW6?
zvBvYg%&fTUWy>pLO@h(x=+)b&?%RaPA{xrN|3;A9E6v_0RT_~jwnp2y)^-slQqLg%
z+K5Ye0&W&t;^vVE&y(Xj3o%Pl`0^05N`IxvnvDjr*rxG8IH#eJnDc5S-N%5n=rI?e
zz@8u^`?Pw%JQAMk8hZdD^FE82BC!EM+2p-9-BM;yO>?VH%QhURjD-+MX0FB&9Yzt6
zwL^vojY!#ykm1c`xfE)|!7etLM@hq*m+i|QV`!%Ye0LoYMXXJ7soUuq(YsT&!+}L8
z?h`4lSAFb)il3Hq5&Ua&{FBf0^Y`UcPh*q@tl8{C8a#PBy9qQS#PL>0>|rJ+`F`n|
zV}BAtvE!vp6>B)12bFzJxuGVz+czc8O{aS`#s1!$*GeQI@(z#+atCS*aBlTeXratd
zUnr(iF51dRn>(HM?3>%^^WGv0eegN&smJ#ZdNH`~BAjaFDep_V<Xq6k{a-u`yIx9l
zrdSMbzBoCcnxc_VQ96}*m+?6ACo$hgVnGHGSgAMn+OC7mhU>mG(L=VvwPW0tU^<7M
zFH_Q+m7Hl-x4g3y{xG$rq+50O5sFm06+Dd_Owc%3kmP}0wAIm?-gqWIV15>%A)uS4
z!R_sKn$Kh_-&6oWsAfoR>JZKaHf~-EaH*7WoXv;(<#pGGl$gG)i^60v?)bY4#5W%r
zJ`D|Y!%OPA-lEo9<ZmTR>v%i*BPa_dQ2!1mM?x**=aZ-HO=@$FG#|JM)QS*Lj7-@N
zS4cUd=O8lxv(bMHKWMM(Dq1Ahv$sdVhG6+@XEWwzBbPK<R{E@Ko%fKY_*A74!-QOx
zRpCx95nqJaF?Ay=ZXqfw2+T|PX!$_zAGGroWVDN81)Y_nYMI@oJe3otE^nb^I>$PH
zH+|(e;yc`Na-@Upa275~ff&xmV-CCb%E(znNj6t9)Ad{68on(mb(}YIOpf&3K5_X=
zOw$2f3yYL(4Hq5^W1W)%Yu8XGqwC6GD55iNC1tbemDSdTWkI-05EOfr2K%#y*tvE1
zwYJ;JrenHNrUr`BG-5nokiwrL;M94xc)o1f0!Ea%6khaBw%Uatzm1;1?u`uRBCM`U
zNs92iHh(;|q%tvGS%H{pXY;<@6{+EY<%fn!6izWxFVo}H`wHx7y~dTU7YgpG+xOoN
z$b_k$!lE}!>KMNK*08e|DW<j(@9rj=txc^?-4b{=)4TM*8yU@#CSZeib)a2b5@q&X
zA;ts~1h+W$MYw#(HP3?YCGsKcBm|r*o%64m&TiSe9QWtC*bZ^DKL|SlEv}e9pkjJr
zrrE?pzeL+%;ZFnI2iOX_?sx?I6<XVjmV<{|QS)Es+6awf74fFYuRsUFW6Mz%al1Du
zC?z+hf`cEI#t|89Ogj&?3Pr?TQIhDOU%x#@q~Ap>DT)P-Kb4Kh{tR2}N;=c;&T)CJ
z!|81Oa9(ovA^fu};#POOMtOzX0e1?OtBWl##F5bV%#`+Q{E@p=P|m~<KJ*`FAH;gz
zhmUZVI&@i5P;u^&{lRpVVLaM^#*yW3r*cWn%ww=R%2_QDp|$Z-E}bapY$VRLdlXHn
z*Io^dwCL+|k9(?ms=1u6v(`xT;zsaKqNe$_`mGAYv+g*Hve}Cn_p<8xZb1uV@sq<T
zK}ETw^sg+#iiMjGnp67#8E{u_jeML-dM=C_VDvs4^H1&$*KnR|&MY=#3$wgh+p-?K
ze5|HN_By>ZS8wG_Czp(Gpf|EddXAgv1W)g`^Bn0rNtKj1373cZT7Ocjviy0PRV5fZ
z5QN)1zDi76g^n-LS&n$)mbn41j)t^frXxhvwLf;EBBjZPEzGW6BX+>Be-pl1SmySR
z*u**F)o{Q71ziSCKDe_&5PqdOBg^;tMfOG>)Xe!^7nN`Mwy)yQK2esk8}s>Q<4J)J
z{JMunxV+4Rjd|dll?JtpdAr8$FI)?LHd{xy3j#Cp<_lNL&sT(ETnOisPbk<U6#6V1
zrW^owUf!&&vn0JAAzrT#O8r}4;73Td$%25<a<JyrRT3@mB)z(41x?p=q&c0rL66hy
zvo3wmYgvgD`2OlH!_)Pr&^qzl1f;^5LQ9agO7>$oN-bc|hkw>-Nv0l99&1$S)M|A^
z&8Pd0sQZv|=XrHq1ksd6(bnomCXUYio1`I0X5;A6_hnC>IW3>1qIr(;og0sPxC|S5
zPThq)7(2K9=JR21WFBZc3CW*cvK-dA{nI7%$eWNl7fThgKGSv+y`BlVLL%N_*EDYg
zyTAKc`ADz|Ch$Um|8!T&N@M}<h~ZPWIMw4TTJS+ZXD7Msz?&oIa_Rnb?Y@Ypym6v!
zE$glHug%fV#w%7>UL2hMIp#jevmIe>VALeEY{lEb_CQ1lvuHZk>}h!u_eIpX$t<0y
zHen;%(;-M3rGDjjHR4baI;m(tz1R2)CY_eZLi`(bvt8X^8ZS?N5Z6jPJ>_Bh&!*2d
zyQIXZ;)T}C`Qt&c+ozXQD+~tUseTM^EeDD3o(zFoLiun7lU#+_GvDPC5k@*9Jc%KT
zN8m@-@B@?MXKsw?<sX%fn6#$;JOW9)DOY_EqW<llQ`s+sqkHY48L~C^j9X4{xY;%~
zwe&=wb)~eZUn4?}ICZZ}ZTl`!Pr{;Zjpa$N+%Pnl3$f@r>0GCc_;Sl7b+a8EXkFTO
z8nHC<CxRLi*~ykC&f^qyna04n!RBiUSk|nZCH~Sh@GwdE`MO~>i8H_l6O9>r_(sbu
zuHAz2d9O*$FEeUle73U3Ls5yBu{|ohAc?pQ6UcV5mWXu~As%-PL9B1SrX+=1T2yU>
z)O1DccSNkpDDOtKbmuyDbOwb-50I;K>>7M3^5*9mJJVr<5z$5-N!8OS>ethrA(w1S
zNb1Y23wNJB@SSj5eO7obFg~n`JnE~xtl4sN1uNRR%O0Gw@<o(~o$6!R$u`s^n6KcO
z%(<lzwNJxG=qPn)HgHxmoTV2Ol|<u8OuZC0*>Qr1+pi^x+%w2zSv!yZTEH%>agPI5
zkDwj<s2lr)QPJ#}LzT;Ka9{l_G1)A?g>x)x5A;PsM36&W1O^pjuPd2B>Xldbi1|J?
zU9BA=q~;PRfG6;Yw}0~B_&F?DA9JBT+giFVm9Xg~xbU)YLc$Z>U53vq9a%K>;=>>X
zVzaY_v4LWADIS%x!aNs?PFveWo}4VXztCXcr`VC*{2m{%BE+vX5D`5f?rZGvgWr>3
zkSp?{*N(5TaJX4XFKtR{m;I?VLg-8L<Uqte%+%SZ?Rp<YBsioYM$DL3ky+Kt%gw&|
zXO0tCB4-#Jc-`0Av8XP$7X1c251AJy5V3ZW`MspzjMe+2BX6v=e3t|_1A|LkhTIN#
zP|pX~>JF41s*KjlzN6J{mpHTU_rXyy<ZNQM{FZ7NaTGnO;~$*d{mDF$?q7NfQ55J0
z_k^vqY&!@Ao7r7|=esK5`L;X#kRRj71K!O*RoF$x>wT{5*F&y&-lh$@bsd1Xv4D<R
z>q7|&>(q9D;H>9V+N3G1FDX;nWp)^ygEd#GAe9j>@7WdL78Hm{z5}eCx*=Tnoz|BE
zgPRT6RDGyo=Uzo{bN5fTIyW6RLU5hY$KDrEUMrQ>rBjs{*5bal=A4rG+7rlnRGB1?
z96`jh<P*iNlsHgEyr2|_h?Y}!^=ukdnr1!O=Gf=@e(;Nhe_zaQ75PCinP2EH3$cM&
zDs-eXB4+_0$zIlMrM$?(8HBVHS8~w9)DZ)vkr^SY!uVh1y*>5JaarinMI3>+{inF#
z5Feb=isP!T^!v@G?g&bTl9)5ac+Z2A(v=icNZ$PVdPMgnRU=zkTB)BAExK@iiD>iA
zfHO`z;C*`cR9#7vk%#{a>X>iDqeK)>DRROvgC?z2SX;#sbJ~IRKYD6qZH0Y3^$FPR
z%+kkh8(-amNS~TVd>y?{-+zv1F-F+GDV2G4^y}w?4wqjXw3{}<er*_u&~fw-V+!xK
zm8+dM5nozSQM?zt-ps2mqTkr$bZ;GjZ*9N#4OyErH7Gdu>`uiE(uIJ4hvWxo-HaC@
zm5s<OBYT2-bSd2^Y)o{l*|5S{N||p{6_JFp3QBovrzy#-Cz_F4l>_|w@FD`4laiFX
z>4z2OsnltUpbwv1{JE`}iqqnvbrtr_2;41RsFF{55s))gmqQ3!O%;svO3^=3F90IY
z=SEZks$;kh);@LTvl#_TNk=01uV#oY*dqe>in?o6zHs7Fr0_5;`F0t^@l8rNRRz=%
zV_5^zX{yebAV};VqM*s4o|xsUO-ecw{8JUTblQ6=Q_0DQ@G8LNQb_|UvkX}vju?4>
zxb0WG%wEbZ#{Qxgt6@&-edKt#|3=P$4Lf?ElxWG1ek?{e?{B@keDS@}m%RtY6kedB
zy2}PFy{8;;;tSW8sHf^{3SQsK<iO{kzw^Qk+u1tz6^OT=%Pa4nj_w#gENAfO=X!=o
z#tr%&?e)mY@yGO(3{X2Hoi8tqNr#<**Hf00UUgZwkK|k)_TCD(h#e_PnY=$b5EEwJ
zCv4D1a_6K3T3zkk6j{>f7x7*Q`w*dHZ{-c6FSocSQII6^w$ej-p15}5XC#lKtmcMI
zrxPPncH!_6QLQQDk1NI=TpKJ(57#XMkV4uy&1*CA0XgrcZI`G}jhL|{3m~@Z1a4|$
zb8cxe3Cx;rQTN>vTJq@j4FkI;39g0i@5m92JNjw2OIJu?GmIyuf+3T~OIpfK(!WKb
zP+!gyei}{h58#J~-ZpjJ-T<pgp{3a^#&g5nV2!ZPHxsF1^)pZg(Qk3wpb&cwTq^MU
zT7ZH=DegN^K2hbkhL+qsx64YzA{eT#EW&4aA9IXXCL%|xJ?e99Gh{08eE2`^hU;`o
ziT5Y^TQE>lkB7O>y33j!-R5e$t2#BhI~7HE+=^GPtU8-Cc{MewkKY%hbz5(#hjvDa
z<vYI{NI%l{WhO8!S9HkBging++cRT6!Sr%dvxbsW%@Thr5jV}CF~tyOmiOUfO1jC%
z;MB81=ad=iyywHeCWKuyn8Ryd_26Tt;Y-db`?iPLX0ULPgQmAN&57z_3l>Xc{fFz;
zw>JKi=BcfNY1z$bk6*^AuP|InrHEsG%9PG1n-;e@dV+C;NXLU|^D3NwGAA0pa@qJD
zHuCTOY2T%khi1ArNj3T&GFcAi3ErI#)|oV{;<c~d7S`8oKdsr#FuYB{%0YM_rs-7j
zh3M$5Fp~8B$k)ck{ej!5R!CbByMAxq1eC<^($J|WA^n+)U#v7ii^88kw-qFc;g2)D
z8h7t?u271D*yxBy%#|kPRWjhR{wY*p!TVu8iJJ$!U07^7*x*?kHA3qp8FpVFB<l?w
zBg*ZTTv5n$GattPIR%+nSz<KFNKsLrBVJboL?`78bSWh|H5b$KHF641luBPYXV6z;
z_bo)<EF7$k#mf))8qgwVqRJJ6q1*a#L4HT%+#8x~28I$52S0dD@w(4l0=zLiijnn(
zzW^<lR4y1E?p(0I|MX<Dhl7SseEK};Ow#5!;nc!K!iNzqK(Sa{8qL=u#^)1TE=fp{
zu92EeF21^~g^0b88+Z?s`*8iu?o<dg#arkpL5hA|k1Z_obLt8?^f~hD=a9<o^bo(c
zfja(9uG~otC07;0nZ3s%Ov|wK?q6w9d90#crlM6xYG;ysLdQ#;MoWns7(0)LsmB9c
zzg)Z;Yo`>yJT6|7*y)1czEk7o=lKeGB3I^M7!dKdLJGR?7Mm*(Jqx;>5TPbsNMYFD
z2!gL&XK9!}$IGAD!hz$B?gv`?WHeVkuGluXe#sy6Y|fDk6kJW_+yP5HjuPL~maHl&
zbA{%z{n*n`Uxz8?3IyLfjx>4rc2jSa`&{I5XmNMkJxBN6wu=ZwvGD^#=wvZ-ID)m#
zX3ohhiE}mV)syZZAV0lDln)1#m>ml!Hj6w7KO@bXbjE<M<dTQ}>?b7>DSNm|J~ikm
zoV)bPW?=TaK>iM=f^~d{Txp%j+@4>u#%f#H#`s1N2;5cWoQ+XQCPV$9k}*CEoipWV
zjKZJeF%wXzX}zWNQ7Lbrt@GuELe)n2<##YIeoV|2y1(%|?nWn^KH@u#Ye~HY?s=!;
zTT9-H<`kpYk)^87;@llu+9Qs%<<v7nT890{oAuHK^qaNp3CU+tk1{=R%|z*;g^hWz
zd-<!KdJAQF)cne@UtRzU>gztP!VfKbK+H3(Th&pQaEiI;AN%47L~(pHFaN81y`Wut
zxSJ@1k&qhh`l>s`AXbcD!H~q16S1-fn(hUrcGsO(H=QNQL=bil)?1!B(287F!VuGm
zDk_(b?aL}_8}1E)YeK+w{QKiZqFebZSGV`$d9{qJmu1RM9Lx`NX5AW@2TT;pzNuZ&
zbFbdFln53=lkJar89Z5H?(u)(CiChFS~j*=^2Bp~xuUD+rC(QDKw?X2A}0^~c7%w_
z)Qp2>-8CywsF6qGiQg7zZ;&5m{m)##-il$aaQ)gYi5tZJ>Y0N7WTcskP`|b-&|k}N
z%-69dFCdD?DnnNA&WOxR+Hz;AY>AdHma$KREq{Ca=^MVzTlY2iz(-H-gKnmZr&2>8
zu{V7(gy7reGufn}sAC>~THf&F$&)W#vC&a_4dhL09o$Gu6ixQikF^wnU*pQ_9p9%y
z(L3fp?uB(GTk4F-wLf5Sdupfqt>#YOk&j@fX_#)b@!kdeZfBWH-R7w(i%SO6!@Q&P
zOM7{5-rRVQEMe}qM8J^jP16<17E(ix(G8A~+fU;8A;zF*c<c=)j|4yZ0d+&L02#e|
z#7HP=t9;bg5$)6Ai?3a1*WLTU9U8s!<0*C=?MwKOpPwHtE-qfzLY>RZo9jcyOqO9L
zBTNZsU!yNXEsyTkQ1GehE_o?5X{sULTBoVbb6_sxp8Cbv$w-nm&KFr-U7ZCUm0jpc
z0$RUmgFP|ag&un{s?Sg)iN4Q~S61=<E^+(X@g+7JF_)-^^!?{CDJdy~J9C--4%m%O
z+4Ru)Z$FTEMlipXw3y#hNf9Pwx#2f`irRx&udD;l98d|EX_pmSc*r66`V6)V%=>c7
z?qUfASa6#`pJHgg_Zh4H&=^Wu#YS$g?z=u%|KiRgVfKE%X|tDhhrUZz`&^(+YTCRY
z@L=tOkmB{q@8gy_RMZpXuNkGLrQumJub`I>2sbwEKGeUvB&&cAhZGsVQ@lG;KCx^p
zhU<&=nP?!x?@xbyRp^LW<xMgc3t9q(+M7*x)lB$yG#7g)S1|RefnT3&zy!qR0bAI^
zShLrE{ir;8_5%MvL+}y?kMYNfh7cr?53FueH)gfPJiA|?$LLUJk~e{^8<ll+b>T>)
zkSB1_sw-o6WXUz`$74I)-T6GptPKX86r*bof5IgY;a41dB=-U84mgITzDQCuW;~#)
zZLIuA?8c8FZn1cD%SN%u%7bf~w!+ssDC`;<8tzqC-czSixdb$~;>T+4OesL!-QDZA
zr($9pF<T2;yn4f;Ja+>j!d1*fTJ!GDHK==X^lXUB{rmSFrX1RyIe~GIcIa(CRvSqz
zm*C|Mi^X<7F+EutHP6K!|NXZ5=BFoR<YdO$k59U1H3Hn$<Jgb%PSko1)IQ?UylYwQ
z`AA9#B9nVH&rm=sYImy_e&EcC)%EM|1IORrSKQ(J7C~n1$SvcD9Rxw~&+gVj;0Mf9
zG~utpi-bCF%m(64PWXT&K8O|v#_yvjBZIH>C7>Va0<(P*lMlcvX77u%M}Sm5Xt<V(
zYgj68w6K4F@5fkIx*|#NTV7ipOlMbCH+by*{oDH>8}J$@52qeIqcju0vjU=p%hXEj
zM2ED30)^bt)z=3L0+IO2SnNatF(=Y$_q6u2jgo666gPGLoa(#%xegWtHNWxK{))X<
z92^=f-{jZUssv5HQbX%*w2M}hT+RDB1u}dlL)BYnxcFMh(i3i>$@{>!34vbCbP6Fj
zhT7$t6Z1`vV<IU%eLD!i$Id(R%mn_6VQZe57kmSph38i=Q{{x_$nAK(3qE0?etxS1
zLnU@Xp@UbAj$*+f2<j_BjXOO|L<(KOlYd5#7bnu0P}CejUezLI1@5ZL8*lAQ!_>H+
zezLYLa~QIR`!y`Q>#vHp;RlhA8Kp@ys_V<lO3Dsxve-z#v?-&rOj8}Vajm@f<6{l!
z{d7XIb(v9aw6Oh{&r(f)ujxKMLU$ztiOva`w68wWS>}puc@s*SyyHlYKo=Iy{}`~;
zZDxQuALbFTtRB(#2`{lNleswkQn}ZEu(01XFfb6QGymq%>Y<-u)*4tlm+;vRkhLad
z4t0XTjnONcBTQ5M#8Bx-l+0PJ5T8vDqubnT%6i)!$&!rWzHqK$#md>48U1l|{(vi+
zJ<Nt6q)UHnmznGnT~tei+c-I8ZDO$WvLWe$ETzh5`tJ73?5w$*l+>Rwqc@*UJfO~|
zhu&Wr*{gk6G<+12^;aBk>ae9QJw*~4j2Fl|Sdf!q>mokIc%z7n)B|8hjmGJc2d#~a
ztp?ti>&k&GekyvgsKBo}W_1c1gJkS)W0gPts9e%D@r&4+uX6kG@o|)E_-v|m+siXG
z>SIBADMHWn19;^9zSlN`2pI(PJ3CzNOpvCKv$aAmI5kHEe*#`bHr@L2Lb{@vf<ID4
z=+h!KO)9qR{QUlY`S34f1L$=Z3qUPR2>5MhX9EE6NFct}6Z<@>?EU-qH-?I@W&799
zruFhjT09_uFpm(XklYky)_=w;3TpX`R>o77$2K<$*W?}1Dp3ph=?4V>=0_2GGDB}K
z7q$Svj!Mut@%$f=B;#dL{dt;%6TisNM)D8h14t^=)6+9YY~q1=q^jrlGs-4+7iE!4
zJ=-n)s}qgZ`&W>KLdFJY{H_^h6OKhs`ZvxMloe!$rgg^!DBtP5xVw#I(2<~gUhA7g
zN6{!>L3wPhG|#+nN6xuni?87EA?s#|Ta0o$*C?_9v$otq=hN*ahc>lBmPgsa&h#%Y
zPRUv@olxNd0-LqU^XbvE!=7W+?%Ics9Kke^haF*G(uvzK@M52~1c7jPP}nQ4-KeF@
z1HJyAf2!iGPj^rk3XoR&xpL21ZgjxK>p-0gsT2!dz`5s-v_#Y5-z+9A(mwge{1~1?
z!Chrm>cz;UDeQ=;VzY`=OE<7ICTe$~fS|+J+oYdXaN23R;EP{=S>oO4d!sm*6(afa
zyFtEP-q=mA@fvRj*sf%DqOAb4bp9h&=Q9?NIzSn)&{}Ep=*f^zLop-1norg1<<ev`
z&45|vn%z21v?W<tSX^BH>@Vgy(Ga|1UF3RYmbU&Yi~+yUnw_0JH5Iizp%z0PvP)l?
zXv8omD+I-GVs_{gF2RdCKMY^-(T`<%%zt}ZFe<bP&Q{A{+QkkZ&lUwryPZ>DkLvaX
zPuvV)r}`HTF+-=LtBVlcqdGF)2trQ}6v`EB@Y1lUVB~Vu*`?t!+AKJ=<uV`^REyv!
zkTx;SAWMBg$va!+JyOon7_!3mSM=V_*~P)4nrLEg_R&bnvW8N~Ab!ZswOQfB^DQj^
zguKSM?9fRKnUJIxug<DXW_8w^)a~ixw1qCY2kf6QSDnvI^VG)k+qZ>MpYJ}ju|cCJ
zEwvP>-b;_}j#znmdlKo9`&8o7!E)!w=@e<}CnPYaRuFhJUD|RDmRSL=SXo&uRd*>g
zs_VR(+ILkVGI1yqkRB&22scv@(O6OoZS`GH!UqyHTg`uR%6qu9exasMM=Sh$>%)oV
z5fFJpyOzNGWcU^=W^WoTVV;rlK2>0=&gzs(aauuFhENJclz$_}VPN6Ni}?6J35%?-
zyL~y@3=ycR*Wl=bmAa8~ipeyFS@okQg`#Ok)QV=(y&_$((exEwKtJA}QTF(Q_T#CL
zLLJN(hk}j>M9jZ64P6}{TPv;U<(sp`*x*~=(D)$Nc&6A`Saz0c`VtaRc?p_c%><nz
zr{(?}s1GCppEz#EPc(*(<S5^<z-k1}-5o5vDkCK|I$300Fv6_IjNrvV&rC-_?9LZO
zw22iI3M~UxpNow8OA59@zg~Gm?+8?Ldpr<Xzy9^CZk=m|eT|1Fa2+OOb)1?#O2_z&
z!D)lmmd^6fbk}IAC_s~}vyIKbj#`2MFSe(<#?ZcwJL_#iLJCmD<}I91@5Rk-(u%Kv
z0Z0Mo;Gu?^UQKT|z<6^aHha`)OooYF4@`ah>4!FWv!Uo(iR~AP_oIVHcZ&gQi_rlQ
zf*fxO-#mW%>q}3KTc4jr?ROk~{I@$iB{MT~cwP!StHt^A0mRrviGHbC#?cY!^*cDc
z0D#xkZWfg|H&RAGiD}3K>6K6MPIrk|c%mIi8CSde?ag`9G&zmD+S(wvcIUW1!(SGd
zssY6M)uVhWhi3j9&|t><gch(FVVAN(8=dJ$jtQlV-Kkg}DCom5{Z?o!1|Pu2Fshix
zJy@~vsL3`_qiBmA(DfgRpMKm6T_7IM)a7o`T(10-)|;iV`C)V$mK?MjAGG*moBpE!
zyY^EO>qgjIpKj}W6;oDNShxiumA0E3uh38em=dhi5mP=57dQ0^p8s|;a<8Qb{jg~8
zcUd~r3g`e=I_k8%gU@|J0W?`c9Hd<{5pbaTRp6tj7BFR9*rl=N$j~wn9xh=xsH8TP
zFttA%-12-h2A?MHn1;JsD%`o92Z*Y!2@aGV2eBH)CvM#bHum=Re&ofWm@rVw;QNxK
zej_360McYLJIL`2UYeyxDLDJ84&a+p&-F##dJeo>3FUSLp~5&z)9V}tE<-kilJ=X#
z=(H(@j`)~nzD)bZBXuDNJl#w;v9xn|xB$;&3%hUdbv|hFwW4h7&d>M)4fX<s`WbkI
zxN>vnh-(aebMSgJEyfX?m?2<u&(4IgM_HYS-2C?TvQP@@+~p_Z0UtRfotObAA@5Lo
zotYRdNrihp49f#CRx3xA3Dnz<im6np7Skg!pQm<7Vt@i>N9;t!>?|lYv1PkfgCfzT
z88p9^AlCEx{HH2k^SYyKu|enKcn`JaXh$9vF9NiQQ}_BG{OW5<4_P_(O>x)@fY<4Q
zh?D?jSDic&I;CAwS}J!D6r?49ap5Zlyv|$Z5AdV@Ve#?tQOz*}(o-<(-mGH0q}l7r
zKxSoH94Z;9zSEae0dP8WR)d(eX%2vW`zs0PMiR)KHh{8mHggG1@9*6VggSt_CS6}&
zFW?^a@YHktW!m!JRzdsifEJ9T*D73ql{+^^&!oj*{3dk5>Ew|*zljFhj$!F*dTBzR
zsgT4b8=zADU&&EDHf)z40z@X|Y_>}uptAn}W)5Px21F2&_<D=LAXoscaS0~>?022r
zoa@nA1v+yT?KM=~`}px=$4O9Ko+fLw{5JK%5rDgDkqfx|+v3XU%gl)764KdQrfw3E
zp+6q;>a5|tE1W0tjJW$24#&+a?hSq-(LPzm{1Bht``HY%8JX0)J*mJXmuuiOf*^J#
zy(kxGqX5(F`lNk}eT5;Y_~T8NAR8MSFT_1Z%AKX<y9ZFvWcb_5PovNKPd43|da&6u
z1*l(&3C=lmVu;|$7_A=$-W}L45Fgs0-(^ZApko*r7TKCTOpM|NG#c-J5M=v1z#Nh@
zRlO<1{yZa0!bA+fQUmzl8$H&xeu!TSaEd0J+E!g#Tl)rZv<l_}ID91HpWD2O_cAc$
zAb-k5#kq8LzGpKTrju4`M83$s@lM1D-Lo3Q@7fT;J6s)wY#P&g&@Dixv$M08WBz>l
z_gngbEM|<a;OjzZP@+WPCZWI*$xi75^e+doEWb6jk7V0N;dp0eX7=_3IiG$?N)XWU
zLxM^*1FX|0FDu&xkk+)qbgKmj%Qb6mEQuRX-t;${@v8T}xB7wLiMR#m9cv5F1bHSV
zCSId$r$T)a^zb%>OAupT(Xguv^vbXz6ZAW^)bEo5oTS(dP&o1wK|DpY1HUfq_Dt6f
zSkTsiR6y1PY10DC<2_y52TFZR%c}zC#t>Q$IVHiZGaXf3t<)Byvb{Vuy~}-<Mp+4M
z+MbL|2AQ$G{PK)4Ur&y9BQ?{%CHyDQ02YAf4Bj6Av!#CE5V|N11yzC|?g?ToVd_a^
zk$uga#gh8|CWYXyn{MJj07B34lP>|_PO+)g&6_`Lb;Iv8><RaBTtU2S@~`q5F(FyB
zaqDcHvV)&A)4kCZ;^;cL(lk?1#$D{(<>6?lMd}40-_^kxGH!B=7|LXNUtgXHd_<>}
z+Se!`G=mo#f%FgLALErJFp_Nyy+ig;Q!VKf&2$;*O<lU@v8WGSir783t~7Z|Di{>3
zVY64u5yq#w$b*UMs==-m8)=eYHney%q9ha$(=z4ojhQJZj-A(Ot*-<KVLC{5{|e9#
zaETlY+FqSd%wX1On+^8Z4gFZ&R9jH?&`_)Ok+awKf<H2nlY#D?m$e!Puzan=u0n?j
zw3D_C?r-euNSR}3rr6_8H`~}xftY)Qho>Ns*oi)o!wV_M^mIEXKzXJ?xtG0X(ipb>
z^PjH15irG|)>xLa9Dn>-c@?(@>cKEb)~?Yn@x0p(wRih#nK4Bgt(tBC4Aej<W^Ww|
z)QzQNWTpoo%_Bg+TZ4IPLE#nLxBQ#Qw%$-7E#FK);QQf&;MjfRxVG`MIn##Ow!@$v
zr|JR!pvxRFf*V*&3zwW_V+_>b3OzQZ8LeZz(&UCOv>`B&KM3!Dr*2GR7>a$nsK97|
zmVv$M(=U_2so9xf`wsArDKIk1?uSPluthN7y62~xj%+w+d{gzGe2^&?g^h&0i=sLU
zhtB~H3>p{t7ey{SYk3pCIj0K<{H$)^T=&BEx69o02oTb04J(d0Kn!Zf-Hm~-D0a%~
zR#5gpz4Vlawh4redo!qgF)qP);%1+&e~t;=D#_24^^KQEYrM%)l93?74$!r103cCh
z;f=12j{ff-9yt@WHUl;~BrBK%xDKcoQF<Eoc3Z+4us*>Y?tu1LxDI=KgjY}u%Ix7O
zVP|*bv7ouZ<Z2_|r+_-_2OvB_!V(?*>SxDox-PEN4nzYLxpxyl99fsXHIU2n40bNV
z+jio9n?Sx+=&H|OK(O&Jl_fUQ%;nQlZdd9Ec0ZT|j>%n5!FyY-Id~F!2>#fpRVGVg
zD%0)yo<I=X>@^-e4Oig3x{N*c^#u@EwsKHrFG0Z9I_Ec&O8o#>j!>40C$7rf^X2;&
z*O+q#nMcFf46yfek5&&2-zm;c7t{5Py28}?X(26p)D=dTX#)`hBtp91WK;bYkj;DR
zAl+RK1F#C648POcCKNNi0!RsP>f#W0SUxw?g-8GV>&7&|0YCdXuWOIqY%C9#5r_Z-
z5?B?Q*oJs7ZhgDV00qez3oQ4R%42!){thX6yRk2uBHOqBu|#mTfB!w1SC5!(7U=?H
zy&@Ep$OPBVO?w+%N0}+|w<bC8gbmY$x42wv9GDyw6m9B_pP!!xJ_>s&q#L~ogx{(+
zc~iFh{jFDGQ9wxu0RFxm>1v*(zWxoQt1GolcYpmoXflits@=Q#T+5kFTT+K4(udi!
zxk|wJ`^!P|3}0m|*z|%t&@{FJDs(R23M<@6nrPg1gl}~#_w7x+K7ZRG4Rox;0%<><
zOXh@8!NDRlQztO~Q#HHWq^aAWmBryZ*+eB$+ilC8?3+RTBQE3XN}{Ds$}El55HZYZ
z^hlePgKMu*>=iR3CYvLXHEtbn2g<lJvx%}dEyM^Lf6VI==Ha6OYTBr>TYI9B*aC>$
zGyTj~@mx=)9l^p5SgHmzZ*tH2y?^`ZxDo$4M?phcTH0y;(DD1BdrPhiFDnNJJ{fuW
zqvqL~bx?re{!@*i{x<<X!M<n8f>)L!VDyS&o?y({*u4$Ywj}?KHOH1@CKR6uS{nB4
z+F74wUElyRNIev^E#tFon=Qny>L$MMsUsu2h~hE-1`p+u4>4WA*+H3~%M#QzYZuUn
zHH?NyOGVQd`hK9rjHcYjj|5ed*CV6-ng23{ng)~35xWtaYLO}%<Mn~a(rYEdYRD+c
zSO>7q^4zr+r1uw6mEa-61pa!F!suV_<~Yi?m5Pi2QB@1XJrN|*c7qt5ToEHMk48@I
zi%k5-2zNRZ5tJqn$=>1%kGOT_Hw$#>Ma>gPKYXpU|L14N1(>;m6}QIPun&zu3N!xv
zaD?p*lY29HF@{NwrrTitAw$>fR%%`Z0D%?8q<esB4^M*zcyNy-=qSGW^Zd_p;6DF^
zDO)S66AsmGP&Fvu`CFeiY!-R-soQ{1<^si5KE@P2YUB4lx%$8V@nke8iG}sx9-Z0k
z^`9Py0{ZDFucFI%B+%F8vo4V<b=zMji`6}GC=9bXA5nqb-xUV{y*Jd2P>wl;oCDRH
z;kxGy1iE9TdskWp6F{E!0*>3dJXXV@o_a^|uahLuhzp~<Qk#7L9d4=+J`etk-Ejn9
zp2loUa1}TjG7lSyjM=B20~oe63Sz$C^4lw7U|Iq}-}GeFd`9nOE<NP1X~_)g<A|LR
zU*v$N_Mml=e^=sM=gom(!NQfM<||hW>IFvq8KnHuQnRWfBKaTtsLp`x_5<z~oGdmG
z!=PiB_8gtG1w5p=PkVcO1wdt#&&Es#Rtt!GI|ocMsjaUL-diQ|ryiLd9s~@hI|VAG
zYdPpT(zg}{xHXwg{4ai%1^<)sj@~eRr!Ay%_nRF!(MS`(z+eznjYgB8fkK-Bt>5WW
zKoh$#fEwRdLPSW&7PcI`H1xTrW+wi>YXJ_q2n)yT4j^;VQ2^MQ?Do(AKz?>HMSSCO
zb(f$wX#P6_*+`X9Qwt51I{Ct;!i7!)`KQSD*Ns*lFhs!zU_$DXe0%eGFu^Jy1o*Nw
zgEQ{WzkT|y)+*@OEbr;lX=Q)_DS{Txq%N>DEkBzAx~-8bU<E<&0$67W&BFC$s4guq
zbwwUbcYg-~PH2xzPOeeo=OPu^RZJ3@>p3yZz?jkoBthtN0eCs$0-)$?rL~2Id&4ys
zdl#3<-pgDXZq!&7)*R5e=atY(RRgMg1e7`YInaPd3^EM@fVD9&Ai%_XuyEZO@Bm(?
zXzDCX&Y^Z*&B9Z$VLq3eSHUSPxWFRY1)$>a*DF#swl+4kZ|y2v`ap!tWh7f@cHBRo
z+?%cCpRMHHxduuV_8KS#B+(?}LO`YYpX*!gp6KzXy5KZ~AXd|0Z9+EFH|pIl&W!Y9
z_6)U;5vV|q4<(}RsW%a4k}dbo4Fz<?PwiuyPyxTFL&WwfJspR`H32rS4SF_qB;N5Q
z0K#)XBIrm#FF*OCUOXhJ^!n`mGGXPo`6FTrOl)oeTtUbfhh*##<EJqNplN&!G>wr#
zyGJBJsGzl7TOLq9f$xR`N^FRn1NvfaZtk>D$mmT|CaN$AvcLCe<&S9ATewrds!j2+
zso^{?AdB0eCXoprU(6?7=Bju9^yFxp99UMn2YURW2NBzEp9)570*yNnvi|eSWuQQh
z&CAqZa|XrQC>o4?r42;kEzq%Zb!*gt$W#Q;Sn#_WmmRScOaLgb%urca1U;(Ak1-6|
zlwGy6(4S5P98Wg(%x>giA@^eK|2+J7ejC>cLiG~pB1W6Q%Nti4Qs$nT&g7@Nc<qo!
zTrn$yMFW5AKV*%4A)6f?ixxzv#O&`SfHj~<K>4&}3BfE}Dzjunt6>Za<e5<{@1bv}
z?3~tz&bRSz&Yksd>i3`ROdWdE8M?Ff!)|77ZZ!wUsX)D1PRJn_O*+I^TPYuf5~~HI
zC;@mn1GKf0;XWfA>fcgE?i@HvXNL$22G&DZXv`Ey-wI0%2QrqDBfQB9NfE;S`o1_=
z<pRYh!V@gy^0#+RD?fa-e*kg}cKrWYhplSoQ(C>`;`VDn9s-fRnN+6&wEOR6TCikm
zxF#|UT714x)oSNo*E@sTUnbH(d}ghf{JYct`ss?eO|p%(mDKNreCDEWad``B6WnZE
zghd_XqWvR&G_}^n>T^VjNrv>oR<{k$zsEeb6k))Mu7Y{rrgWrl<#a_;Zo^SoFNgU4
z{k-^n$&5g)b{n45!A2W{^`9x#`u7tXdsX*$*DD{@AWa(#aYJxh#|q!1K@krmM!YWN
zOi|`--^~1|M@?kqus{C&#uf3SHY!q$W*&S89dl5jx6xyL^<30{#N}Ufc;BO5JM-^u
z!cgl|M>{p;;URGpa7V?EXhrgEN~Yjl`QJ<B5D2ZR;N~;q;@QgP49d~9;k9<zkSWEu
zGKt^ckL{F5SvhRO@<r*PeMfh}+54lC6je;r&u#hCO&hM+9}!SY`=Gq@O+=BjLb{50
z>>jxHQ09eLPjXOL+S2h9HTTmQ<Xvn*@q(;p6Y8Unofsri{8i!>H!Nzg-D$vF*(z^C
z%@oJ}Gvi=v=Q0w$>)v>RWlStEeP>s0gjHSG$2w@R%sN&D6=k#*!G87j_Jn|ss`f-t
z)Y6}yr<48|76Lb>>fK?V61qeC5lpnYw)L!<p~ktfZWTAZV#Tuf=T@HZhx4ou8T3kL
zwCjnP>5n%oxn+vYUO~z_ZK1o5$$?SOsV}}20^5xPIu^8YGprZ%tn@;~VGlOa1iTzx
zdm$EtUSWV&o#x+dlL6M~lBBflgWT5kP$;_O>P>-YDnwPI#t2>VgOK(`$ZM7k6K{^`
z?>Aa}oyoYKpehqXa@lt@FfpxhJ_@;XNk?aiZK(UmokqSEXX>O(&-mJ9bjia%L|Ps9
zPfPKAUy=8;8mH_qCi{}=#ap3MuaUg(T<cPn|Bw>W>0yB+KDlC%@bU4`3DWhX^U&7?
zS$j<;bhX|NOW$MOQj$(ZFd&0}<{pe?h4wU6dd=BRLi`ozR;fInpP7ZxEW7(GjWs3a
zwr$}#)zSNn<h@GNW&RC~b`$?dox;QaUj0y*b!aUOixH3Vr8_y}jFbj=O4GKAy5ppe
zxSv*%t)ke=E*L^3ufxuYMJW`erS`#;;?@5Rv%-(BbKmZzLKj6npXaiW=gt#M#mVV5
za-NB(4)})qTye1SC0x!MS>|u`fbC7dO*VfIIdC&Gj)SWLlXNCq7HTa<{~(Gyfd~(C
zJ^DWS_kIwWbB1dky)sY%@u4IeOPnoqN^3b3i;96ZnBj`xUd9gR7R;gG{->?qKX3to
z9B8s%%OkIC)HM~{{r4D<cmKVL?F1KZ{QK_z=g*}uZ=nCR6{7zCUKav^?8H!3FFI7$
zly5T{7gojGghg4{*bW>&aq!h2aor+pdY9Y|8W_$VI1s1qM6x=`Zon3u`y=<6h)D8b
zUj2i5N$0xR4xA9?yc{dWrj)3$5VTE#6_=LRYG<!H7epX8Bg(EVJt(2nAU9WZw6a$%
zwaGqUq=qKHf9&Nis{_A=9-DOH_w_6<Q+{8}as$Qo8$m_InRk;zAb(1;{4ztUG3W1}
zjQKy|5JXb_kfP0qeUK<~ir^mH@7Y-0au-+nn~2-NZF?=uo3$XU$5a$5jO>u5Esola
znzyQc-CGE`E%Dv?rD(C8%~Z*x^+bt7WBU{4&avyq*AyxmZ5g#g-feaa`)pUbU+||{
zzh6Q8rz8t$L$fMEKlm>B<>E5pHpexrYgFTI7`fF8IqP?fd4NB;SxC2>u4G@KUkNT)
zEOQiKJYRGzi7!dF#atg={dJ0~*D>=4ZR>l63W|+vpQ_Yz5=hrZkb<tR+WxycR^*S1
z0z>&-<HV@q0=4e52Qb;u&ApI77Sq5Wa-0`~ef#`BuZCnx82Asb>xN8=^V0J3sI<Gu
zYV;eb;bzHL$uhR7is-x!v=ij7gEZTi;m$FWji~?8-gkyInYHU;<#QC7!A}rr<A^W}
zA_`JN8Feg?rj&#p6zLEI3{?UnqmF}sCP*(fKoUr(i4a0iMvxXn2rVQ*X%b3+5CRDy
z=S`gb?b+w->zqIP$GOfv`N74NcV)e6J?mNbb3f%>FAb~6*x8+u9K(q?!%M<Kp~P?n
zwpIM}dNoa@j2Du(Q$<_fDck{vM2?$<n=jpNp;}hQ&p*#U^kHkkbX?TOZs;25id=8d
z5*NRY?MrJh5TMy@sqpJRi`_&M&+}!Hy>1O@(QR=5l(%i^`sUa;Ll}y)V%&Jw*^jws
z<Q5T=l6%S_*J|*%lY<x=?}Vn^Okjg)y%3nH&k3KPL8w|Bo`hI;7(@1v=Lt5=$O{2Y
zQ(;CT-h7JyBZA)~!`Id?a&CNKF6infx#Z)TYVK`B9csIgNb7S*x;(ev+(+^|Lotsm
z8mCY^LE@;rGHY6MSa{U?ZKAg7i9`w_if5xO5TDe|2Sl)z!p37LxO6O%3|=DrZpA$H
z!IPhZWSstRyG3C^+-kyJDt_JioIV^D!l#FWCx$d)$6qCpT|x#cD|M9^yN*+z5ygFL
zW~-0R6RYV|(Vqc|jO{Y>gt<jhvn+FIN%F0+e&aUR-S8UY{rT8l<BdSx8=5|p<ra8R
zq5&;$_r$i(yw%c;Zx?R-L-LK!x7(S^D>_nrb)EXRtvjU^!i~uY?g0cW`9RdSaW)fg
z_kATox@f(x9OM6mjVxs*|CdZ8nDJc2hqNDugI1PPamcFSXo>KX$!Q0h60(hGt(bo7
zUJTwDRx}91saL%o{Of9R%+^{Z(=f3qQg&hx5{}&}HxE{64|gMD<Gqk@Nu!_dyq&t4
z&0e3KNCh3@e`X)?Ag`6MDa81NWm@?DK{ZL6lkVG@8~F^#CknxE`W}rNP*i+H?Fyr@
z(XKv;_ebu8K4!m0l~jp*e%sl^jp@x5JY0Afw`wb*vXyBihIlWC$elNsJEU%PDMr#^
zOW>Z^8O}w2b{Ej$mLV^k<AY=6mM6i9ASnfa{Dw${`y6z&cEXn%hx>L%81l+NIddU~
zJhio#<;NN$cu()eog2Sc2J-@r1*kA4;ugtYw05tM*~M4@RNi~vu;YzuiIMJmth^KB
z`|M<@qRtXFK;%Y7omEo@>^A~|WlO#$8x>YiF3)If&iY(JJ1SemHQvz+)UYOyh%OiP
z8y{@5tP&8<n^Y^A;o^l7)RD)uV(e1kJCoH8EKykiKN+`d7TgbexZ%p{?~gBL|FO_*
zL^`})ai)=Oqz|mp;Qo_CjJ%4^qD}xQWm<An-FY_FNkCp9rb+xyVatPn=us1W%#_zH
zgWdNbjPX(7YO+eYuxL0bYShg|*2L|}z7}tIQxf)22GONAe^5VsC4KRoM_Qbh$9AR%
zw3tElBo>sjOq^3l<A(k1-+D7O#~DoIpE5F4&&f+e>2trguF*GI$O8wGCXPa7n?hUl
z_BTfBn=QTqu;Aj>+G_1+U1iU%ZNs_^9iy9tu7#&6GhSI;QLCfnm_S)Y!2ReP6fUU7
z<t>eA?vo*1w|@ZZ79NFG$@E&Vw<ADfqY>{{>*eg=dC}2`47MJP=TB{b^X_SRSPW?H
z^QIn@n)?>-OD!Y|dz`v8cQcuNkhVe7v0Hl$$gWlE8mBe?H*&U1w)2d0Ms5-5S!0NC
zqoj^F*`-&#scVL32Vpjo8>!uM#5^m4-&|TlvlpXG&%;VG8X0WP!KSV`YwS_%3}oiS
zrzX6S8!e%hjg9&uJs{=K5#88q)<HY?%e6n))3gkic4y)%oTViag1*8ZV83KH-Mu9>
zq4TtzO{ujF=3xS9x2^^rr=|b=J8!x7O{}=Ou|JtB#e}K@=XTY|<E{E8r+p~yYQ`VF
zvYOEL54!M-imfJdVtfK3+wAsN$M5io+m?;-*ATPaA@b$+71Z(~FEy(Ve5s;8{j{AK
z4xxCcc{0l4#og7dSitt<x9S_|$t<Ze=|ekg9!SOrGT6;_gpgP`+J_%3UtwjDxDZD}
z$Zl;($BAvTfPO^c<KqxywmnTHAYIL!T-UKC$0=WQt1R^W$~1MUk)cnj$_386VYWH#
z00!TqYTYdGL3s1>X}s)b+LE4<`#cuOH}x^zP{woc2hf~3O#>R>qEWthwbG3`ExlLU
zjfNdeUYJ|8Fa~reW{6F}=sTO_CqShakJfo^<6O0a%y^I8=w&2ys|o`VlwcNnkQNe<
z&S*5RZ+x>-2b`S+K#DqdUo76s5+k^dh)0VM{9JWXoj}CCKx1H%@VJU*XUm30Fk<D_
z{OGK!v~T}?YD>6sI8=l{(;CBrYrf$JYeV?>{W$L{i2)#P!BxxH>5X%;i&7uhXLV5S
zbng*6$lO-37B(`#%bQ#$z_LATa<~t31;1Oe_e=Wp*>(7*am%s}xdH#g=N*IJaPY?S
zZ3X%KIvvT?<R_u8lKH_<W4B5n8`rQsCT1j0y8J#MKpm`I$gkpIB%*sv0@G^TQ}P2D
z-Oel9I9{@O5(Sp9kIM5VlK*5{7L|i{aYos>{enAe1Sys|fjL^rgF&Ej15y=f`NPDy
zML0pzE#-;b-o_1Kw?f^M8U<f|mLBUNQxSVEtZ(UQu7F`<${`!_Qe@du_Uz+w=%;b=
zkZt0q0#zumUAPb?y_?NR=az)7TmkA>LClD4;{4BK>DLwCoZHoOzk47*>n3wLS1|r-
zymT_I2Z71-=9crl-p0)Zu=oDGZ_%)A;;I6bG-4}S{(EzAVkvS<fttXy6*d2d@8e3h
z-m$llZc-gwC>Gq!v>iODmRJ3>@8D7qlzek~gM?9n-VgVkzed}Ok?(F=3!shst5Jo@
zO`H;>9U7BoC9a&Es9<wxrR<AX6*z(!fMKauNoAGna_!Z@CKnY935sX1c`DBQ@S3pJ
z!B2}UNK9dobl<lq>{DKXc)3|Nm=gqCHY)64!;Sd)9Dv*%+`*U10vJ%k`PK?2On`%i
ztn*NAGx8dJf8tkpJdfMeRC@yaO4`-Q*UyMwoi?A`fBerb3U@Sxznj)aOT&l&nTE{t
zOO>jn)9_Y{(20h{ddyF6Y-hUnPx3iA><{ca>@He4t#sF#bn)i>?(K^$hLLZyY(QSf
zIw!d>ZmP65!z^FKchyAil#Ft|9$1;Amm&8^GtNn_iH(h~MlC+@m0$27b7ee-1)s4l
z42=F}`6$K&%CAH=&_oGRFgxwpA_R2&0L>DkE1Q=vzsSf+j!97OH)>_b(Nr+_(|dX~
zpbLhUYnkkGw7L6X(*1HMKLpq>%Z5C(!s3Z{NLq-05rxvhBso-Uz3$-59HD{y+nO+=
zF%5^MLtC>4bjc*QoMQf=0Q)WxBP!#W0n9l&)M&bfeT`;P)+_&NvH3VdD$#_>XM{l0
zTJvD{aMeI6?ejH59v%t}4H$EwaWP4GR!M<qT65PYcbeYeX}Mopcjx$;F<yh8T1Bg1
z`)gth0jwxU97XMV$cVF3^xF223hXfuX2y1INB)>=niuzgA&<J9E>9gGYBWDtFn=a^
z^6N|!t%cjuI)NS11I&2g2P1wLa6aDf0l+Vq1PuB6Hg-A<3>b2sazMhY4_V24bsA}R
zUX0P}Sz8gg@S$Z=sa5nkgfRD~mdDu&H6Ps3{P|P2nKDs5^iwW#_^2xYyT2)6_jcK^
zzJoJG(5{3YV8L`*)h;HWo0LJETkRL`Xu4ar*T^wwm?wQwYi7H*&n*dRc!|7tziFB5
zBgG+Re~!Z6%C7g5XBLKqR|g4RL%K*|Ei4C(bFpz~lN}ehec38jUjiEYTGi{m7)R6P
zo7YR4mq_~Fw^RT~`G&V8+zh-wZ|K}qwOO>ej9*;~PfY3y11SG9cnyS?$%bL(pv9GN
z(D<0{$OAw~koUNtuHN~#8+B4f|M1<F@M?0L`4A0zIoz#q?1s&+4v_5m&ld9ZjYn5W
zUgD@Hi$s6+T-`+F-of4hm<C3QiarIvG&DwyTHCQ|?rPQ__^x}bDCh}dpu+Frz0YiN
zq}#^z)4j3hzgqHZL1sv7uAr40Dpi|dp<MkP{?lKl-5wM%ak8^Y3zO@j0CvW_w>13N
za`_a1T<?D7R{VljzKL^yIus3-$(c&c*U7Vh#vRW{^1dY<d<(Nx{jX4h5&mFPJ0#=P
zq8o$IxVFmM5zsdFax}ob7!p?G^q1-T_+cC86U`dJr{ArPsbrq4)bJ!njem`=K<bVV
z15Il>E{~1e<lj+ym}G-ktV8<M`?vgcz3JI@rjZ?FT-_jXi^;x^Ifs>_o6TU@cl!le
zw8Gtg$KoiD?W1UnEF@szZ5*elT*Lgz*8yPP0!lj(zz%?@#4I<3_YB0Sypo!C37RGI
zy$yL_4>fvI3im{Qc~a=pzixaNxN$ypAe%F-DwO*mH7YF_iHMeM$<cde7C=r(QGain
z(KB#A91+^TpGw-WP5k_K+~?$%YgGNlEi!kag#7&{CcOjt*!=}@&18uXdvleKehtjH
z-U05-aY;H|DPL~op;lX1J%_mn)$VOt8)HBJJLF@%KV&7|%D^={ClW`K{JVwNF02V*
z%0E=2V(WT~l@x6ucaQ8~VlfFW_=32F_myp9WqNPmv^_KtCT+)>6e0jT1`s>!RgsGt
zU{=kORD4f_Da@-oS^CCHF;&K<4sOpyk_Cmcz7#FtiZ`~yinKQRHHOsEw4Lde4Gqw5
z<$pzRmwU|T(*KhEe)ucSR4#!aoV#I*MX{QXw9BuOKR^>~n^yio6MzR98P|iMlJn#M
zz;QfRIWBq^t#_P>Z}j3#vY*a|B)J!!zM>($jBNu}Ge6sO%LJ3{(pypFDgq-G|0V6z
z(dXCB@haGfwC`xzDknn!>RPcumNU(c>leIYf7(0$)qdYnOhnh?y9Y8{U8UL}bF}VD
za!jkOsDJOA)NuOzrU;Op;&S=FH{eqNb@}%O%1g|;_^xhllG|Uydr=2}8dn6VWZaDi
z0X&ujC_gVw)XAp(%gzGEcq?vb%ppd{7{V{7{<|tZ_Bic%h5WM7oA-Fv*M&KG8IXh}
zp<`_?>2oK0ll?rH`!h=_1s|F+z@5UL#z{pod_HyTFPU3@|2LWYSRu>ZL6tDl`wNnG
z$>a=4sAGsG2-5r73MbKjDlPBV>CvK$o;akm)m5Nt(G?F!9<{4!$7EasQ@sF0y0{{T
zu>&Gki|XCdSi{2tz+P}X<PIQ<8`6f_4#$6us{xcK@fUQ}@(EgachlA<b|FofHqE||
zm38J9DRXOPzXpo4e1O}ET}*IWNWta{+6xRP)OX${Kh$LDp4J&oKZa7#Y<xvO0V_vy
zv$2@DAHWXhTmMA_Al1OuNW`KsDH#3Jd*CMXKAUc|M681|5pOl5xk|qA6uG7~0~rdh
zv1=`cXxGi1S$GmQ!u-_MN|dU?!x2DxUSL|wcuw?)*9rA${nf=`3j4%odF)>WBl)T>
zA~zld1BFmLMV=Gx)thzfn8s;4R`k%De*BPz&k2CGau5`@3k{hHzagW7VPTkcUoN=5
zDlXr8{v^uIni4<vTxHwZ7;u-~-hpC|w{ftX@+907<t1;3B{@Efx5g&WW@#LVJ`U&Z
z8A6(e(9(7nai9J=0|;>KgnU@dxL--E^L8d}amK@KX!YoUrMG`bC}l>jpz~OPi3*&V
z_|$t1QjCK`L}$=3Nb%G7>%2!e<y)LIWTgOo&+U!y4qJy-zM~t^^7$#G*vxuxLfwLC
zJD;dwRa+)zwkt7qCp>167<LJN%EMy%!sZG5f;ex=)<{2a<>XU9z9Vt_U!7pS<N8|<
zRv}3*An9_na40^!CXfGxJ<0Z?iE%hr&k#^uLka79ctSL96wn(;^11z^Wg3mltyH)#
zQ7!s?b1M8-a`%zBAw)_Dr&eM@uL<`b+_fzs^$6q!q|nrf6|iT95mmai^fUS4n{144
zUd3c(Je-%tK9L$Mjes302F8+9P%g%qhbmFcUdOj~b7_R^zBVno`dCFr^ShyNg-mf6
z+U0{pZ)HHI0UBS*E(l7zgmC9QC6E{1HfqHbSyY<e7L0DttoIFc4c~szn8>ROcH;fU
z?$(MaP-9EP4#Y5iJ{Q&$>4zB7@{q5<@2_O`X;y9i!h516e9f$Bj2hZ^!UPgiq-2Wz
zw^{(~35{3Tr$(L0jUAj3kNi#V=9p({U-=gK2=w=&!O7BVVlr(rCaC5o-UFHgoM-7!
z38!fu@{c@POM+&EM4mI2+&FpRaQxona0sz-k#PgYOrsyFt&X=ruz)zAgUajs)%3AR
z{-VH+Q1e<K?fs@4;EbFMiQ>JPolwdfJ30RpI6>(uV)@oSP_#F%=)8%v)jWCnyCv%A
znC1s^ER`iST>xOjr%HE#Ojbt|WJ%q_@_Azb+A<DrnJ8+>J!Mk9mOEJHN0&B9KBjCw
zZrRi!_jZxE*O%i?zUFTII)0|*FIOh;9K7%54fK=OwVxJufewkY*keJWPA!vbQd#;a
z$sJq*0vFr)k7l`ih_$ntd)P454C<m^FlbV~c>3=~xsa~#p~%^?$~;7gn!vXeefYa^
z)3A23t{r`yI+GYu+D!i!!UIf%pI7cM${wCplekzS-@ijE^Hyo|ZIhYd@Z`U&UmBo8
zsR8VI8`E%Bt|0&KZIPj+R=2Uf`O!C-Jpt?kfA_r-S+=6&V+H(yT!Gi$f6^RRigZJa
zcXJmcz#LEQKJ-@#MFD_yBwp|WP(I!WJ7?jc7u2%RJZ`#v2BIW4@YONG0Q=a3cZV?l
zBH<RVgiWGt8Tv9|fOA}}XW}dL;VJ#y9(%hmu<=vfAs69B(^g@7qOta`7NKFP|8m3L
zCliAuhVB*nJZ*!1C@KhuP2{kZ`=cakCFA-W5tYZEqj79CghnITe0RIGk@_J3y#Vei
z15ijle$aN#hF~5YhgkdhtA-?(1CrW{;q}M&r0<s6zmpH2t&Jnl(r8SwpE0nz(sD<?
zk<z1NKVUAx#aw~;jvi@5p=CcHfM&naX8zAnU%k#9z(Q%@nP!zRw98kr7zOy*9=s~H
zi1vw=jVZ<q%9&bAMkHf+?;9VMMPW$)>V_Ov-dP2U#u}FLs1~75r4~sBA|ZkIA#=oK
z!6^IntW?dCx5+h}m;NWy6$tpGvT~=T9;yr}5HK!({5mb&2|h{-_ASEKgwTa0!M232
zU+sz39v9IGV^_iww&A}$BpHAY^&Y?*LP!eRr9D>3;_A6aBo}1n)BY{;{(nxI%_%Zk
zeyVdoxFVKUfhB#V?#-xn_ejJE__I(+U&+C}JD48M`2=7ZUWaP+VDwhxn*V(lJVxD&
zJNkg;iw&nXt<BMj;fO`wGMKfS8qDitnYv`u$={9iC-Vn>ZSxH4Sv0Q6CczPx5$h1V
z_y&&#r~rsIAuRs1SOIc@1u5jgPT!Vh2U>E5Q)1$pDghr>sowA4q7}fwg;0F1GWR)q
zkw(T^7srX-R~AX9j2&347#D5Ai-_)OP7K8K#tv)@G(Ug7TG+={2jWn3WxmKJ^PuSh
zd5G9KF;K9%hZgS4%6ICOOswS}^Y&K~+TV~|N7N0R#8-LYhP~D<qF`Q987!pcd}}CR
zXyr}ZJ_$2txk6*N4e55MaD;rOvK)E`k|=2toIr&o?}hN_lS6yL_QYxouk31ysVo{j
z=@CD6O~7l}HhssE*TTLEq?f**0TOJ5lrI;2l+|dH2lRycjgPl^(kA^YirpnSzn0hk
z0D{|thCF4uDUu55r8J~8G@CoI{D~m<Mp)STxAW6LoS8J?N6R=4xTnDCkO0)|`T{M@
zc=EZ5Jp&}g01A2n2)TnoOPjB&w41<rw>^Au(X+PXf@cG@n4~Zu?wL`Uj&C+?@vJIa
zD{mY*px!T-^9+#qq@1nx`+_%04&|dUedHDcdKtShhGKJHJ%)6iUqe5!(Waa@qsBQQ
zmII_tI@1D5Qi5Exoinn7;|TR~VGVCa3F#(Lp-$+hh~j65$9By)i%)*=Y&Y5yOWaSm
zc5T1-0f@lj;Ybp5dKb3Erft)&xGc;3{3^X^h@r@6gM-Ai#T0W1yK}7p>}-uME2o@)
z%a58z*z87D-3KD!z~^YdE+A`!(=$;7Jr@ibw^`J?llDec7MDA)TAL*ek{?TA?CqE`
z+c^)yLNXsgG97wj$m>Y>_)p2@lSo2f4#j~wz#naGNG~e4uK5ogmvk7*xe!NM8x~ER
zl5WY*Nb%r;Juq1n%*5`zxh|Sv887HB-~gh$F`fX2`f7N{_bvbDIM09UY_@c{{wtDT
z(hOF|{+x1sx3At#%UicMq^9)ptx|z^eS_dOk4`&clQXf&{`?hRUEl2lG5{L@P;0wf
zw1ttpc+c4d?7qEh>E;DW2RJLSwmg)YDhx)hjt@g>Nfe{ygn|~8)l7r+T8Qltw*lZc
zsZfU%f-KkmWZP{tNSeryetu+{#1H98>!SwM$a92wzcmIx@OZc<ImCSWJ39zhFd}US
zs)P5*RcyBgSU_cCAmEc?)t$+806~1u#;}#Jc3k*PB~M}pX5z@%8Q)Ua(EgK3t)vhH
z_q@reM`&!XUgI8De}Hc^0GX*sIIk#p0^sF97%E?q`${+DCC&Sc0Un{<))C<*yP9^8
zhAwMYob6TF(z;1hj<s4Fv%^XO!fW(~Zf2Jv;~&Rg5MA0O5iQG?@$J&|>({>g4S9D-
zC$~(nA<*8?8g0%&TE9o=d!Z6-O0Jf@w%-o2Tx8Yaxo>U>gOFAKaE&Og29V%lGyic0
zF>azwgcTiFs%i8jvX?-tXZ1iysw8%G7iL!9)oWE&N>K-7L2v-Jj*TdAcAD)*dt*lk
zjCDuf<TdD>m=t|kJ@s3*$(~q!^+2oL1lHrBg^NX^I!T6!D(s#iv9D(z!VX<^5Zqm}
z{47mE+l#t}Jk=zHAyo(9lSWAay#sSG%LP5O1EVX|12fM0hpVKy2%lZw0tk@p&rZp{
z$t(q;in3+BNmv1Svs<z_$vW3QZ6^AcHZLpmk}-`*h>xoqdk_0-NCWX-e_H0xW;8?r
za%Eth?gvRZsH+Z;p5$-j3f36~6DS}GNaQz3Gn_sp((;F2Yq9P2bGm$=A`Rzu7fN&c
zS4ORS2gsuuLxJ!mj$_g=BF%6o|CQ)&`~O8R|E1MCUtwa=vod#3!vi?02>7&ImsBR4
z1A+!*|BE}MWaLSY;@5(M|Fx26Bqe4GesbydS!rbAslw8U`{9JC(m>z((ne(KZ(Xwc
zPfB&+QF68ycZEH&qc?7~MVi-Z!5ke2Uhsf3UihTizQ6CiLZM?K)*aO1DGQZ#R+2j@
zjnPyD1BO``iJ%9GCTM;MrmQD=;w*W5k=4DW6}B0W&FGQ#j?Geu?qSIWuvPRiQhMq$
z1vo@3ZGX0};-ED8Q}tOw6ZQfr?f>wFiXqQZktPk7R;|1S60=^emXYo-qn6u3xvvAS
z+WwE&>~;^?f{^~?zpSbMSI6qVX~EThnTh&ewz4}=6BTGz535hACycs!xqEqsdHcYm
zI_YiSLfRa3j(LSFjdw+m)E<h?^7LsWtb<GbG*gNxCl7CDPVt_ye`8-DnQjsK+c_=)
zF1?s7GhL}0kUal}<T>b^<UxM1aG#{)GGj}DcgbOhknql4scv7o^4ine+49=-#t4uo
zp*zbOZ<u+OH8NM{>Pzvg#&~-)jRZ}&!TZ<{Bdx`q=SgZWTG>P(ck{I4CuX*b*vspq
zcr5Z7ca^8_B4*v%6c?Foh0yG#`=x%^Nku@F6IN_R7&af9W>etn#QV&iGQ?XV<c0sv
znB;TH#W&?=;+LbRrFsi_CaukQ-D8`$rcULN7rH^=8F*yvHRiXbkpf=hj_&&7&0J~>
zpCOODA&KqSK|QNqE@va>=!w(Nq$qpF@eJ&Vh8y4s%K9B^cu(W<R>bR1n|rfPZ-K&-
zpe#|#{po?8&9KJJm1Aj?Nfnl=$cVgLQzFWv0LhQwp3!CRerQqB7OE{tSj_4h;Rt)p
z5Qa%M<WhYrk;)57sfky_6soOw6Qu9yGPFu$3dH7f42>Ko6E1PNrVF^u^ZsUIK}bB;
z1*#XcN!H*q2P?e{%iz<T?x?Ia!>tzw>=%DaT3;z>%%!BB@EYyqJI#0x6tcJ@W{6q~
z<1q4rar-g;4sDaqHjRe*9;4!H?^PCS{x>`_z@E{~IuF%bIQLFNymj|Qb7^+085F@u
z5^rJ}OO+|hTXu04xQhKbgCY5$#43{ij=>U9$j#o(8;JRqC~u&|jeGMl?cQGf;MTiJ
zdX3pf3Tn_30nXX*(VMib?8KSwsV@OcYQ>4MjvOaUzV{X*D(hKc1Kl&xPumwlHxi|>
zYwT2TjceaDlu~FnlGfDBN*ep70!XD7g9C<Ehp@WFVZ&7%$<;op*edEWLUb_FE*#U`
zh(3i55y&bLKFNZFu;Cx)=kvDQ5mTkQ__2Vyp8aJ|m1u+MO!i_~arF;d-J??>f`TZ0
zPC-;Qw^%=_wl+kt8r(XiVp3_%dtP#PJfR#<;3iRu92rv~O8DPloHq7A;X|_$c0zXe
zmxOH2yfy#oa7l=u28dR2=7$n^IUwBgki3J}|KwbJ*ha_Rx1F27^e(*dB;uM)E5()Z
z<Lce@{~W@=`!t`bznu;~9{x#u(__2f6iFbzR!EME3q*)Eh_%P5fiu^J8`)p{zWA8|
zwT|_BC*Y0?)-&E&DIxK6$Va0@Bnha7e-dd2b1598;%8eib3K$ozvZ6jt$Q4SR+6mS
zo@icPa}Z)-gHUrE_}8jQuXyP&Dcl)kvs3bWZyv+zP)uzw#f)F0YJ7SR$+Le5aJIP{
z#G2#O%IGPzceH3bjlSQlekC1%h0lbaz_*1Q9^T~;ydpb2&91fEsv1_C!#Z?#^K?^g
zNv%Xn^uo}jS1&eY1dZ{~i%8cfw=yzpe^U97c3BEC>Q(1kS6)Nwc??*yH~E?120CNT
z>%-(xg*nW*yxT9=?P<P(VvUK$Bp!34{%eS*LiY4XYA9FT!WbeCTWphJW6s$8j66oy
z-MuQokM~(CKVRx_I}-xe#dH$)#}Qnk_c*KBgpYcl`R%1U`>s9uc6ZA6-=F*a%&%nz
zny-@geRt-rsf@vTj`|<J+=w;(7Wngh)y~Ife$IXT{qEBW7ar`64ZQx_q4zf5Peyi3
zX^FOmU9i#6U{jixOBqJMb(^cOVvs{PiR?{kIR;i(8{-jO@(`PF9Yrte{t(-rxg1Z)
zo($2cZ7nXIoTv6CR<25YJc$JUANsgP$ZExfqGFCUobGr6rh->MV0hz=1$!*D6;<$P
z@+#OnYmy6IbLmPm8L@5)(N!-SA*_$6Ppe`3A7bk@+ch^dz(x5raxnXq1<eFhLH48^
zSiPX8(9WV1Z#|J!tM!G0$qDEl)<_LkZ2x3HvPa<qd_9@3XzF&o>6+SQ1*BfP$nz*N
zxEVExM_HJ6An0t>9SfHug_%)nSSKotWgF>&vO(QG-BF7g!=3R;=WV#J@@>4Rdu42C
zU>$wUxuaQ1WlgX3;z|7&?!YNTg?2;7PSNuQ!sm#*u+-v(Ys_l%h1c<cDT2nXqD*=4
zga!PL`4GCm@=Ox+O_Oiimh0_W)BV{axeaU4__KHTYv2=|>xs7!C~bdZPDIX_SrGCz
z(x9X7C_A#Ex7VNvcDX=FCZ|SNwy}3G-+-)?<yHskF+cWYv=3roPYPSE6^#B)Gq$d}
zl^u{#?sPWdwej_Ty9Z6ajF=|G@cbN>m_HpmW7uJZyr64Y!qVKs<Y{JWn?+udOwa8e
z-$pvS??%>l3%KCLf=JNE^h}ch5!A7u@#%s+i7I<+BNoN7iM))OK&cLt#~6n1JA|){
zEjUmA98h0>5Zj-Qb#jD@6FkerCbl)CTDcG%qDfKS<mb?_EPBSPTKgZFL3f%f8VUaz
zS|3hRsWg<1c7i*2KiL^xs5zD&NfKY3U6?qBF33rnS>a{6KJ^N>S-GRhw~4IH92#3h
z0*mRP9fvetB*yG3@2a-4?pi{2#BKBG5c!}CQDfwV^jj7tWTjO7mrYldC(T?M@MTR)
z$K6TX(X3wosK16B01VlvP8plhZI0V-r@vfNWI!D|i8#CboARokTm3=9wRU#Sq^aix
zhUQ2dINBef&~)F=H`0F~;<auE6k8*oAIHhb+5(-vS2&UH%JU`LrLIUv0akl5IfU|j
z#C)(ZsVMA6{A`@Q!<4PkrUu@^{v>>{cW}{yc>WIDc=V6<IHDdh3zlDP-fi)j-HtUr
zp>fsz?DFX7LuH|F{b|en**y`nVM7sxl*-F@_`|D&iRZhg9Ppju!F|Gat6oLSX5p`T
zgY}s9c)H@02*us*?2p*B&$jCb7-{TRC9**^{V??llSei-rvc{-^sH<Y>p*SyMcJXU
zohjBnZ$`sHJaUSR`Ons`DbS6)t(+<~`rP%i`lHj7OT!Olv=)My3^y?&Gj|r+LB#Sx
z4rjvfgA}W&=93;mEKir{I`otJ9Q`~xai&S;;2v+qzHMOSalhL|y67g$4oi$U$=L5g
z&r#H7|5!py7#}mp8hg5VAHys5s*0wMDwdY@B>srZ`7=5#dk2=66jrI!6+~*=*H8ST
z{ZqJ>nblNRhEm;Fw%a-c<q$I%o;680+Nu6%QisOTC~_&wX#^c>I1?Z$MtfGj6F`<-
zSts)5K{LBvU0SO7aS%0jq(eh%1U>1iQyUuXl{0CAQbiklFU+r9Rw11!$}{+`t1b(D
zzb=XtEGjsac)3%BBdE?VWF#mq5bE`89rA@&HcviSIIM5&T}i3`<ZJKHV8Hp9IrN^3
zYO?Cs;iLr`IiYyWe?DW1m^*RvG2?iL4QQ@Ra|4;0m|JpRyP>fbY2O)ecdjHRR3m5d
zgbw;Fqm)Z5)81%P6~3R0F5RcGvc7tPxq$=@k37r3MpuMu+wlvz+gt9u26ygRLxrk-
zpJ>_Pr-LpZi$o)=6MruBaI;tcNF`wn?Aq2n=CJ&6uu15>1!|PX!)N9yVeclI=!(`|
z_#Rg^|G@}^4=OyG%_^Q09PJ$RZBBPz(~P^7W%9}Lfw2U2)X=K;yL>L9qMQgb-P^>l
zC#Pab>2~1l=^#Z);p7oL-s3*?C(K=^Q`Q(Gxzvy)jM`l4jmJ#=V*oZ(npt!N>D0pb
z#kcU6S1#%t$O+}O_BAOq%|QI%hA(X?U6}T<o6jrr!bsdP#XXH9fx7B548pn_GOUe#
zvQ~oJ91J^YHD|@{z#ojMDW^rw55b0jAZaIOBVO-An)!J&=cW-)$5guCG0}l`QY>0C
zDEg%jM|!TC>$UP3#s227aop$X=1YpRBFAh%Vg;hJyNhqKG7SjKZ3~F7{^lHJ^l9?w
z>!Oi_+HgWZ-+#8*j!&@PxiG@J*Y!_n<W2HXV|pErb?_M>g~jbJJ+~nuTxQ}?vJMTs
zsii$R)z*;Km+haPgUqJ}CwZEDbw_`iLX*5hjY&^)V4N3fuCCwi!l5VQUJea4{0NUw
zUs^iveC;Jpw!+M=WXLMaMc9{lDl%&%;3K`L6#7pucT4|T=s$a&GveT>I~k#M2Q#>E
zkImy0V_!s$+dEn+CZ3gRz#Kn4Z@;pISxl`R<?0^4-aSB|Z@C)Rn(wFAWKCAAqP}mf
zaJ<6@&xUm|H7TgcSD6!`dL2i=_Lo;SFk=mF)V-Gosr8w<y%}9?zPBtp3{Bi-B+D2G
zETfseOx?=_6*uy{-AavSOm`&m+jxg~dL(i8l(2oL#f@J_3ug$IKJ;AhST^Ml-u148
z7((vnMtfG4>;nx{a>H`BWP4Ja?wRDBUT%1(c*<W8Rwk-GJ{_S-_;gK&9(5jd?qa6L
zlbxKjkM#6JFx>q4JT)`*Qsb0Eu>>y<d?ft@sCJrm>^w#t7|_+P1Db!I`AfLyUM<P9
zXfg)1=@UZEAJZru5<4BvwEj_r7*|W&_n(U_sEwAciN~`OLj3v1rb95%khqmc=O5;t
zA#`oI?&ezTpPBuAwfWL|`6)l>u{Xjx07FOQI$6(uN?Mj+sHHVIkw0)R!;R*fcvY`c
zcnm-g)7idz;_S(Kos<HWK}Wdu24p7bqGp_TXMSHQ%EAnt80C==9B)`>?`sx!2_%M8
znlwEB;!hJ~J9_&V`~C+6aY@hKfdar#KN^|jgu1BN;D2EuvD_Ut<@PrFn7?F*x42fo
z5KriwyQA5`y0I!85<Gv9ilK;Cn#@7R0L<(SzOr>ESd?QM@cG2_goq~aQpto(`6klB
z5G*tOpU>aPCWtPg3>|T7(PPb|tVj=g$7Z?BiOfBY%{h~&a_3V%rfZT)U+?2~1dFc9
z34ror(SlrOZ!LpBxH}sZrSXS`RyAfhvQSgwGN-d=sI&>$vy$R(sk=%H7`CTpeI4YD
zFxC5EBe~NY7;u8$npu_lbw5Mc;GyuSxOvqYtM}37gV3JSYf}887p^>;506?nqB@Aa
zZ83G%XUsKTbf7T|A(0iSCZdTvad9>1B5E(ThOb&H_o7?65j^GtUH~j21~ADR^~%`R
zP$S%a`;~G`dUn8C>vq+gG{cK5c-umK(09J2tmsjVR7~2$P3B^LB?X_jIhH^+3NS?E
z0*^i@_kw>p<6Jt#*FuM@<)_g}p?ek<=1XQ5hyiIo64ij6S^py^8=z5H@2`Q?bJJ?Q
z2W`;w=;==nSAl2j_fbCciI~Xj8*}<U)dt3R(|<T)0s^qr0>D=6GXPs}=tdx1QCw_0
z1vsDWY2449$NL$^`dw^?A8P|oInmRFzI}BOswW!p{zl&V8YpJ8=aasPJ)BD`Z!nt|
z1DK%FDfN+cH_pX-nNbzQkguwLyj-OWVE+;E+S6$I{FNyIc&%&*?{KW)T)f6SJZI&z
z3+39z#&^8JBIcnWe2y=v78CU?0IWf}>H&RgC;#Fwp~krM?l$cC-r-;9^-id!AE%5K
zzhu%6ku;FeFD=5$v^Mymx8ds5@1nEMsGRamI(|P$<39Gfi<a9Q#eC}z&9Y++-tp}d
zzk-pcJIqF6k23?9g?=gbOKl6H@tPM)#wte`0U<gue#}!H*qR7RVg8#3*6s1audJg5
z<zdq>wOle{S`|;X?`Ts>yV?aZ5af5l@SV+(=?m27)cSozt1ocA;$(60@YCXbLw{qe
zLG3hUIpFZqhQ(xoy-JM;D!(C&M-8P7GhGZh0Q_dP)@8K1RR6Sxso9W}ry*oa^;E8A
zj!qbEn62h3Tk-$!O++e+M2?@EWLh4MnG7NO=3vzBb&vDfcCYNP47Y&><I?nNU1aSq
zx+(PsGx|e4HbdpP>9nAB7qM&>I18-Ml#-?Eq;+frPAsKLY|+%og^8Ok0Kl5)aBq2L
z`3rfwPT6+;a42hBjnH&z%6RLV>KwQB3n#m=K<Rsbx!;0>mrzF~E<_5tAT!eAeH@sX
zZvgtT%jP58gZ_54u0j5tJo;OG714QA(&dKj(@e1arATb1d{cr`QMQBj#$4uui}>KO
z{K`CuzfxstRbb%U>7coho9Thiuu;wshJdZICoRFu!pV<Wg7+8jb}QAH%Ls?d9M$;P
zP9+2kyrK_gX4fQ9fY->o`2%M9|9}4X5@^|+2iNF#%s`v$6nhslO^3553!N%roNw3n
zX#sDT9Ey7Dj-oh>tj*NZpFFws3kvI4!~zk<l#)vbFBbO8RIOM&_TC;0Lv^9%3&xlE
zY~V;p&9a9-Qn0nERk>8Sqe_^8w_1X=X_;}rcY7z;p)Y#sSBvNlw$+S<&r6yawkv;V
z7TZbuR<!4%Nd~wGJ&DVm_w3lzn~10yu?im=YAC)O=@=j?35$Mt9^^>9Dnwbl5C~6u
z`%<1v-qoq*Wj`(kl0J74_9$NSRM7#8t*{;HN$lTOgyw!!x@~|-d77~@KAr5*`;5$R
zN#NvOC%^cNO*2NLG}(j%>ObjTh>QM9W?{pTK`5BbNbm6?Ka2gw^qGZqbE09#I~{uB
zg|5V){ynqry2G#(hdHA)fs3X5*jY;%dS3W@nZQ=GN)`!@D$e)2C^J2I1bm|7#!TH&
zC)`TnN89+F4<mz7zUFiIz}lmFb49hy=t&#HSK=DY@Wl!-c=AUX<98hglH=kNW@cJZ
zs-sUgQ?NXuQK)ZB=fTUw;-@eV+J*d3jqC9nmOuDCoQ!{ecj#Ed#mfr(<V^k)$(+K{
z=@8pC#DA;!CK{wYe`#ffDqv}Da1@0fOY1c$qi#uIs1W}F>Rym_hFzpsxgH4lgq}&<
z%X&Z)^qR!Z#}(Fa>>Aj_j&J2Y)c<tu^l|t6j!0%6Gb`VJqH1q3XS4Ew8ra^xmV1m#
zeAxcRV68!pZ_kNqOppjf?_+}c;vk<AL}^1dI1QDRUICIIC;fP`$tqeKf$`w<t8j12
zMeUCkZ$R);4rA8tn-waX6Jx(}5ePFFjPzxr3vwroeFY6qj2&!O%5)CsF4(QS)9k$*
zIh<^c{9ZyfY*D&s&&)|0I9;Qk=mET%DPlfL)7s++W$sQ`*UPrV^@8Z*`)mmCV+2&+
zveB!a`plQ(Sd`~*+-3lJ(;oGLFg$&vkizM0d+8_s<#!>f32})L0$xN-`V|WJFCQ8S
zw=xe|)|?UFTOeuJt;p)Y_Ae}KiXUkv!Jbf%DFXDQ7R@)0dFJGM?T-mXRENlMMfTwv
zS-mq5U*cRm7^%|%D^cY@OZGvRmouyFI9dJ&I@NL~hn%#YlV0|hah<M<yp@w;KGzYV
zYTMtujPRg8nY<68i?cMvSq96rqD<}r)=_JR%9?5@Z&;0Q4y6a%Qq9%#V;$mqdtW`6
zQk*hcHV3PtCwG8xmm=djBUXl2sb=!81o78f<4GLyTG^jIVWWGP+7O-^=q~VpTCxtt
zT4wC~4hIM&qw$Z0xIzPH$?aExdE;s(-`OUe4ZVmGqti5Eh1mgVV{bd?7T<S(F4PgV
zRjjM5uGxz1HTsAiz-vVxCT@PfY*y_I_n;e;YwckMwY@!lT{kxrRqv&vv2?LMZd7s9
z;)X4Qx01e%$NauS8+f^<jt=<wwcYPNk?sv30*#Aekd<%Nw2yuInAmN3*s7FODHKGK
z>;?_HD~fu9-Yss^fgL~nAiFH1d?rqgZEDid7d1@nQI|!xg)LZ~>*VRfE`bl(V|%^q
zwB7PA*iwH|&&L#FO&;oy{(*l$1cRT-V<&1df1BNbr7%cm$Y2g;)MWoUtL{J(Pxz$F
z{+Sg}H1&tzKohB-bmz;+IrUn@sc$lQnb<tkh-R!cDzd~8E#Jh?zn;&5e_;CQKp6#t
zRY@_bAJ>i_XyP)nz;>wd!VAZ{e;}Zy_b@a3Z$}bI7SZT1_%ZM;=S~+tI}LH(ccMAg
zB0uBROrs7s+p>^FDeCK>zAQ+=&1X_h=NA({KWs+Y_kW%4;%S>zME12k%pH!n)iC#7
zqSK;c?o_n>(KmEWc!=GBra7I6i1kdt7)|pHs4}O3)=*cD#+qxG1F;}rzVGYto$7U-
zxdzlQJ|9?*7uXfP+LAa(J2vK}Qh?a_>&mkombCmnqZaQMoOyswfMm=vpPQ!c43BBh
z>_~Bh7kb)&e^f0_>v*uYbZmMTco980Zl5o!wNbu`x;!_V_=;@afp$bGf*er}#Kk#Q
z)nwm)CX2-yla8ZU<CrqrQJzEM)CI3u(-WmJ&b-sg`Vo47Ym$U&FWZ-W#adqkt|(S@
zWIbJIKHhQ8QNbfc;dL**b89JQtH|?7V&{5~3Om)sazf!eI;Wl6KpRf<gC!<Cp8QQy
z7Qv?Xl?&~4U~8h4NAX)vF%v79&(zarR|6`YSPZAnRrYl~z94PO{l*7~UivmGb(u4B
z@jB4CnBsOIymAYLhwz#sFDEV@j<L`2x23W@N1Y4*L8j%@gf(m7N=%?<OtRDqB6)oG
z&MLW>jyB&Hq|@M~!98D6WH*x(C&q?gj%)HxK#Hp_@X8(jeGEnJ@(<D0>z+AGS*F{u
zSPJTs#t+dY-Pc)q2@^W?fFmS}KV#onZ~ePme*I=k=w^#)AjrRP9B><LgFo)%Kzy$$
zRzI=L8fSo>;|g^SC`NZR2cyHZ&<+F6NkO&Nt>|qb;H|2k5+Zgd?HQmjlx9Yu$Ja<;
zmo1)e=O7eftlS#?$ow&(Xg2+T`P@ttwmQKRmMDLic;=j{2W^?QPn}X1a?!YrueLig
zcP-jJUNuw;u3ZiC>!x1?H}u%o7%vu_?D?uvy%1iSiXo|I=ub3OoMd=x+4?<IQ)51a
z2)@Igw;z$;w;?{R(8T*-I??<)kOi0q1Q4S)WjxFCOt?)thh1@awPl6f$$2gRZ5h<h
zp~x}c`t);h{p!6kSBI55r$h-?UDgOYwyfuNax92`j%ck|;^DouzgOO<*~!sQ3*rh~
zMnuY1aUXzPipO?}+j44TUwT%W{Iu;Og~1R1aQ|g?)uF7D;LB%;9@F6}gP`}H5)&=1
z??v$Pc@hn&#5X3+<}_X?$2Y2HRxLnTj#~-9-4+(cg6|N6#_T_PyNI;q*2NVHP5lo$
z+zM^-UlkWy%5@Sq^43e)V);W(Lvlia*@-s^49t8LN#6hQcqJ>v;`(L8_RzigY7YBk
z2-hvAQfyG0XFK%>Uc4vX;l&ZwxcBSIZ>+s4GxGikM;TL0Ka1zUTMdH54}WUaM`3wa
ziDSKQF=1V&eV?TbU+$!FUU+6PZ8;z(TwTF^L5kqD>2UERAIstxmTnq>slme;GeEK8
zJ{|7+VA1(=ou~VXEm*|6jRGJXS#V)~uvM=q*4FFAEy|=YNjVWW+o<W+J$d71WbNSK
zsZk>^Fv-o}#5`zPh9NemfRDsb);BWtXn;6Ff8IwRz0s5BeAxp3d-7fe?=xKHl1$!A
z5ZDwfFTc0iE4f7b&EB6qwrbq*?c)jdGcy+*tp<8OmN{A7OpElZM}GL=*dJEQT*PPS
z2p57A$0}d;6Bq<PlxyHo!WI@!y~c!9YJl!Wx0{)gMH}^*n(s|D@PiJAoYtB&S>eOk
z8Co0g08zt^ElL)1GJ|zlSvIP6jW23W*o&a~i*Cb>qUA4#|9IENNx>#RqA(LoLPfgp
zil{ZjT2B`}A*Lju?EUMG!*tUNA?QEDhTJ=&EkPak0oxxEE<Wb65i!wj-*wzKk|`{=
zU)th-a7^jp8ZAFsrRvO-x*aFu&*b73e<>)o*|>hwn;q8Nr%K=D8n4hKQ#Yjow(4_B
z@}HTho@HPfp3vj-gHiib_}9V|!@Qj;`PvTzcBCZqFr01x7LWd3$!%8xqc#KTtuo|4
z3{?*1Qy*@n0n#`ar)2jbTLkw0=w@v-)Lgkv)j1G*Y-(*bq$Z9vD-^x@wM5HAx;s5G
z(87k@2)TZ1<Apb~#}wpk!Ei*j7wx0biN2w>Q||eiaI^toe#WV&UrDneyz})(5uH*S
z-~IGdY{0dwQ)V2IGb~O_1{Sq<=+ARlp1w(-$w=&?#*{f2Sz2oqTGQ-SUW^V(D&Jo9
zQ8RX1pc8*>hE$@Nr{2FI66W?)Vyimyw2scbd^7sNvG!D|mAB<J4R4Q1>t~!Ko+(W<
zk>bG4(-galtb#+Sm34i;<O^@tW|b7@1IneIH3>7wKa@xC%jhtFW6A-6{VQji8$9Z*
zI$S9ISG0%u)P}I+5-mWF@~sM(vgt5~l{%`R*spQI#qo3;L&I2TlFC`XHwA}OI>hf?
z@gIQJGjN}L*;p@a^2dk*d`V2g`hk8@&&CJMGQ^;DPLI6al>}%#Qt(f<jUs1b9ynOQ
z8gBagRrbQ>{Y=eq&91t8f4c7My%b5N3hsiDC#FoTPEY;8P@4_oKh|3f2j20m!22_}
zeV-n!SpXaY^Jb){O$q!_<^Cqcyvu?HU4&W85VmFI?bMeEYy63foh?E$E6IRw3DPZB
z&*li^Iy3Ss3*I4WLshLKbA-@2wEYjK-@oRg6Fv&xPHT<Cg7_!3A#<={+p;#(7u=|r
z&dNn_WH3*xWM5twuTUcNH%E`^edfB+^Sx30SD{v)UTc%DomhO5)~bK}xf>G4ws+@~
zHDfNVq-VbTQk1C=p2J?lXHS>XX3l4HfNvAsDd$7Thd88Jax=?lsAwSKQL!pa6NKKp
z9w2hi<_u=hz`VWgff_zR;HB*!2wU>s!n)e%c_VpUSe}N4Q#2F>%e@B6Ar~ui#YY`+
zTQ--kf6>hkEwlg8NLjnO$a(pbZ|9<IxaXqghHI;p-DlrfukxY7;bkJo8h<HrEpv1u
z5|xBQCIys6`OQAJEEch^oh)j+8}H59XgJ!@O1RQnVGG!kHtH%4*p3tJafy>T;$BQ|
zsyf=v3(Xwv<4s1S`dc3BE6$vlu=4GMS@$S35go$w#qm*Ibd<n8vvi_!$G3aDWk&gT
zLkmKu&IiTRRJzn^lif(1+)uR_e2J|UV)&7`R@FOq^2-r1{qZDXmMl*2&Fg8h>K-z;
z&gfm|XCmHmrgWeia$J(mfy>7_O2AaQGv@-mc=BB#FQN3Im_hE@xSM<1lXY#d_S6VM
znAveG-yTlPnp~VcHbqigkSNyO<@wO2;M)pGj+K-nJ#9}Jxhn;%NOYKeFlxfb5C_&*
z=VbLk4FGqFt1@7gYNa1<YyUZgCXlZiOkHNrY|1Gkr*84bSbv_A$Zn;?ZY@L?I6onL
zZSSSzFz_9vWI|<`=F7$j*{zN*pKSvz{cI<24q9e9WX(6Y%$NN_)aLYUAfKa(n#6+D
zQOuiiAg8u>7FaGHC$4`-u)0bUW0ez)#z$toI^umgW%(CNvmXsVMm<UhiH8nob@=w*
zzWx+U>v(L9AA}XpHh$kRfnjk_lXGCIZRAX*zDpz8wi1D)c%fLAMzX~@lhcWa6M$w4
zBT-N&CFx{Gh@-+E4Bea|N8F<(FBFiRxx0;b`&frY%wSCE?IW@NGpBg5pJYokzZ7R0
z;8tP&NZ%#<NMPyXQT*sZd7G8Tnbm2enWaK7JZ~}%Y}H>ybz6;-Y(NdnAZN^7zYV0@
zAzr9py{bi0R&gzs-}_z4$T((JawaikHtpcnXt%g)tzOf*&rN)}4OR&}WZB(krlX-S
zh#vFV%d=>}%nW=twT9^2NBSW!#-%sma9zS}m#yWfV6Gkj{u)gjk0)x;!YLlcD&75r
z9p@W1FEc`<80MjMwVnAC99XI*<r%kb)540LOukU26ay}pk*c4kt#-$b`vYWrZBspd
zGtCkAO~^+IPc!_o{<2vxDc@qKwSYD>eUWfw$TZ_pgF@}>+E7=&?7+}8cSJKfMtfsG
zk$6f6jLERQ;t->JdUpIcAqr5J+K7a=kBoQg^_CCz7Mv|6<rN$#J&3L~&o)K`y0GTj
zGsA>N%dby^@IX#Mmd4TE|7tFaCYB{{@v$&pyuCiMO|MtQk=270AhBnJGR~fF_R3&6
zE1K3>Oij}i&zzMb8~stU-_<S^W@@%rKh)v>risrvo4~PY_@Rk~qkgoW?R}^7U@b~z
zK-8Ui?Z6=M<D1>oFGA5S!_YZBojq0jbY0hmAN=;w&%_uXm%vkYB#CE3aP8g7JfdEd
z=?{(H3h2yE<~EXVe*~1j`O#$d;mm}ad1~}pHurh^4JT-+#YJ$#H*o=}iOMrwI_MY3
zkY92+FZYEza=7FjotCe-T()L$VU-5xV`_0kSJe!+3<z4-4O_Jk0Fog?Z?!JncXzov
zQ=SDa)68Jzu54Qy)PXM5jIA9rGn-0TDJ~nsArYN_-lY^9EKXtXTSPk!X*R@McAO2<
zfhjz+Y4JiCq<>*Jwe7W{^iw!5Jxey~GWV2tk3Gc&y+VItfmfE5iTt!otagTNZuypx
z4)hnK$yD6BJ4*iL!>jSV_6^pTSI9?rdZS#R0t{UAcrqvRhz~Ox^?Re%RB9OzOl0Xq
ztk!(T^F$lq=0536%r)zDP|8B*iZcJC*`UUbcIcs+KwKMD__XQp!=aLgA;LSEIkSs1
z@};-;+-%iMLi^J#yCfK4OjCQ25Yt)pQ|dg-vF#h=Z4|{OPHh++dBV(+{7EIfQk|RT
z>A|O_o0Q$IHVAi^-`w=<{2+a{&Q~ovjY!y`Id%3`suNX?18>mpca%p&-eif>aa%Dg
zGrleu?olsJRveX-17!{9_S^h4_Ij#WCUt7&Bgk5n5eXx(b5;p{A51&T{9;#P)R`ZG
zgwJ1hNuC)B{8Bo5>@+o~LBHkmNG{W*sW-eq-=kXBZ#{Q$WAVX`&H}~;OLlq~k30Ht
zj-KKXD0yzz)gVNtgVFmk+u5|CXaBanbK7)geoqAN2YN|S&78zL3gTX5o=QPS7J)4M
zx;cCMK|>2RAn2F$m9fm`LJg?&WuZqcPLee*k_(Da`?h@{W*IzSJ@<Z>aAkM#&^<kQ
zAg+5(?XkfgRy+zs9Fx?{`n-|5ytL>Li8Ti8s@es(!U-R#lR>}*87C+0Sbe*dyc}0K
zS7f_poHNQqYvcN513o)*PZtw4`AO%2kEGpykB?9EmUT&4L(7+l4;Ljf2A{Y81$Wlx
G&i?=b1=j5V

literal 39308
zcmeFZcT^MW|1KI7#IglDHj4BjpaDccK(-)+-cedqnsn(sAnHazT4;u<5JhUJ2_+CL
zNC)X1RGJ}#PADPdzQO%H_jlJlxBYX@T6djUizG}YGw*!r^E~g&ywcZIKYsM$Q3wQb
z9Hyaa2!XKpLm&qq9X<rsj3&QZ25*NRX{oD%cd%^VsSO2hN1kZh_kut;BN%^7u`?@n
z5C{(hrmAA>pT02emwwu3uW7~ahJ|Ho&&jJiFNK9W?p*!YXB1fnY0cwN?o==+z3lMw
ztAy!7;E#+j7Tuc)f%i_FWm02iI?2p*%<zobeCua7L};*s_Fqy7w$QYN&!lbt>G*Nf
zb}~w*ezHSoavLGAKc4^f^1mzqED+Y}8i_s?(Udk^c*gh3<PiwO*t~kUm|>d|*0!X?
zxsq|mOJxy+(-6qV3ePetrhly#rp)aB@8`Ej-iBlUu1-3`Wc}~QkLY{0$H8Y|WyL=~
zTNpmPd;lDf|7zYd^dd4QzR%L|jx*IBMiK}SAZo#~w9FsT>g5_e*^u6mCZ?<~R*j?g
zw?layi_wEcyL^u6u?cbL(*jn!d1>f;<T;I09!EU7NWxJbcJXRn6xtkVs*x&4oJ5}c
zV30)<*hu!S*m%tERX3=yqu=uvOj2t{E*jcup;r3=(H9pm&MidLfT?Tb3p(bYWBKa@
zh-YEWQkIz2b4X*0**e6V_5g0dwM-YDybowAD2C8ww<qBaOBHtvIyavNo7tXdH@5h5
z(iGkhy8~xWa>sVUlDk!U%Fe7>N#;2mox5gb`0C4)j&rC9={#;VQXu0*ii09-?Npyv
z89MyTya=p_ze6w1eZdB)eFEAmx+9Z;e2aK>S$NIv@3PPc4!iod!ji{lf|UfIPaAA4
z=a7hJ+K}r5jjNlPu^Kfxh7XZGsq@IDT8X9z%g%SIJsn3{11AK=+xbc66H6W16_xvL
zcpxcdx%cc=3p1yufMU|!J$KRS+dUBy#L1+J*!GV-wyg@St(eUA!=1K0!EdJr;LY*t
zab2Vm9m6L6*29Ksgb#U#*j%r`WclyMZmGk<@x3y=@5feP=;JNSo*6xpzn=ulGflii
z_d`Q_Rd<>F$6N8@D)%L;(A1kMHQIRDw%Q_={c!}n`;utgaK+&J6?OYWyBfRpgv6{L
zmSf@Z<%uK09mk=#B8+xvj068ixQ4y=!<xZgznN#4^RnSa1g42@kpVu6!35KUwZu$v
zD|3Hcg)i(<Jj#>Dx`uJ=_eYgZWDz0mf)i;o;{yj6hYPFgJMGEF|2n?<tfHRa*2&}}
z-z$^7^AMp?Ar7&-^aUg#t7-g5XQTLe;@r=<bMx+}`j!sFR`d!~CSD+?^AR7QP8RAm
zxgm`ORPL+f{T}So3)@_f?#jU*#BU>gs}je>R7O}WdqSo?@ti+s2KK6J22egv@@7a3
zdLG*A5<HFcQ#GVBZybd-1bKy*0mDeGOk7>Up4+#@!h2Ka&9E;QQMd2#9v3*C;9c0Z
zXAaF_8$Tc<*~YB(8TvGVCZI_42v#*TirwKqdKd9HDTPmpK>E(yq;7lejr<pRC3V9~
zD4v+n6>3diQFCjXNC(N2iwPyRS?$EVT*ao6-#lonsrclc&f+H{>UB#)8?}ROEJ(Ka
zfFgIJZB~B)qEbptK!<p`*ru04qd<oUQrB>`HwsTi(pQ-spZ&EQqoHq_&orT3C)-y3
z9}kM)cv^eHRtbFq^q1(lX|AEFQNHmIu9*N9zLYlRg<s+njnnacEmw5lu~T~9d1-?h
z_82+sSWj3&J9$Ut<DU`zmJ#?Qh-xTK5k=k=yeW9NW7=8|$5N;JM6O=-Rjw?|R4X=+
z<-*I&qswf1`c1|M@hE@yWWWDp+9Nl<J&BYH<To)GtJRq1vG4_ZZq(aEqZ&?6Zr9&u
zUoQ}mvl6k=YnDCI%Nkx!wNlSYH0k-sO`M~6=>EqhRbx5nZd^0AUrxyluIYjApUCa)
zK}g0w4DYJ;tZ)&-|5~=rdK;5>$X{w>;%XPeXNftsZayHenm?c<M!AzK)C9LyUZV?a
z-DW1>Ewor8p3UyKa@$!OpS1#Xiv)F5pdXgK>(5=~0-uSW^++Kr2cYwaB}KOvjkNId
z@hP@<4<blq7@P2l8S%8izSv+?e5vk$$jWCIly5;Bg`nUfmgt??wnG2wgFF(Q!9`7F
zz88~H23c#J4l2iwm_s1?oxkIlAh*>vG0fDo*4O7R3rNxDoM4U6V+T2j@x@P@>TM4=
zx_!FSa#bTSG8*cYE5CB|YNy|@^?dv{B3&8dyz;XBu^mR8FQq~~Xxd_;EJTO)_;;y0
zvi)&u<n-ATa*=I#XlYID<yzxcb4^L@GHzm0MXg@YWBElX0hlHN*ZAc(s&Xoap$%y*
zoAv>fpw_%iKluZs`q^=Q0S-uHlB8n^TF)-L(u6a{BTdK{`ZP*)AaG$Vb>h>j!R-K+
zLWK8l<~-fsdXQJ$p34FgaS6jR*J^>q<Z)YvMhILGRyG+Gi1ryC6k6PTC(%?LeE>V7
zQKKp-tPJn5L3XAb(H}@bNeECX&r_NUuBqHVYalq8Hq#HXKg{!P7zb!#-nINbaa8h`
zSKjd#9N)BSF6S*@)ImAjA629TL?0UaSyq?58*3&IWw(?!SdkqtWJi^WtY>#>4cU8u
zGiX3o&q}sw|2)wreIE7WtZg#>AO`hoeWLKAt2hLr5f*9;tZ%m*)qVh`4huB2XB#v2
zPN;UhHDDh*HRUep_sd72Zg96ww=9H2`*1Y{&c!hB@{Ypg)KYV_{ZsT3Cw56PuX<z6
zQNl3`>Bob3i8i8{i94^JUQoXI0uQB4T#eliJgEbCU|J|jjMxRu>AKj=%zFqHsBbT!
zCz<<|R^K@_)CudG@sACe<d3iCU8y<e(DJ%@ZEg8zxQ!pMqnF8b^}BcZ$BAX`VH>-$
zDKxj7P=PhM8Ln8`U`xACh6ATFVb)Wz$5vB3HY5OP{vpl`JCUY>{omc0WT0yQ^gRli
z1HmBs{<hccOp<UM`9|9-7@HeWRGGj%=g^oZmAU7R&3P1k^lerYCSz}&3&-7A#7xoz
zGTwZuK|jiFqqN!GU6GIey=&d`ckHdr6cl{0Wl~^Vd@XhS{bcNbuh!{`m+e90Ef2Yf
z^11hPv6E>kjPCVsL<J$aXX+m(82xB+e*Qx^HiRBWbsdUr35pqvY$cojWjcRM$Rx=-
zD6oInd3>gHNkAY|iC?d$@{@^>$R&Zg*^;_^w1|YGTf(OFMW0)gI&vu{r>Xv}xbH9m
znLbxFu5Lf_%-H1XrhPucwZiHQRC8O+?*&>}bo<4t)M^*_Y>8{Qy3#?ryMkOFf4g>2
zH3L-z8%VRS_~-AP)QR(YAm25T^7yAPEML%zw0jqpa>%T*u5}HmX+`NrUg!v@MAOdD
z;7VqK%L@8EIOwNd?b<;*>J^F6EWNl%)7X%vY8Q+I5yiZH$T7K%av8-_!TFk9%SnhR
z1&a<zOvnTI#c&8%9!OH2w`&ki!EyvP68kjl`BN2z95oCTVJY{T<P~*&Tj<bO={zMa
zjVBV^HRzy0z77$t88QheILX(cA6s@P0Bym0dN;55w@+8Xk+99UBZZAzS@VB;Axzet
z<@?oXI{}GF?KxlhmR3(C-#>8`wNv*-6|oI{>V{iWP&0hz<MOoKo5lHbBI)cFWzBCM
zIlV1N-5Sa%{6F-05ryAzoi;^d>eVwEfl^^mrIaJm0eQbaHOFZ$i@$tumdW~y-j8ei
zj(GKeS6l!PYW5t54RpCOyWv@p?UEFa2;iK9_=hlO!@|ik)i_ye(1rekBW9k9T!cUx
zGK*CO-%k6E2BEYYx?JCVv<>DVK1Q*`i<ZAcdkH9}k|P&pc%i-Gwl?`MykG16dJ~OQ
z(u5L7GxaC36J8<RbboGnC$)x@stLKn<c%hJ^{VpzTIx{U_rDOs{|%W7J}5a!Gl83K
zx#%^!bB{H>T?(`KGbHlL(XLAKW+#(0Yb<+yB4@WW2>P=WS6sDOd^!}y$1js4E&jsW
zT~j{W(kXFh6KOAW(;kh4KVLS371`F`hD|fUIE2?eHC}*5<<nAsR0#o;>gc?i-@Mde
zJIbi*Y@X!|gdN5#kx)(Exkl>N6#U(P&t9TAABtn9Uv;c*H$JkY5eV<uIn$PEH#2T5
z_#`N?y?N}Qty{dc<mr1TF|UG+IVc~Aq6%+L4+=ra$;2kDv%<t}i?=b*Pw95hr#ZvW
z!I@kaV_$ZxrafYK1p4&rl<gjydh^xvN`u*fJwIEwpL9Wm{@o5T^plcoo<uk$3atyB
z+ZBS1^8<V&PEhS}MrN8ym%NE)y6WCmp%%iQA+RP3fb<Kr8NcHD2OL~tm`!^@#tHqu
zUV`%Vv0`BBu)4qb8}M7)H)Je>z<-LM957bzFUt`S$$vi@8~!);@$Z`dPj5jVXOWtP
zO{eb{Yt?*fPFJ7%YkCgbrI#96e7i>uDG1xS+rtKP7N`>^x+B?A&a|47cY`!$mpnYr
zwcZkWW_KW|F##DV(Ense*0Q@^v^&SkaztQ&G6Cy*_|#{C$kjBhF)vJf2wM|yO<FHU
z=OK&ZjAdVWqStpObBd(3?23mE!MHUqig@j@40t>g2p#{$ZG$Y-5=W6Xo}*tNZ?sv)
z$_iMWG2)P<YSd`=G!1NDP4P}a@66oum3M?=V~PM#V4QE<$xV&Od$EQBn<|CbJGk}$
z@6Ou@H|j1ruX#_{F(Dgs64w;Z=BVDnd?e5o{UOgn!f5l`ec`o+zmks_ABt$h%igV8
z;C}a&97p?oIb{GbnSoSQJZI6A`Lm3E4JL;C_(cclc{*_>m)?<bgkMOMeuuR6<Wy6Q
z;$DVo$kBx6IR{wBt<{f`j+I&?hOnljJHy0*+B~23ajv<2)PEsfOv+G@*D)GNhgce%
z{BhirT}}CSOyZH}v#qZOIua@f=yHKNzBvQ!V}%tuQU=3k&I$**1>6^wN`D7^+WN~1
zwRzVyh2|S3J8ba2H2mnYui?5P{+^*EOvdi+`aiaJxwyrMETr=oN!%`lpl9CXme6Nr
zf5s_h<D+Qad-N4#rY0{&uMoc@<WZRRH!5r`0P0Fpilm%WZ+)GKefD0Lrh2cN(4`HF
zE?iebF5<9D$O`ucyIc!*k)6~cVi`BX$k6Pl_J{-)C{E-qe@I;aMH4Q>0elkdEUFuK
z5|+E8!L8s_$QyIiUBU?W9@utj-?o(B<11WELrm7nMn_mq{wNYZ{kY;;`>T4iBcD`s
z_$6hUyN=puKjlb_&bKC0>p7OVl(c#1r{6Ep$2cij`Hy)jzdxkgI@4`t^nGg_V-41i
z=|gU_q$H4m*rXp*T$ah5c;STzOh_X?C1d{W0iu8ul6VP*+17B6#hQyrIk>C!js#{V
zzkp75K0w{JyEYbx5H6@(_9bNJ=+Jqke8N&#R`;Mydi2q;K#8Pv$M99LNbyMBjqk$`
z>6HU(;!wWSsbM4ET{Zi_NS-!bS@LC6KoDI*>cDx3{!<3dTgY<~mWQs%pyEY+c`)wb
z6-Ksq{jkZ^(VoxSBZgg}$2u)POiofJ40^nvQ4KDM(khEMa?`BUot_S8RMr`bt~)bI
zywWJO_KTV|Dm_<eh==KWBea9&f*h_<ic6x<sFV&9?^4vL?cFnWnyjP%SR`@2CB4aS
zVCi@z^|Rv2n&Az==h|iN@p*N&*&Yz3)+T!8Bn@WSx)qSGcv-9qqTa5aUbRlo&Dpp$
zsyTUoR%WLh&0Vx!`Kzbzr5tN+yggRbRB80r>Pzun9W9g`Cr6uQ#98RE;{rQ9??1G=
z4$~6?vRsp@$F~jTB=02^ts25`rnBa?{qMhz5bku+%Vlw$jn>T0IcOr3oRB41x{KF+
zzuq~e<P6tCQHZBtOKo`U4d>0q<C`e~?(JnU_a%vru%gyHgyGI$wZ1*GW1hCM;mNa_
z7a!1qtI=iBYipzQ5t`GzB<IwWtiri1tg>FW?@FkIV@fOjhUb~%Pgo6nPdK*OhBwF|
zYqp#BBeTahzP;#0)>jvjqpL;H_(9gv1s3b#xFbC`#dTL>+F8GkT2j<)!?oN3-~y+G
zpJ;AYrq4NQODle!;qrOaT=+861)<XQ$vJEDy(iqo{u%z6gGw0(edXxxO*!+4L4UQe
z6?MUzO1yJF`dA0*t&!H=Fq}^3+6~R_(UMwNV50!doUHVyJyWb#>;WatryXwD>aq|g
zc$Mgp5;)|gkM$C9FUiKmC@5xPAT#e%i<?KVcNSf{PYzs{;-rj|4RlonucaL&V0yi~
zz92ew?+f0$WHCXy-UNU?p9*acb9tWQtB=wrd0)-&sWLgWFQt*038TS<c~#A=V*PtK
z6>Y`hm75axQE#$Upr1l(L!*4&B5RAw3vG6o@A>LG(jRlKrN{0vG%6lfEjy*eT@ibW
zh5R1fPi7ViM<<K~IQdUMXih?zeP{dCjy!ULlCZrDv>)|umdCR~%|WR4m!?4#JwGE6
zTYIXtp;a44-%_GmHgBNf-%~zISK7_q#*0em+&F+giRyOmu<Mjl?|B!wSI6~4!-8vJ
z`wF?K7g^KF{2aMbG1euM95T+H5I}oj+ezh7gA1+X?0DMV^&b8dhk|0FElJ%f_ROxT
zonC>njp>7a(5FypV1!+`oI)W@sPTP>>f$G;h<w+gCk^r3$_2HO^XW5kqJmNs=Z()k
zw$~RgJu-qbsAWxeH<7+At7_j@Myn|nlk%IczAw?GS3}Xfyng91r<e1XZ;);m#x6Xf
z|4fKge}ZG|KLOLEuU#bR@wJyB9jdnHyq&Jhbw9T$w3&s;8+22!spz${Aq45eB?uL(
zxq_nLe}ryIq%loIoej}rzLK%$r`hg7vMX+T1zj39w&QIGO3&Vi#@N)p39<e(KX>N1
z6RQdELfXA17me!-;Txi1VWg&9^)>c+C5QCNQT~xn&5fS*I_tA<cBc;7B2i8a6WoNI
ziVVNf^k=X7&sc3uzo`mwW9kc5JLnr~xLS7dhZXM}L|01<qg9iGMV>@@*QTq_h$kNL
z_t!C6&hc>>m!6NqJbobR+Zy}Nb)SIyotRJ71AB&KMBO7eJ@)28N;cn{DfOQtDn&3h
z$G@RW*q6TCG^Z4~t}Ek}k%*r^y>-0KaQ2VO*$0_9kD8+5pR}}k2OxTAjha$@Q*Rs!
zZ34f!X1Td4OTUo+@P+In`rePL6w|xn89JHu;dhFw8;(5rfYS>WwsCA;Uaw+RPU@2f
z=v>+3xgoDYzrrKD58{1?vu=LS(5a!hzeAhZtiBA{6<%YpFY~iX^b5mjbW4s(_zg|M
zx+M|#TP~b_L1A{RN!ZBj!N~$WW?;XthT&epcF&<ft8RA7l}|_0IA43GiGAnVE;CtF
zc_lD#J)(M=Q`!9Lz{bG!oL|^l7JUVa(bGDLCnYa2S>v2jbACrO=UXSS-4jRU-CTI9
zzF<?3eJrA}%$+*%VLiP23hIH5DuEm36e}T-Osy;-o#<Y~?PWf6g#%UTEIDjw+hcSy
zh3Z_JmHny8-xuZA<cj(5`{gFha-J6zrl&e<8q$K7C5vdw2cV-;h^xCF(rn+$4NRR^
znB021qUjveG;Wy4jvLHNoCrvFbEOYofm_tKTjDKdI4#Q#I<C0dt5tRS!k>7DPdz$_
zf5oN3>hE5L4G>RFF7&3tM;}6&^r%ZGsKQC!x~F_Zx*+aP+i$I&M1@rt!Q))Q1~VzM
zF_1PkQCE#_Qf4a$91`}IO}q?%@`8oGjhR=xIyHO#LcO@Nnsk$=e9Vq&Pa58PyI`BK
zv2d>H&7#2~>%f`^SJT%rb)%6_uOr*ZsmUTWPC?M9M!d*eylZamy{u>`CgF?SES)TI
z`gOAQlMO5K9v!Ke-IaN$LF+84;kPPEL@v^1zQ#zfxwLU-+@3YuE#(NW!tjN)wc@G}
z3dtV*MJcVW*>ZQ%CK?U5>s0jh_LM<E5U<kJgNN<Db=iz)%__=uw~luBlD5rA)#dHK
zH}6BE+yumWXSQ_)zXV#(mk~Z>=g_CRrVL2icTj4rBcdK9S5GQqj6~|zN)G1Y<fffT
zyk6?6CMb)qQAJH`q=$nmR-~;WrUA*1Bo|38lyI{{kfGqm*$rVISeo|RF5=qc=D^w&
z*g&^V9Bs)EMvuF2(ozjbi;{7^t?6HEVI4h%7<e@_>H|%sCkWY@>;*4r4D3wVoczW`
z-1TAv=ndb{8uDL|x0m-}fn$t;cVA9#e!l`gt!YQDJJ`?d({<{>F5MJbX;aloD(U-#
znX(Gk4TTdp`If2j7ILa)UeQ?Q+^U9yD%l>^YwOm}_0Y9|FVA4i!x!Rf9=@xaupXuT
zy)a9A**5D<%}CDj#A~g=#z2J5;zDCT&{2O3wG6BUNjTnt9-Gssl{|IUz`MR@NUd@)
z=}6h`{cg#d>8-oB+=Bc_+bk(HLu|NAvfBKDH(fJM!W!GEC153vdOK-fjXOwbrt4J(
zU0VVStvUFWcXYe)838C0hD|eLa?r76?jhk&#)zZE;G3+1dmAI_f)A3pjnt`99Y_d?
zqt6HXESLqxwR8@@@d(G%GWIvTBQVA_icB&UYq>SXJWT&4nv9a4lSfg87K1R$22I{C
z>_%oKkh9=OKPZHT*z3wmQ}GT)r0v@M5O&E*b-51-yUGKmRfc-MB7RU|ojJG2qdh^7
zH|0E~8VklQ4y>`mW;YTx@C5q2vAosHtupdV*lKLXvl~j+Y*;t&ZVpQ3Jz`M0vz2)H
z&HFwm_F-Qua(v0sSM)KFi$6Aw4vjMPb4+39MCFUVnn^f1w=uWf&>WdAp&uu0tCFhU
zw_ARQvYQo)=CH624*R7++P(y=M<j3SaaeRUS#+&1O{qeyGPArpisteF(EZfqcbTHb
zuP7$P()bkFS+**#J}a}0J2U@l(<gN?V7m47%Otc37m-W~syY?x1ZEHNo_)+ZGv;Ic
zSTRWF0#YJ~A2G!YTEBe5PJnAWrRxzw*FiH<d!0bySG1?4w^j(Nl&G_9#%<U7CT}(`
zY+P5a)p0+8^iJF;AJy<uW(4AmA6<Vt{|BGK@?rC**MjZ=yV+!9pz_JHs<9!y`h3IN
zC*~}==7gn=C6bRndUn0^=|Eier~a*OOzC1}re4y8BUP5O6px@p&wKXQuMszoUFtVk
zmzl@DHO_2bKZacIstWO4p0!ZRz4Fol^I++QrzdtJII^7{>0448o34I;)J|exkq^HR
zzhIp+*qMAGdONvY!%#%N^}AR`f_BQBP=;dQkYkKtOCNcjxk;lt00&1NxtC?{N|1X|
zvlWt|Nz90#+4Y&NvPqk)4W($PRd;gV?-Eyx)r?h?(ae4^#um9PccY|Ur-zv|r#`(T
z^+=Ekr?z!z#|U@Cucx=m>>1jdr~YDg)I39O$~b46)c!JnS`&QX!LSNCDcdh<H}R1?
z<n}-N$qLi0%=Bx0Ex&S+=#${H;B%okhion<`PM|OCwibOA)(zlxm`}ykaA!k2zfwl
zZZf@avg?_JN%w=yUSk&Q>B2e5%aj-V<2jEXNc(b)-LXL^S=Sze)xc*>=N;Cm7Ci#Q
z!=&xw)#bTwo}{skWMY4?BmZe(Zru}^^LiyNwRaBDGIsQ;+OuCP7hxA2Xu)>vzCo%F
zx!((ddIvM>3iLf{{g1765T*H>n#i<s*A<dXpj_tUe*YNi4fWUQ;!(pau^fr<GBhY&
zvn0uN^Ewm#TLP6#4Uff6E!5i;`(`a|XrdbS9wNDne|5bQKf@UyYFA9}Nb)Y-SgW;W
zJtWL8#TuZDYVc>R?uzB=vi4sUl+)|=DBsvcrxO-5au>XNM%b{Kle*VA831Mrd)vOE
zp9Z@OTA<jSCdoc%|Hy>AQvA#lXL<zJHi3kU$ng7`@9w3hyw1hzXx(1F)m=r$Qha=c
zmp;D<j%aD;bk3DTR?DpQoLTi6ofC>q+)CB7uP{>W77=azGWVVtNF9H!1rR~rZF&8+
zIOn(Ua9q33ya3U;fa_8PRs8FVNb5OYPwwlkk`0@|+FTD&2Ep`%BgLxg)KRoR`E3K$
z*?|cl_?yq(o4NdWh9mw6)Wk#B4}A)A(Or^laA4{UDl&8KMnEjOqRo59^PQ`<Qm(MP
zVNoi256Tq%68+ADDBp;#)$Sft)tC*;yh_|Tst>X}q;N{M1<&<2G<52j-K_GSpkUX~
z$=IeAyl-qW>h325*(3>dV_IEIM>!&}W`!!RGk1%h$mSfB-LU0xJ>ajDBO@bo8DeW(
zi-L}?eM2Qp6G<+(3$@cqqHyRMLY-eR-nM>4(3~jS#%w{RT;cDXRDAb)8|&G6`$JYq
zEJ{-9tnS7eTEA`mR=_v;59;mpJUf%g(9kw6Xb#?nLqt4|s5}u1yQHifh>uOGQq<;0
z|E{F!=IM#W7yNd1twH}YKoJGvKCfryBaw`<-Xa_sAr{+vit{kTt1373yV*5$PG5E|
zR~DW)iBmkyG~sNg^iusYm-95fA=M0G0)5J#ySt#in?D<56SgT50OzA#*XlkQwleWr
z0jpZur86|lw-l;%@%)-sp;;lBY<IUu{yqG|Z`bk_rP3I=KD$r=N*2ymaGo==29slt
z+Pp2g_W&y+ty%Rb#`9#19?(KbNcMZ1*nLoWB2PJslGEoK39+8MbSiPGFIk{xm`@Tj
z=qNvTMGY?1!AWV<THk!#9I!}^^v)pva8`)5S%6^1Klm#a`)I&<PA6{WZ%PPcetTD`
zefwIsOqEu5gTFq_oy6Z-+4aRW5mCr<0DpX{m9kU&RnO+psNSrW0&b0H$!5oDuyh$E
zd6Ca^@9&cg%N%eB6^6(Cow{h6>bgT~&evx4w`hB<RI4ZW0H<aDH0hxyyz!Pnm>D!`
z;dSDD4ZD=~h;pDFI!0%mdgT7svT6MNuM2FbZezd_dT*erp-XXnf+}1uptw<qr28uq
zZ08#lG-vUC&CFq0M&2VYEQ-8CBelvqMlyxGI2RL+Y^7-I+!p+iOftljWOL7ExUnRo
z4-M>XNKjpK&6_%sHu0UMcC$g3{qXJKPtt6Qo3v4q{9_0Du{QjQ;!><mdF_>NginvH
zTs)2<t{UnL>f4{07DVw(^P?yr@eL(-r6Q$GpSHhjjj)@T94+*+;34`7aNmoXoAXKS
z4B%grj~+C)e^_J(9rP=24^VbXnoHIL<=8iCC9&yxDbs{1dv5nMmJJG*A0qdfq8`la
zqgc)*oJ_uup=55K==UnAs}>q!{H$u~*?j07XvokD<hM5FNu2rIN`|&VP$@z=e@H8U
z*}sl!&1yAHd(x}Yr(FVNP?vLtv-^_wuu5O;vr1)@!7#%UC1x<e@2~2N#8HEHcP(>K
z=M2|<opA=$5JQ_#qM6v}StjIqOuI7{h^71U-)E0ASyz>+YelzsDoP};HQ$Boab8)g
zznDpmgurfUyq?&X=ggS>f6f}B2ER32P0A|x?MiZ<Rx&vZ3H{bOmI=@7{SHy_H?a?V
zy_J{27y|@c<l)!VhQF~F_tTTTf6)tyZGQ5nE+*~^Q;^$8Z{!9Z`eg=Nkr|pKecxW?
zqripYgZn@J|KH0;>A%5cG8CnF4O?Z{M!6OHpHs9Qmwl)I*aj<RfSR$dJuoEJpW>~7
z75j6kOjzOQW5pjv|BssX|K$}m-~t*Z=kb~if$Hk&E^(`xipbtQ>Jot-=Sv<rUTl!_
z?HS{XvuDoi);|3556)*{Xl2*Jt$Jqp_@x`ujk%5^6~vP8@bJ<CGmL}=4uNP@-x;ZJ
z^n%^Ioax^tj=Ri7SbF=6tw!Q9n_lp0i_j%jlw{b#@;qye-x9)<vC(wAI$PwVx&^$N
ztV0e~D?F{=UA4jDt9*hkEG&F}wB+Fzi@?prkk)XPWBCCyoT{NGRTZHxZm>d5@iiP{
zemqv?!75>1@p1H(Q07+8!w62SqVMFl=u2LgWSpdT-9xFPoLM21VQyLX2|V1<(a{<P
zgKhYb1~}!N2MVR!>bGap%qyK_c$Fp%a_%Zx85$Z^>SZa-Z53I_&wR0HOBR=8kN9$W
zscv)P&RC3ym{=S5J65}Jtd^|xRSXV6&GoZubZ@%wpBg5IUK`nojPl*v6n&Ht7WuV6
zvSd<uB^HT9=931BoL9bGa<?2RwyF)C^c{5-A6sS5CLGS@LY=ZrOVAF=6hB+-1`$;{
zfWIB~4?8!alwWbV;h@pfrq6tz<9ENwkU_!AY)e}NI+ei6Az^v*NmC4Gh~L6c_hy=V
zef0xi1+y%VZx`srM!B7-TNMuJ9^ZColu`GUh2e5N`mZR-{GGi0Gjepy<19I1S1SQ2
zYb)T^c#f_Am7T#F7iqf_K2T(#7p2M@ve5KFC$006lzn>%FiptmSE_%@P6n?<Pa>#A
zQ-{ZScA|LZ4KA2WQ}Dcl$8R^PhjAQkuJV{-vn;po_$h8);ZM*>yJ2Co8M4yIVZtG9
z-lv1S8-3AdEkRF7uE3<gbaZuXjc<E<J07ehSN+b8G9|3{Xt^Wyw&(P2&V<e+nd!!%
zKRsj3%5)kJ7v?73uXx;jI$i_ar6W6DadXs%NnZ)S^VGI`z|1<xNP!@mAr_Z!OASv?
zPcIvqjEaoZWM${t?Wl0<bxT5*m6eTwa3&;K?0AE5lj4t5$U)-nxq&&LEg8SXvH3gy
z{3VxDRdut-y~swjQ;E93PTQM$GHB(OAplFU4kTV$p?=WK7|}=m{Z4;4?#dn0otDtw
z-`~NHb1UKfDGS577&rm3T2Ec9l^U*4No0qd?S6TZFUEg+CPhK>sBMMT!-qey*KNMx
zr-g2l`q}xTuv`#BBSXVe^Zf;b_my{-@6^3rbpZ+3;{f7RQQ5JVd~BrB8TbC<$HyJB
z9ci0X;5@cO=|N)E{s}NaKAu1SDN56Mx;eh710^-rrxtb2=lJh#a<@7+Eg`F>(5m(Z
zy-kv^aQ^6v)<efHuIudXaVq#e7Z5r1YWfTZ2Q_eVW^PU$9Ands|J0H3kj-(CkSH$I
zZR$dKSHjOH{=demy$Te_@LFHj`nsEU936>E2gKjMfB()IQ-|%i5^>qnq(9$;et|7Q
z$hg3iTnYvWm+}7nosuT&?wf8zdd9Bwaid9jZ>1gVdXs|3;~kD3J^Jm2Q{UsQhNlPh
z?hz1mqysA4!OJQ|ARRJ|FxGXoo?c#Ad4}&>-`rtW^!{Chn;7XKZVffMCrenFzxSH!
zoEs>%qSlX%jm<d?7Kcna=4K4EIjUqgafjj=rKZYdxGV<gJ?=9&ey&!d5kV#UC4{(?
zMcqLgBFYTwxNh664%x(7RXy&`c2n74@tSRy+W^_2e4{^*f1VYY)PYsHM8;2JiZ*+T
zw;f52a~T4)Z^wX(js;Q{0`g5TbCHpeTuLinPg9HUm1bY--sY#Kz7%&KF0*?fl;Pcj
z+8_-Qq-13`QTyl2(<z%o3ThEZxqJy&$rNKvGr$dELTIV7?g*1zB?3KRq2q2W*SIYx
zl_3XrwZHp(etsl$#Vh04{4p0fIk}U#5aK$A$^c(KrAaUJXa6jJ8eEiI&3~4IV{Xi=
zOR*zXVX<ltl!j^rNlEu$6_rmz2ib2ee>*0LFG;W<%;p}~u5^R^czzQ9u>wKc7`z3`
zAKrP=uekT7VpVjT=nz8P)e;pK@7PS(%gfD_zlXdi<FtPN;lqdbclV}Zl@WC`ugUt5
zpt_y;0;A7AOGwI^ZZ$rn8=;-)a&?pbQ_)lE(HCVThrLx2xnHSzo)&leQ9-DvknY0=
ztT~$&TMl3GrD9!2=f&am_=(k-wpgn&+m^4mi9pi(AsQ%6ce~821MD7H`m#)hY+9vx
zOns}oU-jf+xt=o3+a)+4WM`eI6tFt2+~g)EAt7-O>vF#-&8;SHYHMXmd2e^Ei~A)_
zVK_<?zuGFM{?`TQbb5w@-`)+;;HhuX(bhraN+woLZ~Y~dl!Nspg?vn5;ey}XWl+T8
zHTWr;V{dFuOR&W|ZMQ6YDTcM8!Lap^Zg_n3#et327X+k<(U^^iCQ^k>Q%s_ApU^z3
zVSEu*srOHCjQ%G$%HNLl<r%Tpf)QXCmU0c6OcuAe*#74F)B^aBS@Gagcw5FfUhNH;
zlT^RDg|fCz5fPERi{+B!F8Ms`$?hEWu1ruBxRbBm_x!a2!gv_iiTkCFf^Bmg+c6G_
z1=D-?s#a*5lc87ZZ|%nm)@?jh2*zqMu5O0C$wS`#r^Di1hFyF*Jd`FgBiS1M``Bz(
z7CdBawu1^Y%+<|E4&)@^2#52z^6Hg$DI9LDx}w|fdor~Q8RNxMMw}1+YO|~h^eJ%2
z4EWF=C0Re@0Nfi2LV5;URr6u&&<Ar_$dk{9t&IasE^&W6G_H`l@zJZ@z_I_~K;eB>
zEkjW4ljX?DKcAmQIF41V+=2y8MaxQjG_Rb0!$@ndQCD2$8|BdYDGd=Jb?%ULc^K#>
zCTYg=9LC#-{U|nLgo>mjW~;b%v1X-qslN8XV6jyQ&SlthB7z%n)Bi`qQ}#g&oIW=s
z{fw1esdMj%-{&Jj_+@}l;EyCvtq(h7Sp|biJIR0v+`rl`%6mViwj>DA<v?1D&ZNK*
zOGp8&LklC71xt*c7PL^>R9s|IFn|*?E4~FF$b^G@|Hj53obZ?d5^mYIzj-){b6>)R
zP$>uy0d*hjJ5md9uA`=ypv}cEHtI!IzQc9}yzv@Yci7}?vHas6O`Lp>IP&h*`jVsb
zEDMa=^!Ud~OvZu^nSB}uI~|h;a%Qf`grsDB<knELb0*O$){q3O(o0|QBy6(p1c3t{
z?4b{GX+SsqrpIpzd6Y^XD$Q8}Deh-yV9=ckz{5O;<&#g<D~`JqI<gw8rlv*$=*DjY
zL|G@JStE8=nn*oztb{?o*>)7Y(6ne|2UEW@KeR`q`jz4c0N?mz`u}=WAV7rEcLv1)
zY`f|qrto{Pb>i)wQb5hymU>6!&sXRvG8y%6K_7J3-CAMwUYekBBaJJa`j4AgdNg`s
zq!Com^ncFLe&PsE5;PHc*f~&rhCLUn=VDM$yVkg@(7o+o>R2XXhgiUN^G6uVDAu*W
zuT>=XFOLEXQP&6GLym{lot|W7V%nN7z_2Bw8FjvL^m@5N7gv9&O;dqsk@-dh=u>@f
zltKcth`qVaUtgXS6&4=qd3PrQw+MoR?(_Tm!*UC*(5)}rH(-Vg99QW)I2#mlO*nRy
z_C_(t*9az0Tkp-P1HnzGz?p%4GSWCr-gjZ^;lj!#S-19(`Mg|-1T?6V%ke8yO<ga?
zCqY~kv4cJ=v@+u|ftTCenu=vlLJxvE;x3dGysAg3&(6;7(UPq5?ia~ThvFT3Kj;B8
z7!)!90@G>N(hjuDInXjO&J*~6(mL0MFs4ah8{$78LZ(}Qxi8;x>icN_<)7o!apmfF
z`X9BCpxO0a<eb3;3#nMLLt3;xaBG-?;4pHX69{z;(I=;7f#6W`k#XwVLVymle<pns
zz5pL#hO52iLJl7K>%2_p-fn`XmR1&49YrP6U0GOI(m*kRX1k5oJWaQeAH)HR)(YyR
zdTT9B)N!X>d-xfyr?}DgzAkWM=O;)%&Xb^Y?@-7JJD{S%r%VuWn6tLJq9LCcIHc*x
z%`uZ5*RQkg&oFGamK#`%a&<$lQD{^A8GF1Lfm<P=TSQ6xR*U2?C3s<|RO%P*?AfzP
z)=xgMOlEm?%JGr<3pShcO$tW3vXlvdAUUvKxzn63z0n+pG#ugce+nst6ZUvuPJlh!
z#o1iE;c~CcbEdVHk%R>?%6nTJ04GvL4<~|vFwO#Xa=7WTS9?O}b{pJsF6|{P*Y)S;
z7b4N$j%EidUzC2l9K|ngfh}}|HUU3=iAYXLnla5YoSDnb3=}1Xu5yK}ci#bY#^gKb
zPpDG5?Kj|%?wBuZ3JYH}_)n$Y3Rrd9TxKwv{vmclEU?)wKu3cd0z?9xw&c+=AY!7D
z%>8u(5{I`1xYGoSRqFS(UL71@?Ju?~FPcmn*=8hcnbDJZ{8}f~8roYBOs8!UbfRZ)
zblk?m@ESpd#qa$(#=R?3QT8vcTU#nHFzW9*>YP4(b{1F{#~}b#<+>C%$7)}-c2Vg3
z%R>G24fPqyH$J9jWf90fBe?g>c8ln`Jw(Z#yokxoh^)QOTHm(?3Q9^!0N(TE*6c|X
zF}gfDU_5#oqQSv1@WNPixjr)AI1jn}OpU%rBP>l4=%M^H2l7bew$TS|$D3|rRa444
zzjKUQTU$TpsK?gVfEs6(4%|oj)%y+c#lq5ZfkaEw-A3$Aans+(g6&=RI}>oH{dlck
zXgR2#T6zlP3Lj7?M$U%rf_(9e<LC7Fda~RIrQiwLi4MQx_UcU2%<L>SWrM|(|5WKx
z`7v1cHeZS)+*-XSf9RIH-{Nky#hz7-_x3L`ZgKzwO{$PZt})Af#EHR!w^t2`tf%r^
zTto8rJ1~2`Rhzv9LAG+ub)@1xsX|d%S@{&exu7C<g6bKXX4B(NcUPbc*&I~F@f0up
z?#^-ON|!4q|7wh?y$WK)2Eg%ejCB8L+bKCpPiV+8{zH5$N*;WA+|n+f0P@P|P6V5x
z59o3Z=^!B4@f!mcDS$F|c-4gS>>N|xSf=hR_rm>;$P@hC>$mMQf|lxVz1eC}Bec~v
zNmkHpoVkOtLfk<8$tNe?=)Z9S($jd@ng<dP5YULM0KFyg+^pkJ$-|Z(pPms=a$d7u
z41Njs^7Paca0IwsF(W26!?~!W<Qh5D+)#WnmO+%ZrW5oemcE0#9^i=WnFdCe3jnny
z2jF4FcK|}kjxlKEqu9v`N27TD{vwM(59zS9EW}RU2iv{UtK#C~!T|A!G5!FHt>pSS
z)Oaj?w#fPW+ovRrR}8M0#=w_@8t<+14wvon6tjj+Z<ATmjsH909v*&R(Bk!-ci-KZ
zbO|&<5g4DsF52sK-v$}58W-wZ45xyw4&~gA3tjI(s3Gx~r>7?(DE9f6pBhvO8P}dJ
z=k<5gIdS_$kg1uVbC?MPP9ExpZ~d3pZk(Sagr;yQ2Hv_3T#aBP_B#Oq0Tc3Cq=IY{
zk88dw4+I?r0PZ>>6L(u6ttU<M$>6|qzuN)%ptRu9Zn%zw&a@^~?#_Au$gt}PWCmRT
z6Wx{o0j>0ww+CrJ82{D_VD0#9pr$%>M$7R2ebkh4s)FC5H;uJ|L1955n##qwjqi+s
z?3&k0yHV5vT<y0Wc~&gu!u3b0qX1w8%;y3dC-Q^(=NgX&<^Cq((&7hzbVdu;{C@&2
z<gb)ef#pVQy~`i12Q0xX9{u_AXC1_9r}s9f{%@fd5R<q1KW0v3#AG2B#ftew-XI#1
z$=G`QXg~|xg4?(@xdWT&JCQNDH<_GpdZ8)_g!S(BT0!hZnfZ&<O}~kNIXo;uFr+V6
zkGm;8q_m8t4700vM8fQ}H92Cu@{mw^Z`Pr14*cE@N$-a9z(N%m%+K$DK;$$6rdt0*
z0eg#j3=@c6+ta8dG<zeE(l*~-r=Awn-YVeF@EN#IuLtZL@X`+*$>6leW$HS9<dKbZ
z(4seC0&s}19<x6Hg+&&A<@{$u38sycLc7XxA}?ONsON3$NRx$QgSS_02)nCo2l+cz
zED%NJl{d5uZ(jDb)c@J5r(ARe2BZLs-K*Jgy4#EdVK_MyfSLoj6#&a>RqY0Y>Y_>A
zAmOlpX*&yaq+YkV+o78wADt=dZXFByp|0CxJuMleKZb!oEBse}*)SR-jB=xcu`*TO
zC;yy;rT=SwIQQ3yP&(}#@|*%WO(|sOA+itX7MQV~rm^H%t(uvpqrU<nNh+X3%mVk5
z>4Z%J`&GT#P?(xNEiADPt7JsR+m3uR9^H96Qt3TnKC-<!g`-P$?V!CvqN1X_bmfWY
zpMWW|C<d<kV?j<TzvfchnSXkaHRg9;Uhmmg=kUi?E6Z}7d(bWRupP%HU7bBj)9uyv
zwl*;dQBlnRRhJVXDhYx*2r}6GSZ}W0(%Sbs>{il`zP@xGj}h<M`&_`KQlZ>J;h0Iv
zG7H)Rj2#<?22`tl<1@fCMDO-Nc<4BP($0I{@%lpz6R#kWqDAUl0k-xT@;+FAjr2^y
zn<a&W)DN<<d1oxOx0N<=)vf~kJJ^~e#=c^)2PB{`0IC~sz?=pOQ8tPIQkG-*IlaHD
z9HS*q;dXa6NEil+AE)na5+XJh$AfV6O!pe%hh8J`wdbdGx89eOEYFcgaf92S6h@8U
z+zB(_1KgKN#-+H8Vv81mgc7qir%c~AfCe1rl=miP_)Szh_kPf!!aW+#ss|KRkY_>G
z+@y`V6GDoBCP%34@Y<N~XITQhbr|VAi*o3U(Zh8qQO{`gfZ<rFk0v^7>xNeWjt?--
zb5@XzM84LZ^B!jlK#+{O)ol#0<b61n&0S(wP}F6tAPGZ^0RPD-yNZ*8x4}hnDJk9<
zcfdQkCQBaP>D?F2K)PEWi}(@`8pV?xM(el?A~FTg*<B{gx=Ot%#hbRXVNv_2F)|I{
za8ucjw`#qKt3Cz>@l#>PB!R%duDJf4ZVpPv++0?u+v?ZTdY9J+340sPxl^s>gynqo
zfC^<dni^=~y_p<<)N2{KxfM@wT*nah0_#A9;!M(|r8D;rLWUHsvjQJ7ASkd!h&*C<
z>w?OyZ=k+Tft{{b?rb-5!0~@0>p^z=Ha0fa)?vQIa);gq^-p4(I!T}K1rkFg=-peR
zDPosyxHJbbL|7n(27d)3zo^jzO27yCq_s|krHTYJD7Wp}?-44AlK_`*3J?Lmq7lGx
zWyC~8j=*LqUvgrVc0Rv2#^LKHS_`^=?mBSwC=i?QOaC0dgh-2ZA@;mCVMrz;AoEDi
z|9)4tI9^NVfr0KhK1>LuhidkKmW){(E}wH;o9n{3b&Xd7>E<&7Xt@HPE+p8r<yqhs
zJ5`<?Qsjlj-db%g0ei@j0gBB<-<n^)eu368d&bJELx#+zwDp4^*I!vtQSsJw6pN#S
zsB*k#ZhjFc8^?80rSl#FBe;!MI-5NNHK)Fs!3tjM4Hn)ncjqSpZn(Gy@P>R$u+~45
zs~OB5Kn!@Rl_GJeDTecshVnIrp>ys=r_NQ(`|!Tnd1`F?CwjJ>Nza@HcPlD`9w`t2
z%Xb3s{}E82wty1Qw;80XvNB2+@&t7VTh`T!&=IoWCJb%@>DOfg=yMs#0M5Ard1G@Z
zC*Gm`&5A9E?7@(YezPVDz-Lir+X!nhM3;}=wcxE~0hNt|Q%$j1*B>;5E$#O4eXM`W
z5Kl+2u2eug1LiVl<1uTaaI^(GVToOt_JTv15lx^r`@H85*&*iE6%+P$7#GtZXgheI
z-B&8CwM#9*J|Oar=3|&j{2nn&o(KbOOO^)FFk5EVCJ&+tyYfE8dZ*STQA<<vGXw2m
zpZxsn2IvmPSqX3#7l2y$f+!2sR}gSOz#i}IZd>s)piGou9{j}2BoL~-gmh9X9s#<;
z3I|^Dvj#7vL|%|D)Va{TwJgHdTnn6=Rb9Z^>_)n@BhtB=!7%GeaiU6~J}qG^aK!+M
z5JDz^zCxD<L`u0aS8lbLkIq4G1@cNc2<3ZZM!5U{9ccr2*jzbxb%EudUqFR}b=3hK
z)&W$M0CvU;VbqMFqD0G1RE-mpy9)p>&S5a_?+j+b$O)9b@R=ho{<Z_ZTI!K;9vJ%-
z_=_oNNf1zYo&ex<TLAC7Y!xIKPJg`bbSDLO$uq_VZmg71dJvcjFj@%L$XoUbnQKQG
zNqbH=llX~@>61!udX1ENM!yUMw9TD7Ub|_-XsINix7GC3WCY6sPZQI{!cQ8*d0>7(
zxQqf#V`~aXj-z<cE2)`4%_rGx=Jvvw4cL{J*a@^3m-ZdZe*W5z+IT+oPD_R^280vm
zEi1qAjoMq6q#u`{LEaQ710`2k(5TCn<xc?#2m1VmUK#cLf35`>gwwP66#=+W{IzKn
zywW(S3pji;3y0<MJ%Gw4F!#%6{lKh`0+77Xnn|L{GLAjAZUh7^^Im=Mb_1Z|qbmbH
z7?REpfT&E#3`}~E?IzFrr0i$H1VBk#8Y9orZ%)0I+Oq-}*qvMf(ldkv+6*@oFfaCu
zo;px(x)`jYirmZ>n{E!Iq<$cK)_(rE+zHTgXvnWuLiNi77Vfl-PpsU#(n{kN?sXds
zj`PoDzcCL<i<zXEnHkqn(9AgirdoOhlzPW-qkse<Jp>g@GhFBc(^0t$gYe7B`l;*c
z>N*0+I@D#LaCl*#`vcUlH!MPyn@e9C1{Fnu1kS;^zrYNB36KdO^$x86?w%SxD`skm
zXusZcQ^LLnXcRi9g>)~E0Gz*BV4SyV1jO^Cq@)*o@fw=&>}~N$EqTCKoES3`jHvP6
zkD6ZB|C9=+{``F88MX*NE+9fF=bF0(fI^XyVqEO*;o+eRh#}p;+&py<@Gu97+J&<5
z@F2w?%Gfpk<sYm9j^E#^$TmtK&lcRW7HfXZvvU9@zdfB0>I85^F4FtUaoO}s1ZBX?
zKL(;l_RziXn=1+MGV(SUDi;?Zns<PW=ujb{TxnTZ+*ZRucFSv^rl<p@Hb4u=0lt?`
zBox2hxd;i$J-XtmNRa?@Ek)>xIpM;Wl*v$f8X!VmTcGN*Vx9V(EaC#VpENMZA^^iJ
z0@A<iN5DUy79(qM&VW=eFV$0vJKkKk`R6#GxoIwgthZ;9+>~!yz!8s0p-b6O67nm=
z+^qVAJ|jKOO`rtT$CGy&e)!Zl2zPZc-`F_@33?;<PX8rB^7!i?EC;-ShNvh2GqdtB
z(Oml?usY(5>8FDHvS3I-N9herw;Pjm!QNxFQ)Qe@sbB`k3K$R}1m=h+(lZ;VhP$Cm
zke7@Js_&pp?v68v^aLY*9T<HoywwlP<Z#T*&Mv+I8o-*$%n`_8Yk3;PRT26gjpnZa
zk^}{0<r`4nz97N54D}`sIvlt}V}bB+c~wK0s0=MSW!*n6pxCl{b&+T7AS98Su>X4j
zKuitI%>xlw64OFU>y*^f=)D7w6R{`3-Ix1|KCHC)BGgUKdjE+AV3o+j_)6*P;owfw
z{Y6yd?lSKb;I+qLCjVtB_?y14aXMM^-;MqUyal}nw^4;@{&a%L!dj2;U`o)pU>usM
z=Wm1ig%-pk`8yK5^^-{_6F2!Y1VxZDfs7#~nS=SYFwJ){?Gdd!oI;E{PSM#(XudM5
zSC9<eyhya`a53Xb8GY2rA8OElv}%k#z5Y?B)$<-{dMFMUASh$LgMHwntW^rx2|Y9Q
z^p3HqA(HIG*mV%|0lbE(evb2&G%Mux#lPj`j&dv}fXyOcpTXB*4b0bh!Gk6CmpGRH
zc1zd)-SqdLjc*I6G4Ar+UqnS1L*8HXVT=pg_Lnft$p7BmV*itOos>pCdix5Ds*%qt
zay8Gm#}`J>jI=Ga{xfEX=*<Io6bG`;+E5SXpyfpNSm1?8YVKsA_4t#Lg2?}FWqc7l
zPk>|$i`A0nX_>OPADhNsl;r&a&GEJQKRbHFHlck4c2SIIQqAgz)7beG=-Z>;V+*67
z43+(NFTDD1MEo=_o>-&G!v;0v|2C9C5$?pJLyG1uJ5zrmixV%@Ig|K^^`4mPe|`~m
zG#28%JqqvfxlWk8Di!IMh;Bq;9XCZdcn2tw{ED`mp?BK%3ixFtvgY@%LIF<<xq5gx
znV@oAS@{a_9PGWwtbw2-8qN9$dtA5tKSLFjJAj|Tn6-D|a#4dCDZziPlz=R}VEmNt
z5yCpf-dee4no3_74@kFjuv(((<<?65edv_NqtCa5!rnH#XuAqM%z6T)c5?d1Pf^|j
zY?gIgXP8+}unW)qfMi3Xcr9)96ZsB951-;;`h1J!z?CaDxlL<L69F5#y?0CoM=FC%
z>LxLRi2m|T!~h17WS%#_v*@6FjB(cYBAD@OI3Ihw$BRmP0^Gv%-u~+Ia!gCRG*bIk
z``Lk-nf5J3vBLeZdz~KUw>NQ13K8J5r7|#lOA+Dj!#JNXAB_fuP>kEA?@?TVlK%K3
zl#H8c9j0b#eGfa2IYeY*9P50MPL$gN&LKmZ#)f1s6<5@mSFc}}zw;m=3S-sBUo~#b
z3D4-^{~n_4lc4CN^sy#*B6|Pgnur@rOY{DI8x4Y^qLx1IDleH5>f7ZN3e-)O&KF8C
zcKx8jrMo0>?_G`WM!FLT8ar-E)AjM(kUhBhXV{5n@dc#M^RlVW@v;Sr<{9_Lt7_bQ
zY!k#e>rFE(Lh=Kb)~7u0Jlx+^^yy`ZZ`k>S-qG{Bw>ONAm;a)=^~S0n*Wb6yfDDtQ
z){0m^A8T>I+EWta<Qr=Ee%Og*-lr}3!g<^T5QQ}k8?Tt0{(L0}zN$B@^L}eC>vU?_
zjr$`P;~<3=2PRgkjVd3?Bj~)B7+XTlX*DP)(al{mV`LZHc^PX;u$<W6jAt!DAUAIw
z*bk?$_*38sGhlh0sOUep)c2HhWQOtUFwKVAi}ihrQQ7<Z`s<sMK4Yt}__A<6pOE~K
z>dzM){_5Vp9Pk?Az{EDy*oQ>iV$Bybz31+X4Q6Z$DO3C6<iFXzRTJg3e~5cQAT&0g
zqE^H?2KKF3GJ6;tb4qGzRI+g277sd3E_Y6HIDGSi8}C=ShLI^==QTf%jb@@h6|xa?
z`R)GjOAa0OH*30dsIbzjVgH`lhL^*C2KoTa{HL_>3H+&R=S7(IZ-H&lIP&M|J9jnz
zJbLGv;s413e+uOP;9aDh8;9hS<kjeW!Sg&>g~%{07siag+L2|fcfM-&!ofjC$cFi-
zH?$ez>*UKvZr&~;nGWXF%iRXI--Co31RFP!a&cGi9|O&OKHc76tbH)<3GU?p%YSk3
z|CH1FpKRoy#|mXNcPk%uM>C88iQoc>FJBt?cq6JmCqID#b+Fk$&lmKc?HA~$%#-`a
z&2n`Z+dcT}hIq|WpJ`(MvCY*jNn^>(7<SBr(YQyfT!hJH+QeRdU~r|Egw=jr=R9Ot
zn%^3xa4nkBV&^~oZ|%KzSW{`cH;Q8$N0d=tMO0vBqzs~<Ae|7)poXea0#bs~qzgz1
zAq2~S4hR~MUc?3_Kqx|J3D}6#s1RC60#YJ@BvO(<LXxux@B8lA=i1lzegEup?Q@-z
zzaY<A&w9#z|L)&i)|0+@RRLj~Q<P96?71bBU2)C^0JFg|PrXQ~HN^p2jI%I9&ON3)
zz(`FA(jwMN+xnm(7g|Hypv?nVSPe~vQB`T%0Ok0VE#a=tv;1s-V1O~bWfm8eVT1s`
zUIl|~s2IDj;;<UfJ5+Z+^2XYr+nO}S3R{!AQ;RQ$6gIY2bXd_bZuVe{x(-VU|FyRt
zr|56ciqBU<-P9$c;?qT{BlZ=;6JdC=>v}I$0iSo2Z!9IJ>e9Lv-S`ctm4v{aUJ(0<
z*%e}OSwiDg&!3Hbh4UjD`}6Rs<(^lRYrmb_QuRz&$TCO?-6WSa4$X7-iaxd+%^P9`
zt$mYNX9>)oiaA_5v4c_xDq>V+ix9oT)KO*au#gT8opVerDdI~TZ+iSHMOl&dKCvh>
zQ86)m<xf_>rO}Be2q>!Yn+721Zb?7I!aT%RdE*+2o4xVmldjvYL|x-mfQrDO=^{ja
zExxh%3OvY~KgqJBv~LI$M`S!UX3Ad-q8mCQ85_A&EkWbpQ-sO&>;h3^uXqBC<swgP
zklPt`5ECkPBL^;4lXRN0@9;lB2}ExEw`Wr^fFX}O?${>!&N@L^p+sV#P7I|o{t%&+
zwt<_zM8Fzy2qf`VD2kYVMWH|mbz`dH*l=&sb|2mSWxbfST&tNdu2ePl>#u>-4~n`?
zo8?>{J+l)LcY?>4E%D)>43%s5MI(NY8@p(*WI0gjKxly66TD>gMlK)nUTn{umzMVy
zG@@!*Q+@N&yoC601Oq0RoSBENP)iQ1z2GL-9#Hn3_mUFXB;5Q~<uU=+fuX~$*YMin
zmT+uhPWxwyCWZ`UIu}=_h147n%uohq3dlx{WgnGa;{6?A<&`ECk(YS(*e{^9&Tf1>
zaAZt;nRn2Ze~%CYRZz7Lt^srLSZQn1Y+`YYe6+K|Hx^<KqpGRnX3MiU$0$qQ@{D&f
zw6#xdS?tBT9Stk1V12gLq02D3-23d-3Mv*=$?5ZHPAWCp7L8kIV}bfeMVCAfV`NmB
zDYnU%r;32DrfOg#v7Qc~NN`lW;Y!$Y+AWbx1I}G9(hg{KB9wK|t7C$9dwNdY)xcgR
z<LI8WXCuZW&z+l#UxhLzgIdF*F7w2FB!5DcDIw08H^8FV^65y&sCb?NwogYkwKD4K
zpNhTxc_l0oR_ZKv$zUG}J&BYg^ie-h7BNs~hRdJCeN#_oLQup3pA^{y5|LNfgd4g6
zyT|CyX`&v_sUeM$X*dOeZH69nt~FWfHt~cjJ256QgVMyUlw1QZaxROxtO;A43$hN{
zRGhWP2$wP_joF67kphlBS?m_YVBKSNv!?XPg|3Beg-xa*>(-ZKzLEYc&wc;g9tsl!
zd$qIJA_mUOx7E>z9%QLvD=5R1J=j^Ozb!MgpjT!)z1lx*;3lyQDaD4a6`~Oh><!!k
zktLyoQ)=_wZGyvy?x5Id$=R(OnH}|7GTh<FT-Lxwp$HwAQCByNq9f`u(pSl#)m+~q
zz>+4j^5v(`Zf;>3H71UfE1Vp@GG2@c*?K8*&?<l3e0KrrhpVaU2&834@%A=s?HZ6@
zTLd}Lnue6H;1n~n21=7?;pK*)_3pHQ?qZb6WL!<o`sUEngR<+##^md6$9VHHXO^F`
z9$TApswofvG@;b3tgUMoc=a3v<nR_tp>zTUYY*3i>K3Gd-Nn>XV%hEG@UtPGzdy;H
zr)GtZn)$UWsFcYZo5ZNAWr>0!{Ry(pM1yX3sz=!lS#L_fu){3$iNQ)Fj}!V9-cQzf
z^uV7bKhP)q+s(4WM=NvZ!#DC|v#ipQ-}}7?tZ#8mi4;<#1T(+5euBpA+lw2xBL8+H
zkM?k*0wt;F6k&Zd+kr_+jrAojd=CjybLY*rvex&<WB-vc<V5$fCU)!5@f=l5g5R_x
zXv^Ok#df@cTv1p>5v;HwyKxXx<Ut6Uu2ytr?Em+C`V#Ak9B4Aui?a)3I;mo#of)vg
zdQ!Ob5-6z6LN)?mBF^8PL$DlYLVP;OzJkN>p(cUe(V&hoxhgVR)utz}1YA*3<WhKv
z(9jgD8>y3T5!1yg#cFTm$hOMMV}C?Sk}K1(^*BM<LD>ws`jxM;Zo8^%7JrKM_sF^^
zTV$^rXTv_r9@}<g*T0zKzlg<j-B#`^iY>Medl*>ME@q!>=i5f~Z?=ifQo1nthbHI$
z$p_RA=#7bAumoGmNhK3Wl(Pr@tkYEs5=PBsq{ii#rc-(-oa<=e7=rbL72?00+j#<y
z;{ax0%R&!}LP@hUdb_$~?{|7RHo2JzC0AIudt7E8__kQJztsU0F_!1Q6R13qlbfD_
zV*QUc-QPgN#C>_1Z~VUHm1)RlTlsIZl;(Z3O^Zf=<VNaGC^*L8>UTQke-N=*DQlL4
zR@-reZokgZox8%F1m-Ktz2=-8OF_gZ3q;3W!bA(#QMvV-=g!k4C(w*WY4q~dvn}`G
zCnV#uic5c5<1@MQO(z`N5UsiM_L`#}LA`nOr0-8PHgL0Uv49nC!@xhzX!05k7Pfpd
z+&RKP@u<+vEpzX14584&yYJ_q(R~xv*&&Tb#(WF+v!?FNl2gI@7iH8$j_(bK`K-Qo
zzL5+i%Wng6Y(!4Qo%nmZlm0}r!a9de6a$hdzQI%Mn=f`cui&_@EJ)zX3L%cda4vOJ
zeH9Aw3X$z9zbfE~DK3A&)GtmtO>w3CN!g0+%5q^|-N;*KCDXvFTf81=$tKyCag07O
zWp{bCKXf}O`>X1&q$_U_J-1lv|NQ|d2Xv>SOsx~uYb<4b{09{eEIW39fAbvg9!rH%
zfqnKe9p>%V%fr4aogh0mPxr42Z{Eq9ZHQ|;7K><|gUDt%_08YG;^jyG*=RVqZ^vLj
z&F&b(QNnfbA`IqnT)Fl6$Z@ZAwGE;JYMZ2?eIvb?jylgnQnpd*k%wvtalJR=#jLcy
zD?R-cZ3Fu}cB?b<*mwiJCsoz-@2WrH*KYqR`sjQEt~-_cpISiv?W(kiMh}q*=daxP
zkiY9c;Z1jJ*XaS{h{S)Y>H}b}to_>$0_w3#e?oW1Kd0EVhM<z%`TGcSd?c7ei}hP2
z-iniHri&r~Uknrdgf_i2#Fy^nioXw;80xpWm15w>;RbEyDpC6U0EbF;SJoO7x>3UH
zNhBz+yN&6M`Iz%i{~mMx5)1wJp0PzV4)SK$l|m0>t!bQJFWAWqGx%C`Ef_Fp*Z&PL
zziy<nDIjciw|R6SXnM4n_9m2KjTJeYZJCqpC;%UNV-+LLpt`0@ycX75LU&^eqzci+
zV!kA;3xZAlw4-1<1!o&}-f%cuP^BT78wpxR6uC8g33)}V{;7B!{ykhDBxc`<bdQRt
zD}kJn&_2}59&#u!iGC;h@$f}ZxzIi85^sj(&^KR-*?P26=ID%F)bIa^PBqx331uDL
z*l*l_=#p4`Gd`5JD9XWC$9eM}BF#~YtO^Ru*}g1#=g{G`fgL{s2&ihAp~6M~dk>c{
zMlx9mee(>qKYdX|zaf|lghowwd#4B0<Ox>J8pyoe)Qn>Xro*D${*)5ZO-h|wPew{#
zP)^w1rnxzthoX!ZR~IpRw$ANZ>wJF+0L&5}>+LD&<)IFs>Za+XY6i~2^rC9=Q$%-a
zYfHLGdXZA=3t=OusP(DvgU0FahhL_HTH=O(-XXI^6DI+&X!7lqPO%g)r6-5&`7zk-
zTUb*UBWrW#_lj~NrMdI9e23Y=5#{fa>+8tsG6={^c90vdUt^q!#3~pz-W%d?=IPtl
zBKCw`uPNmeg&OHw#DME8!1_e7Ri5dOnugt3DnK-1qt~mnvNVsIdp0&!)vX|Q%MG9p
zQ^lc_YLQ*tgj}Sdqh`e!UF>)bYr)cJ!;Rzn_I@8w`xbwWH_tj}TYo5){evhsn`|(g
z^;F~689ZN)`7w9CUfiE2o{D?>ljuqs16O2nqbFb9UBSy<)4BLnWgXx(L_}vWG`9-R
zZkKaO?aNywdTQ(Hpc<OI5wPbsAEUmpxEGXbyYk}+#qKu`%XQU#OvJPL^e4{0Hrz-@
z1;+sJ%EAfsm|}{8Jt-Lq<vbgh3mjYAG7}m=bVSNLW$(Q5ue{FRkB1on4;-(0H*)iV
z_dZ(JU%}1GXpBB4$dTa@Y$VraG5XXrx-V&ma?J8mX}@Rg_R3M`jF<;10}_}!(Sfop
zIgC}c<Dhd%?Mj0Wm0!;pjk=kUQm{St)@RqHu)<*q2HZk+o#4mVIv;@&%HkZNW>g|Q
zhdDTDH|wydENT(p1<8Rown?tX9ekUDDRqJYVVkxD51^`PPJ|K+r-XW2(xrRAeQ8~S
zx(8-*H-2witV?Ix3K}R+sV<L#q&=;5k*L{d!S`|1l{aas;br3YDCx~GVJBXZwx#X#
z!E5%E$2lI4OgU3dB0iDT)i)2JGz;mVTu&%6$~56p1~QfZx+LyRx7bMjUy`4)GnRW&
zU-Ip3Qx499a>tLusP5qZ)Ot}Fj0|?mz_-RfYeMjZtjS#ON)H02V;|si-hoDLmf`A+
zAFuedA?|3Lo_3N%pGKA^)nZ6Jqu`gqcN)+?z@<Wh#yC4N|1LoxY&YNY^Q2V~6Pli@
z(p=GGT^DI^K&Iw9J<f)p7bml#H&pJht@KO4v5NH5!zw%tFX|>^S_5kq__iKm3<jS=
z=TCnGaZq(lG0ph(z|uf90HcaAJn_wJ@ADa-7R)yCB7PTTvrH*u=T_&!hW8T|FmVJ=
zK;f^vmND_vp5JsM;g@)tw)L6~Ubm3t_1gLF7uX8egHU`$s4MNcX`pZwZhM9nnLlUS
zqVhIuO?|y`0Zk%buMxk!92`<3m{YgaIaTk;##opUWEgvtdT|5p;cnTbkN;PJwSUp0
zq^6G}g_s>1DI?hea-+;5w3PvDQxa4Me{w~n$CXo(b><kn&ajSOK3g${{3By;eN1Hn
ze0pBs81{<j&c;+iK~I_ny9>ZMz3b?^#&D&?rchpXpGfKq!U#t^r-@HY>*Aa;o(MW9
zs?cK9e^W}p;--~ZT=A$C#%7E~hmCWbda)b_@%gBmykg3UzZ(||jA^)O!sWV<ZKS!J
z-U`U^)jfZ2mJD?-g8@56@^1(KCwoZqZ+8Q}=jYDT{!Vub2`<zK{ufY$Ukm*26kO1g
z$|a;ua8BqakpIr9aYaf}%cu(VLy_)0Os)v0{2x<D6x2~x`sU{;{@AT`f)GVRC$L^&
z<kzL@?bz1;a0dErl^*RFVChhXfsNgRF@l<56&#Bzqn(U!oPNrkDP@WW_SoNNG~i>&
zvBlz@{q0oE%9|FK>W!Gr3OWC#&~=vYoQRIJrY2|x5*{<>+ndJXQIL7%-}Z83XnpAJ
zuh8$FzEB=#hOLS5#soPZ#8_jjOC#4dRnFxWd{awpw86HUx`m6(b<-Y`L|>0uCOYZi
z_O|#trk8O(``FX}6A|*Gu6v`tN~U9=>W&W-PV?Qym{*<#%HW2$uC{-AApvJYKi??S
z&<^*1M@!-BOL>&dee>3N?ssKAP@+2E+0#DxZ05}OiMVA%oN38OeY$r&l$@J}gVDnO
zz8_|FW>3V|d2Or~^+SUC-1)PACp5^>?qw}Xn!1YMaqhgw-&+XG?&W6yeF*}ud09mE
z-?!2zM=8wY<AL)#Kgl$|t*IIync4MbPiRAvKLlEbzwchQ-pG6Voipe&``}m4pBtu@
zCaO2~|1ic<aT6~;&ENlVGo=q3rrWg3#S#zrNy>F&7D-ivxYN8MOQVQ;@H4#cK%D4d
zn3FwL=gQ5)ZHa}jd;h8h7(L`lXaG3>1Nlj1TcV0f<QkoVfv%fcR12rOBh3VnqYwPs
zxd+P>lLQ$Qlv2a=jxCj2Pl51tyhWY|MO%@!sr;#?vw|C77e{gvW9-RTJ6GWEI5%Ds
zU<bQ<JTRKa)*K9@4Z1=70NFI7Du1_l68B+udcOGd{J^catFiW#lBN#U+~c^*yoam_
z*~AQ;XiHk)zUhg|M!<;}8DuQ*bxdb73`ScWyp9?=6-F=+Q59yZZL(-p{B$I8YDd@+
zsRFm>B?h`m_b}re$XBFwoko1t?&W|~g?=k4@2=oqu?$J7eYK%c;x#`dx(=&~;+jnP
z?+Y5bs41AmWZRHX7YQXD^*idgfV7>2Ta%q4H?<^JT4V7Fj<d<7s)Cq#keu__GPU6I
zF3rM*HP>XbR5EUfm)jn-#IxgG0ULjvRp2g>TQ)Wd3hif}2R#t%R>L7?75CA8sQSy!
z{92>{#Jjr|+FEtK2HMa1Er@|hK$uz(g%_FyS7?1NS)VDc`spA-lh5B}F3<hvuNWB(
zrTOU6pZQO(I@G<0vu$Q@q#RcI-$^FTlU7S}GjR0qPd5>8SfU2v>kf^<|B8oZXutPt
z$TrCa0#9MC;?X6bIt2J+1$aV#UOmGL>Z3lg<xhdiJ1m;{BQlSDe;crSpD9T*%Qc`P
z>Sd(&6S53m+tzl~k}^FL`%a=E)AZx1CH%3dM>3Bm(a8`XX&-W{_paw1uwDT34$+_R
z-iiT!836F~5&(oJCiunUZmnlQs#Mnk2A%|8Q%P<4SJ{(ungM%;Np_Mt7P=IAh%eR!
zv<cVlmFwPrBoRq_J<vNN?!&}5nPF-%y7r{43stv->j<(4NVDpd5PH|dK`hl)X13(y
zuYWvtKz6%}C15}Z)2E`=|9c8NMSCk|eNwxXZQ4Xb(P8ZG(2acAtlIPr0bl(U4Z9cu
z+kpF7wq9PnNh43M+FNhCwf?+Y?{=EYhMaiz(Z9^v|9QeC-#K>G7E_3T$4~5@p6g*k
zhZNRE2H?S$M8W8-X?NduZwzH)siF`4E1$E|Z!?#)bHO-^TRK9Zedtmgmhtc#-%N6z
z@OR#cc|%+csDqpCTMtG+z5f}!dsIVtyIR<C+uMKRvIDjdVJ_|2+b=`Xb9O*58Wtch
z+^#*(Bbe7;O>B~l;=;dENabT5ASz}t3*OE1YlpEB`0KI-95?~wFg-37e(}jqV&FVS
zzT6q9VAJX2P4DpaKAMh#(44&tqx^)|aRq1JyrR(d(cYgTI*21h(v^rmqKaAHoMx^Q
zqaC@JHH<pif5o&EZ)8&1+WhUD*JIi%tZv}`+H_~8PSZ9yFY#I$>;Xp6S!&Niv-~lt
z6&9@@UuMmuV@dMvivt@6@7$S>p-Ah1aLCdpmaPabVdl4ddhOhT5#F~ndUHm6jkmfM
zIfpu@!*1@W`YZGj0kszOFr!4}wfwkEt|}17#Kl<hYAId$n{@G&7EtlI46qemx3W%M
zl%b(`lL{o6sLn<q?i-AP20NTISK9>m`HzPI=6t$&DH3+Q%c#|<59bfIt2CV!4zkPo
z><SUE#TwpIZuI<capgu7p&XeJDcmwRTgEl>{Y*IYx<b*j{{u7rWkFv#SQ8pz!Ov5U
z!MXwA_Pr~75!kUg+5_9cfC4^@m(#XbC^;Dm<Yb-1tfh%Pm}Ey)m>0}?o--5jqb{b%
z*+cIl$ff!qc&5~JVKPX!>A@ZTu#~qzobN@sF02MICUgoV&lC#0&o4%OCRu#eOjwKo
zSaR~i+0oh1LjDuc+8xp3=Pd>_i$%9B@f)^BF3prirT9x$*T^;sF}Y`?4k<rt2;?n3
zT9jb{U#gtdts#n}RheQt_U)YTu#idM(YuK;CwePGhkew;0Q02l9OLPDRwhZyKgKHh
zpXW8(I?qe|qpWyD)?Uhq?Q^n-#r|y@xL=Xg!Z)lAO0Nc#EQyO^#LG_mOBDb7i^_p$
zXI-ovPEui7A9ubk{E@9p@7D6wB~vY|P>OfH`@{9HW-*YV+CU^#0?{xk)=Z~C=1h<M
ztT6_<i7bVDls&8pTE8y3|Kqcu|D9>VwS_u2o=d4cIKBB+PhWbe!dm9t_$I(|7FZuG
zjr0;jfc*o*02i}L6G+B~yGM1m85pU?0%yk_zb3%ixd8Uw1S8Z1GRVf8w#B;sCB?;g
z)?H1!(&`3!A$%)W2V?@yj&h|eHsbB!(3&-~=_Xyliu-j5F`q*K?-IBNqrC-Fi+o}V
zz}=6Bh$~fT77;C|IF<0_cEGyIiKc!Fl3~pMYtq*L-5Zw0&dHtk-99Tb2QJ3ivCzR|
zSTK(01UO*MAgpPEl7UXSP9c45)e@s@+M44MUYRxvXIRf?yz#G!poK6{+-amtdw@0d
zP)>i95C$fPReCxQ%o*!Bk^6V=FUI`AE7cdYI@54Kz_$-Wa#FM@Mx5b|#KUAAtN*Ol
z%Ro|*S6?sY&%G1%YLsvWv8|X&H$s#tbC<32qEI#fIZ>S~X9_mdPl$->@SJwP1S$jQ
z=@RP^kXF{j7A}U~*cJ){5wD(MHrs$z3{D+Osw)wGU}>a;`uw6=F1hwu7TfL40IFd)
z$Mq#laW~Zo^laqw<n3Uvr-v&jTsZn0s5Y*@%^=L;2FRI{%K9@U^x7%=O8O8T2K=@H
z;9%@o!}NE#^B=5pq}sO5c#9VuMJ-TgF!kD5y1%3Ja>$`41Mjd1r{euj_PsN$Vcpw<
zqs%BEK0QUaKGJPjFkrVt%qFqETME{e{J1?()IqK8UTMaZI+F~BpN@A>vNYU`jvkp5
zU*Xl<3Z%V;v^682#K{LQhLfUMVpQzskn)e&p5pds&&DV~>VZ#p;dXcr!^p2aKRdR&
zQUgIJ!sDa?l*KJ`GHT?qac#u>?&SvGL-^V_7r=26!zv^QqW{{>ZJAX!Boc4Gc$%+D
zGocB^`#dA|H|n-%kTo9o1xR%(^rVI%ekQ?OX%(h1IniqkiFIcH>#mxw#NhrC<nBx>
z5wbd|GAS7NOI9qje2zEKH$Skqe<d`knU$-}kzu)P@$lHVo>O&1+T^u$UHlhzYX1+f
zAo=fB#dB99qo_&n(_@(%WHpL%0G#E4U#Wm^^yqJ`l(}@XX^Gh{VuPX<11IF_DhGY`
za^ZlRv$h{mwm{v=YR{74T}8k>BkErSt!<MF-hI6;^#9>voBz(G`akEv{~E=}>u+1G
zpN;Z~x>;8}a>>Ip<W=;sDp`DO>}Sm}59C1%6$-u#DsSpZm`-RBQduFCWK3ZscfI&+
z2T;lqcaisnwWQwL%3I<yu#14MhIMy(uC4Gdi0_qIJ1E*1BRuzp0L@yRArVaK;?4rl
z##n1l-1H}Q3Qn2u=b)a&or}V<4%iYENY0w}U=>5;YBwvu)|u-$^@y^4nto#K`Wqkj
z0Gu>{g6kbcMQ1p*pe`b7n##Gfh^vE3^R-y%i9)Hg*Ib%H;W5`!?#K552!i<9y^L~h
zb!EHwJt(9sVikc5#Phv;nF}E+{Zl!vpTPMk2!vAoXKl*$zNZIWwuTz$OWJ=5LI$Zu
zG1=i1+{y0kKMdw3wD9HXoIecG7wU;C9DxqN<LVbXwX+SR3%K|?>GBFJ*TD$uE%MDi
z79`Ou5}Sn#ousQKQ*~OdyDql!U*h>aG2+>Rrrg#-xB8<&5?|Qrmv7`5W$00EkYqW`
z9Jk{j31JsT`2N<nN6I?L7&W4==ti>qBv}YMn6ObdO;SLIPYw9?fM*AbYAbtZa4q9p
zGj0es&%tOBKk=7#mVp#aPDof{E>*Irz_2{ik>~RbLvw&;0dFx|0Ftn?izwDop~G6Q
zI*svg21CK)1|%j<bbh+Ohc(p5)VRI%MsZ9b<(~ZvG2z2Pf-Z=g0b{)=UKv>%w{^WP
z*Y<-{!cIAlw9{R@S;_L0E>}lI+36=th_e@BgDcW<(~Jy<L&v>(wv=~?f^PUqxUAG$
zL7o;j2yo+4pbRDZHT$bIK;(b!HECOfRq8MrLqwmU@$6abXL1XY(6lTU+50NAYAT<J
zJ7X4!#|oD=Oqb&$g(;N1X7~_r;DdrbSU?R}Bq?wxBu*Xdoq1&X$wd4otFw?=uUvhu
zcSbjQ3`GVSYk<ECDgHZ;`$yQJ1Yhfsf>W(;&yAmAE&*p_o8d!2@jzF_*m`BJUK0w<
zLNH}8uOHOGTv|~Ko>|@QyhzS&Gy!^U45Vf$n;8`&Iu_1+Kiltb`kjGeI>ty=Uj%)f
zrClmSB$_ZCkwIPc#JZ_d(&qHH=-wIafnF2j6Py5J66127c@j8k0hYf1fsvC!Zdw>r
z%={^^&=q;jRFcfPrCnBcDA~y<VE09Zt5Aci?C-ciPGUlLrSMI{6GdJ7GP_)q8TM<J
zvavkXk#=)$oa+unC+4xMbRPB@bF?e)h8>p%IC8qd0o-_3ZRQf6Rja&QKy;2;Sf#q|
zg|XUnmqSAr!XDOny<#ryz&x9#c4AyuCdg~ejB$f#SozM(AIo1v3e6}+*~hs_lt+;q
zUxVPF-O(ncIL>!g`(l`{ll#SEQ+q$~qV0<N@6m4|<Y3?0bail~$OA67i$fPSMnZXy
zG{8r9wokJ*>eorAzpR&XRN0?5gaef=Ui@d2V_B{few1Xtz~GROa%UN#a&FtV13OVf
zDT1O_&hRj-JGS)7m`fS>3&EJQu!XFzX3ps|2u6F9l%>45o~BE5Rk&WGI9$1RG}=)C
z#%`C{)ExIYCLW{h=2_KT7nhLH(<A%#h}+4dozog37QN22wqx%B9Mz|levC4h=dLVq
zpdd<B@jA~P)-dIN`x%qIj$wYTagQRjwKD0G==(qasCGK(_>Xtr{PN?+;EQ9g<Tvbn
z2-&pvhMdp$6QF<G|M>le+#f!%zf^5{box~8qhEeFu<h9&N-BBxe%=V*a7HKF9bF(G
zaXbn3rOB*FOmmYc-Xe(76cx&)BcOvgOf$-(&gg$TsQ*b9wB?x{?Rb~ba;#$oqGJ_t
z4$Xi>RO}7HSD2>TMMR*tYQnFg$!aDp#|DS|q&bz?*x=fb`C7B1rq0w<>;pATwG~xJ
z(W9CjFq;Lonkb{-@uUHSQ&>}IYaUz%?#t@)|Hf@{@#!8??Fv&NjUQ<~gU0y=xqA}&
zRO<Zg(GHh;Y5p3G$2)GR!K@ab5Eb;~Umj6uk9OKd^kqi!yBvBcj0+Lr=tXq=@eYTK
z(dVZ<(s&=P@X6N5-tBU)S-o0eYWaJzm@iryk0F{vDDYyfs9=r8X?gL}#>%hNJ@p^R
zoT_gwA!e1F!6!sX4S34>%gOYdlHMLQ)!dTSS+CD0yirG6;fIp3bC!*((YUj>1ur2)
z^o(fvi&}a|yv4-vX8kX#%d0Nv3C;c~7g&MQa!aPMuH2`Z=F*je1G8V~2fNHiLTc@{
zHHF^Xn;wEcmM{QQtR9(C%#tj;){3(FJ#EnPU$e&u$K~tiz3PapZS>hvwd;PvBzw;D
zQu%!KH_f?|`H(bahiq?TxbyfTdhrLj3R4X?@F|o=$1#V{^CKAxx8jzq{t_JxXbu^K
z<J45`77!3S+lY5+$pRLwck9NNvqw~zpX--h!#q9&Qp2MHJK3o8Z#-!rgwLzLdU%T8
zE>bfwa>Gx(+`}s0^A|p+M0d6ln^O(WVwk7BbGmsQ9eKH)9j=Af#980Q+wE4l&`6Uu
z$n)nfK{z=T%!QszmnUvvRts}#G1d|6Ou?um0ocrgYH;Uz4j!NSs;io4jQtMpfN${n
zAofD{p-1ay)0WH+-!V!dzl^`AGzGjDfqT(1f7GoxWhBeq7ojS@CQj}?)UW=an@ZSc
z-DZ_(oxjZ2<m&Z~8b_U(pEOzWb&A@n&nF3Uri|S!#cIQ@5WXLBcguPIpa{P&;j!1z
zx=>#(SU~@rHEwpYy>KGmMc_@a?plzI!csYVau8im_~!mf=1x&x%!T^2jm9&I?I4H*
zdqniidVupA-tu;+Mblp-OJLiaVcFHDy}x}0%KHq7CM5-TzfMId0S}K0h&xo7chIlO
z!)Q*-Xi|%{@AfhyetEL`47<>3KuY*b4D+kvCU_-BRm@h?>wH&QCR^kl!#y@^3^CE`
zAitEaM|q;NpBH=-5eF?xlSY5J<7y$*pZe>5xQ_M0DvOIa>e}oPvYkB>q57AqU$xuB
zR0c(OqL6pZfIW~?f}D*##IarA(}moZ-8QXuM2`yCz>`(KI!RrY=xw~m<qFvAcTEHH
zE{tP&b7suVWf#8Jr0#?)cxz28x@S)IKtiD)$*W8ygLKG({dO*GidJ$3g}k%C#SWmN
zJ4V=nZrAOzMHg3(r_c`QCOgeaM_hHC`$8Wmjo{Nr<6#;mT2rKS|CrHi__8zFerW(#
zIN83Z6HwE63WHk=Exk@VhIX&ROUMwN(W}5<%enh(`Gt!=II6|E?mGwx&6-rmn6qCf
z$#gXo^+Fa7LhLRmJrwhXg(<Y(?Jwlsdr>i!d4fIq{0i9Hsu;cSra6HV?Jp=g|9@K&
z5|(FV7~lKMp|aZyJOx@PSHsywkTW~%qEmeDD~Y^$AxvX;zd^f&+jQY82$69WHa)We
zqQkBzezf2*-l=qmnylXF)cO3P5lhU-K*u9*A$_6|4oM1dpDl{qcyI?9e^Jn+JlhQE
zLo!?Bs?;iTBW{Cq-L_s}*hHM}^f@v}^`qGzxWu%}a%g|O$j8`ZoRAnMu|U@bcI10E
zr;P6NF6%uHop1OCAP#F*&zY1P<F1@Fd5|E~g+x5w<~w@=rg==1VXlVF|6XL74WLeW
zx+|b|Rhk{STGYj-yV7x8`&*EjFV!}bDyJ~)A{c4Hk3I^$V?X3w5^)X(FhQ}jG@fsb
zO>y>a=H|p+cdd@I4t}jF7Bij&lj)Z=jt|ddKeJSiC6{-OUbo4;s^~~=9I<gTU_>i6
z9@~9$@w1b;P7?s!!lqP8__PxAAcXErkv`O#%GdaZs4K;d!_{Ny`OT&Cj?acS{FGqg
z>qdN3fj=pzaJy}!1?_A#o=9&czepYKiHN)2&TR(Unt{hEC#s1ySu=~5ix+=ryDdI*
zuJ{HlTd$t}D=jL7YLWdW`Xphbp=cP<tXj|f{9h@X`2-8@M8owgsh;3wPYTeZ8XeVI
z-1LJ4l>LRqA6TOp0Hy_@jpVMET>wrEyelV-*O*yM4NUf^#T;<(qwQ`{xDfG6Pr`M$
z6I%ObpRGnt<`~VUu|}}VEZBQ(xoZEpTz2v>C()bC6A{kCn)*XUFQNYcf;;}r(0}wi
zqgjTgY@)>j*qsvUwpvcq@s28he*g-#;~05*bgzSab$E=EpwX4PvN!95Utn_xsyq*_
zoa`}tAtG)I<YD-L)*I1~eErlZGz=2q-Wgv1X!4_4jNL*mW;EBeXZwZdln0sG<glCy
z(l<YJX>e_Cl-WS`XFa56gs0rM3sS?HIFpBye?mfwD679Mr2!oo?&wrV-xfQ$yRP|T
z$fGCu>6Kr@KZm3gQwDv@)B2*_L!Qf<_2U+81EeX_yR_aN?8#epx9r-l|1P>u;<7iz
zcxUqsX(k&msh9S2R#@K_u#|#HZ`20!E=GrSTB?Z`(u)w>>s=j6?vIJNlLz$8V?5G%
zzORoKXBQ~SnMMa&J;h7fhG9Tk40_eepO8;e_4_1sIG71Gcf-S*@$&!mk{M0}+M}UR
z9^u5IXxHLiC$m{|_(#3=?%$hzQRTNSRgzO~+H3)<t@dZu%%aDT*S1jSUvHVhxjhEQ
zq=}y*$(>%n`Qzh}13VX)^E!SXv5UJoE>(y7cK*T~v|gY>h!n(JjvCo!<+$*%T6$M-
z>*zqy!#DC3AxBG71DFg9PXqc7kLD}VGo1&!qr0EcD(nB`-)vO)pit|=j5Sf~$j?~4
z#^nyG!@ba5)BSWs3@@rkJ>Kb>AAT;~7yoVI#-q^3dTJNYtF{`bgbLlfWh<VvwWX}L
zesU*8IJ2X(*rTHOVYCM4+6lTx1*7ToN|eRGAk9v&j&?I6C^eH~Qy97`WWiqxKczM`
z*ao)C{bec=qLMXC{GAPnxSrBwtco2_9eo0e!45D+pRE2y{Tl#BJ;>HpgTuB!Ly3A*
z;-Dhf$ywKuRuCz=ss6}`S~osPZ}zM4TqpgcboENMM;-ST?`Li5N_Bf|KGvI?!@CsS
zS4|8Z8Hxdjk##7nGrOj>XDa!|kj=t)rqS&H@0lI;-|G9;(DqPX#RR+XWQjcjUEuJT
zAkGZQ@lj#xM7WJ!1FL#`n0QLv2gc&MT$KGw!=wH{r$eJ!j2Bh+ja3B~ng}%_Lvhys
z5;td$L&+)8gLA8q?8A!m*+TfGEyysvm4W-iO$2P#Cwh<P!#4>C*4>Ea@qh2D*Pn+9
zCr=NX&iWEgHir1`vg>+cU);T{oq)m~iSdk<ywppNR;*jYtUds<Y5>eq0Wj-Q^ls>R
z&6RlGD$`#AJE;K4^=)EIZ+4Y#^UXR2sAV|nXueL`J72+6IY~5y)E`Pq(TAIlgCKOb
z$I`J;#Cf;S><R`i!N<mg6o=26T4Pvke>I)!Y-nQo-MD7)qHCv9C3+GqFXYXu8Fh`N
z&?uMbEi8qjU@+vhYAjHSw+i{=v||bJy#MiYBi8$dI4!J00DDLP*aQ4EC~etQ!K0n^
zJM{t9yOtODxv&0nG`nAoYzt2Z0L!pll3fha!cmdvRYX0}P+|0HGxkXefN9QWsig5H
zmu$#D5-zRvc*ofccau94^Hpl#yvfvH&^~U>x89l$;>#L#&1caQmpKD-+}a13&3a))
zlfJ`EjNa~V?~A<&h<`Gk4<XM-7nW}aE4vVaO8sW@+n-Mz%U!hw-t_c^HMK=EFEP)Z
zvlm_(4DYx{;toA2P91bIucrZ&5#{lIjWS-j!%TClG=54<P8*h3lUAjEXsGQFJ*xKG
z--fy=3_S`QuPT--2<_sm*pG6>hlCGOixD3H{?{deYlQ;|I6F>WSL@6UUER(lH%oup
zvN{UKoHd$rnba_4r@!?0Gq7lefRHu6d+J@sZa5%$tJkGxeLJQ<;!@{xwPyA}zh!%>
zxcb0|#m>PGrs}a7diJEv0rjwA<#*4I-U;7E5AO<c2%S(Rtv~V=8NT7#z^Uuht>{A;
zlfMG)$7HV-{!hRaDb%XHQ$+ZU9bI?qb!iv!_k^53V~1I~=v9Z#y~ks$0RLvTG=3cP
zaG@#ww+>jTjd@(S5pAyjH}7^y;?%q(v*dEZZyn_4qjAW0nez>;d6Rt*`lCrUq?cQk
z9?a`hQ?*ip33`<Z&f+o`&eF2ctJdh47jq0;%ol6tT!6y=|DXT$3RD@3G7i}+<Qjx!
zOx5bD@7$>Y4@%+L{e{yR*%Vz&i?bWLuprQ)O1eXvYqHWH5#gP)h{ZI~4^?0IH-RX{
z0R6oFg;UA^Lqikv&80iflEFEX{oAG#*vFECOKYk9miabN5?w*3tOx%7?Q7HK>yd}=
z4Dz2Ht8aD#2-F>PiyCk`M7$Z`efn#{5$ICQ#wb5CXiY3O_E0dM+{{CAO3|Z@c*$)Y
zvo9qeEoRvrNRhi3b&y-luv#d{91?bYv<LDGUrY~oOn-_HDHTay?jpC6?=Eab`1X$2
z`Qu-u80J={5e^=GLO?nJcDI@inWtFhLm<0(RM4{z(Sx*VC8XU!d_nQ#;AOskm;c`M
zJ8VAOh)8DJMuc`o7Y~HPnr=pHIT1O*l2YT+306+PIf@q*+A60N_2inqHzZ9}=1elD
zhj%4zZVW>&>QPPDLz&okRkLiB4H=`F9oq=J?3=@n(s*Q724vw+lYZ1`^q^ossuuAN
zIqKKRZEe318U<W&#v#g+RRTb=4T8O4oqMf}3QJ&Sz(D6zuEp^{$9+>L<39cgRd4jL
z+AVmPNuEmPONf?eX>J7Xm!o%R(RM=_lOG7YR>%TFng7E2AS3~B2|CrXdtMbXmn5JQ
z9W>@z<wVs+-m&<Y@&Od_O(dE180p{X)M}e_J&_ot^z`cRh>i2&z*42Vwp+5tvJF?7
z2XvZw(hMfsj`C~8%{NNrJxi(4quDwhMt26s4bP_6iB&`&JUvvyN`QB?p7`<<6S3+m
zURxk(kV*#-SM!Vc8?%s?T%?lCPYVMqX;|)LIyzp<x8|9y2K*BGpzRFPZluK<4Ytj7
znK^|ftHT4)&(8PHZ-gY6b!cV)?vpmevuN_U7P2{%!uNg|3R$RiT5;1IuI~;U$tKr(
zUv;mE(yV@LlZLw#68c<iGc0SefW8QR3{a;-_;-fsb7HCbaNg@fML*7ap^egBAr5y`
zpj)b-Qb}gDAER*hGm_yqMgvmt3?lhuid3?ZKuWh+5IC)1PnLlpm9C)D8RbpS;!oZg
zoLPkIq@MP8*1gCZ4VdG5m~`l>uV6DL#9uSVk60Aw)z5@{2|m;4ov|9%`NvHrPq?&_
z>C$@kC8Ka?AUafFoMR9gl&rBbPUpA&+R^&LCcX|l6)&pE>}YEzY%v2xmlDX7=mQyx
zX(SE$qc^kq0oNLR;r76xS;^{fd-V2<c>O73c<dHt0m2bU@O~F3DkkVa?!UQ0-9{%l
z@(PE}Am!vvrLc76ptcJc6h=nnHH06o`-2wjNS;P)sqH5Hf}g1wlTx2qMf&*Et3CJ|
zd_x7uLe@4mn>?Ufo}Txm^}PKzDi0H3+ipRGIVHH?#$!Wroia9s<yRk1AF}ex_hEm^
z)LqUPiT}29#5}@^##_kR#q%`iARRCl%xgiOhM)T|PHpdxa;z_kNvL?k*Hj<>g6d@-
zu<#$+ktB@B2pQ1tZSf@6UjxKfbzk25u8f!ci``qI3UVixHwQDL)9<)W)brC$OeJNL
zU#iJdS&^_l$iT?G4)_Oe2VM{_Ps=xZ95jDUYui$Il>0;Uae_Y&6%%+dOs@gMuRE*~
zWcJ1l<q1Etod*Okj~%f4&gmNa>1pW$crQIu`=X&kWuVn`#+dk2=VL2d;&*z*2SWz^
z31R!?$|m^rcM@8>4Rz7xyH1>&p8*hjdEk<BK3KhHPm5~z(qt$KzHCSxg`mGRWMVt>
zETnt)2#s23+@g!*y$)y{5dwuemXT2iG1=<TuZ>Em2ZwzKI4chBlg%~;mZvThb~S9`
z{;CBP8dfl=S}`d=%_8-}cf<SCD}9@|epc@8uU=RP3?Sd@_hu}bW*bIXIWfgAGucAW
zd{g79)9|ybl*{dnY$;9c4I<u2Qh8V<%<N6G<{e`Zejf`c1-o*xV9d7jy}DS}<k@ab
zJg%s()iPXV8(k9NiAKB_HEn!^zG$rFma)elKRP`O;h-n)dFCgpue_2+TjeXozapIM
z@OD7&LtIAN8|`N?8YVxR?`cV)XSw#E$3hP8o8ILHS=<zc0^Fq2PWhtC8f;-{Nkm5F
z&4PV>2uxgFYIeh;3TiwAeg36<_Qh76caZYpLk^5GHu0AZq{D8vXS*B7m`=&}Oi9t@
z$H>*Co`bWIPN2TsmbEVLXB+#5kTVyNs7I5NYjHGcA6B$a3(OZU<itsyU?%dl&VmX)
z&63aIh}4(wuNGc+dmr5!cPLmhROFd6d0z`R_}HOxfZLizQd^l(gH^JS0oF)Peze_n
zeGO)mP6)>Ct|!WBSckF4NG}KbMAceg*C<3B_8m%c^PR2!49ioCo!zvw>##~O$ymTk
z&wTeT6EK*{2Of`!OIXr#>bpd+_}-4O?cA2#EwIjI-CfBqyRR{H5f8FP%(bUd27iy~
zooc=JAir+4Dp>s1IM8gmaLi*8fTw{?Ts!ZpD%Fp>v&KNjODYLkd-n0Wh{5?G8nyQQ
zj?(Lqkao?DhUnF+PW;hS$*$|M&E%dboXer;9?UAXG_wL%9;}zd{yplq5Pf#q9#igM
zWN9_xU5Sum5OSy8xEeFjr36X{U=#&E;O0O0y^sITW$~}p_3frYAdPIh8bfx$CqJ9s
zS@)6tQjGBBgIwAa;;R;Af^h+|nqFhnfk+++L63<q;N}qR<`(Ij!ayf`U#orv5{sLG
z1)yDMw2jlxR%LL?Fssin(qicCm=VQ$=b^s#0k3*g^FGnfZF%PqtO4g|N<jD{h>2b4
zWI!xGhU8Mpx6upR-<n_3&OP=<WbH80V&%j~rxu%+u{UwCn1$98FXjMkn6<S^ln)co
z4lezwq;|Aj-3E>R#Lix!Ch^iDUX)m<)ZS@I>JhEs?EsWU^s@2*e&^aF-D+qjHcU%O
z*p-ea6q?z7H+(>K9WB8~dx1TK-p2Rj6bv@LzAxkrGoHj}bMND@Dy^i<rb@$pa=<pa
zwzDsZhCB{TRjf35>lkHQm3DB9`3bq9`#qVuI*^6!TKFM81sX?y_lg^i_xW#<{!wQU
zaJj*CWOei|$tYtrk|!v;ZyV_EJev{PSezV1%`)^ZNg1;0q;NCbAHK2SA{;7TyWbV2
z2wxjFsC96mD2A`qN*B6-UZqOL4A?6>OrNF*BT1P7*Nou_H?~IY$Y;(DpPL(acg#Et
znBfv?QiRHlubeRhwsF$uW<>1(<-~}Q^=R+cMM_*g^ntxTF2{W-0fSmzNmnXv{w8=Y
z5=aO~J47%0c@I)mn|v|>2Ui-J#ANrQptFOnUd?ja_O}%iD_4E%Jk#F@mgudj7gjEV
z?Tat0yfpw$6%dYlTh~$j2sBv!Wj>W6eC-CI)>G?!vs}LYRxb%E>kS8-?c$*`&3%U+
zy#dZ0w%YBJrvRPRwAVs7qq#<NU0XBa@pwt<uJkaU<0}*R%k6JQE3K0xbo`yBz{Lbm
z%z@@)|Kpi&SG4ZGU^krUeC`2h#KfV(Og_2kRA$|wd}(#?8S#1Gap+l{>(~0Q!@kW$
zS(9QQ1JD&Fg*&YTOpxk*i%v9t6FX|gN)f@YKJQy8iuKvT%+&8YnnkBPv4Np}>$NXk
zJ>Kv;&k%;Z#r~n(e4D3S(*jRTm0baTA>iIu8pSkbP~-9g(en9x-w>4$Pv;5+&HG7p
zv{u)-`jFfgfcG`PELDV{SN-dV_jEr&%Lnp%Qzg7W<j7t)EVo2ATMeENCVuWNssHNs
z(q`;&Cbd+zi;o0SD--9k@@OqEa$$QY$c|Hbu-~Bf`_Sw2J`XRLA60Lpz;;$-5d-cM
zb<#eM91YLUto8+GEaX|3b$B?mKUSej2$aZk58jw5ET(o?a!ZAsB5u#}s3lQT#i;^S
zeY&}0AX2IZy|AE9;Du-KdaT8Huf@-PYP*s#w)u&*m|7f`fxaH+!sT-PE%23ZO@s1M
z%QLnd^tSFHnG0iQQjxK~b&|3Y5-S{&OxjvZ@r6k0gorOg1!e)y?&}CvqARSx_@Jx?
zkZDJ&kHv}pdZ+Xb(>D#@%plVhGsH&QI*Tr+){wG2b6fX~8n#)<p2{VUZ71P0;n%{%
z<S(4q$N#XU3rIbZo&~d4R~v(%v$gcwEy_Hn5Tk(Q0%72wEAD2&G*)R;MUo>Q8fkH)
z<1Av9+rv-ZS4OC~g1Wh#r`{!q12mdkY<TFs8ZTOM)@t{StQQr(p2&KWk>TEmPq0H5
zTbrb%TQy#bvEM>P25%L+@pB3+LDeJMyse{f2|f9CH=@}ymT39*U!^#D%>~|L5mToj
zB=NDvo9(Bvb$Z7Fn@as}1aZ5XW_qt+NK?&LcNY5mwEf{8W^SbPLre}uSf26Y13mWQ
zq(nZUs?o2W-V5qg#`4Iar49}S1ELsMX9WimQOufWzV5dJ-T&gz*ZVxWYdLOUr_aMl
zkPg+}m_zo;Nip-8{l2~HdJzLF%osrGcJ!cI)YY|`z*FbH2m^<-OB?HjEpsPKQn=2u
zgY0s}KBUndfxwNqkYSp(kkOao6NZM(RY+&%Xd~68uM(oSvNx6=T@z)))y-eA!*Oo<
zW{W+6Vp3@)=wt`3yIQxKLAHUf=44`}X!L&J7enf+Jyy2ukaA6@__oX%g#P|)Gl)fh
zm3{5<#Vt@4PcwRz4g8#g#>xe2cKnGBFcj5lz;<_S3dS+3;`XO%&OIrD3M90QNr_pR
zzo#E)XT5jW7HD&VS|16H{FF&zQkcH!9WQmdyym{eEj0Mn=AysZcg}}rYfa(Me%t8z
zepNjv0-=(90jX)#7e2M&-X>i+nkX3ffwqOwn`XlUO~b`+%4vA=vMa^tvPQ9KT=ztI
zQnrRkLbo>p-E!m(8HKa0%^f7I1acV}dSZRw!taRiPN#&4)zHqnmmv`e=-PTpa|eLa
zEp#E<p<T*hOs^JnTU2t5^M~{1ngTi}(YBg+>V*iZJbD4y#Rmk7`!@DUy{Cai3i#Ck
zEW^+H6UP?bgTwb2`WtkGS1D)v{+y2QnAFY<do<~~gH>?8I2)wlKTi)7Z#A9$aFZ7#
z$xCgGXhLzqz9tY*x;6`chVX+lLtzzRf~^-4Js@ky8uC)@xo~2uq+{}H_jP?=n+amN
zdSm1k$nCQZXgjXeLVgPSK;#V{w611j*p_z&bGAx~i%WE=K#Wta=Y7UVU_F!A4Pf+}
z$K}Z{;=@rFw`{u)(%RN&<afg|1Z{5Kqq!x|sqoOsVxX83FgOfRxg<tn)nL{OPu1`?
zE0<n?JNnOYx%p}<4>FM}>5xi&N2VUJs1ES3;5-2$qvbdClA+ey@WWF-b>yqT<I%AF
zm&ib%Bs>fm)~Rq?mjx<S(OvzXxk>B|K$4{EXsA5hHSG=?2wV2jg`Xi75B2K9rH6<{
z9d&4Pec)i;0h4qKU4%{VS0qJv%nVqhQq7fC0uL(~;AXwq+mQTRerCQzt<<nA?C_oH
zx3Js~(jnrbNzJ%fSmzR!vH>6$riZ7j-}<4koF_#jC*G+4nZ`Q=y@|>nN1&9&v@Pr+
z$H%D2+avL3of1YAxy@@PI{`k0Nzcey)Ni;Qr|;|D>PyXX?XLz7&&mUQK!9Wc4q#JH
zUq<}#KEa}2=kufto0J)~MrKZv0zb=W=i}D6PN%E3LVWIIl#-f-W=KrNU|d08^2lo2
zPFCLZEiLFZLz*NG{^g#qDIS#no<meqWsDnYs6%+Slvp#m&oq<^NYT9!hk}hl?N$2P
z%cf>1!jvoDB4HVkXWUMh;35MY&O7l4aaO~Xj6GpFe$2-kA(AITQQBcvlP7PvOd!3=
zL(IKUdQW~mmFvxFwsT^H8C+LQ{7XA_gO?bo-;wTnd|U7D%;)hC$@?>L@6F>i)wg|D
zaOeab>9D?Xo$H|my{lmM8ZU1zj10AUDhAvpu)6Qi-JZgmRk!+*BnkXO)VQ-&Kg<>O
z{p74;qx3Gizi@{PpI(lJidU5fA|ahWE2P`jYJ-5p=lM2Eny1AqApOO*3)VY2X=XiX
zY&Z`->|OIJ<+A6q=k9;D;NKB#7C&TmTxvBa>5jRaWb{g~a%<NVhawVuQ7g`?Rz>LD
zD-P&t7+fp`Vir9^7HyT9Z??-dzXQLN|8qVwQ<stbQZ1dHx3HPNMGN}9W@M*^iTYH^
zLUHM6WkNvb`#)QY^*B>qB=Z>OZ1qM5tIIRCTHxI_)@_B^dTHN69NM=_;&CVjq|s{e
zzSbTq5bm@`NLj`>5J4F?!3%hqSj}|X+*gvlTF^fdjHaTN1JQ!#sh8qJo{eCu1;Pp5
zG3u<zoCs1>FnKrgS1<Zw^u4B|Q^}?28Y>K~@TIrgc<xzxm9vj8w+|vc&?x)i0kPJ7
zEK{@513jw&N9@Zs9_<Cz2WO=;$RE}^(Z?WjZC7@U1wtemJ@tqpq$fWo7y~{hAZA{5
z${KEcp1ODSqZ%rE!J-O0<-YJuJ}Jz&I<9vx5-v6~^DJI?*ZCCmQot>BNUC3}uE}8~
z4aWtUow}@Z*VJfg^gBOI^d&2<EaPyJiWxC(u5vpEiXLqUl&UDdUVFKwye!-Ap<}~2
z5^_UU7uoklf-!LdIGuExQgByUc&2LJd<Vla))vRcz4vGbE<bg4u=AIaaM|T#x_sqV
zq%#RQeE+ONQt?bNS4ID6_jw?U<$p`eGSj$2q&45+17k|uTl)w-D)!j|`O~3k?&qr4
zoJSF9#yOK8(?oim;}FS_7sXI}C4FNpW~26t)D@)N7G~3(ndzTsK<UXBJefpk2A<ru
zNznaE$0gljdq|R;JoQ5E`!HABSCIKLa?nH;BC4;aIt`M0H%!0dm>Fm7-G-{kbLz1`
zc2=xowz+Df?Vc7<U(aL!&>&awYEKJO6K-i!zNMVF2W@WL5tI?Hqn?Wh=)CJ2c3~kg
k^N-_e*ZqIva~v#H1?9vR&&?rK*Jcc{wELs-jMwe|3s{XdssI20

diff --git a/reference/plot-5.png b/reference/plot-5.png
index 5a86a3d246d964138c2866850e1005875b810735..a5975c878162326904b1bffbb3f69a1ad1387d55 100644
GIT binary patch
literal 39090
zcmeFZcTiK?`!^ay#d545C|w{_0R;u=a+DH!@5MssMd=U%h(}RSkkAZWKsrQPXaQ_U
z2ML|fq(w>yEp*7cqUZbG`@3^z?tTAw=g!>u?HRH|va|QvYdz2NDQhLK^mNqDoMJr%
zfk4i{)bANUAjkY65c-EFj)PYS3GY|H?)XDZwR>O>ww=3mAz*j%vAVez1i}$c`=g7V
zU4=s++z{A3Wh4KTr7^!aCgbt%s}4B=*Z;XHpmP2wQ{0U3aijBk${loiCMxCw*81jy
zK4%2&oBlCp@i+YhDY>kte1lu~NW_UFJSUC_tLrhn;-!~7IDAsLl$l?k)88w(a*)!S
z8(xxJP+Kc@uxPoZ@w+wu{qWyCK<k0lI!lAn=C9p+(emW%;t!V!74}%8lpqknK~IS>
z+5z0!f1^BOTN0;Z#L;D8dgCMIn4jD9kWf!ix!yCh185sxI7Ix<Gl-GiQGdEW&ojb!
zKArye<x5PFMYNae7GvgV`2kt|{a#zv+w3S*t){GnfhO!s@u0M(h4{QBY(jlk1OISw
zBAKy>cn6ki?&A~Jr*4p7d`LvisVBL{bLl(?$%MmXmGQHvT37*(Q$zL>)OU@Mg_zv(
z?58N5I3q*6C(ICfV2Tf88Yyqgwn0UealU~^QCMN=>Pccwnc236dAx$=C|OwT9RpqT
z`orSvE$Ewsjr=eq=Ay9)QLLjzPjl`v7tj3l#hRGx<0xnK5vbRAFm(M&fGPeOj3D`A
zyezJ(`zG=B?>k)SSL6S6UT3NY+9fNiVUcvFW)U9tYV{p|{?P1dRAqfc{P)5V9$2$l
zytyfU^s-wDw~$ho)X0c>5=)j9aT*HiXDs4XKVx@9HP~(6EJo_=I*jOm+Ml>9J}>K+
z>p!#eNkI2$$2lhVJ^gF*U6=PzuU{xqI5-@xNc+!Ba5LWVuGMPgSp3*=$ntGC=U#M3
zLbD>1X052IHFdp3nzsv^vnOfIJm~Pdt$I(-=&#Ww`yV+WgGz(F-}hsZ6Wn;EHhzxJ
z7<DC|P1jn@VzxiN;zpM|mq^1c<}#>4(#(w^e#tOw5r=C(hc26G>(OG=nH7FUJ#Au5
zvLoWu4A)FuPi~1(=SrTJD|<k>-H{si9giolbd6ByYfhCNM3Dr$BRCtM@283yr(($E
zmv`ExBvY3cC4aXkZ}P0Z^XF$~bpN=bW6=2nH%txEbQNsq#?cbJ3g0ZdkE03QrhG#U
z<v}C-=*eE{k=tk0n(_qHpQEIpLk-6y45j?twqhOch>sE7c}PNUsg7jj21Ht|p~8Y_
z%+AxVx|Y$n2WTON%nz)SXEZG`hm<1XwjdtnK{>t1C=kj_-z2}#Lm(N~{@V0M#%1S7
zDEGUf^{XqPm9ZyHIwQ;X8(TVH=z_Bv%bS~z?80nU3q&P7;Jrr|18#obRMK1^m6Gnl
z!Zq6BYd9KeI;>LN$K#D;K4YPaSJB)aJR|B0TtS9bANEGym_pH8h0mHdBcTdc9;fz<
zC!!6wEwI@oH`Z(xR(Bmx!5ghr%J$w)YA_0u%=lIG`rOd~2K93Z=<b@Lxa{{GW!qYb
ziC*l#J1y=}VI}b;=9WZO??WfSd08}fL2{DB(@U5bI3tvUj}g+Ea7FZXn*h?xKr*@(
zr@Ar{ty@%E2A}KTVCO}e*}u;#FWMNE_{`XNp_rmDV&WJ-o8VA38nWLVv-R>vxWd9o
z{2`|gA==cAxxJh|P!t~ax-=?3{hpd@&id$^DusAE=HnY96OvPontGfYj?X68zssO}
z{b%GldAprZIwt$QC>&A$u<UZcGxpDNH&v})MNNtpc6--$Klu}cp97BFOE_wDc!_g1
z99qv!=59hN?r?6jOtVZ{^Gl+{MtIWYAHY(r*EUvRAz^BS!-d#p4>3Hi!rC`40<xfF
zHBauEmt`#hkyt#WY@_t*=ms9S{Jl3VXotb?(VLVr7GCLCf6-q)IJMn}JG!q>F9#G!
zYrFdQPDy=|<5Oj0JhPI}<WPI9>rZn&vaGPy`^9G$2qhe(strFt2e{W8p-x_}nI&A^
z>Uk1OH=f2x*%m!6HOQ+=pDxc5Fk`+P6C9xyXesy#d8+MVdhJ1&r)bdHu`84Dc*OE|
zXWa&^m2MF;(*!T5oam-wZ@L0LdR?8W7wXowmB)K+{&sqh@^Sx1qK^|DL#v7ud*>CK
z=^%Gby?e$2fyA*$%G-o!;hE8QzI#dV%IYT6_$Z}O+h;VLr%K&&vTc!Thhk1)%~*MK
z4L{>*Y4f58a!r1Abyr+5z_}WiAWn9Ij;{!5N<96-C6D!qh>^Y|BjJ@@YcA+iuR9K3
zQ&Lsd7_4E_ybI%M?NDGVHnSJAX>6*&&!3GRq>|Pwp5lm~4|K10#_*9gh7*2S4YmrO
z`B+r8ujR17&hUHmF10*g<9afGE5bKB`zx}|Z7NnNtqCu-)f$qm8Hl-n3w}DY9<;C>
ztCr@PF7JMtXNM#FB04l<;;RT5p|}@c5U5B|WL7&oskn2%C;L#+n&@7%>92rodwstv
zyKT*V;!xj7cT2tMc2{i~v@#JLF?$S_-gcB4S+OUxbj#Gu46m&?%ODfBRSPO%%ZYK&
zwA?*f(}X5XjUf;{8(j8KB!^0S>`gs9i^S&yVKFD%+<sig7v<6-%J7_Y{oR|5NRiFq
z&GP2OSBFYn<lA=b9cPxdR;F%+T|W_A6^x1C4Uu;Wdt2fqZLTKJla}F@cyr{DB{?so
z?tuGhK=ctTynP?C9+@40N{c&rj}y8+eFsVkJ{um?Cj3(0G{7h!eSXas6c-4?5gaHj
zxFM)lVa;Iir_vO`d7q}L0tC;#DuUl&&^O&JxXO*Z;$M&3S1R$$VVmR~P#?>PtMc-D
zxBm<cZ*qyFR7NbhoPmwW<Lj3VeX-fEBn)p?Yr_m|ngVV2k6_?UBuVRWS{P62*)&`R
z!=sM(kci~q5ORv^V6T4li$7nx&m|d&SDO%C&UxQ0)#${M8E^ctr#<Jd!SvU~iiFKb
zUMijPExxn!xBX)KHSv*c`&0^1bGK)fasaKYI8^+~OO{-t>f62T=sSB!+vYWi0jg(W
z9+K1ED{~eCVV5{aR|J=p_ToD*w&ydaM`wJ2z@pVHF7lF{^r<$)Sga<yn@pDjVe83^
zxcGk5R~ZT3MO47{u}_AxEO*GPYKL9m>U7F!b)`XW{lqzW*rWL23$YJiTmG(#9tza<
zM|BhI?Zc?$t>NHj+4OMLul6FIC3D*|LL#@2*jWwd)H~+rU9H4fT5G_zY2X7lar3hr
zB0>{43gz)HW<wBu4INA;-FG5p4<FSG^0?TXmVoj9`q|&G%Nej1wC3OGw4187D}WqH
zaB82aPxlF0yS3iR;&Rg`GtwE#)8i#=f)H*$+K=1xij!cQDW2~sDVE6cAHx<Wge0F^
zX<>1>P%f;vv(Id$H5asE@omX1U0z_PqT4OzR(?=$POQxD+`jx7up!~h^x1ldAvAGt
zUv=-y*066m&LcnA;LwYhpk~wjAmgGfBtIg%&cH>-N_E>6@oAcJ4HljIUQ}6qWAqKY
zt|RlIyWv%Jye*$XLC3KGX^p_%S&A@<KVYWD@KAYsM3FGQK0MIsVEKbN02f>~N~|vC
zV|=Y^6tJ(w@EhzdpIA8p30<3VMqhAU6_e;2ou+&c!@OA+A{xM^;i|_*ssslUPo5uN
ziY3qA9x)q#9gEPD9UNiq{$gbecYit|I3F>YG&dijNZ8d3G_hB-*{gTqDc>H*ck(d5
zC)+*d?~2+GK+716SwYKJEOPvhO<DTmmbcP054|M07FPpr!Md9SrPU=BY`POp{!Lg9
z3Fq^9yYZo;CONNcSIc4dKLzGfZ-f@|`rih+us3kk_~EQ`<#z7WdaLm&iS#M`!+6Dj
zi%9vUa<18ch~upt@$>N>rCR56lX#ltP5HOS?(*N6(@JmQ@yfrv*gI#(6@0NCssz{p
zj^xCX-;%pec(~+O`;T4Nk5!yF=DWU4TK2{0{U(X@+#@1x4cW`6f$w?Jzl2zIUVDX1
zBmK2|xZ^t=WXJxcdPAp&=>9R{beUCnC~Aq9qw8CZ*ZR?-8E|gJzV&EN*a_Kq<It)X
z9eJJiMzHq~$1y4Alv<Db_xddJ<J0a!No)DU-OGDvV$wSmtvf5MPuWO6Z?}6XE^iJq
zdxuK%IcbNHlKIFkakimV*ZvP!$OPnhNi+f;b{EP?(1ah!pEk^!euUo9@ypgz&3O(@
z6`y~lCCx|I2$Qv#Q;2HTl~^wh?9{Xz|0e6lSgMX+RV0MBY7OfReE%L}pOvjUeT}S&
zZW@}pWdoNBtGQ>8eiYv*GVi*htMu~&Pf*S?d0NCW(Q4OykL%`hn;Y>r@T55J_gX9B
zCKkE2e5peDk=bsr3Jgvt=HwcOu#@k_9hR2S7o=O^&ay$!d~~+Q!)#0La@)r2dX>*F
zw??(UUEAqa-hbEOb^xDOL;LR3n68RjZP#T>NhsBRTl<(DX|(ydX<j0-;DD2NOb}LY
zSEAc0+?_t}oiYu9e7Y*|TWx%_67o7txjK;bM(YmdRR=E0*v_4NV2qbQhfeIF6j2q5
zpKI+c;L@=tM>&NgC8}aNNL_%4^Lg31Cm(8*dMsjIHY5y_10}p3aq^a%nTnvN59+k^
z?DMrCjtLz%T8&mV)C%2Fq1N1t-&_T~7Pk;6o3}T$R~b$|Ge`jOGoEnLIZ*W4W(W!^
zQh9;&s#cR9RGA)?AHlIQEQfsa-9U~wi%Nb8F3pyPGyg27k3?S{T}$hb`C*Bdk;pPD
z)UXhn_knc_4n1$iQ_y13|Hjnsy7m9`5V|HP^1By4+3>R8H{Vyfb>QDft9M6+(eD5G
zFt^PAt^WGY+y4vuJNPoF4{GBbz&NbNt}&hoW~x~*9pjOm{Sao2pZNCaIiI!gydnBP
z)Hfg~t#kmcqk_;ahhAzuIEV~(Eyp{2$ueQnY1KO@mFMeiNTZ8SY&$$Ymax{Ms3=h9
zs%cMEYYjSEQ0K~)xvj|Mp1#*KuvkCk_%%p`gjRGykz6t>y(@O+Z?CK4^-Xr663s5p
zvUT~!M9RrKQ59F4LPj&Wc&A-;wb9d*itJ-3sdn~gKR)Y={hZ?QeAYtqk}l~9FjoG2
z8`eb~DEXd<+w5f$fMcLlC(py$q0b-5yT`{d#&0zaV8!5l1Ke*+mXK+Q|K7!-$mX7-
zQVu-t4eDg=KhRsP_u2M4Vw@(F|IatUQ0=PeO+8nMKSvo-G+GT&t@W>1ued!@+M<C-
zXx=p1O>*3l!zT**a^KYDf5~3Btjiwo_mk|cz<p^|1YbUPlxJ{!-)QdZK$Hn$Ml{CT
zm32X<a;&rH=GM*<Ab~)7)1_Y8`5UW_&qq18ye?})T{+|XI@`49rb6bLX~q4F;uk!7
z$h=MV{(z4sR(x9@%T2-4zbZM@UR7MXBs1YDIm#?O98rNWfXsd<YgDfKAn(bUuGfdP
z+oaNqAVs4|zM<0N`X6+ZkNF#tTv9ykR101}i(i!^vx>Y4e3Y0gPm|mf3e=rNo!(|U
zJdAs^H!6&=O_?8KYB`!J{`O>`2}*{!26Z;^ih>)1=9p^T3#Nb&dv#~mS@F0l$2b!z
zH0RVlaxa2gGMGsz{#nXFyQTBF7816HOX7z;PhU8a?A$%Z*u39TUv@E%_|fAobyiA{
z`$f?1FQXGA+^kUZFXCd5ZXt|tuJrCBg6*93asvWyI3~62gy}8M(~W2BB{3G4&9!SW
zo=?~))eR(`3sn1-CufsxK5KnmFTlN^^=S52l$h^ua4pZ%z9Wew#v+n#&iiGfbxt?8
zM(I~rI=~drNor>xp%KehRa7Ad+H<}-O6!-j?qte;RQrCA)MLVZLcpxMTw>%@h+8o+
z|B9Q+@bKWKwEpI5fOu%x3$yO%<9a5wN5=-O9w{jC%p(`c)6jm3`UE~uEM`Nc=z9XM
zB@=q#ePsLQVlj~bSG}oLp1ZW2+`)87c_7t!d{|<A^UxAOR!2hagwb&MZS@h@L2)An
zWt3zm6MWiJy9`?yUvsc2g>Ev7C?bAdOOx(FSLxy3NZ4*y!IG}TZ>pnryc_-Ekh$5%
z#b(nU#=CTv`$zfOUe2u~rY#G_1a?=o*<K}!mv5Ht+x7uLYMDrmy=fZL<T@UEh2v7U
z=*k)B7@yZtN+c`A%SjhUc7nsGD71K=%@9!s-D3&QPo~H#weE|;nfc0Cf<^=-%l0$^
z7+&*93O|0ET?J=$9uf9R-nca4^}bMNVEr1}5?!Yrs62<<Hn+EfcU~rq+Tuke$UmiE
zi%<oT{5QI3442G{3Q^hZP^8`LgllCJdf6zwtj#&(QRRnM#za0eVLg&hH2x&dGP(EA
zpRX($J0OwXsbXT8(4L|jOpIFJz86WX|9Gk8HMgk#8Zo5ySpVQqLHlc~cu;V=W~GZm
zwpLpn+4A0vPzbEX?KA~OrN!PhjamQY7LC)i>w4p=5;m&b$8m4m0L}dv%QN_x7L|9x
zqV#7H`i)G=Hj^>^Hb3TA(HUsM-`6Huk>lzP<F;9A9i@1?_K^1#7hw)+@r17e^$J{=
z86}mw5)=*o%Pa{SsCI1mCA6-@t_k7ujgkG-ec}zAu<BuRCcb$+&E_ikIZ~=c?lSXD
zV(}mYe+*S|hb#F5=gx6q&-NMuudKSqMD4gXevva^pbZ&kjm@rmRIx73Nvc>pDAc+$
zgmx&?tmU7*A966_S*^>q6mI*8pqsgO8MP6oyK60B__=zO&}>G2-n(it0=t2Sdq%v-
z*kvU^P0Z8fY!;pBu@*60qA9GmM;;feCEbf}VmgDGf41aO(n}XXuI{S9Z=C(OoUxW>
z_aWfwtF_<W9ZD>A=V9fICeNvp$48!)VIy8=$Qs4m|3n;FKV2nBCQ4+@WU3t*b956=
zxN_C<e!QWneV4Lah)5!q!@&*F|EO{Q2(EP?Ik)B1TB59t(w&%3_|IY#O6<uWw@TYS
zvXnbP=Q%_s7CHBNntRgZ;HqmQr%(5z(2Z+JdGf0bxxIF(&z~2qolUrMgE?X2qZ%S?
zQ$P)YQ+<XgC1Un<`1MST=Z0R`jkh<TO1xVA!s9){I~=<MBiMUoC9(>-{j$4#DxSn^
z-S1a)-~6@2F|ZkHy};(=mMkL4p#m>VHHKOXu0Q+R3=zfdH^7!)-j^3Ys&-_=X;hUE
zKYRGyITG2xnb|@V<6|7HVj*hS^SOY|Hkc`|vYHEgD1Ot}5`RLa1k<DJ+K7BzN$ojw
z`oM9K*P5Rmr%bsewa2>d(Cl(7f?XO@cb#7bUz8}rFVKRsZ551!kV_*fWNK46*a`bb
z2E+v~4xKFk9WPahcUAuwL<_{wro%7PfTDkHd}I9U8VYTH)xubX`Jv1RFFCM=VYxtf
z?O0j48X--e@BEccPUE@C>xrhJ(*1#KA{XDf2r>RJcRnDvFy*Ab)e6OvBE8dio>nM{
z1h3+9BWF27or_x#oZkd%%nmch)U=rQ<j=ltlhYno_7S%(mbZi-SzmRWbDW;`R1({=
zBv6?tyH^Gl4QG%pJK82m?Z^d=MSYB9Dk1s`yuLBm?G;ovrrSimH(>}h+`M?nDVH#1
zG92;xG5KO;=Xi6C*v`c<>Z(a#mE7@^rN#P#uW=K+yYia1KCv0u@{)qxRVH>Ghzrh9
zrVXBEOSA8!MyJDrA?wK|ycV_exaSe0^`7zVn_Ut6f<K8jM8>S?1~`ny+ahob?&-Xc
zPl_*OfWxew!r=|`QT*mF&n-FOP*EHaG`~@c+&Ehk^0bo3X~i#@o0n`^L==;FI?SDI
zK|UwJdlxF1xn_>OzvdEgO(XW@MHP1sg`3WVQVC@Y*QI^Yk5#Qljj}{b(?o>c5lSD)
zjIa8s^M52#wr>b~FfP{Ur1;DVuRXgXj_%1{{m9a1At1SXa|tyky*Ruh(;B;h>xkxd
zXh%FKxH917oiUPDa6hYKgWjDY+gr~@2{b2TY%Qls4PzcLk)uzyE<G|v3rQ}?yGL5a
zw;g5wpg>flSl$=yIq>jMz`T;IpP^oX@m=S{aPP71b%mRccs=d7FtC2fb0V4t&8210
ziVG3pAS|fYn$M*uh==;-2;1g_<OxYOt}qJ4!9xzG4z#ij{4xVl(^LI1Um>COm+5RE
zkc$osbrx~T*6zIKZd{h#&4I3dY_t3-N3Jp=+Ya^Z?<K|UPH8F+$SK>Ooj8Dp)w{rR
z9;}@D1hcpMc^-X#95aIaHm1$#GrS(sz?8LQcWvFj-YACKQoib4Bkz{}KMvoAqhgDQ
zk?<pzgQHXR14xr!1td;eRwjmo7DjfYG)m5LjQTxFCv3GT2#UctiXDhg6!h&s*9uEi
zU1u?m<{0>@Xu@SSU!@xFM7{hZmxq|eCTG1d_1xMBySF&VYzaqqcle`Y$SM)+;i(ej
z3FaL##*hWt5S`n$|L(fY;nRN7z&cqULuht*vB~5@%^E(<`kl4OiS5HsIwOYb;?&9W
zwMg;P1(=gU5~*4nxSyz-)^oeD0^~XozIEQdPKm6iBAU(uk}O*~=uO?XeezA%93D0M
zo(d({t{%R|voYACG>znhzzy-HX3<aO_i-565I4Q|t09oQ`+vZ@X~0YS19d>Xv4(}@
zMv=#QZ26C+Mcuc4T{~#e-5+GwB%T+Tdb(KQ>Xv>eRuiszI1XiGFA&<g;MYTEjJVgg
zaM?}!h3TY@hHG}(px_t(FxW8(uTz?MmXm!>2s6Bj1bMbnqN<6d{iwfeZ8q6mfodB@
zNqMLc@~c!qGv-dEmwr`ARHI1y>xdFQ#viSQGiJk77Mr56VR!>a7Sgx&xjrV5a=2Tm
zB9SE?5__^}Gyddd6N8!CLx!huv3D$Eht=ew2RFo|5cdw&@&-xM>+zbLjR$^@7<Q&o
zLq<gW7n{!}H23Nb`CYq>IR}cJTKoRc)tdBPIlg+w0}gUfxeAI|y;j+NmwityqcGO>
zQNiC5ytcY=H!bb;fBfRgaaEE#ESWL1mt2$?__c9w;jA!bU6#wk`hP~~)`w}bPFjAL
z{#OS}pT!-4Xp#+Y=u(e5<x!uTM>OZyyCUv|;^MJqcm3KC-j?)e86>oP+mIJ6z7C2{
zbzMI%S$x1tFDtS4ec`Cuh21xAu^hj)5h@pb!r?i0hIH-!*#JK1Sek%j<6L7Vp36a@
z?${h|M$l99u!_^kx%KE{X^oK?gNgSM3JVgN{44WCgAK@u0FheDy(RA+x{R`M+eR!q
zwEXRTY{!>}qVQn(lR{<G&7gF$z>jge&DF<?JBaj~GN?d{5b=n>F-66my<b&S8)mca
zy(YBcl}*7#{)YSxL~qM3qIjADFxue<g{3BXzwyV&_}#;UsAM0BNIQ)~-q7AAPKD9C
zb4U^oHD-8`BLvaOkqVtxUnE00olyC5dAP1nrPIZ9kl$hVj~nW{d5BWU#%8X-wre^4
zwREUi4#!)WuL9ciDymW{-Hk~_MvG@!Utce}Ha~&ts<NTpS<d>nBx9eRun}jRR{uDO
zwDL=f8Sy@6-AL+mTKp|JJ*)v@{CN7yj~#-<LAx-|O$5TyNEYFpIpJ92==*7rAs|}S
z!%aagT0;vy?kJZ>E#7}7>RYQa;&s&ecd6t2n5LU}C7@nwu9*>0;jeE0Q&On335Ob1
z+bbs~60Oks17u-|iH}S&itfMrNU<`_axX{YjY9mrqUWP$z4uA``d42z<P`X(=Z?8F
zxaE4q<~1L_FD+7wwQui;4aR1B*e&<1)D_;9pmVF6kRRsNKCKefE)AgKo{iAj-CyD-
z=jb6zAAa{xN=VC5^|LV>FCT8@7c*Ts6ZdlQQcdMRei_ecl=kD{Wg_F^&iR1u3Aw(&
zwEULhj(m;EaRhu$Foj=|yx1<WzAA+>e=M4$@TFBmeemA+dVg2kNu!%4o}Z)EmK{MX
zGaY?ZMl^oI`Q9n|-iF2bADs^d)-4sCqRP^;hy~ciI>D#AuA5dprBQDtGHrh2XKBc<
z?Gd<TLiXOJlWBpS=Sci$<m0i%-T$lwNO4n0pDL{{&Au@(L47%5&&5^c#D_A!?;b@7
zit{Mw!`Ulv@%A$KmttnUDu_Xt{LNdrmfdpjl2^gJ!z4LY_m}!=73{D?^N^6q#OwFX
zB~H77WPj}!cQT@RT5)m5q(-;*383~G%Sd%$pIp?S?_0_ec9NS$Lb&M^>{PR#!&QTP
z`<K_ho5Y_~S9JI=e<7aQIIw7K)s{9Th-m0=>)%XVVU{?ZH%!P>dp8n5Xd`f#B=Mm8
zmq;-h{qTnE5udvFC$B3zT{t_LBiLgz7D)E>hlSaHH!lWADCd9VDf~t_TWKR6bLB(4
zL%j{mzG7Zg4e9>$QzQog6N~2jqB!tef=s-mA21c5doZ5(P(<4&{igDETS2aF*iKaZ
zO)Gvu6YZF3eHHuQ{`6wcJ=NL8qLxyeLCirrs)l2q2_~&NTQjH;@>CZwUW%`X4m>bZ
zL*C`<_o=w0HdnvI@07i{zd!0W8+oa^qPOM%qBj6wCP8HYOBM25M5^1*ED$8p^FR8g
zvvP9+z(%O0^)l`3o5q`8>*0K$H1t7nZ@Ze5HF1a#`na}aV0ZSR^xa}_T>(98ufGV@
z9(~^hzDT)-p1+!AGV`wDiO8LN%B}U{LC%P*;ysy#QWi}Y(RG>mtC9w?-7$)`bAg*N
zytdP~7k0EtyD4U6OftTOaoZIaWh9jDJ%<NhFD3F}iduG<683H?<oBgI*SVcDhi{Kv
z`0Z%CG_*vEJN>*I{a|EWYREtA>0tZO<D+d>yj#ZxrcUy`uwxckcUezYSj)LMshu-k
zD?#`3<7lpt<2A-_XWEZ`NnM(ZAWT0}X>dfm*}&aUwahZj#+;xVXIB}q<=NV~oqCR#
z(kk6b_5TMR#_OII6(b(_B&WN0@Z6@LxMs}stQ7RwIs-i>oDkIHTi-)OnaW~hGTJ?X
zTEREvo}tF?Jot7fr~hg>i)jy8^8*^qG~7I=NWhJs7T=`2f2PYS_leD9sk@CLwzCdD
zG9SE$(=5$>qMn11`Lr4++0=Y?U?hDS`=rl_{A{pg$x->JKbvYld8@%+TcIqyBM4`+
z*9Sf0!_c$rsuG-0v=sAub-~+=w;hhy3ldqg5%9U{{X6zE4)oDMtH<eG+O=RGjpj5r
zE*4s0jQgig@{P#0cF<Rw@DEj)@k4rB1ayVz#_uhjF`xaXlK911hiK_!;<7c35|vEy
ze$la4W<IyQQQn57uMVzSla9HfP<>}^+au2W^@0RR2C1rYVns?nh%WhK2VxormAAFu
zn0zGB{3y92uYCu#Y_P<Q?~&+rD#Ete@G{!TsnkVh{v|Uq>AP29r)c_s)NtRR0xJ=z
zBwD#ZaIK^Wp)WM|z?%&{yldstz2T*es}-~A=yf1=!nDK%CBmx|3dR>1Qq88u)N-f!
z>V?boM$e+ViEq|yLVG;8rjg>KLs|RjtFY<6R=x+`i#GFJ5M(@W%j1=TZ98hn(0(nA
zCHfv9m<;HhCkf){ZWXh+ayRjBgZJj5UX)5?QL8NWK5E$0HMUpmy__<`UznsIwkay{
zaAvL)0WT!gW=`z+3&j9~e7TpteihheNntix^?0lN>ZlVPq;==coQ0)i?-{zOjfM^x
zOR!kvx3b*k1xrHa{oZv{`l{!!9r^b2&W*s|W|#0^z--o&Qf(hiOJ8m}^VF#Rs-%kQ
z#>O4mNkctf{#jtcowGCqqrLL~*A3Rz=1u>Rd@}s&9;_g7-uKUaa!)sOx3!JDn^!fm
zlL21uCXB*Y+2YOn=iRqY*oqlYajAb6*3e!<-3|TSso<%wAkD=8z3C}mm^}99Y3Qkl
z|FUA|X=tSzVRJ1dC50v7nz_O7PT<z^Sog{2e@|y|?d<Giu}Qi5BQQ4gI>|~Q!N!ec
z$+uj5t%HLPfAtrd5(Nki+#~uXBb6?G0fJ1dA$z1MuB3S^<PHwEODE)cMsa`TE5kjH
z(^IS-h+pr+ExdpIcyOPx(9hM{+8S_GNr~EchD{1rZkVgXG9J7~3U?p+cvZSz_M=>*
z;^HUzbGWNar`~YgayE+PZtsj+8{*HvtuCI<p275PT)ISP=re@~S|0U(3VCxyaxV1w
zX%=6(pZ_r0$j!XHi<PU2Pe`y&Oh|Z<$<@@<WIRx8Ik-?<HC|hNH~cKMe!WZ1G9X?l
zXnVyWGBUDoV|i>3XGgevPp&OuY=FDXd90RV{9ey-pZ=Wu51yB6VMb|$<J>ob4?>x0
zN}hlHsc?9X`P;4F>F;l3-mpn?u3<tB7MH~v_t&yJlcmX~1_mfgD=RCvv05L7?i88z
zB7%DzX+X+t<S%V(6jEP)(5X?eec2^8U#_L$y5MZ7_ML#KL6+>m)Etap<1Z12-9<VZ
z?mKs1{8gic;tbyYDP-d`f_0O-#eQ}R%attUR(pS@C1$X{$b5b8@$D2Jf?L(t+ShYR
z)rUMG1Lh9J6CRBR%$-TM7CSJFhqW5QZrUF>F6qNyuQ-fa+q^9^<B3>0buIaW&_wQ%
z)l@k@&X3Oh1-{^Rn{5x*64jWmnLVD5;*i^28Y<1|-kJyr>3MhK(P02M^+A;{X~YF3
z6k^-t!+M8je>cpCHWsri$YY^eU|H8uyN-7^7>dlx=YE(JTOj*Ntm}$x=F7^<HB+f#
z*REZ=a{KY*78vdNyC@A!O)t*CU+<-CcBre-A+KYzpVBj=p*0YOZffRc)sKJn{(M}$
zMH?}}SP~@E{~O`lHf)xaN*S~@HLrgBKpMuX7_{w{rTQjmI7d6#Eg&G^Ck}^u493LI
z%Ci+(Xp10K`Zeq>6z0Y{ap6ds$bp6m%$-co=8O_r{ICq_LgrDiAaz=kBpq}rm7g6?
zH8wJe!>gNwuhbCJ_F!eHq>m52cEx0ilvGa@+pYP5=+%7i;AawTkhefXxVW97ENeeM
zV+fJ6Yk8Y#UT&XS6MV3HlCn5Bf!0FiW9cDn&)(k+=ahOf^-TwL<cg@Fp58|{FnAa!
zV}^Dm*IR?S@49G-5AXTlaCrP#)|-whWp2Qf&aSS$z1U}{t3b?64=!LcLc0;L)@%Fg
z-68L^Y#JN5^A>L+k3qOudHwYtj3LPN&0T2<dmFS2IP<HA^hzLY+e^aDyw2BcTzVp?
zS4c#p(EC#;-9&D!$MEp*1I+KvI?Ev&bpC93`Enh(p(5%d#kskeE5xeYWw3<UUu@}T
zrlZs46)$4S8L;x1;atOXl$-@3-WWu-&jDqy%B3f>>6USU(T*-id(%qiegR9YYe47Z
z=V#GFxPWy|FVz51=C|tRx(XDdUOmB^!SnbJseJP<*xajoYpO><uKN|HV)OC@_YU{S
z9sx_G&6|bRb(_eVnwm%J>+5P<k3Z2>Sp}^&rH+j_dWl7R45TdVrzs%P+&R)&PMto@
zMa%TvYqkxzPiTP{EZyU+nOI#L+~c2L+?6TE${o5D4@&BmW%+x2kM^I-_Ixu@`bbS3
z_2uVdUwwW3FVyBjqkMcGtzBIyGX0}ijCGw<cY|VlT-*|~p6(p0$KLjIv`d;d9_BEJ
zv89%bj*b%7K(nOXNBvtqB-#Y-?@|`6S+-$0T%I<$iJ)%QXeNpg2MdfBKhr8H$S2C=
z6^E23&KqHWzp~1mww~l4uVswsSXEcSMILP;aMhA@am0Vh8$M_zGN%NswK@zInv&DN
zXNe1gm=M$Rd&w=U_4{k>(X>ijXk57Y&)|*=S7U;xr43`fD0<!7R{G($mpLGpNQ{$y
zVI?5!_bervWlU_}F8GY7+A7<|ye?Z7fy9Zbu4I1MJ=mJ?g64Z2u*&X*$_m9-+V7nm
zqf&&y4bMPhsc45Z8Fdt^wEI3>Sy|bsXv2N3GuhN_eXes4W<cypP2B!K8Ym{<hRYqc
z0vC&`qS!#{joK|}Vmay&Tg!?MzWmcDYbzow+-6(r?UZZjQD50RRQgD1Z=n#w>B||8
zLZOOiEw6jn6cN=GM8LWg*0~Os)wU;y%Gs|@HBC<-x5frj2xH8r)hmkdcmtXC6X9RY
ze?A`CAuE+UL+)<Oug)cVY(|e00qRr*EY}c6a3Hhm9lFyF$e^@)MCv>ekGKwRi@SRW
zipgj-a&OaO6Dw+oz4=ZNxm#0gS)~|oxW8jM9O=e~mx3&7XlQss=MxG?@*i+aywleb
zucxFc?z^V?E)B)3B<6DO(pi=bZiAqAuRrihS48R{r{75PKmYq`H`iZT002_VPFGaB
z>29)$!yHn5Ou7ecLk{qHiR-4t7X9v^29iJ#{jnb@-T>x7-f1b>Rn=Ei+}zk*#BfPD
z{>WJOT^`+O=D5Qq?e4lyyD_DePxOe+cQ>X35l8)a2Iu{mt6|y8{!oOx!G&%f<yRZw
zI<L|gyS6B8A{cp0b#-;bBg?h}$INEGZ!a&D=jf&%<~BvLq(2^g(rjG=dNw>YfZ+^l
z-;>W_$Hq>bIwcjn(W9oTI+)lIP>Uhd`nZ(v85h{<zUfj>m|aVVzMS<K2HPYzQqvx<
zNd5_+R}EneUofv7(EyG9gpiAMeVLfEh0=%S`8Niq&(mOy8|FNY+ri&kXTEd^9?c<#
z)d$qUBOzPZxWKg<3{T?Dtjt1Q)&LCO7B518_Kw>TZuqZE^73oFTNwl?<z{N_(@!7C
zBEH-Rs*WNJ*d|*A(s2#;ok>bXW~EbkHuZt2qz*so!B&-3=|h!f@S)5HUtf%sfdq>L
zP}J`LLWl*Vj{v=R{RX~yp}%m;MV_QpQ6z%FdBShAFOOxaM4F?#x5oU=`G|?p;LCEU
zqS9q>-1BgU7@cy{37-Lmj`BBRA`Q7Y?817>`DO@+Paj=5QiXCb2|?*MWiZ0nAW7S+
z(+nOH4fSLiGU8UgoTdt1yJlqwV4jp2DJH9g#?r0$ZMMGYr{d;>6fCOpV!RLXH|Tp%
z_20Qd_E#HkIpOU}`06PGmMq5k25`H<66@|d8Vvzp((RLNd>$Nlj{WwE+1}~B)n+bg
zn;I9=yQaRSJJB{cVA$BynsVpsi!&jfb1*EIXOeut8Z=(mctqZFrezW9F){A9w@E^(
zMROwE-mu;>@gQJ}-N9gl4`p?QHRuQ!_{oRe=A1{Vhd64^rF_&*lixvo`SQi*(f8Lo
zM39+cAl>RiK##B)42RoAlV4wvJTwGVoiYI`^^JpFid;|dL8{+){=r7CF4YiqNTwc=
zH(#7#V@C9IY}AyAw-lONpQ-hO?ZjMKdsDv?(a&-;0I-=1%*!g=+Ndey#M!K`p^zZJ
z4;f(i&kS5RS(B09d`VBR&v<uwa7-!KQD!ofeh#4FY)pZ1VZwI&YOd0OQ!L*l78X-W
z0A*<tEdOMTlm@KN{Q$st%M3LhwB8w#>e(jLI79%Dpb0=aB0e|OcjPUIABFXw;ar<G
zjll<On?d)>?K_?MVGd~lbL+HBmG*6*UBY{z&j}RJRK-_SW337C@$tsOFc}${v__3+
zPKCh04VUN7pF>%!y?dgG;NI(f76*_)vu*Ke9m&!di}14?*y?!w`alX-qL_7H;`)%a
z|Ig<xw9LLwJv<2S1{`4q6h4PkUg(q)Tj-i-OfYwXQOPQ33+s5kVXliwiPcUnhn%lb
zx|Ev**{L_j>DQ0<ZQb1y_|iwseq2E-pR442m#Ss@ePtK4wrg=uroX4qZb1W9yFC@j
z#RcAUADjRY?S=&Ssp!{`prJIwbn3Zp7h00|0*os2For^&2r&i~+`}0Fb{-(x>^A}D
zog1|F8{<;)JnoaKljAO~%n|Jkt5DLKOL<JYe1-q&)O(+WzLR!bhB6JeTn0udcun!K
z@j_FKK=T{6t*u^NC1h)UZrX0|4l!NnCi_{3t@&PuZ_in5s=s3nxw|jLrTYKNvNkRu
z?%L9E-+n!L?8rwzjp(8IfF-d_!#pN-r~fLLo1Jw-fW{wolQyZ2UDQvaW*@odZATcL
zni+NFdme%$*#Sj7<>!9%Q$rfFWA|*in*Pt%pG7Ab`EQoldi==v?18HAp2yp_VsCN4
zp_v&O85Mxxr>o<$VX>?MALOX}>jKrseu-C4hH*i?<aVN!4&U_|aYNcL_x9+N@~x(U
zz%j!CVA@Lq3E&bXw_I&nXi~h}oR>ozn`9b%fK=SbYdj!%sO666SV|1eHgh7DCh>TD
z3Mg#U-44m_Zxmiz;e)rdXc~VU?1a7LHnOn=*l&X{j27mltIL`^VMTIz(=lSNHp5#}
z7<Cj7n?W*plO=JxtEAy_;tRlj6?tIv$N#FPwD3mbJV9YQ1GJXZ9_ZF>Zj%GFylxEc
zKi#_gOT^c7eD4(G^Mj+}=R*O9XI3`=q4_Ad)RBNC_3f+^qu+K|836<@lm#WX*P$yV
zV1PzyR{+~Xz^mNHf^tP3M;%@h7S>qsN^z94?@k*tLV=+1bp^!lLRm2Psqn9xBxQ30
zyRKq*dqRIgg#pUFVP~!e)Qn;5{m)_UpguT{1N`>`+^lluwPcr>DTr<BR~2$CWurJy
z+A6vHC!cnmW)WLjA$T<20xHoIib$^a5HT$&nvHL?uJd&nv*~VcKkWd>?~AWLmq>&v
zfe<oRsHYGuSAaMHm1nQiwlP>-Bk&1nq%t6uU)%Y%eaE|LMNo)(frPlrtNMD|0aQAS
zA{n<n@L^=!uB1I34CGUk_t1mz&IrHdW~1G!xnkC}L9@oqG-gZzd^r~oTkPvx?N_JD
zmNTN%YWYUE_`P&)b9i;!c)DzP{tv8z>+}ToWU2^&4_6o}D}Vm3tGF{$>c1%-OB6yS
zs?(TOQo|PxWYcmR;y*$DHaXdn4@Ri+h_1P=>+8G8Rkifb-|ts0sIiyAzrVgRZBglb
zi^c_rUrvkT_y<xnx=6>49fKXa#pu=yBE{gY1oeubkWi~Q_!Fi5iHdfXa$Y5d(RJZ~
z)Er+0DQDXc3Iwe2V3R<{$gj2SLMu0bdb(JF?s2cBVM-HWnY%q=0At(8h}ihGy3v<!
zxOWIjHv<bOrlngV&J*(V6+qpmRoMaI0j+#_JakR`-#*s?RpJ^aO{huQcliGVx^f@k
z>+Ac9k0vw}3`H$9mTPgKTKx?xFwBiwbmWoF-?*!DhV|wO&d{FLD1MlVic0^qo9X{0
z;sJG=h|y96<z@mDaG!(&Kaeds-w<p_{%%7)@!<w;G>|pV<#r>+;<p-(+<AQYbL1&<
zeI;Av&#k`WS_fFkN-85b2{~LlS&GyMQZ}hI;YPsPj6Q%+rA?reWVeBsalZvz224F3
zWZ!1AB`W~)MOhZtAEZ2s$_1&`YwjyFbQSOMBviUoQOdg&5x$E9#o}5?H}l6qHzfnP
z9j+#7+pzx&2sdo{?I%-l;tktV=rXZ(DL_P7X+*n@FV(9{hFU)rA)@FpSTdH7bjxMA
zOzkgk(g=mtH54813A*fww+zIBz{);*rJbq5>?sXY*gS*lJnj!2-~iV#oa;dMx`4DT
z9>d2aB-94i5Nf<~iHQyOFzo<(Ecu)O;-~rDyQf*nRYpTzV;r{Fwy~j%?-CP}s|I$z
ztN>ILeH%Uyx^M>uId|yGX*rK^ob%W4bBzOsTYKcjkk6Wc7&(#0>#fzK<=|`b#d#3}
zMZMC|@~NIOALL@GHW%*Mp6~Xt$i%GtZChGbUyOlFzJZ7f7s>i8xF%$KR58sHL=js0
zadBZ`VcE<V9f!+i@t_`XtanOJ+(g}!LrWw{x$PnV#@M21I7X|LDA08_<oHd%fAhp(
zK$%;D2@FxRFZ+{k7esQk(3!_hGqZ~d%~RQpiY+R$0j+WY^rg-O-!b2tJNRp~X8mQ2
z7jg5Qq(j#~yivD#mZ=&1h6&TY*<<pcoEP)u1A0$xSZfm@SAW<6L!R`VfVK0lJQG?4
z8rcbW<Or1%9sF03R$U0}-uZ)2(bPeISOIV)>|v*#|6SJ^odmMf2vE0KVtPoIU%Ry5
z@+fO*Thvx<(6<X}ML|H~R5<ozW@&0@q(u@32pPNYqZN1a;<HuU3BYUceaQ#d+>B)v
zDc>g7Fkj9>uQ>~+K9{2IhcJ+70Kq>jkRG)57J&ag;1Ti@0EyC7UteDB24msv2zm+=
zRryl?EwOF91O!Vm)QLDJy1O%tej10Swad)x-O7o#8M{X?ixrH2@`2<A<i5vIp!O9G
zx2GkY2TSTnfEVVo-wObOzIFqzsX&xl1J$d!=$(8(nMsjZ4^O<5{O+$Tk1AjtYylr=
zWMe)zU2%mRLf!Lop9kgM+7@+Ie&y4#{JunWx#Q<JaWiR%<>5-uq5}-DO?VGX%5=Wb
za>WvqJ`AX6m;*56O#EObA-hN;UicCWPZPl*7sP??i_K0+VILH?iWSf;sq>s|<BfGo
zPB(L-pL83oeiALew^SY;pO8~=pThOK?Eq!pr8_oc0F$tOa2u$z7qo)T2+arVu8tA_
z974+)a28n_c3uS(Yga~=46L9)`%FrJbt0yAq2Pnxn2%{I){U_67wgSmdDMNPQu=pc
zk-Xqv)6q(b>)~9%{^fu$)s5jBY1RR7v8YxsS<ns#nW|%dXwUpYq9hdU#gl5F&;YQg
z{xQh5&P{++Dxi)AN&+UOv@;Xy6v-?qyIMURRp&We20ynqdv48Ym_Q*B{77rF?H<-Z
z@TO+8$*+E6Y!tb6ZR4+!z>}HcD|I?@J*L%Jn4z)p0aM7zcXnLfM;b?cagOV-2}t8y
zhSkdofg2l)I=8Rp_jileEtSopHpcuWd;|d^?|SvqEG+<sA-X6acGH4w=Ep;*2WL@z
zS!&T&S_k4C<rez#%e{c96Rn8{cN}a@b8XQ#9;x~u(sD90Gnar?XR!em7{^)JUl}1X
z?Th_|gHALhP4l950qNO(q*DgfOfXso?Bfb;cvArZ9szis&UCj7e#>Kg9|23c{p8^m
zSMc_O=nD*>i>$B0Y=I|}FFo%6s1BgfoH{K8yfAgkwPzjDk(&proQ?OIoY^2A0%`tJ
z-+Qvu81Uy_0~)ov^7AVZID)r+ic^#+^ag8#+W^9(KrD>SQV+Kp3A(AWl?h0W*lYkk
zGf}L!3SfZ$gu4%yeR#@uhC|jnLNcT~O+g8j4HEWH9Mm@t8!M|6D4Ng_ExV8x$pX~&
zSTPOz0dE~12DnF6$G^P5dkA`=TaspGbS@hvh*?|E;=k?y^e7@iQnw5cHW&;R3~CQK
zaJ7krEg>$>4p<^~KuT(!0r=q83_#?R^FYxK@VsoPZzOf~2IuV-Hc<<!?|>WL4-6zl
z;7|koIJgL{(^ZDerMzqwts2FDAJ^SWiK<d)<(#l|uldjkuqja0#%3G~vTMvES~dl+
z$~>T9i}yghMb8gnCSC<o1iVW4>M)3BSMR>KcD8`)GT`?chX;EYjU0~B-$tv>#-)u*
zt8th5Rv~(aH^Nv>&=g@LFy3Sy(d;!<uuNhmLZn1JaJC1C<oXd1E3qaRn=$vPZ?7ob
zvWrEOE)wv<%>gTv(?<b+toC_6Rw2gsHM@-G!1p(7g$=-Pq6eTn;s>G&Ikq+uDKP}x
z6;n_iYZPn)=b_!ZC7?7gJRB=|r(sUZIVB`bAQxHHti9ydPLfZIkH6H_)+P*4>JUX!
z4&#j>RO7921o<^Ex_nusT!-F*rjbnSpB+ELEbaK?K7}T44T_}rg-r-yCFky}ge@=_
z)O-g#C7foVxX~O8PX9@|Zf|pQ^A9xLQV*E(=w#3VP?0I?K$wV50FzdE5X8Cycqo?C
ztp<2P4FKawWwy2{8=R#@W)=`{{ek5AEwOhJ@V0b-L2JZcL7+-(6S_hK2<K!{fr%gv
zBo_nG>hb4e(Kg_r&P_y6aA<qL(qrrrlI`)Ip^sevOTY$%#0`%^y>z}ASXc-XkNo{g
zeQp~#uyrfoM(dSPG+SHF0_(cdEdTJgd}^c~kfVT?p&;WvTHTl)ygRR1WZAd%wxb5s
z*1&W5&*(;Z{t{vTB|^Y_>Hp7Kfc;}n<RNh(H1XsK{3jqLXntz6QJc(coMEujNX0x0
z%DY!vI+d&b08rc8G;A@J^<BE1ub-v*wB#~}t{ly483#$DI0@`zBo>(5kzSsjzIF}{
ziAMPbvqX>^-M{5zIsjYSRCqo0aIbp|C~H63#Dk%8UtX!KoE*YbkZHsm$Re`N?Vtac
zjsv@{QE9D(*Y;a)iCsGgmfoDz>E?6p{RRDMfLb3yfhGj=FE76QT;Wpk7PYEgDW#)7
zE^h(mkvwR#0iK?o5od2thVC5fZG{9iMO>;H1oJZ#rQ!UDTsKHtBlqr8pp+A>eTUD1
zTFmO*qr##v3?zFrtv$j50uE{>CMMMYAsTPGjd<%!G$7M$u&%=&fXy?(*{jFI#1tn6
zDBN6mm+WFA7=r!JU0^-Wo}Ykjn!Mxo%Fp!b{^Yh>hu~<W8xV@mq7*iVUYVqR0QNIs
z6F|jtKroQIm4h~HEU5zM;FftHXZAcdmxfJ&LAcsU%iK;7=Ays>>IGRwOcb{b?(b3K
zs&D>sOW8UXOzb4sMK=R+Sg39QdM*F}^L~<J{XD8eCPwEK`vk2(12=oz3i#;++6hW<
zYi$a_zvlm|66~+hOE8q}x)gT?c>ev16>oqm#}l&#tQ^oso~tejo1$Qnq&_>2D0nTJ
z)^5weB6F`_VaHC3x%K9BV!iu%^7OMTgZEdPrM!q;96taryJOmM43f>?9~|luw0o20
zXM=tndBZNVRtqF(aQghF-3T<%%JU128qd-=!`vHC??rk^Pd^9L`l)aE9odc8_$hh9
zu4AdF=$fLJ(0w35Gvt8$x&o`6OLDoG#3g3q(XeAb27Iql?dB*Bfz@;wh+v4pJVW*O
ztZSXXIefXav}6sWo4-=88{MYV{Jzc|TRO;NFn4+42RzXlyYS=gxr3Ew=M;a{+qI(F
zloOSJh;0W^<E!=V`p*#M#4`|eYvBXPpnqKa)YMebB@q*LfHu*qH0{#mjR>vRmThGG
zVG4naUT@?*|B*&$gkZWKvX;P+qlr<Tbdb-$N1J4gE<+_IvWN)@)m-l-K{oB@cmK5H
z>j|)I^7odPVP<y*?(Sh@_wR2WoO}>d$potJzwZkR0)xE+Si%da0=wGZUv+>$TB_fG
za;1WHKl<-c|ND0zb!qd*dRO-i@x`bzdO;CBAya%4>N|rTCj_!GpoK@E7T~p4;jY;1
zSiX4s+N+>RcWP(tYU%RMJ8KI6Ro(GO1xy`>ddpexx71<gh4V|YDQNc-;IOyuNBu8E
zUqvnJU+47?ht>X#kASotfk2+z-kbTp+il%!S9=AXNfBbCw14M7yVI6o=-HoG;jtkc
z5zf7x;R!C3;^|dpT&&@qldR8QR{rkkU*MR%1a4J3aGKwnMCgB+mHxeA0DOD)zx<8_
zZ7J*TEiZ$%xa;?J=NPTkf42X36W;$Ho|^W<8iJYzP*^<lIbXc`h}gUz?B`$De}5_C
zj*MDp)e`t^3))v7i5kv}vA*xKO`-Lo^IotIP9Fb;R_Li`5RYM5YrG+fwF%wuhEi>e
zw*cSI3RHQ$^zRfu_uQnYh&f7CGw|;C=P^-Ka9P^e&A6M&_{b^b?`1;(UyeM7&vL>J
zIH)!P^A@mhSb^}mB9lgehk}D8hD&&!OCrn9tsbiIZj<@nPY7<ESjm|<M7clS-;+Y?
z<Gw;gQ5D(<otpDl8>$69%Vam1oq&ny<5TGTw;6i+=O?ur>7p9b6TIuPQAgVa<GnH2
z;T)JVTBX+i8M50)aPxiz)8smXYl}9~ATNJ!OV4N*dl1z)_`z(Tn{)Qx!5(6`0M7WZ
zdEz}&yJv%KW3CzVpW{PMML|4vkxScqKbYE`XSWf75%7Ocl{ZN{6&q+pOkAR!OpQr!
zl_%XKb+~T*cKdQ|7Zs!9C-t|~#S;%EZk*K5d`qW%R6p!`=!utk*Q>9u8Q!zIq<>HO
zvGSb@?B2(X?lCi{T(P^Ls>eKhRr%rxK38+~6DRG+l&$7^bVJ3Mw((Yet<QpDdUP|g
zZ!2X_A$`|-At%8PxrhMG3_Zp-X&n&K+eKp1$yYQ(HcPmSz7GuXS=jK`q%0`c{_ba0
zmtGv38D!8%$?BkeW4o&#-Cxu1QgH58&CUp1JB*;+uiyVB`Dkr)fqRu^CxfwRvG+8s
z3qFnTPg--3OE`z@ET1pBSNJY23?`a8ASf*hq-Q?A7e>8K5M?##Rx*|@O0#scnX9xc
zm?(}aJly(({M}Q$@MGkK+KNEm*tc6}`xOdf3kW9Cn6pl0AHD|k5BHn*BXT_w*-rg#
z<zh;xpCZ<XdIS0Tv!5xo_<>1K$ixQeJ$;{rQ?qHK6^9gA)nqui(66KJB=h%krsv-I
zF%~XGEHw42Xud<-qr@#77a8dpSs!1Zth*n%|LVoQO~5s*QONkrI1l7g%1M9o2nQo2
zsmGmiXRSP-@+GZ}$R7FX+F4xk{Rg<RR&6Ltr*x8Q{W@viF}88v?52|Kfpsi|?tby!
z*h2W+*f(}|(}NjYG6ZrIcdgT~aI0ed_>;Y9wL*kxI%~#&hFrx$#k9u{q<c^w4q^Vp
z)OC1ZU6Inn+PvUA5Pi2W4Y?*-H=c7<v8l+7jGM%d2mJg{xk2qdTf8gY%-M6-BX}u-
zQkfvX$VkMxHAm4W2X$UpIr8YSW4|`1f8$x1!|H&ovZ)gJElniFnk+OSdE`^l)k(Ne
zgH?S{hDDXr#T9psyzvM*%FtKDC@Cu4k4<FRWzfAwZ1TDB)(~O5n)jR))fOc-hNIMm
z`kCv<uLYi74ai%NR@!-vB@NvmOF<GyLv`sR7X+eyM)z%YL#c-`i$`oA_urUNm9#NR
zeuYw6&0Oyscz;Jm&`JMhqHQ64?(`W%GOe6Liaa8{a{)GjnDkNk^E*A)4Z!DVFT|8F
zklWaHvBbXvX#ZziTq$`Wj$O}jr2PkZR?5mm+uzSFnzXid_#f@Pdpy(s-#<=@RH#(Q
zsiS(QsD#WZ2}vmD^GL{H6lNGkDHTx>Ij$my4U_X}j6%-Gk;6ubG3GpDGyA=^-k<Aw
zU)T5c`{Vb=_xgSQxOVF%v*+>gd>rnN!|OToQCdm!3+*c$@r*%+!=}>B)72yqFeo)_
za5U|LG`nrMS8vf#qPjxeW^D@lJ=dT#_hK7kqGKbn3d&)<eAOMqcd+J;4LQF#&QMmc
zowc%ydle?(ooIe`7f0r!__BAjPI623WC72B>izJe>_6i`#{jNJs5%x<cm_E=bvusy
zG1Dhc|0#G9{fnD(KN0RG|DVf=bZ`Cl9UuH-OJWlK7!t><oqx)v{%406xw6PB`qU5)
zQ|gf487uyQH>On*brN9abE@d-dqhKEw~w!Ws5XW6)QVf{jG`4#7goJ@4({NVH&}MV
z8w=U3ev6oEuvvRf7FhMZC)m}&0u)1$O@=A6xi<Cv6J@iwG?;MFk==l7RhXYK7w5}`
zu99^D=Ph@aCjo-?4*VWL_}%k_L}-5rJOiVb0}gO8WD$T_QI!z<ab-}sVU)DSqJza)
zxo07TyxJwN3B$2Y0&Gp}d5GQ*wo}imzW*V(Xqx%0Aa-u}%(yQRl3(dr;j<{?vxTc2
z1^D2pe>*bphH~u?t*hqMVOSHXn<8usU#}evS8OsIWS=M3zw6=r&#=hiNWe`f)8lKc
zI7ci7rMMT#pzPoWR9XB<v@=Y>&6!$Bmc`@oQ}{tL1dp}I0k{GXj(tj<H4(d3ev^e@
ze%ncC&5T$f)OG;~nxw*KmSZT5DU`B>fZxcm2MzBndJKbZU9S;>p|^8`m;ASixBV(>
z(0Z2jig&>5(QwcC11*VEbW)cD;!E&7ssWyqP1-K5Hl+IXPa@FX<M(Txy~0D#u+D)E
zs?4Z5yARji+qjw)slF~&mzx>v!NHAfeds-E138y$sNfg&L4Ttrlzug*b$y?7dLCnf
z-68xbV6TFNlEpMXj;Q(7er~-*M8)U$Ab<SM_NW<AQh*Irvzy^|0Qv_=D>O@wcdok4
z`)>Wj4y0hW6V$sn(te~dJ4m^R%UemI?w)ctxTJmA9G<7BO3P-9^{z0@sB6zQpW<HS
z`1oACc7Z*d)dN7$RD1^A2NCa3eoSo&)Tj`lT?^CRqpZQic?Zha^?PSq1nq>_ZI5)_
z;a(g!lh4!W`<?ALzcx&UXHPn#qG3VX-|b^z%5E^f?h$skWqdUH70*C$Fm;C9!R^7O
zGVZ$gxrxv~YA*Q`IS4S{7$GzGyhcw~I4ufYKQgt+Sn%DNAR{Or2YK9@rP-r+!N2Y*
zPTFV2xnt)Du}9gu<}S6x@OrJXISd>@yQJikCdP7y_H`^vqAZPCSkCnIxKZYIb&DaU
zzH3|ne~i84i$>Z{F%_7M(F)?R`Qvy-YCf4cC1Wde{u830D>W8l8HxhsfNJQsp*Q03
zmBxii#-ub;^~~zIwdDlzUi{B~P?Gtg-Ma-2qbPdjhSQ?)C8_fV;Nx|QA!^yJ+qGcy
zwlTDudwfyU3nSH2v0xZ7M-J#;E+@YMxY9X?B!~*D&|hMFBtNCp-nGTYDOUw?N`s4V
z&jf8MELauvk20f*YE}A0@N_;4mXbOxf&7f8o>fL(BS(@mYg7>T;_+4Hn1YPzBv5MV
zmU0LK$K(jZ$<nv7?(&5WWA_(-n5IT14XISOq6VT-73Sk$h+Tuj;Nxx!nEQ%PJp}4S
zweMdo*)FQKrgo4o;A>Lnv5fBkFCr~+IIz>PV6=44qBETOlY9n+#DtZ$1_)LpYam9}
zmOIItc(8>?;dSt;@9^3eWs>4B`}z$)#^NmC@?-6(J1Z+ZNns^JDxdUogS4nbvIG7Z
z-ivejjN@$!)d}^YRu2Nq67eM|h?Yw<P4E5v>^{3c*ZjS*9mLTq*6DDacy-vK;_4U!
z#W3fyJY$YY3tGq`&yzRs?!xSrj%pT{C)7(@Uo~i$ht>t_LVMP#(5P__UxhDl3zi+E
zP{W?-1#Ks<&5ph5uVVM$>JSA@5blJ!QAvgtb9IHS=f>u0>#Gsa`_wcUW#Q3ud2^u*
zm9QCH=pRE(%#I*S;P00$V3e)jsm`U4pW>0`U<ch!`OcU)0%n0T=HSPVJBZOKW7i;q
ze5qaqOI;rA8Utfr>J2&c$amA;RXa311`HxgA3R*iXd7g^bX;8Ui)J{+Pk@Z7BNg~+
z_)0dn=bsL;KnI#=?w@;l4E%Am9LC^s9N$;=80JikoZToU%O?dAj4fJ>TIA8?f&&~e
z#8oQ;0}}8C*^^*!GqlAVaa9tL6+C{8$(b%6t_|s3`jEVAoAih<>eehImCNx4!=D1{
zfG;L5lM){hsuY}+JvhMC>KU*p)vg$fT{G0OJH}YzpRETv0vVSJM=vR8_<s1~X&z|=
zbbyY+<P%`<2!%->!2yn|hPy6$Zi0#H*jR6`To_cCL`eT*7)H0kWX^@j>YR?4@k@99
z+>N-`36P~%3X^)4LRO86ns=DA<jJA`GFUKmEP1lROg=YuxN}V0tUHgW=(iw*-pfbk
zI8%<ty^a<Q`l0)Qlxs^pDAevwXz_?cs3o-DzL4>N+QH4>P{V4scELo!h)F1z+GgJ+
z4JOpLmJ8H8tHGcaE@3&YQn-ugWuS!}z(Mz@E%<je$$nF7Rl+D_UZG?qNBIDh`+Y>9
zq$@uxh<-I6bT4>2m7=lsXBEj7=O)sjF4s9?6y6|!)!|T;3w`6iV=9|G^}slcH<*wQ
zx-T|_^>rEHaDf%#nP4E|L$D6CCbZV!Pp6_A+Pt;K=r9B>IA5}&P-^6&ZE#5U8YO~f
z0ASa_asm8Tv~5?-8}s!|@*;__lPj7{O~FvlO88xBE%_v14PzsSLaWO)_&5Q()vSm)
zU@Fh<bd+*O%?p((XX<c}GB=LSW}|3@Ng)dwN=e8pb(4_}sJv!w4?<1rIbsQKF0jLV
zh@OR{|C7v`pX!TjsMDmwqSuw%t-ngy9_4oYEO2-!LrGyfIkIN1bGE-_zr)z639Rdf
zU_jp=g?)eJ{i8sdksDO;<y_sew3~y-(heL-^qw)EKFCbi;%DC#%Y{1!FpfIHwHET>
z4-}qn0vJpa;783n98=D<eh=82A^>~^%s9#bcz_pRwVPCt{$*Bvq4FIL<+;`!bHgjh
zBvd`x+b%R86oeb$9RK{i{%cSN%>w9;wT)Q(EdDb-3MHFlMRMYya?Yt}0oZ~@Dd?dx
zDX!2+d?u9B>!S1b3r9J>S?K`mqaN}B#CdnV7g$>v51w;yjxW4=|Fr%ON8zYP^fUW(
zEA>zGZSrq&&+O)0cSn^Aw8wv|%A;ium7{8k<OC#_q)eHH(E8IISUqaW>_!7Vm2cq>
zJK2l-Mn4l*w0@x~zTi-vwCR&8mmCYWfru9UkzuG$Za7Rk{&1z9VcfU_Aq!MJQXJ)3
zZqc0{>tme``loPtQ}#X3bWks<cH|$zLS46dB7*-g+-^Ya#BpFo`M0?`HHA9THSC}5
zMFOe--W{V^*Y2mf?n}TcOsWd~L%y;2j*XPi{72up{*dW>4A5TIwOJg{1q}+5^8ZDA
z0bTIkK4Y{lY0g_!A&m9UDPSEj(nT?IQpyTp?f?AyP<IU2%pNfvXe=9OoS$(;LGN#I
z*axgw#IjyNQra9iTPGnuWpho3bPd0SR{Rvt-(=nYY)>FMqrWTE-v=C8P4|FelT{Y;
z3-cUCyHdYkYv}R+sStQ;Z2`YmK3xTtX+J$yk>22qZ$~rCM|tNtJmGkJpV*QGBrn5?
zkR2Qbo%No!!=L)0{*m4^tN4db{n>|6aBph<?Br`1#)?$WeWX2{!a3c2j-PiBt%uA+
z3L6=JsC^cFV5!(AHa`}vbcO$W{iX2$_88%1t~2U^!<yo#D03u64y|FDt|*B#S9c5p
z9%Kbxo}G698n_=cTEN5!+;J{Ysia5EZp@RD)-U=vQ=7@h;ep$+cw1CGDy(`$!!eHC
z2e%vG<**{q!*Gyx<TuYw0=hqYvVOe1R%8wNm0)bCii&sbQqOMnAd2k!7dk3`U=>s{
zdal+@$j{qG9WuQ!g?BQ1Oi(o+LOWbu-C9LE^Ufb&4=(!gqs5X&_2;KeQD)=TfUewF
z8<RGZmd=eUn_;*$gGaumNh8JTtLRPk8THllcQRT_C$mXX4%mCodD%M(e{>YUf6U7y
zO>FR8YNQOKb=v{jdJUVe2zxIE#zv{okYbvC@?{x#021o`aV|KR(Z28kUN|)pNkeN4
z{Fb?AOB(3Dl|o1j)Q(q&!k}#(8}rx!keh2r64Tv7TB0A&loFt80>FgS-!s#+)lD@o
zbe+bc4A)?EP#E{{UG>kP%xc5<3w#dBEnyaIq*O11yYa8rei1rU?z`5y$d{D^2A|kn
zmpCbe$`Q0%4d599m_jsjJU0wZH0)^Divp*@gt_J#Y7yES$;rznZIFPvs!*6vFQCs6
zIySFSpY$(l;mhE;iZw?!9&1(GrpQ}>3zP)!%oWL?12IQG07eJ*gPDYS6(!Zj%_p@%
znT{o{7ub#l;PHw~7*7#vT5_qY(1O{Z-gUI1-ly243J4OVqGSV-SCc4PG^AOmmr&Wl
zRfVvYI|6bI$lKD|fsd$J<gExYK<5~uBw|XWy20LPb6r?(p}3!w2|O_YBN2FU)tA3z
zpcu~UL-A3a1<~)Q+hl|O<uCXe)K^&YDxECbzZ{)9kFP-s;P<-4vI1~YoMStX0>i;8
zl9yXZgH3HCchw(EZw$zc%5M2W2iC?CWt0tTimENC&&ZX@p59j2?A9Yfiu_M3{dRGz
zd=YP_!UWbl{@5xAp`4>(@pTsO>>4YCb}+HGNw;Q(#V@S~88#B5l=<P9!oqwxl$0Vi
zzyKQO!SJ}kK}GrY+LQ*yMq&U@p#WY0#W15~sMJ>lHvxQOj`1#DSvSNVP$1Pi(|@FE
z2dHe{f{q7xZ?f5y>wFQQn=}_}Y5n%G5X0;37@>i7n{|2oD-<|4!(6iD+gw*(h67W(
z4C-RTk@{v=K(m7FEST2u6n?p6XK6HLL`K;Jt`8k0cj5W*6qI&OH@N52fO<_U@muu%
zFix-bD*&dt7f%sMyyq8>b(8n;<tf%F%cPI3am2XD3(#EGIhTxwn+N<Sz}x-*7I-;I
zmhl8)Rr}05jWvoIshmM{T>t|JM638vWoKk1k8$RWdHJ`2()GwaTpRbGxN&^}50o^r
zY^l>^1AHv`2%ZPU=;#RQ^#^htz^k1NLWS_1==v0?dQ9+rDA9Ymt_V~YY#9H6wv2B_
z`SJb>SM4j)`PzVQL4QRnr0lGy*$cz?X;HQDdgwWGWUiq2KY`tM^84A1NBG3kb|nx{
zg-pYevA{p<1+zDK`3wLy#S|u`#s1L^j!9mgcCo7fj8f=7)wi3^V0jwWA8>)Cum01L
zF%>2ma+B4INHNyvKkY?*vKUJT`sB2o-+gKd03ZK3cc?wI*FVIJT1D2PgpC40Bq3vx
zgn~NEojUX%Q!0fr{xT@8s@9{<%6wet#cNuOqJ=Dwc>!jy{Dr{^-u6)x++t|oKXz2m
z`f_a4(kIU-B}4T?HDm3Isv8i=b@&5;FS7{;t+PtEBjmK!)s6-zxY+`d5tWiZb9jMc
z9p;}Lw_I{?)1xlTPUfJ#qP_ycHibXjufjQ^`KWih1KlVO4$e4yX<XzWPsgA8{sU0n
z|7@|8!NC?X@D(YLI)a(3Qjss1604a6`Qtbwy3B;r-peRk){GyVCT0eA`U5U?ClBop
z?kE(f()s!-CI4Hx08D<geM{La>2`}e#8+5}GkfBLe=B5R$b+x?l+I0QmQlWA`|=53
zVF1VE8O(;9O_cmsEkJyM^0SSnIR5?$y?uA~asgLD-K6>%dLOMlTUd}`8R|uR1%&%p
z04~tHqy7M-kxeIe_>Izh2@tpDMZiCKMw=nm11jEvt>S-(bQc;W!-V}F>QgHLbj*d@
zW(O?vRC$QN!wxpTpe5sGbuJ$qM2VB=dhcQo9HCwqd=uKtRR&x+IFyYNQJ*U$kh6!7
zFnQSpd+Ev#NmzRR(FGDf+i9;{pLqu`)*lphms(+14#`!=OsIbe?(2}?UL1kyP|G7c
zm=kLhqIVr1R}pF<C`lZeqbi}ksAMsw10Ek5bpf_vApngpi$;$p$HsXSBZv6m?`!ht
z%jC6WKjT8Ve>z*X)U<$wOgMwwZ@B&oOYBI4SqzzBAS5KC{{I9)6@KVvC~?YVQeEhr
zMxi`r!4e`~#BnkKpXWZ#w(N88J!mT6x_$<{BJ+4hS|nK{XNbcuMYm<4v@t)lo1t{n
zmEj~>xC~={#FK+k9k~FlH^h#(TQ01ODdL~<U^!uLu592e<k5m@vC8w16P!`Lih2k+
zDC=`{nw1OC4g6lrmVGlUu;o2K>v52R#=U=f2I8*GG_hP6PG|TfK&LI@Q*$O+$>x6o
z)Uw`eH^2$6igrdBTWa2c94*q|v{iVv#}TYqP!<{-(oeR>mjlri!2DIL7DdoGO4f=7
z%qDFkS(&;jjyBEa(145g3gGu~y61la;E$BX9f0?M7h6_nBN1fIo1z<xgG<}@RVxAl
zso~f^f7%E9sScH{Y&L3&likV5HCZ_UZg~;<b0m(_WIl?gj>$<Ja1EaL{~nK5qL8Q+
z=<>zst>)7m!|8tOC4UkNPruZ2FAno`R&_lKg>W+Mj=ewrj@M+Z1Np2ov}I7@Vttuk
zO|)fK2?yFaQihX0tQS6kHDqiBK_~pr|49?nJ^h!7X|vc1l$A0o1C+uw>qhF@SHXlh
zj(dgs&JK(wjaGFwZeK+xla%(|;V^*cU$$2`yNpRVT#I%yfHqvlR5f*M$TCTP#th%Z
zj&&xPDSH<iew8+p_nFnAOb2)`K3x52Yg?R&W+-*MTvWY1Zlf-U+wW~c@FN=L7o$;#
z!oqaeNdP}>vFC-LH1oIX$)E8EOjs7kEyM6ae>+MK@OOU${%+BGq8S0oMr;^!c_7#J
zuTFTpycxy|xDurmPeQvXZ#H<%ZQ0lsYkK->R{$Z)YCkBW`S3%Yq5w%t=m^$(#3`zf
z<>T44-KeZOsgN|dHV*JZ(Kh0sjjDzTp_)!1Tjuy6s@a-Af?@L#X8boATLIA6HN={D
zPC#_3gc4hlkz06cbtX;YGLWm{3RnE5k&E^;0HSd~oJ%HRan`W>;nl#{zw?f#Ceq`K
zEzexBZndTU27Gb?3BR9B&x;y6h?~O}Oow>$T4b)b9kxE$ylk7*%;BRSKEQ{J>r{38
zB$|SEMz5k7Q<EIQnt#gm2=c=L`w0mD)sdf*Dfs3fC62$D57|L1n|e%Uppag^+8jd=
zuFg)oY#0ZaAxyDA#JuYdOMAW#hf(Ok@vr|#c*XzW`~J`5130kq-z9McNrCaiG@7DG
zSAhJKU|Jn^m~9QLtd$FiwuJk{SNX-P+=aW441i_lD-7{)l<73#`#FXO6hm%b^^SaZ
z21pgf<_n5fPDhM~0nre@b#*+11mtSRK3AJlhgE3YxNX%sIx$H35?=+%$PibvAIRP|
z`IvQM07;vpq0X3*1OdYrZvJpB-FP9=P0bz1Fujapwkgz1H%3nrc)55~XL$8sNlDm;
z>`zJ@){5)4f5vsz2w1!cPx@RP`~hUkhvMPFc>qtE30~d!5Cr>jPRZ+G>cMd819m0l
zYMECi#wEI0PlC;V|3Bpc|Giji07$UliY0*ri?6pmn<`v>Za|#?=p!ABZvv2aJYE5v
znRs`ykNk4>k6m-zLBy|?lKb)9W-UhDKumfINP@JG1~{Swq?w8M<y<n2wz3t0_e5Jx
zJ9F^b`8O9IO-b4@H#?St@a~2d%f!;)+tAjvDrBKUQrsU4x%2V2cd+^ZqA2B)3}dM-
zQ$H6TMkSM{@wVB1QoZ*Tf!G0ieMOe3(*)d(4FHZ$qT68#Br;hGzZZSRQquxbAY~5$
z&+;Zs$l;yEW&G<}I5i8v3LHa6@2G#No&-{>F5tW{%m`4x5ME{sB()Ym3fuMY<ciGT
zTk*z3AWZqGx|}{d>$kc#Kz_qBWLGNR1x$K=oPtVXhZ}>8sdWIL$`55{wC9qSm6I;A
z#0)JeG(NUw)DBtH_k?H@bIe#K5Qz_iU(pORpdJBmk-6qZkDK#JhMFlOzG+Vr+EG_o
ztXeF(yS`{^O19_*J83nhne(Ifcein@SFz65NwjB*7vUo2HLaF>m<L}S8XpduyG8{D
zvLY!-hB0I7SwcXL)%;OwJ>qwDxQL_v*muvV&M*s&hcAZa9~6@)nnrO*Gj#+m1Ly-(
z0bB!is-X@9zQu+h7%2-ZJa`L@_nCI)81viwdo782Zq3SIplAfmu#`qhY34YsH*l2d
zAU8XiztpkO#dydp!_&;&g!(z&Y>~rmz-*myYtErKzmi4QSb&FF0i4;W_c%2!A{LsL
zBz=f+pz&Ozyo~B`Ne(obdkj<&P;wnq?-XDZtA;s&$?(5zbMwDx>i&O1Wb+JHy~FQb
zEvokWx^;b`nP<R}a<!Nq8>IZD4G5?y05Konbqm+tw`EQJ5#gC}<)QlYq%$7Eu7*)7
z4en=G!vL2sFBKp1$y=~uv)_f3{9jndaGQ>C2+F9cfjW9|(a53!c(|fG8Z&GXEyl^1
zc)kWS=f*fY+6`p*>GW$<E1vDmy7998+>02*%GmZ=38}k=EqLLqmj6xSi*k-h$t=nY
z@CYnMQD~GWO44thqrj{tch;hPw4q7lD0GzhcvVuBn;F6qTSk9a7TQ$9QJ%WmxSDDy
zGi+`3@Iew%Ep*Ejszv=qeq}w(8R5PV5nuc<*qj<iQ5gdoJ)j4`Vlmt0!S)*7?9;vW
zxDmgyMgF4DB4S~jw_s*p-`eu?Ik|FnlqZ{HjLI0ePhEh9<p=+K3F9Q|@`Kn~s-JA>
z7K`k^e|Rw(#mX(5*Yv3>at_|^$4eWMSR);)^(F)$eZvv<=Iel-2n%rC%E9}Xk0x3J
zIn>AGCIymI1@dn&cZQwSe=nx#qv?rt6ylWkgzqYKCkmVoSM7|EAM3UeS^T(g3x1m#
z!^zEOvvczW=F2PH8RK(x4|{7ztsdFJ=BrJ=_Yo)%Y0t0WK=4ZhT;qZ)WT5@z=8;`B
z_b0puGboHl6rsA+H6=M*^vv7P5CQD6fe4KlU3clBvS)wUD2?)NEgTu-#^-IrlK+H3
zIIn8h(*|j2Lw~WT(tHc9km}#Xp0wF&J++_elD<83FyrCy6k|PTCKaiHj@#~2;AOX&
z{uEu$%pz=b7;KmF2x<hfXNFjUvBpF8%bR{8(~ABS9maaeSJHM1dL(dW4(LP~4x`kC
zX4KKQ_KJ`c{g;Q<XRG~KD`laxJ}f8B%2R0+#>dVihw_UU;J=Vi;)S9%pxnvF5BkHu
z_6xK9nk#}_>8eRSKSZ-Q19<GHh`EAD1<!-u=RMCVn7GOLE$}02LdNTQrN5LUDlGPO
z+Sd554j%S$>$G0*obe{veR+ua_<38Ie!FZk)wUe6F`f97?&EJ1k3kwda)n0w)rD;C
zDKC+^Kvi5D_Fzm|daFz*kmLr2iua=16R*-b$iL+?U`_vOQsprRRQwS9sX-*neNE3!
zW%{7F<YH$sULC_6p|e~T<PzyeK^4^jjKm9k@;^Lh$mD2)G`K4j*9opKt@fV+*}#Do
z^jpxT*aBNjy(8x11w1JUm?vfDd;97EdBj|!30ND9t}AzDtUHU$E0z|3far4(Xpm)+
zv<Ww*zMkDkAPY0@&SjBJ`<FG%g~sA++8<qm*2r*E_Xm`&g?kabd%MEJ?uHb$DhnGt
zx(S^-JnwRW?g}Mdo*vH!g**+mMt9{UR$rO?KrWa4(Qf0fL!ENz(5wS_us^E%(T6xt
znODu+vtE3}9^a?^1T~2g&%Yz@iBC%1sxLnzo?83t$SmWRXKF~H1Ik&v7Yr8oT`L1#
zO}aO;_^xH5dcr*qu_NG#3^Mvi>(dMB1%#g8ilsOA78UNzU=s(`O1lyL`;$jry~@ZP
zLUt={F!~-meyGPpq*s#o+pR>$S3iE}z{PN+`-+aI9`(yQQ+Oc)&*$dB+&V`16I#`a
zy0W>NHurcq&T{-q$5B|WhMK9f9~8?C=lH$458iAaf@Cb%Gu!1zT?G!Np}FadZxHtt
zHx)f|<wBzZDg5tls4Jm9n9)|o(IvQiJ~P|Vsy#5U)F>(n6EMkEAY22zcpv4eFHOFW
z37z=8DPt2HboK#&9JLZZ?t=+%i!+K<PqsQ!=ZD~i@ipri_UYzW7(HOs4#ZIqoI(EY
z{n%iQ*3gRdU($Of$6@0Hm#TtedroNW6oTzK{_;fSuUF4sIvzUwbn@=L28m0}?JsWK
zto_)~GP8Wd__6ZuKrNmFCmshX-@2i7sNus-o==5mMYNPAifa}(GhMp|_9^tME137I
ztB-fDc9f_yEG*4g)x?FAU{BO({kNz7{r}}z96=R^58OdV@bL)o2<{N%S&t2?lU<5_
z8>D`4WAHuo>fx)Q9ie^(VwvSGxT6!vxZ^k}+@1*%?l5i>7ld1E#;gzgelCPNv2=C9
zYT^>k8wa{B&X8DoF~|Vfas_a$pZPW|6cK8}s*tFZs@!*#uTr!U{%y<hz%Rc;4<F1i
z@i<b15RbA^Fjw+ajB#Or3M9`tp~leSc*~`NV|GbGI|RL_u;L%>nTbpAb)7~iU9UCv
zRGd3<wKJTxb3ueTE{7Z8U+w%$FUDba+NJA~b-UEUBG>w_yizK?wqdV&H1Kkf>O-L=
z;fXoiQ`~9XVvA->zDY~2dnkDflFQE|r(N>6jy$?Dp6Z4L3k2O8NYE%dr8v@yGijI;
z(*3^p=JbmAT5#_-y?8ycDEGX<%&|jLH3zOH2eTq%^+JyM>w2I44&zyxZ$%*qml9Z#
zR(!RNk+nlyu*1KDKvZwds$cPFdyC`H7~k?%)bFd=272FD4>s~GRi~`0AdHbAu(XTa
zW4_;Jnoj<%+8LrS+?$nDBS0|_tv%Pqkd2qrwa9ZGDAj;=DGelqK?_9{KN|}KEp{5t
zSZdQn74wWQ=)bba;U+?4G*Z+|T4s(%3QnK>ve~|~<&8mX>bBPDl^8HWX;$n0m0NCD
zW^m|>5FzQ~^(DDJZdWWrQU=5fB{S_VVN7D3k52LQ9egNWWZ&p~%RPm(_4BUOTqZ>Z
z_{zIiy|sc%u_MniG_PkEicMS)&djFf`IUW?p`!i1<?4ree24jX->N^_*TR0I?@@X(
z!iUS}c*tQFAxFOnzTfqZn&-|<rrmbQdJWQDHl)clo{y&RgD==}m7fSXeYOk1U&;C%
z^;U<x;l({BMPc|&<lzP*H_n%@sJ>d*n7_37@&2PjOYV()OdfvHqtofD*p!|}HU_li
zdDT<hkQu3~n_9AqhYLbrzljPnu|{yKR)8DL;1Gig0L`0fZ>hx2Dn!n8#7FQQ7%^6@
z@7xWRMwl9hL9t1Ob7`0l3I_At5uTZ~WDQ*EN!?|xMeeF|8mFs9dQvjhlzCbzR(wd<
zT}5Xeo>+>AY)P<rH#BHHV@i5y98bq{PDb%}m9lakSYXz9APeA}&d;*n5e?h-&6*pM
z+rGP}i56rtT&u@lm&%6EeJbr1tI5WTRtPjnKSv09shomriA?QnM!Q11bZA4QE{V76
z17`{EBD-ufb8gIr>CcRHqVE1u=(th{G1otwX}5{jighNZQx#@tkB;0vid^SwM)ULV
zm0ZEu4xYXAa?$PU0?qL9tq_ZYuCZnP4!WX&9@P><AFUTnTXbJ^t5MO;F3wjlp&p_X
zqFNl&<9#iw?>C#<Z!PRy5B;>|<ErHIdh*+YqzA~$(QC*<pAns+(EPqi$jqk)^(Xbt
zyAAT~&$vdi@)m#XszR81B9J;<9MkjBCnwabIK?9KcF4;sScQ}mx+<&aClW1(giO>g
z-pw!<Q`UgrV3fHb6d#3)w6#2t;tTv35@HnFN6?uO?}|<s`Y5xO2Y>g9y*~K67rkn<
zypLnppbQ57qBZODY^aCJU}WmMtcj}L_0e!cbMyOc-f`va600Vq%=T~4JalD=zx9hX
z<g0S&q)NH|b?gIp4?U<xIAdn-l0xI{DlH{$pE#l>WDAAAp1jdfGjPe{UWcDV|FZ{-
zt{XuXE@>O*4`wtAEa^_1(f2gdG~B}8%)1?OB)mSB$U002&B`_yewOkhM@}3Wv|yR^
z&*k;xZ%4bv@YI?ptOby}QK%D#H!eI3@?>b?h}wN}eA_32Sxt8DP{~aP`dcLFmrI1n
zUgo9DOm26<7v-9FPjp9TQruqIWd{Exj0{Qw=^Dlak)1(Ym0(YXDo&DrCBN_La?q|K
zk)L0e-CO^2Wm30EG~S8Ks&l5Mc_VjB83Qhp3f)Zd<x43f4d^g@RgH#D9YplTJPNn;
zc*YZ+ZZitz4YPN$M=jYk_J<u;6a6LXXbUoSw%?`pxad7IqksihTould{X}6JWFd4*
zLmhur-yRkIL@D9R8a^{pS9&8ub8fhz%b0n=L`Gv(4jPjpx_YRk^yUVg`~p{D%Yq3l
z)$)_gMKYU7S~yjkQN07ddi*C#KgRDaN%P?yJmC5w6u5l;r<0-~(0`f9)C{*>G0#af
zvA0bf$&os(u{wg06V42pGmZ1}$*2f}#xvyKW^wx#=B+NDT|6Vx1y7NA(JL33N(K~i
zz(n#3vMOY<nPlD@ruiBE>^kO5Kk#6XcHbc3+=Q}!>|`BJUwX>}PsD{D$;=Bfh3<-N
z%{e#YhxQt9#HXRMWo<{JaQS48R}03cFJ}FUM@Zs<y7ihHfsKI}nZgFuTQR>B1Ea8m
z+%1>jJiC|)ue#@EBv`7ptcu4=HS4<?EuZ?xbQFgurfYS`+zf(1ZXrQRMj_K5!IHA#
znc1{F)3MZ4BBpB?;qHsHd3*Hq&@R=m5eler=(8(6>(Yospghy3+yG}D3H|7buUsCE
zy(pZCT`a>n`MX}dz%|N)`P|R_YO?&^3d)$U!--I`en~?46vgFkwR%)mG~p=4kF|`p
zPQLrgG%GK?H{nv-8?fYU-`gz;8#YYZ0E@>#Z1NlFbU|tTM;1=2pEpqN)!kuk;`$uc
z+CyDE^BXDVEFqeomMtzSg@j!mW$NC9Lm(!|YRKD-D_Af==~he%F~{hUk9TlA$c^<m
zkFC$s>_p}@U#x_hHin=t>c?al(1dNHup0Zk60_S(74<`6Gwr-ar1wqIN<`89vjZ&W
zeaI%Lc|t(N+szi^t@I|X#Q(n9uNucL9B^%}z3Sp3d(k5#;Df@$r61hxDZtmPdc?$X
z6L(155Pe`x9C1@w;%e+bmHAhEc3bOoSBg1^G``_(ETguB>QHC-?b;l)Z}aqwrw;%O
z;U-1LLo%ClTSLi*Xw%s=wBI^7Wh5sttGV@2gTn{LvyBfoHZ5GnM6qfki*ncbLg9^;
z_9EdIA{22#pZt?9!4(u_Allu#!n&7?S|3G5@qA+`dp@Guf}om7zxeKj$ln47WH)s7
zYCw_hJ)Q#_hurVEU4h1!kSzKBigS;+XbX+Y2ua#q^1-z?ciEb&?v`7)(`&$dPSdeg
z{C1*K*u>$Gh3~n`n>Q2cnE0PQ2M2EvVtSe_4h~kvxXH?Kck6?hc{f8PJxq{Co8!7`
z*U(N|qMh2v;4wg?sc2Ig7*IlqS-NeH8Z^QeV)6OnXLMC{8TotxQf~teY}`uN&idkM
z^Xd@ff$a6DRX=}_sq#V4>6+5MdFgk4q@C3C@?8`5{^V`3Ht0Zi+`3BceU(Ri-tCXh
z*+x8?G|kda?;W_*c6jxi;pF@EOonVxNvXw5<#LdbEK@RaZta+%=tR1%_{ighhNR*r
z`=A%SLx{RG?5y20A+LJXTX6Lf#b{+cabWSmZvn5j9;74g;ft(B7SpDKW-Vz7?yS#$
zuz`fge90#@w@umSkjZan!+tc(8Dxzs-l`9X0a>`)C^LmKed|)bq$MQws>5f!+PZ%P
zyOoaLJ>BPH1k>|BqH)OhAo7IaY)p3S-LChRE-5W**|}+!uIEmcJ(?^LNN@Ola!+3u
zoon&R=$%>_O1|1okGC7&+V>{7OJixpkmg*U!@gx7v)A&@0M=#=of9OP(xgGX=(5nc
z+JljDeY<U7N*{PT5SS<b!&Yw-@?qN(^zM>TOxLS*mC74*Yq*K07qCX38&hd-zoeNZ
z_7em(njc6~H?)`CPxmc~eXe)SO0jV61DNAFn>l=2YWMVOWIxF))<_20%e?50|HM@4
z2?D|Dhx)=x<@Nm~R19kbu%L5ukug(Qm)%1Iqu1@hzQ_$awtKgW`DsgW4?w@@grpEL
z8SBsogY*HNHjaLgyR6Up%nwLs%3fftXKrj_WwpYf7mZ@-_ET184$i_=KcV$7t}fY+
z`lUT<a6oO=3H=*dX5+~Kpi0k6GgHcB%tg3eq=UC<uh#49ZqSo-X2&nzv+hqkp`qgn
z7;%tp|MSgK^GCO%bf-c=e%A3RS2O@@REZH5me4aYFa3yk-UbhtE|z(BLSys>BS~`N
z)lgeLgUbiNsgW8`)W(%6usUU~C!d<ve%8;&+qb?tMxQQULIilY3V0G7%Qd!ts-h)w
z6W?_8Qm}EUadOi<>|;%dqCp86ShS|<xk($c`o*$cOZFgYsQe@zwu|7YxS)87a=98o
zlTy$P>VJC<SZWn#sV_ILm_u(bK5q*l9BLD{e7SKYG`!-aQt8X3hq>0GwYSBPo}WSj
z&fctfxu9f5I(^i)oQ*k5n{)D*<a34EB?CEf=niZZ@UB>9w!VX{xu<Wn?G31PL<rZl
z^(h&}WQb8#;m<HC6)_9R{9rbU`ySK}KIZ>>(ukh&%_XZeWkIOe$E`SaH8r!gPH!e{
zeKqAQ!Ek`~<TiO@p8}dKUMa2}IiFF^FYAewJGXuwYLn1Ayj&GKOb<n$(8Yoq>fX^c
z#x6obVqdP%{k0r?;`$s^wUVo*ytM(U8A&2#*3mhkk||{xQ0q*92?jB8o_*Wx`RjS7
z-50WI9g$*F)W`g*sQ5?s22>45f)jy&nNP1U$7O~a%+}3nXqY$U+<&XIc51?`kuSMm
zCbl!eAJ&r;ZhR`&M{cy2Ikc^O=KE@0B$PH16ebdQHeD-%(a&@W<PUUs?Cbaa0Mg0z
z3y0XTn^&?lvYmGeNvZ7oA!Vgc0+n71^jP2aM-+s;Sh~Uh2_{%VyWD1vd*(U&ANo1F
z7InZi$6GfCa{i=UOB`?5EtohcpwZW_wf9+~aNR*d*J?EG>&(8w^E+6tisbG~WL9gK
zc^$2HD;dk_I)Kdn+sh!^(WU{9BWD<q+{AIvc4VrHerAG7?{5R?-!hlRYGVVks3ft(
zC)qMx)mCMsr#B(4QzugB1@v(D9??V5T9|^U-{RdN=YrF<)%ntofkS-3quGD`@e%Uk
zW^d40auA6nmI2OZxAeieT$G=N0Z%Qk1+~nIk3w!L#a95;5!J(_&U_kb>6fxFsX}zS
zVvW;fH4Mp?q~~`5Go%9eWwU?(j(TH_+#!$0ztq6|xQ9e0Ve=$~A&5g(%KAF8372(6
zgh81V@6Iy{B86T;6H5dHiunT%Kiv7z^pY&S>0XcL`_=F>JhVeuaQUCKhwfYFjLlIS
zckUr2D85Hcn$PNmL0`|k=cVd&pV((c02#T+_I&fp7`Zaq1DZhWm&#C-HEAM&8qX8N
zZzXJ64%BNNG9Wxl>C5ppILv0F(QIfz23`Yd`x5SF%D_Y7n{xq6Z=TzQFnsIpvllmh
zV{zQxT*qT3IuhfhabUx_(XwUz*w{V<cZl>=WY2JA)hCd}Y{&%Hb-mSxjg~Jggtr^F
z%o~SVw_gmi9xCL?ACS7@C-GAj?YSFVRdckGYvb5d#_scZqkIvV1A{rXJo9=RT>jFH
z`3as8(mRl6?79*5`WMXIr1|O$-#4sTfgleQf67i-@+9%F_JusT7!7qG?4H-%IJbET
zXS!RV4q>o?S__JVZ>z0Gjm(=0xzz_3$pAj-cxq}7rkr)+-#_T`zbpK&6fonP3#a0Q
zE>G1LzhebYjNNv*O?LMu2hN0F$^7QdH(@q0ilf{mj#J{-op3ebvYD3$d3?^=GdzBK
zE0>&u+V*YPaMg8r*$HJs#3yKxKjy)Y>`&>Zd|yGy9@BqY%s8_2om;a;yi%-kPbF`q
z#0GCsz_-g5r4A~LBeFKE!7^6ywVc?!JbekT`kio=d|UajantaMeK%{Y?ik^-d%a!n
zwv}hWZIA7n*V=f}di?O<>57x#ia2$g>bXJ#!q$;M@g-$kPIzd9;k&_`Z?i64Kv(a0
zd+M*6DSzH4!C+gSnjLyOFvml_Gfyx1O;|~&p&Iv0JbvpAb6J`w%xai+3J*E3amSP4
zKhd>k&ct%jomnHYG;mJ#gnr*CiHUco^qKQJLw0Oja@v2lzeKZ0$CEL>n{@BZ#kq&v
zSC8FdtUX}x;L0Pn?NupxRkE^q<*~d{JtJX=cZc)7*z(gr;k2J5XEwo24DI)vFLQve
zZ+S&rd;(Ez#QppW=ksaJAQA&q+!VA47QHnJ?ZQ;bJyA<Hl9X1}(&&y|QK>yf8P{`{
zzVq6m<fOT+31YARFSvUXwADrI=cp*O&ULU!d$u$x$IMPd|84Ux@Y&P)De~C4y>i^*
z;kMzj;r-!~5lOo40MebZS9!IE%)(&zJhass>HI{2ijhyaD@k6jOysF8YkS!djt=*<
z9rY<JKEa8c3LNa7AcRITGj7su6Im*!05>ZBJL1l35;G!+TjYRi5;NoC^PpW1WgbeT
zYrXh!Rv{3!)B6G5O?@f<@tRBi&R9OVoeK}}n`#p}IFv=o<q|o*rdh4Kp_A`@Vz%qY
zWsPb+lH4AN)#$PizAq+2EfT_dc<3X|>N$7n2-n_2H+N4E3fAU!3+=9b{qXkayYJ4Y
z(<ed7bQVtp>;P}j!j7=(R;eYhX84|}Q4_CSOAi|t7tTZ=_`H26@WV?&k$ex|C9hpN
z8xVES^)1-=Il&w^7mg2I2|Ttmgrn`AYas64I_bphzw4!UHG4HY)n>|}AC#f}Orkrn
zfbR2w0m~U*92^#F^ASJPuE&kwvm><mg`&GAuOhp?94=6>o2n`>r~xG#!GE|!GC6_f
zl$VeA0*{B>^j?GuJH0MIt~*ZRwO4`#2v5#^b)d}-SIl!QN=0gwtWV51Y4;_Ti5CUj
zwD&o^Z+C)Ng7~2v`)9v37i8s}D`tQyf^V0kg+EIJ9$Bl(Q(p@2L>TV$>9&o%ycKsV
zVX8K|fxGcVYGBJd6?boEUT7M55~72V<$rSj>sqt7%EPYLYD2y1*S2$ZL9gb02(fXg
z`}l3yj(5Jtjdj6bel#pS{^M48*q5#O@Y?dDZ@Z5zKgzH^5ej^}gdk;4HRcWGd{rE0
zKc)(;T#H@)2rH@5BR;*3rfQB>(~4bOiw*v+(}Z1WUNdjrl_)srdfW|N+(|rwMMiNG
z^}GK{5YHORxY%CirQ)`)Rn}V%e4?Z8d^{;W>bLb^>4-@_K!k(Go0(mAZGGmuWZ#eq
zBCT6nlJdW9Yqx=otj=Z6FOM0$x0g!2AFiyk33+tB{`&h)^|Q!=sBiaQfBnRa`|C|z
zo1ZPT#=};Z1S5|dWF@f|>yDBUn|N9@rUhr6<jwl)sF#Q4yxWVVvQhQ$PX3Y$BD<vr
zVLM`XYIM%<)avPIvILx3pXguMU$-qK^g}KF;zaQ^E7tmlj4OZfPC4wKTAg_e3OjX6
z)$5^L3)f;6>Cnr`FV|=t1OeO&T<EOYv4`>t>aSI1wDe`GXjRpD<}G;jpynwD&13b&
zC=Y{0UI(4P@L%PTr^S!#eque!WxT5}x{Z!|T}y9T2%cER9r5qlJP@fx4oF!)M%SH~
ziqz_M!JYUSe5hqpR=bT=bCv+X@kjFQmGgOrRryJqs<~S&FYw*dI$^p-yRE}Ktlo-v
z>wz%MY2*Cb<zJfn)v|8T&viralyd=hOS>x#oNuzlH=!wur`An}Q6GFQ#p#J5`V&+B
zXl97029Dv@+g+GBW|Qxtrqo7LYP`(NJkM8WMB{2Y^~ASDck@sILR?<{qE|MU%uPJR
z-uv?V+%$oG-t~KNs+pypv-N@=i;jI|`=q{GYtel7s0wH%d<3@blAi^442nKJ`zzSq
zkfyzT&oj7Bj+-IKhW!yWVI=3I8PZTMLUItuW9+(1k6PC++0WeSJ03K3u3kCOQ*4_r
z<*xmKDJ<$3q@I3zb<Dy#08<~^r&)U=SpTg@W(UvVQ~G@&jJu`k4_-WTQqmn|sW{o+
z+lzC@F>Dv*dDeNn^%GbIpC}^2QZx8&-ie?WZE1(Ay4*1v619)J;uqi-xLJEQpmR)~
zaIC>^#6Bm(?yv=Jq9|6H6n7E=^){H}N1Sqp-)LoK=vn!GLLzpBiC_`3PxwA9@)AX`
zi^`1c$OnwGjiAvdUMId?EhJ_6Cb6zJ<}VyNGPBQUTjY3MX3BI`f$8va-#(W@>9?ow
z&v<Izp31wa##O#4${6Pf0V;bB{^T~1mB}|{{ItUOVC$bRt<klfKqsH`xyk|l(UA?0
z#^O1-78MHV#{-7*E_dbqydfh};Md!NjU9L=xwvwe+)dTngwa05-t`O~Q|K7Co#Jnj
z0S@+wrbAjNeo!<1wRO9cx_DvNR5{^>+x}3)7i&7ciS;i^NvaunAmG{3>`?DH6D1tx
z6NNA7ro6rh<UEFLEA!TWaX+bP?wG9S(L5dkSqJZaa#Vy%M0hZN>$p?7wU}!8Is{Wf
zzuUO4#?X1N0C|BQEcxu>&7!(*8pzDRJ74roGWx^HKde$$z!yUX?~f`2@ytDa4o0FE
zk4|?zfTwACU!y|YBlbt<l&y?KFHKYj0(ZOi=_M=Lwtth$&}X$jE?u6-6Ho6_IyoTH
zxFSo@{B?eAQ=~dBkI5HxS?I@68Q7+!@V4XITOrtOJkIQi;p*66-aK(lHIcK6t2kNx
zw=d^~AuRt6HSe8LyVLg6#$4QZbbk&XaOtP)a?+vShY?S=jx=glm<TTH@Hz}(W&PGr
zK2@kq_^g)0{N}kQDHO%TWKuqfRltsHNxbAsZObjbK-p=xTg@vnl((QkTvoVd1Lmu(
zHRAMW!9KP8HuK)3V23e!OI)u5b8~CcJ8eoVJIH&^EWjr$zFb`YZFbw~qkzqSvvJ%w
zUae<VY2sa^m@7k=*Hu&7O?UDqu41>bp7fwzRZQ0k*C;pggqn&v5Zr0W<4bnaLiyQb
zd3jB8r$3FzLn(BYm!@2d4o+wwfKYU`X*q=RqqlFqJxOqJEkK==l8~m2o_l-Byph{M
z|Lv8IuE;arE0|ZcGH6_uJKVUsk9Ry*ecm<lvYXohbe~lg8Sz!Vg2=k`P|%$(XfpXe
zJYkH~z~A1TcbnzYV>-{5yq2~ujC>Nxn@uhcElm%0{ap#jHiF-4bfk_uQ0b?ZsqKL<
z%fth`@*=wtXY<fk!&tX+x?I4r$+z)SapxSOwzy5@;Db#iWkVJgdjUi;!G$@ce{`DK
zyWSZ?>&ZAF&hJ^Sk<n$7;B)o|Norzu<3X<d8D6aJEZZ;-d$T0HI}6<RjQ=t+Ne+C~
z%}Bh-TH<dPC#9|1=())(N%vsye?1nZJ8Btg<sfHX_Bp9^8!H}o_GjEN<#KY#i-qB?
zQ4{c7_;^{l&~fJ1t(O`?Q*xSRfrL4GS#*=vOTKRy4C>(9Q?I^qX!ZT*MAi&bT)#wS
z_83ct#80MW22PKvNAl^}4+JAlJ(QZ|4btqV!^D>KcDcijb(||ikA9+@Ih1A3y`}%j
zS5G|$t8o~jS#gYVVrgWCIi!1DHpji<vks^vxn6&HB)c0~R6C~82Mpk(w2}2|&MLXg
z!ZZolFEha?ploP8Jy&j78fxfL_WG3jKEb71Z?hbtaeE^$dMw)B3F(#&pEDCphq^;X
z_x!+=VPE{>o9Vgbcj}?m9Md$S`;F^sTFe7RCQgZ`@4^mggF6mJb2Dn8+m~?Kyjiwf
zL?f$u==(I%mGy=P3~Xd_?UULs=JLBsI?q_kgUWz48u||gs6YQnn&p}><ZtVk4eeb|
zPO!BMe6yq%LEzom5&^!J{nLiR-PjdB`t?#KZciK1Jnls~rXc?aK)wbx`ATDo)+aP@
z=-qSBuq*UUb=>4kh<%t@dxX#dUQsaLydf=qrb0&TJH-SvHxrh)B9t<6&Fa(7twDz7
z4!Qd)7!!o##$8Xi>O@YY%kdF~?L=Xz^8x(S4D4Q~=3lHAMDX?Z9|^I&z#|b8t2o(P
zzmphg<)QIipgQ#KCvx36C3asgH*$>(<NoRm11wv9S~ejuab~O6S#jxf<25)VME>C5
zP#Dh=cIVVoz=frH+tKLADP^K|rEAlij*sBHV6DW)VJF^=57v|8FIV7yeRW%zKC77n
zWQBNS&mxXo;4=1tfDM-OVH{T}n{3h;-hI%?Vw^rxX0}wF(NLo9OsS=Jri^{Qp#og#
z=(O}#`DOa%=32$>H1W@jgYiNu3%a7$<WQ^jIMq+IILX)JMOVut#iv%kKc<JCy=7af
zODRuKB`NbDZ0|$?X11-#S#KitKpQCfqE)|fa=@Wc*;|NvFV?=^uDL+5{FJ{wm+*k0
z&a)z}7=VoF6S?qYDeEld12rm1I4noc9cJK<plGbGanFtpHJ2zPYI>a4D>F}E>D|i0
ztc+O@3P+?QJ_FVu_~u(P*)G7Raj!dna$j&c_{r9us~H!?Db@V#TVhTYzv2wCn1`L3
zhhI4-lpMIqyOA@<ICIdo*zodqhG`=Y%3JKMtK?7FQE=*qH22C^zmk6dCMru#{=26Q
z=}=?;Jd96H_RV@(<djZg^B-xDjS#+-1?OGb2F{^8qG#H{sA_mvlPA{op*T~`so5X1
zUiJl+oHQ^+@;~!)<5))^SmLckgJ~wEd*AKTfXk!;X!gF48u1O4pIR=9I~=ZAk^J>0
zSO*i`-VYpH(rFyp5IWT%$TX5&1Uy*&IffMR90RpIoKXCxXkjYHc2S6@uH!y9zV2C3
z`5b?wSO`p*_gCGu4G)?i4srW6>k3o+V;$!7zW3gzI%3t6F77W?b>j7T<DOv}p1Rw!
zRpaOe*UvPsond-c&tOidna%r?%dTi+00|Nj{)IT|l<lMH3g&X)ACuk?A*&oCcFQ!{
zbkL4(WgvA#ua4h*K6yG)F-wRy;Ix|mqn<C26CJT(yiV5^d0&niwXOK6VyTO_CJr3N
zJ0+%#(P@lGpF$3H1I&g?;sMus`2)Nuia&_{iEe>5P0%+SRQ~o9SB9b6gm2W#ITJUx
zgZKPThJ7{X_-g>M&i5rZF|vVAEJu69b|Q5Dqf?fg@yUG-g?#1tm2y!3bH_D?Cg$z`
z4gx5aTnpStEeTwNh&S@AB<>c9PWXGKzZ?7+{eOS{-=jdu1dDr@z{j?mlQoaoby)g3
MMmGwtJ3jt@02TKfW&i*H

literal 39096
zcmeFZcT`i`_b(b0D@RaKP-#k$4hkZ@sI<_fcT~DGkxpphv4OPE3|$DlgkD3jAT5A^
z5Fiw(p?5+FA#cTVzVE)@9pk+_-XHJXG48l)jIcL5yR0?KXMW~fd*y?Umdb_mH_k&K
zkPA>%MO_HwlrIEw^4Zzb;0P|^^9I<Qex|OX2)3XdJZcCAn{zKzO*|kFmUom7Rn*+3
zEd+8C0#$sV@0+qR?h|*#VB*K7eGd1Xzi)9XU8KGeH_LZgf12;TB&4%|r>mZyudu{P
z;jVqF01cY|3B(8!`b<He`pPA0#f#MX(HT6KRZiO-l4cA?l=KWz3v%s$6D4!C=`6$z
zyu2(9u{CS)fBN#j|NL(WP$U5NNkl%2dY2Am^_zg2sq-7c2x82@N_9%+Z0f1N2?|Mn
zbI#q45pLtK60tq?8C_*>^=<PE1Ttspox}7W8LHA!S^m49mvMIE#6SBAr<6h%|2@pd
z!(eg}?0aKH2QE;KP5=2WPURMqg=;t3vl5vAzhR1UOQ^K$uYotk?8GBmhnmH?jeB3g
zwaS*3c=BVA0)<DtT-`DEM)>4Yz8))f!97Ef>Reul`bzx)&>60X*vdf;ZF}fzPOo@<
zz5bU_JuZ?_f9RFbie}^lJhGfM!ZvdE1~gqYNoY|F+MJ`KqEpW41@}xtT8iLXO`+@R
zH#ipcP7y7+3W|CxK6!jjz}%Hn?0-7s!2!L%IT&53K944;&5Lf8AWh)a_8E<~ys7Dq
zJ??)-_`49L@$**7_bl}kFVDEjd#_$%yQt}B?rQ3mb<MCi!LIU07+EO6Bcu2#N|1GT
zM<pKX2Bo-ER>j;CR<7%53x^kQ8>>X$Xs7;oG`e5L?t;d0r=~-<g6SHO%VVk2G5C||
zGN(l3b!t~t%8=5}R@6mq9RBk0OtCdzj|qOQ0qI-3%;i(`O@wH5(LVXI?6C<eQAc2L
z=$aR)mt*py1TJZ<*VDv2ouW~Q!m0cE9cP@U&)u9=^_P0HHTifY<0m((pM11F)DGz?
z<Hh0_Z)DW}Lt;CiXn)AKJz>WI-u5IJb9S`cyVSr(#>JeAzEbUeOR|Q7SSsE%bCowb
zIC}kbdcCgLx2zs__t|dvx!L-;<`J$yKB|dQ<e*48${2ob!+-EPf9Y|#N>lH%S^n@|
z&w5nvAK7JuN-O4{qI-7gV!5rdU5;s+In+;X*Y}jKeW%9WMK@U{8&`R&m?y-Z4aa1g
zP;D~JZwcnBS!p+~s*T1yAu_aUjQp+|*UqGB^h|H~P?@W6oYi=SxaPN{MaZjBoIgV=
z?;^YP);5RveZun)aRJ8<&&WKw6DQHOn+UBvNm>X*_hRo^ItV1B{Lc58-9C6yC$&L^
zt@6}R!Y{4c#$v)fTVy^_ZrcJWo;n;ZD&|~JYhg789j{4WgzvkCBeUm6wN=w`vGDO4
z&3o_9i+Fg|`x)uZQR5Pd-r0nrKR9qWDr{)F$FF&aTgKZ=plDff!f`t`EndnnD?7DU
z4kPgT)^=D~+j2Veu#bE4G@>t?g=<i6*7u!FPcr>1%S~BhJ2`y0F8r-X*R-gG<-Z1}
z6Incz#?ds?{!uGD`!mwXHZ;%mc_+0YS~;Qd_To(hM70{@@TE^~@f$qJ@7J7IC^jhI
z*<ie&sQp^>wZ(5DG)k#YvwIWHrR_&J6tDMHKP(iek%^r<o(YabtNbmu@>n~Xu$PAm
zLJL$Fr?LEW;-$Cw=%qp8lzJu#bdFxl=d8Gsfjv%RP%#VL%1~iCn!nQ%Q~8Z!l9$y_
zE5<W(Iz3@lHfk%ewVi49?8MW%OKP6fZYF^_1A&ng9DszJ@8Q2$&Y7_#yv-vou;}M^
zhIehdV=qI6wHdN5f{5|#V>p;Sr@piT&qZk;(#wnz#M5PhJYrZEW4P0BYkFSsD&tQP
zxc>2p2Ip4VtwShhMj{Y#8ez6ezLv>p*7;`=v9i_a0cu9JpRp@gEl#nL;FySwV%IID
zn)rNo^pitKjey*Mt^2>-X$-+GH1cyAXqK(4%XHFqVLX<z;Tj<wf|Im$QIXp;8-%UA
zHn+Q_b*z3j_Gjx_IC@`3?dZw$;~qj+^mG#Tf2}gcIZee1w?j~I6CUD*ur1rrV9PJ{
z<th9lj%6yz$I~%EXqCfvC!F<~R9K!u+YV-FL|T}fUNtBJQ{4TJDYh`9A&?C8r>V_D
zYj|}#wMbh0^*+B$Ofhlu$m+J0E`5}XC>LodcFzY&FIzp=fe}NzkCL^tT8zc>Mqz_D
zJM_NIRR#EI^KZV@Hi4hJa{RljSnne`zdZ)QG1&bOx*WJA6idE)Ubnty-1vstp{m{5
zh(oK0$L676PrOzQ41MgDh&?|mOScvr>KMuCnA=;eh_BZ&bPTbLpR<?J!%O^e**t}w
zMDlxe)Z@x5PL?%2SNim$O@RdOeC=Zni+)(d?_C<s^clUjbeSz1;;g&b#g27}xOQr4
z604=(<n{<ke{eg0uS{Y~d#7WayYEu+qLhzyRoilzfgQESZG!|KA|Xl*R-jVf>OK@N
znBR!e7wA<#)W5xNg&gmFimH454eq+bE8l(zjr}p4sHnV=0J=6#>6$84bHNEn$kwz{
ztK|xhX&UkYyeaxzwZ1!aMhu^8!$2EmTikoUd66aaVAZUww>n+cQhSH&9@KWr%+B>m
z^E*b=rN19+H0;6*m=9*4^nZJ%HgrfyeIQV~G$8M?;tmx_xYaY~I$&2y1<mHqe6r%~
zj@?0O+}z?pu$|#^-1Cr_)iZqXvA#2_{756Q#)BCGk+}H#6o{6sLI|_&M%Po3*@Z~#
z)yaaj4;xIY2vrMQVD-Uxb>p|0-K{NhZ70GtB+8h>>$UDg&p2$34I{vBnN@^(Y-8o5
zy0jLv=9OMv!PR(M^v!Er9Os-t(O#?YhN<D0+`!x_Gl<Ewb93PU;X^n{w=0JGV&KYM
zm{-Z~*WjayjnyOEfjh<1t~U;yx0AhZnVOtpYIN>=LFyP@s&HwR*kZe=|7bco(nH*&
zk*hHSL$(TTLb6#{PFm@zFRu^CY{?0d*_2LqX{_q>&9)8)>71~Q-?guoNu7Z}T5b)H
z7lBW|5LM})v{8nOMW5sQD{kjKlI!kP+U4DKFFD7C?|lg~0o8$QD#u%2*CkT8M9YN`
z@2ns)20>IiuI{tz%=(WmU!1LD>cScZy7t7}(?YP^y`JD%(^gG2wB0OIP3$g95nK#P
zU&X3R>Db+Ku4czSkJL^p*B!7oD?tvOi;K?(@huJX2DB%_21a&+-ZgImEj^*log6O4
z$2C6qE{&Mq2`J+=`2?6?`Fz#QSesB*%hkg5817!~C~Qj3q#rW4Ki36Otua9~Y2&sM
zO%Etj*5j_vGi1Lk3(PaOTtlbZNnVA&;=x1`>*^?<Xc*Q7_xzyd01_2Y#&EZAI=0vC
zORG)K8I%%cg3z&TpH8R~EWaJ!+v_Rvk8N`JQb81yb}G<}PYPzYk|rIRr{r+1@IApn
z?att}v90!HiNAEGX>J<Cc-lYj{?MhG?n38_PUBkKyjRI}_Cs1jnRB9vKE{QQ8hTiW
zREe*<xvmU#t4CWZ^>aqa__WKPwZhrsU~)LA18161_9(1&9Fe&u-X?VO17B47Vn#3E
zDi=>usG5$YIj?A_;DplKBpABs*Id=6+?=_9V<;w1fygj-C_?o$IbbW|eOAO=w{P8<
zY>#4>O#zI7!92r$1YC%w#4+s{U*Qshw6~d`shVuKEfVgQnnFlWLT-;mwB3dV!zo~M
zRfGm*8)kH~=}p^m(0bjhy!dt*E6ON#$NA&S*Y^fycAXJ7D`m+(s=8;OR-GvgANUG;
zJ##311WkwPqmIqe>lnB{^tpg&0fsg36PE{ZJ$tJu{(v)TOL(s%c46jeI*F%O@1Ck%
zi3Tbl+%{BV@QUPooyeE<G!ZHh`$I(rgnFw)`h9NVM(l#%!J?ecjEgosR7q}b@`iQu
z02e3y-;wx0Kd#8FEzWq~S>c2OnXAT*+klQ%-dApXpxyUVtcMh_HzlU@TK|6fqG$Sl
z%<IXSi@JVA@q6#9oT2N~I&DTG4>*M9WLuKC=oM+$7xWFOCT@si-KTfNU0%J8ekpgv
z$@E(jTbHZ~qm8fZ%1v8-wgRgkTNt>F|K>#PTKR59p!cA*%iioK)kHK>>|C`Q9F2M$
zL^V;quaQfnI5zK-FpyG~*6<{pqmB1<7LGrM)KdK%8D%>!Js$J$I)Wj=vjoX4G9FER
zbbHYOu_wVQ-+`%F5)*duqhB7={LT(-BRV%5*>%1xzb_PdkBKYrhP|J7yTA_y)Hy!f
zB@7q*@OJdE1=PMX`0$+Y)hSVySqd@A{DWPv*Bv4?xu}mZuDQr<5Y)>0rAX|Ho=^);
z7xm1*In56m(d#<UirDsMh57r=C`tW+DSl`-XCOVt_z48*kZV=&yfPZ^%5K*YOgr6X
z<GL)YF5g+%)hinoJoR;wt19N8k1OFFmqkW7osbr=@729)i^}Rtf*yfthJMEyxif#D
zO%mY>%pzp&CBiI^u8h^i=7hhd&Ae9;y9aC?1yAUpKAK3}i9zn38xkAfYd~oHCa5A}
z>LP4heJq9PIT0KlxE-UgK(k<_ohnQaHWtCOeGSoCZ`#4UK4}{&qHcRap~5B-niHBU
zZ|brvuqcDLY4-?W);t-zgGM@r$*W$AE0^(^*{#;l8E|@9Mvkqt$d7Q){*@~7MNasG
zP$(f8Q@6nl4UYL`q3eXWL<r|<<gGZH&SH%D{ySmMrstI$ya#^0)zkpwB`9@&(=S3Y
z2_?Orell%;DES|o{~!K|Q!)7?1qk8B6r^jKoWIyc1i_`0vAxF=0PsJp<5mmJzq?nj
z{GVyrf4=>HvxO%J=Eox2K#Z$2=tseGRO@whS)u~6IIpTN!H;jB7UEod22Jl=aRVvN
z_QxtA557@Yl3>2-+Pc+_b#lBMJ!{qI3*EO%QFFHtgx&LbXnlhCLUo)9VN<ft)=Y#b
zZ#wUxGj5C1Y27<`K^zmVwp?dXVVXB{)ZT-}I$O~fXJ>VDOk+5pDkh7G?aZhPaJd-I
zC`=Pxy8rDJvU_;Y$FK@nG22M&Stt#sOM)cpx@tX7fvhDTwBEemen?I*))R&FcPx^Q
zN25#}`P&@WD5Jkg^-k4Ph?jZBJsg>mhbC85d+}yfpLYECY<(O3?<h>I$|hBacLg80
z#A*EG63B<1#{IUB)F9ISmqE1Hnq?A$^WuX(wGp0%kwyDkvr==v_eClhj*7}u=k{Y2
z8zy6#6w6M2_7iz9YwQ&~-A*6I(TJUCzB|eRJ>?#^U5K2Qv;7rYIm+So0cky;<5RHe
zYL{KtYv24wPv;hj+8N80;93d#kL=<M5-<*>)|`#b*^AfXn=?<HbP>BUvi_(FpV#wR
zDWpdIE$)&qk@;A1;KF7Ms{J4#d8)d$!w?x5e}YFio^+vJa4u$h|L@Fdt_m`n@uHKT
zbInQgW+b22dA^q9=T<Xu9HP2n0*;y~t4uE0xZC+C%-LVg%a4_GouIF|WLdJ9ad<;N
z(u~G>j>7GQfHaMt6>v;hd%R~YX2DU~c<KJA7_yxx`bgKBKln3-XQLEpAtE1o$^;B4
zfgSIBA!?N}q!5Wg_`#Na@MaY4Jw+#O57_cV#_J;$&+4B|OuvgAN@Dy_mOY-20xIJi
zXW$y*$d4;d#AF`gegK1l31MN}UCwa@;>NCbeK_>&#_4gaXH{pCu&AbjaetfKVygTC
z%cZO}-$1)~A4`R~y8PSp(X{IIKW&NmVbr=b=GEGj)$)k9^?Kc~$|_PC=jdt3u)6JY
zRxt>Kum0VY>r^{F2%IDdV|0h=!&VFH^*twLvsX7$p2+rgI`AJ=Ri)+HRbI8Vx57;-
zj<n$$A)Pm#%gWzSFj>6pY2^{Wd-NW;SKJ(xsE_3cR9~)B>}M>yVHWB9*rwo}kPr@4
zk>(bAy^py&t$b!(L{Fa@2*%I83xgxNtu}J(QbEqK1GiO}KF@if^tkDvt|NSIU$9-Q
zV|2)Ho`5s6Ww7QgPkYx6n^3f$cnB45515*~?P#Yz%3(V{K$>_=V1eJLjf24rN@RPd
zO-2KsrVORQ>T?yBWU+j~Z_v${(YCGN$EbHUt`<$&{pLyx6@J=wp87I1E#h&WS(q{p
zq36FQvt>7%a^ty3>UK>!u&3$LHp*`C*=s}FN1WR=aVF92mK%;)nD(`<IhH$cOkT?u
z_(Xz9o?{`#r9aWJ_f3hENpFRXtL+;5q90;!${rCDFJ@io5eyaYLchat7ANj}MBbP4
zW7W4QD^Y_B#qY$#2UiR-*w@DfcJdXHA_fy~8>YtExb_OeEA?-`uWi;Q&cw9Kjhc1^
z+-Ed#F%j~RJ4lu8nV}a<+xu#GQ#7<vu_K+l$e?d!T__~_(f~dYCz&`|c|4BghzgAO
zi<hu4wKdPUEa|T`ZLZXlRU^A46eB5Ke)ZhTGmm{P`l;K7wv2?CfBOT!=cJTl%nbER
zI!4`^3}FwF3vEK}TxrSzxcB@xQvHG>Iz`*mVg<=>`qem7uN$~(r|%E~&~WmV3mw1s
z5l_sG`@@BBJ^D6s9SdeI@4h>1`)sARZG>GrU<+4c>xKCv(2RtUo9j=Ef2Pz}vVBrK
zCe8*sC-~1YA=?G<u2Bm&8v~d9eV{1VtO@_#WA738T3BCCvw!74$)1}C)l>Vl4xz=Q
zxYwjzr>-eEmr}MMDmVELYu2j$dUA_C9U7-*ez>t&H!^CqnK^Q|swQ^u-UwZCa%MEK
z4P5grPTU=Rt`t=H;`wifZ(^*Dmkk&IW6Gf`N7ZSa;c1$Y8c-YHZmECWNwnwh-iF43
zLI?yB?fC)2SQx>ZK(Br0Cv9AnferQG=P>G%a@aJh+EhRJ#Oqv)vX>W5JHg5@XGcyH
zrqqjg(2gFM)m5%aIN|tNiF1s<HLE9NlnkSrhc^$Y!l)Byz4KAc8X+Yg4Bj?=(&UG(
z8y{Hi4M#Oa62@+c!m*adc?p=Kcq65`4|}qyw?+8`Y~p=NG4IPtk?&+j=_}<WM`y%t
zjD^bv&-sYFDlZUeyU)38Nr?3<R&T-;2LM}z4e8B(8j87Y5Lm(ySdtY{Xmg^F;9j9(
zzg}9MzMwnF<AgIuEb{P~trqbu1|Zn3d>!?et*w|S#bhcM&&pl?sWo$dv_xP}+|X=g
zd-tM}oX;)9@1{lxBM}Z0`ti1C_VmU%5iC9IbN(n?neSy_1no28s&x@5%DXDw+u@@p
z;;)U<gV^B}CyW&{>MGC6G4$HkPqV5Omh+!dZf*y0WeJ=>xAFDQFL-;uaNeddmPe^R
zk<J5x$Nf^Pxb<c0k0CM;X~_7p%Req^-zinKOM2sAaMk4foI@0Pd5*QVu%I18dR7;h
zX^eu+J(xZM`_<f%YC?b6rASqwBwItlK7ZE@_4rfJLlG*q8sUDwFzWu}%>CBwG@%3-
zq@ap@-S1-7?6ZSHZ??N8E87xx_}1_Fof&NOtk`mnKZj|{LS#imb&+bv++4`8U(2~B
zDD9ZN;yH0sN1@$(pA?nG9#2<!ySUxs!z!)>86`QhO%1bW>UL}>J!GnDxS1`#$k#^o
zU-w~b`1)COPcpah;`rLN7v-ou0-at^)WWvFAjC~4Frt!JcQp|It}){iHcI^zeB#Bd
zk6ci~0Tss0G{Rw#c7uWM<zDAiA)GY-R@UF?ZAG3wr;UAd{P_8|SZf0#ArRuVd5}`y
zboc(rHF$7wW-|m6klt6#x0OAxOz6YS9iIZw_X2@Q$#iwCPHSkVwtF4@yivaW<6rTZ
z_nr1Op&cl`avn`~wJUXvyFc?>+B&HlgY-9QS|<4Dbt0<2`<Eb}xVA;k`L<2oLbRk|
zXWH`6nnidiP>LvQr#sPTtDO7eo28CZDrRAzR6S|3$R3a9)b95H(JqB|{Y}1|ac|$N
z=dgm%2$2E0+QDUwJ=b5$wvEj}E+5*olp-Ia<%Kvaq6}<vFs0kIg02R9^e^HFDa2oH
zuc{RHnj3e~w|jQrWP8q-7GHGeiqncTo1J6tDG?RZ-o%bT`7oQHJC0o2$Hkt`g&d2w
z5gvpT$S9&90CD^|2XpjQ8>y2(ItbqrjZqu_Fz*%QJ>5uq5<+-J4Kh#F6qc3ek5796
zQd|}2EX%fBHtrIaeC6EV=y^Rtln6&Z@)x7k`SOVmA|Cr|!t^T^{Gu0L8$q;0IB4E=
zQf~(hu5qln?&Ni)k+)l~*>YPW+Lp7!s7I#jT8~)jqS$3*d%TZE{F`)chfM7aUysMq
z$_21jlp4Se&eYboV-BSCCcXrU6Y2t^#$HdufA#r$vtmm`Xod`8>fY3~`>&L;63Px+
z)SAT#G>C2QrLRb`jSnwoG77I`mXBLCR}b@b1=!tg{}>>wJ1emg=SQw?4-QVP=h+OD
zF_~}s``i{?k!wv_aFqYJoSbN7t}&6V+w$j~As#K9L+f#hFDd$>cF50Fn2AoHrfSTh
zO(y<AB63j#n=a!wX>kj|_SqGF{eroPt~8>q)~B;FI1@R~TP=1~QY1Ww1nqwZ?@1u;
z!oqi*wQ)18tc%Pn7qzq3GPa~@a%b93fr9!YFwqh6U9dXMiH}sHeAwOS;8ut)3v6gx
zCU{aWKl{}<<X<wM%op@vaM|2p^;})liAoCr(XBl##BUwVx~ST4+PqPYOv5$@ClT}s
zAKhmhyJ+Q0Kj=01q7iz=@Fvv^E!;P_P_&;gDyB}b$@q55Kp$5i-SPqXauJ?$`QTC1
zs!14iL27utm&cKXiJiVD7a~L}jlHe&xOwB*YLC$N>b2F10l|;{jYWG{lpy?i;6bv+
zld5!$mQpx-+m|%c->KB2Nf^0NKXi^y*$Br{qDrGHTRO^oYk#$wNxGeJ{b<1Qi<!K4
zsZ3hX*Cy*Pet~BTP?DzeZL}Svl!;r5r<T`!@yUYlN`?MikpcX${0R$$4U^tg`1ONP
zgMMd((u1tvPvxYQ{RX%;qPqxhoHan7FP(?(Sq?yZQT@k4#}Xr(rTucwD_=-=87#)9
z%T&W6Amu6?gHexb8fE=Gt;<96YO1~IXvewM`&=&A%<sfwrnMt%nlSeKBGYROQRrsl
z-UktuTJKz6vMDWyKOFVWm|4FH{l$`iIPp{-ajD<F1LFmY#2h7&!_<OPc2^iaZNszL
z1rH|uk@;NoE#o|nvFd^E_y~Dj0*IE}3QHm(5MR9D3WTS0CZy9pm^At_4)ZW{M1&^1
zD(+yD6WM-@+YW3>SAfNiw0$eauLt(esxH}*8hMtyi2I7`S(C>wlP9w4jP{2%!&ybi
z`#B=(njFjPRgu`&OHL^duvW8jTjT&InD)V*wChlZ0G#(A3Zlgb+j*gl*sBUZQn5q%
z9IcA^cqSk@iklOCk<c89;hj7ds5}k1N#Tr_tD!gCNTk|+TzcB?if-D{fP^OqZ-x1L
z>Gv*Nxju45mi)Y2w^*E0^lNE3GFV*->rXma7xlFq|B2<+B5$D#Z0a6vst+MRc&Lfb
zb7qj?ck<3~&L*HQ$X$Ahc-({Pq@G*ZBMjwv5*8Gj`(E~~gkb5mSR*Dz{Ucf9KXsz}
zymiQC2WQMyJi{yTXY0!+fVav#kxl0l-SS6ZqPPhTo6(rB_PWo){kSTk2==|gy(1!7
zMUw_LO;34W28<E<+{ldq&t+=}n(;Sad>@Qy>R1-`aR*rsF9vEFtjcLOU;}Vk_S}!7
zNRO;dqHcF5Ah<~y(8>0({3#!!cQz+Ry_@|qRgxIOm=oPj6iL5F>(0LWX>}QM2Jrh!
zlu#i_jyo64%)WT3bxqHVSL6oIB`ohtr(!)@B?ivqh4S%U?xF2lTWQTcpLS*RV$2YF
z@y{Fg+A)tMH0>s^iJ->i!dda#^d9W*Sfl8Y4`_U0DldJWL(yy~%iMV1kVg^k*~_-Q
zg*Rj$4zPY$sJfYv{H+LI6VT6WE349L;DYseaAJ34r7qK&g{fJo@sANbBmdm2>Z%ri
z?(tui*-c^_)f6F0;Hqi@ivRO2fWKUaHOv}nUEEB*Uezrp=Q^OuS<y1aynMhjsh{sR
z=r5<7L$~qFstfPXdtj-z>ZDbb=F@3TPf{+&`P?&S%ZhZ6gbS4&AGZiUZ7H{57+&F~
zzqZape<i9daA;fbWqQtx;j&-l7%^IZNs9}$dUJWi<;w#K`BOOI-}zCMrt9PWO3pYw
zqL`09I$%g`K$MRhT#Rj~jgxJCOb9^W3RZ=u1Z@~}Dr%IX?o@48$JgMB?<t8Cj^H@r
z3%UX}tCqd_XRbLVV`Y*AuSnj0Fo+xXCmBKCL}IsXLUo@i8Gfi?dERKrE~>`g>)!~+
zI1EHY9*&)O#VA#6#V;!RcVH2X?L==^=GOP^w8KW>R>PzvImghAcmwT8>TMQ&$65`Y
zq9pdVtT4<`fxOuqA-of75@YRG$4*S-@{rz#!d>5YdPKz^%cK*~*4d71yN6|whi}yi
zmK~fTF|ZMfexB8XgSxIxM*bI$xgJESj6cX2UgSd7n;jxXW7-F6Y~}QxuF8oi$iPS1
z2$3?mJX;4Sz6#?^*{?zG)dLBe?l_fZ(w^><xsmL4=I^d=O%|0ob*cxb`N?*OCr-A@
zy8?*CTgp5yHAZW%X8bx#Iv8@IYwa5%G4BqCXICX0r0m39=S{_{rVD$>65C@??##OZ
zIm38=0h2K11_QMK(+|uDw#=_0@}Gu1;6{eC0q45{lhS~`s>Z9|6^R_Jz5hcRzvmZY
z5-VSPY-=3dzCY|6`U=}YeR{0ZoMZ3Q(DXUZw>Av?+fLh7GFv&9e{1GU)QeC}m5$}=
zI|$H!ztBbfHFf287;eT|smTErv4g&)Y?ft&M4hFYU{)HnV&6NENTtK4bclD7egC!%
z<#1L0-p<%4F@PUhJ_CzO%x>R&nGFAO@cI?@n@iKPhKU7_4*pau^H1A4KzJ6X2i}r9
zkM2Apr{*c{Jq{x^rL4nM!d9$3(p@aq-!Kvne}3VPVEKK4pI|>Hm3VX@^2)P0a3Xtn
zK!S4s%lzB75ixCj#%Z<J9Vf9=x%b7%#=G+73G{`mr9GGxI)`;c+fL3B6}SX{TmRa@
zr<z?}$B>}<rx2#9qiS~#r;6x%3FfxUgKmm$RlHJZ@_qfP$~K47m~WfQ|ARpZwqR+K
z`m#%^%{-MkKmCfC0DXw#Y%F&?#4U68ZP81RA5015dKBL)tRlL1BwW{&dNdz6BH$5l
zXl9F}GH*6JNN)egYJSt>NW4=(+3N!#MJ|h-+Bcb}DMZtFzR`W+a+<KH|HfU!Oqr(L
zuekuI51;o%P@P@8XJ}@NiaA%FiX2yY{Fd3}MMI#eNP1zZx78h9Q!_+sdV=Y$OZDhs
z9OnJZLitR-xRgbjc76Hqr0?iUB<7&zk==tF{8hv^9IL_l?rSZ9&9$rncerJY36JYL
z41W8xpK!}UP1I%+qwC7!V{IbR{j{&OY^I8Uv{&4@_rfufT4uJP`HyTEdc##R10zVU
zF-H<zi5NV)!1hQr<FpgIhsrkzL?79#IMA4@B4m<lG*Ho9VDaq6q3qFNW~WFvT~BOX
zQ?Rf{;=@PjGWow)6dR;XaWmKB|Gd}Ho}n082??1Exam<`j97dNard{HPbp3Xdz5wr
z06AnwT4UZ+`rg0=s_7RQ*BbwnE3cjgwMrf)Yfu2t-kW}_P%sgVVCV4nze%}DK~fPA
zEYL3Md9QF%cK#P_A)L0h^;E-qe}9`IaSnEQO8+y?X!l8bTU!^5CkJ17iiS9Zi1de>
z+SFhA=iBz$h@q3v&i`Qz`2Y6RoD*n;meFb#n4^|f&$fDkK>kWVqe>L3{hNoM&-Fze
z9Ua}hz;qv7?6W?Ri%FMnF0dNuP7-UlFE1aASsE<dj&-rRxXQ!7I$G6m@5*`2+SO_t
zZMUZx<ff%1DdbAsX||yKsgqeXr`#iIWOsf(5|r{>xRd9<yA(A$J1di(o=%>8dx2@9
zFJCWr+pS@>`jNQvaCGu>mf=*Zbg$p9&RDXYe{=gIt86_qdd^PG&zW9lVt@CP?iSYZ
zFf7c)YKb2bCkRFm@*|Q3*7EfYO+)&Nujf##FWYb7QgLeP>J{qh>W})Orl+U*mWPU+
zc8AQ|EQUWndPkmcZ(}J&ce2R^?2ERxwi+DnEP7~_yZn{OV%zHKbkVlTb^I_)hv(kZ
zt23+)mlXXT@b$W!jK0<!q!4C~{W`V5N<Kp~eWTfZ=10np2qrobw)xl#n`jkufbUKg
zUpLa#g`1h1o4btHd!6Y`k=*`h=GM5IaNlM0FZJrkz{fIJ$7Z>%HK*8o>Gq~OJac85
z3VzdAMx=jg4vMFFfgfUXnac8}!lQSzjf!wqAVOYCN1!BWF=pDmGPdOVE>dp4@*ZP{
zicGeNFC<dDmWyj9j=s^!*B-M6|F~{nH0jn%VgPzt-a$1V*Q@clXlAoqeGG+uVA1dB
z^fb$i$5&ISYRHg65^t7lrb_#;);=FB@cyjh&}KzG#Hug|7{AzzWRX5x8!pRQ-<u2$
z{xV!<jgp)BD0*zx;Jqr=5!Z~Gmb!5BQc&Q70*WrS!kbPg+y?%ALd1Jz_$26<heM%Z
zvD|ZP$+#iPb7W*ByC;~Dfg$0q8}}B7K-&{S(CqANNjljb9VXNf`3NuHrrw|N;I)=^
zc4K~ZmVkhH?F&T-<~5Hl!5V>18)?W*)=k36Al<qj!K7WR0fUhFum$`r9b3@yLSL4B
zwc~)DnWd$rgs`yiSgrdE3!{Z+-;$A4;BG3a>F~Lt$m;8w!2I6PM8Z|oI4i4*zr2!~
zGN-2x&)u|mDKZYZY2q=D$-nQr@mn-3EKK)C$I?YP;!1@)w7Q~kZvxf}M|KG{tQ6a9
z`7Hc=s{ZupR1!tWd%IXGwwsQTX7#roy$z7|T=<!3Qel_c6hu1wu)B<%L}&!&SD%D*
zzJB`cEem1P3FQU9((Zr=4Gne4QjYK!LqJ>#NFi~fVnLm8ycf85`S}YyH-3NNhr67F
z=yL+2G~rGU*fNnP?dXkXbUNdzSK;_H*xEOnt;5|@4=i#Q`5}<LYhS|8FN33!-h`Er
zTp{z?QZt39HEo&A?^bZfuF)!|?WTkIWGxdDlkAgcXpiIu1G{>9Xkjj0U0pO_G*hoB
z<}(?%m+kd;v5~ag@%~H_uvzI`*YWzjdElaEbuZ^0pJ5Bwh{m}!H&z*$aB*>&U@d$s
z8W;pk%?+~E;>mwM{Jd#UXc$;-+m&E+;kwA|dx~`K+Q(s^QGJNp{iUZZcHzxK`UD}1
zQ7F8m`S?&eCA@33W-_Q|*fBgTtbDzhe4Nn63Ueld?oK8qCrb-a(1q#@^NZ~_jF!VA
z#S!=MnP&@)P(AB|h1mR|O~0nn(v32DEs4+TTXS7BDSnq7nIJyeatE_-Xc&c7SoN*$
zzz8dP>*(zs>&q@GIwS=?>^_UM_urhn%(V85SFds%1*6sC`66gRzveLry>1sGMN})x
zdQfPGE7h(|$!2ww{BiAkI<QR9Wh^S#-qm%j;^5%G`i8ix%+7SUupChHZVAm1=K!4~
zYJdWqW7+D79!`S=yH6-kTJ?O;f1`3hOIp(Z!Dq#iflZ+o^lsxHROfw>>=R0%)2npj
z1nvosuO^fI+3GY}Kw+ZGgM~gm;MxjcJ<X!;Nt(0!mi~*`Z9u^11`7;eUzd-nqO7_S
z1W{oV2^hb66qDpjn*rcVgbN$e4{A1N+Q_nq8RkM^)m<r<EK~+cRoP_kDx{Nv!}sx1
z(lK$p4*shy=t8QWLTX&6X+_|>HL`!anwr`cyo0<mpv}e!jbIW#F3-rwxM%1uzCGVv
zSnanxPvXq?!1t&h^?R>R1Wv4sRKS`6jJ90>(CI3&%Y}=S3q0KUB;h_inS#1{_3E6k
zMSbOfV9i*+Bk;uZz}-P3I_aoKP$<-p(sPrtXG$veO)j|VL5pf<lW|}(EET?+)92Qk
zh--yCtFGavI86;59rIIcK^!c4A+d6_$A<mV6i6flY<G*FQC!~!VFxDz+|$L<Z!TW0
z!oK&|2Vi@RQf?We(kBty&RY~TaT{(5gbBiM34&%fG}7frD?`O*=@P-mNBk?T_y3p+
zFyuP8=l0drU!{?57@i!)$wMu+plU5??-kYkF1?GS5Wx|rBM>HPCXO52CY!jMH-9k2
zKKvYwr+eG?b?Svy(J9lhYHnCWB2u(RA`82WY7TP$)pY=1@3}YtL2nuc0f*yOMyrqR
zt{ORP<v2@I(;NN(yy%!@d^aSeeb$a<XfuGs?*~x^#_aq1Rii!TlY6RcsC3M-rwq*)
zr>+kgF3Xz#-aaNn@$gZuV(&RT(f?pBF<lSDmew-CxuAP}Gtw_-+f4>?waEk1t>M}d
zYqc{%R@KfU-+Ium&(N*aKKT;5cIwnACi%mKEbgpj^?8|bEC@?tMsU7CRIajpdeTe3
zE|o}wS|leCAicK4{`Efww5n+Jz-r1!xl&{2UlW?DDzP+SyjPswKl4jD|I~Z?o)o`m
zPBFKM0LM%}iX*p0Fk$lmcMyl_#`786HwWtJI@)6P`t^93({ZjdeiiQMR=#>o#HRfc
zJ-6188E~m;{*v11SFPcUbi-9nMw6P!_fz*~W3?@fD(t%142z82_17mFdp+kH$j3yp
z663N=Ygbp-J_;yr7Z?-@{kcfctU6_{gFF4N`u*u}HUJY7sLe{zzH*`FqpkR<7q!~q
z41#vhehZ&*F-%?U7;tpA>VcsW3s_ejK27?0KN9+y5>vKU17Y(B`;zCs^7@V__9;6T
zr+1oIwH|L|dEL0tf47SMw#Kz`V2DA$L>7og+r8w9($W{2?(XhYG(xq%$81JVw@f0q
z?+=A|B<1cnjAttM@8<T?DXJ501Rrg-k%y4b*gE(Qg@fn-;CxI0T70DRngfub3^vB_
zfhysKyu;57>s8^EHn!lSF1Em(zH9=!JfIbGTt?>ZttSB&JL*7ij|8<lZq0Tu8WiX|
zZs$GDDj5gX-n@#d*<%v5!-$DJ|7F|=hEOl-zjN(*loZXORI2Lu=Gkdqq&hq4vmnCb
z>wUki+3@o;G}s6hX~Ggkgp9QBh(};h*xYiu4%W%*qIO5QfE6q^lL$EL!@c#%!5~tq
z&qTiLv+wT<xfaRC>tt7_;nJsNM&l3C`H7+Eq`|=qmBctUlUtL+$tdH%2ho5ToIecn
z?c(nZJQec$j?`agTLA3CqB&UtHog>EXOp(Ngvre3-_%2ckLrnSbb<i#TaI}3a%6M?
zd|qxAIo`>YcYz}TeS7YNE$QAShF;W{zV9}{?7x(s`&P9-*{$gS6{{t+$Otog`t)gs
zZK6f>R-B&J_0b}eid^}ljc;-k@cx!Y=L{%GH|HXNx!|?><r3nL9wYDwxK^r^%hIp>
z0q)Ie>5+2K)VO%Lx!bml7VldB(1;2uE-fvc4W$v<-%Ro7siQ>2D3p2K)_Z`92FpJ`
ze`BZ-DKJ7E&j1Wx_hfC@?v)Sj?n#wqW4`mGWdod)YOJff_0a>VDC2Tcr-`ed?mN6w
zEC}@Fo3z)G=U`_%Ke^JnEpllfM{`-!W%M4<uLg)RW>VfO`)jZ1<o3PWB3Z<9N3~Q^
z&simkyN-R@21BqCf-+kAPR-2xh`_BL;z}$Uilbx+!}uPo?&7<Yho67;NP5hhPlABV
zPOPFjdAew6tZq9((6sVeBj6&h0d1N%0(^utqtcU6GSKmBl`C&1M(6|KT}KMN7I=Iz
zgRrGc5s=NR6>Gk>8oTbK2FrlWR-FId8fkWabB62=#4jXyFdG{zwKwMR?#;KU6@bu@
zGUTIe?EWCzY+YzMz4kveE41mE8z8jQ?=GPgAVVmt;0#VNry*x+nZ;GJ;FJf0Xf)b|
z(s^>-_%U1e0bA`U8U{|1I*fhQclraj>B?voe=`UYqABFNFP@LpZbm+$m7I+QtVPUy
z`nxh=`NEBR`<ieHxeyqs&dDlLymIAAoHt-L^w6Yxj=CHuPYP*LtbvgH@Tq%QHm=;{
z%{I2!r1R7Y2k{Q_zq#X`0=~Vy5N-s(X40cq)@Kkv{+=3$x8!Ss6x}=r*eXw4$ckbm
z?KP803;H-m_4)dvVe2Rsb5qkKJwTAG(t{2cYlCLhv)Nu!tcj^dxVt(>Gx>O;*sR8g
zb1^&7QtJLhAXG8h>x`F7KUd%J9pHlbMkq_+Q&-30b>*7?yo}xfuCK!B_`Bs*4YS{D
zOrqOJdG=F)gLd*jtZd&4<iTscaK?Yd`jsWHb*&R-MRil+BVgU7AVi;l7MRz)#0WD0
z&rg*Cp)LlF2!B3-_A_|**Y%f67=&oA1PGgKL3`sq!T?qq);&-4^rb5)DM14x>0R1D
zXirw_2SyXw-O(ZWaEMNE>cw!AERT8^rmo6)q<j=^sMfD3K7P+<Ww;Cna)h6ahQ+4c
zu$_fXEpWTU6z@^T<>y01b^I~jXG{{3eJ1>J2`A}fe)>&)eZvw&T&+<CxkG`CTg9FW
zhz3>Ytr{n+YcX!w&(CkFsHmtPD76v5>+Cw|q>d29S1x}SYYHYG%M;co$)}*gMHUVB
z-2hx)06qa5%S{jRS95f9beL_AG1^<`o9XoEk+iocSk(mDIl!m0@j&0Pcn=r(;)66O
zdg;&ZGP3mYTpitA2H``S->Ag-x_0vAt2W~gSfV_kmGT<%DKDhvKUtp`uelky-X6nY
z-~H)s7AaFHyj}=!8odV(zWktY%raksFhv!Ks#eiUAd&d*R1uw=Y{m)2)wo5~-O={;
zCa~4400OshNrk!0_Q%KTGiFuK#TqLs@8JQ~HuDXosP(LZ_zXQI22>#2e5CtGgnXTc
zm$yS0d;}>ULBZ}a)?4^csvb083lp1QY*u|`)@@J<GE-3^DK3QS<Xdckfm1;2ZB1A7
zOr*4FD{k#(SJ+T<Fu6n)Mqs|{we%|pWD)~kD9P>$DQr}{7vd5W)PxrVseuVpFq10d
zNFB-cp6k66T94unXD-~>A$`0qnnvFEMn{fGO|@W$0$<lg>31c-8-(0OEAw^Wz}RP`
zlG-)-NYQ$)PhXH(j~fA*XEjv!@#Dw4zPR}JH<xLbK+wq~Jr%+Kt+%^0=))P?tFF^V
z3G!*S<Z(b=c{eAN(>u1Wbf{MgtIkVlT1`bZJ@c7sL_sbxD{o|eg}JRNyX?zWQRy|W
zYOInK!A*!y0bUYOfDYJ|+iIlA_;~`d8bw;nO{Z;IE@?0YD7xZ=sstf}G?Vb7+67nN
zbBf7Ry1=lwUQ;BTDS#4;)hoxmS4Uk)D;2$IBlTX(+dy;$;y_F}l#<2cu0&yZe-PbP
ztzfHFyjB4H25c=7TtJyX>_-$EnGPzoTvTxk8?j9coB;SM2rzBTHtO2KP%%!D0_L#`
zO(#{GKhyOX>^{%$4%%upg0{wUo*JMV?)Qt32WeKYG>A^Qz$tV0Kol@*$mSIk^dh~i
z@xW{K3I~3$Or)WqsZs3*)e)KX_h9vkybTeIufW&EtT`yq7RtxNGaTo9U~Ax`)0Tx+
z1*Y5CL+d@JcCpO|c>EwGGmaopQg<?%5%hilko4L}SMSbQKhwuoi%oLfSg*&tl+2cn
zC0kxnw7S^qWtxfFcxYAD&{&Lu{EmPK^A$;XEjcG3-D<8Z;sjwDkK^J%=89yv>@ZS3
z*H5ACfVEEEgWr=zh$P*2IfUVRQmxYNyO^e{0NRoX;*148o)9kFlqUoQ4sHfFc_N7L
zW_JI6i9c5ZkqJ<qS&(;;%&cE*T7|S~4ss&yF4q|LV-^Q;u?rwvwbyyz2`QrXJ>NMZ
zCD_-<8MpNkX1tN(GQbFnIr9NdtT!EPb*<NCAFMU3C;h-x+xS*p2&n;`>;?srQSxe3
z&|gIw4Z%=F3YAaA`a%m#D(3B==ifYR=#EMPUaAjR^c+4txW}hU+-Gg<Mp<X%UVXs#
zODaVHprBIe(3hE|uBMh2fgi$U9Da_HJIsqmD!JkSuzSDf6m#sKHLF?8x6IYcmp1)Z
z393FYUCAbZs3Y8IzSBS9aQIQ0EKCjyh+n#LB(vWxNC#w!09G6QF#XQc?qTbOt#lmd
zhF5xQGmwrpF$$fS?ruRo0RatPYE_-US6Hvn5C7l`p}Ofri1-O~UMVZ!lGm%8uoHwX
z;bwi>O!Uf%vjwovv~89HNdRAlz#&Zd4T~IN)R<TSfvmv+2f$;kf?cElb2~}ij0z6G
zC%A%g!7kTg%Sk%(Xtlr(mDcUix761_4rcL3c%A$xnSM8}WkqK2wC1Z<t{l|^iH}YO
zY->Jcr<zdgEYQ!_jo~b|YM$6-+|10(l!113bhwkgiUD3(53p56WTg7w!{B|$1jSw2
zMSEpDj2#`-Xbj1pf!pC+0d^gHn5QXViv~+ZChNVN76!ECSNWizbTC=mOCj;VD~yKx
zK<uPl9B<g`?elZ1n@bp=FYaBg4`{t=B+$6sBdrdGW@rzJCMqPnJo(_Bp~-{YM}RqR
zE>gH(G6gPI6OIQ9jqsqxK(tryN7$!HcGU^yEqjW1!JtOXDZGI4EXFNut^&tBV7l+@
zP(2w$6a>tnKl!r`&k;x~G|Va;9%BKfu5AIUmt9Ge|D!jQRdW>piDuiy{%o&Mz6t;~
zlUErS{7)M-Qui_LTjZuL@Q?Kwt+=`^^Fw^CX)X1Q-@JLl_3a%U`58!z2VVOEaJ4%;
z$DI=WYYCrd{<7mG^blCG)D=*kAk{q|$my;otm2xvp+1v=KEWtYP$mgF16>-az<;z^
zR;y$IP&Fd?;&)3MSR&BI)?7^~+EM~my{xt#fM1~B3K~@1d=l*$7SEr}DRaAP<HR_?
z(!7ILtFTLM!=+PEk{t;GCc1Tihvd3UHZ^)18X7uAvC0PN{?(B%i1yoF=US}7S}t#b
z%Hqo1=?t?PSANb#;L;KYZ<$;jYC!Rm8EOjPXbqf&o5G0pqU9%M67!5*PW~<i@?dKF
zc%5EV{*LQd?IIX=I*5FOe>eX+Gf1b}VKwt7TeN8KPCO<~l3nG&a7+na8u0s|04(jl
zvv3>aB+S)7;-}&FoQF$!dyW7{I#h)NGFR73%O;2R19?}1jjO2qQeKvjdFhh}bT$65
zbwb#DJ$rldF^S9p<xq)4*HZ=uj0%-Bo+|*N`0q*bu@AALSB}VOpJqe6l}qtaAm_=0
zuQbB<1VFsy10Vm*Rz{|3C+r#lbnxmgN|_bJl{F^&w14^X;LKe>unIuM^dZlcq>aO6
z6jt3kJrs_Syc7kJlDde-bb=&{8w@VnBt9A7k*p13ZzYB5g4ob7$?7EmY9@VvW18)O
zLKy1+B}@nHa(k3;HlSdyL48_?wt(##p!+)%_O6!0Qg#@sj~dY0xw><8Gw$lIO^D9%
z-B8A}3Y17~R_!cl4XiK~u#17&@-88D|G7TcdgER^z@B425HD9PK@dB%PjUa@k0*d+
zst9BzUhzmLRHOIP0vBy03#dmXRRMJt2siHZXHY6i$x>d=<!jJ{%)hF=mWKvGjo1|h
z0!d#*6F&;Q^~$)0KSy_H(8!9k2>us&0r;E>@R`{Wz~f#}%m@_65<w0&UriZ6J)q!r
z6zLCu%w2kC0=7P0j5&SbhJfqOj4w-o^lbH(jeY`lLsKsY`2s*eb?%0hxjC~b(CQwD
z6rRCgDnMqy*48$jGLpTWr9r3H+q<AF`JxAyV=aZipRw@h7W1(Je|lu5Ky8kM2erd}
zn5SQdWn#MDJR%AdfQ*(Y&vygm)?frE6j^0%vw&FdCQVLtmY?+m>4qHn%Ne#5uL(cA
ztUvg(T3wux@ni<n?!D}HTv|ZQH8vloUC{Q;(l{5baVZXHTb)v>qB+aOj+l`KHOH^O
zI`RHc|Ao)*TmgTdW7~c|2x{S2^6_3X4)$Ntr!v>alaEC3YJ9}7C*PN7bQ`s#J3pgd
zWS}+}O?J<3{pVeP@Bh(t`vvi_jWAAGP*9cC<HaLmwGOi#2Xd@b`y<3Bd|h`4l#r7F
z6lJ(LTsDJLj<|8E{+XxDDy1|BOAkIiXo&^v6+sbuPe;cHh0*DPDnxp+xSQNEF#dpm
zo)pPokAz)ND3pf{0HW`{KlM&K|8drjrInQx3t;Q^AXYa?PyJ1ANy#C|o9!G?0hhEJ
z;DUvExmwA$H9p-zf<iExL5cZ6A|Ueypx|}>-M8SQpH(lWzP6pFW!`rHPTbVd(<9W;
z(ZMVAJLKd%-L*TvH?Q&=jv3atT42dGxqNBWR6OL+g)^YmZ}qZ+OI`z@@y01e^9>uy
zj6wnEPeDpb3JajEQ<VI<A4E_|U~WuhAV&#bZ&+`V&H2m&nnhE<fhNW1gPn$o4qm6C
zD4anHu*Hpysc-V`h2rr}N8oJY5{S;H*<dSWC=;)Q`T2QQTrio$NLa?YeJ?~cNy$lR
zi;$uRB=ITRa{yC?*MW4CDXyCwA8%l4Y+RdT-<#GeXw`HSmK7!EAN9s5v|fu_&qttx
zH420<!x*FvPv!O}>&OGKKK}tOga>=-<$pK=kVSny63JOZxq&69@xYFL4uH^i0Z?gw
zML3X`Z@9;2fn3iNejcWOW?Gm6PX#Km&z*9Yfy4#RO=JC6%s?p;2IcL2Y|Od}f?N&7
z>3#yAF&_gw#bq%s>v5K{IC+02%4HrDCQ8AS(yNkBG!RwI0ultfzXOW2n?Hfls+d60
zs0uvzP;OwgqcU8me&KKXG{5;RJ#AU96(%?IreO~5<m+wE_2{*A>Yb~L-|R28e;NTw
z2$<JxISVGMe8$|{2H>8921Z=><jv6&pj=xIh^}=qm`Ku^miGXVh3uz$5usv|1gaC4
zz?4t}phO2jIk*!mF8v3oAx$SBzu!WHFJ`C<)!7Vwe#d$^1w8zxeFvN?{<TmrASj7o
z(r|@h&*V@cCP;L>{vlP#CJ#Slsss=oiA*3z`WyG1jZ+p05LmKj5O)R&0;zo(q;m5K
zm<k%ygQuyfjDRz1g<0lKKtf}k<y*v9ZMndtk-4t!OVAUlmG1uCe|vHXEXMuQbRLwo
z-{>5hK)2uc=fLH!>`uJTsD=OiUSH?qZzTlHyKjSB1|9!x)|RQFygW=~{OrGH{eQoC
zk{%bocsk=@t!cj?y!<2&KPRtI{~P#iY8_Va@M!3RW2?H%Vcv>KYtb>V!0RS2_rL{(
zH#%mzPg`tR`A<-Wo44Pizd<%CIjcoaSv}Nw>IC|In-0{6|I_1hdusI>{b7(!$`3qr
zzIpVHWcKDvO}nRP{~ERzzm%+^Y*T+7EC_uC5uUCR%y=1gk`F9igYbwy^z%D^RlgIQ
zC?qW`JfH~<J|<-QWr8iGJwZMEXL=v(^YQ%Y1?8~L1IpW5e_F`-Fvx$CHRWyAKP@Aa
zvTEX=_J3LI^#3r?{qM>DGj2^;!4}7r4@Y*wAHvl*T@rU1kS|W-{vBaP3P+0>p`4TD
zR(>L~f~#s5+Mv_V50+T$)FJyTYk;ySP2tUhkQ(PyzC~VW#I(lb5C1WC$6_QuSEGP3
z;oo1~xahHw5HA1)3x#g2|6XH&%A54M-cU>8Sv<Ab^sm@1zWQ4JvIa>N8H|ecj6i<l
zG>G?OJI^&(lU7lTrHkLuM)WmQ%fVSSV3g&=loki0_8o8whGM0U0}C-c6H{;o=xB~?
zuK8IrRIuRUV}4RZ<f;+$IXrFt-)`jboZZNoRA`&z;2gi5&-q0}jcc4^@oAgWYhCAm
ze-tF-d?du}Fm+|0*mI?;zAelApJzdc;2obupHDOEUc3pr6)JWA9ws~heLxLw`tY=t
zr>tYwc@zF4x=@am<RCWbW4U{+<))IyX=%?){=J4z0^Dlv?aX>E*BqCLR!RJ1@qc-W
z_%V5;VOs*)TR%Ze@4#^0qB%!>mdJYZ!%ZG`X73Sd+MDGc&Yzk4{Xl6J5+=9Bb3XL^
ziLi{kGgp)t#9Pjo7>p@+pNoc^J4f~KA;aa0-k{^WPSMsoqMp5D<AXj}BFTTSu%uzB
zKA4p3X<LssGqUh$sL`uc-<?{b?J9C_Sb9A;p3{aakY_^vdDG)b<^Ar4SEpWh?fzU!
z&uQ~1(JJgIm@p1RcC=!J950W#7DN`i4s-qteCCCdeJ+<-|1z72;$e`X`&8>o`wF`o
zw{d*t9-phs40^5XQd0-%3-<mR0xk)G+%c_oFqRFrvS{{yTIGenyA}pzNi`i7z9Rln
zm~!X&i9s*--7d|C6F7st0<98Pz40O`bVmX$UO(3;z3|w@zj;jO8s%2Tcxv=^S@Ge@
z&i&Jq2BfIBCYyoli=?X4d4Ci_quJ7>D}(YsY7ujje<Azl3W*bz3Db3h@-@U0?q_Ds
z6cP>6uV*k&z7n`cC9J*~za(~Yu-+>+#D}P`OF8`H={esv{Mz8##r;Ry&eW7cpM<H_
z_m9-8{G=vIn?F#Fv>bZ3qnRjskl!t5{{aPMwSN%djxOQ1g`fD1+O$8=2g##G7f!8s
zTV46{meP|uhOhsO`(xZybm6kcV9)C!_x_6!f9^HI4X#WtT$5l4Hu4&LFoa7I{IhZ}
z#JR8ZhTI*|rV(_~pA(1JsMb4)8<_p?b>JOl(2Ar_OvLXkA~r4XLw{sZx^~vrXd=<>
z#~#9no^nRZ;S9SI|9bz@=D;R_YKcWo;h+AnT-+#$Y-TPU>Y;M_b1M$JaLTVpK|*3K
z|MPn~ALIVP$R|kZf1(lx*FWfZjhE^VG`6Uo`|m|?0s#2`@3(MC@;UkOYtE9^h3&U3
zn<sxgS(T`-C!v##Z4>qFF%9QHj}lgDmkhUEpT(zI1^P{BmP%2h1zOn*<?p}fjy+w{
z77$Qg-;GZG^qMViLT>#wxM06JGOx*)MRZ+x{&z#6Hn@vnT1aBHuaDQuD)$gm@j_Rf
zz`zhw%%n@fnrCX(=$&;f!!MFXu8Us<anJAmAML$oR8wo)FbG6NM8I+o5CbZT1rY@4
z5L6UI1f)v`kuFULEz~385fN0RHxU6z=)HwRRFEQ(5;{o`BocZ_0)!;rPB_o=oOfo;
zS~EZ9UGJ><SS}1Zce(DXx4pNP7l4E-s%5G!pW9!8L%K6D&8cR7<VJfRTC&B^Lu^%4
z5a<42%eZ<2AFbe9<Py^RXj0|AooNut{}dOF7)dILVDj2$2C-P}(m-B^*g&@Zw&%LH
zlUWwU8Z~f>xmp=g|LNl6*TAiW|Ly4hpF#BhK+#+Wwga1ar3Fc_kbxePAlTY=m?f4m
zg2*wXP*{3(WvCSyOM;WCJYeJ{5(F{8A~1*SzRcN4G*?I<c^nq9<BGs%CkT`>YMaKz
z{P~N;<dAer@>>sLTmXxGIO1agshutwgEd%_)vyvp(-zdysimA8Q#$Ed_^$X!0y3;)
zR-T~(X%2NE%VR|bIv6!gP_kxG2h%G1$HV5>msl@&WLoj+j^sF7NpbMrMK`aBVV9V_
zxn<tNXIAJ)(g)IKY$bNwWj4${X(fob5$KY%C<1|)P+zlm0aP6@LqR@5yB0bdY@L+c
zViuHz)2!~ig{9Ouxh&<d)k4(XGXO9_=^~MjWiXMUiPMQoMMDc)L8HV=tiw2*dIU%e
zFQHI=x|_PTgN2>2Z_Kr-_V;;=220};%{B3|KKmzpfqM@)Jw90l8IwD(wZzG97hy26
z-nute)b$hA$l9jW7Ew#*+|rzphs{@`YL`x7_262pQDc_Ks?f<6(dgQ10IYRp&kQ?6
z;kGb!l%q@stao&L0?dscdox)yX?^?PuTaqNliJ^hu^O|JUrtOYa>}4XSMhM}@6FFu
zlp8yrzBJ4QsH(T};n=!Kt7dwX5PXUYhjLr%?d({)OwDP%7fuZbvw?mR!-!XRtm0-D
zyq1Xkt+i)iq2xG^X^z^6)=q)k0l-XnPR7E;8?meO8J_vbse!s|wPq$w(+t1eF32%m
zn@1WuH`zMQ_+m0U(M8%^cwsk`!Ygx86BD&Mat{hr#KVS>^V^t^^3631+D^B<Eh8tE
ziND9W?35W6Gq*#!g%w5k95jV3Kn9nhsCigXN-%>Hc0#jiTa)&Yypq1E!vbo2)HizJ
z76=>N*MM`(R9j7xn4N5o8?PFm!8qoz?Vkmf|H{N~j2qf;^N+TK;G{WDy!u=gG;w3c
z7U@4;7gEpR+51DtviUl>S^q{$y~38;+I#zNi{-BEDE!Q{-Ef0#v!=lc_W%v=wM?5A
z)6`%dkCZok!Xh|YnNP4a=N_`cr}f%MK3qW25=*PruM@j}lWCkS4;D8HFI27WjIhh~
zdAwSXXo@MTGazPJYLsv1%q^;5Q{nkV(96X*)Wu15tFz}2aPm>CjMAtCg)`iLGuS4H
z9wJl1K#}Y~5tCO&EHz4eaG9It`j>B*CcP-o$ZLX!c5PH@A@FWTb|OB6S~klJnv=Ek
zFXPzu=2Au8lc7bdreb6N5?@Bi9i;E<)-wyHYWRW*mdwrA_QYPmKF4-Y*EV(a&57p7
z;?SiC(j%`m{E7yM%JxO#f7~hifP5EvD+%eeJand|r5y^aqk#$yg5o`<4^XO(h2=GQ
zCTY^V;PYm9mGH139}A_@h{{k`a<n9D8Iq@3w@t&kvz^5I^NOWy!y}gAHQQ69NHfim
zhicEP4Ra&EZ3McLf07Il>*jgoA&n11$8-HN9`&)X%TZy7Kng86>vK(v*TGPuo;J<8
zA49y!KG<+LA7b<bYX~~6BoN))*y%ATU>aGK8$vB%G;RkZ@xEb8b;UF6fncA>htQ>8
z`jiPIrpKRYr_rEVqS_cb+2eoCqR2wYDqE2~wB6|}2<NHLC_CZ`modj6__HBF&SZrC
zWy!#DQaE;qG)OAOO2bVG{gn)eY==YE>339+7PN?zm{Bqm?w@4VFudtNrem*Qc{6P8
zKJsa`(Re(&*$1Ms6`gEo=03Rpqa<t-#;59H&=3~e4c9Dbt%}dE&k?a-e%Hiif&0&5
z4+LX;9&^@OihEbpw$Tt?-1q#4KPm3|AUS-3D|Swvz#`>~nHB`G^Vae#BycQz1~^9t
zA!M<<h&G>J>K@-$vOA&dpx_G5|D7s()TO>t>#Rp_0EP`DKX?H55-VRgtq0e%{TZTZ
zZ0n;M0j&*x7r`Ddgv{f@EyL|pz?zXFM6f-b60lQA!gG_@eBm%D>hU60rzaj?8^4Z|
zmyejahGb8>6a!47eUHc7Zx3<(9&h_`*Ci4G4&|dM-}*p=XsaL(aev2lW;l+u0Z>=U
zPiJ>4`waJNrbUOcDRF<l*4`E(CSJh0JtWh$J(Yiu9T=i-2wx=7fAnm|IA;YR(pMu7
z{^NQ$zGst|;)nBhX<r6^WHVgUQ(03qZCHN#oSA@in+o27{-tL%b03L~7>82;Vj}WG
zr+QY^5UHjei_S|>gpesyc74Rhg2?x;l}D~RlnR*(6fY@iFrLmWPLNEny(Be6=7Jxt
z17UxnLV%6Tr?LPt;a*rIVt8?N^Cf)Qr^@Sd&eYO80%m~O-A*L-U@)T2+_2OD9#P-)
zK*ql;4HEQ#JdI6>hOsdR9NN22cD{z>T2wEA@H7-h)gYg>?jm*I&fb>lvfCfoXpn8l
zt9N5xcS!y}Im%MC+{-WFN8y>~&t#@shD-0VV-b#d%n#0Gs#$rdH!O<CyX#t}xP6PF
z65;sc6=I$prEndko)j^>1`@A+O&Z{j%4T=+i22gdI?uiYk`KI^IJqAkDmy6fYMlIi
zYH17m0-k7=YSz4OTeYXd$23vAVYU-A3~;b;KE5O(7^mO!2?_52e7h*J!^bFUk!!nn
z*^-GD_@m-Fbso@rFF}J%_DN<fxuUGl0X+gXflg2(f}R1zo)(y;pY`R23SP~P*KjA|
zBriJj3P8tK>8pw7CTx8S(s8jXagv<NSP+)AA97mU7heV&ZtiJ*DHzmwAr~o0*z0go
zYoXW*`XYT*Xdj#{edq`ly}evooZjs^{0kib@2R=8>+qSfz4<6Qz+K_fJ(~~^mK~!w
zXaY>DX-@A9*y+F?24Q#UC=OgiFkHz>SU!ZIY0!xZMK&p5BUkTb*WdL7t7x=_L+=35
z*K1PT*`bi0YjpPaB`|=c6U!8rf341&3W)snWM7qa`X;@bgt~uAtCXM-`foV}U`vT6
zu?JP$tlK5`e}V(P+FPG<4B%oGll4tb(9*eSo$o!3x1hfR!nA8WpZ+aGsj>XQi3rB~
z(3K;zxa!d9fSqro<$X)L$FLQB{32HLs^xPv(Iva8+%2Ed%NYswW?sS-yP^a2`3R`C
zB*BIbgG9W^WJ8ldt(Q3QQMsKm82`R${GZa^T(9<y{*b&vnmC(9ElXd`=l+L;K|)-1
z;ypT!U}#-En|2^FTW?kz0+6_@B+r-;!z_+rLM*<=FRS0%%?a^30_>6^KaCKmXrBUY
z>?(0tV7yGOh>uNs$TL8<i2YN(^U_!SVCwb$n#4&Lmz_`lR3XdRBASmxh#FolJH7wB
z?vpNpYEku=4YrpGHiYLW$m{IJnH2{CA_<wIAYgIp@b{{I;{Hlznx@%u>m${O?tkhH
zbL6{8+V7lu*;e65)dw!UGz0>nR7-pCNc>}j4;sQI+p)G+oXZqa1Qy{U*8EA#U+l+{
z1MnCp>=OhSMA<#oA+}C1!<2a2A{&~50iE#Z$U@G2`o;#^vT>xi4;;X1AhrwU7FQ%`
zYhwKmb8TUZY!n`JDB!|bT38SPle()j&m#`6Ja$MZPAFHr%JYrs*}BLE!}I$O*veyH
zMbpIM*88@`;jP;f0XvN($8}G(X1?zkYxN11DmgXtY5NO@!cfL%c)ejqk%eY;Vu&aL
zRVG%ONc6EsR)=blov^)}Ft%i9y8$%%jGg5q9Xx!bT!H#gOb*OkjF?4!@Zk$GEDba}
zhf$}@NFqY)<^JU*jlOsM_1zn>f}y4PCy|IQ3HB!7(K93T1kzumWO)p#LDd^A#yUvU
z;o}~Amy@il?uP{fmbDn$H_%~c75?}gWavGuHy+iJsG@mQp3zJyq6RbIq(~*6KgE^8
z7b>xy&)g2NPApsmSfKh#{tS$TOnFY<L(Nfq$>rl8Q)_1f0j{E4A<_W1t|8Y7#d0XQ
zcLQ?t4bh7P+cGb#d}d6pKpCWe=JbPc;vnWu#$;x&R+0vk8jza4I!7yp&h~7UaSse~
zDwjFdZ8QvVC~!m-sJ-tTGJG-fGlc>B6)TA%mUMVtU+AtQy$Mm`tT_1098iPL%>pf<
z<d#g1c1Jr-AN@ajiKakYGWoDOS_xB`o4oc_a@=@uMqEH>!MS%ze%gyxT<hc#mi=VG
zQ^n3oBZ!bu4{i|SSSei^nR(Fgg0gR31gpEqq{2O0>O#l5>Z&E_reqorKsfM}*@ow-
zM&MqSROw~fq!JP9-h1xTW<^%a!y{aF8iriawAMiG7J*_$n2bNfRKr&lQZrVSXjP4f
z#(+OG7pOFEhq9t@9mOr3*<{cl7YAiI0b9#I8FpJoymo8cz)s+DQ?Q;s?sltp65hTw
zyV(NlzmexPR>Kfl^&YLt?=GLPgVyh9K`T$ZS8e4J9ShqqT3>QDdxZMg6}UL!w@BjW
z364IS7KB!7hX0JFWhr<^jz27TjFp1do24#aAsUVYwg1NLFA$GO0AgI;ahpH!nJkr0
zvcwMWgL89)iE6IA%cP!@K@PW!-xsWJpxQ$}DWx!<4^fd**TXv&&DTN3>#MOS`UAsA
z#<SVUedsE%Uv=h&ZFyKHYesI!C!YYoP-F**qW?NrSw#AjW_zc~jv7mIApe@%48>}i
z>HvU9FGg4_2`+{UH1J`&T@sj&|HYsgL1-8?ef1o1=xf)QnZ|?JO>g}=i_KBe$eh!7
zhRxN*77_$&O`D}ZEwe{fb{-Q@;CW)<zn87wD)xPsCm@}ZPObwXL=nXN^95{@YeHg)
z-RR5#OpzH1d!{AaD5-*4so#b}z@^~ph;s&*N%P6-vBu*scVmR%d$1J<vcNXVQ!9zr
zZUWVW@0qsFwD}aQL93RO^1%<yTzog&@Oh(7>DjX14wl5Zh}(T2$$bYvy`jdHmW!HC
zsAa^Y;DrIHSjdHonrfQSjiD1qFlyD?TckxSCE1;=9E39gQ3CntlAr0<oojGQ2rsFV
z*1!CL(nV6lW+RRw#?6N_Ad%il_oy3^u%j|JHrcZ-4FJYVSHI5weq5<OB?np3x%b;K
zY#Awjj{UX6D4@)rzGW*X#KNl(CH5zD^x^-j)R@BHt)V45FX4|vtzm9Y=h;Yz?*jHf
znPzgsYh|RsCwH&K{cTINJw6JyBzji0QN*wqj5?zni#M|`3p7(I7c-xXbm{FbT4(Qh
zKbEB_ER+y9Sq%o2#Di6VrPHcN+E~%dJxIfHJKJoP{}?6d<NOGSCD=B+N!Pcz-j{lu
zXf6PU*ny4cuF(JD{GK!I(C;ur)Jacefd?8Dcw!?1{^{)s^u{y&MTp^{E<2$AVh-iy
zaZT~-b(r{NjsNT#6-0U9)jg_51tQ(qf0{7yED=oy5#nT!%TD<}mtj6|**S-Wruz0?
z!HT*j;QxzR{f^xlSgpj0U?s+Ab-bDIX9mO*fbRTB!u;E0Kia04q&)P5Vn~CMc}q}u
zkf|C%z#_Li(5$|E38%!}tp<mgSqS{gJqBE+($2VvqwpZ}bB5w-ADTJ<wkxs_$jsUG
z*yRoao{B<m+0y+<RA+088b!`khde~Ou5Z3^Ac$?Z_|dPVg3Bur+vp8SKK3=X4;xFW
zBCXBsT0s7`J9}EH0~L4zWbCKP*<`Npjm5oZPmMCGGG-ZYmK)TW(*p2f#9&XBwdpg1
z4%dg{yBSw!5mkalS?5=*SW8SJwFIXO22uyj(Xd^cIUGCjerlh-`On_i=F|mOhBe|v
z;k1Q9<Nn=5wBA)ybaP(kfeELgj^$!+8{49>x<vmv3z@70AEQaHf0A`_9Q-4qDl{0n
zbfgysgfe~qBucJ2E34%6oEYxx(h(83`mbJqu=0PBl$r@7l@f`8i^LX$OMB2iog7$q
z`YM+D{`3mx_AciNvqH&`X3*G6{mbv_{`lJ|ilx4iwDvi;8jF5DK=Ty1$LoMpYYnx8
zPKGE|bwTbS1D^XwHf+C6_H@>7s@hH_4TdNI&O{F@-SP~sbGM_z%sw~7n;6mC{Jm%M
z35eXb0%YN+(Vn=+giwz8J_&?)cF<A(l^>)ac&T0*t&KD!HQt*M5J;W?EZyG=!N$QW
zkdjdP5e#gsXEU3<h#fs72|LO??~cuf8?wO~HzlJ*?TU(U6m6DqP@lsadeX{lMXk1r
zv=z<hpI$8W@s%|*f?%N?nh^z>TL^iN2K*E&A1g`{=k_VKQvjn{w!0<A6$e`_04Oy)
zT@s>#7Mof^sn=@7l#zJ+S1L!EU$un5{3oTRUY6DuReIz`EBnR#s{v2NRb=8fzX|?!
z^HP~!=$@Sfuu#!{5c;~77jB=MkXQ=oTSA&sYl^q?NN>|Sa%{JR{>9FMD}VcbKW5s*
z?aLF=($^Uz5=noN(ZJ+}<3;}+5I#gVBb)4->pea;v7JAq<>~DV(kE;JCvBI6`?dD9
zn2p0-6&o|iFAy)Na>OplPfO3ShK3k6b2_A(lMeK_nxU>D!MKO^IIWC@YR__8*5oKo
zAeIp`@=8l6ADM1MAl88>jJ@bxR(8PHD<?r|!RIvl%4cB@0XrdGw&dJ*2K2vuhKThi
z_Gu~(0E!r|Am2*4roi5DB$1^Ld*hci@9DEAQ=9-lPZN&HOkgS_c7sOqiF=$rSSL8u
zqQk7n9B<)uykghgm5)8(fR`(+^$!h8B8kJnKx!$qXxfK(1w`Hz+a&-tNYhwLYKM|}
zP&$sif$)!4)>qk|D&kmv=*BfD`>*@&0Z61l*j+?C=RZTBzYg?STyX{MRz}fsCvKx@
z+ky7?yzDv453ws*4o}%pQOXf6_I|#H__rt~yQ{tUe_mx73wTAu*Nrs-&|Y@z#Q}Fl
z?Z$DJ;-ByZ8;ZO2Dkz?ClVC6ZqWu`#uyII0Pf7)ALZgV4Va)u=aoZN}oR{noo|x!8
zwRrhpp@nX7v&<r=-5Sq|{@+P1S>rmZ`yn0TUmB}9cV5D?Vd_lslikEgd{$J@ukeD&
z@k=M-%NKuD@_`1~@tbznzxH(AyG$B2&u;GV_lC$bJn&7ocF~RA(;~I}o)oKJ=N{}%
z&PWj(^!B1s_?1L~bZN5zIf_;2Yo4PEn<t7}X?#?D%<>oo6hpK)%|CxfG-FsEYY4ej
z(r1SgeR>l|I>xT>n1u)6^f@YoS!)kreJnIBk?`&3ByPAoadc1)*%bN{t+1@@i%ecI
z!Ai06&S8aqVUhR6al=bW5yv_Wl|nuXa11q%n=!8}OU}-uU`JU=Z?Ykf%`Wpe@<(e~
zsC5!y;RRO4LZ-M5Q(k+P_E}OWU|jiHshY*Ps|iuGP59A7mG|S|@GtBvLEzHDq&-br
zpE*%+C!sv__sjC}yl3%_j?7P669pEOExqzf%v{b2W$$~nbHDRdDGBGkv#Ld%MUVlC
zR9Q)9t}U7u9d~r4EGN;d9v)G;{dA6=XqE_vXmqru`~(e*AF?yjKr}si7b>+>sf3Ea
zG=0`50-5Dz-|jkZW<p|5GvBd6#B$(`jz@M(%{LMAgXJ5Ux%9P`agHdnpfCE~6W|=0
zJgd00L&VS@r$(-pgw%7Qxh$F81w$!bSQ&F51F6IlX6Rn^^s}S?0$VPHPjN1XWm#p9
zJUr`ztwzY0QPV86SbT)u+#gH?>}RIF`6|N|`WdJ=&m$y=BkYJ>_6Qeli)Kuo#10^`
zu_o|xGlAaS9PWq!=g+Z<>QFD}011!qv{)`y?5)1QTF}7@IJz{^oLUy~p>yvj>k<dW
zeV|2@V7&kb5(h!hdKD@=+$?eA!qv`;>ff+qjfp~5?Q+@wlrrUiDpqU^${PQ76iaK}
zaeT4|Fr~nw$$a)d%E93SqO4fhsrbRFfL_7A1`SW_EEcgYanPJz>`VE{EKP7bvL?2$
zNG(*rSRvV20Eh6ZKT`{uJl?5}bHmx9%ZoX%i{<~%Rg3@MAOlcI<me8Z>Di3?yDs08
z&8qT$Au&ybOq=0`W~lq%=J?}67O93lEKH-hSmK<EFer^^X~Jz(s+q}@^_EAt8T?&K
zJWH?z8<s?PYGQoKaHh!1cy{v7;d#t+spSe%qDum%7|62-F6+b3SqSOp(ke;cw^~^X
zbLgGCZ4)BkO?}$e>r*AL49kpZvPUpVnk>n^b6Fh<mfz=LZ2Bz>7ltTRa76gJ>}-&#
zkUfp?(3|8c(kONsv4~BAXG={Ij!e{s-uWEUOU=UBd;W)Oq?TbW?aYSUkY+g1R4v2i
z)#5XRG}fdD^L@Y{aqghaI)$BL<%oI;6k<c_Geqp0Vzw}zRZV0Oeag(FG2L{XFC^E5
zZo-P-6HAxu!(INdxKD>1aT%Ut2_AU(BDToFLTR+g(eBi;3f7Yw4!!Zuc8$hY!(D1V
zE-fL?i3=W8#6`8j4h-B*-NBHB2DIu)VDqBh_(aS05H&=Hsd$<g^9EUJD~D~ZP1@R$
zsjKO$-%~=CC?p!f&>+$Nhb)c2RwMIKEOQp%5=I0mN9sYBNgIx1>{&y+tnQ1!p3PH$
zmm5cd!OO+M*qLi|XeA(Km!j6;;yDTRVmGsy@Fimn>N_>|dP94GY2#){_jgu8ij5=e
zRBF`UQDFPOn@9dTl)?Y3{*7Y&b?+#n5u3?IN8F)?DIiM){6qn23AhQ{P~=~!dNv<9
zU%**TwvGWNHdD#+D{P6QmNnKPj|b?XKxO=$cTr;HU4S3n?ps5w|2h8Cne+cs<SLpz
zv(PaWMS!u%SS+?og~E2rO8mHR*m|HM3iNzk!DdtBsRLNN=`tK*wP>@t#6nzDAJ80B
zrej)O?A`Qb3urKiC{V2jni_^JN7<(9p#TnBl@}ULPN0C7?98(@k@!qevq(#%3Z9)P
zIeC4`BgBvVHhp>>&=GheFm{IKyGYucqn{e+Ff7Z#J|r(Gh}k0_eN|%zz(eYQgf#1t
zdF4A~+^XWJzp$<dsqG<Yp+J3c&D!nkxgDnK>=x5@@`3Xh`v~nf0B0;mRh~g7Ss=jO
zC6(2oy5u+@2nu}lw+3Z(R0(8?pnjk9L_QS%yDOrGEv3t8BDq1YYA=uIOeRxiNFl7Q
zA-+QDu=u+nc<^_j)}c&P00Rid{KkV88%@6TPtqrI-9ASlOU#5GjRsyLBBy%Rw`u;D
zk2hr2x)QHv(a_Y~{`S4WhiwC*%JWc+wY~K5EvAC6lIK85HaF!?=tv?8^*wWl`Qo=I
zvz8nMgx;FD1GWsB+P)<hucqH`q`3od(>bdcaK!Izdzx>jGdG3$qo63FERor>&K|S9
z!`n*#fZW`XibQGgt34uk59NR@Y@yePwv4aU$7U63e(iv0^`Au|SGE=H=D&l2swoQ-
zj~yZn?a8(4$A}66pPmAnSVnFaw(g|*h_fLd0@1?V=D((1>hW}&zlBc=;w_P5X(}r)
za1+S_LGIOPB-AGS5Hw{TWp<9wX1yzVzBFdQ<H)xYZs+BVi=_RRp1axvI-q_hju2Di
zmw%RyHwVvC1RuG6w_Ni4z2s`w>RX;yz);!_tDW!nQd6{P^2%d~W{=JgiN;yxlRG`-
zHg$h4mgwq*^M&V)g^UW0^Kb1~@{+3;T1aos_G*+iO^*_7TK^uBZRCabBd6$FWUtpI
zFSwR&mA7u)=%D#qLCd9u%9u+2)QZ93AC}mZ?D0BqokMvZ0$!b}OsyeJS)=^?psQWb
z8p<WI!UmSKW;<RlUJH!y!V0lQWb8?^Z5b4goNqI>*Ag-pS{iFy59OsCA2Z4Zdcp34
z=Oc@|caWu_g8^ZwBu#<$L!DUH!f8~wSj3^)l<8NbI(;tEsRfTY&e4|1mPt<fd*%F2
znWK7s!BB+Yg0pl}jjF52x{nnYc>`b}S93kv3_mw$Ci|<F?Jl`l{72Qmr=gaJUhg@d
zJ>GDG`=9KF-b-1By4{0@K_>-C&ZXOTl9<CO51f*Qf1IY<wG?#Xv3gLX_Qo6{I%{CY
zm0^64-n=Gkb5vcLEa5QhwHDNOTBW?8s8%$iCH9y*=a9mu0pMsIid)%kue0o>$=UgQ
z*wV}$Z{+zv#SlJe=;6seeTG-v?aLl(s$Bb4ru8G-7U7V-Xy()A#_g56rA&p}K`gz%
zu`n>D56SV@m!)<7-A|e?uTW>Mb8tk0^|kDc7aK-3Ax-vVLzsG?Ik}xAv4}h8ZJm_(
z>xA`WMUyr8i=_B)I?#LoGcRK&2{>FdO2%+YW)0kr$Bi@sYj>MV-<6rrt&(`HFP$5i
zv_b-2|7F(d92c#x_H@rm(nvUjYGbG1jL*<R6b_MT!@4)eAB)mouJ0|_4W`+{|NDML
zF~@PpXZps2&%~I(-HeyJz4jU%;yE4MAhqA<k57U=H%<5Ce>{I8@xlJtzpmIBnBMrw
z#|P0)tPeiYa;k&(_+N((e`z*4ec=86eP@LaL2otgui{Ld49AEo_!qjQr7xuyXDrQa
zMdY~VERNPU!MBIWfy0B=CepfZ&is%0XDY>OW^v8qBNqK6`WYuT=QB>2jwW7k^Hyhz
z7*CjIPiWSGEXZtYV?n&pxSRvu%3Z=C2LXq@EB=H7gfN0SA!ynH=JEIox5MG!6TX*L
zgdLO|z<Dpj`8RLQhi6N3sBzGaK3IQ-hLkd``E3Mj_GN)=4o+x1U?BFdsvJ<RAu~J(
z>*#0EqUSWCdLFl*vuMt$8@$prDT#PBoo0ThtecyYyM7J#@<&;O-lhia^pq++|Dn#C
zjvCCC3p$dE`hwvj<gggCHTzaVTV_wMa2`LNOHA{|wtI%QsMX1xGBBUI$OyQBdy{7c
zO1MEdLZD8IYnvB44c0)QONxUr5zt;?g*-#Pt>y34xbj%_D;{@pt==48baN;skhvvh
zsNark@r5GYe&%;dK;t<mX9Et!txfLFx{G9@lO-UBR3()Dm}%w=raZ)AmV~>Q{3Ree
z*&8^|sOAG}>e-?7xi+hv*vp7ls@f{!56vE*7$}gK3E`On1>5S=vQR~at!f5ouHGsS
zHhvynv*Bt@UvTU1o{XiOJ!mKRi=N!cFM$}m;$fAm>MH7S2c>EepV40w+d`d{+(4WP
zkIxt^O8KCT7~oh|JSwg)R_wHK=mxj{+mRjTKc~N)?a*hwI}*}fu9>-UQto&`H7)|#
zdb9Oe;w!x6V7+{X6hxmCUzlK2mS%CG<I2l5PIsOLzLCpQR|>9v_h+n}PbBvRChZIE
zZg;ce4%YF~!ib;36dqhjd2TeYJ=i#QFKHXA@?h`-L}8{`NvXh|Z_Ww$qxOu#*lV;>
zRQ;jWxGL@nl@-vMeTBGSvY$brobhdN{$?Re$T8(BC8j(@n#1~V>ybZ*s67*rYg(PB
zp<#6#-iZ{t%8IFj=Z&yq)?3-yL1C1%oqJWv2R5%xJz+%0tT(*r-LljFtz23_3kKd~
zK~xM7$l7_AOpP;dReQWF5};G0QSuPrMmVyrAVpQX@qt~ziH;4CHt1|8`pN#qqHMfN
zOnD-z_#CPU*IfwhwfK>KmO@0=8rea`3EvOr2S-t(Yy@-P*m$|=8*XPtPg`%*uj9Bz
zjw&DCyw>J)r?ks+{?bOV|JNd2I*Co0<7*R{nS<rA8&^Dx0nVB27o0&kM9N*9Cfs$N
zxoQw^J`gTH>2rHb5DhgNBgWefc*Kv#{uKD4$z6BijUwa4+TLliYQ?(uw0VD5eqETy
z+od}<TuXCJ-Up!01T48=&##KO93QTOe~2?G+aaCoxI*sT7TKUF^Gd!nq=BZfG0{&f
zj}xRl-wJ=FRy$H@k|JjzQath<W9~8%2h|m96Xt)EP>Y@NzqsaXV=Jq!WDG+T;sbqh
zIwBPA;v(H2v$*{%buSHK#88sYRQl%kphmC8F#XoHVO&4TC0^CseL^w7s0RO9B1L{4
zwBPWZ|D}4q+tpc1<WI5RA6aVu{!u>wxoD}6Fp`Aa%D{=}AI{I(!d_2sisLR8OFNH&
z$BX7_yr%=NR?SdG^Zs(Wnh0vS2Z5Z^nOi!&!AHE+HGl8u_8_vWodqAV>x)|sZSdX_
zP8~z{NGoCXZ$N70JlnqCF<zN)qj{kV;TPqc>vhJR6GASGF`R!k4Vq0@HkI7m#%PZx
z>e-~a)9h+A+~_TnJcb*4H$@x^Cr)NY)$1(hDn?*E^6rMs+LDA7uFR?=xNFsfyS`UP
z$Qgtk<1w7x7c8-o^g#ihE6K3?U@8gWMceBvGvk&Z#sdY9wDcEDQeR@!sfg}>g6C$=
zyd~exX;&-~sz!D-Gkbkt=U78aE&Zg!@+tRJP~>4G^Fi4iY|qcX=BD|>(?@xrbr;7N
zeNk7R-5k$7|C;!<k5a8&W`unE9Mhr)&X;UgCtm?`OqAEs!Gwf8A?EH`n=M=;FIJ{D
zYi9lfnLyz>47Ac`lU!VE>h-ZWoK&@%PK<!23xD^|ZCXHv<j}DC^C-7=WweySXHK+1
z>7on>RaSA?Eco)2JL-_wLBE3)(%D6oWn5y{@ApJthM35Vt>*)ReH6yoeocunh^(J&
z;UZ5TWfauEaMa7*>NF;oykJc%VjR10G&dwwRa-wkB+kj|OjtPiCSkmk*~}e`+e3!G
zFq&RJNdT8F=I{4&Yw{cWp&Mh_s|1?gU;PEbrJlV~ks%4p-()bv(b8LR6TRZHQvF4f
z#3Q*Oix#QR`&!7w9gRvDYgAJwT}Zc|LuGhyEBGz-=ux7Ee$p2=p*DSJF#)Q!(sPrX
zUFQdcCvK?Rehsa>;iS2Q;n-8|^WK6hqN*K!c+R<Ar<z*SvEK=O$Bquy9Cw~J`OrE4
zw16!>4iiqHoKrmGt0r~R!%FVju;U7l?)~;O+tccfEfz<I3E;EZtH)bD+s#K!ry-;N
zjOc17LIV<**?{%BzG+M2nnJWG9d)s6&FDSpa`Zz>onnC@So9oZ{YQC1@=K!u+Tewy
z@A}IY#0AvVMnh@cmv83(1Zyq^f~V$<t3CRKz4%*F8A{`42^ZU%?p1qCY(COSd)|km
zS^<x{lFvF8wHQ6zKNr>RXMH6ETjU*Gv1CaTP7r-VsX9NIqt*Y_`z583xrr_5Ilp?L
zf3VlBOZeBfa(+pb!fBDPQU;8~<SdW(`?>zcd<_31lJL$Q)RN!+5qJ#}jf}fgo0DVo
z(_j1T($^T+0N=qxj9U6)gv1AAono=!q@r$E2Cm#&^sKi2vPn^ck`mHByPDZQ$kOKq
zWxSN!n_I0H99zxO&S+x_XwOSO+llW})HC^2oN)%yVRTt>Q6P4eW*Lv23m`GC#A1Fn
zUA)t5-MKS;j?w$`WcU9OT1W9$Df{J}?6Rt=1cenKYClAjgBPN%>WdP<3fdhUKAilg
z57u1rS*$)0%;D&QwQ3sgm*1YfDysb)bMAf3xuasiX0tiW#y`X6FaN5o)eR>B3?VN-
zC)F7_4bDKIaWMY@8dilS1Mrug@$^iCgUpX`t+0FV!w_=b@pfX1K|*;?S~aE+WxVJV
z^l<{WQB!vsGF>F8E|18Cat$t;QEvOp`IDJht4x>9ZpzhL1zl+sDv(hdAk4buHes&*
zR`3XmyILuqp{iZHj(GC6qvl##X_Jm&t6q%dy=u3?%}X5i1@dUw+kpA>r`wh6xg5V{
z*Zl|*^l)%9?93fJgS4W=<5}RX?dBxK<7v}gEtncb{7}v?{m{y<P;AII-QjAtjg^gv
zpUpGWSOP?P8(R#VW&t>j#IFVi@oPaH9z!pW=7aOYhd^oqy3PB;&fbaaA5kdPj#I1=
z%=^5hqMBVS%9H)Z*2<ld*wyI&=eEH(*UDv8DD&ViRT5E$cEYqOs{vI1OF6j|-2gDt
zhh8_5@UOU#<U+r@v_87vh^3WOz{I9{Q8I(S4I27N^5BX=KA+chDnW1bdaSCj62vE;
zh6}j-nY#`TE<omtW_-=e=L3M(&<&d{u+M(ar_g9PX*dd7Z`nZ5(rcLgY~R2tftO-w
zlvBMU_@&P8!5^9n1oGMb<>LkRYT97L)zVDHb0Zb|@AnPvbpOsl1}Oe=eT0-BTmE=t
zPN}F_LX}rM?%Z?3*M$MvCLQNpdk}KpokC4ad!^))Uyn7cj>h&n%pBh9?iwByl;L&W
zE-BDlj7vc`?B}n&T{Vd~*r=k@VEqBWaWUB*Olo%(+r|#G@PB`h8=59ZoelYIAt0~I
zEVvNvq3h8yD6~+TzoU4MsgT_zd`_FFO*>(8nqHz&te^<MsPz7^LFavgi`{eH+~Q6(
z386cAn>9z~sPX;w^2YjzEA9Yu<gu8e(aG+qyZFE-Bg@<#V0_XSqHbS==x_DxX1u5S
zwXxJQstH+zld*Q-Way3d$HwVbA<u5n%48Z1!{|8Im@4=i1fN2IVfJYVpivBIs#iV3
zNvECJ`b8-H1G7H{kk0Q<0a%Z|-@zrHM5zLx+W$of33$GN1ovU{vC<rPpkeL2?#=6_
z7S(bC4RgzP0f(yB)Zk)fADxVr7pBWFY1%Y<`_uI9_ZvPUl_l<ahjUL?14bO2@BSrh
z46a<LmA?kbZY<H&Kb{*L6*MvYoF_q|=;GK9&6~e8)ch@z?i|kbdQb1>UupMr9tw|A
z0{qk)xvFMi8589>6sX(K_Mr3IMkS?2d)xLi^i<1b6TSY4+aaIqIK2N_8+U5kAw9?v
zMxGlh>UltE{xLb4QD6xL0L=v_`$?0<(#E(pFK2JJrmm8j|6Hm)PCfo6P-GGXOBBg(
zan}_DK%HQN`ZX*Au38v$)4A3Az^|85x56?YXzN>|R=0wc2QMA8D~!h|7(vwj_+Wc0
zNEE()gimFh<#Sw^9vF;(3J&L4l1x&~W^oh1UzSN5_AV^V?SJ;yUNyT46py{LyXZM>
zOgv>v<MVKqwKlaUrkv%)&9uzP98z8LI~T71Q_k2*pUOR2FK^kg_1(x0FA-os3(<eO
zBuS!OmxPAJrej&+r7u4_V2r);)!xU3i{X2~IUzr8)28DQz18e+KCavWe<xhrJ53d$
zb1Njg>127mj(d6byPk>1^?3l+iV_Tu(y#-q{NJr|n@WrTCYZNKRd8oIo6`p0Tn#d|
ztH8vsg?@?&!FDQNA!Qf%bNi|TX1;g)BEbk<5P%O5&xQXqk<%3oJ>q+D>Pe4zK*#L0
zCbi#s=yOK~l~NafGd!8VE5t+W+nQjD>L_0Ix4rMs^f`uX%N})^>y0Wie_>>+N7%^j
z2s>^pK5!+5JMje9BY~0+{$Q(<>I$&PCX_kqW^hJ0n7a$1O03>^RXcb^HEtzf`}6*4
z6Q%qC#gxNk(>fr{f@VIjnqy<*jGI<BkNtCR;?^yKW57O(W6@0Q`$BRFMz-P?6<^}3
zEtgD)e;E$^9c4hvVE#bFF|dH@dBAd6y`uZX;`<}OR&vbJ(6uo+$H}6z(0Kmk(fS@l
zZ+7VkuitK69kZv~o_%SGbKQF|?(4hZA+5ESt`I?FZ(hz5gI6+;#&WNLx4SXIU`N1d
zdSt31|0eS+K`BPyIn)%Zl3T!u1CEf>H~!;U^$E-4jblHweltP?`sSzI1*D26P_EUu
zqF%vVNixF!%SXTrWl?7fH+DaJV84|ZMyHZIZsl4$s>EQraf1oZ>ogCPWPW%rct<4v
z1y6S0_?;BGc^mgv)$Y;QeWQC?4k-Wmqc}~F{@}`Qg`N;}QZ(#9KQeU%)^If<x#*%<
z*!fDsgg_O(3r=q(P^#accb{y(cRjoh?hP)g5_J2iV(69O<py5S+%JI15G?-b4|Ysh
z;yc^5BSpfC#}#;M>Cc(e7sRpH$ZMd<9OIGOB>ffHSPYm=U8h07(qA+sq9k>j>h}_S
z-cy&$;HMQfZnaq`=I-CQGKFx09rBJvakL6#8UB7WVPgbF1VDT_^72EJrw}(0Jduu!
zi&OItnO_zn8`1|;_X}jGB&;N36=KRKZ24?B!d^W#+><i6_yj$?e}15plOhqu(Int#
z-c^ehh{hZ(BcjSaq`<2aL3qg?htNyeiZvN4$!mz{?&!#Ut=H4Os+N2236uy+-Vr8T
zh!Me~&W4$7tG8)HiqW*ZD8<jJ?KSQv0Ws<Fcm3ZM<B_lb=OkDDHzfb}FaUo7MTZi&
z)7R`ryCOAwy$j_e$yZgO>H*g>jDFUDd@rte5h&-Emnph5S%NJ(*+^tQTIp?BxWeQ5
zOv~gZY4-?e4trNcpm(~CZv}Sa5v9E0@|!dI4@0nmug>U>hVur`MCDHMO&qk@YXh?3
z4+Gs-dmtiZRh||8La;P)ek{`Rok_=2PWP_%IavY%6fxAid;x8}?}P27^F<y5Zc5eX
zovr(k(uej@PVRh~d39j^&G=!!a}*<h1&0f~BY0c*Hbn_0=vH*$9nUkm{b?y?ww$_e
z-cCMNeR3UHUK(v1EfGC@2r|PsvZ>;GiC@gjaj)<7&YEVqU|)-VhjrO&t^311)`qM4
zx;}*#BdEZuDS3lEk_BI%aX-Spa_7y5h(Im~=T4gJUmxd2#Yf)P(tTq5D{rSk8#qoJ
zDhLm)p>sM|-(Z$yQwArJlTEF4Kmu-FC{$P6;7Dl<42-6&tXHxKQCxU=Z;Dm91}viP
zTH2>Nup?pR4}y}>_SrUz^q@?0XYTvDMG&HeP3rX=gQA4Q?31~!I$I~~UQw3wYXr{U
zMvT6OOBbW|s;(kyem2ci#jkk9tJ=%Y|I7;*`)qQt?8O;fr&XD^Z_a#A#gX@>M)9Gg
z(Sm4qbOKs0|Ed(v{90L-_Fgh`*luq_>B&WxK1vhN6n8a~|CCKPC(g?GmJ8YpU0b@S
zWMO%jogUry+;wFM(iUO(0VcW3Jn;qyqjYC9&wpEIpu3}9?62;QFckjM!lisvIidI6
z%|~xVTGY6dYDm>$!RFVu<;=M{KtfzWHKZ`HmD3JpQu^tZsZV}xy!+Vd*Q2D(#4igL
zP5xM~Ziu(dXKFTnS%pa5(frLB?lqlbavtw}vZpC*&&rbdHZ+ENkKJwM!o@o?m2Z0e
zvPJ34Xms=bC+b0*nx{&>kE%^;Je}~mP{$RlJQYMuLZd+S^%UfRP3|^OL)YDH;Xj(S
zc;q|F&wW|CNPwbAkP!7lo1O$%4B5eF594(OWA1#N#EF3rw0`O1a(6K1lvaXkw>e$u
z2fcY<IcR?2;IDeVxL^5EC{UUAuf|q0qF<IFaU^a?q-<?ssK7S+Zjr`<Tw4S?p{)P4
z0n~B`a;Bb&d@lQK$PiYsN;(ySj9t2(>{w3QSg;O^3KD30X9@G&s5k{Y?w{|z+K00G
zBm4Gr9qi(Z8@Q~sCQcPht+d`?M%w*MOMKohAWF!d$-2=P2=dh=ygA~$v-ipP`H5z*
zw1j$$$NijgsgL953;R+YEM9MYKb#zMy~;7vss4nr>+PKR-(n}31AnS!ncjnx%Hi+*
z489Bs{9VmVIZN?E^>*HiutwdF2)t%z&DZJrYEv0=>2+(DzxA`gy_3V=EK(+PSm*gr
zoc=H>$hArHl7Q8Vdbf%TVzQUkkGn=u9aQDlm$<|fH@<a^rprpyeryfm@_B*7eCAmG
zK>VYN&)C~A?Uz+umVE4tV12&KVHfw5&h<{MwM(8>UKh*(Bs@Pf9YOq9suV~}{<d!3
zRx&fKH{{4X^-Jd5B|#HPu=ly1%Zd7O=*;&!b;>FBr+Q|^-eQKd9>{%j>|=Diy>0)i
zv9!syRyu!Ojl6ueUylXsRkNgnD>v>hy+bk&gZlsAQ>+!IRNvejTNFdj#*C)$#t6)-
zaq4j8x@>Ub^55q&V=HDpeK@w?J~EN}QI4*#ujR>-NZ38Yj6a?{micQfB;Yz&^GE?$
zQ90EqDyZN8z}MCL?_je_`v^A)tpQ?(8ic4~-%f12`;a_AvzZ)(Pm^-gryrM#zp}S9
zt1Soum7i9>=4ajZhL1PqQ;Anp5!di7=WvJHcJv298Y{sLysE^V{cR_qYWlE4;Swv4
z+ujl79A5is#7*xcpZdkLeY>Pc*wgl8Z)$xPE^CFhHhF$Bb>B?wr911Br<`XmqU<mE
zoUM6IlMOo-DzYlRU(EP)IPi`TQlMb-`7zgtf-7vL-}RuM6IcD7Zm)6jd&Q?hvp$B<
zOC!iZaRR;ix9hNxxAc&lnCLH<sAGCmL<-2h2p09=&Gkm7{4n4-v@cVIgcS#5$Q(X#
ztg{E-HSOKEuO7dbkZm+pyJ62=8zehtwNGHC<Q+`qX?Co5*FE6aa_0RS6)j(11^6M$
z3TdYzD-|@{Q=`JU0k@xuwneR1>TRsg|3>k9j>{4LJWto6eX#s1B4X}U%j0CcXm{;F
zCg}UevcJ~s%wE;mhZlx;BQ7-$+w07U+nq+9(XBO_jXv<^jC(8n{FqqH&Cjx;`HRe~
zip!OI30Dd9QfewEjkEqt7xQc%<pqzx2FVq)(sI+eH_nDwV-d!0cb%w9MW#eC_wF>2
z8JuX`*)`4Bib7XSDu+)zE^)o%b)9N`0XatVO^wF;nFx%;C6zu5iVO7uhf4kg>I(ub
zzray5!y|9hg#Y1~y?9;m@PiY>{>F{nk*6HZgAVWp>^sMNaR_guzdkWkyb$ajD>p2l
z`^Mulr=9K_(=%cm<6-giWzN>2G05{3j$*;1L&bF8lHq!s{mtM|`nyj}eqTWHLO^`P
z8>Zkm3Qe8PqWC|mp;!J{Bf1|1@k*4r9m(xDOOj318CMhf04`ken#*v5VWNvwmpICf
zudSaYsP)m_`v+OpOfFA7j2zphru_MM-21)!U#*2Vw@<5d<8O|wgE6LHVxr6dq#k-f
zlwi?Ed7}3r^-Qs1iUrG7n!;A2KG{0WuHvI}549@NWMZzr<c)g4<7v)#B|Bac54NV&
z4O`OBJH}2HUYQ@lq{Ni-YYCsZZ~q_{W2F9Z<je((yQcMzt<WtQVTfndqG%r_Vz0Z*
zxr4%d{;o9|z2Zvmw(DxH{q=RgDrD(au&-@Puc!K4eoscJ^8+Ew2d4A&v0#eF@*A$q
z!z5nEtz?S0UkZ7Lcd}!U@kA?~`w?GK^A6zW%IcnNwPSiXz&`;DGH)8_vkl%o#R)?4
z8Qoiy=XH^)E@Dbt0(%Vh+Gz{#C|8j^n$uR2H}wvz#i4FQ@J{7fU*Hbntc$CQG=7{b
zdSp0n$v9^q;-|vXZU~k!+!xpvKd#0b!T<G%zO(6QN+8$eJz|P&kS9Zgm&wm<chnm5
zPA*m`Q_J+N3nME1xMz#Z7%wGeEzU5)lzP_=4ye~dFE%TobjM$w(KY#Xgb%R!w|8C@
z>5Bah5M8;`cCebx1FFu=lh0rEC0NH4<qOQ`+i1InL@h=c9?r?i(Hs3VJ@}-j`Xp9G
z+E}q}HLAA_ZHi5Iv9{7ryQR@Z@CKWOi}|ObDwR8L|GeHMS8a|xERc{$^Agm3b8(8J
z{EY5#7uU9<Gvg6iCo!-nsc88q;vttW;G3>qUx^$t;3}q78+t0$dYLJ#{H*3l%j(@K
zjXO*0gqUAM(?X`w?+cVCcS8+m&oQ4MAQR{~WXwQA{;>@p+o(NtUS4<(^~{xb#ijdE
zQr-KXr1HjSz8y3>fr>0JAy$<q-z_Aq=?Io<F`_P{YRvx}9rKhzJOw-w-zbYWXZY5)
z-DocDFgL^3FJly}#SLAhyOjP|@E7pi<H*CY->{D9+ApnHdl}v>ah}RiS@g%b;=3*F
z#BjY2k()8UUiurrO7rE9ZVLRit8#p$mA@#Zb4ex@KGv^i#l-Qczg^LJC2CC`y-9sU
z^eQfgqL;14pS@x@M%>DM_Bb`ys%42>mW+L%_!abX*usoQ_f7jQt{(FF8gDROey~gm
zc*T6bzK2XRQeR&bYkQJ^8F&xz5#_{yCoQ?|^yY(``G3}^9dZ#I#(MQpjvnYQ<6xXI
zYRu0Wu*;3B%e8(LcsST=gW)NWk~~l|-hVoKv<Lqo*lU1jc;C)j99SS;Ry53b6<X3`
zg(&XExF21i4+ZYLyCFC(HP%{KWp(?_)qUK-XLXIsZxNoNhx3`Vy(@`Mv-L+;9>f7}
zU)j4bJchfu3JP#5P&v}@j?5@V6K~6ZgK5{$jT|yL=Z-}uo;@#91P#c6Fog-HK>gAj
zKIcmCs&{++kJB7#=yq*)ao2GpaG{vd*`yMo>@fgFNmb8gu`es@0URrZdz@!CTHR=O
zyGo^6Zg1wJy+IL-7egqR(N6k#oh_B{Q)3Yhdw*fz9XH2^&CPiM^buIBW~F@}<||Gx
ziy^CO9;fe!5&QyLFKfbop`Yys#g~I9g*4p_>!V^bffur&0h)SS-1=T8FZKCG%+tj=
zh2*v@ic$PKA8V1|6_d8ALyIDj-8=%vnqxvSxTmsCtIV6rGN*cecz3w%t&mv>Aqbvb
z>kc;<Z`E}L<IQ5OrxFJ&%N#`<ITjxg-nyXx%Vusc;H{?@!1!HxeDjFoJ7jpP5YN0P
zigVNM&$Y+R$2Re$i?`a=M3+z5RR1KuSK<!jw&UN4lLdwSC|UKnwS{~;B_HDdHg^E1
z3PlUP)qHU*>Rer2c|q_{GuxV)Krc9=U7yP$e8M-ZE(Y6T2%|Zbfp&Bk{Uzh~S(~*v
zvYUTci_mv~R}Sv!ec2!Wypua5NaEm0A?(&=0=SRX!T-(Ye%4sR%e9b)*XfWy3Z$*_
z1FgHV*E6G0(jT<|GyCgjrG#(Ce&_64!X<M>ch%w+1q(DQZ*DslPNq<#jLd1!J2mti
z&aIcnn+>(+UZh+PHhvrUWBYSA4{9L4rul3WDo`A@9ksFOIXx=UEv}G~KX$Q;DRH*n
z0x+fC7G44g{eU&lIHL;}bOmCXtM(~}_i2omUys;(!Z7V+;AD(5BVJZ&wc~6*BTjaD
zq5VqN=>8L+9g}(bQ6BkGNs$@)g(*(6`h&XFKx$#JT>oD0)d}s@y9<DcGENnmQ7H94
zFy&5Z2Bjw7rdhSEoz`>Om4;+A&)I$rP;ewbhw*D}{xU@q3k$iNI`R!KBXVS&nk;Bz
zBh5W}q&5GhB;T+@E-f<rx(GsXAH{WFAuD8JX?FM4Nxq$|m3LJ^oH8d%{F@|JB6+&y
z?|SfTo}TjD;XdN>ETSlx8cKK;Lk<`4Ne(w-KDsk{`#yDTpp^QI)83`3T-RRf{y2G0
z+ridm9+02?$*@`&H$hYKTVw{~<vslHk$rcSj<|H>=n3y1%a(mo|LrQ~0$M+&_Mtdm
zSd_1UA{R;`E6L)p*u_9qvQ<XjFd#wCcvdxE$_~_nt7SOK_bdtQ@RCm)T6R6^C_PI%
z6oJ8<KA7cm=F94lw&VTWpynfDP0GJV6c4-TXg-mB7X<q1b<TOKF&noXbav&hIFhWM
zfj6BN-lk;1<~HEma^3r@@u~Yk-;Y1?QSFvjFa6ncd$(=n2nVK6-nUWfE3~*;p65!_
zVbs*c-FB8@$zyXpKf_IcZxCL;5B{l{|L;l0sYQqP$qa+6&23UuRg(|pdiqkzcK7;_
zVR=%0=euPKwT^F!Gej@u*{wUfJN@t9|4kV9V7bENAkOAIQhdFV$!fpSy=8E-?1sai
F{|mjx1)cx^

diff --git a/reference/plot-6.png b/reference/plot-6.png
index 0105b6d057865490cb3ded98e2bc87d679c2fd54..20b9c56603a45561caf377e83db4aa82722c8cec 100644
GIT binary patch
literal 38235
zcmeFZXH-*N`!|SK5rta?u~C%J1r$&^u~9<rMM_i%Nbe<-1ogH6Vjz?lS`<R>UAmnX
zKzc7q4K31w5JG0h=lQSoewdke&6oKwYtC9igq)pyuIt*ra_w`7Fx1yPdHlk078aJ1
zFs*yWEG&nESXd4|I(7v7WHj;pZ}9ENBOT3q;5#^W?$)8fH#RRVD_<5C-j~dO2Qag1
z2o@Fr7TCSpra>vo<LLT$`~Bv%?G#0*Qi3S#C093R<_Pv*R3W4(;M+)KiNx{C?M2cq
zV(nRpF6!7l(ZnC8BcfeK_rzhM^el*Jd=)`6dyce?S!xW-A~#ZY{JThs!K(ZiBuUX1
zJ{V>@s5(d*+rTe1O0oT&)c^nG|BnEOfPg<b!F0}NLuOp4%ldr2^K4$%rx(+lEG*&`
zdo~u#5Vgd-BR=I?pRJqGLBLTtkGB+uy!H;T+<KmfTVk$&zlZVNaGw9Z6c=Ny|L;pf
z*r}<*|9Ks*9jQJ5UWU_C<NkA6#FdZi;A7I?5d*ND*giXBdzjF`^r(<KcJnzg0jq|q
z7~4gMU}rB$kXiE!0<ep)<CZjrR?i)nrzMRuyF1PD04x<^E0LQ|bit~{ZPpWE*nrv@
z^3_}d5w4vlM%ICq3#p1Zdtca6yx+5f)vxr}LcG}u#7@L*77;Ck=x;U7qu8{R?)GG2
zI}^w}7a<+k%?jeq`gV=BdDX6zE<8ub_jR(iQfqCoNNy4F3uGmZTuo@N*O*9@Gw4~p
z)F+=Tvy+sCIr#Sq4H_Jta{T1-uswY>Bb8_dTMmrVy>|VSFC;;6Y@)2nqP*urf|+=(
zJxL7{Qz5F)o2@0TUGk>tELJU|d~#t_2g{*Xb^_X;z;+JVzs9S+s4ym&iX*S1IL00F
zD4kxd8=e@IZ!%Crg~|-wo(As`Y~*Ro`iRH}jIzzTXCj+`#!`OY8-L3z`Zu-#vEgZc
zFXy{>;(PD5;BKKw8r#5)-eR&_J@oHf!?oQ?j5&uOr*F03Hcc<k&^l|+?oeuG=+#7V
z8A1OuVoO}EXicq=qz7q%@k1Bbkyt+MwR6kZ^S6`T^$N<)<W2|U-<jndm@s9{!!Osh
z4rE(S+-_7>ZPOm4Ng?#L1w3yF$1KG&q+V{44+VX}HpU{uzQt*W4;GSwo^0W4*Tc?<
zz;D|1;!$WOV6w0z*qCJUsJ!C2)e?6ekM}qq1>p{kpFTT9zvWo_@?QJ!Ro|VRB*@a>
zmkbXqn_9!d7jK0d`iiK$%g)x=E$geYh-ae#(yd1}WyS;Jat%uBHA5z+hSxV!=Z!tI
z=8kemOWLl#KxAEdmEaLBf7SW<FNUab_rdz%AU&sAkiBp@h|FgY7=eo(0m#s&#A%18
zc}STIFHT&awy@<E>?y5D!%@G@uhQHFS4|K;v!~tnQ)a`Q59grB`E3|gLrjwz-51QQ
zA+qAze;UN#^LtxIv3bH))uz$$Z0o`q&xw;XIJYK#wJsv>%0_5qz%VM4SgM9Q@0v)8
zXDwZaq@EX{m`P#KqGW4KXfi?rR~2Wf89U7zPnJ)e;S+GbB1`bC`d6l;%dI|3LYx2S
z-<WX^$t!t%9SoGo==~)Y>zAziPd`i#|1om3rOZNzSNaS~YutHBg-?QEmd1B+Wn&t@
zN2<3A%GMp`NDYemyCtMMMydsyNSRM(icb}4*7g;?sFcoD@od>?W`x#Yj`6M-zahK{
z5F2f5xLR(R#{0wd>KTXEfw~P4<wvq{Ve;L(rm<G%eiSw|>A#agA%A3Y*!iBD*BP0{
zQdn^+JX%WD5e7Oky$DAB^vss;A<DRSn|8n<&nl4g)#<<Ue~0yDe^v0?nQ_pD%AdHI
zGWLv1P-R_p0oyF`vF^Sj#I_)l5BV9Zq&!2Ojctt68Q%?=SxbuA<ie>$*aQ%YtZmvj
z+HDe7H^*khP%(o#G6x!Tx5w`-nm{O19`&*1DCJKXQXcHV{(dmHFh$G=+fB-_^MFQb
z?H>s`+MH`&c3=X8v%I_c0CPGYyTV5o8j>Mq2ELQ<nq}pyvR#GA;xY$>vqM8>RFpa;
zK86(ODB49mk@RUb85Hc|mO?iA!JuD+8YJJ|N!}~_oZ(jd&PEUuiaHXMNurJiZHZyo
z_G=C=l_^^IRLn~G*NzF%H>Aj#yO!Bka5CV!f@U^kKVI~FQs)3FG5CKf@!C;o7M2{*
zi9lrNYa*}Alh;lN4Zk!=Uoz^-yo@^b!Us3ntsV>?5{;GnhOmB5vWaUAYMx!I<U{iL
zBSWJ!)4p8dlfcp{PWP%%h48n24oZ!7zKWCQ-I4JnDp_4|j?<jzUsuyWwV6&vXW#3w
z!Phw1C$y5jEmbO52-CmKt}%Rr3M^MQrqt#P{2{Oq)hZ$7LI1qAurZ69@ZOrm|K{#=
zjqZe?)VM~snpAFdGwNc{jDbUGg{$g1;aALr`=MeBft}MB8U29PxcS&-=i89t>3^_n
z$AZVl);l*JThuNF#VZRx*)vgD4}k@Ib<EoJsNelC9?<ssL1$*3Pw`~;b!YS})&uzz
zF$>#$PBOjP1xu1XvnSoZA2#G(G*Uvm%}3h?Rpky~kaTRqtuae>9OUuVg+FH8EG(v6
z(qj9MG)s>QZN+Vt>Xu|C-Mvt)2o);#GE}*!Qiar`{L`NAj^G<bc_CGXHx!DeV?<=-
zO9nflM~~fs2HEa5s6E+>Ag<j7^U52^Hso(wN!+YxuS&blP^Jr9uEDTY&9?5bl&klM
zV86E=x?+&9<NYp@O8Igd6UN;vmVc#f&b2mu%bSJepXV%p(@AZ$aaj!z^HOPw-%L7f
z-LHDyE<-g=%(c4tW^sGf_6HR^qt5BPxms4fU(tPAI_gVxUuJnpq_fpV187rX;Y--!
zB6jr#CTt!SZ030UXto$kJTIs#%3|t<b&6dtcOetRM7d|9Kq-eq=oXHp872_D82mx`
z^R_~juaItan!7_f-%{g)+%}Q|E}K_9jrORT{jg`cyg3wL%DRJD%fG_ba&J<ve+O%4
zxZ1S^joU2zGOelO;zp;t;_3}rX_~PY9kK#)+>TGXLDeF{5VRY%vAYT^EU&OjacW@w
zh_DN7;m*U%ErfHtDv3=x_fLsX4qs<@ZfcDRJ*oBkJya$2WRLs#p!Ww$M+aMR`C^u!
z+IrRnC*hc;ayU2aDIrWg4PlPdmx`5x)$m?FpD0H{j8Y0|&>FYxBzr}Jo=KIe$;)I*
ze0K>la<e}aqcUvVYj6F8=m=YuumIFX1>pwAZ(09;NqH;!?6mrs7*vDKNJ+gkMGA|*
z@<|MiPeES|w07ulzb)K)NOf#6kf@V(2czQQn}nLhq6##}ecUU@My*B#;de2iYZ@jZ
zbvfoT*p<4qMH4gOoVkHcwl?>e>MWfGP^iEBN2t;DOQElI8iS(Vnr*bqS57?=r+;!k
zRgOl^kmrn`+3Ewt$ekqp%)bnC?J1B)rW(+eumf4*92jIY_2O*lN|#R)F}R?9zO(Sv
z*qgghp(YmN8?fz6i`0K#Zx2z!+oun}IGq5#YE@Cbms>j;UC++_2SUC8;}i?LJyD3Y
z3k<4HB9f($s|kVs?0aF{WmT(Ef0lN5>e%u@tvh|k%wyTUi{xKv%%U?vSAc^l%-*G+
z6P0}`*{%LU-wPR<@Ws9>WO-g37KhL{Jc1MD7ds=cSFYLU&DK|%j<oB%O=fkqzQXQn
zhO0S*$|oM!H|yw+0xL(Fd>+3Ov@&1xg!IdL9#QXj3J8ISAz`uuUeP#p3{w94e$j`+
zzb@9OtZt>~O!&$YR@eVr#->G=!=YhQ>p^L{|BR$DltptZi3W&WkFv6@q)z|;GF!q&
z1@$bobJAoR*cL(~VK{^HpbU;-C$X)Aud7>TXkb)YCp1DTGOieO3*c*H#@8irY`-q0
zw_KNwVM7rF&j>{66G&Hl$!}+j=1o2AuniDsFO#w4VoVoH8SlMEQpIlL1^ljbwM(!q
z7aJP42%+($rQ9KHYY6!&?BcKZrERX0O}Zrw5ffusk7LR<k~;nVJ2mLR0G7+Cqep5m
zmhEwyiJXW~sWWmuCO}LazzZbt-_CYB9K;!Sz7nrHZocqw%51Yk2goGdt#OCNUYi~@
zjE@JVm#TYy#wdFsdGy$rWyYB?_~uG6-{>C4?;}ECI0t%GJ6*L+;$w33pkEdZEHJHu
zCURg^6QX4BQ-&>wkQ!Y9f1eA8dO_Jqa+(}9*7YH*DPdEqNCNvWL)%4q<C4g)5d4n$
z_*`b;<6q!&uO{?$+ta-8Vi(KYjai+mw37%^`>XL*uO*G*62?!FF2fqIT8t`U)Ql?q
z-{iL6Nz`&?!Ja!5bO4azr;9SU?BeJ+k78oG!&CHHkH+W~@*S8hWJUWuid#=hxMk3$
z2bcrt#F`VM?i~HPe{78J!X&1N)>nIrB+nPeLQk*MwZ0XhD86cLu^AxfO}6#5x6`#l
ze|{!FDr0uKh0?mQMTXR8QIxi;c~p}WzcY|j>5cvPyvV(z&U?(L8?F1=GAX(}S_Wq*
zU+E#~z#4qgVa~rm*cVB`V9nbP?K#4dB%DpKh_DAMb?<ejs9gBink@&ZzzN71h?S`C
z@aB9Z+6s-R(9Ih6cbNOGqUTszbO!Ne+d$?v-c07eJETlA#Nt!0XJM^>qF=7=Kjru4
zdMJ2oTAZ9_#8EanwxcmQ2i<z;#`xK@A$NK_GUd}%t+rba(Z(Qk$41bI-OsI9DO{hK
zu`K=+wOyz|s{9y%*OK&YDiJRe_AZ+kh0rK9$8Ba32O+9LXdb@yKG)hS|HZ4np!L7~
z!fhll2#5_NL}REq%sVX!sR9T8g&suK6#dW10ymf``+K~@{<T*B^PB&t-*X&}=!nP3
z-F#AkuI$;D%($_;6Qna2R7vtz|6Ps~$u-St`c}76)-(V4t~qb*e9m5wMq3}nhCs#X
zhtP|3)E)Q<<-ApyBl~PJ#n12B^HoEaPS^NHwAPIU@9iCGW015%9;Wyqv7Iq;8h}Cr
zw0DhqPQVTd_3OVY_-^FShr6}=IQu@*yya4H2QX`XO}ezG0>9frsIf<$7!gZcl1#H}
z3h^7Zs{K+_HH0peC$(l8>z6^~EWBBmC}(4wdLkiJf5M`$!fd+YZ?EIDPc^aRG|fk0
zOj}uoc{zWoNo!X=M(HXWXB7+iB*$eaX$w!CPV5tS>HoXTFbma8t7!5YSqiijze4+S
zX}Ux*fhu3UKDJWQv0tDwGw>}ljR#`*GShs7kIy+tdspU3@QK)XN-3d9J@cek>8;1_
zb^lSc8>U#1pRp1B>s0Z#!XnQMbz!jjugVEZ*hcSGeym?@7b4r5^1?jnl=sMAkIZ)Y
zW3u9*pjgRN#O?@ZP0_2DSJYYg4=WZ$#i^(-T6}v+&+N5pS~5PR&QEwisxlq*Y?>eN
z8FEAq2r1>sB@qtT(JC)g^`wcAui<YVb<p4=2$K;K9g0%4R^0i_3yGVyr?-+HWR45v
z;z*)>Dof^i#<DPZtzhvdVPOs{`39`c<6P~BQlt~ug3V(UISyk-T3)ABJ<IgSuzeP(
zqDj%o^IXYzLE6!_>`}w>f8G-x#RQMix31w<m<1MdjkO*RngGM=n)pVlPm#p0_oceL
z7BjV1tb9r;<*le|J3q4N!x@1hHEsrT<x+|80~7aUo@gllxO8#oO~1CI_+gQz3hD-)
zBzNE;jPiAO@?qp5+~{{$@+;25Fx$m1jboRtoO#WmQ`;ldQOw1bbynGp@W|c3K|If)
zU0=jA#Fa*etzKk_xm}agd;&~p!C}0s-C44J80tsrbe1fef8gp_wmdD{n@vAiwaUNl
zjK*Ixj_uAGY}MtRnw70N`q8s=l_BMvxD}`g(+pZJF7@ZfZhF_Zng65Ag<pIhrIR#T
zNDFb$xY1mez5GGnG4ePRd@zL-j|^cLKqq5&Sq#blfOf~NB&qTeZCTvP-Py)h9ezTM
zUOLj4GV3;*UrMHq-EqcC`;OlTgx_2+1D*Rk2^2e+|MX10OD0X<k>lehC9jFEXAo!Y
zhJ~Dqt{hO5Z{T7m1Vx|FuadOu8h?*9w;H8|!~-24L*%SrJiQ&}H-#H~M?$m+LL`kH
zyp!-U8L9PB&KOkD_zAPUMyxh;D`QW0!DSqs_+9Nn`|F-+p3o6-SvV!yhw}oju&?5F
zlfzl(5i#G0O%~n{E&b;Q=qh+Xvs%IWb4bQ*M+Zd5p+#f`vCd(4SPGU5Q5DR6uCsfT
z+o~WxlIV)Cdj@`CD<C|M<U=^+H>@r8d=&SsCDHVANv?iG(i%)t#{Knx?%Y;=UtSkZ
zhTd98xyMhij8Ng}jznn0zKEZ26^M%zqEGD7gkiJJ%S(oMqkQaUtu7ojzB{?3+Khsq
z6yIO?8R-z^Pm$yIesU#@442xmXxWTbQGYsyicj^J@WBgsrCp)?{9A;lq1lg1cs!g}
zOA4RV^OV9Ix_M}Geu_D%jI;QU@g|MMGb-}-X}Z_?3hQHtQ8ptscZFYFNS^1$vzS^s
zzE6p{n{?5ZDwZF;_U4&M`1Ss(52}GTxU5br9Kq~PaAg<SKkAvhBtp0FA>e)#<y(Yr
z*?pr(<ZhZk+qSJY5rfTHA26rZQEKLjGvw@+**X1c9{ZkATv1@cW|B_YNgw`)I1zHO
z_aZX-u3FvLzRahog-uTdqU)3~>gMQ@8SW;Ze~G8Zr_~^=dxit+b4>8eut;v@UA5wy
z-$SGx`B(H9Y-`0WiCxe)nR)x6qctgK(u%etx>)L4*xYC|qZwuCpRxE`6XO@r_MPh_
zcBfnH>W1Lm0{i$O?@A~pWY@BaA6aMRoTl@T!3@@K=<u=MM;tW5pCdH>RXp6eT(AFx
zC7E|@?fA&>r<}yd*TybIEx1(-D6tXcLU#lfB_Z1*E+D(^=GKQY@Dm-hJ2rn1|J-)H
zfYz11{v5&j?yyY8tKwI|_3w48rK_tr{fDTDV+UX7R(&JaL~*PJAI=C-U)i?9$JSHR
zRVy9lUe*f<8}Q@9tq<Z^a19F4sy&KV&v5x-b6GsE(EBj!^nkJ}&Cm%^e-Y|l0=)H5
zTOI)Xce+hvNpjU&6?EzPaxB~4pwWzCv83#ATD1|GJ$><mop8yBW$fUB^FZTgY-d33
z{PX}-T}E&!|Nd5i6j??cbuWT1lh4+}($Q}DjQ`ZD+66!TjFqzCi6H+{xPcxwGVCbg
zDUC`q63?)VbI&O7Ss7fyVU<M7hWdVDzgv%1xiD^3SzMg1oNO&L-~Ghr?87^?lHXFQ
zHF(?=j?+{%FKF@hty8)!bK6UKTo^yy1Do5?HowVQpDkew&sM5>GqTEEg3^!uSOrJW
zVz(94)xeej(B=q`jD4lN6Et=-53ZhkWJMdn8dsVpQ8opmy=`8Rewb?87S=YRCNj4s
z$bJ63xvHbJnYsr1#7v2S@;}O-GIf>DdEvg?thP_kw|+#ZyAXNkM#`e6gek<(zecmx
z;Sd6NKKN@{?kPPYVb`c}vdCx#IVnPMR(<2&0&Q*^g?w7?tmvV{pI61bdST=rYT5bZ
zZIjh#vG0&0R9<!l<-8*x(NQTitM4NwuQV>gF<LfmS6utmBuDGrsB2Q3Wp0I$j4xrt
z-~axYxPjw6=VjMckLfEfRdOrPWeLqI+U#QMR!$6-Isd2?d(@$zm363+fTmA6&3u_#
zjQZ$NhUz+xPSV**%)%(A48_)N<BvwWUwHKkQoOJo;chDT&cn~`=Oo6%)Dl-TC-NdM
zp|c17VG(G5`wZABr*GJ_gbHg%t_MmR9Kyd2;E^}%S4?N3Y&V6HsAFOIh`Cw6M@Oyd
z9y4yOk_YIn1N#{(i96}iIJ$n-sx;2`ck_O_#hkH-2)RON<gyI=?-T*&ce>$i9b}o?
znVz$gLRs%~!W8u~V%UaPvr&uZwgL@>OXdS;1DDYUml^|D5!@}WPbUyJ5E|LSpE+K5
z#-9(n5TLaIUs9uPS&NeSWuQN7AluJ#C#qL74)#+`4_hO$_`SqI7)P&T=F=NI{9#}1
z)ZZAS<0i95zAwsHm<{cuoUIneu_<ZOy22JKKi(H#VLbO|*ghpU*a@~C`X2WX%1>^r
zwn2<{#j#z{xac{~855$cjX+&fG@9G7zF1jUMTE9*j_ULjIMqfvXp9ZU<Kcf|<+h!g
zj+{Z-5={cikQs3Ki29=kS!7-_dla4f;<8DZ#T+M|I^J1s>3dAE+|Jfibv$~;ZmVUx
z@XC{cg~P9xojFp@vPCv?+%t_e?RZEt8VFnF36dsPd>hb;ohpKKU`(=9Gr#NjaHiEL
zYRq95w-F=fRiApqX!6QlPC)HOjA^p$D@K$}`V2QeXPcuqj>E;y)c>ws@o(`wDlide
zHMYp(!^z<vi{IEjSi^WV^%d1IE-l+O1XEr@`IDxb;&-~=Cy@}O%mH_de>rBbsc^Hq
zi$Kq4Zdl(h{i?rxn>1}3Pp8)Qe=|389C_U;)7u>{?sJ0LrV^+e{|9r|Hvay_uDG4s
z56kKg=GPU{2<d#~m@b-<VLeq@@oOzcKGH<SLhoxx>IeF(7%i_KD<SXc)7lw_s6T~s
zTKwcoH<EOE>SPYg+^K<oduV-mL#^L1%b*gAj|wT)y_hAst{CIKBg3ARdX)^tq6D|Q
zaJ&Nzs{NRYAr-#$U40I1R6);8sxJKlaCNK)>{wVh`n2ZS7n)@be02MrH2Gt~Tx9Wj
z$no%KZ))AAS}0`kI*on0>T6yVVc{L7w?22PkmkTz55j%ZYz`UneU}0E;kY%rd`fc?
zV*E+e0=9eK(R7Op6hMl`kIXV3WSQ|wHgetR(7RE!(`>@r?Ih$?{BHK63R3-#i-@`G
zii3kDTx@qnI?GlGT4k?S2k2;<s-s+Nrp;0d0gio>W(X8l?v*n?#7t*`hUcf-GkQj8
zdlo6X!{o%TDrr$5%m7!V84_nqS0sA_^F@75Eax7c&`vMPt?KUFE_2Fj5#5u{U`BYJ
zgWNDe`OdK}By+UV`u9K-?^T1D)4{a1S3@5g$E96`Kk$pJa3i4si@hO`h6nUil1+Ig
z_N-3r-MRAS{mYLZ9QW6?Fm#+*k`|JT@q3ekep^QWXDg5!<3|nMwiE1&lN`m=cT+YZ
z5)ZSq2r!!*%hK;!<PJ3?>@9Sf^3Ij67%uJ8a+;S}7Gu4suAUe^-pBC!D{}LmD&0?X
zvSR~8thQZwt7>4`lHL0P<=Qk}ncLG5e6*2A<07&>RYfG5k60^GZW@VaLq%QV$4S^D
zG+8T~P!&r%_VFQXd0rP678L!piudByb`qrR_1Q!`>rYX<Q9n;R$v-55bSW7fsXh9s
zHt;y9rqyBwyC-ea(}!i7@aTU6eLIA>X|WfJ>+W)Dyn<T|X?T;~`|Sw&FSw?N%kt``
zk<uSUBTz&(@t%N?#x5&ZpM1hi?JR~)j}k_12isY>Z+BtqZCL1|(S?4LbW|ZNL&YSs
zlMgZeQ8oeTv1o<%d^JpU?wfQHz0rMOP|}BQ2aT_!UqYh!YKKM31+ux&TqTpsv0vs}
zRI+Wk(5BR^oTl+P42PD|x80EE7{F2TN<&?DTv-ecFvaZyv?@Z%<dWF7fTpnD`jF#M
z8MW8JBo3Fo5ka`Ey2cUpu7|Zfwa^<sN7RQhr0yzR`*HuBR?Kuhgo@X<c6$a<b)eJ3
zNm@i52z>y7rry!QiLNO^R}}4=VZnWQFB=S#3-#QxPQbDW!O@3E1D6^D-nkW0n+~aQ
zKg7olnIN9}1VArbNchf|x^Tl-Dv|g1?)}*IE&ChgtEn17wH@5N7<$GRo^_6@A#&p4
z$5Vd89>o^6d?l-BA=$;?N>a$n@xiAuQE9MN$(aQ0i<vD;{SuR&4jNWXg)J#xsB7LX
z85XpLA(K|Q4c!E#5OKC*^)VtH?G-^cdWa*=7dG*muTS#lyE1mB$LB`hP?I6;CBx9C
zj=uMt(@0}WJguQsX;SmZRQm+6bv&v{CrKmnQgQ;vsEMTVc)jt=(LqVr+(8zHW6afm
z<wD!*A7e!=>2|}1-zEJ58sP7}06ujo*<3jn%y42xm?o!j`Tl+14UM|dTE?wdohK^{
zq4N8|{clyw3c|E|oH=L}JSC+Tsu)L1kafmP)?=+~$tq_fG_L(n9JTFrWWiF*{z2D}
zFd;tO63weMhsQ67uI)JW#<rstanymZda+~154-X&sNU)2dp=|mQLdTMM2`KwAN$pr
z!@Z6qffY50B*AzoLqU>@2`kj#{%`va9q?bO!ih~{r&Xd;2M2Y;-Q!6ZsIf!sbIUF6
z^K{0`oaUA3X}RcodF&*~idB=HQpr;$g|uESe8_kNU9HTplQNEuLBid|$&^S2@A^G;
zjkNu?+DVBGzQr4n<BhDp-YC!lvC^_UHC*^XrCwHST6|69qe4>OwK{AI^m8r$m3e2S
z7x%Z%_)X30D*+4b2~(P=4ZQF54^u{+4ye`*_qJhmerfHjS?fl4=sxK-4xfr~OsOo4
z(wRssJ)b`2npxAAv0Osza*%>oH7(!7R*1S-FKhKSS&nKW%%4?j?p_k#_|`p9Y%=a>
zH999MBVK*a@sO>;Vn8(MRemVwKvSnB)2U`=J+m^t@-GmLa!PAT=0N36IQjDCysUJO
zO*welLStq7Cf=EM+1}>vTv`7CBXY8}^IaLiqT_|l`7MX4t2I`-#B$~>;IK@l93!Yz
zw~-U>O!NvxW7#5k(_4Ja^&C<C{C*Fh%LOgIz8P0~(+)XE9i>Q-JKy@OTI|`cB__=A
zaE$6>#C=PMg6WG%(!G<l+QBxorYeTZYy{7Tie5$ftypH(5V@5r>grMroNce~({Z}D
zt`{84KJrQ=;bvQ2T`GlDeZn<QXECECXD``)eYm+fH`;Dx+t_V(G`mIRYbkc8z*lCt
zt;tq+$c2%^6NA6-8P)YF436>FH?1{r$a)-+#@SFyGCSOL-?_7tkD)t3XzG3wDJ~fN
zzQR$aIL_BWLx)GhT>^_VNWPd*-B=%%vpG4>8hNR1OWXZ^L@1s={Sh=OU%V&#vyzwi
zd%ren#sn6r_GP_s{qNrJud=4i7qcn^_@?BgII8D$S|mqVqu{&_RhD<E!FG;4Xy_+3
zl?s2*aW>w{rd<pZRyUTZ|FWHQ;9T-PH@>^@A>K&+@=Liu&6R9X$5GK51LTx{fw6m(
zMYx3w?(FD+n<9bV#bg7%ZMX6{PZ|*kL5G9xYS$MhD$E#U5;W$!{KMtpu>nE6?6kAW
z@*4t3sMACIlvaVa_xe{Uk^^I?`)ele0rfQ5YVx^sbc$Eqw@xcJ+~^R3P)fc~<JRRE
z(y-X`ErY|L?W)aVMrwh>=;NsEdYieJW{+t$zR|HXc4=yewkvL>HFRKnHKs{RIA*nf
zA}kUb{mfxPd7ydN!g5ZtpA}z&SZThXwA>ZJn-h&XS*`m{w~p6Cusmbu)Rbg|*ys{+
zEh%_LR1tl4v|36Hx1!mw!L@T+mMSj5Toqc{o45%F&r-pN^n*?wQK=3}A4iRa7k1x~
z7B^`QXU%H#)-0r;cO9h~PRbl8pOrcAI@k{HpaEAEmA$fgG(T-e23I}e?{ZnT>@k59
z8aw`oY6Ov;TzwX4^Lbe~);___`q$y0DDIv0u{I&SgZOS_6k?>t%<*4eIIn`cMjEN*
z$2Ovo#j|?xho-QW`_b18to5y9W8e$5cVVxF<M&O&Un`vCkKKdnG8spkTU4Ct95ifE
z!elk%QS69Lu!dvjL<|Wd5{Gztr(00;nUTL&t>1ka6ZF58j7Kp2`)ybEE84_;>&yl<
z5gL~?DbQFS`(Ex~V!KAjncQ|U8M-HiMC<U4BuRQ;CIV^K#nbMMp38qm;=Dgv5ZoZW
zvFm?-xjR2PLXF2`S>j-t&u|*aDYf?%!de->{gOM9^j^)vk?2zhS-c3`k*b}eUjV+c
z7~LLfr*05o&p5YXb(#ikZ!6b{otGBaX!On`!elI1mMca|?Ho@vHIAU~mEv>{1vPjO
zJ}iF$!t}sr0|&Kd1Fd_O_kUs4Qp@N$fy#;m9cJ(LmbskSH<~e4>xblM`oa2^lOe%u
z)zW`hP-@HDV@Wkd$6{DQBD25v+MZ&$bttOf?<uQ8Ca@E~bq~)>l*;(N#9%9gs?83t
z+!JKpT*Jarvr$8gz^)oOlq$TtazRiw?ECoIX;zl9D5mJY^=%`MR3CTI2aIRsoj|&@
zFbZCN{8EfKYc-az6v@Iu#Sxe{>%^!_I6QS^j`#n`!}xTI|6#A+Y%jlP!t&ylg8y@o
z0`mihS)UZv6x~B4f+$SAGu1TCu(4qM&u<m0`)e<&|L^<n30zFB|9j*RkNy7d*|&t$
z!TnO<+Bt~!#KF1wd1&>cZ;^T37=r(R70=OUpIY5VDr!c=;9p*x^pp@658h4oY!uV3
zToyOW(>-?NS=`H$7YF(zJ6ww!%tg+f(B}X@s`eO9BDh#-88FXT8msXwUTD_V(b+hD
z{J8MAZ<lI^uUT<qo>{*6;1MwXrDOu;<i(p@BNX*=r>-I)7!Hm?&Of-Ou=Cdb&H0q2
z-OGWG;FQgTXD>$;O)FvrHE+6%*ZMcz<y76I?Ck7xadUI4S{WPrP|iE0cn_;?kM)Lb
z%yk?tMJhBaeeinoJn{vzz!FGbBhFx~${b*W`l(8K;G)Q@3+}VCvq<polb6EspYu?c
zz8t5vwzj;j<>3viX*=i3FFWSv4`Ae;tgo*>Kq8UX`h8*=%?s~KA}I~qQYqIS-g#yy
z$~9E$@4CM;Pe89VhS4Ispp3m@U)!e0?<o3AE#Ge(-Okj`jxUC2C>rlXH+V23Ft-_;
zUA(u4OuY;187Q>gSQ;#b{xLxB_QIj`&0deuYEP&**KzLngaqC)@9FQt{Gq>ZW8U9=
z$>H3c{%yPzsrWya#VHQ%S;K#Nc62*|*qx!-rF9e73Dujt{(VL|^Bu{7Kg$s9cJ2cO
z!%_^_SCNkve!QpXCEr{Lq|>(1@6e2WMo9g`FE7|fs@&@YUz!~(OD7VES1)mFzec&O
z&$Rjq8>Fd-Soq)hb`P$o@KyQc;#kc_KbWj1_lYChHvau)(?>Wi{jrJ@)(g<-`5t|d
zeV&B({hD9gI1;+G*niS~!{?2^*UwAr+$ROWZcxrWVf~aKQs89R;NI6W1}UQ;|Eaml
z)12r(UpBCP#qcI}dLgfP5(w`~=*}#TVc3MC8ytLc3;FQj&ms<9rTQ|jpP!rNYdyHT
zu|hiEpMUw8cK5{ztE8n`mX@n_l%s^8dBgTZ$^0sZR%{Vg4&?Iv8DZT&@6@Bt)*WYO
z|ML9#^Pjt0OO%~)3TnHfFV|#pcW<MsSux?0;=re8zE@4<UISC{@vbP>@sz*`B#*~X
z$@?NW%?-qFk;g9RVSqUDB_}_s6ms(1wW$u%_NJY=<fd~fL0;r~<c8E(g>!GbAV$T<
zGmm!X+hB$!Mo%?(^IRfx1#_r@@tpGICxWKsny40q_bXEkK&mD5dnLY}p<HtL{`$NS
zmh0G|HqQ?~U);#;!}u0FW1qLo(K`2uohAIG6Ctc>0_?Da4TJ0M++3pmijt->*4w3{
z;tJbeoV<A4qR0lRA@GlA)9zyaVCG2H=g*(LmIewLyc{nN3TnK59P74!OTbVH20qio
z7|P{2UVEeI-Mgt_<`RoDP*eOWX=`O=_0FX~Z~ED%shL5$kit$VW2dW@`shl;gk8u=
zrBW$vWw3aXytc0Q_S*Z~ZjJUBRq7jXKC*`h+e%p+&D6n7j_la;^p|XCOs;Qi5b}*O
zzQ4WcK4L=$$?gjraZ*C;#zIdq7dSU1D1b6C?t7?rp*sTuB@^S0m=@d7yZ83?=D{4c
z9%SpM1~<@F>t1FWi)2r)1b~{GB$w_?OO_?-C;N``r0i1XI+EF!>oYSmkA_MIO+7h!
z;V=M_=!^1=-*Vkx!SktsE@{X)JnoYB*Hd3Mz;r;&7pL`-<Z5=L(K|Y_)hc%Wk?y$O
z4F{bn#-Gwxc4aGD!TaU~mPVBt(HA(GgQ<p4Roe4O9^<vPelxL}6BdSszuk<Cx_h^l
z2Kht#%`E(xDMzuZX3{Q{WUi0c0A#6D3)|gWAF{_Zl;`9uv_5_M6z0F!x3gsBl<L3s
z^f0>%#Mh0uxf`&oh7D^dpGrsbg)Em&(vTcC(EEGvNw7AuA=a3Co$sm3!)M}&UJ5Dm
zn`v)fwFSHpg4L*WtuKWMBohjU6`EO%KOF;5uChE-nsE#JMl3f=xmagch0cPmX9y<j
za~)qEtC1x?zn&Jnm^&?$i=yum&hqf!vQ02Wwsoko2cMp9OoncBaAasi-;dXwxNyV8
zZSUtxeh#Y|pV?S!A|$EUu_K8#Qt7%~%&GS0U9)ZAc)#=RCfQRV^P-}U!*owiPg14p
zpq=l}=R7DzkB)Q#q%P)F?=Q%EL(!PFrx5BLVSQTYbhP}Nx5|O5p3}3@NQDpf&2MT6
zq3f`5Fdcsa6up1fKxOSK4V6IF+`ku(!!@g>c|_3@5)uxNR5<(6dop$E?m-*<mj-qz
zjWiE>XR0zhl)g2HxuhJh{1mbj|2tjwz2QQ*{Ta{LO23hmrN*hs@eIkoSS`xs<7U#8
zrrMPxgV1LDC7&n&EdJkPF5m5ZE9czvl7QadSs2>Rzn3OLtMLj>GhTs`rP6WQA>(OH
zfS9-mK5b%x0jpz32LP#Qq2x@QqawKdLlff*2kRmK5ueokCRA8{#2JyQ`}IL<Z2aiG
zwcgTMp&mXcrVvu;HoQ(AYDWF}`BDRvyhk5k20ILN|Bw5ijc*6zEMJ@7WL3`xs$lW{
z$EQ*I!_}VfClKDz?Cf<;|7d0585*NLd!^mn1zf`O);YbIOL+kPlb4%9wif*-K-pyh
zUiK<Om}U816)WwkvP7jiAr`^}GXB@%IPDeGafQB*J=4?3&7HL$w)TuuO~DSUukiC<
zGji2@pNYY^D9m-R$UVVr0u;Su0<p$-K0yB_<;VNGlUO2N*2&J;+<flCoy|XM)6!U?
zMA3&|La@c*^7$8D3S+XnRUn@_E1PwJlvUok22BiK@}q6nqq$0ft13b_!y1CO)O4yO
zK|TDa_MCizHMO){sCoN+sMOxGx_ty(Z!y4S+i_5igF?=~zD5ix9_H_nls&VPk$_94
zG=mh3pAJz0*L!0^f^wM0=7&AGC({=2@EyQj{Pgj_N!v!x$woVV5aSz#*3~G9OB_e}
z!Zy31s<h>D&1~c9g=yZ9r62FV^qEq{G6x;AYiG-(iA!_Io*v4Gw)p+ShY#0z)RMD~
zCQ8HiX<;<#7U03rOmI;R4WRhp#a88WeH(zyuQauH`Z2tJIfVe^HVa*E6=8H7UsbyS
zR(5qt4?qKopYn&%Qu}7yd}nINN}6g&@XBy`_Fy&vHL{(R>=FEqNSq1DuFq{e(xX^)
z@`Bu-nJ)DHMngMJnnC~j?umxFZ6>~lO@EJ`1ft`KGNs=4R=vI8Y@?^3tV^JRnSa+J
zcA(IuA;VY-8ta?$hF5vN`u4LUQ4n&6{A7Ll{Yn=Mfr)oxA(RG@;ONL`Gkj+$F%p@U
zuuQw9{>N>$fxSlKugU}5(Fm#_NzQp)ua>5?GZR-0Gq$oCzI^efo0bcN6n@~~oOaLl
zBzkgYxxB0KF;hvctpka(RFz32IyeKOs65IS^v-#i`kbAIuMFVh%})=!x<Jv`Hl4VK
z$$|O#>47&jLV&E|z2+nGkJdsx8d^OOywp00W`xDT_kQunCf{`5eQz-W0Fse#{=|_l
zAb}Itfh6OBhymrZGEyn=-nY!5jdXASF_W|mv8+!%9%!Cy{My$FKq;S;qPSrUyCCDx
z(lFhOAy^n28=nQEfb&KhA9tsVB+GlMX<|K^=u}tZT<gs>{Vb0);)rAQ-x9E=QCB``
z@`r6Y!h!*uFytIbZ!t+X-D{Q4op|BemvidEwFe(Z073hCT{|~MBQW-|otEWT$t@a;
zh}r)X!5FBNQvldh)<3gLhXyb9<;te?xC2^c<YuSU8exm-*V|rBBIi?il>HYg`t6o;
zK|L!szdpYaJ1W{>YIl&#H(K1dySNY16$tCi(hKA{d-g?CR8)H%h%*9=XJF~BEjpFf
z-WW|NzSs30(4G9^hv_#oODv!U--Q%K-{6U`?eT4Z1#_)HiK`6fnq=GQo4mP~zdUbg
zyxLWc4dc%_MkuTgygbO1`i3w!pm;4|E<+_h2mH3pK*$^!4MA(ah(-A2-t4aGl~Hg%
zIZW26vwriH;F};RX=%#yg`F;`#y`KVd+Zc9P)E?(JxnBcGk(*dB}NJh6jZkJ4VS)S
zLMFvQs9kdQ9hkVN=n!tT@uu65p@PTQhvcW9rqV3zkh8<}_4P!lpEi{)W^3Pg;Ct4v
zJd5H9?7fjB4?KAev-O1&Y8p#?aLw|pw0lJ}Ur0d&gx9C#^2OCg+A2cdnJ;+ZJx`&M
zJiIQzJ;860oNYR~EW{Ib*%qU+u9X3nF82+Ou|M?H@j6d$Z4)3HRe{V6lLmm0rHlWi
zVDM1fryITJLIJ+Q>OZLqSk4In)TKsW%rD+=FC<;J|1KvcE!|ZNWCF%Ob*nFgTy^;r
zihmTyd&`msx1%Q-Lf}#`;4i*!2Kl%6Pk!&RNS`+p5)wkV4iubU!=U%36PX&-7~mcw
z8aoL<|1w~$RZjqQ(4VzSMXr=eP8#Le>?NIbib^|X`-vO6YJK8)WVj+z8$+n8<JMPU
zpi1;Y)aMqu&jDovBXE+E<thBnj-0%&;{EIEq}$ksQfGxro$N2qDB~J7I+bz2HMund
zO>5mjfHHniS4Kw#ZCT45J8CVlFF1G?75EWKH$5k8!1El+%O$Dg)qH@`I1r}lKS{1d
zJ2dZR-fffmO`!KyCX4D;YkkCI=AG^A?3hllJX;sPt}EQOsf;cFA7dIv1R_D<Nd4GV
zyZR4}kQ<&8dxF(+wsisY>(bJmaA1!1fed!x4IJ~H%(SpZ6G!GAbHA4+x_)|kxEa>7
z*q?9E)z#%G;rc_XFZAC7Vm*!h`4;?A&hE>obd37dYUCwfkfX<x&(GPHI4&vtHUK1_
zBLPcQ4y2~I%x7TKl+3aEN@OB3mvkWMc+;DeQ<|5&8HtkCZ_4L0q^@2a>j&H{0uNpL
zRz2N+c`)oE6yBP4cZ=LRKMF)yy#V<+(_-oq)EXQB+wjhH5zx26q`mh3tW~E0r+ZMk
zW(FHwisbz6d$qM`qri0Hs1JR(Erds$RpamS;o{<o*X{wq>unrU43;YU_3Kx>#{Cl@
z18YFH$rC||TP)kSnq=voUq~!2DY+K9S~I_I68Ty<mUv1~LrzjcB0pfWTT_}07S2dU
zyuJHyS!NZaKhI2}P?}G=Vbi!3D8$ZU+d66zSORW#SCGr!@*X`k|M}qBhvJF~xz(?v
z+<3ARN(7i3iCALN4OgYk1B;FT&0cC51AV){lu-2HLr^}%eWJcbp<iiNvj3)LyFdK*
z7Z4?mI9=;I?+uMaxx|k>zjGL1^Ui>K6Sbxz>|EH@N1h&t`E<sg?g_wR6HH#mo-t7I
zE`p4M3VGJJzq@p$TBh24)ch{iWw0o99mCeTzBF_{ijz(WalC!|(<%U<WpI5Pd#hHb
zc5k8@dlX-ai;0Obb$czK5=ISUZyq{0KRf#zLf)rQpo3!cPeE^lc6`$k7HPMnpw>vr
z9reG8_Vn+5dFB3X>h`qAYx}{~zT*4&CfS{XqgC$8!5v9AZuAyvZl-uiT32m`U4FH<
z7uxk+_{&R90&T>#*!G4~XG)Zgjt*Q}N=k}w?cwKd*uEw-14l2yK$v#_9L!~+9@+M(
z1}th|v@Uui5`n#@t9HlwGg&|EWB38rm9S9SO7(L=%{OjqzrS!!4gjRr*Garu(+EUY
z9sO!=@ty|==YSSAN!~N9FC7g4a7YYhUG1UTCo#?>jkge9AW66#A-Z|)ZxY)Zr*NS5
zDOl2HIHi80Gb(pgWY_ZR;!x>yCg>qd9AHnJ1(ENcfYTQ(&SdK)QysTg$D5E~5;VV+
zk*ybCm0*0dt$|SIHFmgXbMueCTQv}uXsqe|`$^jqA(a05={L$KIv^q&dC!S@6Q&8I
zrLLRCH%61JHu`*zpUsxHZ+p`Z&q^6A{;zZZ@F;s$$|h(ZkZH1FrsBD-QsQP;c@L7m
zrQb<{Vw<DIq>QQepgW)pAvcY-Z+`Y`R=C&0#B-noR$({cK&n_2+BNJ}BuH2c#8n8S
zDuwQ?HNVt$Oqg7#`Bj$53>Q=02jZmR6W)J*{}f3JS*@8YfHyUo0Ym>rdG&*R&E%~6
zWTWbzX$+K40>-Nxuy?tiZWB1}pUo`chjjrfc7e55d-axo@dKGPx$)yJr(_E74V#DA
z&+aXPAr_Wz@;l`rb`vIHz_nksu3Gkk)lNNS#~u$z^Xq{vIg#GJ2Uu2_u$f|#1Xk&G
ziJ_>e01Qwa<NC^Rnm{U8APD$x9Z+6_oo}z-jpC8^kQSQ_cM@lR!c0r|1dum+X~4!N
zV>fq~iVgAsZRG-xQKx}+7qwz-x|#pluYC8>Dpz2q^dxO-E5by%f^S~DeXt2r*w8Y9
zcS=)a){$oo7-)cLe*1xKI=F5Y-*}S2&BNn4S>x-J$`8FiGpe@Kms_qntb$6r+h1Vi
zTL(G(>~=HKma_`r5n-di58%<(UpzX^+*A%yVC-$6&#8s}v1Rr=rU%uO{T6<2#cJ~5
zH&tr0Qv*hlK)c~CASdT|58ks^Exm{kUSKw(ddDsDJQ}tJ1_5mhdVl_x4MzgpVFFv6
zXrMva+1bN59mf0zZ3~5RgSQrwfv`;I$$UmY?K-CdJ-fRL6kCr4JnY8oJM#*_*HSRh
zXYjR&7aNz6HFAPokag7k!sKJ1l((7zXNuftB7@PYe|<X2-v^e`8@}t0?!_^+F2XJ8
z$-$#`SC|Ib3JFRFw`)f6BlgbSbEuX6Lk2S4?z-DhN!Z_&KlSlR_UrSq-~BtyoBorR
zcBwkI_w>Mr4<7~rvnbf9F#Wn&Sof{h#w-rrSPzynO)tu;S5LmM7O?OgP)T`VUrx%W
z>;iAzVNlJx=CD3Avcc^MP$B$s3U{vO7jzeB9JT-&=YJz;Yhnai5UJ>$cDS0(O#*!0
z5NN#<oghv)tlwugk<iunxd+P4*dAzCTOaL~@PF`&`P6B+LV<M6(a`RWDq#3fJM&P6
z6+CRqYzg?~`d_~u4R(93k&<rYS3**KI~B=Y%CYJBMYS&U!s>}yu+~m?$a{<hr9)2g
ztL^bZ+y8(dL>}Sf8(STAA~f8_f`n|{$)jBWZwd!>Eax@#@f7(@%Wyr?_ft!(U@}t#
zSYZ3g5IW8K2_AUVz%X^d+LKo;==T&OjtKg4yR8nIXZe6p%}iDD58g=)oY?7@`t*##
zbd)Wi!H6;kF!LjlTOtogiS2Y|kID_k3}D*tj83w4b%$M9KhK^ih-L$*fvqo~z4l=i
zv<(^ol_8|ZKx4NYG%uS}fsO_+yped-%9sx1p2~i|1$<=}WJYblVz_1jiht}0SgYC=
zP2bA+{)RWi8d&Vrv66>hc)OqA03=aXfFQN+Qll-O1oFPgA9Q;e=YXh80m!?0;>fd~
zOoI+x`+7#Nac{j{5RTfJt=@a=q|_YHmt!Qt#G&RB=dL~I7Ryd4YXD4K?>pZ)RbW-_
z!g)b%q55xs6$(Xn0asZfRy7vcSN*dIEKhgxK;dXzO_Ao6c|*|C%##Nl#0}tX_a8D5
z*`qhBg9<ozHI5Xr1sEIy+=muCr1WbYr7`4%iPh0|#l)FOWa1>{;P79+#eI_5Y!{Ef
zVzFhQIoi|&T3fPdKqnvZ@U{4EVs3u+&rF;FN(@##9x!?u?TY{K;MzGLFSgm4VHLW>
zF%PKau^muz0Xvlpp#OO;`I4@8jWDWR2Unv3@}OfD_d&CoBiFimbsbnT0rHY=hDBhN
z`Ir4k)~l5+{r71Ad6ZFXy4kwW^=6=*;)>w4eq`wt20wBp779!xHT*$#LrW_OFDrfW
z$TL-b01(Om7pgla)a<{%7L#P$k^J&M_X1c~sVp+Rqx<GUci?M2)!p#2zcwdhV<r;r
zUNxI1eGlgvB~=F?qw6T^6E6}~1$H0IJ{Pz{AI3hNp#~fQrx1CqnV(P%nmUV2O+f2N
zQ?1)x9xe}2PH>ghn8~);B<NOb0~p(RJWyaoDYmIue+`r_CEcprvEg(B9RMo64zTup
zrcv<&jZ<9L+w0ToLt&tVzpr7ZO*4a;{Aj!fTDx!nkzLBG^8XzBmr^sI)-;e~G#x%2
z7t!cm4#zQzy91=T*1I!B@t{oQM=tRy(0Bs@eF|7$wH7#|B2Jax;Y*FIPj2nFgCsG)
z7<I!B`Hy}BnWi8U2_VzEZd<fV>{_^a?JV##)1w+NRd_6sX)aStx7S((F_)SGDHATh
zm?qm5+cgm9S*g9C&i4a{*FQcv2;T}}0R2aM4`g&P6j*We9>|zTrt-QE(5Uvt!<w6m
zoj<)43{6cdGMIKB<VHRitcGVPPx;7+WQ+aB*Yiz4<70k!C9)AY8%r8X0Sv{b3V^eY
z>G18mz1Ps7h5iRHC!Jc+s~@tmQa#y-0!>>*{xzYU!MU70MKXK$tVRNXr;;8XppL#>
z#WzZ+`w}l^;)nuj)q((8*Ij*u)`3VAYCjE#TtiS<D9}SXGiQsOP2iNa`A~uu-`c|F
z@5e<@J3-gT1~f^lhRdC{`JkxKXG1HVz=rpcd$T9KfJba%mP9eLs}FQz5s>x4Fv>(j
zvtFuFzhZh)x+!J_bm-KY0Oq`?a2xgsV18K1k`>rcngvbW30Y>F?FQ2k{WLF{3{xH+
z6yo4m>wLdUU7>All6yTr{EC$Lo)+{g!j}_Ro-MFF6aw=10jxc1rsR0u5VFlz4S+OX
z<Mp|p3VL5Z`2iUkk~TX?yLA(4O;>4V?csh!IP}{>;nMzdV#Pe>Q24X{ZcTpG8L-Y>
z8$ng%->-NayZQJv7-W14M6=4W;&FFBXkbvo{O29&*S=rWwDJ0M*goGZzX}Q1OWq4>
zQ+mU7VEH>xN@=H#KbO*j(3y?zrmX>M3b>qR&-`N<-X`AHsuTxklqU4!mmIvpo0Lg(
zMeoxGx6}hc7d1^LV0mbC4z!mf1%o&}$Z2=KeNj9cQ+D{tttlt(iFgU-&c!=y4bX!G
zEYaGdCNrcA2?y{h*hr_2o@JI_+LAOMR_^hScc5nkv|`F<9!{{A3L~fd9V*<0`IOEr
zp<d9d+X7g_=xSonDQObs1vyE&6JW!y#*>+TF_WwrzsgCVpU=rXHf;YE#Y1531*!d+
zdZuXo>JSUdrAt8iN51sE%2M-{>tu^k)?=3NXM@a5F4z9N%E^>ioNd7HhxxP+5B;J=
z@P5N#?>Eo2--uo4iT}H~8~)4$sQjkY?u_QX?6ly>SRt@msRs6t6#jsx7Ok3@O>gF5
zdG1rP@Eo}4zX$kx4yWo{YP}RfbBHcAMgMswoyEt)73_(0_%nz2f9D8)W(=N<x|Qjb
zPrQs>4;`{FG=4&q5YnSQ1b=enqwQUotWbZezj~S56QT#Ujk$9Uz_pk8OquENmpXYr
zH`EMxn4}?7*q|G;{lGViC<t+H^H&FN3)dC=7<KPNqMYvj>nUC-=WyZ;wPmoY4w*_X
zUs@`q>9_meC`-56wx~obrP){<1gErIV6E5clOj+}=6X?JgZ=68^$kC*%BMCKf)eYj
zq+8&Ymhfj${DLyft$XI7c<X=vcQcqfsecbs<Nwn~y#DXDnE!L)|K*?L_<v{U9q^PI
zSB$B;u^H?(>@uuQhz=1;`4ar!801VH`|Sw7Wo!+b*WQgLCh|Gsw1U-pnjm42T$Slc
zW>~>t=={%AM-dzrY--$asJFVr<1WNKM$WLO{gK1}gy;HE&#FTZRY~Whv1fJ4kTB12
zWwBT}^&a+L$o~!^!1{bxMz@EOu|FwF29IN5O>O6pIhV;=OKM%g^)*Cj%j=2Ct@zCc
z-(8uH5P{>}XTgujNKKtu!9~V<nT#M!*nM$l97l@JWjEDE-JZMVySI%-J!AU>Wc%r8
z>Z*!<SzXaK!&OH1GW)-fLw7#19Dw}G!UOqw_+I^PoZdZCQ!Xx^q6KyJv$qf5Q$NM+
zeb9#QmqE?yMQBY+`o85MqhVZ3$qUKL5QhV&vBMO8vl^3|aqsKqO)UsT^BZe9nqhgJ
ziiCaL+Mf10gD`+5EG*Z~vQF&Kp5S-Y`@76&14H&`us66P+oei9Hvji-0}cI;%4p8C
zTte8t4Qajy!<b<Y7lKTsHq~|Z;VwNaZufZiH-shs9;)A{4#}E8{oCLOg%9#R{QKhv
zGvLSDyZGH@!DaseU3#(R(BE52&M6+C?9spI(tj;3_)~`!Rrd}5m$9KsQ$EMH3-&W=
zH!g&X<?dE5j*+KT`*n`sIheCPSI8U1_khjDLNzD2V{=V4ZzG>QA6mFy-ui8@$mR6d
zYL0$^$1vnan1g>aw<`brFx14eze!9^4iB`iuL{<RLvR;0{TMml-#a+ugx=!q&p(p<
zH)-c2b>!Sq&;=fL^gERy(ao#5s2Zip-Q*|#4M*Gg`io0#i+H^|6Ut`Wg`GvKhN*VE
zy$-OKQF)xEJf@y+CyH-y{k{3DO5p(I(Q<BQ^2GP4_B@LFTiM2@;9&GJOT*ks#bR^*
zG>>Y_kr1n`MRm?kJwb+BQ|g?hpNfjo;I_4qVFo#Arl~4}5jP3)rs$f5%Pi*3Erf*T
zM+RF*^@=1}S+2ef$MpxfY^@yJR`H!eZTub*W>66udj0M^s)ux@zv)Sd*SM+mI*?PP
z=d6?JH|&S^47URHSPzAFtm5(+PThB}e$4IvKiYc_peEC}Ulc?|lww&G3$QK<DoT+i
zH7WukO{Gb1i}X$i5bDB;3WAEF2uTE_CG-|rNMzMW4J~vCh_pZwS}1`e=S|#i@6J7Q
z?%X+Zzcc5%b23brdCOD&?e}~B|L1)Jk&8IMa|72-)~t`P=a)MSIp3Vu5JQLaMs$u=
zScqJw{Ak`tmzIr4uWyVXG{c$j)Pd_=rH>Y`*OD_JjlOt_=y$`8*$o1ssWPVdN1F|k
z?T)vM+P);czT9V;F>06DN@$vH*$}&4Ue&sJmRC-GKi+bje2&zuPylO~x1OcGKLi>m
zV9kJM-1T^dl=e0if5oFt3imH=81I?D@X<3HgH7uVQZdt>N#d?lZHza8&MW6rhaa0t
z9}k%uV5f)7P%(r2P2NFoGsV%rI3^;aH((N;q&U7LaXzO`lkK)(jVW2zu(Xkv^}8fe
zP!xcfK1XfY=!KFw8KiF?!#L?I0BQg{UE!r|H8qc@m7vA^^f^bIylwMkFkBz-xwMCi
zVzDoe0!bF&ll)paY|eka9e!wr>koHvjYqUcJ|*RFgOcFa;SZdvcqC^8C!T|E*02vi
z$K0OW<Q$4!IkwzI+D9DEr@yrr6nDmsTd#6&`gT<4%v6mceIe;Iw~L_&j^9Wq(yb3=
z{yY%GIY7-|Wc_K(y~9V1<GSCBqOiI`KM!chR;t7xN}8i=Xks5o3ViFF9(ihccxhj^
z7I~o*iE{2+mgHU1J-BB?FY$7H6n979=fl5xcYp5tl7H~uP539!IDOm;q%wey<b|J5
z{=c?~{uEsi+{L9r>V$mE;p^a7ad{~S(&XX#;WJBrkO4OW#9;WnkX%aaNfQ5^k5V>a
zPqAMAwfxk9Fze@}vS$BDIOJ5FgpXcoA<K*70NdL!XL)#3+NzN5S0bM5+d`2wPH64*
z*}uoZa{3K%n7F|k0dpA7Vf6E0oT?gih@Gj!!zC%0o*eg#Mm~Pw^-@%6(E|1i56{qH
zNTK@diGu2BDOKzBE5T@5gS|bp=gZ8d@D`S1)sD(z)=vm!@O$>!_drAI&5NN08y$_~
zp*^l0<E6}<Mk1-%v&R+S91j{O5Urd{5)E}i>+6_3Gy4z}Zw9ZQS8y^5e5uS4{lBd6
ze+#9&{$D$a#(FWc@w$=P`I}r}bn4?8EBq|Anix;X8b~wY3c?1tf+&sjMiDHaC@ag&
z(RT_Q?}$|xs+i|Z|8@*DC-!|?qAc3mM#7%y4W>SeN<oUtV=x~hjA~+J&A7gnI|VrI
zilmtOSP7$(oS9EQ#ApeC<mp?1RJ-js-Qu1Igw4^u68#C9EDyRXG1XEcOM4Sfq@aNR
ztU<D2=9R&6OoI+xPEiX!@q0a&;5t3!+~h2_OWUQAO^o-DNJZ2KOntBmy*DF@1x;Iq
zu-Acyh1wOH!1**)E>pgu44|ZzPkwY{4Rm$|F6F*zIqn<5b~Tno*7i1ML|JWcFH3(B
z3t>^!%&^SEqa@7tl}wR7?~N_!#3VOt;T~|Oj(gkDZzbWf1C|kwVaa12ZDQyR(l(9y
z7NB9ko9brPS+jiWoax8rNdfS#MO5gEumwAP+3-yaYYIZ>P#S69^lfW)zyShJv$L)R
zfH;D9s15`nXbiZ_GpdSQd3@Br;rmMVhFX8~$+O!YsEs=EHphvWztiW*yS!Y^hI_z?
zfSKx|%i4vEwBw^#zc17NNY?WH2(91qSp-Q5U*d+U-LKH?8&RANoEGZ8jqEZhM$8=Y
zQU)G^bkHn`fkd!v#Ui4{%?j|2G}`0S+}-=4(txZm%?%zl5>}W3MgTpI97Pl`?)n<#
zQUh%uB%%r|H{la-^Qeyvt<?kzBFO(6H$dX@-JU+)9(B~Ji-7X;(G?%n(ooI04Yd(Q
zba!G8QBER%4x&;Y26#CSPx<GpPmKx_7BCA8{V;))*E(Ep5#yk>Egu!CYMr=g!AIa~
zI{AfSm}=J!Dj0bTm4+gO%pXe8Dq|#(dx%k}=}f!Hr5a-U`>X{n5&Ct~J{Faa{;R(5
zFS#C0A3RP^x%45n;vcm6pZBm4g@R_hxufNHy?U;?-y6J^r^y<(6CX7l{w8U2>_e?+
z82-@D@DEUo*v)Ek6Zs=Al<Nlpe2;D7Hw(#c!~`J(RC9iVZjJvVtgX4bB}jE`tPhoG
z?9Xkixc43}EehFax_Af$wJ0l-MQsq4Al7si*_v42HwU+m(glCHQN_LX%AeVLCiFY|
zK{gbdFADyCN2!*WEABsWwYc?#`?3S=dE&7D=reqbydplAI}Rt}<AY&@cSWE^m(jS4
z2sa$G$B6kM?hzOE9v;-$*93JZ^U2iOtC&dumn`iYYWDhI&FwC|1sK{FMS9BdQQ3Dz
zrVbUpb4F2P{~imZqhHmbf}*TQSCgq756^G@m3yP5Z(%HCqs0qtTS1voq_3%m<x{)J
z=ts2}pMRRx+%r*TUqOKp!}}P?Qr@f0ISg|!JA>(Pz55Ec5ku#hi26l3oNUp@5SFPG
zD-?f-X;iOhJ=G*)S3wZ5kdW{=!MQLtPu?8YSRg0p>mHTU+A9PyV>*E!)L<jP^giM!
z|9dWZb@CKEzFhj%9f`=A2(^S}W{q<V2&eAzJ5Ix!@Q1c6rzu>zLO=Iii2y1&>A*hi
z$PhO?nIGdKeri+{5SFP&WvSoakgt+(;WXzqp8p>EPkRYD)Yd8@6IDAT_EMEGr0^jO
zFw_z!Te>fYW`5hhbC156B)%th=GRF#<>-3GMjRBb#R&{fYxUlAt0Zxkn_a~dSb)6_
zF=me}v!ZMKVR+W^i+K5Wj>Xy!d?h{%GBdNjKtJ(t8t$azTj(PqAE;t!RsiJ`3S3wu
z4Z%^`W_>XlJRPHeu<_!bD)vltF4*^t9)^?7Ph$K&0PAF&6a?6uKXEEG{6`Lc=rBT!
zw!6sZ0AR9gCD)PUAFN%E->maupJi}mAnz*9OREW<=f2$ockur`85&WzhnU_^<|+es
zS?KsORjg2iD?F4EpuXN(au;#X9D*|OuRQx1kK8mbE0HbSAS_IC@9+s<I}nC#X2hUu
zV_q_4i<-+KX3jq1-afSYxar$SEm@ZcqJQ~o9BFG=R><<0RO6mZZ+Y>FoNAex86Fu<
ze@z}EtMrYk5s$sk!ay;2bJMc{+$9m$f}lb4OvTig0`(F`4Y`wuFF>PBHZ?F2)D9Nb
zW<2sAy9HbV(%H{`$b48cC%;20fm<>=JAzYB|H~M6K4dUjiE$R<o7EYNIKED<<jQ{m
z{16+N&zd})(io7>YWuef;=B5xg8^f#3`X6*4@<l+GPV0FP&K0nB>#_B6vl1>To+wB
zRhQV&Uv}!zFPxdi&HzrZ^D2V@L5-Gue`jkEbA4^gbpLxU#5r98jK4XLLGZ+28a^<x
zS&K&n2Q``*6H<oP?66eosC&v!kUbtM*gtoJ{+!5<I);Q9cl}_FlXC$FjiNq)ReT@v
zet(5n&V?=>j)$Ykm1GklyR<7KoK@R5n%#tn@SwNy&T%CVM3$>P0T@$@Im=D&N?96Q
zXQ`@;QAPF!g4{_V&7XGgKy+1bVo|J+1&-L6kn*NFU{BD9eWP(|kzA$_n|CcW7BUbg
z{J4SJrl80C-wrqQ>;J$z3qwu3|0d@9-))|5Dkd4D%uv3>3q<!58eC<U4?m2D1P@zA
zdDFAWNC4*c^)db+e@Z$d#OY(m2UsaUa>;M{M*SroVJSqCRgBp<>+mR{*ujArsLhSp
zH~wgOzWj#Bmyg~&`v^N{E@Hbf21Tt|YHn%~&G<8aF0-!!h}tQOT5dQqG&evrdnAr6
zS8rBtZ14u#(a$K3#uPOS$0~9yLsBU<shIhXfBPJDR5XiO+P|^gmBDZ$vh6CEBV_6K
zdq1VV)86zY(u5R#<&0F`1aJun=w7_vm)K1cMS6{DIf2iej8Hc!Lo7Jqb~=9sDW{@N
z$;lb)h?<!{O8Be`0@~Ftaowm)gwQ{Cw)Ng(K-FXG-svG;&XX5D7o=<Kum~Ee6j&AT
z*nZJ$jyr2bLFkYw827-NT#}-*6x?BinFAQm+ve*F2j*;&982Ju0R#=<F~FIflHvE6
zyE%yuW9Q647$9=h{t>>nNwr$=QzfH7cz=^fA#3p5Mz|TB-p82t8WdW8uXScCE#3&N
zE>WUw!HH-q#$Df;yPSx|^Of3;s+E8m=~KqGzifzMn9$8y+S=g2I!Ork(l#F*d!>zJ
zW%_=AQ08D`k8weOs7^R&D82T~Jb2cKNzUGH_a`Z1;_>n>S%CP3j}91|C`K{nyT1B%
z7BqTAOrMH`zy_gkdOK&eYk11V*g}{_LZ%uonjvF~{3=}#*kWyk5(Me@l8HC97Fd<F
ztup>BP7aet^c<jC0FKnxw#KB|Wp=~B-`DH4jAh@48i29nGNhS%N_{xJz&Jgu&_J~5
z<=^<63GhtUCKLmnfyY~6@7r6$9GQ31mN$ltTPFOyk8U{F+MC(iXaz+q!O7b7FT^-K
zUD5Jh_RD&!q_(z^M-)&bkisK0Ub|XqvToDCVs<|lzYkpiSAg2p1V>Ci#sDmX{+Yb0
ze0Fn6%AF3skBJcu<^sK>QvazYC_lMMFjTqz#fZN-q>y?aW-(r&9@@Z6Grl!0V(wp7
z`taLFPPe#x6(A&IB6h2rk&I~|jExg7^)dEcwYnGifibzx1%~oR02n$B{7CLc7L-lZ
znW5&47t!YaXFbF@EyP~+FH{bbP&dQ62;nha`K<hZhqX;a(O~5d+@Ff{sK=*%MkqBP
zLc!I{9IGBrm%@r@*Zv*tK4(O*Pp4?en~_5P9fk80>CXoO#sDX&`ge#WDVfDA0GGSw
zvo`<TDO<@5<R%1=xcq4PzbOnBRwQAs6?PoIyGf4e&}6Os`z9-Mto+*63^5p)Dds{-
zL%Cb9udR)(Ar1eDtV^QhgL38~g<UZ1=#lk}Rx*spB4aJk4uNB~D8%W100t1h1r%9U
z>d^FkyAR2)VqNR(R=1;1jLOIVgoph)`<pBpQ4)o~y2cXRd2XZrQ3Iuzyo0jmBXC5@
z0|1C!j`P4aG1b%`ZzXMnVEZHrbMt;Y4|Bu!HC2@w6mm(8d|me2pGgX*_yhm<<ir|4
zO5c&&ks4O+=J=A;I_XZ&zcuDQAG?`AK8eDYt%dy^LN5!QT83M~tYAt`rra_3;qG}a
zi(6nS<EN<M0A8aDH#(OfN#qXl+#@(N0$jDgjiRv@r!P-pOgTZr8C*uD6Wxfc45ln<
z!chyQ4q=Q{p~?zNMgNVMg&7Qels)SDY^QO{IP&d`paZABSmje3fWWNC@A%rTY>7OY
z4KwGCqj&^>;ZurqcPk&jF3JSM<lYxaDE-T5AT*;Wr!TebkI1f8KA9MvQ7a*(pN#K;
zC@{3J{D}&e(S3^aR+(B#R#EDie~%?4gYi|S7V{`y+x<(e2BH5XH_L@X0;HwO;UOgy
zI2V~8?Z8jyAGc->bkx56r|Eb4xSbl{bd|cl4Wj3F;|VKs1d<A>A|2xY4<s(OlY?L;
z%rIUyB<C-+d_+DJ#7bI4Eq*gjW@9^>JpFG|-dz7Pn^J75WZf|3j+7`iL2g);l}0y2
z?A!>Jmk=owC&{}<B9t*8=U6~m`sB>a-N**to_L=R_QWa_`f3EQv`ZNS1mzW(&uCrn
zb+19U4r6?xQ%jozC*kNbbrL+d2+gX7IZniGN8x3Gd?s*%-u|YljVtrhmD;H&mQ_tu
ztV3#v_`vV2!aT9Mqxb2HpkSIGWbup~kl5ULs~S~#vgyYkHe)m(%m{Xfgcg4#5@q<w
zK!}IB`}6T+evKm3>q0Pn2Am|MEarQIZYmI9aE~9FabBy(6T9#5-)X`Ah;)ywcCa}5
zG)cXnnX6^nu82QI60I~6#km@Yt*N;ElQ-NcnG>AYu^6?ir=3w3{VfJu<1Z!w01(ig
z&O=k&4$XTA;>gc8p;|oc$_;-3d{=(15C0#sbF*U0)U4nbZYz2De`)>rkG%lO1i&#e
zA~w&0xX6xs@ZKG_19B(FWKRElh4I6?8nfMNkBF&Ogm>Xok-6nS?(ow`BKAzEPT&Id
ztssSdmEX5hEn@0ZfOk9sADnZs^dts?!iJ6H5u=b=i(9}OCxOq|uT2A!Oegvld7XSL
zm?6qNXy~iuz6`haZH)6Bw6HSbh?<AzT`qlZ(@9}%Zv5o&Rw?_N8og8ePe@F9;dA&W
z%bU?7+9i%n-)?gHkL6^K>X|odf^B{-fn(V%N4eh5&E0Nm&<~`*geFZ<8)ZYRBFTB~
zspKpm*`hu1$?ZhS=P!w2exp1;3l}dd|3kBC@sR1IBJKLGu6_Uu%8|4CnHPTO0^~{>
zn;P8G+)hW=L^z{FydaQ(@Wj4EAm?F%Y%X=~+G_4wpBSFz5uCClMxE69yniQ>Zb~fG
z6#?Sw6*$nZpCgb+m?EQ5?E6u{^eJ888a=r}akT(KqtKX0?96&Ec>-8<@d6Ikm6Hg}
z@PsK)z8SOxJ;2np%4I`P+U8nSt#Xf`Csjv~%;>a34IIXzg_QHP<5lI!94xXBsUwwW
zu!c(5uAThatHTq_W*Kh=4S>rb23EjV+`p)+FuFNnWSzBxOthdmOxmGVMizVza8jF5
zoYh#jzxMQ)UofY*@pX*Zg|O>3)!w`c@Dreg+r{w>6&vOt3z&V>2ZPC-QYpjhV;*sT
zqx<<u`{YeacV)FSH2En}A6aJCe}khW+}44xlkSf6F)8;IATQ%a_a{nN*s`_qxhIbt
zTb7;BM?x)9Q5#mNMY5&M=MWN{Gn4XrpJe{UB5xw)iY6Yy?qQH4&EK*)XJR{z0j)<?
zGRWk{OuH9crB3drJY46AjN>CBEXs<z?3cOgcbt^e<akr5$I(?TzMXybKU^#Se~_yA
zFVaL2P<o5K9~ML}e!Lxz>snyG73>C{dE5pR^O$T2<lW<8R^tUD7$84;-9-aAXt8Ox
z{_B|~Ga?h2Di?8h))d$7HCC-2Jqg#qZpi{>J%mAOaI){$3mWX{tbGJt+)IrT1`;TS
z@t{#%fU17sB+wO+j>uY}?!|iIH0lulJn-1({_6|kkj3HxZRZ|WpC@SMXH)R_g}1U1
zf;{^}*IT8*U5BE5IcE+<GxyoA%mP><sPY0Rnl*%e&<0o}9}iAXja-=0(9@FychT_~
zoxOH}$F>L9@|Zpc9S$Y6r#~lf<Jo_i>d1d)BA%;tWsT_k=10zE94F#A1i;sGuq8ba
zAVzTjV>GppF3rZjArkxk&Xn$LIx}~P2u0D#Ofs|~o8-4hLln*K@BBGXaZ>#%iE>oy
z*nklcR&rf)TzGtV!TI~{cp)P`1Ao#&#3Segtpr6Yl12FKG?V34!?~bgdGp2f7*hAA
zL%H@Nzl%*+sS1GQN*x}+959g$1j+YVor}R-cse@(REx^@HoN6V+tKTRRi7`tn>>h=
zwj?=FO2q9ch|O43fC>HGV*waV5d#Y45D*?8-%+Fls*7u~$B4nGs3h^dL%W>yG%bU{
z26aU9Bc+wMzcXKx8<F-z5hL`~-ZT_Vv~I34Di{coP88}x=2w7=yco3bw_87qo0SGq
z>57lYgi;rFfr?rqeZ_d-$G8RaPLfbK!W@%7qJ5*xAJtqNLEtVJhelG0)EOU-X)uq7
z*sfnW^Zh%Ui7drzy;|+Ea(V*{13iE?JL|E!k(Q}qoii0k1RJOOJv9FYy-$bo(HXr`
zytIip1|X*LFO<60=NgnaN9??U%I6t2D;>y*#aaZOE8EUwBAbHW;Lmzg&Qd@|5}<5G
z9<Mt$z;Q~2yBJ7*GqCs=bp)~tBMZ|77NAVRDUQbFFI>sOqPzMF%2tLj0O>=UN`O$#
z1E09){qt5Jdx}?2Az4I$>2c0qx$3Gt$Z0b;KN_bFwKE%UWPE=4lRo$y2a0XDqjet{
zrJk6b1B}T026y@oNjN~@0IWF5k@*M4T3kd_L+cmLZ@&Pc;&1+A`M+m%@;}r6{;AY*
zU@Ez#i!3g*#vf-Z+#TOJolk^us6rz$g2csqo{PM4sI?uU>waqZ29EhK8Et`fTt9qh
zd6}FCWNQII0u_I+rY7|&j`fWysU>Zu@vXb8_&I_JY5rrix~7%9+N^DR`_OT?P1ik+
zI}ZVyJBq)OjQn-NqUj0bXK{=Qf!)4ACOYpx7=qPx2->qF=djLYdhEA9Uo8R@$^Yle
z7XL-C@E@)GclC3?w0V3{DMcx$TB{u-!D`Tg|HO4}^}W0m2z?W^<Sg<{BH9uH>S(5w
zvPvU#>9bz!u>x-7($fKIn&LK?a>gqQY7BaT;*K)41X(hDyOy}VR=J<1H*CqgPZt>v
zsN1P0_bxEAvn-il`pNAkuFnY^RBe;9NX-b3G@yS3%Hhv5BgIV$O<)re%2BK(GEfwo
zz)Ya6uqvBRIoHP;b66s}6b>YatU1{$I#2_8-yG2TCtu$j$(A`A!bZS{S;vUJbBX|B
z|4;84s&?(+yAt<*?Hi&sMVZq3IW*#G{-!EGL?4Gu?Z{E(w1XCM@b4Ni{FbG^R7N<i
zw4rN!an>rR_E3uTU|`JZlpzD3Br6g2zSEt3<m?Wey<1u<_bF+972N!owc+}f{@4(t
zxU7`fSIS(3UeMawVb{YnYFSD1&j?EngMnpX<GSN(XV}h@o+<_HWg|7E)8Z>zgW!%H
zTlQEYN*mo=rh|y0?ZeRZ5$c>~z;7KkzChFyHkn_O)X93Pr#U^7Y6eRe>rkMOsoti?
zq4o{UBr)x+#+d#MSGEh#FoM}QunA_2B*|IB2fN)6K))whD~QbsEQ`G#{%CC&3ylJa
zP?}efdEr8o=G|wyskr@|$|#k*1*Eto+iyIIS*)WTU=$OrEP!1Lb94MuRgt;D^DnIZ
zFDgEmD+?r5X>ZljnIAwdCyU*%G7uW2Z5%pxy(_<JV6fXNa*Lb?6#`y<*RzA<LyTAX
zkGgMOA;#p2sBW|BPtRGQx0mrCReYdI9<llHgd>GLrVLGqApL1b$1J@fBQ2=VXfQg}
zA%uPwKm6Kq=QP8q20{WihS~wU8rHJhtG12U^jrAE;Ak+(9x=g)>9BXMDa$^DvftTU
zs_%J)d0bke-Ov_AC=;*Tc_xFBnN#hVtr{8ZmPVpPmd2se7ob9vBJFC<6$Y`xZ&1t)
z^o8OL<@E(}AO8jo<ksq-vx5}55^+G~>i~%8w&Gx;<zqvtiohRPR|obYRkQHqbNb!h
zS5uJGq4OWaTjDGwUR)jVRTu4+OA=Apc6L!tDe8JReI>KU9hEdt&VFmIn$dS9lbDDi
z!rupmfD_faDo@$lM@0(lAS_|m3jp&<t!04rQVW|yQmTCP5dQS+o5UhwYw&xwDn?iu
zd(hGqnl{*djcM0}Y8@mK)$@*EcD#N)sk{xxp4MR(YwjQ9H$_Dq8frCeK5;e8&?d^J
zv82`8Bf|vOpdDsI@t*%I1v&<tKoMCp9=6~e;2{*bB&M;04od4ORmwP8)mG}QYPc9L
z-xf=XR4r`o@6B`!fLO8>`W6%a-uD)JCd4QWJ!dD;(Tw@IGcIDyD0Q3|$k)Y1&E)QD
znB%@Lsg9UOUxH6s#@I03`3Ikl_WkfxleR<`ca?|_|DdQ>0Yyl!=xHwE%P)Q+P6)&Q
zY=n<ClSfq|0yyr?L;gsf%d{JEx%!DpC94_B25_UwJ?k9j`=Kh<R-4VlVdK(YV<OV%
z5oO*%#EHK*zR?_h=3cpN{Qu`Gv#nNFgvYjDfJi|$a%w?kUGImZ^}s!sq2rAa)9Q0!
zdh{`{xQ4$DOSNI*KCsY9wAw79np29|eG?e{_6J0MaW?QQ9i~av>c=JR8w$Vu!Tag<
z-s3B$bdQ|nz4_@p`Gd@UF~?)MIs4CFb`9uPt`xld8}A>&zdPMCKPq~lLFu=`6+L~W
zKMK$P@#{CCKTdY-Rx}nsRGCBeH4qRIi>Lt}Ferckj~*_nTVN;L2W6uV+VR^7+U?zK
zh^Ugzza7bw9#_DJ=i%Se$a_WrKXd_CU&1C-UT~6jlDOpqKd>~+81|anKY4m`&tyB!
z|7&qf@U2K;KKKZOy?^o$t{^s(OugZJVcLvbmsLHca5>7zMCWOgjm~vBkihj17Mp$v
z(`2gzQdRqbxrLb4T4^^Uh9XIDOQAe!y~;i=(|f@7!BbJ@xqz>tzFP;ElNH)bd(Wq2
zUudC?OyPoK@jTdl(L8nnb_XMm*A+1QetDyzXWU+7D4}Eg4Q2SXP(V%jHCBZHbr5oM
z>3^MN^XfDuz{&0N;*@Lu=8gbeD1sSC!CaDrB=c<icK$#v86$E%ZkWYq^>pjT9X-FZ
z_i0j-Pa-<ysCRl}_*M4EN#s)FV;lARw|O7!Y+X8zcz~Z#`jK+K)iucG?o!_%zHkkd
zAU9d%)r;8HXY@A)cd8r89Lcpxxj%O>09=b{En8QpKAFAdWj_9PaP)aj5Wdh;4czKl
zcqtrQB&vL&;Bn>Ptke(U3y_|1ThWE0o<~LTkjxRNnI2VC>-`I-lw+Bo_Hv25*LCxt
zOW9ppbZIao=R@Et`o<zffUXTmW^_FYP3w8E;`!j(dkwuj7j5GU8=hHmcVuuBT<EFI
zO9ckev!+kTb*eUg5BUAPl26pcL&jX^-CX;JUkgvX7FE7lAPW{$zEmcABv)#t{~mQ@
z>~+)WReYg=TFrUok!+j=4l01oSrYc)jm%t?USj6@+THUwqXx(zvImnX-{zv<QsQQa
zQ*XI?Gmt5R6Q)D*o{X&wAFY$L*(|`F!wn>CT|tvI{-ka#ImB2!yDqqOWh#jkoj?e@
z3L`+Z&ILAPP?^KtcVMI4<AJLb%W-cTI<<pOzgr9rL04WnBJqw|e-#aw(eX9U%N3>w
z$t-s`-_ot?(Ia$Uy}GE~k8s8#x{XQ^AyNo--^HJ&G?o0GE<ABm;h9F*=2hadz$LJt
zSic#|a<a~nNohdu6+d)VBnuvh2+EwT2a5-hIkIzrag8Xuqo&><T=_@p{j?x21DiX8
z_gM*14%N^34*KSPysThjy7?u9cFt{(2lT8>+@e0O?>=4h8TK&ymm#k^!&`D~h_Zo>
zjEWTM=v>$72OFOY#~=q<Z9udqZOp6}W1XnL?}{DgyL|gz$>`;gGxuHCD1!78K9KO?
z_f)kha>-{7i1DxWf6DaA68$n*<vh3}bxK;>TR;*&;#PJpL{r)R=~{QwTz^I2_o_SL
z;48{{N$<`63LBi&?LcKmvwDAUY&~yXVCUw2Ci)uU<hRQTBg!_?Zm(E_dNU-O?B{*v
zbWw&&^+dL-pZkQyVYq?2qo2gRN6O=C@j_>YE1$jSMz>QDC3$@>k0S2BhbPZIx!=lo
zbn|J{GmRqg_eEWvMW+$tx%81-?E9flhZYoUJ_|eCNhJu}uWuBnpUU|bF79>9WL!pB
zJL$F6=G~_~zs>c(2=#UN(R7+TBWFw~vUqq**7DUKZ}r+~d{l&`j?_|$hngG0DrW}a
z?$=)YEG^IbXdAoCV|yWE^qS|OUVHp30V+84w3!EVF8TGDAJLQj^K7}oiRz3OM-jSj
z0gWYxKaCps<tph7XK+UmZY_OvysSN2HwR91!lhUaKVBb2T$r@)VUr9XDg7a8so7Gq
z{Y5L*Kbq7p=p|*D>5SBbzg+Y3uw><^x$>9w3t|kI!+l=ge%U>^^~gK?<Yb^uTj~A@
zQ6{_c!SnYD?XKym#~81B1gd=2JhmP8zYf1!9qD%q=9rT1cr7&Pk>TCQ6ZTY5Wrf-L
zO;1Osoq<&lFXT~S@O$LvIV=zKaPwKjD~xVc!Bgx>wY<-BFGHgJd9>}33W}Ep75e+L
z)B(X|SrvTtco|IY_g?#Lj%eKOsN}vh`R2W|<D_%RObk!7!k1d3zYGOTcm7;<dLkLe
z-xq#*aTZ5AB2^XRv*_;c9QZxNzPicu*7ix=_8m9B_K#YMHrg>aGPH7M%B1_Z)8JtD
z?ILXM$ItFPGb#6vxx!r$=>q67agoRpdKGDp{-DQTv8bwcBaej7RSCT(I(-&uj~xs}
zXCh_FU&`NIGMfA|A(5zLa5wV%;*Ln1W4d>jQ5)FL*ULo9B=UrEnaR_hoSA-$($1uG
zD^<0am*<7<1)0Eta~cqD>Zu0EW4ZPMCy8&4lxf7CboZitw0KU)%%!v5P>&qMz;z%^
zDngeu7FGEDva2LUg)c&mOm{MCZbOo5i^qEx?mQ=4G=|Gvu}Z#6R6T^G_O%O`4y7O}
z#jvIMr|^AsE7rPo6v&^;2`di9S@96Sg@Tubp}u=l+*s-d83xY@h`wQVpAE=XXUl%)
z=#=G@tw8P-(yfAHRRTe9+ss#Hc;sjIBdg?xUdiv_W^;8as#;~T8CWH?g7lAim%uG4
zh|tED0ge4l$TPWDV^e$CE{%I4-^$AY2Tmg>2P73n79B*+8o4L*9W9q!R#6x5IEz-M
zXKDu;CWI44b^?}OR1V&r5!d)=w^Zu^6GN&7dwqQhOvzX;_-o<qsHzWL+G6<0U^@OC
zQ!eZhU@^p!N_01N`h%xf)AZMiHSuUB!{eo;Y1j1?HOb4HcUx)}^d95qlx8*j6Po4*
zfY9v={@%CV)Zm*Xs;pJe73k|1;b4C)#?#B>s=;$Jasm^P8)4e^kfzlCw8wBE+GVkP
zupqW`RVh4&{CVzSuS*}xMU)*Q?n>uXr?3V8(hrPMz1(Q)!dy8PGgp!3mbI1K+yA?g
zd-C0W)kEZ^zH$I!5?(5lbqo((0xU+OXW@F-NbEJ^#Yr($J^ekjeem1!9@gVF?#j!D
zTfQMP7qrtI`qBB@@+Og8&)eWP!X_=+l-aM2(7PJ4eh%May6QkUc(hKz3$U5#kuhK{
zzq5M#IZ>_PvjFrd_0V_O3^FVosSpuWJu#lJHwr+WQbU|2?j{bM8#E{%BdJIh*OA*t
zbq;$FbY*IaXN53()r3~iw{hqc^G33F?rM>wkl*ux4EoFcn6DgrGSUmiUxY+e{W_<8
z^@tvdI$=gT==@8+=4n-JL)`HYb<zEv0B9DGl4@{y_s3Ng5d^VQs@m>)Goxr>N8@)g
zV8s@N+H`rmo2E_RzU!A`)}(*i3<r=?**uky^MiO#OE2k#nH5Gwjsq|;o-Q0!n14^e
z$c;ZGHldE@!g9gXec}oqm#zhbo3m=(*s6=ErDTH>64s8YxAq$+eEfaTG9xO7GG>vB
zNvqu(`NU)tq;HcG9a!C!b`*U~I>6S#bLkgz&Q$IKQz<m>DDXVGQ>{9V#&z}CWm`-K
zo1=DN32W=W8qZI{J}XpT7>CIf!33*5ctuX=Z`Lf_dJOT2xqcbSx2F6I{aK;^V^#y;
z$QR1G0tJ=T3aHZbk*15`U=VoiEnr};`aLd~47uxryVkBg@wIUI1++9t5txxU{svor
z>r8>?gOkkZT#DWO01dU09O!!Q_Q%vA^{Y0t@`P2}ZrGEqzutaVUpbX`snvrj`X+h1
z+9(KN@~n0(|JLZY60gYG?7mamyGPvSF!7LA_<NetlqSu+NR!C#3;Q0Vq)vDbs~jKE
z5Xwu*&i~O5=9mioJV9seota2lkgr2RQ^Y&<Z9_8vwY{3gq?bx^87c}p!RmR1M{^J5
zf`#u~1o@kKJvS!j#8_0rAis>6$f6dsg|+_*dm6EuxAi(<HT8L0OiXdQiT*fX=n20o
zL6Yy{BTq|VO!;q*s+UcM1{EDcxa%+a=B$NKqs!j#x;er;L3iJ;E3Bt`*p^+cgV~Y4
z&P{rZLyrwUC6J&g6(ybk@c;;3*AYbi95GE>q|??!>uy_3{X#hXB6#(!)Q|pG)rW%+
z<&MXRo7sZ&&Oa_IFdhJkJ=V4<m9vQVpY1pLp2I%SFuhiHYqZr(vB6(NGk%4=inXd{
z2?@-wl`gX<6wu0^YTp&xnxu2<U6r1`GaXUdSSae*(6$Y;1rwi}j(xJFgCid^FM6KF
zcjI#@V)udLGfxOAJ&Yo;rWL!4s)<jwtD*pI!(_CLyHL_m;_uN}>bk%%Tf@Q6)VJFK
z!tttFJ_ug^Hug*|Becw#R1C;8_4eXNqeobo@zw#&=g7>@y6jz#GlPnwVrI5`NFVwp
zQXiCHyyxMlz|?bYRz1u{h|`}G<GZC>$=3)R?Hga7O;aj~VmMWIf)Kb3WE&tO$g;__
z=t$ObKw<cNQ^zR;O`pD`l(RG&BB4{IsjCA}W&t&D*OroU8T@Ow2Rw*uera$vf0kH8
z{vo%PWoFoWt2MkOMS97<rQ`}=oa^5`U{F!*xv|R)`cifd9Im#SgS~y{$MkvThmO&2
zo?7FMznkVce2)w=AFN6EQH?<B&9Gp@%-_;9wEybx=CJ9s<n50}-iYs`XRKnSO`I|<
zj3l)|F3r)T{3E#&w&nh=p%Q-YG_(_S!BL^N?F;nW%E~G_d+$o<tN{p;VtQ<iY=B6d
zy;@+R+*MQW84gA^0P>s#<QV|Slkkij5#HJG$l#4AJ*Bt)*mdDBdDsu%V5gYL#Sq63
z#7orFu?KUa5h?+{O(k`xH@H?wj0-Rh2KYVRNUszdkD-9nbYP=LXUDw<w}mEGLe%$#
ze%NVWJ{S=4n38_)H((B~+&QFdpZ_sYASoas9)7S~vOK*j+~etT|5DU^b4m1{%St=e
zOBWFX?%Jjz*6m`se;qI_0KJn}2e-6K0Xh6%+6`{sG*?u|f4$riz8f&<xS;(wrKo=V
z!QHbuC;{|s^kS3Xmc2SmrX5E+Jo{&|s}f4&c;;u?u;yI<h3|dUO%1n`-@i6{+n}O)
zsSN%*>sl&zQE_xyOEixe;pM_yj3AF(b{&<z07-_AR{6Dgr>B)1#dlAA#Lp%r$UUvP
zEPh=6;)!?%TahAF@ognCn73+pVeia$Gsc*TLA|T_<=MX1P((@ro7Nk}7u~#P?AF$+
z>q+_a41yWIKUGu=zlHBEc(!CM4#W-pz9{H=;h<VLL<ZH1bq93x_L*tgD*32_*8>y#
zRnEHS=Wx9rckCPv^Rz5kSbKJLGaN+E3H`l#*VD-r_SocpmuOVRi{uG-9R+(n@_v@v
zh1BeuRQ?G;O~ecHj-==c(e=}_zfG#){Pkb&T%6hr$cspmzHAkF=V8I;Ks5n1SM!`^
z>w8{^{WCf4$JCL2sNSJV9^c*JqvunzpW`Hsh{KiTg1*e@C){sPmGW7Gbwq3b(jLW$
zBYy425mK{6-!yeAg15<dI)IS<O${Y}$?xBrc|Uu!U1sn~+(RG?vd$JUMV(Wezt(a!
zldy+%Y-*rlT;Swi2wu`2S*i19&?ZIC`$81T<<9j)%1&A4&=auojyLOIOEK5jj(%hY
zv?cyg%7wAYh{IFqjY7D&IMWv!x19{ewRv}eU*QYk=Y<|?=Fxg0e|aWi5)mhNxN8pk
z?yih-dlzrBp6I7_C3SpvM9bBgGqL`AV)*TJMZYu@`t1po9J?ERep$2S3C;0{-<#$q
z0iBMYBMUpTBCfBIKXELiSw;0+nF_!d4PO19zyD$qC;}E)cPAYtwfcPTDva`M9^ME<
zTte$|6!|fuN>KAum}<bOfHMKA0ek@h%Ti7RPi2!=O8w_>2ea3L$6t>==!G8G+17F*
zhGhQ6QGc(`(AhA_0RAb%wZg&uPS~Pq^H=?ZYXOQ=XQNhu%SUQMO7-Qkf^4~RdM_ZI
zHc&CTqaJbk`J3}vIK%m@Oo?kVwMH#!2~ikc{gu0%fvmGN!u<C+99rTjKG!i<*Kac+
z5Vk)+c^uZ%Xq1h4^)bZ)M-Z6%kyJ32ZV;{sTkKxTA3MRd#eAwVE<3?CQr}v4{)R_P
zMYR8?P=i-`(9^Ks)iloK3|?mhR)O;P4yPX_fB2mO#VKUMsv(ZXE3v;>$me|WiRNFf
z^A7oaW_SyT2sNCnldpJAdNOe8ccJCIlVDtU0{_NXj~59Nm&(WglD$EK#pT-29Z&iO
z^DP@piinO6!*eO^Aui^0dUj!RG@|clKmbZhwRQv|Hgz*vBV`Cbp-vFLA8I(cQMijf
zM)tvqXLhH9aavl76uYX<veY(*n_G6Mfj158qPvlBUob&{sVN$EeA#+Zs>Pj7O9<|7
z#ih;fI?QNqlqE+u`?^3S#7oxd6*g<Ec2fN|_n`D&!+xwd=CFnYLP~WTrF?WaGd*9k
zWISg`*OhGZ6N!A&v$oy*vU{PAFj8norr-y47X^=|-WK0r2rqYkrleF_eOHNTTNX|_
z>+5Jz>^ih4c|Qt+=sP``Bsl=``=-4y5I*swQevqHs&flQ8N}=L3uUjBLoheR>@N96
zgUzX@ovKcQZ!%Z~msi4X!Gmx4C}+p3&4fi6dc(%>thF2j+u&9v@5J$v;2Yo7nLfvf
z!IrnS4aC_O$f=YQZ=%dvRJg|Sp#4-xvpF{CPQ93b<UunHqb|{Vgldg2E1R8_51)NS
z(ndZt-V%M;sQan*?b0MBs|OZG8I%xtYURa0R~khYD~R9JZaFY?GyMRJIvpAy<h9nL
zMLAndIl6-YmTVb|S%=XIc~y1H%fwHO@8u7M|0#cxT9~rnHd$)9ZCeC+i~DpxGsWm4
zsC629n^|Fz@UmGZp;44Ov|b*=1fb?}F63GbHj;FE>l(-dRyXZ-EI@N=W9GuG#Bbu7
z<++ulzk=?9m!n0)6wBqNHmDo%GnTxgJc&R;imav;IQo}nN&t!pm}|9aQf)oo@`GD=
zYN2puuP^C}&1T1EFax|aCe$SOkmN-gvCPB{nE4eXs3(F09EL9g4?aIMqyTf4C*lq+
zKRZWfYs03u#ypsp#hMcJXpW{^<EKA<W)-wC!)G<6t6>vn8)sX@_S<%EM*+=Ej|$}G
z>lXzr@n!u|vYBwiO(dnQIWXCGeXY5$RA2fvf@?qyD-z<hcQ49uz5LeJL73WMS}uFO
zrtGl#34S&C%Z&nI9kD@%IM4gTnc<TjwGYIaE)5)=J>Wa2?DLC_EcT2q&0d*Y)VFbc
z7ExSo(x|xX%-1H%he&OU*39m@DV9Ze)^=9U{rBJR^3%SA_z7X-K1s^SdAw13E|2gg
z3QyFPs9uXO4|~cqeY3OH5pKdWgeizhOc}|(ZIKdnAg0`aBHFBU+19Oj>S1K*sAl%=
z`Be5%`sTBO22o;#EO*o`Tk1>=u+@QFLlNiotKSgM2DbZ-xLF+&f7~yNwIb~FElutV
zyK6pp^A!1vTdD_Cf3O(kH5weU-0XFG+c&o|bDkYgcwa2b`+!)iOhUNNTTyI0WO1I&
zRDbe4lqF2m2%8q}$~0IUlBr-91;xPC2m(AY$1&_rN9fOUz4+yD#GZzDy)?g*Z|Xm~
zoEc5eX;6_WRpi<eCMCH|BJG~TtB>_>cLcg$o%dWRmQL>9p$WnzPBdUWVjkUwWyWHY
zS?pvlW0~2Vo}jiq;Fl^zG|$xK(AkdH80SKBjgS;(7U(;3H1?2fdwar>jA?b=ushz{
z50T(`_UWz6fz=qw8cU*vHO{kJev8So!^<ABX@+(8(A%GZzXUDCAR7?w)Zkm1<1PGp
z{i>Iwb=wn#F0NI2Y%`V7Cn3MF>s-Y0QrW$9wr%z9w}Xp@uaqD_hU>h+r48w1L2rU{
z{bPC1(pqec*PC#ysYBt|Z5{N-VAL`pR^wQ0xJ7l-=9Y1(&r^By@@QQ9fwhAq9rtI?
zpaPNtqYq~5jOed`6t9{Y&7)@O9FBBkb`b;B8`W+z6_7O0uUE2}0C)KM@b97rR;A~u
z9pZzYB{8YzI-JL91+#{1SV_;oG=nY7)8G|Ht74h-V77kIPWY_0w9n10_yY-vug72A
zBb~Ps-qrWs{;W-tI}+{48?E~;tuCc5ev`C6{d5Un;tec~oSD~0u&>T{YH5UBFzBrt
zLEDlJMhb*ob6%Il<;G&ERb2x;LFBZ~m?Kv~E_dp67O1$h>vuDfR-6;mys-PSZi#B`
z1hicJ>YXzfRBh6w7B&i-Yu5s?&$P^cc7|E@`R!aRf;|U4O~+HRwnfi9#$M12C{6J{
zqAq?eIl))sN%uLh#4=W<ANuREaRVrkpU>UA5M|ZHl*psbW$a{?Yum6aU82&;1+LfB
zH1UgMxrc$&B}+|tsb%$;u+zz;{=$O(70^?Ls&4yV)XOi=7b$S{11{n5&r{s9BKjC~
z@p28HWOGeq|8BGQQHR`OepbfvjVkNlD^nXxbRXzM)!0VuQ3TY-ak6k=b@9%bBkD*o
z%8>+Gewr$_fKI~~4#d?Sa{fW~kHhoDj54;{V?l+z-RWaHpu1g!s`Uy5U&NDi$G)|R
zGdVL=*L-BCV@Bb)kzeyt*arO)kRXx9>6@=Uel3r;dUbxL{Hgqf8@99(_DabZRBE8!
zwK~%5jo$MG!U4%kyncIAehv9rBAHxkr|bUyS%Yc4#E;+}mGn4@fMnpP2wwfquHDxa
z)*d?-q36>_FW8u1zC<1m@blirCnQ2<y5X*&&`F+_iwhg^3oQNT5j5%KNYV|_j40^h
z+)4S}RLS_)BW!1KG`yZ55SX}ez97V4MNz%aFzbe>g~-(ATHV0v+i!{`w|AXt5(Tfk
z$?xab#};IM_B8#@D<xH~SnEIM<_~N%p<l`y?@93Ft9?{mpw~`1^?<q-qAr(+c}(Pm
z@hdSO%U7=Mxh37JcPr6OGh+GEYY51>p9(tcH|+;WVOk(6s|GfrL$jgL-8bQ9t`=yY
zCyT0!rN~K-O~C$w;lfL1AbZMuud+!4C}e!Bw{82G1<gornaQk!^f&*^0MfVcWBla;
z^PKC8G+TD*h25V0lgsl=;8!)fVs5vmBCV~8rm-Q+K>UV~GMUTMJ+q51gbWo|JBU!s
zW{c;UR=`@R)+p?WSz{!vpU*pYQ`W?18r-AKtR0M|Rcg~V)fFzYUw)<x(px<;S4sxc
zyr5^-Of4U}|Jmj7<>pARe`z&~qFSS8Hy55@@^zyE(i$5<^TJ@AaXWvd6iDmQYDPmt
zOf%AQuQ6wZQWn``u_{^OZ`8bQWK1*!QDTr|2=}hwJqe)bY7)rpcGPrX(9))j@m%oM
z-9>lMhc)>x6XEE^Cj{`NJ|&~oExG$KWm{*_{<p%pH1G7nFAe@B;*XAx!^}Vh?A>>!
zNo`}sqBW}Dpt+UkWn5!Q5M){QNPdO~q*gGq3PLJ`8-SNf`TTO%VjHiE19M3co@k7P
z?SE~*&rbA>mST3-T5O-___kR=JmhJB5gyo4EaG>%Abzn)o5tsJ!gtQWDKVjTc7$zS
z4<cmE?TsCWj%<!;yCJ{zd^$8PmKq~^6<gxR2W<i&q3DFwG)<OaKWVYIaJM00mCL`B
zd-u1zoi+qYwWGaS`~LE3@$F7L=JzH=!(zYIMK6J^2y?1mOMKo4`cc_$A$nOPGt!}*
z{x^;BCns~}nKA-vVYM2DZ{ze2i}O_Xt|3Q1L8X?Dw(O>5>Vh1lk`uK5pbEcOgUY<S
z#{sWejeU+ra~mwTmb1lH8?T`cZ^6*F+ogp)^<lye2SmPlVPhf?o4|#8G|ydz9A~oc
zJ#b;0&9F3s;m1a_0jeO_vVNT6>6#vU)z&vsa`~CUW}^u|txHorT1Jq1zKoo4e_a3k
z-z;J1=#<ELnS>*~+-Y7d4&|rSm8q_yu2d>IPkiYIDxM+IR;9wgOXWNUfpFU()})$J
zF%F$4-B)>9I|xviJ$0HD&&v#ptC$VXsa-1$V2v>SR^q2a&awlc!_j>wUJtR&FK83&
z<`U|jlSFfy(aH5BuzJ|J&sQf{q`~4c1o%GFUFttZBbi(2Us8V6;MMFa5$m+`2F$a2
zA|sMlN#{HAwx*8E`0W?xvrD@-H#$G|Ra1b(>+t3tRrg2uAG>wYdzSciiwV%GiO^U_
zJ^~*r@OJ&6eDoQ|oAbMVe;kXGs7(iTO}^}?@LY;1B`9vHO*u^7nCwqr<WK#8v*)s8
z>L{|4he~GNzJx@RtRZ)*H9eZWxyE{;%`zeUis`;mDa{VgV^gg^P9D>n)oQqfF~qTO
z_*hyExhZY=MY*a%7^|oDsASlvxZXS3TI2o3<~wyT=#FIuzb=?4_Ng(Y`~Jqc777`1
zcs9G~&9Z#{CNKYsjl}I&zGWJJpqXkVd*G&`)?vXY7*LzEXNq^J7Iz)L)$#cGe8To=
zz)FK#9Sk?!9RXFp+0q;(!;dkBp9WZcnu{A1z*?b?bh#DmF?HL@BKoT7FPA<gH>3r@
z%J_9hhhuIJOFf@tI-thD$7?0IQ~D?l)(BOy-PK%7(x8!m95U*F7*!@gt>NUS>G{ta
z`^@~3elmE;p>so5qTzIyiS*-3eg6G{x2sFWM1nGP-H&Y0rLvyg<66z7LmGQCzYwb6
z`rTSJdY<hedMj|HIErzfN{Gu&&flw34z?ZkIRPSg0err3u=gqH)3K3_LpGz0Z{-Uj
z%wTth2(;;W`=8_U`FZI|(+ev9_Zml%`+EFJkoBJCyH)xtemY^X69>yluSb6~=ru2f
zhw^d8>Unca-`$*YzL<yK#KX34Ri-86msjVnUh#M2gwKza(E^cshK_IbaPHy``u}|Y
fXGx%Kk<HsBqmY60{5HbbXk?&ka=Y}F(;xp0L&>Tj

literal 38254
zcmeFZ2T+sS8$O5?%N0}vu1HaO=iZ<qHKI~N?;WC4X#u5|fa0})(n2$|C<Krisi8+y
zq=V8S6s1ZHH334%o^XG^|L*MU%$Av*o!R++GbS-4=R4n9p67YrbG}3x=xLlg!*zy<
ziRm0n^PUkC(}_SPrepS}PJ(xc$?w;|(@A@6jeFo39J_bx!okyNZ%s=dCMKSjjDJUB
zXV;yWn65Ix?x~strZ10&)SLPow5(H9s5S~oqOg~jeyAB5qrQ7$+dS3Z<lDI1yXN?I
z{q8xZxBUjunkenR|9PRQgM}f4PUrZ(7c@!CUHXxaB$HxER^{e(m4l7NNV0seGG8oh
zcz$`CRzjlGkpoB#WS8(Eio}t_S^dw4|7ihW0W8T2)vkmH%MF<cQg**lY_&Zy>Aio1
z>2^*sW{DBuOqLguP2$uWLt{f1mL*Ol6{}FM#6X#tKJ;%qw*7A?-3~srcJyC+-ey;O
z!ujt@rUKEU_5XQkpjO(-1zvx|%l`YdtCq(D!D*Z>CHLF)E}#@N-Nf_128R}6&!HOQ
zDr|aPP%8L?$y9rJ4K0Nh@xlh|Nz|Re188E!?cTz{gY3%{B6gZaE--W0qHz8TtiF)4
zfGY~S!W&epxhS%bf^yT|GwPLu#lox-Rgo+yzE#+SBI~<o2U-R;K&U}rK?nwQHc&UX
zgVIa#eUFuO+G|#Td1(dT=~X&GwaF>S+LP9Se-Q9_lcufEUF&r<zY+UPa3y{Lk7=t{
zpNP9|-rLRHkLP#VyrIj-e0YhC!5znWvRub%g)P-XU=l{dq?73T_7G=nai5w7geUGn
z&boM^iBVoPl9%80L4<-{yh)-Xk7tYa-mRvv4E($h&s+P;ss%D^e{B>_6Qc$mRqHE2
z`)29f!EI)f8NcG^p(;U9PGRr7-8InIx{DP;JVGk7ewvT@@WC^aqHMQ)7Ynyy{7iCb
z&9FhSfoZ;thj-D~_ov%~y9FjgV1qY^xP_>|a0Ws#F_{kE&*A$B6MhmWv#|aLnO=L5
z`T4|lE0qs6;HbVRY&;k_c)${0A>XFtB~f#OV?%HpGxzuKHQ9)&tQ+O+dbu&~P7!7c
zCj<LVe{e1*<yv9vcbRt)n_?2iq@YB3vB>&s=-VaM2SnRmmy186wf5BKwv#j!j@aQK
zY$pccYpoR3h*0+R)NV3W_HaHQrcx!!f2w9N-O221?bPY85Y7Ld$BknmQ6YWGg4W&Y
z=#C!q91B9GieG}x+CmLe%oo_{_R~gEFl(ctNq)50mHH|w?8dv4mButJWYjEllxj7Z
z?6N8u`&a-1_1<xvjh7RboxNSJbFAJF){(fQ5#P@Hv-s7JZx!_x6I1JLh=3B9i1_Do
zys`RmzKJ>pH}st^tu~>oI*u2r=sl*Jw|BzO`8Tyl8{`D12zZ-<h4Vyvwvpug!EvHY
ze1C@n(Hdo?B_3D9eefr`Mq#}ET7GbppZ1U@`l^?BOv={FpM_K6M5LVMfMX`^CNEJY
z;rh*=km&|7*__+;O0Ybk){vT(X+F)pL6J#y^pBhsp#kINz?V*Yx-LP?nrUsF^EQV3
zmcuS;^|=z-{|UIHVY$(OuR{Tl?7oX)@xJ9c=Ld8>(VwD*TFX)dd8F8w+7d2H-D(m+
znix66mes4Tjp74}%QhV6IvcL3?tSRIW2jub6$EKwBU~tc8Qfo-Tq%QB!nM;{=%GQe
zr+8Kj_2O74qQu6AtK}A%Jm1~L*&KCzb!b=xd)YLk+{m6ug5{sz3mXWqENRu6o@|yd
zpFiidM`z<n+_6gYntMqoOtNEc%YnIY>y?>P6N(oTlNL`+L<Q@*)WsfSc>RfZx5U*l
z$#*o?wc&7+-o6bMqc>}90T>Nl0uxCK9q+rIyfYK2wK#>!!|U&L^ZG535?Z{Zd)bsX
z1bs%3xri-MMR6a?TB1)%-d9ySX#Vl7vhjd~eEs<SXLXV4-6vQ6#MA~#{T^r&K%)2a
z?!nZ~VuFKcqWmYW{Nzc07n6T7P-5za?FA(FVGQp75g0vDXJ^#)2Zff@(wGg|91G4D
zX1o8m1FyZBJ-vUl-X%D38vp0;omi!9<c7^+{JeJTmaI}Q@w01%m}p06JNaz;jrBH8
z$0z3Ph)G=&3_toO#{6Rm|Gbv}-dMI?N+34;n*gnqX~|^9P~F5x0~4xxGLTKmJH_n@
zKFh9eWOW({f@7UcXF(*srYQ093Am=|T%8s}U&tV!YJ8z+A2dHKTB)i*W<di}4~3;p
zl@=Q~m$aGGudMI5VU$>dlp5PadQ_!j+1o;_B3)IeD4lm}?y;VE!mVkMp=f!damnP0
zCJ~a>E?@Rlr$Umj5x;9p=UDlAyW`x{wspEh+QH)b4!uUnOp+Aj&Bv0ffWnTe5REdH
z6l`aYtQPXa`)U@!B*F3SOpWG&fOLcU4;B9W;-0W8yEFREr4@yt8<^jy3HReT_|@H`
z7>M3lYr=d&i>Im}VfuXh=~H{-^&8z=uI9OmyNQazk^9D>8&sIzSLdodMAly5cvZ)n
z``tN<8zqy!9=V3E#IJ4&cC;sKokizcWF}wN7v6yvG*CYd*;E?Bo`sV6Kv<=*RR|?r
zsAu0z?e=Hn#pR#orc6u*mP)v<uX&m!68rUgH6TsNT+*)fbNh*%pH(A^Hb>^<8ty6)
zs*-j+s7=%BCOy;pF?TaN$3BJ0$=C^;diFD5fifz%A(9|`YGko)VKGh}8Bt~2%f?F#
zZl;mQ&;FTFJ~NywvU#ji7{;f~CBgPe2{zaA)NQnm^(B&Qn*Oe+{R1#nQZXaWnHdcD
zu54JqC%<MW=TqdqQE!J9!jxjN6HV61DaueG?n@IVX4eCq<YNmnm(J{HUN@2xO)4g_
zq=f%fo)s8fc93B2sjO)v7waoST~^fZ)rJ_~s@8=WSqcT)?H|RlH?Q2V9oYjg=KVnu
z{s-`l-~s{inkPflHKLQdI{&xu!cKKd)gCsMU^1o~*>jWN$~k3f^6^JUGIt6lW<`a_
zqRmggplT|3dgOQAf`WB#c{{D<AP?2(7qssU(=8^1PsdXp?b1ab7kBovnCd==SkcJ)
zh>Hr-HP2OHVPfKbjO+!L|LOb`djJt=TkJT_UJ|#(3hTHyqb#yY-&S$06E;kVQ6UZr
z#uLic6j|mw-V|xi&v7#KB*K|!6@nW9rwjPSPL+{_%lgH8x=x^k#qa{c*{Bym9@J-k
zbeGtbFdxZz*j`-^*;<H4OqT1WeraPWDIqVbpFCoAHDB!vwSB<7mY)IT;MhSc+;blA
z$3I7A2EYh|0-h!ZDFK{qI`Kb~aXPEe<PfQ{xyq{vjuE_e+lu`H`tS95aq_x^4Nd4^
z9-^koXkw*kDFIii-*_)O-`Lhff<2!+es4Qpn$2X>ZT|S|%~oq-wjI$)eZAI`s(#?7
z@>u|$rPk@TGqY*!{ddN}&&pRL1muP<B_BiNcQ=|1)y?Z`0Yc~GpNzyV$l{x6U^2du
z(z35kjoug`a|XeUl+6x&=FQaYTHa%f;7mZx)X5g=8GWOA)D2Cq$6=Yv3{tLQM+4YK
z#L7RsC7PT<`Q~7$Np)9A&M;iHlHJ{2-&m!9Ht4DFk3Cgr6{6nu2uFnwZ!BGF)o08s
z;q<q>0!U--vqN|}@^3i)DJmNwHKO(+W<4<Ud51&K&hq?YSb~%KiP02MK5@3I`>*vI
zAHn)d%YxKbRTqxBTSLzJ3?$YZ4=cwWX)^8V^HCulTwQ>ncLP`EOX54HtX-TMoc|tK
zu4^0SUpUS{8XKANHLH@q6H{q{N~@G~?E@cK%+&hM5mctu$H}V=r1ii|o%5rabVbSh
zXso{D9-=^e@kYiHBcQ-h@Z?dHF7hwCUNnlU{f&6J2Hy)uX?la!L<)qmHca<Z2{LMk
zCcA1lWoD^De4K)3sW!r|e4g01)ZZ4~LB7y=(ur0{Qk#SP-ImwAFtLvfhJ{s85oO!1
zg8BU}^t)^oiq@YYs=pmGQ#ccb;qS4pG!7u>A>-1dDovn82ZcoCev5I1p&dgK8iI6x
zTs7@<S0Mfmo)|vzj}BsKM%i9Uzjx_K;yPOAd#kn+>)H#)h=;1jGsML|g~WA!=rUQX
z(xINjrOQEqQYs$^FwWQ4hJ}Q-*9fT+%bnu%Gt%X77pm9QMQWh)D_$zZ2$r=MLdX<*
zT5bnbxn1Uy^#$C52jvkHQ{0i<hrlLKPA5py`&TXG$LQOOGk>%PT01xP*<M~27|KG0
zi7YhPm<yhuC-tk&$?JWiKsJkmyR@xG1Q+Y_E%@^jddR;Z#y3qWU@+{6q^li@TBqy$
zca@{AS#!+&7|q3uJ9~1186xxrk$RKoL%wm0YyEod6A0Ka7Q3VxC8S@3Z=H~>Altrn
z;-G5+)<?)MsX`!n&-CU6^i{YvY_qSGpg9Nez{#7G|B{783rXq6A>wuFZaOunH)&Rp
za_vj@=dbb#_*jOk?V07;g{>NHoaH<aTnI$-*<QRXwP0i#>b=f`RvQS@ibF(T%bg}L
zy{2e*-^g#kH=nLShuDDyKH7-*oc8ls@~_vqjf?NBkGbI}O`;k0YN4M@rlICEN8{`*
zN!C<^BwMwzp|d067`h`vuJt&Kqt)gn4Sf+l2xpf{at%;M*Zi%YY+1Bqfu5!%=omOZ
z*I{~C()o9r>DrccA;MPBR+R0lWPHt5NG>){sLO0a_47Lu$4T8mf>T7Q-Pza5;;wm+
zD?c&|>YO5wc1E_xR|pW~uj8;3O;CzF?>t5A5o+KsKZW73=xqL(jD{fJrC>Lm)Jq2w
zw{oy4LdqrgIe9w<-D@Fa;N1VqN0OoFp%Fgys9*nte3Y4rRDy#qLyra3l>B#)tICX;
z>hO3M#m{ix|I_1x{Yh<cAW|bM!pnOPlnZa{?FQP;1y;8EsjZc#h~%5(5`=13KK9Ok
zrkU{s&wSXwr{3OA>U=^@frUIP(ROm=!@z4iwDB}HN4vAk-;@#IgF-$_TqzWvrS<Xz
zQM$3jO3mrsS(t)p@1KA~N2M1uV6uYe5>4bH%Tg5<m82uL^h7L!lH0g((Wr?e-&h^$
z>muI=Lg8amUa^9fDWi}U_jIfps;Zs)^^g#4!Z77t0jyT9m)R`MOp8l8-!8emTact}
zHoACv^f<!^u$sF@CT&Q0ZbS|bdVq@-y00-8c;Tm76_0q|5UQrD+mJ_y%{x-dE%A%O
zj^$NjLiVvyxbVukTIr;CWN3`{NU|Q)+A3((d&K$hQWgz+d{7_0T$J$C$6W6cLZa^}
zztC)_jPy+MxC&@VpSKcayQgPDt~1;7zUbP3h!rx#ObL=Vv#|~rm-8~QUP>Dpf*BXR
zN6>?HrAf~BBK#SCyKQu8&6)Lxok^lJ1iA9k#{8>By#3SI2=1X~-Aw!KLXJ-d5Ld77
ze)(_GTSU0=QsZPIsm9NyY6Rog79;L=2J*?BV3ut*S}w>V*f$r~**cvd)NnsX{)+>2
z7m{s~jcU|>AI4Txp(wbWeEE5*%eb4S+qDHNSgc4Xj<pzRn|{wqrWtZ%{;X3(=73|&
z-KW?pNOs=J{rsVaLm%yXeuSX7tf(J^d>St%RcOq;{P?b&C2PVzT)EXjkzr%OXeKZ6
z`JZZPy9qk*kZVKi>px6#V>%w&#-Ax8RLYr>*LSbJqm5(-hy;1)&z4Ku8625VfIL-K
z{Lalc95ZC)EPg_cP(j|z@4SA*5auN^JgFLWB84cVnf8XY2x<H0r^b5jt88y#v}!vA
zx^V1gb2;ifF!rAM&f*0o9o>Rn%<fcWvDGU~acVWGEes{}+he?^%T;QB1nS$_?kXkT
zeK0s-GScGWe4Bd3V|<eD`$5?QtbMjFE*Y)xsV8rP<;mp3ar)ifw86;?L7u5{Lhc~)
zUav-^xHZhf*rVWO39l=nbd{8Nd4RhovuxT8EhZizshCi|=`^=OgVI7Z7LVOdy31&P
zxYl-l7MD`0Vojp(-|S1;|18yjY)1(BL@pVi)%3NBsaFyM!#=YKDPMDq7MQd}EsC_7
zk84pfI=P=bt0dJ&wzNyum)-5<Za(_hE?8py(b|>E>2B8hnRUm6ia%CSC}QRJdauGr
z?xYjXqz4<}c3&VV*J9%;3`(0_o!j5|Z`5JOcWpv07kotuWrT6v+a3AdRZGAA40%22
za*mhGPKBj&<HXVo&vgaOxyP&F9g@8jwsRL3j*JGx7aA8+Yal6*fTXR8&dq~oovrPo
zu>MVN7`|hxoCkGNGuWtiop*s`>`_|chAKe?om-7t2zX!Z>$00@Yq+9olA>;g?sJ+5
z&~mcN7I2Eu7dCRuHa?3U+Pqk(0&m|E!8J}RsjQ=dq|>9E)Z^6?N8GQbMG4WG_Nc+I
z*~`mIx6y`$sI4d+cv$n^BuTj?41P{x{r9hS#{@spO}<A@?U4(Uyt^rFThU6*5o4-}
z8LktL(O11Q?Mc5FK^T{OHSgg^PK4`1S#kV6XDQ722PbvTU;aY|T^;=?(Yg^gqax>!
zsq<HVVf`!YrqZa5w(u)1%)A<!$;8C@eflfy)FmadSYdQz%romJe~(uMDhE_vvi{?@
z6=u&4^RC2Us&_K(D$RT&Gv#|pA^gd<w9u-=!dBnacGlF*vL%7Ov6fT2P(BHpjV<Hl
z$3A$X&!dGE?V&nY@Q6T<LQw3-j)X1z@$LFvA&e~|9DV<zEUi#_fh#Bx9>20(sBMi=
z7?#7`58%(QGK4(+{4#W7Illj!5C(pQO__6D!Ezywif!AzbWTLVU#EV~QFoA3Obh)T
zxU92O>UD|wgWnFR!E-Q^AZ^M1x7stuFyE;5{nqoOQ$hhYD?QV8N0NE&O{|)w=JgGO
z79JDRzP{S=z`GEEPgxLw&tpej{O$0!pLDUT?wnXzzHcJYtS(k*vaC&cIZDx9@f@sz
zzz7MKUD;8WgKiF%PCD4OS8{$1f;&G*QCd1M)@b~o!&GtG`O<n$Ov{y!itUT;RD%>x
zT!-SPw+QR8Ohp&(A(=nNiKUZuj_MnuugU%KXugea)k>Tl$H%zZj#n=?yDrCf8=~L6
zP4hSrn6Qwin#fndc1iIdQa!(}bRM^_C2U}3JZJF_Xp8W{<XUET3zJ+@bbb;t?R$c3
z(T2kWAFPab|D$4^`<40jxE~3{NHrl^dv<251(CD;%@Z7Apgb4*`V#d*v%J>0CBnqn
zZrP!d^h&`+0sq6FePrUEUnyL_i#-JS(kX&UrWz__TNrz07d>7XT1r7Fh*u5wH=({+
z6K}ecZdU<+R8Ds3n(Z~IxIX6jxl%Y>+S(6sho{`)l`vbb4zkU-ioRN%fq2&WXhCu_
zG^c?7Xnmfj862-r^~i&A)&g2=abNL6*|XM=M0<?<Vc=FpomxBiX83~koKUDSq4Uf?
z2lBKn@CLi}8usqkG&$v?H1Dhm!V|+o*?AuoZBTd_UtDXSeF7ISlE?EZ<ELRR&mYy}
zPH97>?O3Vx9$TsA2rk4no?V>dL+w`)Tk+43CUu<5w~;ez!hQPBM4=DO#mV)gczOk|
zM(qs7bJjE2F(}V+0pIZ^Q(N6ZgNH9J<mw!0kX&e6HPX}dM-bdfrB%x(Nuwq~sogJ^
zI_zYvh9AO7M_v-=_)Sim6SPzCOza%X^S2%6z_%w{HsqpPl@Y7I3+XGYU_55mYkTiR
zAqPjGPj<(rs21ms*wyW`QrhCVTqdIk{LOYc9u@ir_OL!~s`o@#To7p^tz}4zcx1u~
zVw}2zWbrdxet8<|P~;oHY~g=Cs8GUxf86da(~~pbxIukgEs?yHh|PdxGs&U@{<=(Q
z?<~CRRc8-hPEUC_>O(0`M5pvc^Ej>)G#snRv!KXWQv*iDHr3#~)C`YtI6viQEKLp9
znXQL_xW4W?0y(?JE#Uf2=Sh2q7(_M4i+xNe_d-kKSgv8~i>TE+)y0_|MM<{d&06FV
ziV*Jy2a@HCqk5!P?+7H@IN<^Fm!Xa~D$%vrU7Q~ueNQTJOCQbmusebB*@R?C>Aqd5
zY}(|EUSJ*rRtynXUAL(iDwJ*;E*@TokvFKmL<{4&f-{O3;*{AeL{-C;RHIMhu+Yw<
z5k9UGv>^?X?t)350>zi-1^Ot-9Sp2iy_dH>NMj1ZXPeA1Cpb7|5(Z*C{e)<0xs!Hr
z50qt11!boit2*~TvpQEpsW;wsF7YRn8Fwv(jtg$`{=w7=Vbp}x)U?ir$&I6Or&7<Y
z3m9iiRITbiQgUWyv)gE+6x%;Fn?I^fzRrwzeO$<%nZE#5@T-jW*OJP?*?Nr5sJ(lZ
zg||6sPDr#b(`Y&<h`Fbp`<f4II|Q=g8}shUjB$&HW*#O?l62uoIPCrMkTUrZ0<~Lb
zO-X4xF0+rQv)v#bc@^Nqf!}*x|Lm@<)7(B1J^|&gE`|G0THiqY`zC7Pmnl0kPGP<u
zuK8T{$@e2VjW17c^GP&p?v{sdRcjwWlZO{3VngZ+#Ty&!{7Owz&^H~*I(@I_MDaWu
zhxCl!6rxWXnCA?AD|ZYtcq>W&w5YdqAkyMMUJufzYW0W7F<Vd~)gNii6-3^{vEleG
zpiZ*{BV{VWZCp`bRqFp?Pg@lZv-5!kYY0EF5hWGg^5}4!$m8nR${&RIJR&8#e!0}F
z!Yppp*css}q8Y5+>orT;E$gbq=k2ZA$NKASp;NU+RA>AUJ%h1lnVwua|MLXXeYmb;
z<C`!+Hm0IeVVhS*iHq{Q<#|jNnEQmKBI+^j*b(@jctP{ZhFi~65SzI-D^`t9uQ~T}
z5KYkYE3hScc!x5RXSoJ9GC}7Ak95jbW#@-QV@Gwxn&@}hF0&gZIYj-<OP!}~<B)=E
zI^xObO$*4je#=92(EFi|PJLJAk~~q4e=0H%<Iq(5%gRMmyi(Q6OJ_}5WPbanPLmcP
z&?u9H)4wl?``0gTuJ`aPtRN2*RcXt<ucvu?)Iu5-`O)zy^rX`T<oJcUflG=f$^A!h
zc?Lti#E0`CO>(9lmkv^5T9&6b(b~ZTa!Bd3(6&z1754gV2j-51ntvu5A^a*Iy(k@{
zu+<h_=H`jm@x3i+SdUeB9%RTPABcNI((ZC;_$jWp>O8XS$(MKcK%ps=agD;4>jAxW
zP%@X$ok^PT`X{)+i8+2~R|Vr?$@D~$QTBYAxD%y&_q#@Z?5fU?D(G-ktP%o1FdMMy
zeuRhA`S`v=x>gd?36Zysr62@WV#-&|>pV75FzfUK(Q++nF+_DbQiVF|IGzXTt!nJF
z`Gm6U%zxU+xvA8w{{0;6K{&b<i>XSYoci=YY<d2AEOICMYZcGJPKfClCj~X9+}pQw
zoK3cOAwJ`FS7lq~Fg&h`Rusfjd70fPTNM4dMydj&iP$wtVP>oAOx9A`=-mySlki_?
zoLp)}ORMY{{{Tv$0|x19nrV|1A2gZMNd0K<iJzy2%0A!A?Ne{t3Wr%uCkVrP(SwuP
zAWT|B8l%HjDCMoS&`vqiy=B9}=ul(yeD2e2N^P|#=GK9V>B6IvHgavq8M@jaS$2jV
z^xLVSGa}t+%J7qFxdsd7+Ge`H>6GEf74GnmE<PTVZ`L-YjH!c#A16E9bH|;@;Phd@
zdNnvo8*_`1u4>%$C672WLsYzhS#N|*C*T_G1)in*yp_hU>TA{d|4shT@rrz|f<u&J
zW=H%vk`c96VHE4|NeS7D3rAsu&0$Q#`Q8&+Vcq&6-NTPiyk(0IWcr=muVBQqAq#n7
z0!Jj=Sm25VRaPPJqnMZvotn$}5LNj)zH@%qQVo&4=)~79%=L|C%a3V+Qbi$n!$><H
zH4%II@3rUq&!qfog+>F8+a3}W#fm~mzU<dvG}rKD)%^iltq0rBv7wb0ZM3|-m_ous
zvWq4SJh~rHcd9FY;AmmX`HogQxmp<Iq<<#)5xo=8>aX+<AMf_c4o<R)Zj)kfR8scW
z?KV9XX*Zi89^}|}?DARH^_^F@Hyu^1_2a4a-Rtd_s5<l{ja1<%p0rtMVxN>^U%k;X
z^N_CC+%YD{(?{%>nC!Va-h3Y`7|ya|pVgWFI{4qc030>?Vg}=eDPl1#la;G!?xdQa
zjMa~;ljh4t_o?A+Mag!`()u^tF(}`%4waP?9?PaZ8Os;E3KK^!okLeN-|SissL}b$
z72o-xbx(R>5?>`}ve9%r<SWYjdvQDp6MmDeW;suF;r_TtYQ$9@a(zMfg;cWWS39jt
zWiHwc%yn|CAj0P%(aA3L4%)~=Z}DEsH!nk66aojgUFK6yuJt$z8nO0GeLF!&8Wx1&
z{*FL%uJx-SgVG=La9<?^A0OUTu-EoJ;;A2|JJz%9t=l-JXn1T1u}q0S@i?4xkljgj
z^MTO~rbuxu;x{+8h6ZT}G{t~mxJ+h0j~5;$q=lkuT62c$2S+$iTW>o}FBLlFz8HzW
zb8umhcD_cx@9+Lv%sV7B03xQ?2XOYsU;{DY*`a`{P;@-{W*VibIHsl8>p=a(ovOZc
zz@2h^y2#U3d7~<-=cji{(%c<PSvE=~sU~#fpp1C0_fAXIPJC{7WmQsp(Mhk^EqQ~v
z*Vzqld~JiPXIk;_hp~(33%-UeD_ZCa=Aejvu+kD6uv}P+)pndiylD@0^_>nV>h*lx
z(33oBuJ2B~Q^PDM?XygAM!WJXJJ@K?l?@z;*^(-*xB$NYd~kZeF@l)JL-jekcw)f3
z_fX?vdx{<h=fzVl!Ll7A?3qQ$=>u_S_%`zjk+n{gJZ6(OTp@A|-O&`HSu0hN9+J3Y
zQIE*kecn-J%<aUI?E5OaWEVm!PQz+cP~53rW|{Y%YAa?CSue4V+YxFdORJRghI1$n
zKvhLgJ}s(VN$O{c1fH|>!*6C}4~OXL!Z;;viql_IhmzMHxo5+vv`f~zWYL?fkLpcS
zmd13dHoTHfSB*j!pF8Hh_&6y$H90h$@k^6Z+IdN;d-uD0yN&5uV$$3nOY8cx^xY-j
zx7mNp-*J_4v=eb@eUmKh>mh*((odUiB^n&47|fA;t~JMuTjj_V-)rn-HC)Fu53ny8
z8?GoO83;Qu8+x@VVGMWSL15(s?3fvW2WLOV^z;P1{+2@v8EZ^e9mLOuwS`q3tH-y7
z9*M0TYwVjiTQu?+^4h&-w$v*nuz%q`TApdTk02^rdqZ5dKy(T<$I=oOHB}Xjq9ihh
zwTO>b?VJb{@^LNYx$om+9RZm(55+7ekUg9CBH>TuvSz4mk=h5od-<~ACCBP_qbk3U
zEQawrHE8BGy8P?*;~G=Z+HoRb4}_^LG0wazr^LwVLAotJ_aA*;;()r8+9Hf&k>B#%
zr%D_6iA&}wB`SV_I?liDP)Q~^qw3aK^RxUZO8!@3*GJ~IZFWj0jYk6+t&mlYlzlbG
zQ(ppk)o{v{TX1ut^k!3I76e+r(b_5H4jmkJ3P&v8F5bK`CLRe{@ehpd^gqM@$@-#s
z)#T=s+;;1O$8^amFWY0%im!s_#mLW`_f2mf=ETMMlnE2Bb%DtAr?R+ay)vGko<eNC
z4f@W0d&-d41!F~DB~mcrvMCb^=qDrdkRxwSg0{Chd|X79e~Y;=a~G0=AN5nPb9Vf}
zi{9n0Ygx(GbZ#7{UaI?NL>tl~K2RliW{pFFJX=$#k*W^K-sEVj@G0*+HQzWIF#1Lb
z9si!x7J6jDZQz$Z`f4T5U*xlExsNx`tmTgCXYBfb$zW%Qd_NkqajzZKS!3dZy8T<<
z)b5+~j&HCE`o(QwJJTC3p!xKLJB~k37ZCjtqfl+DAcf9{W{rlF3d$8ba)etwNU9kq
zSOP_COa@gycWlS^xypigRzq9=>^<}WUVDx<=h{5Sf!82~(Ed!)Ec1LbWH`rMEM?X(
zkq|_yl_LKh`sRo^D??5?5*xzsMYreIvf_qL>K91!oB>8a)~H7B%=S4@74gc*D%5q3
z4uw0a;L?q!hN0g=Q$HW*<2RNKmUqv#)qB|pyP`VTnTW;yMM;)laX2!s;j+k;BX$I0
z3orGfX^Q2OH3xLhyBiQ_a&jQH2fKWAaolco9_$|jOP1GC3pbErFyY^3Khlrb&Ad=<
z_-BYSlnKg54neHgm><`SP;TyPswp|eboGTApbJ140n7P<CwFp>J|=0RYrbf}MiYqz
zOibzjZpFF18Y=9Xg;iv?mSp+x=3A={=8;C$KTJ=e4!7sL+X#X=Pqg`g@t&M{RMW}*
z+xoHXg-<PBjvSU_@k>!mOyqy}{}?<?Zadz^INkp*53Eq)(wX~owrF+M1QVH|?SC)w
zc6agV=XcV4Y$FRK!P3L0u>4G{a81c!BbkZmF{0e>ZtDNd-A@;Q-akB^h(|HWfGs!%
zg=WPi?L9r=!6Ri(0~xWHu)T%(mtTIhc@nD{K1;!;zeRgVi;D+OrFk`qY2laM1`2df
zz5J5!^4yidF^R4Y8G?-rH;X<CcuUS}A{~RUY}H|WXSFv++iTt-CnqQ9#EBCLtM!}R
z$u$M$P6M@mONF0*tvSYY^|XN3XI3a#mgUX^@mOu?8m#s$?#$4y7c~Cp_M^oquHyps
z+7G@gaksn9w8ra>R=D<&J{@D-*9Z$c=u=Zui%m*Q9P?Yu&$IKL&j_d*tA6C58p_*U
zN|s4?cwfd?H@eL(`)KN`0Edj-Sh3H1w{e=BS7TpIaeciK4*cVeR=0&tYeaKxJ6c*=
zM%y(6+r}Ywe>}WCDK0RRAZ%i1$15!(!<uK!0vQ5RZ6-BRS8LdWbsk=dymto-)eWUC
zm-kGTyXdcvxL|00B}?Wb!#Ir1(6Oen$=B^29d}XSlN&|!({Gkr#_UL2Nf;=>M-(<%
zALJAE<;6K>`fgA70Z{}K+KXHoEPkoRA0JUxR(5O<2cKM~Q6@?9zQ4a;YvBvu=UESu
zZy6<c$2@O;!72_GCf_zaW2Piwe`IU)Xx{MZ$yAo(_8)$rJ=dKP!mvEt?MGH~%un}M
z?e@RFzbEOX-CUv2s1yP|oPIzLsed&6ZIf8(j+c6AdQAKt3<k5j&b2Zd>aj7?RxYBS
zsibJ`cW3q<d;}_}@N%&?r-R*nsKk?p<z;|v*zWA~Nfz!M*K+4Bf34oV?WMCWJ(<W>
zZJCDt$FuE8(bJocV=BXcai2{(Cjb_KKHL%3OAq`SFo_)67<P!w=mU0KSy-Nq4d0)Z
zMfKz01fOTcJmP7C#nxWozn*g*41av^MgPuwgP;cwe#OM_C^lB#Z;A+?sYN9Hm}-9A
zqL%%`?(T~}EKy6fOs&#(Br4|Kyn%L#ba83m`V<0n-L3z_d*Go!`p$Ihq@jhypHh;N
zZ*F=Fdk>Mq=~S;=y^Os*7Yy=dckv$}PWTvc^58hg0~P*w&EG}k-3kHYwSL>rMqOTW
zNSUbs_m=mzDTeN>Z3?-5dU6DpV|{Q=ZfvdjXVWw7d4{cDoV$|C9sK)!UEesTI3ZIx
z1o3;MeEuT{0j7`inw2V|6=83&P=vXXz;(mTBYY}S#LhR(?pi#UwXAceXgt<H)Ff=B
za;QWrFD%_*1%9jLj??E;K$wRrZTB0Wn+Tkaehs2G)XD%qpBYj@Pl1P7rn}h!bD39%
z?rs?0eQ&VKZf;X5Jbsg%gF~HI;kt==c%6&uZ~L$7`E#t#@64jM`t+eWTX)yzy3#h!
zo;j0w2Ba$VU}ySReQ#1y((?jyoQev_VBp!5CE604Uf?Aoj9OlqO@Wti5+=iFdl@Lv
z$$w6>aW&5?ZnO$`ode$&(M|3DRNQD==TFPXfQA{KQ^aj1V=#2zVynt;6`n+Llm;KN
z;yqDCR!sKQe%sp{?q(VHTFNzIc^d`Zs>kvow;r|d$h~v!N@bm$pNHD!>19wA0#?VU
z837aWq8SP`JYG0!LKsaivIp}pzEWp2eW~&1AYKkJW^sRBN=j-RRQ3&&38EmFIAXM7
zAsXSN-AnW$*itL{^y+@b-DSC)38rJRy*R+<I#@JvE*xB$b>QrJp>b&4Sf+?|0ZrtU
z+Ln<gZgLavF;ccz={8Uc1%s^)6b>zfIr)<&X!UnlmA6(SA|mplqM|Y%4uAah6QEsv
zA8<L@>bF?m&=aYQp*awg?lz(8%>*TeZ6p9v7QK6S->4yY`@ny1ArAu$KiId*=e{CV
z-pE6UHGz{PtN%=Y$rrZ6s+xJXD@~3h2XccFnLj9i_W1zp`CLehD|)V`K}0yaAj1J>
zB;8XU5zP0YwP!HuR?E@ero-6qAXXLMM#YHczbBUH?J6Pp!fWU6CLLTl?K#<~Jp24_
z-thhPY@z%IUtVA?adM`(7{wySYmrJ|D&_EymCBfG^=PZNS`${4ZpHLpFZoz38bf#A
zqc}v;aQ-W!A)|ONI*BqFPMBZ8Pfi5wZ7twxbFRogcI@lz?H%=78npFWt(gy_?{D^`
zU~A)E5vK&>H3bv9BLvBJgb~!x?E!OEw5&@v>Edj3$gO%nBzpkD9??K3ZNCL1L5o${
z`yHa1bot(aV`55*h*GBAYZ@aIN2@$Tx==#ewF-Qb03yExOa$dUNRhVfCd{R}s{=6b
z4~z2C3jTrC6fEm0g;lg#343<?!PkhZeaQwvK|!}JMpjqlo8#>A^wE$U!e%L$Zx*|R
zS-OH_Yn(BN*{vo<?kgP7#L@F@VbWundA>laz5bokXNQ~jYK~%W7Rd~pdbL8t^Np6f
z^t5D#AMDDFw=aW~M#~WPH)bEPau(axRc(KbWV4pBs`&kuaVe2I8z@)(xE3=MFzEKZ
zeFy0N+T8>|YBv_XzrRb;;0>CYlfcz)gr8+~s`IjTRiehj!|yjW{{-Xa0XNoHr);op
zy-^gNrQ#<B26H+p0o;Bz*%bC+87OsVIY*vs2%)IBGwAQjFYoIEWd*(R$%$hw*7fhY
z%geS@;s*Y=!GZGjUg4+xig185va{#Xk?Vj+pjN0D`MJ03=&E^af}WKOtk4{X$Z(qw
z)=ia+$IAQ6`ND3Hz+B-eSl~E212eO^+B;i2>(f06Sb*$XL9j)T7qt(sk9GtRt2~L%
z{r55y1IUq+6TnL6)~S`xOl)g?(HAj|s?Sbhaz~Rj1urfV@ub@@aY@N7TOEhx;Zppt
z(+ap=%ID{2aStaOX%eXGfSL=(BUsVN=;(dD+1WYjBap9h&3ru*4I#0Vj*xEo`7N6=
zN7zt(eZ40AfTj^n84pNdSdGHW7wYYlcGJ@?Rz3UuOWxq$J^ISq{l6CayMpScy+EX-
zr*^C4bdD~Y;cdvzb;iBFoLkA&LH80sa<*&_!NW=OnDB#_$|2;fi87~-=MF6~4LKnA
z;W(?xMQnMG3bp=SN4G!y5u0O(d69)-=;zZS`{QR;RaE>I`-jGRx0i-UmlUgs`Nnyi
zbDe0PffC!gVA5!1A%(%_l)WV!0guJbgy8EJ8c#yxtD?DX?Cr>e?{?UN)9&(tGYy0Q
z0zonW-aYlE=bqhlu5;u5M9*Uvd9H|2kXJ?w@zbz^@Zh00h=D8I$^N@->36}Y7YJY^
zD^VD(fjpXX!>vDX<`D2o>Q)s7y-Ymn3w&dQOuILWOeUGdzqm+{Y+G+I+Uh13-~gk3
zp!$m}%e^4uL#IX3KdPbH+uK<z@CaKifd?o5`13CfR3rU>>P*-hz;zQDS}IGaYIVHM
zOI%FM(#XurY<ePu<jG4wHbtCZ2IJ&<-r}CP;rrX5N9I`fg&S=#C1Mg19VuAgETsDC
zKX+gm6w+*N8baY0l|=9&zlQJIJ8#Z)LD#gc4Gh)@5D?21x@k9wxw>iOB0->ivVhwJ
z{q&Nx&?BuR25SK<ymV&8XMaUoXS~R+p?0(rEi03Vy>|D-*p0CLof;t_A*Wy7w?-S0
z)Z(+8(xF989m(ZF3(3}q8c0U1d~ATYpSIxm&5!}96y!BFYdqo{bnE+FIn7>1HqQU;
z@GW$>K4>!o3}FRSaO;yJ$IQYv+C-jx`L(u+p9n5A-@kqo{o&OCn@lUY#G*8-8sM`)
zmDgn0@0$5c>ev2!W0&e2B0#Ex=~!r3hM=wgh!bl)jZEzETOA83Zx}WPstzA}S$6I&
z&M<hP{v<@X3Xa?!P!1UP8=3-qOp*lZgaaUS0BHi*2gc*898EO&q@04h0)@8$egIk4
zZ>Yhu9WS^tTDkOTq}+v4I~}9A3J74uVW7Y?qzI_mhVfkPM+J-X7DlT*<wi)p4}V~a
z>jSP0F$ypPH4i{snlQJq>h(Dtim8#2ku$yF;g77u5_J8})N8`Z_xE3}!%_hTJb%gk
zxU~<!Xph1?R1u(iOW-VFv7qok2sYgk3x$Re<F4JU)iMHMn+HxUfzoQYxC<EWj8HyE
zD*arl6$qtY)sLo2SYLjrvjg##x3RHdRQrz)16Ud9MiR^i`0zTx)jikAITbp=3f(u*
zMScF|ZJAY}2ma$N3sd;{<k;Xi5C_`hy<_2YT92h4q9%BI(GA@%tGQ4$qA$I7Czm&L
ztw}h9ye#Ghs6RwGcx!kjITw&!U8<bd0RkkFE*jG8m-*RuCX0T2U;-d_*}tyyB%0C3
ze0q>mIVpB~cyqMFw>Pe4c6OO?K-YYLqt*jtJ?}yyRH^`<U1tzCa&N2V9!d{D%R=O0
z+)`sS2ItuGUQ0`>-K^N^#uSFWKOYUEHnbEd8Tu6=0s!t~6G8JClm5TIKlnUg4j*sw
z;v-K|JLHoujRl#VCmmy_jkJewiXWQ;h1k2O;f9caQbALko$@q0IwSjUEp2UM6~4EM
z0jp5vb6gb<r@lrKYzbk=&4<@hIirOGq(g|FcqtwrXsZB8RAmA-eafTY`%$CzpNY`T
zE}&9@+%MTQbhZaIn4jRnyJsw;nG21<<mI4WAeCQ(xNi^V_Uku<3TcH8n3~gQi-kDp
z6nme{x!GAYKwmf9+Y`m~owGaNZMJ}fu{{SXFfB}W|J2curS_Uj%cvt+3c97$S8R>v
z+uYb71Nxrt$xy_1GlJCCx$>NPt*3v%AINLQoAW(TEtFY_O^{YtSeT;aGj;CG3oo!1
zRK{B$u><SWN4&2C?KW@C0U!OCIB}MvpYN9UPYfRT2#%`tU6@iF=mt6|5G%+NF!so&
z4}I)9k17xGE{pt3CV&kdrydPk3ybP(P=3TJQ^tI}1Pkt#P;Z7c8Y#c5WkhxZc)%oW
zEi;PDJbjEE1kg<k03gLYQ2HqW{7BP-(zc#*+ITnZ?bK7J|8<c9=~&Hy$85vdq$7v*
z3;&rjXS9S|LC{}@uP&#*E~>7+RUFR&syFMc@qo#5Vlpx$9_Y|$qL}f?;8_Wf&zV7U
zXgbP18z$)b_0@&0j`nsp?y#L-uYpt3*F|Ro0c#aVzyOxF{eIoD*#m_`7`hA~McpFM
zVwq_=ydk9p)$dHNbF+}dq@;9j{q3XRbP_$;1?oC&!F)g+B|cArN5EwrL74#IOyA3q
zkd~g1gk6zCNb2EZRJHM4dNr$ORfmCKP(`_F3tkk9y2pPaUK9HcT=`_}%76;4hBc#w
z|6tD4UwTjf{0a!+<t{+%67u9>hk4E3V*G@{Z$M$v8{$5|<CTK>KtH<D-^LZZiz%+$
z`A%<i;ka~ZxyY<|bhWGuGD*rzdhazoFOlH$0fj=90ijnML~5X9hAkE2OWHa*UTgL;
zT$F3QM_cFCJ~O8zF;01E6IQh`@w`3a0^ldXUxi*(g(NAGklx!*F&PW+N;8~Y0}e>@
z$2%YZ-CV3#=hMAGad9T}+N=Hj(8>40UtY3eHa9wC;3@zKHnp|2;bPL#(wM&<eBMR%
z6T<175hbyLo&fw#!Q4P=W3;ITh>}|JsJmAj7Sz8qfsuGW2J1V4x~^~q8A@HjKNrx5
zQ95|a%v)UpkUoPG)LptDU;<8BIveaZNZQs`L<&}Vj6ibK=%5W!X^Xn)H4*v_Ud!m2
zT!ks@{Cp<SrTNyExSZJ0cs?=phWlz<=V!PVfkyUOSZxWCoMI68XQ1~3Fef@5{|Jw8
zBjG?{64{rl8&V8vqsjX9Z&x@2mIhZwlO)Vz9&1|zTJkjVxM!nwYH^rR=g<Z7D=My&
zK;RW4fo%`wK;NMucy+8s1kj2(rFu4B5=e*g#q~UP>28m2iJlS!9YrsLd4miEzdbMD
zVHZh@k3W|O3eEA}+U3wp2N)XT;<TX>SJ-1VQD=Z=tge010N8<t^@ih<7e*FJ9=%F@
z{^ge~5ImDfZjFGfODt+0&$e}>$ar0K4HNd;`JElhlGrIu=&qR(*J7B9H)Oe#6nd~b
zpYei~FKij@lDXS8TIp`h$-#lB+a9!rvewoE0C@rMwnab)C@4KBwhmK>YuuZOz9M%M
z!4QXt6sO=w_<lA6)mI7hgA-ujG!>92@X6q%V%z$;X&rp$yN$ZYd;ITnv{N$qVe{!e
zdRk50fE^JlZ!XJL_%8JNqcZ)qH@g){+@K9}Mlk>GOBSfJ%`Ya$UgtcZ3g1<@4<WW0
zp9Or#g6d@n%q>4}ahMP*6AjcE*S_2aVHg84D^8x{O5bYcg!@(lLe%4hhV82eG=giw
z?^O=j^?{N_;U0f{o;d%*^IN5roR*3!6@9`CmMR`Bvg~sP4}KtBtMnV&d+`_Qr>3Tg
ziFhv@W%}jWV?-CgJyp4>BCS3jz<^#!SnpfQ3b>^M{}`bYKAA~v)Y~O0)<L8@PInj^
z`uIQrLuLiBPe3m8=4?ldy0pab5LAGyG0xPW%p~aBZ39FH(P^p+DgLs*yUCh_6^K=O
zY?w>c8q$>;-+BzHY6ac5fBlLG+MGl3!vK5km_K;%;OEmfpnh(M791$D^tlVrsIS&{
zEkR!;pYoMW-^SR$z+kUeTPBC6|1szv%%UosySRqb&=4uvD}KpXAf>K@Qb3=f-@}*t
z^7OSf)9gS*cC6NsR6Iw@X4ftVs9&#}O}0*M$-4|-{shVd<o7IS68O3rY<J%i_S93a
zqTY!^RR4P61h5tCOz%kk!py=QIQ8~#2c>zBb5@6bY51M>nqxQ|M3Q<QAc3iVV|`@c
z=YLMi**tC&!UQb#yW(6gln@T~*cn;o2yElWP|69s0DyzgRRZxIyw)L&Q|GSST$cT(
z9J1|soCFjMFQX0D=ZAGy0A&uI#>mOM{JFD>+-_9z$}eCin$cnX)HBw*>;+`j+`Q#T
zFkkB&LtlUJV92bvC)+{+7@1o3Vz0?Y_`$49_z!b9av&EZE3UeF*k{X{->Y%g`3g*7
z`i(3E6Tq(v8acb8X^2`^&~I9@DuELYEMQzJp*uv?<n=DO$xG*Wm8xmR`pSOAg4?U(
z)Dd6G4a8XW5dh*3e|~x@0O5%*2PhU1FQ8GysHt&6`KE<tB{YdX$g`sI6;R<o1J`~w
z(M4e9HjkPe@?#{G_I_t;0}k5%4E#(Zg8t!TDafvPkQB5Epl0HM#Z5_|XbZxGZnWV9
z7l7}=u7h1^;(a%%`5zk3W5GmM8>gZqc;5n$jb>F^`_u(GZ%8sou=IiVcV8AWgtV1a
zDju}rQhNa4{{W?<5uQ;vH@<7{R-?D6hba2~{y3ZJUUK<{|MGAeK<*$S7#87o(rytD
zAa&}sOxR9-US_TT{?58wv1R!j21G;JIN*R4FnX<hbJP^0vor$C7xeqAO#NE$`IXL@
z;TWzPi-)!Nt0TuwN-C{?Wy5oT7{Pl@@`i18$t|&PUH=^b^o+736dFDxKc8NHt#Pcq
zA0#8Az*z*@u2@r|0hu>Q5Y~133p5eSL$-detH$ywHzI)49t4?5ith~B1`?h13dB(p
z9#Z;Makbjvg|+2>wcU51?bZ*q-IaeUE=F|%Vid{fE99wwDMi=MmFQrLiX^wzTlnZk
zC`9A#TyxVPB#2SN0EOZrqVv|<;W}5BdoQ+aj6=$5`IH--LF%`rUI}ED(Lrd@3V{?4
zhmJGaB|HWom3siqP9)-F{Yq9nieJzOCoC5G^UL=Dhk4U3zJ;MxXJUbRYOyVMY(v3E
z=}^E?;aq_2Rhsld;u9Ff{+}!_LzMT|zTnAALtfDh$Zu~h8(UR|tlW0_?_K~mzhO>z
z=%bC_KeD}e0A8p#9TqybHfOX9Jga64WNvj-%6F6)W`C1?Zy?HrSJp>?c=9s>z5Fpi
z#NltQ$gLiO1<%B5%poS~Yvu?C+qf{$KVFn|A1qQq%#Oaka6*ieCURv3xz@xC1v*x4
z(i(K6@?A3g27^G}%)Rbj(O21>Ne3h}gl3c~pm$pB2xgk{m6Q$oOC>4Xc@pCebpWxa
zT!8*c1rOi+2w*nBU}4~(7)*!Cjq%v>OrzC?f*vg5?RD2)KhV42jVnHpYfvNP!(wV?
z#$i|S&=9Zon!~S-G7+LoVL$V{opJV%Zl%r6Xld%OOm`{vKIq4V@_qQwGx)Bj^oh=H
zi6gi>T|9+|s9RBkfkyDlZp{EbR+XKfKk5QVB|kuEx^Rx`x@M5{&#Mg72~Uw?<-WC+
z^Ns=sO*lSAtF7H3R+*d!zH;9vS3pKYFa4Hq2J)79Kn&<?WMyS}G7A1-&?x@_taW*Y
zW2?0S5T9amv0gDK3S96VL7N={=o)$R5*zto&^^b?xeqRn1CF7;xbyxu2!x%LHWAGJ
z#ucynM_pK0!ef;w?K34^G5hihXS*Ns%+AYKk@}?PIUScu8yXdL&=99NhU{*(bjjFu
z1RXnQPfyQeQc4PEK5#H=3%5__Sci1o@>8JLxAgNG!yXTWm;IOkm1!(!evU#IE(I~_
zb%{WWmYaC104gYVefinM6t|%gnxE(;aT!t3;YT~aUdpWY<@Hnp_OI12=}TNK<$*Z1
zx-fd7all($1&ovVD=!V<=(~)W%rK~8_MPgp<e;Q0zLR>}ev!1hSw0_teZ!O)=KxUC
zTiODoNBT1sg@>MCg{Z_p^ir{1!(MK7jDnjgMg{q;*rsM<129pw9$P6CG*_E4T~2JP
z7{p_WRWluqpw9^D9X&3=6@75W=-Z~o(N719YhPITHh|cwv;h)f7A(YswrBs%b^W0?
z6+amkvVYjh0^;pwp<><?DE5?R(tWxwTLZ^NTUUA1Bqz+Y7lUvbl7ctx_iGA1egIhj
zDi65@c-Xgx!OA{Pe@3Up&`P`3L2ISq7|6?8@4*U9_BhA!W8!s7psVv0Gyo@*T)NW>
z|7@xY@7UrNztCO!q@oLS_Mxvgb8QY6FHAda1ID+j1;;@%N!h52+Ae|P-T=K@-bqII
z)f`M+gFQQWPC*~l4M15ebR8@JjwH6poA9SQ9BCCgM617%RI)7*bi(iln;80*Rh5V3
zw#mi>XqzE7H#Y-7K`oS5NK@yJ{|<a$B?VfaTi&<osbE2@W8{t?6VrWq-2WIb_u=j5
z4pAqWhQmE4>g5S~844j?mxF$&aYl!Lj=0NwfgYHEDbKvchpD$&*Y55f-Z(T21uZn{
z5?DdeLjqlR*%JJY!};^zT4}5Tfrh^h?^<LmT%HvOl>N%sY<qYZsQn$m$u7+m%`2L(
zUb6J>O-E1CiQH$NYx&ro{?DiW-+#JKxYX`<!a!(D(DfCzo{7w!Y;tu00!uhH%m+>$
zr>&YPVa{IXG^+e}3uA2;wc%rX#N;%yb^-gEiv9)0Ml2>1<h<C9&uqa@ImXt(+c~$N
zaJKy^>H0R}a?)Qmy-cveqr;5fvoH{=sGp{r6j~<Om<ue0?R&pZraRb_6oO0VKDEYg
zsd#i$-|{w<VF~eQ?+5QcHxE3vjV|htknHpWH?Q4Jr@3Z|f<H2j9m~Ue>keO=UOJow
z<MoHE|MOqb_}{N1AC6&S_<wlc*Z;Ref7rJ%XF3Bb5YDf~e#E}QW}w1Qm%1J_9L^Y+
z{qgZY3GfphiZB<gy<)7HiYupfuwgGj2q~Jcv{ZSx0SH*#>HN=P3qtZ%9QGtE7dlaU
zF1|vq_cl!CRs4Sk0zt_xB)D)I);Gg5HZHsnvNWL^i4{X-viDs1&$%v$7Kwflq&2P6
zgJIz)-*jv$r01>_?4=bYXmDCeh-T=#RZ^USrIy(_{O21<yhkRM6>WNpW9Tp2-%Rlc
zxkh4nMg!WPf7sqJ0{a*jnB}V`Me_~)3;uJH-B0>R-R3P=PwfO1)7JS<T-@{Hr%tJU
zM#bH`<)NN9Akd!miub&l>LXP{)e8m&>#w4+Bt@#9b_gKAa$Ml~^XxX~Ph7CPbM*LU
zj#H;kUvo_BB<~w0xwJb&d?{lC0|7%+TJS(&NddJsoYvv%R6B2NZcDK^6*x~)bonMz
z7*;S^_l=8MFmFEJsYJnkSw5WApYlgY`^xst`}JRnw)d~(AHFGh4ZMl`(YZ7ArO0m~
zy8m!nLUP=bfP)guWiM!_Rrw@tiKU!_VTivTAD!5x=AqXuUs^7`L9avkoTV7=-=8^N
zze#CFuXhv*3|YF&Z|y%D_+Q64_;`#cHdekzSuAlcdStbY8yM+psVki1!yeuxcE7@3
zvi7Nvx9#@Y;<>&S%7^Yk>7s*o!P*J0akf7MtH%v94906=@2J&_-4bppWwp)|(TCBH
zmi8pTp}ur)QJ5$`=Ka3XxNyFxw7YTfO5xDC!OMs9wW@kJd@I~|ylfA>xOXG7u5NF4
zab}VI80BzwZxY$-uPpTq&=D6mGR?*b3Wc@Cep-Ge2~s@L1rzWLIIUwVtoZ$3jG;a@
zpAO`72=H6ox_OdP&{+JyEEIY_IK1zO8zaL%8L_REarG4NDtdSIF_#?v0*toCrw%uz
z^1T)oo2#qd`?$rXfjZ_7hbPw71%C>y2wu2dxjHQPb6nOynQ;yatD_U^HC~mT3Spc4
zR}L|hL;I7W3f*KSJqFL?#?ScB$4Z{k-~aw>iv`~iuwo9*u6g?3+dS3VdFwX=(w;7z
zo@IRdiPopO3o2tzUuPYT^~X0i1IEXn#Fs?`IAr`U?Y#*!o9o{$tko86QEb&#QM*;0
zRMk9pSF0ncrkbgin#TwtqOEF`(p}UnMHLA`4XGgnEv1GkF-z1G5=jsdgycNZ{on6i
z=dABrXPxyw?|0sBoh)5l>lyCnetyICyYB10bH|aQ-Fd`KsF#2H*o6qdv)YZdqe0;)
z(nw1&7+M{^eVpgx)fU5$sm`^etE${aymYrifSI;*cpsU&!_QE_w(m>RmY@d*xGz^W
zvmJ&8%2Ryj5nSMRBK!YYwO;ozY$2%LEi&wy;2o~}hIa0%mz?U^Q(WbXwb_0Y_Qmk8
zjyk>H^{cKNm;1F!=FsW?{zY&8`ZDW<yF0fb8+awrU;7QkCH>lL=s&p$?Stbyd1ZOE
zkd}H`zhxfPA)WXVW5*yd$%U7sCs)-0%V7~&=2^9KC)I}JaMuow?~*hCLSWO7LE#<L
z37iN!*E)*SR;Z2Hy;e&?NsMo=EP@aA;`k6#660TYj@51STvcz{6bP|?HE8K(G0Im6
z#3_=L$lHH^iE0jFW)O<KW2p~#?Si^W^{PC<3T}mhfKv_SE!up69a~(eeypp^kFuq#
z&+?;zU8<_qRp%z`ckXnsoH8ju%VwJCmehK^3-j8^S$xeY|7`~?5by7-2qCmdjBiJp
zXVA1j8#qCNy^=dRbbyGNkf>WAuw8n+%O2Z30G<N?z<)W9{afS4$LE@bI<i`!R)lK|
zy2zqWaUueRiYVM@B^~5TtH6ZaWt5XfNENtjQWcI@{euzr!u?$2TV5f3;R|K+Asy4>
z6N=e2T97Z9#}UtseH$Hh%SY5x&U16Qw+u+ya7R92FfyWiV`P5KiZ$j3&X+gWSG}|S
z#cv>e1c1DA#VPjau>BakI5XtF4?SBxpf*z7hXhVd?X5)0k!mY2f*tXpH89<axqT1M
zIi>BAfZ&C7(-6j<qHFA%AXf$rCxCP8>Rg8#H4~tHqkI`&J&V<8wT|5KS=PUu?}ceg
zW(cyIG|lJZ8#KwMk0tXcz_coPwY$^adYc{b+`A0mL>;knjl!qNE)`E8guSq-AQI??
zZd@3-<=mDld2c5=<FF&>_0(WVNO+g5M^`t7QmbuK*Jh>v<AN{$SXdbB+YXN=SFkIC
zW8S7v`={XQATB|%R)A-Z|7Au2PU0MJDA<Sb>Evks4zFP0l59t*nmI$Y?+qk5+&77F
zey=@1Cr0h1RHb~8t0;{I%CQ8iZq1=F%yYsBLbr_9_WJ&rgS>-JyXOAHrJM({?^EDT
zhscu2ZwQoKkk~#y%fx+>jj-NVJjKmoHr*!c5xzSe3IWvm3B_RwrV<gitd8nOe3^RM
z1++;QZmhC^1b~dIppiEEKc=7eg!1-3>GH9~p)<n*2#}PL#zl8#0~ESWz~t#MU!CP?
z^|ZzLyJh3E8d)*>d>*=Fvg#$O$Yv?Ya5rp-7qYfxN@g^MIlQ4s!^y1QqiyLo+OV^u
zIdGsf<HsIHGJoP|B^qq;v(hkchOPQtGSaQpS95c~(%Z3R_s0}Lb3VhVgX+V>hudT@
zb853bgL?4$yN3|fG9dG71a>eSHhIwRb9Yv(&kiShpMcoJx05syv<|FTyL%|e?twR(
z9qzjM@uUp1w#i*;O`{2AM1vJQrJ*3pE1Qy=U+xHU(SPd1L{=jVaUO`=QoKF1tUbbk
z<{hw<k2W_!mBPE%ofyTW*aEbhxeOvcYoI1~qfT&MdOJRbD!qCyz*vZD=DxH`?;=Q@
zOBkHG<)EcWF}tiiSrNuQpxRi}{$+Ig+DJLq-L%#aH;^Ht`l$}gH4#^10W;U79{`p5
zeiWvW_fUnmFFPcAHblLfg<zS>o6B3m=O-kNLi`zRI57mvs!gQ^J1mK`QnjAvvYulT
z_K{~lpbcsY4SajIZ{~4|<rdY*@_$1m8BqcT(-uqw)h5eE3e!Q)Rw{_#KBPJ`hZjGE
zfR|){?56MjRpgK=53rC7(imx$cYwR1bi#0d6(3Cm=c#>wnr*9FSZh%QcEy(<`jGJS
zP^u>GM&B<Ui&QbkLK;cbzNKe43=(ic$!b?;U1gXfV~n&gAJ~JW+S=>g;W7lCTh4`%
z%q8Wi0?Rr}|8Cois5%Q&$KGGHQ}uAbZkixI=tbzLr4G9*O;37zti;2ER0G;4b*^=Y
zx2^WXS?#Hp=!%p1XM^CL&JfN^mz3@}-`dHch2299UhlX~^1N-nvxC;=)~aoJR2$|R
zKKarGprzM@`?<Eq-aT|?VhfeF)Ma@T($cx6cQ$?;<Qo3VLd7G%oeR%!)UwGTAfOeg
z*qh^7IK{4`5Zt9-4FBA-&|US84U<FKh`@p=vy6pyRyrvW_tb3`6ZH4vJGKG_$>TWk
zhbiq6jq1j*dyG<2)XB3UGS&CHndUN5g<Oz|%)elct*h%1^mc<5w+{&FJ3y^KbPq=c
z8kb<<D^th~tH^vM;G!e&qKF4!qt+DnkF~$<1r2?T1P@SmL+!VX6{+A-7+cK6wz8&Z
zn<my14_44-++z=Jn)vY>?5;QQ{D|`Ti>awaDQxe4$irj!KQNgi9>NaQ)n-VPZ>st(
zsxF2Q`qdh^@Y~N|oQq8<3{ERy=WZ<KO70li`*xm>pUkA=T=+^u2e8`%+SWM<gs;Ok
zc5z6(u|7MU&Zb_&vLuc0uWS=&QXfV3&?|7y5ftA3Fuz2LdZO?hz#~oz@hGkNThwt=
zd3gPt1Ar9yaFUsC#4VgaEOoaNa=V51EgDwXI!#Pqm}Il2(M~*h4+0>$7sBLWU!H0$
z=`rnsBm==;w0khJ6T6*`o&U(qr3-iI_nII<RlY(Xu3=J@5+a!+n*H1;ciL^MvTXpK
z{|U-QIaJu)%ZuYS;*=b{H-x$0oXfEzEsmU-uy?lw4U+!AjU&v`z4(^?B#q-Fk#r<9
zaASk}09(AyPW*H8Mzir%dS~6Ija5=r;M}jazDX|s?m3D`u|U!2&+9DEr4D9Wt5_)a
zwsX&9pW+HEO|nD7tr;!2VH~9Z%`yj^7{=UVQk<>l^eaoane~|@;U>+w3CbW{#n~bg
zjD3AGy0!B+o~5lT7W-)aGY2!=T482em#SC?=uGpTQU@ja*c2a$OPrlf?*~)}0ChV(
zIA?kwtDS4kybNyml>G+0bK?Wo@TMc(b$+g!`TsobtEG&uh^DW0#U)7pBdl4E)0PB0
zHE2U=ORRrBpuyjDb51priBq0Z-`@vlrT}vY*iOi;v?Y`U*zSv_<DIR6&MAWAKO2+5
z*?yR!;MQ@>6$0AnQ<D-7tvdYXXEYQ{&<oV#+6!6c@=l9|L=~Mcj7J8mS+}!1anF%~
z1%chg#`gYHoKdw0S2+0vEVdwbkPU-l$xI7n?<+t*4TUhilEipde`U~b(IdG(_rQz0
zOQnY%IO4|v3WeTiA$>zatRw`-xWfkkMYdsU)rzBof!ZeOf_;Z#nTUCB&e4YR+?TqW
zb`7iT3FEuW%}<^^Dk}%MT$RV`b&lm?!@ChZDSelB>D;~_-NvgvL{?w<U&9C9PKdC>
z0Bwyl?O_Y@XQX|p=04O*ZFOSYuG%7@RPT<xDF&?t)6#1U`#fpGJ*y@Okg4(aVU(5C
zZ_iWFn2m2Fah#NJz~%}GhojB1xJhy1)EK{`jc!mFgeg0#+ZfjWl886Us++9wEy-5B
z2j+JD5y0epgKeL|>i^V4mqdijF5N=9ASw`wi1<>6i-DU&MB<(WF4sJi6KeN>qK?3j
zb(oOnUJnvOzIU!2SHDQiaBW2)8em_RJdrXUqPt)%D>ZHAje1v}avz}4b$kN)!#T4~
zoEP8c+B%5;n)$pd4z_ZjDx#iu2W{}zxGOSWEg>?6<3sEWv90193#4i)M{s04pnk65
z@D_IuDPxaR1zjJlOM*O3jCf!-t-g!vK~-+afoRorUANKAk>Q|VE30$00+Fg3AGIQS
zjl9ZwEC&^81)Pq~iY!XoQ!)(3%Qr58nDcAzNk5);YI?ZVvkp|bq8mP~JFiw9R4mTc
z^y|_VwiD)AvjZd9m>I}Z#uHMf&=v$I7=dYF?HaiUb!2=5##d!~+bn2EV~3#Qk_#Ot
z2WQrY0DM$y1PX<EwP4f7Feo2@0G1r)u|T|??gK5ok0YStRjyXNu=}s6mc$mEJQ9FJ
ztq<+`7g2}b9pv$Q1V;LVO?MyHT6NXdr%jhkd^DjU+9liN)z-IW=rz8$r8eo|cqv<$
z2(8o}Qb$VHwQ!T4*xmYbA2JbG5}ehB_v*!Xz0kFC>|+B<D3R3kNQrv@;B^ulEqhYq
zdy3eIT8}T81j;2W#F7CA$lFTV5<v(zab~k)MJ*~2QD7k>Qz*@|7#coqg>N8){@jun
zZ;f7S=Bv-WrRd0#w{he^9ET;cs{fREZ4(EqYZ6|WCf0}cdh-+y_ZEy*Lon0pu|6&a
z^{g6wf;deF44gTB@pCrf=t!fbOj^9J=@t~U@KO`@<1u)qdwv3f^+r0V(ARqY6<6D;
z5CtWzNncwyIeK=}3Dmq&Gj4P~J=mXtC7G~jtbKr}^7N#wPXCGh_^;wzDuX=<Xc>oS
z#SNg@B_-Ugbi1GH^`zZK&7(wB0f<cBi3Ln`ZEfxQu(6e~aokpbELZ-y7Y3x1MkC?D
z48>8kX78LJRSP54X$r8~;*FO=UhCZaJXa2+ED$l7_)q@6g+3O#m|8DEB0x70crzrk
zD~C&RoT7lbTj%yp>zk8R;=UrxdqB-eAjOfoaYhAg`n@Omp)kfOY57?Y7xcV5fRa3l
zEV$ifW!_lVL2t;f{Svf^RIs{J-&pGu#!cvZ6*)Wc5LN3xZ5WxVncx7%<oudGLS3+*
zOAN4Y)%&IE6EY7sx(X68KCYWp|Ab{39c71aZ+RuU<7n!)&vM`5+cpUU9E}5Dc_eKq
z?Vp#Mq#mZ(wuG|WTHF3flQ012C#q(m16{*s{^mbGr__7mXdk2L##x6sqyIcU*b@f}
z5hUumxNf5V**pg*D^Vm7qy`aYNzkRQCG7b}&2ezp>g=4dvg$z@YkeIPLlP}v`t-3_
zWbEH{W8qNQK##d_Nq4qh#Olgw3`q*tK!Ta0?fvni2pOrezlhR>{=Im_IF1*Qf(l`)
z;abwYM$0aaZBa-OeSutHzxPy@7mpw#X~o^e5k<8d#{x^UrM2@m;ynhjy1zXAOcwnp
z739Ep+Bd3%^Y0^~7AAhVm&a2D1rRQrz%MefNBwU+Kk(nHLmm#0MQMw1WUVL21GECv
z($>rk?(>HNqJ=^dkP+2k9a=pSza*pLAFp@Nyb*!O4B>lX+kJ?XR08~Z%^XF!^a<j_
zct^6t;nPCV2xC-)$7~JJT;9l++tuP9K+)Jj)S0wNSTal5MYWY9>dLeqBq%NrSy9lV
zi>mbnt^d<GZ9Q>t8;Z>rO<!+OswL*k*<Y&NzylzE)I<7YXT5Eg*3{iLLVihaTq<0o
zJayLU=%~K3!{J2-73GMve>SMAJhgQ;-d;<&Vg98{g@hC9<KwVjcHRpFw+#F+?VkU_
zX4+EB*?3CkVHw?j5|KvNR&`td1wrL0R<TC#VA|69uhP4n78MefbuBMlP}zqYKk=Rg
zfh`+V0v9a)jwb-UBi3KK&<eY=;!V6i%F{d%CYHk4Y0MvgKmIK9n>`9_h02JhRZb4E
zNzS-Bgjjm<1a#=7CczB)A4DB6AyrSkGK*1u(pG&lgkg{CLPBu_OMOcW^qpR6!gEU#
z_f&C$+ihBc<hz4dOCSkz^_ais+m1^jB}xOR>Q;n{jh1Tpo(Om`zGBW0aNie6fD^vz
zG1tF{p%kyGSdV|3Wk!)a_mqY{e@x!tb=A>U7br&Ouq{Z(;!?*iWAGxX1|e82U)ZV#
z81(~mXwv%uz#N!Q8qSmH(<F{1Ah$8zlT3}O7kDPV2b=3o52(|$Y2g{$l3MoS2l_|_
zj8kpNDf&~&qH(oekzy$?t=ibC7GuS3xyH(}vbz(@k^=Uz9D1-h6^Z+V2%}u8lD>#R
zhe<jm8*9#<gyOt?8s^m0KCgO@0!>l$f0y2rLsM5B{W_0ojo6)ETXd5c2Y6r^{C%|4
zzt#e@?=LR^zJCUM_8t36BrRfi^)AYjb$nH&bqi{_=WM&fo{XSS!9wF)m6ylU<*!lh
zpEf;v@qCt}mYPgQtpmU7zZ<EGM{y}N_K#xui7m8h=lCd~F*pt^RdHMTBvQS-=_D}9
z{(shD=|Umk?iwx|#QsQk!$QV7*ETdGfIb6W$ye!#WCHNU(P~3+!L=+YxHD(=22R*g
zA0WZLT|dd&*YTXIV(hB-t^Jc8UraTZs+(L-Fo%7N|8)yicDrwrmawJDTL_j#-QW_4
zdyhs`D5VMV2P27;L~;q-_%*V+<*j=*!~|76{<WNp_*TH=UfJ0jwiCaPkH)3dk*XPk
ze{sUjwEqpdag|H2px<?Zr8hj@vEeHant&O)u3_tHwp^<MwGRG*AI__PjRZUP0uucC
zh3+iD312gct^a1?b5rg}pF3{@t>hg&cQighX1d0k_tz|qVaH#+KmHKVM~3a8_g@jm
zB3w7i^}xT}>=d&t34SCE7*)H6I5U1&%Je%B9#f=@4>wd<)P*v{1uG_5mGfJNEE8m`
zp$|cwxst<9oRMk4-9!9n6t!+!uC~CDGwPT4c)m^CLMQ_HA-T{@Dgj28ku%2t=||gA
zv9tlbF+-|DqL6x@zJ3^(vqH8B+R=S@2Q!}2kloh6Sagxvy0qo&83N_QuUVfY5T*t7
zd4@>FNVYeShF_%+!rPKwwI0AY>_<<W3J+yi89k{GHs{oZMO3-@mz0e7s?@w2UESd^
zo>zNoivN>Isn*n5o;GDDPh$Mr7-a;F8jRRnPG^H#_Gs3SemeTyzb35|{cP(waq}!{
z9%JQT#@6R`3LQguw3Vxqqw2D1<{Z>Mi_=2DkkW<^Cpi>A44yT{*7>vZw5m4dNyB|C
zUzD^3#`7_>q*d}SfqY}(5@VLCgl>FjQb(p79B&<idJIyHVKcS|$M@VKS@ksBYNL&9
z=)ZAT%uZO{M~rv?P%dn=_C~eicYFAH|5bFkESvnC1Ga)rG+#yUz={rUe9Ko$8OJ36
ziayO(bgiMH{h*M#wbL&(mmipKn$pD`HnLo{)WvMvVdTepaFh6u%nsV*LP<nU`x!h3
z;bXt$$r!~c?fYwrPXIUxEvyAIrU4n_GieHEHN)bj+tDXH{((bQTVbF&k`c}yM@CR$
zy|_$hXlz$~#y5EcpE=>j^b;>`o~U~O`s_sD0*YN0<_%hX0^t@1H9#$%1%)QQ<|-f)
z@4S9VXaA?n!u(q`eJ(=$&uA0(f_USam61fXPRX6x1xz$hrnXIZPIZSO(mx#3iB$zh
zuupFv!sZO+KE|!ZkmgRx(thG29@@~GUwLw6gla&WvM%xDLsS6#b^F?~mE!**b6@`%
zR^LX_GS{}wIF!RZZtP*3t()q~y9*<b0PAwZ<ZbklqE!3Wf<U+x?kIbEUZZvWCrK9v
zHb<xGfte&C;ugZ!=C7#|V8T?rbqXPIbaJhmlz>PfLb&cOesjT}?Q?f)wsR^L@m$UN
z3uKgJi~RPXtpT$!70%7eNQ(m}x(QaGiRMQsD#Op2z8O)qGN_0~t^6=XP`A2@br%`(
zE8l*^UsW&L#UAWjYsa|>UnGu@{=~&13ak=JR?STcSG($(EIyeqK1T~=(bO@G^E})-
z>r}Lj*s(yF^m`G?Lb{nlGVEScGLZH-uyKOI4`rn9(NDAUyui1bw@d1A0D;ZiTCGa&
zvn$ezeV{iJbwj}xgu(j;csFz3!fN3=WF=gR6;1l@kOT&G_EVc%IAQZP_2Jx~?KqH8
zwa-2CuMkrPnHLAl5k-P~E^P~mJWlR4uLn2nb(HZn9f7Or!}QhUzBUtNgby}*`*ftj
zC9F1#mUdXC5zw9hK?JV>-CQ8a({;IT{~_ubomri>blOt~Y{DobQTp0?yi=im+c5I$
zR^P0{1Q#hH4IzC814|9&wpns{yUo<Y`axN$rKJs@Himwpxy^Zr12e2x(NCW?X6M&7
zS_<iDq->nBaNY6k+njD4SnMXSNeO!pjG~hgw%j|)u5$mw-uu_o`2V1@{u^}u|HGTu
z&YBB*wUH1N0GiO!@3@)qIx+>f{)!X>%sYEdgl*feENEAKaVrwpw=IIyLIGVEe(3Lj
zQA94iocHD@nwuccZz~eO=bVCsHP^WRFI4b90~G$hT^s;R-R>B%l(LYr_eQN+4w?^I
zuq1xzn`P|)y5Orxdbk$kH7l_G=P|Ga%oOt>%(KduI?gSmd9oAZ_Y#U7Y{q?(=`=OW
z)&oWii4URQ)8Vjf;bV|RpCoUbuN7*cwPn1=GUYZ+cvcMuw}55RxQy`iJHNb5JI3d<
zHVLbq!iK_H>qJ_MKd!xxb^JpQ+(Ngcoir|~Qytbjrp<M_n(nqTSsk|8IYk6ujRgaS
zyGYiUg2bu`9A?6Z?OWGZ3*3B;P|P%`+wK7Ne{CsIj#R9VlR)@?EGEiA1F9x&doI_5
zJD2PIBBg;gwYBv8yfrXkfOg|_WL>tTHK4Y7ss)F>xq{|P$H`hWre26YEU@<>FSufT
zK|3x`W*JQLVu{wjIB!ezoa@mKaOVWHyX~R6298wuLQ(Ipg#&w|1-1>qMC{pV<UtVL
zlf9HnT~|dN{glx)|M6^a3Ax@kq(eWr|4&;Or9~WWPaqgrH4r!PtNm_*V_maB<8mnx
z>>GSb_>}tM<30h2kL!lRR~;af+U`0Muf+K-&qsrDWEQ$*cjM})cjLT_FmO|a@8AYI
zyJ2-oLb1nl?uQBRkbN*_!p2wl76Uu(rN=6DB+v{7VH+>=Lj~<&!!xlyj|EQxh)qVi
z09v|~!=8&QR6JQIuiFM{4VuNOq7Hw;>ttFbUUJ3cbY9Owx7N7XEe<oba`o>MQ4jG2
zC_*3bPq{5vEvu>_n#yWIzNM;Gj_BtxEm^2B40trTE``)H_tYpNqO>&>N6}yF?ddMh
zGIFm%<uEN+dJqQnI3Duz?$)`wPqp`xw<ic!f5w)kyJ%<p@NhJcFo&!<pgr5FB%tBh
zTV87L`9{aNwL9HNY9raqmeVyRZpk`QTLze%NzFJn>^V0=YC&c~92mGkNA^6ak4}C>
zx2AZ`x!^2V%!QiaH<;*PE17#UV{{Kur`35Jdu>z{Xko*gQMAQc9lzi1U;sff0qU>T
zI^an97y`4<_0Vc?V>muHB^Q&%`<4jij?hC@1CuXE-9|e}vJ1Y99mBcf4ZJ(HK7M-F
zt|*4|HwTf9LWbi?i_hD%Y>ryAbr+>{k)Gl_Qe2N(kGscXybP@@WbA=c=JK7}r&>zb
zudq@$`&l5?f{c*Df(YYM<4iNf4BWMh&Bp2@qZ#$lZ|9kl_D1YdhZ0+rToXsZ>D-h0
zbSsxRLUP+g^n`nr%nt4HcEZ(f)039)0UyuhBQ_s)-&5aZf`kfYeV+#BJ`A`~H3E*W
zwg7uA*D*d04Ne0-x*F7`wvHaYSX10i=0Ai5tE}f#HE#3_a3rYRB!Zg<wCC6_VDQU}
z_gG%alzcLbwgTI`yy`=syZb*iXoX>=xusLY=0ifR!jGp&_2Lj}Gf5bM4swEj8aL8=
z8+aK+CM}Tsjr2;D$<8?P>>zg<gr`E<lroP!GsYNOwM-bEYvtlkVTP6d_;m9@Pk~?*
zDw{C9lS`_2k`;~|eCU=41<@R{aMOF23si<t%-WMy6etW7!BFgDktKd`nd~-3{Qu{J
z{qWJdR}gy;WCwaq@&RW0o+6c9<KZ^;l^r&Yb{O|8=;js1KC%uNAHAtu+a!YHMWRqt
zt1L@Ov7RLc6ZViXh;thxtVXA`!A)?hz*OWELJ>*5w*{k&Ln_Z%Kqr=HHAKA-yV9>_
z;gJb1`1kA*6Oq{kH~p<i<be3?T%p?g-<MRC&Ul1edAL8m*6{7qm)9fz;JbM7)ER;O
zyX=pM^oQgfKk`R^2LF>|>iiBG^fqcXetg9RjKgmgvLgmaAaXVy_!=^bY`zr+x35zZ
zi!Z+wNw?v^AKt>2Vk4r4bao8YF)DCaL98nF88+oHA<mj(8MSs)NT-Uyd4-jTG})<+
z8-Lt<v$de#U(>zD#e5?*Gg_2Y8SUo~@w?w0B=wqTJkn7lqUZea%mR+)Xm*m)d{4O4
zqKi|(oEwBE!(+hqZp@)A5x?0(-fMdoSCl$Tew>KUk!z-nh+r!oU*m1u9l>KKV7E71
z{8QbA&u_`vhDP6tZpj!JeMR1CEfmmHO=?u@fj9_0yx*UcY4hd?h2QDnXL629Ka(ZE
z5DeS8LBX6p4vynll|8|qN5V*GJRWBATE(ngx^C!m)RcB`;%iu!BK6b{7@r!iqD&t3
z>Ek-X{`Ck~*4k-t_}#V{mFalXc9#H~n+tt|ZH07Xl;T8%#}D}Wb5?(yZI{-qvm$v`
z@utMRexPbhJ8DI#QaFe1VLqNRIQl9#psmnd6V&cfcsdkRbVyaMAo}Cr?8#}|Ykfnb
zjv~3DUe}_hVAcr4%#eCW_vLG+c(Dxqo-&#Iq)&7DN;%zYi~<li_nrS6#wwX2z|eDx
z+vs)<PVBwA<bL;Jy0&5deLW+&RrgHA>u0gv*!aWD(*-voW)l)fpX6<P?(+G1#2u`9
z3LbNrd+5?XoFs%#I;5&la2~W@^)%|dNZ!er{yWr>v84JV%WZ`>G^<XijyPj2u&1MJ
zO;>kS?`bev7h8SYt9Jfe<*TCsZKoRM@K+B#EZ_HY;G>+A?$5XHKC#7}h#PfxuTa;#
zc*mqmSY{J<oU|{8zqMs#-2{iP7B`-~dQ>PpdhsfUr<wTaZ8s6~__c<2E(ErE_%3lZ
z2%H&SXTeOj#MJq7M1rnOkC%Ae{LJ?O;`2=b9C!0ecsgKlHvwXr8*iOhJUkdm=0_*M
z`^^Z&4&P2P(oi|dHx~r43{(3Ge3R|(hW>mscK#7P+5GsNr;rZ9&ZC2fMti0j)+KW4
zvGPT#nNOb%y4%3m{*0bc2QqNmFRDp3YU-y^(VI#hca0Tp?3>r>fS2_oRQun_@lG<A
zGnAZbNTq7OUpO*x&2%{LEILRFA#JcEF$v!}Ds)<sb9J4LOV?B$=!3Gxh7CHh4<)mN
zWUa|WDbpIb*qgdgRpapJw9hr(HUVD>&7l?VKI<{!Hx93`G=xd51zXSjkLOG|Wow0)
zy%Nh?3sYMNBb=D}?55y6eXnjoH7dT~!&!gnbY`ZOnsNBS)2R6T)<>p+9*1Zn^)EtI
z`+FSIGtF+@%}dgA)<1^e9Gs=^w3ai3Tp_=|{ev@7xM_Q~(mx&Otz+DPwpS;m6T;W5
zJh$-8o42F*10<r%`M9m6i0DS}FmFXwkk;Kv<(qwpy+@VKSQE~jDmU?$$30cXX)0Ju
zRk&3;Kvt~8v_oWd4P%Fz_RTM8%ANsB$g11@rr85zU=bw0)$Pw^lEY~wo9WCWKT5k|
zGp!6ToB8vaReL>~zudgL_~y|%1`F-;_!>1Xtx&K)zgf9S@AOWy@c$TOba|a4uE6<Y
zw)*x+Rqyh<b-J`sNUkE*g#Y-=o*H2dg$)iJsqTI9{b#q{nRrt%7l-@s>MP&Cu82Sz
z#Amm(MHQdO#$6GwtFBfQ#5781=6`k*2(en>fPZ8iSGDhUDU8tsd4wO-L>b5Q{!Z+F
z9qjEeU4JA!x$#l=P}5me1K+!_?#d$<cQ@{A7`DMng)F@Oh<vPW$Z0G#&vWfuPMw*G
z(t8-xd~WoN4V$>*VYa9OJmn5CC2vL|VLW&EjVSzT3IIlNp;YLI%urF)W&&2~Fe&rH
znWc3TtV5JyTY;-@?}Gaz?W*zVrBID5f@GvgvPWlJ2atfO1;(#>ekbC%cGF^v)PD^H
z^({+u9o7!XcO8-mx$e16d+d0-T%m7Kw7D-rR_D;<sfouy*{!wWi;$@H1)08$VcUC|
z8uk(jw3IvSXT8fi(nc@3_q=$kc<7ER?a?|}0K|UpHf|k3qMbUuWF1uF(*HS?I8^Yk
znSyNNb@bd?Kx36uM^w+YzUrZ3`E0&D7n}SZWD%b;C~Bxl*y(jLPE>ne$c~@Rsz$(z
zu5GXlCQ@e7l@kN9Y%|i$62#zFyO3u0D<5fsl0E6UGdQDe#}hYrlQPYSV}(>V*HXwE
z(&tCQ%~wGH76(sUO&ig6x0s^OKEl9qAW@f5ZhcZH1%r<6+LFkO`Gqzi-VSK|(k$C1
zSKDy3-)BDPeXCp2$sUQfXASd78M`f*^D|NMUTr4B@s3t~aZ~PNYsJ5ht=3%*X%0c1
z>~7LFj+@jBd5%%zZMz7Q+P(F@m8dwkz?m<Bm&k6GLSy`piwaSuZ+&z6Vup8V_LLa4
z7r9tWR`7>{&Z!z2^%Mo#Wn^SqrF8x)tF)ZkOzOsOf3o**z`g1nz+Rh!mD}%VxRom5
z;b8HqCqC-WLlh8-Pbf#Z*<E00cZg)xE|vJv^bI5UC%NW&HwsiE`1-JCZa~7}yXS*F
zE4#aK8Jem<b|-x}nWyPup4(u3#|<9uvRJc`*U6Ts|IK2OPO==%m?!vuuN1=jyCjV?
zjYdG<Uq{NjWv*o&PKL<+90@w!G8L#r&r-jP5A;k7AXRHExraH7s~N|oXPXf}sj2It
z&Zjl1s203^-+CI<%uX=H$UiIwA0d67lMB)>2Mv)Ev9&rz*@aq5nr|zIDjN^Aa^P+E
zFmPj*e%GR@9pP1n-o<kES`4tEcKb!+a6@;Gi|NXSa)9|ktL{S*@5P(HrdrK<E&LWI
z3AdjQI;!J+DeVJjF<vq+FVZYhUR2A*5<OrosPp2&M-Q>_wjcXg11d7DF+HKC!(#lx
z^*3u~9WUnMtyAb@Bkwb7OMt|bMfpgo8ya>0!~W?2s!b(nI4kx-{}|IX!}Q%%HAE#0
z)WyDjN4xw92ND=MsBvg5*qnM~*7t$oxXYXzc6`6d&3HMTqsQ38^<U<pK_d|&c``5j
zz0LJN@0%2QwJP*}yc_(qbT~hz{So!hOsVg|>9GR2=0`YP2+3YH?nyeZS%RCurzggI
zEys*V6ABDS`N2;r2#6Scee6&FsX$Bf1}J$E7gck2P$a&*DoXSGRPW2P*xvOFeUTaf
z1Urgy!om;Ap`uAjHc^?iq^eK=5XaVUi3NJx@OUpOzpyoLTs&)^e28+eBv5#<O!i^Q
z<8OJ(sFJZC^I!e<m&l*UO!<D}gU|0L<RFzD3liSM?jw|}O1X)rK_hJIAEJ!dJWzJ(
zr!0-fqt!QE3f_4DuKBzACk_=r=NonhXV2~}IRekKouM7eEZ}p$%tr$_l=mDp{08Mg
z>TV$O9BNTU5=!+hECXO`_EeN<y<B__Kmb6K6{kW$@h*k!njrn6!P%?rNLEosqsnO@
zj115EH|xnG0<(Sp@Is2_tvU1gnw;XiDBS$8*E{pW4f~aZgzCVnO_b@%m(<={60h~{
zlUE*LTEsehjljQa2|K<dZ%6!zJb)8kob9{V4%Iq6(w(u_`#2i0G@NbhHcdB7%3=!P
zZva&_vy#4i00_;P0*ULIdSR%CqgIb-DoI+=>2J&i0i>M9CF%%wxftu-?x+eCStdD?
zrWJuKmMdBcfR~|TNu$jRJ@ZMqH{;H~9MC+KvstfOq^edF%eLqZ#+&+S*A#zXX{@PC
z$&r>)Ce6e;^iQ7QY?a$+Nr9%6Z;HL?9aB0ZMR!kK(Ubbko4omCv_0QAJX^d3{j9sM
zhJ~@*lLQ5Q*gEC@qwTwGt~UlT5gbq?2{*_E(Qequ>%nNct<@j$`;`FxsdX(pJ=@A;
zLr29|lX2?w;Oqw?F0a4cG5xjKtsj@aLYpHjfPKA``_=a$KF{6i)iSwxijeWb=TKvs
znQ}}lyJarE$7P?ld<rOtARTgYiZcU)KiZNM?ci9Ovy+t2s_vST${qFcfK63mQcXrw
zR_BX)SM|(7Y(cnJCSy&u3{ZG9(9t7p-`jF2Qr5unnHN2)y&FYR^-FeX+e~TskjG`?
zth0JX-AH+;bUHdy>uN_;O?xQlrPlhldSd^Z$_oM6Whs%8c^j+6)`a3h(LCX60IzUu
z<Qs1v(0PT(_<WV~<dNi{@~D*AC=cOXA3otu3C43S8|jadeQ4FYRmYs(JDQPXw{-Yn
zb|PTfbn{MmcN^Bq<3|}VrN<PD<l}lqw27=%rAqOiKkv}Lho&j(5fVtlpew^Ab9&LP
zp)aG~wA~R4AZ0GOdpPL6qJP(!=`Xrl+GPZk99+BOtR@hg&)(3Zq)dhgQMT42fGMx`
zYJru*yK5|f-*@{|A8@N6Z~7`M=JnUM8yih&=s)}R)HZ(2HpF~hpDrx*6lizX_kGo|
zC0W6`dJ+t<w7~}|;JA~A={M2_^uD0gOL<IEv1-Uv!|;gD;ad$x#|<%PCql=9-q;0j
zxKILVqw__racqZ!qf179vR?G8Zb7#c{NyYyQBSzb);P-}(=)JdnPd*4r-XJD$+`cW
z)^UbQ;TX&5psqJ(lt@zffqZ^P7cXhGk6L0MV+ZFO=Ja%}TIAn%E3KTHxY8_<#t3Sg
z<*5~05ud2vPf>Vyt(G$a3y9VNAM30eETv0gr{;q8F5G6O{1l%^-9M_3a|L*ZHtst}
z(Qkakm|PQ))>;0|Ny14Xi~e2oXVEaL7izn;_ow@6#gJ)ieuO#?&u{n}SN<^9E9nW(
zmj3uw+wi$qwlR#~If$gC)z*5ZIdqeM!Vz$WgH2#vev(GCrI=5Q&tT}`#kwFZtGAB{
zvYq=5w>`3yIinUss0i&Yy7jrhJropkNcA)@zVP<B>!91d`A~Wr0W0NNAs24482z6&
z2ATa;-mTRpH-W)Uf!lb};5m6)0EUiVPw0{ma1?d3&^hCD=iDAlwd7skSMA*U$(LO#
z`UE<@6s**EzX66)=A8wnnR-$t96~;XZ!>)Nr?f>m|AR(kWq5%QeoRR#ekmcx<lu0H
z&P^$Ugk09@oGB`hPPMemhxGtHIxh}CIfIkC-w#xu>8cuFM8@sz!2UF*?Bb7Nn?9nr
zEtMWYx=Ja0v~Gr-)e_9g%~aaBD0>*A;6Dnxx|XZ)?WVNB(=opK!y(eLMX3^7_m$>U
zJBg8Qyp2+()Z<ZvmShr8VC|M<owG#SW=>4UnZ|6m{!s)yk`$Y5FjrVxwT<5~@KB#b
zooF>)*zJ&zf%>oFa2XN9d3<VF7x*G;{mwb&HjdOPScRvjTA__dcDg7{kZrtYpc$Q1
zU905bVO(-LeWvl`joL=JiSb^JzDh}S=euwGXfHjnHQ^}Rw$&4M`(b;+_kBuz?KO5W
zU(LAh3zi*qg{~Q88>4oB-5kFAXYfZ(`zEZWh)m-bPS<?v_l?vPWlL4t3Fp%@9ua#i
z2V!X~!uaWZQu`Nft?gXlX)a76KZfmWxTFE++&@#&e@<Is?TGWPzP`Dm29o^fB;Q>P
z0-Fkc;?BR3b87`cPW-f7&Wj|98isRT`XN_{{DYfUJD-nq7Cl!+Nf+PH1kHN}iY<H4
zC2qVq@qhpRt65;fdS~e_>JGf}_=n#rbp6zwWtbTOwp>T1YktE5^MJf5JJ379JJfkn
zczk!RW?N33xM3i*xA=gT{-8;Xf*j^~8JxgtX@ow_b$?fMNu>Htv{-{mLYVh1q12nV
z<Zf)8<IUhF&LtL%z5NmVE>JhDDQWcgZA1OIt&vt4KaL+%>U&gtVm`v*HgRn8-D&hg
z(Qdf~vY^IGtL--5a|lh4eTz0uZcc*EdTsdw%ca=9$H;l=^f+`cCHVxXcxQyMhP<QB
zO!e)KsX4pn+1fMJ*SGIf0ikvX9Uq(2?l^Oq?=mZvaeDWt+>Ij?_n_`}lk@=Flk<F0
zP<Csj<geb;hwjYkjzWrdDDG(F3+G=`D@(gzvvzl4ms6Vv86~HqGABKe`&ljGxQ*Lu
zRqWK`!5u^~>7hUf5=z*ux98|>_9zU@8}EaI*;jVG!pcYLKL~Pz#8)f!#hBs-`SLb4
zpvJebvL&lyLmmWB6yG}K;(ZwiS;y_luy^EW?lQr+uwpCh_GQ}vUNEy5ZQG^RsCNdk
z`)6Q@jTMPqaAqmA8mBA2LccU&fpv?_;D_THMY>Jpc}?EaA<3+v#)}*4LNOHRqsnf#
zwy{A6ihk`lZ?Uv#@S8SR%!Z5n_nL#0Kg$HU0owmjF-s8#moyEQpFqZC_b^{Y8N(w6
z3}^28>NlJRUN7Xta@im_l26_ToCFm`ubV}K?@d2q1>Bcvagd28k+(7jcpQ(Gv;7TL
z%hBERYe`#|OvWX}NM4P4TDd;_S<f+KLDIxccRDgJV33FG(Z7411xI`)3WRnGl;fg=
zW|O8E3MHTb8CK7$2zG&h_nr`|%I-g6$SW1z<~_p#GxDLEDTVs0Ukev9M5|@Fown(I
zPtV^-Ht3*SK4h9Y;v6s=8TMhksqV)}0^bQANNT?jUcC9mSjoO>%mY5!OdNH;S0nqu
z_4>Qo8q7H}nub(8Q`8VE^zq7EkPP~%=bem%d3WMF@01~qxtv~)9Wm<DtJ7IuC{{&6
zcwCg}*RxSIe%C;^6?wtc9fZr6x?MssBSGeu*KbI3<VXo6zsIfE%Z`0H#_igkd%utA
z)yd=}7H!S2PBy2k|3UsNrDz*DC4g2`6w$h}I?0ww-jaT6hIq;}z-Bz&5<r8|GUw2H
zhom)rTQfE9IUF-8JzE}SOA$UkmWw$*GTV*4#V=Ktr{#4M4YjYNS_76L57q1oIb4>5
zYp5zwgOwDFl?s4_$P@J+EoRl4%ioSPK{TBakHsg%4M-fQR@+i&?}mj|h;S^vF?+F2
z^?J;fNivJx8EX7UJFj4&CQ5sM^#?i&Xs#74W@wafcV>uB<FpNJ>3D~<Xfkx_(C8cy
zV``sp^OS!3RO6K`%%r}KSH>L&Zbm*^6xRwohQGz>^>maCIw!O|UAS;?qL;TrVMoEW
zhwA>EUax#W3@euF6d5V?96D8L7ok3auk+|PE1>vy>YF-e1-nfc>`AiUqbHNRb?<W4
zQ!Nen;bhIs?yp|~Wixhu>_`Of(`yb6LuUfDob?6~QuY3}z7JLk7e!g{GfZL{J(H)*
z&4eW+S!ED|ILbP}c1j#k3U@cJ?g(!wU$aA7{N*Q_xCuxJ;bQ-**Gr%cFsbx_VTyFe
zob7A<!zFaL;~k|Zb@mt8N5He01;b7I7Ly7~p8|}&!=ZOIzR21NS52IofZ|>(YhQVY
z{?7S%Cr?Xb@PHPN6+eCGyix=oEx4D%0v-55*_1+n7h9z=Gp>_|&X#kE0$|yiJpw#1
zDGcYa2;)_r2cIGq*IWD4Fp+vFx#s<S$<epDwQBOE%D=1||FWY|C8z$xjX*`F*gLr!
zKPHDS9V%iv9@f!67X<5Nue{Jg^T5QnIP<u4%T8wAOj1FURn?A=gML@ZoW=($1`CF1
z=A*0{r_U_(PKm0wDFVkYwt;poz8hFBHz$ARs4l9P%y7ntTlzj*bs}rd&o*`l@vw8M
z!X<O^$SLCFmoq>bfb@IDh*2T6&zK2?zITmZz7>|aGyAZ2ndRZ5NWH@L|DttMhLca`
z3_e=_DL#;L!kr2zZ#UlDJ$n7n;eqUy072)7A>vNX&>4DIv&3=){ke|;(efvJ2l#{+
z9q}lXu#{mpEb8n7EapfNnH6uy=>AQddB9M#I=|CDjr65tV>Ivs9`DieG_p~ya}Y8Z
zR}D>qiyFR(4{)!nZ%Z8=5?NC$nMvpyPtz^!v*k1-U3l(4deQH`=epwA0gU=j=E?3a
zF#Gb-d)-?y`BY*WE3-`Xr||Cwr0)g?UWO+6X)e-AX2e+ajaSy7yVt~>jVHAxh43(`
zdmfblM$T{eCcHH>+`Rclg}E@1eB#EG2f+urZ&7aIMpSPdPXZ4c0tZ#czBPk;v11>t
zI~#OJ8G`v{-&u9o#R<!Hnz9ro%$w(ipNsk^If!B2=79pbn?|WRj@0#a!U02hA#!DU
z@aVN06tB%2l{&z8X|4$<Z7TeH90&@J{l3KSe8OwADN3ja@|a_vgO}$eyk`Z59P@L1
zRvR`m#xW&DWY_cvNaoJ?$_=Os*hco*e1rINLZgM>DNk;gPc)>D#uhZK8NCX-DaA=F
zgx#vtStu2~@#aX2ki(5P=7W0|EwzpxaO_y*yP6;D^cnp&A)Np=YW7SL#OjMkKT`9I
zmm6!~1aGw6e>;k|oD58$2p=7{i2#1G`tr4^Jl!XY<3Y=_%u4pwDV)~#VEH(^Ermsm
z30P$W`{IN!Ql(Ok$xe7fr=Np*-5zbrn;BfGXGT9-U)VqTQd`|jx=ihQ(G*OS(UvvV
zLJfI+qDYBrUrjIB+XCMQAY@qhStdRPL={yanCF|iYy>u6zZog{__#opKUpA8!Kydy
zLoU^f%D3)S`RbvQeDn3O)s}MOd}aNIUI$0?IviqVf&tI@Dmm`X9JFC8{rqXKSxHPC
z7tnZd;b!fYQ!U3b56|L1PEZ#$bR`UItUue&nfiJxe-)Nm3wNUiULMQQZ9i>)cQdYg
zMO6NPA<kfJOQ*DIQ*Qb8SrPqvls|ZH<(~#^rj+WlrAEy2q;I^Ljh3o^bK*4cvr_#d
z*-SOD4>uw&cw>X(R{I~VYa4VxvyF)}WrJl}@92>q0N-0WT%Wr#a(nJ|>@{J%*UQx7
zaU)xhX74ddcX=b#EO=l9-$M=R5!U(|m^;(|{0b(?`1#Z~ph6J+`1~R~*R!ymw%<5t
z27=@?F*301swGSqIjDiY4E_5Jn(c$MLcz{qu&;-vrw1B61z?psG=2}`VA87TwBF_8
z9s?GnHAQ>aSUJ?-RT!7t*_`qekWxEaGWe90q^TXo!9no}3S!ce{pShV4D84pCRS`T
z#J<wFky1sdEAC-ojN>|B8vZO0^!E|ha5Cflh;-YgnD~Hoj<Sva>(#N0vn4Q=QHjW4
zs9sKDIdsAP;yb*yEzS4nBD-vAUZ593sg~+9o1HGa-5C0GYC~W9ouJ#5hw;*PKA=nO
z{>Jp52{II>iIgcp0|0f5W#9VIh94Wl!gD&e%fWbx)?}c4hl*gL4*OO=;jCU81^!n#
zGSo_@+9ID@Uj-BUd*=1^XZvz5f<+7|f&n+pS6!*FK~t&_Uanb1jMDwqIdGLKgHr~I
zJOf)2B<&~B=5p7&r?&|rUq+7nh>VQQ{pcpwAFAi4m8T6O1udQziJ$)@hF~@sO$GU7
z5s1)8P;W4GN3iiD&(eQYRGy)V0%*^?*@u@$rRu+rFRfkcT^=Q=m9?>QYh}{zL~t1x
z#QsRK<KI0VxxN+<etMA)D->kXRF>{1xgru}n?Gfo{D<&Mo1G2P$#t=dPDQIF!$|%t
z5s8(4<`X^840U4nn%xRJIVx0VG%NobwuN7q$7UW_;xVDSFGR_i-45#Q0f0Xz;e8$N
z^b`Evll`kehbYEP61_DTSlR_Xa<cA>^qV`xuHNMsgAvn$rxMLi%SUMNUg8#TAx}a=
zhy7KKeo_)p3u^RxGKo>u35+D&#0$`?kdah5rwmI;0)PGe&aKkEraDPx0%7k{?RI3C
zp*i!<e=)Y|D{H*%d!3^yxly(cIo_mAB@1qyiCr*E1uIoG1Rff1Pewik6O=|6@sDbJ
z_-L<OyqG>4;3T753P?Ky#dF@oYl<vN_G<LWuM4cV8W+Nw27neXr^Z&<R}(6C%dP5L
z#Plc+YRdXdskau=6kMp_EFlMe|5!8!tV$-kLCrbV|LV=^V&Q`AS+84f-q%bU44k-e
zSO57=_>sI#b%DUs5s$9a7-91sdy106C9_6T<$r2p0q&1^tz+_9X3!*ZD-zBK^&J=$
zTJ)T#!Rp7IVjgMrojS{(MKJRFeX(U=x$Th78%?!WTRn`>Sv~nNUY)}l3ONn#8Z|_Y
zCULJ%o!HuC&-I7XSp@Ka)DZ>YbehdC7V=TSS^8UFBvL>)aO85x#q}>ye}*O??$AX2
zjxCx_KuWY|$8=6C812u7RhE3_$5%f;Fw5HX=`U3j^&#SmEM|9@+JmkwmdXbuL0|jT
zZBU{@Px;E~#LZ){f4tvvZg?<Cj-T*`86zW!B8CTjJgNeZ=j0&?E$h*^n#(VS%S5s+
zI35qlft(-qoM$iGT7V%$;cm9$ftU5`M5$-ibo?ZLGD()rH47>eMf>3uwF*RjysD`^
z_)aOEG4V*7FaFf%dy#sAXL4O>b1>J-Z~F6mfM30fg}XuhiCfxxecZ%zTA<G^Tx0$o
zaxQB~57y!RC-*{+UD~@czVuWyD9?Sq@NnamR(xD_GV2eYiaD?527Hv6$jxmFop|W{
z+^J$Mbbx-LwqW3tcUt!;eB#M6jQdEov6rL9Ut_xys?kzUx+aO%U7|UGOdG8w?FW}f
zBO*K@%mcILz6db^);jbk24E*Zi%GAzmuhB9eBZiMze;AsMO<(=e+z5nzIqBz5fJ`v
zyF(~-d(@l$64&Pwx!nha7m)clL{^meM7!9?K;`Dk3sTQc+}QVR)N1=|QL*Dr8NOAX
r2$g^JhWniDQ~&qx-)({A4OqF@dF?~VBY$kKHM()t_)6(zrzigny9DSG

diff --git a/reference/plot-7.png b/reference/plot-7.png
index 9c3de4c61db622cf5af09ff8a27090bdf184ae57..3e841fe08e867549a2fe449bb4e39650c1239efa 100644
GIT binary patch
literal 37582
zcmeFa2UL~mmMw~9sa0l_B#4x#D3U}a7ytu`f{02|NeU_|NkDQuWvNIkK|lrB0+N$}
zu*q0T5D^f`X`^g%l$<wjew#X>yYKDS{kr?zamP3{#yJxH{fF;g-&%9cHP_<Nxw8su
zR&uXoVPRQw`jq?y7M5kUEG$d)e_D>8GzEly$3M&Ul@#RhFaDc8p6!Bve!g?+k|hfZ
z??dvxB|aU)*I8J8VL2^-RLwTHzr`-6+A3G>`(&__fvWuaO5c>`bE=gW_T5nyaerv1
zG$4CMxiq5h!n1&yvL_r4cT<J;$?wzB-XmV%@!Kl-8;!?Wa@;#tUS+&pR-X51j`4M5
zU~~D(PY3q3z8{^o{r>fkq3r0hNypCF7t_wVqc4W8S!8O9GVkrw3*Td$%&*=Cuzvr6
zeD&&yEAuD6MEsb;{Egs_ugl06rv#UXk$;aU{%pI1d~reX?JDx`?-zd1CI7lU+3+7Y
z1=}MJ(X3s&ckd1h3*+Yy)79>_c(B7@H9o&u>iT_Amim|B;oBM;8~OS9154R6o_{Oh
zT8dx3_uBSvuexhPovwpPyG7=Q4h!?@^9t-)o5_t_IP$;0F&m-lsSOvi?%vY2DA{5D
z{f`yJPsI(wQd0I7dGqqj4&}Q9+xNx{zvpA@3BeaQ@6z7Xh=?7XWkH;V!R9A(#uLoE
zdV728O|u8uGU(nONiOq!!(Wud)<jbbd}Jj5d@jaFV7|P(yn}<orywb@^24K_>aFT@
z!n3jt3aEr^?s{RPdw95Dqs&CAexZ9to4Zik+okI~v$C?d4p|#JeqGAvHa*&<AQ{A4
zf5hZVtG}l1rs6FQ#>UhZ>+?D~yxBu}4vuqlKA%DlP6Y>t*&CCY67JW7MYFoYUU}nZ
z>oOQ!Ar7L6nVrw^Y$yBQRGlrEu_2FY*Y$#Pw6n}ZQ_soj?YxP%s)~$6%ybctS#x~T
zS&FPo=0J|!sJ%&G*$w(|F`ti6Vg^5Le$b{k`h0j;db;EU&0$EI?zvB&F*jY=+?<oP
zZpY)&4&N;D&@nMFtxfy)@4uT_cBI2ni_5c)A8+E9WfsQCh9PzW)Kbyxc7FXM3#R$=
z6V9{2Qf9h|v9YnOd1ns|ywN^>{5Zu)IY?6ER=(r(NVAey?vp0m>ump|C1qAyG^nf-
z7MqxsCSfzv6E)eu_<C<$d4gWP^AUR5?V8x;L#@;0&U{S;k2YGC28!{WqfDO@rr-Tz
z8Rzx^PLa#iW-UURiJy3#r@k%KU4Uhc?A3iCqLnV(vruN{JnCvTS>eLim#K65^yx%A
zg;vnS#6)w2^F(ShPB`mioR*fBX*b$A?vTH96Wv&{DPpLosK|LXAt`CdB8buM@V95>
z-TX)P{n|tw9@WS*k2v)l#m$_Yzpc`9+qCb->h7BbT4uQt6ZHl@wsbG4<<*m6Cp_9r
z%pA1hwKG#Hto3Fa>)P-lJiWcWrC}QM&LFeF)cJ59S?-;|caG&xE;O|=Bn6b8v-unz
zIhAjsnQ)nXb}-xQ@b|Y%=hMrm$9$@e$c%kHo;+H9sQ62S(r9g0fb`VYdj{<v9<wHY
zJJrT6q_Mi&I)AEA%(tUTMNS5bpOu!`mzdr5MD^?=^*D|3COy1{Pfx|SnP&AwF454H
zo~Cgur>Fgd*LZ5(k|B=g5=I*PB_&VF7r3!*Ym1VbzlB@hds$0sTTt0a$sn)D$jGT;
zIR>i-r<nIDv79}(Z{M~bF#GoIzIlzh=8nLfXM?#WX%1%bT2#@L2IDvB=|z4jvYYs(
zKPdQ0isen|Z+4j*UOJOCjx+b%G&c4;zD{Pp^TKSaf`Ng-wbX{2rl#JlbDdJ;33r-Q
z$V@g%&-SO%8M8w^c&AgtJ}#S$YU3Ya{kprm-2!#(1!ZR{eFO#qg)eRJk)64jG?{DB
z@lks2`+L7CHqTA_1Uf%G+Zd;r<c-(;;YMoZ=g;ev0)!t4szz?79_oB*uzaBUhg`vS
z={41s6*8Pvk;;7|RALpw?S3jJRaI4SOXNZ`Gc&d@N_wr9nb$U-!xnKv)SVF#5#}X3
z0=EYmR)n++nZR~GJ(m#5bYx6-O*S~=);)<Wgt#!K$wL`S@(c_N4EM$*6}`K^$^iRA
zsWHT=_ENI-K%<E5_TvV7((gOwa}bv+sM>wpt*RGhYdK=ol|R=Zrd6Y9WMpL8U!Q#1
zWyR(@M<{x$@)<LI*^VtUd9#Ck$vR!C>*VDJ=SC}3&I-Mb(bTnzXsXNDdFJWw4&ybN
z4#t$S8<xbtzD&^h7HpFS*20KhwPe%A6J`0d6ciK&U-_L?Y^0bMZOTnA3)JU|(l-4X
zwXWVQf8N*HpvbdietyWMb#BEA1+kp($1bRxIB^2oG_7I+`%tCPhes;0E?GCn)_gGZ
z;g;U<8Lxu}4_ZXMu)h4IOEYi8U$=>4gl)~eG?DhvMKj}5_--0H-dDG?)Xb?*iyE7B
zy)>W@cIR6<+Ew8Lx2xMku`EAeJ~v#vGcbR|KWWT?k6!HKW9Bm3^tj}jeU9@(UTZg)
zcW!CxV9sbc*S;HPd<OPCJL5PWr>8913Lh+!?$8?(rQeWpot5>xuI`q}78^Z^Pr|EL
z+f6e&0@{UUEjvGz^sT!&9(9hs{~R8}M{Z%dqNHQR7TaH}n@p$)y_QA;DW#$%m5~Vv
z3G*S6Z8LpIDgknnnHA#)YpbGEe71Q@ht>5-?G_Y##5ebKMM=ky%fjqZ5vNmYYSVwO
zSagd{KjvuNoEhsW8CmE2{a(V1`V3{h->_svL2jm!OU~IbpE})Gmlq%`H}YXyvS~|}
zY?I4O6~#LxB}KOBE`xiayE2^qV#v{uGH~eOX`{MCFBq|@i8GE7D5Xq)GV~oyn}|TP
zDVLq?Zy%vB#-g0f{KW2aNBt$MBS6cy1EDItwCCrQJtL)JDgpTjYU7QG2y><x-yXD&
z2u20_2F@nM#VP$d7^vrb;PU0mBVWv9|JD6>y9*_r3c${7dwST;f>`(#>|p2jwaO-k
zW5xK}S{Vba%kKu|PBg}qb}*dTjC-qN<|huAHfLO${{DX5(By{_Wgl5%Bcs0B8^djd
zB^F)b-m{h;wm#fmsv<kx?rKI&TPbmN={TIdKxtR7WjV*BpV^Q@>#T3bvQ-=fMoGD~
z2HAQGGj)}fm8`6+54YIg>_<eNpJ@5=Wy2~?QBM!CJZWb5@;)@k<?ru*{`~pMj@Hx)
z*`Ved(G@L+*Z-sC!ND~;4CNZYU!B*|;$h`+p6bQo_}p82aMwvMZgv>^BbeUKE%x1^
zvOe{nIxOI&RkjDNn6aAm_4T!#ZE6kCcK&vsbH-Ti_wZ#(ij1^_p1g;wk64X$P3A8+
zr5tve8H-Lx@Pf07PE7QMF-OP8pJ7<7?IR(u-FjXo-Enud^cs25z&;E62h`jFljGK{
z^N2`2Blj#UyUGWWTk7%|<JYEPeSN7F+M=9tbKlkx2XEeNUR+4cpC2H&CTHZMP-SBy
zw~2{~4#TRpu`!F?=f<7Yu)U;3+j{}q`}Wkp`%#)vT@kNMGb`EufnSHl>-2nhOf$Ad
zDhF{indLh*J59F8OeFWz#q{;|E_Ylw)c^W;N^^aEy<ro>dv8nQgiOhe)RM~QtUSl4
zh={~xcXoCXk5*e-+pgF38gW~I%Jb3kJBNPo^4nEb*AY1INK4I0WG*EbgxaTP*Wuqj
zmqR=z@Y{GpE4!vPD`l!+r~BUc7VUevBJF=hpq)#GG-Bi9J^jue6zr^^@Zs^2;m<P=
zQs@SJvlpKP1RyeHS@pHu<D0wUIGNo#U_Gn6^c~~Z#&X2uAmO^|YU6L)xDFrR<ZQZ1
zlDDE<E>V5;TIsK@?ZurT4uhE&ll4yo_PGpu=xry~c2-S8W1_JRzJym($4cE|x@E}O
zM)>0EUvwRZ3!{^gd=O|5HVFQ4K^#&E;j4T*ZbNGdt>;LeSoCH)mzZtwfAXXNFoHfs
z_whMk*(r~}p>0_vQ8G@Msg7#%lA?861f3D6>QGxnBAV-<P*9omfwcFc+5IX0HC=iF
zemxe6U!y|oW0ii9`t0(;<(#QjK*_bNx#-5oCAUM#*&_mVpKFK6I9R6ymv-pa#$PJw
zP;nmdbD9{3N0+`N+wnxT4c^y%GH)_h{MMIO#0FuorlCD@jA5Syr=MiFKllWU+dP>*
zXSPaf<noV*Gq3zyZPxn-1{UuK)ZReC$_NzzG+rzF{n|sqmj(x#BZ3;oiUX`^c3mRz
z7I%~Z9WUvoTsyr)Xww#$Pk?a6vwcyn{$uf@cDDIQIJS2c&G+doaEs;7S;)(u36@T7
z^7+v;b~MYP;d<b>!;k%aN%^!6w}}Ke@vzv~-I<4f-xfgu{&sF+v(Vz|He$t1{Vw|h
zZR?)vAD>*C1|sgUo>!0^d3?=oqQ7D3%GgAp=xcWHFygnXqZ4pXj{OFFN&#Y6$D6lq
z30{%tnH*YR43&%|IgiJQDwuQLdtu!u7<u*uH!Dy1zU<-P>^eOcr!O~t`HM+cxRTKB
z-7~qZ@G#a&9|zRCOwvc!HH}A(R+i)nJ}?Q*|GN)=37?tw{l3`hU2Z0~Z@cRO_m^~p
zoQ=3M_3dHtM3WgsI|K-vA}4Ismq0Djv1#$w9^5>^5i~#1Bz7R-Ekaux0_cLP%YKuN
zkN(3A70%XsN|jz<=TVhT*|a}aD2i2&TY2%kO`wkTrIfh@GbbT9<F*Gprl%Ma-`2Tg
zw8|coD=Lq&?=B&DQb}*-3x`@`_Qtc)TQ4y_8Om05toR}+VOSna_Q~vk<=>%1&$h2E
z$B1RaNlBiCzs*=+%+e_fbB1rIRE2^Emez>?YFiw?|55L<Fi$7W!OabzQ5t|Vdt6;t
zm(~#+UK$|cAv00GZHw*KzqBjV0oEN}m>V^pXfU*y=ANsLQO*ACe4&6}Ndato<BlCX
z8fnO|<OI$<U0<hTqeG6>`L!S3;$qru^=7~<^Zw-GqF1N=+IGrKpAJ<NQ+#$Nvh>zr
zUFYeJ`s;x@Z6Ae_Ji90^vIlk?D|qmI=su^e+o7SHeY6F}<j%gXD~AR%=)PSJfBd>k
z98hrkFEYK+;aQGPD}w+u4_sn&O66|?EH}>?`=a!??8Y|!u!IDW5XX@c>CsR7=<tXo
zvj)=GF1{Ubvq{7s6(T|MKkS{Jo^IRrXlL6tzUfoJcAd|b_FBFMCd*sIWKRrIC8VUf
z`1Mm4$|!RA70zOr?Y?EbX77IcITJ2+qOtYOtt#JiK#8GAbCJt$cHMF*+GIG^louWz
zt|a5-<&~T6G*+o#+0oGvBy$6BW{B#Yp#En9W5R(1#*nfw-@1_7V9<cIGE11YsJ&!3
ze(^of;)=VoBwIn0-}kk6qC~}cFBKW_jz9{q<(Ao&U~xvbg51Hcv-uN^M%mhfO0v}Z
zf7pwVJpapk)*P*u$a@)GE{vTw%7YKQoCsI&@x}|vZ{22+gya#y^w9FV<&|XhG~zF^
z0{yhO-rn^lMm36<rGAm;rlomhmkeL9=P%3;tw@d?JBa*--e5@anxAaV{~R&a4Gh*d
zL3MX`7nR#k7x?AN7h7x$1B%l`4|PB)Z`dniFsm;tC&w|hufG(|uzpBVS5<W*f$PD5
zoJ$9#C{6FK<TBT}=e$Pu4_|I4<xaR~T6Ps->*(V}o3}bm76Nx~5X&9Apr)x=j5KM&
zw(C$UlCDo~lQ~^7=QSi>hsyT6@@qr9_C#J?OpK90eSaa%lgi4<XI>nXATf{N(*CTz
z#Cvl;jhYc0i9Gv|Mv^Ws!P)e05Ba=Xkqajy;oK!A=0nVWEwV3ZqzY{$nm*p9z>(D+
zHUTR;!1(@9t^n?4p=f~Gx`kY;n9t=fH#fIFqP4i)NIP>+b%*=V;;r`ZO|;mw63~}3
zH&R;CS}wORSGMwMvFE0ZE{stM9Wmdk`EHeR^4*><7jx~cNa{jRx^0C6Z6hD9WB^0E
zH@qIpc(}9t1rP4*;-?UqLu60d4rX>SFKN>FhM83&cku=Qxq0(ueSF?%rpW5rMc2Ca
znYL)4&|TtYSy+r^qi!<4_;(qf3v{4vt!sOqB`|F2uRdg-31#yXC&7bD_g@o7F=s?U
z<z$gel7&T*^1mQ*$S?k5^btAoKf^$MT&Z)Uk)*9!T3Vr{-I~A+^73Ge09S<XNE8-N
zjfwh|$ioce<w=rx`}XcTCki8w4#dXA!9#$HdL9(CO8dOIpooYU{GXfKO*1o}fPjD>
zG}W{}e*8$NBYu9YX&CaJx9seJ<@Bn?#^>&nO`&Q?FWA}HSy?LqNRi?#ON`D?Q&W?-
zv$K=uqJ#iooV?WVKxYA{37O<Ba$UqeMI|L%Hzr09-t;d*yG3hiYR+Fs`BwiD7z=b1
zImz8Elm`zU{Gh4oh#Z?AxBuI4&!fy+L!XpqYwa9ADQ7Fun{$Ew8(Lenf1s6BS5`iD
z|Jm@?ty_3oz$`J*Hqj=*Ute`hZ&uu~V@Ju<o3yk|6lEYN0lL9#Z$r`WEi*GI*`N>C
z_NFJv-lgRs<akyH;9;|Vyjv6%hwXgL@yp0x=FatQ{@%FIlL+FfbT*JBC+_+4=c4p&
zEJLx0iK5f4Z_{gZaM34Eo&eMCla<+HGo;hyWN7Izc(9_yM6bn)W^=YQucGLlPWo@E
z`^k&_A15ElHU2Z!^RF%W-%y~j@t>EqtBMAC03Qj%SF;Ie#Jl0;a7meJZ@%+(2}xCp
zfBSjkN*zLnu^geT4sxX;?1<)$eX;F)W|%eSguqXb>{C!;j%^z$Zgd~Hyd&&PF1Eey
z_0P7%3l#6GIJ{9pLc)ByBM?{WXT5&?I>`&4%DFf<BP}(bAFnGuL>u-dek8P%$=BB3
ze&4w{XBCIAo0}U+g(A;9+khAYh^1d0ecl{daFMRV0L+tkjIE_)>#}?Zy2|<;WE%nV
zZf+%hJ16Tqk$-u+5uRgf9Fy+7vS*Els}zFzUsi14Vr2zx)%GzV9#3Ar00g1TH7h1N
zygCx6Iw?=$B`(<pzifSYy4`OvSwqwlw;61{nCEDh(;;GZ`fLrkI?M0YfBK>6kw&AW
z!r%jzZScE;Ba_%)Br>-V7>y|6Jo^lQ7yETf60(x_ZOUf$s<Iz>)v#A^E%3%CfU`qO
z1rK7$SXmM4!!t88-`21No)OrHV_2^`+F!VEVS~E5IyNV=M-dT`Klc@)=}i+85waKG
zyxG57NN5oy;QA|x4-gXafc3lWk(Cid3>X<1fjclUA=x)^2M2vrl$4Z4uFCO{ZH~R&
zs%dI!I%uV5qosc7(z8z+JymRQ&Bg5ux{T|j>W7@}!?HXoydUz7gM&`OE8w5!*^{4|
zb8TL94R%JhM3@iK>}20GP|GYumZiZ?Y=N$_=W|0tw}xP8NhWeW{N#93MSnx8!;Ag7
zJ<4Y82ym8hM!dV9oe{7*`YPMkcLTj@1-**;<;w+n)kBa7+t0krF2Bmw!rn*rKD)66
z7U4YG#vu5&Fh|paO;bk|ld*FJ>sRO%Zs4x2t{$_0d&1Jv@(<a1!GO;(pn`v1=}}X(
z=Pw}*QvE^0JufmHTH$;&;w`JdX02JXW?AMC@}n^6E3XihettfiDlxt`=-DI?3mp;G
z!<=4JNWwoM4xae?p8&&m8000BcHd&x{unF-kN#M9<$Y03mQ$e-N`Xaqx>Y(Sk2>|L
zQrwTPJ@D%D^;chB*w8i+MlrN>SM;US8-<D4gN<}6sa8iz;Y%sk+~7M&z&4m2po-ny
z#PT~Qp$9=szW#ijt$M7diXNX@&OJ7}iDQWw9!=Y(#fp`6cs$7^xAg3JM2X-#?|vqE
zv{cJo)lbj%?Gy#kAv>Fm?Al?zv2rkbXiHyRQia~|y;pE{2TmM!a&jUTx9uc0&O7Jl
zYVmuYovE&=IZ^OHCC9zPEkOS~EDkXOY%JT4g%<K^$=!1$=39O43Tt39mpoPhofI?m
z{PVfqxgKl$xZlId{{7>Zg&AUMupP&U1TX=+RIZ-)>>E30bn|An)x6+?aO0H;d3nwr
z`OgnLJ;-OLqo+5-wkD|CvE52MPnP3(<}JJx2Sd|F>i$p42~|83X3J-2uO((#-t&KT
z0#(7!uisYiDA&2o2?4xNS*nGNKowG+Ik4iKinr`cw{DVFzO&PAfp#9fA1yPrclYw%
z=4qApQn7iF&+)YEhF66E!j+%Yd3u#jSCUCo$6<xzf`O$6R5bVw=1WnjRL+hmHiMhe
z)$Ze&J&uY`0i?rLtG?j0eQ#OSG-KfCKKVW$Hn;sN*4xUvP5Cb4@?CLRm-`x8T0uF*
zJd{Dn<zHEg3{_t&)wP@)Jh&pK@StpAhMZ)zq#=ze5p`5(aO2@MpNmvxXIGj?=^7d`
zeHfSMgA;ELInPbG)qme+l$6h~_b~|&(c)lb%^u7$q8!~rJk62It36z8K$|8vnP#7m
zCs{N%Xu4LvBwK*_Vv<Y|Hr~w*;5YM%)uK19dM&-eOuirxfH33hudDROduz-qPjHUc
zce-u!c0+D#>?TBNRV-Ya-wS-@m9f7|3@1QThkKpu)D;wg_Epx`bJ2^uJe(#Pc7m14
zs7I!^N=JZsM<g^z>Az1ZBk6Rc3PraYl|}rwyaktKR^HyGpZebyw*E(pcFY_6uhsqj
zd5ZtmGU|VIIb%|easDoe2%)I>#or?;ffjnhF{}6RmRs7bE1DwdR~@rHUfZh^;H9FO
zo|G4Hb-qWNp1ylcg)*vSm30{jy+-rmvOO1VJd*SD^&MyM*;J=-)2hj3^eT1k&PZC*
zL(|qvZ?m6~h?1C?`1$(ki})m|VBQxEzv&+kz)g{b=V$Mjdh_N@0m@vLyZ5fVUCe9a
zNr+DwT}?ebJ{QzDA7+HL?KN*JAdv#_T@~R|O<kQm%QH+Ji6X)~P8Wped@S(X9V;uV
z+&n3g-4BCISYv*o5M+^{xcyl7mZa1&1y62?*t+qkloSiMI6+zDx}qPQlFIG1@*7^(
zQ}U1Daf_nXbq+cS)n?v8;dX9<nlEy9(=rL`{`z`w6An3vxw%f{qo9(P>e@6lHl|NG
z|CkdLgSsSu?@FDM`5<)(90wc(Tsju0diSI!5E5&Hu_SW8mD<YYCD-<rJmli#<%J`-
z_|-kjVIZxpZEtPI)L*p#C?+6SEwcg*UI1RWnr?;KWpih7S9G|yJHjTO+>~S;K==HT
zITS1&8&>Fou5QSWSAb#=urA}YMy95scYj_6%99O2YG^o1VT;}NK@^0xB74xb{dPYY
zAacxBM?A6WctU7sXx+G>k$}Ye6Nq6zE^N345{obZAb!c}r==YP`ott8D0`Rpn#|Jz
zo&`;mHNUaB^)yIQLc(O!GRY=~&P%98Ec02j`ut7Z4)1IGm;PicrHTsD|1x+S{Jrdk
zhlS&r-)Ego&;0H&nRYO+bjK32<l2j;UgW>4q0w}o(CNE=%cJnnQ@=@MP=|ZEySdtK
zyZa3HXeB#OX{xLH|Cob-31~-hvz|FGKYd5W$^;-2J0B_87L(O)w|Q1XG9!kKSkdzb
zm-R-uEho!@Cc$c&4bQW+{(O+^AQrCm|1F}1|LSi1H+jYXCuzk$@8bWciv`v2_KIzX
zza}B3hEeG+6iUk3K*}bYQ8Y3K$5)(TPG70Bh54RzSI4<Z1(p^zXIjz&v`l+OCc#G)
zfnwu;lhkTPsS?ap{1!=a@M|w_jniGERzs!P8^LcDg8JlI!F%@{oZ$)LtibgX%?M|f
zJUQMqXOrZTA;K<`vb38UsX&SY5w&L_Wg$uwI*2(TH9K-Fi=C9Q^R)q>5#))__8D6L
zY{Sgo9>guCf)`P~m6zWPigH;Z5je42xw+ayb$`xq5f6dKtgNH}3w1#Oxjj-Zg*UFZ
zX?3DI4LRtY2{~+cWIlJMH_k~{?}Bfl_F<FpwGxOCUBh4(P8^Ueyr{Z$OmgkBGdJ9X
z1Z8g_prjQY>&+SD=$op~n{hUEA3nfy8zhA;W%hF&5aW!l%gUHhOI6Qt_Kox^DSFk5
zfjFC>JTW#pfTr^CG6#p&2e)<i3I9L?pD)ADPgFfbkT8V1DX}UmD;tK=reNv1!|V4M
zmV1&)aVx`HjxhyN2W~PhDes{7fzGEn)iP{~ii-N#1L;C{H=T%k^QL-gEHpm;(-d0_
zyss`Lh@``bky9iV&1Y%h{}Gr-WUHyJCS(#qRV2Xf=N)X?IyyQ@O`29#<LklKARbiW
z$vU^8q*&881qx`bxkRBMQk@eE3<*Rk#Ib7k`Dvk7X9V!DR|aWR{TD%!l9C)*zx^5)
z9o-EaEuz&FFeYKss-~sY#L-&u`X^hh%a{4}-#@0d&eA5^mhw#<aqK?M=MGi|rH@As
z`JAPZ8D(Wa>!A;}l*!G$Wd}}O-AsxhB%dUy)eF?XP!QMc-g}`3_V@@qaso1?Gb9y)
z8sWa^(kq{VnW-cy@PnJ$jM4x&TEp#ic=SR96q5IukCsUlle9g%b>5UR5BAd;sbwYI
zD*U8ZW30SW^j+HU>EpS_P3TkY76&N1#$VPKM{yZ(-)=BYuQO?y8k3Q|uXf?WmkyaY
z>g011m<b%eo3W%8g4$@X3D-SZy4ar)L9ocmK6(=+Z(Kqa<>G5|Jt{8TZSL-+&kRE@
zb#Ji8(grxL$>f@p<L3_$)7t{2hbRK;KCE$=lc~GYp!&^1S=quY-8DNbtMW|BoA(En
znhejAtN<Ks$;fQ$0%J?l8EBGB^QJoyGC5H#NT^V8MnW;Om==*OprzZtzQK8RU>m}|
zWRUU+JQ|V;R@S1Z6)*C+tC@i;6+{ieCs@f`S*f#?ufGNH7G?};sHtrqx<rEZ=g;T9
zy1Of39TyWuRbc=d_KdJW_vFaXT2x&7CTg0S4;R$15#CR?*pN;wPrp7sKA!7cQPf&p
zACQ1j&GbtnRe=(oqA2L<K3~5J`ul0Solw!d&rf_W!FS@JCCghpGK-D@gCwK=gc{Gf
z#~$X6{X7#gQabt0^Ta-(=2?+5Xd1lA<k3}g$!;(UJ4$n3)P;>680|mIOoHD30fQ3~
z5;8#M$U2<Fr`@F1wNY<&$qJ@xkmf=*Enk4NXB$6JiOJ~ZgSK{avn|!4ZMc5@ecLBC
za@<=<)A}EC6gHd7<ADoPu9p%&rCfgROKmL&NtNbMwQzUq9$9Kkj%K+fX}tD&LUgp7
zQDUY5$-^^n|8$hshH5(@Zu7{Uq-C?zFuhiLD;TqF{ML=PqB43SgW5nyd66BUYu^(=
z)F6bD6Gtuo>Hb?6LY4l?d9j12BE^H#J72Kvd~p}QVV;ajjWB4;TWI3`Cr<cp7J`y>
z{cq)B|8K!e%#s9Q2%(lDB?$q)(+oawefht{3F;!$6&Y^ise9q(2A(r)GX>Nl@g1Ou
z;5*<Jj%zCq$3#c(`e;Rc)via0gBQ7*`RPGjuouY6@c8&CYgOc2D|L?j0e3Q<<)pqC
zVxmAd)(xM2FWY{l&&cs(Y3aj^=WVK*nst3sY;>cxb8&HTLLcj%l@E0lW-PF>HqFT@
zh*x-&wXBG|*cJI=Qje+*>KJ!`2ukjt$H-B#=73Lw_QVEBRRO<E-(6iMQYl_Ujr+UM
zZe2og#3g3Me!f0tq}AHcqa@%b_;FZ}iNh<rhEEq~X~j!$T<Dst)90lmH1u$bBHG$J
znR=?U_9YG{L#~uGyp@-(+COemI%982w){onp)^#riRNJHOJJT__ud(G&n8XrTD6}#
zBXgUA?UY3Wo7!eszney)I)li!>0A@arJ!ed+uhwYLCg|=jTo(9Q0xs%grM?9f<y7o
ztdwXZ8}v6*tomx%5o)2S>l@FENlM~F(xH-^$H40<`0#k!HvQvXgx(~T93U^mc6sBe
zH~FCE-m+V1dP=NGLzPCCDyDEt9$MJ3gR)Co9&Rr0?dx;krI^^|+lAx&<M#WXn(Z;+
zqUayq;0(QG$*6|sEFUXRr%Y3dwrE|KW!0B2Da!fFNet-73IbgZ1{1}1La4@9=3Q;I
ztTLIt=jZizU;XoWW-{|1*i34)r8JZ2z@3W-?*hI{HYhIYg2auK;PbMX0Pm$=&Waqo
zXmcn8i^~rUZ6>O=9ZCTRaAlJ%Om}EJI<n=vE)>+zR3X77K6|ry0c$cU#zlaaoPD(k
zrIRhps~D%^&~m85YLoQ%@@Hz2^r6yVWlg?O_FNhQ00Yz_hLe4qghNnbcWf0Qr++KC
za|mIC?2lmlbQp!wB2+NA^rqf%hDAmS@bi<HTn~MQJ}Czi)rIquHRO|5R;^Kd+tffL
z1Vk$!MYG+;A*#)V2w*EOPbAmkkoR#(+*Wgh&d0MRPIH~LAo(22`;RYI<nSP~FBC7{
z%&ZPW5?iQ=`{lyU+z(2SsIVc_Qr+y(pFAAYIwt@v<AYdX@?qC%?i5#QDp8ckLM&M9
z&Mu^nvd9^R^TPaOF)A2ah<XeZ_4IiroOhL7ZTB{)w4;rPtX0n**a1bv=Z+P1Yvty?
z?}BU)3drJ)Kt1NC8d43{Sv?|GVTsvuZOIUnGFt#mCJj)ob|>OAXOxE&<O>iD`N;Om
zL8U+WT8S?y3@pr5EP%oUCC|4oRcwH)<hc4G*LZvN?YBWtdE4aA>U;`5un(pDjYO4B
zO3<vV=0te%ex2Orq?{2&?K^)gBOHpG8<DMvIS+e?o6&M!QIZ@xgzjBhJl$^pH7BEp
z3{lH8b4OxUZ|ucvYm<>lIHC-&F?4A69{s#gV634S>OI=EX#y5K_cE>Hg2>R4A(XV+
zAi?Sxp+bFIKo*+7^>F9{j?CXRg&KYI@;0&nZ}0p*={gA-Ln%ORx<sXjBta<B&vJ~9
zKT>hz*SpR<<?2Ir>uh&IDXllx#z}{M)g2j0LT}#+xv8Teu}l!d<qVsY1s{uQ(MSu^
z=2(JumDoIdr$ese8v1y08&CvT9p4UcIyq^~g4$QT5$7L=pnM=U2!f<-tV(5wI-l7U
z67<Fe0C7ZTE%Pp4`%k*s&~ofbMh<iZSDDSCcDL_R9^m&s9)QBdkaEBzgm%tND$n-r
zxTO2~%F(=1<`ZzG<MtQ;w2A==$P>(FS+||sUP|hKqyKP;$+SA%S<S8;^EMPBeO-JC
zr&5Qvhls>76Oy{t2|&)05r{?q#pixMzV>v0X8grUvsohCC>bGgU&lZubG^s7L+0y?
z1C}>Z9FfRQG%+AcJ{w^8>FMJk3-WL*4-()UhzgVFwD|dn%65u2na2rzesMa~t3FKa
zj=cO6sK*}4j)nf?c~Qc6#MHTVtP;)mg@~^p^;V&;bD26m2mQ7u)JI+Bg!zJm<i(q8
z;5Q;(m=;~cmi4=NJkQEa{jsjdWxmdZnE|7ADaWu42HML_Unr8wV}(+#_sSFIJI0gD
z|3vd*(B&B0LY!xMM7!EsssT*tF;OAz6^@<z?x{1q-P_j^yC6Fr3!vugAo>Y7KBDm;
zQVYOlQcpeee81oUO(p}+5@dF6DUIj=^herDh>{~T;}BUESCe-yUxYE=p6eg~reS7H
z;{n@42M;oJctn9hwqyH>+L=*U&^nFN<OIY6{g^|l$3(P}Y(L)nM>mrwaOL{VbrRYb
zH^Q<ZRrq~wz;)?bx20>Pe?i0_b+Tc8x+xy7g@~#)3=XC@nI;=Iq>x+B8)81=m7u^$
zzDvkvn|wPUm{QGP7IOETf|(^=Oy<8tGo(0;w8${ev;f~b1g{?~92gMLneD**AwvbB
zf=Dkf>7pK#m(6@5Ul-bCzLHv_U&sAbod4p`(|!VlP_^)~vf|9vbNLIC3nNa8jYjhF
zm+}s2V|9t{hWLu9xh^?A==78vM}t~Dq2<|2^tMobRWzwQ-LtiI$KtM5!mdV*uI{Fh
zk&>ts9P%<w?LA+cx-2ps1DQB}k8sC48#4;9toF*``y(4`;ArzRx&I^U@T6Prem*)q
zxPg4{x<&c~)7<|aGItL}-RwS%vT^*SQ~@{))W*QW@VI9)w?0?xa*?lP9Tuc$J%!$$
z%@CZoX6aBXZSqE65L1ztClYh7l$5O{!MI=7g$i2kiW2pJ>7_3AgU^%%9v@on`>R{Q
zdefrU2Y*O8vR-)qFIi7s$M2BxzrR6I^t{^G&R^6#KZFVK@AgnyukoY2Fx$JDmq*TN
zTujk^a?EW#f2K+&Enn_N)o*)Ro?V-<I6K*;dZK1)ixXN8S}kXCT4gP{U8{voKX0t6
z+L&rs;k|;-@lNXGz~G=a8gGR8`AM<qDYmtsB6D*@AKa0-d<fx(vdU)Nf%e~t_%A%s
zv_0w|%CMFQ-8OoHADQbOt$1RcEvK07X2ME=2f`wn<JZMUSMahS1Kmb*fqdYD$g0KW
ztdmLIONOwY-#3rUO^=R-WM&@)J7qduvXdfgeSj=Ii+UUmK_S?(6tJQ~#BlF3)}j1i
zpIGR`jS3gH`petwGRy*{MJaZp)>u7_M&s9HzOZw-?pwZ}g;`J!OG{4`ZS2TYJFcgv
zXNsLz@-IFO7Zs$nrECYMq%55ZbX(uSyt-OK*oqvG-^QlXh&T=X^GSI#z0D(73BCSB
z2N$bRxs+z$eLw)J7k>UW6p7sd1=h$_{uFrg;+vc2KT+gBDF~pOg(%LG7d>|LWvmh^
z0)G|_2^|LYBeT}cU@)4Uh%y9iP@O@dC3n#NV>;}U!d%_zlm{2$$B3>_+iASU9B;FD
zq@$GXN11C6GHo5ZoL#4FsSGt&cDGeLHY(9+l3o=WZ3~gBR#KR2^74Ya^YWqPM8B3Z
z)#hOisbXht1qrehatn^Z79RyqxG(?$Amz7Py?S-Q*RNMruU*SAzxY0O$80zJZu%N#
zQUH-B6GI5=WbG%KORmkEJ$bF`w_0P{5kX5y$m}00_&nicca!EB!bO&ZFkQjR3yR#|
zO+TaFKzg4L#h=i7^amQ#%nR8y3$8xo<p2W9SnMl|R@{*-e|(1yenqOG<gOu0Qd|r_
z=QgNwewSrT(T6{5^C>`N<pDq9|EE=4b9a99pz7=)lQVKRtx-5J9XD{Ww{L?S-h=3A
zp<&Fd>0>VIse5?KMBW^ppYIaKCl}y4iHM~;Ha1p(kepi6=jQhGkc}zr_ZbSKheCvm
z;g?ks^tPZ>C0gXer`NN0rQ&g~j`ZOSlbbhBU2`cK*>NEanwU?swD`+uv7c(TtH4w6
z7n|y|@VH1<;jyt5*y9E4BnOEC1adQt*UukM_q}vO$)=+vZ)_)#nwq%)P<+%@9_d!O
ziV~3BY}My;0Th+fZ-DF}n@_yE$t-!eOLK#d96wFlKm-M;WvxM%hg49N(JvlcembEt
zFY|jd<hfLG1DTzXC-@!_={eqMd#=;Xo4ZJ*84PU2d=rW2;(VV)E~RWI_?L7p-nF*&
zzNJI-612KIsP}ti)m~m_i{6;Fb=6hp40h~DlRLfI>0<(x0N6|J{R-F9o3nZ1l9D*T
zbNpV-kapu6JeoHk^l0c(W{=8M2zcyfRr{7G{l+jpi?yIJAd++;$TcuCtR390I3$O^
zQtMJ)l^gl$Th32Y7N#kCHT({PL=#6@j@3-)iA&8&%c^%P#|>2t4qQ#|PJN=l=X6(X
zQY+o~5>bB+>n)7yCF6#RVVC(eev#No8L)2y5_uga`V%e_)9bkl<17TkQtjEhJ2E+0
zoN%3rfk*wba40&{G_pM1?dq~=?3}u`S0hSYf8A}({gHrWrmh1zNmHbS#Dk-Uf>&$2
ztZj`{^ZGcA1ou+0{G45_XJ~icudy>jKInl>?Ri@UJ#q$#JIcVrootD-2$D9}mtVaQ
zv(Q@;C#0>M0!x0n=jt)})h;}lNN`qaj%kP<U0+#O7icrPH}YXpNl3xG3e+x4jrAR`
zG{?xwQ_vTNqlC!MPZA^<85#D(?u4xDY$2Uvg@o4|nz(*OfdnNp7hmbfQWJ;{U^#Vl
znw__O4Qpc6?JBByOE1aP=tD<lSMjR_hYs7}iI{PvU=vf*%cbA07kr;O)s}2|0ahYC
zA^7rhb9J-R)L!=RmORN%_tQ_FSSa5EEf?t?c`EB<-;B0Pq(*c6vgj=_8@B&b*JUU-
z+dyqj-+E04z<#skCviw+h!}mSXB@JMfpjfe6FLv*-2CBB%5JEd|MK_&cj+zq=@S=P
z-*2x~Grdm>y1ZZ^$e(FoLzk;Kd)v$Z&2T+0H@D(77h}EqtvvIB&6LaQ*7JK($~JA@
zyx)1{XUg1WO=I0q2A5os%GUcc;bxtYL8&{M>Ml;4V9)=(3KF=-TG-z%ha;^_rrx$r
z<$DFGcDpiN{4XZE8A@Au^lT<cyNpHx9eqk49GXa{5NTc|JCOs89O`{c&vh!yo$^Qa
zaM}mOVMA7)#1gH(d}e1iiyFm!MGongv?wKTclX6bB$357zhO;yrt8Q9z1KMaR3r(g
zn4c{wWnP<2W6YI`o`9LzS?ZKI=mDZ==g1BwbYnAU;uCs{Ez}X}a~sy6UOnue<hWxH
z{AF}pob94NjyXTJ&z8uq;eCZ|DJV2Ng^`^>ia<ol*ms4Zi(bDML2^FOWjatlX%W=0
zW(Y8Y``<y$zhT67&-ld8kIF4r!W!5d(vq+I@oQMpMsn-Flp(N!c#-qMHq%eiSQMJP
zegsE~$Ea*=<;%^@B}J_cMlN<4^eI~)9w0r^-F<zfEfa$_`G*iO-E~M?Jj)SvA^W#z
zr<y!zW@bi2F#P=34(udB6>au%Lkt_nx)lS8b9-=zxFb0v<)f|bi^9bp5KLFBSV2c?
zgv((_%7@9xoq@Pc9)BN1A^ilzzd_%;qV46qweBcf>?CI=bso?!`ipSzoPV!-?mLoh
zWGd}LAD*18Qbu+%mNTRCxe)@pipz{&Uq5rfjQ@hCgnwFirWGRVG;?EUq1+~KAJy^w
z8Y#AHW=FYetoj%;^DGtNJm_F^52tA!fyn(|fCjIm&}`uLGMWn_UuFjwH~l}OS{l}t
zG>&C<1exX8rB3FL*J>#^GPwhm`{XsERl`q<tbY9Q%cg(T0`LMy@r}?X0iHZnpnS_*
zkc0iYFQ$M;YIb9VNmKVjw1K&~kywozl9L^Nq*8!J6LuoyB5ea+q-oat%junkNVCK8
z^8sFDNfIT8%!uQ!PnR~WceLpkUML|}n38s60H6*?qusB|R*lGZzJ9;fgLJJz)%JJH
zBX>_qf2#Nk+Ah4AjaW!bJ~-r~h**Ti>Yyoz>39L)B6cxfD^T71YT6o{fZ5^B6w{-{
zmD#g~0wE|NL~$nHgy0xqZ%^#VHC8jl)y4qgX`)ytz_vf{9mO>N=%06F$}_)WiO>w#
zXIls7Nve;qUN^UTlu<tpzaeK}QBSzG#0>4p#1e>Fi}ZAi2Q*1eLEphkU_5LAy-D`_
z_dnTT$;_5c<@&ygfg>hC#uh6q<<L4fIGE9&Qi?-K&p#e8AZ$B1xofPduhDnX_n3KS
z5_$C_(%;hCd(t_UbT3mKQE&4P`_qEd#$0XC=V*>L3JA^!enyqlh5+jGdU|qg^OM+I
z%_)l@>B<<{jnLG75~%>*9D0_`gOJe`KtZ?4KN^*&cE%nm=`3uv01G?J;8}=<a5h)t
zAF(U~-&Sff+VxNPU>!v)B*=}k)DRdBVX{T(y?gh}4xbNEk{BBw?+Dnb5~BT&pC)Z%
znH`nd7FX%}oU^mC{)PF$xpf8%0j8DF7C;n~i$Jq8{EskP2Hg>1@Q3{T<p=xX!7<i+
z-{^Jc(Ss8n9EC^_P$WVb$-F!C;omF~`;6$LNmO=oBRzS{G!ZeqduXVl#jrB$ggIo>
zc|E?XLGIBx7IyKFr#T{Gw5w22Zsf5~L5-#!ZxqGZURW>huq;@jyq*c=q%?;ziMj%a
zNR`en`lp)RGn;aF2y-yp<{?(}XlKaNNm-H?FdLXY3PwGNT}@7mjqkh9R*Knu1Z|EF
z7FUwMEYisa5M8wPQ2%-&EA&LG^AnYI<Rj;5@a~9DuS6~e=}!p6evk>HJ&dqhPXtxn
z=kJo^SR}|RKszcAY^_lDuPoGzgslxsq}f6;NMxdy31_F|;1Ih$J@eDj1yD)WWM1a&
zf*>5gNXgEw0GH`fJr0L)=1J!$zS?2mU<9DJ$#@6SuBb1^+<+|Sf91ObgJ*D%zGV6;
zy{Thl2nJy>q=#}w+rs(gZ<1jVm7mW|3EH57_BT(82Ra*a_yD3>>zElyBeanngxlxv
z-C*uOt`}i?e{vrqyRfdn5@sH`ip={tc@B<KX2?z@S(6z+-~Ob%c<3(?MR%8*p(|r>
zMGf33&0_ZL+qXO_)#t|i?lYhIcH`3vb=c0@Zx8u`$ZsBKFt!#}VLr)Qfxn&P|5c&?
zZ(`u>UA69{FFHET!BV%mwN>x?#A8A|2>(gMs3+W9GK4l%@yI5r^@{^dJiV3v>`$nE
zTK?4uxre(t6FsnK-8{3z%+Sy<xtIC0S62P$l>VI<{MR8VYR4m1oeCmVdSm1GyPINu
zFS)j%HEw_PMbl?zI+aUL_p59$+D$z+q<s9Ij>Cn?8lHTq(+SDGC?>SN2`*q5Ya1l&
z9;0<ve76oslC!cZPxwrE$IZhtI$=mRcyn1jS}sl2S<<H2bdD|Lc(uKY@4+D@_ha*$
zxVUy%N|dPH)HN{8x;~nh-0yMMeibyY3Xl^-4{G(T*aTcs+0bx_Btf^m;2j7`Pz*eO
zo^;vM;p8?Ar~mQCAA2?Z4nyf+S3C$tlw>s^$#+^b(Jn$vjW0E`JgK(Bg`9MB2}IxB
z#4Z8#H5{DG$sCjB4D;)W0(PihjGF2pza!s(aT%G|`KZY;GYx)jfTUN?;!cMkGs>A~
z&6D@!lan2?ZeJC-%eLRZ^s+5Yvu}wR@}f4M7eX<ip?~CMx+3!D%=f^-F`sTQF9xQQ
z;JgKW-Mu|Mo2=eHeDFXK2p!d%Lh$pub&A|b4$u1j=r<;IFVk9s)EJU-Joj#9ml>qF
zHU&*TK4ryq?5IiGy?ZSBdU{T`GopZh8k?H7>1Pxn-_gtnquOJ#gOT&XK1giHV3?CR
z3EA1%NYX0Mk33?kcY1SGM>-G+?ysju5cih~n1FBk`h>_gU>kFeHeVEQTEy`AKi&Bg
z!*AR!DJ4~QBlS8m%jGLpY_#hSW?Q;+skhV0UHhdZZ1~n@Dr7sQrKg*YkN#v!JtrBI
zFukt0Qc)?%_o4yQcIXN&D<`9B^R?Yi4KfVrsUE(U!-w}ZFKyCvo>9FR%BZ$?U4M1X
zxcuwam&2;AjP}54y8Cv)YRpua5-ezbvj^!}`n6f^(!A*Og|p19{!8J1e#8CM;QxmQ
z3y}@@AJ}&Pyo>*tM!WyKUEGeoyQDCMw6uV+(g!qR<^<TBVX3J^zQRfbB2eHYeA&T-
z>1^uiKjx5$Eg+(ML>3sNMF>P98QDPkeF>vejlB5fF=7d>ZE^0TOG#c+Q**P$hpow&
zmO~n`2*rbN(*`{0fvG;s-ccY41Jdr{k9HIM_i|Tk8+Q&ss5!QEF;Z{+Ad~b*_z-9f
zj6K>+(mIrTFm8afOu#|1Cvq@9CmqZGwIvvSLyD=8!Y3o&EjBi>DJcJ#1L`2N1I>Ob
zHJFcc?y$P~8d@1zRZgQP9yNv{FCJ>cEE)M>XGX>!p+7kMW}12`nwF6E`HP0XL~w{h
zJ!&LRikAA$fEpSqU_G#mom&p+w1ZzY_dHk6opmF*QZwOlK~<;}P!Sd+%uP=m#byWT
zKx2TOAa48qjA|d<&~lWU9`${5SnaHVt~Q(jQo<*h&)Ws}Cy7h7q}a70)mCrq$;-<l
zooVv&L<AOY{|W8B{x;ik+kKVv(Roj3Grns?wS*WLQMzkajKsv`q-NG{?r2f@FiXn_
zuqGo9XpFzoW(W@a)B!KVo1G&`iU<LO=J4W?5*?b_RK{6YLxwz=xge1w5e2<T9Rn&G
zsM8ikW@Z&l6l7H7_L<z-HpfS#z(E`Mz<K8U_xj@IH{WxTYM0XRnhN+1X<i7;jBy&p
zYK%wy$(My@&PEpbE>pL9duacDwm@x*(}YnaO)f;;Eu^!7=NSwL7+4*Q(X+kR6-nos
zzv$B2<)amHd?Z^D(m8p87u`wbV9(UE2t0Iud3z}h%qp3wMLK*D%*i;VP2&bW;M=8i
zs5S<qWDMTXGwZ?S0Em?*dQskt=`pj!%MPT<m4D@B{3@X5a@(%ic#pJnwA?a6<_%O3
z=_YBfeI}$B09k0`0^8@-)+D)QY`s2ms+$>o;`1d*=UnFQmwz|3&119&QL24ILPByZ
zh36mrYAFV{ZK=xSR18foKh3;#_5Mt1O!8dX3H9YU0HkDm#yXj?b7W*oIqEkeGkzWz
zfi-Gprj>d_IUW>GwN-TrWZ}Q&exc0Gi!a#>=8u87O^Jke3}wXY<>!&FTwL-QY@fkx
zQRUVSK3pe1I$nIV_d8c5We!Xhg({c#i(B{LU@hf`+61k@$qVe>>mJVfnFUwRaQ+Cn
z-`D22+NIyi#-nPxBC&_U%MaW>AIf;D#TF7|ZDSM2v_z>EHOx2DkW;DAaWg!g;Xumi
zU=s`mn=>U`a<?)!-9dHrkxcW9>{jR5LeBiD6P!&<WEQJYqaC(2sH{jTf4YoN1^VbZ
ze<TK|yjbIX8LM814B%ll=@UoG)2YRpm|AYE^u0+G-@VDeAAWx3cmU+LNW+PsK*zcv
zLOX&9PoJEDH{vP1k-E&~Xj-icFOx-y-lER>{Vet}8FQu>$UN|z2KM>lfV|7w|Bnik
z|MM>TpP~CdH{6g|<o`byWHEan19u>Bs&VS;y?sb737U^b8&Uj`@hf?f3|C%F0SG28
zlS$Y6mE9;MI9)=bW1ylj&4_%129ovx6|`v(w0&&;mXQ&Gq@;tLDi<!ZJo*L{1a`Cg
zpeXp8=Y>4!0^y$LriMX8H#ckJn<zwVGZ$nZ<eX<vo*R<Bbu)S(#stDI>B-onBryZz
z90<j)<vIuNcMYZ`0<;@yF1g<snd0?Ld~f3b;>FE4N>sYsGU~g}pFc<Q==N<@lMG4|
z=R6lJH=~gu2jEK*1rYC^lplbGy(mAd^rHNkh}<voVXzHWTwJRdSNQg=>#*t(LLJ{n
zg~HOq?VCZc<I!LSo9y!VafUHi@xJ*-dmiorz<qtpauxBD%3d9%0whxy8uCG{@q7!l
zR&G14v@+y$wA}Qp!A|Msdyey$sHQrL%XA4vaPk#{;XE(+5>IF5VsCFxLe|*CMCU-o
zO`j#PmsC`6Pp_TlXL9Dn?Y#fy2|1B8ZKHKg-N`_k_ohq8^07eUV&^35D3>WgYuB!w
z5qkT>%JJgnFw}Y$(+HK&%$`VO9Ml=R+p|OKiIK5fIP=d|z<)L*{<jQejd`HRdCnMp
zW}Za8<&4o;Bts@LJ##X~2qoXmC~x|UGHIumH)D5y)d6N@>cT*p&QeJ|m=prYyqGqO
zLiJ)|T;{Y7U|h>b50Lv8pv}bqB}$@0U<$J#Qt@E6rJxwd98&_dxW6bfh~g3gUvohQ
z8{E0GhdE;f(_09O2<n(jcpLUIbKC|RggKK169Uz<)X0u-4T}ciN5oH{($JPdG&Zdk
z4yfF124+LO3Udqs(iZi17h>F9ZBQ?(Y--|RW%UzK<{p+MW3J@o=NOauVp>cU7)yj2
z`;sA|wF9Ay84Ka)v@Vz>1Q|{S$z-PS;-G^k7eM6fdH^w@LIKa#!BaqXoUDX25SNyI
zD)p1M+f;qAEC<}zHkvhhEfgTSjqb&C5%)RAxSG1UVvGkHrG=DS`*=}#gsm5-%7wTV
zCY^x+YsBZFY&<eyh@t|MHo-8yi72E7i(m{G*&kTxmWfGO69qIGannqno%+o-F2L&U
zUAT9Yq{%6V>BV4HfWXN&<>lo$^S3Yr1@3;|)px(;&|igy?v6~?67efx3wB_CzEJxs
z;OwvS;x=N3ZIZ0Oo5*Gp-of8wwa?>9Q!*@6YyPnMuwcg3w``5CB^gI}xVd*vEg@Am
z3A49NRaN_X*A?GeCo7F&rh--LZHyr~|5}qsF=?`OzaBFTR*-l6eveNWkzpU22XQw>
zMsd@Oepm(~4ycspLlw|yJfq5?Mcbro(hvr@Y@scpc6J{a%bfWJ2u+Rh<e+D=q%pt}
zr*+PzwpceIw&7C;B9~6y{@l=jPooTFQ|3}qU0WMKl_L{=je*UsC+@n$T-Gi=#y38F
zP(5OnCrZvELftVn2U;Wpjy_&IpF()uD=upxk!ilpXH<!IwQ8aZ-QK%!*Qx0E#_A%!
zDdU?ZlY<Zr(eN=kZ--GF;=P;Ag^M3OGiDE72_md*s#i0gn~62W@@xxcNkR9l^ta#K
z+@_{0Ty$S&k{aX4avxHqG(ciyAjVB>`j-bPS6UkL>to#a*&aG{Xd8bU(uWcU3V0`l
zg6}coK}zMAaQkF3ooq2EN2p;RM;dPgnV<``09_?H7zOGty3;%z>#|rPLJ?an<N39i
zxHu2eXv->|V;R$7HlvU-OcBE-?+|5u{~D&(ESnh>RouZZSqW_eY4CFURuKX1vU*0?
zH4KJ$1XViNdBnrP)S#<SdzZdw$v`cqyQe4S&WRGVaRg+`7l2m_2PApNzC#Hjpur8S
zD65a`^a~<+8$bw(AjHkwx$~MFf&9_*-83lny18fFL5)-W#rr?8mky8#xg@=W^myKR
zt=S%%3NbvIWRvkmR#t2&GB{<W<`ZFn3W@#u3y|OQ^M_HXQYc2Nesh;hLdS00Qd(@r
z=T8sTFrb$#b=Wh3LdD5~?-U6ppQUy$_9t614t2K3spm_?Hd@zXg)NWVz8URfVt3}R
zp^+f%Z_ve}yD7h80*hZv5(%`mEX=m*)i#nh#iAIo`v;nv8yWH^;P(h@gBYsJzhfi?
z5%i#<u8+))G{zX9$vOT`1oJb6=h@{_IK&;lEilJO7m7KH<3sB)v&FV*z;CCDH)h=?
z9b4>dVbiPmF^83vm=NE5w}N@82(^Hz(amSrlN!m5iV$9+NE!@OX5M=2QT1gwzR5UM
znT)h1^IkNv*vMsFePf>+EfU++|JSy)aPP)kCkzoj3Fj73S|4BGG-z4b(!%@V#S8PE
zh(L|3VEIkl-0q|V!p~2d1~R_CTP37@J|$i^_h21`%rJu#2u9)yovZF6EVLLuKio4#
zdVZ{m97hU4!`Pd${QM-X|83*hXqfg0hKMmsOsuR#W{0<}hAnXky0E$SopO(u@1N9}
z$Qj50P4b#B7w6}l!RtXAvBT?tA5JH|c&H_k-X$DEh5eTj*$GL?LA0P=H0*7Y6Hb0C
zQK!9;l!0+HhWlM0(TALbj5{RbgYaV#aN*3!F3`geX?<8REENxR^7y@56Gdd4*u_kX
z>!i&d6*nQBvu#9NLkjxDtvU`E>BZeW@aRiZQ&3P4rm-cG>73-5>@ZTua{ig5$z?)>
zkc%>A>hg<-CJR=G=1EeLW>361ibzbINtU%sKj-8$|CC!-RORrnJXi!t+C(~)jCP4A
zJ^ci1Li3V2IMp%3jVLqMQQSULlnK`{Q`OKABNqhEQ8I3bDTkF5WilbUj)DPh-4y~F
z377r*D$tDQJl{YZ9F#<#AIiRx3nG2C7W0>~trHl=ZJ6HiaXcz2O2I{P_}wZooRqL7
zL8P^jmyhE$<nXqj@-WILH1sGA!4$M26!JKrMY7-bE76HTp~DHL#*bmFPtUBI8Sfi6
zB&EALP)-8#4f2S|iE=2)>C{XX?_fIS|H$xrm?%pGv$w(C!66(M`x3GJ%T5&NfhXXj
zcWa+!re8B8`mIFRhUsQ91KB%N4gffWB~n`dss#w_tH7`(^>4_#zPvh}$>@jfogL7C
zj3m}}figsc{HpQIE&K`sdGaeVR!-Z+8WT3j$y0BXk@*2|x)49<;ovSZavq`!asCH#
zeRd?42~S{bf}2~jblV!fna>aPr$;(4BU!U+bF4BiBtK*xHL94gcLg8aB~z{5zCD7n
zOdcNUEWplSsuO1BMc-LW9{VcVOZ*^+8o{hj@j41o$+9O#j_t*oYW46leP6vro-R|j
z`|nJdTjMY4nve<~pMj~xZKPR&aCSsEPGAJ-Kt<_X@A0^4nL%oUF&RhBzzFgpLS~WL
z69Js)(rCl`KHJiF3A=!_fFvUtlabv9K)I-MF$V==X0v{r3wz#W+7Ohpd%%$C-N|j8
zDw1-aSrr)3+fp0v@ZQ!1=0vYl;PXQD`h<;*O+UI@hoO42JCnwiT7DtXM!4xitv6+R
zF2jqAmnYJATQq$eVAgap0`LjKL}Lj3HpV-rv@uiRrlULT_iz*r>NV;vTzsP6ACa)a
zKs}}+_|2{waV-fgl)i&e?I**la&u)8(RHdAsCFI;xCOCj3M0TbRvaEZL-tBKs4rWf
zc3uTuD>dRF{6&*rxu^Lwe4&TFA-WKh!w-_NN>gM6D$!e@H>rS3Fd`k~WEBzR#{!CL
z8uMbfhFrKOmHiKHp?z;Y-D<Pr+fym8*~l{pzZzLTrGN6>q15*4Dp5CVXKJ&gRc|%v
z?;yjq;`>?}i@~La4=*rU7fQ8MpX*j2{?p+ug)deS4jr7G*8T$tk(0Bi2)coKXOHhA
z1D1(^{7F*S>kH$iMs+XDa)$C37(}OGqRtx;ovPNy+b5YKLEm{g$>koHR_M?w+K6;z
zByoXsDL;51m3J>8l4deseeTf~&lAbBf$4Q}91+|Ow|b9?tF`UhDWYigNuvx6%Z_9g
zKgm@vuCJ+yjv1=achvpiDflo_)cG)O!)|C4O&15f5B5gy)lk<pXel->8$S2ku}`bj
z9Vk+2XaLB1cmTw#^3n4m13~WNj3}#CwHW%h{r%ayojwJ1I;oEA4tx2p@|`Kz<QC{)
zm?PfFL|rm6jmU`6ZlIMu?%k1${X^!KA$I}#*hJc`NNEBystj7PtYYkC;4w)IA_5a^
zY8f&>l@w{=w3yQ#@h2vjQ?t<DMn|05HQjgWvWw)zvkFH6@53V>L}Klf{9|nlF4^EZ
zcde`nm#o;l9s}(?$YgU8EIImia3+*rNT6P9`f#&Y<%{mZaxz7W8PULkOf`4Oat_&5
zIFKA{`SRr?#6T=*lhYwy^CjCYp>^%YmV3#&PAq&Gn<)-^VqkXf)#S^IALpO>2}DTW
zh_hx6KN_!A6(@6Op?pFNDipyaWx(`IGCfTUZ-&T4!2r1-i^7?kS(%L?M6=rub0BSH
z8d=T0Tz#@PjJzLyxrPwx0sU50a)s<{0bC#__$_2xTx1BSwpES#@TYwh1?Uf0PXuQu
zdbmX=IH@Mn7Tyd7?Y24`{J=>w!pE*E$IN04?Mdc;Jkj(w$u+R+#|W|Y*oGuAoq}#_
zBiuHo$Tc?yXVq0<mK-rA!mOt^+z28~Sg79M-B;GsFcqls@?@4L?)BnG-Qzb5<M_nH
zvFs4ozHlGS1#@;WsNZKD^ij$1$MVQslXXqc_X*e7d!%`JTF-4C7*~)d!WIm8rT|vJ
zBeqC)zr3f9k~$11425a(_eCK0Ez-(kO$?x&FrK$QH;8>9I1?C2_Qhrgp6$6S3NH*+
z1n(CtBz^{hPHkwhNl~K^t!lbG78|}TioeLXbMhxY$S^=W1wmHte%@ICv)raXC7gR(
zBEacW?Ur>BB~Z_B5#N&o-G>i~G^9?3Kc_&X;AHS1Dpn+x3}d%<ZBZ4PjnvOwxJ8pv
z@3oa)Gn=TmWi-;MRgFg>V@Eac+q%Qwj}W!qu4zmBp_XRqg}>+VgFNnV&aZ|05~>aS
zrtBpRp{^>EDMqbC(qZ9ppNhDDO8MoXqrT~n6{`=_j?)}R%YcN40CQYxc>h>pwR^+8
z<Y=`8-;tSO{saS`Uez^Cs^b4BL?1l!o>XbVv$GEYG~)EELzZustdK9b!&0c_-)p-|
z1Y%tS1Yn5O(A0lkF7fh&a-sdgKoBkVGgnN2lcpf`-kYHyOjr+v(hh&!W;3VG-Sgxx
zTac!?sZu@0g^?~D1S0T+C*|8^*Gr~dy*CG)qh7$cMsM={?a7gDwpD3ADp(G@{TEMk
z%y()AA&v}5W@SAgSNhAET%k1mi7UdpYw};^Fp^L8)rZfhy`NAMCLK}O^xMoa`*jf<
zpZ*PRk03U;4~JH0Gsk)KBNw_JCnTb1clZg%{QP|8J{ubV1w0V<pAU3W<EE!tX<nRK
z(@}~3AYVIO^B;(Gg3O^LO`L5286Io$m8oP0A|~=eh+g65GfO0Y2r>(Kg%wmH-6kEB
zQu9rJXHiCV)tugoZD9aeZyC4rx}NKAZ@l$%yrBM+f&u@7oe4-LoBHSDGUd?gqdAA9
z*`53;590BNTV$dX)P2JoXD@%8On%ZQ8oAvYqTvC_z0ZgJ3hMHqrGOsQ!_AE#8!{j<
zW4bel4g+<=IF9D7(|Kmqt4<#uQcQR%!{wnkE+&}dBK)x?d;6s8wfZjx>9=d3*&?&H
zNo>zN=X!M~#nD1oH%B@Te<GnUFmEiv7P&nuGG&r$Fc*TH`~NBK%cHSszrT+(2pLL~
z45gB!qRev=lIbx<h7=VULS~^NdWaH9p}~}7DpO=Avm!$>+=k4V#|#<zeU9h({?_`g
zcdd7=_pkS?{;BT!KIdH5zV`6hdw({fiT;(g2$9e!7oI_wnh&UFoW!om0H^LjS=G;N
zMy)T&m34urOi|i)Btiux2Dv7gcJBLecsKMu+1?5Zq)ygfzZY=_)In-XbnNtBa;`w(
zQ&LLsaen@ZM_%X1W}^=R?{`+-q3VDEAX8_*9jnvdWNQYib`Kwn1sXkfCg^gM<M&JB
zCRGL@wa@N(Wjc^P2>PMODoymF?p&=q2L2(uC{BA&XIwdd?1FOl{6VubSr6(0pY1DH
zLDEs7<eG~kOjU`Fo>7>)in_WwAF2(>b^@|a&I1~#&Fb*3*f{4n?Gb30{lCLwp<()l
zj6&RH`>d-4&Sm~48v{|?W>+INpq(39;)>!k4$|H3LCvaZq;rR>Z7$s6!)j8$zKMdX
z+p_;j=ed{K<tI=7f)`JA0kT8UI`?;x{k%yg%a&v6)rv9Xkt5epLHLP4$SjUr@IULw
z{*fe-Ae{@cPF19Zthpvjk4cXIHpZLx%1}?RT2k=W5Q+2qe@b{^D_Msik+rep@~Tpy
zJ8hr}Sc{z9LP8VW=sOM~-II5zFSIiQ9rG_HlU!dbzfArJq1j~T7H$=PoUHct_SFt$
zEYntUN=G-EgZm$Qvghw`)U|9c4K0~``1MNmgTp&zPJ1lxTA5iwJ3}zK<5zoVI2W7C
zU&6mbgs?iwX53>yVl}i!f!Y@IeRp0OKCLyb9LEB2wVyj`0dZAs&l8;Su4-+CxMm=<
zIh%9Mlnm+43C<<%hXt(n_fk!Rc3MSM0UElA+|p!Y+}|F5z=L#4A7LI2juK~M_}`5g
zwyeX8ww;1+5AWP-(e51f`*Aj;bWMlqYUoPFkR{jby-;Ha&{EF|R(ER(pMBxCY||h2
z%z7Au>pX{#{j0YU<Nwbz_(8)Zh1cmadzUw=qRel#=$8z;$zB4I2E2!C^ldm*eWo!_
zGd1sVh^VK++x?V^N2~I;>BKkSS0r1<WTN^FUgvWN^H)2}@nUV+Lw{y%|LCSY%J`MS
zqTF~pcFapStb{agV@9lQvVDPlkCR(x=H|*vx0|q$FbbNe^#BMUy3OmxuqZVW0bHfh
zC7ocXHS#c%J#S<a)+A<=lw)GYGTF!x_gOXc0w)#^Wt-gyoJbrjjpQQ@_MC}1i;i8A
zwv-IEw=KPW+d8Xy(Ula;to;}FB`>DfvrX$37aWWjFw(0n+iqop9um~El^7Oz6le30
zt<@ciNTo|d>z$5~N;3af1eH}G^Z%Qo|NUpQ-Tka~W5<Sxar$FxZqq6ri(#3*<#OV9
zbzW>`-Nifd@fJ^X-FQ`}i^PnB;vF|nKT-}=c=9;zLf6H({<>r1r$X+8E7!4<F0yNw
znmyJZ=lt;QE8$RRyW|;tIr{SX@q7@<>$`vb@+7lqsF``o0kw>)HibjRGGXL2sru4p
z{?wqDww(zQ4Nm(_tYpH(+=F%`wRw7=n&4{FqiDt*{6)R#`EI|~5McdLV6DFLH`{Pc
zW1IelnhEXY1<Qq~uIj9TLyoc?T~*VG;%DDo+C)o7CxF1gJ(uH4-4#KwO9>F?5gYjb
z{r4@HOcvU8-z<fG&y5E|?Qdkgw;mO9#zEiba;B#5x6Lmsyf|@tJ^EC{x%Te0n;p>~
zY0qmKS!tYX?fo+R^{ST6YzFgt<2=b<5!vE~PepHKhmcJESK4z)62F@$GvV65e;vWJ
zb*pbqj%bE$XGhB2TBG+B2Jo_}2)$HeA&K#}zRyV8<D*$(M=#g^`kr2JmgC}`fZ7zJ
zyxJEhH!J~)$a0VU%Q1!1;fbKdt>wizSgc)6oWqHt?M53?x&ubbA0MiDP!&iCHtjlN
z!dp0*tVY)7s>G-2CdCb)D|=#YF7<7&;c!h@n-P|&MCeoa=W%*l6O$V+7%HaQ?4&D=
z)YKS=^WYKE$=bQTxUJyEu8kWv65a?qUx1N^Bj76qbp6!NJ&v3yc1(IZ)}BX(vd#JK
zBAd5tY1wIBqVvqJSLKDq542Z&LV;Ae*B?(w^wjChCZ;rTUpDqKe#-kc`(DLlYhUHp
z4~<=|`~DpA2{_0?>*MElu{q7OqN1V*1gN#CCNhztc55(tsr>$)ll(V+UpKpa`D9Gp
zS=Fy;6;JXRwF2Jw`uHe0%oyfe`)KCb($YeB*F;HB3@@!N+#?qW?w$aRJf~59>sCQO
zg`GN0zti^}J*r++zH=<Fq22KZZ$Yysy`f`}_N7&cBEu%+3uu0LJ)GCmH8eCd{ZrLz
z=$z7hSNz(Yyy34+<T7PTnY^N&;&YBtZ}d$Z=aKX0rHl*R`Rm*xacO!=;5b8^eagI~
zHv3u2U5yK^y?-}7YvL|%^>0ftieCA0jy*+n<VU9L3ytS!PzD_kJt|ZDz{~LBT(*tq
z^`A^34<8D+Oq@r<<*p5k9D)*(dA6MeSdrW9!T3}};DIAY7>N535&Gutr%!JzSe`vc
z^c8++)>2b!_(xKnFL7m@|F~wPHIvSol0|l0uockgl>bie#Z0#P?ar6CGhDkIIi^Ah
z^GIJNQlu0Eb!=n`3|7z^x}He0Xu!Oe@s@WRh}sMbEemN)>i11ihg@DWM0VBuet7bK
z4^wnc3J#Of>C*y>?4{S-Qw@?It>{H5RzzHQ{B9D7wo4LJo=cO81oBxvO^QVeT$iO8
zbvDS+SQvNUh4or=+f!A83RXS{hdE^2s~BwUOaA)7zEC**c9VjqrzeO;%7ETHh={lg
zb^Q{}uN75QpYSRZP=N0Hf`BHl()zBEa2}IaP*@NCUn-(0#i$rVE;Mv??=Y~7YwNR_
zEzXWADJd1f68F_#H@fl0wD+xUQl{IRxMQv{k~mHfT4r|kd+@R-g8}!C_f2H}KFG%>
z@8%|sIfO>0RP6T@ZohfMhf)G6Vgh!UOH#7!Bddez&UV)Vmypy<&dW8-imkz^VVnfI
zFYMOB&q4uK2>`~}1^G;a!{m8KYLH6S_w;g-R=ZengX0f+=Wov<tH$Y_Go{2Dsop=6
zkbc*Gh~8v=yie_OS{lE_lgA)D@4>6rUpax>1Bl>2N67|)o<p+vs@?BniZYCqrPWVG
z(k&Z#Bc<GR-8iYMK0E)Ou>gq+b%I)yoUANy;lhR3(YdP3O7_%0`S#ZK@%JkFTKlS3
z*Y}{$<XNY;6g2gv*>+0z_V;(g6PQzqRt#eMh}BcEf!kUHjFy>+Nu%ozCCmCGrV(HQ
zfoc$glc3S%;KnP0JC2$8m!2b*mX_{^hWc<wxi#x>tA`I@&+9#8X?;I+xAB9E=1T4J
zn=PX?Wozb*mswa?&SUNpcd!67>?im`MoxLR^M-2bGK5$+VYhgflbeR)D0c%pGgLC2
z6&ExY?fTj5GM48x7dD~0#5*=NmNt8S$1Z6PmoP@1#>6o;DaH?fZK%2CxLldMRC7&{
z^6(sc&MlD+hu%*{&sxUT-N~gxUj%}(59~or<lRvx!o$ZWnD-UL_CfeLOG;Poh)~&s
zuP5#8Mq7oF*kvs<Sy4fkbnV9b@D$Y)&*eUlhm;MN%5pRvvGwW?o6fA9D`WYrl5CL4
zv};!Ym{WuMc0zcN%R0aO`xhyl(AVUVL1?-m-+j($UQhaRy<`8`tJ8Vf(OTtApQ)E@
z$IQ=Ar2b-XjvKB>-?L{AV6cN=Lcbpsbq^HfR|HwVp@9fnqmXKxU;EblOnAYAoTRbH
z(-gxz{*}T*Ue42{v((LAL87gs@$2g9LT}1CQUj1@9drIp0c&i{whf-2cS+*>-e>IW
zJw0$UVW?>5*c%zK(9WG>uO!|#-b`}&Eu|P_xDat~j2#c5q^@3$(M@R=%Ndpy2A=U8
zIidht%7k|#TPXYk181Bb+YEdWDr@<AvVf0|j~=<Vd3<mZ&8fSO9%TeuR1Wx0YTxJ|
zzdrnZQ<Q{rmP>%MW1+ClKvSn~^ok^X#dvGqJxHI;Q?`$1Fp>11YoAu+?QqaN{&OkC
zH~-iFjr>5r&c9d4L8pn$VEFmxs$`LH0eb!KW%f7whyVPdy!^3J2IK#Hi;FAb!iJ;&
z`4;m7mGu9&+v$Wnd&U^1ye>@Hx7d&Ivoz;abYvvkppdlJ%7@~?vsVwLRFyya;9nuq
zerUkPz<^bocdN12vZJjZ*E#pOF<<34xon&E-98geqL}ruX7znqavX;r&Ga=+h`LV2
zs+O~QO$te(s>#&eWY5|RgTomH$%l&P45Vjd%B*<=1nNc?b{3Y^c-+nUu4@<;eApFr
zyULD_pFSPdHg420HQj@}gfDFI3)p0hq(&MB1_eI`_6ejGyFt)XhK=~Z-Y=zkWVAV2
zbli6?^wzC4$RsluByU5bO;C36r2kFo4RUdsrr_kM{}XxR=x|5AL~E9Hz%$R^8=neW
z+neX$$@i~5xxRphs}Ne5dbd0cB2nS6i-R3zF3XPMQQ0RS?ALSR))N6M#Yb?B-nxIE
z=>yVSq*#I1NyE@E5L)_2(M@Vn+Ba|BtdXF`uJTNpy|lEngJCgxU$_%rxTuslyouVu
z>n@lr@fT9MwthWM&f^`W$b!6Rw4D5=rLNQ5KjPj3B|}9Zc)V$x>MX9y#Wj_}+QH!M
z?_a@;^NS}q0d0k_=q{_!m%_Ng>*5x@0bQbJAQF;Or&_fZ?k~B%O-?fs@1LLSaRDf!
zp{;G!up%DCP+3`7Ynl1@_~oFdF){8Z9A%s_V}ajm#e*n<F3xAWcNuO6HG#<97d;g3
z1xImWxNsH*zCJ4luloeh{anEtcpknWQgHR%;Eq4nP5KLO4_#o;HU~uRk<I1a$Q$y#
zmf=TD6yTBYULWJ;=I;LS@irzA-!e}1Gfq{6MThOVht96eM!DA~FO<knO{4ZjHnk}R
zjA5&XQIrEo5CftJ^KhSQ=gubOX(r0s+aDuH9oP7~oQmtEI~93ClpZ0lklwc+)Fp_Q
zuh?~lhO!Vv$WAkHaE#41`~U3N<5Ps2$0cq9TjRB{HZg?OOz;>^LB~DU;K3gzCMK*c
z4Lz$Mjm+}QmLN8<TOg5edB0Y^AQf{1pEx+9bIv%Z?D&kEl=Sk97D4`Adq_Dpn~H!j
z`UIZJp+}O~p5v>h`|U5lZ`6daO`AW2@!=xN)~%*+dkOEl7)45GN@iGvgS~z1C>C`C
z5h>%v0etwRnc2QI)YNrZ*5?3dezr0U>Yv71c7vwp0IP^iAb2E7N=nWn#MJ5kdWes&
z1Ww}wdVEeK>+a>{rG=z3AX+m|A3@Fjqp1wxR1=)a=bDMJO&_;+JFnBrFX+#SNq%fU
z(ky_bB3)Y?rJrr@k85gaC9C~);YcdX@5wmL7jQx9{}_Ov%zW~bMMHdqsswZ&Vpv7I
zCw7_{dESB>ix(<G_QTL*><CY6+E0D}Z-9yerX4%{v2wCBH!CWXUTO2r3^#p!^!gJ_
z#S6_uCS*tQoX1<Q2cFQ?-R3mjvFZ7e@X*MJl_X~83bRvk9j^wmiPd*Yr=%ReOtFWE
z96Nqo;QCJuLCbG6@K@zsTtsnB{1|1HG^)x&y1OqRz069zfq|r$^+tva9A>6)<mXXQ
z+-AV7NJ&o~{dyMqL)f+>5JQHpkG5_#H8rK7r<ccKgZIW4s^bZ}(4bP}tl<HVWF!4)
zaMZp4%Qpejch%V=+GU(s?)*Sa6dBz!ZQFK7+;Ql9kvA2{4vxmoHDDQ_Rl1O*efOjT
zJBvd=AE$X2PTAS^PG;N*&fR(f{2t#9yD~99@T0-;jKC4tOspj)A+xl5e6BRU4kLDh
zGSz14F;E~{;w93Cax{A|FeO$n#iS7bjHN%b$R>XMRn;tBRf+O7TI!Inb?d%mqXlLb
zmVF2{7{hzf*2Md6VP=$~2fpHsdwk$u->0FW;eT%eu`2~((3kI%AP3?ey{hyzGKgJ*
zmgokb7ytYAU6bc7w%q{@hpkpyP(eY8%ID}T*n^{JyR;O3hPu@N<h!b^zVuJ!ULOo;
z5xmEV5I-d(UM*n@8NRwi78Vv(eEE`*cw~BdI?jPIPe$}~dw8>YP~!GjRf}d1eAc*G
zVT<~G0gF$jfViJ=h2gIm>#4SLj1r?f@@48qn>O=_6DM>jMWdAL>iBfK9)%=}lG4(<
z*x~HZ$~q@2^~Ei6{-o%HszVCpv9w|K;+qpPhkcj|T2ibg0a9)xu)3dy3OZcI5$V44
z`m=Ir+M+2X3KVb5F!P&<5}>IUGufa3*Ld#`yrL0io%JES#S-N|YrHiQwdE2T#KJJP
z!j(}GHu%9C)hO|;1pn3Vd=Dv`ra$Ym>ii7E2I=azw4SxI6L6ayxr<p;2~+ye^>-LK
zZJZY2>`wxl?dmQK_*oxEC&+rQav3`qgy1*ff}M|Hk8|JfaAMY5v&zln)X+=W+qZ6g
zf%5+xm&un}3(X`oUwyYy|CLMwQVm5!MCdjEhB+=a)yU6k3$s%1f%~UqJpP_#O^Ka_
zY7tFx)vK|sej-Fv4ZO>k`Q7@t(XroZ@f?Eu?w+2wL>{&W(b44XlPs*-a+sl>Q;y}n
zJ*FO|6{dgHYqmMtmJ_crn7*KXBiX2B2%soI<ZWLwUE&_hv}SU0avl*#b^pVM4{zf)
z-VHttdGu&K0fIiC>T=PS;GZMBQJWkoeWNwm6=Q#Px#syrKmqE;D@=6cI-dD$f@wUy
z%j%J$d#n%f@G!~cc&<IFA<Z>~`!KCrw@xm#i7r|95CexK58PMo(RYr+x(U-8>FBn=
zB5%&N?cC;;Z;WYH{$IYRywK*2d!WfKew~RR8{|unxAKJm8YJuX_V)VU*|ba90`ot<
znYFYF-D=W;M32)LUp6+;i#NADH#t4M1)42<>OJ1SxzMW+hcboVzMAE#>oUe!*Uxr(
z{b|>9q}$^I*Y-8jIeYuJImV}+plNus^lAAM+V*{a&$M#Lcp8?Jm!F~N@P^?4zcna(
z{(Sc&e8YgJBDS>o#wO-}Q13@aa0v>2>wc91_yQ+cF_=U5vt|n(HqHgDiI?Uk`*V_N
z*sQ%E!msbE$;sU!Pbn#%<eP?t-Np2P0Gxn;JtyxxX8Y(4Lp3-LCj(_y%646!*1<oB
z8~zAaCYCvfD8bQe*X4Gnr00*0BfjH0azxDo6q4O2htV_|{N-jh{QYn`A|L1_#)J9r
zTWib;W=kwa+kRy_nJ2+29gM={zlx!iXwUJMf*W}m?VX*4pEAKXt<5H8FAonlSx)7<
z!UXHHqwxwZdG>2l?efwE?VtMld+b`Zy22`s6SR9u09LHk_%%C(XpWA!0OeT!$FTil
zl4fEFkUN!lhlMF~i$eHE;$i9<8uwAR_N3ASsT#wsfF9`a^Y(=>_oo+u`ZxDVj}86!
zrM>jMneuV?%kXej#>nUoDUbQIN)PCeQxJ>T<*DiE(VAg!>}`{f;5|N;Mgd$<d1J;c
ze}|M?Mrl*F{Z92LadUxA)Zu8g!mrBbo)Z|8C*QB}t+vzrgz4Zs`p=3<?V}ha*08dk
zk%Q0LB<I?Tljd%yVOXcp(d9YauljBgE>#_HTPj&W+m1GC^S9aT6v~R#i-tk@@FPZi
zPV+5W5;7f{np(q>;VfMEwfxbLv=#y%?(`2C`A;>8b90yVGjt#*-uT>umQl;F<>NU+
zL%z6Qm^3f`kD5uif{vG#r~IaCh}}X$I=P!N-n^kl7%WZfK6tRw+7(k>H_UZ<6;_fY
zG$(Oaf&ff%UHlnj=}Fr*PR^rJ<E@#8z~s5{;o>O;g_o!pA-qXxPIMXB-!yNu@Z0Tm
zGC825{-s#h!s6mZqy_6ck!Oq2a@E&@!-j#$1Oc*1)7fp8edTV#ZL%=%MMkUnLN16}
zN!0l8SVQdTEg4sv7zlpb4#|g$JBA?QECK=o+Zv9Sx}3-YY@8P?QflPbe`PVxeeOuX
z($pEE2()sy$ax}J+x!1mWEyy&l!NdF72n={`$|R%l;bv|u8trOlN%Va)M^cAn`dRn
zoZ|^oMuiIpk!JpRTSCLT60EIMis5@MDMm)d?BTP{g>&$UH|Xo@gE8`?-%Hh}!rToS
zykTdvtRkxIcFkrAwg)AxUB6yARfJsGkfUTM^<cztq?sA9U+opaddpc*Ox#q6xoMEB
zFgMoT{>b^6^bI~6k?Z_o5}g*1m1>*gD^r#h<~CRiXmjdU^S&)xdRYq(nV6_&ZoN7!
zM3!9CPn&oxHzs~~^Zr`bt(hgH_TJ@?m=JtxPH8JU=>{Co*3dtQ{i%pu!d68HmU*o}
z;r&qD6PDB(sA@}Sd^%K~@>e=Kx>4y47U#x@n9;AI(IqAFJS?;UgOBYeyH6d=0&%u1
zd^>)A4YzM^^tZtO@llSur5yLVw#5^Xe0X@JepZ$Yk>mD@7v#lEoSeacM#+!!CMd^+
z^!pt~d~0Emc&10N|LSNJhlp)QX!M5l>suY)<aN3S4*!DvPup<@l}b!3EM|6`&zB5B
zqM|ktKPlO%jmt(KYj2`NbANEJqP%=cIT>t*NWr&3!^;{1Rg(na0=^j{)LBWi-tOX|
z;&3j9HEYmlDIQgm=z-wD-07)l?<T^>*H>Sqymn7t#Rb`#`&XAzJZ?xe>aq%3U9ls*
zc{q(!1UN9R^B%pLVw}$_W&8b2MC0<Ns1t;Srsf5wLS&$8<Chj^2KPF?g0GkRDGLVk
zbj(TB!;j4a;0m7kY9yrm?H}p$Sa>@l$;(@1DH*@Rc?@F5vVA-7QzeYB)5Nqzxuiz?
z(G@K`?C9n;6#4qg_gN8#ewA-vrA3(a5k};Gc=(2U3VXxe+3RN-B>%f^T5Zc67ni6J
zdHa+K21@*WSYSz`J<aswQwplynMp<X%78yGvgL#8aRxR~Zva-*JcUN>QEWS_`)fQT
zw=$kLIgoU;%$82Ctmlk%Y12?kQBHB`=5t;dlSr8$(LoUKNVc|)plS!SlzxngQR0px
zff<pLG(}SGvpJ4y<HfSRN5rJQXZkcK*w*-dAlb?4%;LHC_HlxZK5Xyvj9Q9aes4Jw
zRUMzRm|s4>bWKMZVeLWU2dFt5_wDmT=t1>rK4vR3b0ytBk4pkpg55kAb3l;Ti=a(g
z^HbBxpviFctL!-lNt~TUd%c&IJOU`c+@#bZMX3lI8JR%EQao7I&`Md%`O*BXlt_}y
zl&G>z?kMz<@t&gG-!%OIt$3AP8DYx%p;hUhf7s#x8=|H#yvy;@7Z*=(>KppiztT&M
zp78*J$<#+DMVU!6&qz$I8~#<_mP<uNH4l~|J0N^*xsG%=POyr+o!Dfl%@0#E54rEi
z=G0Tq?&9ll%S0cpH|dsk&th*ARV7hGTC?tQ3`Xw_E*FrXedR-cf5TuBFwwR~x$ag%
z16P;r+o(yU#43kJta+ZxxnFP8#oLD^aJ?Ft=qj#v=%J*sn~o|HJ(K_$ME?PcmV&IP
zX%Hd2fBwAkRFTO*3vEai9A}EZ9lpbCk9`)&+Z~mRBX>ZGQ_McL^wdYIE;`3cN1~y#
zJI9y<h>6;hjHkaIKX%M;bNHe6ZdKuMnYy4>2yaAKrl48{up^6}i%?~AU(R}ZhvZxd
zHIu7)v-~zPK`RwMj+6xyB<sH`>n^|n$#A>DzeE$Ko5UWyxzxC9nl{^-@_nx>6x{GC
z*z@`xQs-|!b(S46duSb~cZcMMQ5oCo^Qt!SRDn6t(V|Wxk1MUgYfjq9ne5DUAu0mK
z`zl&5IK%O-zoifSvl!Wg>M;q|DYHRcPrXz&8KfWx*-MwWj~IXOM^2A=zLU$U<|m+&
zT&Gdm=N<yDfQX(o-0`Iuu20f++6CpPous0=y?)agjgEyGusl+t6l7(+1?$BQAKnDr
zYiAJ{l$O9w`RsDUsF*Dyo`iLuh;*>&l@5{`3Tm2W<3=it$ql?FWT=QZiXf!_EIFY5
zk&}C_VfdHs_H1fzq{7zlJTtD+Vjo7*{y{X^!u?+xzINR@ot#V&QPF0~o=P<{vsl5x
zUf@JQ_@Z1)9l<lsCh0V?1A=wn#>V}f-w<${nVBiYlwKESCj<W@M!6)2LsaR7XJ_%;
zKUg+hgOzFOR<b2rkhY8)Kr|tT%1ps3W15>dCiJ_6=>Y@739n{{bKGvHkL<(tLE&*g
zr#O5kfIGn}QcFnI;6XBDBY-xdqO$TM=%a!M8xnfp%WUnZZ?w!r9KeOR&Iey3eGW6f
zu%q811Wx*i637OuFTS&>7_MAgkjkF57>YRoc$XY3%B^^?=BbFf8^@L{w_!g>m>bSu
z{U1khc3N862_Tnbx=&X^Cd$-SBC8zr&#U6P4^hwFOzJ&r$p8g8I3NgBL!T^D0vkxH
zOD}T_)eEJcA84B!rUGy%hP8@y05sDBH0ftU0wW@jO9Jfq#=$c)*NGx{fNk5a?I$gB
zm5FT+rz;>C<9|TT5LO|}A1Hid^qxBU^rRodsq^P|Bk8!bybNj~I;-~Ft*B_>Lm_QX
z-<8TxyZ{t+H{5DPoC|yo)8%hsNtCkx{9afvMcR!aj?C*>Y|64~xdk^!?uCYIoF=)#
z_=&Bv<KL5at%B@Qrk~dO+_^*kccM1J!k3?Tz5~C04iAZ;^imLfQ>04P5%n~AUdzKK
z7-t!OrXF5~A~J!<hk8#r91s)RiS)g^h6WQ0t*^Es5NQ8^05kwBofk$*?D$31CxVH!
zExDJfSu?C!HuHul|DC87C62?9_w9s^K8%H+(0T&!`))up&}^E^*ehz(asF&h-;2?(
zx9GMX$w^k3&AR9gki;URA6*X2Tee_JU}KRT3FMGs#bH%aQJFa#S@1n&#MB=sjQUXM
z?AG-3^q-AM+r6j6uKR7;#ckxiytGvN!PQ|2aku~7yC)D_5RZ*ZK-#M}v_m+MHmejy
zba&>4tBhLoGR^Q&+WOb=-Sw}SOIt;Wc~Q6#a{W;1LC{*AP*Av2dM>!q9huDsAt4u>
z+yRhX`%y$Cc%`<eOA_#(Zl4!WRm5GnnUrRrQ%4Sqn+H96#)-k*_e_iLh_Qw5hY3ra
zm}^eIX!OJr6YK5ZL_AfL#0r6QVWVxqLVV?DfyU*skdTo34<1mV*4wXPhnW37h?K5r
zimff_Xp<Zh+XkE&XlRN)-rj&BVs#fmQZ;4`t*)=d+1+(|@RY1NKD2a)ir^VP;n8Bn
z-l#f8g4bV7Nhxiy{J=@~wAoi9(9iX^+_F^moRF6<2v|kXwQnNxO=hgH<}vEeq)9}X
zmR8QgLlWe)hn7%`aOxhb&<A$zh!+|>{E#HXnCFpnBHP-33=H_qj#7HSH&qOOHp*Bj
zK`TKGAoo5jSsjdNQWSD}o=U2!rSO#a&8t{Q-x>cx-T}Fe2}GPdsMIA3<fH!n9D*P&
z22l#^sJe*p^(viP;Qv9iXISXTj#NBi?X`gZ=&?wU5_v_%9+cjH0wdI2gl}J<lSZ_U
zVJ*Wp?K~#WOYx?n21K=9Mn(o+*B)4RG`7fMRGnZ*OK3;Fdk?fyGrSZGMY@R!cICdm
zVSfIsETdFT5|Hmjz|uB{gY!q5Id0y(2?YgVSP$YHUj(|iISD;tS8_ZSju~WJ>6#S1
zk#5xT33N^dqK;57D290L?M*;ytU~}IMS|a`bX>c3?Q^7b=TWS7ZQ{oX{8dTotsuxl
zlv$BlA*#G+CPT3a)CB1eHoY<hsw_p@RiOzq9@nv#byr{p=%dv95U4yy)~AEvuK$R}
z*|R){vz)q1HVRs|e*4VaG5}fAOjyA?)fRerSx`y?wli~ZFc3UEJhE_E+BkxZQ_D$!
z5a`JdKuva)k-dQXrT$IPG<E<;ecmt#FPKd6xk#{az170^_<>jvTepusybYX(Wcsb|
z1X`Gq)}H2Kh<vM5{d+nBh*TLs84XO!ppB}Qm*1!UPd4YJWfEdJKm(6##-op!({A6)
z6>_k60UX)KP&-;o=~9BY-+@hq7GIGCoaDpAB=-Y?)%@P!0CJFI+)l_sB8T8Fbj@Sp
z(prK{cewHOgVl^6P*wElDi`x#LPZBz$@I{xR#)&GgFKH@d!aDF#*3S4ZXW6YL&#i6
z+W~UINheXl8z40+Y8!Ot0M!+PR<G^Yh8(HcvGxGG_j73Hikcee%n3_LERST$eK~R$
z=6rw>_($c*9}@xMgtSTx#k4sY%xGA3za|N1s^`c7{SZCM^>WBpOOOdZ@n4y7+Prm%
zS_eqb2@@lpWEbywX?X=U5WimrqUJi7M;{*_sOFdG*CyLWP~IutJ1`J~aJhpa%-{d@
yLA5%h)BnBpY5jLL?f-wq?B875|20AP%j+cFu9Qt8TUNVlPoGkgPmw!+>wf^(|2^0M

literal 36975
zcmeFa2UwKpmNklPx3;Z_v>6q;Q9+R)B1r`dC=IBf<g5asL`jk<yA2>wK}1C?0TIap
zl9Ngj5KwYPL2}Nqio4#T`<!!Uo|$|8`DgxcpYb`Tp^D-Q@3-H**Is+=&0WQ_GR!}*
z{lvt?#4P)p^m!(xAMBZ!mS6gD1>R|T7&3w{D=x{)NaHL1&7R1l;LECOziC)AG3{_8
z|6k_YIcmtn^b3=$^f6`orvoi^543Gtq(&+}P8~nKzk1!e*T2$M-MajMiR-5;2f0Ik
zIpP-j{P>1r;r-*k-Tj4q<M-RX<9uFuaLb{P@9#(baeL(-=huHH{{3;k!NWqiQe&mI
z@+rF>w>-{t$SyA~4h|l69yZE3JUv9q=`IgG9iq7Bd&YG|`t#W?V|*90W0?T?=BV5%
zd&WEG<=(C)Z`?cooi6!$<H64(-;r-#Jh-u$eElW-`z-R6Y47eYKaj7#?foYXas2U2
zpIR<kO-;=?9i1K4-Q|3?;ye8W$+De#QgVJXQ_O?X&d{*1ot&IM-9COddh25^LHulg
zrP$4Xb=KPlEJq7A%Y-Pp9}ii#lHLA7WGT7yUw-&^F0AgHB-M8%hx7ik;++rbtt%w-
zKRyyXr=+xAzsSeC<$4pJ<a|R#p#o*D%6_WBC`@tBny8ADl$1vjPS>|ve-s=YvdcZ(
z*VlI`%v;36*ZJE{%XdF)&fgcGb>Nlj%d^3M-4|uPsg08}G$~2wa>cV9MeI(drrDFV
z=hW0TH<_jCJiYdQ)#yZ(LVOB!cD%c&!HBveb2!K8pomCeeS&_+dg_ey$Hzk4W3vli
zRz%5Ax}RE~Jso^VSfuev*sJJh&*`jgiP6~#iey)L@V4OKV3X$Vj6H#tb6D3W)q-|I
z&By8%->(+PKe<_FL)FVOo_s0woDQ_X^zLVt?^ki>4>Tlol!>-&x1pG{CsApp=f?$H
zM?Hld2k7&|)FaoY1{z#aO1sBL^VcinlS|`R__9)eqUZDHPoLHu9!k?E%OGe}{`88O
znND4V-<}OW$?D*7ey*%M@J0X0lP4JxmtT9N^Q|e!`H>jK@86jW>MhQxU5t)R;gQaN
zPS>H(CYMi%J53E7H!~Z&;j+UrpY`_3moHuCpB!<ps-#CoJmbk4E7^COFSR+b%~j#>
z;G5%;RDlgJ2PY?|udl*<`ci4noQ5>ibDU?N7n*#1fBSa!bgR?bZV~h5wD(F162h+}
zJwvn$uXcTWEIUtD_HD*yUUkJs2d`E>*Q4y%vE$^{_&9dQo{$%dW+@GqKlwV*ChPo*
z43ip;%gf8hi(pM}_s3<%?Z@$!QMFE-IB^lHvOLaizy8yT?##)S=H??8a`&mm^h`ba
zeS?$M77rIz-0Hox<zIB<<sb6i4Yq3)*L9uk44rhS)i5e}bG2%Ptw_6Yo%Z@Bi(Hp1
z+EmlczWJfdVV~)o>Fk6H@BVOYa_K(Yc2{qqee?a&&h~pJ*4~Yd%ZO^5@|B#svfX*)
z-SW6a*rbvdhm<6{>&(|>P5jzv`>!_`2BzW8SDI>>hO<Y!%UGn(>zDGSdcnS5UvY`^
zmY8l9I_1r_k#%3iu4hsd`g8W_ptc1r4avS&VWF^UwV)u29PzbM3nS`j*S_AM2@4C8
zi)}C27r))0GhAuF?BbrOleo2`jz8MhRmNx}g{G&Ab8?E*U$~hz)o7ys?vEcnx3;p+
zI!lhtI=^}ICO<kphYg$B+yCsLy?y=t^8>@>hljn!%MS~rOJFtACTq3y8xjrgS9FJE
zXG=VD=z1(R@H)>=L?Cy;YP-#+V>jp0ChKyhlCPMX`?yTEh_X8kB#`f&B{^baV@Hdm
z=z=NDR;4cn19K+A#fH*4+T${ZJnr1NGt((R*=mtBdg_~1`iD-dQQSiQeHr%0Z>XXc
zx<}@E!d&8<aJ7P0D_?v&@PmG#*WG<_X`Gw*G&kUBlgo*W+}l+awE6IGmi1WLUZt07
z8p3~=J6~eS7PDE`{#P!AXU&5~Z8L?U?T?L~-F^7*;fL;VJgQ3t9verStt$%1!(F-k
z+S^0%cD6h`JnavZ&dQqW!3LH~zEs?kkEQc#;(lg-DR;1Kkv?5SJ}!_mbLmY=$|*Rc
z^73-~@iNg4vRFEHEy7pl(<@x~AM$D>ZmKY_UsI5xIIv`52f`(9i^aldgT8f?6E9O6
z89y~O^&Wo1pU!pq(Wl{}_mnYzs?B`b<X~!BPUCeQ0f}|2x7RBi5loLcs^Ga<U}#cs
z2>U;4q$|iGF2w~~ix`+}Qw4=S?vm1E*XH7XeSRQGA-94ruyo_)kB{!Zk?MM+KWD1N
z+qwVj?e|{<I9_9Sa19oEZB?3?&RxvzQpj-Eb?gm)@eH1kY|`98s$*Z|3mR9DX=S&1
zMeyO&Ym2n$X@^CgbnUon`K&U#TwHPOLrG?P1wQXrZaOYF&9JgLv72^$e0n)YG+d`M
z+p^EPNKAY>F<Q9IVpnSS$H(h!ZEamrr06pp9nWvBl`|)|&wg!3kXed`^WuD)?`f{2
z#(B|J`wlUfcLr?$zvm^WUl^0(!sUM#o?bNF1#29i(vnmmxs63)LQz^;Sw*D)%hWn-
zU3xjma<WE^o1-DO-D97}+z*<|_tuCp*TS7_RFIrCu$pgm8um6zZMhyir>&-CmaJlx
za@EOc{zjnpOtFHiA20QK<5d@*NjOmsj=4{6b6J!CQ;}^_^if~`ra+zR4Hx7&R8J)H
zZLroOq4$OF4LiQU+(mlvm@j3a!*)(}4r|J<AvyM8r+!kI$hHx|w%Kw!zfLy}SFwkD
zTD2DQ@h*cV=QK1LEP~=VI@+$~G3yU!PY<QK)a}1o?6>=KLqkJ!S#q6j5HE$+ucr`K
z*GSFC$RJz9uZg^~()o141LLPF`un2e&pwPe8{9ZJ_I<cb;Z~zvrET-A({rYEwY8h1
z5dHj`a8mnowQcqFkG^k-&z{nJlbpOUyzLTOlv&85c}`sZFD~C_ne+}MRh)f@#ralm
zo;i4_Hb#TQj5S+Qrrz|$<hZ+&AbQjB<J!Xmp9ZIn>ir~Q*`B{_zS+S4j9;g$KNquo
zmqA4m%i%+Z9Frd(wlhy`o{Z`EeE_#mJSJ3eM5w1IHu}7G)rA&VWbxQ&5l3z<Z9`L$
z<Wz#eY)X^4XKJgXN%G(j!W#BFD{I1V&V1p^AoD{P+RV(%EW3m43X8d^+ZoZT>Wrv2
zBGaq5xS^@x*>0)=yHInTIlEU#<0g-E0fK5OW!`2!yxweClm=fF783F+r>g5{%~qqQ
ztZ+awUhEQwVD0ORI4I7|ltmitS%Su%4eh-V)U)O+Qyo(crXsCy<-1e$unSz9Sh`H#
znNCRwxFzTpt&Q^e{m4|ke(@Nw3}$9#5=LRj1Pj337U#xYY+;qze*cL1^KRAC;cOA_
z5KvqAD@JfYyZTy*dvkMh=6ZYVa0{_1EkA75zQ;>-o$IAdkqd~BqFwc36XuZ|yYHKi
zok+7EdACx3w7q~hWyHA_x@}EsYtGt=%U9Oaq$tKL`H-{Uwc74CEP0@wXdv4RJF>(t
z*{!BO<kw*%kN?uxx8hMByR4nA*i`W;zRx%!%uP+VPt1|6Wsr`4Z7Iux4Kv~f)wrn@
z+(ss+V%D@g2?`2BXwcNs3QJBFZft7ur|9HmI7?EGn2W`~)AE2DeHI_UjKG0=+9G6B
ze)?gB)Z&8fw194wSh}Re$&)9y+kg3;Hf0CYRkS&0<aXj3le%g}i`517n+d4v-StIB
z1`CD_?e$2==&X&6P5vK0tZuK>vhWV`l{^HW%gK2Op=A`i@c@7NH$Pq9;jQb|Zm7PV
zY*6fHke-v{%13?oX>0k2waX{#Bbl!0s^X9KUpzi$Xf2R6a{MDzDz}N2J6mSq*V*AK
zC24GIJU7q}<MLz#FWNg6*Zr{a6#LoEKz%rU_<l}m1+eFD^uae(XHUAGx+(g}*1}C+
zOTF%I%gkAFIY|&<Pps$pO?Z_!cf^gfo={J}`P_w0fg*r`mO;VCr(ZUOZ_>5@diz-H
zK5B(s=9@fK_!pm=&3at^z1L&E7*SnPw0;q-r|9G=n!7wKHpp;^)U04iu1;()eF{HY
zJXk7_dqjhO!%u=HwK3nOHs{V1xX#ZZAU@{h09deu?=6y%n)|#(%GLSc!GqEwL4zBg
z<jnO&_s?xmHQBGHDe_69D=(WbrfdnaaW5NQ7Znxdp>$S!H)6`Yg#qlo;hLf495Y7~
zo9mM0o%&`C<+aSyJ2h3s-%Pbp77ZL)vmAEE7#KVPgiCZT*r@j4wO>uY<BnDi`iNU`
zvL#|(ZjniR_9j-=jzaJL$=$z8*t*0%g(WY4wk*@8x6<6`O}yu6uJy^u$;E^3p6;c=
zxvZ^!`Jn0jg+F7%kM?fw5FelN!er9wXl^^Mt0y|+X|V9TI)r%T-+6aEcVW-xu)2hj
z5X*_rGTfyRR98p1u7KB1Cgz71>B9!IER>lOsYz+2?>1?fvaaVo>*XF$_gKfatcAMp
zjaxS8-S;Uv75uSraqQQ!H|sf%Ha=*Ih-)&f6G-WouS@cuKfku={n5WWz>wHjzVbu;
zF~@#L;N#;P&1|Fbr)Ou#?>+tG;NDhjp<SsFXM$>nH_Pi-mkC=U_Hd_W-pTH~T&H7q
zhJW7(;!{2V&FC@8)kb2<+W;>V9|j)E97vQ;y*G4Y`C9ket2aM<9ol+UiZ)>|>(n-{
zi`yxvaGgr}W<;I+XtZJ=tlxUTG19Fj9dNQkt8K1UQ@JtqCX1w)27RtBw{>uJrDFyx
zk8G12uPZ|8H+f|L-QQs~v$h<*Zn_;%*b*02&>x@64miY`F!m(4^dX-o4k~aR8H;*`
z%~fgX>e|{ufY#RNco>Iy-FBCr5VuS7gQ>nw!`Y&<mC3+p1k!A`Yqo5WTp<N-Z+=lZ
zz1ZM@B`~S9Nak4hb#fBG4)#D@$E#U0?Vc@v3;?|Vd|m{o;}f*)t0p$OfJ}x*rPpw;
zf3Z!b7sG5wz<p|6E<W1sDZmvd?XJF+o;^`1Z`t83T1eLL+oX6k@q)W@hnvhZ`7W0|
z3Hbg6NeNR$h32eqp<JbHv?05WZ~h8CYPt@)0avA^i4Q@fSx@q_(~l2?_f&ix$zxG?
z$S~I5SF&%ZkeoLiJ96mIA>!(N^41>a^^usWKQ<};cgXV7Ms<B4)7$Rq9Te-XkG-jF
z*foaUb4||UWvh`3^d{vN`*U-f5kRzzT8Cg3%z#rXOsS=v88ubS&CNzlG8`^%6jfA`
z3-t-IV^%N9;0d&RB5r-TvI`47lv+XZF7KkmurU3%nx383)cIy(6B4ObPR;;2zCU|-
z@L*5(RDpnN!fRaRu2k4=LCsVqm%j72`!Z`CX)tEX)F#^Au9W@COZF@A^j+pAIJtI?
z#Tl8?R7n@bK~V&QG@y_bN2;bmJ^4MhnWtS;tlLg7m_B`BHsSNBZOl=3&IgHEUgAae
z@$yWNNKcXTkTunlEZ~pPa`BN$5ryLwQXKG;T9NV^vFp!@h169<MMr14$ZAsvEXJz3
z2Lma+O#!ODsEu%Wj@-BHR5$TjH=68STwDlRxBMtrv?inNlKJWr$x8+smRSA6iyDB5
zb)qdcN?AaKmat5M-q=~B7Os(uk;K$t?2=0WMm|@oUcw6wZVm{zxvg)j(B=GJ-M0UX
zCv+EnYd1mqt2gnwBe*`o?VNsgMBGQL-M!wiKh|w#>!vnr&Fu)M!}^~?Pcrs3JA#uv
z*76dvO<eItlE)3?%nu17jy^hID2FH<RlPef8Tlj^eW6>bXpz>oSTI?q$1&bj<_X}>
z3H))X`qjAva0~?qB%6q1tm_e0UqiOLDW3aiqe=X3sl&ifb$f2@5W0N&$m}<N%L<9<
z`~WrGtt^r=7kD+_>>)4&+tOknwas<(afLINNSpI`i2YCGqR7G=0X(*ZS1dwGyp04l
zf)9|rY<Jsk^&8pf1b>`4bH=3FPB8U$NaIp)y|)+X^?AnkOmCl9-z9X+zt8yW7N&!O
zIK}0!(M{o)dAm}MrYu*ug+M7U-hW9LDI){gAg8lLIhmAi{SVMa|6%4SAe{dfGlEX=
zui9|UD+28Zvvg2YG%)SSn7;EI0%~YZjwC1NQVRPZEp*JU>~dIK96u+gRk}7Zw1;@>
z{JC?ih)t}l0NPstFn7nj%2HNUJ%y!YI1*{3J$Lu<2<Q|=AhF#@v2asTQBhG=Q<IUF
z*3#0lxqh8-6mVX6oAsizYQ)QzTs-^K*ymD_0`dp$vpm@uo{}QMz%ilW;lx9c;{ht(
zFN-@O`U>cZ5MoQGh>VQPuX#X)`F=1sK#o?_{Klf|C9VIEWY^2=aLXZGHMPL)!|{8U
z4YQSWT17s(rmCzgjWh^|%)`;GDQeYOO;>8(-k>tBj;ha}FFHHEdiCnJ!lJ2U$LPX{
zq@=2uiF?Hcrh2rV3>Vz>Rw_HszI~ZL*c3)&BE>Ust{l6waIKGR<Pg0V(K4>7O<<Y5
z-Q?tCOQ8%?tB$_D{@9A&&7xC1JUl*JPCB<|gI9&|qs-~p{)D9TlNLJNLn-mC8SIB6
zI_r&Uiuq0QcC!(CWBM<m9RI_%^DpiAe~S40H?BfOqYpo+BvfHzW20Q+kT(Bj&?%h6
zU&yJ-z`T@I58e8n>4w$Up6yH*?h!&{rp&@ssqcVXUr(5?5By1NHF#vEMXLNk#xnlT
z5w!otb^ecVy#IBFAgfi$<zEa48>fB5Y;vt=N0m<9=x-hUF0UrHv)~0FbBd{RMv}{<
z0CPMwPB<>UawN0(a+0Bj{@(D5^E{i8t6?8SmhRankt#5E*?G>$*%6<P?Y0aY?JaM9
zL+NiyP<Z*J$Iu&l_LO0`_I9RP8IWui+8}en`NLl15a_P!G^o;dfQjjXbZ<|OJMd7l
zW6TRH=H%2=8BmCkrCSuS2@DQAMlzQGj_7=#(se?bxx0fl`dnSD+-_VVkL_4kSctbe
ztv)w5Yqxup$cqGdgUfPPpS`2NHnlP@GBlJEEZWXXog5LhwY5L1DeGb%GF+1?UE->V
z3BzkdMe!J$nC#>X1sp}@C9i$XLbr+)h%~`@cI<rMXL({oa76hfY=;jY#*ZB$V`I;7
zttl%jLlz@PUPnjEpKZTlZ0zAIsYk8@QCJxcLptAM<G!a~o56sdu)3@+KExqkh8601
z=^`XLNKL=GSScnM?PFMX=Y}0~r&YT0=?`jy-><%XoTqPxc{JUfjoO;wYWgd+VjKJi
z509VJ&xH}Gal;}n4JqPJ@eqC<VSK_XDk&+6w@BuSdQNUo9QAcyPtTrG(I4d^abpcK
zb~ZNBGBPs6@sWJl%E>8{Da#FeW!D!o3yaM+reF7!4Z80srRhfsA3V5=(&4RO5&hlB
zj~!J?bvNpovs2@iy&Ua@>$#}erEB6eWqwOQV>#I;zw!MO<%<70Q~p0=L;d$v{Kv{z
z|7muiY#6zE&r8&=e5L62;GcGkeiW*Z1|_`1u32#Hp`PdU;2b&qiyve#N{Kf@w+b$^
z0M>YFTdP4nYBN8p?<=v%S7J(i0FckJFEZkkNdF5G5ST6)Y!O^GygN|)=ZYiar%i#G
zEkXC?9|p4s*pP7T>-)5k_p3X;x^eg*H5c(YGWOwoASr1wDQ?)=Zc`gBA6U4Y<;Xf#
z*3H`2j+uh{JO>m62U_UM!9m`FgG>8*XB&zF>G?H`CD~Bq{-ZsWLZ5YauSe#nKTw~L
zJro}w@9Q$vz|F}?pfQVy894>h2}9}cv*ZK$z1`hoQyQZxL_iF9y1NsST-}&)ur0<r
zIlr`AxXBD=2i(0&iUSzhot*82L4RG%fRp>HPvr;3n1L~6WgU&D(Dt<g!fye1PMz1%
z)NCg;6X^x+p1}$Yqu`CR&aTPRWsd}RUa!CCW*cMQV|d{4w?&m@V%dgC8XZ-6QFUjf
zrB9x`J=z=6KLc>bMrBWISj@6eS5Yx^cI-JMi85D<d6E@1NsbYhAGTeT0~Cyu-;)l1
zl-Qh-nWGDM6pCWjEugcw4kbOkw(f+Xxv2gb!n>gCdQrGDqMx3gknnkC;?v}?!(T47
z(CFI7GfqNw!+Le2vZc*$Ov+567A~JPxrhZ!DCN)0Cp>gS@O90nD47VlF3h;Q+oNdJ
zfjZymwZ{(~KD=^Z7&Wopq!#2nfl;eYaQVxsWapL5Ob7&(u~A*OQ0Xel$~HmkDuYLR
z^$ZOSHCAMGs#jf$hL4?h(E}#MU6>R`P;iUk{2?)0sq}z(dP23ALfgkP^G1l0=8d^;
zycA?)iZrFJ=YKqpl=IC@iCWIjo}2hQP+dr9asgBoM1qxuygt~0;bOhE?Bx1lcIApY
zl|B){NM(@!?m$YTU0qjKM6xr&;v89A7-t=&mWoewgPR^2*Ov*C@jaVU9sAT9yz7C-
zr;(f3gJ+P}Ior?xz7nP3z|w-T&Qf{pGY+5^1RW=O+|?Zl=Mk((H0z!F9O&cYgK)+}
zKy_l`0k&=15FUZckq9v2AIL2dXWg%un0SJdBY_oJY8XiaRCRR`9%J)2PId|JAfftU
z(Q!gbZJWh2@3&cz|C_R&9`&h1I>P!fmnbur%1@u}J#ENBtVJLvX#wiET+|EdDW;F*
zohM$uTd{2ywPbG*;*&R0vBZX$^s5}IWoO1`oq4rVVgTa<<E)h)C2hL&mlnW!x)sbG
z$=f(Twy1G?TTg??Pj($}UuC~jl|Dww%t{ew2W{K@6(qN^vVz1Jzd=Jyc(`{wr?WIL
zu=7@u!0qGjmMt)ODcI@6|JpTKrBx?N@lvqRiQoF5qT5@6MJH9D18<gcJm{bKQMhE<
zuF7`tX=!IkV&VC|xkh2hY+Dx^;LlI?PCeq1Q@34`%qA;lCRF!q@YBCS5Lf!`bTx~v
zp_^)PMe!1`rNTpXo)&h+omTK)c0di1^8-rl#*AI=p`ZPuy+uavOH#$N%kI}1Ew{@_
zs_ta`a6^H6x^$k@;%tR_mc1p(r;8^!0>MLria0?^;CT3i;cPiOx|00DZ%M4{M?^$L
ziwM_U(^`jAHtBMi#~CEPkD`}c#vxc-Flb%peyuhS&?Y-kfT55K%ruuaMXG;<p{%a1
z{+mB#gFglATD6`l|JJJYayktE_x82AK#l=3Ip>94|0%i$#?}4*7@7ZX0TLv5ZYkym
zg90spgKVvi#w=CO%*e<{)|kWq!bI-Y5M|oG!0zzvJ{X*p$gfb8YZxf8-i*{9e#NhP
zv{!+0s1>!ghXyT7Ofl5&vrhDYxA8@JDZ1M5!D+<WNO>lvJD(E`O9}f!QUH>v>EMa>
zZw<UcY~-JlzWm>50#Sr_gX=p}T~osxWhRp8+T9HZAOf-wxkdcJYIV7QfPlgXx6@qy
zylN^c;0iK)6l`si8Br8FO&Y;vRrFpdokjE7nwpb1(yC~UY)}{H0~M{n8Mv4?-IO@R
zRtzUyYSmU}J8x`i>UBIs@x5`00?I;(M~tiYbX1+F7!ApcYf=+8Ukpz%c0TafYgEHq
zrN|AWNA#;#`z2}n?S}cht*dOs>FY+~Lcm2M+AEsv*m8<cFB!LLqGA>*<a4!6cmr^u
zUJ4<K`z0lSUkv>G86FY0w3Q<s35$<!or1;2jT?)1z1A+`4lE7GxaHSBY$sH&|KdI`
zUUr(K_HUC**DfE9a*Lz1A*0$+G<~n^vj1Hx{y-r~2^;3OrIzyo$>2V|P3k*43QhM>
zM|*?PoscX|YX+`A`9qe;E9jeGLn~^oyGV@`caJZnhzXj6U^>?-{7_CL=uG*O(oQQZ
ztB1!=9aX-_Fj2kqaSxc6hyO*SO&z`ZXM6)`v?>Hg18EeFs3lUbf8%}(j#OdRG$Rd7
z#)HnB_=E(($KUY%Uzt?cDQ~&Li1hS^AaMy&+<a^!A}s81^SK+&^KDYer->4&f{YOv
z>hX{Z&Z=QAUlz_x{OVr}5+|=Yn}P>k+#C(cVq-^DeSKQ3uTF0KuZ)dw@6!JY(&T@e
zvmy8R{D=gK^t-j}kl{;9e;c1gWaF1*#*%uFu(Vslt}!_!r4Yec!_U?zso@pSue&>F
zdI+ZYWOoFaCd!(EAU~1AzLbAPPxk9A9$Mlg9^2*4w|v){jBT?4wGtiB6p=vAM(W?`
z{x9`3JdSUhT3dAo_!&|_b??xXSwtU4%JQ4|wbcjW81MIl_AIk7F)?`xyr0ndP^*xX
zxC!447Ga=<VGl9W9_%B}tV;G0wI3_%bc>yybI%D560#5Sqk;pJq>-S;2=1?2an{4A
zU=m-x@Rfz)j)LAP>HKXLS*#=}Aa?2_IoD%9*BjxPtw|1lWAg0h<*n}S0NrBW?wx4=
zEW_a)sOKt9igHWK4iyy-LHqdMj&>|=uz(>0nX1d5_<7{IFo3ie<aO<+BKQDD32~iy
zvVoOVL5gOBl8D49U-lhs3tgh53UK02VBmh>+p=LEYybxH4{Z@Cg_J{R1$Kuy6t(Yi
z@$RSfmpu6{^;JhQ^h#Fg&DR2cAeVbjGjG$#_2hz0TKNzWZ5{8f@Ff;gTrr%{9|v{X
zd9STPTqs?2$~TaYGtCJOEVF>Rk(3XJNUI(-rA(KvjW1;VIfKdyfq*-_gv#Af63_1p
z)RS!9y%b^Izy5)dS0QcSO~IwqBSCDh%ca{&OJ{ZL7<Ts*$PpuH(j#1bjCukmeVVc;
z%AmiU>El#i)X&V)N*LMIZ{EK$+XJW~G2AKWGB-hjDnq=8QpHK>{ADY*uVXnfa+DV}
z{LAG}w~~Sp%7isF1)?aV6Uk&{MQSc>FobtKxt(7Ec-%s(cFRA^uTj(60fB}t^i8{F
z9gsp1kY~9tT2zGMV5<cO2dziOzmupf_WXGv$*@48Wkm5Nys6@Pt3x;AxrK4iu0X@X
z97T;A1Yv&8gmKwhR>A06kzN#!k*bnd?+gESiD8Ed&B@8z0?r}oh79ZV5<AIaUxZAS
z@rY4ZL1pCiSr^(&kre-V6>`B$I~8DFL_Bl9*ubNOkMW*_foDs|HHH@Tg8c^C8kiq4
z2!nLc;8)BT2cGc_rM}BVc1=tWagNB5R+uh^3%KkBV<s=KF>^aP(hb(=o@K*OWBL=p
zf$Q5TQbkL|BNpBKj~|FlZGFN^3}ksqn?rZ7e<z+#Z2b~`_EN_!ETYiMRf?2}{WWdH
zHq(2&Qm7jh@H6(*g?#L(c@%$X5#s}ITZ|4ou94itI5~Zz#FrL^6bCxMWldn&c9oLn
zm%xzFGHpq~5)Lu6I197mD@_-NDT{|SS|LU7&{<0k@YY>r;)dPaL~rpJ3|{EMJnDW@
z8qY+`H2?7kt^@iUo`_BE?rA94bwGr04xp<hg@)P@d+uCKJVdg{KqV(575;HmEWkf%
z+qKvh4?Y$$D$KvTz5@p5P4a}%z}$u1u6QDZf!&B~L%VVaQa`VAVW<9n0INNRXy3S{
zwo1*sV`+ckf7XM+DsYktpJbCBgchR>6|UF!$T6N4?$mX~Fpu7BiCR149{tzPv_5;O
zKxHSjUuT?%BMLMx{+){XRs5!107+piA;vigzelz{Q4rof{;NonX%Z=2Tg5WO(oD6@
z_f}*@lZrW55F+|*xfa7%w+q?(4gPfH7q0v(sr8BL%&jN@Ot(=eB>ka}1?KM3do6OW
z`<avZD>=sY-g55Ft}nq-6jxFKOt|tnG^EG%C}e#;r0&g5Sr{d$6ybUpf+#nsg|GE^
z%yQ=$TkvQC9=i*2>5g1_e{Lu=kFyQPdM>@cwF(+&rGXk-CCG}PqVj-{Goz^jve|Xm
zz!n|Y#%9g1&2t*!2HgA%7rG`WlslLd@_!y8!kE!dCs+zD2brl3)Wp;>i#D&T)3!$K
zLbXtqJ9q;TQDHGo0h(t)cS7XD7b6)*xgfSd;tS3|3F2+D@9@ssG?YV1yJf<4$RiB{
zb!yGBfgcl~8!cMJ{d7NXGUH*aaWhRg{1l6jm{oQ5@W#w}$rca;tgOUIU0fZ&c(8Jj
z*j^8{%pdcrDJyTtoUh@5KnCh)clVhQH>o4M{S05RCplCv^dffuC^Yh5s2&RFu_Ki8
zOz}86V4a9c6?#`Z-A6bZQA>?hD=C}=A3l&+%0HUm>%t2yTIR7yJEC!rr0Na2fEZCS
zMZ$ZtkK4a^&Cn_@#mA?)K+wzUoXV|K?6}rHmr-^(3CPY=q{*yd#-?u4JDUOWPSgp^
z9+V`vYpVq@&+eGZlNq&XIe=t+<H;t@oM0$Qt(t~1C~!6GDeDC*L;KsNQ6`e?{;ogz
zS%;F(eE0c2#|3SI-oW3)vT;~fjj6<q=N$alVq$D8r*phGwm8c|$0{GrRBx^_(C@}H
zBp_Z2E`3S`x+evexHxFm3Y*id8GF@A4_4j`d50w`>V*@%RUJ@Yv%StGdmWt9e3r%Q
zW21BIBbSpJu9%wg+HR%Eo$wA<K6mb-^Bkm#SMgsJmE8lqG61ZoKm=xZC91@Abo^r5
z`7JBnMNgbwen(43N2g)Y47&DxwyO+db8>R3cRxr5IfLw=w!~##ApA5`409KqOVrr9
z3OY6Qt*xyI;f7T?wrCD%My;d7pMIPa>>1*DlJ_OFxy~1$jITXVV^alTBP**#&g|ud
z9vLYQ1?Tqlqmc?OJjgi0P%|O5{WOS4IuLw(pw!$=Brm_lu6+JL*P$~&tUo46PUn>D
z87S~c5I!Hcj7f15QRLWzjl@GOCQ;jU<R-_2!y+~<kvHe5pL5s>Z}8M<C_S8ix%x_6
z;bu4rqBtUQvhpM2AM1^yW7{DaGKXk*8KJP2RT#Hmht=5!M@wFMSkbKyp3F<%N1Q7?
z9uS|fK1iJ`YgE*ruF_@=)V7k%r3Kr!-*s{2HR+E{^#SX6tR^{!VzZZ8!QvQmrzuK^
z<*$<VlQF+y13w|Pzt^)-;`SG!me@Lj607SC&b|K}q!;hBl#bLh6ol_<>$(S^)Z{N@
z(A3maI%b>IccybvgVGdOH&LSC8_+=~<?Hm*r(quuToe8bNuRvDr~JVQMJ=tMkLl#F
z^A2Z`2-+4K@|IZ6`>Ll8^B3ht=Y?lyXScPTNoTiP6KExIoqsbl!MsJ>%+_{BBP3Z{
zJ2zQ1>E~(^h3$*T)nZmu-`L0o$>8>hj=HW3A^o<lL{%G~QueLbzauI-&I3+pFDzk>
zRzCtM<>zjf(m(+Yn_tp$IA#!S5}6g$e#q+7){ituYWU`Riz$oLrOlUFi2L|58FzAg
zwpwVh$rdq6uT_^r+9dn7>GS7v4?B`173iv0Ei8t|?ev{hhi;aRCTpELcmFh7?euda
z+l+GN@4sZ*W}91^m_(x<6<4RqTQn&3zS-uvL))CBWS0wVADG;{=9XrgvM!f?tH<60
zZ_kvpL7(Q|>5Wc~WU~z2eI*UMi937yo8p9!JqN@Ym+Zu!V@GchHP<G=g-9}qT(0J2
z|JAQKzc4@~1KbQH6tNc!St*i0MJ#~?3j%X>r=@1T+yoUKXP0df9CwxB2o?EH<?m+W
z<dmEUVL6GU<p=~l#KG>WOYK4Nl8C%q0sLg7ZRWodA=h)`;5NaCL{&AlBGN*_O?9@?
zM$d*N_~vP2YpA#LeGU()|ItT2%H(08{#`2o5PY|&pG7~a(PnN25?ZDSC$+J8EMb_8
z5<+~|m>k(BkQ1&WIBv9~h^5$Z$xmPW@LekkAVj^gP_ek6urN+t9I$QULFi&3(3_Rj
z5-Q)g@kz^mG~U2f6Ah?s9|Pb#Z)U~=gMBEyJT043A0vgLCZi>1QGd8RtaHzYtYx__
z(|p@@nR4w~`Q0DO{@8Mp?Z*R0*KHA&{&ww_<@Wmp7bku+Qs4N4)UwVC->)$Geof!o
zb+=f5KXzo#;VreV_H4g*=FaZp?#%<~T5WXthZoGhwxkyiHmTH1JJ6$EL`SzeW=3W7
zso6&F<n%ez_v(>|S)<|uMqp92zVl6{)<?YS(WNveP)~)l={62q+ildEV=H$kZ_j~T
z`A+B(5PYIMdczLX5?L)IJb0KLq^kkg=ah>J?Nm{lZJmy1-k*u}@6q;eo$&NFxS^9v
zYplG*7N+l!rywz@9KM9zUTJJ(u^$GOX&-s8SIHwUUE4gYBYrW=*dlvU^&dY&+7sHZ
zSzC`^&Q8tD%zVF;vQ@`<tjq$O<X_u8KAT(iDnE?P(b3T(56&ybMx61`g`?Y-4HIb5
zK5~>j{WX0@uF&O=um16ePsOc0@*v!v<|@jCGRrfsXuI|4e>}G@a*8WguikZc7og0R
zCYUxPCJQxw*vYmDh=VkcxHHlkvQ)iRjB^^R7A$XtSdbK)mRhTbs!3|LSRphuHC3#f
zC@AF-)ef^4BT9>!PWdJ>%xjGi^9jMv3a(@RmYo4=3^YPobShkitcUGg@{9&`T_$UY
za5iKQV}Curtg^7S)%NU>(R_RSLVH?=&pz>T+)sPKW<4(f=dY_zs4(PFN6Xm!4%^o~
z2HC9bG?*-C(VC_H=E}J&`dmNyN%Rpzyh#lYl4v)psikEz`)zrvE%x`N$``VPi#Gna
zgbCHRB+4vlH}=2zvv+N)%Bal2r*Q8$R_Y85S#|UUVa8FC$>Niyfu6y^miFn@OYM3q
zUgF*ANTo0o+rIVFXfy&1iNbqykx0`|0^NnnGZy64RhS0{fZ{UmRQu6(vCaod?YWEd
zx%yE0dyqOH8yk-<<GPNP$qmTV5=kNhy??pM;)4|7S4ph-DF~mVmaO-qEbkX@B4V&a
zI})-&+%)UrxYVM*!IH5&_Pg?S8*2iDg`3=P)g0Vvq%Vx=XyGpQk+{X?7Cl;@+A38B
zw!O!9cV!j;LBYj}rIx}iY5B`rC#jk9_kEpry^~|ye`5H>sxZ@MbUJ;RcNN4~qz~Y!
zO{HwXq8?=n`NWMe9`=zGkX>-zmvb$M!vNlt3uNF;#iXAp(kE~Z=Z<Y{?nJrT*Y~pk
z!<YOb#N)bhnB=Cj&N%~Ope6;;{X+6`QknQ%U$5S~lnNN@!W1bBMU<F{U5q8I|FJ@v
zacv*={HZB8^(VAH@K+b*xs8kWlPNQ96n8)zIR%lf3aZ}dRmNQ#Xy9<9LiV|-NvpT`
zr=<rfq6nuzveKreZ7nS=q_twhVlIfhm!}{9oUYCP4BF|Q-rl`AQ@^t~|Dvg>$+N^z
z96duE1n}A^J3G6N&m<0Wa*`+k1KM{dla&>@3Zv`l>#g;qrb>WE8v4<0FKP0ELfV&E
zp!ke;1fIe8AncP|M?~?VAzy=&P~aIw!<qmAOpl;WZAOO{LEJ>Yi=vlm#xBP4KT%fe
zT}2--lG-Qv<SX$KneSUBYf$hbOUueiY(T<jDS;MJKJtYYzc?4qa8)AWbf`C5+eg5^
z>;2=lM~I3S(;gaXv4tAOGmoT4|L9BVD0hZHhKP#OO9J3(0Fwfi(qHW5BVQySzW2yO
zbg6<b0!t+_x0IrlV4dcPWLC_1teCrK7%fMB9g>sPN~E_kXJ!~W-tfw$Ji9suE)m^b
zM}Zuv?(Xhjmq~||bC(eHE}@N{fuozLqS80y(kJ6PNK@qi+%I`@tgIaK^+oN&^r2y%
zrNsGYS>r_fbF-I7^GL+oj>^QJRWl^oL}$CZ%^D3EMd*ISV~+dBD_}LD@D5HjS@_Nm
z=SmTd%N{a!(L=4IWseAUZ>~M`TiWWQDuh;X9S>^Tbzm6uLpzJd)a2yGMUU`C0&MM}
zkPZ5fby3U-QUT)AP8+m(dwYNW^m^)3Zf54;x%v6^ksU;Q{y9vB`@nFF6HbEQ-ujzC
zD}w7ewbUtJsmzRwP{lpF&KCi1ozwPRR{0!0*xp({87iP9ua526Gc<Jeg%7ATNf_X>
z4_UK8zs_I(2pjtB>w1t>WYp8G42h<-6|fh20VI!?P!D!z-Fa$F&A_q?VMsALpnpD_
z;Ux`5^(XsZmk~iLy6q%n1Mk0rz;~5>>L;S3o$26Md}Oor?jh1k$iM8%4&3$~T6zKe
z;SX7R6$47CXoevks1R{g)4O3QFs#OwTym*VVLWytaT-Zn=(s!YT>AyZZDsEU#^$Vk
z^XqDRNSYR4&x}?OvhVD&Q_7K>ZbIbJ<K%3I?*5@$*HTja>E4S-coz~-NHdP8_)uR>
zbO$6CkQQvWtv)Ac*7zo5&nmJ}o_DP&7P9yKZ-nsQ2;pxCAr?{md-I{xzq9~^<$^gu
zLfekMHPjK1<&lVl7L&-zB!<atNkO=igofgCO^r%#F~e$K|L!4m(++wOp0%5Qx-F-`
z&T!0&N(d?FWG?s$nSh$(7<rRvfePP7s8-7vkfkTXxmn1&Zx^@v$sGI`Br3qkc~<P}
zEdqsjBp3q$m@fV3YA^`YChttxJv3!-^Jk5`Cp!A4&=)Z>>l9?)SFY@lD#h;4soc%j
zZ`ChwB9Z~Owzk?Ww88UiCp|VdT8G`1=x#t5-=%Wu^Ft&OTfm3ODI8Einl3u~=&mh@
z^mS_f=Ex|~-uBF9tD#{?p|KVvJ2pPPj>>EhB83cAHt=2=M3sKjGmv2hM9WgGXtL+Q
zCR(O}>+0*PwA+yZA$9N1Ki5HVX+=W~Ey_x`x2m%8e4-S*ph_H<3jmu^?jppMp73Gc
zM>nKRnrx2)hj~7oLw9jzzjwc#^oy~>$xo0=V^TnR=6JhRN$@oDwAnt+(2$T21Bz<A
zu1=?MWrQ|G<lv*pNIcP%U?Rz#AbPEIz9!t9y9>X}!}7K^@HU%2h>-{>!xza?-`z@{
z?>kTRW-=Z;c8aa8WinTE8ynmCt(VtKa9Z%0fM7|d_f#~xoQ|Zwundk%vFcawt8H6+
zG3li2{(9;=CAK>1q*Tzs+Ra~^ZIrrQ^0K9_O2o^0;$xE3c-*CuIn8oNh2Cs);-&_5
z`)!Mt;E~{?**~RS%^K4?1JyC%%wLq&zXAtzD!-AaMc2o1>7crD3C;UpQSszR!RGzO
zaH0?;`5b5Ml+o;1=PI=c^Q$qDjS#BH4N)ezFO@wJj%=1nU8*=r$7DC=qONgkpJS>@
zwCZl1<L#<(+8S0g%fLd$?bz7bs-!=e*jq>4<-J`z(f;QQ<O9mjUR%r@=FU3@(vT<b
z4<vmX1irkmr1`8nJ~=%dNHe`NpYEimr)ON(a&S)_^&oq^s+NL+LNWcrdd`f@%#~R#
zwl+4Vqqn<`{g5>{I9RaU``NQ+cG<0Ub<?R^)592h=XvOgfD9xR53~K6Yim`VvoY`C
zvMSjCOr~#dK4pCKcL?`q2IlprAjIG=J$Tq(nElVX<cC*x<j(YfC-lz9u<I%S-(jfN
z_J{zC0G=h!|5Anb3+Z24IXQReue@&5eW~lhi>-P`79&+#%CIj8G4p)cA#AK1{?}^<
zX~xAL{nsG#pT5F>8>jxuLnxo9{DEm0(jOvGzwc0o5<Z1Ok(QpAoFp2o^^v4=9x15E
zoF;&yVd+Da^w4A?T1I!IG$6PIDKXtYL|QPwrQUZ?YC<-7sk0>D?#E1QMOX~feUp7P
z1z(L#EG&Gxy1JyhnDZK<RH8$HM(#UIPfeX-R9cBzYDP!Tj1*cC-|;CXgTNb`&+nmD
z6rnH@i1_~moj4>F*ydt}7R|gIsj3JC-wvO{{JjTW9U~e@;?Q~z{A>$IL*!@po&5s~
z&?Nez_QFekK%ea{dL*dt2a5Z{mOs7GzXR(3<zoGt7V*!o_kSPa#{c&Q``dB~@6tIM
zjvBmu*JGo0kYb*MG$N_*6{rr$7!FdGB*^`su<&jjF0!GI7F3^p>-1-f)O_3`1?ay6
zt>RuLap_z{OGJR^&#xF6@bfWat}i>l#yv%jz-Ujz_egcf%!WE$C%(9}b`N`yQ|&QH
z<sJ^>T_^bi<ukO&S#BJQerz<t4kp@i=BrzgQ|tIMqOS+)M-Mp3{nNhuu{sn@Mu40@
z$LG==E5jL)TUom7`>alMCl$?%cI@Q5R1>ALN{zAI6}RSbet{$7YfV3V@%^n^(M#F9
z*As^o!-Yg5Nd`y|IuzLnb*?%B?Eq%}!}Fzj^z&oMh>5SFw{1$dKWPx6b~Q%OTUbR4
zO-BtYX&TYW97l|r=aTkz&gs)WQ_m#01gNR08PWFUO{wUnPr8{-a1Ty08+A$sea%_i
zn`&{HuSha{)?6T7vq`P&hb%I=gOqOpQ9^Tbr8qf>IJldN`V{2I%byft2**|gi)}80
z(5iZABwc2{awyN`1knUT_RG_IphV8Z8!qzC$gS)ua+P#LBued5i;MYNY~Fdw>lJy?
z*r=H(1u#znHR*MjLbp|RNzDrgQ)P<j=+#n19N8*(Ng6NSyiq1E;5m!wSWq?cLc-Ao
zFNB3jxIt6k>ApJuH|)8!b#>f2$3qe5N?b+qByP#|J6LKyI4r7@?PyCt1VEP`ZErv<
zGk0fsi|D~eysx^xL*3U|a?yuE8rVtxhb};m<va1<*{ng7vh;IQo5KXjG#ao5zx{@l
z^5;6^L5g&owJWlTYL-;F1u66ed$gyvkCh6veXA)&Mm??b>*yg;&b=0@yJv$){0tjv
za_616jfoeGi52u#dr;Y!YRr>Le=#Z)okFL(()OO9lwf4f?s=uYc?+@6)Uk<(EYJ*o
zG>z#x!@S4z2N6=8lZbK)`--J#y$YiX;}vS@p%bKL<Fxi`^TF)ymsg)lbq4hN=7>)>
zKCq%Se7Z1IBfNPeE*_Ft!r77nB<a>>%sM&2BC&z&sfKA(OAL4GX(e1b3GZxrbev;I
zFBII<@*$OE?;VFlI4iZng1<Bhl$@uQAy!0dpP75m?<;dq+Q!Z<aG(M{K5FSLDIrhw
zq|0ZD>6nx95|6V2NfVxJbaXU%fzQcJMS!6sFOcHnC-_HR;GpuYUhOm2%qViSlU>(_
zq3hb?q$|`cu&zhJ0f;7`yWr~Qy1MTA*IriLFEO%*VMSF{LSF_=_WPD@+r)CW3>hgD
z5Yu4I!LjVpW~e@o8m{rYA^LtJgTi_~g%_#lVWgI3B2fJ8Bh}Jxn?OJjp$@5P4%=T>
zL%hpc7%Lr}jHgJDktie?29L|7zL(*8I;-=2V>-uY8GIV5Q@rA5p)b@%x9(?Hc0mdG
z5YwHrXTE31ZOH73(GNR)^Pvx5$8;8|K42A@Cs2>%lMvyfAuKL?*w~COSu%V9XXq(n
zmOHN3|BBH^>!HP#&DcPWcq8i7U8AMxU2A0iJ7)a{5Z&Jb&cEXI{u7pyS$6Z=kx2~m
z*b5aH=Qz8#l>zY8^mGwyeSKsInn9sUdVo>}9_1_M?{t5*M&(fkShvz}*JV)2mW@{<
zcIz<E<JK-#hW#>a{R@l!mtOgQ!p;AWAA-dETBwW&r(J1iaGXD|D*8alB($20BqB9d
z7+fU$JNzJU=wyT!^1Lh8u6+9R>A934+ye4EcuHgrNYZ2|B`A&aG*B}RO?UMuTn#!j
z%{J7%c~0rs)3B_+WlkN<Z?ei7a#kb>SZ&C83@E$OdF((l%K?%Zefw~Kw;u6(;EQ+9
zSAoJMxgR4D1WG0T4S3<<Kt*M^Jn?^sJpls>(4&*M0<h~xyJ}_@`?olUuDw5VP&hfX
z)xgP-4;RzL9DJ@=SO_$5aR1JV__uq$<2$*0P-SvMM%ur)CwA-R(V7nimWqq==__>)
z<>{nbE6sO}b-x68Kn_?E9AtiFzs5abOCjXX%xPcYrB7&|$v`G0gZ!$v4iwi$ToEH`
zuwIC3NeJkxfZ~~4|HQ<^g8|}d$@L>u3mB+Crv>>5@z%(9V>MN9&qe_8yXTV}C;NER
z{3WARW3j5jCbc96Yal@27K%0Icc?^3Nk~YLOTttm)dTL<%ilJ7)`#;y5Oeim+y(!M
z?w6f#k%_deLnXnPho|;Z%=?-pkPJAzZBjEa>EEO`vT4K|EmQjka<{LPix-JircA^x
z%!NsH&cpHZpX{l4iKmPkoKw0(f1nm8l_zo3{WaZGPLi5Re-I)Sju`jGik3iEr<7Ql
zYd*8~9E(bJVIIz{BEFL8!~TDh!~Y+*)&B~2@!wbRf0_gTFL)v(?N)At(_pAlWY`^c
zfeqKkXeP@Kzq&D$m+&{TfsDeHqM-1`5%k<7Sz_8C$<9X?2}@0cvK^#c;K`q{2Tp5!
z%sOJ<&#jCRBmjlnF$ay%{Y2E`0hO(wIc8L*^+iRK^5iI)APF6iKR-h__WTkOq3pqw
zCJa3)A~IhpZNMlDkqJZ=X$%~Ng6lB6Eu)#Rz;iRV4i7ok(UZVPS|Bf>!$PSSgPP`a
z;!`oh269|Ta>!5%6%$5wvxP^Pb%b9h;~=OTq&*8WquX{Ep?-`)Fqw{GyfmhCP56@^
z8G=WY$uaT{4O^~Z9i+1d;0?-3l~{gq)*B(!_iUC0yhclw`;3-*rXYwx3~s2ZT93k>
z!Mg3NjGhm8XOX!^Oq*mw@p_9QMD@b=sTK)%o5);?>#WnYiAS`e^$rd)95f1miBh+Z
zhY+8P&;zilot{WufNCtJdu<U6B`?zHZ&Fi9u0h(f8>moM5_gEa4Zd;6-b8HM|0_8`
zsQ-I%vXrST1usUXGWjATgzGHk?;urk8d4cruoELfsV0u@q0WjHx6C_tR<!9DB2fu2
zi_wu;k>LV&ZZYa?+^%)4<tHWcJk7<`vAMaq1Dl&6=hHm!Wv~x38v;9sZZ}#j9>ibr
z6~qujCtKLmgMsmDibFo*?(V)W1Ntg4TrNoD^b)0T<sedss15hHUEHdE+6d1ZcSeSr
z&C@YDC4E3IYt1zM^i#0}0}7d;cCn2L3<@R`75XCs^FlzZezXQ?AXnIa(Or#!lq!i*
zsF>Hi&izmgm3g8RDW;jChf^hPj}FS?a#P(WIOY@wvc}8tjs!`|o@otWSRRoH@TflE
z$MZCtw}z-<6UV&RcJHl1FHI?HF{g${)n5`BmKh_(W1iQiq85#70y}lbg+iufF6(x`
zDSr0jM&mE4X_&242u_^?8Z9I3Ywv!5y@52c97rgFagkw&iDk48GLq*H*e}$k=4?cx
z8JqKGVqQ!MQ!8ruXr94O(r(&eqzh~hQ2xPoBcOBEgsIxOGrPMnTxQQ~Bf{h*%;wWl
zG_*Ct-a@xMNs*UWR@q>}vPzRyU-Smtik1xo6-Gp1oR<d64aM|&+a66WI!7YWnQ`Az
zi=G(XA%B$Jj__YvjsmBk)wd7s>hsxejBBE9?UM_H0*7dM+}**iZp_Wph=!8M-mU`x
zG7JXu)uWQAuFy0J;-3IV!r&rf9%W_4n5S`W|7}}fh=3+1rKN!ez^9O75>2gw7?}LQ
zixlp;JGHs<f~&hdYEo)cwN3(7fO2Rb?j8{>Z;?l*3Q=f+?*pph#U{oH3(}n&2-QKF
zGu=Tm`)u5&x=Z`>OG3(8zSUaK@<b!`T`qoWH%lR-0H{81RK{4|<mX)9wW9dAIX+E5
z^Uam84l+Lv3)^t|?>`2Qg26>aXa<H@)v>N0<u=CjRLlXy1*;@4>6k*!uU}>%O(x#%
zM8_y{>&?*I3rO0T;h~Jy#JdV(vtMd_bCnF`*{!{lq8XAD?PQG0$YN^FWGrJ?F%jxx
zP;YL>BpES=p@8=lZ5fvmpe96)kH*`r7_a8rP6UKxU?X|Lq|fl<6XAWNhr3-MXsMX^
z$CIDzcXFmceUF;T`u1Dv&;IsjhW4w9&|jJd^M6Sv|K9@PjepCy5km?mutt;-Uc@_h
z?n?k3+ycj@ZS(o0Y@k06U>TW03~?_R<)k;?SobqFJyf^40~JKihgo{^X`wa_s5rNi
z<_33nGE@p0;s@7G_KsotS0Pa@V~3*p(2mZAGvvdB!6PbgcX#kh1$f-+aZI8lBB{Bw
zG*Lm2iC{Q_72YWz(~ntMiQFEupLfDmJING5QpiWK3%Z|r$S24V;&SP8gq%T%myr2H
z8cIHlCS@NoOl(P)R)`2oB}eWJ4=hA!dNaD^b!^U&_FeK}%-g-piyAj4Cx7n3*p(~S
zo@D2cHX0)B1K1~m<QJu~!DQ5)qmvWqmmkG+7olFO^Wh*o?9DSkUl*czom~7FPPKVH
z32KNUGX9qAQ8hgmVWLHKcZc*$lk{&Wq=lY|j*o9^pPuV#I1P|+)7U<ljFL*Q>MSAt
zB@`MGa(Dy=d!}5|h6A*Qqlhn4+?*3e%fZwY&~fc@6p>FTCk|9V9gepNnx8U)Ahnvx
zOnCTuDik6L6p_~$t$2w7Vf~M^hPQwa=LQHrGikJ(Obtztf|r;}`W+oP>Gf1NdVB`T
z;o+IrUg#qb(FN;)4(uyeK2e#E5ZMJD!bxaHTTS!PY%mXP%Ux6tb#W4UdfgLY6I=#@
zZ#~F3E?hB)mINtiBco^O_WyJ_gn+932PHv)$v_-~IL)mi$Kyg&9GF3n+Ir}#TxA5h
z=<2DECvqdd!9qmUQ_1``@?x84Ts;-@-*$dQvIBrpKqnOEDnxe@9TTL;r0Mq{r65^=
zF+*AX^qO#Q5=O^to)=;ewzAaTst6fKW~HS`v?IR>B?pDzC%CuFXLg4m-qgn{Pxn1>
zG7Du8SFjSTn!7u)Q$@2LP(fY+`_xX6yf^$srC`za^>|(+6ugj^4Tdjkx0{C=JqV|T
z<XLr~0-X~m;JJV)QW~fr(wbY*TOHHUTlrfCLxaU6LM`=Vc(Q++E{OWI_q+N7L>o?K
zMHAUZ&%l5e)})9GR3kJXdil8zwD38QRwXhrf;_z$(%?ZvpM=Ihn%X|o;akx>Mq<M=
zQ0YW51P!zh8Ky<*Ib={Z3E+Hml^M<1EDj7LeuR;*4ZUPca9odo_F9$U6obb8+G#M$
zl8i2g*B6e(=w=^kCDWAA+%$Qt=6vxXH>-@B4X09UXtGsf@fH|_N~ARm`iyi)SauW^
z5VwKx+6_Gd&2@6aE0T8X`h4@<E1Si{l!cpu8~Sx?9rvrtx^x>9DDFtANkn88!$38R
zfhV1lMCK<O_~6x_(>fN=c;f?NlphK3Msx_AasINL!#$5h>If-+zmVU4KA^G4bZAsj
z^`9(IWcRz|nYAh3t&sAZfBC}4t*-2Fc(M<8ou$7KFgPD~aQpaW%eDvm5PM&EUKl^}
zcg(+-sT+idL)<K(&ue<kd!2~LZ-%lsyciI(SH3g}aF~n(E~z~T&6XH`RkZ)xn}?Fa
z7?b!58JL=AtAj0>006~&ZEwl8s8+MyH}bK=1e`s!Yjx04E>f%(SFYMIAd<MdU*X9^
zN-!3JOpI-pg5FR5>>>$&ckbls+>8y!?_R$r&9Wk_l2+O{#!{#l>!i_lZ1=9wVE>IL
zY(J`&g6U$+bHo^Yzy`9okQlF03wVkevluL+<Mw9VJ>unLgfE#!yfHc|?%j}n$~XHx
z@{phn7hvaUje*miN<$+?E9(a42p6Sr$;FtVAiqQG<?nO$_3F3s4hL1Lu^Psr09lUo
zsAqWCH_Yb{4{0JK(+EV{oP~&T0S!T$FAX)PTcXiY%`D@lg9Q*0RPC7EWC=}m0YtRz
ze)9ZxF%&!>RP=%5Baq)g8OTcJOA?SuFb)~akNPq+bKHy%lY*i-w5npjE^f}^m9^AK
zmr+kWFN|k2O)_rPK2ekT?sO*Db#fraKtE^<Ne)v0&9!pm$vE}k6T&TWW;>VbP%2Xv
z`kQU4&RDkC){)i|gjKz}END5v%>AF?0Z6@zi1^wjpK12$<Uh1nSu^zR!kUwdiEm)k
z5BggVT^&9at~`I3TmDY_qu5#rxS3Hbd=VmL2Qo9+jHmf!R4FMkgO*IhAd-&j$h}An
z4S=x-ZkV0a4LYD>IEJ~bk_^10GjwmufaGO)AGbt<<}^!7BBF!fN*~`tE4c;aJH#u=
zD3I+VeN~=G<%jl>v5Qzc&O3EEk>{xt)#)>`J6Mb^wgpTGT0ERS^Xy%aAiSRzwo5%_
zk;V|xk)~z-Ku=_z{5fb7lELjBSs2MnfO0kJjNSfW7ZmcaJ@ZLUO2+5wGXOcZz|-_j
zQU_wpOM$(U(T-%k2l7=?TqAD4L~mYGTDlp^b!cq=d`9STr^Ok)auj`Zbr?5liLzoL
z8J$ap*`QY>_PnRo=R<DB83B!_R%Fm$n9c6wBn7-BoUt&UyXf@sskjg%BmkU5YmcNz
zko=_yI~~@zfYy_yv)2^|+1C^*P6)`CN^LojJ{0Re#jY%uoNJwEu*avtxB=m_16JTo
zX1J~u|8@`H;5Hd!0c3(GQxAPEiQl*j0x-xlQZjuK0t@+gRp!eaN#R8&G^~(<6=0|j
zJ6ax{NRJn}=Yt3H(Kg!t$7+6e<QYU5OoYk>(`y15`vzO(&wW#@-x@O<bf<`QXq_g?
zUq?pXE+5U~pkMpYt+Ho>hgvw<Jy1~~zGNLAg(O(jFhe$5)VnlkFHn=6B|C_P`foFN
zu~GpCAK0#)W`A*Yn`~(LyR*}ztIXY9+`3D7BFcV~qi8ix!_9ZUMNzJ5(BFON%a9q2
zygeA{N1B3Bm+~SbW$YuqUW4#}r~`oO(Kusa^OXEi02I&^vzioluo#|1HxT)o2%Mbo
z!NOmrYf6Y=<+@a52589`8@35QXgXFqedfDXzE2QiuHMYgXulz%{IqX=^6ezcyH{b?
zTN(bjrDX@}xrO8PpE8)o&_*0vV&m^<A$~PxFK6p0@4MfR9KMz|8R2&)vHWZ6&4Xq;
zJ6~ANf-)rvSXS1Jxuw6X&faDmF!1ieuA1C8yo(nMY+J&ns^3p3?`5>lk;-(Hf|LYg
zx_<oJ8}UF+2Z``;hJF+O^8WT}@Ew@n^h#4lL+}BfACUoPS=*r}Z@H@;-#`%0<ZP3u
znRdGE3)Lx|6UjTO-{<TNp57Rt2&U)AbhGs+O1uXEI&##fn`|%{#+!)4NwL~HXIL$F
zP6a~cg`&$97DV3-{I$4aaT)<r7?oO|9a>`{ef_SZBjJI%Y$I7Kgj!y-YMo^>@lc-+
z6J#|}fFWy%io5bncaugjJ@g}zDWwJIV$m;ou$K%Dho2ibcRxD*#H{$*&Nut^diPhn
zTw}9s>xVi2!cIMig}@#I>|^_ECUbv?8ym$C-cu-hjn2QQ-Z*KM_$f0)wU~X)sq-}X
z(lM4($&P_t?Dy6NIek=(hN-p_?PL~Oy9Dlh7h_9$gt1~BWEL{%t|GIMq~Q|?6mW$a
zleFD$_2*XLr>6^1gs_@d)xbUh@OzO?aI`}?_bE{uV`Q|jh><_TQk1R+Y04O1Y!g;4
z%1VB1@$<+-qP4@s)w@_wY`hMib~g!!v2UcC8PCkj|M0itA+JwX6!rG^JJl_cKUTm=
zziyQz*snSQ|Bvy0%jay%-klbWoPKHPP|^#9Hr=lSU8|jEe54*EzX*})Uo`G+FYqK|
z+8J$iBzq)(SOPoss(<OZa>YsWR}nh0SG6f64NuJse(6_W3#7nASrbIEG7u>`Fi3n)
z4r-(O$n5(x0FX;)PZ-4?j4=04j`0s@We%5{zOdh~M-L%-e^gE8!RZ^{%!&oIV&^c#
zd>t_PQoTs7kIW3ZOVVi44U~_(cGRa1MfDD(r3A(}*B=>ovR+d&s;HTbnkHkk7|N@y
z4q~Kj9)d$pim~Ln<V1_8{BDc3H<rsaWD;{QZ&bfkvtD$Sz33&};pp4tEcuW!pFh$-
z+G9y3Hia2b?JU1D=ZCnlv$?T5UOCy<H#q3+K9(91PYJDRYm<7up2}FjhyE!~2tP@L
z13+Rk=*(tEr)d*c;Kf(QTL)}bYAL}!7fN#%<jHm-?fQr=kl*bf!_>4b3pO$u@N?!{
zhS&DhecpG@=zLY&i-U1e;jqw#r<2^?rMW{26@=j+Vd@g3kql-Kd=b$e4S{i*!Q65V
z00nFE2Y3k9v`Bo$iwCskB(O~*O}>d>n`!>l62E9@Iet|2BuW7CNXXy+Xo#_<e*K0X
z>ZTsO^)Z^sJS4tC^9*p5uehBhyycFlGvD9U10^^3d1@<juD&MztI(|ckY9~Uog&A0
z?fV?rx0%0Z4rS~kDWto4P%{B`qzQ@)?|vU}ax`$RHr9Sy5<Rf!Xne++%W%l4PY>28
z!;lGXX`@f2l26aP-<+eEGc#Fzi6Sz^eGtSRLy1k=Xcp0UwVg3W8C2Fy$5*p|H$sui
z)+zVPG$@s63;ln*f+VsYZ9N@m(Q+P5MxHN5rb9!dQV`v@IAyV@n({Da(tOVv6_(13
z#Rf%K?*2O&b{aj)ino4*1%gWTUd^BO3)k7FTR^DEWjs~RfM4&JZ(DRFDG~X5PDOAv
z+rb<W<{%ASQsl4Eco9}5E~77M3j`dMoiF9WecdX}N$C@c_LKRI!>><zZYuC~od_ds
zx{L-l;DW$xbq$mGm+zeEQ)jlNiueDfI;~S9))jl(Md{FR=GjkkET-m=oI!8?qH9@K
ze_SY;D!sc19Kspt7ztUn9v!}$kIl~y^KtVDZ~W;t$w<hdNy~#6ZK6uFzri)RmCEeR
zA(fLRq&ux0%qNVH_oo-Uvm(<w#v+dxnG`a#_7QH$(YxL`)7TK;Dad$ezSgodL5%^O
zaArdolO|2RAKuTeUsu!d=mDN|iJCt!j$IN}vQ3y!QT}@9#9nOOQQ9yC8k?N);Ng5S
zMH;nC!YV>pN4nc*A}DhaJ$-#1M9TElxGKUA;RKaVj`dup-N|1-xef$L+EzQT_jusf
zhk{m&Be-NYb;az|Xt7i2f0X+F6!)d!She5V8<j#Ll`)y((Ll-+$(TY?lFC>~88Xjw
zM<qiUqL7LRnaP~FA}WOlnKLCb37P-r?)Q8C$MJr7KfdpM_~ah;-q*FRHJs~QYi*m=
z9RJ?=nhfqdmR4uS<@TglEYtjPS;u};@g0BU7qw5jjp~p6S{Eu9%)MLSfM#QE!|98U
zX=~NwLYvh->l;1a-|%&h&7I(|$46@_^+V!&612tNm2{s}xO4O3mF1Q1Qj4EncT8}W
zKO2l3WdG)oIWsO{IT7i0bu3TTQNh_+5*2jb*i0(o`Sa)ZA3pT1jgplWXD*Vp$!<<M
z#ZSC$N<F)`R@P<ymnoruqL#6YnlO7xUb_5vuvmqfSxbhYUtr+7^3`P<-=iE&KW4My
z@nhf0J>@fu%f~14yY4nNT74H;`Q{l=*vFP!`)ZZV`udR8qRsW>MwmC&oPpFPiy^lo
zN7ho)&{(GNx8yqrX%*buU1AqBIk@a{C$lbdrG8yMC0aVVUqn=N6S2>E^czPF!|jXK
zoke$U($G>9XprIuw8?GnVPmo>0+r5ZQ*&vXGpsvy1Oo!SFgM<LOjFZz|3|;#!<#-t
zD#gAAyCY~V{k99ME6c9nidm+e*00mdC|Nlq6=|Ov{C=uq;B#F`UQdU>>H+U3Xw<Eu
zqH^KSWY4)`=e^{$R+bn05a^?u2e%L7_Pc<~_@ty*IRdM$xcNLuJGijV<w4%dm;20v
zqT&BMN*BeOM18uxRPkKv-<6Xn3;OU!U<(V&?ccv06II!pnrE|K#Kb71RBw$A_;<A!
zI-CM@u6B4X*{zN=r{9CC$NG9za4It+H}~u1<>CGg=keY^N!vS#?RDLA-CN7XJ;-G;
zjFySEpm*p#<;^4!-fhkhm!G!tFhfm1CS$^`@jr)jIM0o6=FNBX2emvK6dYXe>!>yC
zTKS~US*v+`QV*u4{`nF5`1tq>%L_B8Hxlga?X~?`&kN9wM%jnq4YJR+liwBZRpczr
zjQ9p}O35tYQ!iRwLqkIe;{5sZ65$bHVLgB?s7}t*+~Z#yx>i`$*1ThmHc4g!;+-AE
z&SOd#gtO18O*~OcDKCqXR_Hhs`ukmH6DO_f{u5f^V<#Xe#2hz&Rm$8B&(2kp!fqyB
zx=mln8JK9zp(X0GO*PC9Yts}KM9I3QZZzomF#DilrTA?GrOvp`tEBpL^=Ye@W?%U3
zb)RC0m`j0VdFHu!KRxJkkP}0Zu8M8IxB+>B;N$0~9Hy?_QTWR(Ns>wF*z_;wpY^d-
z*=;wh?Pq5O3zP5WP*hF~iWpOVge?~OZ!nTwyJiiKj0^`DWlFlb+fWg9Yj&)iUr2}+
zwY3#^;Gr;SgrXY|!H#wmvRJlcppn*E;(J@3<uNU-qo$@iFPWHFc5q#;4%*HpZOenf
zE*lHNN5-u-@<-(9rGK_cAA2A=TW!0!t<*yb!}z8U*%w{kY;P*Rluo&O?;aWp+dn-_
zY3pAn!x6ad;6ZYb3N`Wl4ebudruT!rKcXqW`=@#}SDHNy4yryKl3&SRB_{E({NmzU
zP(x1xZ++)yAuLEpR8$}^BWkY?@hf@BV+K8A5F(*AtrLqAbXqfV2lJ@hqJ<9UbP6*V
z|C#8IH+6M=C9e4ik&NxHel_#l7}dBpFh{<i9a9lL4D7u*(#w%K&wTrPs{%RC<yma4
zjh5S88ccO&5v@wh;UsLtek+|k$+UU%=J)}ZkA)vtGFPMQt?d(n-w$*Q)YO&O%S#D+
zPn~nN|MvNbRF+Nm;r_wFKDdKZ>QP67xD?&pi&{-XLPE&XcI(!y<16KqMd!<xFJm{_
zMt@$oTI1xTAE<geR@qmtbPW#=4`FU@{<AeFQF*a{Z26$e_*3y6_u)UvNF7&IT?e22
z@Vy$tz_pX*v%<S}y?r0T|JmAvY3tTIzzK`T>}j>~qH;_v#W@%_C66!YXSQfWu&|e#
zM~+O?{z_cw`zY3XZFMSfSqih;s2ds@j*Co*NnWb-<Jgg6ak~AW2iKqH5Xte`2kj6^
z7K)a&)py1+*SZjTEy|T*7PdAKYzFcsQ%=%{PwVJ-1M`e~_3B%%4E%t7_&E!!w;!4u
z0uGR?3Bi$MQLxA(XS+!pEbmYo6m%(Hv6q#a@15Sq6YLO?DL?%Y2ieo)uIUS-QukxW
zkE>j|^t@#_ASkE`S+JKEE7tY*_kUlQ88K_k-nlS4cJDA{L3(-jv)uzl+3D%?hADN)
zeG`i`OE%-CmNlhWJEC{BL|Rq6eG7znGrq|Kt+jUT+Ill_vxfK$GLApJx=KCb42zwu
zDg0A9?S<O;2{$e-dAh8O{|H{DwOwMf%U}J|w*qMC0+M=vAipmyTST79d2HLrDXD_W
z>5!*SuT57Jb+j|uE=y19a=v=|U`)st8eXXF1Qtr9>qcD&zsrp)ak59xonu3OB1q0-
zmPb~W6RsDnx`xJ=S9T_Vt@>;{7diC`tj9+7$<>BA1f)LmT{`dTD!u=}fhh!hJ%B4!
z6&0yIe*9<)j35@-Ilp`Nlnbmo(~Noz+v_y*=4T2b3gkD@{DMjAJ&^fyQSj*GX6Aoi
zAGQpB-^usyD^ys}6=3jxUzhK0F#7i$KEB9Hw<7+1$M4vSNB=Lrmh>21859z<6%(|3
zCVMKfGkvcMiHd5u2i854pKv^wzi_vBI4e<v@0XU#$E+ujksHF)?_l`KyO!AZ&Dg$J
z?PMlaR$5-~%nHn8kh(xVz~w$*L=6#VEBcTOa<;Nude<`f>c(<WtJ58(dW$^#jWcv#
zovB}Cn~Ozn*5H<_OB}_&cDCsn+ntr0{TlY{nP}d{8#l@{oSJmsy?aNh0E7jB`Bv>p
z>AIx$$;!%dMW$vFwWvMNnPrUAL4pxCMvpypdF!E$?#9p?wCTF!aIu<}9@lp<inn95
zllOV9Tp#kxD2sSkT^)xA`w!+JWx))%_-G)3`*w{eGxLYLJvFI4Q+T62uM-k)aZSTT
zuwo>$ap?N?yL$=`L>jbrlu<Y_Acybm+qaWI(JrBGM%sCl4Z>u%IBUfvZA$>`fAjh#
z&6gNO8pIo0k%}l|TnrM3xUnY!J&F;Y)DJn5Xk|S-A!FY@yMcj$Z!a&><m!=)f9xKZ
z;toGY9-t-ehlWy@8co=zp+B9z)Aj8HGjqs+uND&4-DQCR0SxGEw%uv?+zpM!A;*UI
ztQF49DypjP1NU+ZhI!|wuNYq}cdUgrAUpcR#>ZD?%s9zx2TVzYb!pAH>LD#t#PW64
zq#!<i4ID5U0<~x#s=v4%MH@UK28Z9QDJ4w5X1pnt^=(Ck-O7^Nwa%igV16X&*Py}9
zMDKctZD;~&znGy=$aG7wOJ<Xk=faTCkiBYc>rs_)XLxs&m6eYKT#)LpgN=Xq@S#Fx
zi*ja5Tv{4KqSmu2eEa5jXYt~`OMLTgD0Yr%8Tpc+!J2E`*+y<#k<$o4fJ5!0J=B#s
zuAZ<N6c0=$fupXwDy^X6DD)p;8$vm#lAaz1ff1Z*bz2N`&6tS`z+G^VCS9+oI1Vx?
zU3h<=w?T&<jjyF$#<`GF^g0nG6N{$=9T<P>>FAU$Ulv%mi8}y688c}j+Via_CA2Cl
znc3Oej~(U^|4rM8cg41hOaaUWyBAg}L}uMajx~g|)9@CwU|<dYs;HvU_2#b_HPeft
z6#HAQISZPUe9Abjt$k}|q=kk+4#(tdVg9&EbVP*EUd-}5M0`iVFlo-=kTG*{1>n9e
zg7&+u&ptmILlS9LQP=XN#LdCm$A^!XmkOUs74tN*vlAnoM#SCgvHEx{S2Uflo&KSj
zx>)8uW08D9e|iB22S-{h^R|5D1mn0nW$%=VvDhH^HpetHlo7VY=%#*w0|~SD?dkJM
zZNmsrV;}VdrN!B?gw;Ho?y{=S^o@;;d&3P+p7eq{`W*hp$<!vZZ!dYF|9!y@BJGTY
z!4IQ|Teqm=6BFYoi?iz)8C9`zyxt19QD}zxa!*f>$8<Psckt7wCHz=ZGuBqc10Sc@
zWn$e)Cw3>u+j!}mQE~oQeJl-FC8kc>T?!&oDTR;`cz<k?*VvFY^noluO;Rr}&Q&AZ
zCfa18X>KkIkRVX;_PQQ`FN(Id`w(lukw3QtVmx=J(ACAP(&f5ySgQPMt+#S>#WDSs
zrl+Teyhq#};C?f>RgJw#YFB$5`w#WlEPo6qcOP9t39rgZ72m$;Dvw|$YFU@@N*IB_
z$w^yrvwDJN9m8wb1Ci2avm-5=c7@TQ&tUH7UqRe*cTs=%PnddTRaKioH#@H67lgDA
z%D5}}otT(IBG%&pBL5K<W+v7UQ%C~S^Lm?gmz5Gchz_qfKS55Aj(Cpp6Q%XM=tm|H
zcIw<YGv_BLe$p7>l5tF``b9&_c&w%lzL2C%*Pdm=W#-MB`EIN(yA*uQv1r<LZ|3(%
zixzry(eZjqJN~>6q26C?guGlOI5K<kr!YrtSGwgtb)n#AttWah6j-9TgLSI?Qim@%
zLN;eRYRk&T_tb=x+5h;&o&|$_Z{};JvGLNvNai+^e4Q3JczeRlm&m%+!2M1#QLf<r
zq*=4FvHg>sojqz%11oZ@I)ADU7(T1gquD-Gew^ELdOa^@M#QgRYNSf)$YZ30>W(?3
z8<%(WjP12^c~aOGafhiF5%M-(US7$?*XjBNd&0BK8wH=33hdnJj|B}LeUP*KEo4Q=
zn+bZ-xv4SF^6V)C0|OKJDF4Q0H>LHfqi1KyaeiwFW_EUS2SekMIWuvNDUb@zrwIuP
zsy+-0^M{$DCwg&ka|?GshcFS9-{0J}+><j{`pF0<VKd<k<suVRn~;q;9xl><O_&v+
zQNjzX|Hp>bkBn>El9bMpOtGq{^uVz2@aJbUjgr#R9FwEb3Go8xSKA_N+VeMJP#>>|
zQHdXvnsVHiM~0a%UoxqwsfoPnWhktgsBLM{r<ejya|J-MS431;Sn>FA27Lafwp<V}
zkz7Xy0S%Y|QV*r2?Rhd_dgaRF?ei9Ffl_AmJmzJ{DxB5ZYl#!8?a3)+{q%EDYD)nK
zO~je$^LieX?E~YI4tc8-;JL?6oCtsM+?H%E8S?Pq<(vRa`HIzgc3497tnOgCNs5gA
zavd{%M>*4|v_x_>xqN1~2L)Ag#P`X-*CkV3{N4)o_WR4`e{kNL=^q&26&C(*Y5vFM
zD_5uq)gUhOImudp$FxMu^XJsi<FjU<XY?zU)$U_qW^S$NSl{(T%0_*)rmjxv{3?Rl
zhHOkg;q_iTI!S$opAQ2g9Zu=!2>f<0g(J|5op=t1szC;N!=W%5r;N18b|cMup=^(w
zKOg!2<HybF3ECXOTRAy*6MmrZ+ym{6F6x9(u3q~4ni>{7Ti8b#_uomal)10#jcnf8
z+>7IPHFTqNo)3?s?BwB50NLSqN@7Wg!Dx56G5ozJ0R{)Ebq&Ede3H9_27Ecq9XNFM
z_V&jwJrOhUd1XIv_jen&{0##EAt8;LGrPiYYO@N1A)~!WC$UIbcQCn^mQ^kERR^;Y
zDiuB((}UDUzQp+wJnplj<_^Xw#qKjHTeJ)eo>sK$_a;j^<gZ4_IDL$N3TWZ3vM-}Q
zy4jOYXKI@8&z?P7WSvKu@LvhpTXW!QHq2hUc#$lv>;*)wl-i^|c$+-<{9IIiP9x23
z*Pd~WRR_(M|N3Q*traL+`lVqvJ9<7j9qakZZTiKx0-I-KtfJF}2eZsKI@+7-G`gT)
zo_!X5uXJhw!I?QwY|7`rQBl_8W@9t6A5Tw8VuH<MwzIPn6l>*pV|l&g5oga>EptiL
za#h=VD|8jLISzj|<k};RNjl!j`dH<)C#Qc6WBls|;?ip`s)p5$sPAWzb$Y>cy;4(n
zeLg$43x>upMMywE#h}>v^EEk;0d9U^t)3`f?HYAQ!1|zh9%-jf8GXrXG7EYO-hEk<
zxjQpcw#HukjmMwKi#aoxT;mtCT~>W0>YzhaPZ9L1g+%MAD7&}T;EP;o`7$zd2@ry=
zarf||>+o0M)R`4{Z4E^pbK?@lL3W!>aSh42grW-HO&aaxsOw?0vGEsb`xLe!b@@G$
z`8*m8J#9+_#(RP|vtbUW>70R9VxxZ<Wf4jk62;vvbaXK*Zn=(IChCOaL|3Q9+qw{G
ztL?=r&6%T4{qLEdOl%78B`rjcMDEvdC7sgd4I4H%3%R24{VuN*;X|M7uHHiBw9c*(
z^g(e+-h)Nle&UOQ%162-P}DYctEnjJsANlBe7lBFP*wGDWH&=V-$j`-Boy@Y9#;=X
z%XvtSmn~Z7)Nn{y2SB0F5RBZ1e%i}n{=oj?JqHh-ckhZjx`}M+dZop(vq)mNC36$;
z2U%W&yVa-c1G}R#GYb|z0=p9J6i+OA{rc}ISXji&JEvq3efX=0+BXuUsJzp7x|MD%
zX>&AFCy_QgzgKGS-i_$bQr_eQ>fX&qkJyOUkj&2+CT911Hgdudd1K-BgRT31`o#(#
zENoC>+o#r*vqnZi@QkSmBpWw<M@GVTS$29A`t0!X@6*KV61QolZQHKc8764^qw5D1
z@oRcIy=zOoezlI%>~DDwm`=EM+{8<#5#5NIoT~1HF#ZgKLOwb4ORzQ5*D9YQU2nh?
zgcq(Fi%!N@YEJG7!&J-UPCL%#Jju?W;x)9i8ktgwW;}eMqGXhHY_`a8=oWA@n1mtg
zSxl?HsTy}vFJ1Ne^^)r0@%DnurU9Qd4^pS2W``Q9d-wN=W;Tz1N(W{Yt4vG0Fn(03
zm@!p6Ju{<goF!r0hji{ga;xalYmibC4jk@S9hz^Mrj7st5gu}c*rrZLGIEH&&scy~
zWF)@5vf~F}X`P)0CsKV0>h#I__3K}z9yh^34vS`GWtA-{Js53q;OgQtyF-`;2Uk?L
zMvTlWX<ri-7KXb8XC<5)pFYxA<OF{TQOxnsF~Fk^i-!w8cCFA7utP_#ToDX?@PPCZ
z$>(28)=rL}zX&=r{KB4Q>A-pYa{EdEYw?3`-n?P78X217?sG<l<7#?UL&}-m-rnBl
z3<0J^^#AxlO~ALi>OgVXC-73#u&6(50S0mTZy4HodRG`}@(BV&N?HPh30Wd#^X6Is
z>rvA`+LzS;#}EpMjfFZ|rS3C(L=FU$P<cU_rw*`f)~@Yfc)#^ukyHn|l`e92R~U&Q
z5Z@6YiZq$l01!?XOxMfHE-d3^UqfKF$+nhau&%~Z%HfM@E14pFELh__*0wW{QNpES
zkB7K<gNplj#;NCj*RLB4hlD{quw<WDwRB-(lf@}LJ-R?uemS@wCr_P1)I*=HYiWO7
zU&Gyf<WWj(*NPNn!BO10gAEH!ZWJ9IUCbA=_~o>+Ouh8xbUTjef*S*Sqi;-ztx)mp
z-``-tUgdwul0jZa%Bt<_co{gqm87fRMCTK709BK3pxR%W)@&orM5KCz9*Ao_bUwJC
zmx@axd%<LB*7oY5m9J-)(xWy8u!uB&Ta44ZaDfLY8wLVp>ON}EpyLDy(v2{ykGXj}
zL|2TP>ebac?%<%;xs_d#4zm)hO0Nuj5<@-8hO1|Pm%|TGKdt#bByll}KOj*)EBu;d
zb>oCwRkd&NFgSu*1R+}bJM;kW!30EiUPHmUDk=h^qy|5gZVN_;8h_Ac78WXEtSwI&
zu^)v!e3Z>|J~!;OoaahOVg;`d`lMA2+m{*zqtOT9RUN#k@01-NH|)*SKj)5$zRT|i
zJ51IqMI&`?qI*-!@czpc*W>Y7$D(UE+`P&@M3VX?>-PN5@649tJ_oSZWj=oPY(j?}
zLKq%B(UwR5d|0*4S#FfwYS;#5OT54u(t`=)EPx|9q-?spPbA(p%8Zz`foV1J|3GSj
zfW_eA#CF>-yW%#PIqKJ_wYG2qz<=i}+G_W1Xt|Yh8m_|c8{KK=4`hB-Q&AbO{<}M5
zM?orL%hs)tOPaF>xP{M9YPC)7+_`gO?#PoAoo9Dr{u+q7K+y_kpY$D60ivQHiUp@<
zq<%3Y`&ZErz)i=v70omN(#E^(*?78uP4#`Ad1F=Y#-ULO>kjFm`J_{EX<b`Baj(>0
z3Ga17Z}jclS?dY1-*mztVkRdIgYo`xVdD(LVjs9g)C5q|J0NBA?%%%|^<~gtg7Wd~
zKlElCpTe|oUF`6Dx*YQ6bb!r5K3u!hk?2Ei4L|1JO!7a}OLulx-*S|_Zg)rWX`%n)
zDap9}LwA+Sd%;DvZuS*Sk`^xxVtOl(d}PHGNoN2Qk6KO9?i@7{0ly2<IRzq3u0P;G
z_JDrFh7jvVd4NCv#zIAS%uta5m$|4u>^B_&-|H<RJ@i4^L#!5Wc(S{<uMeE=O~e_g
z+rzLNO`NycWp6CG2FBkn&y7@sk`i;Byg;1!kcc~a-?Ja!YJ5m-WMt&GoU6h5CXPP*
z%im5_ISg_9h>;isZ>gbVD*V;%6P_bygVxj2D+8k=8h0cuDI!t?woOGmqs+3C5l+s?
z(2V;NsR9}==t8Qb$3`FZ{b%4vo_1e>t#M5_fgzvrq{2^XUcX_3SYfV|xcJu=-VY}b
z=q1eWG<F-<nx<dC2$);pjQu2Blb_w?<-d16TtZ|T_0oAv&ph|hiNuN!{-gD!9xfz^
zqtMhjVAZ+hJy)OG)xl4ZCQk8EL<!hg4buoF`1$#jIZQ>pUn4x&cm0noVrU}iv6Ks{
z2pOwQHIW=js3~D#VL7F(O%1e+ie}v_gM6h}1)%&oyS5TMNI3!TmO?k!yMQ+ye`1FL
zv772Wa;>X`-QVBetno!)t9O`uZlsv95*MMZtxaBpOU_+K{W2ZzNq`B6GpO)QNm+F}
zP~IA*y}j4Ozj(nXFTVppQo|~c9^ej>u>B?=6-YjKmSLN_L$((<NoaI1hXft$V93Hv
zCTbi6bb?_=+<dL;+~+7exQX`OJx6^IhAl{kJ`QM?5ENVoa5*H13d#W9ZX9&+w=zm`
zll^aZIl!rm1REmLLFx=aMDz+xBUVfNB#K7QQi2qM08j%)aG6($q=oE**S_t8byAK!
z$+K@ClT~~EJD_=!AY_w<{BThCCpAq?GQEMecI?19z;2BJ6!P<1R-EI!je-K9n|7{8
zXWk-D?{V`5Y^vGUB<3x2Yq6U6YiMlLY!1G6uL1yde7Vr^M_zDJ@CypbI;7^lyk!JX
z-pTSCl*CIpD)3U#28|%hy*t4-75$p7-?DcA2s61Uz)}>NvJRM3G2~jdFv8u3LDuF(
zIR%ff@CFP>FMaoGDfV+fp4peT0<A?jp5(`Ng{j|71`6R992~J(r|F2Qs`_aY;`Q?*
z_sASTObkyl1w#aG<CdO-e~W^G{UQdhzGqSFu_a0XXyPR3>FGo6-{+a3f%1gO1gmWw
z&MNYYGkX8g34X=M<f*GK-UBT0d%TlVp^1ysTM@OTR8&-umW^erD;@#@0x{4x#Ha2`
z5qWuIbp}q=we5XiSUy;|o$h<kXUDap0c;oNHiC)#es$j)V4f<1$l(8D_|fu2cNrxM
zryw<g`@sYR=@R<2Zi{G1J-dmPk?S?sQG<@#aqsC<@_)_fZo@V!l0f*tTE<k3+#A3!
zJr%yP+an~<;bGsr2Vx8Bz2(65Um+9M$<H08z7tpSYcclh9JTi}J79u*3Yqvsb~Z|c
z<ob~^^}(RKtltb{$>5r&)lP72+2Vbhc2h4pPfS^m%P_ii#}3h*|7?^x_8Z->s9|%n
zWCk=5#Ad-^G-tGQ@OP*vsYa;e_@`P(+4JR0(m0%%hK)0$kxIAx{FYe%3gu-!h+k`K
zD~Y%wfkB_A7kK|Z6XLu+D9uW6o{hnNtZum&x)#?!!d=10h#Lpc7PKca5fmlsQiT$O
z+wg&kuk%MisrSNx!B!nX8uFUpGBq{z{g9BGu!R>;<>%+`uZZ<gQc|ifcCje|?dO&I
z%q~*IBYLx6+*n@t*_gP=E2X9}&_-rJ1l9n&lAUz)^kilZRa%>oygCX;0U}PO?*MD{
zVd{ed@NFQ3CU(2nJ<W24kuBae*6u|`!^F)U<mE+p`}*>Wi812_k18tO6c25MO<nko
z!0cPLDLsOBf1pk3i(A>*{XzO-Kqlx25V$LK(PLkh?I4kS($Z{hZf@l6xX+D~Ym=n2
z3!+70tFNxl?{CYu8kvz^&N6Q0-?#5%k=-zSh3j0Jnu>~w--?`egZ?VZz$MKDwDTxz
zI*AklAKOB^iCg95$@}6vHBf_K@h}8ggqUGpCYx650uh5~fH9CmkdJRQj;XeT?r#R<
zn5sC(MkQtC*v6sz$Swk^otF{dvsbXQ%W}-g!}`V`TX;C=y%-rwhAzOeR$)Mf;ND`_
zDKecx5KtzhGgaM(NRj07*baQ$dL2DItRo*hMbi&o{=Z3>q-OnBw!Drq+sQr3xvFLC
ztE|FkQ6EDEd0qed_X~>s$@Ay^$uM!DSiitpux6uF@JL5t&e_mn;-$x&wLzi%TFuAe
zVrr5s%pr%w{+=q8JY9rx03G=ZyFfa-+;t489ER!%?1A0#f|Ic?e`^_qEM>U^m9ibs
zIg^sY`kVOmi^ymJGps@oN)R7De1MV*`7jGiQjGWxYTqxtPvFD>Q8X%fG^oP&T+i#Y
zIHo$nshdVIOD|4(>XfIA;mUE9=qZ43CMa$=rl$4=(FA2XBk>)@gKv?Ru*zU%V)BCR
zY~*+FlD#z1Rf-T$2-Y^_$rErPdm3Hg@;j9%(~;S$PUgc)lgffF{+ChxXNQK?PctiQ
z&mjOu>^L^vFG5sSSKsa{@$<7_C1YE>^1`)~RS=+%ARgI6qm0ql&mtBUkj(wT8B>ID
zWZt%I9aJ6KA$&*S=35Af9Uw>O(%P0-_^5l|33L2!63p-N-T&;=fBy6TKVA0UJNv&T
d=Z05_LwEO9*yLYOB<nKNj+{`*P`YsIe*iR~&JzFt

diff --git a/reference/plot-8.png b/reference/plot-8.png
index 7afbd327e436a2b1b5364a694880a8055e08d096..653f539b2c2205808a98528c6357a0c636ffe1b3 100644
GIT binary patch
literal 56465
zcmeFZ2T)aOw<U_{n8g5!D4>FZASg(XtjB<xjO45!NJhySjtK)ni6SCO&N)a{QBb1f
z%vMRlCP|dsV;S!I`&Qqo*RSq--F076o|1!`z1RBI_su!x7-P=+^0E?JH_>mRqN3VL
zx_Isq71cTiDylVl8~(yiI)Yw~<FCK;q$JMaZ+y+2EpW$Q8?Rqfx2B?E@Ff0Q<=gk&
zfQsq>73tg=C5LCjUG@%22Foies)iq;6b|#A*<r7|RzYot>eE9m9_!X|tX6rzwrP27
z!B+|KplrQ2e{Eo!+4R?{J!uDC9NX}4)s>@?)VGiE9ki`jTDnDk7DkFW975t5chPFm
zY6*F^&^f!%X<f6|q1JWO|FReN>Yo=yMfHwB({}yu@2TSGb{78retYc2@!zjG{p`@2
z&A)$q<H6eFe}41m|C=v9El9g(Pvw{EYRC1ZUt9H*d1qF*%y!vx`zFeTi>Wx9wxoDF
zI+sT#ELZ)y9QH&N+{ZiS*oM71UL7Mj73-u{OZYboda-e>Rc#92`}0+&4Y+Ua`F;6c
zX*TNo{`Q_Ci^D&3A-u+ZUfeuoajL00O$^(9{hQBO7Kc^8zC9rO`1877-+G)n|F7@U
zEji&JqLFVCKbkkdsO|9Oj>)GN|M>a&1w4PAvW-FL+dL(;CE%!HmT|+8z50g|GGDz?
z+q-u!y_n0Zno~VnqNAd|MfuBKA{V<(cUspPx1?zAXljU+V-noI4S$lg3sn4it?~jM
zKYqD``9hpQWsoF^R2RNH#oFaK5Z0b{<?L%Ge#`D82_{!124Op;k@oCBd@x7KtG}%W
zY68o9?k7b`HqG|>%Ozmxga)Jh1N{6VPS%Pj=5{+J7B7qn=V_%_we00|o*ixSmlKQM
z%=PNDv9YnFoLtN6f7b9hPF!UWw2IQQ_1-5?Gqf^Ryufv}O>Mu#%U4=lTN#8JAB<%+
z$TmC|b!OL2O{JukbSJpaPYw39&(upD&(5j-@L_{SP0x3urUYfDsXB?KB4_)iL{&L_
zfY87*YhITh*Q>*XRnMP4AJ|3vkh|;wo_Sna+KFNQxw$#FjsS_Qn!p6H;-Bk&nwBRn
zjqJYC^~`A~{=%hmskLGY$IQa8mPZ5y1<kYtI<MdQSX)c2uIkGunlup-5@LwOj5+^s
z_d$y5_?z|K<vrYGbc&ZQ=~?9U1wHCJUO4`@e{k>~1$P4jgOG<@`E0|*e3spvR}Bp>
zC|%2P5Mi90Em;x0s>L5#ef8?qCvV^WT~gw_keHROU#?-Wn^S`(Ys82_B3gXxvJa!^
z(5&Dy=h3|W509DqC}|rOryAr`_9bg%KX52;U6`4hcD!=sN(c{EaHR{DA;xjC|H=Xf
zCnxd7=Nj!^)mHU<|Iwr^FTXvfCQ`z8-h9`6qvssyuVjDjK98rjb`;Xue15$u3?I>+
zXML@4=-G)YH~-qUpRVp+DYe*-)7QdX?cPO7?%~vk%B&HRDzG2hraszsExc)Ju%XYc
zz_!gGc(BW!yj9)p{R7$|a*2EEjQjH3wJ@uSqZ-97j;$H$>gqycg_BcDvt&=seVoh^
zK8>Z+;`=ka%6sat52nlZS)NPoA|8BiZ*QD-fnD>!r&L|>rHRVp37WYUjUD;pZ#O49
z<(hZIREG*kWOcEIyXbL}ttvSsWo5Y(4V#nH>4j|KGBPsWHV21?r~b2M{ZN>F_x%$S
zL$uY_(Fwl40*n0g#c`ES*q)I$Z>}a?3KO!O`r^rCGCSJEe)Q<$@*ezAxN_y?%y3&I
zwxX;(*){i)qT;RQ+{<rPi!MzyG%xg5hnjX5I#yIyWAiJJNcZ_II^zzWy{l$mH`*ER
zwlsg<MryifwmT6YU^`fUo<lW7>CBmbL>PS^Ja~|0*2a@<RQHzMxtzh?s4-4K<JI+-
z_4W0<*8S2W<Pu$rTuuBbup6oO7U(t#)XlhhUnNOxN7hJJfqf#Dq1k5JR(jq~UVBew
z*^lYuX>mD<F!l`$n11v-EHv9`ojl?+JDPlJJ@wtL+={27&N+rvPp%YMl8cLiI5iEY
zi95sFy_5|TA_+AI*X<nXVGF>y8}d3?^C`!?gN0$?S;_J|#Y%49_M-(|DcB6<PU7b6
zS!o?~`cqXWYt_cez4q?Pa0%r%pL(;N$!K9_gq@w;56f2BSO4bL`fRLHwk5fnUD&Gk
zBEzZcFIGEWvW;*}QR@5tE4C$&Y^e|WsAAt{SQzUR*qKKUVcoe02M1ML2g2RmK4_M(
z$~_;pmzowJyX;DPHpaQ&SM`Kj|E4VKBY%Izew7i!>Zc*N!M~1;eRq&vSzb!cK%6oC
zWTl;NlU`vv+9}vuoY!SxUCq~`*5^sh#F%Bq8xRoi<nGpe8Y5fxpZfGlE9FIc*Y(n^
z2J`VeioEA0Pi^8mUz$sHGd1H6&0c=LM>k1H^KpB+zGqWR>6a6S4<9~QdCkyJqi>&p
z#r`bI5(KnNhwoosP>Lko4dq0>pZhS;BzXSpv6?_5M%|xr<b|Q$TFZ5PeSPYc*Ph+D
z5Ir<M7#p7DzT!q%>ddna^z+-9wP>E(6@#s!J{Ioq^?sT;uX(%D@sh=iojZ4~PO)r^
zdyELOyQIYR`};iz_GeyCY}~jJK`AzAHq)?rlbS_Cj12LnETsGQ@6Y##TK+v6T5Z_i
zJXUnAD@a_w%qvceB{3^nDp(^@$lu?8x=w=Wqebp|Z#+q3g|M0fPs<ffSe7j1I86^3
zcyB@s9p9(EPb&7gQvzRoR#enEsbDS~<cZ#o%@en%#ptFSil*wNY;iH`wZ{xH+oqZ|
zE!D@!E|b+4&!$?XpHfZu`r}tJZ+eJ3*LE6=Zl2JzEK-hVt#wB<YM#Jmj*F2Az1!7~
zkdYk}d|WrBv$OM2-&oPC4k9<l)er=Djml|COUwB^+B6x3mT$iyC|Kob8C1CZ_~O|-
z;X2#3N5P8s@Zs~37rZmNQ`Iw%4D-|W36m+rzs$@`MMbfQF8}CJFcrt$8JE$Cj=x@`
zqI-mu)pUBOiT=dp(~a%-ZjW`A?P8VPqLG3esTeDp-A&CP#LGT{Fs>OJ?v~GTR9IN^
z{rmR{@dE97H&WhB{Jt%|H5<D(5$Pql?|V-<Mb3TR|G8uTUhHCavy5!su{cpu{%Ahs
zP<xDdM~>TAsi;V`u)7%>PF7A(fh}dXoz2z3x;wJ5nbIK$Dm5B247U4gbXVq8O&tL}
zc37(F6f;ZW`A^)-8ANJsZ1?TI(M!Ps(DNESdhWjLNGccWK-8lgcb}7r*5^7-b_)}C
zFGljGN$bSc<;d;eBn{O^KYYq-yfbI?>sLKawY1Yc<-V$jYfVw4UD(ARzg+)Olw;9l
zVy;L#DWrN18&-|s_lE61@J90t(xuPtV`?w5vUo}L2nJK*rPP$0o4MqRoTeLJoLSRl
z*I}+v=y2op<7AyOI`h`FH%?;=#&kZ_hKiKt<m6dlyWtJ$Vt#cV{2lJpo>Nm(_r7kp
z)>jeOJP{qNlYn=Pj*dQx=XdnzQAtV344vYQdHXD0y=mv_ELINSE<1j(f8j-*ysX$<
zZ`}Ls$vtsd#jXoO504i=V#&7X(g+o>Y(5(MY-jeXN@hO3rm>eUx^8m@udQlDXP;#+
zemYBiC#iuh@~C1=oJCjuP)Ba64*r=_J6{Q@n~3ZUgHz6P_$K~c_dD5tfA1TMoM)Sn
zFs9}P!V}$BmfbeimwHm04mHLP)kS*J^P9z`7SFR^W{9>7NkL|MHzHut_(6k<jctQE
zf2UDrMG*D7p6_bRJkk31vk~=aX=vi&;w}Po-~-cfqk>~D>3(|r>n#V1srkt9sZmi;
zab-Kad)Sn`+S=NL5G$4Rxl)O=U+KQGK(0)x{rEBcf?~&r9riBARaalJ`8bP`r5XCr
zB+gVd<n2?x5li)%o4G)Oel`eqMx&j1RzhLzMb5cMQL$G_@2XQ%kfoXeRdwQ9bexl`
zW}<h+f7zK`Tu8m%pT=&qb!fXc%Z+1!KB)ji5hHq!m6C>UD7Clfx+h~TB!z<BnKs0D
z=a{waoPNb@yF53bNj=<=V;%}LIGo#U&(+&uQNVe1r)y;prxT)4@+@*x%i`Q*w)KE4
zgQ$}d5*eefL>V2W35&)yrqFvRWR|~qKr-u3Dz<*~+51pvbqp>yQMbhHDYyRJO`A7Q
ze|>mdgEE#+N#S8Wx@+go#Tbp&9COWc=16rV@eHz}(~XiBm|PJN-K^JHwr71jKDc|Z
za`qpqI-jwm%$kgD&(QK#)3NR%?)&A5hY$;hgqftCnba{mIXP)tBRF8xm2b=KElP)v
zUeD<8n#gdxrfqbQ*Pc6;If*+>eS2hE{Y<bygAsUQ_i>%1lBE$#p}qjcfS{lwq17r!
zuH;{<eO@v8_vyzhmdtzq(Fq)o<n{RtFNk}3`R?7hF6&xRp_RqS<PrkG7*yDfv^QyN
z;}<tJj%JFX8C*<P9w7j1Q1CJJOWu#yHb+bCF`WywEWU3P3PAau{h7xhQ6I_7HZv#0
zNM4ghHd6ToM*DA{4n~;kzp-cRR@HTFdb-aZcT!PP6T|{sSy;7Zt<<Eb52Mq!muZo@
z)J$T-0_{aSNGSo&o|zTZR#t8<s74Szlu(dg6ZqiYk`>PWx*E}0EmJ{O*4V0f0?AY&
zzdU5yJN+%vSECXT=@LQ!`(6XHE&KSZG~B+u^i<N)((<kg;By$iY&w@W@O-myd;Orz
zP<lDzLtC{~cZ7XblP+HT)ceuj7U%P#U#CT`knK<ullvm~5$?NI6CYU<5q<r94u=+5
zkIFt5X=C3-dfRMo6JPpeL&s<o$)A0vg?w>%X==$beNN%nw_f>#1+DlGe=r0SqA?p6
zSM%+SyADad+r4|Y3E*1u!fC~$Hk4+Wt^4@1x~cb{I?*eV%H%Ts4*-Kf(X7l&Gw(=8
zt|}1AVba3vSc}K1HHX}c+DE$zY*&_M%Xvh!UB15u(oBicpJc+~b&kDMF81QipR5tK
z{r2f)Towbs(v#b_*B1Tw`p{|ddwFt+w$;bOJABoAVmuk0K4?5YbtB@z!-ssfLrN6O
z;*GPHU3NV#Ruo;EVN@3p$J&d}wB#fMPr6Y{aF~~Q?R_d>sdH}n0&Xm!=U4r!bbMza
z?G%7Lhp4Fbt-rR`zrC{=Xi5w)k5s<%pS--hd0^GXmuJ^Ah`Ffw;jt$9FbD_5b8vIl
zS?bh<yUmjT({qYfmYpdtO>y=*axGip6e1TbY{weo{dM2Q#}JR@nV|L6Oa|e`-CSC6
zSe!v5=T9>u9ZNggkR~cBD(dlqMihtyc6Y+s=+lRne))t7Gier2FkQ%gG)Xy8MLk_G
zOxD2AaL7-JtFFVcgrR9B(sBQt$yR;ex$5k8KzG-fwrh25k2g$@bhHTV?yn6`B@US6
zqnV+m$}vx$%YSX9zsgwxR8KGNR%kcUZg99iOg5H4(?lU^Kh~{4_I!EPQ=<~;MKM*U
zh$wJP#h>9NX00!<O^x2&+p(i%-$UzY_CFCtW=lnKT-=9??6anbh6gD{1J9i+lyw1`
zsu0p{Hn&7dP$!xh8XC4PX=E8aR(AU_{^d$#L6=2|!=_D}ZU$+m>)jzqOCPfj=R-^|
z+aK8X-J$C9n_J92gYmJ?C!B#fS2rWksU=oLoPSu6<U4bNW^h2%pn|4k9!E<h-6!$I
zV`hoJI#bte-hD2ssIrd-_4314B^P`d&D(R#O^Z;5JAcYAbv4ux=yuS5HzN0x_v4uB
zsX8H5J=?HfAc}7@vjfM0qen@I_@OEJLkagPT2gg&H}B!vB3wTMfZ7P8%ve_#L2MDk
zPTP7C>v@{9wiG;Lls~-JKfk`0yB9UJN{ZIr(l{UDnR_z1#ckYm<TmHR_i`r9$u<?0
zB%jFZ7rg1CeZ`ksoy3uXispwB0~0buMhhovc-*Z|?MLpDIM;zBwDhCL*8~`;HKSI1
zrLfzfXY1i3M}mZF<YVN*8E34eWMrBi&~l*G9=B>@NVbm7{}T+eO_c4az9#?_%_9hA
z0|Hlvi|Yb7dItdkB9jt$AXziF8K5tUDA=%<6p}R_+fMVfXhl|X6-a|(Xu@{Py83bF
zNOzHDe0)5TvOhxfOJIjHoSHda-x)9H0uoL&sp=Xd=o3Yc+v0bLrOEIWB^8yRox66~
zVjm{zm-`HL7388kGOza5qy^T0?lM6lFwIjQg9rUJVTk~r9D;)CVZ!z*;bN{frCSAX
zbS@~k{!k{C>Mv@>B*d_98I@fAEM35yLQYe0jE=pJ;~JSNK$OJd@v{pHcg%O7TB!)U
zZ4QpcZE0H7bzvmeX&}r#%VzKria1pyBl%!1ZJUpWgN?>NzcE4Cdd%(&0pqbk)0M|d
zjDXSSmlwve%{%y;k~LLDou)n+1Z&5fx%JmkPEHxn7JVt=8UvEHf56)i3!T2S?fTy|
z5*3?xjL|O|iR$0+NdL-5{R`=IzEiEltq5s?eQBsY+thYU()&EqnxAj+ZqNUigOfZ_
z_GD<=AC6Dn&Fep4uKt5h{2$X?|H&`T{i`j{I(GjyPyA^HzVG_-4pf7}g$sB8+Cul@
zh|E*9h5g_7toy5mwh3@OV=I>}B)~RajG{=n>!s2MY$#68(wECi3*|}PM$-z)@92FC
zmk?6P9u$raOMh0mAcRFw4C)~#{@GQZVNlt7{Zu3J_+I@4;r&>@cL#SCD%kXX*g=p(
z^xS&4Ys41J0D|KY>xNp>BP|L>_+6))R4c$K<ri^Zkd0MDiLfa$MWc)kn^8_x>&Bhp
z<$2S--d^Hb!LlR@_rAI=!YI6+nsH0WBK3?A@~^F@P8mQ~pN-ITOUZI}{@J4f`d+jv
zz})nib`f<F%Cx2;6EjjM1S*pf%}R*OfdUbDxN(=9h=QT3j*iZqnm$lSf$<#7%%vJD
zweHI&BYybo7t}L9Y0>%WUT%EVtm6syxk}w6H3SUh(UHT0<dx-I;{4c-b!)SR+4x<$
zbg3~({Ukt+vVE6ru-MX+;z(<HsU%H)*XX8c_rVrrRc+-_wJwWXe)IOIkdxO;Kz$Hv
zP~Xsig7_z0h<F&;UlpR5GREaw9yvETV2ZSBQXh36^vD!$O}SQNQu<uWicuL&a(=$u
zNCGmmocPLOCJyhu8$T<V-M1u#aV+zun>D?o(vBmpACZ1?d17+1vN6+j!7fn?k)Lmv
zG>qeGzhl~emd|y5YG|7v*M8sfnu)&3O4}p{T}I#7{Ij#xKw2C*ZJF0p?N9Xug@u{F
zUzw~GFOT}O5nL8#k@Gk+i!+vHUaz61BN-x-tZEtP?^pSzRZ~;LrPwmumRZR_`BLhc
zY+mNWurI?btGISeTfS`~c?N_Z4Gqn?tYq%;`E;-|J7&qCSFd_;m-SgG$E`?}a1phj
z{xmpThjX?F{^Voo87yY5c<x{dvNo!>fc&~-z<oZGYplc<K*FNa!oKxqP2keBligsl
zzdHZ(&CLO!q5BjZb32kz*-K(~fzM1L=Yt7ioXzcbo$Cv3p1`hcj8|kuJf9ClVWxlw
zsqDGC#iv|<AXwM!=qx{hng0I!?=2DWuMln3DhVEt@4_W+nWwz>=hA7D#S$K<^(eer
zPL?j|>+c`Bvq$F<3sHL0Zce*<uYcC@0P5Lb77)#W0RbPt(}Fq6?|*9g)<W~rr3mm)
z#zX=9ez)Z|Cb1-7F;f(b#HFteI(qDw5_qtHz(CrJ6i`bbZogG<q<T3@*CkPiJmKV&
zk8C&P>%Mq)X?{9UDHKQURQieYXAci|<jOG!*^DpLetvbMX9}b-yMTZy@DTpoxpSwz
z*tI}xVfduBP2Kq>x*O9AU}1li&-n<_NAfsG{QEo)N4<ZzN3#;BEtvbruxa&EzNS6b
zx3A2%lqB^j$_hr)GsowBi)@`3AtUf#$R0(3C4;vISgsGb_Iz*7B7=SBj=DTEF9rW2
zG7<EqZ0XBl_IX-PSoOmsU(uN-$V8kZqHbbtCzuu$D*<k9h19}{2RW8xi^C6aR3;`S
zrWVFZObKQaWWJxj|6!6{f#_9n0yCIg!@`}ZtPVNJ`gLiVAk%THyza{`=Hy~+Bnhj6
zE~o|-m6Z*Jj+2W%A30JBFERSEUe+c)JFwHdIVprllY|O_r&9Y|lpcUV*$ye`Ozfw7
zhFIiE!%PD7__25cV=^UTb>+p<_cOYPOgf0ffXr_mu}A9W2NUdL)L~0$4gt0mDBIC2
z4JMA;uL>ugHp{DFFbf-tURKL?sFB-G8q1R)93Z)u=jy%3S5a51b-ip<n^G`yT*yVH
zS4eIdw4wJby}}X#pdn%;NIpvcR_5_`*<&$AaSz6<$%FR^m<VS4>Q5ZiI-;3lW*n#s
z8u48E`Jw3+-P|GUFKZ==&pQA|X%t7vbl`-1c?=K(ysHgQ`bk?^WqkbjaidzxNG{o;
z=_D@eNg2(<{AIuewLX<(4dpNV$c@*(UWvQ9j#IZ-ODgEtW0t0PMG1n&#kpc`pLu3o
zQ&%lO*6JS<wja%6G|a$7@laZl)b*2Ree~L1-H0snWlC+36|Q#r^Z~`pTd~E%^4h4J
z8<>4XBEYP$1809uE+l>u=_{77`FNo`^a?`NfwB<^;!{z=XaC05W^k5CId1cVtfYF>
zuN5M%qYqIlC0-N(53GFYk{@zaO2KIUqiJ^(R4Ojr9i~#CUGEV<8!S~K7SxSeZ7=c;
zYFMq5$EUFM=NBhy`L2H09%k2W3=*dPBWw82>egv#x@ArGbB)LPtQ;Itz9JL%Y7lG*
z=?Gx3p+QbO8GMMue$%A=NFR=lj($-fXH@cwhlkCIX4*{&z6He1RoquR$^QsJnh@Cm
z)xlp3%7;m1bYRLPiFeJnA4@@gC4dYL=qG3ask9s_YrB@2R(^Q0#7p=xs%V2wXt=k7
znNYX<WravMSmv*5IkyK_Mgvn5l)2Eta3+y7un&w{)2{50F|CVJU`B{B?Y(^CMtZmN
znCSFigG9Dz3z4AnZ7CH2?vT#@Zr0p2&!&|G?y4(a8B&CgMe&>=A$Slgk0fK$?Km*_
z(vz{iIa%{;j>3+>!F?p;=9Y3M_oRK6g$jtd8HkTI1cUJQ_Qorgs=>=0C8EwVTV9FP
z*S^XMcVAY=3iw6o6gdT&c06U-O&*ALT$sU{t3KS#X#@ULli7?6u=uDWUyBO~@~mHD
zx;}BCToJ8b&m)RWqk2*wD_R($0I;XkJO(ML;Z4uCNv#Fhhv~ZDZtnLHDN|E6lu4W<
zlRF)2si<CWjHCCjykU-H*~|vI?8hN?+q=&4%|lIz$<x@Nyr{Id&Y$+hIy@!2EpQrX
zMpK>&S{sH4X=Ixi1lkvL^+TlFJh%PvyLazAB44=yu-;?*`>R!+7x!cD&&kpvWppQ9
z=WP-@K`-x_njH6`Bjlaep7w|x5?PjebR1p^qI~#NTT0-EsF;|$$z$K@A`SGsr&`;x
zO@l*(?JYj#D=I2hV?VzwqnE--%KGX^NzP^0^6ePmM;v?j?Icmzu5M0uoHT5})=KNB
z4HM3-$|+{v!z%mC2&|9{3c#Dq-0sVZj}|6-e8rz4?B4a#N7g;V^r=VK?*=FUL(mp6
z;JplnPT72u51Aml&T4;pW%4)E`JEJ$uZM(o1NPBOShM+7OnGycxghv9v$b7LHExJm
zXHL#_Uo)*86@KGDcby+n-4^wf7~jv{?`QK;mlkH%Cy^Jf=YzzgEF9mS+hJ}_2D=_t
zuG+=y;LUBgyf~+_B~dk%E7J{Pg&X<G%c(IknLQNZU*;1%<rc_I`Yd}rOW%|>BaCk6
zzA&!h0oe;VpJlN1nQ^eToqAyTICy*l7M&qbM8a9mZS4j2maX|m<eu-pzSeK-UIwaS
zHR}3}WguGGO;ekQA~2`ABEd|g^zPP7#@AL2G6KekDF!(sO^Lyfn+V=gPHZmf67`ry
zy59Z!`;7m10q$_+_4++xb|j;egC-)sZrwUs3)Y<{uhndEtlCXO^9}hGip~S${V#vi
z?1dImzNy6;MU2Lz&fUpa@um1GG7eu9A@V{lGDRHDwb1UjERH+_VJHEcPz^iH{F4w7
zo?bAw#Y%@n5I+Rhd9VlN(On(KzCaHOA~UtABZ4%c)Dq%j2@mR^1nA=-6aUP}B?!9G
zkdmOXUeOPT!W(h0Q)ecakwxaeMG|Ctl0wECul<6T#23fUzP2r1!ku^UP?!gEFB9mK
z^nl`dM*02h91gD;C%~<JP3z*K3!dw-Z-8GifHlD^kn`kzik<pN7L~!rbA#3}c#1Fo
zhy(+)G2CV1<3Y-N2YK0$Q#B^_?R%QUd@vu!23v1UBSY)N+YxDEb^PcSYmtdCX3nRP
z#uX<wDf*>%j2I${_m6)mE$jLI1XO&cNfUcRinef~G|I}=Im-Ddjg3l^sPIDZj05+^
z=ex{Vm1M|?P6r?{aa-heoC=f8#5p*5{qt$lB1ll+eO7ghbmnC~zM4`vQSoAFyTw||
ztgn!wb1$6DF=?ux*|SHJ0%&evUIISnMf&bNdqPo3c2+$X?HF#)uB6$$o8GrvA6w#M
zW#vf@m826`^voehk5^thK@u3sl~dJmjQ&_vl{I9Yr_~SPp&!6Q05V%j;|86<)(vBF
z;!BM}cHmy-kY%g)UVC;WP(AJPnNLeT*}!I+H_q`)2tG<mI+o}Ic=^<_c&-d8E?sb?
z!HBi2tj`>_wseWxk~Gv(@(iUVb(WHex+)a0|4C=3R?l|_cq;gz*6~Am;v=|fpuc0g
zN+EMD>ZNsz^rM>L!R{#=vD=6D<T-g#CQ&6h$qKuTU9lDzHfb8_hycO%5fLZ*IF_uR
zh@kz0S#4&kdK?kcmS#R0W`^L<bU$EKnmbJ~y~K3qVH2G`f|8+*`;x7HNQf~&sM)kG
zF0u_Y_g=74%H@Q*l;(3$Imc4Rd4$ij1Jv$G<9ZhVz(4~nuHYL5T}M##)30Lxm}OLV
z=(F};#Y+?$A&y4Ne}4Ep*eXy_d;~G1X*Mqy-T+|8lYUicS4u0A#3@OcUM$uCgPuv(
zYMu$Ym6xue7P+jFs$&kM!oi?#>-!mne;i3l3cn&c+qo;v%bvy=_xrXv_xhK=P2F#H
zh3xP~YzZElb&ShPChB=z76*>YpqrY-iwX8Tu1T|-MLTVMG9iD0jc#ElFjkr^vLBz3
z7T!Irf<TX#_hM^Y@Y!!1=-lHiusY~r+1ciZy}n~pJCO1P%IHc4H$MlH_u|HdiV?d|
z!o|?*MorKA!DJE-h24H%pP(Ip$j6$RZECfM+(}mUoi`-%dhP{*-c!5LI%4Pl<O$u3
zvXCPvYLP^r%hbIaW75(-Nmg9sfwO|P-Iow5Y<bOEKV3C2Sa)m|iB7W*o2WmR>-h0n
zr_*vT&Sj-SpSdgT*5%OS-8>H#=C<qgN4bk2jg$<vWriZJrw5gT&h=tx!0R@(D6|!$
zaP!SJM`l^0Q(9z^KX@e3?1g4a(NYIsp+|>m78xo2TJ}B*llA1asRC#Aj`A6Odf{Pv
zUii(v#4+nsKGSBk>Gk=1E0FOso0&u$PF&Rny3N%UchV)EqCl68zayB@ThGN`DqXwT
zf`#cWa^5%C>Pt9H3@Tt<;V8Z=uCag=8>qYt*UNp9bb3<aix;mSxC1)STI|E~V%-f`
z>wySing?>HX=f?6#!WG!!(emA{<%!hed;YgzV|>MP4u9Eo=nnohnN_HlPz*Nk07Ea
zDmXIQd>Ae%#z=20pr+k1;sA;<5de%NWM{g7^8SiU*C!Za;LC6g^l3B9G#+kE_gh{_
zW7dVpIX|3P%R)jRY{9>45AvUN1WyLB(BFeXFJO^`Cw<ZHP=2z=Zqy+*D2FLU9V0;v
zgIngnS%sK1CG@GvVY4Oott<|R_dWQ6$}cTOz!8---{sdHn?@dv4HOYNdG79&xmx#}
zAb&r<#b(jTs*{kUl!?L)h9e-37b9R=mFx<%{Z@+|GkbG$UGoH(RU4#cPcO2j*r(Dw
z6XKv|i{P4$k(#q353zR$tSpqQm~tKmJdb^5CHW}>Th24P5H$%8rD$nEd}UI6Rc@+L
z2|=7xez6ES&e=IPY$KFt_TY({dw2-|tDldkNFu_oL9_0S_q5F!v(5*epx#wS4p#w*
zxU^ghcLg54bN=_TeN8AS2%@6|ygE&UFQ>j2C{kHYtyl60*cv%z>aVRk?DOLq8ynN6
zg@8ia25rh%V$_;}QT4RlpF|L6^YNiKnyZReP7_3H_Pgsy)pxX3f=d?PYs`ez@;*vT
zEf)Hg4=AZt2|3sKgA?Fr(rpUpxU*uk#SwZtYLPaRR!)e|wCY3Sla!KDyl~;dJJNGv
z@t=w~X27Wv2bivn8s6&E{$|SEeHM>wj-AnV5|bgjV(GM^`{%vS4fdWk?q)g1GAU60
z_xYW>x&=48!lCm1DJ2aQEx}MhI#@{ir~|A`PzHXDunKuhDoR9P;|`T3rsPW^Q_FtJ
zD4rYwkJG4OCluDK)qr@e3V{f=FTEndmKG3jmEl^pNmE9DET?PXz`%eeDG$+}@^<s_
zEqhBW9WCq0iASatUa3iyV~6N<0at=}Oav9QRHF#S4RDO)2kV;GrV&Mhg&6Sp53T@K
zUGX0$q@5rs-Bgzaflq=vjZ4;jU0{|*9g~-nOwyZk)lpN6+k4XRq93En_#2Ow8AJ*k
z>oa~)xEc0dk&*iJu^H45Y{!qwJ=}A=8Fkl-RcroYgG-7%6P171jk3L~+~t#wB2gkB
z#((r@B?w{E-*v{ZVY}0kEKAPwNEBIK5@@8>3%q}{*_PmE{odB(5pF(*$Dfm_$i08t
zdRvK1H!7Y@$!#-hz9&HT{I<3pCHM=9i~p6!EFvxvY_z*~-{sWRNx#Q>@ZcFHw;wOb
z<l^}ujwVPZ5l@J^iO-@_4J+SF5Z_>dX=!Qaye~}3<ODJ*WI+xx&C8&+x_^6C+~*Ao
zE34F{PoA#pPC8EX@_ghWt43{fCo{1a);{O7He(NYbe!x-=h!x4M+h~jcvXOq`Si=S
zOb@py!kwUkvSbRp^LpuTW^EboCEo)5^z;p_(Jf|9b&*j2E5opw-n4CY9vOBw#o@by
z2s;~_yn@2xY3SO7^U3zvoSPx)lL^z(*)unBTr69ExK;+col=z<Vm@k<q@(LP9Z0}X
zMo}lDukY^TREGoT{lmj7QN%ufy`*Bsd+gR{Xd?_cg_aT7>Y0Xa6yn)3Fg>2q%<8&P
z&afk_;it|&;_C1E*k@u|^tdFey8fK&GGK^iC8TQCPlS8Z-^Oh1%=|QkIRTNSJs%34
zEo4)6X6CafR@)05Gs)c6aH!2OF!9}iQz^SeClMQkjgL<m`0`-M0>YVrcg0p-$)YTP
z9*xTG9dmU)@d-l&#eL}f&D_;464Ede@DA^)(Ld;?@cgmQ+Uf3khOL2l$%4BNc@HG#
zc278qBa`3D2^F-CA&)fDei7E=XpXekZ@cg+p5{<C1PoKbPPT^YNS4IrLZ*UTpb4Cu
zOsFs@q`jHMbwK+Pyx~&LcNvzy@}q?=6@QEX&3XK)`dAh-+?P|KCJ{UvW9uW|yHF+=
zzA$-N&ee;rZi*7yv?q~;FzMtYCj^4_yJ1EvGp>?jN#r@s^`S!LEwGp9aRxtkdY`Mi
zT4r*43i<EvVKpyA#QT+J7Rv000#W2hWO<Jb=%nh1me{5*ip~1;21NnpuU-0{2tTY!
z9*Bv|zs(P`uzVxzV$iCMvnh53rLqh1O>nj~W?qv+Zj=I}Qiod8ra$D`-L6px@_sDl
zov*lu*#-d7G%Mf4Fh96}Tv+K`6|~Gv=$;#53=Mj|-mW&xw1~GU=yIFXPzi(xb5{#u
zTNXARX_NCqOEx&fnyp?4ib_q&(7pSb)1~%q5{Xcaq%r0`qYGq4=qv{*GY%rno%smM
zPRK*qpnT!N`Z!_-ic2lB4%nDBi;=wIVbPv-bXcrO$-dk1+J_yEkB@W|<<hn!YYHT+
z<e7tTcA6QEhm+(RF#J$=Q9+cx;u;!STJ=7c*n8teBYA~DCr41XJuW6iod)^LK_d7M
zGPx)nXeY00^xg$AC*5cXFT#CNpKi_8$!0;GK&s^Mmro|S(r`5+=Te?Kk3O~;UVIMm
zX?bbPT{F_&wN}w`r+UH<nyrB&p?KCvUHuf^vTuFCx<NK(LL10=645I&mJmbNbSE$-
zkl%i1*sC7ckO$!TR%1pc#`Eh!h{LWsI_dLbWVqLKGsGIHFBda=q5Ar2FPd;q(;^Sq
zhH(ww84HUPA-mzY!%{(U@StgwxVgSt5&M4R4LC<~)`fq5xZQo(pPFJOot}^gq?X?k
z9id3L%s;<7do%YYlxuxX!uthkB(;7<O75t9JMvU=UxioNBtuhQ*dCrI5fO(Bu3o(;
z6m;U=d!ufna{tg!(`r&jjLbfiF%9&51Pe;oo6b<cB$NPOu#gDsPVh0=W^I=U=BC<k
za^KMctG<eRGlNiKwh9XkI`j*(e#7?+nRR7&N%P7Qc}0E9HTL5;gZciCF1~LZb~QoA
z*qX-#_}_f-9gIM8$&(B+YHh{iM??`FR8uDYVXRU1Cs?3v)95aX7U<b8A@aq8z9gK>
zN{K3*iQ2%!qDtvEddd<&o!ps;&v5Mty|-0>XC|{&EYmOepO~)k8Ot=s+_Te~r;6`4
zCoEp6uy=|;JUPcZT`4?PsL#3nX~H3pKVOQ&Zz5<0J{NAWA9_&nEWC~g-w^gxh?EKb
z8bbDV{<)#bS<f(nxG~B@)Yq{8{0yAm!P=gRGSr+dOqc0KYY5N(Yr<Pc*hrj=ssUpg
zLB8Iz+|z~NCWew&GJ9pF!_pM7a8(QZ0^a)t>z$ZJUuwJrA)~>}04Z6MQ>XBhQ5)fS
zp}!ADN!+Gfb>;7L`ZkcDDA?*59V3J$4!0|T)DN?=Cc?u6PHOYh)8jI|4g<+suR;TU
zdg_MWr)d(2ln)qnn1nK{9@NUsAkpqQp9&w|9qJkoU1Ov}c3Qc~>U%*TwkQ(E$|f0%
zbvF4;fp1h|yGuY`w}$H<Pm+7p20gtTCHCcaS-1jEU+Gu^wKIhhAb8Fhd4`W?3QWXC
zO<&8o)70dIpN9uE<3%BUar42B$s+hl2oa577wgkKKPf8#F35!P|Ade?gBIZC;sX&x
zDtBXH7tLtPbuU1cH(a>Bv|ASUmF!sI>Mrk<9S}f65$5FJs2mg7c9kffX3edvtX}8c
zja+gawaP<KJx&th*MqZd7QA}1?<3U0Hs{RO_Y^GQ19ao_Bd*{~nsnu7+l_T66YgN}
zF$Vn)w+;L>_#nYwDRH!O6`lCN+?-+G>z1VV;Jmiq3fnR~7pkuq@noM}-okzi7jSC-
zHs+5Pd7utSKRNNn3=TV}L~4BiIH=^EtB~!?5t@uyLhuY<NV`Ic%pIzzsydeG2Af#3
z6RGUW^(U~3eb_JR)JUiA1@i&oHXlMsR>jnC+sVtr#n?XC9KCK_nmJXeU4(^bzmUxb
zycE?bTn#6Hh_jnnj~salclO$(L=@u}{PKnp5^v@V*a#)(6LNp1gO2O8a$*$->BVMF
zjjRtEv*0Y(d7<l|mAwJo0-4Bk6zf?B5kr`B4IyS9fnD#o&e%X7WtQwd%W)D<B=bt?
zUxZJ-$sX!xCM?k>;T_<y4H#(MIP2)kB;I|I2awI=gH`$o2(<Tlwh+bq2|sD<?rO+Y
z)ewrjad$A=D=A$C8&D04n{jpn%pD~ox!^Nq)e8Xu41wec#z5aE>>9`ea1h1ZXYX!F
z7h~2%A)S$<TjXT>!GTB~%pVcC6St;fseWkd>NpJ^E?GipPY@x7a8~z&ZsYT-LI&N*
z^ZazVMBD^HJY*LC1hJNcQ}>j$wY4{Qc}+Z4FvQDi%jAIgN*hjS3%dYrAkCFV+F$`z
z)bcU<6$G#lFj1x*z*?nV48qN+-Y7Q%?w983!umpI<@&<y;XB|76P6Ij5qFLSBX(x<
z*$uaD%I>4UGa5`@CIcAt!?$rlHnxGzdbsdIO$|Sx9HqCJspP>8&0!Q)hy1N`izOsj
z$LZ+TrHPqqFDSaPvSC%gyqANHsq29))d#Ekvt?nwa1buPx+0K629IoM-Vlf5N{1^X
zf#eC;E`3gQUqs7-#!`ERfs{1_qD?Kf$gY|<a*%kA=@xg+S6N$GRYC9iGFvnz+M>T#
zk(V;zgkmz;bzFyn>c`_cJoohoz%m*dPs#3aha8pp-v~onPBX{4XM&aRxDm|y<(e1q
zc<wup#eRIg^<w%Q^P7F%4g`QnW`a`mnjfSeS7H^MCaZ(;mS#FibH=WpS`<q?y-`Qs
zD+FQb-GWWw5eG;ogZ7I@jvTq`O4z+qw~^nuzEUM*pNb0hU7iB!^rKCvFg+H&<Me78
zAo=_~I{Aw|K=tQA@!YykCB;-qR#rAdh)@Yliv-l^9$3yrL_~NPm`I`)orYK;(Ms2m
zZF&vS#;|Aw!20cs0vpnH{l-rDRP<h~ODfHE-@JKqWtZjSAE5e@g$l(p)_o2A97t2=
z8203e5usUU)o|;V1yBmVK?e`qvgL>dc>w7vn}7$$zb;pSjmvR{=X<d^P~gZ<eR)Ur
zzRI<haIjS_wZVs8u!=&MP>gZL&Mhp%*P}{;Y?o*g_Ga0f0IqA_0!lO@=^9+SMu`cv
zco0_zlQ^NdzzL+3(1i>)h=a2+zZg2K1`flW0S}H5{Ch4V+iovpN5V7&@l=f6yo~)h
ze62*)P@f*#DEC}M@be6e)L9n6qYbgXWdGJ89GYkkYau7(4GIF)bU_jC6T&Nvc)sn&
ziKtz7#yb69GD`L?u)A(1vJGLn^E9WxY>-LVxj>QRRplc<;80F)()6^?e^g)Cz`|?V
zER9sZjj%_65DBmurrUhABH(EA*%yBsHzfqR{rIw;59UZC5r9io6BCox{K7uXY!lk4
zGrD}yzHi?X9J)4|0nSjyK%k=c;fsK(u>qs>QRQOGBeKup2r{KNhbV<$4fqr<UAROT
zIXo0UoSWF|*^q6jN_k^ZOdYqG)o-$W(B|$lUzhqQQZw&{FR(&NL#f)la3IgfB^(sd
zr+lC3Z*`?(S8TpynYPqC^(V5!tn&{IM^S9&r-WRS>l=7cZ1(a(SWr+5A;k=@*-nO7
zwUtrim89MSJ8j7OS&ZA9fn+5Lsqp}*m>nVs8@wSfS3e#g58+XVJ&kvpXDRU+gdNib
z=j@E-UfvkJ$P<JFNf^zZ@|hYA+Z71He!66Fc3Vs#vqKSVym}~$&osb2sGr**V`7pp
z2${|i$)7o&k6QzlGjPN`-wQ-n5Lq^;uMja7fbasfljZ&CKC8Scf=P$*>IwjYH&|(y
zeFcseP5JThy(1D+_Fl8Ksj5JhA>jA-p1ABml9Lm#E9i=fjSYn!7IJ?V>r;>nfo=T`
zbZ<1}<>g;0m_RUdd_7N)t^f>rV9oVm?$U>2O9o_qH8iB^ukRmLb`CLpz@e|g|0B1X
zV`B<HEhU`xhEPikzr4M3MnVkafAd9@l|Eb4OU6ciAaiY1w?iE3hpfbpu+9tTSeZp`
zD5^tTVA&JGirTvjM`77T9-@Dw)pdqb9GEE0AhE#r(ETP*_uvpi0U;sVT!Ay_GSQKd
z3F?*d%*Q7GpNTjlvI78B>z2Jd@8HR3L?CNGBg09jDWjA2qn#cNIzJ|W65Wp0UfW-R
zLtw!Yg4lb)D5Rd*f!UP^u2(w5uYdLkfg-*ajWRgJ@rd6<Q%DTl#r<RMD+O)&wx)Mx
z5+le1mWAK%(yqV`TG=&<#tPbT2iTP3=8$EK30j)Ssl<a-1eDj$Y$HT&C_%}D%udjh
zXMJL}MX^haUzpg02#rUH%x{oVG^0Vh9`vk{kj=Nt%VR6c$3HV{N%uP1zAy_csYxz*
z>>@#sS2`QZZqpsQMc+rHpXt{DFz(HXzTZ#fkzj}XO}SzU8UDNThy5NOV9`c0lZNNM
z;=_keh^h&~EO>)=JAQi7XfL6*G?feh319p)a@E%szr#`i{{E4Oq6u)_|LThTx`k+*
zl)r+evlrOJ-4K!(L>$ucOILlZM>7f=$!W4$z#z9HQIMW^=Y51j?ZsQ-35iZ+W>9Zm
zCwq=ikg;P3ZB-le-tYI_ZarSMqyLXn?eoJO|5q<6-M=)T{>zX5*S4tQ?uf7dlup?M
zbgyo=gtP#gSfcP21ZjAN75tRs<qza`XjkueFRP~)@zY=eVwmWX=)b2|Zjs9Yf`n)e
zRZ>$^!;4fPI<t`Aasdu*+Am`k9TQVwkt^kg?nHKdX+8-WVXbf<x#AYq*oJtdOA{Zq
z9N8#}B9gvYj(*A9KwqD-HE+7@A?<LGAYckvNMOtPzLHbZt?)BA5MlT+C;4@ZsGK=-
z25+ay_kCUoTLzSal06+2)sKU3G-dJeT)~O?rH@!R)Rp}89^3)PuD)R6C73d+JU=!t
zWR)lT#5K^Sb1V@h<bs*+z}nPz(}NDShjhYaZ`Wo|;rpORk1mfSc8rLn%))f@{kEWw
ztGH@xgX7y_%J=0wNxsSXt6m4OCE{-UB5#Su@bCJ0iDQAU|F~)QKiSRrAG8(!Z+scb
ziTN;*9@uS`(j|GF$;Wu##<GLCB(a&8xQjD>eou0)4Nv#R+e%7z{9MSNPE(JbOL}j1
zHnGni?jLbb%;zUT4y_Da+(X+^#9fyB^L1Z0D)RpPhKlNcN#FSI&;Cz2l>f=S?g`3W
ztKRJ^=;HD$Rp`3%!EnLSLJWr-eKJjSGG^d_OZuLET1o#}kjlSufW7ID7a(-x6`D!#
zs`91c`R9?_JkORrG4VQ*<Ko+clcn4-{c%>vu`sTN^7g)ytI&>_pC?FVo-9}1WBWF7
zD*K1SvYA{I#eZzh>Cp1RM!?RX%RU3g&TfGS)|7BwXFrumzF*Q&D<*Z7t}EvPi@MKS
z@s>p9mx52a4R^f{g$c6?zTcNN5CqTtanMG^o=#FmhVVDB3?k+89g0ySNlIP<USWw(
zOOr>$XxgtZjCA{xd{=^taB*>w2sKCn2L@Zk1AJi$sIGgbLb$(nT!5%7mLf4euM-ZW
zk5HCy*yo+!&bHD$4>^c#2XUJ?jrJ7f#2_G#N%AS#M-+dA9}5c$K8anL#VxUCn`~T^
z1L6BHrMYaY6Q1xlQY$<_EF@Sa*txh;h&VYrk9L@(n>TObD(r|ThOA0rEt`%9?or{%
zj?oH-7e}@0AhOzD8@sJ|%07(PsoK-IeLRTR%dGc+7Pu?dMPw{-%-o2tPc3m1>)YZW
zv1>%+8Y@;F5Y(ic8txeqQGF#AM&~o@+J~iQCD#R%F9<qEXg`z+&%~oVG0OQn7X05c
zegA)A!T#SOXc2Mf6k}Vrn`}k<jt2e*X8g3QEEoNt@tBpp%M!I7?BnTL+1<b|Z_RRb
zME-3@?fb$oX1juHGwTBV?&kyl?1>8CT@lak$`BRg?>A@4ZVMP+3D4YUk-Jl`^Z7vk
zikyk9ikIf9?NaX<bIVpJ15a}GXY+O!B!1QqDV}zmR~*Ubwj?HCoKiq%*qvJ|kF93j
zWo?v`Hl=>?*+@C}p2^i`?<Mbj=RQVCmtzjtIqdm}y|sm1{F>B*#QgJA+-b2VO7hR%
z6N-(kNWF)*d40qCC;@zkum3{XXPL>_p0P_QY{h*skYC2%BFp1|0qvW|hYsw1bpEp1
zTH6bCn=06T7|>i-I=xrxpPo3j7ZKl-Zm(ikcTRon?N#O8@9*&Jrdij<eoyIT#UYyf
zC>j67<<Bjx^+iR4hri67^0emqSTs9XERdU9%@OV*w(5b$pUt_NX4RjOBWu}K|4G{$
z<k$WiJbba?-)3~2-tli!OFRz!fs!6n=U@LBVDew*jT76}(S4*$n*dDDf4)VXb*+{W
z=w92d&46%U5!hP&*zQazI1qn+m9CX}9g49F5KT?tCVtN-hairi4Jjx_g3<BZ!Gks$
z1dENY?fY*Y`79$V+YD0E=FPf2gfeJS7jfs<?uY1TjzY_pEYa-?l7<-Hkty{%R#BZ@
zd)~eoZ5Kljx&v86;Q}RuK3aPEgh!9I6D|V6-;OtdJQMTm*?#}TKfkEI_WbJw44b&<
z2em#PJXElQ*r7vzqk;`)AsR5UH3&Nf@%2Lr&m98@0$5PSBvsk|G##{~3`=hObEC_<
zX;xX<*krK`qKx6P4ueZZ%5P?jtjodoCjytOd(UFJN89Exi!s{0gl>Gj{q^hD2cqGj
zp@|@yNhRxx`to{wgn_>0hpf?zp@4><iY_r>UZv3r8phM8dfw{m%NPr@^v>2E1XO-o
zS}GGC$**>7H#q~{)=8v+P6mc#GewP=R7l((cJ9?)Jd>*#TXnQN6|noN=z2H9$cTvE
zX(yTZ(vhr>J-pGzC8#Z}o=oLG=a`Lc-%U?%(w;8V6}`f_^BaCcS~&LQ%a_G{-@X|&
zjLK!d{8n@d%Y)+moyp?E1;uf8Cx-P4>8q*acb(ZW-i%f>!n+HfLLxB}2KM$#P?Zmp
z-~>#BswGHf8aG&%dmfE0fq6O*1Cp92<TUHVd2XG*c=6($a&1jpnx(U;{CU(%aW{*|
zb46k8F2a`S!dEHXslB_-cT=t3mDt2y+17Gn`*NY=weG2~&ElCakLXA}Kj31{xw}&E
zJ8r_Z@voD<P4q6oX)L31h9@S+V%rV-(;2I)>w+cNN2PZfEjJbh*4*i+%ezi~!~#Lb
z4!|Vfy2WA;?`y(x4R@O^F(c=sp>+BHRu~N$iSF=#pXqX5wXB>**=JB2dc|(<-A#Ef
z6b66BhlJkGgVLf4(<@b6rPo{d3Qn;Jr(r8gV=H@mc{P?SPak)M4SODL^J!3M99Tg2
z#U7?<cs$O-qIfHc>}kzbOZa*Ft<j26?#p!C-dXCf)5i5ru~++)u6Hi)WMoW<i(}Je
zfP59xc;(}^;xn5?807|AtPT4G>l90kh<?D{s^iNqIv4`VorW5ZevArl!*7t~*}?7l
zff0}1rm3SdzUa5GLYQ{iqgt9wvJ=4W6$_zNLOMP&rxCMp{Ce#QT>bN!b429Qw*IiW
zU}zZQ6Uw{wT-|~qNU-SQHrhCB!9&c<he@E#t%PowRXsiQ_U&7mUAveEtH?ubN0zGX
zhLlchn`dDVchfp}WL;r;dU|p93~kZ+M9yo}>o-un&N~p-y>R#LT@$GN^rx<0zCTbr
z*T0W+3FIY5r<|jHVgJB@>M0$~6L-)1)!e@^RH&2cXzNgPX0|=ZQS8>ZTJ(zh)^M0d
z8q%8$JI)8y-f@;V!A)^_-ZeTmk^j_j>-tGSu{}N`F8Si?7Sf4LeOa_zCslRe?c;Tn
zxph>pHw?VLtc3=2LRMsnhcoDi>aUhIDyp(m$5(gvRR(jgvPwWXx&#kI71y5`Ra<uO
zp5-0x%p)B53RqN^p6ur$j+Y2V0j%29bhW7u%)Tei{E8M-9xrzAuCj!|(Xi&3`jGYG
z$B&=#n?EJ`8Y-3l1cMbKFg|$_N9+b7kWRsEbrcR1^zkSlP&UQNg<G-8{=PVm(=>R)
zix=;K1PcT)V|VoTC&_cvJR}k3VdCom-T!~m(ErKEzn(eO{{bieGdNAXn(M$P6p80A
zTnH+V4QfibsgV$y1A(=T(##ia{_y=*O`m)B9)ZV>_F4RJvF}NJ#>(xxciCeCTCzXq
z(iw!v`<8$T`}jMzvQ{Iv>gnnI%B_)+mFf*8TC$-cvJFpXH)D*%MSq=^OiG;dwnl}0
zNh*oP<wG{>J9KwlJ0~F#YA5@!I~>0&9se?iMzs0Ehl}UWoqM7tGqRDIIv71k(Ia&B
z#YacxHBqqAUg~v9kU4m;WL82**KG_HBg5K4WyEU`@D<vi=Nm8aWyijFaauN3kaWMv
zWP0hP4p79Gx!FCrq>*|577FLI?R4+@fqemmg(63JlYZQ?$Q6A5L*31-c$4&eNOqf9
zhDLI+kaPiSeizTcNZbmhkTs0HKX~vU<bS`KMXoC9`h{~Z-=3(dvSv(LWr^Q29+-NT
zC*E{3!d)E30$Q521Jf*F!icy<$A!XV8&>OhmVQ}Cdz-XS*0w2?kBX}1FmcINc{R7s
z^=;VrR$wz%u+*Gj|CDZa$4IdN8@NL_K>4D#Nx8Yn-#srFU6dkwP2PuW)vs17#FHz`
z=G!!ABY9-0>hA5^KUNdB(I6JE(Z1l;Xs{N>XiZ2vX(y|grc*^XI!kOnN<aS8Hp%h%
z=S3=ZxP`N<>FMc*3d9BBY}zKuuuUSpB)lf@dKDA0m&<G{B&>#%RAdV9(=qD`zkK<!
zY3tVMrW;%b0Og-Oyvg12`>c6PuKu6dZ2z^RaX`~Bp(c9xy3LVdwYf@ib}%w>B~Dj`
zmjK70T_JVE_Fi(VN}cUuI|1-d|Ez@?lIQ0n7;38d3$~mU<dP*8n9769eeO>Z{(l_J
z3>=6u!*Yu2cYdXcJ|XfsS+}GZtvyjt-}e@o{(gw3cidfHh)aZtM;RcsG1pR;m`H)H
z$O{60E@MEnbWJzZ&<}>0BtJrjgpE59hIHimAiyRA8_d%D`E|XUN91!DlDllUSTbR{
zC8jN*c_&>T7}5*mQ6vyxDtu7Eb=Aab;p??y`M8IwA0`bf<Mk9MjY_cmpq(e_-n|W^
zdbkndp;{5c5j66w4*O^PzM<26bB8~3Ccp$s3?_iNVHy%v0wf{7C}Lm&#zGJdJ19yi
z@EN!V|NbzlGw;V%SsqMmN`RyF8;nYjgD`^$qeO&+G>AorkydbVDI{#da2ow>kz0^c
z@%vXjJd{mWSt@-0{vFe!{9rr|c=YJA^CDpKCEyb@dBx&dxR3rRkpd!{*XX3SB*K0)
zl+$6Mh}jDdWL`DH{dec~?QfU{2!~Jv>YXW~N6GH~ueG4szTJfLv#8@l6yY9uU;eW@
zkr<1>LV|UrL40LSF6`8ezr$T;LWrJT43m^37#hME2~D^pr{Z_@<>B$A#iOO?`!|e9
z3WtJ#&X-sS;c{q~e#jD!8A9{8Wm||+=vh?(DD`83pBTcW7JNz*BLl<?k82@+F6mLy
za_b;8YGP;|VN4ftTYNPJ+eZpfuM&2DdOj1SQ87X&BTt~HTvRyn`&ZZUV%xy8Z;Xq7
z&uEJAdJr%pOjC<gFoZF%U9N5#7ypv)=K^mZ2DWWGtn~1`9(Tc7sts(|l`C-kxmIFc
zpD&}s=T*cQnWpv@^74#8<gbVFx$w)hQy^MQ?DjV8WMD8y^|D=l3$+8}&jjLb;89h<
zU`Ov6KIa*;TM0iu=kucfzzq&jN>-bJlH|iUx;u9o+qK52w)5imW=A^8TYf>wQ|lL>
z*=o$jx|!JR4b1qQuqwgQ{)zTqRBQK_iqkW9{^14NuQ*WakPxV+IfeK4xzDQv+Wt9k
zB_|8lk70Mw?AY-VJK39#JL2s$+_pO8d7?i(pBQ~Wd~MLcof7NLi9Wn8+7$N|ZTq<(
z)g-kmHiExiLA5;#7Y9=rMYDKuqLM?a1pbHOK0I;qH>=+-IbgJxaonOKr#e>)aC}pX
zuaD0Rr#mFT3&DR5@tMkTkJ)$kca@BZM1AejJQj2*_fO>?P&@9Ciw9NQS6EoMsRa$#
z#eJ=<YLS(H(D@6*6Bs#E$yOQ~8h0pT<PvgjCt4={`&Wbp1oUdcswsJgA{Qpqlq+KN
z>A-*g3TFtqm4p`7jh;iIF=N81C^|(LMmnNTzz-c*F_>6Gr~P~GE7e*~>>#XO5sWOw
zE56vR=$ftr<w49b8HDMg8AifLj7Tw=`c_vwP0U<*|L)zN-#;Y>Dkj^w;R0O5T+?&N
zYsp%9mte|!;OiS)`b8dn%C=Y{VoW75BI3R|wjf%^ByERV6)@kRJzF`}JRk+#wi#_^
z^KepEYFnaC!~a0FWt$<k+11RpfRGUNzMRT-R{2okpvVYxMZrx(q81tVU@?Z{jVN<M
zDB{?~`M8NC1!7V$Xihn(;%d$_!^F)<ir5dgauY)^*Kga8H%9z}%RgK1{U%a9F?eY(
zI(YthuG%qpM}ix%>d87q8pK$fKuUr|2b(t1y%v}19SR1>UTR4ko>6YYwQL!CqxlV%
znij$=VkX_42^yam{DP@ho{P%AD5g`?7y(K6sS%`9%6%9fiVjRWH6Z~ooVtD*7C7(~
zp#foGoWAAEXmx<;67D@)V$u`s&PzN(gJ?!TD2xC}P*PFkVA{@4^{o21sKaLTG)SUP
zFc`Blu>aA(au9Kl=-!4eVcNUt7YBGbinuV8k9Dxfp<sL<8VtJ!EC|Rk#&|Ffyu7AF
zuVrf}LnXjuBn*g|;j|_1es~^vstG2lm+*iSGw1?S5@XCG=Rvnv+HLgEZLE+ugD#mp
zx-RJ$3B*oVad2}-ejivjq9=`1Z`-1+`ZaH?WJSjhcI;ZcRi?^Y=y@;t@uE9`-CWf_
zM-;3#f2bM1W(cJ!<fH@|A#p(2zd!33X@(YWgDG;v_&i*q5(@Qc3;;C6+z^9NIJCk*
zg+1dcn!1EOYr>-o2Us}K2ieb$(;YU7<4^n)>Jge*Lx?5@><LQ)3q_k_op6?$!l{A1
zsVXh)g|@ILv|-X{x}&JZmV?+6$bycCR(m&G8ZoKGY^lO4STn>fF+pfxb=;=;`JV^f
z<AonOhOmy`F!3Ss(4j+;B22Wjhe_y8mB?n5e0cvpQLj(IEj@&$sle`(7;{zLeyF@b
zm3@A|#oryUNeDZrE9OZOoE%J^S^DMMMB$Q=ym*mq$0;WfMi_x)i5q|vo{bJ5Ot>O;
z42J(Dz#<{9r*{$$9B_dcPfH9M+P44H0chQDs}J30eJ+nwm<1>CCDY^kH`~LPg06x<
z!kz&yG?5r#vC77Ti3p-&`rX{Ah1Sh0VXoWQh3u7$b9qdxxI7Ym!XzA`GkwV5{w&(=
zOViO}?l4P3W8W_rLD<DQA~QvZ_A{12bkxR8@n6W+R)(1+otUeFo-k}~DIjcs9AXq8
z;0Okw$m_8^BuG`*;<4TuX_x;2s(|zs0J;{+mMGlAhs!p?@YYPcCEmomyHFh*6y5~x
z(nextDP9R=#KFOlSfzpUlt>I;g0dEbfjVG6h<hXv=8QNreqQiv#Qb2#A|1dKlAq0w
z@hkYAT!Iu|t1KiDXr7zlb!yLPgYCbFbdMJkcw&-%Xk61AnncFNFyA1S>gS@Gz$3))
z7x<ZyK{aY3wInGC<M#YnkUUosZOn-~zf-z}*vAByqNer)+%KroCOF=zpKzbOapOku
z^hw|+_)|%KgsmBDGJ@y_y>zx?$E0z3n&JN;2<RU<@CMdn*C@kmKnw@K;|=Vx2!5}p
zfDKE45O{$Ix(G8<?LxAF+Ej>X5O~};-x&I1gUn`(E>6EFpou-2B`2<?3`W@~o9BvL
z2;2OEXL0v9{LT10Sz;n5cA+hXKgD6A&@TT<VR82fTKE!zL#5U=bUG3GC%R=>NS@mm
zh~6O^IKmP!70?8p9t;GcSasxmi+ISQ%=wuwVI%>rrN=CR!WI?^=vltcGDu7(C3b7P
zIRNbejM5?C%r?)+Mq9#PPpXF)S=m{OTbJBwD&a+UkkpQe6;{^P9LkEybZsdLICEP{
z))o>IDd0((pB+mj!af1bh{19&C#Jq7Ce7oE@4vD49#B<g-L@c>rB;=KWtl;wEHQzC
z5(N>oD8U1wNR(^>BqKS4T4DtCNRW(@vm!Yw268?e$)Myzj*@$BwElbh-oEd4kJ013
z@kY0!en<T!p7Vvh_gZVtx#k*$@==CL*v%maY=U|?B)ljqTlS5>TUCLYbPoj$ISLS$
z;9BInr*O9UhI6@Ex7|&PgUp12bcy88BrQe4r7D5WZND!}TUVFe-gQ-X)6{P+@LYW$
zXQMKqbEzr^VrIT+#iI}3H;}gsA2mu1B)JNGSzM(>HXfo_R2Mv2F9FR=%=r8ZG>>yT
zWzU>Z!gu4xJxsqv-q3APwFf9rzQ)zf9yxNPzdj*=Fcd@ziT9rc;~<!giZXBK`wJ)d
zfmD6{jzzedYYnQ$HQ6W=#u<UE>dK?zk+;`w?MI4C<pi-#{BDSc9T@0#jU|xQ)Npha
zt7Lh+9jW~?s&w=Q&R3rC%--H!zK{ox9?>EEUf?rLcrwUp&Er;r`XvbE25+-TLvjS!
z#)v3Yy?P}ijm<?r9%Q!<$nGb;>BI~S34+#u-nR_r<|8cA+CHz+uGso1l1W1%wwZ$?
z2@4Ho43ap`cwT9DES9cay+E^3D_Of1CdTo^Mg#76jp&gDm0j{`z1}}s00HDHa=2Z%
zujHT*sDt2FhrMR_q?zs}&kiD<$Aby4X$pc`UKV>bLR@uQAo>th9G6n7zb%}?JJ&A2
zv)XJ1C}#rAoX(i+$G_2FT27)qX}BkrH2C~@be~-7EP8^DvglNY<>ho@kb4LJ6kY?Z
zRl2&k5a+XuRu3vHYRRHuezl1P=no6E89?>NxSI1JPsI{TMDL&~#Da>T<<Sff>@sW6
zCQ5v>IH3+z_D(!TR8A>q{E;zJ^iTfqb46w4H1nN~M>)mcSIoAZBasmiGd^qepT-{|
zDDTZxYp<rNPXeelgz}s8?-HxJ&d#e46Teg;YG(XOO@iS8HJqg3jvV;<@k3iZy&ckU
z4xgu5b$P3*WCz8jn7q1Bhf|Fb@u<n)&o2gsR;rn~;?A?Dxq|yNK|4{yy}4D1@wj(m
zC6scUl(kz9R-sQaPE-0hSTtYJ_&3M_cT<R)3c{V!qped}34Je_|8=gAf<VCFvjz(@
zv!KJYE?Rn?wK^~kQEJdl8Vl_K9Dl!`br8qWk%RAxqm7*ZRt6M^ghL}qEg34$g4MtR
zj#T;w4>oHgo0oGhxy}s=lY|obNqF7CVIA#VT-Fk|=aOU!G-}g^(fvxPfrb}z0C@V!
zjwYcIK^0ki=?<UI@IG43V$s4e*EnG(MvRHDA@(fn?3dC1vzBy~*7*J0foD3zkORwL
zJ<@(s&W|tR)2}R8Q2yf1S>gP-(cr_>jh!wdw1T5Ef-n>#2@Z_!K&dQ;SdeYjh7d=p
zM}f=lP)Q>KJV?;_bjtxY9JHN2VWNXRAEPZ-D7km67>0MFbE)9=t2wdDO<QI0k5i9#
zf8%?P+D`P-WT_MIi}9Xy($)?Jh#!OZ6ODQS2gK9&m!BX2kj7RouW-0n5yc#Im{NX&
z@fTkGNwuOWt14%u`KLL<;*>ZA5?d))aq&EO|Dvi&9-g5m!3Q|B-&iAK0x!#4q|491
z4)h!G3kWC?wX%%NeJrntH5^VaKM4V#sKF-j4hl+up8_`@p8_&Ml{jT|SX80b*M9Ml
z0Rl4~xyOEV4U)+UKop%m_Lv_<wI2F$r(=BCC0<Sg2!&zM{EA3na2X>b=EUHT8cy88
za6a>-Es~xo3Ki8Ve0TSPX9IQ#eYMK`&AQN*``fO6JB~=>vvVowOs&r5^`<juI3hVW
zFQxpvy!ZoMkqXAk^5!a`P8gH>QAY`PN3tcj2}q*`F>KAr1l~p}2wWQR&Lsf`@waqd
zW-&%F3MrBb{KmGh@u&YGXwuYSjs{#gh{=`U(zyv_F7|gnwst)_X-Mn4LKCh$5gu5}
z2ul_MANe5^ag*R&$(E@IIwpJO_btEu_5#9tQkcx5O(GU>(0=lu{RL~u;e$RxK{V8S
z62fEU*DI5UCJ{clyKwJkL4Dr$^i(0qBe)&X6G~F@@f~QPQXsi4=`bQAC6FxBH7z~z
zzJ6XyjK=rWpF4e89xu@}qhmFJiqU4+WdX)qrF&0|P4?y^pEa4hFO8~98YcY>yVO3+
zH&8M*j>oYYYZVsfNqB6}V?&Rv^o*a81i2wucNQ7*=Iz^K{+a0kr%%l<X#{XW@oGW;
z*?#I|Uwa;Fx;4*et9f<~{TV+ati^QNdCwj!iEo_Y%=}8VR=+DUO^crsnohJv_4qh_
z{wQ!;Y}$NqYbR{U%qSc5ol{E&Rf;|ZlpiR1BsMLvXW@D70Tby{r{EATE6Tt6f?VDo
z${RbPT777l@4eC%A_`n?k6m^Ts7V9d>|x^L>#G{qR&#`SuI&l(%jty4Z)Dx$p6f=v
zk&omInnEJ2dt+s<$W)wlZmX}fKAPUiHgZC!|MQPpTieRK(%4z<49huy6^50_0qZ(<
zI8PIEts1|i?0T#D_x-a|?~WWFZBbEdoQmpFWmY=<gc4+^sHquf_9_4Ez4$T}7|q=N
zdt2+d5?<7_z=~3q>L?y^3v~k)T)J<RS1Pp)$%CBVHHW}3ZABb$aEfPj`Dm<b&_55R
zuLd3q6L2q7C9kq$PJ)AYi=)=5#fj@YZNs#g-|0jnfHhLH!f)^)K^2KdF0}lrNXeA2
zN-pz@`$WdyC@od|`XP?=Po|p&!O&)N8{u`)*hh!J0A_ccY{C&@a1T3REpO6ScJMBV
z#kP*Br|-(NiJGe=sHm~C)Hd<}7|@eprgpNdRqV6$TAc_bLxYUH*71%GKC~S0-YMJ_
z<eX!gUb0lQ_wemIcep!ueDz~qzh2m6z*5#`&|{^J#-Easm6{*-?w{4GFB3J?0C#Bc
z(eV13CbJ-Z<UO*-qc$&iY`cpMc_!QC{$jb(SHb3s%2uuG<7kGBBI_%E7LL#&X0O1*
zPbD>tcTKZAv<8+zZj8Wz?(4Q+?#-=&4ups_nloOR@f~71{Gekv3la_O_;eUT)FDK3
z=Q>-9ZrVB(y?~5x0ycfUSnhR?cS)jeHUbd7GdjNsrb>oSV0lNwjkRmY3~rC_SBvl(
z0zS>^q{^1GEGqrFItTmA^L1YAB^7ow!g-~Qzb(AQ)1G?a;GVpjyD6(!q~%xVY}l|N
zInX)hB<o1ypv~=|l@hOSAGa@m>{`Q>Zjdl{hi2$KxWf)K0mGi(zyPX)nCariUs=Of
zB(<3GP6n=)d6W_RvcWiFXWYlHyj9+@FM|#T$X!YunSQFa>*9I6){udQ?uCl+sG_1G
z%g?ET3~i^hWYHfwn#sXKyenZkg?c4`q?v_NLH_=+aO5J+Li=6stoZ5R$<d+s5k*HN
z-tgS35e^Ht>Xv(lE)H_G6K}Vr#p!%+IC87R;5a>6c|3J;ZNl74XoK#y{zkp6Mwc%O
zzb<8R`)zT|H@}zIq7iKZ0EX?!=TLmYtxOtPO|%5^T@8UC0}z{3%i*{`_U`?V?gYzP
zuPmGw&J*Z$>qqFr{DdwS+7dj?xW=D0AOfl!O;kPX5(@;KH=Xju4~}pL#Kf9ar9tx?
zu(spc<|y*3y;!h9l7159%Z@n>8=JJw<8f>5ANw?G7yJbB6|tu-dWD6B&eNY@+mk6L
z`f=B)RebsRPhP>E#LKLPp>B1-9YV;^ot27IvjJNOFB;=NfS$>f@Se)vw{6J<*&p}h
zh0@u`3%|o@DAL1OV`33p5mz8@p-!3qzPD{er@(l_wqZjDW4l59q;*>I-ln&i+&Ic%
z#VBkQg7aI)bs_cn+>dVl$ef8SQgwE`eD#fVoctR7qNnm}o`!_zm=-vWm+PL(utM_i
zOIw}$j~#4YfXOn?IPvRj8-e>;?Vqb2ly0`V^aRkrf>U?bfo=I$JFP?`9=|qqG_{rA
zX@o~|wWjhu4!xEu@Qp7IxLQ8Q;+Yh)slz7QTmLgdeOqWo%g{JiyCCDjEy4Ek%1SJ)
z*-NcDwceAL?y7M(%{=v+zWw3~dw|*<wV+?;%loYSw~TCIx1H9U$(1Zv`;`D#fObxt
z_~pLlZoCEIQ8hJwt*wL3ircn)hvrZGzV<#S1heJ<(jquJPZUQKpb9j5{v+#_gE5m^
zy9)OTGxXgMpTGqmHpED6zD5c$8G70XESx@KPT+9^3}On}E|Gbhqu!S*_y&|QG2JLL
z(^AXTP9j<EzF@mQ;TU&tRP#A8LxDgB)){CyYRok<5emR4ndkXHuE``SA0%j(gFuw@
z!4EmEe(^h;+OGM<*9{(yh6Qdb@-A&C!R(PR7>24)1&C2Cwt3^=hmRi_#Bdzg#3bAx
z`tkV+INqUmfCbO*P;sR+&V9c&H%kwlA6kL5&Ax3xUe-$>7A)@0Q9GIVCYWWkjUi<<
z+B71gucuC0L}1aZ8Mg54u|R=RF>=H%AHEu-JD3XH%d$QPwW=>pOj)fZG!~B@GHcGL
z{`XLG>#b=7hjg<U#9pP-r)RcXC@FcF&14Vf@qfp)D>TZ(E>Eab-q7<sf8bRb<Y`Q9
zZddIJvRxJ&+AL5DIG1jD5Haz?{WGyP6TRoZqr~kwy39YHZ!ynq&}CwpFB;j!O3XLg
zEbQ!P;?AWo<;wU+)A^IO`*gd9hX)O%?H?4lc;@ZQ(q*PS4hb37Fhyon-L-s!=8I!n
ziav|z+HCgz%p9{@O(rCN4U@bOKxN=}1~5^jR;<gcLRxA;=WYCw4ygA#US2R$>ixV*
zC(2LjRKz9$2dU%_e&5jlJJ(Lj$c!CAWs~@Ja%nQjb;bwqgW>-ft2cv{Qp2GBYI6>t
zfk2Te1(ZsDWd<qb0CU4V6hJQh^rVpQ*9b1K3alBCN1=~v4+Tzs@_-wONhyfrHc|XF
z)?+rtuXb%G29;fuhVQSg-AV83wj8Pe*24l)xf)VRB48N6?)`nRgkyXQ$@8$n7We-H
z^WXI806gol^SC-}BEI3$oIr(vKM+^b{F3h<>=xSPyEErDvhY|^z61EC!=4wJ`Yyo5
zNDt+aEj}&m7||d9{uh3&n`a%_0?>@e6VV)ho~``*N-wZ~V<!1eCj!&_gMX8gdUZ$&
zJj9iNVry(7!<1I5{6kc{Aw&h7LrJ+lHT5rnsk{hIaY*Xq(e>h2=_t079VVMV*~X8f
zQRR0G_Obd_vq5fFw~phMEy`2^m-#K_p{bt?+91k1EdGNOLoiWh$G%<sZZP}*Pz0mR
zu&F+PTpu9PX2)g}<p|MA_zIwau`M>h*<lqQg9$od=E#6FJm4&Nwh<oyvauTH#M@9A
zN+L4jtBf)k#4`~82M5Iaoo1L30^k;lA+Fg*ZBC#xQ)PVxGZt||JfPldeKipnzT-y5
zXaL|gz!Ofsa8S?t8=rTx2n!<>+~#k)60P>SS!ZhQ`BM{x3jv%^`O&dktzk;0lG%dy
zAA^Eb6rOmmKGwoC8t$OP34ls^?oo@Rl*~!AZOT*Gd_7Y$B2l$rFfRGr>p-K)_U4Or
z>sw=Af@1)Tx`zU6Jdb$2K2!UK2mSOLL}CP%L-e4*=a9>Uut9<bHGkoBcJ+1~b;#8w
zC_P{wc_3Lp&GrIWf}coHmj!80AjuzbgS!3qGuBe3pMHyMqrhA7x3)22kD|S6`BnUW
zEe9|Tro7yZJR79mWwsVbhFp-SGS({b-tbP7la;MS)q{#D1;seAnj#6#g@cf4#DdwL
zof?coi)HSGy*K?eVAYPq7tG{Un*%PE;yZT7R!q3j4Hulf?vmF3cQVUgdE(_WByXI3
zuz`!KpTe8~DyLkqSf)%9aao{-LwL)@$CoVA1jxI=IL%_;ix7T&PrOB9fcselj?qA8
zdi{+O&l61wN)jMe48)askT#{wAOns8us=%XJi%<r5U|xy_OLjNhjYtJ;3ayDI69I4
z3SlKr_5k1Una-!P$O}uFBYQ7oAdHil6q|rj!59v9_@~i5cpz;N*$F8b{r$NpB+ne4
z|70wL;e=_QX1%wEr11@^C|g&YvR2j$#Fkxe)SPjh)R_<|LO{ns4AwUfWpmL_R!_0)
z6pO}-jKv^Bf_CHm?E0wgJr3_i%?4v3QZ#BOrN0}808<t1Y@49DB=C9NmywUD=QYl(
zx5Eeuv&@w5>hMrJCO*OvEQ~&KRr>3%zfz|*Y}^<IX+R(N{5rtB+__o^5u66tOf>)S
zgcnCt&@>Cy>fXBduoOVyPlnQo!=sY$+t8hm!D3{h6MEXbe)<5K7%?o0OgaEMk7ng0
z)J0$wLeZhAj3Z(Hkyn1GzcJ{f4i1t87RN$-jGP;<VDjC@bIZ5`<jFCqTRm@QqdS#Q
zR7ts0{<v(UJ_dwPBg7&hsXjXeIuG1lst{kZIVd|eMPs|5$X1$i_0d95TB@kM$v6=t
zvM^1%2A>6D??Ixm!n9ftBlKl1PL3G-&jIKbvFgMD4yPMd7!_79a1kWD5`Y^Y!*$(>
z3`)c!ih!RJ`8#_&3ww>i()@Ok>?fUQF;%|hH#grPIoM0d!Xast5)xpVMWIfR!0SNV
zD>bRaI^SbEe`IrOxD?UV;xyZf$94@+6TD~Os7OrgL6Hhvdi(cplFDLWA_;qd9(RCv
zU@s3K1&7lDgvfocT0L~+tT|9ZZKu&w<U;_qgWV<*`~VoE8$S*|IXTp#fiZ=;^?=eY
zkP&gBPG4W*6#<DPY6c4N6+7t+VwD<>=U>X0BnC0maI8oC`|RQZeC`r!L}HDzy>;JZ
z9&XgKZHd4L4bl!zz6UuPteJT!L?3~fH)@vy`Zd?wCOZcd`ky#U!MA`lCZozcJfDm9
z&@R8aHZ#@*Y@!|&mbZ^jx8o2@_6X!a@Nzh!#?=XV<OvxDbgW4F_$6jU2j@6iF=L4p
zIg~JyCXOKK*uGQFM=PCh08+zAqx3b2f8$Xmg#H`S<-DBkfqAYOG7|>G3Mz(T@F!wo
znphjv@aKdAqenx6pI?#qigO+SdV<G6cs@eLynS1T#fcnLZmXvG4YL3kgf6>W0jNf@
zPQytH^c-yjU}cE0GojSTvjz_0t7e2;D%GM(2JQW)k@SOEB$EYDGVZ1TYj=7zxwi1V
z|5+JTnoa4IIy`Ni#j&kqgb6Wo#&-i-9Lo5@@(usO6>QeX7p0#(52%HXB-V_KQB~f_
z$Py3~NkU>eYR$Pk{>0ZeiZF~g>QrzGB+J$FkkV$;L!|-r8q{KkrgcCx0bdIR;y06*
zSOw1!5YXHI^^`H%>ogTGpmx}TCj8~8v0FxDYGB@ngDn;{HHAuA91vgoaFfIpnn1*)
z8yrlI@$ILk-opRy@i=d<0aZIVIr{cU1Oy<e1EW}Pr;d-l+vxsd_H9E4ZS2GZa)M#l
z5HV0p5*|fTzIo$DMZIi2t*25)obS2=j#&7@u~Y>*!*HJRt>h>G1j|h!HoYEGq=B3g
zTTg$DvLPN`!+*g}4One8_HR6(Y*{M$?IU2BifM7rr6xB676}L9gH0{9at%uz(&{)2
zEs80-?zVPi_uH58e7`vUECD+5{oZ5Tvq^`qwPJ<%%^leB;j=NsdL+By>_cS|W2NP-
zdo>&6S{M=*-&wm$!-Ii=ftMet_K5C}1+hkJOgtzRNGGJH>(@dKGJAzg;qu2wQ!-D-
zqDhe9FL0mM%U%&6ob>jMm!za5(K*5F>}#ITxAf`a-*10zG4dDQWJ<=(8V>K?yH^Uq
zP!aYr0*HM?_)bt*EaNB$J9>dJx?9;{LT4g5y@AJ`fn7!2vcYqiZ~_NZK4jq|+KnW*
zqAZ{&z$1veMe0(4^3fml?Mq7CUqDpq#3L@Q4KRJWG$U0i%?PVn{o1u_F^_y{rD<gG
z3I!ynH+jFmJ8qb&ZatVvH$;&~t$4UmbHnRIN*Jh&#kQq|kZP!m$ggOD>=qudUB0Et
zVGEl~b+o@KD*s<nM2QnqUAJ!opQ^{XDS$XR*zj5!$vIYWFf4x(QFUq)HV_d#iKTDa
zEeWsb1`o{5;MNi|VWVGKQlemNY#dX$d9@QDZ(6IXNea1m4eP{PFCOj)KFQae6B!xV
z55L&41y}f#!;sH1lOz0@wH)j1BO8`4_JsU_9kLVPegqpU5xqIR7bXY$xkN81S0|Ni
zWiAE0Ak$)l7`u6>%`}~XcS#3G9S1_-02~^T!oia%#Vm*nC8nmJK$fAyWMw}^{k5K4
zMEU_awJ$%yhH}omFay)3jOyI;n*{qcpCyRWZ%EK4a`bd{`;hBAvC{i`j}Y6iiXiSa
zr7aclMm77tr|G{(li6ZaEBN2A4)#EV<;AH57i+;oG0$)nAdo(8^se|Z2mkJ^H2d`h
z_JO~ItBZFNqc-XhC;aZqr$2$XJ4AujF9JEMR?Nbtvloc+3{zk8ORP7+#M1%-HrHiM
z9B4lIzyA7rc=T<sx8TBkC*`I?kIwhJHKt{<%*X8Qp#hG=V<aL5@_vvyqCPo=*<-a(
zK{;Qs^!-p|RJiWnE*w~<LYG6fPn7vw?2kF5w+cnBEnNA|qb@vDzi}Enx)%07#DS7h
zb11#}1;!U%ZX!<g)NrI*<b<g48$FHDH;IV1J|Y9}$N*i0cG3eimD&d_0rGLgI@#Id
zWlt+`RdBNo=im4i6BL^10nlMHfJQ;jDDx_5=;MbEhS*ZniV3QAmX*P)(R)a_NE(mP
zm*~ljC|fqYY=IAbo-^JW1Eotf<^gb0U{8K9&E?Yieo$%jYo9z~(=>wp10MjbpV_-E
zu6)#9RuQQR9|^K3budm!B{R4`m`*S^P{+#Hxf7TZNui?SiOl?!5#6{GPU}96rq+ob
z{X4@X{v}DMK{i9FA=F~n!oP7n4gml%F&9t~Lu1Y(r3Q4n_rd!=P_B@P1|YN1$)N(a
z8Corm-<sOsxJ;ONXk-$>WRxOk0>dIz-qow&Q2pIdXhMaPgo8B_Vx-x|b<E7l$Vtdt
z10>;n7_NITtsG{&0x*n4#bJbXcmLr-FOX6+LuF#yiG;|vm-dxtbl@nAA%OM3d18@L
zw~a!ggA}J1)A+cBCf+p=;0D7?;=pU6sQ6|5dOD`h?xi@OU5S=y*>jn(wPIQTBg~K<
z1DD3{B<=pZHGK7;N_bY!TgGHbmj%u4;k7WO9?|H#O@oP3M^t)p$XpE~nSvF^WXRHd
z)F-=aA^{*X|B$!oTy#w9*JKkEkqX-`Ufc+>uyn`1$i2U0e^&r|MS*h9g0~t;>TrLm
zMi$|uK13NNgZXPhdZwqyBxC5?3~^E-Bs^Lc#^G~t3)tY^c;*p+hKMjW;_NI$7#PkN
z385-MyK3+Ub<hWpyo!h{k=|(-W<$h8BaaBJKg1>v%%m!q0yNNt9~;V$B!@Y4QLjue
z?1s?bpsieMq2QW;uhQIJXExNZLwR!qCHNPKR7HRZ63U@`slvV>f=G$^j`airAySLB
z3$YPo3|nGmH!G?42%$#U9Jpi~V&V%KuXb+|11`zDbgX>_I8!=oa){c0(t&%Ns+8Sw
zUujwsGKfLUJOgSK*PK`#YsrptyfUt|i%Y2Pua0-umm@ln79m@S1$XHCte~8v+iE$=
z3kK0{Fu{<uFfsR|*`OSN8>V;m*`n9$W!6m76i%fUIu;I&X<w;`e~f0oX>aeJd7~Zw
z(E<z%4De;w&INRhufJ-q?|Hyv?m@cgO8XF=u1B{xYB!BF7pCew4zbtE*>h^s((Kb_
z)7uYo8v<|67g;VT)^Ici)YzadLKYUzf8}q#NdvvFG3mP2rbi-7To1g|d7ksEBQnJ#
zRo(eY2rg>#oWIgxQtF2DKMe>Jv#;@D9TPKUZkc$6udPjC(Xuu;Q@s8DaMNriOOIZF
z{F9}JSw~OO-wSh(eqHOBSG`r*^~m!-milT#Uwv439(2Iu&h6VVs6eO{PA0D-zwoQj
z)AxDy-5XBxuSt_Qly4aN=eposxyeu|JH3(a_)ArfjMmP4{W3&0M|;>W<j#D^)dg$+
zhAt(UlVSRtMu7;<%nh(=%8epL3P_!59FhuMgJc>y0YQ;bCYjajtviF|O9nOu;TCL%
z#W~1ZC(JI1pb@PdhqYg7J_Rx|2u2^~@o3Ql7dq_P{x_}`fdeZW(+HyA(&Ai61~R=}
zj*Luh{-i)6azV^oq9EoziTH&>#V%J{F7RkI{nm!VHN@)%6s=qD_WK1F0Yw7l#X}{G
zqnDN5#c|XhEnA0V^i91mtEV2-8d-24P!rT%yFdO`=x+N1hZD}%I&3g9c#hcSc()26
zn<pRzvACXSl)6|1>BiEP;flh}*ixq(u5r^=`yQXTdF|p4m!7=b|I;t5tDfjSK2q|T
zFOmLWWmsA7=B@SDFDc0@Rc**+@r?fI@2<B@TQ}WV`{JkM*t@6yetG8I@0W^$t+KMS
zhB%Ix{F(LIT3I!uq-R{rK3ImX)|!5n7Ng_Ls1rdEqvJ9W#$i(D%kq*(xlPu)v@2&S
zHK`mh@-wI!gb}?Qr<yG*1%p!}6b>M%baVm4(<7K<FNbZ|ozt(J#JYLk9)c>f79MMx
zslXT_L5{_dJHmFsG7;4E&b^Wi_#Df!BOmKtiV8^&4i3gBaoK-iYr8P4aP4>H^>#@}
z8gD4_&wq5CmsVGI7eAemnJIz~vZZnoIqcdPf(k0vHa7<%;}gL3%&o3e<nkVi(^=pm
zK1N}-H~x&C4JP9aw`b%Mw%&HuH*X?hWjY+RY7Cc^%mw>LKtG2o#B7?qtRmneWq(%X
zczZ4cNo!1#7`-jZDq5QbDK1m5LzJ_Z71nO<%9C#Csj1fOQ&8I0A6tNcbG0e{!@vLY
z>Bo{R+cTU|v?tom8YO1xRoO2Cm>1Gx*W_@_R^3@`>N;|Zqb$jFfw3!Up=H>`7mdlL
z?Mq7&=a!TPzlpF}RcI?)Z?TB(H**;ec*@yjA(CYGo_j3OHkVssruZBmI;C;yxfVeo
z>;-=*=LQJjYnqw@I7L3+-96?mob$Z#8)z*MKdH0IWVg3WgeFoa5uEDdVH^wlDSYrq
zEJJ}=WY2PZqDm8XZ3nz%Xz0mg)wUcCGteH*_D>9pi8+wV-T0K#+d{<T_;Eokod^M;
zT&=S$NOoU};~hQq-YT1ojWYf^WZ@ujk3^Sx;>jpXX<M*BHrInEv0><kg1v_QbBgM|
zE$ZWw6AP1B(FW`e$+5j=tx{-Pp;gVkm@sja%8mPUxLl(y`v<o_V<`mz@nY0z;Ni|>
zFeaJ9hCot|7W5#_2E722F{*D4C&Eb_tshU;O+lkZG_zHiNnTQLt(~ecY#Rq~84VET
zN?*3dB6-;`F01BNUU+voJ1Z!PRpO$!y4KJ&h82X)7#S(V&Zf4nU~R=81C^<S3N)a#
z)!U2<p^^O_4X8<hUc%VNk2TXt9ErBu*}}U@w??GU!X129$REWQa(l~=`o;;Xq#Qvh
zqux2r2@rav;<@AAoHOV`3GvyoX7e%}ZG6{1Jf@|kMPz=Y(6Y%SJ;AmSh*_7c&a48p
zGr`Q+2=!^cy4P|kQDtsuVG)oI*L{wkY_Xo(=Lhs9WEE@M<3u|jORhF;b6_N-?q6=9
z4c(=TRf{3#7N}45Qdq=B3g+{drk5<BgNe+{w6E$z6;Hp>^sTMajahZlZ5qDtJ2IRx
zp^3%5bBGH_FMGy9cH2VYzg7*_YI?IpX~E!e*x$F?#3->^&C+;dr^@&%mHypkm<B<#
zEu*EiHShd*;)Pc)O5kyWUjm8v`+#^kGPHPnm$F{$I!mjnt>rg&gpVLuWcU?&30Lhr
z8K-mH+)?su=F8a)`4Idh*?j9$V2<lwF?{6X4u11F@T%Iw6HD+)6E~lOVcKxqx5EEK
zVXQ-eBgBePVPU%|V^1DAaiady%zSb3Y_<7EcH2%IPjXbay6(EO!{T)6xAuc;7qD!z
zv$8DUOumPLv^4~&u873fH|w!`jvX$C82rh4)UQT?)DF;WO}!7k^#~bP#O@r~TddJK
z=5s@lR}4M~_81zTFI8|1nQ5Tw;>^mWr3%};rw|O~Y>6QE7Z9_!xOgT&=+GK=t19(r
zeDHMxBO{G3I*yKxHlHrt$(d^zt^hF+C=<cgYk^fX)#kc{@etyD!^3Tzv+jz8r)Qh;
z7n)L1Q@hYZ!&+~Fx+6F(ElmuHKIPbW2PsRFm*Rpy+db|4d*j(6Y~``w=^Xtc15c6e
zXJlZ{%pKRSchtSf*<&0DOgOpBUyvK6POJL#<m6;Tdb+KL>+$2qXUHSf1gtEAOx^O`
z2I+83o+LrD7^M<=-c;&*w{U(jc2aurBy*Jela$!B4t3h_H35I4Taz(r1p%B`vCb~2
z_x0Z2-)xTw&EpSr=KqkGYp5hg)IkvRs#?~{1cNXA4Hx28E}yAcLD?7N1yhSXc#4&e
zYn|~R&qXLu@Q<sjEARlXVVCJ_3Uz5VX{iL@+tFU9H9O2tpzkSrK51z(-`#rCu(=`A
zX~SPf@&<HyOEK`spIBtHo0*uHG+esF&WBgB1KGZPpR5k%n}1MpTwatED#t>vv>#xi
z3Q<P9mrTU2D2;^|BM4tON=jkx>U++)Uy(w!tPs(!nvsc6AU_73+di@nUK+O4u}YO7
z1)(B*ZO#As-a29BHccGTQ2KIW%-U(#|7#7NBKzF<F@MF;-4u(?C(@=h^Jey?8dOen
z2J9UBsYSVVzBGTnbZ<f!tKj^QZGI`X&ok(=P%a#4K=on?2lsC1UO22~Eiwav=!i-0
zxc*mKqhXwtwRM*+ucI19zIBS>P2H;CuM@Eowo*f~eXg;w(E=fR9|fImIUnrrm}PXh
z-!Q;JvxFL_i(_e&<255OJ2Ebg8$D^o<W|vV;TrUa4sNBUzTOASoF3GvGey$kRG6@b
z;|C#;GdIl{{gB$-@T~D-ieb#u576QGqllfRs03R%X|++w%2H|~=N!C|e<$IrwxL1o
z^`!Bu_gflB2!KSp3xmo_>ov@@UR&qNM<^8U6Cb@72tkF>`~u3YG8~xCURQ{6FQ*e&
zN;l?E_ppfehBl)VH7Xm&n9t4|YjzgF(V>fA%?*Ze3H2GSFD*w*2a;7n^08E}Yj@Ci
zIj6xDh_!wFAY6Z;n`{MyMBcW!*6HOiRHt~3ed36k_0ATI)8N_{X#SR!k%1_0iFs(<
zaLX^jsEITcS2hK<hbqbvH>ab+JXtexF+$vsmsmw#3?jMBJ_=satK!lOfb>r}$=%YA
z-hR85-Fc##Le-i7yfq*vQ7)!~t?jE6Cc8HefRvf(;&t^y=@XToFM*?}Y}HoH$HBqD
zJFKcsswH17E+9<>il%UwSpH$?K|}DT6VuaC+1bYk>pza0a=Nv?uIZU>?wEJdnFxjL
zKLrpb$h@Kht57(%Pi|!?nb<&v8uh@|)zZPyag76+^;tusd1D&lq4n^=5H*@BSR?*;
z<JqITh>iB_+}vxYnX&oyH85u(w&xfs^`;<&O_ALFdOL}c8|&9K+iWA0=HVI=&^e2D
zIu5+59*2YVLG;307oX0o+*7PB?U+}EE(CAROjM-84q5@~g-C}iFc@1G&$R+rQNT);
zqrbDWvm>);xcuOd1&)Uv*QMd5AQJUp)Xn~E80IAus?%IkTLoE)jg9^aF<w(sQ{>##
znJH1_1I@r3dtK2dl`C9Z^`Z+tybDF5qxQs$t>74RL!;7jx(?0YIR$cNVuWeES`$e&
zofsNzB|=TcPw{D<o}M|w&cmhIt&@qIYK-VF%n8g=aj_CcSk9v65g(!S6h|%;GsT4)
zW^%Ve<lv-0teM<u&^Dbn%^YZ!io{b(OUr)Pu`K}S?{Xf62u>GSf|&5xRl60#AL>VH
zLly-V78XRhycFGY?k)fa2;j`b(_6im*8hhAQDh2d)~g>hlFUVFEJHn68IhfZgvPll
zKMAJo*Ep3W)(n%awA=IOAJAtwki+MUAKxCxNG&`OgLA&V=RGT1nx!hzm{jzsu`#Q4
zr;31}?ucT@R#hV-BlYa8{^X(V?(Q3ylV@T*CzR0K8XTMzpK0Uo?_YDI$Yzj(e=m2x
z{J8{cYwJBhyuIm~6=Snrii&tW28p#sHj~a*rKP2Dkn}4^`zS9QN*qFSUJc(Tn?v-z
zBZ<#}C3+_{lxKK$Tj*%jYZ&3L-4c)VuuD0kq0tnYn`m}W?XQG{1Z&5H_{C-bmoXab
z?4`c(t}lM*@9&pU3`&eko=szC9{zb#Y3u_GP87MWW9QOvRkt^5)?oh~zU!agaTb$Q
z*CX28PB327VT$b8jAzYj6SDR<#WFbe6eAgUit$QEmgXy$0&$x001tE`G4l+}Lc-b}
z#AU{Boh5sSSiX{twG%sYVqzk`nm`KmB1^K`W7LyNdHr`kWBD=1iElHB70(Lf(=Aay
z7J)N=AI9udtS*;mJg42#+?*rTe)L)E>UDM+cnYnp+P>CFE{h>PK0cG;i>WoWH8tid
zhGRo2bL4f5j3ig*;F%xDec85}*{-Rvv4-y#?bqaC#OyLCbH0u#jOuF|61#kS1k^WO
z;PS3fiRu~Y$7|`IT2wti?^#H!SJ%*3<9vo~^lgr$M+obYvcw)EUF-7la%)E?#?);+
zkJR*Z`k{6$=lsL0!8)5{ZEbC-%xsTb5I8HBzH%(>E-qPXOh^V1&@W@;xVJ|f84FpO
z31K*esHb`Yi88auWz#4GXw<{Wy|PmId5^qG{>4u!8YbR&j$%<kHtHmbcjqrM@~QFj
zKTM%WxONuv8H`_-3$ZC#3i_sI0e|EVSs2h4&WL)m&YMl(v)xK6>yJ4ei8!t>;O^M0
zVZTwzqlRKkb$&fC)5Ay}!rptxeoUyi#H_)GH|g9CRrM?Zk0}*-9^di#i%r(mFZx6B
zbvHc&Xq)_bRoi{vQsxE*3M;B;t{yVd0Q_}M?lqaW*zX<wt<lkebx1BTDr(;YJj{er
z--znVw|7mS+kKVx<fyZ=vytZCS2z=Tb*9(&zJMJ2dN6Oo!gC{Mj=KBTAoIN?{z#Sm
zcHL=cA<w7Njj>_U5Y%sos^mYbI&E0D_6%lPk6W*`+lO@V*P6QV<Cm;8Oj4C|WiPI@
zwz3M(&UU=@;YFP5kvf!{nB#hYG)!WgYm}zrU}|QEJ6pZ|^8{taHeu7|z4Yu?Sirwc
z-ses%YHFQI^H8S)pR=q!wYBT!TAm`TMGKOg;+Q&{hdte0s<UXZ6Q0^AkgebOdUN8$
zHWx?!6p5@B=s+2o*w##~LY9)7o69_Rn{}NuO=6D4=7k|nE4L?OP%Mfey;sFSnt2B5
z(Xbv47%V|n?}zc-lO#zEA#Gri!Iyg)^h4yR12~rpW9_dPenRulQ%>L%3sPtqwz9Ii
z3iWOUg!w^amjOL-5l4mD1KoxbPtM5Otb+`MaXD%r2Dsb`_P?Cs)UYy44*8F2-k3K{
zn-f=4u-Xynd=MEMNhI$<ga>@;a~)NP9{Fgpy-{)U<1lty6EuX(=QMR7e`zs5eMpM_
zvi}8f?>aBO(05KBtC4joiv>jwC-<Wl3AAL)_6^}g$fS^SKS8i9R_D?>YeSDDm&v%o
zM3eoDIA*EWQuDfexQLhA9cgje-kkRMS|R)Gcy4X8_32bj4kS13Vl9QS)^&fhv&XuF
zgzG`7dYS4i&8-9E#VBxP2G;TV;}i1BuLVc@wy$$r^G0rKqE*};xNYsUZ>{Xq^fZ|r
zDAMuwM)bL}+k9G0OFK}iZtXR`-eu(0Nsdb8ropEn^H*`NZR-MczLN9@*}Fe4X1TK|
zHAP;~F*)qd%-ueSQxPZU;~t4V27~P@sfZ5$Mk+VXVnMnle|%TYEG?-5IYa<DhdS>7
z;tZ`N4J;HY$;u1W=^yINaZNoS3he+w`rGDwVJ9(Gfy*2J@F7(pIfckzN^LTxOx>^t
z@*>fBeAxb68i10Nw6sVit_XxRk`9uRhZI2}U*GK$icXYMtB+k<>^x){qG5lj*T;v{
zADC=*ntCQg{5TT5i==2GrVj1R+2A_M2&UqMyWKHVC=xzNTOHQ$@`qOZy5V}OZ(soR
zwUJiR3nKh6)<$+3h(&RTWP7-1(Rkei(VOI?=~draq6w)NT3TBN044=kCAM|c$pg*t
zPIB6_ydwa5=s{#5gQ*EnZ}_6qm6yz+13o;I)4^=E?Lq~~u84E~(zxql<v_+4PXkla
z3b0$2Xj1Ne@qnj<B%6teiOjJE$S2@n(1Uke6wo%`O=?6SEZAVcpjIAlaT+h5LEYnr
zu)AAMPR<WBQ7iJ^p7i_$lf_YvrB|%$dqV3~*&gb7*xa~|TXPG<10mRoCFH=JNKod~
zr**(Yla|5y>_An-d`WjFl;n$ToW(E@pOK4w#VR;}0#gK0$D%@{C*D>BA$2A@e=d8z
zrEQ@_ie_JWC|$`!jjVA?)b%X(uClVSXT?U}1=@YScdIo&v0(Cv^iH*&EG*e7>x%q5
zWa$<x#7i->1J%TC@@_CQ^!(GX!zeD#qRo#4%JvufOq1VQURlrLSyrIk34>0K;jBoC
z%bUluKZazem@F~@!F#H1v#-_=-3V(9Nvr<B@Zz$X{{x1nWZl5TRw_a~8{OQ=Aqh+_
zrN8u$x6BMJ$whgx>j&dsq`!5aZu0u3+Y1eiwYl$(Y10M?Pw!7pO(l*mzH!3&k)T4s
z7cwz1Cm606Cf9MPCI7o~<i*0H%LNwGiIdEXpIY^U?IYC*z=K`?#C`J!+~Q$6w2ZX4
zc_^h?E`MK`Y~QJ;we$C0GqgUqZ}0h?&rNREvN4Jj?Z{ra+Nng_%}jNVmoJI+OpKC@
zs;X*kDbvLtm;EP0R|sN|3b<V<x7_38drqAE`I|50FEZckC*|e8qbfHoo(Q^?Q<iGH
zYIK^cz2+4AG)8@!9w_t=#|_TP490jh8fKGSuq^^8RKSxX<N1D(V~T=W(p?SVbG+$I
zi(^SX4j=MFa)Z{;?GE7w4~vTYbF=^DcmDPtAL)1gan&Aji%d-W_kXk0`5#>N|NjyH
z_6q;gvrzBXI*|(HC@Huzabk=&wJiz(iiCJjmV|$TGKlns-a-wnGR#NnVW!x~OFy)_
z&ZWDSq9?dEb<qXlzaCg8R07F(Myf^vVD`(ilb<~?DR`#fm_bdr6TyR#2#g_uA3){^
z{Q!wLmWwkzJzz~MNCY9E2nwl5o@Msn;wRSb9XZ4Hm5u~v&;zvva7h;Hms}apC8v(H
zI;W9&k}<OLXitu{C7lld%&QNt7|u#bPuI^LOp~P^)XGY!_y*Z2ebn19g3^m+M`kuo
zS9<2MI@+!O+B3@$mPmz|PAJEh&sYExXvP%^3JPKbnQ2MSpx3{AYDZq!^I?S;rF2m#
z$t9W!mpApztYLxn(_OPIfevzC#2>M}N_yUF-DxzEr|9g_+_aXt-e~ufguga-ScIq4
z5%pGmX-Q_L!|H;X1#{5Yb%sAZ3j~G$Le#6c(o*y|`XM+t-txI#wChfIQj%HK94V1@
zAah~_x}KMnT^*90A$sf>zaArjp(TZX6W$-5bbGUwmO;u!Cjmi0-TB{}Qq2~9)RgWx
z7@EW-q{+k>HT%0B^c{C{E{Tabl=kV5gj>c<o7ei+wp~0HT~SeyvaZJHoULO?i}n6!
z_r|6se$$t9;~4JT2Ml?6W<g<Lea3m*O{Hj7Df86jg$KZfEUG@Hpec-nR;Qw>N)OFV
zOHHLc_ir>bGvnBt)PQsrX;z(Xi-wnDe{!pO6Cm7LfnVOzsz>H!_(ryGWRiO7z80Xm
zbVDL!=k|VrMk)YbAGz<4Us=$7xq^y#vOYh~cDu4|iTIlERD;(?Ew@xL`AkoC#Z9+q
z4fDkgt*q7d*=p;VV7r~^Wi=~Ldkf$&3xL0S)V5CmHxMR}2tob<SL7D~T+C0H{^<!A
z+=R`)CGi0`-}PunD?%a0Sba3?f~)J2!(5^_DnEgRE=e=Iik3V<vv+0;Zts%NChO=P
z*J^?zPP}ztOQBn2=GY`P<V>CO_{5M$p=hu)F_0WFQyloQU;eyFP)bkUf!vEuHS|#F
z5wc#o1+zs4L;6Kj4tmS1{4(d@xtYy=e&X$JJKyOEfzG_Iy$6ozD>P+2Vlo@k-N6(l
zU$`nK&IsWg0C@;O>1I0jN%&z@&QSnyycSg_*Cbfxk-So}H68PB4_<hAF=$*^u)Up>
zCZ+UR+XRx|4t(-%3&#LkF~Iyuvg{GMr3y<NQOc^g(e}!o_Yc^{et8I(=8S{R$m~I1
zwq!a4xtv%MN0>hiqo%3VP|v1M1qZu3K4tXv72iLZX2cllGDvBQT9@joj^va*HYOKH
zTIArW=Wp&WeqcMt$(`PDGcr{t$m8p&_xOZ0&O32?t><PO9USV9NhNyM*Vh{(Xsxua
zGm@x|o{K`R7r%LgXJ{}%Q7|=i`A#ZN8(^)nJ~w2#SSZ-oGk+3HPEMbknGCfV+^BWp
za^FU%sL%s9VS}=;Kgk|<D{sP9#vZ&eJ_dw8_+LN7`bT>ZYAqhLQ_cf2(fksqhn&a$
z^SymzI3h%$CcGdjDoSXB`d13epVV(~mr;oWP!kz)=I8s|?tuhEBm-&D^~!&98C+(n
zJ1hBP4oNqV9}PA`z=H2|9D`IrA5uy`0Rshqv%bsOBVb&0b=DVJ%b(53{2LH;QkCxj
z4G7TsO25`G<WCCjUc37(crzJ${(mZ>tzzx|;~y;mc@qDF(DXm!xx_}ak~G4@;Jn$n
z?&<jGK2ZP_{Ao%QDik_@pd})%Z?@4tVEnUZg@58ori)id(&Xl!G1Irtvii$<diKNE
z#^*+w6^`#Gz?Lx`F8J=E{-2Yl{}g%uXWZU@O5y&WUj=zWo8%C7EiK1q_sZyAE!lnq
zC8r#Nku3#;r#>UM^<%!xZDa8!fQTIqa>-+v{HOChJ{ci2%iNk!=ko5Cmos5b)w%Xg
z`;NnU!*|+G*EKTwhky16M|D=t+nbPiMA*o=tyKC`_k=&YVYbj`Bb(^K#^_20us(s@
zc9Ev$Hk~L#LnF&Zi4|2Be#~Ku0d4?3Ppe1Zfqy`)@Sl?py}*H7E&KcDNuDdZ>{mV@
z0C9^Rd!52#lo*7={UOKI?5X3zJP!ys2*IRyMB;U$#aEgY<C6!XB1x@Pe==?Geow%1
zSj0z#>ps?Y^hrgFDa$IFyoQFx2bL-S1gpsOH`*z{to#n2PWm$I$dH#CB%e0O`_ty{
z0TSvX<}4GoBTA?Xccjn(rHU#kY5+RBH6_~JF#E8{&>(uD_EYN+{<Gu8y=N{QYai^3
zAt&McEgI{xOjPM@p&dd@@AtO1oCe-hR1?AQoS7}{+7PSPX=Q60rQh*3D?U3R!Q}V4
z!J(lFC^Ud&>e}Dhb4pF*gx1j_n}maE3U>?*%^=*Pw5?s;#r)Mljv68B8!1qfW^%~4
z_S#1eYkZE_>7?@G#|=_4qSMobOV=<`v$H!K*bSFb@}}EgkdCBAZS(LiIN_Tm4uP6p
zt5MH#l<kOsTAtHPXyupI)(>+pUPMLdS7~Qwv++(G^eqjbEgHa@$Y@p01GW*7z<J%J
zx|E?uMWCHDdKQ@E*Z<J!QP<X%-+IzsF=EtZq@}Gb#3E?5MI1-9B9AX)>UngX+nWmj
z|K~>s7)C`KtxzrP<sC@ZG%eDeo1Lv65~RpR)R<R(c|Nt)Ba3(EgrEkGux7!ry2Y5J
zq$7_Ep{<bXTeZT>?AGcW$y`0|fNG<(4TS^G9S#Y%Z;c7~ve<Wfh1qTw%9HqJcaKIk
zCXa*-mdxb%aVx+1Pp$U9$$tDVx3rRM?%L2#GU=_4#q4zC=+*S*!1v|l6e^Ngg-p%b
zN!=)em>r{OjX1{eEA~V|=}6hau-DFcWjWRAC12M#FT<=fPLsiyI)~-dU4GTsYVwo+
ztB>ga@`nFaW&iK~=Kj--&=iJ$h&6e0b5$lf4=h1w>%oJh0cnRN>YlF9TPy@4$}L@f
zk<tF{zu4)Q`+I|05d=N6($x`dOXF~w>7HdIRRR$WLdhel^3(FGJkD%+-yK+aPfG1s
zS>(H|Kn}YJj*Hpl0k8u4{n)@uN$|WuU~1D_bqO%Sq$F4PV-AQ;EfAO5(^>Q`WT869
zqJY9B52iJB6O7S6AYdHT9Uru&asR5reqCV(J``ZzQ%-yEUwQQlsHlhl>KSJy{-RXM
zvMAn7-Qcd9EpArr)#X#mr4XByKfLZGD1=@0xUif`F{Rf1x?&g?<;zKCa^maO1a-gX
zb-7OM1P{gbDvMkwlVJ>kl~N(W*XIS_u>&KzLgj=20L7-{>NgY%)Uy$qJnJ3Wj<t?@
z4&as<@%r|bmZYvi@l%`jev8q9^C=At2ZsE)L#Zh#;a3m`j)^9}V419g(}-f$>b}%w
zns=9VbW}YF8w&Ic!KDj4`V>-L0e2HvpKLO>RUoPc)fFKIw#U=E5@M|A&qG-aO-$tG
ze$Q3mV(-(@ymQkq8p0zDRA2f!VT1QnVSS?6sB>7Tk7t?|<KNhi=wa0ao!q<YYmDIU
zv@L~>Jo;auq_MjCJP!FdB7D)#c3{0e;YA2(11|6S`77tnIp2?Yg^M|veu(Lz4Kt~D
z6CZACYHGR@s5f~cJs+(2ieXZ@PfRi|KJSNiSq?C57Nfx+UBIJHvq#`mZQ5Yx$+@AB
z9LeO5m})D}oH}2A_LkWfjM~nB<8a8M&7n%WRKwUWZ#6@Z(kS{N90k(cTy3LUZknmc
zu0yCNf-8a)4A_N>cD$$6963;aYobwDFWYM_iuU|PqruMC3!zP^&Ef}~wLEA>F}iUl
zr_XKtV5Jvwk2hBGR4T2y)u%pE@Z(-@3roo<(Y)7|RVLXV>!0$)y!mjB7p=Wj+EJ#N
z%3B03oJjlP^JUH8<ohe>*D`uWI}@+p8V(W7W=*7D)wNrC>t=gKF=m%zi^biA+I~>N
z($TC;pUfjrbjR3kD71zNOBbX*WrX161(<N_>}!-GM6+_1I^W~E*dw~K73}WCBDZNm
zpaY~3pML=WWWN_wFYfd2S1`S=|5bV!lA}<U35c^Gol8Q_VBy5`H_K7cKWbGpmz9;p
z=ejsA7oD7iihYge1#QBMpD*)>&dog@6vX|g7g5zdE1PXVm>#FLu#X5<)F5CF;*L##
zFAt}hh`e@&`K6$^m;@B?moK)xzWC;*K!L51q`LD`3a)rp{$ZmZ<Ay`f=_q9Wrc$b|
zriOa`i^|T0T-2W>U1J3?p{%t2xv1*HA7Ub~E^dU4vpO%;RaYM|zn_dMHs$jB`%tmO
zS8S%8g(L=W@>AoTb5W$$|NRX6p}8pKkmD;Z3Xn|-()%n`44$@<!8+3bOU_aWrITB#
zK^J(im3Qanm7TQ0=N6e_og%l`-t<e{>K`69>(!QxTbNH!h!L<#ifh{8;M`%9_GD!%
z&CzIrK7h-Ta^2Y^VX~7oSM^KOhm_fBFONn4mgcGVyiRaDzGZZ`&F3M<65Z0J95V1K
z0|JI~a-Yjz-tJXDq1*!ii_x1>)K2ssYcdw0Md(?^@`;v{y}I$+xe&4IhgMFJ{^&5A
zSr)hpJv4mvp=JFZf%yZkf7lL+q7-@zUr5-~D!jiPd(x7Dw}jQ~ljk5i7#I2SkpCY+
zp~qa*8F)Ejw(MEjG9sp=gA<aHj1IXj$F@1`#Jt)5d9YSxkX|o~uTaL@v50g&KFOQ6
zT+GTVF>gj;2(~GRaPx%b&6QpLr05uTku}RkQU%>aJBVNZiz}r6KKv30vD(VU26SFn
zt8OQWCJ@B?b1rLbK!zmbt)f*C3I;xqM=2JNv-P|zWQnspl{)@WV)01GeCNKWobV#l
z0a=kDs;Z<xo(Hw_6tBa1@o~S(x=jB@QRt|^6b5n@BMADEc&4g!%vaNbLZl^}#UI6|
zUJXD-X`L8tvn<jD?I26d88k5~g#<^l=e}VN^!6N|B-qUhR@eg}QpJCY0>XB2iFlZj
z1%SVrwm7M?BuKDNDv`Sx7#cPYFA*JUC-he`)Ig`PVxqn3*N|r>?qxF)OFc`R6lgAN
zyHb;!O|W5uh*FA>OjOP!#}>vI<Z`dcwz9RIE?6{xFpy{``Zdp+IM3FGRKS9<rmoHl
zizL6bi<2TbynDrdo)yDVhNh;3x5zCm3R#?pnxCcTpQU0Twiw~taf4fUG>=v>7>LB~
zLtxEqL+O;Kdj+kMD61V70`g}9+=nM2?~{bRkoVOtm+&WZWPr??G!szjlGC&E<CW_t
zW{G<Cxue#r%kNJl@!A*5vS!gcljfq37zjHh5rUA&VK^zWvZc}6dg@9bH(H%$a_-zY
zNwX#ZAEu#nXgFRxcSH@cv)3A2uW_%oDhD@FM?QCyqY?$x3~0M&sJ+~WH#4~x(jP7F
z!~Ywg{h>)7H@Vkf@F0EZ>TUwR%FvWEf#O|o*nyr+<%Gv>oTXr@AXGCqv~^}{eCx41
zG-wR;GWTmz26IO}%*wd6NAGhQ^m5njD+Vkb0-Tex7*ZL<9u$#CDO!xRUt`+s<cTCA
z>Lv#(uJeXAd?YU5Cx+$B9F{R<PD+eYEWT>epPsW9LeF!_V<Ig@xS;eKJ);mCDv*y0
zFiG3qJ4sY)<Z~dNxS`Fqz<+{D=qb2}UHj_1k!hW33}EAat!2?G6I<nf3lWe^zCDS{
zTV~6_3y;Z?A_6$*9z2QI5If-<p^(>$=$M(AwJuJjn^lPtL1Yd@(g`ZX0mh1sTF#5$
z%J(fxM1URx0vBj9el0hJ&XOJWolopXALxkvpk@5baMA%j2zqcqgCesM{JP)nM`C<L
zvlyKnJA$@73j*!q$Ssvzf7}cGQTkx)mIEdMR?DGhsy{|1&j*2G;~(#-tOVB<Sjckh
zAOxZE$kC*uA29O=YTud5*=A7KXPr@b?}<>L1N_WjlJ>kA;`kX5i2-O9f?498V}mJ*
zgdQf^F(CbHZ%peG8k<PwJ43WxpySZN*A1Tdk_T6tU18rO<dUVp;j$~bTyaBX&=^DW
zo?9H7YSL34;7gQ{1f}CMYt12?1C+Ow*u6d`X$_GdaPM-1sdv9bb(qrs@Cb>e8EyD*
zj%GK!zvipK(S<E0^FK1}j!*(sI>~d>n*fYXge+zZFDuj=DqU=fW}ri}9C-E7v`Vz%
z=$D7zSpLg2W5cB45|?w>=b#)NiwwjeyX}Sme}}1`@{vGoprT{EOJlsiz3W4#^9ccd
zAS#d=8CiY(@FLRIWqoT<E$J;Fianv;J09qE?}AcD4iK*T^dJs75xk#T=yg&Z?p(>p
zW$Cd$QsZ9BFz1MRBGl|qi8nDn5hcNH(TY${RN5Sy-&{ydmqQ|sfE^6Y<6#Pc!HFDJ
zImSA}y@?D<A9w2F)=}%3C$J}3;g@J?hiISDyk+(fZ7UD#@pk!M$4IlNh={z8zpRU*
zzwiig&(?Y%;KFK}bc#)ac{(@j^p~isb_-h!$g?ZnkP~(qXBg4*l2jhMwiJwt5469I
zx#Ov6Y26Urv5hUF&8drHv4ew@5|!d}O0PTR(qeM=7DJQD53_Fsm2P-@5?5Im+)>dP
z*hOj@*!Ts5fNrvX&#nE0MC=bi@onY~3m=5@p5SrmVwSoH6vVHw62q0ax{&KCf`1NN
z4#|6bHTk$axn!u&&7nK02X#`#*<*uV5KBZMB}ej=2@YnQkn&KE*b-4ONeQ*DwEoAc
ziHP)2HH_l|dq{#f(D2$4OQjo?Rh*)&5$oBc;*sF3yG@SfvP@rRH7HE5#dXa<lK6~B
zJqc(Gs1fMLIK~om<5;eQusV<wacv7jwhWgEytQ)NoDX?r((Kks==bv9(0j2^IgU<x
zOGq7c^rp9&zu|-<8Zo**KY(~nR#tqr`cXHoUKa&g4V~_b%|+@!j*i6D!hzZkgj`sM
zYz|?SXXIj)3j|Jk;!RRJCN;uGH!?kp{w1uQXjn>sd~=1&l<B*t5`}IKAEwEbq~}d0
z^lwsG$yUoC<<s)d5Y>I?bcjQLjQ4haJtf4+EG|_wcD6>w{LDBv91){&5QdE*&5pNT
zs)>;0CZE3i-w7=zMN=XDwi+aV>nb;Mv5Y`S_Lw8!{qm<1xtjX;A^fC8-FazcC_c{H
z!|_czw)v8pYSOKkdPeR}p)|T-xyJg!nDp%I>@lD8oQUO%g~QoLR^<!Fak21%;RaW4
z)dkZZ2VE#zmM^sxZ8o0r5rye_+ODMgg?xDC=m$22yw+qwQX7bOz4iq%*&$Z)HjO`!
zZj1vWPD3Z@6o*8DsaCE~<5vUcVRjE8m&Q-iC4W2$VG8w^`Z!FD_QN>^qf}AKFW2j;
zXO+0T$(c<_hESDRv^+LMo(CGAcm1)q(XDfk!UvClGl561C(Io?c5Ix)8HxqU5|3*p
z0fx{MolUkl#-p8M*FNkmquYKr|4qf6PYn$sBlEeWdJe7WV2h<o3E-_x9c7McMOI{-
z-eN-`aGy$Unqkv3?R@0G5vi$y&;$S3^5{(Krc;hPS?IH~WRk$sc(GyQ+((@|66IRb
zPVJCLh@(rf{qs8`H*CYlbEh1Wesrt;v!JP+Xlt%QWYz#ak6*XM%fXl57$7i7+Nhl*
zwX*V8zBLI=uqC-Sa6Fup#cs1jy&?l5h~Xq#N;*8ULw%vyJT7Fd=|9E(x^?|J*Jx`h
ziRpy<W|d0$T%i{o6Y41+4USIYcDl*VrC!CiBpGU@zI>j$*sq#jf*=-j&SmUrAL`<J
zHL;1;DhoT*Rr7IHp(WRn+KJqftvbu_l480E?t#&tTK(al-yMdpy&F}mZslHaF~yvv
zb+zqsHwnQ&wv0u`zHw?_$x(!;<O{VgZ^&$26w=6Sf>MJAD1|-zmuA<c1p>po#@nmQ
zuDi4yWjnRAcwfj43x901y%ZwI+z?}sp`tag%;^GvSiQK2E{#2uy=McDcCK<&(Q0s{
zJrN0O`L}yj>`y>n7#6lAOvBNd&;vqLPG4XDP-*;q<~~5P$4H(-&c}6sOP@-4-ZZ&S
zVus>^!EZhI>M|5;<a#<$$d@C8t3^+LvYv!#%UdWzIVovKz{Mct&BQCWL^O{AxkZ#(
z{MgdF9{s>>B4o{>_-?dz*1;rvv^LAZ#A~$HJ;zp{=4(v!us7)HpX}H^CF_=2;4SQ1
zZoCmyRXh%x(TeH(r3Fp+U5LPkhn|sy&n0=gr`TB0c}v|+xm0v~?WGVXiR2CFB{pi#
zhbFrjT5ihX>`7e#FpS$Xep!$fAA2G=qyfyCn(y+{_OjUZ=HC$;m&e<g>uE+|+s`JQ
zatsuTY=x2BANzu=5G+axp6DS=X;q*Yy%DnIkseMQLd^jq@43P;_vy5^iZ$D7Ep+js
z^@i}wXo+w^&Q0`HCHkl;Ao-%D)Ei#V$&d4N!4AAkTKbloT&97K_xNa-7L!JZE1n{I
zVGBPe9o{XM8Z-Co56=*xaJtlXiG(CNHoeePZdmV^s2BOFKNMW!%aS%<R|=V#A16RJ
z=hdEi*EYesqBB(b50ZePJ%Z=m%$It7I9}S%l<EX?icHoilA;EO1pb+a%}WK;Oiyrt
zaH1TI(~n`$0F#s60g_tbNKQt7NWyhjq<vKDB`2H7^_+1_0+uOiI!PW^kLGR}yx!=A
z<Z6tyl7_gdi>z*C<#e^7q$7O5Rfuwk$f_DpxkzV}^na?-CTC71w8k@VjtoH;-(J+O
zCPY^G$a&xjGO*H>>%|@uan^3lg%3@ST~rsE=g_!OiT{O!d5Ut8`U_KR8VPec6XiM^
z#aT>pL7k;p`fl~558EolBd>jl^5{#&Bf>hy?;iHU*Zv+=Jl{q(;PoHPR%T;9%hULr
z(=xjuesiIc=s?6HL@ss}5=kQU0IcDIY+aCaM+HMavXpK+d{R!!en&z6il6`f>ULAc
zNF!dI>v#2dGp|3btCZg~*4r33D+krbo>8^gq%lydt`n!c!#2h=HE-}JE%ur<Jz;(H
zrn;MEYMVz^{PE)EC%3;lhd9m*t)~1L%(*o`#IH=#;#|c{kRwlVd2wdX)ypuq7#SVC
zZj&JdlTH@mxzwCvzmdeY^mEVUf8Vuh1-kTkjt@ut{?k{8XWXIJW{1(%GuRT`5;&w@
zm%F)7bj!NxYXYCs;yiiyq7ut*AF}ptyq>(b6&4GZ;ndC4U(xdU^W}JrbQUxWW47FC
z7^rI+Zu0ibv{sIzK|Cz}!^)L#h^l>ezVXhULBngaKSwCs!-!6+zM56Ytt9pI_QJbU
z)8;&%B_r+_Z>stsxO+W7U3hN}CclSa+mFTsj?bs7;0-QBnfG})d0a|{e$J;(xqtch
zSp4o{a~ei6u_4~@qXHf*-c@W0gKqQjZaJ8eQ4acCO6>19j8{+nh3VzB>OZUo8#nLV
zx$|-HRcIKu9l87?nNXvos(J+;KxaMcZDb;TLlJif37}_aXsE-a=4(BV4AGP!o4-_d
z|MUZ1sypm(iSQTRPJBmwpPNqTP$>7Rb7Es-nd+SVuD^faY{_Wc@kv27hJByoJI2tE
zCt~ty%p&+1PN3!sv(}hYbaek^hMEE$CaIw>Ui?4|w`9{pj!cQIhy9DetCbgod86ZA
zh3COSYD@Z;&!5*t-t+Wa&7_&@K<kofXlP7R$Z|;zPgKZvNlwv<a9Q~A*>pqoj>HhF
z@G&*YUw?JR9=)0F2X8p=)j~q`5xm_8ttb1HnXJc)U=+Un3_o1s$UB4E#d{>Idm6xv
zwY0FqBk6tBb4j*=((>|EOja;ci77Pn8Y=ztWVdt`EFhc!fJjMu_BM{e{!FUpPPR2;
z3sWdsF+JBd%8)l=91~aP`84;7ck$J2A{LzFSwVWxj;uHBN=n+Rl*BBJ@hG`~`imh$
zDoq$*aS<kZOm3Kwu^J{rDX#4*=I`u3VOR!^wIARVma6DAb;ZDWGO7vNf8V;u^IYDa
zZRF&b#l*x0w!UN@s7_PJ!~;lF$a6_f(~59dnAoov!j6Z+_s&~_``+EVntlwI*L&j(
z1X#l(B38IvH474y5>_Ze-*k((YM_2uumCGFCKaz|+6Fg+r(rSYmP#+!*&W5O6FHaj
zQ=_9Bw(jFgYp2-Q2r-3Xme$nzPpf_El}n6cG_rC>nV8u6D1V()BDWm&+4}wT9*zFk
z>T9fGR+bw5!?v3}4e2mgWirofYD|bpf;JvAD45!p*8U&8U3pZF>)U-IouZLO%>z;z
z6@><x3`J?sd>UR#R5T!(NP}jiIZa1ODrrtpG$Tc$My0tVL^OT-an4!m`~CH;-#_28
z$Wrgy`waJeUHjVCeea#C1Rl5j$n5%m&Oamfc%`>X&FVw(KUKJFAL{(&xl9QbPx8ZH
zK3>h(e`>rfOFuIwM+4%M+PM$JHOk8vpwemmE#$t_$W&F8Y7Cdx5Ut9}g0O)Y8{AZe
zWQB))%cX1~4p>V*H|Q~WB14CHbKP#F1cimIBhErLsCwnW1F`A8CmgDMw8AH>bu8{R
zMVB9*QBV}K+|OK7Mq@G3;e2MQ-1Dc)bbn>fpxQ<5Qf$OdcJ==us#SCb<Gt(Z(BY7E
z?~ni4*IQ8D+^lu){{6KCK3=GqYEt{VgV}a-6%(@)+bvZTCw)2$^O8el=DP*kr?%;v
z&lTJX3OcyHtGb7N8S798Ux1%?jW@fJpFMkaDa7RX@r}9WWkTVM*}1uONYu`r*gQwX
zYc0P?+8c-wn)>+hadWC)a9=U5yetoU2<kZP04O!aEC|sot-kDnhy)8edjK32dZ(pq
z;Z!3=k>z6KR}RHoO3TQgA#{z50$^Xz*3pp$$tKAH-<t?(o6RQ0iiRmiDiG9OK!1ds
zrgKZ{CN-Pc*x84$7f8d~TNwd~nTu;8Q!Mtio@4qI6_-0dwp10D8yOf_A$Gt+<zY#r
zp1Zp|q#x%%y00NFf1kdAo!3o*b7oB)#a`G5)(QuSB~&$Cg{N;T1l~8zse?4fqf@Up
z6PYDMLP6KpeHZ4Zwvi`;uRVr{bj65nGrtZw{ElwN1vh=0y(e=2L2j-l-0K!fjI!CB
z-P@mK>S$}%r0n;jAJx;-yNX^uRi@cG(^OprX`89;<DFl=xQK{|AksXyUxAFOEW~9k
zhMxCDm-DH3^PusyGdAydRt;DabBAw@&^@YAR6Hpg*Qodf(fl(?ptdWY5GD>DJ-RDa
zSz4MIdJpI0*zz26vXxc_5`rb%i{=&N<;{lRf^nws1r|Em%nj8Z&D%CuTHHF)gDb`=
z_gcyP_ZVVOOSfHGnA`ew_I;5!WMIZ*-?4S2JBEaW+>DJicHTqdh2;_MH^Nn~B-I};
z^?qEGOGdiCzt-CYM&%}-&B?>pprD}mTVH3h(=Ug<Hq2bD$Ws0Z*Qzy{PZ(aZJxd%f
zD9%+@RyKZug7MwE^UyOjf9lC4c4s{vlbW$HCq!lfQc?s0YaJQRO#Q-Uw+|@qUqGMh
z{P$^-eVz-<)z#IUa`w!Sxv<FCS<SqkS-ZLNbmmjIvly2+@q&c73Rf4{qH#b=aKE-T
zJ;X+*9}BDGWQkiXu0GjmY<9xz&nf!ox~bXOh_*I^B`l3o@Lp1YYLl*^p+6+&h5$vh
z)YYjWjXt)qq)_FsgQM++BU6*Z!+wy~7v8yZQ+Ic_sHmtahSQogYg%&5cEdVVSVW{|
zg4d?bxThF`Lafl*3WOlc5VW>Ry_ffRdU?&^TC5=)Cpjlvn|@X?;-1?wwebmZ8d7z%
zlcVYT!gS=@`P)j9;<Z!TV4|TlOlygM{yA<8@l+t}{BvxqW&Gbiv-v{`;Nb7$rh2|#
z44%Q?$HBC&>LdTYOI<{7dT`6XzftIx=!MT4|9u<FA=bT@5SRZsM1pz$n~!Ta4533m
zb4f^r)Pn$-x$6U~rC1+5y_<bsOX`rg1Y=&#p)(=hW!E^@;!xC8bcO?pCM^)YnR6!<
z31V`~%Qr)C_c_GJTcO=2(}xi_V=85)_{%(U&6OnXwxz!^G7d<_*g}q}nXz^|L?Z=a
zUgGSj_A@8)GLn}euO^YmOR(HbH#9PO{^re_LFF>@aHPX@;2&B>Mol+2IXu%q#3i}-
zJONtW?Dp@BomMNkZ?~ryWv3ct>!D+5m0VV@#!OAmugnAWO8ZC0R0fa?1w=(@cRTl=
zjXav&Zpv9^`}avDaz_+EW(kTYR0J3RKX_$TH?nkVd3Y$I@TUsX^uvUH+x<bImb@qg
zd!D@)=&{ESk>35nLGq!ry*fuN#iGRSmUJva86rhYAIHXQ5$22w?O8ApX!!a0Yier+
zRa6GLt#=<mmXYl$Tg*fy%sJh=#Ub1+Y0#nQ{YmeZ{6MUqJdoaM1u{s}$Nmb>4%0$f
z0v&t)(q1M~3RGRhiwEO?QU(6-5w=vnN><_L_xi>{ku~v?yL$EN>j%PhMx%TYq$^CX
z@`Y(mNEWSQVv3-dFv_!JARyqi7B~DeINay(!}W>zSZ1|3;?3{`<|Qn2=m>%B+Z!8O
z6;2o!ur4hx>lqkahj^hawC9@{e~+nnmd>RNZS{`=z?FUiv_opy?PS0|dGh1}#8#!@
z42exdArsKx&WHZpHqG88^U`xv1TzcEReS{ZbZ@)8oT#|?T4D)XgZE>Pn?<o}Oc1>p
z%t#9XOTSI{A!gVG4jVW*IW0_P=Me-Q9o<s3A32(*g9oB-jOV8fcx)hK<LH4eKY#tI
zHt^IdABZz5CT18^WkD>-S7TD!PwFP*#K&(YRAICbmeD%9MB*DBzD+FMCmbDf6vjSb
zBU&O)*qKYvL8Kx8*%m-WCh;)*9)R1(>2+Bhm3=-;JZfrcg5FGMT24TWck@RVMf`!J
zlA|<`bQt=MLdwc~Xmnl!{CHn@+7AXO6<{>F3hbE3k&rM11;1?0dhCHqZMA>cq{U3l
zqjZ@SPGj~x#S-KjN=p?`f-{{OIX!tPQN_oy_Xl?ItRcwN$jU#)U%Y&I#B&Dko|*{>
z4`=^D`lNY2&F2&r62iQZfd!x6qT^|C*XDIefI{d(Z5!_?k%~IJ1))lYg!b4H3Zo7G
z#?H}?AA?5VjX*^Jxkn}@hT>L>vaSa{T#FJCqrI%LV#kLDSGD_q%eZT5Ha0>u^qd41
zg5YJ;>(*IgmAwM|%+k~O7<iMhh<F(5`Nbh-bsZ>FNLF?u)HBaWGdXBztV6xb{q0j9
zH-J8T&1#{ac%!N5@Xm;hDaW%OL2tMMqA8bfuBp1BEzn!Uc4r2NW$a#v#}wZ$86ym3
zAWum-xx(sIb?B$|HMQA|$Fi}q3MeXa!8%1(lofGc*COXQW}^0zj9o*dAp4!!@m;PR
z9z(OUQj&k&Pq-F9j+q8YYN51KpD^~;5LCXAk&#y4r%(&kbYhh{U3+``{C5s^_TWX*
zU~_ODhAIbCRefXYx^Q!!Tm9U2>@Mp&^Jm_DR`GeyR!p^TCnoN&FETBdx<3nny5~@0
z4y)sYFEI+64Xv%MYlsh6G;$^~Fl!h&u3Iz0uZ(GVxCp{Xb3fm|H@)pX4Ou=y6`Lf@
z{k)2c4Q<D+3D92COlLte?xd^_6BCm)?z-mG=W)fG(b1>mq)^I#08_d(;)WuRm(zHY
z^!*vUK-|E<AlAzh(T^gR2V@gG_6h<D)KXGXeiKVeOV<&OH6z|3K2YQRx96aIw_fbd
zgZ`;+19sX<u&<pq%E-5cM5eqo+*BC8Tmmb-IcGH;5*1<uGb%})3|Gwr#jpFYEB-2>
z8u@Yrrzo@P`#ZtJRk0wOXcjsT@IiEuAaaaCU`iLeY`ULWn$o}w(;@|tUHB$+toadw
zWC$y0Z<F(wUXsQx9BU`7uzO$g$Job@0+N!9xc^2YvrRmGMteXRv2!HKYUUW(f)nmS
zI!XTtgJnKpt#G;WKkpqE6cbxtR8+)X|KQD?<}gUQs%vP($@@$|tXl@Ux#^2cJPN+x
z;P^{DzHWtmoU_~t)IrI@jA8;EK8>?ycYsA{bVJ*q8mfTYq3eqRgqyT=bZXHW+`P|O
zrvf?wYYF4RSY!R-xPm4gX)D6FJj(%|Os|7)k|QZohKvcaWac0Gj&%gDy)6zMqH9i8
zVIpWZY<K|;HC+=Ex*VeoxP0J#2=&<@ACC7|KGBeAZ)?M;7**8%KmtMOAFSaqe&pD(
zdMH~{wMkGzrZpJ4s5dsWjEs(^!TN0hTUY&oezKcr85r0&ir=}PgSvZp|Fw(1BH(}+
zoM!=z$x1KC_ZoG!Giw#!X@=TF0BOOwv<Q0n!0FT5QFXOB$geC8taj|}gqugtxWoXO
z*BTHDn?oxhrJ{nzG>6@xXfr0lC2X^yA|NNQfGYA~FqeJ2tJ=0*yV!9DYha$IXK1*E
zZxz#*a`L2gOUiy?a!(W!-@Yz;Z@^kVNIRhW0;RyU`4Z4-l$7j>iHf?8n3icE24;&D
z-(&9)*|zNuFqAupK?JhWOLJ+}E{H+W9$zT1_>N$L-)ICVj?8w4{8tzW!TKSTQ>Qwn
zs+<R&DpBvR0(n|Psy=iM`q*F@=>_q3v6zpWvB$CtCQn_>*944vSxf-RaTBS0HCA`(
zw4Z9;u~S-Fw|8{kZ}dait+UrEHYjt#WUmI&)^k)gwVh-`x?fqpB<as`w?n-bN1E8O
zhjz(nyg<hDhs=Xs#kzqmHzFD!HPZMfTuG|sFai%i-@n$aqc<RL6AO#A91}0tl+k&x
z&RjZn5!*LUw;F}DTkQ6tBCL`AX}$s?J&)S1U*iTy<PfR3nuz&y0Hs}Z1uPS`WWdZS
z-q3yH^$eBW+Teg}6ziIKmxqUEW+bBS+~LwLf#~4nlFn7^n3ReAgjU7Y9$*@4aD;1!
z>dwv+r59)KHZIuO*iaMDGpcB@S_OQlc`RvOY9+^iC3-vUd;}Lkq7F*;sDkULc6xn>
z?oaaOi0W&BB07zmdPY`)%y>7;6t2YtHIO0W{F4U{nx;ERbLw*!FaAML?Cg5BL_@$+
zNJNAhqlK21Ru#r&?ZSuMQ4It8QxR||qk(dwqjVDJ=oL_v?+v1}UDVAc^s@{U+{T#+
zD-0V==i%r^it$s=G6jdvkUEi%n}u;;E2g^NA~b*l5u?`<u!PJB@43^=uzUAzlv4x&
z>R`bB7cT2>Ah_K6b}bEk6-;hyq%CxHnIW~#K+J1)O=9doC6S7FL}}c(u&@vtx{88w
z9r1Bw#H6w`P57lYiZ?6}v@<mS1>UfBhAbJUJLNSJ6rX7O9}rxLa1Ev*0rg`J5=+OW
zs64xOAXR$N9fBknWWt01l052wIxMP=ymQANQ^T029-@lSc9j~$YCl>!x@w%|XY~8b
z65pgl7qxZ<Ey7O9`@uo0OCBEG<{8=f7tNFouX2*fhGL2a$pr$=BJV%;-iImV*00dW
z{6qK03t#!f^G>}_y||$W%PH@`iu6kDe}L3JS9Wu~2kEZ1wzk+;q%^1IkBN)pq*$Bg
zyGDvElwuiFmyaGv!jlSi{07LVnq_bxRX2zUD>Gn%96UEQ`i;i-@nhNBbKJuXve@_T
zo^5kE%IPvq)#D$pGwgSs5Y_MGnjZIfMk2lOxz^|1RLm@8vdceNC3w5p`S~L-DOj+Q
zs}6|v+)xeO`Q+qeelWr}pqRtS>z_3X0L4+l#IJnTrS0w(r-7#uPH<vwrI>;|K&0DC
ziOMeMF391#=M(DgIvl@|k1rh9fGU$)KG#5uA>*C<My~X!a{!2=0j9_FlerOHHfbfq
ztK}G}Nyc#Z4LmRuPhWHs7Nx2h7#N@^;>i&7QgRss9=X3!zs~5rynIFnTON~B0N4+M
z8!hA0UYn<C|1OiR{I0<^AyU=MAd;9Z{la4*={f9g1zM~^K)4wh=?f0?RnlG#Dk>^t
zNFS#EM4R5Mmk)B9t#^7Qw$NVFm_W^L(c~Ubx5sk2-KhBOtbuH8t7B`_SRE=7iOF91
zz_PinjU`1%W5wqvaphTu&yGw@i94Ce7U}30#)(IHtgd(wssI^%Uqo<XY4zM>u@(w9
zI<~Q+?yjx{5ByHlgC+!5R|8qZo40OVgLi=~{KN=fq?ojnl&X(YkngN9eq2x>V^!sQ
z`JI+ADM>u^9W|{^1{*aMdItGr+E?Drx}AQwwj?vN;o&|<$84>Mh^3cr-Z;xut|XR4
z_x3wE>$p1s%$#?!0CMm|?+4t9`pJ`9kT`(EsrUN|&_V$|(D0<fVZh#+8D!$1^YLG$
z?c_?bqfQ?Q?KsNv*4)t0@W!lRSXd}o=L*SkkQYPGOr6?akq%Ol)w#Op4{{=J-5Tt(
zikT7ibSv^WWQxzZx~d&L%1GjURApVPfh%hcmis4!mbp#bve<KH=8(@{gHWms%YcAt
zcss97N8ugi<(__Sv(Sg-<)(>u1$XRtHQibf-eNGOX=1`9CN54zAeN$!w27IMGYD`L
z<xP&|wx`)TOlc}i-%-WSMCWDki}M&}C{5JI#>9-(95jzu;i-h`@i`k2va)JRe1v*?
zoCOpH<TIaRZr6-~rIctI2AZh{;M3_J%^eUt^~q*0fYS>s3HSio896l-jijNOryZcW
z$Kg3lSOEqrxKBn^yCK6uU?9JoN2@iN!voQl<{j-dU-ZH`U%$|Eyhp})NjK7sl1JII
z%l<tWwmJ4C)DCabT&8ofni-ZADLN8u7r>JtleSH|@KnEixdBw}Rzg5to((FQMq?xR
z9Zm_RnJ@32+yk@g2!&${sN0iJ*tGI9tfK9|M`k1WY#fe~{6T~g&`EIc`j4aJih!rS
zn82jJ?)g>lQY~5f+d{rn_^c{}l4*T%(HVSh>cfZH)2f~ep9N9ygh6vWXhMRkvAw|V
z7=8na_+fPI0dcNW8cs>b!(!(=#6J7G9$EV-$Qq}&4kEq>1qZt{hW~|9<9vS$&de4!
zXK1MgnG`TU2rJlBtnnSqujeRc6P_r=hAyDG2PLL!X~~$9yY?3{!zC<nkGyqj4e|N&
z=PL9wB_!+n<A-`%lGoedfge0}jAP6Z<No5O-fBeLK#1a+7N!{Mo4}K@l$VR^1{yO$
zQc_F^OsvVBC_km7>hbW%qS24H-P_xHh}5#w4nJ6OA}(C+)GJP+3aKce(ELP9TpV4>
zC!gN8{(FBHzoFKkXOxU!TSG^NV{o3meSIH5C!yr?1csVtmtMqDjhj(X6n76459&aj
zFwGI~cltCk;?JjaSwLKTgKD4HCorZDG=5${#KvU3icL1TmX*QKs7KN_VY5Z;O>zb)
zPI4m{&n*ah4HVfz`_?srf`YhOEeb^h{hKp}T;XriG&Xp`1PO=?tky8<B7xhDgC9Oz
zMR4O$IQKg*mtUN9l9kdbVk0odk;MQJf&Z@O<w=p}-`h*h7qm!%7CXUoVV;<ohgZti
zmVxJyai26G-1SeZYid64=`q{43oDxeUe`OEIU@rw@udB=CCY`VWr0Fa*ks4Y($W$g
zpP~FO^Ya?WY|?hkJaB5*-Jq3aAhcr#y{g6iObj@RY}|t7Js*y%l}&CUeLpRAbp=*p
z_cQyP#p3OUee6Z3y@Z5FB24K*n?uop^eK!dh`_!;*Ys5T<OD>p4>&kDRQBXDobJr!
zg>}Pb9lL{o0?l`oLZD)unwd$960y{MD!TS3Zrl>$4mwcRTjg;3x}NzwGd#cNJyzq?
zZfB;yapOicE;+1!Wq{Rj9RZ(WR*p?O>kxmDMC-6v%GQwUdxybT9a`Kq%{pozry~;*
zLQYtE)K1FsoW|%=fX7&+Vrou4YB-uV6aCq4`WL&NXlrpPoD%>|Km=`2SN`;=zfxQ>
zEpt)tH;vK2_j@MFgVTb})n+(XXra(XW;*xdlf@Fo5AFK(kN5nE1(~FSPpRt|Be=`}
z&`vKpJ2^m$pjWY}-+R$<`hC#`=<TQ?tmH_JVHMg7;N-!nsgSpCk6#Z6fSh{6kmo)n
zrF_stXrxMA!g7WdIAZ{e2yWd<LsmsjPLiza{oBv)xfK}r2RtIKBkc&_XQ<i%u1WN9
z;ZyI8WS<o-Si(yG`jz1zj?;qM#31H<Ec%@=dqfaq1Zo6xY6}I#Qm@5b<jG~fJa;`q
zVK?jgb$CFJV7`i<zv$`Vb}8dQ;b>fZmJ9gTAH#HSdxH#^-OyXNHct1HFkvO77L0gn
zzBAC%!+J~j(WN;X08m@Newwc&v4OtDHF81&i-G`RlzOL!PSt0$FV|)!xLota1rP-G
z{F}gL_hn-%cqbM;SD{VXIbv#LjHmb`_?^!$LgOYCe~*SYcp%x`_55-OO5Qf0K7&F+
zT>qi;LP<&wN4Ets3(0H|^Be(Jcz8kHpi}}1mR200BVTY+p2W^Lc!m%PNmd6r6l#Kg
zSds9&TToHsnfEe@8F6l8Gp?C2kbH4GSNPu)Zs6znd2(|!6@gsh?jqNW8*~*FF0Nn$
z87K-~2x3B{oeC2E4_fy>|My>!yyVA|$Nv+j{P&0bA0L;wN;Q^Fr>=T<PMur~d`RP{
KdhP+MYySiL=73uO

literal 56274
zcmeFZ1z6Q<^ficey%u(nR|P>35S11%0Hsm7m6Aqjknq|fh#XQ{Ktf7dT17zV2I&+j
z$wSAipD{DvJl{O?)%?GidFDLc>k-cR)w|!l*Is+={Zvlo@{X-kTPY|gc8FgQxkf>;
z$)18@gU;W7;U{f=uO{%<UpkVPMesLX3zu?T@z<7HS5&PiC}<xO|E=>JnAE4B_=iGV
z<br~I@Mwpf&pyS)waMz4Q-V*KRpmZu?!T~4>60(R-`dwNe0Xx#>mRp!mrt>-TJJx&
zH}*Qk#(jImZtc1y?shlzpOch-OUg~X@Z&A&|4~pV%&XZZSZ`#I^YVm!Vbze;n;7#B
z$6;GVu5->KoxHm4yMJC11;u+?ngQD1?<vBquS~4}^^d#KUfr91|2*jEhi$*#a*N(O
z`1`G?+eVJxZ(H_l(fa-N?TI~kzu#V$XRu%Q>;3!a|H=c)U0hgLu;dSEDnGRMZTpkm
z#}ZCIJIL|eL8BwbCT%v$ydx)IeB9dljqsVDC%BvXMxk>Zg{^{$%Arj6wz15zO_J*`
z%ip+hL*(|6ck(MQe_e;qe!>5<E7H+ho}WBj^7&9CyJn7$9q+H}=H2mM@DwZ#JDj6q
zQ_qyzDZKnPDQ`-mG4|@Er%#{0QH=Lf%QTH^GtZUwJ}zDNtujd8{b_8~^0L$G*RPGb
z-~XG_eQ%pjaBxCe6~AOhu3d9EM_~f~zN{dNJM6dSAF$@nFz?_OCzTIeQ&S84YX?=7
zm<QDfK0Z~~#rm+B_ZxN^_LX?78~K{5|18wu+u;o3MhRb5Wu-*54C@tgvq@Dj-<171
zFD?bIMJ@ZDO*SJ<(l<kF{7By3Ha!my4u{_PQQKSmY``*GLgQxoXj_&nN8ZG}q<6hr
zUq?s3!BK`ga<h)h1jqK3diuvE?LH=^GFE6m+519Y_cq<bUd!x;LRVMTg6S#&**kMT
z*sSsk%yY-M<5%P4WBd%;Op>xoTDjTS*bMnROCuZOl}BFQ488i8l2##6jSD9jCP}i_
zzW;Xv*Uc)Lfq?;EvfRm%L#*sE^>OX(?Qh%kZr)rnliyRDqa;IK_+^PQNA6!+TAAyr
z%pMFiVGx~J?0v@ZvFW9sp8<(P`q*T%G?o=KIy(Bo#%8$T3rkpNsJZQUrx_Ku>7mq4
zrcnEj_UGty-8naH+Vt&5?l`6DY8h+tSyffl-5JVaVvn+i-^72UQ@{V$4k<@RM-dyb
zXQh3dYxAYT7fS|i@k-(Gf2ph_FXd+CyHN=_y;C&k`S|d{9Btxs?Ds+1il=*-mW3Wm
za^yGA2)pFz?Ktpnn9Jgq*2`F)W1T;6mo8?ZK|$&X?&Slg@tZiKn(zxPvl7zMTt-Gl
zyT=6DZ+@G!UmmXd@@0$qAaStFry^&QeO20Hej4KW;tM-Fukkz0zh%##xi;3C!D?*t
zoQg{#|IXa=*wUU-de?+!90k0+TCcYqWN#2Xs2L@AXZFF#xjIP>qv3`)%iNBf*;W(s
zht2d4P8M1YNRUUro*7+rU0t{tVpYYbmZBrtl6v^?;hfRb@)<nf$86MW_c!lsl9v_S
zpXy%PcXhlopFG=UaoMaeh1c^UpZ!F5AdiK)Go_&8Obtijas*6W%voDm%i*=+`0Q9)
zhJLx<lgy>jwCaR`-0=c+adGkDvWj3n`IV(PJZrI*#niO4##}p#{I%7k-C|k=5e|vL
z!_&W<Ot2$^y`)h^0B6~kFJ26aao0q{oTi@6j(&~2cW*s*8vYD?`(}(wQp>YXp>zCx
z<aJck#zc$N<psyd;)98EJE+dqv}arSzI=HChKDbiwWf=&ERxg8Mq4u^ami1<dktTv
za}eR<<1=o{OoXAo4i2Uvjyo`5v@kVb-17F7;_T|uc=L-Rm(s6)SPyG8UYr@?xBmX3
z-8>6_U{{F(Ki(Cw6j~2mOVr3d{k>og7l>O|FXC&!_OFkYt}k$2>bK19pS1T02}yh{
z=|7Cs$jBUN&4_<a!?$O)rOdb~K{b%uERb9v`s{#Ofm3#)Sax={TDIkDwJdYCAw6|5
z$@J<-Ic~GosBP@;&bMV*_*+j|tzsc0&zYW&>h0?zPZaG`ACHlGxyP1T*S*C3ux>s*
zx2ekKDd`~YM#3s+1#Fo)IArNOFSGkgUVctfySy;n7$LeXezg0;eVh4-o@+E;;`WZ2
z3@C(Z|FpF5U?01KI=RN}?^4KDrgLmY$`q6@T=+K*zD%)ez+angs&*XqxhQyTrNgdk
z)5*c5&jk!;Ok3WD7j2<AXaBY(vXwm2%!5;HQR(cP3<wpxqozNO4I4htnrUX@Yl0{9
zS=TDJ<J-3&i_Ef}G`yz_zZx<#GoRuzX-c1MhEeocX5TBZ#b!Qe5|g^Uc30&zbHlD3
z&knGirhHtaw$^Hr>}#a$)tYS;lOY>ZZ?tvm){B0!va(ra&2jQfhHdOxc`YYZQyIQH
zCP##Qovs$9Pjzyjtljm@gfVdNSMZ7r`2Im%0$aanYSTdtx#%31#UUnIe(TSU!f-~6
zOiY{ET*ve0-dy&iYg!r%a}^pD8g7h_Y1VetynXxjy5{C2O?A62=d7--g2iE0xcg(*
z7v8<&^`H}MbriPxR&jTL+oCf8`&^vl^WsJLc)=2Te+nNBrSp8x<0jn&Jc4J#4634t
z85P5gFMWKXWQA80CMPE^=mwqEmr!_o=E*mQeHC}c^J5T<RPry@*VQdMHkH&7evf>X
zjhVS7RloeAUl{N0h>Yb=du2BYYA0uZqtbLHH(qGIgA?U1{m5-vvg>qUs;iOBR?%RM
z>0SO6*ax0%v^b$@Rms&fMeNMzmxID9Gx8=2&jo(S<vWr;noq+@Rm)kG666dF3_eeJ
zo|}5YqWETCs?=arNa6q<!^oTXq^+u*JU6W8CvIeGB<bi2E%fg-S*X2C@4?I$h;Vk~
z(j%2|JR@q`pt>pAaWa0hW69o8M<)UiCp&gbIB@Xl)sjPktMh$I>c<%wjVc4VaA4(z
z+1*>ZQannZ(^98vxVRJ?mkl+|AMYx3RhZSzw$u-SzamA!g|G?>3$u{L)K4b)s{Aoq
zoqw>R)#s+&GtOD|Us)NFb*&5F)J=CJ55N7=6p@!d%P`cWZsB1VWKbEnkiV11A<%BQ
zMGn4Emo3YpOUQKmb430&yq@{0!mDJAwvF%x7p{_%o2H_rgN}A4MLFH^(X#8X*=<&z
zsGjxn9=wu^9`B5o4HI%4@Kt6ASvJiw{TflYJiwv<JUhy%&89QYf!}e)@VzBWm((`q
z$B<#xri2*0L){KH_pLfC+3VQlH<un$zERIg+Ou~rqWy|iTc%lBCg1)(et$9I_CkK|
z%3k93{533}5?^a*P!O+!uOT<c%MQ7*Cdnc0D@2Oz=zztjrRs^l4^7l6Ib_!UuGVpU
z%;?bbq8>TmkAyh^yC2(Cg-D%uDNEO69;;;-i|)J38?Tg@>PJ`)oE!@mm*ST%UnFE?
zl(1OjDe2E0E=yxgwmr`i)7?2;ws3Ym{dSQLem(NQrSz)Eh#b4|q~wA{2~tCp_+GQG
zsiK3yRx%B}>`#4s_7#dR6s#_e+#S$zS<p9G_`(#nT{VVN3>R{(@r`0*{vEq~{2KV^
zB_-XtSa|9E2M#ReJMG`M&!@*whv`7GL_*asd?|PPMK|01B+cC6iJqdxsdA3Sw41Vp
z8;4SKOV2t?=^@yQ!*?|BbUE#P`#i!^N;ye`uXhFZ*QQ3qQ&nyai}qB%ouB$9RRmAJ
z_I{cHXFt&2PxwU#2L~2r=C@h}TlmK9Q%E}8e_7OK?&Ru>OaAofQ*6m@k7;HF_ocb<
z=E=U&NGdDw9rQx&YOAHft5;1|KHT4&*x7(xJ6!i#^7A5rzu*y__j9YNUrWhT(z)&O
z6Lj8nyT8%mFyry#!*F;_!F*Q0sIiF7hCkY~sZL$L%cZJpU=VHouIDf5fK#tpW<~eL
z_3X(g$n(B0zE!~1)ZvL3iPz7q#6KvAy4E)33X3249LsQ<MWG@BAY+JBFPB-Xf_j#@
zhhw`}&hk`w;+CaMi>@TNmifdv+HDJ+cgA<T6zR&biPv(P6diE}{yJ;Xc@7Xll1V1`
z1<bOv&x^CfkqbX8ySy*^N=d#4Ib+Y*?_jJz!(bozx#w2wh5LUE*G4|ZW{7TY*T@H&
zXj<G%@AOthwhW%H@YMmFm9Dnn9Gz!Z4dr0y9rMGHo(05jQtt6sbdLI4Kbsj4c%_lv
zm-{{9S;AaT3FmBUmW8%d?<7m5gMxww71vp-Z&D+ZE2E>PnwDP})e2plrF3_e^xXPB
zSxTSPDf{wtu^M-YA#g#{(yL34A0?O|A~#vCt*&I~ec7`zX5m`THy9j=y}?KGTxUx?
zpphSW*LpVw2Ws)1Gx83JB7MJI;Qq~9IuA))xl&WKQ#b*TyM3_@80K1nMy~Bx!oc-D
z?*od~r(a(w^%QJO!oDzZ7^-_ca7g^RSzBg?Um-l}lT6ijw{9eS+`?Qp9n1W<=oNs!
z+@(j`y{uCQZ<qVA9c$%2;8fb<JYC6?m~y*0Svwg{OOix&)=<uL{f7vr>PV(Ar_-k6
z&AO$}7s$38&+wcW85vc_(^6AEnx{ETe|s{_o^0|u{ZF_$bY&uRy}y=YT!zy`_XnMk
zCGyBkHaro=2)%|g)#u>C%sbSut5|h?QaG=_r}(+u6kJ?f_{8Kjx(b{V5x8CWOfAYk
ze!M7%?VCc$8h&f=>OTl(frue=F7r3U;h~g9ni9!<p28&zC300lOQy&@)at(IrP)<<
zAmpp*9~Ey|oMpG+54kZX;Icel_kh}Gd~jkUx$qI&c$)y9&s2eugw=1WpSpZ6Yv0uL
zG?#Hh?2|nyz%_wfCVph08o8}wZCT3j_=$5^B1*oh5dx>YJabcR+}S&Szcid>0|1J6
z9TvGEUOD**lXP3!O;5TzqbVwnV&anv)D+{De61~)`~Kaqc~M$zp!`teW;*-xh}9lt
z1(D8loAw?&7}KnEN4i>YE|{G4oK`@rU6(H~)M-lG#9_20rL4NTx&MoIV&TH^_WP64
zB8!ydU7tTb+!h)7V*G1bOvdu}NY6#-=SmIfM)hayezZRs_L$AEeDlW{6<nF{s{`1M
zh5w9$^H+O&{a;EN-hced8He<ao;q_zUU+%pK9Tofb;XgGkQ#uFb!-?J80I}#7wct(
zhmf{3&2_n2Naq0br|Xtd?l^q<)A-f7rbP86#$c8}m_uc#pgLvg7kRS}_cp!JE-dJD
zSuz`INh#v+(v*`6cX!`Td;q!dPn-7?T4au%<O`9J4m|tCQCPQRdxlZH7@l3G4(rL2
z3XYCB{I;X9bDeiM-Yj_nSyZJ}3yttJ8GF(R&J-Wi78yK$v;qEjL?eL5B9s7;j8}54
zhkEBOT|VW!IFsV1?Mznmpy9igYd4;`XZqp8hpp*G-b6AApDea=XmM`bf-PCQP)jq%
zx)w=pVjuI#lMN*v)Q!p7!T{sUy^gxK6NAfsyA+?j_xF^Sm!Ba?EC-&sDf8Kp-etaL
zPopw6ycGaxIQeJx78DvvH~a1YyN01JJ@2jSEmP)MZ5*M&!NKAB@b5-@_Uw^pHy{r;
zZlk`e3gljoApS&`O3*RA>yBsX-nX4SMUU0>@pTnbXJF_n!sFuJ#}^uT^vitexHL}e
zpgYIgeY-MDSU6%#lilA4q59Jpm-TnK`UKT8BP+0(drh=I&P?a@Jr_9Ixi&x9x6=7|
zZ=wmK(%gxkFj+38=6=>b1Z^^0@lXl1g?p?Mt|sI9hkI`Y&9v`~aXBvx`0w^$ZH<wo
zCn6*AZY2>;pDe5~>#<{(h{Vl42qDA-+m_RPI0|d-JoSZ%ipyA`CH0I!#oLlJws@70
z%RSo<pB5qh-MN$QoQg%kg2Bt-lTTsr;VoUkGBNx%!~1Gy5rh`uB^&b`rr+voI}Gg4
zUzu%#hhx4S{7Nc>pKEl`k3@<9GhhtTf$@!iTC;@1Z)DNkKrkmQifIOwGvsFNk(yVR
zklKY7Ed3BjR7tsrC0DYvObtA16cL}4L*$!EfM-a`^rQ**HUco-h?sWv&a5e$W{$$0
z+0m;K5^hLe5@ka8hmff#M~ZpqI6bIU&b2ioz5%Bn1_<_=-gWuv+G2B|kC)f02M;!C
zI!zWEH@@-Bu^oE@t5VbL3*Acj_!7ZK{K%ivf?<mTSJyt;LG1&$jEpE2F)s&qsR*YX
zE90_1ewsNL3D+A5Ult<OWfb2jIP*d#nD<pom<vA(`LX3!eg8PEe8<*8*FtjEjiN`s
z%v;ofnN}7@lGV};y*gdjoCywRX?9E=!KfkAOx<R*<*M`2tRhlJ-P?Z?0Rss@qPEL|
zsC=|kEjDm6JXt)h8Yt2`DSuXtJXC)<E-tRDs*3&g3W$rck>3#~MkbiE#K&&eFH(u(
zE=l(ZK@k$K|02HZp&1ga3-H$ZE*U1xNqIY8e~j?>MQM#b{Liv*aiI84jQ{iV6!;(G
z<(>b73A*1${_kM^)c+;YahVu-{6XMh9lxbh(l^P>#wLwC<QjZiqAul3ppdhp!()?e
zH_NhhmEnxM>W38k*)_`qe6D2=MF)W+0~o9h7(1qBX3e6Lam-=~skvbHVrpjFr>6v>
zCW9pNVU+52Pj_0dgo`9FcuBw%<!0Ofx5zynx99Z2NuI*1Dj<I9;0zN0AQY1{R6(KD
z<<E7-W5<bo{rdGn7Eej++qZIXele$p<q>n}1RcM_cAB%gT;jo%sFl)uAF$0W7tmAZ
zrL7J*cKP`OsnyMQX!$Io@-A^0z`?M<DQd&yrzl^4a_3Z9Zq~4pu9)5H!OgFj((09^
z33frwR8zGLZeAChRlwG5+sMn))d$tXe0>$)m@I%qDjt@QluT<{8_`~KV=e$Ir{3m%
zi1TQ#S=p2`U4{1Q)InB1Bws%Ecf=Q~T~{LyYS~{vT3e%fGvsxDzaIRtHh^FK{hdOw
z)xv8U$OqE*JT%YBRT$HrvFY>R@#9s>YD=sP;vujV)oHyah;gs+AHg`{Sg(G(>!U>A
z$rb6+1fwwhtx_KFOEJq_lc4jGU031}JoeVE2nTYC#wXd1wGO40v)AKH34B)HMA{GF
zk>4#2ufVRn@gsYX?%0V@Rw1EIcPg$jkbMW^Z^E-EWzWpaSn`ru)bs3bi;VR<ONa1h
z<Xy^qSU=gW3YzL^)&BAFzDXau?=H{j1dWTiO_~y>C}Td&j(1rUuOZnl)uXR9j+EaW
zPrHyg9wGY7Es?_{m&i25NQV(e-jiRxeEB-ZZ*IIxJ0VFm&A^wp32|DrZ9BctIlZrj
z&S9HV&DyvN$7m@ZBNKXRsIDuMsXO<h<Cu9NlJtf{x?u2F6qgP<YZA#|=`h!=?)OiI
zleF@4RCBD2S{6a==z!CqN=>R3S{n5!-dCBEw<cs<6aMe*+>RS<L=rxsD9-E+U-O+v
z-H>F940M=hiFwi`0-fd0)JeLGAPQKw3J1=?A*+{{`*SE`fB3$5(XAXE9)2Foat!}j
zbpcoze9`MqZRv)!7vUQ?pG41Ons*qpX~UNSuMRaNs`GpsR7!9CuJFnDa!+g4$uYl|
zFXNQ8ofSzl^L?KF?C-Z84eT2H7a>BCgsr3~C-)B+vuvwDB3GfzH3RWOxN?w5qWgk6
zXasDf2x|hIu`b@Pyz!)WP*6PLJ>uChJxzE@-}<439X##3rUN2Wm5DGBuf(RWr}sT<
zZ8^qq=KIxXXWV{w#m8Mc^zqczWte`g8yCKROw7F|xp4JOMTOM=#_r+U1iNe9sHp6#
z`)OcYcx}OxAr1%}gq>MRe-Z;9O|ah!zmuvuZ1nurS4sljCc`uOX}`a)ywtUp=~s<h
zz|uXcH^M`wJj;2cG5%m<4wF;>aeIisBOkYMY*$|e!{_-ONkuHm30FP1d{kV+ay8hA
zw3HNeGt5~@GuPIaA%DL2*={r8+r1*J*+C$4cslJb3D}Ol-BWCyr49;d5qv2ZQkNYx
zymH_<Nm29j^DoBExh&h82nTVSZB;V|b-9~9Ss5PXtj?#uvAbmPeK}j(Oc064mr_$x
z0W&oUt)?DPo4+XS+oIA8d}hLEH`1J(OoT~PLcrp;=GrCywUf@&7eU9O=E&KI!}4cG
zEO}`N%)?*=;Iym)mi#X1ss3Dd{Y!AySgN;lg(h`TPro8x7HLb;F6?xg_^_Gt`CA==
z_Vm&-&zmd>*N2};wAFv|=!~*2i=sP&{A&qcxG1Sb<QNPu3D9oGy#IjNKK0I7GZ0q_
zBWx#6o|TEIzj7MZr(a`PaOvy=>Z3=3RkqjOVkxM0e$3RGe<uqG(krqHR`EubMMJEY
zq6_@_?&1S+S)GpMZ|)#Rx|%4k2auf$Hj<$OV-$At{ZduM4TkT^X*|YnpeNUj<bXL#
za0CrRQy-vnwb^xTNjttBOp#>1EcQ{b!*>={2{2VPNWdAz72hb-!ZaJgFWi5Uncq*6
zv~Je4+rP8~IFay03ahScy3(3ql1T2<I*Em;`|Nehrqf|ib+3mzy|B<|KgYkrtp(1x
zp)QL?pcAC9``6JAbPzv5X0Pcm;0v~1k$?u+R{5xT@MDSJI}<ERYcpH}gLojHl|jlD
z4T&?a)Qoz+W|{^4;&g0A60;s9tYJ;ldZ&f7)EmGh5ZN|?sKHE623^-jF@WH0o;vpS
zfBQC!lvJV6WyKm7cY>4iDl9Q`bvCPTC^4&Z>%6_OQB?@=zu?MDea^(kZNA{!%Gq<J
zI11(?09y~-PDrsFZQ<0{*B2*&p3(ZY2-ldrW$*D=lxX;o`}qb0#Ddm+BOfE<h$4^=
zaCW+~FT-JiD>EjJI$ZPWKAvMV0wH$jHBZ6W0Hq|2!`4f#tJ7hmDBwC(8#e6%4Ft?<
zM4_ok_2!VDy7`v*>`f>MopoJxLOLUIP}?a5b*9F*H!kfsbn2go8*IkSNo=5N$ut#6
zz#h}P5W6nUa-a-VJ|fvqnL-k7`Pq^6BK5;(jfWz!*N3pRu!bG4QCCqwA}MEV{DwVu
z^t9kYzc<Ka_0P^eVydlTBoq_4ko0uiE~Wi>hj=8C?Aanfo?DmR<;)b$j-edzp6umP
zBD#Wz3dZLJgAI~;kv1iUlzAbJvbId*$Jq$FvB)o5hi@#wGdyl@8n841=}C%0fglb^
z`R-zagJ3Ct?#}!<y7*)yE+2Bl2wtTzNmJmuW-@Aj?*~*q?R%C3RH*M<;LRk<P8I@C
zyY&}KX@6=WdAzG@SbwrnSzGjV)vdkyiK=NOYK11v>}MRN`os4+qB7RF4A6XGPBA@c
zs6OUkeB|xE5+#SMHZwZsnc5fLPZ$^&HjWk(Nr&j!El*KQDiLYApLKHbkZwgooP44s
zF6l;1by1sc?9J-Xbxh+(c0Iqh5JgEV)HlP!9xJ1eh5fZfy$dP5TQSv8cG{ge^D{TA
zg2QcMqM`yksCgcFaQWeu^^2!bCiBao!`a2$5ACkKIRnCEV}dSrZ!`)PH?qQzzNMHs
z-(iR0HNkQN!B%ff)CJZ#;yH+{{w$H79WDiMxi3@G*XDg2^$r7|hG--J#lyVdMpLFx
z)up6T7B!5#q7!n~=$gpjM<iR9Tqqf@`ekyK1OPm=4>MAaav^aGUz#)PI&XGx=qAmR
ztSmh&??u1(?}GNalg>Ygyk*_UoQ#ZrV4$({+NNIH+{7`23TpCvS?=a!&XS5Co@%!n
zpdE^vc2aX!j0>Ff`B|C(<)U*yK4F;ZjDfqCUa$<4cOus7ADx+QwoM2Y1TkOtg|i;;
zzEz&CNMJlK5&&zXW*7pgADg-{$dwx-;81kl(_JYLEJISPCmpp{>c)*%=Gg-Wh#c_7
zoT`$($IbV5*EMZC2}V8!M46Up8y?l?zR6fTiA9vj%Fa48m-+gw?|A=53&7e4dh>;#
zLl+W%@{bO6)T&;0$}&XDhS5z0SAO|&6}}aPTOF#)%muoSGGT8G<Z~16H#EKT?^y=>
z%`%(yaNEIQ1iA(US1Q}3<I;iqmVzuSYCwq(4aX-p0UX_Hx((;_*Vq>!qdEqndVuW6
z^)oL)@*&I}>&_8goi9?;Ds;(b&+gwtE9BIYEOqhXJ$MJD1gG#mZx$J9(LE+8m$n>Y
z971u?y-ZI@-z?~6sDl_BeSD)NcK}k{5p4Fq-&JKf2XUDwnL0C!9jHJ}g<U)|bo;P^
zQ&@EXXOY^h8S8jJ3V=4sU=B3|`a)86M0{~5CM+EpPi(15r&5B-4YtkvJ6)G2FN-sk
z?ghL`yVOL^DqN{lW0zEa`0Rv|4Z(ZVeR)c@Md}v4LZFT9*E$ay2u#r)V++hTlBnAz
zPkQR<$ph!dg$H<s&a<?Ca4-nFItV~UT_29|z%6aj5eac|PI~7VFFwLXL*F3AOt1>z
zz12kbg+AL{X{;|%b00!|Jfm~SI<TUm;_ec{qX&ZOyPikkW4n?zTcr0QqGdW>;x=z@
z!CBNt9&JCYV^#=U9G>zGXB|n(t*Qv*Dj)64Z^Q1P@+{Q_9QaaJcA8Z==`6y0)-cGW
z3-28>&Dy@%N%6Gbx-X70*RWN##y2qG-<HM;d4Rgu<acTf>FpdRwhF%;qIo42+?f4c
z_xkt>RQvAJ*(H$2+M3;tkwUBg)zi~cu{mqp@Y!ju!;T2DB?FeKrC=2=OoP0BiKHd~
zRD9tWk7-MHvjJ%8v&6LkGb1%S1V@0KZUDPCAgmkkPyOU0qx$HD;$8Ig79f0X5-=R7
z=61HmaY4b3v$%1VEO<^G@=5K2cD1hX3mc}vQv^Zdpk7LP`O*l?&t{Wgna4%DPlbY?
zsvZxZp?r)`tDIne(RXKh!O@JX?^HZE4X}H@A^e>l8XEeB^hjw5Wr6@ap&(RH)NIZz
zeve!?@<c(k&EVsMElrtQwrnwgw>y69SkXHTcC1(B>dI0gT{uaq=+TZ1Li*ip1<2!G
z8)uz&x>NjUL|@zM&P2)ZQacih0Kgt<apHW%P1@GAqGF`%A2}lzo6dmXD4g!W#q%KJ
z;Dn*-KTpZKGZD&r`(speG?n<bp2zf*DkW{t^6@}!jHrVZv^n=7wBm`FRLYz3xutUr
zElouJ!?7pt^;>8<M(DeC3e8_{7-lmfvA2k@5~>8EK3ql=_f0k7y0%d%$H;^vSR%j0
zfpKZrO6mh30>|zSL~wplJKHx&E2INLr{qu{kJAUl$bDz4u4H$ZTLCmbJoiJ$c~NDF
zhR0mv>D*P4RVl0#EcE7tLAYb3Y}iiWr`sZuz%vQFC?rc?ym+xixwgL%kus70p7iIv
zQs(AKzCK=hb#-+THZ&Xl+WvMpRy8g9%jeI3tNg(J%`5{5Xt&T*Qu5=vAR8`{o(x-j
zDCI&#;ig5D3?GE>epX*av5+M*>E}91Mkpt1iIIljs^dnOP1;5~a+_N-fhX3D_h=EG
zQIWeQas)6FTkHLpWScnvaMmofx#8xnjmbP04*zJ3kqx_RBP#HLI)2=W!24?C&5mc+
z5S7xK==m%e^fU?5M_a&An<zmOM_d4xdrwRFmCwz4&11GffPz#*{hrDw`xKYKG*}C+
z3#%=u<vV65t^%Ju6PHR!U}a(|b#p}x|8*x17Od*z_FB2EfZ$D*cvfYMkJfGYYh#EZ
z<B=mDEH1v?wC%uy1e5;HUZ|Jfes2RO-4s~}yKJ^;);#!DLqj9D)4p#99}Sai9QenF
zf{*H+*q%cWsz;Dx3~@?dSr`n<dSw0`kc$ZLr%Oj~@t$>>za_pFH2PT?Rl<V#-fiO7
zz=_Y)T-xdD5cVn$2_ou+=I;rSORb`^CZqUnK_$9v$V5<Vj3sxD*~Y(pd#XR)Wo7Y=
zLL5n35VT*W-x{(aKjufs{Tit{`#}K6C2QqN*qkf~n<Ftw-V;(=9u!{tswN{PHKWdB
zP;p4#z~BuwF6ah%SVqkXJftjGM$*1psc#pi2i407oe3_U{<Mq5!g+5i#VGEr2TjC*
zK`xUL?p!wV$uL{(T3d2$nsVFEYF))QxRLG7+RB`IIT*qdnVoF~Yjdt^2X8IaWwsG@
zw1Hcm6$khKaAZGu@|^yqPoEZ-oUoHgGBTl!sC4U~#IWjU@^wjYR|VcC0iMSqD7d(G
zflL%yp14HV9Fby!LJ9A3T@&0|Tk6Ltw)5P?-2*38!lR-XV5;Z3GsXlo6eJ`hS{y@U
zVlp%!74r_zQB?-LX|s0YCm|=|hT{1A>V_38&%mCd5WCL9mMa9UV)E7QT>W|ToT2FO
zk=Y*|CTv}(^c@)ymzK`hlZ51a8y~BaQSs4G@QSKyH(X}F-c&@D4+t7UFVW!sg>l!l
zc;Kx!cH^D=Ys=FB?I!_+^UVuaY{i-OHi(wi&j?0CU*%(z&~;^>H08bl>5C$#U93*}
z9OvwzO{-CcKS)fWL;#IOA#Mn}cW2P3O}56*<PUNynRTTx&qZWpo;N;1;EqJJCWY%e
zmo_FUCx;SuYUsh0f1~Ix#BFKhHBGEYD?lSOIVi<lV>EGUjTpRxbkx^p1oaPK#j+QZ
zOMY|5SMph|0gGoCwoCe*tUcy=E&`z|4#?1@IS!e=&k1=3@q-$-F2p^LOEB~H_rF<u
z3N+<g@p&YTYUQHi$^~Q8->IIacdVaW*?pXkFZ(CnUKXmZtZYPWg+W~Q+@#3+J9m(F
zHh?7;ulrsbdFgpQd&shRn6PUB^5S$<_VO2p<B{_DUz3+lJPqhs55>l=iOt1sANwW^
zQ4J#sSSYfj+m5MWy&Tq+FJ`JU=r$}Jx4E?PkCI=2j;1mwcE%H<0V9O}J-DUw+JrQ_
zk1|K8Rka{bzvJVg8)W<G5D}&q0B;*L2ZOdhW9aE=yL5twhsSh*<R*F!+{$9Vx9sxj
zY$Z=uEa21b;u+woSa>cfe(P9+>QF$<ELIMV>SUfki_}Cz&xj41DOJk#Lq0R^>0IOe
z%iG6?LHq*K(pH1sPfv?U`US~X_O2EPUqSGf{MIjb`iORc_aTSk(o$AIK~3mRLUF$_
z2qSS;gH_1Wo)d`)sAs|J7jMGWl=iGb2f})ldVXrmVW6%2^b5ihmKV(VLztj82-(_I
z7{b)lR01K~fjTB(x?=83yk>4@myi?F9;be<%O6X|)eFA&YwVBnh^k3(BzXBZ@a-rj
zL;-aUgEFr}{p1;$hRib7@1k!;-SxEl5hX2EW}Zbv1{{mah*KL+&0c!1boKNO*R_@1
zn)TYQD>m=PoV}gBW~a#c>~6<Zbr+mh$FjQA%CkCb)oUz4^SrQ5-8>5l|LB`s<b63<
zSn}s%o&@6y@6`%C)x~EumeITzLcjlE`9K|8R(k}V!U;x3QN;Ztg-Zx3dgQVl4U+k>
z;8#`3JLwi`pARWC66(NTTX!}9g*Vlf!&SORNLry@vm41mb+kM59TNBs9Q7Ck!Xe)g
zh6j3*86@PK)797Tx~EN-wJG0DH{0{pr4u5?ND7-Q-)Tkn@P^2sMx=sjKs~TShGFed
zBB>>m0H6&N^BryA#LF$HyF_%tFTXi95Ok7|&Rz5)Qwr3f3|si>cp2#yORIWC7JC=n
z>W6thj>ThgqQ;T8QY7l$-R`q4+QGwo=FFMi7Kxy4Hj}mlP%~<j+}6*N!KoChvGdQk
zS>$e{`gTMk_hGL<@C9*~3f7T1kMSB-l<(aPsk^H4!OFUf=qxz@82iE|zJr>3`(htT
zEMp0l<9X9EakKkT10^(L_}B9c?W{Ih+m^@jY*0Le2-r1MygZ}$T4H}9N_Jp%Z?ufD
z=X^gZUI*C!an`aE>ySZX${S($X<vCx-k@nMeHyWGuCpj{R~-jDBVT#lHa1J$Jf-k8
z!$kQr8B8$Zk#E#n&-q8!!1$T))dYl!E*#&kGudK(vKJ|kb^=4<obb}#<5D$9IxaSi
zP1+}<*m#pjGsqbXfy2wn$_Rl>X<v$CWMwel8AHK>wRIhN4puF?Z(`}}yZ?P5ICWGy
z^A0_067E}J%?c;fwE5AlO2}$qu??>-Z5C&G{MYC5^0cxA%H2InEmq<LM}gGsn6vkl
ze{si`Nkomau<(v95&-|UvFEXakw~*kTM%FvLh>GcB=^p;b)kZ$v!&6kL99ooBSrV|
z&&oJcGEqDnA}GklJI!<a<l|ni>vq=4HD!J!G-x#Dc`~~0TCjgy!u<)_I|_^1OWa7P
zx+8~QwX)H4g?hzU^7_N>u}Qzo$T<n`wTP%;Rq7f9|2`wx^muVyMb_iVC~@!QAD3U>
z1};}G@3aa(`njS)0pgr=C=3;owKS=@O`ptnF?BM$Bc6T}^RBMyQw{Tt`W{GopodhG
z&GWH+@;6p0*ny}}zr9fe?d6XgNkG*Y>ow8G`<RWv!8KW`I<_p#4o^Mbc34VFD-`IC
zAw=rXrwAU4^u8|@73n2W;H2W<==I4|`GCF<d7&graIDy)q$r|N0^-{r40TkQFKc<v
zt$DJKW`?n%Na+kX<40p5*8J17duO79%-vKi`tXqv#J*f|r(MAIuFeuK_qjvvJXbqV
zM|@H0g#>^sV%k-Z-^Jh6`9R-nn?+qDI6+1hdPB>kpKCzwWbK2>5nO`d=NCte!DUwQ
zkDLV4_oX!%)^c1i0qzkn=h_=x7L&I5<2prT_Z9HQ3H|!vH+b^Ks7>3)<i-v>w}>Ds
ztezSWI9H3nbChav0L)UB--aEEyPu6u-|CY6V1F+3v}eeOfVHS<Qnexft?s)BV$s-F
zv50rA?{4e1^v82_(c41r7au-AR4@epwOU;me4O{ZoC@^8RLJ{N?cO{7L>O>gAzVh4
zwUL(_QIvc_f3O3)N(kiI>9I9bmB6D0txrN8M+L<sS*Sfq?NwA%l!_qjK{mB0MoGVo
zY)ha-)djtfF<u*X4t-<q&I#-hWDRByVZsE`V`Qkcgvb@u(7e2J<nOh|4A+0)@#>?^
z;(mej$^=4(3(ZVW9f6wjVo21=SHoV}sy1sq*v2YQC!%*1a$i(+uYI26^WY5Rw|PTe
zg$zB_FJD(#vW_6X-yDK^BXLr?h_TwqTUV6n@boOVb#BKZsIj6SbFhONB%B(f<G|8N
z)iuZqA0?J%IfZ}8`Ux6r2FiR%GS6kzXC|FEP*Z|3O&Nj&KWoc9Idxd7vu3RgYFbdy
z)L06yEvG?3zyV+`B6^1#@+E+sXJ+(C9UoD!&Eb;C9^dJf;ujoj>Kub=!n|hbzX|GD
zRf`4E5)xZ^n{K5SR-=TE?2~Co&vW65W7o}_H`RqN0y#f}u=-Hf7<k30(~bLg;IQ&|
z2<bPg>9`DQ&QtT4pXAoE=0zROBV!p-x&@E4kr5CMnSS|#bGrNGPY#oo?@VSH)<!gt
zv9R6Sb>)ol)Jle|sl>%dw|Qv{kR=#E;e8w_1B<ep+;svP7Oc#w*JoSZJoe=rs!qkj
zZEz!s%HT*+$jBU1nj}wyJa+=I@eKval0&*0?Eb1*<{?HXek(>J;IGr2OEf{j>4>Kl
zq{uEyL~tey$%C^5`t>xcL794xG$zjAVfY9JI){+eGQ6a=$m9IJN^to((J~=aY4)8?
z)6(%3aC|FGY#Q%s)aF1l{pEf}Sh$NtKFvTn4Q^$`YR>-LH>6_*Wj-gM!z|{|8Xp`O
zL1lZ6l{iV-?LIMi+*YIA^8D>I)KAZ#Y@=i2)8D>j&cTz0ukOleLh1F{GKJ8IJn7s1
z4NT7|63R(76;RlqQh0~ZyO$gyz+qi|eI<%R*T-T2S6$k$$d%)1_6YYC*db`y;4TTy
zt0MRVLV8dS<;cjHsSJl{1Cu4d{Ri%L$Ops-?j~!tCj$?maH$-DboqHH(mKd6gP^Yt
zR5#BqzH%C?E&17geF*)lGK<c<?3Tr!o`g`uuJnSZIg9f|6SYIQz6w;PZpy=Z#kbE4
z)fZ)6Jq7j(#oWb?zQToX)ZQT>W@bxpJ^?^OOJll7&5L^{%=}Rgd#FJl+i~<ZBu0nC
zqruT0DM!YnwuH_A`?%SGk*>AXtgcC<A}impQ%-?xC<*XxE(VmS#6sOb6jl5IG_phO
z=GnsP{JDR`4Uj+XruWYh+|aRxlAD<pBsatorGdf+&td{%i1S;2b}(EfLCreOjt(_*
zBP9Ht+eMUASPBWneV>O2v_6eVNK{WlfPKQ|XFk0DCRmS3q+tow%OHHdVFS-Cb<Syx
zq`035v~eIRj!m5Q<AjNy=Vh=jH^j$}b8$6}3oCf#l?8Uwa_We*IDaZF4MII^lY1`1
z7GYiYA*k9umCk|>*__BkwWZVWQ<}?)qshwMbsN_I=tEdfFH3+d{`yNic8<|iBwSsC
z`<XwOxO1Y~>M<W6)4XvU7qe2;_29vSx5=IPca|D^g5K?%Lw<G|n+(tSxMWd2(YrCh
zTYwdfmr_zv`mFl{nShsGj`h%<Tg15}tS!)ta6N^e$dgU-c~lR%SuEt`<=xe1kwEG~
zCf2v*P!M_*qNQHy<gWpbe$2eej8%@<(k_>bO6Ok*MOn^PR#uYj=3eu7G>QCq!l|2j
zgSvh9)!Kx-cyaw(#ml|vRfJ&VdyXMCsqgmf$JM$9;bXI!XnAk_(>p5#QZAF4kRIib
zN*KOuhon7XrR4O7Hh`TyuCw$`6Bpj+5FrDw{$Oni_|86`PpAvKH60%9I!-+Hf50(_
z5t4lvh|OS#otQ!=bdM>eboH@UBg@jekQsjW;OYE3rvUO6HSFHF(z~p~k)A@S$M<bJ
zE+nKybV#80CNp;QjM8L(e}60-2}!2v8p@WR?zVY?o_Ey90cdhLlX3?PKYMq;b1hT>
z7Execq2t(L00twFQ&;T$mlsw03Up;31^RrRQ=Hk|1c}`+E@NA6GSE*o;$S3nfulMj
zLW|#zp@PC_O553U(QS&-TMiW*$R_x{&z2lwQ+a#7?u*sQ7eGy_-sc|z@#?OiQQ-~j
zr^X&C$Z%+Q-r6{n^O(2ueizZ+|JvhYF)DM)9Zu7g7e*{VqoUw6#G_=SdOX^vtdkoi
z(?}31mTkyTm<0s|zsnu(v^9eFeSbvn=z@EZ5fN@vg2p%6^oUq$9z1a15sjx47{I1e
zfA?%Zbm}}x=sN|xG|m=O!)xhul;SY`Jcl)TQH#wg(Q^ggxO`ljH4Z&O7k`#)W>fZj
z8KfJ1CoeBglv*B!P)vekQUKX6D=t-=G=dzUssAmI8ukFzkD}&2j~gYukSW5t!4KeX
z0vQBB)zoLrFO7OfB`mc91+m>TUKeyFnzvcOkr;sUiAE}g)$7!7Sr~BfJhEB7jxJ9N
ztHg!G_<_;a)Yk-6ed5H4Xn4$T$UWV^*M2f2ce9zn8<dVuE`yyvrniE8h7;6`()fk(
zV<`m)Coeg59}Pj0gazhAtg-%H^TC~(r;3WI7x$7edpKcpG9fvBiO!QDY$u|%gE$dH
z<fozFBQ_`(O3WOYl|#5MCZ-sL{08j6^1Ea-lT=7SVrV_mR0+&gjAB#(3K%b;)C>e_
z*>|Tw_yyReK;qwc6oK>e_TDJR07)4T)Hk@8UFn0mP@1_joUxzienDOmdR}PyHT8u)
z=;rI8j>9MFkvt(U&><K};=uvMR=|NCnzDzc!kFPD+6S0MIhXwCPwQQ2Nge1A^ZGPA
zX<w0`nnnn>JnDzeqD**-Xw6V12w3UM{O#w(eKO7R2&Di~(Z&|MNgNzeyApv9$%fT>
z$~!&fZ3Q1%Uo(W=W+p_VUU~!x60Ywy+wCz!hR=A^v4#jxiI^r0)it3{axdyR1x}0y
z=@d87Bvt{AUcyG>^@@-y8dnnM51v$!>s>)8%UxGJx0gvKz;$Irn_#D-UDsBl;jE*<
zBS7370CI}Z91;O<8hsKEN2O^$$BllV{=(WmSAgKz<uvV)J>aJ<g<2X&+IxgqEP%|T
zo7nP5rDWe&ACT2t!g$w}f=I#=lX-P7T6<l1I0e+*7I;b@K*A+0U<1XiH?+bo?SZCd
zAP`)<E7m;;22B8An+=$Ma8z+%W(R2Z-~gr%j!?lP#Ub^mq}qd>p><-=kYY(K$2yMC
z>Eo*7P+1(Hr6fN4h|r!fXAt`a7U+m8v4$ssxa#d|;%B6pDuHokA)$icNYvEscDx`u
zN?$8Mb!SpnohtLs*5Idp9NI}R8KR<;1KmZr01b}YvP*ZO^>5ujt*8^C|2y%T<DZ}2
zm9D-2AJU473nE_s9ZI+V^5)q%i2>TX9Z*Ao!+o1qBzNr^2})N6aS+Ss;Txd2I*L8V
zeBuQ9io(K@@7=p+`JCAn_Uua{9Tm6C)+np2WE*^<6OS-KVnF#4-H?{gk7r)Hc5NR$
zJ%>2|?cN9OoWx;nx$+AxbcO*h`uh7TSM4rtdTH$nfrD?+?J1tMgoFg*YgJK8-q72j
z6@=jg2S@V7<s~j9{7fFSHj8r7NpV~fdh|{ZCp_92hBb_S_wL;k<pv50VYKj}y!m;+
z(%0?~NssXv%QdMKIgpJbsOH3wQzX`wG&I?p4_hUWWR`Socxhb1#)g8secuU(BvpM{
zs((3=eIQ>rq^h^KH$m4Xw<Cr;d7ObEm~${?d{9kA#lD1QWN0;8V*m82p8F9rC&%78
z6af?a`TD=LFZuU5{zHu6e@rL!|HT#imI)A_*f+>6X7YTQ=i5?TaM-cOef<`zDEe`B
zbrma`#7bPeSTToHr3gOpvyEHfJbPblfX5p9_^Al<3Vl~a?+3#Jw1lGdR_pcuI%Vsx
z#$F1F{|PPT#BJmCAF`Cc_BAkc`H;1r-62cfRsSH_D_7)DA>tF;s}ASsQojGyif-|o
z3N2-+k#j=9L#>9df3yJD1=uCzd@;aiY~MrDrVF8^V9ogx$7Ow4R>n<ymg<M%&UrT4
zw-2Otsl+2TXJ;7Ix(Y882zTtuKNwckJc{i-wIU<9cWq@O1;zSjXJV}Uj{F6%(B=9a
z8%O6z^;q3n<!VA+(UTlIerx&Zz>IQ%?G()WJxi_B)_6Bkyk$D=%jo0pAM^3!$0^E7
zKqV4VQa%hJVBar(+Q*7&0vHJ|1~5@9a23JF+(Hoj6%*AINeXCK`00n3GtmojWT;=*
z$CF-ItE%>s5{UUkIx!G{0*QAf7CXa0`J-dDj4z^PGGCZz(GnIeBn}047Du?Ws)QwP
zw5dQdnf>X7Yv62mWm#F>)yoUZd(R?a8Qx@{(-nqV09xu#4prm`4-5{XnioSH0rGl+
zM7su`C*upTgzA?DP)&YTXPFRr67w!mP=p`algFs%?$xHby4Eu#O_|;CW9x4D1mxU>
z1N93k&h(e!nFm|rZXKsFsWq9kmYG<jYa4Q@#8u18O$RL1#|;We@68)<(C+S%jbZBQ
z^odzo8IsC9crf2!9mV<U=hT1oUqW^KA2dSYmwErk*!O>ktNL%BCh`BNN722OW;a-S
zRbgSNG_0&{D1qTF^SX_PZ!!G+WS!n_FVUKpSI^$-y{9d<?(!y|5BqtaQ*M11aewOu
z{;f|=ZvFe%bLxA{7dE)vGrcMjv1`-O2c{v`IZJbwU5xgo+K`6#b>wLe_6JAg4tL5b
zD!Wyot!5p?`hDyE4Eor}y#7xg!QXNl{|x%r75?|1u^RuJ-~9)moxgkZ&jgXX6r%6{
zfS`45cmKe)|CtB=n85yCiYQ%Vn$`kB$3W`xdCYbM^1$Y>)fpz6u~x-LH&ir_{x+R}
z<A&=yBO;DMj;=_Qu;=WdqXUD44mA!z$gZ|-xlwca*Jtl`?>f6~eDrHt28hOx+a-qs
z&pD)`W9nGZDQlqZpW?+lhqMD6S_y=DMU&;vDULEfN~05Y(IAp}hM}iSp*8fY6Pq7C
zdc?xOAOfXj-Co&qM~Y4nJUa_$yk6tqi03`ch20aYNU%v!$U@$rWVS!l)6MM~(I6^L
zh;;8a{pmJY-Lr4qIP|BC;-GkKeMboCI4K=H$$!Od`F$D7Y|QIBTUu1a>j-{#fFBhn
z5(Da+$rp_Ocre#(Zb1pe;0RLGc;T8B{5K&I$R6Cbb*oQcAj{BPcou(FyX7;tXU|xj
z&B1J;bM&$xYf|Bh7hP!!0Re}g^WGLenVh;rb@*`HlP7=U9~?!e^2)h`q!9`aDAnJ>
z@$R{3U%GTDzZ`5<>f1eQeT(~oWtvCi4Gm-UmxgaCu4*yV6o2}pVk=dy^-Nb+S2k!w
zUO~ZU)He5F<NU$qm~O?{bCk2rn<&DC?j7wWiW*>zB8YYl=x)M62StEL6DPT>kd=ug
z>ExME+?1vNIwnRrTMN>cj~`_ci<D+PeIF_1-OSZ^e0sU{ad~p0@;*y3tA}<H=G|JX
zr?>a{2sAGbShp&R$21nGIXD{|9yxMkV0G6HgP8j91m7hpgN8h$IXiX;GFH(i$$l8Z
zM_H3)WwIp4*V4O41uH|nX=*G&5sN!7wVT^*P~1RqcRaz>&Wwc4S~((%J$>d37apKx
zwWyewF$fJbOs(#3h&-4e_ij>fze{?_@M$Ui+}N%Yzjj?r#62DV(D`Aj`0`Vlxsu7T
zoY;Zg3j5Bkv#_>KUtJk@9RX=nM~H8std_ZY^=b~-KO0mdzvE{wW|pie8}Te;-#AtE
z++t>BEl<xGVd3X*rB=eIfs5c00x!}~Zfc{5#7-#J>VwW?ym{`Ko12ffx2`ztj@KZ3
zNDThv1No{tZxQRg?0hOM3Rey^Xm5M|^y#FG;`JeN+`5)AThYsxB}u4I_ut|@ne^GU
zOzUSN0-E!-=&B8iM%dPl9V@hckGwE9<K#2*4OeFuF+yXko+o#G<B+xU)}1?*k%B~#
z=7v#xIH24w{OCq3I9QTI!OH+pw&JvW7ZzO!MMJ9bq3}CX)^~wix86J6?JH4W%%k^t
z7fQKNAFWWa&R`%)>|B}h9W-vDqM|Vxr}`%CPsX2U`PgwyiMBd*MQtHRt;f2p`Fdwp
z{m?lfhpv|9ZruSsnu^kSg_f-Ag+jjjN0%IPw8Em~`vgr+ZJF)bPjP;NpY3=ok})D5
zW)MfIEAt&v>YuMurAE6alF(NNK@m|Sf?LqF;rso|>u%vMBW^%(Ow4(K5-}P(MZ6BX
zm5d!f3~__kjYE#z|8CYh^npL__jm5rOV@17!_h#pD7~)6gEc`94*zt;8R~a`9Lq#l
z_!fI_Z*Q`v>+&UBQz%iQK({?HdSDnE8^srGa3{Zy_|L9Q((kGF?1@0b5GG-$YZBwF
zh}YRC^}WYUe!ZvI<^KOZm;e7?@t?)}Z;jUq0nl3f`>`cYro>HorS-9T9cR(RssJV{
zn7D>Mh@O&is7iLGxchQ8m^ZrA&ShZE#r<@%j%QeMF=?hubVYw#kCoj&FPwu`f<jv|
zD#usO&XA2RRIl55qMnI@f|tB&VtoONt;9mM%XrC}MSc0(%UpUE6mk~k;@QPA@#PeE
zw`!R4G~=V!w>)zDeJ}sdMj8LXCbu;$=k9skthW8Mv}QJkPj4^Er8#(z3A$o&+J)V_
zSyH2Ju5E$ywC*d}Psic*T&Z9ad7cQUy)0jqS%(dswl?(09ywCz8tR9_#|_3+<=Mr3
z)YK-#J6mly{e6cI8{OdAQEHH;Ix9duaw0QI+f@>2tcamub}3C#cY8B{LwQx0cbD*{
zG(>k=+Qisc=56styt{YrPW@`?H$ywPHPfz`k&4GW;l=4eZ-$5aHw_k)6c@+czIdyU
zX%odpruCg_@SSK1QSv_$R2lvDEy+JQHueg;e>sUiVYoRA@?sjw%MVL6W8ROLb9<J`
z4;66x)CqGZCPf}WXtDTA4UarU{xW8(uHm)z;Ne4u`x`M+thcv&dw&#Z=LrCWD3Xb*
zm#AyIdSh}S9}T60jQ8EF*x1jhBmV$4d?J4-Bu;QQ>(t*}2PF$M!z8M?C@5qYH|Av!
zJ#rA!*&N=wWy|F1zr6@w+d@2gbq;eSACN;b9lWr=beKw1^g>oLn!^@+M@=rB*kS+k
z%1pT}-m9aQ`Zadxk=*PehJe^hI5B_Yu+7caxO)x1K<L|h;D8eQ`IKS1S%rDw7J5jx
z`nR4Cwo-Z}FfSzHE1`8MzACJyrp9tS>>l5YySw|0R#?LJM(T_9_V4Bv6*M$Vzb@=g
zDCI@ceDQq{ebic(01f59V`bM>Y%B^H(Te|dir@c3F!+D)?)cBABP<oe1g^bXFRs><
zym&&mI@$Tb<HzL&Y2qX|H#e;KN~?CzcNIn+p(6M;@C24W^Pck?7H6(R3kgg;Yt#>T
z<C=Eh=T8sZaQg$b|C{rpvYDUOIC}gKq3L@{@F#vHFe{4FfgNXm`@Zn)^&7M>egvID
z@hB+}(`3+VzzmtbEg@YdUbkA$2zH~fhGfbe#9#!%f4Dh0tm@pf6w%g5(7|6j{$vqG
z<uiBf03JZ;))Jj|(3D&vS)*+e1w$Et@H#XvE2DB%K5G8^E8Xe3j_+B;f!bXc%C(76
z63iHFfMS&*fH=cYMzfYYasxE7Tt@cd7L@w?Yv*Yf7`xfxLA(&d&(OR@M%^{uqN{)u
zg$f}d_an}Rf?EPwLS6m;WE~fZ$JbfJ0do?<Jcu_MARH#Q=z5;u=2pU}0BCCD!BnHr
z6oJlLDR%$(S7pDydiQSQ^>r4i;HUym>$^dS0M$&t^D?1=MoABiwc&@)7~GQQ{`JTx
zV#V%n&ikcmYedyG7M&sfxjkz5=zhQ=3=fhHJ*SEu14l^PsB?Y~_oVpy?;Qswi1dvk
ze};t4KVrfWn`)}a&)G;Q{;Ux~wR9*dK#>ij(&NhjslLbIGh#sE=9hf`q+jcblGi1-
zjWt1GpM)t&m>TjuDfdSmG%QEOsd#UN6SKC^h@*n$Z)GQ^JcbjePhTVEPAVm9jhPJ+
zowmZ|VEBmF-Mjm~d5WRIDg9Q@MWO{7GP@}u%+Vlv;=dEK8Bk)2H|s+i@&voZ*VJNq
z{ofDl{CQXn`bT5YuqFQ^r7A?g1V}Zf*Nt@_9o-Lk0sUqSaEopt)Mgat-%i_~Q}UN%
zBGTpcm`GZ`p>qwHM&|$q<XxI;xALqKoNMRL_}QqFkV<@og4YbR8X;QK6bt!OQlj?f
zd&tO!&_~gPvfn;hTFvB46yJ5PDgOLUu*=pCcK!ZuDw>aETR(j|aW3k^k4-x%E)>n&
zor4~FaAe=FkF9s9s;uoW3L`#uK<k`}^q7~D|2m3wr8Ax-f7|^w!SEXaLidzO@UVI1
zUS5r^i%LN@zkfwhc*c3-IBaYG{{62Jy<m%PO2~w&a(r<!CC#Z^qC!Qy{#J|LoCi(Q
zEIWF%#<-GP+sv{Y{E6Wtwto=)u5_uL_83GpqFI1RpT_9%9jxjcKw{TCYWeFR6#p2$
zq?<5@7$&zJwSKB*PY(|=yDNrw8u$~8JMp@JjX8&|mqH%8YgW^bZYboi{rUw(xFaqB
zO4Mm~)V!PC71R&Su$K}-4Wc?nyf(67H6X)r^sle4H_>o~4R;PeZ1I2i1PpC_3d%TR
z4OcY&cMcE*-2e0#tjT%TY;!e9GZ>;a^zfh-`jq3WJJ1PWQX3&kikg_XMU2M!Pwz@h
zRCEaYXJYjZGFM`F6hV0qla??~#uo+I2wa#kbZVVsLhVvqT>SF)IeDeAHEh1uT!6od
zpIPYb)q($z_(eG@=;=ifJ?SRN`AXA+)y+^Su$UriL$A)kI5Y*ICS{Y9$nG3zNlc5V
zG^j`GfZCWNF`cQ>K%QWZO9iaI{{v(JPFihFQ!(iLrLEJKBMB84rql6tKow4kf|8nO
zflsnr!K9{ZJRR2F!V<siBPI??Bjf5&K@6X~{csRMqBm%oGQ=E&0lMnXpXD$~6yk*w
z;;m?cM)mXDI2P<X^xE=ZV=@dXf6nO3NQzIw1>%kYbGVWQt@&9stDDD6>W|mFkfQ&f
zK8(K0CRmBGxlE@whFcoqyF@GWvf{7c!n-3|gIzk9_bO-^WtdmF;NYbF+3W8q#<~i%
zA-*D%yrAltvf9va8waVhA(Wpv<R<m~d#-Wv4?*Nlj3>hQzucxs`;j&tir@;4WA>7e
zk;|ad?L|>f4b0#7*Z53gG%cR~R8`|I0`Gb>abb^l(l=oS5_8e1GC}{)&?G1WJ{zPN
z!JoK2e?BV&YmCM<7^95}LFxQ!GwMe~kQ#v&u@-Ww1oSX0Xec``iFG(HPnIhAQ_PFr
zciN{1&-7S$`2x{8yf`}+j|q2sfBf=Dg)iZ_8PL|aDF1Mf5QvFigDID9O32{E)C}=D
z^qrVwXgV+2=5_#P;*Vm|8T8bDHRXo#G3TQdhF=n`1CSyoK`6+|&##8UeG^2W$<XmA
zfBgmz$93c5Mxueh&D~uAN<rvN?2Mo(P&A*00ALtPN&Iv__$myU(8k22SZw+%maG#3
z2Y*1ob{0J|nDNHK&Mt$jx)CjMWr@&a9l#Z!ye>{cH)D^W5F#Z^lr5`(V|rk*&cdv5
zK!qaXg9<Sfnu21N0@$7mOoP&N9C{7t@n*tFO3IzV2qDy_;HnxJ4}udwpZ7&5afK|D
zy9!JKryB<>zYJNI^-U_+=V=$ZP;u%!AST&hJ)}Siv_gAG$SN>gO!03TOqn<?6%bBb
zKYGfV;07?*Hr;j2g^+*|L+l<>2Z5O*X6+D01Zf2EO8{iFPs<Z?ATbyy2|Ghc?aq+K
zJM<)7gW!Ay^Y~aX^a>Sf2$MW8=HV6EKchcR@;Y=$y2=Rs#YLOjIy3TYk!D*s_iw;=
zUhXTDL<~ciznd_#K*-cd#4JhRjHK4xA&uGJ0!<ji!<=VdR<{K-+L;kkGl_{)M3)D8
zrQw$W?x8$3gy|C!)`xK?iQ!(58>-_0qcKV0*K7qmLv*(%5mV|up8G)DGJ3q?A-FK>
z%u_|b9Z|lns%nDdTR}k~3R)brd}NiWm3q=A6SIy8;uFOtG+_}F48?y=gCU&BPaQd)
z6ERJh1aZv~ydSSdmyo|2?J?~ovhU&2^%I+ZQ|5oOU>e6m24WVNuMj;weKKY-NRklN
zB0qf+A=>K+Z4)Gcuh20v-yn;IKis+NYIb$TOWe7y%$ZX#W5N~o^z@wGehFz5(VkDt
zC;&3BZ?4AmU)m|iAdW_@dZ>~h`)4XTg&>`T&GVB9>T^GnM|77OqxFQ)Ph&#J8~EKa
z^Q`hy+u?zTLAw~k^&DbfGR9absG7juAO&K<hysmAH<$r0(7zFc9l|P3^f_mfB`<wj
z?DR>>Tu~}b&|NqG(PCXPnl)gXXE7rKjd!xpgeDQQ{rYzPwCnTEXpod3!D(v_@I!Ya
z59a(YN*ZQyevA<&##siL=ZJ%!B|4hPXpL8FBZhYzi?dyqtm@pX1fRkf*Xm$ZjwVk}
z2618tLE~%5gJGGfg)aF>2GJg{4(zB>P8IMbt2m*K^Bu!YMb^Ewg+h!JMToe=!{5gQ
z8W&Z#RPrCV%dxy?B04690H6?~y&$|krZ`;fa-It*z&v8AU)zylxG%_}z>9&)s4l9N
zOia4OroRF;Jya5n*h9&1V&WtS<%!8UXr(A98^cry7V(VL;ex}sZj8bDSX2b|mY3)p
zvB)R;VaRL@%iucp9RZS;u4wx_-{XR59OWIO38S(`HF<ChENv%3N_zE(MQ+bdV;I6v
zg2`-l?U!GZRbf)10bh3*+`LK5E>VDTe=g-0LC1RZ=*Q6?Kc6&38AQoHgK=1s=d)+>
zXz?TJ6PPP`0WO2k*AUah5vVobzmu%>ZW=B9FYLW{RMgkoEsim2jD;9u1Cba}BM3wV
zksd1!2q;ylD$<+uUZN2-ie+d@S3o+Wz<_j(1pxsCh5?46(q!np4%~f4^L@)*>wVX|
z?mu_^em5&|8HHiye9n2!^X$E!{ovTTWY9D4e(_}JIPUUjG$ejl3R52zQ>ZO=OeAk{
zq;(BmIj!T6&;3Fn=%UX+oWvaD;F7JsS9LmSPaD|S*o0Ig0$L$v`rgm2|GL!3e{cYf
z+CN3`)W<`n5dZPU7Oh@1FndBk2dC)uB%L*nbO=D|5eF-awB}b|AjfY73tGgkzSx>r
zdnU0z0uE#_9Uz7n_$=k<jX(erPSb7jb$K3JGLxa~a_$`dgrvH}VFet`DY!+D?FDN=
zc5-602Bfi&O82H8o{|4Neza$EF;sy}ybYh$feWxF{Ok5@+E`xA2MHOO{HlJbBPC`6
zE1qq5r2hV~P2=9d5q<k2M(oFo()RcGT>|(FYbe}Fdpfc6#ECsa?iVz$X&V?cpCwva
z<lh0hGUhgrtwyna^%{LVH#euuL_>K}d`d}^y=z;*Irp&Caw*cPZZeD93axa15b+?4
zFH{k%LUE`}Zp(KWcK>ZUKCtN@Ex-!#V4~)4>dptEyPZP%d#YJlk5W7?0f#TFJvX(@
zes7j>)~zKURv`5o8*?6X>}e6=FUMX^h9Lk<s7|2KnuZJN5%g=;ul`yWF7K6ts;MUS
zpLfl~o}ttb>1Xq65K4s=2~G*oeYm%Sg$$8qxeO%k_rZk@)?PWqyR{yMoltzn)^p%)
zVXQHIq8p}9K|&;BgL&LUaujk26)K$glF%Q_$iJn5`Y_#D?=l2~$EoCzBgY<WPl&VY
zm9;%=JDYxs?DZ=>i|nzWMHLSoJTQ|oP5@WWhE_F9h+I|q@OA+bwd+fuBX&NhkTg!;
z>&epivhJG4;cO_REg$ZJ;4uTSTn1vmXN1SjV=st%HhPFcxDhBH4K6>eU8`v+o6yhP
z@gd!wh^bSBEFoemfAi*!*52w~#8KifCY+eyj#P#2K75{7h_ou;GTpm(uY_ygX-dh}
zd<}{PG2GJBC|mUrJ_R2IRQnwBoZ)c<QEz(lPpiRd!5!vNJoQu^cAdDC;u9oU11$x_
zI0)WZIvzK%(CC@TFJrl$I(3&M$EV_sQK(Q?YTI#Y*3_+7zdLC(d$UVVuFRKxxvUT}
z?NKqNId!<n6D<PULz>rRKIoqNBB)8cMGR1=W2DHN@rGLgUMK<L;#8bs&?jN%HsUhD
zMNiE79336W3<9(U)60FixobYK0iTTYVxKnBR|^e2ag@b6<Hi6RnW7RVhAgihfkD%<
z1g<g+NK?~Lx_Qj5T(wFY0>hB-@Dv2b9(d1+=2oIlR07rs#9O`L@i}~F=(&y1ooOOk
zj)An)-8k%t&S3ZyQOYYSYVnk55Niz1Bc=;O=!TB&lOeT6$7^7mNles99Qtw*<`2K-
z-%cT!81cEW<1EaUECdN*!Bvq3VDba9Ks}faR*D~&pGm(?E@kYhL8N#+hQ9cLm3GA%
zDDv@kgS6&rcW`&k#Tm=@53i849ighsYui*NG^2@NaI9d~3w_HeeD%65Tf|U3AdTR9
z&Z4a=I5bqJQ+GriW`oF%gwg2Q9GNGXY~ObG?p<Ydbz(N^f{tJj2?;IW8{E4CAqowd
zt|1e$FajcGLNfpeQoyJxVZ$R5F=1jvLK!lztiM;i1RF|xb!=PmwTX@tJ<%$75NvdG
z)Fb4O7#ii#b-y!b&&tCvEXfAK;ey+Y^aX=^)V8rnBbg&HNyV@Ml8}d#T8w>uKZD&t
zT;Eba+hMWtq46DWG>;(_c*bL2NDt9+>jE!<U0b&vL;?+)KFE&4!s4qq^)GLdn}4Wo
z!e;V0B(*NMw_)XGgf>lj{yWAia8@oAqPfR~@AI1>Pbd3x9Gm1<<lV$06r$2(#kr5X
z;{%ZAD4{uG+%B>B{Q2`J0Jev`eWDpd`?>Dw@N1R53bW5Zl0LE)&nuNX`SDzP?c{~s
z_pJ{eoNu>mJ`r8ob*A1Au+R1o@1kzh$Dxe(KR^pvr1|oKsVQS7@Yv7N+8OGrp4;Vm
zmFx|JbP3KDH$+Ba2yNMHh9A3XMl>qZHR`~lU2WdyC~foEY1L;N50(237=3oNeEEo1
zH06z-nR8F_=qKS4S$fH<*RO?t`Q?yC*{o8kUGePOXxez=UE|$7EaMp|>j%sIY#L_m
zg9$kaXBwdp?-qLTsCYd?s`M87u6?dvO|D}4Q}=6!%A4ybRL4lQmXT?h8^R+oiDI7<
zT%QD{7$$<k{C@c!pU7b2FX)I>+x2$7n2GC*&Z!F@UtMO@ZAk{auG!>;!o#Ifl4${-
z1QNkcSZ1ZYy0M`b1#A7n{G_5G<m8WX{bXQHQ-p-Zizt7+Y?qzfSGzO55*H`2eMNhj
zHJW~iT@rB-C&NfU*q+LhRnU2qa=juIZb1%|?dRTO<B(ow_ZX)Gn`iJ%<LLMhL8p+c
zy|N4L5S^a*CXF(~AM~>=PWAFt>bUy0gU+w$S}(SGMNkDbnzIwqg*A8YuG`{v<!VK^
z;$rR}<S0+jAB6bIZ@(A9IUFl7w^GNHrleyO!v`eWJ)z=xQlIo@K0d3)L7OAzF8caZ
z+U1ITiuc;EG2`s4apI99NB%VJt}YvlNt-FMeKV^i5e%)Y<|h|==B;aU->J-u=e~LK
z=KDqV4tM6;>^jGn&pSZHOjHpB{0ra}G&0$8iXSH*N`D<>424p)-|(c~n^$zg;jRd*
zCzfg$;Ze<Enri9yTx$ZjqskqNQaoctQl~w8_7EpW-~ta(sGfp73NfUtL-B0IA_g}h
z+hkzUlK~hZw_b84DCFEJqaT(QA|9<P4*i94WzCv{8;1A-PJy+^zH{e}=EGY7jxznJ
z^mDF<1F2#?rDiL_D(inZcgoc7o<v`9z?Cb)yWi#(EMuy_@}bKL`@Y({@raU=?sx8o
zFH3x&v-%TXy1Xm@VdqB0sT4LRNXf;^&scw8p-t(yMba-mg#{iyk(=F>sq*3n6rTm&
zx|Q*`B;(;PH&TXaSRy`}y7sDX|4!b8xCBi;@k4_-=6*x>k5?Yiqe4R;!DXyyx+`MQ
zz7+o7hra*IEi56Xsi4J<<N&R*%F5gQju3BJQ1G7U;u-_=sDz6$Cq;|r_r<$p4yb4J
zRi}x)(0UN)Syun4>igRp7AF3PEGe`*cy!*RA-tk>fC5VIq`FEmD_OeDE-QbfCfzzX
zR(IK4BYQ8;)MuFCy;GbY<_&~a5Ld<C1Xbvn38aX&287~03939vyuiP+#O~48Y}n9k
zSd0NK!iI^^*OT`{E??=WDytGI@QO+8=`tH+prR4X)U-@vk=<|rt1zuR(Ddo&Nc}8`
zmTQ;|KHtABve^&pyl2`##x0rEzj~%101%dkVq<kh#R|5xyLw2k=5Ysy1QWx>XB-@!
z{3SdcCHeh|$`b38S1I`lOM2x>kw!X3OI*r*Ct%Zdch@OG_bkW*laTeSubEU|U&-lJ
zS4+&QVbXv7PRTR+7BewOX{c+-$2K0WoV$65o!FGbHq$@gg6K}Hr5@#2ZVVtJCNx>!
z`p<v{=x;45XjH9F>U+r|)v|L`rZIh&uyD%$55$o@)mselboJG^)!N}OetuiP3sFOT
z%Z19$@AG+H^}FiM@bRmezw1v$>+G1f*jHu>oY}M9iw7>re2_=DTqCA?(OWfPWmrYR
z-^#waf8V%aniadnFNK}|!_9Mdwf43ICsHHQmT(my8PljrTm5q%KYo`P%Sbuf7-Jx&
zn?SSvt<@qcW%94*o<2BC>%K8k`k^?3YR}yHjnT6b0^X#m@O-0ja^=+ZYuAQar_#m(
ztapmY$~+z$DC6G3fI{9tvJ=t~HQ9yhI(clGxNuSH;NB8j_~ff%*uPf&^0$Sk;z<rp
zjLC?CWk^L3kfA1E6EZUemI*Dg%R*sQfm>V>_a{ZzZSg-K|92OB`ODEel70Tw_wple
zwqcxz`IT;EdAM_+xCj=KpJ*^~*<xs&)i^d*tIlm=CcO%s6=AxkyGf*l`H6Aj#8=Sx
zgI0T(KNFAy3@KVI*Q5kNozLxnkg#d^4M0J96=w%}aZ%ke(C9RdVF;gLvY-Kx6Fl;W
z?=&jzlq%b=i0`xH*W!x?G4!hNqaA=nVv>-AZ4JC)9cej>b@M>(H~zi*b^`w6x3$2_
z*^GZM-$?d}n9Gwe5v;?_yq!XZMv!(bVycg9Al;dR(8Me#gdIp$#n-p*A8R`EUF!lo
zU|Ks#@9);F4D&Y#bgi9`@6>-qbr0CmZ(Xm&1ObAqf3jTz*p6pW8K7yHH%^GT^)<XN
zu-SO5b4hSsaubAYZ^f38r|Dm-bNcu-KBb0qvBf+elu%C%;p;uaw*6u?*`trlw2blf
zq5nR#*XLV%a2mM2Hjt55Be&kATcrd*Yc%MHC9M4C1;Y|g2h9G-(4Km^79FU<C4Rg-
zu~q=W(Ut@Ag;b%C6M63d>R2(wWk2P@q5Uh8{;a3se)2nG^V5b`)c=Z56uYwhwBb^o
zZ#UN_S{$cPhQ(-@!tr{XP#0cdN;fy2`;RC-nUK-gzkTgfbyU+p0k#Wh?>%}LC1$kG
z#2E^ClBg&w{^R$=#l_Ll!%5*<6u=>nG{!+hRiK7%dwpBb*iJbB8EyT{Bb%>Fa8YKT
z&uSwIaTVc;wUD4OQECzoeaGq;IpzTTyd=@@=okDiQmkTZO<}_8TLP(w)}((<I|De`
zLzG&U$nfTc|KSiWg&H3l3dJT1S&tTT3~D+Gc&i4b89d~iyY8ElTv7+nx?%zMso}ZB
zb}{{PgMpbN_|^f;4fy=e22)$%8}YAhix0;CP6m1+=HWR2`rz+fTIZ4F?RoN#@aa1v
zJRn-pM0li{c~tJse}thtZTGeex%Si?1rnvLFz8wUsB5c$yl36)Y2=^aEE^O4*&Qdb
zG_7#WwLl!#p2sJZl5y;~M&x@a?oLqaaDE3}zkIRT#ex<Bli}Fwe#}F?u8oQSMuTFL
zPf@><=~xIQ<^8VAyutsDTVOX&^x!&IKz>Y`B~uOU;X{D`L5$cv>5etLkktrnnqhBo
zs#UFUqMrN#Nia)HGT)$fq_cuNEaHofQ5)~z9S8|%n(!B<@7udyd4zR`43dTXF;oG+
zfw;5TsX7@hK?F<9K;+k(^a2A*s91Q9h)=qMUO?HIjS*gy8q|-(z?6~Csa5T<)?N#o
zq{p+^&KM=9V+!N4=H;Gk?$$4UTS;OXHJ-rsWOJwQ{_=#sU5*e>fF0}=Ht|Kx>Hm+!
zfc%!*OIL1;g=ttS3JC(h62+ct0Zg@BI^L{7siQ`!<wJ*d2b%JHyS#!XM1ViHX~1w<
z8DFf<e~@VPnKUH$PHmz(Z9MnNsu%CjXdMF2cXahV)nNgKBX}bgv<Z?}&&dIz7ugi>
z04X||YXaA?cZ7KoTYHd0m;%_~l5&)hFjd*}_;!+f#p2e-kK4?zPkp$|yF2X*?fLDE
zU7C-MQ|HGL6&aRfQiw7Y4e@%Z{r{p3ppG$2wM7AMvtOSs^)N=(Edx6Z1ghajX{-sD
z^d5lPx&XxRmFb`cdcjXKvD&FN;P0Zse>-=Hl!z!$Q-Iav1AWNG8Vcu5lX?o>yW~&7
zA`p8lnv$T8Rte;GA*|r26>hlFiO(e1VdBq%_hto<qqDvZ<`8%X#EXL>s+T>qMGyrh
z63S5hwy<eSpPt+tw&QMgd6-84uYgQE@p8qLf<jt{Tn;F*84a$sRhKh4xEp>ayf99P
zX;x!4us+QR`u<WfhzKi8uui!x>Q}nK2qV?scJ#=R&xDL|R-ALNllZBENC`d~X7qi%
zV3PvDzQc?BFqu2@@Zm#t-Ld~nL2OvNb{hp8gHn`TjZW?6RBn-&btr&L21Gw&;Kn_N
zpbstvQ1l2h56Nq(%Z~>G#uxt91o{}_2Se5fkhnD%F60WtoZrZ^XFD-x{X4J{ZLV;w
zB#yI|m}E%)5{>h&1I@`~cpFM+CNK$+^de`mW*~#fsHX`d58f&n96hX6kCEwg7>}a)
z(LJ(oFhOyr;N|dJhPE@~M}2M`HDpLs4-s!NROZ%jXa?SM5>Wqsn}%4CjHib%aUm(Y
zGCy)z0!P%S@HF6n_bbE;$zebTPkE;!!&<P{(V+C^rZ@t$;C8A79d`QMLPkDY7(FqE
zh#fDEjOIDTw+d2x;hl@@8$H~BmDrx!&W%w&Z?pmAOm#+SQlA=yQHjT>W2n8S3oBu7
z^)rPiKtWXL_FHJ|`|s%w;s49%kH2jQWk4<^Xl%9dq>-k<$?^{&%aM*UVw-%$mhHGD
zn4BC~QT__&1O#K4FOuwOyy<HGX5AORk-Q8AHxWGpLzz^Ve7mpuSB$3uk^9~@w?)S?
z#w><^RpppJeEpbHXh}>1&-obyUp>j2Si~mr9|m1*-@iDRaVz3JL5(L~nccPk3nby|
zNFh{LFl>=g&=Nk{6PO6fhd(?SS|K$wYIT&XF0>FQp|V=};v}k1;xdQqO%D=~*6}RJ
zS76OYq2lyPVDcC)vNFJASFffbCZMN2pz1*T%EA3){ta5C$Q%s>&J?uJX0rN$W5HXD
zL<B+z;fNFg($cOcCyKzQ*x>-`NF>1regv-qWh#boo(3Lx@ky~%LaJ>EV^8ntd;O$R
zg8O4UFh2a~#(vip#|qIe=on!na(xXE9LTs9lHkeore#^lGhIyCP6qsvRviMzJ;`JY
zj5dHN`8&dh0)2yZQein?rJPjSS<h$I&=d0U>Gi8)0f;Hi9+TJZB^mGF*am9)-anLm
z<-bTCnk(%7HdE*;+_zgo`%joDSWa%IEaTl$hy2B*R#hq)C>^`uNgOV?R8NL$W=({h
zHar^65X)7e9ssFX(CUAx-46puI5pKT=9P;@!K%#6<4*Q~jY@K66eK%ZeD!1qCN&$m
zEDS-Xv@~t+5w7HK8`Sn0W5YGl2_+zO9B(r_<$mqGtF&d$_6AYyw)Q3jCqkc-iI-@t
zE{wYlCNULdpzti69Vv06Q&R%26rTkbrmogwWWu2O`7e1zF+4#i8nBRIGt>y~l&of4
zJ%Eagq}QhA<|5dh*jR;FY9R`4)G*}s#4XK}q5EI6K`-|HPuZZqZ6Ia}Q81G6l!e-_
zx!pyYx11D=Q8}r9Z7L=8MIGL~60yL79m)+7MG>E#Hu<K@?5zCJ%=I#)6Eugto$ubA
z1do`6$I(h7=0*7D-h;7#f$>Uy<=pDBvScTI>`>GZVQBnLz|DiE02S0#MToqKXo)8@
z&ZCv?)|eJaY{su{@jMPlr3RGi&&eGT^+f$c=Eq`ZdN37{t_k(v7?0)MuJKQG{=SN>
z6}AdzTxZ$<!YyFT17RN4m3_=KlXE$cIVua%tb%RH-o-ur<%3Crz0fsqxB{OYk>m@}
zAHq+&sD-~m39>LhxbW8QNA3rBW)3n1f-qFtM*YEd#)eax`hdEjQ@KG7d0N_TVs42W
zL&4tI@N@%aO~D`MB$$F)@S^BnNg=8h97s>FMua~GGVj@Y$Cr1?`;^T0&hX?R<6o;;
zQM;w2bb;%KDCqJHL3hv0;{<#r@12fY!}Pwi{N)Q(4yxGvD7U&flZZ;$Fx0eCJpKe*
zY=zS=-1q7qt05C+0g9-o=_@l-Y^ZB$e_DG&WYqLUCK^ALbKyg#6xh$|cQw5Dd`UjA
zVuI2TvK!YeF=uy+!p(G&O6=QXdezj_LWKyRtYEKsKc#EAn6U7O+EoX5v@=q9{U)vJ
zpFGwxtXk}sGU!kV8%D%SvE&Og*9qSZY}oVD^_?j#RVhw2yT4}jKPQc`87fq6IHZrF
za#N<lEbie<s8-b%YH0Mj^7cjc$l<nYE0kD(c!$fw$=-Ql=tSWLZdZgC3KjG~0;CqR
zb!t7UQvVYfG}b!_gUP!>WPA=<Qd&z@lyvGRMMmiTT(TpT(J42SII~$7_zy8c0n$sj
z6f!9YsHX_PX`J?ns!GF6`>I!o5UDHv@sEX-7%!zb&^2AHXM(=m9Yg5SN|66HJwA6C
zGdeWvga%OCJ^}|!uup0{?jki*yq!5E(8+A4;4;xdt{qREK90#!8Q2**()};-`7;R8
z3US`VM2~9$No1kwQ=XB38>D=wmTQo&Z1-I*?yf<L^T;K^>RTWU=q%1B+&`E|W#LWR
zi@c{t97oqeoC`QdAJ*$yR#sMk<dL=!ldnSC4eQofmQlCEV=Y+75)!=5dU1h+MHnsR
zsakM~<(sM*V=|0L?Pb5Q{?6#A&LgqzKg6-@MAQUx^1A%!{4Go}01cEfnqG^X0f14c
zz}Lr+$EpY~zl}aE;r?8)?qi0N?*;#*$$)35iVy{h;#$;n1~X`d$egkXj|fa9K{ca-
z)JGREA+!ZzFMhkQF@RiU(2bEUT}+3;6iBNp{ezefLv#bM4<RE<Og9D`I((S0^*A5B
z;MtS2+d!reWt9%;y7dVFn}k%B3|4@Y#2LLQp)BG>MQlw`Y!L7S-$`hvD_6*!;Rz*(
z$W5z41a;8zLZFm-FoK6P1Gz2yvTYkNLqiBkYOY(m(+ju|DZXJIbpkR5;P%8ARj3Eb
zOKq59RHPewmJpB4GPA5*h$>%{IqB0&l9fs%lY5^GxywPDH6FdKX*Xi46*5|O;cXY`
z3|Owj*%giJ;Fje~mMn-$wb|j-Bwg5AsB#+k8=}*&tUeR(Z638<umz2%UA0zIFyZjm
zayg|I(T0eOzE)FgXQz0WgIHj#FmqE(%G^%%AHoSZ)(oneF`Q78vWJl>KzdudJ<Q-C
z&Oj2xvLUTw1pWc#Lb42EKPVvQev-(Vpd)lH6>zga+W&=MGK8KGGoG)I<Q6aiLN0L-
zCRZPs5)P?z2n+5CM~IxRZGJ+ENP}m<%oR!b@WY>unAH9UdWrl2MyDB*RwAN*K*`1#
zzU^0sTiyl8t7o@H`diRaE=b1793jH)g#-n~!8q9hld{+-1+R<)q#&V|aPuJch}Zp>
zQOVvO6n3+{D%*3nveI=|C~Uxo(Fd3|cnEXJIi+a-3XQOOIQH7Xlz?`mN+zfr+?lkW
z{t?mGeeCc)IXcHS>5odYQ58p9$NMg!_{rG8sTC)I3Uamo((Y+9+0WeDw{ME^IZJ8t
zt)+)Gn`}uMdOY0YwKw!FJ@)5?VF|k()g~b&(iY!g*M(?}Bs1|2cc;c}MGL>k?zE15
zbXa6)c7=EGCYF!y`t*|jdncPG@%DbZa*UJyehRvSSG?g@{VL%V!Xtui7W>vocqIMZ
zUZA$QHrT%SaqKS(diCayZFX}0(EZ<uro&JEA)2VpAYn>YDi6Kd=T%&9uH;L8Lcrz=
z;3|LSp&U7=)zg-CWyjnT%`2t4oVm;E2h?d7Zaib`kkQE!OPjjd`VaGT@~$73c~Z+i
z?0Ks;Szp1}{M6C;(Yhr?bz(WE=)JIMf7(>=I7S)OVVQTrQN>+-%uPCB?LnWqZmXPI
z{#Rh2WI_?fT<H^yBoPiER%X<*#8DSv=4#eF&Vh%0)1Nbz|Hd~N1oCMw%&vb+m=KiX
zp9y)(_<Zr6_~|N2t<um0B0cCtoa(0h@kyxzfRRZ$xbnq2RND}bcld}&OE>a=Q9JU~
zF$rNv^n@fd;PSm0q>z?$3s4T2)uL@17wy9YZVhN5d9U4l@ZCxPO~@N{0WAVRv7&5W
z8&NV+F4B5YS2*t^vNe5^*EfkY1j+w6%J?5fzCdZ3e*$txtmVVNF2cp*PJP#Hi^0oe
zKpj5jzO%<{xBd3h`t@WyOC7Y6`l-AE{_K0cAwatbVNMEhGA9cucMZXY2jV4H60U-n
zW)WZmaV{C=mWeOpUvjVhpAR%&e6flQ;sF#mIJzH<lv}gIRbpR2rG6@9UzGXl+O<2~
z3s2m-&p9$oi=UM+82b6kh3@>!_uCKa&#e3D=+5tjD}FZ7diUO;$ml1fO)f8&EWWuS
z?$WVuH#l!bOYpV7Ll%kIwVOJeQ(Cg-`VV&xu9R&IQghQ~a<)C%sp688!7fksR;UiT
z`jEYQkd=s|X&*HcWhF^-e<c!b_`PWSz@kAsTF%D@o95dU=S|$-mg%ALEglZE7ovc6
z6x*Od&_(tm1F>#&7MhU32Z7OvTEd-ZQgs=Cs@;6>n=2<L<r!!Ukn{Ee48r;_R_$4z
z6<}`+FnXMe=lY92^O<Z3v!d&LzH#Qgo?fT)nMwaiM0G)smsxJQyTU47<oZoy#W21I
zul+-{C;fBd;!LI{kkIWvz=3^vfW4cmYyH3b`$<GE!D1g|9<~=}P|iQ>`qHYNX56y4
zb+WKq{zYyZXTYsns${zITJ8LRTS*0YO~tu|g)#L7t-&waY&{)(@r%{6yJ?<`RH{&j
z^7d+<0jJSNQboHIr_0p$Vq^+9Y`-@UrPdbq+uBzwTDJMlAAkHIRE9eyDhz|5zkEWT
zSX0|6VT*_)tPG?v74TDwKx6k+3KhVIIiQDaRZcJ0D0M)Fe>_<$#Z8*0S4twd#?QCl
zV`@(?TUnn~K3J#h<3pi0y&Kmyc682cuVv<ym^8ZVT4n3*J$5SH>!{MwdC$9*@LJ!s
zYL%q3Pe>I<qo+CUK`+UKM78lbE_A-CA$a0kpSfZMOd*{>&(}o`;D)KXZytRg{E5NS
z6%tDH@ES?o{oaxsYmQ&sq&tHXqr2~et+B6ulQ6H8dw<yn;|2Zs-jZV}IkqCea5qv)
zYxh2_wXDoI-yWQ6>$-&JO5GiQsWTos233q_YMO!4p21ur(m(+qOoEb$ahhG5s@;!a
z%eN&l$o5I7iZ@ZtO67_DgKzy%hFyehPMMKce+Z$ocIyzNQeVC#cu9lIO$ygR;AXlN
zJ$&V=?Uxr<ikY@MI}0V`8?V?B+ZjlCR)DlhZusTeO&d>7KiUh#=E-TIleb}{_!TQ&
zUYIF13wv^^Nzw0|GE3dgbra8(+Q<I6dg)!z2l*=Cfp$T4ZPfilyLc=?yz}E#U8AAy
z+L(FgCRUWzO1uuw<2`)TM-$`Yok?-G%Ef($Z>&-)fO(nsfK!YLwQ#_NRsq|?=4({)
zwLE%ihnMC~U-g^1`tjnVskqk}Uu@xo^z@08)ff-^k%bz{XInX!ezBmq@Vfhrry6%c
z4|{^T+4J**X}^WRg-*Z<s+g}X&B205dh362P<TAd-ZB)8N$4+f$$+^v)y;;cShP*W
zoPst^Mr$Ycr)Q_-FDpa_*gw>dLuWtbihU)`VQ<j(0*KoCrDXpdbGV+(q7!6g+W?#0
z6z`#%cKTGe)t7g#i!Fm{Z)IDCmheK>!!-d`p9cp8i{&wM!1GIA!n;hokK)fq#rcYh
z_8b;V^L;%CWcS~{wpX)5>8iG3h<R~JOQ6o-ZJQ?x^6y`OO!e&<7*L}YA~mnZ5MJGk
zq}M;JLaJs$j0T0u;F0Qtq>uUU4tH*Dt`s@FK|)Aas__bU?fqt1n>KFT7~KU0>sxqR
z7E1<Pn8K+pDBEDlWk4Fj8S)=gsk$XIj2DN+$u9AjWwjL_dnSu6fk?Q@y;xWXwD;bo
zB;ca8jjQy}(SZ2?5IG;mhI3cbM;j0yVyddDB<EdRTuh-2uZ(D$>oRbvluXu1l?>BQ
z*KvBY^=e1#<<;dVUCo$M;e4-a+UEP)9%W_iLx0XtQs_SXC}&sKE<}fPP1EtL?aZb8
zH8?Gl*6|FtPVAkx`mH0OYZ9d~WpM-}#`g?*cgyl~CI62@X7FhuFh#K=(y8i9_mk((
zpRZiGGHsYvQlhxPX<LW_3Mr(t+2P17U=dovAue5_A<EOAHk8~0W4=w-(1r2!Eu}Oa
zv(_%re<+%_zu`{6l0ph1QbC7o+q4>FYj+hnJ?hh);(qsIIDO{G&D3z)hr&s27G8mR
z+f>Nj$T@^SxHHOGV**UYX6JVYx2a90+xv(xtR(R-*6a+VAjQ*__B$vD6!Rh|m3%9w
zji&zIU<PQsN^xOS@iu#R>-##MI}$&C@WH8w!dVx=Bj_ITiHeG9gz%oM6gdVS^;yv&
zP{lC3xEvvcO2Vc`-;+|u*PG1awS)r?w6Hf^5f4$`r0DB?3#qwxpNXIf6}I}RUET>A
zyC5n(wgn^a47~d3aVaiN)*D4|C~Q+0R?(mbr0A<-b8t?GjU$eJ@!Kfb+}0Z;=V<sf
zG0Ihs+Hue1R%6<<F^8?YlR{bqK5fcReKZ~BHyu`~rzCGR)iUTC22YMXz=DVwZ6?Z+
zZji;*n(Fe}PBY30)Ipy?FKOtOvv3L%v2Cjn1)MPD7evWGvUK%vlOemlOyb)~_60g?
zRH-M>=n&{|)o<vs;7WDb-+sFTGm^yVQop&<KJY3%{r#0_b#THd?ndrnFxX}dnLGuC
z$0(`q0bb|?!Q%(IBfPb>wXn}ryaE?<yGvJNaMM{8hSA^>wTS(qqzVp7+wAP^(?%r$
zsE&o`9|@~)5pka$74!+!J|4Hhdy)pLLavDpph<66%l5r{?2?|2c=<wb{vtV?k^^KG
zx)YYK`{ur?H}LkogN{1)sVDg0<l4l%q8EIl&3K-9NEk<^tNj$z6{aubKu{yOg(kUl
z@R6AH__Rn!IbZwtG~caxygr`~PnYW!XTIJG%hcxvKs)l#HRT9)(hP8n<id{B*Y7#V
zHe=k1dJV19+L0twgOiKdS9M@pVx(ia49E6_?c;=$*-eEsk4ozRdvU$&q}Chc5;;wS
zJf)jL1!!n9v6r|z3YC!?N;2T&X%@MVg~+zgYwT@`<SQNtp>==`tpZ^b0ZHaAC^oAB
zl#wL{up-XkaHRVlzsS5$M*ZmFJ5w9e1pxL9`QkRHuX+Xt$rsPe%#7e-Q4pMYcXj{F
zks{SI^`m$TTB(eWk3CSBy)ap=o60D}kJ@ZBfE^G@wu%QvGwl8Cx8Is;*XbJE#UsRr
z+LZ^nE>oW++d6!!u-9;~90nfYD2gypNe;zYZjex7w5JuS|KH^LAJr|@Pr;5JbT5?+
zu$K)Kz$f2m4JQ^A>IIU0b$Eo~Jl_z@|21Z&E|Gd6DodW}`fSlMA&MQKO;saiSN&sr
zsz~P=O5?e!0*=s`bzc$pK9^rmpeY=Z*`LGe`g5(?n-UJI#1z^y0vH5zW2dmFLg)=8
zbMa>5<KnjA<QW<oa$_fOCsQ;Y@N2hs?Y7oFUR3;SE<Q-D9Ad{AxqeG^6)&&Zi|RDr
z@zl{cLC&ufN6?w+xj9nEF{hiD#i0Uj&3o`_EiEk`ELLl%R8fyAC9~>GDZf6yO)`5v
zEebM^p^z==2R{BOhuu1EM7XPG*T`H;p)wp4sRRA2)`^^t!#Wk6j-Fm#qxHGPk_a++
z7}nrecS<SUZN9~oC0h3Rn^x8IkKiLJ@aPm~-rv6EzGjwLJVB>Wfs9#A6>D#x%=X*+
z6*SWKpFhLjyF}dmAhm9@ikk~r{K{Ef!P<>_l$O#DgsX8Hd#}_DO<m<C^T@l<V0OV!
zrOMiPwn+}rRo!cLMmI0P`0f*=u$Sd;USU{&#{GvAtjYdtZ?A6pB50j*o<Y^q)>fAR
znzKtn$?|at4Sjw6eCx5Mn=+me+DZx3&dM`Rhh5y<5)FFoWlBWTCm#0q^_?(JHwu&V
zGd8sE@9(#yF{~4$2NwlO)c8IqC|PIx1e3$m^UFIW2Sjw2Mrf&;;M=8aV@hU*YZ1AQ
z6`rm<UH=|dFEUnkOf!MF{WaHS6FXI`P8fO@6%|?P_jWfOwhnyJC=61HS!klcwqv|{
zy5IFgGrcrSRVN@LmG`hT6*AK86kJ}nRFf_`OQy+qx#HNq*bt=8N}0^lW>S=?NdLqj
zi#gwlJGu*#p~{!<jJ$JrwX_fhYBzT5)9uYmqmvU}jRGo$8~NG=7w*l^&r5ouN2SWM
z+wb+h=u@Nf+U7rr!?;On{CVi30i`fdI)>(n65s_8`mN)-+G>{3;1{rqHohorlaAop
zM36L=e!0qd%~oelH8nNWFc|bI4yV6gbi+`_%=C#YN&2nztK4N}WowPv?PcE8hjzW(
z6j<BZ8a<*@V~xd^u^Q8}v{z3(`UiGnl%!|>=g$_0Bm<dv9kplr=@JX>aci)4;W^dD
zoT=pd)d@OtJ>ADk)zdec-LA7131}=U=hQYW8vJX0(H+-)lj?QG_=g(EYV+&WZ!2Gz
zF<uZUD_>zljHIN|r#mXRFf8bSAdiqS9n&ii=E{&k?_^=)xSiE1G-4g6hWybTQ2f|{
zbLku`eSHYm-5IQ@R_0bb8a2oC<LFXDjGy^_SS<L4vNI|er^q4BDx*77#|@xZcTmh+
z$FWJRisN~euWyO3JX-6iWU#89$qZX3q9ajo(<ZBOKS{|lN6x#_i+lK2B&-vuu_`D<
zWg`cvXm7KzUVe6`XS$ZKq?dq5YH<%z#QTbVzD|E7X|WY`D%YO{(oPAqm9@M0xO2u3
z<5-qv+R+R-9MQ=g%(k|eSCTo_r6Z=6^wa0!IkgrF2W-pGr4ds_KUA{raQehLwlb6&
zRhkK1e=@;k3$C_@U?uqZ^Db}6O!n@0oy_yE{tBlx>c}0ox_I%i87<xOj~;Reqx{_*
zZbUyXfLp(uw&B8>1?0Vo#~8oqA$auk_Og<y%TxV^b;QE5aA|cTy>f5`ENXyRbJNdx
zeIK{3uua!1#n>mQ0)&EdjQdHTy)EZKEBFT0li`yFkVQ(P+k%vR&F<}i)32q9?~?m`
z8l+kb9K@uM1@MoWZZE@phX{4wksvH$6<okbGIJvGz@AP=X>6Xwix-Q|Ug5iWtVMCQ
zo-c{WW6q|@bxT*5VcHE9qSCzpvs*i+O9T1CP#$(Ci2GQ!BFuM#z1lRayByBYN2ndh
z+Ba)gMrgbkjh-wNDub-7sBF=oFg~g@wvZD%g1TO=Rey`N(}>BUL7!>tk0`V(mF%h@
zO+Z0H-H}del@E4>dtCa_rVM$!0?fT>lfRH`V1LsMqp^q0U=XJV!K(C;PeZ?Xm{(o`
z`th^mbia+3m$mol)wfog#QPAUtd{P$Qz_~eWPRG@Jsq&gZ(yEE$tvIWG!*x0te~dJ
z-N-pr0olYMDsGqU9!<5;IaS^&p=q0|z2hH977tw!znPzN*CuIy1C`z+P8zZO)0<Zq
z4P6o{!}M2rW5usXX32dZ<bgS6ik~j>rq;i|=I<8IhknL8<RMUWqSL*562da}1Z(4H
zA(@Q!Mrv7!_TvNI3+4)=_YE)<+vD?ts{vPq`S|$Ck*VAxX#|CexmJ#GR;)<VbeW22
zR@=O5Wl48R|HR$f-a<Gxr9)GHldQ$8D)K&f$x(i7R#(GG+sE7awV$zAtb!4C=eC=E
zdk-8qFh{!G&h938D}@To;k;Ib$NL*b!QJwM9x$Umh@&}J7uf}6i8{4#>XrBwM*e<!
zK#;nEG|3S%oqK8uKd!QWlf$x$&`1u(zes#du|+F)B}QhxcK_@*^+IqYG`DbjL0_f{
zf{li7h=y3$K2&66J2s+KXas14dmOt7A0_`zbE3j{a>#}NJ9~9pF22Yog(yJdqREpa
zb<@FR0upi)1DHB9l2`2`Mv`2m+3wBn1FSBKHYR=3isSy#kB(vNxY6TGY3_s_wDEui
z1r<9&7P;NwP%%T96s+3iiN%6b&tx`~8zA!sHasCW=uyo|d*Z94c{o|C9JA*`$ng-I
zexu}wWFy`{%p9aX$u39V-}aS&hRE6o&6B+1-G!r771+yB3n&-eT+vP+T+w;r-fvq;
zVIx*J^mj9mBBa1AN#Xm=&tQYulZ(EPtxYROnW&kph1}_m6<wyeR+4EDM6!xTuo*S=
z^`U?uJ8*q_`J(9SBEhwHxI4}AF#UOG8bj<iqHDVYoyfk@EnYTcr8>qj%U<TTF^+s5
zMl4G`joeW9Vac`t?*$NzZ9-*<sFNyFo@|lq!#=wY%{TZ>`twd_H@EJ=`GLU^H@``@
z2(4};?E1N;MW~U>0bojhYRAPajndNzEL**|x6dEF_&m9-E`km(*YrmDk+)O6kpNY{
zSHQyk7)aaTS~c19t>))+QW=q;BT*Za4;`)M2|vii0<t3=^H2^-?}RF;(%yG8B42y~
zMMNdp+XSDmv|dZ&sWjmwx~S}Ul2nm04%P7_!zB;J8Xl8ZCfdroaHvyzGZ*a-*+Xm`
z{X@0H!bTAxpA1xxDiVufINAA)&1&;6)Y?zMUkn^$W7+$UyubyMT^0KYajn;<FjU<8
z<BIVw3b)t<hUf<lO3(u-rMh2DA#FAv{^DKoQ(aQk2cqce)&8PJi?GmIG>Av8OOwI7
zMG1Dqg`aEh-H;g^y?=WrZ%kv~-u+eXJMS*CS<UUO%}yC`uw->VGt*u4`{L!~jmN$U
zR!$h3nAl&ilIOKQ_f6`Z#O<A>!gF?I1B(j;{M(!VN^maO{zZ%U>#rNmSpT{G29<sC
zu<j`VzBljw$_UBbAga7{8GnAUv+GX7mO#U0`nSr@X4+6*=<cg~G9vrvf%5h<g^xe>
zR_tf(*xqKn$maTb-qrCM>HD1pE^|a`?uYcAZYcRBL^<I>FTbu>VrqKxI~Odlf9|5D
z;u%_*U$yOLnb3vtwjmk*{G9li6sbhdu{J4pQJX#U-hNGHQO!e)-KYB}PQT1JY;G7l
zX0vap^*3iVD+}AZ-s%@;H=5QlmKO|0m_!f1HG{>)?>C%;`N`t_evrE!JUrhke*uk7
zp8R6l>3`|R|NIR9w`KU!xcvWK0><{g{?FH<DZ=pgA^<P|N96OsB#+`o*$-_2DjhG9
zaFFYPRF5@~5clmR7cKyF({EbE;h;k%&DQ5Fps;@nj_^J%Y(<o)VFWbY|A2ce{AaB1
z`@2Cz<uO^%7S)Zs;l7-QBe@KcwMGCJcH+lYk{cV?O&0(vGvx%0tO~B6ZzDZ))8$D~
zdk>Xc(p~oeGi*m4v@1l}(%!xr?UUiv-d+1Ft*tA7XWRvJEnX?Mj=Z^8F})oh3}Z4f
zGWrmmDO8A7_vdj%z5X)|!e6e5eQs_*+IQ%357<dT(S1wpMG;=mnP-8|Qi#<koS@TG
z)zzz)cI&sDDGpIKO%-_IX3Z|pp`|zYC2J{LjWaG4?btR}AUPgh@3STMyz#ciCNEpY
zytVvoeL6Fq?xoMFACcQ)Y25r*(GLA;SJj*hPHnyGiABHnTIBgp{;_NeH`4_+jM7-B
z3_ms!l#@JCmJi27+rb7@fB0~=vJ_gZo10zgP9fT!^768(HZ;I6kxjWTR`)iSWEaSa
zgr8L3=HlvV7Qt7P@ws0$b=A;&+#bs%Jq0354A)COh(5-Gu1u3HP|_6@q8x3dCcTP|
z^7m0({iypv>BFKqm)Si%6t6&|+1Y%J9_vE~SJg2XA;WZ|tc2E;yY2A*2(4L%1C4T3
z6P7b1T9Kp1WH@>R&dvynPMqhY;<YlmaztqiKatP<{ppR?xVqP-Sf*k5!;2>5#*dT#
znV6VpnoU&p!|w|*)^Dq=^_6G=PI_Z6!e^Mkj*J3Z-CXv*9XvJ^J;!SS$_bgT`3I35
zgaSRhaQ1BMb#3mhtJAHBdR_ovhSXuonR`D+N!c4=X!IqL-jNM33P8l;viUV2AqvS_
zRxw+%EBCW*y1NbvGpt-j|7yFmYSk)6U%|SP=NUSw`aZO6@(VZYE?)dCfj(>d-UzTx
zevw&klOp{*Z9+Z0$WNFy9w}h+exE!3MrSm<C455t);fK!h3tt<h5R#eT3XxsV|1AH
z_0;a;i%wo7g;@p?g)l?cvQmYKC*B>~1i6Vdu~{JJr(U)VLdoAgnUG@P(vlVRvVQt2
zwlt5AkDGb~7OQYLmh@98OP4Oqe5gaK2&Eg)fK1Y3s&8z_?dajaBI3l-dQTr6VbR83
z@JCcWmaD;v2|^&O46g5|r<Rm-wsy?S&RP$|$b^ckr*b}cp;p0HhM!bzhg#^jDC;Xr
zMFR8&-G4cgw`xOn`YhY#)z2594U&v0<%mv=PM~HDFA{@{4v$$UL8CEcLLDu<r+0oZ
zY<^gB(*WKv>uo+9w*<l>7-z+Eww>P8Jr|DCY6Jvq2Zdx&|2j|lN1sJxcb(Zvj^nd>
z+tlyeQyJ``r-d**@(W7kKFp-sq2IZ8qlzNf4pE^nH`dymJ^M2z;Qp>KKYY6z!Vh&f
zH@DU3?%$!q?XEwmDC)qu52y~xK?&44J=bb+b((<z-Gs3`8+Ewc>$?M@YlhUqC3X>b
z#|e`AZltFbq%bG&6DpM|0GapU?81eKnppY|4avLo7ZUw(Xh+$;=;522kwX(e8-ybF
z{ok{=&P_4^Bp>^Kchdji{hSiA$on5H09oDt!<YO2)=Q)mH7@x{6_-n%LF>CC$+uLc
zO&)0pC!`;1V`wDfRMuZ(Jx#YwZOqserk}wS63Mkr?{-*gWtp9mlM@WQ$LcxL2FYV3
z(?(=Mre;2QotLUc7BeTOe$Fo6KrFFP$G)?^Ic;09ng09wv2X#)f}Bs*uJQd%Db>A5
z%X8?_S)C6a?9i?~tzSF8+`s;p4=qPfpEsIC*w)KdK6I46Y@$_t8fVSmwQA(kDvnA)
ze&$1h^(iSS<s`rF{VO_px9uVMz`Cy&khh~Z_X3JQ8LeE@&MeH*DAPo3_yAOD%q_j@
z_Qdi{R!;=#facX0#WCkiqmp;qlMAS^@r=`g#OeOB-)|II00!Mr|Jh~k-S6b~0K*q=
zoqqBGt>(OVeF?f=y(?{5e?5R~PrcFzXjsHa;Dlh-`&^pWwj=bJ1ZE>Ejir&R3l$f0
zmHX^0!4hqO0ZZogvjg-bnwQ2ld|Mb{0bhwT`Ocg@Z6_1<9%I~$DlRS_6F0!;JS^&d
zQ3`e16-m!@G5aU+zl!jlw4TVnKp}4kqRj1=tp$Q}KlDmcP+x{?1lqDrCC4+W_<MVM
zRT7TTI_KxJ^$aBN0-bcv{$A@mBc&!q+t2b(lL$;yMXz!;*i#oin+SJ_3+HC@7nzF$
z>;QgGo0i+M(V|FPm<08SCMMJKf%(^{yg|%>MCD7>A3JEXi8<qbM_}SN=P9_%-|UAW
zyNAdpR&5E+)z%+e)`mMIx$bj;ghZX=xl?q#^t?b7(Tz6)>#Ub9(|7bAK*@Ze-&Z6g
zw9n<@#fvRQLD{;gqLXsK$8am8=Zj}dho@`liG2{TT#z`>5N=Liy>exIw$3xzcMlg0
z9;Y?3b;iPNU;XA4ZzLi*b(CHI)K5?LgY7HvUDOcPV{Na+v2^<R3~s!Cto>Ey_+OMW
z|MT4S|CIv&zh2J%O0n2h<S2I_aFT=K$%(+SXoKB`Zkr3>{~iO4F$?3C>nEi|*9}$O
z-?tN>{9EwdLq6y(bA#k(z`$pmAq{_^S_Xsh@_X)PWha^jSe!=2qbA)M42O_T7n+x$
z7>ry9_(~wvOk|;EFxZfW89{_0)NQC>Te43)^Mdz8f<|)3c5x31Se)F1f&y7UsIvXp
znhLXx)kJp$%an5Dw)K4)Uw+iX?ccPb2mt){3uOdm`My!5UY?#Kf9V$D|8E%2oj+mC
ztx_z4kuYIK+Z|xuLKIifPT{qi9p7xFP%%T)^ot`bk3~Gb2*Vi4gvtPu0c^8Y8wHXo
zPjQ5z@%9;Rs>2gl*V^|D`y9c%=XbtLH$}$-g^KJCRnMBsAB6CcHBp>dDNF=N6>eR&
z`QkIr-ZMsGi4)DQutFhKwz-Dg^XsCbHd&BZ55{4DuV}TioB7eTY|D7uL}T@z={2l~
zPSvEu_axG53{FW7rfFryOLt0W+1uNfOXl>yyX2TcN)O=8PxXq#29MKe%a=%=#6^;i
zdo116+`?h)_g1lZH5>gnX7qC%)>@i`@7$Dgx{FPX?j{=z$v{SJt)>$zhfaU#v5XK>
z8I9KXc2aFX^AKL-3bN=y_-o;?3Q4d2e*0H(i5Gy*sp=<ZcSUhHmAf822f1c#r_a++
zLqGCN_P57-&-by}d=BA^#VrV+<4X&nm@r(r%-SzW=F_!pD1P(iJzd*mZQDKTWmVrh
z4@e}KwH?-URh^Kv=v!eGJ7GRx<i)Svy6doLy|v}vt0leCZ3+yR>ZdV<diu76)iiNd
zuS$qp;?>_^cfQHmhBY7EqS?RE*yPqHtHJ0<$3UZHrTvlR?-W#%k{rt0U6b<+MGS-X
zHL&!GLYs1Fd4AysGIlOg_0(G{Y?U^UQRSSS?qQ>m9;~0qTrMw<o6J}CL{-OnSzphl
z{j9n^mnK)1`N11YE)>{GJusuGx0$g*y|$?L2pD@U)YY;ai*|^-n58dnb+Ao9=r33^
zp`OYQjG3F)hd^vVZ#;Ulp1_fjZ~&A3;J<j}ezo_bE(ub)5EY3~ncsZ3Uxw5NpN}hy
z51fDI1$9FQL`Hl*?{O4<f}rH2%KRrD|MS+@_qpe?><Wa+09x<;BvBDYn=8`-hwAyF
zMToGm$Gp>^G61~Wc2l@FH8IhA>SiZy!j&<vn*Ykr&#$S?8o3#b7WPCM?Uk=nQ8>eI
zgitcvqW<A)V(&%`Syqnn$i`7JkW{v-2fau{SU+WFdn9!*1|*c+rwwN5XU93U7@Hm-
zKGpbF6N|Oacdk6EB=-?VGeKk1mhUmtso1aS$6F43$+@|1Q+<UxZHk~9d3jIeNTSGt
z&IB!%l6enb1Ae$qSb=ntg>3M!tF9*Nm(N==%++}ssM$xJ6+lHGMA_muH@v2l=>t|Z
zNC+GY>%&OBROP2vH*&8n?qIxlpq#Mx)4k_-OECvgk<y)M2@KzPopc@jd?v$oRZ#{f
z+_ux-ZM;>FBUvyrpUj9Uolw`msx!zK*0k2+*p%z|RodCkexe&^@%(NpI?SRjUpsI9
zmIg5eBa*Ah$aI|IA}Ibk?|y^<u~zxbP5UtA!qBx*=C(M>AjPTYyi_3qxRz#f-39!Q
zSzd>%$7nR136V-Cktu)&s$F1;tW^a3E8U-63&P+YVA=yDmIaA$Umi8w#>-M?q#HN{
zUXFBavb-<v<!V+PUG%yg1ZO2w7|ro)5*&HhG$2MoWG6X3U$UVCltQ(tR7)^uFk$Ga
zly1qelQ#QKeUub0=2mehwGgd$lp%!QS*vzn>(0oFhq6?-@zNp*pb&Q&&spnj|KOni
zmnRP7nzFz+?HXj$eR~~2`gCE1!Kn}h88o;p&6oH-6CD`(yn}_LVdLNgS;c;Aw`tI+
zk+%UQP^e_>(fjHhBx03D)`T*WohUCFGxc(3xPht(QM-G<o9_!zhJzDsaH^dUNO>Ov
z_^CpIb03yvfh&+8uL)U8^q}u#0fkB=Zq*e=6PY`7>kdE@8WS+7PVn&2oZO=HvUQSG
zA3l5-nzw|8nd|_(qzMNCDQhB*6p}`#RBe5Ie%k`1Y;9G93lx&ZB$g~(jr((Bm*ztb
zIqElGy+ENB_NdyI<0|_^ehkqrDL{p~lVUVN4eNk}%>*AQ64~}gPAvowgkj0X&#niY
zKP-o}AP^r&3s#>aH9leTF3(S4{%&hSDmuA$I~VwabjvqL=uEM<2Vu1F!FL-xz4(+F
zR@kv!w59jivu77+DE#eujDl89&b&2O$m;asKoWEZ@RHD(JVDD&UqN7;A8lDoCbOnU
z0hIBJqGI4l_B}>W818BAC9pu~faG)xrxvGJznDpex?<+IA2q(H$mhm^D$8K>k6m<U
zO-xSaj^+43FLl*R9x5uFX!5~~+;I1V4!De)yu$6B?M$O^l{N(Sz!KJv6n*o|Vrpg$
zIViEQ|7}i7%EZSWt6_JTol9%><{rpY%QSR`bgJ5WYEmyiU}cGDD2YkRAB|$h)66CF
z!e8q%dQkvH7*N`aKCPp@af+F37P9OSsoPn$_3CD7bab>(*)7?Dh7Kz}_!LhxSs7&^
ze|m9|Mo6n5ygcsWk48IH!|U)BgCR6;(`ffk*M%rZ6!4?QrTj*vxPdXGx<@Am`X64!
z+t?u0J9KMLL(kWKkw!Sr2<%jPZZ7X+6FX)R^#YvR3C&nB7{PNkTN$@bQ{R^!vj*n?
z3$hsh6`qYl<-c$$T_+1oM$O3O@(}wS(FSV<nUD16*vlj6@9=&(4M2^XRmIgifEw+D
z?$5eNxz#3u)yp1j1N=A*hu&~P_Q4{q5({vTke`5?O-7J6`K-)uI55s0C)_)PAwftC
z`p}+9{@U@~!DY4%y6cSa0bqexHOJO1DTlvY5L)rfQCxDR)kJnw3Ef%N^J$n!j}UP&
z(H&q@-`))66hQ@%qm?Gz2;3KLq%>Q&(K@cjCA<3ill|eIA|c9z6K?kGO`ir47=h@X
zP*fxj)t?u^#5TJ&oa^c9+x)_=p4Q4To1dHZD3xu!i%ilsr*TKybd?*Q*7PHORayy%
zTSs(^)7-Fq`^-nv%;i&CP3?0$ZCMBiyNRip6Lx1OBxMz7PT&d|@!lHYqsQO;>UCqY
z(GB-GiKc$N`1VB4alJqBYoXguJyVtvob~-v%XwZaC+nr_L>PW6g8R^Rx-L-xLKUv;
zyiDEK@yi1M5$=iy&}5m|3GGZ{^N+n|PF|`y6SIU(1r;dZ5bjno+d|g$n7*0e%|}e6
zqjMD{b1GPM&C%fvU?itMD=CKO<cPLkm_yCo30k1SGtuMu3ZpxkYSIZ^R|5%Hj4$7W
z#^3HIqzHhiuNfjCTW8SXW)AbcocQY|3r~UmHp{fYoK`|YBExIBHVSaMiwNu1uer(a
zVnOakc*{I&`ye5fO_r({vXuNf8NImv@ovQhk1BK{k5?%|xI=UXaVc3w>?gLV469<q
z@^09UZFblM4^)pPR)kl^GYe1Arr}zPT<g2HVvez_-{aTW)>Bx>3(lPNJ!J8yNRNJo
z&H^1q)#M!dOf+%>3+5fXT~)<=|NbsfbG<AT#4T9}I{pMMbMW9=)l?}~*F?Aw?5nDp
z^PHf741$DH5?f6j*-aKCC8VsG26Gw)Vz}1>itmUh*QVs>Q8o=0o*ZZvsbsyt%Sa>K
zk(dpn3Gn%}3W(tlA|ZrR*zkG>ol{gWlqX^o<cTgK6RTp5iO>kiDt>-`1oMq_sXxPI
zji4-*B8)Ecp4;%JSEPv8D<KigEE;g}fd3z%W8@GJrD5*7*KMFV5+9U&O?X{P__12+
zz4_g*PGhsiabyGO@9SkfMmh>-TL!C~qDLc7M9WDF2nZx=2@^4dBMwCu?#xPb1dJl?
zXZ4uh#$%fUYx}0ftu2aK*d>_<*Z<eCmjf{&x(#xZj&<k{*{D<O!G1qz8fM5<6N1;C
zezr{E4j$<=QN4g`A-7s8t=RKsL5i(J5%#uaHj55LR9rvZ0>63;{@OIGxi{bUhV$IF
zJSHjS>w6I{lB!_aJ9Ay#8`=r7+PYDo!N(z;)ARY>+F!M2e3iZXVHX5C2CB4#u69(P
zY2;fxQ7AI<%4ZrHi~xcglkehw6}jP~O5x6?=fg-f(&qj-oF!JJ<P?+DW+RbHA<Z2r
zmoHzomUD$OjJEmPd#9q%Gbl3(+m`H^aAx5n88i&Bp}LgO>AY1w<C&gdM4L2gs59!7
zwBL90Q7f(_b}Hl(+*c!)5Wm(rj*T)6*?k6)w=;;UmO)IYxi7@uHuZW>iu2h1x?U2*
zx!;Am`F6xfL-eh+k+(d>>4pWP-JvVv`@)$vYi`Bp<UTOE;X9X|KR(`MRjSVKIcPy|
zPgM1x?H(Ck%TreyI$R*c_XyHluDIk-q)YZz@NcjdQK9ysCYMY+GrqPQb->LthJhDR
zyY8f*nJN@=z4TFcV14sl_VYU^Blr+jC#+cpMCSv<$pIVh#Uw<kANS2$1zv5at=%^_
z$9X00yJu<0t=8j3IS=2D>1CJHTCn@s>~g}m2?6o5Y_U$w#gihnHu8s&47Z}x9`hac
zw;sy#Dp?=q7@-}QqH{}@)wfSt$=WH`cFpaWdZu-%fMPn0sUz#F*XE@pvPE%Sf6<RT
zIX`{Vx_%Ol@LTawDoFYYHRb+-$Ww^sL_QKEM6xU^wJi2oAABmotbD$6&o3>ZP>J4W
zo7z68wuH)H-mb7iGO(U)@*-VfeKV*cq97C6q>yCHO5u$r;@HI^X)$*_ZI8zF&@KED
z-`5e@HDSOgROI*x87`84zn&oeoH9hJqA+szc6>pB*KXz10`6z>;Xs{18UemYiB9xd
z{aM+Pb5zb(&zP3(I+tB+q>tV2GboVmKBtvEsm#-$vGH>5D?@-E6e>B<SL9MtLfs16
z1*RD^-w+Z?1!o&dGocX&!eNdJcp8B{7)0tmlH7wbEHMj{5r#HSohk>jW_(clR*1le
z>S|B2WwwrC-vy$y2M`=E=!fJfNC=Jve8ZO%Q#1OC*l#06TmJ3gb=9GBVHC#NUAT2s
z5;V$)P8FgbiOje84fl*@p<*WLpk)Hi?=HG)9pNu9^cx9^=}=OfIkg2@9uen&!ZN;f
z;L|m7mtXO`ZD?FV=rsg1>IJCi%LyBe7K7brdNn5s+{kXZoiJC~IK2CvXl2)c$zSes
z*$<5LaRvGm<&+QCzAkjjt{K)m)h4p&tjg98w&%#RC)#o$X?$4ZJRHJZx6Rb&I?hjb
zcSBoi)c%A>r7!-yoLqT1_e8eYXAo5{g@nepF^Y=lur+8OM%mkqvf#vsqM%mJ1&Fv`
z=srMaLtDvf6Id1~<Btznof%)0fsP6g)GvAW>R%$q4e|8-yjq0DAWSIk1gXjF0{c02
zA`1k)G(13NhSYE5Uf^f-qUCLT481{@Q5>8O(Q%8&G=x=<toJO*1PJ?zOF<0q&+iJu
z*VW^pM}0uAe-SWa=rYj`Y{?OBo*hW=1bowIG;z|Skb$g`aA%}V1^d@Nqq3#+r{O6}
z6HnWa_QX}=dL1MH4E{PZtofe1RGFrN&-Vxu*p5oNQ1}!g9%z&8Az6LoG>#?O7xb?U
zx9`6In_Umw1VKXgrJc-3a|G^a{40lUT2hv1r0WC;AskljcDao1Y|?0P0qGFYBTe9<
zklZ4;apUpK`IzdA`9Cg<$C%k!?uWp2lPc}#tgNkC5mqj+oBfq-Ua8oYtA(|JyNV<F
zg1M4JWHnN+wd+e?Pc-3{g%qi18k+170@%Tn%oOEr6fY!LNXFhGp9mrKlZgT65$m3>
zE~j}GHs0k(<082C5_&G3V4u@fqat3Z$f;G_P7y;|IM*drvZ;1P>XpUBo};q9O~KsA
zxu<A7XUbBuRaWogRC3Z_2+!jLho$x_NUKoTh0dcIdPuKEg1pg$#Z6n`F>H#`N*$sN
z(gD*;D^Va06Vc=<$JsK1?C@Qmqmn5yr0H&eH*#}BFFXdl+6rEqz%{CZysS4SBDFp&
zElSf#m4@ZY2#FsgFF~tKmCGYbd*eJ2y>+|mfDH!N+b5fEusuObKTmGT!EEVq2thLP
zU}s59^B$;*kP?SA_(8HXg#Df#2`ec)8g-Oj{(3?Nk%|a6?#sAXk*a@BMyA4k(}H?F
z--p0~$i+kI)DEHrB>;iayq}lbmb$vU$whPgznr_i;yW>y7wNwW3+p%>T$6IyXkDEi
zOQl!GC55rK$Wh7im7czvKzfC{IHR}tcAU=9)Jf0YOY4}I3XC>1pFE=V!>Z%Qt7ex4
zjV`4e(xF~%t3F|N2s^7&?rKLzSht+IPH2UgvYDNiOLu}!>QS7AJ#c|`h)7J_iKg(&
zOq%hHZ3n}|jg7XB=l+>KxJM)3H#0t6qlEiwk#FW$)?qeNU4xS*^vf@fM>EEEsk;W<
zzkeN);KL729zXwNr4vS2B9yP=@kf;q2T19no7>eBJ1%P^AAv8{Fq%$dA3y#9xut`n
ztu5M8hk<LKMKi*8D_8DjRKfV+dg|d`J0)kyOFy5*U3b9Y)3<Od*yAyB;j+^v_OS(1
zQ`1eAsj;y<zn6t=(k*O|%1saXL)12;$stq82U#ZHew%Bsx7q^Q$9?F-BDKf^A7lCS
ztEoA5jhRc}ih57RB}HX2X3vBiFTaGv&rAJXMla>_sWj;qdAbaHpd0<U1Pr2i{NW0f
z*Zixj{;ZBNQ#~|W8=pFKBDIbd!!xQNEBs<S=$4j(2&^&<)+r6?Xk1+_@C^?vU<<}x
zMJB+LY+3AuEz*jLikB_nf_)37VV@vkzK4NdLYH}GIvl0RS}XX>1UH68N3Z4~9ur4(
zQw{RPF?T}XF#a1xp0`Or`eAQHqIwKpl;A*F&`utI<OebtH9F*hn5*a~xJLFF8s9$=
zfEIr4Qn?Lm_MGUmENyCfU6aGMtBe7Pci%Q&-`2Ru^9oD_7xVbePn)B!ykUM_?xk&2
zFOUa+L}u+iKbIF}WvLjNqwAL+2(3Qfi8w9cSKY8F#ly=rzG8d!c5z7gcu<0E$jEpQ
zog>WXP0YPN(|2cPJZNji+!@M|BOT=ilM#|<RO8<NEG@l>N64!;ZBPE)LywA`Kj-9q
zk~5ize{XRSbC!92&$VlbG>E7fE4g{)&p&_Ov*&%s22<E@I5_-bvLYHmX(`V;SX0dE
zHY^Rz(Jr^KOs%^W6vyuUYfvR8TRZN3u*c`FoVvCF;mr&hoPu~hIOfvzYmIn$d0}oQ
z_-sM^>BRmJWo_Xik;h7H%AGT1;W^6VGi7TQvNF6m7j8aLxQmalE5mv9k89RYmMks|
zc?bXflLiJiVet@Qfa%)KPW%eqQ|~@+P;V(`E?TrmY~RewHLy2Z9-@2&`E$YBHwR)n
zDqv7^<6>XK9*jO&jQ}D(S#j^gj>FE*5<F+|_)K#){PfdV?DsQ&%5ivj_=Slt0FUyq
zjd|Z&HwkOwgIJZ8of=>F?ZHUvcd(Mr$w`OT%5%(qS*75d`v)?qVM$&0+m2c#W~Qds
z7yYEGxBFe_1iLGmu|&2lluG@E2R0IU2bLx6YfQCIxX8I+tDoS`=}1~Qqg&*=_jR55
zRV`Y~35QqJ%D?>b%dWHjxj({4btlgU=$aiIRr{*j+KdZd?cVyoiaYaYEZ2XHKV-^~
zxr9n=LPcH~k~vXAw2?U(A~R8Dq5+9)q=>|(jVRj?B_TqJgp@f#33)SC<Xn&a+iRWk
z@A=~#t7WbBj`tbv`#W6seSfa)G@z(#)VYi)JB%iQ9k4yNOrO%#7s)#neLgb5e58Rl
z^F)>Z`Q)9)?I7}AYj!wv=#W>mv|Y_=*yjDA%Zd?^kpNGzmOPDR<KN3fxyg#N^bMd8
z85tYX5lzj_8g_QV@SAQ2EGNahrs;aQ?$v+hYECsyPSjvP11`3Xx=gRD-P->oXcHb~
zBVZXrpFc;#yh=x|)-7&Ap#Nt=L6tO3d6}HFG&>?kEs!-wc00i0A*!>}7=nWW5x!xI
zQl`z5zzum%bKItS0fxx}RTE@vwFjp=#Jo2gEwi`^c#LgVz6oLILL=`~JRUHKDNVi!
zd|xO?D!T2qGI_0MKCyFcTK}mj_G$3QVFMUTMgi|l^(-{qIWUnQW+bp}8%W)Od@n_C
zG0!fuGaCNQTndc&I#)Lp%bwJ<v@0NWeH<O#{OT!iVo~w&=b~B8S@vLJn!miYufw)l
zH-`&yfL~tw4)$%TJmVjenjOT(n{aMsGeVcyL>WYy?U)s|Kf=Qah<vXdO{>bw3Ky2b
z?u(a~cD)@jZg<P`1W>4wMmpzMG(F2k#V6_EBR{lLJaKcyfX+Ye>@qDcl~YjIB%&R=
z=Vlm6H5nKhq}(j3*&6cmQ@aRg87Zl88iU}|S_yMF-=8&O#xp24(f#-P!qVimH}i>w
zQ^{}Uxj8wF4*L8G@iUGou4JA+p}?Dng?PpAKYAZnQCp%_U3(`+Mk3Lh35kl*>%<Ax
z0;HCCEYA>(e>z6SY#>n(*?rBP6rOaqRD*j7ax%N3|BMiJVa0;}BP1@)1R$U~Ag3*K
z+^n2*wIBP^F7*fq3dSO8+JOLeNMFAJYbWhh0cW%h9=wBwS8D&aj?{A6^3|vNBg@>&
z{7yJMD6NK?a!6OV4!wvM9<_smLnd+r_`P-5kQEPbC3f;Rh<cn3OY8jceB<@<uEs`2
zK*F_LTqFT8jEs$C1w<mtqyLd}M=l$nHSPH(JMS{q#}=1+e(5T>OkNA7v*OuFzOBms
z1P(KxMq}gy_4ml~A*#G(tG$G^8qM1>lbG(K;ePITZiy<Y`~UP^afn~grT##kXlhrC
zNQwIQ>rj>=qcj|4{`pGKlBqcYg8DySzZtR4AUOT!>oCu{s(=5FmKIk7@qh4RrjNo_
zRe6}{@kh$0%6V}az>WH4Sy|bsjHhx)UF$Ato+lj*UdkVdyKGv}fa%7;%^j)aHxcy{
zF!n0rM_{|$Gb^UWMAd;)U%hXdf@itwW4F;yXEc~6{X7gu6O)qY@LNDSReCIG02Oz=
zI1e*yVKq4DoQ_{6ACu%A$<`u*Ga-9)eEeBkTicL4{yjb-6YLizW@b$Xhu?ssiy(1K
zY4?Sg3=6szii&ENtwqH%O$+Xr78qGsS)EF&Y&^h0H}|ER9T{4(L|<(X<7fMxJsk1d
z)T9y(3c4)J&NTYWRSMBBC@Ks%Vvn6VHcJviu-$^bbTQG@VH+5MOIXUd)9yTj&8zz!
zD;c!ILz0a9JC0^(`S?(>va{2zMG<I)^Bu@{Jh1-JP)Bb(N~sa_8t)#Tlg5&Le09jr
zI*d8MtSZio$CVUVOKjQaQ*Z0F=#FS0$JZyCAM2~(Rc0|oQTz1n89>RI?aq5pnq{k%
zK4HNxU?-L)3hrA?Gs5gZSojasxFJztiY?%B?;d(?Llt@zM+P=fZr;;N4G$03<&7#%
zu57eNc{e<Zb%89@6-edMi5{@WA;OXBMj(_scmBoM*Jm|*?r;W6E_JCY&89jy8rhJR
z;Ns%CWhF2U)JZrRFBqVCBX#2`w!JqYKRrvgUhb&`H|M^l3+uUsm?D;5yOg0`)if~J
z04nH3oa<1RO1bixo144Xxm|e2+lp_MXQ%iHp<TOJtskGo&2~BOMRK4amZAC{Ky7X<
zUvWh1((V)0f)<4vp+J)%<E-DU7q4Ei0d7T0)HgIx8QIB?{(EPrrC8Rvn9!Mxgac)B
zUw{9H@$t)e&i3esnqIwXz@EtCGOwWWqKY$WHz_QI-xxvQJdRANw%U>re5fvWM@LP9
zD`<A>47q9Bpy;YAAH{)*7{YH^!uIZTD*^J8^7Z)ehy#!OC#oX$MBo3cZS(o@|Ck$l
zq>))L%thqoH)3FdFv$ok^!aPoID!FMBHxY8(@^){Ls0cdzO{z|)@$dzrlTTEv~k<i
z;*eN6^G#R)l2+H#V+Cr@!!#o|t`QDE4enT9Wh877|At*X)s$6K8bW|I{oTb9fwICK
zTi1ubjCRT3fihpDg$<8<MgR3`%DC0*=BB0^tlGrmUU1B(uDg2^4!YHn#D3(Yn|%tq
zckA!kAuG#GTtn3kvoksdhB|QcL9u#<)zE~rG*^oB5yfwPubW$1p22L01L+P?v3789
zn~jYP-b?)r8GA24tbKG;_?1*~TXlo0zs~J-@8Lo1e$d}v5tts+Q(HT$s+5;u$@8f*
zk2^_M6wZdx=*!o`{qEFOQT3c-CvgkpQ%Coz;vW!j2aUpwJtt{fGgMr?ZewDp-!aSt
zn`hV+=|pN)E#bxu=ZBQ}%}S?G^BvODBiXJWMn^AZW(t+WpKnEV+_3Eocs}k%-{Yob
zoziz89Smy*K9X==i@1}TLc;kUF}x)R4uT2n#8Y2Wl9NTJ$H&IR5cjY5gf327d;i4P
ze=)g=40#8)Y{zd;=8eeF+iGDpdb21~B2S~U6v;iTr^i!jQTTqUV{z=2z)2J^+F$s%
zxP<YGm@(*Ri4Tw^B@-{_jd)}0u}^$tCHu-q8DN;yd2p>0-Lct*mtaEaAYJn6ZMEcA
zD-JphjEXvpS^crE)6-Iboj!9G7*O2-QsvdLQ5=Z1dJ@?6QAXx+b{m`Z=CQdD%<GGI
zz$Ni*sGwrUx<LJ;-m3Ka5P`z!^qzE_(wzb7%O}1AQHKuuQ4;6l<Ki|NOPb`Z%ndr}
zsbGFjn&uYUpI!YGauQ54$8XM1AS$QAdeNrP6PoJk;my9SpJbgf$Wu(XTIP$IxWut-
zhUCl;<|e~jQxt-+kg~EMk=f<q{d2*+WRaJS1|T_YQ`74QCUU!6WZ<%<m#)N1Jb-Z>
z&H%Rz*?EoeDfx<@{W{?88)RX@5Bi-UoV!ha6}@Z^$sq+O?^+4_P8YdL8@9y2Eiox=
z5V1<ewuZ>=HId}SN25}A^R}L^d{gQDW9~E(;|;m6&!|^r0Hw?B1&H|qDfap=FUf#J
z=u-L%mY}H0OPl}37V1cx4+ich@iW|`E~8jX-puR5rjv9tDd_^p_}2saQ-632R&OD8
zS?Jvp{`u0}5u`IV^=4zab6*GK3gXtX@aV~K-1-H~jF>jh{zr}rq3}Jc2%MKBgcKF|
z@LmpthN_lY?8YD1*m_}Dfhf!g^mN%@2I!}v@ESa+D!!jMz~OZ@+WO<zv11S5lSGKJ
z(7+Dz12Bfew7#InF|n~V014p<eyfFvHb#5H#VKE!ICOC-7BdE`RE#|DLCm=1p0miE
z1-&j&B0qJ<v9@gX&?9|m@t%3fwe)nhtJ>=jp0I2ameYz6cquukWf6w5hUj(OaHmLZ
zh}_7WhmIa?M4iM=G_|%qNB8Vul72pBpMoz^TXE2$hqC_+0a)-b6=}E;`^o{oXD38y
zMlps{(gv|g@!{*&c=+B`1<vbEw&1-*u`B>!DkLVxAeqbz9j^{`eS_-ZBS#Lvgohxo
z9MA|$LbmIqbb$VWAcBK~AB3(1DhJLnfjPBn7DU?NPT{<~yd2sk#P0J#Ocg$(F-tvY
zL|!Z(eVL}v=HbYXg$6<YpdeD*u~TD-u=N(Ape9FOK@eAdIZ5WX-7-Igk-T(kcCDUD
z=r^7WKPj9Q<21#?79qq0bo-`77$2R^bI<kI|1@AWw_*w-!?dgSH4huMknq4QQF<W4
z(@lvuf`E5m*EsH!p@V1BCKhBd-VXCK0@Vg)W;fcEeLG#sZghjkdNsGCk%w*JQ^B{M
zqO@uvoP4#B0nTL>B!A!T5f;Xi4rhlMpw!vEb7zaK&Cv{H&JGv>*5=Ev*KD{gkn6mc
zzgW<IJnk2jy0EYj9$>7wmc@1l^LoEP6J6!kJGb(~FL7l%Q^Xz+nrXs^zI@qP<xj11
zSb_5g9ifRw{le|(*{M{sx2RZS$L!lE^mpcD)(N4@Z*+J+Rsr;4W#e1J@4Q`3t>VAy
zpn*7jS3m6(Wj9%ECRDD?YcgO^12i<wzyB%c_Kw4+QjqW(d%5{>=O_%MoV)UQVPs_l
z+eqxGdp5zDql8xs6JR17?07ynlu~XONTrM*4X5ZkR!Zv3C99%wM(;%sa3MW8C+`8p
z0;?WT*$iQDlt}yzC5@d-&lQEB7Jlj}+erwisPs<+K7LdAVQlOYx+$Ut!@fmd4W?X6
z-@k9}%T=>@1XAJ>ItjL17FJfR`H3!?%uXiDEg0;u9UurmL71Q^B+lPf4N)S7fJZQu
zF8b6&85tRA|MM_+!9<Sq{{5E0Hur5*N)`*ERL`8fiR?DKs(9B~hzxK+YqOkg<@@d@
zRaGws+MYdI2WxXW0<PFu*!2VHR>I)6>Qyv&=2r2biDqKC)_-YcJ1p!j0`GYTCX^e;
zXUowHN4Gs)8fHm69T~9}o6p=VdOi>`FTY~14B&R+X&gf2BdYzWS<RJ4@e1|E;AKvm
z`SCK1tKvwX|BpEdfamsj4XKm)6<25|&NF;K;ds7ojwbx*nlaxd9DUP5)xV-Ll}j}c
zOT2>`gwSQ&?T6jcyB6Bv9!H;ZY`wI!G!-yE8i*vtzaH&-LL;<eN29~aRd0uz0CyMX
z8+dl;w-IIw)#jWbW6X{d5w&p{i)m!$9N1!V%bG@5_;{|`?W`P;UORjH_Ue$JU1kpm
z0eQC?(68&=LqkKMY;lnR&6g*)8V97K+t4Dr_;hB7W~hcps(OM$+*}HA0AbLtN&L7|
ziPMQE`m20j#-7U|Owhp<lE#qYVwGpiuXmS)YjZYBh09+4GX)m92X6~tlJn@Kr5-3W
zg}}(yZ&PR?m)+|tB2H5?I5_CAZ|l~r1grGk+-=HO6GJ2un|*WKCcKAF=eqgR(2#-&
z2Wj4Rry@hKwekJLn@*ZDVSRIR^YARRFY~G}Lqm?YMb=kfW`d596l|idTsd&^<aVUh
zOEB58Lv9=$!;%#b^?NwYy4a$R9ugWHyL6=!n<tMI$tG>`FFt<ay{_6{4l&t3vxM8O
zBC1*P^vPYWoG;E9c+1e{{-KHD_j~fY7v0^*gFTu%<=3ue413>c2$VNvmKkJLne(tG
zvq}^^8@;EE6%-T-XseVk?_egR{WNriw7RSc{?3IJ96BPFUyd0ykk}%uM7h8CU}E|r
zJ9N%*zJ<xk^`&KKNMaq#5#f*#-dp9Wd}g$M!7>Dc?bPJEh2+3X2*dh|gu;_ww<@pq
z4S0C+`gtHed3#u6$?=}$!wtxF2a&?|^0Og@(P@MWF}(vb+r&{(7d2wTIK(aPFQ;3d
z-D>YEI+nZJuk>upwHNuBnd-s!B|ROK{|O^x;Y?BIxNvMXjwFFs(RXj{>+3@)DgA2&
zs{QrHN42!*U}Hzl@6yt}U_1_^=9TX{5JvM`f1UpGzN&x@DGkS<H`^qZmcO6L{)`6m
zlwW3ZYW17DVZA{CR}!FD#dOF*U`SI*?dFu6=p(fzTsC0~nTl_p&u_G}v~ce+si>?>
zW*&ysvCxlMTjc95kMF5JitCIVdlEypU|fEvPp#OxvM;c=%yqmzl>?V=<XDFmyWP@Q
zl2<!^>s3tdsQWNbwMc4&ooF;l?bNDQ)@nkhrtS74chrIgu|1{5d-Me_Jcl)^`n&m|
z?;t(U6L7X-!zqS6H_&C;A^Zc~DgI+t7?ujXw)`3#GhieMyn1F7l7j>2yQO^nV6`j=
zixuad3m5398<(+}=1rE*WMNh-+sZx%{oS1ls6cmFlr~NH2-bFVaAODbdO}D-Vm)H7
z={Q*=sw$Kjrl_>|@{nq3#tc@vl`7?n4u<UwhWTr{88;c&)|#5Ea~Y8SZEUt8kW(R%
z)wQ*;V+?UW1XF(#0!W#78&&{>DHJY*b4_%($V2tSFnV||>hQt2>3&N+&*no#klYZd
zx1a%mj-v$$8;<0jd3YdK+V=ZRTtFa>5{Z=KXM?NDCD_lUu>MBRzz{Qg36eY~{x$db
z4k;<A=a?~6P#Nj*Uu07+qlf{?W8d&>Q`4bcmgTIdPzYVx*Na~V8-Xl~Lzh59py!jZ
zt7p2>Qv5JRQDN?G1o^&EN7V;d(reUUx5xu9w@cmLzsaI+kbHrq<@#|1KQcstsCpjP
z8g<`wFF(wehtcuePz$TBRWC!uycCQMQcUO1-U2-xSpA3-LqoNZycR_TNA&gS4Zo>*
ze6CAhD*>PHfmri32`iU~NKx!J^%yFxHsF~9PH&4{5IGz7q{-Mbpv1l>^%ZptN<MA)
z2GsYy!l~BwBJ2IPR73dHn<b|s8QA_79|S9jR2ya%7M`Pa=78=6n~Z`9K2FZ_DBfws
zH|y_%e}Qp<DH{T)NxrcL5Y5;A_#*=HsPA;&`+SOEj&8E#5<qw~NputT_V#Ft=y6Rr
zS4c-s{{fKVCCH%d<?fb=0sHdruQlPt#l^#egTb?G!66~r6Pt%H4Iwsyns~6U`s|d2
z@qL^AVGKRtRyIg3Nc!u<Z|aJm(#L}Ca4~gpu@SIu$SbkftMI<ieSwRE!*GJ}4w8B%
zUeL_k+yy-`?)YS^nPTOJilzQx6pW7Fj3kqpHf&%p<l>p1osGml87!FlD*WL1&A31J
z56V&~_1k2bhU4|&oK@1Bb$0DhLkMf%uTyAxh28$g!qgSy;34>lJ#d+yg9MAgpV#T$
zeF+X^0-&Ui=F0vCVFx2u8?qnS-a>~SL1(yeTqLo125~1VZ1tCRN2yB}GfWRk^E2Y)
z5!k(%=?=h0<$>Q9)9i<<$F)w-)Sz5wXsC}(Xc>K16(Fm+x_qYK#e;`2N($I$y^BGC
zjXF1{6&R?bx;%5BpvCJ?^bW%S?gNce{v0v*MOALl{WtpkC%;m*<X{yEIhzoY;`m+^
zgk_eBsH|xnHgYBk2iYR#51hvIfpo5pFi*DHD590aehFe5i~#Mrb%m|RrBdusXZbHo
zZbn~y33bpjwDcYx9^jWtVAq{2hA<;^WG%=f_oVLPg`iGm#%oPTPrr)$7m=0i7!Foe
zR({|)sJe{vrc2mWK$idhfq}x3lFU%kHE=6cRaN(j?ut}CemnxdK?M9wcqEy|k{_m~
zuA<ek15A4f@6j+vmpA+8apM!sqdw&9#9XQef%pp4j;2u`l;R?Y8Av=0DEFx#jhS0t
z5;3l?BpY>vdAI&Yyy%ZH(l2S=QHN51#~F)O9};kr!ET>ebY!TCSr|76)&R>7udp-4
zVe4pVUq1~CBkK*sg<8mBtYhu)l<KPv5y1WlO-zQN>FMbi%9e)y{w*lY=nW~ObTq)0
z(-JT-VL~tB>*G#VGJjDL*5{fgZzmu9-<mcy85Qq(VC6x|$~(H70+8%TDZ;_Y$sR3Z
zRCKiW+2pk!<NCm4v-UHOp)}fJn=qj(&AKQBzZAv4OG?N(IPv!kGI4O4M~e7E=#6UH
zZi6Kgl!6-B?L!EMf0>u$mK1#CA=3q#r()M`Db}sZ@7{Nj*OH}s>EEBiTPt>G<|1m)
zp^@qvx%5d^nK0_Hd!4D#TC{=u6cbIs+W)Q<|Ifeue=Empvlid}w>tHF*y&t@nDuvO
RE|Obg4<9tpD$=x#_%CQcZ2ABI

diff --git a/reference/plot-9.png b/reference/plot-9.png
index ee486d34b3789b62776dbb17a052c2321ce9a52e..26e1d4396f4b4c0e1290c10aabae11f0dcbe5a7d 100644
GIT binary patch
literal 26378
zcmeIb2UL|=wl#VzOD!X!A{nYMi$Ea~B$t8|qM{;6l7a+Dr^q=}TPT8n5mZD(L?uf+
zWKanT5|kW|f&_^onL~JUAFaOkc8|XQeRte2{`cB3x^=(_-?zWL*P3h2xmI01eoSTU
zDy~%&3T3V8uSzE=lx40I%2MMWmg6U_p;4dl*K%Vu6(#(QKQjk&eel=H-+t9~pip=(
zk^e0T=^izuP=2JSD*bZG_1<8++s#w;5i33$In{Cp?fJ{{Q~ORh8n3ZDbb(7rc}=O!
zuRIq;58JmryxdyMp|a9)bM)=3qxUT8vd)RVy?po&yZv_+*YC?R6+IMP{Pe_n<*P^g
zGU9qzF0ESI?pS+Dqiwjl_nv&ORv}}Uk?)?&HEqu*I3gcey@GjluXhPLEn)u4G1!-#
z{Nu+H-x-p>eU(<aGJmsS+xunY2fyC@uXsTwrT4yv?I|s-L#C#tZ*Gf5*L82LW~{{L
zk8jF9Y(QCmt<o$yHg=bQz?T`%%kip_QycNSzFlwr{vw*b(|2iix>nu0C9=Bg>?K~U
zetYJ1{_&5mQz%MbnBAf0&E>Hw8Ij3WY9YtZXB1CqYL-0s<wB<kKSSr<Z!cEb)gF~B
zQ4`7EG~-!t&_rV2zT))rwVfYts0SOkw2NC>S{e@|(t_2!`ZY!)Mm%?PxON4t3v!tL
za7hKXggYyZTo`}i?{8si8+^X@amlVH_cq83rEApH)fEfp4sI~N?`D?w`NitQU@>Y(
zUe<JHpb6b;DoEaIwwPQ+ZRCPuo6q9>wTf=($wP@^9Wh~sT$lL0U9Iyc8ct|vY|I_X
z)bsqX+Q^?%%uLC_VeC*yc5HwhZ?)W1+Y5%AN$C}dkopEEx4x)Lor7uB!R;<|IZG?6
z=+xA`_q}I7J&fpDx#Qk(so_LgN6rZbpL<`FpVyP4;SFYmSek0tu_OAPqa}7VzpPL$
z^qzm1nJFPZ`!=NX^^QoMDY}nOU1KA)KX%i{v6%w~w{G1!p{?CGEFZ6xXqlp}!Zubg
z6cEXcXQ24v%gy2nk-~d)yE5J}*0)i~MsuC)L&5uPTGBn8oSbq^j^2|JEwrqUTN^Cy
zKHhg=(LB;iVzHE)`q8E7?4`BA9v}SJdmOU$u}^!sxw*%7+;{C<&KRN5y=QI-YCHGM
zpMG}cmj&OY>nx_)GU?uoxwLBe)+o`yD4oVp+_docOntI<ea~n^zHoB><Y{ll5RE#a
zqb@tD^tNhjw2WWm3L}ce>*-5I{`_RC-+Xy_`4?Q)z{Gi5+p5L6UIpryY{NoaVq&Fw
zxI|)mm-EN@H{JF^rH1+LPPttLQym^cljqe_Mp~1mv&k8GRJo>OgHGx>)xqrE`=hf1
z$?P_70)=ySmIiS*MsWtawArZ)7BGfWJZHYF3!0BNn%}+fDRN;~PjzJBb8l^ZJLNX_
zfkbTluD<AyVE*|&74E@B?6=YrD!lc>qdtsQdTCA6L6hOO?4d~m-1%TeqoF9au`|Oq
zE-5RRyX*N1{wwXytwNjk=+yL2da{~Rhp|>~X;iwuzw1Q3wn;~x8*j@<Qtt5XbgV-0
z)1)NB<m$Rujjrjg5OTAbcorOzw)$;YD&B-V#{e&S=&bir+*{_6?>aWz-fMa|!7yK1
zKp?ZlGJd+b+I#a@Z*@p%Y3ZwOd+gMQNlC&To@14_MNJNQ&Qur$cjSA}_IJEwQQ!$P
zaMt}WfwRWXBKuh}K_^APK3jjEghXkeNC8LW{Gj=2FGHXC38^oyw#aPy&U+%>D5ySO
zyX@7qEnTrs?g!xCD#Brbv$M0VgDGXkmw26bj>~mj5$jB^Jr=0K=e8AlyQ@rL!ETRX
zo>XeH^|`cWoJYw{he(Eg)Z$=sS{Jsi^;i?R+!N7<*g_r1k#BQuwMqHy!wYgovF)*e
z1Dey!%kFy&>o!pbn^H!Hb4M~gY{MTmHQJdRy}NH07dN*_qk&5h`NmKYBfe~XVZ<jK
zgC-o+K<9)nU%p6<<c+OLre=4CI#0ds9TPPzzxiSytxDSJ!o0$lYazwFE^Vx2GvWx5
zJL>7_ah+;+>H2VqKRAD?Be!O%PD4Ftn_8%2=MjmJq%5kMS0UM3dQY!c@efDIk4Lev
zdHc<(`^?sE52|l!y3N1O;`rG-ugPZf3x$^6xaZ7iw&6>B?oaEztwffsXPv=!$3L5U
zk+WIobb3efMHbGzTNlZFwJ3yDbcaayKRrMfK_Ic2<lb+8E{eWGe)RI%`+5@Ll_C5J
z1vO*?+NM!OH;qX<n_a%>|BB!3<F%Q`j~^eEc2=36AMp|4p&_Qwo0_toFI3AjtfLk)
zTWwo==EV?#<`Q(%TG|Q@;ltGLNu~VULo1j_lwgF~U%r@>Lk)Ce-dX!4&b)2U?7Pbk
zcwlMUAEog5%#G^gx!Qf0n4hBai;IifOe)Ne*LJ<y60#?6p@CLVKP<YOg^#Dv(8K23
z-aA?}YFed)UFYQ;nbRGJejArAUt^Ksv;D?pDvpd_qsEhCHW_ph$U_tsT$PkgojO%~
zkVP*2xr3mfU|{6j#~psA6kCMThw?YA3`+8XBTk$+!RpfCW<K6uZ<WSFE#r0B^G3qP
z+WN|VhaT%^i$RhdIu=H*AN*`O6tK`1Jf5GQ+t?aKCnW6g{(OOdlOg@Lmuq>2ywdvP
z7@2ibBoaO|-S9cIDC!d5X^X}8D-@a@Wt{X`7>i_YiDb0(8h?KOd}!FAgiU)>i$iZ!
z5SySDzD2umw%++*v8YMurj~ieRLIhGQfDsAH?Dgcx<_|IWo6~S#0<`#mPMq`!i=Bz
z8OcYFA8$W~1oL7*VeSLV&~U9TxfQ$q*eA|?a=RWUdktCnP1b80wVM@Qym&EdrYC%L
zG8Sa|Ay1)tq<ji8p^8$m9yOnB?b@}<oV43H{RKi3^90OT-}ydo+i#;K?CdQ@Gv(UG
zILeoIX*@0#tCp>`ZOTa!7hASPU}2uVn17Ig!^tOPNSpec&bsALx=auDf}TYPmv~%z
z^X-8u3u{Zu)5+CWIK@^CWtj8Q%<=z5$TRCkXej}1yfR&%U$TsRF-aJ>Z4$gIogeF}
zljC?ke%^b2z(_?RJjmbw+7^}474n~dcP_KFu<%Ddlq&dgE4lcW6}+4Hr;AoRjEmd7
zTToEXGyhAupr2Qcdqj6t-`_k16@}Tiu1M3-NNNHChF%l((=Ck!oB0=~%c6}6y+zLy
z`AT8CnKUHmCRSM+5LB^RB=5LuzL}Yc2U0}mOa6u5K1|RT7pB%uj=Wk^oSN#>cKE{b
zwf)C=hupB<tA?<>_K{?ZZvaksh$j;l7q`mDYK8B{9mn<zHg;s&1Q;8XRka|@2Fjax
zxK9nYcmCvtghGv<FBpp$+0=4l`_Y#7w`V-%ZF4?7?rWt{J^M8x1FVwsC3@c6*%-K4
zC;(B{0-rvbk%@mDl?qRKt2t#8J0giEcBW$Md!cJ7CS|49x2bjFum?v6OUEfI=2$nU
zk|%F6-uG7YxA#R^BL$4pye92NZ-;B2s9y>xtdwZo)Do|q+;N6JY1U<Hx!Ca0KKsR*
z8vm&ac_bY7*T+W}6v+1v3`D1-q<qeBrrfURUd3?g=E`F&$qtUt)Ya9k8<p-S$v0i3
zaPG@QrpW2^r_bc6Zf<To&P`{zc}6+|MS?}VM+5cF>Ex#5%oYfB|I01|24m|g3khM2
z6e`@kd)IhjZf3M-DNC{Hj)+a&-QBFLDaIuM({Ie>H!hO>*0C_KJa%YVhLs+YZd7}&
z%gC|&zclVT^lM(Wb-&Y*N_obhhO2K!d~yNTSWl%@%KX>NhiLLNF3!>+p>Sa$*{<0%
zjLneWvF4Z2_ZL`72a*f<M)Ic&kw8dJtL|NG^zssm$QMp0eUE_-+h9iKlXfbWxai_a
zSK4|Yy%a!)vTG`QoV0dly}J7P5>0Ib%_i@v{k_Fsx-0YsZ@fNtdQZmZ{`h3)KAmj+
zq>^{*WQMc4wrd%A-Z}EP<n(iq{PB~4SCgZStQ3yhI`qA9I&u`NsQ=sZWqyscHhZ<G
zi0WRG@|(N-8jWV(g-J*?g!s&L9bh11hv=r6m9VPtupYd$E~urYWhZvN$Ud*JN=eCy
zO{eRxT?);R+~Vm#r^$U_=5UE4MY(=)PoMfTVQ;f}<cleS1t(^CUElxi=d3pnm1Nbv
zLTc#Q%XJY6saCuM6iy;{s_CfqI*m&|HXCwsEu6|9VQVP$8qV(R1a=JYpKAQUaO!vO
zfzynfV|Vw(wsStK2o<stWq4{JHUnS?=NV?(o8~g|#^0$8P44RUYBgZ)(FZo)bqKYQ
zRBM4PVv#?@1OY>Ow%IWUKQxxG8|hnwaEJ=bP7Kg4YdQzrI3^uibAR9Yll%7VyZP+y
z(b^6?U!-sLEr+i7<09@Dr1cDw$lO<Zt;l((IfhqPV>)LbY2Zk<4K8hNG{nc6w%C$(
zqcwF~8?Akaxgagu-?zWv;^OLj7%1F-e7&BwHhbY*x5(;bJ-x$c>z<w<XXM3zU012i
z=d+pwz8#5>&iL#;^vp#q4TrU?!()V(l{JuEB-m#r%;(eRgSEhN{EIV@i_NAPYz*ng
z7VjRZGNUdpqN*Jp40{1SrYu9IwB?tNEoyS<JqYufo(OLvN-;Jv{ZFG;4UJVR6qYP~
z*WlD@Ln~RzB3m<j+rW;VL&v7`&HTYt?R{#6aNsW&mY4joa$_ep-eo{MnY-cPTRP{R
z6txp{SPN$=smv;7k6z{;0fD&qcv4vyP!j?I0yYoamS33eN?{D=mc6+nwwlGDqT7D7
z#VWbv{F8`=PVE7eD;#eH-nYEN!@Di-<$m*4zE8M3K$+;dS68zZW(T@}IjvaFNdme3
zW!Y-u+3`<yh^1w~rCm=H=Ff*UXulb_{g6~eepX}TK;7J>v1-XD)MX8w3Vbbl&%XHM
z0Ha(m`AV{9x8NwCz7jx?zdrzlD>6*iO^suh5w|wa^8_O|9bf=R2fkb<v&Yc=Q_Ksu
zed6LJh{fy4Rv|C_RCC+Fi9R{bSc}E6Fi1YhUUQ?}YpUH2h|1;Z2*5zCaehq(uT$L#
zyAHSB5~M#oujIThhmnHJE_AI079ypOvyWdv-NkjhTqONs$$uuXr#aQsu0hwlMAtk*
zv;W<y04H7z4Fdz-9g&{)XM2VuvC1zp$+J3W!UISo>N)+{6^BAVpcAVZgg~8Hxj+uA
zF;39v+*mdBK=<O$)=kf*+j`}-f6z3qyj$P-+;1RfIA@^qo<r4nm$sxlsz*OF7N%FN
z>8MOVE?YnmQihb;8nr+$0AS~%_B)x{22Pr7xJ};EWsWcYSSgD7u?dxf%&CL1K{zr%
z|LeO-g9bj!Ke_L2DLYZ8VW6d<ZBSNs&#mXqfLAi$+My7>_Ne)aii)ncD*QnoUS6WL
zPUX_)ZS?`cfqqinI@!L$>24Wz8bnl;o%wjf?#t^PF0O6H9kgMRVIByX0nqSy46>4>
zML;(nvb8I!O=FZN%;$Ra=uyw<m;Av1E<d6IV!y;=|0@txO2t0!xoN4{zUZz6#z@Gg
z+9zcQF(!!gZxO#aM2(fM@X1MUKX#9URLks<XGd)ayu%JIdEvLZE39zNR2FX@E#9bJ
zj2-y!;X?su{Ibb~?qveVF6unkMABtgW1VKB=r)g`4DB_1vQiybpBGbA(gO)WtIn=i
z!?7MQl)tO7aKVX`I-=%P_jIy;JG+MC=N}IR^Kg<&<-tKEn?6a(?<qp2$4I^;36@#i
zp?bzh!J`Q1hv)@fB$e8Yzf~#2X(VN`R^sW56DI6q1x`$GoA?^VfoE(WmJDgROZOp@
zOZoT6l`y^!Grf@^N4BTEHd?{wdL~vq=Ae7a%y)SI_^H3;WNx9tm^sPL{LrFOu?znb
z)D5|lA5Va!QB+Swxro|H*ojkdhbFijWo2b0B?Rbc*`+nR?i|?;T!Zu0Y%Kx6pr+4C
ziH%E23Pz3O@BcJDK2Tp+yEDMIDlRtmGGNuQA;I_UR}jZQ`^46nQX)?2>T>40=P#P1
zB_&n$a{3<I1-w4zcD=fH-t9ZZC-rr8n--3sQV+J4&|`j1TwMQnL8t|}zW*YYihR<)
z_pJWsmiuoX{NIsrcRb}IMVwgWlub-bZr+->cSJ6%x~%+h>B+Dq${Jc)TGMOH)0#hb
z-53_wc&ATKSGRxf1C>vJbV^Ftps&o*wk0k1J#{NAOa*n*4~vI>uj{tYemeNEu)>`!
zd$YukVHFck`7_nHxjX6Ja`|{XHNn5si`z9usf6XM=@V_3J1BdgXm!zJJfn@D4@jm=
z(pe=#eO<&XfzlD%Plly5=b29mD{er!oS>hbT4$;lazaaMGYSqTrM$%uyGR9!bUc+_
z;K%8MGW<*A#jDRig*m#@@24?&NQ$E5mcQrz$OHOsVkrMFdiMXv&GF9{{Et0N5*JfY
zJWN*@Eo@Min>aQ1N!=$vlI|(yKGDyXJD94*gHprp^Yay;iMyAV>10~#0#%BG*}P1O
z-8I}2d*o+ctCxby<3Y(2{^<z0c8c`&8`ZsIF~ScylJX~8rYou5wLO!-{L>v%1?;W1
zxi;~wq>|18qr4@oB9Ug(^z1C*o>1ttJmLxiSA~2WzVFcCP;v26RrNP}oH!}zCTBFA
z+V*x{MUap?grb9R%dD(#5m_t3L`3YADUagFk$i2f7A73@EzI_#Qaq)t?P5u{6}#XP
zZj*VL6muycV@f1itv9l<v4O!P>3kIAkU~#oI6J$c>scnViArcN7evo(z<AV^917l^
zM{fW86KdnmAsRgxOtGw?_e^(I&s{5H0OxJeGUU#EC!E5?U3UNw0REh_E}Wk{fy_X%
z3l@}r?){1`0*F&nQwe9ZN9Wm@e6J_!k$lKHhl04+BEjQE=j2H5y>o=%fTXO>fSn>D
zA=wVSr4N+@5C3LHzJiiCAhBfRB2fU=$qcS%Wo1&?La6+Kve;Q!H*P<oL#2bvEF;wx
zD=RqHPo0OESywtq<@-D=t>2woRADwLHh_(9YiicBp7`PS#td-M%1XsI)a9G~G)V@h
zSl_cvTTZn?6wE{2NGbxtYxvAJ`UDZKv@h-4>+Amh9N_eBhK13jtFHcZ5Qy@Ywqf)8
zQ4pr&O*Xd4z$2-teAi-Cl*XSrEc-PgviaFe0>f==X6XYCq*8&%YDDD+-{%+<F$lB=
zh~@^9fcGoqDTqyl)91_v!8Hy{7^N61E6r^ww!sY?Mdp@}099ur_Cpe~vq|v&$CY-r
zSsyMEOm?Yyo7>unm?VntI#ii~L^U8G{M3mPAI4TFo){l!Os?BT34egxUCVH;`1D<~
zb>{lvLrQy2r|&exzSN>W6{p9OqB}2RX?sQ)Q%c$yV=H-K>H`1VnIn<%asnMJ?sSmH
zS-8ipDw&ZhWqclcP_XB0{@o_oaHMdVRl^6HqY~jl>5}7uj->KlTwu_$^Ib+d!f%#7
zdAuV+P_g{WOdb}4({%5h7xZ6%y!J^-hGhv*PHGw&8ot^EbRqPXva<DNHI=s7wNcB^
zkdKtkprECy6c*=hOHvJGC!0*j>13Zxm|?p}>u`^+wCnHh7wDY-IV-W7Q=r4B`~!o&
z=pEil&J>Oby{Y!s24<qAD6T**<(uf;c(bLY;vv48F}I}Vycc6K2oJ^IUk7z?;7Uuy
z(<qv1KY8VLZ&*7}er_*8kD%TNn`Ycv^!w5+0-X^q9p}eZe7L*f$xz-{m4VJSvUQ3|
zKd~;MDz#emPL9wEKNlKM3uesXz3#jB$NKxP6)q&Np*+i;@b{IBtY&ZMhJK*XVyuUJ
zjz_s&Kwucs!^C7F9xY(yj{*YgWIq=PCv76{deD`lT$Jnh>0MS=uuLbaX#dhup9Dq=
z0s*0__3dA88%52g1aX8!uYsXO1T}J`-rN-z9L6QS;2=ft>l-^%92^`5b4Ln9T|d9C
zbUsEtoHF$KWA5`Uo7qJSc~CrkS<a|yXef0lWVCZgJ6goIMtTfq-<EZ=k8gEuIsYUJ
z#2wD7`;C760#COP_ro)(k4vRKUA_||ywuluO+s9--XWfoKrdxFjGwHqZTV7{D?Uk_
zznT%F7UuqnYik17%!n#J54P;k>*O4@K?h(K3$^MByvYT#mV~1rkqSG@>I%>Py_0mG
z#lHIZp;}&fPgiC&;M8AtTgJsU4@I&lDmD%oXKU1p+si8|B)d47r#?Md@6G$8ew=v}
z>&tK6`&Cw~F{A{PtTF79moQyUrRjVH;tzIg_{==@TMrklCM$7u=MoX}x8lR^^T^+n
z%l~}AKiA>wet2={XZJt70RQ7_5~m%zy6-#=bgWvFwfnp5?;cfETQ_+&%;(L_EjhmC
z&XLl{T{Q<+x=Ka^a^SG^=5^n>ID0Y-A@Am`TP28EY6i>4e?{___n~Z3UR;=0Gx+Y=
zO3*0M`P6DvZW;yr*9jxK!+~neptUi=OMKV8>Kz*ee|C$M5BOrlghNV7$b~sMIUBUU
zSBwK~wR+=gtz<)9kfz0KOCmH+oOt`mYgu0S38>xMV@6}oTUhRKT1jzKl#d)avAASq
z49Xm>A6y->?^W|tBD8>KEF|~s!)FSe6)$?+bdx?ibyb`!?LRAq|DP8Gr`|5@fM{aO
zLJTJqMZo~R&+EAM?cA<@pJ(;vJv%3B$d4%rF?~y@US3}Mz@#Z4A&a5kaiy78Ln_uD
ze93&txT`XYYqiWs-qVBx(9ra`9!UZo%7{co*iw^{0Jai&9`ahn5Y2I47x4HgK)};<
z=obW6DJc=|jPO!@jmbv$9N(#qzE~}?iSdm2@p8v-q5l3r4@|xc684owBOf{8oW6&B
z;N_H*=8)q9<R+W80JVfncZ>LZin3<DGoo(Wl93idX@(iPpWkCtAj{`H{eeT=@|2Pi
zah=#fs5=B_N9T1l;mdxj*`1wsG2hpIIBZ3p6=*ua(qLY0a<j`;Z!V@4&JQ(N#wR|6
z_9L7>{;mmfX(zP05CYLsn+JP#Gt=9Ty5U0a4O$5+e`AMwu-jNqiHiJ}YcHmVW=y`D
z@N18c-0A#wk=)Okmv(r{^Ao!X4@6%=w{2$8=f*2ZR4KnD*kxtJI8EMV3q8HkK!9AM
z;sy!5qFkb6eYnVN4Ba-62qJd95iW<6iV=yr!1@OQ43rYpA22tN>OB_D$_jfz=W3(r
z1G3ZaFNDW4iz?r9j^~TQ@$<_&?)#gB$Vn^*G061gOf=}RvWl9$x$O#YX7cu-vK{nw
z6@;}d0z}Od&i;7)wuH^;v-hZ819}F!+nLvO-QB6=fm-OE%S2NqvQi1uw^j6Ha9*p0
zvwl1V*@<KCxmU}}q&oe#tQvX}>7653{yll?ucz^xAv2T!80vXa;o-Rgy?S3TUI^~7
zuSUvh3N*cPA}e|E1DT!fMU0w*ENm@=U+zSqX+H>^HG*p(&}hy$rJM;l`kcw8=9S(@
zu9Z|;2(Y&AbQGqavjm)CBcG!<r7`Y1)g5LOd}O=E;8`ZF(NWP&S+QyOYpKGg9QmA^
z72Y0gi$);9L?(2hEFK@{%)=@1N4=sl(vjZ<A{NRDr}@1TeT!3ti}_96<kKmRdU(4G
zoIVE_<F)Ek+9EMPL3#}l5HRkqi?st+?%baRttgO~e!M|Hml!R42_rQystJ*cEc|@L
z?nDeTyj&)D2NT4-i%~N{Wjp#>-Fxpk`RVtl<wi~!Fu%|M$8`kc{c#KEcO65MbcbrW
z)k7=zJ)dbT83|w$>>NWa=uY>+Mj-?)G0u<^cm$$V<%3BGPFEl;{EQ;$gGaT(qNnWQ
z+?d_s{8SkP49Los(N@F;L+RU%6TkTRk^)ru*$FG!qL!YGro|%UrNKH;u{%cyq6E9n
zODg^G5xN4-?!%N6(fgp@v-;!WJBgVE$W!o`)Q3MNCXG-M-~x8xTd(jd$ZtP#d%dQn
z<|u43Wq2@rP*Cml9V*2|&%b+gte$Drt6>W$SU!SfSQK^DWxO<p|6!_~L(e8dlPHFw
z#>M^|Qoo%+@kod~RMs3Yb?7_3Rc2)!#qJQmVRdH!A<MxdbA?NMxBTKvwF+Q8<Q@)L
z*WdK*O4v@IDk3_Fa^PmCIC*l<zDdUsx*$ghrE_y}wG7eg>P}fqp$KW}Rw`Bq4L~X<
zL2Yv@%Gx&Y@Hje<c_t=@5TkhPicv5Vf?YV4g?!ipF>K7?!o>x@$vn5QmZ3!im&BTd
z;vMoQ2JHm|PAA0Y4ol}?UkHBxR%Y<o!FZ<M3zrWginFR0`3OqHiE(k)5X8Od5)p5F
zJc)5c5aAy5nduH=Z-M$D=)9kGbYW>7s><K2;5x7-60wrf8m@g$D_1-t!Vk26x9(eO
z`JTXL$cF$WK^sA#Y#^cLrgrMi`9c;-49!e;%b779kINtz_7Z~9W++A!B(&a<-<G~h
zB^0L?&Mm2~09J@Djy-*UdSR-Np9GP3<ppe$E5rrJ%1SIHi<8tvtwDRT9g=eT<NW=l
zdha<jv=%_p_McYqZ@nuX^&8k*kVg_1-XlFt&o&lz^himz{dRB-uXOs$j3-I4Q4djO
z*DN8*@j><9E1%@$we_<2K75*(DCD%VXuFOFXKRq=_%<%Wyer_@u1<z0XOQgkxj}P<
zK>GZ!%P7iMc~Z0y&r%osQ-b5UD7mA-^e8883#%w0JC6`P<i_OTB}r~HuMDR}-Pvfh
zB4<Hz4S4`fCpUR5rz#bj%a7fePF|c%p8gV62xf#t@*{BiR4$Nj>iT2jv7MG{K|PTB
zodzGozeOeZiiWyeM!h$o_BUJi{r(%6f*M0cBYy(`9;^91yF=lvE7o#tf(yYmdE7QS
zDP{iB;F9C+aAB&<&wL*f`ug0e!k<Db&1A;U7*i~c;=G(adD_b>U-kUeXX2cuPbP(o
zd4=Z6eCAYmi0=1%8GA7tZhAuwNO?q!G=@+w*q;J&fE=w+B+e3l)-9bxqI2L4^q!vu
zV*^^czQ+~k0(*$Nz2VCm){$fv2S{MFY_FLw-E#f>HBLu^m^gO*=NCQDKK=!GW;!1u
zen{iLa9s`kN?{AHcHJUyaV_^|qLsn-RYsK44=?=SE--=jN7S*eh6v@eqK$v#hRDxa
zFbilL`L$I!yg1Cr*bwY(l9CyS3t$Ssjp&cNe`TNI)s}XM6NQ(UatxfCW7I;@oU<4y
zAzpicX7ZkM(pXuEa)>Zw0Rt0w$h7)P59FW;!b<`E#6`88JWq0>E+yPLEFT#HvxGFC
z*ZA8eNSFj2oR@41a`jo8{;x=c%tn;Zh_oQeh(cu-NL)gcrtjMY-WzNjuDUNLL!vi*
zx>Qnn({R%0-*5#M)}B5?O(lWsWglPPh|bR5KM8Lt2|n5p%)C={sN$<;)^v)v^~9>A
zwg_}YUdxRVcS%O%)CxOGbXMapA73X9r_~;lDFHi{{n~1|2tW=B4vF(uZtk(NymI6I
zZHOEci?X7){w&ktpTHluU%^2pN-4594m}KfhMfVFUo|1)5nrr;K&Y@m|4&w5l?Q2$
z!BlbAv}S82$ANf7_OFp^(P@tboJ-qxc4ISh%Y7bXXukaX;bj+|Oh~N8lhU?qhc4Vs
z(Ak$4OPW$lOki#17T>}_*5`~v-*wJB_)r25uG_zLv#@^FTZZ$6l_fTNFR=RxUE|Dq
z=62`#YQ>b>&sVpXZ5F@af{iv>y*O8$0+sE>6aYwM6H+B|QC!oduem4%p_I5L{eE*n
zRtqlU_i20intT@}N3m6X%fKUIMc9>&xW@NEnEY6geV2QY4D{jy9@C#cl<@J9ANnq8
zBfJw!WjYk$Ksj0RL&{<@(&_YQ*XkiyZ8v+s+m1gAMhJL#BGENKlb8O@ONj#jxf)tN
zo{13D_6TEs=JWLR@6#My!Y-`OL97}T!ntH;^1uUQqL27XKo)_%NI+|c6#xK@!<dk$
zMu<)BeLGAEcv7*Sa}Zps5XjbzzzG~^uz_5QBJC6c9r+ifNj5<K!W1#|PF`jHApIdO
z9(K^tze4;yZ<)XN7X~JvK<S_rm_6gs&Jx0Sv$6ulkM&6HA+J-UHJC1;;xrK1w_r4g
zT1=Gwj!f|a*k6ba4U(>vzTiCGpP{?goV>&Lqm%00(?&-8CS}1v@Cl~3x=i%fGo9fA
z0;M7R{2-I4+Bd)6+mPdHt~}TZ^?z#Pm2-C_diDvehS{jnE1Vh@)py0?1<RSoyDt<E
zcwyM3Fc1!Mge78gBc7$M2N%`_Ku6VLi~#ugWqx(KKj^}%JAl6ZCdC`|YkS_bi-T#@
zus|B{5q92Bu9DJDGS(e2^UW<DgDEOe5uoIU3MUWNGoM!&b63^aVCB7rG8$J6VXFnT
z(4wNEUez<-?Pvt>!<ZT|vVfTA!}ByKh6|3^)k6`^EwTRs`&e9Aq~7};$lanQoFLcb
z^6}NR5@9BGdC<Dsy$Ii1wz);j?2QF&@u+0`DGPoG?f?1(j62nyB%xx7;fuK3T@hC3
z^FS-qVtR<`!`-Ow0HT^y1Z1rSnNEL7PU6EAc^MfQcJPm4mHxztUHpqu2negZyoqxf
z$EV#`^&(;YqWR025v%6LM1R09W$-3QwmWLvgSwyMI@&1(ps8nV<dWFVj|5Kq1W7xY
zbTux2c1?#kF<H9KPc)iz7GJiR+>gqu3r17Dd*ERWG?|~PNUPr_=Gr)|FgYw&)t^(v
zKh1c<+4JH@e~!a}2bBDO;xLPfwJ^S$TjBnCurtYaU|XWi+E&}t!nXE()bJR+o^AVT
z4@B#ao{YV9dzazQ2TV%pYkodq`@*;A&At7-48}{TwR@-bi(MLdGdAP;rfW1}Y~E9W
zJ~j1ufA5>T={LQ(M2o4Er1JpbUdG`qg|NbdBur|=CTwWf5;G;$nP99YdBU<Nm*mhZ
zyfP9f`C8eZFgF3mdUu=4%$uROhka|Lh&f!K^b&3uHl*U#vg$ixPyYD2Eyj(uu`rEc
z%EDm-lcV3rg{oa{MY%{Zgc1&CtoAzq-FI(8l7XJ*Ao?ZDA-3GgVY1J@-iaTVjBHew
zeH-hHtRGCg&7<Vup;X*}jOB1jD>RQ;L;HHiTTAK^pE9%Xg{g73EhH-`_~`^xuiM{Z
zUiwvOQ<87NDUtXB;D}a=eQ?<%*@u4p{ch8>_&u=p@wNr1XW9h^n4A0L<H04=3MZEl
z(ARsln4#;cuqlfxAa6ix!&mLOFE4dPo={`E7oE|eU3!@LzIQRHUE2*bc5afcH$zDv
z>a#F6{c(qau4i7i1oH($f^J26=-2>{RZ+%k|LP~=RSwPh^Nt49_7O|+GIss-#2SC<
z)Xn^_zpC3gv!(-nM0vpKTpIxPHT?3jq%0?)gaBE{@Z`_92bNzQyYi&VH$EfbGV>Q#
zPkwt8H^0%$kN)i4i8<_jBg1F<l8-<9=O<mbf%k>nBAIVGul2=u3Q>jFGx}|p<uQLq
zJ&7M)J-F^NT=GFdK`9n*A9i-Z@*1FeW4kg64V2r5G&D5q=r9otKSgf+Mkmale!aAV
z$PEY+q{koxRy~AJQVtkrakF0AswzA7M$E3~j(O>p5ZMjna1tDU;A`uV@J_d)@ao<!
z)Vib^DIp)ubnd3wGis4ATzh5coFpEaAkK}V4&K|r=$I;n+{TG(+wG*vy!QE%%CRzY
zC>T%b-~B*7V%-N@HK>D0g$uK2i<tfNBr%OiU(_taI{FX?H*Lq!yLD$7Fx5ZFnup!@
zS>4x~c3cmyVBe#2yv>$6IV=I>nl%rP+rK=^kJv%tj*_-*O=lTkYVxxTaJ0VmBT;4(
zudm2M+}mke9WI&ta+3H7<I9-)tfm0JLNRd6jzM~LfQ@L)lc=S==4ut@HAWfaM8tfH
zq4eEuMIOX*N1tz}!)}8lMI2W03!@=rsC&8ptf)zeB?X*k0-yx$11cgB+K5%{ub<RE
zEo@<M^YuAnw1wEcSh=H^e2>3B(LUZp$j~ZHg3%p7Bq4ztuW$Z!zkU>R-O`bz+X9%w
zOE&Bg^u|;=6Cg<Xm55PG8Niu(^Xr{aZ+R{sk$_rD$PB`!bWJoGg^;Kerx_0_0NG&_
zmVR+(@FgA6a^#6A7B*Tfp{gCcxaRW8joXW1teWj~WIoEJ2a><#amA5=8=QH1G~1vI
ztHwi23nBQEiIVfoyg8d;xZ3r`RwfZMI<mMhk^*guchbh8`<8a8U~=9DR#qZPpVFFC
z?P1n4SKafkKPD|+u*31P$c-PrWJ9xV-azU~B_+bj*}eaL#pI!H8|qkc!gI#k5bt!3
z;ey5akq~eUKkwdsTA>~)Q9vuWk-ng>iD@<l&sAN1=G>b|1qA_t$B?B9QL+-(Bt&vO
z8wHffoquNUvBSvT=o}~+5=_c^Sa~P6bq(j<M_KbI*9!ji{_{a^3>(>@r`oE{?M=+2
zx@lyHTQ&Tv2ef1ail$CmQc}|JyA&K(czek5Jy^un$sI__-An^^H9A&TdU7oUw2~q8
z82p=e!+N0KHvH!LY9aGEKE$iPZWyxl5W6<he9+wE+_V63S;*FBJeps~=_|aL*TQ+<
zcueXi7}K%ij260$z!@$CZOlkjdPz<=>2gB;Mj!+3JMq_(QGU`z2j*MItyY8xOhFY!
zIIFovpLuOK1&qT<!%a_B1UG%Qfff+b4h(NluuHFnxU|ph?G}!%UK8-B4VAGf&Re(e
zlI2%I6<EynawaPP9CV+{fn8P^I)I6|qWbt&u{T>)-wEz&b80kTCsdtG{Umy|NTCre
zS>6;2-4@o2*J2Y94OT&G?b_6R^oRFS`ik@{SCkF$%cgW)#9EFAAQBSKe?v-ssKkAz
z23^9amcqHmPdwZ4HpkFjl{NRJ!lW(BE?=|snCU=6;@h@1eAMg_ucTOVtt<Tw{M3+a
zWHh|@OcAjdqGnJo=H5t=uxUBfgSsUCr-K(aEsYA|mOKP+mxHpROMOGbuqQO&VndH1
zHj)`(;oYH?q|Zr+A{0$X@N&jrzk;FCbjZ#hdvbi|nx~SjzdI&U&iBf3&mOB#c%XK@
zc!0&%)@tSsoOE7~izu33n6xx8EvQ~E25H95N=+D$1nwciXRJCCy8V3Smh5|C`hEyB
zSl7-x+n=|y@k)NW8#?f5^4)wZ*<loa!yjBpQ%UC}9G`0K%;QY)AsGc6polG@<YI=u
z799!$x3+PSUItcH!j_;T>|xy#=7;>~{$aZnG3k0D7GDAIRw$UvNG?HB8I!>E>^sOG
zfF3SoQ&Ywh9&p96YRt>&3&Q~lhE8jr-R2yETG*6uuulE1l4i`j-*@l2$k!r3kxnuz
z*u|_mgRfC40OxeXt=i*o`!?n!BtBDOzDM!!gvD~Ifq}v3MN1qNnL(<p?Lx)n-D>i>
zUu!zWffN0AE4l-gP_59Cq~8{~RaN~M6D<~n>wGhN#(Wcdcl~MF+x1NaKK!k<_T-=P
z@L%(F+#XGpG_y(=wui<#^@SUU7aE93TtxF*1UkK4_4=dS722AbHte0D2UyCRKh-xj
zhRw~i+_8BjUb2I4UNgIb@?hzfIIGUw5xIwLrbWJlf+9_$N_nC`;ZPR5VOw%li`XB4
z@?lOW^!CCI+N=G&V<Su_V7VXbm7V_c4&2j~UfvbAI;_du3>1U9XkQo5urYDWRq_>1
z{d2kg;T`=Q0{MTxDF3@3ajTnILC+pA8;WOTY1bRx``{c`eB8tRq%G-D)K7a=Pxb^n
zP&yPG@bPMNg@Ll=52SCJWBv8UhC?OuiyGwh_C5<O+clC7JgA%W3iG9{{<A3k>vR9#
zy~Tfs7XS0l>Xd2r>YcZ?sv5t%xOTM7p}GVxpEX4G%dgOHwmm);!9_aCr0dJTj&%|a
zMTOsMA7UauX#!&Le7}@57r-Gk)Gf$7mwk7=H+~j|r>J1IK6&`FYt-?+x>167M7%z!
zwPqMWJlJE9vyWI^NJlk{Bsu3Mm`8m4RN+d(=I|h&i9(fN`o+Pp*rDsdrN8NDxI_u8
z<=s0st+IQkx}yYA8&7?GJ)vHh-C;x#AmsQcG`8%pWadXc7@Iv><WaO_Me&jqJjdr2
z&|NSzo<bT2agd}2pS|ovE2ofr0Xp4Fw}t6%cyiDF@E-lFpNXao9(2$7uhd(+_<L$<
z=vGzqg}^LLeD)(7*wldFzs?1C<^zh5Dc32Hw#B+98BB<iZeFw^k6XeTPMuO1#=s)P
z+dMuRzL}L3ytqzeJu?Q?TwB8bIZ2NuEWq^0!{(cq$%%*w)zPRRue$$h-2Amr_W~_f
z!nTcr)n}!ho`#+tH!CZm9q7r{HGe_hH8I#(rA3B#e%z7*3Ulf~i{oo?@zC7wDbhdP
zcwLNeQEmX~45V~cM5S5&!UL9WE322(T@;x@YYFZPE1Ig|3}}zMB=df02ht`hD^}Jf
z^E_wY=2d<YOFK}4+*a`M4xi_7cq2-b16!@T^7VIuTPerA0UQA($j1|NfuT>$C3{WH
z?+kr6ari52v8mXZYf>cg_PFlMq0)RC-i`(=e5bSeJ0@E6J<0W1%=HpzSG0V<_XDe<
zZ95k$^^fN^l^sF;s8TMEKz6bj=EsVFm32vbH{NV<88&Eaf~EaqE6Wo}Zvk?MziXFB
zkp#6vzbD|#`(N`qC?}(`v)zj3gWuiYtXL}lo|h@)qSy6t<%E-V;%PQkRwxiwv}D}<
zh<jshOPi6A5k2}-!}(f*05(wi9{oN(^L=;27m*j&w~o$UD5z^2tkzj_^30D5SL!;x
zr0{Znj<g}g5Q=0F8W2MDY{pPNf&2vpRA>HmR)ga?Yym8gS=BvGR#p&;t!Eb-67{;!
zQ+3(jpR~BDf7Prks(o2d@sf@jprw>E&;LRoKALIp*)17XCg@NBG~po-o^-C8el>-?
z7n!Z(%7brX6l`+Bv{%$|e<<7wlQdE)Y~oh{Th@tAvDV=xLU#JG8*&i2m>4TTg3?+Q
z0Pv<?uou(>)-#iYqX7=!7BbG^QpgU~cb4IBi<QA@#)0-F4{|JkX)*}t;{o{$M0!Pw
zTEK*@$B)f#f1ik_1%56~#p?<>T&hqtN}+I6`=(@$q%8mHtNO<o_5X8`^{=J;e?B<>
z^CNu!o*DTcT~2_CGe5@0#RUL^*@wL~zk7F$i0?ieQlh$(x&Z$y8M2lbm{{Sp(@10E
z(Qe`IeAk(#H9zG&qp(Hul%o${()UR$r1u8pjSHvO<K5To;mX<pc7#YuE}TFA=r;cg
z-7+Z-2jEeW;vDC$$Z8jc&JrqrORA|FYAf=!5+CRKzQ4L0^$0nH_F+qwl2#sKDu5dt
zkzrzD!ZhiJ<yV8a6E%Lg`W?E@mkzt!M0r9+mE<@jR5OXL|Lw*6Z-6iVl_z9(g#ow?
zmDHfZkbt><Eb_K%ug^W)&k5Po&r^Q6_`aLj0y`r-CqC{xM?Z;*^Nb=Z+@mu2<rkGt
zG-o|(GgW;3QDWXSH_j9P5<jPM2mtee`kx(T|A`{wpCR$zAxQt@VSt=r^Hk(Org5G0
zQ%(#tmO(k|TFO7I$OEAaTu+4ov+6B!{yxtq6WDk9U0A^;zXMUzO+>Bu^e%?9td_xq
zLQtn^!g_*s&v=Ad`1}7YG7a$_k0x-53_T*^gzLggZ&!d|(oUB<Oo-gd`uR{X4CI_(
z{L#$tjBtR_@GOB3C&}IJ^|ml#vxMiX6y5w42D{0-6yE|NfG~q9w?#nUnEcE~-bog;
zO9aDX0ge=HowMm*&-mn;X<rwB`L|6?9IUL-Dj~KRm6(eJvIlLd18F(TYmnXxq~|oy
z0DOVr18Bv_te1#B6cpg^4>e~>7v}(fcw_gtEa}b!ZRbC&2KNIv3rtJ0v<&c+r<<+M
zGpz`f>b|MbGT8f<)x!P&3j}tma&ecVWREzGT9&lsyKoL!cARGOE49~NqNs9l4d({7
zgU%h*H+!IlS2ANI361T?7Gw^`vaRy7<^Kl|bgOD7<||pIMWXDL2pI}luB}<3N{!TE
z5<gdex!}A|{luVy&F`0`11n^G_mwvcva-pHnU@cHC{?EL3%jsU9Digq<hv};PaHl{
z)VlQ6EvoFYR?@Ek=Ehe0RC-~YvI=9x^_=ow?1pbHtuR<lnOAgf{3QC!J8$U&$#W_}
z!OtV5Xl<qxMb))fVAR$SF3@6{*=z>lh;{T3M_b1tAF-}?g(!6NEMO3hPOh_!q~Nmh
zMDToB=PCgHTjoQ4qFAW?8M;SHjcyQDAX|X)&a$s2>I8->u(G04gKi6Nf-vD%!HYQ!
zG%ZnRcP#dYk0!;e@~#B^hrdGDfAc&sn<Znq&_GZ-$vUGlQ#X4BujBE5U1yk~V|>4Z
znUd0*a7pT=dBV-Y*}nsg26E#y@`<EHoAN*oa5>0d3N7ecP&p7E1lona%CA<9NkN#t
zL*ybJ%p*ua*%X?6lDx~e@YPn;>o;@#Bc2lzKVYV<6P?gOUn)dGpz!fU5c3T@R3WpJ
z^0K21l%2~Yb(cOk)pl;}YzVTHql`zw9y_5}aknoYv(TH&RO`tBfW=`msWhWQ<fQ>u
z(W#o*wvAa7S(mNpkUzIv<WV?0{x|<Z{x5(tuf}Km-121cuLIHkp0)YEB%sEY+q3_C
z6?;kf`qpmYrG1Y9ti`-Bjt1q$9rOE~7?>mEfZngSn7z>TMywjfRwyAms?bgkdPwoK
z0Wd-evrlQdT)SWc9<uoz!S8Ye!z-Pcz#=YT-0|Su{Mr6^BBvsJ5V5r8!7qRizUeKK
zZr3Z#yzWzKY(xsCjYq{|va_4bSFJhzXXEaFgdB5OLOD;|D`iCD|62XtiC^+#BB9{s
zWSj9=%k|e%&P#aBj=K)%(YaVz5wQdv#1*s8UEiu&ZopLGDF)_W70!PLw`l6Z3+Zfz
zP|6%igqd4prUFVq9(-WnXjfSoO8@m(upkVk3C%r1IoT5zpA<k8FS1<3=vi*Cj<PFd
zXe>JB$adz?HsbEFt2?1m0xuE~R$>o4;6b*rY09!Q@f>!D4AhL*vYDC&-DX^QSDcKe
z^7qfeVv<ht?wzc@H_Lm+$h12Mo?+i923RxZT+_bIW?>EhBQ_KwCbF?DSa2_2wM0Zw
z@cP!vWUdty7h@Pwn1-B=$xNCu$ns6A{F?II9P+@HpfLPyQDz;;?bvGX$uzr<*HjJ_
za~I~Kp}Uxj@FHUeh+CPdEcM(rDc9dtqm3(1B#ljEz{VB0j0oqAbn`e%B)pP2tqhK5
z(ucTLYbAxUMoDPL9Rf`g6N89&i)MK?GB$;D-F#=rq4=h8g$1$wZV@2;f31GGIQ%m4
zU%d~IZHzg8<WMAE*tEI702l22GdZiyr@;qkUE!7l%Yi=guDe!Amyv-<*rWJPB7vE}
zf9Uq*+|;VymlSj1-K6(i-TTW8J9r#Q$(=F>nGtge8T|kjs*p(zL7D0n#?(QgU7EnH
zWd{Q!5*kL)fSva(*!q5%#Y_5JC7lRVoe*if_vof>Mg7fATwZO^*%42P5)UT~JDEgP
zp}&hl`T4467dPEMJ}GCnfWRuh19rOESTr&df}I>Yb2ml)ji0|u@8TVCRq-T4(1i3d
zi-~y^sXGw<9$7N|iA;<kt|a0EC!JH-YG8YoU-gtPd}%^@BuS5!>C@#qci`FYAYG4S
zngS*dU4ikV6nUbPG)!W|STSLxAXfy6Ct<1NM=?)IB3SmzD}Ps$<s$$GU1UxN#AXq)
ztnkN8F;=em(KdB?&06Aay$7ZXw}_|XiiIpkdq3sp<LPUtg1dJc69@C+bjYGVMoW}I
zl-P`cMY~O$6pe6mj<v7~1-3$$GEKli<mf}~L%g}3N=lw%;dE=Kl`9_jAe1`RVt!6w
zE1|481;KgwleSUvn4ym_T`&~}>uS(^iT#HjTB3xI`3z5!lj*}gg_3y9YIfl(J`9=<
zEJ@6POyH3;fp!#?@=_dM!VUtq4Tg~j-MpU_vtDy43L=3Joem>h=(f4!Y-7z^RW$L{
z=~Q?<L@~C43ATP;!RJrrDYb1|l7FOVbxi==lOkklQ!&X-q!Erx;7YM=&q4JjYFYQR
ziCQ>MsFFaa<=HYq=+e{J523fqXmP@*i%3sff~5p@3zjI<M4JAa__X$`4*uU(r1I*Z
z0hDk|2n1wKNVo6~0q3f_?w0UGl~}pMOY2dmZ6v2KKT7}o_vcN^d>0O)k!?I1g7X88
zJ@=q`d3i6ula}eq{QsAes@KUl7YG(B>Yb{pbTrg6@*ZzhJ=~y8-Cg2FbS<6`$QrxP
zIwcPDPd^zbpWt!n&YK^V`<}9VH$;(|Cx#fSM*4U~b#9B9AG2=H$xyf2X?pdCm}fS!
zJ!3P53Spyk2HwV?E%W_7Hnj~Ke`&?N{SE>GWb_xXz{BG^ixGJ@?uc+ac}LKy6)?d$
z=h->)g!AT|Grv!|1yK|i&w?$gF-J2fj^Z@Bj)-&mTNkD>yTPfdR791NYUEDu7%k2=
zrT~h<tc&&O`npZ$h$9P`C!~81P|c6Gum=?*voYTYmZkO)Zx=GaWSzp?XF@Uh`*TzC
zQeJkU`%ypd_ZniwVPz$9-5b~>iQgN(C1xtaH*W%;+w@@?@t-^pVoaZIdZc<Iz{{3#
zSI%S7xy;FtHq+4WP7psCY)B3R!My0m4uNA;e3jn|=duR)ONnL8wc4X&BZPQSyUyZi
zVk2xyKd+Hnt*j!Rkq&51dR;vSP51*bZRZePmN$_|4I(`dn9xUMvl{NZOCH5-IS*&z
zPscDbGDE2SLl$)NpBCtg3?D+GlSoQVxM&&AMr|W6C$OkAJ#(v2A-&D;BN`G<5gc}%
z#H~gm9`a_}uw8_kH;E@?S`Y~#qws@>kO?j%DG<H?)00R(Xy{Gn7yih9VxsVn(d83P
zq96-~>rK~OlHWus2^0&rrR<EEL(wo5^kKyWP4fO}D|&lMO?ygP#K6#NhqfD|O%Y?x
z-Exn1BoCatVgNa8B}I{+z~&9Cs!ChQ14QcxZ1G0~1Y+ak)vzI2hsd1BJ(HvF)I_$y
z$quu$q|aJJ&>7OOGZgHDA)2k$^e?2-qSLu7A4#ah3oRd?o+XjNRh0!H`+IJ8Ite2a
zz_xfkqaDuT>o7ZH&geUys+EABRUH_m?SnYHCj&NywTSH3!)<fUUos*2n5`AlTt1RL
zmfK5OSNFoGd2F7MgkhQ4Ffg?YS^>qs9`RXZ*eWO>enxPuu6=w|4Xf!fH&SySLh)t>
z8ID$K8Gb)_e(R@&Y4z({vmy9P^QefKR~<p&`R?p|1gClo&uRlX7xg4sZnd3ynTbnP
z>zx$6{zfO4GGR)MyqN0}UwKiN;kM365~0{>)lyx+fCMI>zp3z(qYtsm%?;5PY{D7D
zCZWP(rv%m9CAwu8r1d~2aa^>v`9O#+bwONoh3lddHkn||9n@+~gd73Jx;grE!Tb=l
z45Ej&{^sc1r0kx%QbXyr&aQdg)*lf_<%BUl&YAHV<EcKd_()-pK;wt~;PLyV1`;q-
zvJiFSZD}XVJoF3mPS$TEst4&ckf}V<;a|YdAg+1X<<ef-IU>lHz?tEEJ7&QQH!pO{
zeve~MTTDU6$^L;+o&Ls&s@a0IlvwTIKR79Ui%80Q5wo`nTvzw(+$b|hM|=wq>ujmD
z->x&~*3)4_YY#<J@K3uotx^>ZQzMNl+eTc=WCCo4VZY&>)rAISw}`4<CZSLeRvCe2
zoSPfKd@^pp!8Z;>T}4zAzk}m<5{m4-7-u+3wzFO%@~%2Y4&H!-u{Eq>8`8+JN%Th=
z8xc=E&GK}C=Zv#>1m+UIMpw~wtm1aibr^qh43Hyiu3x+GFqzPsDRaR^58gjdhg5hd
z9v$Cl-RKPL(wdQvrmq`-iR{Ur@N^ch!_||NRfw3`4A^<8+C(D!D8zV;xhc%KJc3c7
zH;>$x&29q93;xtFR*N~KSWK~*NEnJgqEg;1FM@AxMnw4n&%ri$hjmgyfE1GTdOuxB
zs(L^4x=H<dQqZEp{3rF2Orm7iW18|d)Q(4xW$mmLFj49?Fw=r@E&^A^%h^_L^~8*k
z*E)UW*=kPt23{e%>RkuR-DJ3O^CxnKUpFnIoZbNoo_l*|Nnl`M7J0wSASN&t8nrFL
zg_6;%Uh&`o?#2xVRL_P6+0HnZ3?Y*l^ntlddd+dY{=-EwXi7gf)RK`nIR-DONA3gW
zG^zL@#49ap@}pcm@d(c^uS@2MP32N!QwJ21R7~rYXw6)cOIjwhT7j28UJ<LZn9Aw!
ztipV)xcUNQo$Hu~xv4b~>GYCB%nyd>EwD!$;11Sn_b}EYF>{wnQR|JA$gKw4iv$(j
zR#MuFabf4+7PQH%=pB0v(kMbnDHW|6>3TjYU9lOHa-6FT_PfL;(c(u$Bi`N_ZMrq)
zywBOA%c;FKWi=(eYQtKJVlsmqG#pH9Fw!6;GYEmFE3lO`SxMLT5^zUMzVQq)(h44>
z-_S3J%r#({Er*8wFjV|oPPdbBbZj&_TzMs!CD&}3OF}RJ8X!1WDTAaa%z2!aEf_Eu
z2_R6}U^qI4Ob;aCDkFyq>6Of9R8lG?1KxU4H$aXm!1%dC$|c+i-h@mZx<9rta$1S(
zrg|^p7=$-^Q(++nPfF)q945AxYR?`qkR1qndD>NPdA-CZG4a`RkwJ%9kc<RzZg*;w
zXw`y6QX8{AQBJ}M-<m(F>(Lb}jzBgPkxNUWotm|moopCzH}|41_e}@Nq~u$III(-V
zRuUH%Rxlm|EGCl<G0%WG!rR{;&0fzm2GmHa!_Y8&^VX?Ju0*N?nVUV8vBsN>>P>GJ
z21kL?1d1hmsl)bbv48j25`T7mZ=N6-#p~3skMQ7}JCACb?ZQK{AKEk2F!rU+nIZk&
zcWFQ?Iwz^EHm&#J&?<&2dn0jule|sFg97+YXV}PfBfBZVJxQz9IE=eDf@`(2@a_KI
zdnA3I(9_eKBV)5jB6Sc)McbJIro<+VK3S?^b$I{g78w_+0abf&Pge@fuu*1!AwhW*
z0mp8iCI~`pI|jz&olk3rI0=dl{yvQ-W{I}pA6i1OYyJJvxfQW1?Hmy~C8lh`yEl)P
zTK7z1yo-ubtK@ylYE+B?t*9IZzl>5RwS6YEZHD#I%#PYINPTk2E92htd|(NQ)*)uc
zod#GrLZ|rq69CM6pC)cJ->+?gLduSe!z84nNZ#iIs3(CE1-&yo7x1KMRT`GKYrTus
z7^aL2wE>D_Nir%(GTd|`+fu~Snkt*|_@+rzzQa_*gmXjHK0c2Dr~I@6>QcoP_)UBb
zoiHYRGfgFgFTdS7J=BQI<V|TOqi)X@%0=92(UaEGrZ&*+mpksNx>0H7(N-oQbZ~gm
z@$v9|{Z?W@pWDbt{}i7>*ZWCNyG&zO*#VDUVe_ZAdqz1<v^0uSYMh`fJZNNAxTI-w
z+Djf4g2d^H<j@c07>CG>EAHNzZDf&M+D!(K$M;(9-{=N!0dFQXJJZa@#wL77D?#V2
ztpZ{u#Mx{x7MQVpsn=^PrL|!%kv2!XMm$C_7?o6KnBJlSt@UG2IBOZk;_y!FLEX6g
zBKdCwbi@=eZh&dnOe3>#J00HLNv?V`JDAYHvs&oZfV)fSXp>&D73F~oqyb;UF<?=e
zUKw~#`P^7dmr%u~=8QS9aTL<28dh9ko%+1<9%zs+o|_rNX@^a8sA`t+oW;Aw)`$!)
zvdhijp2{#syd*UqI0#I>V~*Q~I7>8tOx`oj)x8M2muuMUvGB>##lE-EfvvC-4+Ywv
zt`9efis{Jd2<|X(x00Rwn4gH<Nl_g6{0Xo+0Fplj@dyfZ1~s37oBXBjZ{Q4tFD_*_
za6FM*FM&AS>Fi=_oS7DnozLPk{St*HJjd%stmzvstY8uDu<mCsoL0uPp9hthWT<?g
zSVlFPsGS-4r0W_pcoK)LFdB|{`lh{9ByHW(LaoAV^~Tb~N%71axOtL(zNUH92nR23
z<^wsJ#P^xp*l?G*nqS;=zwl|4kLtutWp=n5ET!Z#OP>JJ;{B~4K!d1^c)S&{XTU_P
zQq2AQBWd<CjI}{%Eg2#myd(k|?pNP$fDt$)Lk?B@cO2W_ebW^wDd6nM>swO*Vu+!+
z3+}j(%5X^+hQ1M_r7@tDV7xBEB(3F^*i@Z{4ne|&caop@`;*z9Xd`VoTZl2aKE$c3
zqX!o-wx%>`iFJj81kV%w5u~?9xSkP8%ZthK!fcfil)<O+L{H(1QJj(j3%z=FPqI@^
z?{YGoGeQ(786xu-5iB}U9|yr&(%A*>3suQ_GBjTwLpbv{1WYyPxTc*%%(>rnGPvwy
z0d1mTfQzzIjudc$q&>WI61~3fK6NbsFJxzVA@ko#e{4f~ZLrC{hI-jUS2$Q<7;$|`
z12Z*M?#6hBewxXlzdRvL7o$}!1!9PfpvRQJW|Ns%ghVC{-|mh{q<u<$G9`qqP+_Xg
z!Gtt2jgk2*-k3JId5VsCFQlW+nQ>3tN{dVkB_tsluYV=P6Wa92=xU69!rtRHYsZTX
zl4x=Li!>L7<eDKno+5?X%N)8-n|Iy~T6Xxl@ss5;rb4SFHYe=Ux~ZUeplSE6%Y{Yd
zhPxCdud`j#(Ghg9%hjlMkZU(@6JvQ|m|XwnPLGE?-9f&0Pe()*P2SAh95vz0%zi*$
zoNMiVp-rnq#l$vJsnip~Vex#6x$%z|151nthlVr@;4M64Zf^cHJjUM(3k?nB;Nb8t
zDJjV+EL6|vI6e8I`rTcsc$rkC_mQy<tw5~$)3sRjt2tp|VdXN8YBBFv0@>%o?%%&(
z&ftfFY?YZhUsY9AnCX@NUN2N=&z|BBAI>6G)q5H-7>rwf552dkUYC-RnocajfA*ma
zr&7cRR}nlmHfBMkCg(Kx`=iV_yngZ;85y3F6Y$}~2lb%EcFo$w+S*z>d;1MSLP9=q
zdHi)b?B1abp7Hxj&rh1%oytrnQAmQ+#r?s-!Mh`IFLAR}Py?7~h79iI6@>SnvSO6I
ze}AT}txYLgKX`a}SYg9oy!|d;zU=Dl9o*jD&cV+gav%z(R3(JNw{PF-7#W4MwzeWK
z%o;INuW!wy(Yj`5r8l#)M<*wXfaAm{ftZuK(B0Dc>C>+5J9hY3<-u-*o3V3q<Akj6
zwxxv&pXTG^laQEL(%x<`J~`=~Lnc9F<>g&@`SRt?TZh4{5-|byYiwe&g^!;<IzE1&
zw#&z*esb~Et6ybhWs65g?T#Ehs;r`-(ml61u!Mt~I}l4MB_k7v{}je4t3)Re&#gf-
zS`Jrg2g~-2*qk>)+_U-EVU?Y*7wpq5VPWBig@p<N3x@i*jG;im?<ms+vd@_<2v`67
zudZDArX`&FQ-O}gVv;|cw>)tDi~S%s@#pFN9bd(Wy?<Ir|IZ)r&4v2v&sju=zIitP
d10S*QU2NFwnc*K+9V1_^s(ef->A;!4{6B&im;C?$

literal 25989
zcmeIb2|U$%*EhV?sZMD)yMZzm6(yXKA@eDcgj0l!Ckh!e&$e?qvLh5lGVD^3nannJ
z19p-rv&|+lgl&`Aw(WQQw$Alj_kF+D^WOJ+KiBj5Jnwt;Ip=Wr@Bj2$zqP*WyS{66
z=T{w#ZCiM@U@(|%n&;FmVK5uKF_`t1KW>DdP-31=!IzDeS{iEb75*%pA>!c6=9}lP
zxM48-A?RO@$f4=$7|ec*rrIz1-U$=EevdADRH#ffCjDM2#Qn>L4ae1Wtws4aOaE|Y
zD?Mc3=KBZ!AMLrP^{q8ulFn}smlV;Bc^oUjm88FO`@pl{1Gb{w)fdhjJv1crqwB8Q
zr4to6oOfsa>#b5BC$8pOo9B$gjVw)8RE`ss?b{#k^Jnz-J^Wyu-^ISY{Vt(f%-G*y
zvV{9LpzqF!a!8`Ds;4)5bD(c_o`21WzTW$FojLj%^ykj0b?BRe4}!SS*Zog_C`Vs0
zn-BdbPSLsE|ErcoXjD{`=#e9LO5H}d$`Yz(m_NYh<-;U`B{2ANsVx^xOn9-_E{CGm
zZ3hW-5%^uxl}B&>!)?43=r5u;g*nLZb~(uC$bWr)I(9uAt9ts!e|+m)zaEahvw@SV
zYADu1fXwH1VRo!d?7WuNGhKr6^wS*yq7A1;5=v$o6b9Q2Ey5jSD9Oh&GBT`&KgPMN
zE{%|%h6wNs4GrZjQ2oe9QWJK{QoXIpmxklTc}BiO>qdNkEEVQgT`cyZp&_@FO~YMI
ziIV*rxAX0V&$+xm5K_fmITh?Z^w5xp-=;R|kjq?~A-U=4nP(=&PR*&hgvCX&ia@?i
zW5k8SEw7wgJqOMFXoW7{YDLIYKRQ1;N9Wa$YISb2-EJcd;bmoIW&-(H-(2`fvo9ov
zD*NDvj^B*5&0@&{e#Z`LKi(Z880f8UU~pDbQ&W+(GUKB_c565#;??6fS!xqIuQXk?
z%Z<^Ha4NqxFz&)(bMMc=CCg(OR$t!VZ+@l``7$%}?vB8zTkL0_=*qVmZ_jd6dYIrK
z6Q7as{Q1H3^z_HQHZ#NZ+vabi`BRH&(|%|L&gCB4FkMVyiu?8dzR6lRS>1xPSS|UO
z-wj@4*Q-~rtjUk%Tr54|a|-<StuHT@u1Z!eTF%$Y_I;eE_u*0p&3%R*c3n6eT@RM}
z`0?WjpOM7kmQ}P|o-HZb;>&^J7L|&AJuYNvKc`K6H&y4Wxh));A1&R>8JD~9>A5Mv
zf;ncWE#S-;f93x5(*(yRzNXQ<q=5OZ^28CTM#X5}g#fyrMc9H#q1|OCQgU*#!{y6u
z+XKeN#$KtgmKx&~BbO~6K77~}CuJk!*AwHfA{BSxR$j3~*QbdRdL_f@R>0$EI-^J_
z&>5Z<Q)|7^nIqGTC=;Rh?Y;M4xa>EX;md|*d#uP2j?G)i3_3|=yUNN`XYSQAVKu95
zIe~jv-y~T(>te(yH%PorB?l%;N0PwoU268(R7D2PK1-a(!H))JTXdc07fDQ-v(@V$
zj=Y<n-&!*y;K3v0$6k7aMa0u61~eCbuj+(TZUedRwucSX#mbf`#gn_@(W`Q&Q<a2-
z%yG;)qgU57!{*UqxYEG1B|m)l;5jN~RdZL4F_DiRR>qmi2X`CJm?Y)|KjhY_q*G4!
zp=W!Yr1{ze1MfUc+@wfNt;*bwwnv50)o9uE>iR`Jy<K24C*m&XU%p%&x<|}%vZr*q
zS~BqX(W6Hn^U<ftf|27nB;|$Wp!M7F8iD~QWt@8U!YAzP>@24Ts;AR*1CDl#gv(O#
zEjkKW>IeD_E5AMLe05!GtDv$Xm$>n6lOl&Z7Y^@&oe`c&2$Wx5pcBR!ON}^XiOR(R
z?}0la$**3W2wWKyBtMq67fRI8kWY+|?emvXhW$6P+(=MSNdhxJ99iMe^=j{NOEsrc
zA?6jn)fEd=UNOV$yRdoQUptZKGc){11sBNY^W}aiv*zx;UG(x9o&1q@uBFNdqTRPI
zwdg@Z1bl53VYB32eRmr^bidwOIKI9nk1P;hY_*4*n+)zG$uD4`*GrbDD-$1nqa|g?
z+`{+k3k4eM_Z|NGCrPYTqcZm!4!vd4@WNW#+Nu@k<+~zPR=v0GJ8`gM;Vq}+-9r10
zqJayZk@F?aoB<@{ef?hk(I7baoVrlCFr<8%nX1A})3$1BZN0{r{Tj_nAAJ@{{i;z<
zFBy#3DEK*WB9F{NGsGPU!oN3%OP3CD7Zv-?jqxk<P;)Fu6L7&6TNdmpCFX34TYzNQ
zX!2pkHh9$?bYa1z_d1Q@i8?mqNI}9l4qTmVm#pA7AAXa3wKHnhbQNd?4vvnF@e#e3
zSm2;s$>K+j)V8;_P86`hBr6|{lVDfK_^>`ED#$80Q_Bw*J4frJ=I;{G`53;G<s6Vv
zTN!Mc{Ip_{;!j0prAodETgIWg;7Et^Y||Oa$#&S4yX@ODYb%nJ{5&7T#>#fFqO>IL
zfMJL)ESvN#8|0HmDaFn`zT;W<&%fa5t!z|gRDoOQ=&yjK5rxNWvt{WY-P2*_Wi~l?
zcb^mwZDA}Urv{xDi!~#9hJ!<NNF6cI*WazORIBS+hw1U7SNH^ocFd#4d}iN~eLP@!
zcJ$MWI;T_A-!}<7igz@Ih3Dit)_H@aFj*qpQTgqISbRB72kb!Z^PjsO_f%?Mym)b?
zf0N4liq+KxQs1XfUK#Uce_Oo69L^jGV5X(>$cM^gJzSn4uoOF1XH!-90~qs;o&>b{
zJIqUNUUldxaY<#8zCFx2$tx5gpd{Zh*_Cg@=hfq)K(kPJVodaO1e>8Xjo7F#u4D&P
z?=k$+9@HE6?c2AK#ep!l9M)8b%1NTG{m_hjQB5$nd1o?RQ#pV+ZqZoiT)8xSm~q+A
zkZk7F`_K=Zpv}n8VsDv8*sUGPaza8>c%?71vm5G4mRijh9G}u<VS#>YJlHQDl#x>@
z-?92Fmc$pr=X&l!yo@*s5Z3S7X^!zn_F|`Nbpv0iGx%Jpu-nXK7p4keZ%8W)S28B!
z?IZUdPwwh^L}|3m%*yJ0+SpU-=0;o2kBHa9(YmU`xQo3>W)B<3J9$J1{;otpD?ghg
zAM0!-n}!6VVkfg_%s{wzw4L$z&+kIWfec#5L}y!D+w|gKBsrF}GPL)U>$QpEzQBvd
z#yp1(9okk2o~f`$nO4!@YG2S1U^2?s29MDxwb`4q|CZp&Z;GpngBPz{*@wmEkXC*D
z;p~w+aEtFykQ;5wFf?_oJ#-ul_pD8k6GWZChlb|06;zr0yi@Ax>eH~cDhtbkVEH<X
z;%gf%FE*<<zfcZXE_fL+e9X8&v_ly@!!z(jN5#aRm6t1isH=NcP;heLvI-^v0-Y0S
zxFJjYn+LziIC#SMj7RlpFJIbD-7AOKT+$qfFhsVJ&$lm$5pi(o1_k=v$zCEGUKCS(
z?T;Dd?l;IW=a-h223$nx&!~imD3hSgu+3o<H7)SMW9HiIoZ@=_=(v?7t-El#|JIJ0
zuc-v;{{3d8z<?f)4zuHStuIG=iCz$%gu^59m>LB{kMZ__L3)0pa_M}GxQS<XacNhH
zBdzZdxQBQSo@w)Y#n<a%kS|6atH`OB2QFWlv&`|^;pkdgN0R;2*)JpVMFxN>%*pm?
zn$d@cy7f|Zdap9Z+A`e6XSl#6YKkNlv`lALE{`ov*r*USZWj{MD<H72JpenfsJ?jd
z;&|Z7;v?Aet?lhKudde(G;0WkH~J4tT{Jc2!(w@5o$l)T5A2%mV=?;_CP)GIx=nvI
z>FX}EccX=dh8iUO!l>0aS57RLlRsP!A5HWNqC(6O(26;BBbN!g_<f;d>6a)`adAi2
zOw#IHjtg_HW5hg?@6-vvBD<W+r_Yu{(7UgbDjZ#_CKF#DuuVWwuF<B|wLZ&_s4z)s
z@MJq;1(E*VGrRZfsRk(VQ);58F>)RkFJImx+v~39#JC=7#>Fe+V4JIZ^r2mD>sz=5
zYO>Y!6LA~06!^URe|OAOW?f3tzg75$MZjF!#sd~wq%oCWoxE)8JuW;3__5gCQ5nMy
zA0yp>WmB}{WBiz<jR8)nR!nfW4Ta$CCLYN;n<w~=r5*q6br1q(@Hzg|EqlZa3g&k6
zv_*x)NXrWQd-WA3ehx$+%v0wzF=Pzhlxg77kjred#t799{JPhu*UB_`q+2^Hq^yD_
z*xyRRu?!5lDv?(+^;=z8+K~Bt&e)efI%QS9ypK8Q<z_!vIr~X#y4}QCy~@x22WOTl
ziPlESjuz2%^4vJFJU7n!QQ0{ReDC$^*GoI%WgP8135FH{g<Z<Q<<1IYOq3d()P|c;
z0P}OB!EU4HGW7_{`q?O67K^Tw0pfI{f%8z06j16my3SbqXrml$ubZTJDm^2E(@2|g
z(u+UZn#Ej*L<d#*jem49aa)K`n0!BXL-RKioaYU#_T81VB8st|Q}mcctjg+iWc_@O
zibClTzqbLD=1Jtzp4?bR?aeGJlg}<2OXu2<clq+=={k$m6ANW|FLs~3yBAyGx6n`_
zpx`;7BWU0O7b8Oyam^VIsqlMz;LPo+dk4-430c-ZmY#NI%{z+++IAyz&9GqF5!+(u
z<B!zfg*33eEqK!i;$)fF0`-bbV%x;txm)j-wUkZ{8<LhUuaH;^EaQteT69#Hv!;wt
zv25F^paNPinA0?ba~TC|zn<c?`#rLbx~^n(c^suC$FIKq69FE}w)D#=ufWo?)|a^q
z)-2XL2ksJFZVItvF3&;AVPL>@CS-f>)y!;wqekS}IGdz9?{=stCi#D>?K#r{(Bqg{
z*+~SSHVS45$~YL^3bNOYKL5h9J@d*%{aLExa@8(fHBumRV4mU}`9dpJ=Eqm|0N0kj
z4IPK@EVuL}8e!4t7Q$+Q*?8TE$Ax4)ol}Rr9MNt9m{_;rxk#e>rCY4&rU#pM9sbhY
zx76S@0I%5<Wl8sIuL%hy7Zu5HNt*L<o8J5d-tb}i$W0WIx+N=SFD1Bt(wv5{J>2jN
zjzU?$Vr#1M9&~6}jRW|*d^Rdo9W2{j7Su5DW|QFEO@jWp>H&hjqfa-E2e*4Ie@#VL
zOaNe@o0?kre7F5^-`UZ`hz>wN-T;suGk*+tx^3^z`?(}c_mTy1{>blu4G(_Ok_^|9
ztSD%DG4y3z{;>9yD_0T#2bIPZ_pJ=ex>2%A212>QUAk|iR`<WTHTWXdJQR;NajuYd
zs+em(%)m{$5yF;cM#y!Nq@LR!ST~xU{4)Jj7bnl1-ViUF^X3+3u@}X$xEee~`$P%N
zr_ZyU9ieO!_LUQ_+_-UL1#oQytZ3m&Tw{{b;2n`9ThA1lwrub29m<Qpg|#0~{Q2tX
zV^>}soQFJ+tL<0Ycel48z)$YXvoa}fgqXiKj7!|E_;EMfeLc!C`Ke-%VqbiAw$PR}
zFfiafmX>Nc)&Hh`o;hDk;)mGj=T7VPh#l{V1CX5BHEwkABDPU!!4y5VxPUBSA#3dp
zL-&<$xLJkOr~*Uc10%R|%5AvHDpHAylUw30g4+=82AOmm4#Ab<o)evmqZ%qaW@QBv
zC&C3)lpEv*?;;2P@v*ena3etw(w;DI-Fw0K?(#?<J*<<kcm1aA4kJ<@Nq|j61OSE`
zmAYPE0oZXjHOcoVO52Po{e9qOrBNESSKi$g*$qPK5YwC&*iT%0QG<UEfU0l^nXvI-
zdCIg7IOLdD3`F8qtX7sgND<)YCAp3n{e+-DN&}`ozZ-le$h1xHXur}7<}oi9JPPho
zab@b=^vX;lx!0?YUsrkQS~={&W^fLBrS0?+AV))c5&7Txcv<?V&ZTnaUN)q)x&)rv
zWK;-T4FC_17+dx?|5H9iJ~-AQ@Dv1wcv6JQ>f&j_`7rmQ8N|bkX8wCp<)uDEC$6#|
z*=O}k-1lGLT+m1UTeOYY7Bf|wSCA*Y%*YUP-=`|}@WBJMj%+hwq2#i%?nWt0R@C_y
z5RQa|gg$+B2gFeLdJo4veTWBZQ5;+?Z2c{!*d}8jFfLxY^eH`HLpkxHnVA`EKd!Q^
zPad8>e?D0&b|KKw;PT}qpN&s0gNeA}bYFF4l0+~C#y&nilg{Vi_~$KO*BSm$U;sNN
zW1&zD1-gG4SM}e1Z~u@nL+;C8Gqq(JpzI-(>Ef1K2$qmTH-pQS`*QD$B@i$!Z#Rpm
zX=>K$kc|MxeSCCWn7tJM9&hD2DXKi57g@boB;lv(>T2TG3+P@k#)lsQV-hSOB#4|6
ziVZDa<{{Z%ob)2`g84f?qi}=gH8l6Re(gjYf6+EL4`2;0fMJg5F5plQz)U$F^fnF5
z8*&RgzO&$lo<78z5+O{~%a<=jk@LahUuI>6Z{wBOjeeT$w=h{+>j1=1n7h1Le`rwq
zSbLVqgNF|b?fSOwnxf)ZOg3AF_9w6nx@a4NksLEAI##(*L9dQFq?HT-&ILS=8Ujyr
zxS@d_x5jXHdHz9iWF$lpHCXIS<HDmUI*B}5u@d2f5egEAb(4Bhcs7*ZbAXrp?U&b_
zs+B)_KOMBydQG|7-k%7lJHfO2hRbV?P4EgepI=IY#7>LPz3CSO05u?O%e<0*I7ZT<
z@?B-Vb$uvYO!(|wyj!D^EW9tFhv(J%Dgy>@^E%<da~eT{mT6v5hOt3V2Gdjm&oy}g
zZfcg^ry?W-<VnwOCXn=N2>2Wn5;}b0QSQ6SJ$v@JfWtkjR`tgQ&hAnmieV4NjPK@f
zhBVq~cru7%J&%J4tm~vzJLPmgjo<C#aV&Nk9=y=%AvzdCOwoGEX{!rP#URUgk5{jU
zA=v)n0*<u+3t`)215h7}MU0`SgoKlOLLhTlU9HMKr=q=b1AA9Y{IQ*TN&=#NB`|~P
zaC^X{VCnTAnZvTAeP?cO?Rv_p3Fj3aARKVADpJx<OG!Bl>k|FTO>J%M74YD8t_jgP
z@<*2EX_QZT9P>Y$m95MeTEokXCUIhZvl&yFDu(+A+597>>Czl4{yF45v9~a_dit%)
z!CdmDuX(Wwiqfu-m*;s;56BJRg<W-Hdr}~*3sFr{i;v9Vu3DdnJA5XwySuw&Je&BK
z&*uHT11?i<Hp!acH15}0Tj@D!KdlQYPph{UvZI=OI&=x!to~Yyd@oery>F5(oa?un
zo14>{<9!$~il<{S1#gP(zLJIu`!)TtsT!77XrSVW7wjpQzJP`5*{>~wkQ7BwoQPS!
zs1hB|Qj0vidG%C&cG7xG?aItai^y{mIO@I+bJB|NE%cGghmzB=z`&@Axt9bLSAr4j
z{rNXlQvmNSkb75w(GH-q?b4Au(2=SUDF90c_*nXs4!AnCa_)`v3E?i3+<1(&9xkQ>
z`I85XkeO0=m2Yv%*CxIoB@LN)nm&&3qzSyg`*^#!5H<c>E~H%G+*B_;jEx;Rob5<0
z@7lTT-fxr~`CKu1_t!-2qYp!+8svnXN{3<~59j9jp&Y^zq{tA<iM4YchF~Yy=811x
z%*x#F9Ji`DSsDO)S|RaBPjjnL632e*t1gGsWyTM|2CQ9I<1W-R3SzPhaHX}5joa2m
zr2zqwMJ;a0lx=EdRgVGtAaTGV&r<za0P;^*ETT^syEh@rY}%42)DMoP)Wb(*@y!mI
z&Y1Hr20ltP-o@hqjol0h$Vg9T=W%%aV5CZAq8PifU|YH!JC$uxEP=Q%@0ohpXDP#k
zx^)PD!PXpnY@dCn!`x3CwjXkS3KOj9NSq?PosaRdk}0}`Bwj1P-p8+hcmP2t?CF`1
zv#>+GD5uy(ijyL1jxRY2aO|!T0&Z1@0i<^gaL_cgaL*`+i+gn9`uS%RM8zXinw*)L
zsqNv^nR~#_-kx&uY*c1W)}2O0#<iz)m?!%xwCV;XuL_F0?`yh&@;*eqW9i!G5Q+P{
z*t-2`WnT0o6Hiy*QQG^1e*pJql95l|K7Fqcc!4T@uO2L<Yz~NVpI#o%(EziUfMnZc
z^l3;9!X2Gfk@ZSfULC)+X$KpZSbuzkMR3ZC>Lapo8_#awX72zuA-g;!jox_B>9L=r
zyrbW4HOI`WSx>#4yuMKqvt+qZ(w)yS?hWNY`&#8?^zOe$4gch74*TBzdj+9?o#KCK
zeeGYb^S}H$UqadOi4;(B_~hh<=ro&*Vy?0uFvd>+?f|WPdGy2+y|GU(!U!rD<E+4%
zCUCjy4AvD~J{)~xt`aB(QFh%y!hIi68}R8<EzFY*;Ahux?dVBxU=XG>{)A6=r+|ws
zB^~kb@PP6GCql!Hal4Q9_l5(@q0pw%FWb?+8(n!nR2GQzN)xzpi|Em#p`Si|!h1Wf
z3qp60f#6n<7bH=Mc9-_Kp9KG7KUbM(Q-Ky96SV#8fBEtM<s$xbmFs`X&brDG=l_=$
z050%ftEK-_?&$xocY$);%1jHAvbq0s^<pnQkQ=vDXJHBZ3)KkrDtg~c01nM%@pDKq
zq93fQAe{4}tml_o8>`8XjiHiIa!HA|YyunZsMdMlgS-Z7!d;-0J2|yVaVD|Z1Vy0G
zjyrVbK2r2?jsAr;P#re-K`iGk@a}$eAg&@1BVgT_#E)vOu<<<=*pMs8tnI)$DsZgt
zLUu#~rBi(%gm0&k-#j9LU#6wq;Wqa^;8gZC#i+pcqT<x+^)4V|n8;;8E@cS?Oh~>h
zR~DxcaRE$t_~i_P>ee*9L1!Gpq{p>R{5aGe)+O*Gb5b3Yui`0(Qi`EJ&E6sm(XDi9
z#j}zUIdEhTVq)0fB|Re>xG#&90dC;Uy*hD#x!@bCDm)fF>sesVP`-t#bvNH_Lks{y
zHi-6})MIBRwwFSF^p2Oh_Wm9o|K2(uLF0`S@R_JQfyJV`<e$$1cdVzYBrPNaluc8^
zL$>6kz%=zo@9~@rB2&I>ydx(P>h*a1Xdempj3M|x@+ZAu^duN!w%!lrVce1t_=R;n
zkj!qRv}c=vtOSaDH@<N=0ACu0#{=Tb;?F?p6HKQx|G)uz1wE+wAyQvW&D68&x`IPr
ziHnumid2>3$D&_L{+1(w5AT6Wzjnk7)WA*vr0g8%j?OMhDQh}}X<C=2f3(bF+yMFS
zb1yEHdb+setwj%INIa3QC8!PKco7mMpAvrg{`zYtP@aJ5UI{+Kaj4pa3sEjCEX3vw
ze%dzI#tM$Bz4myk_xRWtqG`1w+&&yhRfD&zRi|72Nq;5)BU-M1v@LB_`AyREmoMKp
z3QnBuN*BK$XnzIePqk;&dRc|Mx<~iNUAn&KVo%QW3w?!j$9>oB-8+M1A=Ds_k;G!s
ztrn*~yFgkY)13oMOyvej`)9`s@`(+Fl&BO~w_oKvaw>S@D^I00UB@)t&DIE%n=cz{
zKOK92FaHUY4!G2b7gx3Ds_-LbQY^b9`HjZsv2oZvz{Ae9n`lQELcIh_4}POvPYuV5
ziiw2*>0Mk)W6+8%U_(4Tl-hK_p+IfDst}M9I&%w~w}26d0)v}__onaqEne1H;--3&
z&oyQ_$HCJOYEv1`4~qQLGu~||r@ZY&RN?LI{5-(IQ@>_Xs1<%1YE`i5dcQ1}Wf)|E
z!9uyyJC!UUArT=Qsragvh6*+<?&HtXGBWHdcVYbgY?wvYTqSAecV!Y(Iupmn<jK)B
z96Ph6R(WsO)RARU&oCV`1SbPW&j^Md(My?duW?jU1Ge@VNMVG83^I+HZ3*gOT#$z&
zVL;y>?A)aL$-5uQA&Yl{IxON~2VOpl2(`~HP3gKRJ5aHy4y`$)k5ILn%&kaIKPnI~
z+l<G{LA`Ru<RmgoRbF@SBqk*axYZTphx`Y5`)n_k5bMcM=K%Xcb<0P}0luxRt)S2V
zC4}XN2S-qm04f3Dl>v;Rjw9$an2A^0^ET4I4b@pel?SdkqQW{=6Dinttq+o7@r_&e
zHuYBsDEU@F!H`=o<-(n>EvaNkn|U1s<!=7wKbK)qkNeC%kz*71qGi~yKu9DyIe$ca
zI23)ukXL*g%sH6oMZJT(A}UDEjy4ld3hzaq`^{_gCk|)UD$@$A*ij(Wi%xZER_8`q
zrUxPE>@#;8h8W#qtID28{kLRh=xqNj%JUymcmMSa>x18G6Ft3X`T@2%*s4+2Q&Eoi
z;D{b7t1Joduy5A)o4}M4)LIeA#bhi*?$T8}j_7jG%k+h?KRG%JhnPb#4+zwqSS)F&
z-g)59T{?K?JVnxp#GLO0PgS{NEknOCq|~1%<7kW|7OTJ+$==?^%Z0@v_8y8HC!r*Q
zB`kfk89;UJg{$n-%O{p^D#v1>n9kNA0TsrL%A=+pZI_YP!eUjHn<Ih7iGY9PgaRQr
z`ii3BGjJq@TCYv|fYBO&y^4szDoFbU5TAy}1J`&6XeMqY-&s5u{b7S_(=O@@^f8RP
zC=DLD1sKo0Qq~tff?5aoH~}Q!m?-Qp&w~ne32c5VRQ8M3RX&vzxcn7FQpAj7=)*x(
z-Hsg20idnIU5GnzDCXFeoq~(6H@VVPpcH@HvI>u?`k+{-KeHxv^t3GAgmxhkFd)^_
zGYeASHh~fuI1)i@?Rc5$4yX^KPxU}ql1OALID^zW9XaTzJjCc@1x^5QG5vW5P7de@
zV0;kwf_4t1{@gPojgek`0kV~_C}-8wKw}stQ6>sSv3%kOc62#+*K?aUsK^xI+Q*%G
zO3;P;KXY2(QN2P)=xn3_zpm1pKG2L%dwRSZg?2n1$vcK0+grkUN?j`FvYZQT;?Tom
z?zFy8+6bZ+$CWQ4DtC~`3SwKlA1O2QSmk_SPW7Q!)146DC|~!2TeQn&LOG*Y<NHTr
z7J|GcI`dFDZzG)_sx2<G!j77>)Fd8s!e+TS2Gp&~+u4PNChQlb{s1VOxzZW~Cf(TL
zuI&mFxd!2LU5$Y<7T4L9<1Xj3!+X=#y+3uBx@q5qk`KrrK+=OmFN&AhV5n&t&cmei
zt#UzV?0d%!=DCT<{yRi_*uU>o`lftKRwx<VUx$RGWTS}%`!QFK(51X%!6<-DMK(B+
z=w+VIL<+r;W!9h|>%G@0_Hq0Mu%|$)H{2a->Z+N~lq~80!_*H7L`xFv?eOFs>_G;U
zSBJZS_`FfXy00M+XWtknCDJ#<K7qRtJOR?seCY;5wJl<Sn&hE9VxT_${UyL8nQl|b
z!y&paE`?9B^{B5KV5dQ)D8P*dXvCV)(cV7X4XgKWJ_p4znGSOh;pH<~5Zy<*L2jk-
z&1^fmwra>jSP+o^l+J_IQyf@nda1+RAjYfz`zwQtNeU6uLIa>c4W)y65mHEA8snYd
z>d%2tI{=%5dd#58>Y&QspSKulGJ&fzgb{Py$0v^<AArPr170MC*$Ui9By#Z(s(86A
z{9UqvChg^hidy5}E*v|*cXk(HQEjCI$29bx4zmE8L`V~AcAeK)03Rs`XBKFz#TQrw
zn<*!dI4u#TVJ{WFd0b8b{f22dl=l&9T41>E`)UzC_V-$&0_oJd9c0)(R^SKoT!%gY
z7;G+f?n8{7Qx;oojPW!hZY-xjIf0FML5B2xQjU-P-cp;KwAk?x(^UEIo$^ahlUuTh
zI^=Sn8O!;Ju4&l4$_qh-!z*(+ILDkQ_HD>N@Z9Vji~I(2RKsfM!$aC}MfNY6w{y_p
zRSj6M<}9p0qguKUp}agQw><g`iF*1%*cf)S0Gu%ubV;Q*7a(*;fPCpNQ04LTckFYx
z`}*MCce7R+Szo$$D9zrxxfawCVH=o%9b_wcPxbrn*ny6H&8qc#O#u~#DG^|H-#hl{
zr*Lrk2-r#;RH(pIhOo`JpM`eO%7_ZV9;$_dO9BGu^Fgd=Fd$Tve=i&f005-0e9k1M
zXXOs|=2TU4uU!a2HBSKZ)pH#df+)7H-=X+(<xC>IYP9(oq&EVfsq-wuLEZnG*-ms)
z)sP2p(*D=KLq#awuL2DJ(a9T^!WJ5>^Oqzxqa&W2`M}tTG*$pC1bhf@7(f7j@jJ)<
z0nhz~pan3^o0TDK=l2BrAF8240e5cKAl)e76{H9d656i3*x&17gia8&J*#&e6?EM@
zR94t<2M<}1PlzMMe}UK+{!q*i$|?w@n3YY$d}JHd{^{;R+K3B5m0^@IIP`&FwF<PQ
zd$CwlMgqdPNJEo-cl<iU%r}1RS8Rsf_M%h@(u_Uo>fwTRfK^K8!DPChu`MiU`w=<^
zogTPAe00jqT21XPh=!0Xjr{(;Fl0m}&8s2&o<`Z?Yuu}CDTGgug*#^l01iHR%FQbt
z!ue1)4z}OS-);4US`os>!bm8Hm?}IxFWpUAJ=2El$e*oU$2469e**mdjchYx<1#IV
zfuZ3@=^8?a5TkQA1KM<>xyv&f*GuQ=pMg?Mw$jc1*MI+ErOA>(S6a-m(qC~kbl{r*
z{^DzX2DEtbG#oh7;clBm4};3OHF%K=CKouci-3oE3LalF@v6=ekUX9L<^4lEfecDt
zjd*bn;EoF4xljQ0BgF473EdS`dQsMi#pb~??>%Yz>k6orBA~KkO6XgiFFp>M?2TF{
z&>_T?UuD=!+>oi|w^bh00&KB8(t1kUx3d+UzcJ?RX{+Fj??MgZ-#QnFK-^?r=W;A4
zOC4S{mbeTK&~Yq3QwZv`%k1KuYDk-ely~tya3L$8vkV7mg9rd~Y+bBmBoz5ju7Lzu
z7tPJ7No%`mw5eOVzc&RS{C4@#Cvu}tZh4YFV=(=!e6Nwu9uGYPUgKm<TAnw>cpmi#
ztv1$rTCf$*^}~lt$K<8n3!M<%KAQXEiS0!ZTiWl(VXyhUe0}%5<rTGKM%P7m?=}?`
z(wEVCv;BI|AL?6rV^u$W8Kz3%e#;TBPm1qJ`ZD8_q}W)iME7Z=Q_DvVFAdlG!M?H>
z%{#<iaLnkH?<n-a(dC`nkU;W_YpH~wir@T%kOA9GG<oe&>aPH04TzCFm%P8XZjg6F
zNU|XWB$t+!wlSCdmC*};aDPOx2=ZNZs72&yb$teg@+_3q)9Oa0<7(<(NN@ygM>!dI
ziHBudM`Iy|o8g+mUMbM|oMS+C(0v)yled<;8qYZnrO}S~zGi?@$o*w+`sIv)1h<Ae
z1b`xFol79qdzPF$IyCVs=oE!Cqb|iZ+w(l;(K1_y;lFoKv$dlm7S1n=RM044QIm)b
ztB+@1<nz_4Y^IB1ZrrOITJEKb_SULv#k`Ezdt8Vu@GiK~!j>c<ptiC!18Tho>CXjX
z>#d1}{_5Qh4KfdbIhEh98D(p0E9$YY$r(b%o^@1zW%}|hYHR1H&LHMUlOXOa4;W``
zr1;l`A}EI%nt5DSQ|p1~jmpmtUaeWQ{*fg?Npd)^VJBp4@mhv#B&7P`fntBJZ@`^R
z0^U#lT&vJN*Q#Al&2m24OmI?Ns|=eR+TmSn?#1?fo5k4M>RfE-e7w7kGJ*bU{M>j4
z3P}SHBmF;5XBD##ur+C3PX+7%!7ovH^2x33p{Quqxdu#TuN<;SehRQo0w9ey6i1(B
zW`0@w)$_B>Hd9n!Snk&ibvx*pn+u?P51@$BJXDcEpmNM}E#3y0!v(;}6?VcBz@Mgd
z_G==Q=YMtZy!akJkNnlala}0fjV8#N7JLRK)Y)4D4F;i95%giz$H?NK0xtw=6r#zR
z#l2Pli&KJ~13x@#Wo3mpu3)m5n3&buKQ|-t3Hn@`TU+%z7myYn*zPbh)Io(&B^b;G
zEg%(FO-+s5e5aML(7+kN<zGK0DG`5~Jgat4b0=Gn1PKWe$JKxY&k}Oi(`$`LI-<U>
z<Lib7Q1}HE@d@Rr-*;3&YQJ;O9!e6(RN2i6C`&^5@H7bZjw;VS-NAPE(A!b{J&frd
z74r;14Lh7#QvNJPU!O!zFOr)e?h2A*sM;CZtOf5~p%C#sDUQK3&?FR40wa~?MIBgB
zDL?AD215f3;6+1k(e5J3;|iiWoa8D4;M%|Wbyi1HFGO@At<Qg^{;en4i{IUEKAc`B
zQNA!&$!oI5lhOwPPF#SEF;yw>E2RAhrsX%ti$OiM)sCXF@_EO!E^AgdxUmshmMp)A
z4;xq>C6Lq)0zCFsQv-+531DCV^m^_+NQwdNFy5d79pol0swcR9IC9)&pb9Jxp0qhK
z@MHvprN5o25XW7h^8v^yxF0zPDg!9T$K#Ru3+%yIX&CUvU8Hk_t^7Y80&x5QE&fG4
zq;mrPQ*RK>nL(TH!?B<PC=k0taY}XT-k+a{oL`GM`Ju|Uyux5BqENa5GL<kSC`0}V
zAr?FyD}1(!2rAUh8>xxDW3$L;FahR~8_G?1VI$w=m6d?xvV%8u4U=*EUc1BIh!|h<
zoR9sEwzhZ(Dj@3($KxTE%+}msUv;@afYUqr;2i=e@u%smReIjF+9=w*qf6Um!)l}+
z#$o~GUe(v1JwLn^UGbi;QdEi)wfqzk%OFfbbQ~XFlMLeVNCSYLFG+L_7d3vpFYT?1
zy3tDffGsAU*`c)i`gL)F-B@c{Fb&LOR_(jFTmo}JwAFBZJU14LV3`Go?NNzaR8rD8
zj|t&z3*~zVh!FFA?FX$zTtznZkK4vR_KHK9Sif@>P}Z*#uIMH)%bCfW${~l%;iT+3
zd_mN~=0qG9#W_H)5K>L<@6lR0W0Zp~mQ#MIQvf1lYJ{VM!!cKOi5U}M2)6@5*WE>K
zr9BoptsgFJ19%ZkgIND>J`JL3qt40qQJf}?i)hb>eg`*%%dnS0o-gUZ8Z#u>6*Iwr
z4Lg;;K9Bx4M?kMi7@N0UjKwX*0%tKu5wW{^=QipOvaWx;2a82+43Z$Ozq@55n&|<M
z1ONkcE_)R`9T0Dgs%VagE?XSnCY97jZAF^KROb(Cz8LODEfw`anSyp^8g&gJZ2*Be
zVN=}2YAEz{F8O@>5l|$$57xY*&7OdbbS@NSk3o->@=RP~LH95`Ph_mQ?-8WdO*+c9
zvL}sW_xF{XOhD_Q%Xn6C4Qh)6mB}=4AL<axyeh`crjr)RMGz?*oyrF8qb+D}Lk5QW
ztTdylI3>$}!eY_h2T>Mzl1Mk7XklCCtcm&#Zwo*(o#j4mc|>+@+|M6cxseWB+7^mt
z)NUd`1o*R>3F`=%n@K0i5Auj>`9UM{6CisErXgmvvv)0(uu*!Y_73h6oa<ew20Um+
zxf#3JXHnbHbBzWmK_qHAn}*y-sS=va$dD5ZB3(1+?g{w&!Ww7Q)hVfb&~TBb#eP+z
z(QqBqr1Gk3LMGA-XmXdX>#-iA-m=zf`|we+WHb8$s&vuityIuOJ^w5A`~fy92rV;V
zy@%LGi06LS@SXRt--wp^^KCmYI||L&$!WFj&e2fiD{S)ciN(0V*kf354reXsAKSu+
zKKQjEjeQF5P2cmte;m(_d`Cv-zc;mB`m3q+(w^!u+vVA>7Y&LW%@gXaDLs8rh`Kg9
zw6-2M)H%I#%D;VZqV=6#tE@I1|0|bCbE@w*tMK*68)M(bljyQNQ`-d;ON>0&|KFtf
z*Ma}|>VM(8?_B5qn+N#cd5W~qOWeO60|Q3sF({7lB1zstXal~?$%zE^fK7jSzKlc^
zAdtZONQ_=O5~j07!S6McccLyjXzp5=l48H2(FEmPQ_27qrlCj7o=Bcq@(+oyfaE6u
zbTE_x3&@m_;u+DPh@n7%!*-5sg6@Wd&E9pOJ4Se^Hd=%Xjeh%(vKYG2O3skmp}%fv
zgdhmmR|o2Ha=;0)TPT5JSVZDy)NYFO$PFs26@fr5_Ra8j*sP->E)|sTq_-o8`wWS!
zfDI}fTgN6JQWIRyf1ZXWDrpI5DPc?DKfevmvujI7y@eOY*=}bvJrEaNcu;d7n>YzI
z2Viksh)n36na+z0I3^?{5V-i+Jd2$+KM`jt_xFOF=%kDc<g+g?J~MWZf=V17k96w3
zSrX{3g63x#L0)XB-*NxNDRx|_hx}7m2z6jq7_gCvs^9WQo^rE7sH5Q*weXav-`8&f
zLI^5Z5dIBr_20gI8{Eg)3?|_8Q{XiG+@E#3*GfA{cx=YFs{&W~=GLaaF!@zta`M}`
zO%*Roi)47toBe>Hr9&m|ckr6e4{DbAJCJsG=XW`%siC!KlQG#;w&Y*g79>k(B*UXN
z+o{aW%|V-0SM~Xp)Dr};?-%%L*hlzHV_^A~xj{1>w_)SC;MGN`Pce62i7hjCZrg)f
z-AKC5PmTHFiraj&?!z)ui~TAm_AfIDlEo`pJ&^G=+r{6$UCg?Z3I)%$E+W=ha_A-y
zR)uAV^8^a11FmRurYNly>TeuyPZj~VKP~|R4TFdVbs#Beh&ks?v9rS2vO)R)c%ZT6
z0puqVjmDD;9Q53)@1VB>w2E2TIYclLJRVWf@c{@Y0tPYeC4n;ENxN7l-e1{bY$s-P
zKfUW$`D~R@^`&y0)rNDMRfIiENi3=i)HGkFr^CXdww%sk)B=mguiq~Bq+^V2x3;?Q
z0)YnRgG~daPC!<)dD)XEt58d7h2~F<e`x`*Sfr%OX<rLvF@b_pXVv)pCh`!Ne)GqM
z<IpE(Yv=1n>qJTO$2cj#>8B6~>4c!GWqJge&TEU=IB9QLh&Y7h*3>No=PRj$3nW$m
zQt1biZ9s5_*fbe3xGXt|@~O9o<bX4YAkw;i6{0>F`c>KlGT9{4lbPiUpy))YMu4Jb
z6c^<}g4}Uba7Nj?71{<U@rSsKngpSQ(LGS#fo=kGKCSVGa%kLhfmR={4Aj3<z0W2A
zYm?w|_I~<WG&%KMy>{;ZGk)@44}$qyD*3N>@y~e|mmib`tsaD?#Vc1>wXKX6w+-hM
z=v_^IRG*5{)hFDgRLrG!jpz79`f2K@bM#_!r1>|Xd9s0AMKlkfwim6}RZ03+u6zZ*
z{Gj(n>Ps;XET$j>+E_iuY*d7==XXg6QX)Uqe6n|NxC=1^5jj)`yJKvp9(C}d;Yf$s
zs5sOokzf^zEj5)G8z0vKA1mn7!K-@oq^vBkwHIMTM45kPP%*c(tgM$<xNdxkgI*el
z<km+Wyi;=)_uv-w)f|a;c*dtga5lxEJ=_*^>50l}#3cwKLskLKY(V)h&w%;o7}0-#
z!~Z+c<i9+_KW9;qXW*|z5FNFWqZZ!3l4k!e!h&~zk2nb(ds=4f!2Vk1hjLsIutIV$
z^+Jfy$Sq*<e}lUJ+`|sl4s|=XvB%ng!anfJ%o38k4S)*81!?rNNvq49kOT}u0qX9R
zeCvm0?53n3#~;0g2O!6dFqPm&V+0WE1f5^};5a;cJvvIhL}?{M?+EJ01f9-NkF}(3
z1At@gsDqPb<JlmF0sLM}u>fV3AfuBEMbOV7f|G6CY=F`IiN2l+35Oc+j!>iFkxs9c
z0SypzMF7yv{Cn611yYcIApsrK)ww9BoFgOz&F}5@9_(bVAT_yl!-H<%c!UNd4>)O|
zMgc4ql2JfC#@qftj1SECaVjLBfj+1N8gAEe=thqz!h=CjGeM1sX>~z?Y2OjFPio>o
z)+KHZRU*toNiHHV?>=Z8A^t-Mtr_JFqkv3kscC6xZB~rr$LZ;Ep92Q9%r;_Zd-bn$
z<y5M?i2#RRvid}J8_rN4Hxd0JdtH#{0}?}tIcal{!&z2eznr<#;YvvHp;FWATr-3A
z`E)KCjjn4ac66{ulK~hXG73CiRPfYMJ7q7Yv^q>s@)*Eh5byvw0L3!fLhUnW&Zw31
zZ!DhcxCQkaZ|D^_VWVH8d~0oPu?^?5ii(PIuW5n)Djv6YT~IqJ%}Nj_c4D1bvRy#N
zK>)zqC<Ii9dLGo&Kv5YOzu8W;4E3x*O@Zj{Kq5HFgWd~|H-h9j!}-rOrVsIR#%sf<
zXkX!;fa~3YqZva7N|<BO#}7*VLF@lMCz1tz7yAAAu~?XR)a!(U+eR(<SS;!wTCj6u
z+lC{Y$eAt1;st9%y`M0F=uS#X3S1i==VQVtsmJm0Y=sG=b%j-HD#I1^!<tdgTh%9i
zM4T&v#bFGj&p<Uu=!Nqd=R;EoU^G;ga?mH2XlYF78|`+CD3|P&Ta{K&)i!Go5DPWc
zveW*bLu52Z>V*Ur-RN(;)>HFBQr-u8KlX2UvKRfw=wp9y<ue5-@2u~0uP*(c>YCX=
z<vEmiqYfVN*r&?f3qE2R4MGtTLao7_P<>(BOaU-^@c&Yfhyk;XTJegjpo`Q134)H9
zm&2qIB(AA}yxMpmG${M1q$K@Y-a4we{>9Hjw@)H&)Ur8Q9f<7Vc;~ZFkh}w3I$1^j
zDn}$G7nnZ;N%+CYtu1|eu;2l@zXyK!Uc3HJ;M6@YBryBMp+}vth$gpSvCqziA9az&
zJiiRS;WMMwiAXaTu*00o2T35CtcL6nWO{cpIL;r6KB#$VObD$pW>oS2k)}fY3MN;$
zZpf8A=L9+jEx&$xfjY}j&$$(-cJlfH8P&kU^Mblc)FTe_{3DDh<3}Ch2~g=oqro1Q
z@nJ4m)-6P4fsCgU+RZJYgmM?kA}5fpDDFav+MabU_V;hFtwxQOs53v)wA3}8YzQMp
zG7NJff;qW?jgamz-Gv@g{SM~2KCqhLyEUU8l<me`x(28AGKRTXs3TJV+V0T;4*tO@
zfq!*^Rj`d29K`)(15iYeN+MAO>b{p7NpKsepa(cZy<c6K5Q!vDsJGuJ_qX3k5rFV2
z#<VP!FQGXy*y(w?1SmZtP%kAK9fAbSAT%c7pGC;0S6yCLq5hh)=?}=RtI#+W=!C>W
z!@VLJ6TyI>W(7s<2pC3E{aiEpyb&;rO@fX}wcs?XUjM#+5Y;O{JWzwWvw&bq4dPzL
z&dhNb=R)T#9{=d)t8ZmK1MBiS0u6nFTY<(clZ`(vV>u*MIg9=*10FC`@Gh)m5PuAM
zNYtZ+$A|OE)_`z_7xc)IF6!`GM@Z8lMp+J2-I?68j)|A{<}m&h2HFu=D<gvt&cYA{
zJ)KWr9z%K@Bw(9>>_2ZL$*%^ehVy2Jp)LlLPz_R2!iWpd&_RXaDQtxbO?dQB5`#zz
z12&?12gs|8%Dr9C;4#!vSHfCl04-G9>&X^kGGJ)bPD3cO&5E#RFu+s=h>1X77#U=P
zDyVX0NRkYOWb)x#kojRShj*ZnSzT-|uG|9@PM-hLH`HM`fCvnj)<cF#Ks+s}x{*lL
z2BFVr>=FkiDDYrxo#R3$R)Y_VhOli;=QxjUkpuD#G}UFVN}xaDIgk`J*39z?+Qf)C
zTLcN6ZMYbex((U00Fj(fh??|<2uxbZSbD|)R2bN=6+}cHB!a5Sz+rogSuTu%ss?oj
z43NmqmH@`ba3B<xV%2hdstDCO0ZJ@JvKAvz6jp>%;xxqSm|XcM#ei|kY{4!h9Asr=
zVsb&=@`MBpilI6FLt+pvz$+A${JFkKmVF$MYQZ#=8?j5gY8M{;d;rZE0J-;ceBVk+
z>ky_w4H+Pa77_732u>j!EWl}|gAUWgP@^zX=fawy*-BuQT9=Q)a;lYAxW9v0AW+=s
zu)tMZDR0s(spV1S1PLudPEh~puqNhNk!xTM97XFgppgPcq}K;^gf}Q222q6+X_i|L
zaVT9tmj{DUV0TZWCIlG!5Q=IPNZt-%?ra3#K8S=~K<*p>E&UH(sZd7n6h%spm2uLl
zJt}M>N1?7Bytrxk)rF{onrUIkL1CWEWd4(b%@cm4!+?|!kz`P6ffuS)6>&w+OWe5N
z<x~Qi;p8;eW|E_8u_E83vX_Wy@`728LFQ1{VeI)M7*ufi9gWaa*~s01{##5@){Fu&
z4+=qkPs<{@u=ZowSp>bPgX-fMB6|iEVcCMG<v{Q+WYF=dAp$3#qo3nq(?H?wI|D|5
zNcV~{)N=uvZ8$r$ylMc9$Z7xe<9=-D)jIEwL#|4J5`(0Xhhx(7g&Tra(*Pi1Fw8o3
zF$IYgk-`C}PsHAdB-}iGN6fw#iYHzrxz{u@Z)gn6{a#dh7W0JuGiaJerR(eKrxWS^
zB0@r_n2$<N&|~1<tp4H{RC)IoLJeLu2O?bHF4%xzm>}}6LId%D4~C&8gJ_J!5iM|*
z=MF(Jqsd;PV)hdr50Y2qpTJRvB2*9d1~4uq-pOuZ5j~RA{#jI%Vt0jep-Gg;ZGa=R
zA_UB{^{Fxd!+NI?{(>a||9ARx9+b9sFZX;q4nWB<!k+FlBi=SQ5GGl}p1B2_D1vb*
z7vgrJQX6CdjuE|}I`$t?=(~gb5vav@k=hCg-_UqH8H=!ayKZB6t7tNu@?4s3HDm$;
z>o;!2g9fpeO^UBFNG#&7Zhnnl(Nchlv*ypuyDUL^hFqePR}c7iJ{Y*e;zFn%ASv45
zd>*YAUFxMfssM0OGV>3isQ^fQj!q7Z6R7RK3I#^ziaCRLvd5RXF1Smi^8l22cM+sT
zd?*b7n!E-{hzeSC!k{iY@iXf!wKq?&d2X7d9uN=!MR>>QJG%ZtXh*?_lDpG`WHL7z
z-U)+v<jrs<gK013&G<RZ`0LDkDtmpxiBbYYS879fi2F#HL`~-gO!s|ozkdKldk}kH
zP3>DWVdeSMVh1R64+=%WlRO7;MZIzojC49Jm{YlIcLD_dZ%61W(9olaMB85RgMH9s
zVnA_bP8a3`IL5Q*V!o|J<I_w!(D*U~hVt0UXImQGV4R{RSbG=^VoFS=QC@OeM{>pE
zA~Z7UlAd16q+1{~T$=>w!1mKc?c=lvAG%+ki7laSC<-}3$0RhqEKb48DWtLz9fYzu
z14cJY*o#I=1GB&Y9$sFBlUy)&ApwTf<xwm`ia=bR)<gy+K{Xe}-D^TbvxAI_I#{c;
z(9<$0Cl7kb=;h^Q&vE*D@1w&;qZ6gX8Gqt3kC&IC=TJHSK4NucHc{S_Bd912yv}SW
z=n8fhp@lBW4FH3O?hTl(vj<JTROsnL(leNRfa@Dug>%YH<i11VCeAj`UZiM5Qg-8B
zkT)!lVNzm+H;i}~e7$}Ln$S|*=>*jiH2%sfBL->tTs|Hr?~bV+O@~nlGCE3gtu%o?
zUzu;9FW^D|Kc0ClziSax)%(yS)rABkX+|9yS(ANGXq|wzOau#xDTZDs$RIyT`&r6I
z*cUf}mI_^`SRn$<^L!-lG3J@y+LtCgc*DM$kCXqDS02In_}skrv3ZY*XO+(DRmUF6
zU)_jVuJl(Xpk(w8{)-!d(c{TjfbQ_OcT0*Kly)6kRdk~R3OEN_^&afEgYor;byb+|
z-$1=fY8qZ;(EG9phqPjL&r4o;^}KgkK?-ElQ7|%31QhAIRM40lBs7X_+qSJ1M1Rqc
zq`o~?$Xw`Cfos9&sdF$_E)i-(=&TS$&QoWS7IrWD!tIyox4?9(qO`A2uGfNTO$jGY
zo;(QEv>Q%NI!J1c2I}abk-IV=`Au5_gsR=1V?jV3p<BYV<Ys&kQc%2ti2l}XnZ#m{
zzC5yuI5Lrwo_=1&vAcD0+{oNK3CVn>=0JjQ0gb1+27oD8MuXo&Kh~T8dY~_$_|aJ%
z!L5q;Kwiw5qoqvHg(6NAr1i&6!HASC0Z311Ca<gE?zz#~t0pkY-D!mdBbSaA@SnJL
z4)g)V2gM9B&q0vA>Yy^$s#goFfw`?0NC1mX);4eNKF&l~mLzAJKC4h^0R=bee2tT?
zSx4_aB1W|iIpjzaz@MMF7m$pe$VAf)wlF%N+GM9<vS|Y(xV5M>Wl027$<-YIa{f*y
z79=hmPC;db4L_6v5UK@ztMSAL(y++<Le2@C%iyAj!)&CcVfc|#terei8=MTJ>6mnL
z7(b=1_6lZ|8c>}0JyD({UQ|0WQ;UZA4bS+FoFafBaxtL%eIBs9zHKp}G+Os=Vek4O
z*IEEgkJQv$Azv@<Oo1^Y-(LuBKA)g1B(x8l<||0B3@AgcrPsO(ZfL3<1e~f7KUXZ9
zWNg=$nAa5N#y!5|Ij@u=EPvv!S7F-G%a2~gF(~KHmavv{SOApKKt@20#NJiVwHM_w
zL1|QSCcqpfC{Aa4Rb*5Lpw15@FcuO*JsS-%6mcZW-^weKSsj1`8CQ{P{U(C@;zdXx
zK2bi?0Ll&!;plWUb#@wg^RtGXR}Z7PB_3swS@kgah6-@Ds3fPdQW=SgfDr-%Fg7y~
z0%Q*@Ec>oscV;d600b=bX+Vt%NY%u(a2X~*K&a*<sDPo+ltZ!+@hurR9R}DQ63}vu
zhUPsL((_QyL^C`Aw4qeu_SSu$>?p8P7SWs(EVfaF<sUDrY;Uhv`23O8(=4}z)*0~_
zDqp0MhZ+4Por1aN37Pp54QZ(2t;vl6Ynnp`8?Ac+0*ed83dpC>NY1Bohf=lU+lN=h
ze{t2#X<pWWfr*YDeeG}PDl6|)3c4lWVc26Pu)u?jxo-pG6?K5oI2F|$<x<KJiQGp`
z5sHgvqUQ2Hk`aEYFVxaKHS|D+(G4oEEfmzoM{%IUfZ~UYk`nZ`ks=yVSzxWfk>cY=
zIyyQupfnuYsCNM#-U7x0zwH<WnHxK{LZ{bfR4GR@2H)CCgB0m)9w<j|6!sO>=xK$X
z;_8<7(pBnf=)72C1sg?Sj3I-0o(O}$M=vh)xanSHuo<#mXd440qKjMB3$e~}iu>IC
zm@WBvKjg}PB;OVkDyaqwj&Va=)AJ6)MNmrKFJw%b);EFXpevmMkT#@7;!;Vg<0L1K
zKA0!TNdb{BgF>?(-`4}KEUU<`(U00<tpZ6tg2(`nzP^a<R&v%tEi$NHJAfVsT(yEZ
zbW0&A4fyo8<n|mK3|;x;6AGf8)%F8uVh_X;^y|;h($bCqmNWLx>fRzq;n5X8GHhf!
z-lH_<uVAOa^Y5413{s<D8c4?-rFb33BLV7{G)=<%k{iW+lMUx-NBFZcM8^AkH@Y8h
z09huLrd-r1|HVm5V{Ed)jW`rzvV~<ykb!sacl6r$1d5GtsTW)Kv?vM)hx(bV$0UmM
zZ@~C|L<z1p&<>o05!X^j>t~M4JdYArF!E|h7_=_B5_0EwVv%`9CfSe2y$AJ!eKE7$
z-hMluTyC2u1aaqlg1@q$Wck!-AV&8|rS#5J&UA{~$APAt470ZRAfY`EQ)Q8A3686!
z$O=cmjNp8|fQ3VdUT{zW9bs_Cb74XLxVf&2N~gGr4AMu4!V%=8vTTl^qyy}`gpx8-
zf<Lx##E+I@V0LBQRgK~~(o+lQ8sj%^DcL(>M<1_16P5vqsHs8KIIsQ+4sukJ_yDDu
zk5a+37^j|&7^KnzMo~>|1<Afq-!CHEZf)Ea3LDYUdY>}1J0y-NICk@}5G&yTtvji*
zA8uUKjR?+~Yn^eVE2X5?Sb0$?zGGz@F}}CymEgK<otz?ohgPqm<iBw(H6`Zvqfxh~
z<!(aL;hd2pIOkwmc2AXqhAbF6JLF@rXx<yqtB4)}*6_AI1$DXA01ad8$N&JA^|K!8
za&XA+Lvwp$B+R-8Du;)M<zSpLC!yGDFvY9em-b2AKCtKu8Xk{yM0x?Kzi5zg0L?xR
zhk*Fn5lxp<GB?rkbv@>g^{BB}ihjJBFS6R#c|2{L2Q#{l?MqhXWxEPKVK5sw(BBbI
zOhR@FSRZ08_30HexN$Uw5T5JJ7bIa6NOOd#G{H2~*oTx5!XrmsAQ?<9u$MMuXgA<P
zBH9BN&PN@D*t<JY@!L5x8w=4&9!sW3pU+7_Q{vHdBQ)Ontq<zMLvwl2PhhAd2)~i`
zg~*(QbQ4JrPCG)e50RsC;8+Ktbqt~cDE4X~_ffb^w_vte>?wk7wpMS@KXS_6h6|(I
z6b-cp2u=T4)<7(!;V!FAf|!}B1TF?OucApg&B*sFg4zbvE?@v69?fIaI6a-j!%5fA
z9HGQ@lsp>Ddqj5x*Sj85&E6$dFzL^@6KRMP{CiYbGb-Fg&VX-|+Y8h|OomhnLhKGG
zGLm>i*Y0*&l!4Sn;FPnoZy$qNtUWagBLLY=#waBPAu4hz(3~T;*nexVhSzzby%e<t
zpfQw}7gx|+GUz1XYLEh2YY`N)`klBIZP{ulwDedL5J3XuGMgntwhL#2#x*30N7LGc
z2WY(3z3wgAiP^=FIyT6UX95ikEqADY5lPzSw!<uLBs4^Z3!PbBWh{O(n;I69;{m5B
z;U5TGMa|;$;4>Iv3Yy!`<0srln--fwly{?$gHZp=x%n?f-l>-0q04KKUHLnY-}#$e
zjUv{>=*$9iUG)tQtJC6OO4?N>8Lb0((gF8wo2<ivk(R8#RzAw5K;sMJ)nAbtJwWBi
z<+Qu)F=|JIA*=Gc7a=MsWrHlvV1W)XZn{-Zw;EW`_OHgmLZIQi{Nr2WOHkoKKov!K
zvoV}TN8B_HnxAVVVX6Yk;G7>8hbIaAyLE+(;JaT5odgw@qyr3cT1u3cfxswRISnFS
zG>-0=X-V$OLPs>T0Qr4OQ19iz&YC;_qwy9rdld0uPUX`z?7v9id$nh;vcET)@6I0H
zg%V0g5_{!!msrJKWYP397csYWfBeyU(;vTCSsjWp|1oCr^;_zP_Ct95llYsPB;LL~
z_S*PYQ?(TDsdPidP1lFtE{ONLM)f>jEWM`~`R?;i9plml8JF*#efEAvF*F&Uo^DC%
z12Nm(E{A!G@#|N(>V{5?l|@urViR>lz9~k&I%Y-et)Y2_eZIL!^P$i@TRr&6gU7-C
zl0}o-?5e#LBYK^<Bgw)WZ@nq*VA0HbS<$rzCl~w@bW$HRjx^I3Sjv6oIovH@Ps+q=
zCp3;2<#aeF&b9b`wxGIH<n#=BO_IF=DO9HUdJN&dG+`y{1hAT660uqjuJ6O*0wvmM
zo;YtIOi*9l<~1q3+U9aL19WHS_rE#g(D%RN<2?9y_;XIb{iOCx%Nh1TQ}QDJ;HU@R
whtG)q&eo;vzqn2keL^+)hyUbd{=*3@_^5VKo#C%Ppz~;|>!@X){_T(d3ysxqJpcdz

diff --git a/reference/plot.html b/reference/plot.html
index 12321426..4def663d 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/proportion.html b/reference/proportion.html
index 83d5556f..5d3a078f 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -34,11 +34,6 @@ 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>
        
@@ -57,7 +52,12 @@ resistance() should be used to calculate resistance, susceptibility() should be
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/random.html b/reference/random.html
index ce094661..77276c52 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
@@ -193,43 +193,42 @@
     <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] 2      256    0.005  0.5    0.001  64     0.0625 0.005  0.005  0.001 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [11] 0.125  0.0625 0.0625 0.002  32     2      0.0625 4      2      0.125 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [21] 0.005  0.002  0.005  16     128   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 128     0.25    0.01    1       0.005   32      0.0625  32      4      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [10] 64      8       128     0.5     128     0.005   64      &lt;=0.002 0.025  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [19] 4       128     0.025   16      0.25    &lt;=0.002 0.01   </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] 27 33 26 44 31 47 13 50 24  9 44  6 44 50  7 45  9 21 41 23 15  9 42  9 40</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 24 46 46 45 35 44 43 19 37  6 47 49 42 14 42 28 35 12 28 23  9 47 50 18 32</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] R R I R S R I R S I S R R S R I S R I S I I I S I</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] I S I I I S S R I R I S R I I I R R I I R S I S 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] 0.01    0.25    &lt;=0.001 16      0.002   0.125   4       0.25    &gt;=64   </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [10] 1       &lt;=0.001 0.005   0.125   0.025   &lt;=0.001 2       0.5     &lt;=0.001</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [19] 0.002   0.25    0.0625  0.025   0.25    0.25    0.01   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 128     0.5     &lt;=0.001 32      1       0.125   0.0625  0.25    4      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [10] 64      0.125   0.025   64      0.002   1       8       2       128    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [19] 32      1       2       0.005   0.01    1       0.005  </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] 32     16     0.5    8      4      &lt;=0.25 2      1      2      0.5   </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [11] 4      0.5    4      16     8      8      &lt;=0.25 4      32     32    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [21] 0.5    0.5    16     0.5    16    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 16  32  &lt;=1 8   2   8   16  32  4   2   4   2   &lt;=1 2   8   &lt;=1 16  &lt;=1 8  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [20] 32  &lt;=1 32  8   8   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] 0.25   &gt;=8    0.5    0.0625 0.5    0.0625 0.25   0.5    4      4     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [11] 4      1      1      &gt;=8    &gt;=8    &gt;=8    2      0.25   0.25   2     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [21] &gt;=8    &gt;=8    0.5    0.25   1     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 0.5    4      4      4      0.0625 0.125  0.5    4      0.125  0.0625</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [11] 0.25   0.0625 0.0625 16     8      0.25   0.25   2      0.125  0.0625</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [21] 0.5    0.25   4      0.25   2     </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] 17 47 43 49  8 36 33 21 38 30 39 13 41 22 47 21 19 12 47 12 36 46 48 47 27</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 12 22 49 15 47 47 30 29 25 40 40 42 49 19 28 49 29 18 13 43 26 48 34 34 31</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 17 17 11 17 12 17 16 17 16 13 13 12 16 14 13 11 11 12 13 11 13 12 11</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 16 17 12 17 14 11 16 14 13 14 17 14 15 14 12 13 11 11 17 11 16 12 11 16 17</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] 20 17 19 16 20 24 17 23 19 25 27 19 22 19 23 18 23 24 21 24 26 21 27 18 17</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 21 15 17 17 17 25 20 23 16 24 27 15 23 25 23 15 16 19 23 18 22 25 26 15 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 c0eb007b..12fa3b64 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/reference/skewness.html b/reference/skewness.html
index 589cbe7d..ca109efc 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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -34,11 +34,6 @@ 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>
        
@@ -57,7 +52,12 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
@@ -181,7 +181,7 @@ 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] 0.002064919</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -0.03534482</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 2ffa8f63..c8b0d540 100644
--- a/reference/translate.html
+++ b/reference/translate.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="">1.8.2.9146</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9147</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">
@@ -32,11 +32,6 @@
     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>
        
@@ -55,7 +50,12 @@
     <a class="dropdown-item" href="../articles/datasets.html">
       <span class="fa fa-database"></span>
        
-      Data Sets for Download / Own Use
+      Download Data Sets for Own Use
+    </a>
+    <a class="dropdown-item" href="../reference/AMR-options.html">
+      <span class="fa fa-gear"></span>
+       
+      Set User- Or Team-specific Package Settings
     </a>
     <a class="dropdown-item" href="../articles/PCA.html">
       <span class="fa fa-compress"></span>
diff --git a/search.json b/search.json
index bca8d25a..c2bd176f 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: 61.9% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 12 372 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 12,372 'phenotype-based' first isolates (61.9% of total where a #>    microbial ID was available) data_1st <- data %>%   filter(first == TRUE) data_1st <- data %>%   filter_first_isolate() 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: 12,372 Available: 12,372 (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] 0.5813935 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] 16     128    0.01   0.002  4      16     0.01   0.01   128    0.5    #>  [11] 1      4      0.01   16     64     0.25   16     4      0.0625 0.002  #>  [21] 4      0.5    0.5    0.001  0.125  32     64     0.005  0.025  0.0625 #>  [31] 0.125  32     0.25   0.25   0.5    0.001  32     0.01   8      256    #>  [41] 0.5    64     256    0.025  1      0.025  2      32     8      0.01   #>  [51] 0.025  0.01   0.001  0.0625 1      1      16     0.001  64     0.125  #>  [61] 0.0625 0.025  8      128    4      4      0.025  16     8      1      #>  [71] 4      2      0.5    0.5    1      0.002  128    256    0.025  0.005  #>  [81] 64     2      0.25   64     0.002  0.005  1      256    64     0.001  #>  [91] 8      64     0.25   4      1      32     16     2      32     8 # 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] 23 30 22 17 17 18 24 30 24 17 19 28 19 31 28 17 28 18 20 31 27 25 22 22 18 #>  [26] 30 29 25 26 25 20 18 26 26 28 21 22 25 19 27 22 31 19 18 25 30 24 28 30 19 #>  [51] 22 18 26 19 20 20 31 26 30 22 17 17 27 23 19 30 27 28 25 28 20 23 28 27 19 #>  [76] 25 30 25 27 28 24 24 21 22 22 31 21 21 27 17 19 29 29 29 26 28 21 28 29 31 # 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 AMC) 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. Author: Dr. Matthijs Berends","code":"# use package 'tidyr' to pivot data: library(tidyr)  check_AMC <- 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_AMC #>      ICU Clinical #> [1,] 396      942 #> [2,] 184      240 # do Fisher's Exact Test fisher.test(check_AMC) #>  #>  Fisher's Exact Test for Count Data #>  #> data:  check_AMC #> 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          R         R            I          S            S            R #> 2          I         R            S          R            S            R #> 3          S         R            I          S            S            R #> 4          R         R            I          S            R            S #> 5          R         R            I          I            S            I #> 6          I         R            I          R            S            R #>   kanamycin #> 1         R #> 2         R #> 3         S #> 4         S #> 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, 481 631 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 52 142 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 6 February 2023 10:57:22 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 22 February 2023 13:38:57 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. Anita Williams. 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":"Generates antibiograms - traditional, combined, syndromic, even WISCA 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-over-175-countries-translated-into-20-languages","dir":"","previous_headings":"Introduction","what":"Used in over 175 countries, translated into 20 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,  Czech,  Chinese,  Danish,  Dutch,  Finnish,  French,  German,  Greek,  Italian,  Japanese,  Norwegian,  Polish,  Portuguese,  Romanian,  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":"generating-antibiograms","dir":"","previous_headings":"Practical examples","what":"Generating antibiograms","title":"Antimicrobial Resistance Data Analysis","text":"AMR package supports generating traditional, combined, syndromic, even weighted-incidence syndromic combination antibiograms (WISCA). used inside R Markdown Quarto, table printed right output format automatically (markdown, LaTeX, HTML, etc.) using print() antibiogram object. combination antibiograms, clear combined antibiotics yield higher empiric coverage: Like many functions package, antibiogram() comes support 20 languages often detected automatically based system language:","code":"antibiogram(example_isolates,             antibiotics = c(aminoglycosides(), carbapenems())) antibiogram(example_isolates,             antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),             mo_transform = \"gramstain\") antibiogram(example_isolates,             antibiotics = c(\"cipro\", \"tobra\", \"genta\"), # any arbitrary name or code will work             mo_transform = \"gramstain\",             ab_transform = \"name\",             language = \"uk\") # Ukrainian"},{"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":"manual approach, can use resistance susceptibility() function:","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(...)  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 latest implemented EUCAST guideline, currently \"EUCAST 2022\". Supported guideline currently EUCAST (2013-2022) CLSI (2013-2022). AMR_ignore_pattern  regular expression ignore (.e., make NA) match given .mo() mo_* functions. AMR_include_PKPD  logical use .sir(), indicate PK/PD clinical breakpoints must applied last resort - default TRUE. AMR_include_screening  logical use .sir(), indicate clinical breakpoints screening allowed - default FALSE. AMR_keep_synonyms  logical use .mo() mo_* functions, indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE. AMR_cleaning_regex  regular expression (case-insensitive) use .mo() mo_* functions, clean user input. default outcome mo_cleaning_regex(), removes texts brackets texts \"species\" \"serovar\". AMR_locale  language use AMR package, can one supported language names ISO-639-1 codes: English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). default current system language (supported). 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 :   add Portuguese language support antibiotics, allow PK/PD rules interpreting MIC values .sir().","code":"utils::file.edit(\"~/.Rprofile\") options(AMR_locale = \"pt\")  options(AMR_include_PKPD = TRUE)"},{"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] Anita Williams (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 28 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 #> # 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 <sir>, AMC_ED20 <sir>, …"},{"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(). default 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 - default 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 - default current system language (see get_AMR_locale()) can also set package 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()    # this does the same:   example_isolates %>%     rename_with(set_ab_names)    # set_ab_names() works with any AB property:   example_isolates %>%     set_ab_names(property = \"atc\")    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 package 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 package 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 package 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 package 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 #> [1] NA #>    # 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) (default 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  1977-05-02  45  45.81644         22 #> 2  1988-09-17  34  34.43836         11 #> 3  1942-02-25  80  80.99726         57 #> 4  1991-02-14  32  32.02740          8 #> 5  1985-03-09  37  37.96438         14 #> 6  1980-07-27  42  42.58082         19 #> 7  1942-06-08  80  80.71507         57 #> 8  1954-12-30  68  68.15342         45 #> 9  1972-06-30  50  50.65479         27 #> 10 1935-07-18  87  87.60548         64"},{"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 - default 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  # }"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"Generate antibiogram, communicate results plots tables. functions follow logic Klinker et al. Barbieri et al. (see Source), allow reporting e.g. R Markdown Quarto well.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"","code":"antibiogram(   x,   antibiotics = where(is.sir),   mo_transform = \"shortname\",   ab_transform = NULL,   syndromic_group = NULL,   add_total_n = TRUE,   only_all_tested = FALSE,   digits = 0,   col_mo = NULL,   language = get_AMR_locale(),   minimum = 30,   combine_SI = TRUE,   sep = \" + \",   info = interactive() )  # S3 method for antibiogram plot(x, ...)  # S3 method for antibiogram autoplot(object, ...)  # S3 method for antibiogram knit_print(   x,   italicise = TRUE,   na = getOption(\"knitr.kable.NA\", default = \"\"),   ... )"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"Klinker KP et al. (2021). Antimicrobial stewardship antibiograms: importance moving beyond traditional antibiograms. Therapeutic Advances Infectious Disease, May 5;8:20499361211011373; doi:10.1177/20499361211011373 Barbieri E et al. (2021). Development Weighted-Incidence Syndromic Combination Antibiogram (WISCA) guide choice empiric antibiotic treatment urinary tract infection paediatric patients: Bayesian approach Antimicrobial Resistance & Infection Control May 1;10(1):74; doi:10.1186/s13756-021-00939-2 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/antibiogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"x data.frame containing least column microorganisms columns antibiotic results (class 'sir', see .sir()) antibiotics vector antibiotic name code (evaluated .ab(), column name x, (combinations ) antibiotic selectors aminoglycosides() carbapenems(). combination antibiograms, can also set values separated \"+\", \"TZP+TOB\" \"cipro + genta\", given columns resembling antibiotics exist x. See Examples. mo_transform character transform microorganism input - must \"name\", \"shortname\", \"gramstain\", 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\". Can also NULL transform input. ab_transform character transform antibiotic input - must one column names antibiotics data set: \"ab\", \"cid\", \"name\", \"group\", \"atc\", \"atc_group1\", \"atc_group2\", \"abbreviations\", \"synonyms\", \"oral_ddd\", \"oral_units\", \"iv_ddd\", \"iv_units\", \"loinc\". Can also NULL transform input. syndromic_group column name x, values calculated split rows x, e.g. using ifelse() case_when(). See Examples. add_total_n logical indicate whether total available numbers per pathogen added table (default TRUE). add lowest highest number available isolate per antibiotic (e.g, E. coli 200 isolates available ciprofloxacin 150 amoxicillin, returned number \"150-200\"). only_all_tested (combination antibiograms): logical indicate isolates must tested antibiotics, see Details digits number digits use rounding col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). language language translate text, defaults system language (see get_AMR_locale()) 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 susceptibility determined results either S , instead S (default TRUE) sep separating character antibiotic columns combination antibiograms info logical indicate info printed - default TRUE interactive mode ... used R Markdown Quarto: arguments passed knitr::kable() (otherwise, use) object antibiogram() object italicise logical indicate whether microorganism names knitr table made italic, using italicise_taxonomy(). na character use showing NA values","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"function returns table values 0 100 susceptibility, resistance. Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine data set one four available algorithms. types antibiograms listed can plotted (using ggplot2::autoplot() base R plot()/barplot()). antibiogram object can also used directly R Markdown / Quarto (.e., knitr) reports. case, knitr::kable() applied automatically microorganism names even printed italics default (see argument italicise). can also use functions specific 'table reporting' packages transform output antibiogram() needs, e.g. flextable::as_flextable() gt::gt().","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"antibiogram-types","dir":"Reference","previous_headings":"","what":"Antibiogram Types","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"four antibiogram types, proposed Klinker et al. (2021, doi:10.1177/20499361211011373 ), supported antibiogram(): Traditional Antibiogram Case example: Susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Code example:   Combination Antibiogram Case example: Additional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Code example:   Syndromic Antibiogram Case example: Susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Code example:   Weighted-Incidence Syndromic Combination Antibiogram (WISCA) Case example: Susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) male patients age >=65 years heart failure Code example:   Note combination antibiograms, important realise susceptibility can calculated two ways, can set only_all_tested argument (default FALSE). See example two antibiotics, Drug Drug B, antibiogram() works calculate %SI:","code":"antibiogram(your_data,             antibiotics = \"TZP\") antibiogram(your_data,             antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\")) antibiogram(your_data,             antibiotics = penicillins(),             syndromic_group = \"ward\") library(dplyr) your_data %>%    filter(ward == \"ICU\" & specimen_type == \"Respiratory\") %>%    antibiogram(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),               syndromic_group = ifelse(.$age >= 65 &                                          .$gender == \"Male\" &                                          .$condition == \"Heart Disease\",                                        \"Study Group\", \"Control Group\")) --------------------------------------------------------------------                     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>        -            -            -            - --------------------------------------------------------------------"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. 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>  # \\donttest{ # Traditional antibiogram ----------------------------------------------  antibiogram(example_isolates,   antibiotics = c(aminoglycosides(), carbapenems()) ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 10 × 7 #>    `Pathogen (N min-max)`   AMK   GEN   IPM   KAN   MEM   TOB #>  * <chr>                  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 CoNS (43-309)              0    86    52     0    52    22 #>  2 E. coli (0-462)          100    98   100    NA   100    97 #>  3 E. faecalis (0-39)         0     0   100     0    NA     0 #>  4 K. pneumoniae (0-58)      NA    90   100    NA   100    90 #>  5 P. aeruginosa (17-30)     NA   100    NA     0    NA   100 #>  6 P. mirabilis (0-34)       NA    94    94    NA    NA    94 #>  7 S. aureus (2-233)         NA    99    NA    NA    NA    98 #>  8 S. epidermidis (8-163)     0    79    NA     0    NA    51 #>  9 S. hominis (3-80)         NA    92    NA    NA    NA    85 #> 10 S. pneumoniae (11-117)     0     0    NA     0    NA     0  antibiogram(example_isolates,   antibiotics = aminoglycosides(),   ab_transform = \"atc\",   mo_transform = \"gramstain\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 2 × 5 #>   `Pathogen (N min-max)`   J01GB01 J01GB03 J01GB04 J01GB06 #> * <chr>                      <dbl>   <dbl>   <dbl>   <dbl> #> 1 Gram-negative (35-686)        96      96       0      98 #> 2 Gram-positive (436-1170)      34      63       0       0  antibiogram(example_isolates,   antibiotics = carbapenems(),   ab_transform = \"name\",   mo_transform = \"name\" ) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 5 × 3 #>   `Pathogen (N min-max)`                           Imipenem Meropenem #> * <chr>                                               <dbl>     <dbl> #> 1 Coagulase-negative Staphylococcus (CoNS) (48-48)       52        52 #> 2 Enterococcus faecalis (0-38)                          100        NA #> 3 Escherichia coli (418-422)                            100       100 #> 4 Klebsiella pneumoniae (51-53)                         100       100 #> 5 Proteus mirabilis (27-32)                              94        NA   # Combined antibiogram -------------------------------------------------  # combined antibiotics yield higher empiric coverage antibiogram(example_isolates,   antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),   mo_transform = \"gramstain\" ) #> # A tibble: 2 × 4 #>   `Pathogen (N min-max)`     TZP `TZP + GEN` `TZP + TOB` #> * <chr>                    <dbl>       <dbl>       <dbl> #> 1 Gram-negative (641-693)     88          99          98 #> 2 Gram-positive (345-1044)    86          98          95  # names of antibiotics do not need to resemble columns exactly: antibiogram(example_isolates,   antibiotics = c(\"Cipro\", \"cipro + genta\"),   mo_transform = \"gramstain\",   ab_transform = \"name\",   sep = \" & \" ) #> # A tibble: 2 × 3 #>   `Pathogen (N min-max)`  Ciprofloxacin `Ciprofloxacin & Gentamicin` #> * <chr>                           <dbl>                        <dbl> #> 1 Gram-negative (684-694)            91                           99 #> 2 Gram-positive (724-847)            77                           93   # Syndromic antibiogram ------------------------------------------------  # the data set could contain a filter for e.g. respiratory specimens antibiogram(example_isolates,   antibiotics = c(aminoglycosides(), carbapenems()),   syndromic_group = \"ward\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 14 × 8 #>    `Syndromic Group` `Pathogen (N min-max)`   AMK   GEN   IPM   KAN   MEM   TOB #>  * <chr>             <chr>                  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 Clinical          CoNS (23-205)             NA    89    57    NA    57    26 #>  2 ICU               CoNS (10-73)              NA    79    NA    NA    NA    NA #>  3 Outpatient        CoNS (3-31)               NA    84    NA    NA    NA    NA #>  4 Clinical          E. coli (0-299)          100    98   100    NA   100    98 #>  5 ICU               E. coli (0-137)          100    99   100    NA   100    96 #>  6 Clinical          K. pneumoniae (0-51)      NA    92   100    NA   100    92 #>  7 Clinical          P. mirabilis (0-30)       NA   100    NA    NA    NA   100 #>  8 Clinical          S. aureus (2-150)         NA    99    NA    NA    NA    97 #>  9 ICU               S. aureus (0-66)          NA   100    NA    NA    NA    NA #> 10 Clinical          S. epidermidis (4-79)     NA    82    NA    NA    NA    55 #> 11 ICU               S. epidermidis (4-75)     NA    72    NA    NA    NA    41 #> 12 Clinical          S. hominis (1-45)         NA    96    NA    NA    NA    94 #> 13 Clinical          S. pneumoniae (5-78)       0     0    NA     0    NA     0 #> 14 ICU               S. pneumoniae (5-30)       0     0    NA     0    NA     0  # now define a data set with only E. coli ex1 <- example_isolates[which(mo_genus() == \"Escherichia\"), ] #> ℹ Using column 'mo' as input for mo_genus()  # with a custom language, though this will be determined automatically # (i.e., this table will be in Spanish on Spanish systems) antibiogram(ex1,             antibiotics = aminoglycosides(),             ab_transform = \"name\",             syndromic_group = ifelse(ex1$ward == \"ICU\",                                      \"UCI\", \"No UCI\"),             language = \"es\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 2 × 5 #>   `Grupo sindrómico` `Patógeno (N min-max)` Amikacina Gentamicina Tobramicina #> * <chr>              <chr>                      <dbl>       <dbl>       <dbl> #> 1 No UCI             E. coli (0-325)              100          98          98 #> 2 UCI                E. coli (0-137)              100          99          96   # Weighted-incidence syndromic combination antibiogram (WISCA) ---------  # the data set could contain a filter for e.g. respiratory specimens/ICU antibiogram(example_isolates,             antibiotics = c(\"AMC\", \"AMC+CIP\", \"TZP\", \"TZP+TOB\"),             mo_transform = \"gramstain\",             minimum = 10, # this should be >=30, but now just as example             syndromic_group = ifelse(example_isolates$age >= 65 &                                        example_isolates$gender == \"M\",                                      \"WISCA Group 1\", \"WISCA Group 2\"             ) ) #> # A tibble: 4 × 6 #>   `Syndromic Group` `Pathogen (N min-max)`    AMC `AMC + CIP`   TZP `TZP + TOB` #> * <chr>             <chr>                   <dbl>       <dbl> <dbl>       <dbl> #> 1 WISCA Group 1     Gram-negative (261-285)    76          95    89          99 #> 2 WISCA Group 2     Gram-negative (380-442)    76          98    88          98 #> 3 WISCA Group 1     Gram-positive (123-406)    76          89    81          95 #> 4 WISCA Group 2     Gram-positive (222-732)    76          89    88          95   # Print the output for R Markdown / Quarto -----------------------------  ureido <- antibiogram(example_isolates,                       antibiotics = ureidopenicillins(),                       ab_transform = \"name\") #> ℹ For ureidopenicillins() using column 'TZP' (piperacillin/tazobactam)  # in an Rmd file, you would just need to return `ureido` in a chunk, # but to be explicit here: if (requireNamespace(\"knitr\")) {   knitr::knit_print(ureido) } #> [1] \"\\n\\n|Pathogen (N)          | Piperacillin/tazobactam|\\n|:---------------------|-----------------------:|\\n|CoNS (33)             |                      30|\\n|*E. coli* (416)       |                      94|\\n|*K. pneumoniae* (53)  |                      89|\\n|*S. pneumoniae* (112) |                     100|\" #> attr(,\"class\") #> [1] \"knit_asis\" #> attr(,\"knit_cacheable\") #> [1] NA   # Generate plots with ggplot2 or base R --------------------------------  ab1 <- antibiogram(example_isolates,   antibiotics = c(\"AMC\", \"CIP\", \"TZP\", \"TZP+TOB\"),   mo_transform = \"gramstain\" ) ab2 <- antibiogram(example_isolates,   antibiotics = c(\"AMC\", \"CIP\", \"TZP\", \"TZP+TOB\"),   mo_transform = \"gramstain\",   syndromic_group = \"ward\" )  if (requireNamespace(\"ggplot2\")) {   ggplot2::autoplot(ab1) }  if (requireNamespace(\"ggplot2\")) {   ggplot2::autoplot(ab2) }   plot(ab1)  plot(ab2)   # }"},{"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 (default FALSE), see .sir() only_treatable logical indicate whether antimicrobial drugs excluded laboratory tests (default 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 names codes microorganisms (see .mo()) - default 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: There was 1 warning in `summarise()`. #> ℹ In argument: `across(aminoglycosides(), resistance)`. #> ℹ In group 3: `ward = \"Outpatient\"`. #> Caused by 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: There were 52 warnings in `summarise()`. #> The first warning was: #> ℹ In argument: `across(not_intrinsic_resistant(), resistance)`. #> ℹ In group 1: `ward = \"Clinical\"`. #> Caused by warning: #> ! Introducing NA: no results available for OXA in group: ward = \"Clinical\" #> (minimum = 30). #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 51 remaining warnings. #> # 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)) } #> 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 - default 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 - default 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 - default 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 Arbitrary Input to Valid Microbial Taxonomy — as.mo","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Use function get valid microorganism code (mo) based arbitrary user input. Determination done using intelligent rules complete taxonomic tree 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 Arbitrary Input to Valid Microbial Taxonomy — 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),   cleaning_regex = getOption(\"AMR_cleaning_regex\", 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 Arbitrary Input to Valid Microbial Taxonomy — 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). Please see Details full list staphylococcal species converted. 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. . Please see Details full list streptococcal species converted. 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 package 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 Perl-compatible regular expression (case-insensitive) matches x must return NA. can convenient exclude known non-relevant input can also set package option AMR_ignore_pattern, e.g. options(AMR_ignore_pattern = \"(reported|contaminated flora)\"). cleaning_regex Perl-compatible regular expression (case-insensitive) clean input x. Every matched part x removed. default, outcome mo_cleaning_regex(), removes texts brackets texts \"species\" \"serovar\". default can set package option AMR_cleaning_regex. language language translate text like \"growth\", defaults system language (see get_AMR_locale()) info logical indicate progress bar printed 25 items coerced - default 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 Arbitrary Input to Valid Microbial Taxonomy — 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 Arbitrary Input to Valid Microbial Taxonomy — 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. Becker = TRUE, following 85 staphylococci converted coagulase-negative group: S. argensis, S. arlettae, S. auricularis, S. borealis, S. caeli, S. caledonicus, S. canis, S. capitis, S. capitis capitis, S. capitis urealyticus, S. capitis ureolyticus, S. caprae, S. carnosus, S. carnosus carnosus, S. carnosus utilis, S. casei, S. caseolyticus, S. chromogenes, S. cohnii, S. cohnii cohnii, S. cohnii urealyticum, S. cohnii urealyticus, S. condimenti, S. croceilyticus, S. debuckii, S. devriesei, S. durrellii, S. edaphicus, S. epidermidis, S. equorum, S. equorum equorum, S. equorum linens, S. felis, S. fleurettii, S. gallinarum, S. haemolyticus, S. hominis, S. hominis hominis, S. hominis novobiosepticus, S. jettensis, S. kloosii, S. lentus, S. lloydii, S. lugdunensis, S. massiliensis, S. microti, S. muscae, S. nepalensis, S. pasteuri, S. petrasii, S. petrasii croceilyticus, S. petrasii jettensis, S. petrasii petrasii, S. petrasii pragensis, S. pettenkoferi, S. piscifermentans, S. pragensis, S. pseudoxylosus, S. pulvereri, S. ratti, S. rostri, S. saccharolyticus, S. saprophyticus, S. saprophyticus bovis, S. saprophyticus saprophyticus, S. schleiferi, S. schleiferi schleiferi, S. sciuri, S. sciuri carnaticus, S. sciuri lentus, S. sciuri rodentium, S. sciuri sciuri, S. simulans, S. stepanovicii, S. succinus, S. succinus casei, S. succinus succinus, S. taiwanensis, S. urealyticus, S. ureilyticus, S. veratri, S. vitulinus, S. vitulus, S. warneri, S. xylosus. following 16 staphylococci converted coagulase-positive group: S. agnetis, S. argenteus, S. coagulans, S. cornubiensis, S. delphini, S. hyicus, S. hyicus chromogenes, S. hyicus hyicus, S. intermedius, S. lutrae, S. pseudintermedius, S. roterodami, S. schleiferi coagulans, S. schweitzeri, S. simiae, S. singaporensis. Lancefield = TRUE, following streptococci converted corresponding Lancefield group: S. agalactiae (Group B), S. anginosus anginosus (Group F), S. anginosus whileyi (Group F), S. anginosus (Group F), S. canis (Group G), S. dysgalactiae dysgalactiae (Group C), S. dysgalactiae equisimilis (Group C), S. dysgalactiae (Group C), S. equi equi (Group C), S. equi ruminatorum (Group C), S. equi zooepidemicus (Group C), S. equi (Group C), S. pyogenes (Group ), S. salivarius salivarius (Group K), S. salivarius thermophilus (Group K), S. salivarius (Group K), S. sanguinis (Group H).","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 Arbitrary Input to Valid Microbial Taxonomy — 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, cleaning_regex 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 cleaning_regex 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 Arbitrary Input to Valid Microbial Taxonomy — 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 Arbitrary Input to Valid Microbial Taxonomy — 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 Arbitrary Input to Valid Microbial Taxonomy — 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 Arbitrary Input to Valid Microbial Taxonomy — 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 Arbitrary Input to Valid Microbial Taxonomy — 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. breakpoints used interpretation publicly available clinical_breakpoints data set.","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_screening = getOption(\"AMR_include_screening\", FALSE),   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_screening = getOption(\"AMR_include_screening\", FALSE),   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_screening = getOption(\"AMR_include_screening\", FALSE),   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 package option AMR_guideline. Currently 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_screening logical indicate clinical breakpoints screening allowed - default FALSE. Can also set package option AMR_include_screening. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort - default TRUE. Can also set package option AMR_include_PKPD. col_mo column name names codes microorganisms (see .mo()) - default 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 package 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-clinical-breakpoints","dir":"Reference","previous_headings":"","what":"Machine-Readable Clinical Breakpoints","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 × 17 #>    datetime            index ab_input ab_guid…¹ mo_in…² mo_guideline     guide…³ #>    <dttm>              <int> <chr>    <ab>      <chr>   <mo>             <chr>   #>  1 2023-02-24 18:59:29     1 TOB      TOB       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  2 2023-02-24 18:59:29     1 GEN      GEN       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  3 2023-02-24 18:59:28     1 CIP      CIP       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  4 2023-02-24 18:59:28     1 AMP      AMP       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  5 2023-02-24 18:59:23     1 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  6 2023-02-24 18:59:23     2 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  7 2023-02-24 18:59:23     3 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  8 2023-02-24 18:59:23     4 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  9 2023-02-24 18:59:23     5 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 10 2023-02-24 18:59:23     6 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> # … with 40 more rows, 10 more variables: ref_table <chr>, method <chr>, #> #   input <dbl>, outcome <sir>, breakpoint_S_R <chr>, ab_considered <lgl>, #> #   mo_considered <lgl>, breakpoint_S <lgl>, breakpoint_R <lgl>, #> #   interpretation <lgl>, 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(). default 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 - default 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 - default system language (see get_AMR_locale()) can also set package 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 - default 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 names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). FUN function call mo column transform microorganism codes - default mo_shortname() ... arguments passed FUN translate_ab character length 1 containing column names antibiotics data set language language returned text - default current system language (see get_AMR_locale()) can also set package 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 - default 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 returns table ready reporting/publishing. Use combine_SI = TRUE (default) test R vs. S+combine_SI = FALSE test R+vs. S. table can also directly used R Markdown / Quarto without need e.g. knitr::kable().","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":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. 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>  # \\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) AMC      15     0     0    15 #> 2 (unknown species) AMK       0     0     0     0 #> 3 (unknown species) AMP      15     0     1    16 #> 4 (unknown species) AMX      15     0     1    16 #> 5 (unknown species) AZM       3     0     3     6 #> 6 (unknown species) CAZ       0     0     0     0 #> 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 AMC     463    89   174   726 #>  2 Gram-negative AMK     251     0     5   256 #>  3 Gram-negative AMP     226     0   405   631 #>  4 Gram-negative AMX     226     0   405   631 #>  5 Gram-negative AZM       1     2   696   699 #>  6 Gram-negative CAZ     607     0    27   634 #>  7 Gram-negative CHL       1     0    30    31 #>  8 Gram-negative CIP     610    11    63   684 #>  9 Gram-negative CLI      18     1   709   728 #> 10 Gram-negative COL     309     0    78   387 #> # … 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 AMC     332    74    61   467 #>  2 E. coli AMK     171     0     0   171 #>  3 E. coli AMP     196     0   196   392 #>  4 E. coli AMX     196     0   196   392 #>  5 E. coli AZM       0     0   467   467 #>  6 E. coli CAZ     449     0    11   460 #>  7 E. coli CHL       0     0     0     0 #>  8 E. coli CIP     398     1    57   456 #>  9 E. coli CLI       0     0   467   467 #> 10 E. coli COL     240     0     0   240 #> # … 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 - default current system language (see get_AMR_locale()) can also set package 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) - default 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 names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console - default 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 package 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 - default 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 (default 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 package 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) - default first column date class col_patient_id column name unique IDs patients - default first column starts 'patient' 'patid' (case insensitive) col_mo column name names codes microorganisms (see .mo()) - default 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(). default 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 - default 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(info = TRUE), ] #> Determining first isolates using an episode length of 365 days #> ℹ Using column 'date' as input for col_date. #> ℹ Using column 'patient' as input for col_patient_id. #> Basing inclusion on all antimicrobial results, using a points threshold of #> 2 #> Including isolates from ICU. #> ℹ Excluded 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,379 'phenotype-based' first isolates (69.0% of total where a #>    microbial ID was available) #> # 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()), ] #> 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(info = TRUE)) } #> Determining first isolates using an episode length of 365 days #> Basing inclusion on all antimicrobial results, using a points threshold of #> 2 #> Including isolates from ICU. #> ℹ Excluded 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,379 'phenotype-based' first isolates (69.0% of total where a #>    microbial ID was available) #> # 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) } #> # 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(info = TRUE)) %>%     select(ward, date, patient, mo, first) } #> Determining first isolates using an episode length of 365 days #> Basing inclusion on all antimicrobial results, using a points threshold of #> 2 #> Including isolates from ICU. #>  #> Group: ward = \"Clinical\" #> ℹ Excluded 9 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 861 'phenotype-based' first isolates (69.8% of total where a #>    microbial ID was available) #> Including isolates from ICU. #>  #> Group: ward = \"ICU\" #> ℹ Excluded 6 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 449 'phenotype-based' first isolates (69.6% within scope and 69.5% #>    of total where a microbial ID was available) #> Including isolates from ICU. #>  #> Group: ward = \"Outpatient\" #> ℹ Excluded 1 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 98 'phenotype-based' first isolates (82.4% within scope and 81.7% #>    of total where a microbial ID was available) #> # 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 (Clinical or Epidemic) Episodes — get_episode","title":"Determine (Clinical or Epidemic) Episodes — get_episode","text":"functions determine items vector can considered (start ) new episode. can used determine clinical episodes epidemiological analysis. get_episode() function returns index number episode per group, is_new_episode() function returns TRUE every new get_episode() index. absolute relative episode determination supported.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine (Clinical or Epidemic) Episodes — get_episode","text":"","code":"get_episode(x, episode_days = NULL, case_free_days = NULL, ...)  is_new_episode(x, episode_days = NULL, case_free_days = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine (Clinical or Epidemic) Episodes — get_episode","text":"x vector dates (class Date POSIXt), sorted internally determine episodes episode_days episode length days specify time period new episode begins, can also less day Inf, see Details case_free_days (inter-epidemic) interval length days new episode start, 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 (Clinical or Epidemic) Episodes — get_episode","text":"get_episode(): integer 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 (Clinical or Epidemic) Episodes — get_episode","text":"Episodes can determined two ways: absolute relative. Absolute method uses episode_days define episode length days, new episode start. common use case AMR data analysis microbial epidemiology: episodes S. aureus bacteraemia ICU patients example. episode length 30 days, new S. aureus isolates ICU episode 30 days considered different (new) episode. Thus, method counts since start previous episode. Relative method uses case_free_days quantify duration case-free days (inter-epidemic interval), new episode start. common use case infectious disease epidemiology: episodes norovirus outbreaks hospital example. case-free period 14 days, new norovirus cases time considered different (new) episode. Thus, methods counts since last case previous episode. table: ** marks start new episode, 8 January 2023 7 days since start previous episode (1 January 2023).  *** marks start new episode, 21 January 2023 7 days since last case previous episode (8 January 2023). Either episode_days case_free_days must provided function.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"difference-between-get-episode-and-is-new-episode-","dir":"Reference","previous_headings":"","what":"Difference between get_episode() and is_new_episode()","title":"Determine (Clinical or Epidemic) Episodes — get_episode","text":"get_episode() function returns index number episode, cases/patients/isolates first episode number 1, cases/patients/isolates second episode number 2, etc. is_new_episode() function returns TRUE every new get_episode() index, thus equal !duplicated(get_episode(...)). specify, setting episode_days = 365 (using method 1 explained ), two functions differ:","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Determine (Clinical or Epidemic) Episodes — get_episode","text":"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, episode 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 (Clinical or Epidemic) Episodes — get_episode","text":"","code":"# difference between absolute and relative determination of episodes: x <- data.frame(dates = as.Date(c(   \"2021-01-01\",   \"2021-01-02\",   \"2021-01-05\",   \"2021-01-08\",   \"2021-02-21\",   \"2021-02-22\",   \"2021-02-23\",   \"2021-02-24\",   \"2021-03-01\",   \"2021-03-01\" ))) x$absolute <- get_episode(x$dates, episode_days = 7) x$relative <- get_episode(x$dates, case_free_days = 7) x #>         dates absolute relative #> 1  2021-01-01        1        1 #> 2  2021-01-02        1        1 #> 3  2021-01-05        1        1 #> 4  2021-01-08        2        1 #> 5  2021-02-21        3        2 #> 6  2021-02-22        3        2 #> 7  2021-02-23        3        2 #> 8  2021-02-24        3        2 #> 9  2021-03-01        4        2 #> 10 2021-03-01        4        2   # `example_isolates` is a data set available in the AMR package. # See ?example_isolates df <- example_isolates[sample(seq_len(2000), size = 100), ]  get_episode(df$date, episode_days = 60) # indices #>   [1] 11 38 25 37 31 42 44  9 13  8  9  7 48 47  3  6 46 41 11 16 43  9 41 29 44 #>  [26] 42 30 28 36  1 30 39 40 32 35  6  3 27 24  1  2 35 17 20  9 36 34 48 23 22 #>  [51]  7 21 12 43 21 17 50 31 48 26  5 26 14 15 10  2 35 36 47  8 46 16 27 13 45 #>  [76] 40 28 25 21  4 50 36 45 17 24  6 18  6  3  5 46 19  4 37 49 48 13 33 10 41 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE #>   [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE #>  [13]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE #>  [25] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE #>  [37] FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE #>  [49]  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE #>  [61]  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [73] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE #>  [85] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE #>  [97] FALSE  TRUE FALSE 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 2002-10-22 718963     82 M      ICU       B_STPHY_EPDR R     NA    R     R     #> 2 2002-10-20 F35553     51 M      ICU       B_STPHY_AURS S     NA    S     NA    #> 3 2002-09-08 B8CB09     60 F      Outpatie… B_STPHY_CONS S     NA    S     NA    #> # … 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 = 100,       replace = TRUE     )) %>%     group_by(patient, condition) %>%     mutate(new_episode = is_new_episode(date, 365)) %>%     select(patient, date, condition, new_episode) %>%     arrange(patient, condition, date) } #> # A tibble: 100 × 4 #> # Groups:   patient, condition [98] #>    patient date       condition new_episode #>    <chr>   <date>     <chr>     <lgl>       #>  1 032343  2003-06-09 B         TRUE        #>  2 059414  2006-07-21 A         TRUE        #>  3 063988  2006-12-26 A         TRUE        #>  4 065133  2011-12-19 C         TRUE        #>  5 067927  2002-02-05 A         TRUE        #>  6 069276  2015-06-18 B         TRUE        #>  7 083080  2012-04-16 B         TRUE        #>  8 092034  2016-08-07 B         TRUE        #>  9 097186  2015-10-28 B         TRUE        #> 10 125492  2017-09-15 B         TRUE        #> # … with 90 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)     ) %>%     arrange(patient, ward, date) } #> # A tibble: 100 × 5 #> # Groups:   ward, patient [92] #>    ward     date       patient new_index new_logical #>    <chr>    <date>     <chr>       <int> <lgl>       #>  1 Clinical 2003-06-09 032343          1 TRUE        #>  2 Clinical 2006-07-21 059414          1 TRUE        #>  3 Clinical 2006-12-26 063988          1 TRUE        #>  4 Clinical 2011-12-19 065133          1 TRUE        #>  5 ICU      2002-02-05 067927          1 TRUE        #>  6 ICU      2015-06-18 069276          1 TRUE        #>  7 Clinical 2012-04-16 083080          1 TRUE        #>  8 Clinical 2016-08-07 092034          1 TRUE        #>  9 Clinical 2015-10-28 097186          1 TRUE        #> 10 Clinical 2017-09-15 125492          1 TRUE        #> # … with 90 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           64             15            44            52 #> 2 ICU                21              9            20            20 #> 3 Outpatient          7              6             7             7  # grouping on patients and microorganisms leads to the same # results as first_isolate() when using 'episode-based': if (require(\"dplyr\")) {   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) } #> [1] TRUE  # but is_new_episode() has a lot more flexibility than first_isolate(), # since you can now group on anything that seems relevant: if (require(\"dplyr\")) {   df %>%     group_by(patient, mo, ward) %>%     mutate(flag_episode = is_new_episode(date, 365)) %>%     select(group_vars(.), flag_episode) } #> # A tibble: 100 × 4 #> # Groups:   patient, mo, ward [95] #>    patient mo           ward     flag_episode #>    <chr>   <mo>         <chr>    <lgl>        #>  1 219024  B_ESCHR_COLI Clinical TRUE         #>  2 960698  B_ESCHR_COLI Clinical TRUE         #>  3 696587  B_BCTRD_FRGL Clinical TRUE         #>  4 850429  B_STPHY_EPDR Clinical TRUE         #>  5 8CBCF2  B_STPHY_CONS Clinical TRUE         #>  6 559068  B_STPHY_CPTS Clinical TRUE         #>  7 E52521  B_KLBSL_AERG Clinical TRUE         #>  8 304508  B_STRPT_PNMN Clinical TRUE         #>  9 990867  B_STPHY_AURS Clinical TRUE         #> 10 2F0932  B_STPHY_CONS Clinical TRUE         #> # … with 90 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: There were 73 warnings in `summarise()`. #> The first warning was: #> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, #>   only_all_tested = FALSE) ...`. #> ℹ In group 5: `order = \"Lactobacillales\"`, `genus = \"Enterococcus\"`. #> Caused by warning: #> ! Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> 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) - default 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 - default current system language (see get_AMR_locale()) can also set package 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 - default \"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\") } #> 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 (default 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 names codes microorganisms (see .mo()) - default 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 (default 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) } #> [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] 2.980001 kurtosis(rnorm(10000), excess = TRUE) #> [1] -0.02531765"},{"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 names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console - default 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 (default 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: There was 1 warning in `mutate()`. #> ℹ In argument: `MDRO = mdro()`. #> Caused by 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) - default 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 R I S S R S I I S mean_amr_distance(sir) #>  [1] -0.4743416  1.8973666 -0.4743416 -0.4743416 -0.4743416  1.8973666 #>  [7] -0.4743416 -0.4743416 -0.4743416 -0.4743416  mic <- random_mic(10) mic #> Class 'mic' #>  [1] 0.25  64    64    0.01  0.025 128   256   4     128   256   mean_amr_distance(mic) #>  [1] -0.8840919  0.5294878  0.5294878 -1.7046495 -1.4710682  0.7061853 #>  [7]  0.8828827 -0.1773020  0.7061853  0.8828827 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) #>  [1] -0.8840919  0.5294878  0.5294878 -1.7046495 -1.4710682  0.7061853 #>  [7]  0.8828827 -0.1773020  0.7061853  0.8828827  disk <- random_disk(10) disk #> Class 'disk' #>  [1] 13 36 18 50 14 40 38 15 45 48 mean_amr_distance(disk) #>  [1] -1.2438749  0.2860247 -0.9112880  1.2172680 -1.1773575  0.5520942 #>  [7]  0.4190595 -1.1108402  0.8846811  1.0842332  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    I   17  <=1 <=0.25 #> 2   B    R   20  <=1      1 #> 3   C    R   28 >=16      1 #> 4   D    R   22 >=16 <=0.25 #> 5   E    I   24 >=16    >=4 #> 6   F    R   30 >=16    0.5 #> 7   G    R   17 >=16    >=4 #> 8   H    I   23    2    >=4 #> 9   I    I   27    8    0.5 #> 10  J    R   19    8    0.5 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\", #>   and \"tobr\" #>  [1] -1.29633203 -0.33470083  0.68999803  0.04843526  0.30058957  0.64264079 #>  [7]  0.40191868 -0.19451042 -0.15234167 -0.10569739 y$amr_distance <- mean_amr_distance(y, where(is.mic)) #> Error in .subset(x, j): invalid subscript type 'list' y[order(y$amr_distance), ] #> Error in order(y$amr_distance): argument 1 is not a vector  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    R   28 >=16      1   0.68999803 0.00000000 #> 2   F    R   30 >=16    0.5   0.64264079 0.04735723 #> 3   G    R   17 >=16    >=4   0.40191868 0.28807934 #> 4   E    I   24 >=16    >=4   0.30058957 0.38940845 #> 5   D    R   22 >=16 <=0.25   0.04843526 0.64156276 #> 6   J    R   19    8    0.5  -0.10569739 0.79569542 #> 7   I    I   27    8    0.5  -0.15234167 0.84233970 #> 8   H    I   23    2    >=4  -0.19451042 0.88450845 #> 9   B    R   20  <=1      1  -0.33470083 1.02469886 #> 10  A    I   17  <=1 <=0.25  -1.29633203 1.98633005 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 52 142 Microorganisms — microorganisms","title":"Data Set with 52 142 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 52 142 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 52 142 Microorganisms — microorganisms","text":"tibble 52 142 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 52 142 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 52 142 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 52 142 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 52 142 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 52 142 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 52 142 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 52 142 Microorganisms — microorganisms","text":"","code":"microorganisms #> # A tibble: 52,142 × 22 #>    mo               fullname      status kingdom phylum class order family genus #>    <mo>             <chr>         <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> #>  1 B_GRAMN          (unknown Gra… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  2 B_GRAMP          (unknown Gra… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  3 B_ANAER          (unknown ana… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  4 F_FUNGUS         (unknown fun… accep… Fungi   (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 52,132 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 package 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_snomed() mo_info() named character case mo_url() 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() (scientific reference name), 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]] #> [1] \"1098101000112102\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\"        #>  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 aerogenes\") #> [1] \"Tindall et al., 2017\" mo_authors(\"Klebsiella aerogenes\") #> [1] \"Tindall et al.\" mo_year(\"Klebsiella aerogenes\") #> [1] 2017 mo_lpsn(\"Klebsiella aerogenes\") #> [1] \"777146\" mo_gbif(\"Klebsiella aerogenes\") #> [1] \"9281703\" mo_synonyms(\"Klebsiella aerogenes\") #>   Hormaeche et al., 1960     Bascomb et al., 1971  #> \"Enterobacter aerogenes\"     \"Klebsiella mobilis\"    # 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(\"EIEC\") #> [1] \"coli\" mo_name(\"UPEC\") #> [1] \"Escherichia 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\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\"        #>  # }"},{"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 package 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: There were 73 warnings in `summarise()`. #> The first warning was: #> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, #>   only_all_tested = FALSE) ...`. #> ℹ In group 5: `order = \"Lactobacillales\"`, `genus = \"Enterococcus\"`. #> Caused by warning: #> ! Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> 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 - default 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' - default system language (see get_AMR_locale()) can overwritten setting package 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 Antimicrobial Resistance — proportion","title":"Calculate Antimicrobial 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 Antimicrobial 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 Antimicrobial 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 Antimicrobial 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. default \"\" 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 - default current system language (see get_AMR_locale()) can also set package 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) - default TRUE","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Antimicrobial 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 Antimicrobial 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 one four available algorithms. 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 Antimicrobial 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 Antimicrobial 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 Antimicrobial Resistance — proportion","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. 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 ------------------------------------------------------------ # 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: There was 1 warning in `summarise()`. #> ℹ In argument: `KAN = (function (..., minimum = 30, as_percent = FALSE, #>   only_all_tested = FALSE) ...`. #> ℹ In group 3: `ward = \"Outpatient\"`. #> Caused by 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] 2      256    0.005  0.5    0.001  64     0.0625 0.005  0.005  0.001  #> [11] 0.125  0.0625 0.0625 0.002  32     2      0.0625 4      2      0.125  #> [21] 0.005  0.002  0.005  16     128    random_disk(25) #> Class 'disk' #>  [1] 27 33 26 44 31 47 13 50 24  9 44  6 44 50  7 45  9 21 41 23 15  9 42  9 40 random_sir(25) #> Class 'sir' #>  [1] R R I R S R I R S I S R R S R I S R I S I I I S 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] 0.01    0.25    <=0.001 16      0.002   0.125   4       0.25    >=64    #> [10] 1       <=0.001 0.005   0.125   0.025   <=0.001 2       0.5     <=0.001 #> [19] 0.002   0.25    0.0625  0.025   0.25    0.25    0.01    random_mic(25, \"Klebsiella pneumoniae\", \"meropenem\") # range 0.0625-16 #> Class 'mic' #>  [1] 32     16     0.5    8      4      <=0.25 2      1      2      0.5    #> [11] 4      0.5    4      16     8      8      <=0.25 4      32     32     #> [21] 0.5    0.5    16     0.5    16     random_mic(25, \"Streptococcus pneumoniae\", \"meropenem\") # range 0.0625-4 #> Class 'mic' #>  [1] 0.25   >=8    0.5    0.0625 0.5    0.0625 0.25   0.5    4      4      #> [11] 4      1      1      >=8    >=8    >=8    2      0.25   0.25   2      #> [21] >=8    >=8    0.5    0.25   1       random_disk(25, \"Klebsiella pneumoniae\") # range 8-50 #> Class 'disk' #>  [1] 17 47 43 49  8 36 33 21 38 30 39 13 41 22 47 21 19 12 47 12 36 46 48 47 27 random_disk(25, \"Klebsiella pneumoniae\", \"ampicillin\") # range 11-17 #> Class 'disk' #>  [1] 15 11 17 17 11 17 12 17 16 17 16 13 13 12 16 14 13 11 11 12 13 11 13 12 11 random_disk(25, \"Streptococcus pneumoniae\", \"ampicillin\") # range 12-27 #> Class 'disk' #>  [1] 20 17 19 16 20 24 17 23 19 25 27 19 22 19 23 18 23 24 21 24 26 21 27 18 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 - default first column date class year_min lowest year use prediction model, dafaults lowest year col_date year_max highest year use prediction model - default 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\" ) 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) }  #> 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] 0.002064919"},{"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), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), 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 20 supported languages English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), 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 package 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 package 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, package 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\") #> ℹ 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-1829146","dir":"Changelog","previous_headings":"","what":"AMR 1.8.2.9146","title":"AMR 1.8.2.9146","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 Many new interesting functions, antibiogram() (generating traditional/combined/syndromic/WISCA antibiograms), sir_confidence_interval() mean_amr_distance(), add_custom_microorganisms() add custom microorganisms package Clinical breakpoints added EUCAST 2022 CLSI 2022 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 20 new antibiotics added updated DDDs ATC codes Extended support antiviral agents (antivirals data set), many new functions Now available 20 languages Many small bug fixes","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/news/index.html","id":"sir-vs-rsi-1-8-2-9146","dir":"Changelog","previous_headings":"New","what":"SIR vs. RSI","title":"AMR 1.8.2.9146","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":"new-antibiogram-function-1-8-2-9146","dir":"Changelog","previous_headings":"New","what":"New antibiogram function","title":"AMR 1.8.2.9146","text":"new antibiogram() function, users can now generate traditional, combined, syndromic, even weighted-incidence syndromic combination antibiograms (WISCA). , follow logic previously described work Klinker et al. (2021, DOI 10.1177/20499361211011373) Barbieri et al. (2021, DOI 10.1186/s13756-021-00939-2). help page antibiogram() extensively elaborates use cases, antibiogram() also supports printing R Markdown Quarto, support 20 languages. Furthermore, different plotting methods implemented allow graphical visualisations well.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"interpretation-of-mic-and-disk-diffusion-values-1-8-2-9146","dir":"Changelog","previous_headings":"New","what":"Interpretation of MIC and disk diffusion values","title":"AMR 1.8.2.9146","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\"). new arguments include_PKPD (default: TRUE) include_screening (default: FALSE), users can now specify whether breakpoints screening PK/PD table included interpreting MICs disks diffusion values. options can set globally, can read new manual. 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-1-8-2-9146","dir":"Changelog","previous_headings":"New","what":"Supported languages","title":"AMR 1.8.2.9146","text":"added support following ten languages: Chinese (simplified), Czech, Finnish, Greek, Japanese, Norwegian (bokmål), Polish, Romanian, Turkish Ukrainian. antibiotic names now available languages, AMR package automatically determine supported language based user’s system language. grateful valuable input colleagues countries. AMR package now available 20 languages total, according download stats used almost countries world!","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"microbiological-taxonomy-1-8-2-9146","dir":"Changelog","previous_headings":"New","what":"Microbiological taxonomy","title":"AMR 1.8.2.9146","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-1-8-2-9146","dir":"Changelog","previous_headings":"New","what":"Antibiotic agents and selectors","title":"AMR 1.8.2.9146","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-1-8-2-9146","dir":"Changelog","previous_headings":"New","what":"Antiviral agents","title":"AMR 1.8.2.9146","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-1-8-2-9146","dir":"Changelog","previous_headings":"New","what":"Other new functions","title":"AMR 1.8.2.9146","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-1-8-2-9146","dir":"Changelog","previous_headings":"","what":"Changes","title":"AMR 1.8.2.9146","text":"Gained argument new_after_days determine episodes based epidemic periods Fix working NA values Fix unsorted dates length 2 Now returns class integer instead numeric since always whole numbers 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 Fix using g.test() zeroes 2x2 table mo_synonyns() now contains scientific reference names","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-8-2-9146","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.8.2.9146","text":"Added Peter Dutey-Magni, Dmytro Mykhailenko, Anton Mymrikov, Andrew Norgan, Jonas Salm, Anita Williams 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 along 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 !) reliable data 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. AMR package contains data set example_isolates_unclean, might look data users extracted laboratory systems: AMR data analysis, like microorganism column contain valid, --date taxonomy, antibiotic columns cleaned SIR values well.","code":"library(dplyr) library(ggplot2) library(AMR)  # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\")) 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  # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"How to conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 11 Dec 2022. codes AMR packages come .mo() short, still human readable. importantly, .mo() supports kinds input: first character codes denote taxonomic kingdom, Bacteria (B), Fungi (F), Protozoa (P). AMR package also contain functions directly retrieve taxonomic properties, name, genus, species, family, order, even Gram-stain. start mo_ use .mo() internally, still arbitrary user input can used: Now can thus clean data: Apparently, uncertainty translation taxonomic codes. Let’s check : ’s good.","code":"as.mo(\"Klebsiella pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"K. pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLEPNE\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLPN\") #> Class 'mo' #> [1] B_KLBSL_PNMN mo_family(\"K. pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"K. pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"K. pneumoniae\") #> [1] \"pneumoniae\"  mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\"  mo_ref(\"K. pneumoniae\") #> [1] \"Trevisan, 1887\"  mo_snomed(\"K. pneumoniae\") #> [[1]] #> [1] \"1098101000112102\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\" our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE) #> ℹ Microorganism translation was uncertain for four microorganisms. Run #>   mo_uncertainties() to review these uncertainties, or use #>   add_custom_microorganisms() to add custom entries. 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), and Enterobacter cloacae #>               dissolvens (0.565) #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #>                    Based on input \"K pneumoniae\" #> Also matched: Klebsiella pneumoniae ozaenae (0.707), Klebsiella #>               pneumoniae pneumoniae (0.688), Klebsiella pneumoniae rhinoscleromatis #>               (0.658), Klebsiella pasteurii (0.500), Klebsiella planticola (0.500), #>               Kingella potus (0.400), Kosakonia pseudosacchari (0.361), Kaistella #>               palustris (0.333), Kocuria palustris (0.333), and Kocuria pelophila #>               (0.333) #> -------------------------------------------------------------------------------- #> \"S. aureus\" -> Staphylococcus aureus (B_STPHY_AURS, 0.690) #>                Based on input \"S aureus\" #> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus #>               argenteus (0.625), Staphylococcus aureus anaerobius (0.625), #>               Streptomyces argenteolus (0.483), Streptomyces aureus (0.474), #>               Streptomyces azureus (0.467), Streptomyces aureorectus (0.444), #>               Streptomyces auratus (0.433), Streptomyces aurantiogriseus (0.429), and #>               Streptomyces aureocirculatus (0.429) #> -------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #>                    Based on input \"S pneumoniae\" #> Also matched: Streptococcus pseudopneumoniae (0.700), Serratia #>               proteamaculans quinovora (0.545), Streptococcus pseudoporcinus (0.536), #>               Staphylococcus pseudintermedius (0.532), Serratia proteamaculans #>               proteamaculans (0.526), Salmonella Portanigra (0.524), Sphingomonas #>               paucimobilis (0.520), Streptococcus pluranimalium (0.519), #>               Streptococcus constellatus pharyngis (0.514), and Salmonella Pakistan #>               (0.500) #>  #> Only the first 10 other matches of each record are shown. Run #> print(mo_uncertainties(), n = ...) to view more entries, or save #> mo_uncertainties() to an object."},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"antibiotic-results","dir":"Articles","previous_headings":"Preparation","what":"Antibiotic results","title":"How to conduct AMR data analysis","text":"column antibiotic test results must also cleaned. AMR package comes three new data types work test results: mic minimal inhibitory concentrations (MIC), disk disk diffusion diameters, sir SIR data interpreted already. package can also determine SIR values based MIC disk diffusion values, read .sir() page. now, just clean SIR columns data using dplyr: basically cleaning, time start data inclusion.","code":"# method 1, be explicit about the columns: our_data <- our_data %>%    mutate_at(vars(AMX:GEN), as.sir)  # method 2, let the AMR package determine the eligible columns our_data <- our_data %>%    mutate_if(is_sir_eligible, as.sir)  # result: our_data #> # A tibble: 3,000 × 8 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> #>  1 J3         A        2012-11-21 B_ESCHR_COLI R     I     S     S     #>  2 R7         A        2018-04-03 B_KLBSL_PNMN R     I     S     S     #>  3 P3         A        2014-09-19 B_ESCHR_COLI R     S     S     S     #>  4 P10        A        2015-12-10 B_ESCHR_COLI S     I     S     S     #>  5 B7         A        2015-03-02 B_ESCHR_COLI S     S     S     S     #>  6 W3         A        2018-03-31 B_STPHY_AURS R     S     R     S     #>  7 J8         A        2016-06-14 B_ESCHR_COLI R     S     S     S     #>  8 M3         A        2015-10-25 B_ESCHR_COLI R     S     S     S     #>  9 J3         A        2019-06-19 B_ESCHR_COLI S     S     S     S     #> 10 G6         A        2015-04-27 B_STPHY_AURS S     S     S     S     #> # … with 2,990 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Preparation","what":"First isolates","title":"How to conduct AMR data analysis","text":"need know isolates can actually use analysis without repetition bias. 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. Read methods first_isolate() page. outcome function can easily added data: 88% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 626 isolates analysis. Now data looks like: Time analysis.","code":"our_data <- our_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 2,626 'phenotype-based' first isolates (87.6% within scope and #>    87.5% of total where a microbial ID was available) our_data_1st <- our_data %>%   filter(first == TRUE) our_data_1st <- our_data %>%   filter_first_isolate() our_data_1st #> # A tibble: 2,626 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 J3         A        2012-11-21 B_ESCHR_COLI R     I     S     S     TRUE  #>  2 R7         A        2018-04-03 B_KLBSL_PNMN R     I     S     S     TRUE  #>  3 P10        A        2015-12-10 B_ESCHR_COLI S     I     S     S     TRUE  #>  4 B7         A        2015-03-02 B_ESCHR_COLI S     S     S     S     TRUE  #>  5 W3         A        2018-03-31 B_STPHY_AURS R     S     R     S     TRUE  #>  6 J8         A        2016-06-14 B_ESCHR_COLI R     S     S     S     TRUE  #>  7 M3         A        2015-10-25 B_ESCHR_COLI R     S     S     S     TRUE  #>  8 J3         A        2019-06-19 B_ESCHR_COLI S     S     S     S     TRUE  #>  9 G6         A        2015-04-27 B_STPHY_AURS S     S     S     S     TRUE  #> 10 P4         A        2011-06-21 B_ESCHR_COLI S     S     S     S     TRUE  #> # … with 2,616 more rows"},{"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":"base R summary() function gives good first impression, comes support new mo sir classes now data set:","code":"summary(our_data_1st) #>   patient_id          hospital              date            #>  Length:2626        Length:2626        Min.   :2011-01-01   #>  Class :character   Class :character   1st Qu.:2013-04-14   #>  Mode  :character   Mode  :character   Median :2015-06-05   #>                                        Mean   :2015-06-15   #>                                        3rd Qu.:2017-08-23   #>                                        Max.   :2020-01-01   #>    bacteria               AMX                    AMC                 #>  Class :mo             Class:sir              Class:sir              #>  <NA>  :0              %R   :43.2% (n=1134)   %R   :36.1% (n=947)    #>  Unique:4              %SI  :56.8% (n=1492)   %SI  :63.9% (n=1679)   #>  #1    :B_ESCHR_COLI   - %S :41.1% (n=1080)   - %S :52.7% (n=1383)   #>  #2    :B_STPHY_AURS   - %I :15.7% (n=412)    - %I :11.3% (n=296)    #>  #3    :B_STRPT_PNMN                                                 #>     CIP                    GEN                  first         #>  Class:sir              Class:sir              Mode:logical   #>  %R   :42.0% (n=1102)   %R   :37.0% (n=971)    TRUE:2626      #>  %SI  :58.0% (n=1524)   %SI  :63.0% (n=1655)                  #>  - %S :51.9% (n=1362)   - %S :59.9% (n=1574)                  #>  - %I : 6.2% (n=162)    - %I : 3.1% (n=81)                    #>   glimpse(our_data_1st) #> Rows: 2,626 #> Columns: 9 #> $ patient_id <chr> \"J3\", \"R7\", \"P10\", \"B7\", \"W3\", \"J8\", \"M3\", \"J3\", \"G6\", \"P4\"… #> $ hospital   <chr> \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\",… #> $ date       <date> 2012-11-21, 2018-04-03, 2015-12-10, 2015-03-02, 2018-03-31… #> $ bacteria   <mo> \"B_ESCHR_COLI\", \"B_KLBSL_PNMN\", \"B_ESCHR_COLI\", \"B_ESCHR_COL… #> $ AMX        <sir> R, R, S, S, R, R, R, S, S, S, S, R, S, S, R, R, R, R, I, S,… #> $ AMC        <sir> I, I, I, S, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, R,… #> $ CIP        <sir> S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S, S,… #> $ GEN        <sir> S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,… #> $ first      <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…  # number of unique values per column: sapply(our_data_1st, n_distinct) #> patient_id   hospital       date   bacteria        AMX        AMC        CIP  #>        260          3       1808          4          3          3          3  #>        GEN      first  #>          3          1"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"availability-of-species","dir":"Articles","previous_headings":"Analysing the data","what":"Availability of species","title":"How to conduct AMR data analysis","text":"just get idea species distributed, create frequency table count() based name microorganisms:","code":"our_data %>%    count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #>   `mo_name(bacteria)`          n #>   <chr>                    <int> #> 1 Escherichia coli          1518 #> 2 Staphylococcus aureus      730 #> 3 Streptococcus pneumoniae   426 #> 4 Klebsiella pneumoniae      326  our_data_1st %>%    count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #>   `mo_name(bacteria)`          n #>   <chr>                    <int> #> 1 Escherichia coli          1250 #> 2 Staphylococcus aureus      661 #> 3 Streptococcus pneumoniae   399 #> 4 Klebsiella pneumoniae      316"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"select-and-filter-with-antibiotic-selectors","dir":"Articles","previous_headings":"Analysing the data","what":"Select and filter with antibiotic selectors","title":"How to conduct AMR data analysis","text":"Using -called antibiotic class selectors, can select filter columns based antibiotic class antibiotic results :","code":"our_data_1st %>%   select(date, aminoglycosides()) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) #> # A tibble: 2,626 × 2 #>    date       GEN   #>    <date>     <sir> #>  1 2012-11-21 S     #>  2 2018-04-03 S     #>  3 2015-12-10 S     #>  4 2015-03-02 S     #>  5 2018-03-31 S     #>  6 2016-06-14 S     #>  7 2015-10-25 S     #>  8 2019-06-19 S     #>  9 2015-04-27 S     #> 10 2011-06-21 S     #> # … with 2,616 more rows  our_data_1st %>%   select(bacteria, betalactams()) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 2,626 × 3 #>    bacteria     AMX   AMC   #>    <mo>         <sir> <sir> #>  1 B_ESCHR_COLI R     I     #>  2 B_KLBSL_PNMN R     I     #>  3 B_ESCHR_COLI S     I     #>  4 B_ESCHR_COLI S     S     #>  5 B_STPHY_AURS R     S     #>  6 B_ESCHR_COLI R     S     #>  7 B_ESCHR_COLI R     S     #>  8 B_ESCHR_COLI S     S     #>  9 B_STPHY_AURS S     S     #> 10 B_ESCHR_COLI S     S     #> # … with 2,616 more rows  our_data_1st %>%   select(bacteria, where(is.sir)) #> # A tibble: 2,626 × 5 #>    bacteria     AMX   AMC   CIP   GEN   #>    <mo>         <sir> <sir> <sir> <sir> #>  1 B_ESCHR_COLI R     I     S     S     #>  2 B_KLBSL_PNMN R     I     S     S     #>  3 B_ESCHR_COLI S     I     S     S     #>  4 B_ESCHR_COLI S     S     S     S     #>  5 B_STPHY_AURS R     S     R     S     #>  6 B_ESCHR_COLI R     S     S     S     #>  7 B_ESCHR_COLI R     S     S     S     #>  8 B_ESCHR_COLI S     S     S     S     #>  9 B_STPHY_AURS S     S     S     S     #> 10 B_ESCHR_COLI S     S     S     S     #> # … with 2,616 more rows  # filtering using AB selectors is also possible: our_data_1st %>%   filter(any(aminoglycosides() == \"R\")) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) #> # A tibble: 971 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 J5         A        2017-12-25 B_STRPT_PNMN R     S     S     R     TRUE  #>  2 X1         A        2017-07-04 B_STPHY_AURS R     S     S     R     TRUE  #>  3 B3         A        2016-07-24 B_ESCHR_COLI S     S     S     R     TRUE  #>  4 V7         A        2012-04-03 B_ESCHR_COLI S     S     S     R     TRUE  #>  5 C9         A        2017-03-23 B_ESCHR_COLI S     S     S     R     TRUE  #>  6 R1         A        2018-06-10 B_STPHY_AURS S     S     S     R     TRUE  #>  7 S2         A        2013-07-19 B_STRPT_PNMN S     S     S     R     TRUE  #>  8 P5         A        2019-03-09 B_STPHY_AURS S     S     S     R     TRUE  #>  9 Q8         A        2019-08-10 B_STPHY_AURS S     S     S     R     TRUE  #> 10 K5         A        2013-03-15 B_STRPT_PNMN S     S     S     R     TRUE  #> # … with 961 more rows  our_data_1st %>%   filter(all(betalactams() == \"R\")) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 471 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 M7         A        2013-07-22 B_STRPT_PNMN R     R     S     S     TRUE  #>  2 R10        A        2013-12-20 B_STPHY_AURS R     R     S     S     TRUE  #>  3 R7         A        2015-10-25 B_STPHY_AURS R     R     S     S     TRUE  #>  4 R8         A        2019-10-25 B_STPHY_AURS R     R     S     S     TRUE  #>  5 I7         A        2015-08-19 B_ESCHR_COLI R     R     S     S     TRUE  #>  6 N3         A        2014-12-29 B_STRPT_PNMN R     R     R     S     TRUE  #>  7 Q2         A        2019-09-22 B_ESCHR_COLI R     R     S     S     TRUE  #>  8 X7         A        2011-03-20 B_ESCHR_COLI R     R     S     R     TRUE  #>  9 C5         A        2015-08-30 B_KLBSL_PNMN R     R     S     R     TRUE  #> 10 W9         A        2013-10-02 B_ESCHR_COLI R     R     S     S     TRUE  #> # … with 461 more rows  # even works in base R (since R 3.0): our_data_1st[all(betalactams() == \"R\"), ] #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 471 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 M7         A        2013-07-22 B_STRPT_PNMN R     R     S     S     TRUE  #>  2 R10        A        2013-12-20 B_STPHY_AURS R     R     S     S     TRUE  #>  3 R7         A        2015-10-25 B_STPHY_AURS R     R     S     S     TRUE  #>  4 R8         A        2019-10-25 B_STPHY_AURS R     R     S     S     TRUE  #>  5 I7         A        2015-08-19 B_ESCHR_COLI R     R     S     S     TRUE  #>  6 N3         A        2014-12-29 B_STRPT_PNMN R     R     R     S     TRUE  #>  7 Q2         A        2019-09-22 B_ESCHR_COLI R     R     S     S     TRUE  #>  8 X7         A        2011-03-20 B_ESCHR_COLI R     R     S     R     TRUE  #>  9 C5         A        2015-08-30 B_KLBSL_PNMN R     R     S     R     TRUE  #> 10 W9         A        2013-10-02 B_ESCHR_COLI R     R     S     S     TRUE  #> # … with 461 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"generate-antibiograms","dir":"Articles","previous_headings":"Analysing the data","what":"Generate antibiograms","title":"How to conduct AMR data analysis","text":"package comes antibiogram(), function automatically generates traditional, combined, syndromic, even weighted-incidence syndromic combination antibiograms (WISCA). R Markdown (page) automatically prints right table format. suggestions generate different antibiograms: Antibiograms can plotted using autoplot() ggplot2 packages, since package provides extension function:  calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"# traditional: antibiogram(our_data_1st) antibiogram(our_data_1st,             ab_transform = \"name\") antibiogram(our_data_1st,             ab_transform = \"name\",             language = \"es\") # support for 20 languages # combined: antibiogram(our_data_1st,             antibiotics = c(\"AMC\", \"AMC+CIP\", \"AMC+GEN\")) # for a syndromic antibiogram, we must fake some clinical conditions: our_data_1st$condition <- sample(c(\"Cardial\", \"Respiratory\", \"Rheumatic\"),                                  size = nrow(our_data_1st),                                  replace = TRUE)  # syndromic: antibiogram(our_data_1st,             syndromic_group = \"condition\") antibiogram(our_data_1st,             # you can use AB selectors here as well:             antibiotics = c(penicillins(), aminoglycosides()),             syndromic_group = \"condition\",             mo_transform = \"gramstain\") #> ℹ For penicillins() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) # WISCA:  # (we lack some details, but it could contain a filter on e.g. >65 year-old males) wisca <- antibiogram(our_data_1st,                      antibiotics = c(\"AMC\", \"AMC+CIP\", \"AMC+GEN\"),                      syndromic_group = \"condition\",                      mo_transform = \"gramstain\") wisca autoplot(wisca)"},{"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: Author: Dr. Matthijs Berends, 26th Feb 2023","code":"our_data_1st %>% resistance(AMX) #> [1] 0.4318355 our_data_1st %>%   group_by(hospital) %>%   summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #>   hospital amoxicillin #>   <chr>          <dbl> #> 1 A              0.343 #> 2 B              0.569 #> 3 C              0.375"},{"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          S         S            I          S            I            S #> 2          I         S            S          I            R            I #> 3          S         S            S          S            I            S #> 4          I         I            S          R            I            I #> 5          S         I            S          R            R            S #> 6          S         S            R          R            R            R #>   kanamycin #> 1         S #> 2         I #> 3         I #> 4         S #> 5         R #> 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, 482 447 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 52 142 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 26 February 2023 20:26:58 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 22 February 2023 13:38:57 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. Anita Williams. 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":"Generates antibiograms - traditional, combined, syndromic, even WISCA 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-over-175-countries-translated-into-20-languages","dir":"","previous_headings":"Introduction","what":"Used in over 175 countries, translated into 20 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,  Czech,  Chinese,  Danish,  Dutch,  Finnish,  French,  German,  Greek,  Italian,  Japanese,  Norwegian,  Polish,  Portuguese,  Romanian,  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":"generating-antibiograms","dir":"","previous_headings":"Practical examples","what":"Generating antibiograms","title":"Antimicrobial Resistance Data Analysis","text":"AMR package supports generating traditional, combined, syndromic, even weighted-incidence syndromic combination antibiograms (WISCA). used inside R Markdown Quarto, table printed right output format automatically (markdown, LaTeX, HTML, etc.). combination antibiograms, clear combined antibiotics yield higher empiric coverage: Like many functions package, antibiogram() comes support 20 languages often detected automatically based system language:","code":"antibiogram(example_isolates,             antibiotics = c(aminoglycosides(), carbapenems())) antibiogram(example_isolates,             antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),             mo_transform = \"gramstain\") antibiogram(example_isolates,             antibiotics = c(\"cipro\", \"tobra\", \"genta\"), # any arbitrary name or code will work             mo_transform = \"gramstain\",             ab_transform = \"name\",             language = \"uk\") # Ukrainian"},{"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":"manual approach, can use resistance susceptibility() function:","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(...)  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 latest implemented EUCAST guideline, currently \"EUCAST 2022\". Supported guideline currently EUCAST (2013-2022) CLSI (2013-2022). AMR_ignore_pattern  regular expression ignore (.e., make NA) match given .mo() mo_* functions. AMR_include_PKPD  logical use .sir(), indicate PK/PD clinical breakpoints must applied last resort - default TRUE. AMR_include_screening  logical use .sir(), indicate clinical breakpoints screening allowed - default FALSE. AMR_keep_synonyms  logical use .mo() mo_* functions, indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE. AMR_cleaning_regex  regular expression (case-insensitive) use .mo() mo_* functions, clean user input. default outcome mo_cleaning_regex(), removes texts brackets texts \"species\" \"serovar\". AMR_locale  language use AMR package, can one supported language names ISO-639-1 codes: English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). default current system language (supported). 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 :   add Portuguese language support antibiotics, allow PK/PD rules interpreting MIC values .sir().","code":"utils::file.edit(\"~/.Rprofile\") options(AMR_locale = \"pt\")  options(AMR_include_PKPD = TRUE)"},{"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] Anita Williams (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 28 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 #> # 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 <sir>, AMC_ED20 <sir>, …"},{"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(). default 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 - default 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 - default current system language (see get_AMR_locale()) can also set package 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()    # this does the same:   example_isolates %>%     rename_with(set_ab_names)    # set_ab_names() works with any AB property:   example_isolates %>%     set_ab_names(property = \"atc\")    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 package 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 package 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 package 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 package 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 #> [1] NA #>    # 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) (default 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  1965-11-22  57  57.26301         34 #> 2  1939-05-10  83  83.80000         60 #> 3  1953-04-28  69  69.83288         46 #> 4  1995-12-18  27  27.19178          4 #> 5  1936-12-23  86  86.17808         63 #> 6  1954-04-26  68  68.83836         45 #> 7  1982-12-19  40  40.18904         17 #> 8  1963-07-20  59  59.60548         36 #> 9  1981-07-23  41  41.59726         18 #> 10 1937-01-13  86  86.12055         62"},{"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 - default 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  # }"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"Generate antibiogram, communicate results plots tables. functions follow logic Klinker et al. Barbieri et al. (see Source), allow reporting e.g. R Markdown Quarto well.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"","code":"antibiogram(   x,   antibiotics = where(is.sir),   mo_transform = \"shortname\",   ab_transform = NULL,   syndromic_group = NULL,   add_total_n = TRUE,   only_all_tested = FALSE,   digits = 0,   col_mo = NULL,   language = get_AMR_locale(),   minimum = 30,   combine_SI = TRUE,   sep = \" + \",   info = interactive() )  # S3 method for antibiogram plot(x, ...)  # S3 method for antibiogram autoplot(object, ...)  # S3 method for antibiogram knit_print(   x,   italicise = TRUE,   na = getOption(\"knitr.kable.NA\", default = \"\"),   ... )"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"Klinker KP et al. (2021). Antimicrobial stewardship antibiograms: importance moving beyond traditional antibiograms. Therapeutic Advances Infectious Disease, May 5;8:20499361211011373; doi:10.1177/20499361211011373 Barbieri E et al. (2021). Development Weighted-Incidence Syndromic Combination Antibiogram (WISCA) guide choice empiric antibiotic treatment urinary tract infection paediatric patients: Bayesian approach Antimicrobial Resistance & Infection Control May 1;10(1):74; doi:10.1186/s13756-021-00939-2 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/antibiogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"x data.frame containing least column microorganisms columns antibiotic results (class 'sir', see .sir()) antibiotics vector antibiotic name code (evaluated .ab(), column name x, (combinations ) antibiotic selectors aminoglycosides() carbapenems(). combination antibiograms, can also set values separated \"+\", \"TZP+TOB\" \"cipro + genta\", given columns resembling antibiotics exist x. See Examples. mo_transform character transform microorganism input - must \"name\", \"shortname\", \"gramstain\", 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\". Can also NULL transform input. ab_transform character transform antibiotic input - must one column names antibiotics data set: \"ab\", \"cid\", \"name\", \"group\", \"atc\", \"atc_group1\", \"atc_group2\", \"abbreviations\", \"synonyms\", \"oral_ddd\", \"oral_units\", \"iv_ddd\", \"iv_units\", \"loinc\". Can also NULL transform input. syndromic_group column name x, values calculated split rows x, e.g. using ifelse() case_when(). See Examples. add_total_n logical indicate whether total available numbers per pathogen added table (default TRUE). add lowest highest number available isolate per antibiotic (e.g, E. coli 200 isolates available ciprofloxacin 150 amoxicillin, returned number \"150-200\"). only_all_tested (combination antibiograms): logical indicate isolates must tested antibiotics, see Details digits number digits use rounding col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). language language translate text, defaults system language (see get_AMR_locale()) 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 susceptibility determined results either S , instead S (default TRUE) sep separating character antibiotic columns combination antibiograms info logical indicate info printed - default TRUE interactive mode ... used R Markdown Quarto: arguments passed knitr::kable() (otherwise, use) object antibiogram() object italicise logical indicate whether microorganism names knitr table made italic, using italicise_taxonomy(). na character use showing NA values","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"function returns table values 0 100 susceptibility, resistance. Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine data set one four available algorithms. types antibiograms listed can plotted (using ggplot2::autoplot() base R plot()/barplot()). antibiogram object can also used directly R Markdown / Quarto (.e., knitr) reports. case, knitr::kable() applied automatically microorganism names even printed italics default (see argument italicise). can also use functions specific 'table reporting' packages transform output antibiogram() needs, e.g. flextable::as_flextable() gt::gt().","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"antibiogram-types","dir":"Reference","previous_headings":"","what":"Antibiogram Types","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"four antibiogram types, proposed Klinker et al. (2021, doi:10.1177/20499361211011373 ), supported antibiogram(): Traditional Antibiogram Case example: Susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Code example:   Combination Antibiogram Case example: Additional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Code example:   Syndromic Antibiogram Case example: Susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Code example:   Weighted-Incidence Syndromic Combination Antibiogram (WISCA) Case example: Susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) male patients age >=65 years heart failure Code example:   Note combination antibiograms, important realise susceptibility can calculated two ways, can set only_all_tested argument (default FALSE). See example two antibiotics, Drug Drug B, antibiogram() works calculate %SI:","code":"antibiogram(your_data,             antibiotics = \"TZP\") antibiogram(your_data,             antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\")) antibiogram(your_data,             antibiotics = penicillins(),             syndromic_group = \"ward\") library(dplyr) your_data %>%    filter(ward == \"ICU\" & specimen_type == \"Respiratory\") %>%    antibiogram(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),               syndromic_group = ifelse(.$age >= 65 &                                          .$gender == \"Male\" &                                          .$condition == \"Heart Disease\",                                        \"Study Group\", \"Control Group\")) --------------------------------------------------------------------                     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>        -            -            -            - --------------------------------------------------------------------"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. 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>  # \\donttest{ # Traditional antibiogram ----------------------------------------------  antibiogram(example_isolates,   antibiotics = c(aminoglycosides(), carbapenems()) ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 10 × 7 #>    `Pathogen (N min-max)`   AMK   GEN   IPM   KAN   MEM   TOB #>  * <chr>                  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 CoNS (43-309)              0    86    52     0    52    22 #>  2 E. coli (0-462)          100    98   100    NA   100    97 #>  3 E. faecalis (0-39)         0     0   100     0    NA     0 #>  4 K. pneumoniae (0-58)      NA    90   100    NA   100    90 #>  5 P. aeruginosa (17-30)     NA   100    NA     0    NA   100 #>  6 P. mirabilis (0-34)       NA    94    94    NA    NA    94 #>  7 S. aureus (2-233)         NA    99    NA    NA    NA    98 #>  8 S. epidermidis (8-163)     0    79    NA     0    NA    51 #>  9 S. hominis (3-80)         NA    92    NA    NA    NA    85 #> 10 S. pneumoniae (11-117)     0     0    NA     0    NA     0  antibiogram(example_isolates,   antibiotics = aminoglycosides(),   ab_transform = \"atc\",   mo_transform = \"gramstain\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 2 × 5 #>   `Pathogen (N min-max)`   J01GB01 J01GB03 J01GB04 J01GB06 #> * <chr>                      <dbl>   <dbl>   <dbl>   <dbl> #> 1 Gram-negative (35-686)        96      96       0      98 #> 2 Gram-positive (436-1170)      34      63       0       0  antibiogram(example_isolates,   antibiotics = carbapenems(),   ab_transform = \"name\",   mo_transform = \"name\" ) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 5 × 3 #>   `Pathogen (N min-max)`                           Imipenem Meropenem #> * <chr>                                               <dbl>     <dbl> #> 1 Coagulase-negative Staphylococcus (CoNS) (48-48)       52        52 #> 2 Enterococcus faecalis (0-38)                          100        NA #> 3 Escherichia coli (418-422)                            100       100 #> 4 Klebsiella pneumoniae (51-53)                         100       100 #> 5 Proteus mirabilis (27-32)                              94        NA   # Combined antibiogram -------------------------------------------------  # combined antibiotics yield higher empiric coverage antibiogram(example_isolates,   antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),   mo_transform = \"gramstain\" ) #> # A tibble: 2 × 4 #>   `Pathogen (N min-max)`     TZP `TZP + GEN` `TZP + TOB` #> * <chr>                    <dbl>       <dbl>       <dbl> #> 1 Gram-negative (641-693)     88          99          98 #> 2 Gram-positive (345-1044)    86          98          95  # names of antibiotics do not need to resemble columns exactly: antibiogram(example_isolates,   antibiotics = c(\"Cipro\", \"cipro + genta\"),   mo_transform = \"gramstain\",   ab_transform = \"name\",   sep = \" & \" ) #> # A tibble: 2 × 3 #>   `Pathogen (N min-max)`  Ciprofloxacin `Ciprofloxacin & Gentamicin` #> * <chr>                           <dbl>                        <dbl> #> 1 Gram-negative (684-694)            91                           99 #> 2 Gram-positive (724-847)            77                           93   # Syndromic antibiogram ------------------------------------------------  # the data set could contain a filter for e.g. respiratory specimens antibiogram(example_isolates,   antibiotics = c(aminoglycosides(), carbapenems()),   syndromic_group = \"ward\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 14 × 8 #>    `Syndromic Group` `Pathogen (N min-max)`   AMK   GEN   IPM   KAN   MEM   TOB #>  * <chr>             <chr>                  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 Clinical          CoNS (23-205)             NA    89    57    NA    57    26 #>  2 ICU               CoNS (10-73)              NA    79    NA    NA    NA    NA #>  3 Outpatient        CoNS (3-31)               NA    84    NA    NA    NA    NA #>  4 Clinical          E. coli (0-299)          100    98   100    NA   100    98 #>  5 ICU               E. coli (0-137)          100    99   100    NA   100    96 #>  6 Clinical          K. pneumoniae (0-51)      NA    92   100    NA   100    92 #>  7 Clinical          P. mirabilis (0-30)       NA   100    NA    NA    NA   100 #>  8 Clinical          S. aureus (2-150)         NA    99    NA    NA    NA    97 #>  9 ICU               S. aureus (0-66)          NA   100    NA    NA    NA    NA #> 10 Clinical          S. epidermidis (4-79)     NA    82    NA    NA    NA    55 #> 11 ICU               S. epidermidis (4-75)     NA    72    NA    NA    NA    41 #> 12 Clinical          S. hominis (1-45)         NA    96    NA    NA    NA    94 #> 13 Clinical          S. pneumoniae (5-78)       0     0    NA     0    NA     0 #> 14 ICU               S. pneumoniae (5-30)       0     0    NA     0    NA     0  # now define a data set with only E. coli ex1 <- example_isolates[which(mo_genus() == \"Escherichia\"), ] #> ℹ Using column 'mo' as input for mo_genus()  # with a custom language, though this will be determined automatically # (i.e., this table will be in Spanish on Spanish systems) antibiogram(ex1,             antibiotics = aminoglycosides(),             ab_transform = \"name\",             syndromic_group = ifelse(ex1$ward == \"ICU\",                                      \"UCI\", \"No UCI\"),             language = \"es\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 2 × 5 #>   `Grupo sindrómico` `Patógeno (N min-max)` Amikacina Gentamicina Tobramicina #> * <chr>              <chr>                      <dbl>       <dbl>       <dbl> #> 1 No UCI             E. coli (0-325)              100          98          98 #> 2 UCI                E. coli (0-137)              100          99          96   # Weighted-incidence syndromic combination antibiogram (WISCA) ---------  # the data set could contain a filter for e.g. respiratory specimens/ICU antibiogram(example_isolates,             antibiotics = c(\"AMC\", \"AMC+CIP\", \"TZP\", \"TZP+TOB\"),             mo_transform = \"gramstain\",             minimum = 10, # this should be >=30, but now just as example             syndromic_group = ifelse(example_isolates$age >= 65 &                                        example_isolates$gender == \"M\",                                      \"WISCA Group 1\", \"WISCA Group 2\"             ) ) #> # A tibble: 4 × 6 #>   `Syndromic Group` `Pathogen (N min-max)`    AMC `AMC + CIP`   TZP `TZP + TOB` #> * <chr>             <chr>                   <dbl>       <dbl> <dbl>       <dbl> #> 1 WISCA Group 1     Gram-negative (261-285)    76          95    89          99 #> 2 WISCA Group 2     Gram-negative (380-442)    76          98    88          98 #> 3 WISCA Group 1     Gram-positive (123-406)    76          89    81          95 #> 4 WISCA Group 2     Gram-positive (222-732)    76          89    88          95   # Print the output for R Markdown / Quarto -----------------------------  ureido <- antibiogram(example_isolates,                       antibiotics = ureidopenicillins(),                       ab_transform = \"name\") #> ℹ For ureidopenicillins() using column 'TZP' (piperacillin/tazobactam)  # in an Rmd file, you would just need to return `ureido` in a chunk, # but to be explicit here: if (requireNamespace(\"knitr\")) {   knitr::knit_print(ureido) } #> [1] \"\\n\\n|Pathogen (N)          | Piperacillin/tazobactam|\\n|:---------------------|-----------------------:|\\n|CoNS (33)             |                      30|\\n|*E. coli* (416)       |                      94|\\n|*K. pneumoniae* (53)  |                      89|\\n|*S. pneumoniae* (112) |                     100|\" #> attr(,\"class\") #> [1] \"knit_asis\" #> attr(,\"knit_cacheable\") #> [1] NA   # Generate plots with ggplot2 or base R --------------------------------  ab1 <- antibiogram(example_isolates,   antibiotics = c(\"AMC\", \"CIP\", \"TZP\", \"TZP+TOB\"),   mo_transform = \"gramstain\" ) ab2 <- antibiogram(example_isolates,   antibiotics = c(\"AMC\", \"CIP\", \"TZP\", \"TZP+TOB\"),   mo_transform = \"gramstain\",   syndromic_group = \"ward\" )  if (requireNamespace(\"ggplot2\")) {   ggplot2::autoplot(ab1) }  if (requireNamespace(\"ggplot2\")) {   ggplot2::autoplot(ab2) }   plot(ab1)  plot(ab2)   # }"},{"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 (default FALSE), see .sir() only_treatable logical indicate whether antimicrobial drugs excluded laboratory tests (default 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 names codes microorganisms (see .mo()) - default 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: There was 1 warning in `summarise()`. #> ℹ In argument: `across(aminoglycosides(), resistance)`. #> ℹ In group 3: `ward = \"Outpatient\"`. #> Caused by 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: There were 52 warnings in `summarise()`. #> The first warning was: #> ℹ In argument: `across(not_intrinsic_resistant(), resistance)`. #> ℹ In group 1: `ward = \"Clinical\"`. #> Caused by warning: #> ! Introducing NA: no results available for OXA in group: ward = \"Clinical\" #> (minimum = 30). #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 51 remaining warnings. #> # 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)) } #> 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 - default 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 - default 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 - default 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 Arbitrary Input to Valid Microbial Taxonomy — as.mo","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Use function get valid microorganism code (mo) based arbitrary user input. Determination done using intelligent rules complete taxonomic tree 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 Arbitrary Input to Valid Microbial Taxonomy — 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),   cleaning_regex = getOption(\"AMR_cleaning_regex\", 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 Arbitrary Input to Valid Microbial Taxonomy — 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). Please see Details full list staphylococcal species converted. 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. . Please see Details full list streptococcal species converted. 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 package 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 Perl-compatible regular expression (case-insensitive) matches x must return NA. can convenient exclude known non-relevant input can also set package option AMR_ignore_pattern, e.g. options(AMR_ignore_pattern = \"(reported|contaminated flora)\"). cleaning_regex Perl-compatible regular expression (case-insensitive) clean input x. Every matched part x removed. default, outcome mo_cleaning_regex(), removes texts brackets texts \"species\" \"serovar\". default can set package option AMR_cleaning_regex. language language translate text like \"growth\", defaults system language (see get_AMR_locale()) info logical indicate progress bar printed 25 items coerced - default 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 Arbitrary Input to Valid Microbial Taxonomy — 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 Arbitrary Input to Valid Microbial Taxonomy — 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. Becker = TRUE, following 85 staphylococci converted coagulase-negative group: S. argensis, S. arlettae, S. auricularis, S. borealis, S. caeli, S. caledonicus, S. canis, S. capitis, S. capitis capitis, S. capitis urealyticus, S. capitis ureolyticus, S. caprae, S. carnosus, S. carnosus carnosus, S. carnosus utilis, S. casei, S. caseolyticus, S. chromogenes, S. cohnii, S. cohnii cohnii, S. cohnii urealyticum, S. cohnii urealyticus, S. condimenti, S. croceilyticus, S. debuckii, S. devriesei, S. durrellii, S. edaphicus, S. epidermidis, S. equorum, S. equorum equorum, S. equorum linens, S. felis, S. fleurettii, S. gallinarum, S. haemolyticus, S. hominis, S. hominis hominis, S. hominis novobiosepticus, S. jettensis, S. kloosii, S. lentus, S. lloydii, S. lugdunensis, S. massiliensis, S. microti, S. muscae, S. nepalensis, S. pasteuri, S. petrasii, S. petrasii croceilyticus, S. petrasii jettensis, S. petrasii petrasii, S. petrasii pragensis, S. pettenkoferi, S. piscifermentans, S. pragensis, S. pseudoxylosus, S. pulvereri, S. ratti, S. rostri, S. saccharolyticus, S. saprophyticus, S. saprophyticus bovis, S. saprophyticus saprophyticus, S. schleiferi, S. schleiferi schleiferi, S. sciuri, S. sciuri carnaticus, S. sciuri lentus, S. sciuri rodentium, S. sciuri sciuri, S. simulans, S. stepanovicii, S. succinus, S. succinus casei, S. succinus succinus, S. taiwanensis, S. urealyticus, S. ureilyticus, S. veratri, S. vitulinus, S. vitulus, S. warneri, S. xylosus. following 16 staphylococci converted coagulase-positive group: S. agnetis, S. argenteus, S. coagulans, S. cornubiensis, S. delphini, S. hyicus, S. hyicus chromogenes, S. hyicus hyicus, S. intermedius, S. lutrae, S. pseudintermedius, S. roterodami, S. schleiferi coagulans, S. schweitzeri, S. simiae, S. singaporensis. Lancefield = TRUE, following streptococci converted corresponding Lancefield group: S. agalactiae (Group B), S. anginosus anginosus (Group F), S. anginosus whileyi (Group F), S. anginosus (Group F), S. canis (Group G), S. dysgalactiae dysgalactiae (Group C), S. dysgalactiae equisimilis (Group C), S. dysgalactiae (Group C), S. equi equi (Group C), S. equi ruminatorum (Group C), S. equi zooepidemicus (Group C), S. equi (Group C), S. pyogenes (Group ), S. salivarius salivarius (Group K), S. salivarius thermophilus (Group K), S. salivarius (Group K), S. sanguinis (Group H).","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 Arbitrary Input to Valid Microbial Taxonomy — 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, cleaning_regex 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 cleaning_regex 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 Arbitrary Input to Valid Microbial Taxonomy — 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 Arbitrary Input to Valid Microbial Taxonomy — 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 Arbitrary Input to Valid Microbial Taxonomy — 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 Arbitrary Input to Valid Microbial Taxonomy — 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 Arbitrary Input to Valid Microbial Taxonomy — 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. breakpoints used interpretation publicly available clinical_breakpoints data set.","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_screening = getOption(\"AMR_include_screening\", FALSE),   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_screening = getOption(\"AMR_include_screening\", FALSE),   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_screening = getOption(\"AMR_include_screening\", FALSE),   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 package option AMR_guideline. Currently 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_screening logical indicate clinical breakpoints screening allowed - default FALSE. Can also set package option AMR_include_screening. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort - default TRUE. Can also set package option AMR_include_PKPD. col_mo column name names codes microorganisms (see .mo()) - default 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 package 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-clinical-breakpoints","dir":"Reference","previous_headings":"","what":"Machine-Readable Clinical Breakpoints","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 × 17 #>    datetime            index ab_input ab_guid…¹ mo_in…² mo_guideline     guide…³ #>    <dttm>              <int> <chr>    <ab>      <chr>   <mo>             <chr>   #>  1 2023-02-26 20:31:28     1 TOB      TOB       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  2 2023-02-26 20:31:27     1 GEN      GEN       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  3 2023-02-26 20:31:27     1 CIP      CIP       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  4 2023-02-26 20:31:27     1 AMP      AMP       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  5 2023-02-26 20:31:22     1 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  6 2023-02-26 20:31:22     2 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  7 2023-02-26 20:31:22     3 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  8 2023-02-26 20:31:22     4 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  9 2023-02-26 20:31:22     5 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 10 2023-02-26 20:31:22     6 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> # … with 40 more rows, 10 more variables: ref_table <chr>, method <chr>, #> #   input <dbl>, outcome <sir>, breakpoint_S_R <chr>, ab_considered <lgl>, #> #   mo_considered <lgl>, breakpoint_S <lgl>, breakpoint_R <lgl>, #> #   interpretation <lgl>, 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(). default 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 - default 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 - default system language (see get_AMR_locale()) can also set package 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 - default 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 names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). FUN function call mo column transform microorganism codes - default mo_shortname() ... arguments passed FUN translate_ab character length 1 containing column names antibiotics data set language language returned text - default current system language (see get_AMR_locale()) can also set package 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 - default 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 returns table ready reporting/publishing. Use combine_SI = TRUE (default) test R vs. S+combine_SI = FALSE test R+vs. S. table can also directly used R Markdown / Quarto without need e.g. knitr::kable().","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":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. 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>  # \\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) AMC      15     0     0    15 #> 2 (unknown species) AMK       0     0     0     0 #> 3 (unknown species) AMP      15     0     1    16 #> 4 (unknown species) AMX      15     0     1    16 #> 5 (unknown species) AZM       3     0     3     6 #> 6 (unknown species) CAZ       0     0     0     0 #> 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 AMC     463    89   174   726 #>  2 Gram-negative AMK     251     0     5   256 #>  3 Gram-negative AMP     226     0   405   631 #>  4 Gram-negative AMX     226     0   405   631 #>  5 Gram-negative AZM       1     2   696   699 #>  6 Gram-negative CAZ     607     0    27   634 #>  7 Gram-negative CHL       1     0    30    31 #>  8 Gram-negative CIP     610    11    63   684 #>  9 Gram-negative CLI      18     1   709   728 #> 10 Gram-negative COL     309     0    78   387 #> # … 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 AMC     332    74    61   467 #>  2 E. coli AMK     171     0     0   171 #>  3 E. coli AMP     196     0   196   392 #>  4 E. coli AMX     196     0   196   392 #>  5 E. coli AZM       0     0   467   467 #>  6 E. coli CAZ     449     0    11   460 #>  7 E. coli CHL       0     0     0     0 #>  8 E. coli CIP     398     1    57   456 #>  9 E. coli CLI       0     0   467   467 #> 10 E. coli COL     240     0     0   240 #> # … 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 - default current system language (see get_AMR_locale()) can also set package 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) - default 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 names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console - default 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 package 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 - default 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 (default 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 package 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) - default first column date class col_patient_id column name unique IDs patients - default first column starts 'patient' 'patid' (case insensitive) col_mo column name names codes microorganisms (see .mo()) - default 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(). default 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 - default 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(info = TRUE), ] #> Determining first isolates using an episode length of 365 days #> ℹ Using column 'date' as input for col_date. #> ℹ Using column 'patient' as input for col_patient_id. #> Basing inclusion on all antimicrobial results, using a points threshold of #> 2 #> Including isolates from ICU. #> ℹ Excluded 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,379 'phenotype-based' first isolates (69.0% of total where a #>    microbial ID was available) #> # 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()), ] #> 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(info = TRUE)) } #> Determining first isolates using an episode length of 365 days #> Basing inclusion on all antimicrobial results, using a points threshold of #> 2 #> Including isolates from ICU. #> ℹ Excluded 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,379 'phenotype-based' first isolates (69.0% of total where a #>    microbial ID was available) #> # 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) } #> # 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(info = TRUE)) %>%     select(ward, date, patient, mo, first) } #> Determining first isolates using an episode length of 365 days #> Basing inclusion on all antimicrobial results, using a points threshold of #> 2 #> Including isolates from ICU. #>  #> Group: ward = \"Clinical\" #> ℹ Excluded 9 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 861 'phenotype-based' first isolates (69.8% of total where a #>    microbial ID was available) #> Including isolates from ICU. #>  #> Group: ward = \"ICU\" #> ℹ Excluded 6 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 449 'phenotype-based' first isolates (69.6% within scope and 69.5% #>    of total where a microbial ID was available) #> Including isolates from ICU. #>  #> Group: ward = \"Outpatient\" #> ℹ Excluded 1 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 98 'phenotype-based' first isolates (82.4% within scope and 81.7% #>    of total where a microbial ID was available) #> # 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 (Clinical or Epidemic) Episodes — get_episode","title":"Determine (Clinical or Epidemic) Episodes — get_episode","text":"functions determine items vector can considered (start ) new episode. can used determine clinical episodes epidemiological analysis. get_episode() function returns index number episode per group, is_new_episode() function returns TRUE every new get_episode() index. absolute relative episode determination supported.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine (Clinical or Epidemic) Episodes — get_episode","text":"","code":"get_episode(x, episode_days = NULL, case_free_days = NULL, ...)  is_new_episode(x, episode_days = NULL, case_free_days = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine (Clinical or Epidemic) Episodes — get_episode","text":"x vector dates (class Date POSIXt), sorted internally determine episodes episode_days episode length days specify time period new episode begins, can also less day Inf, see Details case_free_days (inter-epidemic) interval length days new episode start, 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 (Clinical or Epidemic) Episodes — get_episode","text":"get_episode(): integer 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 (Clinical or Epidemic) Episodes — get_episode","text":"Episodes can determined two ways: absolute relative. Absolute method uses episode_days define episode length days, new episode start. common use case AMR data analysis microbial epidemiology: episodes S. aureus bacteraemia ICU patients example. episode length 30 days, new S. aureus isolates ICU episode 30 days considered different (new) episode. Thus, method counts since start previous episode. Relative method uses case_free_days quantify duration case-free days (inter-epidemic interval), new episode start. common use case infectious disease epidemiology: episodes norovirus outbreaks hospital example. case-free period 14 days, new norovirus cases time considered different (new) episode. Thus, methods counts since last case previous episode. table: ** marks start new episode, 8 January 2023 7 days since start previous episode (1 January 2023).  *** marks start new episode, 21 January 2023 7 days since last case previous episode (8 January 2023). Either episode_days case_free_days must provided function.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"difference-between-get-episode-and-is-new-episode-","dir":"Reference","previous_headings":"","what":"Difference between get_episode() and is_new_episode()","title":"Determine (Clinical or Epidemic) Episodes — get_episode","text":"get_episode() function returns index number episode, cases/patients/isolates first episode number 1, cases/patients/isolates second episode number 2, etc. is_new_episode() function returns TRUE every new get_episode() index, thus equal !duplicated(get_episode(...)). specify, setting episode_days = 365 (using method 1 explained ), two functions differ:","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Determine (Clinical or Epidemic) Episodes — get_episode","text":"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, episode 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 (Clinical or Epidemic) Episodes — get_episode","text":"","code":"# difference between absolute and relative determination of episodes: x <- data.frame(dates = as.Date(c(   \"2021-01-01\",   \"2021-01-02\",   \"2021-01-05\",   \"2021-01-08\",   \"2021-02-21\",   \"2021-02-22\",   \"2021-02-23\",   \"2021-02-24\",   \"2021-03-01\",   \"2021-03-01\" ))) x$absolute <- get_episode(x$dates, episode_days = 7) x$relative <- get_episode(x$dates, case_free_days = 7) x #>         dates absolute relative #> 1  2021-01-01        1        1 #> 2  2021-01-02        1        1 #> 3  2021-01-05        1        1 #> 4  2021-01-08        2        1 #> 5  2021-02-21        3        2 #> 6  2021-02-22        3        2 #> 7  2021-02-23        3        2 #> 8  2021-02-24        3        2 #> 9  2021-03-01        4        2 #> 10 2021-03-01        4        2   # `example_isolates` is a data set available in the AMR package. # See ?example_isolates df <- example_isolates[sample(seq_len(2000), size = 100), ]  get_episode(df$date, episode_days = 60) # indices #>   [1] 13 31 23 24 16 19 43 25 28 28 46 31  8 41 30 34 11 18 39 14  4 22 27 10 49 #>  [26] 22 28 19 34 15  5 36 40  3 21 38  8 43 36 40 37 23  6 20 47  1 25 11 29 45 #>  [51]  5 45  6  1 32 40  7 45  2 46 35 34  6 41 41  2 12 26 48 17 15 20 24 12 31 #>  [76] 18 29 28 26 48 41  5 46 43 34  9  6 14  6 20 15 26  5 33 42 46 45  6 44 23 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE #>   [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE #>  [13]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE #>  [25]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE #>  [37] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE #>  [49]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE #>  [61]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE #>  [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [85] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE #>  [97] FALSE FALSE  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: 1 × 46 #>   date       patient   age gender ward  mo           PEN   OXA   FLC   AMX   #>   <date>     <chr>   <dbl> <chr>  <chr> <mo>         <sir> <sir> <sir> <sir> #> 1 2002-07-30 218912     76 F      ICU   B_ESCHR_COLI R     NA    NA    NA    #> # … 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 = 100,       replace = TRUE     )) %>%     group_by(patient, condition) %>%     mutate(new_episode = is_new_episode(date, 365)) %>%     select(patient, date, condition, new_episode) %>%     arrange(patient, condition, date) } #> # A tibble: 100 × 4 #> # Groups:   patient, condition [98] #>    patient date       condition new_episode #>    <chr>   <date>     <chr>     <lgl>       #>  1 001213  2009-08-03 A         TRUE        #>  2 001213  2009-08-03 B         TRUE        #>  3 005088  2017-09-28 A         TRUE        #>  4 010257  2004-04-03 A         TRUE        #>  5 035268  2011-05-31 C         TRUE        #>  6 065187  2003-05-26 B         TRUE        #>  7 080086  2010-08-08 A         TRUE        #>  8 083080  2012-04-16 A         TRUE        #>  9 092034  2006-06-12 B         TRUE        #> 10 0E2483  2007-08-10 C         TRUE        #> # … with 90 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)     ) %>%     arrange(patient, ward, date) } #> # A tibble: 100 × 5 #> # Groups:   ward, patient [97] #>    ward     date       patient new_index new_logical #>    <chr>    <date>     <chr>       <int> <lgl>       #>  1 Clinical 2009-08-03 001213          1 TRUE        #>  2 Clinical 2009-08-03 001213          1 FALSE       #>  3 Clinical 2017-09-28 005088          1 TRUE        #>  4 Clinical 2004-04-03 010257          1 TRUE        #>  5 ICU      2011-05-31 035268          1 TRUE        #>  6 ICU      2003-05-26 065187          1 TRUE        #>  7 Clinical 2010-08-08 080086          1 TRUE        #>  8 Clinical 2012-04-16 083080          1 TRUE        #>  9 ICU      2006-06-12 092034          1 TRUE        #> 10 ICU      2007-08-10 0E2483          1 TRUE        #> # … with 90 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           65             13            38            48 #> 2 ICU                30             10            22            25 #> 3 Outpatient          2              2             2             2  # grouping on patients and microorganisms leads to the same # results as first_isolate() when using 'episode-based': if (require(\"dplyr\")) {   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) } #> [1] TRUE  # but is_new_episode() has a lot more flexibility than first_isolate(), # since you can now group on anything that seems relevant: if (require(\"dplyr\")) {   df %>%     group_by(patient, mo, ward) %>%     mutate(flag_episode = is_new_episode(date, 365)) %>%     select(group_vars(.), flag_episode) } #> # A tibble: 100 × 4 #> # Groups:   patient, mo, ward [98] #>    patient mo            ward       flag_episode #>    <chr>   <mo>          <chr>      <lgl>        #>  1 F32657  B_CRYNB       ICU        TRUE         #>  2 A66134  B_STPHY_AURS  Clinical   TRUE         #>  3 001213  B_PSDMN_AERG  Clinical   TRUE         #>  4 126334  B_STPHY_CONS  Outpatient TRUE         #>  5 34B622  B_KLBSL_OXYT  Clinical   TRUE         #>  6 945BD5  B_ENTRBC_CLOC Clinical   TRUE         #>  7 5C1947  B_ESCHR_COLI  Clinical   TRUE         #>  8 284FFF  B_STPHY_EPDR  Clinical   TRUE         #>  9 662978  B_ESCHR_COLI  Clinical   TRUE         #> 10 874171  B_ESCHR_COLI  Clinical   TRUE         #> # … with 90 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: There were 73 warnings in `summarise()`. #> The first warning was: #> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, #>   only_all_tested = FALSE) ...`. #> ℹ In group 5: `order = \"Lactobacillales\"`, `genus = \"Enterococcus\"`. #> Caused by warning: #> ! Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> 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) - default 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 - default current system language (see get_AMR_locale()) can also set package 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 - default \"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\") } #> 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 (default 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 names codes microorganisms (see .mo()) - default 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 (default 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) } #> [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] 3.037125 kurtosis(rnorm(10000), excess = TRUE) #> [1] 0.04694336"},{"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 names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console - default 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 (default 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: There was 1 warning in `mutate()`. #> ℹ In argument: `MDRO = mdro()`. #> Caused by 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) - default 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 S I S R I I S S S mean_amr_distance(sir) #>  [1] -0.3162278 -0.3162278 -0.3162278 -0.3162278  2.8460499 -0.3162278 #>  [7] -0.3162278 -0.3162278 -0.3162278 -0.3162278  mic <- random_mic(10) mic #> Class 'mic' #>  [1] 0.01  8     0.25  64    0.025 128   1     0.01  128   128   mean_amr_distance(mic) #>  [1] -1.3051841  0.3547377 -0.5058733  0.8711043 -1.0776509  1.0432265 #>  [7] -0.1616289 -1.3051841  1.0432265  1.0432265 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) #>  [1] -1.3051841  0.3547377 -0.5058733  0.8711043 -1.0776509  1.0432265 #>  [7] -0.1616289 -1.3051841  1.0432265  1.0432265  disk <- random_disk(10) disk #> Class 'disk' #>  [1] 17 43 40 10 41 48 32 39 42 29 mean_amr_distance(disk) #>  [1] -1.3988539  0.7280585  0.4826455 -1.9714842  0.5644498  1.1370801 #>  [7] -0.1717891  0.4008412  0.6462542 -0.4172021  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   17    2  >=8 #> 2   B    R   23    1  >=8 #> 3   C    I   18    8  >=8 #> 4   D    R   25    2    2 #> 5   E    S   30    8  >=8 #> 6   F    S   25    1  >=8 #> 7   G    I   22  0.5  >=8 #> 8   H    R   18    1    1 #> 9   I    S   19    4  >=8 #> 10  J    R   22  0.5    1 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\", #>   and \"tobr\" #>  [1] -0.30902553  0.37446780  0.09125296  0.28702431  0.82880923  0.01327092 #>  [7] -0.34052588 -0.50251321 -0.01669175 -0.42606885 y$amr_distance <- mean_amr_distance(y, where(is.mic)) #> Error in .subset(x, j): invalid subscript type 'list' y[order(y$amr_distance), ] #> Error in order(y$amr_distance): argument 1 is not a vector  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   18    8  >=8   0.09125296 0.00000000 #> 2   F    S   25    1  >=8   0.01327092 0.07798204 #> 3   I    S   19    4  >=8  -0.01669175 0.10794471 #> 4   D    R   25    2    2   0.28702431 0.19577135 #> 5   B    R   23    1  >=8   0.37446780 0.28321484 #> 6   A    S   17    2  >=8  -0.30902553 0.40027849 #> 7   G    I   22  0.5  >=8  -0.34052588 0.43177884 #> 8   J    R   22  0.5    1  -0.42606885 0.51732181 #> 9   H    R   18    1    1  -0.50251321 0.59376617 #> 10  E    S   30    8  >=8   0.82880923 0.73755627 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 52 142 Microorganisms — microorganisms","title":"Data Set with 52 142 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 52 142 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 52 142 Microorganisms — microorganisms","text":"tibble 52 142 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 52 142 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 52 142 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 52 142 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 52 142 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 52 142 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 52 142 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 52 142 Microorganisms — microorganisms","text":"","code":"microorganisms #> # A tibble: 52,142 × 22 #>    mo               fullname      status kingdom phylum class order family genus #>    <mo>             <chr>         <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> #>  1 B_GRAMN          (unknown Gra… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  2 B_GRAMP          (unknown Gra… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  3 B_ANAER          (unknown ana… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  4 F_FUNGUS         (unknown fun… accep… Fungi   (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 52,132 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), and Enterobacter cloacae #>               dissolvens (0.565) #>  #> Only the first 10 other matches of each record are shown. Run #> print(mo_uncertainties(), n = ...) to view more entries, or save #> mo_uncertainties() to an object.  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 package 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_snomed() mo_info() named character case mo_url() 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() (scientific reference name), 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]] #> [1] \"1098101000112102\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\"        #>  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 aerogenes\") #> [1] \"Tindall et al., 2017\" mo_authors(\"Klebsiella aerogenes\") #> [1] \"Tindall et al.\" mo_year(\"Klebsiella aerogenes\") #> [1] 2017 mo_lpsn(\"Klebsiella aerogenes\") #> [1] \"777146\" mo_gbif(\"Klebsiella aerogenes\") #> [1] \"9281703\" mo_synonyms(\"Klebsiella aerogenes\") #>   Hormaeche et al., 1960     Bascomb et al., 1971  #> \"Enterobacter aerogenes\"     \"Klebsiella mobilis\"    # 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(\"EIEC\") #> [1] \"coli\" mo_name(\"UPEC\") #> [1] \"Escherichia 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\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\"        #>  # }"},{"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 package 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: There were 73 warnings in `summarise()`. #> The first warning was: #> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, #>   only_all_tested = FALSE) ...`. #> ℹ In group 5: `order = \"Lactobacillales\"`, `genus = \"Enterococcus\"`. #> Caused by warning: #> ! Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> 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 - default 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' - default system language (see get_AMR_locale()) can overwritten setting package 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 Antimicrobial Resistance — proportion","title":"Calculate Antimicrobial 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 Antimicrobial 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 Antimicrobial 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 Antimicrobial 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. default \"\" 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 - default current system language (see get_AMR_locale()) can also set package 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) - default TRUE","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Antimicrobial 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 Antimicrobial 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 one four available algorithms. 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 Antimicrobial 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 Antimicrobial 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 Antimicrobial Resistance — proportion","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. 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 ------------------------------------------------------------ # 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: There was 1 warning in `summarise()`. #> ℹ In argument: `KAN = (function (..., minimum = 30, as_percent = FALSE, #>   only_all_tested = FALSE) ...`. #> ℹ In group 3: `ward = \"Outpatient\"`. #> Caused by 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] 128     0.25    0.01    1       0.005   32      0.0625  32      4       #> [10] 64      8       128     0.5     128     0.005   64      <=0.002 0.025   #> [19] 4       128     0.025   16      0.25    <=0.002 0.01    random_disk(25) #> Class 'disk' #>  [1] 24 46 46 45 35 44 43 19 37  6 47 49 42 14 42 28 35 12 28 23  9 47 50 18 32 random_sir(25) #> Class 'sir' #>  [1] I S I I I S S R I R I S R I I I R R I I R S I S 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] 128     0.5     <=0.001 32      1       0.125   0.0625  0.25    4       #> [10] 64      0.125   0.025   64      0.002   1       8       2       128     #> [19] 32      1       2       0.005   0.01    1       0.005   random_mic(25, \"Klebsiella pneumoniae\", \"meropenem\") # range 0.0625-16 #> Class 'mic' #>  [1] 16  32  <=1 8   2   8   16  32  4   2   4   2   <=1 2   8   <=1 16  <=1 8   #> [20] 32  <=1 32  8   8   2   random_mic(25, \"Streptococcus pneumoniae\", \"meropenem\") # range 0.0625-4 #> Class 'mic' #>  [1] 0.5    4      4      4      0.0625 0.125  0.5    4      0.125  0.0625 #> [11] 0.25   0.0625 0.0625 16     8      0.25   0.25   2      0.125  0.0625 #> [21] 0.5    0.25   4      0.25   2       random_disk(25, \"Klebsiella pneumoniae\") # range 8-50 #> Class 'disk' #>  [1] 12 22 49 15 47 47 30 29 25 40 40 42 49 19 28 49 29 18 13 43 26 48 34 34 31 random_disk(25, \"Klebsiella pneumoniae\", \"ampicillin\") # range 11-17 #> Class 'disk' #>  [1] 16 17 12 17 14 11 16 14 13 14 17 14 15 14 12 13 11 11 17 11 16 12 11 16 17 random_disk(25, \"Streptococcus pneumoniae\", \"ampicillin\") # range 12-27 #> Class 'disk' #>  [1] 21 15 17 17 17 25 20 23 16 24 27 15 23 25 23 15 16 19 23 18 22 25 26 15 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 - default first column date class year_min lowest year use prediction model, dafaults lowest year col_date year_max highest year use prediction model - default 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\" ) 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) }  #> 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] -0.03534482"},{"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), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), 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 20 supported languages English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), 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 package 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 package 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, package 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\") #> ℹ 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-1829147","dir":"Changelog","previous_headings":"","what":"AMR 1.8.2.9147","title":"AMR 1.8.2.9147","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 Many new interesting functions, antibiogram() (generating traditional/combined/syndromic/WISCA antibiograms), sir_confidence_interval() mean_amr_distance(), add_custom_microorganisms() add custom microorganisms package Clinical breakpoints added EUCAST 2022 CLSI 2022 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 20 new antibiotics added updated DDDs ATC codes Extended support antiviral agents (antivirals data set), many new functions Now available 20 languages Many small bug fixes","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/news/index.html","id":"sir-vs-rsi-1-8-2-9147","dir":"Changelog","previous_headings":"New","what":"SIR vs. RSI","title":"AMR 1.8.2.9147","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":"new-antibiogram-function-1-8-2-9147","dir":"Changelog","previous_headings":"New","what":"New antibiogram function","title":"AMR 1.8.2.9147","text":"new antibiogram() function, users can now generate traditional, combined, syndromic, even weighted-incidence syndromic combination antibiograms (WISCA). , follow logic previously described work Klinker et al. (2021, DOI 10.1177/20499361211011373) Barbieri et al. (2021, DOI 10.1186/s13756-021-00939-2). help page antibiogram() extensively elaborates use cases, antibiogram() also supports printing R Markdown Quarto, support 20 languages. Furthermore, different plotting methods implemented allow graphical visualisations well.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"interpretation-of-mic-and-disk-diffusion-values-1-8-2-9147","dir":"Changelog","previous_headings":"New","what":"Interpretation of MIC and disk diffusion values","title":"AMR 1.8.2.9147","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\"). new arguments include_PKPD (default: TRUE) include_screening (default: FALSE), users can now specify whether breakpoints screening PK/PD table included interpreting MICs disks diffusion values. options can set globally, can read new manual. 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-1-8-2-9147","dir":"Changelog","previous_headings":"New","what":"Supported languages","title":"AMR 1.8.2.9147","text":"added support following ten languages: Chinese (simplified), Czech, Finnish, Greek, Japanese, Norwegian (bokmål), Polish, Romanian, Turkish Ukrainian. antibiotic names now available languages, AMR package automatically determine supported language based user’s system language. grateful valuable input colleagues countries. AMR package now available 20 languages total, according download stats used almost countries world!","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"microbiological-taxonomy-1-8-2-9147","dir":"Changelog","previous_headings":"New","what":"Microbiological taxonomy","title":"AMR 1.8.2.9147","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-1-8-2-9147","dir":"Changelog","previous_headings":"New","what":"Antibiotic agents and selectors","title":"AMR 1.8.2.9147","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-1-8-2-9147","dir":"Changelog","previous_headings":"New","what":"Antiviral agents","title":"AMR 1.8.2.9147","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-1-8-2-9147","dir":"Changelog","previous_headings":"New","what":"Other new functions","title":"AMR 1.8.2.9147","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-1-8-2-9147","dir":"Changelog","previous_headings":"","what":"Changes","title":"AMR 1.8.2.9147","text":"Gained argument new_after_days determine episodes based epidemic periods Fix working NA values Fix unsorted dates length 2 Now returns class integer instead numeric since always whole numbers 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 Fix using g.test() zeroes 2x2 table mo_synonyns() now contains scientific reference names","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-8-2-9147","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.8.2.9147","text":"Added Peter Dutey-Magni, Dmytro Mykhailenko, Anton Mymrikov, Andrew Norgan, Jonas Salm, Anita Williams 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 along way changelog contains changes AMR v2.0 later. prior versions, please see archive.","code":""}]