From 7f1c20a0e8c033aa1321839eb77fd8b4941aac93 Mon Sep 17 00:00:00 2001
From: github-actions <41898282+github-actions[bot]@users.noreply.github.com>
Date: Sat, 11 Mar 2023 13:32:27 +0000
Subject: [PATCH] Built site for AMR: 1.8.2.9148@262598b

---
 404.html                                      |   2 +-
 LICENSE-text.html                             |   2 +-
 articles/AMR.html                             | 267 +++++++++---------
 .../figure-html/unnamed-chunk-13-1.png        | Bin 42740 -> 42899 bytes
 articles/EUCAST.html                          |   2 +-
 articles/MDR.html                             |  56 ++--
 articles/PCA.html                             |   2 +-
 articles/SPSS.html                            |   6 +-
 articles/WHONET.html                          |   2 +-
 articles/datasets.html                        |   4 +-
 articles/index.html                           |   2 +-
 articles/resistance_predict.html              |   2 +-
 articles/welcome_to_AMR.html                  |   2 +-
 authors.html                                  |   2 +-
 index.html                                    |   2 +-
 news/index.html                               |  26 +-
 pkgdown.yml                                   |   2 +-
 reference/AMR-deprecated.html                 |   2 +-
 reference/AMR-options.html                    |   2 +-
 reference/AMR.html                            |   2 +-
 reference/Rplot005.png                        | Bin 13182 -> 13249 bytes
 reference/Rplot006.png                        | Bin 13199 -> 13227 bytes
 reference/Rplot007.png                        | Bin 13063 -> 13438 bytes
 reference/Rplot008.png                        | Bin 18932 -> 18108 bytes
 reference/Rplot009.png                        | Bin 8638 -> 8579 bytes
 reference/WHOCC.html                          |   2 +-
 reference/WHONET.html                         |   2 +-
 reference/ab_from_text.html                   |   2 +-
 reference/ab_property.html                    |   2 +-
 reference/add_custom_antimicrobials.html      |   2 +-
 reference/add_custom_microorganisms.html      |   2 +-
 reference/age.html                            |  22 +-
 reference/age_groups.html                     |   2 +-
 reference/antibiogram.html                    |  37 +--
 reference/antibiotic_class_selectors.html     |   2 +-
 reference/antibiotics.html                    |   2 +-
 reference/as.ab.html                          |   2 +-
 reference/as.av.html                          |   2 +-
 reference/as.disk.html                        |   2 +-
 reference/as.mic.html                         |   2 +-
 reference/as.mo.html                          |   2 +-
 reference/as.sir.html                         |  22 +-
 reference/atc_online.html                     |   2 +-
 reference/av_from_text.html                   |   2 +-
 reference/av_property.html                    |   2 +-
 reference/availability.html                   |   2 +-
 reference/bug_drug_combinations.html          |   2 +-
 reference/clinical_breakpoints.html           |   2 +-
 reference/count.html                          |   2 +-
 reference/custom_eucast_rules.html            |   2 +-
 reference/dosage.html                         |   2 +-
 reference/eucast_rules.html                   |   2 +-
 reference/example_isolates.html               |   2 +-
 reference/example_isolates_unclean.html       |   2 +-
 reference/first_isolate.html                  |   2 +-
 reference/g.test.html                         |   2 +-
 reference/get_episode.html                    | 110 ++++----
 reference/ggplot_pca.html                     |   2 +-
 reference/ggplot_sir.html                     |   2 +-
 reference/guess_ab_col.html                   |   2 +-
 reference/index.html                          |   2 +-
 reference/intrinsic_resistant.html            |   2 +-
 reference/italicise_taxonomy.html             |   2 +-
 reference/join.html                           |   2 +-
 reference/key_antimicrobials.html             |   2 +-
 reference/kurtosis.html                       |   6 +-
 reference/like.html                           |   2 +-
 reference/mdro.html                           |   2 +-
 reference/mean_amr_distance.html              |  73 ++---
 reference/microorganisms.codes.html           |   2 +-
 reference/microorganisms.html                 |   2 +-
 reference/mo_matching_score.html              |   2 +-
 reference/mo_property.html                    |   2 +-
 reference/mo_source.html                      |   2 +-
 reference/pca.html                            |   2 +-
 reference/plot-1.png                          | Bin 26657 -> 26913 bytes
 reference/plot-2.png                          | Bin 26495 -> 27615 bytes
 reference/plot-3.png                          | Bin 27943 -> 27972 bytes
 reference/plot-4.png                          | Bin 39519 -> 39727 bytes
 reference/plot-5.png                          | Bin 39090 -> 39742 bytes
 reference/plot-6.png                          | Bin 38235 -> 38976 bytes
 reference/plot-7.png                          | Bin 37582 -> 37837 bytes
 reference/plot-8.png                          | Bin 56465 -> 54976 bytes
 reference/plot-9.png                          | Bin 26378 -> 26304 bytes
 reference/plot.html                           |  26 +-
 reference/proportion.html                     |   2 +-
 reference/random.html                         |  34 +--
 reference/resistance_predict.html             |   2 +-
 reference/skewness.html                       |   4 +-
 reference/translate.html                      |   2 +-
 search.json                                   |   2 +-
 91 files changed, 414 insertions(+), 403 deletions(-)

diff --git a/404.html b/404.html
index 9cfc0b9f..2817ea22 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/LICENSE-text.html b/LICENSE-text.html
index bb4437a6..423858cf 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/articles/AMR.html b/articles/AMR.html
index 9d35fa9f..d4ac6f80 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
@@ -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 26 February 2023.</p>
+generated on 11 March 2023.</p>
 <div class="section level2">
 <h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
 </h2>
@@ -243,21 +243,21 @@ make the structure of your data generally look like this:</p>
 </tr></thead>
 <tbody>
 <tr class="odd">
-<td align="center">2023-02-26</td>
+<td align="center">2023-03-11</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">2023-02-26</td>
+<td align="center">2023-03-11</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">2023-02-26</td>
+<td align="center">2023-03-11</td>
 <td align="center">efgh</td>
 <td align="center">Escherichia coli</td>
 <td align="center">R</td>
@@ -427,11 +427,11 @@ page.</p>
 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="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="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>
@@ -592,7 +592,7 @@ impression, as it comes with support for the new <code>mo</code> and
 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>
+<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>
@@ -602,7 +602,7 @@ microorganisms:</p>
 <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="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>
@@ -788,7 +788,8 @@ antibiograms:</p>
 </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>
+<span>  ab_transform <span class="op">=</span> <span class="st">"name"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <colgroup>
 <col width="28%">
@@ -841,8 +842,9 @@ antibiograms:</p>
 </table>
 <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>
+<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>
+<span><span class="op">)</span> <span class="co"># support for 20 languages</span></span></code></pre></div>
 <table class="table">
 <colgroup>
 <col width="27%">
@@ -896,7 +898,8 @@ antibiograms:</p>
 <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>
+<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><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <thead><tr class="header">
 <th align="left">Pathogen (N min-max)</th>
@@ -938,12 +941,14 @@ antibiograms:</p>
 <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>  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>
+<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>
+<span>  syndromic_group <span class="op">=</span> <span class="st">"condition"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <thead><tr class="header">
 <th align="left">Syndromic Group</th>
@@ -957,119 +962,120 @@ antibiograms:</p>
 <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>
+<em>E. coli</em> (401-401)</td>
+<td align="right">67</td>
+<td align="right">61</td>
+<td align="right">59</td>
+<td align="right">65</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>
+<em>E. coli</em> (403-403)</td>
+<td align="right">60</td>
+<td align="right">55</td>
+<td align="right">54</td>
+<td align="right">59</td>
 </tr>
 <tr class="odd">
 <td align="left">Rheumatic</td>
 <td align="left">
-<em>E. coli</em> (423-423)</td>
+<em>E. coli</em> (446-446)</td>
 <td align="right">65</td>
+<td align="right">59</td>
 <td align="right">60</td>
-<td align="right">58</td>
-<td align="right">63</td>
+<td align="right">64</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>
+<em>K. pneumoniae</em> (99-99)</td>
+<td align="right">65</td>
 <td align="right">61</td>
+<td align="right">60</td>
+<td align="right">59</td>
 </tr>
 <tr class="odd">
 <td align="left">Respiratory</td>
 <td align="left">
 <em>K. pneumoniae</em> (110-110)</td>
+<td align="right">62</td>
+<td align="right">49</td>
+<td align="right">56</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>
+<em>K. pneumoniae</em> (107-107)</td>
+<td align="right">63</td>
+<td align="right">49</td>
+<td align="right">61</td>
+<td align="right">62</td>
+</tr>
+<tr class="odd">
+<td align="left">Cardial</td>
+<td align="left">
+<em>S. aureus</em> (235-235)</td>
+<td align="right">64</td>
+<td align="right">55</td>
+<td align="right">61</td>
+<td align="right">57</td>
+</tr>
+<tr class="even">
+<td align="left">Respiratory</td>
+<td align="left">
+<em>S. aureus</em> (208-208)</td>
+<td align="right">62</td>
+<td align="right">61</td>
+<td align="right">57</td>
+<td align="right">65</td>
+</tr>
+<tr class="odd">
+<td align="left">Rheumatic</td>
+<td align="left">
+<em>S. aureus</em> (218-218)</td>
+<td align="right">67</td>
+<td align="right">55</td>
+<td align="right">53</td>
+<td align="right">67</td>
+</tr>
+<tr class="even">
+<td align="left">Cardial</td>
+<td align="left">
+<em>S. pneumoniae</em> (132-132)</td>
 <td align="right">62</td>
 <td align="right">56</td>
+<td align="right">62</td>
+<td align="right">68</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>
+<em>S. pneumoniae</em> (137-137)</td>
 <td align="right">60</td>
-<td align="right">55</td>
+<td align="right">54</td>
+<td align="right">61</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>
+<em>S. pneumoniae</em> (130-130)</td>
+<td align="right">70</td>
+<td align="right">58</td>
+<td align="right">56</td>
+<td align="right">68</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"># 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>
+<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>
@@ -1084,55 +1090,56 @@ antibiograms:</p>
 <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>
+<td align="left">Gram-negative (500-500)</td>
+<td align="right">66</td>
+<td align="right">61</td>
+<td align="right">64</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="left">Gram-negative (513-513)</td>
+<td align="right">60</td>
+<td align="right">54</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="left">Gram-negative (553-553)</td>
 <td align="right">65</td>
-<td align="right">58</td>
-<td align="right">62</td>
+<td align="right">57</td>
+<td align="right">64</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>
+<td align="left">Gram-positive (367-367)</td>
+<td align="right">63</td>
+<td align="right">55</td>
+<td align="right">61</td>
 </tr>
 <tr class="odd">
 <td align="left">Respiratory</td>
-<td align="left">Gram-positive (339-339)</td>
+<td align="left">Gram-positive (345-345)</td>
+<td align="right">61</td>
+<td align="right">58</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="left">Gram-positive (348-348)</td>
+<td align="right">68</td>
 <td align="right">56</td>
-<td align="right">65</td>
+<td align="right">67</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>
+<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>  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>
+<span><span class="op">)</span></span>
 <span><span class="va">wisca</span></span></code></pre></div>
 <table class="table">
 <thead><tr class="header">
@@ -1145,45 +1152,45 @@ antibiograms:</p>
 <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>
+<td align="left">Gram-negative (500-500)</td>
+<td align="right">66</td>
+<td align="right">77</td>
+<td align="right">77</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>
+<td align="left">Gram-negative (513-513)</td>
+<td align="right">60</td>
+<td align="right">72</td>
+<td align="right">71</td>
 </tr>
 <tr class="odd">
 <td align="left">Rheumatic</td>
-<td align="left">Gram-negative (524-524)</td>
+<td align="left">Gram-negative (553-553)</td>
 <td align="right">65</td>
+<td align="right">78</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="left">Gram-positive (367-367)</td>
+<td align="right">63</td>
+<td align="right">79</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="left">Gram-positive (345-345)</td>
+<td align="right">61</td>
 <td align="right">76</td>
+<td align="right">73</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="left">Gram-positive (348-348)</td>
+<td align="right">68</td>
 <td align="right">77</td>
-<td align="right">76</td>
+<td align="right">79</td>
 </tr>
 </tbody>
 </table>
diff --git a/articles/AMR_files/figure-html/unnamed-chunk-13-1.png b/articles/AMR_files/figure-html/unnamed-chunk-13-1.png
index d3f1586fe743e793461091a93a129602c2fd2bc3..8c57cc2599e4945d3f8e00ce04f075bdc31aca3d 100644
GIT binary patch
literal 42899
zcmeFZ2UJvBw<cQZF)JL8fsA@AK#`m?C<+!)0Z}qY&PbG47*UB9Nis^#l4F62<eW1l
zlC#7j*P9!Uf4?60c8~7+`o4Z|jIN=A1-19uYt1$1H^2GK_2#aE^yw4SCr~KVY0RBl
zN+{IfSQP5e=VM3Uoyr%($?)aKBU$NN@D*O0lBHPqa@^*QnjH#7=Y{+|;6J`(h(cXL
zVQ$?}c@jA{<Qk-9=zer(F^oG@Zh`7=hVj2n9Jui(i_{3KjKqn*pFMor>56Z2$#~29
zmgT{`Mr!Qec_(k*J`h~RN`2sv$~bksR)ETr+ovuaY(pInoJDinO_X8dxed6uFNZZo
z2Xb$^bw6UYU7vIJ*tTu<SSN~X_a&I+tDoK<pU<aP>J<CmeeOkjBF}(|{qvy7{yQfw
z|7X0>_Hg!K0aKS5?{(GmWCtqL=VReKrb^V*1v~t^3kwVB+V~RBw<u5bFfK!x3|Um~
z#IacEdy0yRAuMW2vs@_8GoAhY;S&=RtkGF%X|km#R5j%Rp3f4mzVDCuKY8%~ni1c8
zHdJpxx>hNl6T#hSp=i`kWanq4T8X_WqeA5S-Sq)>FIw9>6OCbk$Imi$mdKr0UGm3@
z_=_yx$t;;k&CAa}y~Upt<)}{o<oi?kz0)ZDz=E6ZG&8gdJLITyw^658i<hct?AH5?
zlS)fV39}6$tPS&pgy;e53Q?WinFlL-#0v36m!483wor4GYqwKXm{U}kBX%M(Gc!YX
zXi(MkmM^#XI|T<fFQTr#MN!|uxor;+n$JJbe1{g?nu@bqu46KujB-+tm6c8HGE>b^
z4`zyf^7jMH$G6$aH-0om3W60irabOKeKu)RMWJ*B{%DoBz^2*jbi=t>aB?j6IoS+9
zUWM6kd8|(G=jTIq-=7|x$gZJpNWGtCc8_`BwOxy7VK)Pc<89vgYQGFYFJaOlROP)?
z)wC{kifnwc3bXTEc8!_VkyYgk)fGRdvMaJ7EOkf4em*Um6kViR&)M5sB#E_%tjJ{+
zPlTtd=QBUhb~aon-(C>ep3B{=e^8ElP9}6UScaiut1Bx<_scU87zHx@iE?54Kf-Po
zVk1O}gLb(g6}IE`nvQ*j#j}~EIYv$4WfQzvb*_Z%VHg5E1{Zw&fziTXaq;FvM6UgG
zd)bfkS+&SW<_ZZO8h9)7p3El0P^}Q*g`z7Bq3kUUI(y4yD^ZJ$<~rN`@@%7Tn4)XD
z9_U1EFO;&&hO#v^3ok|r+D(joJFD0jj|n^y1uH1rqiH9fS-McN16I&zRXQIM=O@I>
z6lrtQV{av9a?q}&iOM+g-4#E9j|8{MzGlQu#P*i_i9_n8OO_qR(auI>f@1;bLp7e7
zrH+<<FqXW$ys>`sEV|{8+3m#&@tEB?W4qNh>B+nf1)6u3f8-ayc#?{XoxLY#r>1I^
z6-Dn$+~&y1J73^76vD1EOdLj@D@}VX6`AllvF$nTu^rfyh&5;8*ZW0QTP0Ie)10fW
zYrh-y6K2bD9hz-#<@mX|1ap4!0!!^_{=opk&IJGT!a}nT6}RorFGnZe>-*I;hH)Ql
z%v}<6GjAqg@($=;ab3ha?eF8M1E&XytGp?U>u?Mh+*w7D$7vy5!j9j4`G?=HGz(jQ
z&O>ib@K=O6uS~2(6gbZJ5870oE*^PBV_SbMX)J2dZIUq*yVArzS+>$F3l4#hH(*uD
zS~%pWQNEU8Z8+1J;tXpVns45-E{_F!T>gBBd@Mb;x&F)1iz0Il8lQA;faykhj9z5Z
zEaEsx!Pe*{w)&xLR%?-(!#ZER=`e+6K(XEAy6Y~oq%gjBvg~-C$r!AM_TF+RF=Du_
z?HzWf20KS_R}{98Gt9Q#?plxW`dlCEz1;%ItP!ip(>>;+ob>!Vc-v9VOEtbUp_1gH
zJji^(*Puh>A~v6M0>`Aqx9%|q%lL`!Z5(KzZ{|A~`=tMbMuC+$X|rWThqR`%n>ge=
z8C49kvhvQ~4b2qew(RK)j$oyQw9}$iWTRKnvsk)VJ_xJjyxE`=4Zp}fTJ1Ya^oZH&
zOj>T0Bwve2A`UuNoqw=zB!3Q*@%gRTw2<ELp-?~D|7iUb<qE?OOke!MucVkRzF4+m
z+sIin7No3DmuK3c#2mbp*z}(B3TZ2g6n=<I;F5Bp97a-7vO8TZ@aRQ14#XhY%6@*O
zb?nvagU#_ubFZgjZ>?;1-nvDYu-PmZDZoibN4MGRR32!aS<JY99dJ#wE>j;ir=-Qh
zO*;}3a&x)G!N%=IUQ$fR>TD@_EC<G%>i0IWa~r>|Q>QV!98$LEzSz*2qTJEd6|7;6
z=Y^dYx-zur!H?`5@Uy+v(O@iwuzCC3i9;*wq-FLnUUSv(@Ni_C585`c!g|{+rzpBJ
zuS_(_nwy&w+>B$~9iv@lQsxQ<tTqe5OU#wbW@5UDDtkMV;^N<LT1vwH10SPkRXiTz
zwo!Sp*&4e&u(MGkKKzkskkmxjk-xz5Fd!|f+@;mKr7P{h<U;9U^TyKXQO#RqPBaJZ
zidep6-CxAbw2G}Xw+Zf;X^nMx{-K~OkF~Vo@6S%!w%#~u2s3%8pyun#lO5gNA>1t@
zxmicY7qO&mw~>=Nwa6VKohQNsR~osuCyGgX+bgi7hQuMK7K)0Es=1s7Hd7OmJ5Ekc
z7udBUXR|8G366C2-O;48mf)&8UiG9NtoA|td0p)=^ZLw#3Lzxa^!N84(cFl%Z^sBi
zs6y^*TO6sB5(EKi3@+c)XB-oS-kes%meh@3v}@$ttO0A63+K6(pmc4t#=nJ0Z0QXT
z503$C-!M*nsb+yupK_wLsc9Xu;yZmYyK1WG58sLJPAgVw7hdrf;fmQ?P1<G)iHL~U
z_e@z}1%U<<?D2kner_ufgvPPDz?BSZ_v=|ts;;*W4h|L$*U&j>iopep=lk=K0JuG&
z0WKZ<WkFEG`2J^LvZG<x@CS*pusQb_X9vUQ%LFOXo;!oxgaI-bbeYZAbqQL7g)POQ
zkw7r8_|c<i;FxFIhm22#dq3Fs{w6_u8Mf}Lp?9@V5MAp;Q`wN|6j-PsJ{nzD2o7Zq
z_LuwQ4nz=c!rpqJGFzw}jBYH<IC?r_v9My-5;xWnvuAJA9NAZx0T#yv@zQPV%0;T5
zaT|%Jh&wSRhft^pM$cFCn$ptJI0Fcjl6y_a<|f~;{xl%%iU;GS>Au|vF)hMicRn#<
z*$GQ@I;4F)v_bH@=OG+=(Sule)SXmGCd`6ah7e)-*b;w)=Ft@lndoXOdfG|vJsJsq
z?HwI~PGzHXrlzJ;_QS&^3*a6-Rv2bo&z<fbu;|fpqFbj7jwp>PR4LYMKF$;wfG(bl
zc0D3jv0Q&W5N+)?O0$g@8?^l6^Hi&9FW1GbR<9%TqVC`g&}!oLJtYpaTiS>>#i6aA
zRMITxgyJney9F}J*AsS{2xtrvlFNHsU9W*J8?DTAh~IzgN5{x|o7aZxi&B`ntVOyB
zceB>|!J+YQMpuw_7wsxf^N)pA>=+`}3tqvagY?r&+`@Kmx+95vYYR4fAX<1nuOqg@
zl|7QbY-&JB67OV99_Q=2E3#aB1%o@uUdBP#U2x(ro&(pIvq*AfXY`_$=EP`k_xDVU
zcwMBo-RicQ91&RW)#r9v7}Rtg4-VrtRv^qG0s7-@Z!Z06p93{DHic(UagrK(4HhD0
zQdx<Ki3lKY-dU>2wdl)f1pim(J-H+AD=g}|KDPsIYleo5LG5B=wCh6gCgO<);h6*h
z`{lHVZ5wQ@PFK>48JHszAbCcj(lL`bJ%ulPOg0gsVKu!?B9Qio&ZKQZMV%oZFYjr=
z`E3HpgROWBC*NF=a<bcCO5>I*d%>%cDQdHFa_mN>TAoRpXHOQup96URLi;}UvY$6`
zr?z50`&w@BUw@D(hm#)`8gM-$`u<&Sb0sI3AE3%{{)$a`?I$BI<s$f4>%41Pg@>2?
z4y>F4$HXB7!9P8N!((UT@@%-6y9+&bXURkSVb;U!zcE~f^+A^>XWaw7FON5D;~kN}
z@%1EoV+(d~rCfWYsi{eDIzG_09mBL1<t6<bq6o*i13VU!E;V*;Ki)7Q?p9u#he1;3
zlch<rcVSYDhft`dEhao7V&x^f+hsr~SVsBqpjWS61<53+Fb9`s2T3b0D$jbi(p<tW
zdF#ye(3sdwYT#Yg0<gIwx2Im26-UV?D8b;J#%Kr|Qi#hvs~~V05K5?yhck}CI&P@-
zU(45j0)ma?o@W^>8+w|5@SE<)a<z+Vj5V#2VvLw5jK)Q5C9Gu(rd5*OV`HQ-MqE5p
z%TX<}Y`K2t`%&@Q7}92pwd#<DEDZ`p`Ia+QPq?swt#oxL(Pcir=fajYev}$Jr+=|w
zR-0HN7bTRx_*Aeik-hA#JrcWMd8vL9boJS-Q-+!F*3(5VUFWi;aZky42V2>(n+-Q~
zveq0uDrtrT(C!qGiej4vA*>o95LI<shqWq)q)<L&5HifKpW<b-1DCGDJ~O-&5lGRp
zNhDxN0gHrEt#tr;H<I}I`HdlL2#&lsYm3}BW^X!1DMiJ0Iw90*_=Mk|i0|h(Xwusp
zERzuFu{DjsA#o&vR{4qf+OSoBufRqVh23N`>m1(P5HJe{7em^aR4aS3WYj`TeSl}i
zEXQ5kO<o*Pq){(>@_3JkB{{V^7di?P*S}HEU0aWc%gA6wz#JrEH~;{Q;@f>KxKn!)
z$p#z0U28cJzj#_m^BWTG(0y3aF1OWSQF#xNu`sD&>F4Gz<3B*iBxVpVd(q~o@vatJ
zL1J-JrmOicY{DUMfF}Tueexp2Aks#S-9qu?L>*Ji9Px7ucnXotu|RiG^;h~a?&}zw
zrp=ebuuJX{s`}oZzrG9zkqHu*D%JF=0@EMg-fVhdH@whwN5wb3AQ3?=*GQhQ+T7fX
zT%;Ic=$2M%#hf8~!v`<=a7aAe0Dd(7sPcw{qyt>4^hI}`n2m2tuiRQPhMHl=8{@_*
zNZS=&qC9m25E0O`F)rA(t_MucO9x%`_m>fJt|nqBVV8}xbdmIKeI0(sRg7TYAul+a
zo{LLg9KwGnC@7$H=)8x)`FpI!A)W@gm05mxQnU;6#oZ}s<rJ0W@SZZp(zzU2f?KGz
zi>X!FQnd)AG47MI(b3T?YB^|tGzvJ1L&Hs+e@&8ZAr@Op*(!k>3yj$@KrAH^23ri@
z@H>0Ydu-2#wzx0YY3$G=dyovWBo+X_Ns+eop3?|cv>kSyv|O?+bU<vSV|X|^EwhA~
zv~Eq}8zZ>So9oOH7ZD#II3A?Dj652Wb8g8GIP_@QZH#ys50^O~s_q}`pWmtGFXu$k
zM-PBZ6JFvwdiCSBxUoj=mchB`2Y4(If+Y{e7C<MFX3_S)%-24?rO>pwVO734aiC)u
zoFwt(9L?4wc5hN-ceP`)$=V~3+~eo-Ly4p+ot-Mrl~KrLf+2RgN_J-h?j`SAdBSn!
z@t$Jd0hH%c$Wn1@g+1l)vF0<<wC{u$%eYA^;-o;Yiyi`yL?ajwd{r=lY2mFWgoiIH
zo&D{a1({YRTUvN~Gy@@^a9LVfhKag5Du`{4Auw@Z=k~Y__$?&7@8eR$f54I`?m^g!
zPe@<@&|pp`G!=`$VRv%p8D8+QnznTen{|pFxXqPG<E0-}eU^nowqx(4CoHX9H3;)X
zflP|@KRq;ZqX5GbBe8p%h)FIZ7UoVa26=J>zBwZ!LvZd>C6e8ur)PQj+mUS+m+{GY
z81k-3-YjQ%Hn5XH*prBDBLmpa<@UB0#iv9NmrdGP*#|92;=86EyR$k;Nl8qQ7Fr|u
zBkkjFXWXCKWVHZ775wq~%6<;L@%_0oa1I^7ut0nsN0@?@SNvsqwIAe;aR0WpWR;=z
z3x_LJ!Bl(o{kuv|>H2J}CW%KOiGlde2>JF9?xauVkP|@^#-JghBQ0HX!MEjv#lSFx
z>`4Gnbvv6Y(qlKOCES<wkAIe^_B@g-iKP1UZa=^9@bMW{`%vYx9R)f^_wGRxPfHb7
zS68-@sgEq$Wkrj65>Zi6#l&IPl@W5R0A`t-^ppIMRo!9u?Os*NL)5x7U9hEymtw!r
z!qo(qzMYjWW`-YnHZge~>6OZd5DA&THiGj2Qi*PkUL?JLbCziWa>d4_nu{@gxyDH<
z%v~jN$Y(A)9~-&4{O3&+rK`w`KT%WTmtUX`Q5~X&pP@W&9*aGId~@{$>VNu;TlyI%
zd!xamAY^xy_{_@yG=sbghlYn!%YJm*_xdB(RsiS_FICdIShPy=y2n4!y$7C5U0ofA
zo}ZtGv^(?h?SW9+Vv+PsY8smSlTvDSc6N-4(UItBNcd;E(#$o_=xe2@)RPI%1)$;g
z6<{8b3v)Qkbn@_JR&?qBwg&RY_xeFy*QBF7$itvs{wV?Y1%<kM?7;utH%!tU4qVAl
z&zJy!*}(3x<ShcmsD^iOa2$MGQwF%V8L(Oe6}J)KMsT%_5&Y~3cY$D^#&F*2(sT|g
z2>S-}BV)e5PIpPwDySjIBwIYzbCIx7(Gvqv5^*wsvCNkddxCUeE_Ov6K<4J)q*y38
z-0~g-Fd*Dsm^jv>d}o|JDIp=D)d86!6y<fer2FQ0sBzoJ+f?EnZeozY8^eB<*Vora
zk|Egpll9lL8j*y8*1q*dpJ`ImR#PePQAmJ>R~?2aDkxA<Opuk8RV~K=Q@)zOZU`6K
z-0G=_?obrROPM1Z0d-fWx|6cCM+fVU8|ze7Y&vk1HURJ{Sdd&87X#vya|j;W3>Zj{
z$`7n)yUynWCE*NPK86S}nTp>^Qzk*lvEMW)(xF@JT4)18g)PtJw)o8m?N3f;8s}Mw
z2WegDYPpP<9LNvIw)kN`+u#hkQtuy{z;Df@1^%KTHmm(F#_;2(<uN!QBi+B5?Q_PI
z4b^feE#_b@K+5Pk;7&NF2!jy+F}PhKp@J|A7OPfhrG+y9*o9;%KeqrxQ@wN9zj<{D
zC-0Hf{WuLD5d_b=4;tYi9zYO5I0qW5qRR+j1H;>T^<yd@XgYM=>U0NWWk<EvfqufF
z{UHHiG@qKAlLLPf+W~ehlh7WLc|k*Obl!5CyPww1r$8xXtSIfude>Atn{J-qL^5v;
zEo$Hy=XnX>KbirF%i6ZxrWgkXZI<8i>x<(^(6fb<Ao0ToY|Y~N>#AsHP6K*52qCz&
z19k~YR9wmBdt0-16Jf?=LYBZkAt@`rt2?C{IKDnTFFN2i2j0O=-|KhEhjArzGgvq=
z2iJd^SfRV80nXihUYPQ<udlDzicbju1NBf8O5gaHpSGg-?rJPP=s}it)NStzrzuH-
zn39qb92#sylFg~|b8%9V@gK~H%R^BPRI89rA>`jC1mJgcPJ^5}z>Xp-n3$c-Y2KY)
zmr*zzjs*ApB}Xula+1P)L{|3WU85<wqb?jY>sI|mN7r5fJz)F!z|m7emVlky_4woB
zRG8Q139twBh)T~{+j@I@ol4_cMB+YZvJs;qN=i!sDVb}wts5%OKMVaK{K%e{hiBD+
zxfSM?hnJT@O`KK-;H+<&o@*?tx&}VR8Kuo*bZ`pzxd{Z)<Ho@L7MxrMUK~HW47{Hp
z;@CHSc-tXs05(1!G3;8pjBp5-LkYr}cKZ6G0hQM1I+)iG$cZglO+`gT)e0>7CbG_H
zWN5M(_ve{ny1lsYl^GK6;9-EoQp!-xb=zE453O|6ZI8#eJ1qf|Ec2PO-lc_Gqu%M;
zl)05vGq4cAgFX4=vNl8g;M>E(k-NgdGF8UW_c*~%mMaKAnc`>V>eUFynD%K*pRmgU
z(QeCi=-Ha5^kx?MJ@^F&4DRsB3#-D%iF>;cRQL@P-!sUDpfQks`kzUoLKaGwg@*fp
z{bI)L2$y>6#XR2{n`j{iGsuqu(cTp7^!CFL#rZL|wU@mt95Rp(pa!19mRtm5#B__l
z_=Ly1w^kGb6N^K08#m8UsD#QS&_V)-!2xAS_C6~v?h2&(S~v>Vd;9+Tlc_$VXEcnN
z@RJfTj};io+UcZhpjCH%P*l_+ZUJ2f$XY~q5Ajadf@O-=x;5;;nI!=+hgsfPX_@rl
zZbnEZqn79)3j2R9n=nJ^^7jo0CBOzLS}c#yL*iMNk`yg8b(an(z%09JD&tc^1fYRe
zy0ScYft93jbf>>5u*K(gOwZUj$?H!pW>}r}*46;Py$Q0R-6gaIhiJr*M;wwoCfx^d
zkZ;rT#|L`{2j@$DR|kGxegWAX{mnZg$Q7KNhyhM!U+?t8+mOt!{rUCY1QL~byGuOZ
z@xY2{+PB><25!i%fwj;8JShT=_Cv;Ua2Ajnw1Id454D@fE}8h@&E8z*ykZM8XV{lx
zh;WB~Vp~(R&SUQ?ON#zLR-sqhH-)}+Ku9!JI)=Wr^BH}s51(kPxh(nA)`z1!aq{FQ
zW;e*E`r83{#By>U`4uw<F8mIjl*;-@Jmu}~2uvi1Y0fh9$&R-e#V0k*FLP*K@8&a@
zp4ob$+TLiYW!id}JeKi~Rut-UZHg+l)t;i6R+W(&@fClfGU{~YI%zjWaOHwRx&u|j
zILUpb-N&4Ro=<{xW0xEm=D&kR01U<Y?f!VTRJ`ewk<T{Dkk6yKQpL}j1~oUh$<?zG
zhdQ%mcvDt`%(SXyQ&&4n4#NAf7fyZIcghl%k+Hz*+)dPesqnw^#>edL!zg`8)2ij5
z)GqbVC0uF~Yf3>ucgZREyyyHg6gB0UvzZ$nG7SUfaIYDcuDNv4nO^nK11OFks+pE|
zj1yF5xwMsvKZK`bh~~2$LQygxHAqj+?=w1zwOJH}oh3e@c@3QlI~|dh6{81H99%vJ
z{u$M(=aE=8UKVS`6n*)Oh9{=JzA18q?C$$;YYBP4XvwloPONu2vM$2Zv8NdKld0`~
zKu|0EJo&h!4Se~Jp`F*&Na3{8Up(XLBt-?C&MX&SLw44~KsrVh@cS^sj`A>Z76n~V
zr#IJrgs6vRtC7WYy`zI_AQX>1=OZo)?BDy}!H=8>vfOK#y}0>=USnGho(f}aEzG4u
zm>)$Z=R3pk{a;Xgpy*VRI)tq4eWetK<G?po{Ykrj(f@%(`!~zFzlt#K?yKTNi}vEo
zZo{&8vxcHoIjmYn1IqIrgC{&yY-aw>kCLDm=Tzj!3!+4Ynm5*2U{V~Z{{h4KH)Hq@
zyzzV3>ocKg7wbOexA^ofS3CJA7HcZtkiub?nZvCR;SVBgHHxD4w?YnoUWf83OcJkc
zSB{45(n?PcVq4}beqeF0{wL;d|Gu65l5~m&ro`nl8pC7lV+gqgAnIZ9qp=W;=zt2^
zik{c3$OeryIcTC=>ePLay{RRHpaI6-Zq76pF}uDu*AY%ap?+xrWMH1*)obzUJM#5^
zaU~upyrXnoR{|+6j6E~XqR$==ZWxCVqcI6en0$4*Y{M49>>Lk2k8kfX+`P9OHji$Q
zXwOizPR6EIMnkU%iNXS$*=HNdXZyk6-*Gzs<2liO3N*~a*FP4Q@?oTk@K7cJH{TLl
zb0bA1^&YQ=(WicXk?KN+E4!O%p$=KZT~)Twj^5VZMZ(%gi1iwzekW~eEgJfY)2B}t
zVDHV0m4%=3pzV|`)&0~zJHDfItF%meT|>0+cHq;=3>PyB$SVO5se^!^KvsPy&H~U{
zKAT=?S7)ceHbrvohs$fNk>*g#c;V~!9Dp_T{ai!Hs*ciDj#hUizh?!?Qfg<VXJ|08
z{xw@RCEWB-H{Rj4xo>KpE#N76*-G!2cW>x;S&UA~L&YR+DPJe$)Z!hMA9i0R;IbGf
zvu89=Mlc7br#YOCNXgIV9rqRUTKnq<A3s0&23ZP1sE;qd#*!R(`4Ww9g+BCwLHH0%
zhGxf0O<MWncX><^iZ-vi&iD`A^fMCvIgs<Hhknw6I#4ZwV9;7xT1a-}d!4$pdTlkH
ze#<IdqmV6)Z;RH^!J#WToV8<UNc6j~!F>N?Ou_EC67`Hky!+;Bw%p=e?zPs*M8K4v
zJ!YqpW6l-g@pv5+-t4|U?oIx6lauM1#a#39RU|o1)Ao41rouhD4_X{h@d)SA`}zVs
z+j4YmRW1E`u*?iY;LOz29imiBWpuR5YtBI6+71vdYiXUuS!^HI03&nVu0Nw?B<tj)
z<-^_wnOGOie4)ea%qUaDmjNDDOqAP?P<EYYhq%^5{r(N8L&syK)c}5E$o6JhJdu5v
zub#h4wJMOTI{RU9{d;~-!);rxWdhb$RMg>&M9;QInhNvu!ZpjEi!K`}rg)izoF?p|
z_3WB2FU`8e{PcA8t~Xa>=|MBSPouHg{fD1-X|qp;{@p*`<d8#{DP|2nC1QP_dF|{q
zdnc!)QnrP@KPX*iMx-C@Z^W5^|M?vMKR^row?D$uT>=B4^e*>k$V;CGe!?fzzIK7m
zxpJ5ei3pyYGKfz*{4l%Vvbj*<F3Lr#Fyik6sH=Y6jQbDpzdNl*g5~Gh>-U*WpX~J^
zq0l8!PToP&{FlQ+ow(#l|6Jz!1A`rMHU!}q&L6jy?HS(wg&<I|sQ(m)_%AWfztKA(
z>Yi$x>|#WacxLaqxT-5Y{kpZN?)_iG)%6)z59CU|($EvKGMH+Mgl8;Yiqh;~$W-@|
z{jczj|7}b7Hy-wvX}ne5t2}EOMG8bFw|i&EwIjE36#-Z%pGyEnKU1ovswk=BA!=KM
zDCH-CGzW#c()j-XE&K-}^}qSZuh9KjH{^lpWO`MoU+*&H`};Z3(9qEI%nWt5A!&%r
zx;CwPk@rypn0YpGiO+nd@-6*t#LdloHvQbj=puh)a~{z<Mz(M2{~gtu{`%z?P^z=Y
ztk~Py*G>S}xdIhU7B6}i6F;Gu<T=oBNjCQY4Gy}3aip*hrN#hAA;y#A{OUp019B_r
z$NRt&H8b%3WNccOna9rP#YrG9Zv5htkXmCglvcT|hs${Q$TZia>7CIr+k=x(r|ls2
zSCWfIB3v{=HzO1)$av0`{~Ead7r5YLb5sN(I{{i?volF7xCg{a2z>+TcJ7m<AHY0g
zTQ^lRHN$`$g8Wx>zbpr0E?}h~<z%S~1qO*lJ@2XxX{*O>YdXn)nHp>WaB335v8=R+
z#~@e7ECa31?M3JKP+dbqnK`(<voi<^q!aoVw|ispF<Br0f{0fJ>0Aq<lk%bAX9E!c
z)MS;Lg=W*C7C#6D0E8))cFo_P^qKbeC-%pVMkv?Z4h3P$bvG#1J<xG?z>SRsD$;Yf
z?`$kNcSH;ap^@}VIu?|Z0E4rw8~<p9U*sRMSH_LOEcbtO9vB$NwHkstJkZ-*Qv`&_
z#o&+%%=Oe?Ihlt~7-uXJDj>q3>lB3NgOF+s)V7*HP{9pRi566Kf>4moMVPGjPSfoP
z7#tLrlQh}LOLl-WL}QTR=d*54?U<cPY+`P|=-&+>$T9Uu6vQC)bSO2QQM1C8InR&+
zM-z9tet|etbvUHtg@uJoY8U_Buj>H8zMYr>B0zqEn;lZYh<x(n4WdHou^#@f_@a2o
zse@$<Yhzu$JUhIJi1()Q5ZW2hkQrrle7axKp}bS-^LlBthDkYb)pdSmom3ZI-%ou%
zZ*x7Y%r((MW&3VTRRLg_#}e}zX-O*7ZR6(?ii-tVX)?BTN55Ko9~rO)tWv}I0D&s+
z)A;z4gg(l$NJ&aoe%>vx)kKgA)EpFKC6x&kO!l2M#-eK2;V3S{0a2N^8=9vQg*@D}
z+*EOE+ZL6MUuK%$ZI2Y0fb8SqCvjN^TPq8|MnTuEJYh>$nMq_7g0fzoRUUzwdS;Zd
z4I~!_YBZIV-SbWYh^Z|+Nli2Rqck%m<ysR>hU*G$0zp$pgst-nx@#sG)Ch^DmM+s3
zhjhofq-S!dT)bKzg7TtSoU~I0I<BUH;q?MUKQ!B&(Ev4*5JaI6#&4~S)UI~Mb@mJi
zWqAA}4Rv)89liSUNKvsR;Kpc<VMC71$k!W5@=-#(^72(NOR2uT(=0n$Qc{oyR9>|R
z{2Qbzr+Q-?Y@;@@`TiFA*|{pI-cnc)HBs~TTd&!E=-Za)I^Bht#&G>0V24hhPVt%g
z%bT;;BQ5k|8q7+D>2p+xf=3o6XHPn!O`uAJxl;Tu={T*{j^6h!ZywXo&?xuoM)0h6
zwj6i>PQnm-LGRnZ;nSzp_-$pcgY-hdS;93_jN1Nr{_WWWEsoye$L0=Q7!CWM>zeL!
zMSy>JnT;p`R7oXhp*~BjJx&}rg{N3Sb~qGeR-Tf(T>^|K19X1T<I!c{`m(boS8h{i
zby^P(5BuXUuj}%a6{UwBL2`JM1Z$UKAug1|d3mgygl*6mMs^X@dk~6q88%~LT3Xs-
z&%p4&Yhc&JPZ#fiA_qNNIU~QODGZzyQrROH0`A=iNRbFl|95xItXnp|5)2KHd}@fs
z5-OJTTWsm#jC{i}Lqmbe<QLOmC$eZ1nB58$EKg!uYt=CsDsfQDZu6k2%l_isi9pZy
zvr{JW?dlQF3O;WwR!~UUhq2CfcYR5++$DccvslP$-rWf(wGc}99+EccnteeAO7kcB
z4+8yGo3mUiqO7a@%9Uo*B>!an^#_UCN8HuQ-JD&*b<zv)Y4}%N7Gd|(R8RNDbj{?&
zlQsJx;>H}qD;s!~6oap|tQmKK3B~kCZCjczm{H1uY0=m;IyfrG<;;^$ekzyGeRM0d
zuYX930-pv<YmRN)Zb6FQ-2ohsp9g9NXS(*WsScDD?0GI=Gy1x_RX1Ewoe?fYjszAO
z?VZzWFU9q~dNWnSER>^Mz^~rHAvIe`zr`|;OQGwatdLzN1d**{P~=IR^{2CrXQ*e;
zpXIxC+=#244OgG;cG=BiW=hsGei@wTO!2zGn18X0Hqj;3eRMQ4x?I@B#id}=MLMqB
z#frjWcH3HVZIF#}sejf)*e8EiRdF(OK3|KYdtC$HHQb>rzIM=uVeRLYB_ogp)ghA9
zv8tngSy@!l8`Jm^MG$7Q6~al?^Jd^3iHsOTU3w;EUkI+7jxd!t#FJr=;w8BdxNB=g
z98^`(tOq9zb}P>z!bNo5V6olN#vTwwLM?#}vs}`Hx4LEis~V67brDhH=1628I}JXT
zX2Y+xmp_guEhzy_ULm3^k;A!tKXj3jV^M>!)eT7~QlT&2S#Id)>kC6W?`=*ZJYqa3
zXLF5Pn!&^KxLn`&{da4s8!21sww6LG*7LIvp1A4$1Fc}FTAO_)lx+_!HY4TF?`zkT
z6OrO`BO<G9V6PBCR3jDBK%F*12}bqdJhDwt`b`kE0>=<3kKIxg#Uu#E_FWEAjqga5
zZ%6~-LZr>t<9!hAydsIgS#E~=>S(0aH01CAk#Qi{U@FeP`JFVq;8f-D{a8ym2!T8>
z9+Et!D0?LZq3L+mXeTg=+G|OXIAsOF1kd{Y+ZSBaH#_x9YF|M2Z!qZpc>|fG{|=h_
zKNIx-vkBVo{NM+1#mfrm{QkfNB%&Z5BEh!+^NaOK%+GiDyk}vN?oY5sV&TriIV+Pp
z4hSC-5)y*&AraALzv!Xe?tb-)F`-H+JU?nQ0sM+yDf#vYkm-S0F^{zkKpf+V@)02+
zuPDT?v^aFvaE77*{WM>VbX3kDTO7!G-BP8S+M<{m;rIpUlkNPILf!Yx9yW!92|WJc
zxvR!*8UA>*Iw2#&aO}dvlz8*85Y=SBX2w6HwxQJ2gU~7M?OfmAn_Ff2>t_Ne8yh$*
zFYnOCW@2V0Gb=SaygYZzWA8*~Yb%Fth5VCDF~4F1_lU%NvG84zxGRyK$!>^*-4Wz2
z&GpiYvTOWXb8f<|{8mu%b+2=5GB-cGwoO*mDt~<cj-X~xe0))W{W}@KF&l=OW7oa@
zYzuaHou4k+xJ0bkbL`L0tqJSnogeimoo}jf8;&7vuZ6WVE*R^q4ENBW_U!LSP~-#!
zog<Os!c#vNS%##mrfI%hirjcQWYuCT8r6LF@YjnM1fmoY;^VjS6qOI0<#^dNVpAJ3
zUUK+r*7fNgR=YVz*H7^gB!&KQ+s&Qrn6{C-mv-t$rw1(yBrr|!yMwk=q&o-IGZ-;X
zm%MYj%<jv)B@qahFCWH=ofs|uteb!2vtl9-|1d?oy8Cu+eT)U4M#${6?MuvopHa;7
z(bZmrP+C9FVv77e3$=yYC$~N*>DdX;(l6YWj7L3L7ai(&(s1rG(T+@7*R6MAGn(-Z
zuGr*YeNtzq3Ki`h7VG78wE5N{)KwOA>`65xB^;rKBQd`A2l1X;rCENxW2N0+SNV7O
zB3K*N?1h?6y!a!pa^&ZxdG?Ej^QekPB$7^!wE$6~c$#QGWKm#q_~t?N<y~UM-BQs@
z&4bhSE=5*P13eWK6hbP0N|#-`_T@y-ANDdbZ*jOCabjWYag)%7`&WsFZ=|H0SlPZ>
z{tk!xTl&=`x-UfYMcbI7nOUmWEEQ!-*^m`+-W=u)Ls*-5z!xFk%|f($`mT1>erV1y
z@F2s1`PoJ6$bscAUX>$vZ9`@+P-2sQOizY%QKw2^B$yZ&>ZooVq(;5{+7xqbpLd0A
z{|EZ7U)34-zT(oaQV4we^W^_G-u8Ty0mV74h^8$NxoUTQIF|yEx7LEM!GiBPA74Ro
z<(HUSckk^h8z2XasC;)tdUZM}#;o$y=`B#<1V=`uw<pNXg7!)o;&4}irAEHhkUA&_
zL%--=ow0GRHp;DoKURel4p(mjX8`{>??6e<z{qF@%JKd(=Uk|BCg|7r!4FbEZDBT8
zl&zj`mcXV}lB`$xii<kT&XxP}Kht)%QLEHZ-hFGetv}y9cTU)8A+kA2ILUILz_dA1
z5El<M%2b^SkC$|eZ$FHU8Q`Ht9nNbWQnQ<+5Y?WdoRnwQ#a#96Te4Duj0*??d38Ud
z4j(;AVVn2!#UH=wffAfX`RZLchO%^`E_un4H_mwb_<*Zop5Yi&h!#niXo|QlCMMQW
z6DjPZpeVBR;?$AnN66{tYNi4nG)n)PrK{EOTayZ6M~@x_D66(L9;#!IuRd-sG;Skc
zKi|(ACGhwrh=M!6e|dhMnp*waz0sBhkXf0v#@<LATLH2lW2n>#?xO8js-=*m3|1J*
zrqw=-cjYy#KNrldQ+_P&6;mTp;eprHYNN*YBg4a`PFd7ECIO}uUv%%j`HMm2<E`UE
zjsuo6Ad=Gadi|(++&0CUP%a}QgG2M0v`PTzz8}ndKPT_>i4#hYI^2h}j@PnZrA3@n
z2DvefT1NL*GMYZ!f2yPsoQ8!14qL0H7$xY=$VJDje1!`_;tzIHvRJ;oY~Kjd00X{=
z`*7cc#6+Y1LTiFpSd$U_K_DqE9waaF?D$pOOm{}wr%xQbW}SC{x(LtIEXE~ml<%%<
ztj+c)o@Z9Mo1&D^iFb8k)hX8`^SYwA<nneJROT+AoIOWJmjNH`%F+>Uc#JG4+_E$E
z{&h8$3&GcvrL;=y+W_YV2LuExaw<j&e8|?XNri4X=6fGsz;Loab@EUliMu2F`0?Z6
zf_91Oc_x>=t3HC3U>el5Nts%uA*1gB2tW3xqM?b<9{~Hwc$j^2cWX`Amzww7Uw?gM
zzMnb;5~mL6ZyEP1zT`O9`+-%X;0~@n=vo||u;UXsG1o^t)Lq4P#tyK|4du_YYgQc(
z<3oV#dF&rCV4ewf3KK@(;PvD!X{s%bn%l_7tTP!IMR$gVIS74AJa%3DM$a)C{Ze+J
z&M8ZcUbH(adGqFpPOmSzt?<M=V6#ADs8X<NtGqmMKF88Jw{LYK!n)ol*K~ENO|9CY
zSTm>3VqqS#Gy@D{b#*l}AD=1&)b40e*Bp6j^$gXu`GE&%su}gG8x%TjAK1$`ZbBiX
z&5uslR<y>4Q!q+_RXy*Hzv%kIL_Vss(_n&}oSZJBzWiMI7#W%Nt+m;F7^WyR-l&(@
zr@;IL&%S31m4&CgN7~!v&hIO6NGq@!N+lQHL2|iwbp}cR6OcU((aot8lw270ik%#g
zDr7uAN}dXPzSu7;5G+p<=Gw01t^x%GMN(0b0P>Vqr2SMLJ^ILUK{j)DXbSW(w$@k>
zrXHTD4BSCxcUZe&Rq3*`Ug3aYZl6KnfSoT1L<1eIA0%NM`R1XBZ-J&6lkN5Sw|;)j
z9YuTJ{(<qHkLpc$QUN*O;$c~)K?HGlW)k$pwoJL=AYY>$@PPicg|R8vk5T#q{$g9T
zqjKH$7z}2n^~3WTgDCXc!jQ1+vS6J1_IipdaUl$sUyN`cA@K?Ea=8H)fwW>d-ayl5
z!3V#nfuzO`jflP!BW9duH6(-wJ?vX=Z{-L+%N)@d@W3e=g;vFWp@B#zqo~VjEzWKI
zd{Re>VM7Q8C&w6AVn6*O^zpJS9Roo}Rwqo@$tq;bP6~8K_kn9TM?vwxgwxRLUp{Od
zguoqm@RgR}?WTaltbOo|Q$$4bij+?PT0UAtQ1&nOI3WD)<2p07gohxZ6I|c+$L>g;
zIdew6+^rb4<&S7g36|v1m7*+-vmg0-*s?$GT0=GrvI{zS*sig*iCGVeRMpfpOxxF+
z<(t9EoAu=+bZ6<rB&oGdgSavM=T93}?XupXn1=CoSn$6w{l#`kVj$0xn{<T>3eu9n
z<iNg>gU7X5TqIOrGZQ$qoyX6EkJHxi*i88A>{;-dLkt}4XGzdf@vOvYF*80MEgvJM
z1=erxUkpCxewMbDl6utQ>TRZ#S#T6#VmwoQxk+TS0<o}x!It~x9l<kTV4jr-<?bhW
zsv$-U4or_<-ML&idv$YCeXAZmX2=zmr~Bm~(e(;1?|l%)Jq~Tp?y~@odiTS(z_Y(2
zb=AW{tD#lULdnR;KE#8b%&a>-jOBuNs!Ga7&?Cx$BD%r>wlVECbc7kBF-uEJeT??l
z?RkDnLb(=>2a0l<`rHvsLXLCqp$8=n!cd(q$JMJ>?|_GC2TkOpRUzyrmYVX_cG-pb
zC}AhF-cNeWpecng^X&QauH<`HkSoDj)baY!>~4(u&k!mqN-KP{-Ii_>*I{KcL5Pk4
zKN`?xqH_0c0Mt(7fNhzEHpze>RYn8g5d{4x=;<@#r2S_g{SK{q^bR71iAgftHvxKK
z@5n@)E;nwWw;U|G2c0+fHkZeT^!cm?AI|n<IT0tX>BUfhH&~blX4Yi9J{ZRL;ijad
zAqS-aSC|R}5r?_ndm!Gves|gjNZ#VY4&zDVNl-f4_1s!t4Ia4pLQqS(PQ0f*bbWpx
z>C5w@r34zIT(zcf-VO+GU7z$MF{ZhrSm@Qf>0S3<=6w*hXTH;-w!^}pV8`g=$B)5z
zzoU0wy>~xD{r>zwVGj_rBI}O_EDKXGfsD9#=vj2O$b|*Pn4WCN2Q%p@N>qqSh1LU?
zK_{R!iUMo4K`o!he(f3tOgb37Hq#{wUhikGSDisS$nRs!X{mgvxTU~}t`;G`6~S*E
z62w<2Txv^k<oS(UBX>jcx!+oVCfgQm59s)_qqT$iW`2+vS>sM+{2eHHY?qeP(0H~M
zFl^_~oVkn<vY)!^H|mTF4^M^Wl3PmZj|6rlPwF&0d|nLVP{9KEtT&NE4-{-m{BWeq
zgsfB4JXZ&9OFn=8d}xsu6DH<%^#mG`lfWw$QYJIFY>DH1I0)-L@((|e1lZ8R<Lv;H
zS*mi9Uw5X~Y_>o2V&7Kkw`hZQXybn4!hxYT(=b={23+IR9lpnxXcCyptkdlx9v3)T
zF&vxe$;xVNZPnDr5RoRvQufgzqzA@t_aVy#Cnat^KE9e921L4Bu+7Doh*-PvK(j8#
z6h!#YAk_kN1y}R{#5K`o%Jr8%-b##U^oHT7Ym>0e7Gaxt9ynw>NXRo)Cayzh_`Yak
zi(uvbmlcCdP-~Raa0>WiQ&<b+sURLD0=rW`ln7wut-pVZtp!wE&ePMgVbno>o9GVh
zX;_EdEeFhU(MAN*G`C}JV6=dv0Bsn3#gnCvuBB4G)Pn1J9F&9OCzd~&b*1(#GzeI^
z_|8PeGqo~Y6BHC2aD+NcqodANqXvdqr`*kNG=a(KB<_+ZcCXB}R2%QIDJGT$L4Rym
z%;vj<pXETlIa6pu@U;2&&rhAHnUa1zmyC=2;vA)~R9B{!1`a9PY>P(I>R)Sb`?XLu
z%G{KvkoA~oj+)h$XdSUBYTz_DM@u`Gd3I+eEz3y8z8pG0aP1e&uHR2piLPNg2KUHX
z-=tz<=Pq1f)?XH2a`tV}$;Bl!wxo#H)Ct6G!+vVW532TE-iq8>7Md^~92{G#d|IY?
z2&<j6^E<`x6sZ#^<r)3klj<}T7noX@7xc#6k<oa_hL2u=VJ;FbG2BnS`vnhhdYX2i
zM3aq9<Vh>js8QroH|T)W957DTqPuWmB9L;2L7#O2!gihYC_E>!Rh0TazCWzR(WX&e
z7D9FQOEJTHaB_trRZgJerX>nTDbR^~XyQ}=WQeRyv;Vf)04B%MA$(L&&{(#7f=qB{
zf-D85J;J*D8fNQfB{>5I8nAS7v;>+$!JqyZ)&||TpQZmr@#y*Du?N*TWW}sj?{T<~
zkXp#%z%zf(rOMmGEe#^uY}_@(*~5ocupKW7@CXv*4a@Yojb(KsO}VA&{wXSm0M}$i
zSLOyE{+N);&dy$#_ZhzdS=w&>FBJV*{tDjb6&iLs%YfrqM!5hq0{y_xgmW3bjTgUg
zZIvHD8lPqV9h?}{sw~Wp!!wFP`Am$4AHphlpis0s;Dvrj911LfeLn0oKl4G-Ym4Ow
z7U1T+#bG=H2Es}ktlVs2HY6{T<sKAln)@~ntz$#hqs~7X%n<=3aUITO5Lq9wEnZ#i
zP@EJj2Mwg6v_Jh?QHxcfv5VGZ!TY3&cmG2F2&{sNmNIy;`<j}ON=izDxf)t*p#WzS
z7b{5GfC0LIH=Yz*czTq+wx9)`VG6q7-l2h4#sGW>?z2!OE)4Cua#0z`g%p~!-Sob|
zssxZLK99Ov+K=|07a3ii!>nqIpRn_?0nxF>kB--M-9oF_7Tn5;^F$++C`j<bMO_Qx
z^Gf;-9Xa*W^wb`1@5VDUwCXCmKo^buSnV07D5KDZgtP})W&;J5F3?Lg<gG9P31Tbs
z`!VPCv0(b~u7%8S$Owh@lN(3LXj?&f%!QW<#Nq5$r|y9u`!@J5;xY^Adzv{SPIqdW
zK66ECYSdrcv1O)UU|z^&H5p34V(xJ1&9_<i;-u6IEW);(!5auY`56mx<2bx4Q3eMs
zuvWLFq$XAx-M#+>mI_>nR><iSW`uG)63HRQpz@WXI&}p8y?yIeGC(l(TqED@_YkhG
zGcz*}i1|vzX?tuMV%kS4y%;c{n^nGZ=QUQ5@i`ft&=kmngM(CMH=yT18TzjwG@d(m
zE<QQ=s^w4#5~~pC?Z#&X6M<whL{g0N1Qp~j)w^y|Qc`A6|6JGw{k+PpTQ6FwiAiv7
zf_z7kq7w-VKY9E1Ew0bPF-8ZHBztIao_bC$_R}mKT$Bl58)mhf`1riO8A!vGVN6KC
ziEFzWScisx!FmC=&sI_BbV8zK$QQ8&-xi(iJv^RfP6fk*s9D?+2dVRklP3cK5ZKs!
zLV^Lrk`yo!1cgLDqJfMUI4Nkd&<nnL@9S=f6$Jm%^%97fP|L+TpKyEj&zz!`Sr{3m
zkh*!k7LpR|VmE${?478)^88CI6!qnxTM}Df`SIh&JjZ$UmtJ1m_H)Q#6RG-hZ{T7$
z2HDlxEB+hy7FZg{kbt^R{Qh0H!vFmF^9e;oMHaF_Pt;qpu%x{bn~`S_DP{sG-5gk<
zz1-OeL#`;~w2%%fb`5HXijRHiMPcch>K(DL5(t1mpsw>wOU#|$36}nwH*XMJ3XUrd
zaPmXQgzmvi8}d*?Zk{?dWrF0TAp2Hzb1Q@MN`A001q8l+eQXF0F~GIgQy6G=W_)__
z;>DCVdwDB_<kj!b$(jHD`{suaA7%!NbHz5kADSmr=;-O`iCye5;4%epDCDtI^ytx}
z&Unn(FgZqe`ZORvpoe5H0d6w`9ntHXy)QpNvJQ|Rhg`s6aae1-Av6^7C_rNX$e9oX
z3<)r<>d}%53oI>!<OdHjHQRuQ;WeoJ^R~3~bsioS#y|`>w9eQY&sX89Gq6U`ESfD7
z%sl=)S8f#6ot8GeDT3b|U>q`5z_DK|E4$*P$umHCuY=vqfA;LzGA*)8;m`vl&6x)6
zxDxy}faT6~HEukd!m<S?-E;yfwN@lBEqlP}GstVBBl{r>1l(Y7PQh8XTWw(<|IjX)
zM;iH;-88{Iht}NfxFPPyF<chmq&zj#n;nN>V#^5m2)<30L?5%B%nT?C8Kg!<K5(Y%
zu;7~%EEsI&=HlXNaSw*Wg`OWd-44&S+&QcPfkdgoW0x!VTW!`eZRxsF^RQ+}?2KEY
z5$>bF5;DNxEO66S>7c$hgYzd2`!x{W|EA$$B7g~?CG$d_99-r{vZE4b^!_0|P;S*$
zh31fxx<J8+A)(8sS+<T0g;n@Xy?FKj!e9WC{{H>D+({BZ_|#)@2k&<Nwyn?X(6C7t
z!*=ZGrJn)+IBd@rh@kmqT{1duOD~bug#A0l{k~%vANba$XwlN-O4MFnIG-gm(3YKg
zV>I0Vz&;ep<Hm2$g!)eLJF@;~ynW*~-LK_DortL%xjOUba+5Act%-pc!=HD4KjJxV
zU<V*4zD*}p4h|FC!bcc+0Qv86y-k~a!?m|=eoy)xQ*%G|om}EavO^ML{|aA;J`~N@
zrUJ1P4wTN1e8T__9+NoN^b0tF9UVUYtK<g6qt5Rc{D151a~j|okPYKPT*v@pzQ)2L
z2kx*7T3Qo;@ls$5O-q(+9D|;z&_0W9XvNQ+n{7|<ZZyjM@#BXZU$pqvt);(II)6XQ
zZ9Pc65QdJHOga&y8seEC*f58lTqJ7-kx&Xm{R{Vk!jO#Bf=@82nPuC8F9QB3)~ra!
zT-E+<v7V16Us7nd0+t@L5#WK%PABH}@Y=O&wn9-k>KO>vxZ15@Jze|m3KC|$g)}ee
za{lUqM4_tc!BHeZZiK->MwbkImDfA=z_mg|H2e1Y3^F=6=0WqE-oya7vbREiTsrxi
zh>GpCDb_@CO*GQ3t%hsiKS563^GUCgOVlX0FHf)X$Y~lrWPXCH9^ErAhzCwI{m@Y|
zgMkOIe<0idD;Eo4%zUi&9Xx0V%o2>?eF_O-JK&Q+@F^e`0m<#krlh0@R);I&C!STG
zdg4v>4|jf*520V-60*2xNUjgAaXQXDYzpIM>d*(yt__@9zRk!@1Svpzs^*tFFb^ac
z<Oy%2d???U>bTI&0CRva(?`ka?H31tE?r+3N<**}V8c4T%*YvD3Lc}+8oxKUM0Pf0
z!5Aet=0F7;@xirwfXxt65^@kb_G9nid5}nU!TUC>V-HY`IieoO917UP!upQ^*=t+e
z2}~fSJ>asW{m<_=kn{j*@o{sGr4U5<tcTOV#op&OYP^bj|Mc14|2QPiN29=9;o>i_
zs-G+JZ;O*ogHsVwA7+09Uu*zK;>?*3^r9|PaE?p}x(QmF2S$Y*X5z`kHY9+}#eLDW
z5vK?83@8rB@JxF$wSe>rfw%$T98%BN42{AIYK;;%e(yht?8iV<Vw8$)$9LMXkhU{m
z5H&_Y)3-Ngxf!B6EQia~aH~+bPXVci+B&~Q?_CHO#C!4BTG{{VGWQ@M!?Z(?!p}_p
z9I2${w~D0ZHH-g?LFQK_q2W>X9h@J%X!v|K;LfFuCqM|I$BQ@~!4SRCMzOJF>Mwq7
zPR2M<+#v>o`fEE+{Fm>!q@|(~6fbUegW?-t6rRUFzR?bh1#tt1LVr<F@vUq4Ze-@o
zmnVvkPCYH;Jal;c{y`U@-1{=q)$acGLEqUSs00V`(QH7Y?c3L{YkQUe#CE_VA`u>-
zLJIJI%*u&Az_J0&0!6Qa;}iDL_11Tk;9R?3Bo6&HZ~~LpwRNgaojb?ys~H4!*8+$m
zX=u6(MB{<v-W2nI8N|hdIBy29_FW)n2fep2!@=t&t+7wh(-ov$>jKMiXwP&b&bGXN
zI`|~mvm6ptglp?y|Mj?^bzlUr_Gk<ebMj$ev;#@;@t|4|vn7dha^AxsiDNdNA3H?I
z)|bYZzF7iLZPbSaj1AS4>xA7cZlg>5+6=L>5aw5YUo{4TYcgO}Ssa{zLWY1MP;ljz
zcBXest3c8KCw9al7!v9L3;G%b7S}K?aF#<Juz%o<FaRn-&z(IR2Mj~p=f?=)0(%12
zWYht=#Ro{92iz}2fl|kLCHQ-yGfQ0mh^^%DUqVULIbm!3S_l+GnBv-!A-_q1cnlmk
z7v(9gFu7EvgtyU8ejEn}Gy_fO4i|fn@{u(`8;CQr_V_oYezt&k*0yG|D3a5qr4U#g
zCFEc~)q1%rL*s!|+&zFeVAHBFa40AwyAh`s-#?A~9VsNT^*JbcEP6S#%H7o9T(7(S
z^r9-DL_%OacswY#Kwc0CEyG9QzeYk<fSFw&i#<<A#|mRbPRD~h|E;etuu_#}Z_{qQ
z`3uP<pxp8S#GvdjeArywaJW<|IJ&DsLM0NDGq9|Yf1IDtg`7`4`_an+YeFWlki0;j
z0mpe4=IsPG(#!-e2_^M6eUMO?|NQ>NS&R`Vz)CN2r_iOMcMapq8*oNd%}OU0vI;4G
z`V4Rb#(n%i9i65V>KuIxRAzla%d_f8<roQVFe1ld%`He3888UwghE1rC2Z7KFj#_?
ztTd9Z>H`UEGS&LQ1x^t(;NiLq=TPjw_E^efBuFs3GaX6(Xj7PwKE_x@aI;WTGahJx
zn$-OdHxDBX#ltT~?*RY?CFbK|O*RPgHvy|QXYay!e0jFxGV_DQ%CKaqTBQYGIP}PY
zXfQou{`>uIaI3xavT)ln{TedjI>gHu%hDUqj}#XI#cLQPXeR}>XqQ-{<2ujFfJQ$#
zt8f7f3NT{ekX2~qNI>OCZT2qA054E5?jU0Yd>^T#tn2_r0IZ>GizpHKuRHa{e;#Tl
z%sJR|xWF>PwG_xC`hjm%RnyS@3@KTh5rnQ1=$r&Xl$G`2Ckg1ZB@9|t>@3S#f3152
zv>Euw0OXWM%>ivFGlF9eg$<IWjbDZ4OB3Kd!o{)WU}qoBGAWqA$p>P-|9Zeim`Px;
z33x3y62RXQ-22=xy|AN2qC2pKNVQ{|q~pF8%s*m)d;flaA3!Tg2}oXTElJ?_8b)lA
zAycR3(DnSqrmCbUF@QeuJ{Y*L<6P^@lN4?juKeGhu(`<A_{o!^8a^66ivTo~z>vJ4
zVGokL(n4}tf$#DHVW0w&{qp4twUB-CsUwnpmcR-iX9r?zd-9sE0&4+`WlVgYTGv09
z^&EtWE4Lkg>ndD|UWCJ>&=`OVg*AM@&DmIke6|IwU>!u!CcqaU4&od&b-4L9R5FWZ
zg(0bJ(vN^@8wLj(d_0iyAKdf3r8(^n`-czTyAA+*(H8^NVyNt3+M)Y-B-`HyW17}A
zBQJo9hsw}CpAMTooZmV+Ngv9;XoQq21Fu}e2X+>MjuLThpZ$H|Sb<~96W?KBhqQ2i
zv#}`vE7X!l0y@Fg8c2+<`WM+k8}M{z&Ws>e2Zi`G28P?fXV<N8BBaNvG_Egj)u6P?
zEEw#I@1FZyFDDpr$Ea9tUpkaE#|1N=amAyz-B0OoMl_wM|9Rt8(5}T+guIQ7O(MA-
zy3S;EhCAw}K37=Iw}lLeVyoz8(Nf|{Q?&_?(Hl_#fuh$R>%?^`Cgg5t?;g9(bL9Eu
z*eneo*>Cz_ztQ~l7v&-a<^<3}4||)oHC<gSHw<ZW`xf;#WMIQ<Qbh;eRbIf>Ip%Ui
zG`+MH-MxJEYHeY*Fm3s4LkkoE3{SMW-c#&h4OldFYcsxdY|Qmu3z6sPV{yiWA@5g5
zifyfrp?(Txd8pgZbk;?A0*#q3<wqN4N&<{JG|<;1rNHif83X?ECnGHsxmCcOw7))c
zMFpr2k&DK;edvVU_P{6~{=-l#9f)`VZV9PO(i4N(v_KZJ-ejkFcw7b(LbR(pDGlTG
zxgdMb>)s_Ww4A{|T|EbhfIJM}3~8u7zwy*Ym;!RlSej<hyH;DGT7?pI`qZb6#8gbo
z-MDe$#W^piQB0klv?7ar5nJ`BF37W4(l9MT3asVCsI*U|w?RY}fg)M}$_t2O<IIH%
zjFKSP5V@LiEUqpF%DgN++!D+0NhDCFh~K3pOPCm*Nz~<wFs!!~r=aAwzj5P+nzr46
z<-T+uskoL3QlA{7pnHED_Ptyp3-R#N<u7?j+r_jVFUzN2czMk-?rBn^_BzW-N&zN>
zy1LHwW(&D(eA*_#Ibb9n_bqz}wlhBY(cC~qJ6RiR25?jWI)~#Mcp-YELak#{h;NES
zNHLUqMYuZ39{SW)898_udEKr{YpIR<_z}T_89Ei(P|<H|YulRDA*BWc{0ShdZ^#O2
z$bIYY9*=whg6^C^2*h0G%a?C}8*CuPG$pizh&e527Fgl}JL=W{IDB|qKqChzn{W=j
zXEp9H^)hmDjSnmi>)nxAUif<9BkU%@lOuvlN7K5($6bVI^>9{ueR&`!c=P6(*gSXd
z`C}_F9b)M+#Umxosd`v$TLD^_Db+N!_9pR&?s3~|oJ%E&+dO?1!|rd@??fsXhs=g_
z9F69W`1b8qA6FyDZ1^xIe7Ful<|iY!N^&cDwnydMzy0ygg_F3zLlfnfpkR`Ds-?~$
z&AP&RLm5;Dh>l1NJ+uP{#Vwe+ZGcF~>!2Y{&&<p$#Pwbh_n#AOi9;QO8{xSFlH=o9
zPhS2#ZyaY$`g*x`<d~<EM!&o9%;_H1U3Ob*$7m0$gv2AVCwxp>d$M#w<NlPfG2q`^
zNTj%mEjV=PlC!&0&ZDXmyw<~-kY$CUp<E`~v4<RTR#Sy?0@pO$oT*^1JvUIOh~shF
z_{p-}ZvFS!vuC?<jTJHNP-Yrp5U&ru&V(s>vh;to_uWxdp6`}uG`W^Y5@QrZiFyP@
z1wlaRMq@eBR0O1}AiYWN2aP2vMGr_3;iw=82uPQD5{)3eNpB*(*F!I}-$T52X5IPC
z{4u{fcg>oc<sVr~JbdT--tQ@U@8{W9(paHo3^^exY5_H$Yrmd6DIg+J*)9uO7I0*D
zn|Zdrg5tNHjisV^qG|rcch9POQbYzFvd$;{wS5aMWXMi38R6mQz<obk&TiXqD!`W0
zUr$s<=Wtf@%cG}{%8$*M34Uz*m)}>VAm>{*uWUcgx%YrXyU6^g`cXBr*N_`oq-s8*
ze_rZt2$77;_0V(r!612cvHNv|tzGYI>89(RaGK!|66w;_Jn04ViKKU))T51q4jNxg
z=zgrSw>@%W)1EcPdbFi=j*?$p;{Ltcb}G_e<?@)FvR{A{rx#lx_3O%pT}CAhNx2iI
z=kBcdq+HzJ+PrJ$PSUdwoC#muUT!;}H~i04El!DMkZ4Vf-*StNi1SGB`0Ur?vf5e2
zTl5lpK89SL0Cr*34G%SE?IwlUq~yuvH=slB>HeSAed3gzagwG`enlXz2olye0A(-A
zJ)Zrt76pg<_czG4r@>s(77?EM{gnzbDll*u_)tw;2$t_I8Na``38D#w$c2J}0)oj>
zda)mAgqpqlTRt$3&d6`#ppDG58Z~?En~tPO22@2Ea7vGOk`5bMjh7G`V6W*NC{<V}
zkj%hyBsC72wq$3Z{=IPV^0H=YTJ=VIrVn4JVBf*9h!b~RM7UNy`?SH+%W`q$<y#R*
z%|tuIs+1QpLE))kc;*z!S0r(v=RM2U;Wm8dFUH6A(+cPAJmlZ375{MW&jPt$efWKq
z-PgEp<)l|7MN?hkaSX<&p>8LV5rQZ>3+`hN1>j@${I$yN*s&W};P#vdG=RjTFg%ZD
zx;oWNoi*I5hX^s*ScmST3aoEfR2p!(Dzuse5@5dnqCI>6%QMPTB)m8*h&fBdFYTmR
zMs63i={W6<S)@L2hL>-C`SCrgPSQB7r$;W!U_&zy1+bZZ<O+B_rhk!}ANh?yu^<!F
z=^vw%c?nz8+P3_SA^yGFy0JVVd+Z*CV4MxFFaJhxVlk7-UkRcLbehGg)iYb1pP@&9
zf1H(Co_$9hykk^gg*&hj?M7_qR-;c~=E1PB3(B%U3QkaI$nw-be$C<U6MiU1ny-cY
zX7`<U4=uJ5;y|NM&dA@x4Gr$$f4vsVoumN@6pVvbtq<H}Em|L$wBCyFJCt4kBmqU8
zg-NZQHYRxb^ax~o=Z$|iD;%HrKC7-ITP=w+2mLAmfC&u@fF-y=A?V;Q=s6OYi<v~l
zRIBT%NK^{wH^YHdzVuj_9cn21_&3nU;VH+7>qSY9{f(I$cx7LKU^fBE3`x>sQXX2`
z_Q$200!=_mT8>*XLF5-8bXCw2v#{-=4>1b>Zv|~>P7hDHsDI;^qtV23!>xjfqrd|H
zfwSw3R@;9lvtN)kP_IJz9JH+;pPyK#lkId4)nPg4p>WXs(B2Wn7AZPOBD!_+=Jd`V
z|9*UbD87y5D(3<o?Y%|#7+&IPv}192<D;*<zel)sG<m>mhL}qpB~K(&2sjLSITiqG
zEQluxW4YQpYYf<De{%hk-hFt<QDKy0wd`~6t%qH-i}s&IfH(oFAEeqXSqHEmK!xma
z9**&DJ<QM8{j^H;)x~qIZsXqAX}<2(?c05XKLHnoDrT=9Cwqeq2S-N;nt~F;qKT`i
znnaFvKqr%Qgq6_z?7Q>s+{b4>ot~B0bZesfr_<fztL!oX=?b7YBtks`_W+NE1Owq>
z`FgEYrolS`sOi8TJ4t(1pDJk!=qZplxnoK837#F_P0t5-22g;p6Q~Fb-Z``ZV%I+Z
ziuFsR<sIvO&B5WYW<Pe@FYg5)H}q({SDxH?eN_J7mx;_z?DE@H)JkkixhEi+VBU~I
zMTdiuR<Q3ZN9*pVsLP3nv99jodx*7gx#*_p1Ylo4&HY%~ISHEq4-@DLlB^(nqpsMt
z<?H8QfNaN^29z;0U*qjDKYHq0URiCx%jZ!;Suz(K@a|C&V<zib+N_PyN@wC*5UC&f
z1u=j*MD6FpBSC-R{%<H9codD}x7VKop7aH6LZmDcUwmSE)M#J|o1C~MY$5=N?nbGM
zXM5(%zP7}Fg#S75jN=~@fRvpQaG**q=)0nC3vS5L79{Jq`WjMW3j+?sLJAX&SC%xg
z$?m1L_hlp`G<NLRLGYTyweLWf63z-p)YZnXbKC7#HuTyN#6Xou7c*=~_fI4==W)?M
z>rBWQMYV$hkXz0pTZthXht9+V8)i`B(n%s&Wp~bT`RM<MMnN6~j4CLF2@K!#;N?F<
zrB;Bv1={qhYP_}vo_c|u*Sj`0S)1!VM`RZ$0@Wd5-mqQzB~f0SJ9kdwhuYp$f9R54
z;bQyI=Bfi(!PAN)!ZxIykGOL(c|j`Q_DpyLBBtV{C3-fZN<|)Q5=#Im6^nB~VkDu6
z=-@uVt{%{Sa~5~G2A7q*?Qpw<(W}Hz;Uyy+e{8~u#>+?2;OF*sa(GE?37S(41fxIx
z8?Jlz_N^$|W}=xzl|Qy6oM^u^#6!GS*^#h2XeY_SdSpdtJR*QX?WD$NXC%40EP;F#
zH7wpj+W4W();RE~Xx+)HhI<MO<2)$gdj_gX%Qx`TNgz-G6w;5Y1aF`|K4j!WAQJGM
zB*7t1Bafi3DF>QBURpp90>Z)-?!~vi-I`+2D1PF^iC!Rvz^=*9;l?9YivijNo8X%W
zM*ln#R4w8RgkX>S{Ug8}>d{3ln~s20V;=23a^<go`IhHMxz77P5ZzFO(<7kwHN{CM
zFab)Vxp~s^;<$VB%IV?`7k$)2Ou_s9G27XeT8R&CW~Jf<B!Ux@0a790AQC<h{}xE?
z<Zx>O`M|O;85d02t^0BA<+*Umi`~I868L-~azwKb3p8W3%g_TkWF7Kk32BN|Rz%ti
zI^J$_KqFMfRSmFqL#}%kO36r&!2O6=w!B6Mw|oQ#tn<eD7d*EbfZ?$OheMXoWqh}M
zd(VD{B71g<xfDl~Jakd4t5ldny(;E1VT=%>L_!jf3qY9yV4}cG60rw%r1{|dq}cVI
zpSsrDDFXN{3dq2|BS$VGDB-n&NIJL0ZCsJ?&iV>yMhH^};nIG52Iy}YXdFlzP9ciY
z)snSJp_09zn$Lo3OQJ9#Mu?MjpM8ncuy*ZQ8-^ZmwFzW*d3skFV>q#O;^u<)-8sAN
zGKM36Idf^zmGE777Xb+YJb8%nV3%J(1TuL)0@sX>jDWxy{6r=B_MnW7Bpgr;&?BJQ
zGzgivV+Byc1DTRSMaodY$%7F0bBmF9NOh#*R+LPR^h()nndd-=6Og6{fI#(u^rq*+
z`gu-Yvibuf$P%n;_lO58BwgI^0{JLnNh+FzsaFklMAuc-`w_F_<cs^d%fd{79)WkZ
zY2l(vswXz7x#b^$?%KDY>H1gSoS_q~RYymM+msZr=#{%K5x8`3oR7^f5w*cXjp+$^
zz5!Qh2usb$LCo2{eZKA;KW2eK#aG?E@X%bT+fI_mxT)tEvhHhtlp8ApJTe;sahha(
z+FeM^Dd2OCG>W4rtOAe>+_hctZ@BqrZba>#(l;Z`;C?2s1Jl4HMAlD&kB_fvr7IV+
z(yxTe_A-cor+^4ph@x4W8|Ls(l6B05!m-%P)r}P1glh3uQ`^b3DeR`2jQVaptVF^l
z1mjKJMu`_GR6N^~sh~>UnZJlfMx!{AD|T2mXVtbZHeI11C+>dnsO`fv50?K;kr;@=
z4myK}32)9><MiVo?xW6<-)iBv`1MPKdmney8E@pSFCS)(`9a`EcC~wmWaHXrwk(^x
z(FiJqDb`^Vr^28nHPpN$)UG}&Y23SX#w6KAIaKlmM#ewpl<OiAHM9JMwBJ0i@LE=G
zA>LE=s^s)$15&OZ3K2D-KMxNVL$i)d+J_S_5vJ#%4fQ7-xbKX4qyGUdRG7#}-BJ!z
zoCR)ar`L#`sStonwS$UzK#4i{qX^123^({uh?Egpp<vl_){>zF3n1zd>J?S$@|S74
zAQF&vFjygTFs+g37)Yu>D6B!e5J^=|sG7%lG|;?MR#gi^<Ipy(R8v)=t42hG8lBPN
zF=v5SKp8L~5#rm8bx?hUyZp74lz)#ZJ5%aAaONTpq_IF6F90_}`g#HM`;F1>NSn~+
zyZJsk=VBuv^Zj4}f&hhz;=f3JfQx9d>5Hc&aYI$%Lva_r2tPpruwPI3ofIAq-kuor
zSiP8{A}=BeI@tL<2h}G4wE%(u`Hm1}G^J`Se?!j@Rn!qE`uX<8l_oV?Iu6S#YkMGR
zuO5=azaVJ|p_|AnLByTfaOZeJ?rco{WPP=Fnr2)Blg6arwLG)hr~4${l7|$Ml}Rrq
zaN*@A-QUmB1@&?VRF}{ZVc9mcQ}t=sC+9~Y0?yUjyHU2QgGa<>&Syk6gbJ7*0cq>r
z#??Qx@#@MnBA$?N3TFwTaJlpD*}~kE8BQZuKM`*4dw1_vKu>BL%KbM&+q9mH0N_0U
zM=L*MkV2VyeiMnH5-<9IUn1y{J$o&>68W*lT28v=o|11Ys24DtQ`G&Hyocf;z?2!n
z3+v*Ok5LRs6}iJ_5&~WuSL_UxqsbO6-n_YrK+$wNycFFBj5)#Ku;oD}W!10=0jz*B
z`1~i?a|d;!=1L5c$=^Yu)`O7H0I56~ejqAnBnYB51ODs&{l4V~@W~P38d>cT1#jsY
zfDGp~ZNQ&J(<7if=*2G53Z&|$eDYx+d>>2N$AEDd@|aJp)vY2b8c<GUGU23F0<b}F
zU`j7`d5&%=LKQ<KHa*y(O|LYR71Bym0Eyx6FRoG_2<6nj5kQV(bGTvTc5b!Ge%L<R
zd!Nq(0d%PQKsZp6Scq_j5-W>lAs)nTR%KL1sYsketFB9ne+uUO0F_(#%1uJuGGRDU
zAHeclnl{gMhB6sW5#r6)^0z_iLHf+_r%!*@v;X^P{Rrhl13}3%0Y`(99`Kk)e_dOb
zs895s3($S4Lmq|Oj6ZMRzWovC1PGITd-oQ&7h@x5#NxtSKUi>8bcOf{Ajms+?Mii@
zb08cnSf24M`A{83lYx-*CY$^Ckz>b>O`sK!Kclt(HTHPQ$_%GjG{yk!Bry&Gdx)FC
zz7q0?pwxsVvXx#$g97Fc`$G_XkO-m7D!rgQhs3Ue0*usdq$&k~kDd{cs|p+jsHHeu
z-^YI2y%<XQdi|D9x(7!==Z|D6ckr%O{yRu=RvP^xhc|iX{S+>Zq-k<I2+;wC{%534
zJlYsQ+Ai2EpMucUj8bsO1GzT=L<5n=oX2T|Mu$|wMCC`$Im+22ozo+bftTZJza&a0
z#0)<a=D6NiqGwpb68Y|NTk$RH8{S+CC*cV)q9Q11@a_>-`-;9kT87s=zP|VFRYJU7
zjUgQ}c7xX*2NP&}Gaq6?W4cu>4h8`<P;{Y<i#dL01EJxGARlz_bcY+j=)F|b^=FaL
z-7ERW?(AJ2vF6yL2IeyYx4*L?9<*%m4^doX`T!Vc6<Q~oL4JHbQI1_V<AzO#85CX4
zmW<rr8T<hH%2?AlK&1NtUOn|jE~}fOgOWD|O*c;?5v`&%hWxcyEX>aN?!rlaC3tIq
z75e}k^qiBE?-!K;rZAr<MY#qzxtM(SqwE*)1IM&VV&fpuIQl@hfgU;<BB~SH&L^Ju
zBE|WG@fPGaGC+ocZ)ckI&iVU4d;vHXF93Ze{40Ka4-VylKI?m8|N6;?gKzAA^9`Zb
zGX&|szkUDraLPYT@Um)?{oki*54VN9c)3uC+yS6kf0<LfSFhUHlCk`k@BQnyyh_UZ
z^nAmvGHpTib9Xp?X6#wsEy>~OdFkL5XhAq;{=3PUzJony&R?R~@AJ35H-G3mr+?K0
zsYH1kHW{M5yC>--9lTgB#x<km@$;vVw7>iJ|6EDWf2SfknkeElaw)i^OF@s6!{@?p
zZ+Gtao(w$*2JUNY$FPGC+FJ<wj(N?Ny+AU+J1$9EbW5{9aNCK$f)MeT*ZSO(UrxaL
zd;cqB0+Dmin{4Sjy|rIo_x>W1y!V1kNTL12AGG{{>8fw6UPqzgm)8=!?yv7xUibai
zAKo24>iaPW(oCFtF<iSM&?venCPALB{g#e!NrAu?Zyu&BM^=83P5t~E#3HBZN>7gL
zh^h+KZ^VKIB>r>vP-jALgLWttWSw^YG|)CNw`pr6S$QZGGa+UGa7WR}b(4_SimK|3
zR}BqCyeTjjH$x!Pn7sN*V*!gAjG+o8Xx7f1I|&|$k}I=O7gRg+(`V5lUroG-rj;LE
z;I77N7UCh|Af++Tnc<5vmw+PnVAuB#lKucgf`ctNTJ#HO0~Au<)V|Vaw+sp7E=Rpm
ziGB+Vtjw)jx1v!5A({LdX?v_<tw7)3$J+Dg1g7^+A<C3o*yjQP1|-<`$@CE!0hhqo
zqe;`7Ymec96q(PtO)>&MM_|7O@WZ6RF`-$eKG?S54w6>>`;Aro2QXc!tt`X35E(HG
zoI3?g0>%ucV$MFhfx?m`pQ%NZJ*G$^V8s*cus{52FQJOi_9{i@V+J)GRIO>-+gl`+
zL*<zyvl&oj?@ZAanv&SlP^G9aaM=6x8ItyAkk_j*uSiB(0DV>9gh+o8A90ABW}Ik3
z%vQK6X*q!@A2YD`IDXj(PINy%R>dh^DxHk{K!&}$i1^7=V6iqygUA$nfSlkskts*e
z)zBKjWk?C*wzKou=(!_7ibLNjKlHd3kus11f1=|gZIjt59FM|YI{}b4@DO`QA<k*r
zjef)9PyyXh3;84gD!RvDK2>lf(HJ|Th-Nd(eYv4-S~H1%pV@DX3YCmMDtC)mG>DSI
z5TGA^<UG)9Tt2Rc40Z$s1@R<oTg$2bH<I-Lctr%D&Q_PsN9vT`r9{W!a}EKloIjYZ
zliG(e9zT8ht7V1ej=$FNe_k7SZ)MBTv-e^(qb=I%3N;Rd3D9kP<b5y3<;$m@GI?bC
zW2REoS8EGbM*p<wh1hDL*ur0u{3`|e+O7mWSXZDuIWV3dw4<Py5^Nxs+#TvBYj*WO
zkRx+=&UJD;+f{B3&KbFWVSi@Fw$8@27V^u@6iBn8l@;UE<gKi%h_^v@+qYJy>GM@`
z3ssiBt(o>_Sb~&(759{Fp+*+OV<}^FwJGs_Bc4Ip?yPEO81IY`2eSiKts$`Do1bAU
zIfsq;M|tUu&kDiisuH_d7})S5(G{c*XNjpLwk3vUTtIGaE__lpN%Xx8Pr{>{Z)7es
z-n7Uaw6H2d;SdxhGicH~Fu-F~WTrd*xCs-#RJyWzpUTFXMy?IKr%%u0@c2%Z04fV5
zi{QyM!A}&6bH-j7$?PL8X0~;g?<l%9ri8KACVC#P#HJ!qkHI#VoUp~k#q3^1t20>a
zX!)k?HrYq-s<WiLG@;DDFc)9@prsnBWtNwZPZxHT8_mwn^65<3C%Ed+J1~zLW@$Uw
zyKUGh|0dJk-o6vnp)#E;T*IAFsUb3HB{Lc-dnNipCf}r86EIFHT|K8yRRevJe7a&k
zdv7ks5re_((vkSUz&Ab5?-K)*LEC0nWc2zix+4x!^GaW~R0F~qq!hp}GqKTo%3;Cr
zukTM$sKnc3`&rjX!|t@HlyQYNeIE(K%1$7RigdiL`jwr;{$hMGohgH2tq|Z;eq&{8
zD}z!GvCp3Yxgo^0gc`O!FSvPmw=xf179Ki2dah0e1{6|-V9`yAwrz<Rv5Di$dDa@=
z$C{0xF$VU3rZ|IPqI)$TwK5vX>1Ku=?r3D?xy6Z8cM4A0OL#>$_)&(P%Uo@s&+LB3
zyJ!u^6kY=qL+=~MAoSw9yLG+V@=ifKjQHzjIl5&ilLMKs8!00`Da2rb?EluvWQH1@
z1QgH8YId?pqT!?}N2U(`aGjM)46q7ukcTEFC%Ym;EDO<qh<J!QC)XF3m!EyOFeJUp
z)#$sD0X{Za;i=c#-(Lh$2-loqg{JFjqX(?*f+%p+(~3-Oi8uOLSrz4HlkKNl+ny3{
zP3l;MuZJk0JY5)8ydK^9Jzs&ouZ)@jQ^@Jhb##gz)|THlXVm<_7TNzHNN$xhzH$-z
zul#pCYIV*k@5mw|Vl|lA+Fxy&%3hDo#}uCcqY-A7kdr2MLg^i(OnzR?AFM@qVcrs@
z?Fd*X%R*nWRC>IaQb25rh(Ah0|DojNC-nM0UKeFJ6H@7Nv*B`u7(bOy%fjX)vc2du
zd%rL-WdML9lIw=tmqF&%$+9kNHA;8Tnws($o`Q3lOI64iF~BLPOO2r6$&ihBmKxqw
zi6|v01yZoNnlEC`7fpNf9EO5*U;LY$OAB~m_tv_%jE4nMh?`H8&Kw%6@z%vgnmm2}
z&e90)*i0GCWB#_U_sL!4F^#oM<<{<&G+eX1dwihny^zN>-RZZR4N>gQgfM490xYJ+
ziiFA-^92lYG}!hK$Sjd+u`lo^Awh^Z1(D4Oqetzd<mTVW6_caE3x_NcDbB}MpHHYw
zM&&*+HPsCZLNg8TIWu$x60m9$Ie73vH~P}(+I|wHzy>VNmn}&X1EomqDMycPNBG&{
z^kWHaD3)Yl$LjEueLASULT$Iv_w`Wd3BLJs10NiPE5WWjfHQV2tawN7Z$764F7ICD
zOB~<d16QtBR}X*0+QpgjJF@=7GRZk#s^uX7i%8uBBP`b=3lYcQJ9&tV6cUfZ=9+$-
zMB=wJ-^*Au37aW4!jE>7n<qP&=zA3`1+iR_?+dZi%7w2;*G9(luVllOot<3=SDDjE
zw%ZO_@CZlBbcKu|TgF_ITfbIoA$;^g02xJ87#lb_9Vs!iWllCD<SNr<tJq<?b)mVn
z1dE}95%!E=Kt|jR#s{rvQp&CkRhrcoHXJj`T1QF_&tN<BTb)=x)!5yxhRVd`=UEuj
zI3@Jwz@Vzp=)o)rtmHwA|H<iL&9EO3r7h00-7)9z)YlY|{aV^sXW$o~p5RgoinG>C
zCjn~-A-oN>(=akJYQb^rg4beyMA6iwboTUnp_YE{Rb+*hVupFt=G>86GkQz?w88Of
zDZYP?1?>5<uJil97cJfUVh!_UIqA+X8{)q#QvKo?bK=XA(tr06J?4(=<eZW(T*RF*
zwyCi}C(k2V%KHZaDtjrn_3wT0hsQszE^!E4QGA*Gi#?CS(c=_4(bgdm&*PpPF1JfQ
z+@CAIG~=Ff8rjoj(n$%~CnP2~3?^P{7V0a`dAEL5-Omc}eIGNSrLL|nIc4l>WF)4$
zmcKuJXvp-VE<A|kx-v60Gjq=Kh15C9wKPYXMkX+sax&YLWoz9VV@4$dDNv3V&Ci4n
z3hJNP?Q@^Xnha<?b$^1z+MOlMzi?ct;bO@I0-@UEaX0)5Gczku&Gfjp*wI$0Q!&$I
zu%PxxLh){StXe|4o$Cub2L~l%SM?KJqJBA6tceTl&dPaRqVCmBF-8|uW%E~5t9f%3
z@`!BoyKiV@6eBX2P}~1#&BMtH3AOwaMyf5@Qj(IIvOjH2)r=VlkHfA$y9+6;rl}1#
zJz~{+b{fZRw+-L6EjXv*GxqXnO7c%=*~`8{dVFCP$dQ?x%$+YUGP#2pYv#Zd3En&q
z9~--`zEh%?C$98DYT@{e#0IU4W~WY_)J#u(9wRLjWG<`zXv8f^qU6t*LT$Sw=U*Pp
z#px#Gjnd~$l~hW6!#EzPhiWYF2p>Fn){S>t$l)mFw63N;!U<PPyM&2&>_K}4;6<{}
z<hFXhkA8buOj`QM_3I@En!DyJDlS}}l{j)FXq)RqBLXRTWDg%cT%&I}H|b36N{xDI
zVvJ2aw1xg{JPMm+kBf49n}?NKH8x%-nIMY-np64pYSYYn&m_HcU#<8K{KKKMLcG7h
zH>bn1#!Nf}NT166*x;A8{WTMtTIJ2mTe2m*hxp6tJlv*7#nleU;37jtBx}tD6$M6y
z-hiK5zdmK8OU3)-PbrpWCy1!$_{J{V>ghs5DDvJ0ZROT{5D_J_TJd;z#fLwRn&~Zd
zd3!~y)f?Wx$*CN)dHn{p_RX)t!^1b`HEmR$;ET>nkVDJlzb&*=&P-4%?bWm_M@%HQ
zUMibnKP&N1e2e}GFXDfF#L<6dT>Fo`on^mqy#1mDV9ypm{ohC0|JW<~2Uqbtxp{R;
zCBXkNz^QV?qScmkvzIRYjApG6NCI)R@2;gS)J~ag1;?$wh@M5_tFOKy8JN^y#1;!R
z#G!Jw%+WLBa_OpauxX{Fe`p)V!W83Nvi1>cb2X7CLxEH)aZ#-5Uhg2*gD2tqwB&$&
zmR%T^EdgUk2gSJnm#w`u?CbT+Fu=#9Y91iANb<U4bRO)c)PUEeq@<*XsHi_2#--S0
z1qQ06;KfS|Je)h~y|Jbh1K3cegn-Q;mXjGHZ`TpqMzY44Hfcl)0QV!uJM-aG5=tlW
z6t?W=sY_R}*wf8$Ep^Nt4<(EF0|a#*qYgkhiKU{%W^1XFIUh(V0I|h587_Qz0qC&=
zN;zyNFeM!!k_!s6P5{Vd#Ekx9KkJnqQx?YX9~2I=7f?T?cPVh>2$i|EjMFJ4hKE3h
z)LUqv8YkJDQh-M8$mM3T#(GnN(+PS9WC;7U_mXPkk0$F6tID%<Yn{vbk60nFf{CEg
zQO(;_vh*gNZ-$E)Dv@KG*m0})fNd9d`CJk}7K;7~TRpv2%OWymML~^M?|*oYG^_*Z
z6fiJ<=uRaAV3o%HSQSCNH`Z+)wQf&Mw^h&RJ_`@mFYI<pi?ta{)r?C@<$YDqfE?U_
zu&$uydg_+Xl$vh_Qng6pplVDGjEq=5vzAMGn^-O~ESKcws{_>v**~dAm*FYVb;A5+
zjqO`vi`T_O7g{jPY#*zcC$EiZvMTs%Do!9L|H5Tmj)UskepoSqo&@ghSOBot<GX15
zb__E{{LUf<Va288F2=@tyie*EuBlRofwH!JwW^<>7LEbLEt4kI9zQ=0_g+sD2q{PP
z3+T;@YLC`jM5z%laL(1}gwIQChhBqk@yy5P!ez-?oP<T0hL-B;YE%*MPO?45WKE_;
z2N7$%1H87oWMqP!PAt_&=_V)}&t)GNC*K=6i-KZf`>u5kDTzTQny{wVlpf#{tIydz
zdFpQa6_Ic(^?5@QD^Q>4!LcXO!bd9e@bOG0?HEnX;LLcegyOo^v)tb6(~!FG-Go>&
zvvC5~?XV8{rf_X@bL5pslccG<-^7~ggDc$SUJar>7qRk9o09%`=zX|RAgYMFwvX>L
zUMOC15np{aMtJa>AlsO6`_2{I+qRW37U~(~wIyDoVdGD_6`YDkM(Qw+CRCCbtHb|1
zOShD*@AE8B?Qkpk3d6MH&p6%pVDt49@d74Ay`OCSxO!CFQ1*ri0|%Kb6DzqxlGxK@
z3lloU%vY$kjvqf{Z8>JGT55X+L+bQkC#AK<qTCy2lj{f0h|DQFlItixYgQ&;CLU|O
zI9{5@sW`2*Ni?KnP3mE87Nz=#N^EBW?a@%Wz>_Q)Lp;+|AAaBAhYx)&T%A3Lvg6sa
zu>{3$Y|}ctiq}C*{nO~k$cW|mjkbFwCVZ1dVoImBg;b@C*he2rxQ6%6d(C);K$(f8
z$}L;3yq2w6Zf@$Ctuazj(UsYk@Y#p1t@as;ng4OcrLR@nLqBuiGT3&Ya*OS9h5#^0
z7b4vGVjI{7e|E1P+0+6;!_Vthwd>qdXQ(2((es7b)%W!D=q7MB3vAO$k7Sol*doQX
zSiSqKLrh@=4TZ=2*a0aq?)xgY-kBO07#th?#>^vG<9d&~`R5FM-!$I~lqbQjTEG6L
zdw1|cpe-qwcx`izACG>Uum@#wvW0KjaNW7k{)<5WcmKw&m@NALu9!eSC1qsIkGemV
z+a+{OTOo4%E`MeE@ST#isEp+G1G^21MgrJ{61e1YMoCxYg|_>>P#dC>inHHpE}Hbz
zUTdEz+FY)N@Ya|gzV=}?4yAH(3Y`|26Y4HE7S&vwJ#1wV@q=E0ua}p>+;$7bL!n(-
zmy)L9wn@486<aC)Qk8-jvEDIY=~V_U@betU%g6OgtmzeXT(Y$Gt*sx39U_AI5bISZ
zw5WVktmCQJTXx2Ex-Id();4TWjuYBKV%Vh(k8M{>#J7z@cQsECpt*UI0rbcDG`{is
zdh=H*^P8aKpo(<XmyjRl7_d}YU5+40y8dx*E4N()pvgjqxsWNSAE#C%(f!1alF@d1
zE8l+by{?uytQtxI*7a64JZJYST`whI#<rfpgt4q4NG@mi{K~|Iti*a5XEeOTm@Nq$
z&S<~&!dM`)_>sD9FiP7ec9m2KoOyXM_J>?j3SMxOZ&XH~TsBS?@KeWVOT$Ygt2b*O
z?9`WA9Q8GV$S7;U0-~~vzL;1Wz?Oypy|J94*|NwN{se?5j}v0EK$m<TAHp4YT+-!#
zcw<wBn+RrRib<E*<kar1!Srw^JEvd4+uJDAUQKtlGEk}C%aq&-&*RNc);uMNq{(f(
z<eQvO+~_X4ykNoZK<&yKG2}-$Ht3M3&;H+H!T%~;|3g~g|C9pl-!<g_A6|uBT9)T=
zMc~h)?jJKV?+u5mN=iwogk0~AxapFE#?@>YO+bd^;<}$)VPG-b^D@uznqReULM`b<
zQ(cNxUyxF!a-jw$MngXFI*Uw6l{+X4>>SI6+Fki{?@LL>RvE$2GJ1IIiQNlL+s84v
z>1l4d<;E$NTDMf4We;l%ss>jnOtv_^j;Wj!C_CYIcUUWQlZ!9$>2)+h)x*I<j~TZV
zJ_Wn(*VD3(-yP=?$PKLN>F<}%-(E&l<y(L?0CJ^3@Oi*HMfso4o;nqCFtmSYs5D!q
zLHE&J+Z<g+xs{BJg@EB>zyd}2O{dk>W4^lvR7=6wbvUv`T=kGU<Ua~!f-5GnEiCpM
zKAxSMJM@)RmI=?LI<?Plf{J^xow)?@OM~QS9;Sdjw7&kPk-#1-mSRuYJp~gFu_%+a
zt!Kg?#!P6VJnD0u&{BQ-2dR$q;|02(DTqf&TztoJIjNQTUIe$eD*aSrEOw1V*go!4
zFd=@^6RI6sRs@;FJj^WKfhOSn)H&Dr(dFK~G3~%ES84td!%bI;4Ulm*&e|Fn{1Rh*
z@mo$#qf3{}<kRa9OFkBGE8Wh+Qyd^OYvJ^UyQ28RhYvS})Adj3(d*^Lt;9pPw$y#B
zv~b|up!Ugl>pK3afpq>br{{{*3ax+q@rUJ#iGTy4)Sprko|@F5#Q$3I)V<;EqsRRd
z+n>*8Kh3o_6ByG`g{=NbMHs)4urH@v3ra8}<VsSbXg|5|srj0@QgRN-j3NohjD1i-
z1RdNgQ!ez#umSr!ZVDe3{m3wwyc4WH>>Z+&S<>?G;Tdf`8-RscQ$6W_-gB!LM^Rhw
zB$7Y+!}m<=P#6fheu&6D&%5z*sH)s58?r4_P%xG{89UOv6omGpP+@H9LT5uriQ!c<
zxr=_OIRbhv3Tku4|6>j9Zf7UzZKxl(l<+oxP8gML4YQ`?SNGJlIZ6XFEs~9MR_a}=
zvtE{upY4>W&_)q-B}DaXr-wXWuAsVwV8ct*?u@U4<YUJ7vJH(q&wRhU*XHd10q*zz
zVVL=c80df9i~nTkg`gD0&<<D>dLM2HqAhmQO8@xdDDmsYPNG8?k#Xr`xn#j_3je)a
zzhXiVDuV}vlmXb+PRqi$gDn9Wu<@(_W1Nc<6B8*grow-oeb#Gy^q|xIAK(IryT-xA
z2s36sM%h5;){PaJWv67v9y$cVvJ>W`Tq}F@GO+ik%2?E<FFLC0`F+7Nh^L6!8!#Zn
zW}3j)L3uh3d0k4@1@^Bx^W9N?6Y-Flx0`bxPzWT!zn_6kvAU!8Al(EzU&<A?3=_nx
zD(A`?ML4=d@4&x*G9fJFQgNS7frCY@S2bHhbN1@W9tyS@DbV5JvY+MRUYSqqVyN_Q
zwmV(Q>th1}FOy0gHXqzdK-B`j9peUIX6Y2T=a`6ct9)GzowaK`)=*FbF<Wr3798d;
zYFSn#6(_mhPgQa+EG$H$<!uCL%?9ehKku&(pLEILZtaE5uj0#vOJb6emFB%a74`J?
zQd_rM4EOb!s)$v=4MgHu>nys{uH_tNtgOsVC*Vc@@@Q<RMayjk3a_`<X>5ze!HeFt
zJq82rjs^7kxxx!bVG83FGmnBdBZnRY=IwmNI>NhJL77Hdy1DqO@JVEP`eFG1yL8Ln
zyidkd+Pb^D_c9JLLfpdS8u3gOw%9f|R|jooCu#1`&__Y{;iUM&k@V1&RixJku5pZ6
z;u^u7U|Uf4Br<`!%EMFQ6h|mwg9_>>mI7HyzoI9LdJ=@zK#Xv7d^&014!MHM#pmYy
zHZwHc<l@fC4VXj^jy}om{4g#eDmf>YmTf#bx^Z?qLZ2SaX{Pc^)oV9uT&I5G%#G@T
zjg6X_B|k{|NC<9oO<AbE6G{)Cja*!5nsy{@xOe01;F~BuBk6QSK-TIxEZ!coeI5r=
z<A3^^@7BjIp`h}~?gMTPf;t|*ga|wii!UwxXr^4V<Udor#g>}%hPms|!(C$B-)tW)
z{gC}>ul&A4LOnftuN$*3U7ngR<k`m*UwXM_!MQKl@7dajrF~&FpPhe+3oum8YdRDF
zG(3Cs_vG;p<wrvV8faLf`}U@X)HUZMW8ILok8i(Nh-2J0>>RIjo9zfxd7@}~hgbO7
zAam4zb3}{q9CO;~Ozm3$Dl`6l#p^IBeMm%z<I;0Y=%L<jet*IpomL4juCQ0Ip&W)A
z8J~MG)WgIANtkL=rFd9T;_@tc35W$u<yEpm0_($C9zS2<sd3qg>D3T|M$FDZl6cz$
zHM;dtlZ-2#QwolA%7}LAgQ*BJ7~mgqoqOO69Y7~un-J2X8+%}D<n~`*DAdNw4R7V<
z=4Ljn^`G#MlHLs~YP@yrwLy32*l==iabFmwV6GY%OfKCs5R@oqJUkqtIDUhx298G#
z!M<K4gWwz%m`%?baDE8{XfPljg@2Knv2(*}T<i9w8@AB>Tq~_FhD25)OP(XhKIYoP
z*KZw^*7B0xmv(Xv5FkI*M`p5)E0)a`J}tx>J$L&}mg|7zb0(-f+!3P1ph(Pg_!+$t
zWQPZVjTo$ox<>FDddKapmO-!G05lMa-;WaY#HP?|84^9Kir6E8(GIY?Hdy{=fOeCM
zoBIrX{(sWOHZJ3bN5$T(X-gm`bLu+ws`21(iPI=#`i&Md=T2>V1Ql1dVl}(tz2M8?
zviX14n1YQ>&-#^p)rH?39li|J3o)v)K|`97#U3W~JY)Oq^3tw<1_$~7NdEmD@z#1z
zZ%@yM=O^xiCf(7Ag?pv6#j+&{Y7$s@!uMLl|9<?>#@5Bj_3PIYv!tbV+Ptqr;JY24
zk{Ju;uywcEO^tu^`5q6j0E_sZ^2&;9ms-0W9ULBSE`CjfIx~Oq%RZz?^uJ2B`E&K=
zl#HESnZSGIhlUp|=>`BJW)hZI%rtUX$xn}CLWDw<ovrYVWHOm0n^suA+<3I?H7b6G
zK<m?7!0RCB@S|uzn%m9b!T{fGpbWcu2M5Kvg1#rHN4{SF?c2A#vhKaCHk~!SO_rUu
z=NDqUMSAJST4T|?{H9Nzo(kuCVO`2MdHFi0PTjfo&C$k;Rm&a-|Cm<(@<@LLq3%gu
z%m=MAp{-Opk8QEAMf~mK@2wWQtPF8Pdj}?dC@MbE>93N6(NRjl<vZJIx=b%kVn6xJ
zboM7A3J8g9J9{>vHb4JKt&w5jJ{ZMke_AbcnhwoEdImSgX;Y}}>xXud^U4wAs{OYW
z@;~On(Qa}Cu|aul*TGaOojHA<`2p`qYHdCZ){$jI?nEiTJs2BuWV#mSi-(jD_|$nI
zF`rH$0v!fHsa0e>&;=1(2LmHR&`j?D-d{jw*>MyIYcj*qTVj1QO*KjT&Rsk2dfL_Q
z@j3j0{2A}oX>V%aV2oNU@wk_0ySdS(M#HF6qRd&Tz)<miqG}#WgIoydyW_Kl3ZdpB
zS`MhRq`M``3J4yDVGjcg1pCEH)o*!RoE*k_<pv4M6Psqa>EGl&K)5H<n~CC~7|TDO
zU>SWkxz~~Pl7>?Gtfna6qJu&Q)6NJZq)V<UFO~!b2l+&IrLia^JwT<#?$D<1xavez
z7<<VrBD(7Wqzu4sr}EC>2=52u(_=XoQb=x++kifA5#Uox+7imCheQyRfian}0V6pn
zvSADY#Hm>Kp7!$_A`eJ0S_{Hrbr^FxM2kqYQ#R1ocN4oX^17taj78E4r;$S*`nnJ3
zb8&Ii4-TW4A@`STn}-&MS-iDn<o0}*)uJJa4NKn;)AfTUW!<J__6ZqXkFz^=3sD^%
zr*7tpg_JC`a8`eQ6E-(-jjL;Qp4#57)f@_e9FH}+$#z`O)Z^p|3A{JnDMXWiJ;F7B
z@sk-t5QEx42x<fcigSIDu6VD0?%1P*fyugPx5;Ei2`Nq~64311VlPe?vVbx=eQ;dK
zknFi(%TOsK`mhi!Vm9^He?Oqao8F?3(g|Ho5%D}TFh9@4sDT?RR<DF;+nO+UNoXh~
zxrGTeakB=mxSGb)14d7-orJ`%wxbuKmFYV<P>yu9E-kd$BCmE@ElpY_W9ATKm0_jD
zpychySezki>x1bnE@s58@hh|Sk2=`jyFYldmvEY9SvH5~S8d#Q%;ZPt9p73x9Ve_F
zQS*^X*&_}vp52jIS*~v9Q=(3WKg86%*r{#yAN@Bzqs+8%(vIYMBaI7SCdVWI_MQkV
zIMJZKh+48FR+kl{jjENTxv2WW<BiXn@<~v!g<D)VtYXXxCh5UuMe#<$5Pube5+TVC
zv{yEpWTTCEOPtPAGs`E#2~sTqQ@K+)83Rd0HpRmE5m8b6pat;<lkOitQ;x?w=InzP
zYCbWQ+?(!r(CF<#jn#r0y@Oc90oUhrlDbg#%uSnDU_+;@xX_-WI#RSae0TIv?D}uN
z?SyfhDH1*wL`ZljCTz8QbD3EKPVBPoOrd(0k=&dHgy9d@6w#n3G=%go!L_-+vO;)f
z1ds)IL|M9l`4wBJJBREUKzW=|i{$FDp7}a1@>nM^e%#(O*&w3}LGedQ0R|}F)H1>X
z49y>H)gn%rM4#mzkFb|-0GRqf{Zd%i!hlgXmLR%wd*}II=CCb$=Aq5Ib_0uCc0T*E
zC&^{Iq0UkSdnk{$5;gf%Y{ei7KA`A<s8<GS#n}=${aU`pD#EN%aF|&B{pe5Lwfhy@
zmdQ9~DqiB9xMo&zJ1u2y)<=p<D~`3a*t)cp8Q%$Xy{gMJFMG&7K^~pV(u~{^Kf;%Z
zqeL+WJ4k4miKo2J{zOfFCa3uxhv>H*o`!1m&L`iNS}mHTHcYST#&c{+u;9UM)4&9Z
zWoB<=b7^KBpnCGUl9@N>Q(csqQD2&LZfLaSk+;{Y^5ev|HjMc$P5V;mM$+wE77OJj
z=BYKz#eOCq6{jn=R=g~trKLsNuHpohj``Zu3~s|4?Jh&h|MH6NNJM=(Z`>7Kjz9jy
z4Kg!tdLv9Unl7%c$(T|dWim+&pu0g2)|kK;#hJ?nHC+OM;W&A7+G$Hrq4^oKw$?4d
zWK)qY-LFyh*kNm(lzWrQpm`Al%VP)!&htjT<nbG3-wwq)xvwICDAgpe87pc&UGDlH
z<j|uT=^)J}n$#lGq8&c>IXJ$aqW=2J1^vlu+GXyuZ&=0a^0sKj8GX6bCTKe$d_^?L
z(m6GVciROObHMfM*XQSuP&_R9{Z*83<)p@4Y9z8fykf-X&{fB=TX8?BYrXsGdGWc1
zNe8k#P9K3v(-w0<Y-Johk->n+u+RbBcNa(ysSiNRSDpzfklnx7GQ7~Do8D+F8i{Ma
zd=wC?AQmrB3J5J>lJa}ZzrR|d2YcYv{P8n*95=c`(5o8L)F-sXnHk)&P$BnT`3#s=
zOl9OiW@msCIfJV7iM(J2g=hl4;*@?s#J$i9xay=szDP~NQ~b*OTR(}9v(rf2XGll7
zhSuu5D~LSxQn6a?Lzia=NH>+;Ei;$H2;QCmbZnjp#ElM-sH_l{#V2E%OWHzlO&zz2
zsX1meEDG}nB)3maria3fIO)l?JgzyNcNcUglL9*%%o0klbL?U3;$Um33flYMTKJNS
zo;hgq-rJCh*DFXOuV%~mN)E$~Mn@0rYoaYJ)I-^g-6w3a{q0z&(RR1aeR>6ONAR#1
zPfc*QKy=5eG}Qq)w;|oDb-~4o$QAjV#~xdx<UOm&ml~GNO+O^TrzMN6#c>P89ZoqU
zgkjLpcPR@ZQCrMWcK@|e*}PwDyLX(3z8Ok5YF=1tG`@mE7%S~?95+8`b=t@`v<DR8
zFI`Qy5Xs^(&1XI9Ioq#4ej`}lJa{w5!MP(}J=SQ$V38snm1PGmZSZEN6#rRd@1-%J
zrKH*{#bIz;{JXib@*2xLCX-1wKmxgJEisVwqhMzE5|%LD)v)#Rj*-IS7)UM)oA`~y
zs~}=Py)-Jfn4b-`rECxR7NG|mQ(OHKAC0=F*_h*&UZc_(^Bw&47%7|%R#pk_9lch#
zH>!gazrjSDfsg+r+aIpEA+>$>tldjiqVu1{!f7J8%H(dZGdf!308PV8Lv613;*y{H
zappL`K|$SLaQT^>1UoGT;ifC8a$s0MWAYu7O87RAU5rH5bMuA!GE$Or)f5XkS)-=r
zGSPq4!H~#=-oZ{V;#$$rCoJ{KEkscY^cJf125aqiMt{wYVGIM^a?QLNzv6Yp>+r}|
zh+3qN3s#mn<sBL$O5AjY<>2h_@Z!)goq`1_b;D9T!?PAka3f9A>qnN$hWcKfPVyVk
z5KqodqYiK8%T$Q)Eee*mkoYc7O#=BTP{%=o-a(3cd_;NIJwBL=`q@C7-hmm+0<e+w
zLpH|WFBe{4{u#;<p7ppK$9LZzrciZZ3_i@-ItyjURon_Hon(!<)Ipqo54YzT;{(4f
zB;pmwMkS&^&mIpQF2-x)M-0;E`MB}?fz!{Bnk^el=yv8$JbqRAS4&=`DFh~})}V;Z
zYqTL}5OXZ0<XN}1wT<Sl=j81E^BZ0sW?Pj4L=o*qQZO*FKa@)=v}T8|R~a7zdQp!d
zg?>7k@wKQ0t4MEA1e%hgZ0qor?sh#=qc9eG8Dkk`?t%%<C1&?@wFVAYIm`Q?KqSKu
z?S3T%q|0%JFjw=HPfS(*A$`Hrt28dHL+$LJL;vhH=Eo?8rF2>?qgj?zx4cWEyviB#
z;k*mRvMA4n?P*|>yQ?Y^ms))ph=&q5%lY{$tGh^%Gg=%ZZ&9z8S5thDT<5<$HHjs%
z^@2LZT}TO2Gt+u=se1A>8jUap1UQ>F+_IZISnTyI!($GB`c^9g5_KCQiNa8k#K7x!
zHpuc_yM>&oN=MpmYPfC;s6WbE=7syQaMirT3u2+<QptTp5=109#B?2_1jh-p3lgRi
zX7xoxj7-!-5KK?Vrl*g4C8*Un!q==zd9g!TGgGJG-8Gr1-u7VOz=kBIS6ndkF@Ict
zDW((3%Wk+=YA^3`_WZ~F%l{=DdyyvPu%BGh!4Iu2KhCY)MgITNj;CxK;o`q6xBow_
Z*l_d1uzl@sZRBsMCzO7TKX&QHe*^nw)&Bqh

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

diff --git a/articles/EUCAST.html b/articles/EUCAST.html
index 3e82be48..fe21e554 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/articles/MDR.html b/articles/MDR.html
index 95419422..ebc6020b 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
@@ -385,19 +385,19 @@ names or codes, this would have worked exactly the same way:</p>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span></span>
 <span><span class="co">#&gt;   rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</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; 1          R         I            S          R            S            I</span></span>
+<span><span class="co">#&gt; 2          I         I            R          S            S            S</span></span>
+<span><span class="co">#&gt; 3          R         R            I          R            R            S</span></span>
+<span><span class="co">#&gt; 4          S         S            I          R            I            R</span></span>
+<span><span class="co">#&gt; 5          R         S            S          I            S            S</span></span>
+<span><span class="co">#&gt; 6          S         I            S          R            S            S</span></span>
 <span><span class="co">#&gt;   kanamycin</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; 3         S</span></span>
+<span><span class="co">#&gt; 4         R</span></span>
 <span><span class="co">#&gt; 5         R</span></span>
-<span><span class="co">#&gt; 6         I</span></span></code></pre></div>
+<span><span class="co">#&gt; 6         R</span></span></code></pre></div>
 <p>We can now add the interpretation of MDR-TB to our data set. You can
 use:</p>
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
@@ -438,40 +438,40 @@ Unique: 5</p>
 <tr class="odd">
 <td align="left">1</td>
 <td align="left">Mono-resistant</td>
-<td align="right">3246</td>
-<td align="right">64.92%</td>
-<td align="right">3246</td>
-<td align="right">64.92%</td>
+<td align="right">3182</td>
+<td align="right">63.64%</td>
+<td align="right">3182</td>
+<td align="right">63.64%</td>
 </tr>
 <tr class="even">
 <td align="left">2</td>
 <td align="left">Negative</td>
-<td align="right">939</td>
-<td align="right">18.78%</td>
-<td align="right">4185</td>
-<td align="right">83.70%</td>
+<td align="right">1027</td>
+<td align="right">20.54%</td>
+<td align="right">4209</td>
+<td align="right">84.18%</td>
 </tr>
 <tr class="odd">
 <td align="left">3</td>
 <td align="left">Multi-drug-resistant</td>
-<td align="right">467</td>
-<td align="right">9.34%</td>
-<td align="right">4652</td>
-<td align="right">93.04%</td>
+<td align="right">462</td>
+<td align="right">9.24%</td>
+<td align="right">4671</td>
+<td align="right">93.42%</td>
 </tr>
 <tr class="even">
 <td align="left">4</td>
 <td align="left">Poly-resistant</td>
-<td align="right">243</td>
-<td align="right">4.86%</td>
-<td align="right">4895</td>
-<td align="right">97.90%</td>
+<td align="right">237</td>
+<td align="right">4.74%</td>
+<td align="right">4908</td>
+<td align="right">98.16%</td>
 </tr>
 <tr class="odd">
 <td align="left">5</td>
 <td align="left">Extensively drug-resistant</td>
-<td align="right">105</td>
-<td align="right">2.10%</td>
+<td align="right">92</td>
+<td align="right">1.84%</td>
 <td align="right">5000</td>
 <td align="right">100.00%</td>
 </tr>
diff --git a/articles/PCA.html b/articles/PCA.html
index a7c0e494..95ce8174 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/articles/SPSS.html b/articles/SPSS.html
index 6a24c95f..fdd2158a 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
@@ -178,7 +178,7 @@
                         <h4 data-toc-skip class="author">Dr. Matthijs
 Berends</h4>
             
-            <h4 data-toc-skip class="date">26 February 2023</h4>
+            <h4 data-toc-skip class="date">11 March 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">482 447
+online community for programmers. At the time of writing, <a href="https://stackoverflow.com/questions/tagged/r?sort=votes" class="external-link">483 254
 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 8a4ede0a..7766d164 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/articles/datasets.html b/articles/datasets.html
index ddc8487c..416776cb 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
@@ -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">26 February 2023</h4>
+            <h4 data-toc-skip class="date">11 March 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>
diff --git a/articles/index.html b/articles/index.html
index 48d9c5e6..c740f9fa 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/articles/resistance_predict.html b/articles/resistance_predict.html
index a036c429..8034bd61 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/articles/welcome_to_AMR.html b/articles/welcome_to_AMR.html
index 825c0814..81a2b0a7 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/authors.html b/authors.html
index 3b4598dd..73c91f1f 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/index.html b/index.html
index 569401ed..085de94d 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/news/index.html b/news/index.html
index 1c287eda..41b8f2ee 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
@@ -142,7 +142,7 @@
     </div>
 
     <div class="section level2">
-<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>
+<h2 class="pkg-version" data-toc-text="1.8.2.9148" id="amr-1829148">AMR 1.8.2.9148<a class="anchor" aria-label="anchor" href="#amr-1829148"></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-1-8-2-9147">New<a class="anchor" aria-label="anchor" href="#new-1-8-2-9147"></a></h3>
+<h3 id="new-1-8-2-9148">New<a class="anchor" aria-label="anchor" href="#new-1-8-2-9148"></a></h3>
 <div class="section level4">
-<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>
+<h4 id="sir-vs-rsi-1-8-2-9148">SIR vs. RSI<a class="anchor" aria-label="anchor" href="#sir-vs-rsi-1-8-2-9148"></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-1-8-2-9147">New antibiogram function<a class="anchor" aria-label="anchor" href="#new-antibiogram-function-1-8-2-9147"></a></h4>
+<h4 id="new-antibiogram-function-1-8-2-9148">New antibiogram function<a class="anchor" aria-label="anchor" href="#new-antibiogram-function-1-8-2-9148"></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-1-8-2-9147">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>
+<h4 id="interpretation-of-mic-and-disk-diffusion-values-1-8-2-9148">Interpretation of MIC and disk diffusion values<a class="anchor" aria-label="anchor" href="#interpretation-of-mic-and-disk-diffusion-values-1-8-2-9148"></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-1-8-2-9147">Supported languages<a class="anchor" aria-label="anchor" href="#supported-languages-1-8-2-9147"></a></h4>
+<h4 id="supported-languages-1-8-2-9148">Supported languages<a class="anchor" aria-label="anchor" href="#supported-languages-1-8-2-9148"></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-1-8-2-9147">Microbiological taxonomy<a class="anchor" aria-label="anchor" href="#microbiological-taxonomy-1-8-2-9147"></a></h4>
+<h4 id="microbiological-taxonomy-1-8-2-9148">Microbiological taxonomy<a class="anchor" aria-label="anchor" href="#microbiological-taxonomy-1-8-2-9148"></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-1-8-2-9147">Antibiotic agents and selectors<a class="anchor" aria-label="anchor" href="#antibiotic-agents-and-selectors-1-8-2-9147"></a></h4>
+<h4 id="antibiotic-agents-and-selectors-1-8-2-9148">Antibiotic agents and selectors<a class="anchor" aria-label="anchor" href="#antibiotic-agents-and-selectors-1-8-2-9148"></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-1-8-2-9147">Antiviral agents<a class="anchor" aria-label="anchor" href="#antiviral-agents-1-8-2-9147"></a></h4>
+<h4 id="antiviral-agents-1-8-2-9148">Antiviral agents<a class="anchor" aria-label="anchor" href="#antiviral-agents-1-8-2-9148"></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-1-8-2-9147">Other new functions<a class="anchor" aria-label="anchor" href="#other-new-functions-1-8-2-9147"></a></h4>
+<h4 id="other-new-functions-1-8-2-9148">Other new functions<a class="anchor" aria-label="anchor" href="#other-new-functions-1-8-2-9148"></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-1-8-2-9147">Changes<a class="anchor" aria-label="anchor" href="#changes-1-8-2-9147"></a></h3>
+<h3 id="changes-1-8-2-9148">Changes<a class="anchor" aria-label="anchor" href="#changes-1-8-2-9148"></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-1-8-2-9147">Other<a class="anchor" aria-label="anchor" href="#other-1-8-2-9147"></a></h3>
+<h3 id="other-1-8-2-9148">Other<a class="anchor" aria-label="anchor" href="#other-1-8-2-9148"></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 3690946c..adf2cb19 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: 2023-02-26T20:30Z
+last_built: 2023-03-11T13:28Z
 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 a9bc0425..19814a40 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/AMR-options.html b/reference/AMR-options.html
index 9f127732..9cd342e6 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/AMR.html b/reference/AMR.html
index 24887701..608c3d16 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/Rplot005.png b/reference/Rplot005.png
index c7b0048a100119ec7b2a0622088d4d46de9addce..96205ac97641a605d0ccfc4f350800f82936720e 100644
GIT binary patch
literal 13249
zcmcJ02UJttwr&a#5CrK80wMtfq<1M%kP@mO2+|c1kgD_&2#TU~BfWQ|_uiriD2Vi4
z5~N87kq*h*{Qr6P-tpdjcicP9c^Pc>%G!IcGUqqHIoI4P@9ycSUA(|@0R#eFM5^D_
z2Z6x&^MjlWs6h?4#sVj@$C_$)fiv(qQf>+aP86={58Xf@O1kp{JUA0@3IbgNA@ANX
z@J`v7@kzL9I8naiSF06AF~I{5z9dDfd=0|c&69isQh9^?EjeU03{v@umVx2$wqAcQ
z`SmO`+VKM9<y+ye0ynN%8PkzKAAM`Z#A-dwYeVQr-XC&5_@mX><ok3J=dm|p?op_#
zU}!$0n|_~F9Ro-HvwsGPwh(P>cE$6+$j;QUZ;g$|t@;Jk-Z@!CI=4Vb^9L7)Ridi@
zxZ1W&nWHQV3{2$p2S#|<&^cL?yel9$V`pIe>WU)OG~{L|dmBlojaC)MHhDQ+0;L%&
zygOk@PbvOa6nZy%WfuGcEJfxFnI(M+viwTdr5NlyvWUHE#V^uK-=hchXEr8prbtPh
zKbvTDrhEuXBm)Vnc%0FFT(vrOQ5jN<5hZlnoq7yxdknbJmos^j)-#M}ygKGAp5nn5
zRXk@_nGZ-8b>duW5?E8g7@!q{IgVnn*FJOn(8G3Mv35x=P(Hjx2K)GRUtMt#o(E&N
z?3GbSGN?Ukj-fned%ji0(d@Lv_U?RX*yG8RXo&|ZdkjBq_1M~?eopg9m9=%{o#5=I
zb99PdeLvVEO^bR%iEIUNS?^GVc8)+CvYyL5_0bh{^*2!DvQ|v&0dJc~Y&rm=Qky4S
zW;ObHEacvzj%l=_nhWv1SkBF{zGI&uXzwo}MQF5}-l%xKLgMe{IGtrTt^0UBDeE~*
zr5*ZscU(qlhHappgQ5KD&MnWk5pC&_^sZ`a*LG23Al*(2W{m$(+S^$#1Bqr$iDnU9
zg&mtPqW!mcv~GArj~4cI>6X%1n1PtH`|7tP$QG@MJCW3c9QpHxlF~T8)W%fB7%?=B
z=7;{WK*iYB(<||zX%1P-hw`&IB`1<I+6tN3udl)1IzCr4$NI^cz!G(?!+#*axEv>+
z3(tN2`1}4j9T`7#SK#2OF2wN^hj`hG*JF<s!V3ELW+wKoGp14)D@@$_tk_~nw@(UL
zzc5X<u6x2UX%IP<Xj0Ye{4S#BbSILAhRYqb|E|*r3|mSxl6u{lI683aYxPavS(?jJ
z-^ST_ia$g_IUtc?oj5uLn(OY8fw9rkYkcx6ogHKLOF=on6n~U57yQz~uC7?XT6GS0
zI7sB#BXF<#a}#Tk>mH5C2ZZb2iER~QQ|=X!W7cJAT^3uz?+%uM`2<jzsoQ-*KpiLF
z$&5k9)XKJ^R=w#Gm?KU^oZ4h*XbHE&*YBRKAa0#m&v)8t4JGc)B>rPw5215*cvYn}
zTgl8tXlB!M(zzQZXiD<sRA((2k&m%I%}5@r7)WEEOq%$=3d*J^DUB?KR}X_)mF@OW
zZ`aKi5^n+hn^2q>mT|!w1EE|O5_Bi~vX@J;mwT*j$~rAvbd4i5<@65P2X;AwaivaZ
zs8}AKJIc$mMP0=f@<6v1<I3NyizW@uwko1Pd|4T?3AnBPge!X`AKV6&%0~6Lq0BUX
zjhc~t=UG2!ejR-?jlVq7MRm`gr*Pi*GoK<G-xIbc8ekk~lM7zD%-39WNIGfKELhF0
zP2STY%~^oBBh$YsO*%@+Susnpt~%b<gqXWsq4m0WtF)N;Z?^GB3YpT3<;2nU#%?Q=
z{5d=)p@L<(x328A^m7?WX?B)Y%R;SQh)GL@qUc6BUtOaOO&Je<)3bK8aI96@jEdlE
zb4G2<1*sHQWY;1VXD3(Ye$6Q#R1&3T*R*QDN56gC=||^eWb+K7v-ZWWFVAgx-cyxt
z?T1vEnRG^djO(LF-^sFk9wp`j^2zkd;gH(hYBwiqdq4t_7ma9zo;O6hN_hGne?O8M
zAn4v<oXexvM8oU@*|u6Jq*31dp^{_ON2@ZFmy;SE!wUqSQ7i{+s>^g(L-9Q-X=<mB
zuf+Nve=WgA3$-5TeD*2Iz1Q6+KBKdLpQ9*C<{BJ7Ri!2Va(|iOfYX1L&B@v@m@-uU
zcKtd$csC@FZA299I_ouU@cHOdq7<njZLh86<PV=sWf{XF#GHP@!BV2sm^V5K)b%8>
z%=bng^a0-nXc{E1R*!9eG3op?+F6g9JEiC7(%gQ4-`z!J3WN^Lr5|1j5w5$h+6Fv}
zusC`l!Eus5Jqcb*e^|lyvnt;OGud-YI)`~lZ<Lk0-*g(Io0a97<qPJRkPYq;VtUN`
z+5CeTN}({HVm!=MEmti&A%Ic4igm8bS7`1ho&6Mu7#=5%Dw+4A@S-%K6U3!fv%wAR
zW_AwDu69T<AG*gKR*lG}Z725SZ{#stDwP^Z(nAEhg%jynBP}<pxQ=Q?$(czSqFi5>
z>Z;C<(qTN0sgXOQqQSfIRUO+r+ktu$Z4}FN%Jnz8F9wJCG^-v~ZF@%AyN!S!$=z6(
z#AAjtl_{7bzdpS-*M+(<_mm7uE?8k7EVP&zyhcWIt(q$Dl8IdM;OK~%%0828=?V=@
zhr7D`Cq+hR|4R+r(x!6o6a93qS$jP*D1|wmZADimRA#2+NJk>zk}H>!`7hQ>1d*Jw
zA9oqr`IW90fC+ShV;Od`ELl5Vmr56Eu8zmmI&ctMKX{DoeDYh%%RkfXtatRF<}Tg}
z3PynXE)jl>(JOxN*7`E*_S{i%$3s$(CB29=#f6p2x6d<pHiQ@vfs1hc*1JXPY;;*V
z@zng&NU*?%T$iSL)<;tsO&@AUJ(sE*R?+1@c(N_pnaVZnsX4E>FHkF8H%8ih%58e)
z64n_Q>`I#RREhF>3+|@Mew;?>3K3+cG#;OD{q!wPqh4unsKU68w3NF#=66Ta%YEs`
zXxHo@o=KwUWoU}-R0Mc%%*{gQaCe7QNs|DYG}&Ak3K$eTAl}{8z!iB1;@yJJ5*)|{
z@pHizui~=~Xt>P65_N_ZZtSP|DtWDE**z2=^QgUrfTmp0#xV#?y>*Q*@Yb^Lv1@YY
z4&F<)Fj|?qD>bC}aF27k_XbYtfpWdaoV(IiyU1j12`_q!o{m_Sb$mfj+t`+Q(33Ym
zx4JEFk0;osPs*wER)!N>LA(gKC?<^;pkS_Q|6{1y)5Oi)V2+bktgZc8p|)l6!N@Pv
zNU(#Xa9{B?<<gZl?fS^=j$n>u^Pnzr!G@@T(a>a_ap44?{M2gi>)rB*ec#VfN4D1U
zwGm4n_(;u|de^Qi75UDG+t)$}EG?y+?t;p{UJw}`rRHtzSIKUeE|}7rWUQ=E7RV9O
z=E(!!N?os(;P~MkSbNKXJW!;N+<bK?dvTiIm#qv@!sz{~7M`%7J3{w4NJlxlk%sH)
zmlzk;WOcjLGEVrHo#39xNN)GNynNi-{T8-{9MclSbvU6q5lf-RmJA8Y-kHejHG~Bt
zglU*Ag}@n~z+GwP)V7hI6_xF9xqi@00XM~RT?56KeQE3TyXrZ*1B4r6h)xScjO}qM
z8nEROa=|$=B{5?SyKfy$#8&nWdK@ezQkva}6Bck5-Tzt3t0>Q9=)g)sJ}er~LOxU1
z8_eX%IQMltB5OrDcLSYC_L;D=^fB@*%SK&SQ$qf_6pGWM3nwmR0wcmxu7B`}&j7%E
z?Uce;#V%q@WhhO>F0*Sbid9GEr%V-P<Gb1gnY4Z-Gi4d)YpLY`qe>4F;zD0hoP;`%
zE6{Dxl^&oRRc|&lW^~zpVM441AU-$xMsDZB$Eq7Tz4kj9Fx9hS^hxl&3!Zm0|I4s+
zkHwHpchD_Izd+fp>7Wpf3@a=wWY>M%+2C9^dVj<U5(lqA-sJTbCqrWB&cE@2d941u
zh!^rE94*7x15bC-ze@LZ#|k*?PrLPT^)P<u7M}9#XG0v=59LX&*Wm&}o<ye0x;<@c
z*`HjaM1C1gb^qgsm&!Erozm-nXdZaBXU%lexF2CUQPIVI()nv3d#tkywPuPtUgsce
zhctnh1WgR2AAfn93_3p8YWo#TuT#Hci#VBRV_{)20geZ{$x<bA*&JuTy8rmOX<OPA
zi`)KK_xkycH((!VvZ+d-r*QhW6Rhj1+jmy$&hT$dZ0kThuXeg3N~5-qEh9HK_Zji*
z0P$z1i-WkHap%?2v}^mt4>a=I*w?wHS<_;5*#`Wvdwt#x4i5Q*oP*#EH&pE_v-IaK
zvpg}$6qgSP6SfxH4Fk$+VqJ(jZ=1dKiK;+FkJaJ)!FdxClftFm<T^cT>-ok%^@4TN
zzgLVbQG11Tv%W<E$xgKmzkl}#2bNm41(c|9p#HcSS@<kcOV4`G6Fb&=B~hFe&JBxk
z4lmB?TYh&7oaxFg#`MYm{>s%R?7uQbY*FZ-5t3hi*?z#W8?=F+U}iRKJKLW^v@In$
z{C0*33JQ*Z8Waz3g#9|!zC>}`>;|`GLSHDOz%w50r0TUG82h0eUp$`>;mYz<<fvM~
zs$hPVkPPu<W)tcZI`t`Z?CEIp-l!>}ZGX~%n8+UAz_xH4?zbzIIep3trP?&b`432C
z!=5NA$~|*+a&S0lmO&l=A#kZhrqFX385)jU=x+-oj;^k*E+r);F<`&EqmS1Dq#pMD
zXfcUUKOZ?|jYWgUs9B}f)c|>{-9Q5q&)(umcKkg9JYWjQ+a=NqY!k=k`zzD(Y|WDB
zYoM*&>vVqa$6U?$(b3`I=?C}kyRF5UI{S>uZ8hIVI89c+p`hnUDequ1O_>6^w>(|o
zMNC|{w%ej&`E3RpeFv2+tl)Nv{@(TJB(hX9SARpu<b#u(de2^iw1h-4tK_rcX-?%g
z5>Z#gpKhG%>(_h~LUCccorY-Oo75{cwLZ1|sZYv0007bEM1TMGE-=)o()ypFCHPlU
zz18+ZES}?!Ldqhph%cRjp=4%$I~~kXulLh;MvN?5e3}KG6Hbp<pXzYLJ57D(X+<qf
zj#dh)G3**iPdQd0If#c{Q9Td5o>E022#3+<GXc4d7#cYxT0}L(wcSIpuq3H*xP1YJ
zAD!w|xM3&vb^nU;);SOb@jB0(oJs&Q*68SJIbJPlGBh&U!f50c3OD}B+B#Sn;w8Oz
z|Ne)XCZ4H32Y|7@9kUEX4WAzE>RQyhKAM^J+d_2?03e*G8MA2G`vl`SUH<lg*HRyN
zCh6RO-BS~7gj)}PqVMY}csQS0I%;b6Fq;AZFKt*#>iuzRt$3zwH+4&Xuang!{Oq8O
zi2Bs%u~vBARaGBwHEgSS_lKoTt5Ji^6!qW>)Mf~q=lCgL4IameC+oYU*E}Ze^A!}I
ze2w;4iE}e>x#+)LMOwdB+N7#TJl&o8o+P<e?H(pD<>|K7a-idLwl~(*a`-cBxu&|>
zKs!NX?q^k>;z{$Nt-RaP_W-Zy=Upeesge$(<KKY*y`i>Oer@T@x5~+3BF=iU+Ww+T
z^G;_yvxWEERHAuv!{Y?tP8)h&os@uInIW490LM|^6GZ3pS5~S712bE+Qr|bQJvj-d
zkzI%;Z9yg9^W?$nu*i9?7Xl_#ds0_7^1$?L{#?VWn@wBn&o>*E1CBmZ3j1HdC)%c6
zk$0Z19}{2uDs{#cB`>!0jW=EX$u|BIELXo^b#!zz3FzD)-R+0$(0Th@oe`{cyR9di
zCsE=*Wae|-6%uXoQ(koUjEszI5ij2~Z>;u=H}{nFS^AzR2TXqWeDZzR`*NH0^N2yj
zi<MDtb8~Zkg2iI|-?)ZqB}uFxKJaKC>uPFhjwFa$w|W86)8+O=j{ga$``)*|T2vOW
z1d!s?0zj|cGP4`0PE8M+Je~ITr(B2x+vXY1sk61kcuV$v;DcEc{~>i2&8eK^)@$9H
zBsuaLUhOz#QoHyz4|U!2cQ13hp^^FCAAm>$6aW_7%Ke~Ux;SB<pZdU^!NnHPy%=I%
zVSjvHzmCGjn%SLq4?w1*|KT>NX1oxJ*r$0MvRK|uo!qw5$=Ze*Gxv%hsRu}?4zU<>
zKdrBrYwAzaR_lqJA0T4$o;X3d+T7g5umXeCw*hCzHBwSirG5bQJNq0h#^=lLk6Y7Q
z`mW>#t@w)dSom$!fPp7iV}#P>eSVM5cSQyIj1(Gn>7>c~=r_tK5MCD*Ucl`hA0Lk%
z`o&t-Zd?-_TMpRmm3EQ0>5dUR=klk0X*0IKGp|ne*2ZoD-lXK*0Dz&S(Fz!qUWIU+
zI;IC4uUz!Rp$@#98&~pLj7rUp2)VnJPSSgOdnLln+n>As@@RpC)5drUU;D1-^PMH#
z2Ul#96>;(x&N6#zOKeQ3(UX&tab|l9p>r9psBir_-R&a|*Z@jj0H88UROqRb?}+&e
zknuMk>Mu)WI%;9Ds|;HkM~4Do;v8eGt*(39bM1)jj<XfrKwlhy!3NFR#yqXW-&j&+
zGuMT4haD$me(TVh<7E7||E`X1;IN)rFPrG&FBn=bRjrcJQo;<-f-v-Oa;&x1;hc6+
zC;Kf0t*wNX3hS=cKn9zq6JXfYa=VUDM%J8zd3L|`3UR=L4d_ZqNK9`7e7QVBQ+u?O
z+{PT}^2njo_F)ZA&C+}bqEcp8bTM!20b?WE!pSrZ;&i*=0N|eev*W#!-umbB0WY>c
z!EjY$7Jh;gfFE8S85_e%05~#dWnqc?3`0x-q*yGo`Z0USxn|@+6$>kCnfBIJPtA-Y
zV9`W2M8GL&G9dXrUB#n?<a0H#5W_kAw?Dn#8L!k%7wVCuLEm{rb{3=4U3+z<GA=Hz
zFjG0i;{Ap~qNo-Cgq|QMth9CeGo@wg>F*vF!k>Hu>Tc`eH$$(y!aNU$rzs~b9f?t-
z2WLJWfKTuKzA`^`r?$`g%UfFQ7W?FTbG}nE(TY*h*7s<n&wUYK)XVqJC&{59n+`bl
zTp~b>XHhuMDJL7bXLXkrO$eJ`JTPNf0B_%|ePoKoG%j#)9DQBY&}`NBdQ54I8BU%6
z!mk2pQLrW^21H*DM^cEK7tynx+{Ja<*KQ*9lbgq$MgPC@kOq$xHavr2(EQ~atmLO;
z2`06623k`t!*zWwMTJG7K1aiCeGgmq*HnsiF&9RMgj9SuTmvI3g+UQE;VK=HOJ7Q)
zCVy6!s2vDAcs+&KE=#LD(_B^cous+P@9SQxHFMj;(3|1?)T^8?-qOGr{*z$wKg5my
zt>Qf-g*Qn#jmQm_Sy~y6ae)Ff9LXQ6fyAbRo<c0a7G%^U0nqQTQZi~yei0-_BU679
z=6|JJP$aCME)5nAhvQGsa_FWH7j!T=kBefMw!QJazxMny7>zHn&dR>a_(yAjAk~gH
zx-k8LwOdxc6I?(7C77QZoDRE6&zeT(haz!R^=W?(FR13X;n<ePhsbtsf<_sQ`>t7O
zMOKQYepJKcU<zYMQgm%GOVkUBkB^nHZfhKoaO7H<9NY@c6*NVeuvzizeJ|~8>-Rt3
z!|huPx)t$}(<$yC44m<PZZ%pL^6YoaSggP$B&Iu4-vs+1$oDhHOGg?c#y3Vo!`Wzw
zuPMt>PaR`G7CQ(@o4j+hWAjc4lOt9TLoz+^TFRMElK}&dMdrc*sroYhJ2L(QNdD)=
zkJ`7;*Qc0e_|+zsutNX@FgYo{CJ+KlL_lo<Y!j}G$!P|@Tacbgt4+vfRD@TkV`4s>
zpt+G6XbNLY4oSj4QM~Pi#Hs<QZ8)-H*qm;=7YVzD)E71EKKmzR#k75eEe5A=r6745
zchzR^w&5pQB*s7~7K0A63B<F?c1uCn`K)hZ;EU&7zQ=BmYIz0z`MN}&QDFz%S<MdU
z-&IYX2C&3z0}aaYU0{AK8i4u~&Z~+H^k3}CTOsw!^!Nm=`k-;}J^u5iJ=g(5ol*M#
zp&N=r_sGf(G%-e$38^oIIu73ux57T}D6Y(HIC>YyD6lE;G&Og0h;G{qZz<b-3@z%u
z90unLTTM#Rb*k^%T)xOIU3+6svdpBnIN62RL?QFADqBtJRZyo`=XGRv=A$Z6s@Z_1
ztl^r?nMIX?{OM^JKrs+4EC$YvCHa>ZpM9Xd_7sY@vNFn`O@N(}dS9~B5fMUSLQ4UQ
zm;#kS<v;>Kx?swnY_t)@w}+S<b}mbZBKWdNo=hoBkt~HYh0>97oywo$kjd3B@n<x=
z;O1|SJ2W5*vZo*f8R0^)px)Ifh)d_2WzdOA8IffALW=Qi1sK$J=>{vceE$e2cdzD=
zHEwIar0hr)Wv;E~Ss+p3P+;C9<Z;$aTGYlIF=_WJ<9PYa#R5yC@MCb?P1BRvpEeE%
zv*Kg^J2O~t*%<Y7hFueGRM665tog-=j1{tC)nF^yg)0SOcF_^$4?Tpsa=8j_yJXb7
zeLhWnmGYE_Me$|rlL;E{Osp^AvO=Zo%W{{88h3HWlLOKOySfO)*0b%b;e+X(Fxlhq
zuC6btd&DnYss=OrLkd!S-BjoennsuXpEkM2xYY2*Pp?ik5372l`5&N$gc}~uVwNp_
z85JK7`?*)s$f&3X;6Sxx`=n{4OtdCRdn%#xSMdh;mS4{)T=flVrMF*S?*)h~v|kTW
zO1cl(BxQ#RLEnHl$jBfxjC_$c*hs9M6;(N-h%|o_V&4RiKBpz5mBg6JpFM%Zk@^%9
zeUllC<ApCjHX;8m{2~<tH%#$AL8p@l@eqTIV%^y*04q>!B{|NA%;NNmZ^MzR+JJgo
zE?$in-MsOcPZ<NZS6FZW6C+(8eyztr`ySE70Za+hL@m8C|6UTD9m9<r^!*x-cHv1O
znliE;7%KG`W8ftr{^tG5&_J?QsJya_t{2nx%3ugLQok=w7+eP42dP3>>3$6Cy}S%i
z?VVN<Gt%|D^(t28suRN)6*y8~I9eDS80Bg?YYEcqp$9m-MY)y4|5iEe+!8t92j2c>
zBUPw6r{3Y71x+k7^h1zdupCDR$6uped)X?+j}z9M$jhYwWH$>)WVZ4jH?O@Es!I*}
zVCP`Y0hy*qd0D3r1K$&2<HnZ+Z9&??-*|zKW3ENc2U$WdVimAN5HAG5YeuzjO85Of
zCg;n7!@~fw`nS=iLXcLn;_GHfzq77)iBcS1E~ly|Ge)X+7NL15)Sd*kp?zyfc$RKV
zT}f!0eXpS(U>y|2)bJxeXe`VupAHa#rHZ=A)}W_R4s%eO{waw(#F)0lf=wa658(`X
z!)UB)Y2_MO3BIizpY<UJvd1QRn$<buC0iUY9&I4HYO3I8F@niYORV%9z=((HrSq$`
zlE}a8hmfkfsOM@}d4X&p(qKza8jKpU$vu5K*GD?K`{FOge17&hCWqS4>F}NTv*V~M
z<}ux~DEnKQX})<kJPIloreOYD+p)hq1m-6DL%`C2B&yIe6y`URK}~c*uO}{?GRZWN
z?8d<PM2`S(h(4i!J=!BBX#e6N7*PB)){$A2byidI`u#%bIuH<b0ieJ^_qnG-UE$@I
z6U-ws9>>M{1OOgU1<>W?hNh2OH}3$nzQ=O352oR*4j78sK(Bp8B}D=L|6VK8cFn`i
zvrt#p7&y{|CbflOF&V3uOMVwnDBO}#rmFajt4Hy&Ci6eQ(aiEO>|)&|p|?977}b9u
zDE?<2B?kVkyOAvuK#(fX<a>s=3>N|Yzk8)y_r&&MEwJ}K*Ce@+c!J0}*#T^Q_+5im
zqb=<^RdSr}BR^PFsEfIS#0(EDI)FK-n`1;bCGoqtl>fjL0mezu#RfX9a-RdRD7Uco
zQjx`*p+3cRCt@SW1Dj^^RueEuK}(1QRX8so2xTiyk-EFjQ$X^@pO3kbg*VtzpH@q5
zOX3ZbUQE9pG|QMb!80ly>}TG_(R~LzdFXq<4&Jy<4txk^;>Ysi<+wv_F1xa9lUi}l
z{m_(y7;-12!;$a4jgg`l%f+{a=5F<agdu^XLi~4b_G0g2<I!bM7#(KY4j+PzqWm~-
zh5vrlracHYiunH1VSQ>@bI;I_+3=2o=&I%&Ny7qG+cM|IoXdMin`*VQa?ZA_!p0jo
z(u`1kLZ#>2&ktb+=>`%<blc=gVa(SU6~<zuQVvQWn{U~(gM^t6TM?>Jj*%diF+}yx
zqM+x9nH6P@N!tZ{*08i&2Maavq=!CCKheOVb|CCJ2t;pqz8An+O7{1zY525PS(X`3
zT}{C|TU`Yg57AXjs31-?;BPEu3LJ{y-_Ui%tgzuL#rgm{#B`5A(nzLh{Nr8k4Yvg`
zV)HdFsi7!VW8ejk7d5e4;1fEXJ1j^UL3_`4Kh-_t-@%azfQg*4lrxrJ^Hs;>T>n-_
zb7-Cs*Z=|IM`_@VurMqD>`3N_uDb@bVFwT(QG}mp8ds^U`8WC0lE^a{LmVG283P0<
zS?O`7ujg`W+tnaI>B$T)Y7%wh<u3p+USgKnrZxudp;P**+%sc?j7suKJ4iT2>;`bH
z(UIbi!b-#OkOQb|iobass0(tW@ZPFa;{*tAUhzK-y@61r2rXJMrUs%>jcU(&4w%d3
zBj?V0I=0X#unX{tQjx}tBnM52rp5NrhiYShJqa-+r)+rMfi)!7*vdxYR2JXH5Qh$V
zf5Jn@0U0e;3&(ukKuX_ykt+Pt>we3<TsS^HXdswro5jk!xR~^psg;6t&?~nXrwF5M
z%&2BM=PRPjx;4G3o?%LhQqgR}i0x4z!B5`ROOHRM@jVdp=hJUC{$a$M;cwyTl$ig(
z@u85lBfr1drX?%Jx1n%#wNbDwV7mM<H(vID*kBJduqMjB8VdyTM9M?4%w(T2yXgw7
zT<~3gbEW4%>_-R88k`4i0(;y>SULRxLWZt_9FmmU$o=y$cJ(>`A`sC1>musES8E#0
z93r>5$~o?l%2VA`m%0i>I7L8&BW_(*M><R9NyY<lfpBPF2UMw%3)Clnu^?U^XZF6%
z(n@>Cxt$1EouHACQkzIHPE8dnv^*Q}7%{YN`aSD7nycUI_Ml9GJ>h=wtr~(9`vCDx
z<K(J#n4#r;O^2ZrpQ0!*6LXOc08u~}^)o?%U*4p{_HS;tM1h4DZ^*qB5^^#37`a2U
z-ZGO=+AN(MW|>m+XJ48*vXjJ2@Ayx_?3S=}fUiNts@xeSpSdrw&Q}?T1uH-1kjT^8
zf#aYh6+1Op2h!0Bri9dNM~X+d7x{+DYpQ3*?u#W4FMcd?+VJL&HFa|G==n%{7WTlB
zNbnF;Dbmu9y3a>(7>}hGSo0I_js)unyT}0C{Qwx`huxSnCS#*gAX?xL(U84xi{nEX
zpraM)^YsOL*S{zR<yQdr=>q#}fc2x)3h25KSn^?~3%M4l3ECxA60jbPkNR}m@j<Yl
zCh^X6D)|c%%*MKGA~)0~Zu7Tw2qRay0enS)1E{n}0zkzPopm5DkE}FQ5WQPW1Q}s;
z1jLQx&javV6yyS#59$h{3<_zy*Y|3GE13)3DWt-S)PHYi62DG~EyWt+X_ewD0TR&x
zRu%Im<S?5V4fQX8Daynqpof;`?Ey+*FC1*l0Km!kFO9?b`M^=2eK46yyBuU0>c`Cp
zWD#=p0lm;;weSMi9&8ZS1$~4a!9GHg@*ws3hI!a-FNj&0S}|pE%hOy&s$&npULY^#
zdwoK|3#q-vN-wq|jvb9Hy&jrtd*B`$jt?i9G;X)#Q4Y38oE={^w=}FVtyqN_*ZXda
zXp3YgcxbE|&qQBreQ0hfU24ZzTbnm3WN}PH%c>Mr!9K<Sn@POIeHA$DNSVI8@e(Re
zOT;S6b|;|?%`cWGv&v^#o*4<@9_?m!6mI9_Tbe?e)6pk}hUw22g9W`-ElpE&V%3%e
zZGMM)rFD?pj5YVr-gs!5PUsa3HN-Wmv75Kx2il0V^saIM9}q#|kf&<x`Ot!29&92l
zSYM|8xO(vj2ul--m6vpmuJ+K62v&xYR<PuVcH%q_HS1SwV^Y>I!eP-*r9e%e*}$;Q
zS!BOmrO0`rI^HDo!9yB-z7upGH1gd=-FGdD5Si>|z=a!_oUi0zry^<-Y}m&0^_KoO
zLk&uq|33id10O(vu)~&bs;D{hug)>(F31*S3{({)F4oN<<aZaaxO0l;fTV*w$=(1!
z{dU_B1H>Z+{543}r3p$I3Evhb8E`$Mp8C+2cUAVXjJVo_D?Ui5lNTBLv%Ln&nzk-(
z_v#?jaISGt;6brz>tn}?)%%YC+d$kIKWKup=zwjZ@sKv~vkS$`QGcVqG~@=7@zL%1
zE-ww#uVh|lv*HWs3P&l@V}h1?#j6Gx8Y?dqqi8k{NM$$@kc(gVkotNC8jt<JhXIk5
z9H5JT<s>kGAEN3{$#*oI$N~tQ3P7?ZX4v7MNk05v=?nLG68|TIU_f$OGu&X@KN2@i
zgW$V5e`RifWRM!#+$~0%WOh^Q9w5r|tN<@`FBm~G!T$OgX_A|~N$BM=AR~0n=%Byy
zIoMkk5$qGqFbPlobB@UYF5H*9foQ#Xp6B5KfY|pd<20`FF5t5h4K&UZe@6dj;_rW+
z)A`E-{r%W^dMAMvON%W9oDqpPT|Irm2P-pRm1GW#-3$B_bQWX@`G9r^x`ijTids<m
z053S?&m}$8wDQB3gzrQ_>Og@|MYGNSokZgb@{#Rky1V#V<}YYm!td*Jl43)b;uhr)
zf2QTkeO#n8lSW6N0z>U<`UOX}@+UabvuB0Lp?btdgTlOq*Ao?b4#XCB8xPU=M})~d
z&lcX^g3&s}(bsCd5yNVxY(i{r2`b2@LT}6BWHrUGuBF22x#%j)PN$}Js;1(}%H%;U
zaUG>%szw>#Y(!J6L(^7Q>$7R}!{>1T3f0^Aco;W4tY25k$7uORGSKf?fXDmz%o(YP
zq-Us;371sqOX`f-gryOOW5`ygs-WMASDU5S$t<D;Sn4D7VxKt&$SFSCz~uJ!rTID!
zj1Dx~Y>h&<x~3|fXVzEZ&1K69Yik=W@8yq{tPNF`auZJKUkuzQYP87BBUDV^EEyI(
z=nOGUPG*+(TWvsvJs@@)qJkDz<_Zm-ePfj-IL{@wot#Z(eb30P$&;LSdoU+N7N9at
zlR|h@29Y8OpgtAJtOp{(foEtJdID!Ix@KTk1K*_A0Qd@3!$I&9(M!KH)j#*hB|P{q
zU-tjo4%Gs`W7>WO1)W~~*_g$hy-F3M1pgip@<Y-&X9FTht)Pn?=@e6a`laPFtW-s1
zq++&4v~U3VK^H-C?#-Sq$S+pB9^z3=Wi&wWyZ267d&}-hWBY+DPRu=I%S~;{*XvNR
z=(`T7&YD#cixpq_^Og1$jAPF>!?vN)dNMc+{b})Hk+LK<7b}v%l0Ys2JrmCpSP<7x
z;@nBU7|t^-<$nKj)X>M@ZxW3-dql2`H=JfN7WZ%5P(<aT(&bFvY;L23+)c}dx2ot|
z#n@b^=z{`dIpa#Z&E8}N3%htYPA_727O4uu*kPXCmyN#K8t_$ia2yEWhipP~i^Sp|
ziOtFNr!Jga<v%+~iL-dKd5ro|Q{t7s<rY#Xmv(n;B{}$FYm>SQeZhD=lhqi><CSnl
z>^^o42^&;l>uBSggRQJosto%y#LZb)ac?tQ`8T{;&52YD0mc<6tn78F=oR8*&X|!M
zXnpn-mug5+DcCN69SGjch(L2=6>K7l#0$xZ34P!6N7)FX%V9+LXPT!MWO7pOc<8s<
zprgq>$wn^)SPAH<_4Ko~_D#fPHu<MhPK-9SA2e9iqBYk*cqS{4!65g+H0)e3N3tap
z0bb%^(F=$NI|Om?vZ*sKh7nCN;-agCvR*W~$sJRfP{2UuQ2#tLpojn1Xj4zEg#-_N
z@L0;#WAKjqCx85I@GB`rs6Y9kTNdr&KJ+tf4DV2Da!Jz-IDw)3(jlL#RpQUp@E`tU
zCBYkogAA2au>?SPR5L3lW-C*^dWmRFTZVG(ZAV~qpTJnq=lz{4RxbFUyIkGQbdT85
z)ep2<kA9Yswo(nNw6j8N>8MK!;B12JMieLTB6Ji42V&>9MU&omB3Jll%dOSsBk<pA
zV$_f`5}pzj7s{@UjTg(44cX59XmAwwh~&|{CB^h@nW*x?KU`mClumZV5m#cFe_!D)
z$UX#;KWSX<e2Uap^_DKn>3<blC)MIwKRKf;xAsw9zsK1FiakSZ{sr8IY^RpNw!Z(K
z>HM1w{i6_TM<((yY)gUqP#;L8U|X;zR)`D^S6c~xW9DTeoujxC-a|-vVB(SorAV>X
zu0GAZRCka*j6fx!5+xhFQ0vtx(FclP8@}JfE;`&#XrfEUF$bxkTlI?5VZiR@EsH20
zrb?}jXqSm@dlQN!Q>Yo0yxD@UTLYC$*?DtTkO#R%P*3PF$Qr!`iHF30$x9@^$#1Q-
zbA%%CW>QyaH>JlI^Y@%I-imsm@vL=i7seOgLS8Z$M>m|>+k+JiMp$BoH_n91tvl<3
zf=Q(1St9EuBgrf{;qQ^vXW#rIYK4By1v8B;$u7w<cwaK6o>o`iw~UO<UzU~(&3;|`
zz}LT?)>!3mH+tU?|8?B8)LYy%>lHdVC{}Oci}3^%-fiH}D~JPRxYpnZYKw}qX53b@
zx~pm~PHx^zgv3E?Ri4kXb;!o{Um*PmD-Dm^WyE8R72@eeP2D8gT5csMDsI`LrQL3>
zNn(GPht~-VLA5wfAI0Q23!6f8qHw~#XY!o(5_AE@Fgm)4yH-_pN$wDKZMy=;6x*~u
z`o_4TASVjsT+fxo6}SguxnJ@6pt0HAtq5LxnVQGYo#q`MkUZ?F?%^lg%jiq0opBq$
z#;xU6Y<9J-yt#R8^)n^?l=q?pNxad3JPhIjjiTRwHA-ia4!b*~NOGzx5dg(gSS1^q
zi`%8+<&uE=9%KYYE(X&C{78LwK34yWhfmWIat<a)OEy<8Dkj{}$3DX=WW@I0N@5hi
zy(&-l)V1=A^p%EJN4tcrdQ%*#q{*1r<2n*$Q*_mK@*?1HjHQ}K8};cW8MsvMYi!cT
z(ls>2!_}4b%G?@A6s^hUSK{45p-T;r`qX}inU-DeENERx-FQB`K8d1aYd0ucZ0Ws)
z%WEwfSMZ~=MX_6<4OKg(eSb9V*An0V76TVGO~%k{7MW3{XxUwv(NVv*Q#uZ8X3jX;
zf9|Yq=hWF!Z}EO<RIk~-om_CJKHd)Zx>bJP_u|Asfv4xhK6mxu=p~bGdh(Ki`tL_V
z;TNT8_vc&~6duq=QoBG18i!O>eD25GK96DEAvCqol<&Myc^q0|C;cNA6VW4V7eR{O
zU?1qP2=bGSNS^huFt4&r+KaZEy5Xke{u0*H<3ef|*6iBSU7x7>w0aN4J$bjng*8nL
z&lT^f1-sI_$~;0Ha29Wwoy6@v)cW;oOLx3(Dx_Q|vL~Um@WYUod&U%wq6Ghfg{Jx5
zl22hWFk86-o^|3WeI}*n9o3@;!KheCV1Fae=hmib8?0h5SHS^S(@`+gaj;~d#JCOR
z8<^>YnB&2&q`sAQV&~*TiH6$K<K=>PqvW#1dqzXHmmk?MOetciQ&TJ_%|46yzwZ|-
zgVbi9ebT71bC_ciLve1t{V9~+M@v14x%C9{z-ya|Q&#ZW)hm9Yb8qLg#3}N@Eo|Cy
zmkw<@B^)S_TXaeSw~||ehR9C{LflvFMhtn?JW8%#P4zeDoqrt-Isj4gxc4YtYWY#a
zA(yTc??mfLni<=Ic14d9Jst(vpU`7|nJH<?quifQdQqtP<VU^Sv^!RkSN)}TV{B_$
z3Tqx*`V7cu)Nt|PV#}Sm6T=ez=f7LYao!+8iMOikB(=Y4pZ^jEsiJeYRLSb){{o9U
B0ayS4

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&

diff --git a/reference/Rplot006.png b/reference/Rplot006.png
index 1f50ac08ef4dc563bdbf72166f7c4ce239f92283..0e506b0a4531f1361a7e165b9a6f135b6154b7ea 100644
GIT binary patch
literal 13227
zcmcJ$2Ut^GyC$4M7eqnnRhohnMM4Kf5d#7W2+~2NSEcu$QC^jzNRb-pz4snbLFovD
z5LyuFEeIr`gv{ps&N*}DT;I%m*FWdyA_=Qxul4l%UVHQUo`DVv6Au#z1Y&{Txn%?b
zf$=9V20EZ8Y_vTQ_@H~Fr*jMV1b+5!wnP9QjNW(5eL$d7Y$q@9&*|_Z5J(6FzjebT
zFnwb>=p&y)Tf=sXll~ncDH<(?@4*oJMDH_q9?}XkT@ZKIqM<Q2sei%1m!KUdbb+C<
zwedP0X?^Xo4zkj#niq+v{s@}5b4SbcDtxdtLT$`Ie`NA*aC5NZ=Ahr6!t{eNLuzq;
zNe9K`fmx&|JpTXrH_?Y<h(?WvmzeB~ZQ5`z$r)U`JZ2ha;;#Vb6e{}Sia1zKdLJ}3
z<Ja;ZBYr0%r9^l$2%fh+nb+JR9SKJtEfaa|(n?)UgYrRokrU`W^iZjbfS5ZQh0*)Q
zwP|{^GWr!-AKi`SMK|*Kt>46!HgkQXJ%T)J;NQ7_%8H>6>IkAHF6}qLrjx3ftM|Y*
zZ*d1K?<B8M0<v8Mzw;c|IFt8==ht$&_0hU`eY_4@MH?4Y>7Iu0`ufWH97~I;8_p;x
zKqu*~Xb}wyl661tx@ITmKo&X=jT^o;3$;M}xGqxZvYS4JCOot>)E+6LfkEiB<17&`
zk*p;B$6tO8s>}2Ehnb8xF=SO;b}cxk8Ha0BE%sT%x~ZQ}+kEeg4cd4}H!-+I+sdXc
zBNhtA#Ep%}@|e<>{`G1jVP4q}{dAbDSZyN_q5+zD^D2Xj*WcyZdM36K*i})lcKj&#
z3!~syDMUYcmjl&a`>KmrH8-yF#WRkjmxj<^WxtbSZ`SonLHkSlIrUBUkQkQ7BhzPD
zT5^&5e>GG+b|;6OW5qw}6*>&+fMmX&mK6(?&y39sxV2068J-WK#l0T1Jkrz40%-`t
zE~_H+tY`9MIEzNe`Njb-UNUkhCxrJ7yVfrTEg|=_X+#Q^!kN=SzvR5rkEO)<FuT1z
z#@vpWuekC8(_fL&KZ=ruu_F=RA1v*NsWo7)e)qpBtVCs1O2q76`sOe^E1%sl`xnb#
zZH-u#8AZchIrMJHu~@r7R;sS`%!aj$(F8Yob-?s2afR}#Qud6UBHk2#9Tz@S|GNoQ
zrrb^Ob$>{V3OL#p>dYT}<s8VwWr?qQcByV48W3+XdwC0t{>vXj6tYh_lBiUc6Xwyl
zbTAoLS+viK^!Cc#9g2A?JF*Ui10w$WTAMSp1{XrfBxvSeuOebA;qs4ju#`T>nZq8!
zfmipY*Ys!DOw6<(M@xr~Y9`8y`^n)Y0#u9jnAUFIf3%5>?BPpoFN|?QC=XXkyHNUq
z5Cqys6#rV(es+ziJJ#QPq}C~@!KvwvgA=Gkbxlu+Cr~bnRFgim6It2xlyUB&1%fJm
zwCQ`ZGxA1%!jkA2z3h5$c~)KbxS-OTp*2azOIZbU-Wpls4a!Z5@13OTnX47pi6u6M
z49y+jo4SD+(YCiE8r~e+aPX1_u@ruWo$Va|T8iB+=}f27I0k*gd?cZ{b+W}Ow^|-C
zmLV;6bZ$Wxf3x#`r2>-mSKpw6j4;i_!SJqe<Rn7hd2+{!UGsuF#)_s-be)ubi{b5(
zuKWCGVTSB24?aEeYr`vfmtVL`qc5S`koe%2KNZh+=(@!z>bp5>ecWne%sRd{cfEvH
z&z)1Na)xHz0NW8FG<UtqJ@4{X>}Ae>B%Hdms7bxCtf900W0jevAz>$3Xp|EAqTWgA
zXTb*jI_~q5!1c)*kHnw;RZG63;YgIlf@{PXZ(oU4N3;7nE>8E2&+lAxuG10mF#N_a
zS2(9zoaN;HM=b?Yqoe>XXqazOJu|R7dNx9sZ*MBI{;hgJj=vD!MtTXQ2GPQ>^&`L3
z@(p36h-{e7<9yh%>Bm^WW6i8)FgdG=Iz4qJB>h4<n~%WU@<w0nM8)K-Nm<%Uo!XKd
zposj9pbnC_mFTagLQLq$B5!-Dw=e0s=pI81Ql9o(o6Q`LLH5Rvi_ImL2rz%>W(IP$
z$^X@WyXxe|?@iI%Y!#ta39rE`{bz8;bWM>M6zAC4@DHuVqsJ=fD^>NjpiPPNe74T5
z7_Yx@%Qwjxfm5;!9TEfeSL9TJV<^s)7+>hzsc#lYV<!U{Igj=^5EUFM-Mv=)wvxW|
z8TXyF*HZPlbrM(K8IsssbLKiOgkB5>MZd1|Q<>AEk7F;t_W@E)o6jCjA95v2kxkP9
zk1?-!Ur`pAG20hB{n$l4f>4fR?dfyaIlfLtM{5e=Nmctl6sb-5vW`xj50a`gZY?V-
zfJ4rfqj#N6=C`t4{3Svk4vMEoUwG_>uRt!G?RfI*N$2@j9Jh8-COYrAP6l*k`nZ@(
zy4z9t_Lf|IRPhdWGG&}q2yg%5#Zsd1k@&t~RrmJDi=;<7oz%I7<)*hT)73TS=SYJc
zw8WTKWui3xi*IzdXRaV;RhNqG=?G7*ZF(VR1#T+6?%86Lpa#ArILuMhMi$+morj+i
zd|vCV(lPBJ=-FT+c?=Tjfd~w!SqMhyucgDJAqBK;JM-5qIvWn$7~2wJ7}EqBM66(y
zv4pI!dnUcME-W?tkvDS=Q}kn+a6EUE(ng=wvJm=n>Un+o0^ST+s$7AgxD~%GO3WTD
zrMADTW6<GuYQGzLhGmQXZnSy?e<$bLzKQg|R&8%H#9QFAoR1IB2EPf*nG3k}#TW_V
zfs?hu15_u=mqx~a_1`_bhpDvkI4yc)ixCf<3lc8Qc~9ujTX2pL5?+?CV7E0*vA*y!
z7;P&a`o4GJs-0ygCQ9egmjH?M?43hoFK?Ii%{l$g3P|}B2bqyZRl*pStm?Ai;bfRr
z!NSnH&NJ#T0X1#nhgca*&RDhvoS`cB@*`Py^N==Jke2hzEo`^Ki^@Sa`Of2tF=Uuy
z1zWI~u`}hmMI7c7;}aWMGs`o<-)!?Y5(95TI%0AC&O+`?Hd>8;hQ4R}phAwRQ5b}h
zxZ8o(tuOP35>f2!wNGS3-%b2(|47JNxZSBudpf%=3T0l-d*=C?ATrZ@5!Xe}*8Zta
z#Ok>hpYSdEvoN>z+YIlWvi)^ET5Q*qW03t-JB+=TTg*O|^vj+hF3G%3y6HGYhb%E-
zc~gg~`U_)htraNfkn~`&I=;k(*OUcWB6wq2dGWv&!zSaW6hol$-dgiS6fj!a09EzA
z+xP7wD_1}d^tJ7?96yPCSl+XX>f^QhI0VKaSEdJKs_X&Bx|3XAk3_sAWKEwUUzmHb
zf<n(BNsF(LLn#K>b@Q9cE64O%q<Y(H?MapU<cw0HPaW-?$p>5Ubk8coYwkQ*W_%sZ
z1D@!|$jnKZbDCtS*(0iA8F;(Hd_J6)h5E=H`+eE3rVgYTyV`oOSpD!Xf;1v3=dcdh
z-verk9Apn3Pbq-{4nC3KoLa`9KGe6HSIZwUK-0-t1elQYxpZmCwI+!`E253f)sRjd
zl+HffCd#KqMhx>jZ-P-}g!q=P$+;fNbtWq@9NHRQuwuES{ZKp#n#0hxa~^qpD%Lyo
z=UJqFTZ)UK5Gg6%J;i3tkRc1TecC&8w5toPGfc4hJ8Y7ZHARYazRyf0H<6E;YB$&V
z)8)ygOkU6V?_gPpO7^p}8dWX>BFF_FBx_-+Gj0Nz`AbyGP^jj|z7}rNsZGLPv!yIh
z-A!l1G5y?F`~>|}GGVUc-H=bOyKA)U3l6P{inblw?+Zu)$3=y!$$N)ByMZdg_yacB
zY`M5Q{0`WO9rD~cT%MkJPmJ$jt%~bB!kN5MwPIMX*Lf<h9#9EG=buBVIf<(iX6Jjx
z90fZ$5e~@Az_4o@6G)N7`Hk)}J*8ht;~`hsV<*oB|0TuH&HFW^Yz-Rmrc3=MR#E+Z
zeX`xUA=c1J4|`$z%G?pY+i@)3i+$JMypAFU+XQu12d5D7L}zRFUp0`lIO3bLZsX|g
z7FE9}G8qdzx10A(;(c!Y6it~zU3{f^twf953grKYm?ibAN?LrSq@|^!?%}=-iNV#s
z9GOV&-C~oqG7F9O%;SJQj31S(x=VP(94{({1|^AJ0@GEB!WrcdVsKpqxKw5#(agsD
zuyH<?mtU~ncjYHeoiw#W$aRe`)-K-@8Su+%-mT5~^uS@WgL>>&V3475alBETh<&{I
z@ycV4uBfmj)!F@Df>d&M3uti=Wvl7f_whr4ewx3>Y<pO;VTMZG7ID(6<C+GA-B9fc
z{&1Fh93BUJKLhl*JC^bbE|IZ&Bpk)}$KI*mIK(PwA<;6LN5S>j*ic>J$?o#7&+RyY
zwnBN^t&D(~m9S1miO?%GYR%8{^Ycf=)7<}1-paO<yP^cc19v7pTQ=r$-B$nVtBxRi
zrr(lu2v1E-H8QjI8%tX8JuuefEUTz6MEPONy~Z(^`ma$=m^fdR3>5+z+Hv%i-7vJu
zwj;bIoO(EGEAPLK4GKFX>MvQC6^E%u_EbnE+JU6*Yc}F{jWtxWpN?pRt>Qx8g_4Kg
zq|#GE$KS@O@BgVwv0w33QE(baXQ5*iXqAMO(%}npVuSacG!G|Y*(D4@uO2G=T-oi2
za2%SNN_TZ}DN3E~F#ITIf52hs{?xo9oU(`t*=$PoBd&SWPdG|78<8Q`u3f9`OI28V
zgaFj{^!Cp^Uz5;@<g1#bDWAc2)TYhUEtaW;8WfM>R?3o5j!v9L*tm57MM?g0d-&aS
zrKi|Z^BUMk^$?<B@?~?QkeQ&R4HzJtjxe>^sDF*oB;2})M)8K!{URRMK~2B4x^Q2N
zf7q{v5efH+7V)v4ixg!KTx}i3HiBZ_gyU9l9ewUHlJAkK9{Pnv@#yzP!|Y6cQBhHU
z)DI*h`)$M2Yc4k#)MNA&r+3qVvE#J5y1E8OPd`6DMW=j}($im+<i;Z70-`z|0X+?F
z+^C;~6_%B?DP$NF<Qrt*6y^Ir-FOlH2QUWn#cLbGI)Y(Coi9%fwy#(Bdb&G1H;S2g
zEv*!wHaR0U_IH-Xp?sl$p%q}TW6$NGEKbdX<s4`=inBt@Ck^Idfodb-+^n+dCskQf
z<^rbu#t$Si1Alpf%&eY%<KVOn-G)^ysOKA^G&%&XIS(0nHZ3G&Z7+eN`IP@8oR=~k
z{hd}3FcVY|(7GAA;kc+eLTr3GuMx45mmCXH0^FapKH0D(>*nU>)%NR+RQ>n5&_81Y
z!WUN7Qb3}*tStLq%fmTwec>6Qy`mak+d<zIH776=p(A5o+F|X6M30OZn5%BVh(~@*
zg&^Q+)(XDMLlaOs=7;K`WJ2NxasA+H^48^kVza?)()01-iR*j0*k_WY=AUC+=+&#K
zX=%nqCdFdojX0bI(9qlqpK-70H$B<?!c1GptdXT<$QqPY&6^pWmuz!zG-hVorimVm
zINs^+j^QQYM~jT9wkjDK&pznGf&de)6podc34U;Kny7ZzlR$<3c>u)B_ZS$?S@OD`
zo}Ru+hJm&3u=YG`%KOXs)sf9M(o8`9$cl)>-H+af`~g!{L0K+4EOCdwKNz~Qsu9ps
z{elr4&khRF7$mNrT07U5B0o-D&WSTP==TcunXc*<2yrc6a&(2COsg9+9lX_Bm49!N
zKP9S<DzTHrwWUs3F^u5(v)QtBIzu&J(*=n0U!*}z>Jj7ilJ;PkG|YB_Ity4PUmY+z
zY39iX{YBfrxtCs3e&g0_9e;YHECJI}c70XCek(7f@0hkPd?<MpMw$+|aU%8c(SA2D
z9=4OGmyAiOzWZw@Nu9N6ro!z)dpMsz*4~FS6SC!UxLz~Z7)G2}D)}H`Xe0Y?_JC|y
znmsF%NLjf5+Apu<3S!J5n(NA=iS>z^s6`%9=PB9US;sVwnSO=MtSEtl$BQ@psT2~5
zM()I}{r&w#O+HJQS?@0_^j5%rx6e0=h0Nljx#cn3YJr5S2q0%g#HmttM*=i}<t0d)
z(}%N%BLd;%IT2S^SI?Hs=CyV|tbJ6QwKGoZi~KRMp@W47=8n|?0sg>^pKwC)AJkQZ
zZEUxP9p-n(@~durFw`(t9ru~9gP;!n#FuQu_T3aV&cDlj+$TTC_p>8{ng*K<-QflD
zcGY}OqLiCJ@N$-jy!t1_nZU<)Ll&iMTT%qpfr|tU)eLH=x)Si8d%*%B>s2wan#W}N
z`4-tf(x^~{bRct0lGetzFj+UF47Pya=x9Oln~P@%hftKZT6d1Ozl+38?GN2#rOb^2
z+1+8T^QGUr2<snpnb!|7iJ?F;a<pD?Ww!I<W?^BGUMp|ej7njRZ?yDma)|N@TeGY<
z=a!S7zc?%GL!5BZBqUaZI!smCe{t;6yjN59uqxZ}&+i{0fOg7#=D#@gr`CN2cH81X
z3~vRDPO4vATomzaUdi7XRGN=|`ZB*?QO#}UwxCbn6|b*RF+jy3dNfSeTN)c1ug*mY
z3SftZDzmYz$vFRzrL2!Yxtf1*QWnd#-*WJ$85G31ySQ-AVzJoPt@gv2q<ysDl}BGc
zY-CheZ4fu7Te$5eJsQFm%Nk~yAN~6J`mh7=rWinD-Smpnz9Ta!nQp@Su8l{3dJAOC
ztABqN(@WZoINo0xH~--9ll#|NcfaCn>s;NqO^XW<cKzwfHTFPMQaCR^yvLeXCbhjs
zqibpQr%d8r6X1W_LYKvgy=MJ1_(FbtxasPO^R%!Tt8I3yDZ4!|n0dTfQqfK-tr<9&
zVo{tTY(eiN$yi&oc+Yhnw*Xo77qOLiUx!~|t#-s<a$h!LzgPQZKCi0(dKRx&>vlAd
zKPvUrfxo(zJE)Yc+3%Se2d0|sK;Z1ib{zeYI;EfH71(u>`hixjEtkAw-;%KVz?54>
zTW|-EeLK>xx%~X85q2=8WQRtiC<3|Tf!KimaJ__ns;_a#qVVdESTCRqy12N&_0yHM
zEmQO>XDOQjacaN*5^*%lR15n2PWWbc)uV53j$hER9E>MiG7e8DAt~E;B3X4$v^|if
zxTS^wL+LITmz8-m0eX%I8M;BwPsO=aP*#EDk*N_#z}r?>)D>?zBwG_H6w+*jf9ArU
zA|4qap?JtN{f?)W17F2G?dBJp0j6-AKo+kmhj{by7Qnb~Q~}>jil-{0fzo6eeuU$d
z_6j8(3OdADHhDkYIO%j?9V`{>I9iZJ9sf*j-(3Lw?vOqvCPweH`tFdb;-q`c`HZIW
z-<F1-uCD2tjV=T{YS7Ws>oo%)*_*yh^%ncBHj>5yVDqD(pLM_G0oksY*#s@rZ0i@C
zBp@@vpx^IXikmzTH(Awp5kNdFWZkW(mEZ-mJ!h!4{dEIAK78NW-r5|xV_Y<v^G!lZ
z6Z%m7C+c{&KzP0@=I}=h5Xr|tHC2~i3C?vLgJRA@8`+rc;6_PbA=noH7zA+OWw;Rp
z=!$2v1L?&lATo)ncoIPf=q>T5h+F*cPHl}+{2F@C3NWX?cbdYXW6-jarZGJ(Q7pj<
zM_$F^4+cgGG!)$Sdp`7FGer|iTQc7KvB9^i&3A!cNIiVp<3?t*bgx`lv1D1ZE~g7<
zq<-5Z9w(2!^lf1Ml(dYQdAbU5HJUTM-7Y99)>R@5k52#rXf~II3;j=_?EfmlKc8Bd
z4-$Z^LwDZ_Y|&VO))P%`#EbeRxiaF1(W>b2$nnT3Kng3o3cfOz>4RvIyh-mKB7!-Z
zCZ9dH(+9D~2o9C?rYA(@z5xcOiu34O`Q=2KH94K>G@{jK6kcbG!dEYkv7sYqUUdne
zz_qtXYmqMJEAGVS8kk6fLuCkc*m<|dq;9B_X+0l2Ud60#9hwQX%5<9IM;EhAuFx|N
z%6kT?;@*oE89&eiasJF4ntFevbJy#1YwO~|o%q_tW-Snu@AQ;Sr*E8ggH;#wMXyPm
zDBLaAf(YjH@%c%6C*H(h>?c%%yHHxLz1`>~&}IbM?R3tfp~Bz&fNfhA<$E8RM?N((
zDP@AkOMbElMt5AidffQNL>O-L(gavlb+{|{KU#Y6qdET%3ja5nabla*c$rUqL}=`H
z`AvEB&iPX9_}tcJOOWQNyMywZpO`Sf@;9cX5y@b$C2mD}F00QLqVS&Hu>bGI{!eQ+
z>w*L`eZldfiN<D(__Pl)*W+`a6(_BMQOu(pT(-7FTB7h`r0WXU7J7}X{mk!X;C!&m
z#JG`Cif)vWG<_)wKYveJ9dCP%JHkr4_?>8x7GV5P$M@SGblnR$mJ^+<MBYg@T>wma
zzmCSciH{*Y-f4;soxp^-05`I+7DKSod&=53A7)$1%twB9C;>b|;hyv%;_Y?-IzHDk
z4=}><X@IHanCP)$XTf^$!3nNRiV^ge;j34GmDaZawx+Ioa_MlO&nWqx^hw0VJvs^2
zf9~o3N%;R6*Z+KKp<XlM>LfQ-bFk4PGQy;i18yY4!Xm<Ru$yvkKzyXcOf$o#`uEJU
z`iTj^UW0Ttee#~tcILEh??o5x8CxFi?-3{Fr;aD1FY1MqBuhIT^fzcafF#@;eS7mX
z<r<&Y-HHFR-15WwV5`!@9A~k&{j0jx*ku#?U%BYN62SkOSilXRGc~8Z<tlmQxIRMF
zXpXoaX4qK+*0-+PMKsXO(E8IPK~VIIU>y)u6j>t*pS}$YM4VdJVz5V`pySZTk+Kjo
zu!N{_swjNHOd71l_VH|p+9@TWW@-dlI)MXC4|Q7LZjkYg&^eWW84Z3BVvQ}SA|`Yl
z;HuY+Tr3jHEt`rL8hkXYjw#MVbkIYEb~Weg^Tp3<Su5>^dki%!YM=E!t21L+9pBvX
zoW`vsY}9y-{ra|7k}NYx_wZghY)l>q#o5z*r>3~n1q|ID66tKDt1?1P9EkBSDeu;<
zYbh^mTcX8_fKn~BOd{HZQ$%7_c3#g_-EfPUak#UJDd~1MN#X?glYh3!Yg*J+TlGJ9
zd2mb#+1TCbg<UiY83_;kmQE|%forkaC|W8^0%_0((vbaj$?A4@eIvZ=tk|@HC@?P;
zLvVss&%)TpK|YV?H}!J=-OIaLb58-0z^2QH_e0c0nz&=>GmK+J;p#WDKsKx&*`{YX
z0{O9Yfl1!g%)o_iyo8X0@ywl{@9QH`Oko1pa){qal$}>$$1Fv5XCJOO<1V2CRGY-%
z@y%^oU@O*ZIwV^N9k?xfE?(4y<s6QNVz+)QhL%F=4|4{%YR8N2fq-lNz*6WL>FxHi
zH@tks;70Voyf7EceB6~#F}UR0N68|0&vVNHf%yshS}AphS@EM7+yeRy(s<gr=$0^W
z4bg89CF}Q+K<2h*+JGLT-aG&j5v%b<glw_NSz!FyH`t8|dOl`%=2o3X*w*@hjBb4H
zX8AqVKnc9G9mAYS!iz{3^pZ+Zl6B+<MtfFo25$+1bn#a6w|LRA_gdg1DLW_N<{t_n
z2vr4jY$8=+p`M_F+ZB8Icp7{%nhXEjAW$DDyvBu2O)LaYOeF~+t(q0PgRn+uBk{Jn
z4D$|Xd9-SR|LN6!#QmVIjW<qz;z##ygC>)k`j_^1m#Uf@wZNgQ-sil1+_7dka|<w<
zhiucf0u+kq!Q`z*HseddV9HMP8g$gZPz5e|ze0G;=2~FfAU{UQ`MUW)R%Pg7`sni|
zn@0bIBU-1V9Q6lr!1W{vO7<f=z35nE=88bZy>5QAIdC^rsWUOTo#rkbk^b1|k!pnw
zP=eTrkM)e4bjuVEeCpg-1Wl8=G{QZ2PGyE<UG^wyKF5UDMuxTDWK<uDLzw#xq*M)v
z@7Bsf%Ax)u?!m_*a;O1Sf?j;?EnuQgWN&23aGAWcINu&$1G~M?pGSXCA1vZ?Ws1}R
zGj?8JwuKuh{$5}>Ik`#}NdS(rY+~;LfFMv=3s4~b?}6)Cp!V}0-zls!*gaWF6oDHJ
z-g9Q(^+2*e`Z}$zB$Xt58UB;!;wV(0tAGSngnm)2019#ND?nm-Zzhk3XS*y~gAW1A
zMQWvek`=O|nG40!b`}X2u*<{>!Q(N&<!!*xU<^G5x(Ke3x}N~#4(qB}FtqQrXS`_9
z-G2mwON%V|#lR(<_}t8_d!P#XyRSFB^csgKFmz4m!4hy!s)(P7pEgY0o34_-@(p2f
zmJ+GytHs|9<@MESi5Gp42e?fTzG^vKCDosaFl|scKaNgA+eXSl+f?QhfKtWt64IE7
z={?&{!+e3}ahSx|RR$$~F8a%~;X34%56Kz{qJF9`iC|5xmIy`y(-4i-6uhpKE)yC{
zX8`G9(+cGBRwj(F;gq;yA&fskAa>1@X91L?1{?h%564^k{_swf%q@4BtY(`WugNy(
zof>`Tnxf<rG{V|DoN037F%LZpnZGEty7bibH|JQ((n-TE4z7lhX~4F>{aZLf7X4kd
zKVQYiF2k6W^1m)kvrj+cW@Wb7`Poy!QQs821OQ$q%CUpXR)&}qdDXZgPEzl&kps)@
zGNHn~`8Yh}JWj=|dNE(z=78AL+dG_O;@JdIniZe@HY`nB>uaHnB7zC?OzoMX@C+F<
zP(>~|eCF9!vd3kg5I})-R$#S@Yula951WWTejEJ|s1t?1?mdszs}@j%3YvZ50Bj!p
zh+ZWZfzv$)lxV~F5_&bc154J>^MS3;0Hif4|C<R<Vw`JXB5UHmzcCdBa>6?)#Gy$m
z0Gfh7Rv%%3a#9!fUvTq3udn~9W&z367)`)Zw$Z4ZN&8_rAl1cKEj>5ri~z@hx4;D;
zTd)iSxI0Vt3e<785C)I$DYt--L-9S!$3bWYJC|MZJ-mnRDf|tzV^EjJ>C%TUc*H)7
zqk|nhceWZmi|LD#K;iscx0k^cLRL&yL|Aqut1`}_KLc}VTXDk>u^V3y$0TOlLd{Kh
zqEp3Cb%egx!=t!{IOy@cB)UAATwpO~yas*^9T2$|DaZ!9fugs%|47yFA6Rp|P`4;b
zZ)|kb%l{|x#sU-GoJ7fU+18vPa<T5+0dTJ@r27m^ev5-em4l2p^L1RpBpEPIwgrUC
zPO?(<9_uch>1{<o<_uRReDs6b=@Tr|U@YAMK!GBiEB`o={_j8mpLi<hndP{YKzG2b
z5J7QEpeijq0PtJDBa#ke3)%w7L2#pq@N+=Da4Jd?Al$Ax)TAXsGh+n|X=Lcz?`f&h
zgSr>F71c0~EzBF!f!1OAuD5hD(|U`OlE!M1D%4JYd{7RnTZHjdW46}TTwyY1A^Ize
z`Q|=J5iI0FD-YdO4Zzg%L{MgodN?h%=IfFMU2BrlRPr+Z2s57B?n|ASJiYbZ$ERs`
zYlL$~<ME6fGrsue&^;gYO?Hx?MLBVRJhvm*pIQ^D4!}2M6Jt;u<R|*kCuLac>A;e&
zFjI*9PPFB9Y{QhN!cgmttyi&X3I!T_e*NDHjPl0D#&V-ho0qt6l+~GrHM_^aSN(zQ
zL_qljr%~w&n{H_CL}?&74LkryiACg7>Z#IV6QCA8(5mC3wf<{P3YZTN4FLLY{P#fr
zVs*rlxHSUsg)EsB$OSWSo${*+`Xk64=xmWQP{{qV0SfjQp$U!n&J)f<xLDcz)qgcI
zJgBQoe~&~4pzp0Z8iF^z0DTe9X6JBCjb&;q)}IJ9_%83sMF<2Sm}pTtAYd}I05S<S
z1Zjc<manTu#CkSG)6)e2MK8Xm6bJj!&a_S!4az?QyL2KMfV7ol^p!5IacG_SyZspC
z)b|Yq(9Gr{*GU>_0AByih5xTH{eOeke~15d1zN%xJHb!3y8vu}*j3~*0Y#t&fOv6q
zGvM~qt*4`DJ!uvp<KQT<2Os}kPnvYMAtQ|HbIRxgB&eOU5lX(vxDgtAW~M1x2pM8H
zT50o`61V2&7Mxh}Ie#(f<&K8S!B*e==QiG0*SmTCvSx>%ourD^a>kj9PHFLPN(3th
ziWL0zCKJj&FD@n}3Q8Xd+L=z^*69G<H9#G2k(MC+=%}jhmL%+eDjFKpr2y_#v9RMD
zkGQkdRbg8{??jhW^7#WQ!VOH=0&Qj#EAKd=rrnq7R(ophJM^T#wA$&Sm$e7!oi{VK
z3?Q01r8m1Y;nL(6h8F}i4o20v&>n!q)U#YnebN6xkHzYDgv&l>cwCfg%%S!7gYvkh
z`SZ6LOyJgxEk_d6-A6ywnYXmpU*0gZJ%F3z-j9NdQWyPhyPMZ9a_EOiTi16rzjvLW
z|7YZS7qJ9NUg8d*uPi<xA)_k*m9qN(!@VydLZGS;icj=pNp&8uP!pg~9grZj1L6pA
za*T<n{B<6Hy03v=S^C>R`hiAnAlwip@qYtsfENo@a}pMok9F~ZcqV)>KG(n2N6y<c
z^XVFd6i}%FFXphslueUTUWKoP8kN(TN!wIE8T%zB(leH+Yb*^ux@JGS^?dp1DS|CJ
z4#69VW1DP}XkNG~z%{kaCRQZ5%YB4WZEz1?W#k3Smu#Tw)K&aVuV*Y>*F-w))8N9$
zVHj8jG?Nv)3dGUnVlk`@t;~S@Cfg^k6N*<f0-QuZOJ(Fd&A**6Ur6yKAW^Sg`Ip@O
zzaA|8^I%$*1cwIx<Lm;U%3cP*b9(VTMJJg;0$NAgHJ*$ehyb-La4RwecBub@lmP6T
zW#|rB>)o1Zv7?<|re$;Vo^E{YZNS3Jgc`_v`NgW3h}#WT8eqDHk`u-PKOuPcpBz0s
zU!)bE%Txr=otp1t_<ywiyDJc|`$w-p^O4ht&u9pq8*gA|`W)apCjP)_%rPH<u9Abm
zi&w*sUsoqc%857q6opH60Ot=>IxIL)){UHl7R5ic>(%A_M~MR22!8zFG!->WHsWJW
zPjs3B;fekjT6Fx_%C{B`!XJA6AWxZ7siq1LJ^47Zy4pVVoxBFkb!*EJlUO3q9xXSJ
zmxnno%yP(-U0PD>vz{1rT>G(_v`8XlT=jF-f3x_kw!_!YN7nAX`%rhs@lvTn!|~de
zxSnARG%yR+0^_)Bwg2O^ZEJ&juk*}vL!QWmlx|e1mHOe@lA3x$Rh9n(Wt^9*?s$c(
z>+8WUZ@N)wmd1-g&O4dzo-X0kysh*W*pLyh7g^ikQ)DHW^YCHAberAcZ|Y$#-^Pem
zvBTafY5s+&%)`cmJg<n8RmQU8cq1dm(`@Qo7i$Hn?gqu>o_@e0=5@uwZIb-dP%9N`
z6Q`v{_`IM)TGUu?l5~uwr%l_zm*jTJw2gyX#7ead`d8n)iC*vX2>n)%ndr$)8}lU2
zJBs5lCkpC8vtH!vSt8U)18a;9M$1O_vKIqH#IuZpEHKAwbgJ^|LCd*DIUZR6qP*8_
zM(|<ig3oFNK7H>Z_OCs~+MJI9=?`yADt9R^G-jAot?C*(3mkx{ao!5W9S{*ION$@3
zA*8dbt*lrTSUHwj$aAh(uye1<OUSD7*u}0wm%}Xk(T?|dkMS3-t<J$!LCZ{QK3RXi
zSCN9v9-<!D&(aylUe%YbJcF&=>0NnewL{<|uxM?hfp#K~6QxTEEdb&v^ofp(wv-Oj
zlUklYz0amwMD0M>BYR(Y-X(kUSWH|!CFvkg4)!k#brF-=3p<Bu$6=Np?F^mAEE&zN
zjWugXfeY>*^~*5@Rzd1~O^9mDqNS}0Rrbl??g!O{r-LC_Ww(cys5!3hOO6|7!G~sr
zNwGbdQEm)t*UgNUsrNS4d~Q1mG_M|{Z?(-NVwSR9-xy{q_)5;P%szNB!!BKA?_$?L
z<LVMeXGf2p&ONpA>Cs+=4*7a;`=j%9w(UV5nJ?1{K<Z0;?{Ao_Q%|-<R`dO~*Cf?C
z{($yP81I_?T)+94(70MFBcC>wuNGVhA{)P4bB#MU$GfAf+~xPutMBEiW(py40>WqX
zBO6yVq;{R*xpm}uBrDd{#9g4d^B~<#pt<M3swbpPs&r7ShJLE)G(;TS`#~l*gGke^
zORj7JH?n=qaMpfF;d;n;bZtdTrSKl5gKm`}%kS)1i*f^&`YFX5G*p3*H7PFimxQre
z+lLJ(uv$BWZAxzKRbN4<j*}2;@iN)aMTo61U+p%noey62wQ0hv7*gb-Va|qQeR+1m
zfh8gcJ%+|Ijg1Lx><eCFpD`QDPY5a`?kUfz`R464;bzwy96$k?S!hSJ9-Q+hm>wL)
zuoWi@3W1U&Ebe<QolV+i^zA1|n?&$?Gv5<bw_#O!>+P+ta0$jbE42RME_f1J0Ae+%
zMzbaS(I_VFH4Ukm93D(Pm{}`ki((It!>~=KB64c-m7^++OiHwN<dB3#Tc)E-f@-Jh
zNZP%C)cTIWDMcRJ9_mH-w^e#BXzN{UE}2HIy;Fh1xJPZB_O96aVHg9ps9KEUL66#X
zzly<ci&=nDzs;<TwQK18@9rb~pZmw`_`K~TYE>DzMj1DBjm9RSA0d}%GwEVMMz2kC
zU5!t*a#N%m-lGjwvaI37u5FP!k#!7Ns%HY}lth1IoA?oFW!7`+jnS7snMu^i!mYdB
zqgfxv9fB3vgTKpyavsgk{MF^;&T^zdBzt~hd`LG1WxhmcRs~mpd?E!$aFddwU9`iE
z>yxxq2VFWFGBg7@bpcemN`K!Dx<LL$Nw=BIf#U6f!VL1Y6c`L_6QeGJ39r|x)0(5+
z^up`f3CJZ9A`qRuOCbE8QXT_3gP!}76$opeQOlk`mtZXDI`pQpjTyj4HPB_~N^E6v
z<UB)U<E6PTJ{0FE%UTw^2O{lRttE6_n|ys>$?DVlA@<a}%YKQt+blzDl$elHjVL+W
zFvKB)a58r?<9Cr9|B4^Mn?AKNZ^6U!dFCUf`eY-5p`CZ^(|z2aX`$)GqliEnC0=JC
z<n~i@k0lEY7IgRZuw5nE(02-~+!avX#lnV!1!H#}1bd{hPiwiTUx*plr88<5cu4&z
zgCN1DCZ8fe=wA*m0;g|Z80xqq?T3~|cWpR}Xcwx7%<fs<*XI^NbU&WQh|?TvMpDSW
zAVIn#q2N#+?~AC@A3Ye;?kV<f<6fR@2BSrd+C%K0oAE+<?OV~Vvrh{$s=Z&@7u;EK
zuvz_EJ!X$b?(oM`HY`^q2SjORRgkQ!B{_317}9Rd+dwtgLypdtrNM0bQ~fnNqYmCU
zA35cWe_sl-d*GWk_8C^)A3KfGX6S45iS&Y=dZtFY(s{!r27L*c=~%DB(0g%Kd{k>A
z*}^T0`lT5uD<&;gi`t-qowP|Ej8^ZRnpqjT8J~ni|LQGBa5-RazjcS-5mX}O22KKL
z(rR$BSc2S93eypwt#1MqI@t6H&^i}y`{^vjqX(VhV{}@7om(yPIHyk%u#b46gZmB>
zT|2HV4N^W6C^zV12JH}956QSihP>O5LfTA!L&`-<VZw{*I1V)XBtkE(e1E^@#TJZ1
z2?GIjanRRr*uUY{?Mo*csh=OQodNFS@ZP~{rcAJ6lfBQ3r3}xX%PR1D;u|5N#r1?p
z&w-;+=lXbK?JVZ-jF!fD{z4==;`YcGH2Ey?aT4x!mi|zL&~7^qBRz<ppO>T}j1c0Z
zNor$k78EwI%nsVp-wZ>aX%AInE6I9zIKeb#u}&qNBS?3eMhD{yOY3)}1rhU+0WKi~
ztNJj;wAPw6X@~uDX>^@QaWi+^&;pgsskG>4c(|QdRsrlKMt?{eIOfDVHy`F4%Ii6Y
zXxM*P3_(XBbNo?#@*7%q^qu!x<+I1znle0o%X5=??Tz=>kT3eK+GG<V?QbdMF$mxP
zE;YQyH#0!gE2_g%=RlpwDB9s2Teohv<H#ZC@q<Vg0cfl+$;k>((9ktYXuw(jD|h0X
zU#|-UE9eztB9Sjj-}nigF7O#Z`kiWuYfTdMv#0ZZ$_ZK$B{UnEkBI@?G_mj%VWdVP
plEPjNFw%dm#Q#0*Jb?<<`jY4FWwciS{I3lNu5ECuT+8nH{{XJARF?n%

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~

diff --git a/reference/Rplot007.png b/reference/Rplot007.png
index 0d8e1efeb118837a2f47645bdbddc1f013504dd6..395011118e7e6348435ff63af7346c019ad37747 100644
GIT binary patch
literal 13438
zcmdUW2{_dKzxEH6o)+3eN>5QqmTbv3l}h&PSx4DoY-P(bLrMN+E0Pc@Nn?yjwvd^Y
z2am*9M~JB;TbjgJX3Wg_{CfW9I?MGx*LB|aJ?A>-b&X~k{Fd+cv)uRnx##+6bHlZ(
zq*fsavexL-aVrE7`iOq7TmkRkyQvxQwBnqJ;c<9|uSJ8p7<l?E=#)b+f~?+vehYOD
z5|$8TCt`H`sLiE&Q-cw=ZANk%r$-r#O7;5o1I9<R_OH5PdG~<Si3>(5*ClIzJNw(-
zg6k%GS`K$Kt7a#qdz>sEGgXz;IJsBqc>MUu<<c4lvn584U%znn^eruk)aN^|wmu%8
zC}rnjv~0W9_Vm~e&QDeQQ1gSeLhJ{*bu{jf6T7?K7T==QC?SNz{=PYA5U$a1m#_qa
zh_3v&`~ByJM0NsZ=~d`9B<T3A*zFQZuQFS`8VnKSW%A9*6)DHbWOAt!f@n3aS3wZ5
zH3JCY%Pqs%SK@@z*N-1-ZEfwR;-jXiHv0=nrM1SxO{u*dhBD-srKT90J?Zj8F`weM
zPNe93Z@;p9wT+V#DY)&hp-$Mhc1jEn*Lv@;b%9|jBQ<7e*tMl)<RYipg7DBiRnIOt
zIhoU$NqyAT)^;P392Oy(e?{p))I>K?N-wg4@FPxUUp_t4<tvLYuj6v*Q+$1Z$)5BZ
z4d;yaq^CVnpuDiUb?P`g)V$uc7u%9JCE;)7i^h@<4ejIS85eH4e*AXJ$o?G#7YEiL
z?E_EOd`w9Z(>Qc!lFw!J@_AhLOuoQ5Rd4RhG`}Hnt3sBk?<9lFfj3L4nFOay=Zi+u
zd_PT6PC%_u^-v_Ke_%i(a7foIU*9F?P$1*l+HDn~lXkq>B215oa=p#H(H1QKnJb=c
zTfgu*vA+_RIW$k5_ZfN-v6<~O^;3+%V*hMW?hU5v6?x}HoU99*>Z}>fBiUo|mETn9
zx-+JF{DtBEa&L?2s)QYpr%dh~u+-g=Da%`E!q{o&+U?Ki+-RZuU|ZmeGWvX;NxjU6
znG<F)?%hmH5k&frH3r;y)}heQ(8u-lyqny)sR3`<ELhXe{!#PaHPTE~v(y_U)Zpr8
z{@k6qi4`-`ddN&gLcPYh^wK~$jq4K3ovPlNxwknaJbd1fVQIMClm9c5Haiel+2Zun
z?Q+Bfu_%&kVQ6SbYdQSh*51W4pq|b6%qq%@EROhIGs?NgFKgPNhUHDzC(`D>4L?dZ
zSEbPSk<--g-vf3xdz3r4n+n`*0&;{9F^vPMu^X>nWl*pbO7w_AHYSD|@ch%Y&$ov1
z^rCK`lD%OVk=EVbzSAgjelR?-I$2Wf@j|ecw4IAfuJC06cJPvJsh_5T5xT&#KHX0&
z8?37!0tTuBS)o@Q%r#mjedfOp-e?*whHyTASkEPLypRJsvDZCmD0;@sf1t{&+^co}
z5bf-n6|MOgESt@~aeNo&Pec8OWT$GodTFBI;Qs1X1WAyp%D<vN{wRGm5xaCOZT6iS
zee92o7Tqb^b-Lfbf6~@w9vU95AeC&K27w`G1ji9^v~zbaxbo`0wvNtbu>_vO<yFZ$
z^c<T#ta5X6jb%63wnlxp&?LVlaoi$?lm4A1AmC?mn72nG(!^dQa36@{Pth@g7mv+z
zi%;)+WGI8T!EpQN#*9_*$Bc`*8kph)L0#Ix30w|4`gdVO%k7N5TEJkfEyS$Cies`H
zELF2?^z-dunZCB>6tSX5b>4cY!XMUhCP-|e1-`h~@`A?1#KbI|-Lcm>*lb$F6au9y
zwAaO($MRU+Lj8P0p&~{=kaVxOFnp4?NRKH!&kw{;I@R(ylc79z?_^g#KV*6iQX)i~
z%;7}FANzgkLya{=aA<xgdQu(M3+u&bSE7wEbFnSlc&@KkB4fY{i8WZ;g6My}>)t&$
zNVYaVe4^}(DVE#cJNqTwV*e)XpqEsyR;fZO`Q&Yf%ni4P-uA`e;G)9U`eucHxR5Y3
zlQ(~U?pq&ZJ?G0_3^A23ThOrEIOmQswiK-(d+!BZk&=?C2%B;=%Tg_T7?G2cQyIN@
zu{BT6$-OB>aVYPUY<GWuu^L_18tNFN^>zLE8amryG#gjiaYxZ<Q({9%zfaNl(hMEr
zk$v&6l$!l2IgoG(K|Zc~^K+#H5_>BJ`EIyBBW*N7PJkD1nKBnzmLqG{njaNGJ|c!M
zp=usM(3e7MkXVsrzv^cTN%lvL3i9$)NAL37Yn}*=`@OE7nowos-k8(@v0mcBe@Y}8
zL4sD=#mtU%_yz{bwAb1@#xSXPIohVz;!qnW#>Sr7(H=ZF3K5hLZT2vqo!;z0!JXq0
zsZ>`N7Z<z9a0tQ-V+SJE-o6O71@cXSZI~=oXu8L}lAxJmYj2;K!KtjQd^>WHF|l{U
zZ<6jeGOtuxVzp<re-N%W4gb`oJMzvPc<iR~gvXtCBFU_Z@9yZZWWSic^x{il+TrDE
z|M`a2{iD15e|+<*^P%>9$jR|c+LGsqE=G6vzFhMbi7r-!cW&q%NCoZpbBI>e#Svqu
zZEPJEr~?q=N|}@4{nt0{^@ZX}s1Tj?c`q7dXqccG@L?=!Fx^7GZ$2BjHuTsm2kzt(
zt~Eq^3a58}(_ee_FB|k9U%JWx<v-T1SuqmfI_>S$vNFCH%Y%%J)(?EJyLsk?>tsM2
zdHOb=7Bj$BsOju;n-KS)X<1E(3Jc-bB9~vra;m2<P3ux7^o1{rG^uGeHA%RKa>YFA
z6*jM2R?=HtA0t*Fpw4xim3$e$S#ik^Cn@q<;)9i^oY<=^?rZjmeY_k{ZQNIa*~A-&
zUhh#SIEK8J&iAz$NSNaL_bTfil5XRP_i_?!5pUJ3&umAvtfWfMM^?n*uS7E0c7MjR
zux0m7$=)_EQmD0?Ix^|4D(PA7puirUe%?7=QOSYa%Q9{3*SCBUIH`KI<9v4{&rj2d
zO>iM*XG@XTyKns&J29ZO`&<&SHZ@4A#97JPKl<2%_E=8wW2!8Uy;^i@)x=g;>HZQ3
zlod8KO8ZwmHw2O187qVY#Ud+^mqLF5j{hZZ?)*|K^fBm8t5+y=6MJ=k2q2|QYyEsy
zct5mL%c6V&$8BKS7Q0esVu<!2E96tq8K_{;7nweL&9WcOwCT`O=Me9eZf@(64o319
zNAZ+xi47-Bk-!hnEg=D^jCUTY%T&ao?p^tBy7$XBvGJ}kyik{jk&FhWfX5WLr0+4C
z&@c-RufC(?vft>&$pXhp-wbS`DDv^?XD7rWOZ5bfQF2RmkiebF*1B-6d0eS>Y!?6@
zZsi#Ob|%#2K}X?_v$M0mM7TC{ot&I{7>viuS8qB0>2-X=fIY>PjNwnGrahtothBba
zPM4Lxb!uVop)9m6Vrgk<afB|t>xpH7M^ag*_0Dyt{r$<6oV-!^r884ZFCJ8q<GUU}
zwvY+BJM7h^$d~d$wEYV+Psv)b`I2i^Y))*jv9@+EEP|XmvhzN?-4l)1+q$MI^Yy~}
zCo*eRkah@8?~`>;dj07si?=Cq|A8hi-fKaEJDxsMJQScb$)(EWM^4D+;^+G0SPJ*T
z+D2!5{r!8Y{0G#}zFub3*-f-g6#sU5pH1|+77tZI^**DwUgfr!h-+`o7IyjIgf@yL
zWHft}EKi!^JUD%-&(E&Lxy_LW1(QrrktnI9l99HNi?+RDE8^hq;9(cJYMC(9Pg~3$
ze_hv~i!Un6GR0<{{o#66sACI#eSJ$9%DRI75>~{}StpFNn1aL_u_pwqa(47uH}|AG
z{G3B>)b|?Cq8VL_PeIRnMP|CKEqaJWdHap}>%u9ub#-!m1Cpv)rWYhL045k0S}}wV
z>_|n+`s%v?cVP9Ve$(EPC=YLtWRg#2m=}@j_a&YzfCg_n6<ihB4A;iW*U->FJAR>+
zT_hkHk;o74Z_-B-@$XkH`1Y!8TDA6ko$Y>!HE$){E73$2xg4uYFSldP=q`^tN@5pY
zPPWk9p&U_1$-hDvPCgXEaPVf=lI!Z6S~8V0W&Qh}olsO%bgT^_S`lrg$F?G|yOSw~
zFVpv0FupXWM-6UCJ{daQu+>C6D7)szQAGqv@mSi9w!#0n%KrO?`Jewy=o-*)qj>;P
zyGvc_IJru2Uo*O%<$Z)AGKti+v^c{%A9!Xr)FhFrh#9YX05DXBG_aBN>tFu2CF*}k
z;X78kZJ(S}@|~qV3)s(Jpm8`N@ALHYq=R;mKXon2&b*7{L2eZicCLHsijhj*zv22B
zvW!@QLf+n7bEk2+O{;9{V^+i+?SWRh>DkOvh+>F_RV(7`ChP}H&PKzgJJPHm?q9i`
zz8sr7oc)^M<BpGFP{Qi{O9FSUlW1Rv%Cl!j_jJpg!dfxi)end~hbVxGxKO?pkR1RK
z+t!T`gr<AQ%T3M9l51;gZ%hV|0BclQVg7zGJplB60T7J`zNAdFzwjkj#osnAgh$Ej
zrZp?j5Z2p6y45mrW7vIoIgZZB0K9Gsr5W%Fi+4)&N{2*&hpG>m`lz^fpmFA`em<o(
z^%qPzgI=3>{4tSYnbanep*H#R|3~dd;*R}mDPxp<?M~IT>Zs^AZ$q>xeyG5T{wN~T
z4y8kMV8;siTyq?IqGz+&w!bK3?S{SzpRQT^7`A<!zxd1b-~J|!a!EbjY!|gRSJiWs
zknS2FISK=Zq>?whL{`LGxBmk~_@58(|AF5;(uZg^uCbMY#k738;)l`R+((-g?4FQF
zxF;MT<m2B?YsMZ5`0%8$@nkMAdk&W-h^pt!4H)!nMUdT&eMkzBs6Y1VKfo+~66Y+9
zVkj-jH0PPGtz9zw9|;d8#F5zbM5g#zAQwQq@Z16B9a)#)R*lHn4#UvJk<9t*Qiw7r
zF}^-N9i4?H+>&Yl^okD8j)xCL>}4GkK~l~<xEMYJEv@#`wQUt4<5q(~Z`Nv0JSmVh
z^1gTBn(*b=fV!zF*Dh>G#|htnfbZo)-v{d|gGbHD&NWuh{PTw{4X-uJ)9p*>0a*XT
zN;9@8WjkprHrf)c$y0fwJ_0^3!ITM9p{hz#@yydBtG6oYe&GZ4HwhkXv)#F_{odi=
ziinx7=MdIlojuUHv`xt;H?LVCX9S5vyA4eO(u^@_|7PYz@hFjTgi~zuAjZjCQ<qMv
zjd7ZE9NV2wcOqii@vr9Qw%@>4bh`S@AH3N_X`L2dCal_cW{{dz-(;$>;u#)_a}V#F
z3ODAgpSYc7Y>{c!#ZwE^AFo$OvZ9!z<GVU0=gzBx;9)19PbXDY+749tcU}E$W29Nv
z%=JD%s5B<5Q{>o#1X&{myOQ=lu5Xkx0uI0<55)d4d_hB9{R~Mau>n~2eV4j0pTXKt
zYOmm<DCS9)nzx-s>S%<NhOdq7%(sDR3z9vh)Hl!gUABfvxi`JkcL&;oT}4?m0+C3x
zG%$z*P}#$2lo-6ww4K_E0SO~?uFu;ZJqYktpy9Uk(Q3ev*RHKF3UhrlUjpmv_%qgB
zr7=~8vRe7Y)8dF#!)L6mhjGerWSLbN$+v`lTPEBv9Rn9%f#-%{CxNq|VURXU!t&nE
zi%S?5SSmVIl`KSQ<|rO^>AMxh<PL@ZxzUt3fYwLY%P*Gb4YNPSiBBvHr}kP~=oLDY
zdu?U`<D@>O;HPkDV$y3_L-kIbmyxGuVblBTqpB-8P}tBHLy?n;yt!%`yPg1KK1H81
zR{T{Gv?!2NdPmz%zlES8&td@;u?NK0u3bCv?Hj0H#7lXv9X8W|qoCr0E!!#?c-R!2
zqESL&nAz9oz+U~lwpJU^2WSTN8u`g;yT(Im7o^V)A2<%$8AHHh#ZdI3<`x_U&xcdG
z#*6Y@dvt-UHcH^QA)oFSmzS3pFBV7W6K#|-6;cF1WeZ>p79441WtM1u$r;i0xh0x`
zL*H;bSYMZ*SIZli!_$|V<1Rl_-fpto2TSp)_Up4+LO@5_zk?SR9*$f5Ig<A1pnKz!
zsw%t0EsCAMNG}PtB0+!bk6IQz6frs;!yk>gVOR`WmlD-i>phglO0U*zgMieN1+}1X
zCDN3gpFjP=eFGK42r34sZcpaxS$N1jJIEE@Jd*<GI`T=R2;%m~{%yaY0tf^JW;vP=
zwUb|S0;_&r(*cDto6x1cY1Q~`YjepPu2445-R7-hK=mkrT|*IPRaMo3ID)J(@7w+H
zMB)}7D9qXVc+P3I2=a3MgKz79)Ul`}0P`1~M52YtQZqKNc6`rZI1?Z3Ba9&W3Q?;f
z`<{6=kV<D?+(;m|uz;yN+PCdxk89c*1hIMikR=iT)cfz}Ui%SN_Waj(Sq*`OHy;_L
z?{NrR1@ynv4~dyh#C8V~W^!Cvg75Fk-b7j>NK%NW$R!?!ErlUOPlxAVSFA-|-g@MR
z&~tUFd*|H(fFwXoW8?@UZjThW@?%Z`H7Je(oL2iK&7>k=P+L#1G-J@S4T`8;ACjl;
zleQyf>4JZMg%RM=>JJwacisB)G#jYJ-)x~^LC%=&3I20R&%#6miJiz;`*CWZdTR^{
zfN{8C13>o+_x%v)-%E!j*7!E3$z`jYy|P8o(WCw#f}DItpj_?gf<OiY?#C8})hmG2
zn;q}UX!Vjas&O{YGCk?<?{Clvit%oTK7;^(F3LiBzs_L<d2+h%xLlAB^0dJ3wWg-#
zoh7wLS3ob|4y}V7yz)$FNk%X&0|IfKzF?7Vl7@;ifLuLW?yNNei9E^1Q)J<WLTNjL
z?35V8jT$e^^!@Ps0;-e5+04;z0u`wM72q=qWW3(H2i;2!EeGAies1MLY2EiuQT-o6
zuqmdOj~odf49(mT{o~bx36Le$ll%}Ggj^3CwbG|+kEHu7U9dziCK4r!hY&=fjKk9@
zfZBtqL?F)@bU+jtUVX+A3IsH(=G|svU|=v^^^dlATl||(pZx(ZJ{{TwYvIy|xY7vq
zP_nQ~gA#9g*9#*b<DLoWhJSaS>MJi=;5H_2>*?$~S1%8%T>DJsb?wL<r|!<qho*Z9
zOq6PlgGAQ_;u6NDUI<Y+2fK#StH`D4CiJR-oey4YQ3R0o&tYKQj$M3f|0N=Ch&wdz
z3)0tBWe8*WZ7(sLj?~$F!D9Z~QHYtp?nY>-k4~^_xua;UB4H!DL7p!HYid2G_Yu_J
z2NNQ(yP@@APk7hty7jk<HTN#oIMce!v(=4?a>KG^rjjqwI6V|lNjg{|585vmP5NmL
zuqsfcK4LWY(AbcheKW#mR8tElOY6+jW<kH61Zh{Q^D(j}cW-|5vxj@xzR(&Hk<#{Z
zx;}}vG@?vp+|!Lv6^fSF7qN<SZCk)Tel&>3Vh(ChCgk5zMIq(Bjm_wNoSO?<E-W-M
zv-p|5yx?TARN7bAF{1H`9}CmN8vgz2MxF1=+!HZdUjPN4(}5bYOhNKxAa(KU>3JcD
z-1zx_Cpf%y{jFs)edb1f?f1oT90raJkBB_)Lc-bYer#rNl3$y5RP+i}3yqn+9iZGK
zAGDh=5nwbmtEP|4(|x7e*a^c6o909Y@V35B8ol?;38;4t9uU!z7Rv-I4tN>hIHT0x
z<w;<j!1q#K5lUM#aqp$_-Bh&h^WS_5vK4=4tdyg*E}rjSe?-2!dh<&WNrCS&VIdMb
zYA(=y+w3<cW8>^;HkRn7vd_qt?!9|oR0{pm8SI2!3WzyHUE`lOdV_pZl~h`)gk!5^
zy}Q{>IeB1<_5O_FiDz+D96FHLdFVU^Fo7}MV?C|gqf}rO&nJz0?qvHf4?;Uba<b=a
zNnqR*e`ay%Z$vEd*g9LC{Nz9w32ov&bzwFp&gQY@<aeZI-V3;N4+k)4rNO^bE&m6Q
z=fBcS|K;pQ{tOk7xQFM?jiW=R^ZfvKK;4YiXR<0FbGxta&C_~6$LZ<mDNK+dJb&ne
z^Yy*m>Xp$l!pWVxy3xG7d2hb%`?=URLy-KB=jP@JMXM9uq&+g-1JWYY0&S?eqJsmV
z>gvCfOE=P+{$nGxmrS9^q#<n{s&*Xlgg47nc8<e<rfVROHic^~NY&3q=7fsw|2N58
zJHOaJfEqWpjk$0Q1O@dNezc-X?d(k-PzMsL0rQX@E8kKYM3A5}(~7Z0FeA{wF<>?}
zdB-WBW2io==l%Oje}-&z3AvS>lf!d70P_nurttHf>sCGir5Ns5juq(dZ|mfgv*o~<
z`|+!{ZV8N8_>@oyO21L3H-_r~pzu`Mxw5-VMPu!tWv)(6I5Gj)9rTsWsZHeW)Z@GC
zCbx1}6BEy@XzejdndMd>bdUf$0QL=cSj8*q@=;)~=52E?LCG1AxeVEeJVRdGvba~j
zlLFmk)-Dsan-upHymD)w2eD}LWWq6OlN*H-&or41ZElp%Tl(<^u)lnb<*K6GnZ*~5
zPEM8n1Da-cRgR|FV6l|x;ij!Qt)nvy*_smWw?WuwIqW>CroX$6IyHq$Z*6)y#TTES
z9(Q0~yQQvgaFCC$|Jx76*x;Ibc4Wsw(6(lyY`#Y1z5WzL*^C)ZQVVI6^gfj~_kED$
zp(;QAlG~{H$Vwg}0mcvt?5^>OwB|>Ze2`Yr=iS|J>IE?M@KAO5kh)|yhDmQeL&T0T
z8R-UpPr6N<>V`1EV)T$N@4+|b$<sT&Lw#xBXtup(mnakIrn6HYUgh&=_W}m@0VslU
z9DqW*$B)D+VX4lYm&JW6D=N;9cNT(r(@=l~^+cVzU5I}7pn<sVkC(rr`|lDx-H^r?
z#5qD89beG-Um~ymk14(@YXa`yKCkZh(;I^-9UwhYfYFqoacW?N$ue~$f+msyI(VxW
znK7?R_vN7)Bw^nst6jHl9&Gf*8`(8J@y2h1X<ZLL;Td7)N5O^Dpne==$Gy7GV(Zbj
zJ>&di^@A}P9vbRE%r`F*pX2~y0^Yz7COnw{p<RJp-d6&{KeAK&mTUysv;p-3EDw4>
zl~rgNgUAFiy;AU?B%cl{GW=s`$~(4Yp;<!B<L^Z|<^Mm~;2#$6|NA#T;t+~UogECn
zJ95K^<lW!J76}1#o>7bqXAO~ab;5l|-ev%+j0A~th5A~+bHYsF1#<OJ=ZBhL7_X>%
zw@KH}_2X>K(#?xNqJoEK(yh^mh8WHFC4=Ss(+A#oO}(jv{x6yT)>o#KXH9$7pMVqS
zP1Z>l+{CV)jL-)DoK}?CVipV&Pux_0W!fVyi-v8I4o;Hw0bc^AyGVb;KIa5j4o;)e
zxC1ffXY*bX4Dz6|`SOCa{6eE2c09@8bUijhwet<vuV!$pi_C4|HNYItLOw9%qQ{T-
zj9fgdzy@aj5ME0{)x^Mn^afY37qe(IG82KFq>bva<Qaj$rZjWdm3)}V+m>LUEkK3B
zPNr@<RC-46dbP3*=w%dkPImSUHVP_nKxe6pKtI0&48fQorWjBbcCxppXY*vtm)$aG
zqrj8%VaCpeNpo~=<OGq*NNqURz!|+4oZ|KF?k5}%a7~G_QI_fBFClr;<H_$qAQ*oo
zD${*ptIMNGT(2*q#35VN)3~AQ%f*_{=|>C=n_~0UH6}?Gp!AjRVb$K<{ir@_-jRff
zoIPJzU9FJW;y|JWGWD7HYgS8b+t$<3ah6N~;Q;c4`yq?>Bp-GO)WB{Eg<=mk)594Y
z9Hb^#9)&?RXbaH($lWp;zPDAgl6p9OevEk)#V5a%!g4#zty6#gT_uza4GV*mPi-2B
z$on<pLfgz%H9hDuzlVl|*yshqC_;A!=1UmU{AjAA+DWLyjuq!`0k|$`PTy;i2IqmH
z@y};nr>p=+!<0x%x+=0Cd^De4R$I5HRRn!HrU-Pm*B|sk(2Oqh`2bVx{1N_NI}^=2
z*!Vo)v)k0v6d-(CdUZI<wWqgNVcpZ2+Rs2}ZDCC+Iq*|dD^-!GutG=0pb^_mHVt2h
zKSm|AhFHQJ6x8BMzK*sw47N+yG??h0D6k~40U>P?OCWc%K(UbBP-X{3a{s1P@}Ss~
zVCDkh>0U^H^#>iT2OZ`}B|8$CgM*6hN$1MmpLM`w7z1l}BImb^Y)Wk+<$=(7=X*=i
zPML+5qVG^mq$gj^JEgD6Kc|v2njb@rfb1dn2;Mj8d$~r8Y{s#j939^!DpSvs`Dt<{
z?|?1f$b9BfEAuY!h)zddi&EV?T~Lo06EWi^Sk+YBpMR_5*t<p)I#dV1J)<rehx*9M
zN)D4hSKX>N|G}NiN1Y4bpAU=S*n$x(zu^P0D8>Zy(s1%uNTTbN9MrT^1JXaa2TP5v
zse!TpuWBw9)_l#zMU$I<3CB-^mEPU5ApA^cnA1;L1qaW>nqzDxHs&u}MU$1xZ^H2Z
z(#v9AE#G<`ru1-u3vXl?Ku*X#7}krKkZwyu2T}}(Ubxp+e`8G0v(yYX?lrHl6C|m%
zwcnPvF6sFtt)VoNB;e1}m=k=ps5w8_S0_>o*p<%c=qVckroCOZkn#nUU4OxU3*G7C
z;D>;@)Nm?hXnu6`?BWh!%e%M#LZEK%MSp++rqZK%!yqs<Ig8ZU&=}$X;1)+mM<AY7
zOSdcY*`WA5ymMy@s-BVT$Uo}|;1wtn6q;mmRP72IJ)UZqBpcBAqx5{r;>_s0Lph~Z
znYQ$dKNl#l1C64;Ixt_kWq&1~*CYMsf_Q}&_N^lBz6Q6GsjqqlpPbChi~4@pC2FeD
z&X;ng>{#MfWk%~E=C>A~4;S5CCoY^Og8*X>$8mgdsjR=h|3)>pM)58CaJ#={>#*(5
z@H!i=MCo~*Mb0gye5kT5AHoLw_hhzYf!brB|NDnt9<MdL%j{4V^h`2ADO2sxU`YBR
zc(Xv^f&~H#j}*3P;^V>Ta<8+}kj2*D1mEjeN;($U(7M{%cb=IpUfSB)#-)NInYIBu
zlOE}@1@T8-SdJ6ph_BtlZi&V5k7d)=+PRuE?jY+O7`616IM#=nu97olufNPh%)As4
zzuW5?)mq&zni>(48s`f&88u1eKs{vF;-3yYbj~OEB^vLb6jqtQVL)b8QGW5`O0$Qm
zh+~*s>L=ssuBZCKgh_I>I>-dTMOz^-DD`uWC+6o5VL!1h`7@y6^-}TNXbP%1gC(e|
zc77-dFG-4P`h%4XkiB02?oYUmu|u@QWN-`d(`IIE=h?kj`{3Ymz~$mu1eKQk)%-$V
zv5+NEt8(Rx1El`!jHm2*mfoa{t|lSk^Yy*x9B<Lp-ikt-5<@G*>6woQS*!~x>wwNC
zL_f#<+Z)l6W_at*NSfW#vZV2*0@1NBkdjTfkkHW0sa~rE)am5F+b$^~W5eXmDfJS}
za{tJCsy}pDH?3FV2bs)h&B<-<vp?R5lL`w9d#|wz2<lQjAX5We?}1wcg=aIyR;N8w
z-djZ@D#hT_s|_$UWcS!BfF){iG`|-Mrn5Bs?c$u4kqQv*T|mi41uW`fYv2~lEv>tr
z8BX(78lu{K5u!tB^~DycQ)a!!$;nt0#zUI8=8Y~oR`0xZ>O3g>sILs<%EHx3&tJa1
zD4T(ow!wi)!kMtA6+`u@vp>C12LJ0~RPE~tu9?~Zb7`@JM7rW~Fi61koalSFa7SQ9
zK+#$JGTjTGkLxXdNmp$ggQ0CFINh8&AUwTE5SLw0iV9Gx<MrdO9R<K^!t-90CMHi~
zE=h~!d4n<tDmZEzFqyL~{tmJ<Fw_b#Cjqy!vCw1Rh6F2gHuG^C#@x)t6GNQrth);N
zjb2alE`9#Z<wC<|lqqo7lTr1f;|Z-fhj5(PaYrbK`g3KeK5%d<gGHn?y*gr=tBv}=
zx?o5J0}3xAzJDT>Q-R)JO?McCWG(17vlArB1X`282MPj9#b@1-lvq;48vEMZYoW+L
z=!x1t9?$<AY2m)0Q3m9Qg@q?jd8g9tl`v?I^j;3cYkoHb2xxrh55ZK@%ZV2kIskVD
z1GQdDGh)Q57NVDFWL_I2pNy^;6if3m0Tg;uas7q+sN=#}Dg0C`^^VhD^bR-uxuF(3
zQfzR-{h8^R;V8JML6Q{Y!8W&2XRFB&;u!qYZo(MOZ>jUchY}Qxv}S8)!w|0We8(>A
zq)gN|ko-VNN39H?5^KYr;fYSZ@3@iDHj^3lu9hYypmaMv+Uk1gXp>CL3t2(c2`rvd
zrnn#f;!eL3OzmLCp|p6YCw}+GJV7aq&w1u<Juz)J9`>1q%1v%(WQ0Tz6|43Cy6dkw
zR=BmPI$}Nk`AocW^wI6xw;NDDz!mJvX%$<%_(@q<LL>}qe?9=5>I0$xD{j7ie726s
zGfx8>;hB#7S2o&%r?sGubq@`>c2ofJqH`lltqZ&Q7S!&=NxFxy>gOlmu4q8c+q5T3
zj`9vCu8rHC5I0&~7E=%E7(|#I^_Ab7Yil_T%7l(l>cWU}OkZze$;-8HZm_<favF~%
z;0I1uv0?;V5a^ZoRKU|W_+!-0@oQJGhFe4&<}V(d%EVFX?O@`=cq5J}@lYkAK2!?~
zZ}zG7L#c0C(z>){-X!YqMfCzKZc7EQS#V(C1JVx%e;w0dE~aF7x{s}UDRCP-zmv7o
zjoajmbi%)PgNm*m{Po>uo?fah$dLR=8s7w3WcRx)HFl?oa>l(<m8onEoI0dg?!~wA
zkzh^@g>T%q_hs4uOkrJ0ot27)Q*=u0G2n@_?Rj#%hAL<K!W_yaWsm9I`+eo!lOvf}
zeUDny5)7OS<T~(#f{};v;^&Pvy?JL4ipHO{C{2des%^MFM&*9*f{P02v9%ws8VFDr
z%53&f$8%;4l)k{Qh6rZkI>)ke@r^0l73eeSzTNwNd2d?g*wyc-jCtsC-QX|)atQ`w
zbed@-s+7Cf8^h}bSk?n|3>*E5VNbRi4*J1?ZsrR)3*xS?pI^5<_2=fou5tZQR*m9{
zT7oDJTr0orz1NN#GM=wSgR<z}#T)1iK^9PY*Vi39nX1n@-$!GLCA=wyZgEMbQN?!7
zxZ{W<N=L@OeN%@4t6H;1r*jOC?FFyDLR^(bfUul=uu*oyZ|U?=cyUS<y1y!TTweTq
ztKbDty}?nhNWpY~!SDh_IO1Dbld0~GL#VOHAO2qh5!H&}bz!pnG;t}1zutA#liP5;
z*mt(ll||(SP_*%EmvL<))#|~poMx}`ua&*7YQv4m3a)FYqam-{o-PFMNk3?2X66OO
z7VYPC-)mbcDk^3j=Wc9PFoi}w9?}oxs#1$(Ag<>TK;z9BF#O>FUw6wao@!>7hGZ2o
z4&}6FUl?x80-yBb-mrIY7N}b)5K8JGvG_5mgQhu}t-wE?x%UQ38<PVqfEScF*L0#7
z0^f#}7`|M}d<8z8TQ+Qc+`G%lyCn_<-0Q0iC7~A#!JGA4!kY$d3YAm52<~>bq{AWD
zZ{xxF+<Or)i8dKZcq1Z|B%}j;4E0T?V))N%7Qg1S!c;CG>7b{`^U;kAa6jFkWv=@8
zngt%P`YT~Dfb|0N)aK<uF&Z34N}R}Z3RS^nuTeb}Ke8ITa0Rsw{NBZm-Zk|6LP5=J
zr%57hbS5Glm{_PRdsPAC9Yc5K9XR*h^=BW5qt5NWR>ceQORl%B)ANoa69VLwGpIlb
z-RS%zNfvBOi2~Uy;9a-3*X0eLhHx9g&kv$IyhmCr$4%wDQr)*_pYfvfzpn^q)kovG
zYCx6Q3Z>5<Uc&B4AMat&ARV&-Xg5e$>&GhB&sqsDs5Hr+U(*l6?pblc5Aj;C1#@z%
zM!7-p)_FX$$p}|UVuPsg<<CCNd0VZN*)EkC-DpB8xD@4e*>d6~3YD%ggQ<D**swm&
zU$c5}$>)ak`^eEUM&G-q_MqdKIXwpdj)N~g`Jmh=`f-4F-ueY0+Ad4pN%B<N)2;mW
z@@km-8)A6l7L^2EByiq(ShV`Ncw=9ia)KxrsZjo{nx*???zI3v71(&!U@krn?I}d-
zqPe;qIhy{RARL3CPlCGAZH=RfL^sSgqiKx+axH72BLk94%>TB%4ZQ0+U~I&23LGDb
zVokC6XJ5hisk^UF-Uw!lwE!vg{lQ}m^X(V`Pka7DFn|OTh75!g5JonX+cNrA1K^Sz
zPA{z#bv)FCv+jg^6rM%7$Fn}ao|XezsD&Jc(Y_!E)Cc6lQFjm)&H`)*w#Bci8P&R-
z2A@<&Y=Fn@W{+94&_7%httAvjPgv)7k|<P1c63*fMK8dp3il>4=m&JY`Aw{WGWUe9
zZMg$1(Z*+eC1Kw`5U*XkR-BpoDGq5o3(j{l)IXa7_K34H@)J~;LL_h3vD@k#beX`L
z#AZf!3aVqchrlm=0_r3<fq-sY1uOJpnAwT$xP*?`?E5^O@$4QjjCVq*<lOrTbN0M8
zR5buA8;saG9K3T2H=1i5g<1<k_X$s9(UeW#ru1=vziMD*4n92_2L?V(zxVfLcw>g3
z*+3CQ$D}lNWQqY%ZL{@b(+;1a+{a)fhb}&OGjVWULvW;*avS|H1sJ~#duC4TTCexx
zrAW3;cq!CkaGkp>tz&>~=OKJtV?LV-n#-+Ij~0|OTgE}|@2&>OD#T9y6sPQtoLuRi
zL;#*P0j~XSs3YUGxw~J>Wja5U5iB$nlStdzm!LbKtq%QA1xT|+Xs4MSD@dJ-(x%qb
zIKYo~wBORXCN3-?Ap{mDAieAwI^G-u<IMzKU+nHd_!LATI(vuPa(o{2fG}5{?@)7J
r^xC0c{?|+5Fhmty`EO;~%L0+Ij9q`~*Eq={@MUzu{CMdxw=4eySVe(u

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(!

diff --git a/reference/Rplot008.png b/reference/Rplot008.png
index e30f5bcb497e881f6921ac9fb6eef6f050572bcc..156a625cb4cf882a2fdf3e3a5a897ec4b3a4259b 100644
GIT binary patch
literal 18108
zcmd^nWmr~i*XE`{x{(wFK^g_5K~hCTRJy@Hx|<u2MgbF$1{D#cyBkzQR9YIOTe@fM
z=Xqzo_d903Ki|9)$INr6JjA{4y{~IsYn|&n&qc(|8)~Gd7*8PxLVERz@+}0x8b|*U
z;=@<`dg@-m2Yd^4HD&k+|LrMN`@;t!$1D0S2trJS{>Az^f;mKxGssot%Xd7JmPUMh
zZ*LvRul0ZaEUEFrgh$||jh4bY;mfWPEOE4W=4axtvA?XQXN(6+_SjXWcUNl6x+Pw^
zdtsB$nOe*K#rbHB8j_^&0Bk&`YaQ=ee=6obeEz~0<6n>@CVI8|cS$OpNmqMNdr;Db
z<Z9O}XX?zWB+gkLMM5kN4i0NA@j;w392`>gA^{QM;hbiOVawu{V!s<h=#Sra`L){y
zOT(4Lr&Vc3N*;Evt+%t4d)6x}w;*LNyJjz*jJ<TcNP=@gE;6oHh>{Y^eGo^&FxQ~;
z+BQXacoyxHv>Chuk%2bi@T-Jq>|k_f+__TNX;S&+%a`$sGOXm7y$(|jX=$cpX^*xb
zaybU)PdmSC@>@HE>YrSFagLpf3$HnhynTJDeSM{Rby9k4kL*{uLyY{<ye9k6-VsJ-
z<Lkw_!D8+U7cQ(#v7Io8iHV6smFDG<m5;bH-MV$lasFML#l}o0`(l?2E{omR0=44!
zsLyI^jn@VRAuh*u{k;x4BcJ6`_6}<sn`cQ$Pk#L}KpI9o#z>9((-)=ow-&=*@#(65
zykypZ(9qJpE|+B`cQp<hTfGz~=I-BG6{D=&6Nz1soqdX$`$kKGn9WUN<EYlh^kKoj
zV6ko*8a{4nQaqfMKWY5&M$Kt4w>)8yw7FrUOFo9))R&T$mX<-xn#S41MeF9x09b;I
zf&vPmnl<e0r9n%3mv$D9@tpj+Ciu}nxcqqa*pZ-W6eDf7=ldJUkz_1VX*D&>Y`$w)
zbV4S~MRmusIZ?}oPrGK7Fc`U_;^OtC;;yFqj}O;KPG7)Xt=(b$($Ud*kLi+q%jSId
zgsT)5;=kXWI=<ySvF^Ece6TuIIh*o$x6-wuD`>I40V`w7v~cAtCFN0^6<2mnj*5Xn
zLRMCmRY6`+(Jh1m7J8=W1-IrYB%`cMhmgG`OY2Qbrbgn|tuS&nI#`eTF$ckeP6y0c
z6YqJhVtFJZFHi7o{*7d*2#5M3@ddr0sdZ18nIc#oEQMWPIrd<R7w0sn49o59T*n?+
zn%4PNRxA~Dv{bn*W^5ae3Hk4-uTMS}N+=hq{7qG8*&ZFfe92*yAG2MY`nr5ErqE%m
zZt{D&(o9uVl@yi&L#X+RQprJ1p&utl#?9OrmV%DyrZT%B$Jt~Li>6>A&p)5JJ3Tz6
zr>7k&jb@~!5v%?@7G%8V!|j=(k01FrP7*TN1q1|s8^7(I7xh2qI@v;mC|Fx(d&TSY
zyFus%*W{4Ahs95pa(k4wI8(H=v{bPzFsEC;PkZb3Z4vw7iY&kWOE!J{hsQhhVHDV~
zCT(!j!j9t#E=S*zrA5q}aNoXvuhj2m<kBg|!_WVu65|&&Vp_O_F1TjSKAr1Wo!`3u
z$&Nn~usl>gmGM}x2@b)tXU_r?5*T}WdmASl5rmhQ7eVId=bg4*J$r_wt*v$YHjYMj
zLXF+kt3<wXsg-sQA3p4{<5)Ia!)yBTim%YT8UK2!91EXrUT}7HRP-C&e3L6TZnPaA
z9TeX0WE}Pw^Seod*D&AZSbuV~I#t{y*NjJRgjA12qo40?q@FOB#g5pA^)HG!r0(f^
z^6%(c9$ZmYKIyhjIZaGVT!=aHzKH8;tY&8?fE|eZ{P|PZx`#(*^Sjd8p6{A>ZM?j{
zTwe7`)l-jFjc2j3p-lD_SmZKm0ekP>efe@t=Fg|IQODxq0s^G{MlPhRK1(7Dp@FcS
zhm-X6;WlHBL^Ybv#>hOn9E5MaG*Cz?=O6VOqPCq{Qzno*#S{C|uwzi=urP^dtqCk`
zvwiuSk`kJY*{&G92R&I@dPR4M;GQ7kuzJs>;~y|DA4lF}iwK|dH_$+4fB#N{oPmQ+
zqTuX&0du^OI%RsYPrtO??eMcd&zLx-Jt&$*I^*{H8&|GgZT$I8oqAzoI?hxTzUKr9
zhC@>FG#rICa{pbW4dc6_Hhmu(8-oZYoGTp%@5Pz_e0$Yt)zEKe*oFL@X3CR^=CC(;
zd3isat|v)Q=GXi=C0M(8%XRL1qtS|UWr1}sFM3=B<A}>gz4`1br(?t2SUtzFm}NXE
zK7amPF&W7xY|#>qZ<jG*sxhd>!GTTlV_--Yl5CMtju*joWo1`Eb0iFobQ;gK0sk1e
z{W-~t_>kn~WN<Z<l{vA-u&601uQnj(I5?gMV8PFGV&TMxhhM?Q7pA0qhCAzv#JzlJ
z_C4+LOe-Ef3BwtmE4iWO40F9Xaa^UadT_HpD-Ist^WlTdk@MRZ{WY5;>2gzByLV|y
zl1U>$^m7f*sUMXewebolupMgF@$pF+gMa_NOTr{h0*70y|H0k6qTVSpncn;J?+eWu
zv0=OJXlq~fvMBJbhef7yZsjoUZ%#2N5@~I1MN<I(>D#w%QOO-F761LdH1zbAbKldo
z4)_HHLr3h@tgU$iy6IabHjeYMvlY)!1mrD}ehbT8tn&J{)HWmIxUX{A+uJ+H<_4q)
z7APia*RGv9r;%{iWPagmA?}Ha6zx0#n{NPw6+0gvF<M!aZUh?W>qmTk!F?9$5A3>%
zo15G9Ar(UpG$Th{8Vz3~q)3*Aq9%X1)~r|SEq1(JUY}l8Mu#R>I4cepX*5%0PIr4H
zBxa<gA+<g`_(+FM`<I0yULKwXA<Q~4JoU!dOlM-E(QHE?j=-P1+dBTw+8kSHb(QTl
z{&Wag&U}46W7jNv6CT~!pP*TCSVe3>$j0^?&im%2nM8*+h<fw7n>o3;t!op_>v8pm
zc-q?9dL>qrIv@40kQoM8%FvaO>cnzW%-%G<<%bU+8sXDID0h&)&cGSl8S}5N#4PlD
zh`(^}6fYm&v((fWCWo5BAIpq6k`Rvg^ap38xIb5tI4Sk^_9|-k;?w))=NnhMb5IZx
zvIl<`)%a*qYv8&vtWUx$adT;;y5IKw!vxQ%s7uP__9HDe`L*Y^SI2}x$=L)UCANOl
zFIK*H?;V9=`0I-gP9cig+9K{tdJv|{kmx$4CdzWMvM7!YcIZn>OBD&(`#$QQMZ?}D
z5n_x`qg%P!eYpjSc5wTYNI#U#2uOy+@4L^7YEY;8ZR-yl{VFgIn)_9nGuNM=x{DSH
zUsE|qC>9;D0_OcJQm#CAN!T#E>1zi^lThZUVN2)|G9wNTeWCEg$Zp4b9`DVz+Le!-
zLvPmDW8_|+2#J!+{@j}koBq5Eh+K=kKhrZ+@6_WW)mXihuS1{@m!qF)B#INUD;pj4
zjvaSv%F`xf62aZBj@GtHuhvkzo1Wd8+SqH!$87QaD6~}j@m`Z{{f8Rc>mix#0ZWU6
z?Pb#akE?LIbg+9f%SlPhPOu{dM>rIidilGO2&|7q!iFNDi1((I)6#&569izmtj{)V
zfQ0)}JcERzS<mv9FJI6%d{ZXb-EZnIkXJdw2oDA8*fj5&;BpbR>wWiW_nNKp$M;4c
zYq4(2ot26!NkB$xeLPy^$Irxd+><+vP&}p+pb9k)&#yl=a9tYEf|~3y0_cPH5K|B7
zo8#ie+mM59?jB(ZUFUCYMCcK+w?(t?T~k*Nq2S;Qcw@KonL9Q7{O!zer7*IHRlilA
zCgCUemSQAIAN2|q`3*`w)r`Oj6#5;wyRdOA_d`7j8C#_%AY-OD+TXJI<L9;2t4o~N
zc|Y`J-r{}2&DAkew9q~=hftXO@IJ+QtwqXySVF{a-&IRXi@nAqzXJAIMPL6l#8jor
zY^0|A(H(|R_JxIomR?;`fg33@5Br$pF}|Oum(bvY7Y)NE$?;GShC{Xak!M_8u%lFh
zi4`&xBx$C&ckdqhh;S}lj4l1J2`f*Wot>dd&KxbfN`+8F5jgU@)P4MT-N<YD6~t%V
z?j*5@$BL25$k*2wTbBllTZbI#1)`ZHsoaM$@z)?yThK2AOB_9Nf@H<MNI=fYT$aB-
z)LOI>YiLgiJ8$!IApDznE^mMuK)TY-*OzHYjM#(YFFuIg6RbFXF$%{OJvCq50~Hi1
zj09bV_BAWEa4HIyF$Y6qkv%<k)|bo2LZ3Jd7TwD*t;Z;Ki>7C1$IJSZ!FqJbpBN4M
zZgh$)>mG6M^z`)L2OjkqI=t50PQ2tmrcY0Z8{9lWc#xqG-o5&Lwf+PH_(_hE5Vz&h
znD1HD7*-Z1qb;S)gRzsktPdY3P5qBb5wqya-J)0u%(C8h2-(pi{#BUl%jeIOq2?{U
zrbo*m6c$r$Q5FLQ=1mihgxcE8q1WGH>JI;?i?}WHJiT{T^@;GTf{sqCmw*52MV?y-
zL3zGVEMjl_O-Qdm;iGB&=#n%yH}`KmbVGON-o&7Xy(O54TCuIS2f@s|e$diPE^j+3
z$j@&}(Et%<N73-Wp1z|>zxcjLz<g4_wH(e0^a0`yqs$v2bAjKxY-v@9_BdX2-X}gd
zIYZeTm%?Gk9oztE5l|w*9UYy7#mL8x*PDoi%!bPB);whfiXV_es8<?Sct<VkRxHlL
zkM<oJtuEx`lj-Xk7({jmR+D6Bhvpvb&8ALe=j1ir)|3rlR|*|}EL2D6BQOO)<+|8M
z3UCYwP&gBik-;I6d;=nbAir+k`qP6HMMBs4pLq7=Oz;x`H<R@{mfha~kiDrbmUq11
zQ2(HdwPy3DI;<cL^df~Wvo}y|NV=f1@BmUIgQW9YuOVR)^n#~PpU(RDu_XWt2WtG4
z)UET8k1$93P=CbldQ}_@-7f}k7+kXDDN`_L`&~5mhon$&!>a#@&+pk;K%AUhT<KX^
zgo)yI7luk4##n<0$lES<$pis{fns&WHn$DUO<914_CAV3MTGqNdas-+X-8jQUklA4
zkT-t7fvae{=2~VYhL!aE`6Hi|N&>Xwf>eYgVJ0!_t@LLx7rh{5QYempdUh5q3yu>Z
z^x{@85)&UokWq((git7&btZ^aEM(`TJv8W9R~}jclusN=#uCXQ?QRAreddx3v|G<#
zynx1xB(;m)xi_QE?_e^we6$JfVXF4AMUnprhJ;N{X3|@u+p$+$ccQ%hNC4WLXUWM?
z=)17SaK9Z%R|*q`TC=eYEf|_e5d~<U3Ov^*@!4sH?Z3v0kjCQGhp=e+Z<UW#dk6_u
zFI@ltIn@$D9WQ1>hfmHrY{vX-AHY{5fI?kJfB?TI9>^b@#_Uv&UDl#umU8`ci0^tN
z*X26m);|F`^HlZ9_2LIzZIAGX%^2_xG4jx$JvrGqIcB3#dFc9W74}fq#ET2UZBOw%
z>_v>sAHr+bt`#^>UvpVuk@cp5YNx2Gs(O7Yd$o~(4HrS7IeSO)4Q|2uL+6i%<z$M4
z=)YcGURSPNi>xxXTqJ2$0I>h*v1o?N^Orj#l(^Vfg|>qie$@N>r!Q9Ru6We*9q)Dd
zCqN&3ry3IYv)8ZBLwC#VzWnQ6nY2qebew15QTdt4!YbC=n3Tv|RHblP9#;DuY)4R@
zRgIq-sB*LU#NK(ZT5k$*{iEE$bRF7S{bA=0ftf0Yx;@n=OqXuVRk8lz`gn0Cr|!v<
zC-`A$Z@d;hyk}%)#Q;hUrck_|ETwIga1P!hUfBGRgzs(vAujghrTV?7PhY>%WM@aB
z#hyJ_51K!{a{FX`4>YAfep3wZj_H0ABgZS{v0{taK7Gj11LdPrDf2S4GBix$w!-Ch
z*06o~+1XSO&UCO6ll}F_r4>ipL!CnBdg(u;j{hks5p{wh|5(@5uXp%9geDqD*57Ae
zL)PX1DE44fZsVevM)%j|68mz)!_D{dv~bWe0P##lORosmH_$U{zLoSpSS}Acx-bH$
ztrd`bYbYtx*X6vLKi3LjC7-$%GCvaJ<GWZN&cN(*a(vXe#jTk_1BV=NL4?y(TSR1J
zq-wd%$C+3G!xrcx-b6p1Gx~mCNX>JG{Xs^oD-Z;en*RIL&^p~Ef7kh1!ihsC-(+M?
zizlO)<u922NN3};<82Y&i0~`{n_?8d_r(5J+U9=@>>q`4Gsd<|tt-n+|9&m15oFE~
zdr0%)xb+8hI?pLljpDblo%e&Xvo)lI+&KY2YrTwL9cr`267F(HCWO{c;mr4Oi>>tn
zZ@FFHx?r2qzCE+zLHP4+e{X!}z%p;zxsqz-1rKZPxqw;|hoFsZ)#rcHDC#N7MuPN!
zEU7U~+S;^aVL0~X(i0-{Rb(37{2yMyk**%GzbX*dfyW-qRSHjR#-RE&#!Ix3m{m`V
zEgQCy3|m_p<z!&dg4^FdWHb_t<Jzfio(Sl?Uu-Lf6xhn)c6B9Zy?x7BF|rbGS|E}9
z?J02@MJWyIqyM10A^D4qVrWoSR;CjE2if&MUbTO;L;ukW%;IE`Yfs0>!#~l-(96*(
z;v#3iw0@?=yD3Q<9&SLD>NX@XJ+^vTf7`cZYxLy!v3r&h2@9#y(XtMCRCsvCIJ*@}
zi~S33<UefW{-Znp-}{0<<81^Q_n;BhAx9dO%g|k82L^EYNhf~`w&97yR?z}FkSgz8
zHm|L?;q_7y<1nGd<0W_LO@{rSqNIw19or)3&!3-Zmth28xaUiu$a$yv3mSkHDdbUw
zL(8$f@M8#nB6(Gwl6G&f&7iHg1T)!ORf?lP#!Qbj_Q32$<J5XfkNElXL{ND{%{hl1
z;2xm9LQs;*1p+eSlsRYI4{Vra_B+qon^a3(!X&#Ea~;#pmk9%Z{rZLLBje}qC6f}*
zwqfOJT<$B!$vD$U&!F>_K%+Y?JDaPNnT!BEL^{HrxmoZWH6}uUK;e-dGQ!94jn?g-
zJrj<c!`xajo`ebi)@T3Uu|5B;_5XkQmdx}KpIQ?DKxgIU*#Z^L+#La8Q5bp&e*Gd`
zMZ%maPM}DN`H9Vp`yr7VJYf*wxa*~_e;V4GLW|Z@Q1O8M)$iq^q|{2hS6-FYNtZCM
z7R`Lw#DvvWoC(D&j~-!f&i%ka)}UaULCrz}5*_MJ2jP=Oejr!Tdiwmi#bEV(NlD3H
zai7>3y1D79Zv7#f@wR}e^MwVfCCz>}35z*(ZEZq!fJ2XDvE8d#va<n9a~gHrFV>f0
zgT|CP4^lQji1oei3~(R@EAD9HIqnyhWW;7?0}!w-Sx(PBRdJ-+venL~34OY9GWqPg
zw`T_*IZ<<E3_dVBl7|zgXuq>E!qoj<lK^pD{zVH(IWqwc89-oYj)`J4yF~%ta2@`(
zD;|WyX)zjR1qbpu+<!Lhnm6@Im++JLeP;<~3dM%0b<W}U-Sfd+vq4?6>J@aeN=v|v
zXJ^OuJQrh28Qvl~#ruVNVRim;v*Tq0X{4`aoFh`Ln7|R5hc)O%(mhs30aftflQCQ6
zE&hB*T;)2CjbvqKTmNJ$f@_*9V`ztIO!6;Ha1W1HE9K(_5+-3Xp1g~l+)=t(kgsqC
z@h9<nm~ekkPKucf7~iV`?7{Cg8fQ1ec3G8f<BhQxl=>Hb3v0t2!!p$3^W<#F{;OhT
z-)ntFmfH1TaEw>0hX07p6@!r*#T?i{OHTxyju>(VKrbvvyp$U+%9uBILA98@pgx8L
z?1%W$A82vSzPvoI<E{d=H*?+Nf4-RfD^hax64v*@t2tgw**j(}TY<dNJqifaZVM*U
zFQscXvgx{Y_=zV&pO&+iz8_)+Lc#~y62)<_#4eUKmKS6s@E-2v_)+%rMMA|jXHabs
zpMC$<+&zDIhJlq*j2d59j5yJWIeRrRd@+RlCX%IPy^Ae|AiWaUiB6){xou?AgcEje
zLuBmh+3;R*Q)j2^iPCXcOa6m#{AYUc|6Got5DN&Q5D+8d;K*=^l>PMuIth#8!@Y`?
zs>P>CNyE1+IWi=Y*Lo%v7rulqx}jn0SUo~H<a)(<{P1N7x)8n8VgV4cQNZ|^@2*Yk
zHj-lkyf$XifglnP6X&qbsHL6u+;5mA>aF0d=EFEow9-MX=ZNqoH0XB&bWFzPe<*_4
ze8V<THe}xnddXi}TF-8Hc=)$#Q5Yh1!a$IKP~a+tj0Y01HjJGddknh|*>%cEz+D$>
z_b7cAs@<eY@%?kv$p2^y6)}E%xXq0;j5h|E7Y`O#{m7s@%}J?cGYx{y>hm{QV!lrO
z48tFoZW#*_tEt#s?m<?*r{kd9g;fGwk&zb79bMf^iw)u7zar~>D+$6feD`ui7Ny<G
z97TI{lx1=KM!jam2*<kQ<&W^l=}mJfDJfe8tPy|cd!inmhR?(_dXPTR+{Vumkd^wd
z;48_*8Tf0!zeE9UeqBy{>Xfka?@#yHXy7|#Kt2L_t7c>hve<M-T={r7)$KcXf?++y
z^e~%qTDHR#T20}<BN!XHWOtZR9_wq0ER+2Hj~HRw0bv<nfYEi%a2W-i?R(|MywJnb
zp@BdrHbJrOcLTcPlA~1eK)t{GA;$lc`^2v=x8#jND9klom>=#9@>?T2ke^Xfc`91k
z5n%kAiV6nc(riBz!p(I<>-rs<rH$<{HcU8lJZV)`j3)W?v#I`a$QdCaa^O+#eJXqQ
ztL)`|`cSEj?h_X2GL-`dVn$iYuo3%qXvR@>XJA>&zZCKX9pK)JUG)e;Z|q755I)Ld
z)QbS*rkOveYzK(-?$8xGa%PDDAm|B^0wBdU;sih?Qnj}ibTw{&&ZQ2GBXGb$FJIF9
zo|(axILh;08LmX-2M8RX+e3g~_?jxuR_ndh1P!*9p&>C+aj;S?f;sfO`{2PPiJC*u
zImN8lX!2qpa+F8P9oX)FjSmEhRY_ah#mnUoUr$>bxPaDSlkSqIB3vAh@+Oqn#1ly)
zGejS<aR9m!=|45FymsjquavfSdfWH7>}(3f7EwVyRwj*ZGtFy{I%9XWFS;2Mlnk&k
z8TSjkCPyr)@?DKJv+~XSuU@@MiV<gs(L13FiYcUZ83k6tzP$ZTN2JiBg(~~c6^Vwx
zfjJ%SI&HYe2^z0~=xYvgO2ur754ty&AV5ffOnl|0nhn$ZO$T#x_L;8K7$WL(XJlnr
z{Erq*(N4Z*t%-QX@A#Yas@X(S2oeBYGW}}ZLEc_RJhXupa6P?33%vWC2_f%OyiZ-c
z|Fr=aCCA^No;{Vb6E_UnzC5uXFXL6DvSYPKB^H2+s=Yb7iTRj>7Ze$d4;X&nVh5fp
zjcHrmG29*uqO~j<v=td+<Dj@wQ4htZ<!8VDiKFG|I1NJ1ebLi|xD;y#=>~VyTXqNZ
zMA#k;wL6>Q@q3a4#XNphYVokYr+&z))Z!?yY|^0@z2M=Gq@tpt0-v3gr}xNWIMSy6
zs}~>)N7u*+4^V~N;hGW&qbQ+anQbFT;{#>~j&>|$1a<ohe5gbOw3QOT55(-#Q+5Vn
zGsJymI086O8i)wcB-w>LLGJ3yH;sX|^K}QPbLkHmX;cD1tz>@U-ka;V{ASCkEONhZ
z5=766xs1mKch3YWAi(Brl1m&B(AXTbS?g=H3zHDC^M9cBwFrNgtF2j4xJ|n_b+@(R
z5p1bug@9~%!XF0w0}bUhkrIwO@c)Klvq4zHR6Dd3`wZpoQ|&RA$c0ehAK-bv8%knS
z?9C)jxL5zE_E^PdqYSknMkFOAJ>lg3fFqS8;nZYX@40Sq_x}CKs>M7dI1Fu1P6zdy
z_>e+8%_J6xShkq~CjiF)9%KMQI-pxk#l_iK7^v7`$7WI_U}?!#=6HAN)3<N-1)3AT
z2vioPY~P0SblETV<pN4+etG_O!qKZM(2&ZoYF@V+0y6;+ck0KUuo*XjpIm!U16WE^
zlYH+Zmq}G#^Smp%Kv8AdW%i*(OMAil)JkefM*co;mZ7-~rXZCoCgT1uNjo50&16Tf
zVZ1M}#%=uM_n-G>7o*SJ$tgRkrPyxM+=+YjD&)_fKf$I7S=o_56r_Lr`0+;>0k7sn
zTs3udlipYfiOhv@AJ3B^O96Murt!fwYinz&1#(nF1%X~mSGV74Xs`Mt>Ill~!s=)B
zJmu9MS483d{riet4y-W*#c~5L2R`tqJyAR8Df1$o0a=}fhQ<sKX;IkE8A#=_mTe>(
zpUaLPsGH=Md#M3-agBY<*q~EW#08~AH-=UA`x`a#!Dg!$7FDc{Dk>`KPeQ`O=aMM5
zSk^$x6oIq2G*sSo_@o3P7ML5y$0X4}K`;z*5lpAF$=63KYg3(kU@4W=!tsPewc9R#
z90+oTi~iBrTS%{qG%Ah1lUR|-6OQ-R<ugh6J!Q(I<90)3A-*9-gyw^vg`f!HD-sS|
zV<qri_rERwbb~qI?jpX5onp@vEAU+G@;QUGK9~M%Y$W>KgarGbI%_q6UeG8UBpi?`
zhP@Ya_3AOQs7x5k$V5r`L!ZwYfojcy8XvY-Mu?k*3mhjD)xR04!=vXt_9m19*9izi
z3UG#0pD+Z1Ga((wu76Y*;Ad_c88H`q1W~UePAE<^Ba@Gpw+RdmjQsK&yjN!v;O!Sp
zM|mj-$G7@T9b8PCijG#e1LXgl=i|FW8#%_db@wFDv>r*C=Ei%U`Qgvr+T)QSjyzo&
zJn=+<h0536Vk|La6&aGDY7d@?+4PMMK1c;9uge2qq7``kXvovet2<#b+)SZ?gT)V~
z;7!6Hd;@U^3=X9lw*Em9HhI)-Bp19th;qG`sb`%++D{+N*s#?Vlby|*shJurXk29m
zdQzvHaL3D7Bvfc}q0e8I#yScYH*zE@m0Frnlc|D{{Ij->PAGT)!uW~nvN}3Cn&V6{
zr=3fy$mI_yfVXb|FAo3oHN^*^mXJ`<b4o);XV9lsFf*8Twf<P}{Q1ky7wUO6m3ye5
zhB6w>UdjS~o?Y!c);Q`~Sm@1JhXfsb_-Gn9GnCp#5IA-1v#F_dX6-<sJxfd^hOX=G
zXDU#XC0%CDKuscXZx%++^xL;@%|N_|o<LDcixRB>Cbiz?kiciea8~LL?4Pj7j~?z>
zA1Qr>Z8rHUp@(zo6$7OTz5+zBW=#<~xuzNk%?dvr*uJBku^6BjQB#fe&5K4S?a?gF
zDD@3=J*aivHTa5e{bJO5kZ+xxt>AnJAQr4Z03o!1YBriF=W&L^x&z!tZ0o<(xNjs`
zIew^z1s*T$H&Iem#b2qMWwix`S>d-*Es)!&*8!SGr*4f)6R@9cb3fi_LDhyX5ov(;
z3JM{n&&bTghiWA_>bGND+y{qjeRnFxvNe(x2|&vgxClhrjp|JkJNwf7ZdS-~5cSb0
zHq36A1Fr}w5g8iPi22TCLag$kD9|%AAlgZ-Ll^7S;{?%qfZ1(ko2)xnxmX{-{BUfF
za*JXU^&I^gU@euTq}1nuE~nZftp4LH#QNHJ5V^SCilYRoKsVeko&d3>CGB$1xT6FL
z=FFkQ43yb1K?*|<rBISaKsyB>Q22s|56%F3S7UNXUlu~#0x*C8(EVm)qNfL<Z5q!h
zSK~`UV;{14hwVKE)<bRBa%1#<Pfs`AFHYA?l>-q&)F!!-96>Q+_?UTQk3Z}(H0FKd
zw<>>ZzdN5%hf_AR%zgq+vU^c%@^y{r_u*NA`@iIAGFtMG8oJHA#R^zMN2nk;2q<=I
zppB!7EntV>LCpGOD-g>qZvDn#!^1{TPG7_aHyb1LvR|9aE!&<nwnZ^Ld;T07q7URR
zM8VBX3@-8CYRQcg=NWLjIjnt?xpk1{=m+6x=ulhwc}KUx4>{gR&IBjUu+M63)ONjP
zJK5XJObd8FPmrn<^T<4B>Vk7l6RRlioic#n@U8n~YL=asXYU;L4BQT4p06mc0{qd^
z*MG>m5z{8VT9swm6scm?_)E1OFE^X_XLq-foJT}l-Xb7c3AcqiZ|q*>CZ_ZI$fX~*
zeE&hwKQ?M5jWVao*HgVXwQI1exexhv^m^ZoVf+7Vy&~v;O8o3`_aT;o-GLALQZkd>
zKMIhlX|K+HO~w3_|2fgjZEq@*{ZU8eESV6~m1Bu?1HbMIYo7vECypFqCgyQcx$i7y
zFXioi4xzvF>)W(5R>4fsJ7Ctq$di?0bNC2<!{dbMfKT&W&)ADNqlnQDpnHA&`Zc(&
z;P<*G1<$^l;`<C5$*#uNKLuBo4GjRnoq}$_dvoso;uLrmc+Q`1ga}K>jq%j0T-6%u
zJDwKuzYNADv_rx|OuVLPfpr0O=Ilf?xDq+$M{A|4JXQsL7&>{akulg)uuX}^Bz7f;
z5rbZI7onz1ZicG|vdVgYVhyFyS@G%V3b=UjBC4BjxWk2^!t>9A)n(3z4vHGE?@oKu
zaS#TlK_=6yay>r^H3Y34T8U>$$h5jL`*~8VR+DJN0uFXoW1ygd$AMmv@DQXFC2$t1
zM@5Ba_1>nzo0A~WkWRPYNzA=EtDI4+{H9(2NJ&T=%3wQDe-aj+r9r9KP(4Q!{&TX8
zNiA*cKw{icPlDGP?Ueqhw8|W)0WsqQoh99AW=bu?Gm*zQPQMd(i8&oF5Lj1du<pc5
zdk7w{$M`FBi@NB)?aqL9OKS-(0>(DQZ&n<^RxWX8Q64KQ1K-ia#N^ZKWOry+4-XIj
zx&Kl-@vST?i>3eaFbH1S6haKf5AxKmFbHj0G}~Lo$oAc`Jj)?9o2y@ppDdj!7qtLp
z3ot{%xk3A|4btb`^DR&>I&JU>RIJ%@_T#8I7gb2dHRbmHvKgXp%678b4h{tdI8O%F
zw!oj16ct?nO<(@*Alva80ji*ZpR*0^a9zBh0)tZAfiK2U8lwU=2Am-S1&foU4?_1(
z041PCipY#K|C8ei&__EL*{LYCHu(7XlmM_R#&#xNA_uCBTy~S<93*AIRnT{Xa?Zhx
ztzd8dyN(Ju6yk5rt^T(r36=Q1yRkq_?}9XDzikZ7C;_V{LT}>94v+w=Sy-`5x*wiQ
z9#-9RxwN0EYLp#X;P-$NNHJc2P=Dp@#ssu3r1~C{EfEFjba{Ri#NX-Z=um2bM?j!e
z<I)H&G83}eoa+NhSOK`~nB_6d0A^7m7uv?jA5M_ZlzN3wfX4!DUD2Kol6k1DM;^el
zix0lFK-vPL*(>U59q~C1>(ijZ4%A9-7NtO^27kcd){d^I%O0fhza-Wy$~?a{>`egk
ziiFp}g#`KfGEiLTuAtAsF4mNLTN`LUCZxb?<Gu_ynBc8ZrE|e_S{xc(xTxR$=4CrO
zyHD=3Rasa@w{Kh4VgK>2osoywIt`$n+{a21j8Sr_<X|!ZmSk?E`qI+&r&*osF}Y#9
zIeHK~zka=L<T2vLPG3Yo;co2iF3#$I=mdSKB~+4lNtgTA*|ht{R*dMke^i|uAFO{m
z3#edwc_<86nX<VwIHM(~=y=lg?OR)n|Dol^HnC8R+3`smlY?!6UCFjk-7bfT@4Cu{
zJ4oXqP${1-SGz5S+K*IWDFnGeI7oY}h&r5rK>6^y|B?F6)?$B`k%@n`i~fK|1H|du
zy%Tl8%D1ifN+=i{=aK*phBJIO@Un^u+IfLSv3HUN;sBh)XT8T>hdd;ddxl5nd$~JT
z<&>S>+{<Se7%7u2oA4ds(e^jzamF_tIv$;nTQ8MZDd+a8pPX079lMgHYSNxZ7Zn=*
z?VdSKd>j)eM`Tn6&-Yb7=E*1B3gRIPSIQiffoQ2^1?!#4`$ehE8^(9ZgMOXC5Iz1s
zr4$S#RtJIS66Kc=B&<0Q+|S}3aR9h;O^OMGkzv<vjd%$s3w2kN{XAE41<5xkJq4a2
zDX`Q912r1ov9mfBlWSOxjpS8Ko=O7VLj=u|ApZp_r2Qy!bBdC9**%X-!)48s*om*A
zb)#J4X?8ZGV3FQGi&DNs+Yicz(p$gBWj_3iY<Sm3=1Ik*3pTiMS4GKJyWCtL#0SLg
zT2g;}w;L6_h&%hZIdPXC`nenLH#YrZ&s2}Fc3aw(r(*UCtiLT*ktOD?w@bx}M>$C_
z$398qq3mRBXl(ulrz1xvw+TGC-`}azBLPFpZII-k?Bw?!HfQ_Hk%dn-Q`)rssae?_
zQRHE%=9IyV*ZZ$U;THTE+tQJd6n&?Y_ROjES1M?y5|vn41I&fS!gFu7<3+Hp?e|g;
z#VphxI27yOQrDv4Iq`c2EwgP?LcZpJt+@Kk^@3R11NtQL!V=<bn-IN&(nqg$q_t`c
zezOQW?#J-$lJQ!NHw1#W>kI-1{}j0I5G1i0-?5*w$5g|Obo^u4Z^QfW)hPUe?CkHN
zox@?ub5&&E@wzS0Iu*ehE`qvkb##?>s?za>iF*HdTTkTVsXjO7l&KuHzjPRA%}Ibw
zBp(fDmtu6`=^lMP>L}J<Bhw=cTv)K~>FYaZ)Umqr2KX;Gw}|@0?LA+SPFVmO<C{m%
zvOq=mtR0PSW9xX9-v&RMVbF^-Jb#0)yl%A}7XU6NWym#o^~c_xGOQaTFcAP=(7`b*
zQ^!U;y=r$m7Y{gbJ(+7t6#swdpZ{|L8w^ueT?|G@On+}-(Kan&`Hp_Fqroe%0rqJ(
z>XY$hg@N9zQkqtL^Vw!KyC5NofbgQxsh#G<on|INW!pErr|zf}VjPv9ubr0BdH=<%
z+Fz%o7h3@@gY6c@YZi7)IpMA~o2}*vuZ6UHK(?wi`wmrjstipoo>orb_&96L$#P+^
z_2I`ZE{31!FI3KQJdX)W`+Gp-Kc$)fEpw#xCJ`IJsh~@-GS+(8O)qbyQu<X&C%S_}
zr}vaxv`$a4^9?;&wvTo72DfuF`wE>|%kG%!$d6I9zUjq<fOnK&Mv(jr<yz-!{D4T(
zB$(GY<wQaC4S&n2T=`AwI>^k=n*#&#&VF%wWnob~m?p`*X2Zx~s(W4>3WIwX&Nbu7
zZr~!7+$7TOaA6!(x<#$X2yDw*Y`D0%uoBKrNHrQX<)1TB)1pDOWH;}oQYZ<3$-L?n
zI!E=bR!b+fjJo`G+G6Vm>Y&>3Cy+N9C|@W|K*ULKSi9ycN+Oh}=(BQiaE9&GqYJ_1
zkd`MnVGyV=?kW(|USts26{c+3&J>E3Iz&1m{B*9n92L-(RiBTguKk8OyT)gap3pH<
za2->GN;mSJ<Y?1RJ3lX^QfyduPu!5BY}^~)I=6peebd*mnyEhj(Paj8!+Lmi1*p$(
z#Idym^%?kL+AX7p=3UPj#qh8mlKhj!`#-6{7<5*qMP#`RYMDf`aI2rqBR6@{D9n*c
z?{o7Ynj!w#y*;oq;={F1E#@qos`2~Clg_y8Mt~E~gZ;nWj`6Qh(Ej$n|0fscU*P2U
z&Vf~9KO%(=XsE~X2U28k{Uu8GCddYKeED({9Eh}hIt}x#88C%YS&icSt)<S9#%E{M
zp2o!yfT9dOH`Hw|>%EB}NitqkAax)C0IHhNQ4wsq&75prn(C3~25<@r(qc1sZZ5kM
zYO7?lV*xD;Qym3^{2$)^TE0oc)p!jYHbL+uAoy*n$1SbEhwm;OJd!YPUq19~_v~)H
zfje;<Z4!BQHk67l?ZSE;Un=i`0bL+Rg+@q4bVFnU)St#_=ZQzef(=kt3q4j%jNE#)
zkpR$ozy@f7Aj8nWF^JnTAPu{|(!tf|EHnTOuq*d84{jgFDP47M+WAZn5YC(oXf#k^
z)2dhS1S_~7$Fjd&Ye-B-{pz*Qqvxxa@1#7^D&TXjK9SQ`8Im{j#_y5Cq1Y{BICdc8
z)Zwe<>)8+tSYr)bqv5`zeszINWffOzhmFwG%cAtTk5b_Sg`Sn5HEwY>8eMrFac((Y
zNo@Z!U;HSWeHngLG97XR<Z`uVl-YcY&C9O1P)*ER!@4B3Zuh&e-O=<NH`m1TuhBdo
zavKA0M~ti{CkYz$<jIn0Yo`@tUr0BYeV3W8W-Yay%G<vF>r;$5f2s4u3qM2#Ba*r6
zRNnQ!xS1QMKX|3v4SCPT&1Z6kLQK1uU@d~08<oW;G865aP<H~zNho|G{RQ6e#;J%y
zHEq+sIRm0*VJcQr9UVI0+o+Ch4YakvulS}$=Fbb$k3fk)clP1-d#b#@EmqOsqzbmF
zG!i6ahjFw-$6U06|Ltdip*d(l?hX3#2njie1~a|M=KWP_69#?qf3sU{fRz;5&5juy
zq^C!jtWWRVyLXL?Tj(&?gu(K!QY8%yQeQdK>&wt+pKjn(DfY)t0<IfWEAaZ!qJr1N
z15|O<f9y+m;ocXT>#r}o<Enz%Z%w`X_h{h1;N<-C=?~<fs->z`0s0(xb4zPOeSIQ;
zF524E3%3aaVU!~sB9qAUDL82X-h-ATn=XZnZ|vRF)eVC$1lxGYu!6%AoMf<OBlciX
z3TuAmm<tFAAeKfqRh~9n@O?K?wd{J1O$r<`F!L4+Qu)|165`&3nGxXjxn#~Y3y-^3
zgB}L@VW@e@m%X%yj3A3u=m1Jhy7Ay^7rD;i5p}Cd$z{|T2oCrw718DllSLhZ)G$^@
z)V~iTRtt!F)OlxvDbT3}=$JCON@22t9i53#x0YRRp`L7|H!XSFr-}JPy$-I|&gTYu
zdwY{GSy=wI4|tUpm|mgIa{*OV1bTL2RnEWWQb0c;QMaxI5e8<ec0;&IQ!x7v*AF+k
z3~wz$v-FqilB*P0?sL$BgP|g0rbX#HfPX4Z%-0)_;MIE0S-M*2F19{bii4q&itS%E
z)Ok1v2-m76CMjH{-a9LHpVQTmir>%GL07lEnhO@`Gu+(m&kf$F#i-uD4-W!UP`01V
zZ^m_mfS#u(%#jAmP_e&|Bk;iu44T}&p0aZ=5^-bKGA)fSzlI8C_Ta**(aaGrnx}3}
zDfS*bkkB#xs0{uak63MCEp2(D11um0fbrF34^rXRX;=?bs2DCq#(~3Zg?Y>Zm^W&1
zzInz-Aiw{y8k-+Yccw-&5Y(2V?VuNOfHeqgG3}_ebQ<KMW*CzMTKai>8_fruk|X<t
zcn*P>_kD1-p2Ax_?9QA3QeHDl=Q}vX*VdZJx$orU<$bHE2lTriYz<R;<mF=+X;3VH
z<~!|?>pA07;w0u)r<P6Kl@zmGamnqQS~hK061TO}g?g+$8u0AbfCI<8U%;eB50u9W
zn^&>1k>FO<M!mc+y0;E**RE{?IRKq%0eeq)sZD<*tZJK)8X-HFiP0Gr6)&$!l>CHI
zpl9f?3%rIt4D3nOIu(WIZgKzm<7thDjkW$$T?o?64bD3qi(8QbhGpTv#O#Rl6X}8}
z{V`lt{DW$R0oY(cfHwrflFKNkF7w;YMH{XQV_nR*Ib4pvw#z<~WP3=~dT}<w;98D9
z0W;0~<FkNRfN-__xw`x9<Fup1S0RhR<?VAvszsPwaHyMubcecXKxzpA$MCrsKw5Bq
zKy7RPcU5e1F~2_gpKcgS0r<8Tb%!*0ePBP1F>elSH9BjMtDU1W0W*XRFttSKkqerG
zIdCu_4L<x6&k@C!vWK~!t32y-B*G;%yC}aB@omr1<x{p$5~Wy}xV$uYkRsU*#|emf
zVFFYFb0~4{dVD~P{4oP?iBEEFfJrdLbCL)~oER%BD>b~I(<q|C<EO5!FqkNiJj~>|
z_BAZ6xi?bxt<-u?Oi3q7<iSuUjI6XU-<hyZhSUHwj=i&RNDeFn>g?t>EF%HR1T=@o
zIk97_Y7ZZtx38K%hl==+;EAG$!yW2U_4!8kSnaQ`!_CI{)VRo{NGkU*mdqn`isLX{
zqynCBMK_>PpMr}XI1NjnM}e0&RNMmKr;}?CGCVvS|Lfog!*5#045Myg9n;tdI+Sp-
z-(P<wxS=oC@CW$PD{NZN-b(gdP#c;-3pCik(YYkh3tNmAK_Y2~@uv_tNwz~}0-0Se
znTF_9w7`;f{W8V8$mYS3=kzOG$D^%&OE@A?eG%e$5ZDz4>tA=;R&p}`ao=?xT*9+T
zt*Eh*;H1DQV1n7+Q<D7JckcB3R*Ieg<T6Oj=C?_Sk_I5TPr-4I&e2(W%!az=e}8^q
ziOCoSGR(yP$PFG52Tn4Npr9&LJO~6~pu=Cr#X;P&I`v1EjIH-Xl*ptQKpItm)-w}C
zd^kzWu=vr{eL(OP0;c+CTup}>?%|t^9=YT6(f?>rVIYGm4-G+(i`&rr0FHUzlY=pb
zk{cyXtry2&e;?GTG2WJOk^4Et3a3^)EI|t5ozWj1wB)>;==s;cWtlmU&;K@4=}Q-}
z!-4&*uG8q&Ep%EHyeS|Zpxy+?lIYt|oxWdu?RVl>?q?XCOxP@b=ZSjHLd`{O*pz8b
zN5?b7!EX;xMt?5=PQ|Og^%XUA4$#F##Q)g$CDhlF&!0Yp@4sMxp$Ofvt@50)c8HEB
zJYu?r#jPg$@+p83tuX%(1V^N`x9jy*qS9NxhxR)7{!UIJOYYV8bPFSYgQ?x>PlRgt
zCn=I~ohD%t2wijtR@!~30ralu6j`51*opgzFc%`er%bJ(T^|GL)y4xB2x9PxKpz&{
z>tHW1s9Ph1Y4QRpUBqSPddGsw1=qQo=*(~DC^=<FJ7TYL2h1uwpku({X|LTGeXR1B
z?)75O>Rd2|=UjT&)8u5BwHLAao`zp~46^YgNG{|<UFgg^h@BZAW})@~r9Qlais@Lx
z0?30fokm%ecNT_jftUvY2*w2@VaCFHYoXW75YcMaJu#k#m!k6^FvLv?Atg>;xlk2d
zf4r4H*O$u#lIf~hSBfm<YO&rn9s<h><ThX-bU3_eaVudN3^;&;e&iZnB4H3l(AjMm
z<Uq%UVAO;eh7XY&sl4D^Sx4PCU=><}<-$f#ITD2Efo1o(Dlpzr6xtQkqa!FI)gI(N
zqUOEX+7;kdQHpJqYSSCS3dnqjnhf++l?Af)G%slseu7ad;ImKZZ=+*$sAr?xSTGJ9
zj{wyabOQ$(xm2OIvdF~FK`UDG!Ic_WaO7EQf{qo!h#9*%L#SSbV>-<HTxNrTK^S<^
zGBDr`rU0mR0}5HY?E{8TcoU)z%g&X-6daSg3;jF)-B=&gP=^(?0EYbAXr8*K=|YST
z$dVR6GE~@ih9Ef$La`aB@hXN*odjnJ3@e&~k5>5*reJAdgb;G!<DWlo!91Dr)vFP6
z-S1<(GW@l*JL?#2h!tv}(3=ZU!Eax{4Zxp@zV}4qA-!vdKzTTj>LIoM5m{a$0frG*
zuw>AY9e3fIFyr0=6IViXf<`<bdD_V7<4^zxO^*t`Ef`?61^$}d0G5hU$!561DFcS|
zV1}f9iV`j;Hl#v4eq}1h)E}f33z#IF`J-^=49pP#MMjFv188fh*FT|12<j;{Od(Rk
z^rp5pI?mVWzIEVIJ;HwA2I`Rr0M|ko&%#o$O1OWFV`f5|g{hA!C9S)8irWCWB;J-;
zLJXEPI-K|`RIHEeTTzY1Y;C-eH1_?wcjgdjApR22@#_UZGI$+kfc{1en0S+$jEf1T
zxT)Ryd>wv~#m322ekG%w?^a0lQ>6mqqb~a(-4&vK@mRm}f`V<peg}YA0=(>K;Idoh
zkELcDo@}qgbGz}7;H*6s2rI#PdMC)<m`N|m(b-JI*7*AM;H%4HW;pMggd7_x6kHt<
zd^##-q8Bd&$*xx^-lFjiOMLqHj$J3$J<rtO4{S+0{6|*ZDb)G~-~DZDh&T3_iw8+s
zEhxL^UDutA`(5T$<V#KMEGQ$CpWb*c^7^_=j9;9H6$>IGCuiZ{&~=fRGMAod4d0oW
zVA-8%+WB<sJ+D5ny7<bG50$NNZ<k4!n;IFNN$hMY;_r(31iu2Kvy)Wy4W&`PO0CI>
zw(8aAXFC2&ddSIZ9B9}1y9|$xPEJqb<t<LGtdLb2k^Qjh@xZTlabhNj%uvvxsj07j
zbL#_cO1?RRRjC0334V(Ixw7(d5!M*L-s9i9y9`}jU4ixVJ?-rTCZ?v(uAl3dqod5V
zz!^Q0kO^{mv+(z2_w~(9T#as>h5aMBV2Y>rPQSj69~T#=p5K_1l++-qL31kP6`w3S
zu2eEvbaZq<adD6y|Mp@{lqybGSlGbGh;fz0(RsGD9jr8y{IKrs+wiOh2R=rorUZ;*
zt45GO8XK{wB5=2NcKjS7*CbaRC4%jSZfesi6286lp>^FL7vH#_Jve~#D(IfS4-c7j
zb#((T;%}|31>E{jvh5W9iRqH%nO_PI9z4j>$q8?tvSDf#)eueWRPbO4pF1(ff%%qG
zSPqw=?1Ti60=L71Ym54j+0Fj`4ims*Ey82uLX`~LBgRzdw-My3$_?c_MYBi$1tz4=
A82|tP

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

diff --git a/reference/Rplot009.png b/reference/Rplot009.png
index 8372ac2445c10027120bcccc42d0937da564340f..77f83a1d8a4b385a0c9b0e26ac75fa862f590b68 100644
GIT binary patch
literal 8579
zcmeHNcT|)4n*Jz)g2Lz^I!Lh~DAfW;FceXNfPgfS5~2cvAT<;TgrJTx$k2j_v``fx
z6lnnh1RO;`svw~^DFH)=5GjHEWoGU@dw1ua-Th<toIU5}B;R+E@O}Ks`#$gUJa59T
z8|m)j6XAm(XdgoFiYWwfjIv+5dB7*wFO)cN;<;m>dj*`qWBqa!4xILQ>RF*7i2nfl
z#nCwwyaho=A;gtS<`0tRh5|j!8LXY99$4Q{^icHM19dXxCUTE{J?uh%a<1MWoJ4wi
zO3!ht`&qoOn^*(sS!8~GW=rD&&C&W*J?4`254pB-nqnUI^ylHe+?B2gUZ1WieEi^v
zXuPYKIwG-MJo59;h9QQ-Nv$hq-(OpsotoWDmJ66l9=Z>wCI1LP)20*#7~$#_?!yqo
z#a&Tn8#q^HUFDb0I1GjO{V-~D>!)`P=S~x-ydhhM-ioLFAa7UQi{XZ}2;;liR|Fg!
z?Lpy?hip9`9*_A&$}lktySe6A5Z*9OrV#>_Zq-59Nm<nALLAHW<bkjjh9%NBj(}aW
zG9<vQUcGunp1>QF;&y^-mOdU4QFeK51e+96U+cA(pI402p9zyDbo}I$obIfYJK$br
z=<Dk%t?s&F*75sI0;YaKVj0~s^77puBaTE>w&AwMCCJ}$iPmz&!a_>~BJ3NTeiGq7
z9FVP@9u1ytFq){mb?GU3a;9z+wJqi1`S|gr3h;$#j(O<XCDI%>l*}D3Eg%k`#xTB3
z`YjC4b<UI$b924<h2<J;HNC!GVV0z_TEt-9AG*7{E01_oe@5b1j0C#$wU?tZva&-?
zsn{ueQ&y6CASqlYbAG8o!apJ+Vt(}9WB1;-x81v58z!``HyH<ib+2Xl`F~%$9e+(~
zsH9zk{xMQ4u(QO70PoAUt5G}OvpDvCh@c*5tCrG69=>vX?6<ME#Dao0Ih$gKu&S}A
zN34fPGr@}F>Eq>hTN70l24FOuNYR<?&5@nE3Kx`=R1pdZ*GN@@{R2i&U08DxB%u-1
z8y6Q>$;B&BN@kD82u~yuo8>mYhASpZTB1-3$(!eV3TwA9Qya)Czsih^<MH~h&NT;b
z4vdhU2mw-P9NzZw<;&rlYK$*t?0NeAR-)FUR%Z~e&`PFE%~#UInc14G)9Cf;l{U0Q
z(BiwZ9y?SUkIJffGO=~jPa!a5V1RedO&S{gFxh%~T2C@V*_ApSd2znuL)eeXPHm#y
zU%u$!U0hs*!mbDjgq|;7=*o_g%ps9fO7g&HR=my}Hn*}$2-?A^oD~(2O}M6y*Vx%Y
zBTN^DYtCo;TuzT(n;VF)9QiS!O#U&i;QX|Ehv9{xtWb&In{BYU{xXBWwOJEKN5`oR
z2EE7i>xVEIMMcX~LY@AU6|uSAqRjVnkAfPkXZ64yXu6^rb8xjW<^*F9-HyNy<94w7
zBs6R{nG2RmA{Izwiin8F7ow#iqVpjauOwn}a#Gp{_1W3{>eZ{cM55W5!!3Pzt&c}O
ztW*mWGF#5Kn}bl)*qA;ck0hHjhXXyPRzTP(P`=;Ew<$MrO7VTc`7mTSXvL7V6orFN
zZGd+oM~<A2PtvR%h{1Zo{}|D_0@=PnApd63@gKvlpUT?Uz`W|Z2e$OyZ$IzBj6URj
zt|$4WxA%=z$=!ROo}ZSFd={k(maCv@UTp}s!%8@$Dh7XWT~K}XdR47%k{i{wVu7-`
z+IS*rI!0Igvu_VF-imNdsvF#4dD(jxCx?ZF#XV}Cl~s@O?ccatQ#DaDKGsVQYz5(q
zT2th;wM$MO($K0yz>gr5c!}xhmq13{0MiU!3P(A2=UVV_Q{Z|i(*1<5OBEZ;g+eo9
zbqkQ~)^BgULk!VC#1p%WHIeooe>DLY6;L)@kO7l%bK*s$3-qQ$rF(b+ZaX02r}H|j
z4MwwE)Rp7b)enj&>#ZI#2f~~`{`wOcphZameS34E2kg3qsc8nkV)gZSRB%8zy=3lj
z+prNd-30F$9utS4#>#3KCl8b+eXP#ymxl3WcwsB9#Hqb)z?Tam3mRSGf^LPJxCKFb
zfB474ME6CRMa&sUD;-8nx;SYVA}FSo^#Ja!u2bGq<?nIwz#e5KrJGTY;FFt_jL&bt
z&gbi15)Z#}+`G+j__eUKf~Y|GI;ko75f4-car`eFwCXS+ZTM^144g*)Ep?rEexAqr
z*<JdtOsSbZIS<E74J9xy0El<o-gV?wKxh9q7S${*JUPX=RGb5f<j+pyfX--z{FjI5
z5~zntl@Sh~>31#c#-B8>wk;1@_clyYF<haw$D<W2{r5mx<#8ntbEZqT`GtH}xFY4v
z`%pn5nQV<WM<Vlcb7kahE3S`u-n{5P-_nibggV@OKiQZFA37u}#D~BGoXuYvZ)j#>
zBaU1!8X&oKMIXB?i6Gv{@BCUHeiG4@ZEOh=x9zT8Rv<O-7}E#j835Z7@l2aVxG{lN
zR2yw!ViF^H`iE14{&J)jUFucXO}`-!(;L)s%B#*!Q-q7xr@obH;dtJCB3TQ;%qo0V
zFl*zqcoglvFy~-NR@Y3jMlfFjEr5^b@vNeQg{`d|0&e7e^zdF%B|Mu$gPvDXdZ(VR
zX5$;NpX8PVpf(3&zRTpNI1!cmFK-mu4ShZ3zc~6%1peTy<V;8UWf)6a8mZ<)*WO0A
zEk$HJ4jOU7r6V6|g)|@{l~N2<t=kA(+uz5d4u;n&`F=BZ8>}#S|8!rhq=mWpo6=Hq
zvuq;^Lqo%<)$<Ufo<LikaBNEP;)YM6JSOgA8$F!juPO(w?xNcJ@KY9KMI!Fno!V0h
zifbb*8^8kHgk0mPn&+uH=mM+d-3x_qaQ^G?PuJjK2m>;F^e&C_;b26pUbZ($VM23+
zc`)osUc<C7ygil{00QFRUY?WK^#|{omRO`H-xaC4uq)b258zI81F}yW^~qMeQMQGp
zr4vAg(M`&;@bErVBLJ#@za4{9-hUts#D<%H9sY6+jyiD+Q?ONcieA1pOnQAaT)3>l
zeSLW{QN=BBsy$VaW?L|S019!Jb^0SC@AsW!<BGDf@&;frk_bS_a&vP#e|y4zP$0An
zO>46qItf8TH;(qmu3}N3N`s0YFF7|{V>8|m6~C}4a=|^B<Tmj7*yX2s_+boPUY)ht
zh0kIWP5Xw();`fN9~pKE%gV@@Dv6YdXAUgFqgYS!f_9q6OTK=+GmaqFn&-}mPb$^5
z!PwaFto}}dqTPyF8-Z3<5i}e?o0+d#cp=GH7(s;!O0@OKaaTAYvb$i|EunKGObcu4
zPutozYA_7EB&Z~ZjXPOSw9{4MX>xTr=KA5n@|_b+v0x_Wn|TF9-^9sUx&R7kU4tnD
z*mu5%jT9Lf+IXMoj$`%XOSTc2Sq8NG$VOZn3{)oiS@M(D5|JPdrG)sFc4TxPy}Omv
zj9wF6)Rn+yoiupiys<h1hTC7|UznwM!p7P<H^(eH_w{Qj^u|w62%!ZXuv|RRrpifJ
zF1+AP(wHbOY0gMg-{1r{-UZSlLI=<wH+8<{o011&&_)af1Js<rG0%$ngfXct@iRaJ
z?fySK6!Y}LdMtp%>d181x5%b$zws+hgELZD5MZb_74qnPdkJu~cw6tVdCI0cc4H<R
zseA~6cFDVj0P;xLVJ)}<nn-3)ZG4;M%bfwO0xHPOvXvLoy5z#V3Rq-5s@AR+kPt}(
za|Bn*W^6;=&GIQ4q7d{%CVK7@;K@4Zsg}=zxbK%+6VB^q6EL}w_hx&Gma(Xnsa87v
z7zcDp$%<>R5T)tXUy68l>utSv`-;!^#W5o>fYI777(%O{df3$akAIB7XX#0H2AFX`
z*GG$azHhCxFLa!PAkUy;9#v46Fy{r@SJZruLJ;Or9tUN%xOsIfRN}6J#{kK2WP7dr
zBK8yn@gb>)ZnL(TwLR;!)Ivn8R1*`+CZP*8Ogqa~9!TrmCy`MQ{?33y_ZK^c1GPg4
zT1M>Rw9?!KK|Wel%KNxEMbt4qmMxr+R#XfFHVP<fZgH{c$&)96?rPoQf*@&W`u@c)
zIcALVT9)AnonNPO1AwaBUo6f6c!LX=8&a7cY=I6H+8GnXbu=s14u120$N|+gHDUNL
zAP#d23*)ndJrI4py}gxx&bOkVpny09L20@(9V1*Co}*fS4netYpxmS9jY;O0Y~$3H
z;!BTtd^fH`kPtbKg8)On9uD4|6K>y`AE~YF3gv{-Ivn>cCMvrE85+{!NK1b9?3vo1
z3j;+-XxahnNN7Hya{s!VFnu*0f=>BZp?0bJ&)e4DISbOL_2t>ivN!TN$i8&4iwaoJ
z8&pV8L`iR<dL@!{zc0h#TuvpLCL_e>+kGF5Auo?Z{LHP817TN)ldZ-ETu{h(ymH;<
z+FWx`vQc)KL(>7fpLao80uKA$(gG5q!k;};a_d)0@$0{rsOIaEEeJN`m@6~oZ6cUr
zs=mIynyWC_-e2={>%g*JKbP=a@|EdnFf4hi;w%FmE+{P;%nm@M^{{F*gS}Xfi-qGx
z0lk713#*7kkk(!G3<V@Kl1nauLbf2$W7gT%6YmGJIYbV%1$@V<EBFrXSkmi5W6P?r
zfL2t;pe+^B1Q^&4{=It*;|8L0rCqZSaEY0&Y+6uVMn*<5r71MtXp)UH1lp3+S+R)#
z@lF0Y85tQoI<f&FASzo&K+nGneFk*qmq@XT=V~|U$!0s-EFdT1Ryh7((1a4kNu;&e
zHbbY48oK4@XJuZZivk3eT|EB-k<Ot|hB43G6@lObY}%T@7}Or2V9T7^2`!VrX2=1;
zklDonb-Z^_-&J$#9Vd4D$VkWPOc$W!!g4p$0BHLCZD%x?gXl7#pxpY3ueQO|zmGna
zQBauA?E{;Bb8eepa^~<Cw$re@yj<{-7YY$SMo0gbq2{*h@w=8{)+W>cZ!u_BayNo!
z@8Rgm+2rx|{hUxVmlYR0c<TWWw+;$y2r9an_h(>Y_frQe(SQL0<)&o+aW9Z|+H`6@
z{f{A8acwQ&c5MDJ(w0dM5Hz7hHS6#n^a_QycU95|#IyDve?UQyx1?)`1a{S==KI%h
zAOrr6dpqA7G^t=o&f{|4%t%0xD$FXCtAI$P=fXgmn)JgtDL@IHlkgZKmo-1-fYP#^
z&({GIYYJjlFyB4^<Euj85*`Rj8GqT%A))H21z2Z_k&}jdXO;hg!cPJKxw`L$@a{dF
z285jr{!1aMwxz*vZGLEAJr=-MUH{(20%R2#BLJdVhyd>NzoUo#=OPOzD4-AhhcEj#
z5-;8d63S%}j=(P@FG(Wv;JK?qJ5>hr&mb<XK!KoRVj9lsSL46k?qf;p?C?ev)SY(z
z&zkW+NXXycPRP{(4+s=n@B|wpUtmKCs$>(K^%Yt(qtxF5toc3|zbEh_iXEGM)+gi3
zZ`%D6*)+M8e+pac*yaD%lBuwI`I&g&>{&!-4Rd%vW?1?}%pb}6P)iG>%ik!Buc!;Q
zmPS#w7aRu}2ivZVxh6GWJ3O<nYiR3v2`xZg0p-`{xY6H3k(y~fG2P$apFE_<F>i%$
zaxjw6eA2?i0^gyiHj>LYg&{iQqek1g0Q|C7x6bss1Z49*7_EC~UFrSm>AoYcyNQ;>
z;^K2?q~rk;z!W6K)O_^V#F?4oH?!vP^CP_e(q&6srTU*)-ChLrH*)!OXqoNKh$>ak
z6NWwLBPZPDT3GYCDOQ>uYpfCTm-5KUC@Lgg-iA80>{?K%(_@P^R9N5xEHPr_{~*xV
z0aS-i?R}Lofy-~0HYLzb&*%%mO+D20;|-qZoI;x4R_8ey24#lIx@_G{C-1m|ATtnu
zu1&ps_?P$*t&o@ts=N06f0Agw>!qL(%cId%Ubbp+CBEr_sxb}5CMG7**B%A=W{fy>
z5_<M@9YswRU&T*|jW5Ms;8=qjQ&OwAyip2lI(_kTLwnJ-ecG9IiegILxa82DlA}2A
z*QOVCd&<O_+S}W=QR98K;%5-aXzA7Od!%Cde1C&C8aFwZHvRY@`9WKlL^meVmZYb)
zwqLoO<6nB}pUTNJ1J3GyZ`BmYvh`Cnb%q15s~eyq3oF>Z0p&gAqQBo7uw|l|oGDmd
zfa%olOXHw~0#`lTZGd#IG5Q!j3viu}A3wG<5NMOL!RnLl$Z2t-iKW=)22nCEWy;yu
zQe8()y{^?57D(v%x-45*ox!$V@vEbc1;2fgEo|vyy-x1>b$=$BttPfl-e$}VFe@|_
zk)}C52rubtKL>pw&fX8GZ`e`2_X|t0^5avZm%G<L%7(q^*%PGLBo(7qibWN~raor@
zCS2w{sn43$)$ClJY__nn%1OT*s*eZc)ime1B%lo2FE3||W0>>#SB|GY<)K)mx&3aF
z<dVt#7@>(GJ;~#=WY8MO*PU4dzWQ%tp2W$NZBfswSD)w`7$^fjhFz_VG!0O}CwB8Z
z2A<F|)-HM<G!0W@?XJia_`40*u>VZ|`~7Fs9-SB+JbMv%j8OjmfAkRlsvi6|KAUJA
zVV7t#d;3hr;UPOLwgz~@pijX#)o2EMSYAPK^HdFN9<UoPdQbcUZ#OHntH}eiVWTh}
zkH66jEQ-bmU<J>B#p>y{M6spX7hu}IzA5+Gf_TFUxRz_ceggK}y{VQ2YNn9f@9xCW
zTk4QE{Sm)H2XNr>8nnb^PoFlC6D|?w3YZ60xW!Se<7z+tZXEtu{YE)s^k1!h_|e!I
zc+PA+S&OS>=rx*^m6c)U%6h;p2)X(B768YAmIZ#l94G*dn5;+jd$u`?eCanL#MP}e
zch=h{0|*S&M)ZtNKZbjv=%@2{rvpbe3Dg24QBZU1IuJu8;yFjXxNVObX|jCst;_TQ
zQ5u)N0ZBU!R|l~DVqtmfEMNh5V3HqQ4dMTAIy*T7wY?nA?hT>a)wAaY%59>!(~`)O
zFKkLdN?=)Zl%<<nK0|d^ICLl)_~~QsL+Q_C3p!rN+vGKHbQlH@q}x}N96t%kqbP&m
zuJ!1;U3Z4azL>>#yeAQvIuV_P!3^2)Fv!OrD0tIz3FdHdOE0e?aL3R;D$jp1SRcY)
zgxAtVBXsHw)I+u|hnQRkt)frO%?uh?f(i}A17W2VK^dPv`OY2)`+ykfDWZB!B{+Sn
zd&ni0I6lp?AA4}unNHd|`Y4Ef?J$;!9P)g>4>J*?8%a-^`y9h|L5!y5)s0tIj6O6C
zTb@gpU34p99qq7)bb3m1ABaO8NjtIo4Bw|@7}}>@oynHC{bXO)a5@OBZJ-ieLFH!m
z;8Z+@<q+hRwu^Ckl0v(=h4`L4H_EmVm4BjrOx}icjm_<+KWCP?_9&>YPsD!fDNI$U
zoWKj~#<XgB^b{ZyRlPDmD-hh2T8K)u8Uczg59qXeOJD0ZK+yTlyo%4w%e!`ABnV9`
zKY#^qefsZZ-)nh!R-Tlzj*U2cRy+N=9?<G+k_$Q^QGp|3?c04df#t!Ak0sdtC@}gf
zw^vekN^csk%<(_bZWw%;=^>lb>RYb-zKFDWv)qkR8(G#KRVEl>G#0{Xf<(?KfL<9b
z8J(o!=6&9&?Y>uyWV!Qac8_bYK>{}mI)lkAzQ9*j0K8T~_jpW;Bf`B2e^Mb%zp{K7
z)BNQ9Z6@$?$mTMWz=>iz$RlMN)eh%oKyPCK^oLl&bT{h}5P+iqf(#@N6nKnK+=1|8
zU%!iAxZ<W9^(zTj0O%%%PV91%bOCN>TLC~?ViDq8f)D>81t0LGE7~UU2yl>z`T6Se
ztXR+)>@BiK0+=?)?s5mRXMVP%-7m*Hw|lLue4p-!$^bcPTf}i-9&Bx?MDg6p{Y|J*
zne*32OB1oi<b$#|RB6fVD}gUvke+@FU6Um|@;s0^pHeBhm&YcLJ`G*K22C{Xzwlsj
zv}JB>wr{+kaw_pgYPr|Aj{p087fNB-S=T&O?)Q$mlV@JH_=4oTdOSw>q02@uj267n
zl}ZM|P@1@;h+7Z82$t6N%we`OKR=b^;SOfvDqAPkr<ruY!w(O>Y1xXM0KGkHH&oz&
z=c-#t{^oR{32DxqPBPp)HlWc?5)82&7gFMTgf;>h{}xz)Abfz63T!;1n(Ym`ynD|U
ztWGO!eBnd?Y5;+jQ(CH2GRS19?3AXWjVQR;9lO>-SGr$8-%3xb(;B?68$AH#+70?n
zuK@0x5|45PD-3!gV!gRkwPGCDrUKCHR8~>ByA7iWMd(CT8upRW*WE4_&r3;{4E3$8
zhiT}9j9(2g`2nOuUbq5TFUMSFB_fd4mV%j14^>-TKX~vUX!<nw!B6N$e)$w9JHX03
z41_w{;m8xa=m+fIR|fIlIt>&p!2AcNKnI+djP3_vY=@aSiDeK`Zbgv~Qr{{hgWnGv
zV*eHZUVz6Rh<pD0RE(Z8*bmr1q*N=j{g?eGR=*JozdaVzm|o-`>K}M2)knpy_M95<
zbK1oCIizwSA3~W&;h--|W~~j_$Q@B~jy@3fvj$M|ewsU=2i?Q=l&YO+4&0m^Ko|r9
zL*xZmTbt_L#L8P|-rztgC2C;FpcdK%OdgcK`8KWI4N$z=V3tq)LCfig3&YL_pMe_t
z4R~ti0Rff3fb?CeKa{$<rRz$;0Z*kgn1xZ_n9k%2eb4tqTjb0wEo!@m$B{(t$M5V5
z{@CfZHO6eC1}~Wt_bZ21whO%!21T-)PnSYCd}JbnACms6a&BpQZAAvfk}l0d;DNYm
LbfrN1)}wy|h$i13

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

diff --git a/reference/WHOCC.html b/reference/WHOCC.html
index f2decb28..9cd0e709 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/WHONET.html b/reference/WHONET.html
index 9edd929c..c46fe44b 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html
index 4ab9a22c..0e10229f 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/ab_property.html b/reference/ab_property.html
index 702ea6c4..0705f6a4 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html
index 5f586ac7..4580476c 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html
index 0a15a7ae..4540b9d7 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/age.html b/reference/age.html
index e60835ed..d87caba1 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
@@ -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  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>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1  1974-07-07  48  48.67671         25</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2  1995-07-06  27  27.67945          4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3  1965-04-10  57  57.91781         34</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4  1954-02-22  69  69.04658         45</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5  1944-08-18  78  78.56164         55</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6  1989-07-26  33  33.62466         10</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7  1983-05-30  39  39.78082         16</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8  1965-08-11  57  57.58082         34</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9  1971-01-15  52  52.15068         28</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1948-11-17  74  74.31233         51</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 a488b6c9..9dd6f22b 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/antibiogram.html b/reference/antibiogram.html
index 1d6c312d..492e22b4 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
@@ -290,8 +290,8 @@
 <p>Case example: Susceptibility of <em>Pseudomonas aeruginosa</em> to TZP among respiratory specimens (obtained among ICU patients only) for male patients age &gt;=65 years with heart failure</p>
 <p>Code example:</p>
 <p></p><div class="sourceCode r"><pre><code><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="va">your_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">ward</span> <span class="op">==</span> <span class="st">"ICU"</span> <span class="op">&amp;</span> <span class="va">specimen_type</span> <span class="op">==</span> <span class="st">"Respiratory"</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="va">your_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">ward</span> <span class="op">==</span> <span class="st">"ICU"</span> <span class="op">&amp;</span> <span class="va">specimen_type</span> <span class="op">==</span> <span class="st">"Respiratory"</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/antibiogram.html">antibiogram</a></span><span class="op">(</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">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span>,</span>
 <span>              syndromic_group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">.</span><span class="op">$</span><span class="va">age</span> <span class="op">&gt;=</span> <span class="fl">65</span> <span class="op">&amp;</span></span>
 <span>                                         <span class="va">.</span><span class="op">$</span><span class="va">gender</span> <span class="op">==</span> <span class="st">"Male"</span> <span class="op">&amp;</span></span>
@@ -458,11 +458,12 @@
 <span class="r-in"><span><span class="co"># with a custom language, though this will be determined automatically</span></span></span>
 <span class="r-in"><span><span class="co"># (i.e., this table will be in Spanish on Spanish systems)</span></span></span>
 <span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">ex1</span>,</span></span>
-<span class="r-in"><span>            antibiotics <span class="op">=</span> <span class="fu"><a href="antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>            ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span></span>
-<span class="r-in"><span>            syndromic_group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">ex1</span><span class="op">$</span><span class="va">ward</span> <span class="op">==</span> <span class="st">"ICU"</span>,</span></span>
-<span class="r-in"><span>                                     <span class="st">"UCI"</span>, <span class="st">"No UCI"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>            language <span class="op">=</span> <span class="st">"es"</span></span></span>
+<span class="r-in"><span>  antibiotics <span class="op">=</span> <span class="fu"><a href="antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span></span>
+<span class="r-in"><span>  syndromic_group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">ex1</span><span class="op">$</span><span class="va">ward</span> <span class="op">==</span> <span class="st">"ICU"</span>,</span></span>
+<span class="r-in"><span>    <span class="st">"UCI"</span>, <span class="st">"No UCI"</span></span></span>
+<span class="r-in"><span>  <span class="op">)</span>,</span></span>
+<span class="r-in"><span>  language <span class="op">=</span> <span class="st">"es"</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)</span>
@@ -477,13 +478,13 @@
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># the data set could contain a filter for e.g. respiratory specimens/ICU</span></span></span>
 <span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
-<span class="r-in"><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">"TZP"</span>, <span class="st">"TZP+TOB"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>            mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>,</span></span>
-<span class="r-in"><span>            minimum <span class="op">=</span> <span class="fl">10</span>, <span class="co"># this should be &gt;=30, but now just as example</span></span></span>
-<span class="r-in"><span>            syndromic_group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">$</span><span class="va">age</span> <span class="op">&gt;=</span> <span class="fl">65</span> <span class="op">&amp;</span></span></span>
-<span class="r-in"><span>                                       <span class="va">example_isolates</span><span class="op">$</span><span class="va">gender</span> <span class="op">==</span> <span class="st">"M"</span>,</span></span>
-<span class="r-in"><span>                                     <span class="st">"WISCA Group 1"</span>, <span class="st">"WISCA Group 2"</span></span></span>
-<span class="r-in"><span>            <span class="op">)</span></span></span>
+<span class="r-in"><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">"TZP"</span>, <span class="st">"TZP+TOB"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>,</span></span>
+<span class="r-in"><span>  minimum <span class="op">=</span> <span class="fl">10</span>, <span class="co"># this should be &gt;=30, but now just as example</span></span></span>
+<span class="r-in"><span>  syndromic_group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">$</span><span class="va">age</span> <span class="op">&gt;=</span> <span class="fl">65</span> <span class="op">&amp;</span></span></span>
+<span class="r-in"><span>    <span class="va">example_isolates</span><span class="op">$</span><span class="va">gender</span> <span class="op">==</span> <span class="st">"M"</span>,</span></span>
+<span class="r-in"><span>  <span class="st">"WISCA Group 1"</span>, <span class="st">"WISCA Group 2"</span></span></span>
+<span class="r-in"><span>  <span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 4 × 6</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   `Syndromic Group` `Pathogen (N min-max)`    AMC `AMC + CIP`   TZP `TZP + TOB`</span>
@@ -497,8 +498,9 @@
 <span class="r-in"><span><span class="co"># Print the output for R Markdown / Quarto -----------------------------</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">ureido</span> <span class="op">&lt;-</span> <span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
-<span class="r-in"><span>                      antibiotics <span class="op">=</span> <span class="fu"><a href="antibiotic_class_selectors.html">ureidopenicillins</a></span><span class="op">(</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>                      ab_transform <span class="op">=</span> <span class="st">"name"</span><span class="op">)</span></span></span>
+<span class="r-in"><span>  antibiotics <span class="op">=</span> <span class="fu"><a href="antibiotic_class_selectors.html">ureidopenicillins</a></span><span class="op">(</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  ab_transform <span class="op">=</span> <span class="st">"name"</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> ℹ For ureidopenicillins() using column 'TZP' (piperacillin/tazobactam)</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># in an Rmd file, you would just need to return `ureido` in a chunk,</span></span></span>
@@ -538,7 +540,6 @@
 <span class="r-plt img"><img src="antibiogram-3.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span><span class="fu"><a href="plot.html">plot</a></span><span class="op">(</span><span class="va">ab2</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="antibiogram-4.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
diff --git a/reference/antibiotic_class_selectors.html b/reference/antibiotic_class_selectors.html
index 8d9607d9..67d43c17 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/antibiotics.html b/reference/antibiotics.html
index 0703e8e4..b046bea7 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/as.ab.html b/reference/as.ab.html
index a6dff866..8cca1401 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/as.av.html b/reference/as.av.html
index ae678ec0..57abb576 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/as.disk.html b/reference/as.disk.html
index baaca508..70c1021e 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/as.mic.html b/reference/as.mic.html
index 640f97af..6f8465ae 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/as.mo.html b/reference/as.mo.html
index 87ec3213..a6ae33f0 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/as.sir.html b/reference/as.sir.html
index 6abb8e01..cd129cf0 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
@@ -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> 2023-02-26 <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: #BCBCBC;"> 1</span> 2023-03-11 <span style="color: #949494;">13:29:32</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> 2023-03-11 <span style="color: #949494;">13:29:32</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> 2023-03-11 <span style="color: #949494;">13:29:32</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> 2023-03-11 <span style="color: #949494;">13:29:31</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> 2023-03-11 <span style="color: #949494;">13:29:26</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> 2023-03-11 <span style="color: #949494;">13:29:26</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> 2023-03-11 <span style="color: #949494;">13:29:26</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> 2023-03-11 <span style="color: #949494;">13:29:26</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> 2023-03-11 <span style="color: #949494;">13:29:26</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> 2023-03-11 <span style="color: #949494;">13:29:26</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 63f98eef..04cd81ff 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/av_from_text.html b/reference/av_from_text.html
index d18e5d40..5191fe8f 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/av_property.html b/reference/av_property.html
index 172066ad..43057d3e 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/availability.html b/reference/availability.html
index 8374972e..d0537a80 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html
index 622ce9f2..111ab768 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html
index 88ec9e5b..c87de6e0 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/count.html b/reference/count.html
index 68337220..d19ce19f 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html
index c167f747..86ec20bb 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/dosage.html b/reference/dosage.html
index 9df05df1..a1751b97 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html
index 03b4e734..51954e6e 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/example_isolates.html b/reference/example_isolates.html
index 93210aea..7c8541f0 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html
index ed49d264..d3c91d71 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/first_isolate.html b/reference/first_isolate.html
index 39df58c0..788c1bbd 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/g.test.html b/reference/g.test.html
index 4b55335c..fe2ff13a 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/get_episode.html b/reference/get_episode.html
index a3fa82cb..d7a7d31e 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
@@ -246,27 +246,29 @@
 <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">$</span><span class="va">date</span>, episode_days <span class="op">=</span> <span class="fl">60</span><span class="op">)</span> <span class="co"># indices</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [1] 13 31 23 24 16 19 43 25 28 28 46 31  8 41 30 34 11 18 39 14  4 22 27 10 49</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [26] 22 28 19 34 15  5 36 40  3 21 38  8 43 36 40 37 23  6 20 47  1 25 11 29 45</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [51]  5 45  6  1 32 40  7 45  2 46 35 34  6 41 41  2 12 26 48 17 15 20 24 12 31</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [76] 18 29 28 26 48 41  5 46 43 34  9  6 14  6 20 15 26  5 33 42 46 45  6 44 23</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [1]  8 36 16 50 29 40 17  1 46 26 52 49 35 37  3 37 52 40 21 51 38 22 29  5 21</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [26] 37 31 17 49 13 20 44 35 29  9 49  8 11 30  6  8  5 41 27 44 12 39 12 21  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [51] 33 44 25 14  8 49 30  7 50 11 34 10 47 21  6 15  3 39 19  7  2 51  5 38 28</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [76] 32 29 22 43 33 49 45  4 31 30 18 44 48  9 47 29 44  2 42 23 42  7 24 19  7</span>
 <span class="r-in"><span><span class="fu">is_new_episode</span><span class="op">(</span><span class="va">df</span><span class="op">$</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 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-out co"><span class="r-pr">#&gt;</span>   [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [13]  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [25] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [37] FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [49] FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [61]  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [73] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [85] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [97] FALSE  TRUE FALSE 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: 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;"># 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> 2002-12-14 144280     76 F      Clinical B_STPHY_AURS R     NA    S     R    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> 2002-11-16 762305     87 F      Clinical B_PROTS_MRBL R     NA    NA    NA   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> 2002-11-16 762305     87 F      Clinical B_BCTRD_FRGL R     NA    NA    R    </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># … with 36 more variables: AMC &lt;sir&gt;, AMP &lt;sir&gt;, TZP &lt;sir&gt;, CZO &lt;sir&gt;,</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   FEP &lt;sir&gt;, CXM &lt;sir&gt;, FOX &lt;sir&gt;, CTX &lt;sir&gt;, CAZ &lt;sir&gt;, CRO &lt;sir&gt;,</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   GEN &lt;sir&gt;, TOB &lt;sir&gt;, AMK &lt;sir&gt;, KAN &lt;sir&gt;, TMP &lt;sir&gt;, SXT &lt;sir&gt;,</span></span>
@@ -304,16 +306,16 @@
 <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> 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: #BCBCBC;"> 1</span> 074321  2015-09-20 A         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 088256  2003-01-25 B         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 097186  2015-10-28 C         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 0D7D34  2011-03-19 B         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 0E2483  2008-07-22 C         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 105248  2005-06-16 A         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 107DD1  2012-09-03 A         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 114570  2003-04-08 C         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 136315  2004-02-02 B         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 144280  2002-12-14 B         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>
@@ -327,19 +329,19 @@
 <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 [97]</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Groups:   ward, patient [94]</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 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: #BCBCBC;"> 1</span> ICU      2015-09-20 074321          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> ICU      2003-01-25 088256          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> Clinical 2015-10-28 097186          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> ICU      2011-03-19 0D7D34          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> Clinical 2008-07-22 0E2483          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> Clinical 2005-06-16 105248          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> Clinical 2012-09-03 107DD1          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> ICU      2003-04-08 114570          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> Clinical 2004-02-02 136315          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> Clinical 2002-12-14 144280          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>
@@ -355,9 +357,9 @@
 <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           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-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Clinical           64             14            44            54</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ICU                29             10            22            24</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> Outpatient          1              1             1             1</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>
@@ -387,18 +389,18 @@
 <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 [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>    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> E32B78  B_GEMLL_HMLY ICU      TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 672020  B_STPHY_EPDR Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 578848  B_STPHY_CONS Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 844790  B_STPHY_LGDN ICU      TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 409788  B_STPHY_CONS Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 789292  B_STRPT_SLVR Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 94BB11  B_ESCHR_COLI Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 495616  B_STPHY_EPDR Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 329C35  B_ESCHR_COLI Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> D19627  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 d1044b1e..fac7344c 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html
index 99972545..3560e138 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html
index 16d9755c..6ad8924e 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/index.html b/reference/index.html
index cd9b04e4..24afe186 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html
index b4e338c5..0f3c1140 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html
index e3062308..5e1dc36b 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/join.html b/reference/join.html
index 9689c55e..2e55f508 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html
index 0552ceba..3952a3a1 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/kurtosis.html b/reference/kurtosis.html
index a3758d95..d3e8b4e4 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
@@ -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] 3.037125</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 2.983707</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] 0.04694336</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.0116776</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 3997dff6..e22e2926 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/mdro.html b/reference/mdro.html
index b7d23ac7..b7781abf 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html
index e0ac993f..14f4728e 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
@@ -201,30 +201,31 @@
     <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 S I S R I I S S S</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] R I I I I S R S I R</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] -0.3162278 -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-out co"><span class="r-pr">#&gt;</span>  [1]  1.449138 -0.621059 -0.621059 -0.621059 -0.621059 -0.621059  1.449138</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [8] -0.621059 -0.621059  1.449138</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] 0.01  8     0.25  64    0.025 128   1     0.01  128   128  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 0.25    1       8       &lt;=0.005 &lt;=0.005 2       0.01    &gt;=256   16     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [10] 8      </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] -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-out co"><span class="r-pr">#&gt;</span>  [1] -0.27951091  0.09147782  0.64796092 -1.32641444 -1.32641444  0.27697218</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -1.14092007  1.57543274  0.83345528  0.64796092</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] -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-out co"><span class="r-pr">#&gt;</span>  [1] -0.27951091  0.09147782  0.64796092 -1.32641444 -1.32641444  0.27697218</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -1.14092007  1.57543274  0.83345528  0.64796092</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] 17 43 40 10 41 48 32 39 42 29</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 42 21 38 40 46 49 41 15 29 48</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] -1.3988539  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-out co"><span class="r-pr">#&gt;</span>  [1]  0.44150742 -1.37646431  0.09522709  0.26836726  0.78778775  1.04749800</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7]  0.35493734 -1.89588481 -0.68390365  0.96092792</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 +235,22 @@
 <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    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-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    R   17  0.5    0.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2   B    I   22    4    &gt;=8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3   C    R   31    1      2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4   D    R   23    1      1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5   E    R   29    1      1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6   F    I   21    1    &gt;=8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7   G    R   19  0.5    0.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8   H    S   20  0.5      1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9   I    R   22    2 &lt;=0.25</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10  J    S   19    2    0.5</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] -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-out co"><span class="r-pr">#&gt;</span>  [1] -0.55862477  0.58537655  0.77602663  0.17820212  0.51539668  0.02637631</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -0.44622659 -0.72591865  0.07694007 -0.42754836</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 +266,17 @@
 <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    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-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   31    1      2   0.77602663  0.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2   B    I   22    4    &gt;=8   0.58537655  0.1906501</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3   E    R   29    1      1   0.51539668  0.2606300</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4   D    R   23    1      1   0.17820212  0.5978245</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5   I    R   22    2 &lt;=0.25   0.07694007  0.6990866</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6   F    I   21    1    &gt;=8   0.02637631  0.7496503</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7   J    S   19    2    0.5  -0.42754836  1.2035750</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8   G    R   19  0.5    0.5  -0.44622659  1.2222532</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9   A    R   17  0.5    0.5  -0.55862477  1.3346514</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10  H    S   20  0.5      1  -0.72591865  1.5019453</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 34cfaebe..e71d5a79 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/microorganisms.html b/reference/microorganisms.html
index ddbdebe5..8baf9dc3 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html
index 0b7ea5ce..ad273f4d 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/mo_property.html b/reference/mo_property.html
index cd0549ce..a05cc85b 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/mo_source.html b/reference/mo_source.html
index 5268f933..d48847bf 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/pca.html b/reference/pca.html
index d6ef0875..15dd79b1 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/plot-1.png b/reference/plot-1.png
index 6c99135eb91d0964fc0aa719e08d17ac04355931..6a8d84dfb5c229e42c5842144ae99e2a4b841622 100644
GIT binary patch
literal 26913
zcmeFZcUY5I`#%`PHsgzB97Xy#1{eiFMF>T}5m6%oDkb#5ARxUNAcQ20j_8aO4ZS<k
z6Cf1nkf4lUs8T`;Nl==E5?Tl(gzS?z@4N5!x4*si+VAfE^V|2iE(x6Hl>6NEbKmF0
z-|v{;ko;EWTM!5&3B7sk9tgBI90Zd1<-k6mg_iv1GVrnQ7t<TpfKTArGH%2IAKwJt
zv<m@&<e!TE?TVjXaR-5Z070+)Y!#llKu4zf!kpMbE`@UW%GDPy;P=n}<LXJdsB3$6
zN&jQ*mCK!<-M=%5KB#lb+2rn?y>Fv_{CwC%eb1fm?|lDEQ*-yrp9_9)KC<W90nJkb
z_1Fcy?9X|G2%W~p26M7=Z&gl}H@kXxClmq8Uz@Y`krEU2pCA9<aG)ia-^qG?yk=Of
z@+b9n?l&M?`={y+z*;)b<n85a-t8o9ROpZ_KrQ`x_xtvWX(+$2+gbEWl#<efxW)F~
zKO3f%eL$dc<Wk+2-M|cW--Pdqa<6REYi>Yu<f#Zkf(oB}2aV_5;rz-npz@XD$>lxs
zj#UlwQ}%^0rtM;SjIftV!^6zlm?0o|@2yblTD*ciy-cql!jtowwM9+w$PAfhouY=p
zRIG$H0zZxr#@369FfU%Zmvv#ZjIkUk^yKhZ2x=#xSD{5qn-+>PhkX7+(um8nL!%I_
ztP$2DRyWm$0JF*&`P`sQD`VY~By#4pBI(0E;bzX&dZdQDPqS<TNoLBhO#q%$85L36
z??KY8Wi_7wwmkB4kN1JI8@?`$-7>_W2+mtX48NFVKEElmLuz>NWKk<&vi&x%tAPO{
zu*U+r0*V+Fx3aMJ<SXLz2>w=CwH<W>G*j8DA4+TJIRj+Jd=k~M^i%vdJ_5sSyH>@Y
zv^y!K+Z@NiBYVo;_jc<D$+Elu@;dD6M%*%>9wX!q*$xmqV^+d6dF=EREk`Q@*Ce0j
z`)$YoTes2-!Pxj8Tzh~<m#4@`*~7@8@ATyC4cMSIteMY2oU_s7HklCTn`+&VJWuz%
z$bekxnsDsY6%LLqkppjXXtI$CBuDx$lEWtlLb2w<vT{q+1m2d`P=zqG@2s0*5u~eb
zX>B-gsVw@3u>3V4_gleX@C4lwUGfRlkRH3Isj6)2FY7W+rSxpGd(v9X=j!@Vb~Qjv
z%b|zOhtvXX&pp)1b4{N9)p=5$`iT&EuIk+fNDav@T}WT9;2b7awV}%HG0UPypX}#3
zW>$0{+TH*hirv@LYv(CHK=at&6qX%C2Uy2J`d^+LH;^<vw7|Y1!2q;K*610N6&0rH
zoRJ}LJ_sziaHrRQ`iD<kS;Idtw#J(kji+8U264(sJEu2l(=>PDyH8?I(=2Va>^+{m
zlpWPxJ<5tNxyKaDX7agIFUhK`!^LIP^N$hvq#R<L^R&I&=u+?8_1z4%ehbm9N`lc!
zAa>VnWWjW~bDBM<wU`~01j7Q-^?+p88Z+KZ<QwBBWs*n3*J3KTRz`1bIl1j?vap@D
zm;ENZ_TRX|sZdwc+4eO4Se~9h$SLzToZS3Ivg(lMUKNdi1E!&nJt~D(y;%cT3j`}O
z!sg&<xKTahl;nJ)p?A^_O2+>@q8BPO#s_#E2=d?<d0qE|rN&hE>k_@ErC&VNMwN~g
z_97M9-elzr*kc33eXCjrnrV{+pEnwT`mY<zTYGsawC$)`R<WjcMs3aVpWQoSOL~);
zjW*1Iu@ouYu(r)vV(?PxF1AKla4BbBlLKdLyz6i6N<aR(37fZ~sO`3GKs}ft?|a$3
z2WgQN(u{PS+LHX0zO#eq(M91Mh=DzJAv~t{b-y)_i@gr5>mHgtcAf2<_VTY(=>$L@
z^+NVEuE{wGcxTaVfr}o)y7SS_Os!m@Nhzs`VxOg5Sa#v@CUO^h5>_14yo+tqqz&PJ
zHN>|b-J7Uut-kFND_GB%=E6^>KUB$$9=TQekTINYKnrv}i7b?!y^QS9Wv2%9SkGpL
z2&HGYX)!y*>^>}!F1wljFYfGQ&X4=|AWnQjmpaZ0Jl3&&F*_Er6$Pg~9NjDsow~EJ
z(#wYaNixH-%l=fK5KX4ft|+rN$?<x-Kx+N69t%c|ykCqrS01tYBo(I(^Q#4MA=j!r
zjY$=*219o0B|G=qDq0E0h64ox!9a{aXgHh6%&HJ-hV<xSo}h1BLV5iT>yaizk47nG
z1udK)WHyeT*vj<(>yxCW8*G(gL%u+>;U`J+u^vEgYoqhILUydBxwTZU?uOZEXV2`;
z>vBG-@Qe%&p!~iUg+K2D)ifH}PHz@VYVN}lTm`-H)^NYN3m<EHb=mlkxKgE?8OIfd
z0*zmD>R!1n4kmRSJkgs`A?(+kEm`L?n`S?!NmeDz`oVI7mZU@4=)TV}fD4_{ZFe3!
zpAoE&d><o#cpnfJ&@tDU+*%&5PU~_zbF93i<Djg6(MECfFmJ~wzRJ;0a#*?*P#_zU
zHe?<U*~on$Mz}PhynGHu705dq<VB#2ZBbp{qCHBF*&7E|q+4|9Y6l>nCUx<7Gka}v
zvMaF7<jHQ7`XMjd_E3g)XOnD?UyX@Tq3i`Vf0=ZyZYKhr&uf)#Ct=C$@#%d5LuwPv
zhl&-(j&$U}1m>K_lDnp@Ws*(PcI?ItJWFzI>W4z#BoVv2BZzxJ+G$Zzpphl@V)KBF
z_NTBlx}>J$aInfICl!(r+&s+d$aGutkihu0?JTb9Gx`oTZM@}W`a?W>M}#9A&4=Ow
zHh6EVJa!E)XOf#-zj<1b{C4EqwW~AficS7WNzNHG4zOX{CvYnu`YqQ-%Yx}85&o?*
zb@M@u(ebes{CAl<d~8#s(wd>UQGkGvq{hqT=D?RZkk;3L&r9W^H)qZfenB(r(2-9B
zg;v8^Z`g*D<Fi^u&x90~zpRFz>mm5`X7$j8AsezbWb$9!O=~LURYZ?^!u$^Ws9<G~
z>w2yixzxa%wCI^bWoQiscj~qqRz|PYCLnv?bG5rZ&^~Bu?3rDiK`Sl|Y-CE;7e@5Q
zxEbkfaSJ`<a<urh(L#%@SlxDPnQQ5Mt1_Cq(<QGrn^_wCF4OI)bz!MniAl3|d*na)
zX?q>;OXcg)(RNYsWo68A<=Lm6nQNtd15){@&mso4X7nBA8e%BMO$vgNA6{hnbRWw1
zq{}^DL6?pikqfPA$KKfwO=e2-75mOV56hR%QY138Ji9DqhK8Ley;c@pn`zB?#GF!t
z`OJz%ukcdhR76NmW49i$>dWxVNg&v?C?8wi4cebz0pS;zsqQse^Ej|Gl<7wPxDf9u
zG+!GIr<ZvcNFDYPK+N?DcvJ~h&-RGbOgAip)%jG;Mz9b*T|(JQg_o2IQ&}1S>3_{=
z<OY4tberwb4SlZL-nK#1+`w9>5`$3pTPyd^SIfgQ=Fy_vzZ7eef*t+OYG&$}F`!rA
z6SPLUW{-r7hq7}iTG7X_IKsoYMZdFZ_|9bL`fHm*1&1@_92VQ|TDNOQKHPc4a-B@7
z$!*GOc-U5POvWR`uz)8V<2G*pDfyNyfsfbzt<Yf+;<-TXi4kbnxi@Ka>uzLeZYaW<
zU6O*@*W0A|hrJH`$&FrVBm@w4@*CRrl~q{-x-OCp2ozfG3F%sIEQ&Bk6tY;7ntdB(
z&;|l8n)PP_N!8D@=cMpcNRJ(RW8IpRJlw0pX3u1X4C@Z-d$s94?bV&V8&bx3r90b9
zJhiP`hVJFNO77Z}mwi+{w=>_|H-6oZJG)MmEn<^G%BqtWU+cOBB-14!dmK#B*qZ2-
zLjeaGJkjW|(zx_>gV(P0k1R@p>BzU->m&<ia~m<Nl;$ww==1Nf8)f`dV$`s2!Os7w
z6;b+=4BP8K`poE(>r`3vvR_R3trq=}n}KzN2*7KX)|~rsqEv)ZAU^w+cCtxrD+RUi
zj1b?2sIV|j&U^`5BSV1E)R>_EF+Afz9^MVg`0G1KP|Mq;*U?wJhT0h#J_oiv=s$U%
z+bPz|{S<w()7Ju#!?Js>e*Y<LpnUw@kID8rM|$DLIS26&Vo+1N7XSJ)Ro%jfcX~X<
zBGU|X)%&ETng-ZfN=~Y~v~vcbS+vBpmV+R<E@x`_%ru$U&;HEXFYEvDjXl#Vr+Dcu
zy@0p6k41}9FIBipe-$ymuLlcjZLjq#ZSELzv=RJ*@aELc>#((`l92q@RCH6sz*#h=
zxtjUg@;labN$eH7?g0Y7mh<WJyo^6S^wU{EwgWx?Q?o~I&6-b0sn`f~9*S^!$>&d!
zij5@3{l>e-Ul6g;)j!00ZQfk=B&^9M-`dPzDH1G7*X#o_W6<8lfvBqJw0h>q^3gd9
ztv#v%KAS}xl#Iuad<=uOTn*26HS5lRL5j<m%bD<zBQufZr*tALTh~_8Ouk1Q;d7%o
z?!>_(V2o!g_fxbaq^IH4r(6McVEO1yyjnq*aD^c0%2b<g{$^|z?*NJH<`Vp2SwrU?
zcF<I~6?UBD5_I&KZI55L25*Ojk|o_}w-MSjzm9O8WB|CuIm2N~4C(+q{+~Z`C86g;
z`AX<-2B+hu!$vqO;}RgbzYf_w+$A318v|G;@VG#MY((7h|L-5EHK@M5%SMO%HZcqM
z4YfK^S_1-k=oB|taGp{d@y}(h(wk{}srA%en_<5LXfe-hU-`g@U*=oKt9{u-|B9#x
zacdE#gtNbqAAlV%Pm~Z(<1m$tM&IFQvwo)%t!B+OobDNvH{9YZQd{wmu&ozdYr9JE
zNEcH%`Vq#G<O6oq)f{~@c`_T*UUs*+gm@UKSa0_pNFISSviC7fs>={*PR_|;g;gJq
zGs3Vo^CjP25Wjr9RV182ArbLVK8uR&`Vw}JLxJ)s)X5Ef5+TA5!KX|rmN}nyP76W%
zAcU+JN89uY8az0!JdC7h@1fMwGTzRiDcU8Uw#!VUc7w_v(@71Tub-c!6cP$yaHof-
zK%k68G7Yj8%FV<y7Vyybujgc#bC92&X_d_=l|~NKN{nK#2j`hZLi#40`(rIEmcI_r
zY_90FLOvE(Pxl!w+z%RgW-R5@a_GDPf2~qiuyMPZXqelJDj2J!2b#=tHL0@cig$=q
z1A>F~Z2b8l5B&yKjAqXlWk@~cwa;jIJbJ;cc{Pcs))@f;sg3nn9}aJk&|?+lo^a}n
z&O$BKXJL1?rn#n|krR8GGT5zDuY`>YK#IJsAB;!8jWLumuO@#2&D+PvTY1J`8luYX
zW|&@=2^gS%xej45xFqiD&XIELRtk1h7KoeWlH-g1Ve^}l@$iSzvKzl1j&unCJejYA
zTS;+8WI(nzGuWKWPW2{xUC)`*N+6ei`a>j$sK<|@!q&1hsTgxrY|Pv<62@#ys`sQ%
zME%n+MHeSgmloWyWq%U3$WYG<-psoK1fe|ES*pCu&`@(vUatu?r}yOiVDW}eoBH>n
z?zZYf6ZFWGOXIc+@(JxsTLCq5=kS9Kz2>kCZz1Q`6j9L%q^7xxJ)}cyFyXLRb-Drt
z+vW#7E49{4BQJ7DX1}OMl)KUdT`!=-ybM#WM`%E{t_f>cNz^sG1@U6EyR^8A@YDp|
z9$uEMKPvhYbm&<|iC8_=&g{|<HMHQq12_Xb|7rMd@PoY(dLQ9i?bG)}#6l1)J}S&7
z%a%@vc!;n;OdsbhlquXpm+-tf?^!q&m0DU;a9h;RMOv=m%kIz@?N$~JuEJb=mC9l(
zVc5EmJv{q@)kWEO#JWM~)Xu*p?(3EMwoyxAsN<7AH2@Zt^9Ebk`LvPWh~;#iriyD*
zY8Q5~XY^|#W(FtYt>Q}*?xG)5vbU(k)W<6EaF|n}Ykf4GT8$@X$xKSgB%?%=f<n&&
zV;XPpvuDF#fD4SwvCgt-&etPFY+2>dw0|R~nAIGHsP!&y6bG>tL<7omzX^vh4GP8%
z6V=lrB7<6QGw`xzJLvnZX4H+aIV+~1T}({q4-h+ABvYGe81Q70&0wz>^-BBjwgWOo
zeIg+~brrOe*dv=g6!MMu^Cv(H;HOqbPEH{}jcHnvDztleRBiAS;FMwTho??&6|yo-
zq;`Q8F7IY^b7L(RHA(}w-w713?me~xXp2#ie;Hwwylih;`Y^c*Ks+?Oiblh;qXu=n
z0d-;H5&iaVyvjLIL(wNs-e-{yTu=k13JXF^<uwz=s+cQIs+Sq$-dku5gXR9Ag$gQD
zYkYa}3cb-a_7gWoOy3QgtjJYL7Gh`w8%DS9jTowGn8KMJ^2=nNvvmeu26vpH&l+;=
z9-iAjwNTZ&RN1MnuVy;23v}=Yz`_+v{4aXt*rFrIldy62$}O}Fi@zhG;n!{GEZDi}
z=2UxX!pSSk736Xp*3h5Sz^AA1oe8~|jjG0}@5tYt*ID`NSjvtk&#pJZCdDaq^mQj7
zxSz1DjL3j=Z>HCi$yyqFrHSd!zDUK)N2`j>_|Uf^ZdDd<R1suv8<6}tO;A2pS(r`l
zn4izSq>CjQ$r7%J1nQzI5~z`uTF@QD!`87X#j+lv;T@+xUA33o@XwXehU0-ZK4qBI
z09d6~&;Mf2ES@}yV~Ko6lwYR|;EXF-Jyhi4!9lVY5VNRuR2V_A)Y(d&vE6u|2??7O
zX+xBJ?>`u&D`hMml?EHZ+P5x@<3sQ{K-yp;IloNunBs{jiBh`1k3F>If*4?JvTjjl
z3B4sB*<RG=1U{^`B(9hN6Bu)(BsIT=={-#cm+L9oz3my~YvO<$r6ePo1eBcgKY*a*
z#Q!t^eEAnB{J+N!2-6MEw}#n@U;+>&-kP*DZe=?*zv4wA%n?<W+2Ym%dtx2tNmK`d
zg>^i>tZ5`z)EeckJmD0h3hOO0bZTyBFsnwr+$#<_oi|z<tNrf^<}m!85}=s;iuazC
zSgI~MvjN$f?`D*8+E|9vZ>SbFIhlH)j+{<vJ01cHD?Y?z?H23ZbkLXA2S@%igbDmP
z3#<aF1!NBym33F(jtD}8a!}N#HFa%X5Te4k4)%x+dX;_Qbc{#~9i|(oxrEbFP9F}a
zmu1^EqX2Kc-@K*K7KO)_k;r<aPs+YzD<?$ob~&peVX+ygL&I#~qo4H@t+TXNQ->9h
zmw{PEZpW8kBKu5YzI<U}o~!TH3Ea(CoD7lFMC4e~^G*#mvj<lO@FB{JMh`YkX^k~S
zH8-rWR2|48{;#Y<RMhp8qPlHO$5Cs~Y@`Qs^&GSEi@o-OfQ`ixBd1k;Rym+fi<U;g
zLNR0L$ZtF0tzDxkRw@=8<nn!5wTTvRqA>Zgts%&`1vPhb65vKla{VXCfDw6ic)LkT
zh3gG@#2_O|O6O)BWo%<(i^U~a%6M?HHOczGs-W_O1K}yH*(`bLVH;Qpri8b7^{cly
z0k9#GbjP5!VeIumv5-!fwlp?vsEN`IKzAzkG>LyPWqmY}Dwi0?k5zy9QPdLk7zlN1
z&B(vcOZKanUJ`fw4~qD2posrrNHKt@L7Gk#dc|Yb=t|!9FP&n^)W~Xv*QPj8xz<7(
zbQ$joX9vm`P_v|9V*gi8KLEJ%qf~+lO>=gg<|IIJ3aQ1aa>5ywHFa$bE%ue)eeeGy
z1LkR7!|j^m&QlFGXj}#h&ZTi%sl9PF@*)YAzX5=DK!n{BY-ZnVBCD`aLFkXTjd;w8
zruI=a3*wB5^@9GA5u2kDa5@rMo9&H#cs+-2UM+mhqp(yh`6PRT23|rjnyZknCz{d4
zOB84jg=6rnzS%g-#rAIL`4!7Wlz*LAlO|pTimZCqp$u;}exu~$EUjQ{wpV(-)p9XI
z;pW}>hXmH2v1|K(ZHCWRoQ`>4cc>Us->~uepfn|ez;u3iw56aSv{mtXyfXk9>hgG5
z(Oh*vJ+;JmoZx?19Dbh|-Y~D$$krUnw_H4SsJx7``8x?C{rd$+pQ#m{Ku8hBRb<yB
zs-krcJW#%626>ayVj^|nwd%ty*)kxkb^$m$a<)ce!k~(0q`Ho@MDU^fWtNX-TY9$S
z_V0J`4jE5jSm#9r*AhM~49_rlXB#uWj3+pA@CKW24?By{L9qqeTgo}y6&e5dG5wQu
zSY4~U-eioXDA7TGF;--~*T4jskpQd}#W{lUB^ePF^u3IlBaobY0q-&yy^Af059>NP
zpHVnQlqa-kZQkbeziiWMJ~h$oo40auG^kyE*e?J@Gf4@$g5lR<Q9x$102;3EK_v6g
z^CNEYbWsKbD#^$w@gqTO6tmtC9R+@faSYn|9@@7b6VYnl%%@o!AXqCoF}A10s_<Le
zx8d-?^O}|}kw_H2(agZG*%kTZhIPBehSPZ9z#9wg@sA7nRH)@_q%r4Rw334NfycGM
z`^4&8E*X3L1EVp*)HrbK8SB>RSX6m9jMHivA77k(*Xd3(krx}iyaAJa$3Q`TYlfGS
z90f3rL_&y0ng2_rLt?#Ymqkf32v(jJH87wwdI0m}CFfCeTCGMvo#}@2aE7m{ZZNrB
zk>Ec}Qc@HRh?4g$k!~6;&bCFndovAWhH?bN=;;IIa&z{b3vAh|%k7uO>wHTM$IlbE
z8TiBgSHGri-^uvwW8c8TOECGalB4a`wK;P774Pr$0RH;xT>w|zN1j$heD?T}?WIAx
zX1Gt?%P~#7k2TZYiQEv17@XQv0SXFhgf)DxdLD4|08n`cS=8^*rg(COFY^wa$lGFd
zBus{-`AS)lZ3ID_xtBh+Cs6pqX4+!15wbqNM<+!#xE8RW)H$lIWxIJ%_E>pjD}mqu
zYlKvY%NJ3Pdp!0vp`2_4Awc0DBE|2Ep5DKqSH*i8?S24L)4)10xJNWw`FhQd6ImPY
zSzD6x-8)}^n#0F(L$(Tj7*aCb=%e~v><4U>9psl4hDg$l*h_`Xb9ihC!wEf5023ql
z^51ICP8d|XhAySZ42?Ha2P+^}OiOuDEsrr+A!{`YQwcCj6Rid6bkmez0O<>!Yb6{i
zCoO$SaDxNp$!lXJ1H}~+DvGe*dbo(8HP?vh7gwmF&baMwYQ)RJdi_#M`Jbch>QK!U
z4Q`0{tZ%68o-u2EtR>5MwpMVKczRRz$CquoB-ffHjZ0k}S)s0+K_IXRZQD~7v%KM3
zQF!rYbPmKGU*3VWN)8q@=ev46As4GNT2fJm-uxt*B<k^~?Jl;nlvdXe+8k~q5tggg
zEuQ>*E6{EPv%ovk+C1iaR#%U<1NH$N=@-QuUuOK07bNawn`?YRtR^6ktme=^7<6i@
z%3`l|FMJYdWaWhH-^pU(UW!pMNaqVc2L{E7f%2t>f*4ThfbwFkb->ADasCwbm|<&r
zY1|Jl7#0;jEf*W(twe|b)Dm~zrUjKzyzvwmM7F4=7fFhmUE~4dBO~#GX+$QMTD;+P
znkpu*{4_wGf)SPED?huK<_!<Zvln#-YJ(NK8NzgL#frE!z?EWM|GgbzR!yvmTO)bl
z1v4+b#nEKsC@_C-mbY+SuLreY#jpPN`ANQbw2;-~qcdAWjR(x$^WP`ywXw*I*v7Ml
z3CCbxQ2Bu5nC6Qpdk*DaGar%<Dp9fVv?;Vu5RTvHU*q(!Dp?MA%ME?@B6ae5iCAnd
zH%|KO3!uKIhFSO00)!N*ylk@Hh|##9m31N^L`<XY(+d&|{B!(%>q2W!7~J{K-V~Mg
zGPprGZ|sAXc-IRD+|>KdFQTcr%CrmA<H~&U$BsY6{s%Pw#%>q;jfTpgSgB9aNB%}A
z<tG2cG`E1-5Ac-dY5Pbr5(x8FOk4T-xV7wB-D&aUa*=I^!UG%(3L7@|^-6uMl9cqN
znqJy|$f@<aJT9IXkW>0w6rBYrP!Pg61mJ3ih7Ia^gV=+Cj=Ak^GSa1DOc4au>z8ff
zvX0AMw{v}gGgkaxzX=oXQkxDaqE^9}qMVnwH~^YC3HSqHiFwu=4SBDwEN@tAEUOHj
zbq8G*fkj{tqr2zDnoyGlNE~J(^yxRO`PAlJ!ptHtiRN~nMzD|)#CHF=(gUJhjMTK*
zzy9>Za?v67(#+e>T2!W44H83$uvUHAXbmfKzGqPDn*7ycw*vZ0D&^%u`G@hjH)Oaj
z%XCYcn$aDD2-eDb$==yXywSpZfIWADg_%qiPp*~=kRbD@-H1?T-<;df_smVPE6(@8
zdoC{{x;jkpjzN8d3nH@h;z^W2quwusZ^a&}rm$jY0!_JJ;s#ufsgHmme&!6ZI(_73
z`PcEDY2#&R&1@l|!G}{V;6+w{s^7W;l=f%08fD(Oi1y?1R7Q288HO17mTytiBR40r
zpD=faU)T^?*Ep<1bt4#MT_(Z4-qjiD15|vpPP1T)u(w8do=T!@=65}8WVyyx<+U^N
z^~=t`vkiQ;<Rthgs)~9Na!PlA)WG+2BY|0D4%A0aBaJzOKn22KVLzS?e|Yrt7MtZM
zWB6&G{(DJ0{DEz<|0oa!-4-Ak4M#?<-mtRp+321~>GqLLZpxh9MWxriN-kY4j{f~O
zxc$RZz9fKElh<WDRT==<4-nR~b`&2Cv}w2(?6+p>16hU~^76<t73*Q32y!~YnA2n;
zRS_F$%1IUB5R?r$1^#iwF8iL7p9rb22~y`84vbtqT)Ncw<;Uxx<=cJV41GR9wNMAj
z9rS#v1fx(ZQbypi0z(T7vUF|&g6*-liK3#b(LT%04Y!z`%Cu994(xE%?Q5LR(W8O;
zS-(oo|AXxLTgAi`c^l4`8%7Ps%xxLMX!7Ud#4n2hSX60~RgZb7E%i)cX1}o_@1x0Z
z@D<GBFs77<Ui8uG;h$GaXvJe#g&4f5qbt&Ad#y61xg?CVmNkt|&zddAH!?V7Ss32j
z^D_5V2A{#*_$)=PYl<F49wIahRhLoYw?D+IH6%;Ysyr>o>!xHYAC#n`4QI36h;%y6
zmVu(>0GA1x*=V*Z+d)5CN#+DQmP%J%q)YlN_EZwko#04U&F5piv$AXr!`k$1)acF0
zx4RnW%mT(JDmhJGE^0572B7C(n~n^{=`Yp!BRd_`8)76XQMHaKS&O2Q^k2s;97R=O
z-Y^-{r_^%Qi9a^%%oCnyH4lCFjfxt_C%iY?VBT<c20}*OP(-h@<ZFvVdJV$*l*-@l
zX>yovbHXGqVS)Ph$MU!twE--0i(B!B*ie%PJoYvfT62s2>4BD|xyt<bELKP?YZ=Nn
zrt*Y&m6444juju+TES;CY;<IGg-Fe#9*>XgV*f4)iQCN9T5mStyo2(WsPGua&WjB`
zYZ8okvub!PMRfZJs5*|P?qXZx4_mccEb_-O@rBKA^cqinkMZE7$Y)lCD-MwmA+WXg
z)^W@ryx#?gXvoMXz%;_Nx?1S6Go*ruE1ch1uWd*^OeG-q^}M7u92mh#e#sg&-*Ne?
zf?;FupnvD%vq|x0Odpg)arI?(kKxzwjSsgnelMLO0#$cB3K6)k$t_~^wb-YEYT~Qa
z<;&9*v%QU)s-HAk3olSf(>K4;3pnk)O|@y>Ikn6R<U9+5zza(MJ^DC@;L)G<wd(}`
zCe9x~p)r8zXqZ)p5ht5Y1AbDv_$wlkcLhi>0}wq|KXf#6txSuGnf&)9h*{oDr2MSG
zeY^nwuh<0a8-9_xQ_31CZm;rK&bAQ{iw*z2+lM%*Iw5$-@k!WNq3Yx3d%vI_AebyC
zOTL07`oF|kyvmyZdHTE+yVTHH-hetAfImnG84J^_!4_p}{UXkzK>F_hsVGtX@Sf(2
zjU5;?08?FRSz!crvATSPI6OqjyLJH=ls>aw0=3!%{6U*9Ye~^ATprcJyhj`vT(S<)
z^MZuU5!?6mD_xx!#xg^r)k8pf1E7Wj+Nu*GL_|4PC!VY&?&1b;@GE+JkN&f?F9QUU
z077W&`5yi2C;u6BRVUD<h>-)6F-N}>H!U1Uejpxad(W+Vq8aOR@OgZRzoS8so_{49
zLRh>06=u1#1KdGV^!UQt90zJEVZW6{v7zR@bK;@;?_MWAsH=;(&lzKNP#t77e8sVD
z<mid|ee7(Ys<Kf`ZI-hA3V8bM*3E_t0e{^44*zxGtIrRGYXHYD7*}g?gW8`a`<tGc
zRma=e9v3&5KIq%U4!d35>+r9Jmq5@Yq?jA*RPCjTxmBKcNZu<`kr+z+lL_2;1TIk4
zrj23max(v2_PtB<!vjw7rUaUGj{Lv$=%wkU$TD6maA?Ch_%}K^6RY)@`zCr?YUuN8
z;6mkdYWB;r!y;PBCjkJ4dGIfMXrkUHCb4DLk^i%Ul;R5i$_;sH*~_-{X|s&WhNAv0
z_oM-3D`b@ejqX(v_eE_{`M>-w=3A7K7JxPRm%7TEksM7+J{%@6;q-Z1AUHtHHOzvu
zRSGpy2XYpA!p7POJ8>v37XW0DUN_it+7sFXihA&dq^rzA8x@#_?|}$x6EBO^dYT%1
z9_i0dCjCt)+_OYUCa$Q55JzCpkBpanzqADSfkRXpA(wzHj{7$=lWy{~RxuwtT&(Wg
zs+(fYBjuhtNrD5M1zd!=UQ|e-#AX7ab3=cpSmLCtXeYM8fQd8psXHkEdR;2sFwobL
zU<e;DFCN_g*$6gL>+@TXNM@paekWO*?hStvx2B%vjUE2q?YDa(OJO#ZW$JU8{h@f{
zWR+uW)pOY)1?PBzMYYk;bjnwq4J`+J1H{JDe5{t>T+z1XZ$QP+Z&v$4a*E5wj<^Ut
zIE|$SMa@O%w_IwQhS)xAJpkG?yx}eU8^EbZZ&`59ae|b&0cJ%stkA}SfO#F^!EuwE
zm&As@G$|dBC)~KygHssJ*halS+O`{{lR93+n`RZ6NL_qniZ8Sg)_u9^a!_Ejv2@wI
z<~2!H=()7-NBxc;e=2&rcd+R0>(~2Yao6@<TJmvxjjQ_$JQKv1j%{oMTU_eP>3c8;
z|FP`uv9Q;d_n8Im-+T1ZqXQq5=cj`;xD4b@Q8qkBx}Y{~0L@!(ViIBGf{2C&ym^jZ
zVI$e8&^slO9a(izXLc%)T?x8f->|z$B|pZTZbV@~F_}|bT%@79_oIA-;U&s`+`{k{
zGYPswP9MAIR|DQ^1+&doDT#-djG(KtP)6$ewIddULNL;{+g-sv1PKE9al63r-VH~!
z#uRor4<+-#$k2h<qP_~~P8d$!46-L`hxP5UziIG8T-CjT??+T9g_Z?B>YSs*<GNJp
z-8wRy5#TRzV6H{5v6)|*4f9qo4NQct_V+M7!Mdj?Tv^}Y^Vu2Sy@11w?GBz!EktMR
z0xA0$<K)7|gLHXcax?rp{{YTh?6z9=8@?FiI!TQEVvHMK3a#x7$zIDWpjDpLK#g`@
zG%|t@zg_^v*kvg;09#%L=Rkw+&tm6s<Fz3UJ@sc!8)F^a^b1B~q0Y%ukI&>H4_gFZ
z$C1rfSHTV$KKAg8gvg45%DBbtEMoCxP*hs*i0jkoqTD#|25HqAx2JD0C4Q*IY8WBj
z6*N}31fS}$NP9H#^O?0(b?%)14P1QQ?Sa7dlB@}TsvUGG^CAuc-2qdObMgoFi)^6o
z_aMjM3prDXU<>oKGS_%;?XBQIoPoX&71{CIq7U->**I{3d0NdU6!g&TRSuYK8oaiT
zCQd5e{8cwQm}U{|gnORgm^d|7viNE#xW{zW2Woyht!i`b43SzPE!+{iJ^S0BAiag#
zt7YJ);4S-(o3k2u*(Tj00-~g~%0TR+Q0JtnPN=%6^m`N?cll|7+QratT>XXWTt9X5
z)u_a|bN;?{VlUp3NV^k)<maoEg)SUH#+$G9X05M4cS>h!-cTbud^Weh$<AW(BJd%5
z*gJrhy?;w5@TP<}ajJTAwe3GK@-b=OO*z$x=~BA~iwaYB-?PNX;=^wS6TtjDu9XBW
zp{XXp@X~dH7)-?E0R*D^B|l;ylvc}`1o^3ur1e~H@2_zp{SGXO>y@;=mauSg6)9!a
z&m|Ur4L3elopv$Y&d#ybk-ntfwHmAbu>bO^_3WIijcr?*)|;<Eue(UK1?8mFjQMSL
z@g6h>Zs{Q64`}E&z-WZlP<HK9MHV_cjDL$auT}jutuq6L@ZaC!xt_kv9!*;J{4Yec
zCauF&Mw^4l!v`3raZN?I8-E`)?Lq7f*Dzh>nMbg}Ola_|0ulm7Ud3si74j3n!$X5#
zfs59kWuO3DlO7%U5YR{)oGEU>h7uBtlf^~;M1MbG4qXk?s`*N}S&?Q-qXFdtl;OO^
z+%a9<_GtK(4o2R<xg{(VReVJnK3B7LF~Ic~C<hb9)hV(#$NJmZ^On0C!^~HkdVsID
z0ME(3=nF~w0Wk5)c&J%gN|;l}<;IyY@lK0=26#!wbs_pQQZ9!Y<KTl<;y4GA2lhg|
zRaudcJ)d$^G)Yv%pw@n(iTLUPNVx<M9Wv?7(XR&t$3(IMgqN+A^o^+R6-8jQ(LJb9
zQNcyAI(*3m`eRkLYR!2D7ykHqU1<SuBFdE9gv`wQWU!|aG7u-Jl`LNTiiy*%k;7pf
zW25!hK%DSq<!OpE6f*=py!s`5Ow5_{YrtT`vy?XX9kL<CKu-*7LEcug-;13*==L3_
z)8X8rVmT2tplB;V8GX#A#eba7OWG5jlesi_NU@~it3t)NDD7f!^BZ5;6G6lu&WpOW
zq*Ap^TJN>)`BhB6?Gwb~s;|Ri_k6u&Wq<3d)L-TQUpHu%tZtEU_h&B(;YQc8r?-30
z(!|>+_bAbBLHU~@Pjr>OIVGOor`ubiDaPLthfZ)Du0J>X3LFm&W+(Q2WSXw#_rOJc
zqQI&Lm$OOlW=j!#Wuzyr-Fk=10bAS(#!O>1%vV!Pv-x>F%mDCKDHs-894i`f<#`GC
z4vIbIaDnfiHuV&`;jk*?E9t6G7IBWLQ(pC6fwin6u+gow;mt8#hFFGgC=CU&<=3};
zPV5gOnuUG8Q~(y@?$?6Z#ZyRQ+<q|f4zAXEhb+YLMGLgZD7ENg!pwsoPVclPEF|5}
zNjzE)QAWDpAWEYnl#pI9@n*0$&JwqMSEct+VPVX-VyH88eivg!|4$qSjq6oTV~-0x
za8eKZ4`PKEDPbvRW{s-Ce`6c__?{Lm@$u^Jrb+ILTRe%*%R6&QTw#TU9lIz`k@We5
zSVo$zO3zC!q@Pxu%Q?5UnQyw3!z?TbIXl-_QfHE@Z|I)>Ob>HpGpUD(_WtSM$!;fH
zldgLYyN331suXI-d}v(IRfW5pKX-j_>JM<QX)ts^au+Bf0)x8&p+8G^#7!N>IgUOw
z23;3zlyB4Tkb;bC$MHL-?E=wC=%>ePA3ThQu1=(PUe23Yje47nn{qXSyuMC5-O@Oy
znjkm!@U-_j8@f|GcO3*WXx4i!^?g8NJi;GT`XeykFu2}C#UN#AyU`j_(AY8-9ACIP
z(v$e|O$N(*;z)MFMIoCf(-Gm^q0wYi^rxE3rTK)Zqw(R_6t*WCuvKZ9ih?gUki(6P
zq`?sLbS>az5;4+!W3;eY>Q?5%e&|*kg!FQx9dv$iNQE-*%xzexaX@K&hZ6FcIRh3(
z1}mcy56*}rp+N_+)uA;duS@D57DDSHj!(z1qQUUe;*v>yEPrq1uNU+-Cmx4z8=1{>
z9wj4j@}?S*F2$$gFJI)d$=HEz+X@ZzoT(XP7wF_$Cw9c}LPfiR8N}_qp<piGbQ1LD
zIf%W9)}S=&%{hSUUD7W1_5_l@_b<uAR(6Dng?LT{;D#`1imN}T@3949^Pj5u5O48f
zsF%bC#Sh4U9hTo>7Ujj;x!XO3^%s^^s)>~2)pX2+JXtaS_P`5p81SmyrXTL$ZYO$m
zrSqHRIA$z#>8_=nrwS6IBA86?5Z9sr6RveK_ihlc+_HXtzqk^1#q^u6Q2tFK=syhr
z|NfuwV=%fvw&jDrGc>@zM4ZfBnT(mpD$!TjY6qt%0U##=m!dR^`t4%b?O>-_u}>&*
z-QAS1<_Yb&wb})){j$yWhcaGF^@A-Q6p3r`B6`f*jwLkZ_fE<tME*oUx#fuD3^M-i
zwj3ES>&9T=J1{xv;vd`zu!U)`$!rCHJI`#0Va549zy7tNuT6i_r*4+^l>7Y5&-CZG
z$n%uagvf$~*$Mz9E{ao_R~kS6wZf<0;{634R!{IW#ZBV25wkm!_jZ#v#4+mQ0I;+2
zedCR|o#33vHR#SIN?hXJ(d@+gJ~&T=f=_>L!ZwukjgI2jR%3vqyGX@a(v<<_t}V?Y
zO8+P{#f_^`)+}eH=;>332Yg>}>%d$>eb-}eb@B8=a)3F~F8s2y1=hG-3Bwf@+@23K
z?l>@3@TjTRd^HX{@AK1qJnpg~B~2?)9Lass0oxM%NKwdIZv)e!!+Roeg@t>N@fNGE
z6MtZLwtXbIj;Bt^Y9(>%(mO=tL}~T@=sX!_qtrZnpqG|*k-K;%i?3m}iXZV5ar4Sk
zmrtAMMjVOrte2iWRhtl58zQ6@d!~y`$7SCEz^EXqbB)A_Er*25KTzs9m&Cv~^*G?B
z!y=Rf4ytNyAvl!E))p&69PPTkeO?mF)WBUXPEhdpwc5GNJUAY#;f#1LS}rQ>2Y@+i
zP(v$AyOct;8nG(4{2~5`s`>8g_b>q`<t*-;{GSF9AO1W1D3&yXM7^Y|P#jeRa5HVO
zG#?~`0{D?yls5B-GB^;QfI@ps+H0{}{GY&^uDAM_{Zz;7SE7qCpcmJo!oKUV2)ir3
z0unWLVEJN<3V5q(P%~n4qK7E}f~~0A(4}1rik?2D$2vLan!KcRACS^j&0IWOR4<;z
zHe^qDJ9_~fwwT9GyWe@Xla@@e*jgS32bflpqAQ&<r=ZXt{GVc9Z#y9BoA^+osn$H$
zJjBedLLS;vR9s@}b5^W@Lrr=!R{7+Cvvc~8A#vDnnGtC#S9K1h_3sVHq;-i5Z!rhX
zqeQzJ1|uC+nwz6nLreeshlpyCnwz)X@KN*ej1}83=$yyjI{6<J6uX`Ojtc%CtXQm*
zT}q=`6o2TD%IM1}9vA{n3n&-+`<BFG6Io65VD7C|eW*g#ls|eEV?<HGMXFOE2@7(l
zd6A%^hK>MXWfIqPnsUl`hU_!Dfd@;eAYF0s?H|N0<A<4vti*aC&0d{bKeXhWF$I((
zrsWc+a-rsb2*h6I+N;2OK%kjR;*)Q#7DAVB_qmCJa!=L^egA*n1;9z@VTTbr*!wuC
z{1}GQaveB89`#)8Op8UpWxop2YkG%Zd3|LEfzwRz?J+BKOl5BiOTbtfMY!5AiOrn)
zxN=FH|2#MdczBeJlAs5SeR;qAnLCihw}G*iTg)ohsJP1eYFeb{u1V|jHg-d@s6??H
zmHZrH262(BQ>S$6&vDM=2Ep81^`6Qm_}aVMwMN+EQu4AD_iR{H2HYy=O-rn5F9PL{
zhGA!vJF$7gyWyN_XsaY-s^3(@48Y=-i!6rVrmLl~vXGfqj}N6^8)%z(bWr`n_I4z0
z{71@hbR`hgT=&aAyrb<uuJf`cw)Hs7@nv@gM1}G@ltERQhbtpj7$|m@0e3v)M?cx8
z=NxLLfIG0JxP*kuZ-BWiR+*YZg<SJs1i1uysGeHtpCl=(eLK%@GH`1;WtKNjw7@Yf
zR^x$%%y3fOcXD<jam={{&KGc@|Ja)|`xU|`u8nD{8}UbbPv9C0Z<|s(-$M^&{%I&!
z2ea7;oJD9hbHflV;0({Mc4}DQuol6NIVI$mQ;EQRS*87Qfi0rpNrufnxKeZ_PzE}&
zUokRphzK{@2#qz(w=hzo7;!1Q9<a(%Rj=WwTfBjCe@#%)`kaL_(h8U;ZE8w-en7Tg
z_$>h87tkM0J-ttF*oaeXT&V1<bni?SW%q}H36{WQfR1M5+Kgk+`{U5n3UK^l0;d)i
zNm@ilU@(SKj)8CA(SHsq_eoeVG)D6-Kn)pzA*oRtn0x;5e#IIs4hD_m<$nZUN07lc
z1|4TOS}Bl2Mz?VUB1sPhr)y;Q%NWTmZ}#FGv6HcoC7Z1)!yZyf^&VHU6_s@tK0nt`
zcu{(MCSm)5sDWY)b4ntVHF$5IlOg|%<~4yG{F_$VGP-Z(kbV$IxI&R;hw8t)ey;p6
zvtpnyfK>>s@u$`JrAW^W%(^enrU6q1Ugxd%fM@^lp=yY?E(DL(Am)e6H2Vv&xQhpJ
za}<@4El;GJb$*?Rp?^g4I#h{WNib*KofEOlSB8QFFvOqUfS8O~p9{IT$)7)*FwUJd
z5^n8D*9_IiW$OPBZ%FrlToZPxw|V$Zj>q+rfZxrz9eh;<9S>$YnAqJ=-uhWZFbX~l
zH?hmLO__q-+dYU#wFnM1bC0F@0o9_~(RaV2=N@597ZdX6t?wEz$#375OF|FjTyaqT
zgin0w|5z^le(i-?_wUx{rn~RukS<a*qCY@X(Ge(|qGw<Z0|G@o{iqg-*(aQ6yfCf#
zLG5IXg<|rQ$E07!wSUkGxu*5+d`@Jh-0zIKTs2sTO`F1<TBU2d-w&bDQ@>m2FO;Gs
zPHjvEbX;?MIn{q^wY1t0{Ty5?hYeBwTqwOgxoW>K&~hX@4fqzR#cEbbr2Ugvs5v2#
zGj4A$4o_Ds8Vj~r^+!?#{y6<u+ctSzWYxX4ny$77O5%`n{C3}Y>2~l|2Tmxs<o@%c
zfIP>tFzXJb?_}?t=+iw<xeLus&2Vnx(4eIAq#1D~?GC3rvHrshEQt29a`%s-SwP2X
zEO5i%(e%FEjWk@-1@~ZaDJTkD@Zdp5TOty{&YAj<cw8nR>mr;j301#sC%>}*E)3oA
z_sPo_Pw>Cik1G<PW~(t^K+%x6d0b}llr_|!UKufe7N|^wnXXQOu^ChAVA|Q*$FLE@
z0#i#gxu=0ct#<84SNCAFQl?sfgkmb`3@y+cM@aBB5B9)uRdfyA^`EYwOvg08Y$P2W
z(^pj<^<0RbgTzgicZWiQ@1EdxK9=#jiyl(aRh}Lfl!5_y;HJjGiqO?WFzOOjC%O6p
z<>i^#1?){-7(5wB987VMx|B|w+=Xj)aU4qeMz86?OexS%)c%6Z@mc;jBXc!cbCEM-
zJ#%`0B2Zg?adhncLvK8L*shk;pnbc6I7A2T4PHYYtnEGn$L&MT9B}VZ(M9~W(ent(
zmFBzgVkdQct7@kUXa+#=Ii2A6;w?Y?22Se!<(#0CbT8?h&Fu)B1TS&gHYZa%()pHX
z?cdxqw=+KMw`5W<a-1Q%f>)ypKER4jwIn@$`WJ#3#?`K_Ir2){?mwEf`u@57(xvPr
zZGS=@O<|%LeJjc%5xtGOZr^b)cTf0if@9i!vVyzrkV>IsPKtPY%~hMmi*wMWwBX-A
zHY6UdH@yM59DHlOw6SXv2cS|wUN5E&LH&o$gee6K-A*as?dY(EV4e<^=;|d5;%dh<
zws+x;Fd3?isBu-FUTwRfT(9%IcKU;%yN$#j+>?Kvt12rtbi>{z2gyD4dvk%Zb%wTa
z<4lT5FOdJsfX8vc^}?OGrAd0ixY6_8>9sTHUp>&5DDw%IFH!vNF+!v^XzI{I7OUxM
zi~vRU-DEd(@Yphvg52dI8SdVovaET}@Uq(M@uWzp{3hw!4$TQ;$!rhzzbOUJ1C$yk
zpb@&0OR&a5+3+-rG|#dW?<*_*ZVyZyw5R~jMlW;(ja)zYbERfNN$<j*aBkP<iQDAK
zKsQ{D)vBTYLTRqLDe>bMM4Vz<iQ>FjdUCHRa6i$fC0Z>zgUBRe=5gCiIPP#^2?Of?
z{FNPFi^^321T=;*+n_ot_YoBwizhn_xAuG=fTsX_=t_2tZ>^~}ZlEEa_6R+cT2K!f
zXhCm}Y*~~{39ByDJm)#3Es+|jXFJoWZp~f;tD?Q0ZPVJlFFiE8K{a4U&Ha004SgU1
z4;Fs1v<a}JUDekbg|Ay*udv~FhMk>jmh~~wFQpn93p`eI9CM~Dv)18xCAW=C%gXY?
z4!y*ZaHOJ{n)A*y)j1KgQB#=rd4HbY11z#9Jk9zBtY3KX+Kz@ze|&ajM8?>Ov0}>3
zYvRQ}s>k4=rFp&HCC3-v*zo_s$#_4xYyw?MT%By6m-X+|aZH+`Wv%<+#_KH@lF*&H
zV4>-x_xII}H*gLQ`<XX`yU%uJymv#Fp7dbqYFv2&(x_rRyTyBRYE|e<34t|y-q2zB
zc{@BPROUY?b^J8sjYS432|k_53HhbtLsKVU_0sh`5;*>*-NOCS)MS8>Z<x2@TD?Lu
zzyXVS7B_?49{WibwyTWxL35I)Dxe!s?g&GR(tPj4lLoh0pfVdgw}<xO%TVEM#nnu(
zkiG@fq_XcAk5+}25S3b&^k?@*Npp*1wAl<tC1E6vUQi?wC(?{YEG8Mv0p4iCr;@mF
zaS2oHgX`3r4gfG-P7_rZ&ZlduAa9MyE-RQMpxbs*<#PcoFIT73-m97Q>%45J8nUde
zb4lrd&KKJzJxZ>O_i2HFE~Tc&uO1!d%|af|u69GG-yWFF{|mVbck*rLf?get<o#^h
zR3(7C^*xm~So6``Y(om2bho5>=VHoK-a`!obOa7QbZ5yQYM44Lp<h)XiS{_R7_Tie
z95j;Wsk~L4cywAw_*8TI-4;|Kaf;2G($#eTi$ONeGl}tb!o_CM?y+wN`*!@8M!4|2
zoo8QxL(evW&bMM-ENLKWWmk2n)Eb!HJ6Ac5d9XvwAHHP3<nsVSq7f;iu}&R6HAB@M
z`@`cK;fC6PcaTfEPfo0#oYBJ`zAe2A5tQGx1#TZ6pYa;FpHpGv(OT60>5cvm)BRIh
z;KN^DdJbJ*HZ-Ip{{!-Rrx_aRRgkv6GPuqRpxwjN*~Z%G4;6M_LW_N$$5>du!SBqV
zD~!D<u<Ve|Gx#tZ?Z*d07&DFlI$TPufhVA=Q-U}Ih;@hHT1Vt##7H7=gjK20;|tHy
zibWTAmoCQFNCP|p5)U{Oa-hFcZErX{b*aV|y1D@77s`2toXK5ROr2W4MGDB@KGOOy
z-Lzg#c*rg?rbh)i_ibk;AQ~C7fmtQ7Zp=Njn7ASN5<7Ppt=b$DB4*B>?y>)(9{0k?
z>87iYq>D!BJo1AjAgJWWxk}{tU5Z6}6j#478vEtDcGqJHDU}95Flx3S*?qIDkhm1;
zYZ^Rnh#Xeg3RV*S?iL`AsCQ_{?Vqg*_s;&=gJYX>{ck4_bJoR1P=`~PX<aBYU4k2#
zaI~w>y|GLXn84k?uVTQ5@*VeWzU~pk-(R`ai>NnzeuFK4H)Lxd?{5W39SqX(LKSv#
zG7}f;%ih-9?npR02UDqkWx<e#W?R<_@j;iKRp^|i%&T%0ji%Oswc!VCs@R!QudYU=
z*l}8KMWo&}tS!#NCaw|~ZwCKOIlF?fqc5i1&K!IURywlw7g)dDiWz@)&dyn>=^XHX
zuq?!TLFOg{aZ5=Tm2Tp;9XdW#JJ-UCFS8$Y2(j3Ev89y0q;**I2duLa+NI-5#Mmd`
zJgfQX2cyn*UWvmZDa1MI+g;8c)M3>*mBTNEe<rQB7nCpxYMz98SCYRFVtxT!pE{J3
zI(0XT&xWp|q}fv4t*Uc`c3X>qv3}2?j-6BwGr6Z`b~*RmavX?FetZSntY6%fjD}JJ
zstks>H#I!Z$<J36#EC4Se4lUEXWiwcK^1lbcow$G2<TvCP1U3hzY(Of_z7hWa6hs#
z-&=#Q2y0Z@^2m>=_1snsW2e7Yz0tdx2pSmiOtv@WyV+|6$(fHCSB7KelE$+&S8Rwq
zw0=MS`R4)h>F;T@ny3x?jiHBJS|@MH;b1%O%|8RnUp@l{NURD({(bpG@7XPN3d>@B
zvMB5xp#0jhA_7Vr;8~JDaG5SCfG?&^Ep)6gaVyxq@fVdH>sPbLT?-mNY5D>Be{Lz>
zYv8XSoka|8G@Kl1)j+Gz63l*bPMJEGv<+84%@=i|t8_qIAA?ohp$x5aU-7^*bb_x1
z7rfIA4uf(#ht63t?%)-F%>G!|bZj82c7d&8%dYEijC(ce`vA|5&s}m}r;dKS4|nRh
z#iwHIF59nHs}?n~egg;G3cih#xTo&sxVc)TK8--##|bH)hi85#l+;S5f6?5yp+AV0
z>-i&O0Bsg+ml<e(PF@~shAUJaRiRG?4TH(qQ*YD^PbglRYpeX=me8c^P8t+g&cr*9
z0Ct-)D)h#Mxvv06LCJ@=0qP-tQ~%;v<>oa8dt+r?$XP}vHL){YnkRn>QSa3F>+U%>
z{>6AE_F3pfd4P=wz=!mZlzHBC0jt{ha_z8Ryn6@AJARSbHz`~@qk4yUJYnfyR05W&
zOsSCG8G+XcOi!Q7XX#1{<Ra`MH15p-aLr5Dl&GW8BJ9mP-PK@`uT~j685J2|v4ucl
z{tyAFyC*)|*43{U8vyH076Ib(GviQ$HV8jISK#17UQ8R-`kC9Id3L9hsUBw8Ogw;N
zwaX@7%tT&LN)4HQvBi9`Dj_*g3{(6380w#MvAg@c-29~0qDWw`TJgV@>{U@2)u&kE
z<Z`BdJ(J>{nKq?)Mm02h824`h{9A#01=f6(dFHf>!iy>YGw@LE?fUOmy1-2HRU(v>
zevv(HuPFj3E$f^2I;_qB>35i=dNaxR7U;`6FmV1km#FwiJ)kI7Z6h08nAb(*w><3q
z6O|v+bs0k8CPZqx3!TSqs(NXPzkT|@R{rw;_HJB)wN*xwr`Dy333F9v@)dneSAUo3
zTpT8+7ag-j4Y71x)hR;g=mrczOH6wchATZoWD~W=Pej=`3_p|rr6S>`5K#3wibg_-
z;-{?}8pxd*u*y1xToFPm$F-H+Ifl4sTg?`9{Kk^I0Y7QwaSYLu&mY5-wnYs8(Tux%
zhQd_Y@)xC2&|qhrg%Z*Phw^+AqLeV)HtCY@=S*OSkQ1hU&02TJ@V)tXC_gi|MhOX>
z79yJ3f%`16XVCL`&n$owl|j98lx-YMa6NqIMhbH0G=GXYL<yO>CDVA-Y>?tYC53J7
zVf}B_o&7)4`~Sx&(fLSM=W{59`sAAED7l!>QP|Mhh(yMbbY(J^!%$B7oTI31NJ8f@
zb4}!8Gh5L)+GIqCu_>E1SIcH*8}@zcd~e_H51;Qp@O}Td_iI1ApRd>J`F=hh&-?TB
zX8BbKhivm%4e7S9Nci0|nvxR#@Xlr=%`*MmTzrQU-JoJVvI2S_H#4sleU#c?2Svp&
z5@@ZTT2Ba&f|Wv0zy@M8WuBUc(tq(A3Q)3Xk5mVKanZHm=!5l-Gw-W-zb(TCW8X8c
zD+munm`@iksV$4&8fqjArgN^i>9*cxl26ie|2n^6jRS(J^~618Di`;dKvj$d;l;FE
zuO`KAS#CqQi{ll&BDlM$p2JTa{x!ck27;CaD=9xS4$_Csbl62)s@drYEwj>IyM#r~
z5xNqx|J3%O(7NL9M~u)ZChO7QwD+MwPfrwZcR32AhGPyD$?T7^lxx`V9zQ^JX#B^p
z*Xkky>=O2Ge#sbJQHY1py1n14XkLx@?f&(e<Jb8Ffe$HO8`^O@nV;Sb$SzNIBvP-4
z#*+@de9`BV<VtQB!ssHsaLA&?x!WJGJ+o=Kr-;75u|%8der2FU^VLyYs>9?V50|V9
zCDNk(&aXh%2tpr$)jvePGL*tXZZ!5;QPaow4q$dZH)rcO>8T^2K<JriT10_GnTFP0
zO?;b(33Ga>iR>JZpa(zV8ek``yRwi$Rf=s}+YHfhM#@g&tsr;XFTIhInBM~$<83h(
z0uC*|Cf3TxbhX})pID-tGz5?prTH8M%~!IT?XQvI4!?;4J5Q9&f0K{fibZzBceV-=
z-kHqVy)f4zOglwK5Z3Hp62z;0xV2Kq1}4kY1-!G$De|Mm_Iz<m9D>-4n<xL)lz*H#
z8ry<NeC-S_$>Sa|>rss01?GUyY7@ZrBTtjou};<zhsj=!QH~%&gF*4@0F#lHVvAEd
z-GMHk1GtIK=5+&zs2hK;;Uw`{G$*fV-^GT}(3YOX=TcJj@CrRf+7p)QI5|q>bfJbs
zCGB2Vz~9LL#eihPvq!s=`OmTf#*>wYdoQofx11vE@*mcl!ki{h+-{lz^}<Tn<^*8Z
z5_oAe*sX>|?$}ci;pdMk!squw^VQ#s7|D7pH@Z5OSDL6n_Q}NnX+(iyikeq7=VCQp
z3g0+KbzC;$9E&w@X~oh4GTN%5_74MKhNGg(UhZ!*{;!({e5q5?8gW%krFdp5qn_<d
z^(M7>iX?|i6g_6A{|L#S%PO6RjVuPHvSr<eTV2dl3-0kJ65sMI<5#h*2VZodlJ}3!
z1qN^?ABnoVj;-_l+{!>pc1NUI&{e<x$jsB%C|hAwL#1!qmJ{3|uNv!5TJKAE+~O$b
zw#%m*s~d(D8eE{Y^9`s%Z6GCYsTZFiIAX3$d9|BoJPB(txH$8Iyp=NCZ;=jn1Mo%H
zhxzJBb_CGfFT7JF&CU^Oj8sNt#T6%2HqO`GTuXdU`n&Cjtm^QrB7#v_MBF(>MJ&Li
z_FE}9c0TOk-l@7W>j!1gM%@}ov}dnhPu^o4_rJ)$arcaZ_`v+YicGjTL?y7+NzT0a
z$9v<(CP?WpxdsS_+DWd*DMhuZz*$^Y*CUJnk|d7|C)!1{yK=hf0RG60;mHZ>B{}kL
zJdQy|>Z!8B<Wa+os%#lP?L~l3RLC*!jJA4bhx%ASV@?%b_E8mGlm?|lAsx+ANUV4>
z@r2ccf$^&pS0klcKAN(qA|%Ht#&-QZ{qF}=qT$+Bfvec&;3;le9^W$f5BnSyVb=oI
zu!j)r=uoite7mo<h6%HV#1vzlOzFsC$3!0yNT8lKGqeuxI6~L-y{kP3zYEQ>zK&>^
zdjEe}LkAfI)NbMY3jEoQjTA67?TDU+L=c~cx?gA=W0l>8PQmiay$jA`F6XPiNbB%G
z_eM#raZct&jV-|{QrkRAqQL;b?V*)PUuPuCnGlD}EoaM~yqP17Qx!}j-g+#y!-y0}
zq{J6D2_K8XIYOx^zrj2rxX@;}`%dx7yLd(i6yj2=K5qP?xJ+NeLeQqUagttIK?zZm
zKNvwI=}FIt8Vy~l@CxSBaZ2cb!|%+$?OA<ifVJx_i<|N}n0)gG%Vol}uX}nw!JBov
z!P5DpHnLkSsYdn7WIj6epm-u6y*${_2SA_(p7XX)66@BYNrygn>6D6x*1JFe#YPgW
z8~q&4FkjI`@2an29`~gt9p&F433OhB)Z_ypIAUJ`D<?)H$%J&YOxN~R$2j#^4mIzA
z_SEDcu~uTHscpr@jQ(TJ+Z|C^B`@n+S>CDs&PX-~VP`k5<+^&cDLzlcz4;8!@)Oab
zUD>m58DTSxm(STgI$4^`|4vW3X|0qjBc7h!wajGum+-%!O>1ie&5?*0CAHTEJ<Ddo
zfcMxk3w8@qC~ifZM-sAYwH!@8jG6SrTj`A?TJW@<9`B$_--MgzBJ%gHBA9YR#6`Fe
z*zdj|v<qi?DF(2H@AMA&VW6|%$)*i}U>DrauMo2qLg>l~hO*~}DZS-wpWi^(;Ww(q
z^_bZruf*ivWQGj}15~>UA~w3Pr$=uJ$X6cR_@E-Z@wXSblpzh=Iy=#ZO-xC)*7r-y
zkap&+^&30__Qkc%G}T807G{2TlqXp=J46*%t`?^wT2nmYSw%J&MNOpq5OLIYant{{
zInJ)sZDq_79cPnC84K`XF9xFR-E@33AE4&CS6Hxvm(7%S0MP?LEQ>LyVE$m3AELC+
zjT<dGwUn9HW8nO}HpuEa|GA1#KabK`@4U|=qfNe_`zru0HX7H)Isd3~@u3WFZ3Py+
z#Cri9yB^7yTkjUyjWYw(lnF+Y(Xe*d?Aqzl9zHI$43$p0=Tl9+0G4)e(+*yZ3YwkQ
z^jWSee~>2R7YOQ?sm>AQJVQTP<*r!`fPDQOr98b=B=i>L9cC>(g~)%=N0<14X;mI2
zVU}7Y(Lm|L>!DauTPE};1|^9zn&g42kA=T2yNp`48%8j>S7cLHFZlL+xeJpom@@0j
z=MLIBzipRu%cZ`OtkTk))d?`o#JOIa7>TvsX*$=mTtdF$K!bsmJ=^<(>JWZ|Sw4*+
z86)3MZF_EJa$#`TVKwBvECt0VkjKaq7&`T7^)=<uBgX?e<Nezdf2g7x>dQ4QLoT(o
zv>q*6zGo~S=`X;K_43UHZZ|6vA59KUmg-cfomQ9KFv7H{qWis3(~qKLDC`_a5MU)<
z4JK#1gyh-mnt?<iQ0YVFjB4G#mu_<$5#H`()cA65N4e#A9v(Ja1Zq24%o<@t1@T&M
z6W%`mQ@6<8y~Z8b-81mkIUbmb=iZ%&Q&?AV4Nj!ry8PFeiAiT^wJIC2{H}!prkb#w
zCSw_GHo*uvz%6TATE3BW;tkYI2;@GE|9#>DW7#E>b)e?!Hq_}CYg-`MdGkr=$LH2J
zrz>M4jXF<wzVw@1IPm8sRz7Te^bE%_<#xUyz295_PQAk}!|EqQEPGi9P9G6j0ctWZ
zmjoQu7nvNmbjnxzD}=!t0yU#Xtw=Ow6Up7~zZWWWQF`CBrhpJTw3v@P6f=sS8tIA(
z=e35Of8OwG_{$skv?4qy0t!X@AWFH1&BPGv$qfnMx$QKB_79q&=aOA6c(6IgKF6BK
zO4?J5FwET&<Lv(R^?58e5hMu7mOS*5*j6b#e#j^^ex>P()3!=?Ha8krPAOK{U3Ds`
z@3K5_a$lg9?mNjlTm9*4(>Z%sUrQizl9zrB)3-A&?JI1-8|4oK3?zpD-=1SnC-@e_
z`3dzOOxF|o4LSWu^7E79Y?B(*zrYBv4YGT&sLWgdb6+3%T{o0t^NFl_$tOc6!x&>W
zX??KYOGGSQRu(1mYs&Y{U}&SW9B&#(t5A3{Z@IGIP{WycKh_>MXQJbH{D;RmnZZvM
zd7rF3r8fLYoKp*K<0pUT7S~}D3&Hm{O|1*5qp?-9JXvN%<`*KlSXsDN?I6#86u)n1
z$_Tt7DDQ`gp5UjGr1(7@bAywM>$=xCFQs&tpN>DMF|vN<0zw?7W8cv?WeFyWA1NuZ
zMdh<^*RxH6?ObaBtZ$&aPLNZPa+X}MLaiyBc~?G5NmT%f^uP_CcLVFn(%R$BgF)b2
z^1laA>X?#=XN$W|W?fn0O>|Dsu+HH|(Mu2)6_xG9o8Jg{s5{kCOe=z0#Y3?k{`%?R
zJ`z72ShDAA;0L5GqQCm6KpidZXKc+W8Y-Uo{Kc{)Ell^JIAyr&k9a#nk{33YsCL$l
zxaN6DPpboZKOmrp*a^+~EQiQjAGZA5I5&hzrzIlEmD8v+U@g`{Ma$>pdS!MuhpSIZ
z#JZIR&szZO_}ciO^=_T3OA&pw%)GqiObzbj6Yf~}EzN0Z$5wp~C}J4_-{QuvZ!Edt
zdi@D{Z%wwjbDAq#B4G9w8xnVnRluHM7d<=g-P2Q2#L}mR#1X%K2QU>zQvUkkcvIYX
zNI#)^D0Gc(&OjshfE1M*bk<?oW;UjA@7$2yLh=puezCk<H8;1WZ-{M@)pC52R92JS
z<5MKVyQaf)ay7Kx(xUHce$_8rJgJ;xJZ&`}wt(F)4$#lo4on5*cvI4gJUhbJpRHH>
zX5s=CsEFi-YUhHc)ej}7^oe@xLT-kBZk?Bi9p|FN9O|y4uzT+n(-Ij|{rKSw4$IeF
zCK2hFX+n(XJTNmDHYaD5aQljv*WokDkoI@7hx;)hoRFDj`;YNg7@x7-R!F-7uCqX$
zr{m3<Bl$_p9~49`RW8BSE*q_H?6A{VkmE<!5-e(C{mKq%SiCqsxsl{2&%HwRz>e;K
z&Df7SEnrapL{yeX4_~xUuc6Kep~A;Qn*5D0r`Ao|xm*hC@Sjf0TZW`(s9nT;o1IjM
z>y5CDSG2j(;yr!Dch@OXb71`^$VZ{!kO4t6ION1w#gFQCP!Y~B7m`T6FEJDUoCcgd
zzfIY1J}19KSab1IzRw_5fUxnSc<V(d#O}}wE2BfwPjMa}jOoX1*1BBFs?|L<s<3H@
z+4vA_r2itvf_`>0#VRp#skXGVu^lC)pTY8p^Sw$4>Lb>MxFo4{G@-lrEv6r)cld7k
z4d}n&!`(ggA9HhYPEmy@rjLlW$(6Wlr`Cy|&8B{`^ZIUpb}S-9fU_YvBzZXO^#9qJ
z9_c7J1#tT|ZA)DasTgdK2|6At+!6RcMyf3rjsZqeK(GdhgBtOJyL=MW7VqQDPMrgz
zx_0yW2%vz~6dRcVi;rEqYJx$k=lTOls_~bXc<b-PU?h-bZ&bpWKsL`Bp%2|WGf)9>
zDAoUi4Z0w5iTo)(trD9)pd$n#q!scSsuXfSF=+)&RFkAKC}~5$+p2P<lc)WARmZm|
zeq9mJ!fY^<q11(Z8UC!6V*dvsz)qiHut!LkB=ANi-2I-`5LISq;BEJHEV?5LU6%rs
z-W{PKX9F{xevB}jX=EWfJCh1UPg7522|9|fsj+#iC-_6d^dtD@+d;_puVZfUO?zHL
z6bqBdA6`r>`~XB>H?E2AhB3d3m(XL1y|P+T2o$N-Th?;QXFZz<bc7d{Ry{#e+C)s&
zi&jxWrPl>ccw#v#U41fSvW(Jg&RZYbE!lcLOwC&59n}K2=1$I@vgEJft?8T+&+L6!
zH+}D_C>;)QVsUF2EeYmi#F<t20Z<zudn5SS-fPN{-4V1C5#ztehELqy-f$VEl!VvL
zxtXap_U~X^jPYR{Z0)@~cI;t8<Zvs#w~o`qK(!LRm%euGup1Odnz?K_H!Bit29baa
z-+cdVV(NWRYtQh^bs?^sY{j#MFig1;t=5P~v2bmV&T44MP2UQ;szve9UM>iTK0L6(
zoR2y2f|b(KZ6OVOpcf}M=c3E2rzXI03pcyd3EuaX2JzM$5d5x3si$m9N)lsx$aqu`
zHXKehRr)e`vmhXmNp)jT3X}uWq_XisuBzE-LT<&H=P^^2L@>kY4=K3NdUgJsqTXje
zK}H1je<%9iO|$2<?jwr0HR1KbLHv81f>7(HT*U8rJW8bO)G?N&G7b`?b6iD{q201+
z^rl}!dW*1z8WrBE;xWAI2PWR2gqqK__LaZqte8bcGAQ*a1VCVXZK%XG)fM?}35W)x
zIEHOLqAG8z+wIutP$PDcO~b;Rul!wC+Qv_eT$MI&QS^RfN6*+dOI><Wmg38JX+AWs
zFpJ&hVnR<Zr|i0Q&Y|Ghlf`ANEdP?&>&P3Q7&+-+Sy;qi$bd}IiLZ*<RQc4&ZrX_P
zosaN$&g&4L9dI7L9W)Csp1sm$l&g0hJ%9LnWB^d%ge$ubRBoPobFC_|;((uh`=?fw
zA>NOI5`&%B+K<-6hmu)M6HW}Mk8nz8NTK;kx5ecB2n;_~9z;AaU|+X6JQ71LOF|6Y
z4)PBVXDlDr<ag*;;`kq!;iaXa9oqB7NLEht^EBX3&m(5wcRM&>yUN?%IIS%eBi+8t
zJuK`5<x{IA|0>uSJIKt0&+ItIKqDLQ4zzx#G`Q_(--Ce#ex6wr2;?r@gj6t2{{rM8
zNkRhTm|AER34eC2BL1JkzNR&F-RMI5)u&@aY_sgIeI}&H?x;<ou3`eE%3c5?IakV5
z{KEh(W9CG2k}hz=H3|s+dT|txw*S^~;VYoOPISl6#q+zFMbE&-c{i|r>E3VO{Q-vl
x=e0^(czyUAu=iZqa5?N7m;9fj{vZF60VUg|7A|fJ?b>_@@Y!=`>VFEo`(I?lxk&&3

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

diff --git a/reference/plot-2.png b/reference/plot-2.png
index b50113617860dfc71c7189d5155f14e7cb21a141..b63d05382cbd8f94be412726ad33ea7f17ee8a29 100644
GIT binary patch
literal 27615
zcmeFXd03N4w=Wupc4!-E^>Y9O+BU*RqaY$6Aka1~7!gnz!aRt8j1e$QA%NOq+cIc^
zAY)WyN`NrWAt(wRrl^nv2uVZ&kuZcQ1PDpao6z6heeQnt*?--0_qor>^GIU7RrRi_
zwbpO_)~b4R)y7ipTg7idAdnp7(y!M+pl_l<plvsH?EqTn887F7uN^n6EPn;QfzO(G
zO9Jq<`}QU02oOm5q4eLDw21{@5a<UG<kt)K(YZ4WMB`1UJ4X@~WduI9W$T3-JAOQ}
z>(c4ue;Oo=`hvDxxbywszMU5bFZ`OmMSFbLQpXl;w}0&X?ZUO?t`k3+ZaouDI``e!
zk3YTm%?fnzs*c-<@ZAvgDY2+Z(-xX-2k)P%nI0^S@W69Qi=ndw+6W6<gBw`ObqG2v
z6Y&53`G02z+N*>-${U5W81YRD3{AW269wy!Ul4Bt8IRocm2Ul>a5wmd&a(080#;SB
zUQ(VT*<)O^6BJo1>d}&kl<0H(^Ec98PS>`2NWT*gp7>WoXf<GNwB>SHSN+TFdq6Y4
zb~!|qiQVBGZU#jGJ&VDcI*UU1Z@2`?PBpR_k+d{gwq8$ansZ{j9x+gmXyb@j4`T(%
z{2>qW#}$VteEjR?D0n#EfMSI^4E4<Y$Wfsb;1CWpytrbWMS($i!@rmqRH!&H0v1=Z
zlLYG+T!Z~#`vf;p&!K*u3?#)J%PXB@-FA0#%kvc3@Hr{Mnvt^E=17H#2TEsKyF8&t
z5u2Q2@9CcB;y!$v(bh1gyO_kCr4(9~L>Zr2)SXq_39NB4Hro8_?bRbHg3-^0b56)x
z2gc*8k6ju4U1F!~HomY<5~J0-dW~qAxE5u-NvH$QrMMGnuO3|4iOX{(Ao9YBRTHkd
z!_b@~15ktQ7E6I~5te})g@#>yOX__Rh5qW2AOT!=ePcM+R9$c5-i>lvSY<5N2tVPJ
ztHX(lVtzhirduHKSKRt8z-*|BKzpd<A%Q5B1=R2)Z3_4?FMc|_AI?mUhcUd$AB(TR
zx>#gD>h^Di+R6y8nx~+#pZTv&B}>++@(-A23V0X?b^YE!#-B}drxkcOEIFi-oI@Wn
zIkW;L#wYXMWGec72yJk+?k7A|lvEw7HKHa8oVHRfdbH$}b6!tAj59OlxViGF3Y@rw
z9qV&JyTWN)i6`w`yyh0(7#E{x>N#zgkr+iar0t=48WymOsZ$Ngn5l~r(RYAUWsUft
zps_cxTi%M3va(!Fx>~S7eTL++L_vsLk4f<Ic}&3S`Wg#X-S)7zv^;5ks&yapp>cUc
zzK(mtI?b9Y$WJ<<<Ts1)uer;7g=h2)MqFR=M@WW7p>1&a=V45oV*DE&a)|%3$!I0J
z{bRl2yh?>upvd!&ndQVNW7C1hZJk>ZPYwO~ojfRUcK(2FJ+X3qB<U(Vq2uJ}05*aC
zy(0l}e_V34BRky{-jX^yJ<A&XK0ahHKyKPGepZdVG(9~dY2$k9t`JI_t>JMUv@zJn
zN=J?h^Qt=nU8jum*Z-|GCoGiz=Yo~<03czCQPmg+-l8BR5@YB7<eVQ_le7BvXh;J8
z)bz6Gt^%EP4H;)cB%kfoBKW`yQd?PFdTs_$u*jcdaY_8#h8~AKM<M{ZjVENL7+IOX
z)%1@OMRd&6@^i15R2}=(y@#)K@0qi1kGQcp#vD{l^sfKBhjTQc(I}|1$*=D-VSOQk
z)U;wymL)te4K$BgT$9f_N+cdTpJh<MUskAdk2*tjL3+?fh#O`fW)xnM^TYCW7}1?g
z`1sc9F~1n-&e(?FT?#aWEoXQpxeqV|rE<=@B&BWru>^VM&!>E1I+GPA%e~`XG{;#G
zVL2Z;bgZy`!56|j=d&S?ABgHzXeCzmH|ZPSk80V}fD|_jG{0}U^?E^7*Jq=HVwJ6Y
zePb~<p8Wiyzel@pEGg@bC4!Y}`VfaG`^XuKNF@rBN8+PxFfl<-ncfzdl(^Y=Tzq(E
zle|IPDP%c@A*Ra5aE7nEX!sfwb)293VdHibA*;rx<$bP;ALGMxsBWs+(8h^t`s2BV
z@V@oJODRh8;!JqpXmEQAV4Sm!om&8z+`XtX1XaAv@RuRgJe`%XBzUO5Vbw7&7aVJ%
z+{8H4qa7hp;ABPVRz~%GU7|#+pTA*&A?Ac_O#2;8s2fim(@U9nWyr}t+?{7q%zC~B
zc4}qB9iM+d@h#EWn-N*@?ELID-N5xWg)ZWq&IR((s-gpax#PFtG(%D6chR(#Hw?dR
z%-QuT+UtO_>)RDuLG3mRR0TT54*BxfpR1xIg*-vXDpzbFJ{9o`d_Y0SbQJaEwq-it
zpI7l*<bte2J9GSlC4c6cl5?s=H4*E%&7JiJvI%AHaGYcZ^Q)`!x+@Idyf)Xc7v3p@
zb#6|`a2uAqfy6{{;8g_!uqP2Ksm?zwveNq$Gsb}9@>&wrxuRZPHT&8Re_yx})?Lc|
z!%(Q-LgiN(o&DlOU(52K>g|<!^%Gu*IJw8U=9a{tpAykYn93C^H93!@^-d)`)Jw(a
z-H-Bkvhl%?vs$EpTp>@@Q01OZ66B$J+wU!@)U3CK^-qsrmHG^M5&9n_2P4+WHNy%y
zkDDWDIDhK+V>#XDnNuTu6}(PMW#J2jE~M9e&WI<{yyN;w?NV{;u?<N$tu@KN+yT+J
z-efqQW_VxlXu36{ulF;fpS=7E9|rQ;v*CCUG^8PZ!G}fOsSR>St6&a)U!U9BsZ&GU
zC>!g%Fx?d0-xkwjd^Cg^WPHw_RqP+knjww@LJF0rJMsn3eW~BQ)#(*-1!kcyBo?U(
z9l(zHz&So8{=u1!L|q?NM`~;vRT~_*a`H277Pk6LuRksdN)FleBX4lN=k9@)Nxity
z+$tLvj+^nv$oQhD5n$)8?3>>TGJgL|&QlM=clgX7#l<=(wcvz)y*aZ@SrP6={;XDf
zh^MKK&}J=av@X-V{pfVxpmL8*OJqXnn)hU6mR`^LZsnNrVn;!K*eGr?nLu10Pdagw
z>Vmpi?84)m(T-+l1tI@>+pvbj2L!qbhjjF%&Rr!`ddrRWHAcbbuF*}+II9K3pV~5X
zCuedRX5N0R*9^{`@XU-8`z*XS6pH(HtYZuvk119D9wcxwsf>;3ku-=|_Ntyf!W5jV
z&4_&dN5REDN#UPqdM9Tq11swK#W}DWWwYWdyw^cTLC>pa5|+1s$|mqw)zIgV6^h);
zw*^>FoR&*5uR)o=aUdd<z}XSlG@Xl7rT;9+yTKIB=gH|#r%vc-gIL&@E$a=zFC-?N
zKixdg*&0b(5|h@biyye}PepaI$G8EyD<<KKoP_+U1g2t-5a*dyBk{-P==CNsSWkoa
z?YXA(iS>vTWzW2tM^zd8EM`9-5(qR@*oGaem<?hKapbAz#H#*B5shn}JkyunkAACL
zR}P-_%pf<DM#_}VP`;1t#qyRFkQI8x<T5>LGo$fUPJe00u<7UwEvCaz)So>7BMcAb
z5K5_j!7`r@GWad9#)GrRHS|w}NCA$l@;=0K5sFLK^e{k7j5W|Pj<F%e7`UC!nD(Yx
z_lob>V1&J;u=1QTO8>?c)gHh&g6}E-0PxfLof=sCoiB>?$-Wf?H5X~dn!($fHLL}3
z=6<bVKD2CDQ~Dv#P4tD@J^bWw@2~);ThGJA`)sL8_O7AL#cG8#mJYdC9kUl*<xii|
zW{ceT*?RRDHxVh>)HWo3SHegx5|&%T(~4M0nMLZ>vl{AXjX+#UW|ifwW~m4PB%&Sz
zC|=|rU3$G<^sm-Qp*lXoht#2!Pln?gKl7`TobN&tF0L&nCC+m=%fddn!N!5s+B);8
zadn}7xKD+hV%t_p6IW;anf^db#EMDvIw6T4jTa9yYR4vB<(;B^El)0vFn&)cZ!49e
z(nfSg3N6Fe@%x08<k_Y8Nxg)SNr`{w%CtR^*x%?AOHZB^D*f^mN0XRVG`sU@a<n<W
z5SoANFzp<m)XJ^#HOH+~TVFIM@b%<sx-0ULjrg%?qS>bz{23Okb2Tk*z`rKVpB$rX
z9P=nik=mV0;cb&U)(<yY%vWW7AD<9p{<A)E!!Ptq!$IwQyQM1A$>AC3;$h@$Xl;Iu
zqi4RZ*p3>*cpUC#zKie_)1w;V)f(gg?faTgIkt4?6MN_YHUaA1+8@*9@oLONDUP6o
z>~(qdvYw+$__}F!-&E}VzJ9T>aafnS0>!J29p#1WO7R<>+QMr~`aUH2LqN+`rk#>s
zNN7?0<Mr<Oz;zvR)s9vG4GrwbAy`dLE~l6lGy8ZkA|fz5zBl_WqShaFyLGB70aF8;
zNfb14F$T^o%vm9?BxVaQGOp+_GG!ac)$7p?kXOUGq3`#)>-k7tCjG$obv@UWqeL|(
z#O?7pNgw03U?o*Ezvz#lxczcw<0-559kI9?Y9S+l+7f9nj-)UqXC#FH`>LluHn`o(
z!J4r?N1M@a>(-~lI@68suVJ}kIp@T3l&G@_Ymb~A$8=350V_UwQJM(sz=y2Hu3J(K
z=l<X#l-?5gsd+2ITsjWn=xLO+9+{0({H2X<pAhz)h&3>;H{%>sqV*PmtHH`wM2sR7
zJ!t-iJwBe(kDU$eq~{QKEJP@_QHgn`%&WBXwy<#nt=2N5hOtbo>6l3V@AYPGtuMxk
z0yE1aM)%-$>TihdltnK3$L<5{<Mc#(^&SxDd310Gw1gPCc&_2thn9ej=2UfqID)_v
zD{rtpo&%LMth3d~{Cd)mzuxwSs9Vpo5a=YlaYFSm@ER@&Z%u6A6RE^Mn5_9`*kGmp
zq=CU`*)&y-C-9Yf+51p&>x%}1R`s55ZOu$eXH_VIv%fv}KO4svrckV~l7UY1w^hNM
zx|VARBi{%1H&+e895U<c6lQbykLA2sCvyn9M^4Dm-WoWtpo|uyV(%o;PAu*0=x;o(
zSC|6Yl{s`<?tIyLf0A=qv3&xRfQ`L2&WsMehMCHX2Gsn&_!xg|ljeBs6=Biusy2+_
z3mdH<P~_eJG_(6(O!a@(BQMxU!_3y_(`Jrc)+5?3e#d0u2lv0>&a(m52Lj!e6K1+^
zuBmGhFp!~bHmqM*Qy_6N_ro=zS<{0mX}83K{&LLs#!kw_7geGSX?HLAnqOsG-u_4R
zuN^B*ubQLL$TmN$$QC)VUryQsbn?S{${1<9DkPM;{HC-`))riY#&Y1`5~MAhx<mMF
zv`mJ`M7qJrn%i(Mp{HxYqwlt8OS`u34U6M1r~X!-;F)L4iT+n(gbn{?>TeC?G8?Dy
zbN_0*<HjFQ-hErUqHV3jXFH%;;A1@0hmDE?|0&AjvPq`@>e)bCf6B!sofdfFgpN?P
zztZ>J{`Rn`OC`o4*+{R4bSM%=bIZQ~crWmYdw}fGO3Vtq$_V4GO=mb<;>%HvD`udE
zuxKT!a=bl1?RfIe${hFN*tm{_11qQod%RoZK44&_Yc6f1$0B%Q!K{IQ5L8wq8Cp${
zRJ<Ku<gV^xnn_7F57=_A6X#1~gW#jPd`w<9Blu(cI=2qZR=C6a4LNtn2xbJSFgT54
zCRtN9y7SdVPSV$OW0()jK67WQhyE8IN`!^tbdcg!rlzH$ycexytq;aDS6%<$n^|?r
zWA*Lf$gdw>`ep+ATzO`DD~{8z>}<R?+jDZebY)IjTX@`<2utdTJLvP$hM|rX?H1_|
z5GXYzQC5)hy108&OOEM!(T91-PrD9u%M||%$SLE>jvHK*{qIQ1@C(^E&F=4*v+!&1
zTaSHmbuErb+uFGY=Czm?DNX1V14mEhm*|}}Y}ueEClu>Da*9U$wQ#$n9lZqmfR4sR
zBMlxd2W2BGj(>J<-BUc$;Q9B4G@CkJTiK7&9*K-DYw9815~V|JR@3qb@7=ymX)`Et
z%NE{B^!wj^loi8HN*lZ$IKPrv+us)SA6EFkw?Ww{a!c87T~OkObHwk@g)MSYERV=6
zZsqJ2ZKbeSxaeuu`7n{WRQ$x4?XAJ@$d0zKT!mI!oY;;Zbv#*CjY4E6W~QufTpD*X
z{Ul`pph`{?Nm2Tv{g3?s#RfiQ|NP7T|2H?3Yy%v}+BgO)DlBx6ZT`tTg;x4B6)0a}
zD)vGX>_owEu3G1+8~;5?W)VXj+gs_JM-(AWwJh}9a3q}@Kq<!ZJg&<yO1hxXG+t@E
zZ@+jPm+7G0O_tg5&=1~UzT{mc+~%%z;e>j<UkG7*5yf9!82=(RCSF#(Yu~YJ|6yL6
za+A(Tu_AR0X>ML3BvW`$e@A@Y(It1enix^AbgL4Ze*68gAkS`AwY;seQ^&JH#oi=n
zal>2g4IU#e?Qj|m3R{>vO%c}^b}3inUlnoLYXV22!>sGozhsmeJI$ufjCglEp?O^u
zy&`W<w)#`1*s=@lEz!`B#nleVxMIew5^v{+{wLTnHKzUuSi0?%x-^y-rh{@E2er*N
zYl|KET~D@eEs2Ry3rsUg8&hpv87oHei%3hD()?VEf=s4kC*4}3Z0g9PH(8oESUJ-q
zMx4%V+!rX*zSI@Ih$ifXN~td;6BGtkKFv;=xB~dJ3-D-I(TKpfQ>V?I@p8SmJX5BI
znL|4>+}l;=A8`@tv-e$(a6EBz2T4rN!A)WA0mAHsYAXelk<X3>hqSokn=QwCSKHRu
zWaQtia<z|(uuxeVecC0RHPOVfy~m=p#OS3_=x<eRZ9rCF`9B%Fn5~oK4^6|HntcLF
z_<(%=yQ87xJmc#4S$d|k3?VArwlXP8liXDZJ*8XP$4j10oRKXPbWZ<2lx3;Fp6Xht
z*#1+dI{U56+4-u}(SOL|lh;|l`+s?dZgd0fBIV_&)Uf^|52Uk#aJGP=4m)scMp#bB
zn8)MFy(rlfKWE$;+6&7L#5Da2ek_5Gyyt<e*Z>FF$d*)KbF^{;S@C~wFg2pI39J8*
z^@vn8aCU7aL*;X7@khN|4_F@AH2rS?(;q*0JQaTHU6r(B;z2)vx5|F}Tb=notnhzt
z1Kex)o67xjb|yt(@yF)*XfwR=TPLZAmBqWGhkv(plvjx9%}F`0B5j*7lm;j>2W?!c
zl6RS<gFII2sJ9G@hX$lz#=)5>1e76c#OKfdOW*%r1D8?d%+_0;@Eh=1ZX*}V{lu;2
z&hMYz^rCY>K%qk!Z9sCx!_oT<SKe~dux91rYNOhDu5`VL&yIa&c*tY(p^TgNyD-Kv
z=bM@vW8L`Oy)!Zouin9n9EJj=c&^{W7S9q>y|su_{`UA1k9>@hbo`SSw_DDa$Fc{P
zZ0&7J+S-f?$2hkc++`9*s7{C3nY4MY%u!SvO?<`<$_&OhAF#AgH&O54Qt{c|FGxpz
z8`lasf!gy>ozC(Ut+Q7#IU^D@#rm+F3<4{E+TQwDP*S=AbCi@f2cmtBV)H#r5P|f9
z1}9mlulyqIkXX8+9U4|vOg<uO9J()U<lD{K28Wf|{=G5kHWoR0s4H6><6i&%FN;+N
z9L~VD4p#HND%6D1=JnV|h8UVQfS2I&1BoTgRGrj_jn@*FaoMgSo&am`7<oGJk5DL~
zV7=KoHLQmlLQz&H<4fWb(*N35soBkJ$#f9l#boxHfxT*p>2<>E^~Xs$c=u1=kE)c|
zl(#h>`yk5a9<oeXd%^v5@vscwW`Y5aUnt_<mFxM*4Ovi!FOQE$j~~UVco5=iW$=;s
z%=b~!>=F@+$L6~zOW_miiM7FtZ?l_aDn01CgSWEo#@{1%jCTsx;@D6c>XGN1H&pq$
zqYEbwJ?sF>+GrZSdPLfJXkvS7PuN9%8_CBZcjTOIo9$6=Y1uz9#ptSg)bPdN8Y?BW
zebqa60iNPnk$-maxq)holy)T+0I`c`6g2!GxJKKt0dLwk^NZ7YfC4NmaCMcNE{r<5
zwTx-gw9`%>0(!TfH^BCfG{8KIbHlr<gN6@fdc+1m-Qg4s3Qhep<ABt)BGl4!&bP$I
z@yW+iq^CHiX>zdWj(%fatCSYzZ?M&)-h6ZTWXpH;PZOi~qfH|Q0YqfhCiL~3`0Q>1
zt4PJv3i^z{2L^+N82^_Q0+Ht5iSU0(69=8P@T_pgspn)s2%0{k`2=~3pUVBh?ZPP~
zpOv{INH^JJe8qwifh9X8xFMIe_RC!5=kW)4f2eJ_R^G_UEQOA>buhb@r~j!*stC}n
z)<l61McbZ+n{&@ItK-#jX;`v-!gU7x-IC0oW}JO?mO~vA92v#lO-Y!*U6J^ecJ4h=
zy}G@p-r}0<4J*kP`(KOh@MpNo+*C?W(TY1U)}muk4v(9=rrgl(B70M0-ZuK}RC9`_
zBg<8EiJvFeW0<QqoIoUvFHr`thoP9Eq~@W<90&P;T+dx`<81P{G?YK*{mf^`vC2lQ
z#LZ)_zDi+{{18gp`)4)dZXS6wJz~Jl$JO{T$rf1i%rEuBEC;*`kSNW_(f7^U2!An8
z6e1`BDABl%*G$p2R)LkrS9lvf?4Y~;7_>UU^*){!lOtskZJ&@hmS^NXtJ@blkUz9l
z-t|dnX{O)k8GagiO~kH#>}A_%&41XVbpsOWjEo+>2&xo@M71O0_^0H?W*MBLQJGwc
zBM+xykG#21sM7_UeBH|RJv;aD^kc!ReVy{B<N@-$c^6<SXwtZj+p9~Sv59IPkop=v
zrZ1>@W3eX<U%}`EN>#YI2#oL!VcH$`vTk;}>e`={u^eNTGnytx|2&8turV%2Fs40v
zDtujY6U1xo;iMB25j)!Thbs~VW!#5Ke!JbKOOUW~v8(wDgcQz1pKr246ywKZ{NM}q
z_T4UGzr`aRHA-8VJr;`cAg8Wje+<J%W$C%pjr+|c8(PGTMQZ_KwL_KZ?4MUBzN<M0
z$0Hb&E!x32-Trev<PDQMV-F5{D9>+guXkcwQ*ET+*WfLBiJ809hzKw*L7=%66eZk!
zfO1>vm64b8NmX6esj`6O)jb5%<+$~fJvePgZC-Xzd0Y89l^aX3!={msa8$87ZA~Pp
zPp3C?$rztgA8!YC1T!$O=g*91tGNmkNtrn8fRDJtUYO*Hl_9d%VMWc)BKr=x!6HI|
zcff-srMa7}&JP=0{voxx_E7y5YwBM8UMlheE%z>>>|~$J7*BTq{^aRsi(`JM>Z9gO
zq*)%7(P=NJg63yJ&!ur$ugVMmUdr$*Q)umSNXx6$F7;@A6EAt$JoZ{N%I$v2mASwW
zzw_H@V%2&bM94At$Xn)Sp;tT|V9KR3e@%3Um7P#jIZF`_ST7?)_QE&EDKhV>R8KxQ
z&y2u|8gjl0o|e`t)yZZHehY|KUNqu4@+(o>wvMb&D#dAl?|K7AyMv`GFRho2&jZx`
zQk_iBncY6$v|<1bS@|fw%eKzU?D{ELJ3=}Qy?DeQ_KF4M50bdoo36O>Th&&kflVEz
z@lm|(^FX5a-`k-!b*k4)ytX0R3fIR<Ioe8pf43*L*v2Fwgul$K)k`H@;HPsFY(o%!
z{6|j%H@D%y-sE#sJDkuVD)+o3i!}a)3&WaIh~=Si_QWVhGvgb||B_5M8dp&==ScWk
zGV5>88mSEK_&7GlvyaMJ-ZYg1z#$%PuVWgvzck+t8Ia=Bc6K;wn!6L%V_6}4mzV94
zh(}2QswMF^7SA;`&)pPqT#f%4j6QS_0D$Yi$P%c;k12%B(h`XNYkNygo>SiSS=Y#8
zQc2qTj{_8|*nxFTbdx{NO$s_1;l}^U#bC9eo+TA!9#94n(JeDa$2YI2pG^vkTACpp
zCkInvKi=pb-WQLJom8YJyo&9AjV$Nh<EC0F*ASao>;)l^aGCEZ-7kHIlY-CAR@QaG
zi{|$_E3EP@JRcY1(eiaZw}67faaNv<&3#n4hAdmh`y7;pR-mk-lX)xp2A^oWayzQQ
zsBk@{l%tAMfr_H}4d@W<ihgYTt;LOdNeUmjtW^wP(J=hV5^&rv9STAWIq62joJHLL
z=W`%Bi5xl<s~=h#k$+lWN!>nm7~leXZSsjPj`b4k_+55gaaJA8El{B_X+nW{4LNX=
zejmqjcA`jUYELzAY!~|2%|@La{ch%SvX3Aeh6yyQ4{Fnfp6z9e9&<-Y^<D73+k!x-
zbt;lR*tnTF40Uw6yc&xK^4%+q#<P*Yczl)QZ9%?dBDbnY$O@gjCYwF~H1#`X4v^HI
z{q(%$y)|I#ZaxxUOB3Rqey4#h=IV3Jkygo7lTsipxZTMn;}xYuKhZ((giA_EIIx^I
zgXK<9VtTZ;dl?*=Hv2&`dK+D-D1J5hyC(L4`YaMT5&KR^jW!MH`!eOzu!L;0gwvmP
zwT}Hc4%klFD@zAf<;;&6pFaYnC<`56om}n$dPPIdz|m*^W$NgW*;?*^<t&kTjZ8E0
zEBDbJi49O%DWKpTx>8P4(O4eN^t3EZ!G(HK##IE3p$K{vhca_sb+vtG?4&Nn+0$@%
zccRZ(U}jY6_SPu8hw-PzmN#_c{qs!XI-y{NB`o2{SHvSao#b-^CGV+L^h$Gz&{A<)
z_qeq1Tp5%bzdn@*r#eroHh$4+VxxRBL6y_VaC99J8z1#aT+SUip3<V!8jcm^P(q6G
zS=F&0UyC+K7;?ZYA8w0TCXT44IQ$^p>dI^YnsD(Y$L?%!x$cyv<EeyhrW-QuQ(GjT
z$>jm0Q?;yuep*RTjJ=Qc5x1>RardIT4q($9X<5k;5V*MBUY4oOI7#uCd9w_yPKG%U
zir3xDqt^=MQkQs^Sbudxdy^NjQI=cz_`Hz>2f&qj01wD?q+#O&`k%8Hx0m##vUtfW
z0cf4dUDd1i1pMR@$H|vh8p`;d89#81PQ-afhEjU433etvo#%EcF{f$TuP@7n6#_6>
z-6rqQfVU|Ry($Xg59_4@u(QbBM{!s3yO{7I){dW@l(ktBF8HRE<}CSMO)KmC<XZGn
z<}K+Ipd+U~6XITn$$k&L5KXJf0Jz--{I45VkAdS!LrRo`prpUtCOg>7m-{@&k(OO6
zsW)nU@fU|FtlGvLz-BrM80}S2r5<Mj%o=0+{`?Dt1AVkU-Y!VWY8)7wI}wp8xzDXZ
zufXhu7G;}=Mg!K)pq<_(7$hyxppv5Ic=-EdQ>deDUM<Xp5%zZwHjV<nKt}P~xnJa#
zg#)E4QOWkQ_|%?%8Zar5-E5a5G^CPc29zz0{tosg#Unh^K-QR{sO>yOob+#~3|Ih6
zpVBsSr0ZPRZ$`2`SV;zgfVNFt8vR$|fua%B{|V=3?Pdp3N1|@o5OZwnSo(i|g;LRo
z{LrsNOWQh6<G;7Z*v<Mo6kjm`t|tg`xeIn(yEd!5`qG#+KuN&V+LkjN7_psIJF&b9
zrf%JAF;@HU%W~zXr<N5UBKcx2no?JsYDjD@i*>dP?OZA1CL}w_pb=QVUpvAVPUadE
z)ib`9_{=e$dNpukC`cU6qD+>=0%1Y-b9wWtSU0#Mzm^*TAbNm#C1RS}rH0&8{_Aaf
z6a~jA30dcDf}Jv>5;}}ps3S5J9eML=D>ER|0pOxbvc5AKKH48;zuNVsw+nzzSxHsK
ztw#4c*l%G>y|=PF4I~S+##n%{`X@h;HF?=q{_8YDF=z+x_=uBJ+zmLLdyN8DqsQ09
z3NFDbkL<)oC~*#NHBj{aE;4o6z5yum(`QgzUWWU2P^C}u8f?M=$o`nz57m3NfI9a?
zlj-aQTWr3AN&Wg=ZYGYLYQJY3ae;py;IaLGj#nM!VMD8=sCJBklTC5FwK5i<uTJ+o
zLslh5ATJVN1_slAaKq$uC3CK#i*Ovb`vj#U4tDGLv)jOF0pm4LK~uLp@_3@<A_X9Z
zgc+my_r+$SDJcM9bF^CyfxNmJFH)>S!_hWIQ?Gq#)wex2z8r2@{qi^nCPt}(B47J{
z)9UaLr*41P(ceM7+@tMX?DN@HeKRXW22hA2C(pW1tdSGqg)6gS1lW`pL!OqG%*$uG
z?*LV9Gg)|KJ2M(Vc8^rpBh4n4+3IF7!@eMpT?WBsaA)xdtFHDZnKB$dRBvNy7L7<k
z|0;=<z3|=H3Q!;0zsmd|&0E7;czJ*~{Ch*z7G59L?0Kx%(h9q2DJPVV1mvogr_$TQ
zgjgke0d~%?PUOYE_jk9!4Cv7Y7|A#+0xCK4Z}xsB^H$^Q^=Cl7bIR&3vpbO@HT<NB
z!}?#m_)XkBl(DD%GTf}>0N^E_{Wnh8<F<Khka+6*|E!o^QWaVq$q8g#^66V#`6g|1
zn(Kh}SZ>N@pPXt=8vQJ*<2h+$Y=WE9HJ`rEqKYJidX}3>K%np&N)R@oWG~dy&g4Zi
zZBU1teRR`*)Bsu9utqiO1kzH2qdeZUt+R?;(qH27>HB5zE&eDM0P{_>vON;?DCqz;
z3Q8;03f>h;`!)(683xC2^C)#VVug*$-)Z3(B>=rrfYa3fz`>HL^tN}f4E}Aa016rf
zaL?ESBA|w8cJuEj*J4{IANq>*%x<<N<!|_AjI62eD=S2Nu@|oXT@@?<!31jS3?D4=
z&EJ`?c3z+)6n<ZBxygf%0vN06-$k3C6syhJ;fUobgCjj!M@#0+3@2+z^<)PL>r&m!
zAsQ~!nGyI&wwsMf+on~kkL|>@*z;h!;CN3$vynYeL-K+*9xH2&pnaEt$IiL9189f~
z_T=YL@b>=7b-eo&TL(!{%VQZhL>2*{s_4hgcPG?0_uZt6xjE`-b#aZc?XP*E@L%{?
zh_Hi_Mm{&!$bCj>AP*(rk_0OPtm>^@WGSajGzkXsgo>3a?kLwoZhFz9u2To<kZ_ez
ze5`h~Z!A=9Y(=D&Cg?Yun?m|F3dLNcWeD)M*b*nrg;piQElZ~ZDyl;_STXhIhY#%k
z<<Kub{Ot5r>sRWLpCfg=Aww5`-gj`HvxoD|ea^qB-n-XsyKn1{Kg{1t{6Sch_+!p*
zc|SW<^QO_W&Xc0?<#T!+SE8(=9DsXQoCtTMHNUsiu6PbRWr`G(+JW*YckUTz*y+TX
zl6<Zg1q~UKUPAU-Ron8S(k?Adc?>%X+PfDIJ8DEYq5PrwL>*GT#_^Z1XE)Qp3GQpc
zo|<zBI#O4zw5fagxjfr8{35Cbo%@WvSeEuW6yRAI300S~EFCi>dL&h~rs~BO9BKoC
z5;(*Q<}F-grhLbdzS{#;@KX=8{Q9K$*#u{$bb9UC#DS#HebR|fAINScxlm4_bDy7y
z<x?S%JW)LUa@KKF@YE^Exf<i@s0iN}ElnP0R<oxV7+TxCsB9T9s3^LO>MG9C-af|^
zHX&HR<^L=9`!wtxDH}~CB@aiTTLV6&fIuY$Q`>o4Gz_3Eu$1a3tHpliBCP0&_36j)
zD7HRfVjC)#_)Av#vx!+G7t5>-mF%O}9g?V%Aab;rnxyG%pcgTMON-SIgB!n?qS(t2
zgRLO1Cu#>;cb-`DMu~nRw%i<E!V1)4+%JDx;%g|EI3Im4EIp|f&C6tC9RIn5fy5IU
zT2GMlaDxuYa&eKz%W<464TFCA(9iDj*5PC@E8qoC5E`jNppJ&U+h-_V9am>$R7DHv
ztb8kN!r8F*3ZM`BtWQL9u3LQ80!NrVW$)I2{25epR;S6!5=FeUNYfb5JBns&%GLR2
zZ&+QvcNXH2!@jR!kdKQ+96HcyslL|v6Wqa)5MN!ogL>N{yXDu}PL176WjV@D(9b=4
zO;<nX-hR8@_|3bWrt^0nY}W?;tbP`tuExdSTWEF;L?u1;_!Top=lDzJAzAvKHj9s$
zi+3TW=$kxWUnUy$tM%pm(V-7wCsD1<iyKI}dYMAMz9`z?N_23J56tL4NVkRC;9Br5
zSo$RTeB<KA^K`aA)j#ar(n@+xQ68E-ls=_d0d?jyYb+gP;RPGX$x(n5QnPrFeR*C5
z>MWkDCh~Kj0&Mr<tYTo#S)&tH=+6j1hhbqTo_dVK<;9gk4HENVF&KfrTz!fZ15u&4
zMj^W8xREIZW73)0mjeU$GL;-N@G8}*<#M$?*@WZ1?Ae{@8M`%6A0qA9$uyDzWG?T$
zhN`rCBD}10Un?aiDa5TKwgs?n=2h3CVGinOI$Hsv1+*0xCUXhbP^xtwWW~G}_p=q>
z2r<sN{ws-Kj-!|g`{}L{G=n$_3}fhN7+e=lr;CX0C|7V31jRrkm>HiaMFe)IA3kjT
zx=<#ceb7&Dge1UhRG+ii+0&v>-?aj{x<buK^hTpV+5_ZNQ&e;-+SYeX53+(i?-{b4
zcM5%;aNUn^Vw1XAe6qhM*Z^x!p8_mTNn>Dd*-bl?rJ7;kVv9exsr6;OY?iV;fGMHb
z@11ik1x1jy%rl31D$mYfY{KEFDE|e&V2tfzgIxV)#C5-Q=1t3SQW(U;bB%GKr+d`+
z@goK}#s?)-8y@yDmd!_f0Js9A+JrQKI-13vfM|KV?0u&3h~Wxe>tJqj*RqdA8C+2S
ziX%{QwVRw^JUK(IXo{2~C`j>x{k^C(a0Fo@q(>YHM@1-($m&EO6F^FBNOe=gc^du)
z*^Bya1@84QfXE~}QUh3*8SbRg8yU|&y#weCSSYjjrIBpfuLY&sc?iuOL2!wUZ1DOm
zV0S$s;<|<nd7VFG2OBTovW@VDnC7t)!7QtAJUB<OY*YJx6z#9%O#z`(uu%!dK&o{~
z8j$#pt}1MAxjGq~c}?m)`;5GV>eJka_d(GGn|0o^B_s_++53(Z6j{vAF|KZ=`GbRi
zX0nquYfmqVy!W@Jk-iM><=v{>to)R{IlY^Su8B6UqwH@*waaEW6%_bK(gYWLSGJ%d
zZ<5|zl(j#%*uQzfMft?$;y+ybR|AD=O?`ffd}Z;~*`){V8BFrD!Br@I?irf_S;@U)
z-Z?W15#I&?v1AI5+F-s`((!|;#l)e~tecMk4#I}aU+)q_Rze&5`{Zb}Bq7-F)~A%`
zi3g6q*>k6>qek5JJ6)F~`ceAUU+-ncicgYy26}ffiB}*h;@Z2-onIEx65^yp6Jk&R
z&vXli0Cmc)E6oZZbHg<@I^x5H8cE*hPP5fFt-u|X{5!gUmuZ{{yeCkDM3&DBQ}nva
z*tPrU=7@^mlKfdYimrZ+0jz(ng=aeDS38#-RFQS(04S**K23!$xDb4MGTuCp`l;{W
zc3!F6Gw0I=!RE3JqUNY+HEk+sYm&knVfitd%w}F(=l}=u2BS4)y_|II&dPq=N#BH;
zpDiJq{O-SR!(b7_AhAsabJGX5R*nvq9f+Ssf}@M+Iq2ZIQ&yYuTRq<-tS<_ZKZmgR
z7ETS>iDqC@g>Qmc)NjpFYf4N-15c)$bN#&k;a@)A(zJssBauekn<wKSW#8?*)nME3
z6-8Rp1>d#gjm6Ce?M7;E=xuw@M|00ucSthXDW#Yag8t(H<D<;Dfk3($iSViScV5m)
zyrPNBi;ts#_dJD&e<A@OjYO%d8y+JCpo0e$3CBnUiWN&#IhssS)2Z8G!ivj_J0Z+$
zAaFhE!+>MFQBd%(PE!$OMWAs!)Ik3#EyT`ZAyVUwoK$Ugkb6;aZ5eC}hzE*oNWj=$
z3Xem<Y%AU2aczQIZ-db1O-K{c_y&BYGh(iG1tCxzHA5p(sm5-W-Swi|Lm~HOWgO7x
z(!HoNZJF#t8pmVM0Jz6zu(i;EvU9HwCi`2_HM%5C;KgEF)O~cEA&CUIl9%j?RATCr
z&v~tw6Kl`FwvcK~5=`AtlY|^!mA(>$y!3<3LwAT+i^NCAX^>1IrcZB<gOz04e8pfl
zb1FO%g#gD~>6ROo^>?fb_$2o^vsXO^ib_*A^m`j3bNiy_+j+~)|2#pG!Cd8!|L584
zf9+i|tF~$ML=_hSL@xBBu*r5C{7r4k1r*dzl*d!GUhK^FO;9J9y9D=~ZT^uIfzH*R
zN|I8>RLGQPXcho$x1cDZ)#6i#!S<rN+qL26wMekDZX7wK3i+as5QvK5l*N_yuu{xt
zQ4e@$EiJv2p9fCRnX-`1AwEsojShXc-OXTs>k}!g<)hdbRB-T%3YN9hqztYezS3e1
z37@IzniLC&lGpT4&C14+><8L$-qrPj3~R{Zy~C|PwD8&xnzPwzn6+O2#Vm51+Jblw
z=;sLi6C{XwUOJsI7l{tFn(jZp)Z02H2fMUfI+n2J{~}cr71#Q*Ii4VhXnVQt#Z`_R
z@@`tn<DCZ(FBC|+&GCq0V}=G|whnr~Jj=#xV@zJ10hG7>2BkSYe4)u4qZpr+Qzo;O
z|Gv2OU)#Wc-XkRY&NinS6U`pF8ueSLt9X4${;Z651;<#Sme9TGYmR_l55wf(Zn^9~
zm@=Su>H<f1afShI!+r$WJ0qIYhj`?(Tfu3$SO0Qc)Mw>1%M&pU7$VGOaSjYi@sUBn
zAUJENje%-yTHNRb3#=60Bb-pI%i-QUnC+!Sa+sU+^@)n${q!i-N#>E+*7TTYvn<r0
zi6+Sybu^z{KscjblspOFyhqliZ{56Qy4_r}#}*R4_N^}fZ=b<<o5f_X`ibGE>@Y}V
z9iu&a?-gAL$3(FN5Im9Z(*PFQH1G3?jJaF^^7>&nbA?e_y)kS$0kCFWQh&NqwTTf)
z50XD9q=N}Yg(23!*#;lb{b|GsBs>sxUyboZr{z2eNTPHA2N|%v*MKy@p7eAq*uba_
zn~?16CfNivlIqQB2os}bs7~tK!5z``mDKB9l@g+w+26rC`}+EPSx8#f56m8I;(6n!
zxlv<MA3y*Ge*l0#FX*Hhh8>`hi8-(3>+%q1R}}i(9wm??SRMcpjwwigXo)E>k>9BF
zvO(*_DOlDW-Oex0<XFeHg%lXT1b%+l_!KJaFFgsXXzGtcv1}H{m`la#F){Z;WD4+0
z1mZv&)Y+ql4{kx7KS|;s$QU&HVpy+i>s3f^SCEA2xK%|O<0)GVX&Xj}zns;T*OERx
zy$u{q7f^r%A<0RS4HjH#3i_wCy<H*=LJdFl2$ZT*Lm+eih}yg}_|v9-3?V8E*S5&t
zVzsQpg<!H`lZ<7xUwHxyibF*i*wenzFmRRG)#=Tfu0KQn+Y^QV+@1bY(Kk`}+!>Pj
zK=0wLQ8S{^Pe03!<0>npyigOBJ9s;<A-GYhV31Q3IJ$Ja5MpChU3Iq^Aed0p#*4en
z(nntI=+A|zF!h*Fb2vf&#2W<MTxz1A_T~w-AZZ8Cf8?$Ifyn-MX}A<@05FKny8!>S
zOwthkLj<H=ZTL8;6Ksrj%VKlEO9z5w*~aM!0F+=_BxUsZA4naj(IVSnnEKk!sI(Jn
zfhhO#1{q)a`rU5kLhNmnfm&01-(tg@iAOqn25f7!C_XYQV*u}twvM04WV6Al{ag~G
z2c1h3HlfT>t=c5Bt}vOA{&?-2mbUF8{TYV?j`2geo*34gI5&f4#}Y6~u|zW?(tw=>
zPwSip3Fiuvm2^(?0l$fILUHW#iRXO(!3#pg8IbBX7Ss1E5m*V@B-V1b8Gcb<1_}Sw
zxKGAkntuU+#TI*#s6oQ04>xrhMV^Sch@v^h3tEoX%f$S84!|yx%Ib|z`=(>x|HPL$
z_w<L+*GiRp-JaegGZ#D4KQ<OPJ@5BMpiUj9I?!Mz)&`_W%LPaU8dVn0Osqz)`~fBp
zN3oDKyfy$8PnFY~BK)NR6l`cVagL&c0Y6lYkDi?LlT5I`Jl5`?$n?xlH>~xv^LYlW
z{5=!F%M%G$L`uE&efO5giDcdWOQ<09jAKNd6>0_{n)WnP((e^VrAiYOt(KW;a0~=>
znBp`cK(~gGO$5DLb{_o@NH2P?9l!AS!FvYU&E4l2M!o6H%ZDo2);aoh)QW(xVc*ON
zl$92w`yV#8_^R##bZbPK$;zL1cd86YhfWn^^4HeZz=r2YO?r-oCrIV;K&>Ts_)J9w
zh3yvh{$SJBSC3Kb5J>)2pJzZd=}&68E089^TXr%csm}hsU8%~D@EWigwyHY;+$6lP
zQ#rldwV{5bKA<v$Uhft?_5iY|4N-CNdDf!7mUhaup8fg0d}v+Bz?YdCa{tOK9nF3Y
z_AIaJwnUx(+P+P`R|PTi5fJPR1?ri%)NiZ}G0kB!^&FevC_DX-k#uu;lUiWE9!HQ(
z3}|i<)~Hc+QgKk(Vm~;|^mF32YEC|4@5xcP^ijWwU=+L-i!!D<P0WLdLzUj<6$v8B
z9Ky_1dM(8bPH})N&VzZF+Yv|Z4@<yjV`xQ9+hKabYaDZ!-CQ<111PTfcJS-Qgply5
z!pzA}R@!-YTsx<PK_?N92W-s<<@Hy9JK%+*fL`049P!Ym#_nY_WghXc<#v932$^gM
zq3Z+x>h<ZmAvRVqlCp6jB=k10Yjs1prS^iO>(@~3P>-02q|1wOCg;>A_PjP~!?3BG
z+z6Y;eOZ^`<d>@I!(n_16z1a3`B3N_TQD9EbeJRjMv1)+3=cpBRS;H27}R0II}Y8M
zPvCK8r2ATppYI*Y(S63vHwP*zHvs)0%4;HSAyAmXlR^==Y5wh=!)=Mq+Q9R};3h9x
zh5OD5;JH7_PMxu<(`V)3s5NsZ`Y6k_4T+K{ysRid#}zkhz%-8k)ah-G%6Sbg$vKT#
z-(3x9xp3&f^7Q*OJEH^eVC(RLL-aE#T2|qcirt=OB<86)UzfZLc6PRJ1Gp*daY&X0
z6zts7@Z&@9FuoAjHcYYj@ZB8LXzly@D+QW%W&uyYG^?gqKNCZfiG=dh6b;UYSngp@
z^rDM9?JlH(e%-Z*lBL00gP&zU=k7gtH?Wv<5kG09fxkFSGK6SVI7ezt9CfkXR(WW@
zEwFK->+8Qx10^=DUl33b%UW=sFqVrf%r%Ga{5YGpI2>%!bb8|aR>k>u=?3L%VAh}W
z+KoBrxWel+p5CX;f{<gK-o#?=gLg9q=+U?o_|a!)q6U6Zrm1C#NLkVlk=RBZD7=Nf
z<Ax%i5;AP<)P9lhPWB&YFqx>%6<rVgF8m70ve2!<4lV&>Zs9kt#@U;kCZ(QCeXBfK
zr8}|rcj+{s<Y6(8ABOupt^-Hntr!1DSCVl!5LoA8$LnC5ET~`(l4{d*e`8EziSLmY
zPGj1kH+~=;MwsFAE?LP~wQQdM-~1`+1~3?;8;AiLTnV2BcYzJHNT+^EAYMc122tU~
zU{A$rW$jCg-N$Glu4o;X%44J|hzg1w3Q4_OZ3xsy+p^dl5Yu$_;#iJ}e#kp@h`!od
z9XRcBI1Oy6Hk=WjiUgBdV#&qBZm8)KO(B<q<4=%IA&B&Jwm$^O)<qCgASUWjiFzkY
zyVRQyytOFwhAXN;%@8@Le9Jj!Az-@oXhV_D;EJK)?vR;*$#mL;4{Ed!DtM~Fp`6f=
z_+u`|<cU3e5T8=RRF@d2jkBb?&sfS&*)#j5L+q4vV&hN-_?L2?m&3_=>8izHx#kfX
zk|1?(lPxMoZ8)E832A%6_JOqJva`pc3LLDvZc*0<LontwAMl~N7BwG=ZAKDcbFoCD
z0CkI+PsKZ)2dIOyROcJTirf~vlyyhMd2co$z;Zz6GOEDNXxDu%Ba5AOhT(a;ZXf3L
z9gBF=7ffY{imA(zYt#*{L_XMR@wwh0G8l#1F}T)!MX)yD7&l_1t^RWANL`GL&1EIw
z@-w}`wTX23&jW#@T^n%CLByZG!|Tgaj+lhW!TZ(MZulSuTI<O#3+zzP`&{wNt(K42
zBA>HCNUlElrSlZD;b4LDl%4u)n*sS3Kx$Kqnn9`51L>a`uuVyu!!GZPI90Vauw$j*
zR7mTTs$D@pcU<0r@#s)_VNKh7v`0!TuO(@n`E+VFx&9!=nVYPyTu^_*6apx~#q@_h
z^`>*AEpl^q=+2m47_@YJq<it?iRkIhTLiUeTI^nvSU}Lr_Zn=H{mN|_RKv4YBCh_8
zvsOs%Yl2`B5K+Ca>sH44AV_!i4Mc20va-{<X!sLp4$}5)#j`S@t~y1~P@AH5R=I$i
zGv}aDaB}bErrKaFl7~j-c86(<Z8+m&RN+DEF8?%erX$tlIB6StCT~9`g|P0CQMh0j
zxa=PAegVSFS-c?GtI-u^btp|>s-aw5Q*$RFb<U0x@0@*Z%@IG>M~IatX{Ws~emTqt
zyrQ%KQ4@|(YXMVp>{TyKLB@J^Pxm5Gqd!b+IG4a0(tEl*tIOH~klIQ#9air9Yc~>d
z*@Kte2l79M9Jios=grasSD)9y_bRuPZF~b!q2Ur<&Y7S_Pm<8+*3vh~z!9*gYs-h?
zP)*aCL42r@MGX}&tr%E-VLbd(T(V!Otz4xRQg@c<MQo`~X{oJE(U&_=fUI8bTh>Q*
zhuSQnFAt-WLZ@zihuG_2_B{sROs2j#);|;OH2T@35UQ6Kn;HZf!h4sS?5-OHX~x7C
zjyh}PwN$uR#jPuJ1<_xhJy=lB&DjB{aPgWMPGlDOrewi+wSAS5{_aBN{#JPT2@8s#
zCY~p#xp`_~7<DE*_~i`k`hMG*iWJ(*3PCB3x*x+I#8+UI3iu<%Hq;entBR09`YOtF
zp0nH5n_ll(DJUOIxX`?|!|+OSYDt-$KT2F|-))BYUEk(YCFetz=Cnz43VkYe>zIf0
zXy#+6MvaCQXXms*wXgZ*hw(3G@cPtGq>=-e3w{LMkzbQX%3zAMRkjo`<)s|OP@|)h
zQ_>x~r-10E^Co97?PKhfc{Tp{lI62-XyLLP?XjBYL%)}{1UJTNS#dl+9DbuVUpNQ>
z430mFx3QQe_QqneGy2}<9K3q|RrSUn;1)FskQ>)lyZK`{nmzt{qZt7XvnP?|7Hjg3
z842ZM^~rRDpL%e8n5cnt<8?KX_H~Qyaz7{v>X2`CYqiMwfzg69FFW<)$<Edk<j6JW
zwpTBW)P0|?2487Xib8F~pj7onUQOvfmK?Um9>6q#wad;+YEI4`?`u{A{B*{SmO_r5
zgVG*-jGePn=QDF?5k#Y`a`7HzAh)jZjwu!^qXYlk?uKGd6DE|vO&2lMIlu|(#9Hx4
z;9&Z2W9K4eFEa1r?t$PS-!%j-y~)*g&0UYXanpCrJ1F$yozepse#O+0z8k$c%woE_
zVP(Sx3!)YDq93BDW@>92zLYNCr5)6IalrqOL_6DF-PEA>i^Lk$T3+6HrL?I3rSt6n
zBq0q6^B{}3&eOyXI{RBsD}`d%mCQwdRIb6B`YUgbU!PmaNmnw@eSTANl9>VzLKzob
z@k^*-^75bY4-~^3GhnU-8^9Ou!)@2jS&wHEg0kzA8P6v0gC$Fcd#HJnE#ke`)lH|}
z2XwOEKR2kdRkG9aM24dzTu~?jWtsY+BA|-8I{E_%hv?}le&<b<i`Z*&!Z&^C8%TX&
z@_u7O672P8nP0}>BU`>m)CJKB#v9s|meQ!eGVmu%r|Uf3)h(vWZB2R+OR>{Xa=7H1
zM2A|#;(lXIIICr>xho+3i1O3LA|tcUVF9RzX9t;KO8Tbcva%?bjMS@6bEeLUcx7a_
zO-nhXpZq1>&Hq`rYWK*bIJ@W5ky)K^EA@iVxE)y&Yj3mvxaP{YzV9uV($R<-s$lxI
ztgqhi8;^4diqn7XwqlG4f4DH=7q*vK_pZcfCydxt4^Fcd?EglyR0+fJ3zK*b2pN;<
zk~-VhZa>9c=>BJqzpu_C!#&w?A$iYC;gr)0&hjEIwpk~eZwf!a>#JenLu({wdXRyM
zS)ZtTBKe^W^y^xgT<kTUS_F#bKVd)z_!cDVFcz1TLf)crT;~wgM2{5ae01z=Ci?}L
zOuDhNR<WV4zyLWjd41Bvb?9N&iiWW6a#Idp0}8+9n>W3e)%(cV3RoSb{}x&&u%IpA
zbhoLA7LMph)ESU$!KZqESu!S6bNnaXy(v2pO;apr-pzc_HSHR}UoAUf3ady*+~`IO
zkO$06@sI{#3Zw>K4um9+)0gA?2%ZG5Q~!^(;G}CApzc}WIdYMZ`+S?X|I~^7HG^4I
z#~Lq(L<SX+eDp5IspP~~Ij?#-A(`);2xlJ8O1hR+-}Cb>4<8SrdJ@KAhtVg49+7{j
z8Bqv1V-YfFAZIaL9IyIh($1pud=;tFrrl*cf^br8?;LPT!P#oQLeD~7EhYfvbAA7*
zoY?8$%i*`X|HRhREW54u9Sq_o)xG1mKdVki5AVPk@zzuouJUSw7VF?Z5ZeRnYwTC3
zwo_2DUz0849b&0WfTPb4djm0wUX!&8_e?SiVZR&PDq%tt^et?0Wl;{7cZy!5Te)*k
zGg<5*FbkV5bZIs?<1-lXbswYxBC#R)*oE%BDsrcNS<ZA07VbQ=nskI!l8!h2^!Z-5
z(SYeEuco?A|0m8f(y&}KowLJwZosYk7NsDAVvITSE%q^05x^Tv&y9QDdNt{vL#8D+
ztWaqVTvKC4EALNW;o*fFZwos-uViV$8`2|!t-6n9Qa=8%1hENhTl4w2JpJJ$zZ5+-
zUgNP&A<gT!$RoMltQ?vhv3W||b8@A0i{Eu6?V$$pr&I?T5Tne0ns&=!&zt%hsI3JT
zZrDkK)Y-7Tv(G+_ZCpBJY*$=-=W^=TXH6l##?a&s%&;7*#gUWsB4jD;n&h@`o=;$C
z@yK0z#B)F?l?w_tGmHwXyGqKR+kQH?GNtbvyE`~@@>^wnQ+0l~XU3utA=~-VeCqho
zk>gfqZ8-Y70k5Ie)*r1{Ii^p<k7kLb=_2Q5y+0aKBBTtX;GI8V8JBbJA)p!ps+rd}
zUFb<bT@z|=a-xt{FJi%U|4i)*Zl|v#V-1V}=Cf#kP2UVzjm%|?+i*X}Wz+_(e{<?<
zq2RUn_i~y&)ws=KjZss3eB^=rd*BUEoTsFU(DyOb7#}Upw69-E&Zz%b>pfoMc?kVf
z{>at(>D|n*d~=FnKuh?6l51b=@;qMjuT^ajf{DjQjCY6RnFGX9MbC8lrKd?h?WNXA
zsjzejh_34tQSzsRtY@Zr?Y%+k-ynFGk;Z345k?f9E6M+(yDN=LYG2oO+Pi)BX_t4`
zIayk{JE2BS<yhuW?R_XfG$l3XQgI#%r;w>rJ8f{t1!#^`&U0d7I8nRQz?4!^bHJTS
z1d19chKSr{_jkYEZ}-djvOe%zi}fGg|2sVI^MK<8Z;_cbH(BaZ=P{Eqs9EJE6;Dkk
z<ujm2-YxRmF*^xgyUV=BZ6lN>bGn2^8U+u1{mEi`(R(nzNqTE^WkQvZ%Vi2C!AC3*
z1X2e>kZB?)!k6`|AH|R>9;?F#pYtAvc`#Q>_p}UB-DcwbFq2VH^=P8HQ>e?dhW^i>
zWB=(0dZ2@=a~DR|hHedk*~|)Q<(HD>Krj-6>5P^7rn(eCY>0UTIt_5lZr-APYp(g?
zorNYvsLR_Ef@N*qz}JZ<Wl}3li%Ez5QlGcVR&&m4SmT``bFHJ2#kwulu_$nf>yu#x
zneR^oImS{(E84u6tAd;s>uR?dG-_$5>N<9->U}x9@K>_e2f-^}!Ej~%9Uh6cGG}<b
zCLZI0zuD3kAHHX82-OvqqvFqy4oH0Ht(kV9_nc^`52kJ)M^r;vb1;heXgr)0P_<b3
z^IXB6SFb-O(h?<L6#FeFiP>sZ9lJknZLunPX2>}?C^8EY*cXHkq<<W1JBbuUw&Umw
zJ;pM5vHsWSIKTXRykGnmHf`cs7opIw*|niQTFwd{6+8vURcmzJ=-SR06zk+g9UvFg
zE&73$Qg8cce%QnztsHkRbu)?ojJDlJ_m+COPA67^S#ztT-If>0ZmoA)GxrB`ty*JL
z=VdRWdHQXn_nx12m!w@BW*MqQh2sy(4)1n6l>kPgK3g@5nn`G;kfL9WhYvV<ubokN
z?5mqu*P*p`W)Gb7=QJ4$`}>wj4<hUw)d|g(V+rZq`{dX1h8#W2;|g76#=)bXjv4+;
zr<eL_;wX3C%aBjaxyyoyr{b^s1FPmbwn|bw%I2#MM>oZQ#ghLb+V%k5d)_Ncl~@@C
zj(Aumu#ZwpT47RlLNjOz3s%aTY6ZeSYhPo1goSf@PSya+gfj!o)4Te|hav3zS!3_D
zay+kG*U_+~@K<*6?DtUp5CwN&bm9>5)Mq(dz(0*2$DYn1jcasv4B?8XxYa$9^+>=M
za~yhg(nNjCo<5tsBf4cr%<BreS<<GpJP&r<iCP@I3$;DS@&#@I(VXeIcYa*|rnrK#
zTt2U2qK~AzTS&3{<loBi89Z+1C|p?XDNA>mG`%nCPNj}~*$outl!@Mzz$RjS$4;c#
zl=<!IxFD(kChYEWXc_QWz`{Jl8a9}MZoIZmPXwJmj1XEEI0@TK<|l{oa+8&so)2Vk
z5Eux``!F%KFz@RV9ieKxX-yPw+qk0{s^@cO?{J;`;irC2J<+=#tMm)9DhTHbJ^5~1
zYPy1&djv#DT_!<y`8v7WQcQiOLuUq#cUOUx;y>i@$P{{UAx)E;R4-F0A)?LaV4HJ6
zEP-~{gOQ5f`TBln3Iz+2$rDKPtb@zf!<yRol}rt<Rq($>$l=Y}Fjn4y^IYS{yxw$}
z&e`?a|4xf&#@#;0@BYU|z$!}mnkyRzqV>ui?;Z+Ccg!2u*YW%lGq6~8=30I;Zs%r3
z9A=0+ji`8H?0K&%GK&Oa0giB{_L0>!s^*pQV}ZRVs>{fFIAW%Kf0|+N5R5qZsE+7U
zXTFw6{f+;xS5`@X3H|cWHl|SYjtOBGVdb^;;YS^v;QPu{a^3OuCTB9avRWJ;?g@rk
z@L-uJYIM@)np<m#lJ8O9jz!AL%H#QbN9)~IeN%>8*kGM_|0qur>b@iIEFHlLkqh7l
z^RePlH)vdHn4yvm(RUF?`yG1>)+A|^3CFS$+!PggR{>Y1D&wKK6!lbs=2c&%V-dC_
zuczFPkrK@{NS;E;-a|AGQj)*hma~=yp}XeM7e*m1aHZC>IBq2sO(C4(u*=$u>9*OP
z7JY)eS++au*yQ2+qIZtaTq@a?GeTKM0%X^HU9AB{#j|gyNRF#BRzv-HDi=)i)Q_pF
z>no%BK95`{=_F7J4qA@_w4UXfmB9ZPnCbs*BIPfckP$<rCvqiD%qPbH+F<4E=5XYG
zOuOnRW_VY{oB{3(pg~|G$`%^xFDQmLiI7yTO+89BJyu6gXYvZ_BiH<b)cHu+?Sz??
z%M%JD7KMn8J@t{P_2T?yM^NkZTqkwbrDk0!f#^V#E`a_f%uU|p!n8E9R>Pa(z-zpZ
z$~@jXdS>D%M$#>Q4x#%B<9bJkBp|omJ!$5K)OqYge}P$~%x^lkuMDC9WVbn!hNC%5
zhhIQEjcJ5i`F-3X#tbgI2Qf#;cpwDHh<alBmm7y-9wuznx-lHX4YCMV1}{QvH+#lB
z7v%%ICtwNK9|g`)o}T2Ki^}P6^K@PKOO3DgkFpH7V8EAz0gS;HM69$5cm+ndKOuU5
z(JFZWN@YB;mw;rDr;6B>{e=|+IHdGy@~|d0<>_{XNURF+VXAVojk5t&x7}u#hqpDp
z7_vLWdD$Vfb!Hqw&p)BAwnCKSUkRX${RMRm0VoACp}iY6xfVC2H>j^a$|;3`P$xPT
zD$kyYoIlD8X>`ypzUu2K*8>;F0ahvcNK08b^=Mwt7^8jgLe4_C#4iXF@14HrWjK+;
zj6ToO4bBg=tmvv^Tz8xxgPfNekH4nw(i+Yc%D49$fnD-HAXCmV1|$ulR3q|Cp>kR5
zzH?iZQ!Fpa?DQ5)a*at`)ML}rwv(p9W_x&mBPTWCMKVx0=DTqpZnG%^^h6(ci66AC
zO<puPz)_`v&<c{ZTiLR02sa6FMajNU-(yBfA;;Nyvr_oMg09lv_&%^D5bzXrD8MZD
z%2w`3qX8JW2qqj%hYk4%XEzWnrBzu<u+1kX9}Ng1-zIyK*aIa#Nj(g<N6WPm?xLo$
zd&9BToz$$iY`RzR&~SxUP@3M_9t^O>w5c5ItruBk^MhNgC8I!>dhN7uHTk@3j0)j-
zz)4gKTaoPNMO3)TuX?O-HVLX}TXd1BA-RfSwF}5^VQuj$I-_6HwXgs^%pvFOYFmWh
zVgaIO<;**Xxw9;Ts=Vy#DqDrh>GfS(psMA^5RrnZx+U0{sN-$Se~8j#ny9C|#pF2$
z(`fR`$i{U(giM=l>#%E(Hski{_fL23$Pc>?^d$N8*En&fl~-Tu4sX&D8u!|~ETC$0
zw$Bw803p7dvy3loL`h0gj-khzgTkK~JNezB8x-Djd-n4+B6m131ZYw{TX{nL{d7_3
zMC3KQC$8#Oj@9EQ7&4VpeIw7vle>ys%EUTTQbZ_V|HIi{YvvatZ}ol@Noha!0K2=I
zl<1gzdVDx&zsQ|`7KnQ{L#0BzoqB^?pd6KIh-Hf0J<GOzw8M4ZQ}r@aIjyUbH$z5z
zuc#fOYTK5sledotK6N*7dXDhNuT+Iu^VI}=hKyp*9K0G}fDAlLcZ)v&TE^Z2kT#hX
zCQcLbKTRK?MpqmDG-sa*UlJJHKP<0DVCOpVp1hCH!|p#cuik?+RN4n>|0r<Fif}iF
z<1TQIjI@s9;_0o`qW2YOSj2FZ+>t1$jq($@dw{D!K%{%w!M5l0Ec|LMX+MIwD4U67
zeq<7vY$&GsVXG=Wg8Nxk4Za^0O+I6tLr3lF>+o^FuioWEX4ce_gT4)y;@Ge!`pvXv
zXf=uoHlc4c>KcICAl|uWnEc`>W}895o>+w?9s5D*22XO`7jrUKc18UZqU|CR)H=Mx
z6NHPR3TNE0Y8JJ3s^-Unv{Hy4Sn{(SLcQ}00c=*)P`qeMj6KVp8HlaV_-?VX$7<jH
zESAXu7RdLdQ2dPU+y1p3M>?6!8p{Bfv$<J(R)@_(Q*FN_iOfV06HyYswE_swnf34K
z4Jg{?Z5gCVft0;~I8{Z@p@<S6Xp$2Wq_*QjzC3jL#Q@%MA@tDQPD{?9J5a0#L_6ag
zs=Jj<M5e}zhSf2`xpOnVw_Ra&;JKP!FJqx&2f|<PD5i5PtRN<aTy`mdiglSU*eH27
zS4lc^bw*ccgo`#J26zzL8U0Vu!QgJ5Hin^=I!hQEO`gy}^AFFO85=!mqoe_+m#QCV
zY&B%%Q&8Q^L0XErOP=9ng93nX2)X<3B@p33kE*bD>n(io_iGu;QFbrKUmr(p>zS#i
zN$1~4M2mjF$VF~(5lwW)zj$xbk0AeOyYJ?I1UTDS*BD`K!o&%GOIw+ubs8p`@f!`0
zyL<7=P_LMg<fdgqvPX}Z@hST3XAf>{-@xlhM9<6}?i3lIiLUsAZIyGz!no$Mc`WV}
zd!VA8$z<E-gjHyCtfE%>5R<+?Q2nbMX6%`i2?f@?mdf_ekZB#WmP}mOqIGF%lOGdv
zFO`%80+)6{zSSV_-h`u<!ei&m+JI14<;GN%iPLdmYJf55K(lwmG#QWCmG^pNy&P=1
zUZDcHMC){jXr)-caCVQi#OXN3g-45am5FGgSA>&{%lW*rD0C;p`)W`AoP!01dzZF;
z4(K)J1s3ynzxOgyj&tQS*pyQhgFxWgkJo&l?V5=^b&ju4=+iB#+kTAqc6JHK+cnj!
zD{3q%kMXJqt(32A(L(=vQ%7rWX};Dt@y<$%k&+OnghFi}jM)c)W}LawE|6zcOmvW%
zLne<^-_6U~c?6#X@T{LpBSyN9`*T3mh~~tj`Aq8Xyqox)N66mXH+)iOfVU&z^aoP8
z+HVcysD{?$_#}7(i_1zC0npUj>nF^2dU^+lKZUSIRJe~fq-G~<@57gaqp13&ebPll
zQ%{?nd*Va$z;0r61!f$Mb-^f8t+`uY42@ceL46UrA^+I($TxN*V@{U4S%G?lTH1Y%
zLa@N}bK@$~ZE9v72wJzxYdcLbzfaa58fO9yXtM(@2;k2#-8ll}7Uf=3DJQrUa9~`=
zDf7fmk=U1Pubeo`NAn}-T`;wuoA<#_y%`}g(SRSq<ls<^{1eo+zNTO9b70vmtbf?9
zu_m+BDeCkG$!ES0{kQp?0YRxVo3UijpNes!l&PLA-Tt*mFM4HAp<oxtZo|>l4I`pj
zpU6beXa=82g+lErw}V>csUWra*c($&GPA?GM-1PIlHCNu9-L(VeD%^2hl-qL+drz8
z7|iOFPbAcEW@CYQ5AO}PzC@DXs$RK2r6<9FfAaR6fWOhsl|A~o#p|c9c#9JsF7`lp
zWv@1aV%^Bo{>b<xPySv16*9Kx217mFPKX_|lh)|k9Cxah(mT7?wpEcj^+HopuN@qZ
z7*ojBU=DFwmusEhYia%D-oY6>Vw=!<f@A3pdpK!ha9q`Ck{R9}2bh;l9Owwt7e;v(
z9{lqLL{o?rEi;Ee`Fv5x!p-tb@h^$NzNpV{>wF@y-8eHT#o}IyDQ7amN<1^FP$cg(
zV;J%wm?F0tlWN1Bv<{!|wG7XK2KZUDL_PJ!{OgsiiLtI&1Ym1)5{mh4;<$IA@0U^t
zdn!!bgx-}#7dw!{fz^r+khnz12RG{x)|Fe}&o32iOhqO)2KH5Mu1fILUJs;c;sp##
zoSpqDe~7hQ0aq-VYV0|$(6Ls{UR(_~l{zc*<LVd;R_n4gZ-NTu9GM|j6v2j%hv<;g
z^|#{-t&(tQlMzU2+<Oz;rguE~(O#X9=LL$N7?zm+VSMN{Gp>&Ir7%43L#gwY2h>yJ
z8f1O}DblyhG}*aB@6IIoYWCf#{x7&9U16N<v?BFnd2dYb^!Y#M!o<`aH7q<vNwtn`
zFs0b#k}6i^5=aX|xodVg0H1^rhl|vf9ZUmOdkdk(ifqX~Lr)t6HQm+nfT{4Hy3gg2
zXV=3hk>6?kw0UeV^)<qa-EkYDh)|aSzF%I~sY?BaFAq;KD<)1zW;#S*$axIH%f!G%
zQ7U9r?}8!)T5_*~-$fJRcN}Ru$yu~{Hrae|P`JTjtl!xJ;)XaEp;9#Ryv`;=<u~@Q
z>;2onw|}xw`VP~Iu_P?`CDiwSmQ;6U^7Epa#nP4tFF+$U^@_Xr=CW;riRN-poz{&*
z=RvA_Z;Z3-D<rUTec>&H>=Z&wGr$FH%OZ>$b7E3@+bq6rvlG*BSAi@=vC}cmxdCTj
zwT~ujG58Qi!|GAigSU=H4Khsx#Ep7*Y&(L*f`D=}b~D{3D}wKIF(;620VG?K1yNq^
zu!1a$z=XkWqM4LCJyG9Ns=3xm1ahsO_SvXoFiL5aZNpMG)%^QRKjC7Sf+VoN<+QuG
zQEfwf!XSM9jydI#TxUYJYxLB5@n4YZTW;)t#%l{KIF{&7a|B=E;y7j9XODSqU=0c`
z0D#McSim<#T=aR>g_S+-#W<0*@B>-{L!WXl5C3{~QK<$`T}dE9eH$`0XR7~T_8B$e
z&>q-&41(1-nrk^(l1#A(`l4yD*I>DSmEPVP<AO~9$Vl0;?n*P!S%+}^zWi$}3f^jO
zcYn}2`aJwmhY?2(HtMe2iD{0Q5DgIau$M<c&JTK*bbAjzn7ck^?jp<B#vEaSyokg&
zTrhW8uK|hctM1q7z=IuioDUH14!hA{6S<D0t&yD6=VZ*ynKKxKv>&6{_dfv>Z7qbz
zZCNSbGMF_4;*T&#%*Cl@TF?589v)*YoL7Cj)l=6R_gjYaqW(xZgkH_OxUuoEHaZy4
zzQ`rol^Ef&oZ-J{N6J0m0sc4=2yD;kRD7RRJrrNDMT$q3%|ALBx$4>xOgaOw_rFUu
z%mg#EgJ}>}^p5eAa|lMLL1KSNb%7Vxi3L-Vz&)HS?`G~f$ZIfR$I{k@b=#7?xZ3&K
zK8_e;yb8$I(Ok{4Pk)=zq+`BQV(STIFO7XI-qXS!zEsZ-psl&qyOF>Ic|gR!sprXc
zY=fQBx?H^%6vWK+rcz5qe*{~D)H4vAe@0J#-WOP!j#$5v!4Q?EpEyOlBb|4~1_7FJ
zJZX`d9bE#!xguF-__vANXe28{B|jV;w%@G(&+JhrSNM4%@wESpT{)apphi&$b+8Yj
zQfG4ur&;Eh)k3_O3URG=Td<|HKmND0Ip!Sq<m$5_y(0B}h*8K&{PL7L%-OtUY3w;6
zKexPPs@&LJVGcbkuvI{jUqFqg%G30FEi?i=*TXF}l)`Lm31h?%)FIecfJIgXRlE;o
zO`k<WbH7X-!X$hquSz;?I4$T;s~vH1<5S+Oe4nK9w|J#MAP1`=!xDc(^(`vPpH8*7
zxhSFjHeuv$ChZIW^s_E#PN>5Wcsd0*HkoH(r`QU;`>3ECWNwEP_E^FDQF%y3KU_e?
zIwu*j?1c~QErsPl4Q~@reNQKz^U7MvUu!VX!$h4R#a#&RJa!VI$rn4{X$bM+`n^?^
zuhZFw4X$-s8e|5#<o&v429Kp;2fO$Rsirx(r*g;Z(|<or-gT7{+E#do7~A-i){uR;
z^jdE4EDSoBI`PNtkuDRH?!C6hfsY-qGRL$0V*~r$C*Py1AFUS5C5xO^+08r*sB(*<
zek^V`pG%eqzwypbTV_=)te*4;|HK%now7{NX!?e7K;TU8?YZU|P0RDfi7@h)KmM`#
z@#oj*VKagMj7f(sW6#TqZtrjRx};Z43mrM}GbRpe=Zko*35(x|kwv)kJII4w_}KG1
r-V~r;{qsMNcl2%g|L5ff{_1xP?N<(uY#2Y;yb=B{m*48oUcUL?S=?sO

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

diff --git a/reference/plot-3.png b/reference/plot-3.png
index ad088aa8722bb6947246c2da5527556e74024986..9f3bac2bec373ea6f6ea8976a90284085ce51ce2 100644
GIT binary patch
literal 27972
zcmeFZXH-*pv^N~bHjbiWufRASK%{q(5*0-b5KuZIMS4d<jUfbe!~z1I&|54N=}MK3
zgMfk1L~0<Ygbs;>5+RU0Ct>E^yVkvTy`SE-o)1sfngLJFIsbk3fA8Pk`?vSu$#sKk
z0>7R34S_%ifI3%<5Qy!;2*g(VUw6PqDDf|Uz(01_>s`A7|AarQTGe>?$F4g%mVO9?
z;3M9DTcXDooDhgV5a5+dCc&w5BiKYoQ;f=-ZReI9JAS{Pdga5`bFY}%Pj;HbJl<)d
z{`-CHGjgx&e-Zixx-xc>ppq=p+$q~#Gb45^^2Ckn2X8$2^UAY7UmZBS_4(tSkAJ&#
zecL5R$0<g%DQ=mbK2)7s9b~#puVc&(_2&=Gz9r63m9`od)SCUm7w`Z5@BbeSyj{ns
zlO{A<FoZq7)gMLJ1z4y|!z;ZQ>l3jYoeZh>i;N!Kjd*)5KgoYn0LiFrGQ8g|UITyd
zx5hvA-vfh=0tiI>#A9$L0?{xd@?ke3-^7EUu1*>wsLPW!pt)W(ydN|Rg{YD6B9)RP
z6>O1@&DQ{W673VUOemd32ea;u;YCOyV8{G3WbI!pg;5hoi%<iQzJbc3m5QV!keWt|
zPu7e^F~0-#>5d7&D99`VIhrJu_}@FJV0tobf2{*Cmbk10O6xOM0b!6y5V`eQV5~}J
z2z?!z6vJF@tqt^o5;BL4t(D_w=0>3$wixC*qqACUP=V9|(se3rrS&RRka4sKFjVti
zaEn_kF|%-l3qNtgx#hW7TFcU(QH|j>&5eW*k1b<12{e*c)J77J!ag;*JfpHQ^u>C%
z9C$=N=~n^R3jRK}li;c57^xtXO7qy-Tp;8kR@wGTQFr`}k)y(fiwqArT%Zm*6eWp2
zKX=k5tk(VeU@G+MspdTQwb2;pV*Ul`2)2g47{(f!4}UyKP})F_bh+eCXm}bhC$|;`
znW5@s_U!kNU+esQo@28OmeHcP-*Mr|>)Qv8&qAs1{#xj4`%3%rPzi@jbJS|pAGuw)
z{FWMAEmG<GNt4)ihpm_oGAFKb_IRV1Q>P2Nvu;q2Mu33w@}P8yLLFQ1wayPwk^O^=
zy9}XM3=`cyD{Q&Q`JPbmUDRm+sZ&{$BB}hrDyY3*;63$`#~RM<V!dxjy!Hm`Po8~9
ztaN^cjy*VV+{4J(Mtb>#EvgvR7%+37=4hsRGIB1!TL-g;Q@uNqktFa=L}XW>*>2>c
zYkS@sSZU*IOOw9Vo_)Z0hZI^%lOY?s7gmUjM?4hWsr<1zh`6GM`e7he`DeSWw*To5
zYEhk^-!Sk9yT8Me(zYPF4i~*q#ovXnnSg132Knz1B@$N`ZQtt9-57kMbtqA9IcAT+
zk!8O-x(B>1s<ZNmn4;}AlkHn)hzT@Hr2ry|*#OKM3$=~PIJI|$YQ{OTw>Os`dtE0p
z<bP+!sDtsDs+H~HdcTn?L`h>imT;HaS7k(2Z)??`G;&VRR437sHmvhe#m21{dIENR
zhE1y@?EO%w(>8~wv63&DY+>FOjfch*xTISXz8$c~W~#4+-a&0hi|<OWI_@nDqKUCr
z$jPym5!ouGL<2M<*`v4pYnrO`oV5q4HMz3rSXbx;r^Z~Dm7yXK{674pD>OQJAaLec
zZ&7FEH1eLE^-Mo^#KbR6`5CiLXDIE1l?FA{ag<CouFhEBOH{cs9I>30Y?0h|5H{SE
z<j`w<ORDmug9_{DKLTd=+YWXW%^$BR68wGgvdfRC*%HMWzQivM2RuoVxOsy6SMowO
zp+;tz1l4;_v9gjiQM;%T#?DLaK^QIKG!Q-CGdU<QzpvoQ{LgSrl*cR0{H#vdXs%4t
z8%Bhq;x6i_r(E5`fuhq{RRXnpbKL4x+x4DHe%vD9+lxEi9eAImwAKEW>;0fp#7y5|
zp2r~ya#F7TEC$phedqGro6`*}C(Ap+W3QOkhgM{BC2O;`F$f-oi{as#wguBAE?Ptf
zkF}8OtPL@bp-zkH$E+{$ut9_DUDSQ$zxNn@@%mw=;`fuByn^qs*QfN|a=1Wpa9=A;
z`-)o*J9aert6;y2+ZPrRA$z;;>vn`0B=aQ|uLlt`0TXHpEhDz2k=1dBt5NFJ`kJ|M
zE0!iEfWtpu`)fp5+`~rt<D`2BzUXBq#l3_3{$*zeuEQ&3%}GRaNY>2A`6wp{HS&7r
zl65ZZ_pgf+my;+<N0lbIZ#paIjq3N(x%~#`LC9A5i<JMf>87fZrgzP&mNP>e`(PF1
z+jpWi$mLaID?qjg#<lT1TX{)X^f(I&=j@y9sq^09WBli;%E?0C>nNg<0B+5T{AI~+
z0FpPZoPINl4Q*t*H@=}q9QW${YF!bdqVfwRTeG}&vJ7_<RqeAEQ=kNfDmm?nWr<Xt
zCk24sZNu1X?}DLe0B@uaNKpvp-f6&Ee%eFz4UN~vaknB4PNY~O>_k-6+Eb*LOHj~J
zu~k;=$IfO=ubDm}RO=4y2e&BK`%7yYLr@GaKdq5vrOBgfqxVtBM@r%zTU7)DW}M~A
zolICwXK$rJ<kSqJ7{(kmQZi9XXI;H$gxnq6oE$5o849B%NKJ+whg^Y1jN_GasA?W3
zn}@s`%Hm(cl2rM_l0^K$i!VSBfC-)v28C$-gjB+}zy#aJ)8SPQZ_Q2$1fZg$M{WE<
zE7vQ&uAC9VS5l8|>{)#H;e1Wz#JYq6+iyUX^i65L!|fOr1x05_e(UlPj~UMuC#5Qi
z*|cPiy5#7=w($1XwsGXl7Q_&<Cl-|;6i-`<Rl0NZ`ww7WdJuggd}UGClHpPmUX|r^
zbizaTP;?{eU>(OO@_XH}hgnGjWBzWC@QP}E5X4??H7uIOYA4r6o|kiSdsH~+d~pMJ
zM7HleQqQ`QyCv*u4A16r^`KKk`PW{Zv9#vhgmo`SV#ksOnmWsMbHxt){!TsXC4J`f
zn#@sHp`$uU-zpMci*KQpD>EYEJTH*A#>TAbTBKo4PEpE6N-UQf?4Y%g2MsGYDbFR7
zsb>f_3FpB;w>MTqTG;kTrJPP^6OiNr^#(QSMt5kru-v#C$>lw6q2v%stjK}2WlAc?
z_I<xsabKzGm*)Zt^EY~2Qo=O@Jo0QzdpHFG?*d4d84(#HsJ`U2NQ(5d@R*c%Nie&2
z<B#@e=IHCJ(HAJPvhq?MW$Mm5<X9Dotl8Z)xl3SSnc^sAnNG}Xg5A8~uO!}1n)PRg
zo_QmH>riUHpkDTpj?d<F(<`zt^TXqB=#xLbONN}udIs9mm)^Nq(Z#kGsg@(Ptfh`7
z(#Di<HVG4%R|37>sMWuucMAk~Ro6_cAMC+DqX>w%2}yoIuS>!je(?YZ-Vzo^8y8oY
z4j-9wtxsMYo$;fS!+mXHXdB`s2VI2XsAou{nLzMt4es%=gcOAyq<C$exr*zVTww6@
z_>J|Ggw!q@*D`I4ZN5L(r+t;(mx}ag2(D24qLDkX9tBd>?1q)CP5eSpx(Dt$kIa{W
zLJ(~hT(SNb(7bGkhOHKXNLyGTOXGe-0fq*}hOKkuNiEIUMnYPl;EZris8Rb*8Lz35
zE(haAwnc}YBH+~CQ58C>4~otw4iZ<_Vri`X;!mpF*$+MCD=grsWHxg^00iZ!vES&^
z_!IUuUXW+bbiL}hCbh6XkOD*-qUXbA%WJPR7VTRNeWWoH@2b<hXnA6p4=r8ENmrVU
zqCAX|e%>f(@#gzxCvj5Vg*J^3YH<+R<IJj3$2JBG9&xE(Q4o@J2l=|{`1?h#6X=?m
zg#=j5V!{2QmCpATaO%r=6-ANYmW9nb>nDnwKp>pG3_v5VoSb?Ek>_h1ky~SapwRQ;
zg`4ImF;!6#H_iVK2l=Twi)pIHm6Vf&Tk5pnB7?z5FAIhtb8@$9y@?g1j@>d3Ct`0D
zWso<rcM~&vL%CZUip5}Sxt^L*95un(QOb6LCbK-(6yJFDMibP}kZyzC)>lH>+T#Hk
zJvfrv+FarKWT@}8sfeFN%gy25K^wB9qdQAi^Y+x={wf2hpC50Fx}+?ksyt>W9jfVM
zTxPq+HRvvsowZ*3)}n2-gvQw!ws$inzPa={_F5&2A-E5Y%dy*<yv02X3Q?HY)z;X|
z1JXK7AF$BsYnk-SGaMuK<9wr$^8{66{N3!V5u}ump<aixZ1t>-n?M><S#)s^e>fq0
z0qoOR#c=-=QC%vCz4GO#ai}O#$RTp9M#e0+KO11rNctZvC5mD^TWgs$z#}o$Kb)M|
zDufV85>`INBK70P*hOP{w2R2<vslY~HDJ{M@`F;J2dW62>~$gWH<%Yf|Hn^c=a&rF
z7{i`UA8ENs;ug$KZbMvz9NT#Z;g6j!>?*upF6iDr%KQ2MKL7V<AdHlp?2qvOTZ^!6
z04YgDmT5sa!l?w>%#mjP?!EY631(So8VJv-@q`XL>^hRM^~FI0kXZ?MSRI>v13VI0
zHQVjL-(Xqo6eH&!>59`AdFQ^E9kMV!%=;~D7cU&apQ*FEZ1@MFpnnd$2_WnZE6^7i
zlp$?}<{(-&Sg3<`g4D#0@lE9|S?7nej3J1=3DvT{jKt7B0xr=?yovx}H&}e7aVjl=
z)>HeMlmm94xHNBQSczl_8W<*x@8+G-@Yu^henf3XJ^QnBRsW<lXcI?^1S&f7kb!?K
zL=9!dvhIA&*vc3EnB(>PL0XWNgja`Qv9C?aJd|qe=BEdFUE4o*Oov~aUeuH9mc=|0
z8>}<u+<?v)AE+~}5K+BA>aFcRNjN1RKgC-z%t`Nl5HqSU))2IedP!7+YKoa_Z=PS}
zl~V}+``Z{ajaNo>b{8p~X01ERgn4`lqD$mJ4I!vgcXe%xmrs7|K3C)+-r*N|K(H92
zTo=<8Rvxqx<v9CnfOj~|R2yDFIybg|ud7<z;OJoc>s@e(3@i63tV_E%RavXf&l{nc
zHO3H8amm(J9A8xMg7<xxH}qAoNN?vCMgpx(V7Xe-PLg-n?qSK5MkbmTwzeTL-We_b
zJOALS!h@hCgHJ!6ED;6XAYqa3_<rMkfslB(jWJaVCzaA5^KY9~ic}O|)&4Qtd@4eM
zFZXLGlY}$VL^M|BHY%+)?V=%9^h2Ah5P^0{D!S66ZH%YzhTJpou}~x_i*H8J#?ISp
zyZ^S}t?)J%{2?to3+angDv;uLwaAaAxyhVnkHt6hE(|j@ydNaLqJwsYvH{hzBo5eT
zK7&s#emS%q<fxM3z^s;2XLz?k9A=BCiiucO$BaD%qSG(dynZV_u95a1#@VNTA<UHa
zGdAhHq3xAXwpgVs32Wb{#JjTV%m+20PJL!n>uejLMw0}S47EYtdeG4<$<aI4p@W1F
zH|qJ8EHWbfgwtEz1&BiwvC1<gZBnOtvfBv~%k@e{-lKqU$FRsr8_it1mWBf`mjPF?
zE;VOMo(!rema+-D)!3yRJSsC(Pxt%#h)skH>-edYe6T`c<xMF&PjlHA>?kT#Egz9>
z(cT2koAv_KZmfQ02uiD3*tE5C-WZui>y7KbCMj~3cmEe`Ao!Y(LuQDJR`s+lhM26J
zVA>jO<<BSO8lL{~LGA8nL99w&xvsKFt4d$#>eb8|%BwSc;>B#+E^2r6d!tZ&uRPlG
zIOb80A$`+U6`2d0&ITuxg0(F3R)$V7j;Kr#RV`qyZB=qGbwO}j4vg{daKYey(2`~f
ztsXm$gVxO<F|oljBs!QLQ(R8RVZD^aRwt9+(XGXa{edrd(q62{Pw_|OH3)*~NwmD$
z{vbo9jKI8Y49v+?Wt+AVE$+JVPfGxQM78uo>K902gt>t(`g_1d`HL+?v#e!i&i}>Z
z9}x|*%Ay!wXg4&ROj`kLb(k$+V2a(Xu~Ko8KSU4}{FQf-f7<byu>cRK2g`St255lC
zFu_`T{cn6~Js_~hBf50)VMFtgX5Lo_JDuz-&&a-sh4OcGd_1(cNB-(`%v^N!HdF?q
zr9-PE)#~Okz6*rO4aLeoU}b<i`RcHCA@GeU>_4#Xzk%QXEeH4@Z1CO#63a@H*|x&i
zm~P|4b(mZ*9-5Ai#vhwJ$<szy+^0P@;R}603L)R?cK!n+=Y`Phhwpe_A&$L9i7Nyf
zCj0-v`&U;$-=+Tr@&9jm=>L%8Cd^Sz{q=xQb9O%D^p77@;z_H2Hgt<-jAPaKm~itd
z-^%Pn<R2v5uKANx4_H$Ew&(<ze*p<)vPXCijBqFwciO=K348fSU)3S%!9Og>BQD`j
z*Np=;n+Kc-W#caANTHxm3`{v9QRu6X1Yub9!+YM>VNNi(V=%f<4>UKDO%DgeL{+i*
z`}rZNEneh=Hlqe0(gjflD75#QSP+3$2zKbA@3u<X@^7x~=?6_yZ%nGjDfiTBGIW`_
zfD2HGv)OoeCNk)Ya%Rn=f1RP>j|T^8PC~;s-TXqKXNyCg3A9w*6f4M6?WtYM#ed=E
zNjOPOowQQRjD}7nK|czrq)6RhA-$HGNQ-%Qh3`g^R=nWlpBbXtir%v^{G1jC<Iyuo
zK5D!;$cte6<CxpG4uzH*X};OG&VNYh=U>xmux73O`&_XwT3uwn<V$b;=La=Rg=wqR
zj?&taAi&D!BVX<L^1KypWYz>;q~wq}9o|By^Stvfn)C64d~fh>Je?&mo(8RFyyULw
zu428m^KH+cu#FLHec?rDJz(qoX4GlRW|kt6Dsmz|;S3xH;n)}&dyl_%8}WF}>!8cw
zh8bMXiyN@W4<mN-pU*-Z##Ba{R8#;69bIW1_4d8RyMR~!RgX6k_VTZ`&;){yH!pxT
zgup&eY9(tjT`H0<LSFJVnZ{r{2zOJ+uO+^P2O(jmF7BVVbnP+*S9FWZAHL&AIthLw
z5Io&)tMl$<QAUQ5mOxlPOS(oFR{v&k#Nz@<N$S~h$-nWh=$VqK+e$dVK%tExmKerj
zeV88@kLV~y_4|mdqG3`THd1S`lYHU2oM83&_5=77+8RLAM_UY8j`B^`;wli_GTRMm
zDl&!%P>3iAE{Ibc8NNIDl3&E97%PjX6sA&CNxQ(pf-k1g5d?K5lCr}ye|6ZBTK?0L
zHV)J%WEm-2tEp`qtW6=*h?7Dcp7^W(8`E5Mf0)gu(pW~wJQgz+HB>HanK5>H(=Gg)
z-|nIwHwHtcNbDYDw^|x)3D{XKG#$K|av{>*b#3$MwT5X7TJn<NQsDE=?m#K8xOmZx
z0uA(vgCH1(midqiJpVfXbQ-WlF^9<=GaKKoY=$;bgZmX&Zv)E*ETMngGf{FEl|D0b
zg71<S;XI4)U>Drs+}2|IUX;Aj^dG#v2#dQ0hcxN*&1OYItK$>CLH-YjUc5u5F;N#y
z<i!qYsYw3q8e*vFJopfF!0|2V?Jr4i%2O9hd&^6CJSqS+u~qPY$(%il`78Y=YQQk^
zHUuTIS^;D4`l_SWOA$4(N{#WLwR+K0Jb;l1+a@EcYYYm-(Vl^I@w9kACChV|)y!*b
zP{Ql5q8K9xiDEtnMs;DUu_D|`r;UK=>5h%R*jEL?XmcxtHUSW)tR37xaZcXFsfb~Q
ze<`d!W@vzREn3goZ|jk1L8RRSZDN>MjuA9#4CVHVtolRdVybpq9O$vM2B1ET)(%Re
zRyR6NVP)*{iK44{HmSmo@sD6-ghjn04b3bx0OpzNbtzGX{Yyya$8`!V1>&TNOUcdZ
z3XLKZ>aYjC<9DwFdoSHTF&9X)+93sTG`-`Ty2`U8p{>o>iQmVDarfjnx|xjUyXSb%
z?BIJ5Lh6CTw}|o{jChRK3JN_+m7620R>3J^K(KeXVR0FZG^(78(^*)Pq1w}=KymQP
z8Hq_-`L8&QH~rpzx{!BlRN=?6$;U-ykR<RyElvJ)NPqZ(_H&q{U@foN94_IRhJ|(@
z3T-pP6?>nXjJP{=%)zl32sW?NQegSvwIBz=Ts-tCS+^JmvBgw1NfeJ}j5bultD;`k
zawsOv1L7j9$_i}(6Tx=v!T8a<`tw%!9Dj5Xy&RZzUn@ePor*>nUrOEDl7arE!sTwB
zwpZ<-dVM&)urmOr3q8rpxXxrff?VsOIV?-<fkLE7v&M81d%3wrQBUtjM~J0t8tw7b
zCD_iw`sUN+54v;6Z>qL2s$e#iyhpSo*nH<MYMfUJd1KfXa%|ae>-PvSH!obJYkKu%
zkgL)Rm~)!TSd%s6n~4hw;MXF@CA_Vm(UZyNXX^Ugmp_s>SUKKOQx|O8J|Cz7EsgI^
zq{IFe_9VG=xWyis<XB`gGO=X%LzE<OE+G0CE>Uvpff@-)n>;(IPKspR>_5`tL_eyx
zIDadLQBm1vlF3QJ&9Dl;FesO69)u{C=w15Ke%3(uGS2yR5k5B+@7n4z-x@pGSOL|D
zIQ1!(<l1Tvd)>oPbbgp2WyanY!CU{L??ciC@k>#bdaL?t@ekN~-I0^pr_QuJ*v4p8
ziYuO2A-k-_In)Fy-J>uV$r=&?>@ShNdPSVo#STM}%D3jMWaayUb`HrdXUx5f-Pql+
zNv(KdB{E{QCGXmS02Nb4f7+3Oif7vxwGs}gHJ{{oOCI*=oa1I_y4zSA-w&$R9eMbA
zPmGh+{h;wG^9j)dDawWJR!aA<7~-1yFF&{7W>TDgXyyfRW)wTKyxMNZSwtQ0Do_4e
z`v=K#n)oFRuYP-*Z&16IJbUry=Remfo_{Tn7G$<xjp9K^!B1yr?zNz5OhASC%vw@T
zwUcA_yHuSYg||A%bCavBa<<h~Zet$oFGV2wS`kNu{e@vRjkg<Lw>FADpJ;%-9<iXj
znx`wt;xCz|TBtX8%riUo<8l=M*Ew%KPvpkiYp9m!T`BcF)n{}opf5{O*gwRX>ot_h
z9RGM3b{bSGq^&UKdFyXpq<sr0?24K8tX=Jb!CFoGh+x|jSk22b<j{1(`81gStstZ(
zFhx29zz##SZfjM!*FenRwRwgPQ$lH;+T*cUsTok8!117VkRNLlWLiABfjVXT^{mFf
zvjE=5x&lM*vs%B1?dlh+EdN~ojK~!qyzrqWcI@pD``op8vS>8hn(?=!rYgnkKC3Lw
zH#A_^7%W%=WYRB}%Kg<3-VloJNRamNKia}!(E{!|BRg9%$xmx9wC}1lE-7`|mj#7n
z$Ws?j4AITqSlQyF?wr_+@8mhExyg|5kXn#2G%qp9h(`<C#?kr!B50|5yZr#QC341S
zf5qPJ(9ruq*YE#eo%7mf<sVxTkG;<t2=}O!^1~PH?u8{vY(3v;`LC+f>I}sO9c-)v
z0vRDYO=hTStq4nH7%mDG6xf(hOUOh1mJwrY@a_cnEvHJp0~>y9@~VI@>;@;Vc&7}?
z6sF&r##=yAN*gktKKD<Jyp*RpX_Xd<2K%&rWpUr?vK@{JJG;RAXtuHQicrO;cNE<p
zLPp11b_Bx;(I@LEu#s<M63zQ#zmAyXunTVN_*8uQLp8cq!tbDqLUaT7rtaVV=R{6p
zE>3j7en0rh^Ck8AG}kZ*)v=fP0$>=T{d9|0{J^NzVcOJ5>{#e4w08ozV+j}3dZNMu
zI+Z$DKWS6{n02G<+JtE2&K%KTq{bEYPFRcepN>_#pK~Hbu$}0&3TZUch+{<s+Zgxa
ze5u8M`NG24fw?CSjU>wN_G&JeCVnmGppL*;)cuGg{|79fsmjh2uu56V>c@mGB~N}`
zlA1%_Ku<k5u34!TnFLG;f`J98+!E@l@KUACvK(8mI2dc*y!!L^`Mh1!Xn2pf`P6N&
z#+~V&a}Nu_VYB-cYd+tm^^Wl{(KPp&(isBt?4s>TD;#y*b)gJmfOttj{SDP1G(;>r
zv|IN0Xc}wnBM_uqTGWv|tU|I5f8U%8XV0q-sIw&mbv2P2{o!TePo@D32nn0d*bK85
z8EdzSk|S{g2MTRRJkeb{JS5vy<k<`}Bl>Ibjr6Q9<u7~qv0pR}>B7)RgdNYk&IjV^
zqpc^r?fou6wi)U|8VXzKB_PKq!WTjCb;?|C17KDMZv_>Z+l+c$WCwtnv9us;(P&Ds
zxT*00%u55Er17=oSP`+@{`918t8tne-feU20yf&?Snspm71&?L(CAk5DKEiC&y?0G
z_3lWD$kiX}l~LY-bLMS<gqLVALuZw^Sf7_l!P%rVDzLgIDnu~$Ln*kO`W^i{RR(qI
z%PQG}%A{<gzE%_zB#H?gLec4QgG$$<(^Xc=<Ip2LJNlP7!%n;8)@zLF`VTZ8Pf!B;
zDuN>^L&?nZrUzHeQD=GC%LUIIwYpAL8z5t7DZF?U3`JUrsfDJ~gpCe-FfR?kX*)20
zYrKMU@7%`VN^J{mLy<Shf!8hejwXw0um@vkr{ntjzm!rmN%fhYeeto!xiXzUNZ)Sj
zi>Q{EbC82~E1$H(@|>s+n%}_m9QgiTLF5J}Pn?0(YBk;$*{mQjUW7Uuz9Hen6@}x~
zTS+gSq?i5`13CqHnlY6rE@i$o*nCEqFGkxL-{O*u87oyW8?v$Nan6p-=-01S*?Y|I
zT~zDb4)*{BuxP$aHPdh;TsQKOj9k2mNplq|tX$dBs^)<Li;?l*$UuDos!#LAaToEE
zOE84L)qUo0WLySX?>tu6I0a|K0&bK0C$43Zzi$*BX|`WgRU!3?r(_8d<R5TpvB12H
zG5sZ01Y_Om9HC${=#p=3tn4|Q&01RqIsV(^7>P7t?D*(Z(_rVQ7zPC;h^Q+0CEPjx
z%80`}Ft%y}A?b0#$ptP4jTwmm+)%J-P1u1%t>O%_L*(A#pbewGn}wq9xf<g(Co6Ku
zp{MUSK~|!_^NNv%`<}XBc`8hb9ZO;Zl^Ttm6QQ>4=ZjMM=}~*?E&DI09|^YgSq~C$
zt?+2Q`>h8~xWOX^nZRwI`84kA_FEMldy{aT5A5Y11Qm|;aD^yFexVmg%V22cZF0_1
zeanuKl<4DsQn6;uKO1k40=yLg@JHcZmd>E8`*k!+DaV$AfzkDJ3mjUmEf0-C--M#^
zMvY3ygcarA)GAaa=H1d&Wj?5xTi>ss2OGA^|AERna(;sB`|0<E^y_Z;&X?5F@LkD0
zeFp@h$=~~%QVlGdzpVjC5N)L=w}r5{5xeXlOk@tM?}zIxj3Y+^>;RcBV>6MWPAPye
zJ&s6#S47G<tv)f&^y~`g2ND2`2gGur7Ypbu^n>Z2BNqChkep0k^Qs`xRnOM7sz+Ip
zJN<}25SV0&b%MA$Oc^-tF<(EqqB`qTi>yy)KIG9&`sDQ%(**RbWcfU|YM4A~ZrWpW
z%oF9T5}@Yt_dD4W`##OpV-3^p@Hp5m_xnK;?=_ruoRo;zCbMT!MS;!wCUh|B;YzLA
zco2Kzi;OX^zS&w<cBN5HTu<>-DlPilBr>Hqo`z@HjGh_4G2dc7<f)5U4<^DcrEZoI
zdyUYEV!j86ZsG=Eg+C_&F2S)ACKE98j*Ty`!Vs8dv-+{LqLf#d<aOEix6iu}frLy?
z@1-IzJpoFca;b<N`9@5pO@eT5sk*kRT4dF(b>bR0n8qm-pI!QlOM%ueK?JPRkqM#W
zHR6XPT3&+Gcv@7fOjrJmI{9@QC?PAqn!V(I<^#Jn7GK26qF*X!c*ZNH(em7=%QzTZ
zkFKl@$2zig;hBf%f)4fc5dD-7@;BmLEs{%mIOwx`A2rHN1@mezi}<0NEXE)hI3EY6
zuWi5M==w}{Ez>|;A=JgwV9?d8`G%grCU2eTCh>uNGLEToq>GUf$5fc;#nl;UI)2!s
zQ1b71i3I2E8ff1=6?83geE<WedaXfLcdoo;=7W}FC+vm;H;cWcezW^Q)|oZFTQ*>-
zbat{AW!|c6;ZR0VaU#4v0~3mG&*3CVR-%GDw#Ui?Ub9Je_3iehJntdqLreh0q}5zX
zR>;E5Cf$(;?%a}p(-^clg5L*gK~fDCp))s@dm0WgQzcpuKEH<3P_t$&F^;#rW%1U2
zy%X-6nblhHS;{sYt6fxhoZ_81Rf$vqsf+t1cqhJ7CXE>>TTn{inE`;2)=xhj$6S?a
z+GV|50dhqB`m=T%OTZhV^&w%y3p}k%%p`<aM^Xstq9hfg1MjQFRvjTJP}%_P)S6ty
z7QvVlp?Y6}-YG%!RhX*b->419hNBaX{FlQPGorcUY-TKP8+Py`8D6v!4;ccLK8)d^
z*nmJ`>z+w0JS-=l5>z*K_uBX8n;cI;DQv8fj0^xHOf1f#$7_1QoAwg8mO-AHPZ&ly
z_pcF#HyOg$?Z;vMfUxet2=<1afr0vi_t-9=_jK8oBlMl(CF5I~-8k~P@cNPm;Sn-3
zR5sx6b9^DduIxPKGptY;Y(q+GukhJfgoFId@tXWS(Rxg_+vM(hTd{chz-CtgvFDC+
zEBd6D5aYyuw6aVTf>i*FE3`ovmLRQYtzgF=f4}W!po0$Nk-N*)V3a=E@}CM_YDk<k
z>a<XD!E#kObG`6CiB7MMuOkwqad;HMSDz;h!oBpJTOav6k?e_PF@b1QF&2`F3#=A{
zwZ=78Wz`erhWNcS#IX`B`D*gtK?F~$9C$QBPgXr2%@KEa{CxbwX1|~dDu;`RA|p6!
zq&%8hj01`oamXVI!7gxX#0Sb2!-PUE8RWa(vq5}K%C<Nqr9G=&tf`vWL(RbiuuW!c
z<h~!P{wfZJ#&Aw{SrciNgxv*7&ir;(gS?-I^TFqfF3D8t^@?&M7HNOWdGSfOs%N;O
z-PgndCxx?JX0KopHmMl+bmJM9X)~7$@423!Ec(Qa9p^6vc>HdzA?8PT53@cJq&&D`
zTnRF#ftDy)>J7qIo={;<6Ddh3%|fDi(4|be(Bcf-t)U<DjU>pQ{ZM_{&*dVKSa9$g
zMMO3G^F%y(<Wu24WXuZ@FR$U{WnIi8OaL(_7Vl*<!ii%ZmTLN}^#9p`%Y)O-qvyu4
z9eHXMMw}F~s6xLOMlX}@z`OdId24otWJ_14AZTNW!F~DkCl6W?W^s=k16RWcu&0Yu
z<{RNXU<+RbCkiFh@T=i*_FV@A63k~<CuI8y<I0a#9R8YU`HF9QH;W51eWmr#Hbo&s
zOh6A9rNa~k0}YZsU_aI<%Ye*{c>C$(|4wx-1tkDJ<T>()l(+&$Sf2Ev*{*93_2ju_
zW39*vkrHIRwEP2p1N4G7E?m#Ka2w3l3Kx9YmwZFR)D1fpR^Ar(4-=MAAadf!Ov1q`
zdWK+PfeY0vA!|U~Dcmn@IgZI1`i<XUoXRRcP{SyQTuzl+nNR+29l)@cdv1o?nc%8l
ztZ{Y-;^pk}Tg--pR9*#NYyZ~{L8<Py$*D1p?J-_><rkdCx~qRTJ><Jw{-HK_Y_g<q
zF74nmdw=TScMCXx-Y#07KhnZC$_uWacTojkSv;)d#{QEp6;1k@rpsJ!oy}@*O3X*<
zFo*Wkf4M?P%HUA{rA~+J57ZDzbH%?Y<QCP(ObGO3L7p%NwYau1_NV=A^%b3By*3B$
zW5ayH-IX#CyUjbLrOC}D@a*dnV<^q)JT;!FhIFg>#!qxw6-Xi{LSyAAEDcaIKCYTH
zbQ#6(IvY8+a}-I3!9ublUR$L?v14B98UM-hsIUMVLItC#j$TgWERhq?U{W=CITpyy
z<gS&f0IG7NL5FAj){;f}5yIDbm58MdvS@IHq-N^j=^ApO()#=G!0ar{;QeY*RSD8T
z=#=m-G|$*xh^U$EoyaY;yfvwsR4uvyqd1?c&ye)EUhq$E+G2D6rygH|)dzYPl&T_0
z%^0t8J+wh)yA)V1c=fFVZ)gqvNLeaOBR+it%NcR_SlXx9zkGVVz$gQB0AslscoZpe
zfp?PK;7#(0n#KVM5{@vev7zFW=hy7|w`JddZB@&Y9^Ea9)<u8#b+e;qmee9rDeu=G
zVg2dd0fC#ZzA<$-Y_>KJm3?0MUjEA|1wBRHoiFMQ-Fo$H9}M8o#?4cHO7C4VLpH+7
zRp179>AwwbFRAW3^?(l?>TW=AjSrAIi_tEV<=@K5g&(S&E>hI6Ey<p>`}tj_x2>gd
zUp3wt!91gualEMA+XzU#z}zOKxX(K+hul$Bhl#z!_q{e-I5>)#wzgbFS9JXET>xYF
zES$u+PnqI`uKzUL=7vHlUxp@Z-(=|Fl)$Sv5S6L!Drh+)sQMz5tGl}RdJo^IEI32S
zAK2XhzDt4;Q~d5k)2`#1&o!qe1FvL;eEa@4UkJ0OJl|^bywB<aCd(D+x<5LOv{V74
z-Pm!Tiu2Mu1-)&pm4N$@9t2P1Hw%k8<;-C*F%&;CBc`g#`(Uc^sK{zIfE8VJhjvz+
zSLR)VSmeb|%i4F!D>kD@k_t4ONNWSV1<|Rmt)BAfWV<Ff?ZBEq%eW2(L<6O)`9>SY
z6DQwu)+m11)d7qiu{y>-tb^S}6|_3>Xna}_jPk&cx84-uje{c0(%{o8Dirs&^<=}N
z8-;LJuR5Fjy|(Z_yLw<2fma{!&i<b!I^Mt!kj4lDS^#*oXsCd)+`+i+R^kJB-5vMI
zRxjtT(hxIb@PnRABLjw~Ud_KI)D)SA-i4ye_{=o|p*5sIdM2*m>k_qEl-DOx`7(%;
z3mA|67W<@_1do0p5TzLfO2M#vU+KeGoDEbhGAKfl@mPbKO#!vcI#7se#Ghn9<Xwih
z`kJ%yC3YELh(gc*zM*+TT)6kYemj~IYn$xY7PC>u<(3377Cy%XjQm8hHGTblR~0{d
zF6&h)phPkOz8HPfI+5(=Holsjb7?KZE$4}whUh-C6+Poup+awqGa~hc<e7gZ|BCtJ
zN7&_8um1crFQCH0F-C<wye5Yl$?1(Y3Lef37JK<-5pLeHf31)~&ey<p2HE;qUuFLc
zNApd`;%M@@AL$mQGW4N-4VJeXw!6+ytCIJ?i2U8C!@-Q3r9LMVn)`Mz`Nu3&;M1uV
zr7BT(hJ~)OJp{)2k?XCrqUqO-;BL*g9;5WbQPtRPj>Uhy3%AUbsQ$Nar}U7p>)Flu
zEA2bgCJ{MOqLtDl&89l9rn=8Jn&zvaVP~eB{MHVg<y7KO;GeuTWf{pQK486|{@!`m
zYeY%UackbX%m`3g%3Do}wC_YKAL7IId+9Q}{k6xXMOV%GS(9Reg8`I!r_zUmi9Xt!
zqcd9YeNs#wQVtT<eSInO)obeNZ4s3|AoH<_F&{(_viC)I*MMkv$U>yj3K}(3685PO
z&N??IltkOU%;R6-BJV{3xEDmXSm+3BUUbnx8TIXyipc83p$@*8zb({%-O#pPZ3P*B
zCdUe~it4@H{@Co#ZcQb)-+U-qQIdU^Z<BTc?(W+daDT+6qMrCIBB(x=_M&Pd8w^yA
z-Pe9WgJk}h{44cQ=7}b(<yL-&J8Z|PgpN;S^T<2HZcV=X(l6SOg(C8K8+lPH`PlO$
zAX;i@^4Xo?oe?Wz|0u@J+BLTUYv0ZJ{A&xY%=P3ZT+80i`mKg_Ziu3N-W%JknfsF8
zP(<`Sgb%Y1&ySorRbf%;L%Gh!4#ezb<Ly+Rqw^Z@*?!%1FaPV=!ZJgTqVEhxAD%Z<
zjOB-dhN>=;dn~Q1?94N#SSeT8wkQAHqEj*Mnj@L>w9`=-i+;IB`jTqSq~kg13}cD`
zAL}Npjn4$vC(z28Si(BYHDKq;6!s$A-&w71@`9sE%0JfD@K{-_@@(5R-d<}%i$;fW
zMf7*v9?QCc6CNr1`TaE6*W~~P>uvpUzM}RFNvOb4*Xj_z!S>=b3f2AA@fzeJs(Ox;
zLhzjJcCo5ud;&Uk(0+}o{5_VvUklAy`;4p@pda0xEh8>B{mP#hbN!SR{yhYlhVegI
zwc@46c+Ou6k$>6PSaHt%>}DHQ%N<s?Q#V~`Q_&6EV9(Gfa%E7iZ)WCZJmu70Wxo_?
z;y<(9cG%01&uPN5E4ClcZce5>@yKmR0pQUhB<UVl7el)ZDk~l1y=j36gLh)UZ^oi}
zmaz}T#1rOp(QZ&t|NJEbx3j{@0chxGW)l9_;XF>2?Y|Z?r&B!8(CoQ3tGKuxPf^SI
z*YxBc{?Z5+JN5d-@9tT2>cM>;qQ|D4=6k>*`Se^r<=nH+7**N**R@{TX9GMsEzFen
zbbeYqDit2LFgh6F%tw)~U$$>buIlu>Y_KGTCIpu)#dwGz2EP<_7!0gP<U75rgD9+w
zb0h+^?hj$b221L2#8;iS!SXNjGEJ|nKr|Yv5LGR0sbc_`OAyOI=?rffyOHy<q@Bix
zJ+NMx*+(jnflpNpC_4$auKP$}jMtFf<16JRuR|t#kLS=^a=gU&faE`{NeZyEiJarq
zm5TSi`O*ZKJJ&_TOAJ1zvg(&&CGn|&0GtlBK{aPc?bd9f@`n&H)IGkC&g9Eb#Aoq~
z96d<w3)}46ox1k}tT0mc;L}UnsRdy9VVq{HBco6gZfD}VCz}DR{-1;5*Eb7|<BJXc
zPos%o@w<TvqkD_#O?4{z=UdA6ocp0!HNY)ck{R~aRr-v{VRR}ck1w752bYu;4^2*A
zW%m>2PB7o+JzZH47?<}k5Fe5Ol?1h9)ClX&3mXST<M;EFA^V?rUGaIigc7lWJJISy
zjWsq-MZRD>idZpJB=YlZSwkLDm${BAI!6`=(Bz`&H%p5IyI57Pa3+g6yjvj_Gje`m
zJiCf@7`3S|>ri2rg`<;H#?MY%GPas4tf2Mne#hV3S$)|1qmRyStzMZozz4|;345GP
zz!*~*mzowH`lpwi6y2Oakd=W~<al&zV*VOSw=P9?U6(GI8vw2ad5vH8WA8m5>etP2
zFUi@wXYag`R91PSd-?$GDUR|Km-+fBB}k;Qoz~;o6y;B!d@(P!^W%m0Q`o-0m}#X7
zM&o#Q$`hW@4K*GxW|o0A<`pbGW)g61Wx86NBqLf``hLC?K)S-Btt4(?N1q+y%8{<{
zt_T~f$oXJVWWekK_UWR7AsJvb5UPEuIVq+p*EX5FaR)-W$wpf)uJ5tc5jLS|=D6Y=
zYj~6o0&$m;N^3tJs(T;V&crxb4vK^6H+)df>e7`r!M`Dr{m#T#IYg#Hdd6GNbU@=3
zqdVW}w@nDTRxYXvkOWHtj~ZFc+N9IsO(MP(1X9vq4{A1H@YRri3DpjlplRRd_Zb~t
zh#%%*T-u{RPXi?%=H&-+DVynG<hO62^rwRC+wWz2@7p~M_GoDiFXtZoC;psi7W@Ef
zpqilmu9Nc!P?aGe#V{ec(JbRZh!zH@^jXOS=`0TjCFGu68w7-N>&u<YwS149e9Dhj
zk<-sAm*0$pQiv6?WBICokW?)>10jl}+twR45GQ*4D!7tjR7bFX&YvT5?fzhC<6oml
zS^!rJO+!NLAj6>LN<WFDjwp)As=?i(UOsTMeuOcNTZ^Ss$d5+!if`I@L^{FX-GX1v
zk*_o5a=orSnV~Z2Hv>zi&sf@Iz&-JRCKfl{u?SFmO0h}Ey%TB=DO5)xJ#q(C^fsOH
z<`T#Mw`XGcU5xjyMjvIx^ygoYZ;L1q%P#pj>^prZVD#2frBC$vebZ0<s-HM(sBUgS
z*DHx0ad~>#<$8^$p`%G5Hx*``rq%amvj(`sk}|`bZ@;~xnk`}7OWX{e{UXEL@Y1OX
z|EH0=x31C8treP2W-lHaE@|PYIb!ayY$QHb`E)bV+Ot%Z{<(dOfWs-{BLO=Pdj~z+
zk1tTIa>duSaD0Mr+_J;=);6Wl$JI6w40SBKq0M#g*&!9yz^L^$20YHbFCwt0yMec6
z!-ZE3Y8aJ6^P$E!hmA_jPaLjIbPO7y7AI%$z7AWhaEoghJ(G6dLFVMOsT9E0(n+&q
zsukGTJFfwG+Fs3n=>L@2U-TzG{=D6hU*3Sd+KAgJHKZI3PeO76*e?L{<*CIvNttj~
zNu?_u6Nlj&pPzTxcAFjSV92F+9-A3-k2QDNVWiyEw(8$?RSfR2Ah~F(B2CLuLwz*o
zoV}n;DYEz}fAXo}gsj2KmG{jh78H(Juc82mCZBWAc;$-skK0UDR9yf%Z3Lp#2{ctn
zlHeF6qjcyX?|~AHf2&y>*YFbK{R2Uc68Pu0hTzQ*jhKkt+>C})`JFP21IaY89x`$p
z-2`v#`fnn&ZVEMpR|M%V3-RvJaKaHDar#LC9*GVPT!gC!IAaRfjDLv4R|-bYzG{@s
z^Rd8=YPmUZX=ku$nFz8tKXlpINCB%X0G^eu8ZmMG6bgD9ET?jU+bSDw@v%K@+Cm4d
z16>ncO#`?wG$zn5HW*J^1Un31hTF>Ov3Zph9J?w-NpJ%?ud_s!wqP~u5CmWb9#0B2
zy@pb+SB#$ju$ZsJ7FAUy$%v@JOh8V0Ugmt)kue;SG)sWre@%a5y@h;=pU%zp5{znv
zPdHQJb1jtHgBw5M%)=$#r&+S_+?dHzEzKrZX-z;;+E+|pa*NB4WpwtDF%i<1rgzcD
z7c3B>`aGYpDAE@HB<)qN(G(1#Ctop6LUT#EF>8t6S0?v9_SGNpxt4o*BqP;`=KpD1
zXGtGf>z)cowG_rWMSkC&D9$tRhP%stApD--agd%s3k1tE&e=$1tR=F?dXC3cl?rNP
z27}C%s!lQV?DR2%JeHSXaZcv~{NBqA7Iy8oNy`EOu<izNa(Gv|i_~3DXg47>eda`!
zOYUWk$nl~-DE>7fdfc-X`smNcTDHob=6jR`MU2${S=>7fzV}I^XI0kvOqvjTpS1m{
z9<gSl{XdP5ht^Sj)iXYgXNv}>FVnu~7yhC4Bsd}4Ph*Sh8NT)Gz*<~rdN-SO$fy2Q
z=M9Y&nL}*hQ3vkJCQhSS6624_ECqop_1z!!$}>s^`q@t@jD#7DnZr5S)Af0C|IPx4
z*rvl{MD$+&Vn6#MyK7{~ONHWy@QemHyOp)D^t4Vjs?mD75j5R1*?gB<<UayuXlIt|
z%=488x2$xyzD}t)w9$!DGFqLt<OKWs$oQzw4yr_Kr&b@9Gw}FB%z~hA#GCmoaq;Np
z{2lv!#H5r(e}{kheq*f=XQ<2k3fLMlca8z)?~grQK|Twfvw`0ccc*@jTMvbZf^y33
z)$$~%0QR8N#1@6kl*PHT@#%`H@l*KS0qcP3{>z>_56>4Jnx|wbKr54kxl>{v_Ny1W
z;+;P_2&dDI@?KUSGa9cuO_~5;a}MridF6fbCAm%O@9X{;XunmpoiGAQzoA^qU?GJN
ziSqm~lke#Pc^KCQ4JFaS#GBc9Rih6zRy1U4Cu=pb>oyAV$bJ^%*SHmXr&1(|uWrlr
zrdDtK_ArHT%Xmq{Kd!P4kTBdXHxu~%+@d8((x5vAsJHr9X}A^hA=!aXA(EpW0u0cl
zbgoErm3nAw!)9aoG#Dyo>g#{n^!9?`3{D2+5=-Beqy3Q@LsNNrK1g4wVX_sVK<iey
zZl}zw9$DyQe_}OC63y%_40HWv#^Zq^@j7Hk<WI%fh^y>ReZ`2c*CKg2+7x5o=i>!G
z&2xTB?ulaMF9~0|3lshU^%|d#A76j-{pUer)N<GC&&86ECB~Z-aeJKiCx_PkHT^C>
z9PZre=DLk#swOxGY-4CWJwNNapca<&nr9YWl#`#u9cyLMF1w#xhyu%w4SuevIVy^|
z9Dnp0_iYr;#+Sm$(Pl|!B_{-pn!FAaPIScL*we2^<Ahbuw?#BHp50w@bk9_Z$?Gc#
zYe<*KQ<J^<M}niJzSmUVt3g<Z@E|H|{K#iaQE4L0r?~37chACI%X9bX`e-{}cHRu7
zi0<1Vc35>@VFI&H%6`2krX_Ye)$h?1{>q_Xmz)Ny$^Pso4;gmtayxlpuGB>f3d&Hx
z&tnPds=aRpEg$!{HJhBdPoPzTbyjoOz>Vc-Y-)+*Cn1%I%Ah0jw~tsjaN>;bdiqH1
zPQ1-`)vhQM$WiY7+XZC84NAz775Q;?ay3W0GE|vlX+4Q7^~UG6_)=gCsA#d4%)51J
zK1R+#zsC&>RD7RyzIV>0(JNv_35We~p*;CXI~<R~7UrJ2(}y1QjdHK%P9u)E05-@V
z9T%Z5#>F+kHP^a1mxyEarY!?_@*?-GD|aAKF3U{|YWEV8n{2(ziSrWKQlft^;r6LG
z+R+~JW5&*2Tb9K;7zZ1M%FH3S6UAL)BSGe?NZUxS3pU8zAl0<OmOzuRW(UE6*Kbb9
z1Ya9UJ{6dKLN$bF!oqq$Db0~hA?IJY+V<T%g7BlK`im<1xT|4vS+D}_l;=`%b;_2g
zQ~Qh$8@Py2b<<-yesSN>&a^RRVZdmdRS#+LTi&NJfvc(20^Os3dNd~WUhV6U_#Rn(
zUO3-P`-gm_dDhxKw}W|wn&*1YQe_l>ht0??{Zt}N0i+w@;rIPVlU@c(RH7hmkvBI4
zvRYfhVj**w)`nD^RuAnJgX`_WKNIjMrd0qtv~pR;zAx&bKj-+JtI$a+^7gQy<2jt!
zz|EXlf6$|3o$yReVg38filoamaWy5HEtXr?1QQ+2M_2_pSvM?nOdpS!jdFILNw218
zP7hCBS_QO!1X}4ZJnXy5{MTc@$I)+o44yt4bfAwQI$^@2T(m3XdQEmVdU}1df`@ht
zQKm0&-@@ufQE2o?XdsSE2e?VO18m_OO_8b-Dx#~CRU2Ov7u|Z6ox7B}XK&tgn~BaE
zlTxj8<+lI6aogI6UqG$FDJx@U#aMp$8V#8U52@ng-3_Q#P>4E72F}(uvV)E_eLnkD
zYFbjdWvFCn-@{JP%Hk$<#Mwk%CnT(QD5m4SH^WEusOZ=y|16hae>i^YEAS`D6IH5v
zj*qOq2X<B@?j|f0ug&&?9Ucn%69f46+@QY321fHKGOh~yPgK}K+5PiJGtNzI#d*4`
z@v<e?uG?@#2t1(Q4!^$$|G5n@c<?l32=yp@-DqQZ27qT(uS8VtnSBb_da3Q|6XWL;
zeGpL(?Mwk>15)gzsby0JASZJk2wy7pKf-;GBYzc6z!iN?lj_9=pVNBhvx=qX6Wl-c
zM*&DG8s=RNX2QsF5t>V(4VVZ&QRgKLPaX6qFY_YnV`!H^jx4DO^o9p{pokfYP_kq6
z7CCx6tsn>f3mT_;?@L7IJM44tz|BPg`%X%2Jco<!T><Q82Q~X|mYgO%$ti3!%)VP#
zk-auGizTe1A<CY|3mZU@VX-O+`yB_9Q;HLjKm=&urM4eNP9fq9Yf+byX11R@1(@&k
zjoNMYcp>9(6c)}(zQS1vap_vHO4d0*CM)?U^i430mb<6UEKme<+f14+e0>Q|rWrXm
zd+IW?_SA)YDyI}o&r8W2KKIrDE`S^6k{s``a<o3$46VUz<E<r(7NlW(_=d3YFlTrR
z=9Dpu@;vv43YdKeBPjpPuRtWo(Dc3Fc>9u%z~j}9s?j*Pr$HJ6Vx*&=L!Y-emlTZ7
zKLsiT8ct-x$}5?C2mjF&^xfR1C-kJ;$9KUCJ{Ha5Khk5yeeTu~Xl|f)Lhe9$jAM~W
z-Uw2RL|lHdxU%-skk)rb*^rmdaBnD_G_L)2Q=hpcM{hZ+46JJ73jM@F3d<&5`4`5`
z^CrgUdn(OVKy*mFZ9+s&VRmSg_*$A`&c~$VhWhL#!IXAq>`g@9dDt8hg^y-0Iy-&&
zg%NM!HzzH~o!*nKQkEc)qdg)mia|$j>^wSoKFcsN^UojI^6MeBV|R6)cN(I8-tr*T
zS#l<R4jf+dHeimwV#w@yjKf^$M0!wg3TtQYz)N_bDT4J}w|mjzTM)9lI#I>8BX#1W
zM#8#X<au`1xhWA6etCd9{|XlkCsQcqwE(P}|0qyf%DJnT`zjD=_1Ut4j%%7{=`HVD
zkT7~?U#m_^F@A=&X<LyQG8e1#e^^%mtd2IZ9tk|&Xj$%O@9R!j4fM?wH<ET!+iz#R
z7LtqC#=n}sVu9zSV6H=#p;4(iyc0Cr5?KZl5CaZed&n!G8rxC*5SToq8sbGh6<!vU
zJ1q^t^WTAcr|K)I%9(4`>+>K-f#j2M&ddje4$f`*D6Pk2P8g>jA76fleavxs$vL@o
zE%b#eQQlvhoQKz8MwJr~eVO$JOcgJ&4{N;q>|zhh@W1%ZuD^9PKjrBPm6!ij1RZ@&
zjHP`5>(Xia%$_&CjglDRD*o+@%{=!j(&1wLs3EiDwWyL<9WnQP8az>hru~M4A=ckk
z_{8t*R+HDE1lr&juoEHMw)u206CxwBN<U{m660>r4yabOaEfEr<6hw?fJ^^8vGZ$p
zDJs~pn?wG!MMJ$;y?7L^5KJn4bd;)Cp8#Q*wK}gjRcTeJH1R)^2Mj#_$QH|9yk#4k
zd$AsWVNuivrPTiS!)blX1xS*Z^*Z4{GBKFbnJ}x53*UmUyEQ)5hR?6}HZc7dayR!z
znM!7|%;JL_?IMmL(V`k|g7-fCBBRT!ux2M{qVakVtb%$a(@Q_RrX%;Hl@gu?*0Xy$
z6QE(Sv}sW&_bkp5A{r?Dwi;vN_eto2_PV#;cA6z~90)R0Drnvc)dW4PQvp_c0X))i
zE;`dV45?0XY0-PGy0E-QXi%W5RDxoFu)EozqvSG_^WefSh=S_#X{Ik3u!emkqOjT!
zB{P{Dm}#0l8q0aGkA7o`oo2E-Rx0C!QdLRaM$=X9!5K=Sq0+_&NG)s4iq>lK@C#l~
zNw@TgVVTq+RqvBsDmip5440wsg1h*%PAn?TsYS1txhOZ&cf~+&MZZk;X1yOy+Vpwg
zA;WV~cwL92U*Lk8XA6rC!@n@9pN`oiy6?}w{q!$xY)8vNwT8#Tu?|%6J9@=6$YYiw
zY<dcBpBv3(UEr+ea|m8+N6Z#<OmPDiw>HWUX*s+>R1Qs6V35Fam-n;df-qNoTRr2v
zx0H+--2c)@gJ`dFCh%7C<x4vlWyZBT5e?^?rGBKFo~n4wS$l4>m`#74n^-AAx{+g6
zBNGu=|Mv2*gAr=-t)dL$A^cL}cvCkre=OB}xEz1s46f6l*eCZ!^*;UV#Nf4GSw#sC
zN^0!Xbu7Qu+FcyYNM5ne;rca`efw(9rm^2^^xk)^FkHSRC6yI)8Tvo%eP>it>$Y~V
zz&<v%dn<|{hy)R(s(^ri!ch!$D;=arkPeX=BZOc>1SFauAT5dl29Y8qB6W*`5vdA+
zP+|#$9tj~Kge3P3?sM<CcieHmamM(*U-w%-7-40tmAT%mcfNBz&ogJNlH&<om*4T=
zDe$q8sBa)onGqi8jK7J{A$wUb0$>0xOKRb-sEZm!O{FKczIk|>cKGO(W5K3Yr+e~X
z+tkA_j%a@WR4Uu!e0&s!pwpe>u=p)Q`M#9e<_5;LUQtqk$W8snm{NyG?J@&6B5TJT
zUelrmrEiLkY(&swLrB6y@B6xm3$suJ`33FQyWigtO|Eizl0FiUs;R>swB~*NK7oP;
z_5R7ur*40rt%Wt2elrlS7^>t*d>m<zJG(NYSiFbleki4~y$}S+@<u6~0Uda$znuoP
z+%}?r6B?<4@-w6iJ;$v!um6_GjIG6H%R^e!?Lj3Z@J_zwl};;zDv%1j3+Jj4u!znC
z;V?c<FYi{{d{*z~hHg9E4@*_Vq9m7>YW<T`D9?cq>4gv4Fm+$Hxy4<xH0C`BC#R~R
zhyd*|WnA2qo$aQt0;Ct^FSu4q!)mg|q@5=*!v)5ZS2sYi4vXW3V{=t-`KnjRi=#?;
zC`UUotM|?L{tKp)k*lQX-Td<Xw_m<mhCfd_sv(C92{XF$X6$rqZt%q(e)h}B`0?Ef
z5)i)u%gDzP;CX2x(3@vC6Aqj2z3>gj@nvWj_{~TSD9U*>-X_f0_-$IjC?ak2#&kIa
zdwDFZ{*zh=bLXdHsng9VoIB?1W-AI;e;Ww%dz(VCZUP^1OAMXhf4HCKW2;zi0-K$y
zo6~ziBotS2{fh{S^1uqfX!(r_?<nv|MO6tSyDW|)v=r5;$P1TBVX^QM##7>4>+_Gr
zxfx$|iDw+RAX;#*PxA;&V?a@V^fnbBz~uv4%y6yv6Po05D6bC6{XkV~Bix^cBa3VE
zet~HByd%aVw2JIE>>Vg_P`u@3C^U!7=XF&><nSZP<oXno6rxaa&p_z;PIP@1UDbIK
zd3BT#T(?AXz-o1&bGCPlcC;qD*va=Gt$Mj5N<NC`*qE+E5xI`;_MNvIq7)HlS=A1o
z{M2`#74Fds)SLYj4EJ%&O74#UN)TZPn2CdVx%6pu(T(W9dc9!ywIkxi*Vt-f#r%s^
zcVB<3pj|pCmJ8J{=F;>UYmsSLT%(!qU3+?%EztVuCg6Z?y)nb_3+?v_Z*1=#?Eukm
z<|zZUA7fPIBdawps~eyDKOLWUV`Bn8!PY}6IB0G1#-&;il)p0dp=CTU{dRl?tP|cG
zbzk-yst@k=mhJp5R}&nG#z{`)+$ogmZy(JPM$Tj?)V-<GvX0!GhJKWWiYz(zL2&Q~
zhf@V>%KTc^Ou01Peucc40-vW|X#-3wYJ4&?J|8!ZDV;y7JN^9<Z0x=}HV#GIu$Xsj
z?e_5MKMpv4<!mPk&!HVldRu6)2u~cNu<HF#MJ=?hsOxK`vb<RC1FK0IPJ=31v8)1P
z4we4ihl*3EFf-jaCe%|kQ0otvu{v)vT5+_0haBix@-a20Gft=fdrWv$;5tZ!Ctxwc
zY#2|jr4T2IhwGnr=M#ll9@CL<obiGCvpd9OM(|K#RC2dC{>CYP;>oS_YA}E|&fVki
z1szo>a-Z^gHRt(^?!Tuq;wl)Ugqry~Q>&dZmpkf8k^aS370NzPU8{FPyk#-;YG!(Y
z0O#;Yhy4<S1Tr+!i$1avkcvGR0zD_1zcKW{%dWXh`V@F6CYLrtTHcD|^o-8V7-2$_
zq9)a}H|`K<y}<dvqIFl$r2u8i`T$k62(R3VENvc3pGIA|lu~zWZiaXUz~BdebS2cw
z%mqua8_NO|``6CYgBq00L9+rkFJO7Kp`BE|L1XCf-vDaUx?}6yuxB-L>twxAUa#Wl
zKS+|)bqKzRQ#F{3_4m3WV_JQG>H@)pWJc}Hp--00mH3F7)~C@fR5w#Yf`D;p)-29i
z1XB;?;h|F1VDj{sME<a)sI2GEl*y{jgUzB}Dj^Bvs|XyNYP-%Qv-SX>#n8Cbpn5>y
zgdV+)tUck^B2-T1V62a9hj>4#ug-&1%%k?MgQR}RyUy@mHd~RFoHhN6&`3yZ09|U*
zFj?#82J`7lp{f#)YP)MlmayPY7lJM8<7+{oB+%SEi3Js%?;4g{*l1(&IaqDYnq3>m
zcr2twerz{%^VYZFoe~upam8~R5~8Py4$iNG=)TiST%bbBLyk{ib>%_&0D%nb-RPMZ
zj>gI1LqhfqOgK<RHMYJLb8`vNHXu)Ls2t0_d<I858xd{30C{8sl%bF2pb628qFMkF
z``up0T;&m?Cf*3H<N(@d=m8xswii<B1+Zhr8;4Kw&`$NMVoDP7k@!CX$V~d^<F8ke
z*W?Q)f7>_~xa?+_@>4f=tS#WDPSoJi24<8;hA;OJT)zMBYpR2mReMk$+<_aZ52I<3
zIgV|^+wD!n9|zGn-FhtURRFE-1yuZRLX8C&L(M}}i`tAeB|^o-!5_9`n4NQc(m(Hb
zaGHO!7AHOfG5|Px8z88Pe;mdP**7*I>9e*wH~~B=t2Q?(WAuO4erLgp0@mIUBj#%_
zA}xQ?A^(J4r<CfXqVc)+{DexENh>pEf5G0nzvRJW<i@(Pr$LDmuu}K^FQ95ZQ(W&0
z=w1skLVoFAL&zZs2I7nqY}&=*NEmGY0o4CY-`Ajh@f(o;#?ZBt`2YGv^lI`TkzboY
z8L4;x%mmTh;-CX;+ARia1Calp2A~fry6+2LUzvZ8UW|_`yXGQ36k^>gOFZ)<|K9~C
z>OU)A|DWGy4%Ie69XgZ^m@hd%<+EC$`YNs=I$((fSP;lmZE0X7Qbjr7>~Zq-W;Br{
zs|gCku@1P=-@Dh?o`igb9D0d^a-_v*(|!6K;-<HYv#AbT#^>7K#e<N5^4~TlCN7+C
zk@yj!9YGOLpMsi9&{`=6%Dlr^Ydzr|<VhHR5?(IuZ}S8{o9B-G;PDJ$dWf82e|Jm+
zi}~d6GJlKm+83Ts7aowY%irrkkWS6Hkh3gS+40-DS>KQSMe>=)ea5izU8_av<Y3(F
z9-X#%hbIZ$2g71HhZe;%AL5;0_w?vAC+&G^bp8GTe<#`J8voGdfbX%z@UnHUaN-c$
zV_0?msUQL;df;~^PVqdZ(c|Zn>g^$(lY5CW)>b8TNM!kbCs*&rz@6b|qRnuz`tp(4
zJTax&l3m>svu>uv6a*))(KIN*<8<j0@Lh+AB5@Yw`v*WCU*~+rOFpddN~Ye9wFlbU
zC;$HV;zXG2(XI*TuAP<n!)If}_CnY-x@1n&rm9`420m|hbFEp#{cUN`Slz5OT2K47
zb+7hb)dM_I*>F=fJr8E`FleC$XdYDh8{YE^-BM>&@h4Qs#;?<i)yPt+nE=!QTOl4l
zYwf>d{`hbi)GjyX+uBZ06Ul3Y>ibu}nNI^SOhgace$Bc0Jy6?2tu*Lwc-^iF+Wd@y
zCJp^@jX-TgfI35gw!&h%+{lf*W9=IM|IfcZ0tp|?z^bmjT~1uxzW|FrM7|HJJy54a
zrr_grK(CiLLr@MjG+>(znHjLa%R7^>maIaVNC_9a4-{Hh3ard#iNx9n8b7gkYU9BD
zn*#dAxp-xL^k8VW(~*rX&6<`B-Q^z{f1Po$7JI;;;(@h9V)f&>4UmA(WS9w#-Xor^
z3A$fS!8GqkY;W0O|7D|#5yy&!ea8jh0UL_6eyP$QECf;$l{(N+$(Gd&KpHT+mnML6
z%~>;4?)=UOW**)6bh9}=S8oaMST0D<tS$?3+1Z!K`YA}oI>6yn!_z4=5|By}E_*`Y
z02B3|J5CH})|Rnd4}jC-jR8!#^#>DVfs3z*838PDVU0SNu%XMg6t25Fd7U?8qh~_R
z_nFiDBtWKsRqyMgGA1HKWpjcASPTZzFq%|^T?DrFx3xRjd76)hg@qjkl9hqm=<|0E
zxv1A#;ZK;71?M~EBwIo%-tReSB-tnoD?^B8)J63x*FIVP3T|j_&>`iy24|?u2Dq$t
z=+N^*c?m$j23YY{>JO9Ce1;#4Va!dRg8_uNkT|fKZ_LgquSt)U8yrXC88wKu2UsWP
z4timvLK#ql>bzqTkbOhxK|zU`P+l=xafr{`-MLviRD96x!j@P#?mHC*qK&IY+T`!J
zIQ0(E6|{d2Orq5Mugfs|YY_IhR$T7Gam-;wgU$^AeQ_!F<0+O&)#Pkzd0o)5xhL(S
z7Y0)`iM&+u4fySk>9?S?vkYm<ZPGs<07$p$N&YIiSW-J*9Ic!TmchprEDOJ^jhAdy
z7M+sJ6dfcFWJE}~++U~FvQl%8uA0njra0{fOW6%gyNIySv241l#sqMrBv!RL;Rd%S
z=97ervvn_|C+(aUhUv<tW%y#rg_7C|z*`Tbk)SAhTzj$G6jB5QaNcJdK{OKzBs0!S
zwe&Yo(a{<(h~6fk2ZPr%#}k%6<OAGOX*k1=mo##bG=rBPG;4y)JOpyOx!sH<?)z_9
za_}8m+Hk<4xzL0vz!k-j#GJbK8MF?$d0?&)zZw(^!qSS2P+MJ#K|H=eiHrh`)O5{|
z&Lsw|nl=%(IDcrJy;0!0Ux9tpBl9!N-gn{p(4oG#pnrYh{%<>@<-%|VP%T<fZJ%OY
zyUW$w&yRxYkul029|KKeX2$ETdtMMhw3YQh1_0642ff8L^9n|G4cn-0KCP*^_v@?9
zrP{#XV7lG|i^1nVaighN4?Ke(U`Sas-jk%C8jeW}a2oDzbwg`xOo%oILFVoObKGoZ
zSI&wuZsd7?>W<wmMtX&j##+`34e^P7=EL3Q!we~i)2X#XJFbp!8i@WN<Y>VzOKU*1
zQ`h59ybNnr2Az69lD-iZf4JWwmhmp5_V?Yuf}BeVMp+7`2w<<6lTBRsT!8k&eF7*5
z6q^;lRw)440#_pI!esClJTT2F{m(|<K^5hWboJ=~fkyc&K|HMMEa+J@2h+4;O|S_x
zB-601Yh~4HI89;Lz&K5@nibo80&B<0fI1JL;x)+~_}O8glftit;ku!9I%G%Cix*Jp
zr_Ojhj2A&cm$k8#6Z)z=C!kVj&?9SWqsAbREiK~T2(T-1+gI-1%;zOQfdX}rIL}CP
zz@h?`$O8MFd@W+6&=$9dYtZgbVxFpfx;T>O{?=Fr)LHY~_Hw^L%Pk}LN<P~b2SaTm
z2o6uWX?a1FEi*H4Q)dGI<`(^5eWv`sESj_T(hH&#u&sRd!v}=Neb<Nz9o*vA^R+-b
z+5dg{OX=$IwAig2!!TV+S5UkRZb9)zzlrqo#tc<~o15EQ@ELoGI58=;;#x88VhkAb
zImyG)d-vNSPa({4B!AS!$FZ>}?D1IX<3%IFvi5}=$q}Qdu#)d=qxM>Ltpg{JCv9&`
zXeiVMyi#*q2+C^zs$xFYjR8oO$-8i=!b8*2h76)Bs<L8!5n;>HTJK{L+5$iF5ssYy
zwGOE6jyXP#uVKgyRBRk`U6!>hnHldlmlsVzrKC}q#74=SrkJ))cn$W?Aj0i_&klUN
zhDYDq|1jCVcrg*x{EZVULlSX*4ga7&$td2lUC;XBSO5BN)>SRB@g?U_H@X%Zq3-rO
z?<bOH+MTcDCaPtJkS}k<7l!_EjeFfed1_@ZCTAsUShX(f=3-N53;JNU>9dohY5{eV
zPLBEy%>nLG?ei0~iZdENYI#S29Q2oki|`8sdd&Ly?h1-``FE$-xm3Kx@7O=9H#G>#
z#sha;$+_AYFiiyzZ$fmjI;5ewUD(m5=E=^MeB{t{%8T0cfL=)~G61aUF@yabDVffk
zJEX^xi$o=+-4VBIq>o=8!#E|2pby+RVv(22bd|&9!3Xry#AE}x^TwLDk)y?%o$h<#
zR$ZOrnbr{@IOCaBy{A{LnK>oP!crwBgNr+%<f!y47CLH1K@UEp8^E@`XEDm^C!@|2
z_&qbL!}gurhA86@BC}QX^~#dmc0=%W_~B@!-{%QIY(^iv)(eNn$JHID5MKQHymK8Q
z$J89-AdmDYQ8U{{>~6d_ZMaG_^m9#@n-G>pZ7qu$2`bAAX9Y~_oNdD>WWUZ(U2>z+
zgU@{W`C!R4r+3MX-`+BaSxs;mMf8-PW%AVVC4Yq5^pYq$e%<^|m?D{5e=O)4cM-;!
z{KUOE@_O=e6rstzs$t&<vq}0xxx7JRR!NIa+B_d+<D~Fn<xv5%i>+m*>C0p9r99=z
zKN@Aw@`d*35Np=80w#ewMRE8u+NuO)mf1z3-0aRwb^a3i-G8#;Tb5QaSN^k=o-c1P
z#+o8LSN>;w`wI?LRu_9f@rLgz<>kdKks5Z>HPvKFYEd~FObZu%$-6xo7R$ocHQWC|
zBX9;yU#w#`Iwyg3HnD&?Bbm$JdYY!({t?)^+$O<rhj9gQZxe*fgDb+7gr=PHcgx&~
zlcQ*%|F$I5$zG&CIxWmb=7QpdT;@#^G*$CQ?ZQ!Z>^q&t<OieMG*<{?HHgo_6}0f@
zjWK6BpOD-4Jq)>~FB|t?Y}>WXj<J<x89B?zc|vmMNeWg<8z9a&d1p4W=49WoO%1Jd
zRD3U{=fj-c$1eq~k5<dHeAwBPD3@`;$b+EI!<MzX1kQUZU(o@L;`uIb{(7g0@k-R;
zUG+vK?ZN|_%cNu&S7Qh!p)yy7S7hA|y+QlG7s4V!(I0Plkg!*QPbs?-?Em>hcH9;m
zdDj&wj}H7*jZl{Elc``u$UwTMV&;0tSsX)Vt_py(e!aq5N^p^XiT>E(y2=2if$vDs
zRy+fl!qJyxH#P+z_9_#MKdGh1sXF#P`y0V7qGJ=EYpF^T!E}RA_g>zMo=s?S_UA2*
zcB4-7E0k8aRri7(*Ms=>G1OV5&cN}{pg_J;uW0`Hamv%`g`?FLC8w)!B>}HqjMY+x
zJKs5(WB};2wEESTN5K_@qVl{`C~KC^?)FmPRN^|bGHHqsZQVu_swAXBrQhr#jJ4o^
z=;4DF@cQ-|+H51D(<#OAPNeA#AL~|a)VwCu2=uO>Lm#sE6CLxQ($abBASS0k@J<OH
zUmrk2ouqXnrt+2w(6^PaQ!xQ0J~T^vMAVGF6e5SZEIXZ_8S}aQQlZ)Ke=?<C-RqxB
z0TJX$EmM3~d%9v)(2haNjFmC9{@iqt?&F56R1anLsPQW+-1x?fQ;Nm;fB)#ov55|y
zH*Kp-<z`6-rw3PjX4f1&h=rL*KFKN$M!A{NAQfQ`0ctflzSDz-P^fqysZrFr`~hnq
zoy~a!8&9h9jHGcQ_yxuR4)pEjZ4Fi+kp>!|_``YE2Px+}@5E&~M7|2MULq}+HSRrr
zX0@iQVVh7nuRUlmv|~d=_CQUQuam+J*JiM&WJ^GHh|$?F<XP4uM|n}HHKwa|iKXAk
ziAruDb+b3=5BJHwLl>)ymZMGEcg<M=*whNwcsq%gR3PYAT0zD-36_c+lsiS5j!J><
zCFc^ye|dx2Ej#q|Vtit)3h&ft{N+Ti9ih0Q=sER=Y`FGp@X*t;@gh&FpPxO@k)s6h
zzS7*+uMp!)?U;t^K~d}-EcMO1X9Bn9H6NrF+blFN)u?s~JxI7A#T?KzNz10D-lBMr
z0vvFZm`PUhiD6V9Gy9<|djiX_w@5X`w((>QK~02Gt5(VJKzvioYhA(a)Rhy*(BcUh
z)mJVHJjF?AIoyTJ>8&E4k~i-w<}b)2!?6PQWC3H_b|&DzB3IaTug98?wu(T3Ir<}m
zKdY$Qjoc-W+G!DAG#T5M#T%_ks>~W!{@p@nS`c@(7ZVHCE<vs*yHT%^ki%L{AtpOW
zaqp0n$o%9c|D`EXADjd{v8#0i+DQS0qLqz|ooM5oi9Q$JBn|~`5gxauWEmYDS?48Z
zzjJwhAe#S-TTBVJxnT5UqL&V@@J(l2{EfnbdRpax3GlW?8WNlcqV<zg*MJ_n+m0pJ
zp4NCMZ|58nPp^<%oMt>(qTGcE38mQk4VkK_;01vczCpwB6g)7#`xZ2|Fq_Z%47Edi
zX#l@4Lw?<;+kfzVanWM$%vNHuGsBlB-_@M)RmPQSM9##YP|CxOAgrSmg)f@W<ii$r
zrYSI!KS*4gXtZLEZFmU01arQ@knG=<WLkbkAp3ehS)`T*B8A=`&!Dk81RQI}U(n}D
z3=lLt`Qx8C)<Wu(-ko;uf4`lz#{lv}L_%^k&#%8IpMbfgr{{FtXWZ0V&B;e2VsI$<
zqEgbcN8W0i@*gA(-g$I)@Zj}*%MzRP++5`cf1xJb-Q-hswfNlyZ^Y1W=L8Ovno4iE
zIXdKf<e~M#e9qsr+b$Qm<1fjiZ50Un-XlGHz4vx~n5{0^lF=d1X`YIeMD(|jWHIyK
zKh!yLbb>kbvUJ^q83k^bWd*7x^4WBwHLGC4#LhN=R^2pgWIn95EfhiDGIoI5lac<&
zXSAvG!{p{(>iaLD@Y!R57VhXj9xfSH8)wvKS>ZVoTVxv`+Sj&tLLR*|K74dEUw^Q@
z_3_+MwNv*rjbSXmj|}%rD@wb_OHxhiO^z4idpzuKCQZ-Jam_yL>B0nm7CijzN4#<2
z*d3^T@GL@?iI*=whX}jdK7|J#Pe?C3Wn1W3G|qrp;Nk)X+h7C1v#CM#(teHGLb2mz
z6Mbb9TOi&^5G}|bvhl7xjp?MS?rC?NbI0&7PTnQ>Ns)`CD<fQ!d;>~&iBgyo<k91B
zc`)1J4FT~_b4A>Ai?Z@UcK4DDmaj{0g7WbrmaZ9a9hDxAI(aC4*GZ4g5Q@(|x;G9G
zeos&IHD&oKksm<ot0DrEi^7UaH(oVRS*!@+e@Zu1iGiYWJAd~Lx+<-qQg@%#-qV}@
zu>-3n;2l<RIZk*v+mR2&_IVJ>D1);7eu~HHwuPpRYckgC2UGRY?QG3CkZLNC*HmMW
tNi_)|kOa|M{%`+>9pa<@cWeO@iXDFULPtuIhs8GxJ8k>-GBfY%{|4My_Xhv~

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

diff --git a/reference/plot-4.png b/reference/plot-4.png
index 9cb4aa5f9306ecb6e8a0cc5192bfa0efbf29542e..e773bff19197269821856c67d406b7fbf2eacf79 100644
GIT binary patch
literal 39727
zcmeFZXH=707%m7RB6@_Q2pk(ARr(PN(nUcCRVmUzr6^r`i3F@DASE<I3qt6<3j|_A
zIw+ll-a-p45J*DE?0C*ub7!qtbMO3^KeO&$EA?x8zvX$K_uc#Z-rY6OI&tjmF(xLa
z6A*0;BPJ%+Kqe-ZM@Nr<e{e}3H^9e{N4i=X;1e|R?Z!~>!Twa+!iS0JLOA2a95cV^
z$iyVT1kq482~1lZ4@%?Op|x%XT!)sQj~vT9^y2M{Lyt~!nsEH`4t!vGvw&w_%Uv_a
zrGPDh&2r<b)$!Uxe0M*Znp6i0NF0hddPw5vA-<zom%K}*Ru^_#1Ae^v`wB#~s$B-1
z5u?R7+TFGLVYGd>kvyj~S$6n$r~dcb|CRudz}<TNWRsbVK<U)PueN(i%uHcAlJYoi
zhHOX=>)~7HZ3BW&jDDMBXJRt3R2VJ&PiUX{52644eiw3biuIrGVLA~^vj6^VVkA|`
z@P`B>-#>?u+z$sbGu?DNl{9eAC;-x`>mptdw?pB9oD&YYxa0#tYFj$dnnhZ?IpTcZ
znBLcdu<0JNActOZevUFL-WPH{9}^#oG8VQGT{3|T3QH$lh4g7aaMpdkNmtE{%pf(w
zCGe}peHdYBNVX~)WLbLY#>y{^g=b~)+Zm`)crJt@{$8%bN>eEhHjq%iW|-g`g~}E-
zO|FLYQQ@}9+gYfcGo@xh$03E%L|e663!h?QgAA`H_=cgz<%TE4<U*5&MY`jwg^urZ
zqqd%TIu(}uGiYiYOzwHa+Ze56;m*ZN&YH)_9<c*gUfX{VDfm9UnNrh~QWIkp)B-n%
z?ySl{y=GrlFf~#?wv&Lm4(AbRlP6u)6g`S?=BA|?7~aKbob7yuh+UnW4noLY>d+NZ
z;5U4yC-C=~;YQTtC9a!Meg*qYcp>;59SOZkgDcisft`-@*v`ud+6~v#Eqd!wsqqdr
zozy18yIP*#YFK^zrZY5F43T-e6eZT*M!zP)s~y}cLbHMwwna(k=c4UP_LtkEru0O+
z8+ANrug^m`DaxEU5kH-*C;`)LpA?s=pFD71d-%@V&;;Q&B^w{>j`R;2*jPP*VR2eF
z2Q`mt>b<t5BvD@fyJ6UHNmm?u2c=B6f%6oq^E<Q(jkEmSjdFg)TT86xPqAL`cDQ4a
zYxUz6gv=h@BK?<wS;70hu}eNU%JS)z5ixbzExGLD{VKezBA3LpflMeTHlp<__$p$y
zI5tS$;&mpX`D-~Py4bf}2`rm^CpPW~OPg&x-_v6Yys|kVwf^6z^Z1f|1Rkb!l`ICy
zeTga83o9)_b=KgeNjAz`^laS&7+-+M4W7COC}cR&Dj%bB7Za)6*8?B0bgK4=M%{M2
zQ?5v8v@jYx?8usvg)~-Up77~cjs|^L(8Vqp`IjAbUJromHg_KQhwZW<PIk9;M77mS
z?kd1t+VrY6n{_8nLRd}>NVYMpm#}wiaZPpqkFr2o7B#VOtT-Gx@$M@8s$L(v49>MZ
zO40XtnqEA6(ksV9Vr-D$ulDq}s0F?q$mytOy%MroPO#yR?4+1BslNwXv1(dIN(g~y
z+`$R!<@*lv^9c*w3Nc|_iB#*FU1(akfa#o%L=NALVwv5k0@`0P1qE5mb%!9~?ba(e
z(*tH(NW*ZBhpo?Xd<Si$=PAVJ4XQtJe_PP=(;P-l9V@_s7CM!X=n^JxyxzU#=E}^_
z;AdEe^xI11%zfE_b~V8zi+Z-}Yg^g@Wm-YDU)mDom>UC;!KioI%d4@u$kv0rPXpbq
z!d1iPb6XowkOhC5fXP2S+D?H#sM32dtZNF%KDEmAipRSw5BI`WtZ~&Y>wABya8Zp`
z(~Cd8Ls<jE#q#Y7nJ4tn@|}(UIojpAlc8oEQT}?Z$udD&4j$_M#+ss+<`X>f#<b1_
zvkE~QP-h5nP9Jqs-talgctff>9O-9dsJO4H(c;?N<TRZEHV(7k1fN1Jh0$`oo}K-s
z&VzVs5=Qp3BKN0+lX1Rqtp+f9dEtL9-6Y>x#klnRC4^NvkU^z$bms~u1S`R{A86S(
zC|BprkD5DZ6_2GpX{(nbRy2?``%IGE$?#?2doXl>jji2m+`{GL;m~bupRjMPtlmh=
z8Ic&Y@RhS0uF3A{r%=+Kqsn=(xz4C4zpeCBvRz5eb&co)`S9UmA~sW%bhQ@+(ZbHj
zt+h_fNwYappV4yU38^xMi6>l9bO6x(i+1G;X+citNNZacz5c#YMc3Dxt$`@l#R_S}
z%@nJRyK=`*<vfb-8Y?hBABv2$ww2w{p53M+hwB5)ovJH`v)}1rXO*`=H&pXrSGPv4
zYdT$Z)ZEJITyb}J5n9pExY}s_4&R#CrJ!o*WSRKbk;eolK(vj)@Qq(J$VwA9d}gY9
z4mdd$IQiY4qZ@~q!Xi8(;K!L-I+CxN_i4#}!MbozKXr3SAU<mfuBg-o>90?HU)e(+
zy07*Lov}<0w8aZ*Iq_MQg`X>9#dlcM_7?ElhK4(yxuj;QB>X;LbVz<}_oEE8{{5la
zdF=*0(MuTZzHGQm0{h*eWQ2?e$?^~Aoau+m;)qM}z*b#=t=?1+`h1*;NupsKuGm2%
zO=~oCL|xT$64^5ee4ndVQ+$CBhrYwI<{Q+MwI5@4Ez)tl2w$BQ)Hc84${oUgUSOB}
za{Lan?n0VjJmTxceCNOAzDTx71+m#)X<2HrxW~Y=SSETt7;4AVV+1QaI&u3|*ZEHc
zH%u35H_fe-NimqFv3J9SZCm25Vmqm1H?_(d7Su*k*!i}y{xtB1OI%6|yxA|1CIFEx
zLH2q>s3<hc=568hC5($jJn{kcqxf9n(ymibbqy0!X4>z_n%I5_5}I%(WazHwpO>}!
zSmb<yf)#|;cGs0j1h|Hc!PW@L^wg5%sWP8UCB(@f7@w{B)75Tpxn_xlwzjt@OrzVm
zCo1$o$41<uUHdbGdYnCVN6yCvZ$ZTlTbF93=oQRYNE;fhoZjjff?jF+$HW-%JyR~C
zO{)B=mec8}LAYgmS9?8Hr2Cf_hAy~U?{Ap2D?QB$r_{UQM0SI$B@5HNIdvMPqhC)E
z5~w>+5Ub_PlTGyegJ?R4duRh8MNDG#ccI>w4hzG6Tk?>AcI&Ff8vGOYx~uAuZRp|j
z^V4yqx)JjZ)TT2URzQaxXP9l7n960z>T8kL6<A^DJ4d58NBrZ*JiozACVbHMO+4_A
z%8l%96r`Y(`igpBuGOuzya|DnSY^mCt64hA4ZdCZZDmkcKLB#YXrlkgz~%_)O>3<I
zAMORIpmQa!GbkD-A=l4`Dimy%H+KJRJ%ayOPmyqX?i{bE%k&oISopAL(45b^uD0(#
zA|{5$ZuWhXW44u#5xdtK@dTd6(jPQbt8rWlV&sRg!plP%ls^>`%rfFlmMcq=x}Nm7
z3SY=yq;J*BJ)hJS4w2?k?yiPf+p2ZV@8D0()pgtB-pP~=$8P(+c=qz@@chnsgh2I$
zC{A4$*(H03en>)m0kDNyU<;r<P$PZn(#D*E*o*8W3}`qBr>KZIYa6LRk}ar;DzFte
zotw|uC}a#dWj^5p*-g?)xuV?RIt3S<6zaYkQ+Xr^WhKhBU+{4;s5hQHY&Vv@xMdBw
ze9H$00=?olEgA)ykl2;|U1)vlRFcJ?QV5C)QUgYod+t#;t90AwV~ywYE??Fau7nlt
zJrXfsIc08Bl)CphyZHYC(&9>su|4)pV_Nm|+QHIca;kCcSAz-`zqG~bZAia;z|Cy?
z&2Z{(VdpHZpm%(uM>ll(-b03Z-4A=9sqtO1B(J>I+DsPoL&%Cz@uY8koxJV;5CrC5
ziJt|rsJT=_G;_`$2Sw<8?sfl|?|_zEx`AMglc;)&B8sTM2@$J!LAicu2m8X;zIu9(
zkoxcmLpjX3x#mB!;@jaK`VZ;8i5(#onqp<Tidqx>hMx-lSnt+wmu6zRB*x&VpPN^?
z5el-y!Xuu^sN;@2Pjsq1i}^*;r3)fa1l47;lZiSFMtyPc1*$eiV}ayeS8o=VCI=A`
zzLtFT%4^>;h+?6&OTtJUd_iR7aRS`#)dnxb-J$s=WDW-56(@da8I_AHq`@Cx#DsT}
zoL%<|+t#}s7|?Q;!@J4`1l+L0rLpw{%F&fycSIKlO`K%fT;%Ew(}kULx=h$Tb%M3}
z-kk1Cah)Hx7JUjW?rIx*>F5zRA;4vBA>dQABPJ)bLefOGpNEDZ6s{S@Zl8mcIP{QZ
z<-VkG%RSEylEcrpx|#U%*WPi0jbi1Vf9G;M;LvK9N~?h^3?0ZjdQ8(Llm_<)FUWmS
zl`oKaL8?Oa$hGc6A$dA3(pM~D2vo9&O3sr9m(Q`7PsBP$QgkTkU6c6@C{ws?!ZzxK
zO<*y4A{Fs3b@*+%|I@eCqFeuF3CH7@h%=!h15ek0AV?4Yr#Z*}^wj@1JFI3~>PS@Z
z#XPQ;Svo7`bKXWS7KyvA<}A^02!e;C2rfxNltfCBx8I;JI*LX{mt1Rz>*@$ht7p~S
zg{3o6T=`NNu_jUlx!-E1BS@z2_-tnSWU6e}(={&eSWz<w6^79$12`YNTD$K$L=BR1
zaY+K=7k^1hr;M)Bqf=$QkmjBMl9GFJt#%Ixy!W^8(g&Ai+8YMo3U)ENmV(ZaUF1Oh
zKK@6`GdU3`N#T$O7kYId*&>6ot(E-{rEYAU%L@vFmfgHFCN2I*MJI8Lts-LCPOkNr
z2n)I1ofw}hsHETLq*->duJtm9b8FjS_FxB8Zh@7!@ouigr7Eu@orfh}3OP=k6SA(2
zxjW<E2J?NV>xx<L(az#ST2e_~qp(*fQ^>$H52PLt*nav#=V3TkB0Zxf^v1i8aXz|E
zmg;NB95Ein!xuD=Ts<V>UV!52{J847GCqdI%f!qc0^JLXYD+vL&J|Z}&>qTEntycT
zNKtqezpqyN;afhAcUBf4irRhk@QDQCrQDYL*abwsTg0)`Dz#{-gfl&N5jPz-le(bU
zwVnP-pHo-!`^?o@1C=Bxd<P~*vJkemsKpcQQOc_7eW4{G=;pC499{Jxq0z=j4RJ4D
zVJJHVH3Wa79W3__ty;ZxHuih7$1%r4J-ltzJEo@zLeX(&RNUEg(<$%DvqF&y@D%f<
zNa69vP0dzDFT*+w>L!e~Q&c`XDltsQLBkBeDHiGIVH~9$EMIi3P<j7wpk#cZT(+kW
zyk|R3)qlWT^E~&2QLaZPFb_6{dHk{ZtC35ERKbFV<(@hw_}R)z^yd>c1Xl>8-@xfC
zt2~S{$#UA=Oi6e|{tUl)kj<YrZ9QDt4rdF&W&B5AxmnK#ZiN$!O9(MZw?~(9RcwFB
z-ehLtwl=7h1WH$Ezy`x^I-SlHPM0dM^)GyR43*raom(c{v?|o3f^})?N^9cQw^|8u
z;^}xM^wRNA;+Ll@<S)fTUvVK3(bsA5T?5RvJe}0e^J+9t-IaQiJ}vf+vP-oRii5X#
z!wJI{y>;zTPof_ij&6z}mc(C3DUDYX9Ty0+)UKlsm~OJ(3FBodFDooqZ0kyPyi+*x
z91UadGe|x&K6-!QddEpQey6%lsT)48NoYb-JPNSJ=cB^T@G(zZOm|~0%F2;PO}sWE
zydy;8iCpA|YjEFeR6W+`VHz`*%3>+X>mI4HRwx&lEPdCv0ud)C6d{+By@Qr&@!fHV
zo;|m+dj|`h&e#hQPt|F#UN~PIudu)^$DH`mx}$!yV`W#aZjcmPFCAQ^cbDuHB-_*9
z9-Q$wXI;?e{ekcjn`B7Ir>ocOqrW(#5G93)y`)ddk2-z(Eu9Qn4IO*FNg!UoITf<x
z9oFSON{<ghy2sa#lf4yW%@a$uj39Nk3)YQ;IX|(@#y#|^y6Wf_+rzH$C<{44JW{q|
z-#X7IxYetyrX){~>We@HIFy!1W1(K(UOG&i^C=0rt{tpnl<HlGOgIRVNjRf!DbXKe
zqVXDj#8S;pyg;gZf1iZ$$DF-CpRzEowW82W=ryjk*+=SP=jDd)L}xLt33PA>x+C=d
zyrI=spt{AzaIb2b`ohpxPJ7!G=cd~GP&ST7qsP(KB3vjh%d|Ot1ZSp@O|6#03tf*O
zJ87;<Pjz;4((ulDS5>|y^7*}MD}I~d22=0-;>y1}?``Mil#PD*SiO>$zIkl_hJwKa
zKG1aRpjCK8O%Ojd&^T0U3(p9E)Y<e9uj~B6l^Z5*XX#RQu+Z-xRLpYCKCh~~BGu{>
zMh7nHoA=qv?3Cj(-PVmJ9%y=A2nbd-WA4zsW)ruAHK?!C)Uu#<d5!odYbl&We0G!A
zx6NDP3lHhuzb9&b&T2yPG8HiWE9$g^vh&Nl--A>~u>rv^Xq5_@-`FGg;&IQ43-(?n
znibMKzkLX{Ua7FFl)J3?(%}W&pUzWM0aq#mvZfp{_rL3GSVamN(mG(caBS_o9KQ~X
zpJ=&S*k|y?%xd-6#1-KSf?N;U5)o!UIcB;N+5aRZlQ($aCR)E(pMbgy^mW(U`_SsX
zxCq$Z!Ch$Rr=4S&q{#LFR9Bl3^kE8C&PTxZLbB*N=83mmi*-qIt!Y9_Pc`YbyGkf&
zPSSoXX;vIE?r`W<d!Wu*>)B4#b*(n*(2Iy!RgGUO!Y~uN=&rXiN`J{|EnwSf_%1l!
z`FUqfIq$J;msf}o*QKi70CUj~RsC34ur)S(o=+-1i?~`kkRi1Cs56TH1W(d-N{xFW
zD*m+1MqV_kVyIRht2FhjF^jq3qFi<oVWpw8Yb*}`xL#RpN%4}`&-GKTNZhP#9e#V?
zNHoyg=GRJLh92~4myn$pthC=ZMu?R1u1#ujK&L00z14y$mEaqZMR50n@Jdi%&VN0i
zy-@5OHYfjKSZ;Tn1)VHG$vApTk+#15fp)zuE!o&%qOqVuBWzbgbLE^}E9o*0{bI0J
z;_LuN;c8~z^(xZnhe!V^VIczc^c_FDtL&2*Ztd!qamWtx^<P%@TQQum5T`<BD0F|h
z-+uzs|3vSb`A)~r&eqnm-X(IM7@^s=cissLK%}wUDB)I-U5du)+|lm4;LVp<ry)8u
zeNF!h=TNg>fBx;}tmg$I;%aAG1{_n;%D&hRk&Fl-&5z3H;TLLkf1T{w%zjG996B9-
z^&*1FN&^}LtLd~}j9pXI#B4lO%u|SnUS2yz43=N1ahDUCh9@p<?W3siiB9>gs)kn7
z?I>tS(hg->eBkm0kKuxZ39s+Ad0g`c&1$;8WN~92ILt3BzLIH$cOWRkZQjS=1#Okd
zQv+&9Jw<=3#<_GPkWi`Ss~4BzOP#z0k!_hfN!?RlINs&3-f(H1$ulm?uGVI$ys0Y7
zZa*-n4Z9xA)X5>?ZmQXfF+(0@Sn>0!JpB(V<t2?-{GVoCa$i^aYw5aKrplIM>e$Ou
zoy8&1PurDX)#k<LibH;UZ(8HINqE$h@nrd3AYj=I>HX(5)imxC3w=MkwClAzWK(&>
zk2`I1Ln<i*x9gw78^zIn*i{hgBV3@;@X34)|F!hC26U2wYYh%*8pGeOW_iT>qX`x}
zRz^68I5Imk>Nvr@8|<g4h2~#0;%z_tOi-?FS$Cny+3;cCsE`d>FBvaCep<4dosT*~
z{=xR%%&IH^E2&9-+Gvoo9X;!|vV%OoRWsk&)zYXx@kK-_mS@uG{wt!}E^DjEi0t*x
zsf#Cl!*+RAu9a!_!xKH|u1dE=r<~~*9`WbEdvvp8=Fdz?$0D>;1-WAVmyg3wY+(B*
z#?Cw#RnIvg9z0=8o;l7G`;bw89vObcogN_o>#=~ONLM}8Za&54aK8cF+!9=?c1elc
z6YPK7x@+swuFVQrBPQh0Xtp@v!5z&7e}m4`HZ>}UmPfYn#J1Wh<KkXX#LApDf*jh|
zc{p2y%Q{3CO2;_FELP<)Zy1@1{;g=?q!T^!p<xZq?(J3ss~dfuspRmY&A`89AFbq(
z8ZsW`J>pcwi-@E{i{rN=yDD0v4N7IWKFh`t-^w<2?FH#U+tFfTRzZ!U4^`-oR0878
z&{WNwew8>lHYj73g3Wt7ZEE>57Z>S!vw`1(VTMj|mD!0m%#+?CS4?!)Z>)Y^(hh&-
zZsWN2Heb{yGO%K2#93ukvDuM;k^W3!^~)f%2ywBJ-3;3f52E>UWSryKw-rJTs6)Z3
zQtgTj?3HvInI%!g$uFiNHSy#_&dFVu^%4gJyD5}J$*Lz<PvIu%C2NIUR;v>Hvmm<r
zYFv*RD+6Zj>+*0tAXz^NlJ(Ec3xS$iDvBkRO`s58g5>HB!1J81QrlSd9_4F{IagNC
zL2%#UntNEq;^i7Yrj|O^Q;>k|AOy+8_}&@th`VaMaG!8;$kk8y+B;k?$LAis*hvdG
zEQvj3Cz(CN<Xu6L%CYsDPrf!K_~iK>TWj|c5=S&`C>Vwlao2X5KJUD)nI#y}V|KPN
ze@I16JxNW1!WwRjKz?tU7!N)u)3tHavg{=Z`uR76ifZ%9TFvLcOOy0|dPa`nn4xoY
zEv(Xq01PV9p;@EhzLP}6DMXl=R!+~lU&@--$Z)2ylKTn|(Z{sN*3{vU6Zx`VwOYT_
z@p71Y`eUUwv)H)<k1tlT{^w%5q_+o4@FTS&s-89Qu5bFWG|%EkhytI|Wak({^R5KH
zpPrd{uKSvVb8}bnQ4jj@{XdC5+%d|jR=tyle~Xkl;Lv%9$$|N|NJU93lQG_W^Ujk=
z6StwgajD|A^F!4c7(=--{u2DleY3p2B~g5wd;g&hHGdB5-sh+b1H^`O@(qY=$9|dL
z`>q*>YYwUoIKLXLugz!TWl&GTh9w_(vY8-bpekm+oJE?SLI6KCuZ8qEg%ipwd-KeD
zOB@&EXeLh0eSsB@KiDosS7czk3nJ4eGU(gJD`epn1=R;>+PR&?_|lvm<!|=<N_%9G
z3%x}>cF4_qBDEB7-xl9)9UrRyQ3h7V2ry<&8pXmTo7$epNuk6*6IX3f?F$+-)Km}g
z)XJ!9N38Ft&}ORWh^$uMm!aMjdt3UA1-x%As!fAVO-E^!AdL|`B1cJW(|mHF!!lcZ
zh^C7RkL#9B+=55f!2HiSujPSI^W(_A#gh|E5`TljRagkb4-#Sz<LnGdcwFpr#ki&^
zZKk4|!$nw$e@1YHtnBFYyppEw_P6NupJ$D(embj0xi+LSL`295F`IRasd~g1adb)@
z5hc`@-?PyyyI04Wv*nO$7mEu0?q2m$-cC~Sgt{`66<QY;Pb|h?aX2umemNd8uiSx^
zAsQ1@zIO7puU!i!SR;OYDC>+8PRz>wqp44!-8Tr<H<}%uzHli~Z@d&z9&<W9fXa#p
z+p!p0smr~~i}@W@M(pl{TwK3t>=)zy&Ln}j-@G%1^(osPG0qmYY$L>eK-#mc;6(H*
zVKDOpRUxNJdJSg3Ywm5|c}Q<Z+H9|5ZjQJ?Y_7&H*qF2W{r0F2b-kT<=A;W6mg2#6
zZ874xEKCl6{kE(*y_KNku6(YtINLgNsx5L_5nCEhJY;DqGVVrGzSfRj=s54g<tY20
zNml%>??HC~)`xG*CE^G%EmxvE-AyHQp<G&c$wQMRyMj2y$44YWuvXfAA#!y_Y9%V3
zkYrSLG2U#xC*#pqcfKc3?2Ae5v!Yy=sNrcbBDGz6R(m2olI5pQM6|jJI=|Q<+C8$Z
z(>hHub&<2lK>F?!KhB$S)Ly_=X<e7mzi0>LuqgZ0xe3@JpxF-%>C@L#UY=IL@QRG|
zLNpb6j~C-LB8zioSy#x1AtlPBDryod7TP?FX**1ciQNu66>8v?v)E=V?nCn|Md_9x
zC%3SdZ(c+wv{5?`|4`}(fo%tpzQ)(J1*Jp-gcF<FchpjJ%!?oL&)?a<6S2gIzn|+`
z<g!cFbRSK!aNC?wgndjpGqmD9%ViN=U5mlT(XC{HMuN18DYYisx7Q!Sd49M>^1r9b
zx?1suZ^=Zr2Yl!ntMI1?BCM{+8eUYZbBFb5zw0e<xTB=m{yseTzqJ6T)3dUa(=Iwk
zbsb$+jEIJxXiv+tg*R6(SKNb6-m=bL4DyiOJ=sq>^sE<9h@>+uyK0Cj9S!J&s5F8#
z0)%0b2OQBa*D8@$k!YtO+GaBNaqONB+6CQoW9%0nwd>yYjk->#aE?uGfAVpy);XUy
zSwe$_x@QHre!kT{dp5b$N3TKhbd%^UIXtlr?vc+lPlgN+V?(RDcK(Eu&+$uKp>LX9
z4*C%tsC;h$tHMZUsn{aXEADNa_frlyc6?`d74`8qgqI9fb~K)Ea@lYFCBs!+6Qg9d
zpz^E`<%Oj-ncwTF8h2VvqUJwq@1kCa+YW@2f26wQObT+j)!@Qo^+I<)Wwqjdh_fJI
zUnd^Nb(!3__Ndk@nLLAcc17}C#=Te!wd&lLhPFj*McbgNZ|2ei#YElr_ZK%+@6M3^
zj*HF0ubs<LEzu~=8pcXN-WLxSNl;YZwgpAx_;!HozLr|7+@n)1#L(*Dj}=+`2`OSD
z!%m@h(O1@X^bQA>#ghg2yaJ1XGq~C^o3tN>r)^d7Zx*Gj$1qQb&4)y<wpwdyG5vr_
zdrY1psK`I$jX~2zmf*lOo1QdHs5=Rl7*n_-Ca#}bzwL_rW>Xt}TBX-k9ry6^{0?zM
zwpOo>o3IYwt#AG*GCs@RS1(<ju(V~QZ>H~b_HbT^T>^sTT2>Q&r|Gh~f`Pk*Aegk=
zYB1<m;1Cb)uJ#?_k^MD!MG#+KC)pQN`?8GiyUyUBI<xbs=VHiny}J`d6OD<1XTxjd
zn7?S?A5>>O-^`>;)<P0o`NMISXy)pN1JAW25_SUKA%A@L&+JO}_h{|UIp4vNHK8D9
zclFNP{%gVClIc0P+kE5o)$x+6xEg$0ArK6%BTBW=O!PsWmDBUYhu(HA%J;&|<RVw2
z;1=4BbzR|Apm^x~9(n3f=pP&R6D_{48X$g|oO9^u_ea#rttSk~sPny+tHa48TWn5n
z{MDj#f8kR=$Kf!fsW0|5uEZA;qgyo|tYWyJJSNGtAse?{s`puXILG#(rdpTM_%SKS
zi4wv&>snk-HOWd~Jt*D2vuk%`T^PYSeZ4a+26SHg6KkP+t8TE46UTrN!soJu=%?9h
zXXX0mb5cs<x-PYK%c-}C%MtErJE5#KoK8CzA3~f=<ZMS{>a1F8&HFCssFw5e_WGst
zcnSNY|8SM8DjzzAAU0|)eB!U_@afFUN_*Vc8`x)$9AWaz3h#g7ZKZ{fy|7AhPSyL{
zFTd+hWc$(1C<A`UD;<sBJMhv7)xw8k2kJYRiwM=#M-VQ@7wfp<*}Xft=}jikXbF-L
zAG2*)t6;LSaR08gJ)z3V$z=U6O|9)CHvWI^tUYlzbTYZl(8Q`4oZa^k-9&c*3)tS(
z{#y|f9)#->yf<A|Woa&sje<8p6|LTB#i8-H;_m+XbL#auX!XD+so_~8g&4QPQ<L}P
z){CY`OOM9By8V68hH=uTnqj;iGRgJVW!jC%^$`=b%?4}6ubDO;qq_*?d4&@v{(P0}
zklXqei3Mvv06HB)Kiy61s@Y8aBN_UMlo4NH`euc(;d5S^n;GK^9MxZfA3P&dSF^Jp
zi7JpgeoMOZ=>^7?{~xxSIVU^(S<C6~Gz8gn3XueW-;5@((tPaot<*}JcO6n-8O>){
zK{M)GrOj^%f>PqR=n6B^yOL4j|J}mW7}E{ETPFPX)YI?piKiJeU%y*EiZQSHyWJE4
z8arx^t96&{?CuWnCvVN0rq@meOooKlcuvBecBfq9Zvz`@Gc%{`-k-g8cC^4a@3?0p
zp;k!VW9$PjGB=30@a@>KV?WAl8fyi{DP(c}CD-B7amVSWfm`l%<Ngl&wYNDG<|t2Z
zD3jlbWjs0S(9Bf%t%!edqKUfxb$VmYK3c))?*4TwrNcec1uyw1JuKo!iEKIb`JR@#
z`j@>s@6V18H3qC-;gPkq9+zAfphOBR$B9=fFvSAKA#jsRRPPDecd5_VP+>lSH~PlR
z+Y1%A>+aYD910RY9ZFlVTi@TCjWNVkI(9zK$;k;45fQo8OkHdIhVMzo;fJg|n{xD1
zuM5o(FI;izG6q`*DI@|kW3nN~SQTC4F;3}VbUg#QrqJb%tgNg|1F!PV+Hzd226~G%
zHprG1b~>c@j$mBV0b6!S*{3J?U6Ye5hfRcSL!Q$P-i0P4B$$B9ty?xxmWz1nJSPwA
zgtVgF=95DIq-p-S)0QBBOLM>y-3r?LSnh6u$=O3!pY3hviYy&a*O4bNB|i9`m(Apb
zRDU2*Je@X4S1EUBhwJ*Ujg5MOA<&w^X(L7TK0MV&s)ihx<{6~>Z8}r8m#U^evvBVF
zPN0JxhmuAg5AF|0r*BP{R96phjJ&F};bj_~ALJCiWo75}tA|BfQ!YduSixg3j%JcQ
zaiN<x>YTiXd5L9BbA*QAM<Au0g)+<m*6WqW?{4QuNq9qT<5rGj&Vb4B!wz3xvA-WK
zu?lb~x?eKk3=E)l#2pzzvZ%??P0o;j*#C&C-vR3XfXmfSC6LMFxc&Wo?RZgr|AT4H
zP;=_n<FaT{yBJb=ErD)#u$2sfUk%)tsxmII`g2}LMK@U{METUa?WXx8E7fj)8>R%#
zGMIox^-kjO!9dTtVe2uJ#AvgCm{^_^X*Vk?tMAK8w$N)n3tc#og5qMSU7&8)*(BBU
z1HranhePfhOu|X2gkwxFY4h^TAF&s2Q%>{p=IxaDMYYxAGpHGpq37?!@X?eY4f|W2
zcJC*zgWlhPpFZ}w<INvuDzSzKx5GJTkM=gETY#LDpx@q{Fg|znNx9@`wR!?46ZfyT
zCxxO@uDe@`L7qqXi%UrX?mza4cmME{CdzY%UqdW6U37GGZZ!q(P{wyBL+xbqm4W0N
z_YsdXf^dddU+N1I0p}?#Az@kP*qOwlMqRD?0q40vNHz#P>7c^I#LXfUc+VL)<MMYh
z5s$0~#_z$bVh}PHrv+QjlUW%q>!t4#dzD__`dF#{@<<3bCIr~YLkQ5LLQ6|a^F#nc
z{@@xI?spou0>=H!bSAUd?!gizOf!y#e~agpGQa!HnM&DNAre~7=;-S19<5GDN_tb;
zxYpnwe!+iaaInyns&(Uhh6xyhmQ}4=(9X|~x0T#RD_gt_N&t4$4LRqf`y0GGa$>Em
zvlH5onfc%u(DfTT?{*O}&yn&ke$O)*yWOwz81E@EE1Osx8ym|5mZP|~@r{!|m`bRt
z^T(?BZ>G+7CYurZayrR1uEWjp^76Z**c#VK=#a(Z9dFCn#fnxwS00XVu`=_DtaxFY
zP}+`=J#WE{jn6Ee6}$TWUT;l04DT1>S@QMxLbiWJCd=3ayW9==vQH-VqGzL(476+b
z-T|FBR+aSLQIaLQuF$GoVc1}7(BpQQOLhJP?28N)XomCP$|$g^a07raFwjd0JzwQ^
zTds=v5^&j#?S4bGJQ$4<+6!>90SG+cuot=@ZL(9QarG5|j(UJ#F`sNOB(m#h<^13#
zZvVirQth0QzgrdJ%45>;jVtPPka$sAdv6d&p=|m5+?>W(ZBD=4VT%|O9KRavwOMMs
z8?2xj4>z&4PV?Cv52#b#{LaNTP+*KNMWfO0SXo(Tfv-Lzw(#1Gjh5IE`;X*W$}>Iv
zdeqj$60^Uhn<0GRCrNuyWofT^OM<*97D5|4E+H-5y&HePa5)or%2nqruZIsGTI`eJ
z4HTVU(tI#5T5!{LpKkxR>GBmd9S;MQodHcEzIUCrTvV2E)v4>l>-OB^S^L_;@YE+>
zEO`WgtRIu4t!x{Ecb20o$~-5Vn<hFEC09zU>$hG3GuDUEc82ZVj!)znA~R&0_hv-A
zM!BC6yX6T0x_)}J!$qW3DT~>goAWyaTpbk*j{X<b(#y;1O9Z#1;<$IGbh@ZP+TIs2
z<TLNk)yZ~SlNm5vWtX<ti+BRIW@Y%^bn10?m#wW%Sx)7-*xSLqh%5Mpk$WMIj*igt
zKEkKiH3j2FCVlKl`k!uS6wZr%>m@Tu#P{{}DMK2_9sVvR!&jUVekPb<25|h(^3s(8
za`!)~-cyT>aI4utc;H1s-J2%;yA7=jmDZ&Ytn)wuUf&r=b<Tfv<Q{IK)^BC#P1WOG
z)jIGcPu3Q_r<3>ksBx3%BYLTswO0!x><m7t&7oO{4HA{WF+pJGgKiL%3>217@ulYV
z-t)8y=kPzy^mzec<ni$Ecm(6q1Nh#{_O8=~sa5W{+IdLd#lgZw*%A%|CCtO)X3l07
z!uVu67=#>IDL{Fl8Obv^!iaM4fqEbOye*YNkbP08Rk+THp}n?Bjo0~YL<41Z7(Q8G
z7N7UzhD&X53rl%O4<f*e-E_6kgGtLek0zDUQt`Uy1w$v8(*AzIn`m2;`+l$-nq1B2
z7p-=^M6oMfY4<L~<yThAu{;9M$5t<hpEj2uWgrr10%$k~;y@koPW1Uvu#aZ6P)^$U
zfHAL0NMFh|S8R?ECbMIA7Q{o3dWNY41qHQaYsEB^Q$3sOpX{!U(`r4&gTFx6o8<T4
zx-voez_Zj^a?dCSZoDIfH^v*Pk}>OOVvi$9P@O+?e>2C-HfUZ2fFVV-x%lJlaQ_dA
ze#`vCt*u(Sp!vjIDS%E>YvYYn_X#xlg%*Fvc7tV&O9;TN<=NofwUB6I8^(TWCIv>0
zou9mB-w;MBvgy$mlqfO$Y6n;rjvEgM04Z1e)+a&&SMzHmOFuo*7e2KF9eEhu9gk94
zNBd~33UUL?#V92DB)X8_p`fZ^9dl{@sZXYYr2B%aRacoN=f#%UjP8wMgVhM=xv~<@
zEF0aNlAQG2buu2wZ&Kydy%R5H$k$-s8hxM?u(rCpTGe}ylb`>@1_FV+kCbUx+~2zj
zqlUJp;Q$P+Gh%)BP>Gf7TMfaG(7oyKm41aI7<@oangTuFwu$nF;a;zpS&sPD*o6{m
zhL@EO_GX8*`_$;<UfkJhE+#Yg{eKR39Av^0m2C!!)v+;8+vv{vd9s&eWO@)cyk@?P
zHwIuJ@Iup~+RehEQp=^pAD*EnHB|P|YWpN!52JS3K@!>^UD2vnjlS>IpJ&Lw1u)R&
z2|v!*P{nWO3mZQ=kV+!#^xT+@y6EAjn{(oihBD^z(3w;L0Vca=n=K#VzM$J>ez?bg
zo!A3~`R)MQfK>silLDl%tmHZ*_5QERw*azP0bkjpH0@4MF+eLGUY)b@7;?80L_%D=
zpaR&&vog*j^Y@zhK_qsSfjkD3<I1p2z&M_?GFW6LOBg8VUjRLA;%%e@;^7U9wOvwp
zX(mz@7C7_$U6p-Zo$Ig%?lz~&_QPiiz*gARNF(;QMV981?G9EvL#a4^g1e@sW)AQ&
za>|vS8}@<VLda(zKv6wL%BOSo>N^03EoD8<V?7C6?-8};ISU)CANb!nPNmgyvFjdV
zfr}q5+gtu|?z&q{sQ2xUcM-z#3k$i|6gxc01}|>#Gr^Pf)MAS}hKBO6J-P>2N+$N2
zn!7aN*|N(}F_{3+Zw)9lxPXRz&1?I~1Gwxi$N3viyRtMR4P>RILpt~z*XrnX+^0{w
z+19#^zHRVZIpQ6)lr{<MoRA^s*x}v;-G%w~p&+5E#I7sJyUT+sRAR4MsD|*V_NC#n
z$q_)ko~E0T)omr)(o85XV$=@ys-NB%)2H}-UnqN7c2?d49rz>a4CnXe&@@!P9m<#!
zv(5?P60aIr1eXE)1m?f*$`BH-jqk5IfLRmyz`az^PTeUSkv!6cznVj+C7aY-?2%lf
zf5MUg6OhIY4S0&YXLGwd5(LP^VqmzXfB-Elg9x-lULGu>D;{zLyycoxZ$`-4W-CAa
zJMv&#Y?urX)!C}j0iu&#@#4~C%$pPE4x}KG_luuobR7Ua{+tGgUvFA;pSA>on;r#;
z*5V7AD+eacTkSULZLq3_r~vV<lK56AI#Bl1yH~je=?%jw&(anBg2hQM6G|+qoG`vC
zLxdm*r8o4z=TW7fj%n;Xo0lq+uq*<w&cu=u!<zKu{hA&18rM;`mEAo50|1}M<<n^m
zQ!kD;Cw6U1Ov0#yGGlu(f$pvXCG8vD{bD0dBjNz$zkkWb3A+hDa^kFSCtxqo7U0O|
zu6a&8JE-barQXlls-mf}??Y;2%RTWiCkhwvc+b$qkKqSrLRT>HzLZGc<>_78bRK5o
zlrOAZLP|=?b+VZTg<lP!__o$w8@&D}Q(wzbTLGp<e}Y5;3b?T7{)5X8K8Ja-{qfg^
zCxmW`frT7wFZG|(|M2%T3eZn_6v$^bMk?&fmOw^k%1~dxrn5a}T3SYiv~!@J_e{;B
zVJY`D$g@V8LE>S|$nLVByK7}TYvWL|#pCoH8;~XVx31Ht!}<9HAzByw2GAx8(P?U-
zuqzNt5ZB(-*VkWg2zd_Z<|=jdmXYs4TcV`dP+Ya>^%xnOhG#QLp}?%wptJAgtAWuZ
zwGWA#sweS(Co{#OO(@SbB&@8EF2Cv7OPArw(V=xi!ZR?wA*H4aB$A)~^)1IZZQ;kQ
z(HAg1mzRJqq(A-2{+!S1Pkg}YNCk1B3*ob!hlFAMS|Yg9j~zcQ56}e4DdO{N^Z<GI
z0M*A^?T+(bm}!Y@)p3#;1yR(LU(t6F0l<jhvsRCvpi@@L?7Wr>@`4S_{?rJa&f|kk
z^1~>{W!3S#>ovo+MLxYMyS}y!zRpx+=mB{On3`Ed-6&XN-JOCpa5i>lVHU870(o6*
za<Z8e1OS@0^TP~=(f`4xyjP^)v!F~R;XLUIAb&A|gZ~Hno#`X>L!hhu2YDicTeK&A
zsP-OCwf&6_fPn^d5H-#jrYr8mu&{BQHwKb`fB@X&HP;qb@@RJw9pn=yJXm5i>~%FP
zl@TJD+V|}(EG;eXT3V)<F<$rYC%LM8t%&5k(QP$c{_ys2+$lD`z$t%-gp5onLZ`{-
zqyt$!jgzUJh~vCb;N~7u_)pj>wCQxR!l=~{0WO!;{S|)eW7DBD5SOe#l^}ou!Heb-
zae&PSk$QnMrNpgDZ~rIZxw*N03Lks;w>XXqif-vw9`~T`msoboNJ@4#ZG1gmX0Y9S
zA`7$eAF?E85+knxqb*el-VS^Q6iCC+52)XCb#w|xTe$<=qSdh`xSLGcydV);qE$bw
z(7B!`pE$MV&b}VDWdgWsnLO~Qp8!N$$=$Pa@rDQF5BAZBiU(g_Ot=meI}@sVL3R_`
z^ga);DromkLbO9iGODstt~K;(00BEs;d}z5S{t(Qh0PG`K^R@?&l^T6_&8P70nh#f
zz~zG4qwjCC^MKrEvtv|)jo=Xx5w$vfKi;3afH@Ih4EP0M$ZH>P^~n&K#Om60481u8
zS9{{zb(2KY*RNl@VOu@E3>jz2oq?_28Z3^RNRQ?ZnUO3N7XJYIoJ6Q?M&p9o?X37s
z8k4$fGyEsgCl4l*;<;8oB?|Pc1MGK+I&)>glDaED`Hc8N+&C9KS?6ij8W&PgNmGM3
zR=eP2DgA8DWj+OPIeI_x$S+>>R)?fv^^;#S-$7>%HUM*}8U%@|st>MCnR>h|7)3nL
zuEDhV!r)h-q*WGC{gmy;5X$&EY`3fv(8X^c8FDW%EgJHG4)`+Cw$y8`_a{xZSi`n7
z!gv$!k2m<$X&Y_Ut(fk+f2GIA8=0FgN*axUn8+<@R;oM=B95AQ-!shoaG|Mdr10x+
z4_jHe8H_&#o`6JR273jMEhQj<gIF6G84<$yVPI`h<J0M{uR43p%50meL+J-A)ZKNA
zZQs~v)nopjAi`E0=apGy=(OO1qVEV6kiTnyf)=XMw)<ywb#;*jr0d<l+=>yG?cXFx
zS$Ly`?fAb>1mV-~!&UvT*MT7j2^To`=h}VBiK&<FPWJ7L;Rcu^@e(LVI#u3Nbuo1P
z**WtJe>yxOlfo<xbq~qK2DMk;E2w2a|59~LHF3VxlWv!>1Htd}@bco*V3ehA;L^u~
zw--0{`g8R${J`6<C0l#9zwJp^vP*pOrBR?PF~kOtoLcR^7^MK`<zcnM@W-~cwqEz{
z-Rlr~^m6eyph6^F5c%kU1{F7Rs_sqk---ImD;?K7KWY~^DD;*jHhPeS-Xgm6B7+tX
z_u;<VzqBw1lk|h#Yk48fpl0DzFd)mlM7qXAGCT}Q1uFX+-+Yz_3aV(W{Gr8XHDC$~
z3Jr5`U2D7&(fcdASTSrq(X1N8>~H%?7+~)k)OF0HDcrL9$+US4C^5*#qCg4H;oYS7
zev%#Vu4|xQ1ym$`AOCiu3A(;BNtCV|^Y$-v%mDOCjhBb#pc+tFMF56D_rf_Y)B=W{
zD7p4&Ho6a_cEcCs9NN6-d#!2{hN5LaX)<)`B)u65comzC-=Jx0a7i^`0XU8db)^gg
zs|TqubZnuXk&82I-5R2ZPxD!5$tyC-(Wwx2XA~2v^@Ebuh2IjHO^$#nL-15VUWQ_!
z)cccW6~6}!u+=Wjr9ptgY*mY;=+zM6b9p74g*L?gp}wAEH4H5@2(r8H4?_Oe_vhrV
ztAXl)TDocorE~h}jjajjdN&26_>CF9eGs!^kj=u%wgFY5HXP8XWp*_#gVKO|<exLi
zuxa$CRp#fnd3}DJoJo1X#{X<1lmQ-8z>Tj#_cvQP$wVNtoPOAEHD)Vg`Rfh@7x&@L
zy^)cTL=ka$`QgpT4<Mh(nE)mbt)B-oc(Lrtw2J)j5;zzzk0n4PRZ{>#Ulrh+ZH+;O
zbcpBeS<?3VbY*QsAb_DdQfFpnnkLM+#0-=_f&L`Z2%e!z!2m#O<lV8`E6I|ePS6FN
zc_)?vsG|3G2KUhE11R8hBayg}>RFqN;!_Fkg5<!|6D9#!2@qZaMJY1ovi*<hOXA|}
zkOk5=P&Qec5z$TZ4Uhm;*@?&7E5qvpz>BVpfb^-17$<GljCSeGQ0c{1*lVTVy<{>3
z_~*Jvfya15;}O6?I3Z^lJ^^TvvMWdrudUWM-Y+)4w^xUu5f&Mdw60=)02C63Gu2=6
zC7}Qk1e$}iFjW3=Pg;t90~lZ$FiBn;D4~YHpnDj_!J6vo>Z!%-nC4mF0|D6}r@!m$
z?EIu7LHs?a76nj2{&(DUuyAM4Z`fux=vf1gwB<7q2yoAp15lGtihid1W^yb2uop<&
zG6B0u{Sm{@udVjC*Ua}(Q2Dc1tX}pL1Z5%<hoX{RlmB?gesvA?krF0wb7mP7X1#Bm
z&)5fP#n3KPPkj?d7xM<Cjk=1M{^s2Hl9)*%l<_l&PTTG6=<S_U1rF3m_|*nP49fxm
ze==mVg;(s4zy8iFG9$xExiPH`vVW;Q+pEl^Jb%X}*28z>>6AQd`J{>6Pe%Eak;a~b
zZ|`F0Az?Tej2h{?@-Hcz5yOB|G7xo15-LWOl0SSxe(Yz@nxVYgNGy1jlYsB^jJFTS
znyT+G(1F29e&*lHhJ(^gk442J&AiRoR{16XA-m?9^(d4un!)&z0eCK}?2gT*7+yLp
z0jT>(J}8TML`~i|GCBoN;+`4gEcmm}WSLik47k-gBOn!HfjxX2wuKTV%bG~o5#KoA
zFm#57c9ApvAs{sF@W@z?_y8@H&f5aV-~B#OxHn*I*hB=*KL`V4PzKohkM+l0A2z9b
zS6k2fbkS$*fB}!8LD)5=19E;PkiiXrVT?`|C_|rr3<NZfQUyxYL+di1re3hgf;W{~
zZbm6(XV7f@X@lc-ifd^QgFx{!m?j4}QHbK{W16=<-n9VL%A_)@y0z=WES&kz<d-r4
z0n#1d6qZbN4-x$*7;7{x<duw@;ewzaGb6l}%0W!lwXi@u;?>Z3jU+0C9_(k`%f4eH
z3t<F)g0xkwRBat7h`8<oyAFV^Pc-qTUv=(7_9_#*=7$J#&`qBbluz-|J9k5#3%}=6
zp^$<1rPTtc^9RQ1`V*AOPE_Ffmt<e$ha-2xCl9{9M>607u)e)+ptXf!P=D-S{FNOZ
z0}hN)2AcU){P5cwSCGNY_QMMvSN?+^f3Ybpzx!`3z?b8P=iZ$22(*RsL02pIHgPhb
z&Q}I5r8<`tDHB``$j$osr=OW8=N1;${&;;W4&)FABgGa~tTgIw=(G8b#N;vHN6~dh
z)VA7$!q>1hMV5ei=K;9;B3^R(f(H5Z>(`E;O4?8W&PfMH4-NZc0ZF3l0BCb;13I1X
z(b)xPqZ-n$FaKV7;e)*s<ASa}-roM<-==LqsrO(j!PtPj4g$ado<U8s_k6)u%5g@m
z!5hGrK~;xjXJ;o4_EVirnlOo>&&r}Vz6$4Ufn<*I17OCDe83tEuepspweav*>q$#X
z>vI__r2PcUDTo)8Aif>vk=n~P#u9s!)@K2p*!`-oZ%uX`$gfa6eJqiio4aJ?S9a?a
z#xS<P>`{Pq7s@!#AXUsTL;1IaOiC<wTD65wNk~c_*Ak3NXx1QFH7EP_u!Ri-?y$6X
zI9VZ={B5^}hw^ZxWM|ji7JnEZ$%De;=Q-9Dr(bcwbZW-7X6op&vY=ATz%<}c6DEM1
z4})S@UfHLah^7}%fRST2z%&S(u=CGM^}6peaPA`IQf`Mke*$}<kpN;=fMk^)()V6|
zA`rGu0_kDqa-0h_-XL`PyCJN-7SEVCc><2*94fP;6B$wfh4vZ1Qe_#C@(qyn<`Pg`
zfSz~@F}oXbB8+DIdass`nyfACU5%>7Un+jfp}3f71>!`&99%?K2H<=*Nq`6_$xF`z
z5La<h)}Nl<1+^X)8<Bv}VaALbh|&6uUb8<=oC760v(Zw{unx&_SXgcI{?DQHCC<>j
zZ{mian9R+{^<O311}_b|4YM5?UTXfRtj0C6D9f~JzYu=wBBkjns68J2*%l{+Oh6Tt
zH7n+jr%wfu=aW}LK=s94(SP+udgv5W=8R4re`bl6Xm`3&-E9Mdo;9GzW(1*uiJG!S
z(R+}MVPg8un4ww(QIfXMCKU6GT2eQ$-1v;VRj59%<h%G&*T=^v02GOK>NB?vGaZ&q
z2fgJ(02+GAvMJav9JrRA$3Xr)NbL&Cs%yu}mFMpd4l(^1Q*Rv>7P8eY76S>EYEuU0
z7Gf4lW@hS$nf!e`9Hge=($Y<T<93)xj`N#{&v!zZnclV@{Xb`6%^gmhsgsrc{X_$>
z^gf}y%`)Y$FW@f4-!1X9P+*)_oHi;K<uY2zSey@9rH3(q;2wXGe}4Wiyfd>uZalPS
zTG}-!BrFb}Z}{&J>#MQ8&8SUylD0$~`!Y5L+|L&4n~qA7Q@8tfmzz8_vD;asKNduZ
z<}wAwJj?&}EOuKfhi~M4sIthFM3#Sd7Pw7$(tDF21|B2OVAChKQZo*hNi?zRy9}2}
z-fkvwY&8q!y8XLvSX90Q7rKU}8n3k4f5iz(|KzSOLNkLN2`ZB)>-cwOnknnR@Ym<n
zCowdSsL`^DZ=~tVI9<m>t^b|F`>OuLb1SQtA82<J+&Ce@LQcrY7bT+qY!2&qQ*Yfl
zc?aTGj0Cp@O)9cOB&uKsmQF5j0xt-MJfSH=`ohANKeSzgd&DJh-L!oO34+TcT~!F%
z?=Y4}5Ur(g-0U|CP$%Jh9sZi&oFx00j(vW#?4CM!%0y9>%}JoIt^37``8W;cosl5_
zbdz_Z&8qpwM5hf1O(RwfG2vItLos*6_hx$*?C+Q7G1MncQ6Hrv|8CRDJ(31~a@LQQ
zeuh1$K@AP{4hp;bjF#zi^`=^r$RZ2)VCNx8?r$PAn=esQ7o?ZGO|v{HsoP3gZ0UoN
zxWNr~;^WSp(ez!%#<So=<<Cf#{DTFiu>_Yr5<Ycum{=B)NN@pLjx$ee?KWpIPs9s%
zKOJ4)`+&sPuW~6J3e-*6(HSI#ZHcpALPoW>cN>2y*O|E>ng3KBR$BxP4Et-aXrb^<
z4cg7@TOHckiNeB^DaigvLloW8b4BUCk!g6yG2!=-WZI-mP)iSM<JIoqf0rEO&mG-J
z!qhqP=*Oc(;i}04#zriV9g%d=B`!#~1vNoC_=`x{!euoKkFVyxhnUFd{knVLue&Vd
z{Bei+1#Y`DLO)gC+Yfc>>ZR*q7z1H6i7SWDhze~ZF$6ilHE=bc00rU0@oF!-($F_P
z6#jSl;-ec*J=T!-!Zw!*kSH#P9{M4OEF{>dkCT1tfhFVFA&iC*{G4im6H$2xrc-}6
z0qK7pEW-Hl|FBZxpPiW`#Q*0HD*1oA2;+b4)Km1)M&jR{J(UxaNQ1|MM2$m-4n2hb
zeLhxQxl1rpKtS%;i%V1Lns@L1;^%A07BrY*W@Ss$ym|UhjU(!D7mpr|`^--K^OV4=
zix++R`qyKce0FsDjs3vd4YR=zyW!?#3)iNVlDx*kzC^&HWSAag9scKuEL<Z0JdWkV
zCFbAHV)?AW{?8&4Q60w3%}h*x-#hdh*uvhO`e$v4UIMtK3$*{QPr>OHZl+~=E@}Mt
z{R~g1PHvQzautQ&S2I>ARCE@zzkc7aw5LR-Os4d@nP=fzg`)d!fH8i$QF+c>E8`w*
z!k|a3DR>&((--{M4}amud6V(b14`;L`{2-P-`|UD^xklzi)O{JL7%L4LW|gXSJYNh
zzl~o|DWwd?QGTCIRPS?Rpq8oZ+4D#nl@dz9vN6#o)HK~v#TmBWvka1b*Aeq^R50#A
zw4F=s_;1WCk7f_#UBBo~haNojBRV}_=QqhST`!3)vQ_$vXw&_5vV=JNIK{np<M|gf
zCkI24=EH~4k-M?#w1MTqFPphel0iitS3;@PPMoH=UrAp^F5tm~C776oE^=B}p>dqc
zSNy$c-;~Z{Lgjr<tYc_0TJP60oviLeZL&P)y7PgBX{d;+&^T%QyLL~hjFmr^)cCu>
z_D+vS!pI^SJAW>w&lI648}th2vMxAJ#D(a9T{XpT6~q4rd+#0ARND28;@HNqFr$wm
zA~5sN2Bk<51O$e$A_z(gHK7P7RlpDkgrKOXGz~~^DpC?46d^!J0-`XXMTHO`B%rh*
zkc1W@2_fGm&hwmkuXDcZyx$++`<`>Iv;WNA`@ZjeueE;bx60n3&!#$Y-c!ZLqWeST
zqRph7=ZUXJ8m}g5;-5W>t!}whKW^s@l)>wR$p<_lm6X=+)U$^hweUYlc_MyV)MA#h
z7NZF-MjJbL`%R3PVZHsG)idIK1z<+i$oBCy8oAF&5fOa9S<2J2ISe{%k$C)*ml`By
zgs_m+Lt1=~mOCv}nEUkFphLI7RUZD~MPlS|gRN_%w$OLe2$9Zu%Icnu+%8pV_#N}T
z@H{jz8sW`*E?=#P?^x>ZTnJj|S|c?O)@UoVqu^_zJNQk#@1*cygI7ZagNuYcJt01%
z{LKX0p!w$@cJ$BlZ$BEEHqbW&^;wM0xM;o-vLrYUwsv&CRQ&surw#98D8-x10(TPS
zK$kSL&t3ZXAQ#x1EeXV*wgA*Ye5M>?#|XF`yTYQ(@)Y~Vi8TdWY)tpYCWZj7+_eQ*
z@wW~FTVjIN?wwacr|HN1)~$v~19j=Ur3jN*==E%h0|m{|N1E1;BwS7{q(pe}h8*V<
zwx&4aIg$WdYIKq=@4p#S95>m{)1<ya_h8f<khBHMw-kC`ep<-dqNLN$Q@;Kl44Uu4
zs1vl=^cM{UK|`oLm?B2sS<q>EBEL_GFCiD!PXR5TJw%nqOrj5=LmeP=a?>~leBHFV
z#SMCc_mRb>&gWIFN_6bUMXmM+D>P2*3r6V2t@TKGCP}b0zT&^w{3W>m-ws0m`BfzG
zlUa#qpTFR$rty0K1fr?q4&&uuFhd>5-P+5x?{}nN=08$}Xl;-!*2XnN^3HHu#-6CG
z$r2VHgLMFa%rV&A@0{f7)$gM~G!G1N`16<ugT!i;$2GSw0}LgCvQCeOK?}g9FO7);
zGOM+{;S~l_)nBz)5BG%r8DJ>EOzW=po)FK1mKt~%55-EQQcmxU!>-9w%|Ur+87HIz
zBH($<apUHZE0a2LKi?kRsM1si=FLXE%GK;NjvYx!_O$0|_bu=7{Ia99r=bdt<o&^F
z@{{%wK4%%P%qKcf@=5*ZxGJ~@bmRno{VnUd5+!>6*(`v9@fYBcyuASCoY$_1^B%b>
zCA4jenP{pRxxz57=6ui?M66B-Zc%$?c~%%1NB?TOQ0<^2C(>$JYc^C(H_j8_gEdml
z73pYB)R&Kpsq#Qgd=?_I-xqX80MbIYtkuPA>-=THbTJ6Ci4nB3)cc(J&B|Ihp1tWa
z*!`Hnfw?Ul_@FUCxKpwYjO0l@yLPc`^{c(DOQf7|p{QTPqHfc!xfu1w=v}rPwa9@M
z*ofv&;}s=nT&eaQ^VYBvq6Tga4dVGwWim$rQV6iIJKr(qn#%<PtX(}JlF#BfYi;A+
z33dZ;2{_uq|K&JY6TXHu2gY8F)+|U^Ykb3{E!1dU_-05A<RkmHZjk!#alM1i+qmP7
zFa@COxpl00R{pm3nHq!9b~JY^IQ8ipD_D3td@{tg8tjayf(P>o_C_g6F4hNsh1aua
z=$>7(3U&qH0*EQWIIM&(QOMsWceeV2Nbd4(j57HA#OPxx7X+!=Y;K`H;(p@`jAJG@
z8SWxC=(r#K^x0P~H8Bmlow)f$F@8|?vvKpS8QNNE|9JieQUShJV8>{NuTP9FQMm?m
z;^(VWQ%uU<1<6dFeJ&|g2OFKZSOt4|d(#(!-U)#Sg9AaZg^nKnhv5>(o!v@h?&W`B
zVbOU8d*O+x6@?%~1snuT3+M<ikig<86^#6%!l$*vcw}>47_4gZmEbmX@&QaT$0-ZU
z;l;wyASg$rZ*7dwH8G0E2ptn0ap=w1+iWMQ8fL!QCQq^{1GRs|^IZXuPID*<X(Vx8
ztORB`l3YDVEp9w4)sgB#J%o{S^6&2o$xr*Xe$Rz?*f}VjWt&$ugt$=e*aR{m2nt!l
zEF-E+qz+{kvs*e<(Kx(FGJHC0@$$-XT2KQ=`?TA0lRFo9ctB%kD?R%u(ya6~j08gB
zPv5MJ=WqB)Uz3A})>j>7x(1IX8sXTX_L4CIM*yKys{<Nc`Dyr>AI1>Af!}n|<LJo=
z1d=z#(qz*E7#`8=M-dXlt5o;d*$R`tC2>hoYo9E7LL>vlBlL!TV5U&*XL%2(5cV`8
zt;!~shI9^As3>Zf7-nSzoP&aSDbtM-lQ@2})3sg5rgA9|8l)XjwvQuBeFo2|+fVI4
z+ZyieHU}euz{pB?+p??V`UCpFtkTB@L*3gEp$dc23Kdz~`@E=mGguk6-&iN>G}YXp
zj58(BL>ub6$p;5Y%*gXi@gg`uV1KfSTb}^0GAn1BV1yKi;z(c9c%?#JM{_+|jO%Fb
z0#S}tN%qRKuJebeP@QsZk9I_sG;81@Yx+06F{jVwkvcPQDrJ8v#9iKnfo_39&({ZQ
zRIiP(-u)CKv3J$2O>VLvKzH(k<p#S_2Yc?$6eb8(@JCnvv=9kcY8b#Uw6JldaO%WZ
z$E7(5oMczle#czd&GP~l_|yXN@iO7~%@DJ4?eX{7T-iZVQ(kkSX*mg5WFk@9q5BD!
za%WAcN;AAZv@~}bVW==DHnP*+o$K|8@FK9<l{m`Y%~ONv(foJu<oRV4hg;6^I#O1)
z-^KThg9Gn3AEiBH%~7vrG5)yB1c`Wcc~VDM0_s3+u&+Ke4`Uw4B_#qAm3Quq5;w57
zaVe)`ejH{-%@oJ=Y%R>pgAoZXaz^;7`5aIj%!|~cWm+IM;?^1tnnO&(r;I{tXMd@z
zb92&mjy$9!SOO&S9>7B9?4mz&V}@-RI45%n=EdKVJvkxjV%g5{Cea`Vab02z5hJK=
zU+TF8Z1xH^SUQRe5wUGLTgwETXv9tEwAReA>Rp?ker2!={GCFv2C1iL(hmG;Gu^lD
z$@8v`i3L%=(~y7i0Y4yG9wEhDaKpB+iv1wAZKKdC^dZt@syr-j&J4r*6w%LaH59w8
zvw<(4c`7C!UW_9M8dyk5+W0k7tboA^G)XpnF@m+<?4B6oI?kqS$?$YAq+;V9tAVOw
zlDv*-+}(;pn>8ghKg57R5${);sa6+r=T-Z@WA-Wab^UR`Xf3;17%$Yqr-;JWPJ$Uj
zKqFP8T-ynwnKy3M0+O3d?BY4l?kyZWX)ttdDB#E<G4%GvUMdKq?GzexL{LcASd@Q=
zSG>uWK9bimfT2+4)6qD`OWad4&-*q*0*-taI!rz*m%DQY6hnPvDp89=`hIQrik2AO
ztVup8gl9_Kj-F@VW^1GIjY5QfS%@32xP7T0FBrSH%4%9~zS^gd8wrM{vA7ufU3_;u
z+@)SqA>ha^EY&ngLQe_6cy3Tf-aFPC)&uHm6WNHljZsaEVlYkESFPe7*|(b_?;?tW
zS^(@2m-hfiVrncPqUvk-%YI16EXe!}31(k^Ep|RYU!vp3n>t4mV3!nw8-M4`^}X%&
znVHoNn0>*8FB<kaF^P?Aq@v@v0g~PO+2nW;;2+p--<;3<@bJkf*vuzCw}Y`2JUI@S
zJ&sAo_^<X!7tsqS`fk*iN!Fpge@f!e?BWYm6TY<SptYYH#<3xGjOvld!-iGWr%9Wy
z{a$d?FnVaL_G<~=O~k8CcoYUx%Vs1YZTw-k6wR%C3cxAifk6l%(0Y~tv6ApMa+V@9
zAbEneQN74Ep$ApbkZv0Z`Tko7y!I*EO@G6k*SKan_6@mr0)Q5?!ouzDngx--A`mpJ
ze)kgzVxD>ln0XNpDpYZ<dz}6>CYf&GowJ+%81kC<=3B{IH1QAQPEHU$7jM4193hcw
zjU0e@kmG}H7jqA7-4V~&0%S(9SwM-AWZ2;pVA!nvte9kdHOY{89j7)SgbL}O&vLqy
ztpDx%srXY)ZEn007L^J`OFQ9Y!@i+)<ZZxpw1%6}o~=x}IruxtIB+pqjGkx(13QVg
zl?r46e&HPETx9s>SJtKyL0U4!uFkQSlF8#8^#QbhbtzZkFXIXNPHi#Mmscbg)no;I
zix2*f{J^omq=)haPNpwg{GoX)*Oiq^`#Z;KjPHPv=+VYvu;jL(lG^ReLl_c<gOl+A
z-kb;mn>WEle-I=mD{uLPe7qfA|GcT=HvcUnSuA7)t_UtrAN~|0S&#DyfZPD|-;EjD
z_}WLsIdp;YBEu*%EZPijKgJ|&P@*VpEx>NXWHs)7<L@9{Nqi&2Cxl%yzf<zp0v-Sx
z+9k~@b8=f-CPwVyF)p&c$#%;Z5@^L{K8b<!$cXXj1VN+{@0*mg032c7yauUFi#Trf
z`!_B$-wEuUN&<ZH2aZY5wIWlyZ=#rg3;Z0ji}%>Xo!*is4ACvORjiQkpLnPj;65}H
z1ls^lKu!?s|NH2~@N4EI<AB?(n8U4Qj^?IEmA<8AobLk5#X695uxS`GXGXY;k94(y
zM}g59Almry>|1WmSq9h^OyR&9;!dbjfg_^X*!$E>74<N+hrW5L5eGt^^8RbdZ?m^?
zXINlr(ymz>rdKawRAIr1(9{BH@O@`i8gi~aexsc#4U(=1`Pig*a!Vwh`;VIFk2#oD
z88^T~pzSuD?n4^G1YchIN=$b2Z%`ZFV^$HGR0(!NRE`Avc}c5EZ|km!_=v@OGom2g
z!utH8pKw`WFsO*ZYou}VFz04?Fu<3uV2Vb7-q>8zUJj9(=19NzFJF0KXX`nT!)(=C
zq0u!49&k|A8=ka%FhfUej2W2G3J-$*xiOlKhB+YhLL~I7ZE%<dT|a;Y8=r{_gvRoe
zs4HjyhrSa)6LhpI4!Bi)i^sPE;nF(CUS0=F!4k18cM^57K+>50LPbRv2EwuB8~t$h
z3I2Fn7|`+kO6=|XMx`@e2?Br2NtjMZ1h1E+IMXE^+&C%`rE?ju<Ky%~ehx#<=l(nU
z75N0X?Oir~H&4DZ)Hh4vqlUYJ&wD<uf4uP2TlX4LT<b{WuYv8fJoZNfr%ui&{<y0M
z3T4&F$&&GCe+~Q|XJ6aUHH~`>6PUh6rYnU~>M3fr;?esWU+1-4rHXH@{TS@5of@#V
zQ8d4mj%lqGJU2~Vf56)9^dgJ8IL%t3?(7M<|K2Snv9dPC@Yo<^q+|HA%=E2ET%FZ8
z{dB^gI_~~c>z<H}pg^?^%m|HDq=kRH{SR@uLc@aU#XDtw<4?h@3l}{Pi(+}2TVWf&
zFCp9oqCguRb>(y&T&yF;`tw9vCtx=z+ql|JU6sFUW2V8>z)Z%DBtrm_n$|*~v!AKK
z7&WI$6pF|O+Ibma{c$>YJOS3D&>2!dIuOt(%?((uWl3XFlzMr3)K{4Li`jeEXcmsR
z*r5TF)%%lKj2<%+J{8Li<cfY90U<8!c9aiGn1|MvnMH3UiloKM5p?*p<Frq&AmSRe
zDsIl-<m;cWGJ)HK8z&(HI~6ystsnCf8I`kSZCf43k>jC^f#K~#CQKNZw7Q|!O*=JD
zv2cW)`TS=gOgLAaG&0o4;Wz39a)}S6(SouVW{c7R1*Pv?QfKqmo0+2Lp&8<ksx64d
zx_|y!ge`=aHjH2|iJf>20FBKZ!0hguPw875Wjx;>EdJ7Z^VwClX9Vx=vzg_<$H#A{
zwtn1ivXK#}jZZ;D{!tq^x4U<e9`*IJJfc2N10WA|UXyo7&G&VVM^DN{{<+kpDY;JP
z5;ywCN-x8WOeOqeu#Njxz?uXh290R8*~v%z(Mc5DpW7^k(#^&q@P|@a<$HFwb}6!C
z)@XUYh<ApLwwMqw0}F>hlOh#Es(0epd7!yMz4EkMjcGI0`}jti3(i+1SUT{g_cGG0
zem4x!G@_6#jRn`fi*xEoT)QnQ?R(4dklj46UN|E`zo8~y?)#-B3(=$3(6EWGL{oQh
zqlKvdf`R1h@voT#^{!G25yNWUyEg76LHZMa^~NedZMmbk$SpL8Z}1-3#=UJjqLelC
z-kWgeUGpXe{-t$u@X?B7ghOu@;`v$$g5=jw9eHunUs3Y*E?xcv;X03>FbUGGkT|Az
z%VU8%x%TLk7@d%F(BGk47C2!A<cK``cj(=VpNVTZS^K)hmdE=yK4vyw=y*U41(i8)
zUH`(u^exv0&|*@JVKrK-T&ub<Wpz`sGeZRc<l3g_e{+U0w{qO{w;n^PM)7}|GEd<>
zZNNLoUCa#xG3zIQS|M<#`T}S#PyI!yui<3Ufv4Yod;hP{|1>cBC4Dz9VF8sQ-A}5A
zBY5ipZoDdh*u_Y9o<*8TSV{b0z$b~ySi7hYfWUf~q6KelIyXb043=--J!Y8$(i!3R
zZ@<>#Vww4`UVz%hn`bJGUm3RuZr4vl`hVt7i4F{$;5X=b-Z=Hy-yM$`+Bghs-aHDp
z$)KFHDwk6m=l*_rv#ofOdw7ih*+L}x`|nJjYmkO}LIY_Q{_k$rB-WXJW$lw`>@`K*
zze{B<L0|@WJs?;r`1>fEjN;86)_p?L>@dq05Onh2Jh<=)Ya<%r&Z}Tquw&^D5-{9?
zG^4OM&OKJ>rGI(sGf|XtYu%1i4@`aTgO)B376W7js?5N_n2_s?*MYlw+|YQ(|1hds
z{o!Uq4D%WzAdXYaGMU8|tl!|pum8nA+5o!d&R$>{PzibL9y|~QH98<64BW^Q@Mo<p
zp|+VFjo8VhHadnDk>bjY>x^4QuX(t8=qi3o0mV6=Z!>vQ<x(6&zBew>pSBjgwZCLu
zi2}H%X%+lDl;Rh&3=WLjEN{=2b4u)=xAmoe3&#)3noOA1j&xA}7uZps!duDBZQx@k
zCm0`sc5=d2CJjO3cJh$m(f6i#7)Tz?u#)p1jKpJ9;X~nd(;C4i@EE_-LiFL^mICm2
z{!)UVf|^Na<>p%;aL?c_R&h<EXRzYfoRqCCklI}c*hkEMpbF8{7;MP*Fe?u=w9^hG
z7da*N27W6d-QK_DNncqY-kbG@ef=IGxvx^r2jJc%>}~8$AWuP;OWe<@-wFpKy5q)7
z_Dx{jf*GF$S$rP}clG?T<zekjZ-R@4PbYNApCiA>+s7J^TyI0ixeO~p#>mb4m3k$V
z&w3CLT&wRjbI+Z8)97m?uYVgwt<xg04*sz<E{=!xqZ<rXgBA;&M5~^?uF_G4ju6++
zrsKxtlwdyQ#g>7_L;V1wJxL99pj4*<e9N>%@TV4YwgW?w&yTqXUD^tfiB2A6BL@3P
z_Jk&abO1I$xOTwsDCzXpBYJjsj=p@sG728Q#P_4R^sRj{{TCC<Gfl1n^6ZMq!(Df0
z23aawv=&LBTqSFt-uoJtve*KruI&e55mND%U+=P(JJ$;XN3eZV+Y}*f`|j5CJktc<
z5eU+Uz|5Z|N?HHK<ATT0FZ{%9`+<O}J*3a6o{1gR^;X=S3)BG0ISz;yEmwJSETkdx
z2i66=KoR)Ukt4HA>gTOWkX#<3NP3<ejGouVKmLh<4b$5$b>zlEe3(7&4QnSg9j%B>
z9rx<>r9uq$-ZM8?7Mec=f}Kg0*nxBkDOBoftoyonP&g__(m|$t-r480RA~AsR!S<c
zyTTUkKE513=l2SN6w4Oy@+go<xf*?#`T8(8hT;bJLQyyy83l9GT-uhMd_knosqb5x
zG=0(H1b`ZxszZH7wQVH6WnhBQakVXuJTg!}%b{`|6E$lk{Mz##pciR3kBliarw0N=
z(Ge2FHoF}Bx*r`f5~LE^r=ZJkW|?GBf83JCDO138%Q$8ALKX@%siCLc-V4X6&|K3J
zj`nEuH3`B3n!2+!aQ&BCNo&ol15|r-74=YVRb=A})2#t&+<bOEDv}!nkUhe5#8nhl
zuU1XOkAUBYhODi6ZCw#x09YMjve?Y8#EAAv+YyFUpfBA0)(?RP;w2$JLBLe%YX%@`
zvwEbkb=>4zXzAY3uGF_uM4iX%cGRxgtZKPg(oDl(L7}cdfetv@?gsWIMwwQ^1EJnf
zC!R-&%+C^tsY$5)ZWz=*J8EoB1oIjLte}3p^=N{cv|NRR<Hw&41vK~t^%oN(Qf-T9
zLlWAwJ^If@bZaUTh#)#H@@w8-0hi}a;3rX1qxrR9&^GLkN4Hky*$ybV*QJ<4-+fz<
z;(^f>$!<#>IRC$1DVA?7;*Y->ZAo8|<9qk^$NHHIC7#mRaLsT+G<Dghy3q?F_wA?9
ze@C3cx_kU{!oQNzN~2lDYc{W7l8f4&jC_k-|94lXC9|o1OZCgU7F_et1=1T(Od<$|
zHgFPqh~_td4@3jsUsaj#xT@x$U7=uf1_tM4vliN$i>l)M6Jr#C3NMkhNqw-FB`Ggt
z|LCS)nHmTJQfZ$<sN(!05_9A05+b6><jyQ>lKRo-ubNI8Q1@vqH4eKS8%76xR&W)0
z08af=d-kjo49oE#+`hdzC7-&rx_IanpzMIF<N%S8Co~8e3%I^Kbc_Q9b08;eeT_9j
z#iMZ`+oH7QQ9$K?kT{Vcoo(DNrZ=vMFY>ghjq6K7Oi?ZkL=qLSk_&oQ`m3AY@`*}o
z--=656Du{DFliQvN#@ZUk@{yg>lwYgFD%Oy!QH;bNay{#LAbzt#qZty?;FJ<>R+@t
z)@yo|hFI}l1grotP@6lZrLQjUD4B+~)yD6U^2DVEt%;^x7oW50dqTeR1d=c^p<mM&
zv#@Z{hI}dosKgc>9mFxWs?@e<McL^7fs2LG!}VqQLW_^COKMaxU^=_A4WWISh*Az7
zjg*wSwFs|9jwA%WhdFS8O1RWXKpd@ycJZE@G!uOjCks-VM|~3nGs3Zt-Gtf^@LRb{
z+4F7~d-MtrnK<XzV-A=s-9KQo+2qTwqcpllx?~}jT64wGO%LrcsjZ*)kKX>{mzZNQ
zZ7pAVW^wCZtOC|%e_H&m?e6>_nyvu5Atw!#fWW{f8nl~V?!}I}Lnn8%w(Z#J4tU8b
z0gB~pA@@LVfN=XzWF(=zp{lR(%9%TryZCQ0GgzJ0V!<41Ua4<)<p-!@6scM)l9t2*
zZ>13>X&bCiAX?ZydW#b0msoJ;OC5dKzi6W)Yi$}Q>Z+pyRh>_Poap&flRGU-Zx4xA
zCJAf#SS|eic*M*vfl2&k3&e0-f5Qv5VtwestoxRpw!OQ`zZ5jEG)|SD<ylgVOk`K4
z)<T2{pSg7S;=ZjbDkFf>dyFlwBpQ(vsj#?71@}W2ip?UBxe^v%N;$GW$#AS#1Qr_6
z`gZZ~YY`I0<oTr%*zz<(dg%++5O7KaoZ|oM6wvm7t7s8jjzCnxSwKYaS5^!tj1^>p
zH1HA|n`rZrmZWa}%NtO>nfQNq8_a)p6$ikP+bf5kB?vyfhDz?yC;-a-^O@8lM@U7Z
zqaxr>jYD=uxNjkq<otYgJdC&D4tSSWRLhkJTvef40mKw;iIAKHWZ)rh?krjhsaCWj
zv<g;Yt+%uwo}g^)-r9jSmp_ht(SodZY^7loAc){N_9ZlEO7bCZsfC>uU%oJ~Z0U<8
zq!F{$&|61XakW6-%`U3kjE};{pwrkiN#&--ZMQc5E_7n|E}lI)38p)<%(9?zohaoB
zF2U6bpkR+X2{E4qeF8b9JDZ9tI5Ym%0zA$4j2z+GBnZYM^?<Z@Z-7<(?mE-bmOA*C
zjZs|@xc)=5HBJF&UjYth7@%OVNucNgPDVOGt5_9%Yg2?e!Q)K1<;_Umbim~I5sZeB
z6U?-<Q<A(+WgwvA^t!i8baa_xmjV6dXLah~zQzz|r$2;u*8}%y-#oGiHrrx405=GU
zeoFgNU|23a(i79@Xj=|8G~b+KH7l)2YDJaXfT2tRuPNg134v}74Aabf^8pLAtRwN7
zGxr|=#xgpw>trh|0!Zh}u1wDA_nS3s4cvC@XTW0k(kwc=Lj(d)-OR{FQn+e!{RKE7
zy`LrIP9D#4f}#5v;=dy7FOrgTKFHJ$3~5kYiG9_A;MEHm-|E(?TS)50TYfep2&hl2
zl8ATsFE5GypT@NRs%HPcMgRXdJV<#$7DMV}WL7{&YaB)Q8w<&_YBMqAmLJHW$weAU
zc#kvv-@N4i#D7cytZz}GsAWhp=qI*u1J2;t(iig3cqiL{{M+j`E4WLk|JFV5TBogJ
z8qj~0mwHyh<|<9TV^Vpw0rPSFVWUS=DnAUMk=0wZF(0(hQBl@Qg0SDZ$7)#yD*`&E
zhaW-2xQa9t`>~smCCwo;jR}VCzZKS#_Jx4BHty+ibxYBt0s$_u#55@nZGIyeDgN|U
zqk^KsAk5uWVh?S(Tb*JQp!}eLku=PCvX_7cDSSD6s+D>6ZERxNDanJ?>;gJfx-b{>
z1LyxLARqGkt8M7Nx@%CP-TzN-8yW=qa^ejffc{1YIgBRN7s%B&s;o)4`tgY!t(eer
z&@NUC252{6^bJdcu+>aw0%^2mk+UVWc=C@xCnOLyR9BO-CPVd*qAR@DtXoubKVM0R
z^wcy8)bo&~jixos-v&A$HrD9{%^^HA>+pJ$WOw%b0P2arsO?LoU<zFrEylpBx1Y0F
z8SC_fnb}ZB(VcoK9u#wfhu_PU5dJe0TZ{!x)!vs{G!S%{yMhseJVp&en`?sOHY-`;
z{w^^xVcy)Haa)Hip#t_>Fr{{YmE}Nk4R-l_+zm5c2|jg(KP6iDR>?cFMKV3Fi3M@}
zygYpNx0HI~mIIq_tJ=+r2515(QiuD}ln8g4-1u!`YMmaKc8Z4n`gVSNIBR^f+80+#
z*?Rf#Nhc(-zZo%6R@hJ+;?y+*ye{|gYuee_=-@L^En=gwJM9ln9uq&H5npJN*z$W>
zeoKwuu=l`Sg64WPu@PZ<`MD^^e4~dF+|fA~X9Iz@?>u=7$uTEZuP~RnTh}`Yj1c59
zbHjDYTonl1@N&uDOh_A|&aN6^1!ct5PMl_W-g|oZ`ZRFgQkj_^a9wboA@=45K~Xq!
zfZ60_!O&pJP#_~+Uk$;re%hP#w5>bvU+bPDV#mb|N%)>Tv!<=Z56K(;G?pMFvU^oG
z)EP3`kP(s<MTGR{f3b~~jPgDYv}W~QyiQG06&VrU+RmamBCMh-sA#}?{0_;!FcpJV
zn&ox+(KQz^UTrOPV!1JMalLWyl##YvH1WdMo7B=It&o8WGTQhoo!C3lG;@eYBXnR!
zNlW}z2K1`vg3ufKQK2*TP0Po_5HsZAQJ1D*Y8U^M9|2*5tZs>&FYTkbDisaP>eGu5
zFf)M|2b=xe?*S#Wk!BG>Vusz7TX&D#&1}#7&UNPt-G6BO;GXf~g6xj-JO06vdU9ys
z!cXTPCeeD7vp(NzQ_}lBD(9ykw`pcvKk@qTO^aVOfv*f{2ah*$n~{}Meg*m+{YI9N
zsFn4zwuSW(b#+6dBP2rlMjhYPG9-2c_I?$0OlDyGa`-wC_-sq(lXt5wvf}8Ola>b{
z&$5|WQ^`8MSF9m-!m68Yj=2rn9hDh?*w(?unFKsYxs;SS^+d;)h?=T~2xqPwuRqqc
z8a+<Zm_;B9ArL=5cWb03`EZs;LntlBJYQoEvAF+_uUss|GQ126sXI6y<A~Dh%2>aG
zisjawTqg8%vN={Su3JM|jCD*y5O`lsO^SE!;o5hkOByLKRioPaSh_M~_;(MS<??j@
za!mS||6b@-?@K5}Tgz*<9dMNI`X2G4)q|@&l`)=*{K;u(`f)QMr}w>~o|$YT1odj>
zLss-x3sl6h3=>`QHa$+y*++BjPU@xF*7YYLPAa8X>*XR@#eqa+qo6fLPEx(z=I(J1
z^&$%=i-Bz7EmUBC<H?AR`lZrWxj$fiVNR$p*}INU=JulU>BVDxs0H&$VsFP`b+VQY
z;Uh;GmyH0;d=XmaZoF(_cnjbzFHhd?`g*Zb+|(-XbL{5CNA=ykJmn%QBj=#bYUMHq
zsA<Rvf`rg`mW%A;cnhk2=0#K4d*;s=ll{@gYksHuog|RZ*!JW8m9XDYn<Eh7vng(R
ztnqX>L9xXBpS2KFg1wfynHKq61h1!wQ!ur?eqQ2?q#7VsWRrprdvO@l+1pHciK8jD
zUR&u^&hoE&$6+c{EM>=zZz1B(kAFEfT6fadoiUj{rt15!Gp{6>7UO~nE$cmigrjWl
zY;uBMmu+47|FPq@y=%71<;q;^@HmteDjzjPNgumG9xZSx*sWq@K<-FyJw=vDU#JQ(
z9K9iS)(C#TsZT$Gp?9?UKy$+yGN%@m#hT;+`=QUt$}%b==~Hi%u~);z8N8q`&k!P6
znDEaXB9Fr>$+C2xr9N+ScR$+Y<$|E(9=XQRS92rJrtW9>@GC5}Y{GA$^wL{Z$XJ!;
zNVfu?Vh_uZ^zh!e3*EJX7)veN@DNm*a`Y9Hn_+nm(F~Hb8C}s6w#`$n$*sE`fq2J`
zRu4Us#N@nToxNo(KW`aw)`H}0{r9zwhVhDb@t~nGc0}crl|T2e+-;X1?-AgCn330O
z?D~@TKCY=3oPojePbf!)rcb%al2Wi&mcRDS=p9HmQR(ATY*mbc&U>u!)?FS_tR7E(
z*VcF&tB*VsjVOiH6kWg%RN8+E;x!x=4urT>Ruc`p8~U_07<ButLG>4B+~st9Vl;>@
z3yD3fvH`a)0(H4gEh_XGsyOF{oO#9b!cU>79b7nSZ~AfL5-p)%L!)C>rE6z=@We#W
z#x>!4Wt>&>`J0!z6UR2|<!oF(M3#BXicqm<$q-c|9r8!Z>X8bVUg(fUW}`RqNN^IB
zI^<MoV^VEn-Mqp5Sg;8(Cv*CZD_^@aqwG)-bzvSMdN2EpUqsRO2<lj)PyQvEi+EIi
zL0t%`C_H)@tsFJ4{Q0$dmT>%MLU61nYO;RGnOJym@0<B5%ff=GLd(?4(G+$FE>PL%
z1X-^>3{OB@{@3oL?v<jq4GfsXBh{xnI)1jrbR*QJ`#e967SLK^&GKUE{c7JF7On&V
z8}-n@2l~{di+XoX1w3TmnIl(f^Q(eM0#O6g=-s*IET0Z+S$Uqai6*(f(&iq&ILNv`
zc~lyvvhM1*)~?5uANYiP&{^XXdk90s;%;3x(>BtQ3feC8og9vR7007Ln<Am>%<-P=
z{s<4{zD$T)tvR>xJ!?5o&nYqIlDjCD+WA1K+wxbiM;;)d%FI&X=Q<-}0?)W*h2&_t
zsF1^i$6_qj(=p3P-HtdNgiI-p9T$`Ygn>k6<AwFssV1rCE(P1R2b-KA<QS52!jE*E
z5~9wMT98FM194SQFQW!oZSUunLA0oK_lDTpfdpq8`f0yxqT`q}Of?$hzpicPR|-*)
zaqJNu%Ya}rNqW=7Q*I!qo~)qSYR54eE=iT%AGYnp!PZJN-k~S-=C#cP>M~Zt86RA=
zx2K{n>L-k~H@>rHle&z2Gn6#_#>3~~oZEZRqBQDw`toE~y&I=TW8-JCze&#}<!0)m
zI-yFmH&lGCp7jrV7+sk*uYoiszjvz)J<R8^Uy>Um+~c3@(8jQasLSg6yOH(W=DdQi
zm?P}Q1}%*;gUJh(x{uk%w9O%!q~^~NHa13j+zsUzyMA}XRg|#QI<n3~<4W(aLQklk
z@bf=S{z=|z+@q%2{PI1q_tQC)JJrQZg`9wLHzXip#L76^oPV$~3&f%@)XL28B~_fB
z@7JeYs_VFZarNb8jD2|wb&ba^nFH1&<s8QCzA?$Ea-626AFugsOPtApEgPdxMA0}+
zrz}Wbt#NPVWPn(Ce0~t=i#m5PqpB-b9!gK2qSdwT;aa|t1&o}PeZ$;+mkBCAx6Vxa
z*y2}oayuP*<I2I?W5lTIsNHV{FI|j@LJ<n+#lif?*0ZKj!2B2LE*E7T?Am$x%RaCZ
zEd@QTsQ>G@d6cl>NI9c%=)v@$U+W`B)mp|EUrln}C;aNFA<F&K1Gx0%Wx+n1h00vt
zM>*^_rkNir*E$SLjmeaf{Og-#ntWe{ew%Q<vbOB?nKv1FYnxtLrrvfYs0G%TfS^Gp
z*r}4oo=BwM7i0q9z~Qy&)wl|LbjR#qeQ@a7zC@QdCKPCx)6{a{$+cNVqq9d`a3||j
zeKKa&5lYgUq%6i>Ly@e*-BIW4J5$}}H%!k??<<c$8`ILqklhHdesE_pyY`qWLt2L6
zrJkjVRcoGqs*8!-#Ra64>*qg6GA5rMol{jh`_ET6Ix|C2tI>OFGwQ!>M*Q=&=G6mV
zaL4T%=QmhJG)QseY_k^1uLoN-Tm++gL)RN*F&(`pstety!qkkVU1eS{6yFhAlujiY
z2ma(!+Q?|^s>`9q4QDneD_+&!quYBzC93Etv&B7k3Wpl|+1K37%E(F_Vo7v<ZgP4$
z@#v8O*Y4@Bm;5KqS_-Ggd6f}ct9b*g=D3WM(-<Cr8bw-htoRHfkB}U3<p0vzBi*Z1
z*7MN)I6nbIU;1)*H1*=ZyIEb=ciD5e0$@4WU2($?cb8RC-di3xem%tTV5^~ZsOg$&
zGx<@SNqTI_3FLBbOxK&^c}$nN`H?gFlrxwXtoF-qAaL0Ka1DC1-Zf;|D^g4pn!6T9
z(p_aT?emuY>6G&h-PMzt(IX`;ju2~(oEcbov+=7Og^A~7%tMrWe$=DiiqS}jW+$#h
z-tKm$Sh^Hhxi0jyo~?9^yNC)+`4wP`T@HM4>=AiyRbs}}2vwA*AIn5@=IQNDjRlk6
zIVeZD0O`5Ry!`2i^9>u+3<&SY>%S{!_fj1Mg?nwi50!Dg_gwly-t`zrRVFApbJ6#4
z4!h3u2&0wX5#565e)_ZJa+<n-^?tl4ir2~&DE%xYb*#wpyoE!kqgdP*4UN3QvC~Ap
zu~@H1Pj}e&SNj{aVqJ;n5|%NoSZqsG2%xLwmlx~MW{#TXtrqJsPCcP+HUD8JI(tr|
zVo#B`8}lL0r=I1oW%^#Br<44KZRnXQevLhA&NqS=5`#x_ZqOdlYwCK|OluUpef7+T
zj)q1=FC4Es`$yy`4b-Q<_1tX+H7Dh|+D!b;kH}m0S9dd`+eZs#TaME1tOIU%1#rV!
zB*435S9MaiS(@2Xm%jAI&2U8ZVKaX^Djf6#)Nnj<R3QnSBD&4wJWH9h3Y}RSZsx-S
z!5QH3JZmEKhCwyAPUQ`*UZw=cDAav97CB6lp}vd-hr3G+s}??IwhJSg1upwMkao{a
z4BgB>P+l^ktCC7a0&W6UIRaSae!wb!c6*FTK;?r?#p^jDnLd%7Uk;}++QJF>d(MTV
zoYBZJg``qo2_F%fn5DS&_|>7w<z$7#m`f;WhiP-&6{e0Rth;l^7-gTjKO~g)uh|b+
zhWo;IFHw}&AFmRFJj|!}zux?D38Ta^F(QQZfQ`E%{T*k2M)_f0048~X-tmY^{oq%5
zpl+neSYM8E1TmyKT3wJm)of{VIo!G1H<_VrZej<<rK4g^<sf;9A+-$O4;QzP+-p?$
z8GS~NKr10n-A_V`7rrK?pZzBpIzU(C+b-8z+K@XDOPm#shTPd-$$IvkRs@UguOAt~
zjhW#&B3qT{XoC=(z22E6VDaOBE&ifSZ|C;)ayng4K<gN<>Lv!Ivio25H|#5X^s0KY
zds~{~cyW=&Lx9VSZgCmf#VsyVNS$cz2%#xl8&`T*DRw-!Nh;Lc&I2O+>{`>zjyL=c
z$J!Hnn99wdT&|Eomvfy{Z|L${_W6^q8;iQK#i#_cuiu4j+cft{tzj{r>yo<<`*ah|
zOauRbyE6nf3boPw=@NhDQ`z)Krv=K(;M$ha(I-@wPHK~7+QSB;{$fgSjGbRmT<?9T
zX1zgL=xMr7ZC`+SiK(Ks<&67q%?9N4#%~?o$1;<m5Gc2jeGvoq5s1bkq7~HScXzT+
zKV?sJSRSxh4z%PHO@*ju1>|Q=nX2nW7B(pV&9&}Xs{1&uwvNlt^W@}C4QFWMcfWRj
z66K1bsIDg}E85D!Lhw2MfK+PEm}TfXbZaZGPiI(R(`+oI5GG_9RIAB!V)2R_OG%D!
zg4|`-Nh7$yyFSd9<mW{Dp^&!A%nWu6_$1**4nUIr|KtB=2UhCU$V_F3`;vjnS-kxh
z&rlT1x|Vr;iFz@wZLQuTZBL-(rjakB=wk@T=~FupqC_K*=xL&qMuU7XOe+34pS*hT
zT1>k8(;KrpC?u=#6~3xb*!gvj0(xBD+=6PuN7YGcEq^j1AmT{vsp@`%QFkkDgJ|j>
zbcR-S`jnR?ICpBrQtJv37uBO$Pm%{vW51-!pUffoj!7HPXW0TXev&0;$R*AqPyW`&
z)C`|Nrm`)1(>#Sgtf(Aqb0vtNz6^8$)YRBm)-hrDl^qo1eEe*Y{>u%fP0oP9z{u5r
zS64eRKHPGnH#mF%mtkKCbBJyD=x2vI&xo|m@y25~ANHA*?#CAtO~LFM^^*K$`3Ze%
z(I!L++dAB)8*Yx7Ccc>)UiF@FqKeCS3Ne=*DG1y(>9)oh8C|K-MdAJ)4-@is|B;{K
zjiP@;Md*#_us>wf#}=JRSJ?I{KVxdKf7yz@aJi?Yt5Q*Qowbie`k5?e+@t;Y_|CS{
zGg052vJIWzK-@-cm&+g_8#aa>1~1qcotrZ_v>J#Ry_wEFXRS?2EKPpqF5SGyQiffz
z{KCmvZjJ@bZU(1s-0B`lsQJE;uH$tHB?HS8>yTk?3s!kUsEtVfbw|s!zVd?EJDUnQ
zQ((OSAp0<UPjHYpiJ683FOCfu`;MoF4~3@xa;!Me@!?J9HwNj`t@;E_Lvp8=3~_iJ
zatI)iPyM>AW<r5I@m+|J<K!~u?{sAfKb7Xa<{KZU@rU1x`%wNI<mIRj0x4N*7tziM
zfu$J=jPl9IbD{_z=-V!)=FP>x;OX~TUkSu))W%hmp1rDxu@X56H5P7dj{7vTWVvbJ
z>;2j)J=Tb9k2<bjoT_NG>~Cq7HHA_hzq0&9xhr{&-iK~fG>^QBf>d;md$kYw+D#H;
zZA^|lRB^){bR+-y_1NHy>lM4jY0G}jS@DYH=2`Oo#^IR;V=+lrE<ICp=oik_<(Xfg
zK-i5y$waco({+aygm&JmA9ji48rg9MWJuY@nLD>;mjYGRXVDwK>}T1c<Uo{L@SW{H
z7g=+m#-`*=xAMTJF2q8&6h`&DYP2;9SF*s(D%%HITFR)s3jD*$p6LTum+|U{J{HjD
z2Ikaj<Fb-T#$m4W%_AbuPfb<qE6W1~Jfsf_TVfsCpf(*PT+Sf3JtXXMW5Buy6l&wK
z^!n5;Rb~NmUN6AA`9id9>qXQh)o3ItDcwE0uITIuf=@pJO;i}KVRHPjC+)p!4A?uk
zlNdp2{_jBFSld6S5UJ{{DJE5P-M`lKmHo_ehW6pBhgv69@6FjhVqacH)-CM~6|>cq
zXt$RQ_*TnnKe5^5PVLGxxdBr8rB(KfW3#G{qIvlVbB%WTP>VI%@*XU=_EggF71g{O
z!JuxLjqAZ?-g|?=fz}Kjo4uVy0HbVAM{y3>qt18id(r_rP%+;Ybe6MWlfbjR^7geb
z-{f?x4e6CY5I%o(NUygNOu6-*6Q^#jq5n9`T*02AcWf{$0M#*5Q<%q)R!yOg;uACG
zNeFcnuQOF<u+s%Mf{*DKVWNV7H)p{nr3?RTRb%R^+%>CA-D`KSlwY+8qwV6U66ymW
zh7+~7)S~7oCy{3!2!Oo5I?HyEW5eC|vg|-~e}9JFwITcdfGd9K6QbMQIajU{7nu=H
zf{7T57@3lM=*pvqB4NcZ!4%zg6D~AoP<apY*1NYF-Y0JrPKYjds=@HMOTl%0RmzV;
zo`yTQ49t?k&x-{#Awr4O9C_uFukGw6OxJiMC&TB1O2P81|H_KfSq!#lKp#VkaIM9g
z^c_Sn=rudIaq9j8gR0q=8JG)x&4mM(7Y!cMT?c@K$Q5^Y|0@Vi0BWMZ$-1BAJUY0?
zu|WEmeIuLyE^nIU?<?Knfzs^{=EtS);CeZ($Lx!W9o@r}pL;(7D!m4jzfN5c?O~Q!
zyMGd)^nMKspJf>_tp4HuY=X_~zZy=;uu`Mh9sHr|hEMpHN_AYc6AG_pR6H=VX&I+O
zDES13Ljr&t(EW^(_o*3E1J@BExy-Y<zV~3vdEE-jgRKLP9=N=7`gFFPPVmT`@^Z|>
z+pDq#IAZv#{yWEI229ZK+~lmL%#`_9UsPA=zN~8`Es<qqjC4il2RP*E)He0URTg{3
z{GEJ}aBHM(dAC{E25UcW8eJIn_@!m<ByTKWfmn$pl5E3+P=S#w`o{`kqq_N;Xyz$=
zvjsi*HsgiD0q&}QmHoK9{g)b>H+?>)Y7A+ELpTp@>*Y)8SQ^y6e~3#=-L1C%S{Z_2
zAx1I;W2ZUUr~0z<skLt^jDLbB>G;xPZQZRxtM7$qFwCbHxD6Rzav?=2!i)SN<#so<
zV(FucUo|?LvB;7I#ZsUuee+}S4z8_}ST5uILfn#(2|>HH{>oxu&S%I!BVYG=k88%f
zQg4WM?Uun>a56E-pX3Yz*EdJLJ_!2&?=@}*qSl<jl0D3-&Mh^{(P)pEd(;wHYVAC%
zDO|qVm}6~r-Cp#n@40N@K4+_Cb4=arjm5OzMDqxn>bOpCk{oCnqE{M(@bgXA3EEw8
z=+=A==gf=xI8)p){3SjWMhZKr|L&QiJfvr0$qFBN!zWh!E?##4OtA!9*E_ayWLa{w
z$q9Abh~-eH{Gn3W^m^k=1Kv8C80O=LTQ7|Io%fY*cTk+LV7+{o7@4OWW&TlJxcN&O
z<)7_t(kXhJ#4|wiK<X})D4LyCI+#N3!kN^Z5I3>Jz!szmzY7a@T)VH{u%50o>Sh%h
z^@OKPQ5Yc2I{Aj4o>+2Givoh=^j!URJk>e-LXb+jN|d5rY2P_?9mwzNl*NF}BC+L!
zIL4=&lNH{`EZ=ci3?Ii>D5Ah`y`A4GtM6fi<cR4tOpEFPT2}(@8i?dV+Jam`C0!=D
zA}~WxId8Ft9>-IeMjA~l-NMc~9csNg8F>&^!6MP)yZoO|6!=MO=)6d<aO)bekN=Ja
zN*njL_Mma+XPb6#8!&Yp)n?fj?%_7~Eq_p_?y1oXiae_$HT;B^p{-@PJg=TL97*+u
z6M{QMO=l+kcZh=;&IZ^uS&nWjKkA~T$F{VX-}Fu&t)MQKTXFoos6)QFwI419sZ1~M
zuWMZnE#()e`@8m^6J0>KA9v50!lH#RMf*4iD$QU4_?X)Cp~Q@6=Z1~3Uwm`_v@PT^
ze)kaht;21UBIiFJDKsptzTRTKj4*$dcG_dMG&v`KpH0Zn=;$T9>SMZPK3LOsrE)3E
z?{NAG(}pK)KAyeUyb@;{dwZ5kR8~B0PXoe$KT#HO)vlXmdkS%m<5iiVK#a64vv7yD
zRB*I?gL_&#|4sk6bXjag(Q8U+?B`?CsDSm{b5>VzeQ*LvuXN!8b-BsPvMd;1)uX)i
zSRxRlpaTeh&BA|Rr$+n+r?1e}EuV&&fi)EYM80KNeRDV~O?myg|N7i^1&CGXa+hLB
zaze8UXpJ#Wc+e8?O>1e^p}#&@u|H^)7nYJ?iARMy@e2FdpZeL$tVUc56#EZZLl+-8
z;mu%F6Wi6w1zV=Nehn2m&6-DsIL3{AtsT(q$<F6Tq7Su>%ew~DL=)8jg74E^aqEZ`
zH~3m9#^k428N0Zn`C0IS&@=yjU?Xj^nn{l>XTwyNV09W7?h<N$73DQatn7Q)hoFWd
zQRU-rgocmVjauoe*py*oMC>zVvLR+VIu<aChOs+-t1IZ~F#jn;;5{NB$@PM1pKxB!
zlh7>PyC6HQB4$>f&}&>Iw@>4A^EV}h=Z|UYmj`BV*4~o7to^JBc_kbZLw97fbgBqO
zhc5-DzYDhO%oKTd2MQLH`IluuEl3f}i6X1Ni>uMAt2#?4VCiwS^8lg7+Mca(HG5@&
zCwKaYe+7yOJq6QV>Ab!xqFig?rNi;hRonOeme-PHu9$_o3BNJBc{r!<HKwLJS5;}g
zq<!>*UA<0{8Tho9A{fX~RC8<L`K-;QcC`j=GSY#6DW$LZxk2vDh`i9Dy48q!K(`W5
z8!>)|&J8p!=(wmIokggSD^D{Fx*U7b^LZxwq4z|ejBxvF29=|>-5f~#Bz(Jsx7;kR
z-yrERmQJYWwsNKOM0xLF)*a!O9D35h_gZCnF2T5vula%?nkD7V@mczuhr;I9RjmlK
zQ)DIM9>qUq5|5M<>RrvEf8ePk@!@M2ZHl7d@pr%S;G0%Jt`bFm^YESX4%;i?jQ-^j
zONHz@MaAs!PEP9TknqD10q##7qsMUXhK|I{0Ej5puwY{0(b5IfYUeLvY=3YeOOH><
ziJ(haUA-q0Z{ShL9Jym_#r?$0!VMHWcS!4jT~1h@^gHPvCJ!V;E**%#21I|dyuVA9
zA02KBHy<85Bd0y9F!MEF-70Tx@F<0~!6*o?h(YB$m~Q5)H`v?0ypv|_Z->7SYZ{nU
znl(kRFeth`md-pS10#2SmO1iKg9L)ARpB+rw#v~lSM2WCcBhr_MrvqRBL-M2FT19I
zKPuN8uWSG(^Ixqp$GJ~2wyZOM5V~Z@{mP_|E}L{RzQGr2C#pO$y4%_HV5`)dD)HWT
zxca3!zs8xj0qe6}8I0#sIKN>TKW<jn*|^)?BS=fjeFe?rNY;J3^ss~TmWLH}OBecA
zJEv_>X{Q$kl+7}kIdH~fd2!_m|BG@_ww;N;^7<X9SU#se=XKH=u>XneUpq7hS91Hu
z<t-IjAHe;5KZ!i`B1>V7^J=<r`Lx88=vXHNcr`mH00Q^9L`(0|%;jaAN7}8W;nUQS
zmvdEsf)dkM+%ePAsKR^x>(#F3yMNwVJJ)LD%t&{{0Oy7q#?KaBA31K+;qM5s%86wa
z|B_aJ9Gp2-^Gg~)Pg$#GVuDtmA6RjQUO?))vEkos{F(nHqkiMzdQ!%o*Qlut<+5Nj
z?3G8(RCK|#Nm;U(LFipK%87L!_Zq6#8G+_ykX7<*LU5T=sdFmR_#<rI0toV8X)x$)
zVC+HdicCnJpU(%5b#x!_mkS1Jr^#_OvcgYEU6Z;3>lwOgv|oa;|Hs)2X=KaTV%N>u
zA-E1$luI(`pyYEDgn3cdX1VVzi7n)~;P(ZX>Ap=Vz;#$Ie)pk~mvP?#w$YZAtkr)X
zsP5=<1P&<t>n!Er=cqgIP?ULgC@uRg5LmdPc9$5scl2k3>6nkMBWcKSZ?__#ErgwH
zCbxKKu7KQv?3UptzRr3&{0#LpCIaq1s7ikIDb~B#l1hF?!em0y=ZsI1Utxqe+vNgf
z?JME<3`NJ5bn7Z8XWW>~eI3+;yc#}0G?rQ7RTey(I!qNwD@XfY`Mow~*NSVmEDC3{
z{7#u<R$Qo;zJcvu?$1!%0U3<8F$I4;ZGUjp762r{)tvF8t?3K9^J)vHObezHP_bux
zgC;pTvHqw$<>-qj%IV68BU@^a|86&Cn%*#!v}Be$Wp2`AxS%nvN6@>?x03xd>0@aV
zbZy)f?(=|jVntkw&?IZ$$)xmtt*362iZ?P=nYMZhN6iPnC>+PKjBcH8CKTv)^nX9<
zL0#eOVk_7`M=s=##`*xzFT;&-@J;lHi)NtHUWLq-!FKWB-A!>x0A!c@gnm%gA4uI|
zr@F>OmVtThl#(o|fM&0H`na#VwHg_bI7q-n#6&>W<x2Ap50xP2Ex|?LFA;-q!|1BS
z^_!49Mdt4wlV|m(t$R!7A>*4-zafH&g;%{{Fcc*yOg<e|B3RtPyAztADxd!zZd0ci
zWDt@j|EaT>K7gK<x%z_QaW!?%z!S2Iyj7lY4Vrt}r@K*97<DIC9;9`3nMM^&?jD4$
zu$C;iViAi!jQNyQ6o&KPSHUfHL?AyEBcx9BPwb4R!n{yLR-p&;E&6TNQ5z%8tB+j1
zdR;j<0qM+$aYtp@yddyP^M7K7f@X)z=O{+%kJs$w21QdDUWbNopW1g7Qeol|y`cFN
z^*uHG<de?LXMFe3EkFhuU*=utdEw2x6`V8Wx%#=YXMgGIU)}KQPK#e@X^q`7Zy?dG
zv%i~R-nYhf;YEM+sjINE1HUTCf6!Y0o|qhQv3siA{OI+WMD>omLHfq0ZPOguI*_QR
z*bb)w(Fdi8UPD{SLQmsthRxa<)qu}-(mP0{`yMP!WoVCsUMr_DpDpiPE6>o2atVn^
z&wsjnVV!HI1tOjB{qmM{MV7yO_*|h$1;Ub(i*kSmcBk!_Ja`*mJxcJdjHP?F+*NL(
zy8o@s>)rS5D1p~#`h(;}AcOKXnQo#zy+A%^?}tYEcFR69zY;A^ESM6h`(!Yi?Kz<+
zcY_k*g&0$z$FnI*%LBHu-^~!zAD1K4&Ceoq$>Ph8U&*AF`$M{$s%e(85I)b6SXfsb
zV|Nflu?;U%Hndu<?$r)!$^o_qT%cRcuDACt#TS@AT~=!2rEmD;R`E52t$*r_IKYxQ
z4D}AQMjg^t_T<-E%T4uZgr{wH?bOuj!_YHz@c!uL7S40|(97#yzD$Mjb>V}I7r|TU
zxK3kZIql%xm%ATdn=Lsm$E1csN?$9mrs9cR2QUiwu6*HhWgL2xWV@HW)iGkl@|{yT
z95NUViGRP`wwfxP*f5q=Wqg(ZmDNvOR?<DYZX%6kzY$e6b#HZ{-TYH^PDS75R<m~7
zmysN%ODiR)S=+7lr0>@p&Jt&7dw1;1m0{uRFt$2jV4f~=ySddr>8u;5PG(SEXE~gC
z(RX0m-gz_iu15sRYG7%*n*D%O$Lh*Mb)Q{o(`R4>89F424c8fG$j*OZ(mh}=h=k`8
zw%XN_Z;!8{j`P^788^QFjnt4~xp9Q2!3^|U3=1A+$Fc83lqcA$es;4B$7c$+dR;V{
zEcX15-P4|YXVF<d#vDgrPk7!I<4Vx!69=wb;k@^G@*X3#Fy?9ZA9~8ods6DTZ_cDM
vd$m#zZ8BG@^zc#wFSrA$9RXm*-2d!1Vq~pcIHN$Pjxl(;`njxgN@xNAHQ|bx

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

diff --git a/reference/plot-5.png b/reference/plot-5.png
index a5975c878162326904b1bffbb3f69a1ad1387d55..173bb7f6ac416fdb220d5ddfd6676c25bee87195 100644
GIT binary patch
literal 39742
zcmeFZcTiK^_b(h2#Rk|AK`BzD$s>qJ4Ls5UNS9tj2t}Io8o-JoQbIFyrS~4BV?nAQ
zL278yLx5022_bif=lRaux%a;Fn>%yg_m6k(9+HrAl5_T6d#%s<eAYfEZ*{a(>Cdp9
zfj}ViFf}DT2;_tx1akcGsgqy}HsRwMcsco4Lsbd9g0^?RJ_Nj+eyV2T34ySMQ6Dr>
zbL(&jgcAZ&QZ(>OSswR|yZmtC$GUwE|6ea}@GGCEy&N|yc+%k0na5XX;1A>Gyp0SD
zEsQKKS3dq)bhhZ&$zi6UQ@2hk@d|LA%Hsroin=_J=eoB@ZN(Muatfp$YWKG6cxt7)
z*0~leE!B0Gx{V9b9QEe^KK)+{01NOCTw$&3jL=xQjfPZof?*MH+3nsT1d?Ii)!}s<
zd?57BX;|k2E~`k@Cfm+4fBQT29=@Jogg`p0mo+8-W5Vd`#~S|qUq|u6)QNxo4?PnO
z5&d_wfsSwmH7u6%?Q^bCw|##{-FGwUZ=|&P(hZz3OoUgWYGRVh@kN#!4?+ZY7N$`Y
z;sz^Ad%>IPIxwKC#|<+v+)Gza;&%*x7<}&)`E3F(jt7>gv2-gxJnI#bPmNX7F<CXO
z2YFIsNiaVriwT(n3sAv)x;*Ncku`ygEN6cYkK8teiSz!9-b%`{(#zw%X@Zo7)hXzS
zqBb9=X6^92kKZbPPA<Cm&~V{acdd@b{8bJ<+$nr*Ox8){6SYwh&xzn0n{587nCmbZ
zv0oDvaXo}P3-^vjxHh28w|HJ_x{tR<T3X#SL811V?en+mA8zFj&#j}Y8^hy&6qazo
z_*LRfj4%gRT~c@ipgng-N7YQvXPPg}@WKWz6>+Q4+a6O1b~#Xsxf{6#Td=P<fZi9y
zN&Dso%=%CAYd`B|yzENS6~^^kB_ShT%Wtz?vuBYEn4P?NNx`d5vx5y^)_utQeI!RI
zDkPyz{<21$h>8Vey<JkM2c1KbvA8m1f7Dl<XY~3TG|7I)PDvrt9(+8gVUoFdr=OcZ
zLJD?7{C=NXl3Bd5q&|AtJ}v6<B<tA`B>RJntImlmie`fh)k~4N?+fA24LeTy#h%W~
z*&KUNC3j+jabtW|->AUO|F@KUWP^#=GxM5*n$KC<ye-!fs#NAlOd4V|mZj1*Ck83_
z7((pTz0PTw)K$FrQGfEL81$cfd0|ZR(is(l${)OKrI)5HXGt?LBGxbW-K<vyh3z%s
z8*ZvZjPjwT`a_Q0V`%%H$FKGZd6$2<Nm)$)uAj?Jtb>B+_<}3fia<2QfpDWKFs-g%
zc1a{=@7XtPv#17ZlmOkQY}P4y4by^QXn5QX#LXlqw;vIC91?o@yZBe&>KVf4w*5jP
zvU0=}2b@v5pR0MRV^153ys12Zc67r~1q|w|MB<F?bF1|N5pg$oKP}$>&JQ9~eF<N_
zavT<>(HURM)>PYVp6WUgV<7bf&5OT*;&kI0Rb#yxq;H;08hdZVi|Q?W(MEj7E64IQ
zwQnL8rN?QC&T_rIVY#$^$d3$O>!{JS^Kw*$ZC{p}xPjW7=kcdgV~j`j))vKOeeAB-
z)k;kCynbY~I2)&`oj0Y`VwwGq9dF_SP@DzHNn+2KN@L)qFli+|2vS7c^l60lep!|s
zySM-QscH`K;<^g>d^Z~_H^SKNV_s$P)`-}bOU)OGx8+6+9pdK_?8C=G4tisD-uw!a
zTRM$-#_o-cqI3!;*wby9cG9xgu<1kJMh<H()LwH76zmW_w^-e5|D!J<Lw2$6WV2L+
zC(p`OyKkLpT0d{<r`=p$|Dnu-x!W5jrJnoxMbuqn`(kD|iStUIIqHAL^UX7Fgz57R
zgk(DzH3D=e6vIhD8*+;=ZK5sp=5{<5RicQEm%QFS9sRxhWEi^(37Re&>>0zp62qT{
zUDfw~uR3mx!eYkd>z!K}HuhmW8HuZ&l4-=*F6ifLPP5Lxl1NppPKu?M<S@)#A?k5T
zRYb?cp(rlBGMWkN+oK;GLTiMZ`r+=t13(K!gbpn>7V<oNP`>OY*U8Y;?TODuYKC@*
zOls9eMQ)y5BW~oix!o??WA|s>ovm%>?mZK=r7wrU{ta8!*Gbs_y}}&l^e0xd9a0)M
z;VEetxB-72V)>=6B1LG#@vUm|;bKhibJhK@W6t_bs%+LU{@vNLVlCI4p4BTIZ-8&7
zYVAldKp=W8*8z}bG<=v_KeR!jJ88wz5?T8E!@G+~>jzeRR(hABTq=3V)3G~}uvl62
zTt~MA>P?jFgw<T^f<V+x2(d%|+gxLizqZi&Yi$$c>C3@{@?!mnhWz#z6!*Z-$FOTb
z8@CcEx6kO+^^6;{s_*OAt&TXfig~W@i}b{6`T8|bd=q!h%*rvYLSHyW@;D~;qLmiv
zv<w|X;qi06()$0xU7o_NILaH1Qr)UdUN)S*>!tLAb>Re`TJ6J33ryhqsqM2oJL~x~
zjO_juh>g!8Z)Sb3tJ@xX9?V$UK-gb4Dp=b4O)|c|$G<nb6TA54r&nry`RL5{FrWBy
zsaV0TdIX(Hw%j-FapXOGXTonwJHZ5IUWD+a15r%cLfMdcwKa^gCID@}&@j|CoT#L-
zmH@goPVJf+O>@C9Na)5iZ>#0<P1CfjFG%^=(`W;C*o?$NuI&|uaCmC(-DVuyr@a-k
z@?Lbh?1c6f#XY#~hMAq~qvkN?(hDyY*Xp+ey|3@hz%IQ69qW+3`<6)S5|&lYj@?%w
zV^`=-cLZK86ET~o`;g8n61|nB$+>YA#X%?N*z0+3R^L!DqOLQu;y^30h8n8(&QsIj
zT&pX}>}R9viOB52tRbe!g4MTc*H%z!7BWHTy>WD7{|tF!gYvl(<r*4g%<cU`Z=z@X
z!Um&V0#m8(;E+H?sRWPrj@nM0_Q(j*G8Xmqi;FsPLvV0mn%Pf8$aNC8;X`%fu#Jr{
z!0tkM$byx_eSOFu?cL9k->f1>l^SbW`4L;i)2^)h&YQ_TH%v`VTx)dhc}ngWp08X9
zy|=+}+2H<kbfl-GNh2@W3`J=^{2K=S+z^tHl`x1Gm)npRqHrj`Zr5DV>6>jG4put`
zkKgvKlT8J|+;XFzf&*uNDz1&8bVTqtab7aG%7eQA)3BM9lUtg^BQQR1Ge!7!ok_PM
z59VG?y5BRhv$YUoY=-z192{F5pKhO&%6rYj+3qQsP+GJ<wiR>762*3#CBZAI4gG9z
zb5a&f`dOYLf(uSxkyMv9w!7nu=300Xshw7#*KcoDk~MfbE<T^&Ws(MZ1KJ~whemb-
zJ~gipmL9O?QEnIG!y4a(F3p&!gi_;GXhNV!#e6kqtnG7l%auYx41X_w)KE&!WI$F3
zCf5apHlHAwwDDVsrw5g*==0a*8FF2e^R=nYt<co@=>%=NS*bXY#J)1hCmufJf@R&S
z+2f1~D!+2Oa5}cv?Mtg|4_&Ep_u)e9f^9mnR-}S2zL(%7_K$CJ`_VuYl(%9Vm>(6)
zZlp~*G{?weT@!Xh2DCdvR>wBl@%PRdPM_s`7~^I1<mcP2o9Ql$ehuloxb-_#yr<r#
z)t4_vn;3Mv2-3p#W3yD_YdHygFt@q}D`kvrl&o(j^pq9W9$PPumELncJDSK&Naxy-
zauw>Jow@Xoy}4hJ3cZ{ssZ=ea;j^h7ty#mna}qm#A*imvOF^Br1{fuj?lYoD=QjM<
zOG(mfpmgNj1j$RY>~THS=+?@=j2@!QrX*o?T%U9N_oI5z&yZ=csHmaC<)n7uCU4VG
zvnw$@o|5dzykteYNCQcu4&jCb0n2B*SJ(+8#5|HR*4-A^VD$jAdApMy;~ce934i{J
zcrbYb#>RuV<+;jNP}J0ql5acGCkiLls<`wyB9Ol|^lVVNaN^=OVH7q*G;yc6J2xOM
zBwIU8iGz`}tm>ckNm0xb9x!Ldb?JM4PW$cN_|w5Vai^oD`uQu`%>{pus-#B3(v{pg
zw0Ed^D@jdoLx6(5N=m&2(zBOrdY6!UTq1w|ckYn#?w$M=^<>ffuq^M}OY{NZuS?ul
z-5MB61z58x_Ox<I*XfS@iv*DgjqbEbZBQ+OYXr!IN;gHHX7v{!&`b!UI8Dzh=2o0p
z8;oYDMyX*Fo)D(uH~;XDK->V}Dvf%6&Kj5B9y@O{*+0i2@8TifpfaTrc&}$8C8{2Q
zKEXCc4>L`EMB?irvCc71iM%6t)%ChVAkoKi9Kfkb;g3;MI&a&EdMJgZ@0XW$)$E%3
zQw8E;9Iass8UbAW^stsvWMuqa#LRkTcTl0_CMH3$9Go{u0H>6`!fl)5eA8)WMy(Qt
zX>(8TjqQHyyq=U-!S=<RE8prEZZgIz@*p>TP47Zezall3IM2{nLLg<LAGkaN_Y1D#
zd|-7te7np-#r?g=9^`Lj43THlXd<qic8?+Pj^x{ecebwIh)MS)*qWk$Yhs3OV?>Z6
zrRn_>pB{Y=T;+Qld9c74tT~T9tk`;?S${BAxXXO&;DO#X6o<uQe`AcYAZcp)97=g0
z_+5-^MAn4ZP_AwK0>MkV2UuV#Iu=LO55^^NwXgR7!uP50Weh3VS+)s^)$AN{Io4{n
z5aeJS>Z8$*EGmmDk~?T!qY_w9Shabd1$r?>q`z3rgOHEIHQPo?RvpW}a;VzdQL!dP
zh4J0%hY$E}R%-G=?x@axuod*%e=&gWLEef!{rGNN?Ka5*CaRuPvisttqCl6Yq$MTx
z^pgM)VWKEZCT{c%QE8m_(()#9k;|dEGv#bZ^p+}YoVPlG#kyGulPvabZRA<2<~dh?
zqxtKLLO64jZqY;D;^?iqEPJNquQs@St;Du}>D-ZH{Z~&4{&Dd~R(O<tfuT(JFg+<a
z2uu)!6#8FkvH$AO|Ev9+jw3)H1!!W9!LU4JF2{nbLf!!&CrtIqvu@$s9#8#^yag0f
z_u-X#S^1yFc(L41h@25*`0U}HG8Aow6aS-INS|w$cWBIxbhIiB?bLvWyl~a<x8+FJ
zH%IeUQfzFS=msp%^!h=Qj5P%EMLc=Jy~_GyJW)9PP6tt<&s2K!z$?X{2X|G7tcRMZ
z*2QSTb~={>o48%n);Vex+ZOL(_4Vid<U|}}vP8t%W?W#2O4V%|yf7p6{qLi>b4V4n
zg2W$n`W>XCc(g|Z@d#QjKK%g1;n&;jHR_#jJ4q4M#TqQt5^6R~f$7Y)Mcyd=KKcIr
zaI(~B6prOdrtR}v*02X+!zZDewxhmk3Uk>N&5AYIvK3Liinp*A$0^^6x4y}t8I2Am
zV{Wmp$jU|f1UY8QVQm5h5f7?JY&ea}GUt={FDXSn+|g7`)47goWbgg%5XHmvUAIRR
znM8`y)yA+!HSi)MweOGTqw7)e^YZEBW7G-Z$*ANCiAD(E?!Y8$RZQR=5hrD|Fd@Zv
z<T5vmsvcz&zvdz#J`>po{PRw&`E)#$nTDr-gTm`>$Zs%7O?rrrU6C9KuPW7p{HN0%
z?CCnc(Y8biLI@!ewc-;hIiZ`arFg<mf5j=q!&ar>HE;3zN`y_37nXNBmgQNJi(G-)
z6A{NZS@t$@Hl*KzZVD;5@yqSBsiM)R0}PQ;S89=waV&B!bQ<F-^{+4Q1=+!#IM0d3
zv7BH}s4|;ZE#t&%Rgde<rWF4vXQK}3e6Fcj$iYa+t|}etr|Q_+MsKTMiih0NW)2kk
zHN!hY9YKbapI-Nas^T1HFq)EC5zB6*<eQ}3po<D7#KrM-dB<gl8<#%I@bgn^C&!1p
zsyj_@ia$^=#;D5UQlX1%Oqr{Gh{y51iVAbJ`Fxk688qsK;Ut!DTD`O8XzdC#6!p4J
z|7T!PHF<((^dw|h75;=>0s;}N3%krhvt^IMN|A*P{-Sxi(ZbHMqoHE<j5EbnuD8QM
z=%BhfEf-$J1h)^tPAZMGEsR4tS?%PYtO_Q$i(Xcq@3#-$WbG6U@v9f$W16zJ&AD)u
z#_vT`(Sl+F_n8w{B#nD(x*{AR?X*YM?NI`P&x>RR8a(0iE983Oc+5T|l$KgI;dDQN
zJZV~5iZn>Fl?p!V@c@mkj;|#X@1k0b!;2O^Zlp=}p=xw655z*&+!F7|2(I*rZQ_C#
zZj!WM^yZy%1A;?o_Uq;=wSQ?j^~#N#ac!r$#W(9sgmjO#Z9sJiVYaRoP1=|s<tvr`
zMs{8XvNbJ|aa)Win+5aV4xt)>(_~E{js4{AppV3_^1huB6M`D!#S5=s^KJr_oH314
z{d}mHaidO|M?8Qj+~*-{+kMP&5vze&eQac|TLPAfsg19Fm&FX14@<XIF&vEO>p9Ll
z&b7(nnpmZX@kEa9&id=bwAKm2>dY}=hKC}CW+^zWO@YE9qNJf&lCif|Laki08b%Do
zIsHt2B}$cx<lrQ-Ievh7(sc&4*+Cd{XHR!n54wUrhPsf&tA2Q(U6ZlTYUYiKDMW7N
zbk`o_Y!ian{g<6=)yh>F6D6T$y?ve+7X&@n$-UeJJ(MT6Sq!J|d2hOy8UwylDaGv)
zM{+tkW9D)#Tp~Wch&=C9&0JNh!sjrLKHpX2kuHfr?Ih;OZWi4c*|54<tb~!$og;3+
z+<M!D_O$z=H5-wqf4t&toUK^7t~?(b(KO)W5OFs|h~j^)G(3h<p0~9glw;H$Xr2Ck
zgW>gwpZ@smFR<BW?ZY-ZlxowL@G*~7;u{7Gwx+~4aSLc?Zx0T5q6lvF>dHZ^s?16N
zMVpUvxm&!I*CN)VtU1_43X@Au{3bfJ!rzBD2Mg%rC%dBZAC2v<*cA1C#VHA`%wENb
zNh#mgLA+nNSXiXZM%b>HFw1O@x}rsY-Q{x+_k&NJkHb%PY$*~YhH#v%zUKC3j{7H#
zeRce`(U^u0N_*5Wg}@R!4y>yMT0N#upB#Nwfew$zkT#Eb)RH?&Vsy2i(1xpYC`ZxO
zb&OgHi~o&o5zH;=PI=*TXn4y-=F2ECye)X|gkNm2q;?F`tf+TGNHo{8?E4l=*Ela_
zI6aQH7PaeF$6yVchUAEu>6voC(PpceuN3f#hs;Un`U5A-?TR>a)?|)NQ2ep5FwQ!P
zNV~!qN%VVva_4cc)n2h?Mo~d`IekY^0-4u>fFv*oO=_X~(m>TD$J<urVLCxA9q%yn
z1l3Z27+dZekttru{rp>Ubo8#3opgAop-iEAoz6Vnt1=?H)M}BWaAlsZP;C@#RsZMr
zXrV6h`++N@AUVw#f`uC2XE)KF_-?g9rJdVZ?Y?fWy0^buIuxL@bi>W9GG7b_ldd{Z
zGA*34*{*O~@EvbZ+?KdYV%=123p^2iU9z<PFFqNJbF>s6e@BpI$IW*T!g_d>R9!0D
zb?m{hK~cer!wdzh85?)2G0vK0K~%ekwjREq^c$bHdBcCzm`D8Q-&F<S!rwiLR43>?
z7{?wyoMmk9VRs2Mj8U+?6KH@M`f3j9WCnPbA~l7QY)u7w)V5ox?uX#N#c0%Ppjn%j
zPiK*J*WWFOTX5|_44jnZH#t>0S~vJtq#jocFStK7n80l?6a?bz=0^A~eTY&OSG~rO
zNOG^896emf+v%;ENEalSby2LdYsYdY*4ekE`)kP`bO%ZJp#<ZN%X_Y^;x2DRP>2S_
z7@s;3*ZAW0Q#-eGvCD`Q0#dY(obHhATW{0S(KLaDYVOH>>a8?5G7(VmhFhr|#OIGK
zp|_aS0=~se?(M?n>HcbVl=ze_(_)s_J}zWb>r5zWkGdGlUWplJdNi&$S6tL?KSh)~
z23fZM@PZi<`h7$((O}_zMB8z(O@s<Ir=26!vL<Tc#t!t+N-bStSGT~@{1trdJIzOT
zPvsAi8oS$dIwJCdwD>+86mY%6@O)B=!J08fjrufb{=(Cp2syj#B1SXdpi#V_erDch
z*;vb~fuVzz*uV}|nsa$9H}tl$#9TS}{Gy1sv;;fxxhWp5lK$EPbMQn?rkUR#X&JqD
zVfMPesHQ|$KqP|!R-efki3)BhwzRMF{WKwpCvQ7x$odF)L`;+2Z=euGg5vPksckOq
zg?gya%&MKS+~BiCymge7;)QDWoWz4>?zgl*W|(ZPY$o^qQVkT1mg)>>`g@h6%d`0c
zCWPvBi?=ajgGqL!R3eT!*cWWT_rsFJHqSz1{x%`*h)xic1ikr_hW5ta?ns}542S)b
zU3|(4m05nz-9-)%W441D+I(tsUG;d*ZzO4tK-(kKV)d+o1lj}mJG%b_W@wiArgq;s
zXGwFfBSQE(-=%Jna$w|VQkbd*@gQBa^`;C<m)wNtL!S04y(05&U5LwnOx)2kw)nlr
zD9Gzc8zTctnccti?W_xl8;noAV^EvE=!O?0*JV$VWi4GRZ+_<XvTTV3{7O+Fm*-i}
znC)Rlxn-N}rubkp0~9gpg0G;5d|i3JvZ&>-GQ1ujU-`p?y&latmOaa?cK2c4%{`ZR
z%AvHk?lj3SsXOZQr8y4caceosmF*@(ke#DjIT4!Fm|<O{n}WU_PL^BWzi}fC@)0`;
z2Qhj>IVbjQnxr|>uH8IjV;152$~O3Aor&F@r*PPc?fmovZ~q|-nUp&`XfWF@oJ6F9
zai~dauMQE42P@@VqL>4-JiQcT<Lh2@;>Xe=IhY|3rgK!Obg|}pKuP}ABr8sG=Tl>G
zS{sp#oq+AXP!y%RBaN?3ax2(9@vzkMV>SP*{Fz@q`Q7c4^|sINOU%hn-;&OF67|OP
zQf6_-+L<xf+xNdWS(UG9|CO&`r)3S>7k(z~!l+TFHecDGHILo}Ce~I>+>$F=2;nvE
z%^%#PW5-rrK5Zbw?2VuIvoNtU@Zv><YNc_tbsjdaU0mt7wHb1I)IQhj`gX7hq14KZ
zM|^+72-Wf=1Qlw{s~q`4uDS(#x=I3tsM_><KzLlK`n&W^z@)sq)#urDY>1>!wCKcM
zZsYTs;N?gDvgDVDw&$Ds=MAhpi@KxOkR+)_eN1!l(0Y%g84sZ1*Wfw5<sr0n(j9km
z1bM_astdDf#oj$vq5K6Wb*$GRwNXEd7K}6dFXhYRHT2+v8oX&BL~B}59Z?%wh#X3l
z)jyH>x);4PxEaNcZB&7bWX3GD<zXk-<?3#mDE>Ax3p^RNny|BoJ}Kt8uWq-Vt0ETC
zZ7(D?y=oZM05ZPfdrPe_R}T*5MajQMeadFGJ9(BEx-_3X4edUK1DvaFg<HINe)1D0
z{gGV#iTQ@vn;sg6lI)I8I%uCt!bX?%CjW%XEs1IHt>KD>nh=xzLUm@OWv@P(jEV`X
zX7qLL%IHVv?yrwU;K8z|1u7`SpmYN7uL)b?x*C2@CjE{SGQgy?If81xKGI?h#qx?t
zFWq;PJPYwAp06pt+rGa&ps{B?802X2#r?`L)k7->7lHsKxb0^EN?DATa-jwHIEsB$
zV6)aIhSI)FA30>{scH|C9t~*x&HX^kn=XEcWzGI0Ag)xeLzY=!Xz|-K>TV$c$lkj$
zEgP`s@KG9+I0AD=$JSPV>!7ehy?P?L`_#8i<l!w`5T3j<Cx50Z&4%a*(>9qKIh#8Y
z8PQ`~&irI4v%+MpsA%L_#lun3-Cd=Za22U$!Ao(KP%QQnW+d|xS*1(4^0?(>SKyrh
zC%jaE!DGQq6|5rJZI;8iydRO@sQN2@`*6Q{jt0MX4iz)h1x!?~`Yz1&ihS%vvu+{a
zV9kNS{yniQaB?KqCcQ??%nUjzm_(Sf!Pi#175pC7hkZ9~UwPE#v=DVhaZBQ1^@8k*
z<)({FYNC=Hk?}oTF6oY$UD}o>SS0k!eLZQxhCJ<nCC>}6JDT3~FmGZgTjktPkKy<`
zMC&gOPzqEI1({;@;31!a$A>J{%k|nW+3gM)N@E%A#noEzOeWFJ$SxnKc=5uwS-B9x
zQ@lT#t41tWa3FvXkIH;n*WFMm+ST2LRkQn2?La)mU&?85$-mb~#*9y=&$#1+OlQ4K
z@q)4bd7jQRBZ+{QIdT{KoD>x#RgHVy_OhgY1VpA4j+PKU(Jobvcq+F^-wl2jaAcp>
z!;dpUi7qC{RU;n1B?)L%vMn(zCD-de*8;egYzGyz5q}_mn3PgjtAEPNyY{Q`RJM%C
z;`gpj8sz&A1juXUFs?ne>RNCl>{$W<*Hf0}+hGo7r~>PIXY6Wbq=OXlR;6V#A@Q$A
zCSqruiHWBpeB(}sx6R1o_Y7;b``webWmQ%YYJ%Rm$RVFM+so)FPAUiCq>+QGWW9o`
z1o`x*ZT>KfKdmP3mW^YHr?BA3w{F3;z2h2n=Fv|`QEOMk&gP9^KdF8g^~ZK%*$k7o
zPy@>=G3o>G!?kL>kI>9Lr{p2oB$3Nfe2N3u@c;@l>{aB>rtNb*Yh}Z?HEgbpR$Suh
zLcIaa$Zm)J2a)?@$DT3Epshs3<z6C+&cfw;g+6V3-$d;rZnYXFEy+7RUwidXyP0;A
zP1G?<Q>Q42t1UCU`=9`7GDnQ+95RWq@z3TWCGvX8?B*j~!#h1o;}4Ie6B}Hz9XYlS
z*CY2|!wT>YPLbXABNiBk6*9RN+sQ2S)G^nSM3YgpGgK;KWr{t_Y%kuq0Tcx#c2Db7
z^}=t+x|$saR%Romq<yEkp!@M#;wjR{TiYXTZ7v*S;-tp2c>5YI&-3*xuGxXo&ni;+
zO!sQqY0ZSp2=*PCjO$fgZCUTlXw-{KO&l6`bgsL|wdh7Q8k;(y2rz9_48}ilYfo&b
zl96qXwN1B|KO9lghRkj+H!J?08{ZzYcSBHk^Tk^^et)=fPj1;*E)JHxYJhbKY#01S
zh`dUjFol%S&V!gMk?U7KHoCod&wthLh32D{;_hk>t4AVf3Xj$7%Eg-RQu?^v1ZgI0
zmx_fhooDDmsK5FlrZ`rn$vM`+Q1V$$UT>Dsr8Fi@vem(9D}0OTMoV(08cAbTo+Eej
zPm1^+#|D($8a4GMIoD?Pon;D6A5Xq7*V&~v>sR}rW9r~b3;xett8xpst=if69w&yy
ziZK9{Uh9tE#KoWy!=;zv7v<H<p^0ZLHq>Ge&a0I_@s+@PIiz>9%8xf#w;q;^O(CIH
ze9@~lz4c6itYNnMNiVJwB=v)q9~G13T(7C766O8gcSl}P3azm=AP1+v8~V*n*WTTR
zuhzD}==h#r=`oz8>+EEAq?!bB`b<ZJ-8Eiu(-LF5$gUl@O7Pvac4oJ~%Fx|$v|YA`
zZ!Dk0<Esk-WS=RS@uU19LmjlFgm&6Ttb4OC-j-yC#v6qxWkk!*UAQ2jpR6nX<`r<2
z*dFAbIlBFNI*s9o4`hg7c*rM~NyprAo$^5JovuYrV{UKWDVhnsaM4GX684-^YF;jM
zMX(2H&ijGYbMIOmj72z`lC+_n^m_@z5hwU_=iDc~x^XvDuZ_8;Cv@)!7RfPdh7THo
zga$W6m=&SrdBvyi)h+uiz1K=yh%FH&N~WQ&i+CQc#F<3nl$Gb#(5eH0<cv!i4Wvb{
z=UGO!SHiIZo8$)A#M!_0S8N*6UtJ4~qg2W}rqE0nM=f6H(qe-jo7R9w_TNQ4TGBza
zZcfKISlLiGEC1@*;N5II)fH5;@@hA!eXQ><OS>(~T_+C1F5xL(6!E(5bq(ob4Z)1f
zO?egF(=X22mJlX8#<G9?;@1}Rrt4dc93=Xwz`-gI1R{1bhEoK>DMjcqcXG0;yP=RW
z5i4cyWM6lKx)%}x@;oOwGP-t<q08%j=)LR0Dg<h^OW~Cwfa=a4#whG&f8*yH(FKyW
zIqh%K*iTWvrNi*=WhLL^*kn#qxBUOKal%Yct`H*h&(mE9Y+Gy`EDBSb?U=8LiAl29
z_?vKH{(kzF;QX|#D=1gmfA)&G#%?PhE5yhDc?*o`acb?Tgb4mUK}~`GG{KwykJss(
zFGIUvEz@FSH>WY#8bUIYgucwS+K)O8kGHs?&=BWuuj$<g<MlhZv&^@OmW+HV?S7W@
z_V#kr+x<*Bu*EVf$#kmM`>fnzeD(Jww`MZ&oA~$@$eTnXD5rAC7gcuu*^EUOMe`XK
zM!7AWcMNRO=SnOa_qnB{q(tWz7NGrQ*571gyqCQGtkg^f@BVmy%~QYlc;E)Ul0klV
z>e~H}zrNqL`EloT#h9i*l*_TsYt2mx>W9(~taQs)v=JvlE5H~}{rc=a!(iIr=e`rG
zC5;>S#vtPy=4s~W=;#9;H}`eT&CTKD{q5q30DSQ~8L!2)?o{82k<P+{7;aU0Bd|By
zZL$f0bg?<Va#P61?8VJOZ~UOqtG_?KWt3f0c*og!R?pw{TIl!P?c;j0bypa|cJ`sh
z=ZAJ5B;TF8<2vRyJb6g0sfSVyHe+j?M?4f@Fqo^myL<IEeuzUlkhomwR=<Ka!&W(p
z%Z^ABRoBgP$@{|fr4MpeIZ0nL;@a-g%)Pa;ieB<gYWj5WsT2|oqxE|Rp}%%_Sqv7i
z`zI_nAlng(Ua5L|Z)_;od7@$Od0(cgl@*u?!q-1f2VH8$eY$ogLkRot4o2+L8~=nJ
zXV~@VNs+QgY!4Vc)N^zaT*-T*uCBhEp>CLiy;v+;L$C078b{Ya>;9fFR=b%oP-s+h
z_SN4ql&xH-T!lm5Ck@}#u@Se0o>X!ZX|?_xm=@m^aA>mT%4qcvpZ?)tIQ~wk((d+&
ze9K&%P^akr83;-V%tZUqOaOQIQ*2tjUuIG7gKfaT?d=n#DHg`Y#$tYJQ-quc$v$B`
z@d*j@vRl9IyEJT2wo5}&Ibkp880CfHi8-T@6=i<w)4AXNJe@RiYuxVAL?4GtUvPs?
z@y9zI7+E16!q=x;$9WxR+hc0pvxwXIB;R#GFU-xo+T7Uq`u>{agntKbVv#=>`{d#D
zJJB?)9K;$Lx@|)0Z#wpN1Aq2;omRh*fw?)9CQCyLT$IM+G|}Kc@cJB!hmWqVt~$2~
z+t&r)Al<ey*bCe$DMBzKbN5!Wj#<L5kKYEre*Ic)hIW>kDlBc1R>R2Fc-;?UAlnU>
zSu1<acS|{eaEV<SDs2`*?d!SFLE`AXg)u^>tA{M{JOa^uVkb_Vz^X>Elb?vuj2e(=
z1gf15e&_37Q`a*zG{i;UxNpdZJqgKE3xD^nQcGr~$}{9(y{%3$egwp5wrIoJSGtbb
z?SDrAfqc6CoSA8`ac3g%V64`&*BC@VO(^?#y5IC$C|`d9aN;=xd3z|eqM|~bj-GYP
zqJWf;n#x8TO-M{+UIn8e{dsy14`x^cf6A=XCuplrRiYkzE7x_rj@!e-BdyfDc4P5A
zqny_wo9|fPXkif&i7dM2`uU^9yDK6k`Fh!%P!ORn3mz8Ak5s_BoL)1q1^iABF(W34
zcRH1aDs=`d<>w~727%O-AcE(CNly69r1rGVG2EL9HCw4tR#qMXejCy?69o-c0Io&o
z`uUSBJv|*t%y_|2HwPwfb$L)F*;DtWgwx>c&e8z>9DcU}IMVJgj6e0aTWdJei5}Uv
zRrI0t{OLg()2;8AeHjZxKgjs5viOdB54pcZbLRzf0=MZ&mAQ|+%n(-jE<F6=A=ENk
zJ^sf_(67z;pM~>i#CV;zKXE%hE6X-2G(c>~539<7FxZ@p)t1i<Chz;s&4Z;G&@Ht4
z%#Uxk8n-Z)B%Oz+>RPnL$kCh9jb1oRr$wEYRI_2RDdYU?Y=d#3VKH5=>}GfWC+5rQ
zv(jvYW0LEGsc&o}RRyOlcy3M{{&ESKmg7&^7BNE?O~vVJN#$BL2JFnv&dORf2b1fc
z4^JvZ66O-Eg3WJUPBI3;#NW_kMcS=)9@$7*bm>URCDrR@sz{{Xbun#*Q1}+_T7P{B
z<@ZdnISNUaEv7A|j+^+VV+tdWX+;H#CWA>LMo8(Q(vO}D1+jW2PQJ^($yUXN0I4j+
z_R<l=k^4OPU~uWomoLZtP$$@Zd46I54X`Yf$9r|mKi_I}b-bQxS64y}K+%@~ID52n
znryLsHExsav3w8qp$mavI`^pa?1e9`@nMAqAMjfRt+#&bnbFbHLk<A{z#EpBkMyD3
zNW>NIL{TeC-@T2wHW8Bw+i4j>!)AeJJ@zrp+k}Uvrh@h?z%zxeGssZLB=0g{61mBs
z&7aTuNDUzC-0g)@f|89a{j&jhc~U0oyzwi2j?2mya<ozdhOH?3L&-kN!@6A)XyD5y
z#qGN9l~SEr*!=UFU|{)srO}fu7GU5dJmVe@Wh9Dbu8D`^vTK?K=+cXZfS+7CoDp~v
z+>)>?arp9**6^d|)uGZ!N4JLcAG10)XZK!M0~}7d<n-)H%}R&m3@>iEvUhcRA75(4
zy6tngG*E~I5L7b$N%>t;2yw*TCiPk@E^HZ{n2_+=GK9R%4R8iyKU8ATJV{gK*q`&m
zt!Zz*<prbs=ba*xO4l;gr~DqNk@nA$m6jrnD%!Z2)`c&|sf8@YElT}4FfVP`=#+15
zZGCthtGB9pNv<hV<$aR4&5sDEut~*i)+Mcbe>&&75~0i8GPdo}kswN!EOm6saNiiA
zL1J}HwmtpiaR4PAy!m?MfG1uPECPu&kAL5LrJ-ICLRzVL`69i1|G7bFzZTVjg5gSC
zN>(%(&QzGO#4p8r>-O&p$n`kHnTGlimq)_X^g#F|@+Q^f<=qTC*j;}gu(jx3$@4H@
z4?Z(sWYst(+Ize0dZi^2#KpiZ6p;@b`SE@jyM!Yqivl8jJb2)tkzAQ=2l83HFJZOD
ze{=q9TF^$vwI_o`d*se|p(q^S9a23(M;H+=WaQKR^QQ>w?Z@9m3XMve9ClX6$!OQ{
zfG^!BaXPC2Ty_-5V}wM%QIvxT@Q%3xkS}YL-(B(K!L8O4*jLTX0j_!13_Cgvl{9W2
zZ1!@<`EShLXg=8JdPQuK+Zpp5y7T0>!yn=b7M~WpkGp>`y3;9CdaeKvmb$l>_!-8_
zZB!|#GQPyX^;Twzsj=~A^ykl?N0pf^yn@ACj!zL%^jV!ArOyk<nbzdRc#-qBj`txO
ze^Bwb`K}|zwuz^4d(e!TY9Fr}*YW)^|Ao}zE5gQ8(ip*0z?bM4*@GI$bfmZZX}gn*
z(1SIPz?gL4><1NvC3Q>BUejMYT<nf@hR)uBcgAT5J}jt$Z!Pu>5r)e%DUob4`%!E%
z#5z`qCq*Moh`@1nxqx$(s;MUssajd9ezfkg7~Yt}UkTk(*2!Y<PTp6qUg6uQd3~_h
zO!QRPH$=!jF87-LNSJLD0eIq}N%fNfdTP|UA3u3+@kuj<h;<tRR>>`m&k7;qt!e-S
zmJJuKX7t9B6)k@*J^7WX(HT_~?Y8{WsHlmv9e)RL0-@Y67ry{d=;>9qleAOGZcS23
zNCvsBUlQe*;KRKI{;<D4x?%{;tA~g8yA<izs-7<W&W|zxaQD450($U=F_|}?YGDxJ
z8wZC9rwZ6W>OC#D{agP&FYc%0<N-E6H*LSNvAC?PY&Kk|#Mm43kI`wcXrz)OXiaev
zP^bp$Ub?&XJt=lbuR#<0#;4!tC4*cR3Qen@z;d-x@A@wNcC>3)(Lxu2rU&cn=vY5^
z7_dBCF8*m!NLX07cJb4@;Klyj-bMf+h@gRjhpHmRWf`Vn3~cDSc-_W;Z4Qv{d=fW@
zE&Tqxa`Ign-JWWBc3kzAaoJQH$d(H!+`Q9{*FrZ$VuCpn3`*7kNnjbt*UxnVF|i#6
zoXId<9uY8!>(6!CUHjA7+dDa0Zqw$=9<=s_;=MfVu2Hb!^&7S3?cbmCK&n@sv>cE0
z0^SFe4?ftHD`^Hv6H6Sni9##|iN*)F3tT?4k|gOi@gC$A;fl#?_nuBI0Z5}{f-tbT
z3#e*)dklB76~LipN;}JMZ)sFrBds^^jK0IaP@Z1}0O8+6@@l*u%Gc+QMrh9M4i=es
z?r4ZMdIb_D8q-t$vi@q-b8jXhJ2%y5G-@F|<Zumu;drT4vo5ox-yhTF{TH%EIzH35
ze@NCZqvf-Jq)+|zocUA>a9@UmEP&rG;rxvVe3W=zQet8axx9^?l!#rS^rcXqnN>OH
zVw$V?F_HXfexF)F&e^1Lt*$+e<GFx?5@eUlzS+J0_NNFI%O_DW>Gmt*ODVF-d8ERw
z11-S@=lztCkx>Qsm^iQF#%#y?225(m;a=?6u`Oovr(YP6wW$YT9KmW&1kXd}j6RV=
z)4_ZI7ZJ9s^_W9Y*_8x;%G0kx4+{;+2!noLx5A$vZqGE>M9KMdr&A6VZ1i1nMNs$+
z>IMfOEwtu3<3s!b$j|J@P^}N9Rm<%j#Q`nhGP(r<ysnW8CJNwXeSKAf3#|L+&BWY#
zOAPBsxugXgCqOE1sw8fIePy6<=*O2AbWkqXi<9(q+l!e|O&<8cV)|qWr@K3?%vM2A
zLo!NMJEp;J|9M79{}$8gq7czwf^-J*#hO<1d?;YqOlo?dFE)qPeE{7Zfl|B9i>l27
z(4ynL`0Jy6cbXh&d9z1`@W*$;f1wlDm84}fb)9TtU=a6v=U$&rcdATHK97i1)4^^^
zN=l|JSpjgR73_<Oto9VMu0+vN5yyC*aR3IJ05WP2eV>%aodya98UU4b127zjL)oX*
zHi5aVzvSo}4X^bF$;1h|)^a?RS=4gu&yZD!OI6^`sygv*;3RPThh0J;KqL&-Hl_n6
z+SA!7NFVlXM8;=X?QjALnbYGUD+mM4NO$h)+P6@e<A)-+Nnnt!WLbZYZ-AwwuC2Fm
zq+6PrvI4OCT`d=|^|?+vT^_IR;wCmUrwt`<J%UM3zu}($F0*4JNRHM|yk*g}R*(sB
z->MPl84&yryZlDu@;LDfOMY1zpUBo?3orZK+t`>Fn@+d~slI2Gum_&MLQM@NR7zt2
zlBnZ+H!7;NGUR9~l#bHm|1=e2V`E$}5WuS4uly@2D?^&${Fl$XijrQ+D+X#U=@($i
zEZN!FQq*9PD4#d*a236IQ%x`r<sd`FLssNoFI7DTgNG~i0h}yy`;-*(xu^3kW1rW1
zsADStP_K2kJ!Hk0bjJ}xp}&6bsVzWEKJaFi1I({|p&ypb)}-&`xvRgb?R(R-wUh4_
zQ=*`UehxsCG6gn&BK{OtNKQ{D_-g>wVW{`c0dkXa5l8p<^fW8sHxT60z#Bs(0gN(3
zcc)%7+XDTjh5<4ID>P7%JzLV1Ea`SDE=$^b>2oBXN31$@_IHNj@m?7?mDaBy4M3Bd
z@ZsfZuX`cQjzc<xeCU??V*orpwPSYk;_r32m8$;v;ZmUb??_Bw7sdV4xcobaCpyf5
zlpO0bTF!6SVS+sWcFNMgk5Gcmdf%LV_tDajMLsz_50K<ZHz0sp02>(8L`Mkd=R`Py
z_v_HZ-I<TRlL+6!Ki|Sa=Hh)orf;6a45MAlhH4+Za8(7!U87;={vt|#e-ZjN0b62G
z?*b%4hbI>866g-lVjJkv!7V_<?%4rZ(Z;|b-#~UMZM?f(*()E2F6ycPSdjVS<>^a@
zT(E_-z*TXoD$J$guPK<?apIe2AmF!AR4`ZCVN=+r4}98GV5H$fjO}ew6D;8o_y9O%
zkJAA)x#MG0*`>UJ>TEOu%xF~PfumzSp2HV}#Ku9UXdm9g+WL<nZ1l5x<^tP=KE0JN
zXD~h+rc#${cb)TuVL(JC$BHx;EdlS1W2VB%$jFySv(wYNAnjy~0*EiIq>v{92^MP@
z4N=5cjhod_-%l;zxIzV=s`1;~+}gS3TDX!~O3A5DqHh+YMf!~VK9&ymHw_v=)>J~7
znG92<z0qnINwarbKL;CgZmO#*Dk|!oJq@tdV`O8lD~bdN?=@IbvL*{Cf{=?n7kVhE
z)S(02w1osyFgX=Z1cocT1@rg!H;XlSB!cp}?)_W05ODMYkc`-b1aOq=)J&fhLOI0m
z?+}``Fv~#9|6T+IiM+kdg`E_T$Fedbr3F_h8GQN)Gd@{z0HV(p^W-B)M8d&NGX;Sa
z`3b5<*gF$w-fx92kOrp9mz_r%{5PLbNNZs&omHenCE7m5VT}L##H=I<C)b!Potsr{
z2YI!%waQc$O{FtIH9?yTy`>ilm;Fk+RN1_L7XXE$VHLbLSI*1J+uP=echMpL0(oOt
z9h2ya&CyN|{=fq$?CiCg6;S=q&zCXPY0GR-n@>^m4b+orqKeww6pzj_ko^zR<N}+1
zWx--O0Hk=YWG7lmHvm%^wV8gu+3pFq8UhMH1X<<OWk}xYTUO|#@>@Y+VNE|;H66sW
zA@9+*O<&m%t!c#SremR!#~Sj=*g-+Sq}pj{;&8JUYQ*rVVR_lv0#M(yO}0I$)z34A
zS|~APA)^xK81-xHh^;<KwF?$a07(e5h09Hs-xUqn>#A|Z&G)35Ss|$<Hs$=^iE1)H
zxYum~rygkpB>Z@3)BaK>*|0+EJJng86cxXS0&LC$r35QxzKc^2vWnE>1({$NkkQRU
zE{F;WcE)C<q+B0@e$LTE>G@Jg5<m7Xv>XU6EC?8O|JB-s$eX~+zbs7-lvo6kKsBNK
zuf8BfP)`deT?Y|(76IxU7QFf3?15YU+e-sxfPEH7Q<JB4W8luAfHa{POpD>CZGVfz
z(z>MrIFR>79aN@%jzw(tL&)J~I!BwJP+l;|p7PRo5}`Sy5@d?NF-$YS-2+e}A;`j{
z7g9Xu@70{}PSwhB6~!$jiu0m8xl;AyngCkVKb`sU{wEc}MESMT4(x7TKGT~b<+%?u
z_{cU8f}cHrT4f~7F>aWT3~du8L&n3Igj_Aq&L*iDoy()u_!_{d)4n_uRopreJmko~
zf5vx<DR6WMl*T5&RVz$}*4EY?AT*)ZfHIEYWVdOHOm)Y=_<5`M_uz>Z=)aLoWFnRU
zxDPC(bhOH`vXScM>KLF)#9M^M0||{oipT)svXszB-9;Bw5THKvtaldOVFAE&rAI|u
zfI|hYz5M`x<K#dA?<>BfzU14Du<IL=_Wr<%@{K@9k6Q(<RPSGtbUi?5Rb*=<8m<ER
zfuc@tYJB{|(M;7S4yOARa0$1G0~uJrO7&1c5QV%G`2ODFU@=x5whz><Ry{5!dDLaJ
zN<bBP*LgTP5IBbKJz(3_sGVJ2K|?IW4-f2-19<F73}XZbH24P?_X2=Ai9gV#7fW<m
zv!&}ZZBcH&0SI?d4tJVs(m@?Vge1)e10CyT6Svch1+1E3vd-J-IE+<3XxG605I{fT
zCXC9`(p4g_k2HM#d{0BH2F2moeVbX-v??#mtg*v95&t{C(y1ksh7y+r>LV6dAQZ)!
z0S)@Ost=B6xCT(8kql&)Z-KaW1z-l$WDCj>gnh7Myeun=rw}~wAW)wSgU-qL5GE{i
zsC8D?&wpN*ruu`@A-{f2qCr&K*H>o?sf8_2D4)icHd~60AkL_ZEhx#l*3Ko|B?gZ!
zcr~oRh`SRWT_P^>AUlbJ>LePl6moFj?>`YN;y(-u4PD5RlR=(ifa28c52Sb3BA1(1
z&w{8z^W+6T0)krnB*XR3(kP$rOK0^OwPgB?ei?n&H`Hl6uCUj3%94{>A{KTXshBH*
zcO`hU%J?|P3YC23!o{nqMqFK2?@g1_wyd;!P(NfDSbRvW41cL@1ZHyr#T^S?9JOST
z@&tRyv2C$0bHRDEDu1a<v{}{<_#QbI<ibgj(#D%FTtMO6d!aXdl4_KVBEnK(X)x#D
zN_c*pH*k*N1FAp<d}=`VLDC(6c_AU86yQdtpoW!ARoMt2h6)LQI+##d)SbuQ-&k|2
zL=0zkpQL9Mb_Dc2i7Ibrja@ViiqI!4I6|jR3@~PSycfN7i$MfTDhZTP>Zm+24M#0v
zmNp;m2L^60Rrl>3QZqFH^vSdw#QFV_`qjEX@FZ=Y_LOo+rtA?yD5T!LIo?2vV1PdI
z=FP!~V!#h}o&%D8=niCNNJNkCY>et*?;gRHbR!p(;O1IEp@A}X_SWaAKJfG?%7vO}
zsfz~npWKc!EJ>$ojtA?>u@k>a&%whpY<u1st7~Y?nJe79&~bgT)^p(sK$%8Kbquv)
zstN`ilCD}r<;0ZDbjnyCsB!S~YkpWu6z{oblkfd*-x4Whq&H$j%WX)l{g3K0AYL6u
zTybJ|`OmcgubMd{=|VT6E;J}8xYprvjCgQB?=GR#rE=2(mU^j~R}*=-9u<<p(MSeR
zea{xul^#m_EZ?gI&i}0BDx0<p--D#B)!g)8>BXvkt-un+7bi79W!J|Ja5{s0y}8`^
zpUJ(oyVLJD(m}eVNOc8(*-C5Pn@=XlZnSgf4(93FJn-=$u7bn~ikhaAX<ir@1pxx+
z5bTZ$x}sf=6e$F{Cz)R><z5w#P8^`fx3>UPA*kcAo(cQbO!-v}%K2?z#<v^*32f@=
z>ABU>(IFtcnDOEY!}WV>>wwHnSk|~&3{h-z1=G+pH!05->GW!USow^=C$-aL{o6r_
zWy=<vU%p5;^T!g9D5+pk><=grQN}<D^(T{dLIMChCr*GV4t#K5S4T&u?s02)GF33!
zQ_D$>sijmn)kKNGsS?<}ZXmf+;t*8%{k;MAyB`Vo)liYNhNq`z4Ts;<vv;H&0%eO@
z;=dtHE%NFEkuIMO%F6L<GCo0-z~S*!e>{8k><}n~ZN6d>GBo#IE^k|{e)>RX2xq$#
z$vZMr4pNvP67YW?knh}^c}(5vcs#YT^tsxo1+#jiQcKCixVTyB0rrGS8$3!m0s??<
zyRS`siBiL4D86v@z7;nY7wLHd{;gD0ZClzUvoxbrEvw<kD@x4|Oryve_pZB&3d~8J
z>B>9~7OMsUHDGU|F5rOGrb;<BZ&1)$OyCIK{rK}>Z<7v)!5hDrJZT{LuxgGe)^vGL
zs$@rllllPMZwdw{o`l!Qp?tvlNy^rkXRn3-4;K4~z?t`w6P;dxkDjKjUc-<jX%Nc$
zYizV)cCLnoS0-yOQ`P-1V9LEB9t+Q(5~qJMGM55et6T*otY0}G&&BTbUWdd%K{?EP
zMmNE(Hw|Gxbt&MZjfprkgp}lU_&~vm2J-Yibuo{c4ENW*F;b?;JClTZYH?jL5$d)0
ztBdNmT1j_sP{l-gh?<2U88YM-7e75cT?9OceW235_bn9>d%OZe5f7v(d>N$>$Y<+d
zRnAA$LbN_IO<i>aaH9rMwz*@F=b(_*BEb&l6%`dV*VFT`&cV_wcVV`{@J>Kl=&85t
z9kq<-k7Fd_Y7zc1;HoQX<9Vq(sQ3L8xc=&><z-y`@=lDIA*@iN0EzSYXCvp#T{s-h
z1h2dB@2CFXe`yI85bCE_c=fb6%ZwLJr(>w5SCaLP_rbXgS;%lA+YQI?EK`)L8cQ-C
z?mFy`rsUBIa%X)C_%R2jzg=LjXcGM!z}m5bFu(ef6r74}iwSarmz;omP3^jZziKG+
z`c*z0`w8*qzAv+cc#6+u2t$TN&pDV{?NqrX;q2nxUMUUvG_7}X^=MHa9E-~mdt5O0
zUWNzcQFBaUYIcFtM@`90O<Mtt?(QRdte<@h?pOb(aUTEQUAX@Div~K>>j#fo#&fQg
zv;S^px=g)7?x^Ku{BIpv5G=C~GtR*Zcpa0spZ4qMallkH&cOD~G4+H0{B8lb-I%Mz
zEa3?(z*NtwEwGZ;;wDa}Eifc@OTl2`Z1F-ZQY(JTDQWcR0tRr;<uU8<*A==(7_k+W
zvG}Z+;vL4SLqs`|VO82YZmT-$iK68Be|oCWdOx(rxj`@=Ju_*>#PTYc-|=}CxO8t$
z%`+<N60djs)`uB#fG&l4LmX&j>0B+z!Nok@9tZU`Sxd+#oe}Nh)E~#-=9x2fjEuXZ
z3GSQXP-A_PT}~gz!gTJRZ#{ylgi4ZoJQ|wJFlNZFgU!%*ulViJ;9v#Ju|y9$hu#ZK
z{B*-A|9lw2be?8n3;)NDoYF;<{B%f;&^FPQvfR<CQ~vh@#M88X{e-7&c!C9*a&?<P
z2<kMQ_d_UQME<=Fp-h_-^s@STsc)UL=-d4oGgd4^8$JP6^RIbY<ex#@_Fdc?dN$N5
zyVj+bV}48+K56XH$kzS$6oT@{8gM(sGv2}BaJaw*%0;VO_&?n<2P?Q`9J}Ti-9fxG
z(y%^@MOfgns!46+kdU79zn4FRo{0o!_!+askKg|2!MGL~RavwYIrL7?@$x^12@Q`D
zV8ytV%y=hW?t1daPfonG;@=Y(JdK!o&|C`7ag5I5))U5GZo``hB>ekBCmGMV!O-e?
zyies7Wg|<fEGf`HUv~QulOfV!KW>%Bmf@KK=lLHB7oTUm;jHv~(?X+cs}uK*%jg!D
znE8pY!h6sBbogGqSh#*Kl=IE;>9Z_Kt*4b<a>no|oT5E-`t*$(r&;Ry#}}d+Nkpyw
zaRcvCi+aNW5;>rFD0pz9cYj;-p^U}AWWbWi0=>Z{YKN~1&=5X1cq@jiz9>k4@<Bw-
z=k>ViIh~`R7?hw9)mYzJdV74RVWwr9`te8O7uG5+LoLYu)$XCD+9m9ksS*kz)IG*S
zv<<?oLj^UA2Kf3U+6F9L!O=ai_=Hg3pjCaB50TKS_9Gt&ZN0b_*$XvH&YhtrB^}La
zkqezj-9Ir&jQZyqlt)>#;O=A4xZ#WwdotEj0e|~b-)3A~D|vZ#)LZ`!^<C%}V|wbZ
zDnb6zGh8bv@+@4TGoJZ<gZ1cGKeSt9LKX~jwc92wEdoQg(Y9SjUE(ymQ}mLtJHo9p
z?+8pnt5v}VInr3tE6Q#i9aVz5!Tx6iq58kyke<#0eX9F_`U!|rRC3YJ$@qbp+?i`9
z^L!T}M_3;!q0mw<HX;A0c;)!ukvFS!U!gzx0yi(XX$v%9@S|V4X;C}<4+^QM{{umP
z=^Z=5pU{|#|6Gt05%)h@;=evb@|AH&=F`n3Yx>RO&-8PmuF>TkofV%Hnwaee-~Rs0
zX~JbHfZCC=)l+^Z;n2Jiw+|0%b_!Na5k8@&M>|SRgB>Ah?DzdlVncV9o-I+W*-|&h
z=S1BN`Tu4l{|Aimf8Y>D_H@_$cAzlzi{{DE@PToX5)Gixr$1#F%o(Yh2H8NIxLf}>
z?%q49scnrH#f{4GSlH^eAOdbh5EW2bq;I!JG-^PpQUWRh(xrqJ+TLtIH%$q>MMYXd
zZ-Ees4Wved1wu%oQX&aSBoG1#N$yHK`<!#{8+W{S-?;aV_bz`p99e75HRt@k@Av(_
zk}NLZGb7Wy3%jebM(OE($%QK4A6&D)WR1H2O&ePbkBNNx{on)I`VL7h|F2Pj|9017
zWfvj8C}J*PUt>~%7J5D%@6-E(K&qVfhMU;^r0ZmCR0Q`Uoh8Uc+H+|@J2u^xM59Q*
zgn8!B50or!1swn$T`i2`udD&|=X6H^?cpI+;>V^D4*iLAE?`VTwj%8ieJep$mK7A&
z4t=!r7oU!t|9mC#1~)TrIT=f{3&*D2<3ygr;NPw+JzZEXw<88LM1`04I%pRmd!+}0
zQU6rb<?NuHYZ!MMWk=|8h-u(kPziId(iWDz9BOli#c%T_&^ASS=rD;+#@GM&ZJ27~
z)l*v;TRqh>c!-2O;GkU)$?}%_C`Ix!!9P&ek#7dqAo6wN$vkW6hc}Zp6PCkqc}s8Q
zZZbY}j7be-{O3*V4gWlaK$C8#MszJ>SDjRX(g_5;c}w2ly_tDS=1RAuXm@KCoP#%<
zx5U6C@EV8b7r01_6f>(XNS=7Ly~U>0i~E^gGf)*2%za6xj4oFL1zQ)SJ$eVLVX{Ev
zRW654#WH09ms2%ZWmlnaWkVS-1ZWtDgLr&)-q%5ITL8qa*B0lu=d0*GqMYnwhQWJM
zQl*`fSkm=PY<c}{N3|<s8=?2tn}74E-J(*VJbBmoK@r5MJ%&3lP}SHG^(&Y1(nvbO
z!uPA@p|o08N4R}Q37&melv2-U(C2|bTj%UZ<~SXPb+(N&y6^T~F?Tlq&Km}7K%phU
z&;rpykR$5)a(dYecacMP$d5L$tYNto*^}$>FvLBSrjk~bnN5+iaCs%D?b#X040Sy_
z_kDAHuiCo7rT+$K+ft2Vn5}Ap<xWDB11hUR#V>{pv^Vo-R}LnqR27rXVCw@RF@yz~
zX35}Hj|*G@EFik8IJ%U-q{fTBld5>~6L}$#&Mxa6!m3oq*5fBvxR)%`2e4EO<tE(d
zzWfgM2q4^A6Zz`9p9%}d(iM^e?wqIG6uy04a7&c9E5Zxz)KL|6k*frR<WE1WrhlZD
zj?Q!d$pwBF>-^J%b>d?Cjxsppq4AGIpWu?#`YKlmYwBS<#SC3in$*ya7=Dg)+<kzs
zZpG)T6E%5nogniH-&jo@{L9>9fR+9_LhM$_Iuj#ISqSprKBw;l-ky4Y;3}?}|LvD%
zWyp1jLB$_8yth1`hG!HVAou+FF?4o!a=>r<T0THUcCa6&jqR)^naw5ni$vrN6hM&o
zYq0t|Bn(aO9F>ajX0QF7E$pfxfTuT^!~0E;8{S1%$=^66F2N9ox|&DUdeu~V_ry!?
z?Z&rQpNe3=zl&f201_Y6D$oXAV=iDuF%v_CFeh%_^kb>IekvH${nP<P6?hB1xRdmC
z;N|E<4QNb8_4qh01jwc@(klTdpk5XNv7|{c<Ba>)`0xt|9z9Oav05;$hP@xozr~FK
zMu6?u?L2GRcA&&-SVi|f8eD~pObKL6nb0e_a{y*PH;k`|^5!N12XbyKZ`YkEP`VzT
ztiQ6l=TwZliRA8ES%*%3>UaT%h^7`^!&>Yk;+hfWKCE-^%j_R*=g+50J!$tQi86R!
zMFIF)X3wenyqszU$zYk)I~DP~V?Z*7*BpmM@aC0d$DMW#SGTC#O0`M4CyibXc0K!n
zBG_r`rD=pSiZK$c(x+sraL1j^q!YB=p188sJ-B9&>?7UkO=P0e%l7)H`OxT#+{ELq
zyccxO(IpjI$c{0h59c%8t8B&wW2IduMU`7S6@0d^aSqyOWp%9?yz`(74=&4Xm@3rQ
z?VNapmp)*;5PbZlp%iLI3WH*-8@uvLN0q^U#VUx`o%Kv2JfkT(HG=8Eh-yj&v!A{+
zy7Z&-^}|NLNcVqh=Rxq01_sv@2U=NEc}tbxQx0{+bUTscs-#es@)4Zmu&%DFrA(W<
zXEgW_>BFnGU)Vy(8BCFP>_HTG^*HPrlWIUW{)}z6;91^MQBDw<>D41Px-Nxw8OAFR
zVyJ^DaRCYwrh%Rtoxep%S=G%0iGPm}`+07-1IM!O5Akfpyuo!9`Tv})nGW$BrrLG1
z8U@IQ1Uhxp@kx2KSR6j$>Gue0`~ggMoS#Zhk>KKiYw5`taYc&*)t(%MW0*M!U&Y7#
zwc_ee)l*KUPou~IMD+C-H<Z6(n0&aK8#ypa3d1r3XYO`8H<w2FAbhy}bVX$Da_WL_
zxvlvPxcPmlZw9yV*jaDTsGL8!5EOwJTUkFv3i<kd=Q=~DJ{56O|M)JP1jVRiiNVXr
z*gyP>c-h}c-rB<+2ITbz<u|iTV@x7IiqzfdUsG(Y)b+{Ceku=e(+*-)0`j(s^Ap3E
zkQnYI;B9iiABfu*xG(50E<ze!nK^eLMqn~^W`n0bnLdM_h`59Js#Aj`M^Am05qfgh
ze)BChx^F7;si5eF@bmM6oAHX6nf&QbJm>Y-^w?#8FFQO8rFir;p~$tvdjxj+T^>-r
zpS)Sdm->Dan`c{F&1ynVY>#j#ck|_>_VsM?i3tQfCrv`(KjKO-mTfa5ru1hx$58E4
zUwG7rfC-oI*JX4yP0dfH7`jG!hfS{vt`M<pX62C;oPrd6ew8l@fBpA!82YCIWs;^c
zC;lR@;b<oZCw`xOs?$|uE_>_24ux+b7p}4FxI6b;J3s3;AEj~CPaUx!yXnT==fl&O
z56qG27jgARUB%@z#1H~<T7dW3_zK69`g(_-l>l?8aZ8g$o3yJcE~Ixws+%qZB?q`)
zbP+8zhHWLt)Ex!&%Zk<WnROf%TP>zyu^1&;p`lbV*Fdi#S{Yn>4aj0OWdW(UZprUD
zR57feJA=O5DwXS*a8S|Jhf>T<ufwFHR&RsAFrYi*FAGRf;Fb#b1gfE~*`25P(i;|E
zgQ^bNg#6*}0fCG_ViRmYT%0EMLVWxM{L&%#_c0l?UJSZQM8JD+>**_-e5I&dS0M4`
zq^9xDqs#5K=nAb`Q8mA355pwvX{>biaRNB5*S3K7ZKEDgkQIJMtHFc8f2rI1>!%{p
z4_AEe-@mG7+l>_df{+fqdDK}N=E(dz7D$|WemlI}J{_xUTjWZ<%iq|o8)AvsRh&k|
zh7DFqp(tM8`)fR}m97cA&OXD71;9Y-l0%&S{qJ_|I|UlVVd~xEEWcLdTeEpF6fqgP
zf$@1A|4*EV1dmTKzpC5RGfG6E+XMB$c!x{K1+L=g5)|l=qf0^N7F>ctR_#Ab>0&S0
zAJF`a4=1Lm=>8{mpuBpBRV|uJc<td7w)uZvN$~3NVy#Z4#khns2iKHA0Lwn?r1V7i
zNJ{EVB{P!;*YLuspszA9RbjpQlYf3O1omPIn74;}=-xn1{^m~(`jfS5rhu$NnN-NP
zb{OomrP0H-G$|Hr&bS)%ZgB1Q-=Z9=i=Blm<zn$9J!^F7C1B1Sf)pB87`aCgDx<D6
z$7sQ&Kwolp?C%u$p_FtIvpHtVlkh@?xcg6C?Mu9xX!mjSeLzY1jQQ7s*JQHrCf8S%
z`xnY?VhcQb(r@-4tO^vme8B+)$1n~<g>KhPvHUzbVId^2f^}UuyRw1C7(*O)cGN?E
z6ybFBW7`6EeUR=H;R!0HzwQjxcaION3Ac|>Zci-EYqXc8umdN0*Q@BWYQM4BkoAMn
ztwv>0y@Q2p?>(Xbv@#7cS^T*~7oj4d6f$)+60YSW91o4Y2I^&I#~|6(w4VQFFoftI
ztXtpwRUj3_U}2ztLk5n%vM|#)wXEDj@|<V6{*w?V;`^LH|D9w@CErE<)(9phUwh6I
zx}xr*{N=*<zo39BU%*r%w9nkeFk|sMGoK;XIAX`}AxaOuDRKR`?jgAfa7T6ahsS1d
z#z`Bzbst<c|89Wtt}_pR?uws&uSyV-!@ysoiNzVplbIU;ti}7I!7UZ+&?DU$Ebkj1
z(O%@%nC{dV64*gi(xm!g%Xhhi1fC**la8aP{u8SY7=7S}+W2kp&9}aye+=+%c-uXY
zKYeCVT$x7pAJzxN0S)rZHd*mHc4VCXej(yQvpa4AviLcgXNb&i&v>>*HE=vjSaOEB
z{O+vp_!jw{Yxb~)?UIz^8V!`gKH~C?qewL!HH^++C`{bG>D>Kg{=UaVyDmM`UH%Q)
z?DMeINjMhCWDS*L*|}-yjDm!rl5k_{Xrbv4yzl--dU@}UuH&=iqi#wS${+>%O8%^l
zo`$iTGmMusd-kE%?uyr6QC&k7WqkXuLmWvt3`{as8z*{2#(=t~(wQ_7PHp)zBB<cL
z%UkMVhF_8hw>!UX&n9*h&r5e^La)tn(o)@_wjfO`SXs(qM#hnyuhmi)h)F)ubl^dC
zFoqGjBR+>l-{hi!b-)W_Wp_#6F;y__GY~qw4lHBYg~~*%nVXVfb;w1oXx{?3PB3-T
zoH&)hD~?$ndbQfyyS1Wz%CA=WM$u%=FTz#b+CXgab-1%>Y1CE3{pMQOqvDUV^ZwGM
z85w{m`PpY#29fx;M-C-cw&i-;0=wLCG?j_FhyYl?-7qY@6j|9=8g-3JX$zHp7b~Uz
z`X+7aR5=z2hBlDuPFHwKQe>BNM>D^kE&@J-0gxP~%ctSPcTQCo&+B!XitS9Y53enr
zsuA85JENG}$CFQ$^>(^^?8M!ZtV8DD=}$2v9hx|riv)DBDz&a3iX9rIhFy2ysw2W!
z-Wls<$HoTO3!Ph%r6X=;U)(~lGse(hz2OzH_kR11d?~|6WhoddP!_oFX&)Jjdn23J
z<9SOwttzQ&iQnxM+T9-M8NK#TMtAVpML7a5PXXQbuMtl+v6&e2JVWvF7Oup~WzJkF
z*os+R@tN5(?_Uw%WcniN+u*V@X5zmhVEq9EG%MH-tLzlx|1XwwF<|8WMXoWRiHUQ)
zr6OH<i5>`5HU)lNbw4>{2EO~ob?FtxFzCc0WdZ3kZ#~-Ic2Vk{WaNJN@8y@qKrL*y
zYQFu{3Ay~ta6+0JJD3GJtGFryd0XGWd8H%LDN_E_l;$gBEPN{V5z>7dUafWlM52k<
z3UIS?s|d5jRV&{xz3Vy5hS8)?uiF8-kT^$jS=7wRh8srR_5kZP>ne6H1_Id@g?1I5
zM~MDSO8rOfUq*N$?jr0_pTev@vn&tTiA(wS5I4C?^l$&>oK7zVf6!YO*1Uo7<1UT<
zMm%@e@1IqwU5&4s?_ewd+7gDAR%}KIj(0juQL#^V3Eg{Y@lUllnvnKfag&VwgP*$`
zl#{DL8^CFTMV4Ew@jN-N9u>D91eoWGJF<!GgP5R$=`m%)`=<M6ypscNZ3?h6Jq;%7
z&Mdu16uU<5D1HpmLi-2c`N$2Y;7-rr6+L_84^|vV<Q3A7?`%Bz`2k>Mk5+iPAN$5p
z%rz#}eO0sXzG}AqfU?q7dm6t}=GH!=Ia*xHF{{p*=cL^B+L)D@B3}N4rsPr&C_KNS
z*H~;$1o@fn%#bYi)weLcNl4`1uEqqOIjTNA`w6l8n{s{R%yVaup;k&l(w7j^uo3@3
z^87L3QDvDbgA2^P+?RPv7eFGP;MpVA`%jna41Um#U)WP)h>uzxTzCFYHy-1Q=mUKR
z!j)UGN?^V!qkkSX?1iD*3H*|OpZ`=s_*Fy~eNpWhs~#}MGJzzlmHuQeR||;22h|a(
z@o@<9zlFMmpzrtuNu$F7?@=1~%QeQPtFyYmv9gKpX1U{n|47~F7GMFmkiR(4?k^wG
zVi#vE%uzcP^`Kp<&4SzS_7RMk6=r8lW1EZ!Mh#Qf;dXG-BB?@lZ^{J|w7!>6zwqdW
z;_uXuG5r3YT&eEYc`$o1Mh^lK@)!G%sD<TAgM)^ZMiEA4+9U(%?577n@*D&Us9CK$
zKG@;LonKfR0o-;@e~%0D4QBW6?1eZ!GFD`{)~Bz>S;GyKx-fmPkJ7<6pMxr}6K4NO
zS=Tf!5P_wC^kKNxv_0deF08$vQ)Ic{_o%gF-#1D*d=oPdIEGEmzE5uV>Pc6q_|DJ}
z5=z>qJL6?f%8no(@ao~I{O6UIx-+`rNdI25n)Wcy==6U};qMWF5Z%rIb6+qT{0;~V
ziRJ*hGpS%S_;~YRKKM27@9zl6TiWuUONqVb89g0P>rd@P{FC2EH#08LXC4T7^(l;E
z1Krq(|CC{1fr8i-@n&$HV*jn!$rS0C_Rqjb0VL4c)v>-tW@Y@Fh<?}k08H`Cm^<@r
zG<%1g{}Bl~b6<rqOF%tXq@MzA>xNL=;10r|_JE$%-{?HKMfd7+c%wG_K&Z6PbpP0e
ztptdze2uIA^7K;sP~^S#-^myU2YGDE&X2vh{uRp3F)jG#d|L!7G&*2_M3sJ{{pL*K
zjtFdvJrTpY-cf;%mJg}%-m_S2EnHd2Z(e*%Hyxd^6XfFR`N?#vg6NH~@EkS4PhHPG
zn+Hnv-~BiLd>itA<KqL7ObX7(-AI%iGl&f03foP?!<tKs{O8-$A&x_WFV7B1!-yX5
zimazec1c$0Wbi!;E-`J~1FZ2>oZVVd$6y<R8=_0A4dB$9RO6jd>Ffb7*SH&q>3Hxr
ziNL>O5|V!r!Cm>(qpYVZdgVHA66Cqz0Bfmf^_*|>#nNm8(UGjAf8`6G{oo3LlG;H=
zJ;CxA2E`S3TctVH+jB!!K$aGa>(q8M%du(LcFSJH{6)oqH}iQ*6f*-J$YecLnupN}
zRK_p)PGv|3^N};=Ken=0mg+Y7Y?3+SCg;#~lk78+T2*IdUwbnYtO;E`3wm`{*cQSi
zJ2#aImeaRMI%{+(2q;D7hI}22V1V4OCev(HM&Oi2v$s0fI^Rc)0!uL4SL5jp2gq-J
z-?)gR-T@H}0BO7srG|WC4|=@bUl0?&cX>N>BQ3a>z$pD09a%n;fMxKkeZ%y>T}%kY
z=@Gp-)OxU}lwPas^u4Gw&v=h{9e})pi+b~VKY8HK)=!#&fs8B%Aoaj)FuCpoIvMfQ
z?EI~CjL3@{4rIv!%I#eh#So>gD(G5=VCXC5eN8;CiSCZ9MC9Zx2@-woZ_~nvCVpwQ
z^xpGr3a21*OLbS<WDuEiTi!8L@zj@BT^kHAE|uv%u>*l>#`M~=0~>a}oCNc2F7YOw
zDt;5SVdN@4iT;i}PpIa92;FE<LvmRG7q}ntmT~eQk4Z#(=GO~b>H-VKr_4;d5U3*|
z&fG_8n`qYder8}iXpd5Y*&B#^S2idx`!^zMR93r#-efq>73jsv8BB7%<N*$46Vrq~
znDTILaa?5<{G}9<s=!GJ=?guA*Q};`JZ<GB(d>q4o5Z_*qtA`bOskQcH@vt7F#B(!
z_baV6(0j2TuT@tA!l`{_Qov+nYW`$DT##Qyh`EFq8(a=AQbsliQk6Q#?uwnlm!$#C
zR#F1*o!af$-;{mHREJ&%VzriY4}u1996<``qyNmA9DMf7{+p}$^&;*kz8dKafq-{M
zq|-C$mH@1*)@@96On439$!&fRDlN)Y`3UxYGpmH3ilge?(q;^$fkDxCg5U>U=<I|;
zY)Q`Dr#4ID_}s=w@?C$8pQZU@8M#{(1y$egef3u%@c+L2^;hj<Dl~qAS~=C3M1yGE
z9xg77ix)~{BU7a)cj#NJ=Tk#t|BqA)=||&=e}&j%qN-2AbRj_+hsSSXT3r_uC4@`Y
z(A_ZkT>77XS0(?SFXpduo}>`ded4g4X~pf>Blmk3W)e5~43Wy{FM-e7S?;AmUvAb#
z$fmk~Kx#XYBe1GEUyQu1^MPrQmeu`TQmUJ<HP^X6G%8mEktbHT)_+yo|7kA(l9_Gs
z$N5-#`+oA99|%vQ`kohn``T~SJz!1f#6u-z+HfrEdQ5D*({k9aKv6mY8g9Z-OJ;en
zTKuRKDm?n4B-pX|@&|_9#@<GregAXkwdw#`+!B4*kVpkUjMS1;v4sDuV!;isceEkD
zFw@wR;if?n`r%MZoq|)=1Cs+}Z*>v)6YNp{z~<HPv2`J&m_+QFr5+%I0<QbB^?zs8
z>;FaV^e^T4OJ4t)!T_vIuP%r&29~|G&Ud9&-c3LD2~HMWYK}D8|GioZ0aJw%g^GXD
z@O!u@AV_9nc<VQHTT(bucuMZNqU6#+u-vv0Qx7MdKKxLd{kd(Y?DyJ}MgLzlVvjBC
z{z)fz)1YsFv98FPO8Cy<>(aJG(AW+1Y%pwI3k0yvQP<l`;Uwc}&$aPvj|$$Ob~BRs
z&-{3sv_XCrH3X(}=rg0Vo>_;2KZcy;&K2udN4D={NQ1rc6W}7ic&>y$On*gRp*I5J
zi3t1a>>c5_o%a`W$D6?1BKUV0WW@dJJo+jSXhn?c;D@5R!<&&oh|8#7_`jq(T=R{+
zJvSwZ7~-3u-R%u6_FdPD>|@HHp9GKS;RTy3By-4IRr>@_X~f!K4qPLmont!j?B|E1
z{;%g!cl$XKCP&Zltze0s4ai|99Fnx_qzS59s+$SPymh)UrX13?^w9>R?I1rulLjXr
z<u-kx*L6hsBkpjufg1o4o0{w=MVg!B%>?YgML-s#uHW{v^t8KO{hdN(qfttLGlpl&
zbG<K}z567%rjE{W)mbU%eLVJ;_5VxS|Kn)$e^HqK9aH#>K3BZ;6IeHt7N=`QdrQDl
zKb8yy;;?B>p~p@w!U1gdIbJ!Ycd$MO_(}(BJ6;FKV7G<ut8lw6TP4C*3;Xx0^k%?K
z3%~p3PD%3yD*q3{1tafIQFVt*1EQ)R^7j@tu><XThrkKw<`}A!9#m~3Rwy@1z^80m
z#Irr$8uZNVI-X6PyI!qQF?{Ih{;IG7OsDuw0rv$-rRyum3>m9RrVDPvm2)xvleS#G
zfYSULQZ0lhP-0(}b_loUWY>1d$n5a40UnuudzWS=9J|QV37?2v|1vCg3p`1+c`YW2
zcbt9;!E`B9j4O=xD#-!~cI>cAho>$CMlMPMDF*nJa<;EZXmW{h)bf-#V`Zck-tiTz
z-`*_Q)kXF<UM&uH3snrktydg7K>nf1$TzM{+Q!p;=zko<|G%LoQdiQTWTq?X`DY?x
z|M1@_S%@p#F3eS|5)fzGbzP}3<xq};gO$9*5^pK({Z><m#tzrSJ_l>3ZS4-)7_G|U
z)~B74tCE(JniQ3qSTB{@z#36&6x>W*@_F~3ZF_0#$Mm?tLbsbmBX{?)tFh`1IWRZS
zXsg%1x940va=EZ8shQDpFGp`Oi5CO=m8|U$hzYRi@HL_Z!R(j$(~_xV1Wn!}JF8^P
z&*{cSApqrh7iUupSPYz|F9AD&bUPA_{Atg0SybrjKZbd^W>IX=-PZl&Wvi}@f3$D4
zl&%B|c)zR8>erTa0a$gXGTkX@^hlsZ4RTlTgwCf|YKpX1atyW=C57UCV@SrXf|C)!
za>`?*oeDg3Z#P}iT0kPE)VjRdpgBM)izn||Q5aaSrjL(#$AOCgSUE9$%5S0D$c}*!
zb<5wl%x84>rO;(LO87`e)x!Z1mrCL^4`9>aq1xT&{uV(C1XE7zieft@^4;Xba)$~1
zN&M{_KeKzwBw0YP9ug+)_NWSyqpsw#)=*Yp;_uX#Deag@ukP)2ssXe5aR-Pg5bdRW
zCBOy~Zb?MLJMMKCOO8s?MFh4V5mVA#dk`dSuf0lcao1Bm5Irots&V`C#!$e+*a8n%
z<OQ(ung`a<3<k|wB^SF79RatQKA6I;SDA35&i33zE0jsI#J8#?y%KSQDa4P&L)41S
zM*fH2a8hzuwGZra={C9_#632;lr3Aa%%v9tVq~|Hl=1lf9v?0WvyC&kUR`U@HYrU6
z#s4!O)bl^Hb?LwBe*Y(;X)~yUXa25;K^>Zx`{9!Ob?N>ucMp6ZVE7AQA<hIW#DRY|
z!zFOoi&@5`%8nV;7-9JQ@0|&^;CsRLx7q?pYZMoZX=MS)4DpBo*tlezjK%*S59EC!
z{XM<T$Gy24Uxu%ls_*`e33is<?HmJJ9!A;Kv;{9Pn=iH#Msiv7W58-#xYQk|B*uq(
zfL4q^eAz=oo0-No<13=<xn7Qw(xBz8JSD5)SyT*ncvYF@Y6sVenMUo!;2Z*nXgei`
zb@_U$wX3bE$Na_@(3HuTWBJj_X_$bsF(&u;8{R-;K^29(umoJCQO-(kucJ#y&^WN&
zbJ*=82t{l=bXDgFB(SZ{oxd>&oGM$63!3NBY}JR#;gb7rotCudjO8u)V%)GTnvFfH
z^tZr(XEAbW)9UvMP9dn-%4c;&L$X!eFMss`J%fR%Kc!aE;My12D#U^-eVBy8(jb!T
zBHD}+9q)AN3xE~{2MRZigH=mbz{h%l<<D2c(Y)xYowBP9PVgw<xDd}urMXSJxkXHN
zldH@~@}H*IF=F+p)?Ji;<S=Q-uf)##-s0%K2|1Q00v{_7b)b}&_Ob()%bLY%{HY1t
z;sk;%O4{B|i;JOwGg#)pgM_hgUaOY~{51^1>V%m~5FiqohR#jWR!7j0BFQ}y@Zbep
zag!W6YTei9>*EVsS)Sa<FlMs=K?cymX?&zSJg!e9Bz-1`MSKIwY_)87Bj2wpRxB#+
zTpc)2i{`yAbtBg@%y^0BtnGo~f#2F4V_E_yyVyxBF0pUH@&0X#zLItoH4tJnfzP8$
zJ-bfJ<_R5o5pvhP+B(~f5TEiVcDMv84_88uG<H23yj;{c*j8Q`4v1n(%<s^&yeM<d
z0be<qkiq&QJFhnOGeKb-7mt+J<hrj^`t)Ww<NRBL8F>D|BI+!IW?g7QUfs}k)$a|n
zFD>m%GK2ef(G{KL`D3(kV4@)zdkW+?h4hu}T7;-HX4RFeG4e$Y<T@sv0f8ew=g=mJ
zKFi#I=#rTj!*aZs>joE?50ZzhsTa`L8fqtk@iSx5G}IsQau2Q8#x!_fLaSB_?RE)E
z1n+Re)KG$6@4Qsi$a_CdZ@gNFw1oXIsO3h_>C(Sl$$nOA??$eV5e$t_BJVkK^DaVO
z_TLm-h3{u80X(Z-UX@v_OtU3c6k;S|(4WX%_ca7lpkt~}m0zW0D?iqp@+a+kUweS2
zzwUWQV^?*6c`sONJcZq!b5{I@{?3rw;8f(MiSX`u2`JG%4)}y3xNkiM&7T>0T6R>~
zA81-p*&KjjnMeltIz`>Pj!34Lh0r~k`FBv_iR?S>DBk-he&-rrEzUt37&<f(=(cnA
zm73df&YiF9tlnE*-<rnUHnn@Bx{}9H$<9$u?a%s<_e?drrxZ*yxu&7lQ0zzJsH>P#
z(d@koh$J<tbL|0A0U;?H|B&@gO>E$7rRB>p`(f+1y|B)EV1jmBB!1A|#{YJ$8hz-6
zvBdIZR_X86x{4chb4?rZ3?1J>mzcX{J=yfSO-N~MVR6q?cyj8fX)9kqE*7|O=jqg`
zW~=FOwXW=tYls0l1(1iVBZ?fU4jtjKOliPyhOryi$#i<qZr<BMPqN1cNuL9@a7`3U
zf4pP(l9f$=3k$d}^;p*@pWnV=AV#mUOs?f)YYwc1Q;u(g{_Xb8O+Wvrbz43D$Gger
zH+$?naeH^G>!#fuzxM}k*}Y}+Zsk9=SGZhstUYq%-eaY^d*5G{dAa%6Zq?0yyL!a0
z_BX!}@i{V%xMP$IknK8#Xv-$TYuv%g5KAEjPhZKiDYu>nI@~h~UYML0IcY3&W6kF(
zJG|5lPxU25nQJ>;hDnzuUG@Jl|77>~JU=DlOMj?30S#_cRaZ6C=u5?Pxk37J4$UJ9
zR8@)_l&xT#LX8QgQ^8p}2{rphm3K8bNwxR*BziU(YW8Gy2x<xAkQcC~5cnH)=&IWK
zlB&FAut2p}&G2z>O2_d&-zav@q(w$a#$oiH0u@;B428XVd_6j26h%1O*Pam__i95P
z`iR&EWqm}(d1l~_M>=MNc#-4N5DMs;w1G%pV+*(nD*63e1E!UsHu~{wSO*~;W?0ji
z#W6&N_eb(atNo#n`3|DIB?k?yu@<s&RcS-HfjRhxBSiJch0Tby`x(Yok7JAHgLe48
zh#qGZ%+^3_(6e7u(NNBBs)1IVC#vSTvARY;Sju=cjLbdTw2MjCa&Ow@Pk$lsUB=+B
zsDL|7TK+!q&0$p40(Vz)o%69c@5j`-3GG3GKKY)p1vD6yx%E|Rv^I>Bdzxq7ihpQj
zedri1rH_}P%0pN4i;RyCnaj#$nB4uHV0A6{bFK**HD?x!|I^B$ckXDDQi)c2-^GPH
z1(pRzi0Ln)H@Vp_Q)t>#KT}J*1{;0hZ@zxsJKL5wd!gDNCcv8|24sAQ)YoG3hFcVh
ze2gs^<7F()L8qKBgu_{<90SA$W^<&{6aW>Rbt*T|?{;!7+eJC%Mad5UAH*otJEQ(+
zD6pkY3BK^<dEBoFCX9*NLHur|{1^3<IH7$)G~3{OZ0#_|z@q(6qLID@DeZ>CS+^Hj
zE#4X0mkXd;mxJf_<0@gx(==QKr?tY#wR{()?Ceyv5&@-r!5nhFnDOByNmaTi`a!4i
zFvJ1LG7ulg&5`i`SX6s>hP+y&XMgpH-s5d98CGS2y{rCM9m>ZW3jW}Uaxd~;W!~8D
zaJ8-3?^xew4cum6Jy5NUzF6?#QOiuoy^KHyM(o)>(+n50<7P<Yy6GwBnUHD8RTPR~
zHkT}_wqBh%udv8IW_`$#=D}E<zWa~jOP;>Hed@?aHhT^k8XK{h977^kjfd_=->s*C
zZ(mU=skzg68SXc#Xg#bO4hv>x&Xl96FEw!K=I+k115ug<6J}KO%M<l(SA)Z{CK|%O
zh3ziEC-Ac2cF~r2qeRQ;Y|)0QJY$YZ6uei3T;^1$YI_G2OtriVVVrELlpJR>mXtPv
zZ>~fI)C@7kYAmPHM~s>)H-(j2y#V*`i`5C3xS;^gSF^Zum+^LG{@4eW6=(7!Q?{&T
z;m$SW4#4eUSMF@xZf}P8L>B_@qm}x)k-OUFkaih-VGp_7`jD@2<drPefJSP&ojx?$
zBCog2|HjmxHyqYA6F>n9D$uvly>@4Oqxo(JiPsj+-7E!Vr}pSZjHx1`!%ZFahW0e`
ziMz0=ygbyTx?OAd0+v!%1KWcpkJZ%Zr;Y^p+o+@91>x@PMtL2=hGB_GeBf>q{M}AO
z^U~LTJo@Hqu1}E~YQ-FXMf^sC-g<dd`2xFQyq)*QTve3ZE33P8ng@138?~Kja*Fy5
z9#&TpI?<~w)j;Uk{5M{Oh`^MV-6dMf&RgMrHe@IwHJjrCz8fgSrP(!ZWx^=zo7#|j
zlcI41YJMs5S{Cb7X3OV*k@?7bN>gJb{ZwLrk3-;uszrMntVpljfIT0E&~X-ckV#{q
zeo9*Qzbtk8tWQF<oNX`8FOq;@ls}~^yK6cao^40IY1DC^-&LI*N0`G8l6tXox0h*f
z^SMWzP|kzQC&8~>vS-@>0n*Z~x%9oNSv#C`)~Po)ydIWgJ3#f-AYRN<Geg=G_Y|pG
zS#k30eVd}kmqJF$)RrbMbBqB2b$Hz11KxM}pi{2_Vsvd0To~#ga~?E_#VJAo3FwPz
zcDs4jDB`xMU*K3*)Qx?P@qx$JZw9VUK?C%TGCNhfZgj+jJ7hWOAl0wALB1BGSl;wZ
zvK>GxXUy*cIKGEw*K2&%z0_+)g1tde)i4iMyAP5mWzK4CR|413sOa(sCI`Ip*EXUu
z6w9IwW9=FGPG*d?wtF_HH5c^+A}oXO9NT}ovL)>q!rul09%O(6<k3h#fDNQWa*SO3
zU+bIJ>C;CJ#Gz+>w^t1uH90fq-35(2^F{+&llyY?raUy5k=eHx_+CKX+kpJC^2ld>
zZR-NtMw5z>9IKV>K&@iNd=J3!-pSc`)q==buk4!9dqfPF0D<@HyuaTlC6nZ!k~NEt
z>S{mNm*813(U7ZWHetyjP&j=WK{vxk4_`>PRG5p7&5+e7dE;6<>#8A-bySCav2mk{
zkM%+D*kNlQ#x%ahpAP-f=u84&cbZ63W1}tw;m++*MI>#R9V2svMsbdFF*(`G#pRB7
zLa+5-M1%}7NKn><bqvjaYR#n6i5)U&^y}^Rc28e|mco;;Nu`WghJs&jR^kg|ebkV!
z`APLzqtN+$EsN2$l*ZxZ2hICRYB<hYM*~JLSQpp9%{|&<V@@!FOkju5rW!b5K$*$y
z=ypV5LU_hOAmm$Gl+tC+`zqH<FD>(XXnhKP;$>tP3C(dtdi7VoqLrsKME|<AD3|ec
zD1?D;s`MRw;{|EL`oi3sSpIYa5H=_^tcJgg57^coJH`AhlT>OCRGr1UWE}9KB{MxU
zbgkzfIN6Q%(E2rwo#iu%i)LT!usG+h+UAs@?(trxdZs0A6+<Lj^b@><{t;gvHkCUp
z1e`evXBGfP4n>{$+jXJQJ&qGS_dvIQw9DhnfPv}h*F+#LV@~}0#2dY($h+FiiN=uL
zmw3cC%R?n)N(A9VvT-GM<Jul{j{Smiaj?IUddbZ4Mpan*S#RDn-qN4m>G-9Q#iVya
z@BG(0sgc0QD6ma%02_@Sxzm|^=4y&^Znc%45;w{*BY{-OaT(f!PAYTs<Gbx(@URE5
zw7b{E4jwBfYIHG8y#|9_v0vCH!y3Rx2aPwMo$^e<zYA0tY=D(?9`P?rNr{Xo$b!d1
zXF_bZD#?W(%J3u`MX0BaXh4heX8*`c%Z}+ltR8QR>VJn>Zs6X;6VnW<6QN(0^G`C}
zt(7By*?e*K*>$aU17bB}tc+D)VAlQ&PKJ1=jJLtaJ<XA168K&_xAA7!&bl!Cx!Zwv
z8C#a7H0Rn+jP`u32ciy?>AkPb4#qMpGG1pC=FXz*@S}}U$8<eS6P0sk<rK&#BJcIT
z-bANXI~9tKB;dO=W>g+7EuD-RU4XpKl94%71O3N&+b3IT4vf=+%Lz=iRN^_t$AkXH
z7XCEcj;ZOg)XWh~um3=}uE$SfC-e2p6rq%y^K!4xFvebz0NR(Qs)4l}Gi~hp^b_dJ
zGUsHBc>J?`>0nmW{r<CI*ZU9WaPPexQkB2g#Sa--jl5ShHP*MXG^xp?-{l`<CK_;?
zCgwr(4`>%Qq)Fs7_I8^fdvdDI2?2urSD&Kf3>V*jOm#ph&ZTiHWBTwEgS2O_Z4MO%
z_B}m|2)I%*o9miCgAMM4TmY@>Sl@P?3W4H>s%N5kpQ62ovTY!rb`_L0Y(L`<YFl~F
zJ<?hF$@yfxbbowg3+0FQD<Kao7144lAL7*KOiTxjLca~q95m8%Hg?Mo(>a~sJ+r*%
z*J041Q3Ae3;AW{X+TQo4zyrPSL67PM!iu*8E@($}NI{f3tab=2w{)N!N!p+Yx8C%H
z7uLDK=i2&lM^<5bOZd0tx_~1>1a^f6yU}|wf8wI}hMggsQCDcd>>D4sT6=YQw==PJ
z+_>#Lf55}{Lg>EEpNJA=D?K|EmV!@Ne?AyeP<$h6xizi-9;s=`P$yydU3)sTovuV!
zHc6x#dOOVhe7*5ad9mx$zNLDhQ`qcJT=*X~Al`c05D%fr?`w)_k@xnI#!B?s4A_Zb
z+!KvWZsVn(My*bn-FEBjXJ?!i7h1xu?Ja@WF{W$I^X-t<zW%NUg!1-tCZ(@~ry8`K
z(-Ry#8WN*~rE88mKTw}ui5ve1C=SON-C-j=35lCH>-I-v;3yN*;7Yn}=%07o%-pd$
z9J<wtMp<Y!RAnA>o=Vdi5*&>B)o%Ql(dlw9;X-@iLry+aV}tGrG&%2>`~f%%(qOuQ
z)Qlt1N%qw+Q%jWZG9HKR%_|i)>;e@8{*1YI^%!Qu_5H801Gn-Ib~HNCdJnyzQhqeO
z!|~A?G|}Qu^pCGiSCj-#1>bL>D6f6ECPIq~Y#DK#=lO2cAIw8uHOyD1g2+lw9cgjb
zOV1^ej<zhM*+tf}TdN$04x#Z0;ZqYOP9M{>G`}3=k75XdaF_9_sfWZT03T)?!D!RT
z=}5p0xJ!6}|68vj60mG0U+q+Yf21}sj{tq!*{cE%fHWr4Zc(7Vn5s&%T~~s*#}3>9
zS6ks!-gkGk_pYzIZ<YlxH1k}H$D}kiPAl0Ysjc7J%8dys!?HzwMcAc8kel_56DiE{
z6M?Q%e-h7OrzJ+Y{O7ufrcXh!L)QANVJ4(na#%`)<a3F;ppoasPKLU+D4-iL(P@WJ
ztB?Im6SE`#Ow>w$hPY>gQ$Qp99kjogG+pthm*u+@Of<vEW2c!pb^$iBizoey>loVv
z6QAv~JH79OcKH;k++8UQK{g=nzCxCR<S;aHgnHTg-9+ljUT3Wm7)T!vOwv4C5|`O_
zkufHvL3ot9{X%E{obc#qShR&BAhET4uR)r}4U#RD(waAWiFSQnZ~tyI(X-hsF!|xg
zs&ide-ds%UZPJoqTR(&A&WQ@hT3uA=Uv!%2eUHIalG2DqEm&HNvQx7G!UhE{xEfq=
z6}Vu3aKT34g54bFxQ`W<ozzC(+!LHAVyi~p%sA!wKEhbF&mUEoU44LTjI&!Z%$qyN
z?^Csctsf1EXz7`^pPc%IdX1C6pH9j;<;92-#MM49sm;`3yP6Zz)!Mwb7X9EbG_L)+
z0IEFpYa9IQQL~b&JMK-)gG0vi%kNhWiZVE#o)}9!U4qUzdH)wC`hqpTj_O$5Iq%hh
zk2g9iVmhzLJAz@QdbvrF=tbs8rJUD&zeT8FIu+7KYv3M?`j#>mR+{X7&#hl`L})@S
zn)Or&tyzR|sGw7=a&M}V-&fE}^UETQ!QXh1rvUgH32MUO1rwEUQ{w8t$(G7Z<5L!D
z$TL^Oh~C6?l}^pt*G^Re0vLX^`#a<MeDf5Y;fF8qV9Or~$L6knX~10CvLpFz-Nf9b
zS?BcVL%b;W?8Z@z1uZi;nlxX@Jc!u0e9;G4pqy1?el=&;y3jMDwmcf@Vv#&W!-6Ou
zmZF^9m;NjV(cr=SN<WBq1<lHo#m!*`*kkdc!s@63Ja(<hrX8Pru-c{oKLA>66Sn=K
zB=T@PiZ$D;oDGPST$?+LH#!zBGBbVF_d$0JT90h;(Y0V-@lJ7lU$;oLT7TX;h&M@0
zY&+dIgPJxiy-|NM>`!pbC(6~@d^3`JUm%0}Y4=0;Cy92>OV9)a{+t%=+0a466ROTv
zpMu2Zi*7|9Zg7XZPqxc2T8ShBqWjlhGQ>|f->U3J?4!*Jbgu9Uw|a=evb#)eeFb1N
zYk+WbUUKXWLr@9B0CuX3<vTM)r~&zdqk9ub8TQ}H6BlgWs7qA^n~&o@KR?;<2c;-@
zU>=^aajn1Fy|>h5M_1;oTL!_fv_44mI~a=uS_ThfCeD+s1CARVoiiU~Uu`Q|tZn4G
z1$c6cGv$U%^yadGy*2@D#=3pZsEx>c)7=L@(upX7xkoI1FKXIkZu|Rv))u9OMYC=w
zg63SbsQJNIhR13SRHWgtd9+C9Tw>bgw$hD3Rh6q82-GZB=T8LA2+~)tfy+Q=%$D0&
zM8}<)vtO{WsabEYHyta7ndXOa?@LGpe~Pe+>BE$^7XBZTUHV_Kk$8{$NBS;8%_eV%
z<hY!!6MY3nC*AFNgHO}K4`+C0oKwo0tpvr<hr<%g@fW5Fjz^x)u(I=T$(;30_3b(c
zSAk$LO!r4*-(!7p4Toq6k;XcG^451kHhyRPAfr2p`wBKA6bbHUkanz-c&I}3Y>KKE
z7>j|a3d6uxI;%Xe+g)>&7pvtf@Hm!W6tDw2s%NQwJd*mj%ioO$GD^^oQqInuo-#=*
zup67Ne<M%2yzudAzxg#*T{k|I$%0y53l7SlX|`$&%sWFtS{{ztu*)b=ZS#Ak|3kb6
z(=l|5O#Co&;HmCC0fPgrDVU`|i*q^0c>~bS+}W2SkxhRSpa{*krx>0QG_5n!0bh?$
z3ud*W28d8j$Chyq$Bqi#aw3#f|L#wj?$e=QXNz&0XU2r;Ixo}mAM>;|)uh#I@mUYI
zMVJDj^~r3u-4xPpKpPtTHggqj&a`Q-IbGKTCdlX=bLOshFJ^y)*wi4PoJ&hWuvIhu
zIKnAoh{D)P616<h`JLIXp_F6=<#105!7R>T?=ZIE!r{#GF%-fl{Em>?9rTED1x{OB
z7LnZZLWnxa1bSb1Aes=T>l;&dd_hkA&iIgJsuAvfdf70<aau27=5zT0mjg1ne~PdF
z(9LRyiG4YwnxndIkzu78X`fMo+6c~?f0`K?DW897%epwER;_P~`v+wgO_i@twYq)q
z7y8>wps{gJ_Bn3yQZ;PHZ6K4YB)?3t!zTSxhiEf{eBAjGM?~E1E&syg(FV$$L-5IP
zgK^R_M?B0gFTC0KM}~u1<h`v$dzzsZ-;|4H3teNhu5cRNKR|-6XCxhuL}sv8=+WSq
z%fXkmVW=B9ufK#cF`bA(u#2{96l;m>Yv}?!TyT7$`yoFz(S9Mx^l5G2R7}2}c|e8^
zoih$~{dhI*$lRvi0<9*?PC?$yJl)a4fC22ltl2FZMJ5N-*01$X7m_X!aL*X-l`ZCd
zn$~9E3qv@8&`z>5FGV#jpCePFmSC3nmX)S)tj`?1r(HM0#ISF-@oW%?fvDLTjpdic
z1+zr!dGn$-SvD;TW2$b<ETnxPl6B?|au)*-XZQ&h7gr{u;Bs@koyl|lsEykhy2wb^
zG3>G)rSN4-YJpe86G50`8n*ZDeUrnwFQdQ%Jab?aOuWop4UQw%mW4JjUr;9_<{uJ@
zbE}I^mbf~6YzY|MMVYZS8sZm~sHa{gjnzYk6>m%gmWBRJZy{50f4RHGtT_{_HS>|q
z>l(jRU{(1co&KrF1Fbfc2I~EljC8#dGZ3APgRt1Ob5khJkC+>gW|7y(YG>2kE63Th
ziafsU74^vzBzK&4pW>z2$Rp0i@8Z0#DJh$lZLIW1J0&m;P5W*bSH;~-KnQ9fVb^1+
zmh~H?pCgsbj-qeVnb95zE6LNif%`7UorzD<eOo2%frCS*l?Wga)gE{~|3ztH(l-2{
zK_qPSC9-{o#XLNB*7F#yD$lnx<+73km3=%ij2jC9gAcqF%Cjib;i|&iR8wJ2+B>XS
z8U>|ur4d>juJpvqX4xp)2PB{nn>&P2GdfAR-9~YCeJJxrpS<tQq?t>ZUD_U)+%nQK
zGjn9F+0iJ1bfT*^Z4PN;5d!u{agN>58H57-a=pu4?_%1&&R_zqwxTQJd>>1?zUF<h
zcTrwk3xDq>W?lG@!tkS;FM8?DwUA~qT(mrz<}CVixV~y_;TaQNoLv+Ywkyumqd&pX
z|HZV(HT>hWcIj)6V;&;X-j-7D5*yafc4p=VWog>AAJXWBjOPjA<1zUu>Ae;$Y1195
zeI*$#8VPcnDQC>5pBOHlhsw<*@c;)T!sL!~F`^BvlHq2PY?SSqJG-PIf1PlrKXa#|
zMv>zswYEzcM0<~pyZ>-Ce#=^hvI=M4?^s<Y+Za^}y!l06bJQaJu9VY?RgpHsmk@<i
z<L4XZRUhDwXLoDJ@1LGHB?#6)=cL;&P;^ymP3`j2(@r#EIpKfKL2S|<ow~bnVnvM}
z_oGYgdHzKf>7t55bGR+znz6p5WiRy3_%4cr1H=6G<@_5(uq0bXm;0X_)QA$H%UZ~6
z^fox)yEh!e={Vw%A78Y1mGhe)^J#9W4eO<y+01-bb@_pV`X6eUyC?>$-a2N=xZ3_U
z)z|6NR8?Z_hvJ+f+Icc1FElJMe0GsIzVK6xVU&%9(^&o3oAZ-X_0eA+h9WwK2gj2(
z05JU<rrQTgd||5UY|ILqQt7pg6Z?e~Cp8CJW#H@=0`V8}Q|avT)OT1c2iMCXq=@8(
zUYOmOi+PvZWDIx}3&Ngw1oDjS_|AHl<z=px^vUj%i6=8h0ZyI9A&a&qClOJBVNJTs
zsZx!bMuIPZEKpy!xVmoM;1nCtFWV6o49$>hww=d(^+5+XuoK*fpP)RFzuLMRv~wI8
z$5n;+JQizcgha8x+J{vw#cG-Oq|?JJTkFcYI+R+iSi^gosi7Xmk%x9VcZh{M*bhaq
z8Ri%YTaPc=4S*TAWk2~4$15p}s9_9)6KCdO8?I=e^R;lJHgnC=2V8w#e@(gPy<m|{
zeCadrrM(BsSu*X@KQY`2XN4Vem&#3k@e!vW=;ntRxF|OD31@|Wy9hB_VeCmUQe#F!
zDHC@l2WpaI@F+Qlfm$#=%E;V>?lew^ii<LzU`wK_oI1yIn@Ua#>O<|DqZ6K0ZtX<P
zrtFRbV+A502okVXd?L$}P(o`q4bqcI;sHC1My&iE#};Z0be(3h*ikR&dgACjUMD=e
z0X2IH;2;jYkc%&^4;<MiBa`h)zJ335X5<DBtO~XHud=O;^eOqXaE%aqk#GOKMzu6n
zH3{v--KTMEQLCUKz}WEg-1z4gp?h4$ZWZC~Mwfpv@(w|>K#dR37F%1S=PwFwHAgR>
z^)}D`a9gjRhne#hwLLj4x;b_E(OXkwB<0N9O9a=XmishaEu_WZ5!^IGG~oVWyu<-#
zrE&lMl^B$c@QN6H4%s5S))HVNR@ntpuG7CVF^A6M$fH`uXpGt&F#Ty09#bc)!&Er3
z^Mh>nhxcHwc}mEP&w7Z>d{97!uWF>k$1QBMhO+@)O{Vh~a~-u>{(es23zJan&@frm
zkl-Y`_8Kwl-L`9>W(Yj_D9g`fR?T9kW|M$7OkU51sRDI!?9I{iQeQRD0~WyQhU*L?
zd|`JS;?%SiP+_)(I3kSnBDUGvc%Eh_YTG*xu@nje@)s25+X8bc7n$orQPVQrE_q<@
z^}+JkccZ7}L-9xYW~?o8mCzr~3KDl%=utHLl5C38%rL3JQCml4T@3r&N=*zqPQi@R
zNvylBTl|@$LHRu`toM@#^L>KmBTp+m(fKnfW0>sG^i}u~v7VD=rSk-jYUr!5+e#l^
z@@Cs_ihm$IiQwu?^>;iWa(kp};j9hRHGg)K#<4G!?`{RWl~Y}x+s#1dd<a_Ob@0ql
zv#H)V6iyi$=JFa*GkRwo?(;#UxcZaewkNsVxRUoWHK}Zh?gIpqjvXs+u5e#~pj_&4
z(Uuv4a+7=XZ<)mY;>_WJ)Xccd(^#We@#FJF1)aP4{r1B}FX)l)YRza34scT^0pS7g
zgG|{|iD}H?Fmubg<O|52Ha(BwabvGD2j)h5szQP?+RLwPdYq|F62qnnV(Guw<v5p5
z%D#z>iq3dYNn0aWC#>Ld>j~bg3t@6P2e)k;n>6xxeI~pr+g$&4B;0YVe&rp7$X0Lj
z?l(U_%mio+eoD;fx2J1mp`9e>&I?m<xztwn1$Yj%C5pmeXjSdUR<eE+Z#+M_NoG`X
zYCA&iWL$D!ql?4lRh8zubRvG|+v&v4lHO{_r$t?23p-GS`Kv~e)c1lm^**aqDAKmS
z;)@63eDlU$cS*4-Su@Lc!HmqR#mvd2LE9mHC=8E{uB!qwOE;$qUf!FR_t=;&Gl#fG
z!ofNmtvGY!QzM4x4x4xKZ{N^@XV1Dt4R90wuE0(NIF8$~TV$@Cm(!N1BU4n((X+Au
zyEAi^ST@Mchbk8@9i#5D3SOpgJQTz-MJpesjr*LDZ1$5`P499PuN2^%$~bJO3XZJx
zc}CDudiQBz8D|yT@$-T~h6%(&G=2j=UEXY7o9$MplM$X<&ZB2#j?6a>9w4_ws$9+S
zID=zsQ|Lek<jpE*W=@uxW3t`yJeU8@hOL)(*Iy8cN-0D|&yDcsTK((~#@IRE(@!_-
z5CWg|#eYfpBT9$&DHUa6=FvIqU>nzUt}or|e*4A?u>PH#xvNKAz3oPe*&`80Lq6D8
zGa)imY%rvvhk*9~v@?*^A+A&nBrf8t<Bs%|BL^HTIc=HVKl6`?R!dE8E*^7ajkH4*
z>eu2hJQb+IGjjTfNEHI*w5WDbj&#-Vys1e#xxKu{nU*=|fm`@QQw^niwO2WrJ{@kk
z&%euJ@Dp13l4_*Yl}pIDI5sTLP2deRqx>2PH#Y7Qvoyg^VrMRD(5j+3zYZL4i_Pd&
zTffr(w{N#gddywscmoQi8MtIl`wCcjvfq9pZXZ}$xEi>=n4-d!(5n!H)0HHmXMaU!
zGG<~vcY>xH-Q-n`p0F%YOZUERDa4;BU9Jqf&gDV8ypSkD<rQA>Af*rKcSVlVYUGwX
z8wm)EclHI^7V?!{=0Xb2as|mJnXQICwYi>xjSbbfGiAH{7e`+>nxt8BinjGfA+C50
zS>_+U)U0T~paQ0HPb`~d2T-0?i5MCHCpmEz;=SnVT&e=u`O3=PJ88(ApgpH++82b&
ztHzNhSo*3l;^{T~t^u@OHp+<5FP_`^K|7eL#c!pzTV}03+6n%C+SRw<&wqY|uf7P4
zT6KFJeZ23rwM9uxSdUKEW1W*rRK*v<EBy}}L&{IkKB$(ft}`ft_7^F43rbPE5DobX
ze$PWaIqQJQSn-az!Pi?9%=ZWiGj~;{r}p`}3iLFr8oOdMMipPgSog0SEwIT3N<j@o
zU*SC5y1+usraPag_04&4I3yj`I5G(aAk-b^7SwE=J5lYTwjhC?wto(_lwIoQW^F-W
zD?Q2nq6}~p6U%;xKVAw#O&E@?zJ8}aq2KlK?9Up@g<xzWF1toNgB?<3TehW&?KMg+
zh}&{_7oj43k63Cux8Ka2=648$CCb1AkAlveW$TjsKX{c6GWicPcSlpL79L6}Rpd*@
z*V7d^=<>L0m28x?(KT(Df|8&tGwms(Jmr%T$Y%7Qo%uRBi|NNBq5WoUdVS$Gt9>&z
z$|qL~Pfv{PAc24o<#=TSo%LU4bZNW%=5n>lm`SVEe(u&uZ~x(^=RLFgCzs~@&*95U
z=ETnuI^MtJjAHF>mFvEhFCJ~k-K~>a@})M&>dU#4Ka!W9{j~eroO|b+C*7%XER9g@
zfBE;%^eNWiz+?#AMw8}$>c#i7Q~J`{b_cRuHojAQ5ONlX$D}*<&3&gAi^WV&{dQ`K
z#9Zm}9pHsZ-bwjvi!0`De6UMrwX*RGmi^~JLjypt;izq}mA<}L(T%3pn|kKW5}Lm`
z>5Cp%CdDjB?q8b7dwy%9r;L@eHTk>2%cMYZBMO8a{LdV7K&vZ2Hu5>h<DRa5F6*2U
FngAqR80i22

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

diff --git a/reference/plot-6.png b/reference/plot-6.png
index 20b9c56603a45561caf377e83db4aa82722c8cec..54ea6e711b0d417e0c9e129ad6d414b91f6790a1 100644
GIT binary patch
literal 38976
zcmeFZcTiL7+ct`d8<j0=8>lp;3J7ciNDpEG0!SC6t2B`&y%%v~0coMch=4-qCDMDb
zARQ!h0t9I>gb->XgpltE`}dyn&YUvu%=z!kw`NE($zrYNxyyB5_w}r`UK{Fbojh^z
z1PcqxNtpI+V-}X9fh;UX9v%Awe1lE<xCY+-c%-X!8@z*U=T>6`csu@7+ro#1h3_Tv
z?@+?xx+4qAB^KCi4b#B)E7M_3_w4r>>r^Oh%2&b&&LN)7bLZ~mkBv#94w`QYJKvs)
zHk56de|fR{@50M*+VJ#8$KqhGU(0pbz*cn#FW`no?>rLpNtF>1)~<cFq)|(n2dQ$P
zaBOpZJRz*#cC5OLXi1w4LDMJ38l;aO4C{Zt{BH{Y3tS3|NHtyZ+LRp<8gR8Jb{Q!e
z`21p?lZ8d1de7RNX`)W}CsdT&#$w2VuCtv+f!nsy_|v^ZEH`q~5JYAU9Bi0dk=+0J
zSajuZ(|<l1-Z?dU^xw~s5;1p1!N<t3jP!s1txK?t9h@fT6UranpEPW1YyuM*oyQ8h
zr)))|&cUI{TlL$FnzKDhW|*U979?T$#IqF?#xv9%xNSK_pznj)`c1eptU=S*940Ru
zCPA=(C5w>OI)^NbBVg(XQ!5MtzN8&&jhXv%*{2G{57)2tIO!O_9RQz6+Nwbzgz0ZJ
zE@&KwDeGc#;r+=3ZlXx@WdaPwes;wtwJ2t}@$ayY@x9kPqzS^rmS?D4I7f1oc^)oY
zbn{}64JH)U=xvCLN$DGt3*$YQ;qPUsGrq6#yohjz3nbxEy@#zlj#00>-bxnsE>hlA
zH@`^zUR+`f<C~`h{ewz4J?vG4$`&R*R<Xcz9H$VQnlBN4ob5Sg9EOZcvg5-Um&6N&
zVRn$>4Ezt>n%&i_395|sE|aU9N20A%x`n4xKS&X-tc#<=;Iz6CA%-ZDCEG+jH=ra3
zLXy$1&@bd?Bx><=3!gMF9NpyKnYmk0HW7Z;{2NX7V5|jSUP#a*??Mj!6ACkM_{11X
zQM|GhuQkKaE)tbBRzOzhS5gB0K}o@uf^@q|%M-;2&*Tz5>myH9ohwD&^N09Zi=C;`
zQD{rozagI<rZBR5wnx1)Al<+M89bKL+XY)V*~RKTy@6W=US#n!#VA0~=-Zg%Yr(0=
zE1&kWwOXiW7K&eu`PSgF4lxIG<J_1v@+jx&qknBUXgWDre%FGZ2<u)yvDP9cJAJES
z(G6a|7wQ+YO(d8obg^b-a?UEg&;0QI(g?gtzI|6J4O#KowK2=|NbtmJW8rZA{r#L~
zG}S$5sh@g9L2&cP%Pcegpd5$^$G0B2HlEZf>(>!y3s(-!#01`TZcI4B68WdF?3W`f
zEFT2_qy&a$qvjo=ih>|HPdO38mZJB!z2LM~ODFwqy=tRiL~YjPGkZFtKOviD{D?<N
zoWkSb^(b3@>`cnAvGKFs#Wk_c-Vwr*A8f>=#HvBFhc(E=s{(~%vpZ)F2G><2x*1x%
zJ{}&8@;ZdLKt72SKRUMvQ&!QKkyPW4u*1|scdmE6k#-j=%zV_La&^d4xl857q@EKL
zA9K3v3siDW-o|2A@n0u?YZ=@ZYH3NnkddbS8kO!CRp9ZYht;e}Gp$*e5Qt>a&g^~s
z`8AY~1HTxOvM3)l*3xXBnkpviygJKD$bqom*{;IiyI2kOG^_XtMzxlsEmIp!f>Z&<
z=AxRQ?;9;84duxkHyS0o_fe4l4RoPi;<dqR+sVtX`rIhf4n&i?P7iOODf<$y8(0^i
z3Ch3w9=?}xsnu#>SKY9_MBvs5w}emd)9g|&c_s=4lC=%Zii83sm*q1^P>zEM6e*%3
zJ2xUJ>ObrmMzDcS742)?Vz@B=puNLQ#SWU7cB|!NF^R%T-OL#%K_zI|J<@=`wO-p`
zvopEZk&4{S(V8$q_F$$n8&yR(Hg;hm9}uLLbiJ^c1%au%a^BY;i~SyMchx}0^Ama&
z)J1e8EPoI85VcN;kF$_}u=?rAw^^M+6JmbNAB<*}WmZ0ztO=8souZoa)8e~(eTcd5
z^dh^Ya31vu@tf>xnRugk+@l8qK?{ZCWPB}uP|KFKdXG@E)O*eJsEVn4x7H8VXR<;y
z{s_!np-kW27Kb0-4?0Se@Gf)6A5dHNt=HOhR>LH4eA2(e?|56pZ>@{lAyQ%Ra6sW7
z|EFCbZM*<IKU4&c&99fkYkF8AnQ0dWci54-)MO=uIDEL%9vcD=-|C_zqYC-4CmfgF
z>9t$&C!p86Ha~UDr-yCuE^XXTYRXT2`?FG}i}ge+W!&E^stMVNKnjiYordv*ZIr<o
z*G?GM?0N`_z?Zsu)p*c)PSUom?e!G|8U6%@I-(<HQH^s|=(DL7ysE?5s>sJCzgV>`
z?B0T_viX1D&h#>(E8<)3*p*2wk?QRUPob>6uB&POaR&QC)|imE*==@VyE&8Z;`~7y
zp&NBluNDG$1tEttTGP#ECZMElQ?)Gb4$-8DB7<=C!uauCGu&NM0W_8ad8)VG)MjQ|
zMOm68qlO8~bg&45hl|Rd>yd?Xb+R&at33rM?~1QWxV#$Jf6n_k#3!l-1&OK^v>TdO
z1&+LQWUmMmC5c$>l)&<wz-aic?yjNexbat!xUu(Ha0|sNN!w8DUS7Z}{8$Yn%UwNC
zHFG)WJ<h8$8ObwFUTSw`efZa&G~E_)d*1=Wdf6&W!G<(cVDQ5q7L2jCLf0{-!xIym
zv<_i69Ks_ft`h8F^A3lT@_Y6a4e!z@KR*q$#GFaFcLA8Hp<>&Mg=O{zvoM-+g*Zyf
zQyb1ICS~cqD_b!0Gl<4L9x&~mT&(OPSCFRp6U@^#>z3=wA`@;4`8_wD*j?4$VUv}N
z8M{Jcn3R;hge@;8c{B{PYzj9E%SJ!PrU<9r%f`3MtXUdO*$$<a0vKa?*}c)uL{s>_
zWaapv3tY6j_gQ*O8C*X;#4X)m_qT5QbjokH&2<0Ews(&5G`sbA?GVO)R+0*YHw4a@
z-hn5^wWOEv!!u1*2e#Eyw@Q2FwRE36q0=Q3nhZJ_T1iC5JpY0xC(gL3L!-m)ZMil;
z4rZ80OooEfD#Qw(OuPUyvlA_Nnu==GwamOkKI+BrT!UdnUN`uzP1Ia}y_Xt|^Qyb=
z<Y1$$(8>~+w|DrLvshBAkKs9(Mq-#klA~FWhjfyhMLplO3#oEn?6KrhntG5MHQipx
z0D}vy!ATJABL_bP#cYp+W~rXbtr@5}43jb*H!rgtIycdAP)ynmEgwVP!%lNif<BAT
zl@qFrcI;+sgk|qUS+zjw8TVJKJKv2(>A{Z&2ab@n6I7$2K}}0?^ll-#VphZAq(o3{
zWtl<Ccj(UPxIBG7qf%4UWWbq`{02S4zT%8Csn|=N?g_!9#0%}j8WPXW*v|Qc(AQko
zihm{#cz$m8-F-eL=)|CO4fW-Rd&HK&Qa9x@^aa>hSS~zZm0@8ihzU=m9@ZPN=HKKY
z1+)&-sQ1v|%U&B<Dz%UeWe$4k?qO|t;pq$uKRD#GGq#yO;ia(J+vo<?%Ofr6ozloV
z?NfO-6H&z?Y9FSK2Gwc@Pb{uqggXT<)a&kR{QeqN(?^-`PaCNDAhjL1$L*apU-M4y
zZ|J?$b$LEt<|vN;#Ux~@?U#_J+`6P8zEK_Xk~aJyqt5E)I`0H!C4RKr-qA*~{LBj5
zh%?BJPwlJp=G5W7kA+*jEzau|S+f#8`DfXa%uMP8B?M;B)!X5!9uViig;Q21%Exv&
z;qHDngXMSjCM_>%BBRH=+a>?PeVg}c#anM=rSIq^bZ{nZ`9{(IAgCpDY&t~!pzPru
z!rX<!1cw4l?iry8)>M!(U^6`W#@ucB`EfwZh7Fxek!rht3;XPl<u_VV>Ki-uJ=eOD
zm)+kOs7P#{Jqbrj4oTwh5RLb@&Z;aOg?uLaI39NB-zvNcLA>sQ`sq`b#71;CDyIXI
zOAUlzxlo!w=7;Ht(`m<VifuOSNxr6^(}@XOkVy_wJ7?t*E1V(Z5*FqCJTv<z9#*3;
zkv?|zq;Y#wUgh+Eg;yKLB?oUS8S*LDAh<32hc^QbQJS_AA(0BgXKu3#{xUT>H1pL#
z(>>3)c5zZYaIt9z2AEQ5OS(>&rOqVLq+jazWTw=u(B*}upt!a;1<@|Ll4r#B1eb55
z!c{`!S)zi;4B06BztfYy?||%9G)(DYeJRL~?QuL@Xac!1FNNIE4{-jJU>&Qo8;5%7
zZIg6%&)hf!iC4UU>*=jsmX~pNOfDejJt{=SL$O-jdJ~3&qGCx-v8bc4Rq}OY`#4<w
z&N-DRCBedg69hYiZ_-|V)|&A-+aV1KaKm3OSwO%l{?#Pt6SVt|l|a#M&=dWQxKcXg
z1mo6O7IbmGxLxcf2+?RsgLCC2MU?kCj@$YaIDK`7U(~fsx-|U=>F?ej92b@3hP&Gy
zx-+1mYPM9dAG1}e*St5Cv%oFA2g7th)Ek5$ni$V}>z~_e9l7q&#5EdT!-G4U)N_j=
zt$CG$!0M5=!pkH<R6MBHSUSDDG)i(nM%oG?gZNxKt!HRCsC$q@tqQ_@$yL|dD`qMw
z)AvQ6hP><TocaTTQ1MJT4!;VociWW}X(?%yuoN9y4TkvGXI`$nKlU2ZK@&rM=+!~8
z{0K$b!;5DLwPw@Gx(%kHuPg+82vj%NfcCrMh3Efh6n3_xmqhfi$~SXyh3H|V9j|Ar
z-3#bpr8S`%j!i~5?tT%6OCyHw8M7l#QL=P{Eu2n4RX)9q{VY}KxO+ne#@+MgQK>jV
z7d9lK_VEEXf6pjDPo|as$6Kn=m9cYR@3uoi7iU`(4f>#&z&(*iZrC2!=HNKmoQG`x
zbHIY^V{3>1#|Qtv>{#^nBfMKUPFIj*S6UU(^CXUg7v05+Hmff?#t|3<*Kypn%EszT
zhDobF82K0y-)-PfrAO~)e;k3VQux(is!3VjiSDWK#i|=ERX=?^HBWK2fz(QS1*zBa
zlap@mWLdh%=p{-UU@YL@b$0J#IAH2J??hY@Q4O$^kzphJIt&${Jl$Psy%gvU--<_t
zI853(<g!cd%yUJA$aOUgrzzRai%6wa-CerhBuWT~ZxA-Vi#Z9OshzCS!Dz#_D@^@G
zgX}6WW(Ge*nUmA*=7VSJtoHoWmL&Zu8U59^E`XfB6UjZ{kJ7W#UAnHI<(Hz@CL&R2
z#6g^OPST^tbN;uvD1CRdQYLx2EY^Q@Os88QdP`%q)MT`?Idn^_)WR@wh%Lv{+vi2a
zan9-yKB%}W^*0`W)cZ@xXo7s7@D+yIZcxj-9Tt;3K9B&{*Z!0h3~yQQ6iD)QYIAh9
zB!4%1XLNk>piPEdZcA5V4Ky-R?%Apq@U}T6a3|&{^n|xHw?a_N2-=c`)`M!+7|Vrt
z^JhJs0_OE>#gBSTI7N(#s1yy0ce0@AmT{IY0|?)@dg-?yLJ0$jTiK~g)(P~5o;SvE
zI=j-*yLYQFt0}`)PG_HF`W0U;j7AYf)RMlnKtEAN6kb=^#yB4CM|88Q6?gHzJPdPp
z)HJ|;HbxJYJ#5d+2@sL5q8RBeg^EbHpnEwVYNYbrZYJ#qzwPipu<^x1Gb@+dVq-QU
zAYr6-Yx8efCP-!J0lzyEou5Z%OeZ?<^hk<x!c4|WxmxN#L!iddKw>BBMbZtQ&<85E
zuE@f(Q+rdB1No34*4rY@sShGvigQ^*<uxW9nmkEF3=3s(@h{{9UsKFEewXmV`+rxD
zJJL*DLm(zbxt74)SSu_U=7nR4yxKjY>I6n~LyQ<6uJ8JDVE?1NzP_uG7e@^xuNw-j
zJTi$hB<??pq##b_oa1p1F6|vY6b?-dVA)F;rQ<}JAJV6dFsGn2*C*3j!eMU<DpJ4s
zx90`_noC;ZBJS%Wjr!JC#8t{tk=cTs6|!B8hE6jxjQk@3C@71m5^LZgmPDuSt0kB(
z_Oz!#GLK|Ux@4v>*!&eU2%?SyVX=i8ZV)K>ws|H~6zC1Ed^@XfQ+17&WwQpd!x9T`
z5gmV5)yU~*Y6g5soGWn?WA!@WIj(co6gx+q;*)U6tqzMcK#IU$Xa^h6q|XnV(;|XS
z_E^7A)xl`RNa@R3IaMX&L`ePk$)2YotCEDRp-bHZFUm&g`4?7l3VW}Fg?!GJf^2Tj
zTq1uMd<a4KFsz)C^7tSKZO-BSSnCYO=>bH2N_{NK*&&lE;Gk(Xs-K;yrQ2YWmqkjc
zGQya^^(opBS&w4<?j`%i^}0UI*K1{O&sD+Yuf#m@%x;jY=zcH0*<vEGd67@bClr#T
z!;@jz*_4T}+}<(HQxDqa!>_+a^!ofXy($|cm)&c7WWw|+fjWv)(2*E;VE3~7jb$nd
z;kdieGs*}jD^X}q?!VUJG#5=T4XNFE#eSBk%b;|&TIYE#h;uD)WUuILruDEs+dLb>
z3HS|5WEHE@o#)L@b#)Q$aSN@l?m}W)#uaLNG)Q|9DaZBd7il`HKC(+)thbuGW3?MP
zbNZ?sq6*f7_C?M_#&lmzIpYQMdENnatiCqr^_2bZJS-O)UzYYL_K*$P1jln`*w4*+
z_w#W~ga|q4IH_Ux=zX&BTnk)Z{bcsu2PK(C==Hv^H*m&gIYz`FqFq`cpixZ#G1KDP
zT#O>{2aWcG{9}`eY85TOk4W#Vc(Zk~KF=W7dBMgybM|7~&~`SHyThMbf{r$xfmO%B
zkDrCEBs-%~*QIy%Cg1+5e>FC!qdp{dw=Dm$;;nM~Ie9VHig0HV{z!*C-Xb9^XFP#x
zeOYCBEdok_-#cX@r@f?=U(lQI=X$)6cgM)S>gu@%6B-L7r`#aXI67byENu+a+km#n
z9=<9eE7&Xb)Cup$&N{v_8*lfcpF^|S*e%6ax4p$T;sd>Pc*e<HGI@&!nPheundCgr
zOH&)nwGO%bp6bBm-&MJ)?za%z&3c2~LGu)2bUn~Mp~lw3U0}6>Bp<|fe#ku*>x2Ju
zb@ymahzcbH(W`A6GMK3h*}S!XP1?W?8EGYiWI;45#;IYHB+i`mNiJpagwW+)Z9Mmn
zdDNjkc*ReFvX^St#oGO(n>Y58UMoR}uvDiSQNFQ~c6@KlWJL)rDSLvZ5WjUaUb!s!
zc**s9EAqiFs?h}~yTIX1BUhRhL}<3yYP&?70BH}u?G<kvkj9gW<fh#wsb}{i``xQ-
z?$jt4*{qD2__W*N9wKL3=;0~-S4K^-SBkA^+q=0aju@%yes!qhqcu{K3W=n#@3%<-
zKFO)!1qSb|*#c?e{#PN@Q<Gzh@f|VeR6K0W#k56_dE{fS_*g4<soE75y<#CFV@AeG
z<K20&^uwbASGdMGidHUd@aK;^ALt>dQo+Mbt==gQBYu+Sk`#tM*c9qLfY{z`=TdbM
zpw`hvN-DML4en#{-_^oVkA#Mwe0MJ{8-M(n$A;p<_DC!^#f9yNN5I6AUkSuTchARM
zxBSBkE4q7h%+VrgDhv_xN$=AjG1jZ*a8nl(5;!MEV${eXi|VBv<<dKN%b;`-i<t?t
zT|P(-9zHm3)WNpM|7s__nr?}3r})U~&LGk_%Q4pTMyn19-Wdd_|Ag5fX}efg5S*Za
zo~QI0q0b{COD(i9uWc^{GG5B}*0h!D;R|rF42nVdMxc$$<#s4>MWAiZV?FHZD;BiP
zJ{xUOJFe2R-7e{ojOg|6ViD!23-Mu!p6Z=V0Xu#3#1s;Xrxa5B?jH1|vLxl}KUw8w
z4UI>xs=R6%Ij6GkfxgUg<ET9cU_4Xjbb~E1(nzC7WUtEGHzAHoBX4SNmCN||>&lVp
zX=Yu$J%sNZ75;-Q&!%KcX@t@J4F9gsK}1J*{(UN<NA0(L#GvG6L2|m@<ecxT-ovV5
z+82e-eAw{L(=#Mk9SwNx@~vE^*5z!)$84HQqO$TnM-MBd$phE0;OQ_ub~n4^Fw!U+
zK77l`_X%X>!S6qigZV}XIY#yByj^QFeE;(1k-j?{wr`hhh%*-oYVsw<1!d`xR`vb9
z=nl2aSW57S$c2!zD$p+5@GK7=*KX^35Ai-$Q$^OIzpju}sD-%0u7oOv^C&$4mPZYV
zL!CZv5KRwt*q!mt^;5SKef3N3bA!MYvVThV-UILK-p$(Y<)vjP^`0%PE~LacFUCQ0
zYAg+D_xt0YROi+|l7nm<jQxv(GSKM`GtUpPSZFd8ihzjJ3w;;^8k<Jt<#30hA*)e3
zGQMNw!53ZVPRqTkKbEcAIy0Br+><`sUb%5w9wu*HBj7mQ5%G+g46|~(dzO7w2NT8W
zIBD{v=@NMsiLiH&E(|x%%jiGl3tFAGCqWrXSX4W_HXxotf7bz()`LVS5cf|YXQOfM
z3A*7bEbH;Tb7tzv0Z$8Jd*8Q`yJ%Xh3KZ8>ex%WNk4Xz^@f<&+zBhqG-a*=4y4s_$
zG(D_o-##}08bL|m59nfaoK~3*@k$6VYOftDmabWb3_0l~A>;E*%k4jDE!AVo79x0;
zv$rM#TY5Q9m@Ej~pLU#PEClvKq2wnKuP4b%e-WXBSM<BA<&%E-&20<agnZUOlcOmK
zl6T?bFA<*}4NZR=aFvF6_OK?hFNU3a>0aMUGqp!TM=YJBSJfcyZ;Qt9H}3QxEdC~)
zg?Z}iRyV2J)mTv}rs}pUgZ;0eCMMz_aQjIUyIz1F9A>VOte{`_A_h4f4@aK+>=}M5
z#7eEho^qCj=k!WIvtPuk`D--x`O$@<8cydQ9qu#3FGj;84$bIIK1vteY*KTiGd%0r
zKDbj}<#@siKYUGvx1?RT7naxiSgiJxFQLRbv&fg`k{Na3(68zQhbUM(#D`xuZ=Y~p
zNLFU+59E$GscqY|Ju0!rr-asFd^M-hkCE3wNqqEd`S;Q3e*Zlin<cHJCy+xKH{0V8
zVbJ}0q2}g+2(f~!3=i1UeEnmzInY!#Hd-MXBTz_K?Yl=EZJCy{)n}LAdGL@zWp?vo
z;=V92>u<wCp*}G3Sh$RTq6|{0pgF2GeC0t91n~oFuKA6PLJGG~>7v)UpvP(DR#`Jr
z1SKNbbm^T`YSn#ADI`mc!m05bQ(&L>1tMe@A?@-J<+Hf|HHF01fg1_MOl(fm;tUO{
z0gNi+@X1o_EQxoR^lRK)yWHj2?v?Gq41#j6Gxp;soeF}PWjX0M#nm$)o})^p)IWVw
z;gs_%#vL^c8>L@FxZ6TpVuu7A%v@UUi9S~Ca<)q^rI~6@jv|ok_aU)L@`*c-GV{le
zH9QnwkvW6h8miggKcpxaO@B`+=WI<Hud(NgK<j_h0~Pn|F|DpQMJ@N8%CNc)OXGXV
zLhfwD8L7YVB6j7v8?9^FCR(C_jdy5_2agaF#;>((m9qvy+oh+eA$&?h?Q9Ve4b9<x
zN;Xc)Z&iWd^R9bO`;1u$i4vOSi893bPF_Lo%fMX2FblfM1G^!GAWVLLs~tqs!?~=e
zT040<S!&%4NuCm$Q*xj7Z3SvcXF71s;%fboM5p?Mz1H5z@UlI1&Stkni$oJ0)f0-3
zDV;xk95aWcbyOuna^^kJJ2|)D`jz{({7kms_Xo}5@kthmLk9s{jOcZY$ywxE=zO6M
z#jf1=P6KBB@EskxF=FoKkm$oPbZ(SRfQ?Ci=l8oXxc_YQaC;Q5UGAp=5VbslghNDf
zh@+;UjUYjRpH|6zv*C8vz>dqn1r=M&S2MMh=t(#Cd09u8D1F#d_`#jNpRR1(qmOaA
zvqPs+FT7T}D|*G5n)5SWj`HhvYm)c+O61sBj7^yUI@*tXU1HF+?APrT0lt>Rh)?bM
zwWhR$55>PIyCaLX#C-%Lr_8*oDPTNI>T2aLWXj}CTSAplOtUS0gKp`RvQ3QXQ*sj!
zsJ*^hjZ08V)nmt*5-RiIYgXUxPEy$rzXi_u>jaPP(lm1S>E(Hp4D}h;7WaI+{QEEZ
z_D@v_YNY@by*WIo*O`d)&Kgs)MLx<R05=caF1N4z;uzB4=={z+hCbV%)E)}DlgnV;
z^M;;1^3*%*S*zjV?Cm#n)&IH{U`miGezZts88Wi|TV*uNhoqu_9^xCNhs4;NaAU|I
zM=-@5i-k@k6L|mAiTvisxaQ^hWVJc>YUA|m%gHt4PKx`|h~uK9qzmUPa3b`OQOXM)
z=-USg_cu;7$Qy5L4mb%S=yF#`rL@~7f1$o+nvW>m9O922*X^p*%4uCr{=T2|&7H%&
zu@{$Tn0GckS$VL0ojFD<(VUz1GRaXIdlSB&cYjST+}2JV*bt(=uU$^ynv*wm+hPdt
zCSW@{+%LZN<$i1BFrB|?Zw1M-pgLc<PHo_!g*hZm;Tjp2Hd`jGF}T#<Dhsb;;#BFm
zeyv(dYQvsq8^ex%D?Lsji`qF!dy8L3`_)7Xt4aHuYrlvreCVH-7?I+quD3em`)BQ{
z*b~M;hF*7n)ePLX$&Xw!dkK(P7GV+xl4bt4FebhV6Hgh3_Dr^PHMF*g^aZ+FaVIpD
z&dDlHIp)?6=dTo?3>>BHYFbyWCv7F!TkUBNwcf|-IGR1L)!OBi*lZu1L0e7Fe5&cY
z=Vt2hy7yQ^!MuNU@2lcapgmLO#Z)NwY%q%u9|cXv7P*FX6*EFj`;4<L37ua}5~^nt
zE_=E{bwaxEt=L=ZE>(;?i{FwetvZD~Wga~`KjILDwc@4uurD7SF~<<{HBtmn10~+U
z`AJkg7`2DBox^BFsra&N>oVtLtAM}RN`1XXxr@O~2;zAF!q>`LiF#$dJg`q^Qx@Uw
ztlZ5jK{%#mvkBxFb6CO?+!)2KRQ2^?guimFz?B0Sv3TMhVq30D{tC%4V@RDbeDj1+
zc<3}NKdxfg1K)U~ocit^d{^SSM8s2TDD8J$H4{f-N<%}=rN30PmB{k*$h*H&+r?@{
z64<^8F}XEPzOYUvuJ`+)X!o6BxbUIB<Q>Z-0w;BR%C>*!Z!q@!l)H^y_<XG5f}oRq
z95r>hm21v*HRbpDEVy5ZAWLpi>k}V!cw}JG%>?b1UKacwm|Kq7E-avA>0C*!cwSKJ
zkU3DWhi+mj`H}yu$TS<(58&}lcjhtsQXY2wmxxy0tq$tgLo-$mEs<I_9yk}=(NVXD
z$oA@~jq%zZu*z1~%Nw~)V2H>z$V<o;U73R~add>o%+*ZGki|Lo;_o%Mud}b|EMXq0
zv@I>wusVK6|Aw}Uk*s32&$P05-tMA~oJQ;swoIJ&0j=LW=lT*?{>gLQN0u{*;lq`B
zJpLO`)+K|zFM5;&zCP8-Q^qSwoA2U3@67)*CKcZQsBtQfpZ3OJ?3;baotiPUv`y&p
zT3zp#XbP@Sr#i0Y_CSxLbkNH6%-PC5V*Q$ZYkt-GcxJcrw3PHttixeKIBB~0+mNF6
zQnS5HRM;KIa8s#7q}6D6NqNuy)c7+?yldUP(~gs*wTnp7+1rt|!ewr)_;%0bq@0C)
z3pZQ?tF`}M+x;dhY<<6rBbhmINu*z<xo~Dq>$K7lXWQ5e2bDIgj_9x8tls6V5FoeK
zTf8=xR@ZfsZr+9*svK}UeE#Gfr0Y#fgMriSUz}!Yn~u_+)kNQl-p=f1$mSjYSgcl3
zZ)hV`-2Zqb!Xf@%kvr_{td!YGkB@7DT%3a@UMkH6&xJhIPp@KBT?m2*-NL2r9B%3>
zx@CvFRL%D{g?%m0d-KFv-lRdsjt}3gJ8A(dFu0$Cj&;!Fj86^f_wRnwuxE;y-ry9j
zMr@_@zHdH-6fnr^WQ~3=bmeQja-EH`6tYk~dorM0SfSK`E5gzxwZ5;2_!S})pFuOo
zoBHh=4JAlqH+Ky$$|3h_t(NFZ7uuJ&YPHBA^z*6O74C1wG?v&(OU;^RQiJFXQj}j~
z-yO2pWhkkK3<BK@7AyPRzg3iG^wPe1w=WrHiOCw*>z~*@+4Ckd^OCG`WAEfxgaZ`)
z-ehiE{d?%UFZ%<a2{m2WVee}4w7%?8(R+?XrNqA^+2R{Ii^6BLBF1ybwiVmKPy2Ka
zVG+4zj~w_=4N*@^3q%c|RxT}1>#yR$1!Q2w<MO-WjWBVT=r6Ou_lIm3UZ^$yGsYdt
z!p1>UtMxCFJS_Y<bB?fPZO+Q_*H=MT=D#GWP)tuB2{SoVA5hXe^^>YQ`5s%u!t(xK
z;rGUBsHh7ZrE<>dDo4Sa@7;P?URv4zutdfH)e|f*Zv?@dX1cb4o*Q|>E4P>Tmz9sr
zskRQZ1DC}Vk{H9nLP^bHUZ-RD45%x8%+vk<azlxv5Kqj`o{7^;fSbyUZT~yT8~vpx
zqi<Sy+q^Eisy=qEO`|XqC0bv``tPsa9<hG6-fei>94t42?`{PDA6om3a|h-<*zy1I
zLMZ0C%00tkv(mDzfq@98osGqjjD#$oekJagUw?|bV{4kbrR?Uv$9YOiNCa=Bd$x${
z)UG@kDZ2aT%daMyZ_W=+N%n0KhKSK%@1IWtyVInsHio3wkuFnp>wOt20js3YlBy~>
z^oI`*HZlTcCLre9MJ1NC9$|MCc{9J1D?Kf-Yi;=vF7r;-S)UKqu(#2p!aQ*YuTSp}
z>oLyyGmPMTG_Pt?|Ht>53H;NjyXn{IoN8M?TJd<ix{0ajRv--4W#gRTH|iKL6Xe_j
zen0mO|C5(E`$dUMf``czO|K=3-c@$$f{WOw_$|wo25l}izQ=a=^lXB^uY-?=w`KSD
zY2=8al9Cclv~9CAtak6Q+&j<IQyZ`|5;8KJ2n6EgkQ5uzb-H024$gxA^7@R}^_bg&
zAEm5n7O1}l=jWP3s9eksMC&nhMY)}NeHmMnV=H$m_)lT+)2|mS7-!Bay{d1XAxN@8
zTYav;CMz6u!@s^b`Es9N5V1=X%L>H+W4mkJ5lo7zsHnJg-EI8GN;7qas1*8p?IHMi
zgKs@ZsYAK$X?%3|3r-0Z7O*UG>m?-_b<O^Jua3OuK&F}kzu&NtE`A^@;%9|pkL{De
z?Dqay*H_(-ey{x`_rAS0A8_Q4KkEY8A`dY_n!M(}vm2;w50jKiE*f6e(b4J79(*i=
zw>QbtL#jA-r_|RMoskgR?f!guP{|bEpMeHWvmriJY@&5bqIuOH|M*~#F7H~S3kM@L
zR18?160!{@<feg0dcvE9@F{uDj=x{vcK$Qqm**fZ7?gbrZM88?EHiMfX|&XetnDli
z@%yVlW-4l*PP04jMlzhJ@|X_ph?Qyi{qn_0p77HrqPyAI*#rE4{rG7Y%O$lNQbshp
zAZ1bE5Gr?(B=0u<DYw3iJ#uAkJA4i1%Tet%QE^c>?J7Y6M#V)i)_{jv0yetD$XBmk
zeZ$4YrO9*Mt&SGZN}E;~%DWo^u1JOQkj?(U-)H0OFE^a{I|=}iK$5nw@BR+nt}dLu
zyQ@K}yc5e+oEfrOuS8hjQ}SMDhs!;kZ8IN|iddWdZ1*Rha+UA?-VV3NRGrsyk`{lx
z*mT&(4dO&4{(GicNPMTC8Vd_s78d9O+mh%eZ%sp5u~E+Vc&0DyJccBN;lFsFjFwo4
zz|VrIqFeWOM+n0S|1q2%({Paq`B~u&pNZ(!@CmqxZa>E|Wp8tU^Y=!NOsq+1&+ayn
z-Mr+!m)VuG60OTc#iM!Hyf0tAJWjvvz7Ig;R*q(zveL-jN5cYZc=#?MKPCLi3^VF9
zG(Hb8Q?)}NBJ`Dc1Bf=m?d1`%e?Zy|<r|=>0}=a`EzNJWD{2Wt`D3v-PsTodKrh8}
z?%V6QxlO*1mGasbVBcgh1CLoVoU~2D-uHfi)`AxqV?jLtL-Nc@E$u-lhm6=KsEz(`
z&qAhL02i?IB4zXSv*#z8LjrK8^z6UCQQAi(>tDQI>D2dfb8|DR&?LV<EJN9EnNGoa
z3Z&_0OvTrZl}0f3oxkG^syZ!2-TVFQo)WQOb`qa9)6&vbsm-%9A!PT{953_b+$XmZ
zJREf~R-VoGXe*TiEeo%N;?E<qvKYQ#ZUXM3B@?r)dy6tsO&h;Q#9M4U29l*l_RoA1
z3@)r0Bkx$Xga&O}RzFeu3mj}~d4$N@3TgBs`VRuP1_dw;cIVC=0}$y?f2Eq+PJrBr
zqg~xfA-J}s^}G52WRc&ckZ7A=!jziq?CcKq6DLH+ip|Mr{Cl7Nc6M&*lGN_<BnT0^
zPSf7r?vIN79JG+66)v!#G#qu5J=~8v9gvmwL=CR=auV3*J{+WCCN|^|q-atpO?Yub
zRkDEkEN0KG^p(%mOMGv@WfN^36cUzQnD7>Uv4J$1GB8BljG%^oz+?%2<WuoSHU0hl
zjDf0GlQ77WwdA?ExedML!D`pB1a4{T@%a&RJJM4Iof?s%GXo~={`==k0S=4uN9{@Q
zG~sl#KWQ=qTkT3EQ)fcZcv3BH#$~A&iLT4#Q497R!eB6ye#B9mu%FS~;fycrGAXEr
z#8=okVURp2{ZYb{o1z}H(8W~q__qq4GoGgx<3bdh!f2FTfrvdXI!N{ayH5{39|4vc
zZ`4R-Mn(z(A*PyX8%s}3O^u!efisQC)ooN1z=2@>T8j&Bd<fu2re(E(sF}c{;PEiV
zPbYW~sV!WAD~J78V60(Rhtdp#f`SxJzpkq(G)LRw^*}I1g2fLECOcKqJd=qSm7)fj
zd;88=Vb?|X=@LkMSGe?4X1*_=YO%j^hiuV!&&BT4_WTV0$pT<S-ib=*feyRCpV10a
z-76rigFt$;LRNofgZWoy%DEU5+he)2)`0b(7wl{yq~jaoI>rD1Gv7q4yc7t33~}y%
zFG|AJlIg#45@tfTN6Z<XKR>!r-6i9k)i;QoSL@EdXBG}^OnF#{IKyzD&fd5sVw2C^
zs^(^609jg3-V=g1<Wu$?f6SN$Uu-%95SQiU&@z!KmdLP&b%oWmvoEVk>XMIG(EnR-
zTsr=8Od3O$QMx>DW%O_t$BhdiH_wX@{yC$6AL$aJuTI}O39oYMdpBCR<c>jg8N=Qw
zc-rDkH#r2Ld-AY%Y9SO6M@Ppp$=e2R|Nbcm+a-BM(4vGvBDsbE>|Jbf$F<OnRp@0l
zjl17G$xOieO`p^#>d#Oyh%>-_(0FcO;;t)v`uH|+%rjB=92eKFblx=plU6Ggl+|f0
zEhhomP-N5SFQbb=!II1T*r5)VPS-b7QW5Foj*((>9B+hb2z6EMvkdttxAhMn0w&jH
zV`D=Ql&|xf(*Zb}mH_h147ruDKjJ=F6(n^r?gfAN@1N>w^sOPSd=mxydVH(juOAOB
zm1!vu*Gj8t?+I>*eBj!wNxpuD@&~|0@No(=fn<DE#6AspfHKqIN6ZCD-Ixo4-wtqq
z-7S8#VC%EK6BDcI9#L-O?o`QG1Es~3R*TQmVtIGdDetTrKrCmuum!02ul$~C3fe4V
zLK_+QiMqd#Y_K~WI3Gt%OGybOrzuR2-4N}lG0M>tVC)P;v=BE3)EV^E>BbQ9WOeZX
z6W?t>QNqP(=6nR@!u>(Ez0hKdr;y6BzoO0d00BN&8vHy5Bdw0~DTwD&*{@|D35F>~
zx9nms%0E#Jrc5~7d5_ryWc*T)xNdt-xdSeN4>BtvA4&aLnM##jxX8Q|qv#bVt#g4e
zP;({}kgLQL6a5JZ81VB;K*6B7Nq-+<<9IIy1I06}(r#~#n;V$6@RAX~RjtSLbwo<a
zRVhiyVlQ$t^?CxX<?&$lt<f?Y>~&raGCwr54v#p5fA!8mYb+%-by3&Y)^;XbDB$Kt
zR6mXCHFQ@YV}nk`DbOYc!ml?1`bDPYN?p0~+Yb5jsp2HCCP~(*H&7ogn4~=jVklWz
zOgFdJZ6N^EtgJ80>PS)9r-<ZvfMRLlPFa~i=wLoQ{JQ$??}uOZ;Ba`wQ$=3)v9jr!
z1nBOsbwEJ-Q&CnP^^42C%S);=g~L)wzSxy}wbqnqJvVHP)ZW@xPR=wG!Y+Krz`i4%
z{(;E{zx;e!1?pDPvm<O}JHUM}K!sEDY}s)_|8`-87@eYv*(Q=~>b)J;3-dFTpLE3Y
zY1sl`Fwh6(D9x&Y$ph8bznxLg#+);^UY`YJXgd_dTFrQ@S~Zi_jF;Q1uU*VV^t)af
zvZnz6p7sI$l5&}8+9_k!Y%edbAf}xS(yx=Q72ky&o${He^_tb8@IC<)gDMa(<t3nl
z0U^T)IJE~_Wwma;zs4O4wJo)*?f|TI80+4BD!(W^USx4!RdR&-tb1*uJA~Ui?ttHa
z`yR)WkctAYo?X6nA|fJ=6kIH~j6UIAS4$Wz91C79z~w?HE4m4abFZ91s%850NBFO|
zb0=(fNA$^$?U}$u_1-@9+N*i^^@U#&3S?1V!M$QL;nZM}jUfZ|b2`SLNY3a{s{wQR
zvOeE|JJ0blfI+ll1S`wP$mHrOnwMEygn|PyG~pNJ9)A)>);L)@<?YPVfj35k#d$dp
zB09vaPN4+3WYjGVB?mfRz*Kp!RNoO!)wQO3_%zqilEO5Y+Vd2TboxTJjKlKBm;6sE
z01RY<_-Vm{B<}NYxd=GkL44iL8T>I^XkvpG*VB^TH83R6nVgA9ca5;Mp6CcryP!K5
zT^TyIxVPNC<GQ4b%m6d%6AV<>zo{T=E?>|Pz-kMhwyb;luoN1$Yb_09wivy){W|?{
z-=9fN_!KY$4?tDIZio8*dItxe@?J4OR0fYoD?qMWGvPFnU9BfBe6D)T7CQqj6vnaj
zD5ag93NrhPeB>OR=^Tk8OQ4c}h#9YRHYgXqhqhIlKZnf7xu&D5YXTK`s~)kh{^HLI
z6@dUFTW3B!ytN6M67D$B0O=5{d#x1DwMXA5%tB-f%IQZ?Wooq={+R)w6D#5Z_<q@@
zk<%jpDt^L*PhUB(NraYQ%1RIc5Yrd{8_TWChyvH4A2^pZ>57&@Jo3q0T3oyX2!Xuw
z@^GPnV@~gf%?_*9@UWAxqI<<@u5CR%bM3J_I>tR|QtE3uLx4{RY;J5&(hvyHbEvoW
zdv)+pZJer4YBsq07yTJ_*#r<DRed<fD?g|3@cqJyKjIXZg#Lt;Q$W-%KZUu@QRh{;
z)|-~RdhalfXEy_uU&L%I)Ioh|ZV~odotg+J%HV|lp1luKCZLj+@@3f`D@Q5UXz_Y|
zQ%hIyg!X49G<^E>)EP8C0ufB+ZeaS-n(EG5XRP*e85G`HKs>_qF+RjGw9I=YAEz!*
zZj`Uy|1=u~VtnnN<7YgS0Ym%4#=A>|XKs`K4gS9IQ@jPCiF09>37n>XFuB%;m@8mQ
zMS+6l7+PQHBV7RqRh6dYoAcKmtq0)36iS+!ngaH$T|sr>TS!HJ`t)}&XcFlrF>#_v
zD1g=SGEC&$8q|^@fS~-IlQQo<(^Ow3JW^;PmLtP2)3kQ8>q|6y$Z)Auz_w6=DnuQr
zN_lk8OQZO)BJYfqrRBS)kCuG9|Ae%rJNBfdY8#U#t8MTs+ZCW=yd_)+l992kYi@1b
zbO(OjebV&32jBBFkMs=hY3`^`0NlO`52a}f8=rlB`vgE(4M37w{IL09wmt&N33vAT
zidU?+vMx=kfcl&cBG%S>?zLEdG73PLU+0)zgw4XeCD5?B!i=XI{D$_gUB0KmXXU#7
zkr6g<^~#m;d%%@ypop~K)Ff{)QzQA`)Bwd19mb@!L7ROF=$@XQSZ$0Nb;>8xYWMj%
zukMK@Rmo}UGwbl0jhX1~s8gU#7yeo7>0f}8B8%$3ZR@uo*j1Z>ltcqm3<iw6*wxvR
z6aW4xINgcR%dhtKLUA8Og|y<|D}?QCNdkfx;^pE(=xJ|nUpynG-*g`isKVN#AKOW&
zI35LlAs9etJrzan*SBwiInf`$E~<y`;gb0*6qvN+4-VeC?=u~MYg#D=Z~aOZ2wnS}
z&O{4+&{5WxF$+Z0RoQ|)TTom^Z^JEX+>n^;i0UUJlb2yuHEv47GSdd?bl<nae1L_d
zItlB61n73OJEqh3PCh`#=_k5&CYZS6dl2o){wsk56>?K0F3Pd~m%2tZ6#*I()CF0m
zCcyovp0*M6?G_Mf&EX?O_o$7zrT2<Q{l%{fmI<=@xdBGK#}F<Af(jXkDTQIg(%Svy
zdVFT1Kd2J<5rAP*>lO>IhVe{P?!I<DE8Xw*J%v=<qNn0%2#BPL|DGpk%$%hxs|q^a
z$-7$C<tm*YC`a`UR6>FTI!Bx($u<RgO#Ns!2edd?5kf1rduvK>O!0R2m!E5^py4Fz
z%i94|F)srN{N|jbnOtJk-^t3Mpo4g+<CJQLxAr=h%QTlhz|mTD#@=Q;@R90z(`G--
z3B*q+=;&*4!Nk%mHk%nxzrLvN|DG?04+9Jqz)TMStPJ@M@cd$}>)5JeZ4adRo6P<o
zsP)sJ3bbV(W#{U(u6z1Z22|J4e%np2JS!)|o$1?xNk4$z(Vv21m#({%`v4CU%cx?K
z1>mQNLU^W^?k2OP<O1<8TnGR`-O2hVivz|fA25Os?}L{MT>)_C0xDmNNbzrz&4_OE
z9*l^3><J<-kExp6g9Yy_BAn$O|CrqZ6e^o8KW6Ro9e@XgXWehPFuyDa*uC19fT1L5
zFA!pmU1SY&Bi$M8htgJHpbZEt20&SZ1o~D6Ak3=>bj6lCE4r>O5DU26GX3{wt!Jc4
z+vZ@bao?E*B~y^wwJ>8vUjK}-?FT9y@F=B$?uD>R=2@jp0UsgE^}5Iez)ko5_Fm}3
zBBZR$WD3HV8VQjJ9E(A|1B&x+M$k<gF(7-Ug@=@{TGy><!;~rG_T0`5Xy0kyp}Rvg
zDjDYm+Ia*M3eUinXPRm1z%xfU9i~9tau+TH2H^p`FJm<KIZ!KL%}mXrAu1{ggRl)2
zCw)jWQRJ0IpuoHuq`OmGkP`)!!f|{mHS06YA+g2g=zaSvXqfRk+!iP-HsQE1sta+n
zR4d`4e9?qd?No&zv=8(M(n+Y`?d4KM5COW+ivmO;E<TV709kT?r!NX=z8MD%_-GE>
z$&0>|fIX?<-A-D9QvMgId8ML<Ya)UhXgbV(Xt|`aKiC0G=DAGk-o^l6<8=d7#{j#Z
z?`|Zws8smi`x}@kPe@SR=#0|CCTU+!C{?pt2V{uK^Th(Wdp(NUD?9LJAJp-m${2Y*
z(B@#*nK?ORx_Q)(qZ)9;HD;484HJ9+^oz5H3rCI5Rq@Tu61Qefhs}dBah<u)uidr_
zZ^Us=$Bd%wT4fk}cy%IR=ub=ld;@G=QQDPC0;9@_6V3pN5^_K#DUYcNP2W|Rt^%ZK
zTS@__3)#YPyh<CTmwJ9LrF+J_;NY8F1su4=79LDprCT@9n5fkTupGqV2P0&B=R}o@
z$>(2#IaW+ZBH+VC_aw*mQ(cf6qZ1Thdp@Y|uOAFCn7NJL$b4b^RaK^~0bAUfV-i#%
z&<|(IL81?SeaXo<pudYf2y^ln8g~cyyGurxu-ZrtzxqnqSz_oqt+1fwFBIsLR_lS*
z#Q$E=Yfj(*CqWESrT`i#<%8V9GKn;RCr_p(0=lc6c{`vAmI1E47^ab$5xbupN2vj{
zi0B9CFQd+D-eVQCkM5v8w~ky0EVQa!%>-l=y0JB6V5k1@%RlL$$)F{i64LB-A1NA%
z2TcEfgfo?;l^&T^)s1#;yHKfD{!ZR*%y#e$6tH`H0O!94UGg((N<k@ZVET{8v&kr+
zGx2ik1OOeQot>RVnY!7ixb;P|yq36G5NGZ_Alqm9{g@KWJJlfX=QMI_s}^;28b=1n
zF$N0lv{|D+sTm%G&IC2q2q?EiXPLCX4!(e1Z%N_<C6K5p11EOFJ_tgsg2*==nfD+^
zN@Sx@s1lc<JaU<J{YDAMqKc%ksnpk?Ns5L7Uu5ZG`V??Y!sC{rGN7%eZ4ox7zP&!f
zI3K6E`A*IS(h3AfmFJTT0Es8!m6qP-lNd~4rW;_-jBY>#vv(Ut+c7pld#|ua)XOMV
zu_}JZ@H~_TWVDi?wa;uet*lPQPJivpXZT`ZV^TH^TX+hCDW5YyRrF|mkl)<d1(Xe$
zlP|wE^8-oB3Lr!4=nRy}wo8|{YKH&oS^&#x<z*nvD%yjLtBnW6s<oo5ZZ3u`Bgk8%
z;5ycOg!}#C<a@`l0;Bl}!z*Xwu$f{o00dAqUJio`e?Mr$btDKdT7=bhf8}h}x=k2w
zW-<1h8A?7&TTEaSsP!hbzlusZ2oLDik6Qvw>)(0EW%tYHN|?QJHZz^=`bo@^+;o!X
z`Y;km6uCe-YzPG~kLV!h00)&Jc=IKv?V0?44oEzd2JkZK*ZR@$N-#bnV80sBP#RyB
z+m?Gey#YkG7@I++>duRLA3R6z=wXwBDd2etNlA7Y+on1q;JJ-JTWrpxoru1<sDKOI
zR0GGD5k-XYo&_B76W&6jh0}D{+4Tqh{*Am)a|rWBrXKg&X^IRi7!<>Sy8!}BGp~c5
zs&Ej%KJw&6Iqe|n*-LTfugyguq&P3i{mT77wF4Thz$9?##(aYMegrdw7xc7j^?~!<
zlxa`PR(XMr3#3OZkNlG@BcQ`}lmKe*%Ib&E5af9``E0FnHB%~_md5wVBseTBSp!b;
z7G%rROo>HhKul~bgXcta_DlZYU$xHfy}oZTfv}+~SqvZg4RO{woo~kn101K^J>cVp
zL)-MrbdK87LId7{@L6Hf?jok~3qjeB$jr)WRRNuxF>}EBia-C^F}L<=e-0S?#YFuu
z`a%)IA*UHYEuWpKX_a69d!;bYFaEd^P6Ofw)E&r&V_88<NSP6k%|yd1rnZKLpEiLg
zW{b%;$XW-4!Y831Pkh>51&DM5t6-(zA>euMOqeHI2?lt|*gO~sr~2O7^5t7Eu2Llw
zezjyQ6Hl|fyp1;m=t&D^o`XTDZRHDK@>i9WPhUqD@pv*v^vyxQ9zCI;W9Hf>3_0(C
zUZ#v!{z(c^?iAY)XvtQB%nU(Gwli^YL5VA|&~Q%p#;5GTUjxF9su990ye08KRd@($
zm`x9$2zEgra`II#=4d?sqc(y@suha>yxzRpWk_^wB*SE#xi;eU`6%}rrqrKTYE^4H
z0P|iP&At=MW5$%~g-_SZA-4|H@4R%62K=CwKyy9h&G)+<pU0O1$zbLmf!_C4@j+YU
zH4pZvL#fjz6{em}GJ!G^kU#l1vubsY!5pc_K-^7oy}0@D$Xt`S`M;|yOhrNMGAv?m
zUZ64m9d!|52b}|VWvJDIkdgJFx8H(;cG5dZ*aip}COdOr#jrvgu=U%D9fuQjnH8+y
z5pZ>F8R#)Q_wnisLUriQ#&Y2TpK9RK*1f&GY0%&F5uUUHp;ykKGtmqv2v9Mg|I~wF
zd)NF09_68^=-_=YHWu&K;uj5nzRf<w!eI*-au*t?`%oWETmK2id1fRqSC_zI<-HCi
zt|G;}$DM}%-c`k+4>-ks1L)_5z(Ub+%`~uh$@-7a)!Esv$81d%HUD!PQQMbeGk;!V
z151|&o2k*8**np(nmI3Zijs)I|9*K1U-Qq+j~Hw7{||Q_{pa}q(>KK{(O@Cg^tb}-
zqNun`WLUJwIqim<m=qQY8w*=SvT(Ctp?;a9@#iJ&25n=xv2zC(mUK&lRoxq@PwP;Y
zhsc|H;2{-}x0Q9Cyd~-^v_->Xgy|{F6>wlHN3BTiuJaNu|3qE*tYVD00V_T-&dtJm
zc9%Z@rMb6KXKkKy6I^A`(%pTb4m`|)g#|p+BG!TQ=VNupLnJm9j*HPjmq&Uz@eLQx
z38E6;A`e!K;oRR|=`nW(+YOF`8#0+6!4oo=!qLHIYQ&6<gY84i!E-VWKHoe0e|LY^
z|9H*$|Mk$OAR3<jC6QvPh2bDB<=w`J!1a^Qrft1OO^kv?wS!Gtu+1uIS^J_)A!5Fq
zDr_U_QaWF*4r@@5COeMOZ^EWU=nA@i|5+wCjQ;!1t#-wm7#$MNR4Pigv?HNrzx5`K
zEg)kOW_g*A_(Ag6zlZ&>e7Vi)S{_V!YMtTVuKANoC)mu`50;>Wd6PJ7gc%Tt^Ha5z
zS!F&&hq<wdA8wim$VOH2GN_5Z1*l1gkT+F(S=a@IIxKnD3U*7RS%`2K_PkWC2G{fE
z1oNppV0-$PP+NK!xAOjeG0I%zPANk@(KlziHY@^-O0c19)=>C<Vx`)Y{}Z_dQODMt
za7yv5S>6*+p;bml7-jp@Jel8R>_1~}JM@gjlTz)wN3ZUokmvX9h?VHn_*Mg*M$LcE
z$HH-pHLwyLKTn>P#tL~c8gt7@-h0z>q>KM?tm37=X8T&)<6Cd>yHFJO<AYJ<wW(GS
z+)l>g2S?af4+-EGCD@FGFw~I9#DCm`-3}f8=a{ft%pa^t?Te4jSts7n;5ear>2L+_
zp=kWEm`f)Fr7r~@XH%5m|8|uBI^wI~tH{pNHxK{w>5rQ?@87)CrRTJ?lOy3A;{+Mr
zni?4ipc4H6Nv$nfrD1V;=6NAdIuUoW?f3COZt{@q_C1b@u{26}e@l2`Gjl+ZN!Xf|
zL)V{in&sam7Mo{bJFX=Z)btO!()8$E&)*+}|9PcD<hka?;NMj(E;NonyKBy9d#7bi
zfnG!A5!!xFWItS=Q8B?SUJ98G%nQeBP!8;^*?(fDopwLj{bG@aS7P(3vPmoPQpkaw
zgv4fTnleU8*_Qpxw%@vhQEsR)zmH5hy%yh7jM(xbTdP$(tl6p)T!DH=9^C!-sqlCp
zvLmO6jbmza;Tes&dt08+tRsJ`G2N!xFVdE?aMdUSdgCD3#)ny(dgmoaT>n_!et!4x
z%=D3M=AAK-cXiuTptZ#{dc*)3pTEF2;O<k_ruohIC$y@Wiq<0sgNP#~I&w09V`*DH
z_HV*!>M);b!@}~nF$E0#%pa>_Da_lFzy-8q%inq-GBF3?`1ko@jf3fFcAuE3_A{%d
zT?(0Ud!@nLGjjdRn)Hmth*bVI)f~a=(8@Qlaj<VE*Gbo(?y}pjdoCVWZ9I^*DVCav
z+#Sxnd$5C>;^CQgm9RXhyNe0)NGv?@kpb&aH4~hR#jSt8NQf<wnz=JV&);4=vZdk{
z$<O?{?T<_UtG#!DhO&M8hHWaep}30@Dp4vSyX-5)Eupdt*(KS<*iC~mC`w9nM`Sxm
z+00;MH+I7$QFfCUV>?I;V>1|Im@zZ&HPik7ulswyZ>{H9-}k)ldfv6ZYiTvEIXB07
zoX7Dyf5&m2*JZ+48g;Oou*Cnj*6%&%$;nkSudl>Z?x`U_3GcT7X(Qm0R{*kfz$K-`
z&(kCJPm%vw0Rp_)_)j!Z?B6LHbEDP-On9>S5L?{GEEQbi_8Mbhf$h|17e!294M1vv
zM%?}eL%H*1h3q_Z55MSxSHhnVn&k<Sl^K&B-WU=g-LfllTce~{KW}2RlqDUw^Z8#Q
z=YPRa|F<+T4EU+k3lHfTA%XZk-+9CT6WbY?>tql<k$XaH^tWbY_*JOq(TrfON0{2!
z7oR=&mN&)seFa=*R!sXw_PSg*XkxTnU~%>5)))MjCilaCVm|*12FWvt8k=}qQyV9w
zGk{U%NCWfV7;}rXt<eu;4t4}5lbi6R>mdts_;Fhhf@r_!vJuJ^5$i1+Mi{^#C2a{=
z)D0&~P_qN<W5jLRaLEGCR4Rlhjzc#wjS<fFvP)6<y3rBMDkp4E#V$EGX>R?OZv#v9
z8zN@Arl^P$w&<dy`>WyS>+1*B*e~a(+9=uayR@bKORff>NeP~E=W_u!D|0dc&#b6t
zmi%PJk`u{UFjCqsXaO#|Dq?bNWBg4xO9a2?fcjZ(`>F>3KZOonWBnv&;L8LTJ~l*H
z(jH}yWP@IY@En;3deEMJobFc_T-~cCZY8YZaR5-9y81~ncTu~_-t)Y!Tm%4XQ2R9u
z?Fh6xs_Cr3SDw#dzj85LMxE9zRzWCMr!~vm<rR1P#YK$z42CB@54lhYi(oe~c<!C`
z5*Ps+ff-R6t&gw*ckr**n$akD|H5R59qQ0sU9JFbRW-`c7>kJ?WLdmZVRAD{w~}E)
zOUFaAZbvTqSv(r?$A83EXKkxDvWMZG<<iTVs%JlOhZ94yz&2<R?Z%MbYIfS+_FjBX
zo)a4Mg3w~f%CQzaoJmYmJ~YrHjr6l=c3wP>4T0D-J2@j6BM(VD@pvdn!;_;mM8ZKg
zS%F|G`4_pBT(0P(o9zU9r;Cgrt+6i{yYs}11>m#2hnzU)zdNcR;?C!iji$*pb}fGd
zSC$1_8X0(CZzD>5^Cs=NMcHQSjuXJjKrdcW4a5Ox&Bf~SA7F&33iPQ$xF?O?Df{S)
z#PYdhQyY~4%J!e4ZBv=IPX?SprUR|TtmwY?W%ina$=2}l1&4uFFZOCQou=M%ia;FJ
z0C38~i<bK^Vhr@wkSke;&RynulHqh)r>uv?eLeD5&t&W&iRC2RV&;OJHEqdLlsDKj
z=`oN<;thZJfYUnU2)APS$D!vuL;HU~UnsCvhzQapI(P+M$3T=L3ow%0_eB7hmxzDM
z{hv?Xb30{N7dZ@jX%L{*dW3rmyP<q)!k~n~Ca03$lb`C~pg$hXI22SOoGj-lsIxj>
zqx34Kd98e3=BM=HDK^Ot)0@(26EG*JAW;IDtOELj+m4(3!C*0>1i}i<2!!NE;I6^Q
zKMJ^C@M&e|2@s7D+V=KYhKBj|A>eLbLRS!>B*4)iWTvjrzTFBW-8-jF^D)ojje9Wg
z$<8p!^)X+^XawUSSq8ry#=P?7tartgPwI#fOE}aH+(14p*j+)4j3{X8t9bIem)yMd
z3E1*A)*!h;rzhMsC6GRcpU)~;!UVnzA4qwQU|P+03e7?W`FJEYW{=y~{f+ivgs7yE
zik~ZRT-VBkRvF<MDB9=(YD`_5+oqd@G@u%vf+p`Te$ad5*Zh<^b(+DBOdfW)SxcRo
z$Qg@p!#yLMeN!yvx-z`5eK5-Vt;26|9s`3`>FPgCry2Ma7zTXt8}TB`|FbS@;u`GN
zGXhc98ea3W+WH>3k~ey{0OReAm6CQ1lfZYX)<(^k(_plHv<7u}Ug%#v-@1%t%Aa;&
zUI({T85V!bZ3H2jY{ooiY9;q*@JI)?pgg#F6wmmRmgWOneMVTffuJl=N&Xd&=u4-Q
z!tc`R9^T`60pH*sfUY6yIeCjEIPIbONgw=YB+|A48I6d_XJ9V?@2Lc?`-cFLs2dTq
zUvvIYaCi!yM+5mvS{!#YRz5g8H^meDzLc@^-96m$F`8QaJBj1S8q4_;d-{P;6S&0!
zv;~Vyfq~6`)1q?4v}z%hso*KHm-IdJ<Pl4Xm<+J&lt+&s<LwByR}cFw3gV?n`s?@b
zR<=$Dw0r799B9o5YMOy?R!bWBB7RSr!Pi5~yEL+=7(aIA2;jRo#F9}UE^804tZi6A
zkS_HLN7E)&$s6Q%I!Q1eaQv5~%CkAQSmcU$H|ttY8p2cT<8iqn!<vPoe1jWYGLd1?
zq&CDD6V3jdtnKgxI>Uzn<kqVv_opP!5se=FtnhM>h+gfeWDI;r2O&j4O)>OYL_CoW
zt)E$mR}QVAO?;MP`hudEb($>76V1R-d|tbAsUPdBOu9j5=EjfaCX9?LGh&moJ4W*u
zAh291yZr(t$y<EcMeDi|-$X<0v;}HFJ&MLmGib{U+Z@jwL!ZTL#o(Z8v7lk)fZ*!w
z*Q%NtS1Cr}SFpSM$mM=6`O^BcQCt{_q{SAx?pqeXW=h?$vgN9rFU@V==`dHvMaSv7
zX8m7Bc=x1-jtLM=54YC+&B=6zfGh!UxxGK|@e%#0FJ0jGI-u&YkDsEjYnJzlQ0&ec
zZ(i;H1jifJx(=7$LFNGlHXAT56}ciF&EN|;bZWxc#~geWOd_Y@6D(?CH}2E^BKzYV
z@Jriak=zbq_JDwHlkGW`^-B<)rAD$OQd`CCHv!!P`ue=Yzu4tAD`ebYw6y*Kt$;i&
zh~r6LuC)7LiD6Adf7jG5q$@JO$vHYguQVbu1>ayF=1ptmC-KDR8GAC)<vex63L$|Y
z*;0^(+89i9#ApIO#4CxzA|Dd$=GijP;QnRy)-_feISAjwIvgEN>nDS}a-qu?DtS%a
z-wC(`{Y^0kTJ6<C&DFIYhyBy><+6)$hA~t_P3!Tff8PJ>MeY=vXdF|g?fYj>QNaa<
z>wG<Wz6I>o3zhee18m3639xi-SLJL#lqUazCJKKx1eL)k{hU})CgaKqrNxo?@pyVr
z*8q7Ro`f_<fHWNTvu4Pae(H+cimE#R5mA}NoRa$9!=&4VjOQ8csKefAG;#ty9odXz
zzl{KcFVbo=JVZ;`m<S*6SOy6Qi0m+!Z}7hB@ZSj7`ef%$PGtn8is+bQYfu3ie>Az%
zzLf2hqI|Gy5JDGJCxI>}m*qw)pfKh2xGHuZxn}$WLB$4l*qg_2+<*qc=r%?-oHbyL
zm~U5!&k<JkpdxlNIh*{G99^S5%DR_Qdgz`?ZOvbY9at+}Q#cKWYUUt$VJ-yaT|wTQ
z>}%)Pjn$gnYO2<)V-PZAtw0rHJnJe?@Ru$pY{y$<ZmTQo){l;z^LI78KGwka852^X
zZ8StY!dg>|*3v6TcKva@i}i^tcQ0wPSCP5oGMTODq|sQ&csLhAFLr4vFXkFi3>Y+v
zx!v4<_xVFT&7pJ%$uMcimSszmw`l1I5z2MPY<|mNs4eb+_^pztgFdj%Io1?oJ&?Y$
z;a-yS5RWhq#yz?H3HsQ;!tMj0A<P5RFcA}`l1p4aGci?WkIpSNDo-X@xvp%$m~)y?
zb}4GDXwttlfT(J;x3-P9ENMM6Mr2jLiH4)>8xZQlwN9D>CB;{w`G@+5*CCbl#A_Io
zQM|dmK6@Z4qFWX^W@{BX;d0a9{zPt}D3{H<CFZ&sL=73A`DBhTRfY=3yl_)z#RWP%
zu#2~tSB8<u)5^@SNYB&RUddzN<^1kN*Q^pLqV5IMyaoVxp8&w)<7-=LvyFGAlKl;S
zKBYWZm|d*@_2Vq-$0mSjJ0>Rf0Dj>D{oD)ydr1C97*r+kLFD+s`?8fkSDKQB2p_#<
zw!A}_^K<^fD&DJob1J;>%WW-SZ8<)4)W@$*Vmvr<@w3<3UtU{F743Y3*PqmX9iBp-
zumnXmKydx^!S;Ehnx8AM^}cx*bsE&(U?10(%Z~z15IpUoXWd-;?Mp{lU<%o@kAW+o
zNB1|;37CPkt<zg$*9CdLJH@5P5F3)^ygVY=hGYXTGPIK!yQ{e*ft2a<=qb*<Id(4s
zdXqLsF7kn+Y&jK*eLQ=bPveHfOXOU<(6Dq7T!YFe0~|~NZ;Wh4h?cISm3jrUZa4B4
zxWU#EAWoJmvn(YxCzC(}cfkP`XYa0$)BC(kQ($4^=azYJh81@B)bPO2CC5(2NyZ+A
zsSlKEH)xIk({|y-?Cnc?N{Dutrf*kWxg(5uysc5izhV*VyS4f<w#3>si?~i2U2pzP
z;INl|qjuKOzk}^KgTI4pUjXBiiyo-F<?(tx^#l8s>xHGbEn*Lq|6Tb%j>!9K9x@KA
zU6<q5<D1d2!9q7Lz<9sB>ATKP86d0mG5qkSXY?r-@%Ta-T=66il;4a1U()TP&zHiZ
z_(;yk2zC`4M|KwM{_UWXd%%BHLvj5e-54tIob|ZZKcTRptKUlw8xGGbrl@wE<n@*H
z8EDSJiwu&*hNpM+YfF0{;NFk1a4(9X4`xKr`Loep1%~(j*$mZ-j694D2gud)f08R0
z{Tgga@I2*x2E*u|_gSP220?V9@kDn&N8|gM-`%zV-THZvU5l1<+I5T^Sw`x~oQQvR
zg0g8gpl<BJhajftK?^~6bwqSd_|hgm{NEscAAll|Jp>t`2ixkOTYQd>M~va=b_C}j
zA`|H<7sz|D2QZMYF%U0sJNXM<1wgEpwHg%_R8)Ax*?B@fqENHz8O5W3PLO>o*-8IF
zcIlM8^8!{Hae?-ir<ed=$u8q4l>PxdWT8!T<-$e27<WM;LI(dCLDJkbA-Usi?GIDC
z$ts!KK6&lgT5(9UM$GRSr4~J8emtCfYn{(LFo#?QWM*O#gfzG49UjXuK>nuu>~5K5
zm&q=&eIXiYXKDp+E;7S**6Qa)1bi;J8fHRUCQAse-N`x6IuX#92JodFxzIK*D88_%
zYC#P^%-Q}E9ADG1U%xz)KL)ynbA<GmUsRmdrTDOkR{#>*lyM!hu&yy=9xe=|VdBBR
z;y#Ow{lQQ9YXp53x{c2*XE<u@hD^jL9-uod{ze@tKl!l{l^N>^JU<@1{y%4!imxnw
zg5ILBfMkyueqTr~{r&cTSsE}7S!b3d?Tu}&%5N8W)Ay2}B7#4NZ)*<a%`;Y4wO_U|
z-Ubu^s)d>WBqS4GmN8Z4FjfU@UxiqqlHf$1Iq)oA2H|8eZ)IzA9g|=GcGw_fbUVHs
z>1Bh?F{GD}AK>l(X;c4~paV^&IRH<O09W7z-f93@B-JXPmgQ`A9Txj-1$6+1=4x2G
zR{g9tP^Wdo&_^>Evj4Uu0O(u{ttk#G!`szqlc!y>Fnl9o|EBe-HdyB4jEIqEgr3ae
zWT%zk^$f<-e{y-F^VV{8KdaF_{e`7ri>VomlHY^kdg{Vsd_e8bYVEFm9EfstY!NTl
z-MG&9cWnSV*k7L!3~h4MO09e=PEfKCDz=aeouw=P7OrGe1yXHz6XWR!Zv>>rk0JYz
z9$6(580Iq%SqE6aKb<BA=H;z7PqnBC2y+As$rrywh9ky{3{}4$=82Tm28g8J<YEvW
zG2-G(ybJz~V94?`eo9G>Iir?WO1ka(--$c77yn8u`Z_piERV@pJ0pj0<`C|Rd2ma{
zaQrm+DdQ!pjp3t^i?H@P<}`IY#a7#KWw;%2!VYwMK95(7=A(IW(aL@#UiPkF!&O>x
zA43A~f%;WjiuXgp8($GXj+nd%Pug?*62BL31H<I2Y~ynXlN``DV!9qO8p7xar03%g
zp3a5-NlX1yz=JvyKh4u&l>WP?ABHzpVverLAf1sLVIB}%24m4axC@MQ%?jUoH#Y^c
zkdt#UXo%QaTd72*+ewOQ|E6kMNRcqk6kpO9o)|;VL2lfV{*>cI#<_e`6U#1kbud5P
z^RKf2uB?t{1eN04@5hCk#@FiLr7q`MW4Hsup6BM&1I}5y1}n6W!V1_l)>_2yHI_9p
zm&YRq_u5rHN*7dzUaVKf=fVz$%x5qpm~-HsYp~%@yTLI5POO`sw=PVq_c7&n0GU+m
z9T`WK2{^}7tfH-_$fXvDlK5th0!yov9XfNM-ClBG>dohS<Q#y?G{mpqMT5%(S6a#Z
zg7n635Tk8;F$xVHv@8I^%-Bt*$M5Xna~s<WWGIZ#+7lQjTJw5PR|8TGJ`iu0?req{
z!ed<z->u)&&-)hLKENL_*39@wUgIWe%5TNn$^^0I!8uSOc&M@@0k?(s8xHT)CXYzE
z^YN89Jr@l_N7Ie98k<qM8l|q+S;pX%XC5xSfF5V?(Lmc-Jpj`8$AoO$p-BqTbgRv<
z4f72$!hBdp;A^DZcr}F~&yAn5naQBWV*qqWJ-*^{cX2i&qFunKCxT8{WZ}z@_8do<
z>Ys0#iw%uRUBBN&5ntEX;D9O3{FjY*|HV&1b@g+au*sFg#o{Gkxu~(QYi0X_7uM=>
zEY4;=Z~+&RqwNWqI;2B*SFNF+d5Z4Kj%B~GY9Q~*w#Ih*W?KX3OkG|SCc4D%w!z(*
z6<efHNt0ua1Is04dELRHg$VZ-Uw63)tJG~~)XmlDA@yt;2zLUx)9=P|?3Qh$)W4#N
zmRc_9q2)I2rzi_+HP~x9gfqo}!$JECuYs5qt1Y5%P(^epa+8O#$em%JzAZ^Bz2<Me
zeSqDam+o~!`q=@De&B93RUaTrw^<(G%{wMMD{s38c$j`qK*jb?aCDtYd5ucCX5GtS
zIW32kEf~HSPq&G;a^>7fxlWhB3n5TvJb4c^yE!{qV4;1!ov#}o_@t<={;;*G3SaOM
zkAq`?Wem7tf*dWd%;Hal++X-6Vq_{7_<0|10hwv;hyO2I3jYNu=>ND0UY`ANK2lG+
zOKh8EE)jI_E#aL%a_Ocs_*+OxHKzzzFOTxgguWohrx~;!V%pJad`YjkLFoWLkbDmd
zqEP|AnZ~jXO!ne;r5m)0N7gckKw?9+kYH~FxL(>sF+L>S0KlX2NN!SomAvdb&xZ-P
zF;>J_<HH<Sj6+Dvfx}M7fj598MXe0*987c%)6s&{z;E(14k3qOiO56^?sY8~K!+GY
zg1mAUDZYTsr~sXBq;t7?k*uq;#`WgM(1|^$p((36VnzF{S{2ut%B-d(OsA}%o2|>5
z6MCLO7~0SyZ+$zBBY(rOaGuG)M3u;&t>Cdi)tmq6jh_ElrT^Qun^+7Ge!z!S@psa-
z#7KZhTf`w3`y78Ksv@PGG<epSn7SWW2TEhlL$qRy@`f=#X(}?8LENu?XVdG}11bYm
zo63_(HM_s_;z22;e^mAJ@}hka)F(02X(VQk@*!@;7>jGzhA>GxLO_MZXNtXwFZ=BS
zQz#m8K4_y?^y_uXeq<Zh6Worp)O=~ULHp^$YR3x|;$D0MX^CV(L<6CtRir(H`@BOJ
zgKvS9KE#YROV+~I;gykAb{x=@!$sJY+6KRvkz60E5esgY*o)%JWjUM@OGFfev4KV;
zYl}n@8b~(wKH`7MEi69n%Y{2PtnhmE07{nT;8FJ4D8t_4%d9}!mLfm!>@(2F=(fhr
zm0of`xwx<0s>INFEXskm5z03uKjEC~j1S2Ic0b&}i{x=+y1l*g&)d9Oe+q}CQ~K~?
zHp#xhlON;l0K%P#6XSkWE+^1RbBEAgkt6U8h<qg4#wlHgmsT!s5(y)utJpzGWcz<T
zy3Z}lXYV^wAvB@4P|QX|gn&mBa6Bw4@c?FS{<Os<-p)r1tl~c%{rs=o()@p6>A>y^
zpUI>wp0{sTc#V<_?8lWrw2Pvebt8siOFnAbMC+42CYMA-Jgh7+m9)1iHmu_!>EPa|
z@(4%pY=w}6aRV~lhEuz6(am5luxBBpDH0wZ8vBs>DI&Ca&cCwGgDTY6Y%SA_br?X3
z**0Uii~HbSS?4h*H+K*u5|$^vd=74EhbpQO?Vm41^Z|RT^J6s#cq0)H{cc9My?$mF
zI)t7pyH=(?k8P^CkBrz{w3<VVXtB#KqBgs>V`eHPQ&G))e7n7z@U$qhi%dvomNGQ$
zal@}&Byf*wBO=mj4=0^-@J+imGFrBXFb2HPZU-57JJz0L{Svu`bzV&1Tmz*ea2RTJ
z(%hRxX^#01W^?dLg(=_>vGwFVsBwG%49N{WMv@JEhvc#Kl`HAyT+`*dfBpY3DEhaV
z_&crh9WF=q^pD>j<~acN2arUlu`Ff30|F6iSP_Fp9u%Bi!IOW^{hf;dc0ox5M6zA8
zjWdifT8i+pC)tbL{_(fZ0q)a=vu#Gh3BWFbGv*AOmydU|Y-#-h&|i{zGOl!K;O>43
z7#VLPY3a)RLdI`4a&^cTviJB|qVovUy^5N(@R@04acNA&28FOJA*ve8L|NET8Kb;%
z%xeE3!TSP6>efaGMw?+$-TQh7tr5sFvXGrfS0FbLfz`>rHCE_;iA2c)4t4@Fq?+7d
ztL?D9*KdJ}H`1uPpofhT89G4GQm1i-a6wRDv$Y}f_RB>QvJ(+sz1933PB<Pa`$L(x
zEr)FjxkH=bChrSvDX!~#u?+6Xkd%Am=QHQoO87~<N9Mver~_>_O?uT3&A7we)`pZc
zp&5g`^w!{DQt9x6Uw=7IS)yIn&-ndp;cVa@L{1}Z?VZ-oV9yH!`=|td+2+EC+u&;{
z%7<iaormwvKk<Cr$kD{?ncNN>4KW7{6#&5<klCR22dkguDi+iRS{2}&mi_!H*xR?i
z`mOY26{B(CJ-OsO{Cq*OoT~v8o59sp%$^GX;LGI{C+oyeAK24NiCRFufUHf`;SsEY
zxW~`68;&v(gu<4lf#6E)nw(L-`}!x|hLaW__ec$>fZb%99qo{^D+PAv4e!0-F)uE0
z8wC<6H660!B>~nj_Qda3)N4Hh4pg{FoJTP>eK1o;7?GHIFl}5c;uu#JH)Ad#&n2UT
z$h2uiNhh8J|4JF%`d6&=?^7)Q%^ivdn(pS{SnkOYHNiFR2>G}mW>gmCIgzV<u$GtH
zy06QnPp<eOJP>BcxXL)gVE;TT)LDQ&+W0milJ?;tg9{|sg6U&^yr%vukGcHY+5cB6
znafGN&B3?8m#eFXZDGjzs^QAkI__9vz?RsC^%|Q(TYAxw)0nSOd#(%18Q4yN;g=p;
z0hZ0jm-r+G;y>09aRZFeNp7BAThGEvBHL|I-5tEe^$2vi?6oq!6G;G1PcCUAsD*T9
z@FiL%7dm+OE3%W&QHhN2(Kkdz+yp--e^rFX=Og_rC}J6g(Nyw^g&oCZl8vVxFjV7q
zSH8qOrcjT!_L4Qq|8|OKRMb<{Gz1B#EB9h-nIa`ElQ}PdL(JF5+|+*WHF|IZ(fICu
zm9kuiMP00H1CnH;@+N#~pO6lZd8q~i2P+y#*H|U73$*OhbG(Gi{YxWh23W<w=NXK~
z*ah&X*K5vI@itaIZ{&n;heSlb;EyOxkc4gR!Z7+sD$vBo4UxExoJ%v125pYL(~fLU
zO#MVf4pNu$nv_kD#$f^{d<NiSLP1N(oX+@>dJ*Df$+A_0wPBpgcRd`3f$b}?N?&vt
z)0@j}UQ_WOuUQ$t?7GQFvdy6vpy4B>(Lce0R+Wa%8`Yh5U?NMFx-P00I)#F53~Srb
z>EEx&86fFK>q@*b5$x^bthca@LAj<{<Feq_4+|Zl9{|ryB6=D&QsBcHl)TO!*Rf`q
z9(H9qnzn2&rr*t|WLDr=2C#3EJtO**yj@L=2&y6NS@TjQYO|eJpK@!AZmieDsJ5WW
z4hK!zrx^(Nap|ZY0%<v4Dsy|sIVLK}T<jh0AtR4`PmsPfz>QD3GPj*W?(sAscM0mR
z_g6N5tOu=`E|duBEFtyIQh<N16X)l99yY}XKzhDpEg5J~;Jxh#1IfkF>gjhmS!CR7
zwxX*MDd--U6=Fz#u^rfx?Pkg{!m63l<D=nU+YPIWo6>UZVg5ZHxRQX)j1UzUpur!F
zl)Or0C&W2osK&g9)pCsF0BRzlU1!6hdrG1<DpWvQax-PB6zHrq-m?L+)Yzd26?47`
zyT@&1se0<PYiUz`ZNdONeB2n0I!g@MoHh-ppnvZnr-aBt?^y_RHR)2)oA<aA>EF|l
zDwH7%lbp3+P=#c)0=vcj_qRTr?WBw0tt{uA?B(>Ix&Vy$T<Q3&vH2qrGEa>W6d&>H
zV(wN{p}U<ASnE61vc3#iG-bSLcgLIU%Jwv<FVdy+B{jQ%`ybtNK=l;QIA;cVgI5K-
z9FotL8AABl;n!sv-U3t%r@;{uK-+4&Oq`kTMQ0jm6xG{-N4b>@3BNei;rIPlWrweg
zOS%sHqL9O#G5c8S_zOM$tD4cW#?dJK9roL-sO+7CK^E+@!`FKJ*i{eAQ|RCCImUpm
zENcMD8B`pX@K+?+Q`x@&XvkL_cLE(~$ZCwAalQ<(Nr}$#SCrHWm*W@Q_@L-owQj?~
zlQ}J8E*P%@g1k`1@8soa@U0YAT-~uZtP6ji4r<Df)kDbd$mVU-Nab~}A-y|8)5)c$
zbD6wTK_d-TqdkhvX(9K4c%TN4)7%8-I6zRuidx|Px4`uVj3ckEm$f)|``gnh{S1Ep
zQ8REg8J&51@aKg`V>0-V%-a(@j{mme%IAvvpbFHIn7uYAc>AVA)&c?QlY(52`hEB(
zs{N)SZ?)vM{J;B(;WT5Q$!Q-DV)|_0aa(A(bJ<tx$e25qw{8C<{OH+6;ydA_n73}?
z;P3lmlTZma)OH*{<q_DgS#j9rvcPWLmk%%AFmpRH`{U``Hffn&;difuA05#acG0J{
zFXa-));u71BCCiUH9!W>=MsUl`lxvuRs;~P9XEUR-{2=x$-FNhrvLi<LsJ1O;MBb+
zzpNyNb8^Bf0c>z^<LZ|O+1n!D{;dBnop@v9)P&oF0@nKB*h5XM)x-IR<}uG7y4r)*
zTT`EkVb3k^p13t}#7)~xep0I8kMk%R$DwB1!nOtDr`;(r22Ms!dq#zC@477<d2plq
z4Gm=Z6gP?ObL5QkMe=X_5g0NibF$=a<W-sUlr%k)56RzrPJXoV6DCcw9tt{vDJJL3
zqONQhG-L<nvPt)$H{0GDQM+U01V>DbM)uc!xQ9`A{j^t3z$>Zu35pMQIDhN7#ZM{6
zV_Ri1{Q8n%yO*6Ngs`?)PpthzD3W4wqt2thD!YD7tv{}@{%&ICOKamzKWZZhe_heU
z;#;voac>fzok^ptp$W{&vIz8jKG~0Aaj?!~Te;-Xi&C}}7B-=4U9R?OtI(x$!F^97
zwE0a3{Gx(P|6=Qv?+g1P1(w@As{XD=Om(E)s(2$+_iW)&Na9u5l)jsH;7x`mIj#zk
z{BYY8c9+qQLSl~JfP-U#6vj7E*FMItV$mY%LvkKQILAJp-1LqZ@W*RC6N}Dbb6I1H
zZ%FemMM^rFsTeKoqo^laYQ8rumCasgnaebe^b@nr1-H-k#%&A99^7(x!aDh(^-44+
zPIrm`?;lHe)Mhp?f`T0HXEv&l_2P#tOw7L&ntk(Ll6w7YQ{(kuVNlbNQa%XZi05cw
zw&G@nks4yE$-B->rm<Mfv0s!Jl$@8Ie8bCHH_ZADS9Ser{qGy=FDGWES{v8-g;=lp
zewE8tobG?s?;X!?FGx`seO94YzJ1xh170rnv?j<s&;Qp;J(HCyblH}Z&t*i=Y0CWb
z!su;ZXed6t{_)qxg4?~!Cwjcc-~#aiP7*&WM-dd8`l(7N%US7X;6!Dd3WK@Nu)R9e
zkV1=VojXuZ_d*>72N*-zzoDJLC?9--y4c~1kjuoej)KdPZuO*^(ctzXo36s*Rzp})
zU~c%?@?Db~J_Pi^$@P^KC^-x7H}u|+su^mMfZ11j6yZ0Z7iQjia%o}=ZS(oW`+l|1
zK&#D>m?<Y3dY&jAb|z3D(4-C4;h#ww2>)8l)m=DvNlH_>@X0yCjN&x@rICq6N3nv>
zQD>`7iS^B1EzvVoFJ2*9y$!}B`!MmT&VpMfPeDVc(=1K#9w*tCwyfQdM?I&SPewO6
zs(ZER)`Q@B&_9P#-UW{zJ?_V9R6j)zxfHC8YD>VWrkqDxn3qYtMt}~a4i*w`7{x}(
zkuHvHR5hyx90@(#Wi13k1l{?=75BuMl65zGqeLO`uwObR?fXi9s6vl1HP^ZwXy({7
z{$szCvajWyDd#LnHPjQvrg=(;ubFS^j}r?*S7_sHkuFD&t=<IhoK!Q0tK{owr^i~B
z)_9NE=e-f}FY0o_DHp;H<iP4YE@a?CabjBc?{=QLbZPu7^g<0Vs;iMEJ`v|K<xLdk
zv$iX&D=oc`@Gj6T{NNk;AbG$qneBT=NacOI_ddO_qXvd2Os1EP{(AEK7%99+L<fxO
z-#ByAW$d(q$@6S$<KgP?zo@=N>CGAjg?8>1wYSW6x*Sk$7TXxBI&{t*V$!L^Y!p}K
z=!ZDnv&}x^oOPWYeWerkbu`Va;jE*qMl{hw*lKP2hKT$7(Id@*me1Lo7P3W_h|-Ef
zDB$+3Z_)<~EqYhSGRWC!r&b&k^<PL&s63Rr>)*TJ^OJJL5-2A?lwon4LrH&Hq*JAQ
z?^xy>f?w%DTHmSqs-yWHP@WFz$-Qad^@|UduXcaW>W8bBKDqV@6$7eCgYe60zhCg3
zu;}HesNjnZR_fo|JfCNEFh{jhD>;o5ej%gKq?4r3zd7x_`N0G2UGu)xt9Lr-hJ;5h
z6u!Q_2Bgf9543-xTUdNGk}Xj#G}*aQJmjPS&D?<gWqmbL){I2oG12$oka~WwjIl*`
zvGr8t*KqLF!~;5omKZISV3)C~tt%mIqAtY;*SE_KII^J=lGgV-H5wC_t6YV}o}Awq
zrkYduF+g<7(ES{<y(Vhi?!XC4f@vr7cxrS|Rio=n&9&jmchkFeqz<V&vLi0#7|l6A
z(%=let5+irI0F{1HlwrdlM%=yi(m<kMoi_vYcC#jKMR_w+_n5^V0AL?yoU2@k6Ccs
zjUF_=v`BtykATbNuv-=>?^{naHF5TCc-GByy}wNwD46M=YW2E7C8tU$J6bwV4>x*;
zgHx!IW<U58Nc}q62<A}6eC~T)wc*!+H=GZy)Jue!6|7r2;x`%<94W4|vU%U<>q6W8
z`d(I4282x5=`ueq#so{M6GvUmL)*POhm~`Z`Zp4P{Mu{@{!(NEt9(l~#5`J#Tij3@
zS(!hS83fEb+%|3`%3jz?ih^&|32RTdp)W)!Czwo}pF!onh4*i4>Ey7CFtkd|xbbS0
zeF6KMUZ{4gmWJx8WgQ{aRV(R$O8})DMJ0V`E1J^*C;5f+QOU3V<yALNENu9orA5%@
z-N<^iVS$a4ER~wmScnMqQa2K@GL)n$o{w4DgFPB*a$cEVPXJVy64MssWZgU`fxi5P
zUV;&8-=hTew^{mV-0>wKd0;3nLcQ=_HZY|AyBc1JSNpYhkr(<_b{pD9Ck}BQrN6rx
z>C$%!dYYvtnmO=EFCg)zUf2jK`G61YSE2+QFc{Ra+gSm3!Xhobny4mUp#lCkEywIm
z7+T<vBW(#c`a;Nu2{Q#GyaF{Hn44Gq=Ra5ua?}v?Ux_qJ;~XhW;?jv4Q7CNZ;2Eow
z69~)5Bza}6MU^Kj9Fm%y+}l1jPBrom0wlkpL1947Xaypx=Kz4tNec^;khY@Bfckq3
z_b?w-U6MLTMP$Foc?4LDL{3s{q1eg3Dqwl>2>oN(#Vz`hX(xF=7x^%LuZi8*CHlv;
zpG}(G@j*XBTx`}aw+NzcmTjgd6+s53_0d{Ng#k#wF~0)hzqpa|e-mH9a?m`~buZ+b
z4W-KFMX&Tb+S1YEvwGqIU}u3E0nr^zGOrubHl;Ie(4`^{2C95cc{L?0CiFB~3ltP#
zlvCr{IxBSNw&ZIo8J|F>s-BW$jakhA0ORhQ%hWTe129g9?vtSE2&U&o*bC>so$0Lw
zaHH&f-zU{SQ*^`3SuQ2p&pszNJ>HC5`dYi9bNbYH6L{lL%kAQ<eMujy<8;nW^*%d|
z?cK~W+E<4&2G)+eh{yv9s3&AK$GGfzGJt`r<k3xY2`)mv7eBE8o3kvLaeghec(^n~
zZu#j^&-90<->ssMqd(@p2JJ4DKc1cb!>k}c@wh@mO~-<SKWXXEVosI9gmLIFTjz&3
z&HNpB-D=b_8|!cT;iL6+O~ZT*rGwov`8U<z1!fC<#L#g8J*$w%b}w+frK^yAJ$<nn
zd9e`j_U~z-kf!$!&UHOPtc1&r5elA55kImfixysV5l#}t+pnhtlXX`dQX6&K)%t&B
z*Teh$sPBH^S^eFv05>qdng9B{J6r;<gF_#wu}!$KRUQigpvK0Aown0IyTHtO&FX1Q
zi07xpUscbRdd($Y8s3>-^TvFZ=m1@W9MctZeKz0fm0EB-zs|$v+)UrOZ!le*;hwBr
z{_<$Rhn`q^{n}-cnnM>Qn#mfkWLHsF_ZI1{Bos>isbd&{@*J`8rf8(<KFNG#O?ZJj
z_6wh)FV^j0X>hHhI(*+M*`54Lg-7>Rg5l-pB=Sh>0&Xt#-POdi&jxf<^R^ldiZ!*0
zpR@734~PzddUYiQ%<~(&rxeI5=|8O{I*hbVaabQ6b!EU)>Q^OR^^U5YlA-#%Su>Q8
z@}J*&H1h3(Wn`{IDLSF2FQ@|gosUnl-0bp2n>r@OZbvH2qJVYM=STZ@r+j}PMe!iG
zSQ>8p4ooq>eZnxBqSIDW@B!dhsoA=wk(s&nRt$X&kPhvn63<2R`@gvYoYnltrLVA-
zD6q|V{5b#(5UsQ3y}DLrC&#m%mdQ0|SgR*IXUoqf;ih)_pGfbXZ8BKC*2d}Nxb!l#
z6;nBVMO)DJ(Si5VPjM%yxrsALy~pnu`t%JJ7G4=)-k<cx+iDvI34!ngk%Q~`?|lv%
z%O%%9?gIB;%Nvedo-cf`+!o^56Cnip=D}UV%QiH0P`PX2-o#+?AAp#OB)k%!UVwa<
z|7iId-r^z<@Qgfyxd*ER^lK<ynsP3KRTg@}iK>AJ5SL`_dgniiy$`wOFA^DR2{pP}
zy<V0FEo(D)lInbgP9a|0U!$#5_fx$?{{&zO8pUs{epIc`#rA4(A_Yc}rvM!hKk2>j
z@D6GDoHvt4J?9nKB!{o;ds>mz2ix7f`o|uH<UGY+{W?>9{f!ZaNO|EM#nvBgXJuvD
z7a~%cyFvgOy-R^4qL*9VEP}46vQ6j({AI~=BRd2+E1MB`h0EK%J!gv_{VO?TEmj>Q
zUq}m${>$J+L;_XmhBIN`tG+1Wf!9p!d-UhmmNs^)qqxl4;@nKixl-6O5a>34uEs-u
zJGB=ZTTeF`uq>0r2+*gdoFQ1P`_jf?Sv+lMA2{d0YfN67micKwV5nt|o}on-@&rsa
z6P>M#KMzd%G`^TTqe9K(>hoK8i=iH3e!u2?rh@IAe!Z)tvlC$xpF&@uyB`Y1p!#6F
zuzO`stp3dLnAO3W#i^W|pH1qH3OzA<8XJCa*s0?|D5si}x*Hr|{sw|_CTYjC_s#t=
z_;5+(^J%Qw!5XJa3BVYVd1KI>NqGr{Cmqud`&Eoh!aC}b@)TOEE9rU68pi&Xz6Gz3
z`H_zBO?`vA%8*3S6Z$#qy)Co--Lr?Kk;0@E`IzWDrj%~Y%mZ^J0HqX1#mCC1!4o2(
zy7p-g$K|_r9c=fulRKrAFkTtnQ*7Q^=o1c3kkV8tJPY;@xanf>ZOc=Zi-1)MZBW&#
zOj=bdgFx%Awr#7iy1FD~fbtqkC7e0oq>z*+|7UWyq=>7ym#zLOx9ewiU}|J<g7jB?
z`sXj*tLzi$_)@r5=l|+=OrS(J%(=iB|G*U5irilNdRZapfW^Ezyik-ls-~N}lALF?
zf2dObs*G`R0dsx!1}cQQWM}sw8aHE5cy>P=VEYPp`rl{Q|EjK2`di$A-5K<w%RfpS
z`$+qpmZ(wWD~m<mn|;_e-90()veg*pjvmCw2aQBu*?4!p^Qx?I?3QTQu4REsH`P=a
zU+#SOd-EK4RH|9##!`OV_~+MTi`=5yD@WaP#M`Hj|4O`(z_@p@7<MT2d9LwnQ9b6W
zN8#H9cz8gH{u3>M=*wYY1C3wz!{zpw%n?(|yCG`K&Fg3AT<oY<wvWtwV~@5V-!?$$
zfSmw_VofF2)T{aWT9zthPB$x>MZ8s*P<h9R4s+a-aWP#YU?@^+Sxy+8v}jo?#ooPf
zAW-D!P_6$}0IJPnU?K@KRAIDu>4~k!n^#*F?{ZQ$Il<P1@qMxyA%`c^@UbH3QGY%|
zvB*bNCj`vN>mn1Jy)%V+QiEB`+h;Gv-B8lz<7|t37--1(V&hos@ZjvSW{Yq^VV};E
z0f_)5AQ2$3;WXjfa&q7bSiM|&(rz_Bh9qGU$$1utT>BE|$38Y=>_5~0Im2FT08sy0
zb)86f_2%>c21%6v4vC!qvt(eM;kW0f^HVYcte*|-s7)_VW?rjP=8d5{+jBw*>&lld
zh!&*tX$zzafOf2B+f5xeGnUy|vRBuLU{R;6fQc`Mj|<vapp|}CdeZ$+n;#<Hq>&uq
z&$pg+)m*`hbw&`qx2LxdX!wJ_IVoc8of7&AxAgmrz@0ua4H%S1LGLZIAA*gkJ$0K$
ztphrbXPXZaq=WuI@ob9I981@l0Aoe5hgu?&%uA<pV!Q+h-^ac+bpA1X+KhZ_`&M?K
zWz*db|BTf3zB*BZ=nntCxHWyqF1rq!)P8}a+Qx~-txL|+58O{b?o$O=`UxXXp!j>a
zz3ZFOeF0B-{ZMDjLUkp|EKfFX+cI2spBTZKw0KW;#z;I|G%0CLTmJr*8S`9xWaEW*
z&cy*ft8Fe~*;Zk0xSY~;nB>O!K-EnUXYAnldRq>yS4*1@zKtEzo3p4h+;r`PiLI;L
zGx9uLN0~pqYB<Ci2_s0z{z6YZg)Nj)v-f$Tw=F&Gh}O&NS)czX;_o0dI#NmO(Hz-&
zQnc_o4p;5#T+D<cT1i88^z84r*oXFl@a+MLk#-!D^QJih3sxlhDf-7~+cm1c#0YcQ
zI|uio4uu(x4s8&-W$<p{crr(v)^7PeM0r@#U79yb#hxHtSA>e}jR(*PWMVFkZV_h*
zj~XzUz6mmFGIaEM8FqZNoB)Z5R}6rpV#Lv#)=wa}et9#4@5p?1kxL-Yv$6;HTn~R_
zuNbd?ME6ilQ(10SW76W}`^|=0?*fE#;xY4k$P-t6sDAH)2?AO8qFq1eMz#q!iPdb0
zJ4g|kE~WN)C#U&ehU;)S)|Qp{@b2X$?;Jr1!kibfD7R9g!)kRy<wo><bkRbVM9ooN
zhSss?J@o^_*0^KwPpKPkhTVf_Vj>F0J~jLpP8L!KfW85;WBXg4jw<i0!Q2(1>`z?U
zeZQF<_GjZwz4MSoYsz_<M!JLvR<z>sY^WSM*6(^&@|+Lpt$+F;$40>rcbgPf`$8$_
z%c6a;C=nlb<Ll|Tx<FI#H5EY!x?|h~)38l6VK~(0(x#a#M}eGNdMt6x`RM4EBRtV|
z+&VUD*dUgdQp}oW-fKzUyg1(~qmmUPA&gd0*{6GXed3|aYnE)9H6oV&8S4ZfI2wYM
zJA>Xixa0gE8#iol2NOnQXM*CK7RBU8-(ei8x@BV0_vt<`1rMeMFz@x&dnuIlp*%>k
z6Vq6R9dn>&c&j=$SWlr}rzDn&u*=XBZg=zr#vE6?!!E{Nw3zcPG--<Zj)n|fmOX%p
z7Mz6my%cA{Pti#k)a>sMOss}NL?BZ4FOul)Fysr$k;F{d4;>%H0)wlF5~isJ$4@Z}
z)M|Qh%y;#&IoG3JwMQo~pkJcH22bTR@;qhN<mu3v3ypAgKB4K-?5)GU-WDw=PN)fX
zDDxMZdV(e&<_}#3FBjZ0e<NSzOenwMU8jF^nAqUgZ(X<;)Me!0p7X$K;`5GF=N*P}
zuUTQ2a$<GQ!)0FUWcPginkq^(pB+6EB_C_*;xc$DMAzMrfRJenasu61D_Z{dDEP{u
zR!mXpYU2dNVdh@8TgtFnq=QXON95;^8@JKnf6o(TzZF;w;o;0fo5w3)283L;*CbuG
zW7g@V(f(3up?pV~qW<n;=O|Pzy>RH$uI2usvRL(g?GD-A^`v44dwdOPb^=Cwx;kRw
zsol$&yq>Rnp0HP!&t8~1cvdY+h%(U2VS@L5S==%}K$hENve7RGKHA^mSOf1e4#}0^
zW9vq6V&%!wrg%}87{7O5p%;o{XHSLre9@{=z5@2YcH8T>c4hXzHqzp1H$;Ul@m9g?
z$4>_<sbae8L4Bh$q0znUuBW=%{L%YaoD6)XT^BunI<@eVeYI?uXrSjjr}^%h@q$T)
z%?Pv3t(A!ul&nrm7m+{L4nDU0a$q&cX8t=zb6LA|nlnmb8Tqmr_&3}iGx*m9*?HBG
zLV5Y~>HFuOO#^NKHo}dP;=<}5izOC;ZdxX#iOF4`neJU>`UR*gU1kS;(Y-3i`7qBR
zcyIo-e*i$zrNK|9&0gI+d_n5qK<?+@J?>G1Btg#LDe8_E$<-)oe1I9rZW7K9QMgYf
zdxuk2vThHFy9Z1{_|he2_BW;X{IQ?D&qTcDLsw7+`9mpVB&2{i<onq;rdgqj0L3NN
zz*6DjCa;o$9jY4V-;4~3Zm5(_TYVqPFevMD;>eo6`CRLN@O*Lg7BB!}$}&`f^s~9_
zmi-wLJNYJ^+6A*5e6%dIPgqQ;K{Kz(tX5!yKCo&ABf1{jY7*Fbrg|bsCP;|VEP6)M
z@!M2$D2toYl#fy{%Y*_6#aHixszK<cJsaZ7f3(ax$C&bSFgRT&)6QZmEzXsyt*1w)
zMe`d3L%yH(Jlht(Z>ETKAn=s?+QMfMv=l1ArL$L8hcw|2$yK3mZGnXICE~%377dH}
z?;mKfk2}huLuNeUq}XZ8*gD;&E3;v7s^T?^ZkOWwSy)clb;?Do_8j}L?CxxbyfPOZ
z;Ex^D!@ZV5S{UA&P6;%N7R8CGfmK=kz}!jJY+|k9{tf5u#V>tsTLmTtACfp`{mn{_
zvJdPNj+?!37Xp@0gx~fs>70E}+s|!XyiqJ8wNg5rKSOs;Sw7@xuxu44l-Zp*C5)yt
z$Fe`ZnXW;<-Wc^@3YenPT&;$me!sEkoqS|#mu@%}>d1*o^p){$*lSY8J`kqAY~*MZ
zt+Om-duaF-lw<mBGd#^ut6)*0chwI{gnCp(K6v8)B_~Ts?3VQV{uQGH#+2ofJ4FA_
zSN&2v{T|lZSFp`{9z!!~dJV<3>OvCJKxIFQ)K~}Ik1Jp;uz$t%)Pm!B(~z^*tZ&ej
z=&_M!huhAT8@L}2U{2mD{E-1ar?typ&NI0+xZ!eVG5QTkpvO;{^JaOu?C@|%SAf{z
zKcy7LqEyGCkvAw5Vfk_Gy(ag@dl{RLcXOt`9{I2y{f4L3OQU%P-;|thZYB1tiJuTR
z!5eR|^qZ=;6jraD-e+`c@u;Br2PN=UdYKVhrv8J8!K>rld-TE-%x<1tDJrfup!grZ
zmcr*g98-F5(WiG6@+6h1pVQOnj&cWIAn{u?A5AQ2eEB`0>aXudC6i?%Ri{}=s=joJ
z2s#L@*UqlD8glT18Z~@3Fq%Drd!A-3Ck{T7Bie4iH-OSJd3IuJV6`EJP@TpB&D-KX
zVZo)-FG6LTs`m#5JZ4;7<f#63S?Fx&>e5%>y&7s?l}e)t;%slhz{_iF<~e`Tc>c60
zz69C7;#$wH^<cIgqW#GQ!>a28;=1?k98vM>N+A!{*nW&gIVVN^)?(oA6ObQP_d?T;
z*Q_wSbD~E4njztEIyC2x*PK6puFo^>R&{R-%$rpkByFBJT~c|;tLo>G#X4<!N6vbd
zqkI`P^3n+p=3qnU1q=jD99NxSxwrSFyrU2M>lGAsDc#73qV8y?MF(7v?s?jA*x>Co
zkaOL-k?j&routFxB4;#Af$P3&3bN5zhn5ylbs|5|D&@Y1zqvzc909kI>Qj9VDTZ&Q
z7d6{QCXHzSy&Sy=JvD~56RUi-)Kd)ai;n677NMo1iNn&rFgJ#r!Y7<f>-56Xh5N2m
z={Y^=9%=yD;NI(o5%|%4;aKOx$-r1lg(zcKOTmsZfwRv#a$jF3ZDQvVm$r)gJ~doE
zUod4^L=@6~(f0`)1*h|CJ+BFoD>uI66tWnN7=?TFga}}l3>Kj-@N>4mW+xsmCAAwR
zrj5Ix9~uzl@EE6mEk$4ln<(c?;XJ}{$VU*^Qt-s8$N10XK62Nb)S8tSMqh2uITb7W
zNA<QygHsT($xk;Ioht43>B^s-Ev8(7kvqmd?FtN9W821s>yPAo=)}qki}5+mh2s||
z40eSnxwF4@zk{ntm|T)Gg9v@yj@f^$Xi-NuY$WHAN?r(*bIx6K02$F?a3-(EDStZU
zNmah3kV>48NA<W-K8^_#<j<9Q-H^$SKB@GPQyd(ftAi8qm!l{_72O%n)~nRav1z4R
zz<J{7H%#ZkScsRbRP+8FkXQD!lA<N~e>1j(WuaI5TZDah!#rTu5DOV4NHc8@3}vWN
z?)bt;s`V+S?S&{rkE;2c-rzA&VpiMW5@wBZGVAil{DWl|k=uU_wh>MY3`LDuLP%k#
zdYQ)jU*w6$s*-*U_nUooU0sMqh{L_gLN1NAff8?wJGGBebY&X%{yYHUP<#gnxcVY?
zrTE|capA8ULulK<G+<f%7GDAc98sd%4Gl~-LE<wvx0as<qxq8g%Pfkhi=hD?xp5Re
zl;uhKomKsdQMZlw*ZHFcNEio@9v1ZwSkzSCjrtvIet|k38m0Zo3@hf;Dd)BP=ggqS
zx)Fht!ORYn3)~y%cnQ`QcooM9>DfQ}KzsS|z-s3=y=F>@o(8ZXHeUUkJ9(aN(N8Pn
zC+wA-sY+PByJ{{hPnC7fBPvYrO?Y7Gj$q=Ius;X04(fBfof7sn`iseT(KpA6oL!HP
z_F!PhuGn|=<RrkRXHPQsPClUNiBPW-O~R!5m@$H8%+HbEY6vo)M%dYQ{3=Hh&oq9(
z_w0#FDvJ^$N!yWj9=-S3B(x>+1z@FhcTxGwN)=$LuSVwjgK;9Ph0C?b2`W}5ZX7ux
zLaOMCDO8K>A=hNfhY>D=$F9`%5MuKe539brpJK8cg|b~@>>S~RS!Oqr>t*yZm7{WL
zUvlV_GqpKDxJBRJa(L|dL{R7*X3O8<-tppn3w!S$Fv2l<-b#E<ozo_tKR0Z*BvjY^
zu2O^<p>BEQB+qJ6AIm)I{z-Dc$<O9G=2v;@=7(A3V%H0Nq;)^SR#Hcf@k#-!h_Kdk
zxR<7RT+i72Y%ArZh(Ggj>Ef8_8*5Cl<*@}4J!q|rr}CRxyrQHqm&<g)m0=V`o$)EG
z?hL={hoSs1^ill_pzVVoZp`R&%Fe2RpNEe}mI~Do55(<aVil`OeKz;I>+Uprbs!Cm
z;=<=mhvsdD{0lp`y#J0gc@pvZ;#U6ufBq{qFiqp|ozrR1L>|R`;GRh`yJC5{?2_A~
F{{craj-LPk

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

diff --git a/reference/plot-7.png b/reference/plot-7.png
index 3e841fe08e867549a2fe449bb4e39650c1239efa..6f2d06b60ef0cb114bac014c0dfc854e103bedfb 100644
GIT binary patch
literal 37837
zcmeFa2Ut|ux-E*Wt*y4wsEA5xF(OG+f+EnZC?X;X60`^kq7qAT4&642L>m!QL=*%B
z6a*wEMS_BWfW(4Aax8-6{KhQ0_w9S`JMZj$zO&EW-*;Yrdn;D0TD8_(bN>Gr;~)Rz
z_L<Z2YgTPp#lpg}M&X2<3Jc4(b}TGQ^}bt<S7?Dxhw#^OJw<sr{Eh#nj%K>zua(zN
zoVR3Q;l4%wzr?R&*noxQM-~OSpU>Gn>Tj~o`fQyoJ2dggSwHHVWzHKS10C00IJxq7
zMWbh|!Ve5tRjV4!yiCt^&KY<Lsn-Sz=_Dypn)ZJ7id@%Q;C?dc`wvIH-JlRKc0J|6
z)ZANx!z;>`e>~Vc^?1ZdhUf5P*@JSYoWUulps6zBoRN@IGNI)wn3oo<8fdkI`BOjF
z5)tylPbXK}F<((R`DQhF;f~5TI^=J+fOSLPkRP4}xN(rbe~eg`MgC^t-|^*J^7je;
zzvC2nEEBhto%mv6V!~o#_6($VJ$_cQ`Hr_JK7S{`^Vw>aX?5{npPHH)50Cd<BP%ub
z@7=7A_kQFr`#-<rl3QkPOe|k$xx7rMUr$xkF737CI*x7sKM(!$mMD1^=~|7h3v=cb
z$2ba}-s0J^?YOr`Z*T7=X$LDVr=hpQZ&_XTx(wam%~w^HW}kA(DVl#69IQXkm~LLU
z`D#9A^JL-kmoI&O-zHxuTEw+pcdl1Mf9h*Tb{wNS+|L)c^*koVQ+8phoQ<ubre>pM
zSDDZL^27Vb>-7D|=WegxRXEiZ%Bw6hbvbHIP&?y*aL#1w{R4(72M!#_@2&`Ma~^cm
z{TM7Eg6oX$Gi+{-2-4Xi&+BaE&2?~}k+HFUeD=7fpHurb^K$74ISPfgb|O9Lq=C20
z@NMM{y=h&K9UZh65)DdPaizyU-WT0g=)<X@Ik8rH^poVm><GD$;jbkk6B)VO<W^su
z8DVvue8=t3|Jtx>m0yUItyvHQUoEJUB}uN#u1Jc`_U`5yuj%ROI%@7*tM~pg5BrhM
zW2&lEaWeOQb&}D29*YMlPNg@t>5H2-d~Oky3e%(=Ntif_NAc6|9sCN)vU6506HT_|
z@^bO>bJMlC11Y)bmR+63Mn(?(U7uBzOQ{X7ql=~^PlsHv``G3u`&c)}$=+pla=2-H
zF1_4YIJBhW>y(pBb7iH!?A&Norm1W9(_5VqCUu`;hcBdC7|fee9Vve0)YK-M65095
zw(MR@jeEtLwC!5l-N$X4oo)TB<24ibWDSY~bmARfzI=IXbNo0D9`=!ic-Of;$GJ2P
zU8jVhw8X^3+0N47RO70enm{~4*T>DKsZ9<}W@BSxQ@G-B$!5B@xo}Y5CtH1eeZM)g
z#<YZY`VQgu|8~2>2XCY<dwD)-=UT}xZg!a6o|VlUxswI=MfH!zmmSZVpYCf@qB^L)
zprppt&NVwvG$l;X9g95K1u{%+3)nUC$-{VmCCPktxTwg_b+*lnCObRy&b-mQXiRcp
zVXk?>XXW~x`C-Sr985ATI*KKS-YjjSI}S1uOhtD{AJ0y{`1aRLvMx?!h9B#*yvpU!
z{Z!Pd`;$kV#m?c%2t~)SG~M~HLGC?O%Cgc%Mn);dmLsJSMf_2Z)(L3D^(W*i1&bGW
zZ{FW3FFSo!L_k?xG}T>i5D|;TEobW&p`R@~Ki)1ZEcEc$wD0nX#_R9aB<K}*UNbbj
zttHHnt|g3Fz*R20;M8bYE<HT#=Xw|m>kz|ues;>d-XMtGY%qmpW<JA6Et($d+ZI%E
zOi@u$@O<KSnW0};H#8icXtW%DJdqVutD8FiIZB4z$H&KfvgKCMHowE`@bm8_9bwVY
z{5*DFej&H`lwsc$!f`d!*{0FDR{N~3F0bs=2jx~Q7f%y$&UzE^KoMP@s1TbAPv`R7
zSGTp?;vKM{3#h;F;8%^@Ags1mFW)0=AfvC<okPbXB_-v3hed*6RitLx^-EmW-z;%2
z;<o;L*4f7Xm$K~#F|H={iT4j#8{Nuv?%W@Gdxv6RJ|+se1q3S}PKm<ih53mz@^rNa
z9cqQkQ}=gtbdV6kZl>$VNS2n_8CqWS=%D4QRFSkVznal<r%I{5eet<6kL^1jTr)EA
z#Q2$-nv#`SG&?oY_T<LeJ+GzvUL@(2J`|H0J~geJfCn+ad=N4Lqx|_MgJbco&MgAv
zuJa?Iwi%&;fq}D#9;=<XE#7Z!auJ%HX<l%-6;El#AZe$Vx=#J#@m}}(+i%?!SDP4*
z^;XlI?>Y9@B}@hBx};Z)<I%Zdim^Aiv~T0>No>Mk>0Fx_PtDDybp@4P8QFieOz^dA
zoA>?<6Uz4ydyh{!4T|ty|Mc?;_6DC#qI?UOsrp5sR&3HU{YmZ)2!daQgoK(>TrrDR
zGjwMfYHbeI;OQ#6y#4iCZF;VncyMW`LvOUZOzv!lh;6-ikK%#Rf|N5a=6=9T`;_|V
zE#+5nle#naUD`OC)2ikUPky;ET&XC+ZdQ)Kh#;70;#zjFE8mem@;Hwn;yP85{jeNM
ztd-I{t$ummgVTeqrN%nzqe5LAZsj)FwfTtRVV|a5-S=R!SR+Rz%^7pvwqYPT>aphv
zUZ=f$e0u~0+RCH``^Ts5-Md$y>^fbQS34e`Igk-H@1bt%za^aRfLm!xDB=-l%)x?f
zTSK*bCp?xOYl9`6_aG8?E3G5mO2+)sv}NdwR*=55X~S!~E~)1FjyVcB=|s*%b{=EU
zWg;_MD7$&=FL%2vYsa;Q*M{yrg9!anb0woBd#tV^Qx_{SvZzSQynx-k=a|>V0xTOx
zr;kxGW6{r_KkrUanu-6)$}4l2KAw_lP0Mk9^7=~Y!)wOIw7AkW5{+l>&yPmQYNuT>
zw#AUO^(U1JH_lgu&VO#x_uLvS+WRsT@4NSe`^jXy>saYOig=f^*SX`KhE083diue*
z^v;KQ4As*IBX*BauU*@{tSMIgrDtbnXV@sk#5qA|gR$%~JgrfoCDfG}ldWE>t3qen
zcj>q0-xj@E_DD0!&OH8`+LtMhD>&GO=O=P=HT>!kilrI{dgR~o@$FQ8+^8R1n=_v5
z-h%}>YB%1Va+MeB(5$xevEu;$sZ`mKv|YEjZ8R@AeDL3q?a~-`!pPg9FK1>z;8dt(
z*+Ho$<%E#yc`My}qz2Nq>DYJNJ58G#u+8<t;%8&K#p`01HDDdzmoSJczAx+Q;`H@y
zsJCra8~1QVZMJYsY-~8D>APIAeB6shyR3t4<#bI}7c{UEq~NUhhVtt;jhah7`MI`M
zK0lB*cY3x<mY3?>N71i)b&1R<$*xD1dSpgFPJU3{)%Iams9@s&qn1%pT&x%BJkH<f
zGG2ex(diVmY@?unqqQ0*AV$1V&j-XamHUsyO)ro$J3BjnuBkbW#eYsigJV9Kwg0sA
z*yp@9hml$4Q}RLHYx(-U<6yRJZfWK&Yinz6moo#A!DhX$n>2E#tMf+t>yt7W`w?GQ
z0b4d5vbx4)`B8NECDmDAAa`M2Guy%X_3PJi#>U3CTp7u)U-#!F^+=KDr@#38q?b@{
zAL@M`P9n{4aP#a=UkvElmga_J<F<#!akryYqY9s&4R5WXP@4vZX)YldPoF-m9dW@5
zQ@w9yV$z@BtbFz1fYD4@yG455=3q9;uc=58*Gx>jBO@b8Y(s3y%N_qkTR2GIa9nQO
z+u;07V>Ky##do$Ioi?Rso42@gY>X7io&Cxf(8blZOlKi@iaJk^n$2WlVzq_y&RzR(
zGg&G|B-LHJY4HImzoF#eA?kO2d>~4tXI(Qj^<fuK&u64I^YG3O-3rgjI?Tsc6R*p=
z>#?2QY_02@#;qbV&L75iK5sI@jk@<_WMpXDEq9KRUHa)4Gg^YlLfeIiFL#cv<)5u<
z1bou#sf^$rSm+ROJ%3*Y&p8aom*mW2A{QA1X{)Er`Pyb}8%S&OB}rbdt<dN9(kmn#
z$b^c_(vW<;X1yKaC2F%7eWsT*#+gv4dHqnN-1+n8`;i1mp4%0N08^J`Z|Q_6x4Us+
zaAAHh!K^7Gk8x^eRg22%l0c!pYA0Z^TZ&TsnD@t?=S=Q<aOzgyx3XiORk@F()8~yB
z!Yyv7?Y!GF6mCRY{#-#oK)|cqWjx89R;%M^dg;)gNe57|(QIb=gR+ateVL8`RWfVM
z`xA-^e_OdeEGI{%xVSiF^{G(T`6+GZRUy4fcC_IAA7?Bnbrp*dd16@>zuNipn>n<t
z&T=8axaUewcZYK~aGB?`x%b4yX=l7Nb-iW?#Flx+p!p%D_9cgwJolCA{w}Ifr-e@G
z+BR9QRIf}QuWgwtUzjb=OEwR+YrU<RY@{Y9N5+FTcj8M*L;S9!tK|d3{k6%)wKkIB
z&Kij2&yn&Q2TC7QDoc-@i=RGiMCmSd`3`HeoAM1kK<bOz(p6Ql8u0_yn^yov<c%iR
z>d<1J^tO752%l1ZeE8taHJs|d?U^aOB|F=~Tr1&-ZEuY#8Ao}i&s>^L&7Cv7m6WFV
zHqE?E?`!)9wuFu?NtF2XnYlsN+)t4bznIP>DFWa+JQ`Hf&`#dpVG}(!-Y|cQkx@C{
z-#)NCTgWPvm(Zf=K)2NV0}>KN1n%j$Cj?7SeJN(?5!W4QCH$uz?AvkNXNy{zw0@zt
znX6{+bYHw*Ff}LrdZu$7#dGbU?k9N>-|9H@MA!{FHq-iel_&{%$D9B)dP=^kbi7&0
z+W$jkcAxg3=RE<9othQnSIQr2-_QA4EI{st8L693%S^TN%v@XGqUrWN-A_=jmc@r1
zQfCco$%4*9Dwyk8E9;x`vM(-E*erK$TxX2ZZ~FB9%vN0e6Y5Gk#ZQ>o8^h)Oe9tFc
zDbTiUVi(Do)O($rd@Mh(&n8YcS7sn{Fh>%(SfA05%*J+BRdxN{ol3o*lno3FZW;T^
zkSrZ3k<CAlJwea&i|epRx9faxwH1kHU`TB8!`)rB--Vw%yYudCnpvh0X55QHObZQf
zZYlA&oatV2?4jT4kxCN)Y||Jiv{u5X2;3!8`q@d5gAYzczB4$i{_+AlF2qp_!!`VI
zfB6RC+dm}im~}+3Z7bxYdXr4+Zk-g*jWFnL;9SIQn@^C}lAh6?Dk-P?758v^?2)<i
zWAR1102wvGH4wQxJV*>7!0y4-$D8H3t<P12z5^~+x$n|?dzaqyXwNX13j1Su1k%G(
z?RSTNVRibk?C{7(oe3HT@c-f(=PglHdHC?*SGI(A3jm0<2BBCo6>G=DZ2IeFABq`g
zytM579PK{tCp&NFSKfyK6=vcp2b0j!D^j07*VWY-(2%?&b<dwqf2S{g<>P&xzB=}Z
zeaAU;>@GbmZL)1{G7ViAkzGi7*H+ii(4haaz@p~-RqG4y02>|0CXqpB=Vu0+`?_op
zm^#6_n)4V}AKOP(x!7LzR+eJC)+t|ziH+;Mp1G@_VdJ#kViH($btv#Y!1`o~?)<u4
zAyz8!4Fp&KAIF)Mr_2>f<ggLQ>R?@3K_&;7gns+`-?QTm8s6Qd?2AcR2&m#PcR#7@
zkN}T;`|mO@_JsJ#Om|Oq$u4XL#=2e^Vbwc3Qr<j3RZ~;rcG(^$g&02!z$7!??7F~1
zb72_kdd0@)90C-xO)DFi9l9mkZ$1+j8|y`eqfD~Ro4Yp?yi&J+k&T@F-D-&m74XX^
z*erveEFA=6saBvkZ-SJ*c9evtq=;iqgxb+)^o{$keDpxx-M43tgN`@gtT$+;44x1N
zI3q<+D{Zg(3r#j8S5f<~?F8sQ0z3uWa~yrQUofXOt5eLp?`3M?YLToTQX4H4XORc|
zZt*zmR*%!#40Okw_B)jxZXol93&eqNu4jtE=Lx-R1E)JW%b>b?=`KvyCR}gHBjCuS
z;WhuwwcK0Q?>g;$gH=Y74)8MJFeebWl=bjljOKcvtv$1hUsns)J^jHZ--B%!3PJ(F
zkCPRCE*mDAU0t(=@>Vl(2pD49`)z*Sca>!h7W#72IFz>%L<^AM3FY8GT=sZF8c?f#
zeWC%mFmiufz~-XHH7}mW$NPcvF|IHCui3Cc7i^{Lt}s*WapUe<w+0d7|6ArMc`{L(
zJ1@5H!AIc*saB6|{w{lNi{2!k`tZZQRgLz6%7ZJAH(B~6um0P&|1+Y}->XZhZP#X@
zyiQ3u4*FhcU=<6L020inr<jefvr^;SA%Y9T^h!EEfBw7y6tczhq`bDaHqwxXN8M{f
z)x7EeUx)-m$nhYg@tT?0>9nxQvubMWm<3RL8XLKQ<j9!-xx-(Wlw3_piPA7Y8pA~P
z@BlVdQc@ys<M{x9?&0IRaN$B&a<UltCcG686eKSvhpTHJnfgs&4>yDdHZ~Any^+q+
zl^Uvt_UzdMByxz4Yc(}?E@5bqgYM8e+f$bz-7zxd@0wd1+K`)SCD?d&M*q^FkknUt
zq9vo5a%khg^SOlUTj&%hYa4r;&VEB*_j`Gl;=Gc^&b$0vX~`)mflk?T(Q`kWiU*5{
zGstxSs_dy5o<Dc??4^*NBP7O4+%D`?!zH;V7#bU|KQR}IvB*oStF=0(qeE|5dP09B
z1~6$Y?e0%TUgJfM7>Sw(HVuQuP8TcB;^uC(JydaB+7wz`QnKyt2RY%Oq%xCtIgyr#
z2<{09bgOgf>gto%Ytph}=7t|zM~06NW>#Gn%o@AV9@6r6%)x(#TKV@*tbe9v8gn9p
z#YRh2RrPXGy-O$+nhECPku^|6=5&tA$$>!djeN`ErgE>mYrT|A`Q?2Tm6hw**f=H3
zPPJ#BJa+6Dh{eQ=(~>6}zOz&9zA8I&{_t@AdWE3hJ&->pdo^+&T5V=wIir$TZ}Rc6
zv{cW&bt7Q@(gaUDD`9byOOsnlmg+xS82%52IvU#WMXysk*B`z4eUBQ=)AMa+oz3M(
z)2cHDDj-f!Fy0!SRK#SS)%7e9=BE_x&?}kNH_@qfag2$Mz74XsES6sgNyokC=FOYS
z1o(v*sZn3QTF_Lb7_|?Ov!B0saq88>%0$L^00#0!<bnFfSf!3*jU+Fw?K{H{e4v!^
zo1B`udSLr>uV$JhPN1S~qhRPNs70Z0R##WAR9C&w5HC^lK*OF|pZuD8b*gTll+n;m
z_Z`nuSKQ7_e6!+L6{-8a-D#%)xR=lEGosSIxC^CdU-d4HHBrvi59!g`c~?}N@!ECU
zXP1YJ2{Eg(@zI#^zEk`I#>;0`E{{k%)gUl7Jxz(GDl1qBHcH`}>K|tWrs%h5+Y50F
z1$sEi@b=Zb>{Fw?o$W!C^#hRJsa<HZWy_YZ$jF_1eBx$}lFx%GQjYs_ZN`e;k=gIB
za`r6SyIZV&9v<LnV75o|*zfsb881dG!sHf%oT$^}P~LfNoZhmqWq}_|0~+{oIY`iy
zcwOZcH!y^G^>ob2iqD?|t{;maEZIFt5*$pn`(46IFc<GFVhDJN^)G4%Y!GNyUp*!!
zCUy+LE|1+B95~Qw<!vKFL;eqA)}NoB{r$sbzMztOmz@=&U%c?X^zJ55s6ncETj3vJ
z@hpa%jV)N*l+a7^8g%^KzXc)M-U|vU@?hJBsIK$cWp0|hmz<nT1VXkR$&K`TRKq|Q
zCuU0fDe9k@Z2p-s;lHF5^B=6Zzo(k>-*^g=yIJ1+Gy$NKkDA7ea}GN@`&n&mZZ@{k
z(o)n~Y6itf4W<3VUA*_bmY?*vX4dT2k~x{?ahfVnJ5-QS$2CYcAg%JF2#9W#J5ea=
z)(1$|eW8`U50Nhc)MTX{e++?;&GX}_&q_|@L#Dtqd1Ip$#w;TJ=gMCHjRW<M&pS0>
z>4HxU4O^0~l=iC8?w&rxe;Q1IyLSOp!jI}Q65N!M-s&l!Hwb3;)P*?=HU)0q*=@sW
zHi>UwfOJbW+RB6uCM-C~<i49@-fh3<61E|v^g`57YC7gX(tQa%Kp-G>HYw>)6bU4J
zrN`?+Tn;l6jOZFq;~`<qS3mOc`5x@Nk3w2Qj|&22!FA?0@{4|1UxEN9(d=X?_gfN`
zyy&*X;LVCGA`Ki67bh~69v=IN3@&m$x3JAW)P7-<HKto47(ggONU@2IpBm{Z3bCbc
zCZ&Luceje5tZ-IEDf`Vq?+cnC6<n6Ovr7+sF@=D&s1DPIeQo<KTuB-0k^{8!1pJXU
zm@}idFgM|+D4hPRsBeaj8pLo=?rcQW4AF!oAq_dG$=lo84|?&q_4f7M6}}|J&K_@e
z5+N_s>Sfn2k#o0rBhvc)E9W25F_8-+Ly!~ltUy6-F^pds=h8n{SMShNFf_~!<rfhV
z`M@yTt6P-&;>C;YK?*Z%(Fo9_JgTnxu<}*^@?a=N9s3MTO&MKCg3-Ln)~Wr=w-x1S
z8zVQNQr9wmvDPI!p)709nxZ@f!v&p0e7@apt4)XWJ2#iw*-uAVkICh~UBSzR>{{RB
zf{fP+=Ha1mcW1#+%R7SB5T*}9^BE2qbb_STdOHo`gZ_Abebe{|XnLVgYGY-OGm%|C
zK027BF%~ersy+yh8gYH6_VHAN`@6zVw?|y6-w~0O)mUfs_SO#Vq%x~GazjN|UaiO?
zxh*z2S|Q^%|8p@VW_zj1P29?B##!YA_Ut)n;oMZ5aJ?4jclq2jjU6|ZByC;hSQ1B)
z@$8D7nO`q4+~{8O05hH*9+<y|LDLTo7*^W2#)97v-Zzetl;mL6h+ak5u>_;)m`xyr
z!lg{e_#}0?$s1BkL%EQH6s3vqD-&EkIozgS`Ai|fZn#x5@v=hdl0hiET+p)^qd9;O
znz=5{@tyTGH7TmeYnP!=T-)|J6sUi7=rTzE{8$(yk#0SDQx1v;rY)zq$%%|d{uT4F
z`%!^RKn;~-CZGoQ{ogpOR{PTn&>}&lAMVbzXwU($YoTyOq=}q52`G-xCYK*r`yL7T
zH-QHFMHg+933sID)Kpiqmu;e@nd?KuAZI*!YpsumP>ESpB#1_mbWvUiRr$u;SYkv(
z#P$g>Uw5f4%`{_%$W(1zo#gWgdzulneJ8K*4XG2)p0#L7@)I;pHHv7-?uc{R=JaNH
z)<{=5z`^>{l26M;4ELJ1<`eo#BCB*tu%x+O)CA-Gk`EVMmsX-YYqgRVjS*ay1?;~g
zx2jti<8oc;iHa6r(t_A)E#a#s4TiGfco!3XytWBHSeJfUsG9F<z>(W)AH2<aankz1
z>xF4OMa7aQvN;VgbJnvbt?L2Cba&p6UbbhfEn21NF_w*SWxYvF-RmkB6Ry>^pIsKH
zS~}G-BYVc2FS#1aiW}C|_7@wRU*7)h3xaD=HbPK=%q<hz40V@}Pj7Ol#aV{wcyN2U
z{PLTb$u>WEKp7%NibAZB38{@yi*5Z~p69lQhx2%SlyVo-<M4^@_Prf7qbJhC<Gz@&
zdVE7OlzDR~M1?zov=W<~0gt?hz{afWnVAXCW<$T+D0E>1?@V10A785w)n3t?QzGuW
zY9^>2Epy9UKA2(Mhci6(@bGxIR_cX@--iwh$jMUKW@><N6l%7ZS@u|sFmG<T%uGJi
ze%rW<Bucr39Qg7L-F-F3aXBP3u8NkT{WdjNtxVOPBrsApg^27086MO9RDWu-i>P()
zXS<Q$=Ejy4{Nx-g)9>6?+bK)7{D%Lnx9Juokq}M_Tl)o4Ps?Ag?7d$3u(?JF^J;fL
zTw=dw(G2qTr+<n&<X<68623+LJJ}2osqH4G*@??^U}XYG`S^lMcdERDFvPin$L=EE
z$jyQ8m~NTwv!G>%;jaL#x{oFLOwsLh`o9Ek`WHiS|2R1LN6bb4>FL9+!0{#)>8f(a
zvm^YzsxuGqvqFvLD({lm6z<jfBYX6#l1(SXOfH4+J4fW4%CgBV7FFu$GkTz@9wECt
zLPys%F}^26g)3MkcGt=X@#Vhmnd0{gkH4YtA*<_w&SV==)YPhrssrMXs{71O7Jqtr
z^lupviVibDhHqc9dEfS)bth}y|Gtg9%jB9lkzsm6JQix_&IPpZGo(TaR0ulb4-rMt
zq8<nA)IOq@;aCp%)=*v@5C|IPuce#X@cV@04IllMxO=S$LqzL-rg-Aq(W6Hds3i#S
zJG6EE2`7B|bZgJPjAViMjvEtA$8Ue=8c6`t(n&ToHtuUPI$7)Mt?CN)%%4?H%6TT@
z!Qge__z*@10#zN7PGv`c_t&pqnPgE&Y8avB&#cCp2sg-Kl3`+ygA)^lG&J@UIA2o1
zfRK_CUrN?-GkVs+7SEKRk`902t)l5#aocNaaT`a5a^S@HIomrWS?tUozKq{u>q*Y*
z9?-M{SU>{Zksfa@*mw|7KwwP2g}LNToP$X6fni*EUGPx=2!Gy+6HuLYEL=zz*k!fK
zPEiig6xCpzWbS_IPYzP2=c1us{V)U_FL3sBxNLQ8El1AsvqU`rAalRbz|2+zd}X}?
zf!rKyL$do4BET%Fb88r^Mz-x^uVvSC)()sN6;(qtj0_GAUNN@MD1J_ZL(#f^O{cT2
z?M#}=>R+ZQ;=w|owte+_5!2HF0pnL&gL)GcPtH*1h#-lR{#tE9eU)1)R4}oN$Q1f5
zD$b14B=y61Q(%z#QhO@UpZ7d75dU3HyZ`iJ_;>w&d;Eo2JPqM?yZtdWE>0Ljj6{ms
z=OXz+HM<D&7UYYC@{*D&4CdcFI3fh0z`C#I7*i-PfnloI815k{8<TVABwynar-N*_
ze!rV2<99I!NHqbzJF%X9Za??`vVzA0XnmLValQFA+4*-XOrb*MTHa=2^#39|;(z@h
z{a=w3_BgzuRaUC>3p1Hgw_L?idPTT=5l7CDyCw{B37O0>dlC}1A`45xe5xa;h`?Le
zPDnihYpmcgQxSRdL}S!#BFszJ6A#zJgRq=dApWbwOTjG9;GydF*E7H6`wPcd?Ycfb
zSPx|DLC7+xop^ZI^+W_Q$pa!u0jav$ef_Y@OPWGbs@u;|-Akx)atJ}B05LrDj<pCZ
zkVAL2l_MN8ijU7_wvCrb$z}#Jsf`vTLM{Q!3%j^$OyLcUj(T+~j6gn^Q3WV?=@$p^
zv#|}g-&HPD*cGyY_!LE)dlZNBR*Q^UF9MvUCqq|cQ4l=y&&qSNujUZ;Ce<p9oUiwY
zh3$ZtSOGCuViJ472y=*-%Ydur)HagK{mE#If-gmBxOKJe<PqZ5q|bIraLl{ZYTFhB
z@T<ZE!UnesmuxX}d_VcPu-c7GIT7>Z>4O~sWSZaN)e7Eca54ZEAe5q8M?x2zykVIl
zHJN!~W)b8^_GzIn+L<<{P7|5UK2W#{U}LJ8ED&*FV`B?H?&BF7m$8Q!T!=RfD@b&{
zd11ztSr%yB#yfQa#x3qSwVjI>JZktRxTK>M##T>4<Bxl9V(SWZb$M|i8PXelEY7xI
z&~;&km_oEbcZjCv;X%q7WY|P)2O5va5`W-K;wf@Ew{tPwUZ-ncoL`UHv2t5tiLjTL
zam|K#9aMfp=lZX<J+dsjouFT2Ln{I#qTF{Gt&pDy`{Sbm4sWDT?IwlXL$eIGrE5JD
zMY0bNz7M6fUrm-lLxDJ~hLrMI_WrOz`b+jmnbhhk7+B4mFbVuHK*mwi)7|p27hWWi
zgIJb7!t`Cg^OOhkVqrx@@v1o@2j$*D_UJw22)AD_zXsuL<VLBuu8Fz0U-=~J+3s#+
zqW5mzW3o3S_((Oyy$6{lESdT87d5<W+fjV)ER<w0aP#S&sENAytdTN-*RCibdM)~j
z-?*ubx?zmc-Fsj_iYgjMUG>VP>CRxPu+2g8^(;%(vGBkiv-di~95+@skK5jTsu>Tc
zK$vN`zW4?!bq1yQ=)KY=b+346tef{e+dGHS9((4ZXt5*~cOVd-_0_1$K-xC+!{tNH
zg3NO_VL;)e%SqYW)Y#alKLf!lZxnF<uNOD*>fKs_QzF_KuZzYPCW~ZORZ(4MX#d+0
z&+eYyb4nNGQt1>pZc0{Hfe7#Z>*a&!Z6!r9zE)C9A9U)HLZL{?6s6j^=HaB!Il)D{
zT=P7m+4C>sLK1=t%v2m^x@2M@6r{D><ZNZuKH&uQCQIDB?$WISRAjZUO~BK$8|BXn
z{T9rdTmHf`r+bvp4RXOu>nI=JMiCvZsK*XQL5$q-<oJRWvXgIfb~7K?#W%fo-6Z&0
z^?7GBxojKFhwIB-#LaT1I#1IcSyd_!16rMNe723f3#pSjf$;RFl(K7Rv4N~joGvH+
z1#{wiJ<2M*0YiPVA2#%1n8&2(Q)RZt?d_*dPcWr{SNo?0zxa66Ax1H)sem^moRY#T
z21OtCYSBx1zvU%6cx{CVT|AhPP+E2YT7lDmWvG{yu*M{fB(}*u-Gwcfa$W~5JLRmc
z$4*bMlAC@NK7HUzQw((Zou?mdLLIn&#6HAvAe|U<HYS&^;S?ui52m7mj}P|i*St7S
z#7<(#&~lmT7#^)sE|Ne^{=T%M&Dk#I)qV;ITX=t=vbflDA;2t?k%B-b3d{54r&}@`
zAUlD;=;4ZCCe)CFFeP~5^=|ce9qmzjCZw=3c=K&mK_Gl=FnXc7i88;btt*P3E#56N
zgSol7#>~T(ODv(X*2HOZLUUzf6NO3pG>w>y5rq-VCn(Tp@t?EJ8ais7t2YV~uXq73
z=3*Y}i?MbRdxT4Ei66Kt#I_K&XTrl1kYx&C&d`S`1r?s8cXPx+&Bu4#kB1vbC?sVw
z;;5Y5EMjNlR30<;*qjMnmZcaZ>_fC29Za~K?i|X$E6{C6GP8M}JJtsyU+h-1tH5#F
z_`)J0c0d{k`gy1$NSB9H^`j=ZT_#(GQK`?=5My*(ZaM*+>&+v5sAu0v6@&!^KOckI
zWN2g0<Kj+dc>5o?{QeF}B9#UK&(4M)-=UpngEs+X&u)|V?YEHCI9nl-YfSNnY<1FQ
zLDr)E{WHbg3K1FoDE<<#15&9id>8B3B`)$cD$W7z(-~^EC1$UngWU#>e^#<v0csA4
zPK>P4N=1dBJI|pz`O^d8M>?62vmJ7`93VXk*~S_=C0_Gp`b`<u!-1(;JK)1VH%Z%i
zi)-C2E{hYP%Qumr_V!zj{AUVx$%tbrge4@1@;HxHOqSW^fZ`9!X(7XiI*#{q_uBS-
zvC(jx`AH(E#Cr2k=g5?Bx!yo&ed((tVyT9afLdv=y^|OY5X`sr>{IE25m61PbcgnF
zK1lN%#HDaoc<J&i;*3_3XbP~}eET4rTCHDhu%6ENe$&unC@Pw-F8+-3Tmc3li|AR2
z+mb<Mw1DSMA;UXAYhiA1@awyWOcT{N9T)dIjCK=Qb=pY=xFa5pp(ANCHzl&R5+gs+
zHxjNUg+Q=0ok&Hmg5=lt+w@_3^F~q;A^HpK6b(-uQXZQpjUCDg`EVu4y(cj-PQ>^y
za=5Urro10d)6>v_@5lG1rM4ZY1Hcz9>MY>U^#%Z)utYTrZ5O>?w=^hxxV+DO4mLkO
zE#aW+$7mGX1<d@yq;>$cya-(s{=)iFc(m>%Ywoq?eCkt8De2gEzbG=ZHVQ_IHNCY_
zNId8cSW1qgD68+Tu8n$|C4I~)dqJe2)_wkSQ%)!85cxdw5R$YA#xuxjGdEM3WB5(L
z4)}pFvV117xWhtwz&KFw{J!TCV#GO|IU86C@5r6^sk4f2Iq5?;B|GoyFAkRLSgbC4
zBsFWe&}F5q<321}(r`jh3O=8BR1Bd8Q60XjYJ=d?U<tTg_ol@r*>2;5n<k5>riZfQ
zv04ZQ(WyRgKi37NW=HAMH<WDqK9N#PI?ASCZ2lA~raGBlYzYSSrgg8jg4{{mXQktY
zPnw0?_ShujWY=<|2t~=j#w80iweQ{<Va96ntoXk3%Z}hun{??lHLSm<G{6%H8i&Q#
zvTiS27zj5Yo-~wj6f=Iyhs-Gqo$#<Q8|C!-9wU<m*G#Lu4WOd<2o~1r!DK?g=4S4V
zLv~*s^h+b&Lg{C_)>-+8$(#sTF>!I`7g1%+lFgb9@gA`W%hYc%#bW9ojd-3R(`2<v
z2nlp=HXbKO=1hn<6i=X9KxW%+a|-MA1sLzG9c?b>2cF|0vF=GOu#4$}Soc8L6$X{)
zZ63QHiPln3Qx%zgUO!SU+iVyt&tt!xk8iD1|10h1#N*C#u?U|EIcRz1QpUN{O}a2b
zZvCx^G&6K3JUF%UG}Jf`kI7Q1OH$JuaS{SkNJGGsZ1vE7v4Q!m6P`RnDY=CvuG(4-
z7)0|X>)HY|8dbFveM0CUGWT$TpEDjS{cB*Vm4o>dQlG?WRQ}W2{S^-}7D$^Bd<0|_
zA~Wai)>_&{S|>!DT2_<3B^dkm-0oY$jQKa@D_k#|=KZ#EAvD|3&p%vvWX0@w<Np2k
zZ~wmO$2(l#)&6j2)5WIUo7SJH_xVB2pg_&<hh^;Q6T8>dg??fC`MgJK;+`MvepU+j
z_E)d8?|utjwsINA))gC;t+h}(va@~P1ILB=i&e|-KmHgYqSjPCqOn6;du*JhR^2>p
zrDRPoHz@{5!gANvH0Wa651W^}PN^}>5SkfQhUERh!)QBDhKcL?o1&EYReCwRI|=fB
zWJ|X<2YM@NE%4GGZY_wncY?u_ba8plGEoGJ_fKz^(qXLIy7plEok=t%@sKCL^yd;Y
zE`br`L>Q)@LvyQ6ORaGXfnd#KPbh?Ok_r;=$(}<}QpEPoT>r?}B#qGqGFI$|gL$%r
z)pZldf_5W@pO|}@-YXd7$hqa3sGG>Me7^QuR=ZuwkGZ6!rPEz)ZEOk+qe8^nyRIhH
zv%Rz>hI-feSvb+>Uxi8me%{9)HeH-(fe}QlY@?v&UjKE>q_`^P%fw>m+mi#UT)rKI
z=~uP;7!w5Vb^CQi7V+Ud<yc9+@(oMlzpZAf4N(q!e;M;P!G_V$-O~fz8|K064YACz
zdVcNLYP+O*6D@SJn8R6E2z8u3RHq)KWuZXe5F490-M;IXwzk5)tK<vZJ`0?D*ic`;
z88iQKBl?_ZA*tcv+j3wcgj@|k%t3lejw_I3S=gg?PkfO^<CbO100xk>Vt7A%_=|=Z
z2B~qB+qUtn_aaQ(qk;!U_#4_Aet^p#?E#GreJ=^FJci<z+sGg)k?DquZHR9^u^;}D
zjRrxBDzA+~#f}{V>d=kSdvHb1K6Nm^_}mK&3vRhYNjX?tNFwCJ<>ilDX}oHbq$^5K
zOPGWDToVr`)MdJ-(Mn&x?bTyib+y_TBj%^~Xy9s~9yMC&>NYp42Xv{gX=-Xp?}5{m
z<Bt-gr`nL)1oxnwyqUx9RIR#k&t_si{p*{6{-sLIHZ)UO$N~sfi4re}vHK!hc%p|p
z2tf-1ax3a{JyD@NK&!>E0$aXpTvHMx;s=!<uM_KX*`tGh{g5eIvfyQvlaq6FbUYPu
zNR04md1Yl~v&S7JK}DEf;}0qO5d1IK{lQ(&l=gZid?FlJII}--5_Vy3<JveMWtXpa
z<Lzh1CK|F)0B%Ln7d*a5tl49~XbhQIgWMM(?yM*Ejjv^v#8gfgq-4*tyPj%HR{T@J
zo?`Ya3IkqJ^Ra4;qI()Tn@N)x>G!ID=EiKPfDO}2RKIY8<rs5l*<U_iGN`_0%^Ct=
z?}I<{t26o28Fo(_FXmZUJQe?}Kzy5f>>Vl1p<uSnWge$L|K=|bh<C1AzM`sXe{0w3
z#m3f~kzbY!LR;FDot<sZJ2*J#2Zk&|lFSTQxgrooS80>pUY4JZ3D~`HM3tA-FsC|J
zqX^!q`pNi(>3EX9TYF>U;gk6_h#4c#BovQtGbZGe+&y8^q-MwkfQ3p<i5jyn^{3td
zA+U+}m-m@}*byWrmk(&Pf%Nogmn}|jm53KmNAh(X8TW$c;qp9}bA&k>Dv|w522&=Q
z<^WA8g(sP>RYvqLp<cau)vQ7KkK2m-<Di+`g_(Wo%w>N^*qRn<U^hK8bL)UIW&-Kh
z8}3ndJv6aEJI%cAy=hnhkc7wx^>?8&LVz&zBY(Ro7q6R0C#2J*6=hMshYu4M-@3Eq
z+uUC#JO$!Ba&mHh?uv$lK$Huji4adN5ZI9*=K47^yozVYrFDJi?omGG3!B^=`IvX`
z@AnZ$|GMzLK^87&c`B9ql!McYLqvFTAM?|dFYxe0T!y_6q4-DjzFq7<j#nLWvjs!K
zU91OX8HQoLeLfc|bEH!6M&wgs87E7#hz^-b9xCxu#l^*a4T~FHt>fLjy`K8M2SGYW
zDj>CO%N8>^IW+eX11)0g4`jJvt)~qaa=I^m>AN#SZmIF{B7`#*0(t#FsB0UVmu_cd
z)W+?+`yt<DhQUEB?p!u?m&|FVsRaa)qtDWtIXt9$isj7jMys;lfUAnz8sFrd{j!4k
zoo%WbyiFQf=Fm(J!`eHL#AJ7SzsI$@jwgk7L;^^rG))1B4>pdH2Ee~PE(|ptu{FKt
zZ5+KR2L5LWXYj0ze~CA?G{<f;II}WG&~wW#(e&xlneIPgt2&|?7;1ZDJUYJ^Ww};8
zl}kdIU;X9_PW%{-==2`+gY7_njcj|!1gVWGJNK%lyOOICvRaft5EhmUqJNNwck;~&
zKE5yv5KQcBWEHkSDe;A;T9EXm6~HmDju1nASnJ7iV`D?>!%VC6puO_nLN9HB{q?0{
ztd~-;+I-R%Ni5AC9tB<;B&m<cT!k2ufa}DP`d`s+b`{-2!&Q($<O_nENb7UfNa>@4
z`_0lj0w=MCIN?$&SX2@aVsSiir)AAF4f-~tjgz#2+RaaOwGlkz;XxV@kdU~5-Hs-x
zGnxCDVb6_2X0fpCLZgZ&O2f&7L*!|?#heMC93VfjoN?Nb{;Tajo=%;`+^YWc0{qvc
z)+K|(MZBS_ZLgL;<|gVIF?k{)H18cmTZBaw<da)y=y!`rb(;E0Al3OA<Uxxn+I)vL
ze~3+kVFK#efd)-ylt32NvC2$d!~wtQm!fZ0GjQyi4Hy1jQ!;4|LD2*p5P(6$Y#Kc%
z9%R|N3j=?sM5Lxl05z3pAFu!3iq5h@Cz;onWzR9CryO?GY8oAp93X6BVmIQ6Gg@K}
zNoT`fr!Z{n={<Nz9g(SXBVMMaGvP*Xvh)9S0zv`|2}W}oSB+(6M#d^FriT1e#~!;k
zG#Cwo2m1PEXr5miW1ePaN=ZmVtorK`%4y>se6PgX5xAPBGc1Ku|Mg8C$Nq%$9@xZ8
zGe>l7>TxCLB*^(Io*;e+2t^(qlIT@$LprjzKDM+m>=NoG3D+vZQ2mfF(}JWE_16!P
zwwKEv{LzI%rTUSUJJS4(826zx36DjR@w!UKQt>8I`u8*P^~?ztb6#6?qXQII(vY^1
z^l}neYZyIVU&Z`#=XEkk1mObI{NHCfvGp?Ba)@?Dw<pp5Z%rQw#mqB5*F1))8;cGg
zG18brqG1~tsUIefFd|ngS__StJ_#&ry|_gZ6b7UsjjR}!l(ZkJ%3**x*x^5Y`aw1F
z)NRsn?O@G3w;Bl#7%`rZkPvsBMP(Dw+i_?BKCJW}6LCs+0W&cTxgzH9poJ&B2lRA<
z!!YyXv!TB>hqVx$5WGqj<$}3B-6Hg=R~(5C<Aco}UR9pTEKIy(aXag8HbfbzQYgWx
z5?Jp$;&z@#W?z@u^!f8FV<!16J<|e_r9=3s3DnMYQ;$tDo3;<%I@#CPSE7C7ywB$S
zI|pr&7>*jI%W0u0CiN<<M*h&6WvEn~<)oHUA~0Ef=<>aF2af_)-HufVL=|^Nl?PvC
z@7UJ3&%P}?BO|xq0RA06WxGx?^`ooVgVz%=pO<s4mP+MnNVsx9|Ix2hXJ#i8h&9tg
z^6R&&ndKHTy6!qk1rQsK%AAkyWNm2~jc$M=tb?JH%xc7m`^au01~H4=IJ^mdEoZi$
zma|LYMSR0jIwoJ%prY6c6^MTJGnJn|pZnXj$2hwhnV5W}1#fq`5?Fjuzr~1kqqqAA
zr-`5zgQm2uNR{S0;h)A(cYM>ABqgig^2-|WjV{@AI-M;(>Wlmc;YxJ$R~4MU8p2NM
z3a8LvM|yK6zpRkm1j}HRjqFbd8Bs@q0#?n<%_YEpyS0?o)TG^R<S!6hT38~It2MB1
zU36@$0F??evL>#~08S;egh4tBd-+m`Pse%Cf0AcxVj?K>x5S>F9)(8n^<*oJz!@&>
zR5R_?0qf?9in9ajies{~r3X2-g>QZlp$(ispNj^d%dg(9C@(Khr<>5T((#WHNa_Bm
z64g!-My{@|^qg}e<~#YwB*oW5BqLvs+K;_s6<|I7vEz1IHvGg|n_dIW<cBP1%)FF@
zA?uNCX7qUODI+Sid}eb~ZLJ0l)qj;R)Q7$a-$}1ufuT^jba}_=01H_mBBSK9u3e1F
z>}1aV7#%aQ#gP2u79cRmo<;iL|Hi_|9EW`6uCtvSz_RrEBDi%6*iy}&g_w}edyca^
zh&y#|NA1hp>BT5^$$O7A1Cim|tfhu#W@XZkORvZ$?O}dir$}fE^9PlGeDjv*j3Ywb
zq|EjCbC$TsKu^!+(cxdToQ4bB_aJ0L=LlmL{4z&617eC&|L`Aw@AF5td-nNx+!t-a
zntGYvfARit_90(xsgyH|%YN@EvkK-L|HZq>Fj4Nm4<!H9%`es+!Yof7G1X8^?GLpu
zpmE}<BXR-|QBq9!?booe5lhyoovMM>QEHgkq<6#Mye<3!uvUUfLx0;ATpE~lQ`@F~
zN1-3D?$@zzSOO|R&jG#S+xtw>fDP5y!_K;~o<^g5-9jIYlpr|$WRA|o_GMW=sR8%6
zZ~)$l*lspRLV|i8J92g}7t-Esf=4@q!%av7sbv4Fcg5PvS$;Y@*S<@ZpLT3hhd2Zd
zq93J~u-tt6pPzXD>JRBpJ4B3t?GpDyL^M0>KBI?f;V_J^$HA&d2WzBqC=XF#^}&9)
zhZ9lJ7|?uut>VcDY3}xbTI87D$6NrL;yk~z!`3xF5bWDLv_Qt$pd_$>Y}teM^V`JF
z2PxxK5E??m<KwBBBg~Hls2p3C1tx<H9@x<&P15!@gniQ0k&&6%MywKE!Wp|s7o%dJ
z;B6G~F6H+fM4xyY8kt>}2r$|6rgYD=6@ySAe;_2ajeOm3Tai38El6C%;X`^mbLVKe
z5EaO-8VsG=QuiM|^t{I0@9E6e7`_B2(oD$~Pd|PMVbdUvNyVS+jp6&k7#u8Dk`NaU
zOjt)Q*ex&G!*R&0T;ET6BO4pkL{G4LZ@YuUyp0li?rkV-;<y1lBL9Oe*P&+~He-{C
zDb~Y7KamF8r7|gK=q!XO`S7ztF>~r{V5uF#TYpFKW@J1Pdk~ON56Vxcd$-@>5KsWC
z`&_+d_UzsObn?Trlpxz7S~nj+rNnGx=492<(54#<<?8tYM^HpM9d?zVw1<}suU&g3
zyXMEcAKDp;e<bp^g?Zyqvom)SURjp>vT1c!SL421yjJaGcCha1#7`r>=L$k^Tie<e
z9plj6z{ZAcMy8(!jscXkM{30Da1*0D;T>d3fH#-fuv(G3)^eR2!#$13x84kUsQhIA
zgH!PR1ZH~6!TMD~3pV0S1EOH&_eq2S1b39>OEXK+QqH_gX4W?19ZG%ux}Bl)x8z=5
ze;&Cg0-h`?HAZ^Y3<&|}lKF*)Wb`FeXXosM>pVAn++(L2_wg;4PJPjtFm$miFg*QQ
z+&bLaRl&or-j)gJQV)c7usa_hVh90N4K$iQJ<C1;hEwlr2T|%9CfhJwDSNaTbA=R-
zAmx$GCDKrqB0KB&dWqynZWPwi{G!2}t#@<_mS;h^7tZJovm1K5f>bZfD;0M6*qw*$
z=k#s?`%vU(HqQ+ZIh3@y-zMs`4wB7%IEv{VSt#lIK)$qmQ)B~s16nVu?)3IB7~B_H
z*mnn)u1#02js%6kEzhlrE-Nd`%8nk7)kZ=Om?-uEhQyFy?T8GDR{<)J@nPZ(EEGim
zHz0$9gKB!Td7ht=doXJ=OaH(Ming}4bUG~f_b@Mdy1U=gf+23K)40n;2GIFbRaKR~
z;G-gfnq*SFJ$^11inSL!TnADqL?MU}<C%(bXO>2<`Uq@lmL;{ErMjCmF24yby`oP9
zdEBuuQ);dZ$~pI1Hl9>rV3*%dTqkx=&X%vSgW`Gcq9)gH?=KhOW;uG{s@UKrw&mBR
zM#sj;+;IIgIyy?8f9Uq(SsAh`?V6wG<~Gj<6kVKAO%B~|V{MJ2#9VDyQ)JZ~JrM8#
zeye$EFj;A#%kPw4;l44l$wbh^wK!LHGl8KV3D;f9ghu*$dy6^GsE?#~TnufBmv~6)
zJlCkSH|1s~B=oD~3dyq_b#%rx^02WH9Xl*;(a%6(XCE@-9N@9~^fS@ggWBac5upG)
z#@JY3deKOm5_6w&0D65I*~|l*UU-rgXBcqe1GYCh4;OIg7kIL>v2B}YlVC=U-=BL3
z*rl$qjUwv@a>}c`CK4bC{l{vuf2U6>_FC4}YNQ69+bLrXDq)VgeqruxNiI}1bfW7)
z_28NFTGX4uJkx%iK!M*2+ieK&@saj)Qb+t+HbZ(DY*09<oz(?6#1OBUe76kKHc(i5
z3yRC7nIBQ7+=Y9W%<E;I>v$yJH*`~jr23y-)32xHHrAq2f*q9Xnd&}v!m{g)jw0Za
z6!cLvcO_JtbQ3_I2p8fecXv^958sy%Joj1jDr^}Z=_(81#2#^_Gn4tZ*XyG&PPBfu
zcqRpU@@Y>i&!W~;ed15C^UrMbKd=MrU(`GNKlq7M-^*mtQG?XeR^-Q9nFYNbygMDc
z0Nj~5XS#)xb7cGqYBame#XeLvG(1d|Knf>3959vF+Ip$$22j&MTQ_u++owK&JA;|%
z3=Kcff??A|+!X}IKx%ZFIWGo-aS1JGF|<cu&l4&Yjq@bv67WT(qSTAfZt=ZM*{7;1
zRTA;xTE;OH3W@lDu4_T@>gt$u6M-3UI`8BkSre&=e82#74lNJNQ*ufO6O_DWQWNP8
z)ZQ1_KfabmllnU5J^5h6Imp67l$7rc=|~TJ8h5qc^{6HzJC)Lc2L=Ynbwg{mcijJQ
z>9rkoi3W=FFVPQg1mV~$+9?eU4Wbo1e*Bo^tm{viOcZJyx%$zGi7tjBG<_VgaYoez
zFvZ?cU(h6YGYQj$c~fg_%nSuh1P}iZ+B9WtW79deu13@D+QFJ@!mHSZb`rJP*VlJz
zDW&vAgU2R|&=@6MU0pi;+{~@(%*=Lf=1EF;_|Z2~Zu9f=xR&G<Dg+S8JZ~ZwiQ}G#
z>*~hLDboJ9x}Rc_lB(tpxV<gg9E0pfqZzvvhch$jBg!G<4$G3+N0iP~1ZSi?yg)P!
zS96tYS68NLe8=K{@XGlAW7Yp9kNSUTzmAD({B}bFcG7D@LDGu|{uOjO58C>Cw)sl(
zt;Kem0NzUdiOht5GdAoSIw@I@oqoCj7U!D5to5p6)Xdv_BnrRf(E0)7;x<K`<n}iw
z&V3Zx0#$1hu^D5H5tz}6rceV?cVpTHz`L29UMSq|Fk94dx2cIP8H{%si|y{~^RmSz
zPfYhCif(+UAORGBKTaM8*DoZN18|GepBFFfcnq)zOm@Tt=ix#24W-&;Au`f&NAhy{
zsNIL$H(=rj@8jT(eYNp^adB~ITSH%@+sLA4&m3%Z$CbtF2tUiVWDxos7aJSZzJwB%
zl4A45o@jY6zo5vwKFNgKqg%yuLW5ty-0d+*`E=+GXdSA|h%W@8t}*sX?<ejr^Ae%d
z?<ecXPNFZ8%C@K|A;uql7{3k@Em6PIOHMq4D3O(omQ^8=9nd=XH<gw+$o|X@`L=G`
zu)RZ&ZLlGgDngEIY<!d|L6K#Rt*%SZM}B$U=6muS5qU+x1)0gkd-L5?32+wj4-&?K
zh;zzc8l6#9y_+fmiv@X|nVTZf5=lrTj74-h`YQtf`#1MD?z{UT;FbU7==k_@S}?aw
z9U+<}0R50vNo?0I^7WC(?onDQb4Bp*Yp&SbT-W@*Xpf4DnztV>{#=^Esp{&wa7QBU
zh;SwZ%6Yg-)=t*Jjw#;$kQBmDBHmP)buTg}$K{huHY5XqOJW}q;dbw0loH=}%s?rs
zO}IybAJuMU!MTT20$*Ntf4`vlu@8Ie#P?(0*%*3g<f#W3G#Ey&s!c?qP(q~=aR9rk
z>fRPOc@z3&2ZP+28g)N8hEB&OWDzlr`|E5h!N`hG<yUF-^nV&1{XtXG@9lULdZN?u
zHmrDA*~F|DGp%<AFQX!Oj=`}D48E8a47EUGZ2o|zO%j;lxv`*HaC&5GGHcUU?~X0K
zXuHfVy8=Q5T7|fWlh!j;lInODF~Wt36?VlV?9t_8p0{!rfcjXiu55efMvHo)BoC~h
z=}?i2$;O4oC&ZYm?_PLR)YW{~WL-85A|{IS@ezucHG<7pcknIGz3{TGL<W1Lex=_e
zbsSXirP?!!p$u%bO`iiNBQ|e?xZMYV0xVs9hl>T`&oLyjm)gS)%tidLrB^H`UUycv
z9RuH%lLPN7z|<?zI%@&W-8Fx}9#@*qo3Wj!(i2f1utA~4xi_}2za^zD(i6X}wfg4@
z$jd67p8ny08@2{DC^yH9$ISjhK#7E#PO4ec^SJxZw?nrfkcW*80yUAu_h|%hA#9|}
zDh+gZdmv|%7f5|^KqL$e7Q8r&TsQd)J_>X|jGZ0AyF>wVEV~-OcSwzre27TIG#z5K
z1Gy!aZZcELtx&Ff(vU`Bv9h(*VBAYwx*6uk0dZ-=O{-HOS4)0*>A@*z#{F9yOtym^
zLn7IuDsoxVSNj#tFGjIz=j51Gvdn|nuuE3<%VFYk^6<!=$;?&!_($vsN9@5~EX368
z;bDvo=+^jA@lZ%P2C+w~d8#a5kdwrViwD4Wd_0I3k;F52dDu|RFKddrLOj@DjHJX*
z7$zvw<jpQ&N{V1-c_=S*lhbd5n0ogaLH6No#%%HPXA1w^@Rv9jVGf^tSVUY?S09-d
zx|KtJ@*@yScKP0ii(zkrV!^VkMfsF$3CVQGkr+x^bV-E`_3O=OBKR=ZpjR2l{Hnd{
zz-EujdGpH3kdi;_%bUJhb}lkBq2k3`_l(dRc+12$XH5UYT=S2s@c%7hC{Z%AVo=P&
zW?__peapv49}d|z4>e0{-lYc`O6DJS-3u*%3WL(T?qnW^9_+M&mdBV5x_r$0b%?QK
zi{pElkMix%G0~=|JFOxoM=}fcB6kY=$iAb<M}oddqRC2CPLAozN4Oxf7VKPyxPM?8
zBSshm?gxliAkqiPu(Pp|-k(QEJ>((=(1r0wFAVS0d-+yest7J(n0H~Amnp!?<&#}x
z$&S$EB!I4s^RG*UGxJHG0aj)k1VnElWaG;|pOr6=MJrigdRY4;ni9Ck&L47FhsH^V
zj{WMBcZivyqN0M0#Q4?1X-^tEwil9OC23y39uh>wLg)&>$C-OQ2U5)7k|X_Uh;KUN
z%dpFU5B3rl<>SkNtR9&Du${EpV1jPO*S}e^BA@IiNDd?>d(tP)$49)Lq?P=Ez!ohC
zzaF6iUz9vf?Yuj=ek}M(x%!`80J1SRna0?N3{vTOT`E7>Y&o57Wn<G@Dn5^9k3zJO
zqhPM4n^Mu#6hga#0o;{cPxh?4(^rbfHg<&*8yknV!VQ3aepD;7r(_{^q?RJ}b(e_O
zyrcy6m7<aSsRWHwg#G)J#DlAY7m^tBkl%JX*B9a(me~(P^>5Nm+OSFDhq1`aObRt+
z7`sOlAo8^myK0U}I4DhODGGz<sVNBz%{ueRWMaQ5KRj|u?l-&*T`skhFfxZEA2@+K
z9Y$CehFIB~I8(2YfbsYI76CGUpB2H&-b%KH#gv2kWPq$roF1qE`!Y=%0qGAfPm!`D
z@z0_^)FQ}#H)v`(Lz$$=2OwcYhN=y2vZ1~Fhzw8K!-yE~LAJ%(2hoqjR?{o6jFM9m
zqlXri{E4>&2kpxp$xb649vZ1zbzs*C#&AQZB=4c|f)Xb|+QZ3rkR6AKpO!7&*L*UV
z)w%m<1**7`BZq(k-ZPX@+Yzx8`(S}?p<^Ixa#;>*%bLk<1yEq&j%bBM+&LcgQlC>N
z86^dECF(vrG0MQAfohY?e>`MJ8*UZnu~mf@j7ZG=7KyftJt)a0IQfunWny$Sc}p$i
zzcvC(5|CKL>w1{EaWMA9w6e0w|9J)X2HmM*jguE#MT4XpyU0eMJXqtT5r*stLeMi=
zFW5Dp2nHT{9kw1-yx^J;DCOTpCmY<Or1W#pj1Sdm+c+FcY`KvYHJUFvHafWSANMKH
z8oxH~^}C6Lg2P~HGTB$9Co;H|>^ejIcBngT!j?3i1eotUb${=d>B`2`3X_Ql7e@Hm
z?wp98WWVE2X96Q@b-s(3*FtN9cflSt6l5M@e-dQ(aDXCWV6X@}Cyrrb3f3Kglvb>A
zOCkc}l#8CN=i$DdaJob{?$~(jsqc<=+-=W(#?v5oNA|a$Y~x+nL>!B#xEH|x!StPS
zp;ZL_hP_`bV|ygec}&PC6@^*&P*dYFFZik|Y+?;+O$$VqLH)0SxBX!mDQd+|rGyhm
z92UnO4A=yIH99a7o|SO-SPnu-pG0TZ_|1h-{GNbEfNMKqbMKe%3|KM?MQ~Owf7$g%
zacJ#Sd$NDZ1EOUTb^YhQ47hLgL!HMrx5mB~F?1GapI3RY#VfhLVu0galbQcGT|Vp3
zrcBJ-Htcq)K<^-X@t)O~NFBT`@RYi(X+UwVK|cJGvtpGouaBMy)zF^du2Nin?*M<H
z<_i@_oswi1dJrBIC9y*g*_*sXOU)iKbUv6(2S$1qY@fyaQKY|8EidMz{_7(D-@VN%
znVYdU!UAW{ONAR1`{cB*`B^!c>(;6<dK5$RT->m!7<W{$G>>qHxsbE`@x@kR(vAwA
z<%iObu*_@=t339dA<Zx5Fk}^>PlXLuUQg7>0!`Ax?ad(1e1v6@ta~!;$QIn*h}%Dk
zI`hj@;prl8Vyj$kJOFvJkuCHmfTAK|)+1XPzrZhrAf~2WkFFo7cTkNpy%I};Ibl$&
zv7IW}IeJ2=XJ!;djRk8GS10R!Z;+|b>d4E+bfb|?;z`##Ng3t<)WlUdIzC<q$iyo_
zw`=2kth&;fv&TlaxK?IMK{w@s&djw9Ba-aD_t)y2h?v$=2tMLhu%{k0jhpPNPRxgp
zoi7Yn6dS>-jv}wJoTa7ZBkP*;q&@05v>@~gZZ$&>2QwQI-imCxSrpv~7D1FV;nIQL
zm%Z<9M5R{$kwr1T`Ilc-6q5A=;zdlh!0RT=4Rf&}>p?<hiK_<%xAw;xLPbxHWKS4M
zZM<jfL{Tb=w(z^9=%7{Nf08tPCIRA=kOnKNoe6%dC)X*34Y&Kz%y}-cV`E|@F|d}F
zA!IpXOa6ct^LoOWo2;erdnxKs3o($zuXebMCF#YC2oJkp5)Wa%vC!o@e^qmAt;V{U
zaffY9x+l-;M+atS&*@3RhtfQQKGVxxrNKcV5>%aW7%Dc^IIOsPGbdZ;Y0Auzobig?
zYRxJ40?5$zQnu(gin8}vMpS-pE2tg~{(V0Lp6PLaMd1%JBOmuKeqB%Kd~~D1y;6CZ
zufO+a*^eZWt#RG_Lh<_-m<Ny^nR{u8m7HY>&ouQP9RS_>Ncy7|KTTL%U;4f4Tzft{
z>aHvbQ}Lm4N6kmxZ);KEt(|>;VHFliW9nIwpqq#6@Zbi5-Qn!X++HH2uJrEg$1VPK
z!Kv8{)ASB1THvpZaTup7)Llv0b=z6s-m?K^+Wtp#VO(OqHtDrmmP$xyn6Tf@iA(o5
z3q&5t#@%W(`wun9Z}XvX-rK>LIrG$MhSuy$+MG}*)wS%nBvyPSi2xTiHWF+8r1yj+
z_*dUsUa$oL4GZbi!vp--0}$IXyy#NTGk<<&Fk8Q(`TG>@4SkZ<KzD?Z@xt}Zt?<D>
z^`rCi&qZQOsH<Kg;A6WeK0XWHW$~%jx3VxFPbN0%RUhCgc(L_%cKx`{4O(XE6Voe~
z{p(Uy6nf`hOTViZS$;QvLI#!}?EZxcuspU3zf*f~gTf$1l)V>mJAFFvH#|PWM*4F>
z!{%G7S_n=fTet`1j7DJB_&v4<4T8|Ih<&@zTZpZf*GQHZ-xB}|gCASuytabIN72(;
zNEjjw<ZNsRh<&df2amU%d5T}C5>~s#By)F-xe>fY!|!<K_u^M3C>LCn8F`R4Rzp!8
zjyRey{?vl2w(*99yAIkZp>dQ@=>8MOdnvBNLkJ^+A1u7os;+NHy19M46ICxS4-I9b
zBIp_2S@*I^8=Xc<nS13E>jo-aUKr>C>@%j1YvUc0NLA>kL~pltZFu~2fP+Re^Pi;W
zVz~j9VY522;V+4z%qT({P=Pa;s}+;-tIK-X`_Cp`-d{C_pseMrn1`UOGAa}uA98t9
zndXaiP<;zY*QaFnlN%P7maviC?a9`4Ib$!W1?UQ25Fr~~k%n%tO42{Ho>hAE3~2xW
zKvN%3Ss#91|Izl6D@o+nX%vvgM}TkUuROvpIl7hX{hIgIs$sg+#_Wpm6fZrlDAI96
z?Dyn`6F|F}-Gbm$kxE{yPG6Rq1GlUZa9SXqTBQDon}-+FS8!eb$q9?;|7q^K|GECZ
z_aCIFWJJV^qGc5ol`WE;QQ}orktBP|ZivXrib%4Oh>|^$C`1`i*&~}Gdw#D+@Av2X
z{r(A`=gkjwOFSO;an5zFbIx@*r&p@j>7)A#4)mN!L<yh#`)>7%n_f9pj!#YoBU0mD
zeL8q8O7BJN-K2-d_GO+R>$8pQB~df^*H-8~i_Kg8`I&?PqhB-n$>MVoVTcXIF(J*+
z?#a2hFUKb3LCdQ7VH~!+d=&4M8Ug3Xm#|+2q_eVv48h>zW^|yCEm>p&5xe*gIiez8
zuTIB&STBcp`VG(2zrqibOK`zXcRyJA!B^fJT*cowU%tMI>wn*SwaxpG3*SAoWfZ>;
z@tR30Kr;zB0Liu91e=zFSv#T_obWWYdUvz+`4>mjw@cyar+kGxxKRQ*SzAHMr;#%n
zIuZit&+mul`@6KQntzuTRfnXWUNAyZ37pH%@6mg~PR2^uKq*q9>^}-qq?&Y2x7V_j
zi(1rwDndSvbwAI~clr2-#X(X(xIfnCgs+>M+aBZJbszFOFOaqVj&j1WKUp$TdwH*J
zQyx8o+-4l4{Yjuk2JEDo)icrpC1s|AFO;4y{7iNjCxPaG%OoM+>U{U^XhS!1vijT(
z3EGf_=;p0jCh5_7O3IxH(`=(&M_MKZ$x7`Ci~Bn}ExMP|hRLY<Z-YOYwf=S-qt^yd
zcL$k4ULkEg2|)jza{$WRPPe9}d}H1j3yr><0}1;`6L+=y!P*bLmtnwg^<>ouxoxEI
zJE_KH9~~~bUS28@68L;+oq8>nLgA8T@7@@#fI=?8Yq86>V!e9KrCv!jkJVY?J67Kl
zUZ?wNB{}NigUhkz*JJfYD(!1pK6Uao+4Ai;(NV>z=@)i+aAmFM#Juo^*S|Kr9O^$3
z^5JdieB3;OU16670I(F*Xtgde+x0ciIB8Gz`N>X`LN?nE$AX0fI?bI{mz?56?I_XQ
znz!gzIo(7j=05(Nh3$2kVZrP8Ewr@q`uZGweSMnR+IP?dXoAb~t=wB??pJq+j}NhL
z-@cxN^%b7c)=Ud~DobPIJ=>X>IBUCECd+osDHK=J8Okv4{MY{5%#Ld2O8RYj-cEK0
zAU1J@-le)nz;y4Ow>$|Rw`A5=m^j59--3pHLH(<5r+#GC>mxBms|xAbPr(O2(|x%M
z3a|I>-=`fI92A#KEEI}*l(sOscqKq;$TxE~^?ZhoV35}57|%8rwGl^g#CMy}-x`~H
zP*im1y?gfxyw}TSELKn+ez3Yv<LBV@$!{)3mKJJLZ#-S@O4QZ2&BvE~Xtc<gmQQND
zgaRH=SPF7-xAH5e#Pss5NkgcGs28jb6RG@i=@OSV{|+>j`GP{*&c?=unlXxZm6fHO
zSsJ#pSknxH;^P?iLp&J}9)*cV<IHA6jJ}@BbKFu=ovC3#KT|mE2fw{Obo8o5>F+BN
zDVn@ro}U-Uz5aCzf8-Ww`d#nA=1-yIyBDQ6asY^jZ%kiFo6e5r$?_|0%$h1=kV#(O
zvH!%0wsCP^UtdC{Y~`Ey#i|&`&K==W9c;ISB7ztw&CSif<=gDOm~R~fMT;Mj>qe(D
zgc0IBY+)E1YO%ZX-xbu36&^Zmes@+uCO=JxbD97AbERMIv!kZ=?O6uJlte><j8KrZ
zNL6=jePjnhwr{NwgMrJFv^H;2%CdhAaAryRt$fEjh9x9~U=*Cr$JKoKOdWD48{q?*
zj_dew7Fk)@(3&fn{E-;vL~ZCfd;Zzi>ma<o)io~B=^FMggyRskK#~KDtelz}t)O9v
zKUV5OM{3ENQw<#Md8gM+uu4}YcI@0qlXBMFv4b<_ZUx)pqQ9q$vBc}y#W&XpAE-k=
zjMtdNkQc&7+-cNx-~XJkiC2v$<ceK>Dd7H1d9TlVI|s=>_jgJ~&4;LM4`JP1W;LHT
z8QjR)z-r+!>v42o{Kn;gvaaDF>**8|8`r=VnYAhU@DYJXl@U)U3*WkRtM)~I$j?Fk
z$qw=i(JqdbNmM?vO3$B<m;B5*6j}~FC`tmu?OuI4x*GQsSMAiVu2KpDMOA#)+p;Ed
z7D`4?`a!4+=C|Z)r8td$BMaOr9!t50#>MIPN~98P2K@^HF=}^FQO1H@B%e~;Wzy2V
zbov{em;-KRWOlxxYf*gD^*5r<<2zByeXG=co{~5H4|GGBA&uqDec(X#fb*p5$5@`1
z<cV2yvCx$D_48A8_@iHFUo{{e6B9%D<XN@5{oiG(e|5z2PAr%Oq_B$>xzE2B4-XID
z`?H_d=->&Bsjq*OKkpdw<y&$)m!z;QL1HeF|NJ}H_bol%;d9ZcXVdSu3e_9c)$iyi
zCC|w4;`IMplsW?}O7rzMriOC<N9zQ(uhJY;jptr6U83W)uRj)C{#s{ut%bxufQeC9
z`yRznU!EU)@#=o#<CY!>k%pVo=+B-zm*euw&|Lg3=#@T9#8I%ceDR%5XZ2L;xMz&}
z$Jn&LFPU?@jUM&o7$3w$WFvEMv~KIxty5sTD0vruRxynf2^?@Q5Z+`HiOYc?{M}?{
zapQ{%!X+qWq&p_JBeMafMa5_|{zi|_fA0oW!DI<<eRh`!2J`WRtRfPkCs4W-UNbXW
z9?+Jds}Y-^{W+no{=(zG=7i|(b}@6!I5g|)$@AH+O>E#ZLbV_ug@+y^Hp}PvBb(N1
z`~AfnX8s5n7CF|8vo5VJ&u<`5dnV@A<|EuDx~5LUU0>IptjrW%SLYxxFr4#p*eWb(
z_MO2*1Op}7U8*qntr7aupyOHE^Q=zAi#z>Ba^(^<Tpvo_IC?Sn+T~`YukYW>zy8qH
zWC25Vz~oQ$Y<61vG3Vb)>8L%TI~qe<>^$)fIX_r)Dx%=Vp95H4Eq(p_$S*|Q`27KC
zi7&-2HYMw8%YtXxzFQeo#q9LRw44e0f)mQX%Ib$^P>HWJg&CG5T4vLK{y<ICI8MT)
zVYpZP`Wto_Lns%}5>Jp1^bZKI&wd}*E#ndiTiv%f@ae{7o6@N&=RDL0(QMsXxwg8T
z<FROO?xhu^a2tafjWJ;d{QAA}@`y)=OowPRCX0nTw5-=&{c5mPEL^NB3tui=y7*$x
z7Z)n+mf*UNAC=5i9wLL&4`<)>QTENnAPoFrgSn(6X2#k+Ti7k_QG98)Qh^_xjo+hR
z;;OY2N2xM50ekKWq+&Zaeu0Xbdatx}*R}S5FLZ_d-=gR9KE}B9W;tKS^16ao%dzW+
zB)I$~XaM}1XUT@V9>eb4A!ss_m~T2(azko-#QDqt+7DUbMq+L=yjaRJf`P%o)!>}&
z_w-y!TDR#g<3!EBe_){SV%^=O5%J>8i<#9v6dRJuoy6lE^?ZA4q8~iCr5-OHqt^YM
zTxmgrqJ8x|`@(AuHf#%j{5TjxR$>h%EmvA=tDBt;2no^Orlvn$7-VQ^c@PP+z6W}o
z9+@5bH_HC)w-^c+9`fBgTg}@39Zh*$`}bE3N9{Uwd($?4ha-dZKR5j=D4{r3nl|6b
zmL+(?`-h8JK~cwwdAC7Tc!I#%d(JlFpYdg%-o1O5ZU0ln#;<<|L{%_FR0O+RpVeIG
zV3wSm98uiBW7fW1F$?PoWra-V9^Ob5IL6m%5@)r3aO>`N_cjkKvGd$okQV>rwHbYu
zVQD}^yo9?`=vh>k?3%k$tW!2=_3RShMD~@M?NFMa!<i}YTs{s^?04r5mE%}zeXr46
zoN`-?RegP+$%40@SBR}{q3EML;r7v?gq+-t&Z%Mn1I4^;^z<LlUlJM?cJtFGwSj?w
zlbJeDmtw!xl)Smfc_ILREF6FOowct1{7|5TiKEMpxyP)*E&muv&S`0BkxtobWlC|p
ztMngMAw%pj)nkuo<$e42UntF<AF_AI>vA{lj_+;w19b2|3#L2WG89m5|MktzNbla>
zUg*XinVakk!~yGr+>2(q_A|p$alG0=Y3*l{wyB5g=GZ^zo>qX|+sfhs4A*a*3oafW
zYB+7O=2{c*9^*?WtlIOSxZ`}o+OKvDV-eF{+EC2ro=Z_oSl(l!Pw)Q9+JQ<4k??;$
zVF`U4C3xKbe!K!W|F`{3y}-7Yl6lKUd<Aoe_02Da&R;P#RsOI5`BAzw6IFw$D~9p|
z?)<fd65eYbyv$ol76%`7(N)<@wCB@r@udV1^kI0{-pjxk#x~xwf6r6<5!mXcXx-H@
z-1OsC77JCn>qVZ+c{NVH3}3E^;46!^+hQ^FNTpjuT-<nT32jJ@u+dYibb-gU0umB>
z{N?Z_et-CWk@gCffR<C78D{y;*>w4T|J`{p&+;A!t_>)Z*f7~${%hi}z(Mcy4|DG?
zFiM|;6y)LSExgx6*wNKO-9`6dFS<K7_{!ZxWx4XY!<@qlP{r3;U$A+t&gx<WPEBO*
zcE+%+Ik%(z{c8`clFy7GK(EY;(dicPJ&Iq@lk>~<H+b1!1<&^umK1rfOB;HxI^FT}
z>qqcfjV4g=Uz?w4^LLbc?<SETPB>|gwUKcEK)R)pGVT0rM{s$IiTAzcbI8)rW$FZC
zHrh52ntz-{t>=3xHjtN!J!lgcvKNg<&160xOHV=UJYr1cC?4OOgU1`&=q5ox`_<(f
ze<Yx>bW?LP+R~Y+HpQ+lz)?4SWL<TRl2=r89|+7%o9130swsNsHd26&pr-)Zyu7^G
z=AQVq$T`XZFJnQhgM({}r@bawn3;Rva5oXiICj-Fx++|~dVqKWCF*BJ<z=ePlT9gG
z_w3zkw>WKz(0d~?DEC#HdG_oXvLnxTr2EVAHSq|Q{Q%c<6YXYporn_86vc-*%rS;Z
z=odS0gTXm5+Hpo*y*lxfpQffJ5wA`SsPOKc96S&m*{A^cH&o?+Z>0`U|4H-6mm(*N
z_wV1kN^pcAZFP*eH2ftow9a7pn2}&KnKMB3N0MUn*lucn|CJpQuJ`<(b-3eRhCX;e
zNhqnRzAhlu8z>0ylClshZ8zEbG~hJNO(eXP&mDSSUEPPVQTVn~Kogos9-t@|U!+G7
z7iR~At-|{RVEouqQi8N=!=7#(ef@*=g%Zx={|YJgvx&#k8F{ZBBc342x2{RjNRTFc
z&<W88V%|}8m%RLdsPpgbj9R|Gk)xp?zykVXx=;8RGc)rgq?!pI{M7FeS#Nm4%y7Bt
zgpA5)i2{$sqwqnDOiZ^!Lm5OYT4@oh3I7v)Xz^j)7%bER$lcpO$6N;w(oRiHk*T~G
zu^HToDM&Gpdkd8Ak5*y`Yxn>%#|D|BlKHGx>)`Czm&6UsTYMckN;r!%3Pu-V`?HFS
zYk~N&R=2R~rx2*Yj45eo<cprUS6G7JVH0i%GHj~r7Er8-@nc1~N$eR3SU(czW6s4B
zZGKp^n?Nhb5793!F0#y5wb<3v)X0J?V?SYipL_-MmB`Ux3r{9$_TU-w(&I3diV8nD
zJNvMt>+iRzR<x#9u3Y;3Sc*`wPHFh`iDVtFH|L#+6{*0SgHM7kPBDc~1zA~G$Z?Q6
z4__jH!GlyH`MLS|w?JP@M0$P2fltT*XAZ3<H>Ew7dXPq!RZ^lcHZeJ3KcIv$sk^N^
zc@)QTGG{+)dU;8MZM+{5!JKQ;y(=s%jArv@Sp+RmehYx&VGQT75v91fJf`5*LoWGY
zsVf+Z$IH)8NjU!M>g+4(S{i4RyVRCt;0g|ck&W#h+0=v%iv^HDDW99M5~MLiQMpdD
zgx88Fo@u~d4Ccw)$x~5YL1B-eARUo?se&?pKvrH}odF);r>3mzO@fQ?r<YmLq~VrF
zg~vt$393Ee?GTdDCK!;KVU(kR43;Hd5JK6QjzNH*CUHOd-GZ-IdMa)=O{01t0J&IY
z?`SXIyLSU%&cq@@(o3L>Wqtf59tqg+;2nlO2=na`75z5g21CQ7o27pwrW+kT$+I)&
zG0nJT6emdmb!o=pOxm$V1bUg(6T?$ZrWfa0BT+l%zi}(e!}<&?dmrw^D}k3C9r~|F
z*ve24Vszk4rj7ve_7--<Po8k)T&-t+R67lv+f-<^;D*)rWvHHAKs<c4nOV;N>C-*w
zW#B0q`ich0EXr9gu)9vYK~4GwR~uSQOHp$h2ym7$+tXF*ejvZQz%J?Q1Zs`=wD}|T
z=4u+<EHze{<osb+zePYH#Y*1G%PVHpai6;0kIme1FZCv$>9*4Dmewa2KtAiV%~qR*
zT+C#Va+^8)NCQ;U>C2ZBhkv}uAh;fTuieO4hWfZR#mO1?txrcS5=`b6&_G2+Bx>^B
z(aCV)KLm#PP%!Dhwvci<gbzs782zjSqlTtua%R|{+)v?|+tRi9(eS^qs;WwT2-B`9
zY1u_Sj!#2xwhuihZ$TF}q6`QjbNCo)X0}SX&pC?ka$m3*&yp;A{6YW^Rf)07*3LI%
zWhuz;4Fz@L@zIBHAo-;@YB+$b%RHCKv#-<qNXEOYM(gELqbvH{!Ui++iyYIda`SAu
z1;6G)?(IT-yY^$?Mk>vtX(=ftmh`&OF#@CAI4@xl5&O=ZzWfq01xbiPbjWqxi{!k{
zRYz0v$C7EZ7c#u(7C^E!rl_)pG<c8J7X3^&`3=BB>C;J1`RBb^2m__Evho7%QvG0f
zWTY=*iPx;0K?K1hr7SP29ebub0=i@yh)wjnEdLx2zSdnPMLc=-j0W8V4i*4sS9omZ
z52I|O@OC<5iOa99$hGMBc&pqsxS(WHz*IS!wve8UDhj8Np3C1OB&0FyD4y7O?LGX2
z{OZzdic_9-=XDq9)aiix_nEQp+Yy#CiN~umv#<mpGed)EOF>cBzrRW$xDYw+M?D&M
z?!`<1i#-AYsx8mYtE!AaUe<Klp^e5+<Gr<jyTWgA2M>dosJ%}6xwuY3dQho$tl~Cx
z-`krTa7`Po&BT`8SjCDoO(PfDe$g%v)KyXe2Ey|r-7Lu24qqCOoV?GxP%e>S<Hn6T
z^Fksb+Tf&5^Z_l)r8P%h@SYiNr1U|ZQno06CcFuy&u~k6_*c&(KVekQssSX~;@o$f
zzuM696K;b&<tt!<-NNt7TL1pb3s280D9D0(t?h6(42Uy@gm2BmT{Yzax0179`#{>j
zdn-*;;IUH}gNgS5;GKCIbFC)sW8H``<Y7~*pf)K4LWVaoTs@5m>%IeTQ%pWbL>Jft
zwLuGwxV&7ZtY>;zFLd)h*42eKE#la|$oe&cB&CG2Qr+!ax9<Pw#I?1X8P;k{JVl-K
z@I_G1F-aq}px@3vX$&jfOh$r%MW!YHI=jLv&u{C}a4*gO67qSKuN$&k#*knrb(?*#
zHZ)3S=qtx<@8EDI)nVetG4oPPYx)tdF;XIT>Qohw!I`WRE|dC6LLpEA4Xl|}j}zt2
z6h<T7(YnsB6=V0lS($23zZCw^7HKZlRN+4GkuiJ>s@V6CMo<|uD6p-_Dhb{#W1aIl
zNu-jlm<-zW71NsU!O32bEK8aH1t7rkN6K?4H+K>jN-fLe+r3vNALufcjcf5B$s%2D
zV~6~jc<VV$2Q3XI+g>K*s$aYq+qpc0Dm|Jf$(5_v*Q;$nS9`d*WeJ9D>>{@9(%<z$
zCNjPAfC!($d$35XjNfaHa>n$iy2#mD7D)KLSz5d#D6Qc!Hyo0fV&8`UjbL?<jvSJ(
z<;j&T^yl=yS7e+`PtHYQf<amnFkM?8^EP>T?L=gDHW8tZ9}9MFx`^l=V33x8ukOn$
zK^syq=9+vPc!eEL!tW(~Aj(*{?<FYu82u>R^-@HwQs{c(3m>a`|NaTgHsM1a<B^A!
zKVRk)PcOK=$|_kJ&1kyqpt5z#mUmB22PT*2TeS~5dJO`)j&~F?5ns~J@x0C#t-Ep*
zJwtl2I=!gG&3$OCqOxU|_ljNMSWBiJD|U^VK=9Z+AiiXelYY{}qoiu)(-99|lrP>n
zWxms1H%9E-`YuUVAEfB$h~G#e_rR*;S(&ZLz_K9;LP1EcEtmj4KQho2jP6J7VFPi+
z%uLUN>j}?izEgQEGI|h$hUO(Zk!@PXlGgmXn$xL{=|c}ZO4IVQXS~IEPV;7;aY5kB
zfsz3bVvk#0Kf0|@KNySrgCpO|K=s^2fr^rnzvBzB(;FDWY%Ws+t)AzFiB@oR6>Yix
zW@g_q2spV+9TEk>fm+u|aC2Am*?Url3k-@+v}Qi~ZhF_~(jlpMy#wKiNTLw;%SDPG
zl~+&J_K(#R8Cx|}L(x&`7sf4Ur2P>d!qseA#wZ^>ecg0z6;5@<V2&Ko1oH%GPs>_w
zC`1!JIG;Vh$O?4<fq^N(-QZ;Qa&f&)6;aL5PJb{XEhE#NC_-D9@hL9d`|gqbJUpik
zgkMn0$c1xI$-#hoN|luD+}zwv8D~>eo@u9V{~N6NYMCu#M>=0=G-SV7qj(E9dl>N<
zBH~2Q69774bLSE@mHt#d*XC#5v7=C=p{YYFU(${iWbfWm`puhjEc9Hw=XOSoV5=>U
zL}7*ZiiouIiJw%Kcn}k_nNU(w%PlGs7?#LJK3VDk%16K7C4*P(L~@HjX%UAlS`QLr
zLV)r~vhcgc;D7Xn%b8=CI5=nt+!6Jm^FZ0J>t>Q{2(F_?n~cSm==c<O(G2i<2X#h%
zXbX2r0nN?=^?m!63|)G{cJLD$xty?<P0M&1Jp-d|HYx(a_tiFc=!{s^%HS@JYF@}O
z_HBICS?r>kbn1-M;X0jT*IPonij9+>71w@>S#p838Sw<=`1PTR@OVe_H4y~*ni*f4
zGT@*vjqpDasKQWwin@lz<8yZ{Grge^A`4CqvrK7F<Uk=NCPse0S5%j7kV1cxsH%fv
z=XC3l?8ssncUo^BWE=7x-o31tvwvy#bBnLfO&tuN<~n`v@7nYZD>k6EhmDK{F&&d4
zqT~6b`dsjpKk|l;18N?t>lg)g>SW#j&^8+IGV<7^j$Y$?nKD19Hf}`S^Jc;a4wC)r
zcK{CxV$(MEI{=(N*hbcO22*wvyS!{yyc~7aB1X)i#Fs%mr9Z1d%I523z0q#?iInsM
zE0KrDoATCnlu1>__+j7V=UtT!mFE{dR%hD?A4JTRVWAvn5rw^7phg?wwDf<%m2(hH
zFwfP3gQXyX*pJD9qby;I>OQhEZJxPd9u&YNogar|@4JnGbxz`(I9VGBYbixq#j1EI
z_tw#+yL&H9l<sJc^Il)>2BgoF2K(*vpgnZfAuJfUFMZ~xy%_eA`bqL$2c+oKQ--#h
z>csYPWPJbr-Pwj5w?488wt!n@*A}bhx~Q<!GKI8eWrwj%)YSa|O4Wr9L$>pi2G`zP
z1Vk`jN0H!APJu^`_4jw#YXxpa+sxRa(%(3<5^GCiLV<OG0RhS4Q%GQnvBm{$*}665
z^D=@R_k9`|`?u|Hz$h0hYPW~r5)%46Z1XC<S1G;8A&MQ?DC)CkW9mAe6sN2?u5Vb^
zKV5gNge+tzcXk=BeAP9&ps$NG37`q#Q&1rKNHO&yNvxaiQeAC1w8Ur+_i%Xmu(&%W
z463-vT=~clNWbZIHEZpsvudb}is?*Z+3h}lWMmclsm~IfQQ%e^jjF&tyg1<lJv@_-
zq1<E+3+x8eMH}hOmkM>H7}JDW59#e}-L2?#$(E<muPjr)u&J*I4Z1l$jt_g^Im;hO
zcgC-ert*gGqhl6>VUqX!KOX<fzib&nb>xwy_TQVoU-_q!rxNv=^9R}(<#sw<3;T7U
zz?K`%_Oks2JG&!Lgg>xqHNmZ%!X~}%<B!ToIOi1Vt5#q^K|3EUo&<_Y(OWcF9aw-F
zq##g$dK*Tm4?!E{?{dFvu?&(<f%oxwKrM&ZrKYd4f;L|AM$<EskGi#=G*eU}zvSB(
z5&Jncjl3394BDxwsa2EWhCU~RzycRZD!6|?7cyZg8lB}uckvvFT<ZU^2^Q#|sVXuJ
z{QcV&j$*^%HM?5YJHEb&a36$^P1)imklw<DkJZy&FI|!#NTR^!!AOea*L1J7l^Y|g
zDAMZv{}+P~Ms2vY3jwH&-++s;v2pU^{%(&yXI>bVvV#IYsr;b_aq(~T@~Rri)wlp&
z(xM>hfx><BHy1gi+?Wx(R|0cUL`Yul`f>ZFig28RGYX$r6x}wwg<s|^{q^IG04SLh
ztvNXwXn;~|pr(^|a})P@aKiysz*1MOHP@m|y{Nv}eSQ*TjE`L6$(M$u`VY#~4pf3I
zexk`s5SX&zy3p%)#Jp)Ahm2RpwJuHgORTgp2!Y|QQVDjEYnrLewXC%-L_E_(2`Kne
z;@7+fo1W63#!a<l%a&~KbuSQQxu2A(Vua09e)aeFhsDL+2a|@l`{Yu&PR40N!(EU?
zqK5`|<@KR7;qlB5b0n>qJddGk6$rpm#JTRgg`3PqKnl9#qGxt&#^0^jx=K$AHIOO$
zeWq+t=7?c1AzdYXZOl<TPV&a~&6_uWEAuR_a&#2uL{&U3G2C5VzDy~&fA8K6Cu?$!
zuY`eID+&3_dk%!YeS2~R1r8fEr%fH+`cS}6-BdepN>h`89a9|0Fbc(1C-K&$9EW1U
z@dCi2WaNkh#BF>Za=GyL{GF5>3=BTt=)T40olTEOJ%%>Nt+-s9V$?umP=IehS}bW+
z!f_bX{U#;v5TGlN7szXd*8RjyJI-EF1Ug$g{cKfO=W$ga5nz96!u(Dr1cz?}KouSg
z0oVWyDx0oN$t&iKNPr}$xkW^nQKP`0hJPdzSAVf<u1Df0l?=W7EG@q=K!L_*T6@6@
z@MV!~0j|ecp2)=E;cMpCX$feh9|upt$iZ<i@(f-D_>ZK#P+3ZW5Z$hQ?Hd3l!qMc$
zScC#{fLGeopcKHt9kKj&n3<Jzub9~PB)cW!&R4$DgK=>j>8F0^f~(*JD%_}f)Nc~u
zOp5afE-nHF=`FI|w-y#$$>h4v2=zBpGc#4r$|bFeyobVO!P>iDl&UqZUy``?h3nDo
zOUm-{k1Oa5@j&+9Am^Ff?s`U9N$D+K;b51WT2~}C;`?i}M+S!Q=p2%+47eqZysKm#
z<YIzC(oKb=Fzd17GxaCBmFVM-^56l`v+)*B!x>N$D|>qr+o}|}ndl*MZIHJ6A=#$|
zMh`rbE2`k?4*u~yDsX83{*AbIa+;c<r^u-URPFgTCt}CiACbqDB=<zi9c*n6({YO5
zf#z*(|KC+eCD)faWsTxqncRW!%Q%@VX~pIkQGq1uo_Zsz=gs;uLw3mLPOMES8{w5z
ziorwA9G22c4aMqzHYTAVqu4=SUK)tnzK0(O4Gkqy&fWtl0U$*R&wl{xbO(pl#!s0A
zbRmh;aal=v7LELjR8&+wsMdN<7DYh9Mq>7GJX9chz<VpHt1BSA@xpmwBNY{wloV&0
zdOTb_(-(+K<9~7CBw^R8kTkxFTzC(({_I5$SUlHGzlBUU1#jW8&fhoBWg+0bi&~IA
z_&qXvfq45q$p_+jLA=}9*||c{bP9!p+(<NGkiucH`1?W;F?_(s(Q}!gs{HTYmp^%u
zzzJ$Bb$3BI>Q;c8CZ)k9yy{)BIwVv<@Th>8EqOHnE+}M|)M1A~wJTSz8k0nEC#(Bd
zj+C67Tqsxo5RQr9Z)mn{^Mwub@Q_4$%@$~j%(n{IEnV+?wU3uq$<1~Yi&VmK=0Zh9
z1$i&M))viXCOiK*|6E%ZJBV@<RB|2V5O<`(H#9jO1PLS*UX2_(MZ^k2A0JS{1_CjB
zU6xiQSXH*8mwzQ$p(-8({So90VaEXm$e6{T$Z^V9N&br8%8JLxWecpY9A2Y@Bg~Tw
zCY?qhFu562P-v7vsoM^ukO&_TvLNQaPD}wyNJ5~!z-v_;{Z7ALiPgY)I*Hzbq4M|t
z_!eaxTR@oO5IDl8A+r${{~R^4XlbK3>~$?c2C;fI8c_DEcIH{>CH-9T`yhon$Xz2H
z8+}0dkg*CTf!@_J6J}&wq$6P#K8eXDrZ}HX6P>8M@40=)e<fmV1%WI#DjFsxZ1>@~
ze}LBK9h_*{l(Vul6x>vR2Yxss+~5%r&Pfnle0-Zh=L!E<Lym%yKq>A&y8>_g#f>O+
z%{jAg&$L$D)0^Zw@aN`txot&v5#Jwgjm|YS`N=6L=$U|{AWtrU>Cl8NL69jFxA{pc
zr&82(#L0MfSvmN$U+*sS1ZBWP=2?)a!<m0sjWOS;Q|aiL!zTqTaIE7U`==dnyx-wP
zOE~X#K8NNE(rRv{4y=T@^EgfL=DmBSJ^%YKG(P%*iePcFk(>ov*MSAq5(DHHB2O8Z
zwri;PL_H37bRB4|ZKRs>zs1gn1oaDiU~+FrXalV7{qK(=-_8G{#@_@4xvKbtCH&_W
e|L_0Xk~j4YCnKXB=l@PxQ9i9MpCxzc*8c%to2Wnl

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

diff --git a/reference/plot-8.png b/reference/plot-8.png
index 653f539b2c2205808a98528c6357a0c636ffe1b3..740b0e96d494be280a0b7676d82bfe965c8efac9 100644
GIT binary patch
literal 54976
zcmeFZcT`nt@Fj@)d_HqlM8Kz@2ndRRWKaS1A~|OSBqt?j3{O!MQ6xx4kemfXvWfx{
zL<BAwL^78kS-6DWM}6JXGd*kl{^_2cnIB6R>2l9G-&bGNu3fw8zP#+kt()jJ(a_Lr
zC0&wGq@h{wOhdEI;IBXNllGwJ-|^{BgUc5s@EIQq;)P!LwDH;{4Lced25;j38o&NY
zBO02&X-E?1l%2yzJDmddsI086HqV?AxgWlB&viA)9e4I#UoWvk^v$hXJJ&Fs+q2&9
zit5q`8|N|kw={npKlbqJ-(UUrtT8&LPQN24y07T{fn$!ZOT~UTSj|f(gbYW<dk^-8
zizb8?I8b!*k_$qdx(oQ;(i;Bp^Ok66?g_n8-|+iun&S++ihh5+BjM8bwZFbT8-C>N
z=HEZQ?(@gV-(S9G{h#^bvjS|#k3a6T&eJWYE$zSXkXAoXqsaMsWA6w#EiAXxfkKw_
z@Y*Zm67~Cb{G_tB(eO9$(?5D_q|F(u!YRLt-=BWlMfK}%OQ%Hje%&+U%-8k5zPiCL
zd29dg2l=*VqwZH89?vD`1BO>0Dg<-t7%*)6^*dQ5X6H4(zWiJE$^XqCchTAwc51$|
z3}N*Ap|5lO!`@_t*t`BupQ;o(TIHHIN#%7qh8>d$eNh|6S53QHHDdkx^;MOXpWobE
z=diLc$j-~F!fQcRo*(a=ovJ=*fzSF)4QX0@tz(Y(m!_2`>)&TD82IGL^K-ZVOuF7%
zE+r$=6tyzLBJ%ysy0+KXW4m2nS+>TQM7mtSi&gup0*MRjcrDL{a+^Hruy%6Zs`8>_
zzpnGS&H@Le+ECuW(9q<~oUhN$ci8#uJE?mjp+V!DAd}JR%Cc+Eo&EL;{lV-aB09Mi
z&8ib!#k|+QNks5pkCAZjrR92OzWU+8{tX-leJRIj6B?3MmZ@5k5pLu8ZFx3&A(UCi
zcB?#%S-A+oT#0sFyINlIs{;oPtf|iLv`*74)~pkrekf=^6m8y=%!&)_*K^PMa~pl4
zfZf1xF|pEAZv(D!_3L=#{{H?u9miN$Don!s?===Hb~XRnspE%lKl`yf-(4#(7;eaX
zXLj-1gOlgUAy1y@D=I3UCp%Eb^1{Ny!=oJ>MjAh|MTLZzIZkw0(eaw^$?0N=bbj}`
z)7tPM*PT0e2F42~wrQ^Tu&1ADZEe-hZSE>|s|{4sJuuZmcgE86G&gryGPh!fb>2dS
z*vd{G&ahhJ)2B~M>*_|<mAbX1B=(efYZ>k1)TX`G{iNWLPvpc|*+}7GJ4Vj@zFl_T
z-rx7<Rj2h_9A>dNBooTB(XH_IhV6?vF1ot9S*K+a8r&C$6XNyD?jCN(GIw6Rdi9?U
z8~(xSF-K}+L|E9tmt9X;<Q6S=Kf2V#UjHNf*BN{wxxI^)-?r}_afz|ETy_J8M|8YW
z#cuQ4QYv~X4tb?M;4C;ksvEa?KWF2e{kn-FZgYQd%zwFj(rlzL$u?iRV6M$#_U(p4
ze{huA_Dju<wsQBac&#j$M0m`#YGxToR=#wbA8(2a)=i!$T@}6Bdg*|$b7rLLNK&*B
z*1>gtJT0%=Q^<hoTkf!@=DE0cV~pfxitx-(y?E);)PrQzG`dL-!t_;(+w*LigE+L_
zwT)-jiDm~)lu)hXUcA`D&TTg!Jv-5@hwE(B(36#YM7-&Ix0hH=r6Ifjyp)UHuR|Jb
zf9ZSqn`c~x0byb6q-4c7AJK)r-D9oU7qN3VX!uuEUENUPLCtZVG!U8jEG~8V@@Dn!
zrG@EP7=-4;!84YL`F4YB?Ch7V^R)0sZ8>I77iTFZfvvd~!LBnyJIPE<e!^4tZ0+ot
z(shK=G;^i$^Ye-Kt*dLE>aT7}RA5ROg`s5}7Qbuy_SSkSIl1PU;Rc5v-}UpgIq`3-
zt60ma<GQzs=kS*}&3s#<Hy#tXK{XwRK;Ce1UpihMt2Umo?ovJ31SQ;qfPjEzfqfFO
zcNT@|I)x)c^|6Qj(o&6UL*vXE;x#6;URfsfR|jiZ1q25t1hT6o%Y>crm(4YLA+ad9
z$+#h2MsRg$I@g2hNJ{uLjg8RyroZl8;In6m-iJI}T*Zv40v@;18uA#|&{CE&^~-Jr
z*^pC1L|1+oj<sZoFDxt&%O)5h=sJ@)P#2Yf-O)7Ku$?iL>Efe+Z<ng%UQ7f|6_xyS
zb7v2{FW5X8l1O@bjLcL^A*Zn`>0k-t9mm>KJ%V){H9lV~WswaZn!Ysa)a@~t7e3EK
z^{>G-*KT9EHuU+qfuA{_>E}1}BCd@#E_v-%u5+VifyIN7Zfk=qn$sgx(yiKGo%5^h
zw+#uiDQRUByY}_X4I`%KnVFv(lU1tQ^x$!P8-};gwvXj?>!wCjot6-1&WODt&T~RD
zr?SQJjK#7=MzLzV#~q8oPZ4C%F~>sI6DPK4igI%|u<7MlciylngjHl;{pf##BH|O7
zJs9OBdZkr;d;Jbyx1;{Q9mzdL*YD+L9UWigy2AknC#6SuE|R>rA9~eQ7s7opDJf}9
zb+_9@@ko8F_bgrzwLRYt_CuL%)>Auw{#N%a{7ZeOQ}@`kjh@GBpm}3rPo6t|alwz#
zsyIr(F236~jL(KFC$`K?l9H18f`!^YgL@AXUHUFg$y&meOWOOV#$X?eXGf$-r22#?
z&*b;--`~iR2|0Z<rtE;kl;Ebm;`uHm#G+K)=WwE;A{Md3;dY<0TtoNm+h^YT(&${Z
zTsVKdrsuc&ENgW$nzikG(@Pdd@YJ|am&d#gWm#yVXt(uHKPMdym-g?&HA);V-ezq>
zj#BD)p;4&IN`stOZqUT*-fc}8dSZQNP8QGj=N1`!*koj6w4P(NlF2VoF;41Swf%TU
zt7f`xaY1^~R8@3;@$L&3AJIQ)7(Sso@KjDTE9j8NOns(*EEbRM^woQ_o^Vo!^H@11
zCUuG5JoZaqVPT=<4@N#{Sz&Tv*lYB#zSmF8I@d@_%7>h)5vyD5DrtGB=R0G&^Jq)f
zn(DoK_LxrfeI!#6VlG}?#hD<x3`Ve!1h0L)v1!w$BZ@XJBztx556u3G=SRH%u-PI!
zUBgYDs(ivUn;^DKGAw^sKhl&UeoX7Z)J)sc7g{#Oys1{9d^Y3LqK$oKk&}kEleF^E
zbKRR%)b5NjXyiCOFY7t3YCQSv1No&>Mz8$JVsoixb=VoJO>Tw$Q{xJ*oes@+a%Es-
zM_#R7(!JRAh+ZH@)MIf7k>iQ|NQZUnOG&SVYP~9YJCaKtrO0L4ezb+Fw0|FmCUbE0
zQIa|AA2+oB<@l{zYipy#^l<!?y|*zaXXuI?Wnrnd&Z8E+vT8TBQex~C48FiK9%!ob
zYuEmMN$lE({d%bj(}P58_RoPY5SeXMDp;DTR!P(3h07A3?{dosI-&6`&m_g0>%u`k
z>zpn(SGv0f*{zK^W=fRhPZ2`5gSIE|Mc2-iUQWV+*8i}xDp@5ZwcjE=|HAO_Fx;G)
zxp}gpl2TxJxMgwCZd<sT!d#t1Lv{|shjQm@++y;td=jLj-utj4iISK&-dRXV$tdw>
z=Cx{5h6}52HHl)#dVTo_2S=@mVo65nN}El7u%2hDrsvd0mfK{rhxIQ=PnC1cRU9U|
zbnxo}oPc<7?`=t$neyAb%Le<?GBY!`T-+^E|5`1^!Q&_Jx>0%L?Lj7;LdT{jOc!VR
zgY}Y4>LNuqe(A07vw(phx;+J+h_fkKD7RsdJQR|yop0Mh%}`2slpBs{nq!&R_1cJI
z!moYomFrchgk2VJ^;NaCDxRJtQ`6Hb>guM%gGNpo9F?@%(iLNM$g)Vu=HthYJAIjU
zH7jJ&Zl~vu-pwK_!871bC+hw>cgfFdDYj)1cdb#Jw0rmNrnt+&(X%2jy#j<f+4iee
zB*|&r`{n-{<^vcec9${`W<w4au<N_VpJP(TSQ{avw(id@na3zqRaIZM@^wUS+<k0M
zdx_~0Uga2an2GHv(&36nzIQG$-;Yf)b$4-b(YM;~`@%pYBQ~aFc}{C~yi@B-60>f$
zacvHu@VyF#^lHw+n$S!e>fBh{OQQ#Mt`k|0C*VS&Ni#r&BQTzvE~lZ-=cWg08xVr*
zr}{`lEbFvBV3?}*U?0a3cZr_OCLQgj-Rw_Fe#e5VPri!Cz;W?fw#bXP&V0@>Yajwt
zv#wjM=IYnm+xJmOA&(z_<!*d8H`ZR(7Ti8Icc85YUW(Ol=1DN|8`bXvwP7Cq);%Rf
zECCPf-GM@%&x$tkmorl5C;X&@8RPOF`4Di~_*1x<NJ-Swr?C%CmZ(#jGyKd)Z{pG-
zD?fV4R92;U_3Cn3-viv`u448>pQ9aQqC`72$Gb~QVev7g%VRcz)Y0s~$Vgx{)f4&!
zHr*vJ8E@(JFx!+3-_(tqmts!;l_%`bV0M;MB9Tb0KYF|!T8)A=R_CfumhNG1F7a@)
z5cvG#h2-7{Y1WQO!|L{*Zt|o~0ngnhKbip)EEWnUN;X@B-PyeV0y}-_yW&SaFIz1a
zzn5+2y;`}0l;FykXWqn?Y8Cs|jlPSo+r5GApwsf=Ea9@4lK}WW9NTfCiYn-e!yrEA
zDdM^GTFz$v<)urP<lzKoMTLZf`pzJ5Y)cte?_`fF{=Er(kH3o4>ezlfN}dAl@=2R$
z&WOqtM##_fS0E5|Zb!V#L6sEMw1)~8DsHS<m)g$~<*tYvgX>}ZYoH`eJl>LRWmnKP
zZ}B=&s%1{u6nmpXpBw_#G1+heL8$xp+2%LmU6z0J`YGJ!dY1i3@Um4~&J%mK*3koY
zPC&G(A=h@j>MFiG=IB<r{OFKa2~W?TeIAUS%d^do`-|OJHteRj#qs<bUbpNj>Q9Z1
zJ{x<%cN6>0Fqn)+wz0;<^47R(aJEURX_{mzAom7_!C3?Wa!m+#+I-i?t_GRvAdaS&
zMwdxnfUuiV)G{(zWZJV${21LvvrO+Mz}INvK?Cj0mwIom+ps9D*<VW=yMfX9Y{esn
z2bCpE9t`XKA3u(Ueah5{%!kj~FbFwrDXg$%7Fp<fN}g)TGE|Y5kN)_SMQe2+;@bB2
zLOX{=8gGjMGe2}bUjn~Eo{IGo9pM(xQ~>5`=*YJ#OXmB6==s|ZNB@kTFwn%dT#FRB
zC{fMKD>t_>3Lj5sU>#OIater3Z0Y-L&0O<1xTnkhNJ|=#b{jYxJ$iKEA^Rdi?QqNk
z&M!c{HZlbRj)l2~72CERIQ@Q5Vg8I&+Yjb&S-y=MH-?J1y|R3z6g+%u{pQB#bL)zi
z=i1w{487%!6v9ywIDND=y9pU00p1sfV*O(1qB^ac;agMZxs7Y&+h1Aby3U%jpYbkx
zUQ;9OFxsL3PoOI?f9NCScz{Wi2iYXGiBZIL2v2ga=H@?1cuR8bDVr`~;($p#b{zil
zVbJ^0*~OV*z=Xw-6gmOh7ep#`bbw)-H*WL=k%$7$?G(F9WPsH;u4+IkIWE)26n=WV
z#JVHVb9vG1bM!g==7YX&al#qDPM`drfA+J9iFIFMo3!{8E^v<Gfm3){S?~6rTWjFn
z*OHr)lvq+Mjg5^fdo^EQd#>X7_07%0)rH!eQO-c?>w<LhZR3Ec*sg~WXyq!sQNLrM
zOm~S#*&{RLBBB%E3%Xr_<g5cVUSHGCntf98=$%OS(_=DINj6mhtZ^2v73q-JboYje
zd3mb03q{B_#7I8KY_|Y(jdz(IAX8y7KhpRb7p5F57@YnoUY+c%pcZXEsyHu|Bbx9F
zBKOtb&I>1eI>KXvFJBj}>H^}r1rmkBZfzw<v=))am(i)^z(NRLY)(!+pt>cCj1xB2
zS*S)NZEj(2Z*Q<@N>)*LI1=#BKYw_@Cm>N(u6&6N%q-95NHNv9_<YNO(-Oq@UE3Lj
z)e+&0Lq<;AhrLI)lp;+A3S~_lLkJ=cQL^NNeTS<{rmEOokp>3_1So&wza1jzU|u;L
z6ciK>{A7-$Jen+o2TA?y3*~JC4H({pB;vUj7bzJ;dIO3=uG?ciLN!(Wd1s*$!A&3)
zX#&DY!huaOtay~H%v#i7KV8d5X|u>^LPX<no3rH5DNuHs8&%NM3`MdPD;ps=JlAfc
z)?4ly>;BIZwFFrP5Bx=2saGkH@*MJ7QdkAJ%5j>|#mOL!gd#GHrd$g(hq1OMx`TPb
z@chN!-)$}crr|aF!dwejj<k;OlWni8Qmi`*e!hQ%pu-Wt1(lriSoy`du@p9y<RRQt
zATyB^3c5_ah(r>8ukla~k=7Cb9*hgQbnC%0sS>0XtoBf|o>vpLe`bX`VF5%<+#kUF
zb3mgcJn2Y#UIrbH=?-6Uc|}G4l9fe^W$bI{X(OMK)fG3Z?h+k1Bb5Z%2>Y?z=49+O
zfuCe$W$#n`8jE+OP<~Nn0n)d)%HIqA^@~KJIs1`IV%_hr|6>H&r@x3NKY#vwh~8Lx
zp5N^A@F3G)u<%C9-~U5iF6j@$hxZ+J{JzQ!|JV)xYbd+l7yOSghkM@xg<Q>RH_B_z
z#eS-L@8(6Oj7$edDH#7v7_v)=q}!0eTAN=fJ;J#$LGBP?^}}C|lAi~($?$bD7j~vc
zinv{fTe<W~A;-{HE%MBGS8-wYtdUjT{oO28)_Ihh8yJsYe)i^o*}_!+%l0t>f<NTS
z+C~tbT<34i^IZS-ext|IWCiy5F=xThk+_6}6x;qP^Ui|YakxyvYO6ke-1xef>jJ{(
zHt$&aSWdli+F9@(FrH!piZKY<R|*<?oeZwtOL^RTUb4vX5u*ql&%&W02e>44?{UMf
zA{SeJ`bCP~Djh{$>I1DILm8mO?m!QTWZx<#vU9&#@HPg)Jz8pHM<IZMyIT()gXc!(
zTGFyOIy$P@S&d}<gsR$w3wLu@^i~(u)F;8ny=yame{ZM0jZ)CW^x2iE;8OK0_=^|s
z+b@6dTA4mX9G{WT&u_!|BsDfF^O}A>3-f8dy<If1PHa_M$Z0%h*B#E_6y-6!8HZk9
zz94?p;OaP>e+#&MB8Ah;vra>pBV53K?Q1v3ks;PbiWEw8>tLUNB9*`wsvvd2l2J0e
zmQ%_f9@tGD^kO#jQPv@@L>#&(uN4iuftrnl99VGqx3upY5qxV!<~u;&xgR}tEL0)(
zLI@~3gIOefWo*GAAqQ?V-7f*h=-nh6+1!|<WXNEvop`VLlh0muDf={MUF$p{@^h_6
z3_`{qK$@nidn>VR*hZiEs0YjS$a(pRWvpohT}Dy6bA62D2Xb(3C!^QWb*7J7==ncS
zET*QW5{tQw{<vy$sz$aHJq6x9&Fb#<LyS3=FG}i`TVGj))y<s=tL+;YxSN~CmC%-j
z*r=b^jRf8NAy@gZeNgi9gOc>Z@q$dlSz7*USJWNUGITpn03=@Zxah~o3wm|4W;0@}
zsx_D7OU}AgKY~%z=2WYEg(W!qL5XxPghT7q5Il*Rs;a7bzYb?Gcmob>c(AVPM-v_R
zO!lQ%7d@}#+}tAf1>0P+h6{kJqDGrGZ$?_C-SzxKu8;E=^1hQtyW)+igQPB9x_6!@
zF57{kYOw%U>Z8vbhkGhc)YbaE8s~GbYqW6yoMe<NCNmFDRvbF1b0gvYCC?}-x5J1s
z$-mQDO<(^xc)o#85h>th1o~cq^D%FT_eHSZqiQ)h@ME%<FeV_kIKascn~<U+;at=D
z^H`m&nraBZIK~cTTlpkJ;xCb#vJ5YRSkoeqirDIc98OEDG!+GhvrGE-?$Qc4e~$)4
z>YuLodT%Gw3?-xVH&q9_LZU+;osu8lHtV?b2XZ~+pIe!ZTD{~COH6=Y-g|jm%D=Y!
z=vK3j{s6>)iAE1ME@*=99$h=v+r7Gym+L;)YEp5^zhS@C9*-_7?NZO>dt?#)cYh$&
zA=VPMe6Df|2+T`;{w3Fi*^#CPrm1fwsML;O`w=ia!OSAaBu3hDQ<#U5G;Oh<!j))#
z?0oTpxp=btAaQqr#~t&vt3G`qgDOoA;Wi;6s}za!gc%n7r_~^AN{iZxl>3cswn^PK
zs<Q~(PTEeU3#M=c#NXT6+5&L*ITi*+M5KYKT-1Kb*KzGO*1x+T(qm48J-;2Oj{xID
z38a0D3>44f{QXLbB^G+!w&y!PK0BfjTx;Bbbv;Td^WHvK!A3)KgIj^8a6_3GNH>t|
zS42FUaSOPC$jnzdrJltL$k$@%dxn#cN?-8L$D^zZy3S9pT6kKbEF?uLknMth5{RBu
zan@Www*#bj>~+?*{v64Jl2Hl1fRqR8U4j0x+Q%;VFTnWL1x+)?9B$mP@wtTIy}MIC
zMCTbJO;Nir?s<3TCyM%~qXe}I)di%dclK(%?sBDYq>RJ#C342E(F-}&D5q&Q`SkdS
ztsGY+xPC-@rjIXPym*GpdLfNcLLJW@2@!U&RW|gwptHI>;gx2Kgn)Ba6s*9GsG)J%
zb?mCCpTERiK1xzaR;~w+(FhhY^HuSWZx0-rb)5E6=kcT(PeW2c^0X|XnxRP%DOM-Y
zE0E?$hh1InF}ed{p6;JN>Mb>86MV|@OoMq>Q9k&Ob)sy=#7~H<{+kbbKxAY(@Is<i
zp0(>>gp)mjMrpfc$LspG?w=%IsDPbu)<$h9?&q3j-v}Rq;tz2kC~0|rrmYe*__t|c
zU9Cn~5)Xm0=>-%)gU*IrF#<x?e0i00-}PHxfB!I!mNM+semsAWO+Y}yB*GzRWiGFD
zI4!Shit21`mL37@I5M0NMP9#OI~rbH$8|W)b@Ic0hov8rnTEbW$U4}CgtYt{@w(H^
zPG8@wk$HCN9DrOi-Yl-K?<$fiRA(H%Y(7~OT~kwYqd1xQBW)~xO2NCT5@Zy}Bq9g`
z+PIEpRpg*pP};tVBN)I;R8^8q5J-t&Ntkpfuf<4^mi46Dr?uAwBY>z&?ru2%4^(go
zEFgf?lRvl6?jUFG!yi5YFn8UyZ_l2gvh8BZpwN^-niJJ6z0wl(1#m<Hro{>32k-ba
z(Z*tFhC#k#4?E*7bLLC%nm5Y3x)yL2&nOak8~~tJ<#K?8S^EbqR1u78o;?>De=SZd
z7Iw}7t6|ZYsBrj@TJTD9d4!PDT%l-(opvLFUh2Dd=Shl4RHSmohMDQQ2PIwF=3Z8F
zDj`p`pKVgp60z*|T3wEA7mSb{>&RE~NAhL?FLxxh<(niN#q4Nyo#uo{#mE?v=FIV@
zC}1hz*(SA`*)xj1ISJ5lNyetbQE3f2ftiZyVu8p1ro828wMi<GCKaCmKbvx|@ql?u
zDq@#9#c8a~=$Kq2{k8LcFyGK_PipOirw^IiV{yiyozLvcdy|8~)kch<PotmSV@3)&
z_F=Z&rt}W)#`_J!Jmvp1-_AQRNo!bxFqvk12(`yI(>=pnjoBu0rB9tep#I}vsiuAX
zj==SA|8y@qS65b^GBYzfOc8RKx|*e4R_sWmbOSXZyEDgga(J?FSRZFyXdnndXYj|J
zip{;Y`8Dv+?*uOrS#7r<s*DCGa~`mzqGb2Oa<J3lI>Pp`lPBx!0m{D40TgU@5f)qd
z5u2n}>iL-;#XZ_<w-_fPAA+Vj4QAsEs-bME6<LNAK7o2D=Dv9?>wRVu3mL_RI}RKV
z4~dC3CjE688{3X(ZawS2bp~i#>djbjdy5|Rj@zT<$(bM=yX&8A6Wq3UnYu^03Az8s
z>|PXk+5nr?>h@ZBt<7mcMVycnE>WNV%3J;nxs3)FVtsteA{dwjcklHLSf3P2M@Pp-
z10Pi#u*6*c4?C?*Uh)zybc={{bqG8z;yv4N<1;f*30C<P-#-aWm{vYMyr$X2$S4U%
z1DS%?XIIcCLwWNG^^8WyUtb>kvhjV-wILjcdf4-U9Bt+R-pDoU{#4(c*#YXh4g6A;
zRiXXxk-T<PoXm&e7T6p5blBC>l|f(Jp6m0VzIdPrWdBPGFqYR;=w#DBFc9P_*7Ho%
z!}YVp89qKWq^AK5!w=2r-#YspXal*R*gXAa-FEd}-Mdp@y^~r>IkaEP6M*d}H`C5%
z)z&yz>?HS@$bUvsGRVkx)>?ZJo@wlZC{imOkJ%<LRA)c)$IFDp^L06wO%z}Y%QSTn
zG&oCs7%&LfzE6-w1_aAcO?9vDo#xkRJ07mi&CM-dew?&j^v9JDmTb1mh0X;3UNB46
zO4EILa|<%opH}10Wz_*H+<;PMmUmWkb;*w8L-`n8)k}2;U<A>w<x>8z_T$HT)ZNxu
z+kmV7$^XEuzJU9d6fRfS$}+f<J31i<lz!-F1fATE)a*~2G@H9u=J+h~Pgw0`i#Kg*
zf=?hN93!^_d4$X%@w&mko3dp(q}=Uc&ZW6{R?<mn(lIWsdg%zkCVDZ?4pf4b<mIoZ
zX6RaJ=9uaS4wKWgDqX*P`BHQ7kjGV^%YnS$5kOA&DnCY%HhLg@7*_8!{u&V0(kM}-
z%{mr#UNtgW5Ge;^Pe&TKd(ir?mTk{nzh7XB3=gMI;kydXLHgXeo4MAo8}b~;lC88B
z_&$HUfKmf{tO0p0gM#w)!p`zB$FR{+%WSw>JJ4eq4jk&4Z)`*ma;F924_d!|d)3%j
zb5<Zi)~>H|=QKQH81O()(uNHCWbb+6py&vJJYPZvKd>}imseF2B_{Uu`b8~(g*5(m
z!waKzNZr!}>G-TI5mklkhCUzewHHh?6ty3Eynsr$G@el!_^)mbiRO2+Y@}47;0r=i
zQ&03d7W!8ApRVvb#0P4QJFmm`!N=<A=~ho<`Jz-gX=xt7Rc-+4Q=Gxo{1LKGQF9OF
zF*68kFLKH5Hq<C_&sQNz-2-4K&J;RLyn1w$oxOSqj47Ev;!IeYx^Ow|beG%20{zok
zWA2QiDWAPcnNus|C|RpLY^sJ251hJk!v(e~ZEPGbfYhkT6S&kE6t{6-<r7r~Khed+
zZozj7WJj*@XCTnk+gXkt4FUcNL;RylXfT5iL;&n-(>i5;GsL6rI-7|zj{Nj-=6qg7
zZXfQB7<mcKfAuBL=X<x0o!LsqbIQL44609BDUv5&W;+yo)~A`wjkbz5ml4$}6simX
z-m2&gQ{BEt3cGxHk&tkZG|=OHXilrV;XyH2&chTfaDooPHzRBANl4cxS5v9NEiGh3
zf<)&_--CeC$r`H*p5NbYU{bXqJ+;4iOcMo6o?va;`zqETna@#xAGAKd7IYZ78W;g0
zu=})iUOP@x3)?&>6)MO6`y}#_1Ag=0cdpy8ji;P8s%Nt<P{57rcdJoUMIbsobbWrf
zp`vZ@_4Qt<B0qm^A=oNC|23Et(#V{uPK3ClkC+q@p!GjIdG^fI-{1dyiO;bNfPago
zou=m_&rVHErM`H`{Pf8ag>Qzm*n?aTxU3qO(Q*(K_0Tli>fn<nUxBf14~?$SJJQyU
z_;&kPDUl;ucm%SkoR_8VWA*9}RNJTJB$%s^sD3#HDai%@;m^;-lV15r6pps%)wPu%
z@BbiLR@@2qsH)-Rm+sc~8a~D|@1#{BfI(NAP2zKgqTNN2e;6gIMTvSWk@pV&VUtr*
zCqhowv1po@s9)MHw)9!k3wd_513~(Tjt#jW(|toY+yPLzo1C|=-(CGj25cDKg!fC*
zp$rs#9s~lesJgFs=5U%Ltg9}77C*tpC7GSx(V@-0f}AwawHrHjPi#xQ{3lc!Q~QDb
z-ZS5YH~%Bt^e8LqN0alJ>o@QFBh>_UX#V8|>CG(4O61s5LIHs6P_VRr*REYN<p*sl
zjfETpWJ7sa`?vxwyy!IpzAX1UbaJ>cb_|H5h}Rx)mth7RZjgX+SZzEKn#+sbS1)$q
zsxwGGF8L$iCV4ZuCe~253?QXeEOwn;N>2f%MIBa(w24wbWm<_;1Ti&ro-_FJHgBe(
z6wtHD%1QomA~?pcf>x>Bt3k)_s_A(&(RKBK0FXy^DCJJyHRy^YYRw^bBEz!eKz(+<
zmKY=iDEuaYO@u@VyqDq|4|0Z{-u?Uc{rj9=W}^P6RSmwp5evM3hE}+LS>}_&?;XZ&
z`d{^M3{Yln>t9_iT{YpzP|rG#RM_#&OXORnxkX4u3@9R2OJ1w<UO#u4;7&BR%~rZA
zJRdjlE>o};e~(Pe{oOws{bgk$L64>wAU#}hwP>9d*-?ov(*c|k;Y>wWOT1P+3HG?4
z_mL2gzqkWxTczOT{)qA8G<?YD{B;f7=vh`)R!umeZfd(*WnxZF&g0p(F-MEmo@)|E
z<4wsBLYaH4oFWLhHz9Zauo0ME*}e#nZm_E?!l~<k{j%Q5)P9RrH{pgD>w@9<=vl&S
zHA_(EyA>oOCzrP?9kk_U0d99Q38qN6ISsE<Vn`lA_7GIyg$oy2NRg{6^QEECPZ4UD
z=}$s(fW{!A$P^srV0*T6I`+ac6NYqL1`;9Vi(4<&_nlRUWR^&r42zB9xxcaQ*_|!`
zN)um)mLeBBIH*eis2-J9Ga7~)e<pvx@>4<=9FD{KRH>cLy}kuVf7S)fGAiXEixZ`Q
zjOQiKfKmF1>*M1!Gmr#;y4Gseh?5FT<uZSuI{|fys$x3!hDdd8G2V7^k^9o!Yi}Ma
zpS91|4JP8+_vB(D;0tp4_Ce9y_A!D3WgbSbIXTHC8KZ+El}3we>^r?tFGZEZB7bNw
z(CHwx@Zviw_*t)iNHQ`U^{*11ntDbJ_V=41cKE*_^jH%nZtxWZuSfcVr@8&8fiq&!
zItqEj%k1pjs~>k211}SF;FpGmw9^Pc4e&h^^BX=i+`$TdeS4eGra@a|3ax=4#B9q!
zv(|an$`j{hNB*RqOFh9QS(pwJl2WYXYDU&5pY`}Fhe_AQyXv#H!h_*<x6<=kEmbr4
zCP?-^<}I1}pIwWd>h~}U7r7lL6lf>(6zlJ?96o$5%6;m7T`55YCPSs5&P6b5$vA*_
zw=pEQW}8?b96>lVKkPdYVaeVWJA@k}+iI}bZuB19UU3#jh?#ZhAH5anaQ-aymo?PA
z*6+Hh8}1`kd<>_%du*uMZ5NZ@`}Yz?#>OLr<O3B$)YEw!#yd0+EL4!2%pkN=Wri#}
z3!0?fiQz$yny=eFp{v#<a$;Gd&@nAno5v*|^bf@qXySKmdzS6Q31tLzhX|LbYy^`Z
zjr}4|zB2g<@0y}5s@@+FPO&#<Vs%bs>~^2_B?{=^?9G7Yp-qdjhf^~UxhNvVov+8-
z_QCDQYZM;uo|uR*wfY==vrcg0%-sC@i~M8z%zn`*GoV7poZKd^>b)*@FP?Z1Ln33V
zy-<fBPg&)4?#t{z7Qq}ba<dp`By$86`j?QZn2StRG7(M&Ivivc-|T9&-Ntf7n=6Lj
zfr%ktG*NCw?E-mfbT<)IUb^-#QsQ(Q>tipb8OixAU8+#lb->1ctvP=yFWhDR8GNNi
z<$+JJhr@LvdjckfKQXYzd87(xzVjV~TxZrr3^j_(S7D+a1$DiRaXp=e>}|zPvH5=T
z;;jEzK*>e6P1KEl-ZJL673dWO>Sk0KbCCa`L?;1|^BK|-m10*1aD3G!2Pdw^lwM3O
z>xnpCF!SZ|?V#B$0$+$?Uzv2k(H`ZTyk|~2T_Q{CCOP$HyrUbEe9wPiBLOWn5XF8F
zk(s^hGPn44jlJ|-cC`>_`7Xdn2<dXQ?T7YGI8O4_h$lL3E_dU<?Z5O)jXXg=@my`&
zAyInrnXbWNWDla0m<5mV;aUz7#Acg*^s2!K(`+Y-re%}n0#O2;t`pP4caL)QKug=U
zcuxgIMP)z$Vu5^*kkrAaXFvk_cb_9sk!OY?N#?MdfKBVm51QL}=oOd^e2jG>Ey&&5
zH3{VMS~lZh+krR6*&2Sz1*pYc=cuj*dw(n4sjw3{ND9c@_k7QY%(r@_eC9-zkp2$e
z3dh);b*MZW95oa18kGYL0g=Hb%{=HZ%E>a&JZ(7S9*u0~IQR<0Er6U6-!tHfZ_GNc
zt|>s;_PD~n!1e3R4Y%ehH}kcQyQIE`neJTfeT73f(vhD{C~CoXq#%I$%WmENI4a5}
zaGLSRJ8IUTzdC&W3#DF3SC+?1Am|=36D7_MWETUHv~)mq|E83|*g=(?ik6o~)yxqE
z-sfk(&2e@kGcq7G{oJVn)MZ5gQ~hN-iQ?f;LOo?Zecoe)#C$pKV9a6co86$Sg+QZo
zL^*Bo$|41~u(3yEE^(&xjCEU1QuoEC+qqiDyF{3(?f8Q)_|j=qcWs=#QdIYVMPX=X
z+Qc-7iA840?PZ#Q&N62J2+^pEao6WNowTYWExy`64h)P#N<!Eqd``g`%a$E;t8&7m
zYr)n1+^J8*x&}|R{Ae=rWz}?mtJ07w3UItfRG8kg>?-0qW!ZAdVgV>b5yhzd@zSBk
zYb7B8`J~BdC3pH`o?5VpcI8?4Y$%Ek4=ca>o37Y}i9t?PHHi0^6`?1wXy#059s-A!
z0D;mUsjJ_6%0PAoF%xx?0!n&e<Lm2&D(Tu9pve;wM&V%J3C2V1z<qV%g-?x}^ye_^
z0#@^4UnI7vr6BIeZWH>ai`))+b9cpxMKU{pBfQNhj>|P|o=i?jXM5FE$fvK-{_ZJI
zT<pdBRn#!THzD}Ain0^MO^92G+}j+hO4KsIRBBLxXvr^p2=@rLAmuLo#B~3Jgk@r6
zmY)0Moxq|QzR|l0zUzDo8B#mXPs-*54Q^NJD%*`pCP(UXy)J}Mi`H)Wz4bptx6dx`
zxz|3%Io*=qC9W1QC-^W2V%=nzUt6aBZGz<TJ;--gy2N#Q<@zA>2b<K#`#wG;@8$;$
zsQa1vI+aT@g>z!#7IBeIEdLE3fQT(QHr-vJIRj7|6pSRNyz|tyN9bCdX^^YVSw%fl
z4vMm=!cW8Y=HI`*B~#^}ny-Hv6l6SWubpH11ZtLDnWIFmF)M*0%%!Ax4P8(?_6;(8
z!{Fehq@^3*-Ps%nuSh6dj~<nTxDztPi*Nq8<#f7&+d54QSr!?WUY9N>D@*3c>o`Kv
zEOyPsVcC;uDLL55F4Q4$Re@mUAs4vys1BMef<BrDuF44#F;u(!g{WP8Ti(BCkGf&l
zU8!&vmhvT;(*wg+L$>)dNItzg=25?Wj>=AcOX=X?pq7ZZkmD%rOU8+*m1Et45pBnn
zQ?c~{_U7(wF@A$?NSr&Nr6g|Rh0@c{ffqE|B?V{2pH|>G*8R*Tnn0$I5<HpN+6r3>
zecMq5wxr6B?UMpDPrF`K>?Qy36{t>%kQEYd(fKSfV6N;E8zn`>rANH<!xMJUp@X|3
z2{svuhIh>a&5->bp9FECPTlt2jS5**s<eaTJLKtv435wdqXOJm?tAco?|z1=pBf)A
zDCtCdM_t_z{SaB|mQ6qG@hxEP)?ET$eZoP6$OXjz<jO+5+Ay!(mzn7!*lKR`#^WH(
zE<6njvj}8I4hI*^4Gu4D7|!X*Rn(m(4Q8RgElXzS=FV<e{7Ly8Jy6(bExJ6TWC#5f
z#ORQNgP;DCW8K-t)_^Sq#Ox{->buUVSJGipijsf4faB;hu5wzi`i|orW{dL^HfeHF
zQm3K8{jQ}}=s5O}*$&maKRcqzdNM~sQSfj4<^rgacADGd(~L&mwVv}9ouHa1e4n5?
zOpzw?IFROP)8meXwa7MV16Y-$QMQ*Wik$8rLp+~nZNPpI52gfdToc4U8?#Y{;)YPy
zz#*WoZKJ~{2tUo3L+m~Yu$bnX64ccFbQv$tA3YnGJ~pOlHA_bdw?d#&0X_gv2m?I^
zac;-8vaMuK0Fl1zWo(7}rw`Ea8qZC1+aO2h8ff%z1X(?eC>;Xasxeq;2r`lU3KhPM
zbd-NK?nW|-Otopk3aVexsFR4J(*lejM<Ky92;$T+x6be1$MlgXihUTFeDtw<c4z7m
zlxGjp#C{qi6u5bE;Tjqm<<FGn8<8tXqn5frq&-lsn+F0BxK^H-N=O|uc*uo(Y--`*
zD)f5NpqF!(VeR^;5RPX@p@#6MRWQ6r;OowJ*P$??CB=ifJXej3NrMV?pmWk({}@q(
zs+%N;<N*{nPJx=CwHqnn2MytZje1yXoLyB6>FpZI!@l|aLGDZb2l%YDTPpg^<h-K1
zpt|BBs&pEVi?`<4M1gJLMrg!4Xnv}yIt`WAX(;<=%)(&9|8z`1(`pph4aTfx@-7or
z*}NHc9Tt1hFQ_0LNn@o?Hj-K64?cUz0+7u;&R<=jdX&tI%)9a!`drmvB(%N`TO#XR
zVc_8hpfUXn6fX^~z3z~5Pf=CqR|e!Il^)C})FORepT^R`tcm)>JJDBoGX5(fPcD7G
za3pe;(yT?<u!Z)k(;<+7(JIF1K=G{Zz?vVPx5;bw_!M0FM+R5<Lk6KUqlU?e8@nKV
zp=y_+sCifqJk4BEnb7q^BZ2<@26Ip~p4cY$lBY2R_x<4&p|?nzMT^Eczr0`k0ZjMJ
zX$U8e*PB!mYPcrQ{d(#Yk&o}L6jj3;Q90}ia=<sNF4TG5eVBzb))=xMG8N~N<Lbfn
zib0N($3x$2oeLFmO79jdYUe+BGUNT&zDKC!btRWgGlhwDZPX>{cQ)hhAW0NhL!m!o
zseXU_0iv<i$2Vt>%zA(YakA*dGn84^L0x502=_}(WjB-duYta#tj&gJcM;(>Ci4l2
z<PzBdCtV4vmifp7jA#EYC1whnrb+Z_7#2PHn*j=<2x}qRATxztn>%&tOqh(H$lNnv
z29@~bN3>k$ZHgu@yqP76Tqw9miTtUp07dl^-skFnBP(C*dn#v-t{cg#QT0A7Acz4h
zg6z$Mh%|Pw(-VlE-1`N6Khq)iI}Jgddsf)bTmt$+Ry{K9Z1y)oeB;9d+)J`|?c={Q
zx}9!$A<>l)<Cls1k;iSQS9&aCDakM^2eO9AUak51i-(df2Xh{#eBZMFTP?T5d2rt-
z=Jelar!&mC@^&pLAwA<P!p&8gwR@$3on}z*Nknq7#t=Eyb5N}3$^ER|2tGG<dqi-8
z<^z{gID*r*?X@2qgE@>x)G#o}$e`+F&Vvd>G6#Z%>m<0Ms+yWD`~mk9bU<Z}I{g)L
z?bG$+P#WYNbDWNh<_x$tZsA?F(_VOrD8QEe{m<zuZ)iC5N`(6)Y@hrP6+`pGtN<DM
zvZ#+k=gsP4?(iO?5_KEy1x=F3<on&BC^jbL`2}73<QCm4i;fSQ(&c@&Yu|%nlJ}s_
zaRfg*qSH*I9v}55paaeHL*_xVSK{IBnjfnKM=l+9ruHZAR5bmG8|G7Pofih__cM@2
z&k&A|d}X>_9+19&05G{0G?)z5P1?X9CV%v0wL7EuTmp}Xv&bv!PV-3TZyQmCVJ93O
zmP;A}qZ-uY50GQ;KN)$e)#i*kab52M!pp_ai}RzchP!s}PGkHh@}fa8IAU(Y@+}Q`
zNCHxfIu?R(yAA&m&dc*2Vl|5~Zu#xHejs+akzqEZsGU?o<(#+>kd9S>{jfp#BL<6C
zHr=|GVLqR4^YQWJDHk`EEKOZ5on-)RE*;AA(VjxCRZdd;fO=F2JZu<FVd&AzK|ee?
zN)rjznD`zduMlLkPa`9(p|`h2RnTDH3H7gegM?T2W#)GN{x#acP0Xj%vi0TTEX$V6
zx59|P@xXJ(ARQ;%4Y6=Y7DM2CxrQcrEXXyNP9yP^t6X*tJ_KA;`7+Bo_L_l1VW%-j
z*#Y3l)4+tN`;~Q|bY%=F9$AE8{Mx!6Hkrab1eMSLxgbuV2l223LyhF!9Jx@hlk*0f
z$TQm-IgbidycNbJP#XsgvNnjd?KWRWGZyWj?J5Eu7n-upz+0Vxx8noyg=oK%Xo*<r
zJ+yiXR6Tps^}spm61rAiebdXla_;^-eEh^>@qk>Uu-VFNhF1vq>1Uux!%$Zb0jY5i
zO=aK)q_HA0@D4=bVNVWUu2_HTZN6}|#0d}3*Ch)B2kojk@}A-KVGtk?W-&cqy4pFh
zdrSR8)-M%+lM@=<r9JK!VT{oRq*KQ(@$FZpB{-+FjY_ERjd4E^prPp>dw+j7`Kkr#
z*Rvs}+s^)g389D^jns*iiAfxkqL0HRSo3j{E%o?RrGq+M>R|<4C@^R*u6v&Wi96IE
z;>jr1qMzpg<r-eJV;#{Mc~)8&M~8r=z<3D=JmrG)e{&MY$^V5@m&-+XD6U6DH*D5f
zqTTYn1k%-1l32Ci&EwaO5zL%wx^@zLD+LsXp94Q$cyDL<R}t}3fBu%N$xnX1{ondD
zMgCheX8r%+#~XF=hs4Ld|46&$|IRN~@o}Ir@rSKd4cnLV+{Gb^i;E+nkJTFqn<1SX
zk4loFq(31YdHQsBuW1YmnnBLACv}^HF*Br>nSp@fI4dj3pXi-K=HGiSh6&$-1@(Uc
z__uf8zH<JUq0ClG@RPyJa2LoPKj0?<N}><oYXY7<J3ibvAHj93e5d3^TiciLAk3^M
zPo5m;rqnAYDISfBkC*a?5D7Bs9eD3v>BSStU~|BAUwv2)X_p~`Y~zdgcrXk~Uf=rq
z`YOj9D~JoWk4eeO1~K;!4p#SEdyk)dUpXPDv*HLTcqsnZfB_~Vit-2(?s;15kKMa=
zrHZ{_*mkj|_x`?#UY>I0mfiq&zuY5-5|}Ccoz_lwM99bkBX%~Y&x6YUw8<^}OVS^T
zP_*OC*k-P~dhN^CPTv%!Op=uqWfFY=4_SAw?nqdz<Ard!;l`{s%U7E`MB~0iN$#E9
zpkLVOc|p4{p|_i}oE9C;HyXBy^ZYU=n*T>SpMPKbKPIF6-|fY09|Um>4=&_?3F?k1
z*qqmy{w`n-$)8<JkL|?6k(HT*2QOyW>N|gRoZq9vdhbY&^)?Fo;g>l6j&=A&Gu2eg
zxW}{a=x|ZILf0<|?Y(_>O@BY<|G91Z_mBTyfA0UUt>tefm#|2`v;cTYqDTGzyAS*S
zy9fL!yk~~S3$B^V_pLEiPgV?$8X*6^K2JXr7gyH#Lnc6nx!O<svK8Z2J+u)AK8`sC
zyKNbtvF{J0>c$d%fUjt^iG~rft>o&7DS8Wt#P5j8PSnLz1+rt(K$&Sw1vUFP1ia{H
z<9c|^wP>~Uad`NsohSP)yvS!|^$vG`b|sY&wXBTHD9Nt*t7!ilb#&Vjdq0wU(gtx?
z>NcukXmS$uy7d0m{0bbi`$~iIlk4G=6FILP*{@K`lQ|xE?o;ivXx5Sx{S-5Anp!#$
z^MU%BZE9gg^p(%j>Yavfg>m8Gtj+0sXwA~!U1U;~>lRNBZIqGZtvq@JppOU%Ja++a
zQ2UkfS5{X~MEon~i8LjlGYWhKGYR}K_>OzmCF+$xzo+C$;Zk@+Es?KwwzdX#Cyh+h
zUSGFD4?gRMuLVh{d?rIFNJ1G7yd~#6ZPjJGOTgpDe;Z2QDY*z~TRbjCQiKFZ+O;*<
zduGO-h--qg8n`ws5i$h?T1Qxe!I*b+bkJVh!+?|w>`edy=BG^_egK>eQqHP<y{VeH
z$xUIrKM8DhB4UC^K@?>sp&AF}CUL*l1y{ztE!3b!VXs(kyZ84AzE=J}7ViH~9jHO`
zeKpHzuty=#X|+O(<!OlJj<wIvoe|%uyz89NZi(m<FLN9YzhpJosUdOmd=^`ES*+wu
zHV2nP({m|jcS>liAG!N?=-+GN*00$p&%E#5zQb4Rt{QuKeXo9YDTriytXH}#gHqMs
zQzJ0HEW%<^{?TMSfBVm;y%BZf-?14qVv=wEog?DubK_rFx3p)?zx@e6Sl9mR<-RNZ
z@$V>-uL*zsi~HWl?fiEt$$!4_e=Y@)g5%>qt>S;$0lDR&b3+3ZEvIf#BDf+#;f;|7
zs4P9QMn4)FLfT``5WfRAIs*A}BYI;o$br4nlR7rinw@ZB--BSkUz<cj!^U!DZ5QZE
zQxI!vsZN3sjAN@vm`6UcM4iZ5My=Hz955JA5^`cCbTydG{yTukVZHPli!61p%Y2=-
z)f_};0wG!<1oez#VId(&!wm`5zbYzpg7%4sgZ2Y82Z$zX^kg&<VrRU-ZQ=OuJ9%Tb
zX7_i3<^X%-v`R&n5#vN57;gdkZA54KcH!?g+?G|;bmAa)u|SheTfSYwr$~{MXU}Mr
zC`2}kj~mI!vwRwM`PyJai<!;PKd2_m$jF$E$B^=e&T*P9ZR@XB(o`u?)^$DDe-fRK
zdj5?N|0(9>_XlY)lR)1mXBGT@RrHZu(XWWa!OTRv^}YvjjaJ~fPLLMsyjGM9i8?p&
zaaMAz@wTpHa5erf4vBYs`RC3Zl0U>k=qs<<a%=OZO*v4%o^BsT{{k9M)P2oUGMvZW
zzI{7xxcTb2bLZB0gr7Sn<|(_ovf=>!Qg5{ix{vRBHSbX~P26N&q&Bf><3@$Y+tb6o
zHK%<r*f?a-d}_<Q*SZD%2dlE;;^O_b`R?kz^LxC;vRW;TN?5)-eei2A&;4^WS8HY|
zVD0Mi`ANgAeZ41E!nbVQ8rU#AqtDwl-OwEKE#vLY7e6MrKX=2?%QN6PAtLeT^rkmr
zT`5PkKWL#M91W~!cdfECLF0~(uWy<`+`#5-+vZ4@R2y#Hy0vHL&K}BXa|tVfPoF=_
zN6k?F+PwMo4Zag6q~D5`SesW{Jkh7k9T1>nWE`jLGXEz-_knq6lX+m1`K-eMU6U+z
z^AC18XNvbV1)Vy@%>1IG)Tk&fUbLKjgr&t?<=WPweFydmGbSFQqoG;S`noEqb2CQ%
zxhZ4U7c{fZ+eUi|{+tFCS>?26WX?CmUFU9+u~U&u9-j^F3FYw|%|qYG5Z3$Kqjl<Z
z-pAV7Kzo5LoD(U}2rgG_#W}=}&%E&x$`2~3jr1Q%+tBt_z4g_y4%9o$Ix7JpXn&|^
za+Y&;eoei-K~GPwYtOD--x$vYg@tK-yB!?*ZPOddaZtN=u`$unf6pJUgODA-smXaO
zZ`@FBWxp*elz7BDsH;Dtu08=_zD7t=e6l$PT{_757hfNl_-orXM?ZPa<HzOCos0JH
z>UsGsRs-!ER1v+ueeT_RtWUdURdWv=-3a5GCtwybV>ztOgkg{fb9eE+ej$sM;%9ld
zpvn)=f>-XB*VBB}GxvCNkb!}NB<M7*9WqUc6aaAs>29@c`%BF<oI%qNKa;Che+a)n
zF6tLq*7My(q|1Qz!u=b3@_rav;VrH3<{)8$^rPEUV7(m;DUFFsOE(I+jWCy??>6Na
zKYz>e2F1Ti_mPf@2c8xE^!V|P`Bm}h>FIT6l(W=N9MyfWmiNb&jT^6TD7v|HSbEYd
zeC_h`d90utV;5c?^BGss?0c~NAB*$y@{g(N9=xVZJJ#tIcIx&_-v;#fZnwu&Lo!ZM
zQZm1r9{Z5oXoaSYqa+;^UjF+d$-70I81=(EjN(-Bw@F9@t-6Xd{0Y*%$<*rCsicvQ
zgbhf2!Am&GP7%X7(zNrxEIU+B57rTq3qp(ok+%cny$i1~Chq6Q4}~{1Z;9r3P-o4{
z6EXR#M2{&%UX{fw+r56=E<m}_pxbqJWH`hmvK~$Hxljn>eP6!s&C1H^?-KskzqlX1
zvR0R5)Ftu!$KuQJkxmFmix&pM&=r-2nqd+;1I_^eD}rT&)+$QTrsTH^qbb>QYK@Jl
zX(IZy)zi+z#K!VEj;i}I2$5eT|GvjJZqNQJ&-R}<?SCy~O}e57h<qbMeQJ)iGO(vf
zbd%R#!TPiq=Jj%5z&ZD1LjQi-mh@7C;t!tM=kBb|T6d?*@6|lv|Ke*nJw|(E%=RnZ
z+f*j#mgceKZYg4?=<?`%My?I*-FGL$1DO%(aG+h%PLj-Z@*>mI4)!;RV$bENJAkjv
zIV`&NzuZGVX@2NVh!|&kQQK2x+P0FJ8+l)Eq_%1mx8-pw*{-Y_rKq|2qZ@f`m!?7g
zeI}{8dFvs~r+0Nae_{K0L!W;Q?SBoJm)`D-09a$a>$maBZ)#ZAg^DIY^b_situp_1
zUi)x~e67V0F-&-9O4Z;drl~;0@EBb+g$IBCapElAE<q;jSXk{b5_*opz<ej6C^w8=
z_<9iHHRHcW1Y)&t87M|rxw(~4M=XI$JV6rlS}7vNYY@j5W7ir{FRUHk_xrug85-8<
zl8D(MV>VtXD6O(0M~C!A5&nyr==M@{cQ5+Z+l$6%LF=l27g41z@J8Ni)Le+s3B&+F
zbRiKlcrN>61Vti-2oTL(X!uh_8k%?cH(t1L<J;_wyi3=vWugHj0eY(;=vQ0^LQK$v
zr2`8v!+Sx%d>_Js3{<Jkqs2MC>No77DOE9CV{;ud><I6J0&UFe*ZlK~P~BfbOPLxW
zR75x1f4jl{#K{}Y{kCRxks`z_9O#64ckyXc(2|V^9srbffZLdu3*m~XAr=L{hhoxv
z-^{!3yKa%Q0`y^uC>TPH!U8X+=)Z@7A#Igfy4nCtnFB!<s_es1Ck7F`HJp?xF*Ggd
z^uH;9jWh382f+wp^a3G(Bidb1s%!%8J4!-BRXt=a@mL%}tnK6D<Ijvn0y8N437Jn}
zgxK=1T-S-+zqe;eF_VuDNURz0Yz94FY#7s5EXu>9EGInq57gTksD=i&m!sO-Sq-_(
z+=Y<efB3Z-HZxH`;k4>_o%Ox|NQ_usI{s_Xsj4X#$M$qX^%JVe^z+p>KlR%#6#T=+
zwWX~2D9sH$Smg4c*y^xu<SG4I@zNnN&_}$IvLaMy#(_VFT%AqpvCR(%4D6?zPJV%M
zXbI;UnkAp%H8z0Dycohjh!%J5+}UJKX;RbUO+rg3KFp)s6U37=LP|Kxm^elxs_2vr
z=WlJeaYNtrjoZwSLW%6p^?OtI#w+{uEVZu=;O1gbCs}T*c$vG-pEN%h8pP<BI{qD+
z8LGedmo5nsQ-_n@cLG<3wTxc<{uRwq<;>0^7P&}e*zmiuGBoWaZp!n5a=4>C3eD=H
zwm)wNjOLXP#_!fB`4tO$F8#nuhMs$OR729uTS))=C4Z^MZzw{$nB&QA2%q-CujvTQ
zPoeduhVvJ1OhdCaXm}0eKyIDYPzj}6qe6ID5WGs1*>wIrmVcbUv4m88sl{3TL+!!4
z3L_=I-~YxGZlV1B``5w16YL#b)_H}&u`x0K{kz+sPq3pJ^xR%+FO2b;pTe;;cSJGm
z=f{pr>@?W0j%r92lde%A-6;qrc0RlMzaBgzEHP$?9Eaif&!A8@OmxYk6FjM%Q{$xs
zIw*pfHPTdx@h|`V;@tK2rXVeHTy=x35=uv67zaclQbbQUQ7_}vDKJGTe|)UVeOgBH
z!iCV^&zu++E}KBi-#`aX(4sTczo@Jwz%gKihJ?PpK45hddhcUVt{p-_P;C?qe)cFY
z4x#!U)G(ZCYo#)LFb?u60{OvkmBt;Hsq<(wR?^heygy4!1j5&lwZ~C8Q)Hr#t#^y9
zN2|dw+7iA%M#M~lytW<^F};B~V*Czfw)Xh`0y}3ph|WI`D1tF-^21)g2r(~R2o=bv
z)L%}OlaWyY?+6_LI|-f3kJBOepYOe^KSaDQRy`RKyb%;r)9rOs#tz0oY8B9FopDVF
zSZ>5Ia$GOu%+1iNi+^<;A=HKr&@S(?n_5vuF$jX$RI!ywzb>|4e!mgkO<XBKRt1mf
z0F6|zb4|Z(lG8+&AX<xvxgvxL+r$KkBr!Syv4POGqB%b;I+{K9DONoZqU}YrBI^w+
z6g7<<r17j`XL5d!gAuO~Dk-3w3lwA$dMOcob%6DVW@5MW--H&o6~@CsWZwkM!I834
zQo=#-r&!)<<7_jCngGJDiS47OPer^+#x0G&!zNiBy!#xMHG(M?pNSEH==*ZjR&$_U
zX!3wk4d?&+ri?#}B+rYBJJj+v4~1H!C*2<>?6WlMN6WcoiubhcSeZfA>tG;#HL6cW
z^I1P#x7%t|8XCD8Y_I<eMp7*LR6s;^gfxQFbpJ-~Y4oRYlD;5GW!w8*HNhsKVVLoR
zn!LRH$eE1dxlpvLrb5`$tYf$n6AK76BcV|Pat;JrN{o?%Hs>=jV*-8F0c`_FhG+6h
zmt_dSiflOlb2v^5G|SV8c@{wip*A8)sLxUBe*xjpSv02>41`+nV3rm_4hcZ`xI+})
zX9yw?#MJOlQDPZHLJoI_p=pX}Vu3E;1ksgTyo@kN3`CmWvyl*aa*en^^0HWK<Jr67
zgfSJlR_$lzxdI4D=H59i4Hbd?XN?*!{p2E}?e}k*3JePFrJR;-;6Kqp_+oIlIp{7~
z9#Jb*bp8>TahYgfW*+-F&NEv|>9E9w3(1%dgC`(56ZSGNjMaNKjTOc$=wh8wGS%j6
z-b=w*NWw_RU6lEW?hNcR1-%PuuCA{4-_(PsfG7#`jr8w(V3t-oR7%jJND6u^=Ah50
z&l6TToK?Zt0Nz60APQ|>LkKm5VhMFj%vPviH$NG=*$Iu<5Hcv<uOOx^;fR<+^bmT+
zN;7G&tmj(5kH4oA2BTvZY}p$!Sd#5HZ&Y15{fltjA@4Zaa+pN)%}9AH#fMq@Go#N;
z6*KOXam)i351_l7c^IRizB?bvUQAC#0;`TPzR!aP4FEBy{QeEWB@#;pVP|>B#tRMD
z9$>tuEIe%hMiL-o20&Mgm!RH^-i#rTJ4HvLbur9I*STl?hYueJYexhDJ)jH`9_A0H
z;*}UktAza6h%AVfRH7dYO2GhlGj!V#-4B=jF#?Qh8RqIww4tLQP|ff-QqVyPUo20z
zcdxus9IR~c6fr8#rx8B)n`B;F1!WO4X>xKhPLy%)-hReFw3B2cL$OB)u3(A@P<Ln%
zLdwJ_D37H4389Oh1$W9sRa>!J-ivA_esmyQP)$*}tACuCSpxpA9ux&S^gZV1FsLRJ
zeFV&`sNoQE9Isw|v8C*BlR4pA2voHEZGY*tiKozUCn+Vc2?}Zw<B<?jkYlrzo!WEY
zKoT}`1c6!wR9Y9<veOp6etyk}V}!i=z+Fs4Jw<f1xni~uGYL+MBg;dfQoA$ki^{9+
zsb>zHG;+=yUmf`A2_F?N8<7m3KZ4;$#P!*ie#M}~M?dQvmX?+w?dU#HKow}DA;FJ%
z7!VLs2kyf`KgZ!aE-I>v(BhE4yo4z-0m}I|no~>IQ%pa#LiXg6vy5?aM@YnBialsk
zR3GbswK|6VMF-D$1eG{zJ2?AG1;VrEG=NRj%+-Y;j3s@+M8LqXFp7dqpuTabam3~~
zyLC4@L$uq7MyA=Zb~QqK2t$01#T$a~d+*Axz^YsUFK$ChzNXtjOp(H9Q3Y5DS`=ph
z><Vq3Ie!0oj%f8FBsX@eaF$Yvais+|7rFz1`u<L7N`~$&*=u#h6%)@0xiXx2P=SZS
z8|^*kU1LlGzT*v9QHL6lQ~2x-{q^Z~*3j~A2_OtR_#A|A04p3^s%kuI1^3@zkuLqH
ziECrDbq3mV=L;~SClQJnP-I+%LO<{J#x8V9GLuwOnqtS8n3#SKuu?(!N);kX+*~|#
z_vR3<3j&@cF$q0rgJLk%Hce%$@Fk(YA!b;DG9q*^qKiXJBt_)n1oR_J=Gd|F;$=90
z=A`#q8`%{1ctYoJ`gZS;4>ot_l%SO2n9hRA&J<0)x*>Vwe{Xravz|-;UZ>aX(1c<d
z3H0abr+=q3Hx%#Oy~}p;q?}`6u@v><#fuj3AO!S>4<iKSX#dvD(ovxHufN?jQh1{O
z&K<Z?OyjF2#w-(|n;2#5fAlhs*NVFc_)SbaQGl?$)8UfXpT&{KL(7~1#fZT{71U*A
z$opgd3w!SYR8`h(jbfX+RkRHl0JYU)00ayKkz}i=2Sg-^WEGJlS#ng`Owfaf1VJTd
zm7GBo1Oz1KppucCIfOUXq5He{`)|GaU)5jl)vZ^TvZ}Q?Z1!Gztr^A~bIdnyBpsp;
z9CLf{Al!`3UmmC2^~=y-J@S0u#|aN~z;}3<ELx<h@HawQm6erIO=pJv?sv4eKT9it
zq2KE@SM3;27g&>*sdoAD<=!lG<ix$51OC^Hu#hyR5dksM?oN~qp@eAXhfOC%L$^em
zX){_2pHOgQ3x7CB%GZeGLJ)DF=TOE|L7)1+?_`NF<izY!yNzgzTaP~kQ7A2#7{XLH
zP`%Q;A9qU5MG#d_8VQpWQUi;|47c7xVz$>y%-8^X1Jq-co_XYWdn2xgnq!TmXe8vq
zF0rY)0KA1lC0%Pc>743kpCWkhIv$gY!sT8nQDP@xl|y-WWABB=i}jAhS~aj;2+JFh
zkWRk;bnS4u&Eh6AvlyOui8yMJ3xKvTx%wbwD*!v<v)|T1Rv;aHJxbU3euAj@3L7H=
zN@TQP(C{L0fI^bws~oj|Y3ZGSey1+A0v$|fdgSaG5s3p`oyZFp`9J>--PUS!yN`jo
zT-ywfuYhn2_Hlnt!9}GscHj9P87JDU>3b%Ka!ft;p@I51Bqfkk%U@e{KRo;Z(Xy@R
zkgX#6EQiHS5eMO@1mg{Iad1R@F(+?{Xva5OqW_)5ui*AlX&Uy!pEfjZaeJkac1zDI
ztRq}9yW?=58dJa_(Kfi2w|1$=JzW>$HU$ZEs8gs=8RQ)wo}INP5-+4J@Y=O&*ArE=
zx87Tmck$M(1aN;yl8N8lT_V_ekiplMq#3363wRhr8MSWQcm5)H<B7(tVxRK!iw><(
zKDe25Od|67is)6ce-e!@X<$fK!$MgFZhkBQl#SFnK)N2Q)AI1D)4?ON9=+iTt`$Gz
zcqC4r{(NZ7@|7#2;J|%R*I?PB0luOsj*T=RSitWucJJBKT(D*H=1?TgP%O>-qQ^_o
zgP%qRmlwJFt@lW&0)l%~8no*4;MyWSPGr-Y2PstK6(rh5;*MZkz_>+im^YYp?$SqB
z^Ci|F#+)agp50-}>x2f_8-k04){C(?!M$*nO5hF9&kTJcJ{3}kHAo~wkpz&3)=w<D
z5zoQHa{+thEeas9;AkDmra3{rgEU6q%p5qZV<ZvTXayu0>_k4WNr^zj`^yK@fG9HN
z6Tp3=5lb;*4rQOq=w+!O;T{@gz40Mr)^e~&i8t$yYzO205!(YCJ0jK8KvX>KhyFwv
z{XA9{y~v3mLkA(qB~0(V6NlHFTD|!o(f6C5DiDu^1Prc5nk<i7{tPFchWIXg+NVqR
z%ZtM^NLa|Kalo$vVka>tv{+uF8B_C}gPNK@+{G<4=tKf*00{u?w!q8+W+fw=j3vGt
zgJoZ%q#b9u4!qMpNn-#E)iWARE@DgCiRb8ekBM9LB3D0=^m@{GoVLG88?BsGV{sVa
zP!F)4|NiN*SnOvz{KzJD_JchIzs)a|MXW-+dg1-D5c!Bzwl~a5Ci^5x3p%B^<Kl)L
zU0ht6N3VnVM*qpS^YxZ3g>ohz_dD2~x~gl}an?QKS|+!hConaKi%+eNahDgl1$203
z?b*G1)8@_ldPa6yVEx1*@*0VQvRYbJSU6lCPCH!K=oJvy<(ZXvwXQmbPh_G<JZ-^V
zH!e%WDSsu4%~|>k^SHR+tubor)2B~?GSPxbaAvpP#%VL^BWoQn-vHsd<`4dM;q9ye
zQ>Jc{ebVxA>9V5NBm;F4&w-e{Y18$YV;pfNkhy|9@jIz4{2bFqHNAg4)lMFl&-;34
za=|6i0Qe740&218+RnWFc-1q3$3&`QARCc0YI9W(QaEp%EkLC7Acz-<`d3=~Y9^MW
za|`DY>65&cXkDoT_#wN2h;0*&DT(_Ke;3XD9m2#CZ6DIG0|bgolor1mTo9shipaBB
zu<{os&EEvlbL;BrHtgwxFXlt^=rK9@!TB1)bY3jJd~h?d7DT`l%>_~L4}LY4AT~lN
zXq@nKOTvwqZa-Gq#rXK|Pl)CpHt&16rDdGo%~JUw8-|FLW7n?S1j{9_BG2#4^#gy{
z(sR*ac-?q+nMi!gH}b7HMUwLf!(UEVyx1{hiJau;zQrf_z0IBdH-EN2mhwO3Kz)hv
z^>6j+BMy)5RX}g97Xu8iRcXNhI%-ZQiwl#C{lSAFJ?48?Q@l}*!KSmnEM4;U@<CMb
zHb4H9$^N-OdA2lxWGBTPk;AP1%3Hh?e`MM!!MkV==UR-K+i-J!S@k?BXTQ4LT^N(g
z99<Q%TX*Xuty?uhLnSFt{pcsn$GfrHurEB1XT(gI^8u{@y?MXIvIg}+l>DSJEn_Z4
zt&1Gq5mntA-=#Y|DS=HTW|Mt!<0ElF06nNLUBXThT4j(SW4wCZx^>-?l}ye#9f=Gm
z5UsM9ArXec?rp9eTqB6#xVX8cX=7iGmK`{ja^{Fmpvm!<VosjhHf?Hm{Lu$hG?QcY
zDjtF?+0TC=RmWW@R4R+y#D_4yfM3JqC{r1F-=Eo=sNK6ddr;%C{haU3r1z!TueOJ^
zr`M(j<{8LJPo-vP{+eKroFsDA!KP%{^5v<4VUR^=D>XEg*cBI7YV}9f;H~2?S#~JB
zau{01^y?Z8s{H84NEI034p94NG4g{7HFWFBXu(K$h~{a_fgYV*kgXMcRIdai558wV
z6|btOXjQ`S18)TDs7_J6TkWA+OKNQoA3ZX=Uw_-)A8*F=J~KMfvTS&?byhZI2*5AU
zP{nI&yE6IUM0)c;yn$fVw<jUrOJ?sugwGN0=hDSjCs=oO-#YHqA7rp^_wEm(ay+R0
zEnmKbZ&}6*QU(jJfDc5JG!^WAVs#gI!BP&HTnE=gKYapBQ_N1?!`1cH!opt2N&j)`
zT+hh4$~TivU?ibOJ#LtF<mGKEBRnl<h9w)H?!{Bppe6~$U1x!coYnnwFu@u9f1UeZ
z0y8z`g^YGHTgTtK_QZ)Jz$lSIC0xy0L6w6~9z${$jtcwUtTP%-iM1Mi8vFjm^(pfa
zlgL9rVKq5I^4M?e5@a=aoq)UC@Mie+6EQ5R`{fq8TUu+ittmyDzxQom%Hahx2i4ZF
zfsUyu>q)a=Kiabcuj<_1BF54A8-1%%$^G+B{ognAlH#kz^_fk~M6niu+&-m%kxwOy
z#}1?rO&_s4f}EU^sCfZ1MkV*x=J%oc2OKAR>gq24jnloFFk>k)(HOB{*<rf8+S9dS
zuyrZZ0Y8AM&|)F*JJmZ(jeu>bjazmOHv(e#*AKoAD>S<i5EnIc?&=SnkhYmDo~2B)
z?uc(G6iSLWB+gX6O*qjTTbqipwu1i-Zo<UM>2`Pg)&#&b+SS#yc)QmTiBr&6A~`Zu
z4wkhVstEs%C9<tw{~8QADvd`i<rSI;^w1oPO*mi(ZwI><yg~j5A*59ZYA;4jOwJ8o
z#)Pc*_Mj8Ef~Y0A?G)GODhsR)mkp8>nXgQ>^oDdgT%=9VroZ1XfLD(;?juG`$3Ted
zg(@zuxgR12R1MBUCu{i9w!c63``e5ocaqNmh)G0iAdy8RW}EQu01A;B2{y?kL=b}M
zf8({z&YjiH$q!(H0P<sW%wyl~e_&eu<#E!a>45Py#Pgr{U!gMPhj1%G?CzX{3y@NI
zarvKu=Q18ZyU8uKQb=Wr=o|mV2-5*5OS+|Clb%nfKqCW*)UEkho5?i9U-dTy;{T&6
za>F(!-EPvzBT3k9A{r8FvXEY|Ll?};=e(M+ZC5IbbIYC$2O5WDsKrfO^>79EkA3a7
z`g)E2Vip!|{?vvj2L$9)%P{%Y>$7duVz3Gy%#Q4(qT7*ZLFhZxRy|Y*_F6|@!qjPg
z{N*DL52f?xUBw^{vcbb`hp5T|qMR{AG5TPa42;94q3$e;@E8hlT+5d$_D@LQEZZ*3
zO_Bw6z(l4rlW%trF`{?zEc4bwC$K~0k=G}>$0c<GuSKUv3`$QL=&s;2Pe+%D0rF!K
zq?1l5jaCRxlXr;74gAS*&T1=V!<nM8m>shsQwL?;9zG;<GKiiws>G5=2`UgsA~cPY
zJSlCf^Uif};YT&kk;mM2bCJD=FUEfRk=YCuU)puvuYp<h1UDnWh5~{P2NVqcU?LYG
zZLmPCO+RVjF-ZJ%{N>&nfSHgurQ*Fs5y%QXROuCS0sk8$ct$c=vvJd0Ea$c4Myck~
z$~mse(74&A-rTZN)nfOKJiNNq7OS(DO2$vn2eSu@%@`Z$gtN&pc67r=5*1r?Po>Jy
zp=vGxcT_p(v_R+jrR@<KlS$nV+Ez+fEHX?7Sw2cjP$dL3eF42P)xag9M2>G|dNexC
zRmiY!ju4}=S0b(UOiaISWz4*Y*WpJpCh4SFcW4b^96{iPG<$O;lJpV{uDp=-!3BG3
zRE-sp@*!72OpoLs9Dr*d)WZ)vefVmIv;?DN!8su9l>qx5&a$p0#=|yN;s6D9URtci
z{qhMB&|YdbdWoVE7NCu|mx^}4OCb}&m<q+vH$bouUX_K3LlD+#Vk^&v5IKTB6u9zO
zJg|E+k#@5}%ZUIF4<7sq!qx>qdnrhl6mIwtbqk<E7O2btUq6rj#VW`rMFIfwN-aZk
zf6&<-HmC7_!H|7nl=5u_(aN0pcK`h#%U5hJ94+nZ*9lI`Dr^}IRPxQ|&*#v#r>@jg
zs|UsyZiCVP0bP?S2gkqjnQL_HQ=s1*+}tr=SVel0-$$D9L`Fty9dtB;Mr8=vNw@?A
zE}^1Day_xBHkVb6v0p4MW@IBn*F|XrLvTWHEz&br^zKq;2FXW|4Ul^-?nwJS3|=p(
zi0<08YX+g7r07~>H8%cFSXm^fcBu)A8rn=Jw1Y!J^Z*jiRL41n3=Iu&CawoM2Mst_
z19kMtOj>8dx}BrpRciM1Y!80abs4Z5Hnr0A0{|Y7_f;Wr9ES*4FIw867F~|#8izOz
z7Z1<Cqbpy4_CgIZ?b|)J1T<rB7qh<%ky}!6Ah<UWHCdffe~xz^#T^wu8><L+1284|
zKH^zIh$CZO^ifZo_{lNnxJ1JI8+yalaL6i%91@BR(2dbYUP}UMq5`dED&}bWQ$k!^
zbS2Bn^rEr8hrRxfBO`SKz9IMOhGDKD0s@!-2bnseVGej1kld)GVLWH;pR!I6RlNus
zJyx4NuZW}J-E8EY_bt5U@THCnEXgG@1q2AkcoA2enyEcHyuwfx30Bguk>FNINcn#3
zOO}AXZ`sn8@1p|{e2o!{UK}&H><;YDIU4lA-1QTW>VEx$T7iV^+pw5m^DR$G54y!6
zk#sFJh*~lRl|Qb-hh<GoO^-t-m@wNA3V|d8Sq4Zz=qa679ENtQ9#m$rt04H|gUM|8
z2pR0~?AazF5UTWLV+K~5$n+4<8j%BlNG>3juC8miu}6?n5o+T5_wS3MEwGQ$a4-;?
zaFBMW_V(^Y4d977Eo3C~d#Pl=4ET@8Ll01im&4!bU8rdohLk=Ho3kx)HNTpKR_k*_
z+67W0<Zk`^qCgOkq!DRSq&*t%WE5jvb`!BW_{I>tGW;f#h|ozGr&4gyYgB*hJu(6b
z*e|J016MN@pYYC}l|U0|FD9Jqu<d>=N9UtAgzKR$dLb<SLm>EH#73csH;uHWw^%{{
z_3x;PN#6&A0xAtCRaEr_Shl@XO~>hw0{UqQ39@RmV5D1$D6q(aaKH!uN~TM|Rh&-L
z=aec}D%7;==O;SJ|BYbpl*|7Cdw;P6u&Ylvf)5{p^S6qbxq9h3kymK8l%b)0?eaBx
zq}s|)1Ym}lZzY5)McdfE3Wcr=F!ahqpNx^Y1mKwAG{-?%L=DLdq$w<X_<d~*r|ux`
z0|a|;vs9`$p8xRS!^ED^`&JRS4h6;f#ZcH;WgmJqXq(vBRAVe1G4V+<QZ0vzN`OP8
zmH>43Pzl|HOs0YAb2!i;`2SjnB&o+VI7W7ds&<t4sW$yTg4Jn+nsq}Mkro$!^mcyz
zXB0i)b|{~Whlds&ir0)Uy<3lMCfhsnMkoXkxym&M?6vP0+{Yn*r+Ig^XdJOmh=a(9
z$K;3{QD19Z?2)VM!FWwgb<Z2a)0>#V3!Q0Qn9~I664Ma)k>T8!W{GMXMdIvPrKpz-
znB6NYpc&K*GO1!w8jhynIIT@qns)0yK>4Uk4xjc?D4+Ec^)WVvY+qEqMI8{yXqPTk
z+jMBlUG0Datb1qCw*EY&UU@k1&8uk$W@#tM^SOPJx%<~&;n=;j;L0!=J2mgHFF$Le
zalQa_KTFHf9kV3U6~+})*FG}98}XXpdc3V`95cl#iC7I0F$R+tB`{xLeo1EZ@oIj(
z=U3v{)F7~Y*ONo_;AX@)ny^zQPKJxmn6pMg@K&5r%f$pIq;6h~4_sXpVDt%&FC8GF
zjFDIb#Djy0#2~<KSgFu8)&`KkB1Jn8O9+5Y#;<_G89ELKkq=_(pFk@}7Du&`LSW7v
zld_vkSfq2614iDz#`%6eeHjcF2*ilReNi=F)J5Wz4?dTx_2w^1g8emx>z1T@eR3UT
zMJX87(_Xk18c*3qwe{p>5spanprg(|0K|^f%*=}LW@y<-Y~X0f&KL@N4;CMZ8A-{X
zjP#O{k_S{G+LE2|_AOMNh`}T!H}tk&f_WiTATkjUDxw;oIuMVVzP6{pxoo3E&<Q9m
z#NfHDGg@Upz2V^GjI1hn^h?%*2M;)tH8;-rTJYG%kbAyyq3jPIa}7^V&vDF~OkEJQ
zpE7}ba(vS|IeQCf-@8KV8LNHb1c$E_pQvc2O20pXKDm0$*4W7Jg3+AwUen*NDg*6^
zpp$`4A0_}hSP;P*K%C_Wk`iIe5x)>I?g2woV|b~Av{PU~rdiS8GxvE+a!4zWE`wxE
zN?2YBY95i*_mk=TeQHxGim^+LBL{3yFs=X|MNtoM{0=Q&a!luEyTrZI1LI<;@r3e*
zFK6cpym)E_sUTIC0vX&-O#@9kQdRCZ=;c`OZYkN%slL|w2Lt}k*8Mm5pJBD*(qT&X
zQc;1c1pF?dAJ%<O@qfYpXU6Ghl9!}mFN6*anM}^SdFoX?_sCwG&h4Sx^`SP>Y`e9*
zMQIeG0layOZy%Nua2){{g9HLnlL27Ufz(tft<J=6tN-qh`QsHj(UoaBLF&cw2~I%!
zNw`TEqy*tL?<)1$`j8G423?#PM*WYF)gw_-d^m^PotsL)G2mf))dM_k0le*n#)ePj
z1rg*bAZy&6haE_x11}*{XTj*A%x&$YdY`H!lqnid>+eR0sRznGA`(I+^(*GVNZMP7
zBkKn9)%%vsvoQp3QeR~J;lWWA(VL&02Bgk8%1#F1>^b9VlB^ELjU@qHNR}2%M#6ra
zkk}KBEJP(nPmr_G*apRG;D^0gS1(JKmZ|!}ya!wcNf(hHIBS|cwvo0Pt<aVyq+FPe
z>YWQyXQ^_)WXBMZNv5&y=z@fDgRx?)TjY))!T)&GL1))dE3lwZ)ukxp@1>F<y#@64
zuC6{PilDncbJNC+d!njQtVJq>Lq$ksWS9~*9Vic-EOa7ihO&DNNEz@1T=*Q&Kq$O$
z?%sV;KJ+|CVk`3gJ|xjfFu2VQDhD?Mf#-zdAhDa4hv=nifn;_7;A}EV05*_i{rX)f
z4ddV=GSme(P03Z?9-3Q34G^>x14tCYZr|Fei5UF*_wRf_Xg0{p%vPZ$4GIb;EDy>N
zu%O*p^Z`!_iO><d$Up3VJTS?pQ_7AY9kmKVH^Kd|ES!uiH<pOB<v~0{%tzM)0V#Nn
zN0^_?xl>EAv=93~3~Co#zypz4IzF7XM35AW8WxGx2686CBFzjHy0(_wn31kI<Ib+!
zDRQ`4mXERsalR5EwL?Twaf_51rJl}nB(X%1x(Y{QgL&trU~plML%kc-<WRyWB$G5z
zu-cn<i1d$;0V`NOB_t10nLSV<VpJnYT^)K0Uy&M+!L7)4B~jJ+3Q%6E-wJaZiM1w?
z6xuq-*b)@O6v4V4hd#D077y{_-ihB<ts+Itv7x+meT2!07~p4Ck1A?)Wd1W@t$h@`
zca;o%B_tK%!U;76!nuUkMwDI0dn(UDCc?vxr@8{dDGb63s6r(E=L7*1bwk`iJUUg9
zxsp5{KSsryMl3AEUe&hns6EO1_N2IDq+Lm_!;wp_+FX`T64XX_6g#{IYHz}`WkkI1
zsy0gYexd)SJKyvZ3I555XDC(20F2Z$T`CwK=jMAOg_C|W+_~h&sHB;pnU5Hvm~W#B
zt^ZUl))~YN%<?O0f1$gO>>Y)I+-j{?YIz0p;6c>umao*Dj}Gl#!E~Dlvmk`>YR~Jc
zh)`HAdNj3@KE4|=9U;$aYpt_M--t~DK^W~jLigR(P@_oYt`8jRXTJNo-)pDP$fNP3
zpVIGd7?1xc(ef?fG?$?5bD0_U2M;ddeFYagjX{IH%9^=n+J@CLBQu{q9=eeFI;f%6
z*!t7V-!%-EM;Tg-8b(qzOW*%(4P#*FO}EFw34&4wtUQEO2F${*aVo3w^|yyRai1-G
z<7sTwdqRQI@#%hj)s+5r!9PVUw%BQq#Xhd>=%~HA``J}&;#|vX2{bOPXZ-J_i-Za~
zQK}^xwet|WNi%Nvt5@$omn|MP=ngccV#-4(Y3z}7TYG={7m#lpmKg))Lo){<KOxRa
zsa1`+^r@hr_|O_uzg~UZc^U;Zla+#t!A7+O$bjJxH6ho5Bnn7iA}xe2ODy_&S7#p(
z;G&RQ(La5@IJFW07s4?PbPSOaRdDe}zihy4113sovnOq!;7tgyaNsDo5wzx48wTp>
z%z=I@6jmk3gbDa8MZ}7tErOt^0F<zWy5QW6DuLNk2!9FA1VUPf+|J`n9f#)>i=bSe
z)M!*mL>Yky96$z!ktC-cKCU^mhH!RpjEJ&(B2-2pOj_SnxmdY(&M`$e9*HS5Ncw37
zbYLoE-~^I4Or;sRW)bd&!WCwn@Ihc+)B6t7HK1-cN?0eW){xZ2&gg3e3N+kcL(&N0
z$|!h|>U>^nv91CFgKdV#)vb)-WXOci4E{2-Iy74@=JLA_ne01vYJQu*kcgLd(_l*q
z`~$Ggu3;B4I}Kh?obipjIU5et27@Vzx=(X)hg%ExwRe}BiUS=ceEtsQ(NL8GR69{6
zW}pMAMv5s8U@q9z--!QTc%&Hfn&J(Zxp`GGMjJRp(Ep)G7wOpM2%}6iIRXzGjnn@;
zwV&W6FrDcOa|jOqA!jl%F$k%Irh^ECfhVFAt$~*kNX7yaf(0>$Vi?*?=DO=67ANW(
zdO6NZQ20##*ZszI^6UM>1vrrtImM&v1qVe2{t|g$AW}~$ggwor6noG-5WYztd;Q3o
zynm;dN_rbVPeSB``GGoQ93{Y@Sh?iBx_3dK@Py%Ee;;5x;1vObJuDjhm9>i2`5Sur
zRso;4+1uvD<Iz4x&=Sad%7L{-(qhm>_fV0O$s)cb)N*5s#j5lgzw3s*ec&Q%v?qeS
zetf|rQ+S<c6wm1L`Is@^0m`ypf8EDQS)zPbciDFKUzC2AP+hX^Oo4Wj$(g1VTWU<%
z)<3!Jyh=m+%&m*3JMQFsT3^8R=GSvf5?lFPWH<eO^74DD<C)cSdo2B)^d4+hS9jnH
zEE{cq9gaJ4=<|=9sZN%eD-EXBgQ64<lA8OcJe-ppfihn0B}vR$8yIn)7C5>4=jN!!
zGBHy1N2H~xdR1l1*Ik^<r?jcZ-;fTlA1NFgi>6<!$J;OXeiawE;&;h$YO-g}d$Kmo
zmAhgp&O0n2??NKa(pM&tn}?nkcc`IcKHa`w+y{uHQ&8gy`vLR7Vz_*iA^M`@E9$ym
zU1J2C+_GMLiBVBpan_%>(AJoI)3Uy_wA6kgK2Vy*4uK~hVJjnkNyeK97gqFX>SQ*y
z&byLghpK<qe(`DQzVqR;7kM#rcqK20lqJ;%7;yV59yc(Lv9Y?K)+t3}2YfK?v@p2P
z@%7X?X~fo_I^e&`a*G$Cc4uzE-X(+D-s%F@Hi*tn?MM~A0u;i<n^5oV5*F#q;i^G9
zhFZY7aq-4=TXTy;<RZS0^aU#PKinU%wSN(F?X3x4nc*~*w{rz=azet*Y6qlJ!bK=B
z;j1c67Tpx^HrA<eaf7_;_&zEyH#axCcmw<rYFJFGt77mO&6x`CwHhrnjx9Cf-zT4(
zTD$E(UmX^-sjXw@T=!W&_4#6u4%#{dGrlngEfSzlH0w9b?pEyrSwq^uz#uIxZRGYk
z`#-jC-(HW5kn=*h33|4)@sLy+EQT0axU(=DSJ>W}e5#lYyziu0U7)M0dk$+v3r<cJ
zoSU0dbjVoOZ*ii5^^8Lbj8oN9*@gVCEiD1Y4t=UZoiDWBWAe$D08wEiP>vvXuJBIn
zPii!S@O>vn(eKC@P;H&5xfJ+}ndwT-`+<R`xv|c$`<%3v`T5}2HHui5ANAof<xePa
zcFlb&!ZfUL6i1ViGYwsw$)nf*(KMIrL{HB6&Jq`Xh65kZ5s1vr7DvCB70zjLx#6I7
zfpA4rQ$}t!hCKd+0k^62Yk5Z2hXYDFwGh@d$D{$W#(QQY%FgL;l{&lKLc!P1Zxn0f
zrYm}%-RvCs7e-Ba$Y}@Zh}5p|llO8Kq_)frSarR=zoW>nb+&Vtw-AuKk@VI%o<ZB%
z^hiZwrr)~F)Ffwlp*N%R!)o59?xdEMma(z1n?rHw>B3Hs#To@;@aS1~)6*K$MM|M@
zamGRP=aM06x5_1uvD=veX<nC}^|b2H^A-AR5w{%+f`--DZeFPVrG;WsY98PTvV5O%
zx}_mlBe}Cbb;OHh97jA4s-+xy`SN8d5Yl)+K1%TfUcACqRa@%~VxJJk#GVC5=V-f`
zCcU9nj>lV`$Gft%HIRktQahLCv?Q%Pd%iE*3{!s<Gg_rIMra7W_w1Z)TX|N5cfrJf
z>0IRRLs@Ohc|Ql(4fu!x0vt-~?Cd1#D%Aeulo>h|IuRc@qOa#z!#YxodP12lTbM07
zsQjKnbnYcXDxMU_ILS@PMLa`{+O>Vq`mET*JCD6onEZ@KlU!bsLP)DdaS%H|yk(V*
z$&0xf-Gp<6*HsS?!$4)_#d@3~GSHaNS_J^bvi2*Ig5J%iN6SQxQq9K7&myZJ8-_+9
z-yQk2`L_1_bl`%`Wsk$G{Iioz3sx77_XPh$7T^O=6?j*+eXo3A5r)sIn@(qCW!a-B
zS&HQ-`uR3*t)8)Q*=miUJN{-x9MgDX^21`MN^;Xf`Uomo+0^8ZF$WZCs7zp1Zbn`!
zy=8KRcYjY$A+pQ7**x*tQxh;H3CYQX8f8-;-11ZttZ6^{tO=k#e1d{xT9gg$c$Az&
z2$G(+v$I=^u`o^GU9mOm80fnUN17%mrQlt50d2LQS$Mh|_r_hv^D+hXg9BX*qwAfw
zR`{Typ`oIkKjuJRM<kXC?(#^Lyo@&NL3D$bC`u=#XT?UIV)BWTOCQpe`6kG3lBG6B
zz2*jf!X<fpd|Vst;;D#Y1XIe$W${aS7iQuXe8U24IfbzpE0UFbU91dh69M4Q%m}<t
zvYy*m=m3LOx+QDqE_2N^d2dmgbNX4R6?9_Eb)jC^%S*#T+g_ZV1h8I;12Vw8;|I!m
zr68dBLv>Xcto@lYbB0vILmF-~>oW@2&A+sDU^}`_^pc28pa(O1poSk5q*d3K0_r`D
za(}yJ-3s{wB9(Z0Kgx7{nWMnZ0@e%6no}RP9C4bTML&{QDsTp%x4vVOlVpWcp+8>P
z-24(fXxd<AxlB%fTj%tG%)7@5$T)&F9fmUrYu3}-TSU&4J)WuM`_l2G8|u7{!;f)F
zR_1j=fbNz_DO!yDYco3eUHTkaMgzQ!QNdQmq$Xn9?9xzCs)X^wP7+0re9k@tyDs5Y
zA1e^nY>=mtlZF~YyZHPMaT-O(-}DTaECMM%YpPcKsK%xqwC@sL$t&!-9S^?M06n#5
zY1c_e)k}vGOD9|F)EO~g3i}JsoV63HMBW@onZ{|$s`lV-Vnw~%i=1$<fwZtpu^H`q
zd8x4Rk|A;+%+fZ>h#X#^!^z#km59k|n;QVllv3!OH5T#{rCmLJeaQ#~pkY#EXZkzM
zPuGKUogAM3E!yCZ;1-1|WI}z{UmGucjt>}*<`WQDJ2944o5=$L!=xt#Wy>Bs><_lq
z7i7Ka!B3ax^r~oR@J-GdkC{%-rl(WtyTgBQ$1vwd$+j9GWS`7EDhegXhc+-8XXv}g
zhQ3EyhW8R#(bRfv_=V)PrO6Fnzdo6mn8@hhHl~`Jo5w;inppd~_PM~xjIO=glylM(
z`DalqzTo8KbOG7mE}3g&bgmecVL&wmO=t<k9n>XW(o$FLedV%NlJ>~FCw9>FXlc0h
z+>ANvDba1?&XeRQkt6SKn!l~*=g%)NlIGy$5TQgHL=(gv7>?}3P3D|i<L_!9)%9`v
z3oXmJjXs&<F~-eTSc_gm-jbVY2CDa(po(wRhfKzPV)G~E{+3&_AozRETL1kVRCl}`
zK7RfVhe10{_v5|cvw;hvP9w;)*V+7%EWNG7LbQ^x``Y*HEL^gZ{Rx=(%oA1EP!#19
zlW+S57hvYozmG3HyUEJZ(mmzOR+%%RZ7(rf59TKT+um-77$sihC?)~)rmjw$jfs5=
z<Q%!4R#H-;1xG~a#TPsF8)&lm8z;hn!#)<liTOHV-bfh=OWF0FH<?B9!N9$*K3G%p
z)_V9mh3e$AaOKeK7pJBBSE(-ibrpu-6!AIal@vP7)jD+;&vzP+G<AttnHV*qb?T;R
zNl<F+xC61Nu(5tbMjzY(W2ESrg#ny;C?HtRJul^5BcXMTAfK;d6OxklDO)N0?2H!g
z(noNClk0`T-(gG<H2|cLRuZ78+jbbOH6lN4laub<-Xohm`f~85$;r2p3xp17WYP+>
zxeMf}GL(Er5ceFxydWx0W1PR;K&lPm#g}BGs;W)u#&S_H`{cI$h-ct=oR2JUNpFCE
z>2j7I(($*ftc<H?uBetDYv79JljSGx`?64*^HMVy()Nh3fT9b+{R!2RXxO`k@iIm1
zmUUNc8XH@lV<2nTyLV=PjR3aaM_r4vzrH$ypFg8pf>)A8q@AbzvZwv7#$+cZ@}tUd
zg?z&QRs}T`+`CbTQHo<a-zA>(pYMI6`(Pd~(A2EI-DL?2wFc8CIg+RDlB;bF9UT+a
z2}-}=rrv(iKp5DG{qz}dUcpb4-t$xn$eTp1=Md2WC3i>OP((`8&iSGHltT2_iZq!e
zr`@~l8LRF%-7$@I3jkW>Hua7;97>=2o0yt3G)nkVloRz8amaNHZ)Jl|vv-@Vz8l`*
zm5+(c+UAM5);Abk%dcYDZ+-ofN`sRyuIoGCB{_}TdqJ7bSt)j?{nomr9<pxER^hDV
zf=Dg|3)6zU7%%P6N)v2F$5Fr5c6i1TjK2`Dp3@N<^-(kZ#9{-^dPQTS-}PpZtaYMe
z7fF=0pXx3$@|io*@K_(~v7%{WJUPVzmp1kX?VQuaAjp8+jY$Lr66#N=9EpR!QdMX)
zxfv_4wd~BMp`_&GbpeHMX)P@+xlUL(CarHI$fuD>8hcdAq!3M$cT@xx{|K5RGru3H
zRWp~SwQKX*)j!yQ{c)`0nfwU6#W8TVOIzprTZvpUhnFP&FoFj#!~H?Wg2`adAd${O
zJ7NU$fKmT095#*+ISyuKW*?kTQgI=1@XqPaXOqHXzeV>AF3b;lQUam)N1}-Q{CtE1
z>q-yXf8KS!9rsuQlo<hmKlp~h3Y^-g>`vqK`(fVW^<bH<Cjv@rt~z^GS=WjSYwd;a
zhy8AN6VISRxZcBI&_Od=5qblCQT6fARbP7c>pT$Y$}{_?fmus1JOrMbwMH#_PsD2l
z&JL90%dmHzcsPy))gv~jgxy7iri0Kx2#XtkCHKy3+`@SFidu8DGm>#`pAmjdhz#14
zN#Vqj*SyF5*lrB=vjM?%9ubeRtaP4oEf_tXjYjF*aSr_k@$kxFadEt&qM``rTtpFt
zix=(nR-?hk2?D45jOrAL?zxGznE$qO{wiNU=_AII28l67bf1WM2~~9=1fHXrgATm=
z{Y^fcNk;XyBNfW#g6RP+YmZ|T0#>$l!H<zj-c$UcTlKbJhh00oT`}BJa{V~0O(&9b
z3k^hK#)>adVXq92=?md?&?B~Hm$C89n_sB=Uq17^a6ICBzuzh~LQ*ptdFOHL`vd6~
zCvpU|*OI2iR|B_h-MWr9BZSIJl-Z^mh_bfn_&>$+uL~Gk^J+7r{BWWf7Vq=jEgCOS
z=j=e)x+6T!iMSOwKC^O(E!rQJ4X?T=+;SUNbWR(~7tA^g=8s(q?3G8aEoPP>%$U3m
zNT$%VV7p*s--<ewP6mmvR}KsBIGNt+He)LlA_u>#={jS(<X+zTMP0Dvdl$Ke2f}6j
z-h6{$YSEP&4hRU4ZqX>jc+^8Gdx>XYs!EhyZOxn3{bqf$*ono?u(#Uh7a3<4FYY;h
zXOg6_F~`Ygnd&65=aTV4%Oe>iI_DTZeRBp%@I$Icxw3NTsky#-{&Sz7e;jfC#;7Hn
z$kc!N;y1gUmw|DF?`8;@Rrw(Nhf`PaUXrtQ@k85v4_!SOBgJ%O&9By;O-S{9#GU5%
zc?-dmjiL_a<jToN^6NyF7}F4dAxJL)n-E`z0)Dlw#R6sQkF%|g(g(o`Upbo(?<%wX
zBE#TTY<hT_(YOBU)!)^i<=Sgo#*x#Ae}+=<9Bj)TA4~<9K-$i3`f+_dDt~gEUi#`g
z7*|@cETc~uBW~I{;MuOjQA`heIQgS*N<47t<9?D_7SgS(th#7YJljSlNMPu5SSvrg
zTA01B>}XS6-3vUYZ&f+})ssd1HtoH-H*cOdcMB*UPu7b`>bk6}ug{v%)X<PtU#rFc
ztg)ejztclS9d+_Af3f}){LztlfQwCkG)1uj7s{6`)3&P2uVtx&+&|~Q(U>R|gNxD@
zaN}W_dU=!S{LJH%OQUOQY92Kvw9MzUK!@dB*}Z!+w!HfAxb+Cw%R121EYDM0`Cycn
zm2F{!KQ<&p+ht)V7qdgA*8Gd=nu-d{G}UA3^+tlLs;b&DnKh{m13;DsapZAfVRJMM
zki@-|qzA;Wlkk0CEn1U2xnfhUeQsQbJP=cHcbtlQrFLOq<Kz2{`J4L?<m{*DMT}^9
z?KAeS_?YAtI~Z_imxH3?&v#XLFq9K((p*ht^DgV^MN9;!Dr-tfH80QL=H}Lme%t!Y
zF5rCRtj-Iwvb2Q@=PFeCWrFTaJll73RwKdk!(X|RA8o(FRCTUujMvfD>g_SGMigss
zSIl7eexSaAp<&6#PUhB8mA+n?;64~wrP(!6$?!|VCq-9e{J46)qCv&UX)Gf6(x5}g
z@io@>ZFyBRot#A7*qOq&Akju`u(>-8uaG!;A5l9?k~lUYFeV|tCDP<xmWozy?LoR<
zZ%p=zhE}^9DxH~;HQG$75q(x+`{tSxd9<qd`bR?f28N2T-La*DF65*`=Hbg{)I`jI
z&T4~l;<v_HRYipu14A=)5RA6Y<n|85wV|7+uwj+x<al-EaC%>8S9fDoQp_6?6wWs}
zc+!<}rTveYn)u#>iLbrBlQT{QyGPj&j_N|h2gl0GJYfOueto^Yl1_KU*lg|kdT$27
z^7cf2?fNUxKwhR`<;3Kql6`jH&!5qM@>fGUr|6k)jghr_tuL#hyeLovmSnxmxeP}P
z4`ZSK?2m~%e8t$<xFnwfcjHpYLb$Y`hu+lF+cqmviMR`Gaz{J)qx;d+VgG(0wW%pf
z7_8gFkt>*z{GF{<fAQt#vTC+aLLX0$G*2yFym#hL?NcpT_R}M`tc<@uNXeR6#Yz)*
zx*GA-JfieN(ER-Tn4x~-rRjGoe`%2(&|F^I)FiTX2ydgN{V|3x^bYVhR94Cl^Q#!|
zT<hTA&=c`h$Kd7Nv&iFET37ng>3y7S+(3Eg(aBE^Do<v$%(*rv>>G4atZ8giHTA1e
z&1tNyReD!uyLuPiJL~E`nc}awp4Cd0HJ6=An_p{)SJ$ZDUt$~+AjPyb!nT+*@;id4
z^EAvzP0aTq)`4mEuCvuq&;=Gj01|JqDlyE`(3VE~oOC>y8g|Ka-p=_ai3STt3e*w$
z7-i2**blkjLK#DMdB4O5&iz#JB0(L~+o!gEH%y~I&LiPTDlT_l?FRAV5cLqdr^v9@
zH(LaD4gn_QN6Mu~E_Lwo+doi&y8<u|lbX}7<9`*S)P~lP`u!ntqAxeBA(swciTm-R
zAi$Jz2X4a-ASjWCp-ge*vEk^V1!vmiwW#0oAC~eT4{~5pfbztjzb)!au994I6c;c4
zjgj72go>FqJ^P<a{Lfl2{h#cM$agi%Pl$3k^c8b7&uA(-xU%%A$IfWp3^>X)7v`T_
zUL6wH`mBP*TVedQ@WQO|iA1~pg(k(qWll?%G9{Hd@j4w3%or?H@je{M6F{%64@r!H
zJ!U%f`_M^#FS=(|mWtA0+nM{_A!@O4VoR9rMW|hvDe54$`Q|D107m%me{%Q#ZypQ<
zt(ety&l3_8LtekW+P!?nHvZ<%qHP;dP?8*7D}3|TTv2}hSoNy_eYB5JRqu)ktEyC}
zyVypCynA=plXI-Txh0^KwMt%^Wbj9>=4Ia=kI@M5;*ShdArMjis>WOWo*}_%E_Wh>
zV`8|O8<py;l=wr{&hl0O<V{ej3&?bc=}}cD-^UF!8;ot+7Z;Ok_2xQ&qB6@8ZZ@^r
z<k0yUf6NK_WO)4QNr&x^51D@d_{c$kpTGK*Z!dC`$hxk}$Q50@Net;*btoh|d7p=>
zG;kkvOWnYXTnjW))^=g|iGI@HFFEIawJx{_p9661p2n-SE2+eP4fZy4&%igZD#(ix
zs~`DQM^jVuQ(a$@>L)S5M9nP<N=mh7mKdj|yVG*S`vwLs7~eHiiF|wW>G;@KaM;Pf
zp24^%jr+E4p~=ayGY5%-_!*m5+YORSWwmBYcbYArLUmKW=fxId-l5TKt1tD&x8zHl
z%G%;~8%2cNrJQN$=>{WBHT`z7dH0+igcmp`hlT0H(~S%a`nCd!R=toTd=H-yWE-lf
zHRW`g-_Vdc{Pax7@zsj9c6OEW5oP$>44q!798oqfkk)&&FikAbroGOW-(r%rzqdDX
ziq(+IyHDS5Vr)#o-YscjQfFg!)B%6wu+cGJb<B;8dwZsGumaI=c%mx;;i>g*HXJ&w
z9G@hRtIQvxo}To*ODc1D23CtWKk;8D$Hx_anja*n-HHrE6%mEzs!=BCzVI0p)2^~#
z8|G&kj6GksKCao{w)~1nWODRkano<t-x<VzGyBh9lLnvmYF;$OR(r?gchQj^<!K50
zZf<>9-+^z2W@l&XdsH_a+I4?WBvZd{B~zUoaZ|5)9jC`OPS5Pz5pJ<9=!TiRCw$e`
zE&ob&{>PR5|IV%NC_0k)4;SFSt5yFm5mWW$zF+b^K^f_QUQr>$ZATBPr}06WtrUDu
zAJi~l{$JO0EQW(8a_Bl}Uj72C`8iBNZXZEkNO30>{S;=ULS1Oy2)W0?IL|jfZ(^oG
zU%-ddClDnI%}@ORj%vRjX^1*c=2!(O<RRIW1s_YZZ&5f-;Ey(Gf}|7NhJd{<381fM
zXeb?_Pz1b^MzB3VT#SMX(qk3xRV3Uzr0ciN8qh^-=0pRf@VEW`Z_E3UHP}=~X?RhH
zv~PZb0QsD=bbaU27Cfppk_;*hd69xzCx(Jupfv&P(7MDR(d2aA99=Rfk73;uAISHx
zqLrK{9%DB5Q60fx_fS?RS7P$3#IordVw4+oe#7@mN8orURAlBeGc)(r+(}2N8;QI9
z=kW|w4$DA5yT=n6Cm5CVs&hBN@q9zi*LTm9-n%zeOZ=yi(FLiyVzmN-VPPxA6$rq!
zPq8~bERo;9;Gzt_w9k>fK5xIZ=FBLam(+3Vjd+fngioO^)IsJs&5U_fBn?^k`RJ;y
zqN2eriE7Md9L@~S4ioCA=06ZFx$pdow*&k$YYbm<k^CJzY9V=Fl!0Q?qaw~!E65=6
z{$!{E1mdFfI0kYn{EUD1F6&;qo0}U8{idN|401KpRyZf>TjY&{-dqP2!KXq%&!sPI
z#@0YVdK@}7@4gExcOH(-I^&u5wKAT^(Eur$na}vrW^9mMOIrY*kc#T5*e+DHWz5Y-
zzkV9y57uiH@{35h>>JkaABvpwOM~!Szb9u?&<o$tvgi~;dRW={UR}Qnb>l)T^n=oN
zmuC9H%dV^IX3ORX>zUd1#*}f$S6;m8XPMmZWtQqVFH&CjUjCDqU4y06!e2ALJz?D^
zw%?6q=BHsZzeB%W?W;Gv2l$ONhck5xCMLf4OhmIPD2N(z$LJX~9Qta=HCd}3D4dX}
z*c3|dPmzBf7ZdpQX8tn{7P`Nv#r^03@=uR`tLVC}`28{wQ4YzO5fRq{8)f`2D>>|=
z+DY}D$$U;{?Tsya+0ZCB=fK^UweFW3Yk5xZVvAd@H8ttLtVbhc95@O41juulf*Kjg
z$DjsKrU$IDg|FwNL$l-yB=$ZR%n)3g0E_JsNbL@F!gC+J`awXvau?H&F9J@>1}z&6
zAyk)PJ^Z8?`)XfdTA&{@%fP-q#|m<|bUrttht(hbE^Ff}%FB`V0#@Bm1((gQwA7ZK
zJAE7=F}TLk?*~5j-i|odTMnZ9ipgS?LCxx_ni>J?;5n3IDpk*(JsuMm5<;+EYE9<k
zEenh4lsCO#V{D!M?K<}PrMTFj>7@Zz5sS&ZA6Wfuq@&nC4cj3=#AZmR6JmOCjeGn_
zVSei9C{TUo`3>PM07SwvGery2IKo%tpB%q|vGOijxqt|xRTPaqwdX#ON5fdIU^O0{
z`uEz2JYo8z(;!rQ=Xln&PmjjN#~JrvXt>?~j1k0bdIkn!O(m+!tE!$+J!|HAjWg`6
zwpo`kIS*flJwQRTfKo^*k?pf-MD>|=op5E^)5wV^)J9*a#whQdzJ)}z{?zurvO;~j
zHx+x#X=##|7#-<+QtkMuceVrOs&;N`$5aO$)^_*xMKwCt+Q~ZDbgrxKO)S%i>=Fx{
z)=-GcG~ybRXe_dVgO4zrPu`0K-tMo{1HsFflrP`|(<r<)g;M#^Jj8T3mk=uiOw!%m
zol*!{^ThIgg1rF#%A^z)YfQX9yeL(EMLua7(3m@jEzcxFn!qSZ9Vy~k+4YuC#LS@?
zijYzem{AEVqudWkhJ~KL<`?K9s4OTuudMUZi%=BM45m%WGt5STC=6T>>UhSg$lxVi
z^l_dno4{a9`|&7E=+&utf|?*IBR+TQMp?j1CDc7_&`YB++-^Ybnkf<dt1^^;UXUe)
z1jx0R#qQm@ga737|HZZZH_hh}&p8`XL47FdFq1Zzg66BAhkg4VDvhvFN>TXE_pOoV
zMmQ^kxoMshf}R0qm;Lc5vMV~iw20JDD231lo#qp$=_;Oi#1;FQls+iaU+;@OgXl>u
zPD%=R4d$O%lqifUEJ1qtW5bW{K*h;^ENG`RjZp>?Zk;Y=RZ<WgM1_`snrH~=(46nk
z)K|Yy4eo?E>ZOu2Qc@&*DSb6r3wyxtC4}oiDYOGl{}axaT$W(*?X3%Qt)3LZ8qgT^
zt*yd6kZRt_g4LuRP2hcUTX_5O?|~B`t0Og-I13@gQIsM$NC|}2zA<p>G^xaqW0nGX
zt2sRJe(L=E;DQ(_^15+Fx}%maIQ?Nu))R^l7#e0De%p`Vbp6A!f^$m_%~$Dc;Je^<
zzbytFIusswJt<|l%?fxVtX+^A&$@|J*txX+qtwkjYR9%lB15P#jDz;gP@4eBL>k3F
zN<UuLh5pChq%s(WTrqNr$q$gq8_K0Rh#g}3#I|s&@TEwj3eh|<gHk#3L@Fe3()!I|
zR$UxQeU641(bb$Xz5<g@cGy=4zJVW*<KxI&Bm2a5qL`d(*Z?dpIa3TT*u?=_cJC}2
zyMQUKpf`aQ*qbE7w3Wljk%8wQ_}?cZ`q36kTw`8ZwFo&)S~+=BY;sp?T>POQsY48$
z!$3mi1^(F3G<QgfiJ%>lS~IR5w-)`gC|d1TPTpA?g|aB~KstF%Zcz~d=#gJLK`2=@
zDZah7d2B6Dkcpl5ITBJ6qEL;HOem!+DJhA36YUBY5O2`P4h=WuuO1~u@gaWOoFyx(
z@NGQ<1Gb-#E%G71T>~ry9oS6d>Jb(kijc(1$gS-}i!rg<KT!d4p$}k_U241*sq3w7
zx$WmDSo+bX{7i=&y>LuYYa|#(;O%(tq=`8BtMhRrZ{#okb%6+^a>%U1ObE?3#e}g!
z#&8lMUPXO9ij$q~=oy%LGT8^-5{cC$JWTPDQ9QOO*7XuOnAdMUM{mVXFjzCFUjIRA
zp&)GeZ^`VI5JH0&iN@Uj+@g9T!Io$p#G8EkK$tfKiiE`lL7@{ROt(L`_$5O+W)`ZE
zMX>uX$<D<XUp)k!>Ez%D^@az%C;Ptqcx-B??=0#*WuyoSVL)~qQy)QN4a9!=uO64M
zQ3LlhZ^^GXH^c7caP=T_GKpz$wd_Keb~6yJJMn9!wY0Pdk>J!iQ9d+uNFmVKYIQWB
zyoCom<`Qz!G3XuxJ2@cnSsSG=U9*iEnA~J0h2S^4a6FLk1~5WHyg}PIvAiKb>iNM!
zr}-|YPCWM)^hdm}ydc~G3SuFN$+6Q?i_~wnn9vV4q#W2bEx=t=r<*XtKKSEJ`IPH{
z4=K77hs}Cw%b2tx6hORA<hk=S6-13u$cEVl^%3Gyr)S`7`{Enu!~JmEx)M);Gd^S@
z#m<t~e^j})5A*|Hln|LyzmOVuC#cgq^n8M@Q8F!2e+pcf4kYXTC^>s9gtvrz_P34t
zc1#P9EgQkg*mxF`dc6>uUhdDHe2-81TQ=zR3=Xz-C&5B?fviQ9k}3|b5?0&OZ%(0q
zxXelo40^}w8uGNt?%UlhodpnzeKKhPHe8q3WUA?Un%jgWs<=WwT#uTed2v6LL<YvF
z>j%m8opVhnBSu*W%>aTn3xtybK)d!qFMC9JT>$nWI&3X?vS&wj)tbW31;2Y|S+LGE
zxa^JF<1=Z}kB<nj>IF({+p>4h0$IER+my+_mgHPEUuL`=5f3Wl>@<jK9U~`{<N)OE
z>wnJq`uZ|RKQ?r{ppjUsOfH00IB(lZX!o^KV0X~Z;)4|o0lG!lR}2oGfB+f)OtPgB
z_#I(FuN{FoJw%{#FX8%(r(-jBdF^+R91Gl!XGcqq?LjGD74<MYpm+x<D}yT=fb!Ry
z&a*qbBtzKEKJOBODD_c<5GWP!qp*Dr@fj((6IzurW}hO3GVYKzwyiufshMh~eL;L~
z4CjS}<7GI1Pk=IsAT=ZOym;O6H^lM4TG}$F(H}pZ5Taqny9Jj$6+5eCNEs)_Y`VZ{
zwt(FvXGuM>Kp6LDI1Qw%$sm`&YP91s#Y$NtUcNhJ1sWQWei;>_f*GjgF0`*!!>)hp
zt+(1|qRPOh5+xZL|2;3`m*w4~kVY&L%o=usQ|Jtw?+F}1Abcz)CZ?yqzXaL`zHCM*
zS6$N^>dwtz*Xss1CLGWTdOD%}%vAQs`?3dbTp!b(j^khJnOswkSipP^a9G{C^-O&D
zp^U4pD7XX3#ULJc5WZNDeBFUYU$KP=<AtQA@^aZM&=)yd92@k)yo8S*FG6GR+yNz_
z<4Fm%2&12q|3!Y1ggilwXu?z{j3(8A?(PF%7^O*8ySt-~A4M*-_>KPl=I#k&`yXr_
z2=GP-380)*$#`~}e>u$fUq3v=#Wr`WZS0(}4=uNK@JNj5H%}N)U7Z(`92FHc3!Ymn
z<7yy+MP2^ZUFR#N6sx8z1{y+fAb3_qgsPGn6cjXz;=QRW=XRfx@o60BV8*k+AR1h3
z?ygr0ggW}iwl2bRzvQy&Y{9t}nS6Rmw~s)rL3zQMr2^GDqHd1`Cb!A*{&*8Jckp0u
z%CAgkRQsJCe@ubZFvmfm6sCyn){}zW0?f6K3hSn5>T^3#ITmtxUwR|&wIZmX(qLOf
z(bC%3Jps2gTdg^#NNb0_!;Z>^q#6h%zqsB!p^ACbE0R%Y%H6}nRpMQTJR_yG1!Z@|
z*jv}hjldR4*ty?RH3lIN?bA;w%IE=C)Mt}ct?ZK6qW&y2G&BRuso3eKg{o1y^i~tY
z*s`eDG|s}bRGor6UiEthDUI<De!MAhaIYz@)0KCqsZ7z84_rUa$HUp6vx~`f%P$(7
zGsI}&Np65#MF@8W>ZK15SATgaEJz&z3UEp7#+8fNkV<h=R<C{w*Fh;X$9nJb_Q5!M
zT07Vpte(F24CbbP1PEz1fx`11g7TDYvm1uaFV#Fh2rUIp#bd|+?QvveWXQ4IOC<~)
zS6lTULn->~Ctk;=VDYvSLXnE9YQdWXS;d4o;}rZP7#)NWLm_5fDAd9&>6=m6ZuiFm
z)dr$&ZUTZD=X$l&5+ceT*s^Y6>5u=NDSx&3kX$*4$`~GtBfQ!wL2)mkb()iI1JJ;~
z6oBqb3gqB~oYT?K$$}zxms0kOB8l)(R8Ou6asP}Uz?0&E6a1uD1m2ZO18vqyORmy{
z*?)~TnxOz#d|#1FI8Rg>B2;FZBd=~Bby~-sc~rwNvds1fP3-XmyP;tw3@u5s2(p7z
ztpoffhlYlRNcGMKhSWfHb#(@z2+n!UJ*q@}NaN^A@u@!<H-v{RL}z3?ls!8i>~a5$
zg16FxvU2J1R7R~9lq^#F28C^DDw>H$!1(3t@9!r(Ib^tp+n>JrQE1XC^XM2)gIe79
z5rGDU!0|&o4Kys%)-5dKjAdHF%SLFHj4)jYGQI|CCEYLZTC*k+<2q2tEanLFf1hhe
zE{l-R929Af$43u%d?Zv4N+AT2NMpfGL>O#MSvUtx$W*EGBX@6z^bQQ{5aF6zDK>Q5
z%}5hmCt9|>a>VsEDUH9upxF=dQht5$8pPj-o>f&DB>XskG_ep)(%HZgFx~@as=`({
z8HofK>ID*qP*Gj&wa%gYl$K&cc!TepG1L`Yyde$+H}kw5j?t8nw0++gcn%a*MT6}R
zOU8vvHnpSmdIWpw0Gf7(+YGVbsXd{7W@y~*z}75oY?(+p9AR_p9^FyyqiOl`F_m>%
zK_%nLRi@=Nj%PMms40w<on+c7lRx;t&6f~;11N|uOWU5V8*jr4im<NwfYxE+AWX#z
znx4M^G`99wn=^%QuAf72;{aYi{y<-&6ytGxz?z}Ja>r;#oa!AMECX-tC5&lkP|zOY
zn$HxYHGxt;R%TGU@csRKnP9<V?w<G^f3K0Mv8HD03VJ7rdk{I~&e>%&REVKl^M#*L
z8pr;Zx8z6+cR1FpJt=|2!Wqb)ifi#I8Fg%UK1RdGshu+y(Mw?t?r+DP)jV5&^d>hw
z9}^m?93;5_7Ri(()`SL_iXRyvz$D4TJSl{dM^q%-_2yLdDTDhZ*tQr-$4Ts9!#0-K
zvBj`N=k(J835mIb%bbVv*Js>p2Q$r*X0acNmEu#m>&y!GdS3@|r4*Lm8=*ZZhmjr$
z=yi^E@Hz=W<l2s$5mMK$>2Uu{)^DBTkc$n@=?EYIW0ywYCNBtCXc{r8I8WCHRE<b%
zGx}I_b~kAeX&H~TQdEn5XWFb<d!qXy70cDuF>E<j{(M6D)}Eet-Li7PPFSN3$dAgr
zH_~(<oPUte_z4)`2!3h@6d`9*j-~bbU<r|E*>@fdEQC!QeWa9e%{7WWHKsnmD>OhC
z@z>S++n<w=E!guy5eYrOJ@rWU^u%6WO1}mcG)ZMLerzAynh^uGDd}ec^H$WFubYtc
z4@WpjxsA;QpM#I-N&Ku86w^cObj+OVf!>tx@W8uFhjzGm$XMmOk^XhE1mHM{49G;j
z3qiGrEG301Mz1d;yaY*kd3nS^4ULVXi0O}$#G(=>1uTT<{AzbK<nvj`IlI8rGbUXS
z%rmp3StmFC+`8GDrWpski$U^oWIQ}**+pl?XUcbV39%nzH9@EHsaRw5%~NX}dbY`E
zu1yLp+l`<fF6oSlG0<3Iz1y{R(l3+iiXhk`Z2*@(J-V-BFhlYHpN?YhE-<{e);7k{
z%9|G8!VYg`TK8q+agCeOfoMM8mNDB<o0ata2u8$M)7m}IkCSqI#3;Bi{y!F`IaFf%
z7Unvbt=%4vS8VDac8lxm(EHn@A%;prEMY1%#pX^1C{Sxk_>&Q|TvF=<b5xnp{y-WR
zE{ILHe^|butO^vRAot^(co|h^lA8gVQ)yc=+Lmjn*F0H~6+MSmxI)4Q0zP0o(oMVF
zKlO2=%o-t&CPXFy6w+81B|moPreUB2Kf}1Y-N-~}8)s8uKsp|p;BSx}wH#0qwpVTJ
z$|)O@0Zqxxh;52vAW|{P3@>pXl_z891Ht!$G^Z{_=FbZvIwe16gI{?zqUdR3gII0g
zH7zt(gr}ehh2GXNFfahFx|>FB9v^FC`~mEd{={&tpl?Q%2-7+1t8`Aw7D#xxb`c|0
zmOq$$BVP@Z*`R%OkHXO|qksR138g$V>&2QTfq+g*)IEYaqp1a$&P-Hxq$z)2^n$h_
z`mP9fQjosa7OnU$`wz1A1|NXV?Kv9hU)a`s=f2)Cht1_A!ZcTKvn}{x_x0S&&%VCK
z?$#W8@4i;C9SQ~ucIe>pyxuhX(czdwQbNLY=*v*&%|0aR^#1(WbY)tF&8;6{Xt{|k
z_xHMR*0}Ov_@!sBlO)y`NcbJmm=Iz*uwe<Wiz6_%5Wsg3R%vZlwtjr$N@*T6y{>=e
z4PB~JfbngeDaTHRsKlZ%n1n1)ANbL!n@Tuv;?spPW=QcFS@dQHWi&DMba$VlsX<(a
zt(kZy`$4-_srLiF2CJ|c1xg%cyh6pG7PC~5V_UviPjgI}&@;N<JSNGD(yZW6JmEOy
z_UX?#D#Xkg<1o0qF0wvGZ9}^I!pZWFA^T8Oqtw0BBS?&Mr%xDYCnV{t&zH#SPt?^4
z($73o(1=h=V0@cwf@yiEYXRQRfnV@|<jxtjn2SFo&(y*<t#ER;M2kZwX-{ceuSEg^
z(iRhE-@!%g4VnZvE>8A#JcwbrM<sYsgCd+GBm~CD<~z_`v|UiM#&M=H@CNd~GPGM3
z#>~98{BpQpc41;b<iMk&b4#bJPOP{w<m`O%{BFrLs~$WWKJ)A8)#@7$<)<ibh*RWZ
z-N?zMsxOd|nvwX0lfz26Io$fTHusr7gH(<9uASyvk)f^l2WR-NMORl`f6>#rb2ilA
ziO<yw_XJvHsudC|jk5<U=L8OkjZV-{?(_TxakX<YGG|YnTFUh5&6{7KhYYgiN61FJ
z%nE-R)5XD(<R4ZQ`!sP_%zo;34719C0>MW*i!LiwNlxw#P}WVDbq9H~6AB8?_Q>QO
zbno5LJh($=VQs~`4iTgQ?`%eIJbU(xPqisLD{BYzLZ^^{GciG_@+3qUwr|Kzz1d#|
zbG{r(ULO&6J-~k-jM_w2>$8j6Zh{W<FU~hszZG&39c-MKPyPJd+^a%ok1Ab(Gw|`v
zIksJ1WfF39)pMs$Q&^7txu8s?1<%abLMijXLXCdn_dRcCb~<~~XuogUruWvpw^tv|
ziseM5e)+1qA8xN#EqFc}9T|D@+O^%0QBiI+3s4{XgM*{6XXnt8#?o=Pk;~Ar$+ZQ7
zis>?sEDuX|pyB)btp5?8FYWBM5Vk?L!4{PaC&;N#_U~sC+4sjEOfV3pGm&*k0b!z!
zS&etNyYxKlUUMt68Dp8E3V9JpM{8s@^mP4tELQW@I3^XW$F$D(Qqf6ov0Ranhak$~
zIX>CnxCPLO=}f}%iSDxZ?~hRS?&bG$|FacE4*!5P3AGMfcgrM_0-_Ye*y?Qq<fhfG
zdt|Sdn^L>pXCv8R4sAPG)gQYJWPZ~!-`bmP%<1hkJ7vlw?O~W$u<)__vR#0NhDKe6
z%~6cOFu}AYwQR@joL)qY>J%nbL!tdXM11dmGGsO=8}IPe%tsI6J*cdFgCK_W?9|~q
zE-sL=dx)u}Vf(kOS-&i|UBo|spc#`TkZdgX@$vawQt}D9YG2c>j6de*UmNQ#?>E{M
zp4g?=S7xOeKOtf$TC?ro!`j-~Ezl1*X3@I|2F>OB_iI(v)vwVpZVjsZ@9Wp!{+4%N
zcy8G8RB#P%&HFKt%#4h;7^VtI<L_X+AK>9R<KQ4VJ2zJjg-J|A`2zzu)$L8g%Npi?
zwqyz?-K@U@4Z1dXDzatU`up9$vfl<+L3fMh`R@3REU%)j-P;XH6(0q<MGMJ9*z>$E
zX#JIg{KrPJe{l`le${}A@!ww$zq4nY{@2%rPs@0*&Wm49Pan>e`q#H8vge03{PV5#
z)J+@zKlm}B&n13>yS$6>#}vw>=`}9{19@xIRaEqA(_+Z@MNIR|KqJA5Nk2Q$1Ql*<
z<oi&<otmD0SU@5;<y4#-D3>n@I^%W{==Dc4VXJ|JKAZaeEma$sm^N>vp8ivz=;`Eb
zg}T0$S79>x`kd`%LI|0b>fHa%2Z<hMHJ-DlPcK{cr-|h;vJ#X(xBG8rTL1KDbxqBO
zf`WoJu~fCS;^N|CeYNYL|MB4A!(1p4VdCAE_}<OSmgpxo1bkzSPS2z)tJ3GkA67Ot
z<^jPj+h6ZEW17<se3pBnr<UGbz6Z+7-ypunU8I+&vU-oz!LuzZ<*s_z{YZ{}`}TLp
zJux{0b$W$q1sB{ui$z984uRKsGN`-_V}x&69=mW9V?@?$J;a3a;%Q4u0Vv-1Z)dzM
z>YB4d8_JCXc&5kN`v9Iu$jYvVoSO&AepjK?J2WzK(%xPK${7CJC**se;km*yU4}>*
z<+k&bSDy1`Qi`hX?_9mz?sBTxUOrZ4H00@xJx*$UgbOTTY5}>xa&j-kc-F5^bYjPL
zx&Asj`rvhK?W?E}PNDhg7oYPt-m0BAkt6Tr`p!$J_6Kl@<P?XIcIuiPB3q4&jJS(1
z|7|_f5Q4XFprqptR;sBrbXh&mSf^58FxH5;(glr@cw5{m?d`h$y#H5oXZ{Xl+s5%*
zNhpPE*{P(mq=>|%5@qRWBSeG;4<%zHWh+J2QI8f;vXqjoWb9jIERU2)qwGuB(kLZ7
zvb^8B<L!s{KX~Wp=xCX7%zfY2bza}|yw2<M)zy`Reo$6bWde?~5gZ!V%fAMx@Egj!
zwwd8aZWMSg%JcS_X0lULQn*A#MajvCJq=zo+&&5L_TGg3){*M)yJX>pi@;|cz?H>i
zOUCfv&BS?9><&Rxmzs|XUp<a`lNN?n*fN<&#1GfYAAHo*)WB3)N62evT($SZoEEye
zduqF*V(R>ZyJd@rTV_1k+S*f7Qx3rBSTWp1$)|OB`^*j%9GqDoA}(Hd`r#khVPQPP
z`MY=TvNlae#JVP(t?AVex*DjpWlJP&RL$yfRXr<T4qGA%2oX`yUL2VB_163~0N0SS
z&^j3yoc9b5aYrX7=A=(rPLj{}0dq_S`~ohl%V8zMhBW0+R8Zi$aN&a2nKQ;1{erTx
zbd=PW141zc+E6xKTVLNU`Zx$YO>UZ&>{YxAfNG-yVeG?c6SXVx*@1xpR_;mPJ2g4A
z5Yf!Z;F&RF!obBPeX1)?9rW_C_ei0%+Qk8X8f}N|^Hh+BhLNG6f!a+c>}(h{ShXJ+
z=vb@^Cdw2l*^$7xJZo-VfgKZxZN4gVIO>*GQquPE2>>;Y+t8Xqnn@kRnzKq-nU_Cw
z0h|h|P{`SDVex04nyJpWuBIb)b}<OSitE<#!3pOQ+E7IJ7r`!ZBdo2Y0Uu-^hz-kO
z0Tnrh4ZxMSa!^)Rfdqf$>Q$Db>{~3AGpOsnxT3JDXLvXQypKLiA4Bv}SPwotnDLl4
zxDR^(e6J}#Rq%7CCpb}Pu<6B$$rD?A=8G&s1I)villblEj2XUXJHzhKyUd~@3Fr%6
z0fEq+DF&d`S`X%aLDi=3=2l>!pXl%yPu$x4MF?`3s-fSh`Rgefx(J1b2fc82cjqP+
z5DFR^tj`}tyUI7n4)*ma;(UIgGDt{@H@3nqIBFI+xe#=*S=GGXps1M2d;%1d=wx@v
z=>~^r?Bt6#;EOD=$!_`ly?JtK%;`8lf<_;JmKXmtG7^f_jO+YY93qs=s(t^X2;MZA
z@2Wa(&M`=j&Z!_Lr?h&SG(5lwgLvS7pUoDGTX;-fkmn6CN)r-Ov2w`s#85sCi%O%{
zz4iWzFiv)}LzY(*@NZtdhvPEiZmDTbeDnN^Fo6{i8;uo4&{7+1-a&pbe-0pt3Audv
z@-uG=g|gGy`d0l2{214u<1_9_EtAQ&{`#X<>U5>6Svh95^E+PwyIk~G@M8~5mHq_~
z+j^Qdd3+uBM_xXeop-X+^;L<n4?xKZgdiw6$t<^*rOD)+7*DJf!eV6k_51f-hYyPo
z2AS+WEcaqE>bwh3A-f3I$u|8h*_Khz?L9abjqcvId_HXaXq8CVdf+5!G^UDVCnjSo
z77yJ1qZYybgLM^eZfSYkDC)VU;m(kROHmDIG&b4ls;UAcj)yf1ZNXx(peyrrECV^5
zuDXVXT`*R${(jRF0S$SjM#($^JdRe*UBI$K1{tH*DS3Iq0R9+_v_hyLN+%!8Fsp&_
zRVWZRg%YCfs{VzE2?>!nHCE`?o!|#=I{`vWeRYdv;6T;{;Q+rrA|X8!B28mN;@1kU
z<QEa1r3t1E^(XjjVln_3+1XYEtRw`*#KLeOhmXNvG`W+%9%g3l+Pjwzm^(U->?hhG
zcV38ZP+jZ4T#gcrcz0AJu>zdi&)t=B2F;2FP%(Z}UsH+*J2;_};cp%V4~QVfqZqgf
zQIBu4^LD~uYZNPM4%QEvvC{KJp}BG54*V-W(X?s~1m35C6MLew#A0R=TX%5$TNY!q
z5P=D0&fvyT2Ji&=({+=OU#w!9E7XmYg@pya=l)E#va)iOsd_m#p{%Z63)0jnYMJgy
z>ZzfqsHhiU98zKE1XUIBuCky&418daD7l-i9j17H+>`kOfFy3L4|T6z)ub86R#$Yu
zOr^-J8&yI<Il1N6O+tb1>B9|htOULe?~o<Vqn)k~<ulbba#2Cs4){CAj_olu-DPdP
z@~u1LeHU&HGh??mjh49P>+H;oJnsU4d{<YT0aRXW`wGy5@;yq!RO~CHP3{Y~7oHOT
zX)VYd6?cWrX5$Vj5LPqa--rbcyqqB9<>d`J2K0VOC+C6!nT=+KZ!4^}?Z$fb901E|
zr+2yb-Hw$RwXbsRN|i=SsUO0aRH=m2l=O5X2~e`8cq^U^6Dkm@>k4So<9}WJ6N(}f
z+|=;?tiYxAg4_!5Kya1`k>_=1*klcY<*i}!>ewO8_iabA?-Y7YFCd=d(gj*l*zTdY
zsHlF}KQ*OB&*?*9g<_saW{u$fIu;}DSqD&bPX?aKH%eoLpIqr-JTHl3t|_Uh&mD^4
zFrw*SJ%<`t?lW)fxoEYn#U{5P!HdZL93VRL8d)&@2tg#@&5rZn6;}J^*cLnH%la2)
z2A31ONE)&WIujsh^3U1DFq^~zTUt2!d+#5N=m!9}!~N{9k-eiwL;$lI{cU}|c6N7m
z*H(a0>~;F|BH}hBux-GK1XaM{K}1BPs=Heb=yb~%CH8}Z8CT-s77~6yS!{$i;>CVj
zJG(I)q;E(sXxr>Rd7Xk0fTzBIfZI?ErA9Tb{;_ALF7<5>I!6A8`+@H%926A9GYK4?
z0Fa9WfuC<BbH=|^+t$|hN_;#oq2n{OKeHd&flp3O?&8mJW3yQ4t-`q#(~q8c0}eaX
zQM{SZl=^dtrg_E_SSb=!XkF;(@Beet1esS7z`eOOKaZrlYhx7yU_MV*?asGv-qcde
zTrruEAqvgcpGe-XTz!|ek7Yo{w*kkHo8fgP)3l@OMfjuWr_Y?Jh0x_9`1trFHaYXQ
z<l4uUmFdAn%;xYrVDDHkL+Gdolmg1T$h)6EDR}N|)TfR2va-xN=CHT#mUqI-Yonf?
z-U-jA`gyMQ`g`XkU*FmG&e<ASoCa3_baou=E{-54WNpyXlQw7;U)bW+Qcj(p3%r+}
z?uf1l-la<~?M#6*7Pl|?_n^gkQS8K<wP?XIEA8ia+&P??EMR$Wy0sK6t%Xf`R6b3+
zqjqxT@QV66K8_Cx+M}8TNh0p_u5nm^faFqg(q<mu>`TzB{}ewyobF1f%$E=Ux)Hi1
zmmaSgoQ1Q8V(J6a#jcAD3|CuBe_v;_IZqYZ2B}arCN6oKXF!qfqu_~Phg1#|dW){`
zArj@&HvvO2Gy40d)v%RhauCB4R5rxbH&q4)m&SUXRR7FA^2eLXnv!CwIp6Z-wa^5F
zW{l+I__(IkzMJx!jBa|d-82Wn+M+j)#-PF<o@U;dVws8Q5;OV*uxnOeiO@BoXJo`9
zPX>DmgsQky^B+l>Z1;z3_qZQJ9;#bUMy9m|qCqB$UVls)YnW#&n6Tk>HowI3eNgnN
zKf%z<e?*7P37Ki4ffiX{4Wa=?TR8F;m1AWRXx2nL+H745966H%8Q(c%GI~Rp`&sHY
zr0r@P-HTfz#Qn#wXOG93r0bNZw@r89{ZEyPJ(kg&^0xST#0?gBA1*EV%j85qhokCw
zRF@Hx_VZ;cXZY>gw>Y{sJIk^8mH6qG$M>vJ%-ebPY_ZOnM~Js<zGE`b&Eub2SHOVJ
z*u;b%s^u}TbVL<^j{9wGPmI5>t~S)Tfm)dpmZ!$_*vFP&Sur^{ip_Mk-XVxhf<=k#
z`D<tInR@VbK;b<dVcgG6y0`sX?!$)<+25vV12)rsrTUEia)c#j@o9ZMy~jZ3p}vxg
z_zqQWlPwk^Zy?C+?BZh6NiWCLH1C*0{!=Z{c=p!jgwk?`hj~EDKA~@#D9x-0wr6b7
zO(Jjdp38j5beHgjG@a;Tp`SQBwx<o3v;-a?)xp7Nj#O%Zv)?LhZE;BYy4SC3VW~8R
zTrAPC$)4C-=oY7e3wdgKur+Din4jYJwaTsdpMOjeh0SkFT&K*|b{#z$omEkB>~@lZ
ziptB+tTKAjjY*^3yBE28cyJM9RSEG=NKo)RSn$u8%=^}gmHOsFsoR(P!tb#2(@9PP
zzcIr@eR>QI9J(5RPwWj&>f&0+qoW~A#=pOX$)#Nd0%0R<z)Twcp#kb!x3>133!3}a
z6=L`0$F$Muy&5#J_LY<X9gOsacuck>K71%b3CsOpggtn$7M_({L|t876|51L-e`7b
z#OwPPusJ%Bb7gf1H(FLkg4_Yv$W|Jm8rly-nNhE1LRj}ezZo^C+2rpDn3+R=*5*<H
zW48wXUBSz&4@EIhP+5X{1|vh&|J&EERd9&#Ck&7|qwJEEW14X|vOQb%?VqoGWu7%O
znBHDH1E;2a^z>PloX~cF4c8j&BxBma=O{E><-?Q3B1tay*z)1R>?P$wN2Mc^pPPrr
z#`cuCxd41T$UyGw>I&ECoo4cspcx4mv((0GI8}cepnrrzgCM~7@+c8-+O)>MbXT@M
z`f-CqHy7#Sb0^vh%%xXmvE01+{}DAbHZCk(EleQUj`$uHgjz9fj^N;0PZS4naCQt#
zqGj+Z#qBt3KaBO<wu2rKu>)fsDIB4>CK-KM5x~`mLw^wjlrb<bjj3CtQ?f?|W{2eg
zUZF!jQAaIz2=2GGjsW?V2mT5Sw;fLk9-sT+PTjVCJwHgl#>bB<$Zii(smNuyA-M^~
zMxFymL_BeCA(Z<jP^CAc?<sUGrEfa>&6%h|mfMzPo&n{xsA-($+1Pi7yn*BG)wqMK
z`JdQCLNsFDG*h~W#=f$$GBV8rK((xOfy>?CxviwQK9q>0VYjWVG+fr#Q#?m|doN(1
z$*Zd`H)u8)g$Ybp>*)!UFtCev{$NHmNQEJ&dmTP}*h<vi$43)ldU5h!tNSsWS%8|?
zIdAeAYU0BBQdeKU1Bx5Oa~BIK`kRzpi{Vo!P9zBbz}mE5S>#H+vlorF3+oNsrK90x
zvIIf+6oX<sE=uOz@(Kz&W+u@T$&GVe_^CxjMG!~?N{7Oerw|H3W-fS$p_l2u3mGNW
zduZS0@W{xJgBc>ZvB>_gQ65uMV}y|3tv2YQ&c(&mSRn`#6B*=gq`MY==9tY{e}5-1
zoTt!HbHLKFB~=#d+@;V^E;p0N>8U9LrMQU7%%p(Gs$(48$k8BmH8ruym&r;c>bG$a
z&ap6*Lq?LJ;aYOM$}((JE239wdrI>!Su%!gQ*=U{b9P=0AatVsxs!VUIK6WBZnt~!
zTG%F#g~g$_87sh15|WgR#C)@seqg5b0$Yqx=(K5cy9M=v<g2bvqWw7;*VhRyjfjca
zjo?skJTKFEgm@hI)0e8C;RFfu1i5J$TGhqgec?TM_Ef+R_7TUBW<gvd^hpn#aTysh
z#@I90g+29~6Z_mekm6)8{Y$znN;0-dvvO8NOKS0Z7fTb>ixe~d%JJ&l8CjyPMP6F?
z%V*@J3E@96YV>Mi<ziuki|O_OT4D;lB2ZY)hdOg|a^g3;E?d801Ms;Qa7_Psd%G0E
zCd5EBaBUYc(0idu{P6s<-P0fni+qNK%gW0k`{_s*uHCp{V;c*w7K|jmqTlEPm|upl
z*y+Vkgu&E_uCkT5Wf(G+%po^ag>=r1E5mtDNJ?r69v32$S^P#W+(IU%-(@gY<FnFK
zr~hHIMKKkNd^wUN3G~wC%P;UR(BSYQu&NBw2OvEbt{b(Fre*G9iiYmI057n64<4*i
zQsTk8aK#4>9GC)(%<D^+KKJ6q?Y+^`PN?e^`A@1Xlhq4HKSc^MFvocwZtflus{^e;
zh3d|-Eq>!5gQWKbkk%W6=K?NWx>VKJm~Gy6nj298?E`BuQg0_Kp=wN=m(@S#0Q1~^
z^f2;n-!oK-x6%6b$GTU@nVC#fqe{b|y0>jr9DL)>@B?L(ylJ?h3KU9Ls{&bDyL!nB
z4+$>@>1Xc%OaPIf&OdqdG%DcNZr*&+-QE2$e=Qjo48wlEvlw=ksR-04?mW!Oszwjt
zhtF4TyG(uUB{NkQ7bR?D4}JC`ztkQ_9Y{>P+HPal3s~L7gjJc}lejc$68IBjoIuqD
zrc!0S|9yYRf8S4jCVWro|Nf0s5Wio4E0e3(@7L-`azXz6iXe6E@7JEx|C^6}nkU|u
XypWRciTpse(C*#6&oF1FL&$#sn4Qd9

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

diff --git a/reference/plot-9.png b/reference/plot-9.png
index 26e1d4396f4b4c0e1290c10aabae11f0dcbe5a7d..e659895db99c695d3f0d966566b431bfdaa173a7 100644
GIT binary patch
literal 26304
zcmeIb2UL{lmM&asw{{yFm7EksR0Ko>lxP4EQBhGqNeUv8Maj9_MoSP-P*IVfn2;nn
zhgO1!2q?Kof&_u0Kr%)6pSL*YOwY_Zch1Z|=l=KpYh0@vgsS?!?|t{X_p_h<?Cp(X
zN7Ys=<ylIhP*$iPR5?MR{OC-fEHeISF@8e3_jnLr78`4*so*RAOzzM2!k3>f9Xx%7
zLgBwo{#y{(Ib=$qtfQ!_>^<rHsK43m{warC<-v;Za|7>GkL}%is4AG-LXAhdkoTI4
zU;Cw9&#ODc#MWq<hHtDkGWG4EpFeG3n5FA{LVcH^fY{H+7fBd=*tt>bgt+gz<v%Tw
zN*<q``EstPV7b(CxBA!zb$u>fE_rSVjH&pFhp88?b(cGN>1>Lq=w3wb&%1id7RLqT
zJBs~HZ%*=c-LoHZ$XCi1;kF;i*MnOYNRY344*l%Rdg+Nn1<S|}Zk<?YNWOaCSv|Or
zeDn0qKkyOfmJD`Ec<p{)Ru+|$qafhcpJ*Hs?3x#W_wRA4imLyS!q#9V!^6wFK}aZ~
zf;02s$neY$_+{2Isega@`UOqS9sai~x;I2rlq8l2==EPFH*)ajzkkauLhm~-4;8M_
zEcD?t<hssx`QQkhCcAa()+b(bvqN82dF?tXPfy9{yr<XR8DbRRuGAVYS(jiW`1yrq
zpj3r|)L4UA#LHK&e8Zetb}P*e1(ww481dWY3?0ZW-aU}f@dOWrXX~i&n!Tc;vQt{R
zXyn`Hj@aanzU%j2Q#p0&RDbSx%TSSor=+3lw`UwB50t$;_3@pg`@~>RL$cWjqg7dH
zvQ)Ep(JF;sGWFv<T3;S1&Yu2W){)T>;72a{>eVabR*!N23Xg#+L-i(ss#;o~wF5Kb
zmfG>>Pj^a)v@%Kr++u4?QXLHhJSV=~-(_dqpWf<uTuW<BXJ_Y7e{uy!Kc6O^@oNTt
z%+#j4eUL2K$UGXMlw0GOsV^SxHjtSy@=2qEx*>qtlIx~+<j4{Cp8bNp<mm;R+kVwe
zw`oobKZM;8akv{2qUPb@5z34`o&3B(iGJ!GGfm3VmTbb7doMN{x^&*1#s@wKq&j-c
zd=JUDy7J-M=NH|gqM|fD%QwIOJSlBiYh@Q36{sZlZEkwFqPrFQIUTw8YTB-WB2J@6
z6`hwS-IXG2FJ62X;W_n9uixw4`s-hH5)7N;8Ff!o{k~u4chn8-PQxRAjqsYCbo4*{
zlTTK_8j+)ir>3Xs@@+<)Lk&ZIT*mQr981L!DD5=R^jlcBmZsv}o6Onq{QPES?r5!E
zsi&~8@Z{9gRQVLPtkY}A&nU%JfY+<0y87!2&4`vRJKFHfhtA98#fI6A)}bAF%z^Ck
zF>{T_4BE$uTyiR&hH~eqzILDdzN^i9QTu3}kwpFd5dF*QH@T&r8%XgnvijodZS!ZI
z#K*@Qn9SkM0=y<eyl(g`<FrX^J*8v7j=gX@pWLwP+tc9o66M(;4yN+V$Y;Cu8zN~g
z)2%ZDPLj@pZTF?jj)r=0ik(@zM8Ns%hex}U%kyZjq~bejoqHZ%XPJenA4h72qESIZ
zD9g5cYea>F4BhCd_hD$+W7)Xz;IVHnA_VT<y&F3EK*svaBiG*OxbTwV+St=;Op0&b
zkZ0s1hu!7W#IqT<lASCFP%qH?LF@V98>yCc2UH4iKGrJFO;(tEdE--6SqUo@znrOG
zT~}AqUgXz-Q|Kq*@lE5nfkAVxbD+ZagZg$w?4^B*WAz&Z+z0>AO|>}n^>XILix;Ub
z!(Bx$lxMy@t>{b07Z7=LX<zoff&|aem_$c<BbTJHio);*Df0ch&dZ55%~{UhOGQj7
zBloJe4u6!F__(|Kk@HBETGaFBn>@b<M-(QRmI;NHACL+4mu+$0pfvoTV&jZiML55v
z@{D~<iDAa2b2s^R?>PPOv2AOfT?xNaVUV!Klj!IzxOrIbrVDuK6241&l3IJJqB?NW
z%!ixOR39gF)IQv4eq!g&orOxw{xyM0Qx~giYE;ET{M?lUVVf%zM=F2gk4Z_{DV{U<
z=If|+dUI`;5`OXg@PnWnB^Y&j{oGZ->9b7^bq0xN|M(5Z$ABz=g@vzEi;IYW%h%hx
zdLpj|<(WlzIBqk_%UNWW<Y8azq1Vq`fz_|y%kD6HNp=**syzAAXvExfw>3}9<Hze;
z^S$QI=UiQ-<m@LXoj=(X8tU;o^UHNX*i*5_0u_~694F0tM)lR7o@vN3FHRKvx@ee@
zGknx<=1QjiEF9qBDbIl`5tgZ~RJIL4o`N$yYU$24@V&}5O&L$;9+-tUShVij{MDiV
z-Rc!=x0S#AzUI5Xzdy~se{2c{dYdmDuCBGR@^#T{h4P&HC$BmKC+pE!m;S_(@5vX+
zEgYDO$%mbL^ZWS^wjO<Yabsd#t5<|Mv~qm+B*7<ZrIfSZ+0ot_b+-tQv4-b~;V?wq
zG^^9!r+!@`K!uyrW=6ml*}eH$WMe}9Xq;`T%gqIg78kCNZTjWG;pav4?EWN;NAL>e
zE!pEOZc*4)v9XHtJBH}CUh*!A){Q9~%v^ZRp=vEnRTY)fr%x+P55Cn?NDm4Mn)s&a
z^>EvwOE81ABaih%x8gCWNl8if<s2+Vda6va`D`l`#}8i!mBGHR(Nst*nw4#Jzw#+>
zqTNp~*DGLm*Zp1J-)+!q&fBZ5u0B0@UHP!#T-y@mi?-irr_7qui}G6Wh~E6c-swM)
z7eAeOD>!>8wD!;pT>tG9Wf*6n_ku-@P2I0z;tw@m>H2WvRE)ND;+#(YRE^C3!sAf~
zgy~8iQ-$ZQ^QRTf@-8`979tV&{Q2|psp;u%Wz9xT1N-Aii~H}LJawuF2O&6TwnK2P
zzDtS!)VscVHeyQIc_Ka|qX}M$=0;A<4sm08`JNt4u6=Q(eeiy%>q;1iHvOq}hHPv@
zU7@zcQ=@&$cG#?UsQsJaN&f8Y*Y-(6;z*;><a-@sM(%jP7Om%nS1J_iF4Sj5uaNq2
zgV1b;@@$j=vp;`sw%jm%bTFl{u~DXkn0oNoyW1ORkNh*g-7LnR@+pO}dJ}cGj?*R9
z4bJoioc&a{{5aO#ViWXV2%2b#M7Ur6<VhS^kuSU07_;2ga}7*LNa(;#F1BoXnqH_$
zf>FK_IWZIOb@EoL@!6^Lr_pSVA3x59&{m#j=rLMzKH{UHS>^`0J{ZFcL1q2-f5=fC
ze7wqIGkN37=;#|%YMr5*Sl;YtLW!Ev_uD<q*-iuld{i87x-V_1?I)!t`>uO<2#+qD
zAp60}N&{Q$-nK~apy3=@WaGK94|}$4ne2|M#u0R^sk&zt?y|{T@X7~C5@e;NrH`LF
z#g0&q`?~FJ%||(UfBX7|gt#j-4umHDRK%*a?jO=l>gdS29c=L!Z>}@Wk4earGuMQp
z@iXZfo+Rkw34+|vP@tEBO~#jN&Ikj>-#$Gf?iOdTJWr+;2XBSh=iUG2Id`~@^yucf
zyEI{!NbHbVc2Fl?pF6$9)soL+q^b(*moYcX)Z;Vx9C&fOzahD~q}4RUAZV;H)iS+4
zJ+2{x)|f90kN#i*&yeA*bU0sKM7&tNbn)6aJ<b&?R+QEiFE}e6qA}L@LE3r4(kuM(
zuX1v7B0Ey+3U90l9=oe!nAtG)Oe17d-|)SA_lgk?vWteqb4NZ+z;5&RUrW`CNk(L8
z`CDXBVP|KTgv$Z4Sn?z>QIbPkpKF!U#Mwu7#oWiwoZ(?(i_wnr)%57q8p>sO*e1`&
zt~fzkY9X73VEfbnQRWY+9tEGDZls4;=LVQx8T@03$f`;NygK9A>B;gmK&57NCf38>
z*Vk9OWQx{0%b}xhr}}-0;G63LuA2?LW=5UIo1Hcwc(B}hg!>9!nb-oeYESDpw`|1$
z+JIv|iSc@y+M4&?xpSuwsFjU`t0shz<3>h;FemGznctR)Q?s(NE-=sUcyK{OEN86I
zk}jt$9%7l6@|J)q{~AuiO~K}A+Jj{h`AyrtS~BX2SKXlr$X(|D{QT+Djd^3K3Bn1s
zF!ZNk72PK9?`$dUlW_mCOpmWmM0ryElhSClmI<P?-SkjNN!uS+N#Hwl=#b2^Bb*Y3
zIdLy4pNuJ_#Kv)dp)vF5E5~_p$FEHnw(y^X=hJuo;$ugahM~ypPYxZGhbIhnwCsCq
zWAS;(t`$5xUtT@s7;h4=dy{!ZxMDW-uD!lnf=Q!Q&Ps+;KC}6}brYwd^ZFL|;c}|8
zt$*7Q!JhPJ|AfA$>cNeV7Gz)Ue19RiB3*S;@QwHH-+zTqkD)bn13ud%Yf3U`@vf}p
zi(4}|(6$oz=xMI~w@>A1UQ?eVjy9$@JL+Y>NzjnU-DzG|TYG@flAE6R{v$TuY$IJg
zcvBG^lb>#Rnt7kx^YVdghCpg+D%((tTrzKMQ!b)juoC@eE7@>^#&BsL!0_ULJG58+
z31tzpBQIo_whHo@((-RSXf*?}SdK`Cg)MZenW<5`QsIykQEFU5LZH!1uTE*-y?ZCP
zc3(a}(pM+pqIluMeKDoj-uUbRU-Qq;;R9B>Iqh7+Z-0!>zH%=cTcTA%(nO(%Qq{oq
z=VJO9+h_aoXJbBFh>kcMj=~KK)BEDH#cPo=kW=@)Wy_04pSrksm)z!+7_AI40`?o4
zn{A!z+w&|=9(hRcm@211b^ikM(5#LC`Sw4S2m&L2%&=`H2V{>WeAlHSb{&<u*%_1R
ziJ_sMsK8=9+gx^la<U4M<ZQIgj-|6Q7i}DMHZ~wTl1-EsZxt1_Obhr1chAnGryBt-
zO1bo$fr$eT7b0kU?vEP9YL+jI=Rbn2<CeOhqF(Bj{talHXVH?CH$!c*ws36It=#s`
z251eyvIuClo#Y{ZTwPL9;W?IkG%n#*ouog>gGB1O9$y#mMTFS6;2TaotzTK-jFji}
z#8Z!Q>AFIlyzh4b?PY~R$`Ff9OiVt6%1V5c>x<=5bhZDZ;tOK*kw%G}!F_zl2#3Cx
zNr-e;)z@=L<c^$hhn)l@f0T<cFmbH)pfxjCTWPB3Yp}=35mCAje*1HL9w2~i^5{NU
zN4ULWlG_1~7^tY^O!dY{T2#Lj3y{6kR^UAG<vMNpCy&Gx0t&mjK0H`+UC`5cr;JST
z`@5nY08D|y70UVb&xDsc;7leZ?f3`?dcKP;n?G58o<=q*Uh?R!FTZoiSm^@oEn{P|
zd%HrGAd*UpIdWDd-NyQzhl)7UAAk4ak+Z+(-X#^GzOUwV@&#$POJAPbz(6zFep}Ya
zmoCMi4a}blF{!Ig@-Z)8Qc3zI>DXWIILbh_sW8<OW&JTF7jC2j?!&xJ?s-Q^VBq9%
zcgaV2MpHbGZ(LfM3<q)npmy_dB@*)qU=GvPGBYSinQZ6G&uDN%T(kp7PPd%<^y!l`
zJcmjB>vPs)GasGu*2p&7Yu&kf*V-XW?=lP|-Gx6UCZ+?)K>$*{<mL{J?|BaMCs*=n
z`cGr6UUM0YJf?9&lIc+8{#8Y9S1I!m;6l(g&|3Q<!HGS5j`imxaej97zKnXJ7C2_`
z+^(azNL?gs?J9-AJs<8%ZXhsw8K?No-rnAqadG}6^V@Xz{xAElE&ujaYFmzhV}tMj
z(1dQ%dG+aDoqXRCK`(`_k72xi9AdrzXe5a;o*4Whi9kn8X9T|Hvt@5=-~CM_C0fq4
zeI4;qUvsgMUNQUX^E{Zv#1~8uFaf-6rhqe-c)m2Khn8-8lS@)4K(6=YuFgAJ#%%?^
zhS@DWuC2|1SYV_8H)%(+PA~fKKnk(V(9bZ}wWvx>kY6Os{=`i|WhDy>3*+*K(t6U4
z>lu11XImL`SP6_p_D?vt6iQmSFc0gS{}o*lV)F%A?#;uSlFN;VB_?V<GsvP%5;nzN
z=ijm`CkbuiXF$jh#G;Dj{{`~pzslc43lxb*%EO9}AU$T!ULJXs@Hers+k}MR=osDH
z-YZMb??B4O&qmlAKn0zzzLbM8hz6U*#QdpLq}E8{RO5f@0eA9hvxEerG7r8OXlx6p
z9zTP{m3v{SRZ%5w`7(WdeKQJ+D%T?=FKrEWS7I=JrtoW@I(2vYh&hrbD;d47zTN?^
zUcT%Z%)57ywHp7;9Mykynf`m3vj5;NP6irt?y0+5)@^bmExO)x&mZS`d7nHU$(`eL
zOHW+S^N`0+pKa|yCF2)KNya+(tUXJ9a{jh6<{?kV%}gn*IK{e0=QwwIjlik?9azL3
zipEaXTK+>U-hXg!f0qbk8Pc(&2CHdgCjJCB&jJ<_;vh;bP#_ULyo61Sk4;U$<@}wH
z5L>&OcSUuIQ_Mdb4>V;aA}!z|KDFOO&~HPa!j3~48tI=Oux>g<bN+CstZ%yXcEu1t
zURn(uNPMW7;VJ9b>`=#%UV(rkj{FUF7;4Rn&pG^HhcI}*wCG}>wu@F)P0uAioi{bv
z|47p5LSh*ua>dNntsQBNQN9L??RjC0iC`VnSg{KP0DGno<<l!*^wRTb?4sH#GNEFZ
zRrjck_BTi&?8>LuhCKKqyG%U0up3m#uEByuCLsonH{ihDT}V#6^ySS2@_E7G?utM^
ziM(Al`7>kh&hIE6GstyyIQ(!Yp(Jkd%8C~U3JL-p57p|~76DjuC**uTqz|+zY5wV{
zP#8aX@`A3c4?H<*HXWv}Il5~-QmjB6uE4{a)+oA;P{#q7LB8myAc5JYm$nVTBBdcu
z>cR)Al??9&wSb!>h95tDn)|`wO^cUv{i0*waOw^~`=i}gNVcK6ONv~=`<}xCZzl~b
zl`+T{;GYZS4=>QZPr5gG&CRf}Ng~FVkHy8u2Xq7~D+vjOBUw2bzm~i+awbc)fL33B
z|3eeMg7c+hYf+u}o1=YoYAPGo4?bVH`|B+#)p_dM3;D!DtP90`Hmz;5R#%D$1auO$
z0W*iy8nT@mvn6a&Y{mD^vJFCnr$t`S*uINk6e?B6$iu;}#L~*@@P$R`PZ1IU7}KR+
z+_GR7evl@=;FvwQg13H?i}Y+ex(Rxpm|PV3_Ryi(-L?r&o;)d9QnKjV>u*wT(+-3z
zbLQNp`-{=+_Yly@BKh@;SMaQDo$iwDh`hS8F?R0NsTiU5i1jua@L)nhPe8%Ij$`&-
z+P{ClJ`&HkfML))>#i;5{xon!J^R|I!NAzUo}e8M4v!$u!U0V`dFD)S_bTt->h1UO
z-V#S1$KR5!AzpQ>{6gqVLJepdla?G8G0!NO9G&CGt9qSzmt@V#E+;Eff80KE%RPo$
zE7Q36CO6xVVP&@SP<sXp(xkUKdgyDgW-&;w`fac2RUNsue_V5L)(a}X5Ij~a=v7bP
zzx!y6k+lQU2DwGU{+6ty)D>}FnhWafz79{?jE?{8D;8Y3F*z2GgG4X>9s7b?+5nO2
z#mH@JPw+ggzjiZeKa=Bi0RK^QbMxguP9&ep=y~EtQY_ZhZ>tGB82N@o8h{I+JT5>B
zX%QR4BT7#%e7z7M#gFdpyrZ37S5qTw;;^KThu7C&A%%XcqC2~$Rb+D-D4C6I<(-#3
zXDL0eaY{D6AT(L>&lY>KtjdxM3@+cecEohY1Hz693AJZ-hg+suzujNa;>o0Id$Hw#
z!i8NT4RbKQOV*2`_d@T79JaWGFdFDvaw4eIB0tXb`fku#$p81RQcStx%PN8Nc-ycI
zBIiDD(6Y*-XMcGbxy<C(;0;3UKwS-1tq5Jt$ei%^!MCdzUk|ZdUt@yC4>_XRvDEB=
zEYTJK+lv<`y0a3nr-b6m=uawZM_s@l@o!f<b%P9TE;wTPJn}-qFB}e&ljb%7ijs8s
z{*};xU*29X0U4A=+s*py6ye~C?%^VFV?8mPS%N^t#-c%6Fx<Q42?@-Ro)73r-prA{
zf_ult_{^?{FJohyp3C+1_9BC}3oSkveAj=4U2)TcBJoAuG9xiBZ_+FRw>olHT9fn@
z@R5&=apd63m>AK^s+6av5)5;LOht5Jb-%2)yk)Ei40~y;KB^#ftt!%v4zNQ3nOC}t
zlPi>X6LQDS5T;i=yMHwhk|se=gaHL*I{KL(sJO|cJ4_PP8)2$*V4srqJ(UEf-Q<zp
z>atas>`g(y6O9jTM@T)Vp6$UO%V;6oIh3`&+&PsFR%Odu#1j`wkeBUo+W1c?K>b%k
z*Z<bw{&xfD|J=ntt1$V0v|aQb?_Zb|j1&$qp-9{$dyAiYq6Q^03^3f!V4*!P@0{EA
z;L<;ZHehqWBR}00uf@`YrPdDGsKKLGMX8NX-jj_p(&=AKxp*B;x2GmXC$Y@iCK~7q
zzIQV<T+xjq`-ii<$cY`DTL*$=1}MFKeNpf~^`=&`4*n~O#20uwl7bA-m~qI0<^#YM
zBLmIyLP8H8J~RgP1CZ~X(hG7Ksimq)A&Huy<s6wpkx_`7K@r_?!fnj~066JWrnIn+
z?Ca>$AFt6xA}VApnTX6$GP9uwT{B01wRyk4miPh6|0;p{-%_&vCtAkcR#6B8=CuI-
zi6Z(x@s9rIF8)ihhW`(Cqy}P%weJaWHQ5~z*g7{mK2(=KE3F-?%a-dl9&qmMwIcA-
zoP_$zF20XS?}spXS@PTc*H`)ndi0yveZ8%Az})<s<1Ut-!+Wf6^#TUYYkP9UO#cN2
zk`kIE2%h`9uUx3Ett|%GduD}2cX=o$r;)oIN|;}B@r|UR`%n?#?u|iMqvBCz-px9-
zuT!&B3PMZI8y8a92m@8p;>O_jm>RhOuS1Hz-QjNH@gTRHyB?UegVzoCd{o{#c?KC3
zfAFRwzWU;v9mW2BLmee*s;a6v0C;wtcXf(I6LMD98F_giNs9i-YzetiWfBkrgA}EW
zn2mK$<`V$UXV0G9E<F!`&ng~~O1z57Gz17Ytz@zrX8++7h{mjDJ<XXK;e{<}^ZYO5
zCMNK6<!PO$)S#HMLC6@)Y$#n1^at1~cNG=FF@@=GA@AJ7bnx?b>Tqz7HBfWve1A95
zb7qQ4<=T1im>*N{^1Jn@zKwj=bUNX~x{O`|VC!&KxgT|WVH4ct1=>9cBLOzH8Rod{
z=mM+1B|NEXVpud#y_`Y&dP}`eC<~Dqgo=v(r9XZQwKhVHU1eIT(7N>6v5Y_GDeg!i
zEFB(cy0@>1`?z|*mxm{7n*L*@Fpw#sVhBLQw@(e`6P1;;T`Z}KyOL3%g}3$b6Q71$
zG7RK?E$h6@+6-TI(RQRB0jp3P3mrALvbsqv53#AQFV5eN5_8M~ThP1!yQwvH2LkxG
z30!n5)6AU;I!p_rY|aAd)gYqpO^<r|w6H>u@+0!l)BUTxtzWN?>ZgiltL#S_Xqk{J
zGmGLXY-mF}lq)ugMu7qh(<a{9FV^ZZp+_2NLC;wze(i~nM}LG?bazbkB?O|5xWmHI
zvg3)LxW(A<4F6G9cp|i*c1a8<f9m*~w%QAf13BaQrXVj(VrWZN@Z8z<a}|HA%|*qX
zOU{<uF6+!<<t0d@p_I{@SMs1n22O=ye#*cg?6AHYXu?p%^8yD$#Nxy&?}>8`+Wh1s
z=rujWrR44m`!x4UwqcI;ak8<2vUH5;>-(21#d@e7_Bm7)=$TSOcW`h(S+6qubI<BD
zQ;XdcnvT;H^M|6eq5LDmlQ|K}6gjI7Q*N+ip&kpl4~BgI{(U8b_N<n-epk~BmWD4_
zM6;#+CXGr(xp-r1!M{y&UjN8iAgin5Ye<@tF_6{QK2#zY2z}9Zp!g)2x6RaXB_Sth
zE7Vj7m7d%%_ODVN39BlWZPFr<^6<WCMr1E}eeP{LZ|lT?>^IFTh+63Y$_HQvi>jlw
zf&)uQ+OkPAoDVe$Qi$N%es+&p7)v2mpk0QP(>>9zUYl^hM8+Z15_&uPnY0tB-Eb*5
z55n*KHM<$--f{k*{*`j+^4hd-q_k#}Do%f2d~KJ>zz0B&J-kz=P8M6Is)3N~kwGQQ
zxI4@#fI42Q5&Q2Fn{z7+4<-WJIPP-m4_bAa_DLe8_Ip2Zkq~-te|~<%Z}*k1LP%fM
z5a$S-RPyq8RGP(s?c~u5#wvv`3_kZkn$l1G(B?xg1NXYLXt`mJT$<@(+YsHWEEp0g
z|DVyp{i6Y6slXsK2y0yrvNqw&u|e;25FjM`(@nW}{PA^C;-;M4@Z&1yIuvMi3|&l@
z^X%MAGza(bxbV{Z1X(YMq=`BgxP9^AJmBKu0$(_mp6^dcG)Iu!8Q-3V8rQr!<)g$$
zK5b9>sX=cixbsBU(cX#HxtUhN)KjUZW%r5@Y6MAik@~o615vQ;6XYOoiKK0DF5qQ5
zZfGb#2yz8Tmxgc@ON2x8SJ%|kz{{a>zKfI)>OmTHK;>}vdJ=01?2o;GS@D7%()XO~
zJg%d|Ni;)5sRP{-F2E)4XhrZPu~kwOFbiQ>Qspyy*MjW5fdY!g&})#g3t0|#CapCf
z%&~C`DOQIVdAjsNsbz;UZBekM5_f}@quX7Urt#Jpnpj(NJt-&R_ymxe$`a*?KfcbW
z33}`#&dP&1=8saA^JY>-r4(>jjeE;NVu-3b=ZkPxi{*T~UN<bNBd;XHAXz3zx&+9(
zXg1l_ZXv81z^$dYib3XO(`hI}sLf8TiclK_7UGbA#WKqGv_n0t7*5o9tiNFh_fkyC
zOjIhs^-yMohq!=i&tv+jP2>jlSlC+>gqC#@8kAIsNb$lQBDI?krAR>^;m^Y!s66Yu
z%^+*%rlXIzNSLJ(1q~r0jHgDvmF{Yts!f+mK1@E$`($qw_f>JDJOxiw){5D6^w}UT
z(QHYGjlG%RLASIU$m}kLS}1*2F&sri3Q^~L+`NXobzKe2F@RfY6G}W){ke-)?pi0F
zKmC2z>_i8u0kH-hR!tHiW$Y;jSl5Y<Y+JxUmFQ7Q{-BRUVMyM{vwEji5BZ(7gcRL3
zzZI38YZclEJE-s2U`liV2t*JAc}~0)<aoJt{&Qa7b4cU|lUMncwf(Pe1npb3xrw!z
z^-4>XuF48M$;(qR*MagPmPH>>=1vZB=a8xqsWgF|{H&>*TRsaHVfblIaBf2FaT&}0
zdXn%+5I-uK6S0x?m~vlia?ltk=cNWK$<_CS;4qA%EPJ?-lz>BLR*?$!3<_ZES*+9I
zEgpm0!zY$J1^>m6VKP!aA5~{+dQEBl`4aKEiR}?rLsAsop#iWnA|Fjji^V%pW`39g
zg=y=BIp}1bHiNL*7sB${m8R?pZ6H_n2aiop4%7PQ#`EVC&AmpV1jX!kvp&E;`;SE}
zi6$kR=#UiTl+Tjm|4BgV!r?z(Io$nwDIs~;*hq=geU8~02vw_bD^Yw60qUWM1D>j#
zobzRWdgk=3EICiu*v14Am=x;7L&Fhs3Z8g-i6CM!QA+W_LhYc#+c+}-u7~83f4i=$
zeQ{*ORP~GY{5fWRhlbJA3BaQ9rYn-rgK0Cpy5(DM0Pw|0&C_Yv1f{D^SLdcH=5FTC
z&~3|WzcyFbCz^B+39g@b*30Dgtmo}Xk~3Y*SWJ90gp3m8l#?A6*0`qvv_QmR&_x8N
zy0Uh*-ZhM8(B>GP+>MdzM0B^bj!UeMY2NojY(A*%>Hc><Th4k#3+Qo3*nCk@Tu0!?
z##OqOWu(gfaqG|I10prP-SKt;_i0ysT&)N)LOBzt!13qFnuRThQxkPwGj+PCmVIx5
z;qWaHbiZ`+<jJ*Df4Mr@G*Vmt*=fAdGQqhw`h{_U_W~a!7S+2aU1#um>`cYnwB{#L
z)Aq?`eR9}Sm<p^Qcnq<3siFewhjZj$Kr9>N2wwK_bru|Mn<M8*)<*vmDwYm^{-Gk&
ze?epL`6qh5uoh@S8Bt@&DgnOnRxd9Vl}A@T?j9PW*>X<(!uoU-mB4+gGEgcd-d{96
zLx{w5oDnk;l1WLO2&VuS#Rim7S>G4JLZp?#uvvb@Z4d~X5=cmb7T0QgcG5ksQkZpB
zd-WfUp0K_H_G^=Jj)3fM-zwI+?pcPj==pVn;FghFtXoJaSWPsLe-fid|0YpY83cer
zl!CYh^r%GLs<@Js!c;zUI8#7iAl$y6t$PRWP`jUmO`7#phUa)Tsrpi>69|0#imp~6
zAGpZ}uN##UK4Ji_`KExP94TGMFyjrfW#aRudI8h1jLQX;a-<HioKDYWInxy_T1R&a
z|Lg=EXb&l;1HE?c_XoLYUhz;mM(RE5!Fq!59J8qK1VZc#R7FZ!aP79Bf&-`^aX6Ta
zY(}2C-n@>!(i7XjfUM%F$INJ5y4)3~t3WE7=tK_SBhK2?$g-`@1@g$np5`Lq5TPI5
z^x1gMU+~o!*{(mkq2GR77UZa^TBJ?SMxwD$XWoxEU{C0f6ny%4A$d;Wn5c#{<g6R%
zP^7Q@^P8bHyq7{t{`#g^%2Vwo*YO^fn4Xdq``eNnlahcVje&||x}h_+nVt8Zdc5EA
z;$OY_h0<s=SZEYvmWgMqgU@Y|KekL`=k$P6z8~bhC=5-F(Rrf|_;3BQ?*eC(fs>3B
z3X(s<6;G6EdaeeIt{%bCJlnMwUeMUl1Pzzo%rP0VVcr9=YG%vj5P(=3eIo24xCE+&
z7Axj+OZse&nzAndoYMhT{zZX{G#j+J1TR%cRK}>3T}-VYu7%+>=k^GpE$kEvAMFXL
z*&^jE3kZ5?y5CIMk7k>%$RT0KNBj%i0g)f}qwWA{U(JL0lh*S|PP(l*619z%@w~Z;
zmavk#Q?-+e=34rX9MagL6|H3*_iU|3+1je#_U!dlz3}|4&<+d!%5!Y?eA@zk8+-Bb
z<~o|j+6yWiW$oIp-yh>(*WWC3GE#S$<K{&i+fMWx2)!m=TAtfIu11%B)^H^_G&rX<
zLB}v5*e!hc5hK@SAV@P@!Hkp%<0X!=jAZ-fk;cCqml5&5HbAg@+_;8wEkX#lzzAr-
z*<x5G%9Y_OFxYarOzd{~g=~+U`NoaNEBGN1X;(EhHFeEG;A~kNdjn#)wKi@vS_=m>
zjYZ<Dn0t1QsA~apq9f4hRdbd@U;CPf2^FG(y&0|bqSz^KIPB%i&93t?YLRxSc#;g<
z#l#=0Jbxit&n93_B2GLIwDH+i)~&B|IK(k{Kn2bu19BadS|BjCBfI`=+x+iO9pb_7
z4?o(K_}ZgPEd5SA%SCzL+2T?_yWHH&WaQ`nK41BHs!_VAg<sxrZCczP^FF~vc$KqW
zd^R)QDL3YCFH$Wzl7*77)2s65M?#IOo~RBb271I^ax1eYMn-AW8Szesz78d^kU)8y
z+x9zJN4Gfs^@*w$wZx;$>&KG(*tX<s78RZJWy^g9#Sj=-4TA#_(c)!yvVNe*tq(3$
zK^_!c=Dl}A@MM|UQ@o@?nUWP}8gK$N&FHyTi>F@Pi(t!s)w6(s8XgyVKAL5qg^&bs
zLi`UOxD{4vykqJa5FOUl<#~(QL!KoS9zPyTMt+K5DCWSueKT4y+-4UXTZ>5jOngDl
zuLH5v*BVu8GJw>h>J3(M>80Dyt5%6x%b0~8LhUUKwKtQu*H%0eU$oet2y37p5;E>y
zLb@PU>-@0i47Pn6E+`~qjP!{AXvU(cgva}@JG!mzFECugvZ1q-HGhvSJT>AMJM&q2
zMr)_6EN3=N_x0JmRZLcZIGFroNzPxI9gLks=Sw`!VfZ1D^P&R+mH3;`k8;=$JX#to
zqK4d(RM@q&w9d7nus_CPD&`OOqBGbD5~YzIcRUR~>Pw{(<CFB7o!*zh%XWLCI>|)r
z(6dv5xVM_%0HTcDH!qzL-|R|2b^E&wf#izC<im+TEb58z_-ELnGqHK-u@gwq6-a1W
zsAck_Q!(V=L}OPZ&Wc%Q_iPn)yskV|6}VJG%DqpowQ)m}bu^p;(R!i0jp!bmJa33U
zs;+X*M@^nRO++)i=^PXAIr8<)48tqGzLRBj%XRSzh&lD^ZEVK|#9;+KbbyD~fBv;z
z{Nrpmyp^<k2*Z5n*dgP2w7C5&yJA}yxVm$0Xw2YG%SU#tr^w3AyvOh>68@LcK)cyM
z3J_bKmOnSc=!lq`j9Av{h4Z`l&&EwuS+idRXV(h}!Ex|`ry3#xYUC(+y2xjU%#4K`
zkD^EA{tnYa*fu;W$R%Ego2?S(ku}$AxxA%kI8jg4MeoHUpiov4C?o_;;V;WLwp|>)
z55=Q6fsU-I2J57sE!aA>yoDOZsgaFaZ6}=oh@t5X0|4Cqpe4=h=4Qs#z?s<IVZ~%T
zwZ~$HI0OtN<z=b7NR5&-bkxQh@UpQX;`qwu4f&Jm@M!Hfmf<eWF<j=XNA!8{D3ZvS
z>N#JqA|=;aSJNA;a6oZ2iulmhoaf<YRsWh4`c+h*`pT0y%G%29k)yyF8`Y;tu<@E3
z_X>oC_9xng5FNK6@>nW01Dyg<Po8M*Fe6**UEOzgr$b2qza%^=>D7_5%C@GZWuQF)
z-VWkNe^Qc`oyQqmmt27}2&?9EX&eF;CxwZ&tH-r#r=l->d~~|rFvr=>_73ZQx39y-
zdJu6qJQ$=HPy$X&R(K%;_&^0wq)pQ#9#d@okoDx?kP*$_IVPG8EUt77h!fvo1>!hg
z;?KKJ6c9di96io7sRxH+=z4}Hts$B%n#Us8A-MiG%OuvOK`xkWZv~d`_Md{v0D!g+
zU0-}WWKC+~$^GQmy7kv5{`uW*IAok@^2uNKm2u~jWB)gcLHr57{C<lM-DtX<6E-|M
z)whf$I8m^~cRn*I@IE!-Jx)kRAP0as0<>QyB(%Cb(Y|-l8GLp-9t~cAZNq^=DEIvS
z^se;ycyS_tpi=8o%(V_flm8ciiH{6n9Nf`D)MTxG4f%Uq8K>#IIegga8uc0HT{0eb
zGErZm#K9|LMGbWSx*?nHHFGALu!kRj`()?S7_Qg8NMk}sywwJeP0}Qq)fFJ6Lh|h{
zWR|WmERgTLAV%lH;K29ivbkH}uNZ|u_9jphlAZ(7S|pqEWyMgJZ2oSdCJBAPIvHG*
zSe&EK4j}($Qx7U6<#;wWgk+L5lSq-M$$H)Pi^L}2@&MWRiw5Q$K;4!po*c+WC9Fi$
zPxKG4PQe86#DuLm=T>%4tY|=hWvI#s>~7%XInvPZ(PHehiu|-7V)Xs$6-2H?rQ*8s
zcshfAE%F=6FS(a)r-EwP-NI}cFD8jSt2YA|_b2M2p}g^2i35QG{Os)Xh|8dA{>5#&
z7RcjKpzz6Vc7#Z&5JW^(bcexx7UBmK$x~r%0>vY$N6J|ct&GM_^I3u@79y{MpRw67
zgmnukZ~mGk)I@RTz&H3Tdj;(mJuyEsb5cU&)#j=5=g14a_r|G3TSBSzLrz?DT%1_H
zX3$fS7c4i@=CEx(A^fj2wVqlV69s|9fzu5GE_O1Z)=plQd59VuF&kM|T_6^`QT<c%
z)tFbW9GBVoJzml|;x;-us_K<9m0aHNe4@wJ^K|bHN>9PN-iH&l@_}0r%`c*OT15KI
z*yKf2Ah-z0fZWUi(p}P8!p0UVV~q-lPP?!70P$kg)z$WWx_{O0oeaEpQ5VfViM_1V
ziS%<_JGc^AjIIOg@0F_mhl?g(J*g%bb5BHl((ZOIFTLKakr$KDJbNNhs!=`7#{XTn
z29%AZuR*MbYWHqEv7e+-1#N@xBcAlr>h5GM-Tbgv*^A!Zn!>$(<#gz~WzO>DR|Ltk
zulr~8Hb?uO;5@bs2ePX?v;)n-{zS1%T8-+{to06u%82Dn6iGO~3M>zm1^8`ZC4vM}
z5hldB!mVWuI7fSk9ml^bvi7Iv;es`T;$ZUfkpD64+>|01)k8!$2Oi{N`WO)%A=nV6
zeocs>t2x?;IOFE=OgPW>1-4*!`RoeWB*6i)WQU-2?Y=z=aqO+XcJ10kQ@0W)dJ~T-
zj#lrYr+j__j)#Ov(%?l@Remw?SzDFHGb={F=g!|u4sJ#O@!t-8w`=pfP=k~6=r?d`
zW=D?@OF9jFwJe<hKxfxr8d5}*gQY>jq+63q+T!H#K2|X8iQnsWJ9VRaJCsLA5J{yk
za0WP)wDzf}6e9Fhzer{Iq!hEtB{`w8wgI5Rkbi^8im`sK8G#ZQX}pZ6VI?1xxAzJT
z!s$s035|hOg}lo)wHyj0WRZAU4{a73zejB`gptjuz)I8!4Jz#+vRxGwf+E3*%Uo?k
zia<KR>n|vGKe}?1G_WbYlrgw`#2?_<`zQd3Bm)15kOfy!+t^|iLcsN2!S^*aMPny8
z7wowXm~Zm3;$WCe&mzJWv9W>WwoWtW?m>-3W>E{um#dVgFNMw^2#ElrWt}#kI=N}`
zL<nV%2$1F8(b*qn)K8y&&@DLf*s3UUj-3mUye<aLc3umxxWnwJ0k^5s@+o1St+UgM
z^DX&XYH{Ol98Whydc7li<Lzl&IX8J3T6nPQ??n?*8ZI!$!sZIKP=oF2Z>EN6>?UF?
z>wyGl$Bu-`I3#}>iLQ{j%gRb3DptK37ulQ&;Qz*~va72LCE3k(s@t76hTBr2p-5cP
z%-9S~KI!MN;g`ol-rk@Ft_9jkn!b?}y)kuO`02<bX%KKnN@jjuUY^?LxX;f!KbTRb
zWD^=Hoq{$tSks1AO&wEWwCXN6@y6`wN8S;JHG$#hjs&9hJEgIU^b1p|Z&%94R`kxZ
zEbk&$IRkfm2v>xt1l?d6C9`x|dI_QtmYFDnBWhVzRT&O5IH2;#{*yXNy~acBMMQgd
z(V{WavH#i%USo(Uk;n3(#p@zzfES+Eq_#;AyFlcjuJRnb>cc=e>Mzw0ln62}F@NOL
z|3!`8q1vVdK2w1RhVRST8gA2ly$-PTHEkAbYq6O!v%xTy6)z+E79TF)tr}}D6SK8-
zIqr84ZNIBYt0?IWBaD;tXpN2uI^7BIBbD?RP9s)W+x!eCtG2qj5QnKIN{N&bNCyXD
zBvfB5B^w+${+GhiKgDhR-zFqGsTvJ8-*a$N1J7;Stq$DB);@T;EyyZqIJB$l-Pg1`
zY3nvWPkMa&{8kNvA1TYXqIn{+HKs9X`&OQikgVmKAM*FjB^l^t?TGAgT&^s2xn{}T
ztmPE0lm9Qm{(s9j43^jX|FgpB?=b}boarL}+2uTSym#T^V8W}sdIh8McphCkxbWar
z&?<nD&%~oV^hAYtG|+RyPZYQ>>G_N;nGYXc-0Hwav44pq|JulS>v3O{zL>+8gfDr#
zgy2L?IW{Hw@b|xlk(t?5x5`aTzZm=&IY*kjl02uwKk1*|()Q!QUw2LioUod7ANT5D
z8ytI#d{7nOl-z|<U(}jCJSdfuR3*CXqK<=B@G3FNmO<1DRK;n5ty(-hv06XfT3B9Q
zJ~Qid$C&gpoYdBK?9*k;6IlPG=`H`+_5D9%m;dK3{wIWBL<<QaJ(@DMEqqQ*c4}zs
z$S1g{==Y!3v@0@PG)aRz1Pq3~-%0ePIBsa0Nha+_X!~4?^3^38Db=G)VMlsSpM@Cm
z0P+dPvWV!jPWLWSss9FGv-oaOKH#;<SYPbV%Zc~t>pRq(dM3^)rk|Bv*h0-fvoLyM
zi;k=Dv%iS&bVVws0>Ts0I|-tAAgv3iZWe-LKV-0#+}55y0FQz<k})l){F7Nlieu=T
zF8*2M(JyRls}x6${TUwFwB+n&%~CQ(?`2%t10@ev)M3$C`Y$NiqaYhSnqWmxn#V|{
zoEjOlVg*pCm`5>s+JIaV&Tq5Bf@f)e4@$#v5#Zn0-%aYgXsdd*q~^KCr;Mm-VM^uC
zrlzKZ%RUiO72icab=!8E13Sh3d{5=$<1#68c`tS{Qgy7vSpF1Zz22!w#^$1XXbbV^
z$8#nV-VGT}dc$+mNYwRc%gLidb(mQ02?D55!ExQIxL3Yft#Z-Trj*KGAK$$@GdE+N
zkblFyvu(<I$*lvO(zev3+3D~k2Je%u-^W9;mQv)7%@)g!&%tjWE}$)9XO|cJ&7f~E
zZ4-nGX=U#ljcT4m&0JMCP&Iv|JX(nipn!5LZ~P{gz956rv!XMqM1`(aA>qJHId<}@
zQX4<SUg&xG{$A8NtpRMIO8@e!G-lsk3??80FMz%wI0Kb#pL`;+8db5S8fmD#r7*9r
zr(Dz^iZ{?HY>C79rBGQTt)-YtBke#i0*3|Lq|5AGaWDk5`vI(CdI0~(=ObzWifn9s
zbqTh^K9~N6!<AxKykz7C+)^{}^)fH|8PuO+tR2hWEB3#BI~6NVHqtv_`INV8aA_er
zl8`NdCwA*^#a%byE$C$ie>P@5A3x~aBMWR*3Q6ZKxK&aJ17l0Y01_C=>vQ`__x8ik
z&<?`N!d4Rs@3OG_wCX1Hpr_TUBTF<DM^EBK2{j&s34(ZI2O4Hw?E_F<0OO<ByJ#~%
z=jla}T>9r%cpKZ@mgd@ZZ6$vry+85EyVR$(ceZG?leQE2fs76Jww+#`kT+#P7&~;H
zu}hhKya{ge#JtAlHLsL2^A>l`Et?C23y&=)|J!z9@J#_`sO8ko^Pd0rHivFKWKeMS
z1QLAppgUR1BSWFxizkz7FcX913LqEa3el`}fY3pNmJ<>p;}v#c<`B!298gX!VfD<l
zqj<rZ0w#<O+<2^>ji6{7eLp+9fX%^Vh(?6(iRp2$u@UARDB>QAY^r0bySQYIuc2ho
zktc7?d4W=no93^3K?D+0`bXL@*q@10)`_iDnrNG-ysosCjSc+t7CUKl99dgif0_Au
z#MV{oM$CeB<*4Sek#aq368Wrf{Nl}b4+m{jkM?6-DYEZBM(J28${w$KsOFG_5+cYF
z2%}B<YU79k@&5L6`$!RkG$s*RBD(B{b*7|kis+WxN!vdrUp%kwq}*Da-J*Qv{rMe<
z6-vxP6d$-qy^=I+p^fjx)*lY-Em&504f@7HGPeqoQRp`5WF!rl_MmZZ8$?$`v5IPu
zmhb8X2cy9PfF=prxW!=$<>|Zl@xVG1;SA7YCr*m2M3U|PO~c4HX~$g3imN`}oRRFH
zk#byyjd8At%!^E!V6$~8(|ZDRiiS(8<}7JLO!CmpaZbz+KpKgmC=pX9$;brw5T_8r
zX^^h`I{hn0NWUle*N7r$xQyDj{+cJ$_T$B(htihqfnL)lkfRimj#AL1J|3jn2(LpF
zJw&ei`zf5|tH;aKH{s-ifL=}3AQqws(xwWTg8UCT+o|^xR#o~Ug9m-{wKUjinG=xV
zRLs2G0a_$0?sZ%$7$g{1&)%B{J*N^EL5?%`$TN~D2CR;7qD3W47>p9xt_NXwR8$Z?
zic<HT{RLfMdqm+d+f4+Sx}@oK2rYCQS%bbXScv6#uCA2Zyi#GX;4fQBp-{mZ5shFI
z+5+LuP<n$YM@k@3YaDd)8h4U}oPeeOAj6dWd1Z@HVdf;={_x=6j@bGV<w6UDa`!S$
zKvTJWvav3YgRG^B3ZVF#q|2li7spQyC1dUP-@TPKVM$A7nG8WvF)^W~Sra9oGpKjg
z6tvE=kP>wFa$V{E=&vE3xe?63iQW?nKtpc~DWlubV5q!geBIBl793KCUHuG3jCA(i
z$G9;<e(><{wEK$2Q_1IGMh^!OM8w&F0UNnNrs3FO(AI&%kK8#haz9dcB4ZO!3kk&N
z!C_D3U-uTO)>5+0;Y_ohOGt=(9-0!x>{LOQo~_!EBL?b*01Yz4X#<(DM0Nlg!j#`@
z7I+-N9UVb^Sky{ZM&{n@)mV_%V6c5FFwz2q6WX_PYlQi{e<gQ*1XD=1S~1{)gkw?U
zU1$9LYbj|92B}0P^HrXSFX`E^(9l~uRzGt`S4D&%i4I`38Dn(}fF{<ghy1zmIka|?
zS}Y>_iMyaceqoajvDpRD7)oT<cx|@(aW4KDLE|E_B!4oE1oQtItz|)@5|76uVnOe*
zBj}zc^CSq?f@v%w;?KKT8zVzt1ZV6j_bw?SB1@L$ip;UNt~|Y7#-@o2mQIQktRXKB
z)yE;PJ+JcuOFeZL<zm@cdh4thspszMx^MF2$-e1Di%!o6I+lN`boggSn#y0Hty4p-
zhm89IPj$NYGWwuvtF=(L5p8+_9ynSJEyx{Gzj&=&zOq=HvYd|)AQk*@dOl~rK-u+e
zcvu-#TOZc{`O`FmHln79vaQm~_$yN;M-*$mD+#NgNXc{6lKl9&vho;LAk0?G*dea3
zcj86gp;0cU&YVR<(mzlZi$Ypb`63*{7KsFFy)GFnXw{mRt7*CU!mXc*URx@3UCuOA
z4jF1?;%&@^g#~RVk!4f7|1gDOj*X2}FrbJ)7exnJLj1`vvckKm)+iCqh;REAC_O47
zAb2sN`zJgDqB+Imd1mHrG8zfJWJEzqI%v`7aJf|Y**0LFb*b<&dm<)l5<J;)06`@}
z=c!1B|G_g6)z<`s$GY!7VWUpKR4F45;GGnNgj%5HE4eRe(vE>T^R>hb^exLPc)}`t
zmi>hOW(`<5%5g=+AGa6YkUanH)-KdxJAyRCZ-C2Z*iEA!06lG6A&b+`u!hO|C^>oH
zH@4{=a#@Y;mJXokudw_D>EkDwMNHT!#C$-GNq5h56O6g&ury+x$w4KHh}+4rpYHaW
zHDlpJUHg+-8c_KLVnSpw(I%4$ACPbfWcS6GK(mG=tXwATWFJAcf^d|F!xc!qa5eN0
zZ2~2VI1@uY67K5>eGW2%U~r<-?Er<~4;1FuXH`ymhse|_;D1#BRx-TBjtuL8u9Bu0
zFBPbX(Vo*2CCULT$2c1AR0LfeFQJ8RtPv?jD>b(tr^JBl+?$rqiGo!#L-}QC*0gqX
zsT<}nJQ#jZVU^rkzu)-NQ+3uPGXXS5h}X5YI9tSdIF;;kD!J@5V5=$65q`5|;8D70
z!g@s)Lr#<ag%o~>-KPq`;RaC;3JFbOD64M93@Xltg@mGGVl)6eY4na8BH^1yh`w(V
zmRa))v(BrgwssTLrgPb}Au=fe9XXx`h?d#><D^<WCWhIHZ?p24&5X2$aS|0XB8cu8
zxb%#*?B%cT&Z7Wpt;E4!lTrrH5`;;1D;dLx=e1oPXCS4#+!vQVVD~|<<@>#TOnvbX
zGTO&{oDWKn(l<WK<VT0e@P^A62b9uK2aRSBX~=5!bVv3jFU^5X2#3QPpXja(2YgsN
zHStwMxxpedJYZ(yx7i6C$4toJ<oVQ+%_@#!5o^!QgmG&Y@hvk@bkZEKbrS3d4<L&q
zWs+WB(KirafOyF|9maqB_M;Cye}<MXW)<O3dm3%<4t=!~aL>)(W=Vm20t>>F8MmdS
zi4ldwMH`!e1F6kUWNZ<f-xrzjR^)v{n2sYwbPKyM7mU=6<I-pub|#2I*=ni4Wvx^)
z1M&5?15RM89ms61eml3ahIbg?aX}w5Ug=}O<rg}jWbxXjpYi(b6(Y$b!r+ulFL)>d
zyPPLGL*)BoL<Epz6E8Ydez=92C&(mv1ag_TcJ>IwB`BAS-+wmiiia0tzp~A~tEnh2
zzo@#dBN2~1JC(J960OtwJ2!=E0&s{7yW1!f6zDk;<<_r^xvNX1+8aM#-lRM2($zvw
zZw<FX_FLLLOge?&30j3A6Z3jpj-a2eri?n;=6<6@W7X@UPH5s#*@*SGy^OH2k)Yt9
zitb<(9YoNjau_9$rODN#%N-XZ<4H?V1qp@)z<r*FyC@CCkcfRoWXx!~#hFx#8XM^_
zh+Rs7yjh=tdYie8%~6Dz`=kTyu+reKt5W2zI_Z?RLQWKfG*6f~gfsxihB!KzdJBXY
ze<@;?F*hr*8?9u^m<YT<FdO@cUH3vlL{Nv9n0Dvo<qalt_Ar=rOUiDX?CjAABDy6O
zGz&i>;(|F;%K|l&A~X^CgGgt?ZcJrb>OsbKG|Oq4**h$@)hzZ5vbUa|1FG4YLK4Sd
z3>T9ogMQG2yUc`%Lw2wQxP!B?8KOP@+WEt)S-el@qd_c_R#SK1*uNzHQF>G&O|POa
z{3h(ufkd7<+->ZjACs6($*ipxljbu+lDN>Bl;ul7w+12Ucvj<RSr;#s!i0y(E13oR
zk@c36`Yis38~~wGoc=TPZsP51_LfLMh^|eG)6Yz*>>_<f=@xRRUM^)OWc9vU$uMyn
z-y#$Qz9O|D&9ok~C!JCoq3shf3v<xU9`76N#%_q{(PPlz_5c|7aWiH{jbGHd*(M!Q
zCQQaIt!67FQ`#&-PyCRh$jwy$lsYtbJJ5aO4eEJxo|OXbHCly~bp~NwgxPNMT`E{W
zuWhX&ZtTx1@v-NFbN_o6d7kXtp`3wlm}Gs%3}T|lx*4ccbW~LOp`Ic~L2)bsB`Y7g
zAlgF0dWVzB#94ZexB*_ICVbF2An;_j&cw%bj=dwe4})5)99ppxy7iuQ*)8luViiLt
zeI9^k2zaOl+h<+_^nKGYA?r0PI~1+y@v6)t#r}^UISYxq(jEsxBSuCyQ>kPySEIhR
z5I6udi;iv<rA-bEqf^Ir{1NE~AX5k924bMRLr)B69uOr{K4(=knTi!L{Z#}&42h3<
z!lNkpDT{uy<OUX+W@96L2Si|RoklPv*)2T72dRn_w=c<K#afoWyEz)=oot(1jlY!*
z)x%O)N7-Bi3A$^|7@gMfJk$n^2P)i~cRctYU=3+o0;x{}5W>L^R8IwwiLy>}@#`|v
z>n?iaK9Dj?%S<pGWCqSn1X`z+o!>#A9S{O|vMl|#$a0p_fM2XfB^kbC2u_$>9je<j
z8)YI6iR>vAiLi2_e<B?r&3R-D&6_b|qK>pQGcp_y<ARE?+*~A4Ays4Gt}m8#uI~xH
z00UwZ@!~$79(^xmJ`q(Yv2v2|3V85F2j7r-yg&>h{n~(L5GA2AX|myGv_2$Owq&~X
z%{r=jaR=s(=71KWQEoYsqoUIchvY*?|5-|d4f=bqa#-VNtz>Pd@=MklfJ#U|E@ytc
zb9yJds!k}=vHY@M{qE3AMbcPRHRki8F&Sez-;AL@H_LsxZ)VhJ&LNbEl+>uc1xFoT
zw{!UboEb!PaPwEPXNa~0q$d`4mDj}n>)qDnA<&wWEb0*^q}k#86k&RrAgPgJ*w%!_
z<OFuwChSE$o;rPb1AwCy><Rhl@d*S^Uo45XV9eN?l7F?+AA2=B64S%$W=0)&DW8rK
z==BQOP{wOAUa|Q?RO+-G2IwxMlOEueO!~Lj>XuIpS+2>m_oO2I?83oO2#hCcl-<f!
z42#Yd8K+|glGwDLN8?CMJtOW`TuevweUrzzS3-82JUr}tbMwG352Z(rKvT<E_>*G5
z%2N|c$g^a4<2rT9E_IT|P~^vVo_t;KxRWQOLSguDLU>H);ji!;4)uARjSqoq%#(?4
zppkI|CCFw_Z-xyp26GaW9qZ0KN3v`O@p0iYiJV_B2^CGEn1QaAG;CamTCbCRG~?Iv
z)i00FOEw4Z+jqrCB`e9t$d>HWc$SqvW2Z1yOKy^%<sXSsw71p{ymE!OMNOFSFH_m~
z=B|07SnOnRq{G;uL*9ZyS*$?*jetinQ4!+-{PKU%_?f=H<7hVHK{|QU6VU3K^t!px
zhuN5nW=4{4QK<y=k`P@8mAeVyS+%v(XxrYxZNzGLyg1_4;RP<(hmIkTBdWXOddm|l
zPvB$*7@D|RDvW(ijl-r<_Amwquw%YZ24wWUih(fL>b1Uj-Ir|{aeC<-Z+=BS`?Ss?
z1|ljer*5GOGX}8y90*F0*dr!6I-4@X(KRBbZ`BJ$n9DZ9oQ(1`Ob|VH?)exQJZY0p
zlyk@ilWIIr!m_J9ZN{F*=y`J_q3$yMlYymYS{Ip%M}n<Eulx!*`B_T~i=~{(Ii!0P
zV<vk?8_#?WHF^BZxTQ5dr+DoZ1&+SI-uN#_8gkp=+#HCN3JED;RJoIdwhkz#M=o6t
z*b=4gPk)Y>trku7=i8n1+5^A@UBP*cqTO4bVG3!=YmXL}Zo+4`qo+XpcXd!QV8_}q
zS(}VSluEN{ZlquF=oB^DVui`5i~!5{%uT2%=<lk4>6m<cw3|R^C{4iFlHt_+V{Vup
zRD#BgrKz2lGfC!21_=d<ggXlf2|>`w4ZKA^k?S?8<8`WDspF;;dBCmuTwB$H>Invm
zP*&b_<YBd~c&zqe1MWF8yY`$LC?-javM0^+nD~=;$qk}GA~3=TE&<Z_nDBdht~7^X
zg6?>_XrP^BiHW2U&9%>E1!Zjnr2akZ<TdBqaI;}b14gr*MoVdASm|-g#%EAT5(xtv
z8wf5<ldtb?LtC+w(;aDzVZx(yiCoO#ZWeQ%BIC<!(G)<KDR5)Nr3`*~+d<NIv^GVn
zb;}XaJehm`g$ZY(40_@CE>_<Kvvtn%++4YAD<8s_WqJis&Ow#K`X>em(~ar}CiN0B
zinKEdIT~*70~I`QITvo2v?fA@f?^7oMDF8ydU`K`UdR109aI!qfeGBF!9ad~-}?!8
zFRLcBH~26R<q}HZ8=pYVk30R+y#tw$GaAsi$qa4s?-slq9qZC{&NXgsjaMix;tMQt
zx9;=rmZnsK>7wkZ%0)|pkF@=qmz&dvPltGX7EJuUw~Fd>>%cK9tF<Z{eZqc8T@zch
z&r<Zp-QSe={I)oi-Abih;&l?|5B|B-yOC6XuL8YoycyYsW5r?mGi@%fZf$RU^kBDC
zQQ0l6=#!p2h9gFsHf=gHH>Vp@BPY`^+tbry)atQwP|Oq%uj=B?CuAxoQ*QTe^A)~R
zcej<bkBp3@FPyVT<zX_J`O~@i`O5JL2`0|Y8T2z7gnCC^MSo%*)YQ~$YH2C%@9%$6
zJ2%kyZ4SXABO`<Q>#x5mpK8&RYhE^Yr*0_iO2BY`i9&?6ODoR2*-y?3yqm(p!cvuN
zt*uj2tsftw(&=<4yWQOU%F4?0Et#~D5&NdrR<+bK{zF4U%Bz3#^g$=PvxkR&M@NUQ
zzW&V<(Q8lNQ;k09J2^R7Pow!=zka=^ua9ZOEKS<Ba@DGifdO$DSy>Yci>*o9{EQR1
z?j90expHNDXQwcbt(Rr)zQ?~HF|M+bF|oE5TfAh+hO(s1A!Ui#*}Q>)fs(s-PkwWC
zb90MousRS`MLqOvb&;`TR(7_@g$tYT;0nshCH3|7FY55b^tsH;OlMbD-{Ik5-P5OU
zR8>{gj`DMJ-|+GAd0As&=itCDFE5{2+xX3J;-EkY?c?r`$%4b~X`LR>=Y$=1W_;B(
z1)(I<+|;C!sUJYizvc&@??K43))3kGe~M)FUkl5B9oKXmz8!zORsNz4S$)dc%gbx7
qSBS+EU5xmL-}fJW@PGJ%(M9)f-PX4%z2Zu)t*&}hC4S%8-~JyVmq3O9

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?$

diff --git a/reference/plot.html b/reference/plot.html
index 4def663d..4516c019 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
@@ -155,8 +155,8 @@
 <span>  ab <span class="op">=</span> <span class="cn">NULL</span>,</span>
 <span>  guideline <span class="op">=</span> <span class="st">"EUCAST"</span>,</span>
 <span>  main <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/deparse.html" class="external-link">deparse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/substitute.html" class="external-link">substitute</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  ylab <span class="op">=</span> <span class="st">"Frequency"</span>,</span>
-<span>  xlab <span class="op">=</span> <span class="st">"Minimum Inhibitory Concentration (mg/L)"</span>,</span>
+<span>  ylab <span class="op">=</span> <span class="fu"><a href="translate.html">translate_AMR</a></span><span class="op">(</span><span class="st">"Frequency"</span>, language <span class="op">=</span> <span class="va">language</span><span class="op">)</span>,</span>
+<span>  xlab <span class="op">=</span> <span class="fu"><a href="translate.html">translate_AMR</a></span><span class="op">(</span><span class="st">"Minimum Inhibitory Concentration (mg/L)"</span>, language <span class="op">=</span> <span class="va">language</span><span class="op">)</span>,</span>
 <span>  colours_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="st">"#3CAEA3"</span>, <span class="st">"#F6D55C"</span>, <span class="st">"#ED553B"</span><span class="op">)</span>,</span>
 <span>  language <span class="op">=</span> <span class="fu"><a href="translate.html">get_AMR_locale</a></span><span class="op">(</span><span class="op">)</span>,</span>
 <span>  expand <span class="op">=</span> <span class="cn">TRUE</span>,</span>
@@ -170,8 +170,8 @@
 <span>  ab <span class="op">=</span> <span class="cn">NULL</span>,</span>
 <span>  guideline <span class="op">=</span> <span class="st">"EUCAST"</span>,</span>
 <span>  title <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/deparse.html" class="external-link">deparse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/substitute.html" class="external-link">substitute</a></span><span class="op">(</span><span class="va">object</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  ylab <span class="op">=</span> <span class="st">"Frequency"</span>,</span>
-<span>  xlab <span class="op">=</span> <span class="st">"Minimum Inhibitory Concentration (mg/L)"</span>,</span>
+<span>  ylab <span class="op">=</span> <span class="fu"><a href="translate.html">translate_AMR</a></span><span class="op">(</span><span class="st">"Frequency"</span>, language <span class="op">=</span> <span class="va">language</span><span class="op">)</span>,</span>
+<span>  xlab <span class="op">=</span> <span class="fu"><a href="translate.html">translate_AMR</a></span><span class="op">(</span><span class="st">"Minimum Inhibitory Concentration (mg/L)"</span>, language <span class="op">=</span> <span class="va">language</span><span class="op">)</span>,</span>
 <span>  colours_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="st">"#3CAEA3"</span>, <span class="st">"#F6D55C"</span>, <span class="st">"#ED553B"</span><span class="op">)</span>,</span>
 <span>  language <span class="op">=</span> <span class="fu"><a href="translate.html">get_AMR_locale</a></span><span class="op">(</span><span class="op">)</span>,</span>
 <span>  expand <span class="op">=</span> <span class="cn">TRUE</span>,</span>
@@ -185,8 +185,8 @@
 <span><span class="fu">plot</span><span class="op">(</span></span>
 <span>  <span class="va">x</span>,</span>
 <span>  main <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/deparse.html" class="external-link">deparse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/substitute.html" class="external-link">substitute</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  ylab <span class="op">=</span> <span class="st">"Frequency"</span>,</span>
-<span>  xlab <span class="op">=</span> <span class="st">"Disk diffusion diameter (mm)"</span>,</span>
+<span>  ylab <span class="op">=</span> <span class="fu"><a href="translate.html">translate_AMR</a></span><span class="op">(</span><span class="st">"Frequency"</span>, language <span class="op">=</span> <span class="va">language</span><span class="op">)</span>,</span>
+<span>  xlab <span class="op">=</span> <span class="fu"><a href="translate.html">translate_AMR</a></span><span class="op">(</span><span class="st">"Disk diffusion diameter (mm)"</span>, language <span class="op">=</span> <span class="va">language</span><span class="op">)</span>,</span>
 <span>  mo <span class="op">=</span> <span class="cn">NULL</span>,</span>
 <span>  ab <span class="op">=</span> <span class="cn">NULL</span>,</span>
 <span>  guideline <span class="op">=</span> <span class="st">"EUCAST"</span>,</span>
@@ -202,8 +202,8 @@
 <span>  mo <span class="op">=</span> <span class="cn">NULL</span>,</span>
 <span>  ab <span class="op">=</span> <span class="cn">NULL</span>,</span>
 <span>  title <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/deparse.html" class="external-link">deparse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/substitute.html" class="external-link">substitute</a></span><span class="op">(</span><span class="va">object</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  ylab <span class="op">=</span> <span class="st">"Frequency"</span>,</span>
-<span>  xlab <span class="op">=</span> <span class="st">"Disk diffusion diameter (mm)"</span>,</span>
+<span>  ylab <span class="op">=</span> <span class="fu"><a href="translate.html">translate_AMR</a></span><span class="op">(</span><span class="st">"Frequency"</span>, language <span class="op">=</span> <span class="va">language</span><span class="op">)</span>,</span>
+<span>  xlab <span class="op">=</span> <span class="fu"><a href="translate.html">translate_AMR</a></span><span class="op">(</span><span class="st">"Disk diffusion diameter (mm)"</span>, language <span class="op">=</span> <span class="va">language</span><span class="op">)</span>,</span>
 <span>  guideline <span class="op">=</span> <span class="st">"EUCAST"</span>,</span>
 <span>  colours_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="st">"#3CAEA3"</span>, <span class="st">"#F6D55C"</span>, <span class="st">"#ED553B"</span><span class="op">)</span>,</span>
 <span>  language <span class="op">=</span> <span class="fu"><a href="translate.html">get_AMR_locale</a></span><span class="op">(</span><span class="op">)</span>,</span>
@@ -217,8 +217,8 @@
 <span><span class="co"># S3 method for sir</span></span>
 <span><span class="fu">plot</span><span class="op">(</span></span>
 <span>  <span class="va">x</span>,</span>
-<span>  ylab <span class="op">=</span> <span class="st">"Percentage"</span>,</span>
-<span>  xlab <span class="op">=</span> <span class="st">"Antimicrobial Interpretation"</span>,</span>
+<span>  ylab <span class="op">=</span> <span class="fu"><a href="translate.html">translate_AMR</a></span><span class="op">(</span><span class="st">"Percentage"</span>, language <span class="op">=</span> <span class="va">language</span><span class="op">)</span>,</span>
+<span>  xlab <span class="op">=</span> <span class="fu"><a href="translate.html">translate_AMR</a></span><span class="op">(</span><span class="st">"Antimicrobial Interpretation"</span>, language <span class="op">=</span> <span class="va">language</span><span class="op">)</span>,</span>
 <span>  main <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/deparse.html" class="external-link">deparse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/substitute.html" class="external-link">substitute</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">)</span>,</span>
 <span>  language <span class="op">=</span> <span class="fu"><a href="translate.html">get_AMR_locale</a></span><span class="op">(</span><span class="op">)</span>,</span>
 <span>  <span class="va">...</span></span>
@@ -228,8 +228,8 @@
 <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">object</span>,</span>
 <span>  title <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/deparse.html" class="external-link">deparse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/substitute.html" class="external-link">substitute</a></span><span class="op">(</span><span class="va">object</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>  xlab <span class="op">=</span> <span class="st">"Antimicrobial Interpretation"</span>,</span>
-<span>  ylab <span class="op">=</span> <span class="st">"Frequency"</span>,</span>
+<span>  xlab <span class="op">=</span> <span class="fu"><a href="translate.html">translate_AMR</a></span><span class="op">(</span><span class="st">"Antimicrobial Interpretation"</span>, language <span class="op">=</span> <span class="va">language</span><span class="op">)</span>,</span>
+<span>  ylab <span class="op">=</span> <span class="fu"><a href="translate.html">translate_AMR</a></span><span class="op">(</span><span class="st">"Frequency"</span>, language <span class="op">=</span> <span class="va">language</span><span class="op">)</span>,</span>
 <span>  colours_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="st">"#3CAEA3"</span>, <span class="st">"#F6D55C"</span>, <span class="st">"#ED553B"</span><span class="op">)</span>,</span>
 <span>  language <span class="op">=</span> <span class="fu"><a href="translate.html">get_AMR_locale</a></span><span class="op">(</span><span class="op">)</span>,</span>
 <span>  <span class="va">...</span></span>
diff --git a/reference/proportion.html b/reference/proportion.html
index 5d3a078f..da32396e 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/random.html b/reference/random.html
index 77276c52..8833a6f0 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
@@ -193,42 +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] 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-out co"><span class="r-pr">#&gt;</span>  [1] 32    2     1     2     32    0.005 8     64    128   128   256   16   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [13] 0.005 0.01  16    0.5   4     0.001 2     64    0.001 0.002 16    0.01 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [25] 128  </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] 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-out co"><span class="r-pr">#&gt;</span>  [1] 48 19 44 26 24 40 49  9 50 47 12 47 34 17 37 13 47 21 36 20 29  6 30 49 43</span>
 <span class="r-in"><span><span class="fu">random_sir</span><span class="op">(</span><span class="fl">25</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'sir'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] I S I 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-out co"><span class="r-pr">#&gt;</span>  [1] S I S S S I I S S R I I R R S I I S I S S R I R 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] 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-out co"><span class="r-pr">#&gt;</span>  [1] 32      64      8       0.5     0.25    8       0.0625  32      16     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [10] 0.0625  &lt;=0.001 0.25    2       0.005   0.125   0.01    0.025   0.025  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [19] 0.5     32      0.025   16      32      &gt;=128   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">"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] 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-out co"><span class="r-pr">#&gt;</span>  [1] 8    0.5  2    16   1    8    1    0.25 0.5  2    8    8    1    4    8   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [16] 1    2    16   16   2    16   2    0.5  4    8   </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] 0.5    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-out co"><span class="r-pr">#&gt;</span>  [1] 0.5   &gt;=8   1     0.25  1     2     &gt;=8   0.125 0.25  0.125 0.25  1    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [13] 0.125 0.25  0.25  0.25  &gt;=8   0.125 0.5   0.25  0.125 0.25  1     0.5  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [25] 0.125</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] 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-out co"><span class="r-pr">#&gt;</span>  [1] 41 35 31 49 20 49 19 45 43 17 45 47 40 28 19 48 11 40 13 28 48 39 20 17 23</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] 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-out co"><span class="r-pr">#&gt;</span>  [1] 11 11 15 15 14 17 12 16 17 14 12 11 11 16 13 14 14 12 17 14 14 11 12 14 13</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] 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-out co"><span class="r-pr">#&gt;</span>  [1] 21 23 27 25 16 16 20 20 23 23 18 19 15 23 15 24 22 25 21 17 25 15 16 18 22</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 12fa3b64..3cd881b9 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/reference/skewness.html b/reference/skewness.html
index ca109efc..d333530f 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
@@ -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] -0.03534482</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -0.0009699827</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 c8b0d540..d8467b85 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="">1.8.2.9147</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9148</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">
diff --git a/search.json b/search.json
index c2bd176f..84c3dcbe 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 !) 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":""}]
+[{"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          R         I            S          R            S            I #> 2          I         I            R          S            S            S #> 3          R         R            I          R            R            S #> 4          S         S            I          R            I            R #> 5          R         S            S          I            S            S #> 6          S         I            S          R            S            S #>   kanamycin #> 1         S #> 2         I #> 3         S #> 4         R #> 5         R #> 6         R mdro(my_TB_data, guideline = \"TB\") my_TB_data$mdr <- mdr_tb(my_TB_data) #> ℹ No column found as input for col_mo, assuming all rows contain #>   Mycobacterium tuberculosis. freq(my_TB_data$mdr)"},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"transforming","dir":"Articles","previous_headings":"","what":"Transforming","title":"How to conduct principal component analysis (PCA) for AMR","text":"PCA, need transform AMR data first. example_isolates data set package looks like: Now transform data set resistance percentages per taxonomic order genus:","code":"library(AMR) library(dplyr) glimpse(example_isolates) #> Rows: 2,000 #> Columns: 46 #> $ date    <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2… #> $ patient <chr> \"A77334\", \"A77334\", \"067927\", \"067927\", \"067927\", \"067927\", \"4… #> $ age     <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50… #> $ gender  <chr> \"F\", \"F\", \"F\", \"F\", \"F\", \"F\", \"M\", \"M\", \"F\", \"F\", \"M\", \"M\", \"M… #> $ ward    <chr> \"Clinical\", \"Clinical\", \"ICU\", \"ICU\", \"ICU\", \"ICU\", \"Clinical\"… #> $ mo      <mo> \"B_ESCHR_COLI\", \"B_ESCHR_COLI\", \"B_STPHY_EPDR\", \"B_STPHY_EPDR\",… #> $ PEN     <sir> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,… #> $ OXA     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ FLC     <sir> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R… #> $ AMX     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ AMC     <sir> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N… #> $ AMP     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ TZP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CZO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ FEP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CXM     <sir> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S… #> $ FOX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ CTX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ CAZ     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, … #> $ CRO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ GEN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TOB     <sir> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA… #> $ AMK     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ KAN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TMP     <sir> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, … #> $ SXT     <sir> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N… #> $ NIT     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,… #> $ FOS     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ LNZ     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ CIP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S… #> $ MFX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ VAN     <sir> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, … #> $ TEC     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ TCY     <sir> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, … #> $ TGC     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ DOX     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ ERY     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ CLI     <sir> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA… #> $ AZM     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ IPM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ MEM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ MTR     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CHL     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ COL     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, … #> $ MUP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ RIF     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… resistance_data <- example_isolates %>%   group_by(     order = mo_order(mo), # group on anything, like order     genus = mo_genus(mo)   ) %>% #  and genus as we do here   summarise_if(is.sir, resistance) %>% # then get resistance of all drugs   select(     order, genus, AMC, CXM, CTX,     CAZ, GEN, TOB, TMP, SXT   ) # and select only relevant columns  head(resistance_data) #> # A tibble: 6 × 10 #> # Groups:   order [5] #>   order             genus          AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT #>   <chr>             <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 (unknown order)   (unknown ge…    NA    NA    NA    NA    NA    NA    NA    NA #> 2 Actinomycetales   Schaalia        NA    NA    NA    NA    NA    NA    NA    NA #> 3 Bacteroidales     Bacteroides     NA    NA    NA    NA    NA    NA    NA    NA #> 4 Campylobacterales Campylobact…    NA    NA    NA    NA    NA    NA    NA    NA #> 5 Caryophanales     Gemella         NA    NA    NA    NA    NA    NA    NA    NA #> 6 Caryophanales     Listeria        NA    NA    NA    NA    NA    NA    NA    NA"},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"perform-principal-component-analysis","dir":"Articles","previous_headings":"","what":"Perform principal component analysis","title":"How to conduct principal component analysis (PCA) for AMR","text":"new pca() function automatically filter rows contain numeric values selected variables, now need : result can reviewed good old summary() function: Good news. first two components explain total 93.3% variance (see PC1 PC2 values Proportion Variance. can create -called biplot base R biplot() function, see antimicrobial resistance per drug explain difference per microorganism.","code":"pca_result <- pca(resistance_data) #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #>   \"TMP\", and \"TOB\". Total observations available: 7. summary(pca_result) #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\" #> Importance of components: #>                           PC1    PC2    PC3     PC4     PC5     PC6       PC7 #> Standard deviation     2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 9.577e-17 #> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00 #> Cumulative Proportion  0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00 #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\""},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"plotting-the-results","dir":"Articles","previous_headings":"","what":"Plotting the results","title":"How to conduct principal component analysis (PCA) for AMR","text":"can’t see explanation points. Perhaps works better new ggplot_pca() function, automatically adds right labels even groups:  can also print ellipse per group, edit appearance:","code":"biplot(pca_result) ggplot_pca(pca_result) ggplot_pca(pca_result, ellipse = TRUE) +   ggplot2::labs(title = \"An AMR/PCA biplot!\")"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"spss-sas-stata","dir":"Articles","previous_headings":"","what":"SPSS / SAS / Stata","title":"How to import data from SPSS / SAS / Stata","text":"SPSS (Statistical Package Social Sciences) probably well-known software package statistical analysis. SPSS easier learn R, SPSS click menu run parts analysis. user-friendliness, taught universities particularly useful students new statistics. experience, guess pretty much (bio)medical students know time graduate. SAS Stata comparable statistical packages popular big industries.","code":""},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"compared-to-r","dir":"Articles","previous_headings":"","what":"Compared to R","title":"How to import data from SPSS / SAS / Stata","text":"said, SPSS easier learn R. SPSS, SAS Stata come major downsides comparing R: R highly modular. official R network (CRAN) features 16,000 packages time writing, AMR package one . packages peer-reviewed publication. Aside official channel, also developers choose submit CRAN, rather keep public repository, like GitHub. may even lot 14,000 packages . Bottom line , can really extend ask somebody . Take example AMR package. Among things, adds reliable reference data R help data cleaning analysis. SPSS, SAS Stata never know valid MIC value Gram stain E. coli . species Klebiella resistant amoxicillin Floxapen® trade name flucloxacillin. facts properties often needed clean existing data, inconvenient software package without reliable reference data. See demonstration. R extremely flexible. write syntax , can anything want. flexibility transforming, arranging, grouping summarising data, drawing plots, endless - SPSS, SAS Stata bound algorithms format styles. may bit flexible, can probably never create specific publication-ready plot without using (paid) software. sometimes write syntaxes SPSS run complete analysis ‘automate’ work, lot less time R. notice writing syntaxes R lot nifty clever SPSS. Still, working statistical package, knowledge (statistically) willing accomplish. R can easily automated. last years, R Markdown really made interesting development. R Markdown, can easily produce reports, whether format Word, PowerPoint, website, PDF document just raw data Excel. even allows use reference file containing layout style (e.g. fonts colours) organisation. use lot generate weekly monthly reports automatically. Just write code enjoy automatically updated reports interval like. even professional environment, create Shiny apps: live manipulation data using custom made website. webdesign knowledge needed (JavaScript, CSS, HTML) almost zero. R huge community. Many R users just ask questions websites like StackOverflow.com, largest online community programmers. time writing, 483 254 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  1974-07-07  48  48.67671         25 #> 2  1995-07-06  27  27.67945          4 #> 3  1965-04-10  57  57.91781         34 #> 4  1954-02-22  69  69.04658         45 #> 5  1944-08-18  78  78.56164         55 #> 6  1989-07-26  33  33.62466         10 #> 7  1983-05-30  39  39.78082         16 #> 8  1965-08-11  57  57.58082         34 #> 9  1971-01-15  52  52.15068         28 #> 10 1948-11-17  74  74.31233         51"},{"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 2023-03-11 13:29:32     1 TOB      TOB       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  2 2023-03-11 13:29:32     1 GEN      GEN       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  3 2023-03-11 13:29:32     1 CIP      CIP       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  4 2023-03-11 13:29:31     1 AMP      AMP       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  5 2023-03-11 13:29:26     1 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  6 2023-03-11 13:29:26     2 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  7 2023-03-11 13:29:26     3 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  8 2023-03-11 13:29:26     4 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  9 2023-03-11 13:29:26     5 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 10 2023-03-11 13:29:26     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$date, episode_days = 60) # indices #>   [1]  8 36 16 50 29 40 17  1 46 26 52 49 35 37  3 37 52 40 21 51 38 22 29  5 21 #>  [26] 37 31 17 49 13 20 44 35 29  9 49  8 11 30  6  8  5 41 27 44 12 39 12 21  3 #>  [51] 33 44 25 14  8 49 30  7 50 11 34 10 47 21  6 15  3 39 19  7  2 51  5 38 28 #>  [76] 32 29 22 43 33 49 45  4 31 30 18 44 48  9 47 29 44  2 42 23 42  7 24 19  7 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE #>   [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE #>  [13]  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE #>  [25] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE #>  [37] FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE #>  [49] FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE #>  [61]  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE #>  [73] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE #>  [85] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  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 2002-12-14 144280     76 F      Clinical B_STPHY_AURS R     NA    S     R     #> 2 2002-11-16 762305     87 F      Clinical B_PROTS_MRBL R     NA    NA    NA    #> 3 2002-11-16 762305     87 F      Clinical B_BCTRD_FRGL R     NA    NA    R     #> # … with 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, #> #   FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, #> #   GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, #> #   NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, #> #   TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, #> #   AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, #> #   MUP <sir>, RIF <sir>  # the functions also work for less than a day, e.g. to include one per hour: get_episode(   c(     Sys.time(),     Sys.time() + 60 * 60   ),   episode_days = 1 / 24 ) #> [1] 1 2  # \\donttest{ if (require(\"dplyr\")) {   # is_new_episode() can also be used in dplyr verbs to determine patient   # episodes based on any (combination of) grouping variables:   df %>%     mutate(condition = sample(       x = c(\"A\", \"B\", \"C\"),       size = 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 074321  2015-09-20 A         TRUE        #>  2 088256  2003-01-25 B         TRUE        #>  3 097186  2015-10-28 C         TRUE        #>  4 0D7D34  2011-03-19 B         TRUE        #>  5 0E2483  2008-07-22 C         TRUE        #>  6 105248  2005-06-16 A         TRUE        #>  7 107DD1  2012-09-03 A         TRUE        #>  8 114570  2003-04-08 C         TRUE        #>  9 136315  2004-02-02 B         TRUE        #> 10 144280  2002-12-14 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 [94] #>    ward     date       patient new_index new_logical #>    <chr>    <date>     <chr>       <int> <lgl>       #>  1 ICU      2015-09-20 074321          1 TRUE        #>  2 ICU      2003-01-25 088256          1 TRUE        #>  3 Clinical 2015-10-28 097186          1 TRUE        #>  4 ICU      2011-03-19 0D7D34          1 TRUE        #>  5 Clinical 2008-07-22 0E2483          1 TRUE        #>  6 Clinical 2005-06-16 105248          1 TRUE        #>  7 Clinical 2012-09-03 107DD1          1 TRUE        #>  8 ICU      2003-04-08 114570          1 TRUE        #>  9 Clinical 2004-02-02 136315          1 TRUE        #> 10 Clinical 2002-12-14 144280          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             14            44            54 #> 2 ICU                29             10            22            24 #> 3 Outpatient          1              1             1             1  # 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 E32B78  B_GEMLL_HMLY ICU      TRUE         #>  2 672020  B_STPHY_EPDR Clinical TRUE         #>  3 578848  B_STPHY_CONS Clinical TRUE         #>  4 844790  B_STPHY_LGDN ICU      TRUE         #>  5 409788  B_STPHY_CONS Clinical TRUE         #>  6 789292  B_STRPT_SLVR Clinical TRUE         #>  7 94BB11  B_ESCHR_COLI Clinical TRUE         #>  8 495616  B_STPHY_EPDR Clinical TRUE         #>  9 329C35  B_ESCHR_COLI Clinical TRUE         #> 10 D19627  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] 2.983707 kurtosis(rnorm(10000), excess = TRUE) #> [1] 0.0116776"},{"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] R I I I I S R S I R mean_amr_distance(sir) #>  [1]  1.449138 -0.621059 -0.621059 -0.621059 -0.621059 -0.621059  1.449138 #>  [8] -0.621059 -0.621059  1.449138  mic <- random_mic(10) mic #> Class 'mic' #>  [1] 0.25    1       8       <=0.005 <=0.005 2       0.01    >=256   16      #> [10] 8       mean_amr_distance(mic) #>  [1] -0.27951091  0.09147782  0.64796092 -1.32641444 -1.32641444  0.27697218 #>  [7] -1.14092007  1.57543274  0.83345528  0.64796092 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) #>  [1] -0.27951091  0.09147782  0.64796092 -1.32641444 -1.32641444  0.27697218 #>  [7] -1.14092007  1.57543274  0.83345528  0.64796092  disk <- random_disk(10) disk #> Class 'disk' #>  [1] 42 21 38 40 46 49 41 15 29 48 mean_amr_distance(disk) #>  [1]  0.44150742 -1.37646431  0.09522709  0.26836726  0.78778775  1.04749800 #>  [7]  0.35493734 -1.89588481 -0.68390365  0.96092792  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    R   17  0.5    0.5 #> 2   B    I   22    4    >=8 #> 3   C    R   31    1      2 #> 4   D    R   23    1      1 #> 5   E    R   29    1      1 #> 6   F    I   21    1    >=8 #> 7   G    R   19  0.5    0.5 #> 8   H    S   20  0.5      1 #> 9   I    R   22    2 <=0.25 #> 10  J    S   19    2    0.5 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\", #>   and \"tobr\" #>  [1] -0.55862477  0.58537655  0.77602663  0.17820212  0.51539668  0.02637631 #>  [7] -0.44622659 -0.72591865  0.07694007 -0.42754836 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   31    1      2   0.77602663  0.0000000 #> 2   B    I   22    4    >=8   0.58537655  0.1906501 #> 3   E    R   29    1      1   0.51539668  0.2606300 #> 4   D    R   23    1      1   0.17820212  0.5978245 #> 5   I    R   22    2 <=0.25   0.07694007  0.6990866 #> 6   F    I   21    1    >=8   0.02637631  0.7496503 #> 7   J    S   19    2    0.5  -0.42754836  1.2035750 #> 8   G    R   19  0.5    0.5  -0.44622659  1.2222532 #> 9   A    R   17  0.5    0.5  -0.55862477  1.3346514 #> 10  H    S   20  0.5      1  -0.72591865  1.5019453 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 = translate_AMR(\"Frequency\", language = language),   xlab = translate_AMR(\"Minimum Inhibitory Concentration (mg/L)\", language = language),   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 = translate_AMR(\"Frequency\", language = language),   xlab = translate_AMR(\"Minimum Inhibitory Concentration (mg/L)\", language = language),   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 = translate_AMR(\"Frequency\", language = language),   xlab = translate_AMR(\"Disk diffusion diameter (mm)\", language = language),   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 = translate_AMR(\"Frequency\", language = language),   xlab = translate_AMR(\"Disk diffusion diameter (mm)\", language = language),   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 = translate_AMR(\"Percentage\", language = language),   xlab = translate_AMR(\"Antimicrobial Interpretation\", language = language),   main = deparse(substitute(x)),   language = get_AMR_locale(),   ... )  # S3 method for sir autoplot(   object,   title = deparse(substitute(object)),   xlab = translate_AMR(\"Antimicrobial Interpretation\", language = language),   ylab = translate_AMR(\"Frequency\", language = language),   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] 32    2     1     2     32    0.005 8     64    128   128   256   16    #> [13] 0.005 0.01  16    0.5   4     0.001 2     64    0.001 0.002 16    0.01  #> [25] 128   random_disk(25) #> Class 'disk' #>  [1] 48 19 44 26 24 40 49  9 50 47 12 47 34 17 37 13 47 21 36 20 29  6 30 49 43 random_sir(25) #> Class 'sir' #>  [1] S I S S S I I S S R I I R R S I I S I S S R I R I  # \\donttest{ # make the random generation more realistic by setting a bug and/or drug: random_mic(25, \"Klebsiella pneumoniae\") # range 0.0625-64 #> Class 'mic' #>  [1] 32      64      8       0.5     0.25    8       0.0625  32      16      #> [10] 0.0625  <=0.001 0.25    2       0.005   0.125   0.01    0.025   0.025   #> [19] 0.5     32      0.025   16      32      >=128   2       random_mic(25, \"Klebsiella pneumoniae\", \"meropenem\") # range 0.0625-16 #> Class 'mic' #>  [1] 8    0.5  2    16   1    8    1    0.25 0.5  2    8    8    1    4    8    #> [16] 1    2    16   16   2    16   2    0.5  4    8    random_mic(25, \"Streptococcus pneumoniae\", \"meropenem\") # range 0.0625-4 #> Class 'mic' #>  [1] 0.5   >=8   1     0.25  1     2     >=8   0.125 0.25  0.125 0.25  1     #> [13] 0.125 0.25  0.25  0.25  >=8   0.125 0.5   0.25  0.125 0.25  1     0.5   #> [25] 0.125  random_disk(25, \"Klebsiella pneumoniae\") # range 8-50 #> Class 'disk' #>  [1] 41 35 31 49 20 49 19 45 43 17 45 47 40 28 19 48 11 40 13 28 48 39 20 17 23 random_disk(25, \"Klebsiella pneumoniae\", \"ampicillin\") # range 11-17 #> Class 'disk' #>  [1] 11 11 15 15 14 17 12 16 17 14 12 11 11 16 13 14 14 12 17 14 14 11 12 14 13 random_disk(25, \"Streptococcus pneumoniae\", \"ampicillin\") # range 12-27 #> Class 'disk' #>  [1] 21 23 27 25 16 16 20 20 23 23 18 19 15 23 15 24 22 25 21 17 25 15 16 18 22 # }"},{"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] -0.0009699827"},{"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-1829148","dir":"Changelog","previous_headings":"","what":"AMR 1.8.2.9148","title":"AMR 1.8.2.9148","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-1-8-2-9148","dir":"Changelog","previous_headings":"New","what":"SIR vs. RSI","title":"AMR 1.8.2.9148","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-1-8-2-9148","dir":"Changelog","previous_headings":"New","what":"New antibiogram function","title":"AMR 1.8.2.9148","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-1-8-2-9148","dir":"Changelog","previous_headings":"New","what":"Interpretation of MIC and disk diffusion values","title":"AMR 1.8.2.9148","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-1-8-2-9148","dir":"Changelog","previous_headings":"New","what":"Supported languages","title":"AMR 1.8.2.9148","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-1-8-2-9148","dir":"Changelog","previous_headings":"New","what":"Microbiological taxonomy","title":"AMR 1.8.2.9148","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-1-8-2-9148","dir":"Changelog","previous_headings":"New","what":"Antibiotic agents and selectors","title":"AMR 1.8.2.9148","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-1-8-2-9148","dir":"Changelog","previous_headings":"New","what":"Antiviral agents","title":"AMR 1.8.2.9148","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-1-8-2-9148","dir":"Changelog","previous_headings":"New","what":"Other new functions","title":"AMR 1.8.2.9148","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-1-8-2-9148","dir":"Changelog","previous_headings":"","what":"Changes","title":"AMR 1.8.2.9148","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-1-8-2-9148","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.8.2.9148","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":""}]