From d65eb144db36cc3571b6ba8a3222d939d4a38c1e Mon Sep 17 00:00:00 2001
From: github-actions <41898282+github-actions[bot]@users.noreply.github.com>
Date: Sun, 3 Dec 2023 15:57:17 +0000
Subject: [PATCH] Built site for AMR: 2.1.1.9003@7059568

---
 404.html                                  |   2 +-
 LICENSE-text.html                         |   2 +-
 articles/AMR.html                         |   2 +-
 articles/EUCAST.html                      |   2 +-
 articles/MDR.html                         |  58 +++++------
 articles/PCA.html                         |   2 +-
 articles/WHONET.html                      |   2 +-
 articles/datasets.html                    |   2 +-
 articles/index.html                       |   2 +-
 articles/other_pkg.html                   |   2 +-
 articles/resistance_predict.html          |   2 +-
 articles/welcome_to_AMR.html              |   2 +-
 authors.html                              |   2 +-
 index.html                                |   2 +-
 news/index.html                           |  15 ++-
 pkgdown.yml                               |   2 +-
 reference/AMR-options.html                |   2 +-
 reference/AMR.html                        |   2 +-
 reference/Rplot005.png                    | Bin 13425 -> 13456 bytes
 reference/Rplot006.png                    | Bin 13094 -> 13130 bytes
 reference/Rplot008.png                    | Bin 8151 -> 9362 bytes
 reference/Rplot009.png                    | Bin 9553 -> 10271 bytes
 reference/Rplot010.png                    | Bin 8467 -> 9577 bytes
 reference/Rplot011.png                    | Bin 12989 -> 13454 bytes
 reference/Rplot012.png                    | Bin 20243 -> 20264 bytes
 reference/Rplot013.png                    | Bin 8534 -> 8483 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                |   2 +-
 reference/antibiotic_class_selectors.html |  12 +--
 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                     |  28 +++---
 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                | 116 +++++++++++-----------
 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          |  72 +++++++-------
 reference/microorganisms.codes.html       |   2 +-
 reference/microorganisms.groups.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 26875 -> 27286 bytes
 reference/plot-10.png                     | Bin 25381 -> 27660 bytes
 reference/plot-11.png                     | Bin 35507 -> 37955 bytes
 reference/plot-12.png                     | Bin 60264 -> 60234 bytes
 reference/plot-13.png                     | Bin 26123 -> 26160 bytes
 reference/plot-2.png                      | Bin 26396 -> 26421 bytes
 reference/plot-3.png                      | Bin 27742 -> 28142 bytes
 reference/plot-4.png                      | Bin 40843 -> 41063 bytes
 reference/plot-5.png                      | Bin 40160 -> 40180 bytes
 reference/plot-6.png                      | Bin 38474 -> 38499 bytes
 reference/plot-8.png                      | Bin 25035 -> 27081 bytes
 reference/plot-9.png                      | Bin 28236 -> 30546 bytes
 reference/plot.html                       |  14 ++-
 reference/proportion.html                 |   2 +-
 reference/random.html                     |  34 +++----
 reference/resistance_predict.html         |   2 +-
 reference/skewness.html                   |   4 +-
 reference/translate.html                  |   2 +-
 search.json                               |   2 +-
 96 files changed, 259 insertions(+), 252 deletions(-)

diff --git a/404.html b/404.html
index 1f2050ec..8bcd3f5d 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 ec68dae0..2a47462a 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 16d7cb8d..e8c01063 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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/EUCAST.html b/articles/EUCAST.html
index 90f2c512..768d407f 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 685067ac..fc269918 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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">
@@ -401,19 +401,19 @@ 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            S          R            R            I</span></span>
-<span><span class="co">#&gt; 2          I         S            S          S            I            S</span></span>
-<span><span class="co">#&gt; 3          S         R            I          I            S            S</span></span>
-<span><span class="co">#&gt; 4          I         I            I          S            R            R</span></span>
-<span><span class="co">#&gt; 5          R         S            R          I            I            R</span></span>
-<span><span class="co">#&gt; 6          I         R            S          S            S            R</span></span>
+<span><span class="co">#&gt; 1          R         S            S          R            R            I</span></span>
+<span><span class="co">#&gt; 2          I         R            S          R            S            I</span></span>
+<span><span class="co">#&gt; 3          R         R            I          S            R            S</span></span>
+<span><span class="co">#&gt; 4          R         I            S          R            I            I</span></span>
+<span><span class="co">#&gt; 5          S         R            I          I            I            R</span></span>
+<span><span class="co">#&gt; 6          S         S            R          S            R            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         R</span></span>
+<span><span class="co">#&gt; 1         R</span></span>
+<span><span class="co">#&gt; 2         I</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         S</span></span>
-<span><span class="co">#&gt; 6         I</span></span></code></pre></div>
+<span><span class="co">#&gt; 5         R</span></span>
+<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">
@@ -454,40 +454,40 @@ Unique: 5</p>
 <tr class="odd">
 <td align="left">1</td>
 <td align="left">Mono-resistant</td>
-<td align="right">3206</td>
-<td align="right">64.12%</td>
-<td align="right">3206</td>
-<td align="right">64.12%</td>
+<td align="right">3209</td>
+<td align="right">64.18%</td>
+<td align="right">3209</td>
+<td align="right">64.18%</td>
 </tr>
 <tr class="even">
 <td align="left">2</td>
 <td align="left">Negative</td>
-<td align="right">1005</td>
-<td align="right">20.10%</td>
-<td align="right">4211</td>
-<td align="right">84.22%</td>
+<td align="right">955</td>
+<td align="right">19.10%</td>
+<td align="right">4164</td>
+<td align="right">83.28%</td>
 </tr>
 <tr class="odd">
 <td align="left">3</td>
 <td align="left">Multi-drug-resistant</td>
-<td align="right">427</td>
-<td align="right">8.54%</td>
-<td align="right">4638</td>
-<td align="right">92.76%</td>
+<td align="right">451</td>
+<td align="right">9.02%</td>
+<td align="right">4615</td>
+<td align="right">92.30%</td>
 </tr>
 <tr class="even">
 <td align="left">4</td>
 <td align="left">Poly-resistant</td>
-<td align="right">260</td>
-<td align="right">5.20%</td>
-<td align="right">4898</td>
-<td align="right">97.96%</td>
+<td align="right">256</td>
+<td align="right">5.12%</td>
+<td align="right">4871</td>
+<td align="right">97.42%</td>
 </tr>
 <tr class="odd">
 <td align="left">5</td>
 <td align="left">Extensively drug-resistant</td>
-<td align="right">102</td>
-<td align="right">2.04%</td>
+<td align="right">129</td>
+<td align="right">2.58%</td>
 <td align="right">5000</td>
 <td align="right">100.00%</td>
 </tr>
diff --git a/articles/PCA.html b/articles/PCA.html
index 73bf7b75..f4ffbd5a 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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/WHONET.html b/articles/WHONET.html
index 9115bc60..e8008775 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 c474d318..3bc909b6 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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/index.html b/articles/index.html
index de46b1c3..ec0b326b 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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/other_pkg.html b/articles/other_pkg.html
index 2d8b842e..ce50bba9 100644
--- a/articles/other_pkg.html
+++ b/articles/other_pkg.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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 e162f8d1..ad98a297 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 5c536d0a..9f080d2f 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 613daca5..d4b074d2 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 ea336816..7472beae 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 2334442d..4a17b21a 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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">
@@ -159,12 +159,17 @@
     </div>
 
     <div class="section level2">
-<h2 class="pkg-version" data-toc-text="2.1.1.9002" id="amr-2119002">AMR 2.1.1.9002<a class="anchor" aria-label="anchor" href="#amr-2119002"></a></h2>
+<h2 class="pkg-version" data-toc-text="2.1.1.9003" id="amr-2119003">AMR 2.1.1.9003<a class="anchor" aria-label="anchor" href="#amr-2119003"></a></h2>
 <div class="section level3">
-<h3 id="new-2-1-1-9002">New<a class="anchor" aria-label="anchor" href="#new-2-1-1-9002"></a></h3>
-<ul><li>Function <code><a href="../reference/plot.html">scale_x_mic()</a></code>, an advanced function to use in ggplot, to allow plotting of MIC values on the x axis. It allow for manual range definition and plotting missing intermediate log2 levels.</li>
+<h3 id="breaking-2-1-1-9003">Breaking<a class="anchor" aria-label="anchor" href="#breaking-2-1-1-9003"></a></h3>
+<ul><li>Removed all functions and references that used the deprecated <code>rsi</code> class, which were all replaced with their <code>sir</code> equivalents a year ago</li>
+</ul></div>
+<div class="section level3">
+<h3 id="new-2-1-1-9003">New<a class="anchor" aria-label="anchor" href="#new-2-1-1-9003"></a></h3>
+<ul><li>Function group <code>scale_*_mic()</code>, namely: <code><a href="../reference/plot.html">scale_x_mic()</a></code>, <code><a href="../reference/plot.html">scale_y_mic()</a></code>, <code><a href="../reference/plot.html">scale_colour_mic()</a></code> and <code><a href="../reference/plot.html">scale_fill_mic()</a></code>. They are advanced ggplot2 extensions to allow plotting of MIC values. They allow for manual range definition and plotting missing intermediate log2 levels.</li>
+<li>Function <code><a href="../reference/as.mic.html">rescale_mic()</a></code>, which allows to rescale MIC values to a manually set range. This is the powerhouse behind the <code>scale_*_mic()</code> functions, but it can be used by users directly to e.g. compare equality in MIC distributions by rescaling them to the same range first.</li>
 </ul><div class="section level4">
-<h4 id="changed-2-1-1-9002">Changed<a class="anchor" aria-label="anchor" href="#changed-2-1-1-9002"></a></h4>
+<h4 id="changed-2-1-1-9003">Changed<a class="anchor" aria-label="anchor" href="#changed-2-1-1-9003"></a></h4>
 <ul><li>For MICs:
 <ul><li>Added 4096 and 5 powers of 192 as valid levels (192, 384, 576, 768, 960)</li>
 <li>Added new argument <code>keep_operators</code> to <code><a href="../reference/as.mic.html">as.mic()</a></code>. This can be <code>"all"</code> (default), <code>"none"</code>, or <code>"edges"</code>.</li>
diff --git a/pkgdown.yml b/pkgdown.yml
index da263654..a444aef5 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -11,7 +11,7 @@ articles:
   other_pkg: other_pkg.html
   resistance_predict: resistance_predict.html
   welcome_to_AMR: welcome_to_AMR.html
-last_built: 2023-12-03T10:38Z
+last_built: 2023-12-03T15:54Z
 urls:
   reference: https://msberends.github.io/AMR/reference
   article: https://msberends.github.io/AMR/articles
diff --git a/reference/AMR-options.html b/reference/AMR-options.html
index 422ef1ea..9f91e95a 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 279f1556..9c849023 100644
--- a/reference/AMR.html
+++ b/reference/AMR.html
@@ -24,7 +24,7 @@ The AMR package is available in English, Chinese, Czech, Danish, Dutch, Finnish,
     
     <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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 cf84930f4f7a82277428b652d0ff85e3102d66fb..3c198bfee58b5c9670ad7b7823b3abc638bd9510 100644
GIT binary patch
literal 13456
zcmc(`2T)Ycw=O!vkVXWQAXy{}NKS)95rHA+oE6DA44FX?lq?wp5s;j7h7klLNX{9N
zoP%VByZQakIq$sts&3V(Q}3QB_TD``z4z{3>sxDmy}J3Rsw|6tkMbS}1j2{QNvnfE
z;HA3*7aOSY8*5DfF4*P@veLj6_*}ef3IHxpCpkS=5D1Ux?f{R>`(J}VPe5?#7n+`F
zyYq-NYWJJ-!^woX978!4MoBArCi1`stmaSO@nd}p$9=akSoT$J92fV4sHEDE>64Lf
zxKE#iWs#_zud&M!W@6qC`4SuxBgMJvg1EX`;p_WlUpsH%Xt}@l*H`qkLS&b3KP5fo
z%s2NPBRuxM{kQ6hJ%lMrWP4^hw&z3htf;EPJwZNPmaSpy8zg4TdR#JNBQDWlZ=-R3
zdbv?Pr;{PU(0&9g$5zl1M<A*0JlCYDa^}1wXli!SKtTR^c+el!A<4i^SpSGTeRz-|
z_+^#^)<EQ?4;hSV>Ono0?!9IV>J+A^7HFuJ1m+rs2J#_Iaunr~uPON8k&|}RaurP@
z$J@tmC_&963GOtiT3>JXrEkPzhS6d9H#KU<HEOQJ<&>VJ^_aS4k{1uw?1`ORis#>}
zHTm#fa=i6Nyls_>4O5k!`YD7Fge#Jk9T*-Ms3x(9qmF8k6f5`QSLEYn)}Cm~LTf{)
zARSq2B<0EF&WoZGoZ>s1YE8D;$$|`wk*jLLo#z&sU1Dl!vt3<(%X9;7BW9GgP@+Gn
zOS!nkY&e-OFzg<LTdIzgKGLPAndRx@u0efNJJaFqiW8;*mA%?+eQb+x9@`tN@aXK{
zH(w8DEFkWBAoznlkVE(Cg9^kRz7kh)Iw`a3Pivc`jH>IJ;fR>I5uGSywK;pA?<HwR
zP1SYT#AkDsO`pqP+(l4vdZq>glcnOMbQnQIA72?M9x3@equfi*_nOT2dhx~O_voWV
z>z*WJ+aueg6l8e%V&?!^L0|n)0n2BcV*0TSG+y<C*dIK>!Q~dkiwN_!jE}8^1L|u<
z6^ja0)e5f`1%?s6)Q-a*=K_Ao-oBY1pD=!~a~9Y7QT~4IMXn<e(fT6Naad5Sv;Ns7
z+Tp~{rP!}AEt=S7eE-2(hx*KYP8ZP&b2blugj=f|lO=|>N^OXf^8WMo21(>RQUnRH
zYRgnwJx?zX=-W@!ZR(FiG<%UR4_O+Gwcdk09jvcp6&$FRI({-5yWt~Y`kA2X^nuxz
zo%_D`VRSYD1}9=4oboT;NV$~doqwYu%g9S1i+v>0Ci2|mTPz>AD9?xH9NTO~0yp_l
z&0>O+9H0Hl)c%Rt!j(KX`nJp8akhuoIQnMc>8Vd1!Tk)|$8RnvoWPmbxBTUBoZp7{
zhO{@WTx=`OOD*~H-PW^fqLNdAQ(qygc?h@5?wQEGjXZ|qCWhlzY{JCl-j&$x<ERd3
zO$KRjDq(O(WFM~1Fh-he{l44F_V+JJ;@9XN^kr3onMbu(sP0oM<hzrO$mJJ+uaT<T
zSLD-#pDfw<33+RX28xUo<lF`!4B_M|GvmW&+QVlS6%JMAZFMY)-Apu3uQE5T9<<lm
z8Z4nA{KL{0Y0|mWds2|&aRNjRuP%fS#FyEoY}!L$eog(+xF79;@^2|n`$#2glVd}&
zqz44HoZz(w13n8fa*tFV>2=j*xtvD#!oE0#tI&1!2Pv{H1t8lV5KX#;NoZbU*b6K?
znu%o&-dqTe8KY$|v~t(@v??W6TnEZvPFoa1I%VqSny(=&*TVnhX`c7@?dJ5x?1!z%
z>#DGeiQjpktz@nTdmruS4Zn^M??QG)dd$wUZ{qU9ScMk3(`yLr>z-I7O58tp`PiK|
zLLqO_Z0`KT9szRG=I9jbQ4;nfbvxsIci{PhFz}XXE?X~*ly#Ti6nFVMmXU?g$_=x<
zijWQckGY4l#9!WxQ4kW9;_k^cT!d^E??QFJmh89pm-aRgV!P{F#LG7O=Oq&E)oqKy
zv%HBOln0-6T}hYKKH=f&2;^P;qP8Tq-6zp}XuMR8Tc1&D$-n=~)2ltMdLA8LWP<E~
z9{W-5(u$>6gUlAFr9B1RYP~c{Uv4H;&X=&xDTt&Tm!~+rzZfFw9=Fx(efPE921?bC
z@xTFkfx-Rh^sKV9NUK_G=PN_>lbsLlkqCOTjx_)FVJ;Ea^#BTIn;Y$t^!ZmX^-3YV
z=#II2@jR{dXC3$8TLIS&1FEZm+K8M!aRS|pgL4^NmEKDj-JxGptqxsow>!-;7LI$h
z7(ToGeY0*e*s}K5@N9JHleraNYI{f3-U+T?M*UZwu%5}lqRRfbdYhRqAF!s^l{kZN
z9ly)#k2t>>=xD1vZJAP!<_kPn`=WSAxvJXk_lfX<ae@%f1lp#(*99w+xhh7r1K#Gb
z|F|7HAG#qs+Y*y<6P0EEQYcWB;e9z+q0M}cuv)BS!*k|KPm@BTXBmC-M=MUckEFcp
zvR97M7W`lznO#AaW*H{|r<Y?6oK8<OC-{YJ>NpOh`O^<|pbH99<y>L8`<(3|c~dSQ
z@m-xsW<{kcdzlRyR*>t+i|UpoI{yrtMqgZ6E`RE6Z57XI^EtU03F>T4ZR1jl`=+)_
zp(rwIpR|=n)d6}^xjiI0B9zLWzgB8db+30bOn2p_8W$C*AZ=0pgPi7R(`)#DjF}y?
z?Qf>;o!>K~`K&zqQZ`I)FoekC8Smt<`D8{v>?e`JTk7fF$8xh#inY9y$Tr!IXM?;M
zBNLF){!yc?hXhQb!E_qFuPEg`c~GGCd;CGFvlQ~4*!!UNkP|j3drRV(fxp_rXw^Ru
zuS>Ca<(b!F(;0rv40O(Z5ixRG$3H|E!fc-`r#D}m<k_!`N>7<e=9A7TUZu`^2jiPu
z!eynMpRw4P+Tc<pn+?11OYiPQ<kp2bk$Z^DoSyG*N;cF~nEl?$-b<-W5Q*m=#32&Q
zQX%$;8WE99StZQ5k*WD|dTfh*LF`m&5n+|A@*wtF;`OeClBe4$AvBa}B@B)l4^BP$
znqSOdKEIb$Qky>QIHTI0IYbB*3GM6^bR1c=FklWHs$HoUHj~XuET6j;5VXtu`)u-g
zs<ze5$L>$;iJsKXhm|YaX0^Ph3_EtD&!0)eoQkAvfe2q9XkDQ_Ut>go)2DJW2}YX$
z*)f0H_R-)}sL#aau{G;Vn-x;@2K}kL-B@bYtm*lZM?2kGUY2ehBzFWIV^!*zG>p!{
zW+4`gI81-X6Wrmr`7&g4ojyQrDt}(uV0TAy#^%W;`Do4At@MgM{*J=b(6cAase5wm
z*v{mcj}F!OuesY@Bf~lBgasAFmfs)XWpDPU4L1Cq&u|*B*W>4UL^QCRyEqZgDPa$_
zugR(wd40Scr)GGFsVxip3u;KJploLcSrRSyiAL0}Zt*P9w|8A*Kliwi6fkQwl7uDI
zC!Y?6EF^Q!PhTn-NP7lvBnGo2yGyS%f2<ocWHzwATI>R^OV5)%L^rt+aa4cl?29hT
z?ktr!gdKIpMg>t@Xnc*hWf(&TC`|G6S&-QGWk3IkIp?RyLj9ITVTyg1kUBl5tpk_p
zx)rq!r4q368Q3khs<&xxXd>jp`_MDKe+C^~X-TfdA9O%v2dB>EWszGFeJM)2=t`|W
z?_%^P*z;}=Svjuon0>YKGaiF8vdi6!ztz9kPi-I(C6To#5FDu_Aw{&OGHc+fO-!WM
zEYnJAFEC`@8%gMjVfIoatenT^LyZM{T}XMH+ACPGc$_MGYKoe_KKlXoZk*}cN)ze&
zA!{qmz2#uM&ri;H1p`(2OyF`KR&b;D=Ryv=+uYi+txOF1Sd2d3u+kmAgt_^uYj!aQ
zAJ3HtU%rp|D#UPo`zzR@jG=ND*P>R1D~_Yuh(s{1d?znjQFAykKU{CHvgF?dR>?e}
zQd_xFnNabr{0ptD?xbwYJi3}gV(SU9(^*A`Q|ks%d+#Clu28GB%0s~eN3i8^t2Wfk
z;tHv1&=^|&ZaubSF&$SRMmGD1E9}xQ#K~$Kvc#v>q!+x79?0CKo@h*#oZ?fV6;y27
z+e7DHv;|*`D#Cm((l^dr+GQ-LGiC7TD#VEXA_&aTf)5E=9xAQ(RXFcU=Ub2&IQS;t
zLL4Gn5t)7^0vrYnXCO;r8+qA>f*)3pLVUf^R}{O0uW4UivHNXjRX(PFCfhXRVc}Y8
zu@iv1ZaG@hNf$hG`K%q=6M8^z#$u(HXx;Z#M5@v+vUYYsn|Oe=O$pX}6e)TmrCZtx
zPc}KjFbSI>b0r|3_#$-S@t6_{f9h9rLfw8lq<H$5d(k`5o`%B%FwP3CF_lc+$^@FE
zl8EeV=V#$v4cvWBHNokDSbB}{mI2ix6A}h^;e!bz)Bkab;2kNo@4cg@UNA;i9dMS*
zSb#9l)zT8+`1o+$N6An{GYG01JH}j*w$w+?2$x3!-@aic1JC@AqKMh$$tFZa^zg^v
zpY6Pgii)?<VyCc1L!N@`N)DX)!+;E)*D@XcSGx$gw^!3umS?lBgJRK&^tpTAhE7oy
zQVl1yMPI8L(U;rZ&9~^Qn<{q^^!du-lvQ@5cK58`y2#?iHSO(rcN6##r{R?4pP!%V
zKqvc)0n2cfbhl%!fQvp$N=iz7;Lz?*=E+`;%ea2?+I!0IhKy=^XS(XjZ`ao3>N-1G
z+*`*oSF2&wfFMd_uU@Dtf+7wfoD6gREo3>6=A~78OY=1+Cua)@A%MBfR9k1z@>>nf
zQ8_j{AHBe#ss6g1`?Riq^=X2<#XN<hz5VWxvbTz^_@ssK&%d;1Yrrs4kb&U?&sUnc
z9QdnM-d?SXTNQudvm6-L$W`>>oiDw6cFWlUm4NU0fxxDRfn7zXM%CM2jYqq6i=~Sm
zv-V~6JC%K3wkOL>wkx_HU(J!~7*=%r5?Z_tICOFSYob_ucT2A!Rh^EBU(Ibn-j~MP
z4nhfEl|zlTy^d27iN>X!<0zPEzkc8MDlX1q|Mhf4Y3o|5?GYyq2M5PEr~!Sph(4{O
z8b}vGj5T{5qZcbJ`fQk$Qmbcy0iMX|C~FvAOAORemxPzH)uj!H?#~^E_<OI{BnDZO
z{VMgk+N~@5^4xukc+(@L??vfty$~01?=LY5Q>VqtHN3icE6KI><7}MC+S=N|aA4p&
zZQrq}`XlR#3JYzpHWu3gY~9`6U6quS?wPg+L&M(=q&0Z0oxA)EXos>WQgfT4#>_fG
zZca{T9cl3ox~Lpa<~>&;2g?cy8d_>=PbbdlzV<tWKK9<|0k9wuRRGkl+_i1-Jp4VA
znwlzNV`Jm6Xdccn;CnWY*tYCX9_IGHK93Vz^gVXHJlntB&bYl^H^6v9U~rT&hku`9
ztnMO~uM7X@HFfi#jGuaeW>e_I8v}w7mz#Zje0*D(=*MD?KV#^Az3z(8^P9D=6XUg?
zsiu>qcK*@2=`T3%HoD(2b8Fvtb=XbYCj0Vb$GCui0K(A!V%4C1rten-1s|oylv!w5
zGzH(<92kPF*mC|mye{zc_OQ(VZ_%&cp+xJ08EyV=Z=I(Tf7$)+vn;jXn01rp$!Olp
z{j5CWwVtl>Q`_V1{n1<{D&D(+fJ&Q>dO1dpc_vTDib^vxy|P!LC}S=tD*Kdc-X{jx
z-SV950Wj2q?0ub>m?#83n$7dYdR5{^Mn)~RYzc*}2km%Vg+)aQ;IXl>H)4iyCAShc
zn<@+@0XKh#kJkq!EWW?P@;jZeEgJ<acuFBpIlXcGI}Yvj(x0BV07DIpxKuG8k2bfI
zlzC&{!%pxWn$|nd%nkJQTgOI5)ZKpxOxk&`rC9EvgMgcQgcdwse_qcBKsO86PH64}
zptel+T1z2Ybe}X5dP7h&2e=1o0F`$rMgtvvy9d~ZEJneovWWdm4Bg9#Z?*$!4Fct-
z0eDV;N*%fVE>G+MZT=b^5h%6m`vtW28Q(5z6F==FGr2ZaP*4~@KV0Dw-!86dCA*Eb
z9Q-oBKOnMyZPo19da)*$a+BAzsxNrl|2ew%S8cJy=O_xkxBShAT~x14JeS|^TR#5%
zi?rjjW2@I@FqHT$fV51U{xo6NZ5?B;RkBTVl~O>3Y|3+bSK*(hw6rci>Gsb~Eu^MX
zkRn!}?Q8YK9T%ECe0&TX8dlpZf2F#w(xMMSKdq;^4(M>!FTG0wfAPQE{QkN-%1^8+
z!tO@m`k>?W<ACeKm)L}luitC3Y0b~f=vTR{_HI=Tiu<h5g;KkWO;x-`Oj~C7q5Up5
z^V;G$S*m6MyT5dCcmFF(71S9{Ho4chU7FYbMa(BJF)>kmr8_#{fu~D14dNQ%&5%?Y
zdA2#qLQ1QBVev0qgW{N#E(gEC<Itmkz69R(C({}O%GdZHMkMIBz;WDmGY)|B1n`xO
zhk$oIQN1ol)ULk$yEQ&O>)3W9dwa+te)!SS7yvmKun~?1^ordLAqDR@;YpZwsiB^c
zqEz2MlFeW2u4G#D<uUYHO;Ew7<lhhiyx@h|bJo?=FX<jL)_R$qB5boRF&DHdX|O}0
zB&fv--3GxOHY3S-e%}Bi3#dMhT_~saKVLo;nse$v?6qF);e#C7ZccYq#Qu(LrdYh$
z4ggRQfB>*s@6!{@)LmCy?aP`VPcvR-(l)W&5nAT8Q_=0<bFrFK=6y0O%hG%@q5v6P
zedFP?TQiCMOt12dYU33E?K(;!hq=DKBrXa6BjD>u=;#W&p_Z2J;P&>m!Ab{Pn_b@v
z{nFc;2?NLZgTkWHnP$M3+YF=$y94*o&fUEYfQUsApn<}F42p`1Dik$yyQ`rhR<p3z
zw2$87sGF<QdHs4tHhQ41c5ZBPvhL0pGxY17%rY*H*U!8*bCNu$MLc$nd~cm=(Z`Y5
zQ~36^cMbtGHZf8AhmA$&zR7tz8G+c@oNItL(E4!;0F~%k%ZuE+c-|zy`VX^Sg<sYy
zKBXe&E~oD-6w7V7e6eP^wT-78LC#}dLV8BlcHU_+09%PLs!DJl*V4K9+k8AA(s&oJ
z4}W(CN&GEP@k@4`+eAkTuO)ji$VM6RyZrgdIzB$$M#W?PBL`%1dpU_H0{m{a<6>(a
zFo?35x1a6B8^d_3W&?2^*E>yDT5RVGuH2!ziq`ATr$Iq}{<-s^mxQZggtgOeKbM3N
z8qB3_>m`;cmdssz2$lGGZRp--+ZS#`aCEbO=UJP5_FE8F{x_HN{2n_~8MM9!-ved<
zx<FRahQ8b%pPXFewdjqX2V(sr!1?Y1=AK03+Rt-bJLl3T6iP@dF5uV7HR)2d972Ug
z%+))u+gJ85>twj}veumfkyQXqyBKhLL-pfz-$X#8L8&w7K7dU!H`UrrIfSjXbphpb
zw~f{5ke&!L00{>7sbNl?SY(C=M;1v&9^32UXARj$N-Cw5d|Cn}dKbOI64y_TkI^QN
z4S9f|-qnv@>3dOo>j^Lvr55WH)kE*u*%*S3B%b`x&vz6Eu;iBgmxN*`-KErnMP;os
zw^`wwGZz3Gc{;1r)cJJs1i>;8*ZfFZON*)Uz5yh0jMc!g<!_k0LaRE$93P-zpY<S#
zj0HkGV1?Lt5OFyp9LjxHL@t~%7S|opKZC1(ZXUFW`6o@YT^PFuc?_k-+QGh$^9URb
zL4fs4u9)HK2rX7Jvt1wwdxBF6VFqgiilgvQKmJI?F&3<QL&@W^XP=sCQ@sAfSTL!J
zJGQ!^9i&qLkM&NLZ(y=~a=^3%H)V>UPs=c-J!d^2KX5W1hR2S*90x73jJau`wl<m`
z!ZLcN)nXV67}+-2HmU<9O{9oUhpHG0WD9>_HgghSw|>r^m0V&rm7mV~G^W-xPDthL
z2UjTm9XO6hmaaC!n?tn08J#6cdG7b{<_4>tOSX;ydPb&DX!zOD?N6pb<P2gkc8-3!
zvhm}(@|C{^Yj3Q4iTWOd^UnnRl>)d#SiE3^q2QgCmd*<;YjLe{vE3&b-m&rp;DESm
zGX1}-I!|I56JF+wAz2`QgK6H`(E@{umzQ_Y`LpTaHJt@?{61R_w&6zaAL2fu_l$50
zbyn#43C6r5y8;PtCp?GI6u5)*{}KOx(!~E?MdZvk$Ok<_82J=FIYBedHBCLOl`=Oc
zJXXF`-3B#_Q3eqKMWR|!7)#`)kLZLIV#i)<%HyAVFKGlb-ak-S(qDSAbc{+sO@=+S
z1eKdbIb<U>Syrg<`Q2Qm?9@?qUu%Lb<O}ZyAwMy3x^iZ^sB<zvN7dxZR)+gM*qFeM
zIL)NCU+{LwZqQUtBArZ2M#yYew^?e>LcZ_#Mf=D5gT)g0t$ZDCkLR*1{yK7*v~V3L
z7WBqzQf!6br;G%w;GgY}21vqVl}1N$+}Rh?5k^2^k^Pld=LVlui*-=9gGME0^L0tx
zwoZQvg7Ltqpmzrfy!iGp>~TG-jrhHD+mj~R1F27|*9@0$TuGMaTtZ893`V^>I1Li)
zqEB}=%L1HM0@k&9mjmETnnnf&br(8qqQ$q#k@D&bBS~+0ZI8;f<<)gR6>e`8);-@I
zi?u&qK+lWyEwmm-I~?<B%43LAowINY*!WW=liw`h!FWG@1=5;q39-l|kO&3?E?LO0
zX!j<rPal9<DWG=r5hD$f5x%;_;{}f;GQ~(_WCb$=$oL;ACd4BV;8v&|kpL~m99G)B
zF=o<uMz~R?I?=&Llp_**Nd<KaX~j0gSHv7p;^YT#Q>Jc{7>85=D<Ex=GE%(81fm4}
z*qm%xX&+H!u5MQ_ZohN}Ep@>~E6Mry<C;l!dCp3<eSF8-r5CRny~cj}CrylNIvQ3U
z8K^6{u9v}RboC2)a~@}^WYAm@P`JskhTB<N`&<3OQE*_jNHHn_IwEJPcjEKvq=oI$
z0V9M|Zvd~tg?E=KA!pCJ2_l0pay0cz=9EJ#u={2^K?O|9wA3grz;eAuMl739czs;v
z>pwhq!Oq!@`K(66$e`&8J7e~$F9<=d6MK}JByhA?<T&*PKN%^qth{u;y<a3&RpSQ3
ziR(ak={tR&p46WHULxwi9Zv&JIo`%8;BbIcf0Pm?H1@Z^0sTktrGobP5Z&KjOzetV
zW|vXaFFM|SCfTdNwN3QbjdkfQQ6B(jKz9h53GD9gU|&D5!t%#UOE9zooPjamMkE*w
z|5Dd6aKpAH^iTgC9j<z@ZlG93_xJL`m>h8RGV}i_e5eA>i_uhb<b_<UCMN*fJd837
z6;LRKUmU({?6*=aRw%hCxf7)m;}Gr~Xa`r5iQ*sN2yTMRQv+E7xgd)m0(!>fNMr^`
zf;}rDD3H&^jS772KHm}l-15%{bka#l5n86LbzCXuspELzgm{s5-Q3ASd%}8rmMDBx
z3l4=j2s>GxOyi!*f9kjqzO!C#-}#qu&+}SKz0Z{-ZcYx;>uA+tkG}X^K%&I_W-ggn
z71gJ;M(8#S7qB>$p5p>&El?+N@&lF}V>Ok5F=)VXp1{?Gwd4Vz7U=x%(eS_GgOR>>
z(y?m50HU1`uy}?8;)2{_68zA?mXFosy&yW!LRl>}p~#SgfwqA(c=uW0vG<<F%V_`+
z^A=<Pz5)$m;05xPYXgxtk3=3-gZhGML1m+IP!E<?P?Af5oDoW~-yXA@V4!&pz)s}c
z(5d_N1iDiBU=wM%fmp_ZAe&=1AL@E?-T9tnCb&Am1<}T07<p@sW}qAp?f-~iwvakW
z$uzx2?ly8#15qc`S72!o++<PRgyTsN2M~a5MiYEbh6(W>bN3=P8=U~(M03QnWB@(8
z)$W{0SInnSi2dds!v)U3qXy!}98N!06pJV_d#uo^NKXeW3qF(0=&)`h2E<vHv`>8?
zZ%SVdE*CEcB~QL`PJ87!)IA%le`bbyAI##MjuU}dx|ijw5Uc5NLF7bQPFxNdV#%J3
zC+a?gEUgdk!%>2gug9)mEwzy(A?q+adWLs3P-*TTDnyFt48I%qUezW+5J}~t?t~d&
zK`!T%0EV3<g%<f%{3F6Zzd#d6coQrt2}<VIqLlAd_R-xIeECREnY4kh=xnWHszY3`
z{5F2HcEL%t;NJD2f+I(ITV)UCT;CW#A9j;p6U?R-jub10X9QGuEw_C+C)(>TAnOH|
zL-8Uov=iu}_K61g0!U$@2NOB8!~`!?FgUp?({!>`1p5M)w@EC71RG5<61p84{d<TG
z;&1!)gaa%z3c@smcwP2C#<PLzigl+O4@^|zzdb8x0)S$f#Bt9P`~2QAx9jD?%1R&t
z`tf{*w~HRGp46A*Jtou&0+BG@?FB$`VKWnRhUg~_b5u2uyuDjebKH~l#CC68R|Uef
z{i*^=M|%FOZ^S;X6pDkp9xzF=1L(}*nF+>1mYo^^gT6mL4?+RO(!VD<&_-RL+|`at
zC5@6~7~$$%fM)jz=kALVq><DZD0^CkFv9h50N5r3<CUXd%o=<$##G+ix(j5R04zQQ
zQ*s64m`i$o%LLlV0_}bUvuM2VBy=KO{v|Es;j9?T_}BBo;bp3{r|6%HU`rke6;Dui
ztHlSPM~^v7FpRNwUV4%)W210R7V4nzSWR`{8OwjfrTuisegI=H`vhPJS87-2<^pp$
z2bZ|_K@uZeOuv=#VC>nh!;@wf-B`wwpeXUMBaWryQe5fSZwml^VO0kB%bU8RP5%aQ
zs3+F)e6uwNDhQR-$WITzKF}9BmBb0i;*~EA&~@lf9f)}9Z=w?#rm$DB-;5bd-rZn2
zv6r)OlT4)__-@c311q9|F@FWDWf&EO!mIFT!{o#m*zEn6B*u}^Amd)EGfF4j$@dI!
z^?g8$x1VEzR|qh_Z2X%M(YzCX<cV1yac~g18z_K!{2wwvgUDq88C;?;OpS%#VT3@g
zz*?x6s4EM~GJq?pZ3t$UAj=?&%#4dho2fW%{ZTgy1fDok?+?~;>X0fP-(f)NRIwGa
zSz@_1yE`y0%cC!l*6jr~Rk#1`{i)EH6BUR!{`EJ!E>^g@`4P>#)>u_MBY95(epEP0
z0*TkeojVUDL*^*uogWY2KrH`k2x6RXKmRdq5pq^?<>IF`{$6WHKkP_dRY_}WO_)8^
z-!SZm5uTI81&(GM^8~98Bb3OxcmrcLB)<947c$MEH6tWsGwh=1(+o;s0^Fx$B7Qrs
zeH^*#8CQQ9*TyoS{~iU$k}$)!i0<Hm(h1y#LM!gUsOu~6e%D9UcFdi<brT^tcOZ+<
zi2e5Cj{+(Vd4)U<Y(*WTcuf~MKdi)y&igCW-@pL`F#rg9RZni?*Ipt=3v%s!sEHj{
z##qn|*pmsQmT@j<+piu7s;aHCF^L#AeWXAzZGpF9`cfR&nrfJe`C<XOXNuc@aH5j6
zcl$YKmf46n3TX1O7E_c&7t>B{O&3tQKqbvPf3EC{Ru@bFk&fyc7WY9J+ec4<pDchC
zF~I5ma4`z5f!K4I=J!}(duFl+0d;wOiAY*VX_&yRNH#?~6|mB+&SIf}@<|Z82jGul
z@PC_e{a0ele`<@AS3w;EduD*Z8?);k46i53?-}v;Grc#2;hR_d5SU242S{%KRz2<c
zOPMPTq%3lczK`O*U267huA~NwxrVb0m_fmC?V0JTk9|8mg~;8Xt$ee3eSwqmy2ecB
zu%Wi#)fx~NxPU0J!1yb`xyY9+QJ71zsI7cIJSPNBF9i;DV)kqz>SO>r|61m6QIpTv
z!=Sptq5`%anT@tS?FoshgN{-CC83<BWl2+Z9iMLU{7y%+!{^O7Hn?s*Lpv38qMvGc
zC48+lLlJj)EEMEuu5x&vUWnT!KAV4(Y*5HME&@<#W3X>{jAR~|3`FRbm1aH6@;Mv1
zoF1l(HLk5SY1F#1)`5M>s<gU|aBLmq!;>gxgPXO;jw`Q(lzs6&<YW>dI{a~rxAx4q
zvC%1dUT!|-dr__E>L0ZnHpjJSt^mhjSFZZv!Vb1w`eHI*cZoeRG*G-J1Tf^u{CG+y
zL%ijz;#ufNV4>o19ucwAjBhjk-4s=8hRzDmCD#AdiQ9it{=bU(-+-M~Q&i+60{ggW
zcsnVYk^oCgkcXo(z;7WYSjN}{7!DX~kTwhg6*g{oY@-yg(@L5Xs}{1rI2ODJ5q!e9
z)CNLe1U$`3YYEJDaht{P^frPdd<%}urxJIQ*K)Vdh)tN96ZQF<^~y4#Sd=sefB|f&
zWaG^DE2*c%M6<%DRZ*<|U&FhguQ^p<dG+^GR!fNnJ5`$%viDiF0=Z1Cs}Nvfrxbu)
zp&7U-lZgca$mCwNU4i(*pXELD6p*cRMjZ@bKZPe`4I;rDH-gkgl}Cj(HpOX-Ujd>V
zKnUZU@YUKmQ$ua8{u{*<SUN*y{MqneDbJ0l0KE7rFuzWV?$L=ITX74;1h`Z5>;Ex&
z42Ra!mP5E<3Uy+ARAIoJsR)plAWD6S(v-l-TjngZ_wgs7HXo=}K@NhNKraKGQE$Go
z_53qCtbyY^m?nntU$#)fp!L-CkIrRqdWFeh46=0(Q6?f38GyqADE2u|d&A-@HP?GE
zyn4)fXg+IY+CNSgBLpEX4ba;>b%GZG(KUlt7-@L@xh}us?~*4>6)hmc`0UweWXQjW
zMNvHF{h_qH*`vx=bZ~VFP4zqaJNZur=>G$=|68@ly;x+l@^jD~M{i<WGWf^2sTV3e
zNB$HFOckBKBGDVrUE&jNjKo>kl2iQ$>jhvH>@BDW=CpXHKGw6ILp_4(c+NNQ*yq5U
z6b1Sx4fDyOKT3SM8lyvm<WBnEfFCsOxk*$!0|=2OGcW~E;TpQBt&|4jkOKId|L!#4
zzc0Rj5;PS5uWXwuaRfk{VvFJ_4@yz&k^xK4O^LWkHz3U*Ma6%gf}<wEvk*ti>Hy4o
z{Bx6~l~n?HAT|IACr`*y3-$a$bR*e{=~4vsd<dv*-_#KF%+}$;DGBF`PaTkKsmixj
z5*tc(FpiV|C2=!-+qzhm+gL!dS_aRtNi|3&wyn9m=!_iCNej6s`aDA;X02S)dORI!
z8BtoARYS5WBv^NfmH0!SBmKGU-jT?H=WURBJmFB9jXlEV_3-cUqT1VQ6M4btN6+-N
z2#e)}%Jk<&_lqng1lw9$-zu^8I@;K5R^_siW`rc%Uj4b=B2szTWKk(FemPLJc(EY)
zL@Vs*vrh32-m}OW{iA{XJw5*Qg|-s5R~O&%<pqS=h#hOZhkvhGw)rXdtPQ6!DQpKu
z|F#EKfd={&$~?;oYik=wR`Vu`3vBg9i5A5LIem{(#zu6|8kN>o#;wgf3v)Ab*XXK6
z@jS<#UPr?PvUBcv!vuK_@0GPh4$p1GSutl1uD-slJ|EhWbl-dlyWMN)B&6k@E^R&K
zIb6ya2npGDS<4-|9OU>gg2Bmu%m|1W*=(DjN^;y2CdOq#yc72S<I?%Rw!;DEF|ivG
zt1$7!fsq#0zI7^#@KV@CuGxbF+xFS~FQ8g>N2^z}1ohDm*8Nbm6%Oyw+43=5{<!<y
zX#C~(f1|-ZiG20+q^Tk7)@_e9e=CgQDZNDKW*q->#^czYH}lg@@uPy3NJ<Ex%uPE=
z3G*~6l{(^3J;75#SdTD*`lz&D2%wly++Qh^LHv*#zDxEs33(}=!wf7tX;1qN=HSTD
zjiZnh9iCBcim9CIOwy96)u#&qIVFSdb2CMX2Rk{p6B@<Y!Fj5lX2~E?jS$`E?5>m{
zMA{2mx=npZ;ywyZ7A1&Bua!=JfF^5YCoKYU(7@X{qL(ttol;KV#&UrUl{d*49T^=m
znp`*R^G4`r;1zPtg-;CE;o|usJ{dEsS9oxs9H2i&9=fAWQQL5w5%~MxCK=@~ybJ9f
zx*L{S*OOUcq6s3{5vnwr8vz>x+pfNpjVqUPG>Pt7NGYG!H$MYlMMQ5thv)>phK2^o
zfWwgz3BsjO`Sd$H8R<C&h^f~DWVb?4s(unDIpH9H{Evd@9`0ZiGuoN5t7Tf>J+an$
zgLB)J2k4(}M)+I&dRrRYIqYJ&=P?8`5ZJr&iVOS$S5xDxhpPzIa>{ePkw7=~*7H1!
zn&5BlbO}DNx}ks}`FhX>rRl8;*7%Em$!<C~v#EG+-a?Oyg4k*DQv9IkThK=E1<EWq
zT<{b}99uNmO1k0ZgA>*&YqdoGgb@jGz&%~iH3lqS(b-*E79FJB?xJNKmJ$L8&=pX*
zeg?ar9v3U{EDC%(u;^#EC+SX4xRK?)JA^w}*8;;IDnCmncbjDUFfaNZPGXpVFQtEN
zLo6R~mKHF+&s~1gF5JVm6bGay2;=!s^dwc(T9=n;j+AVEetVpKjch-LgRh3whx4H4
zE;169#M)u<367L9M32xiOET?#!3nvvbbF6x7BeoU^L==3)ne~;5^_KEjB=85rgYLw
zdHw8Lvh@^k@(bA66~eUpC`4D4XB3psB2JbXvR5unra!^%=a={&cJW^+6iSzYf}RWS
zKv7AqZd9N-ykhKA$R!x@>Q^Yj#dkH{amM={Lm;G=zjPL9W<i<cjlA2Pe$Vp(V0gez
zH2bm$|1a1fWKZ)vXw367ZyJec7n1;D*s4|>3Lec~lPK<hg{S1I{}i%c<<G|JGJqJ~
z7uEdj;@UuRZk*DU^B9F$EI)~J&aa9S0Up5N1Os~zEiuZ=BX~Ny>jVy-#e}8S(KA+P
zCN4i0iE@#*Y2L03itqaptZ%JA7N7pB59J)3&IR8(MMHOzhaH~KeUJQ*x>8g7I#34+
ze?w$1l2SJ8f$NQ#*;S(#<xGIJha-;Zz|446_M<VcTZkzv`+cpxce59)JgxjV#)H?%
ztyoy6IYnUj1*!zdsn|;u&$6S)vQHHPX~A0CFniFM^k@b3IUhG~cCij_sAmTF5yVo)
z8BG`FdN1?QJyk+OU;|ST>P3YMCtZKtnw785qYVl1y}ZCW7sYJ>6lZ!w9p4Sa@YR%A
z{5J<K0}SQ5MXpP+^cemSV!;Ur^rIa{x%HUJbFiayet})GW!fk-G_fdf7Z<+FPBA|U
z-;y^y)2&l5cYcUZrGi@g(3>mSeCP}k#YvN!T5?pD!=?I>yDQUvvEGVes8$h8Pp_@E
zky7_%%D%Bh;Sa-bF!n*Cxa%)$p*4e$Y=2r1+GNaMY>CHoR#&fVNc=n5)~&#j$Jc<U
z&ET5b^{5}SDQIvjfiJs?;)tv$X35?b{)93Kt0`9bJu9ZMsD%jk3^|xe6Am|vY{NFO
z%}<21MKk90a~0~6YKb2y(`hQ1o}&P7j$M?tXd}4BgfUJGVxfBTi){lB^?Elqs&i^E
zqKRzUgZLVa$Rk}T4t%8W4<J(Zr!uBGQtgyvo#Tr6en9>#jwkBGmpwNCd5n9^l@ytk
zEJC^8c+!?!%fP2@%xvne^SS)Ap_dS;fh&-`NKB-8t-4c&OEu)X4x3~d6I?Mi%#(9v
zCvlwNmiooN$23DLb<2I~!!$iu;NdoNN;h_{O=Q!%0<9UY??2@HARzQ`U^zNWoE=vu
zR0w?g<T`YkWcD^ov;xxHHzV-J{I^jO8Yiw_*v<VV@M>8Ia<I}v9phYRxp}@K*wy*4
zqlh?rt^fF=GagdqZS`#K=%;qdnGBY>C)qx&o}-T}K3dx@?Gkj2e6ltmtjB`A<8Ir=
zJr<d4SoIZuLT$6J0;@BSO=e+vbD}f;9gSzSamppdQJ>ZY=8E@9+sai$Z7CN**ClRc
zV3=3_GG`pnbv9dEs&!^y^I|m<Y9W}t;EbW^A{ftZq-K~yug=vwAa7T6xeJ95dEhwX
z6a4KY=V@+aAnk)aiM<a|iUu@h>zO=Z_Kv2Z1W{b}kIg7srSnsCz!_g}I^`Wlv^zL*
zZ_Vc@I*pV~7!9kLNZ(*+BM$iKgj-bVc~8yTNjp;B5<5V=S+)>QoQz~7ilFfQ?qGd4
zK_775!+{$ksrsS9D&>#8`pWgsFJ=h3@aVwGm;}t!i8r<<B9mS_zG3OL?@_D4`lnaH
zct6l(#48FS$QbGMhc>@Ai^4|-SPWOwTkkch-gbJ%J4t66&NkQwHbFxRnc!RFQMtY<
r?TzVg9-9Mss{e|+yKCH7b4!T5B4v}%B~5tuk45lT%F?A$rtkh2x!F;1

literal 13425
zcmc(`cUTi|*e03+gouEEfYOoPdy}T1VCd3&73oDOp#_3~h%`l-R1xW2kzOJSC{+Xk
z(h@{^3%$b*zu&dH-=06twY%qB-^WFg$;>eGl>51#`+esnMo&kb{0hSr5C}w$cyLc2
z1OnqPU!)|!9@I!%3h+T<uc>|y_ym5ARGY(q54iUOGanF$jQa8g{xuzX3Ief#5clpH
z24t;I2Zk{DogHqCak~uH@+msI@aYK)sOK>i^f=1h(;-e~lp`9dy7HZzPVdfN*vn^h
zS4aaukSj0VvqiIwSG_~)^*%hL>m@Hhx)@9pBK8luol>>xnlrVVe*fmITAZs%oX(z;
z*nFG)_8>$_LLQpLiTMBgn=<(J4hJEieDdqV`kmXR#!lU<{3&#ahEI(UFt(E6Qnb>L
z0LM;i{-po^I3jg<<ioz!Z4jb!Ye`>mnkRfR{Ag&E+twW`dICy>kYn8OYPN=0(L|bz
zdtpSD*T@fqy(y|$yafpCge%^r39|c~l4Zi+8CW6ctGp=dC<YlGh_S%@4(u8n^|j~|
z|1J#*;NxHAW*Z$K;1Zsrc*{<V9v(2}Z_23Q7{gOAWf(_{`Fb*?-EV0KPiK=AlV7up
z+qiuy(kV5TvxF1{mp>x0y*7*Xx$ey;D$1}Re}|!gvY1#prppDQ*4=ZgE;*%P3$<Ne
z3T809H{R$)y-LS=5LY}I|4@sOCaLRVahCn5AphJ9?|_iQ@UgmkL;L+{Za>?bj}pfy
z)9Fwm+((eOxK0V)6=SGiVv|R%Lly%*viL&4Z1_hGC3@_=r%kM1v*gNtk!d|(R88;w
zq!D;NT>j5Irm%eGkAbe+uW@edSy%Dorv0=y{IJfn{^((S?zbi>wduBNDAsG4T|qUo
z8!wws?JtgAq}5oJpPbd^q9-j#e$QD^D$t%-YHjxih%;<{UpmnlItV20i5avweOI6d
zId?6$@P`Qd2hulNeh{A1D9Uk{u70MMe<Jcz>jAYoHFZb&c=hHtr3?8WWl{CFRb5J9
z>G>A7gQ^l{=7s}eu?u5b5_^aHkDt9_d)j%VMKxDffAu3O;+5<LBqSDl#MebTbh12j
zf~eVe`&0>0Y}Br5O2PWe)%*1@-*tnU-sb$?`}}e=)j=PPB7K5ns0N53Ygeb>YwDBC
z9=8V<-G$_oWV)LiH(Ta#W89Lc{rILLH1vv=K$`trpR?D^M;1)2n^5<mFXJrif^T%8
zxuAr^CLBvE<t<66+<Ol;K3~uEo%_=(d7|>mJ`Jg!7HjyF+jh^l3Tnf%b_xR<<ZsAR
zWk^a$zd#L0?#$RcTfUIl5A4-R-3nUUIj$L$_q9C@>1CTt438z1I&=yrm%p~CfnQkK
zIeU0h7tDHZS_!eLw8-88{`Q?BIaluIGV7|euv>b9;AVm#eaT-f%*u}yxCxY!V<c4R
z)<_7Oq>M0^<$W@5_SvwaRVv}Du6dF&;ie2J<f3n}3^-f7vr0voB6(y61Li2w9LMv0
z`d&^&tK+MRC~xaMHI?@#l%nrnEI+cOZ}p;UwV_&-k=PHk=m@dIhJR#@nW4P3)?7!l
zKgduxK0iG)e=8*mZmMVoovq?k1}{)oKC8I3_Cg{o?MR9Jb$s0OT#8e=@fUScgHlg!
z=9&435m;th7S|&mZTY@-3=!aW!WGBOk%n!#=!*5SSuh-O6P#53WT<UvcZ&QppYte`
za#2a&R3t^5Av&Ur+l4<`lE&;A+tPO&O`nm{zjPFM9Eg0}_~0L|Df!wmWnFqTs?xbK
za=rG~+#7&{J|rALW|d#ZN0J!^y>CS`%gb#j)wa?YiOth76n)x%zbEqG_k0z3NaMF*
zgXOCQ;w&|?udrbtUt{vl=^xsG0kpmc;#uCo-?%V1_{ET5=kw$z45E?VbI&S27H)+`
zV4E4GsO>jic)*c&SK^}KCPc^&Qz~=5*t*Cvv`<PHx3}(YV`^P*>1*%L$kQril*>uj
zqUEY-mkXxK9%m3~aesiray09vbZ|y|d8pp2TM8IEb^=?)f%8lRfe=~ba&}_SW(THV
z7d$AbZQXlR2aa^~0nN_*Y)sW|%=jhX*lsa>&gSt*<STav?SiT%lS$P>K7nTwCaU8<
z&R!AHUWtHcTuyATQlFn;Gnsw=(nT`Y6mIEouBA&LWsl8Z-4~M8w&aC28&7@SzH&0>
z>*yCY?rJ&<$2yLt5t<)w2DsU8HezeEes_abuX*A~XLZbSIXT-}T~OEUADhciv3UPX
z!#3MF#&I*rUgbEYUHawRP)2(3MCmz}hj*TVPK8>V%2Q`s3ZKE$%USsnD{p^y!|}}^
zTZch)QmQZ0?_L7f_(G&^WKN4sp^?X;MNKZ5(EdG|G_fc1#%d_DokQbGy*F=li)SCP
zG$TdqU?Zd>L&rAX*X+g${M2Q_^*Excp}h+B8wa&B6-%<_n?ITz5(28&6=If9D%5U+
zeG1d<EGa{zYb<_~k%SvxQZOR#JvX5+8dvQ%`JNV#i<b)$=e%D|NagpVhEAuo$G5Iq
z2-iFjj<?U$eqh}AmmkSQP!NsA!W6z;6lL(Ss;qWW+Ml_?>y6_W?@neq?8c|0c`-xB
znGRV9_szW)8~8j}Q(t+AcaA1Jr()S@;PQ4YKWR5@O?q`xdXfY)m_PXGW%}bvN9fVm
zk4cwlC242^m%GETbL;X0vB}?h9pU%)rOns&-!Q}E?0+P1nEZyiD8Bk(q^3w1bIx*<
zOEu^+$BYsmYbthpiOdvz6zlas6;4TV<IXs`kj7PB6@GwczBB&XTD5rH{lRME%yo~<
zTde$|Yk85(^PAsMn{(^%m{pp{d-@h^Gs_Pxq42@KLLZbX<a5<JD4;48c)AJf8ur%O
zp*ZsPt(Oir?O+Dl<6p{|`_)k+>y^%-29b})E1VVowAVxDvERC4%HofmdW|;pI>cEY
zjK?Tx%F6aIfgID6lk4P1%_oGbmapqzj<90ypVV=49<_HiZK^c*%^#5VCq3!p#Ph3Y
z2kIGO^J+qo!{(C%EYc3Vd3?U`S$wrAfbGH-%hkWv%qf{<*qN3_u3<A__5j~!&rhmJ
zOiYIUdGa@wi$&eCW$n;Ey(|tz3bR<3>ZSa!4lcTl;a=L9L#c>64_ZY&`guq8B`dXG
zyTnSH+^naHb|lAY-%+ik2dm_raJ!2;<F+FCpMO`1c*F8yj^6TOE6|M2lbjhMGX=G%
z;xC9uW{1;vwvu^ALc@>J8%4IMJ9O5^YOw?RMW0EMV#ExBFWQ~W1;PiZ{3Ult6AR_z
zs#PsB^<o3~7{WX{Q1P)s^?cGgLbKt7o&2Ld>w%*`(`nuV9xk<l3p4|>k8`lee98`D
z6`$Y7HQV>PW%5=AKvp$n_drKuHLe{TAg60Ns21sp)zLQ?uE^{ok)`oHs8Qs+Kh><|
zpIZ|%j6u+jkVOV*2liRI_YR~;(bX4gjx?J&tvYo-kDKG61M$ZqoTvV8IVWcP=|q&<
z;^~^tzRmGo*W{+yPS%!pJQc~?zhLYLH&a^u)udD!p@o_5mQ;~gHSKul5{7@l^c59T
z{dE%-l2dIEfjyZTi0dl5mDOyKWOYZY&8}UwfpxQ>b|b)_B!_L}uM%yiY=pPX{2CAT
zTPKox%g0-W#-AxS(QlGJL#<BIJ5aJBHh+1v=J>vx1G@YD;AmvFHZwc?plGi|P5>Sw
zXw89(!!kIF&3ARn<y7ZerBzD=M~=?(*JpUYdVNc>Y>;i_<>N;wxkHeayOW&h*=zLF
z!oCa)DZ!+(9o*mB6SUbz`qF5q|HyW%%h$6h%!o*=HPu#B&?c4ipTBr@$S%p8dn2WW
zeO3XfnPbP!xfBm?52LZ0UVUK+Q~5nDvHD1&f<CIBt^DL|YRvVDn5Oh~!LiFaq)E)z
z6D+g;`j=l`*yJWFRo#vQ>EyBZ)5$dKo;vd*$cB<J)d0Pf+PExB!TNyA+|3S(b0NI@
z1eTxO-JOi|b3rhiF+i#D`E<A*H1HPxMUwM)7)?eU9Vj=Zsh@M|;6TxQ(5@l|9?7wz
z^~rAG36fF`ccE#jq#e1?>!qHXM&-{xM=E7b8neFT+~5Q6t$+P<S4}ckq`R;cnwM(j
z-njMToW4OQ2=}_~Gd9k0eugYdwV01tXPi9&I@8CmywP2&n26%^sM=j;OxYu~Q=E8>
z70BGFaKLe9<@S%zMPGAWm4M6RJ2A155$_7*7KG9&NPp~0nP2SJC3)){d8i2Vj*(p@
zvuiyju?w7rG12;oEHH_dx4B<oR*<}wq_^O@d_dZ+O4C^XRb|qw(xY~Kork6S@sQ|x
zXHwZ(gQX)Am9~S6WdD&|R~b9g)xO>#-4(y*`iJjtAoQcENd*62#|R^CwFl+hEL_<-
ztoJ6Jyfl)6>W@kcU0tbZgm=>$a*7#fsyR!H?B%{hi#<q61_9p?a8a=4|J+1&)Vd6l
z<al?$bNM8-R8>{?Je$}3?={-%AI+OnHqC7+p^kTtQTx9a->q$YdfZ<+i8`33)Vx)=
z@uBrBd^K`=*~8@SqVG<>Ok$2a((}UdEJ1#wK?Z9Rc9yc`i96q~SH6f=KAmMjAMGq2
z+!V9_RslLYImiwB6T>sJ{VDfs4Y#+q*AKi{wR4qPLo+?<T)xL;dL7z3WJ|djPn)zK
zj}9tteWO7lA6uu`<n7_p9TEZ^c9({T$0A~ActRbz;%<)pJ)Q<<WM&$itk>hd7!>K8
zoxZypF>^655+-+qhEk-D`S~Q<><1<56#1~k)z{YgF0r-pl;^sAWchm%AG~ur?^Fwe
z;d06ioTVRQo#1Ic*&$;noeAD-T`kmlyX{+O;p5}85<#hCiX5-DD-)lVp7tGfdOVO7
zxHsw5Zq+=WYGLiyNo%5DV`4J)bazNyIc(I(!~@wYRDbH#G8OWbo@TW$)3ZqdH?Tfx
zVzVCB!&kI+vXq}VV{tlFb=08Pc_4Cou|xHMv2`MdFcrH~HWRp*y;|9Re*A3pE^u$!
zJ8if#nf9appw0G+^QKQugXg&gnrX(m8ImrGPzqW6R%KRE!=aa@v!XeTm64}hw%;r<
zJ;n6=a9IUR(<amCY=6?r2Kkj-=2T!<C|L3P0pr1fpUP1nC-mxu$)xXA>MG^;P*ms$
zJ|i`?F7OEu(=+u1rlZgn3;q;W@Di>)Ow-87$iUCfkHNMh5>DpSmtKR|-`yq;JKgHm
zWfHZ;kh@j2?k8?E%q2Vod9`e*wUbMa<C{lEN0B2VBU5x<ke&@B;R`HxcibV7+04oc
zwaAQ|5_27Te_!hIBx4g3T+MP}X3pM5^Cnj35(=k>TRfg0e{e7EZ{jW%m5+Ytc7Yxx
zT5Ly6kzc(zQnmb1qMQ7CD09QuG3wW{Z)PU;uQ{do_Spu2r82d62C3lhuh_No)f24)
z0az$2`K^qwN3#SiEsB42uN_jug^z|!YF*4F$a~fwA0MxlmX&R`Z1)IsV^&uCcbA7(
z>nA;%tKwM{kDq~|BvanY1!<mOkFYtwP5-$7gFjhvKG+{DO2T7JZ#6^C`syp^GZJ|h
z-bhb4=Oc2@cZ(7Sjr{$u0$5}g2RxwxSGFCmXiM~p;ZEtxyilm&<8CG;wiBsG-OR2N
z7L-rVx_&l~!RMXG8zRij&GkzR`I$GReHQwxN=r-qHHwCf=xsu_xKJni+E7CilSTK+
zA5y2R7l++cRRBne<$M;h6uf3yl!Di4x^JqpqITzHro6vChOq?TzrNwoJ--WNE(lw0
z=J!_o(@E>$pIE4l;@-&bt^DzYg@ydf(isCPR?R8f4_mpI8+JYnq_B(b;%G2T*z&Wh
z(dZ>9?3)3Hm#2}qv)HvKDK?dRY#g}F(C_TgeQ$yL$0;66?TE6WCR~cNnZ+M{FV*Uo
z<F98hzVHBVG%n`69xl|L-0qjbDF_+N^?aV0LgG+kE$7Fp4uD^sJC_#Bbtks(u6-I*
z5;<oa%#bSEsBGRi4ruafJ6fRo{nhDs-a>{<Ff*~Y|MO+6mxqhS+PAtG9c%(-qc$JO
zY%Rpj0+(+I0pKY?p>&8pdkkRoNT8}^d$^C#xX?asT$u3P`ZCfmX5XJvGi^G&GBn;W
zkD5-p-p7ME(+kdCed%HsKwcL{+b_;G)w6;(8oRB1hwh6&6;9SZMRX@Hp9J~eY<wOD
zRJgkG-iQH=oJHY~Y}vP|w4k6M=xBT1XfexYATEO9_7b7Sp?jlY%sTjNCr)A@fX_h3
zzuiDbZhjufXCjg$mQJYjw@XpBPL7ltmZHDEAJvt}dLc^K1?u!-uun(WfN^uUNcXl<
z<(1ygERX)4OoO?#Gj_w?aBCm811TF>8p>}+0<5dc?youC7g)cB+n;c&LhZ-!<c#CL
z(ddS7&a7p-wVE|PbsVjm^gP+}YT3hDT%63O*|6x@*x<4@Wd@{9U$LuZY->AQTHeW7
zYq@G(dGz8rO59{y==xWHsLuKT8V!|l>`C5qOww;V`fV4@oLWckr4F=)k708F{aP>F
z&a-rXw(+hFby65j$*P0|3YwL*<&nQ&G}F`XS-?Fvvw=7|abkdSuUY3S$`Ti9j<fu_
z*|b`@xdB*qAZXok*l>x$U(36*08E9xJ}0->`};<B=6t(X#AXL@Mk@fQK9a{hKQ;~k
zC&^7)0=C|1UHo{%<0b7m)sU7ZWZ^&c7x?O!F+9g;$xmCiGchrddf31(I2QEk^(L}?
zy>1fQypZ80?c(XF(|Rxyi~{~fx}mbNaz4ex`geI_qugG%^7)f{s;XbFnV!|77T!8x
zC;Y~&aIJs`2hPsUhlkj}Mr`Kgb?L1GW({^02R9|02C|eyT6cf#J9H;l87~154asx4
z-kkprLMhQ185s*9^M7M!$!rcgzR+8Ge@PT>Ki%d?6|p(rIxqiunykMc^lDYcy20Cy
z6)1jt2#J!$C;R%6D&g;;(}d%e#OIW(Z4=K=h5<qubjk@_&}r;x0^DF}Aasx=0#+4U
z*E$d3bx|t@npd>tB~D+fv&aR1dqG8}9(O~mptN+Q{%bU4I}_f`=44{vl4kc;$2PHm
z6SNlYHJ#li4<8>)hFzR3idZ%IR`dl<U9ur|E>ZdXH$aItO55K#hzBD)04fmpGe=kd
zHz?Jyceqy>wUkfx(PnJwc;ch|ScFRZNnSL|?US~Xjmf&o%8fA~7-v7iVJwH;09T-9
zZ?Bf`-1YESuP`Xo`rX*p7D`oqKAFI(9J>6EwLr~zQ7}rcJ?}ee3t8<CTCF(1J(=sW
zYM6b_XpP+d!Gi-@!x;5NJZlKy%j>Q2r{uZIX<iJV2omyRKIUXMCGoRM@=%SYXcZrm
z{Vk@IqYyY84uypRg-hQW0(9l6Iw0Y!*3O~ZDcM4!4X#H5i=ke3_uV#fS<jt;eop@T
zYxh`$W&73(Dh`I=;oEtC_Uek*QE=9-cPyUAyUTl*MZL%p`1iH4@4T-yYH##B&GPAw
zqRp0_fsa<7E#jW`havxn^Erp|r)WH4t|JjFF_6ZE>?HCF9cfI*NZBIvy&jXBCViOs
zMkI*PhiGAvIJq$YYz9qcSI>0C0;;Uy%1qhae`aVZN;HmH38Z*$TPNEa)z<>9G%$WY
z8ka#NHI->JlDhHWY2PV((}=<wz)R2;kd~IPBqAxOG7UWxcw9l&C+HxU#JR_?6W$hh
zY&2)&n)HFCQ2#th;+pK000vWv5AemJF>ol60$d@()E78Y5170qm712U77y|QwQ0^X
z@jgg0q)zdr_3gUzj+GrgxhsfRdCe~du8M(%5AW<UlAbc8>(@DR8f7U(z!V=ES+(kv
z^ticrTDQ4wMF(Fqy5Sm4u9z)pBVu#}S~k+YsCx1v377m)i>s=6>g6^m9(n)jffN2*
z;f#XgwB1x+1Y6X0{$}!GZQD=NXr?#q>@ec~V7urmUT{%q8FJ>2ypy|_?d~xbU)WZx
zM8Nt4V>gr=f9VozDrfQ~lQ4Ph-@t<Wv=1|a#2@szg6Cb;v{F8vmgZpfO5!w1z+n*`
z^yYhfCTvNZDXIk{XddY2Na!<55g=sA8XBG^v`a|G-|t^>mR#j8Aeov;`N5OSbp?^s
z^Fg1QaC3dPy;blb5D)H@Bs3Rd#ppj|?muDEHHm`lu@L^wB@PH+f(icaJH9UsoCy9#
zz%eg)3xzvO5|j&F0INeJ;6KcOOy}#nVq~DXw>a(4xnLOx43rD2Bxy6hCkI9EZSPvp
zKd3TGPiiu?vCXGWYrK|j^GCcu%9sxxAb_tJKR5_Sy&nt%Z@g<DYT@v{mlK)^8uXRE
zgK&J}e8$aaWh3+S+qVAoR8JR#P@vyJW_+yjsV`s88;uemRGa8Iw2eBB62|gEX$&lK
z-E6>NNFePid@+^FsIv6+NoZZ@U+d<yzqJOM4jhBoK{IRm$y_Ps1)O*vbURq)9u7ys
zt(JQLqR_m>3jkdMIM9jN$9Q){+4<O?ZRm2|0`}?wd*cFuhZhKaIl~^@E6)G)jQl^7
z{w|4{kbsdvljm>3z({BjND4vBh0r%CP9_S05Cq<_9C8qpF(%=w=ok#~k0@@0{<4uq
z|5IuLpRJg;;?Fw|x_Z$?Xfq7g{bUKML;IZ2T%y8?Cm-~ADkN(CbDKh?x>G;)rNnC^
zLq)^pp+jTG4>^u6YIXfeZGQB4xdvWDH1-Y(83nGexJFYb${QJc_P8uXM7`vIjW?J8
z)a4qSp9`D_N>$5MemP@uZ!;b<aX)oOr98_$H?}8<dZ?;$Fm2!hi?v~?|NSZ{S!S-C
z_300(3zuh!G=vvojFo-_+C@*Bwf$d}`8t`mVWQvzX`>LIogE$A2g6Pj;im&DiZIq4
z76EUF*Fa#9U?)gDB$~>#)CRNx-6vrY{+QMIw`x8C{Jg>}{a41(Up2RyemYf6ePbWZ
zpwFHWussicfF@9GWtker=}n1-Mw(|`jCIPEua7BiPFa5J2zsR<2MeLA79iZN23JcE
z6yFgal6(6<%|j#^2BqjjtyLOU<UE6Iy+^7F)~FCkBT<0Ai=SkdK>#?er2HZhLg-i7
zLsB{Uz}&WoBx>9L)J;7;_~AX<=?@VAzgPl>;Xo}{PRV3%ETy^2CUN>z03idpL5slA
zU~-HIdOh4Q=`P>`?Pc8;!UGr%#R-0RkP5Xm>mfT~?pBZNHCYMW_E`+njpgw&c}0a>
zbC};KlEiSVT=0Hi{Bmjrt@g5Vzje*FTLEv?4UX008Rd6{K_TQNsrt$FA4*C*H@3Sj
zT%I>;KC~8lezg>Z`c^$Lp`RiXys_OxnN~T$67b2;QA6>rUwde`3FT6lk+#+JOa0_D
zPtQtM>&8E<@nrO@#xJ*f3X-V3r^NA<OP`gz-Cxh0@deF4l@)dGR~2YgDk;q~No4)B
zpThx5`_N*hndY;kY;9e{8AV&7Tk87FA$R)mUyRZy7vE;R*TDSbV4HL`aLi@==S0Th
zg)hEXKjk20Cnqa-?96K<rBtD$=s}Twg!EC*L)GZ@j}C|)i@jOu!d&Fm_oVB_YAGW`
z*@MlPc?66dq0eWe0TDL)l*@^jz6%hA?=LVw+&ce15cfMhh!WKe^U6B_q_nSa;lY?*
z3=Vy^^*S8rDO!0%A>^W0WSI{k)kF<c<89enoCtmS3C#~Kcw&4e<}t<^<AVvoYn9V#
zCXMi|bKx0mk$5FM9RJjolz^~hBk0k00lbiC#0$OvnSw&#=|t{Vr-%f(N6;edoK659
z?n)6KD-v5?DPQ}0n8eAKr~=;=xgUqKEdAFYsk>ezOu3`$4{XaS$<zUnk*7y~SdSSn
zo8BPK>E;wf{9@oUgj(m0W~N)9hpgxmI1#O?fYpq%7wsjc^>0X}91uyhB!FXNXjL>N
z`Xu}ye2!EG&-?j$h1BYGZ&`xv?(N7r7#KioaMS=;1jY^F2=_w=V%CIT&bfEQE6s3^
zG_@01YBkV|MPW$sO~M&<oCrq~po62C(d%d>v$Miv*@T_&+<Un^U<(lW(18f#q5$YJ
zH>({0%7e^UMBLy)@V`}o9|mI<(YesrCfS5EI6bOWyEw4w<(?bKF|C=-%hUc9X$Q?D
zel@`O+ttF=@2@_|(vclJrNvuz7^H>XaekIVWNG$Du1c-SG8Oz4%d3G%()geU$uX+q
z#p5tFk&hSp!V(jqGhCd17<+HTaw4oM^&oB6)3q9IxVjj(uW?Yg0c!}Z6JM?c*enob
z9*^>FYfSK_Tv0j5OXVz?<WELdy*%E0laG6DMMH6I;G)tMdD*RMRNw)_AMXPg6fsy*
zV@l#IiLFgT%Rt-6rmmxK@TJLuOJ*Xwu7Np$)Pr=vJZjUaRGt~^h$QSCz!#}mNO`w2
zivL$E=7$w?GABaODvwCvnciHKx9U~&N+tQfNrkjUc{}lw4uW;4a<q1Pg%g11b}Ust
zaj&?kJSmT?I$F~&R2#tEi#^xUfJ>O?5w$?ZV+p(+uQ8C244_wXJ>&(ms+@DLG&i%+
z0rDqaWf2`s<J$gq3>z7JON&?45>SKAQGkH>yR=W%;q#0OK+e)q1uqVgu=es_tc{{S
zofy$<BuG`PcD&{+LGmV}m2W9z%H-b+xFK7r$pN@d59~uNJp+RKt(!_CDH*LIh3DWE
zzKb(^ZweHU)D^DrittJFba)=7GV(;#3wAZG6{r{(Z7DbrQU|ks;dgS=8}99CLIN~W
zU6W#x5MF}5as3!t#Lo&K%{_`JYJxHxA?q22xB2g2=3dGV?+zCrmBEeEnG&xVKL<q8
z$`F8PVvKA@KIs^E0sN_#>bmIFao7W(8~XH0S^*O8eQWzo50Vga*_2ly$oFoZKg9(0
z0|oHRw(1>k(q<ra%g<Z@`_qBs+H~L&av(Z!O+wIt&1bT}^XYT3YI^qNT#_}&X*F3P
zA41>$iqu^j?c%7=2Mv^xm{^Phrqk-iaAhPJM!fLhY2YF>TmsO}6bjWOPZ0WCH>Q!_
z-$fgYN^v#6v1=-rkMD|#-3J;uzs|pzCw`#5IcjyImq_Kk4W#y4)7Z81bHh00`&E_{
zJXsd0A2!_A^XBRq_hY<JpC6rkTGUZ0sxx#f2>@avgfT&9!r@}XOpw!$WPciBu#Y!R
zJ{~hevu22glR5dsfO9zUq4?%I%6MgrKHkUbTK5m)<Fn_410Dbsae%wTv-ZV)q+$2X
zK`kZ;?>?r7a3K@{!%i7QpA%RuFQNqy`f%E&mm8|@!Wtm}3}pC#!T^O*xD%B}C9)-R
zmOKK2aUr^LalmvYy_Vq(B!l@W7d{W8VmmC}eGBNwugl=a5#^f)X&l#pd%m&vbgV<B
zssB?zAA@5wl0N*IZzgq<H)qA@W$!WLqbjcg`*MU$AfeO^-1}#_W;k|*8sK@hpME7#
zi~8?;pueVVF#)5f|8;KrKTLD~?{60G5-ITfs~;L!)YBu&!dIB4$H0Z4eoTEY!)pN8
z#em?g!9-v-KQ-lERiKchH5;(C%~v>+hA_xeF=Dzf?{pF}H5me8ylwOTj6f`s8~TVb
z@?63ONiX#Qu`Nk(u!Rs%wvHpMHyete^KsC&<RaV+TXUypjK-4(n<sG$`eSdO_#7>^
za7k6k2;vw6faLB&f)g3_!zQs2uyTH*@WcUYN#wxGu9^aV-K5*^0gKcz^`vlLes3y*
z9#79(hSsNYOgFG^@g!J7DczY_nrsG{G@ruOHvHa&ux?6ei}fu(0*Tv!qQ|E_O)Rz8
z#(9{CppjY9$iozWDl8dhAhNJaJzVL>a}I?5SaC9gx244F`*L~SGdX2M62A%;H$G18
zPzESmN+TETpFmw+`7h8{|3S^^A>aW-)jXoS$8Cz<;Oby~FhIFRfaVG#Nhb~=4!9``
zI+PNHHPFd&mS6xtUI7+CU29|-zY_39kLDb71MvV8MzR43wYeu342fP`uID4HWspp0
z?^)E_;B0m)5PKp<;qlsn>8<wj2q5Ous_Q@VoD2*;X)bGM6`2fVBb5lQ%RR|W(SWF%
zL)X6_^g+|vdaj)$0DAi>Av7oS1l*rZUY%*-W_U5tS`^~iSFKMQFrxgp52l4_)qYu(
z&`K!pHz@gaU1o7BtUTyoMj00lH*LKf^eBV!(FgB0i=fL(KCHHCujeISg{iW@k_Ewj
zGEmpjjIOKY0%n1qjf}Lp1hl3#F7~id)Q<SJ2w1@+tH#P({&^-sAV#cabL^WED@E4B
znahea2h{U9=+G`F6bUZt`L`PaqsOR!s~!0On4{Q}{V&UDN-}yCkPctuj@PYc^!_n7
z30)6TBC3SVKnK3BTs-)F*=)!F&3DdDxM{0y%PUHee)U7rB~euBzwrgVo(_-59~20u
z2^R=w46j1I%lhvW{PV{lForGx@}x^Wa3&nrRuujzyccZ)9R*TVw3O02i>V%n<^{%T
zh8i@xf#sny^mvFZT?$RnWfSF#StrIPDV()Yj--hJeYDB4fYTOT2sukl5xSf>0EUDB
z54Apr1M@vZ>A<Xk2Kv8X(>IMM<la9DSYsL(ccl7)0IjO8{R@1r4Qxoige&^~v<s5^
zW7tc9V66TOh-8_e26=$Eo~A6@_dR~>7278{W8&``ec1ug8VDeTq@naqyFjd<gYj-p
z0r3rN-U}cnw}1mVzjQ8WdZ?oS!3S{LVVwUl&N`JBi0>O9z5s;y|H~ls&yDo}B#3dK
zzlLXr+hJ7je0Wd%8POcz?_XfLr-g=a<tJ+cptz?%yI^Y&H#dC-C!)ZFPYR!j_6V>4
z)TB%?MRCe}_^2`B-w;7q;32Pya8t!$ViZIJeZyX)gl38^V$83?1ul7|-P7jmn=)xp
z@nx>z&-)IHmUpuxa!xj<npR4?^po59vnxGa7g|k=QZCF!^o)7}(?5Nz(OyLuG|guZ
zD&0mI8odMtYPg>xzbH+aR(EH~C2%(Wi;O<>$?rqZg}$1Z28?9%6_z#Yix-w39VNYH
zUz+3d`wnOYG9QP{XYnkKV2!Kz?;pj-_oIxZ3g>pJPd9Z3OZc}GYRz~oehF<V9IWq<
zK$d}9><(>*VxntwVsS_7tz@|7O%VZ&he|H`%Y}u=KiS5@mLt1UC5x%<H!I9f?Su$V
zTKdb3TV>|9%ak6Bj!34wKiw@gLS>prbsr>>@8q5%kH4CnS3TPKS?g}5dK#>0Fg&%W
zqga1AM`4PkG)+3n(I}#Cd(!K**t$0vY?As2(l0bBWY+%aC6bRo))9#BOHd>(hg!l;
zMf3hqZl&Ry*Aia+!t^Y)f)71MYyhFlk>U%IyGnFSl|)GLf1dKcw{UhPCzME1t;)b>
z*Q;;qIOz9#0iC?S8$_pB1j*7cutkbY?RyX6Dcu)4p|-uI?faqm8hX5;q=Vh(w6o!R
zx{&5HiH2LBWhhOC{pE|lMHkk~P)^$g3rk+UdwN-IQcQm8Su@1f`adrrp#!h_p=7g@
z+*oo*bZ&iXqY`DkVL4MhVMK08Qci9Nra^PR3&VPMo~j{uxz9g^@4*wvr(ae3Fc{5<
zlJP%g;fBhmD%ZZJZD#3=uBu&cYz<tZnDnVx4Jz;))WH0xZ5P)9om)LOy)6*T@|;?F
zrqr}v08P_#(~w7-*#G;Aai5nV-_uUk12Wr9@y=gnzjs96R+IbkM1@9c;Bq9ycYa#c
zFNUlJAt!PsjYiMe%vEu>$_dl=A7?-4Zjyq^^IX$Z-zBaiw${D<O2<{?+>ccLgHsRP
z!|wUa=o2&vz6#Uu6gGNy{fq+eJz-E4IhE7=wW;#T&zT`p=BUh1{C6vXWq`67KbdNU
ztFIk8yq#Nlaeem1Q5Mr#(^o2=yu<dp<93DN5rDxk+6)+J48YiOb});WiOlGYYQ@Ej
z=#7tx^q(q`(d&)QSadV?9J7v5N5FpFZ3ahE`%AY&LLq%RlJ7f&Va5<;9l%Kb>lfsA
z$k|=2wC1H3>kb%euL*;iSjOuu@A~Nl@BC!0TeJk6u3OyrSeX_FZ*W7w8l;!mZrf21
zxH|4Lo<MRzEWq_n+jJYAZ9kfj{^UpB`7M`Z0jx|+!E0d2JoFm(E~?tvD8qgs?-vld
zouAotiyAg#nmt5Qern)M$RHQ<1D-X?t8C@0Cy-zeB`3o}&o`vKhh@Hle=T^=8Stj>
zbazqAc&(03sgw_`fwD2)I^x9c&=8t{;w&&-Uiw}!a9x!hz2!+Cq}iAedji`js}OT5
zL%4_&xetY1ez9THf*q0FycZ}GamZ>{P{Q$TT5IycDO7sJrq3(+!~P#wI;n!LvwqeU
zChLV8lLmbXYZO{MStn-#)u=#KcN4K4P)e)fzn}%+3aoE2`@dRkUP9dpxw`VVp&M#5
zFzf~LX04_khZRFN653dQ{a8*h$O~MBQ$Y6xhHE`xSmEK?5vU;-GkPQ;I=pLXPl>&8
z{k2rzCrq_}Pl;K?jZ&B9if={8>jd7PA}{weF||u4n`5>aoqYy;*E8y$W&nef3KC&r
z7QQEqN0t;>CWS5~z^r50uRX9o_!yEOz6nVKE59I@0H@n@rT-b8WD1~oppKK_;H>dY
z*t#tr|5U@Z$?YjRd2l1r+y$V{?A~6CSY(6<VrBoF#65S^gN^+Qd170)n}<5IiaO~#
zC$MC|CjT=$NH(wQ^OGDG3St(bR8al3q_diu^S8^|F@Ky3Kc})>C|xyGlMVPT3zxmM
zuV~ZqTWaVo#^a9ah?hz^P5?unzrhN@!Svl^y+8+QJ2h7iX+&?8m79=iw_E_!;B^1V
zxzbCYD|vKR^vEn}`xnbF;F_nx9D?<Q&Oy!f5+NZIgAlLzw_~f}%<?zAoSwrPo_k|>
z5!2R{L|Myiy1Ri4>@HV1*2-bj)W3;r^PSVRAlA8h24+<l$s($Z)G~AhDPnfK{QNDa
zpH#J5InJ@B;YC+G557v>aagBi%LSxBnRRD;)=T99DTl+(zGlbKVkl;=R$C!EyFQLz
zML&eA?8FUN_US@FGy^WA9*DqRbIm<b|EffEsyl)b!Xp5FG^_e4{iR;!)D6RY=R#oz
zUUo6S98)jx6&gZ*XdIsYA@Mg+#l{M`vF>#jJcljuPnBZk)|;!!uf22pW$>*E^OdT_
z?6{jLThxRd$dlTibukhr!R+gunPN$=9)!}SoZTj<;@^#n(t~^RKY9{%E%FxS@1zbv
z6z&$oDF;@NPbB*bFq@=O7~;r@HIeXaQn;JtbrYJreH7UH5mr*Xw<3KOEX*m`9)eA@
z-*9em6F<FWZt?iTGho?wrSzjcpA<6m8@N<Hj*8l_g>T=ygQE7Q+<+hH=l%xzG2UON
z6R|Q59xDBk8(b68c_J|=Yn=_BQs(UXR#K_9`N)jz02L2O;oznndmJNtkuVl-%}P~C
z;YGy4ar(URy8LMeqz2m3yCU`1{*Tq0S=gICIbT0jZd*TfD0=W?kUr#)XSQW}DAMQ2
z*1~Aa(81bH%#$6o$Hyj*q2&j5v>t;r34JRzyfbl8a`qEU0nHILzK>?G5Km$XF2T*V
z$ocHesi*8@q}uCe@J0U<>eL@wW?u_#%Z3EgH=RZ3kqH+)!H_xT6gKhER5!ih?>HK4
zzm{RU_;`<1Ro28zdt6S2dSKd7kYu0J646T8*w1ch`=%s5!LEN``78yy6UhiFLbA=o
zQIaB-i+Kyl(L3-9<&75!eHJzyUsP4EPv&6!nX|0B+&dJQ?iar;LRYv1*Zj(?#d^oy
zY`cNH|0Hdg9!~FpSQiJE$p@sq$^sU+$F(MVcY4>EMYe-mX@Hgb4Sa6tDUlxzpejPQ
z(-hIeq>Jam9G1?bW<2VSLL8RaAr7#)s8=8!2#h+zk@bbrqI{y&gPI*_)`ld#-W<(_
zJ8NO`Vy$s!qzGT>Jno#vCfT{>$g&m*le_gYA!0dfwY<r0%Jp2{lzAoQNgu4&Nqdky
n<?;^}xVit&Nw37><f0`&;dvO(qtBQBIfS^cbFWgx_St^}+kaY)

diff --git a/reference/Rplot006.png b/reference/Rplot006.png
index cf2bf0d7defe00d8ae1b38ceeaa665c39dcc2ae0..54562b9c48e81b5eb9b4a304867a6947aaffd959 100644
GIT binary patch
delta 11639
zcma)ibyO5@{4UE%OLvQOr*sJ-EU9!i0wS<<GmD~FgtSPAfOJSVD+mZkN#`!o4brjf
z9l!T??>YCsyJyav*?H%!=M&HK&O2Y9^gl_kchuH#&&7L-c*OTe=#Ewt@!c;7;-B2K
zlpT@x@h;vx!i4%LK9bXTv_kcO`1y0b;5(1X=0BUqJy*RSXQ-_+e5{qB7YT$v8FX%G
znaTf@rS~^fneA$NK~DK7dwA$qn6hMz<ed+r_mUDs$N<BOOo$Yu#%bY(DnOw$$t575
zb<fcKP>b6v#UCfJxr}&&zD+^`rO@IP^nXi|z!L4Jp}!$o0&!t!Tf6mmUihgGDm@S|
zi!PR1^8`{jBvGYNJ{DcScvz?~ln3Q*jSUFNs6m7st{{RQ!RhxyJPU3fz<nSM2e`$W
zsgE9$hBKSrZY2v>8a{6#Tc9%{n8Ak(Yq;1z^W_<CN=K`Jp{+>RIA&|(YGU+^=QCw6
zr7Szc?EMRht$Lz!wX(UhFJ3{;q*l5SK?Sy4FpVPaXDiGIhiKLzA7m3U!7aTe$`mc~
znJk?lcI>w9>y(p6Qr4VeY(??yKm+aoA@kXrZ(fH!9drRL@^9S*)qb0^rWn0v*L0bu
z>~2zjW=Y=tD(uddbPaVtf|4zO-rNWi@1wULGgs{T;&l%>TQrx<vOJE%`w(OIGeiG0
z9H*5fKtXPDcfM$D-)Z4(@xJ4>M&dXgH6in#I^^WD;2*AHo-n6%^Rv=CFQ6kXV1Nt#
z$#Eg|!GcI!fXCbBh`X1{eA2Olj$I3M0Sbk$FJ7m8^C-Jo@Xtgjl)TtEjKo)@y0X^&
zI~Xj>w7;=&MUc1ujwG2nr{P>*C-+vkVVPARu1J0RyK?&CxyF%V87smv&e{!I$nR(_
zA1MxwXVlE6gVXDUU(VG1k}m+qoujub3zm6jTN_Hsu~Y5}*BNrUZC09+_q8aR{)B&d
z?E`lD%<eANkP2?$h_p)g`TgU_(Bqqz=4ZNcb5xcS-c(x-c%HB5<+9YoX{}IMV7Du}
z$gROi>Y+^=by<4CDtb5xTx?gSwmd}H)uZoFj&(b=?Jy3g<Yl=m6OL^zM5q(%3Z^+O
z|Jo=bs}_!5B*(4acYbz8wdG8XjzGRIx*8n466?%e6QxM1gfatn9hOyYr&9-D?<^%e
z={cWX|K+gQq9cYebIb|aaAd6PQTArLx{lI+pHoRzZF4I;ZR9yXHT{L!M;dqPVa|SB
zfAFnf-mQ9J&$&z$>cBhFyALitvSvkn^Gd3DwF!FASKItVNl2*-vu-0(Fiq}B&8v~W
zQSe3|+9d%?1eO!H-uIX6e0g6ud5wpn^h-5Fq_DhOhDA1YWNS+cnp=Q>pr=;4AF-nR
zAiF$I{^z8cY-Q*@j!_H=gV7IviAbh4Z~8Xuoyq-z>2({zBcDj#{&tyAmG@P<vygN1
zNwMz9AlGdT0Xgcb&>H+syT>B<i`>?DRvOb3t~VlnWE>xCJ();V=o@LL6HkSQV||pD
zs@8n%Axo|6gVD~1!5>^QsZx$ci{4Wc0*9Mg;sFWewC8qc^5`EYQr+wVk+16Myff<a
zi5%L`c)3nui0CaB9yvErP;HAwwGpick@F<+;PC*@4^)2gbZYQN^g)o>HVpfBW>FtX
zrhOHdDh0{zT(k0<&72^8xU;jhUAa;Y8@oAbG0dM!pYrWhc)dLo*yxwZfb0B7L89EQ
z?4IzOG9ktgl+mAP{nHG)Gj?@q7dDIDh>2!>BUy^m$~vL%O!=NA+>m$J>`329`@N}O
z8F~(oF1@1Hv#6w5+SfSjfK2?p=@`C9RU!<)yqi9wS*ri=j>t^b_0?du7xiusv0*fU
zoDkm4gR3y7eS_eH&gEJRtpMsEhiCkPWft{ZBJF;3vs^ShXCwT?h%BX=v|=wfA+UP8
z>La}F;wM$_M5#%CeC7O8LgpHFsvG>jMD~`zxabZ0WbC)NdS({?wfYxXk4wyJ^f;fT
zO9m-i`tbImpuv^n_XC`onsf&x%W0N+=wnCE6x>S-Eu~vso;=jsQ9@Ox;l>e2bIkWs
zcG}d`b8~gXkDQwS_;p;tp}6xowO+yLek1SESTuIoR`;CT_erY`AvQ=kyS|Wrm8o5X
z@U6RF=L@FD#H3Mt2B2yQTV25BA+Y4G)Z}cL!byaEc`dng82mAIMXOVfa*yNLPkl~B
zMYMOV($jsi&R1WF7*yx5?0%#Qsr96D#%kL&sr9}6nAFoCKKZEnutWVqVMzS@{-$>$
z%`=?RL#}xt!5Q_;ax{Mpv-j-Y_j}=<zH8RwQZ%fhY!ul#l?LFlxvTym<DLX)@y_dB
zcEMnJhvoKUvC5CJ`rlS@=h<R|ylCds)T;ZrTju+aDp4A>N*j>b$Qs=lMoz@hm*IeD
zE1@UM>Q~Gz<30U?wL5L>=VS-4S+Cv3d%NtJ3A?`XUaYZq756AR_YYFcJ9?+9zEiL(
zJ1HI<O`lz`>H^e%rwrO%PM*w8e%k%nHcwY`6U((@{$pyvajICUg+a68Z_G&jvU;2^
zJ`{-UvB=8HmUxAmMz<k)!WTpB)taF5q<|3?%z(Vp`Boxrq=XUHxW9L&6aR=e+H{bv
zHfvjN^d@0BhvYU1{FX!BxyP*cj!|^)pbH!InX=mNq5<G-3SULjV{3K7;iZ=e@S%5K
z71FD|^Euz~WX|E~#D}Vtq94vS?7X6q4*%G~M%i7TQ599p^UQuLFNB#TphfsCuZHaH
z7ETVL`O!XKNf&38ntG9~9O0Jv578(GLI>!jGmR$eQbWJVsn+aPt4L|Z+QS7BZnWCK
zA~_<95OxDZnV+>0HGI1!TdH!l)+B0tJEA?L*bn#a{F~~d;90(F-4)Npf^P$xw%B<e
zev;&E!Fqp=%%y#`KK(=#)l9-#X=L+;Q^Ynh9P`U$=zeroc^ugQc`k;mP#(>((SIuK
zkcav(N63u9?_9imU34Y<`0R{&@6NdH^oyZP8IJ-0(}l+;CKTP}zfL*CTu&uET|*d?
zp7Xl9nG;Uy8KZL4-SW1+3zZb*{a_)Lip<4D`k~#OXAH{<B-Dg4D2bTcl4n}Bu<4=&
zRjchCr`kGIi5NOb;Y&bMTnd+fN?Ph2QGBt2#M|8Lk+;EdRrka4lUAiQT@=-(5w?u6
zHE&6QZ56X7cJ54XE7}S5uUF=sNuMNKdW8!ykE7=t15!ec9VI=9-WQ>oH~iXNDl-mb
zks;CRcc#YbTa|8y=TwPz#QmuhEV?^B6XYHy|65`f#T%;Vuy{n;kqj3c^&||E*e<g^
zN!rYAE2|$lZK1XFQn1ASS#_OU5U1(T50EAUes&B!SrJ&@kDkO<5-L2oa8A{v?Ym_?
zlg-1TpY>q+iiI$orx%`c_u2Fnl%4lk>*w9YdQpvK6Uw2{@?Ro%zK&G6Qtx$zi8!n-
zk_0D=IL>9&E3Je?U-7%D6!UOq`%Ap>VDltbA69JRsrlX_y>)sx9jx$hY-p0$nnDj7
z20XqEoj&Ny^h8|c4NpYJ4&ta$u`#^M{gCieqC97pQ23GjW3TX5#%R)A^Uj_fTj^vQ
zuX_#zoEozE%Ay&=o?nI!(<LksQ*JuXo2W2`oiFt{^Qu+!8mox@iiQ~x2_Mx(N6UVM
z3l8rR#a|BGR!BW9uM3wDo?)j<jO+<V9|4aUttf{48dex16?3~6w6%QI-=%xigP1KS
z)s%g8Z{aH&@wNJOnbx+~p7Up_c&G<*F*WnvdbNzB?5dY~5<9wb`gk^#o%TD387K-!
zvJA|L_MEy%g`<^b!id7A><Tf<hj+@1_bDwkf+$Q^!vB(9LDW2!zq>J^cz1SzY!=cE
zgR=K&glAVas?%BqyY|YG%R)Fe>eH{+GKL$Aqm_<+IduKX+rOcS0A4=w9*pivvx`-(
z<auVyLv2}0kvO;G*62;K)%FIyAuPE++^g)GjkS8#ba;3}Hc!*G-}g#*%6fH?C==$!
z&8jiiG}9M-`L>rPh|+WYTl`lHVAB4vt3s19HS6AE#gBrYj)cRoI<hLC+=b)r)K1#n
zeTGPb4pRj_H(h<rKNlDM;7Vc_OFX3|TX_9Y3Tr1Mc8%M4kEoPLL8kXdzvvo^*h)oC
zI1?d^&#%s3bHFqAL^Nz6;`yIRFqKgdtk}6OkM)VHOO8sJYK&PYI<+!&QV${jD2}Cs
z7JQ&-Ki5b!j(P^s`LM*waKgx{g!*RY?&+!Y{&e_F3fGCJj$!e5W)e54ki_wr0lD1a
z59+fwK^|^yMT<eG2$dvbU7w@%VfHFi<Q418%;DNlmcsex7i98f3XZeWNiRsbfxsD;
zIVl+#?<!6UBcn0j%gw?LYb&ccrv00V%E*)Dpv91N?^Gd+-`=ye?sge6-irqK<ZPL2
ziopjNO@8ZSU~v}npDvZtX{W9yztRtzrfkDJ`_bQDpfG>aI>MLb)-#W+gNDp>baZH=
zW9MMJS;vP2?BNHejy)cpo}Oj^`Qf30m8sd+sT2pvqm1ZZwJnyY?(;788?nn7*PQa<
z3GbYU(-2qiWWlzdYM~7Iz3IP&enGArK9niP&My6IdTz=Nz^ulw`2PG8J|Mu=&>E<@
zaDo?6f~Od9d2A5m(n@a^?t_L9upAT{_U6S(hMn$wFtZ8y{)@Zv<`O8|nKCiEv0vMn
zsnP6-F(@1>2B+-0Q}j4*QWJ%3xn>XB`G##>Nz@69CGYTE?ca(IU*TNO44ifE@OE=M
zpC=J*Js1Rs7g$s0F_r4lHWRvCKIQcbUs(C3C)I@itp$Bcb9<mBYnJcYM?1_#9-wlS
z0VoEVUI%pBVWp0MWABI>8y)Gei)#??x|uoe4yC}Q&X?Gul_Y~~8IQ>_R*ti3;y#he
zHkS*52IUll>u`3&vw(mT;hHCdOfpVA@7-VElQx&Mx8G>~yF;+_XvV8$_w?coHE*O7
z8X2Y1asKCB14Q=vpsNFp#hkHcG0My|J(^$HeORG_1>X0)sJ2t+09GzjQ&R(FGCqPP
zM(W8d9Pe7M>SoCYY_^6R@$|IV1T6$u?X{gPp5^%V^H)wxPS(dUN<P741ub5rL7@1x
zI4hQ~;^fxSG2U$_liw($i>$nwR&^2T<Vwd*0RaJ1vCJ}iS2eD~Y#%;8tdNlS6La>}
zAm?Tin4O(vE;PuAT>0^ay5oZf8=bIaZC^AIb^ATHk<X<=N@w%C%ggKX#_6Jy);@z-
z3PA@IEqj+Qg&XF5*Vw{##G&?wf4cLkr&7YJod))DDkG~~Tr1yIww+pH50`s^mgeSA
zQ$4*wy{e^4%c3~HiJ<mf)o&f@3MR!{J~<aF0CF&1<>{mAduvVvWNx0GSnSx&s^0<n
z`*v?Ea|8UxYsx`t9$mNAi?vU#>U%D<@*S;oml!_`TenE5O-Z40i2*m-eeCN(s(xWS
zqb%J1a>C4xwZU!li&0MKR`GuL(I-^q2gbF!DciZ5?*#1Ehc&~>VV4DIwxLqU-3C-7
zu+V<B7ZxU;rMx!n6hGJmF<bR;cZU@i7EfAZN=i#*WLe~ao9hn>yR~D?I0LD|rNtE$
z?FG%vm#K1oYwyn&_9JC@rX>7NXIxc6R#VLO2;i8LDFazF0=hOIel++=*u2Vq>})Y?
z|J{_e-ykh-CcKj$=c51T+or7^X770*e0R1EYFcLQ^XFe|{;YNxkRH58j=dNL>Vb{i
z7?IGurgi;}<KH?esOf2Q+ML3jive1X+(z55-8z`x+OMXS!(R3dO7zK;9ZEXPrjJ*l
zKDboK4zseA-6moiFkv#s8oH4SxIcX=U(wWbQ8N-5AzWLro>-h`Js0H&05KK)w#q7)
zO#{(c0E=1I%TdPs+#D-p_nEQ@^2BXQGugQQo3Vd3Y_bm`zFJhcm~W5;)h{p<#yMLE
zLPcw`g{)L9b;ST*>gwuB3JMD3Ywo8@xb)}!v)X%dRHa?VWJyU$CbQQG3m99Nt(PS9
zYtJA`7b{6!ym}eOz|+<_Q!CH9>nH-YV3mVPO3sH;g=3pe5@8n?skxwcH#vNNj`yk3
zrlo$%Rpn|1(t+9a_HGA5DoFn3d>Jbpe1JaYh+(M?UT6+b|C=&8J>B^I)kz<34og@k
z5wD_@Jra?+9H-#*-NH4iPH0MTr+PpHYZDkW>t0y6Hk<>fR6UAVd0Y3$+UToph^&$F
zimln-zPR0s^&EC7#~-hsAT-${{H-f4`zDmaY!r`vNv^X7PCIDoWk|UXL&;>(qk<KU
zfBMeFUQV8<`3_ovu5Gqzsq8wGd9nXd()C6mOTUmuH~k<BbkeLG4~v&WqLp)Ce#o%y
z04hTFbpfToAf*!?Fk7~UU;JIdVZR)SS)2vA1E>N2Q~%yP7B4R^*K{Ep`(d_cEWq-;
zthCg_qCNbg>cMl<zVUx)fKBOg!DS%TvuarmOjkDT*Q=r`towm~`*=Gp&pNOeB(qne
zL%4jF{=Iwm3Q~owq;ZKw)&JS8%{9{p=o!D~F?oCTX=(YS9++;H${QA&&5VqUwr;NW
z*)6QBTBPk5!TiC@Rn2eowN(=AL1_$RfS``6h4hJ1v-qEE_~?(W*)tJ+iQE`4d5rHj
zt!IC#KAE(t+$_?sb`qvB)6>(FSN5t1;Lh2Z=F!Wjl^+xRAODPF0`5J<bdjrs9~}*=
zV3R;*f^Sh9D2hud>*k`06ReL+clJk2W(L;5j95Ek5cxOh&8EM6o&ERcvtZ}scFq3o
z9o7Q^{+=O&Cs*qwXG7yoK~qNFzYD40>grwK>wzsROG`^bFiTs=gY+m^T2kW0suX&R
z_p)d1-;~P%%etpU>j*)eh=_<WYQMCqUdpq!G)(BVola3@rFTEuI_NJ~U5u`ohu?!6
zUcGv?1wu`Via1*^>uDipm6rmqf`GGmzhR}4ii*9olarIQ{nn!)EEw@^Y3vclqa;Vd
z>i_8T2xR8nGy~bC=rG7Xrk<>F=wiYLNSXY*#CutePD(4=FAH&A_P_ol?$l#dY+O?J
zuRVI(;3bH944F-$N8_OM+)heg5-%1-XZ7rG%*a6L<ZG}5NSRF!XYNc6{YVi8o$X;9
zONfK`w{Drmi!ENWvz>rLkjNHG|0S#&%oVn}qQYwd^uKxmWnI^)6E)y1hV{=YQnu?o
z9aNM3CJE|%DC}?wsAvqFdF{|8&7jgA&gS!jl4QSmyTS(lHUqpXAC@Gdd_1BK$bhr>
z`u~70L~T=C*j`epM7n8oE{1<%Z)WY&Z3;5QfGxSo^}%2k<330pvnyMrm5Z3{u)U_1
z7Qk^^r}<Lh_?}XJc51$1@eEA25lk?tp(VNMF}CebCrb2s<JndICD687$>sLL9_D~v
zkay&Q=6!k_ng!e29i1X$+JA%!#0lHO4DC|QN&c(PRcx4!;D%`<h|Cl7Z~b?vYS%)1
zN!m)NlQ{n51=d(6kqefX4;Yr@V<GX7INVd*hY+18O~f1|N0ke<Wg@V8w~QQD6bBmR
zf%u3tQB^@R+FSbO!V<^Bln8Wxj(H0m7&1D2;3^(A#~okWG>bJWh9!o6)V|OeqChjC
zee8utv}IM=hz>Z>4(RqDUy`_r(@OA%cxwHvqIx#kAm`F4HF<Xcm?6ygx5)^jI^rgp
zYbVzgW>{(bhA7yHg*Y?25JiE;NB382a2D5`mXxxGc*1ma57&FRb%y!eKN=DpCtE#^
z#6J(9?7~1A6%0DS_Y|V3#!Wn4kt<7cu}bic#A^+Sh3OwE1_~|3G%m0LQ+_OT@28#y
z*%Wc@N=V&9QJ4d*kV(pOV|Ux)lEuITgEm+}Eu!PW5=r@GiXa6nu|L<)5}5`Kh-Q6m
zEecLAMO)h|jb<arVdhH$SMhCu0=)Urls?{6ZesA+d_$^(Dftb+&*ER9t^hX#gt{<k
ziOVf%gatpzo*xf*gnnc%YEN@Oc3_4!Q1yH614}gaP5F(x2nnX0rgg(1yF~}pgo;uo
z)^Nev9)e@3#0%u*azL;{&7d(j>;xis`T}1v?<1dtT*l&AwlJV%?cwyjg9Wtd<h99F
zy08=n2C=U{T-x>AGLr`wI>VEHmj+AKWNE{Y3b>WM?LY@93r)lgr`WqcR0r8e*T2V~
z2o*<D&|BM=O3lMOU0cl(ULsiQ=90bGiC6+$gOn1m$=v$LA1TmY@6*vpC_xI>j8wV#
z<CfF%ms8+{k12n@;KL3F_4V61wGn+(2U}C9BC8tvr4Pu60)ZNtqU2lG9_U->R~uK4
zkxHY$Is_@Hk?IE=_pkDGHm&M2TN*9+E@aQ8Jc~d5RcIHne`!W%+P?(AxQ$JWj1&(2
zdMC&;t3qJ?R)_P7w$0b;b=>@AS|)XMCUX&(0q(iAHlJ<Q(#6JI{e}<=5bzu#MI39|
z2mqQ+7qQ4LEAi$t=*l7~iQt0C2r(MXOwvWyeYudO^&pWeAP+=^@m&&`YGS_cL3(S_
zcOH{OUb;kHCLj#g4!8bwJ3Ib7SvX@6On?uT*sNZ_g{F?O!+8;ff?%PUx2|c@@xuk1
zMz~-CZQAY@L$?A6QlTnPCc?hpdk2JK?B|h`tGZC-kA<ju<QOw+aiWCJ(fvA9>6a2`
zh1hMY-v+|gEjQJ_&b~R}TR*b0x!3NzT4?oY(YAKY13<ntOyNHv#$4)csyI$F76?gX
zd9?>&!Uh0~eu)+<a3tc$l8vK`jR>3L#arX_7DJNXe5+X*QVO!&dtdZUvWM)LI$frf
zAeY%g9fHsuM2+=!C-1F9KS|dcuL4)U0pW<S(F_uCh|aF@#CW*dSiG)^*Lg*fj-l;Q
z3BE0Gu^C`hMnDaxpU2O<U)0J;Bs^h&69i$0+!#8`4fLV<7w{SCd>jN|P6U(n`fSQY
z$r|-N9pk5^PZvY3K5z?$ue@$vOQhvFk-+r6e{x`I<*#T@Iaz6Lk?7lBZa4(Ns-V@8
zrO04=QM7_8$$=nxyYD3r%y830n<SWfsf2C4kso<bWT!L3naEYlY@&@j<Qf9*{3MRE
z3anDpCN1)X1F9eh!%(LrGBwb8#mTqAX%FrmFu-L;RA%6y$!$TCL$;#E5S&r?XnA|$
z1Z|1`jHve;@<R$55AB86-6G@QS{jUE$EBxz+>?uB1S1N6Z{}RApn_2{6n%m5C~cSh
z444@4_ggho6rh@Wj!E~0EUsb=lX;6<odozHz2*2`?`%IobA)|cQJR(3akvjX!`%$0
z#wSc;o0l}h;%A-GM!EU-bA~&5zuA4Nfx(EAj002r3FUJd5G0BJMs8S=e9Jlw+}h$w
zF#;zvnGXdMN9O;VI4F~lEO>yb-sIT_jkt!|A3phrLnQO)I%gDhUgQ*R5KcLF_E#?0
zn?8MHji3L3i{YE)bNPci91yJN0Y5q{I{yklGN23p&qMVJyi|DM)Qz&2iqz9=Zm-(`
zs^FJkHV8y!hg|NG2~EFd2&;aIc6&S5Bry3DEyFEjpwy)dAb(J$U1-|-yctLquoY)s
z$wg9h<}ST&&}Qj9Las&A9a!2&l-V(D*g4Db|IL}wU)@}!AMfz~i4-&l!#?TKHbI9B
zx3xQt&hk*h*^SIp{%#Rz0$Z4T?HYOEtErkn9k$RNhWU?1?8bZ$Z`^tMh{ZPvT)dEY
z+^`hcxUndpC2AOg!l}R?;$(TAO=NBEEGPP~(%KZ7CoRy=_G}&z=@Gx+o0Q0n7R0vM
z?|!M%D-h6E`kB5t#%F@7UuiK{y^`CjZJ0VfMx%{1qo!Jhs!~^R!taPRG5-lBJ{MXN
z^W$mCQFU|Aq3^y6QqW^Q2)kAz197JVT0rgHxfxKKRViH1gj`frvBw!<Xaij=fS4N{
zsj72)!LF*b)L9g{z%mv(n*&YdDxTs`M(oE%C=&!{!1*q(7MMP8!6H<1Z(ScwuZI!Q
z+n*571$clN=mhf8OXFhEnAr&jhiVb_;J$Hg*}foQpG->&*nYkm(Rq}9U>dl$*dG88
z%KNEo2y|kfSUF;Na4G|X9)a}(o2U-D9-lo$`^@s9&4{p5DO@mS4l~?E<xni;QiU-X
zJwJ$T@FM8MXs1(1u~%U(4H{f9L-t2}5FiFFQ<eiA=%<bY2d55h;>nBAnVWqz2YZ$z
z#*n&4{>O0f;8ce3L<oQ$%4`A_02B<dk2WZdxgTR+_#V%F3F`#;-<vNP(I8?)0}e?1
zCE0zQ#C$KqBD_rTWcZ>C?e?kZSu`ii5C!)6zwJsCoPD6xsTtMQEGGgoY8^Pur=36)
zJ5C0Il}KhS-TGA{IED%0ET}R?u-Ij0+^aV7eSlmT_WPk=GU96Vlq(;=IZ*|qTSaUC
zgDK|o{~y>{81{o+1bqu9$m^Ghaa3>>dwIA_70WcaY{@4lTn0QA0v3aFg=CO<wJUCA
z;s)NCCuxp?PaF*s(F4wdIN6)8Wd9i0&Qb<bPgAE8x03D5la3{G@rySNL52zFpenfS
zBxP@#3tPnF4!;f4LkMulHBsKTA_>E5nF~c>h8C;ZNODLFl>L@1es<pOzSimiN9RJl
zL^l4Zu<yKjl;q!IX43f@N}qsFk1-zcr8))eX#$Ux8q{cuCjn;qGI#k(npIe$wx}7d
zGKAq43+}vXu)uFx)q6_^{vI;j5-B77wy>)Q#_H3Qd_DVlW+IxDL&9d6Pt*Np2o}%U
zH$Rby!a|PB%2!{<Wan5Wc5=ZM?xHlzOQYrx{Lx3>OA)k4L8NO7N1O*@7|II`aye1R
zhU@5pHi~tW&3;vja}HU`Jg7kOBlWoBmd9~1k$7dIhsRtnZDZFPf_b$@?p4?VjgB&p
z9T-4kGt53(iTPAX2RuCgu_{Qc;K2YGAm1haqujkm;FkUeov1CA1sO=it=~wqfq!wb
zPy~u7*C-W8$KY0^fEcrzNOk5CGBxDVlSnFnLz>oA=s$gPtBl|0W19u>^oMzc8k;(%
z;HKzRljojMQQShJq>2Q!sa3is5Re0Sap)mNQGSS?qA;d&J>LG0!0m7WgAPwbVGGk|
zVrGTUr!wqJO5rWDTgEVHwaPL}tF!UMl){1~`U1Yn`01xL;?VW*;C-L&y{f<s^0SV<
zYTDR59TQvbB1XlHZ<k8S2_hyTI@rU?$nCVsX-II3u}g_JD0*7)WnU(!uH&HaZsuS5
zu08mV9Rm2}1ArnP6O*u|<ToekmnmNl1Ga(1)naZzBT&}tYem@qE$S&9TBvc>yvueq
z^#^ajR4P!>p4?Eu@x0knAJaK)m?E>^N`kVu9Co!fop4VHq@0vQ+9FSp5vm=|U0^VV
z8Eccsis&`+dC+lCg7S0Q*b#TgJQ`33qth@YkPy!@F<<$6(tS?DBr#4Q0Hm>a$X96B
zmwh9IH{^<&HUTt20cZkcWH{Ond4%LZk|OV-iP2*)JdmM-ieU1HpLobPBtR&<6ok+t
zB048WdLq5w@wv*wjk6=Ipax#gJ|`wujn?HowEb98vT0UL_36{PjEK$U2?*>LV{P2*
z+pTO7wzq0=_IrT#eG6wf103)5%^<Oay{CvoV-9Kd>cL5ZXCXV3oFHo)t=k^c+2e}T
zfMP8OcNu{<dNvj{x#6c`Z~I|Y3P*HpQ-+aYVgiS|*zlNHiciVD#)n%_)WpVfnG2HQ
zOLi`VKA%3z`Jv2?7hc}F1WJfDP_ti?9ds#Tyr7c|Y2tLCwzC0+Hi`I!c4BB&b^^F+
z+6qW|%&7(fB=Zj-a-2ZCSL+)Mb|LEj00!mABAYqn`nEYvxNs@9%_Sc6b1!3lqVpJ}
zGBO*fWiJ7j`yaib1L}<jr%<wZWf|NY0+Cx4`0dkge97>Jz~z$^+<<-=TomPlbg!Z-
zd)6X#xkwz&D$CvoDFU7Te=CXy4+E4QfH7ZaNOnJ``_*~`q#f^9q3@q$u3|0$?r%!a
za210NKg4&J_Zr|J%=n2IJ~yd$Ot`!Q#f3ys{w*PxVU9ymE}Lc&oJj^y;)Vb`n>e6k
zEyzwpcx%h!fK3;JckbHZmXI9pn>ouRAtL@B&yfbVN!|xndNv?f;c|~Or#XI}2u#Y`
zhh$GZy<^jy07BHP?GAxj8-7N;)JPRq30?h-`+m##pR7&-3+!g@W2pzIUUI=)3_)N#
zSdTlJgdUS<B^vX9K?&A2`X7K5fzM#~jy*V<kT6Idq5#Q>+(ph2^>D)s`#?2#{sKvY
zR7Eq{^T26G$b<3IQ?1=Vtz`ReVFU?;EdJb5W^QCWXRh*WdI6GM3+P>oG{L+E9YJ|F
z-Lf>YoCY0~$Vvr9dYsaXF~PIzBNfzEe`Ax7r==Z%lyiHfw<ln8TnDuN-9v1Zd^Jq&
zXs@elmK%I-Uf2T&dlU(*DhfS)_B7}?zo~9@kQ^lwWNq>`)i4<~GXI<1oNc@IPf6OM
zZNz}be!JP*zmN<otJ$ns^X4+21rc_vdf%4Y;qQO|WX7+vfY?D2m7uk*cUxN}Q)yqW
zzkc=aIMFLV3^50;8hYtXQi9oLg_DHKg}rZ<OI=AJmO76kk22~oQMBLm2T-jiaRnrM
zm&?tD$p(VOlWS`-%UMimA|5xrX6=uw<t;nHHCPW&!DHJ~qUk~3x%cy*KFt6d*uFfe
z+dSDkKmF#=w`@>X5xj)r{uHw|QOf)WWtH&>g#oLNT6H+D%9QPqm;DMisH+!=*zK{R
z?}*%HjphER+Fa1gy&YtFID}WhnMpA8Y}yFPgEWX@=b-^bNt@+8xU>U#F|FJCMP`Jz
zCI+<XJwq5lLKnqn6%L}x9eeW+qdY)<_NJz)(eb5r+>M@`t(}mSe>cArc^4vwI{V=X
zLzPpAy5MXaav@rFA3yN*$@Yd!$*q@vVh+N0>?qjGv0_zraxVl8HILx@yHhW;!`(tP
zk4C5`*VX<z$EBWRS1FhNYRAo-{4O?ul(4VaBQMnP9qd|0(i493CK7@GuhL}7$V{N?
z&=XZ!F<Li#{3oPlzr2&99sgkEtK^ox<yuil>o4~+Ke^xit7{dRy-Hs0)-9rWxFqNq
zvFFAX+IeZcQ*B$kT|)&IU=_V>6jj(yp=0JU+@;moZw0CwDEfyl{)C1fg4{Vka80I!
zrs+?73)#8WN5{>QosdRIW|E~4tAth|1>Je}-5TB6p5}t6HmcVJl24;-5N1ha`S0Z4
z0@R)On{TQ$#zQXFmP9^7&IQGql8S}yic!j@!e!lrs%Xw_J}z&)r*VkC_{D+=?PT-I
zJ@0rhZC{TYlOkE9FCaxuLG?WwX_<5-#0o%UpdN{2`AMgEad)0Qi-cI;7Sfga(kX(&
z6a_MG{yXAsfcBBPmL)3BmwY|&n7Nih1m}|RVAL0H0>(P1ZeX6{2c9i*!uqRiC3w(#
z0n%`d-}n8$-$ffmzmrxbZY4k0`9irGM7BiSZ{!Z7mlyK!+?d*n!x`aPAOCCt?}x7O
zg`-zXe$z;jr?e}te~QVjRW`e<wlxGBlO&sFI@kOi<^R1GaX)%>IS+Y*RC@C;c}`c2
zHRr*wZu`qWl@Mi;<9F@exS97G8^0#{OOe~%$xPCPC_u3!*G-uay4>{DqTf@X4?No3
z_A{`UG~rNIKUcBzIDU#7H^5}RrrW9gxR`jbi=nd<g=1}S&hAjasQOw#?+WW0DZ6C{
zT0cMT4?#NqbrM|5hYW7~(P>9Z{HTguEDgSQR*6jh`)WGZaSb96_CF3VKU!_Oxm}aA
zBq5wgN(3a{gUVJUEw|#_-hO4Sy{d&qOXzJs`P{BMX2@;|e!yrbb51fs)3%83MQ}RI
zv<9;`{<w^rZuM%@bgbTsULoh+0{zg#Qv4yjJ>juq5u71`v0AQTvk;tgricp2BDo@N
zW}5i}&+Ni0>Vx&D;XvQawBmWXgP17qG@nrZ&N#pdE1_=`ksxe;V!FA}#t43vNO(>s
zCcnkz&A52qO}G+2lKg$2z&2hd$4DW<1&QV4Dn;RvB2%JGnN(TfL{(fFdmP_<hX?|3
z7T<hru%ldd{)*u`2+a;*CAuauCz>bJeSb3G1z~5_b0QK;nx6w-o1|WvBAsXBc${rJ
z;(%Ao`13Rq-;-*0kDdSAIFCo*!O1wjxzJ>+Khwkk2Dc_ZqVj9H8XhhL2~s3+@y8G#
z_(Pi6X(A#sNUgq@MCYHYnVzLZZ4(NA;Xr;uvUG-YGIh#z#cOokFrMcWT|GiOsQn%c
zjv}QF&-Jmze*%H}4Q~PKtLc0<tVFQCHNdyXj~6T&T7l|c913x*dcI2Dp3WT6MhW%$
zmk=uOT|Go$i88gctf`6Kc#fHeOhKW9C(_K*H9td!QU$Yzu6b-2Z=BJ|iicW>e}ASS
zD)V;#<XaQTF7w+5?_3QaLACIXu)O#^IrY%vNoq2i9;?tRN=txoCg!sO)4I!S1K?KU
ziQAeovCF%nk35OapAprR6NTpdq_mq4LR4zx$l%eroTz+CFqT~H-riwAkDIfwP8iJ-
z&tln{`iIXx`3IbHBKFtbXbKRK-!B!Nc@F<6IR}ZJ9I;f}_K{I574mY~IkL3(*$JcO
zD2|P_^#HZ&6=u}`i2q4u0bUP^FFL_^IeSoYCoGyv_8pwzLTr}zJGf|6Fk@xal}(GZ
zIBv^Mm|xE3OwttR1*A(sK+}*S(WG7^9JvJ>t!IQW!9jO2HGlEi28u7!2zZEB`I$EC
z&WQ25Dr&o3m{3=ovOY`)5bn_{*F128XPIxJkEX(YI9g;lHSZHF@=<s^{C1LjNU=0>
zKBsZpN+5~IY*=TZhpsBLML-Srg7ZKfy-;>jhYam}DO-*N{OzYi^gTG1a7&#C6&m>M
zdF}V7#jv>%I+f`@#1=H&+3tg0>NkFAa};DJrqpY7Z{W&OPI3-69eQ(3e>tL?6%rm9
zFz!=OZHkZj_d-#(_5|*(4GkT6KDJ&xhIJMb7SE+4CajcTT4MWD74=GK@ComFZsg18
z)VY>ulB64;opMYmJwca_%ChjIptoeZUZbyUB(1;ZvcEHcma=EfEeLhbURs0BQCc?c
zQzdTSv*?m~=#!->>v*u_mie-_gSa_m#8EjJ&UcZ$arFGI)7~#d74Sm~n~L2DGNd<Q
zOfg7%W6J6md(rpaIeY!S3;Xw0{r?^xP+Sa``B?J8ACCwKC{l%Sz#mLQ|3QVC{qz3?
DAnEF1

delta 11603
zcmY*<1yodB{4Fy`i8QFRNC^mtNSA_iNe(3;(%sD+K1WcxhE6HzZU#pD1nKS?L_iv(
zhkBRq{oh;ftu<@b9qydNIs3QI-us+8z1D@+vF4Ua#JqUD>`$ogCc03(x~qGK;*Rdi
zK0S(q(wu(+o5RicW0|?#f94W?d8AEFEx%m)_)Qsu?5n4Wuc{JDkMeC)(qHskU!V1-
z^?3W#DvL;@|2__sJgIK=2waoO&P+WGw6?Z+nE(+KNXX%%1oHoi(c40;yIQWqHob#6
z`!+Xy7Yasy#i>+qZ{V<b8PW{%`&+$l_wioX{l9lOz&ptC#C+UXU4~P>iMgFS^Y~9A
zOhuq8wV`6vzBslx_n{&<mbIa&%A#!8AdJI^s|#cuY7Fhb(TDIuyVpKh-d(QRw8uDM
z0CDqLjebR;eJ^vaeQ{TftOF0m7T!YA8WLz$Om7Q?lGn3K*cXeN#EGl$uNNdEr}SU;
z)L*jhQ@Rp8tNZYmW!#BszuBPCMSfv-kgg#GyQ_jyiVuIII{i}w=R2W9hGj@Z2+PQ`
z2RL>ZrX12~X}TiXhYxtR;%u7~%pJ_%9|2M%Wse-^8)Lj8VlF1W&XH)T%~8a6&3?^z
zclnHaW%tS8+jnFBY{K($T8$f-Uq5UgU~z|DFGVP~<5<2sxEIkffwT3+naU$l2RDQA
z7!Ys}pE-o=tBmi|88k-ZId{Z(@f5a4ls{0`eh|s(_-P6vQFTC({jH8YYQb{B<x>lA
z#XZTJ0C$P<GQRKCDmhuswRuwg=OSnNMMETskInebm&a?Yrd?UpyQiaW(A}@3HQg5i
z`v%({0zD!tBerdtic2!&L1sod8E0)je2nh0V{|MRYN%YRkKa0?loq-^YM^Fh&82eX
zPhE08#<EGPD-lX`g$4<poEQJO`pIAgv{PTSaVs`@9+@(tx{D6Pox;on!-`KgZ8->u
z9>X#;?+Z99dd2P#BmO-H&>M9FngZ?gsB9V(z&9zSGpK>kKTb#Z8_1_G%WD+9QTs9J
zW3wgCEk;%4UrU1U@E4&R#Di`V&yzAJT#z`453M_vi%B>B+hXO?wz8y1NgBjAsj#4f
zUdAg#s#d#Md+HL*=qv1nK_z^iH(U^T{SC%Qq3gb0IE`F9>&#snqDUymr3WM}R%Gs_
z(0JY8EXP`RO;!)3es4QXGRNLT2Q3yvShdkzwCQah0$%Sf1Hq9Kphbvy&t}lVnV)JY
z&g_oP7r#b!!{xOaGi~SAE<ab{3KUYU+IV~7ceSYhq$?CWUP$$iQyIKdw&?h5*&d^X
zm>g<FB#|M$M>!C}MxdAcz%o!|Ov!i8zTkyOhyfLn#8t?Nw|yCXfY662(h>3s!HJ2=
z+fQRJ$o(3kHy;`EEVpfvAYPwpntM`=^_*!PT`&H*YON0D@0Q+sl@g;Wu61%Xa-oHA
zn&Lw%)`oI5v`ZNCYxktWC(?z;ufyHNst~R;jx-i>8RxD2@`%@_!W?wLq^<;2ygSBO
z>AxsDEe4QiXs6%M9AVF|&yV{=rMTBwwP-o1u78ciy#oh8clLOnK=zk^=r1xF8CBzm
z#*Yp8L>BF2kJ*kDi5?g&n7nT$r`+O)UlFeRkg+DP;c)<w`!YW{I_0^7dcL8h-#*-X
zGrJU5GVLxwSII-lammO9n_&&1QjwHGYDgguntrg<UD4;1+CSN?a1KA<{?pq?yPWi?
zG^Atnq@!QE>;;Yul$B6~=MP^&xX|1K+tx8HDhpF_OR`SJIPt9^1kVQvV`s$`uOCbZ
zo_{=$LBjihLnjG;T8$}J=TgKk0Jo&z3dla>4Itmdu2=7Ibvm&oQ&zYR-b<HS;F#T`
z&L<>uHz2)?J2~x={<Qz-Bd6^Jk8b<2jPln*!@3SUE4~keULK;$(j(`aMGV|l^d<`j
zk9OWqSqLuMA7UxHCrb5tV=CwE3F&M1D6aAU3YfbC<NVjmlj_x34fG72YYiVVwMq<X
z)j1qf1brm17M$IH%)UnPhs`c`kJLmPBd*RUe!9H9j)L+m`<b((R9d0x)4)E{yP_1+
z8Lm8vggyhapJOjcb9bsgrEV-J`A)y$b@J)@$5nI9hVZ5vm9fwKyWbZ^e!Um?*hy2T
zS19l;A@!I(f<VY5Az=vrAy9pAQ&Yg?K)Gxu)%@NFd5pM`z5cxK+t#m8ljW*|xFDpj
z5hNlbJL7SRnQcw4%C&>y51-t0n|ybEH5ckcnR{%gI%l~WEO#M6ovAj5@+}%pf^X%y
z@3Pc6FEZ1w=i4%6<?bkuer(Gqx>(PvNr&U+InPlsaZMA>Q+ox_1EOie?k(b*4aECC
zotNFrJicTWEA1K2DpR90s#l@&G*Lco)N``3HNEVu^F(MF_>VfNKaknrTD2KkR`}lQ
z>@C7ZgMQ((6OrDpQAn8WOaSvf31+2kVb+acRz{R%Zo+e3oKD_%o)xjQ+*ch8*sS2o
z1)fEghh+cTKD1>Hq{Vgw|K%-h9BEi=OP=p@64)}6vgm%?zI(q_V`uD#s5?>y(ZI;;
zo+$Y6O!dX!EW>f40CiK%OQ2X$EX{T0qsem~b*W_woDYONy$rIYg;}mk3$G9)YeES9
z0pYsLE%lM>*p=+N_lRBZS>&DZ%(`srAi4*vn5a&fWJkaD0pZ9e6=GT@a)d+6A7fqH
zsnmU*S$xp?`EpiRS{*@xkI?_dTjw%E!tQwX+(+thxD&4hu8#UNIx=$2$#r~Z@JSZ+
z^0Vy<1e>c5XWu|#+*SW<%U7Ub+kK0u<2!jLzNa^}BI9ynub#J*lb30-9?av*?ng@3
zT&ulSv@?S326$6;5sa8K2>#$|@Vp}Lj)}Z2yA6|76SIY1UsFWGq<6*g7E#zM7?t;Z
z30AYK-^Tii!wqt&F3r5wSAC7UvfW#{uwS1dl9|n9JdhqwENn{izW<f8?f70y>Py&C
zgHoUV<MUgiQ=ucIs!{s!W^lmxRS-ggP>AXzM{s>$v<?88aPUGux~A+(APz(AI`ISW
zm&p$ksttVbDvEaYZQ6aOhP}@MpsuFlPGZY%^sOzA#5zL-+H0RhyPolS{CwsS^Lqjj
zH9JsOKPatYOw8tCD=_xu21YH%x^K}LYR$DQ;MJ`l5Xn~eIE&M9m}%>M^MDp@425vc
zl>ADW#R`BO(%V%eT%VhKeH^>dj<?*So?5e_KYIP7Gqbv-SJJ;awc(D!xJ%HH*isEl
z-IoDl(&-VaINH!AbvH1*N}%KOuS%Y;kH=?3>;l9`@6Il9`a5*Ct<wjKE$e@gpxk{w
zY1q8UIVKfg=v1)d!&5i!U3dMXZ>X#_k1IHH(~$v)1u_SI>J2Utmfx6BdiVeBc^bnV
zPt;T+B`2C8o>g@!Mt-Iny)6A|y6Ti5`jKN>&dy>3zv7A>#o$Q!Am5|H;i?DZyWax@
zELImE_@)fMn@ewy`rVAU;Ife^=3ve8eE!*i&xuTKND{?S`|ZMReCN}&Z>z%CU=luG
zRN+lI@FW!C7-%sVjs7sQZr|Bujl)7j7WrdXN$Gv+<WK++?;CL~=fE~Q1j){QMAXcc
z<I~o8`%Y4Ya95=u=NH)$3fU_HR&~%rJJo-hDX-i+KWZFn&1AEgOy4<AMkynb&c$VA
zGhQ|!*9Q@bS5~15|D2T92R`SW*`taN?vljR0$Q|l<bxiKYqY@*vJn#MO73zIDb5Wh
z#|aBkwPg*%-dYB^s|_i~`~eF=%s(o1GNW++R@Txs_xdGQ?Pe<(+=#F?#tb)HekJ6<
z^frXJn*O2eRN2R&cd=#J?pf5FZj}x@iRu1OrHzuarCCrnh&${N*#;<#=*bkv&V(&o
zfYUnLvnxxhHnpauJ!q$bzc#^PDy<D<pU#3c%|UvFgW{xLFpNYnrv1Y5=xPb!@4&&(
zeyMWg$7_-IJY@LG$Jlvmh3}b)Ur#wYa~u2HI>;|F{H|T9SG&3|=v^I-+R5k}O+1hn
z#uyV>o(<92!2yj&<1op>Cgx#pri-yUprcpBWBORMv*ys5T63*;EwFF#0nxgRVEGRU
z#3Z&4L9(xc5$n_#PGb<TnnT9gn&n=ZEz=RLCoX+jyE()QHGA2_VZm#$Zf#$7`EC#*
zw~Os53W-HLhpGD1&EGe=shL{f<Z{lOEh2V%dF>@Eow@BUO`~q9{$jvNtgDoTYWv>P
z0l~M$u#%$vAN=w8ci$5m#4R&EEFSvv=R6vvBAzK7elqkK$#(42r(HZ=mcUB#^m)JF
z&^@u$M4giu9|v38uhJMet~63t&Fye)fZi1oLclaPbFkK*DtI<5M<!Jw@osiJA%uh-
z@S4z*5Ec<}sbe+N(HZlfG_*l!8ylxb``q-x+TH(k0WRju_>CLNmVWmnbUgFfop-<|
zW14^NyN~f%2Yd59UGQ=FM0Ru9aT7FMXE!D-2wY!&{y;!x`nP~TXd@|tujSAAZbs$g
z<RmvCeWQ-1)bXb<=HRo6cU`JQV>_Dwi*&F}-)8So%X3F-(}dVmZ`fArL*VQ?vv9y}
z^SZ*?X5-EE1^+B?a|P>awMx>2CnY6~TZA!sAC(!_Mn4pGoSu`pm>H^&zTE$wyk@M=
z(!5$Pv01_YM~4@~rBfJK!?S`q8db$5lRm>}CW<pTrKJkmOqu};1!Ku5Y$<S9Yc^$e
zy09T=GguZwwlq2#Z&YVHaz9TYYL-0cX6P?)dC+~c63$aGEhv4tIZ;|lCiXivVo>t1
ze@W!Ui@>!X56s&3o7c1E+!tEBtPEYer!B&~(raEt#B>x}>4m?W{83R+xuB(3lpU}O
z3;uhy#MgAN(UHvzyuC=4(U1YQ93C_{^jPy}cU-RWHwIqpG&UMExh?}kSyC4gg4X@h
zH17PRvD<%Vjgu<fw{(bn!5VhMV6Y`=%dW`ku|oB9N!#I^EyZYt^DA0G>&TC;UmYt^
zC)n+iITv6N9fAwEpRXqG5xO3YC<43R2$(NDt>@pb%eql58h0$m1GSv@dV_ba+p#3&
z<>fohF9g97hk#xCNx;MsIMj`#!OuG@3(hSYg-)k)E*%7#GFM0E_4V}|BYBE0XFDK4
z)*-m~b<p2Np@kZm{=<wu^VN6=lFf!WX9-5kPu7@A+qZ8Q)GfQCuTDV1D84U@D~*gi
zTSom?9iXA%BniA?XuUd_%T9J+y3cRgcHZQ+(k^^EE&leiUzsZ_>*Bg8{F5eb17Pj;
zvjTR{o1GdLys;NcH`jJJs>yteNL0n|1V&@;o4{L*3V53#0|eicH!fngCMG5X?d|QI
zKSoVW$$Dj!wjVkXgkLev7OUvRT#a`F1kPT++&=uO9AUoNhkLeM-DuV(4}JSU;POzy
zd%Kf@RpCVpy-39YIGc2$=48D;mw%1^oDLlaP`8$O7iVF=N+aQ%npuH1s;YhCA-Dt$
zZP?9H%Ln|XfnB#;k0R!8b2YL~IX_(A0|`oYb4wM8tzMqW%NOXkJ_}yJ7=(bq6?WTt
zB}PVi9p405Szj-NR4Ev3#_GXfE}o*Ei*0_#tvd~K^f)~FWnYUcD%w#kE!Y#oTI;B@
zg}q=A@u}yY-|!yvx9_%Wk={9*844EO#e5*k@|tsMbZR#L_K(0`+u`84OWQ%0gQoQP
z?=i?<WE_ph1aISf4KUH-wS`}1RKJ;cJKky<9{tE3Au^Cbvx6~`e}9r?0oUiX6>Wce
zhlGWN^?$5$1zfxTFnUq}&L}2Z$YGL3Q}W<nUYyE~Ypll1mWuRD^$%mK^G#0J>9*Ga
zcB>NDi&JDeUoR{yq)JOq*Zk*X%OTfGDp`$jb(WO+LIUvAJNbLwqZ#ae@QQ%(g->rH
zSGPv$^VJk^_1k#-M3u>|Cr<@-02TiH-iEx2QpX%Yn+&epNP9eAP}91kPNpptNtmTC
zEjK({N<dbM*S<;-b^Eol(iP<pNw!pJ*5RqAr#DE_=pUWwv*@?e`H|S80d5VhuE5K)
zgUT+T^_EFI%>I9JPa!u90`FdX+}5JsG`QW$_Ey^rs?5LXZ%vk$Vh>_~z<rnC!}K}6
zj?2Fb8sNM;NSP#?R>8WOwFF;bS<ZWH9UbRwzl#T)Z1ZY>NGsysdpPXhNyPb7^ycyq
z_B$;M9Po-v->!4(&dGS2-yuG5za++_`hK{>uYW(sSoem_{|6yzzmxcbLgd4dd;4cA
zF);&rGR-YEl@aS8_2=`V#W$M{R=W2*_P-Ix1n<;O4Mvmc*uLdwn`)!Eknq&2SoOdj
zZQP_u2VpymjEuBFC@#7{_2%!X{}0Vf@7aw7lUqpl)T97!pIb~z@Y>v0dzW#TFEIp5
z;-GZ|qrlGz#zsbkg<~5l&%^9~@%V7lM4L}eOw?DHG`kx-epUM3-oark-Kk+F9%PMU
z-HI3}8&d5@1^iM}d)~8lMJQ}z$JNevk)^=HNnT#wVvqyl;WRu7FPE+UOk2jFZnkTO
zfUKk4);-c$5RggDv=?^ExANhlqocD4N>y29dHLJq%E*?MmMF@Ze?L<_7)TMY%)WJi
zz2MW<5r?S?#yF{a=-MIa*3%lRJ}j?kThr3vkc^BQXb`@QdE!0M^>0jsgoJnx+&s#8
z&fj+n2Vb7mrw0EILEXlIuUhm>K}REVroPJ`9hid8mX&Xp$ne>-kHJN<?gPfv9da-$
z{_Ms@|E;7c6OVzD+nL}I-yg4d+v<JxceU?g)kr_rq0gaWNd|`UDP(GG&GJtGRe_$4
zE>s`X>BZUEG&(8&qraeDn!)t+yt=K^oxJUGZzZMm7;u;@qumt4^geh0{j-pB{pre8
zx8%OY$<}9p&+_-rIQ5wo6VRdo(TnX`8S=f!;o&!^cR#|H1NKm-o4%*Ha$ybg;Pq15
z*{_J0($dn4A3x(*D;I-K7jEj8f-lN1kBVea&zAlZh_?N)?CT>{&g{UyS2&<^1UO7r
z0Tv`5@JU+yHph$C=Uu;($UvXyD$IjMzNk{5y#WGmX5u|#ML@<V?rB)D+%WIn2WX}|
zTLv0utkan@-MVNSyAu3VRaGmrgv09v-K-q!?35>_rp)x!91o&=+Oa(opc+f=)Qz2#
z-|7}{bKUW^wA8p`$~2(-GuhY-;Dw5P2ZS|Qd?4WWJEu?P0}k3;+Nc1O_l_nk614Zc
zgO$E@fo_J{Tiu_rM8hE>G0Za8e_26w^2t+-aqb0KJq4ELKSK9EA3^$WmSSheVx>dK
zEZ>lMgX_cD2XxiFi$8fWi<7AbV5-O(aA{f&MeVijwF3dIJPqynr5$o+V{)5zhTO=s
z)PE5d<6`E;MRekxe<p70Q+qsVJk$*4kjm};CQwEd77n_ZfsqBDrK|eg@XE^R#HC#R
z>a1`}VuSecWcEquuFg7~UAcss!(2I@(j1z^#s&HBa(JrZpI}`6&Um)^nLjcU@odUK
zJX>)h_XdE@3t52&g=nGOF#;GqG$*-oygyQ<nQ#ji9f5v{Q7{*Gl^Q1Vy`Pd{VjItP
zY&wLy^7m*C>f0PytrS0Q^Ay+q@Tjz_bev5YrB-msZbSHt#usWpkWaK;Btp<mCV5vf
z*1!{Ns6BzkLnJxV*+wqX!z*>f0ExTtY{=K_Z}Bn(?)=eI>SXdyVmnqrrocsh#GwuS
zup|&?{0bi97xCO09n0UDDoQFA+jLj!8|upDs@x}Dy(S55(K@z?TRSY^W`2aqJ--nu
zH@)i9PAdL=oM@~SeD_R`;`<e(Nkp^5lIVs%X*}$hUedSdvrL<3gf{ZD6JVDysQbk{
zM9QLqF5cV>@@dDEN|4bt>M8e{ozI(?HlKu|aHw4Qb+~SDcYl`4lP2RE9kz{*sA`)f
zLA8tHQSOWiYRH<)l11+vZCd0g^cwn+zity&@sdrMJ50q6Vn#Af;Z5j{VpHDK<K}99
ztCh24WfdVCpa1-8;(b@eM=*|T?W8B*mlIT!(Ple2H%?ZcZ|@c--g&C5o&7FB;E7_w
zqbD4|n*>iB0l5&?klQLf#_*$=L)PxBfaUwmU4Yj{yw6?5IgAtd(BN($oo{Mv2D|d6
z5P0KK$J|Ah690_F71veN^^PmMt3Ac)J!nJ!m<#bEl%0jjrgc5Kft}Aotg7r^E0y#z
zZOZ)$^j7tr3vQS~_6=FeD>ouvW{i#>fOR61oSDF3NB$7_(71HcjoTGz3JdQXEiW&g
z;745=jrHp7@yq*ZbLpu-uW4MLyLud5iDKIS9O7VR9{2e@^IvsmSe~!R@6{7pV`Zk3
zT*Kh1y3ZEZ7k@752)yppPGm<K6|iAELc+kKqCFR!guEdFNH+&XBAfC;fi@-QzKZ$d
zD%$1Fsvp(CryP`V;Ge&0Q@VDIf)54QiW#_9U6eq6aV5NZA_C&22)t3g9XK&sc;qw>
z-U{c32r1*SD{K3FO}rC$ci+SO*}ep(BSa^J8IllE$^IaXO}PNZ#oM=mUPP;4x?ke5
z&{pCx;jTfJ_`YQHpt}!`=7@Z)6IhWgwujgjIfA1~vEoE4VPT=Ac3Z=hppmI>mWpI;
zC1zeFKu@oWa}W?P67RK+ERj6E=XHF%o^M}r@<h+CynJJ_&}uJ`#J5)0>I5Gd4_Cx>
z_%gJou(y?W%3=OKZdH<=hjT70uO|QX5%cgK)q|||IU=e>i7#xc16QZq?Lh-F>B}-i
zzDLn>_oT3`>*`OvgEof81#Nio)zCreG-iQI=Qd7eCBK0CR~z4a9$Z<i@e4p2b-0Mm
zG%${s2k2@<(q=b204@iYd0;rG^0(35=Z=DtQOaoHjNM%BcDqNAa<9Fey34rJS=H>C
zHj?a(F>Jx4rgPjQzr={ZrBS{6J590ozxzq?G?Nj!y<(Ufh%{l_T}P5_;y_$mykMe?
zgj<C`vYQsv!C_Pt{?X}#_hPX1Nm7rM<6E@A3J2<QFv7v=<!Y0@N^pMYp3w1-7|;bt
z^R!BS^p#S)E*}U>C4njQnv{X$NK=>rYzHe~Gl$2aQ_<Bgab2mNQ7Ey~-tr<Bd@l4Z
zErb(J1s}o<ql(|xN1wu(skOS|&@+BmC;a)w4T~hxptl2JxTU3h4|U&;8HE2$8!Z>w
zN+0VmYid8J(@um%3gFrUdyKj|C>Tlcq;_IsOS2z3-b?*v0qTSN()KR$q5kRSA1yDh
z0&bV(crRT2@|kB8zU8`BDyUapH`ik&1SD-=tkLk}f+ze7quLXrP1kXSt2{Bh@_wt@
z%0!~hyskD!vv);{e;x!gquj+vAJ%W=B27DV^{$WsJEHErDa-5tm&;<B7sKIC;`HSF
zs>jEwdQrjqa;-76qm?aes|yFSl<m(ikSy$6WlDLQTlUun7p&b$C2d0)H-F+K_A+I1
zbXS#8^5RIkhAW%G$(UJ}?D)h+iwXO+jGJ-+uvH}Y2DzuS3nZC?5djiimy4+Beb^PK
zNkrW2kfH?AdoSbj-An#|$q@mB441Yd7+m~5uP8x`aa~?#$KYvv#$Hi3(JLkUrp2?d
zb4Wmj`bT(^57?tu7%&#H(Cf$hL>Oa4{zV7c7Nf|XRmi6Nxksa+m6H<BgwTX3D2sIa
z-iy~uGNhN_V}$*HkRZ@R=nblf1XSX6pZ}=y4ui@OhJ^|)a=S6`;{e7KM(8a#Np&#q
z%bUl2%5gVs-rSl#%JIDwZ=j0|zK^hN^eoEJ1WW`cylxCOkW*+jWDwUGr(I6;juc~4
z*-v-VaMR{8JOwB1M39k6xpx9_Sp=l-ynwVqnejP1<rp*}oD`&jHt`odh@=yrF9skW
zLf``}GAh2%e_S5|P2t0jui2f}9aQZ|tC)w$9^$-E;0KK{Q<exks-X)CrhBN|^sK~8
zq`NNU<koRFy};HNmf8H!EEb0FYwtz{Hf3$*H%}nU6c>Rq%h0lG^e^WqrTBd5oZ<2(
zGAYMFG%V-VKFMsx85kTK9Lg5u*ROPyGj*RpWX5}TZ@pQmne>&-F2P&*vn5A|vL#W>
z#4f2yeEuRI3>y4m)09N=)y17l?&X`7cs4t#-zsPc#l0oYd87(BUxVDzoFM`ZNI=a2
z090-@d;({O3y#l)d^hx%%3R*BGmfp;<w+uZ2-<)%nap)xIUXA+3&^Fx8bCkVw7d(j
zV<JLIL#h$$kZ!Zv{;9?91j&!z3^QCcY-G8D-=PP)W9+^Ekxe;BEmv+B9B(?cJ;df7
zd&a5yj~3K~)G`<jM5tBJ>9}OCvwd()A4$gVyO}RJN2|u?o0h+UwukJSVZz`X=CIBv
zF^)tQJ`mk{*>$+gJxPWj!1;4%5Y8SMukt7dPa^zc<b*B1C?xJBAl%Mh2mbeb*|40w
zn4oq--%!^l*trEd&=Z1Qf>nOKeRzc+uEOF*v<iW$gM|-Xlbz+ERTn5g41$yu!RkH3
zYkFWn#6ZX}H6Q?qglgbuuw62Oz?!ndpc;@?@?E@zmq63zIVXa+rVujZEyr-c$-i^A
zjc6NJ4Qfi@y%%by5}%l2vJPK|sD|v}F5Q`Yy<prT5M6GW4d5``lcWpkz&m5wk8{pw
zXH&L9@<3#u3?zX#zIeV_4IQk=?*v3XbG^R!qB%d^V(ttMj=2b8+SNa)#&dsbnx*)D
zol#Q31<ar>$I8nO(~~Nz;&iN=DffHOk1(zn>rS1x3cM1cu%fXA>OMAEo&cd8s$dce
z#hiy7F{aH7ilU8Am8E8WVo@gy$5~^VyfY`;8-j!7JbH%QrtMN+>(QC$*PZ7WAv8B;
z39Y<9-W`Ru2^oV2)L=7}n%KcLi&7BKlrs{-2~2j?@YTe4VkRlO-4sHCJMIW4`uc+l
zzeI4I+=!?#U$Q>^T~GIK9+3Ge-jpCoXoeCj*cIbBMFA-YzlJytsNHhvqmb7XcS<}w
zml&EzZ6WR2lebh<099TSdQEuAQF=j(R?BJr=^lzZx3<y9J3Rhz8m9p_)XQz&WE6df
zU?ip~BAssj*Qy=2&G6TA_TndC({6|i4hQ4`BueY_K4vQOi4AoBv*eZcovaNN*St;p
z8^Hh1GzD(Ub7#I7{<m)r?Z3Ck!DkA%Ysp04cYgZTxGNku9I8!ohVG<My<h1<XIAo;
ztymBg!w%vM4W*n~xC<26lohM}0^{q3<h4-F$EKAemELSgy`>XL_Q>`-&V=2d`1DHt
zVn2<K7{`=J9iohp+e-f;0<w)vOKRAP#cH{C9+%mK1QCVNG2>v_>7zlv%F{MNyYV6-
zYOLR2J%Z!e^bjg)XxfMaFuLTMmW-0VQjH(yOsHnCO&EGe0s0~y@RFS!GlHfzH#iD<
zyf1`7=R?6o)(X1iB6A8sAigg|l%a{%;GPL_;PYZMh$^X&G68ur^)!ud)G(##ok$io
zj0}zsw1ewQ1{d1R54(fYzc~If;On*VQ6;LlfkUWD7f)E|aIo_@Dw{{oZ<BU&b89HK
z(7Iz3nW7=W2N?RZ>}zlICi;hh@stWal{XX-qHnIeeIYHMbjY^vofRAg;eAU|6-F=a
zZN9)VwS!vus!bz$9M{|j#%8mGU*iWi7czUQ1u8o8zfxlyeYbXH`q6jFq`~aA?-Ejt
z{^!qy;Y)GsJXwnIdohyVW^tT2?5Z!N26EC$))>QJ_nsK~dFkm5u=A)%^`dzj;gnXG
z+2!w}jAZP`8c282vp~$h)29yavSV=DjTUb;$rKFXrgsAQH9I6iNUS2|<MW-tT!)cW
zYX>nd;$#E2ME;Bkadb`zv2?@$Ttx+q3zrI67Q-?cEWY`#eLUO;Umqfae;>zeU=sjl
zSV73?Z&NK(l}r`c8!|PV3Vdy4{M#^DtSk=y8G;@eSy#Z+qOH&*=yeIL!T*y!rhc4(
z!M+$oE{1aOiZ7<UD8vJ<3QrH&eB5^)bkw^l`r2G~N-mMD*pqve{OH^<aPa}g0WHbR
zsi7RN@<p3+t`^;Rc+^Vx(4_0uo4i5ZL(+(kjE!#<n)?Gdb};hN{@=(utV6k96i*6z
zx%MU)_$L2L#)<avq>ujM<+QxK36@V1WSW-I>_EY%B&hy(ur@-&aJ>+W|Cm(1D<{xj
z^DD<QQh89rru-HJ%^c2i_o%h(=kqoBN)w|Ht;}$cr_S0Snp@(302RY8VDMH0*9=hq
z;kpsYPIwoz=6r3+s)S62K#;-s2^f&Uwir=!Gjr-H2H$&4#J&>Wn!u4agBv3N$Cu(E
z;XI)lK?MO8G6YRXAXbmhA1zJ1zaNEWLem>A*gzX00G>Cu&zpEQuVfHKOSrrrmiIIE
z!HN=QTk=W)r(^TI2~L@vvmt6I`}#m1)-_<|Hx??M!*UoTCyqKR)w~G+df=NSh1!W7
zESVL~&K9rAbXH_wFLaUaO!q~E>m~e0Gn|7C<;ePJjgepKsaHi_G@cOXcZrv`T(34x
zElLAD5(Q6~bHo;AcgMT_(nM{YivA*FFU>3Ho0@{`S+`fZIGUcNdN98=fx%F@V0)qZ
zqq*V4=%Mc|9n#fJCI144!_KLW>W%H^N3cO=Xuhge@L}r~7Ps_WrhdC$_d?%;`9ok!
zRZH4Vdt+@)WF?a>xzaWm+qaG^ixw_3(IdFzTudUNzu%8K3YRZVOIy=U%wDX@ZLpG4
z4ZN7H+WuTLT55U;>sib7+nEhLJDprxW7h=E+pgJpIxL(3a~-d>0Pajv>6;{pr1s$M
zg(&vam#Pk44wHLjzuw-Pe=SOuF;i6p9{~43OR7))^Iv-mw3A8uf41m~SGPM!ZDoSk
z@8mf$EJT&N<_2*i96)~7#>GpWBdmM$wYr#THovjw53Uzv-{INhSqjHK=2L$|_3Pe-
z1Q59`5^qy(O^vwd65ahlwtPM<!zo#Ht%X6m<HPc!*OpagD+!Qi!$;9;xtwJ~AOCx2
z<lU082+~gtq7|+EU{Ml6gHBpt)<_1AFFD~u6z`Vq8{h-?_+1PbOYr$13Q#>XTMq5R
z6d@CV4lT;?&o>O_Je`(ujVz8CT}dMS|AeQ8m4*cMG8>JAzm5L&9X5-dACWvl95+u}
zp|q!L*fICSO&@YVG?6Sx6}?$u{++$XG+QTcF^TT@6Y{n2o8C?ReivOX_aDqYRIW;?
z?>0)d{hA<Y2|-U8pT91mx__4SxK=ISa={25MD?ja&^|;84oe`-j}Q+BJUa0=dTQjy
z{m$2xnQ|a!#LsdQ`ky|2MkJc$Dr!q?F1FwLynBL{tCQ)lSqyuOkXn;H079z1so^Rj
z1;1)=zaS&0{3eYSPhjF@1i;m>$HAz?$%w-sH$8pH(8OSnV*C2z+q*BIGD_mHeB9JG
z$1h1(?1CbCr84G5T!vMeaQ<|o6?<uPIlW$xfrt(Ykx5wv?Q-?`_oTpEr|{7A$^;{L
z5h0r7e6weS`%IXj&kBi4@(7+MLpfI#4Ow4O&c@K%7;en|^B)rcZRR<N5mBFB0hcus
zcTmuv2$_^!P{olIDqjt~VDq#Jci(d~#PgeC$ScI^3M={*Itc7-&Qq2#TV+VC{o}7n
zhz1lZza&C1$YEllqb!~lHbgO~TnJwX;fDg;HgNeT)?y_`$IZi%x>)SIPLh}2Cryg$
zn51Y^bu^LTcY_R711@g8T)&6v250o}Y>I0|13P^3U(UH#H-a+M$NXy|D%y$%xN7*<
zuSD}jiA5{=1-$c9{6xu9<U~&|9+<!LliPkHaEOCMN&EZ<$F8km<<YU>?GF><A$UMf
z8h*xbkDKY8P%pWs1@2E>4N?oe=%3Z7jcGIMBRz4nMa~3=sZopgyHL_zI&0x)CH-!Y
zey;DHrFO=R5t&12yTmpLdbkg8Pm}HZlcDR$c1H2zrIL5l!lkEh_HdbS+Y=04JDKKO
zQ0?1<B#4U*^5{AfpUI)8`b1IG7TrKOKwDHS{qug6e`(Sm6s<3jDD@d#mEpM5;`6rj
zVIx!GOQwgJdXo_dxg1Rh5B!FOt#k>81nq^;r+dlhidXeCZFjlaoe$y-xi3E^QT55~
z`B)Z7Tb7OKR)`F=G{hLbV|i6$jP6z(Gr(8lJQX$<lvNFqFo!xRQa!IUlLx+&;5tWb
znhQHgNToK(FnO8bnY>nVr2V)iHYb2Kt7-9C6WQ3_-}riAiuW#_ZFQggnSkc0T;9-7
zm78i^cr3M9|K{~wSE^k37k7NAQshN74dg^^&PHsfWgs_<Ud(qTRg_w2`D@(%;eRp6
zgT7~OzH4|j)TS2oYY7?4rWp<#_w}zok*%SpB#|JR78cI5K<gv)u}NZuI`Y%6#2#U<
z$uQKizh<*yr@Q{`y`X&tf8!z;5)_`X?%4cmqu*r|xvYt+F+>%^#uXDtbjC5ur_qaq
z3uZw>yW2aUj*J)AxUvr1D{=^rL{_3Oz7<{+E>U?$<hI-}W7Of@9b=$wMwno+chj4s
znQC82c62*AF9QaTS^dPnOOJ--4Ozl}7+&!|Vw+I&I}lI5n?Vtgm**dDEPt`Y3Ohd!
zPN*OS?(Gjq9w@Odv)CT9&ykb*Sxcl0(-|q+O6|(qB7c-e2bmH)jS6yk%vR6)Wvcb1
z<@nV%+m>@`!NI@K6_+O(N+oVXtF$MKvXmLGL?Z*KcM>r5h~Jr|(WI;ysvX=|>G;_B
zl`PH#djU>By7b}=c$&k6AaUkl$u0QP=0h0f>%)?v*w1O|EwDn(?Z8zPpx>A|Bg$nX
zsbPDrM^$mcsE`7{dl0`bIb6*gGj@p9E0>rfto))k+R1$LUY61RnAGWFMaZqVJP>d!
zR(oV3#qULLjNaTSAtd^J1K)$F)?lZg^n?$7<%a-V=&ZLHld_$7`Z`1sja=p;<{i_|
zDo5H2VEv*WpGLnMs<ZZCyJ_xYBntB{B|4maEy|o$9slgctZ2vNoLqJxDCLbNZ3N+G
zPx}`O<srl&0pXvTYYzi$s9|D38Lr&2o5CFC$NIHIMAk&h_Hz+Luq$h0d=mxRW?mMP
zl%qyKJ!RJd@xCT@SbV_5Fa5x)6Zswdv9bhR5i0@22D(MWJHc~!PlU{uKlfuRBG$yE
zpRQ0Kd`kksiq9P0WvFyfnlpyDe;9|5>p7Lren3s@VY7B_(J8j$?o{aw304<X;{^|2
xPq=!0ILm0+JQBdYG28$9io>uz9Ot|O-rB0L26I-;f}3|7WqFO)6|(04{4bzq=Z*jX

diff --git a/reference/Rplot008.png b/reference/Rplot008.png
index eaa189bfb11c567fcaa09d477bd86a69973e72e8..7ac9eb770da6a0292b1ef97a19a95e31ab2b9908 100644
GIT binary patch
literal 9362
zcmeHtcT`hb+vh=4Y>_J#6a>7AAR<L+(!~ZSUAjOlAWc98L;?Y<hzKa4TqyxTdXp|C
z0Sh9agdSP|rG%O&Aw&`qz9;uxGhdlC@2pufe|_uCN>0{EIcM*Ep7JXv!puY;wMk?X
zf*>eEgVPoW!Z*r$ZCDSVV7mz~;BEbt^ZKXZ9UjZ4YD3{|;|&8lUj*4K#C!2|l0#My
zWFKO9TF>%M@;o`{hJ*id(=VcW(D~9o4X>uzUVPH`IDXTcT?J;|>6X<M8#Cn;!_v1L
zZ`n~eR(|+^#^I-Kc=J`W_qX<(yKjHO+iI=GmDlUeU|c<3pD1}a9II@YG&jL!XT)(T
zj7yEHq$$(hBgK2JO?j7&xH6N!1u^HYjj2<T5#&Tp@GKvK92eSl2|>2RpAJ)~9v8y1
zX7YQl8pLK9CuJo#VVW^d^_aaEeWqJvi@$Q%q9eZ_y;MMZAK!-TNJ!uC^KSY}lWhNY
zb@ABa-)<>xUAp_Rz^-hOObbzC(uzYlvo*_;Ndi}ukwb=OwvDdayvuFn_pjJDSy@u}
zm4(*N+h5@DK4bj-r7n?9!N1O=1TQpKcb$E@`*n2mR{XEeqW4wyBk}QYn&Hmra>fSz
zx-*fxrV~Ajru|tJB&Ym`n}tP;)*_8fk`4Jb#l6)OToRl4QBb$wj#g1yn~7l%?lMY3
zM=KzN_`PG$yVf{#xfQJ(vT~<!?51&&svJ|L7{8R5Ws;JYmv=bDw@sZz3tdeLc*HwN
zT<Xf3*ycA<rnrJ{Notg~R_9}^35C#H77NqU+dC7jNi|bVdQna>^VAZz(Gn*$OQ#kr
z(<v0kY)Yxg@$YL<dybt(b7pLMIy#;+gKBDO7CT}>ELWF_p|`TXy;9Ogulx|EhoEE5
z%U7Rf4*A9;CMH_i+2zuSx@a1niQ}?J^&VgUI+|Bl*h38E(y;_r+Nt*CqIyZrn8a#(
zU&+;=!k`wJ>?xSDX!}#-#wXwP7P;|s?8y8;MXNexs5|!p`Q6R2q0I_)qshM1k|uHA
zs-auwsk2Fmi9@jm&nNq~IR^h~q&5nphbRkDCdpGK>JvMv0_oQhnw^JIS65sEK5Ud;
ztPQz%F-cZd)>l-`+pqt6uUCI*BRO=HQ%x_f=VAiBsgT}@Y2JQkBsKO@-f_k$l*7Qv
zXHeKWj9k-sCE{?I24%=tjXX|Rswl2rTDqNJl1<iT4vn>G%ogwupPeNY2l)H@7ZnzM
z!!NYRQa?&?-wxt~n0)oclqEMew;QzjRl@n~F6z>tPr#{1TW;3>Zd7mx2&nS(^z_X*
z8+A+8x?swe%I~;pik-PY%}j8bsh|#2c#^|)7GLvy+koh6#37oLA(sTlN#6J%h%_hv
zy2_jX5QHhL0rvZ5^o`I}_J}XKHejzje$k@pdVeWa-t)(&2<mipY8yv0#YEGvsI07v
zSTQ&_=xAm2!qdxZ-rot<C2ssSbx1vJ_SaV>;=5N$u1o5kK|!@~-LlC|;z0#Ls~o1_
zoqjrn7~qNv%*xNt_Z3yWzPnwkZN6H2mz;e(o2f~w8{7Z<F~nAN7h#n{tF5IGYDq)<
z)m+ZH-wYBuKw@Q7KtM|x+1vcpiJ$P}iR@zc1ygqpi{atn(FsAoXy6w-l=LmHy4r5+
zbChOIt0rqI!*`X&4l3#$4mW1LG?HTch*a?S{B%#EvU@DGj&9;LLU4>N_Zn2Svb4-8
zEiEO|u#6rX757d_D;pbG@tD~Un-#h*`rvygByVz!RjgmPx&Fk~i%nxUy@u=5tDJO<
zjg48%`qe^+EVBdp(ORP<T47O<Jae_g*2v1rs(SED?BT<QS;5+z6C`vjoH+w>jiG_<
z$};wFb32}+&Nph{vTGF5O`k~)NW}$C?lHz`*+rZ_$zU)rE5Djlu6)`62V$`C@?mKD
z9&JoH_h-27RENGqPaq{Fd%LNYdH;3mii(Pvxw$#I{{WmkC2;ap%~-T1Z(jZZ0kd%B
zsA`K4G3Js&=KA{KHMO<AtLhZL9}&{xH$Er%5bV&)EjH!X`;W<8teksROg}MR%hi@L
z`s*!mwoBW(!1gr^&sDLBKP>k0uF-7=&bqa}lw)z3^~FkU$4|v7V_AOW8y`_v!pF*{
zUriFlFJx`RI|qF5jF;lrMRORzc?&A)5{we&Q8Sa5@7F3<m4nASwyEQn20ZQSL+}ua
zFeocW$3i#dJQ#%A(p*2MsLR)`Y8c~5MP2jq@?t^7@M{p*wpm!dJI^YAu`88pNz`E%
z$k$Bmuc)jP?_`KS4kpz%eTvxb53$B%4GTyFo5AI|uX6y2UsHwz;j-PkvT{pHEnp>{
zOJZ4Fsl9AkeR0Hg=_NLHWMxv#AFcosBfR+jIQ89Fc@Wb(1v8U-SV4iLR=;e_p{K4|
zDvP|3gaT+PoqXe-4vZ)-E9-2G*23dxUGANOafWiXZ%=HOGBP{%syl(Oc+e3FKp?4x
zf(u%FEr>O^+7xXS7+5V|zj6!9rKcuP0y!bXL7)0UQSWzprdq!q1dos&Z#%G9JHjy$
zMhD-lCsy9?9%0aq`c0Xo+#YbZuf6R>Ee{yM+TbX;1VTtVg+I(<w0d7O<;P?CWRj0Z
zZ-Jft+e>%fXiJ<4oUb6wly{jZ;kF=8`1#f&4JSIVLe{xcGc&Q2KrA<aJzpij#Uf!J
zHxtA75XA;1UeEjEKW|RU?V7-%k+1`2LM7^UV8SlsS{{<feOCqj0qRt4e!gtC|9zpz
z#&{#Cp3crI+UMRwB6fFlytX{-qJ2(JDB=M;2L=WT^kr<lybA3vUVPE)3_Av}3Xtdn
zgM)?ejiX~qrPrW!x#>yhFp^?FeG(;#f=6uNl2ZjI9%QDcpWL}~C!Vs=A3(;w2!tQG
zbdR?Q`+wN%Ye$Nm@b+qCzc>+5(MT38YYn2<m#*j7P9@heBGD@+eJ8(~ySTWR32#PD
z7;ZY!0JW-pyg7~?MVsl#KWO;tjsvWKo}A`B97RSb;*bbxZsyTpp@=W^y7Uw@Yt?Hs
zxlBBBU*=nsq>M;Z3temW=%ycRLhy(}gqK+XlYfLckt{pl)m}ym*A5M3suS0hOBdKz
zoGY$dc!I+&*`Y*LjYhajRsOtk)0PUwO41$rAM3qf$RD3#`FqQ{cFda40&!Zz3#v&K
z;+dDO^9MaQIQ`1#Gv?{N64cx>7iS~)v2Q)Jr5Y%%JAO}KaB7Ei!=bM#2mk)5Lu|cP
z_N4I&-b8Tja>LK>A1nN(EqHMa9i$!Ff%45CpN5uWbXYl(GB3`#H${u_rV80RGc#jh
zV`GyuyZ+#=NSRkgd6}7wnD~1=-Q77E8K-hBTVO|0XyZYli1ZMjQ*h1U>OWCMCMG7H
zoZj!@<5LAgg@yBQcb6FuNN;wwh=p)DI^~JyEDd6oClUm(DQQn7baevQbH(ZD=|n_e
zNvdFG#sej$VF?wpi(gPHO^K+bloPogNgH2_*|!4qycfFm-tjYQ5VP3d+W$F%|4Tss
zYxh6q`00;M?R2ddFJ3&Xxdx5l$gVg_;OipMcmv7(Bg|9v<a^_6!jE6?AX6EK)<vAn
zfJ=A=kAjff{~XXBMBTm)xAG~PK&+*;wG<_pv3uQg_3EP%)RmIHwBl`V?r06u=6|rU
zx+Ii#Lz_CZf$*AZ@K=qLtypr}rz=Mr$9_Sw#J04yzA(vtaW0crCM1PqX1vVI&7p*3
z8cAJ+DuM}lypyi_Z0ReyfG_$iQ}V!5uV*IN0@$d16P`w)rdlTj-@a*=xT{GShm6wa
z_Fu2ep(Mm7)!P=IDxsay?pwcvp%NQ&T&aqcdL`tu%hZb;M`0&^sE7!!EdFcrPuAeX
zC?A(p&&`wo$f1Wmf*=PEwj<dVG8I)-vO*DS14|GjKH2S=aOIs}R~&D=UuR$HY*<Vm
z#>~nYT0le{d$r|OKmUT3DS;+D&(AA*fleK53MHtm!i`$>H1-+1Amj1r{kPieV$A;_
z(j_03Ni2ul#Gg#`8JVjlFA3X*1<dumHqSWAsBKTEUvV+)l*vv`2bido<09~hu5~8E
z{6#`Sc#XH277=1Muc)XJ>I;FE4?u1z^k@7B)kD_25}cct*MfO)k8%C}9W9+HPk?$`
zSR#6P1qE{fBVV15VR7QNVZsVd1%dR*X1bQku{|nkYMwLQxz+U5rK)<v0pGtc?4|kt
z+Y-1DFqvJ9#oK`ad%!14?2n^2Y@D2m3?$P5`p=qgvbM3w2DY$cp1%W^ksu}}cAsEf
z;zg;ftPB~nh77Ph(mT7@utqCz!I_5$WBs6I)3&1#<ZZE@`WKy`rMdU-Y!T$#*_-$l
z6mlYBUju^d{_nEceIqQz7q|=QQrjA&VcP+pFrvon5yPM#nm|~4NTk{C;SyV26k+;y
z!$>`l=h!zYp1p2!eI-0NNua0D9vs_^Ael#e!hmW(ePIBQpXxlXV732wyQojyw%+Ry
zy9I6pC7-%qXUTWQqA=WLN=5MUEyGwEogjxGTdjtX)lmW6b{GYtER|!#>sIA%9qDH4
z`H{5v3DgZcyH@~Ond)A^j_@Y@2qHj9i<YJhRGjbW>AA168A0@)>ZI}kSID%GF$CCE
z-8DNivzU+O98PXVkVBWT>)%oSpxe~5tiGjas&(JxX%FMtYmI~XkllL&`RgzY4;5wQ
zoVq#(DALud2-5JnnJqt+dm(!Urr&EQowB1>3qkg7ThQV6c6aZHkjCxqg)H5BbYcDP
zqFQQaDtA#@o$?`305IIi;ZyB4Xl#XsrshGz65AsI8WNxQct;c+jgF3fJBc8pk3XQT
z;Up)9T^mk?Z~cbd-#c9CGZJDDDJ2wfip9M@!RG3Ow?`UF3O%Q>Uypd}&GfHtWRc1-
z*AynbMgwusEK2+5(u#Zf`*Zy?#HL&{DC2=RVEb$E4n#VSI<3RJT9yQGfJO5TvZIao
z)Aoq!q57qnz~QtZZi#Kd$Y5rDY7opFa&c(YwagAuk)qo0`it|i@ZdcHAJwz3tBSH}
z^rrhSKk*OA{avLeDRfzM?}HHzHNYN^VWbsre~_3c!uj2#yVVPCBz2*j?{?e6+qz$_
zl>z*aSoFUaOC#)zT1pwH;~QrCrX6_xILkD(=<>%$?vU`|idkzyorCvjYZ~><uFsTi
zXw;~;Tp8TQLwHfU=2x^kr~TY?wIU7w8dOO!5%vlTs`T7lWmBTv_l-%H6OUmAvh>6R
zhw1{$ps5Ll9D`hqsUwD&YD~Xz=ump~6f}v%q@+`Owjf~^FVz|VZS^rD+3L|K2se}A
z^rf{`ebJ5l$c=ZcY>^v4o<Vea$(A)pK!;QVkou5_Lc(HSs5L0LwsoHoc6<Y(j{oi3
z9s}j>iAS%VwJqaA%t{y5-{Gz-*GwjQ!qRIIyLQ?*ybc$~94hFCh_egiUx_dcJ_%^{
z3nvITvWVvGxl6>g$cg4wwp2m2FJ<PbQd>KyB@=<TT}N!wTvM^C|Kp!pm_;|QfpSAx
zo1%DM6I~E**qqaX2%>l+P*6_+O>h9li$bB`kOdG3po)qABgg9a#MQiq!v0PWX`isG
zCAP(as1dfHGJ=GC7)SkFC+v{6x<b*d4lTATJDbXxa}~fIg2Z+lR=dmr;ingb8)Ybh
z%Y<UyN$qR=-2dlP$G<#>*~eKI+Gk!@&4=+X2<$<S4GnyrB2j1kXL7S29sN5E>x%jK
zk+AoAJgRfzzscqg+b~k$#q7_Y&l;!dID)VuICF&$l*PO2-Vq2+Jequ8P$y@8u*#E%
z-yLDO4@!$Hu45630Z@awLGxwx7G7j7Cn^Twq>1#CXthJorx`1;x9GyN{)d*ZIpH++
ztu_=(14ltYzue?o`=|o7{uJ>xYHaE7XKYSA<m#-D8x(5HLbG&FUtgc+vsU?9qgcNI
z_iT6SP_>?X&<Ws2B2kB589l1;eg@(A?9WpLO^5!1W?fKFE<W}QdUQ@+-X@nq$iRoC
zCV#ZFv|zkQAdyVJa>X4@oEkO$OHq#pKY$tFo<Zr*L+AH}IN4ZRFR|%=rkF4TF_-w-
zoR1~yh7x$9p}Bd-?8?FOFAZWL*ML^;>MrHCu(HYoRRnm)Lx2ibO8x?#X>V`OgPX?2
zQ5qT=@qc4a1Hh*61SJdLZhm2*m8WO^u~%QyiW$^eng<Ap_X#F{0Kvs@j4<?XYK+A$
zQ&D5TpV8V&eP-egjMF6<8CTm$e^(imd)>Xgy*`7W{OpKq#H5J#-FyJOj3-R-N-%KF
z3Xmew<V7H#xA!TH#+mBa#b__tnh6U4-IS#U#3P8=n+?$3*8gkse_Dg|U6G&1euWTc
zsno5(_`i9>b{^l&wmq`z_XJge$G_|9ss_IoQ7RX-h`>-!XP7S&&_cYrzJ+B^Kx@U!
zvuk`F-mKqhp)7)O8F|XXqz_B2l|5p!?=@a23GqqbQU#?*?dl|2pwhLkUxK*bok6*;
zq1Mc<hqxTfu=P&h9;ZB6(s+wHXM?UX1fp@-i=;>s$Sg;(G|7?;K#i~TH@KAbw--hF
z7UlS;QIesTbT|nB(;^Swf9)!|<#R_G2Y#T3K48H1A^B-|DH~os_R8oG`x;THPO*2>
zRb7+?A=QT?W-fyp13xAHk2TOo`2Mx|-><>l6Z|V*4#c>z7N={N!$A|tn-|idM9SP<
zcW1EDYXE!}@!+3&zz3%0t46lY@V-;;NRxMi&Zt4FqXYkK(_vpPgti3I?$?x&{(c$5
zmTP4TS_(NX#(_BD7XN9xj6R>Lx8B9?+m(V!+C~n{VJmItbbXGtNlbp7JB8m_idwVz
z=WuwkHgVQ}0}C|v=aNxkn%4E4c^@`DS>{64T9wW;P0$+`?+OO*D`$Lg@Km(4va*Z0
z@#P6tw7a|eL1|mIF2_A;kCIe;#a1gjJG<g}4Ug_ajyD^3p4~217iXd&(~T{ws|#)?
z?bgQlnS0%j2_8Sd@xE_)xQ8;0+dS1cP<XLw283CXKgls$Up(+Mv}q0Q@39~hFln%4
zwt=Js+U!3sk}UEz!2pn@;R(`QZ^n*-%1Z0P!on8kW3clEKq?4O^Yin(Z;FacV0~o=
zm|rC03=KDNT<@6ut>_h2dXq(FYCzA+%gLG2HZ|Ma-P6NUWG`f!^eoK}GSyd(%tsg;
z*>yo#q%Y1JL7rHrsU#nBIcF|*61e)NdV&9c5l;kQ?4X!p;z?;sxh$Ux7903rknCn8
z)H#im-Xh0Z9$@7WxD?HRJeXdGTUzKn4dq<Rb7V64oKzfdq7@ZZ&CaE6ReCly3CCVN
zg?bNNr7v1c)AGWF14KIPe1QW25`(@5I|iyrysq+S%O0Vb8ju%3Tc%+jX|5RxrhlX6
zX5&i5GcKJ^koUK7^nbzF9X<{wqaR4^W$>}7&hndDa1RTcj!OzV-FbZxXbvbYL8EsC
zhP<lC*R((Xxkt@A+0)&<1b$t=GNC{Q!_gCTN9cm80iXA{+QvNp_@^7FygMi*Iro(>
ze^@IPFm$u`>W;a;u10sb>d^Ph9^Kl^dl&QldX)G1yRqo52OGB+z$p_PZbg7E2))}D
zi^W41<b8M{%SdJ*;e+Hfm~eMG#B~yu_HH5o7b|S~vXP?#N?b#28UQZyQofM!=lXx!
z1VJ?Xz^Y*gm{!mP0W#!VS54Byl56}uu3zt7obFNy<pvvef(q-44w`sj^GGLu!r@^{
z0Cp>-n%y}<urOi`gg)u0=Q0#H;DVIHRC%rm&zuGDw-l(WJ2XDXv?+EfO!BTVjEai#
z@bpwl{MlY!UY=7@Vh$4G1z%rZ>Y^D<;Ux_b&8e(Z1-p@0EseW#<pjFkf7Y5gIKu4(
z34k;-G^cvKubo07L)OU0H7{yt$2GsJrPj29XGG@tigFI`em<KNywdxYa=80$5%IeQ
z^X_6*@s2n=GxbUrnLLjR<pwfgZTg_+fRENl2JK!x>01r>VPAQEZtnq9AD7LA2pK}D
z4UK|*=6<|K9HNU#U{ift6BGh+E%VY^fxA!vg517X<xQq6DpD1~=ab$h`?4sytGQ94
zY98G=<|4Ww!PEfa?fHSs{Gu9<)M9v$OhXp78+Lw<67?Srq%<?JDcGfc`?70MXQTJ_
zd!j~YY$S}v2GPXY-@gKgY{jMbYk+O#=jIy1h26M_+3NTA%uc|}j{k?5mHoTe69Gs`
zMpm|!!}V7dk;@x#84ev8d3*Uj&nIqaIhPT^MIYJKr9yJZ0HI^5-!;|h{P{=`5fMu?
zT4B1OaO$~%<c^porn=lycI#!)OYTI0HsN2<2(ng+cQ1gML6G-2KH%QM!Yx?pw<9#)
zAhF?JTC}vS%;S{B@i@~wxTK)=6ciW%`n6F_>bbXW^J4uf$MoYPl<erh-hqP4M5`Qr
z$JCGhREw%r?ggXU|Im|mGHQL|n%@iu(X@>dl?|aBvW6%+uG$vwA&D!m_ovSm)tbOT
zx~l3qUUb2YpnQT_eydF;u6f)<*Gv|0b2d1fGY~pt3f~!9X2M6+1{`I&j@}kmPT3y3
z3&bP<B9Y)tg~fP05|k)vk_dGGi?$QQ_3rj|#WWRQgOkM6;;gJJrx-JBVyoBaK9sX{
zWc4PM_CfBAq}uq2!O$n~e>6s__zY_W;<AmTGA1Xl8K-FUU<&S+Ui&7J;@4>iwC~YN
zxr<rg7Mb&0Rfg@lJvzbfz}(FP{_NiPWG5(c5nyM#9Xo&cMdv9&?KeEA>6+qiUF=#S
z;Bf|WnP<^yFOSFl9T_p=Vl$jf=TOviOz27mj{y>~RNopdYZfGpOyH3Q=-(fNC3%?z
zb$<bi2A`y3qjYkbRlc=5cy_Q*v2S=*8o*#u6@ejlP-vqgW;&DS`mG6-B&-(TDDF%u
zxjL|L@~|agFa$C`e13>C?TMC=!p`gR9Nwtx!(X8t7xkC9UW~cfew#L2D8ZT1x39jH
zsk87rW-4fejax4Je0R!cBorJ>Ke$0)=J!|(sjzC;eh0}Vl;?R|`<9pqPGZT`CXdOl
zubwoGy&guNQw4!+Z`+gE#(lsp*Zwc!Z}cDF3w|dN!ai=G-Qy|4#IU>jcf~YS-1vSH
z?wfGP#$oaxk;Fi&vC)^nmbdAy27|CSNw`gOZ2+_oEoGee#`#z-+-(6M->OZWe0$JK
zM8hu!*szH%$1gvB-QH~*6wN*XGXcMIIE3xRyY)eW-Ph#DyLSQ49PSdX|9u8-lc*n{
zcxGy+K2`A^I!?yS7Q$-U@JgnLNVzvz0}pKg17)v=OfCdzpnt}LPk-&2Ejy|KO?-hg
z_wTjK%g^tEbdwulGWv-j)=)}l3<mF}5T0ysbL{gxUB;Q`%5#6kgie8djkhj#`Zn9x
zu<t;_iRZHsIg~(la9SOWjpd@QQXEnx6wv?=z}EEu!kwrYP{f<h{w-Vfui3Kq^4S>i
zLSA_1TNlpwJDoZ6PzPALXIrxRK#hMss7)xTwb5JF?mqqjV7HuOjb)iDv5h0v&08ad
zx}W!8b<#U>c*Z!Opi|AxofwBS=U<)%AZCv4`KJtsEBH_{k1i(X?2f?0@QlgnqLY{I
F{Tm0)e`Wvx

literal 8151
zcmeHMcT`jTmc59gQY?TC1^g65Ktbu<MiuEzs_mmok=_D`0yesUw1BiwgGe=0K}A41
z(whYWBoL{AKwwUsH?!W%d+V)PYi9nKSucy+kaCmXJ->7I*?XU-+L|gXj2w&zg0QHm
zD(WH#-30BvhXGze_14D11H)Z)6-9W2+xoSt0C?EztZM9vAWW>ZJ6-pv|0aTPd#Wj3
z(f3N88})V8Us-Qj=ooAh;(7R3^_<1!9Nts$SEAFEw4doJT?tmap}pIwFoTm|^KmAy
zFIPt9LYb1i`t}>U1(BV1?dx>~3bRB6_E>1o?^B{HK6zlrAA8kS7pc_ufA02IlX!T4
zF>;h_A9BpagPe^2GX_OM*L-kom!?AkH785B5F}7vF^~?q5XnwgJC7rjI*!T^F7l}H
z^72xCHSRBLns%1;obPRu96zx^c^%5tO<5tZ@a+i1W~uM2EXxUQyptlNWtpm3lKdj3
z=EnussT<VA__`M;>Qd^q8F@S)K;;DkqL!<^)2l08i9_7(B~NZnjw&~5Ich<%8NWt)
zV82QlFZ!BqWIx^cy4aFRS<%zb2&>&(Yz^l)h(uqH-}7ss;oF;)J^W7Vi<5~8D4&?g
zicycGmey95Gq8*%p86z7r@s9GYmEc_PSpz`LGRP-?Cp6ueII|Va2g+&2@6%>4%mD^
zopTE4$<fJRXJ#<a)n%3Kz^4Xazkk1LX=!Oo94Mxc0=AEPhD^4`6Lw!(dnh6*DsI`r
zJ~vt=>0!dd+C}mw%T7*C&b$z9?@X3<We-#4<&^E^U}iYP%pmSItsNT|_i0q~2d@3n
zw%_E>pVPG)^Kcm&Bdxvt{e&PUp-$^m)L~Nz&v~;^Z+yxF;r?kH0o&6mTvw2uerjd9
z>za|-gWy`~swpjOy>IR`x_vrkJw0fhD*sKSi90B~Vmo?y%$Jm#mX_A+5xSonyS<6O
z<dAp}(M5|MU<^*~z`LqMaJjcj8=09AmBUZWj0J3?@X4-DH7jwxzuxb@hZhzQaLG3;
zbZM8CwC(1p@L8E<>yy0Guv5}+&AGypWO3xF;z*KH?F?MO=HZH41_qNjPtv>xX>2aN
zD`&Vapw4%$tp6@%+`Zr2k+M94efzds>D0jrt2Peu$0N6r--Lf^HTRjx;``#dS<o)+
zNva{^QmI61EDkpWIYM4+6|TB??^7v7eyA2z)!NqPZeU<A|0Cj($2fJ~(;RO3@%Eek
zKk@h~)W+&NXDOmp2YSxM#bv~`Lmtn;!C_`#5PMw8Ic@mOoogko73JIU7CyNfkfBNU
zJqdk<^YA%`o)<{u?2R47WLL=%=SQX`R$)w`jq|eO>*1zlcCks)uGy~b?_}pI8>t)P
z+u<qXH7T<I>WbssP#Ir=eZ%9u^K%vB;V901p(#8@rl!Nh^8EaKb3?;8S%^_gPhMVL
zGLPra&ruR{O|qowA68#pbrB1`e*LPc8{>?2?=yBBuSFGNt4d3I!?@&!Ca0!woU$IJ
zwp{pd)TKD#TU;9yLQ$QEf8Ds!si=Xsd}yzRn&obFjVZ_0tqS%D;o7x^6x8aV1DXxd
zO&D~ommGDA*;<GQu-RCh6A%#4y>jKDMF7=ZDMKeVHr5u_!B<u*+94;p8jwmFaO4aa
zSoTUtNHD4KDdX~*pwmFh8F$Dnz1YVkcUCPgCr4XZImBmqx{GMpUucSHj^PuOl{I~T
zTifUss?1}4WRU2+|Af>@wb{WEYvLRC3rBm$YJBevKgU3^rutKIv9%Pt71DU!*ClRY
zNlD4o?aL?A3JMAa$V$;7A|i<>g_<&IeCD0T5Zey4yha>puta5jeVtF756KiLK_WP1
z4U1|id1KyqPK^W+rBR%_o`nTEPRKmZpGx5u6wLqFirZP;X8k$x4+>O30k3+T%}C_~
z4l$cZNWXTeAIx9-`Xn~w98jnl<p{1qY1gTo^z^GNEG#}7a}}n{C{4}qP5(MOJG<V_
z&I23x<es!Ek4#b)wl+xpS^*TjLh~v^`+lqgS)oFyn(E{>Xc?dKYtt=*<+y|mR6nOQ
z7sA6*f{+RQS{r0iKbw}^YjvS_iqT>YLt>*S@@QLaLwQ#q9ewG8AD=uNeDUomCU5_I
z3Tes1zq;d%jaXhm!MRbF*7H74YX;`#=kdu3+ncTq<F)4BoNvM^TiV+VO-vGLP-k3X
zRaTku$z6VR;9l{OS5O8sGniDz6-`BN?{^XoLz&~7Q<5gdcZ1JpCg(JU?ys<~8$LC*
zC7F2PPLKr@;hA{zH@6=?d-hDmPZ?2Vu%cU)(*m%b?aBy+a^X=kvhdDYog2pNWjj;q
z^)8g#|E6%wvTlsgmDCqY?Xp_#?(Vqh^ye?pXe{kLZogj-ySux2)Ryoa{p<MDA||2A
z%QZeL-QPbx7r$MPSXx=x0dV7pZ(Cm`B;;~$uQdU3Reg6POvc~pdxuBo2L<iO!e9uW
zjyv_1^;-ybESXe)!J1dOkw-lV0IF4f3aQXCFqfz^T8Uz|y3ZrZ*>ba}PQ4f{?y#d5
zubNgkhMnJ-N^<(?jb2<_l<`x5i$be^STM^>hLpK3YTe$OuJoLTbK`8wuw#|RY2_Ce
z(98&qVFxo3_)v(fcC$IXw$=iT7vFGNB@XVbJAq6rH=MqWAk4q^97PcNo&WJ*d-(XI
z`z>gCUERBd-F*VDz1GI~WqzOej9=pC<5M|w=ulrq+Mya>vLSQ^jNjr>O>@Yty!`w_
zjKKveS56ok8|M`j89^SdoIL}n|1v77>rd&>H0Yq0X>IhseTeKtoW`n+$z#4nj7&_B
z9<iSd=#fbMZq~AN98N}_JmQ)t>xqS0j>`GE1BraedUyhG#-`;Z|M^dzOH*ypnYS<G
zjEsyZI+Uv~tcP<ttE#IHRB&>BSdz=i%Hrn;Gl>W?T%-C+;|rQDpFrcANn{MsAr<f4
zChWPF(mYyf8>6tbcCYYe@m1&`;auK753UdgMLj4#n`3y@8bermVzd+zo@jWe`Pbcw
zQvKk=TpC2kwg0t2eWskje{kZ6h1V6xvc7xCtQ||ZWp^>?k73-Gwq6QzhbbT0vorCW
z*1%{~Eo#HAEm1u2Qb4W5{ed)td_!A6;NqV6bH=CC7=y1uMcwGNs$x&=z$7OpzZ~7h
zW_pswWc=1HL<DNNN=r-UVX=m1+23+yHKHgpn7rKFVy@G?yLQkWVrNfYoD>C6VsUP;
zcpRIOQmBBU<YZ<l7S(Ox06bE1Xz_Go^||cdovB94$x}vqk;}T@QS}IN^yEJe|CJc1
z5{N85Xw3X*mFKFJ784quwY=rYi^k_dJ-f6UM+8a9b?99<`f{99!L>{63yEAo@EFG1
zZxuUYb`MBnLPA2?$B4G?@k_Mx#9wQxcG>j$Fn@>wSkgY5=`3|$o-z8S7>7guoQJyS
z0a^L_Xx%O4^Iyqzg3j%b!Y=0SbYcC6I(3FNMEkm<#L)B%NIgRL&%-~C!Q}?}&1SyT
z>0Au@`m!AzqAIYL#rZ6|uDg5LrhmXGBPQf>x0?FT7DKFA?!u2I`>%Nh8k)oJTroTV
z5`v<lqESh6B7U{^$exvtTqsL&T7Up(JnXjNnxBF{rt>w2)BYjX^(mfn^}ta$q?~U*
zKTc`Z@;{}<q2O1E_MS=rP-<O}(;6Hc?7Nr7;<HM$mEU3VZE0P1YM;C(!`#kC?>$Bx
zRZc}&#q*u37c|25Zmus)dZSN8J!xra(Kj<YAxpirO^MIE7n-}DOYT)pn+Lx_susWj
zf7Q0R8fmP?eY*1xphyIr11f)o+cVDX(u9M$hA0%O%CQ2#f&DiQ-3-LsNJ!_`%T|pB
zx{12#M~BoumHFwQT4L2U`-FzBVqV-AK+x^^|8y8SaT}i;4iq)21VJK2zB71zd37;5
z{G(@Yjt<DGp5so{UHp&ekjO9J8P+l)6ry<?2Hz^}+O>;D-MLW9U7E+B_x^cUvL3dP
zF|^TVn@V=4uJq?a?Nfm0%i2pINVK6zH5=(2o@*2cybq9pGJmR%T69Mfp&UVMiYn>+
z7n}LY+zE!%)rvX;E30fCThMnYBG9dy&p8HGd6KXI5^QlAuP(f97C{ha7tL9(#jXq$
z0Q6Cv-wRJf?K}Yrj4_x;4UPX8ho7371qMS(ZblH*yKM}W3kQ~fx?#MRrrc*QBS>aY
zWoX({VFckk?HDNOG*+;==?BauAB0meX!JDH_gY3=W#vtph-BE$HDbI3(P90rutULZ
zI+B5fqu2N^;aUT+`DlyMU)Lht|CbMO1=X(fnHrdy3aT;g+)+H~cVIVy%&|$<i;Ih=
zh{ON<!F2^7wI5bS1a2(Pux%(7{NR-HdSiZkuCLIvN@(1`8_n0Tq<eS!bd+kmLXfV3
zK^6{&6WXAJE-x>$p(U;xqVfC(_GEszW!w7Yi`=)Ey;ZdCN+d<Q5qtg^cC8xi?yw#~
zNFZ2Ow}FqoeJip~-O}Sq*WqEcCClJWrFQV)&8`!-ftra~x1S(f)a@Jf94~|z5H89j
zyJ~jXi1GSkfI#DxQ--kTPVB27Sf4vzxZeAT<<|na9LROHMiS^JX_b#3KVsybxZStb
z)%j7Ara!Q)UwK6?MJc1}<(hO%IJF?FrM5xYHy}0W{q2%gt=Y;pP!h3dw13Mr2*5pv
zCzqU;a{(nvRS{qxNY=max#ax;=kE!oQsFEJqNzEOF2N9whdod&sO+FKGBV1~%;Z@$
zigc`APzF}y-gh5CcnT}&&=>E8(5UjzkO^pnJm~ZL<$hhZZesu}dv<)HyIWTyRY880
zj+LgT4hxwL7?(h5mqKcT2(0xW4jkO@A<vHroIN`|odCb)^WfHFai=jkbC+gb0U@C{
zythfIO_X85&8~kt(F;_{P>FSfgyYCrd@>p|+u=)H^axUFK60&-&|hS9@_@F#e=Q>;
zBbW`4_LTNS79=q9kYxR7mGJvLzmxepgVcdNV(V<W<|`d{=n!X_r^hD%aS0_I3eQnB
zmaT`)-M<=eDzHN}2+co#<G;NzZV!?#QN;2Y5b80%C5=MklI#*IdgP?-M;7PpEozx{
z2baT8saltfK9Im4`{8FIk-|Q)^XD~=n7ip%f7yXtZh5kC0+4L_=zk)`|F@s+W%^a_
zFsy&`W{z!7c5Z2@m`?sK<Rm}uxq5GJZvkQ9{-9k<Zoek37#JF!vHJ>HlTxZ!)Cqha
z6fhtd-?>Z96ZL!XYD!@nDU2Jbj2m7uOLD@N*hl(9Mgumaj#og9g|ut}Q(R5T?<W8$
zPh$3MWRX;jR(^5xHJ}TxONse2ymw1;b1_#&Bd3Dj%}2ZUmvf!AT$%lvuW`wGWo1P`
zP>}HWX=gH&8%WLq4ZOpQ@(Io&0L(eu=)TZK`2L%Lr=y-+S64UC*Ux<M;>8(EkxAM0
zXm-Y6eI1>szv^?v+oAh7yW`HA7Bs0EXQ)It_H+yaEUv5$J9#!J+Gv7w2j6Hkdr1Z_
z<Hb>$iN#{gD~699I|jg{5ywZYYy-rBP-L4r)ffZ^2SX-nr5m!)I{(1wiPzUcXA3LF
zuJ_JDDcDuJ(EY7~`oEnl|9h(kWy!+u2;(=X%9F0j6J2jB6XB`}+Q08!srtzSk$7(h
z8E#@`R#w>zxgdW_h?l8ud>lJ`&23+5M?aR(Oe4owHAph}26sZ=47h4HCd8zUMJP~m
zC?M!(*N0p=GpNa~4!C`lXyOA-H<r0~XcJt48fe%~{#$Xr#PWu3&PmuhXQ`3BJ!!mA
zs+k)7dcOWgy8In#(6<FS!paFgb~8^-cF@ryw*!%X9{zC*PQIqIsP-zRwLS1ul-l1Z
zVmScDqu(G#`uaA2xF+DkfCEqp#*$(o?X8%&oI`VCHKsp4g~fE>d%z*Vo;NMe3E18$
z?y(0?nug72Jg;5J^q**a*$m}}OnK9a8^xYLVk5eLSb6yd;STsbxKw>6G_5I|Q&0_h
zQYVvewR6kxF_Ax?IO1lq^=jpY@YPH0dPNpBCi1D{B)pG^+qd8&d7zC8(ZcME59E(=
z$hg@gVfoY&<-FfseXYG)>+uxx(F<v9{d%q@xG}l-G(LN<`qHx0<7WXg`oZXlO-(JL
zqh}C1cJjo&{Os&A2a|{{&}G0*0_?dX&uG$B<>>^FA@JKfkfObAZm#i4)AjF~A>IvW
z`u4Uqg9t8pAnXNzg=ptB<LDg^1L3c`n{_qqI1(r%_*y(!#yu}vGbQn}0nNtvpCsCH
z_+-KtmXO9foYF3tqLSW2Pn0vjzSV`Ofrlfwk5eWS47+u|5f%9q_=w`n%nTs!bgG0(
z(AVDHvj_H^J<4nnaW~zUJbztF%js=(4}EeA%~We>xQYUck7f}8^!5RSW{r%P(Zt4s
z2gNj}1T3uvANCImo!9oyv<MdUf?aMc-(nkFMgHwAq@0zPHwVzAi63YrUu%3l2^Tw`
z<rmeOfaO%qMPGPS-lvp8COOG}vJJh~2`5b){H;BlcbooIGPZHvx3RI&zH{d_e9Ix2
zMyL&!JLthvTDF3pH{eZ}oxO{%S)WRhb>P<R;!E{Q$Dn;$Kw>Y<<`>ae5|9M@RZk`i
z=33~!+&6Eu!9#>D71O)uVDa{JJ>R#_QKx^?_WOfxB~t3ZY^52a&`H7YdTn0iAug?T
z;ix`Xmj25b+#lm$IFegkZN_x?T<7~oyA6zukIBwKb{RnjzD|wwC<jqLJ8QR2^(W|b
z2GQ@{_jyU~yji6-KE)Fa6zq<??)AOFSAAf_P<p44A-M5d4DLQ`mcE4rHkj#19u8*$
z#@qv^rRZZ$X!~GSO`2^9vx&lo^t<gqweVF-;itPYaFcPSnw2-eS@x$AZrKB?<B)Vr
zhD;W!+jId9llAe)DKWCY9(YPb;(beN>!!H#CZ#kE3jTU`Z>}C&X}r`}ORS){|E5==
zNm+{zdubZvKrwkM8=SM#ryIS}ds+AF6hl$mpd9QzK071>+F)^MDK<X-2w7rb8zvFM
zdr2&g^Af@d2PY(KszZ-}(0nWcyABa*vzT9Nyo<)C^xhG{CqoO*ZfIy2kf3QDpsAo*
zX{!OIx9!$0jZY@dpA2tlxz2JzO8d9QmV%+w3QQ>M7JoLkbaeEK_nX~=xGio_p|iWR
z^RqrxeHM^3U;~+u2%Ok|t&uwqv`utkU7Nbc&dNh*ye(Mj(V5^W+fTN{g2$F9^&mZS
z4HU`hTt2{o-9-IE6Bv}h^$eG`K^KEXLD<E8JI0o(2lmUe(oUlpc3|!Bu<95aCp>%p
zys)WZy9D?u3zytYAl`v}So3_^G!I4%efP3nz`nx<+QDp(L)7Za-iW)yi+iMmg|*b<
z&UL-DY%Qgo$su-j9+K|6gDNLcIAHzx;qxY$_SkpIo}=#Z@KfgIDL~tEU~Y4*yO^A;
zeCpu3pF&%TtPxz?*8<~`yB$|(V-gzcDRdYvpE**u7L0QzWW%t<qGH5_DWX@VlQ>u+
zZr^tfYV8HhL!~bF3{^hJs3q5sW{m16vA=zY!$T9#g=;&%B#6Q^FB@#_wpSOgK``xC
zC4>hL(hTLyPY*pnnuz`lhfqBT4n(y(i&WSz0h@nyo~Ii)E~!^Ljn~rL_T`oSTlQns
z-ZD#HMBP6gGMfJNt+9BCYkSWcEV2)L)mZS>!L^0H-z<%3;tnuFQI>Up$pHztUuJsw
z`pNpsM-DRvv+oNnv+oz8T|Bs~uR~xE=u}5F^c?fXpaZtn+1dp;H!{njPOHooH*x8K
zs{<2?mjLdxumHw%OOE?xgoH4FgjX@F+LgI;2eaD)J&a~7G^<w^@4ZY)%7>Ap85k-?
z=I*h5MYqO(ZqkOUAD*&_yZrpjrl2-gP}1J*ORB*fx#gz=Z%wV=O?!0kcG`3M7dd$E
z;{E0E;Z)0nPjweja(J^6ZjaR1*sQSQl6lYq6D6I{LbYpmPcgfl+`*jr>W^z7HZ$E>
zL#4KQF+I>m-JtmG=11PiI8Ow^0GH*=PF;FB2HhJlHt8!c&VaGW$1N@bZ0s@&bp($x
z3xKepnb9zgg~^1iyijDR*Kt<xYP@!WJ3&Vq5B$Bu!)^LUA%g_=gTXfuxTz^=Dq^qR
G4*C}p5R7L4

diff --git a/reference/Rplot009.png b/reference/Rplot009.png
index 0409080c4dddbe04235182e225eaad8528ddc95c..21c3a434fffba306efdfcc7cf4c5f019d881e515 100644
GIT binary patch
literal 10271
zcmdsdc|6qn|L@SDBI-DmO46bfp_F8AQOH)=vR6pROc`PrLv_@Vq$m=`k$un3Fh<8g
zwi)|iY-Jft6l07rhTm(>`98jndw=)d$M62`<9Gk~{yZ!*<1?RmFR$gfyu+@V>Whi&
z5kVjjVul8K*AWPTQU1>+VR$8|yXGzY5x#AtuLpnOi*vCm82)U&Z(xH)AVekjKLVXY
zxK#vVKf+M&yoG<l>`?IiZdPLBJk_tb%J9sAlY3cm&wYQhxY@1u?bvpqyaxF<vVzI8
z{diWwQhGpH+VCyP(!GpQQ$~LkzI71OBR?Bfn;?*8Rgl-)d;1gPz;7Y?=k>O~O4|IZ
zOR=sFdt<bA)Hft<c8+VbfNec-v8JDgbsCXL3Yz`yPga*hAg*2&V89Sx=xsj%Lw|Zn
zfbw3|h(RqVe|f;r!^5LfX6?sD#x4w-toeP)JZZ&ZhF*YU<qT9jaPDO+PoK4!!XRI~
zK?|J=$j}pD`(E4;**Fm!Tf}?gx?k8<IDDsKCy_lIzrs;);yj<LW|DKMg~MvSp;R<C
zkG&CL2X9igD>JsXe-J90RakhffVlMj!G|yO@u0z29+%DNv3BZ7HH^OALe^&6mzI{g
zH@=kSJU{EQq5rZ=!``2~$KD(rjjkViz}R$XSX8<CcROTd8g+q!TPpTIBF`B7df~RX
zVCAjrwef1GUbt7{3a0+~4v}Yd8o1Sp)L$Y$HoTOMUkRd`KVuL1CE<gXY#bic^ZySs
zzv6JG?y1;&-QXH|d3m26OY3Q~;1%=UEy~qD9<QxZ*6m$gU9$@csKdlSbCQv)#0oul
z&CSx<+H-NLBVhE=#y7@C4k!9&tL_z7*emYYnQCZD@Nw^D`9%1B5|Yf!&c5Qb(iz*`
z+Z)5u=(uYk>FA&A7|dgJ7d_h-U{F3B;MvRYjZ^iE*%$cjr0SV7+11tdr`*WhH9_1(
zr*)Rodo4`S<;Z<`uszR)^3e-j#r<VYQ8;$hNLE={88tw;X8AU8vD1jxJeANjvNk(1
zgIb$J!I-#%W<fk$Z>~+iVu!w}8+q^B*jUDU&-{*jhbk51SguX+;-9~mxoEo$&}hCi
zuktMTU|4utVnFF1U+y;u?wP9Wh`oC{`%?JM=@+NS?KW<SX(${s{(1dv-tgpFLZE~5
zAePxCudGaMh>$8q>ZhysjpWJ|=r`+ZeRPYm`?vbrO4~M7EG<2xC!m>hil2G9@q>cu
zh39B;r5hg85X5P5GIgjaEbPd$DQ-}w51ObAjXMUhdXQs1Jw27-$~AqnLsitn5}VnJ
zU1n9HN)`LZR_yHT91IP^XvDyBqa)GC^)=pHhAz*2hMly&N*_`zAN(pVqw`H7>{q+0
zhk1dFymCTBhc8i~pt{HoA6=|LACx35U-D~|_N#Did|BzQHOF6dHLrmZ9d^gkEN$rB
zNoRXc&m8Xe^)(dx%hupNUmu@E3^AW~&D?xUXL)pkWMtzIyN0c8ZD~2NHl4KA<ooa;
zp|{sc4Y%sgZjeHywhd92oG=q_=e7nfUo2oDhX_51XtKHunl~RA?30+7h$JVd&wbh`
z^No$0VX%FDediL;<Ofv`XT8Jp{?O)rE6)G$;R8)a6C=e%lUw6XHS+3oSD%DmPMMqP
zNI?!)V@#vM>u=kzgM%p%cwP2s*jYb%q8|YknhHUtp~M^F?+)A;`NyxD=XmT9zb2O=
zC)#VI_>!C_g|(=<_0bn{YDgk_E;?y7mA_Eojbn4L#(ti?xep+dy~*nA*kDfV9F?@@
zJ5wY#a<vVwP0O<@&!^qskC#JFGUM%yY)q8(TV0+nVdy;k_AVzg^S+{@A`wR&7?_c*
zOwo^A^3;U-;ZzwaDw*CE@U8EpT$c3%JZ3U(?#Wi2DfG8AqgX<gc?RL6b3;X<rr(l6
zBwB8%5pw(03TMb~#P`yrOG>W|Qe%P`b|c=AIxBYz26_E!#zL#TcBsl?vW9|Z{`zv{
z`u$jkhjDh5-mV@PCvKK5w=h4StY}k|O8cH+mNe%WFrDT|>+|K@qqlaX=qJij`}^Yv
zt&jpMmbDH=iBi)z9UJO{yPlp#ikjHv`{ghC%PL89M+b+*z?JEYjV03B^qaxOOZH}E
zm`yK!x>F!PiB~ftE!QiT1aTko)3vP3N|)PDfrKl?a=cCAR9w%E1x;O)xx&-kcDB$)
zPS-1-sHjMVxH6MNVB}Zvw~IHrQMWz$;xk#pja&Cw$VyzpU@!xWd?%74db0JJt83aH
zUq0rO)&hu3vZlLo;ApLYVQ1#Gl<Q{Gp>?+)C)7%7?(LD1V(G3kbqQ-L3{~V{g^qG?
z5v*kDnv6)O#kFg%2b80>hv{|dyd9t}8Fh{~MVGo!@Qy9)Z~ZCn-@nhToFlRwBlf6X
zDR!fnDl$BES$?{>@k27xW%(`2B>V3*Tx7%XesbKCjaz40Pf;w6ZolF%2_Zrucx_Nu
zTk)Q1Kk)YL+hY=8sWFKU2i&7l59z;5jd2~Ae3TFf!8rV?EQMqfm@h}u`suP@v0%+i
zc20>)lLT+ZnshxVi1<7_+|t1zuP4WvF^HyE!d5ph2z#+U&R_owTWK=GeXz{2a&LIC
z`~8VqkE5DW`SowCF)|}4@ywnGkD2Z)e%bZ;F?t@7Zw8`~a%0ib(h{oobvwIw4NXn+
z^#nA$^+M9_@6}9G9?NOHv^UQ#!<W%3hKc7MqAmGx`Wkh5XIaxqsOtHr`GlG5+%aVb
zf`xg8Y0qOJ(KiONvMgfKN|j8x7p>RJ0lMV(CCA5)c4-HcEX@tkW_??sXSr3co})z6
zSXf$S<>i@3OG{_DLIy;oHa9nS7a7yEUZE+>Y5`PZ6Z#{`#>~+y)|k0%*XCA{Wtei$
z8js7$Lj@Vh@TT++8;b5c-acDCf?tv@b0F)kW1%3q8!&LNk|iEJ@-WLQ*Js3THo08i
zv17-|Vuo&>axkYw1JW)5J@E0?lQPsuzQ0yJnQUrlWhK9BiwD$20v#WylAqz3i^7gc
zWvRU@Ei0R;8sQ<6{0_d4k7rHBqh_E8bwioWltNXX^i7E|>n|f2LEz(7x{{U%bJ1ox
zSq%*hxns{R90-DT(=!q4lpl{I7#7~hrSQ5(c+0GUU<dBfY&6=Gw8knZMGk2=K;e5b
zur=j!M5mbA;O`q{uvf$MF2K^PRFSw<FJfi*J>~gx5h(D}HQYJ#<2%jW+%o1W>HFH^
z)e2~b+F;rG&;`1*4z&1^nx|R+<be|s?{<8oW1vr8$6zY6v$JIlsrKjyel3Q(xK<BS
zYnD{CSEf>k!$R8IV8!qn8X7)LhhsBKN-UhX1Jp(C$hzaFn^&V2m&YO*k42T*LnR$E
zyG%7r6s+^RA$kfRJrx0)$Z-1_Ev&54X~-%Qemg17XB0N>>r$sDqznw$zKw~Qfko?;
z)0qz|i4-P$8z?m@AHh}O)|g2O+cr({H<h%`B{9<Dk@BA-%+qJTdpGQMo9Z-D?t$Pl
zZy65!u8qNG%*@Q-^2X+>zW27NjpW9ia?4IhNrClD;`swKVJNSCRkpCV&*gVo{?=Z(
zaz&UZW0iX&*(Cl9<XdKX`Vm$MhTyFB4xOUh*4Fm?a*76TvCDTd&JCbLHB~z49!={V
ze+~aG_+~+E7jITD?UnG9`rnvKG?Zt@hfB6)_u^82ak|9mr{Jt&9Rsoq@lwc&^X!q}
z;DJc}iDqbpzO$uN4>~y!W7l|Y`|Wsv@vVNUCy(+`i1~y<;&Q^m{o-47!a`5Zn<SjQ
zrvGwphqT{dG9NMa79zI%k};@uOH2~r4xXOIV-vpzDbuNSwZ1`{FF|=bV)8yUGI({2
zF!oY2*I8iK5r2_A2*d_0L19EKLTC#jL_lO0JpT))8tXj$lUsv89J-XQOgiQ-(0IGB
z8khDJT47ITXVeP*mhR@C1R*LqKRwx)my)_e{F$Y#t?jFV>b)-vWDio9E31LcR!|wX
zM_k?!aT%_tAKaxi+0g!CvGQlAk2pmIv-2AeH$(n+pMCTu0<q<4EdsIY_YeWZiE{$~
z@r9pl1Z32`^~A))_>g*Gq6Mvb9D%62P8L}%=`uA(ql@tYGsmPgAGVeU-;hEebT>^p
zw}1V5JGAZ#vgHhF__!fp(mki{80%Om7%pg;LEPf$6JF9!c5F(hApNps(zSf6&9!US
zJk~ka_HX;)t3zR0SXhkHuu)iTwN>oBIH`-LI_a0wAD{_pWnQ#&XudNuIR#eGVai8_
z&=l>c)TU@T25n^Rp^`N%f{uBvcMu~LoT)|Wf)11GoH~KQ7ce<IapV2lu0k7uLd{>*
zO+UfyWjRZOOzz-Rb1b8>qoX5Pe;>x>^O!$gjz0LRaku8fT<A&Wy5Au$AhGr?R8%%w
zo13RhcV)0`@y18G9Cb4Ea|i_69IL!e7)S=B++8U5StEJ2rB|Z1hu8Zo|M+5`UltF2
zI;XVMGEDCvim@yA-2UHTd#jP_SAVU$1xc8lmR7HE#rCy<9-a<$w+$XcQG_gj@7`;j
zw~<?h4HBiiyu1P+fW0*)ukO{s-T**t%v*&F<j6Zg6**#tAN0gP<2|~4rc}%AFG%4I
zX(1x~Bs(v!$Cv65o0BYn*!9rcs`i&7##hm3bpBJ={2OEF|5P;mUx&?q=L^q$Y*$#+
zGCO?uFh-hGQE{WmMFsZtIEA@T!@KzN7GZvHB~mC{<!zgeZWn1{YB7$wpHve@rII7`
z4!-W1f0|7{x#?NmQ}+N-;V15q=l8!_D@1-0?NiSnlgZT)GGTfUJ6ON326EyTYRZE%
zAAkBC#e|T6JB>^#C@EPTeEq{qheQ!GtQqnnSN^Q7wpvEiM(K{tSKN9(tkSj)aJwW{
z*<Zk@nXCQ7MP<4rkmIoWUNO$NS)ES9lGP2@hM>=p3pldD6fsg#AdVt8fkQ5P1qSZh
zK%#46FdEgu|8vp$e<&RPog?7i*L27#TA7#m#fwjN9>4KB@PJ8=8wkYHw1ge^eSJ&$
z=wv+FYz7dr1|ahMr^1MveVZo(YM~M|H(vpS^U!V$L<)nb=g*%*zm8$_JT$kkctf4v
zsIcwqgWyIAM~iZ3WIy!UbNfeWSPA;@O^&ux4OyGyg`zl*vaxc?W^x%q-x9j`W$5YC
zrxyJ_oE*}Mf?;R9q*JcU>gdKwdoQ&|=gv(`O#EFO8NT#Cm9;3$ARR-I&<hQQoy@;V
zb6Q#&l$>Vjpg)gA1^h!e5IKgyC!jCxqw|UO^8A>&?)pM3shfr(X{Vm7A`|V$Jv}^{
zIeS~EgFoy(2|f9Q<SLD9ofOWls5m=YXd)}&uAEdXXeuiK8bTJ-(;Z=Y0lc+X(rTLQ
zv7O=5uqKtZUdq6(tc;9|8pO)BZ~6)F=UeKm%=7zab2~e)mzI>=&^J_&2>Z;`0yb}>
zZ<zDp!v$!>9e2B-RC3-+p@^&)9G5mS>C>8AG5T2abim?NH>H5IMmTe?@8-MAs+@uv
z4SM5#Cv+VOo#z%@QdYJneL62C<<RVGpsa*pApNLYfdFcVVA&F@Om)*??p8g0`Yr{p
zUAbouBIK5wTV#kFG<cx+N383hZRXCa|L6PYzd|}p`3Y#!yN8GI6ln1;cG-yf18R>l
zee->XwSDr;j7Q#$!VmjD0eT=8?fl_cKs;hNP~;l!;ptfl)RD&<Sr0n@7}h7jRdE#J
z8ni9qLbJkq?Z7f<x}_jbTyTY!(<gF=J0V%~Ln!Ers8c(jLMgAoifd8ZH~pwlgq)H0
z6bOdB>;;6$iUc|=ub>buOZ@SNsL*BrQ}<&^LyWFm>F)3E_p)(ta42v^Aa33iUc`C_
z23EnP%(p^_ke?6D9vX$<xpDjmAP(hHZ0oy|D;<$Ii+3&nf1KS#D;UQnm4l{@Y^(hr
zoZkQ=mT(CYdA}fp*slu{n{11>gf<VM2l`URngAjsxoJgdio>LPf&xdF`VcPib|ZYs
z0mh+GAc=QDlL&1VMAZKFx->)vw|t8SYFb7DI<$EMy!MK9Y>I-NJjGY-WRGnZNsD1#
z+6$vF=@mqJdMa3mBM_IjuN4V`fLWv?s5AEB)b)|ye2XqV6Blz&tFN!WNWh)J(~p4?
z7iT;Ps0q-F7Nv!;wDr=7T^4`grDG&I<?2s@kC4he%O*9GmjO$eHcDmRJkdA1cb?{S
zU-Vg>y6&^^=g$|sX%dl*XpP~ktm+^DWzc^V<mImfZc#~m0$crkn3%OL7j3BMRHG#q
zt@zqt3+BbWs3w<T>d9SDImqfxccNF5l%a|6+Yp)z*eQA;mV!cce#3}!6`c;g{!Bah
znN@+)j1AmpN1`)2S)<9+g?mpDSGC)L58Hmc`G~B;z1Nnc8*FfZi79AS&j7iqYQrJk
zI&nW~DCb+JWi76a`qwX2&gy%!>yDF8NAO-Vm@TU}YAbd07gW_&A1bk{gBEDBSd}>A
zDvFBsb(wbbsX+FerMvs|XcX+#o7~6{W!NN(csYs9^|w#LbgKUuv%m{Gcz^81H-hH9
zA-eQhl?W9@Dc*p%pWU*u=SQJ)V|RD=n-v8EwGnKwt(~3dTQSIg1y2EG`--b-!~Q8R
zWpwj_w&!GJ8Tt+RS|=Al?A(KBhl)QFV}~>hdh};ChPl{nZni@nuyCDKuVBr$qLvag
zo!m{S^80QDD6Zxdyy=^3&@a7T>%&?6ummYFmX6^w7)R`U{ohH_Y+L(_oM|^+JFvvP
z>xepiu})^K-EV}r8Bv>clwsa3rG^9v<GP7S1f(Bugwh9N4gY*6=#tY^6$IkM2^-O~
z+eKI_2bt09=Qcp%CgciX0Y%QRs7_|QYS)&X4lobQRw2l&SeD6+^#$ei568^YGiz!b
zVb9HUc_qF14tAOMAY$zxMg}V$3>3RX4r7@nhaplNtKaE<Mj+0KFZQPcu8+0%Hat)S
zAA8#+NDTPS_{M)neg5@P#3ShF!0-3$+0)(L{=17xT+S^()iKcjzudn8+;wHSQ5T<c
z0A4G5T)FSWHiMDrjpBQ!m@1X6qX{hx4dE2eDkhN!<mabx3?9YA;OSiKqjR<8oYd7;
zF$&WgvXjHv`}$2RT9)6gxP|mHE7~JXEnQ6*0k;JZlCDSjOsB<q%zv#X;?@EGPeX@4
zZjqqFC#@hH+K?MrT4tafsmrKYuO<{QWR;YZ&X`8w=@{Swz>U<Uso&8lsi}XSFhPM0
zFhu@x|LnbY>E!@Ds#{OO2z1mgBwzhU1@Rhz5-0_|GBs6pwlJb`%w5^(qo8B)oru4h
z_voi5L>$SvQCxd{l^OcH3-URiNzv8Ka26I6fH9;E;dBA$$}1{b8X1NAz9~8Q`Zq0#
zIY?UY_@5LOtuaSV)C^KpyIpvrys~3>7%6!q0=k87yfcW)P^C1z#|_BgivO;C6N%dY
zXy2{qm1Sab@_D|9+1=Nd31oEK`!4j7w+T9eNF<Uxai1T*YXL%1P_`fc7ZKxsg@*oL
zFvj!YZ=cqEacp^fKKkfxAR%dO>f7=3CT4X)WV`|-{a?Wg!Gk5P3qA?~s*-^IJH#?G
zKSPLpy7|!#DE*eM#hoId-Z*i(C-TqRg$Xa!EVNNc7m{D`2@C5op8@4JwVL{Ec*~H=
zOZFyDXhqX8nB8izpVdxtKGVx*XDcIc6uKHmL!W?j^488zO30==rY9T`N|<xBJox&A
zL6nV3Tq}o2$`3q`tFrE|XKICBI509P8hT+ti&D-3%~c{ykIdu*A!VZt>rN9{TEe)h
z*Do8#)OGb?958AbDxgF9nTD|k@Nd_d2;<0Z48M&1E!+I-*RKCTgZlq8nembk>wNnx
zA<>;vVRHCaXr<VT8vrhk=Z0+Er<KV!MVsW3z#iZmcHXL|p+TCSu--5`Otd?7r}gUo
zZS9NGU8T^JBBa!mSX^kdMp3YDfOmR4nDSKhss<)wDG(pKLe~ez+QjB~*HdUyZ0A=6
zSk5kLBzJz?7}uiK?)<7)P=|BOjlvAj777CQ(=ZBpB<u5`$VI~1ubY24jT)}Duisq+
zfR@*v#Itd1AX6(Uzp}AF=zD)w0b~RYAZ7X;h_N+zDWDuR8b>4@e6E#aVU&ES^)65f
zzzUPAd#9rqV^<v==W@^}z?efX^F}5cfFpGc;~H)kmc+8{UpnS$-yaRJDsarB1kIss
zE4|Y|M)O(Y(Agr}A{%?a-U_!&R}EOa3pD5GYlHE>gfTt?N&}<97I@EI@f?tSL52rj
z2IU*@YysdskjReiiICqD0hfeVp7d{xNZvh-J|q!#R3dEA7GF#aX?(FeWnsY^(3aCP
zap{?*#bQ}V2>2Fyq8`{ThNwNJkl}UKZ_d6gQBy7Uq9EeL@&6pHdF!)u2Z7jF%s&h8
zA8CqsG<k=2=)AwZe&oJ3+A_=ru)6t)cU`)^3BP~|ke8RIF<a7=3&LD@JqVkYj*dv_
z!+9@XimTHtY;8T@aaq|J{`l)|{Vk>_?+njMNx2BRe9_COooMH~0M~#>r-SrS{le+`
z^{BPJid&Q5PoXs*e*5_Hb#F}&cd$FlJkQx>aiK-I3MBD1jmwwUR|?i=_F;dVXYATN
z1S;7aU3b0G_r(joSyc|ChOx_ic7!y-cL91&s;tbSf*D96%L=aM(MY^_?i!nfl0KAD
zfSpj7>vu~kefhcrY=Y4>a5Q#ciCRK{9|hL6B~|snm0FtZ(gS|&arZ8ar-N@k?>=?s
zM&oC`X2_TNPO7{=<MkeN04=zE8ysbTYTJ9VQ~ubodEVeVb#yw6>xgMWCfK5Ifk~o<
z<yb>RG29AlW)~GTk+nT=3}V!w9f5dv_OOWmMDtrd<i`7d-OR$)4)^zGJ$)(+#P(>M
zae54jR%QZ{F8_iUC5QL6!yO&339A`8tgOd(N}Er&Cs@U)H2J*X9qf9b{dN_6-fq7d
zX5!CD2NDk(#bg5f4VP3l)wC8-q+$E2F~v2|d9$Gd!#*D2yxgn)f&Wpes_vYHiL8l?
zKeUedFFhvBdWMMaJv+J&GyZd|f$Shi$wSchyBz1fJd=4CruTumpm*@gJ|Iwj;KhN%
zK07;`Us>6I+Vj9Eu`_PTr&#*w&fg@pBXdH*oo0gzAvQJC2MA$Kjl_wlT3_Qb-?gsq
z?(8Q8lkAFI!oW?Ri*v)z_sAU&AU}ge^W)BzSAo#5Cw(Qj$)%I;u*}sy+AtI&c^mHo
zo;S>_0xA#T#_=<w3`zN_?<2pK2C|WD?@nF>EZmi0MvYOl>#TVvvne(Bgbc_OHa1y*
zIb*IZG5nc&+`HF3Scx_%+9}_4)qAIkd9aH{1Mc-)#m>jzC$00o^Y0cRqwB~&G6F8h
zbrK4NqJr5!jihGf=l8Nn>p_N{U{>Y=wYCE1`q<sMXwHF)8c>(AC?*@9oWFGN=P|wY
z0y?&@5}h%_U<9$$*S>!J>X^h+{8MXl=mo+=j2&psw}{JM!!^9ee}htE;ovYt#~yWg
zI3oE`bNZMJg(<wa{T14jqRmbK6nkCH6Am9h2xPmcWWhY9!Ca?8AUk}_4JDuMO9&<V
zWE=U}iyqq-X{7O>t_#iMM=%m;8c3{rw$&5NawtgOcb}TS_|zEIq69Uf(-wD1ANHQ`
z<*8KeAy+USOM=&NQ2Ua);J$eU!92#Dq)q*0UhzAX>@z``A<(2BU!X*P_;3*NTb<5#
zDqWMdhLY#Fg8M*%=a<g;u|`iwi0rJaX(|hjLh#26MqUZqR-0lZ{qBNm>F3k$5_ZO!
zx&}#8u}m#x)_h@1%ueF!LJOSjAj~&N9d-@=37#o4<{M>zYh3|3ss>v*Nl9{QU&VcX
z)dV~1({V|1I=^bJli<r(1c!m|%Jvp^?tw7?2g+%b3AX23t8n}zg+S1VJ`U}{w$$S-
zK5$OmtH0<Hu(jh;T4D9K17@-^PP=!?g1ePRJJesZPeAf?peg<{xp;?yRYfJ5PLNq&
zZd8gh)}WhK56{Dig+JBl_Fy5$gHRHG%FT2p9(TrTVEhgRKMQ;80RIVG=dt0)1rN?}
z6yy{X_?eiP%(>}uZ9J;6uG;Dumg#HbKKWdo($8;92#_LiFBw@4TMI}l2kM&OD9jKX
zV`9LahW-c@b%3KFzOr=tNzTUrmWLW@Sj!JQx_H+if^QE0&OlUs!80Sl;S6%6xifyd
zhGx<R&|N}EBRwE-oVfJAI7Yh;fSIv;cK7I^YSDjqUZ%JHCPVzCQ>q-7AadubKx!$}
zSxnyQKG_=Q;qGqeur#h;3#NQNJWm0;5(;}B92X#~!$FB44Uj>$i$C+_OvKuG?L2Yt
zYHjVY<2%D42tBF!4gpii&lX=tV)Z1Qu!Z33W!RyJmbxJD5FD&D=Gz>A{dTC;%!>gH
zFoyB~QPTm&(IOay3|I-iJ?j`atD4g$c=;!hU1#f||5@|%JKdxU&dQt)TJaGQ6yVch
z{z0*V761!2eCnTW9M?I^jiqQYjd^o^q%N@goV|vt<EY_}v{FJIncG{31>mW(En^!V
zof{w}eM>Onx-LKk2L&w}P7`I+hf6Xb05g7lr1c@>(a(3F=<^RI0hMlZB!%l8T=`Q@
zCz*eM3ix6>1iYAp;GqpdTV9+1j19By(Pj@uuf%77aZiQAKOaEex_Kg#?|ePlxbLcs
vO`Na&&AzYB2Eg%0f94;D`#(aw+I0cHqmSL^Q>A+l@MU<(R4@O+&By-+ic5<4

literal 9553
zcmeHtcT`hbw{HMJ4=S941r-DvVh0ON0_d^OgeXONu>jJg*94V=97IKf7{QQ;^qQme
z62PN~fD#Evqy}jrM2M6SNGNY^zwf?r@3`Z=cgMKzk2l`<GGM3dWbd`-nrqHq*>M+5
z_18;olSH9V>kSRgnxRl41IYgx3HSutQJVx$5?77&&%!f27S2>h!P74{4Qv8XsI@Z4
zzX-h#w}?XRKpCDrZ4pYG=!*z^!b@$Oq8%^q-5|Qg`eK2nk@&-ujfXu>=MXN69~BYt
z_Wn}V(`R1j%@z+YB?a-k9QsOKR&M0;=rTtNZ$HhfDl79jytyQP#~u02n=-^#$*ie7
zTFzYl)^up+@x-_bquPf3lmN}xj}r^Jbss-QxE@OmZPijnp&ps*azs$5E%(-_!l)(A
z-EyOm3O_y9uuIr!Xj^eJZF!NUXP3~%o%`OE{@kZ({}hi<x9H;CSEV))Hq91Z|JK2I
zf3dO%W#DsaRYkdW-i;G^kqeiCbwAN%u@Bzhm5gT#eX$4OSGBWBePOIrciJ_odgmMM
zpp`;~*sVg5-v*KnT6v6j<l-{~f^h0A-T3{6^}5OwKVPZsCo^Q7A}=NGH8GM8?zC~s
zEi5$gt(iI>^&qrc@H)ZZLC=my+r@U(E61Nt@7%pEjE0`M?^8MQR3SEMI+1K~?OOVq
zvmcavqE7`sTNNsto2VMRcTmQO;z+ekyJ#>mF@f*L-c|GIt#~)(pt8Lm%kLBLC{cyE
z=Gg}}O1gjhJ0`B(tLyc-wVMxJ3=6Bw!jOwrmY6Fh+Ha53(CN}q^KTFA-`}=gqxXf_
zYN<UB;~TCD_)#k%mX?<8^FO{-tt<;SB{V8gg=U`nHk$3*NY5}#?D+ioQLwfHA!xFa
zmG}C!8F^)q^`*1(=^R?wl=ykI?hg^_bo0Tuw+fLzPWJ`(c)QaWE)e(d**1MkCnpM>
zZuW_&WxiTkp(ARE5#>HT+_X(MvO*zzEX{|dwJ_2ZC{!_wS{zceG&gU*RxN9P^EPfp
z$aFiA9$@Mu>}Jfys6?Kg3aw!TjZe&X6}xdB>npVNRt8ww+U6zTX4AW?LwuXDM4gB-
zg{Z};5=QdFhf-KUJNdG^d#;CvM~P40$(Y~Mo`{Tdg3>jR5yR}B2%o)?8aA3NXbq`N
zH`s%p(slFi<5b-bn;Fe5DY39C_c5tf%=L**ZVjIf&X_Gf<NP^5V%Ot8XBHxh($n8+
zv3l=_ukC%Z-@JFNO3eT<VA85ikR%+Gt$U`4#bT*Fe_e?VXszTFRku>-2jlyWP|C_W
z9d5o6pRB(vc4TvWWi~0iIW=Oka(_9KP9Kz9ySA?uE3`)|gv3g1_s8fiTpyh8@vZCY
z^KUs^R9V^8aP?@p-_ZH;09LCHp)IJ^ScO{BgI1=E69ZO$NUsb`Rt!loy1U0;w&g@X
z)cZyVPW_y16aCbl2l86<(vp%2f)lQby0XL#Nli_qk|>@!dHMN$+T5BU_(?UlR%q|y
zk|`{Y5^Bgw#X1-p$HT6f-h4`JO5kMw+zQ=TB`9inh8*F==P{f@t~EY1(PlU;Ps_r$
z`(P^i#H7)WrcUyLBfY%~<+Otfda6SxkXn<By5-V?Dk|JES7q+Cy^Q3_09F+j$FH7z
z7q?+@erjm)y_|0#-hPjaVfAvdqlW2{zyhu@D5O6un=>|c)7#rSj%%DHBsK*h5j1(G
z`etduH!5H%6rH|g5G?ocdZ&4Ld(XtLTfScq{Z0&EI7Lx=D*V!6W%+dET=IE)cfC&&
zUa3`!<wNCN=2tV9j`(#aa&tyMCJ(85cXv#@_VD!VYscuN&Y#@&;G|&@m8!!ov$L~v
zfTg1y{M|ZX$kx`DxF=+Be(L_*P<h=_p<PX=AKeDNlA59LP5!v+{RmcSNI3QT-MbPY
zT`q*m$Gi2b%KO6)riT$DLUc%wWA7NgbxUP4!X=n^B`IM)zm}yCHvCv2!0q))u4qjF
z%QR#(*)1!T!_r%+6n>7?9quf!Nz|+<4{E-?-4n*Q0fTOvn0If_s${M#^L6H4ac0>?
z#~TwAL-6*R@}@*rZa;Oac!hBl=lp95rjv)g;pu5C-`U&S>-|1ZNM!$voer<V9NV2>
z?u+E~crd+B>ULWFcei?t{?K&X%yZ@q{$yVsg<@r5@&GqiOx~}M;Q1xbGUb?y5%~!B
z{_$3WMJ6jEq}&wOHG5r2Nhy4)K{klt5aK@BTPaxP*HN;v_8QXR;w|j$3+Qxu2CJ_o
zta!9FHUHi{$(Y}FIs|-D>n?V~(AyPsBdzfkmoGmJHB{Ls$%X|HH<4)$bz)_Kywaw|
zuuq>FYUGCt5+QYuO|3Yq=1fmdzpFAZ^Ying6U-udq6|~eVXx=7Oy<y!B_Th*s_Nu~
zJyXzG4hPNB3K-oZ#qmx(=FljO`w`t&76b$3Oq4TCwIDtpVSp!GKW-6^e)G=#{DW;t
z!AyaF-<XNHd7_b#5e%KE>Gk!mEBK6b{e-<z#-%iD<zy;$t}DA#@!v)5P<?wgh5@sZ
z#$28&uljaZ&f?<57kPOH3hl~G-BN>|Hn+5xlNc`FtTgtT5_8DQv)Q)Q!G*3LpQZh>
zVedYOEL2(#Utf$MpnHR|>DcR*BkfCkCS|w&!+r8n9CkL@bgQ7b*=S)2S6W)SUtUUi
zS&Mbz%HMxDy4B3IYV#6S<`cS0+!G;#1G|O9xIiIuWVhmiK{*){62|*trG^#EmJpbq
zKfh=>QhgzQ^I@|?n-e>PCta!@RtHavmmz&JBtqr1(>g_6`_)qNV{-u?&%{b)qOndC
zIcjb}frXvj^OM@z8)CO>2VcNjT)OmF^UxPv{zw%o%#q9=8t!q<DkjyW9?A8It-t8J
zd7!xUq%jmOI=_~$<KW+<gwHq^znNW3Uh<-cd=1uO5d%uRd&=6o=SVscCBYN6F>kLJ
z8r6s#Qb@?Jsj=5!Mi$r3H>l*5mRh!Ic|-524&h!tvCUFb{_?qVF`~C`AuTo2n2^<L
z%5cF7dzhv*ZwfLq&++>sd@0hqw#muyx;-iE+p85m2hen~Jt<UmzwAyjeIkm`-EFJ0
zFr28<S{=FI|AoQGrY=H4MR2KB(4t=zRZpHgx9hPZV}E<l;(LwSwCL5+&05*arO6N^
zzDJ;~^Gl>$TwGGrJfA9r4v0V<4hjw)=Qyo|H@EY7Rc+6wr~RQ7j!#x|FYl4Lq$%G?
z&omAidY};;k(KO0W^#g55|=c2>d)8(*+wDStL};xqasMzF>7V>pv@#DCE+uW78VXQ
z1PS_dq|-vP)$E7e3A3THflfq7z47ngJ7Q!Vb6&pOg&jHQ)CSq4EG{ZyNWYe#lLeP+
zr8r-|`h?e4)k2%%^APEWkjN$eBSzTa-HMkC4dZl|*ci@Sk1wXWP+t*i0j1j#miWDU
z_fGisqp{zwDH;pBUbOU6BY0gj++}19?kCf-kr~xno=OPn3u256x9<P49I{m>yyQsL
z*e>d_fD`w2S-_}ko<Rd_a4C5}!SV7_G<->}t;F2c)>epTV$kBaRb3t?a&W_eefvI#
z)Xr>2CN?G}h8wp1GR3hF@*%gP!urdXFL8uViCAub3pD2k4<4NK8~g(nX6Dgr?{i8@
zN*cw;fx<<{qKM@0`mcMO_ZE}#%6oRIehoV!M`2z45H|Bkqx5={Ord?H8NFKZ+(Huu
zXC~_q__zsO?D~nWw=$!ryu{1#>Y_%J7u8G~QF)zqOJxsgKQ!oyjp?B~-bGA9?b7?r
ziQUIESMVzH>Uw!<$BVcP%Hv~g=~Y7ywP#0EqPoHlp`zd267DH@GYD;K8m+>v6HZpE
za9C%ULD)6tAV>F_q-5k5RT+i!`o;3BQ4+s6-jP7nqiDB8P^zc?<HmW~)1ZdC7Nu&D
zA^7o}xLQ*tZg_b2rsA}Mf(wrgGJ4#42UjdDEOOG*cTZiqGgbt63o1QJ`fIl8)pu*u
zG&E+~5)DI1*}v^Rzh^l|q6+#-UQW&y)u@{EiV973<Lhae6M%?+!s7q-=Jn<QsDyb%
zMW$<Ik96c&=D!Ylg+i?}vRV7%uPbjmzJATwbntQxtXutu@=cxR#89eZs{}*8ua%VC
zC>c|=y$mZ1Hbg+=hsV2x*NSaeVWdS|o#<_xO_oRk&yeDYePp}i*NbR{a)sU8P?EA?
zZ<*PTg~(~NZ+!uV!Jo0PNyRaW9BgYsUlmmhN+vyh8m#TZrf1?ani7>cq=?C;QCHyu
z8q0jguVYirqTK0)$O8usm|eM&#@iRx#G1*?$T-`o#kn+%XN3XkeWkEFvr!@9!r{Y*
zU3>Uw+HBSF$oZSt!3|`Cu*KIdEgRz-{53CybaIaS^lCz}a0~3VkUmztZ2&3l4%L@A
z3<~4ncI^{>gYVw?c`}NU_MF@G8_bAXK=D%6&*?i0=&Cb)I1_qaP+%YwbGrmQ;lROz
zL5uT#=h831Na%n5wmb0q*H<-IiJGt(S2Z;?n}jRS8ID#>R&mS}R;F82q}iNcKwz+#
zlH1(l<Ks82lZcQfm7=)4mFC7z4_$(Y!3r*_6^wg1z*I+@c8a2}{Jly|UEP&42$ZN}
zUdI_G>DPa4{wpz9<xf3Lo3XIAe#ZG!tBk8-hW}ABI<o-zJ3KR`d1Nc>gXVr}2TP+^
zB>lyUQ;<(b@zZEp_$qTTc5U|^8mlD~Yu2Yfx=4DXm3>o_h#Urp+AC|X`j5{&q2k>;
zpI(5GgfemO>@IkX<Od!v1F6$6<j1*$Z)U8Ps66&)GXL<qp{e@}<u8d^iLs*#;nCVj
z0j5)-k;qXBFkEZ$tTlP@J%2Ct0(>W0%Taq3R2UJY%=~NfzmCDFPSF}q#u4Z<?rks6
zdEB`1p|e30rMl1Ma6R-c4==Bd>5-N%9UU2?qoe;^PCI&J$7U7)^`t?U^oiwm73<@N
z`9e!GGyKG-xwW*J;h6<^U#Q<?T!0BDv6{I27A-d&qyA~(5|S9}GU~mxON)9!va(I~
z=8FWkx-SWMK(`WUjHM=?o9@h3L%O)E{R?i?im<EKt8NO7dE<N$5(P3t33@LckFQz(
zv6!Zt56Gz9OGsiRQ$Bf@_hmV&KAWv5^<<0>youH#jjh;FFi=|y%llKN=gqlq?{2FD
zNF29u*~zI>8K{O1+Ee6V!&SKEi$F>5#m7g;uY5128R3D)rlh32F73iu0%A7U^Rl(o
z6iSmBG_sQ?Pi~ZoEz~z0PqK1sdwSTepiwnwiN`p&QBp}~5@@A5_SV(HoE!sSTG-~H
zp{Y=*>?S=V*L$t({yd+)m4ZQF`s%-JBL3tHI243K2<990iV%>_tkbiTU5~1Nkyz?h
z5Ap~d!3|yx@=(LNKQHfGjY6IOZ+EQ!mkw}D>aPIP?c296X{|+}&YbT?IpG#Y@cj?C
z9l7R^e3@VJtytF)`Kq`?1jo)K>@ft&iPZLBZhxUKkTR{U>rl7e9P++(@W6qM56<)c
zUaNAWm8gXvDmmYp$N61Q?Mp=24;On`y4f`BWPsqU;_?e%Ax8K}-TrcMR6W~BZUAHk
zeM;SO)iTd%#S*9+KEsU^wpTeCu*tK`^*5ng{{8J{`CDMZ5%M4f3bHU1<&eNYK%6h@
zTt!gM9}EQRG&f4_mx(jLqiM$Sy8gokI*UKvRwl#fP9Ji=Rqc2ck=rOPEl)=Rcc4&r
z@06&V-rxj>((gw3GHV@R^&x%P-Q9gq5rxv!ac7ECo!^KSf~3VFN8yS_;VcFQ!gce2
zx>8iH{9g2r)5;AfF8_!X00i65vj4X)$;>wDE&G+0mmA~nym219sr#oG>Q;NAdOc_b
zl_~IgXG~oI3U%vt3GJ4Cf<o&2*@8sqvZ<Zt0hO3?7Aps#xUX3y@v){7Df8<js<F1V
zx>LfX&fU)r?%Q{9-$tWw`&t~+KdvX(gT#8J>pmY@i<D)j-!8}vAez~-VnSo7$Ctc<
z-rR<(ui9(x+uv+$($a7q{iMbs4o-#k7fJgnMDoj?jtk!k))9rf*7ou<83f)htMJ+C
z-nTHgKfd#Yr8nAkQMp)dJLpIPRaZ>gjoFe`@<E5S=!>JDN+X@fp=ckSJC!$1G&6sg
z?slS7FWli7e$paL2{OVr<zdP{!bo!9q2dNAs9^?HB64|wr7Wo!->@<6Yzd5xxDHx_
z^HcSDO0B?yzacwxyBrENYtsEPSe&5vP7L+P!97Z%x4SzJxX9jRNjjjB4mutGkDQ`Q
zgiZ}MRLymJe(CQonwtwgY+rFPq%X*-$W;vG{27u8<PJofnnq(tEH;QZ9rrdJ^jCvZ
zL|^VkMU9<Nq}%yYljg&jwb;DeTsAiuQgxPI`-^ASYnQ5Fym1N4e;6qnMAY$07OCZn
z7a$f}cQLfz;8~~>PCaGbi2<h4I<ps>F|(jTCmRc8W;+Y*01H_<I25L&Yy(XJQFE<|
zA&qZjuKxjA+_97EeqJJKb@UsBsG~7@S0DszW4VEYlo~S%!2dxG5ZbZve99z04aV~w
z#=}{yH-9_pLh?&FpNiAEWDG`!_e9;N_u2o911LY;by0PW5ff_AuMgXm-PikoLY>+?
zYugS~0n|TL*<ut*^?JtAR#?hoimSGuP-6eTY;4xv1QPDxkUBm-zA>S(_}|d~P0)rx
zr!J}*&+~HFgG~C*rm5sB`1Uq7LdT>QdYUfT7lAGdwYnshO@i|ga-H77tx`k4{VMG|
z!yu<8JXE$nv=(DL!YU!Khtg-fddf_mYxqvmUQPESN>E$6zOs%_dzMLeWq{j5<z^L)
z-kZ_Q&CPfPS;-iGw8`>Tb+2cVl9KR#&G7e8G67992fE%o6dNA7h>PmxZ5@Xq@t(8c
z!TCf5QnB5@-Su*N3?CbuMfl1X8nUb%#J@iBwGk>PGZ{7My&zgG3+q<28#jy)jE_{F
z1x@BccrP$Q<ZSL99!3kN_IyeG`{FmEafH)}4rYr?e8aI?io!hs{q^ABRS=dGV%>N<
z9U*3js+Ln*>xgLo>MGlyRx0JwZpq2X6^HpD*?vYC3$1U<&U)DF_}AwD5Q8<=UMDt6
zx&&68s=r=SBeQ0eSrLKBrA?h!P~NgG%U+S3&`3zTxNk%dN+JxoPh+z9=gvH4g%x17
zVQt73{BwI_0$N>ofzjJJk5U32@(iPSh6z8jksD~|Dl-5cKpXq&D{DOre&uD`jb`3e
zES-WuEBrixOk74}R!geqC~{2X$LT7Z4eKNl6<R$5|2294h1~t%af?x4la8o@Q;mv*
zfFsX;;y`>UN>R)IEFv8O3bzF1m>R)%<-8O@Z3z_Zs(OJH&bhz8zZGbGin@17@b|B2
zAPp-&^!4>6?15q&Mk2JixQ17~`8ZSagi|m0OP6X`t~+p&F4w6{ZFe9Sh|dobh&nXR
zl+5fbyIsi;aDbpK0r#ZMrL&_W=f#U1cDtnvSfq%e^Y^zro~-~2%>!HmEz-Wa``fiI
zXJ%#^CRBs@W?^ZWG!!@8l5;5?aDSpg$R{;MPHyggs1XULc08IcQ!*@eqg~e5*O!74
z1@*_B;nrUhcCT?ri@2|j==XlBJA-xovB4hpH&P2-+EZFlw;+hq$u=hoo`BS!K0fY|
z8qlhxBLM&!P*}_Ry+Q{Yn`D6LAi^RvW7DQh<6~nNHRa0`lvDt-kArjaPbLX6P)=oK
zm;o~`doYXy*OB%%RvJ+qKxhHXCGk=01C*srva)<lw5Oh!f|h@Vk+E?pbYBQYP{e`_
zpr{w{CwjX;hb@6}u5b2O&!Ukj3o|UYnO!guPyTPJ3E<rlTGpRWzqDNshbBCaq;+?9
z0~iB_?W|+u=;7^c31}3CvTVaXoem0bLqh{lXMk~*;KxMjl2#Kzo&`n914Md+Y7uKT
z#5Z&RhylTcbLp6wwKalT6=GTFda)*rx&;%5&~Guee>vX<J&4U+S>^_{n`pR$`-6~h
z@NN4*Kt;?1*zFFAuPrsa6!PsFa5;BhUk2n%DY$MR!*l$mTS2>?O{PVjr|Aj{c>A(r
zVo9*{2TEU^&|Ul{?b%!5*LiKYaO-i`8=&qWFKvol5l0ny3FHT6z%Jo20mST48kyEY
z+y5jf$sMqK=QX1GKg2?n(*UiUKZu8ZN>Q^dx$zjxOh2WFX+^16ODn5vI0R(8A-%|<
z+5+CfO3F=3+v(R0a_c*H-zs(%apLW%AK-DHTbaeIT-Uj~zjH^aGAZ<TM7RTO3*Rpl
zoAMvEygiw<%+n!Fou=Eia5OSRBZoiyw*6$FK?^~}%FplB++<(R&<PMSK$L|xpDz;&
zlqL7ot4qj!@Jmvjzc`-khC;0xLGA^3zM&C2ZcYYJaIGH~UHlCa^d}eLaX>{A0LAR=
z?A*CDX##_S{A`&)7h1pi-HnVb>ksD~<~;tmUQYc2oXGao)fihlyP)9@k4yO4(I$Ij
z+7dCkxmHeC%eEUO7?w#{u$e$O5qUJD|Bq5%rtYU_N6!Fg1Ax$u2y?i_`HXCnx#~Fh
z4Ax8^j_)<noMc<-ku=WXs6{O=;-2d;hymaYFK0k)VS}5}j&Lz+>wSi>z&?M{VD?VT
z!gUG=Q46w$^ck8S1;LJT>RulfdKd1f8B7c_w@a@Qt@(|!QCwUcjIB6IVjp*%IuQ8s
zUjO)%=g)b)0W>!77!XY_L*2;e=LS>}tAfpN6y_tzA5AJjVI)R2p_Uk6TZf|ptdj9N
za_Er7nKRLYQx%ts>~rhD;0$L9`w6_t!ayCF^%Bv23Xe8!+619L$_|4Q1oelp*0V!=
zUrVr)o;(=?Eg(fZqzLo{HP6mNoX69DLyDAyPF;2IYuI$)@ZqE<i#Rt`P>CfaqWxR<
zBNikkguGkHNqhea!`DDO6f)Pg+$XhFn~V1T`qydTYFkrPHpfGHo#yd)hKhv|Btiv!
z-DVOCsm1sVa1huFEV32MO)10i`CQu=5++1fC9&Y}?!frZOh#X@j-EvdHtcf(;y?he
z>gQb%1c%^cp7m&6ULIlu6*|>90_Xkw$1fY`{oHkX0ht}VT<{t0IS5=BA@D;t_w*be
zFlYFH?oRFZWkv7gdm7Y%RfHHEuuP20nYe0zd~R;pmkX@ZfN@yTtLw;H;u}DsnH`9h
z&Pz`}V@x<=vWVvDEf47hg3sy!g0i5SQJZz=j_9Z4<NBbn9H)hw`ZKY7caWAluRXiE
zJ{&P!sQmtXa4Fc_<w@>r9~Wl@g}w7yG9c0Hr-y71p8#0MVaFP4@YIaL>!K&|g$^Lc
zfcx~6&(RnbU57fQ`=L&@0cxn|e^Mn>&w^PgmFJWsCULS~d_C%x2uVat%$ngwNRZPo
zm30hF>&FSb0Tf?wBl_J%d=&`0l4->B@d3DIJMhRY!Q1&g?UA}~{~;D}vR3HxKtTt&
zK?L!&zW9io5fzb;_zM;u+}Hw5b^Oh0#SWl4r;(cwPKN$?;Zr@Kj*$31(}PrDlaG1U
zg*I@vy-Ul=4#-P^r3!}S1pT>hZEoE%k0KfSHXQVcg5b4VWY(yv)_)@FIl&TxdkQMg
zKa|e}XBxzIHt?!WplA_99CFhNa7Q(l<|jVnUUe{Bm!=Y;Ixx8D4c^S!RI;qt(tVVe
zn7!3mXwGw|XLZRcQyPVsQKGTn&Fsj?NOzg{^Y;mgh@ik8j91zF_bI(y_4U6V2Xcs5
ztQ6{;ZNd-;TD(^Y*bCCS^Dm^JqXJ8f{^fSsZIsxV3p;-O{QiMlX&{^aj)_5xXvffR
z;=mZeI5Q!kZTQTnI<UzK8pS0J<JSKIgbgkBzY|jHS48ZDHf>^3$D!Z>-9Be}w&-`~
GJO2Z5itqaX

diff --git a/reference/Rplot010.png b/reference/Rplot010.png
index 9e860b0e2a3ad097e0d3e2b739e86f1efa4bb5c3..b3e0b9b5216cd7ec8f3d507916c41666a9377243 100644
GIT binary patch
literal 9577
zcmeHtXH-<(mhC|lR6rC36%@P(sDPjnB=e~R36dn|ppt`<3kpy_11caHC5dE7l5<iK
z5uwN>85Jm?fKpOP6-D(r-+kR<^t=7WyFdC@_jSO*s$F%?K6|Y_*PL^ma6KJ$w!PeY
z5d>k=xT<1+APiIJ?`|e|2RqOd4}X~KwbWJMFZ|oQQXdL`_IO=2^F|OB4)m9yZ#-lR
zK~5kVDwhlcQkKSpyexe;+kfF?gE|zGhpry5Xc^ISK6;{5by~>$#2<I1v$HsvIK@v!
zo)GT-`sR2~@7)81kB5{E-&$trsqOt_bYSW611`=v&KzSc1`DU;3TLr#BALwGyeN6C
z%z31o`n;d)nj_KWQ8r0fl&vR?Un~2S;;n-qud;%P3<x4~?0_wTu>Q&4a@fiz+8Q%2
zNIWl~;XV7Mmqon6R#C0DahpP<tmP`F(tjsUZsi!4Si8#{*f;R)&a)jP-;?|;SH|sb
zTsYcj(iBg#etA>oVNkAu=Y#^56g1fxx)tFWd<AbE<bGZ)Rz>ojq(PMa3AfinjK>}`
z^9G!t)6L3nId5-mzLfKQ^G)SjImuTcZfUfdxJgLIPj|$w=0@;b)UmR%qToWeGk^VP
zPwr+$PPRS{U}`AQ7IIL0XGfaPO&^Lku^uL^;eux+%&Xj9w5lNIR+4rG&?xJa52uu(
z>Azc<`3x0!P5k|<v;5QTB96;ThlY?kqb^PaWN!a_q&zopUDrr~7oQ^Qy^_BAS)?Sd
zveKxsvQmy%SiVLIrPoif>MGMJlr~DE^54AClyK}GsB#}&A>1rCqb)irU%ztYzFHJ-
zQL<-~S*@i`U%hzc#~-)be%!v6reLO(AbO@ohjcT7PuV(CEuzn)nPTMY>&v<I^2UYy
zACdAp;>}xkwksa69A0=+P%Ql0w^6Kaj(<Fmkor=}HQ`M`!Fq>oh=FG_t!n#syYj-H
z2X%VpdNbGi)3+;d_(dv*XU0-uRx@qM)5XPQp?W#|R9as1){OZ5Zr?tQQ_h_UXR%ch
zyip>_U7ErTx*MT;D6gN2^;!9kOU)^f=I474H~rjYWMWc~pRW<6xRLbzS3oPX*Zc8m
zW^YCI%8z=ZQUksL=SI)>_>JFgKVG-ir)6YiRWw*&!nZQk*F&AQ^$=-!Jj<HZ?@XXJ
z5$ES`cgx~OMiz3kUwlssT9zfGct<Hw@~9Mia~gdMLS4%#Vk|>QNmjzv1#NE<sFv2=
z-sSSYJ{%l}1+|n;_GMRGd(NXAvh|AS@8eUmvT}z!o9tO<DMqOBoLpY6C8TX_rqj0w
z;}G%rk<n2r_=<>0u}?|khIxAD9IY-Ybn6FpcG%yTkWx=sC>WQ*TmF!fm$wp%z3o2{
zm@7|P>wnY`DeXQoy+9;dsjEMtwkvOcQ(DVluWbw>@7wfk#H%|vWCjsZ{Vm};7sq{K
zd8Az8>X)iUErsLn96x^C<K3ToJi1;=F?K5sY{1p{{KXr7UT*fa!LA#TTiHXrdTNEP
zy_}ZzoJgSukR}>+VmfbJxUw8eE5T=MY(6_GF~)XrY$~QBYqZ=ks(!JoGesa#C-+gq
zHq&S|RX&Lrm?!mcr1&!a+uh<)y02`H-?bE(UK(Vfcdez^^V(D%O!~1!_gzQwwRbG0
zcqdxk3Ad=dlMUBceta_0OT@U)Yjw8EyIYoAi^F!)HgTcdq03GFM-JNrms(W17^Qc*
zczF2fDy|!D|IiltAyB)#yv%@KUR?Bng!dpXkJGw|b8}uXV`+*(`<&#{0%wo<)7J6q
zGOmt}xv>M{DeAm|S?KaSY*-Z3N~r1Ak?$JB<H@!C$2B3AL6rI2Rleq5A9=($!fV{5
zla-gKlk}!~$aG2<_Qa^NX_!sWk`(zad$4U!iO8X-%hVf_n;YXFTQ@f0im(=%*((Ik
z;j)=9#xYkqw!Zm(ZQ3JqhdhTs83tar2*y1>udpjh&TTSFkoBn3dY&RJS8r@6PIZda
zYwId)W<g}LAkXq4pUD$JC3k9562z?DoVlLEN?e(sZ<7gh5|!jJ)Fhs~TmV}cr%Z1s
zu3whxN;-d~dhE{Ak&zJr4SD}%!<#p=Vec_-jU-xQG$d|)(c_QS1B8HWnTxjx#kSza
zOC25w<mcoF(O0Zs0L23$=LOdxQ5w;P-iLn9AyW3%C)}h77K`1cmzrwB`fd_cV}wOS
zn1f~g@?hH@)RDPlQSGthPZ=t*82tKGrcgGBbL7<hqi*S>vD)kCO52y3Zf~88l(GMG
z^kKPY5E~hz^jzwpw<vSvajHCJF7pxg#e-R1m-J%`kP_ivb_5TZInFQFlZGU43Yjk?
z9N!ccYO}GiVHQi<B|XMu28V|BQ7Xm4Y%-E0onu0%qmv8K>b$+fj_F0=2Tutnb~>-k
z^)4(g^ZpFd?YLo(o9g${qiOTj63iywP!qCd;FW(Qz?gvmhrWqPqCvjlcDlFD!BaZN
z?tFADX*9Ok{A^O;GALYRUb*nRdh?G%Biq)FDYw<qNxEe7@>>zz0I8Rn#keDc<27fL
zCqg4yH#%C~-oxs}>Ey2Fx=%JWPT^VoCwZTp7&@{b$Lb=lfMZ#^@Mz@JBa8)u8h<$w
z1@&OG+7nN1rejm@d_EJCRqIVy(6XWQg(=&O62Ce`4USa0vO6z9n5?1{R<3SG7Lu7;
zAQ4<#U41ua5{VF>I)H?klzx$thS%4UrO(86oQ~H#6MgkGcZ3UdW9g-G==Lk(n)T4V
zT`VigwnM$W8GN)k^(#t`O`*C#sB$el8;x*E)Iu@X`AZXx)aO;yzizdwtE-NX7DT}=
zVaemUkcVuTVe~y>aeZ9pOq{lm?~jO!vpA6o-G>D^IckLT&?b+@bz`U_OXKw>liM5B
zEFoQ9<fYc#_7Ruyc6zRd9F{XSIx@zL!=gWnwxki9yHjM}6c-QW>1PUPq*DfR*Sh6N
zd9}5HJtfqk>ZWY@<(kY%-AnB4z3O~L0B8$Yu{s8-st=mhi#+GiO7^<fKlCi-*Y`GV
z<&cI*`DK~G!NK`&kNAd4+j(I_2rK?<VU+vvOv2E}C|f-E8w+KNplst+Bu{F%&bKwi
zJUB3r_2kK3E6jX$-PAo6io*M^lL==n*0RO18d1vIHNlhkn%h6tV2RE9b9LF*jt<*0
zjc!{ze0<j1aVovg#0D!BIs&UFh+7@#={YwyBWC*c51xx2hA>ARz@$_FuFchlBd6Z4
zPu*vCSsJN?1U3Xf7Hj%#ufe^BQBK0jTV5*EpM%_pu>W$Ftx<R1Y}JluH`0E56WsSM
zluotjO1>ygXF58$HG6UW1uj_aRC;jPuN#b`70$6a*OGH!*dpp4y<MsGTO@Ka&N^_0
zQ#Din+Dl1|dcUQ<nM9jn7O|Q?Y^WpnLUnn!Qr)m7wJ~|S2dWod#1R4+x9jI~l6TPS
z-d`gns|s-JK}$STxya($S#DzKKG(&Auf)?B7!lhr#}))Ra^gQ9WNQ3nWmLAt4kJ@u
z=f5U*#3{7Q`_u_|=F22q6?m<}f8g`QfRciO>n0{9>Euzi`>@oi{0Dsf{LG^`PEV4h
zFl%#LeEhp(M@GyAV>RH1sK3L=*f^)G?1Hb4k7*P~4TXY%%_`xe`yBW{T*r-p{(cAP
zD}ZJIJJi%}t)73J)c-ZdfA-~W$rd8M1V3nEQ<RX9pts$qjUe|p*cR?>ERPqAdemQI
zIV@%fOh@l93-ZV5U0hSJ@UF98Ufloa55z1cD?p)`LZQ4$I9_#CfM4T@8sq31nH9Hs
zC|qSarJPq#uZh%2ru8gI;8q15teUQ2ZKAIV_>6MFaG>>ERe^~lH`_Sv5g~Av%-2}G
z9+F!hR=ng(9Gd5&|9+H)-7q`uJD^YL_HW93`SRt~CP4UefmE+|dniz%_kW0C&h~e_
zQEpa#^hp~TD@(W>$he}QTWi^kiEDusk8DHSCmPIvyEy6Ne=3yZjY{4At|E2u;^Czg
z=J%zh3PAHpO$#UI?<xu$k@qV!F1E~XdwSRvXn}dDy=waXIY8BDf$V<g%Ia$7%Eh;E
zPC<@6{YRf<7Z;;+Rx`JMD0AnV*RL-}UsXv>O@$Yq$=C>9MY*_kdw2IDpa(G>H#G2A
zVo~+l_ow0sK3%V52|l0C<i-V$qcI+-6!Br7a{W&+o(W^vxA^ESNBEx~pB>GV^`4!Z
z2qFzS=caNEs4^n^<?c3}R>D$zuB9s}etNnSIT80;5#{~+|8@9h)<8w8@f9IZSX^9u
zy)3Yal#J68Z6b*PwdM$ijhE*nDLhHOQjJr<#b))mCkjBs+w4oXq!TnnxkTnMZTrJj
zzC?xtp_L_Q4~ZpAP_Gb|S+k}Y{JPJ?YsO=S8z%0L-g#)BpO>d66ss?nS_NC6k=gU>
z#tTB=n>TOvj{9jCn3=t}@It$z`fO}RoKK_ur<psq+Es+(_gaNHlqQu87Y6z>8LS{$
zBwkx%3}3WGx1MOX?983o9`|H#v1WI>K57fU+o<61Qu(<L?^jH5Gj}p-_K_X2^z&bR
zKa;~4kaK_h>+t_sgZ%+vbl}trkCeCUdQfD`+sQrUBUjYjt)t2xeL|cI3F`@G3bgA?
z6vtOM_gC1rJqaP8`Z*>G5FP?~BJp^_1T(W;@bjs_wBSkh@v6-xzRyd5J{;}#&x1p!
zLruC?70Mm>Il@25I+ppB2BbM+Qa8HKJ#X1DHZf7W=b#X4UC-g>->MpYsMxdcs1?k+
z$^L{y)fC{!kvMdpL<NGdZ-(uv!D;e~L*3|^dz}n9IXMNb@GGrYJ^Rw6;q&?y7OrI;
z=L6GnO+|AcDa((R4KIe}GILGMbSAn2J%DcwyBTXGiW#}Ox^8bN)8=(!Ca^E+ii*zW
z-F$SGBm8vq)g(wT0QiN41%tomD_rD>3jf05V#Qzh?Fw8nLxBrU5U(u+KU#ImK=C!}
ztj}i|oQZXK-PB~gw6v5FgxFlao~bRAnU=;|eiZ5y=hHSyDz9mnV|PkEP*}|vkGlCB
zrC+<iUg4bK5n3hS#6Py}K=yN=q@Ut~dUkjBIj~&*ryTalv~<g^udN;OhI`w`vx_kp
zB$XECVpDD!<_$MD_ZkqRgd1?j?+R_hY=VD59z0~@831AiRCd08lIEeo#E6{uvX@B)
z7;S9F9Ej%rw!=1sKoV#KW%`*xHw5|9xT5EU&Gt&-`iyv}Stw|hO3b>8Mht1y+X6w3
zy}8G*4R!Jr@HHdl%?@2)gMu0ubRHLuAc&puxt5uk8O>}1td6UP$Fe^+1R>3?F>S2B
zKU0NYzt9XkB{$aJtTKR(sXAIQ3b@X6Ya5%AQP1W^$yf$tzda*U4OwuuBxJq7yuwLe
zgLwxKBE6r2PTyfi-9E(nld?_(J(LIh2h0fqodF5Ea{XuSD+>z?lou}i{t+cUr~nh?
z+N;DkQR$i}q?2O$=C8}bm&HSVvCy{3&57Q{hN}qTWqSHI#~P=JWybQdhlYVDS7&-R
z&uw_$X)jYvK|z5b_;OyR-^4`_#P-#c#MF6*kJFO}Ty5Ol-5*srnVAj$4NJ@TiM2^1
zynuuU%;$O<za?&*ZEyxid$m&{DJ7N=s3{byYLQu3SUBCTLaP7rVAE8z(n->M%_e>g
z=!5cMhufmd^?qMNX4}bFpqs0J{kCD%?xP9xP_e}RzHBYdaFu<5Np8|XpAv7MPVt_F
zo$ResYgs%!x&ifn0VJK+{L4$p((W(C8rS+_iidfo&hb8t(T-Flq=1^ys=L)=kRLaE
zKDz7HmBY3L)Hb*bV{Vmg7#Yhq2hfA6q`b^X16Nn#-`HY1ZtR;Uva6C>Z^~XNT~m5{
zdc6t?MNF{?D|$)11u648oW0RMAbX<foI=v=?ia@ozR(t`V5n+HZTs<yL6>jHHGZ_p
z{kp2E>Ip^$#7joxH|uW*3r^YP)2(u#H|BLd&i|f>0l<r}%7M@Er3^^R&qJavo?h>N
zVmT3HLThgbdrf)ybub${=$u(v;*Ex2v1Gqw0~i%PGDR$DwmxhfkWp7(KR7;Kgi;#N
zQX+rP;8sN3tW3qkIxrh*E+McEtP~$QjhyHrjUbl`Y{Qf(y-_aUg=|Jm2CrU|l9J*K
z5`vMEDR#TF<`q|SQ|>%5x3HKAOydohVo;<mmjB0PHDZOFi9<!^^?pSu-jsRWP`NpI
z41$C)n*0=;1M>?qAiuO!|Jt={PuhQ&H)4GAZ^4eM*vk~~IqnRz&tKc&9z$fCJ#8W>
zXi^zCH?mKs712$WzV?^f_k)c`dTz`ccb1|DThXeO?;QndN-?+;PC$@Xur{a*2AtCD
z%`k%OF1p8{G@Im5;Y&0wvZ#if(0R&r_B;Cp1lb?<H&^ZmHPq>gxBoj@G&8dz$dmgB
zszCmS98SDNJjZJE&z?Q|Qr^EfLDY<607ZYzySr*KGA@C`*^i3tsw!!eYy0-Xit0$t
zv4CI*SGgRn@)|cU@3%O&Dr=0vSEXc_Lavagf|Js?RkRSTS^H;OU*AhAbqY+4yupRH
z5T7K^q)3?6CyAEyPpmU@IEAHpW4?!<8tCim^K=leo#xQ!LzS%RQ?6aLi*KQT>Sw9}
zF0iH%^6TpArinrvdya>0{Svgfcy)b!off%=kh0uJw$2Q{rw>AHXlN*=gcz9ix;GEF
zfM#8B+b_O~00j>}x}k}Q<6jl|6d^@eSoq7WPd6?wra=`fA1;J@evO)1Wo2cPo8a(0
zN0kLC^Zuh0=M@!oxdD6pOFJg^&HHHJ#{m|l-M;GeN!|CGj#l507xS)7Ny99hL8k&H
zu5*7*eDL_cE6I+S;(AA18j1iNH~OZP%Ahy{0A>U+S^ihW`K$K|S{r~L45GlAkig|W
zUMH3JlnFWEvm=N}A#mk}BDhnRtd1c_23J%1Hel<{e^y!kFaO+Qb5ZW7c4kGKY_5--
zRYfI)4Scp<v1XGDcTp}-$WPlq0&CzSRQLt0N{;YHUv0)Fbk01>*%L_!6l#A>qCQc}
zywe@;Gww7nEp`@!bn>Fa{5ZA9fK+b3iVbZU_0M-a^0=ed+=&_dX$yczk#CZ%@=zWs
zY+HB4R;GGq)HB4t`vY%bTC85M^j3yz*zr-5hiUP9>&8)sTC4j-?i^E>ab8b17pvn$
ztKQ4zY71RosVpCM)4+>7v=qaWFf8vSrtF(vOD0*0<+@3CmImx+lH<}6Ox1ZLhEfNi
z_U}#hrC-BxSO29z|0w|uBI6NXh^aTBdk}gzf&|OwhuKi=!8MgC?~i(xb)+V$p_y6!
zS&OQj{|5u}STL(y7kxK|9XrX`)WF#nBX8~QN6OA7ZEcQ^kH=(Lik-r(GBu`LCPWAr
zkA4fZb}D{vz&(dy>0EazJ>H0Sgn%2AH_&$%kiS=fe}lETv!PHjsnz3{>YE^dI_%Aq
z!4fzD*oIbq!#6KQ+<XnDCUpI%+C#-46gFSRp3>$0MRkLY32ozrx5o!h69wLj@?4-+
z!Ccp>oBa@eP<=~@2ON}PB_2ggMMqtmF`y)zuzbCLel09FNbHs8(DK*PD`;~{i8_p1
z0I$**{J==Xjjth$&~*d>va59ZQn(5~=pV3nC6Y5tL6Hm$4ob~+^X3DTf}=DrJnXEf
z`3l@g=$%zMwC@Ao%*4W?09tT2E}$cyKYuoa3r9x&H3C!>mi6H>!T-N9(Rja-!-zy8
z+6WsOG6q+FJ#f<uL+<qw<+?7({WaDDLDUNWaFq$#aO+A_l>hIyS^~OdO+N3Ec=2pw
zarpXYK`Ia~RD*$SG|bvo=7{4pGBiBR&9;V9NY)aH{d}t&JXBPX0duJLC!yYPq52+6
zXy?FP1DL=IL%CFA;dlchqxkjHcU?U_O{}a^6>z8v2bltr4n!HOYeR!2C_I{ucwktO
z`$&Zb-qJfFEqDbrA^lNfooDj*!yKJd7g)~_a{c7|Bz?QuqvjhEj1Pixot?b}ZGq3=
zuvrByRo2bDemXEV<^~@|&!M<E9a~4+7%fuYGd7Taef^6B4iqM8tnhf$T!IF}064QA
zikiv|JAoVC^=dQ@L?GHy;AaufKlz^G-Q9aY{g|Di+7w76w3fpE;PxaT2<SMRZUBtw
z!uH)`s536nE%P4p>~)f2kl$R>%@d8(FCkx&&JQ`T&b<xV!;OTIcUa$JLRt`p?NDfY
z0P}EhcGee5Ea!DJmG&6RgL2oEDsKX<-BR9DUeJzIUVqQG-Gh17G7eParG#T7*IDzd
z=&j3!8Kd1GWqj8MO$xHJRe{>79pr7Ws`tx>S7?JBh=1mU&;o+&q@m9O2tFWe7&}e=
zIWK*&=a-oiU&4yt(Gi3+T&|8vwDk`Mb&vHB+JD@JU9JtTvrhCW7Heoeg9D#KU9Q)K
zg6-nzS<QRq+`NxDxQY-tFcw|fBaefeCr^epM}naV#^x|4>%R0?cSk_~I%sVAfXV1G
zTz2#1iU}POslsje!gwg120DJAJ5~P5?I4i~&xxniwP14uE!V2M4i)MP#U35P@AC=X
zSH-S{t!Eu%x05BTK}*KUvuQI&NIU7(bzMax0Q$|<APj~=JB@)^hCr{Bpu9ngrY3zL
zV+A?F^=RC!D>I!^?jw>#rls#Naeu@oCK5tz<hdgre)mryH!rz?2@BnJu#qc!Razkn
zuPgB4LKR{>Js)gz1^tpf{ItzyCf?*tW1|(?j`3R>$^Q6JO-fSos@!kt290c&@0pmf
z_iffzW~Qm=5NU5oUS3bx%}&ve!g7R^w1Dq>Dl00U%`qRK!m{E2+;i|U`6Cj8wmzyS
zLoCp)A0SP)GCi0Wy(6*fRwb6|hutVoAJkVro=plS$iK<YFXeUpilzjrRMLNcIJMHs
zJzrhj3bUh09}ZVRYjw(X>Ey2TkaOfE#-;ZLlVVpmZE>qU0Lxl2ex!*aFkC<+6W_6j
z$NAVs#i+e@lP-Y9s?$)RSw?`+pzG$!jCn&qsR9m8GYI)II5Og*XnX>w-K!r_%GP)*
z|5W25^K9rPS)=W`b<?}t|KLcsJq~t=eE5(_apluVl+9Myy=Q{9^6d|k6HKEQNrR)7
za;ZZ!@?=Rz>?z_}zb<K2^H0ew>PG9b1cY}Q|9qpjuMZ|BGD)uV`gR$fWthNZF_w9Z
z%XOtIE7KK|Q7eJc!mMPgVDuK;8X<Ul61Kx4bhS_!R^hi~j{W_fZwOl7=lIi)Z*VVG
zt%6#FzSwdf+IiZ=!u71iJ$R`SF9}bMfItShHcugz;6Fz2Kmzi(pnIa4{%g3uUt&s0
zbbToX6*soi*wr0K*#3VYj4gKd19|!<DQCs+zWtW_3Y^Ur30vEpL~QzKsY5&36zk*h
zY{&o-KM$StW+kvVT%h&6i5n*${fUkomAC~2UGrCfrFi37Op13MG_<8|eK~?Y)R8JT
z=u#f`eg^}S+FRHXU|hj=bo%!00p*jr((8k|3i{~N8pU-B!s8*-C9IMOhHMoEPei<i
zo6G~P3y2aC5iwG8J98Ua;|oRA69&TZeNV-koGQ&)?Cv6rC&w0t%V(%fI?6O3Xsr_g
zyy8eyIr5k{m^2F^>CGWDj8(w52QM$&)J!LDp+Q}*Zl+yp-t6+MfUzbU)<Z{Ob~O`^
zF==>0$>{GU<*CUkzizhQ%sT-zD<K-ky}z6X>m?fSTJz7l{QNB~cDMSnG|)bAg~xbZ
z2m#tk3-?&Wdfq)?K?|m@zdw)m?qj^W&v_+<t7V$QBL}m%^mIB;HM&6PvmUTC5I3~N
zQnxvqY-D6~Q&DWc`4%@k@x&Cs#EOJ5?D&5i?$Q}&6n7pJl~+s$rHp8(>ZlZ7w!Qan
D_|X2r

literal 8467
zcmeHtc{J4h-}e_GNv=Y5DU?JKx{73{L_^5Fg-XK6lHD*UZ4$0TYAi!`S;APyk|dRk
zecx3EV;^H1%k!S=d7k^6`<(ln=l<jO$8+xUbIyE*FZ2C;zVG$*e!YZU*VE$K!@mbX
z5H8eJ4MPNB8fCn8v%wW?XH6`;vEA0u(tvmPTE9#ThBprPs}`OJ!g+x4Vrm}>+C-3J
z2uefU*e_vr$ltw_w$-@U<Lg`ecj%+5yxdaa&zM<6u7qb9UkPL06Bd0vuM{mXo7MIF
zJZs+1zZA}Py(>viEJQCJzW!#9B|5#tRO?C|%Wt1k-|f|p?YeXzdS-Ebt4><+vq-%2
zE8l3W%H21|0|IO3=YxvrgIjlt7ZW`75G48XdM-O0<Inv!5oE7dIHI4ZVB*}9Um|!p
z+W5we+$fceub3A;Wp<7GwkEYypJTDBL%z;KmEKQyQYxZ<2k=hLRBD98V?OSFT0eb8
z_50klVuFT_P>e%MQl9P3Mo(cu@OqQ&bfn^(V^o{S{s@ifoRL&DU!D(w$?`V-zhirM
zI5|0G9Y{wLu`7K~c)V2`N51Ix)dtt9Y%W}mIQ*BekLAwxmWhEuN<qzrb|^ddg+y+o
z@}bn?o;ANQ_g>7Sv@(z1+Sa~9-ZZ`1?F~xFy}omyVMsFNFOG!_-T38tq3yFPwDxoz
zzP)TF78c@Dy%M=WTN@3e0_DxM(NOMbw_tkwmxv>oH_v8`kB|4-*Q||m^6~MBpFeNJ
zBYLehc!xexem?g`g`w(N9d}GpQvOs&rt|!8bs<&w<+Gqw;)v5^Yl_0yBOV#IVWlgn
z2B^k;C2XPUtfA^~`b*)5q$7z*{q|gfO6GlieU-DN1I7jh`&SjZy1Ep8zBs?UPR2Lt
z#j6I_xR#GSDerkN>pg9(ygKN4`pg+)P0c4#m6O6)`fS+-S#>A-<q56nM?8A1Jo-uo
zWQ9hd@rbgm=;h&nd2wm!f?<qS1b_O@CRsItB#T*goE@wvCi%cgt_bf(Ev|m^<yL37
zwncR(FqOVJ*_K*4l@TBF@?}a@l~SI0;ibBpXXkjzU(XG650Z5AQnP2#(--bF`^XTi
zTU6+|`IXbB?eE-i1joB1S)nm_!bA<TmlH|t6t9O0zC}P8(<x8BVPNp`h@ww!z|Pic
zq9>^u+%cC=oO^Cv^Wj5lo@L2*dGcV4xY6KKp&)Yy|Mm5C*Ul^hSFokjfv|EjTWl9t
zsl1IA7aLoVq#RH=MD!&(%1p1!kIdTyE;jV8%MJUb66Y!@H=LZZ!!?9mY13VEP13cw
z#V`6Pi2-vJ-V^dcrsM=7t=np7wrphXdsCD3#iKqg%Bu;A^Ho$_RYa-Fz=Vf~#|;aM
zxAb4H^arOh@pCyjIk+mD+1Z!z@syR>0rID@5Alk=`AsB}kG{%!+WYM6`}X$su3!H?
z?z*)><01`puHa#_xzs3?oU&m_tJ3lgJ;l7XiYMGcbER%0g7nIqobM=)G57Ls*rlO?
zl5DJsOTWzOM;7k#>1nj@udn;%?7r+3`}X)!il|%dmz7!Hk_jJIw1BRQS=GRY3d-7?
zOJA!G*O3c9w&sI(Jlh;)j(aNhEUP%pVp%71t})Lfc$(Sxr(0EHN;sd^&!xT6^LTHX
z=TYp?;zN>O?Z9t$ZqY|}BCS`}*k4HGmw(E$D9#`iEm5gM@oqJL(iM*GQ$*z#6&2Na
z$Hm1FfBd-SyS6ZfA;-qX%A@AKzC6*~A6YhxX==vFzOS=rYHCVM!;C4d|9Ul4gQeqc
ztc*8=53Mc!wARzpOJpC)_(diYV<pVK)C`w6$5H89;L6W>0h1|D2a8)&tlZ`%?giU1
zZ7(%R4~b_cv`^B;8%_+X$OqN@EGsO;27xiezIAnWekP4ncsdlb+@KKYD@03AMY)p5
zjG8#B1EXKM+Y*r4jp_FhNh7$5_<k{$>H@_2WnW8(!;d#9!J9vY`b%Bn_i%E?#>HjE
z*<es@j>bB#&$RUx+F22*AXENY3C7R&-W3S$UG|%rn%Y>X=k_Ki`l9;@PMDd(Mz4iY
zb(A3r71rL~4%yH)>fMuX{obh~LtH{ar|#zUSE9P@kphbRRWlD**hNs)7}|4z8cw#*
zexk42J?tXy!GqKApdD$bS2FISA4R2us}d3tdUuQr4P)NE{gY@dA}%SJS65%C>ryKc
zaa7(gU}YxTcLTyZCO&@PoxF3yfLMgnJ$zhD%p?S)skXNE%;qaSh3s#CS{AuEmn{3Z
zk`yeLsuQwi*YcTI*a&`a7bjfNcmfU5@5)`Kq_ejk+W3vJRzu9=RIrtp<;j$k%TLh_
z&-tcDYHQD(KYye;U*}`N^}RwWHyD+8x!<{9>Jz7Q=Z9PMyb+q-pE$&_7{P#7HZe1E
z-dtP6$+;!LDU6MbQsqVhi`&>6XJ+oLZv~kc8)s!?h_1BAXWpuRA}jw>z;`eO5B0{f
z_-;gZu0_X$i)*CcfGuT{+EPHJPFYfV3VI%K@bFjL9Q)(CuCDG3Wj;T(5ZjJ(=_UFW
zTuQk<L)fCPHdgEDthzXh+!oLsdJ@*ZuZ4P`@<+xB%4%Lh&Fs#>(d{j|Gc*7UM3fgv
zo_sa>E-4`!0=c5jx3`;L#_{h9_j+41yytrhFBep=Sk64iUE#V3G4S^7+sX-?vsaI`
zZ+2DH4U#-|vEdLSR3N?4Xmm_$Y`WU9WH^8`DM3CWKhwuzbtE`=hU+fmI%c^|D?ac*
zA+!>g!E%Er)$Q`(Ai9fgl9I(!ZXwqWA33e6*@9R+zTl8;VBxU$AxX=$PoF+%#wEff
zGCtS__J{h8S*T;TBZGq_yWX2gTl@6cV>TBX`NA}eO-#~lx2DbR7j(2^2l&IxwW<cr
z8c-ag{)z7ji0@d75akx5`}YgaojVtkm?(*=TpJHBaT`(jO(x4>s-gU7K~=t2?W<n)
zIfRLYal^J$EeTm!6KEY#Dr=)Gtjvu;l9;hikNK7SK9DZ{+)JAqvTBNx?nzb;Wpptp
zAy?q2HSHZ8-W|GfQ(um_y&&e3lLGWI6Mb+?OG`00T+aLaO6@q?Apb<O!y_f0WWAw4
zYV!sZ7b$#8COC%D61-iUaqVq%tJxh0V!AVJcEHT;%Zs+xl2*4fvD9X{B_;jKPxrZa
zRe*78OI?R@Iy;Rcj>zf7-S9xoRDqjyB&{ork6gHS-O>{0;pyq!z-M;VFtseedBsKN
z<ZXdP*Lsb-RON}+xM8t8hGE{4{V-vsYa5gTS;(Yb_B(@sM=GJw$lm>N_~$o%DDW3_
z6D}GIl)04&vhGA-AKuXlX_I6)f6p#vewD!TeF6$;&iNIZ@LUx^i{ZBBQLhQTVrtgb
zbFV&BhC_w+PwE-1t?(p^ZeqS=tkerbcF5fRyxZF2+sVqUm4UoQZS)LJvXl)I3vx5W
z>?494KK7p%$Dz!lg#Gh$W<(_Xi^{c(dX}Sm9xMDh!KZiv>;~Bs8xvz5^-y$qszWP1
zJzZewo1J4d<8oYF&-x1|FBOR0ocw%KZ|@S?DN#mUdi~mT-fN#aV?*R$u^dZb8YVcs
za;>JB^6~O!<>sDw8P0|zTXBe>)lE!HFiYRWWP`T@q2Y_53lYRihl6tgTB9s>V_EYj
z(WyfeWq9_mF?4`;rUU6~fsOkv?qcR3_O<CLwHQIN%kY;7vg$6-w>(G|Q4cOEwY(ac
z*Y9ssX`%9~44s@*^!@oIVgst(^2&f4rPD~it9o_#y-hWyfrP){f)jE2_4OrYZR{ye
z;diNT^Q#gE!px%{h<?CVvek|hILhF7nSwT^GU1%|%9`)}l6ult+n((|A#;EHv#G5r
zf5hpNC)0<gI06*f3v5;MY^p6e4@q@y&?qJ7uG5e{jilh&k{%IM%Jr*Y{4h(GxN+(_
z<X9qMU?i5|5=ik0K6kOW#KgR%C6C9Kj#CW!haB+OjOWkyQ2vTG)Yg6u>0yc8A`l2{
zED0avZb8Y&b{nZVEhW{hBVC=g__I+A-3De;<WN4QYX6;A_wBipFT;0%fyY-vn2@t-
z|6cw(Huz(5k`I%V3BA&18Li0LLXF1Ii3eJ!ovB*f^5k4C^`l8-@xyz+&#ziVJ#_ss
zzgjkRe}L?~R^s}Uy;nXHhr>nMxMYq}*BPI~T|brPD1%mJ8^5ZnYjTR$@Z!-@LDnI2
z8Ge_JpFVc_e<&(_jE>$`m+UL6m2fAC^G6gFREG3pSqX8aK~b_kA`$q168>M3fB%{+
z{{QnX$Hthpmpk-nUr$7dU>L<if`iK)?CtMgu^ALBe25cKE91HP>(elH)tXVM0lVh-
z4g<szK?T*?0zwPB;Sx7S6$q%Q=ZPV;P?Z3C_$N6i@4<m5%k@*NyHo@Tzc;v0<{>&d
zI<j}Z{;g@YT#Vnk7A=GBJf*zSYsbjxpZ-Zuw{siS&M!}QHDPwi)RCztn|Uxvd;2-q
zLR~O}ks88WNl71&TKXK(vfModDwoKCw&;^O#>U3HiaRj6#--Uk&E^(VdXv@VBd&ti
zhw%+X+_FaDRj^)@t;b>1qZs_5<>ONdV+P-z$GKWgz9f0d{%EM%-tmHE9Z<KUqYdYm
zAF=vvEKf1OnTXTeV1@TMCxT3)<7_YE&O7J@oH>YuxQ33fupu82rvE{US~1H^LI;47
z0ndX760JFi;C+Yvqaz!92g~pCD|ltSma^_Y9?FEYCN^*_GztZuLG@Sp7ei$wKnox8
z9l;8mJB%Ph7K4a`y}kI+eUz=$ksdy?8!3Xyn927AHPcyr2r^m9yhy70{o2Z_NwRD*
z(RaA5(wicJQW2UTU`CSj#@V2a2z3J-SsobKHih7dPJ>~^=PPg>UJmzJ28$i^VfyHl
zmsC})9d-gX#NN_NX&r{+@87?p3oH=iDnC~Z^FFrFJ&!L%i?mR0XOe&Yn#|5jntt;Q
zcK^b^*M0W*#DFVNm-FD6$<_z(0k-?x>O8gx(yuj$xb+iI6%`fQP;oRhHD`nn#4)~n
z{-M<A)9t`Wcz0Vkcy}rxh{)sef)H-NB>=3wJI}y{h;pWFU@nqtw`m0)r2qRzPm>uC
z*TmFRJKE6D@OCCu{V0MEbO#YLV`HyQ_+Er>yNDT>r|}x*IY2-&JcW_I3!`-x@(5Z{
zf-aRoX21QDlv;G2&qiqoTW{>vhA|kX-^v-fkfD(gWwSSn2!KsoTpYD%)(^mt5_s;G
z*|@(=T(vZ+QAMCgGHM_K8~lZ9VYoWLZYN4J)w1YL7$o)kr&)k%`>S5$e<f4D$;a^y
z)B;#+j+bk}yh!r~7$_kp*8?OHIl)MVU-voP_{x<laY2Ryv5)J=E?}crnYl7AxQ0Xp
zm;PR*%X&^+OT<iPpFVvWO*__J7=h9(X?YlZi~eeisqlQbW-4kCD=I*@(RuCiIHGhI
z|FG_6XlB`Dc{7|T7KiJfK8_$C|GIC>ga`8$PHPG8hYl`jQIvL>jRi?o3_mhTqxAGt
zyPs$*WJ1)MK1-rq+}HgD5hRRn)#2ms-@l=YM{e<@F`zH>bphEU5QL_KNDEF@=arbg
zsl9#rX$c7vV8(z_C{Dc#HywC>WV9jt5T%lmAC3+A{Nm5^%P{ZycHV;^v%1f+31hr1
zcq0m19kA*OJCv+mgWm+)A9+-AvyK^v5_x#oXtF=%cdQu$68*;~l>*n?XZwn^03ZMD
zsi&{BsF8}FWI_;}^xW}DZ%ViL$&*(gyrBLVoju$Mk*|QhfFPB5<xGN#KKF|CSP(>#
zNA4pyl(zlI*lf$w9QGBx&!TjHTm!qkPA)8va~sYD$U+!;3h6=&r;Fx}>};>{1Ba8L
zk7uBKU}a3+n-$Fc?_?Wi^Bev6pugB5>VoTFmZMA#E9b!l_CF!&LqwhVckhyc@iX!b
zD-x2nAB+j<g#80DZSVh-MdZv!t1?#;b@c~~ArxFL_8;iDHRUpcd6cvyTxI7LG<I@w
zdUufPpH8>Tj8(xyFM^5FguKkczOS?5+PR#exjVhvcY42&eo(8@`q;k_@;<b6&;JDo
zSxGsC$%ix*Avb(oU0uEURzcO0Hmd#2Uv`9>bq_W2OLZ2N7_F$=jzFw`%1y~FCKU;u
zaDwTMXffgdQzzskiv8N7C%+~p&;X>HEAQEHk2?k{10@JR=i*V<1?Wf@4}>``jyEt!
zMoUY}YN}bD<w+FOxYLmPooFKOKXb}VTJJVYZ%)*S=59NYJb<-w6#5JlM+~I2;!R;0
zk;8kZV~q5iy6qD^XO+14jZ93sG8$(I1ex@-v`$6kTa7NI_xj$Q)OkG#tOLVtD>Hqs
zvaYZot+OICd<%fumtm3<L*IwEbguG*&}P{MMgQN+|I-Fc;jwI?b*8p*2f|Jr2orI~
zMgB}M($$Ti9d?P1juvjA;=JX1Id(BOSK__9=!U*ytMq|ozh{wS)b)M>KCtGAEd{r$
zSH5SOl5b5kZBe3LW6o698HBbw-BV!uD$cn{MRP!&j4R>p)nDmlx4vw(Vr17N%K=CZ
zP4o8u;wo`;pPcg!6!3j-*doF-gq!>IT`HvC&Ekmon-csHrB14{J{o`j8#Dj6Ms}FT
zZE6B57*&NqACW3Fr1$|Ap1j2*B`)3uOiPJln=nkPPGnMIn57JKB)ww>oc+}SWl~iO
zm(~MStljHa8Ssg1-_gU0wM9jS(ZKEf_P2Rw8oqDB-==s)h~6tm&!#%hZE>vh+o_&B
z^RZFLV@0W8`1m9TkLcBCz+KCL+RHG?P*i%Lg;`>;gl3?YI*GpXqSEA5dY}Fi5`+m2
z<d1Jn&{J1lrR+bUDdVh)Y9}~#@a}os?=LKKr!`r<d03yz>0YhBl)7rJvioR=X|8z+
ze)E@@B|ITeJhG0h$rzSL^|ydf6uBL19oa%Q0uT+9BTRHWfRt0)+YSE{xaKeti+lSv
z8>kteD6OJy3rkAsLV=~}>G!sTczSp+M2vUugasHx*O&uJ8-+4}8J+pTqv}Z@HJY>?
zxj;HXTVrWmVK+p;b_Z(N_aY%o%>R<5MV_qS@p#PLTr2YE)K?Q9e&GH9H%t!^%V?(@
zKLHmDJg_)Eiky|$2l@?zRZ}~b65M~3!xS{Fm>6CViYNxxmS_5mva_>kdh-j2STUBq
zlAWBaCKR~vfN>L`+i6fICB(!UF}$A*jE&O*=BpV*TcIH9{eK8BU<Vn(5ye1(FKPH6
zhR8P=s5T&XfI7K*>eQ+0ElJ9GjcPoiy0|nI>;{8Sc_E}~1MP?)nA$P+2gDScAL~CB
z8RP&&oCVvnva&wcOzr-u)}DzE%Fz<^|BTuDc@endtnzYmz`K92FDXPpgJ2xBp`n4%
zcxIOKtjcoW=kpGvPEZQC#AcLsyDMJw`1NeqpDa+l_~l4*Wgvi+BKsz<Zztmj)H{xm
zFqs3<0%IP<p!FG?=qpvJdUL6X|Ln~W&{JHM4{Y0lM#N~ZjLWB`rOkk@7h%Y5mZkUJ
z9t83gWWgK)q0`SWU~}ol!;9qQI(8w))wv=5b9bJBwr57xRRRFqCEIhuh^zo7?s5yF
z#`pGYlAOp3bHsV{_~$Uo5_BxsS_AB4S4%&#dl!&yca`?@x2I}FVQT3V;D~@S2Bs`E
zTY;N7K@B_}&6w|`Gr(<(b2OMcK7h#O`tzJd64@f^;Y9$@G*F=!Lc{mw<}Uj<>$}Pa
zB8$B#rXa28sBVsN$G&{IUvWr26LhW1VC<^VKjt609s>TU8z<`w871StbO+TOz`B{K
zx`iqm^xzzM^W?R(ZT@4<L*J=NpzCd|HL5}wy`-71Me4?1a02N9C*u?iY<%ifk$2Fr
zt)d?P?EJC<nccH6Ru3SZ;J0|2@8Cg?&*MA$NA=!;r(RywfZ>)(Zs{4V3(eEg#(0uR
zT?z7@FgvrraIid?8ilTZ!nHBHT9e5x=%)q>NRc3Cbj{|HPIHp7)dMv(e}8{C@9T$k
z$qZB|?>X^#*lMv+b=x2_!Q=TNp>+3I2(F9W9RF3$iG%_1sZz9mQp4}i|A|4bTP64t
z!@%7|2r63=eP-JqvT}Njhlx$U3zupitAA=2r@YO#0BuVKWVQ6nOo_vL(X;&}9;7Mo
zy75R27L;D^l|Bb6mxF)v?mpZ47>d>7`dgrf8Zn;otwU0_L8|7|fLm=Bm)u}-W)dcc
zWPef|5YbF;Vdc&?-NeQwABW@r4FHPLowos*M{AZF0r5jqD0s85>n;=O0m5QE*kxub
z2{?&LQq-51m#-Xs$oY%9?lqMjhX#?9CRGbGDMK-^YmCgJzU>ji2Nr=+tE^1@aULRq
zM^&1w|4$~i%JTkam?Se9oI46ZkW9w^0+2)Ju`0Hq!M6SKJ)`6Zhr4$f_UFIoo_z3(
zc`}Gi@w-`OpCK%$yP(@y7T$ggRyUuo0@P(4wEi0hQu4l|7n8q#*AC@)@JlV^fe6p;
zT?7mb-Td~v*vhIUL*4>5-WIei@ZAZm$j&N%DkuW3%2KT^i_TYp&mQGtc<djy{a4F2
Z_gd=HETKC{EgTs^UD4CXzjX8A{{jfEkpTbz

diff --git a/reference/Rplot011.png b/reference/Rplot011.png
index d4414a5ac82d1a39360698652fa9ced304940ad9..027661f3e88818998d903c87f8b1f5b06cfcc469 100644
GIT binary patch
literal 13454
zcmdUW2UJt(_HWQZMj2s75fCXB6hVqKL8_G|g7i*QiWDhQr3FSu8Kp!(K)OhiE>a>b
z(QzbVsL}+4ph##!gb*MhA#We=egAUred~YMdiSok9!nu6@#LKE>~HU1<K86`{hd2R
zcA!wGorVVI%uy(=3FPmO+u)g~etatY*mm`T{yF#wUyQTO(ePutzkyvK3dOq%`OEcw
z48ua9_M;5XowmH0y)YJW(~|xxXQ|sUC1d+<Kl_^d@;=#r^s3MN&Ns)beGGs8<;yVr
z{@92P_IbUN&rn{-(1r5Rkt(77%HN;%{IKWQE$@MYPJ85Jc<;Y`qVt*i^nR{0M*=yU
zn-lHV&m2j~XwQy35Jt$2jdi7DcQbMW-6_h`9f##z;(tM*X3Tv@x1vywrJlrap`OKT
zS$rkmx?<kSpky_~a-p<$abJIxZytH?+?f_`6zc1@5%E)|mKGK+5kI0(B=s%ZP%m$t
zZHPffE}f5>)+C!96)?B5N^S5X=i=o>66T7teBZzPb!+gSzv$0!*aS|=O|4w~=94)2
z_bdiY)n`N{H8pj%L%F*@QA~4XT9;!tNyOTa@lnCl*0lzEXQhmOYBVPzLG+~go;`b*
zy;w|PTU%RpTPAgxPltP~<!f(&ez_kR&m5@66j{oo24_^`nPu`#Gf%A3@4UXky@d<D
z8@~V87&x`I0e@6|KdQ?)x4al@*}nV8Ol`N$1&(-v9=wohd?avOZ+LYt>T&(q=P~<4
zL@ceWimv5FS#PXSha@(?@wBp*Nwf0`urzQ;pRpz@iD*_0p0#|zDT+QYJHPwhZ;J#D
zMw|8R^+<DQb+K{AlNuMIS6}f(dz^1hgYQ@wk3{4pD!e5;SAw%nWX=>|SH>f#o<mhm
z8cTofR&{&#FuN@at_!n3AMvgp(4@8$)O!zkA+OI@)UNdEOSm~vJWFzlt;(%StE%Lq
zhVSn=rV>ao=DvN}@O|src)6`?v4=|8K)KC4eq+&lt$y0Lng<tY-q(9#&LfWt#dnqO
zG+d2ZYsA`#h_ywZz%hK(M!aO-hYyeN6?pu(fzl`c55g`T52Y<S;uP?Y@SC)5T{Uqm
zQ8J}{#f`w4Qs^we>SQ|SO76aQ&R)*D=kaOEd^KUwzl9<1H&W+GNl+X2sMp!KbEla6
z^kucNpp>xnPqBRjSg0jRPUyI2C^H?68D@Qv(lz9a2Z$0y1LGMheWraw=MVmJs9kc`
zb{Avf=+RM~t*E+S197g{GY2Z|YW0TD(XGWMIb{)xjZ#sCHa0dTmySNvd6!bC6U8)0
zUi~_pg0DamFtd44I(gx1i*mRPj<uTbPclgc2FW1{BZm#=U`3b@E6LQM)rC<9u`BK=
z-OKhPtr56euO7d9_b$NR<ie0+V8UVx&1A333S&CYd$hqPQ~kzOCNp?`&~vW8R7OD|
z9sYaRbWTa71CG)vS~}_T&<B=uM8lQL9o3;ooT(v-r$|;hHCyXTMAe0S{g8kE{tnd>
zCuDZrds<g#nOql2bv85)3ky3<Dtr3$0ddmI#ibxp2YthOzkbxls+GE^H9T=9|B^E;
zQAE|~*Hfn?FzflbQLA=r*0gRqX(~BrLiMAC$;tCn{6>`?R$Q)ns65>{+>l>;<x-n0
z{KHl@ZIeAQL<k(#TUe+~Glky6Bbtnjcfk1$EKia8?o6z0?BR<|H7e?F4)wd3p>!TM
z7LxUO>BZHWN4|K5yHh~>;mXAOv|f1CDP-8KX)4X(z2D^#oL}DJi%CABuGDDD*L}V(
zkP=E))SDlw9wJIaXZNuP>u(f-*2Y5T7n`YrZs|^Gc&#LZqk?gzEiI1D)i#=;i~XHy
z<d42xP**GKlh|XaM$*t38b*Cd>Rqc1W(`r>P#*ISWGAk?zAL@!p2r`*ZQt?3mZqh)
zeRSr0HECYk|0O<X*8F^d*-qi(r*3cCX>Mnik$dy=JrlXWO%{XMriRboZK8e})=UT;
zS%cpgqZVV2=G0w&wFT)L`eMNknJcpeZlpBNKY#z_WTyjcY;In`ncsGsT;go9`_iG#
z2KxJF>iSG9jtaaVut8UrS2dN5Wjpvbo_A=tZnppD9z&^|!oaSRC7mk64QBcqKcRXu
zN$OF*^Po^8>XKOKn=^|?C-}}4m^C_9#d8*$=Bs>hj;K>Z69OtIl%W3)KcQl{1&+hR
zOD|C<+f!UyQI}sGV90H6QGi1^)vetziFM_o4%e0pkn=mO2pG1djm`9jAqi`1>oVAx
zH7wYvUexhc?#hTN#>VObX<h%o{ro3S&cJ3%oLZah(@#i9@YyuJsz`hN$-kWXr+o49
za)7F;>d8!rd*=ia^u*e!W6e0Y)d!P)Kg4Jar<-euhpOl%89Xg5ZBJr7f39>m#gMps
zHvd7q;%xP;;Dczd4ef7lcKx(%Dxgu6#Q(#V<(pa~glK{b1MVq&YFPKapSCGutdvR~
zf1esK2e#}GhRJsHG72U8_%Ea6U+`r00xa%$ZsfAz`GQ`)5*j1YsL0&tNQ#(=X5_km
z4&0PC!s3^Aa{u(w3`Rz7W@hI7%nYNV`<Xl+UP^oXv!}w>uJHNWO|5`NNAB+V5Y4rg
zKj!HgtA|nY>E#ylxx857q-_XoJ*P{pO+AxJ*9gST@G*RGqcyBZVlq`dTRfUJnV7<8
z6V_kzJ20wh11~K_maO^ZPx?_6$2PU4Z&UWj{g%St)5$OqDzsp9@}DvZXo?y0#TAhJ
zl?cb$s9#bZR|!Y!xU`3gnqUcyFZqU86yn|2VXRoM%_~+fmu_m6P;YD7Ry(%}#Jv&K
zJR^7Pn1zZ+u&l1Y4r#%-J#~wX6yoHK!@IWYFW-Lk81AzR1NOtxO~<91AA4E0vJ?Z1
zr)TLT<KDT0B~&N*bB2cZ3K$H7BJ(ipYwDV&1}3k&#8qk57C$tfs@T>WX_z>rCgG@_
zN@jg|*B)6t<ZuZSQPk=EAJxmn{SQj||Lv2%4y~ekYGF7J3=UFMpxxUXOu8hKI_E#)
zg4MPrvnXnsnp&dUyu7^n;p<b7PM&G0rBS=<^Vgm{{Nfd~F3oD;*IP%<bG<G*cQ8Mc
z9EF={gw|XX6Y=}beJ7sg=jT7oXcW-svCF>oFR1H(xL5pYZt1XBEIM;E)z}-+EMm0B
zOizAgG2FCFRqr&(QkW!hLJIPOpSe)#6#|tp`tic~MHkYIlEtG|%y#ZolAH{J3ENm!
zR(8MM7KP%waQ@hYdy>I909jDld#kHUOU-<IDvK{>70>pSlqjH|yWZPe#SsjXH)r!U
z4dHFvas-=dXQeg)6+eAP`Sb#)atAjzx5Pv!w38Z}k+d9<I9OR|MYK%f<kC^$pUUd$
z`jZsHTAlT2+Tz$Pzs49D8Hu*9g|`MYk1cPo!#an?T~1#kzHf=ndAoK~D~yb3ajuS6
zTxLX`moTM0%1obh#n3phE_l+^y9mN?hXOM<axy_JsBP~x*On?qU7$>=`NfL~4L+mA
zuWs{PNQT*$gLjE&b$)ZTrX_s=h8k>$h)ZjX$i!*b0!8Mfua(I2bQu3fGmI%(rUy1o
z3bnf`QLkf(1pq#W)@hxllDmu~cg4ElM;EM>GAy;kyS;U!78Ob@-_?vJ23_u8P?{a9
zZc#{9-*-tTmSBQB@%jXLn$|wKB=C?to>Kfo@R_2XAA#M_1?a4zuhCJS5s9}B?U=;s
zI>TISku1gIos$eCsZ;5xsW9&}tdHkkdfUsoeEg}0%A76N54TUhxvEGMf4ueAm?ym+
z&Wv7<{-hc}S}lHOi;9Zw6DJdOV%n}M>eD8dUbu|*Q`e4(V}*jqvJC)p#bYhF^QhtB
zt>d?RLmQzpD?wjUXs?OHjmiO5u!XfuhP6;Z%Us|1;r6TebRoJ^PhQk&hKVkF#X23}
zmYnUgQ^|F1Bta`18}GpiyZd?tvju1rDm?RvI2XT!j^pcCe!pNl=owWnKJPH(F;P|{
znDL@wa^t&^;q~v>>EHM7$%xgjy?L7}CE3OF4K}QTQd^Mv!gi_b$2TV1f<tbini_?i
zZ=P-rS(x+Jz!DFolZwtA?EO>+n0Bzg)FMu?<*W#I<fFKA2ko0fe0l@1>YsnyA$%(K
zgwIH!sV=+jX$Bkc%P-s=|NLzD7Z0?5xbgqy$(P;{sEmhcX@XjQ$Aup%7XMzr(3MXo
zEwN7Re_(^z|FgfVEQWy2zjU-Z?4vIZ_MCaCWfx6y*S*NtwB32#Ha+VXE?k(RF+}&K
zKpAKe3JcFUS1X5l!-5gW3NXzKFnAZwaRMB(m*oh1H%v>sajZ*pNQLa1ZA(sL-1+Et
zzjGl(sE+L<Z!=DtN<qJUQMiihJz)0|+6l6q81SioeDZhZet>=d-dvw{!N>@@>cgQX
zH2@t-X+TAQsI^d*HrUW`-<4YeEWw{88NyzRU_s}Fqtw`TeD51XT0!sAC%sP(F~qU@
zHMYh{27e~8A`#B98T@(s4HC-{`jT{pu|`PVPj}8hrHABs%BMvRjou@^hP|++4rihz
zZi<{Gl^)Lfj(^+jy$aLcf!BYct^cWDMU%<D4dnC>49wmUa(?SMlAOHj&E!%x-j0$*
zW)-)^@9i7@6zl<T(H<WeX3|Gd-keO-G$D#dT)!5}^LVR5%ePx|b8{NmRgChZlS?;a
z`&I#yL8V?PArAJLz2x)0e!btpuhnYh<AJkxgABR!3(WM}$T&ar4&j+C`%YZ%fxd0m
z-*G5W{ni%wEheY`O?&KrT84jq!T$E0cDx^i`>ZBGN6>G#e~C~#L3zig#l^){BK#<n
z_L;#g&IC-WjEqbWCF9y$9*2=fpGrrE47>mVV(_`(GoV*%_%&@rc3{5Z$2|6~Znd^3
zHtx;x!w*|cZbdzInEQ6owoMrLmkDk>yj*fN&B3?#f<kK{yt82-2nuRoV4$q33YuB%
zNGIU-4EL_=QiP%mJpvgcO>Hz4tHXS{*f=fTnW=hV?PDNz+fETlLk)Vr#Z*W${p}%a
zUFPO8Nx{a-%Bnf6Ig~osm7`hj(R)yv)}hS&THCD>x&G~71xP%x=@r}`3DMEOxg%hX
zRmz7pIs!J1&YJc1^0IVrDEI6u_5{x1Q^ollr=SwH?AkcydofeRFzbYmk&u5>RO=s~
zo_(L@;@FbMdtgu^z_T+8aAt}P45bdKR+zm|)2~KL55WKy$4);Jiw?x#GlxdEBYQTo
zov|d;<dh>E*EjiAjXcYq4{3Ibm-cXcLs?l>kR&i_IG0-3I2`|T!Ee9+a%kis_3qAn
zK_71rr3H4>H?{l)Mp*y1!HUZ)o|3+nP8yh70|sk(%(~)%q@3f6OK0!?)PALY1Q>9)
zH&aYAWCr-Lf`{F3cKBSWh_ZY9-28l8uACu&%_!yyLTV2&V0_K>m0$sc7BEbtCCO6(
z^^H!Ul1T<LOGJro?_5XGsFhxgg}Of1_+C(f;&P+EB<at^D|bDR)L#AI0{H%XC#RXP
zp_vmrXqTKVjmqmzA19b-8D7soTUewl#S|!S;}d-vC*K79FB9itXlUq6J85Phj+JVS
zY8kIkHUAkmn$v}@xq1h5DCd6GrK5!z%GW5`d88??zI%-ZlDqaQbT>_zXf6GDg8yZP
zJW1uHaW$21uhO}0T~14)*0Mn>u+Q%bK+`_I7BjW+n+1hi^<P6xbe*V+XVT|Om`rAi
zxUmk0708}ah;EOt6PqOy2+TEVHB2HwZHHvI!y(l1O6`bM(KN=zix&~S0py2i4~!1N
zM*Y>Zhf)%6YAwE9kP=Do`ud>;8e1~?!-uC!lf(<j&Y&rUNbKLCdgJrCi;s?<PF5yM
z{T%n^{RZxXsd(Ymjrx<E`XOfvCnqN}ntp;vQHrU~$?C8(-`Sh;okp2eyieElaUGK{
zQYr00n<Mq!^+120P;4wMXFdjYcY5cZ+P@z(gS&U{wxkOIj}r_XEJ@6`U)OrA7Iuyy
zf$Nw^LNOL|DCu`WS=;@yr%tD(rg}mbEGsXcq@z)%u&MMP*I`Qxa#-D*Lae2l&q)7b
z?Ojmgyl3AR`N%8C$qnqWaZK8TqL|%_me>aJBD4ii3G!go&%q9BUkI5WzLqxtO>w}=
z1@`yoJNuUaI)-e}QAG*+)MQePW`E@o_y58jil7p{(f)aQG1-_4g}Q%eKI@sz`oz|Y
z+3IFccK}akL0)|v_*|T;`;p?=3u&^wA8K5hy|hrMl^^Y}F>7Ck3qZJPDsS5YXP#6a
zgQ#%Gre^j20Ed&Em!27}y;ed9E}FvKRHxH_ga0Q0{j8L_7tXtcG)v{uq(`;EvzB(e
zH%V!7BD$E!a)OL<(99(w4>#&mXnK82b@QS|>+-u}mCl^88kg9~+gnhNn@RRB0bKQh
z0NO_oS|$QhKIH(1SpL#pxyeU?I<j(dT3CM!EpYKUo;Q5FB@7yvwU+o0B0@}&(&xuQ
z$qQvQT&Nx&H@S)Yv$0+SWmY3EZWQdg_Zy`%ONu}y|Hs;G<>3P)>lYYC>U)nu?T?1f
z0ij?v&!Y69m$!G=Zb204{Db-!nCHxE0EJ;gczv6Am8s}*cXBH%zvle6tY`Kk_^4(W
zrxq?;sK>;&;Xi1Fe6<BqX=wf(CfoS<_@w^?#BWj7n~MO=uTpB&Jx3IU`f}8}z#KZc
z`XTSv)|Qs8EMfCnz*$03r(n^3u;X3V<<NBNqc%d=6Fj=mR{(szx_qhjTK8h|E4Vl(
zxHv>v0;%d^^y*rscU5l;*H#2~X-Ll#AIKa#R_`;~utFWh`PqOrA}S$(LX9~M9-5HK
zy?KTV1V%Mt^*XdzZO~D}HyP8-Ue{5mAXw@37n6y7gOv{THzxH!+VkVA`pHO3&;Dga
zgV#?U0@j;!NWZM5rR7-LRw#}KbkzwthXxPU`VzD-Q|5D6a}AxNC{$B(1$X3F@X&0k
zjdN1>^aXT-3z6?XZvIe(UBRzgd5WXe+P+Qy$b~w3tAbk#+CEIAqG-@2TVdab{q?NX
zu5@OpnZhdoZkw2M<@J$_fYy!0W+&AX@G+GY+$<b{eX-oS@_k#vzElHA6~A#U279wg
zWFK6#^`J!$qKCqGNYq?Z&+-3b`$Gk|vB~u@(Ho=WIczkm0I;A<0uL%i{D3zXI&3O6
zQ#+zTOB~C>L^CCz(y}Y6xztXa2pLd^QO&I8Fcnl)RD#rVh9V|DX(Sm0srw-E`|9wu
z(D3Wpuh1OkP&S_Y$K=x6g%DsMq3S+BitrpZHB{ZHZOi5x1x&u?;)}D%E3bGYW*)gD
z&K~~}^?-mQV;s=Y>|itHSbhl;u4#|Swgga!EzFcjAh^m*#Fsp#HcscRbi;g(T+#z$
zK{EK82nb~Udd<~bAXR~c;!tWS!w#%<ZVeA^pMVpuQWE?IJ4w~OL)g$95$<%=Hs;E!
zvnBSz!q~#OuxQ=Q;ZTh$M_ac2cXi?cJ?q)^8;y>Y<H0%D$mu|swsKPGj<`{$Z{eac
zjM$CU8{s-7M8EhOl4vHOMzaR187R>;@F|;|IvUH)(k2{hFpVIoOY(l0rtQ(HCR4lT
zPm@Zi4Xfd;NIms8eMzRStmnJ~e(RK+#}6zFM%IuRxm4<C=VWME4DYZG!@qefk%AK?
z?A~}hwVo3mvnW3q`s678TBKp6FDKTDi+EM>FtERA-aBXRpF0@Frh}HBxfT%c3zi7#
z<d>Tm$z3;8$fZkCpz#pN*?Cw5qFz-57MwlZJ{56=w^s{OvO_(?xEP1sP4dW~{!l{9
zGb(Djh~FLE*)<#KVw^rD^j?+m<%0Qf;SP1N>VFrH`<Gx$=h3Y*Z=upFIh#~Ym*>c-
z{f`YK54sq~_94ozh)(29z}=`*r}-FLs;aApK;n<f)r5WwMC$U+f14!v(xKupw1=5%
zx*HiFbEpK*Spt?U?^FSz9~A~<Sx~{*vEt2El)tUCJr`D7+x|kjyp>Gq`<5`8B^q6Q
z|Ni|GeNGt06eu(@gI7Vtk=;`nlLf6SH9ft&rA2F!4w`LQW#vGfXP*kp&XuWSHdXyF
zC=MIiIENL%Fg;`l;`kf!CxAyL5MgF2{o%u>6&1#>F83rw63A2!3~fCtGgGwPPdr*F
zBS;c7i_(e;+3!Tr2uR`DMqsX#VBC~uUA79AEIerdir+;)iFDHW&C`H5)2Eup-b5en
zSqRb0B&SY7L7(pibUPbajhj*ZAVXlR8Q*Vmb&0pGax4YH6R%nH_X^EB5=qs6;^oR^
zi&9ICzU^(opvkNR@C`*an65^aOly8zt4_&o;n&pOd>yq>*AaRqbVSFghCLdw%dBpT
z$&nQMmYX#oQ4M}F%K#mtd)mAHb4hM^nTa;t($M@b;JI*X^*+r5A_+30e1KFmk|pBa
z2=K*<?TgkDw^0`j`T8T>Un2Tok^%W{09^5&1U;HYSpspA!6zpL8*=W|fxm8(qplm%
zuV<G^%%$~d%>gVEfvHcqe({FWnl}Wi-0+1Vnl&ENd^)UeXF*f+0;>t(7XVNW&z|~K
z9){TaU)8++UO*A!!tPTu>DFFz%pw5~4}P=9#(+ss^MXm&w{#Mw+P_d9|4-onj9Jt~
zA@J_hV>T2Oq)P)xNN#tH?L!Qu|IJi+3SxRIVWK0bRXW^PDWfokD`>~RZNtPM{_y{;
zF8~&RtGT^>=F;?4=(gZ62WaX3{G)m@b#0?rQdg8uGnq<b2<*7?&_GhO6N)vF0=-m!
zxz)M)J0MP^TnKR8SKkqO3$&0RmQw~bM2Mpf<192Q5=*pLi3HN)#xUO)odFNZK=O&J
zQpV=+_FfN5uv8MK5jg`XFTQ!!O|7c7Ha+4FeEBkHa)&@mKZCqwkAEOuQa`O%iM+kt
z^?mPBs2fHmRVEz1&7EL$I$bhudn8XvFT8yG(G89N1F;vy-*rc!1P&rufdB89|KIpp
z|9tyDZN__Z__<r=$-rXqXx5FrE<19!0k*92;BJXn^IiDZXb~qU2g9l#vEh5bNL%><
z^lSzE`k53d)3nir!9v57m3FDTFRB-11ueiqRToWD)RRv?#_ry3;*RT}-gi^ah<cGo
zo`ufiI~+T|-rg;@;V%|Vip&~)>rip0UMuHoK%-KwbJvMZK-!hSkqna&&lU83<7Aca
z6%WJ|eIw!ykbBb7&Q97e@ry*n*P615iie223cdAYQxtSnv33cd!hlov?6@P^E}<P>
z2amyZR?uHY*x9>xM&Pc&W033N@m?2r&xz}{%+=_nH$q^C?os9JBd_ae5X#Do?wxz`
zlmRBE(X?rfhE=ae-BZTaZ9f@Iql0?SgOVoWg@LZG!TEz%p?{OT5toTJGfV2?EQS)w
z5#p&z2&InCvcStQrPJx>JNfUNnPq`TrjOt(GuqZ)Pib(Fv;|zx{Y=<DeT-xQEK%8H
zTIqcFfe?rqAn-YuF_rF(+!*y6PxRu2lV~a+__6o*^z>Zn*?Q(L=A_LEPl`L}a%I)k
zp|S)>U?8D~<n}@r#GO3GynUq>@xN76)YHf%rV?O;H$=F|>tWy~$qEKa62KY;PMk>5
zKbZ6+-K=)JHR70H9GJgAfnn9&Oy1C^LB^n$RA&cX@At#zUovPD26KnFe7LlxrZ)+M
z^rMK>4QeR3>C()UjI{siOz!z<+o+>+okb6uNw<JwxOY6tZ)(!m3Ceb|z`b)|4&SQi
zGXRksz<f&gbl^%IL||fXJ;}M1RtVOJH%!_}`D1@&vB4rhK}B?U{yV7r?&ALQ)&95d
zoKR{L9=)yp0j?c9`Q_Yd@C&A<rUIIC!klZab&Jy%$G~5Nn+nxt3?Ymp7MM|>-+-eF
zrYv2M-fdh03k(j7#)-|lceg>-h1f=j=3r9O(lj#B=H>_Ux~B~=pvbT=;M#ujw*})C
zLm<~c(<=b%sJ{#_;Y&!f7}7Fj<m7@Sb$8u+D_n4DzrQU7<jEprCZ@C(-g<R;oR*fB
z<}RK-tA}SV_+^bXhiazjdcK#e^%*@uT<$(OElQg$$<s)Se#I^H2-uBg8rsRp2f{7d
z#N}&U*`DK=4Ikn%=aa<dtCmgXeBxAhhBC3jzW!YeOM*S4PkAGR4w8*t{MhKuSe{Cp
zB1h6X-P@B7!=v!|N=j)-Zh$NY=+1H4ba$vMhs<G<I{{FMk_eE6=s@y8#AQfJTnPku
zK{Jg2zJK7x0uCO5b{9yJ!wIOSSoDDHKGD&5{DM4w{VLI<+W=BZR;NoO0_d&OC{ysm
z;Ui#AS?1|PiIQ|VEZP8Qvv6%N#OYKrxrPN<AqUu&@HP;tdL~}o79-`UcqZTdY42y-
zO*qA}Uk>T1r*U$qk@{z2`GRFR4rFFp0WJ?oCcte?oHBiM{8ddKEm;M_y1ADJouoy1
z<N}KM7u!up_Ed6h-KL&~=wO2ECB}ouox;!v6ThFezpc=>=WZk<=%c;4KDm&q(XMil
zL`su)DnmFtBp?vYAUHU<JoGXS7jK;IH~joO&`YGP0vyfm6Q;Fe&mEjq?8;O<MqNmn
zWkOQqMyqB@hNotoC<jav)!@1QmbFpHCp|_yN6X;gdQhrt9=f^Na}t$!hd1xIH%N^>
z-_d#9R3Der<>fajFp8v;M*Rq5Nnh{q3eCO0pfIqNU+dDtz8Av{VZ|G#@qm>I=H|Sd
z!e@&B&R#@2cSI%mWZzkgi(ITW-`qHCVcoD{QL=X4jQoDU5L$*Z5MCt^9cTPUoSH*2
z^u@#eluR+SaB#?48hf2l>=`zEi<`H@4@1kc9wJ9`*heyq-JSIGNdOlqn;TR$$Qa!i
zoL%np^YT&x%q`~;5fl_fqRU&m(?Xt>mtVkgU{J_<5CC3hoD8Hq0_7`y9zFB?d1Z8Q
z#~EDofsBP&54$_Al!JXIWQuR&E&{PqqyfFmge$F`8HN;#3a^m73V4KCARSXcaFe!Q
zeKp!r02gas`Y;UgS)El*&Gt^?!^oAs`5@Hz5v^H^ws)@&jh1P{Kg0ZRJ-!#T>SEo^
zCG1E`Sgnc1tfZ6wEAC{z$i+%VhCYpu$NqXPx$y4?TwQw?a?3%(MJ!NU6MpJi<fsE&
zeMs{d2(v>F4tZ{3V}JphRKEGEk~Vv#@1)yQcvNxs386!^AZ`rov&fBDJc;O2;lS)^
zBYt@pW+{*mOKa;vcV?IDu~Fu=cd|hnLdnCqj%lfW?(*48Rs@L!3sF<y+-hHvhY9Zu
zJY3JbjvJbno7<<#KTZ)F<KJOsZvLLq>69}jG~8wvAC*L6*DG&KO;i}Km0bNAclNVW
z4}hfPFEoa|&j+Az;8nY46YJD-Cccl|w<?{l`cyf&(gm$<t_}@~dO&uN>T9?@oE#j_
zprogzc`bc@1A=f_X=yJw!0)ML3my?)p11N1W^|;D=7;4$W16F6`B_6or<sZ`uO@72
zOtaIBkJJZFn;=9>MC;}=%qA_bgq-vt!_CcM%{b$ZFXxO>ZGs$BbbFm#UxRcSlNl}+
z_9Y2K`174Uf;;ZifwprIrtfYklYWr9sc!=$-C8BSc!TuEY&XR`QSMSNu(~JUg053D
zDOo*VtSHok=IipJZd%4iU19(DVJg5ABtB#V!uZSw9N<0mE;aeN1Q$O@%y5cL@rvPK
zajOJ;d3$2`dvSyNlYzP1^{{tbX{1>z!*~ul%KMCKxuutm_J0zlb^0}SLRhSvXZ%ch
z>c#vF9>3Vz({{D)31Fp<N3D&utU5qa5_X7+_n<7IU7xqJJ~FIS^hX0y*zLh+N{Xk2
z{%CQnb+vOPqO?JOu&;H$SU#i`cLgGfBAOu;X$t5nUilZNK(Lhd!dzH@yQ>HYNDhs6
zbZLw41wtmd#}Bz;snzo^%Uubs4xHOmQz-O7-WNrXHp+p3-mFDWr~XSh&KR6jav_Gz
z1nz09?A~$7$tmY9zl8kkOI`}EpsYt|W?DKD=n~gbk77eQlMcPt0|X-G7!@<L2y4K4
z&97?-Q3dzz+)C&p;KfzAbf^&+JGa!jcN$?^?TW)RMg%)SSL>S}TMly9A5|Y(_y)3R
z5mEPAm#8*$aQixsB;Y??8<l`IKppKbN_Q@k%8N8F$qq0wOH1`+*Sb+^*$(pM4?0$Z
z>Z*C$zZyi~lCF9{f4cw~)-*YXVkp?TFDa&j@q3l;XX*xgF=$;K@y<NqlbI%K_XJ`k
z4M!U@8p0yhZb$lzx7Z<F7xdH<*FQWygw--PUxJmIlx@SmrEl85#k4;h7-yFXZ`}Is
zpuaj045_eFrl<d_4AZl{5pxosf_}mufmweoQ3jb4Qo5$kNZkri?Z*0(7_(<)1$v&J
z|GZZzAHk*%0kx$=i!w?ETP=VoY+ThBd<S_0q%%%l+=nHOwpLAPOljoomUw;gk)j^r
zf8*(ue$qexI0LauYCq8~yJvX8{~OEXBOcG3JBEZfHg63#1<$X1aKY9`IIxzm(T@EJ
zcE5@V9N&=!S|n(1Y$|8dkNLcLu?ZH*YL+}dKOe~Ki9$a6s+gU?^Tpx&O}4%Sc8Hwa
z^K;0CRu>(Fpz6w?gP)kWzGcoBA5S;+&joK7TusoA;xdOT8X6F*&oea77(6m0m3WAx
zH<TaKE3M0(H4P?5tr;eVEPgCS^luPmXXhQn!`9gtcrE(<g9B%gf!fO1+}K;<of`t~
zgNTyr??;RuJq9nwkzeau3}VVCcWYTd<kbpx$y2ie`-GAwPYzbcJG$<}6=o030(cpO
zi5~pNcBz@${5l@WV^ETNu*4~PI1&vuDyj>kxg%!O9AxMy9T|OdK_%c<!a)n*oW5tA
z;eIFM<;Ahy;N<w=-x6wS%#grrz5k~(fFIEE=@$TFAUWhd^ypBp(x?L&IX#OK6RV>F
z$#5)Kb4juFIRwpY0C(s+Ih~z?`D4)8x)|;cbFjbP0}i~r!G`c?4}Az-Z7Vyt$dX$-
zpFbb^EHhI?EV$pSlF~_EZMgD!8)6vU=HVZN^Xr@YeN4Cja-!1n(>Tb-f;1C|-#PiS
zRgap%*0x8GxXb`btL*kpU^ibZ!hw7sDB+4>kkFZsGT!SFzx~dcBdNex;*(qfOk4*j
zJ`AI4ubQu;M)_1wXW!u9V5X~*r!$7(w)>}Togf$6q!=IRgcOYLCLPTgfKadU^$*Xi
zU3T0#6XsTjbB26}XKLU0i$EtSLrRjqxZ=HYe?%^~OS!*{;W{FY#btC&E)_;65+{qF
zKYxFH`1u=v0kl7Nn?3@y$(rQ+!av{eNXikxxD4=`N~WE_<3mR8XvHXqWStPuO~26^
z=;U5ZzbLs2Ii&l-NPXq1%fY1Mh=erU7+7LhWbO`t0|<d40wTsj<Zx$;=|0j)29M6i
z2_nd&aEy_KyMe5%rHq^8R8*EK<)y?S;p3NMU~7~cO75cM$BHZI<SFFP0J>$M*>g5+
zj~Z}q(yE%~WN4s+ySDJf^6U`i24DZt_So0%VA@c?%Q_Ct=ec5=tK38yzb%x55hu4a
zCMy~si<ea$M2?lgap*3M(R}CO*UBe9vD%h1uVH8;RHfL)`WCCJvQN$Ty0R0A=QDkN
zD9gL5@d!IYCRGVSAUTIht@e&#HWrYJZ4-7eIXNW&q3#dvV}sRbKS-h{|8-5qo?W@!
z`1$^^kuS)8(Mw)pM9xMh8-t5D=ra_csiJu^Di4B|$`F#Dq%q=&lZt_q4nuV4H&*B9
ziypeBZ3K5VX~73573J<eUjwudmgtbM23<J;DGJ3S5nSlNPE~<#R{*)r)ETB6nMH3-
zp@UxUR0CImH1sLcG%Q4=5=>aS+mbI%Gs<2GWH^QH6`WUK``hykH>R6R<Vr|%{;RG`
zB~}`(z6H~jeX?mfH=-_h-a04fz2ROClk_&}5FK3lYf%aH1#a&4Xcyx)29)I{=)}%7
zwzBMQ;S@~i{=Ct%q#7_$ZNk63;;DD<6oF}jTy&)`Z=>s%S_}_(03vF>*$`pLB4u&>
zsH^Vro`DV?p1n`bi)e=j#m?7&XC!vx&tJq$V$TY3$D7b@YQeNQ#ti*RdF}|_sRDte
z#O@kDJ8WU9eb^-)zy6sQ0bZ5Py4PXZb%F94CATGtm0!yZ(*t8GRZ*{7XQKkrVzLkv
zi$_TPCCOdA!9z|%iz)e#zh$qK=njCXTMqQ<g2p#}Q*od)hj-m;Ngw_SzDh4-@vFfX
zZD@*wPGH{{P&5wJ-ZFmYV}PdXt^~b3KmH&9f3QS2V(64gbpCvL`{8uQCy;qKN@XA?
z@+MBmo0ur3)HVa)!U&B+Ku)e!<7>5e&;t>*1@z|j2!?vlrI+dzpm)x}j2L=+`Zixi
zs2Ps95K<n^+0=l!N{3(5I5~NHlZsi>Ob}K)14$$Z?v&e94}Kl41>Q{uhk5p%RPNiR
zcnELU0nZoF2=Zv|`v~W&F`XS4G-!9N6cR~*DVq{C=8nSDffZ><mljhukDRZ>1<^Xy
zN;vqh&=LVS*7=TUupy#o1wkn2lVAX8c6c|6Ddm(3D_;E#!O$R`d&5Zd!T(5TW$-!?
zjH?<cBAt1<x>N=*Uj+BUW!ixqX(KCmfv|lZ*d_u6M!cAy#elQ6LU2_o1*^dcVkO?E
zC)@IG>$ahScDD($nL~s@<QFdbOf)-J{lkANug2Sh>sbxIW#H7qg77F}HG}m)pSaCG
zhsY`rN9q4GyAI+$48Ow1heAa)j$VGe&%?_Lk+H07oQiZ=pZH^0!!fyw$Zy6}N3*>d
zY|O<x9Vf`~KRAS*`St9+@5$P~b+G;t8Rzg|YPiZXCopa(_%b|ia<1yk<y-#?;Z&u`

literal 12989
zcmdse2UJtr*6v2^^&Uk9q#Oaef(Qsw6$>Cun)INAj`Us<EJry~4=5mAr9`?21ZfG%
z0Yn6(ml%T56GNl~Bp7&ed&?XD|Gzi>@y5M(yf+>r2<~L>z4lsj&H0td!)tmPJGKgL
zMG$0%mgXfx1mPJ$zc+7$Be)(?0{qx$tF3Vfe!|!C#hNhqvBgu<+y_Cn?MA<Ox~TXc
z2=XhUb?Jg}U>cJe^w@Yjy=j)vH<RxYY;Pd;`_bdJm##IZUl4y|=lfK^E>5QTQlpO0
z<nN<Tyjq@jcCL4PE+hH-p6J^VzrH`C8Dp@i<>B+$&CkVlZ;m(``L)hCposP3jd9L_
z{QQD^#v-MD$!BMHaehb`D})uoQ1$aT)o&jqy$(UF-@DcDB8X7*<{Jod^4Ht3XZ9S?
z>rrCWVrvc{o;~8XYpw-sCI>1@4liy(R%(e#p0dgE9vMtY1gYK<bDIZw`P6d5w}KK<
zzE6jW>9cE^*yfntMJA;?_OjS7RQw8)-c5*&QCT$OE)djAPVUoI!#D0Voth?O_PzER
zH8wDKrY%`@Ny-1~M;3lbDIL$5&MzwJy|_!Ir?~Et*t4}C9cw-HcX&;VjIzRhG=~}c
z`1oLOr*i9vgr`Psy98x5D=I2DjZ*k$c4v~y+T>}WMy6I?muptuetx;`&TaTtVmi^%
zJ&wjoqOtn_ICxPuVCGYrT=-7md@0g*F`UxwhwoTRC*m~6Dq_5&1(08B-1pQ+M@I`P
z`4?jF%l2||a?}0f)e1})m%T6wQ)8?k)Hl(V%AQnPGq=I96N+nQsuxCQ1Fd3JIWHXA
z)AG10ORfu3or21NFZ+rt3B3iTuJaRZ!9Ql3*|W`}gvqMu+|+Wf(JQ!xPK}`o*Q9pU
zm0QswDvBkm`?@idB^!^|pEuU6t*!*K>Xyn~`z5V|r*np0dtuwpN>xvZow6ZWg)Dy6
zqAk5YQR38<t+jRIz4+{KkzmbEC*wHHODC=ueiV;Yj-SU(>aey{;%+{2750;LKyC}^
z$anVi*d0G{qHUGCvMZ?Y*|RsAXWH7@rdJkcsIC&po(i|3q#a=76TPN3x@si*)_w3;
zI%08}w15tkr6+`p;N#Q`iSD7Hq2eNJ%(<peF1ufNvGU_0oc8Ip*95bYrYEJ3#bU#m
zLSGjt9`Z7d?K(8o`42K}D=c!RAE)0YjQS~<Q53##aJ;-*PbM>LHHhQy>RJ$I_hmLP
zoN=`O2@m4UIIB?)VRXW@xCfTAP#W7b^=6Z(j_5RVuuS9GwWGMlN3?o-d*iclD~mZS
z#$cHv$0M_Y@c#YPDr%VdpEpjwfMC@^XI3IlBUWVEh=?cKP%1q<0)9)@KFF-72w8I9
zaPNYNvGG{1o+XT_Zic*u7r>Uo1G`bHO&T8T&{iV_vt6PEWUiQ)n9QwkIg6F#qPA4|
z{hwlbW)<Jd!E}DA?j=0cS7OR?C*;X-5_L<%A&TRVjmojjJ9~S2UQ|_?7Znv9my$}7
zVA9Ao@y2i=V@n98xj<zXclX4O$rEks#`1{7>z_T%U=g<7SKrVU^5x-?<A2#a)vd4A
z+bTsagiUB9nWWuyXURkA^~ky+i;AoFHtvY;XY{(3)a4$@2o!TwDd{h8l1(8|7rE5X
zJ_Dk(>x%k%#PfxFS<<K4pt5~Zn6#a|WLk-*LZRK6JeVVhG>ZTluF|&j$@9(^Gp|Q?
zTKn~yFk>RN8JL^r!8W!~TW-<s620N#<n)ourI639stg)ax^s1njEo*dNn4aTM6dps
zRGXArvksu<Om`DpISbR(9C|_bQuC<S<tzhu(iR5WWwJfJdNfAKyH8LhxZI&FB?s=W
zDe=69MOyg-?_jA>)wCV?!?JSA2t;E+MTN=5@SP^V{^cY2M=N=%sB$>IKV;-r+~>FV
zBQA;2sWp``=O*}J$(-8L&ZWD|quE2AG`nMmsH)YS^XJ2s0>TKa&`G6f@vBEvAm9WQ
zJfG^RF58)yn!0vAzbttA#(B(qjIh%64(0i%2fIXzF?GQ?tX=ojQy`T|!nV4veh?nV
zuX=IF)u)fsqK|*R<G|UYuokuR?cMfC)_pUxR*V-}(!7Y><QYBD86M>JMc-6$NSQH*
z*L!Y+jVGCOVwAi|Byi++w*$yWKZOlDklP!CczBT;JUrXs*Y&3nWZz%v5yb3c5nlRE
z$2}e-Ms^VYDUY(=6N}A)ZT4o~<ofmFg3<XE6}@*b!mpp+Z5@BVqWI=MMBvu->!xW$
zNx9hOEIQqVIb27H<HQ@Ani9}WP;$`91!mpF+1Yt1wKs=v>wUW@t+h`Mc?n5Lt*oU2
z*y(W`Oo^#V@WRL8x)9+!DdE@O-px>JghS%2Ag|`;=K3eSJ((k-5`0u7wjd{`F^&VX
zDiW)qtE(H+v$Pxv;mMR#HM;6vX$k3edfQ8dXArt8ORRufYG~YON*Ef)jES>rxQ%uf
zPV;~GUe=O!cOz22ZXG`qyT4TyC>6Jnf7L-KL<n;JFeE*ni4QHV7LWlTz%Ge(elCXu
z-2cKLOOaBiCl2+AYA6=>F-6YxDn@=^q+cX5!6*Col{k<5FP7)OJ4mJ5ZkJ4IeP6rW
z<DZYuFMh;jMR4>g-|&wWGsZa1l$dVzu6}#Z;O%L{S8C?YUDG_E$1<w+Ch1h`A|Z8{
zAmxm^lk<Vfu&CZO>F1un?0Q#O!TQ2P!_h$Hm|lIilX|`S#~^t~H^mL*NlQAcybJGV
zBuF=v`aK>ZrsuQ#YlNv+jG(?@v0uHb#+Fvb*@|V_=*b)HOurua<i2#XF4MEh3KEd%
znPlI>=+M3Mp*n7<jfGjaR*Ip*LA{F8%pO^;#gsrAu&}fgo=V``zH!{Uif?DQa9(Ec
z?1(WAf^BZjyP2-N>Fm3{zP{Sx2J`JO&#;S~Wa*l?q}D^&C@kYeZLJlaOe;krOf|Ek
zQ+nsddt1XBVm5f1n<tCK{SKK%oUI}f`(^gEg@uJsp6KTFW3z!m2Y53(CY`v(ua&BH
zK%7F<<viv7W;B|hDX|sPckQTz*5#tC8>gSl;)i+vRYCLb2*m&Yu6cD|J!~sBo1I@$
z0?~78Y2f+gNLlADiMA9u?WQM&a$Au4n+0|}Uzb)YJU^>j`w!YPXJ~aHmHsoI@+LFG
zFmG;Lvk9HOoR_j58F@K=%I`vD4K6oqc|1w*oOkvo_ex39ciSS%O1);$uv&>}KpVYL
z0x}X>P-D6q_uE(*hd?CVIsbG%y`z5Iy9HjXB_#`TZf;W?C+}=pv5mR;v>C3FQWO!f
zwYfbbn>hID)fo-Cl1dX4OZQ4jr*vXAB*|u%8mc>cs<WX}dT}`vc0xcl-X#jBnwiB;
z7r`E3z`Kw%_sTZusT{$n+PrB^8Pex_v|&@`UcF;Mh-e}6y?>Pz(>>Ft{T#i&Br}_r
zmo_k9=E=QwG~4WCu9NK0a=XJd3<gp`PGaId4f;x`YC=LnUS3`c3{%)rnsL?!P0H@>
z?bUI9*Dqhb|M$JXd*^nPw2PaYUp5I)X~(3Ik5Adp9ism1k1VW}wRKT$?&0QjuK>J2
zN;gmzfq|dqHx`S#Cb8Qq8xOlmStJ&KtvK6glYm}yY3sP5iAh2h<(j>HPD*MjY@oS}
z<~**uvWQfmxzeKz_V)GyHfiTX>YIfF(h?=D^B?IcYI%^k#0NVLB;pGpkwZ?8&u(gJ
zdY&QF#DD0KR&&Pd7CS?DvQiFY{3iZpdWf=!mUkiks#x67sfQ+;u~EAp{+5OPI>sMZ
zhtXhYd)JbDZyfNRlM&L3DeYd_0$Y0uFXs2{%NSnM8?W|DZ1c7ueT6C&J_`!J(E`7n
zl8fXt;VFZ`U{h$T*GHS8VeL~Tv{XVCjbWk=>pXc1zh>*J@gknGC#HY-_V)fZnPD~j
zkHBbAHPT~U9>mjZGz6`s|6Q{FpH=tY@yU@2Di4y3189!|^ziN~s{1k*UqK{Jm<0U!
z`!7Ibn75V?-`zaheT@#tyZ5ZEvepu7XObN=v=Ck@LU{TwEs6h%O!RGN1{ryrEaP}w
zXlH;zdh=+U#G%~}cM^*7@;aADOLgu3JCMycgsN26e)!1A%O{mP<S}>@g1xp1{xfy{
zuDg%)#-!E+(sA8q^HtQD=M&7GY^r-eXKRKBPAjHY?X~a`KBbKika4SgL$S8a11+oj
z%TxlDMFg-yQ=L3|YO(3~jC1C+(OCrxwThy1b(u%YNsn4Bzs{Eqq`l9ktba*iqL@37
z)Dv;iVK_AVY$$;a#aM$uxZ9b>Ii{Ma-(uGlHv|a;<FPbg94Sy>TX~S+uZk(De8GG(
zIe%_neV(*G{j$dKPwAl<duw*JhIg+#>j@6<R*MrHRmT(p_SN7RelW|y*zHX<N*?=E
z07DB=7djcId35(fKIKdr%k<<vBOp7xb)43#0r}|Ohm#6P&7%gt{>8J9Bmo%ovRGVg
zF~Kql5*i@s%UPXwCw#<CMoC{TYPBVz<3Woy$=KNV1z^NdWas*I5m&Mz8f|}qw20=>
z7?oXyo7U;|0;rf<4vm?aL{rMj!yBi~zTw~ItxD{Ec-NJH`OE%p0$N#D)1)rz0flMH
zP_aC9>eL4L%?R?URiNIruh2kOH(Ii4Y&VxdUM(O2x-X{hM-YwjG&Jk`jVGAW(*uS(
zcj3SBQau}Xpb&pJXfnN+E(#yY-bvdb-{i!m_i_e1@Q1a2!s&iYh_3u6&wHckyvT@V
z!u&-ueHHd&k<f1!bTgFAcd34ViXwciuKW8jM<KNz$qap^tq6rC?yw`WMDNuW1-g7t
zv<6bLi;IhQITjR)7VnI^BByWEAN;)XIb8Ir;<w&C2y!hgjVE+*&~efahH_M=9cf=^
zmA7U$2>m=^<F`r@`xNfmn@!UcMqg1kz(gSyAB?#=P4sQ>Se%A0oa*{@pwzpD8Vm)C
zJyXN-H=!Yjg6;VBk<nN+429^{4csz8=P-K!8|#-U+PDcB@$9>q5?qJbA*@(_g0l{(
zHxPG3!sc3zq@1yjP~|Rk`zKu)RjR)&i*4G@oz@RK9z+G+q=aAAL>A$)TlO5Dg1Xl;
z^5$MmzFPfF@njuE|1DP#4N_X>-Qh})OucjCkBjNsV3^TqJU^zgV(B?a81JuQpWvB_
zU=lLmHmJ~15uy+DL6LF-Sc&I3-q%%CRRtU#7|x**1`mryCjVn_9s@5g5Hj)A`2|4R
z*o^*;Sv|<9*|7HV!+}}Dfuqs>thJee(uRK=2Rd$?sfz2WjFx8N{{X>=?}#H3D|F8A
zrx<|&R!C<sg#R{|nRhd!u@B(D-pEE@s$akJ?3YH13KtVoHjpCm>+Q%46x2j1o8k)B
z{^KICu1!$_0Eex6<ze7vFmU}$RS4?vPqf8y!0Jb>+L2)rE`-AZgugsLIe+jZf?WO8
z5vj2%UZ4R>d30FUkVeAE-F&;gSqZRWTw#OFTNl@rMJB*%J@y9VcGi`4ahTpsCq!bk
z<Dql`CY|WWq#Y7QgE3L&wi;(DYjIDAkKRrMnR_f_u*C{N(&A)py1KZW!S6Vh166%Y
zcpWn0^oPvtwH5N3CFlwLX!L7!$#f_KOnq_8DA!kO5(32#R@AvW*BLaQY1S$#8v%Wl
z2MT!p$oLOILu>1FqB}K(REJyfXMY{+{@Vlxtygwz7vlEXa1(NMlEq*Lob#D*n(oT^
z$E19Zl4<`4!c&Dm(?S623*u0IVPRnb2cGwrf(Dkra;WMKA%$xIIjlP&^kcT3qwx;E
zym?Pgjq3|En80GdoR6l%Lue++-1exQy;3*ZbO5r%^osx2fzN>i7<<>a@`gI^@vH3V
z?jtw~f5Z6Z`EpQ8?l*kiNtg!=b>c+piiR$Tc>|Km_g?|(VGdQCC~-a@DA+SF@WSrQ
z;OBC<KaA@ul)a}CL^nzzY2t!p-nL&V%FBHfiXhd&Iw_?|PG*FTy^9+vbJXW70yQ(X
zv7y~G(2U$K)wfAB^x9mt+S+mlrS8zk;||po9ew55ODzmc(0GENoQrV_W8Haa(Afqh
zC$B!s&%X-7i${1jOuB%K!wV7#Q);HuZyk^h%15C|!S#f~M?W{o#&4o)-!)M2%(cJR
zL|LRC5GT;4ntTs<yVM)MnK6;-k=Q!kCE%F$>~ll-9uk5))@(;sdrR;+`!RtBOLp}m
z$n76+A6D4f3c9fYg#EyWabT{e($ZKw^78TlCTcK=`F6_dNQ~l|t}rwOSoaCQV(b&(
z{f>%QYq*JIM}Koi_?X35=q@0~3gV2AdLYtC6z26a$vSK4U0E8jlGBi`H1eZ(J`waq
z(}U@G@!6<gv8FdjbW5FAfuP--D1E!~)?imJ7ePi|y#1G9p$ko|za$z$l{&1SaZ(FF
zbN{C*pLF-#$ZhwIStK#rwYcgE2)xXQTp0REUoE5~RbGF7vZEigTv_~&?+aXX5y`Me
zwBzxd?o9ftR>>2uiLK-DfOC)X9d{yC4hL(4;1yJPGiusX5u~X&agD3-@87@QO4lbr
zZa22HEI?QNz}Z{xTr)yfq_i5scbRr%sPw|bwEM~wod5M{b+saZEcz084TqWM>snLA
zah#?;K8u&AtXW{a1Xuw2pjLdSs!a<++tAIe=m(w*Tv?o5ke`ogCT<ksbXKhDd)Ey0
zrW@Zz-T=I}xqt%}4v~A(DMOvH!o_d6_vY?|WtDf1;DH14?&6t#WBxUf!~HqLx=^>_
zT65S;9W{6oR@ryz8tfLrQ_^cIN!VulW%U(=B%L^+%iPt4YF}Hm&;B)(Xb89th4fLL
zG(~2b&S5!^VP#Zk1nDM0B3e-H>STIAMmDb!u`Xn3pB4G}b9IPeDD+R4_nkC*T0&l}
z9$8eRYr9N}MqkmkP_kcJ<-%AwqoQjs4rwPot_5<?1HyJsPml53P}iMG3D9#3;Ns4G
zh31D!)NT!&X0vEE@pHq;e4sLAS9Y77ygtW?FZZCSLehYVR5T=)mKuaC&Gd$g;$MKE
znpC^`=fgcF1_lNnmH*CRvgn1tw;D%h@AglBX^gC}Z%vF}Y-F(Mg=Ou>okTzvfQ5PR
z-~r59g-ZgA{Mtw5<=3F6jW4ZiOCeNOTY##5RbqEgM|W43p;Q0d?#RZjj#0Za=5w4D
zyI0m38`j;4k{YNP+ib>ZjdP)-+n(mvA&a@)x^+_Fl2Md&OyQsH3k?s`$-XykJZj0t
zvW;3td~t-Pf`X=cExlN;e7tGY1=9~n9!y($K$6>0Ib6VWSSi+I%D<mV>{Kr=%5)PF
zTH{*)(gW$Hq~+0(6T2T~$KrVqg_Lvn^~z2ke?4XO`s1_%1a{#!6;SrT4Eeu_&jE^`
zKMXpJ&t!Ysf6%v@g?yr}L<{x~&hWfz06nk5ce<-Km%YLT0C@HABVoM|$UBTrJ{>6e
zU{u>92e1pnA*_VjnPY@Bb%fVC8^B}Sq?ft508f<oy8lO62tb>}ljhG+WY{omC7A@T
z_!c{ipGWod;oiPJe`1IyKUCF3*|TR|`ird(l^lp|*2e6~X+GxkPc2<_TO&Vc6rrFK
zxV^S2_B{ZsqZ4SbOo<yDgBKPRv*0J>e_-IFUwh0dsWEAwDA2XrV@vxPe!fvglt20z
zA7*l%;O<kHk}jkMKebL1tknzkN>_s9@y@+L-9$W*`2v(*y0@iuXZvR>wcZCt%xo7W
zYnoPg2-o;Pl-JDJh-AWvCP~peyihUrnhw`Cad6t+^OZk^Lsh-K=izUl+esYT7kRGh
zf%IYO1L>wt&N8O5>>A__P;VYydK<@q;OTVgDK*W=^YV9zdemZ<6kWQ9okPC0rWfty
za=_aqDt-9DP>dG|vhNTRJ2$>?c5H!P3CPoL(pO5&3ZJU(9lAn_7<{m`(?9Bvy~=?<
z|H(Szn;I#NoPP!)#e7OT(;v_n$bN@PoO$n@cdYR@)|A){vIqn_RE3F&iCW2~k@;n)
z(n^#%_gG}3=Roy*yOK7(@i`Cg#;MG;<xFjhc}@19W9Bq@l|jCW3FvuYP(Anlgf^zK
z=JP+P^8R~;gMWt@@qc*D3rXw0{RdsI*a@xjZL<iZl>a2<-S5_2hkRs$gk*zX&bbmJ
zbPND$q!h_4^{m@fwSBS8dv}JTuh?b~kh|8>zSwbz+<@vGgwq?BD?dMOz(u9cgqfDV
z;U91t^`z~pD9S9@QGzX6BNGW}){>?*6z#;5o!ljwlvXvsId(cLR3rYeri5>=iD^Qu
zR9!F|h0PMv9;*``XqD^Y;K^#%VW6-mPG>*5YGD)4S!ANP0DxOTZEfusd(#fVYyb&6
z1ZCgFNh>hRyV0_L<Mfm!mxNo%ZNBm0i8K)BLq#?Q264q}MIJb9$26}iS`6(?L?ckW
z!O51VUO$%Z`F0CxMniEG61XSRu5i)M&kyC*J!+qS+tbCc|M7kHP>BurV#TPs=?aDU
zP!VYJ-E67aZ|Xp+O0wC9@@&ZX{1-1ietdRu+&^hm;RIXxY?H#7CPJEb1f5Rji)<9i
zYhS;phakHR&|Ux<K|gbW-^#0TFNlA~oVjyzbN(Gvvy+Klib-DIbqB3${GEXpR8OS1
zmOFKdflZ~uyK(y;5+K_h+Py`~P(i4DsKpAP`rha(y+cD5@7)RDc2-$Qf?T6yNCU6?
zFPk6#(8>VE)fl8kYE6KWp-nuD1n>Z9!7R}Nv_#P9uZrXz0~fZ5|9-<w4SLf5&PYNf
zyB^d@TG<X}QFKa5O2p8nF+ge}P>c@II&;^r;{nGbp8P_8_NR?B1bkK;w4F*9S65(v
zk{a}%2yKx?V-4V+zj|c|R$83qpK%<xd3?nCyFjgOoDO`)jmf+_GgwAAe*CyB&^*)%
z2KD%djbK2{IOK@=Wuc&ijmY_c@$bi+rt6)>j_B<LLi_s5OOOQqb^cR5v`i3{4r&2@
zh48uDHVMWH+<GdZXK3g+HtHwF90i-h(h{)KXH72f)dvqY0xU$Q7<wZ9YErk?W&p1=
z3<|2M#W+gZP`kRsKtl$nMT6dKD-F622tgY3BfGc2)T8=!9H)t&NvYHaPz$&(ro0ol
z){WCgW1B}cxt<CqAzpv#-x^s45C9rWdqB#GiS{~^+yl-kv`vg^;<Uiby>^t(`&Lwq
z&%Vb=NsbvELKE(F?CWKLv$oCjj<j>#I+?1}Qci1<l)Mo^&#!RR)COmNC5r#E84epI
zAZ`2GGQFTgs6fq+Zd?|+44x2Bbm|8<uapCT|15y|0xY|~$kMU6cJ693I(VNxbH`*<
z$R2uf`sUrBHSUsd!5U!Jf!c3xx5e~ks)P`$CNn~W3f9_{SWkdHD_jo)ls}Q?(+;AT
zbdwc+dE5b%;h5evLKx>r*{#7d&2*HfPk`7X>8z`F0ld3AlOWKLDE>zjv-0w^csK1t
zH433TT36Te43!Z2pL_Iuv+=Tm2_2K&K<xgyB4CBjcc{_8KP?YnVbVips$$aLx`ErB
zN65&Bq@h6%hk0jGOu<J4<y~U8!BM_05D$d&L?O||Hv6@on^Z8Q@n->Cr@!4M<@?p`
zHmovkK9vS8e}XtWH6=y%Mm++T=#S$-IK#RL$~yfa&c>EL0b~IHbV3tm750kn=llB=
z4Q&*^UGFt_OdlVxltw#G;IqL?GfYh`5Nr_9w2!2Ds<*iKr!D~;RY>vtDYfowMKI+~
zaffwNvdzmJj&<{d5}S>dXny_HDYDM-8F-F!Z-0LQ&|!eq4T8xt6i@;{kC8)NoOGp1
zJDd)cHZ+tnGdkAtNSmnoywj=AUW({I^oz52038=jkRZ)n9ST-jj$}@Wu6`CpAv1vc
z`0@njWu^RNx9y4+rpfdPnd0T*OkzY)s;&k_%hBF)GG}phg~cGP%=lz4ejO~6^ZIfL
z)B>z|B}3ibl9imXt#JSn06G$Qr!7;>#&Eh>%+w}QAodnR!j!yCI8{BfAYCLj9-m#X
zdB=_&4$E`n6A7l67FP}=zGRREyCimRkqKK3onM~JoMZ>mSAif6ee$QcIe>>S-3&UR
z!6Qux@4l%Q)JtMFMc}_>jvX0#OQlRMYzznJC^%#zdTf(a$T1h9_5LP)U?a8}xW&P=
z`7w*tJR?F1_#jcOHn%LR64i{_Zec%mR=>MmndNngSY%6|5bq8?o^!81=ooC%d5{cJ
zptFTMRslUl&JG<JZt*SI=CAA?4wgF`X)!9TS;8v&BjR)pw;AcH*!wew5=bR{*tmj{
zbm7=rlrL|%Gam-V=B-XO2AZ8)ceZJ2SJ?M^n842)ifqUjloOIxs4S2*uLRJ0uh40+
z_=T*6+W%UBT}+iIqj@Wp?$ms*(WYsLZGAQTw<S}V-l-SXP^a_rj{}GEtB&17Qv>E{
zjbry(yu9(|Amh~lacGh9FeT3~?v-83Zd49JHBXuLM@2JW;}w@;n;p0rzNK#C7OOgP
zWziQj7-FVWtI-rKlG|Pjc~L!FbB!ApCL^!VtW;h6O4QLHYb_=zbA}zfcl$<d3o<j9
zHC1)35V8}nVShJ&|C8SAin7r-H%sN$v~+>g(2Rl{{#}l$T~ET%mK~_9%<E3j0|IVO
zS-ay<+7)oH!_%(6TMVSE87(=cs98o^F$J;ZkP0BWTbcd=)v<0YslSR8pveA>SXWSw
zoQrfjk<{L!9B8e%DPwcMC$DhG&DV=8av)d~S?ml=iE^JwJ!_vfIXb1XWCof%7Gi(#
z%`LKO&B${gO1}%>idey#w_VT5B1Yu7TITv;j-&io>qz?8z^>naQ$NTYD7}bnit2oM
zv%wc8O{ps;JFVls?JW>i(PX&FDOpn#7RwQgyoK{~Q9i@287OU8s<Mcfed)a(EPGk+
zF-_eRSuH5tA0zgjKp#m<Sd>Q;h6)W*m3C2`6c2LI9o|jEf^_C=51016NxmEw356sA
zlO5N^Eg`y}LH^WBvhvpAjIz_2*xY5{NTI5<gGy9e%KMywl;pn4l%VUyR>z=82(+-h
z<-<T5GJ+-qP{|z(K!2^F_Y$VM$L9IR%J4qDZD)&<`OYNX@2SYk@*6fsJITtOyV0GW
zaKbc~#yXkc$LMngy~Lt6uoR6=m+|)rhl*&|iIeK38v$`OM($_y80q&``!TL6?i=LL
z{|vNa1W03z-uYDUjo##5G=o4yH9QWVFMacCCK-ei0)h2k%6csnQ1%Cz4m(^elZy({
zpx8q2i#kJns2)$WT==_b*sx12t>Ox?Iup;y13k9{st76pf=18q6{QSka<0`we%t%-
z(#~@!$B3u2z|hv03ZZl-4^C+Hp0b1Bz&PutE1GC0N%q3NDY+!(rP+3idELY$59(Wr
zg4c7%4CdCJZ#EoqKG8il`M1P3$QkN<?GUeT+1SI>Br>!4UKPz73P8i)zEc+2_qXk*
z|CsOS|82{js}^A+^>;W8qN`8Qor!Av^5o^V_+Hh(I>!)6J}j4mdhF;&7oe!ovmfal
zpEk2&xH>kAcN3o!(Hf(44|en#Ms1?~Y=K~CfaUp*M$Nx!6A>R5LmuTk*%_WBy812L
z<<_n8u0_z|x@<=O{2Yy3G0;<<{qth+hq*pW53tc0&{6kT0vgoD*m6%o35>1KMD`r8
z7wxleso6;e)-CA>Z91)5{lgpR^m=*Y4wO2f4eNNK`K6zC+c+MpXfKYuHz~zltZ<{8
z4XPW~!)=mJg$73kJXUn}SIQS%d8%=jsQDe0^`|3tL#d?a5$O7193u`1I-|~maHvZg
zt_kqK*u3rts4l)1As%U)sAPIAsSCG6CG58eHE2ydWdseDCrWCcqg`s2!Ulf+)<Vi*
zd6gyLS6I;2R+qG|mlS~ufc99%zw(HLvr2c)7rgtj_DlWIA-76uu7*g^rFe9I0|M8F
zvw}IZL=6#W0$iI{Tg!r;G(FV%P+k2#TG$Pe&o$H*e*pcUr!0w6rOhHnC73$jmP1L|
zv72{4{PSSHQ!zk`wRDekD{r>BA*NMaYumn)*HFJ9^t&exq?T}<fYwu$NB2cS+uM8A
z5+Te94+~qp(4}nxtx-@?a`l5aTznlIKpP1_!zX}vHVvg!r$4&U78*sr9o(<)<nG?5
zvh*nn<hKIT5)2?cdS_O+d)0X2g8t&TiwtGXeel&~NI3vU;-Qt5-dk`;`1|w+v`5bb
zOmoSaudn>2`d;<6mP_u$V6@QwQ_wv@QT3f~lRqIC?T}7p2HIJYLYtKAhiiirm&YDa
z+LBnrMF>3?P*2fJh=E>He+@<xe;o*S!uSBn<a{2~j}kY2s+9rx*XS$^a2#FeR}cWl
zZ%cTC1N8nmP^<kWGD2pgQSjKrzh~=xoioX@P5k05AJUcjp<0*Wxxq4a@BZ*Dx<^P=
z3nOVs?4Drv+sMDU@$tv%5?LO!iVrDrtv{fv5IsM(Sr=|XVISk$nzPnksh2wroWUj>
zCutXy5jv*@@3%p-fDUW8PWR+KB|F8~=0~d(ek@ozc?AVBARC`XFPJRT+|%DraBa^}
zQR3cP03BqzpzKkVHHa+R3{ccBCb~g8%LHT+|EBSFLYq!2=tsJ6(Y6p)U4QY~Y~UxW
zz|Z#`ppEfxvQ7%%?^Gz_a@0vH25ZF6>)RX=pkNy$4G$PpOW{1B&!>2t@k0L?_{f@y
z*<s7KT;ZMzGgQj0;o8AJN4@3&jPhr8Bo&uI@9%u1%?~kL4U-u75?VR?;0i|sL>oTe
zgAL1BVtIg>XHE*O3i?{w`~hH&N0c;}VobdPw4IeQnCl9Se*0fpR=UfLHpj|0jqR^r
z?rABva^qF7%-m+4VLT#;{Qy|vVi`b=i@B~f;%6t$o1sqBGzCTn#cNMwV^sFVw^M-A
zWWY$ukZyXiP4acUth~JFt6SjyN~2Qy>rcz&$<9G)KfYa@uVdj@e^>zFDvh%PS)G%a
zy^!GIT|~aue>ry?z>{MeaO9|tNg0vYW}S3JIqtxlf6R#6WD8$COb2>hfN0uk`yv~M
z*le}cQMC^oHlw@JXTvuL2Yp18M`1&>Ue)(6k!;`zX7E{t=Ma(AkfRqgk<vcyT~X8Z
zJB8^UU?B%8_FB7d(NHLZk20yAbc9{mGS7P<8NXdkj`b#17cx338=mzTE(F0yYBa%S
z<4&O$(CMo(8B$m^^r5`A=}FW)L?)L@TdM=?%gL0^rBc=}7nAR;ps7s-a)SkE|Io36
zrU`$FI>~P*h7`&fgUW69?k`}7U7XS9X1ZC*tcCGB(Q(u2Ld(a6dmG0qgur0xg*1}d
zUp__Je8U%ba3^q)tE&fUmdhFJU^etS<){r(tYNeJoN1bZM&F*h8@3JAg+1v4KMYtp
z!DE+*vYAxfLOeXdNo*9_hK@FY13nU<OhGR!lt;hOXn6;mG}=B-aj*I5toyTuPfgc1
z>Z3jAfyx-q)Og<^X9B7fSk(Bt)vRpVB~pmy9aLxezUSShlE{0|p%2*|UX8ZWD&OjP
zgW7C9%-MT`?BWc}YeU;3m#~>nRYRrN@F?lvnX1XISQPe%Ys7qDG}wZ=s1z_`43fwj
z-1#MSWb~yRz;Y-R>L5_-R4()R9fsy15O2E1lWdA<+?fD@fbYB1e!Seco&S&3kw1Ww
z0rHWxf6oW{#3}Pvm(WJe!FJ(*PtQ=|!|0b{$S%>s$dK61ppOrdM)&W(i2K4`X=SkY
z_2xn!G?2dnrJ2{_IO{<uL#>nZ&S=*G>e&H71{D#Ep|J|mPR#yI2TtF(4MY>QGn@xr
z-VFb-8Bbh}G$-j4lUIXPz@y*L0c^ekAXYS(+Nk6tE2tZ`nSpkvffuqXnNVD;2eK1a
zDozvaPwwi#FP7b<Xe>1}^4}J}Eq#UjV_eFw^YZ@b=aG`HsOjRvz{Ln?Up_R2U2e;S
zet|EMQZ9`Vd(poH@Pqm?Rnd=Od^4`~9f?qfKSi*8qcJmu-+W`_bU(0HkUbzVTqm!x
zOKRAIx3j3wTwiRnI+p}Kxu6=pHY4!M77hGI-tbL7J8T0{EAc<;9Cz1v*E!UcZ=u~$
QM&L{9vfia)^&5Bp2jeE6qW}N^

diff --git a/reference/Rplot012.png b/reference/Rplot012.png
index 60203d382a56740b107364f928884b95cf08f8ab..29b1307a8adfc8058aee3fe56b8b50aa67dee114 100644
GIT binary patch
literal 20264
zcmd_SbyQY+w>G>80i_WI=}<rsMCp(&rAz4)l@2Ls73mOBV3Pt8f~0hZh>C!8w{&;I
zJJ){BGtT>d<D4^|GrseV?~iYft>Degy4PC2-<<QB*LBSmq^c}Oc!BByf*^$V<fYXS
z1Y-jIgNFmJc=go2hc7s$3Ubo$75+Prs`7y^=k4Wno*@W68TtpKYt;J~K^TyG(h{0(
z3Cp9;6E5zYh^_Z4K4uwwq@qn^f^kVT{Bm%x3<f@CD${qS)TiupZYjh$Hod7OB>`F@
zPkVKPQ;kSwG{y06^qv0EJJQnAl&&BrcB!}=V=%xxcs1(R++ItZLlev3va*ZV&FZ9x
z0OCQxvE+^?Z-24>rTCbf(GMXdB`vwG<$@a+7#J=`V9dnCbe^)_<|++Q6BmIrQ{>}(
zk4_m1lkN<q+}}ApoK?$rs?Sc3Y{ocAJ2!kWM*J1KLyv?<eM6*~-fU>qe0B*649AUI
zCZg$xzGcK&g)c3A^}HCyMN-mxzgIEcP7ZhWCy9OVk~;!^M6srQ{`@P6;==3oR-;og
zgG#4{^Q;NJMrVip4gp@bD(9I*_C}t?-Z8&W^ySNK_wA=N0``bq?Ey>eUd>vHQ~=R_
zck(HDzabA*^K^t*c$A9hm2>CLmCr=$P3#@TFF)a;{7T3_;=8<To!~NL;kJ}pM#a&+
zPU4j9z<brN-oSPA^{{OfVYK-*8=oeB!t(jlkg1Gwm>7@D%^6AX5M;G{nuyXLqlt^l
zenR}FK_$;jEDkYUXjhl|(V8k7b`uv#m@EcQO>P%0Gu(*}2IuURln7Z=l0vm|9;B3(
zUdF$CGZ4SDm9vWAg*bBU+O_@7)?0<HYlix+qt3g>@>gHh9nai~74s3z&d%oYK6cw)
z8>b&GvunvvidR)ve_40BlRr>o9#YeuTQ(Mat9BnV+57N15uHdNce?=AbW4O?Q9i7R
zRHkyGs*X;O&&iUJc7d@(nz`wCAs=rZWjBwSzJ4gDLFGgcYm#p^i_gAnDCLW?ac}DP
z@89nhJ1_N3!U+ZRFP%e<7gT&C1!!d1$Q#d2*UqMXuqG=WBs!(8`BB$4+$;IV6*@>2
zGnbT<F!A$0)YQ`AjeBTdz=7b^joYaRJI%jA7%*S>l1K(_Ot*yWEcFj>xvq_I=yS#d
z%BJlWkDu<AO$|OtE*ZvRc-dgAuV+x@a%tRiOR3BI+acM7*UJ`gdoX>ul)97n%zu(^
ztX8dM&^-J+4i}v&aI)Rq9LH<*w$$UJ(dlN4>FM#o?zq@~1M$}Ydhb}LgR(Jqh2!Hn
zF}t3+SPU$jKsffG5AG`o3~|`IEM;06W>=Yn#)@-P>P3@@*J}3YybrsRr=~(~ePfOg
zG4*N8`TW_hWA2OCD2BMVw<t2PKH0Qe+}J32G+vp<%+Jri8uhV%sl?}o)B1!2BCf0)
z?v>TYMey$Z`)02q5p{F(Yn3i57n-=1Enxpzbe5ZX@8lRUva;e{WK~wIeG$&}Y*z7j
zBf`gz1hdL@?OUza5!a*dBp#=*%Jv)06wJ475&N4=RByFQ-Me>hXouR;QZJobmML}m
zUX?Jud%Z8le)8D?JyQQIjM`!GQ|46t3oJG^HsA3zIW9`SmT3$G76Sv>+}zadc_1Ny
zq0dQi>@DVGVAtnvZf*{D)G)ugT4YA($G1x!&kCFt)T<8{bIOl5TMOa)a{WA(qs}T<
zJJ|9jE#b7<C6<>~E9U6ff-ygw9u0r#5T&N~+9o~RUBOrCUXSoO#_~BD9)H`{%|ke8
zKX6(#xmUGdos;oRUfM~-;-N?F+qY!Zn?KoI@F@LXh%+FqMQ`HcLtsZtpB}9RSj6E4
zG{xTa;Nz^>?kZPlZi|E;vgeg!?v1)8drhf3JM+@HO}uzl7tz+%*8F@fF(81>9WPQI
zyTO>mb==D}<lCjRB_VhV1_7-Q`9&7Rr`z#Uk5+5;3`LIDo80y%100$!Jsjv}j>K!6
z<boUW!Gh{%YwK-~-Q{;B-y-NEWI`@C6y5ZbWE}B1J8>{mhJAenjognAxgAM&NeOUn
zyR93;t(c^!+dl_!a7rBa;7r1MXR#OWwdDLr1vjj8Fl3XPhSdXV@f~;fXewRT!n>7)
z33YUI%KyAoux~g=F_3&UN(b(v%j(E^NOIjOVze3=iWVE?RFzxp!VW*K#TakTcjDo^
zQrh*|{PRP*+@4Wcc!kGlvFAt9>efMl=YeA=<!wyZtNjM9w0SkVL+?r;9z>T%DlWpu
zQ1V$*eha6i<`?TNwQQ$Eb8WEvj#@b6Ze5wGLu^H9X{XP|NSP`d`O8$c^JgCG4Y(2U
zq9mkht?MK_q@<D9$LM>mVqnDv2FhdNaG@{#Gv`Z7OOtW=#;ZPf;1B6_h@XN|#I0bN
zlU9o<63=~ab){SxGK58DV&7D0myXy)c=Zrs{ZTysxF@%zrKN6-nA5g}+r{WK9SI4E
zp9+hmU*BEVSdJf5?H)2f@EO;;6mFT!|4#Y-^Jk;rT*50@RSNMXk(5)2-sw-7NoWdE
zQVM&=`Y@xV#BRK{T=?=8|IYcMw+lmcK4QvZCr&M1j#Z=k^Jx*fBM*d@o;04G9Nl*O
z^ZlL8>UbS}Tn2<)OHv<%ne1?NMg~r_c>`CdZ2wn2f~Hzqm+Uza)w2wSTeoga-p%B<
z9SQ7AlVcVZriRriTyt@LWMq1Ft&(N|((WyEgS~$JdVjFP`?Bl#p2bB=ea9cyzPGo3
zE4JuX)zgdbb2sunxCS8^FfyW#{%oMm$7hl7EEi6w<ph?-Cb(lxA*XHj{+`gvf{^af
zU-O0KJAHZ^a0djlJwz;AF^&VojxpFD=6$uwukjaW@I~{n*x3Ow+*Bf3p`Jgyaq(71
zyDRhEnZ(GWYM078ck@<;%cfx~$O_Puo_Rnu)74yp5VeX@NwOJzkS$6;&3AL=4!SM#
zDrV!#Eg*Ct1vm_gT>c&8;zyX7?%s~IBjLK!`*3<PfIi$K?J^!V7gSY|tzX<(pFTCf
zRt>wR5RLeb26!FqrpQIm?T*9I^!3Fcqwv{{g%VQpefa#DprWFpd~eKS<p8QrQ_m+2
zH3NgNsVP%^=Uy#~+%m_R`_+(M1U&b-VDsuKCJOP+@Y$5pQlHj+vWdT+@v5kVCig^f
zTRW6S@Xjg=M-TROS4P{Ep&{K##|o;^?Yk^uVn*80*ZfE%`<)BnXbz3%SpylR`~!3K
z%7$7ODme|^7p|#kYF>@z&|~o<F@?Nk3K7M`$}0COK~O{U;lmKCyb4)_gfm7#F_udL
z0{*sfJyNBd>g-37nw@>0fQm;R8xw=kk0j?oE*YAApl*4e9G*Amm+|(lEng`emS!U_
zT6!NH9egi}<$QiWyORM0Bto@DhZ@M9sUCZ)rW5rqrr?Iz?7DbPyukhHPe@JHK3rn;
zP@jJ6`5Y}AyYs20$ibheRFz~=xDY7CH~mPaQUd76d=6TTP+Y>`=;-)$rEJ``KUkV+
zssWdte6-4yLPkbrs+q#5c#gxcT3ckj9?SISyX#DFOGM6&w@d*&aoAdIOf~Nw(tEDI
z`0Z>kU&(p6y}&%?HXY_?rqmZsA{m=;9W6mY?TNx6a8*G*XGh~M^nA7>16ghUwPz=5
zA#riAJMMbyk_QmeD-YlzOjKdtlyDg&Q&gimo-_Ihz$Z0Bo%#y6<3_3VpsC7unO!pF
zok#LrJ8%b6Qzf?-x>;xL5PHE@nu5x8Q4)_758Dr_WO978Ioz-Kf_%b$;jUr3Iu6dd
zW5=U4_|BmEWOw*{OxR9|Nph5p9^>0OL)*F&m!p#0rOKKU6fLkQC$M>nCR1`h4&7h=
z(y=VGu*~h3<+J>D#Ut+MP^G8ik(Xj$$0M6VPtl`71wNP7qOW$|ThyHu&z__#RSHap
za;uA8mc(<I{p$&fmk66-)rE<+cz#wdQhd@gKTL)s??VgHAnC6Ujl8Z2xvt(+>@J?W
z3zxbZ($1sQS!gC#_-sy@G)%U6sySpOal`NYMd|0yD@Y{2p0m$}oikN;c9I_}<eWWF
zVikizST?3ed3_`1$UyC)M?r3f3z;i*!f;r)(Ug0s@;rIz3NgV?d2WavEpy+tpQwLq
z*K*tJ8=jWd%9}d`O=Fi|;SiERc!nLK0AT}C8sAWba)N-GzW2clx=a0<Og2u|e2g?T
zHSrkzVtK8i_a|NvOJ5};Z7^01y}~U^BjofwkKdx}>d|W<IE0$6?$A^zRUW;WPIOjY
z-sOXXgY#ulMJ}c`HTzQ`Ok_cU!NC{M%OoWVX#N^>G2Z&3c-l(;mnSM7tl3MI>+Us@
zff-okpXNMeWL^s_ef|Og=m}S4+V@$<VJp94mdfE|r$40M$j4xL@C)nYVcYo$fX_<R
zr-w_Dfrs^!f{u?yjwhP}SgkWB-ETu~xQKkc$C`K*&~4XhQ{-D%bM-nyea?p?)8w!W
z0JCJ^{ubO+04Nm%8+9;3t@0>T^C-#RCjIEpZlFWcE3{;^Ao<gfb-B^K2d<C|r?PYM
zEPKv0(Zo_{GhA96BbHQBh0uw4dv+bj;b=9nwzct(Ti0E@>lje>eCzRTxGzU`twn(E
zE@oJKTd}9cfweUC-m4l9N=TsDfHx0x8@f%Ng9Q?YJ!D^@KXtg2Pa%o70T4JaC%;xG
z*MZmb#q6IyJnjv72337yS85=QJ8a!-C8LYT?4*7fi-wrTTFpc2;nKFeqn&=EkgX0e
zF^h4k+TpuL%U@oPuTdCz?Yu{f_oVurF~pZgs~+MpN~@_w(teZdS(9dtln)uQEUzVz
z{H#}|obaq&uyJ|=FQ)Z9m$7=u*1=qIh(&isvl6wi%jc}DEE1?nyDk<3j}^0#@VgOI
z_gQ^4?#?B<FJ9mH$(}!8vj<DlNgWI;y|LzVrZ?g;Y=a`*@Fl%xnEM+o^b|h(P4vrk
z3it0*om>AN$xMB2J#vW`U5@dyQ`aLUX@#r9rIH`LYvwEI%9bI)l8HWHXJ?Nu>5wm3
zv-q)8XCkJtbDM9BIa%SyKbEEJ<45TPr>=XZW7W@1m-=$UlKO)n4M$-lWVr0muET8%
z1%S@JvL;RCZI{h<e`L=rn@Vh(`3&dxpCFY_eWa2TO~hi-XcJ>#K=|SGPXgNaxB*Tl
zx$wHcy!EnlnwrOf^iCYr!_=b%<yfwUQ%>t&v9}$z0NXjh{%o3Yy!QD7K4)U&Sx0kP
zglI4+vn+y$LfhcC$DNm#C#$AL3TO~PqRmr^i^&c1DfCLM8(@*>_i7SIFh?t$r3?&c
z;T><1lZlb6oE)jS_C%;ffTx>w%B%-pK1uevoL_r*1M>yKXEWpnC@x>E=FtjLUurYV
zWji8pv<AH#hhAy@jHAi=M1v~%wY)Zea+wA|>W~(xdX<XXw6tEgOb6+Sl3$R}(weVK
z+7!IyS5JSoFGo&60Zn%KsO`JxXf(S!v^vlK#%Ky4Zb3U*XmL$o+n;Yq{3%6z*KcTO
zsC>-<A0L0CQ;we3y`AFn<qZ9N#+cUEA)J+XX9pcVaL?jK^ZlLQS}g96T?~`mh)O>G
z*!isV!v{osYxhdf?%luKPWXFUn2+9Pw>d5DE)QOWn<0^JShL=vp@L#FrB#YD+tJ{Q
z$^t1r3RQ)L&$9zWavgilYFj>u1Gxyv8h_rAEG9NN_yf8#CO?vaFHeZkik#s1^ZNOv
zUFczN)g9ltfB!y%UwyV#jy?1flKnd$y(}yfpCmoUVU`O=`?kQ4TeXw`>{C?n0Mr5?
zd}$Hq;Chpa%NXg)GYElFFEl8vG*oDYD;q(V=v14xBmh*vM%DNU5p=JJFWT15#2DEY
zzZxmAqF_;sH9o6>);huG^Z@aNKs9Z6dG7qHr~P>b<?D_3)2HEyuSefPcC2$=dXV6K
zxbVHTwW;A1E}9?+=tZm5=--xLAy5+kjsw_o*smnep0Wj^$m89+cLPtv`K`C)V+`G|
z*FrY++ajxgw6_8GjFBvefhr9BxP55y2xQdMMd$bdD}8Af!tZ?AGiDJT9X<5|mkv=q
zyI2V&Vdx3{+GrI8z0a}3NV^a%y4f)hxHOd!r~Lj9?hcWQR(+pekYK{!4Owbw6E0O6
zc@OpU(7a0vc(&*!qzR%|xWuLXR_u@r){jnh$4`#-T2K^8EOuP*;|(l*^3(C|u6~~;
zZ@$b3M7{L+AGf!?JMJ~*6EE?(TvzpJMBKP0t%O*}f<9DNk9a>*QhNHOW9W*hPPE(P
zYl^QQBwqQMP)>z%x09T$ot=m=N>59bgizeNOe^d{j)hA^4kuq|z}#5?!NKHDOwTDo
z7g4^LowF>J_=89yQ}@Ymsg3FKK%tA#nAe`c(VN+ty)pJ`!+c6EW8eMZ@lzHweOHaa
z75!cE37P_h&o6y<;MG)BebZ->5-UgVRot190b%z5Pwgx*A#Vv%BD!I;m&a;`$7btJ
zR_tT|^}TMLA%l<s^sxasE|}M<Z=j?<SC17<=pKo<^q#5-_KoLv_ns9-r9^WYSkm)_
zot!M@<d>G``Y-kO^aMh&3IY7S5kGz`JL1%><iN_(eAOy*vKn4==(@)mG?(l%w%RmQ
zVnto@{3Gm+Kqz<|wnLHQ8jF>-^x3&NITyu_20NrBBwl>)=%9c^m*a(L^jRz@X^Vf<
zNux1p!Jps?6GRy^)C))<*)?N2PZF;^$Ty@dv(^asz<%S#lrm<QvjITT?@2!MzZq|v
zG-5*`iWP83Ju$G=SjyF}P~TqcRroVI8w^YQ0G94|Qq1Dj&(i8P!<zXqGp#Wpjg5`v
zQ3dxE6rlapXN1K7Sm?AmB4|PjAnR0P#Y1=VP^>8V$6@T}<R3#KoAArd;{Z~Remf_N
zdVTFmSI`i>OwDIa0&A>XLEtqPTSGe;8ITY()aBf75FmE4pz^(~?fvMbR6g$Kn~y&0
zSKOKDlsY8(yMufQS>FB`muFDm{8N`-Xgk^28H({=c9>V|xE9q|Ty*N4<}1UW@(j(h
z%(Y#rs<lqF%H#`3W&6#;!MwNA<4hYV|8=9|Gmp|MlE#ezO)XgNpWg=vntUzd<a*)X
zN^XC3&)y-x0w%(NR;hbgK|lzy_6N4$>B8&5m<IjacS;p&ZR5F1`zj~zs%Z=<A$rlT
zD=RCDqn)cpA<aLdxnP*zU%Os=F)pKF#bub^+@6V@9sh-R{YE&quyB>mFwYf({>iNa
zntqJMGFNC!r)lk#m5BiM4cQI6b*LI$cM}P3$zZ$SDN3K6lS5lq0a-;#fX2mX8WVw^
z{dOKj&s*lfgUf({XnHa39905wwo=$^I45lr7Vse`Fz~7|f-<A^2n!R_Ti^fszH#~f
z3%_s$f5KiM6xuPUEc}&;Bfy;5AI!v<b*9Rkw`=L^@nm93HB<VAIlx8m*|3JntR*}w
z-cU{L#pb5tO`RfasE+CB>1}Z;KqK7jx?`ZKHO=h><On6NCAp?nGeCwyuOk=Ok?gq#
z(dH(rBNd1*-CcL%u8;RD_8O))>bVHy%sQQEkqNkOy=q#Qva^NquzJQv?y|GDZYVAB
z!fH-sQOEZ$IeU^zck^UrUx;a)$qH|sdD%L1-^x0}Jbl?!fa>+E3u|IN+XbhRg}o-u
z7<}6@>bTkEbn0PQWqN)YwGV9fW$DfoYixOyTD6k{bv3?W?aLFo-VaXY8M?>IE5^@H
zfxLPDzV|cj7uY-D`goBADjc7>*UyyAO|1#7Xl<PDsIhBA_*6Qt9!$_$=ZOqVmEyS0
z`vnAq|9YAnm;LuUH*?a`3RR?YQ+lz=v$F84`txKUT-KohH~s#G(W2v_)+PQCdn}}$
zM+qpn^xg~;N}!2=aZ?+wF}y`CBf&souPN8fGhew{PK3CL&_PX+W+IhyhOKO_6Mpn@
zNeIBO?6>Im?_paCwIr_^a8f9COLI{=X|}AmNNM#Jf154tSP;DoJR}VfGx-HU%Z}-d
zM}Gvb9QDKnwTyESV83bzt+ws<8u2}xqm3lEau>g{yqq$>?o=p4@voPVa4sOK+S-9Y
zSt-Wz5kldd@6Dlj*}!m_Q$N;86hgI-GcDQoVZLFa{UJ2+x1AR?kL)5^UZKEu;qRS7
z>x27W#`3?kC;v}+>0j8y|5twD1xpncM@sD>=K42<dvgz4q7_PgC;fNkX8u~e+^IGl
zZ%k`DvR@Z&IpV#@aGdq5j9>>(Tes*U`~5oWjl;NX)0y?HJAL#_gZt<IQ|9bHzBGxd
z3KGa`>j$2QZ(v^2FV$R4Cj(sH;%FrqEv>BV#d0Fm@x9NVZ)!(FAY@pONYXf)ZMoNw
zND5@9Sy26*_#8D`W*i%49MgGA6^q~73ZwB5eVD$abxH0711TFGeUwZtbMI*95vBjM
zXnHps#bPf=KEvf=y(HfLi8tbFDHJrmulvcZ#*i)eZC}x+r*Sw+-DqNS4R&^R4wPmJ
zUwriBNmJ2HAV%bzoeTp0PPQU+#K7uL^HXGJH}WXK{*`kcv2`7a@Zq6y?s>pNrIZ7S
z7ceLMug*z<*C)70jQB~WupE8Nz5#F4^&$>v3O{<cy7T}xb8-}2M2kgPpswHloy_+?
zwrM{6SVc;R%!gUnw&)dw>Xg+GB%9=345@4T3pSziW1EA%fh-2?n&fU8JwcBmqy~gJ
z!wfI-t}G2%9y@Lw-J4?{7cG+sp8K<mkvWNb?)8;5YPDxvz7blDn7`~d2Q7NDjPAcU
z&+r_PLhk>(bG`4HB?|_FwNALDfL=Tsw;NvEf5yxH$GhvlAaeix7k)36F{3~km9&o5
zSd-i^@49anoDt!?iWT(|Lh7eCj2F7nb4Ra%_P3yW*H=YkLmDj)$T|=)snB?K-r;`y
ztNzcAD7`9nV2S0z9uQB&8gm7P(+W$fQH@u69c<2M7n+_!^Uf!Y%%N_M)<zI~2oXvl
zC$=Qd&3lNr!Gp_-G}|(2<mAw{)ObF45dEsJwmX4O<3~q_o#Pd5ehS_zPg}IK11lY$
zI9D6q+U98)OOe8T66V#q;sQjiFmp-Q^ARTl*B<}8B?~doIOLR-m4BKuVbrCaqQZ>w
zkk|buf_4XnnFf+3CX6?D=f)FeW@m@IE=vmJ$@P%gT&`cMJ>vA<PNV0IqZ3}X*0~G{
zWjM-p>Xq3di?23I`VFyE&zL@a{`}eKQ~SMRIyLs!5SVPiFU1)qvhFH%ze!9CgXT;)
z8>=V46x6=d&Zmp#tP;N6Qq|Hrr}?09VK@R(dC$kf8vF|v(AQdvXi8!d6A8O{NH4sm
z>Ag*W1)<ApVXe^3LtA1V_gJ^%2M`fJQTjDazH6l=67mQ`#BFSBqP*0#E<Jz#T)=5w
zHEL&(rq&kYLs1b~Z%J?XQcq9M2lae<JwTn)?$R8cEEqTa>H+ThKI^#4zi=lwRlI+v
z+%+vYbs#xVvHLSI_M_Qfg-$M;g|z~N*sVnje)jsTEQz|oBu_m*ryQ&V5^Prpnw8yb
z-Ebk3eDuiU$>N5}!^)M9+@*@!6%`SBLOTgRziu)2#gw}12fek3`tm_RLBXM2aBcuq
z*>38WlOP4aoB`f2111tm;BKZ73Nm!!Juz3I-nj29nxCy~xY+|+h=qWbWR?6;2H-{Y
z#M-|CX#U$_ZR02`$%Jra$A}Z#DF}&arZBKKD-EaR<>6f(8KN>^X0m)nU~I`9jE5C1
zws8EX^JwMJlIsgC;IF{{ZxhoYQT@+<dIA0uX$Fnx-;Z1w41!Z3W)UQnlOmO9so63;
z)k0UliX%PSQg#v98E)Vrc&fV?(B9=NO;c)4iSv$NTc?|+T!b#GgcHRdN><dB`t3tO
zoje>Xd|JJO9)q*Rpshg(sYuY%4nM>~D)aaUS!L9^ylwe+5|jd3reBZ}3FSq#4+QG!
zRh>6x#BQ9lKcQF4+z45-OrJnu_kG>g46@-3fm|#EKxBpm(@+mzpg@=rz#|44+2|&r
z&Zp#&_LTnS4flAy-NjYsBEX82zsfv33{_35r6}QClDUpW$+SyVH&whv9Gn9~t_z&$
zk%PsiV7xzIT6vtLVZxSK6pu4o20>}7cNMNsq~TY25^&v5-eWLhhz#QVVw5t64Bp4n
z(07KyOjuswJN%`06Z`HxsZg|DQ@ne+aK9|k>05M=DE|{`zPX)0e&#whqI{`+jsK0k
zIMlo58}az(E3sGVdWrT)=RU}4rDW`nrcm-xnNGdW?Ge6zl`?Fqk9ho^keEx!C(Sye
z#Ic-P-<%ebHfjNKMPMW_{+zu3Vb;FuK<VU<(I@=+1M>@6g4ozMB-=TyC_{znZd@d#
zP5y6G0tR#B$rR2IB<3FNp9me0C`mr2&wF}%OSF7YQwRQaEVr%MHqedh=R4CJK=LqK
z8>_MDy@kf9?oWh|01MRWuSaWj5!80oq$sRK1=GE8pSs*bIfzw7%gw7d8L?6NYy5bY
zAMrg}ts*|#ZxOR^BIGZ2pF=-H>$=@Hv|LO@%F#7|La_0jIpPDLX2cizY$+`*5n$wO
zy6!QhrfGqS=<{=ziR8APzL{G;)1Q6ME6bNkaIa&PYh)_#Z+EMtEV++3$*6(UHk?fh
z5qm4?z!Tr)S_Q3zkl})-B#wvFip)=`-%!X*)7DS^M5zGbSF6X&#AI3c4#V@?l_wv0
z!{D6aSj~~8%CUJ<n&8x8%K-E&;X~YjKHT4*j##NHvl|b0nEqDco*5XJH3k|^fzR3L
z$1SWEh@t03Gv9I~H#9@qb>3cS3!wQlS2{0U-Ih;!hj9#_hzcJ~K==NA$L12A5hb}z
z1(hyRlXg1T^=4}cO!^&06@!=uS{&8Brzj}mfXk^MG=yE2#d)a0^lo()74Z-}WRz}f
zrD2WtzNv7fzQ%nAl{6hd+ndUWxBdJ3%^$2~TH|o$R+lbaqU3q{=44NJt@>4=SxYK-
zOeXCqSreVe5aXUKwf*C*j_E$7Qfoa6%1Y3ZT0p`WDn8l$@B`oBX`DaMU1rd@{4Rc1
zrwi*&ke!uPkY9%&nMv+99^~l<@sGJ-q0W$P<wT(rsCu@ek6OKV2cPWk)twO{1LY2B
zC#}(N*fh9D5WFFmIV7)=H2{Sk3jGD@T@gRYT-5pUm=Op=aHq&Yc?3=Z<ag`s)lur{
zZ(&jlBnUF%&_Wx_V?kEww9qxbz6rw6=+lE(X&agxb)0pSPz7@YYQPZ|9zKl#P1`7d
zh!)fnjH5N%(MprSB6D;TC%TRWg;DckBB&b%sKBqe@z#Tn0^EVu=7P_A7ZYRlE%b^T
z=oJp92Xkp41%s~!7pdO(Mjgv<M+b7|7?@A04`$<CaubDJP1_R$LBu(aOaQOijFOh1
z?#bF@XAk}Rse$lY@aP8_#1vS2`u=fh05U)prQvS__%u*pQVxgTXct~Xvc%Mu-f1XX
z6nm$d-=!l4#t<Kmaed5VZLl>Qr2XdR=7H+x+_UY8_^n08l(HkYjBd6zvgcQ0qQ$xX
zT8uupFDSSn#d8s8N1sDLmAJR|G3Mz(#*o0}bb<*0Ak)A++T_%dkam)?D8)UZH#DsC
zz6;cd31#_Qk_TF8q23XB9d27}UouHG$N2E1BgqYDfX*5ZSCm3B^ajFmexQ&Uycp`}
z+EEy~F#_F>Q#Ncv|FRY+(?Z)(A+#glA^>575P?VxNxJKSib3U5!R!cvnza%HpHX?;
zQ7QFQEaD57xn9?E0yeIfF-G23AdF|$e1Zb6Qi5)AvIZ6q=wSJEXHorzhhwlPJKJ<+
zs3a_-BSG*Y_mlR}_|m7HOgD9lzn|)L^ZZH{qu-kAD9e2R?j353X&R|`Hr?0DLm1Cz
zQ*_38;|8XxkK-D=NX-H2*rQv|9#OYsL)g!}e>i0HhQIo%c`ELEi1n_RJH990K6*Q^
z0t1Iq3~Ss|pwlg?>f}s!7MlP@rgR=3T3`p}8P*AO6Mz|GpwuSb>%#L0y1NAs?|ch1
zq-q{qd3q|E{x3NU$J2QZbOOUAAc4SL*q;+S3xYc`)J=8D5*9e`mdSR3b72r)y!BSv
ze3$-TOC%-K*Rl42`V0|%t&fKh6QV-SluF5n-BTp!TT#}``#DJk^ZQTz%q#=>=Obni
zz78>5!>}!aLqhawG$56?RK830ookB^05T^wBWNkFiYUK&^MdH%eA;7P5-_WH9XcU9
z4~=!ec9dogr}WCaHG1+TK4-nwLxbjVnv0~Jw{Mvvd-I)Gc6BFwrNHWgPVsze7EBS3
z`R|y0BT!5d5uB_Bu?P*AA8)UrI|O9JpBh8J=EcI@t8!Yv?B=0XvUAfGqHT@2)e?M(
zoeK<FsHkr{23OBz*~6BV9URFlX9_xn3(By8OuB|F-nuUefsBav%t#(5-iim_f~O)h
z!veICTyrHE2m1@MC(asSJVQYSVkMu1HFPS&17DpYS#?C#R9=?B85Z`>MO3X46U9Dc
zTk1y0RL)%w%<AUQ6yu9%!P0VexolNwrlHka8*Y2U)+9Y4naH3&emN?Y&7w~Bjsrh0
zyoq^a^Gldn*OlQ;Q?T|4?DPN)iisu8KvKWho8tgG<^HnHA*3gi%?hO#6SX}YtMS+X
zg^a$e?1th&@KZMr_ST{T)8^pE$$ppqpS{BFvA^qgRr2&s10mD0w#CsZes9p#Fp)+g
ztbtlQw}luyQ3s!{nbh&sKkB1bhrF6b?Grce@_5I0iDYMIcsWV*@37eE)3%92bt3BJ
zqNy8nsL)p>#EQ-qa*rbO7>OgpZ_1FoLFRC(6_8;O#;H9{wh*qQM*cLe@6iPH758VI
zn#rcq%9(3_3B2g$3N6psEP0o=#32;N+638a{p&>)B<t+bPxMl|mH1I-7ufI|hIz??
zCP3aqSJ60*1%&A@e09Y3QiZ-JV|%qR_Q~&;+Z7jjL@rATn<M-qpUU+)Q89YHFP9q3
zYJJO~Y2Blw0fT~el?w+zebm3xfBFu;XHA~|`5#gQh}qq95}E-qjpULaDl4f3obW7l
zT08Wyk+aDracAD1$D%>ukK$M)dwZ2Q`=PZ1W<r|jlWW0!1|)N1YVU|KJ7@BlHY@ps
zhR=Ea!#o;dlW}0NREz({_w&b(7vLYm(eP2vZWWh5_euh69?VqEyL{KE!urHm<KM)@
z1VB5g%J$B&BF=cK>Nk&^fq}u_T@+YCEp}=?iDsrjYsUO=9v>UiB^iqFe7T|3r>v}c
zkUOR_nv<;3WWySP@V6aj!$43EM<}iEWw5^_ynAL5PSCPCT9pPlf$jSBAKKJ~aV;+&
z)AEBdD)r#Omh$`Yif614w`fE{E9bP-M+njuJ_RSXs!vt!zq4=0r!j%^dQisaK~iXZ
zo+#uTEPVg`|6YKZS92OWM2~)n#+5rv%X&iX2RF`tQgVd{!0Gn5E$%gZ0Bm78US3`{
z4vuD20R~53C^dh3?UQ<1*O3?CVHp%Ng2UYE%hkh0+>Tbts5$7Pvcevj@}jNWxYyn!
zl&gk;LbDa(YeC;sb#;S5(WU@xrHR7Gvoq^)>h9jka6k*a4^6cBWTx23Cof{ZM`m_G
z?=VKd_q4q-OaV=FGphBX0v70kreCP$hs$n(&=H0vCD5je>uv>)QsE;R%89}6*&j|-
zF6B*M`7JFyzW6&usw;$^7My0Cxu*cJ{NXoJK(ItbM=(?1F&w0ffwAyc(p~DAnVHzz
zW;ljEC(fwShnkjlSB5bVyXp;`*|vDxy736GqFn@waE5^usJOA*Pl%##>R=*n+nsWS
zmc2KsItYigv`RWRZwC#@vpmqi!|3J#Bt<mhxz#pM>CBE?rFlhy35*!}57^?}=(ShD
zIRSW@MpNtEy%94X^GNGuj>yfxiOghgsycfd<x~cy)NP<OOaWw{pR*Y#z(939YNb6l
z5!9~-#5oGtz)}Z7)&vMy^?{_!C>}6@w2%0LUTqAz@j$V~MbZ5UEI><Doq}LXo_zn%
zumPMJD+}O`-TmR~Z&F{hsB-wMLO&}<;dn<bbw;BkGJN2CH7wMZI`x8^?32&00M1fy
zr;{w&g_Q-9RL}fUl%|<QIFu4zrH~^)R6~6q??qA3TIPY!4SY7$N=$#g5pffjF(nRa
z{XCL41G;|$oWbaJ7jRquP;>n99R6=Gj_A8Dq|cAniE&o1OWxEgC9o|U7WMQ1#n2Qq
zbTG&Hn@~alrA9sgE=8r(&pKc1L6U-0Gy{%+e?o;+J`@yJtyVs^%a=AoYK1d@tnE$1
z3zkJEAYhUR3JQXa8H3K}c%%FoaX5`aYTSaVbg;(n3l}bsof3R9VI8_Za!)(I7T}zE
zYseK_Z68A-&lABOxwyJcsv_GX+TV60JyrQQmPBRMzq8I4WEhP9F{Kr95{G5&W;L-n
zA^E9MXZPF(2^@UJ=$=4ebZ`sl+>8ostli-9nrkvBA<<>qs5)@c)^+W1-j|7L<(<3w
znSIPFr}+fK_ph2GGs!Z;{hiFS=FEWjy{&5h+T?N4x}($IEHMguS3ldmbcWD9D=im8
z?2kUZT2E^qRp-1Yd_D(!Q$O4l6kJSY7GgM7BQmnJ1&>8Mwe-T0?cB?noso&!wZs?Q
z7cOA*B>T<$$;k(YdCoR}IH$~YchU29F5V&ic2=+t25P;8+r`d~A6S{~t&Xy>vp0ce
zU&lG5&eD>!X|@9K_o}Gq&Is7S5Z~AM6pc$erSaoQq@-#`J!x{p;FFVb;n&sriPVA{
zOHEzfl%79X@EHsEp?-k`B@_-`*QN54=KJW;geQRAGaaw>+8qa@65Gw2zvP=xT=E4r
z6U8mVzhnKO;qz;-y}>e7=&~||wm)iB4BN`>oSscaMszWN=^?$MZgx=gCGlQ>%Vf{K
zYSgBeAKJjAR5tOscv^$Uy$dB@KY3YhmdRA+O+#xXYiMYww;d3eWk4#)aJ=0_GIOM-
z-&Afz^jq1MNaC-kJHD=~BSA1eU<OL_Zm*d8_WTP-Z)NwO4;{i%JG}NaDPPa9zM+{W
z>*PLwtAB%QS6ufZAH~Q(mzcT@Fd8h3w^oB$Bm+Z7)I$RS!2#fr3!}Q^Di;Q}v9$8?
z@?K%!sdo6R2YgSCG<@o4@UY{&(rDfSsOpI4Ck7bLrF0QB>4G0O1iZ>wxregSP9u6?
z{#6#3#71_EFsS@7%7(+h)YEe6`)9`;Qcbw*Ub=FrXC>8`@|!zwq%-KQnxP9qKXW=-
z&$a=KcfY?p>G(${nVXu52}ZHt_}u}s2u4bRsc_-dda=_DVvj>G2dslVh(9~h`oPI)
zZHyKi(eeWM>65=y&y;^6r(o&7Afv|h^fv=K{IGjM5)_!Vqot{dw&V`owpA;9&=W$(
zPanmqQkv<y*z>8=AnGkg@;uHqYM?zkJu#ugxy+%9JyBJl$g_riFeHxO=?WCkK#=&G
zFo4Kbx8Ll_uXr7*j@RC3fQGW*Ik-TXt7JiXYv|pC3>*T<T(EMS5hhF&p_8LJ+5Ce}
zBUr@!J#8@yS)a=!>zb};C*l16*}Ngf<NKnW)!+pn2bX5!3oKl)Lfp0<-~#q@qOgVT
z%UT<|9~=jY7OrjW!DH*uN?zNVX#?2VvvF4Tfbbe;90&Je|7037rIxCJ3f2}sZ8!NT
z`vz=RsF53RLkGM0VQt~G)78SHTU%_!?Q|~tk)TFyF4M1gD4_}YJXYA%RzFO73yk%P
zi;D<iSF>~ZbUi=}crD0k7YWxKh5rG9!3YLpBVTh}tD%$kt{eBpB_|44IRj?FMlp`(
z`ee!s6lwtS;PnL{0<{G<F8X-rK4FZC6V~B5{L3HU-j#26DNNfwB4(JZhPKui`qKHX
zbbNGf11zm9x%oG!^WgCNPe-9XgI69GK_CK}d@-<wilJrDv;)`0ZXKZZmwL7p&8J8C
zJ|{URYdLw9*`1Z2qLX3NWVR#O?(8nuVd31lZBJ5DnaD3)!Vph;9+!a*`0S2@uh~3p
zmwK$y4tn6-ZT}_O4l-{{?>~`p%RXllxMEMgw3CpM(%y2zCqn0h-g=v$G`E(PiB9y%
zT5Li>0@^hJ%XPy9ab4&-2c-!Z_2Hs{I<vUSn^Z7qlpf9H`@Bj5Vi2Ybrt<_z<n(ch
z1oe|X6YK^Kg}mFtVqr?{fmXJ&B!1aeRrqUE0km@enoPbgJdqZ<d3a$W?My+aPPW@v
zCQX%U#15{rnyA3`M9Io|Eo<U}&Fe{{7^m7S$A)XzMK{?O?lc_7BW`*ruI<N5@h93P
zb2%TkK#BDuDNql!X(#qCk?jv;Yoe@|i<iPpS?0JWB<zw)du<RC!zaVTsYgwkwnu)m
zH6>X7<V=HZjFh9!Qq3wm$WLu5-$9f4hX*&4(9eG}-v291wnWtdZNdMh6^e6!C4n*E
zWYbN={=gIO#)X9AV|CD!Md=&ka9Hb9OIl_(1nBg@VnCiu%;x%_g^PevlzyUr;-f}(
zcApro->^;q0;=DmDM=c@4|7`}A_`_tJ63=`Z;XnC&ua{&73Xf*K;vIG`&Nzo+G@#P
z_gTIH&HCn&^X6jy+=g4%BDb2AB|oSPzT-wdk>`7zb}@^sdV2*$v7rscsH4wr%Ap5k
z+wa7iEmD3n#QGy6AKx`lv2hWI7=~kSfl;N`O(wD4Vk41(X{-t6I$2vsDX%5%sTyD~
z3PS*2>4UemcO89a))#ya@LoY%0Or>guM60ic(LtEfT)jnV?wjDp>Qa1;xaIsZ5^GU
z{tbOrN`fJ;T`*xRk(OW+OZ?9oDgKq!`j@n~e|S5@qed~9gep%CzNi$`alUb?&9DH~
z_1>J1SRIzP=hXGwPTo|PFff2zf4^^4&}!5@wSzyJM6l~WKhPirI2j$x_;)?;f50#D
z@<AQ#BzVLfpn`$@Wf~obf}}J(3!;ukT+4I=_=5DR6yTG)O572^{}ee7^Zm_rPy!7G
z6#QPkd|7V(_*d);sKaG04^eKFDwnSQc6K%sj5L9C6*kLZR7=DD;B!3;fpCGfz@rj=
z8{j+g0T{$`;4=_}v5cA#*GrB39JrMOXaCf%eIP=Bb&_iBkju?(bX0}y?6$e3_Jop)
zwg=2474WWoPdtGvECgVp>eiM?v~bS!hT8<Zzqs9ZX5s~aV{l>xXsyT;59uU3K6{qJ
zUeO+mfWc4_hP!y$1{Lmg^Zc2cV_y2r56W~*46a{dwd5j10lWxSQo(F9N}SEDtx3TN
zhmp+VW6wWqjSjRh#{=KCI0q&?jY2L<PJm7*<=}8zllRnWZouRT5Ct%`vpifz3_uu5
zmHu7Xh$Fu5Vd9H%Bl?&|Co;92lZ~Sj5@+Xd5N85@U#g>*6d1k^5d%A28$v=T+PpV{
z<DUV8)Y1Dl34wqzzb6$UO_S(ELLE<aLC|}uueX=+&5KMQnCyY+FOc9hS*gMr*Y=pn
z9qAGvlAIq8I3x}X+-&`!*SeN8oYB<Jf$X209?qppH5mUMOa3z}<ijl6Lk6n^T8u&r
zXEQ9!#EyFuCvaCz1`+so053p=MRtTYva@d>zKiw&BssVNFOZ%!ed9P)TATuNodU&+
z!R8VU1FYRVDbi`-OK=r<-A**<5i=|#OK!>y>!D3micDH~a(RYDqm$F;6i{}+7PCQJ
zFffqdPrJS{aEHXDu%&Hbz;yWgLOeM{KWTuS`ir%1vR5^7lF!vZL-tv_5EI3g&{Y@u
zoOy%w02La=Pwwdg>_Rnb$&@!I3o!uOwEGs5eQ}9s0>Rf`(zy-R6{>nyRtu;!v?}|;
z|2E9}e<6PU!_le#(Fpy2_X#|Mv`>K-!$2gpOw2z-|K@3N1}k0aQUUq7MhRLit;sHD
z?d-)YB3KUnywFg>aocCCfN?%7scJo0+as5Gk+-C(6f-TlP)S7F-jb1h156>QY~hOo
zX3xsQd|@R;Ll)`F6hk>m!}RlhiP*-?dX`<HrjArxx#4Rjj5${oZD%DoQuuXIU{r$l
z^ZP=fD^z+civuNhG+8f}z~Nt}t!;D+41}i&73%(#{j4}FEGvCT3khg40k?|aiS?ch
zN4JwF!5AtZ`rk;X;V8I?5Bg!SxnTHfdjW!LWDfc*JW7G~0~>Q+pYmMbrtu#xqOq|<
zl}WB*d2D4d1*kcGq%PX&-qxU!G)_*_Q|m#{jAVkASg_z}jjE4kUv9FenQ<$1qk$9n
zXv($z|A#w;^6!f-zK7)Ne8B<sUJyn7jM87`prF!Pfg1#Ds;Y*D;V>IkzqFHDTwJWP
z1sgqF1`-JjSE4rM57{?l!)bz0m;B0sBpxampuiNEj1&thblb6oAEjGR!94J^5N8ZK
zvzI#gxws4otwgb_f6>JQRC)Z!y3pobP+@bDf^zdWm*Tspe^KgXBn|)Ne*EH>SQ&R=
zVxaDQ(p=A9d*F-r@0gQ<w$uS+A%OjtK=H8E-QsAKgp`aJ|NbCR4&&)dnQI4Boi*3)
z4Tr99y(Lm|mY^3ROU<|tX%nw&i0hj(Zk+Y}T%hMRr{5K2T{$&!?dbQ_$}y{|D$61B
z{mD&-kBz!!z<Nc(WRXnXL}>F}r+`R{;qV~y-qJ^Iz7>y5sg0TX>;pvn?QU0A8s+GR
z@+v9pIzKLUNT%7AKW#pid#YArG5mT((vTl@D@Hb1bKF?^!{ph|U7_r5VSaj<LBRq2
z4ML&-$sfa3rq&0x{5Y=5p?6Do_lph-(nQ0Ww-VkU2vGt0TmXR-yo^iDK19o|nwdu4
zxK0~~>k`ke9B2IHRFUzm?|N$p#yQ%;oQw<CczZckqf(K%gx^@ak16Tj(?4~fMfSY|
zB%RhD4W4v-jdxF-wcJ5O-PRdUll>L$$Hr}OyfD~>r!2DZwRo-tx)abviidtd2_0Vx
z_{%gik|@6)Uq%h)>1lgAc?hBEf+OWknh*l49WKhjk_8E5M}fhmwTJ`{o2D16pD?)^
zK+H-U=H$FQKngwI{R(asaOx2KyS1@tdgJ3swSue~xf8qv_|WhU2h=*tMJdfDGmr^C
z7Vme4J`enF**AdpQB_l$%p!U-*q@u7ot2xbWOV?<#2w#z!4Jf+4gPC?W1dni@|TeF
z4WJz@|7k*DO+?+TpQryDlK($K<X=Ky!S?O}>wKnhkB9gN@;-ff(Z2-7WyhY!Fs)63
z39}C2i|BX(CdQvXe+06zklx;2Fb0OVJb3sJ7m~6|6>V>jG!u*m1EJ;u2%3mqG!*RU
zS=nFqUIRvs%Mhdkdjz;_!dqDU>OonLjERYfEWF~6VbD+8d;YZ~pi1<Bi`vnzVQv*r
z4Qf`Zv;9(3^ueNi_3h9|zYQQEav6sn7PXK-v)|@11}vh->rySwz@W0yc`V;<aRwch
z1*Y-l%VC^QABLJup_30^650MOg$_D|FI59~GQ}!(5Hu|W^LBkYPl8}9Kp>ABwAVt@
z<_j&(efdVWY%69iw>ZCS_<&CE%dn9<>cdQf4m#u#zI4;5Rv!r6VTVNc#XuNh9d>}3
zNpynZ=9tf!SBrDf^Vuu6pR^|sYzJmJZ~S9$xjlS`CLNu*h2i`bXB+}b1WZ)@*Fg_H
zN1;Cf9$T39#DL+{@FjvPcdjxAguysoD9qdm<Y6H&<m=#BPLF`b?+;CHi!;nfaXJ2x
zZvi(<Ggx0~>Ai7$fv3R*U!1*uCJb$&=I$j>qEpJt!C#KRR3ZVeWXt8sz>G}p9dZ9=
zPVMWpt%Y`#Nhtc?I=mH8-=;|}z$`HCNzhufyinz`Qj|#nL;`Si9I)CQVrL@g12jZH
zkdmMCl}Ocv(nKt}&w##oe74&zmJxImHoYe4A+JXHNh~H?aYVBDYq?+Bulb(XeJeJD
zjES{Cas4}^zsQ(<ZpN8+FVN4^r=C}1{_yr?U_rr^={q_sl<z6cE|I2D`qZmqC)h@p
z_Rk#!Sk)El=es^6;pt#!EObrw@EKEC+n`(BADPooQ%hkhjgG#oo%;a2S`hw?$N%Df
z0`}?GQ&Up~Ns<fSn?K7cv}fw7jE;^L=;nU^^$Q)0d`wFp78V9m_w+m-jzWp2Yfoo>
zz9(&VS?c4k9x6WnAm<WH*_S=0EvewhOy?_0cPjo<I`aHM)Nl%X$JbQtg7XYp+s6@d
zIGK!^<2dZ(A4@xyT@C^?n(FG3hWRltP(BZ$T%3qOh2v{Z{c>@54YJcbh{?gu(rO=J
z=cqn;!b(c&?BoO)f;1>q9`u2pp`q-1az8U4s5l<#iU6(`a{Pk_?|9k=5}VUx<2jIC
zVob#vbZ;5eegu@O)e_M4qpdA{t|M9Q<HgXp5u(1}t7RTb(r<?w=5(xV1AbC)RJ3vB
zhwz^g4aj9-a4KhBWQn@B;o;pN1j^!8T})R0k_itL@ALagN-{P!91lKgzgS(h1!2VK
zy|SbvCK(w#=>-(!Mxek+E?s&J4v9(hvYbA8TJBuU%x{J<$**6<NwC|5fS`uwBb1un
zN-4JHqkfR9`x<m~i#`h*8yQ(y|BM%QDYeWc6||o(S=b&qG5xS%SQoKsTQN=?6TR{{
zYqRgk6ECm3F!!d>*3#14JPCG_#v=1p`-QG+ZD~AaW@hNKM__`h-n5Xd_9+>urInRQ
ziS7x-#l;<M<;?Eu?oMp!Y425&dKLqOGSbt}wMKJDd3aQ%zPy&+-P`*b+Ps`3V@7Q&
za5|v0Gb{sStBpTDE-DG7vVk4U7$O^buNIK*c^9^2ZESI^Ea2>Db1OKUlS=-S4tbiy
zrZnmq_y*yD4liSBn))~_BZEcz=4GfOW-!zI^y$;U?@=%b(DwLMv+4Qa2jnsd0uGqZ
z=GqBk3%^_QO#{o!z|_;<ZwAu_aZbey>FF{?tFhoxqK7OhfA5}zhDH=iB9LpK=J3N#
zg#)T??>!P!5lRH7tAv`wo37jOZ6q?JAc!{oe9x|CWJDiH4;wT+{aV?K$fqAR;)fzR
zZ7Zrm1Y0cY532nimFO}CGXZn2s;WA1rgAjVfK^;lVglZhIOhaqR`25txq%gztE{X*
z-O&bTyd&tl6W@l$ho`3q3>3m-@2>(Xm<E%Ev2vfGA27CMI#TX{_`<R_JpN)b+4vf=
ze00syo9^>Fqn|n#mzN1X<@aekd&XUAH%?Sz7=a2R<K9QP%Vtk220UT>i5eQ^B5z5H
z8n+EhKv+_WiUeKV-5_mc7r45mi1haLrNRUNJV|69<`f!dBlWE<pXuFazkOTH*;Zj8
zR*|K*g2Oeb_fKg;_|VAX*#0&>HQy^B+{`LX=XzteaS=3eNnPTshc5)&c=i~NDg8}i
zC4BMM^}@UMo_a9zzpbdLp|!BEAi>6<i@=4l&;)A7o4@|L64Zvl+@&@y^%|!C)HiYz
zbPvFdG`Zo1Qw)zcIVGl;V7%|>cn4cT9M1UWWqfF{H-K&M<qHZ5GBFD2EQ-@uGkbXi
zk5Lf2fRB&P@$@ehf&>a-M9&wm<SeFscoL?66YHMO<<rtqzWE(@nY1<LN$B>}?+)TM
zaScskib!v`i9EHl3+P^uHZx-ep9C}TuIE7pKIMbIvRUI)73}Gil$2scp5O6(*le@E
zK1s@9wI^gs8CrX2c13|6o_Np<1>#WY7WtRQZH+*V;8xT7n;15nbHlqxOG}ujjGNYp
zm(v>zqsJ*(S>Nl%UFGDY2SEIV9f2i5@+LZ(<<mn|RbNoQwg<hZzY!`Xi_)T=;~VVk
zSi3v5R=?WY;RzRnFw;mVK|(@J9Rj{2DH3cL{$vsqtmr629-Zv;+1c8DtA4&kLQfwt
zGBV;Tm-muk0)%P*LF;c&sZ7d-ubF95g*2PKV8D}CRQw8abc8XjmwrFg(z^8PF*iu{
z{xH{{T2MfOD%xO^*;pRr_8EqYGXak`rKqO>kiZd88;xOH7dW-wOFJak4Rw^hntDb)
zU;v{O^RE6Ag&p7Kj}|85c=ZTN)7z8;^78U{1K+rQA$NX9DRB?Qa+zQ{ySn09S9u=X
zNzcqY5`&x**}s%NJNsC-%$BxdbX8OcktdfK0#8e#$n&BO@0eDZG`Wb3zP<;*W;(&J
z+Z{>*ufz31uvIw<&`3-FaT2<I?b_?WKo~nvisfPy7v|&)50vjqRvBM$iTd#<6{-}O
zOeDBAepr^^d{uk!KsJ?49fBo2BSV%Zq+<_K6bSlkAM#<C3EtSAET$rx7J0fqO$`5n
zs=NSl#~!^f%zVP<fgJOxrY2EcT^&v`I6RzaULlVIxpDn^q&^R{Ib^<~weQ_EhuY;_
zT=;<2$%%k{d`Qn(FZE9KoEw;BPCIH}Zo-{)hf!sio0W0`t-v>g7tyVBx_$!ByLo$}
zU+0|&k58C7TXv~34|(A3E_B!H@Lgb_ySw{Sc&LN)RU_Y-<Ad#dqdH~4)u$^^-=N|i
z%Ltd_g;Cy4RZ&yF*a}(pIxp{g0vs$EIXMkmTibfqS*yLxnO5KOB$4NmMxGnzNJ$4*
zTrkCPUNxLy`3eaOV;vIq_w6aXC3ifh9VC~&;t5C-B007nTkNIxt9^Ll#MR7$kcfzY
zQQH<!mr-d1S~}kb*<`QXD=^1)4VQs5(nq&Jo>ZpuY;DY{z<+B_%91o-Y{mWTq^~Sk
z#a{1PA*8{{iH28@VZ~Kch?iYFeD}t@7Mv=5&!48HrXCjtr3RK*^@9X+;1ekC=sjz1
zBgJ8y(!KB|E-n~w!9zzQfe6bXjQor^0{$?zCax1KiV1$-q$IjyFsQ&27APQnULB?k
zlLb>49ymDW=jZXtjpt@(r=b5s^}%HPyk^sepo^^Q`x|B?q%d#H^E@;>+}|*NZwWgh
z<Nem$mKr=23zwMAH_#q@aKAvYgBnnXn_jrRz0JVKce${z5O)7SjYrv`@7dBCG`<p0
zB$JKU&`uWI1#)V`_JC7q;L#Mlf7A#e-4O*~t$`oScdwa?01{f*k4G;V@CvkV3izCg
z=<4bk-}&pX_y`8e8SyZ;-S47K2Y6!Frw%JFbj11l_j;Jc!(83PmZ%^6@}$tX0W*|}
z_rmSlx8qKt!G8r)qF|P@t{=@N^TBzA0a!r0#+^4*mKksmI5M)cebNiBUb}`C9|*;I
zNS^333bKfI#X4ZhSqdKg1n-4o1H+0`yBn~BQAG?-A<sqt9y4OUJ+G!j*K5BqB?AhP
zS^W_t{7co8SWVR!)+6PlVBM1P^gQ<6iHwYV0d_Dg6<SLFP9h3pC=C8ru=h!Mpd2(2
zVCw%}Djt(eVE#f2#Vh2W?<hyO;orX)*2U5}%O%OHg{RR6-rbW?md=wje)+!ui<4&}

literal 20243
zcmd_S1yojHw=TLs1r($^1(i@rrKCY5L_m~oq#GnfN<}(E6cnTtkOt{)L<FRyTS{7*
zk2rJv|2}u0eeOPIpEJ(gcib_qW2o@uTJL)2oX>ooXRhD}%5oPk&|W|g<f4MS%tHjh
znnM5L;lLx_{k8AlKb*&kax(Bg{5iN&<qQ9vca+z5Mi6{b^e<NLxX%%STtXCNq|`h<
zu8cqZNVan<u`zhxoORe##qGBLIW@`K*T^Zdv3;Z@)y`_mRO_fV;CpKwmyV<kj5PhK
zGAxNnU8MRncEM7rWrWGgCc88<=wk?O)tElz(B<JP(LWaU+TxZ1I!i=`j|(<D#Tmt~
zI9E#~b(`MIH@z9J>Q9U<BO}wFt~ShYiG@X&<c42xP|y`4q%6u&h@gp=I9MjGX3Z>C
zKj0#rK(TcG!>T`7*&EY=Or=k&WMtT|aUAJxD&1OhO2?uKmbo;W?Ww&=N|J!bH_RXu
z#id;&<3<$}Bv0yt&C0^^CVBkaMjXG*M$Ni6k$4gj8JlwR$uVa2Y03jUiQ^@eo(gA+
zk1o9m9*Ze~Gs+kPw_!7ngSprnR=rn#&(A;p{Vht!aSEAuy1?MIATlWxNXW2JHF?aP
zS7XT+Vv+b1CnM4GT1ZHUV;h5e{rX<Uss%4~GgsNzi?K1ik5A`6c<hb4$8+}Wle=U&
z@w58Zd+&`0R;^Xy$CzEU^Bu2rEng|>m`Oi}jrIHY?@PpZ2(nhOz(^f{)x^tdRF81#
z6tmC0$GJ%B9};o_<HSz>!k9WpHVx@@$iCkuFXO^usfnehs~Z|j#`gH&?_6efcGJh(
z&NDAov$FG~FA+;VeR{k0@Xz&?;jg%T=}KQ0<XA|KPWI~B-~m;A{qRDQwp7D9pP^Da
zIsw}uzrTN<98DAX*3Wmwuez>PZz+4hLJSpKkr6VA27T=or!#4bP#PWNc$A(DKMA=k
z5})jr)fsnvysZ=y?A;b;uXiKeDm5pEAVI{v6<&|8u5_{u7m&baFs~)$PT-1Fwq}0o
zfTrQa{6TzVe|+61&`~Jt0lWVxrtY-KxmzsuugH?j<at4#m(kX`X2P;8sbm3`a)18(
zk@4}V&CJWQznPz&E{hQR)J-P3?Qa-kNuI^VV)iG7qoJ_*l~cH|K2oAsuE$LhF1u<~
zx8K6ktjJSiT81lm_GLqPX{E~&%gOP&FWag`4o}L1Z;2WP2HXf<AK$7~G^-Nndflnd
zcc+%a8lwhmr(fC@!SAkXvfAxgqMM`z7HqX`@_5a?rH#q=qsxK&q_^hLqMU?vZ{2&?
zO`&kcBatr~l95X+QSu{p@9e{1o5s2AoF7`)ntn+nUQgt^e@D;fV9sMEm~BPH1GdqN
z?uD-s<5-f$#>~jnx2Vgz#c$q_VkRr|E=x#AtVMqsTrN3PTbycmiTG7li(=Grjj4y~
ze0|5*I?@<~UGVfPU7j0L?-q9@h?H6C-H^ZxcfXE}4cwe<W1Eze`ylQu?6Va=DT()7
zzsk*ceWHe1)T2Pmw<+La`O)7F)dvrdCSJ#$yHqLg*|Ue?pGP%5ghd4{%Ji<~y;z&9
zlQ`M)J*7ele2zUo`W!ByApo0vY7av~fHS@6fgl3|1Bl;?7cVGF)Ya6^#&(>mn<Ua7
zj4zph)i91ruQ4z)GyCrPCj9#Dyirf;U60vtLC2i|O%wRpu|m%Z)03^q16$q*v%9KQ
z>vek#7N0!?J9!#2y45Ygl;rjI{NeV}8Ku6B*t(;0zDKi6(ZBlmE>1fR`Hbyj#_yM^
zSKfe#mme93%CD}b&#ybFWWWwEh6t)J;{3g^V6rk)=&?T?XdK7dIL&L`^)dK*e)Y|@
zM^4W0<m`w|ehXX_OT%)tM~`00F+ROAH`z81Po#!&H>O33hsa-fVZ<Ahbo85zi&ir%
zZ`tE_lpL?=H{vil!MyfKa<;AdGelFHZA@*mkrEL$`8B^18G~hr2?Nn}RqA`7tsVT~
zmPL9FzuSt|k9a|%7&B(_3)7IkBIum6?B2Cg2L~x%S-s}>Y<uxJe2>&i9A-8)T#1v-
z$jwOKqcd^8Hl%!7LMZTFNiX()A%lI3W|JRD5|gU&JatDmD_xfZZgq>2-M)Rhd?w_&
zWBb+Ep`<I(+OUppD??aG-GN`bgz;fdTW^-rR_#qZb84Z`G=fZqhx5`3BjGe7^CLlo
z3X&JAu-l5I%HDyOR*~s>NVPcdR;#}B>GXK}d)mtlxJ=Cv3?i?iqhCU7Ql-g7M#Lvs
z>+)NmdmH=ir$cr*$l<xE`rc@s6*l1Y(`6^WAf@`&|MVF0f|&84q>Kz#)1#m8D$)%y
zUJ{b1vjj#gEG*}#>(PgJ|0U;>k&r0VBg!VHC&$z`O@q9hpTmN*ul3%zN*bK3nQsuW
z>@UrX=29;E>(M)3nc-1~$y%$?>f_x}RbAcDKgTI1Dzotg^c}E8AD(=&SG*s`*IxVK
z;^)CNY%RR!nugvKcLE5cl$8m&%crobPfsw2jz!>M(&+({=;_fGUSLyZZtlAxXR~K|
z&-T{Bt4yMdsg-;*Za#v|*`#feFjuZ(Uq%OeQXzEoyXmp*P?-Z$01;DSQKvU%SE`Ti
z_uSlYpXbh>0n_-wU%wiQIH?1AehU^}Y%1^-6d4dF;;ZIhXJ?o9=w8}c9yEdL@U646
z<&#46N|iptc<hjGuusIcgD-|IfPm%&<bWdxJ@+w{<X`>$Qz6&$B<=0F)9-(HQCV3T
z_6zeTJD=D52Q|8j;ko)+Z_Go7eFyklp0(32rFO#NNjzx+@z?t%t-AXQN4jOYTN2zS
zf?){;RL98_s_>@_PY(yRN^NN-ytb4ed*&F_(4HPGNRR}}IPPyu7uWTS>5Z1!-GPjO
zb!oZe*1J_FyV25%5(n)nu)kMNC!l~)4jdkBb&vjzw$LqmmqoOY_{{#hnSsxN*%*Ym
zQy)td-tPJ&Q(318R>Gp>kcUo|H=`w9C7pdaR;0v<#~59S<R!nT%W^X<RE9Ekc3cJ?
z)8{z#%88K`@s;r^s_`oKGNw96QclMknB$>xr^~-`bpwwvdyp5Ih(tI1#C*TM<#;dp
z^rp47^#cu!faUx;@wA;7F5Q4ZgJ&1zBIp~G1jqf`quDaX(!<M4W=}IlOTU<ku<LgG
zb3rzyn`Z5MrLE*E@SX4>kDZV082FvOe&f}vS8(m1oK)t8nsvkkM=J{}5HN~r*$x-A
z7$^_AZo=A!^!7e1^w@k1uNp0Lpt>v<LH_jV)5nmjQlT=T=a20N`R(yoxf2Wdg<wdA
zqpqa6m;H(1dmc~KKW8Dv!(;Y`j91~Y(SZAkSbBSPRCBSC`vN|Gi@y8#Rq2%1`uVA;
z()Zu<N_o^6q%2qN|BhB6jaUvBoFE3cKz0U)fH+>j&U)9)3vyC3qyo~eQGhUdOcT%6
z#o(}dEY(E!#+*~#*C(V0T~#$SSpA7-dgPcW>Q0Z>TL77`{Qdj4dE9-{ZZJfKWu~m|
zICt?^Zag~|7X>~gXTd^el7yk?-*1;6_kL1HEhu<U{rCIz$DMKfT=q66M+duyOy1j3
zKV9|Vm_7H_xF&ostNtcRzaogG-=}l<?0!uOR!$HOhHV*wexDoDuYAMFPo1q@LbC)A
zt8{|7a*>QPQ0>lR5J{P|p7JF`hMoLgRX09!Gi(RW-L>)SDDo(Lx@G?CaH8f}3lzZU
zJT`w~b#-<0oH$~wR^WnDQt7)y5laV2Ub<9mkYD4V6LYoNz@f2Lz+l}H&;|u;_>$xO
zwW@VW_)V$YHYYzE^U-$nLe$aD6F_96`F8q)tE+W3gWNZKjy$Hf4w^Gn)0!8OeC-F@
zf4^szX>yKT8R{Dw^iB0)T{bE5VYTU7*c$HEFDlfHx$1MWl@to6d0v08W!NGq;co>T
ziF4O&wxunp%@K9PVXXYdQm9tYKEnk+kLg!bGFM1R41JGFAel$=D)lAqeZQVR#3S6t
zch||OaH!BE5Fp)Sba5LzVBe$`6+Ogbp1~Fx5SGT<%=bN3yq_qly)stu$ZP%CBw3XF
zmAw2Yc^Qb92h|&m_`9>{f<ppmt6?V%w}JY~LK_~^Epe(xK+T&xJnGb+sVc3mF7k<>
zsi3T-{`r{}*p%_x#;|2)+S!t9pExo|_*9o4{>i@U>RJN1a3<_l7rNTgc+BQM-ZP6I
zE?$P@Aj3;do+cYk8c4)cx~%k`x4|bxIZ0e!O--%1JhyeZO)KUqgrkhHv2o+->zo8F
zUD!;-aF}Z9>cKNJk4XpvTlP0+`%2Fzx9=|Zqy>5t54h^X4ri7*M3l?AHQ{AjT_P%Y
z%+2Xa@y%g0w`S%iA)039;~gj+YIb6VYM!|_?!=STyN|oZdMxLb`4eA!e#2{jqx|4b
zAfeKb^6buI$dW8b&?DvBchxjCEye=Vu8x*UfAXm**UKwk;2;eS0t_1R{rf#*?HE8(
zb&I2A`N#BTo<HPWSFFY~((_MSkM9q(X^n<sK4f*aw!JfTEwdYC&8s^(WIa}ygQ7^P
zTV~(saX_XGc@(fSYl5%K1O5ps`?_N{Ou}LhZgn`FV8~Km#%Ls02dQ|H%KLO0_j)_}
z`Y*qN59WA$Inc<1uyA-bl611ob^`!D1URINf1D*;Y}ib}kS3YaocMj7&(F>AKW)g<
zDfuo=U4vPxa@ykTAZ3iqXs3Je9*r+tMh{8b@ydZbOsC*vP$*BWkkuq@-KZE;v{#es
zROp67jP~vfx?)KJdX>Xtma($>!q6(sM=x_ZN<MVNy1JHF8q&Xxo@2wg#@S80*p7F+
zMh{E2AP$27<2NhOiFp=hW@V9Hy?S-m&1%g2eijn3Di>}>TK{GD0Aj_U`s8rgZB)#2
z+wyNfYF5_0BS6<w=mkPC=Ir6GkBHCw2a9Id!!{Tv$dl0sqqvtxG$QU<)5;PRw7->f
zYFig<8EiVL5!9bgr9U_uGhY4FxzT&~XUd&UVe?PIyT!kECd-q)98%M*_h23t3D7ZV
zz8T=+;i1a-_$1^#zfH_gk(pxjv2YYD`z^m){du)##CQ51Z9?dqA0^1ot}9X84aWG~
zSDy{^*)~C}c)aeFL=a;$`5xT>@DmglM~hsfTKGrXgUjUQ2hB)lyx?Wp@R1I-tXO-!
z;I#}9`!OL*Re@@nY)fZ$PEIobf^)D`1-lkW-U0YL)x;{|2Uk$=gg~qGs1ZXm*x#RA
zTugrM+&Kj4D0-l(ij_YY3ZFXrPSYS5W1x)pJWszWwYHWKUct`Ec^2s(987zZp$r>=
z%Q}1WFkaB175Wx`!3mEu&m|FP&Fb$`>Gyv!C~`xLmVf0^-!cz779tHc+M4f#_fYd$
z5|iHzw)uMFt}5Cyp2dF7>Plwktga3~eI|zJmGlF3ipso<v4vN}`<)IxFN=z9LW5a0
z8V)^Wj`mkp`wBt^VWY*LDIRdK00bD@j+Sywmp^*C-6N-}sY$gbX0M=dUi{#9G%LnS
zK|$gB4y|*A9@@BWbo-u=r98+SKDO3l`{0rv`yQHNrlTobrQPAwR2HOD#QcizuIcPf
zQeLFo-gWi(5B;k>X}AIo6Jd#B&t_l`wY_G()5}OzW;^@>$}PvLR%U{}|GW73&}Zv4
zRApmN@UybA5I*{9i|)jPjX%H7;Zs6K0|;Rjy2uu*jD(m1`#PWF4FL4e$k0pUaT?ar
zQyF+LyXdQo#JVh3@6Uu%i+c%)pX{5U-4_600M*~x+fguhzb~xHL={kHq4mHuw8*!!
z-m?M7M}7N=Y41U0-fSa2Pcx72fg(M$TgXo+vemmIHcEuAMcmh#%bjLd+MeyMG(fZv
zAd9`9@#lYhh~^D)TN?|8i^sR-v~q1Frg*^Pm`k^GD(7pLw3HMU+DF4aHG#EOI((hO
zfGomloB;ZQ4aY)FFe4{%NaC}TZ3yQwR*PNf6fai=)pv6)){ymM>^BJWFo-(FDEYHJ
zmgLU!KN|MxPQBSf-aJW~1eDYzvMwr<riw|{R#kltJ<VCkrlg--I>p~=4?VgQ#rC)R
zRAvc(eL#5*1lgN}PSOSTC6q1P>zaAM$H2eMK_r)_+^QbsX=-W)mQ8p9OVLooi9*De
zI2VfsZLUKm08F+!>vMcK4TsZ=Hp2p@y)`Ff49|294;{w3=eJ2DzY9b%suWck<Wt4!
zJvpD~b7(~`>cKm0Bg_iWy{M?y$63j$c-Uf<vyfBqXi3GF^b9T`d4MrWG3XxEo$j6b
z6Dz3<bO8_{r=kkQI)f9WB5~ZG7iBpFp+O1ho`Nw_PS5vvXZK5&X-4v++^??78aE&d
zRq;R*U+aBu>p3nXu|dA(_WGpZ5wuj~g5xgH`s?uywIs7`kyHW3&HDbX+vT*^uU-4T
zSp48ew2JufZ?!(SZdd#*_mu(bLFhEYvU9twj+i-Y86M40rIwd#0kzOm=3odY3$1Rj
zV8u51kU-IMC>lIib@08eEWC6DgQ*y5c@Rp;RiqN%%2>DAO2=gnVO>&E@)#~Wi-d&X
z5nOVrvT--vu~!N=+DEKJdEmj1Prh@^K#C~D6j%>rvDz`(?X8W^<W;SO#Jg?py{*g}
z&9GixUT*O{-SZ8F=u*|vVn?A82D?_vmvnUt{k+D|B^)9~PPg?5E+`fb)tdn|z+5G;
zY2;{;l-X83`U!R2i3)3}k{gGR-m+W5YrlY6(EcOfxiBV)i4$(YanFtzE;6xayW|H4
z2Qz5N!zE%YUie&zcRfmNRA6A)to_Z^)K8zTtg+-k`fyq;8N3kLcG*RrDnZ!wo|atD
z@BCA>w#1m~lgKrTB5~Gbiz0!@TD<G3<Isk7i@QSTIuu&=q`vKL0qEL%bg=b^`_8G;
z*hIv};-fAZJFAgmtA7wfM@L@6`pnk1P`a+jtm9##*r!t?1Z@?6LW;L14B#OlVd#h$
zMbOqW=D*U1|7<1Up*Mb5_hn?RV4ZmnMBt0x5@DWU!N;6NtS<U;e6)5?qPONlj%#gG
zCzb68@2VzS>1eDie_0lWnlvl%wU_UrG+vv@BXtVy*z4C#ZV{YGw_<z*zw=Lbe-wx{
zrUsA{v1}P<e(Ik_#vO|vk}AX)pUVEzRqVP+$NX~XZ4;YZx(Tw+efHQxMVye_EsAqS
zS7p7uYi1v8c-HI^t&5~s(T>@dc|XEod-CK7G2U5vCLw2M=lBw(=pI)F@uY4ihGgSs
z&`S!7BMWnL(D9lc1#r=njb4mwoAs~_Nv&VlN=Z*|-HZ=?@dDew$yiR18$lx5&gc&^
zL@KGP1F@1^SvjK0aH%R^S6%%Uqwb2Ua{n)n3vsX-*f{?|C;s0r-+%Ci*ChyHU=#cG
z>vy!ZwWCMdbM-1n0e<rXix|7%X1g>G7lpy0h_XJw*a%1sYB5ijvF)s^Yib%AkEa`7
z!HQ&NW?KI8Vqr<0jfz=)kwu%dROP<j3b`mb>l*ag5zv69KmnlXEaSw7Ou&aEL!17d
z*I2sMd&5JNm!PRf%S9g-+4mBcb5&D+m6a8-j9e6S?{05*^6K+c>bvAGG~YVE<H?Y8
zu7~epR#xNOliOLdPb#LKR21D5NS<z8Uy^wx$=BjccP`nIcAH%A^<W)C8BI8=zHASl
zRtWa^tU_g2de(!!u}lM1a!9!E?B3=M=B8U|FO#X-eD_3{ayJ{)RNu1V4!Uf?A1nlz
znNDF|d}ey4Z?A~YWBT6+ho7#i3AFwy|5d2LMpsg_b5yod>XUY)_+#YcLz<gLM4TV(
zt+3_O-O@B+2^yiZREhm!X=Yy!26_rhRY*%>G>E34^UTpHxlr?LuNAXi7uoO<*Fw}#
zU486H2N#0$@d2Nkpc)Sp82N<;J|#KtJ<pm4<maAC)~hlo^(8M?&I%(_n;ygjH~{A9
z2UOK5V$5!@MdvL8g3zYXg>v|3d6~ht(!D~DSBb2_=%*?JY&UgvhDfg6_S)K7aXWUQ
zaSN<ufGs=bLrw_~*x$OX6sqJ7dz@TslH{fS@f|jPH+)V++HR1}wQNnP*mv)ybIT?g
zf#}%S88keD+`D)0HB@$hmQw)l7KgvGy=b^J-<1&WB2Gfmv&o&E<foaV73FyNyFL2G
z)4!&e!nWCli-KdX|M`4`FYdpF^nb7?>(R9EZ*su@r{3_Kwd#ya+si<KWBm9Cdh5+Y
z3v$ZYSPO#o$B{vcT5T%-4n6iiJ)e<IA}3%^le%MjieP)sZr1Y>$l3j=Bt&S+;<F^O
zvV`-_@Yx1_{7A1AGnW~WZbd9j?`*Q=Swk!>l$B;h3qjK{_e8C2?n%Sk6EnV2+v2&c
z1N!x^$kdJ}$&G}%;&FTTk;3aCy*Y#C*M>dEt>a=!#7Roh-N@>t@%E2T!eRzo85)d;
z)2)Q9`<8d4Ro{k<D#`ZoS;ozm6aI93kdvP;cZJkRjZILH3b;^WJowgSVLfh5jn;YC
zlaT-FDgdOWTLB0X&YE?ry5)0{){~i5n%cvD<Rj5IU63Y>z{`kv69&(}lD2Bg%eppZ
z@2-(}YFlfsuHIR$!Wh}6`b>ta<)38ZzhxHxr%U(mQIY?7Z}@5I?x#$@7AMg_am2ZF
zWb_$3l2~B@zlQEls1F|3{`s)D7KPZa*C@&nk4ODkJc;M0uYJ#)e9O?`=KV_ZdhI=7
zLsjx1^6n4mnOcq~i@z3WqsR%2tVLL`&zXlD_`R`qCD9$cr;97EA39>tTtr-&Lz(Ze
z>BYccQLgy!m&X6Iu+TYn^GCt3*xs0lgwO7<MGti87$;D`_y_gQAdsB3iY--b+(Fxu
zuY?o^IE&`GMh=2hZ}%z!<78L#qyNjJ8%{ILzt%254!Dsva7KugngFmCbx7~Fz>;DN
zXG8C2MO2If5@a~hUYoK@;?xVNhqhs3%(>fa%s@)&`IfTpbv8D(>#mStiXLVZ6!2bm
zMe3J#%s(r$n&9J(2L<R%1f*NBb8tu%3lmMp#SCKkLCYiA6M9QWAi8j=axGJ$GB54Z
zr?a(xUPk;uB%PdY6c@a_c$`~ZT^+VJd_QJOW6f#gvw2N`D5HvO$9ZO*5e62y5l+8J
z-&60SLEmG|=%vEGt{5|8Ku1GW?mU%ls}~SRzYetrtbPGh?La)-w9ZaWMttg+qU4)Z
zlboMCNoK!*{oF52I4+$T!UAYZCpBD4yvdOf9YFDNu3q9yoE_2t8q$P85(0UEpMQz0
z2QP5?$xjVFT2xM(%F2H^olJ<(-+pqoabUaC$RxnHe$_3lv{b-OtdEajr;SeuT9<E|
zwKxdSHnQO~Zh9GcdEBcu%IfOm<m6L8gjy}%9v?(ZT?j1KmP<1;zgd+9>3mW`S@THo
zLp<oDjC;+<?>^64c3PLExQnWv(2XT$WoO^ASHZ*TD5Bq^^P3fpF*ExtabuWPSKv?c
zukKF~eJZqtCt2G2&5yMy{ohn}4||^6q8L>vQe~s_w(mOWq3rf`9#JL#+?gnt8e<)u
zoeV%6NC>JSkkHVIfR6wxhWq{dcgIftK`3a$Nj|WJA}s9wo-YR=0Hq`2cxZNWDuB)&
zLAICr+G^!PgFw7$s5|qY5xd3ht9X&x6oyjXYtx^;zMMRf7^ot5>QG@mB>1Ki`&QF1
z;BR+{!h+`Y3;x6km@5~AC687<<m})g)woPAUi=mmjAXyy#bIB{*hO=`Pw-l{cPiPS
zvM(3o$Tp80zmLn|C4y_$+8;&+>C871PFa#~3<_UkPVM1zo!6Z<`YwJovH~Y$TDj`J
zGY7C+zi01HCVuE#`+2^L6uTt-tM|2ze4SLvX%5T(k+AX~t_bI4k+c8R3-I5oGXLS5
z|F_-%`uw-IsqBy!Xt=Vot`Ri(b<EwX_~=+=EW84^`a$2eqWVA>hRLbI==P#Y`YQBW
znOSk00<O}1eC48ynXX@wB3&LItEX1ZidT%=ODc-FDfQi)kj?tDQe&ILpyYh=X4^v)
z?@P!m#7qW5RVq8py7Mq?JPDz2UUd6VkdAKEc_Zetjnj^@9rM3`*YhdKutfFa$0^TJ
zU9|hOq`6Wbt$NDEwi$om*6YduySmsmB8?FXy6q@O>HxC_g|ls~^C2=uLMzc$al{&j
zg;jcaVWm-X8&5;UsCq2>RROk3-1=JVkU>|KmGzJB)*Tsi#i5PtT-dsuC)ZE9%If$0
z2hkI0Pyu>*9JC|48+2pd3Gi`se9w_U$eE|Mzbdk>npTGwT>?s-<+`g{psSxl64}+A
zA(LafP&;y)wk3g(fp*!X_^-aEmEE_mn%K(ZzM91P&&cloZE2LJsUI$XE<i#;!vF53
zA7*boQq$n+xt*OIy8$Ku?qt&6kqO`+K!D?Y@>?EsQ;7y(ct)}eLFj8Md5dLBjnf**
z6iFm;L=3#bb^D4r$}k2IN+zFuW8Ixdh~4tHyDXf{XHgqM66jHgAFy%6z6TwKjvZWO
z#hwd65dWGwSs!?6wn-B`X3pz5ck-iJ`p?gbR7gGG$&{>XASAsKSh_;eQ+N&J)+Ef{
zCE^>pdM1{!4}TwcX|GU};BuTP2d@BXX>iexjL&1g0Nw(f5)&Dj)s|30+t#kiILiD}
z&y47zAv$ZGhlwvx(ktG79hg2GWMgxjmV`t#^fSQ}5EUY6R&Z+lq8w+m26tE`T2t8X
zWxK&aF3P;V=_eOX`wFEje5&n61%S&=9}u}KBXbeeu|P4rn!tp2w!o<2g}_SGEeZ+>
zt*_=v(Mu?o^k*RZ>b891TdX6fZPTb2jdDJ^4mA{1dX89$-n+x4f9Obh&O3nDLulz!
zKxi>&ZT8ihve5ca2Rw&rg9D@s2vgAHhU@b56czCiYBI|-Cf!yRIQEFS=?7mD3`Z~x
zTv4-K*?0i@bN?e1yX;yLl7yeYwedPmNn$pEd<*PJlS|9XBb;`m2B!4_Vg%d}kZC+K
zzm!UCb*<<tL5ykw5^SXSc=ysOk&|Uy0I0Uc8&eHGi{JW|z}jd7^>221f{maCf`2HE
zJ{UW+9wkl`n#iqy$H0Evw8Q7*U|}CKc}j?A)%nyM$8uG<tx5x>XGDzy4P+n)@uLoP
z3>6g>i-Y-0egP^2x6FT_ifC&DLnxF=&>xI|cv~FErfLnNc`11bLCQgo<TY+Q>#{ah
z(Y`cSVlyb9EzYV+*OvVjs~U(Rko2gbKLZW6<n%I2>aW3+JGk^hj`cocB{rIz2G!Je
z@7^s3wxSTc4@f<<S>O;_D0hwKieFK407DIGCIAuPeu6Ls2osdtwfm3zfBkw4qAB86
zHt9q6)x7h?<a(RPdi6{9iKlIOB_?fzqPs&TTIG&l^Z?1F`R`nZ6KH{~c3D{?rU&n3
z?CA3!;%vO-cvRpxEsc`Fd3jwY5EMFwz`F|Ak5LPDLa~D_(E|Nc2pk%bWYa61j088G
z=p8by<xP(bpsJ+to)d}h*BiJE6&OP^-qadSw+WPA6RJj&?8WlLm`y_$7HFnTERCwl
z$j=R`TR<f65@^MoL!b#%SpO7jdEjD49|^|->a648Qbw7&A?#>&GS>SADm?kxUvU6n
z@Dhyqo*tJ^2hcq*G>im=PVgXvx2FjCnI=bs@}nRo(gDwD$s=LWMGySlEQnn)a6uiH
zKF2=ZoM~~a8sFtu1gec3%s{emB?+49AuzBIS65e*4h7BWIXEgP|3SE->!u|BflXJO
zRhzCY?XBUT6V$#0&j*0MyS45c9CQT;h2rCNkYiZr2?e$150~>DZgLGnAM|)2OI;~l
zB}ts2FGD3V9$05lFk^K4<XF*~cPEAqf3+Cx``*#v1TL8ArHrHqUCll|+o6I6A3YtN
z3(3<n6B$W9mF<5&fBw9CQ?AUZ`W{XFpuT&;@wun7NuTa-e<Fo!F#iyzU~=6d+lQD$
zRiyu`h06BI@FO?Dk<VdkxO)2f<CNaaz)u-}Iyzeo+u;63Arg>~N_dz-UKaswaey`9
zGK)1)tIO*1J1It?gYFzw68Jq%>}E*!`x|F`kCzQ042hoU%+@JfW)be;2zhbcbNlSi
zze%btR(UI~K_lUiD^Q<?zBISLUq=hWs~~n+JS^8!6Tga#7XBQ@Zme(Z1{s<9@=qsB
zAcCDFj7yznW#7JgSK^t0rc`$h39RloetdB2K-&)<u_h2#BS6vkq<!)1ODQzVBD6lw
z%|KREiqWoka+Q+DAWV_F;_RTIuh{84uxk;|AgKZ$qgCa`RSGmdM6C8#^9J59bHSUg
zf7IZVZ%kMJE8XJ(j3G!O4T>Ydko043`a%Cd(8m{l-@!{qnC#!;{oD2vYvWZn9yjA<
zW`#^Q1)4yNxgokSuOv<ny5X?idO^CODak;QggnN(#IdT!#ii4P8b>XX&CVBKD_iFi
zgc!iYXZ>px_eY&7^d6}5r<o-H!N(nmYn||Q85ac;o`<jcU|0IZc&@~yn^lLa4GgR2
z6uK2TwcUtpd-?j$5$Bt{W#~EC@3ms|N?XnSb$v>l?z5VRXyoXY5u%&KTg3>$JBzJV
z`1l;~wZg$-9eQ`~o-4H*4S;Rgsx<l4f=K*$yN_&F;}wS?Nw|Z9nKoq%xBkdVXu;!*
z1g?(8H&+!4+}90UU`HfQ?i134r1DiL%B_@Y*LdzU?$z$qgpH_?f1XZGO3znSa;C4Z
z%xii#pCh?)>0SHqnQ2}>EYtpoj=2}aENNzGR<ZJvG1k-tY6A_fsRd;zcvi#1vO0eX
z+a`p}io!qo*pb?>(J_Bs7ABC>t$dzt#Y=ETLif~NZQte%ckH6;=+JEjmBxfKI2Lo*
z%1yj8KJW5ByXZUg42#ml@EArwDWsHaGEB+QD#QlHnXc4fGQyINwoLyrRHE7gb58wA
z@^q^iFmX9-VGuz|{GoO5O`hM2GIclq{%L-bc!a}nHJs=g@TMq92#rz*5JG%a%s|5~
zdq#)r>*$22XKSQ@)bDY6yf-U8mz5R5mgs?v7{!^lO5i=z(gM-{^+w+_`Z|eYo~TdL
zdzj~Ozj4`;rzK;+{}#vBf#pX^*wmh)hZm(P0)?Z1GrPS;h-~xrA`t>cqWAp#j`8My
z7`VehtE!BnwQTS#XW0ExT1wgYu2K+uK8%rUX{$8h!q4JK!(~;$A7=FPAfpOuO@CaM
zFLx;rVEp|2LksyWjJ_S~FWfF=P{Qw^Dn($xOcwpla1sA3_U@LBp598ifz217V?cAC
zO>fyPA-OC&1u|`c!&?Mdui0aijbsduHT2Q`Ucx6AXi{cB{xers@NI0YMQ*rO=d9G%
zzZRfPeQURx`21JIZADUBn<lSfo)%64wh0BWEzS#F=YBd?<$^ct{)e0Apf{V@tCNwL
zgfcE2%dJn^wG3U<nD#wH2=-o~?eI<1Fy&-OftK9e;byQ5%mR=(gRG2{;2$6EyfmhM
zFX+ICA_~y6!TUPW0H!uxlU4%M2Mc~WFiAmaJcrglgBnlcAMbDM(j7cotH4F;!>N}m
zh)h~Fp8SZ>>PU%FOr%1;?9IYBz&EJV#c9MU!+3kKCrF0HEjO(VG5|<49thx%7FdGe
zPv)t+pmv0!hFU08ff7e4-bDVUCJw=GvBvTZC7{Q{^*-Eg_>z$rf+|)ZzgTLVK_3aB
zxLyE$96eB-clYiV>Y8BH1AT~|?frOxa7{@v67OtKj1+v2P%EX9{m$UOQ{~%Lc3|(A
z0FcN8ffp!=&0o3>(|~ql!JY(RjDvV!R?BYK{Nmg{_NfM+g2zIF`-%=+m3os%v4B_j
zRM<!&*uy}599wgqbyHWjEAqOZD)1(FfLt274BN?C27m@4kRP#;^KF;>CB41H&}500
z&o3V`k3kTA{4-gq{P!D0N7(hFhT2!?o+*A3#VrFn-u|nv8^`1%BxdQrP|yk5-xqQK
z@P+DlargH;M1{cZ1K=06{}9mJJj)n~Yv^-u6$I%3zyru-*)Rkl01qxGc#ZJj23Ubo
zz*NUeaCz<JYcp++<DEg&0`trhppA=uS-yTjPC)ieJ#H%CgUNOUd;WeI`6jc1Jpq=3
z5kn5Gm;qA)1RQAZKMgWpv8hW<#fJK*R4cB)A2Tn1a9*pgxcoFIDANusA}Q@(<_`}&
zw;_kOJ3R#p5oDSG<EazOdRaH$?qbiG{F+@_)XVr1muRG;`@=2VmX?-s5IlR}itBkU
zeb%b>xPi(fhr7LsJS_l@qvGT7n|KQV#37RU`V5|6pQO2EJ_YLUxt|-cw<5)MwW3w#
z+yAlmu8=kqTlK}&)R2Z_?RWDl0rLXJAH$Jd3DAPUv4`50QNepAPfW;@GuYZ(nuLQ}
z3W$h%PnjR->v!<{u*{n`@Soem^cni^bW%M?vOJNs`BAwXD>?a`BQ_WmMAR?0^Y)dm
z-`~C88HT;ru07xR_OUpWf0-rcJJXreIVzf0-X_L}+yPad+{F)O+_ydy6x(O6Z<ts%
zL{8;dO=vR9;gd_8oxXOj;{FOt&i&o0e=@>+>)l(lB54KGb)qJC8tYtbAM;B~$_Wup
zVP694AH4SZr@j$?#=H+Dk2EvD*%Iw*lp%58{$%N+Veh%UT?{wnyY>3skbE6Ixku`p
zbe<%&dD#t-0@af<!pO#ydspj+yH^of?#3s72Q<M^uD<CkCvlg<Qxw<1-Z*s7!Deo5
z4t0bBu$|GcF&WI$2eXU>coTcA?TTdt+7kbotU?+K0!Q8~gvmJsHIo|TmyI8-(vXmp
zsDQ*L2eEACo~911Qq_7b9em*K(=8CbQI`WaKvT)IAXlgi)oHcQ4?Xh1)zi}p16gAv
za&LVy^39t!Ieb9$yRax8oc91%l+eOQXUB2ZK`C{0u)s29Yvfv^?Mey0^!nl=uFJ>V
z`>X2T5k?`$O918y%{nOYm^ZQwkF%x)U4nz~>dQ0--73d>B7KFAxty$%dQa@4@|K0b
zA_89BpOnYUEY_O-#CUZZLNar;l!Lr$&j>8p1Ot;-6lm(v_-+m+D}?=q8I-7WUylGU
zH4r^PK_h2WHz}Ie9CQro8zOH%z`Xlg-3EQ$#$eqE|JvH+b|1=cKvy>b-bn0Ey(~B6
zBat?pi+K!P5nJV3EYZi!LFFRWwqxZafV*-hI9OP$3+%^*S4LwGF>{iaey-5Xz4qcb
z<MAs;8=(A4tGM?9cC=`@kYn;L&OMZsacR^Avo9((BNq*^X#e5nz~?TnKk@s$<5>t<
zm38m4a;aB3?WFSSA|5m3blLE<aiqki5WwG<QyT-yQ9|_eXm|sxOaW2Yc&dJXGS2tU
zmkdydoVvyKroi|RD(3w!U0+<ke5n^J8mmsTtyez|_Bxtpp2~ekPGDRB4{RsUHKGRH
z-DBe5pv)ckycES)Zl}Jr%4Ke73<H^bs6odq&Txk+(ykx<hAU|iXZznYdbm>(#vd#v
zfJlCMo{TjaN}G_!#wCI#Ngp3^A8>z<+G(n(O`%8^OdkONiZ7D}H|M&iD$>-WiXfoU
zl|`RGXvlAZH{SAZEL*oYhVy=3GmMZ>3a&%uKYxd-=WnpRq@jU91bDN-WJd~{@f?C_
z-3y)<gEk*^p%c;zA>fNa-Oixad%$X}Jlnd!Y!t_e!W^^_)h)FR)?Jr@3k%}9ELwG-
zb=nnUJ>Yl`N$>63w??zA`??-kSr^pRZ#d827ln<syY0ICYr42k+2-Npp1*~3KXqfy
zF`vyVpSghMW!M3z#}!->OXak|^A&pB&;g!7z-Oy}bU0WS02d#;{IrvEZg4F?B!P>2
z_Y!f?KM&&Ar|y`Lot|*?6nc?SR1$4`)B%_Wusk@2fSy?#uM*_rI&iqoZy?@tadF|t
zhnL{S(|bDi&PlKc_qC(JLRZ4(kV#|;tT?rREj5q{tzorUw$&5C+|yroP@8*{93vJ2
zMl%Wsc!xe^_@bAv{-dYRUf)_PpC!a>GM#MTVkV&@@Q!wU#3k`;*Y-!E*r?AKOe@?^
z5*M>S2I=x`OboVU^wYR>V;Di$oz1U3%u>LFJB$m0W9gaa?2fYOc?m3u&1SOQx;o7G
zda1Sr1duX$^*LPBHm*>|3*#RFSy=^~9VhPh4Gaw8T_T`0^?1jNHV|P@oUkqTM~jB)
zOnQ$wfu<q(9K-APv`UI4^#=%Jv$buPA`WT3%{xAQQZ#fKsGSKSXk8wHv@aW*S5zgn
zO3c#;{OSLiVrUubi<^_9d+unKM`2;GLa%@m|6ba&hpo8|<;LR}4;$C@_sL!r6k0b$
zmX4A;;8K%!+^Y<GziX1Lxc6(35%`;YZyd)?5+jwTy$6Nzs&BZA%D;|(Hc`oB42iZo
zi=+oPI~WHAed9=c@xM&JS_;B30n04Tgj`Bp=KcDh<9oZ*H{&`{Mk2k*-Rm?Eky%;e
zr)rIH0hupPhD^3R=p(s6R0!$DRTIvJmWd#VXv4;_m*W}Tg$3WX)g4bPuAQyve`w@4
zJty?IW7AHWM{W=_p%kYrN#B9u=&D=O!j_bJLXFBGAD4~E)`8czVBx1bf+)KymM@e0
zW&CUbF*cN4Bss3jz|SrB(6cQA0m*m6uI>%!@NwzG$0~P&Wgeai$<h`Uax^4^$OoJP
z(ez%^lJPZq!lrG^bc783s$+~<%EWpg<%I^Tv$)t;c$$B%0Zb1`HQlbho2EP=z@M`Y
zb18&@3<FrSt(5<i2*IUP@bE}ShZV+geznf;VN?wb*UOc8sf&{|!9!8bsH(U@&i6l`
zNcdk~{D1mV{-4&I(P1_N_U=>Cd&%6>qABc|kJK}JEPHoarc#L0cpP%qRI!<_hXji2
zCb`t6vO{L*A@3gb?~T4u{7KQqB{C?&nP7@j^;#{Cb>x+gY{(S}f8Q#fZgFDmb&AiA
z&p+pRBWm$Mn3T}$i=d24>i>y;@IRISq_QU?9NQuo%GYZ#;45>2akAMso4nw7CIXyQ
zD?qAXlCoaqhHzCF{DU=?p~8f=l^~-*;S=)O=LI@>Cgy{%>or#8k3%E+D3xMf?1{7i
z9RR);kg)hZl$o}ZqCF=}yEFpuUtNNC|LOmN_@NfHTFlxR#DC0QsN!N}Dm!>BW%ziA
zd~jWnOQ5*v7&HG-qlkGa45Zd0tsu`Pzoq1etgLT5oHES|cj~1WkZ&CwFLku=zV>P}
zfValbm^pO^hZ9%jW5=8{3~lAm@}%;29llLUBGPYn*ElN)9tyDWr&x6v+>1+3DJUSm
z9eBE0t^zJk$)Wb^pPmmc--WlKp#)!<49_Q?VQllh21k|1LO-~9-~|Whq8IorSO66%
z(D@ADX{6L=>)av|+)6p|QE~(th8}2Okc(1JVc-z46r$cgzUahppN(YTClicdsT!_l
zg5rKbe-p#(|MjKx71HOE>wy%(LRlu-8cjnlq~u%?)WflXF&4@(iDP}?BT+2h+;bPV
zu<%RD>sf{}c<wpp$_WfdLgb>7MuH{Zg}TYe2j8AL;$UO-#~Zh~J~sKv$>zXy`qd$x
z(69`FsW7@-#lRO0$=;&v-R>u{VVPHT#Qf5F*eSVm{1u{E8<uxi^_5l07b_X5OhZyp
zCKDQ0;{3ck93+#F2(Qr7ks433so`Au`2l0#cg}V2rD#!-2YTOBBD=@;r(7A%f>)^x
zi#}Wy=DE~5o)p)-B8C#D!}rIdjbDi@J<a*zMb3^FR&f!Nel4x@d@FtqZ85J3Ld6`E
z+bBn6l_x(T7fnKiyrM50S=h?lEICyF2_$(ND9cU&EK^`&5p17#K(-#MaK_@(`=kz2
zB{q2#I4~r5vaJYMRMciir5;8v>ggP+drS|(l!8o2P+7soO_1Mr|KGK1{I|l&e@^cB
zf9o48i$s3XSLQ*GWi@`6=1Q?beQ`K<2aP!{_`ehe?&I?^@KCA@Q=^5b&4{jcd|nwu
zJ;}k8tgeZ5ju349!IU`eHg8bdgg>IrwZ%&C<IxWkw#_@zVHFnM9uK8WEVB*cDh=1m
zTM>1Ln?EX`prebr55sdNxY!GUF%CAjMGKn$2#HDZ=e;Wt)<!kxp^_psFj)Zs_`+oU
zpnGH}B1O1RXE@6_Cp<j!GYn`1HW?#+*0+ys_w2a(T)YS;_>?Zu2VDDD!v(b*q`nZd
z+Yzr|5cw*plOBlZL5@S43(XJ*x99^#i|FkfPzjN@SpKZCgyO3vV}BAit<T1`pp-OD
zZ!yv7^3l4EWx!x-!IGCPOlw?+Xt|zka!aYtDPd?R$aLoa$NQyN0m=VYdmt{e%-4l~
zxC0?wWM^febCbZOG%oL?!XND|RP&e1Wvu_VZa|pz6rMeh#zP?~km)VJog`b)@+rMc
zWsksowxWduJ1dMjH@zQ{pNmTegd%Zum@)oQR+hP$ZK2)U@WPhes~L|?M1FyND2z)N
z<5rsJdkKSa6DTE9Rpdy0*L?t3JY(a)Yq2kb2wDO^)WHp2@E?}7W@tZl1|gsqA_ghb
zv1xmJ@=|4ns8xL78QI>HFWifF0`Bx{TrP6G)3J;=?l7D!Nydv(nF!=<QdkBY+EMgo
zaB1w~pLTEJ<9U3o(}{SO+v@7f{_-PiEsl5Syclzu)PsBqIjj=yx2Ah9aI)8mYLzfy
z$@d!IxRR$`CwCVNn$jemPx;jGB`fX;9S1iPxq~m>)+L#Z_}?97KiLKZ@klK9+fKwH
z{%n8ek+HI86wo-Plao&Qz51C(H)S+jswJVIDv#1|fZnM;!<g+|V{z*K!gFtUIoFC`
zmjRQw_uz*R1R;z^?+cJMMpS3Ts?R+>Bs}lYQ<q6CVw0_EpxZYi&T+x9L1A3gCSX%x
z?l~p6X>JL>O@7JNw&p7^(ND&&nT@*vx*2M#M^!S^SPmVS)8BSB3sH$yJ|%QIr{5fE
zA$YrfkmH8mhdCfvow~_&)SCuXw+YC8Ky#=`CpkSG2X)LO0L!Tr^Gfp4RAxlpGF%*?
zCs&?Ynr4Ah2)r%IPkFm#3=A0W_raW!i&`6C)7eea>dC8V>=!~pLsiw)=k{p>0W*N6
z;s4Ozn-y>|Sb^>Aa?>D7rDkU0LC*@#pJDHDpqtT2%NQPRZgV7>JlE$!CD3lrQ@y`w
zb^#8^A9QW7sXd1FN8ulvcYWkH{%cU9O#c3E-!%cn0G;4j`iGsB;gk^dAOD}3tp4x0
zJQ**;9nax%x|O-t%T*wzVfi4b%Gu$(l)__%aoyQWWj<PH5+w}`5>ZV3=+T*^?gbB9
z=m!BN+Lw*0Cj4{28P*i=06KjBt-CugOZb`L;vOc&DsHy$LwfK7_TL`1(s-!52q26$
zO}sJnPuFo(aDMvBUjeV-LS}@ScIKBaQpUz9X~L3C%L33(#Lcg8f9dodr=+BmRwb9S
zBP|IMBGh*sP@jQGX*WF!0CqD#;y%%{fI*hWNBf%*%P{hFHCB%WCYPtnyI}bG8qlz_
z<z1lvU-H>_$t08q)005ACc+FlSdDI3bddwM;HqZ<gyKwu$eN=b8#O@)3_L3WGu~E$
zj;z6#zz2Y)(){DYEr!9{@J1n+ieeZfkq%n-J;ls45lOh{Ex_akIZ7E)ddl=y{);!-
zV!<;))uc5{N<krbRv0?oKmZlX<P;RsZxp$m^l%Z7RRh7B-sTEE`;YJXTEX_-2&04a
z5Cnj9Zi|l-A)r5V-&0g%806sMY5|WG!yveAquEs4ybfs)bOPCBePY}x0iYagQlY$P
zn6HMBoHkd)2uAhL8A14BpyMakyQRSsq*--<ZlXnx#(#Pni!uLdx2ECjD9TtLtLRHK
z0K|flQ<;PaI<Q%e-Y(;4>$I+BE7EGlXLi5pC*wnM**}q1S$CZ9DWdO^ef+R#alfDA
zUZ=oMJ6e(;))SqW_nU30H?I9*T4YK+uX;K8&5N+-#=4ZT?Kh2kqFS`>nyQ9L)RSYm
z#(!SjIXIaf;o%xw9;Z0lxphx|Wqe^GX|L~Dbs%P=qMJgh-S7F^7(mG|@^bUmEmdvp
zFYKkieragsYM7dtp?4fGnuHrD1m@&$0%PL8Lw-+=+nitLiTHr$pQWYe1J-xV%~`Kq
zyT-S?Z)t7a2n)sk?1^x~$+{(6bCMRft$8Kek>c|j*%Uw{_T{7rl+6|Q&zHW9h#89!
z>9&3%g8pRwUbE{#7eXf=!6<ewmHpnDh||p3r!@vj!i0e@;qtUE?>q!{f4st3E;<9G
zXaIw;-{3J<SAH?C{Rf2+>>)1Oi>hD{0e?$Zm-01kZdO(DFR7_7Mn*>XmgEom|0pOM
zhO*@kDzd30eu7tYLe&Z7)~}QSdyM7^o7QqmD3zMNe(s<B2f~m)wqe*I$mNA<^k{=l
z)NeP#1o<Z-`=U0Ts}BxRua?owt8b1>Vqx(38(NlJ8clC<_;m4TSpesAvOv1yDHbN1
z#;Wq=%NO((A0Rn~BqlPB)jZRDrz|avO-c$kbs=IDt$%S2pO}K;75Eiq;I<HPE}y)t
zcdlgQw~!&t&CQa;XFG)<1%Swt_?Rc9*j9k<SDt?8)<U<<FDo0HJF>C?>A?;uwU<jO
z7;-4A>@hAZ?RJ+o&?7WTs1>I%FaO(WVR7fqnfdOd{Db}Sav@QlBWj>y8)4`s?`e#=
ztt}^-AHf@;#Yx#hrNkl)9DR;P_UU73?|?ZM;e^zbI~)wT{(nDdNc7*x&CS&+v8E6f
z7AB|Q<VaXsvxi!tzJrb5paYr(dgl@#=+6Kf2Lr=ay(D*`!9TM+m@fx}V4{R?o<atX
zwX=wpqK!T7vSp{;m#v(uWdrm0O$1aEi`e&N_95N8e*ZpGJFTLEhBEXVNOb#)X^}sN
zhNz?1;BFQ1oj9Q_q@}s+Awth~IVrjIs0$uP4coTPb;L@^SXx@vgBycy$x`Z`VrcEy
z4QOAvVY>D8n>R1!=gpPSRYVPnIU3h#>F6kq#k9Mkl`~#87(GxU{^at*{FaPrGqBx-
z=AE>Be0(y$<?QSgt*qMZZmU>7p?fXf4F~>~u&J7`nk150J69tpH7GNaot%>L@U(BD
z%AII+b@jd2vr==0j6T$?(_mkA`SxuZpi3VXe=;qI8Mc*$@R*sMjRLvzw)4FI0N8MF
z{%&m@cPKN8xDmjF6E5;m!k1AX{vu?`yKZi!EBb*Sl{43s!!=;AOWwOAQAbAy_2l2l
zA4Em3eHd&v4NRGPyak-NA7nReiIn1#6O0249h2R-aYJ53r47jAritlSf1!2<!aWME
zT)jFC<5cJ4<(?FN>9pg>ZEltssNni+a`S`QLh0C3jp)%Q!q(35#s>TjGFax+0mknK
z!v#I@;*8<Pfa`jGGy%rc=6F-sKVW==_YOieB}o6dyJz>VJ*%^v9ByxK?<G8JDAIt$
z*kCk?@xzA?QA6~6ED1YedPCJuzs|_T78iN$SbK;jO0B*$et+6@&g1=?j~@x7h>6L_
z&V;tSfYT6t`u9q*(&NXEhuR(&S9g9{ztP~0mNQT>fwld)yn_<2YZEp2@tx_FOdQA0
zWw~P-OfTX4K6@sz?OBsLycbmfdcr_xI4?mO19yEBIaK$E2tuI*Wj=m>l%zm0L1k6d
zu^s%Q&cl}yGbJ7uAAbXU4+u}_DAd)<p1I@MLB{Ndv_XSfheED@$id;pYvh^oUc{Xn
z#<av1k9i5UNa3^JFD_axx(HK;%R-t&7aqj0ljB+uVU|YJ1OY>+crW~li}@648XKiy
zo~~eW7s3Hb6%Ek7*r54h@9xyt{OIaJHA`?u;loPM(S-s+mLfh2L;~~e+ZB_Ai0Sdp
zfP<A)L+ag7VkV}@v9U2fW=$z#$LYp%?>o(bu4Qnm(!VMDxe#I)z7Q@eDqaGdPR{@H
z#~NOO-aez;_f#CU`oWDT&>J^^#b&3>dt(YqBTp|FtV5{Hr`#JOP<!-Ov3%TqamN$y
z#gs3NpPH_rFW{{ALQbi_<zIA~H=#qG1xjD!_zS5CaIRdra@N*_+MJkXr6rxl4({O*
z?CI%=U8-O4<W+B9-*M9H%nX{K6&3Mwb;}~a|1ZISTnS;Z8?Sr^*>2FGu4670{1y-4
zHUm#Z8HIh0glGr?e7yHjX8$%lAqxxCkhgE%NXy7DMh3Df#gQj7P*KUrTv_hUSa+*x
zH@&H9V{<(?RUR05(-NICGweJ(Y>@Fke}47w(WA=|EejZ+2h{ZR=znU03=S`(;xV|A
z)*}z|;pp37HnP~FoAI;|CNr4>o1_f#V<EY*U%mQDM)lL;;-X*e?-A@|pu*c@aR9Ml
z|LiV%7<q5>;9FN$I?OfhZZ+`!s7JRkX%@l*Lh|Xb=7U6W?|Xn8(jlh%V745a`xjz4
z_x;^=2Tz^+uJh^$|46A_{OQ-x)fg_FrKP3q-|b{9EPqy4KZ9&c66J*zv$fDy=e9~a
z_xtyL*|Qo0z`|Qpt5Ym|*8Q12cNLU4ExJCY^!KaXq$VI|U{Hc8g*Evhe>{|m2YUwe
zArkMS%{Cbs)3VX?2n_+wDG>tY@WP^?p6(=%!ozPAMpqf%m2O2vM>m~&Z)`v!diI!h
z6PzE4+*?#kOhi@V5nzDBg=mw9Yxdr@_;JgH{{DV>Wl0v6)1ntqEFr0<lirgJv|UL=
zUt)!}?FRSO%WBK^&z7l^Dnbaty=MYiTV=O*cN^`?a6Q&XW0f=TwWcIsXp<?PJp^?l
z!UWfmgv^x+ap&^V6a%A_zNOc3aUtMO(|lqm6lpzz#S<GxP!`7C#Czrp)zqNh#Du}o
zMiUV#Xu>T1l~IT`PTf)hxbw&2$_hcb(Zc-v4BQw9fU`jzeqPJt1~7?jZcN`HCIQ62
z|1>NjBETSjZ~1I@`n#=#EoduVf%EBk(8g-n<YTZ%@d6SRV-NpaRz_7=SQr!@{tD*)
zU_jx}?{s+`hPLD$J$h7U$c`#`z9$EAs-uox&=l>1<%;jA+RL`t=S|b~;P&KS^aiH=
zoQD=<Q%HlHl=$2R)j<HD-rrC6S%o`p2;#j!Y78Qw#>|4b<e2eg`}gnCva&ehfO7q+
z#H5Hfp!_|CSp*mrCxHCNhsn*%tgn8$wXr+ufIF6ZZ4>TNhT8W)B_ohcBLT4s`r1bW
zb-h|3zw&~B1HotnsbwD)YXBFMv><VML<XzGOB+sn7HhUO{LIE*r(75VJ-xzNQ92C+
z)eT=BWxz=^K>;m<TW(0L2O7VR-RPjEp63TiEF5KXp=9CaMuvqEz~o;Rt*a5WtjPaW
z2be9P9t6-}mm{~J9{eD#hYf{R3WU2JekZtf*72W3gn7zW=vq2|`FQpKdT%?0yUH?o
I(nc@-3lfkwegFUf

diff --git a/reference/Rplot013.png b/reference/Rplot013.png
index 7d384ac5d104638cecadafc459d783c4deb62713..0db4485cea30d3c2b4ce6fff3b9ae7bfe3677dd8 100644
GIT binary patch
literal 8483
zcmeHtc{r8(+wX%AHg*F_p+t$$K$(YBl0xQLy9k+E%dnQYwrxuYVUsbL$H>g0Y$Wrv
zmT6f+re%m_k@>yX{_Xd=&O2P^ocDUq`Qu#Y=gPCn@;vMNy}$SUxj&!JU5JjBIy);b
zD}o^G8n<ugA_(0$?fW1T{KU5(7XvR$4>i?qz$<)hs@3?z3ybS*6BL559j1NL^%DJd
z5abl1aYI$#D{+zN?W#}R-CrTd4q1FY`?Dc~@i)UJ!Oy?{;#e2MozIoj`|0hen3Z!}
zlHz3sEtaX_1NfZw2A6!Tybm8NUva+{J<z7jEcwVC$BI8RRcO<Ep6B%MAz!0jSxcsn
z1oleme-(OzoWr<W?P~ey%FxKrhV0N(Jj!E;nuyXukW|f0JOhGsA35{@L4NvqTmiL9
zB{=MEQSpxuWLPyoKG5bsbn81WQvu|u)n{yg&)O)e%OnCpKD#Z@F(404WzpAqESg&Q
ztTc&=iuS%fucv?ieq>cX4rhs)=^SVXWT0k9`fAkgQHx@33tf)WPLR^jR@!`*sg;yF
zG4b%;9~V`bIZk$q71h4&NmnO-=+o+3_TS%ryb*UNV{JA)Vt%2t%kTnsUxfe8qk;bZ
z#MS(JoT`64L}BIE!n$=+E*>5S%5;a51jTB9EAH&Qg#Lxv;bBuQZtmRja>FR}2GLXO
zaEQp^kl7td7h&~VTWP24<Mv~VV|DVVYFK6D?Hf7zxoRV)MzHjVR$$wqU`_##I`0h+
z?~Nt*IrK1}f>*b=MYR-abE-9(sO`6U#B*Wrl98U?q%&^wvFBviwZ{@EGH!3h#Kcf3
zep@-3XQQ3>b~YurL#x#JF@H)+NmVumbJl!q46YG3E4Oy0Gz#o>6Wv<)mS%g>CF1W^
zpG)!SweZ{gmZd=M&(R~wFV{xLJN)_kA!icKS0YKv*1}mZ(zVB~-@wrDai`h)he2<o
z9SlRsSI3q!;w<O!1!jbKU-_BQ?EC4X5I9o%Ty?eFxmMxmR*wo*Z!!xagONQlo~|BI
zjq}~ERtx4Rt6cd0wNWc7FDIupabt5PMP8$@yc{pPadQ@5Xz`}Xrz=^pFg3LyLpP@#
zQ@`2aGI~T?A+kzYqY%D^e3RUok1zFUD=W^KBsm-(j}bF^Y+<PfXYKC4M@hMoC0YCZ
z`o`Y&as_6WVCmgKM0F_^j8&k97)Hmg+PNswA?GA*IaGPLxa^75?$wk|d1=h5wYYhu
zj)8$e)VthVQ7$g7^Soh?3rR&*${M=v?(WmIy}iBdgs-l~zT4|D%lrEVFK?yaIU61I
zu1jhAZd8Akc1rtc;%%X~FJ8Q8-%W1MFF1bL`nLMD=gw8%xE0sB)t+|R!=8pKTvEP#
z`EohVWwi2s^f_%q4UG`N$lnXA=S3HX%kmoATypE2T;D>(W-Al1Tx$@}wSGNqji@p2
zWyP>#m##ye>RjN~yLT^aety2c#?wWkH%%>ANJCgy_?)}dd*i!|jEuQCIrn8waya5M
zKe|j|EQ8~`J7s6Gi;Hyx{C=bv7MkZ+?amv;j}Qn1a!H#+-*4<E&`VV_qDNWh28^r*
zQufxeuEz1O2K9G$zmg@cZ4$#BpC2*NMpRn9wY&6K)Ogra=Lh1AZwMY5z~e=ewDMd?
zxCBT(ey?#l;lnJDY<6R{n1riNrloZgM|vt;tcb3S?A6P-?aPv`ay~zb{WfYyW!w99
zr5(J#dN0=*v2kDMe#Xd#Ze+h^-Kl*x`iY&L4#eep+A9uzg=`FE+QtI^XULH<>(;{=
zef9&e>_BGz&*u2CtFsH#0|ChwZ0QxOKmR;1Fks*Q$0gAV7y7&W*00p<u8*(=;RZFV
z%AUl`lgZK#8W39Eh)PJ{dowh(=af=>RyC5`@g}_T9{H?nZ0?I?g!u%ALVM4MvN{v(
z0f3RUwLWc!g~9jpq<a79owsiV6*opvTU&(sLil?}vZC2Fk`D@RL-Zv14p*VFXb))>
zj+^}=R^;e`DR=4~f`e3ypBWw@0=xyb#VZ27VGq-fzS!ErVv-5-^_!HLnS~NMgvcD6
zN{3u*;J*mKeemC2p3d`ec_|)4PLV!Pr?sGt&ZviH<8Wixm(E4nDN1*Q4(I0OmGU#n
ze%bJCdUy#+)HLGRNke^op&9dOrzdW!Z+q<WT;8;wwxfaTUkLl(y&T{kpPzryH0fsH
zze_cJ|L|sM+n2UB=|{~!iJO$%=&VXzI*bH3ixndqWP)~{VL@+cmr@>-|6`?vnvxP-
zW={V85}UT8VYkWv;Qs#Q{^M+m19@>!4MO(IV|BdBeziU%LZ#c}hes_O?0j<iQThf3
z882S2YUn}%{PN2$wlDkAf8dIqJ);l0p0rwUlmGj!<lfTsrDu*lq&QS~iw2_5<v`t0
zsaWVtl6UV?+?ahs+R1zo8Y-MKblSFyl91%LT(`0IV=_WBYN?tm;X8SPbkH%@TCRYE
z+t+%~MwSRCH7ho~n!r`tOfljgp-&{w<Uqjjk4^<k@1=2b1c#lQ+v}Y}=goLajFa%4
zvUO{{np>-J_4)VHZ_UA$`x2xb(0%u=<<!=i+t}FfUUf7M9pRIGdV@-(26jZE?eA@;
z7}1;YKOMpNK1xuYen`Gl%}Cnd3T?WqeT1&yyr^gtN~^4_44;nn?y=MFvHQ@p8K}s-
z)1xkMo<G82Yi(}07E}5gr+|cpsJOU+o7?+L?UcgY+}!rBwvcEg4I2U4+};f`Tnlub
zC%qeH53UD1@0;^(fQ~!)?~?4F|NIYi_g6@+;~At_gRZYCNEUw+?#*e44H})nBx5(0
zoD^?WjAQsm*cRdKr=2S@ECI_ExS}^#=(I9)0l^MBVh&W=x^*&c{b;qtEtl4H7d9gl
z8CiD#=z`>wD|8;6=aaOUs}<HRs4Dqk>C1Ua;FtHjtFoTtsVgx5gapvh|MT)ck3nDq
zeJyH63vj68k4tK8agr_`CKycB<Muz&gf(M7%kuPF&?DVWjNIcu0E}oj1gJwpkuK$Z
z^C*22lbq+woKJ>IY+l?-AFlKE)bV6NR4TYwv4Obdym#+zKvRu+2Q=)-&X1K<U#rRH
zu)c6Uc`5X)jz(|f8FhG)um-dneOj{(N6Qu8OnW0DB2vtsX7(2pJ@!6*qKh(x;g5($
zya4St1DOV8v*IkV8Y^B36t%InSEVzU4z2pl={MN59(7?28d?37<p}*7;YcmU@(WWq
z9g)|Y>70ucg^0zVeBSg!AvazE<ml$$y^k6P=;geQm9#3%(aRDAs?sSSW%ml7Z;}oq
zkDTN=cHhLrWV%En`mBBLJB_G>Wp?J%bAyaa8d0s*sSznY)7!X*?~jeJnE=yimCXXO
zO1@uRXD2m0e`u^BnPKmHBWKyeCf1HbS-ll6^X-l0IH8!>*xbrW<Gw6yBcMss->)FF
z-u86Lrd2)s<b7XX-v@1))gLl<yTk&;Ija_HahEy-Q17Y7h}S3iU2SYKs3M<#S)gM+
z#~XH4{_^PW`~Q_34{T6oXF*oOHvZg?4IeO$=*gJhCuN<&IQhI%855O=eB*8uH_AoF
z6tM2|!DOtb5wC~NX^tqh-+jKeYMifcLEURV`HTr^K<NJOUsUjj{7m%|4nW}7`E>l!
zPhKZurjPRh$;g|gLsHd0^N!zpbM-uTsQss3S+>4O`sdKJNZvF(a;nIN-Bsvtk7xb1
zMS)elWd+Hv#<^zk>b*pn`(#4Va_%Nb9?eDE!8X~fuoKtjf#Jpp(Z~muI$-2J;#A?j
z<%%Fvc{=56DP9vSG(t<--`gm2{d%3oe4^g9N!FX=mK|0mnk+60fi67j5CBJ81e!qP
zB*!&*5=DVe5&J=pnE^SKVxxFK(Q&YtdxZ!+MkpscJDZXTOYE{?|9+FlqCbs-APpg{
zyyLDiqZ1u63SH#>Km?g(DPj6yM%s#1`Z4~zeGWD~J3`v?$l_!~RB&xug89ljUpcF1
z--b%rd^x&_D+1;|4;xs_9fF;inJ_3oZ7q`Otwze7^yHH8k0KN^bwtSb5JZLfgIS-B
z$h%9An$b-c5M=F`NeaUl&bJ2Gw7#)%_Qb?QyE1~fJ}FtEgC1?M|D#1&>gm+y!USd{
zfd4d^-__l{?AFU;JqD#9G#^>Be;3&Qp<eg>6Px&+B=9X6X%kM>F35Ihw^98I;^$BB
z^2&LNBFL&a9wGGiqX$mHhl=oRDU5@BasPV>3^e!&<5x5Vt=g7&&FlNPmH;af;1eg^
zfSPXaAs5w;Eo_n)m^si}I&d&G`E-^Q&PhxhC;yOLOw?0VRsB0KyyGjAEqS0jP}siA
zdoNlhla!9a@ko&KQhCHp-+3t%-VjV1Dz$Y0<~>~ei<(gf6QVNqg?Iepb7pebwXyDU
zr;*op&z9Ns2;LNoEKFcW0%~KV8{mWW1taa;{Q4Su5oG*N$gZ0=Z%&iZas^yshUnqY
zVpdkx{yd|ihS-@by+CocsxC^cih%NFJ4K<Kz;eecnR;+mFlnXq>vlo?sHj6g-C53R
z;9E4bwN1uk0^8u9b?xOTD%Q$)7W()FvLT|X?6(@fyzMBN$`1D(Vl7J^&Zkqdym>PS
z$T7q5S+6;?X{+_+;%^z|a>X`j-UnA#qPuwad2PMj6K4_!nU`dr?G4$H%k&@@;P~24
z<>=oDxp7_0F#jMLjtToVpKh$gMw22|@%{umf@s~vzddY@Aad&XPt6bC8}6UbO$VOI
zr1Ud@lPVtBhw{1)AMRYg>!HyLim|{>DmVU$tw*aXtx)pmaOA?84-SOF_Wj+uZ?R@l
zAQGVMHk|*m^~+CLDSp-Kiz9Ke7WeO`f&BaXY~4A%=6W->h6%V#coL9qQ0Dp$`24wl
zO=6#qj68OPLW|*Yr#Fcx?Dvn%m~A-(q3>SWzrq^aD7U>(YM`sz%LNtGea70C!)vB9
zi5Kc_i%O1@b@@L2@$b`j2$*S)PbXOUTYPnuL5&46Fw6OhKt@{Nks`ASEjZowM2}AQ
z;g&<t<)R^_f2>RbGRMf)+4r4onq2vNz0{k=q1@uS3j9Wbk=+YJrDa&^6Q{xAw7kbq
z<}Ogq_4~Unbt}y$XzPt?ea9_s+CM$ROOFJEw_y)%b;>Wlp21X)KVy^fSTLSoLsV3t
z#=iy&;Ax~DEk`u?$3ED<bNYQ}pfwC&I4^Dp#)IWF-VfWW%*@Qpn+DFQZtu?d1B7gc
zPaTM8i4#mnK=RuGMAE$SB83{nV>xKk2ocePTaF<JI{a!1gA#i7l@G~CE~(Rpm`y9|
z%TR@zxdRC2Ng17)B+q1_9RK~j_@Wbl8TFKNu%C!B-f>0mbu{&_a{YgTM*)i&?e0#6
zB8NW^&xMlJ)u*%)(~P|kiIT{!Ie?^&uwG~YlZZxPVvH<@7IwaTxdWz<PFt+Fo)quE
z60zoawI+g87wB}et?f?VBh`2oHAe_Yk~XBBM`acV->U&?#Rvf&-<6h@juPskn2xUN
zd)cpl@J=`d1)uLG|GrKuV8%o{K;r0H*KdU8$#dTkfugy82qnuJj{@!s#-}u@_xHBl
z2MmlDJ?cZyqQy3+H-fI82GI$^g}sY1Xn8JmnDS@Jn&R{2ZBdpVwrAq+mSg?0n7z7F
zRZUADhk;Zn^c=&egBXf$%e))^_Pk!^?WpjRvTp&#8<wuk^)6bmVBjvMf3RjT?oisY
z;<Q42$CFP3UEV7_6n2Y?BiJlaVAjt=fu8^!Egu-r{A7f3?0?EoOj5lYAZ$;lJ5cFb
z1Pf1#SDw&}Dw^{EPA;khfa?C|<$o1}pG){t@2jc?3P#?3&_?-J8D#kvkpvAyJ|L{v
zg*|{yOZC6%Q&06JCbATxv^uL);)0WdmN6|}$vi*0PQ22lLAwLZ34{g&_R-*Klmy>k
z$*b)|RG^aIs>!6)q+?MyCGp61gQ9x3@`<mml>-zE4Gb6#{C{>iO{POM{hmhb{AK~h
z!+Fw13_6H)0B;=8`>nUGQ?EL;AN!NaVY*wKMmqbEg3|jf*q0J<3Kys){GU{OKVG1J
znfUR4*X}`U1hoB+-c4a80LfF+EauAp;M8>DXm43&)pLCttMiuj+wlSl=Qi1F-xAKp
zD~HY&Iv;;GH!bZnrECThNG!@4j}2P-YR#{#sx;lL_+3%myL$do?;M>o_VW;>_HmY#
z&&o}jH5&DfS-?k!V2--?VB^hGty1-H7DvoyCkEDZxm_^eet67%x}3r0Jy9dLakp+e
z`CJhpL|JQoVqdc`t?<a%>xC<<q5a!JesR{(w}njWB?+zCU;NgGY-xbwTs?O-CN3^7
zKmTsyZ|u=R;2&n_p4ZTIa&mg3_I45+lyqQL)pC9&sqVOCfSa6>l3wi5sbcW=WM}lv
z%p4T&RPX@ggxxYrB+Yfv#r-=GRJztA`OH-&-&N)UX@+qKI$C<)Mu7bM=g2d;MMV|-
z=_8Lgk1hVfd5rHPzpHEeI^d0p=Kq;&mZe}C0L;N_D29*8D=07!IjSpk_&qp*;L4K0
zU!NxwrO;Ft98dHNW}aA6K@}ldg1Kg3Y&<xzlgM)M*(H}T1q*b~&Fb}GhXQrg@GS^9
zS40bSeZ<8KT*URyXCh#1Vb_@`i%*{t=TN0hTdWn4XYMOaJPxznX*4qo#vG#j5oQsA
zCq04rh#VC>loX=YjUdOyf0m-znXbvVGjwSRSK4V<3dA~}jB5i{Xet{l#JZi;Hh6@(
zfsv6II9l`lISJQXlA$3yRMZ`tK>U*jf#Ns6Jf2Q-rB@ZYuqUS~(Q;0T^iKzY$xxG)
zebhl15g>n3ArDO=UdIhGbRMxya*rMJ+jat$6IgFdml)L)%H#0m^?6gAPnAT~)M2vQ
zDmPu{k*p+-1V5M;<bkTyR;0f6SQ@ogJRIOWUq0fj2R;IBYoI8%x_TI!SbH;oavOx%
z?}yHyL25aGJi$*QvzZEA5La-z6Ttl}XlXT5U`{-D)p0PDmd{aA*fqP|37-0k$p=+D
z31HO=M{9t24RqTQKRLy>CjbqIN#+1>^?&1XOckF}BFJ)whQwi!s$k?ykx;Zo%<I>)
zMgF^F7Gfdju%Wr=FvITmnnH)Oe5{<rClgVfJun22lWJKRY55e=-aYKTsdk|1H@LT!
ziZ=rjG8US*>-Boah=~4~6n{TmmpM#Hh5Yd>tW~RZNh6_SJy^v^HuAV>jkA%TOF;Ok
zhR<SbJQDq*RVZn<i5pHNT-}fj(2xC{8D(j^?o%|yV^-lDBSiGwAcFr44j2%=J6U}l
z>#^Y98Wxyls6J<+neoG=wh{c^3zw+NB&#E5@7k5~46S*1uP^k#9Lqpo{~ZiWK*&Ch
z-T1^NX$Q;~3xfzq6oI)HVud;|dvYfiRFr#(^}wnvX^NvcoNvT2DPfEN^~Du(<7j9T
z8fYM3&evZnh#`(CVAjlgleUw=<#qoSV>GY4zo}jNP0FJFwovc1+(~~6jRJ|9vg5<1
z%jJb5Xz#MJj*(#|l`L14<m~)>G*~k^sLm>o!<DkAX{j3XC^N_y%?#b8^a!P=w8%}D
zfc%Nm%DR#j`r^gqEa589QI-0l-z>csr2!_0W-fK#baP&tl~{*tFS_`TOn9_f_p%?m
z*ig4I=G_rHQdtYTXa)$1yDc<2mzA;vIX6eUp&FeN+A`TuSmU+wFz)VIVvB(P0@F{&
z#t(<w2t9rRGN;0E=yHYE%7h^yBj>c*Z<R3qIWndMPp6ITmOu!?n63ugaiCW-Rr;a>
z4|j*nf+vgKI1RP<N0bzJIuM*jIflw%DuEmbL(aG;tPehN2000%q29R<CoWt6o|l)`
zW-gI4B>7FrcgtEUxbf@bHaVq+Fe?Fhk9PLsmxWL?+Fk`Ts<3JCs>|5I<Bo)Z`NXKp
zABR5JN7sOQx*P}iYNW5<e?ceA3dVsEersncoJPvIuk1lEP!K>ITW}+7u1Ca@3knJ~
z^$Hj(&y3iT@da8@@J_sclsO2PP^yCqUwFN9xsAZS;+OKV0*eYadFzqsp>n#Pr8rd2
zNXpDW*Wd&T9EPgdP)^|8RlwMAWC43Gh2f_lxjn7?unLNTc2iSRJjIvfLQUUIM6K?Y
z``0E|;)ki`(KAJFIu|gOp)=(h%cLTRwK*(7&)2uE+?kjQ!3!*Vl5=L{T+E%h<`XXZ
zYHDiJDJZS?#`zuYU&=Y|6^%sNT$78Tc9wT&uV1216rq1vTkK9Yw7||~+{w_5w;HYS
zT*fcMn2Upm`|v>@KsvDF%9DPP0+Z5AB4^Vv*{AO;y}mNS#80zRw!pkf*T~#0=+jr%
zX*t^P^jl&dbXO`&ipgSDn|DfW`T;axdKVNF1htl+?C<x0+><@Gew_9g?3{2NaB@Ds
zG&65<x5!9I#6G``*|H<4E8G#4D)#e(sUX_+JV)KsODRm8{Q1!CXm%5EM0xe|8QMgm
zlxHz<Uu}&UL68dC{{?sfE(=&nMljFTE3&Lhs5_XK0}T!)U3yn+JM}?rgi7MFGc@C_
z)`Tx_!(^<!5V!TDZuN_p`1MPUgBLBHwhD`$KM($6yhP1HiC*%)*S!jDAdgp_N3(+k
z{ET6e0a!v)uh{K%d>V`@ol$=$Y5B6Pdbw;j*N=-#NK$TD<1{kJ0EXQGI;QTN!EHuU
g_5XyffA^<bq`c({vp-n{TR=2!YTYQj{@~fa0F0+q8vp<R

literal 8534
zcmeHtcQl-PyY@2#*(oGa>?DGSBtozwQAYF-A%f_2Bti5#>Zp-dydnsKMARUHVC&sT
z5kwh%^b%1A6Lm1kxo7YFo^{@HzHhDXkMpi|*0+u|o>^m_`OUA~_jO;_bqmo@Q#yR$
z^Z^7x4lCbQ&_oc_80~jIBmBj;yEX=%7$4qIQh;ap*}Pfh4^O{1-8R4?$U#=xFRF9c
zZyQ0l5M>29ZO?>-VQ(kxwVk~sBD#P0_o~N7Ccg&_suviWe-SU`i#?iqBC+$+Tke=8
zJ${U#jqO?QNQu(Y?JuWZ=l590#P%NS4Ah)_c<QX98Oev?!}&&&=I5iV+-z4PjpRkE
zyroBF2Ihs%Jveqhn(}DHr{%ZknjZ-ibpOv5bXC7+8~PH0v}gE`PzW*;vi~=j`F^L!
zit(psirp6sNmmhMfvYaDPB?JZ)lf2*1ql)|my*4T+ufYbRYH*C;^6@(<Tnisl;2v9
zo?C|^+hvXj+ix!~4-{Fdua}w&XL*xr^yrzGyJs_#^OE<rr&TgE`v))GjZe=^!l`Iw
zs_7UQycWc7yDSV>>E+j~$A5i(Doslbt%%=U%QyNU6x}pa(7@4UV40Zl?AZZdviGQ#
ziOH}sVPO0CYPxdt>W^skxdGdJ+9;w9L!K=by9<u~UGR>fv7V`EzSZ^Ytu35#=dj0U
z?rom8@W2wJ^zOu7X+lr;6`1Mh=)}|{s9rn~qVTfGT%wr8#r_g9##2t`{Bul>n4>48
zXJWDt$mK*`U%-Z*6!<nG?X_%9*&+Lno<+NS>FMdIFmkA<A9n4!3u{tKnBn8+AI?nn
zo9mn{_MS-%Hda&&@?PywGBGntyW#mW<&3mX`CzfN);hL!C6QcygIwKpR@$d8qsyAo
zt)*UZN2q2s-7-<aE!(ID=jNVw^!%-IlPdQLuKg~8VlgTe`d6lGY-|dN0w$H+!#*pm
zxr>9vBX<NVzT_Gdy=!&5F+DWXkv2@*xcSMaPZ=*YWpvHDQ{=n{e?i2Z8IFy84rZy6
z_T4In<0!6N82k7{Eh;A`rx`=poKC)>oL5^rf~Jt$YqsWc`UgE0hh|k&RE#f1H(@Bv
zrn{X|n?Iua*OXljA3iM5>o#DWS)neov-&qnw>-CiI02(rtXJzWob@6FzvsKY3?q_9
z{P%XuM)tzQMw}=^SoJp~cR`PU!9k`4+8%#7(Q<jlQ!-D)ws$74YO(W2Q?!WVh-6Q1
zuS%4St?h}3h7+Ong%t8&Sw4^T-tJC6X={xXw<FHZoTHvBy}YxB4=K=Bg_w+5?YB%;
zj>1tE6QwfFgeVkS5|ZX|>gK(rhfWEnr&Y<7jo@;o+7kJv6WkYvvS2^63JZn3sW6-f
z9A->h96xKwOm<FQXZYD064l#N1A|NrRf$MIIRAS+xd;v^Ls(}q&uk54r0efT&lh`;
zF*@4X@ALB1U?=8!a^iSq>mJibr7tfpA2Za01BYGH*3-*$5>F^Ic*BGbnf5x1S%f*}
zLM*b#Y-=>f3Um?t){Ew+FGVYdoe@(b_2zb=rxVgNc+-id{w8?pFgd)U8eV<<`gMMC
zia>TZ=VT|i(;*Uh_jlQb3(SIb^Ne4IDe|!1^YyI><kCo%_Py@>T|w$+9X({1jh$Wh
ze1DPK&Kj{yoKKp=cWbo+&fq<jjF+L(Wj<kInfu7ZX-A=0VO!%_D8nAf9DW-Qq_%&4
zu3Y+hrihr?Wi28qnq6IOoT_mf0>29e!wTAd{}N(Y?vx5&C;Ymbo<;ISO>T{Edq2dM
zl)=Y+o{cZg*;rY%Lr$FtyS0|axpSLby%H{;>d%=jWLj(7WUfiic&ObvGw~hlzJOuz
zOnUDo#Qbc_H9|K7n{f(kj%|L8F3}i!w@T^5LMyKDo$BkimPuZ>*o>VXZ&Ap}HPRdr
zB#0|w9DduPe!{sm0j*Uw<Z`FYVi*E#pw_qQZO41T+Kq=MUXw8b4uldG3GW-IluIkd
z-of60wB!R*`+r3OlKzI2oqMhNZKYatPinROCPS4SB1`#r*$P1pIqf@y1fb~t>GC)b
zr^J9^VMKUE8*tdnz+Zl*-VA=JdNH?QW*veqSDP;ba*cFPBgJQwcKnc`AC%-!p$!u*
zZ5E^NPF@l`8CtSg+LRE;h}0pdf5Anz5f{)F#Z0$D3_kOrVa8?LNnh>=Z*-G>L}vzL
z{kQ98edt~@L^k+@eZZu$t?jy@`eQ+1;T|g~I)t4~O^zP9AAtN%yl^?9M)3Z4){wtB
zl#eq4=oQ!7^Xq<SU!CnLaT=FLPcN6KAV_6yHAA`MXsuzbPeqdVA{qcyUqnPCDm^~_
z^ul17dMKNa?v;~Tfb{Fw1nhx8S-B%e9wD+3taHzUVq;_ZSVL|{W#{CGJovP4vo?Qe
zqQQ2s)c&-D>w6yS&E?7X8y<N))@=#s%BgtUWzm~_jX&%Sczqx<cDB}vIMR$B_@|zK
zSnz6X|1gFx#%5)!n$Z(-rgA#T+qK^!F6+Yw)hI*ueq0jq`!W<I*B%``3nn`Wo3`NM
zgUh8?j@P<MBoZ6cudeu-`cl_*?Co>cczN&N9na0r7fVW~hTcj$5d1kek6;@>Ku@#h
z8dqov;kOG*?FZj=q$%09r^p$W*u*?#;mqsF(aWLX+TLxRC@D>8*O?blP39wy#$UP<
zJ`=0W;@6K+4=y;jEs4)6czH8#q(w+;a!0loTXz*ZPux1pDQ(<|SI5T0#8?j$TW9Cy
zwkG*8BDCCjyzLoHK0j7`&~?WjA6qt=`g_>CR%-B9jJedUvN38cA0MB%MMT2)rSb%9
zLSWky{VTmw*x7B4KX}CdUKr(j>8~$Is@EptlbNdPChZMoIF>gzE<d_U4cQ4YmLx=f
znMl8OLN(wHR1HJJ__E1Oxr>g?^5ZMMGWik752w76Bnf8z=Q5+BSe3s!M=>>QoqKnm
zE<r6bM)l%KnIzjE;V5@xrZMt-ubJz?S~CCXutNNcXfo9W4!h}(^H@tTLjFbA^DhCk
zfAOHQBXrfiRQC(4zuZlhHUTbZ?L7GG1pjs4Emr~Ka*ehMpIlZXz=7WpB|V*hE%w=T
z5x`XImz0!jZLbh6C<C|*lsP7!6vBMazw$0SJNvKOUy>QNYV((uU4T;D`M*I*YrF_<
zJcx})xhqqh2*q+mROv5Bxp3jaTvukq+ONZb(_YJx5j#N%z$AE-Q#D4eJ>F|1CAqcD
zPR5z)a!0-(Q~OEro-|0Ik?Ll_D%B|0Z<jOQ7ZhA|EIKllbYXvBwY1-}*VA4A9`iy!
zAh7tR*(0JQ#d}$<dL4AS7~K(d;m&opIqmv~1@mi97!P-5>*QSb*>K9m)EEK?^bbBg
zeCFMQ&rg-3n9oY2KYjYtCoJtxz>=Oe#&F8iw@`fRA6{K&bK$#ud7R3p9xql<Q7y%^
zJs!+yGuxRFf7R?M;I=z?L_m3tn4kRZ+qdiX1J{AwJyMsUN7|#k@?<fwJ~Qp-E<`s~
z3kWUHne2_`PuhF&+MTCr&!iTx8ANmlY}Doh=kMw6){h9JI~mb1nV<!PXN`9lbLFJ%
z3h%2?`v13TD%*yzHF~=uh=!{Rqvz^O2X4LJ((OlMWL`WI62NiJGC<6(F9p8J#BXzQ
zYgXMqTU~lHJ!gszNqIkc$ccxwgNWaC%QdOe^%!yvcEInr-%Sv|XG_dm$lgQ{UX_K~
z{mtmiH4>4;hTjO{ncbz5iKKjd-k>ACrYo2pNztF&_O7FiIo0|m)6s9`O}v<6lG=@h
z>&l%OaTvZ)O3D3?fr6(X>K;a4tJ|Sci0|&cInEqxWK8x-V&;?{%6fuAG+JDe0y#wO
z^m8^GhRW}C;`elU5Jdbcn%)N@%6&!xL7aSC7=a;|;jS?gRwUfG5Cj*PfZF}0SNoPl
zdV|Z`3rgG8oF0B)_9l!s?^3&;B!=G%6RiFzfA_VpVlCOWzt92)l(5Q16hWTpwjnHM
zCA6zN$i1-wM#uL(=J4Nk4w6U9Fd&GlY?a92Ao<fb-0k#Q=%AiPuAs+qjmokM3pG2e
zv@I+w`a$!K{rDkCmn3Q*zq=fVx9Wa>&qRa;L8Ro{&X2(=ZNL2esENoy_`|I>WN*C;
z-xDfS+c)R&7=_d|HF+Jtffmfp&yPz#>8RY_*Vk8}1+)I)!dRY{mzOhzASp_-?W6S4
zPGhovP2pPIV3{NF)m5|ZGRM(*H=w$UtEq58?r3^R+ukcRn^SKDlJ<tHJRCDw7!cWl
z=2K&!Kdi>;9``r5p<`aYbluVbO<R?KO8V_VAjpuSI9g;VaCiJ;!w%qfw>MRISb=B9
zqp|ORh-bNxdfj^L;A6Q7C=N-_-$!sH0sH<!MbIWI(*!*p*4gZSn1dnA0cM(^nWak<
zlMy(KAmOHM2)V!L64m+0KEq1a%R-;%5H1!M#=e#6`38W4cxm6tHt~r=ci)KSb}>O1
zy(*!q>9;QQ7b$gE6&F21At?=wUi&0%x_LoCDr;VhzQ-5S*_m;t${Y&sGlMq7aiBPB
zGVvyw%#0usyq5QV5k!#0^Og_z`S=F5Mba+@vv9UiWf5d`ADVtUSY}(!dwss+tjEwf
zW!%(j%j2Q-*Ui2@7d*ubaT#;1PG)<YrNpcue4e}uYs@|U+kF~PkMT^xP+ICzI#uoq
z-!LOZmJuMWh<|Mu2TI7W__pa*XB-T52nd#9=t=9IY#kGkGYF!{Zy7+iL6%(TE4XP?
zY8Q!B!%nr_v$nR@Yh^@aJ8USF=by%ZZ<8Qh0Z8?zmxPJ#M<>&d0M)0t1hu}@NBL(Y
zJ)QpjFb#?x&9;h-YKybCVahznBWj!nVCr$#>TG*Ibbv}Lic7?9qva7~naeWZlIp9J
zfq{Yf$-`$vc_VNC<LHGx#mo5D0(fa=y?oS`kQKwPXER*s_U{wkTZ-_X$t@e{gp_!x
zmMP-D>!ZjWsdkEooq2jKLMA;>btE?E(=@dfDA9ie^LPw(!YgTsXE>Q$=39uiTw1Io
z2|^7@^ce9<6xPOOX{$7u3p+O7sp6v5|AT?DZf<UWmh_zB;+Iek{@_@U4`mz~gQ4{g
z?J>!aa!MRt3Ir)iLSVov6!JnH&Jp|%qic^^G?bM?xFc_`a{@-7(du&8XRsG-9aBet
z`cj7Dc056Knus2P+*5pmN)mI#tWT^EWbn;TA*wPPH`yVAX4o5im^WbCmVHg-j+o0h
zbOI9m84W#;0+^WD-$$NTQi-Bzvz_v$#o?+O{=yJ<`fpGp6<A9>0t!&!6SfXPer+6m
zBSbG7xjeNelT~Y3BP72p<B#iYUj0Dnp=_<{I1Q>-nVanU6&YBO*?Z}+k+OE&BzyMo
z-@S*DAHJU<TI75`ew}7Z{r^NKmrss;{dx<|A07;H?cYmJ@atM_t<KUkb^><O@B)Gi
z>yFT|gP{Qg<PS-km!Fl@0YP8vx%3c<FDRNxTW}g(jZ%Gt+9^h&HI<cy$1bV@Jvc4n
zS54Chpj1_&fS~}*zo^^^6j9`UUBGthQFEH~kS44EF|eW0crOkrNjyW6fZOL9*|sqv
zUKx6KnPV(XL}qf{3lrK5J{N8xOpHgFR++#yS}DcQ4Ha$Fs%uCzQq@)aOwY!xOD_E|
zAav-Ao=M~wlY7EQ%kk6TcIoD0a-sOo+fRHA>neAi;86}{KHK^63DZjHNb$h|{IO@Z
zz+oayWWEAyfC{`$bp9DA%T7-@)K*dL@A&26RY)#f;`EwMdx(O3e)aORcWQ~*5i+~_
zQQi5bGXF)eG?R}0T%K$mtlq68<#o9cP2vbTD5O1i$(t63T>pFda}V|#u(b0WKhQ*3
zp{&UMML_?M4u(LxJd-XFR{BgE&dqxDo=*vN{Yv>-FrfPzs?A&&yrq`G<^4_s)_sqo
zn$iK^<hxk-=Y>*4rj1ANV&NxN1AqA)t0s5khnv@o0SKMhUm!ZB(U-y>dx!h@JF@2}
zNizMDz4D*jmjA|=k6)}I0T~+0^g-d<pK;FxC%uw1gFnyp^jS%%#NDaKXRB`P=|`Y$
zbJxv`SFc*~Sr0vux)s;Vlfg<lcPKp#7${=yeBd;K&;v64&$^hfj{Or{m=u5!a=<W?
zsr=o$TQtk=dwnR_r`kF?J^eZRkd(0?32z3-;&@;+KBL5qrHK;zLD9p&?V_NB_4M_<
z+5ys0olDf(jSX5HSKyG@^l3@;9<vQ!ckMAt@1h^tepxi_B_N1j?Y#5PmPqNyb8I}?
z#%?4w_>(Bt>8>oW^@oz#6!=dl+_~|9`}kXo@2zkAQo9YxH^1+F6A^X&q`-R}b3G4p
z>8HQ4V3v0Ff?I`}pXPcILAnb>$ZD>m?;S9CG%PGKK!aRV2E*pH>a8ekQ&azza~;k#
z-V|NF7|omXdj=`?wd9<)Z{<Oc^`@l+fFp>WhT9yaRLa;w>w&x&nvBj8P!tkT9oT3*
zkWd<-%S_Jj-%~om9DIT~c#>Mpbp~n2x^5kHQaUT)db`QI6ZhO9;{5&M;Pdxg_bJtF
znz@4|h!~##ZvhV8S9m64u!aHnNmnatpfM37e@>D6D9t<!<Z1_0>?w0Jg;#aq>}YPn
zN;fnhNcSphFg3W#r^8AsYwy!N-+m?KT}mVDCf*Arux?r3t=UW<q`2us51Q{tYd1hQ
z<2uk?(!FOVGgF{DbEJ?1*`)PmtY_L&d(Bb;6w~fO*&$uk7;6Li6(rB|_S&D3{+CH9
z$O?NNA+`Q055J3pJE1p2#PJ+|5BN`t(ZdvQ&@<S8Cl6m656y~4grwY?!OR8`+Qs4@
zO?gTkhO-OI8s?Z}HXch7ia3}U1ozXCj%w7=*i^pnf~{edd#?1x7=0M0%m^<1?U$oY
zk>V>Y*VbSzR=~8jzZ@7#zoP%UtkyeKnof0Hs%JBb&?<(ml=$RJt<?$6J$*P(9IU$p
zWIQPnan&jt^{dkTpr0z_w-h=6p5dCfVkp8E3GE)}q0G$X<kJMqhXz#pT`wBAxq>%t
zTmS4t@5FI|Bzi$snggGJ#_}iw-R)5KTRt`sg1CCr;MO!H5_N@NYg=1q!HB_s>dim4
zRF>BD`s?zTE;a&BGQM~39A*x6oP+N9+bFe69SkN<W_Kx~Va;pFv$}rrSeS0DS)59S
z8qTD_@GGqy09_bYtMD{RM9{?R)LiQFll=z{qi)^vqArxVt+a}fKuSyJ?b$_ycTXq#
z`^h`bdc95d;8t@tnRTuFOle(vBCCf^IyF$~sOM)N5YAP{%eOMs+68TjoRDLeY+y+O
z4xtL%TO`xHY+6BXd3AN5+N+3XcCm^~OCjFln$SQvvQHp9mOJwAu|lSKw7cf}3I-~$
znKYrdMjkbdGxhTdO>3M=F2hc%-A$4-Sn@QEysbbY<`V?T^=ziyzy{X96(ND2*m*+F
zI2GDUGo^zLwrjd?g{prB(&USY(?<q@(yYzOTVba&;6$Lx4?|&wQX;wN>mf<a1llsX
z@l{M)SN9!%ezhj_2xW|}WNxlQ<u`Nq+(C)yo+=!SAJ-rB{Xi8RHEkG}ZWv&a&2*BD
ze4mx&9B1qjf^Dk+kyHWnzogtG%Bavhq}g0}5$5CqKCHufDVS$$`{dSLBsmC}*dMWi
zCUe5CpL+wPF}`vV>xkbeh1(s*KLsu7;kR^h2zN5O9O?p4i?Elr1w-#9rKs~og3(Ro
z+I*90Qft~}5Or})=0k54Lyfs3t(-?04xTJ`l~|D8-7pioUx&ac8@BfGB&t=_hq6_{
zo5d9Zt%pwue_0sC@0C;3B@*4^Ft{v`s>uff>p;#mgt4PLEreg|4iZO1*?mWio29pG
zU)oj;Ryub1HI@*t3-ny0`+s}(bs|D$5hUkq0v1wA%)IdkvAz98jfrOH-b-)jf7P&q
zWpITwgNXV1{AfH1H@ukNabFmiga%usZgLqoy&e!*W$4UwJ10@QzA=ZIwcdGqxg+JC
zk|W$2cR8Vh=z*u2lvy;#>R#IgBZRhpc?W|(=cT8A8FnAEw|BUMwPz?lPql<tQ;ULk
z`u(Gf?d|Pz7Hl2Qgx5OeS#a)%d@C8YA1<?&`7tnEv5tD-m=JjDo>QCk;!p)3#xn(O
zJ^=w*;}zq()^n0#yUTu`w|mLsq5YO5Fj8IJ*qRZ_j_6)-GFGkDf8H^Fs;tCKhB&`T
zn!4tlM4YPtF!CUUTuN$>7qNvtOo`XZBiK7;J007z{W5@CV#BV{r_Y=z+DpJ@zke@2
zB|16qbc(XjSokKP#eI5U`qI{~sBkvOv_41&!HOTO@acK~A(#>F=SQX+tDE5LLyy^H
zWa^X@u2?tMuxF98{#Oy3ml&TVMVeG?Lkk#{s_Gd#2YniMT9@|UT}cLK%FV3=<2laY
zJATmAs^B>^zM-Q94`>esbKrO#2RqU?cTdqq@!Pbh+1sU_^-DGK9&G0!7DnuvJ+F5}
z5M++_zW}Ev2NsP@3E}hD6#gUkj|W{!@{|XOXc<akWa0P#!_j=CVS+#4yog=j?<a)@
z(5IjsL9V6d(SW$Kv-70TCmTU_W)j(duj*0Dt5k3)A>Ndse{JK;IZEll`}>=~*5ejR
zh=xHA`+k2=x$!-ehF9OdsX#w+F@0LbaAikJQ}Zp&(u!2#xCi|cFu_0xV*UJRb7p7e
z#P%!shq>5$<mFo`t)tsdY?vKFBy@~2TTy>B(SD&jl%nOldayX`|Ik_*^GE$^6Xjbu
SYt;p-MwAuR6!PTn2mTv?8>4do

diff --git a/reference/WHOCC.html b/reference/WHOCC.html
index 3a58a540..b659864b 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 1133d7a5..e2efe491 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 81c91232..5abea53a 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 200fb3c9..d0d111a8 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 fc436d8e..d60885af 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 3c0200f7..4e7a7c27 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 6cea4cb1..4551abee 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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">
@@ -222,16 +222,16 @@
 <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  1996-05-18  27  27.54521          3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2  1993-07-19  30  30.37534          6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3  1988-03-07  35  35.74247         11</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4  1967-08-23  56  56.27945         32</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5  1997-05-21  26  26.53699          2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6  1991-09-02  32  32.25205          8</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7  1994-03-18  29  29.71233          5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8  1985-08-18  38  38.29315         14</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9  1952-01-29  71  71.84384         47</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1988-05-05  35  35.58082         11</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1  1984-04-27  39  39.60274         15</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2  1935-07-26  88  88.35616         64</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3  1975-06-27  48  48.43562         24</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4  1961-11-22  62  62.03014         38</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5  1951-05-07  72  72.57534         48</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6  1946-04-17  77  77.63014         53</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7  1972-02-03  51  51.83014         27</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8  1981-04-25  42  42.60822         18</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9  1957-03-29  66  66.68219         42</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1934-06-02  89  89.50411         65</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 2b17e038..f312ab79 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 34ce5723..91c18e74 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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/antibiotic_class_selectors.html b/reference/antibiotic_class_selectors.html
index 22125702..32e603e4 100644
--- a/reference/antibiotic_class_selectors.html
+++ b/reference/antibiotic_class_selectors.html
@@ -12,7 +12,7 @@ In short, if you have a column name that resembles an antimicrobial drug, it wil
     
     <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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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">
@@ -626,11 +626,11 @@ In short, if you have a column name that resembles an antimicrobial drug, it wil
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 5 × 1</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   kefzol</span>
 <span class="r-out co"><span class="r-pr">#&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> R     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> R     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> I     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> I     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">5</span> I     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> S     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> I     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> R     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> S     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">5</span> S     </span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://dplyr.tidyverse.org" class="external-link">"dplyr"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
 <span class="r-in"><span>  <span class="co"># get AMR for all aminoglycosides e.g., per ward:</span></span></span>
diff --git a/reference/antibiotics.html b/reference/antibiotics.html
index 84a824bb..50d4fd46 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 cbe82e18..606ea7ef 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 30b6906c..666abb8a 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 ff3e6c4c..f18c0939 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 cee05c38..686f0673 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 ed623df5..30ae35e6 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 b923a7c4..49ce145e 100644
--- a/reference/as.sir.html
+++ b/reference/as.sir.html
@@ -14,7 +14,7 @@ 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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">
@@ -567,19 +567,19 @@ 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: 13 × 13</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    datetime            index ab_user    mo_user    ab   mo                input</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;chr&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;ab&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span>              <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2023-12-03 <span style="color: #949494;">10:39:01</span>     1 TOB        Escherich… TOB  B_[ORD]_ENTRBCTR 16    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2023-12-03 <span style="color: #949494;">10:39:01</span>     1 GEN        Escherich… GEN  B_[ORD]_ENTRBCTR 18    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2023-12-03 <span style="color: #949494;">10:39:01</span>     1 CIP        Escherich… CIP  B_[ORD]_ENTRBCTR  0.256</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2023-12-03 <span style="color: #949494;">10:39:01</span>     1 AMP        Escherich… AMP  B_[ORD]_ENTRBCTR  8    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2023-12-03 <span style="color: #949494;">10:38:56</span>     1 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      0.01 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2023-12-03 <span style="color: #949494;">10:38:56</span>     2 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      2    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2023-12-03 <span style="color: #949494;">10:38:56</span>     3 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      4    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2023-12-03 <span style="color: #949494;">10:38:56</span>     4 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      8    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2023-12-03 <span style="color: #949494;">10:38:56</span>     1 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      2    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2023-12-03 <span style="color: #949494;">10:38:56</span>     1 TOB        Escherich… TOB  B_[ORD]_ENTRBCTR 16    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">11</span> 2023-12-03 <span style="color: #949494;">10:38:56</span>     1 GEN        Escherich… GEN  B_[ORD]_ENTRBCTR 18    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">12</span> 2023-12-03 <span style="color: #949494;">10:38:56</span>     1 AMP        Escherich… AMP  B_[ORD]_ENTRBCTR 20    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">13</span> 2023-12-03 <span style="color: #949494;">10:38:56</span>     1 ampicillin Strep pneu AMP  B_STRPT_PNMN     18    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2023-12-03 <span style="color: #949494;">15:55:17</span>     1 TOB        Escherich… TOB  B_[ORD]_ENTRBCTR 16    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2023-12-03 <span style="color: #949494;">15:55:17</span>     1 GEN        Escherich… GEN  B_[ORD]_ENTRBCTR 18    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2023-12-03 <span style="color: #949494;">15:55:17</span>     1 CIP        Escherich… CIP  B_[ORD]_ENTRBCTR  0.256</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2023-12-03 <span style="color: #949494;">15:55:17</span>     1 AMP        Escherich… AMP  B_[ORD]_ENTRBCTR  8    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2023-12-03 <span style="color: #949494;">15:55:12</span>     1 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      0.01 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2023-12-03 <span style="color: #949494;">15:55:12</span>     2 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      2    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2023-12-03 <span style="color: #949494;">15:55:12</span>     3 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      4    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2023-12-03 <span style="color: #949494;">15:55:12</span>     4 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      8    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2023-12-03 <span style="color: #949494;">15:55:12</span>     1 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      2    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2023-12-03 <span style="color: #949494;">15:55:12</span>     1 TOB        Escherich… TOB  B_[ORD]_ENTRBCTR 16    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">11</span> 2023-12-03 <span style="color: #949494;">15:55:12</span>     1 GEN        Escherich… GEN  B_[ORD]_ENTRBCTR 18    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">12</span> 2023-12-03 <span style="color: #949494;">15:55:12</span>     1 AMP        Escherich… AMP  B_[ORD]_ENTRBCTR 20    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">13</span> 2023-12-03 <span style="color: #949494;">15:55:11</span>     1 ampicillin Strep pneu AMP  B_STRPT_PNMN     18    </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 6 more variables: outcome &lt;sir&gt;, method &lt;chr&gt;, breakpoint_S_R &lt;chr&gt;,</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   guideline &lt;chr&gt;, ref_table &lt;chr&gt;, uti &lt;lgl&gt;</span></span>
 <span class="r-in"><span></span></span>
diff --git a/reference/atc_online.html b/reference/atc_online.html
index fd9296ce..e6200465 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 01947582..916fbe75 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 1478168d..4a4276ba 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 b44f6efc..9a202065 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 7ca4bdd0..3ce3b621 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 ecf299f3..74d70ef2 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 a3c183fd..3188fd23 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 192e577e..e0ecacd9 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 81d66473..832fa59e 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 c8cc019a..5ebe5520 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 33f5c145..e3211911 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 aad83e5c..96818f8f 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 42c6e066..a4569796 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 cdc28a27..c842f35d 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 b1914385..2bc1aa2a 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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">
@@ -263,30 +263,28 @@
 <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">$</span><span class="va">date</span>, episode_days <span class="op">=</span> <span class="fl">60</span><span class="op">)</span> <span class="co"># indices</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [1] 47 43 22 23 10 50 15 34 13 20 19 47 16  2 26 37  8 44  7 14 10 10 34 29  4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [26] 52  3 48  3 24 48 28 28 14 17  1 50  6 51 53 42 17 45  1  1 30  9 27 39 12</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [51] 21 33 45  3 18 24 35 52 25 37 40 35 52 49 48 37 44 42 31 24 50 52 19  8 16</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [76] 31 22 46 21 14 16 14 10  5 41  3 13 52 33 19 11 38 45 43 41 32  2 12 36 52</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [1] 16 46 29 25 10 45 28  8 21 31 26 42 16 10 47 22 10 39 17 23 44  6 42 40 43</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [26] 37 11 26 14  5 35 47 22 47 30  4  5 30  5 29 29  2 41 46 42 22 46 12  8 13</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [51] 11 35 18 24  2 11 46  3 39 17 18 23  8 13 27 18 31 47 43 12 26  8 37 25 36</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [76] 46 24 23 32  7 28 26 19  1 33  9 15 34  7  7  3  6 24  1  2 46 20 10 21 38</span>
 <span class="r-in"><span><span class="fu">is_new_episode</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">date</span>, episode_days <span class="op">=</span> <span class="fl">60</span><span class="op">)</span> <span class="co"># TRUE/FALSE</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [13]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [25]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [37] FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [49]  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [61]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [73] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [85]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE</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] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [25]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [37] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [49] FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [61] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [73] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [85]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [97]  TRUE FALSE FALSE  TRUE</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># filter on results from the third 60-day episode only, using base R</span></span></span>
 <span class="r-in"><span><span class="va">df</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/which.html" class="external-link">which</a></span><span class="op">(</span><span class="fu">get_episode</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">date</span>, <span class="fl">60</span><span class="op">)</span> <span class="op">==</span> <span class="fl">3</span><span class="op">)</span>, <span class="op">]</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 4 × 46</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   date       patient   age gender ward      mo           PEN   OXA   FLC   AMX  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;date&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span>         <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> 2002-10-22 718963     82 M      ICU       B_STPHY_EPDR R     NA    R     R    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> 2002-10-11 974319     78 M      Outpatie… B_STPHY_EPDR S     NA    S     NA   </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> 2002-10-20 F35553     51 M      ICU       B_STPHY_AURS S     NA    S     NA   </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> 2002-10-20 F35553     51 M      ICU       B_STPHY_AURS S     NA    S     NA   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 2 × 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> 2002-11-07 430011     82 F      Clinical B_STPHY_CONS R     NA    R     R    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> 2002-10-14 FCC668     54 F      ICU      B_STRPT_PNMN S     NA    NA    S    </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 36 more variables: AMC &lt;sir&gt;, AMP &lt;sir&gt;, TZP &lt;sir&gt;, CZO &lt;sir&gt;, FEP &lt;sir&gt;,</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   CXM &lt;sir&gt;, FOX &lt;sir&gt;, CTX &lt;sir&gt;, CAZ &lt;sir&gt;, CRO &lt;sir&gt;, GEN &lt;sir&gt;,</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   TOB &lt;sir&gt;, AMK &lt;sir&gt;, KAN &lt;sir&gt;, TMP &lt;sir&gt;, SXT &lt;sir&gt;, NIT &lt;sir&gt;,</span></span>
@@ -323,16 +321,16 @@
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Groups:   patient, condition [96]</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> 006827  2009-07-24 A         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 008268  2007-02-20 A         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 023456  2002-02-05 B         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 074321  2015-09-20 C         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 09B453  2010-03-21 A         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> 1435C8  2004-03-03 B         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 1710B8  2008-01-22 A         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 197007  2012-07-17 C         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 207325  2011-05-08 B         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 001213  2009-08-03 B         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 011307  2011-09-20 C         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 022060  2004-05-04 A         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 023456  2012-11-24 C         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 066895  2002-02-27 B         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 083080  2012-04-16 A         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 097186  2015-10-28 B         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 0D7D34  2011-03-16 C         FALSE      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 0D7D34  2011-03-19 C         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 125492  2017-09-15 A         TRUE       </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 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>
@@ -346,19 +344,19 @@
 <span class="r-in"><span>    <span class="fu"><a href="https://dplyr.tidyverse.org/reference/arrange.html" class="external-link">arrange</a></span><span class="op">(</span><span class="va">patient</span>, <span class="va">ward</span>, <span class="va">date</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 100 × 5</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Groups:   ward, patient [92]</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Groups:   ward, patient [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 2009-07-24 006827          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> ICU      2007-02-20 008268          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> Clinical 2002-02-05 023456          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> ICU      2015-09-20 074321          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> Clinical 2010-03-21 09B453          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 2004-03-03 1435C8          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> Clinical 2008-01-22 1710B8          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> Clinical 2012-07-17 197007          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> ICU      2011-05-08 207325          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> Clinical 2009-08-03 001213          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> Clinical 2011-09-20 011307          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> ICU      2004-05-04 022060          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> Clinical 2012-11-24 023456          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> Clinical 2002-02-27 066895          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> Clinical 2012-04-16 083080          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> Clinical 2015-10-28 097186          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> ICU      2011-03-16 0D7D34          1 FALSE      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> ICU      2011-03-19 0D7D34          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> Clinical 2017-09-15 125492          1 TRUE       </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 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>
@@ -374,9 +372,9 @@
 <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           54             12            36            44</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ICU                28              9            21            22</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> Outpatient         10              7            10            10</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Clinical           62             13            39            50</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ICU                27             10            21            26</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> Outpatient          5              4             5             5</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>
@@ -394,7 +392,7 @@
 <span class="r-in"><span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/identical.html" class="external-link">identical</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</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> [1] TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] FALSE</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># but is_new_episode() has a lot more flexibility than first_isolate(),</span></span></span>
 <span class="r-in"><span><span class="co"># since you can now group on anything that seems relevant:</span></span></span>
@@ -405,19 +403,19 @@
 <span class="r-in"><span>    <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_data.html" class="external-link">group_vars</a></span><span class="op">(</span><span class="va">.</span><span class="op">)</span>, <span class="va">flag_episode</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 100 × 4</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Groups:   patient, mo, ward [96]</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> 557266  B_STPHY_HMLY Clinical   TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 280834  B_STPHY_EPDR Clinical   TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 1710B8  B_PROTS_MRBL Clinical   TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 800264  B_ESCHR_COLI Outpatient TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 696587  B_PROTS_PNNR ICU        TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> B46416  B_STRPT_AGLC Clinical   TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 904485  F_CANDD_ALBC Outpatient TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 807228  B_KLBSL_PNMN ICU        TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 838434  B_STPHY_CONS Clinical   TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 008268  B_ESCHR_COLI ICU        TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Groups:   patient, mo, ward [97]</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> 550406  B_ESCHR_COLI  Outpatient TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 987C84  B_ESCHR_COLI  Clinical   TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> F8DB34  B_STPHY_CONS  Clinical   TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> AB0003  B_ENTRC_FCLS  Clinical   TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> C56827  B_ESCHR_COLI  Clinical   TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> CD8C33  B_STPHY_HMNS  Clinical   TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 0D7D34  B_STPHY_EPDR  ICU        TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 022060  B_ENTRBC_CLOC ICU        TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> F462F5  B_STRPT_PNMN  Clinical   TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 083080  B_KLBSL_OXYT  Clinical   TRUE        </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 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 8b35c6a3..49632ba6 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 46795456..19e75a0f 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 7344e078..071278e2 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 bdbab2c4..5e0a7f94 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 a581cccd..90ee69d2 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 f54bb196..e2bf7c80 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 5bc0b8a4..82def528 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 c66c6185..f854ebe7 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 77621306..48d2def4 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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">
@@ -199,9 +199,9 @@
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu">kurtosis</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="fl">10000</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 2.989289</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 3.080482</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] 0.0300932</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.03543362</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 db3d156a..9be7869e 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 fa1726c3..a01874a1 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 c2dd1275..08cc990d 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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">
@@ -218,30 +218,30 @@
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">sir</span> <span class="op">&lt;-</span> <span class="fu"><a href="random.html">random_sir</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">sir</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'sir'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] R S R S I I R I I S</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] R S I I S R I S I I</span>
 <span class="r-in"><span><span class="fu">mean_amr_distance</span><span class="op">(</span><span class="va">sir</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1]  1.449138 -0.621059  1.449138 -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 -0.621059</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1]  1.8973666 -0.4743416 -0.4743416 -0.4743416 -0.4743416  1.8973666</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -0.4743416 -0.4743416 -0.4743416 -0.4743416</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">mic</span> <span class="op">&lt;-</span> <span class="fu"><a href="random.html">random_mic</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">mic</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'mic'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 0.125  0.0625 1      8      4      0.25   &gt;=32   0.5    0.005  0.25  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 16     8      32     0.0625 0.5    &gt;=256  0.002  0.001  1      0.005 </span>
 <span class="r-in"><span><span class="fu">mean_amr_distance</span><span class="op">(</span><span class="va">mic</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -0.554873825 -0.827456801  0.262875102  1.080624029  0.808041053</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [6] -0.282290850  1.625789980 -0.009707874 -1.820709964 -0.282290850</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1]  0.82035688  0.66129723  0.97941652 -0.45212029  0.02505865  1.45659546</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -1.24197617 -1.40103582  0.18411830 -1.03171076</span>
 <span class="r-in"><span><span class="co"># equal to the Z-score of their log2:</span></span></span>
 <span class="r-in"><span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log2</a></span><span class="op">(</span><span class="va">mic</span><span class="op">)</span> <span class="op">-</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html" class="external-link">mean</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log2</a></span><span class="op">(</span><span class="va">mic</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">/</span> <span class="fu"><a href="https://rdrr.io/r/stats/sd.html" class="external-link">sd</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log2</a></span><span class="op">(</span><span class="va">mic</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -0.554873825 -0.827456801  0.262875102  1.080624029  0.808041053</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [6] -0.282290850  1.625789980 -0.009707874 -1.820709964 -0.282290850</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1]  0.82035688  0.66129723  0.97941652 -0.45212029  0.02505865  1.45659546</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -1.24197617 -1.40103582  0.18411830 -1.03171076</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] 26 32 50 20 28 49 12 20  9 42</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1]  7 15 31 28 22 45 13 21 18 41</span>
 <span class="r-in"><span><span class="fu">mean_amr_distance</span><span class="op">(</span><span class="va">disk</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -0.19375217  0.22143105  1.46698069 -0.60893538 -0.05535776  1.39778349</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -1.16251300 -0.60893538 -1.37010461  0.91340307</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -1.4040837 -0.7472024  0.5665601  0.3202296 -0.1724313  1.7161023</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -0.9114228 -0.2545415 -0.5008720  1.3876617</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>
@@ -251,22 +251,22 @@
 <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   23  &gt;=4  &lt;=2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   B    S   27    1    4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3   C    I   26  &gt;=4  &lt;=2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4   D    I   19    1 &gt;=16</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5   E    S   20    2    4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6   F    I   25  0.5 &gt;=16</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7   G    S   18  0.5  &lt;=2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8   H    R   19    2 &gt;=16</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9   I    R   22    1  &lt;=2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10  J    R   20  0.5    8</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   28  &gt;=16    0.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2   B    I   31     1    &gt;=8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3   C    R   23     8    0.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4   D    R   25  &gt;=16      1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5   E    I   17  &gt;=16      2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6   F    I   22     8      4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7   G    S   17     8 &lt;=0.25</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8   H    S   19     1      4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9   I    S   19     8 &lt;=0.25</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10  J    R   19 &lt;=0.5      4</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]  0.05387469  0.12082470  0.28731436 -0.12784750 -0.20825803  0.12342200</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -0.98202095  0.60531151  0.06239096  0.06498826</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1]  0.64013819  0.32815059  0.24520983  0.61863348 -0.14954667  0.11665680</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -0.69000290 -0.43800225 -0.58498190 -0.08625517</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>
@@ -282,17 +282,17 @@
 <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   26  &gt;=4  &lt;=2   0.28731436  0.0000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   F    I   25  0.5 &gt;=16   0.12342200  0.1638924</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3   B    S   27    1    4   0.12082470  0.1664897</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4   J    R   20  0.5    8   0.06498826  0.2223261</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5   I    R   22    1  &lt;=2   0.06239096  0.2249234</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6   A    S   23  &gt;=4  &lt;=2   0.05387469  0.2334397</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7   H    R   19    2 &gt;=16   0.60531151  0.3179971</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8   D    I   19    1 &gt;=16  -0.12784750  0.4151619</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9   E    S   20    2    4  -0.20825803  0.4955724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10  G    S   18  0.5  &lt;=2  -0.98202095  1.2693353</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   23     8    0.5   0.24520983 0.00000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2   B    I   31     1    &gt;=8   0.32815059 0.08294076</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3   F    I   22     8      4   0.11665680 0.12855303</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4   J    R   19 &lt;=0.5      4  -0.08625517 0.33146500</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5   D    R   25  &gt;=16      1   0.61863348 0.37342365</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6   E    I   17  &gt;=16      2  -0.14954667 0.39475650</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7   A    R   28  &gt;=16    0.5   0.64013819 0.39492836</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8   H    S   19     1      4  -0.43800225 0.68321208</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9   I    S   19     8 &lt;=0.25  -0.58498190 0.83019173</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10  G    S   17     8 &lt;=0.25  -0.69000290 0.93521273</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 bc131c9c..8894d33b 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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.groups.html b/reference/microorganisms.groups.html
index 89979ee7..b60dc8d1 100644
--- a/reference/microorganisms.groups.html
+++ b/reference/microorganisms.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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 ff584c21..15c6273d 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 4f97dec7..1e3e2d4c 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 9a05f48f..d9d419e0 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 07b18284..2e485702 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 11963a6c..2df5b33d 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 b0008357863bc96edb1adcd1731d27f49dac071d..7b7c810e87c5a2cc778bcd40d6601c5174d928b8 100644
GIT binary patch
literal 27286
zcmeFZcUY6z);An=Y~vt09z{T49D@vkpd!6FqGCi~q=gnhY0?Fx6JT_N8Kn%NOH*l*
zkc1*cAR#Ct7^;FH)C8nNLJ0<vKthuDPB`az=DEJ|mh1ig`QCG1mlAh&S$plZ*6+90
zzVVM=tjwjpmHQS10!cxxU$Fs!c1MFiyYB4U1GJ3iK3@fX?73rMeg*gmJey{%iNKHj
z_pUpIgFp%?;y*hw=DvD^KtF&WSAMpOE?Sz5DGG$bn1TTksp!!6yM8lz|HF>olJvem
zbnb_rZX@0tc;I#q^z-iDP(M9B_4vnU-#vJoB>mVV)$Jd@U=9`S-|>%qLyyk={@{ln
zF6Zx8>4*`IL>T2{VHnd(D-1>g(H$!=KylpSmffLI_69V!#>gEKx&Hmv|2G;?#}R8d
z=vsnL=G9Q;HfvD(ph4Kj-5^l=!AP%&Pv7#PV_ly&Om=`Q7Jt7gBN3s!b6>RNOmef6
zBrP70{r_qxtl0r-pKo4!aR3DJ@IA7+6V&4s65+sBAfu7;_EpxLL=5i=`&R~m+=}Gh
zR7?ymrZl@na7m5*$hMX-Em=`1N*9WKno1O%Yb(iFs_w@ml(%?BTxN3^kH~8%&0alv
zB-soZg0-p^R`3cLA~ZGhINKG~;u6tAiY!j1^G%U!NQ%8C)&zc>G7>nxtUviXN@r_=
z*21e~_>lh~UnR?<!ckB=rSZ=#I^!h_l$_h5ZtzA+$i(VXyiY_}gkQ%1tyQi~lkO1m
zvh7t;EN;-JO$Wyy90N8#a&?7r$Zm<&9?92so2g;I7mss~kYU4LwYOl6A&-`|vu0Fo
z@_JjSd8iOWuU-^11Xj^+icWVjFSd7w!fLx;4~<*MUw)5nnk*e6x>mH2U0bI!_6G<|
zwwyYYe$we#KyR^^0`~2seD?J%61hw5`21>bV;yBn$oNVQ4X9ZlYjtjn%L=%?8*&``
z43{})&4N(sBE@mhhp2=5ye*@5LR(sjQNVtM^qP{W<1@s{#@!HlpPncL<tguzgPDuP
z<)=omlVytn^yx}2;{uuVmkDtX2RdX><2BQAo?|Nm3$YJ7c5&=Xah(PSCpqW~oatCW
z>Y_=a6n({SUl`OtBm@1DsJDYDWSo+s4Uq%EQ^gT|UDz;mUY{P*5><pditX!5zyekT
zihM2~75!_6|MQN+QVr_TT)7Z*y4zep!O?3aG@g&25Zci-Z2G!ij@ftv9w+A`qqq_t
z+GCH=s$3Qp1z((ka|{;qsXB&JgvHcFx8Xy`f$%k1`bvmn*wim6@~bOYjliuR5>UIS
z=;)wePTW~w#aatvRL;ZS=ojPvA$Xa*>KS5oDQb;z8}{`S2Un^kH1ka*W-dce@n;*}
zzE2C3-`vMeaj0hg8s0BxW%MSG_M6-JUhMUNRWp52GtlN?$0mMwOJ))`@x(l(ZgdCp
zBN^)B%EzFJ_^)K?gwSiET|1a6@;(=7FK%C3`IRC#OtzQH9gUtzt>Z#P;W;gx-$g6o
zx*K)AUk!d25Z*uw09+>t#yADjzqgT*{Oq=N71R_4Ure2;y4Y(_bhHSMKdC-CeyGS5
z6}wU-<f9e`s(l;JvG62z`dvpF`xa`QM!Wa0I?j@(bPEnQ`&|nq^3(D+^t&*8gN{e$
z_+C;RU260%)uG26ABs${3J|=R{-f`pwRE7VI}G>m*YWdYDfxt9;>i47yggC7tSKho
zfYRs^c6mjszd0_VYPb#mKEO9sB((2W(3%od*kc;0Nl_@b!op2Az^6e=HVv1v517k|
zZfkYY+zHP{-*jfp7ja#}F}j+m@rh<0Q7=V?^dbRy*+Gw<5Y8(#2A0q&prdccK9Qx@
z%+2sTNBRf+(58AtIPe=^`3I9R>DUC%923viKQ}BEMVMhj3O^>gqclV69en$%1A2c*
zaBJtUp+3C^#83mo5NwNar@?1S5Zw{j)$uh%tCLDwJv{4_9<;B*!GNjOR}!SBnTqd8
z4KLB77vcK#=ugA@W2REWh2Gah(19<B?Nq6FOV6=wIpRe{ixZ*!5yz&D(XRAeKI@6o
zsX}Ynx=K$USI@+VaeaE4?5as*jvPgPHJBU_t_2)nlI5%141*mY^}$8orNnFO&E(Y?
zYh>x&ji0!U&f`qIu8VOxvzt<jb^T$X>?*Bv1z2%Q@}d$$$#+Ra(Wh@wQw61fOeD1>
z!^%+$3w&6=qjv9}t<_XdoYUMyU(<W~{Zr||`LeBu3>+_Qv}p|6KO)LM%29yAnW;je
zsg=Gx>;ip+k{VqcqoEX9gN_dh9tNzxmbA537BsRV^vIc^gyf&!_>{~xHMM~^Pj_CT
zU8Lg!`t_S~e+Y7jcI=S)V|eC8a-%Kuf~F(hqf^*3U(_(jOk68@;;Yl^P=bT`b~fXR
zv{rmv*27y4R}JWM29rm#$4P2FOQM$K)sV5M-Y;>tQP>QZj_@rhsU9T$*>tjh>D_3w
z5^qhfr;&26_<XADYVaIj^i;9NPEYLyHMQn7HG%GqeFS1S?&5EaDm5Q`hYg-<e<Oq1
z94Yc#%Z+j*;jtlZ9&C~<pTak!;_07%PmTzr2Q~DzPETyhveqWe9DT5erQ{(^i$a&;
z4#sS~loj~&?KGMConbC_dGi30hHk^2e1^Z)5vDq1`Xr8?8ZmK(GM3)NYHW65HiFtu
zi;-!3vV!c?C)`-|ZzJ5UoOWLMhN!saV~gQByFU#Nqz1NQdq;?{gf7E`Un$h;frZGm
zR9U{#<QvKxQlFb?^sxGrT*+FVuKM)3;S0xIZo9pAU3*AD*AE7Sx3*Dsf>5hZ&VXva
zGa0(~u-wINAjol7l<~^QYTZ}A(!wFphm1U|7qx4Fj;6IDt|qPv8xFJyeH_KQ2KMLP
z>TO>$FpkQ=)QX(RX{VRtdiA(5ebS$m+~AZ>3zz&dsD47HLFX8QGVg;27Aw5jegLGN
zr3;k~%7;hxCI9gG4S7g@m{mkyNih;`6t&^p&CcSA!<Wng^b!pVxUTV09jW-Vz_rWw
zFo9I=bX(9sK_(xi(yPaLn3b65-}%+4Au(V>l&vzLFKmzsfGKG&#7+6Gyw|4(=Ge}k
zFhm-JD|nt|H0sYw%<daJsQtni5SS_`y1hHM{$QMz3J6QM*Qrmt_kQL{Az^q0r!6`)
z-DSru3|&I_U}suj@QTsr>otX*=i9Is`E;%`dDO+RCl-IVQ>YY@|E_f~wLimwnXfh9
zC^t-jkvx$q+FQMDV@G`h1`K=y`SNayYRzAZ)`E%si|V{a{m%ao!-_@np}q2}nDV85
z_ok{}pL3v3c$~x3Nlc?G`wxeI{c|{Rru+!f-4GuZiB<u=r=#2A-`^q+q(b|jy4nfe
zeap9gDGck>&j=A-kP}ZZrr#B;MIRXvGE)Un{&U*F89|O54?@#SKsZ5SP2Wqb=@g0?
zxU74+<xQ%9>2jNPfS$T$CZ%-*JNYI<yVSwB;p9~DoKwKdP{Y{UYI}#wEz{AFGcS=x
z<V_3`Z?S8?-Wa!cvw`W%jHpUA=qJ|m5)C$5w=$*PFs1F4g7;bD#zOi>5<6u1?)BZ#
z?)l+M!&)5o3SO3dr^3*t7Dwshq0379X1ESCM*_NKWe=vo9>C!EZ49pS)$Uy%hZ?uG
zSVR;j7u?-D44$-w5qOypT252APpas5eR@q)+}IiSo*1ouJ`nOI;Qef?!qEPYK;ZmT
z6kr_PAH($9mxvjBLkB<=wAC;5X6(L*O}!gj2H+!)A@wOrq)&0~X##{S^bYBedNXW8
zD@EVNm&rd^adM|kqGnFRGyEJeQ=TZ-D7RHYCUXdf{kjNS<W%ZY#vTJ^Oa#mr1Zw^C
z>xb!kO&Qv8x#Ne)0oPu4w#q%IjA@EH-duGuUj9_g_p1kWzCW>2U1Ca)MFl<C7mZ2?
zPGk2y{Wb1o6*?5ZuuP$!kT;x~bP;qJEb@DNh_v1V(YViz&7+c|s=KlaR6DdBurJ#O
zz9e8Jw}%X;y%_$igmTfDIOJEbj9qaN^uIVTI>*sf$JPE`aqM8%8s!811tTrs1hcNM
z?eOOtZg$4O3Ib`0cVFA+=NtX#mC3C+6(6T$%Dlg1iNk<37>=UkhVfezdmhXN?%pBw
z)M3$Z*_LdK8i%@1T!N2(^d@IOb<sk<U36mE^vrm}u$y;~1~#F(C4sHGsK?YM_vuu;
zAj4n9cASAHlr_?RTm8&floKY^KdrKt4qWVbN70_JTmEd%Gsd;Jbj0;t3!+ozoh8t8
zZ&u8{9p9ajGl_)HkT?-i&nXe|!w-y}PoH{IilNeK?cTaWMZduB6jKZmFL2oM55`?C
z_Vx-G$r;xZUz~J|3GXmR)5zx<f>mOpIw%Qg$=ce)M&3N&)B~IEq;B`~<sOvjUNq7y
zb-q$(I6~B^(d64p{rnj%-zKda@H%w(s+7{QXfZisHSz9wZz0Y-U?1PdiD~)4#C8%h
z9guJ-qD#!QSj=kS3*OAZJx`wGx3k8Qxi6U2ZLQOB_wTSZ$!bWt_DWip&SGP84xrWl
zm#5vT(N~<X)T%8hg{l)fFd1}3kjGxbJK{#*sXf2bP5d)SSs6f}e?J~$)T=V>iH7yl
z{JN?!;*_&QCHd2@7;R)F2j)wT_HQkFo1P#k4yLw~FHf#ZbmsAE@GpcHYRSz3qZ&2A
z4N0+*1n{Ne81Lbv8O}7=v0KuRbT$l&IsT~)6&ue+k#}do^Bkm+m|JjLb}b_fraud}
z&kd9;H}ZX#?r*rgJIDB#4|EONE@tBUx<v=g#m6U^r*u({l0FA{(+Br7SQia3>6JWK
z+@l#&(}=D5<f-H0Z%5w6e@a{<;9KbB@L~qdcij-p+b9?O#i&a;L9;D(fp^V*<w6rL
zxD|xM&DiaX-w$^hJa2JhPx+VvIqOoqxm||;hC+;r9(Fn_3TQYdBEKoX#nIQtwa82}
zEna+&U8zi}<BY%CV7D&ZuxMG|#nBX~mkgYh6Gio24R)_imt)KB1UWuDB?D{MxcAnM
z&(argtQ&p%?nG1U_y^@NcLj>JgtpBcZ0AXoL2ImNpXUxxEj}G<AXp!2<r0{LFU|*h
z8cNtT940OC{!IL5DUb*38+;-2A-N!nv!5*Evu+rPgnvvmk+E*XCgew;yHb$8sTYSM
z<aSam%;ls3la6Gg+zI>%y2%rbnOY;d{=mYZg&-E70?EX4A#J)=jUk3rG<4-yNd8CP
z*uL3;pw~Z&wdY}eXf?P^#<H<=a<=Slie9A}2EzX=>uIUUUG49v5fgooW7tk=AD2r`
zQCVKJW1?4ViXC8Oy#D0Ih!DMn1}tw)))SV96g;gvAhD3#C)&}vv<fNRV{wbKU!hQu
zua!v2pJPCvX_JMh4oxY^oIX3L=+K`d3K?Xw{rq~|FYLGaT{KttK)HYudSjQkOKwh5
z)MAl)u^Zeyg2-@@FA)I+ftnI1JS^wDdXKmVXiIkobC`FBJwXzeut4`5C%h!H|E&)3
ze<@7-y8+PD|CoYsI8W3Ol<^adZ^YUiN%#iuviONXC0pBZQJAL7r38SyXV|%8Ox$74
zgcgIifNn|0+&7s|G(2QBNeorBr9?Y+Dun-q5kYoJS=WDI;9D<%upg4=3T>3dlh$7B
zotl)B4w$=#W8hG6H^+$MIUSQ`#<jehOoo^SfO7MbqSlHXjl&sVQ;_BkrfzVLdgdmX
zjZ!KxN`OUlB+tol9DMB(wX{1mFRF9JLqJP+0E>+pIA}7!tO@>fXhUcf;lr+E%rfR|
z;VT~w3EfOaO-5>sI$SC{3FhJf?U>V3lkwalf72IZy6g=^asl4nU3Zh4DWz*~_{rNA
zF0CLbaE&~g@CsUk&)D%F98D*#{n;fl@~v4|BZm55zfQiG?hN+a`gv+ND=|aqwmJy&
z#Sq1GyMOr9rX9PSu?DEo<4P2|E#TwT)Y&ZZ5tQcHmP4SCWwE29s>a+Mr#IfE7?p4j
z4Bn>&1igO2j=LX0%<RH{d_S|}7b0}h0~q&Zl6*i0_D%cmC6h-*B9Bo5n%8huW(TOX
z7*G<-M#0dJS<O)jiy!zlLwdQvhSqbC_)0tOJ{u>c8*k{!^>g&xfSpR+-F{TTh5c-=
z@yjeL1HX@rfg{cIlJQoWU<EmI40fRXeL6b!i<#2c*Dg)w-Z3EqD@1bJxP&>l9H?pc
z4(5EEJG(E7Q!S<B{S>IdDPlkQR<y||Cin$#J~H%ArA3q_Cq-36B=0!>rg-Ik6(%$-
zK)oeZDvlXPi~C_}_ekbSJl!ygMN-iex6%!1F+eM*X@{=Uczj5{Kh68mOq9{=SL6BA
z2wXMC^uI)^JRnlpPu^#IO+(Tx$B3qzTv;@}ZN)EZO}cjYsO0P=(JDjA;)I<7t(LR2
z7sZ7#(N_NXY5Xj58>CcPjAnQ0jazs)#+j^86I%g0*G^hIGSFohFoHa26k6kp$1|a}
zZI>o$1qyHGy-99;qy#e#?a$|iypE9<_pN<W@TOIpdTX*gw&z>^Kwy5c3;PA5YCg3A
zds~4@Np)3{^!g33ZUZK&t-_$0hh#L6s~o;?vl;KmyHMi}Ds}+vY2!UfE@%_?1?{Bn
z-strzq?h%%zmQ;P`}dMy2I{#1q;$YjTMi^w;>Y6O4PrI?FY2R^se;;Try_Pf;PLQ1
z3;>{tL!u45DfU*x6C<X$wSC_=X)cRGvZ;OPOGSHFP~Duk71VRHz4bU0RpJJ(kBx}n
z`!jz$upOf^lA;18D|xh(CBrISE8_(t4;c^?aZ5a=Jq#%Lq1RA+hblX1tcgj)(it+=
zPN|yeIEglhMIG1<nO)+Lna8`yjw3f9L#h*6PHq@a<&xbU^6Um)CJ=e;$0&NsE-0no
zBGY|E+MC?p4tMSxrnitikux%|_xm(ZB`^U{Yu^F#puE1tK0>a0qT%%oFt&!;{N-{|
zhsqSJMj$o)7uQZjcjC9@JXC;Hx{|$dY(1eBAZXnT3l~><8Iqr7G%s~cQQC3R44HDa
z<@dv|h8F&dCxDVD(O&GbCmMIIbU~#Z?;mcTKC&9DRO**s&0k^^%3q%3TPv70#C|Ln
z?3GINi|aZ@=DcKxF7~HQwc&XE3~|V*88~l`Yn@zfnL8WO{KZ*ki6&g8#V%O5J7;0{
zf<R60&)d_B!mZpzY<Hsg>$e9qmvXT2-kWf_$nSPAISs_vaI-_aGAS)zC1|k;nVzCD
zUUC+>aD=RsadEg$ekZl(nw+#_l{{^ub8@pk%I&_aJSP{2pSvV>9+wp#-PTbXkqLYv
zv`b-M^sfqUEr*+M0}cb#81gw3_v%`!gmbk1krbuBgrzzIoP*4GPBxcuJ}f@O+Iu^x
zb8F3vW^$tpY@ejX5ol>l{AX(M<ewRZqm8*nOA>5q$$vnsH{gj>6bH;1aUv%E*J+E3
zk`L;tJ2b@~+7aTC0r330;eT2m)d1lJX~7x|&||i6Ja6evk0fk1$!kU9@{Gx%m%zo)
zO*WPB7Q%naz&uftMAMOAG2Z4g$XUw;;k;$E5YMBL*-$-UBN>IH)A<r23I1u~TbgsR
z)+klGiWXwWHS(ghqC|1;+nCq{pH~d=c_mdS17I=Kb7rd})r5jfh-G|b+N;><qJte2
z+>POkjUz}~^BRfqI9?XBk=ANXitl=}$d0dxOo6&T9plb1RFI~{V^O8IklTbe7Xb0e
zyGX&DEPTa?oUl|>mym#jW2K;y7_qc(+qPa-b4vV0-@@BMkpiK<C;T2#a5lyElR+^|
zaD4`#AMKRb&-*;zOt<(#i+(2dn!p5~XWjPzX->$TJXE7>tuV2{3Y3J76k*~u3%uEX
zFl3OL>Me(o*g%|WORNqy@hf$PhdnasvTD>U&>Hr(UA8^gfMIXcbxObKKOSQ=^1DjZ
zJ10)hN*`HCqviLY*+o>T3&3((+}U`>6ERuz_>4mVkpKH(ff<|rL}RB1L9Fveh3}#V
zs)fX(Jp6NBx|kEHX>~p=k=m-eg>)%2zqn-{%-rW`T-##7?myi5pd;cwxkE{~YNhY3
zCi1y}ff5+@uzPQIYj4JGgqTB%6L<^88*ilD?w?JWBUvYe@j1)mS)$-wpz)rM_8AH5
zWri!S0XEZ<Ojz>PTr_6JSOcNN(MD(<x+aKWe}VA*$UUtrbCVL<n<Q=}*~K@N-DtZU
z-gV}ll!-mzetBKL|GsEVB|js*KwKLeZ3yIQGO*5M!g5fE;i1*~2Pii-a&r1$l;eHb
zl(}kaxIZ9(%Y9i8NX>x1)X>^(kE#E1L1ziS)(>>wsje($H*}~{$t$dbz%t@X;-;Go
z+-oqwxx<-|GG;3n{xoB%P<KsiS|0n;j;^-V-u9_%-{|H*Y1-_Kn&i2oiuT1uqfw9!
z%OMl$J+p>9hbqhBa5iJNl!ei!LwiEzzLOL;Jmlrn{v=qlE0d>KT_~)a-$e0e1v1y#
z*yMR^Y{V^gNwU0gv8}+_Y$UEUxt7}_r8~4az=%_!yV*>hwm2bPza68?k;7A7mYg4T
zyJcK`a^vW=;nIt}cz8_2;3uKHbyLoXxc4&|b|d?v5B}?2fT*P@goH%3Yv>GrN0oQT
zz_wj%WR^%R4hVfa!oI0WzmN9jL`j|8u-2J)9f&p=0itdIk{fgxCRy@(xqv;)6r?*c
zq4BoVXqN-+ynduVFwPq5W^Y_~a$^$R5KA1qcdD@p>T5^)WwN~G=ZJ~fjV`&%lU%5b
zU8&pk3duZ8I-KvN@*ENkM=@^3&v?xY|E!s)!ax~&qMIO;P09>#JjO>!-_#LzPKuBZ
z_#-(dzW#P4!;kDleo~oUY$I^!U`b&y!O-Q&zqSh^kwtyqMN7ZN^XLq|T(Ab`p;V9b
ziF;OTu2j#Vt(y^jXJ76Y&*rfZbhX`Zd5yd#KU^qyW0qe0sn&$qg<O*xW=%HaypzNx
z^(^$k=nTeG>ZIw@A*K%5)Q(xx<}-wnK!D?Am!qq0#M_Z8GIGNc#&L|1MU<g^7Z6iQ
zbR(83$$ZohuJWct^0~T#9n4U&02yU38mw(w`IzMisIG#?U^I~fP;hc9YA=+6rk-DD
zjT)3BE=j7Md+~9|Lg+wHKAQhI+3EG(BKOJW7Jv3@MwwRU=DmoF>MpKgvz27}im2J^
zGer~UT(~lFK66&BuX$*OKADL1?3D%z5Rr~#$!3-=dG5xGM(^!v^b7HKbe_hdEOgGz
z3p>t=-!K7rsK~2L8GE>Q1Wx)!y_0<{@9igvtx2+--$sj?V=qSp%NL>zdwR@INU+hv
zTvlz0(06@CJQ!4a&0M`j@(HBlQzMIyLz&+kE3GaecCHD1BN`$6D3pac5+Dx1LS4W@
zf=b3h7DuyY20w(Xx;YktaWl0smW(%~I$B(e2MRIR;ZSNo<rw#PwfF)bxO%-^lf>oN
zE=vQ05{vy?Z%K%KyFu#F)r4g`{u7}1+GD${Wsa0oH9Vllf$hW~;hP5i<EG>`#`W!5
z<kGidCFqk)d=%&kK!Ic8@9pAj=AHr_-e<>8J4Kcpa;+LLe_JF{Fa*s15ePVcpC3MI
z$N!ZKqulFPK&Dt3Cw%<-WThBIQ}%{EImE6C^MQ*(cG)LtH8NGnUH>tIUf5rCitfd>
z#o94j4Oz>c<Z)CF3X8==ypIW~Ry4e$AW=x`TSu*Elty(MI+l8pZ;f4<s159RKE0yk
zfGa4H90i~Q*;R*Sl%i{~lW`gEGk`UE`jJ_TpUKc`7|GEC+6ez68Avu2wMVU9T0LD7
zxvHgXr!h4{KD+TlZTY`Q?9Su9r{+h=vhByj<#6EnPo^_gqaAIH4R(Y_L@XKf$5H3A
zB{*2Sma4EyeI6Sb@ldKnbGuaQSav?|?#2QmWGwpizhu;Y$h}6+s!|mBvIEG4r~xS7
zyafnIz;gsR6X{ZaVzoi*nC{?+q^-uIMp>@Ez~A(zX$^5hdnI5tgcUnJ{8XC6f!j}b
zp8dG0L}sfj15`2`H#T&ytfkPd%C59pqlW4ZYipMnWKxh1U^h8r(bHGffv@Z27NaPD
zZ!|jSaonW>6o~gN)FiuXLf7TqjecXtU&`#9P<68vECZ=sH5uisKY?y>W+NEQQVsE9
zT_(YTf%yO|wH!<QMZt9Nwc#6_bx=cm2>Xa_hNfS%iIR)pHWcqSDGilAj=Lfj-9r`t
zh~Yy<LZ^s62AWJs)8kl02Achp(C)5VL*SbMwS~R*D!S?qL)#mWo=wP0l0k8D0aDbw
zo1F%?SsMYUC_5Q@ksUlVZDuPYli_q42;ix7QO$}QeC|&Z_u%MYlV4!C=@?;C^11CY
zYX#u8OqfFBTcIs7{8~$=m9~Pv$PRiiO8WJy*!ygvN(QrdyS5hDShv$nXC>EmnY?qH
zSn8j+wo%c0zl(7@t)aZ@*9t=`x!dZU5m$hk$Q~d|0fz)`#DXFppF+`UFt{m$PF4na
z$YEiPK;{PieAZ=}i0rx;|28PM(2nS+fKxsigt3rsBhocn4jp4ul7W(s1#U^h@AY|(
zJiyEYpL-&!F7ka=d*&ySl{Ig7iDOFl^_it>ltTZ+u$;Cbk=ea@K%Dg{Myt8vTI{Ke
zuIW~%4s}-XXE!nvS_->=5_D>_l94D*e<mBI;k=%3o>6rRXE?h-S%`_9E}D_{+t(QN
zn2;ti1ozCDBl(&wzh^FIn;N`vo~Vr7c=6=;$I0(k>&<{8>UVZXBEy>>2CTX@-JtcT
zj2~8TWk#!QIkG<|yAMiJ9?A{NCQ$Zyp2w6StxN^$2}@lGfgc-B<f<4=?}x>IeK%+O
zZF_&19k~$;Ush!OnWEm3+rdH+c_>)yLPeajVCZLad%-7bft^b6E4-30X0*p`c$_BW
zILAO5{LJkq*_fzh2V4uelG5m;fnR;=w((YGnBlHzNV{bpMdVK!F4i<iC=!2?x$;gk
zA#}6AhpUfdq-ynl*_$x6SNz>FjYxDC&bez~Z%3Y+?)qHp4yTk8aMLq^4L3y@ALu#_
zFU{hcV&|@V78`GJS(%-lUMeNgcW?kv0{GHxea6eOgFxNFupa^lxy~Gl<E|_ZTF?dE
z;a7p|?5rXCldW#ui@peI3>}RU$@&)6eWJ|};=1yWv|EKel%MdZPOQ9*Gn~t|&E;w1
z6Gg(Sd7wh8_SVK=YL4%MkZ;E?XAFh+mkI-hi0StK5Sw~!kq!{68(KmlT9XU?6<*)^
z>knHv#zn!{FY(!!RH97`|4diNFM^?>nW`F3b%}GN05^rkjnkcnqnyoX<93?)ub8(4
z@7_16m$mAeP7%J^@goXdC4r~54|og2tt>Vqg7v9w-~=mr%mU?m@wc}FHNyI0{U_Yp
z=!9LFyG)4PUk$ArDY*RYa?uPB^;i7ze~tU07jDTmBX=R;==iF)7N{<$b%-Nox`ttS
ziri4V_2zd^LUZhDx!~8sT@u{M{ouHRiI&?rhCG05jhHL^13ilcs{Y70oYpStc|zbn
z6VUN4=e&cCeO&8CrH^B8Cy|Mz{<O&Qe-fBSbvvo!){UBavm^M32%-^Ln4IY^iQ^+j
z0k6jDlG~ymdY&-6{7-=B1eTPk9E+obQqKeUm}a*faWUr{+WE=Voa&Slt?~UazBd~O
zl>UyT6V)6TFvP5K3@i8dc4gb;DhCxS;}RfX_c9VK+5ZHBnhszc6_k7FD{BnZdD80|
z?G%a+W#*4@v&i)S05t%1(Xp<3DIW>9*(;1A<gd1HVN1*F*jsx;Iyqc2Gu2VT0@^X>
zeA?suBKV8~TRtZAdZ}ULxe+vvz_22FpBk2I7Jyy=)?hZq^}yZgZ;Tg5dGXaJM+gx1
zDoI~#1fZIs%)*N+L<dvT(exn4JTCCUNhG>aviWll`o4{>g1N%u4`8-I3YAI#Zji-~
z%awoC=Q^EVOv|H;Po=I6AMTWVCs8oXPC@&vp673WNt#+8D9cGc<bDExSZQSJ?PpI^
zAygWJxiHEtB=0`8&8}(##6-cXSfFHU+!%WWu$e5R^?w{i?Mr1jjcP@wViy>iSJio-
zP_o0wM^`a#T8Xji<!}MR<Uq|fb6HU5oep3Y`_rOs+e;Q!e|~i@KJR8D(ev*O=9Z1t
zj*gNVD#+sQN5JYT$7IxNBxEj0`PKU*ZY<dXK-<v=k^~s!c*8vXBJgTP9OXlZHQSzy
zgW^iZh}c-p-$~2Bm&c0>m@aR!0?In$Uv^acURln>*0|XKZ4mNn#I{Hkbv~;lqqSRa
zotID=NE7#Er3K!$y7~bt*?Ub+a>_~C&a?llik{f>|5!nhTRa_rLvnx%GWC#=*{>4S
z<aPtom^zrJ0gZ(UKS)ka{pi1{G1b88RNU?4Wt>As!I-Fpp^21+0<YZJ0rh$~a==za
z@VZ{<CC6%Pz{TsNSgdxA7T^qz8DnH6Yeixkko$f-$`d+7#IheGKjWICXlN5Xo3n<b
zaKqQ@kG5g)^#gzr=vB5nYq`PhPo9dbxW`S(u%|f@Dh%3UT_WE8r`r0a2<{P#a>1Vr
zgmzK*XgSkG{=xKKy8*@OlI7V~9ePm)Nv4FBtv_c<^-}A^yGv5m1*Dm@UB;nj@KIKc
zOmhEs(IUdb9`zIHBFEdblEa<b!Q>Obg^P^IR9yO+MzMe5_}|woR;2&NI1pgO9I56U
zZ*avmSjY`4x@9chY&0Fr$-65#jL145l&qn`PcIufU>#>-aE1QF;Bx1tLTh_U;J(_#
z1|DHp@zctp$0~gJtA#|#xubyM4;UU_72b-t#$K@RZ}w+DsP58Ktj=AY=aFrBqS037
zIj3}<+Xl@Ta{2qm&yv2`nYwSMXL}Oq*zt3F9=<;<e~y&z)vmcfxVGm=+MA>_jXfQ^
z38x?KD7^b!%kkQ)RzL1f{q4|YeMN<Z>+~hQd7sHp>lmbUa%iZbbYQKwLEBK<o;*Z0
z)*@D8ZU`@u3bTxqz1yZmehs&dv{)m2D@#<3gU9%-?o!@>GJ78z+qA9q^-L8FRK({!
zN}KhAqzAPg*49^3&JBl*OFfT?*d0yFyI{bc0`pZi{Sc;BtcC3A{SL!1STNHn>=$OD
z40u!I@8451nmM}wp=QpOK@zk`1Bfke1k`)e=&uIGdB}=Oc!Bw_t0D+=sc_}bSyGVh
zi)&mfRum+%aq>|93uwh(jq~q_UR$W8h1!;#c*Cp-eUh|I<v;X(Aa&VR{-~!)%E(eb
z-3;k2bc+Cg!GNP6VKJ6L1@`XNVGqHY7Ob~ano9ZBg>}NQaqaY~%5P?MDZdO4?wfa_
z`2gku34|czsjJB`-L@rzkuJk?S2bgA9g~q+bk0_2@v=aih?)o;4IeNzpDT7!DY|#r
z%4Bo4yzRA;7N%c8Fa$PN-n@b+<O*@BB!xH}xd?IL!g`f<lV>ihVD>7+>bh)SoE?IB
zItGrAQE7fbsFZ3nCiw=OS)>w(PqcFYM?R;;84n>&2syZrR@rug2~HEP4|vg^9H}+D
zbfk3S%DAORH$!-J<yE%ozLk;-noR$^u%#QUU$gz>f)Qy)CeXZ}5rq}0vz7?@X?^X7
zyQU?^2_5=NS>_4E>L%+;_JSOyVAcR~(7HZ^nV6V;;Wq!8;=5)wQh-fm&_*P}EGu4_
zWSF%yd8bu8G%5XVtX1(laNcQsEfUtoVP*XN&y(b$r?WU)2mguU*{`ZtGGS;-DE2jw
z!A(%8&XN^t7;FyS4C}s5A1E)m+9#PtpKO=J{Q}msWNm`!<*A<u_pg?mvOOkic!qS@
zZ28Mmt*i-c@7O15F2UQNGSl?QmHM;l6Vzl}D&V)Q0mAw&#8yCG&;s&ZHf41PqE3*6
z-|y_``=}4Iwl4NOY?V1k68#FMvj;=cZ{gL7OTV9mwMgVrw}mD}o%H@@MM&V6Lc)r{
zd%afec0rY6Vl?4isSc6=kbfPvgKAi=^i1G@;8*mj7<N~kS=dYdw4D)4J;#8UGiMfj
z!K2#qYgX8Drj4zOcw-=)-vA@*vKyp1e;xV!Cw=*4d5HbrzoKBvHQhaB<Ay&-60jtx
zGGL~Pob~zrB{*|sxU*%DM8uIpjBi(kP-Xas$6b-qG1L&dLHFwFvyvs`F9V_tI1F-&
zr(&!z<)*j(K0gy%?q2Fu+Aa9`RCI<FtLTQ}4oS5uY5%9AQ^qPJwb%tgR=gIeZ_n%`
zV)ihk&1I7op5^*q#D|2r^47j6=FIMe^xvq5Di6>Rt|haC1<lULI|w4SZkznbUoPH3
zMIlaP&GzMp6!1R|Fe{WsED<<xa})xm-6RQV9%e7K+6%$Z(=iSR`O`7phyxC!aHks{
zTpx(AhAXL|flj}wcpjEmGOJ7&FHv1%1>Y#}rMOCr4W#{2t-TeBc;pt#f83>E&02iQ
zkZ9*w6`-_7kS5OHfsPB3B=VPxmiej1>k_)qo=pa}+KAA3u$@?c3#c*yiJ;;tFGxlo
ze*ui%duE}PVs~1iP%}U^0w!2AW<fN*_Ej?9MGOWMf-nM;=!g^9fF4#dy)4q^BzyYh
z78Jx05b7hjD@rA^rU<~@Cc(rn73MM5Ete$=<i`PlPbu4L@r;O8G&@X?5L=KoGW@Zm
zt^=xki8+j?)KqWpztawy#C`m9`)wePn*sY*O$-Fp3RW-cwvYR!$Jrg!s7m6qv1cIc
z59h=ZU+Q2yeQ~=Y^g+8s{+f=;k4}<L`B!&tUwkSkZ!@hAxBmMLF2kE!^2(!xZjo+A
zrmwcBp`Rm}Eb#cE)M|;1ZHe-MfFQ|LPW!b6Do8VHJa@JooW}`9>8q2>vL-aVFRtUV
zw&IwQdx=Yk#@*4xhE96W{k^C4O-LPTBK{48xw1Z*MWm~279Jgd+cBBYtgQsZ0eSyZ
z;*~y}4A}pPis3l)5(88=ZzCQ$Y>BKO35eNbNdI-#YggmfE)jgNsZ{}G1HmhllpyYt
zDx2hMf8Mgl-5aF|N&Lr36PO99hazxL7XAVWmNj8aDm)`HjeZ9gFW~X;`>7ymA7Y|8
zOq2lOScF~b{(V9%YvL>kl@+)=*KCQ%18>?Oj$|3N=J}FlY_IK>oV0vCC}b@iUGAPa
zn*})*$0g>@77*44X+#JMZQs2)psq9>hm{+bsOJ8kK;IQ72r(|F%e3vY&{Rrw@odhz
zV0hw3i3A!o-kI8`!`E)`WKc#4oX7kspo2=z`q|-$7bN2!yr2GVkZ|brEhck^b~gD{
zSr%T0L=Dm{-_we~pCqY_1xprT9|pGZYtH&nlu=v2I{}%c-duxd`})-7VspyrX7fI8
z#arPqmeF?eA?2??;?rn9As+D#f+1UImYa%#$vF#}wM}C))kaN*-r(h?7CF7-s`6e@
zYjbkF5I9UkJTbZcX_G!=C630QFX7_pFY#K7*|t`suU$%|pBhBsz2u+ViSIMi7J!(F
zkjR>{nn&wZkk@w==58`rVE3Bpn!W47g9p3@vKL+H2F+x2FaTEKVN?F!L>F?G!MNoL
zY|iYuJ<CXcCJi&;3=stuFH4-xz$ytBBg&bD5Y{d0W0SZr&)i0LWx*u-q9nF|X|pVA
zsI_uOmuEQnI+@!Kx;y(y%>GiB?3;1CQq|3&KfyRhUnef6oQUtQ7cZKWegsfJ4kQ?6
z>tuR^mFmW;($3koplnbG4v$^lcFBg06aVv$^WS%^|HFH@j0J;^jJG1FRA)8GX$;H0
zF@)(_u&g2dkox>tLx@|pmuP?Y>mNuJ+flOi*Zr$j1Mc9>pAjmjn;n>mxSOnQu&+wY
z6^nn`$c>~Z1I2>bMl(g)(^&$9dYCV{^E;m9ac7Nloz>JW&_Zk_bpJ%{$THF(#Spqm
z{+xx^xl5KmzYna#q%LTtMn-lU;Y%&|GlXYI`yrUZ*&_raX$#rD0GhZT$ZY?QI?OjY
z>q7|F`Wqz*kd@L|Hi0i~y?<-WRQ!GWC17Xmd+xQ~;H{al&Yq5OL%>uI)El269fb(X
zxsHX3h}q_9m8p0yCi7SA?A`61k%i#IEH7-6yv;)t0&gHtiv_0X{IPu;FK;&}laVRf
z4Z&p2%BqI5+yE-<r~5um1dc*B_jdSeNGGMyKSEYMMxOFtU*9LL3?C6kGtB-qO);zr
z5Jo!Q&M7^%*FH2IorQ-106gky<}_f%GKW}Uo~wg&nk*r#slBFp+}Gw&GkBHK_^@<S
z;l^K|fD4j#xqYiTObP@uY7t^tAD&8AKOGZ)<4^7mNJ-ZBv+!HHmgT#0AZ^aJPSRKa
zuC60a#D00M&-+p~9^Cm<Tv`j#1rYDcxl>o4av$D^B?2VI(0gV4NF3Kaf3~7-K&-4J
z#W!!~;bn-nmEQ&kXGm~FJSs}``jeKW#OKfL6-R}}8$5@AXyH-F%G25G*r$@{+k0RK
zbsk=Y5DK(&>x@auh^;U|p9Ej$A6*@O%3TC!Fe<v7Jc9mwf|xKMtx+cb8EJtng`6kF
z0W^9d``xfu1klax%K8WU|HB>ff9LiaE0*`1t1|>UeW?(k`jRDjUjBY+xkU5y=$M7(
zcRdow>eaQ~evjjRwgiztm(2HX-)`K`){icXa_pPi-z_<B$T!hd)34TLu1fy(G7Y3T
z6MY3iylz~UBFz*Szf6_{bPuy5Q^go1fUfjkNXL|&JksKe82{ute^NV7QumNg2XHtc
zdsa7YVH`YK*UyUd;n|uPX_E>Oftcqtbd?x8gwm&9Bs4zot{BM913(s$wN*~2tQp0C
zY3S;jhJZ7YxpeRb^LE~^v(79OHV#W5$YVw9FL62RK`sH3G~rwnV7^iQ%5YqhoWwSJ
zmBN7@pTW}13M}$D-Z<bV$#(L0?Zgj#eIwU12J_s;tdxT7ch{bkeY5?Wzy>$As~`Vv
z_+QtDzs0!J4+kKen*bH;gG4GftCQv+$6Nv>iNLuTz`{aKl7h0fenzB#o9F(-(=1q-
z;L;ncMsTZqh$OA~APXE+ga{l03HvL5GqE8m&JUrLWu!(*@${1L+e6{2Ry~&yQKvVf
z_lUV?SR|9_f6#*U2F!dSkdTQh&+a+970iCk;S;S`crfmIy>)lOh%V_Qfmj$TvZ_A|
zl-{@hUjSkZ0!V(TFxDy<8-RZ&cA@rOF=3g8xH&c_x&kOhQPI~Yaa`fukKR#5iGR+f
z5w<ijMnXCyJ+QJ_Jm3;kY?9Vz7G|=x>=Cddxpjsl0AilAh$`3yn50M7exOHN&bm9c
zp$01TlSCOKNyj7*=7%7}E%d3Dg;G5I3{upx0$qHaW@23tGUX*o-yF0G^SDvVdv}V}
zv}_M?dreE=dlLnmfY!4Ck(+aQ^reOYD}Xgmz^@O}tXOQc3vRx)<6z;nA-QngLX~f5
z=y?bxXBL0b2}tjFDGHH}O~I1}IZfKrz`Gtj;<r$Mk`dJayhsDb2!(uRDV#eQbtWj_
zZMuSCSie49oPA9M0CPiFwD937u65YQ+8Rju>;4E#)>)UPn-xKpdfbWd@KNtPWl|Pm
zYv|9Kkn5}>ea29!6>G0qJyoBS*Az6KYbI@cADFgVGvVu#idhrYAseYMAA}kGrM0OE
zsWsUP9**$6Pq2269QD=*=F6I;Atn-mWi7)#E5<ElnjO^l0*2qHux1v_K0fI*00~2P
zNShiJ>V^d4VV2eF3tiw%`bnn@Yu2k>2lalS;ODig2AfXX@z%|=jP3#xS+JV*7l&Lf
zY<>Ib0AeZeufyhELzOG(O2BXZhSz&`rGsOxAh^;bUK7}w5nD=agU9t7GV$QeUl1u$
zBpx4PGRZa}yndZOWNivxSb3>8q72~_l$i)VfR%!q^>6+hWvUw0)&EIm3S9!za5vY_
zYOzI>1!%0*KkW&4E1fm_;)zE0RT-%#Rrfok&5eXlz<IEkxtutAlMfN2ScI$5j|0+K
zuA*M8ffJeNBge9~qOX)FohCh!amzoTiLc!}rX6BeYNAa_SHt32a^Ztj)&r}T9BtgP
zP9*@pOJVt3KW(TAyF!OxMw-LvisJA4z6@xY-VFOvw7xJ2Nx1Oiz_rC8QI;3|DActs
z$cnc<V6*4;cWWKsDC3z_t;!EYooSO*ny;rGxB{;&_2FK|lmT53t}*Y9LIaDlOaxh}
zLLm?6y)~zqsQ+mGVlWh8du{e(Rcp{Ej>cT`<Q~RQ@BUK&kN61g;tb-fSj!RF5o=%?
zv%<7sHevCnfbR<t6F(_mf~C9zn<A@gDq)%e5IpZ}@d#Ao3vO@+q{UdhhAAVf@ZC6K
z_ND48Z#j*dvI{!hnGlZu>$5}8fUEZs-uiAxg7LMsn1vyz@}}Q4?!vCD`%(M)!m7O2
zrQ2L=O&<0>4CSx0vs}wYD{sDg4d*?}R>RM~sfI+RzSpvb^R~dgX_?GDs;3HDSGu!3
z@=OR5O<>~3c-zX)c(89qW=I)(Awan~a;>l6ajEZmMvr`*^)AooJKg)k&y#c;IDSW)
zPU~0O4BH0yZ5Lp6Ef=s*bQ=$j3nbX!wydrdtMP`cp_Wovj@avyBF<gyxDQlk4yXya
zYuUT^w>=GOran^5yB-K}x<dZ|##yu8y9zE~_~6Z(;e8S`d&X!}4bMD_nQ2n<-YvO6
z2URX50aenltEUevt=wcGvq{?&43Yoh`B@j+c{0HQQCL;;A$l@<Ggi|&>{>Tt709zH
zsI+4AE6Ek3SeE}EJ^A5rr(-T7GEVF3k}4opH&}yU%CaLNBBwb7;c`0WK0^2OrWK+%
zXM8#!P^qkuth89;A{sqjISE}{f*ybzv}ExiLTI<n7MOGu(j*S<tq7wZybZg)cBzeN
zF*uL*j~QxKj`}WTj8gsz9r2C>Z`$W_dpewhZozew^-s@D2-sj56`)|A4OY6&$_G;d
zQK2>d0l+B~HHgm7xx%dwYKWqo*)<@yn*DOVJFLc<Qj_TB{233rgkH&IU8~od<vh*S
z#W`Vdm<vjJFH+xu&2N6t=%hF#)-(hQd_D6c-O;V?#(}A<+~9HBHgFp2ul?PwH(3>c
zXwsFF{~=mPHyIbFxD&5y9Tt0yIRk@~6!@6ToNLayApE+S15Wu)wWrR+7`B1W*3$(2
z(h%pnrCW2vh9+-uggxw334$4pF)|`3Xi+awhv#4D?wA{_sd%e0>9a@Bx-8$5qaAu8
zl8hdtbr$E`r|iNLGrxaU;H5--mXljQ2;M<`2bKZr-@W9N&R$q}OIN1v2KhH8<|ZZy
ztq|kouIaB-=XSBXGyfr7^f7i(V@(KY%b&$&ohY2`gkV0ZMV(dp?l>AP9Jw;d8;B1;
zVTUKN>6Xn~%s&VmcSBMWRY$$tmAcUm_Vw9pnuxz%Ku-FMR=L}Q4?qo!C^caJn5lgN
zc;i{`@si@O7#9AUXw=Vd6;$*1N<~5wstO5KzvbGNKPYpTvNsF=BZ+`;)tobq@*^&1
z&b|a|_OJw;=^fts!&OxWO7n_jKn5$<AZ{hI2Z4eQJ+VUM^K~*?`V1-tqD;fuP@{d&
zRKK7eTz!-Dp44H7#3CQD%y%2Gn97BOMr+=$dGE4g=J!<Z`A5j;C6^W?+Qc)lHv^L1
zq2c^3qPWN_d|su!H6(YhZ?Zy)OV(Bmf;*jtOK+*cU-tZ<pKxL=p$6yYV4|t73A+F3
z4Wy)G_S3p`Z`Z4l5@nbAc*XYH#B;$nSUoqWw1T?Ws^+sh@AutBnOe053##~IOQ?r8
z%iJf+Y`S)vvG)Ju1}lVj3iCB*6jNZ~GwZm;PJ*b<B8mHc9XGRno@I(gh&1H_W!SAh
z2;fo05xLsfuGn#(xi!SvPP>cRJNGc^qYx?OH=7b25Q*3IGc}v>Y1h#A?0Ben;g8;b
z{q;j@7QP9n;AuI8xQt(hk4>cTq_aj|3g!xP)`jTC7lk_hrOy-AqE7@{vFhA-!1Y9B
zsfvvKU~AWVq9a6voNxM2gF3TdL2(*3Qe2dOr^oM)*>UEZ=z^;*h^^r6Eu*~C0OT4v
zD2g1qA3p`IteDlf6~>Aygy@^PRf;ZGy5PxfYCQX?st^8BbncI|F?UW)dEy6WwHCiJ
zjx0kLcta)}F>!IOD+Bs!BKNdw{;WJ<osF0XyiPp}5#qx{v_k)XG`2<{0@#D@mSJBF
zdJ4Nd;bq5tY5JO%9)UC)?wll@Mc_?H9I#4dO?~t1EAYVeu)E#AEip+f_a%q5iR?#?
zj1~p0XC-B73t87*sj4dBQ3J3=2G#P@_;AxNr8?Z_!Va*Ge`2$_vOsvgbnR7kW(>U&
zZ(dR~TMQAN^RCNEyW_x16?}vT=pXbQWh3yIJQE#K&e^64NMg1ZR1<E~9rb9}plK8L
zX4rW4)#t&3eA-%8k?}j73(bA1Ln%hef;WahJ^zZ7_rQ2vTV`r26EI+(zYeRaj5O58
zKUjYUrdO#~hIMG?3byRKT?dx)cd+AY5WYqF!eb8Et(0>4VGqL4#If*~I%Q~~-f|1O
zbS0nxdvNeRaZHP(PheqhgiZa4C+oYXOg5>++-#9wEx95wE2XRxDX>~q62-QOZizB7
z(F|YJ(7&L(Pmke<|B3X_+5c3Bv4LR6MbvVuW<?2iD#mcwm!uANy8#I3+~AJrs;o&M
zu*&1EB6Q2uJ?g2N=0m{600Foe+r_H6pD?FSDY=V!ijI0Z{^KP|Y7=kf)#7ks3%0M#
z;Chz8|0Ky=ZO+u^iZFE7V+))vWrFJT$)~i=biSj!>$Z%Ms<%r&iDmILuh%6>SM}5!
zt>{e`jcYr<<fCJ(^N>5~VlyE9dM&zU0O7>Lzxpd?hex+!9@+Y}N_j6DS3#&mk8+1-
z>7nNGP$<W3F=hEQqj<h)-v{mgsv|FAtYyVg6u=Jhr7c(#NO0-wvutkHBPz|z_S&Ul
zCslwd?D7v@tMO|9;?&yh!I^u*3WzX2)*VFa!S`ArJE*2N>QBuGKCsfkaj%AMCLcdG
z1-mIb%Ks~3_<Tqgucn}4z~Ya!otM-c(vgDr?i2bjPTxAtcjE9>x<g6B@ZqbLpA7qg
z4nGUH<{}6ki&!+Br1-cilvgLP88@1hAO#_{2gecdNE=Q;0h4{yr*6P5=#}83wxvQ!
zkkfV6qxFYBJnXei;yNva^rVBWt-|hghc^A`#RNxLu!?WAVXF#vUDtX1C_9s`+t2d6
zf1Y%J;y902SZuhPD~V9AycRChGlI;;D2u+o_T)p?5G{8+BQtTXC@F5>7qMOKXz_VP
zR;q&GghTKk4*6%s3iN>L(804gn{Pgeu7+c?7t&5k#YcWr=M?1V3<|$I_B)Xh6dm2o
zaV_#HJ-Ior+#cu18?VzMMWoD57G3z=OI0)^7orf+?9D41l*u|_@+O?lb}YTxz=|Qo
z?0DwAx8j>vl#*UvunaiH4<X+;+(IIl2slcH*MtFHn>tuF;`#sWS=#s#$FgUOb6C{U
z`GmjXT`Edq+>E@A@q7Jsx`kgJ_$j6gy-MVnf`J#E@N3^s!QFV{Uq7E`(Vihhy?e&5
z3fyvqZE-940|gXMxI<sJ7y)128!VHeE62ojgXy|AS$PnRCF7<<#EgweDiGF33zqW6
zk@QO9<p2eo=ble%9XDc&4qv69$`gxNdm4`Ymk=`FObmpuo`W-(lcpz0r?QrUU$!Yh
z2iNd7qM^LrYokgH!DC(17bkVRZ~O_~q<kmZa#pw)8apSle(K;E5JtbjJK#BM+K4c@
z$X1_tcRwD9Xq!B~N#ArVjar<H>Ob(y$##qJgp%1g)w0+`Lz2j<SzSeyxJL-<-l<Bn
zW_1-TJsGS7y9D5+SUER%A3a|!y5+2=biOz3iya?3#u%D6`IMOd7oL-6jEL6}WEO^4
z$B6t{PkbWmzdVtvsS1KEXvupg_SWm9cQgg|Z<M;DVp=)IwFd9HofB%#(|n==75{YW
zyJj#<w^R_LfJbML#ik7>q?z}RgHHgiz`AzMUxScypasp1>`jVo^4W}!(^VL(w1ilp
zW(h;T2OY2cLyU$Rbs_)76TAole=OJ?mHl=8qUqW85~sGX^^Z~%N~P11mReA`;5=$f
z?_kw(iIa#vR&|p=UR<kOA2;d$>|*Y-$%#DsjF3st5C}LgPHyVFtusMwW8<ZirNY@;
zPHhZtwobpd;m|5;eSE#|JH@YjnVfiEZ$Zczz$?*Wue1WZ@+shzYtQ@ITAs4kQShv-
zm#i&H<)-FS7aNBHH{{w@ujs*rhGAc%&Szo1cF>8Gh85p}3~tZf3ZeqkYa{+`AtJsK
zAzBl2go*P##6<xoDL$PYxou)e=>|sLZj)L=rstK0d>)=Tcr(nNx6E@!xRi_Dt&FLL
zxRIsRGmhI(hXzYeEh|+zhS+*zt|ejggV<fC8H!g#1=4_1l4}~>6|(2buDZ7+x1iE3
zS&VG%Ycm@SPgfqRL3J)7&I2JLu_*JIi#50dV_&p5=DMHYC;FenIC%xlec5=DWu!ye
zv7nR@cY3oJtm3^{7tQt4&GlByt-m)`oOPlotZ{zO#N~SPnKc{&uie8Pr!>S)4b)NF
zoHp^Zc+iK?1FCsOZ5ll)4k6=pPQuFbOPuNNL=~)oGcg&ZcL<i*&3}?*1TS+#$!9WX
z-l>rn99;x;D*#e$Qm%=)F(E#cey{}=p2Jc!^x#KHZHVq5qP`oY#%L`A<m<lKWY;u4
zKM%M-o-mwI;#=UjuLf<9B?!{MbGp~+m>NJ?3)qr43zBN~#Rw~LEKX%=o8A4vBxe{~
z=hpn&-!JT$Trv3B#R&#1+z%|Ac6$uoxRTJwI?w~gL3ZL%lNy#z*<NL^rv`ohxnvb6
z#AbLw&va&)0{!cvYJ<lwNl_n`Csp&lN`H1N1&mIDJa@>KUXIfdOQo~tA&P&_2CL>h
zDk_>iqKbN0GM4!__xep(KVb@xpzOov{#$!z9@W&@?SBxJwhmNn#SyH8At0q9GDAcw
z5CT%h$Xu;a0f7S200DyaS{#@>1P}<s5(smmOd$*jyeKpgL52VlNKg_W42gyzhCp%;
z@B91nx7Pjtu6zD|&RS<b<KFxGdG<lO#k_$qo=P5|omh*K42Tk_|1|&ovRQ-A@Y1<;
z^}bB3dx4=q3fNi-h8B-k^z|k?0#W+1cpyvHidt@7Kgr3#KQm>BriNf?qI}7vDJYzt
z&(DF%rAxm4@O;qs{J-aa=z-w|J7zzGSFmDTV8`*?=<0`PSG}CPwpEj1eJ|q{vE<NU
zV3UW7fRU$2edSv@flnnMW%njRb-vLp!a^mLCKarz{bG}YC)`-sIlx$XJSTRIS<2oF
zz)+3DLzj*<dFT^;<lGFB_pL+8*SrfNMrhfJaJk^v;*`NULQ)A)X7dB!KZ4!^vkGpX
z$Jd2N<}>?qT|+dp?l(0j5a|P34nW)55GB0f2jdA`Oy)!Ze^;(@?6Vf;8h|Wct&?6(
z0tKI&qj30P<fO=~YX#QATG6(IDEos;a>j<n&xaAOIM%Fp5J|uAnq88$DgV#7oP!Ym
zYH8WA#bUHNYAtr+Ifn)YDfNT`yCB>BqZ6|kU+k$?%mOH9OOnSKA_b6mw<3LI*dXys
zbzhdOI}WGz;&0i5y{YE5n7*VjSIIlQ<2!!R)o_XN)w8KMi8EzM{W2QAIx>3?zJIw~
z`S;tu8t(}4ad1G};unPs&K*W5lV2IAjJ{$XN)6rkMJ(>lSvu#aQ1>3vKGBdF0oB1p
z+B>w3f}`->a#bk8++dwxvZ0zF%a#dsm^6kG-cZ*?JZq*h$29TVn_Z4J`gdF;v%wo7
zIh(Don;S!aU=r4cqTK7-{!R>L%P}_qjD<HSKD^k^Mtl)@!eQ6b=Sn8u$RFW67&D-X
zg_uP>{>!iCoN%^W7(t87b8q`p%vJWhgfh<_6(=!Q<33x?UoYbPKDHWCVFLys=2NG(
z#y+eYY1PQ>*7ZB;>Asgh`QgIb>JSxjF@Hs<ZuJ)}MUcSA|E$(d^xfd*DN0XiS8XcI
z;oNLK4);0}q%hpzk*1}^i&ZN>!nr{Gi=j*ictV##<YdpDxNTG~N{?p76v7o}nQl&v
z%e!-1305%(AkACCi|lDec@pVKqf?r@cLj_MQ-Kd#;Z4?>o`3!s<rd>FmhrapC+d`j
z{tk?iVff~Lo{{Fo$pZeC8Y|d2h69OO%4OK0$nV|DR|oI++~z$`mfFA-=7Qe8Pj>Wz
z5Fpk_rg4I}39%}b;G-$0uD;D5#ulgGZVbfXnhNzo2G-W6bk!lU957R2#ickGkUu_w
z#ujnyXN(QdzW^|V-$PMta8U|wXiSVBMCy+=6^GmH*ITCg1msLsQ@6x!l5X4sN^=E1
zidR^FWc>NquN}RjN-3>+ZJkMy&sf@*C>GD%xkmF#aG%s+YU|7H0#9-Rvt#hKKWu@;
zF%2U+-aHfhn7rO<SgzsAJ~^Fyv%Fm=+(1J`rP5<<PiGS_SrLOBH)x(oOdvbUJkLg^
zqDL<NWTbYz2-x(>!+r=#zzn`K#<%wy!|iR$=*xbJ2WwS26Vk>XBHFI$@+@PuVYLAE
zmsDz9M8SxqD%V<m@ldi+!JVf~b<mJcX>8ee+z3tju)c<>LGctd0ZFLzIYoj?_U&x;
z#s?>LT$6hq0bOF>v#zf@UB8OA$&}%9GyMnih+|j#Pq}rJ#H~#kLt$sqheJwWrTCd1
zgPBG6{(^t~L0+?b*2wcsPP#zPrHV;A_b!I50MVL$HAHuFwsx;iavk^lY4MqMeJ(^d
z)xI))#cKx%dsW5@>NT(sW>qIk4YR=Bs7nb>h}<UtWwZ1CN2jOq6knBUYQ*=?;tm)*
zff5UWn>*SGh@(|z{hC}1`|4b8Po;Ph=V6g`vLTsq{<%m`t$moA2X{L-T}L!A8>9l@
z6ebMJPm6|@zYedBwR0%F@8y`AA>X9#3+1tv6(iX(<bs2D-*elPHG4Jw&=Z`v8)~AY
z`N><qHwhW+w?4m?ILby-QSBFaLlNM?iC{VNqTkRA1YuA0s12DaV#}@Out^p1br;4s
zg=B7WRKGI-Nw~8RXifgj5M_VoXKpj^P*iqJ7Atu9k5HB^6(I$M7cEaFcECrAV$<0M
z-AY;SKDI4(hBcaY7U3Qs^6329!<IBJffXX(!2G~;P-}56Ujfa(@?+V~?;;BYU5~uN
z$txoew0k|0(r*XqRvQlGuU8o(A{R+5?)hsHZhF|mXu`rEe&j|fdV*tG1-b@ts}J-x
zd`(kQ(ZXgOCebi165-dl$UyRsn-4{M6S&^cTX@+)&HAJwii-q?6CSLlykVUhP2P>8
zPW(Y2@ExG{>exG4Oh7-E{cv5&iz%NYT0}h;BUwkkHz<5%7QDTzW{^1P06emQf2>1d
zzQ&@*v9glgkNwim^95lJRCU@V%(CBnE;<dDa&=#$+iy1dcKb(%qVn6%GFLo<DQcdM
zizXeJa(ysqO7LOX(YL!Z?n-5YyLsG?Cz0pB-8c?PPO_4gY=!?WM6ys7N@Qq)JZeis
zp7wDBHF`%oTV*x4#+LiaR?60fBCmLXM@!@FCKYj%uY&6TZM5*oK&TM=A;Pc|<FXt3
zXuPog@s=mIJG^>@k249bp#_i?m+!o*7GQ45G<(?1M4^h<jEeHi*&RQ|(JY(~VBS#W
zp1j1Gz}Nr!!IMiRzwEyuc~Dpln+VM9>Pme8p-}jCU=`V~8e`f9`q)lq$ajC%RoXc5
z>oRLk+}h7!d#i;$sX~{$BmH|1I!-YVD~RT|Q+13`n`deULRxQl<w?Z>wLpbv8NX_I
z6~QQe)r_->O4}tEZT)AWGWKF|a(z{-u!ZgejT*tfkD%*oc_nP(ah=LAbA4Ig<3w;~
zrBvlhht;&APIm?rAG<Z85e<rm-_?Qfyfe);@hp<SizK)xDtr|@Z*W)9d|2Tl(em;t
zW>}TcL13%ms;lJjfn`jbuK2BQLi_*Ee%MiuxqG?oNYtl~4sq)7JP)Qa{Xhb>EPeF&
zVHH2xlvInj4-WhFtii7UE>+}Nf<4|a$zGzCX2>bT+T6kvyD<*Bh8P)QQCfSgnn*_O
z87F&plx<Rw3GJfgz^GL8WQxrr8zhUtG0d{~e!A{IgYUGq_TJu6Va_|`UTK@cIDiVm
zT()uA6;JVF+RQK9*8_)s;8a#^m22w0YjrD+R67&TkdmU|KyP{A^|mtWp~Xx4s52ka
zgAZ&T&s}|S`Ga27iu{sRh^vIn+F~Czui8k|wOaz@dVQSck@uPt^8xUqTlRf2A!U_r
zg(?%6c{3`r7Sur&7$s0lIq`YL{5`)l&!BQwU#dxLtAUiYRNI-WM{Rku%zEE2$yzN@
z>{}5K|Mp@1Uw#`;#qE1xV`r?4Q2qQHVA(XEfZllkzOf8R@rI9Cg(cxg8~jG8*IF8Z
z;QnPYl%a3mE=8k(flPx%{N9q*8|N7TaAoh#fdEPfa%c<;L{y6PJNH}&;3DD3xoHes
zES~eEk5~wgLql5rqBR2ap3Xopl|Cg3$@mB3p=-C`In)}_7*SAW(l|&>p_TSI*!#e>
z{VsJ7mvR*fTOHl6Oj(*n!hp<wFfo8p@0~YuIf0`HCB;W8#2Nd!caPU>S71$z?<B7|
zv%dO214wzQ@cw3wv@>t?dKT_-8(><Uh!Hmv(QTzM96y?I5Y#m6WsiC0U^Qqa@Bp`*
z7!6=SIe<*xy5%FuDugxv=(f%b6<36%O2^b<>mm!qk1!S3E3(9%MFio5&HzyUz>-lc
z>7H)%rpjJGNB6u-02!4=!4f1yR30)8Iz$`!VdS}bZgj?>y~o#3b&ssk$RVTqKO3>9
z=<qw+#wJIs)@f(-0}$C|UAqjEcVo3>y+KE}mRu4?9<~pINqbPa<{)@xNJi}!bLgIU
zU4cY6<=2MrorJ}+19QSR)&iq=b^ST=<+H3~Hw2T7m~w6B$#c3bO?Nr|G~=V+&xg+(
zsVgkw>7gRui_6+c6z7-$TtYcAQaQ_t^)0$j{rW#PF8m{Zqn{));~kP#m0?XCJD8GK
z<xdz$!tW+_w_nmp-XS@ySZ~#5;oBC53MF%#6Gq=;sHl8%aQhzt6*~(REoddnELLdP
z*ARXGDf@ZXzd8fo-Y(5()4S8nF%njg<7=(m*I2SH{U#fxhM_d=?OR%jb<g0eeMk+t
zF}6-mNR{i!f(wP0;uaGLwY!g0B<sS5d!{U?lrsIJYXNCtsmZ;##ohB}AQL{ZDh7AR
zFAb1^#d0Njp_l?($4j>yYOv?zk=8fqi#|g+!GE?NW_v_9Wy$ahx5wa4)|oMm8~JIl
zb6aolQn+M1c;lR(d}N1@c3m04_fi6LsiVZm$7DeJ{K;28v6nXoaHpmr)K=1~XS+<C
zV=kB^`vYmD6TR}X^zkX2;t%bSi}ITt?pI1%UrhY17TZMGD9mc>e81*+3qC|h+syCa
znE5z~2v&ktRcp6Y%~bQCr;6LH`DNHpaR4W@Kd`$04SM>B%&E?|f2lP%Z$R7XpAI7-
zT=Rhc+sc6)&akp;_Td=@9+TY$()d9LqGNo5Slo<5Oy%(J<@Sd7c-gTp<3t6ChntFC
zk2QojZ8&i6H>{`5tglwkT4M9u89zNK+zf7Nygem=M?U3uLmdW}p)kPLsaq@iGn+l@
zC(g-f0r6Xh@VX<*^<atdsQem}g_c5jdOygXXz>sXl;mbz!1>CQusv&Pck@8}DI!3&
z>tp&EMs2|w?Kwd2eb^q5V@vC5e%G#^6`!QU4PxP6?=7zHNvu{akDi6s{#gCRNk<ZS
z1sg1aSVi}lyM27M>a<{Ny!0i%gQ2M}^K@GSbws3*6b2+Z=Jii3(mdmirO%Oxc=Wmg
zRBb-R;f8T=FhCo}D{haOpjK@uS;s!ddqtG2*;|pPgaapof@~v4&yaR*<isRRCjYEZ
zI~SZPofSr{Fly^U7d<-;q9zjrst>_$UzSJanKVll8r#N4+uxTcs;AJbX&<(x=V8JG
zx^w1k_h<P2qo2k}5V5mzh1YhD%UZmTACzN;(-p3M`@VEop>VuY*xdT(-T=Oc&p+Q3
z*#crJq<p#;Ym<k_w_+IP3@qCGb3ISJEwZG(h2QdoV+^5P+HjERHmoOtN5?laul?-9
zk`7`+;0&Xgw+?Mqg7b_I#}@G;klrr;r7H{=*S_1y6pXdr^cXZeG9O4J;9uP`6fQQ7
zJ|3*5$l6Tf`Kv=T><Nv<tqx0O*4Q|Ct;I{XV^sT;sySqPMKnJ<nC)K(`y@`gb?YJ+
zDI7DAxd3hD+K}<WgLgM>0CTR?P_tjf5Ew%8e6=Ec9B*netC^?_ktoRxWD_JyIlNbU
z-<R@Lr+QI=<n@+^P#t1kzLU=-C>~Bg5-#?H9J%eup8iK!RmLkwEH30f%9T^yBsGvI
zmzW})wol&S0{{6bm^Jg9=ehu^OH({Hz4dvmb^{JzYbTYi0JVJQclnM#nUtkx+gdXT
z`U#VU0zAMMCxZMA>??Ye6u*%4Va)mKcQ7MVuuzIh1WBcs6Sc3e1MaIs;KmThn(RT$
zeR{jiyS6}hCmQ<nCTtgzV-AASR@AV+k)!+~00yIJYeP~O>`qZ04r+G=%n=+B7?;72
zUpVKsv^ko<X0S=Ji({5A7^9Nr%Wx)NKY^2*GGj`DpmX;wNtTxcN6k*(4k*WGWqF9*
zjAR7Ua%)}y6SnX~YG;(79+Z-Bp**8Ia*hQ1Gx01cnBm9CgL})lVNc$TO#R#O`lRrw
zTiFA*Ck)CH&Z0cJuYot=AhK>%lz3^1%Q&sP1||NhCzN*`I5eb@w4Wo24qkMizbNe=
zs3X>TXcd?qjGbJja7!322@&*?nboNXIK8<)!qp{vA@&fi;ch_0V3%HVEHLwH-PU@Z
z+QQZ4b--oA?R;Yb({k>=S~qTZ%?(8_dqKs;paZ&90{)W#9SmyiQASd6y^Ehktu1{*
zu5@8b$Ag4Yf(LbGHx9k!l7KzyxTWHKq-&4Liug|mqeyYuvc}3-fRI>_hE5m$4tM!>
z8s@~ugaO#fzv=Fz^shof!^O=pgM@QlJC;MViA$O!*P<6KCA%{}2#iFG(ymr+(LojY
ztIG86H7okAwN2#iYxt7ryX;Dv+-O=nGgpm+YL(=Ec1%>97C3c=1_j#-qhB26xk>bY
z<6|!L*fbY~t<*}H(LsfhW4fsCB(sM(rYv{Q!%iK)JC`6lNfuS2m6J96Hmq0-5|4#1
zP8JP`VZ_M$!2@#NA#MDNl+=ChAdG1Km(r!+o0$jD5IVYiXO=x6*|lDYJJ5cY(+}w_
zOY%^_p!qdo7r&NIxzLqh#Z2nll`u5WM%tNc|4gFORjD%R2VHr{{Q*Uv)?S-H!&wGz
z=gQ9*tQDSRRbM)PBn1wbm<RISLy7ttE`C)CFqv}N1d(vE-x<6nXyqavc_aM`lCNP9
zH5xpSOLmp~2}vWls;)3`7e+)l?GiekALY3*csvc)V1o)C+wz<pv-^RmEW5L~j_YKH
z=S-eu?gNs}4DUHX7Fb?=f-dU|mp{hYH#8DpITD@Drku_29*#(lv%LfY5wUlp5Cy)d
zykrv;dAb53%fd<TEkoykigH+T_tV@td~r=cYyFHJ6jtlXIfubAicOpLfhoIAXHcm^
za8v=)Zx{M-vE9A?k%*vbui~Yqk1sD7W=+MimR*1%p9Mw;cnE;)PsSx}I6UYE@3s9D
zpjD`leaOz^)CZ@L7{)bkiJRkl_baY+w<X3Aw2())oB!4txuKeo;}15+m{-6aOHsyD
zlwC8)71nT<j+r$~ppRgdhQ02L0b9dS95w$+ZSbY}XAf}GB8y=S!xLbRKD8i@fs1|B
z55Z6;^VoqpOm)wRgsUuk7gNdOp8RO0^0UWx9nRl5+uF=qU(mU_$EfSt__w8pvtFC&
zo^k{;4VQMj#qsesHjyFin|;SIqSDWE((s}nYFPgR(GMCK@AGKfZFp#ZOX#7PI}DW0
zUezz6T91$DYGnJ|GB;e#UB;i4w%);gO7?xiYyexT=tm+u+sNcw`*(fX1KStREAlGY
z1}8t=YF6V?7tX#8>r|qBrjYJzgihbq?eFsJkm!t4|E{gE{r3O<$Fju4%JIszD$>H{
Qwz2TbSAHg6{PoZO26SB95C8xG

literal 26875
zcmeFZ2UL^W)-D{&*6mina<&Kv=r+JcrAhDZ&9+3E(t8mBArt{aXaQ_sp=dym8ViCX
zK<G6gC<;52ASI!MfJobf5(p%L1jzp;oO8bO{pXHx$3O1<@A$?y?mLDA*1O7FbItY4
zXU?_alj{~&M1GR|2?PR(K(78_1p@sL0Rrv0`QtvIgqrbY8Ti<D)AY(8z$fr*m^8%!
z9|!JSbqoW6q!R`IcBM|Qc!NNvK#)Ivvx&%Ephe<tMY@jj?JYUyyAHK}d#oy|Q`!3M
z@b!y7Vt&!T|I?w{4?lc<^&vi<_tf~ux4)eF<4N|ZhoJjUPIaj4R{qoIb$on$$?q4#
z69PThB-tW8SB*M)P*D)Ak+TtFK$?u5&t+`nTap*ibCI07<|3h3|M$oLPa3eJqU<wE
zD)4u+F0I&Z=7Zv2!fp@$00Onhgn!VBP2PeodPVLOk5(QO%HRRrixd8G?plkmr2Oqa
z3OdXwAdpAMQuD%Y5NL=B6t|q!b)ccSDRc^11g);XGj>PdqJmkLWGV)3ArlvyHn`D7
zA4`MM(2YJYj|<yU7y`!af&_nAE5FA{)CT?`wxBT#c8w)Q4#W7`y61+Fq%pE(&83b0
ztY$hE&9}qDq*%GOGz^zQWQ&MkIRqPQnVT*C1Gh`7tm1alq9*MrMq{&qKS<WLAGgVK
za=ngkj&fubk*~n(%P#3(N0_%pZ(SDY;awJC5TQ0j_L-WS8`06NjA?K3JX%HR%~)Rb
zpPU&B1O=E9i1+6nsrye`orjjHKSD>*Q5^ZUtq54Exj`bKSlQ7ldTwf6G0mu|j5AGV
zw<kr`(9P!uHrmNgaz0eiN9o=3N&0yF>(dWM{hGEkw^m_v=bjqLZ8gt#ghXS%_5DvT
z=~mUCdgMfY`*wMAA!*f^eE5_F?@cW`-`K^lwy|#?l>1D=Ym=3<z2C<m(G=ZY5>`6f
zqr=dYDuMMfy7SgcfQ19Gv)pgbpYFN*d!h+hy;g18l<M$&jV>|Ehrz5`OZt*s7#V3R
zWfav1VP|G>mC3ZYiF$fn-MXHO11)^d;EDdAICE{OlQJQ%xf>#skcDufo>%Z7)W?P6
zD5b->X!5*?vO%c6%wOD#OT>t2Z(0miy^M!bG-aDeHQR$pt0ebXTwX-5GPmkDAk0u=
z&;WaPBJ20WNl72aAdX}(+QMPBq3fqJ?$j9{zgYAHbe=JOD|x)e0<mAxM?8pAvrd7P
zsTb0=vx79hZQ;!+YIr5RZ}{1ydAG5{=$^2CiCLK-htO{hRFMcVdxrn!so0`DWB)Q@
zQU2J-Z=V?2F+XpuCoE5Tm|TSS)35M4utVd1q;-C5+q&~`EFy3A!nvf;j@p;l>v4DT
zxH1N_ql99T->bzK7Z@!;q6Es?a)B}R1|>TIebgOY9t+Ot@#@{hI6(fQUWVv_kr2$J
zRX5{z{1@?@G!gae;9=D>oIiQn_4E#r&H!9&+fX7VuFAsIYacGvan{NCKm`0dzTvMP
zCqOA4DDEA1X`M||ldt9Z>KGI{NPTkMR?0`3^pl0#z~PLO0Y6%h(Ayps{8ln-W}wRL
z#0H(nN`X3zvyRNK8IRt1P|V0)GcM!Wi<~BvY}=!5Q2o*>D0;JUrG|T$*Q*B62xjh}
zr&-g6W@-2S#3C(y1H;O#-(lfb_S7B2%xiBt63V~q$Jtuu-k{=Y9*Jr48fejNx}375
zk+w|n$c?0x@hVaSAUOd?s?m~KUK)rFdx5F}ByTa&JoiWB;MGa~Ur8%s_C0>#<ty`H
zr0DOt^W6u}jQ1ZNlAf2H%?+z>oRpk3xMRWB@A?opW;PQEeKi)^rYp74xP2mg73!2D
zCOWGGS#C)1faj-S+?|VhPLvHU==svV&a>6I?tR?OoPYt{FuJ7rW0d&v_9s2fAgyn-
zC!CqL#?W0LP&^`ll0-<<><S-EiOW}GPb6SZ^@LiP{8~ea6xWWfoxrIwWz<QLpK`;-
zG$wPQUAnU$fj`HSw&T>^@sI4UTh48(&T;P_iMg|$uRGYVnmd2KRo}UZ#6b1(8`Qac
z%45wuok__=47FP~xGSDBODPVi+QqQIYm09<9t55Ldbydt7gTPB&8pxRlf@-JrTW^Z
z2CSduI0h~-bQH9rwWgp_6tBGLxAausllpJ;y!H$lQ_lG=@LO`jV&QGumOZ+&Yntr5
zp@sKyZU(+3l0JK4Z?{!Y%fgo4Dy;&bbxhKyt!&z>G|sCwkInBM;`c)rnygrOK)B41
zj$(SBZhM*Gci2qB&CeC#Onk!!z_L6(Ztj-=4J~fZ>$PM1RlSxWBJ**v_1w_b<a^DL
zq{zWnVF71;8!1^XPmnqq*!F`6w5ZJ6t$ck(f~SHCBRR$Kb2v^0rJl7++@h3EF0VAK
z&9&N_Q0N>olk25&`;~b^&gNXw)~wdUxc+9Z%j58xZEn3Lu1y>2U^lHRqHV*A0Kjy}
zIJL?Jx@S<L^%bD8W^HknKx41s#R19OzP9at?WbU4?6QS0aft4aMEH~*b27P8k)KM^
z)e4SGtm%r5UEPhbBn=UU36tH?N`Te3hmUD>;-pAQBbDy`rLQ>A5woH$aaGMWTN6oM
zv(l^UN7T99*+0a54k4#%JY)u!#TId!6XKckk{PC1+f@;FNDlT+vrbLD|1V*bF+t=7
ztQdBdD(NE`G-a`zqZX_+?So~#*KMEa-iM25+omz$xU|fOMLaiqNy{>?g5N8Rkj))i
zU_J2d-d#4K8F!=gsHm6HgNkD_kGeFCBWQ|Hyf%E^R|!-u8h7IdkljaLsi5a~JLzJQ
zlonc&zIIGt%*gEQnlXER8k)s{aU<tBW!}T?c>X97OZc%f*1YcK7-J$q-|oxIrU~nX
zCL3i-^XO~tNeV;tZl2n1M>9sF{7l0>0Snypq#~S)Z+I+Rc%iG^Xrhm6cNsNZHaWcV
zm98iPjU<IJykz16EXMT%UuOWe%>F2nOUUVpR%$NtygX)C8+lsCJrEdh*CHwFMIxrX
zM~A&94P~r5xn43%(A<pc2=3C|t`dQ|${No_PxXwx(VbmL$B=ZxV_+otHYZASSl53H
zuLl6r`$3Ja-7&XdlL?x-<HnP@Il%I~^}*Ie^Je1YHb2wvsi(M(EWljgm}fcC*LoUt
zk54w$568v!pqI)Y)C9Dx`Ygmbw4NWU@wppY5H+8x*5mKs4p%aE-HK{O4R>-ohDWMM
z64Z$V4%aD2V-}|$$kgkM4Nh74r;V8!4KL(qmHxchm2-N%K({@%V%WWPJ8uTN(&*eB
zr4(D=J+3pCP7k4;^Bs@+1lV=J*0f0qHr59hsx@_=-a{FRyT+OY&>~<pvscH9RFT=E
z8&1`2Z)2ybv&U;{2I~!rJ%=B3k0dj?yJG=Xa1i|M2T*uZqCMPiQd_=pENP43<~1HR
zo3#2sMEw|++>6z8$45DtwCH#CYhRxXS{tQ^7N(+3Iy2gC*cNHUz_#zxue6^FbdQXM
zZ6tZw2fpgw_&}f6=fXEkL_RXa>}36aw1kfYlZN89BzPzSDZ)KBY{9)}C~lfQ3^0nq
zLEw#x$&UzdlCc8~7i9`#A1|x2W^`W?NC$A1p?)bJuB+9S8!VMBCF%24+A(eIBCLjf
zMIJFYvZUp;DGO|VpE?EXs*9W9B#w|DM#9g!FzD(eT~Cs3>Z`EQM3OEePh_I!r9QV&
zcKmSsIR3Ol&Jtl*symPjxJUlYgDk__fUBr<z2LiWNq6XuNqO=BM?vZY<6*+<Xv%66
z9QGbEeoQLk-1=X7gL&dH!PA_zK&hB!DH;hCR{F2bJLRD$b=YWR!7-@aRz-S3GC26h
z?3v+3&#8L}%YjMd*IM+4uHA`v9Yx8VFRfEL^Q~0CQnaD_mMZdbA6E+N_!_fAL%SFs
z>F!#N<0x(3s*hVs(=k99ae}3~`D7o+1KU(^`g%62hI3AGH^EW+>BwVyyYZ1bCIz<P
z^-&jDs7%*Cjo|g34UfFf*OW{5j9f$=L=Ohv!J(k+<imqZ99=EvFM?~PHqYl7G}4hp
z>voU5nPD3dT*eU?;bQ^Nb-MMH&rI*9%)$Lw2odeuPFO~@r9G!~$;P$*5jXr@CnnKW
zyLhbp(dzifGHcNhimg<$w;{N=7I87=xtu<F&T*?=u8|c!D9?yMX$$^6;sz|J)%bPE
zERp<xx^ZX4Rz%yj>2_Xh+;tjeqRIyoz8W3&4sg*eyH7fW10HgIeI)_wI)1w{k9s&N
zk5+zsN>Rl9z_uAHKjHn+?Mg0{KAD4~Koz{)i%!?$y<59Rp;|GQxRW9uJH|C*$lOH{
z^?CMNxP}1-3w5ww)gsr~Xa_}ftN8yIHby&q5??j}+gQ8sl-6qWwY+&@P-E{Ce&YmZ
z*<3`M)cAS=vlTbI{PVV$a#0t5Hblg8{Cp1nz=$tKU&~;Yfn9RR>sNK&&Lg92`S^Dq
zzh96q$KfWb{=(6tC1bA?*)cHN)lrux$07o50Hk(PNNNE){vSVV^qx*-0V9}Le(bfO
zzc3TAxC_KCpUiOGsqcIS*qy-RaAUXI|G0<$Tl()B5GXbN<((?}M{>xSBq07uCYxaR
z>Ysqj0jOnN)GoDz<P?7T%tj9VOQ-T~5666WX_-jgmNY_;RA})TZC<I0_YM+I;W1m5
zLV$R({Ag@rlzA&*EN~ER3;!^t{>ed5@n)_ItQ+c9#T%V9>MZl_6qPj^?MH2|WgEy2
zJ==>5_`Dxel%he=*4T^7OxBPAnv|a!tZ&n7<Zm$u3m0kw&VGAj2u;$o1uPy|;@!Jc
z1fMNWk1h*<4^;B&f02R@CV)E4^}W%Ei9WPIGEfHT&{!U9l1{Kim5AE&uLHpbSH7+O
zHTV0iJ%aaawOtI-MloHQEN1JjKMX`LD`W-qex+i-)`0-x5)e+baqI|cv+FD5RkZIE
zTsYx?=c*wq7A4yr^pz{<Ce5zzhM1_UtT|$ZY{~Yo1aV>0J$s)#gAb;Romf03GQZqh
za(ZAZlRiwYL1&tA4PJX{VaG)T^;9zthpaUFqPJwxDkZ(Mon*M3WwwD4_VhZ7C)Bpj
z(NoG}N<|}ahDGBO^j>l>5YD@^+%bC&qJ>!jhmhT4Uz*r9yj7{c`0C`i{OY)JF72T(
zb<iTY7xxUlA<vU7lNl@j!$hQ0&p`3CaPjUO+Qs;e2tXq?_x0$yY+Yo1Cr+Y1$Qa)=
z7f972bLyKb4sKS1t$eat_F>5$f0McHi*YA14hderI(%GtY}~R2TSE*}!7RDqBbn=J
z<c~-Eh1y|*fUfZ&XhfA#Zd?eAM>m{I7b<(<XFyb5VlkUmtJC&~tb`E@CY%)X=YiF!
zUf9jPmufq%?dM*Z9Aaco!*Pj$_Im|y7Y+c;^j!>t3o4ArcdBag!e2M~Om?js1wK?0
zypIPgxGUIROe(TVD#<`#y`TmC-ELan1Ihc*4}fh&24Y213qd)Edg`0^nQf2C<Ow3#
z(neO+&p=Q*RJRw0nWcz8zuV!1vK|SFK%H{mTn(`$DX40(uK6DYB@2P=N=<@aK%m;o
z|CW-G1T;PK=j4HD!K%ey1Dv7JP;bs#;Tk5j1zZ<2i~resAA=NS5Bs9kuH&2ZyReL`
zAW*gKti{N<3nDUS(iJHuD|~UjxBQr!8o8NL`Bd;Xh%E!?HP4vbyz+PBW}g%&IuMO`
z_I_t)F6asrJX_3`&af4#uAEa(9QlrD)(h@y4tTg?A?YbxKM%3P%S%bdG9e}BE}z`7
zBF~K5!a@Fl-|;`o{bxY`uTvmMoRuG4a@lHc+t`0EOu-myAcQ$kIrc!r@Z&@~4qyq;
z)PJ=EW4j)#Xo@OD%#KqwX7sPMdu-$M`W)&|o4A_=D*_F8jEAOa3V!*oGvR-(L?YmA
zr9W7pEy_ggGVR^b%4kuQiXEiNJhr@q+e21DN84~PL3%|DD%!Ys8_sH@KYe7sUof8%
zV8@fVBsv@e4Jzk1q%GQ`Q|-jC+x9+e;kI>r053ZEVY@skq^gysVuO0M>C%}+mbH&d
zAD0refByPTkwG)}UU9oDM#dFk4fAhhOjOh9X{dH{_ILsJNy2c`m0Wwnkcpp#yS*tt
z9=<|C$A;5ayd-^AjEcGK^iR!E#7C`ZTZ|8}(S7{cq7>swy=b**;rN)}Tbd%o^8AhQ
zTr!<$o)U721tq5yvA34B=7kmPa)Az)bjh)!TJ78#iAiB0?>SJ|?1$c}2uxuS|Fuv#
z%U^bM#-X2peO9Y0$dQ4{zXNc{=_qEK*XmM|i$<^`GXnPrII?5Q%45%pMtVM241L#W
zw<NhkT?8*YWRC(b!t@(+KXqePF;an{Nm4f0(jd*q7(dp+4YY8cc9b{X;^ks4Hnex#
z9O7h-Eu+Nfx?xe2No)6@5%ut>4B7%))_eG`*kBmmt8J4+H+`loM7<%}UY!2q&t$x0
zhOJKMyBerHp`I90_Cdx0f490rFn;_??<Wa)t9*NVjEu8uB>=Xi`PW_XT-2FCA=xVV
zRXqYu`GKsW{Sc@9A;_Lq&=>?Or57Z~Ndug=D9WBiC-!tFd=m84A_L$|iYX>8HvseP
zGyQn7`XsM}*t6$MlCUdYc6?^?`WF54nbgR%7wv+X#Urk?bY8}l>ovX8eoRf7w{agY
zKP0SNE+T-}OKE|JEX!iwdK%so4B#Op1W{>M0vpzOUI;%9%DWi!NN?C?!Qy3aL7nnc
z={iBq8hC~z|BW<20cB^MNmWP}+Ur>$tFi8JCHSd;>wr`xt9}z=sa?7RPJiB&{4@3B
zWUkszJ7p-q2*zyl^!?((Skr+ia7adspb@C@k5-F_FE%`<&a=W%hoS`zaodL1W{TM<
z2osn@Z82LZ!)|A4vp+_NGf+h%a0k01f-YLdCgq{eI2&{vgjac_P2cbDPRH<KOOyI`
z#OI2#6CvjX6+Ip~?x4MZHT+#vAVv|q`7c*60GMpcwFnPZTJoyc?!nwm1Fwe_pm_Y^
z3o=Kg>G6vgG`Ei2p5Y4kz369vVbnum&tA0SxPMOW0!8;&+PM=Jez|-X;4=U#1)5y~
z>}2yQtD64pM=wJ(x0x<2uqwb`+8RzAu>5PS-_ku?$(h~dYg9Ez*k*Z0YJfn;F9MBR
z?C{=zS6A!P0aBhJw0XBWb&>;(oM)lGSnZuDFj}AELRJ2s=9(WWc8|^4)k%g3`2mQc
zW_K!@Pv4}!Be$Zl5`IE-0QsDVZd)9*wJbGq#eZl75>-M`FHSzf4q|1T5q|#T2+W`z
zz^ZQKsr3ocx**VPTs-FiS;gMI81P@y0~PA%A{!KX8`nsCytAc;>e@t|R506(ji#-k
zBB}mAhNsrsv(UumEB5wM8(d*0ytZF_`(i7jLSZbr9eK4d#kD5tW-C)9?gp(wuf9c)
z`UcoSZl1&0iKp~rGRL;a*7!O;j=oMq+uNXuV+$MGL<e^G0W&;GX;j6g2VFLJc+En;
zH0pO&FP%)5M(@#g6oORC+2Of(&Uy-I2r-izTUWYW8|4gJd}P0~S?-6Q{f4(_T^Lo-
zDrtyLEcU_{FFEBo&<dj31}buF7{#Q~tP4APq5YRVW9MweO2*vqSWg7)n0z~sSoDW=
z)5FP~7*jOdMy!-nHl{ZheWy>jY2p>V_T%=WbL`^2sycI0C4DAjRz~PcWLtE*jck@0
zDG|*xGu&Q!F~5T}k%^ZjXV(&j8wo?I=inok$j30_*zF$qw#~0LJZPP*v_K~<G8%R*
zyeA1#AI$X$o_>!ACCao<uM+E=2W0M8wKD$tgcGvI7PYpYB2w!MJZqO2te5I{>in@v
z-FGgxip(@ua5I79FiYwFSr3*88EfNf&UWoBw^*6P*f0I@MmWZjHZVq8!t#oDU|{Xi
zc@@c!67OgmmNRrA?hn=gg!hncOD;{B3v+?h5!nm${gS`C3)*?eN}c<LU%WcGU~4=w
zG7&@$w&%Ox>$!Q&dZUJv2N*Fsf`(m)a0Gw7py3}D_7vG!=0t6Nq)z}g$+2J?Vj3-2
z4Ea=dTUUZ-L&5_xOe!Nx=exL6Mzo+6h+R?Pvn*2*8`HNPVB*nmgkQ)Vf$-qy&dX?D
z?S|kh57z2z>ux(>>Jw_7y4xb4S2Ev4II|WJ)6kXeglP#9w%NOqVMLZyF7X$1=P@V+
z-N(?jx6BF{9Q_<=kJ4i}UqO4<H85;=?fQ~SlybL~R{2GMp2-5|M4V}BM5?rKVTTgi
zvOn_jtQYzCC#!BFy)}LeYrIcERCrpBWz9IQZKV##WhRHUWgGEl)T5IbP)w5+$@s_7
z+suCEDNw-ROMv1is5Cu4;zC*Gygeucsd6V^#TXjoSVoBjbiKtU8awtOFzWKE_@GW~
zuUaZW!?$Fv**CD^ZFjU2>-CFv1NfFQNOkZGhF=W;+1djAv;fFJ4oY4>;Iwo7-RqyM
zHXD$Qd<&eyb4)R5@{4=O6J9B@4wEYt2ns!VgKbJJANO-``VIgd!W77(a*|b#gLqUj
zx-4#M9?l;2RcXpl;tdoTk)6kw89<y3xYO<v=e`*t(1<Vcq#V%;*b7!lP2ElUQC`*R
zXiJ%}&7oPm9jb=g4CTEK9KtC4TuwRjkGlZW6ZxZum&-JBBtTk=%J2qL4uQXw>Drco
zpAZSw5k}zwBM$SgV{f7n-{ndAvFNRh7yiS6ZrbGLcFA@|OxGl@OJd&5dQm%|;TPfR
zSpvf3iK<}<KzwINj@`Oiv!KO?qPN!Q^46)r*A2b;wpG$lmVt{b7}iLa|JYapb5Yn(
zGx_J`<{0#rb&d|B*LpEC*`_mSwS#^pjZFj&n>qH3;_YI+Qcta=caoF_!^qH0U$nDD
z?G?b7T_Z1$d@+R47jyw4#JfUE;^9#l^aq&epl`Xs*zK)>$yq^fVV7Fx)$m#?HJEwx
zkxYBBfclA1F*A1Fb;CEO*AXVH&naUcO6k9eXa@`V7hu=B$9im1^P)D9^g+p>6X7M;
zVj1f#@<8)A+DHq~a<4N+%07<JwjCzvGfO!f%Ad*&QTB64<h(yKxI>L!o195u4bg+h
zrShq9;gyJLZX$3t$wy<|+}R8aN~$7-MyCPfx?k$|r5;<9+q>~#+qePMW884EB3U0}
zZxcsR?ceeB1Mxswku9RVoA;)8%h#f|X48`;OLgxQi3zXlUVV`uCn-=P$VU?TB;ky5
zvCK&MLW?!#;NV|j6H`OUQun_-yRzd)Jls!&?PFBK?NVO_6xkG+v*Vg;OoUJY((=2p
zi&1^>7+uI=l7bfg-s#Zko#flUgf7pV9&ozoT(Tx6O)F$f{kXNLVDy$T_3#$nBAH=3
zNd3O?_eLU;VNVhOXM`?I+zE37J~^mI1Yh538<@1TNyTiJaes$J6wC`c0I8}0DCcFp
zxQfxY$xPDF5*9*F0F!DmuZjD0LKu~_d;*LmA22V@RWG5as_hQD7zDE#6<I$sVT&2S
z3=u0fynz>OLLB!<5jbA68is1$D`AB$!8mZTy22vgDbJv<ey32?uEm+f03w{S8~9>9
z_~#0tkI`v+^P(+s`&Nmyd}^=*zNYbYlnHENUwgy&RhF>bLL{)i$6ciXQ^454rRH{)
z!b~CzNLLG<@UOsxu?7Ge5j+830b#$KNfugJ%f5eOcLNTl8Yky->AT3%m@v#ab2dtk
zJi9=D2;t$$6F-oJ6E0UsmR`=kVOZBVzmGds8$~KWpy)l9%Y^RJ!%_;+I2QWqiN5qw
zY`6tq+y<3jjk1$IQ6Y?1;thjK;yD*v4~_OKq`EsH5I`oQPh!@^&M>^ssHu^Bb%#?d
z9Q0D^y)&P(uR6GF$=0>`-YO9Hr9*g4nAI!j#1O+0?t4L?Y%On0hy_@lhjBwFOPj2%
z+>la%dpnUYveXJpmI|np_q>>$T32zWj?Oo$3D&k!N@6&X$(LnTug$}EfeJ^fxf$N#
zYHxYA3ijg4O@{%z^ak_^&-N#5yz*N0@s5cEay~EoJ!6br4c~<LEE|<^U(gW(D##;=
z1V^;yFF#Z`-;@*>`SpR!z!!0M+`<LGj|h-xtF4%Q$@%1D!4bMgh>?1qj=LM-AV{_I
zjY(o-hizG<pklo1&61oF1dvy{b8I$GhrIpmKj!}f4x8hT3hAmiH<Jse6|<pN&^sD-
z7ueUZ4KE)+tHh!a6^Y^<%_2Mj&2CsPD%ciT80Kp};N;R33b{Y-@OCZ1%QSLacC{sT
z%!U?mdEA8j09|LX2)OY_FZ_kJHY5&s9E?nVFYGSX9siCS0b|;r06u}vAMDMkz`y18
zoS_ISma8O5EW6p!ilasbnz8zYJ$d?)3wB#Uut7TW$j)8@`Z^SWQ7)Bnb1jOBfqj%j
zG!GnJ7^~-25OsGfvG2_f$-H}Xad|3m)XbC_EGL!K2#y~>pv&bS;stc4J^Y)Vur9A-
z$P}R0z*BDWkNZOYC32EMZqg?_bG4IIg{I!p46Ju_ENt_~$)qDkg%-YW@SiClukLS%
zJ5mRqS)zlS467TNx5k6*f@O+@&=HUD5~wa`f!|L~L(5`g%egFi|Czs=yUKY00ZcK8
z_9~d>+9;FBN8LL@eY!CC&Cta#s;}7s#+MVGmx0PdGZ1MtaoPUH9)7mMY9+6|_v11x
zY6xEcTF`D;W9jJhv#`dw+rzsUr9~sbZvQOE+QrbeQ+XA&sbIPjkbB5UOZw&VuS<_!
zB<vv%+Kk)L{QUWHWJa=$FxMrVJ8z&l)R5L)k82ELW&LwaGaCMA^~H>TD09O{J%R1W
zQ~=PX{QmOIPnTV+T(2YE=o$2}gsNxf0a(RJ(Gb1j`rb2q^&Z`eJe%PxR8l$hJb)?9
zBlqaBWS(S3`FauEHI3bbj#kDB*T$pn2oUQ&A-kK^7In|T%cDG5#+dn%kj(_LDcX0M
zBU~f!ucHU*ikGy8$Bc@`v9=sTpK*P3oPp1{2^u9)DqL+3?*kFy!BLKn2zkA;CG?^~
z8GaD#8Z3>TgHFQ~nMudRz}g;b3X$mCZD9A%8a=m$E=AGqx4Wn7Thth9yz6;TI1*>K
zHKf)~a51br{bOv)t?@0)xYWCgg4ufry?v!#ET9C(yYaYRwO{$iMP5cVS=Ph_ksJtL
zd*eAxJEs6G&02OXodNwHIrr$xl87K9a%@j-#TbyT{1X0jq+njV>x7IKB-PnGHedCV
zL%FLqW~m0?Ud$}I80J}q>z~T2_)^4VhwWM$-CIb;*yU84@n%7l6?zug+qCR~v;d>x
z6PB4;WJ}A%INMg_9^lMYi}1$Nd?YxBzFGhw;OPjmxxymAsLFb03yRtA!`d2S&<JuM
zrA8tZLjn?mVV7mZYQH;5BjT2RK$Yg#E&LMxLdw3!HT=k##sWS3@c2GpacgJec4FMM
zkM!wI<@;X%Zobt|cOu833}Ve$E%Y<cHN;Jr4=Xp}HOJV7&~Hz&pqaXj^@8St$b5%)
zJ@aVBt7^*+%K2)PwSaG_DOR+$O@H7z-p3<t+>``I9OI<t`Ju9Y^iuRVFPvUnqzuqS
zeg5WZG$VT4Ic>I;ALc}s@XhU|F=9C^?vj5`-1chILS?7L?e*Y?f~>;v$!hc}Ngq3z
z>b`cZ;Mr<j9Ip<s-YToiPpuD50=%vRzM(OcRh#fFe9UYh)ec?^5cu~|rY3(yPh4d!
z$g7md82TAEMBQM0@!}d_-tA(TqqVzF%qLhc=51y#>7(6c{7TZKc8@up)>^0+jHKRU
z5H{=`Fg$C-X*jb>1zJBsE~A*pa=&_KRw${&OdM=lxG-J<qyzGnpC=j=q*)q83aTd>
zt-S%N^FE(|qA5TmmjvM@=EyF|glrT9i+o(>qzd@sNP^s1baO2ilQ65lxIumaA4_xQ
zC4>Su)r@6V+Rspg`S+67z<y(7`4ha4%>r5PE!Q*TO6tk7<}mt7sicpp@mo)&FOs`g
zoNZ(=OV{zQ8@U6z_E3ku??GwpvZUm2p*@caf~-~>9@0EldkfJ|6^y<{p`W%+wJgrJ
za{XF<d@)$#VO=Wp$7sg*VRmeT=kQZYjnM<m#1*QY{lXs;f?isdG%1p}{>j`vd#sv%
zC&YS@?23=_%BQKWvM(W~e;L%y4uI;;y=s>&TN>^Cdo~{tP$GfD=J}6D9-F0wptsNf
zLGRu!<Tlc8kc+fK#&}XbEVrg8&h8yjXD6T{C~7au6Xoby$L-Rab$RdUI(%mPHV~6E
zJ(CJ5V5cVx&iRm&G4r<e*(dq8O+N7Q>D{G#C*gMI3CN4AEn!$ypS4EElL@8UZ#}z*
zys(j(bb`IF8Qb;xR_Y6X$Db3W+=@6tZ~ph~?$&o_5)0QTc^^%te*t)`K7m`5aB($(
zT*~Wse_9zEWbdkBr6kY;i1*21SFnis4Zs5yG!k!(hmZs1sZL|oH1#S6Aw+5g0-z(k
z@)5X{NtL8b<71MIGVN7vI}}bN8~`W<@YLSwlE7U#S>~oT+)U7W-xvye&HbJA1p)+2
zg14SW0HWI}a&JjoJVqG8$0L0IgY7=c{uph+il?WDK$&iSmHdHXBVT(3vk^vjrSNbK
zlyf1xCHit}H+iY?UUOY(zPjId5x44v;BFfTw5<{imyvh29ec*7`rb@<@au!u@jv}$
zmLw@BdRtZU*kt_e+;<^QLz5-j=hIK@k(@lK)(h2;oEA^ianZby`_@IpKk0*x<>2Gj
zr&c3$a$9wa;^F`Z4%c?I=9G=pV2n|-#YJ#fsS}S@GdO0;qKrV*`spX%PPC-w*ORA~
zULrd9P-LfGXRt-qL!r3wr^zqB<(baiYibDN_d|m6Z}Gv}YZ0Ura8Y=Mm}RJod4)i3
zP{{$9Tm&OK=sr6KBJ+Ie)9HNpW$%rG?3==AIUJ*3TQ+L0+)M)NC&r9zyqundKHXtM
z<{!>|jQ-twBga%Ghp~z)eL1Z^@V6bGn0?*;KId@<lXb4dizE46IF5rl(36|9qnq)k
z)o{#tKCV!hE(cK_HXg;f7oV3cpQ><0<iqDXn3?}N1TU+DDVws4GcG+J_x4}g$bL9a
zI87XT$-_r2%j8TAzr1i1CmiXFKrjf>K0HHPVdFBA%Ko+KH1QoWrin?Im2W8g*FGRj
z=H2#Q!?Q$AnoIhvo@-e>at%gAUSD3)Vi!2h-AkCY0zrSzJ1`~<vAD{d>EMgHSj`yC
z$%m&+DM6MpZ}}z*(<lNp%15M4C4hUbg<`;QX;V+VJ6tl6L-;_X@w|aBFpNJEc)}WQ
zY|88-1#;3uA_w0mRY6OHS03Ue2VQ=osS{m%BRi4pWtL1#HbuyzE?#jxFm^62VBvfZ
z>zoj{E*jD$9}I&1gi}3c2Pk^9qf%yP=EnqjPwHScYmyHL-d~i3C4b~$A^H7|&2VHY
z#YUyyD|Mh>zo|^CJ)*?2(z*Y)fhjJyDO>dBPk|i)Cli~j*lOCfzWuufrV>aZlx&*S
zSSpninf=4aT0oBJMw!mRT7}`>yN5yNZVYB_^39mR5SjETX^0VOik?j`j4YDhc$PhH
zP&vbOz;7XARETfP_gkd}%WagNID?}XfMu`3t-5M?onY*3y~m2H`QTKj-s5bSv?(_T
z$xrX_tJhcLbPcOkOh#jn{H!UM`TpPE0ZWTg*AL`ILdJ&fp0b{4RYa-zezb+e>JwA5
zMfZBxz?yrF&Jz{VMzw)eG%zBHPl}g2-myhOt$S1$Qr07RZjSx4T617HoLWlDi>M2v
zW`GG<x5AK}*~vcWQA1)IYkttTHGv)5Gtv;|8@@2~0!%n|VC?-;9#~2{sH+hMOa7!0
zNv#X++1%VTQZ(+nx?GttdWM)Fp=;UmKDS6NVz72n-8qo!_Trl>eGF3R*I(GxT?5z4
zC;6ssAxBRi#a%o|W-0UTS-2j|G?C4oGPa5$A@qrn5@q7FR{y7oQFvt*LMP4(>8r4D
z$((hg>RpQ7+2Cj@HCxon*1}-rvrAaFrg@@TO+GWdXzE33%&mUUk|9C%8~5!8mD_qr
z#o^q!qS=-`VgY(dKSwV@-E-7QI$n2vdv(Jyj@uvB2U%P^%z258kyAm;ndXQ-P-Ft>
zb9J%U<p(*UUSCLh^Ch?iZxtxe5&NT9tSWDz%2F_bVo@#<@YGdvsvx_Pvb_w5<2G`f
z1z8$u5Q4+?cWgI;(9p`;$m9og_`#b(tE{#Gj5bh{yc6p_w?lqI1T>oH=el_b&o)3w
z9&!i9;P3U{IAQl<!K#8bEw9L$f3#Y)g759X&Lh}^XTrWzlfgT2l+(5Q!kozK_y6+(
ztrF%&9i$SP#mCCN?F!wFez_v-tV9B1zPw#fc;VoTouqI#wK#b_7V_ix3@3=#Sa9ib
zq_F0|ZMi2SO#?DQr~{o*|6^x2nnHFisZ^)$6<&O}_`gyxkWjeiHP#qX*<7_rlHVho
zcwy>Z#@*;>WOAPO0pZmrk0T~K0a$gq3~mzs^=!Vgd5bLa)*!N7#px1FAne6X$a2c~
z#++zXxup2{RYHR#T`0Cw$Kf5vrSyJ3V`>z@_9LjU&JLzZ=;Uc(vLas7>^^)`*);S{
zhpGtHoSAV5^?{bZv@BZp!MOU3bmkPKxTu{&(~IMjyeSLO*C4K@Es*b?(j3Z~LLqZ$
zcTWM|Gn!As={(cdtR5Cm+l({czYA1icodKm1IAi1jgdHAqIH@9m>8!mG4MD)XX=IK
zVC3CyR&uK~(x7SXl>gm7kUVYay`YQjd1<*LcMoeu-9(BNxHRksQbIsCb(+NfG@Lr|
zO4{aSBuYNg5os?s-0O1NB-#SmQ@B>?P<jR2V2NZ5Tpz4o%m=s2#>J%QFVz=%IaUB9
zQw5-tiAWL~1TuR<OPl&?BG!FI))DT0pU7<@@R}AzYM>@vA-h2u7w^MENfx2Ekk&}t
zhgHrKh(*_$g1(Ab1CAhCXAW6jDk*NR(4Tqb#cbJcR6eI_w~!k~a=Z$df?3`kQJvRh
zR1I>fy5FL+%(bu+1j?QepCLA$_Rg)K%56Zded16RO>`D09xlSgItW?Tn&c@OI=T-u
z@JGS3h5EZB1TR`H9>sMJXHAh6@ew_c&^t&Kg-ClOUNTAOQg0tPFgBByGgXJo&Anxh
zQ~)=eB5vK}3mSqDS5H|ax<bY>rpm$Y*D8aNmI{vJw}f{@TJENO%TpGYZ}3I77n=Pk
zwDhn2fZct&Jd=jgCKe%24&D)N%9g(YHQh3cz>z{b%<%rlwm|OO+_<f{V~_C}|D6g(
zzTO2@e`*9@d7m%4H^|CFzzX~SiU;r4lK#V-WL{Y_cM5dE?0%*vPL9~`U$*)xdmaFS
z+kHnMvAMUrT0;h=+ZD&DGdZGr<F~;P5^b4jCsDT$A~5M!ea|?Rk^=Vq>Uf<+Wk?+@
zyz{sH(gSzTV?xIwXXS{!iuNH(0a}uveJ{RwpJ2?5(wE!7Jr<!5b3s19EZHm7PzwvB
z{{eiqT?#|-ZpFH_yT;e55`{bWe+hT~cVO55;XVRy>wzxU+IvTudfgD(d~*K-cx4bk
z4UjI#TSmk>Bu<AobA(?13$Z3oc)_GR0kUjCI*5!hCZ-`rt5*qBg^e7rxI(0jNr=$G
zmE=C^OQc#bvyD=N&6v$#MiN38>l}hy2-yrbWwJZA+~qg6(%o>0@{utBwN5ouBl5fm
z!r@JxUp-0Sz1LS8`Jy<v94jwZ9Q_-zKW+3EqJo~cOnzU^V6$)#F7l6)ghaaxON-F%
z3*NQzk=K#hcbANRkz0Z8>|H}(X7QTelwVHwa1^ysQ<0ExV4t#IfvHG?hE<2OCDc?_
zvBE@**(@Vga{z$#K;~5XIkwM7IY|D?si%c|?j2~fMrO(l<L55=-!TF3aU-<<9tflG
zQ9t&IT?SU>?dHL>g%?v2ieAhhQ|6jR&J-fx8nVtJanJ>bip-kq!MgPT5U(*HMqlq9
zR{bq5d+KxHgActmFTOp>ItBEif91R}ifOtY3z5mZP}0hR<mb#Fah9gcCGb2HJ%1X~
zY;6K?*st<Z$SIZW?<$p?v1o1S&Zl_bx+m~`ncTE>BjQNaX^0E}#xwPkDNwzPpLy&$
zLLR4?gMZ?6!YAQD<Yw&4TPPB<4NOgpAjKdR3c!)Fxl_Z6kTLPp)6Jd%5aw_;ox4RE
zFHFn>a<`X9=PckIGu=4?@?yjrZfusks4pEk^`g{b8TdwO&D`Pm?5xkw&1QE;8Sl|y
zSBOl;R2OMWTy6t1<SSUei;1@;))Dy1m}f^&O=pO*;0>SI?M~|+RUw&aIrRLSGy!Xy
z!6?5!8*Ccd12!&J5JmzXc2@yUTX(jTFkWR7=ZqYcBdXZR3X^oI=gca#VhPunuSiVQ
zV<oImTco~x<Tm<@(5QUw?`5oaD@3}R!(6j34DWc~9VZML`Tsy{{^!)^zkCgUlm1;_
ze~OlCxI*9f?b~D0I6zkfI*Qjc7vQWC(F<C(s_kDUCRixS$pdZ)D=HASWo2_hV+tfR
z0*M;DF64nFen)YKCX+~|qQLfD0Nayo0DA_)Sx0$CAy@nF>9GFI`3gMPEoI^ogl$%7
zL?nR+1p#c#gB^^swzv;x&LBs1iCbv~=ZTBF^eqcyDp>anaS*v68GTovu#3>E%NCRe
zNWjB+nq=^kNsCyd%d06zh|x*IXaR`DpLhVb0=E1)Vtc=SL$o>anSNYm*j1#Md}Q2$
zwxEksM<1mzwITV}8~7M-k12B=!nPO|<H_rOScSXJ^6N~Q3+ZD`2W4JN!MsvW2wJ>~
z5|G5(NO9XGg28)nhLj*y%ovUM^U(&0Pe4tX=P^}^4YqT^iKz-g+Volp9MR4ozMQU;
z-<SvMUn%jj2e2;EYdS8ju|s%{7oU*^>{{cJ^Mso(VR-ZvykMLbNsYm~AZSYsgiSw>
zwVX?Cl04X|JNo=q|Jiv-%48Z&m9=^5jt4TQ0E}_J@H(L1?d8-Z-eR3Qn;J|I(8zf#
z!ARUaCIp$AH8qBKx^0K7(heGZWg@D3>*$;0?u}U#YGY#q+~9}o<o<2T5qU;bcj|e-
zXnn{p70IpMnXVa1&b4475i&+8`U{$75VtzhJq#a5C>*8g1kf5aGhIR<$QWfJgQdu&
zLbk)&M%HG)_N;T+Q{_41n=`}Zo~?h}lu9&J!D|e<!d;ODaztaGD!>%--)L<WgE<WA
zH5mwLq}tC~)M)+Nlh0bpvw85bPv6HBTdi23p2!EjAIyzU6PtK{LxdrHtMTUU@H4+%
zbo$3#02-`et<N-VYCO&0gJthHBy=XIX))u0sNQWre$)5njQm^9sJEHmhPgM1GTA_!
zoN8DWQd~V}Y{67PJZ;t_5_t?l9B@Mc1q?0vQfsRhtiV!?LV-~^6<YYyTiA>EUx+iU
zqWb6ODK&NcA-PN!WT|tVydWL(+OoG!A@UlPx&|mIbl5vnj+lrvzyV@dGK*{L<^a9s
z#%*sLgzumJSO%%&9IBZ!zQ&wLX!YDZ!h<vz))qDI4Q4HbZk?q|kZEZrox}15$u8qM
zo2MXUjdZjN%$5*y4Z7*kGC3>Pu#_MbG*rdBSJth3HIx;9!T4(E=SSjhI<Fnhbt!E0
z3JQ?tu(%8MZjsaLXCw)~1#(Bh&cXE)35Gre<|x$mlkx(be4^`}k3Ssy(RZPGPKxJ<
z3<Pol<AIk))*2)>BWLzQMDt&0;m;9M_oq8T1G$8>fbrTuYN3Q*zp?q;!`>b-<mhT9
zVRL%#CyAyv)U}`OU0$3ktG~H<BzgR`)>c1QJAhgnIGI#EC+oY+8I%P#P*q}Ao*j6o
z9<HCZdF!G?U?Xi`AmjI}rjj>W-AP{`z&DKmXH8;Cr{z{XZ4AnsYRa3ol#gsVOPBo8
z2(E)bt`B+|5yhZr=x9(@(EaA_;}#313Tw{(eDLDZaSF_BpF*n^Sl54dh1sW<iiWg(
zx(392uN3d2)pOI`RyobT5U~lrYM%>?x`O0qehlQwLq@2Nd@4}B%VNz^)<zGy?t4<#
zjzHKaf`2`x7La3fYWgRv<DDR6>7I@*pt%9@l1aM$h4;Q;RN8aoLSLacHGPUQDdznP
z5OJ;?o~AhEH2WcQl8tkptUK=G*W->;HB=ueyw6pgO^Z2mPS(!T3yHO(M>#3=3c1wR
z{v}01lrq?dkTvz{$h?joaquiGpeJC>PUvFoJ{}fH3;7+Xa(-jH$4VZ&j!o}mm6<jC
zanqy&thZl5SDE-OY_hX{`xLRLU0hj!4j|jOR~L>CZ=Ma(xAwWXb7kNY+cNYTGEhRP
zqREhmvD&9COx<0*`8c64z)ZrPvktB^&3dzPTyc`+hMLM$tZL<}5*_mhRH-L_6ZZMv
z>&L5*CWXkG$W-|T4Pp(XIeQ9=9DVCOUy30i(ngJlUTHW*fP4pFM@3ydV-8VlqD#VP
zBRwOkbG1&sP1!(Z0SWBpOhxMQBp3+YkyFu-vAn5|id5YsW5=UCuc_jfAH(g;D!Yed
z`-4xmd*&==Ox-^pl$od@c>_U_(>*;siU>zy(gMtxX<&Z#RBGBnzs;TVP%k9)eQp(B
zZQ2C^K?XvUuQ4~kDi)y&hhe%D1?Y&eiqai7&}%!+wa7DBjId}}M-H_bTUVwhd*OA;
z1~@VKuy`vJZAfVdTJ#Nkb=SGH$<;9CG52_ovsE)#gb7?6$h8cm{J28852=&jd2y2`
zbpopTLnjyWAMIr<B6Cq6c@e%yUmpWh$O)Rg`sT{UG<ZL&xU<4YH*g)7uA>6FUd8iX
z*DmR|9+&FWp*Z{I9lhIq8yFD}jL6pPwaeN3RCy<w7_TSyw`l0we@x$*cA6GA>gE^}
zeV;>7Jdk~Zy>d|MN&5TCkY$oW!!N{LhV;-9OA9F@6<uHmOt<6aF0M0VDLquTuQ6S0
zz%<&*;MTQ4XLz32HRcyE_G;)&<h!(m-eS0%?4c7S#cZ7v7}U6`w7Av@-lV5C%_Jzm
zz7rgg=6QjyOdD`3ry<H+-vb+5QF@pIU-VNBca}iW{}4?o(2Tj79>`lw?@aTR`{-<b
zG|0SRl;3ifvoZ!r>G>YE5p&J?wWF*Vvja@XoXSvSG#umw)`5Y9uG}*YH8FZ%+5wfT
z72_7_2D;rq1?y@OU2{YWCRO?oRqNp$`(IAus;!ppr)%f?IWr1sr*j;K_wU&UERsbi
zU{C?KD@&DqM;HAWcaN#8Do%E>tjvEMi#=3PZEGFaw*5-_-O0qj>gQmd8<Lfkx0liE
zn43EXKN)1k?7udACCN~aUb*&DvfJ#sXBW3(tiW#rH-!Uzwc0Nq8HEh%H&jD?^)NW7
zb!$Z@ek<0~C38yrWhKGTdp>Wf_UhN<E7pr4ee;yq5iGHGq!T<sX4p|eE}=q?KGmk&
z%Be8+=`3&eq@K)M3YVsBjF*5T#4O?ZB-yu{#FtE!6J9O@jHh=wv~GLH!4jEy^PG$x
zQ_{ifYGpF2*E8`*gY(|yThEZ2CLK>=KC;aB)_lG(M`A|0T}yr97A_ZQKE1uCFaU2J
zc#!26D(i2YbhETJGuS3WTl@GW{Zg`)$(GNGjtgoE_+JYM7Fk)b87{VtW14?#Y;-Vx
z&}{z{vH0P<ds9cF&x(WlxrAXjF!1S*of;$0r{Gp?vkh8+#~m$Yu4`NS42<SZ87s)p
zl4<WMH^)wQ#1&R<mMc03TT_2WWRE6}h`n>bEP8V~m>j8RZ}TYrId5XCB2F6V6V()r
zV{w%e4s8=1V0Xs(@e9O2UOMghl<rF&O4|PVz)}h%XvA?F3%18rHai+n2A_Je^Hz7;
zTwT7PG=toXLTZBnT<A)7)Dqt`AgUuz=3Y3(i>_K1Z<;pH_WmWKF-T#}p{L^IG*<Mn
zqM|Gg)8jg~FgEG%pCy}ypX-!8!PHeKTI*`km^NerK=X3yo?=>F$<O_LJ`1UAXJUv>
zcMx!>kT-oSZS<?h`N_3E!TS20?&ce1<#CPe>?O!j?ghmGENq*z2^P0hy?nXO$b0>H
zQ6V*QHUXT9ezWALPZ>mdascUD(sj;H?~T53T<ke;^ubIg+62{nYD5vt@-Pi~U%d=v
zG3gRMNrVPD){-tgAvpw}HhSFaL&|@)q<SiO38FkVKsZA*=cbpYY>Y5!<%qIH_ydd^
z8pD9h!)hHNL;L2Dxp|d_MM|sdB}pHfzAdMNzrFTaL4y@E|8{1}P>Uw2(Ye@EWz`eC
zTp~T+e=FNaMUHe3lsv3LoV4;GxJC8RGNzIhkG(2kclH(>#rfKIY&xZ0Fm-i{Bz0dN
zL9dj-k<?#))0Q!>X@_8)d42?Ws=vx)fPEOm$f=kQkQrI6H}!X-!@5^2-pNyww$!99
z8;VlDDJoJ!A;;bOa2}MSILMqLe`d{SyE*5Ak9PlD$Q+(AhK!1B$@}7k$TsVreg6X>
z(lPe>`M6JphTZ{qk?u#eH^<H4`U#7HE_uVz?`3P-E5;x}&-T(HG{`av08AEbWd~sJ
zNmZ`+Mk`MAh4m|4i3*XIkdw%h&zajggB#Izu8Dm(cn(HVh`iISW|{L((TZrlDQ3#|
zC^Yz{UZ~3WhPZ_+l~_v5fXfe;W~t7-bH5Kadhg3fvuj8J*0ZQv1L1ZpR~#f+QEk6C
zYu)yzUsZ5HlVL0S*T!mQ?o0NslI>9n4N6DvPWIYF=dLx@pxDsLQuc+ia8}*RC}ZS>
zF%M}iIo)?woJU!U8$TCTbla5HZ1rkQ>WSuD$ecPjC;wYi*89DTDqtB(A=?_ndy4JD
zE=W9b^c=D7>@>+jU;lZ@&tZy&J*lMcUU!X%&+Hs#1q*o_SV#cnHEvB@sH6u%k=k+_
z-PNnlzP9(%GA0v#CEPV@!57{Z7}kNt6~MJvMA1epd78s^(Zx-!-R}?SS6`pqZ_O9)
zHndLAmegDszT7|Lli_5_d#5BHa#CyJh$1x%hQ#yIb6)OcoSqA6XM^khvJOXFQ$Rjq
zDx?CC8OpmG{k}&*_wK`461~DGxo~<Fxw`vetLRn8nw{|-<<YXMOnXRBzr)>k(I$;6
zD4jnx3@amY?4yWZbtwA8z!$HjQVT~W3kFNKs@>B9Yclny<VG+9J;1q_#JJK`XHQY@
zOiMKL%5fw)4a)|q5-0B<wTgHqpSJd==K(^k_R&%@58c@Q^V-J2_7mbPeK{I}GkO}O
zj%nvsXYau1M8TC~%BIYe^xsIqFQ>dAWeWziy2MUvqojP)RFC?IN}X+wIXr=)Cqxmk
zvz@JLzczY%Z(LCfRz0Vd!qTcy8Ym(nK5VHzbqrDg!q8ud$MiUsmdwAvO>Nc9vbGt%
zkr~Ui)mb%_#)Y(k)K*AiSjY2I5N7Zkf1_r^@B3y4A9bPF0~(sz!4yyUH7>||<J*9G
zh<I8EaEh*Ml8&18LQqP>$8}XMKH95d4nz&s)=?}Vj^w`#b+oXG71?Y`KVa3IGi7;U
zMEpGrqD6_ApYD7)-7b1$kYSr)tntSNq^zzu@QsoAlF4G?jV}M_VI`t(-(tf?N;(G{
z65Crs`UyckG1y=IcPSl@wR>7qeAj!h<jO#vjDz$|Z#iSiiQp<j3dg~X<fKROHJ9d*
zB6oRo1YX;)Vlx_64vVfrnqM(5tKL43e}Am8lpX$iDl4Gt`wBZ{?pS{#*ch9R=fwy9
z<$F@#66-(58f5#Yrn%Lz`(w>($!>PkGPW7B=9+XcX+80WA;V|BDMsO4^Uywx+N}4K
zzU^0b<|h-=!pBRt!xpU-WLWgvjG%>9vu0St@!(+U(GlqId><`XWyv~7>EwLBzmw4L
ztN(0<$fSL#a<dC=b*WW9P*f<@uie+w&Nz5>TDgNhZqo(;Mj!wf>jJ<KFgY!aVY|$#
z?C_dkq^w;&KRb#4Y$hSyu~3{68R--EdzK$m-4+@WXO<(M((1?Q?`q>sLjb$4U6Zwp
za79|$cf`dm^xnv9@?L@C$LTjn6=Nm;APR|vNtkHQMunMU*@r%SmxqKGNDU4G&a2UV
zAt-}6W4qPD{9we&%UwF*$5;KxO2Lq(0pef@5QmfVOP6?AB7mBvOR8O^pMSw$vUL5}
zh_NtZ)+sVV_P=*&t6!e>b?umEKL_*kzF@oG**A=7-4<}d#p|39opV0k$eQ=~Nvs94
z9?UC>HdxA^s!NN}e3tqp+EUNqcw|J!XFtx>P>&9B_Ca2N@-(_@+>BX!ek|D6cjGh4
z3m@6fj3h?xdOba3r5qXKdK~!HO!cv<b@O-Qi>-5f(`l4#M>FjHy!4g4Fvr-wRDWsx
zjL6OP)87l~3P-$X0Ppc}KNeWC6b_>w?pcIUeY3bSgI+v1dz~MH1iqXw=BPfW<rP<L
zLTjHWdG&H{FmhC-RhD<Kj-z!{(bls2ui3ArebGxlw{xe=c(d<6NA8MOYX}b7EUa4#
zciQKER?q+U(lS=cJ5s|g*jghk?4g4r@YR9}62MfUtp!wn+M@rQc*c#Oci@Q3CfA$`
zibSU-4Hyz&eYX=EQ(GO?Ob?6|boGZsCQz~1Bu}ft->`dwvD9cQ<w?dDfROiZNBZ4c
z-Tk@b-u1d3x#3(OhA_|};;obqZnz;W3%W$3f=nsYf!gaRdCnVl>#x<BU$*6lafNH~
zz@Zscp#jz*K31xT?Ho+oWTZd2Qm1*$=bb#@oyA_spv)!CP8&gFvP0)sy^50|{0l@~
zh*AEO7eqPm*UQJFuOQW}+XnIWjk903tYSa#AV>yy<5tHuzW&9pe(JM3XUqS+;>G{H
zv+)S61rO&>Ijc8Y6!sLRhnE`?ulSeLx%2#Q=CzDe+4UR<gKS%N)Dm#R*M0hCz*H-@
z@$mqi<MDRRK-v$~g;RN-6F-ta#RSh^iP$#GFo=sr9^<8t4>bfjK+4YzeAbgF8DR&h
zzk#K^Q#ypMdIBT8=Z(Y`x3S88=E+5xA>*zxy9I%3_S7g?7Pz<rh7)hkUX#Zrw|~6m
z<%VH|aV$fzB<6hfp@QG$tw=YhX}EGGUvJC{YsN&U*RuYf=Fa>tsr>unwvS`weDvFt
zr8Z5dk*0>Zq@tB=;0~5ru487JxS+VB&Nyl{rZa%zZjIuuq!y$IYMKsMYOW;;V@RZk
zC@zExd~Y+~Kj8D@mmlxreSv%5=e*zNyw3C7EC0(G5Ftl5LU1x2DlKPeOVw~?D{~dx
zK}UW{)K`v4FZ9q$yTRtTs(WxNSsOM!0rc%jcXgoy9Cy#3?%NPaZ&^$=NO2NkXy=$C
z$aRyEs(*j_H*u5@@0H&?U1g?ej}I#gD{4jY6&QLSrvfp!%iUGAv+&blh*IH_z%Gxi
z$2|Vn<hxYm^I+x`4N-B-1-KD6=&{a^Ps%cZ>QBp-s^i|_XX#Pnrm*C585>5<63&2x
zdo6pX-<4wP9r+ci7`e@lOpYBPB_dFkX4e>igsYO+z*BWXOq-m>52D((wM!sZ(U`x|
zV|80^!_{K#SOUr8lFe8Li7hfg_0>6#`+{FFFwT^xoC@j)G0xL{t0OyJ!jA=gFs)o`
zuU(NYfuzSqECH$cMzznY*rY(2tDUtpv81`|`rMk;o9_-h^ShaGFQ0<Z6}6wEgQ<Ug
z^gqlPlW~o|Dg@mnMg{F=+WSyht;$jdDW>gychkAKzoLCJd3n}1rHKnURv2tUMjP<$
zg*Q5srVN;Z5l8fD)Mb9Wl)0qLxoCn7hsPp*&vBEdRDpn60-nIkPA;B(4(c%bVPLF&
z<qIerR^SnQA1Bt0kHle#HM~zSpS;;)utrC8GA_jd{U-H&c76S3!7eH&-h;H|T^V}&
zs_EzcplQ@o_hyVKN`H>a$h$4@f+Lntuw+iW+#J7k1Gme)b%1WE;QsL%)=yGD5#}uU
zSsMf3XHhtF%sdwd8DG!<qgGQ-d=$PA1FVw}nXEZE8J|Q3DXI{1UAxgTQ09$NlZ8XO
z`(=nW@`x&af$^v<&tzaC>IEvcOAae7p7lj$OHttN8%bvaLq~X!sbA6iK(ZFX!_S8E
zJ*(XWN^;JAhGkQu$ZIarIE2<PnLm55M3mx-J6_?C^g@T9v`HZ_I}_-8>!30F=z}*J
zD2~}Q=s_NDj$yN{n^%-4?U}}H!Enwxt(MF~;6A4M2vlJGL`}GCH%?pnaQ_<oSbeQk
z*XA2IHgm!SsLr^Q&mT@L(v_vLwv*}aeK_EjGkp8k&wx2l?=o<F&|oid_S{O3ofEyr
zQX^I|Vb>)yHbOS;wXa8+*$)i-W=~8Vyw$(gCOM9j1T6AMHl)^HtsO&2k9^*6=Q(WL
ztTL%fOdaTJ_^BlEKs7C4a|yD>ImaYs#w|u6aF^)@69;W4%Nyw}U)onzPf%>Z%EWZR
z6zVU50cI5Ek?rjdrDqA(>gz;d*Jb|x`RkIc9c%4L4=e#TWN0@fiqzLtEuzfxpsX`=
ztd)+xhx;3qjFLMI*=FY(*DdPj@2@TH2&|fcE>9rTKGWx4TI=|4bg~qS=P%P?RCiHg
zUVQx`pjfZ4qV87%)vfAx!@I?m(TU&DEBd0wt5z=*IM0m{vh=z(TM4D`?xdM*`q2ql
z(~0recsRMJ?|=q3eBDE8q27+?<hee^{oPQ0CaWB!)2$b0Th_&#PG_sTzZ@6?Jv`_z
zcf)L9h0D=nkl5I(WeMvrSX=1aRL2k7_!8Lpeco(vO7Kma@Ygl=AL~b6`V6*BE>`X*
ze!l&34Zak1eQwSw6zWd$Is+LH)t~ILa-cOFHS4k}8#Y|81Ap4n4T=+n;CYtBgTi0o
zC920=D09-DlTkdM5B<}Pjv9iP8M(1CC+##}Onbw_=*3Nt*aOKfc37a$^f(V7Bq5Q^
zSHxnznqDl~I%r1ibtEY6aM)p;co!B49|cxU4`{YH9~t&mXl>4<;}<@73V^h{EDRBB
zGi~(33U95i6WnB|Ba0rzeM!*C=`HxXWR;Q?Lf9Ryh4R-#>1)_@(IVJ|vuATLVeo?I
zLR%t1I586GQsm=a6O_NKCIIr2SWtWjD?)i}A~0A@sG2ZJor>`-ovk4BjH8vOkf#I1
z#y@b>PY0Hn#H}Q3gpo=wKK#?w;p3VYij5WGHh|bb^i@&|_pVyBs@!@nC1|g;GZ1};
zz;;m0sfR(0NUk?>(%<x**+Y@$nuQ09=Dk!26vg7{>&|lx9ZQyBPLfPm@~s+agX!yb
zzoGL_2LSVlntN9JaOk$`DfVpQN3XW1Lk7=D`>IyurS=+>nE}tFP=hIeXSk_8lxVo}
z#iF;BEWh3wov)^(^yFf{PFza4q=S!ppyHd9T)KFe6A2u_aeRL?t9<iQhs*j<;kb-W
zF!Jedbr0LRQa@48rv)t^?&VVgrj&t{yTAIX<5DB#A6qKdiKDL9+{dzhO;-uzQeI(n
z%biEvw%^^omY}x_C;>i2tiLi)oE#b!F^tU@n_iCk=Ssv!T_}?+==`&xM5JTI+bT}v
zrM~TiMv6$AUcuORVDgFfLGtMGh=FbL+%Fi<DMkvm0D=h@R0BKcr0A3xDJ!0B1f~wd
z#`l(}&xihNSw*=Udmk4}1NTBXx6cR$+cFBm;+%~<#eiwGSO(gT@s<I*sk7tl-#7tO
z-}9gF)!L)LMA)F9m&X=)gB8zNioV>xwO03hoP~c-54j}>U-jgAwRI!k=*9C>zsM<w
zbxOar&Ejq`h#Kftt{dmh{=WAe1@}&^-C6tDK#?13@w%9qHt)2=XhFy|cCi4zXqYhC
z(Hu<^n_u1!GxvX|?~fz=x_GG|<#;oPzd!}`zOAdiUlZ9;T8E;I`B2HnT-<HPd?xyJ
zLW#9L2>GKxZE~WOd(q!ol<Dk`+PNFz7jJkx_|o_X-9C(=*44GO50J|;U9P$_*;<~u
z12Y{8D^62u-*jWGw{7@oDs$$8e!n*1rq@~cY<Rf`=%x9DWV{41c}T-1Zx_+r+S9Xr
zk(j-M-^kZk=nmM-RZl_erZu2Vb)1$Gi{4kvF93(_7yJCjDWn0)&}B9pE|w-6I>%N@
z*TB#-dQUg5U|p=<|1leWS}KM<*=hv&9)V;dPxtGspIXUhNx18$OtMJK712Kz$tcA<
zpPr9UA!`E{sbVYf&XWrY_Z%i=6%-qkP1T3YAMrMZ)3jcz>i1*Tj~>6{g+dQ+)@*bS
z4qi=Ktl+92e<!2qGlTYyVqgiX-p$3_8Usb&H-onud~f9C<o}(p<CgO;hf^!tJDR<;
z(!`{ajxu{0!q&;xS@vpd@I@{knvho;TQVkM_+9u=ce+2#P^W=?i=F2>c}X25JZwL|
zzy~1c8PodM-=QPZfGP+3WxwP#OCs7w*!IC4%gExdz#4@H3O*!fkYpv}<$`&BjicE*
zQuBsC8*Bfa!o&(I!YdN^Au0F$nZV)3iq%@qa4SkQMY$2i)~BZHU)k$8{=VnzEmJtn
z+nCtwZ3HkXAc3Hlo#IN`da@iKgaCd$x9tY-rnNyed`Ag1k{mq<4hninS!JGJ8F=JB
zSScnP->XMzdz<MHL04+f>r%OZG<1@X1|+{(rl>kg5pT!5#;xl8asFwH`o{q!wA20e
z_{;WVTHI6PlawqqAT1MSru_zj4b&Mpe^gnu$>sY8UF~?;Y+ZegBRkpCe3E(JvcGS(
zmw1Q^4%|tu@Uo%n>&L#uZSFI?9TLq;hPtz3>UguICYQbG23kcigVE{_X;mQV_hHBM
zse97Yu#TGLH_D&0E3(dPHV`IOb76}`kjB+vk*{z306JhPyW4_2TOmch5NxlVw28%u
zA11ozrC9eQ;9m>m$pAw&G=L^EH+o4Mg`?Z$-*!6S|K9Tirb4tmPQ=~Yzz=L6gOu94
z`{x}f%Bt5r@F^khXij9$%c!0s99PfeNnBqo&&^2dc#<0pm(+po;Jz=e^XxM&oD%4d
z<yp9g5MpTIIPo28RCO3C96z?3S3w48Q2nKEBXjw#3ESgO3;np3Gnie^3jtQwaU-<N
z*FtW<T(Rs|=&xnF!_L0hOeTNmC`j(x@41)<oMLp*=V1t$5|EkX^nxm^hHCAT1-210
zZB3^8i={yFJs_!Skv!c~YjexfrZr;&H<t(nJiFBPp2dUYTDB|&iAyb?nm?)*aJ1c+
zDKFud>B-6oyf`sIn+C`mi@T0K2%luno~l)^)0%2=Td5_>o~aj4qa*$RO-KHf@~+hp
z@j@U^SNAc$PzO-PLlvmG^~%KHHsU&?p&;Te0^`Lf+V7e?6v}rX-@d#2<q!H4x!HOi
zbg5rsH6Cr>X8T?<?=>+>iUz`Gw!U&mD$**&oa8lM3Q`3~irT_)WXa_@Mokcc7MujF
zqf{2nLXGu}Do^v3l)j1m`i=l+i!fMh{%?tQmX}mU?iF+EzSnJ_*)Yj^vIsaL+MbnD
zF~)he85D<kZ7o8f|7QYl)lh>+U5)nA?QO%><%;ZnSz6Z>076j*x==Kr^*8Et#|BY;
z+e!^OfMt*5CI9>-@O$6!H}hVup7QA(f#$lhk+Z$8zCfcpc}2(sg^teIRs5mGszR5e
zcZ+-GD<0)&y2N1HcxXLlfnD#pM<yep8me&<uir9-?1aAQD3nFw6tz=|>+^G-mMF3S
zG3IYvN_&*R8I#crnI~_$4!+Dv(5&y-h)$%H=63PgZSHpRc#q<NNq81RvWkhuNL~h;
zl;J)*vz8;pJNV(a8MSt*eQc3J%DQwrcRu%2cecLj1puhJ$Dy{wHRpCXw%}233(w3A
zOk$<0r8lsKfsz_*qP3G%yD9S`RQon@wq!0@p6HxW6i+Am#>lntU1i`p<qq?zRf&*K
zM(PsaID33j_?35S)RW9QtNvJL4MpM%MBpB{8i`FT82or=LWIyIYQ~^xt)N&(b8v^>
zq|YF!*4@7y^jqTNoR4Dte(^v=z)+)e{M+ysuQ1sm1fHKx42i9sW3MQE6YGFxeTT_D
z`6)OYU;ZUJQ1<EZm9rjerYW%r(qbPAsVZ-#q{AKH9lqt=dQ5ZAbKlqn>MR6(ot{rf
zHG~RppB$_Bn03!4(1Ewd*9lFI#_v9c#5t7#gcUQR_sBx-Y!%E29{Vz54b1x~`RR&c
zPf8ODgRMa*6xS7;)F2WYkC76L7IR?cCxAHZg!Jd=#8F+Qy2)10h4Nb6Yx36St;SK|
z6v(-Dsv`=gf-BCObpqSg8aO~qCt6cNaX}Re{bbPjpI{{;UlML^TQF^JE~Z#A*+{iV
zD$slZBH=pF4{{!+-QfVWg-K#d_-kO>Z%W7GoiOc7P=wm*AY8+JW4mDLNPD^FKK-+0
zv+5;$EI!FFFqD@b^<@J@y)Kb}xJQ}?B76R^;<6e8ZcWpvy_=VCiV07fik7X!x%JNJ
z-ys!!1u!SgKN+pjecz^B(^Uj)df_&s^HOkK^syaEsmdE6EaI;+kh%92Xo^pb-~^V1
z5)<aePToV3N^?}3I02{mWtLAd3^F2l=&*S6Ca{ffp%w6~?J!~`J1xN`(5oXD*s<K$
z95jAtbo!s@LoTXo8!!UNlJ<%PG6L+trp9O`e)ebRJcrYY^@GXm5DDoA)VWExkrBtF
zb@rmakI;wl>`qD9?JM4C)|Mo_PtVxCbZAP!Mqv^(GniI{OG4$c^9i6a!PV-Z8Ol^e
z8t?spYj#>Lh0T_F^r5Ll08Ons0Yvr1>hu3b_4mba?AMSEi0aEtrs4zd<0jk}k9RnD
zQ){gmQ=1CJnDl+g9Hs0cl_;$iKY(M6j{LN(<o3BsMX5#n2-JaJ1X=XU;#tI5vNPz%
z-kCxIs8gZkvKS1LKh0RfLP*!XAdXp4NqD(eKJK1v;)6C{0wna+R>)4xS2gyF9q24!
zf-ijMf_mDwH@!+A86vd1nFCMg`2xyE3n%KEOSQmu*4FynYpd2q{A-;sZy%fa$Y*L?
zC&}yy7Sy6!i~WBt-sl+r7&ojEk4<aZFX0iGuH*&er$WMvW9?>=5W?3UmC;O?=GU?U
zyxT-ULk$x`S!OO@HTz|9swohBD2->3EB2=^|4A_7S|M@Qj>BX-6W!pdK9eXwLP9W3
zDWX{Xc`Kx@;Z_kL(n!(N7j^B0J`;+>!H+x0{_u0zx%ej`^0K%P;<Kb`wd{=_wqJ<{
ztHbFD58CEk95a0Jv%dxSa9@MzdaBn8YUCm?^hdINyjF6-ED{_*;Lwqak712sSMGCq
zsXb6lO@|$H(%AV44}Mtf4Ysw$f|>V`lkbW)MqBg)y(YF1y%SI<Go>n2m`CUtyN{bX
z!~6qglgHFK=#B=KKr3YL3v3{~$n*VRnUm~4f8T}4&vc`t76GN}X6NhE&$gM9_SZq)
zGVqX_OGy0IHP&Wl9wCa!oIA*kh~u9>Ea?5daUm|8rK*o#e>1?1LS#_BA~{Dse>Pw|
z*ZKjh_0-Veym?anjK@4eYyQrlPL7sa`~pKgTOSs7+f%XYsD~{ud<YTlXkHU>ue(WU
zgr74EwbdMrFsQ>sw6U9~ELb`A;#o1Moz}9r!aE%R4EMWEy>9+=c`PGfN97=}h%c88
z1H+rn27W0>Uzs)`f}?H<nw%Yt8}}5AJQj5wsQ=1;-YaM&%r2S_JDx5cx^tm(G~oj4
z{7;EM8%1{D`9zRxf?%Nv(=f3B3tEADwk3~m6SRqfD>K7vQwr4Bs^!wJT+mS8E<ho2
z3%7NEWhfb#5+M({&(}XrpX@K2NSM|hb;yUJfF{du8)nJT?5`SbJ{riuVnMAK{KXyh
zuK+pYOz>UF*QTA+NjLR&qHcVmL;c?8+%6cfbU|T^BHWwl0*L>IO`J7E=Rf;_asSn)
vQ^D2N1TsEgyWUVt9y0r{PWpd6$Y#f>Es4U)P9OU9Z^EuPU2eSO_sjnR?*zes

diff --git a/reference/plot-10.png b/reference/plot-10.png
index b945e09dc7470b349601387e2cc3e8c6579d1830..9c553b7d7fdf0a2c9fb90a0768f64d0c13aa0f1c 100644
GIT binary patch
literal 27660
zcmeFa2UL|=wl0cVmQ}{445$cLh@eCf0m%jw5di_oNd-i*k~5aJiXu>gfJjnMkt{ib
z7(hTk$*?6zP8(#C^ZnMg`<y=So%hCh<Mg}Zj(fYt=thD6-~YeXTyuW&o8O$?JFg_S
zY5nf?3=9mL<j=^eGBB)lW?)!z`In#Z9ZK-C@A&fbWd%7|e8qorC-c4V<=1OxwCxxe
zSniVlt@7_3H(_Ad%OEd%{DO1%NSn(eJ(EzG@3Av{zrL3}v+*w)r-B)_?$G|}=AV1d
zXmRe{>)gz8T=wRHr+aqo;;e|?c%S{yFFViwcE04si8F86*RN{X^Ow~pZoIzz!7`#f
z!PBd!DP;7y#Ndng1e=+DJ0&G+heC(pINg~d^{J%ncHKu+SJGFJM>{sOjVX`(fq}vG
z^qURj>z}Ht4anCUkAC~Un*8DUqZ>@*>)sbX6_BqC2lscbC11}RTqQuh9y|N1GyQI=
zXa5VgIA}NBVk+n2;_@gYq%2&_&bDY-K9F<&uou2<SLE4RIWr-@@|i8Ne~a|&UUPHv
z=z;=C&*}6ihi1;Os!}Bh>bBnA-toB_uOfe*5ySa+c0b>`-MtCVFZU+wFdrY^m-_l`
zii(O4tFG7;EjK!d3JdqTw<R9zsNq^V>E#?*Jl~K~S`ow(^YSI9lGIfFX3Dnh+dnjD
z8nWP`@mQIqgMC$DO=arSY)jw7Zk4<p$DS7M>s-6DGty(a<*}IE6<Jw5hX!>O4UI-K
ziqv%TKF4;KesAxi-~PHa`o)VE%H}^|NwAm_$0F=&<;!bWRu=nZnDX{Ky`dvF(pX>r
z{)-%2VfD18-t|+mvIWcY!}5nV;)+S-)$NaRhvF01!>Ue;g!wMbbQIS)R#jDbj{dSF
ztLaa1ccztd87(i)F*08NcAF{LVbVIGaJrS}v_C5gBco}AL*vsuA9O$Vlbhw8e6!)u
z4Uc|_4*6hZJQ;u6%bc8}{5hRBy}h*xoKpi?v0ukzIPqFf+dXF5X6vGsw=pv2Q?qm}
zbH3ftFQPt_V-4jswEuD{mmh1(@R4zqz#9e!idFXhO4IP`r?ai|zujRj6`@#eWn}bP
z8aC{vW_MJZS>acYS^whf)`{!ol`b~A(xR)wy_V<XDKr|*dGJMuxRtJ<VN?dQh^3bC
z;ZWh$a4f2PSc8{HTWxKC-b{kw68GFf(ZVPt`A(yk#K`NDA*?o>f%g{1ie;2!mL{7o
z`|w4_$8(qZu`nCX*M?l49%*4|Y|#xp$I0qB`|W6dxZUOKHb<Xt@9xT-I(6#79<jrY
zt!A;awIM~@if2CvbbVkl9HH%bmr^uuuID+|dwt;9-BK>;@q5M|<mJP!m+rdWAK_Gb
zm(8>JMQfyU*S(zYZ#Ni?70&ogHR=k$953Vdd;*ok4_>KyvVCf5s<7=Ri_D89H*;pY
ze2i+J%a5n}d+^4`$9DxrEgFZr4#f$YR)=4;wyqvtl9N3mEc_vhVjVW3O!iCMCa~PN
z>&BYRXRL)>Xgvkhj8bN`&wGl9p?PZw%S=T$weRAJa<_fJA|ZlX;@z{ng{qJ4i^Pg<
zZb~PgN}di6?DzZ0JMTX}+}Hj3-DV%{^eYZ#D~6Qo`67jFre3tbD2~dloaY8;OIhOM
z@3#i_=8qNSQ?kd-h=f@+ZE0v|NSYAI%*>=(r;FqcN#xPO9WuOnV~X`MuBzz40#Yma
z^JQ^3$YO4mn*5y8_r&7zVC@S=#xI{gf3{}srR8uNM>QGxebdNo-!a#Gm*(*xvr%iH
zsl;K>vSY5BZDprvq{|ie`MM~-Cr_Rn5D@S$WAk$NrNn;AHPA0wxWD|peGS76CF8-Y
z?K~RB``DQoTkw09oPmQG)PXNCfmw_Xmp%x1eleqzvAXe^IZb|%b87DUBvf;*^;4u&
zewzbM33b#$e<iEsN|<n?%%!(~?U~0%a^^daca1da+7vcUjTB5&x9nF4w!c1{M5z=g
z4sSCKRLomEdgasO)W#`3os0tuEj6B5mBSY1=0n${aK{#0CoF-VJ0ufd;f7UCo=8om
z&W(HwvndK~!?9^{SurYoP%b_3NoZKl(54WFxuvVCtH*OJw?8s7Yo%n1=bsrYf|q3-
z=V-kyNj6hUbH1YOAFVUKoVI9l#0?E>&xwkNTp>p^GpoZYyRC8VXx@Tk|K+Hq@9(w*
z4p&xGG`1=`T*0X?7w0_p_%oH_#1UAYk>Jr=5j0$&NRf83641>ZA)D5lUu<b+R_=*I
zuRT0}_Uzd-r#1)8tmNe6whlL%dkR0Bhk?^nh|y6)3On1vd1?PtcdNUj(+~Ai=d<*4
zu3d_CkeMHdaq)UEHzGi5)11tnVP#-Qlz&4x*<I?Z(EgcUFX#0;5uAkf-8P<0I|7Jl
z3~yweKipYJtKdmLd3Q6lHm}r|ImF7$Y_N34NHflCR8n&CXk)jQ?inGQ_5$l9p6H1i
zPNffawHY*R68-wRv5XS2CDUJ$b|(K&CeKRWr_l6K-*yEd)9Q1hDT#@^3)2)Er{-iU
zt>*bvq8+WHH6ezcE+I2X!^!#ll&@dIBaU9b9MVc2$s#F6qQ)+Hx+T}HpR-B%$&olY
ziVbX(IvSgrnkqMJ#$)(REqTFj^yh-yQ30XQbJMS4{Dx>oxn1{pTJ@_>y$SP|PRXCI
z6bw(Xdg$+QAZnFOQP#<#HSK0~FHI^dD<zV~Q@oZe+S!bX=6&2}wl7T;u47=JhDJZT
z>nAaqV?6ilF6+?isu+LiC%pbRt?cZGB22!%zARZ$(jKm91^tn3j`=SG0s=lJucuNP
zM6nz_V^%9tGhgj2^gBe0#(lMl++7|pi}I5TD}(ub&T(#q6^>W%q>zXakpZV*+THFp
zMs=)82otf=Nz}_d%+7viGt2JmueJ)ju;AUy%=-e%W#KOK^75RAld>;Yhl`E-+PLqF
zPe_o^Yf4Bd$}6p@EuKoU`LIEt;4cmi4iC$X5w!MeBx*4V6i%w1i;|X-p2@9AN-wp_
zYT<mT9L2hUiT}a;{Je3P`|P*7*Rj^6c;UwxQoWDVx^8We9{8|B+vH31sd1bhk-6Ny
z@88~a-(j})&hGG(coG&yp2y0{N^<h^NkQwcIX#YBxs*m~Gqx5Anp8aMiCu(SNnKf9
zNEsKZGN}yy@c!P`z)DR67n6MPg}0)`b5&Zowk9JuOoCp^i{#IU8Lv`O{AE_=YEr+P
zX5Me5jG%Cono-w<nBfDjku&%*tP8QzKR7t}s+rk6(RSy3elp9m&zDYak=o`jHLaCm
z@u^o@0{1g^Y0`J1sz|-Nv&kndEv<)!1K$ucHF;Zannmj2@G=da!*`d^6^;-bq$X#0
zoxmj=Cw>wYW=BkAm>C%nZj`E7D~Hy9>34W8v*+F1*B!T=tEh3yY0CBubNx|luz=JC
z4c!lL7EB5u{J!Tn0}I&ZKbcBnfoo}c{pl$KHDgi?3|fjM(Y=q{<_BW~AFjW<$M(!!
zD<!esN0#s7qdqK+`+J>hm6_`aw8+p;GnThq!XitssC;Qq;U|u?c9a+&t=nAUEeCrV
z7amVX&M$*S*!FwLI;S~L@pO2_{ljiBAGd-?JV4v^G8T$eo!9b2RAIaoOr&KUtMkQ(
z7=0ZbowPC*Cq3Jaj+NyZ2`frazYro6Otj{C8>`z$Y9pgI09MOEwU-=JW%0r1$x?3&
zu3o+R(K@#`xZESsr7x5^)Z*0P5jf1L6#h8y%We3U<;BbtkEFT<g97LA-7$Xxtwa#t
zCSe&>Zk)D`&HY;KbiJA~HN!7ix+eFlDP?p`cDt*Ee^dHIwFK92E{p?DtueP-|8YW5
zjJlKD_rA1ZOMIwk-dMAdKh?gFJW-vJgq5#{C{vl1^nAhSkU-hBqO4QW)6d-fI;)x-
zKhFHp?){O~{`2vO<rA?<N2HqOMtExJLB}6h<yC5`pvzWWJ-vd`yLZS24MrX|Ih&Q0
zC1NIH)tVvPDC#yn(lcMDEK^vwK&`yt)bsJ-@S^-QPf9^R-T6!STY28l(2#3EWNf5s
zyuQA&)YOHPp`qm7$`Gy@Df9YQN)(6Qxy3S><%byud3Y-M_4Di_W{PclD=bY+Oq8-r
zG#y0P#)c+F`E|2+$1*d_^JV;Z3!6u<jcP_EOaMq}TP$QPw0JY_6Sry#>KAKTu%wt7
zyKeo&di_0PxvO>2T%SS1qpCgu_X_onFY)=SZ1@){7e@01%+%D>(yPnrnwuY!AXL4k
zeRRM)s!4yTcju_zsHKnl$jP~l&iOGCl;1m6*=QO{<R^TtagbiPpHVQwz;JQ9+L6ig
z+S=M9#VgB<sYARU1&U@b=MOXS>1?YE5%Bl+_F9?o^2eL+l<{<z&4{}HtU=$YEr?e+
zQc}#&#Hlj7c!}yLQ!{#IxPEJrxrK$BL(~E_+c0f$B5I|e%=BjQTwhpVFR*{+!a)<m
zorjIjSmIR}8ON(7#$->PJn7%$P|26gQ3=;+y%B&hB0DZV{yw&vFLx0pGc~)MZ{F=I
z>PNB8=gV~`KrXY%fSE*9S~kIpaE(u9bi`>@Hl9Lf6%?9UUsCPY4W|@)j=)z~SQX=3
z?yqEX|9(8BdT->g$MjT_p>l8&oB(Tz>AOv$=Yl(2`XjKi$^b>K+#yT7Hl7n<j>Acb
ziF(7Xh|FCwD-$yLT4tU-X*I*=ykzd^PTA{j>k<}?n!g-15U-{r_Cg^gqD_6EUp&g=
zluo-F1H<e4H?o<EfI~SeXF9yR@`qciD=Fe*EvaR!@Fq1<GkS?ysnPli1bS$X4!Vct
zHU^2`_wg|zM%LTzc_SZqFR(XGEw*%rt|epLOGO`?clKaD-NxRjPhp}0+RGK1MYRb}
zG@3qt{>x0eo6Wfhap7r1jpl3bHpN6ovsYABQkL9$gERvulUMlj#O(SwD%Y{tpAWHV
za#Si|GH`xYHq*h9P*X;6q!?cRu-$_-BfBAJ$>r82ku$+Ez(}W6RKDm?G}YBJ^S{>D
z*Qa-}&n#FDyJs+_R`0!J#m&tf(2H>KkIS~tUowD~&AIar{k)arPi*aK9&P;&FaNLJ
z^0|Cqo=4abN&4BGB!JL6bIa{ZNz-jk9maFS6{lB=^W?h2LCL<4XOxYaeb2?tPDjtl
zE0m7q9SbAr%1Ee|rYYs+VkAF!cXvyT)1ZEBgNm1x+=VH}`mc@@D<-lidvI`6bak2O
zS-`cDwbx75GU29FmwFYs4}xWTwz^R>HSfmcrnOi~E=|^z_bPh~sTIb@QC(Azam~&*
z6}J!GqlwqGO<QG3X*YT<P(vj98UrIUmOFHe5)gTe=U|G%<4aCW!cBTxx5zABD_Epe
zmUm2dc&!}8gDxcA@v}FXRJlGb*Wr@6bTPtauGuspAt7)nS+vBCa{iKj%)9jmnLOld
zdg6;eT}h{UGS*JNzhoigIyu0fIqY|0$|d4pkKk7(O7ve+a@!g@rtc;17P@jSgz~aC
zx7x|@VHN>zw;~t|3SZ-tk!)L5*7o$6x4MnBZmizXzQ;xZjIyvY2U`k~0SRquiPbSw
z?ZORK`un12CSC9EjhC!rd;cj^sJrvl7VDWmJjw(1U#Dg^mHzeDt(V_>GmbBGta$oT
zQkH)|psBW6&-vT!akiClHjnw=g#k|&Ue`A_@8r=;eh|c?dD_FnqrRz0&dA7!mJ%0t
zaIR_kou;8*0WTk))lB-bw|Ahjbne9kwS>`jFT=(#e-Fn%MG^Vn5PtpE?x_O!v2xlg
zl^9obwW6u4u>oH_szAcnh6MjZ)N{iYD*W4{Yjnd-u)gz1<y+q-=XlDC!j%^<Q6{Go
zIqW{Y-OPP#6~m$CU!C2aA^-I|+U4Ct6RMF!&Pvb@t#tPD$t{Y>0z5nlo+p|dS=&37
zr?bmx{eTw*jo~}CZ{JQ}%41;*4U)J;pYrJ%%^&mIsw^o8c+Jj!xh2iyI#5xWvL`Lj
zF<XD>mfEo1$HMVi1%VIyL-YdlaPA7bBi%;tH5qzI5sZac1;oXkKlJ`K_z5Gp!?CHQ
zm*3W`482qgYaCVZ@%9$*nyXxLU2Z&4Stgy|8?4J&>BA&2I_}z9-AkFb_-j#Pgv$NG
zL5cZW4Kud^^Z48Nn=V~=HeaxO^^m1c@*bmGmn5DK3ihR^oauJk!f>NR=?z7}YE8S_
zXfBRRUv)&-BGL>;i>Bp|`}`kxtJfVaz=^;^1q|+CkA7<RXj=2bhYx~CdyWn~z12ma
z96lun)*ConygZeX&HPO3!i9Qk34X2DPn(3l+Se(T(}?@pBKiH8J0OKWGMuA?w#mtH
z_T3_DQ#hrOGM4@7)q$)wM@kRPN1&*A^p=U{%2MY#nE?kreS+*}N1ckp^sJ>yR1mSw
zg&!5<;ySe3o0eKLnpVkQPLo+4_h(Iso$^WWo_=;c*?zy)(BOgBqth%)$5m8R^0#_9
zWU`q_)QU5$Z|I+rGwaxqD_&Y##FEFZ=r0J*+e$p|-cD*JVN0}^8&W*CGqOcAI8RiG
z`orfJ>n~(YOiLs886QeW;fy(0>d(f8q*yE^HKl)`G8@p4PAe?Vj+b|^dM%DSS|Jnr
zleYsNwXmori>pdvWpScOEYAdIq`aV_FtfvR!9h#TE8Tvu)^Xw-p?~~=u=a|*n<co4
z+-$wNL2<~iN0!x_{Q2=O>VW$eqFy%qr@zDIGQ%pfd18Vn)j}=I`edV#BM501jTJ5A
z*@tBr^7ftheW75P(~81wSf<sQbyE2`mA>7T*lq?5#_!X$I4%K!(Rf>naayH7*}E+=
zdl*@2V)FrwIoS$4XYxBf6u3+T4Eu@oJq~YBS5ph*&92@g`I%%N+6GHp1BT1AkicG<
z#cx~27bj|FhKgGKXu}nY4x0K7>M5fsN#q1*OXICKI5tjZmKz@y1bv~EWvMMIyT|t9
z@8dx!?)wpFj7Goamg~JDSAE4vxXqi{Q86W*j|jfM(yqUn`h`i#>SPqMH;Iah3LNl+
ztTJm#V5P&A)(kVT+z!9dV$nLyo=O!t7Jv$*JP~E?c6N5|V{RRlW}9x0Ow5NJvG#G_
zV`TPqb6ZeW+2fib_Duv#&wAg;4!M8-ewTvbysGV-h2eru+Sf1cdBm`Bp1r*v3mTpN
z>fG&H?qDS+Gu`+y%=!s0!PjECHqydJj=Xm&UTEX@y%U#^;M1`*>QvsF#r5dEck`vY
zEUg}ImcI5&Guaf)UGcZD$WJDCBFW0MCNiS#wFzR=InN?M*0Hjl^1%GXz^PL{J3eF=
zO(lgl)P^bu6du|2ExqJt=J&`YhmCvcnp2IsgLSRtTa^Hr`enG-ez7gA@f<N?^GP)-
zJN-UhjLD5A)Rh&J<p-XTwJ9wR3D;3=X#jFc#3xe+(XZD|<_D@ea)fxudA)|!?Jw>l
zS)}_^f}xdDS9BKQ7m5G<#~tV<<4a+0D$|k`&pzq&;)l(=BQ_(=-dXiWc_0^8rIj4l
zC<r8$ESrezYyjZ4@>kMJgUbDhFSBH7mcMQB+F2vLV3{AKGou-zXDd$#AwTh<S2?iF
zE`({tJ}dCbsg^Bik40?^Ml$M@QjN<4yuDu~CO!a_mD}cuO=+#NS_Hs*&lzU<8<U~?
zbzaa4ZCL>H-c1H>eAdNF(~l)xZ0}CI&tlnc*J2X#p*~IxD6jpD^ci6|VUo8JN{o03
zC+40Nnc)1<Jf2-^-8wvGK7cIh#u@gzy@4r9;?h|}oB~9@>qzrH0A5_iZIoVaE)PM{
zBv~!9ve-id{oLxi*WddnPJbXH71>%}H}zQZOpCWS!i12vXXhX5^7Pdxa|epQ-xjc6
zn2s=G-m$VY=H-tRQ^vr3`~$~d;p^`AM|nw;({vO@l%G+pHwD)O`Z0g&n<ZLE@dIRk
zqLH5SgT^vf@yo@y!FAlYuh;nce`>|i#l-}F3v0Vl4OiwhMc7-HD7b}$)e88KlrNrO
z8TIp0$OGn9jkKgoSZQi%h7efV?Fp=yj-;XmPHQCRdUs<^8v=GfR*mGuxe#+C6&Gy7
z;O<itvIoz2cr5Lna_sF$)J_-I?~huZjI~Zdd^Yo)IwC4+iu`?nKcyRdtT|9qwj#d(
zp?~J{=H0KPw~QG1O9k`s@C;9P@4`K2BE7A(^6bqVm!_JOM~CARl7}*^5cM6?t3(u9
z37hGXkf81~)8CSA%0*8c9kOi9%*?WrG?VpOF6EC(*5tODrngv5qc$<58dd-BgAWr|
zHVe{<Y-`GPlH-Q3b*O9VH;mGTXkVP{d&MeEy+V;cl1w>-trEeucGMPpp{i6wxhBbQ
zzU{2;&-&Oe{ixE2w}bLb!oyf|%9!$UoPeV*Y#juObMAAtyGWi`iKD^XYGTlDw=nl<
zzdbFZHgc>k#!43Puv&^uv4?N5>ETeaZ!cfI^c20B1`xeGwz4d+>stXoIsIRe@KQbR
z=j9=j`GB0T)>^tY^1#7^_mJEk$|A8Kt8lVbff#0Wi~EZtM?xkb_H+Y#P-*lu|FMjh
zr_%7%psFi`zg0MWk`-u~`tpFbJ~5&>XRpO>wleJWgsSe7Uh^nl@10Vv_J=D*2ql&6
zS=!-vWPAozWdIMcvedDn-=Aurug^lrj&PJ9EUZHOi()<#hX}`}50BswVP*R1F{v47
z*ybiCT1If))d^Yt+&{%+*0v*8vXs3xpCxafufJf2$0;v2K&WG7KNX0j&Hvg!Cx<eZ
zz#ue@(vU`l4F_}qA^JJByKwR6kEAtYF^qr@#fts)kU>~=gDmof#R*vF4Zb9&%xI)J
zRYgy456-fn=i=;aYg5sVbkiC?x>Zi>umOcTj>M4<s3~{0eWr*B#I^UclXp*FzkWTj
zczK>&bd+#UpwWHsVWP<FB=l+s?KBu0<%cjM>W5gq6JU576H^(I=F7C6^6?n9r33A*
z!%0WsJKb24?fbw=J1;KJ6#Ij1;7Kl;A38epD&{hf=RI(Abcf~GdXUtINX3U&KkrB~
z{7f@q#)N8RZ-`-JIL9@-rl4KlCkN?KMbWk+;L0NPBH_9So^pv3sItwhIi2_<?`Z=;
zbo^~zB4LWwqQK4ti`%f~yrVhYeq`UnQJ+W!&O0qHFTasZT9SZNlwg=Ik9U1Y1<~nf
z<}o)Qu|DbfbB^SkuKQYGYW0w^5H^h%8J4E>_&SztNJUq+juo$X?s4gPIR5P}8)vqK
zg#T5W$-B}i#b>&+K28blcvwyStZ(N2U~ZIWzK(c)ag9e+)1CE049`okdA})3j>}%q
z&?pVquMiy_eL0EBhI=(zq!a?`&QA_fl{n;A?15Riihlw(rPh@<oXm6W)G@E;DGUrd
zA3q>9G98&WO-)T@fS6g;(}W$9m3^>Fh?AteeVHPz0)gf4XNqPDduWKI8gI;RET1(5
zK);6i;m<iNPG1E>ee8zn<H}naBxqoFKETiHU}O|+v#-;M^?Ldp@hlvcMnH)im@)IQ
z^cv~4$?FGfCby&f8g_%};$L!rdSb9v(zBhnNTEX9G7zV#Cs5hXTYbVi#e)AjZCIjX
zPHAyKPUb$T5d`z=?*=SqOf}!uju$5dC1PVaBWaZZlyhMse0MhPQhgQh$Li0T@X4DE
z7zXYVPd^J*p{{otq?nu}KI*joyS*`3hBv<$?35+8dw`pJXsWnz>$Odjd3*T~@CtCG
z*Zm~&MrV)*#bM5|QHG~%dE=$b-NS|}u3x=Ymc~Ph7x(Yr)%uIi4!--yA;}phOa<Xb
z%dnxWY%8;68}#|l@0~~C8Kv0IThuRHc#n*P6)BwCGlf<2f*z>L_Wiv$Yxn<4`4$zv
z|EvNWv?K3EhBg>lWJ=rf9vqL&$T$KrfSo-WDIbXG4SI~X<Pn)3J$fW7E32=s4<>iD
z=KG7PYHG;FP`(3?v|ex1EqO{)dui$EF|PqND?>NLhg~g}S#P!rYN1Md3WT++Y(_?g
zEP~4)JeLoFP4hur(c7EAhrYf(`VH09w}1GMQ2Iw>W8;pTCneyFxChtdDZHU5Vw<Hq
zw;e;y@F+OBRX^;>6UlXxaloF&D;8#E<EMS3>$1iytgRbozQiKI3s_2d_N-)CE-F4b
z*}7?E&Niq>RZlN`#I40rqeCu=n~zU%x_Exxb%D(z9<ia#B1J%R!C<)ae&nH3Ysf17
zm+RuK=imALT?_EfDzlRZ_-|F}&u-8^Ce3S5aC8*>yYu3BnGrnFIJjuZX%NCkJ8!JH
z3{vQTy$k)5!~~zNZnqR+cKUi<?yLJm6?M7j>X+yoY8Hj=sYzn3q9gv4RfPQZp);Fa
zJ%{TI74(>&Jiy0?5=A#Nz03#;0s1HdGqFAWN)@;Ly&pFhy12R>gqaK}NH8)I8tQWW
zE0uBw{JNmyNb}L|2SV%B!FXWJt2aFkOsonQ6Jlp4uGH}xKl%2XW5Rps&xcRF=k2{_
z^U<eo8psbACVf{4o_bgNLLmk;FgyEc-`(3c?BMkd6*4U)OabCl>buPn?|;9$=Qmx;
z)Kh=((4`L_BUUkf=hsf-z!iRbc9FXh1Xb>Uim;&5=-0;*PS%8Fy8iZ8u0%Tz<o_Np
z_{}L4c+<;uilSAgTI^54<EQEA=<G(axi;}m$;*t%m}CIAw7TV0w78rcEf|?OWhN*j
zL=GmqD&f-WgZ#?_ckigHtAjk)phXT#QMHAD2cRFXHot&?O%v0JqNL?kg^uAWK^eU7
zojZ5Dy=TF;cSF};w#Tvg()FH)a;1O%elq&mv)@_WznA394<&RX-dc?Sc%&jTOLCw;
zEH=T(xebZYZi;2PVl~?ueNE^cMsIJKSH+q?Q@Hpi?f1YFv{r@R=l%?3+q7v@dCFch
z8&o-O^FQlIz<Ck<<?dG=RC;kdPlS~0?X{D;1kW%vo3)0$)5_x#a>i8fwjboVA|B~!
zYHRNb<W#)h+S-~!JR%_-q859&Zgaz}Kkf8fS?DN3{-8Z@_)HvX(Yz=istp`wm|gi<
z_Kk#P^J`;dpXDrBp@%M=AX(jbt3}EwHMKe&xo3Jn=n=~sM^KA@ZezdUDV)--!Y|;%
zAGF>nI<KxCw0I#mE30warpY*hG2X`u2wGTJY#@FJ#5XNr?Y^Y}3oE&G$y%vzw{3Fi
zeEd=rM79Ti^)0;Dt5>hQL7*s3Upc~NuZc?5;Zrxd^AEWSnR^}ksQ~3SgS1760TSPd
zz=+Cijf}c=*0dALs~Zj$Fyz<cy<sU5_EQ18QskQX3!vzJ`;Omq5Wl^lM~C9VAQbwb
z$X~j_aRnYJYvoHUHxEzXjqKFaFChsool6@FFMU?@1jON5mC|G|uLGmN<su^UwwVmB
zLi<~9JWkCAQk`=-GyH3lMmCBKj#Rovr`8$|$H*wR=Q{lGrzgS*?}pi+obdK;wyor&
zL~P$p4#nQ2pU=I?N3!=Gw$g<X#J$0~SUGrY*~EFVDUb&w_WO8Wby`h7&o_=RA3CXx
zGHEyX27kg%Bj{ho|5;>+0p}3O1kW)Ca$|;EQ5<D&Z)Nwct>cx3%ZEo1;ha&ZA}O9X
zR2yZp<J|-q`{M68pZC)RNIjo!task05iw;`@#rAQ;7^3mzyGJ^nkxv%`0qx^|0MAe
z`LO?%SN*3*sQ&d7|5QcV|FO%-&1b*LOie|F5fSJ%ztQJ|+}vk_d3bnY3JMBtG#qN)
z#^g1r-~9U0J805&yx#qD0&=6B((caBDlh)UY@t;bP%9`Xh#tGB7Gp@SG~b;4IT=c&
z6Cua$Z(<htv=AV96~CviPd)lVIn#rx@3l0wL94tosNC4qU3Zm@sLA0_1FLJUFTu|L
z>Y%Hm6P=rz3$*w6QBcq+ghyGTnEKUuP%UY};C=kkmie>zChfFLV7W8(g)$-|*Ks*J
zyY!}ZN!0oe+W-1o3xAv0?{Z#8=SjhIRz}8Ar9m~&_~mJE%&m7u+V*!+0o7gtFCMVp
zbxY~*PWKcv3ItH@61TtlTN+m5SFr&h<lg14%7MhSQZFO!Up^EEMgZaY2iycJPT*!5
zvdaBzwulOwUVpq*T3Sk8JxMRO7O(ya<;vPxbwNcGGf@Fy%3=RGu(r0=cqJhzX{y(J
z)y7?ipO<9|uM;2!_+JK#|H}Zxf4Q=MvNHDnSU>dtU1%Ji@p)}e9Vti=%5@)v!33yi
z7SF3cQ=nsmkD;rWzk{mG0reJ>n=8i74t2|VJx2Obv)<Hva5vG5IwQB~0`ldtKS*DE
z7s0)2+95)GAEarp3mO8iJ=fl>y3G%%7ch~)&&F>^<@_|DAz^J)E?l@UU`xM_cKOZP
z0%hqr<4;jCGSw*9fI;H57yEHfp{J|cQ4*ge{J8`*Lybrg|9;ODl4bGh+f@mx7#gyv
z#VS!7T$fR3<2f99?rxJJ$^uz%Fa}9cqddU=`QC6pL?}T|2Kbg<B_zVdSc>SrG2fYp
zjCImuG7c`<I2ZIyo5{!QtOZ>~MPGPFXf6eacXV*bgm?TrZAZkh`IUd#Bot5A&a_@J
zY!kld>VxY4@2C(m6M=6cc(FpACXmC#CBGY%o(QdZ4>GSgVX967GG~K<1CPNdo(Orf
z7c7MF(C_O@Ed*}&WQxk{shd1~>eOv~j)sQD4rB|r`EMPs7}wtkge+A=z418u87Kk%
z+@64)5{{PO@l!wd(idv--m39TDk$LHL@7n8CHMf>!Xl(hS#`rC&BFJoQy!nCa8nf!
zoQ#Y}Jg{zto%eU1gTyV+rbhEek#%jdcggc9&4a+!sh2<8U#<D>qN;%bD?l=YERySR
z8T~gP>)drI&wY-j6ssK5j|VTM^;QyepS3S$cxWgZ(m@i8;HNo4UzXq%3U`S~A+LhY
z2h0Z1BMT>6cnIa-BaSY9f47gl3nHKEmY_N#l5M``T#qowcAvAgxwHPM)iubST!k%J
z@)D?bD?n8!#a{bE&w?^zlCJFm{(vfJiD#XbnZH00a{@8(Q-Mq81`AhMZp-iq){F@8
z4<yf{TG$}I&5dn%Dq5$y301C!y!PkLTj^xOMh;|FM2RU;I7+ZI(Fvn8(bZJE?7*f$
zPg?&pT+_v2VNo|f(Wv*SiZ1s59JO)w2bQ|{^zWyz{>I~5PF5nVS7UO(Kd#p&O%2Ga
zca{84X%C>A!hf&B;9s}+J3KzS;d(42D>|s;dU+F$p41~qeeD9ETl6-vP&eu;T;Aj(
zKb(EbRP+}{MihJWNq+-1nFv?+px#Yg7D1Bj3~izj3ObbzKit|Hjq2E|gytZ!BcfRa
z`>&8cyhFgC;B*c%BdG#{!Igsd5NrBzzBg)#ZE;@tepiO05;VP2{%|t*d=z7$WhI;s
zC>Xku-JAZP%d=%NeR+H6Pq>Pwf7RIH+(ck65e>IF5Iq2qWTUQXLr@~bVZ6V3>|=*$
z;o<NPhx~J5__~6W0@~R$Jr$LQyk5vK$4OcC9#`bf4<FdtsIk&hamI6?<`t4F>3Q+a
z*S4(R8=IP(W@m^p5=|E1YqJ*I;A_$fu6=u}7eYT~hc5O?cCw(lkfx`r8^%i{(~OL%
zRU(uOi>RtAfxVaB-Rb?u$F|O=2J;GNz22)`1d<;_Iuc#$re)OR&jru4m`45MZ6hE%
zOmjz<M;Tn+4yd3X%uV!{ffnusM`qPj<X~(g`<@AH4F7gfP;$Qf3@N#%lB#mo2OoM7
zgp?z4N1=(D-c1$A8;C|P0n->5<j8+t9RE%}kTWQx9?ohtPw*doo^zDw2!;DPJRoT4
z#xi+(d-EjcM7RCgL_bbi`QiSZzFH2^nJC%{wu9j#a$L)_a!!^z*+>Eep^?O1f`XAg
z`?vL^{r3H|ENQMe&1{v?C_?O`Xp~OUU3^nHw~e%w5c&=GEgXpUAu?okcK)K-u7WnY
z$GP~r(&Y_gMoS%}qlZy{i-nslGr8nON%h|@KG-0zy1nsBKK5vBGHJVE*ekW3_4_g?
z^|Qg*c(K(QKZpYU!Y8+8P`2^26rqgTBymA#<2?R>DJCbUd!ra?60k?65Cc~;TK2uI
zT+=UqnBDOgnybdp;SzW*<?};?r99nX2@s90YMaR?y?Lto{ornPjkP7jT#TSp<oz3n
zi2IVjSU_~iyVZ^mNd-c=tgIw@1XNU2Yrk3f+`k{4mnVMu!A?e^nOEFJ?#6ISH6`|{
zmOiZcx9@(;qRFBObkK2;&vpBE#~!H3DyO*V>pE}@I?gXKa{aDB^b75u8ipIw*Yd__
z<=ijr8i*s~Z`N#ikI>y!&K30lm0UO9t!WNDFG36|H09|RtvPG3x_#}29q+-uFxwO_
zIZA7w90V`l^Lr=Msb-6m;w9bs#zsyrW)xu`u*pb+%eVD#qF;PV15W@gx%jV6q8;uN
z&Y%<t8=G77V|R`V^mA#0@u39tqI{~K^CN0-;p{W<YW>NJPn*s)kYZSrnI1iIE;;QO
z^Y=Q1Jc+i5wZCjsV9)&}?e1TGS@mn>fs20?wA{!3w)YVGtG(+#9avp)H>T^(-e2y&
z>fHa!FBL@__ewC{{%NC7;vM~)C-(kf88P~Nc%kI{&*kk6`r$(h%MJnJvGel%lWy&<
z^YyxT?l&uO&LtA=v)^fsNXK48(GBs&irV<inSE??XI$JMgbwm_rN7~_@`nxWF@ECl
z1s*8WYR(VRSAJGzyMVLYgPjk_yUW|+wfg`Q<0eDs*X)%ydJ*-VtT7m@38FbAGKKbu
z$eA9ujs*ip=o1KzKnbH#%Cm5)F~OdLTzBKih4GtCL~e>A{#*?U3k!-%MjG?8s&N+v
z&(QBI9M`3Ag#M?`qTHVA#vm=>S$ahO2Jb06C_1l-YL$N_-*}^{6-|HEr%$*2=+#^M
z;zv2?z=5?t(#+%PizgfXBxxT(?K@}sLrCvsP;w-w3GG+Y)6?c<15H0~-t!n)uX)*1
zHYi|tY_=dxy)SQ0+6SIM(reS;H=dl)GxOpCx4K|?eiOQm>RMZMs5^y|GBX=y9!qET
zWIFi@H@><Y(3*(~+!bh78~lzRPf9J_t!<H%8P5{)65U2Gl9G~I1qbAU3#OBE`<|$u
ztMx(`&ecr+B<GR^AHzj<u#E{Gi}{6}`shOvmyk$ciRmyy-X4;NpDb?~P(ycwnWej3
z&d9*P87C*Fpsfv&r2I>IL^6%epXUm{c0#1xx!b(#l^BE_X4ck9w7$%2t_cY(b(|60
z>P!Hy*6TL{Ij$wpfzU?MYcwQ&UQ2qpfZ~f=1<k8(tXflBU;l_>aAagerDaWmdFPuy
z;!?aOe3w0o61tPxFJ<~AUG(E9hn^@{;ui-eCsS~qp(q&7jb))zjkb5%IvZ6GB5`RI
zoR0wUU|W(;z5(rA#k~P#)Ws^-P~YC(pqSzMN0)abio4H-PY8g^aM3Lpb~|SX0LRT#
z;F;Shq31Su&(Cjj(`^HqhG=a=Ly(`JA9w6C8N3!hQvuAzGgoJ=l(yj0uC-%pwn)BK
zT_}WRPYmH{BCKPPlSn=J5eis^e^fn|6dR<Ct<Zy3PKw#2i!nssQ5#xY!(tY?WofNd
z=Wz}cWx6IkoR}WKfi?;wQUNzFrJL7*NAWuf<l+3o9%Xxt`xssj_=Q|`tmFc=%eJje
z*E;vb{U5I8{)?ftV*yd?%{KjDYJ)VVZaS4?t<aXo&H3YQ!KK^B(#-Sn^EJ(*(7e|B
z#3H#I6^lVyhd?gfwY{OK7j&YpNM2L3iJMIn4PGQM3-Y6f<n!tAMY1PR5?t<BS@6n*
z<UrNXaOx=iHEyenOE`O>^Xv+WSCwtCD0^5Wrk+MJ5n3hMA-U$~jj!xU!z9k3COkpM
zGPQ&I?`P|_Fg;sjSbz2h@;!fkyJ7St?Xxdb#l^(~IAB9K9>WO&^d~$Y=sLBz9o<KL
zC{EWvrxOJ(C?hKirY?+{&@NGHJyHaQqBd4^AN|ql6AgHrp+Fu=gi>#AGFB!6eHG91
z^B1iAl_A=SS{gAG0`6Lb+ITc+C;bxP1kK>9q-2whns707j0n3e;cD?5m8AUi6_Uik
z(NQH+Q&Z{bRR8_*o5cs7wtN3rr9f_j??ouD%%SqFvpj(Ud@$Sc_q%2`HeM9ZiT;`z
zujPKPK9ax$^!#~pb2)l(gzN`CpOvGB&{y(!`ELOV{MZ7`0AomyayxHsXjd8Klt;zH
zb25f4Xslho!1+3f;AhL|`}#^74l^ZhD4sSAZAK&*v)<7as*P8Efd|rP4RLxLJysR(
zT;jH}G!v37ij-sfs0=g`FIeeOgTC$5kr7@S(IZC^j~-j<*k^BVf8if4%>S@M_fa-F
zEOXy3L4>E@&Wft}i|)TI@O`EN^c(Tote{&*_uI09=j+yhGWzShcE_X1%#|w#XOl)2
zB4e%mI)lP}Ipp8z^+xpP*DAsdfL&CO_vX-|pz9cO?_9(7ksKK!?%5(Wc^)kR9masz
z6*wI_?PKUFtAMgib?}Gz2`8kAMotp{Z{X4!M5ApW<x!&iFd6kC3b4fV>LK3)f7{zG
zr|+Edh)v|2qQWYLde+;+Gq|`t1ePO2{Mv42w-*+4bo5HQur+=ZOhPUmO9&OmPmcxn
z2^25d!aN(bO(QH{m;8*a9jg+c2ln%I^F3KDaPS1NH8#`J!B<ZWw0}g48|_UzPTz^5
zw?OJz=bQV~AFJP?zPP!az&*HXGb^hqm`v8&C4VUXXv?Hm;UCwzNzYEvWdUu(lj~<>
zkPBB*NE_Eb{svJiy10c|Wods~Cho{(p_JJs(f$Y4dZLcghc+f|5^aocFKpR;<W<2E
zd_Y|7b^7v{Yar6WH!Llc4{I(?B8)0X?I1t7(VO60l3y)8{3x3VEsAfK^7Ha4C_q+8
z|8Q$3C^LC&h{TiU>q=Z~B)82N)QSJ*KX5Ww;ZJ(0M3;%*z|VMW#-OVB$KOzTa)j^+
z@&O%-eWGH}wy1Ik1?P<^!@H1HNcn(OYkuwl@cPGB%Esv@O3AsJvORX^AS{kDTpH30
z6?ZTv#aBJo!51x%QrQ7Y1kaGx4k737kw3jey7Xf%K<B?5B;UgE=Y)^){C<nA2YHoZ
zz`4-F`|q;44ji6YA?oRqo5h}sM$?z7KJk7>$wIJz`?n2qXti0)5_VbnvTEI)KS`^I
z;bIq)8_$oFzG^5)!nq?2KB1qKfZ^3>T|NSRz%XayE>xhx7sj~n^rNOBy0u(vm@>y*
z@!tao<zi0z4*3YsA?jy=$=2Wg2Il$y4l)k@7zpt1u5<kl?|)PEhbU#+qnk|RVgFxn
z%D-;$@0jhMxEArLt*f|ApmGXD-u+T3;tA=ghSs721nOb5mk?ns`kP%c_t72Yg$s(?
z3Tz6-Poqa(xuAQ@5Ps|{8eVeIXCnliR5+R;0i|A@I6=1aMn%HCo7w1*glg|2a<^q-
zD>#dah|Yj;La5z_LkCN``iQo-7jY)eQICG*{a5#do!4#J{b;Mw(cinv{A+Y9l7b*$
zsBdU^0S<xOP_u{@#6brv(1&V@@saC}nbgac<g*i%LcsG2+$KQr>PkNX-mazSPU`3=
zzm2G<sHPwtA0O+T^tm}RUdE;zK(uyi)@@F-r5C(!RJ^*kMO!zry1JS?pnL$L;RKJv
zr+x??O_Cejp#3%hGm@kwt)b+x)ZxYS1?|VBrB7rWT$l(bwaL!TjwOIt(aPM+EY9(#
z^@&MId?)X0jB|YOB)umy8`&-RPxW&rz0!O1Y`%AvBo`LG$qb3ydF1M!7B&J}v{s30
zoyW$Gy;SC#%X7ld2_T*_E>f%G(+0T3*ozDl>v<rPsJH?js#>Oq^ADw%&X5uD3qNn%
zdA|H{Vl;T>Z2j4%0&~45#;(F;qZJ^Y<<?E$$e}NpbLb<k0*}J;*n64CvWOSXG^&?u
zYHz<Jt*CrNM8sA9Yvu0UyW2H#jvLY@^jYW_+_I`V7*U1LokZ$JWELP#l+`7EW2k%$
zW<+wTUX1{JzTC#;p;@$71VLB#@`Txd@|{i0!I0?Xw+b>a4D_3)b@BzB6+;Sbi#Fmb
z=%<+LVk&+NSP&$=49$jqpAnFf3%ZQB83WZn19&4|amdTb-IYihP@__*z1f^#jD{=q
z-rjELH+>Qbt%s<qy_G>@zT?GHK#x@$+{&LGzg6M1r3zyK%8EaJi-4Z)JoT4t%@FF2
zPFT4fS5Q#U(bWy)nCPpj1ny1<_g*&s_~-Av`K47h@iCd14w++-$+<UYl5A%X&?_LJ
zw(Yvdl}w}flb~uMEn-@|=a%JmUa>fVptGEFlX3f-O0F+YZMNX4-Rj97$JuW%Hat2y
z?J>GGxp4BYQI+lA9M&~V`u9f-%cj=Xzg18O&NnLat3qCGSw%Ozr&k(2u@t;eJxE$Z
z>}J}WAj}mc>dHs?bn{kBxF_Kp!s{f5zF9S8DfL`>Xs$1Kl5=YYvn)PAb|1kpM_3Z~
zo_%cEeMP2G9**D^zG!|S*E6^(;`}Ob2c4~4`m39dEd69;Msu!jht{GWi=O281O7^@
zZ?*X0nkVJ{<*WY{w-}Bc|0f2Wf6Ji$w=*sCM8_M^$iEak3JnTU_~xW$yp9>7lhcio
zHIYIKUSxe2PqUL37MpR68oNGrjrxLXXn1(Za(Z=&#H*KHk}G;#_fM7N-$!w>w!OXG
zqGknZJyS&I!s*5OWO#E@ENt*onII};>Mz1^U`|>Y54<k9wa&B(y`|J{W@g2^T2gSF
zQsOgOVg&a({`JWbpm=k+r*Cl8<;7tWs2j{IED{_y&L5QNX!z35kl9-1dMf=sLNXa5
zW8k)Ik(|p(AC4d;lbL{Ui?Jpoa*_Mkt~!EY74}X}nqn$cZOG3v_np5;@<os)8Vj=|
z6d&5OB#$8FUo9034oDc~HTeLf!Y>gJaShm$L<B_3_5zpEVgusWQU4--UCzIPRZATY
z2lu<=t|<r2o;h1Wr~WC@@z}urwkIg^e87|+oi?F>@<1~cx!<_d3oZl=*CWfh)3sX>
zM`KWr)cfw}=s4eNE<lzT@8#=N__U~~NbftpPR2)&Qo`g-N+>NYW`25Wk@jm*jra!W
z2<QCy|Lu(OUm*Kmo67JHywyL^z=^**8z$xEm|Q@sAZ5jms56ih(iDuqCsOSKW;ZXR
z<BV62pKv>~p@Qf>S81pO2QfFsijER+&--$L<0g`9Ja|Cjs-L5ihv&tF(`DbmsSw2V
zGCyB}ZSJF-?Obn2KcNFLOeN~!KDjICLm>z<o@FaRH%~8{zw9K9uIXgb6PktR-g8Mm
zM2?EA!32F)Rmf|pM~)onL7k%#Q=jyxugGN#+f}x%wLy-Hv2Q?6AjIbV%5-&g)6s&s
zU!LgQrK{*E@@HN1=*|ed?GApu#~iz)-Dg#2T0nyYaFCX)1zvg%d(0;O-pv`@N-=;`
zBH{fM+DyUlD+G|?0Up70#P(EFSVQ92{sOF&{IWq8f7P2m)&z1yL_}y~F3Mq_P7bl}
zZ`P!P8-5|}`1N|3$=kpFDs?8`-Pc!m-0;M9@;Zc4hfBU-xH8jmdlReV(HE+5_sz`f
zbd8QA`W?UZ^VHQv-S%*X;Mk&~_RQ=MUKc70lp@;$>-n1uTrVS|fb%^M@~++5%4*8)
zY?KaC4)A;2oP))Ld0l9Ja&;@XlK>8EN#$45aweO$wzf(S!I`%k1bEXssRIK84NNyt
z(pfqj5_+T8%+m6L>f|5$1zaywqrmrQi^sr8I?bQis3Wy|g-l(uL^Id+bFyAv8XNms
zDeK(*-O8XSA8M^MXO?t+9aUw}2XNfepD#Y|QL@mepJJ2rF8>I8Kfg`N)u)LmDJiWN
z^Dz8Fqjz}qg*wc;c*vL49F3YmtTYTyQ)2b8C}t=~FzP{<x|8A9S|oxCjg+iq5T^Wg
zQK)WlXy@kd@q&6>U?LTRE{xG|knk`d61{vCoh7#d3+BAU5ZSo7qGfg+xq5z%Wi=Yn
z4Ek7>&zpf>BBhD|4rEvrXuKnN!gHi>NJ8WTTqgR0`c*$OA-EFGheO@~<04qwzN}7&
zE-I2nB)t+K+Hr7xemF%_jB%+P8(r7f7|aohS~)0|c#?7qe16u;e8A9SN?l>A#&I_{
zH<#t~c=N8vm288>B4{{l^3|N=y%s1P0R?j+7m3B*&^f<#?bzpMC+iSDIYI@EN>yh_
z8Xd?1Zqg{KmmbOn6opm3H^Npx&&mg&eA~t1PioJEdJ8bdWR%f5OL8-Y!WYN@I9jlm
zs%Rahvx}&HB5Ve7pzQn!=}9<(tqCM7+L(Qm5KdPLJUwjQb+R!)RL1)obI0syCF!{y
z!eHe>Vbw7`3(@{#0gpii^|N$V=)8-t+IP-!bmZD$SYaQgSbahZYY6G_Wwt4}E<Iba
z<qEPLW$>MUXBy5aZbwa_QOBa!d!OG&DAZM`Kd8~d_$!S+s#pUy|Bq#@|HaWN9VdcB
zRYV4wX*4N;(Jhr^Yy=14+0)TLtwC(127@k1jx8UM;@-=n4Ye$>yl7P6h+aRy7!5Zc
zu{nCZvXfkaT*}oco>K`N<q$SRL35T)N=hI?6D2X^Pd<PQ${}#jmVMp_jkh5wg<;lt
zW<}^@zCb<-1??vZ$$8xJwwRoAB{sq_9={k!8gX1u84oOQVPQRV@mSE-DH%Al1dqg<
zPTSEMKRLi$Jrs)WZZMC}ILP*HIx-oDoHNUP8HYw}Wk3jS$$hjcLZT^GF3J`CwUzxH
zXfEtpj8RsMk}h;#-hX}hOKiLN40wicx5+p(+631Wqjwh9s`<+-JCjCA4tN*)<?Rpl
z({tqiN+jEzz`~$WiZ;**(xD`O7+paqj}?Pt(x)k}Oe)I<pus~!3_ukY=Q5&;BbyL@
zXZZ>W*S!-qsPd-R?w$`rHZ2uqnI<1#j7p!n(n+twfQ__f(zZ#z8)Of79k{y-y+<ZR
ziHhY)b$Dn`Rnza=6!hZ&cO~d51crp_zcHf!_wwuC?Ue9vPX!*GeSr~%5=25U0q(d5
zZM+_RHBnLM|L=RGrWEHW*=}+L!v)9~Iy6SQjHH$wwrETwmE;&X|7zHd#}i2p>&8TF
zF_7+R+(F19B7OMtUbQ}abqTBjnaW{}SR@JRn`|4sG=TXZEqL~HC=1mk*w?DZ%{7yW
zh&Y#m<o69Q7n9Va2+>1Yy-=hOFPs&4`U={faKaXn(M3c(NlFf=tdbUR($qYHmRvJ)
zbDg2ozS|d6_m>JxoN!fBPHvE55vV%u_A>bbcThabYF7%%lYbJTeQb6CeWD@|)0uay
z>(U>FYV9LR0C`_M2vJUEOu+Iy9^GF~yujW2JW#2E3=-l%QKJU^-m1M9>d}{%9DeQX
zuc6W&^M_~VFjXxO!zA*zsHyO{inH+qot;CmDU1xWsQlUw4|L8b#!bsPpES;+4pVlR
z&Bnr_612{g-_5FmKhMV1&zcyVvC(a_a8NE&u=^Y-wD8OjO7V|qp-)2(m@%xR22;Qy
zY&?c_8~i@xp9eigMuCvtYqAJr;1p6edq>ABXjBnH@V7%<s~@nW676HE)SSsRXg^ja
z7-$5lj9e7*5?Jo;cEVUxf@?^Lh4e#_!X%c}1?-kB&QCv4@R8afsr1p^F-yqJ!9n+t
z`#i}XK>>k=EZu`X)E2O^b*SPK$Bi;HL0E2rQm3+jj{1e7=FZ`JfzXjiAo=+SrO*Uv
zHzggqkx(2FfL2W?PXLV|e<6YAs@%e!p-i<DjTxP`y7AgQ8U0^N6pD{v92yxe;jm61
z!4l>;hwklQq+)s?g-08oYU0$3Mqw@!t!8Dhrp0Sz$qqyF^3`q`Op&3eBxfWPF61Q3
zEdEg-j<FDOw(l9`NI6mc%o#rIpt)vLNGHmbJrB#<F3<J5U6=cS>iHoup$Z^ng0yjC
ztdrrOQPl?RT|v8xKW&J+bJl^2XE!mi>br?5!sJs3DMsq24SuV6tiYDa)A}eei2iE5
zN9yy~7CYDNf@>ohi0n3%p9-IWHpYNW`2dxe(|it5ERM~;mxl=)ekP!g`B(A*=-4`z
zt8hwKR5V1P4Y5C|7WSR^;H<A<-|b8zbe@r+y@$~OXf8J>RLP77;d%EP8p>dtk~ovO
zqj|$Lw8(SHWAvSV?}gViyhtU5j(blexj~plHxe9nc@i4BKW`ym50N(%YA`ZVFA;!h
ztl1RHEgz7S)f%7D{l!1%><co?N<M(@_RU@i!xHIkn`7VU2U;)4>OB1MfF_v1l8ed`
ziNaTAzkd@$-zMFdKR-R*;MZsoENbxNmd2f%AqGutZ4pEehHh&$Qdlg=<o*a@!(oi1
zMK{-#xUx1hh|R&f7E~D8pu4TQsc1Tr)#Q$e3wlEXImomvKpi+$orW)89*(*9OY9=(
z%$He{dUSFh2I@MKY6CfZqy{#D-hNS>?@+YBli2{I)0zN-tgp6}=5jCCTwwWiEiIuO
z#NsE=zuia7J;SW7wy{woQjLE&nN917YTP3tND;#%8j=7kLO5*!dRKE$>=Rjl2V{rn
zc?mQsb~wHLsLR(tbQi(Vf_DKky<m)S1U&jZOfN*Wzc`z1Znax);Cc#)!9<Nr6qzeW
zbPsdUStP%KZmAM{u*T6pz@`$^&h16TSxeHmqV@1UlnoF!_;6e&YOxI6{xU_aDg&k>
z%@=Q>@>cwDEtB4#1SZ+IkpcmU>ZFKY-=y2?QzQfp9Y;CZZER6$=nc24J}A?OdjN7v
z5fRsB^xTpjPW7|$jFK24Mq2AogRF20+=N<&;X-Rug$<j5Hr+VsuZ~PWn#6n&^v~~c
zs8=D;!xmoU3XGN<y#NkURl&zc09aqn9d=`SCaN7uKkeHJF4U8lK!$^98hSWDN8`(3
zqx1v|SWH?)QKbENR;Gz)1g>|zZ@<4j0m=mlpa>m|MGitVH-S_%!I8tf9xD(@-awAc
zW0Ko9^!!0gLolRG)+rZvkM&oDg`ly}6gFC|IA0G!K9GaRRjW~6B_5LGM0E&gnCH?*
z=y)V%VVJOSm<-*;?3{$~$4=>9yeCM&l3bqcCYuU)oS|BC$>3wf&>-Bo)&ev(WNO+2
zRw;30TcqekWT)q#tOtNn0vMn}QIfo(R??ojOv^76)ea(yV2VZfG=_0WG~ErPk^#-8
zBv~+QP<;4CSFl^Xc#Rs&<azw-R?Z%^sQC+-`&44!59uQZnXPj!k$Y=MY+m{dpB|2q
zz9_^rN<|B8V+3~q<9tFj#tyi=k4t7w9-aI}WL7q5Ldxu}RW5MoS4Gl{9luM9{lZ1u
z0~xDH(hsO%k$P(-y(*~m#Sk8#YO4TIpLQJ69%O5uP{Dmdq)@<R)3#%e6mS}ZrFOwt
zE<}M$vxFs+U?GIbTVy;CDczGJNJJ1V@G5pgaj_Z_eP?y)tI+?Ia_$3f?o~(R$3Jh{
z-Sh6}0|hvDL|zW%fei`O0-obO(wV^qcIEp+dN5I|22rd>GN#7C-o6L;K`{WnpragR
zt6-!{&rux&<u@1NH5anDP`u)XoX8gQ_b!O>EtMlJtE9lRYgA`kkheu~xF8va+Npsn
z_dphig$FwY*@^c^hw`ZBHj}|e67Rnn;r-Nqyk9m^-DGAKQupXYyx19UqF#zXUU6-m
z!1i=xl2;OvvMMlU;vsbH{4&^^01l!@;8c9_2zo{9AnBZ^2Yha;<+tY@0G`n&q8%m7
z#IRWYobUthVieVsUs?P7{?@O#c0`23C0`9?Ck7#<)zDNZWbgxrP4XXE8Oj1m0ccqk
zLrcXa(&D-YfWzQgEmyF+1SE0Ex%D1scCMhnB1tGp>5mqH*YADDwtTG`&FrMMPZX3Y
zJgxYL12%Y*e<MJ3Mo7TeVbXZ<u)v5HL1H*iN65672r}BOJj|$mo%HuV+y_pw5djL#
z&#2wEusq%`r0;3av(6WnF<v1Jh@hV+d!X?dC)5O{w(8=@WYYzT0gq(%+moi{Z?PwK
zTuQxW-W0&}g1}JtUW0GC(@#}3j!bVK%%0g+rt7qc&*tr7qme?9#X47A%;X~OtpEei
zLqV00KRb;O<I1LPNw|E<=!whk2?@V-hqzp_LT#)XiiG~J=oOr68}rm;^(SNDu;J3X
zq+BO$z0BsUhZQwU3y!Qy+PGGrFdD`JsX>J)TK>sk6Bpo#%Bb~Xz#$yv3kV@2Ks4mo
zhlpP+q%duWbwp%zhvm>A0FvY$#8tt2vC0Kg)puXGAO_mv4zcjOMD5G=GP+m`;RQ^<
zDkhTq56xd8P9cEIz#k2Ns>}jW2svnvQ9hK0rma_k)oWNO7C`l=udhMZYZ}U;F8b|=
z%I0zYVNA7K6C{9|8k2E8$Y>GZY+a(N1|Z~gnb^}zs1uBB3G$qo$d41%ZX-_Sa6#-}
z4KS?pPa^64>sF<Y+cotblJWBRI1{=@u&p?FTZbdOHM{4O+2p_%F;rU>yIeG#E!d4t
zoV3}6LHeXsgQNUcuJAv}09tpsGoaQ9=wHO-IpqL%XtR=Zl;&fx(0m{;LLvVrA}xpz
z_m+n_EddF$%nd4gJ>jrfVAyvifC%s)1F6QOt@HdU0irb#a3dnI;kIYV2rEqAFvdJB
z4+;VKCu~TZ`QS)w2%?lKoS6{eMy-8Oz~VI0-Ay`)NNS+2(@JZL=IVYScGaXFTcnzK
z^0Q1;s1O&S(I(z+Rqh#x@gHqSA;<y#G-t7Ybab@9VMsu1fOH^`EY4MU!<aRR9TmfR
za0q-(Nl#%l62LnH30EiWY$C$&ZfGh}hzFz=MkL*1_eqeTh>+YGLQ5hJwfmga&{pm@
z61wu~BQyXjS=mAYgXlG%dh73mF+Y9lebO=X%pLRbJjx2HNk%Y9LWm{C3^^Hq=^zPC
zZ@y*+-@l{c0<U}VgglzY>m$0ddeEkVGx@6myvAl&c+E}??-Db-p+jVJWM&V-mzyn=
zNX7vB1ox0+3~|Xdl<oT}o&>ImRQOIA;xVjYe_yef#9-4(cUu_48C1fHB<LB%kw}Em
z$O2xo@7QQ0WehcqEWf#SLmI*Pg<~EzBqM>3yM9~2?J-QUR3LcLe{6?y_Mn+lnmE~k
zn%rA|)7~WhUD)_fB#8e*?SZ=oysa{ZI$|<64V5iT326S7ju~ZwA3m0JAn5Gi&`><7
zK+iKe{Y@Bj;XmflEdQB|S$G(E;@Q957PiVMPe_B6S^t%fgo=c0U<B%=ToA&wZEZ_g
zMus3#!U3h}DH6Civ}^FLOc|~OVbgghhNXZXdsAlcd?|-Iu#~nRwrlnPvR4ynGjtwg
zTgXIM0!wWByhx5jm=iL*njAecE)5z2f_6zxp$CtGRJ)zqJ(hvt05_?spHC-pFH86X
z`2f;nhu_d7|B}LsQ3;?YqS}Op<RkD!I7vPr<{fLcNy==jKo+%PjDze}W<>OMM77F6
z5De)FG`W(G9k)aNV2b3Wa!K?Pk}pzAlPLxvN8<PdNgQ*_6dxb@ok$mLXI3#ho&Aa}
zKURDVxs^crSH%D*eZ)vR%>-ta_IhBlqH1Luhs#uhrVwC1tBDx$RYFcBzy7);2RZ?1
zX4(2meeBm3qV!sNi;tKhq&FFGLWYat*wTLcb!y5+$%p)-2E?%wOE1F6yMBn}%aM52
zVfi3jJ22)_;}*bymd7drl6AyLuH!yx7sa#Uf~>g)P(KZ|Nz@%pF<zcLk{w1*^&7D*
zG*kTW500!nApuaHE?GjjME*rekaHaq_{_d&AAX(Dws#B+^_z)w{(O3{Ui33mCTnC?
z+(9rVIgQMaR1$ZvpQ|Bm8liJbfl)D0$Pq*kAUgI|3IoB~r)Mj8zg+u#d|fXlTPO#R
ze;<UvZjJd4A&jmmm?<1n3?TZKma8$o8uMBV4Ca9nkiHEt6(KJX$ixbR^~^3PBxD@<
zo1#by$E|f+lmbW*Bq99Lx3{-@ZJqFu<!~PPXUqHnaF7+r2M|$DP_Z-vL#7;k$6k|x
z-V>?&eL8M!7QY(002gQiG;=n93>L<XklpuEA${ft3zuL|=428XDMvZR_=clFK$^^Y
zqxV#yPNCl`$-uBJ>L$~&mz&eZ-{f|@xjySPdhcTMiJeC-?rW*wR)14^;`Msjj9)lj
zGOGUMbK|i{l9u;v4SAz8F~RFwDb4$No*#PN%6V#as<yZ6n)2;(!5icXCLT426{oo7
zFI;I4n=Uij!I1M?e8Z4{*TVSbc8-JhyWTI@J2>o@FY;J$9FlThoJ|#89*@6Um*=(O
zIXyEYBrF{CKUTGO-prXZd3br3YIKRXP6X~doqvsy;lY1j;Nb5AuOHi&r}|f~`eyR)
z*LU&s$n=5&1K?h>6CXboO7*&J+rIrXaNh-RE2paee7mz>Utj+W+{*d+h7kk9^f{nQ
ziE=Li&n1)EU;X{vneVGZv?L`YPQ1Onee<3@bJ8}?Y;A2-IeFzuh*a;fnb#Q@9ux@!
z<8XoRuiw9;9|Px>=lgw`*A857f9Bk|ZFc!NIdfkBWn|b<!vb8=m@ygH&A+p^+T8Qg
ztE;PRfQ_Dz>(^g@1@0G^Bhkjs&maAmU*7J|_FiBaAjJgCF<UPF`BRf^V{1E?onP(=
z&;>t#ea)U^fB*9B9J9^4ch7F;mwyHv!C3qAY?`sMvhvQ-*I~da=@YP%6!yl}%Ief?
z^L!&Kt6Q_`feV<-Y(*Iu#Giiez6Tu9k5SzkrQ6-z4Lk}5IC@!HY6@I~uz$|}J$wFa
ze;>EMZmxCtyE!}jwnm-(@#Du$Gj4_hOP{lV+;j8In;hUtS(k3!tX%cFt-bv-aJB=O
z=jL0i-@e@(I5F|x0(gk<Cs24iV7jxrT%Vnt9k~7Z<c%8<U5h-H26+aGNXp8dee@^^
z7zyiQcVFAJENJBuU|Yu0!r}z*Y%Dc3wM&;TPX-1aa706S^5n@k@7y`_`}_Og)mQaC
zyaa~d^!xkfe~<qTp1!ys<_D<+rc8m{BNY$?3Nv6;;mHZ922@oCP{CiuiweSz{&cYJ
P2f4}9)z4*}Q$iB}Z801m

literal 25381
zcmeIb2UL_>x-DAfy>%-a6$1!tF`y!_5dles0YpSZL~>FTQKEq4sJ4m$P!v$IA}U!x
zKr#kIM55#(s8|$|p@1Uu<|@1U^nK^uamKi#`;K?UX~*b|7FG5C|61Sr!kqK_`o^)N
z%FCB>FQrf@%T*339;Z+ixlkwzOn+I7S7^bH2JvIDsj9Lfe&Wx>fn0C=`1QgeJqHSf
z_d5CCya4);8HMr(MMZJn377ERR#z88$9#oB^}w^|HMhlb=`XvlbvL$1_*U=gM@o0n
zS}yy1dE3vW_{4X<F<-X5Yy2-Cl{6NL%wKbVjp2*vW6f#4`}awD|FQZn^UZ{gh4r5g
zeAv#PFTK?|@a)4M-+7Ev7(YIXKOB%|h?`e<h%+mk3!d$as$?u6cjxo{en<}a3x$%S
zT(pe*yt8edDEYbf@xnavlX7F_;C%9P^WlHO8^RtiD`Z9E<KrL4$L~1%>4C;zUjUDk
z2yND(QeJ1U@3Vetl)BsXwm~`X@vql43=DX*w6uoqMNK~`mz?mRIyISvJdcmRS?M*F
z-t+2BTdb6v`Jl$vQ7paurY~woG&MDM@7}F=;J|_J%!$jkg|4*a%a<=|m$ck5##9-o
z;)!I4dQX(4S~aCTPfGF=uh<h)E@@_FX4jX|a1bAw9t!Zjt(Sf_sbhzGK-*yWYvrpA
zJQT{vZJ)K#0q*_T4Abt~I8IKxa)|~-d@R?R)ohv4=-6PiN#NV}d+PpApFVXwz6ncJ
zB|pE7+Fh%uUS7~Y{!C#eu9i;OQY&Ygm6<8&FWdhrBeSqOK0!d>o1awMhs&q%iD>7Q
zgIha8Et79}Q2FtsD`dK&ooEu0l9M@`Mpj06wyt@rsV>izZeHo{>#Hh~{p4+RZuwY~
zdDJ?phl`?OIPQ+WR3k6eqk?>y5Vp><F{@xS$!Y>O)?X__r_=3*iq|SDzMGz48jbvd
zce#np6Wz-{+U7n`%s)3?;_KUZIzZmE!%wPzB&TG9!;wsnAF&~8a(0t1+)KW&me;xE
zu5gs+Sd)>yGv4Rlmq`;55SSefDSRh8kf&MIjJNu{`F+soZA<_))Tk(N;+s-mcGu%u
zX_;gjhs#8>Ximj9x%6^J8%z}Jvs<{WEAYX@@@|tb8w-n%{ZqxFUUz4veHN@Zr*(i?
z7~#|$rx_p>QdYcxL+*F;2uE$Jj5_V=LhreC()MROzAiKI{qgx(mY%sf-9Fpp`(5R4
z)6B{M`LV`Li#hl<)Ya9AUF@oAGz&5Mw#;OF|8%dp!mSWvx46#^zZH%(TiVfsbl!6+
zZ{Q?PpWfG(TEb_C6)hR?V&&?-IJxhch5K-+U`L>;$c>pXn&{%?+^aJ(GE(2JkbM{T
zERy^4Q?*-MLfRuEzh6985iV^9(=2{v*J1j0xzz31nW+*%>d=in9k<0z-``$e-PovG
zJEN=!<9s(pvyF;SXGFU9>o=6r?d=9%FEQ`A&96}K+(D6j6dG_8)^a9vKAUYF@LKyU
z!b$)32I*astO0%l>kO@MEdS7(Wuh;$BcvTV-)#voT$$USWBJ>LJ%3(X!ON}8=X&Ny
zm}HVB!&^Cp=07qLK2sgwbzeAOz4+75Q`Sx&w?8{m#%gg;4{5MfrAFD-aR{3fcqH}g
zG|G8dC&Rn_M0@o^Mq&~(VciTp&z1SDMpe|W)rlr-VQG1~tLl;6J5J?yF!;5Oj*gt1
z8YU*~-Rro99ko7EQ?l}|g>g;luI|{dW6Ux!`~C4bKEtrFiU@`2uN-z2(u{Y{sH1V@
z*x~TIe7hl7ud8sC;pSM}FXZ;tor6MYzWb~!c&A=qfUk$*cS}o4X0~B+Yv$YQ9F@%n
z6=%&Aru1^H`Msxy5?k#`cpJ`!IaEjsT8ev4j%c%ROfs7rnrE%ETAeJdygxk<YnrK~
zPFZ=)PESnW#mu*Ty*WL}6IYk5bsFwUY86V!3v9PaFPE9w^YqBWny+J><zXT1&h4JE
z`T5tbU6aiE+}QY_5Sz~2bnv!rk0c(RSvb`?P(P_F>qeyw*BjW<v4)iy{oe0<IVa4c
zJe`tgJ#kWPE}Kk@jU)S5I8%KuW}NYv#J)Ql4-DU0D=K2)nGjWJTQG6f>z<nAkHJPG
zm#<fwEZyiIgC_2&Q?uWWx%Z})=<_S<L^1Y<r!QNx^Fx11Vq))$D2vM8ru1~{+t|Uf
zo~$h)Wf}3FO=)J9M$gQmXw@l6`^R`ATq|z2nn+4W&<jVCY#m?a-@JKqy4ATo0(<M4
z5arC6Fg6mK%uDVTPWfued&@NZK(U7(SI?e{83Dbw>jL^(vK?qiQ#b%Zlc#r{_15Su
zc$}5bqEJq%&2O{emasT6cFn23CEGgFb#8{$;KHP79I}UVNS1KX4VgDkr(+UfVw*oI
zlaZtmbC4bu8k%!qA#LpS68`T?ep^=+TT-oYNZhhMQN&v4@r;l2$NSrj+UnJ$T5G>{
z^nJYEtjzEEQtLtULvQi3pLD184Lpj|e0Fv!Au;h&A!3DfrseuIlHNtanZXQZkdATF
z4~L#>JT@PsBqcjb{CR0{V^e)uCf6M=(p^PrrDYvDKlRSqb-Z`%8OQn0Xs-MiB$S&G
z5qy)2%9S(FWl?0%HM=XMj8447QH9`y<ed1rzWodHio2LO@i5_AjyrGKt>6;>B3y5`
z&8bNCe9aRN_^w=uS-PnAcC3!}&($fw>O@Ou?!Nf>hG66aVTI|zw^$wQU>g1ICgp>Q
z?-KRWO|iGunNf1C_L+Gw3Fqvm*p?3|OZyD!q_7;l4B<KXxoIY^&48uJl>ScRygdi~
zxVh5LmfuvmwMI!K<ktK5?_EagbUI#j_?I<PVR3fto-D`g&N{Ww-P?nO4c2gS_9SH)
zw>Z(P#cbR1bhGWvzT0PipxdW<ObokVT~5RI3}vKv3NOY!bY>d6_hm>F1kd`}dTcWG
z7%J9<b>IW%EG%wrQ4M}qCR(`Ro}jvarAMc@Ws+TPU*@gSdx9k}j?-}3O=5a*iY+)L
zsn`~CYjuU0k@3N|9PSqswI52^8u#W76f`wREP>~hYQ3nLhd06Pt;;qP2^KNp=1`cv
zaGP6Vhk<QxV`@aG(__J{M;_eq3@Uu|Xd{Vr5(Q7Q&s9HOC+lS8G}WDuVxz^#85~XV
zmM4$tK9KMHDN<gR{2kw?ov6#{J^4Ax6bGraInuFS&#v>K`TH4GJ3kz>U72v!yCwV{
zzpJUKRoB+8CPyy(VvUO4)OCKZJ$&#Wd=0uE9Gu2_o0^9Ea|haz-0DrdXP(2}Y{;2!
zZLY{U_Msp~c4@0go7YsInAyksy<>PIA8ba~sZGSBKrYf_xU>-~fw+%|BieXc&uIH(
zUgpTPh6`c=O542{PCB;If-*giZ;9Lt64H4uNwZqTYWMc8t*gVnmbGzs;`WVCN1TZ4
zF&*Q|pU7WAp(JeAdl~-9JkoV5PuvA0KUqzkBcIhPl@llRTli-OuP4qR0mwK=gA+T;
z;^32e)yl~g!ArNVQ4uuj<MSH-dQ>4fc+(L90dM&Di+k*!(4XH$tV;Ymb-r?qfz@5>
zfow)(&zO^awxJgz!r8|0$Q;t0k>{D~#m|1{_ZWPg;qP=FK{yJ|xv8L#88PsZmMLCo
zj{6<RXSM0&=8rWeyyeuxvQGTEYU`Okb@|cS3i%?w=P4<bIQ{ia)Jkbaxc2CV3q|vq
zJWrSS3xDW(Q=vQKG5b-cpr*foQ5jYEe%q6;Z?7}f?$oV1J7p*mqR{)|ZCa*zxZS~A
z$BSdGBHyKH!y8^<7jp9>jGS8~XcdV3RegQCh3dQB>>uSV&yl&Db!W!R)6+Ad+EXz6
z!v0*EHMwcm&WD!axmR8MxMC`M;@gI~Me0+xy_Y`mi8HTQW<@dIYaY&t103=%TWz~u
z-mh^m`cxormBmM)OyBa!3e!$De3*YjSX9)c<G=y3KNr;>{JF94;*XldnI|gu>Kofe
zetb@!s+8$TbYkU-ENFXl;Ck|?P*Y05q{Y4iSMjg2G<}?<tSlzBEGxd}$Frz@4I`rr
zm!WS~?QgFwx6Cp!kE|@5n;Fmx`}pyrhmldMKC2LEs0UVapTC)Th7|umH?v<v|K;<p
zjNO*5R1O|Vt0ew@ou~w6wK9L-3(qp9)rUJ9`!ahzF%7#MGU>Z_$^UvbfI8V_C7ade
zW*K1dk#TD+l`DSWTz#DnmWYny5S0;^lw{P?p$Es{g-<WyaLIj~?rE$u8r*O}=OUdQ
ztj`wFxQwJ%6t8fRoytv5I6PyxHa+DKoo$j29zP1(AtclhePzW>UoD+|UU2&QtqR1~
z<hj@>Do#%o&bj}#?$19C2JrE6COtDf=frAtHZSWa2@ut8G8!(o^-8a6NT=1vjVh+c
zs)mf6i(10xax!!JbJXlhn$G3I(Rzb)I^1=}<PLhypBKIqHQJX6<5VGb`Atqv+SvAF
z(rn6tMPGfntm{29+K_W}Zg!^QlX&GP8@Qj(t!>`s);T|(A|r4<lNGS%oA0aH$(|0R
zN1wdLiWYFZbL&ac8PRVZCZ1$+w5Q=zSny|Fs~4{qry>D!YV-=T&MXZk&YRC@UtSfX
zm&Z2mt}qAp?&maGlS_wRIvs~ROId<T^U<V#|Bf@>6A30W@pVp?78WusHYfq68@y+9
z1cikC_m8>u{pd^idbU-5P3(At!I7SC;a!QJ)fMtJ3uSZTYbzbarf!~SbEi;V-1Ny>
zLY<qaG%w?j8{C^PrC(7-lN)IIRWST_PR>e&Igg!sX-~AKXtss&2L1gW53klgl=)az
zM$)gQzDO^h-0y*^Y4jVb1m9~LSaaRms&-Q_%463WVg1@4eV;-lgbZeDLJDeQLbY{F
zO!%=KCP%{eh#~7NY{V1A<mAX80n?vxM_@^JblbJI6^TU}ot%x&JsIJegT-r2ddFsS
zO-CnO4EUxHAT1)6^a(q5dbFL3@k^t7vRb8J!ZKFvNW-<Wc(}Q9bPQL|hFeDlND9<*
zZ@%~hS1>S6HB}0**zpucwpHuH>@aM&JeWiJ`<<hGh2qTX&J8*J%;wKL#&3(|$a2w)
z^66ME61QC=R<oVrc9MVm@wnsBpBKAO4}1u=g>&A67?@6TZgtdAOyu4-0{<l^Ah3cu
z{-t-eaCS5$b#8VF6%I;@dq&}>zkj$Z<}&!jG^A~N1n+2ry`q-7a(Um2w<|~#Eyf-3
zs)mU8H5j`KixiB1do}*;)+W=sA-S=}(;ed-0RhOfpOse|q85vUbLkxuRG28`tE;uo
zZ7}in^cYPrv1aYZo?A=>>l^Rvfw6t!b6_f|htAn1r|O{)&77X>NpW_cGjeQoY$)Co
zAh$9lZ}{+7`jr(j+ep>psC~ddMtt&l-n5A~(_-RvPUB6xiWN9ZvIA}_<+6U`s+W{W
zyuY;Cc|`HiWSIxGy_;vFu;_;RyKtb+@kToL<0~!Ix0}4}`KEjSo3oA1Xr{yC^n<t8
zRsYD0lWRa)ecXLu9%WF&e4#CeRW?0XLraT`^R7{~tZ7{st#kWYe}&m`tA*7#Utvk^
zmGZ1JWx~oQU*^R-$0b>R*G#?5D=SIG!FV9!XtBYm@l+D^g{a}HKa&e*C)bIeJ@VW7
zU7Nk8Xzj_Vv)%lcmr86YB9+M85dU22QH3X+_SxF}!EmeV%Yub(nUw`2e-JXoe^Tw~
zAA3zuYLBJRuctUl52u+FdL<Rq^K;rYpMMtA>tRt}H<ME4cFV59CbQ{mlf99V5xgq$
zX|po;(H;%8;`+}8F@j4S3lhCV5~*)q%9j`3S{k?7(^&cN1h?_plE<~4XUs>)TI38*
z9-?Q9oHe=o4fTajfXqBwj{6}YCD{3hRxea~@H6xflZv)={3vUhW`cr(nsmDKE8b4^
zn9Ru-%%WU0s14PGALm`Q^@#8J&rg+b<DbH$M1cfE?xEk;eEj&afPer_-fCrD#}m_C
z$_f@4{oVZGp^W|gH$qIjq(us+hkM76M65;rXPY<lo+tH8X&}kB3`N#XXss2wjcOlw
z!07;1*qbG@B(ICB;};eXC>Spm9qXPjP)%CL=ta2^AzT@yP)MVlmkzOIW^vYMB;P*X
ztI}T0gH_mRQjn+F!zFWjhgMVrkLPNCC3EA`{8u~f)F7At$A{1O&4omAU*8AP4rj;G
zkpahOO`Z0a-YTH2RyH_0HK2^x_te4HH?TsZ*&ql@<;%J0Th##$zc8De?ZKiZd^*Oi
zW=?ZDg_CvKn^u>7{HV`cf+BR})inc~>@5b=uYXzf4O#zf0?(xWcp7%l+S(c`o-%Z)
zt1qj)f5;0TF-2NhX;U~-qnbq3bsB3YMG{{_&3b}t%uQF$eX@}<`SKiD;d_5xgFDOC
zjVcxIF?Xg}XIV`Qc+a_KWqqE$p5R;m$nMd_t-9U)+g>D(@#gQ-)6+{>=Ixlte_pyK
z=l8>PjM0N}{I~Y!zdx?9BI54Pg=i*K=E6MrOq7Oa$_lZ<0^dw@B-#*hPG4Crd9JR2
zo|jK@ygYydP3LY_$0(Qh%v-S3xFaVrCZ&<YK>{z}Wpf306tMak$ee(Zd{&bhb%_!d
z4Rx+oTASo}vFy#&W$CU#?%nVSR_Xq2Pm`0A>*PKw%=}RA!0LQT{&O~~a7MU7VQ%_(
zP(u&bWVrSE!Q|P=u0*8$8KH$)zi)COYg<mIJ*@sH%y`&)qhf?N|1}OlgmAm}+)RZ2
zGdlm8WsmNA8r$A$PTf{-xih1qiZ@RFJ(9_|JW{uD!Pb}aNbbzt*4&d)$VWVn-0+7@
zoYi9<IbpU1u4AuzqzWh0NVv!yfVUIXHA3p#WY26V@+^O@5tBx*E4(}lG)Bx|c4okv
zUo>xUf52Fs{e2Vj&Xio>RBkT|gO5tn?^{hE$KBDS^;tHsjBYwTK9q_q-q&{zJV%GB
z$>i}9T3W@Q9*U=DA4|#Z41G0FFm+hZB1Nmq!70SZsWjNUBD^U{VdUj+o+Lma2k+HT
zE7vb@GB7qwmPcsY@xhDM?5+MZWm_=7DM^FK-REocO_+XuenWk>-nr2d_tPqStdGmB
zDR%LTb=i=!Bcnw3A5**Q2vuzG$7xE)Wa4mUOT9-9pfIshZ~lT~u*>XliwCp3$x~Qa
zVXU?@%r;V3VYF6r5zR)duA!mCdzRttWU>3xFukmUtd>(QeNn7{fD=5GHva_$=EKT&
z2DbTiea-qdJ@0vJdtdY+O<cyE*^Anzsez?1{^iO#1uu_hg=th|OylpR3N&8mH#-z_
z>73c*A=!K`W*jx;Lq890fLWx5)1wV_l&qcZ`R=*zQDxI|sg^$v`fpedB*?J0qQTd|
zzgY$cEXSy<dxdQ83#*CC%S6sOibd76Ui_?<ByzYZ7(hV&>q|@c9(abG9{N^1gm2HT
zyLwCiBZDbs_wC)~C3UgAOt-Mr0ZNZdPII2;yPkZwPizg6y{{zMn-lmV?1LLHM;s9B
zfBE^Sb*61(mh+FA*d&pIqjBn<@h2rRrrpL^1+07*ctCy6x}2<>>0x$aVu#9?U4X&C
z7)#n(QlkQ3%*CV##y;Ob&2mst5xFjbVUjX6wYBRlY&wuzNue@If7P2-W(s&A$zKV1
zeI7D6I}|AI$A>y-;ewg;Z4Ytqr^pkyFw3RVIgSI<9r|`l^!@o~5o<`9(K2tMu*9Au
zo-ro}3g>1CBx;CKSIoJ;MSJZLlKti((X_)mN?rk>!+|Y-x?+2<V5EzHNKV(|-Z7*=
z`hI@|`0nX{)se>8C!~|So8Ojasb|GO#c<$EO~Y3>sEzbc{R-uY>`KpMz%b9ubf?KF
zCbg>zvf{Mg<KeT6f$8j-9q;JC^7Yh8E~|4MSqvB=Nrb{;M$O5s*z?851*K{lGa7H6
z9m^^#-gte(@%y)mgZ{tF_;FStIm;5RZoN@+)8<qG{TUjtL7Md(@`uD~ua;MK5zP&#
zx$Y$q91JKON>dX}&Gh$P$QPN2XA?00X&mQzU0Z-+dNd$anVJ&BF&HpV9}LvWSnH*l
z7ITj}b+*4e|GBxflVG7DwkeMuo$~A34T#0L#jfP2ZAY2OtUbaQJa=V;Y*zWC%#=qb
zb7Hz9Kp~QVRKp$XC9QNDB7H@kCnS{FBEN1bNXB`0q9yC53KSNvF*svgiJa$gU0q@V
zgGs8Xv1gHP-TE{s35g7udSnTf=|(z{jQtVmNVw+U44mWDEy{pZr#mVK@U_M|%62@t
zceHz?gE$}kRsv`FSIC(c_li1X8_oiT7#KO74P!KDt)SaS#6OcS?gpSE1whAj=S|lY
zT*DIDy&Y9S)U@;*2P;jF<Xf#eBkxH8m{|Ht+x*6pSC*$OQ<3O7M{Paz_F8e(qXPyr
zy$??NZ$GCdAt6D`F?ooIm<`jbuBxi)Ie1K4N2g?N%6m?f=WCuMkJXEPE%3}q1vhhY
zki`}!q!ct;P;2_1KY#9fozHEX6P7U}GymI7p>LoJgd06#%bk#gx&XfU=DW$iYeeQ*
zl$RSYj@)R+-DM1>9l-3fF&jz7tgU%JF*zqEr_LjfK&huEM}8>937cJ&LH=@$gFn)5
z*A#ZACN^a#YlGT!#DK@Ml8nTD?>Uxt!=yg{pC|Xq!65Cr<DNM+Ca?2Uj)Peg+j=p>
z9_+FErcXQiaMzX!c~(oWlbIP^Uvx!=Wd5Vd9^vEiYp#0uDLrxDkRwoBqWd>IFxq;4
z+kCy5%N9`+pUh>fyb%R6b3BXYEm@uOWDiPtcHKr!s~ui@2jp{ULBM_*45uSGxBn&(
z5Ct1#qU4mBB0JYGHs(Xr5px^q8V5wM^7AwG0KBJAlexw=wRp`?L*cAMr1$K^I1UOg
zpsX7>#G<4ULbUcJ(HUhn9}HBPplj7BCWVq&;z;<(3Hw$s)qktQUsePOF(+~OXFl8^
zzIgh}5(VF^b}v~GRFr(zh59BMOQ>rKXZmbAkfHcnrsPZdBQI(MI82f|3kwUj{{mCA
z>FLo3ZlK<F!1v!h4YSd|ub-SZ{IOTy(t&Hg-2h~nJZcB1sZ-2#_<Qddd{+k!nCM~8
z?LxKEJgFAlm(MDo=9A5s^Pom0N#S-LdvIl^rS0m6M|>df?i7w9BNY#*tfZv$8=u@B
zf0XvGkVBaQ!%m&*&vmwNFYb~(oP@hd&gqVSAnR;n2nbKiq82vx+?OoP3i*+$@u<0p
zC<GpkZ?N7Qi<Yf9gQ8CPrJ&n^s0M>4E<2abaNjz_+;dUuW<YAAr;@E3YJ|PCkHZr-
zhWfldXL?Z%41qB^2w%Mu>3)1df~?hCS4_Zr>QwJ4PENpsHjW!r+@9eui%)I+Kz2Z?
z<-&m*Li61=c`l;tZ9lURq*BB5JTGMXq%2R&n3-Ti@pEzlO82ewo@FW}`9+q|?MeKW
z&Gm$x2fyHs&Z^M?&t^2=*oSI^!&cGxEqsrisgvVqCb8ZjWGJZXgtJ;|zK(F*H%4?>
zCYtvr@wUFYqgs7f>B4tf^YZ-)=2N0=R}IehSZD~eKtn@=lizFnlpzX6)cH77h}=r=
z;$7c0NQUew22$=R<TZGO|6O_|l?Pi(&_d3HgI|U5;7`?~_+pd;{E0C)$&e!Z(t;(y
z@n@nJj&M;Z53a1GxIKdRqk5qbbj+CnvdYCFKXON-VURN>g+eL%#d!P|*Vze1Ok!eU
z$y}&tQC&mN<r9Xu;~{L$QY)VoKZGch$*lwZl<2iN6P;llKo%_I28$L9F%;&cJ3l>K
z#q2D<1xArXvtAS&c2)b96l*B+a`8_!mK-(`IVLa(4jQ~%)iN#kBJfG;K?0h`sm^l~
z!%k2-zu-R<%AxzxF7wD=R&M#*3qEVfzyHvfZ%lsrDE|6SdqZ7H?V=}VK~BI)9_!u}
zeGc)*w8&>3C+A&chZDo}Ey=$`|A|aG2JEkZ01Ct(Ea@uq+S*!0n7v}+WL{t;L)DOT
zZ{>v5M`bl&J?WJ~Mq9r0J%0LhEB`M)KI8Pqm=tOmp%A;m<n0pQv13QtY*Imi@!aB(
z^$QmN_F*y$pg9n5av=t;u0LwsmRsF`6>@er(j;8wKZ(fxr*Y+{Sf>yCO$+c}G4Id4
zVnw3Ybyi04lX_Ba=-nzY@7d{b;=>3=z5yt1YV|>$pd2~!oIaPgA(TuUU#qbVL6ta~
zS8x+FYbL30frfb{=L4Vwd^<2USw$oD9*C87oSX=98ag^U-)>Tt#$eqSuh@VW306Wc
z3ki88;PYnP?sLaZXlsMzxV&hYKuB2-UW^nH61uhVz%@lh#nY!x!*{%R$F<LHIcP~Z
z(VPsMDDKy?j@`8nz=n+UG>8_AWuz3_<o2yWcqKFe&>bhjTh?x2YYkQ(iTc%rB+%sj
zz!|>_D%?O|He+dNYrp?+cT-1idZmb#D|t%Fq6Tx3G)a6n%8tpd7=x?1?3X>YqJD8<
zPIw^k+4K`6TB*a<JiM?Oxv`zQyStn4Gn|}aR*f$nh?^fh5+NfI3DAd7lpvnBuK+CA
zXpxX~*l;`fzP(>ZZun$P9KLzr`U*c3e(wUjr#pHJzyQBPeRCQI)^4O)xujGuDzCn;
z6-Zm3v8xlf@*Cw+?RmeO9Z11~1q<t(IPnfO<ze0B_`V)iN|rpranhu$&;6i~xt?1O
zXQo&I9#oSz7L=tad3i17nv;3Kg_XJS>I?HmM@Kn1Q%}CWJPz1v1L1i$ID8Tl17?aH
z3?NBHG7$Q4Af3FjeJ?eUQ;UFELS1qjCqi<7hC=5wC>^2k(>`q<L^YyQ00Z8{|Bi>u
zJ9Yur6?Odeg|=$&&Y|nS$e5vgh!wf?j%+d_L0nl^FnCd$6q?}tHgH(JzJy~c;d3WM
zcxbDRrsk@S_kOnk@V>{>vgscJhV0Oa!@>>Tb%OAq1dI$kf)Di^S(Ee%nS@%iL<diS
zFGg_G>2%Z`Sxu0Gh}j{<FCir>F+x4Prv5RJ+}<>&xzO<&>K<=Q?sR=7F6B$nB~DJ_
zu8}wj*(p)3Ru894V1b&ZXDD$!Kqe)Zm)FFq(jshDxGiIBJGK+wR@&(#KXL68jAF@h
z?)$>FufATh&ICSQ9;}mW*mSDM9YlPVd;7QITj|?3MvGcVGgL)F910eA9xRh~=HcNP
zwcQ(4J)H0ymw6*jp{5<L5R>`1qv7dPI%`aTXbnu?`TCAGENakhuwQNJ=r}49$MzA?
z4<;3hcsPl5Acm1FJ8$l;GT#kf-=wUzoI0(SZ0~%$&U}6w;+O(CZGiR99f_2aCXfrt
zzqSVwmf9^B-Y)0lOcE&~G9${JG^+Uh3^-AegCLVLJ%T(ATz1;QuvO}EV!-RJuRMXG
zjzT%+;<@#UpYQm1i2@(VKspd8ZV+o}gF&p5bGdMvPhQ3!$ay2mNL3>N@|nG_N8)^>
z1Ri_O&5}y_!Jnu0Bdn0$Y>TIgFJa4rij@8Sb#dhXLa6%pCH}8v1^?5RdQ`}9UR+#q
zvj4mVi|^<BrYnfpyY|Ww4iU%oltmgOEU*UJ6%Z!bVc~wroMlTz^u8;Hb$`8hw9H{m
z^c{HVz;ZDVw^<EbgH9SEAwAXeuD2y8ChkQ30|Yv%QW*&ma`0`p;hlRInEliatkhX}
zY#qc4P5i0cJUpACDiOT99yqMt77XUq%D?)ag4eGuxddzN?&(oATD)4#)z$Tcj*haT
zBKQl!LcQafx0(b1cCc3jI76^z^5T2kUmC|%Bn1+GTs2lNTDDphQ9n)kebJ=_x%tTh
zO2k?Ga}<OBv_$K_O0NE;y2pP%#lP+0KPze3`TT>~iArxan29W%gnvR!95z}>P7oq-
zlaEwXWeJy#Atz98cGhzOnK}8hG@<fn_27q10q>ZV3j8D^{9Ba1NnJlEqL;QgxnQz;
zPgnGnZ>Tv%9HrTxQmPhQ&<1=;h7cNH(r>4wr5z7pXU4}?dy0JV1XN@Sag5#TMQaIU
z8&~!-@`qBn1__!6_;;WfNf}#q__trilfvW<K%N|3Fux$2Q<Z5G19P(jNEEHxq`Xk0
z0N{3i>Dg7Tf7J3g3iAa^R_r__LAk1_p>bCj)kap%@;hn4GkTzmpxs@my};6Zhf=gI
zTqRtUhH=}B-76$Ao=7Fxxi%4M!Uws%Od`A-d`}pQXlnc;oZGUMY1dDHb_(lW*maM%
z+L_7Ghgth550Ua85208h9NmlsjMB6r=Fy`^w4O0k1Nid|^3(umx866Vo>nzlLDs5B
zrdse?atrV))I)1^4DFQm&8~ET3;}r}Ai@|BJ0(*-ZSio#L_*~2d(O)0HnQMgQX~<b
zGIXHIz#FD|;9pVIo~o^{KihtoXeV+%m+!Ovx&e0r_uJHxEVwCPO0j4P@)0D7dgc)s
z?>);4_dpN1l2D7NfCjHC+$N<&Yo8ytM11Si8d2lk1sgr}%cM<)Cp-cQ1SnNuFT-Qf
z;^I_DEiBAvu|9Y1_sDsJcZ164fu2$A_s$0+5v3)@#l<PJ*xUW&3Cao`6HmunRz462
zB7RlI@=rt0Yj|CYz3Z!WqF#N_R}N)-cMLj8mLxjm2lKCv)Z}k*{Y2D*D_NVutkP6k
zpbm%?VSjn_m)rk(yy0(maR1+_DA}At|L5yam$F*c6PDe<DxCAo1KyYl^psFAF(?9J
zx3Qt5KlC6V3V{YMYN~}HAYgi(*U8s1Is0kd3{ef<J1P@bS4qwR0bosU31@D*_|xNf
zYQKJVj&RO|ZaiBa%7rr2AYlqHM&ZF%Je-6vYy|T9I5jo3icX$`GD5Bd<gMMjjHo*k
zrfk5SgRt9u{y0d9gAfG}B8ii82(rBQL>#yOrvs}p=aTyp0W>v}aP3(+1J@O%$)N-F
zHsr^KNufJm2H74;rQ==p>}yRXUS5(%lw6sHMn)0Br0-w|H0`_X&%#&I>2u9}MEGF^
z7d`9n=%*{}N?*!HT;b#LsJRaQIbUDj11qGT)Kx+ilt#~XV64g6K)%_Bn($@b04Ve{
zI()*}GD0mYDq2CKoYV|h#>y4TE&3lzw4jX$*9{^8MPzz_?^aSIFmrSK3^0~7qUI|6
z$HftZdQvzFROAp;CMC6Tny2BihX`Q{kJk|I$@~DGpTMnGMc8FvbY*we*j6J4JYC5_
z9_!UiMr8oe8cMc%FnbFKA7J^Ckn)hg80Ha|^B>Pjy_|(Y6KqXf<JmCl*^GO`!vsfa
zc;L+<bu1Ad39vC~?X`wKkN)XJ{I7XZyZ2j)=8qgF)EnuzAeegdZ?;$@Q&1SdT~O|u
z-e;pd*tRXGQMUm>Qj=_Y@>Y@<NU9U)dbCn7xIAvMYhFsd-{pWW`MAlLxVY^C0t6U%
zoW#E3poUIxC4+gj<s{Nw(gC8VXfD+n;dqz;8BnR4u(3!m2Sl9j>kBLcJQW)_&;WLU
zP=pr=dI6@F?Wcd^-n|Xsag%`Q>&uG(T^=^%yx2>m@Bq}2!AEm6Lsk9_gwzhl^{u-A
z!APc@s$c)~9Jyi2p%W>;xP&PWNY2%*C;#@z5OOV=!d{3Qv|#~}){|HF>bqUMn?VE>
ze$ozMqQPr8hj0NUS6AA4d!+A#D@lI!?Pjkz7|uSGp9hJ5yZlm@ESgQOmD|-!D=d;x
za~oPfTJQa|Wa{z5RHXy&;{O;|_ny?Wf+RCm<mbU%FVFpY3b8&>OzAD?KtOh|E`oY<
zadBBE8)$2<0e0#O^o|JT8t&~PH%Ordy2&ZM?IsYbk8|yF4f!TEsz~AO(kP+@Lj{#z
z6}FJPF?u;p>vax(<1V$I+j-5{?ydy+4J9Y}%O;mFV0^`3P}h-`3DAB!+FThC5+Wb!
zgQ1bh*4@I6G$Y%Uesy60_Fl5#!d@aCE^hpJIgP*gWF;qy%=Wd<3PVX8I%74`RZTdf
z=XiCbpM4o>8crlpH|xF1r3V|lNBapk<U&FtHa~woywvM%26Q%?3<~hge+3AYjt}}c
zqjRHa7%e%vUFAP5$s6BflUBO5$sUOGBPq5G7R_yUDUt;eV&w{Zy4v>h#^#>kcq3E?
z36s`=|K(L2Ty8=tL19EPthx5Ki0e&S4bB_pS{|03`LO@;uNyxEZ!KD||Cc`&?q0g+
z@!gl4$2d9pRF(Ga+X?MPtUjONABjpzt4;ZveX0dy176NYkJp;lX&)c=8XP)wXxGEi
zPksFN#SX0rY$+`t=p3Ew`PAajKe0l6k2tfy+=~?(VjMrphAk<FYL52^dVsIr<<#34
zZufq@G!zc$SftLx^ooy2Z9?{4SsZ&jE^%-ilKhwTo7lUt`$_1MwxrDF9T5=`^88s@
zSqx~<-DxE{33d7b>`kw_V^N^UUNVZMa{0iQd@QWx%zgHH?>tbxU;wV!xaS%By(ic2
ztY-h>*tSb-?jicvd^U)*_tC<i9K`;*X|;NPneOKo9UYU#hC&SORhB=C%g@Zrgt|1k
z?R1fT^)vM-h|Zqi?@I|mLj>U6@{6xNKe0n4&>};hp|Y(w*udt8xkA0GtsyP&OpKpo
ze8=X&u{<c5KLOa*9T4+byc@)usqFi}Evnj6jd;W;Aj>~`dky<K8b*t9q0PE@@nWs}
zZ(R~WhqoYYR1L)a#OCMcSFCz{a|XEUr-$Z|dgO~7r-w?+5dfdF*1gz|r0Kkk&C$S}
z`Y%7+(oJLnV#_96Zrf?f@^j;ZU-e#HIF(<bD=aLW5Ov;?&!yz{`t&#7T3TBpK*&pq
z7Eaq&GTKIyonyg<*2jzxG5#4C+9=-wH#Jn^>R<Z$`zszR-RhR(OnhLut165nf9@N5
z^6KmBBa3KlZIS)`{W9tcg*ugfiO#|;b4{hwLZ|C=u?^sMnVDnm$eSIY(dY!Z<VplA
zhG?a;JQ2^{?0P9j_j?%xUT=PRemz)H;&_OP+qjs$!Mckam*tiASb7pNRwa<f=G8{1
zCavX5*q`2Z*l2-mAn``>G(<}EZYk1SM%jC+o6jZ0*qMd1fPG+Zzge977i(MFfu_vG
zKQTm_4y!14Bqw*kbHKf4&riRor>|X57XPEa-^*YM$hsg&Pzf0tSt**ooP15u%{sZk
z%#4iK^mK<HbWGV8Qityb)Vlo)y4SQ=Tew3*Lx)A@hDAX(8`RXEVA-)Re^Y}g>*75U
z!ylWFV0m?Fx0IAQjN~JU&`_MizNi^|><5niiYv^%RhT}I6vZyXe9T^KWxKY2Fj$Wp
z*XVKD3FmEX)dOGRZg7N>G}#~D{lqx`3M-?*xC~m_a7dZuvGYF~QcOrO14fUKtfVV$
z7r2|Y4eUjXJ~gWUB_BP3X~<g`;9D!8k@(!&I`g!|{L_QGB#^I{xp>YCMn6`+j{P*t
z6Lf;ly~0QAK@8^qa9AaH^b-oO(8IBT?Q^rNY{+Qv1rh%K{vO{}MI8#<VE<ubz8Jf%
z*?S<~^-BbLHmI{h0W1#>G<AJ%hlAa9vdBlOFYChBH`7doIS0axZdM^;qa%>R1F%XS
zJ@oU^ah<BW310&5pc1HZZz~<4%Tu*QEzoObl0MZw=T+I7izIC#U~W7>KY+a`RWB^~
zT)c;FnT%Me1KJKcY<6|Zn<3l&qp4u{qmZTi&l`J-8_P%b>7J?VzahJ3Cacl(mKh%#
zJYjFU)5VJ;R#H+@DIPvg3vXAowHf#P+*@vK*m^DwQDh&2ifil?(W#ibBUEJY{Y-#P
zVNZ`f`16tZREfgu&|#Lzq+eYybq#qSAFc$~FEb<06k5|g(LC`Tq={jF0b~O1q*?PH
zukVjeJkoVgSwfZ@B7-oLpJpI`9s;8Gnq@|rqd}??k*F@cU94ooJ<@zu-@#tA-HE|%
zE;V32gh*CYIlL^Z#a@-jA?W?ypdJ{|77qAnu-}S@8Tg_W*xA`hj%hZGQ{1NI6}_Jo
z$;uNXYX7HdVZp({#^=k~Z-*r$bWgRh3LrA!bww}hFxat9*xr#0{qh&5-ljF34Ko7^
zoA2sI-tTiQQ`;tt7s`;#Rwo0?UbbP^9!vSFkKQ}b6C~@h7IO0R{MU<lYOPGz7nyzB
z={!hfeiHwiA#d{EV;Vcf4-QIx3cP}M^5~ww-c9Ev7CLzPn{a4jbaDvU(Uk6qX3P4&
zA5mtJJCa|P)c^v$P^&k39J`wezFy<8_y;qHz=~Fmp~lzFnFFfmo=NI~hJW}9S%?P<
z<y=31j4C)Dx09*MzPff|=OyRH7pLTD=(#igF-zhXjcvOBad*y8JNGtuL3)6%hODqY
zeFlq{_4j>3TnO;^zDb@2P0r+D)?9s7L0vjgx%`KZVX`~JaO}6JkX>h>;{c0o)LEb~
zSsh@HR?safNOJEX>3Q{!UyI{mh8ALZetyVS)}z;H7UxVHeQzJ&=^Q5LA?CQXPayeW
zKKHk4reR6`zG!tdJnC*Z>U6AG*-#08)aHj&PdCC+!hle5bwb(R^N$AP`Bq`(vjUy)
zlwtMXXDs<AJ}3`EDa9aSE?Ymg->G`xMqkk;MRLW7l)1^2mx%NTaix%%g;->SLTYY~
zLas|MfgQGev>FmFv6ds;tw&EFn$<ESkFpQf3*)YgE!^x4=ra0$hxT9d*uS{C@~6yT
z>6V}E<f{+=#0R$B|Jhdlzt0W+=X}9x3+}gn(*pbp*CV6W-ADf+6h!@ygA;z7gF1c^
zPV631K{L3_hamhi+r6gT0@$JanC5a$7Y15n)jhj4Z6~HxMf2}NL@X`U?%@EHb{N`M
zrOV699UuHoHadEYTVPOoN6>Aw^-uMttHVr!USC=A3^f}sFE6p#t?H5CXcm+ufEtM|
zZUJlue{9wA)Wrih>JFr(4Q+z!cbz#D8WyG*ccLcCww-*R$y9Sg;AWH&sz3}Qp0SIY
z=YH3I$rDOP`YEUqUuOYS!Gv+1ZE+T-^MA=}ZEcmOc}|Tb{WS;4NH<>CM)*&aG+<ul
zkdi%T{*;AeRR3<9mMK7P5~72^@3%~*ztLnm(K`OdzM)$lI=!@><lK5Is%}We%a>=W
zqW2*|(|!(oWR!2-xs#yKS^0MyjaAD`ypm{OYc-R!d-g0(G(={=`epBxZphnG6>^hG
zSp(NrklUJoUkQSI{!Gmil_p3b+Uo7_mP+KN!4r-9mhj%EdQR5bAJH|}+QBu~rpL(0
z%sh)S3>Q6jj64vH^@bEvM4D|>v3z$XGK-wHSDOdt=f?u$YxJ*fYRYadk+k}fq@6&X
z=xjy!LueD};!zVv@|EXDuf6E(%m9m@z*@KWihi4kW72XGTL{#W6TyGZ5Z5S&6GD<r
zk*K+IDN8@86Jpp>G6W#?bV(<?$ncK*{Vo`Wu#=UFi-U<4%2>CmUIn1>dJxNjNT;`u
z&d><K%Un#`)m>@;9a3nOUVVLij?$u4<Jc`D?-Zj+zp8l_aWOG5jW)=U3hN4{UVWWE
zz+=aP{?8GpaaK6GEzeu%XRrd%6xV3aAEw~#g$y|F>FNEP@pbY11-cA9^WVGFu%?bv
z#$A((N46?37Wkd>>|2+hO>MAKEon7!YTAOuvVb}D$g^Yq-js6>WL*;KEGnREBkF4+
z=07Uix7&{G>du{8ZntEh#?BeXVFK{c#(s8~T%NMA|BENQtv&8*i^?p^drpezv4V34
z7OgeBBb?c2YVF5HE2=DU3`COjlb(`f^JfmU`!Y<G$nS|Z)-8bTfnR7#yyO1qA$vCc
z@2aE!IUev|==J`+h(7!O8?J%yC|~q$l;dh@IeYvpe|Wdg=4)O)9D96#(`RvgZS9Gy
zfvd^3du@jus8zo84gMPklOO*bHb@L^pYD{>OFN^IUqa!oLMa+*o!RtMgwY27++%Ni
za)$Jr=5_E6JTJ?pH8*$L8%Gam?Fh;-qOG)B-zA@vl;oISkaVM>qN0wf{l!QL!9nlk
zISo?GYd$?v3TPJYvC0PxLn*)UQ}aX^#whiTDwZYkqbuzG1e4J~Z^38&=I&aPtyOAT
zTaB_D7WgHlr>7@3WJy#vH|I2$EX_+GkR+1UWY#)L&wV#zo`&yQM}2~Rbag$ajV2`|
zB#1B;)pK~IEaKL1c6Lr}$U=BX97~-QVLPV(jHvuK-0FX4j{n|9B#{3J9Q;2vW&LjX
zy-DQ}vQh+RC+(`pfux&&aWgM*c>F;mv+RTbZ6E-w%6Fgm;@S1+`fy!}mjp>0qjJ$t
z?0|xv0kWwK-9CLIeY7n9RX+>nqi=Us{|N+#WG6tYDoUthsZg*A6TFHv6yIJaCP0LI
z7=07&7Qn`~j)^Z{<wE-40MXIWR_Be0IO?%UNy2b%WYCRs0W$)q6az5M$6KNWx&s>Q
z6v5*0#`(4=<+XH-f;LY;$Tu5YG6g7!$OTpGL6D`?HL(+3H`N0R+dx2_1$d|u2)5Yc
z;S@kWK9#`snSN(WYZ3A;pSIfHFFAuPQVGQ1f~VQpt`DcI&_k~hSi<k^kyOr3s;k1t
z(g~Ah*HROK{xs4uuM!9>33o0mwNS7W3|Tl5)t$`LRY&)1g!<Fq%K4sW$Kz_<7a}k2
zqe<~tztk|QkcWHC316$LfcZo(Y#K2CDii-a=Mi)`)}|qIGv-bBx|jPn9>csJqZC`q
z$GN%g83ES5MYN`-hu=qwUdX8~X$7{)wYZIBZ`qf!^7B2fmL!*vZo$adofM1n=-`i|
zW!Pc5!9>+{>WL6|R$J8nj=2TaXc%WZUVVN2?n(DP(mx6QrnYZ-Z^vX<;Y65eZmS!=
zi-ErJ+%E0aqPaOaZkak`7N>x?9w>P1@?qn3Jd95$?aV#3VbcBvG`F?|sCFa}y?pjR
zlu_-w1yHJvA=O&}%1C9HOapO_LhIWWSND_?vz=0fdi|%7_EuuHN*=f7{`CZz^moqN
zRNCWQQaIV2;28N!v}AL0GlAYVlX&9hB}v)V3fQ9tfaK;<j4Dby=uDZHD6}K!@U`F8
z2W}?Otg|A*fN>+*c{#vw#?(Z-g)i+y`#AqBaDTVY5jm>vU$3qW+`LKI-_X*5Z9*eY
zk$D0JIf{i2{0`2@sFMoP!a48^0EhqOO)y1tI~)!q)3V&lI&Q5I{)k~+QJY=cr`oc#
z&DmI9moN&#d*9LDdL;IEDP*jncsx18Ih8=t^-Q*kOmZS~qhQu4%qMK?%+LT_TIa^0
zA>=*&mfs#S6A@-A;TXtZFDyZQ*9Nju5a2d+NuM<8RIB$nNdNnPzN^*iwu1~fnUQOz
z>SrF8K>BboN+=suheLyrGw>zjQqn2S0obw$a`Kv%7Q+F~p9kpaqy8^OJT0$PV;q)<
zRy}Wdy=?am=fU}YA9(W;ZR@|UXuGC|jt=}e>AC5@A38zG`5Gds2QWi8gK`Y+8g8oe
zl2l0p_sV#;N<ERONXcLi$pHGK5*Xmwd%6kR-y_Gl*P8UCxPG~`ByclnL4%1MXWXFF
zw_qFGy`);v9L<D3%&1tkZ@|LF#<=hFqBgDCmX^GObFWVs>BODbg-J${0aGJCG)vA?
zEEw2@AK3+c;1-dTy>)^2aZ9$Xv#1+ge(o2ziyzsU!XQa9mTMQSWP|yX{}L8@9_+?J
zNi=|g440~V?P8bh4X)fi%|JD+@#QGQj3S=Rdmce1Q^4ep1da@}7gl_x)E;zjaHy=b
zA;Of1XO|oeV)apx%d;6NN<zhdb^Q~n=!)f5hu_5jBqp4$7#W)eqP+dg*Ed%I@fq2J
zN2zm{J1H*(Byxl6_q(v~^?+3gG3^$x@Wn?IxMWnoG^#{rKKy>C?u$P_OP1xr9h0FP
zXsbV;ue{LZ7KB7{Xm-d3^6h}OKspYnnyRWQfli?57b1a5-@qWmwHLSIfEL@cnCs*U
z&=?aP!wFZ7PNj;!{JM(J7%B9W#r3>FV8oJGzwbSb%e&i8{&-eMXOb~-kU`ic&Rn9*
zn&7#ha0r|lC{$1(&IfRQMuq0Q#8BkAWL%Of^y(qGl?*B?8@FmFe00$ZdwiD-8?mOg
zVfaTl*dVlhHil8jK$s1fKqTbRPmlrhj~&I*tDU_X`0C2q=h?uq33!9~X37LrA{|{5
zXz4Q=NTrbI#lYl&O47DWhH<rZP!fZQHzA>u@Fki@rMJM@d%}tD!evM=*TfHX@6cN!
zIorq+p#{=-z;2$K6<~{r76vs$?DYFH7*2RJit1TYR~H1MmRW1;a*C`!nc7i~ITeA2
zV{06M%ao!UIE_pL0IWg~3pnSR>gv0gDQG_Yy_CHhXx9D?U^Ssb=a<n_%Z;NuoiNRV
ztj`3kaE4|DqL|>}fKxs|!4eD*N))qiDwj|PFS!LQc+qTwE`!abWJDNgDM6F23a$$0
zZV!RJ8R)}9!;>aj^Ac4FjLWtN>r6fDG=aQoY68(S_x-kb#UVr_W3LIi9ME@L85=Y|
zNdI_pkMOx<3I~w4xDz{m(6Vgb`|*UNc=lPU1cH<R$Cbcm!d)9ww5;b6`ma05ARU4Q
zy9LZYCWRh5V**gefOA!I{tM;)6aA#KFl&`SOmGu+h$2#y(jHN(dG{aNwih&^Rp8!c
zjHJ_zJ?#wgeshY+CQNfOVK$vKGQY9-+%cf+*=X&z2lsFmIHF1*oO6Tg4xdS)`soKv
z5)RJsyrpFVYY!zM>3G~3h+xXVa53SVz{qa2Z8C>Ns{{gDva=3Y@}gNrR@S}0r4=e7
z?H`Ru03!PeJg4d}ifp@7v~2m4++5iJx#2?uscJzo=h`8^8kFii5K(48Gt1=qQRsan
zaX^dO4oK%dTi&u;e6Ovgt!*V4I6<0Yn!wF;%daTnmc?KMakQ6`8JZK54TX^?5w~Jh
z32V!~1dboc8Qp^|D7qn933YjcZ<b|aT+C;fSAf?@OhVqLcFfPYznQF4IyjS&_YYni
zUFGuVLY;+#I(6a<X%eJ^f<C0ItQ>d-J--#i+z}qFW7{_*)OA}VX}$zlRu{Vdq&51{
zZ`FX&MH@$ITMVW49lTsHp{cVWd|?Kqc+k}XMj;oEn&~wcM^F523B9`<x>6V};r$UK
zgzi{mG&`{R$Sec$)beG*c}57#e@8S;%gwK@{3Y+$^R*@(V%P-((oD)u^LpBU$2_7U
zk}s~`eXfe;l}x65z4^<p`4T(!w#toE9mob?N=W478b{8PMl5I*8Q>wj%g8vdJDB3P
zJ0*WCw1@A-(W#!W+ho2R`B2lT>%2A1&6AmtG1#{D$@;8aa9T$&z>YX>+uKWf<SwmU
zI-)SgRLK5vd0F6Q^jDRGG9pl=8AcvCM*eoP6;S3w(t}F|U{c3lbJV+U@CltD(-R1D
zOnT2Na5%_(IWVp4eFLFEB$J;oL@WrY^d3S1v0hPZNtceHL%D>CF-Ja5z7>REfyCXA
zHe%8fjPyHYP#TV#fYcAg&CjCA9=*=gbjgF)9RmxSF3lIt_whhuqWFa`dzY0im<~eg
zZzm=ovb`yRCG02`kMr~8kuI?UH<P?O9UC1Ba+#b`v?k5=56p^y7oB@BHa2GH_9NCY
za?MFP>X3A_$$v*{o*QU6njBOb*^uCAyATqO^!O9`3ba7wA%)Xs3InOe$YfS`E#M#D
zOM1&&5T~<&LO9@bZc)Ke&m}8NCYkD-*Ko5<TfF-*=Gtyi@t5v=kR><vewi8eN}mib
z*j$Pb5e83_m@z@oF2SGy9PgC3vN)Dz{Y;2`MG4S>vl5I%f8Pk@=CQaF!w6_B+>^sD
zc(h({2xTn;ey`YLbBJ>5C`{hcKfMhnU@y|i0S|_(1N<EwV|>q$`FkzsG|EP6vwgju
z`4$x%eD=u&33%ARfVMQMGAwbPpOsldR$cb&yjoRrbMwH&Kbs4N8f&~q7ahACWRw-`
zpNkNydK3fbf<)+)o&~TVl8T@g+5>D{B?z=2%o+QGbSC$ME^EOFRtaR~G31-jq@Rwd
zcu%k&wyrDK3nMj~b4riM+cqFVksTsadouQL6@{)x>uq2|`x$EtrzkT#&BEA)BY|WD
zZGYRMn;J>l?)7vsj5K6o905B5i!KZ|Qi`z@Tl?S4;ps0Ci2(rQ_3~~P0a%A)3+P}*
zD~M4db_tQNz1}=rV?b<Pcfa44jUJR(!t!J4$3vVeIl+jF#|wsoQqEw=DAAX=yStMK
zg@*Q}Tad%SmBVtrh!Wlg4RchIBuF-$x%V<NbF1r&=qfU`ioDeoN|n#`^}$Fadellp
zc%3m%A;Q+9gXf#b&e-GN21fbjcNvk)o0y9n2DeCK1eW`Z2Vi<>9AI}U{BH$MNN8-~
zuVgl$D`?pE9wucvgM?NxC>X;f@8A@WL#ee3GdDKiFu7v97}5t)bHS5UFVicM=^(?F
zFbw$zSN~^{KH&bE;NaUvCZxQaRE@nac_~@;8((<6$X1$<J3`jwlsp0FFd3JMkZNMd
zFl-AAR`qV{QN^r8o53%yi9>WC`2%D>rDWhRnT&|ikE}Lz<PnD%9841H0mXI~rT`mW
ztX7&p%Xt`_yr8<=kRrk7FnxIs=8ve6zzLm<xUjI$Q7s{Nj`fA+X;k~4eQtXn1$(Q@
zvJ04=-TxAN{`p-Zp#;=;-rBkxNvsZB2t}9UWi5uqQO>PK3_*BBUQh8E^_BfAhpP3n
zlwwymMe%S2<OcskoI(JT-59=d7K$PXd^48XJa98fsR~A7Ma?jKf#j(KwKTLV<|0~~
z&#6QL%bo;*a7|7DaU#<_g|n7Kr`y7m#~^8W257q}ZJBmxk%rTwW_E%{JBJAicM+UB
zF?qs#4C9GNJs~eXmw`NT`rF#V509?%g+f;U6nO;V$}uv+dxK-G2Ju$u=w{U#7sc3s
zxWw1XD0|<AB3)>M%M?^0=W!arxZ>+|{(?`QMD9wA9ATLx8foY~eUhI)BRsVn84xKa
z)8J1E=&7W`alM$SV&HdJUO8g_%a$fWwrp_gdW5*}b!p)>GVl#t-N3{+9A6nx%rK(7
zy{?w{Aw`AZmMmq>qeOf3gPcB@e*v;7QK|l|rDSY4nH}+g5aYy=l-<r%+2uag3pab-
z@9YHh10f4m>^T{0$4Wtl0HgJa1(D=4dL86;k=s`?$rBv|4oDv95C*9N!L3~RtD0KM
zoURAhPWoH^*$Zn#3?HCwk|pvG%=@T8%)<_vVVvoaKsHH>@eUT{==oOvPR3bRmMr1D
zc#JT(kezJ<UPXX`vz3t%gqn>Se<#>>u@`+IWZ>c-D!ZVUJQ78f$1ztY*KT0qAa2KK
zo~ES(e2Xd3cDj$}K^T%TvT`|OZ)IR~A3_b9XI4n&AF%xabrxk{5X_S$JS7h&K^V)x
zJg>F=khw6Qmb9auy1v2w5K*RVCZWOp&98!aeJKnw`42;W?rkQ+ad+8&|B&UmlS1(;
z<%NkRt_9#Vuu~ihJa5T{Dz*&6`JtVZ3W<PLNS#$kec~NtPC6#kTUsF-b$~ZZsTne`
zN>xJ0ytf%uENe#;KgH)gDcoNeH=^<no@%W5%DQu4g<iiG;t!dx7Wf@<7(@Dc(%>E0
zM<6Q*kb?XNkTsKPZF4EHQliu%wg5NSZU%rXK@RFq`YIebT~Xjy7qSELQy1F?{>CeF
z@oME~P|}-KoSj)aDv=I~j#w3@*1nCGfx~117x|8%E^JK)kqHt_2R2_EZiomlZDsJa
zerjw38*~~7Hmb|D&?&C5^-kT&Xo4V990O+|LtOY?g`Yy1|A_p51bjyY@qxfFWTXlF
z@<ek(K`<Gl+CDeaZYc)6ZqFX#o9Y9Je%gErhsL;rVQFJi0dd<dWQm4wtAvWJzzEaL
zr5JjC7sHQC{VL~CigL)E_+*?(FPH3TqGBF#`$c72;oX=e4NL2t7>`d#j{m`2KzY&6
ziIq4hOezgBa&dDhN#Kd$2Kh(f+}a@J;=1+~PI$};qs^EJQrjq$;@{bynMAqQnmz-S
z5EVs_E+6I;Ty%2U>gpNM=F04(Bd&fTDId_ekzP_vLcc_9f~~R73&KYKjeIa86?i2C
zo#L!s1!%`JM$n>iOceAGTwlFl%t618Bnpdq{!mp3BqO8&b(FQvVM>-u83XUXi}E08
zP!3}6PsqMsoqY>)sbG7~L^HA&B<kad%!B;h5#gdpeTk!B@YRb{?C_=9IoJ-iHAJt#
zXm|B9un=}m{*}Xf50X|jl)RY09*P@e;QlKR_U@wphqd8zOG`MylME)@RiKUQE*Stx
z&{0&I<UDu`-;4T0Mrf08il>P0&{;w$YCaxUKbhptcJR7F0IZ0E|NkR7I?F+I`PC<E
T$@2nohbl@(6_fX$y!`(Fw98w}

diff --git a/reference/plot-11.png b/reference/plot-11.png
index 23a4f9bb60eff7766baeae30b37428332ddbf503..c1aec433ddcd2924d136626718d503154f48592e 100644
GIT binary patch
literal 37955
zcmeFa2Ut|uwl#|FQIDe17(fM~MNuS*fgs6%(1?f#2ue~Akyw%>ht^h61QZ2SR73;>
zlqfkWNkK#jl8YocODLcS|5%0H_ndpb`(EGs{&?T-cTNLUwfA0o?X~6{bIdVk-8^wz
zan*`VE0~y=Rw?}|f0Bu5i6ax!V#B{L#VZYgp~LvG)KFPb9zXGK>Tsq9ek{NA>uFmi
zrp-6V{}=gnj2JU9{lcUqe^A5maetH38&2bga>J2BImTNLuKl~?-uFD21)c})oIS6&
z^!U>CZ?C>e^kKHLmM>TR{n^&uU2X4vv1%z{Te)jd-IZh7ZCCh~$}hij!0T^`O|zG2
zLoD0NvOhHWk7a8H(5D{XyVu)5$sM<|*R-!rjcNY0-n-C{F|a$5>y_9UzvXqZ4*x`c
zJAdDk@yah@f6HRLvyr!D3Hjwc-y#w6^MJx~$3^6q-AD77$<I4#Kk1X7p10Thg;Q8`
z$j*)UDXOTbR8>_KG&wXsv6Ix{E!}_<{CZTgVhPh>fsk@V6B848`E}lfzsJ<=KV5|P
z4xH3|`){uH?$lt3K-_J^gbjs;q8C0rerP9226F%IzkdsqTkBU`b8Pu-S=)1O;ajt`
zX1O9C4))~aWYKf)ey??Cc5A=NJ#*G|$fenPM~Lh0TZL_&v&pHc#f|B<?PHayeyKU*
zuhk3;xEm~zR5+c!-5m*tbLD;NF*kLv=5hh6qWq8h3Yi;=^z!QN?Y)(5+f}sD<S{G7
zxia{9PL6EEv4?BALOk51M+(+vPmETns}>fr8*r*e9`}ieh<LE;+@YD$+!^JM!Qvt-
zuDyHK*F$aQ4boks$Z0KakyIu|t}Qi`W&7Cv>uGCRlY^p`R?T)=dU{3TZ8EPxy=nBp
z%w>A>d&;HP8qn)&+9uom{034QV&4o7<j&7o&rNkP?j=A=f1{m8a2vVXsLmmed3x|z
zZT@PhS7Wsp@kCwh3BH2oN4(qGyhVHuua?|abbI4kA0MBDVlJ10p2!fc(atjOl#~=9
zH-6qi!vqa2-1nQ5l+}|DIy*bL{OZ<q)U%ajbCVTS^6FQ9nc1<*!p#ni2^(0iPSZ*a
z<`SFdIzK(pc@-1G5g(#{Tr#}2u~GW0+nx&_`D8kA5dqoxnbE=z_jb@4XeM&>I>XQt
zd~8^?ii(OOZz_-XrOwXLhYWNxFAqEmtVuL3y*1?8CAG}e9n0Kp1jln|gy`@Km3F^l
z77<L&?TgJ^`pTuJBD}DtGLpr)sjt6Zu=IQH;M--~g5&d@;<-+r{akv&gO2*eynM-r
z+p?;Ph>3}rtPGiZlQuiiz%tpmiG#!2w(Ha8OF36h7hO9Ps@N<fB(!)U)2b<h%Wvat
z8!P4xDht^b9bGG^>&~Rx822aF=#MnomIs9AmV3;NTF;CWv-Fx3U@XFy8!S_`vz^DE
z(aF#ZgY+)n3|9`CXc+unD&j|;#oh9*_F5x9AMUx~8?z}MldU(}9~k*=yfimY&z<Lx
z>W@DxsGD{vFQqh4$UB=xl^RU%db+3~P`#q`V*Fs{P|gEsCmZSEyv4?SwzmCv?4r(N
z-Cm9ZN82KwI-8yFqfeE4=s%JjcvJa2Hr6UkOuWfX{jux!4+$1^iHqpwR#v|4esZ$z
z!&kXiCVoF3V5`GNbN@T;ZNsLYsOui>F**L`%^P|7f|qJxZPR^mei*v~9?ZHG*Z9`0
z_)=fLNhE9dP@@~IX(YkVO>onuP0j=6b2C%cnBhPxO7850HSWLg(LVF%C-~%XprC%1
zgzWHdEcffBX^pnlV_y_mru)8z%zWDs79P&KdVlvb$8UEPpX2ux*ABf&PA;T)&>P4t
zMh!T1Jz+Nqal71zU%u4VZjhV);@8#@WZ>&6%RS{POE#v&c$K>H0K1^}&n$8?mqJGM
zGwe){g~(>_)p(m`-QHGj9_Pbu;3}GtkwJ!IYild09skP&Uan|gEjKf~g+u?DxRP+#
zTj$P)CWhDsGIJ9>svi5<MD#gdzkaR8w~i;Iyg0+YuMMNp(RFCD9~pGWb@A?M2relp
zN$Y;@eKa*Lz4Or`ZgoL6qfd_|!yDLjFCEMsXr4$(Ewr!EV^<CoyoohO=B~PWeO%V}
zqaQ=$<R<D&LQXbd8}B(4tDC;D{QTs77ahK?h39Zc$>7k(BI-=1cu>ywnMk==(<FLN
zcQ@bh2fIS6YTW4qHA6M;a_LP&6O$R^*mqw{8V2)O<mE@MFbngo5{eI9FL26YOmk4_
z-uCqdP8|XI4eTqfwTTvUs++ldJ#8{xH~8%8YW`#?EFrp^`;=>rg!$K3C+U@{a@pl6
z9e1u9YY47(pY(K_sEe$!YD}{(%$=Vd3Z8pW+Ko{QRjcD)e|L9x>a3BU%qg?^>*86V
z<;GYL4(vl%ow7p)A|d7Ms*)Y|PlgywR|HL41dsi^>TrV4eRH|LVV$iM(i)EVT%T;N
zTuE{3(-F@MS;p<UOW%E}qhi2mwDX9Yn_I|0xn$R4iRtbz<<#7<aFJDJvTJu7zfEy%
z=eS$i;hgP(CFWCkT<UdkvsGH_^$p@yx^V-x>AJ2{r3S)HIXw}<&X%-q_f(6nvdC^=
zWv#BRwl3c6Fh-x@=jU%LmYcKgF{jt)yI7v~kdKj562jx}PbhBl^O!N}nUJA1uBAAY
z>ZH6Z7fNpTl?sgAJJ8*|;Zm*FkUK3iyr<lKBFRm-57tzWIv(pV?lkYbe*OB9Cqu50
zcJzkkxrwvN+?W6HZWmEjY`$E3F1&|WvEbR#F}c|uRpm;uS&u!Iu+A&i#jS_;Tw`W0
z!me5B`f-Qc)Q6YR(Y>iEw7Bnno2A64Q=c3r8gg8oy|L*if!nEnSvFo{VDM<JDpFQ+
zQ&O_r`1rUrmQ;|{d@GB`hGEf;J5{?kz2csqEDrD4>MQZQc=&Hh(~VuST90ztZV60V
z)KIBhS_W?RJrf^xgy;rNiSOGNaAmNgG)U3K#pU$0P-<u`TS+p*z+Vsa6u%~%Guc)#
zwnKL0=7ZhFM?XH?vx80O%-)xI_SxfFV@Kq(hTMiYs*El-yV1B(mrbk1dwH2wMy#ss
zTt#s_Z0bBc*0We&CcU-##*-&cIFi%h!;~wV;-tCWFIvWV;*oxq<H$4aR9^ox3Rmri
zvT1=4<l&bMMmG9N4W?C$4Wu@6#pO(!#SOTR1~<#ghWL4TU0uDOk2+Z-zPi={BPKfv
zV^b~Ia)WaXStMa8DPm@3X3jHoJZv@RkC%KKvUXlK)ZwdH*kNa2>FTk14Pm&5VPTD%
zoviMXrKLL-OHo;kif-SuZZ9gtDAr<C8-!?C!aF&(-Q00Bbs62Be(Tn)T5N6Q)DUcu
zMcjKH#4Hiu*Egu%e(b@nmuHrKf4|l5egq5b_(cng+eJl1FXQ6;rl+T0zJ4t<+s9S=
z#HM6tc=4*vser>LOE2!aRcPqfy76+IanQM7+5uuuXP!0sh*N9LGvi!41j2jjl1yj~
zCJEZNG0QH*)=S_D?HtXM3d9K{7#7~bX7u{>=@Zv{MqhV#?G=+fcH~}*7w*;hC+hwE
zGsF2t9VG!HF$S~zzLK2}xLTW|HDlU(W8!=_?zvDfInv%9sAE;vJ;LGsBqJ)y^z$ec
zZcMq7xWN?e?xU4Lmd(M3LQiBE?rBSIc@1NI=FFMrxS{&``sZO`VPkPFXSMj&9rfdg
z6?MF)t#CEDHDk9~)l1ycSzX80YvDbm!kHFfbgfs~YMVk*6BBm>(A7~qV4JO7zis7K
zveeFux^AOWTQU1HOvJ9Szh&(_rK7`<bfN4Pu?awof_fQytuhBvCWmjxaRvkgu(Bpx
zD0?K)`up0p+_}D7XX(rNo|kqV9B>xUiWO85-@Di1?Vb($^EY~04T$LmHhJg@teOCT
zsPHS38U46t1kh|eZN5ux-pO?OvF)duxDi?UH1;oBkiN5N&jeXYsm<;##Azh}2fXhZ
zg*SMvEf_dA0Do-ku5eZSfi>lZvreu5pJU#rrl!V9PztBp*ca=8<QfHYb92`La|3~<
zdCL3*CBb>Tw_-q7xW?$^HIt`K7fy`sT(?5z`sG@fr_vK{Qyn(xT<uHbroP>iIh6i(
zu5T{PW~-)fP~S+H8OPF>O298(<*uE3!W(LIZB<M&v;~VEOWGbyEr-o=r^7kAYdn(0
z!R<NpLGLi%{roUHH^?HyiuL)al3m~NoN~yaxTR~Ut2<YWwl=bf*)LdRnMD)@O)ahB
zBkX$X=mV(+JrqCL>5G11FwB=sGvX4I#ox5D2&p`kc4Cg^Jac37Wo=XEw^x=dKjqH=
zbt(of1If!zRb&pf56^XZ%$b;4G!}DvNC%1-aIup4m{pKllNLJI_o2ToX<62^eRZ^^
zOzg$xHR-y-y}iBIRkq<{_f%yiU*_DaXIq-|r{QU5c<QlR>jj(}X}__!KHKY7wy$O-
z|I3WYHgDz1$_N#2QKO>!p(UXsFnnoyWn3ouFH$I~@sBL%pKr)&JenJi^Jr@4AKY7u
zrDItC#*}rWenV1UapNr9xQ^ML2YD&<>hOL0_AUNIL$J~K*oI!^<}xssf7z|a-QLE-
zpUO7fz(9V7mGgpk4-5>90A0DCIA?(e+vqx47T&<II&zoW^w<VDch{bYkz(%VAr@sl
z00rwx74ZNo2Nsvgi?9<rR|yRR{}sD5)2RpNFV<ItE4KrHj-3b$+UYWs?cjH3uxZHM
zK~N_}@a-)zG2BPR*m^BpR$S3~yjm-0Zeos>>yam$Ay+(jSZ1s<HgoWni2I0lu$pNz
z+0YeZk+S1aTynlCq2_VfVq~Kn9M>7N41G-Bf(>={;&Mmz?h0iQpXF<}7bxv`vd)3(
zWRf*jp-fN-Yn)F*IyO@pjp||qBkrRabLxn^yq=!ku_ybJ73JVRWr{f6hTkqTN^QFA
z?J}4iITmJq?a-D$!P9F9K=X@xs~Rl!T5&^w0v@SV_W0MAFC8uS<&1ukGOBo?WL>SP
zRp`&F?EHJ(=FOW44ow^Xs?+YZ)z=H2af|!+dm-C2oqNAw_2)YZ8stb{ZnQ2=`{u7~
z_`#nyd<!d&KJ)xj*jl)Hj<)O}H;QQ!r<o#`)A^p{8~}I*?(($j{P<8t_G5P`?hX&$
zZ+_BmvS5cSYq{LKE5Iw_wf_Fwh2HGNBwoA0?e16?l25$fBX?kTBOE*ez!3P91g9i=
z8^Ln><&#AF?SnL_bq$BoCk|s8^L`fa=eM-9EcB}V=7AuoFR9#HIOF2I=|_7_Pg=vk
z&VH4I_c%#MAZ3x)1t;MUP#@wx-Lp8QylYg(%*@oZCuty`MNaosY%KMgv6yvR!J@v}
zHKwN($mWbH!@{Yigv^i1%_n^*O-M?z%{x=t@o`UifAaOI(Jq$W5j>MZ>~`DF<pYR5
zga)zG!yAAxdy<<~P8R`F^giqI=m^l7NRzo^W669tVPP*_7jRUMfK!~C>5?nHx#OwC
z%L$`mUmwJpNzE~qxf988g>mlVF$aAlohy`f6soJrtVax7_RRb^_DrQ=U7|4~!kHE&
zR!bWG=diY>*k<bj))9cFtrH0JUnuufPj!XleraYn-d}Y+_yq*o5y$zF;pU|n%=T)v
zN5{n8!iHCgO_FP#pQ9~n%;DIysjc+}w=ck_+r4Swnv`AZek@oGUOgs`2EfYpA`Z&!
zAOm+vD+Cv5KxBrk@2-u^jFu|`DfVad#g2S($l2p7H#aFU_W5vFv{_4#_19OYtpVwZ
zvYbZSzUokXmN2swV9SMthHi;a31&ki>V>$JmpT?9zAAr@V6J6qlbBU{N1)?KA$xmg
zFvXWXlq<*O+#RZ&VsTmnmJu6@jYRQycG4tp*dXI<SHT<kHMn`Ea}qn3-D9@Tpgkye
z#+U>cKu7D!%F2kdCV;6Quz2JtEz9I{pFw&*LOM}AckLWkrydHTg}2+U#kUvvc*`Ne
zAHn&ylhCYSF^lxiA~}<Ww>iXxNsvVDt$lXL!{Zp%8sF1HWKci}8;GISwJqCc4JS}Q
zc!4lQKZZ{yaq^BQb|?3Wi7D9Hj&7$J#9E%?V=UtXQyoAZqCGrV(o9UXcGoX5e))go
zfE2QFXF5cHzEdWf5m`STQ&+leOv1)HEB`ff#IGgD=B>oeppo(-Y?u7^AeP7g{{lCq
z0gh?-5wOrjQ&Xjk+wPjDPAOo^$Y-6RAQXMKK3oyIS3@&9MG~aY5!n#cwC7_NFJAQ0
z6=r8;#S>eub&m^Zp71cNtT^xsmHPD3ksdX5^|dg9tgQH(3okF%y3Ym7t)@0oZ<3S}
zSp&aK&f?{Tz!VoCZ{y?SC{|Wjyf1Zi56M}ywAdNI<x54y8q9y|t%|UO1W_Iy68NU1
zY}ZwB*3i^Ef)mI~uu)IYj9R~b{ffYmoUSi5(lFt{^k{Z#)of!$!X?!vziUr)CcZT$
zIy99FZDyub&1y?9cLicp;m+a#p5nZC!o8{)HtD)Od9Ra__Kc6OjvQQ;JmpGF?1=#W
zLL{_1!Ivd6I-0-l7<Y6`jLYL~XT;*nEi43uf@5Qaf2*300$n1+PJi?iJK4pR$+Rvj
zHZ(M}cw*ZfXBAp|Ha^sMOuUJ<Pj**NRBUW)v9(IQ>a!c_#>U35<QVtYhpTF9#$rKy
zy|H*DE~BZTabNavvaWv3tYi89N_E=kpdcr6-25+2wi})^7R&!AR`M?{p8vtz=)W@x
zO$JB1OM8KCxbBvNAW6pTqNU~LiA_8_V5ZhS6k)ofPgvMr_P9^xa~B!RPuILC{guJc
zz@@8XtYrGdc;y#z{{J9d`|k|tKf?I_^C_bFdwz0M5f1WJ3sd0LQ^@7#*PoYuh80je
za5px(cU&b378uL@O7#n6;UIz8kO_NNiPr|gtZ}d90?Ya`D601M_SiS~&hG0udHVF}
zM^<KLN8|nNdU9m2mFu5o^yGZ1s;)kQPvI1$*BjImw2#F<vOC&0Q8k;)VOrrVo!pub
zKhR_Ed(EY0RyQ#tqaN#>KB0jfjV-_Y)IH&*8jik?eUEh}gv^WIH5rtu@V2PjeZ&3g
zvcVVZmfpV2$D4vwjvOguv%Iepyy_~uep2gxqq?k56MH4TKBPK%^hVKd-eI}mQh&+d
z@K8mXjL$dPwKs$98I;4=+&@wEUyLU!-?A;uk!0iI#Dy$W-Ad~dh68m4`C3$Ng3XT6
ziaQR|R1Jc>`%BnXp6A)wGLM3T*T(Y6JbLs9oSiTa&)3&F>{th6=MyekLqkIz0U%%*
zuikMp7>UB#1S1?77Pj?ktX?o|%a_lee_pj}6)Y5N3qT_|5*}ViuNpCj*^bZik<Xv=
zq|{vqX7JWWXX!ncu;&Hz2Ac5sA0`XPWv5;tqN+{JIa6`~az%dJFoR&)0*0Dk^^}6{
zJUA43r=;e5)l0STn3XlLdQ#5~!dCfk%gJKq%nUML5U=z=)QG1o*{IdN?<=panlte~
zaMbehd&R|JgNYF?1~qrIH5^X~GbRd(X#t$=Gyl2`2UOM6C>j|V$)9i6c2v#@e>gX}
zi7YZt{r^vyiGN<F|Ec80|2@TD&J{%;%|K4cA5WCz8P+^>zzWdN$eO6LP*zq(=I318
zxvnP;zDNPE&|MdG8-Uayi0iIX&DXCAn$_l!fj`V)_I0qBg+O0o{i91f_x?QmQ{Hw*
z1Vn_cfP*ji_-LPU9`l_;g@)GhxpJG4`+@ON5Inq>JgooW>8}3;D<axo_!;MZDXI1s
z_l|GUXjrQ%EqYvLyh^2a{<mdZUU7h-vnlB_-%E{<cc^>e&hV=#1*cet739B-P902c
z0GBSFK4h36adsY)c8|_T|JAP1Ig+QNr?)wk>TDiDi_=cv`6NC1@qLF)!cY#~YQpss
zP`pI%%P=we2xDeD_VN_9WaB*^o;L7Zw_&-!*gZFt7ZfJ%|FSh@x*!q!go=36I@Z=+
z9rEJYn8fO3S*o&A=aFGxa84=|XZ4B>@*dOuvQwf<DIUlhaj>!y+DBJ7^@v*;n7u(@
zer?eDx50!Git&M@?FC?aE}ceka$sIWee_~#U|^sl;?u1>ZFdf@u4|inilD245C@>g
z)?0&BEPCoPxY1yyjeQwsGwqYq&~3T-sq$=UD&mPEP~%C3`&Ng8(yy?CYk|qyuC0;*
zoS3r{IL`8mcDYXQAh)}=iGGdSu^~XF(uiQhF%@>OoPGlWt9twT?g?*EgM*3EF~}S;
z0bo+sGVsr!$l)a!YN=wzVCi#sK43DqE%hTuj_{{O-p%6bvPqXUxCgj;E_S<Kg}Zch
zv$^;hNwz^9U_YcRxKeRopO1Yfycs<ZCRJ!^AkOZ|zM#6pEHdj@SwW;yZg1LWmP9uN
z`eYVP`z7M|lT9SB1D*wSH_{#ES447}jtFtvZwbVDF}TF?FhAK%;?1tMakR~LpAU+H
z{V$8PPSe!X3>UUj@&*(3LiyKlU19OIs|h(8r%s&_bNjXZ2Jcokj=0#^<Kxq3)YR_w
zPWx-<=?!d4V8MLBF&s5*daCf9g`4Iey$=*yOmy_g^qkl(?KH|KA$nkuQR|y{nek8Q
z#?6VB1haU#zgkthd?8PRmDL)&s+F1?f~%%(3X+i4-Jv&%j!O-!gMrXTB5W67k-+{p
zK6hd)=9OPhaSRKCC((BuDSR0fbyH@d*64lb9;ff$9_+U_6Fl>V580bWN**1IiQI*c
z55DxdA%988!chJs!RY8{@+5e8_V3?M2uuRPb8>RVlujTygdOs`z)?Sdmm|P1@YUuu
zu__sltGef^ACjQL(ozKh10iztcpK{LA7#Wn784iWp?ByxCha86T@&;lt_3d)Rs|1$
zc;S8fRF%pu{GkQd7apUIBZ=oGR|cN8E38F+3E3&)`)+f|?C0V6KIO_!bNb<N4HIg+
zWF;(zg7RPEAp0RzRxE}#Ol;GQ8FN$9jbG?lt3Y4{<P_d58j#tEH@_kj@aYQVsQ0IM
zTO}_Vnwc8KzQ8Wx_%13&GSuE|LbtT^os9D?t4AE`8P9pcb!Ut>q+Cj=A4n}?-SB05
z`*8P2(Z^V>rpJB(hRN_)_4)*#wpf#$ff$Arr6ac0jefk{ykfSm9)XPgIByg^QK{WL
z=*6Ic0dhk>56^`RwoiuJyKMT<&P$^`x;^$Y-$1cC7Kwkwta-&JA(!49i!29PmD@Ze
zJuB9wGho84^-^DsMPhtho&UMhDJ6?uVHQZ9oQH>zs0(Q(!7VE*R`=%b3oGGOs^-!x
zKbZ+_?AbzayWID-t8A)p5(0t5h2*`L7h&md*fHb_Oih*ni-t(R%*u+WxZ+uTvu(MI
z^}FRe3fOZe3w;K4vYqX9L1QMI|KJY_QDin7Pnk_vcLVoqw<4x*fa2j|0rt!IB^R+e
zXD_8>h&pP;>2LOvn{$+x=e(4+=&JLv&=Uv&oBPr<F)M^mNb3mHnRcB4Tem66q@0>{
zg|URiDo?Qw3xg-Xy~$3taLGMQNH6l;%t2BzWW|%wJ0c-T*5~%)k2kpVCn$b=Ai9-`
zq~iGGK_X0H@@$Q-UXq2ZmM*75qjm7ugNJ)eijbM1XL+<0++^I?@*R)k!#fyPgmsl%
z%80}Be*NVyd-1=b_#k8X3xWCnl-NoWl+giy;x8QGAb>g$iZX;I2u5#K4M(ZEMQbd>
za6L%oIBjmaZ|y?|*kVMhUS8n$Z&qEZQd2CXx(q#epA{<ilm1zGvMREM-!Ur%-F<-g
z+ip3zSvxzs+ufs)a5*oOJ@q&K|2TI2&j*VCO91%G=oic__6Tfx)=Vd#b2E)|kgzvk
zOV|CB*mJeRU)EIDpfV>;Nk=BXZ2vtWYRJ;!M5ibA+h?bpRkBX?7%Vl7qe#@%tUSIc
zNTt44=}>*(Z$_;-J}->7BCbP*g=7FKj8~ok)I|uhLr;O<2iCl2&B>iXrQtT4<6>fz
zGk%vR!BTW|1^v`6q{m(;*S<#{&L7wTo-e8~uqFl`*4|CYY$0=qC_fyTNM5}|{0M&|
zY+R!8cE;}oV-BA_*VRdWL=+8k%wJP8o_IM&{IQw2ImvwX>^`~Er}*7GxL$%v;E<xI
zf#zmrC-n^cNrV{{wX<jUbTaQyM~_Sl_c>VkS9+N^Y8nVYbA`PW93w(f;-0|j*d>yP
z5-}k88%HA01Af;|wbHBTusHuBWaVodYrlvBBm~Kqyu3UTBe2Ja{8Zrpfec<k{ZN%{
zK-ixvr8waqP88>QvNts~^+!xd$XwI;n#M*><P~r&Be!QKkZ(&*muxi?3rwM1YprUQ
z%>Pv`qNl(ASQA}ce%kNaJx~N2Nz^-1Z;rqr5OHv;wnP&rvxzbQ6uEA$R&4WxxMIeq
zUd4FApXI$l0`DiPt8B=$^9^-Cmz^yGIN$7XBZ+-tVu3kJ?-to#cyVI{5p<N!QbecW
z;wcz&)obysGQTJjhxbg3c_=a;k2GMlnUs{2P_R{o$JMaxY`r|XM}~)e!U?_?Hcxhf
z|3t7nH(_nc{M&V%Gv%sZ4TL&pqap{j+9E*@9Hn`S0K=<0^h;Kh+gCaY>VoUo&vv}e
z#o;7~xueDJOkD@W-tab`{JzzkSXie2&N@AE%D-T>37yJxI4(JPACYIscS6<F`w~gW
zA1ua;>m%*OiiZy$<{ABzhy=!#?^84J<*8sW0FHThh^7Z9YfaQS$Tc=kO#Q@kXEo6@
z9SYsmKl&O;aeyW-uZs{Q0b9wNK^^xlzMQ?*^(Xxei{=2{imSB@p4e@?DG7a<5prmV
z_dcfgM#mTq)F;Crdo^8d5F&BOi&CJgEd<aI7FADyX9B4aiN04!{=$oE|MpkNZ89By
zP2l(!C+>fWyZg`cAukh`Wr3IO7+k}+bN%}=El-Fx5&AP1q#$24>MbU3d{(JjOvN@z
z>rX5-{Qm8OHPXq2SC%jXtloDKBcEW}s*jH#2gZl24?;osP9KNtKE075@f^|EjNWMN
zqzfwPCUCk)>Kg{&GSHV1YvYb|NikAOIEDziNbEvHh(r?Cj{FoK&4Phj6dq%wtgOJd
zjC2mNFPzA8=(^60hWK5-e%;G!k_cXqUUBJ1Cd_)YOq{O97?@|--ffF04n$0+Yx9AZ
zB;Fs$dALIwCd0$?6j}}m<oJvLsz$DfxNae#M5r($5<VNV``e-+XIMgO2=j_@w<P;D
z(jnp@so=9|J<Oc-2i~9ps3X{kYI9~HHP^>gR!-NLG2ZiE<CkPLA)9o0WN3bFh^Pz-
z8vxrXnuoGRKRu9jb0+y5Bpcfm>&ToiO^fy~qJY{v;>}iwY$PWjZ12cb^U6q7Ke!a_
z%*&RpgSH{wM2@nrMt4sm?}Ci?9WE1d>h&$6l}o@rD3qd(_)gZ9yR)&fwio;P3B+aX
z)^{7P){Z}W@Rd%g@Lcme-Gh}C3Wl~<NuO>Ji9%Wl5o0mEzrAIZBlERG(#S87P^^f^
z=8#<TBWfV|g3RXG!L<1~x)BMZTd%PfEf9~8y5<aD6DfdjR(aO7N7-Og$!^93?2a<y
z+ph<K<jiEToUgJ-_Ff3wPEWhQO+mO2a?|#c<c{qskE$r#iks~<OsBsna&Nx%<Ivar
zi-wRu=O!G4UoO-W`i!YoQ{22BQTWx~NS91VP`++rTqtF!F^l66pkBoo<QN>>^x9%W
zM2yr|IkMmH`rVHaBsb^z`Z$&+$uToV`|5Pk^MnLz@9Ak|4Amo|$w!z{WN{*rqXpT~
zF~~+<@?52HKl5-agUQGn=A*C25(FO=-UAg+VlrcywAdTxUU9(6_(8l{*PBMJs-43_
z7D)Dh6suHkH91q>sTkZ8nWf4us7yQu%8^m77y~P!F=#_Rz?5<Zm!}|IUoj?2@85Hl
zo;^_?v)2tOjG#<T@&V7+r*IE)Gp#J)JrFyrtaT^sYY)j5b_g>z*4Y@%2~Qe~rCavs
zt=}@sJ8ZIL7w%&YJI*bigL>kT6@|hb?}psAsdImA!ap78xp+07iX3#5i&N(5&GV3s
zLRR!o<Hv^7ew2fJ{89@K=#&B?5c5ph1Tkzxd^NvIo(uBzNAQT{<r^>MuNrwxaT5Tl
zh_fVuRw<I+2QGlKo$1S+GfyvNJil5qjLj0X1rbw2HHq~1v0yQw*xb2kMn08j4%;CP
zK20JkhPbVMwq==|*OxHGf0>x-6L!udgcHx5Dx0h=mc{BPu`Q7tmrpP<&QL(NoVd@}
zH1XA&{5{2a+QPVJFLK&`6die3?z-J7qbEYY;V}$?sL7|twIIh*9o<=^hYz^IKn<mq
z!8mbc!u(w*H-cz|XgUfB_d@h&{g5h?tnLmX13Y4E+;M@%(6Gl#UKecf@v1eAe9}fz
z*D#<Yi<||?iW)?sh|Urq9=u=FD|7>XbjsB|Pb}Dcq^)oS+gA*^ksgW%WQ=Vh9#agR
zIE=+wut>p8a%45+MQp0@bjSudL=wx#9_}`N-(?Sha1nNi=2uxz@*D^>eGi5HOgOZA
z$M^39C~OZ8(!T`(mLOrpCZ}t1x$qg`K3B$rm{y<QKCOJ_$dRd#ir1;sO@Mk4sv%t5
z)1PmUTm<kQREna)xijBizIt_=s1(7fmB>;UYct0J&@UYo2PDviEe9!C-#rJ*T(LeM
zvzQnEmDEfy-M09Ny6DxJWd8bwhOIorm1L*XdCbouu14~oD+OdR{$00N31r4iBzGk6
zcgJcBazAVN3v66-%Eg?~u5v#J0=EVV>oGVkhz?|+BHKu!V}x)jgA@obSXqhGA0zEg
zE_<PL4?`j<$WC^~Zj8TqeOrrLZ&YO4=ff;S7i`U!F;oNTf!-ti1j8e*z<LfMJ6{WC
zpxxVG#t?wk;-m-Yv725sha0IRF(FSR&7g^7ueOj0waS_5)E|=Ses;BuC;_%t2fl!f
zs2Gii#8%y=_fA2<kL!uu@|z&(G@uR)t)T-MT~B{x_Ym$@LvwuY)UI_#k4#8DE2QsZ
zZ*|NKyk}}=)@xh$;-E)d)2}sERqFuY0e6m)L{J6qi<59oU=MK~uyn@|zJ|mpnV59#
z(z+*%vqb3{)btG{bM)Z-lTOESB-y<zh2Wc~bqx#v;TCvC!A9_m6~?W|uTV=%xrSr)
zv-eZyCQ=naf%Q;`0IVO<H#Hp{ox!?*y?gf-EMeFEnW!HUjEWUNj1jUGI_`Fz=IK+?
zbCd1Xh3mBnAng*5Y941!&8^Q|mH`ocxo4`j;Dpoq(X+8u7tRvxHsTynBCR!qB2+18
z(E$+#3V^Tj2g<9x5Nt_Es=o0YWR40!)ku6qsz~2uU_IeE<7h$<ipsOh+kX@-mN*!0
z!4L$2fc6*ODoPY-{qgy$aZ;UZcr1qz2>T>^cCQ)Dp=FB%aDTnM4pue}c;sXnJuo$k
zm&<kd?E?vG!*r9RyHV<qR%5BqX%dNrQRI`$w;)>*A=FQ4kVx|j=dT8JDejz9DxP|U
zorhMI4~KYcx%gKIsm8i@_+WqZOko2s^7L;Kpwa;yXh>)s37AorY!RQh>P!6VR=8Oi
zY@N*y091G$cf4CULA&4fNrKFHwhXW7?2Q;xHxFmf*X?j+${Dxi`!NqhDNl1YfONyl
zeJB=anK*_TIwV)Sp9htJ?B4*<NZ+{ZP*e8M)ygyq*YVkGhg@(KTb*4P+E(}!oQo^*
zl9i-C;gmg!-+8l(aDAaaqG48|=z%IUH<i#6wb6cCO1OsS+SqgXRGMMF^VbZnK94Ob
zYTsL>{pO+?1pJQo%+Ac)(*X%+brFaLAjrB(&v1q@g~Rj4bj8)sf%MzpgErggDS?&U
z=~kT!mszY?zb!7;{$f*xeZH<r22y~ubc<>F@P%z8i9_dner_9g7MAUJ`lnai8740v
zkWy-*YhqRR2uu*BkyF8F+cFVo)XVL+lbJNLv>dRlL;hf;?Y^EwaJsTVDkze{>gT$4
z=&eo9VaS0)N-w+!NhPqyey9n^A>4LgVq!w$X`Dsb!OsT}chPlCkT#Vf;WH)$kdBP)
z6*hr=aTWv=3yR?E%cdSa2$#jAwu#W0yj5k!HB6lWJ_Q|U!ImZWce1hu&y2q(0g!wF
z$tB2YRY5trlSe<(a3is1XVVC(MjeA`MsCdM8$|5CKwmb!DE8Sbl`@uZK`JTYEEl*+
zi5AbrDbEg(BRy5rCp;Y>VG8egqnnZII`#eJ-!oB4VpU-TjeF0HgHUQ2SlW3I2_kSP
zku<&}$1&xH15DCHFWfM{)KeX!RSX2hL9}6_PcL9E6J}8syHG)X#$#eo=BRJRNABSU
z+uUp0<PUk?`F0@fx@W=9UpJ>0{7vKX>E-dl0)ozLSK@_VIj>texmKD-T=adc;*!4^
ztF1lvTOJRm{5#7noBq!AJLk_oO9<Bd{N8OhZEp6i3dj4g^Sdi6^{Qv3Jv1w-H7VJ8
znzI9{H5vr$PiLjiv=w?2y&l1m6X`R^z6kGrcuab-<!Zv&w^xWn&&w-7K&yyYoTjEG
zQ79|r8!4oIjOiuklKH#I#Of1y6=6OfTNiD#M)3$q7ie@>XQ_$>`w$?Fl;ihLNw5t>
z|26GuF&g(vC;i-&yIU2DNHrFz&|s*v1ht2Aig$ou?Lu;>g^^8SI{Whqu3=d9U>NY*
zlfa2<$;V;4x8$JWq6iSu@Z0;lcR!q;cAFChGHB}Kgl+$WIVO3kEM(t^PKao_7O$50
zh4?5WH|ND_nnGY}ic~$OE7MT4MZ}FG2$%}LeYn?-OpWj9qzk;g?)l_i4;b?~=1D;%
z2DN|5Y$oWKPe6x@<m7_OR-0v$GfUcb7C`))LQg0D9(f&#Z4QP^5V_G{WBS#dtmHh;
zo~>|aSs=h~g|%o6%c7*>n*KKO7Lyw;;qrdVjdjj<8Gnzx6=EdXnNI#e&YfJ6d*rZz
zm{sEjXsU062LYuH)%>l5GvolzL@|p+6im4eC3~N6M)z|l+NMtVSzgA@E)J&TY-D96
zE*1`q*Oq|-e$76oq~6fbz_?i=-wYm2LA?)wSRmz}IvOH6bCWpA0Bd|w`8F(6f?wDN
zv0U~LE#AxMUKdTkni3T`te1Q>hEG3mn%^-mlSoRjmf-G(^I7~*gpo?3H$~!BN0I!a
zrkgH^)bjGyOSfc0Mru<DI8pA>F?aziReBF(jS-)JBqCp5S(XJu`AJ3f;ci~>CwS72
zJxaMXT@YQYSU~j$>Tpdu^upgF`uh4(`_d6hFNtN`<1<%WU^7;PRYqiFmxw@#ZOnDB
z8;xTHH`g-%<Ixan?-iH1qa$qLR7k;24qbr7d^$^590|N`97FkHZrK#6E@~Vjn%3@x
zfT85n+r?BoeR=RM^705OBuQ#ZMWDBxZ52s|kYWfP9^j76m@58-nn@<(n3hEpQvC!Q
zz{7Lu^_lhecBuMtP_js6l+CC#QfLHY=MDv~WM&s`9OO1;fcux{_vI&iCJWXYy6XaV
zL{Zv^?MV3T(cI3RRWnFRoD{j;#x5;j>=P*~+?NbpFnMgPkTu;KV_mW{^4pUxQl&^t
zr1f-9B#RcqIx-oXYsa=USpe+$fkN9!aKB@_k9ZpjTl}6reM*qH(YxQ5!xl?Cj$wp3
zeCGL^hOJIPM28^x7AbBrvS%#Z_2+7>7SvC9!#`3MFErj#zFb0Sro5_Z`Lhs@+3_0o
zc*g4X{H{6dNvaM{gQZ+GJ@tda{%t2V!{>v`jyO0tP>bP2FcvDYM4fY#`B|DDrs+@+
zV`ZNB3Iibv)U)^z1wrvhp@_RE>JR1wRs-**q4el^L`2IUOUZxseLW&(hNKjGwD{dM
zW+MBxJD*f{GJr9~clPr#J8tEH5!6Mzj&vwyq}XGg<`I;*5dLZGG|l5+O@K7JE5c(U
zDI_44R7fy+-rwnzm)YUw;DMr;`|%4zjvZcWfMhZF^1`O~{7qotL-W&d^BKPfFsAE;
za#@N=oose?_Ton-B&dNYWjxx(d5YvJ@{t4cxJ9wOd*$I-FUt}7kEm-8Ed+cgQjWTN
zUq>1>4OpX{hz_Eo{TJT4vuR206&e(|-gtuUT@VHOB3vs54mZr5ogj(jLqUv~<HQ6m
z;0|I#V2p?HLibe{o`u2v!JLKvC{W({qd@uBs4MgD>*Z!E<;WunW#?dK6Xcs*U=Au?
zwJaJU`ej&4{xQQP3#HB6Cx$&!F?KvUl5QoWPvbmj;-dRI)p_oHDEQt{8U|q`tyU3?
z0py!}b~_nEFn*1*5&124wKWNB<qK|a+(T+1%dcg!vesFM5$O3q%E5|k8&S~sX>iWJ
zFI==C6dO%dRn@iMh}#=Bjfb!i1tj6VZ{LuC%JzFVNmVkJ`(&#GYH}F*Xi|WKBG>1L
z6F>7ZX5)T%^dXU9=$6Zx9<`Ecuv4vrfskvpld1LciVShDce~8^WXfR}1CmEi@7bvy
zVPQLmk?jzlB{jJJXwFz<C(l{|hB$~Lzwk*#4nX(X0(bDt0`{uuJ68rW-A$b{eKm3A
zDm6%6XDlqfL~Is<MACb7tkR8xM@7!Od0F1Y*ax3;VJA(V*q^eRhCux`At;=>+>o_w
z3z?q^v7IM?Qu`lm77EdL<_3r6Aa8Sab|%FbjEI*+T#krf+hAtY8&RWejR38vDoQ;J
z`ZOLM*{KhzMX2H$`E&WD%nDp=*og-yl-jt)w)5jn(0{P}Y^VXnns`rtOg0)I0G{!>
zf4L0-$E{+s+Vv3FuP5O>C;W+1S1<@Ua!H2l8X}GxVe7~e@WgEkIEt5^-_1Z5r0!2U
z*WE2^q=;h#d1e4U>}ey2`+ZoqDy*#OWju-!DmXbE0N9(jA*l&CcEeW;5A}>^W$6#k
zy20Hf>uuE^T7V<zR~+EV6Bdg4Zh3h@$q=?7hQTkv*s%>?NUCELN|0i9U~kHJ?FEt|
z&H_4tp@38!h4yN@O&CM2;t1TE44mK#7f%XmQzR+Ce5AB+L1Ixf)>C;SjN$n`6ZZ%%
z8iGr?&+GuwV<Wqso!UqESZ5cP)JF?X8-+h781?^0;xDznbNv2chjQZJvF-PQso&#V
zy?Y=f;rrkmTH#8|Z1zDVN#wRZs%z}dYNrdto+p*dy42RFW95*#0azDQ6O%AMcXogh
z2JsA5GJT~Z4BAlR7%9*sL=CeZv0%fCfcb!(^m})E?ymXrMJ=I=h`FD?ij4luob`v>
z_KS|5(?V9tKiD11?H_$|rsGIk;~4h0O{LD<RAFGsj9T(Zy;z1(yQ2WlO3Gmy?cCgE
zFUzE&Y%Q;v%pKGHI4tEb1)mZX+Su5P@ewDh>iCXqKBV6NX#xAWkA<c8{MKl5u2jGs
zllzbU+yx4ot_YyKIm=HT@Ri>pRQLD1_;by|+<ijTB&z!WQAoKNlyMek)VNbGl1w!z
zLj-X_ss;9$pZZ565F8wAglY%Wx|vl+lcMNGI7x_00)o4sfbzyn)7?_fYo}dmy*$sy
zLGI-MZ_GyDYwpvPA-?9Z>Dw8+XGwrS`)J7g44rGfCHrz*=3oXXt^Mc4F4cZ}4~gG=
zNL|E`G|IPJXHe?ID9lJJA!lad)5*7ib0=(jqg|znHJoZOAOve%?ws@3*jNEnRh(<L
zAM-*e-Kr}?jyP~y0B#li7!=@9;qy=l$d0@aXeDsK>)^)u$9l=<nD_~(AsHkPM9kUR
zjMrqJwa{9cfH!R_>nu_}dow^l%lta&78#`YZ7N~0gvQz!dv8(>NHrgX8(WaBjgq|l
z3^*=dQmA`P|H$c*^Si%PRcZ7hzp1D9BsexIO35`lAR;4U|B(4f0|SF^X8z$A5rtxA
zc6~f```S1}uM`$I<hwCSzVLHd^kaZp_#<%6H}YhijlsZBvOsur^c9cDr<-Q8w{wVF
z3EJ%Ya8gGnI8_{o^GNLW(|FL|CnEg)BIjiH0B5XEe{{ZQXjCbjTNUx8lS@*EN!HQ(
z1)_)7Oj<k)DG!`N^09Nkr#x`6RLx4`+v>yF6hJwzzPMajgf?%8uVFY8a1QI7_ZjcF
z_dy;C*{ISD@$jj)<eYdx5vfq!UREqi#sHjL)O+aGMn++tsVoS8ooEk_a#x|0VEgoJ
zbZ$-TeSXP1xUCZUBaLbH?X#Oz!7h?9!~Tt8%!{KiW{v3wMcNW_My)f`9D6n}b1>5i
zT^!tzYISlk5|oZF-+_I4RbTaOc_$@j>Vhj6-rJ+)a&p4y-~0)!@)#vz3<(N}kjljG
zK#CatnK%3pg+f7?ol#{x20tWBsuR6x?F!4Tui`kW%c|3hGFBMl)be7;pSAE<jAy4Y
zS?WD<E2hx*pi@SKHZj4L{^%PCiZaU)ibcGy-d2jEG1nTnB}3Sn+g>VhdTR{ALRWsR
zh|SED8j?-I*5T2*Cx|$ooUHv7_TIMv6<&iVEVkfOkpS1HH*5Dsl-2}~_tmiDNu5S=
zn@WYsV;y;Hlw;~2a(7mtgd<fvz+K?eT+zFLxgaHEUrOj)O_i1E7-(2-!tk<WkP_40
zS71trTCI0UHcGAWzt|0*M+K&~F+@A@GW@hnret+{-WQl$pGAsyLfIiI!B*jhjo`9*
zzva*H?2<PlqVT-GsbYN=YE#m=HPG6kv3Eqs<HNq@xHk;=xJ%;jQN2uH_(c>0kQFw5
zCP{UnuNW8eWcl+9f1u)j7hLW9QNwfeM^5o%bi@hf>F+_goSK?L<=(i!v|E-4Y8PoM
zG#OA6J6k)CPU?|<^`mTGP!w(0kh)Mv^lQ`>y|;9jgLTmi56F~KdFLl8!f&=e{=TQP
zAkcmB*GKecQ2GP0-~W%_)c@wE&`^nDM*in}i9o{Tf%@lhsSy#|@l0Vb0aLM7z4Z@c
zcZ2f}@9rZY!E@UhG*Uo?9J{LAtX)(o-%VKFG_ViKdW@B`?aF`sS=LnV{b_~qcyBc$
z)e0Ehh8`89ezoxZ-K|EwRZ(aQ;L1Y^zYmJg?<#WL1VKxaDmM<&W25&cl|QRt_>cJP
z5}p=4BwHUl_eSZ=&Z91JEhqqAQZ5SYZ%EB@Qq$ARZYnLmko2XpGO5goY+0sTQG!2F
zk8NKIqEi$=l;jPJkepM~)Lf6^4J4RV3rHs(<se}nk~cwqSGs$g^!6DcQZ&zn0C1)L
z>Bg%L1zxPyRHv>&ay4Y7akb44k8OuZ4nqW!GvY%5hAI<p&MFhIrmJgevJEgWc!~Ux
zZGj|bzSXeOavianZDx#-tq9}!iHd&&>$oi!VB93%<HWROLn)-79bm(KW)hX*oMScw
zf2V-}YXe(I%619?pA0AZ>#Xw@EtB7VudZ`61d)>?+Pi>BBkVXD2Aw}(uQC0a2VU-|
z-XR`Tj&}{HfUPAy5)LUVd*eqIk-&RM1HFvup}#)+OINZDvKAeVC1JVkQ5q?YT6C9z
zqf!3NnRFKw{#kDtR(V!-6x5Eyk7{WJ!P2@)8}ELY<%T>4KVGb8)~+47KkvAk?Kt(p
zmgTpvjgHps&PA#PhkYv#1oKVXH*_|@Ew7=jZX;2GEHER7&wTY3$$1d`M|Ot`^#MdA
zOj;5&NPlTU<+U}^9ki+1PVSo=Uasek@xBLIsHiyEyL(L(wjYJU@77ZB`STr&NVbJy
zYRWo15lWDQY$YHCK@P}-f9YzS)6YB^{p|pJ5dV;Q1pw@ypn_j!1~p724Z%m}n3|fk
z(<&BI>+8=LwMtKOFl&7`DfQ}h_;})jQR|$n&gzcm={BFqXYz;8Xo=if!tjKv?8Xuo
zr_&*2gp}}C7QRUqH!1E!R%GZ|AjYwdME9GBWPk{l5n>{eWGKFu$xR-VhInQKHPa%U
zWx-l4LX6e%KF(q>)k2JeOq4YG1(2>j2r8$a1?>ah`g$%7sw-bq`4Yg%=vO6pI&mA-
zx$iY;v_kE0ZCe}SMY0j)#g~!xMrbta+1oQX$fezC%m;!pO*J3_o)>dByCMqAJ8onw
zM(=B!K?tqH$3jepILye%9hk$fUr&*VLUeB3)ryS-FUOkDliZMIZFEzhzCJK_*$Gob
zC8L9TIK(XY$U0!SVFcy`uwbdd@kd5R0tF~~7_}bdt_0TF$lUb>bLcOpqadGa^ynxT
zGmTca`)2zq!(NJDV_n^8T+ulH`YNZms@LBVE_s9q$zA00IHw!P;4)zzisxs_=NTct
zIeKix&pH*NJ%$ldR*VgeJzhh}lfpd7=C^sN?6{I_UbFh*lK|JV@*AFYR905r!%~@3
zs02pGFEd+24J${swKScfP~zHm`Vj;f1?;sXYc~6GxW<Q}CIH9)F2l<X4%kBBH|$|I
z08%~_tBVDfH3S`H>BTaUditGPwdmakjy>CcOSt4NtJ8_l=&ty)R|_lO<MRotVD-!5
zZ)-URWf9pE&isKmg&N=7XM}tO2z7*76zv)YUWgA*T;Z6y(gBP>OIphn=3C#lt`y0g
zv5}W2g#@)VjNmi=bY0`~Ex~0)BvIr@`ct7{&@BquuN+zK(2R9JXm0s}XC4KN$m;B_
zlC~98q5LMoZli_78@<-Sf1vrT#%s~nrtLb1l@}IVha2stIfdfKs~lL6JKK;8c7;@!
zBbeSiUBp<OD^Hx(eM<-3QcUDNtgPkIqsK^s-l=Pr24buh4zHQ!0s0D-xU!37E#eAB
znhL`Iq1}i-o5{`pvcMT`c%1lm$JF~}7wEGc?*?W&a)^QZkrp9cxr$0faycRPBSmN=
z17nD6djjMxirepPH0xmury(3h9%-Iph)X29yUL%+NL?s@`h+$@G&x}44M9&|)n^1J
z2SNi1Wft1!@&D1K?%(iO{|0LPZ;nd-L}+G5^_ZAWa1kC2Rj6|({Ov&$p-lupy`6(Y
z(2MIHGsPaaksJ1Zg-G<JE^ykjU#MtT<p|nWSx@1^<4f<BhYQ0Oy*%)Puf22fzbu&h
zbK3qjkNJP}^A9Gqrzr4Tx|gD-*MzLA?!#jj^4y~LGQ!$Dh6P?E(n3nb$))o1=M%)#
z5;UV;Nz*w90N}n%8&R@D$Z5#x30PowImjJ|i{B0UBP;uZ!(+`W1P-`;xR6AQJbs6f
zPft%Lp&l?DLR7dnhQ$xr2x|A1u1X>#3JB;>SxC+aN)CSj_AnxLS)B{JAM%{?7i7f6
z$W#w41a*RqwYA!0cw^mPh#Y5ZN*kZRA-;(3xT~6)WG2T~PCs0yf&3N{{;MxiL1{xh
zk)9Iyhj&9F*&7$PyL`EGpzOr)XB}rj<~d!BJi1yR{O0$;JwMzO28lbU$iK#Lg*^iU
z1m%2Tgr2ZG<Q_Ryr|j+Qz76;!#Jfd^6up~*536Z5Q8qdUfw|+*r#SF>LqYLH?-rLP
zT(?p4@)X~aSZ9G=d8;l`k8)Kh6nN5+o5nS9ndaZ2O2#CC3P8a76IFI>VxqIjgkR=P
z?~r5k*uepG#9VlLYWif4K|N}HI^cPhoy1stBW1^E4?`=8_aeOajg&@8|NBIdiE@Y#
zy)DU@IG$7(y)3QXX$#4C+p-gLZ7*!*=NMA6UpoHFtn>eSBmZ@xauQPWy&+l%XmZ&|
z&qPCHyhKqeNQzn8NCzSar3!=@)ahB50L?r)wBv;}xpU*)0|RBV%}5d%O?~gM20kf-
zif$9^+)W*Z!Z`2j{W76Vq)?x9OGT_j6vd209}%g=0v|s>Qy`-H6ZM#zDG*w?M10Bi
zmTZ_I0)SUTolN>ylCCtM8;LTQ@MSNNHY4Z=GP^a&76gNMzUBA!6bBw7Sq+}HJt0#c
zPx+x|Um@xF<mE-^{xwMppA6;m_H;lv{N>9RR)!RK$o44&i+g!^h@5W6g$32|VKufz
zq*o(KZ5cX0n5c3m(t-{rL!kHP$zJZXUvw=VWQ5nG^5^;>&<hKd`mlvr=LRkb({ISl
zoFfSoH(4>u`Zp42>$*%s9TFYSP60*4ugF5{kw}U`ucBid!&r-p1M2>;&qxm<htW(~
zqN!;-vbp6M6nEd6Opu?}Byj;DVku&|+^3?!9l^*RDBj%h644jM0LG4SE<&5CpdTf%
z^+e`Np?uG#XkH<#?H{2dDBh4%aYPox(qarSl29_hfhYs|?|FEPRp5`HRn$<P;WLWf
zf#@WkR`BKANEGBDejAxt1dDqI1~!j5ml)c;aoSaBa3ZPWQ&mHQRLhf?JEi)`W|}il
zd{aXFR8Yr|w@(FHR+RKfJ##8mfgR3uoBp;#?sNeh4v5|N4Z#4D>Cat~kT}GBkv525
zwonL%OC^aI!u9B??H)@lM?Gl9bH7L=yE_KTAcfQXl5J{g`k^5hbZ#vKl;mTiW|U%p
zJL7i_sYCOx?-Li$b8CH5@Z3Z&Y3>V&P%4ks{gt?>qIXVX4$=ciBjRm{`EYF{aJ9d%
zU&YI1kM_kF=1Oct855NX9wGu4o=Xe{+)jO-n`LET@v$M8biH&~WQ<;xzD(SE?|X{h
zE4z?%)&24z(zpzT#*{)N?%JiAr;dk=5&_n6w4?L#B3*WuH8vg+rXzm+FnhLPS#nBB
zk*1d3zGsgZ>-#%W8vD9)>qWc;92GI?tgMvm@l#YMB0XW9HFz!7SV~$3k5aFPf(nD8
zT(TwOXed^dL;5WInGwP{-dH|K%z=%EFOfssnvF;m!9Fx14RsJoHB~-R=_2d~@G1nH
z7+P$>dR|4WoI%xlHLrDo=%LCy(Dw(>E5Bhmi<h)W)n7pm&Rb}&D#|0a>K1aK>TS3D
z$yRkR{yIJ0ComB`lXII>a<{mrI}|3@?F!{3Kb+q!ih^V8k-<S<7@eTB=iku$v3%Cl
z2I(UB>j1p40n2Pq<oPL-j>5~5VUxlKY!(E%?cibr>?LnPdKpZA)dUd{60#!JM?QRM
zY<!$KP>rUkNLeRg=*58^O?Ss;UOY(2T5<rg?n3>Y4P<{dS~^$kVzWYBuw9-Go49tP
z^xxeeT=}lQdY)u13nJu<R7(;We9-mh2y8GV9agL7p;I1{EJuedD01WGdgK1I05B)w
zNVO8aft5A2(Wdm2AEhx(ln6JG1}&DINGK3ouxxyw(0sfkkeVUMb&}U3+1RGY3sB`+
zzg@yEkF-50zrgcKt+r)HgI$ulWfsYbZz*Nal^cYON4A=?2D|gMlfKlXB;-d<*Ri>s
zw0}kDom$=n6s3R!JC%F!hCgmZx$bXJ=o5h|5tB3e52M>$8z$2m9VUuNjRCYUCA0O0
z6@iS1b=L;t^=OEIMZTRh93ZWqhiwtnZHAVLD{w*kvN9+!ec3|ms_-A()Blh2i_sBP
zOX8Jaf3Q#!k#*@qb8lZ#(2SfgDRm{?BX!kmpdZNff{F)TA_q;rh~`!ki@hDL&<Dv9
zd2AD1Bu>MHcZec@516d4$$`@T2AA_iQk)`y^~vy$2)#G6vVur*hLZm^S)@d5N3w(R
z@<iN11jRUQD?aCgHslKO1qcoq4W4fb9QDQN_=vC(gn-;kz8u=EbE=U+$j!COF-To6
zFH-G7HrN_iGtiCc?WUw1Ar{IraxpZJ+z73NDm*0GY2PH|f)E)RlsrYG?Y39#-icti
zWK-s&cSPKJl)rra%C=R}_cn@D-c)sLE5cIznWUo8R|lLcv0S(gC+Pu7JRD{Wy_1P~
zQ^t(x+I=}P0)Ykw%i=MazeejGr@*OKURa^xsOg3fNCG`Z$p;zrK@C=!LOeX=j3|D@
zm}Zgv;u^En8UNY$4=uowtmV)Yu9-jRhMd<3+$uU0tOg@dP@{7!E_>oktT^zwbJ8_N
zlQ?9~k0>j9=U5PHm|BkV1Yx{|jt+Zjf}x%6DPzMnkSioN#HgKe9`!)HU=<P-6H^%^
zlm$8{oyuunrAE#NSynpreA4OcDLEZ3RhXNaAR%J8Yl6|j7iS_>M7k8jI^!3Zi3awS
zL9Q&?Gb*~(9ReX~74lHR`Z?9&Txml5=rEm>-jf5o{*Je?Nv%RQiW>fow_yX)sWWBG
zGb*QPMgK--`0tVcgmUO1RP|)Pi5v_N=|_4KuAU$oMrOni#Vow=)W^{Wwl@CUVMz3Y
zGVaSmQgdw3u^tXm84(&Mnp~t)iTu6*F(jIOtVJ=_ad~+(D+)?`^bG}wp9c28zzACJ
z`;bU<R?bQIFWx#og(4;{<T6d<E$VO7z5w*VY@_UhToT=W@AQ=&T(()G&CI`p6qSHM
z<L43cv7YqKMd6E|ukY%vhb$YUE?W#bJ_6}X1`e|*0qnwKSZQ);u8lN-2b&v!J~qUw
zZ&nR17PClfcBg9|qwI#xNT+u}6jkf~|IU|?sro?5+=#Ta%^{brN9rxMt3Kr<g;tzs
z)p&R=c_LI0^622Q3+*J+Pjo{gkk*JY+-$J>=~?t=JX)PXl+b7-8AYmAq>&{d+Jg;}
zHs{d{bRVP5GPbQ&)$|SX^s7#TTm7WhKuS5}kdB5kr;$>Ty-JM;{VJhb3S>aWLvsFY
zDC_b=PfS5jwrwp}S$qkXcr`Zs&c4-Mi8pL?S6;|fsxZ$DN8@txr4fvt3Zxo?(975|
zKBSi|&{RX$bVXxoXg{rxIuXie*Ie;t;ILYEPPl0Ub7<|5faz{dqXe5|j8+28?|FjD
z6hH|Or9!xH)Ol<GMyUcC$P^+~%?(1?3kax)h`%H{9~hB7{X6Y%=088t<v*~w{l!5n
zU&hVb^;Ar@bI)&}WQ{10FSQ_jWJvo^GNtI~SJP!H&?BwkRU@m?_57p{MLbUGLs!&E
zzjKw2TqU3;Euy@ijp3MdM%@iqOC1?8gdb^Wy-NCybeD2{v?X2hC((AkzUzC%8zJZ#
zhzKLR*%_ve6~P6^@Qs>Q=O^B?n-t}xp&GL${XKPRNPAe417J$!$$c?CK&F@&NTjbo
zsFE+hs2|*aJ*=qp%)|*l?|~+Y|3tl_#irX$;BQBWM9V3Nv{65zQf82oFLWbk)#Y4~
zi4>M2q4dBAOYU2(wapcY1J_P|qy_BV=$oLOUUDIc@%<8rh+rX6+Ut%grNFf43S{FM
z6%(Y0GHdj)Q|Ci#lxVC=h;G}^n|^+m(c?F1DB5iU(0osKga(zk0-Ys_0XAIxRpucn
zwrWvDsY-IH0X8dxJ4a7^=YQV)uY)QCJjbxJi;>0KOavZ(1XUn<_0@u89(2XRU|B()
z1vNol2v#oY!Q%=^j*nE;*9ZQ`QgpAzcSMPB+YxI|;;Vt^bnOOl-O|?x%YeEWMIuDa
zNW}ia*<%%lENu>&amE_6_oi)l?|!eKMwX`y&8coeZ_`COK#~^tq$$x&q{9d<6sJ0F
z9Rvb@{ZxIm8%Mvp!ACON-8yGd-1x4KO0U1$Zz=M^FgxyrN@%fRM?c9~Li<$=9Thuj
z+xYDzd6S`(Cyh!;IsPZP`I%a*ABax3qo#oajfb^ndbM(Ik+LUI#AGC86ICkoGW4nP
z(mh2=-cK&q?<B3ABv7+tNYZ)OlHEN$H_;AXh|zeIYz_l-7;GnB{s76Bi+=dy8!N`^
zBL{RfqDL$%%GJX;^CPCDzj0+1Ad1;2BO}A83yy*s1|bH#m|<g@wKalCQq@E<N2k#A
zyULiQGoADt1|3y&?#5=eW^`{iBm!Sl{G*dmf0Al+it%5}f^y=T`DZ-*r@w<#%y-$O
zY}ib?x&r6wlW%QFKzvQ0eW+OK*?p^P5}ECFSDw#RYBSG;(ryx>Cdkc+T(O;K#etcT
z@&h#CAP|$ZRMv3|)BOui#*8>>2q5GLZX`A~s&r@if)p|5dq7+P*82u1Ysh?{V89v@
zx?&<cHLMJ9>A1kx<^R^#x}xpA`th4azLMKXjP^1n#v3aYwa%iZ6)za22S}O{b)X}Z
zR?W1mqY^CZdy`e$(_d<|H@pamJi70?3f<}DZiUx7A@5-$Ux0yc)sWlxbsTo5QCtT!
zfk}#U7FG$(rx<qn<Qwu38k~>gv}P&BzsO<^x&m|2-27aOM_QoNW4eCmwggI{f0b>1
zibc7#+O17DG8!TL<mKTZ0^lp|!B59>Rz)7Nq@TDSO;M_y2>rEvPQgiuE~>%TjDEhP
z`2=a1Ow)7HzPZ3q0wBTo)pbb|v?y<}B5cyTbrY;B;{V&|gu%ljW>FijS1xp8^hBt>
zOV{(<Zb~lSjnS%012j`PmgSCXUqA&KD5S$iM2e5?DwK%vBUu~iSADu*k)hI&m;Xl9
zki9tib{5sEITwjREj86yYFO&B%BqigC13OK8v4m!mHKrOwv9X12M$OIPm^5=It5Z(
zZdSG7m^J>?)K-m`*oP;_g_bwSt}|_H<`XfMyS}0oIs4lrPgIjLkxVqg*nUBHqR0la
zvO38X3nA=X0BX;x1MKo8i<GW{Txbl)#6qB7mJuK53O$rP@#)A=4eIGucSW_<4wbe_
z-~ZTmOW;@JO{5XjG8*@ia4FbhlzcJAEhyf^NXK8<5E`Q!@K_kPC2g93%E_;(IRSLq
zzYsUysrTJR<96i0nKUHF!nyA%7mBpp=!JB?FprqqOG^ujeg|r6^p@pr>3}rtcod9{
zPAb{a`spT7%7T2@^^Ib6uQt_Csdwa!&|(?kl;q}ZBgqCR-3Pf1m`+N<(c9UNECm7#
z2x7{ne4}sAky9FKxWr!tw-GfR_&X-ha7-ww;rMQga_v`~<eQa9aR)(d!h*lF0XTUZ
zmj)Ih@LxYaG(VeL3%24pKovm+WN493oe5%jOkaEPy2I^z=K+C8{wNJy;0a$wK;A&k
zk>mB{hp#yfJi#{<y`JS6z@%=-56pM^HBaX!96;bFDOhiZXZ$oEPs$ufkr7H-bIR7b
zhy`!1lDV9bnb}MmLgH4Tz(AI1+<0_RC1e9IDvRgrKm9R_b*J=@j7>MVCXLGN>&bJi
zXRJca>c0oc;X4di3D{ru-<talx18he{db{46HzJ2h(bhBX=$KAp`s;J+I#OPNode8
zT2yH7r>Q}^G_|J^nnEh2`rdD!&-Zuz{)BIr>vG}oJn!c<&biNh&biMH=YM)@Wa~0C
zNiFP7{#3Xz7_c|7`jQvrYkpS;=?px<#j#}aH58JMMCRkCMFHe9QKx#P*x82D>+g?$
zg7VLE&BOgl7$RgW-8MA|FDukxMFkhbKU%z=r8phb2=x3r!cETRz(5E+J_o*gSqLdA
z$pXiJlIV!jJO=eaaS1w2_mN<R|I3cSHG(?&k$YTmU4~H(ZcCa3b$?PDfE>C~Aa#=a
zjV<iSaITYpYw4}YZ6=Z`44rHg&PG}4fn8eYh9!|k=C{>6r>`~_E-$T?=^d+~qvyX`
z``}{3x(jl<YZ-*k3FsV?J9e%ur&YUP^p2ZuiEFB*sbzt$(yK&G{;iTJAB(lVT-{@u
zuXcO;^f2R_BS#t{2f6=#v7>$B!R;);9poZTBN@{q`rS^_s1dYR%&$Ll+G9$!kTkqi
z;<5Bn_AcX_ORobfO)9IacS}kROh5T(DonJs_53cSH_6qyU&`72m~yCEBO&WafKqb%
zFtWuJZ{6mDgM+<iW~^UVeNVBUddRnPCjrSYd3$@I+qZA;d|az~<%<9NyG;6Mz7lcy
zx#vq$jMcBGHZGR9<Ti&rDjpK-`ePICS{;93O-02|mz88q2*{VZ%?qIK@;098MwvGi
z9X-9Ajg8>kRPSAwh8sX=#=Vq7FJHcFs2Vx+`wM-bK+(QA;VS2g-BX_$Tz}1GJ$CE<
zshN^gTUYmPy1xh!EL(EPd$nzs({u!#N6YXuHgl7j;?6VPzh70e$JmH#R^HP>4qHw`
zW9zP6yAG~=%1D}(or`vxlONS=6DZD%vHfDymDJ2+<J~C_a`sMW=oZ4Tl<onWAhn>N
zARQy4qnS=&(drmSHqWA!bjRP@`YJsXPVKO|8xNSN(X7LQOLd~6qT(7H(-$8nYx+RN
z&VeWNx}I@r{4H<YoP^9e=piG1+SOIe*3Qo2>dyYn6Cc#YfB!b{7f#=()cXOwj3do#
z4wHOEme&-M+sUmWn|I<ZJUu=AgMzB@l@jzu!10(L-D;I|;*8ztZi&ov!HTvzOD3a^
z=dUB8`l>scZm8eWO09xs84CwTKz_c6)7=pp$LT&YFaOeWc@b5w0?&?H1fVV<HY0=C
zZGO5E+(N+YtUW>l>QkpqO*3}x7Fj$NxX%oX6ve!oI(AZG926NeM;I<%x@0pmbSgeR
z{>idn7XNSAxyb)-`R4vDpUZ&z1izvei*drmds?}sE)~1J73vw_8$X=Bq^7nucJh9x
z@G-whh2C)e<cpg0EHc1d>qMMqcJ}011)&u8O7Tie_ITCi@ycLyddQ4_beQ<n_3pn*
zr4}NQem`Kg_o1bQii+4gj7@m-{8(G{P^N_IEJ5fLJ70L#Z|tAf{(QG+@?>;Zr`qRx
zhIx037B8yoKUr{hx1Q{PIcUwArX~_LBpLoWcOg*?br*cZH544sF)`guPd{vMls!~&
zuj~Bu!%jUfF8;6$&=z*I%ZYv;5@S<*FDQtK15pm*c%Fj?=_3y7Rh9eDp4)M$eUZnl
zQDW)kYUV3lXML&BfTj%f7u>`2SGWC{&|3`v`qoF415^nQpwV(IfzLW4BSVdaHEO>-
z9#~kb1sNcM$g}J}T6nx4$x(th_e?00|FBytWwa|vEj+-Us|QyF{WA|q0f=l<-bYxb
z{@*tgF|fnta}7)1Ter?}{V1-u?D}cC=tb97qwf>%n01R<QZG*{e7(B5DUsBgr)j&)
zAJ+Qo(W6ZZi;EgP)!QdNT!_?kRGBoJbuq=&YApHKZ@911uF9-N+wkU1YJ$u*m^`<<
zSnos2)SRv-MU<i4ss*(~5tU!h<1nsOrIaM31tq;fK^}QV=DrUG;eeP*9a$$beJn8V
zQLO<PtY%y*r{)y3U5ncKl=9`I8&U>M1x9*mRXvWMES5u)CEsh@@vJFwTXPsZC-bIF
zw|Qj!b_xr#qHj(v{pwrmFAeNaFJSVaVR(l)EI1^DDooMvxJ3WCjua2I;#w_L)!>}Q
zCY%TQFJbsc;#*f{oVp=ppM`c{s-j`b>%kZ3e!h;%MU5sITaEtw?5hp6wY4EXGsE_E
zJWqBWpHQe~N#7`IKlSRA56upzX;Pj5)%u)etCrNN7p`BwUa)-NXNRlZTBC^m%}X3y
zDgs3keccg-MU$Vk8eJJ&PRb4n`!a`7F7GR3*L_7JYu`JYK9e%d`GdW>_o*3^dM>wZ
zML~L_N64Y6ch493E_?c=Zdhifb?%quef$DviA~sa3z9v)&>P&v%S%%o#7&k+9<d&h
zh8FKes52fDn>r~exkH;`p1DYtMJi_Q*@M25*49@3QQRwEG)e4`?oJo!2yg4iu@LR(
z6VyuWFspmH+qVo#m6=w9k~tQ=TY;a_&T&1<Zp<zkuZ-;L(-~8GD{+Ezroz>ZRb^^B
zQT<kczt!pl0N8Ae-6{xUYJy_Xd-KTh8d}sGqu_`)RV8WU!|$7iFNc!rQ1+|dlF8|J
zLbR%*Etk<-zv6Br(;j$sHtWtMtMMnSs#mhxRekHQEw&vEXra0t<F&$0FmrI!br-o#
z{&_Gm<;&b!Se&YzcRc?s|GX?EdTD%MTX&B@aYBr3wb2!;?{`JYCT>bfO8P%|P=SWG
z;P7xibYwx{k$P-w%o$}#KQIg9q=X+Xh79lH>)!|7o;`O?UO{0!F$70~qC|d0B_#|C
zu$bZ9Rpj*jG>YnG!1IZU<6H<k(i4%A9zDr*FDIJGQ(IGWwitlI(ZRS;tSK#R8$p^0
zs7KhKSH$*2CoIKT6_sj~tx*vu4HlA-kugsZT&n+?LN4Cq_DL7rE4`NH4_lsk)Pk_N
zUsfjl7m_~^b&JX=c>|jqE#a17JE}<Rce+GaC7w_kZ`_)8ut*~Eg4Mu9M>mnUf#QV8
z&+dUM2Q|39(NHH%lo?uEXXNZ@z!$fk?h`fn604Z-4qdGNV7I6U8b;1<6cLG#R2Zdl
zc<2nveleXA_kzCpOSxJPac_pe`Bb9v?$g(=cM*+*our7($~KD3tG_**^;*|iR%$e@
zDPv%Osn&O;&*3NA{P)4?7qWMQqoN+7<5@(a6d>;gE(}fH9X~&^R(IytujCvHXrY`{
zP&j2^V35#VKKIIEBJ)hpjzf8k*7AjcJffoPsQ3Ndm8DZ)E3c(;miO^f9V6bod)F~+
zKQ2_*Kd|tj{b6#G&DE<r&|YwJhAj$^0@;gxo{00#T5l88`000uUs=>nZdZ)p87uvv
zZazYp$tfw1?!0qosw$i<sy{-f$dLgTcT|9-^@RPmW<~C*;uix%n}d;Ak&^?=*EiQ+
zud;gIxBUKc&d_Pg<&t#g%|`B9lgI5Hwz9GL<1zA2a^}_4*S`n)4vvYjvX8-}ovnCB
zb<3IBfs%^wCXoU1=-j0Bg%2R-Z6<Oj3M>c5HNDN7m7iX4-{|ZqHQI=U>tk{mh`d2f
zd`wc`n3<XB|L~!Mf@erb2x$Y4TMaybM}<Y+hWwwbjt-~m+@wioy6;@PMSUhx$h;#h
z%VLK0R@>7A?gv_aFuc?o#Z{W!nmgJR8@rQq#5mqHH8s-b&oip=vvLR-d&7SKJnS%T
zPYQcK@IX9s;6W}eN~(wFPm=d`LT?)-XS<5{JP!{K`2Z!WD{hm$c}#Fmr=b$j0$qkZ
zyu8nruYJeewJLIwCqYyvAVdF-z_b%q{6)J9#yu*vMaQ$*)I9(KFlkemkyFGQqo0N#
zL9+kgK{*8GEbQz^08ClYZQN+*^yIw0ir$UyMw}|FoSY9pri{Q_R(^VaH{|hSAIRd9
z^K6U<ibZeSxY3-ce<nM^{qm^io1|zrrToQThlX@v^rhar+G&5LzX~|^-=D*k!p`?F
z@#8-~#an3qul;G#daQLXR7vnFQpD!n#4n!t+J=VE#ve#bTAZ(bL>aCt_OJP+7STBk
zN)HoQj{uiC-5t(l+#ybPN9NX(Cv0>)eO!zzEbQe}_~J>ArUy$8d$_bqG)A2?YRO#G
zb9V@WrQ+zjOh=Wg#gMBtRWs8c&J^}~F!0f%N0HAWRzxXZm2&o4RI0le8W}15j);n~
zu#;5R_4Jg)8M%u>tyqMEs{COjpawukh4CSGlw&(D%)BUipF$~4;L_e~<lwAQi(7z>
z%NH8SKQMX*_afrkV5!uW4eNm34=*<j%x4Pd7RFliQWg+yW?1wdKjWt)C&yUv@3FJ&
z^{zSu!I?L_93iu~V^@+&>@B3@TJleBNgsK0>vPgLj)F6!<5;$A@kM_nD?rp~K<^D3
zH~yIUB+Dt`x&`5$Gz_xp>+@VlZ86-rb7z{dsOLwx<K!Wm!a5wg7cIs!$f3qwed{{S
zSUnbS|9+;+ubXe$O-b?CQA+oc<Z+`$PNED$Rb$&trvk(L{W;V9iq^nC*d7P`yvxMB
z3&saE+-DwgR1K~OaXb|ICGQl;2Z3mTKf9)q+-sH!vQjg2d#2IHNooVDs;XXVS>bp{
zgIPx2FxhyBC{8YQ2leG#ZB<oo49%k=&}8@di?Pn`-MiNis>vFMgscYoUYIz2AK)r4
zFVA=V&F?tbbqnK)2VRVAW(ipq@j?AZQZe#Kn-vo_;Y$MR#@2eEdH(X4?di1cGjfgA
zBUKD?^73KZ`_3sUz5{=b#~@NQHl}La$3?=_S}HxSWnnRg@y}KM_loOL1^lAjw57zu
z&G*io0_<B?zRhmp8fd=Ydafm5J%I>_Y_JpFs47z*<EHiZFJ>k0(VIFql(Ym#M$!`y
zu)9_3v5mx`&{q|QCyPWTTK%vL9X+Rrs+K8_xHu=`C;;<~%@hl5(!?EoN{?TNMCce8
zD!r-J%z!N#g2sk%cUHo4CF#%IaI^`+PWzOf3}JC(5jleLq$Tiv&3rDi-v_w~>y^JQ
ze~~UfU+d%VZ(jZH7;PPJ9$_HLaBxE+B8;qKZr`Rh@5xzD9I^d;_I@kg?q@@TvdI!I
z7Zm|nXfjdAvUMwsmzNibW{w#(3UNg2{liv2fsI;#I?+9B&dwu8m`P<Eq{I)TzKDp4
z2|}C5@KdAcTNe}MoSj8bqrIlD)N9aK8F{&gpU3jj^put9G0e<<M;}*FoXGfJj*%3~
zjk7qcj~+iJk?#I5Mf=Gv7Ct^c-UvRy@J^s}$fzO<$g>O~?U$_|HoiU|Whyik8Occ=
zWG*p#Z+sM<frXnp2$zvZN@~zRlRhefd_j~LD;&Vg{QMcHcJla2U%A4DJBO5x;ROlQ
zw=itDLw)Pkt^EQ5ly7Vva06YXUQ*s!N{Ppg9-V>YgSNf-L`SNi`+{{`FPE-Spm9Bb
z?O7$I_e14A<g<QE(cBCt@U1<08-nXyqlU`Yuk)ZwAZTTIX#@oaKBc8n@7}*hTdXms
zq<dGQVOB1ecF)S}=-03MOLJ3a&!0d2wK0l?lan5cjPrFJ$EvHyDZNGWF{Q%9Lh8C%
zudO4uNe#-|ZzG5xq|MD!YCZaY4*+NmymkA5aeTW)MA)EmbPBp7GsuW3e1HAp#}CNP
zI{z7s`0&bU{3G9%ty^UTB6f?3^-Qi%5Lur#FxW#}gYLt>voX07=07PGXMswZVCME*
zc)=`#zoDa&M)Iw0ND!$URzePauD;AN5T-w?I0D;A6I5XR>2^*NYRV5-NV(3w$ytU0
zzR^puXdPtbrlO)6#>l!HK5khTz|BVEt9YcBIXP-{JQesKcVc2<%9jE4_ND0--8@o?
z5n(btDiPs=Q@wfHh>GO&l5v(yZN*Y9?0lnnA0=Av%qv3+?E?F;M^`d$+-WH`Mc1t7
z>atVv&$<sEgr7yPkOTU{T0a^Kk!(C~(68UWS&@v~+CGXfJie2%G6D*8i%h6hH5dt@
zXRnxLLPJB(PFX<4j(1-83;LTve)i^=JU(Zl_W7GwcHW^0cYxhpJ9n0upLCuXx>Y`c
zK+$w=!Pj@~g)3LSg&yth2~0KdlA*hxqho$S5+{)P&!0bGbtX!Y;j^1%x&|qcEolud
za0BZI@B*fedQQdjZ@^0wIa$U1f$g~mLxZw0CMsz%4Ro^}5Rjfqnx5@QZ0m8m@A@5R
zp$;c@;*vBQ3ppmB-Y2k<y%@<}3pCEHBaYa8QTFL|n5cjHOx-vLQJ6+yc5cbLcQW>{
ziU|n`j$d<cRBoW>68rdVF6yMyFp3Jw(H7d0cxfLV!e^<!z>aqP`t^?0`V3YN`$N8V
zhFKV*%bj!|4=oSx-!HAEwk1uk)Y9o{I0}7y?d|sHgSDceXY<a<5y~Dzg1m1i<#NAy
zF%G%XggQ2{!$1pz&VzPMqG@w;wu0O#wMRX@@OB@*SnO{C3`E;?TkA(rieKDB1Iv^Z
z=A55ZRt^n+nbCn$5E2}GZEgYQ<fTzlblA<y8lUSrLnZB|nmFz<a>>k3_v;p}B<}7F
zPi`!SXIn>T4AM2QewUD;1I8MLDA9>$DmQQPZrZfz!7Hm8oI)xBoI~~ko3{OQA3LFX
z@#1a3a}i<Kj;u#TXPyA?M3y(x4=l}pS6Z!aY!t{_MHm=w;o{;VLs#82w65-^tS97u
zb{#&fVw|f5hg}IDAqR#m!2`^WiG!ry&kc->TXEJmGwl#()=Jgb!peFNOTz<=?Cy)G
zTBP=dPHRcN!$dgtPX>MkX0_$bh24CqsP9p)YtlUCtjkiAkn*(Ltm%(~ffgl)m7ALz
zDQbSc^x@$fB)tIer)fF&EZ1cjInmZRWG>$8INV2vx~<KJCxWtl#V(h`@?NXNY`u5y
zUaKSIrZ;TZ&=7uj=T2#N-1}kJ2ASk`jUF$gv8cV@pj50@(*$Q>D<OxF;zM|4uy&rM
zf?#DuMOWx)`NpJUZaqH{ywoSOgLAO?yI^xcO>Iw(R_gvkhgiYKvH&A(1sjkg<TH;(
z{MJl>R#L%u7=#MGpVD$UPICf#AW;D~5T(Iu)|;6-m7bq}1yQp<x~oqkRMJe^m#T4q
z#9xeDVtWc5r}z`*L3gq>bUqU~G`5ekg~HjLb`XwfU&1e3*C+jh?cXKvx#5(h#l`Rm
zCnZJ23RFVLD=RA(JZbna)b90`?b)ZP8usGFsj82xR7jz?at`g?^^(7~nMh}rq2~}f
zON1Rxj}|g{MC?SeWA}1X%bH?{Q-1#Pg+uu(p0w2yAfF-@6X3FL?OOldJge<}<k=i8
z5nq{YQ=yr!ud91Br-?qHgsDc3AqV-%@T{BOozcH0YI4lkha_x<ean{>6&2;4AGe5K
z27EJ6zY5oMRJ7|t$clGAGCuUvaqXsL7w5U?!GO_|UMt15?x3X(Ou2#A^I7`F^m20G
zZLhf|y(r!*v<fYo`j<FZMdKPLE$YDmgwAOKI<y<QLPvJ3>hU4Z?*kHXOC~}bEZes2
zBmBW^iKHrbQ1s1uWA*}VefyaBxRy%FY&a)A##*k|)}SbhT(J)&Qf&8|aAOMXjFSe0
z@w$%G=ap-Up@+3ax_i?OKM`B+yo?N0#R=rU@^npDSVHt|?$H1(YhZX56M+CUL|_g2
zAu2)9&=c@kj#>jKGOL0jjH$IYA{ElI5M{U}oR2PpSEIL7!tIX6xnW1|R%H3|yLazY
z(U?k<aeMu}?xY<4v!jd4#Z$Li<Eq!P!&sFl>z~C%vZlFp5K}3A`v0(B0>O=7h#+uG
zOlLP4JyrIDCYnM$zdPD+c-zjMKVJo-W?~zIl<6*h{td(tr)eFm=lBbR-sIP-oqf<!
z%BKxbV4)}rg0mrd?y`=~1JrSG5Hk=o{)E)>UBf$nR$<sM7knd829-7DEn5ui^$K(X
zQ7W~KKxl69)6YB2{oGh~{IVuIs6H+e;mvi@u`4QCw9!~QO@tQyBsBW2ukRIyl<Yyb
z9g^<;2t<i85*@rcx827Cdb}w*1+ByR4olCpOsg;aU0zPG`>>b8-(P&=Ka{9tGFT!3
zRzLP)V*JT&ZV_t&3@DJRZlS%6)_{$T4R78A%@fYd4ilW4hn8)t)Mn$;eZYpWmo%y?
zO4->iW|M*9rD^5|zI2$A%aiaGnf>QjM7yaT;jurcH?XkPOSs6#AQFWT>a**s<)!(&
z{i|bbuh$SDoNtp?tXBh-w+w#~$1ctM_>sB)Lx5>V3ZFz%Q&S!vgU3Jf5aX$=pr8<3
zYrxx<^bNJ67tJ9ya{FSUZ)s`i>>JH&Ui^>scVwvU$Xu|@SVl~Lp6{{5K?^U!dZObH
z=HYU}P#L$zuP0Z)7Fv=f9$oNQ@WUcPjRBD@6dfBz<Q4Ww%+Jr~jbAY;o&VN31|(p5
zG#pKzyWxsIOG;}>JtEmgu=6L1N_T&w$v`EB?jwwM)AU<Iy2n&3H%g?ah$CJr?nD`?
z0oTTwA9>etYrE37k}d2bU%%24q(}V<O6O2quYI-^u~S5(%RsD<ici~;&fviRy2AvN
zl=9})kJ^%11cIc4ihdpY0^HmRH}L#<zupP9wFHph>3LE!<xriJfWUbURZZyY6j%V(
zJ-&IDqwsoo0+@uI5>6G$7mV7UN=$txEzU1+Fxi6Y@lyG(HIe})=)x5DO2CG|O;P^~
zKy{~oTbMStg{AX(gi}hfOL0gekbEV%S3tDz;E9o}^+_7ZTpUL7IEB+<wBfdMZb2(d
z%m}o2+nTxeAD5q(b-?x<v^2x22-|p*F%egP!&3mP5zOoBD)Bf8klP+r+w;^st)^aG
zQ1e*LxJjmVUC0k-E$tsUg*0_c`A=?)1sz9u4?1X%64wypjP^_-(LqHZQz9QF=8*DW
z)@#;?YKMq**;w|G16etZlcIw%bVaQ;9YfJYJsEqatl%PAFK!;Fdqf!xu!}97y_d)>
zNN+<vtl9euf81C8d?mc0lwBVlY<>|zSnOXW4VZDye`ehD_4W6Dt>$s)aU!IlRM8x-
zu-VCP)M2*N{n_?1n3<&P_dMHiPij9q!o?ywA-pf??Ox0tZj6z#a`ake&*-=sV9@}h
z=0q4qXmEoLm+m&KDa<_!xkTg3i?#)vu(7bK!6@WBU)1``S1kFg*TeS7N%Q2ObiLA&
zS@*#b4`#y?cPQVk*D7C9fh|wSby)uUcLs^%5%BW)UaOKq91QT2Y9$|@pC~#gpkl5a
z-fSfmc-8X#)kO!>w83@WgI=rspXUJH6VJ^~^>&sF9mFKWJ#Pn3SoU@8b=kxcVklCE
zNWZ-mDH4JJ3K?DDtsiR&P0GKSSaQ;7+PQC=#bQiAJ&T(CaK-wuf)&HAj-&Ity;nvn
zF;C!Wt%o{4D|F*e64C|+@s-CeeK2W`y_FM~U^%w6pS}P-g6%DPlrSpK4oGlIy64w!
z_jx+9-r*)cDS+KIu6bWY{D<$XxUg{hbsN!uxCr+GkEP@A(o6)XR4zXz?m+awt)-!X
z{fk)QIhWUwbYOV4f?~LSc}~BciM}!3t1S-*?@8Z0R2y$D(ewwYDkikP8dWuaQJQ8A
zkfi%)-~YRyha^xeFuKxFDtlb>^lIbGp!=_f<sQDwqck}+zhEdgVT1%tURZcIS#Hug
z_{H*(TFA<|+YSAb0zfu}*HIl*l;QOKYTVNKXF>{vp@S_>ficG|IEbBx4l#75>s3Qf
zh4}lO*L-fgHDRNe(=>hTq@OBCQehXnBOZWD0w<1q=5u(Jj`Di8BiA63MfC>c^2ekR
z>}tGbBgAs6Z>^!_+!2?XVp84Fs;6{0k0zA|yk2+*DsV$=TyOMd$*HO`!(tPmt2T9;
zu`-y#1*ZbmhKzZH@?$iTh28W|5+qIFoi&=EBmBnm$FTg^9}J`(eI$O*zvk$F&NtG!
zv3&_|5q?fH)=nV!Q<zY~)MFZG@%SnxP^%t4@+2(m4%iq4J(*g&TtwiBiHWTb1RtLE
z0a^SKP*TKocE5Ml$%f}ef~xY1W{r?%s{X8OMJ%8wDB&w|bkd570Yddx>24ugbiF2+
z2d<n-m0~%aas7QtKyBptC^3Zym+Gpjm$9(~B4IRL+nim5b3yQg2$JvyZWV{h(i1E(
zUaM}|uL`moalAhE6%;Id@*#c(Ef<+6_4w^uXuV-I$xjh+7!MVC!sjb}>J*jd${!=-
zbYHeASK^i_HWfKc_{GQXF)%WU13X+TJso!n{4^uC<O9e-KH>VCb2oRYiz7(<r~j<`
z!ptWT>ml!tG``IFc3&CMQFDj1o+mWy>?5PQC<k9G-MWrkjb&i`(PgoR0RitobH~r6
zx0~85&YDzIR$d3x<!x&*%_aF&;#za;sm6`@w@C+W_*HB54ER7>DaM&iz&qz4fOm_W
zHAB>un1efHTUl9wtDE`#TOPDQCd>mlX5P5%)x+m;d-cH#l2HX3!PjB)@H#MJG?0SZ
ztxG*QGU9`3^D9x5i;1D(!U%zA5#i82Ri^pp8gHSyJiJ#5DJP`&!dG^H4I1xA-H3k%
z>S%6mwmo*GGey$}sJa*a^|P%gvL_Nm@Xao?Dy+(Y+~x{DbM`FV(PPJ6sPR*UzV$?P
z<t?lW4=M!EeU<b&IG7obdk8t^qff&3ebkgEdw5O^6<&W&lVjSx0pK?v89Vu{HQ~O{
z*@VMFLTXwZYX}$#U##rHpFau?m*P${d_+SSTtzhqCmVo>*J0H?j^+#qGCp(^V<Rx?
zjW?GHPdqeq4Mq8z5OSVXRJ1ku&!*fL-M8-yNATPh(Dd*%wBA|5_IK&ocl|CxX34na
z<vQrM@3WuiAc-5|+5(MZcXxMdS%#%X#$L?4JD5vr$0Tbn)rBD8<M8$?u#aZer2q0a
z$Dnz+v7=XIhlhuQpFK1E*{W}wj-v!lqUPHia^IiuX~ta{FV2mn>l9?QlF;s>X|BCy
zUJ2Nfi-lvMf_MN3JxKV`v){O+`~?AWPTwFR07vH?sam-*mX>Li$99a`ib6BZAGP*_
zVHEOYhek#|fKCSTw>|cMXFk2p1R-K;=27&RJg8_7IZ{xP9TUTiJ}EzxEH#djHsa`O
zT)0*uVVdS%G?^&_Cg6=ILgZP7m&N~1W?*<W(IGzFnI6+FB{|mPQ*cHejAoeSE2G*?
zo8SSpNDHxR*K-mVlWFW?mt*+Wv1<G*%*@^u6>{zf)fdnYa36IA`6rc+p2BDRUHDPc
z&_E7Uo<U7^d37}{yzhHpAYX97Jr2jBu%bh-(by^(xYGv4g+B`mJ3-h&KCc`*&BDsM
z4pHdZwQFOM`B(t?Wdr0^3?-lA)duJBUM<;1DsZH)CI+qRLa5iQfxwRz-j5b<Uj>6g
z`e!T>rd#YhdUO+zwphu^qS@!s(d)?d(?mS};VX8`0C(_WH)1g#9GwMxJh&?y6GY3B
zwzjtMPa9Z&Pj+ucqcn+7aGU*Lrxivwppy-`Mid6=AQ5&Ax-RDr-6vNcw-jGl4!zgt
zyT0Y^O(0J|o-FK28Mve7EJLNdim^nC`Yn)18>>o0BG<-FC=6T;X*U)SS+^kE{fLs&
z|JHm!>-QEaZOWUPRI~yUz(0qzLJ(NgJBwRJ#}Lqu@J7{{rmz{>qjY3>Q{Ltt0=dX?
zf%VnJL}oTNI#^~zEwQNbm}b1^pMs3n;ePWeU?$^^l&8;kXPwv32p4)&3UYsTx?j7;
zae2|f*VmUIe*XMP((<pXfiQkx^F9G&E}-^{lfo;?!XnCOL>eg;mNCtIYaZYYYHI4{
zT(iqgcV*=?k}kbw#CfHQ{d;by_>_qWANpQ|(b>jzGQ2mf$OQ?!DV?MF8ws!-)qp51
zv8Q}&eLmobiT*<!a&o>|x59AOl_H!}qQ#${*tF~C#lp_-2=Z;_r*)m~?(WVt+lF00
zlf~c7pwpRUr0H(aGVQpA4I$IS$YBx$p#2;0#RVf$nEpYOK}&fw>&ZTqXi(o2bM_^<
zUAven$(YCA|BV(YL7aioF~wD<_R%J}&zunVE=PJ0qr~>30k5I|6G4<L&uPz2bT&<T
z;>PAX|9IKfLF9cz;YSO;HhSO4WWdp<4XHnvSwgNCPptp93F~6qf3Bx>x>wu>t1IX@
zdBy4O^;`rhWF3elci%d11nAy&?+|%$M;Ie#fvon<i29Znvb5&?g{&w)rWC#BdCC7q
zB}Njtz%rn*W`wnC*R4Ax5D|wm1+u9$!=#N@r_g>q%!RGf8?2&yzT6t})BpX1M4<lU
qzo|9+cDRxx-T(LJ|L~uFuM);pI-}p8=9ZD2j0*DS<<exX-TohIb<5cR

literal 35507
zcmeFa2Ut~Sw>7FU(Ii%Epr|CMsGuMyO(_}?1w<6-RYgRk2}qG@VpIeH11KsAQ30h(
z?_i;*2nf>K77&pR(%HcN$K06jeE;*^^WF2Dd(OG{KlkKGB5u}Rd#!iPcg``#9CPk#
zM-CrY_~YsyXU&?mP~o7Q>a1DcInJ6j=j8Wu@k;%z2mSbC?n$Ksa`+SfjPJ{I#~(kO
zJE(0tYu4JU^xtgXwgKZ=vwoVTAh%b|@m_c1g#bO{JF@+cBS(9qm;5~Yn*<Nu1fyBP
zXTG1yvD5G6c5|m2+xYX2p4<2P_!gyxwRiUJ*Ve0%U-`pau4l);|0W^g$@V1y(%)|X
z)jZoleQNsEN7ehGfg39I?J{LUY$xueW-u~_E9ys^+?cwlO>dU}c=;FhmEG7?Ys1O@
z=~)3s|2OngXuw7GD?f#Oo5lXkmW?02qaO}#nJq+r?os@~aW?&+s`zX^{dq(68w2|D
zV!+aW!zmWb4q-O5KTJsxJ8NcU)Rc9hP0-|^NCDozXJe$G!|Z3_MQt`^wY94_I4=M4
z;BDms<pM7J@`+sRKVHU(@6*OwydE9dEcf7u$G%09p$%<xA=?lBlS?n!98y}CJ=(-=
za^Fs)JC)%kY~Nk;Br3|wy0swRz`18dA&<L@o>NEIK*ceY{SS_Ot`@7WuQw{WCuv=-
zuqnU3X(Z*$nKK^6V)a{P#-1-O5YHVib{xp(WG3I=w^({7Zt4;4ZTd@KuAH2lrl#iG
zJ3BTVmKpz8W0~3W@WF$h?HjBMC8mbnw_e*6tkq|~Qc#bpJ=oc@JF_oG_+*|3m$;>#
z%g`ep?{wRCd6|$t#-5nu?7>o5*+tSr?`q5wi`nOBy3j5$Tycz+udK2%`FVZA@|z-u
zX<_|#eJT#B?wbg49jP5FamZQr{;`U0OiCSpEo-FSDwjT)?qy)2D{j=;nY(4I)mP<g
z&g7RO@y|K)cwEleFeGh1MW?FBg!mp<cVR<`^k{>`=RYr<9Qs^VR#CCM&#5}ICDi}0
zCtaSduI`p##fL{WZ?LPZs**P|GYc6twY2QD51Hm~i{HMaofi+|_>JY8y>eMoeU~j#
z8&j5WOifHw#<^9ti?zSKbL>f6T%WD(rhDgpd61r-UTvX#M`U9MtH#K8g@MaJ{tK~s
z^Y>l$xsz=|oL|OW9vs=ceAljBKW++kJQ+7Nio1F7=B5Bs)WB=yHvTo|p3QcZNsUQu
z%IL@Yhs}b*j%;b&X;u{-%F=getTbs9${tcZ@#W*|sV3I0-Mf`U4Dx%i=BJoAk85t<
zXlGO$-)Pb4#LHDxRhF`Sot^x8w#A~C>4c^2l@%3gw|AHv&Rv%(Yjm%)J<}pLEG(?F
zCsrppvfgz<wbSUtPbK?tx6eLUe`mF7tA55#dxq<%WAkNB#gyVWx8W#Z*NGuF{XZNl
z)iv`4GrN}-1q$_5r}SlX#X2(U8SCic;-+#2ysh%u0u9%t>k0Zuw0Mlwmbz7$vG82B
zFOr??m*HVeGy8n6E!$KuXR*{f6UKh8mHF$OyEz;ft_~rtN-+(s6^4WDrM?y^wS2v>
zaX$-NBesd3t$5<0<+^d;#pU@-yWFnWOhMBS$ChhH4GkGR?c$B+kF9ZNu#P`d_E09Z
zvY}xO*^EG3mdzY~p%7J``E#rV$u-OkyqLP+bZPL~ZQHhSa0uIce3drQ8PysVXyEDN
z<MXP0@Q>2=N6E>e25#eRx5cdtTt*T@ybB5n)}=ZWu64+}%xQ4Dp~<N$rnO4f%6o-?
zh8%1RZ*=_fdQ&SNnes$d|BLwp4;H&VF|<mZo@|%p!3SA?_;p?ZHr8_dOy`e@+2+P#
zG74cD{zFqkk7d_BzHhJ7aH0J^nWrpkywvEm-^M?R+IRzt^1Xy|xOkW~g?`iXS8nyd
zGCnOVbn6HUJP9*+E%KdFp^vvfY#Q%MA%ivg`ucSG^mNIUTeX(JFr002P2YzvtSSFS
zdJ8sNbz-Ru_p$Ip9V6L7ZUbHeAN^Ix;6DzIziP9IKU*2;IMMOAH8LvJGve4|%P+05
zD(dR$_w1^)U-uLVW=5AX;d5_GJKNhZukR@N9N)K_F<z3^85z}*-X83gl9Gbu!%kii
zmpknQ2eCFDK7r3wMY1hGE8~2Jyo~>I;U$H?M3j<^?O7GTT-wrnhS`&<($qT{m^*3g
zpHf*?c9eWaLN+%3<LcDQtg%Ab-VysQGVkmu&%kva9xqmz-1}pzz|)j!180l-sn#mD
zw?F8IBZy0NmkAMC<2Kf;z&NHNom}WPZ=K`u@;>Rl3o3D?+X9PL`0VO@63SB8bicxg
zp&IeKHUIL7T$g9T&b@brxOdx|H}l}ruE=&Qb($V;_jiI1oWFMM+Uybc?s%-du6caE
zQ=t=l)#dTQ_R%qCt@SwNVTtD7PdOX1CaRr#3`;hyp4+Llxh?&B+~4NKn*Fz)V{7}5
zkhfK?t8yJ}<W#z~Rl&0K{%-Nrt5-X9%i4Z=JNE9XOhCi*M0IXcyU}$XS(zMonFD<O
z+I^*rFSk<j;9zTdoW&c??Yq8qw|(~1jys3+&OQI`bx)9GhH3Uxuv2G5T3-&6m-k_K
zIG<(i)QCZdu2_(_;baTvYu%B!v=&c4cMlD#H=>89hGC!iA*{*vk0Xr^jJ3PFpT6X?
zusvpx*^~0RG935nD#$(1%EOW{bssM>H0aafap`|PkTaScSIBs|y4GCG4K96Ss{7bW
z9?OFrm(Kp_b6GXm3-@~Q%M#q{^Lae?lH75KMfu}$L%1CUrV$TQ3-^>$%5-VvW*n?r
zEY-g!S}Xox^)y|^ql}D<YMFeiR-PTF50UH39uZpPe@jq*HSD-+C>{IKe}prq|MIEq
zs-~SgcjlQszJIG>`hCcBMZ2qFm>&E@?pVH%-^hcj>*_47{bn=qMl;7RDVN#UH&PRq
zoh|M5#Xn?2s_*XJG-bDLj`i{zejbHGSlZLm6O>i_>-HmszigSY#S7m%7JX>2Emf{F
z3BGW;JFV46*m<Pe@#7x{CKl*cr+kXc@$hh;s&?<GcC~M`tF}yKP8@lv;LkUlUc4=(
zhSyXjSSr0VYEyO2#M8t?+xs$upNf_0@-^3v8ndQGRq9k^#!uz;rADwHLMCHM(6scP
zr0t{BV`q7$`>*<sAf~Le${HweVor^u=6dT1a&mBx@tUW%-O9F1#hvuxk|YM-?r8n<
z(jw1osSP5Xot;1*DfQJmN~Qc8OrBO-^v3<VYo+1^wv&6a@0spgc9iqk-Rlt^9`2zv
z^<2|%{EcQyW1pMD^>xy_3Vhab?=UI3PR8t%e;UvO5s5dwk&#+&l343lxV!0(1~+U3
z=N?8#)OU%s;tpZJ7Q<0KIZ{LK_9-wwTw3gd6Q4PI*7M4hD+DI+G(Sd3Jl);=`!YY~
z@Nk1-1fCEN;7|RR1y=6N`guL^%<`I=nw~jrmblsB5#}fH+&f97GR*FEshXRYx0EXh
zc^`cwpFdA_?D^RAXfDrale1OUNS#?(SsBOu`}Z4jJnhb(H*ZN?#WlxnYe1*ox3Ikc
z-;1e-!j*$oDg_F9cg5w(P>ivB&PXsWYS|<^u5bv!Sd8H^m^S_?s5;X-nQ>Se0L4vq
zTDR%Tt<c8u@^UjqPOL~{#dMc{X}f=v=z5KLR&dj0<>gB)EG){Zs#aoWyw<cn9*AEv
z7k*lu)yDOBTF2RNm88wbKc(&6-`*7!Ff}}1w!dp~GP&A7sj59DGSZWPT%pB%=ic;z
z*D7uaCtubr6nzJSV+Yth<1n}<v9W^Wl5X#Iusm;XU!WwEy*#za$;_o?S%^nuWTc0O
zR{Y8RYc72JV<3bz9@5p|-gEJ3+@C2m<zY(x_C`jn@-mlx-Eq6Q{oK<L$Cd7B8XId&
zLR|e82RV<_niw^|xZLRG24mgWb7zN%yuN;;Q|jQ*dzC_V1d{<8YDuZL^x`yd{8=ML
zX9Z$4zzN*@N3+yMd+v)S74EJv&bzq^Uxa_%;vD@N9_C`)x>nhCE}!@Dcv8&K(ba85
z3^@E{u+-(#j(C^xFP5XPPl+_%(s9H!Fo(6qU2^Kn7u5qb@@W`4-8mFquk&0*dRQ&>
zNNlcQKF?<Hd&VMnEO{bR>@VtA!o%bYg$Zp;70T>R2-V-b{NzY??du*D*Un?omhJ7b
ztPABb*a#=5C&#U8PF&5;ySD66M8uYa+U{Myb`%Na>UlnXYA`kaE&<Pzr`za+(cjiu
zKuuX$nc`SHtZf6sd93+rb;oGaXpXq)Xp((LUDfo{Fu;w|>!a3a5q>N;czS&ADuGq2
z!w=o@OCl#KqLJX1-_<|;VV>;4s=}!*L)PlJtS^dka^~5?Rb4IzkH@!6ee6yw?H%l_
zHZHnlmwDIq7;X*FP{nezU9sbFuv28IOq<lo3Fp7<&}Kqcp;L8caWR8~M!wQwriS*l
z##28=5nc_IHZ2e3l6D&6(~#UQGjL61Ll1)Y?z;N=A}!?!*U45Ml{$cSs|@{o{U1{5
z&PLtdX{st0ZD3+wd^b~edcfCT#`SVazUD0;!IAOXoQ5s~1-I%SwHT$0UFvaCb+NNi
z;cwRZ+%>a_u3mJwk(`=ZsF^!a>GoZGOiauGZgE$7c23xGK9%4#K~^~zY@8>CI<no;
zD%3UgR}kE?$&~1h*$E3bNr-;tw^6BOJ<s^TT-lvH1f~dI%+WUJMDSeK_)7-xM8U)T
zUT?I@GD@)FL#N<}#BnpNv`4>W8~I3vS2H<fb6bqpRkg=v_IkRrM%@{&6rJtytTy!s
zxirsjl=;cRLQ~Ire*@r`tEKHv2tOhJ5`JLUT&)G(UXOfbcJ*crmN*VqYCejJ65!Q{
zt4%+d?>UA~<aF*y*4(O_vXz&&v#ZMp%dc;uD=I45ShY4ALG)yxzWp_zrrqQ3l3iqY
zN~K5j?%CIwHa5+b#lx^Xp%w<?wl3P1DkqldB%VE3vfJpz?>|_--WZr?TPj_>?N#sR
z&mIJ4;h~paUFUS>&^-xZH^kCgQA3^quTN!cYaMta5SNFI$K4*{?j|+bVEfwddyc4=
zx%jJ$Wm@x<u~!FV{JKNt^jzYZJpP55)00w9yFg)&)2pAao_7{Da`lm!_#8UbW97cP
zL~8IYC2i|NN)O1%nV6VJjeqpF1`sVwGJQYyqa?v$1!kQ4l-U}aS3grGxUuw6V&aZq
zfM$u28lzUgzus!GdH;f0{J*&riD$orgL_icp|`;x)wxRB#Nyou-=W(_0O3r1>w`p$
z70XBQOuGgzz`5uvu>242iYT2<*NfGahTD`LaqWLTZ$8$PTk3Rcp>JQJkFxsL6sD9?
zm@Y9^yZf}srNV^7#Kck^rM7@$hqbL1;!+QUjP@X5>Bj0zxfz5^R6PDMvp2)ch6!-l
zWnIrLQa~tpLjPqxr(r{ny_MpHtqUsHSc^+9_QTp!c6N3Hsku|zPQSff7B_8FaQ)iu
zzO2Mj0gK$h`+a%C)rO2Xy92&hJ*~8JC(N<iry?IT@G0M2j?Bxm<Nf1Nrp)7eHi{J9
z=4h!qt#$hRVBqbJ(mbTo>b<H}U2vX;?as-`$&E4_VmAR8Tf;!|;M%H87*?)bx?~4(
z%F4y^m9;_eMp>msS@H40>wuvoIzs&?vKv*`&7Wg8lZ0s6rO)T`AuN$LP{2)K-@4u+
zHNPj7v5qiO76^f|ckh<;Ar7}(oU_OSmsiMSu%@5H_A=r+o;>pLT<;^n`>EtUcWQ%;
z(pIgfKN%p|KVxow9jGKfV9T+V-<KWsfbm#f3^~z}-L}o@8n&<`<p{ac!*Q(uz1L(X
zn-^2I^dutUn!bJQMgoPm#Vm9V1&M8w_g<~u>nBi$Y?}v5c4o4VHElK7shR8f-Tak#
z>wTq`78e%_BO$8+%r}b59`Ri4@?j2l(&Ae$D3!y5_tiAGuyMQzV7;vGCy*2I190Gy
zZb%agZfY8=qR_t8AWMQ^2tl1Jpk7~i0X~@8_9H4kpFMlFrGVS&`c)n`31pfHY0Tct
z)>@MgHb9{J^H}fWL0kRq-MbId(=S{~(SKuf;?J+?>uchM(#ba<b$_Y-kj?b`Q!Z%L
z%60CO1Fuer1WgVjE~GZ7gqdIe8E@ay_79Yj2r$i&UZMqN&8j)~f5GVdKOmOqod1NI
zB5vuWf~~FXc{{tCnb!@7u|Q~(lS@iX)pW_e6oDiFiOYK4hmRhu2SHGzceL#Z(8$EZ
zgonrH?(XYIRYf$_Bok`Q0jR<42nJH#MlXrnIkZzuOkR!>TEq;zq>Z&jUIAiCNlEEW
zRK!+qZ|^@|pW;0^%+P46fO*z-cUJf5>geE*fb@rNkcjhOTX1lc)zz&=<bS(D-KS;T
z5CqFdn{@h!w=5?Oef_oAs2nGzQ&U>ftwAl}$^+@mQmh@~2Gw`RL(&H_=s^5ktTD`D
zy87L_8*g`u1zdDkf;Xy9hCanTMn-A&t%RpqVc2&ZP)V^MJUhFwW;xvR>l$FG)j_<K
zm6fk+KH5kc9orC8TxaEUBDX_ZEJ&H}Y(aP?()m-2j?T`=F@^5qkzjA^^)7HQm9e$)
zmca{!>VLH4wgUVWgdZGWST~Pmc$VfDd{WM59b56^cSeaT2XljQpZmpvk{QtljvYIe
zYU{%}#=wfavoPnM+TSfIbkA~u(oTAo|LII5ea1gAn12hI`5%6Y$czRy*}6vuPi()l
zv$OWP%*aT7JhVdRD2*6?rG<DpNAc`$6`mHI;bYB15Ae9|jLn(ov<W;a22#Lq4fbQ3
zppjOh+?@aL3;cI+zW?5<P}lGM=88YEBY;66hgI2;v2M13_}RCvu1-Ybm^SR!&mI|X
zYW(Wngj#V!0fgWld*`&GQz8nPuyuK#HQEJd`IQE0)YR2i1+6=ksBM&ZtHt(ud`Ik<
zRiy{VdfI>Sci*TUJLaFn&CTtt{6~wV^1glhDk>{)((5jVDyyp<>V2<dw%A&K{Up!A
zg2}kugSpefEZyq6uABT{t9`6dbE?|$j(=EBQ>xJP?Age%al-*gn|%sT<{lLbiW&P5
z<!JKebYf7F?LyJ~+)PE5^rDoOsSW<GZ-3wv7Mn6McQP?|I&Gez%j}b|(%G%{Wax+K
z<07oCJL86>Z>{kg{5w%vb3S#1F`&4kfhDijIdCs!+)yTj@tOIf<GiDwqOf^y!LNzJ
z6B!v9!LKDH$-#i`2t=+K9v&uc8L4chxrN0x+x<6*qei4!!NGx)zO11^?~^D00hrwN
zRXg~2dBL)tY<qLlIQEInsl-}ho#D&?2yY1)8sNa9H0xHlGM;h3^_%H;--sYco%+Vq
zJcC=>>AcP5X3rG@Vy1o)71!5DilmF!;Y^^m)aetj<7b>F`Qh{D$@NjNh9BmLcd~4#
zs?wmNu+BEg8gPEN1-5rQc5K<B1HL{Um4mtJQ5rYN=c%i&Ao}k6!Q5eCR#)xmAd8hq
zf)Km*`xKv(oo9dkyxPY;<BBIoU+;de8Lh<ul=XJSu%+^+B;hNgN(;y$XKms8$CAQ-
zUI6-k#h?6_ZrA_VLean1Y5dPs{8PC0-^9Sx12ZqGZdv|gym@T`n|g1)%HzZA%azs9
z(K$(|8YyO+VZBz0xpu?(_sRoTWhZ}ba%}ak0TbFXhi7b0R-bF17Xrv^H!9lBn#Fzk
z)wfx`QeO_<bDtjXa*+)w9qpPX`q2ehXm1s3ppeHGr9mmm91=}ReGrxWD%thVrx8~U
zJ=29lA~a6rmUuA!d0-cp;ke#vSIt<xRiKybQ7e$>eDbK}^CMq+H)Ywa54{9EsW>)}
z-RCMq>HTcCmF&xz5<m4#6G}L{N1B}bK8}EVE$?GC*%WO<X@kla0|UP9QsdKO1FfKk
z$K)yG4?LT*IL|CDdnE|>{R&;|+p5ueIlZq6Zn42-u$Y6xdZ_F`+~6QkoCGLrUfvd*
zd~HP?`xBjZbkAvmM<>()awf4@tg9!H;l|uAmv?EgjKmo`w&e^N*UZZz!qCG5q~?Bg
z^-3ou@{XT*d4n%}y0bTmy&``K?K{1&$vYX`frsOcJpB;xoB+{C@%03&jh4XAuQX)V
zRXa756oPL7A+=WQLEW#K`ct0-2?+yD8b^mGEN3-!b#`WWSDp(j&KcJ^3Rsz1|5BgN
zU!e|3GS#0519%pOM@GJ>8Tu)xcy&<n@(`oz$`1MNQS#ThsD6CCQtcM7cxYoy%^7pg
z<)x;m9z2YVjiuLHj3RS#a_Zf7HmnHo!Y4UR@|a5Suj3xf?S0FdD4ev?HygQsi0{}R
zM#l^d4SScqiBv7M%eTD;?$$GewIXD_c*RqdCrB06^E%yswNga`G@!44AUJ4;MmtU4
zddV-Z)*ll+A@*yJww+z_#ngE~zI<cSuTCdM?3%ZL&***PAwER@6Xs5GvAW2(X0!mG
z_SXfk3JSi%8h$$&XY$EidWV?w4mJ|#<F_`y!em^9(Rxm1FIRdwX{9WmE_`Lm*xqc>
z3(>CrSQWuQrMlQdMDEVw&+m7;_A{@%@4B@d97MSJx`K0Df6|Vtp*|{Z;lhR1#iG?i
zV}rs*1$kh6#S+S&W9>t-lK(_b=x-p+b(TumNW*zG`j%y9Z{FTeQBfg>04^8p7JsT(
zc;9Ghu${fVJR&(kj5~+;B<DHU+2u#7N4?QIYP<@C2^`t}>TcWbS{4+C=f~chcCdTu
z%_YoxVZdqpOWO*TSkiNxG(hiuhE3ZWOQ8>o9rBArZnNxwZv(ehSAj<cflMf5c_|B!
z?;+9_%8bC^a|xf617-Iq4rN$+3H0hCd2dgG1KK-SP!Po9j3S!HwCVnFZP)=@4Rf*@
z+M<opc-nE+6_P?i!K@gqcwRiUI|7}JcO?r8xJG98eG0N#$H9Rbi>$Mr;b^6%p|{6p
z+o=Y805}m_Us=N(mkaqSx4j%jz;gkquq`#W?M2ObO&8<L_>MckmX5jbOa<Zd3Qp_i
zKM122%x%*3@>NdGJ2s$&%!+xK15dwYqrl|aKFc2C?(4-tp4CToiZ!n26aOTUjqGg&
z=;WHny!chUwy}!R)iKiIu|u3=;%zP0PvkZ|yDe<wXD2~**5Q#0pC4T1<vodN2~Sc6
zdm~SXU?U&mTSgU<^}H=8@ENwZW9h8BVp8Sh@|+pWroAx>>CivLU&?&&_89woKz212
z=~HP450wTJQmTlPyk}c-4O^uK)n%i}u>s@Q<@BBp%CxtU`4SK2UvE|=JC4E^7sLXm
zzD&XC?EvZ7(33-WZN6aIPbHG=_tH>^Xa!f>GcpKXMKA}Ir$xty>14CGE#Y-J%34pp
z&S&(QK5uEXuiqA%_2o@kM|e=nuk+S<R2#ZWgGhYLrdRIovdiYlVLP@XhxhS)h(Ri#
zYnhJnXFO^Yg%OW~SA#7+do7Z!WZ{yi`X7AvpP}N3=p@n5v;Kat_5~XQ^;B;amQcop
zTq^7eh&Y+UvQt9|Qev}a?F)pM=;Gd^o0o%CdDLJWpbV}xT?y3E_X+=}C#V1IaOTWo
z4K1abyd%8In=dtpvqW1FC-!KoDDlg}p!3;}c2B(Dyev4<qsY@bi@%w*;uNUZ0o2Js
zu7M*<EPkVpUARYX!Ly|cskmTUBDrbhHXXH2Wi}~sB=qBKE4)<?)xUU%sq#<*UqdPl
z7a7COt8A#jHKMq#KbRyGOBYgQy2Z86*^mmm?6LupOvR~lY$x(k<Mo`T+C-B=Xa-uL
z$5Q^%ydH9aYdcCL)|3Aa8Oz($0yV(q;g@X3uweJb#g6NF5#L&g3nh7t0i-%(AZb^F
zsN>)mNDP_UN)&Sqc>31r+-bX7nW@$ESwHp6-SA<Xm~~5@1Z#582vH0yu0!6(*Z%Od
z_y`Zgy}Zw5k6KYi@yhq&Tzq~D`x5uPxi~M&Oifdh3JfQo|Mnfx$7vsan}71-t3Rkx
zN<|Ix<Vp=|EPO$qJBtZf$t86*7Tj}oq0Td1^Qf2<Pxc~0G1lkOzT3Z#0o9A}g)bku
zAi;PWEM0#!VFsd|d^e}*mj_D-%u*pk_{59fEqT(!a3rt?1;#N{`<GJs44`WMREdoj
z4qo0n-!X8T?n)xJjp)N1u;|u3$<>G%6ckYhvqtfmJO23d-?Wm?s=M@<8G3o(dGV0X
zZLqJmxTMZUH@oNTf-}#8O50l(yN~UAds}!tG1ZSl+??Ro1t1)<>#RtFyrl&dUoY*%
z)A@*IurNsDN)Ta5WtDIrm6xtL+B0Y4S6`eb9|&m!DHd8@%on;IrWDA>h=cDJa_vwO
zh7#oV_7lIv)f1%k8+o}{R%(ec`#Fc|z%GdYM^UM~$C*Su1n#K<x*wE7ZpyI+p^(zR
zBzd~(ix2+%IJ*ztD>zAZI6@`gM3z}wg9R2ps~a-;ag)-_vv_vn*q4hAEqT|hA;HT(
zdSLAeoG2(MhG**YfsK(Q2%UBO^Og(Gn4`HYY4E}k;%q?tOeZ0%>Cba)Vde320h2#A
zd3b;+4sCq*?npuoy=zuh#7E73L7k+{tGT%eb{&=)$dh5ZPrMH~JRu8P3lp42haG>#
zf8p6~lahO((ztOMW{As|*9y$=AWMEbN&5Y)eY6=+Y8Ex^L-ogX1eLl*woXE$(}sP(
zm1t~SI<Mad43*g}3m_djg%U%01zSBiYk~Gg){SKGCdc)rCW)@?g?A2}&K_Os3E25Y
z|CbO?vuNN?U}^&2*Of~>3;76n^A?1g<hQffi1tW?k^hxk(>W2UzU?jPnxK_XR1ncf
zjP2~4J?_G&cv8`t?wBiF*iiz6i}wNK3dk_jIwj*(xq^B<Sq@L~;)TUy-`Y0T*|tu9
z3}{o!3bA_PSDKJobJB}6bubqwJ5Yy&#G<zLPEz6ZRmVDQ_!V7UT_dOCP0h`>I{dIA
z3gEuf)j2H^+D@cfAV>c|X=Gx!<Vy3CjEp*V#-Q=BX2GcKalp9!(*~a2t3@LlHb$c|
z9?`HdKz((I-ODM5il7;h#%70AD_-Mzk(`jr>lNWwU0rgmO;ugp7~0&|r}%By;2bcO
zz`20I-al1Wn1pgiel%@`XfNd3tofXR-6;jV6R1D44%YQuD0NoUKfK@6!UC!l&0Y~F
z^)-58;^J22C88A-RaJZs9Kh-Q#qfikoZMH_)U*yTj_@$(%#H2`k$OO|md-8~yw(P2
zj)oi&f4e0r<n(E9oHP)3qO_~e$(E3UwKl;Rr10AH^4AX<95NsjtN(m=gYO_9o@av<
z@*=-;cpjNps*fQfw{G1+y0)2^LdWT9_$MS^><@+q18vHV2<wq*1M0WIsl1+l<CA-r
z&c6|P6GN&PyVU^Khn!eMvm#UZ0(<v?eQZG;U0qI&y-%lxt4HftNRbumphy(5lU%V8
z^;+C#deLX~fs)`WPQcTZ)hJ7}q)KuHA#LjQ91Z&A!EtjZnBcJv|3|-SL=@Uu{~1)&
z#@{=t)**C$anjB;rQia2&9(-%qy}kpS{n-HTOAMs;?B*l0*24}n;jx$urXI}sZp!D
zXZBRRGu^T$-8!svKVfh`A(?^Z#qJX6-wa*hw)9|4#IZW#pM^{hJsv1BoEG8LPzj!H
zGn@|lmkqVp5$tm7U9(td*dThL+Nq|YtDV%u=SKs`frVqUN9t<aSyQb^?++_Y=wE2N
zxguR-4ei%GVXJSbOzuK1*(y8TD%*n4WG*Ksg}+<B_+isf4NI3Xp+V{YoTF^_^X!KD
z7XioWu~c_vDiI^7=B|<+xg%^OF9pT$z-1wq6Mw#8I|8nRxgURQ5+!s^rBsquB0w+q
z;uP{h^q@i{l%{-U!)#yrw0Z8%5BiUCa%4!rOts5-t<5w3<nnCYnKI$UU;hCUEv#9c
z1%V7@7Pjpr-lVR0JuiB=t>baXx+i`!?+=*$gCi#eHAFq5ftLI^@fX;?I63Q^rnaKM
zLgFY;GkrY@VN3`Kbtr+aSj={Z+s$uEeaQakr{s6e;p$p@57at^3VeC~4nj=-Y{r^m
zu<j>Wr8UoO7Nj#jn*|S@d9L|IXg2gUH=PFAA2%5SzgTacs9YY4ip%gPb}Su}1yt}Q
z^<y^TrmEFCI`kq7L0}YOy7T2Ztv<UIvuCb%S_Ph$#JR=dj5Qn_q)VL~3}I~@-p4-w
z5{}BSrVlOoUPk4i3fAyq1xSsS;Q}mNP}=n&nFsvwdjHo$POKl6@dadTV1L%n5eQ2N
zuAGo@z3xHYbz9U-Be&0R^KwP~rF6uz8){!RvcbtLt~DETe;dtV_6iL1&7P%7#qoID
zW`dMGN*eGP`{m@2u4^}~N~UzFo-sYL*IjScEUs7ptsEdMLH65Pp$GARMmQFDy%>wX
z4O!I1y{E3tWna(VME+{81J7uMAc|=voKisX0We|F?(Xeei`fxxd+0)~XS$H4Q7M`=
z%9=KmnjUNInl$(3nfx#>-f^JC!-Me8!wjb*A|~uJS8(6hnG7Q&Ng5ToLtIfHZ3$vb
zo|cnaV^?uB9y*gnatd_ptbHXHqn|s!6)xP++)FiWs>++&Pc0S&xCoVYpPu}6^Gvur
zh;YdS<5mFKAowm40phk(t>5jPH$z_a0jVkI&U7-obH~#r%~I$T__(-20f%ZL8p@}!
zRR3G}B*Arg6TqRrJ@lqltd_;<Ohi!azYnpbfhmuTimLyUnSw%X8v7Ald=Q1lN%_0I
zV4(BhuTEN#4^VG5wSsi)?<2hPv)S`D^h|F}KjM<#hdfy8@6WvB^xK1V5K49>vY;2b
zo8&9`>9%Sh8&+>Wh5aB6=RiA^mse)+PHEnkwjxd0cTTs?q(MI3iuB(=P7aD#vGBw9
zb^iXeKr+_Ayx{w|t+F_F_aq&g1PkI@Qc@CHIyK7RNjzQLnX=&}?mG23&l|QiY`;g~
z?6FG7x<CUb)3^5|cih^ly_5v{sUdOMRlO>}8OYr0_5`q<2(Av$NAxhxO<|mr7N6rw
z$?HQveBhqVn=NzH+2?(BL-Z%j{#aq)?6Ec>_f>(0ZWpr9q(G>d^ww>+bPGrd1R873
zICa~=BI+*A%`!veEPz9-a_rJ&?Be?BBepK6S<2=+W-ZwIR}afgmupwvAKtyqCcQzP
z!TM4pBy>V_<~*9%#UWGOCg?#)FB|2YX=xdvy)8XbvmW8mY4QRJ{kSAa+^nBV-~B!p
zlZt8gkzOgq*o3z{W5==|XYSkeJ=dJu3$ESZ`eu&YJHu1{%Eym*EIM%cgqqxby*o!w
z9X+>v?<xQ13%@-t_uEe%uWW8??0&xI{1Qpt%&i<Je{z2OUKdR~=gYJs-*?1DI$Kq@
z+b(Kf9+5FB)2BO{GhCHw)mZ=TNGBSP@Q-{F9M@uKH~%z9#cqeLdKo*4b|aPyzyV2h
zb}AYyq9dPq+^kWqBFS6Yr@Lmv8Tm%V<?L!=O^+Us2~mk;2b_y<6}EyD!ZNc-X<lAS
zq+DZAO$D8rL6~>^&OGDhh@Tp%=Um5v0$(|H37!1nbp@#pJ?@Q-jHzCSA2iqx{KmFx
z>?HAWVeIo6x8R5urs6W0tZ4>I=_%WRT{MrVA5#)jwzBH`(;yL^VXWB7Z5dm|#A;#*
z)46BO5xDTI9s++LwV<p9^wbztMQi`HVe%9wmo7xAYK?GLh<*MroNvH}{Wg(DA5Hp6
z-&6Mf{f-Z2$oPz)ry;Ytz02-wMRj#x2D=n^ks}V<GMB~74RpH6ey90GGhX%&r~c}P
zdi1raXw}ys_v(os<5Ul0x`**FGc&Uy${QJIpv!&so3u3$AV`=`ViTw2ul-SW0a73C
zOWK~si}{_EQLTR*Shr=;8K9MPLm&vrL&EB`9WCJvyg)On2F`4ohuGBjkr+iAWbY!8
zu7^hrP<?Ack)XiunH(JBE;7__l!u_qxmafGcv5n5zOs1ZhL3D1LCv>tYR*nhZ2dCf
zhIquBd^C_i6k=w@hc*cc1ZnWNY&8;cupOkXye;eQ#$KSAFvJlNULlaxo6+@S;OQ}v
z<r<{b8v+zkrxX%q<o)R-Gr^T_Gd!-B;pD@9$GRj_Zr3S9g(HqGXneVuF=JpH<O3m@
z(sz`4SDWdIr!wD{l9j)F*vXrdt6?M$GEv*%g0s6AX9%d+Y>glmQS@tM!ugK!&pkac
zWA@x2^GT;j1#4sk*)5_dTtJ>?HdW^FTsz&y7-31nC6ic;(sn3&GBbDI+NP^9u$q?_
z*wtkk&njTAgM$O8VX1hF(Y638Yy<}fgN#KPg0ME@F!ntxT_`sF(_KU?VltP3Pq-MD
z+kAz)N@pck2C<w5?o-2|MF-zPv&BJz&vN5aC8E`O%iIC>{RP(xll+b(k_u#Q7dhI&
zRC*FUs}T6dZ3_G-E*XyW1i*}^ynrHQrH`NKC()M-@Dk@V3x0C**Ys{ZrFT>pg4Ajh
z@mxhkr5jB|1Aq9sY=EFH1p>uQEl8iu8r+yY)(DV!Lmltu%XYc-IWxFtl8&#gK>dr-
zPN<96w?s4uh}4a|ywu-=vXx-QTVyRf)I5P>{_epnXz~yh*UJDsdlGGAP%}C&cWopO
z4!xT%?fkNu8ZDo%jw#wN<azxjSQrXh%N9!y9YF^JFZI{N<xHH#qXOR{>^3<Dt?v1B
zvBk7=wzu5e_Ms;ZT|_A;J=NeI1mf9V=VIiMTVHiSN8DCOUUI06RHtb0xwF&LgXDU#
zOM}?v&Q<niWk0f?G}vkjdTl(YpD(+8!UYS!?n8sM##p%uKriVh$&Q()47AHDpzf0B
ze>SHT;Hmzz8S*8zI$KW8rT-ESZtrx!qp$nm@z&}OQ|LTV``%#`HcU*$!|>BDgX*ku
zr+)MWMCr8&m<h$av}>`8CLz|p=HUT^FF?%*H7L=c0m1%K9PY4ZzVCNgY?pCK6E1_|
zkdzFveXrp3`o~B$`^QS%r)?vOW(cu(;50%VmkZyAD+@@KwApaGxUzyGr;t+qZw3N3
zDW^XzKHl2@mVUOA-L<Hh_ow6iDapwz){PDgWyL?>4Ll2B+g7Py&?kTY5jkD@ef(1E
z-frZa2pQN7n&DrbKkf+dvw7gYLsNQLHT4(?N=?&hd<7EnL@pdyV=cE+R1`o0Hat!Z
zOm`Prd|0}Wd=n*h;DDeQslkFN>i^9cFQ$ftZ6p;FWGY#X)`x<nB;C+Jwjb$UvnajO
zrPnq@x65w<@}^-vM!-w~<8I|mqcBPwT9HLryL284@gx=T0ojSN<Y2>5olL?dYaBoP
z+P0Z(9mn(d$5&;!%8b8ODVQ&mwFJ_s^JUf4hd|{^@Psl)V@24{Lq0m;-t&4BXiL4R
z<HowyI57XW@8-9lJ<kU&!f`N&v4*s(MyPGIHAvBWo_WCSI{^28?T2G`kjlxyZIPOp
z@9;gX&SLh1P>aDTk(i$}Fiy$r&x?NavHC+Jw(yU>gX1m{n(XcHjD<{vH20kAgmhD)
zE27w-_5%xn1YpEyzOPlz_$Sr4zp@hS5IyMNQRhXDmXG^8(y7co`4Z!_*L4uVjyjSX
zAgwMt=Rlf~r<wER=Ge1&GE1y36zM*Uwfvx*1I_CeyBVClUKQ>JhoO_TT8CwiYEB-q
zmK{&p8gH{7!0~g6ZZnC&EPjf$GT1|51{>KQ%6$YDy|&NsK!oNG^un{bX<s>i`Gs8z
znm{qUSXc=TquMzqGn47Pu=3{QsS|YI#YnN}isY!Ms3rZD2a&KeBf!btQ)VyS(?x-2
z=$KirHh+b3@?YDLMgG#d9DSMby6&JgVp#rodx-1@Gpegok(pnOgWmb9;0Lh3U=mbR
zO63jNd-Vq2H%)FnveRP{V;?U03$kB3enfF)zgvfrA;_*|p^o~>O3@F4+&19-ZbpVG
zi`|}}bImG$cxrYZ5+;f0cIJjvGhp%5wPPCWixoMmWzs)Qn^S#l#5s~Fqn&g$^?Fi>
zCMK95*jk4MVS=H7eH|TaY4QFL2J}qO=B5Xrx?*9`qvPAv)wL{|Pe0+E_@!Ev0|`K(
z71=^lqe0-|Dk?dfzw9)X4ranrt5sFIGh*fEtSy}gcVtvls*m>Ylsid{veq1A-qteJ
zzRlXWpRqjf^0=Yugs-^i+GX#5VMNEU92$3yc#nsS8wv-BR4v@}ewBuhFPxr)*W;4+
zDzf^HS-_Q~{ibH-aG-r+b0OqfkmhpWzyTs3_J^>ZFLq=1O}_5jmYQ_6OaWw@CooKC
zk(NsGJUi3_4-coQp~C!TqFiEcWay_)pVV@MTyd1Dw&Ci+XNqS*+fC%CG27es80kYc
zdpSxW5cjFJu5?PB>Adp>^*22^`p8dk{mriwX@`hv#DfPX9x>KOsjn=^3kCS^4B`cl
z;%A1}$?|~Gddu>x35btnWxo}>uzR0@&9`Vz_9=y5LDl>E88b646zd^?8|Z(zxDeX-
zHNMg#TK629E>J7LCVhU60kkTMIypJvLxoAgQOUA9x0%`u3(=Ata(>md@wX;2%UDAy
ztbp^Y?hFD*iBaDzK>^s?O6r*?H{PS}2qvpFI<Z7C<9Z%tUWd`w8CrDw_jD=%pcp_f
zfVw{7hpTjRQ;dKSX0!mC^yNS4K}<04Zr|dK`tm1F7vEh$y=i`eb86(iY4SiGS>VK?
zIytjDQH5M4JK<|cw{3+|{2BxrEA&+WnISOe**q~>6K{k%&Z7;Yfl7F>d1xFwirU-=
zq1;Iu{;tRXgusWhtz<&t&WUy;I1P6`EUZCxu$q2-SwPYErhw+ko}jZb!Ms)1?eZr+
zfxeIGoH`ATVLvk*2>3;1s1mSLS<b2w!=?kj!ACxHeGfF-e6~C+H8u4NgM`+@jyv9&
zIQk`Q7eB1XDiS&G;PCj1?eodFu}PsVpyke`I9OR(@yl{Xx=yD8=2i5FbC|hpxq)_G
zbnOE(`wN;#e{%oip1)ux#FyvXfEF4(sNET@3mjDN0BgQ%&fF}NapnZKW#SaXf=a&G
zdI$FQVnYgY?VEE<l6CQ7*+pDcDqXd+vvZkIbY1R^81ScO7@-66wKt+&ZaMBY<VlP{
zcE%Y7%yJ(hQCW^cYv4o0&fZ?ZI&tW6Be7KF@82IwvO+_SfSz*3(8x%*O|TT?{Vy6M
zVtzia3R9gw+X_`r{?QY*tu3~#WTh*=Y5KJwZ^IpkNLU+T!Vk{5mhY@GLFd1v`Ly_0
zd~9;E-K+YD-ud>6z!mxPo-9^rpYb-oJlp*}5etxaCMR#yoAImM-)3>MyJNC03_=R<
zCSc1Cnrs)=e<gCUBb81lf0hEQFw@@=h9w{kli}D4@aQM2^&PwiS_?PLly06mMNadX
z!QE0r4gTf!%sGwI&1sTid;6b$Q0E~4kq@`Dl=!?0gP-!zzr%?-ps-~_8oQ+de%Bk#
zBt4fiMcLU)=>E!iSsWZx1EkIi>T3^GV!KDKq{!U4GczSL{=tcV&6%!$l}-NZY1qFW
z2mb$eiYFPN^N+hmX~y{wn*YP1y1M#+e&gN7hv;2`8{;<s7TLE3?KvwLNY*;u*Vl7X
zC*0CnQc)w@Lo*bt9J^98>k2}PBRcI32YSAlRr~V?<p=N<RGlj~-VmOgtPaL(kDMI%
z`^aE!6bOEFoPBY2`d{(=f1c()fhYf!W=RG?o`b)gIt#(3Yr142t)Mm&4vxmgMydvT
zRxja*M+UXBa~9VRY?9;1eoNp=$H{?0N?wVHIf+zWA#nB)5y3LHBf4?)zw$2rBe!_v
zM*Tga1Ox8g^)~6mTC8}&mkkth62uVVunx-N+cJFBm^q0Mm9J=YK(C8)8VTFWa!k*h
zc|s1qyj(TO3fqGp4*xo8hG!V`e~KiK$Kfzj(+D~YwcR8u=nwQM0%G%=VbI1(yL&zY
zBzK*-w6ye!SU%Ts3323cmkUY023_X2QMKFIaRPluKWh35f`r)5<nw0QY!nq2e{A!%
zqeztiPM4Xb<>8Fq<)HSz%UU7)4q`>{ANv?3Hv&)j4-XA3IQyGU&$7uGi5lG&_V@n9
z6A)L>#UNUL&Us|zIa+*9MoxYvR3Gbk@QamI+*J7YOl9jiJ<y@$w@VO+RAUOS;E1?K
zn|$Rrfe7nry)O}h3-WJsbBe!Pvq^#Im7+G)AWk)=$<1*?>!5_vMV~_7o%}XyMG|1d
z-s}hg&DiKYq8&F&+l}%)SJ34MXhd&z68j}tMIM3Et9N(h^skv1qVdr6SPxGcq6yuz
zf1#odlGm~vZTz3mAl49&0)sBT-oqIN6+CI(!Gd7jarw0F_4QplTxOZx;}#baTUqi=
zPj~mBHW~Ao;A%3h`F77>?gr7FJD0mj-YA%O>+MzQUCi<<WmQ$y)$RJ&llC<t{bO43
ze~87a2cD<sx}bQ=<9kc7@`nIA?;*Lxp1*s3)sYp%5~YDaCrmqU?fE0r;fk%zy@AAX
zub#Tdgq;6j&&?;F2Ey;wAD;Lhmp<!ngztZ(8~UFJ`Tvm!`40~@Qn2HjauTyyBWo+9
z(L+=bf9=1iaQ3`4OPu?%9Y~&`&*og7>3o~TYA4+n&3G!Suh$(*r6vLDh-FJyk@T^>
zx&T{ar4^*KQ;zC!)IaXi)YLSFp$J^ndbY}Jj!;Q3eFIn`E*06S^Td-W1hVn{)0MNW
z<QmYsfFi~+ni;Uh_N_2YJ3?W-z?++s5>u*sA?>5t6$*iD9l((z!<5YVa$BZBobLnT
ze>m6*VUM)@yu38}%Cl;whIfOK1eEOvHhNGpb^20VEru$fTnZJFYknsi#@F1&VTQ<P
zQG7V*wQYvM1ZC8;l!F7KA<X}F7!b$7Iw;C{TfCPz?3-yaL+u(De{ARXmR452k9>AT
zes&^_=xrI-33)UAl<Bia(c*>3i+EbG8fvyO*U-Ar`;Y}jf6h6ksoD6l5v6gj5$epN
zKmh6pfdDhP5D2Kp3khgnV!RFl(TbiR{E`AEN`w2Er!lq+K~^7%anBKDWT*6QQj)vA
z$K~MNCV!?|D=XWDB%;i4n;PB3#_pwC52Rb8DnrMjyp3Z)X;a8aPp@rA^Z4~s0n`X|
zR^)bw99(CnQ)BTgA6S{ZQu0}XK+7TA|5KCu$#*b^^AB%82nT9mYep~%se6@0XhG0=
z;w(Oq@keAtgy}Q4U6?3xCHcJx-l<=9ZIWitQNTR6zXL8?=R8z?yBVwe`IIB^3hA6V
zdxFq(9u~=ZA_DUKq3aIRyfHp%MeJs2)I>8Mx+X$Jtv+m0SrKu}Yor)0B2gJu#6Bij
zH4TM}tJ^V%c*mLn{6sRtp`bs*0Cg1EOKj5Y_5x@qteX97E6=c3*5uRMJ0#irQ8Qp2
z>OELf2udi~qZj6Q6B8ZPx$GyJbAa=Yq@=13J7!UBuqTKHzC|)mBZbAgpeT&&%vtrB
z`6=Uyzg^px%E@9;Uebjy#tOtwVm^lTr#mK={h}C*CUox(6Eie2K657mo|69D%d)G*
zOUMU&yX)9~c51s`9*pD-_Y+5lP5OP<O^XH)%{+|LhB8quTGH0$&x&ji${2>HIW!Kz
zAdS$tqGf8lQ(T<WP3I}*twhRjAJ<3Cn~jCdUQFgp{U$0~*uyk!Knp|U#MTx&wA${6
z)QcJovUj2PF&Gbk43#Q8a?#^I>0++Kixpu-Xt?13O>s91waedOG}D2R3e5JiVcx5D
zgm2&C0yh#h!70?W8X&TnQy0qAhzTX$p=!X$wC*?9DU_WxdzKTEe7Pu=Y9y#<Eyd#`
z@<2Az0()j;WaM{nFaU)wM&E*{Ayxw)eSlRIf14FkIvJM+(TNmbEO-*m-mtvYcvP2_
zmgb;dr9L%3&yIHW<sDx*g$(XZ6v-r~q%4yB5YszfEdOrEkMeRPN!vUjpNp|k9Pyvp
zpRggm=}|Zk-2TtShEI<_pKJKuL8F(ZmU&jPne?Z^-~v9`q<?KBg1le9Zyep3`7`R5
zKDW_K0Ud5S)3ZI1)MX$s+5ZxQzw+@A*%c-|2lKLr`>i`89?N56Rx*s)?F9!zB6DBs
z<lW`=b3qB;6XVE4zm}v7y2k(FtACGV+d$3s*?p`#GOkW~*7+;g-kO*%jH~b^Zw=K1
z7j;87TQ<(z&(9xv+Lf)G*t714Am6AzBbbs;|L*|#DnnuJ=w&H4Uho-mT^|}+0l$wj
zK@IjHSyOK=Ynn#R!LQSZl&p5?K83W0Lynj;(}MQ66LNChI6@dB6Uo#d_Z7~;<i!JW
zc~stmg*d|C3MqJ@hy~#<jmvPKEOg(XcQk`2;IhWXwbXewS?xZhS>6`<2Ay^*X+Q`_
z+_cXRz5E0>sguIj5&ImMK*Nu4Vm=stE6QH%X_LIX#EjN}{HE)T&7GdWN9_jvRkH)*
z6X~j`I*l1O86F-q?qw-i*)kkTegx%JZ3u-xZjgqWPw%J`hP>=x4}e0!2~{j=zN7QP
z!6v_$?1Q4gnMM>UV+}rcbZi+SPs!Hbr9C`9j(Yl<qFdX%KpRS6){H+cJ_n;?3TdPu
z2M1<9odExhTX&l5zbbnjWl#*pDUoQsUUN1oW@+r1<M6Hf3^bnU?zOY)=<B749@8k6
z$>-he4}&s?b~}`Amvd_2(T0^};qSK&6@xyucYUt{2IhfP3Wk&5XTd;$A!V76YVocy
zJOM6b>?oKzx+eM%%AiUr#I+Nr*HSHsw1n8bT<>MA#&9MLE?X@8kg_a9^_z#3ot-mt
zI@-b63JeuPY9DPip$!S>11l)Y#9iN)f?A#Lng|l0Gsw-!*?B(2yeC!@9kSvE8RsK%
zv<ra;R?=`n3_#OiM7L6fd2%?;-P=QS_JipQFF^uoO2mR<l0VP*Tb1Yk7UQbvmw$Id
zkd#I)AJ_AsMv4u-0ZBEO?DTiyn6{{ZY`GRQi*Riar14R{y>@rXJ8=x)+edjH8r`u}
z7%B<38v?ocOM*zC_7yS$DlUM$;-P{*;(e%+!hI+nwv)7s!^}sxX*=wJU<`z;NX7|9
z5D|)W8AN5FuL?f)4#xE~C!R&C<qtp;%lHm$9x8@#DF7K}0<x!-6Q?2kKZFCJ;N3P<
zjEP$Q+xU#(fq!&;KYAgbo49)Ftw(>``7IH){m<tWBJ*0wal$RjhwAq!2+|%}JEpIn
zN_SzHv&*0t4_SW|+5PLRCuMhP?Dj15b@^ga(q@`W<G)G8XU-ytpQu^%M5BeZMAzCk
z37~P=Z4LQCZXBfdK8cb!48$^r6Jrpc_N~$G7}=_wuwk9+P#K9mu-DzMp=F^ERRwlD
zhD0tO5JI5ticO5e=AtH;sx`*iElz8qaa#7MsQsL3nkIV7EE}Ihx-YgB!pjd}!ks)w
z^==4M24EzvuaW~YN^la1!u%s6K{m<R%exFvO=>_pE4%c9v7|<r4Rsyk)(Qw@>slmN
z&P@$$%i%7`G3-cLtQo-+H0$AsR2|j5RcG*{f=kXR1<6mw>uaH=q|u@D;6RkH#ZDc5
zOEHbEmD+(Z`cgU=`gkoUK?w4&g)cu}U9*%*oqmGxTNOpa%{yH-&4=B#2)U0dy$uo<
zBKZXsdLc3uB$+PNaJuopFsI<@bK@wBt^E1Skg$Qx5#yGOl=o5^VJBpQ@&`6!#ND6*
z=U!u~Kg?8(oSmzkEnz;^7zDK+hPuCccZD(f8Vn0pXzIm3w_Egh+sg>kCq;r7szsBj
zW}DTKs0W$Bx}m_TKZXn#$Th5c)7uwmRg+Q_+hNry0Z%2}TxnWkXryej-5J!N0+mC}
z$6asKO(d&!H?*ESHi{*7P3tOYA0$0rK5Md+wOpTtxX5E?XEm8^zhS8H$mo<}s%x5J
zql2~?>&Q)`kRru;rLgi4A#GKk3$+JtVFfW=>Kb_&Xk{>~4wFq4(;`oibO~cXms5&T
zR_y|-@}Y5kzp1MioGT1?@YW%r<+u5Q9+)A#+%*RQ2_rE3VmP_f-)pHwj>?*~9#tJ|
zO!8G%C^boxS{D|_`{pFI(4g9O0CgX>)&difM1Xr*sq>O%9}dvuJ=Ae|Hifv@<X2%0
z1BDFNp*f=zap6~2Qn=9gKT3kY!kv9(p%5vJ#OQR%y;THc58d6lg#bUMfuPh(lB;X*
zRpP*foFu>|U8%9fzPgF<+*V&%nME>VACx20ReJ=goglwA0`?_|OCGiXdxTPA$*w3h
zWkK!xZCX|lK3>ne#oNw0Z>e%cmkfJ06TI;NwwrL%fR6+9%7XAKB+&r+Gjy!({PE6V
zU*o7i?MO5K0fuqJ)IRl+R#DzY3!yMYx13YT5$(q78)?+3uk5rVO=GowxlnwA{^5rN
zfM~&iT?>iSIrjLlH+=iwdfQ2JP6I~s5gAvb&Z3mj;J3Q>wn)JEVGZh+8t71Q-(`=&
zbqzKg2}8-=i4R6Flb{X4?tG&7i8SDaP36v&H#?_F>l_0rT*P~f)XN8JxB4E4eXhHa
zhEF0?Uc->R9X=49(g;gbP^q*-E`j8Ay$$IkIrfZkcv3|D_Q;93DQCS0nfX9fYK|y`
zS13C{tIcJj>vy)4dZlQdG*F>5WheUqTyYgNkBhtkVF(Tm%8-YKh60ionsx<EpFR6#
zw_aF0gBrAGKG;djy1qtZxM_|_<fmInB{tEiYK8g7$<<&V7$luJs)ytN!XMzCp8$#D
zxJ1vY;Ztao3}MP2Hr;#LWaxX~7sf(6M%=0Tt5IuQ1H(oZt4sP;dz??r4&Waj_Y(~Z
z4)i_+;?e`citQr|cb3G5i*tZ#tZ9%KS{5L^iif0hKT=1W8^X0iWL?4*bx9stwpC3B
zcia;;elmDJS^|N(^~SzM*B}Pig(5X374JvtM;Z~DOGiV+NW(&d(5h@IMc5Cc<*$d~
zH&`3(d!SO-OF05L0jc)ba{#qd;q>OaKc_wc=qVr_=nwRU{m1ckt?f+U){PAhKYft4
z#-)K$_=C$9)D3NJvANo*s8&48ufQ!TbZd(HF(0qcvuk=YFh8Nr!-It83M{AK%Q*sp
zs#r+B&dj7F_a_NyrNUoMO-b;F(K?_Br_~<~QaUE<G#<eXBjf>Xp-FII&S_NBJv>DB
zF1OlT4JV6DZ4_jYf+&wlvSoR~dgHD9l}sxsr<Z$}1qZpJ-lX@LxJoVU*U$n0dK)4u
zOK^&2{4R&Y%8~~D88?1HU-HS}&PvRNUwp%Ecx_%TYViUIO%DWhV^bD5G`2-Yu|(&J
z+bchZ|I}1n&5ZF&sF_xl6rAf<C5+~gLg<!OaUA`UxFq47Lz`+OLW`<v;4M7qm7|jK
z!O=Vi!oJX?2yzdG&{Wcp%nqBSfta~~te1mhe{Ov-u8fzLnjkzYZ`!xr>pQg<*W3KI
z5h&Z<|JK?cjE+V$xwn?p&8-ucf1#xw>*~JY`hgCqILF^RdV0L!#Y8MI_>!iwJ?hJ`
z2cWfGgIF3*^D0u`5}+MJYf0McdbDLsr;6?UOABzn1#=z>bXa7*P*x%5JUaAEx2uDK
z3)&*)Wydr}W5?Z!+V)c4L9)vUDnPi$z*>kV7LL_nG2*(w4R}#s7<=3uqz{;GolhD+
z%=m46%2EvaY6DLT^9x3Tl6<xhbOL4J!5&G^o{vag4uj)XV_4zkQs0yA7kCb?oQTy^
ztQ=`pjO;5fpMDT2mm8Mc6IvMlwn#n_X)1dHJ!a4;{k-|777EmunOpcBr`{48|IlJ#
z<*W<hil71wrW_!dJ~}Dq%eseW4c^~b)IK<XsqEBYycETffnOK99QPNxxnpU;L)9!+
z!J}adbH=L)tnhgEK`!5KvyA-^wWxQ2k^~Yhzwi-3&m-lX)C4nUQOmfC+rjGcj^18_
zJ_dxKD><xOI%;p^%%Rba@QHKnHkJIfj0(vnMhF=59WIgx-mpq6z$`l~GJ>TqL#-<4
z<+_d-N7~9(#3x_0h<T?xy1jEmccXF4RFI9Hxp}Ta*X;z*h#p$YPBo&B&>G_=Id64f
z1_sg>0f-Z0X@AkE|Fvq;uJx~fVcguerC#RV03Yw2_@9-5b`*znb?OiAGVjbX51lCU
zbz6?Qh&3K8pT1%w%}yOa2g**t+{rK0&Y4hUi^sy&eF5R9idIjoyKYnLQ2rUAlieo?
z1?+s2pDe*}VaUA8qkjLv=c~6E9o;fxtBF>pE=2RUyV%24ziC>kOX!T?2O`k&91r5f
ztf4|fDD38-wXASj1lEea7(h`K{G6uT0+$IVme3y5pe{}Pvh&{XBD-3fWMc`@g$}hR
zkx#TzYC}T{p#^?=8^~I_{z&ZbqSSDtS7Y5KvfQBGJkZ+$(RmrV+#qH@r-v^9AX#xf
z^<aM79Ul0wKrt@0BB5gX`8v)^jaE&A!J`3L1u`#9-5I6t3?aa!?}BKBlE;_kO?y*g
z3hG7}O)Whz)#olt*(CLI8X?Zro{`q>%v2SytG~LVNlwkp{GcxLYUI7CTO%p{Sr1^l
z4{4$^JtGp+&z;H+zne8wuEah09gQV#Me~vm=GT8tb$cVsP)M{>`JjXKBIHNTelKgb
z^#<?#FI`#Q2h8dNT-SRUsi(Imu0H|acIzF)UL2hI=a0%oD@?&Z*4&039P`mvUaP8f
zxSE@@)_&iFd(DCoA<>uGeMbVfY3g3d>{YEs<*XHV;!VA|l**D&QTW_|apa@rU283s
zxhEo&p_oEDz6|vPkC7;e<QYgijbCy8qGFV*uBuU&y9F_EG{_;Vb?wHO_}u2CT%mwY
zxFlS~dbZFw*bMRf(xP3PHDdL&$GVs_NOrN7Qq@Fkz?-s>=Ii1dH`j8P*Gxa)H^~jl
z?%81WWH8~6a0sv|Y*7y`2`v=@{dd6=>bfBPVt?@jO@N>YyBMiPqh%U^aM+{LNrV!p
zXQMzu7JW<p0H+-P)5|s;zK-S-Vcseijcpjs8H-*VnwsC>&YH3=+s|n+<m<QIV(aP>
z#0{PL@NRDHHBtP+A!~nVJvvd6Uh;IB)7jG0^jg`Y1CLvNYj<_OfUvI#4a-dCQ}q6L
zsr{-Fr>hy}>2)Fvc7X}8h*;ElP3Mn?M=peBziqGKsMUt->Pys?zI<+fj@iJ1{p?8c
zZn}95XIEz~V@Ut~4hy3<;3+g5CjoJ-E)@}>4uApSOU${`tyfOP_hHPs1y70NvZLwo
zKgVD@hZa=qOuGLu;ETkYjHuWQewx-!O3ccUy~5bpY$N66&7p~_=xBNfn1(_xCZOeE
zM*C_KkkepF+U~zcDf_CHI3$Foq=-=#IaXJlvy#UA)9h1gC<+&+y*;?b>&oCXw{O$a
zEll~9g^`NICI?<Peq7MBbQeI&Nu;i9|3v^h4d1%3o~;^UkA_B)D)!myao`s|Gb^X}
zL11dAH>a$tESuOtj(B|FLMPq7tP=<>V`6R`5Z)$IU?IAF;k*CqPQRwE=XdeUsoj9S
zYQ*YGLR)NngN4w|q*V8`!u%^kJ>ss_+f?drm$Nn<1ywaZx0(Sy)Ug3y{DNt9UD<q5
zu9`o#nKRE71bMu<zR=W?b!4MtIA5c^-%+2|%|&c7MJ~?-dbi==uH9wEs4H9NaX`P4
zN8tr_6s_f|FafuNd_UHrR$L>di{`hpnZmy8(SktC%Z^+e#;YjKcpV80%lab3T1uwC
z?vSANvwpkvc3&;c=-8-tw72@rd!;_V4{T;W`o)s~o~)Y-8{lEyJ$16R&|G)4!t+xn
zUBOkEp$F^-gFY=UM8etCl@9*N%2QVR#I=!G2<W{xf$)#J0pE%M%~lI~h}!?G-~E^4
zOzRTduRk>}j)uz~mL9H9mSLVDlN^Q<Sdp?r&9pmJQPGyEWUXg@km0riY`mJ!7HHl`
zKzJaJn{(1o)7hD+6KAM-i)oqmrg5ZsS}&%-XkT5uteS#PghJ~5Hv-kxJC~Y&-hEIe
z#ISGtY`Ftp^`5;48MagI!`eNH!i?7$-aU)gU!Zi?u(-AC$cXn=tUxfWz`<v_d`Ph<
zJ0@{Gd$=u4exaO;M&7cSTgu}~c6CLMG0=XwFu2Z@=mc-^rnJ|r`=~~9N4GtFxd=2O
z)bRG%?V1>*F3xbV1+8vY9G+zT<ST4VO?6<yR#z~aY+)u14hD<3#t=g)uI_a74jpyc
zPR##{!13mW<3D*%X(*UlxFa{S8)jHhhg-_%nY5k|gjzW{8U)6z$D$OAg9DU_pk7dt
zN)^*mf*;<k>0RhOqkSvuut44sre}8FRF7@l+$D(bBzef#JK^@F9_Bfg`iEnRp0I1R
zwF|yQ#B(kvrGAK@F#qa#ZRXShlN^6L3~xQB-vlpjeW}!UDE`coKKEqb1N$1q6GSvN
ziiQ+VIBxh5a}gOSYF|$G&X+6DgoOgi>QG4W!nf)aVBEwoknfH+1}{Nk&{x6qqN#wq
z;7pSx3K=aJNbTL*+e`Y-xmIgzz+L5Y99Y9avRnnC8N9Y}a?n?lEK1*|ko?%HKZCk^
zsK!#T%4C6A|LU`AO}m4rDneg2Q;P8dI^vZCP3ZIKYa>wO1F=9;fA)39H>bww6&~Ce
z7BlryQc%n;cZ+8I)%VkAXaI{v)Bug?!F;lWa~5S^C1Nv8L1vA!j&B%lJffD+^HSX>
zewPxQpiQ@}w(0JhM>{WQCx4zlJ5n^Q0W92)fmbAv$hF)t3Epx~v<BZ<fGKLuU#0B$
zDhWOflhq6Da{RxeN-RD~guWdpIVaH`Yy^b3JY)>){2Y2?YOko$Nzo~IXp_ZS?<%#r
zB<GLI@%*pH&8rF@>E*D-yVNR(#HP|Fo55UmSmHr<y)&`kSk}f5w&40a2#15ZOKP&E
z@!B+`gucLMVerpV6~l{6Y5`;89AcWMC+1#UrXnRgf^E4sb>p@FRohobMHP4L9xwnw
zx>OozNkO`iv`A@`5-Dkr7AZl7MwAdq>69)(M5F{AI#r}Wlt$q0<Li6Zx_{oazVEF0
z3mE3i`R#c2vuhhg2_bfPb1ro_rChGyusQ&B8*T2*eu;hGYyf?-XzKQYac)1_vx;`M
zs`<0TR$F9)WtRchPIJ@(C8<B-vyK>Dme=ML6LYso=4)v``fHa|liZtx98;)20Kk$h
z*Nh{SGTLG{V|}$ATEBj~9v<;hxx`<RY~9ts)+b+Q|2Lb)M?wC=(|(6TQ0{fx#9eoA
z?xHz5rKPUlj}P3B;id?h&PD@QRz9zPi~;o}!z!CB`CEV|U;vc(augfN2kamAfAmUt
z=PSx`_+LU`Q8KSK0^Ns!hQes?I!{FtI&1^jEi#ze<kyoXDT?+*B}+YYL4iRzw7Z<$
z;}81lI?|viG*D#gkjj=bphY!P+Hb$4N$S(@IY-61A;|=*@v*V5aT<J2P<aFc2}^R0
z0(?9fu-_ApN&ciU;gIWyW$H>$*`1=Qkuap9!RQfV++&=Jh9;{e9I+B&-(0HtdTPu~
z42#WmmoNt&RebJc-wf%C4I?#lo!V`wL@CQc*)Hzv;I&9mF!7Ae9!hY`Uz8Hv@lT(x
zQ~SWNQzuGs>eQ*%Mm2mdL?2)rC+nGoga+?2N~L>ET-(BHZ;@Q`l(;z&(7HW*_3D09
z<@V%MJ!GcKJys2UA>$<G|3_?XvLS3{<_<bdeq&>U`uO?I4~u&brae2F$Ge{&_FdR+
z$)xj}XuLm7-<9~1f9j*=@iR><DJ5mFh~3Z(aLHoco1BhQjRbEDqa@syGJp@*ZD%|R
z3=hZm3|kc5Zp<4wIL73iAs~=<aS;Nl2rpIAllb}b=O6m}RaVE|kTEb|Zlp=PQVb)e
z=HDt=S-+aN^~b0kd%TW2wPiYoIlk9aC+l*OpX|R^?6a?QjWE>u-(?y>V9N4;FaD1I
z=P%8Jo`wJS`k*2B7Pyap*Y}*{8R#MVcm2}3_7jQ&{#_5b*u5b3|93S~Qv82?+=tVi
z>r*h#FHqrCE7&dW>qg(bq0Vk_uPNZT*~HyoiKoOqGF(bq_u(o#2Z!RTTQd--x_EM$
zE<!*Fd&3sK<WmHbt`UcYhZns=zm4vBe$&R7=EkK5HI9=NOLd`UzFRf-`qLA6ID!L}
zvmPmDIlJvTIz+0|Zodi+KABf-<>kM6NbZndT3b>4^eNFIt+l1)2v*N3F(oB3>VhbY
z*Cn$VtK!n(rougOVxZc=2vp-dY)?50qc#@T1PX;8EOb`C*Qb)vc5!hLfFzEM@OfWo
zSY0FTT-@JYB@9t`1Owt#11fi1ivYXZU<;_=1VauFb|i(2Sy)_H(8u>$Yarkub{f`>
zSYdw;m6#Z<)#->V_P6L2y2E$vitYOT@dp%n0=-2U6ci-jws5P+pz`$M;^M7Btk>Qz
zw!W9L(Ga<0+<YnX+Mtph95zVU>RMUhMYy{u(yYSXG)Wr6VroYJU1li9n_F{AEhsA^
zZSYtPQqFpdZh6ugUpb^p5rshrcc5VM)1sS4C#>@5yWKm9LrO*_{#ZSy4Knz#2?@b4
z+UJ#_$Y;ifhQ)g44>JSOx$fS*%Xh%C`my>$L%ErE^YU=%H=w{2Oij~u=iV6dl2cH;
z8n6-YTr=iSQPGvR3RwLyH#ZCR9S^ydCV}l85SxY;TO0&icw5EGt5$J&ZR`z?f`Wq6
z@sVG4Zthp$cARYvp(fdj4wcEoLFg9A(8|xyD|G(#U9Hk80MukLPPuPXMF|6=Z&<59
zv2|QTnVE4Qq?a=_WpI<Ahu5#)zn$m%=<TPQ=`}Sq9iJwDIDj<CNr^OKP%K3yrQn8k
z=lWu!8e9ZsOybl{l$$NjxroEf;~?NUUVo7#A|=zMMnzvUN2kHV0T^fkq|9knq0)+m
z*YG5gEauFjps1L}aDhyhUIBk|ecJ4W?JLNp?gB#(+^Is9E%pq2$FOVckKxtQBHPy1
z_NmlNHdVs?3~=Ad02C%0@s!d_YispixOzg0!4dO*5AJ-Z*a#0<m~Ls&9$Of%;YVh+
zSI2iDSWO)~4M#(aYMjn}KZxUzK)-`+W)6-{o9B&<QY#a66o`P!chyLDL&Fd$Uf%0t
z{_seVR**da7(}4lOiWA&G)J_pj8<NMZ+X*5^ug3SXc`t3wjUvV^ypDI2J;?1yu0_+
zGtdTT7$JYqv_%c705L!iFxQlzYXax`A^VoSeW`bOIA5EClk&_WU9F^tipzI#zHS4*
zj^xcvH}p}RJue)no}&;(OdpewVCn44uk9f1y@`J&1h@BvNc<7BeOJ0f()1+@w%as;
z!GrjjeXQmINuqiP*jp&~IRRN)MMuYy(d4IT@jX4)U;xz#c&DPA9P;9d!x`q2V4X5<
zaek`2ce4=lx##(1SSv_MOB>$Srcjt}u>g$DF0f)a3C8Mv+baf*5cKFt*Fc{Gt&DGN
z*D6NF8ne*3x3>qqG<c_p>B3K+J>S~jPXaw03!{}B(7wo%&#V2#Rs{T8aI@b*rfHKm
zK0dx2l<x>QPKbe(?pr+2iS@U4IKG?!&*}so@g1m-Sr{p21xY3)B}GmAQ(8gXP7M8J
zaodW_QW2vm4yp17&s~?ka8XiG;UMPb=JpZ2x-X>_@aZHyh#;6@fVbYNw7$N%HhE#Z
z+96&>+h=nD2Mh|JzJ7a?;S!U_hQ*=Ad@AtEV%upwW@cvSoIQoyfuQNe{m0?qbgH1c
zy5tWZKEw?~n4nwkGIKB~JTwaA9VCXMWH{K^t|FvoFI=Hiz=x(=mVC_0%8JX}`a~V8
zLyOmjdEM%K|I269(+!?CSEgIcyp6YWb+|h~hOe(h7dtpi)DBo0!6A}%baW`-GxM1?
za{f6yj7lohEll$IIrC`1YpB$WKk@Uhl{#3OlZYVLt*mhq`i3_|2cLooD2gJQGUrE3
z{N})`!bn*v_VEJEtk7Lxwj3OnXWPTrmxUXzr%Alg@dHx>x#Q6lS6@HB4h+5Iwf8z6
z$D5^g?c=cDiZ7URGMZ7=-@f7ob1)}2cijFsz_6Yq5vxxb@>)KUo@+(7=&i)}lSjvo
zH(_%OYl3=Chs;litk3x!!C79Ck>%sN@fvHJx2k=0znu5*CO-2SLc(I(EYKzvQPE*$
z!83RF{1ou}SB;XOv_Tmm1&e3hUAJ7Bmysd2xrFVuGy=mx4dRn&RO5&nxC_%6?5msK
zy;H)G+XQnsam7xivq<D#A03VyD99_U?P>vpNU4#me{Fw?jGqK@2kuO{W_dpOvPi)v
z1}-kHIlbyD*?NToZ|rVU($I`g4#8wR0m~1h@#7>6QsLkYSb2B|!IE)uatavoNlIRD
zc6RpYv><$dnbe#gN%XUV!BF5Z61rC5{B0)dMJg&PRvf#}!=R`bYGt9TSpyknE&d?y
zVIBK%@$pd@Z*27Y<?)(QVKXar^@!1WVtR>-TQ$%`WMn_Cp8~$&^ev&hoH|!~LKVi#
zDw{3fuUTYdOkYH_=|OFYys>d=$Nc(u4asDKC%<TY#iiTLxkLs_0o7uzKZxo*R+k3%
zZc3LwFt7k=Meckyzf-cmLZ-kZBf|gyvbakK+<tgu<mFLigRZVFB=Bx~sDW;Cri8}|
zJ_3i@mL}<$ItDqT(XPpJ)}ZSuf>(leAgw((EhHqQp4j62EwqZ4gF^*>c7&`^uFK|w
z!+7<R)Kt1xpBICbv&g^|kj~D|YCV69M{<6W(P$hn7TLkhIt}axOaXVtA}ebKj!zMo
z%KO<maEqr97>I|v@x6-zjw=NI1KeMx|KYwCs&_oY>6OYq>sdVPMkRuV(_L5kn!kSg
z#;R9p%KJuFU!T+e&xQ&VK}Bk14Ra3;L0%+#6{bZCde(LTlGf-*Oit!xkoCWmHV(oO
zxx4FizX2vQ!7)+q+|1XB0EG+(#Uo6+al7OX=*dZhjE>IUL&p$|$0=kWM}wyF;O<8V
zbhBaF#gfh|iH|Nrf|Ys4lg)k&Iwla$kU>2dB}k(}-sdM@znaIUrSW3u#92-g(cW@A
zxenzX+|r_)oT}N`+2MF34ERyXtWAumvH_;&C-LR%C+o5K2bLPL)o+fesHr1rYsJca
zb{w78r=%Cf7TqMEyoJ#DN6$I*Hu9UHFTQV$Hl*y&*w(vdt0;Kh`RO2<DZI;={&ZFq
zjH(rAtVYugH5Jtpzz>lCeG((OC-6<%ha=0$rZ9AG?MCkPM2*36f*lJKe{!F-ATVVP
zrWLpoh<r3Nmxk{LI<z)vw4cIC(Fn7<q<nWj*%U=HNXLByc-Cig&S*@FJMk+#SLI~`
zDBR2E$ApnEUNPj8mNwz8ghoM^@wCJl_p2*I6(Vo#`o%qNo8d6=nbccWrDtZc3{+TY
z<WGRag2fqoV@FNF;2lcP(B!joo;U0B;=2#Ati9>e6$hRN+YXhh!0Bjn`vB(1vLcG-
zRKd^*8olI#HoUl{U0AovqoZQbCuY69-EwqzpkW0IfNg%uG{I;dTu!xG*?^-7OYveo
z9&-x|IKo<Ii@ddX-oufhqkCuJr<s|VJ?sbePc-qnOHFRB7!B3>?p1X3<6D&1Emf)n
zFkGkiWLEF<baSiHC71$nI+I~}hQxyI`HXHMR?7s)xhE#>WMr|f&Jm|sSPC|wC=?%2
zRab|8QeHm3_fSoNgMg=UQEvbQ+5v&KxtkHp+$E)=I)&6a%?2rsL1|>tfCGdygNI;V
zzrwggc!De}xFWV+5X897rDH9*Xl}J!@+)0KMJeoW!W(Q8x7yp^N0WAg-~LL$_BG8y
zEE?Y+8IWRP=XXx(ved7#i4`txgRs$|dJ3S>EI=VqqaQ#Q$t@2R;~=oqR@6{AfG+6q
zV={VrhiVyZA{r|~V&alp^r5NlJu8a=At50n&3=-|43r<f13uvPvd53(&o;vPE&kHT
z41h?LTeo7mDqQO5(4hqga_-zYYnqd8b9NtNL=OAU!_NV{!Oof7+%c1vZ0RF4MO(WY
z7K{GS@`LSn?YP$uQt7<Ec^_$QeRKkqhO8|x0Y%1jg9Wu)b3NDJeB;Ey!5OUkL3vh5
zDLo1ep9(fXQo=Gam?$YJRSQR7iaFy#RCz03XJMkQQhN;OI*|sGmv>y=8RvmuiPrW0
zBrK{+tg{k22T><+#wLyRz284Z=-n|Uqqi^hj_$%1E7SU8=*J$NOy>gI9<v5L+VAZR
z64lw*Qc+3ukogR5$`E@57zsgqcGfyzl83qj4<BC=c?(b^Ei>P~S?H!&fyS%tKwmwN
zP8jn&`pZ81FYjI>ARvGthStm6JUl!a5e{JD;6?$t#5nr<`~Up?`|;n{i!;$aV1F}T
zd%KHaXj>NX`t_XvTml&*V|3`*3oJ}ba$`3`6qtKS3iS##b#%Ue66^j2>Rt>+N@M2)
zE^d*3I**JGQS|bsSBkywDHs@tbF}VPUvqTi>r0m=M4%QRY_tNCdGn4eC@&i%k~KR7
z{zCb5W4=xZ<ZuuuW<wAle`pYOa&oHcQ~H_5T9y%jkrBB<?AW9ph}m_=&(X~|fiW?p
zm0JrGT2uz4uQMCPJyg;xZf_^<Z!M=x@;&Pwc&*8bw1P8UaqLgvxS6iH)85|Bn>5T3
zgfPj<PFvPNaR|0x8_ITaEI$HnRd<l||5Gb0@ns~)V5q>Tro?TUjP+{TU(rKt%$n%^
z!FT4@tj@7-3!gs=`0vb_GucL(nq(0X5g7wE@KSGT?q!oU*?S>m>2RDPVn@C7CI4}`
zMW^}tRMUr^9wmACM^!Jac674_#Dx1Q0P%^ZpwB8G7@v^vvQhNiiV@1g=r3E#!!`A#
z@L;g5f1_A}{n*QR#B{AdNIB26;vj6#3MC_pSx1crk_yc$*t(YPmyfq;3O+P6+jwW;
zknAMbImT!YrWO)a!iXF|$$S|}pKW^>BFC(VLdXlOhm7r-fgCqdo%T6<DvG47w^JW5
z2b*k(@s!V($k<;$)=*Q6&3*AxuVle!NGSE_opD9orQ5#Sw9PVf14H%h%LoFk{v|f5
zSGv2qPu$wagXuiKr2a-ji9aEAt;ULhPeqW}*jSs9vR9Jrzqmuqq<Gn!)~8$6SDao4
ztxYxAc{JR}YX_^1wC0zRv}-!oJeaQ5pK>}`#QW@S%5TWgoRaIx!NRx(*1)*#m@U`X
z8_$J0O7<ZP+uvCy$+#H%@aK$t!_x0y3%#V-KLw!cS7ckjr3@ZIai3zsC0qw=?~B;n
zbmhFyYOc%s2L~3D_3pK<^Tei&UO2^i;7~bMr%Oy4jH#b-Z1dWk+h$g|S$CJe{hRG#
z6Ac2pUGK$@!@*|DY~;xvb{)+W#k_pU?>8lC^Qo210ph;@NUiRB2qth4adGh+hIn@(
z6}7~)$}hJ(JgPhWx^j=XB<bZ1P^YkV!8Fu_?1w+TKx3m9B&w{Jxi~pn)1`e7gx9dD
z%zHC~&3$<&(J^>f5<$+eswv6q{3!o&b3Bugc^LddeB{8WIS{pjhk%4rOlW(^Z;Dy}
zFgI-UD(_{b4%lg!R@<quAN^WZOV<_qxmn6@AC1Nk#D1cdtUZiGMPDCAl$~IH)~#_E
zll#&ROC{)SK3$P?m$BeOG*O>r?ZP+|L-Zy!xi2S;?%*9p-{MXzQ26{xCwR884MIW}
zwG@ad1^;uv8^Fvbc-z;iUoo(*75W2kJt~PzAwk_Hb{?iNu|cPNlp|Q9_uwvbI+LhX
zh2A^GGPx2?aV4tXe_I4()hxhF_HH;S-TH*-0iI4+_Dg}3S#%OxT3OwNc|*ui4Sq_K
zgy-5*@IJVl21EtmLab@<F1sD>Z)vG9-xRkF{qyG!8Z9Gi1g`<XjOETB92{t5rN+m{
z!`Lr^uugrlp=}GJbA365F9U{>!Ah#RzEAo3ffmJ_Je-{P^78VC`mx*`!9N}cX$9Nn
zw6&z0HRmRg*3q$A?Kla3)t2iv8#Z8Xj%M))@>h#-SAd-{N9nIjZpD0^r20lt0fA4G
z%nuVGpu6wi1$hyC!2DO`<#7Vz-re!`mhj$Q;qoJ3>xU!ffk9VS&-3PDZJqaiSt(t;
zI{RnuH=?eWTvNpvBN+O-uP;Tkf5m1U`fRS%PZt}BUSwlaV_gX$t@YXAJ59pyG&}nu
z!uI<I@r5frKazW%L`1~#ZD5`}kq1(P^2{mkowe1Neef$VbqB&i97I(^qaD0^5L8%w
z1s|33g9vLQ{NdxrHb5j~6cmr4tLT}A{=+sw9*vijc!8*u`Z@Eq)?@WWLJy?^6F)x*
zl-&fNE2UhrD8ab%Pr@R1Wd#LXfY`SH)*hM^Vp%vi@aFr{+JU~k#4fvP1wipC5GXkZ
zAatEDB{N%wDOk?K;|gHmD99w~8|(KD{73YTR6?P(M^r^Ceb(6l;*l#4rY<#|27aT&
ztOWzDICK(jA1vvCkAY|c2O**rXsyG!Am+C(92n>7u*(YM-b04JT;m6Bc((13emchC
z+^Z0Gnvpws@}wMl{M+GSn(HZ7I^I6U+km`nCM-R1#3Wr)Q&p7%iwzNdaBHg^EHu<9
z)H};-RGq+wv%RtL79JFjPK$a@T<#Us>({S>>@&r80h0hP0boh;?elJ=3BYG8ZUBD^
zK%D6LwbOORn6k1mEmnPq1tOt3QHPUH8}>gO?ez!X1`5}&!Q>)IkXO1PWg8n#=qm=g
z3Q1`62QrE5+&O|AEdtoeK%WJT#P2#fW`EAMD=+^7n>GX~a!u~p`iH-*m6VlXqoS%a
zw#a}2BA=u<olszlvhxLzI}lgc3}zERq@81-U2G_F5)UuSq`?EmF`j3T@|yYLSCPXw
zj=q^y1onvntm56mf%^r&346Q9AyijYRdu@m6oo=L(UO-hYpbjKL?kEEx-Ac#mFtqK
zhX5&Q%E!;w_Y+tlIvHPD*fni4-7FpWD{{oj(o$9J?qT>HJD|>#mPnEu-!BA|(F<9I
zV+r-i$zpx}x^*SJ6d|h!w{xX}<>log6;_d>8qr-%<mBXKz%j&+qy1<A{Jx=sMw~97
zCGtC>QOYME3NaZguDRQO`u+fywy&>m+5OM#L<~~3ZGz7)TYiYqitdcMF#q!k3oGl!
z&soJn6{(MNF0jk`6u@7#I)`+po{vCFkyqP~;+mOFpOFsO{W&WJj6;51^JH(5zkOr~
zA<c`l>Cx$FMhFI5A^czc_18FL0Vvjr(}HwG9zZ2Ar<uS}t_Lqf>`-aFPrO|5#Pu+>
z6QZi}y-p6lfHF6pWuPku&ApRyqnZ92wuBe`SYD0<4`~h(5grv4qMj3^m9OOME9J31
z6%_zA6R-7(Uo^B|ylg3S4&ZrsRh2N5yi)_Nc^n;00$uoz`I~ofch;wCd<4^g-}?r<
z8yu9JrDgVL_DlGaxVSi{Z|~2-NWrrJZ$jZG3F>U%mqQ^2{sw(9w8AzqCW6nkp)kyC
zeTp6&>0&714RNhIcbK%av|`fIq96rvXQb>dxMyZS3P6v2%DhU5VtEu`qvZ92DXA>&
zF0VGh%hs)dID^ewz&in_^mAiFUR4zz+&lCO;aXZ-tFu1uUYcr>WRUR<M~N=<pMH5A
zTV)2c$O5*I2D&z)0il{h&FGD3MkAn9aB*>Cl9EC}=`z4rf~x`Q9wir{KzsujTVaQ>
zFT8@@w$l~whKFw(in&9i<%r3Z3J3^531ApDMgmFB$DnAfaC*QvwF2IAo^3m|3obyr
zA~-DUnYTYAOakK=6^yOm1du@ZgK=To<nZtbMRt3Wr6e>z@tPbwupB&)DnnijgLDKS
zfqKb{EESqSD^`t+0WkRl@KIF7G6gtGuJV?EV}Brb2$7h?#1PO`MDE57V%wpj#Z7%M
zT0o$Mbw)E<^ruS`r^KfMn-hkzIq)g!2hjHdq`@m4ZZt^t3{zC1LNORBt8$AW2aWR1
z&Vo|`$9{{>z=mcY?1y}V^Zyq2oe%MaDi5zeyMjwY#XM{;o`ip>{FjE&)q?jB(}^*e
zj7bB;<GZNzy<@X<7DB<VAdG9oDlQA3n1S@fyIpD;0;C`*t^)oa7~Lo4u|f-Zzeljw
z9zlSj&@9yBU95u%H@FDoF~+PmZh$-?W%i>&%^c)p4+(AzK;BzPN%tcZ06b;WNJ=<p
zDC7%ZXPMuFiamk5Z6{Ho1t!+jQw#}2%R5lL(C_Q)#DfBs$j?7;YTkn^qF=kpVVoNB
zE+N<0U5zd&DOpOF1S$Oe*He(CeLlffa1!P@M+3XmAW4*h&DX|LiDq!Og9Rjj0*Ej;
ztSrA|QZpZ+$5wC=%onJH#dNbDRNp>rXlKq;FMzy}n9uelm|BH-{hIUSsZ;WN&$58|
z_k!tt<*xHLCYpR`0F15?gQISw%(DSi{dfJ+`kO~UZw9Xaz1$Xh?{NJOrP7v;akf>L
VUM-lKMWAhOYFBS4l_=Z^`ak_msEq&s

diff --git a/reference/plot-12.png b/reference/plot-12.png
index a2c68230969034d6a3a473dfe8922e66ea921379..0b2a1dc4fd20ec8bbf60d3683901eb46b4bca8f6 100644
GIT binary patch
literal 60234
zcmeFZ2T)aO7bSQNASx&sBnl!qNRS*92_ga_2uhSB86;;=M52IzfMf+pk|bx445H*5
zCC5w7arW_rzk8~ttGcVFdupmFU#ZV~?>YDT_P6)iYps3!9?47NTq3)KLZNVE?n^vI
zp)ed#sB^klnDBpwBR;ReZ<xBW(h~4H{OsQ=_kiCnzP$g;28E*ZLH;`vJhN_qLS0A6
zNZeI+j9ngeawO<IxUgnP5S`8XHc$DH*OKRRyz_Te?v{MVIAb+&!57Py5li$e^Yxcj
zuLH=_O2VIn{0g}ALGGCWfj+?{Pu4so#_(iY%jlo`$DIsw@<Xg)FS>XZb~lBc;$`N3
zMme<o<YBOE`{{*6bKdvgAI9ILN0cbMe}9Os5>!b1`*X(sq89BxKZ&S@^Bn)aQ1O+n
zXXJlA<_>3#?7wd#{Pgkn&41oTd+E``HT-|yhrvki==bmY&JzBQUyktn#>U2TuBd5Q
ztch<UX^KhuQNLpN4u!MZ;)GhaDMeie=9Vr?-K=c?`)Z8eK5ROmB)$Y69k~`8WU6Ga
z&aqz1OohM6S%ujAm#=&I-~OJ;-algn2*k0lXgYHBq&<A@{qx1O{(F`LlSxwXn_knE
zch|Tv;l8CGKE$rAt;HoL4`>V|pNcY!ugQFt|8lbaA{Na^Uu0yYPkp@vHkB}`!?+tC
ztnH&xYXjSbE{xUcQ&D!UiZmwVYuD*>)#!6gtWDIRPyr2|@DAbAqs=tUa$b1HTCm5_
zIgaWht{_UG*>CY-3<|nkUu2Ai3YZd;lJMZVP1GI~rhS>O6B&odzdnjvtvX~EbXa-!
zBb?<1E$!Le5u2-8juRMWV-?qk1CpO^J1@b%IPQ)*taseIO2f>2DK|HFelVW_A0Pka
zQXgCJBH3+|4`!$E-O5=rb~&em8g%bgyC3VjI*d6nuxnQ>+f|yh3va&5^cbsfqGCIo
zPYV8FKtMnc7okLmak#tM^hGADqsUxcIrAx=qM~BS>B%9d<Jxmr70lAo(lHBvN*b?s
zzx26i7#Nz+T+g08vu`y@o^yKK)kOMNv?)HqC$v2|SmLm1qIAGQMQgQ2u-QD**40hK
zx_Z??UE%cPSW-nLqP1+O*pj1_;pR=h01^&=wL+89Oox#YD{)0a8ZX=z`OmN^_&W^t
zXMe>6ynBcJ3mqOC%c@pjG+}>e(wE5;8XC&ezr-e~SnqMNNgc^~4I4A$<3~noYwP6`
z3)7rhsUQlbR<n_k&V{mVLGMeemWEv1+~}RVb>27)S?a~Yrnhk1kGEAzg9PmsjkH!4
zdo%3K<HN#~zPxMD#gmO-#k+0VXFIF~3qZ@ssn}gsxXdl7xV+fj)@HA6`Qk;&DA&ro
zTm1a7hyPz;o|ZX@HFn+R_`r_GYyg*pU85tp%8!5{(Xc%}T8e^QtpE#lEMZnLnVip}
zzH)agKsJ)Sly!H^WmN~Z;hS2P<8p4(+AqHG64q+Nb}HMYJ~<`qa(i?wx_CTjwA`Vb
zEhaYh-32N~lzNd_r*d(zH}*A8zH!%XgKLtC9r=a|uImjXWm&4Ldd+b?J<9b>O|5Pn
zg{G>@!^KyLn3X%+G$9}&x8p@zh#3^(dkRJ%o*7avT%qt?TU(<wh~Ur;az8$x5fB(O
zcY{4e0;=ldz%(%AFmh~8MuuAN@55H}?&8>WZu21?eM7@@s2`CW!3_Z<Q(eijw_<)h
zBE;}dyw1RIA(BJ85%z6;F(ZeTi>pgVbE!Y4sV7}&<}muUsV;h`@V<hAPa-3{h2Q;%
z|6m8r(yCGN@{DG=eXp&+wrNhK_wuq?fl2Qrm)#XfdwX6&$-5R7?Di`oeh?)sd38Ql
zn>s$<%hjpFl#rCvsqw@pu}70-s<6N>IFr7H32rJ8<5g}#JIjMp*2j_Tn&L6M=F@DH
zy1KfzJWgCSO06-71CV=VVYxh9Y$**vF=HJkXjdCZ&NpI_z^9V=^!5JcT<dNi+3i|5
z-v;C})6>t}<3)`6GOxYzEb0k}Z+b7(6inST>M$OdZ`gj8aiBdzHD_l&;!ON4vOrSq
z7w=rf99A@nTd`<v!D8*Z*ZbnJxAx~~QwI>U)t1`Ky^o2xEUEZB_vy>2Mx0FTYAU!?
zsDTe`Dy$WWp6r7lj<&E+;l{s{sAqh&4{li1Vo&-F4r5W*y_dO5oH4&>d3ja(dcVjJ
zL`J$X(9sQO${%+thzYQ!R_Rl}BIVTe>K?xMB_)Np^q~+tyF8q&IvpoTMTugIQT;&@
zEyo(;3jai3l`OTEJT~<r+fp}`j3>g|JxX~;ziZyaMQle5nh`(wsvNSkIGCUM?J2AL
z)Z%!xM~MB#EHREV<vsC|D#^MOxfrjS=kGVg4(4y<^d<Mgvdsv}(TBm_vLk1^Wuy1&
z6Pux;7(Pp4^<oP<!SR$eQJ1+m2aSl`IW{s^yZCvhcYjx@a65_`a(yl?uIVjUSIuQ-
z<b-OJ+sjpH=fA*#R2m!M0a4xwVZDD6=P+hf9t64W_f)W$)cyOub^A{;R2sg&K8HTp
zsk<t6ou0l?XaD_s+=o$Il2zJOuBF{}3H(-Pt4|JCP}g~Q2*r-K!dlVXq}Y2q!<IA2
zaO3U$$9JFc3fV8ePZF%FtIO4I!6yxrO11hGC#2dxd~MziyOhhYjly<$Kr!f&+{1?j
z<j<C59Y(BtD2z>bV{)OWM9vW4<4<puSWU%J<phf!Fj6^9<E3tzV`HAHI#^`cKWXKt
z?jdvb+w66EjQ?-Gd{%Sb^1ahmr}WI0s<zvcWrlW+XL%CxVu#E5P+q-<4IoR12voZ4
zT6{@OEoVI)GR^r6G0fg-JKu3vUiGW;MK-m9m(R4~Sj*gx1zCv$>Z~E7Ms(*?pOmGX
zH<pk%?|p@md7N6<u>;N3@VRPyX(^|AWv6TF+qYX4+kNU~S=u>z&3H8D8-plWhB<|W
zDIhQ038oUURreuYbRkzr5FIrxt*8+G884z<wc4MPz`hh56eKlTW_Jd~#KhFx+S;A0
zRk^<?ygtJz^;e=$?z;thNiP>omnt1h73gs^Mg5W2oNQ-xe3f_N>kIP23#ijwkJD7+
zC`=r(fl7znHJ;&PJ<W8Ltms&a?v%jc&XBj#B(rmKLafD3n=b^isGOGDU|U&JgQ>-`
zodgupq(9iNj%jvt#R)m|nIA(uzSXRDr-W5%yg==W`t*D}Uf2o$s_5RQeomJMquEP+
zS#^*CDI<mpj0qr7pguvkq-j-_4VP_hY<zP+c78`CLS{Z(B)PrVi$_e18P=k|D`+$O
zPNT7Z*Or}KJC31B@So%OgO=X$&ClcA@!;*H{u@w(Q7?CvpH=M7#yHM@_IZ@6D_LJ(
z|GR-iyS^_=Jub+CNc{IeuHHta`IpZ^m~YO&6~b2A6U0{YDk3^xVb+~q=^Pk%DraSs
z>#(XNa10fh7CsYd51jrxuzdhfre)(kER9!JYqX8-5c%NNveTrvcf_(3kJ`sraL!v{
z3qT4cIa;f`ihAX|y*M%55>|4&Gi(<yOb`+hVt;k!aJ`9|GQ#Ebcz0%NZ+%L$kICyh
zl=MfR#W4^#L+x?IX}>)18cN5<UoWQT=7uYKe!j>jQJ)g35>#-t`}c@SK3v26EFUL0
zaj?C#cXGJenWfHGPG_v9M)smTK5z#;ob(`^`6+;;keSQc?39$0Sx%#{(Ba*KgM;j?
zW#oZN=1zu$k|9Z4uRW*LAh+k`=ZD0^1dNPmz=ii8Gt8LnzQgz(=CVd?;i9@L^yKp8
z%iXuHSO(KLaHf3u@*CE(tZ_L#<c9Q*h0~)sG0EA~l$0NxJyJ<{ltT8l&&C10taZt9
zgb(RwrMkMs?@f{dib8&v13v!7d90(O<G7ff`t8LpGWeEA;d)wb&jv9_M?DZ8A0MPu
z={%9&E}E}i^pdt$Gg0N&&dXaUi&e@c4(FTxZl7P82?z*8M{G(y?u^lE4(ZKMO@a!K
zx`gitfzXt$lxiESo&izcd|@Z<uTonOlT$^q5x`M3&*kU7*u(TNPVh;U<!})jd=sC`
zB<$C@xd~J<i2M`pV9{u=udfSQh8T{F?iSIt5mW|q{&nk#dA=9NxV}HX_36d0==q^S
zmf7}%;MiCSSI8OGCoDM?x_r@GbaeGADwpAIdJCuks#u_30{oiCtvnGI7hhf>aRo?Q
zULVsJdGOiVxzT>La`(sX%8H(u&CCy?Q9Z@YEmCVbdV0*(NRG(JRYL=V3E12wSY}tU
za&8D&y{4e>)w#AfmejYnaFC;2#mok7McT1F{dPxwcd7p><o1T@lWmR>_d~R=zrSbA
zbH>nHm9G1F#WBA>MEFFMlE2pKn)9RjPtvPDZ+VT7jLe@?ukn*i82yIj;SM^VrMt||
zQ~<qv^5rd&`^V6}!={6}q+n6z)MCr=m8SdtCcR(7V^0=R6L{G2t)`^3-%h=cfYkM-
z=9OjjG4Va0D;SS%Z9w{MjE>mG*miS-B(LOTIdVff==#l@7^p}2FMiQp#btfyh8;*P
zHin+>FDT$C?_3$Xbn)U{N5>M^gpiO4`zQhi`4YCN_RTLP|GC{dxZS>QI`;(PqN7dt
zaxBNItesazN~1#+!|3HgyMIOBzSrPSWarz(hXhk9fP-Mjm|{|TIgmcB4<J`K2z7CZ
ziBUrXuw(c1N~xfC>_xb$xcJ$|ch}CL00JWDqR4!BHjGrW6kRmjk@<`ph0*e2Si}LU
zivBgWi|X6WkHcD~?JKzr+gRmG*wc3#KHu{hiBJuGWS_zl%g`Y3Z-T;y1a)6ai;7-8
zHjU|8Ojy_(A{LeA-M5yh9Vv3;%IS|Kt5%_^w;om?*c93I)!vq|E~qzBMm=eGsNX|{
zrt_UiKC?b4nq{^ZEi2Z&+jU*0L!^<6fx_4oqY`%fHhM3k#|zhSe<spub5>5XFKdo-
z`oaDCSfT(1kixQI30SXNSofL2R8w$dcXz6SG~5y-3bxY}`4XWddGW87+rOKk@p21&
zzuK1-6AKl;A5HaMW#DLkGuIxiR%~}LDa@3v_wFB*{oIqU8IBMU()5g$^YNMuyaM1=
ztDB2rypNfZnmQG!?S6NvF)$<|0uu%OhIDs|9D4C04o+ct&O8)E|HGB?s{o-xu4+QN
z0|8wJND|F%XW5q-rJ0w`1A$QU^-0F_TZFM^$$8E0XlPJW?M+^e6>?}}k22?t2>}!i
z`RudJTpN;Lb6-w~Cp5g^in?Xm$GY#n(3Na7n0FJZ9LLm;_IByVkMUt0eFSV~=gZtH
zgJ{_372?et5_+>W%XJ6y3=YhErC@bKnrh1ImJpm6MS&j3O&YyQ*|xPZsxI%bH{nAn
z;>-h}mXTm{e}5l=%kds3PPU6ZEf>@kU#@D$Kxlu0!^v+o=`|7oO*Ms(J+|B7PQzxq
zSWdb;RIQu;;wuc1yLWH!@u{NL_6i~YrP>8u<8DkUo1C1ihwK&C*I(u4@{E^>sRhkz
zK2o9>0f6D#@P<l8FMB-T@yMCo<-r@`_wI>U<OA#~32Yz-^eF0fU=(Ed;{%iZDv_ks
z)79pX8wODTqlu$(b~(S;d9+90Zq2R&z)PGn;cwjhp5{ui>G8pKbVTt}R$5U}EgKgc
za^8nLMjdym05HdPmyM#i3lRWsgAGAm;Iir;Kj+7BqVNwyGysSw0rhYVy5`@04DlVx
zRgaS&41Cjh)7n`??Y4zib$a3=w@ZYE;E<0O_Jdy8G-z-f`VQ~uY5lF`L8VQud*0YJ
zW0fv$q)%#^TcCZ>o2>V<Xl2(dQyy#YQ?KIpE+7@OWsG3gWFEhIjq7}Z`$4*@o%7^O
zq&E8oUs#J5R5??%F(V$`;UaV2;9v*!oi|rSzmsUYzV7IF6g*{X2pyfpuBN^|9W)?s
zYTjHh%~oyStk?cGnS5iOTSeX1#v$YR9xQg^YucZ!16XPg(%Lrwc^Vat$hS>X&7p#R
z_cgR^iO?A_s<s`|to0}<Oyo9E)xr1qX41=SHeN-Ev^Q`m<QpR8*W>gAhzI+NW}i>K
zs$}_xv#3tO8a&O_#eme}J??&p)6&v{g@e=Jwma$r=si&>ReruFJ*+}3I|rKR3OFB*
z0J1qDo{^Rxz?-C_BfnaanIAMle?DEU@N4$f>Wv1@FOmtOZjY*5orHG=^iZz=yAnl3
zMSZ-b<H^2VzVZFs)lX7^WcOucLOjT!7m=13`1p~OLeLfqnz7n`ZI3`G*uUZ4eEt0H
zW~gL!WNQj-Ep%V7va+fgIFG_8R@n#4l506G`ZG?51deRYV7{RuWS;`dajIaE9a87r
zmB6$#MtuVVC*@*@lIoy;%H?xHS})v_E1rKhPw<;?lR6LX-!X)T_uof`g^&bMiTFb&
zk)sXF!Om{V2Q@RjzX~%g?c9HZa!bRd@mh1h{fDNKkeC(-`d>iM{d>Ltd3<20?L;lc
z$A|RrivkF~@rk{6iH!FjK9qna;ZdctUFokNt;@u$YYru6K4(%0z5eR3I(94S;XEX>
z>U&H@3<<9I^kEkv#y4e-oeM*9rm&TZcF<kP0d@OhMe0xjAJP+Ox$hfT*3!{3hh3o(
zW<0clFg>~A<4^PPa8k%&#j8bM9D?LJEGWN(MDkgJ3WM*k8{dpNX%Vii1Xwd%BBHnR
z^F}=8Lxdbvd$cOq8h)9XtUj%mXx=9z0Q-1^A5=CC<RP?=!Y6xEPb-~oKUPwj;T3Xz
zmZjq1LxJnLK2T~k<=r}6o!<{B?1<7C54whX?(U*g?t3~OK95<&u}X2EtxHqr`z%c!
zE)o0sM;|-13~XHO!}hYWvUR?AH;`)q)7GRD_3qs}-MDtu9Ie6(2H=p|4uQ(yX>b6J
zuJk9=Ssu$szJ$3p;l1B)xFVp8I~QdBO7J-G17d~*X+vgyghRl-E-B$JuoxxFso1!5
zayWiE35C&KyEaS3G<T?aZ3!~q8Y5(Ae}F8{fe1koS2VA=KeQ7N2EXH7R%!sv20+Df
zIvPx7yo7*G;CVhnj{qpG78<H|#@#8U$JiZ0DFESgpjz(jR-Xm}sg{uTB!d{j9NAyn
zm62=OuIm_3w`MzcoR*-dStq3TkA8i^k}4LzWEZr!Q13@zEy5rlyEf?j;GCTp`%`=)
z1LM;(uRBRN+D<W1Mx$l7sT_ZwSsp693}h!$`b$6G2Yh$$vT$=NRjk!sKKp7oveSqV
zz6K#eDcV87m5-2l@L;1mVy_Rlq}zod=-ZZK!hi46V5Lj3bdx_inbE)cUa<|Jk88vJ
zL@Y;?^0{BWd}%fxC^U^+*<R|EstV$Mls@bL-%E1VW!M5LB@wFr54|Cypuo|$9~&B~
ztJ)546%M-1#wN$kZ9RRzcxHP~*kSr-6t{gV8Ssj6aaqRXUW`U{P$G;h%5Ta>G#juH
z%OV?)0Ss026p^?ulI6b^Ln+b)7#qD<0dbV3U0v0BPz4DU->)TX${EO;n>+hkyW<|M
z+b4hrH&y{O+6)zUEm8D#!_G7HZ_Xun9GHnD*StP=VY*1G!m)FFVm^@-Ek<)Ti9;*)
z6o<EH^(7FKgen<@bhS5WnR|b{_Uvj2WB5Y_i->3{IYB5eDWzNooCyd&{>TLY*qd2Z
z7en`>b!mZ0n>l$N(?0CapFi6h{CvR;b@YK?!7Ji-O>I?fhr(JZfc~&9UR=KmVG6B^
zfZMp+?mlPeWCpa!3c(zeL(TnGn9L67CAsosfLahe=TYw{g^C={M9@0B#S3x42BBWH
z=z9*Dc_%U=ja9t!B2zI!RUz0m2s(GB>^or*5#CUXbXUhJk#0FQqP3;vuB&Usu<XF$
z*1|a`u+W6<3)5%OKm3S-?*q+&lO_qCq72kYN?koY1cJeGU4tsO?Qma0!o5MA=lo|t
zfr)_o?X@wb1IgY(CqN@2LJ3`!BH{G(^zV^Uo2Nx)*AV`=$@h4vUF`JyqnAvNAfx!x
zIh^_a{CB$ecQ_j}TZN|m*S%gI3OcTRIz4MY37Zw!T|DkyPJM%^azLso*<u{9&%D|$
zKom?MUIC(y*tCm8M_dziElJsYhV>el<R!8kmZ~FRKsbAQQ^99YfcT|S<>OE&D7C!>
zuHxdJ4_Casy(hyIL~8&U=_d}D6w=<fan9p-n*<?ZA(n0k3u~}j@{nm3Z9*6t3<7=@
zVk?G<FSkBa-`J?zlcxBz*n$#DvJd3Ar&%hD{+_^+XKI%Bt$bYW%^)F^v{D-f7Qn!o
zkDdJr0F`@yya^?1W?CaT^_y{@6+u^W#q#rKhliAcw%=iStfrgsM#}6i|3!@Q@sU8~
zZ-Csv(u$;=#&M6+tgQ8;w1W5~iD@xt2$fPEo<Y5%5C|1ZBT$TL!mGF|h4mUx)eRvb
za@3<7EfKS!f{PHVtdDttP=aNZf>2!F>Q(@{?gUu63l9YKtGQ!-p}upRpYDSe;0y}x
zO*)+U$q|LzIwU|G)T87FL=sX`2LjPue&*BaMLXt$dCA#gpPsQ2JGGAjg3#QwmP%4A
zCf%A`YTrZ;7q^yOz$SeKJ+clky8O2DxNQky!H}!UHQb@cvOa*z0$wYQp~lDegi0YH
zEj>kjy64k$L$$Z*ZG2Pn;cE4&t(L7VPnkX1U+naV0J&{vXTi|0FsqdjfgUJ0_xAw`
zKTHs%yi+53|G@)sU^i!uAV<S4U4V>z4{8}4tO(X;o(REe$QjO|O$B(z`l;&Er=*Mh
zIl)Mu3SYDd`jm^PfjooDC#$C??Z(*2ca4Rm{hCkXT{mPZwz_2bZDz6EPmdjVjJxW`
zY4Gs&aeQuKMu8Hb2MGpsNpK%BkKLX@M+n?Bb-T!J?CAK2h+A(bfjtmpL7hp`Q*A=4
zAFf`D60k`bzEI8ob!mApAF$b3G9IH>zP?yKksF8@0L`oT=Ik##sGWe${^WW0{hJn}
zWiu}|n}R6AmJYW2wa=iSLFCx@6f`OND2tynPQV5ecoZn`7S%=oBOSP;hHn;`0Igh$
z^fAs`Mg?YrT`M|laLj~@EJhbQb4GwR`9l{KTWXIu&Bb9|zFcQsJnHRnyrjK%wAoIn
z6|EMl#~>hZje!2)cahzZH*kx>Yv0d(6trXR>gwWYT4XUcHg0ZiMxlVOYFIjW{P^+O
zpkcyi&{0aDS;i$M_J*UE1g#IEjfH&pFm3Hc{OCPusM<pe63HvbIElIq{!sPAp*>u0
zB)3Ee{VPj*`*R6rP!K$k+sh-R1jsEzj!^^#OZa%JJ367nEcYqG4q{UZ62b>lBNZ`2
zg$<jG=YF?28$j$!VJ&phS7v8sCWbAmC849lqol-vBnFKczeeK|-7h=^Arbs<aghy0
zBo$#$^o$0+(SR6%AQnW@fz#a-#cimO-K5lDP7(trnpLxmuwC?!)uJ5DixhH%jDZS!
zTf}^{uL@4=OMdO86E?H{Y|Rbnau61s3(Sm@cITXQii*s;zFY*F<gGN7lSI(B<AbFf
zO?Fez8BUIzP<NmX2GBX2(@cI4e#>b?AJA9U+vAN^jt$``LA$S-)~aY|WnS{513nf!
z78_XwJg(JCgjIpI(WrDL0tg2!%X<&gj!$<(+^4)Q62vyXkBbXL6rdJ{1PSPsk=22-
zk4-H)f;Kde*$L5PvY@73vmEEa-Ax!n&jj%eUqC@4i}WL)`vgF|(6X{(12-W7yTWNZ
z|0q+da%jrul?f=LEMjkfE68#=mXJ6jnd-9tn5i>ITg)lxfZBbpX6ayLG*<f<<qaH~
zFLVY%Zu63FFA>!PlR06l`F2U|+w;32f{0+!s-C5?4$DyP+973Rl!k<1K;{x;p^mK~
z+y`R+cmY0L321<}fmTdR`{Y;euainY87vw=9t^E+fZXJF{c4`$q7d!GU-<e>4i1Ix
zBETaWkTP2riUxo>^KWjxU%YMb;svpv(o(L?`lMv;pC(&cO3EK#M`>Vh<j%?{%hW!o
zV*Z-Jy(UCSQh{9s!S4GrB&pl6!cLEQOnRElDFGejs>>gDL@#uw1^~&|31W9@YmRzx
z+suyHaFHCO{yGEfHy5tjAFN&8u1f*2ibg(`pXMu&I$wf7XA#tR0Vg`SD6pVW0P?R~
zQynBHP4uN-{=@9Nr^l-vZ(H=OOCi{8;<B|ztAd8)P0Ix~fHS>_O0-80S+stg>e%?i
zZRooF{&uBUJ3>~>ezO>^TS!byoSsX#0V)yuI3Pbwz5IPRx-){%XD@`=mE>8B3YMOn
zyKse{m0PzStGu046}FeY^@YCv9xHuTTf7L%QnZLmQMWa8_Jn{zXnA;)m&Yp02bMN>
zXiUdqlf)gz=N^geb@rMr59D6yuXf%xDRxU=87&ubYu15aip%Pb5v{5|OnF^&x@E$V
zb@kb%{UIW07jeulZ`Q{6WDY)z;gv(-(uet7RlkwQ6$oNw;z_N+Em8>!i|l?o=g=>p
zzs3$<-GNTsYJbDPXX$bO_G}ESnbvW#+wbqMqXorK^?3%Z%Y8E$=X3ZtFVc7=GA06u
z&x1uYT%L{j^}?!O(*c$Wc<w9JbS7xRv$)VK*zcb=-zG83iZdct2#Ny;R+3c>VkJb=
zxN&Ax#QXQx^#&pL<u2vk{8UiEu2J&)TM#fGM%hz9O695V01IV3HrWthaA9aL!*zc{
zo<L-g*J?Lf)UB*eu&1d;Xte_2UMNq-A5o_+8bC#oH77UrQ^KP0zDk`fpkkSDfR-!A
zqNzu+1V^<&E3pq}+VC!P3zD;@=6uqSZO)@OfwL_(khSsbCrOwyNpRbV%G5r7dko6#
z#wsGtJ@Yw`J}Z6LseU5ACLhfslP$)=(y9}+x951~4k%&8G8o%C6e7+&hMdORAeyz7
z$tQ@S3r3*5n_OQ<-#31+Mx5F{A?Nw%c&f5o6_id2D%qQZ()I=NmWO*MD>0ZuC*2v9
z1$2&;qoSZg0uOYXZ8hWkD&%H;%IuQ_=*)$HxAZ+Y_c#adc0Ap}D^Csqh-Csu{a_0S
ziHu}GsI}_zXK+FFu`kK4wUdO?_4YD}KcX)#lt45MXD@$XQj#z+`3j6Ga+r~mh44OY
zd{GZ+MMOp?oPkSVLiqy76fnQQc2TQR_s5SPq1B%K=}OYmGczqt03O3z5w%Q*!pX@g
z5eh974x&le9a;tkX-0oqLP@-9*RJV8{YaFH;mwPOjpWY+uMfBVaCe3(2NWN=rTnL?
zlc164g83m4u$I<M$Jd^n)X4Fh$qh?L;J895NVigTXz}a?EQ9=RNY3^`w{+uY16#iv
z(D~X^?gQ;41LEx^$V$Zmw{Jf=0_>Cs5V+Qimn3}(xLJENAXdIya0`UQ$Fsvd!NL5J
zoa_g*if?LyyBgr$0X8wvyUR)6NhN*c)bj=|26@p_R$}B9|K!Fveyha$6wqLQ*hJbg
zVm388gl#Oo3EjjI6b2+CoMpWKm?%gRvf<Aty<!o}H+NmvYA$e5N%yq!Gb^bLHEYK5
zzQM%72T)!Lm<S(sD-Y+_i+p~UolKxF3o_>bLpZ1f3yl>82r`k9kukq$i)DfkehXd;
z<ffrx!~2Ai$Wkk$a9>a^&Pk#+FDO#iS^Sb7p}3PQP_q=!ZbJ?MqoEOtx(@WZvi=EJ
zC|F*3cB*A(suliNaU>P8&*Xpk`&GZ6G`R=8BR4m<q_Xk{)OCP;Qw6c+bS|*SqkZWo
zRt>$q*Tx#B{o%@B()cEO3x`U0V1~>2rei>z>P0Qf1B%7dBsKhPVAVorSe|^PBW8Vi
z9a0SI#^&bRL`Eb4DEMFA0S-zl0y?|Zkw$HNH+_-Az2sy|GOR;|_8sefl72lTE3wWs
zG&KbrAMPUlm1~yr@<IH-a6T=0`8kDJJK$brT((p)!Cw*IJW=N}<1-9OPNHmFJpIBI
zYSHCyRQZFfWV~jKtpa3GPa)QmRC{|U1EG}{U`=B5C*{)jW!(jdk)O36lJS{4!t3<}
z^8KkdYcdQUF5)#7acD2LUK|JII5pQqr%+82j-!SJ@G_M%2QPkpoLCnW7O;Ov9|kdD
z^|T#?EQ3<G2^R_<EwdZv%(bn}A~VaJiR87iG|N@`LZYJ=L1PpU;Nm=Y2Wp+OM$@Oe
zZ>BfE9bhFWZVfObZv=V~^sIrtzV9U>W-OFEK_{<jwuV&{h2zek;mpy-OsgrGla6`{
z+=t|Bcm%ICNouO|);tZ$7fwI1sgvcf<S|Azq)d6~$jdbKug;v)e<mH7H9S-dUEP&P
z4vTV10c(kD)z&P^o|YS^=(YuDHb~N&NG@t=yT%Vw2s=LNL(g-a0=`yht43&?GA+05
zC*`~al?cHNpI2Y|_Ob}oTl1-$^Eps`!S8{8rzTjnO-ezXmAF`jtq``V{2^)qPe})8
z%#DG<(IH@~z^X8E;`>+)zXB;x^Zj#sz^u=>vZ*Q$NOd*V*AI-Eq}|#UtpY2LW~27w
zKe3?pK>`5JPJ)WsP^+f4Mlf1Qdw(LQGaociE^lxJ9o)pEP3(FF#VHTy&HV{pfMS}9
zTXZsWMs)A<_b5ga6gB(dgva%Tg@@m4Ei~?KoDqbWo{5b1#vvDEO_KIhUH$0ASh5<I
z)dt<Ex>ng3IJPubI10hDaE9<5Eh8)N{MqHu&$Dxc+cbvhHEA-8_Ps_Ls3xoV&uftn
z*>Z$dN|Nr2EXp?f+xV>@^(FEYDxrTN+aldK2!F8=vT}0aGnd9gYk3b?fU55{=Y~9j
zx(+!)SzoKrq<7jG#6CW`;pgg&iVRf+1tI19A_j-FV~D!JwrXCUMsQ}O>N}_;mU)>;
zF7`u1yMcUTFH2mlhRq?VQ@L4jv^k;YZEFACLU7qVMd5<5bLF%+WB4WwO^vx57!lrU
zAH`J;6&T+D)p<rS_T>H=vp=n65&;zzJ3xx1g3Lj)i1pI4v&~X*1=?`96m*$cxw*S1
zPQZwx+n4#wy%HTl>>T%J@7Cz<8u7*$c(nJrwV3y)HWE@fEe4C`#k;v?xsLL&p%2@Y
zvWH}``rWK5i#T8fcQ!!<SRBQ4swrZF=-5@qmXJ7kfx-v`=Y)V}EW?zu>GxM>*yqH6
z#IrsCvcTcl4j^G$!BvQN>x1M-E$e3r%F2Xg4y#vC{H}Z7T2pIX>PR>PNjc9#{GCNT
zj2FHJa2V-IbaiR5F?Ul}pwkZY@;VPCi@<KFPf|j{Yc$Lge0f(t{Z20}N^KJwiid5b
z0l61_P@yv{Z%D>xfsHD$p7B?hdN~VdV#6^if=!)xUme&D1dO9F6fE6O_UCc|6dVEX
zWP#MCk6Z?qP@j-)X{B^dIEYC|NWEx+KtcF5w3Fk;gC9N+3TP%t;RwV5oq1L42p9Eo
zYhDqt1j5e-5j0z{0#31@E?fce8s$&11gzbCIXMgzIM;B%XW>wevuFTxiw<ATpTM{b
z*BmIbW6^R!-$wXM*k;N2WfAa5p}vFJ2ffbgG|UeELhIujQt$2nQ>+=0^ElZ*mi?&u
z0btZNJcXgGZ&2iaEI?xz9KmMpy6pshy*FUnIfnxFU~bLBWq)I)v)sWF^&J4fh-mV0
z%?0XuD8XQWcnzKVB+yj|Uky#y%~rsd=MYCHtc&4jk))-6;&afZ?%%(E2gDBGpMBvx
zbQ?R_4I3C5mS>3pjF9*7_CBDa&jRa+mg~Bta)t_y%g*w8S=Ak-$8JF^s@cu$l|Vay
zA@j$~oB<FnGkK5}ZPaqa4w{{}*UzO}E(RZ~e&j<xc|1%4>j$9aEDC9Xe5jnUpw=@X
zS`m1^TnNMXn3dB|->WR8B>uQ)Bzi7-Bame8V6|lGO5`RE=DC%!C3e(zXb?0bvYzFq
zbUQ8Te~i%g$x6CH=vumsp<Pc13<tDj!^h@8N~9^LhYlYAm#2x`HBcjUpl~9FmFd}8
z1R+)RP&#dsD4<H5x4*^~-K2AC>0`2?2i69qpZF-l%gc+_k84}jv{w7rzyN>{E{3XD
zi7T6CS;2h~uwBT=$b95BykgRmhIr<Z;7-$2vdEAQ6k4nSW3D-XmwB5fed?A@Jy&?4
zKMv~#10Ng|U}3G*j;m)7`01!fhymHL`PZ*-j{HGvsC>cP+oBmDHH``)jYFz7#EsnW
zC#{sbZ!qmQAIOrI1}OdcZjX~MfsZs8)?x}6p#~5#(QQw*_Xi7%m9k=JA*kT~tert1
zv}nyB$O3W6ezONnPED<?|11lgQVccER4~WmXuXNH^=lY5ae!p^LC@UVB)Ydbq6l}-
zmYU!+do6Valq?KXiR}VDXhC1oSAabd-e|tzh%W4Z|M8<Q6nKD{T`M1*z{R;76x;=Z
z^ly;!8UcR;RP_7ug|0Zk4YuM<i~iY19r7#S#k(i3nxXQGfs=?B0y0<=%z(TxR?wuo
z!chg`x(N=^I{0~}z&J$JyL1(*&)fa4?TQ}-@5Ux1tnO?+9%Zl!&)k%zeikh1{%Kq8
zIQO)t!aOH*qzbs)Nq842|I6zLgH&;_nBfRYGU7D`T>#YcUW-#aybw!pXi4-WrGW2e
zVx6OOtZPHItWSG5P%5c-;=Y<xc?3@ur`vX~s{N{JmfDPc6vk!Z-{Yqz4!M(giopXL
z+gKO1v+GO>iDf<rJcusMcRREf$TIkl&H^+6qOd}4cpn<-2>@AVzT-2%j2ftDis_GU
zW<JeLY_?w1PdvCB#n9uI2n@(~gF$WgeT+MI#dE>fc4cWzc_PGcv8*S_Lqm~6X;1dC
zvGENDtD&DCInPx(Z;LxSm(A|^hyC6z(hQ;ypiN!sNuMe@g0DSo3)U+1Z2IsF6djJd
z%m{lMdU|>(d6xsdc)kRI&dyHjq24sM+uNc!z(9oTR~-l!2To(Y0@xvNyq*F1Hg5!o
zxo4SnZ@s*J1H5a5zcKZj7OLf%4RWpWiW@?Tf^TyUp=lw-Pl4QBG8gZ{!{!2X`&kq)
z+$;89Hi40!v7OiyZ~yqm;$dXOxB@4)0`N2l_$9G(7GDgFKN(hkqdU0$YWJe9;Zdjb
zPEjzmubYy`ikQeyD`jOd=vgau5YRaJgYUp#ikN0e1+3|;ZEQHLCMAFaI0t%Lhh3aW
zQG0K#SP21M_|0UFP05BAw@9ZA!XYO^D&gqdhJsi#<0`5666k!?OBcT!7D&9-2hge@
z#{sjd%&eb;6UZ+G`xP_o9dI0S18v)4TnYJbx;X@ON$Qj9<}W_LtrTF(d|s&bnbDtO
zCeL->I(0j(+BKfvsusQTK?U6Hd95L$YSjY+>@3-jK6_?UHX&pj<bsd4O^JcOo1U>$
z5MDnIw~zku)so<I)z%;ZG#%$r!0!&-d{6NV44$Y@z~x<;g0c;rTeOt&pw?UI5hPLH
zso9^hka#j-FgZ09K=(uzIs62Sq%DFkev+av=5|4+UIXRiM`!t>O>e|~On-*}Ov~XJ
zID6J=Zc8X>FWf&oJmJWdTHe~u#=a^D5*x>;vT}PHNfG8eI?K7oz~~8N$)}VjjxiL`
zeU9c3Z3lavWn`wq_PLJ-0jF>U|0A)D9w3ls3ecpCI9=V{oseM={T&$VTA*7+783^W
zCqRl@*|3uCIqc{RCU<W5@&xv+6Iw#J&A7hKxi4`C9$kQUg%2uN+fJRoJGkjv9_+my
z;@fUIIk#oX;bET8X;cr^RzCzoLAwP;>B!<8GX9sJM#BZ2p)m}(@ou{$5C>23lP)-6
z-jQ;O%ZmY_*!<%MbFLjpRoopNpR3dn-3^qQ7Y2eC^F~R!qQF`QkR6&h(mfNNoH0(2
zZdzUHd%=qg9nkw5(&Y}TD_ij@P_6DsNnxU%cP2_Apx{xty*V)Cb<b}N1Ue3sJLIIG
z_n`$H4SIO##iEO+=>Bw=DEV!CO3FY4fmU(2GQ~L8=SCcg2<plzI{-&JV-I%K)(~Iw
zp@Q6)R4SQybM*myDnzw$j6(FqzuN$Ywd&JIs3WNg35~#lC<0<p&epgF$uew7g%AS(
zjUdnfdcc|QI>uC(li1PrjW!P$v_`Z{CnpVC3=H)TirgA4jA2=nFqJvEm0V@(baJ#G
zZE?H-U1zJYbn#cz)~T?=?%*_N)>`92s&T@sE2Zhhh+qW8gx`6K5eQ$a<pIt}KUL`-
zu@%HcDc=f2-Vc_Si+x#3Tf%)e=oJuVz*sv_1^dNHo89&)opiDJ28Q-_Naey*WM7rB
z@rcJOcl3<BjCn%M^Y4)9W)2|%E>>Mp7o&XXu}y@hcuNm944{viXvr+libh~^ot2eR
z0FMd?ii9qsHnTr-%cm87>E<F(WTUeDf<_7cwQEe$f)=2pAoPqHWa``v*;ZJkfXcSe
zcQRP75oI0PZ8$RnB~~{;L_$ncUOqlx^C5wiwj1Dl!eDK-``%_6*o%)qCUx&44#<Or
z)H5h#)&l}NHUh_f5${$!9c?eP<cRMh^2Qf0+>d;g@vhoHgO~3YxeTBA4rpo43LE$k
z>mIN2=_o+~l|350qE2r}HNdA>m5+hDOAlg5@+3YchDF-D$h5!BT;y$})fOtdt4nHS
z^89sbGeQ!Qxix&Z-yT=R5nle4=Y>uppJMdmYIiq*O7Qcg&H&x;76j@ujC-_Nj{5m!
zZt8_5PxPJG(6bMKvz`|D(c6Nl9pLNxKEW8FN#=5IAn~41I59CHzj`}!ZmzUJw+3c=
zP=xQ+y15QSSy*K0T~p-az8Y7yrA1nHeeqMZTbO|ShuCXC&-DV|3{os5S4grN{x}WC
zT{q>*50}5neY|y7epqVdkeNOUT93C-9N@0RrY&qmiJ;(r=wAF1dIK(NP3I1ZUWIr~
zq-(GAPD-$L^g&9p+v%Bu!bl-!^Klpy3Ig<5_OK+wV_>u4UPq5AD2P4xcMtr0&w79Q
z6b550(1yc68rGYd>tNSo1UjD{T(JfMfPxSjK?P8VpzZv1lrLD!hkETmiF&30@<)gr
zB`^_f7p}wGpXTZNHUIocD2}C*`?MZFjU+Jr22nsV@xN2o><1{QX)%R#=zWI(OAxl~
z5{=g*LdjS$53Q}GeucKHI5<SWZG%n)UBh`4a0v?hn%eH}Rhg#XW)cltfWd-rWK6_@
z&k~q=>oL$416X5#k(*NmGa)2G%@rG%#sS5w9++iGef=+*nMKFRJ5a+0!xz9a&Tqf`
z5vBt2svwm8fWGqwnUf^F4qDUE6DnYdP}h+n89C#M40J?K+0H`g#Y0IzpIHyeRYS6D
zq!*Aj;#lAoW`XG+i5#uUX=i|b+FyczPaZ%AgE8Yipa)4%Lun*xN+Uu;Y26PN7&_r4
zr3q#TMII>v6j7@qQs({;n$N0#0c1s=6a%oRvv7ggv=aB@vVkhMa^!?ypul`oMZ5H<
zX=;A&r9|aV=-eNeBft}j2J;Gq5s4w-5ZuJy6=hDFDPV>nbQraZOoQsjTKv4`tQ`7_
zLpktwL&w2zijpBghqlIxx~YL=+GRm`;VKm?zE7DZ0vN2T^6hQr-jZm6yZ}w%NQ4|D
z{Uve%YyI3l!%gVNhRsi5w5$Ojt|V*-az%v5OK{!ryG4qfHv+z^nLMpZ(RTCGHz?n0
z+%JsA*wtMG4^I0*onD{tq4tLY9{>s-q9#BAhY#SFS5P1UUXutKt$fp%B<WxiwOuHo
z?2!Ua8((HWj)L<d)LacJ0FW;@Ig2g;0}WzfEAXLMK#B)C5|MaTY{u=g51CWoMSkFM
z^ab~Z@6VqP%TE=6Dw+iGXcA5oJ}~`U*>LkhS(Z@I5Ypev+uI+MOcJ5&$&O8m-i-E;
zs3@i-Fi!>`>)%?|<3@v;>{Zn!0v9a_Ag(-QD2vZ>j1akH#MiI~7WoQ)?VOV|Ew`ht
z9zGne<5WYJ(xrf<T(_e3NHAdg0;3gBeR`tqPCRf>0$4h7>1l;yZY!E22$&iLs@9?t
zMm)u5U_yQ`=)9E%4S3#RN8iQ<eT|Sw4`cB*d7zXPc%FPXUAp%1#C-u<nv7)wcO{S|
z4fGKeCqhQGQS%tRud}Pz_`#9~qY*CtKRp(bBS*4_^|=Po9MW;(G+s!`okd^lJHPs#
z=H7CU_ArU2Y7V?~sQ0t{P`S%nAD;lQCHmwJ;HRu0lnQg+GO!37bB6l*B_p$NEU-|C
zARb+ryGq8>oOH^Po}Mnb2u_BmSW8*rvLr33<C%L|e*{Jk=wypI8-Z~IOTmv_=xBVr
zRjhx;*yD*4%KR>K1|F|ZhjMY}S4#_%N_lBMkO9wp*gj9Z6LdO%r@NFASXm%TroL6-
z-H>v$)dCllGW}W2&Z*4`KxG@N&<gdA#3Q{_DTo^bzczWOP`YcsYohzn8RsoQ$r2u0
zh0Gb)eE@TIk}%A~V}1(edN(Si-IdXROx5Eg@d;IEJteiY;=9p@(_sqwgHWKH`UbSR
zAO!N;@t`I9LTSF_4tO)*1HwcJ3o1wjQ=y+6iO+XS_vHs`s~x0>pKnAYgpuur?E%s4
ztG+8*fD8IZ<~F_19%=cj@)8mT7?z#5Ul64oI1FskTX&S4z#Yyv=6DXGlXXWBafc{~
z?$<9Ml;jiuy0TFPi;3E|8#L$JK;vN5svt$CBiWa>3?4#9ih%uBo(#P(Ud6BBez3K$
z5xM|*bXX*i5{1yHTk%16YHmLBxk8y&UeFT-Ku{0j_zJbRvP#;j$im`hiAB|k);i3q
zRPW#(>d<9K>yCJIk;#p^eGo4tKxQB9(<>KN`eh&E1y%w8T|$~FSg%1<Kr!-HQAFJ~
zji|_M4~I#`-;fjUg5@BtQ7Qk0T)8a0W-c3;N+?q!2_n6DyYLOcW`cM`1vI5WcR-|p
z@>a4+r;boQ^jX5AaiC1gTMaUvWOzeEnp(`2TPO?olv2z7Yx3xb@n~70>$jML30pNI
zf#S{0%{VP@n16ZbM1I}@ICn2d*(T2KV~Z2!_$)@=pEg12wcLG9=>VX7#*G6QuyZ*p
z-GUY){BRV?2U-vPBivR@%99ck5vy*ATUr;u!~n1hf4Kkftw4Fd5qL+z?YcsH^&V?h
zg^2VvGFp|ID=@?XW(Epg^C2(rBP28?r#Mg#4vV@QFUDZjo^<C|j;z}s0vq@SD6(R=
zQlJ?ofJcZvbX6OLmRgi`4LG;1LJQi&S5xChT;X2O1_eNl4>)rU(-+i8f6)DOxS}M$
zZbS-)7dc6FvY2lf#~6QtqUsL>jvwrR4iA|m7$cV1@7gzZYZseC`M(Jq69X`9tU!T6
z^(TasN`e@JQ(7suDQeV5J`zgimP?#M&SPJywp|DXM0S>u^sZ4k;g3e$TwkbO{@~6H
z0F4;AJkUc808*w6907&e3q7c%2f5{Ef|&N!eCIvz_gqTqL_nz|ghm5=WCQ5i0jvbr
zHyrcx^F_#dpS!rhAkUR0upAP=L<2r-4dN*wflP})T3ui}biuhO$-eCX0=fwD_e20y
zwMIPtV3_UvboU&hv?KGAK1tN%JVpR5XzqEJWU9LfoB;aJh!BC4=m-Z9K@mtPhi`+J
zwUKrMB<No=J3Tqt+9+I(iZ#y^a}^G5__Sh!<3TC4DP=-Q=<me~jJu`4EQ&`!a05yu
zBiQt>w;L;12z-*X8j>`JdrO4Unh2x{a!(N0zR1cW&bHFRdpbosc70$u*H#^KUSd0Q
zhsSbk#Gte`>0DtIKp}MA7_z6xiXbT$uoQxum_eyQFcP#Cr@n~1>@w`X>2w0}Nb>-n
z*}(60!D^Vl^J#K{(cL}pVb+6xiypYaKU<2t85{F`+JXB&UVt}1z>t8~0T&2TA~>VA
zLeWDZp=31~Gs!bH<FKEA?fnsrP5G;`3{uS?IszcRhbNw3SNSsma}4sj5;E!oPJNl4
z%x6kL@)b~b8h~L%RJ0p_>oU_TA9Q1BS+*%w9#TN_Lkvk2ALy&%@=?hNmWdvivag4T
z{i9rHI*i^t^Z{%|3<aBj+I8(U(va1iCZEjTSQ>01NwnRPGe4_yy3nbmCbhPh>n2V(
za(1?SwTb}f5`0ch&f4d<{)9h7fgg;g7rS{)Msphm32PVbB>DMzKJmpBJ&B6(90Ih;
zPW{mu%nRiVdGPdr@B%<<Ek0R*OMa}-*nky<kOHQ0!RZ0kSs1wXg%Y(VL`O?oJ;_D}
zK5IrJ;D{B+@MpkAvSz~rub;k*OxT*3Fe3yZ_=@(0z$8+w_vax7o<Cqz=Q$4<Cmelb
zI(2*kJlY{`u$UK6dmui-IXtGo!3#P4>nR6!ZvvMiirXCH;{&&*xR4KaMu3JFfMRL@
zSJ}$yOJCmy7-MfDD{5W-^XP;9tJ44Z3s}GZYYR5T|MKT2z?%H^^FPm?{jah8fB5MT
zY1SuKUR4;vd`ny$x}OvpNm%<cDA2)h#rJMgX)|h938TPE31<6!0kUHV|0{4&)TN+w
zWXMVSOEt&=aNbdO<mIox0BXXGF70m#0W>r;zCl5lC_v=Zr>mP7@c}i+0}C+k)vV5g
zfbJe7<zX<PfsKQMj2NJ<b8uXJkw4gux4;41GvqSVEAZOEdy&t7E=B^pH#ksGC~znO
zymSw`?~#nwfMi1p18zrT=O7|<U^wxNq~g)Z3_l&61bsMg^@Nh^rovUvhec8DmX_L&
zR_orsfB$Ku5pK6MFnOY49Rrd_ai?zXc>~Y>A~$j0Pvbs!UY=Al1<dJwQ?%6nwsJ42
zlXPp2_SL8H;2L-Kufa+gy<{JTpJr>6KHeVW@Oq8}@&n4T#Ml208!-NFcqqp|F(vW;
zwV=AgA&v#6gb5(|2Z}AJk@g-omB(U)^z>x6`gSkOX?a8Gnu6qsjOQZWnu&zdBL-w}
z8sJ#Y<fg&szh6$77emg|0iGX$So{KxG!+TuTB*g{DWD<EXYl}m3d|{lU}LC(@&krI
zmCOOxXMF!2j&RMpHUXGPoyf$y4is8MwP*~c&T$$9`u-iY7?pqG@ATC8T5$g$oIX?-
z#6nrG?Q#4TnkqsTcI`g7e{aP=y@SpX67gN|KNACpx`#{+U%Qs>GzbEHjY%P53?aSs
z993IahX+~=l&RlfS$G5N9@v@w$mtqYnKA#y%#Wf^GHb+<xMx8~I*)?kukz+2zzRo8
z+NWnx!1^Th^!(@A%CiI&h#9iA^&G0_>l1M30H1UY1p<<i$s+wflPR>!n-`H%c@70g
z0!}k!gkM0wIaDn8LI0}{Ya2harw%69SEo1CDB%$?C=?9h!l_GbQPb`daoI70k)7s=
z3Ph#?3ACRuEawBuZuPx%Fe$metKWsuc4n2#`PEytB>4EWsdDs6k1Uc7j{N=oL+7+_
zDO&1*Des8XZ}nPW<0oNx9PiWP9Wr9#7rUpopQZ(OgQ4@2FiGzrCuy)!q4mrUj2T{H
zQIdQ2?j4f`S`waU5Q^_{l8s~S*eH&AT*kq{`C+cSTDP|L5^{~06g&V%SrB8qSnBG`
z+}t_Fd6+0%Ib!xt6aWjvC?Mfla0pPg1q24NJ<%3?pu}2*3(rW|8aBE`x-8gT(`>|J
zA{u$_$v5g$VUjoZZqa|cyxC+G9c9`0*|cUI)&DXHPcB2?G`E88!`pW3_<+|rtgh@4
z-ZqMggw@#zzP`;NzgJsIb(Ve(t%NW6_L_uu$Gdj!{1vTjbcP1HN0#cxl-}%<v`M*{
zn^PxOz|!jLo7Mx{qBU&r5a#CPTfw3ObBhDc8v(iTcGWOGjyyS{&I#<*fU!F$A~}@P
z9^Iuc=<e$B+=xWOtkgNi`#;9Pa@gKE&jF3}m=;3xL7RePL(n+^wp>8zzjy&2p+f=M
z<Mj3hV0th|XNr(a?B{m|1=b%Yme}XITv52-QvGBW8vuED!$0!*W0+arFI@?4pWpiV
zTYwMJrY!l__6A@K1`RLM!zlX3gZ!;6TX;W`jluiYa+mOAh2szgeeVD<ha%Jv`3SSK
zbG}=MxCtl|bq9vKf$u*i3(l&9uzC>2mGC<Y)H>8}02%hqS4RN)o<$)9YjK(RMI32(
zEzkf9ie}DA3?YP)7k`}3QM+ZKJz;*S(VR7)$@mt%W3Q(b_19t<EW8iHX`<|J?8tkM
za@}frQceV=e$-m2^%ZfF7AqW=J|*7K`DSqgphWSHBat%*ytC}_K-_)?X1i5xAce`w
z{U@nN7>L%8!x4oucMb*9gNL-6HrCePfJa5$0igPSf9Yiu$eR36a=zIusenyac`R}h
zEar%_n)S4Otg=a3ar}_lqunBJP~*PmqiGYyvv1E!+@#gQ^&+ew4Czr*&ydb|K9ll+
zSn|k8HM4l}u2*93CCLtEJ`RxrBHXu(j|#6Br(O6ge#;XVKlB%U`R3u9FmrL<NVyl>
zKWDdHg&l>x<Dyn~x%0WpV|LrzQ}Z6f{x1TBnPmq}dKPemi}1XN!V2?0kHA8q(iDEa
zt6AFI-^x!oX5Bjdnj0m0vZn{bcf4DBIUdKRIM=woG?qa=afV?G9%%U3v?`v$^CPl3
z#ctfV2dwVZVar>j%GYtzHd6?H8I%A0v5N%UXL9H;&c4Q^&RO!lholBsj><34=rDsh
z7R2T_=bb-u`X=z`6CKO<eNQ{h{(kSBB_eol|57gmbWE=lJmm)J0Y8{1vpbjIL@+Wi
zFff~+`|CP)+(_S${C!CZ|5yL#kFXa1XC3|ui2sLmU~XGE+aYdm5u&I_RXBUDr6qLO
z?MSSlf$Jf+iCa{v;RBf#MN?`N>bL&4rFlHctS#v*H_HV@Ct)fX(3#VjSy+<#RD-9L
z3uV`&vW~-gJ1<q+6H)uA9kNHtGR-v6$%Kc8GqbS;s%dMf#vY_5;1}+k0cSnlS`mID
zi;X_T5xogIVNbEkS4|<*sfpmRR6FAi-0<`2n=aKIsXkYSG`(-Sj~c%6cFhSJH7F=?
zGicgsE(%7o*Wr(bKHeHX$5Bv@jY8pK-sAZ@@L%1-TRZ#LpZ_=hv0+dX72Q5#&BVr*
zHD>TYgFT8(S)m{q%$uX%cchf`c8UqEdE-UzI0|!?23iyg@x;Z(W<s+`p5Yg?IQ$VF
zo1k~IbI!h8Z-d{i^kj3?6l9ahN!`_~vIDDVAnU^Bbhqa^gQj>SYlv65)s~&dMb<Um
z1qWNMlP%A4s{!Y4=0Sy?7Zdnxxh|SCqt8!Lsg)y4r4Z1MLVa&x-#IZcOZKc8*uR%e
zQ}g_h_pf2{fWz_wAH!}QJ$LdqHP0Vf=-K5~xjkOC(0hU!B5P}SI@-9nJW%y?IMv=f
zcyun&xba8-CPUL9d%Mo&VD8lnZLgPH6^6>Eq4=m*VHjsDv9U1UFf%cwJ6yX#`ay9O
zzfDn1jhTp~*s&TO<PaLPx~fZl5L~pkx7YFH8W9l@4waI;^6}TvygaV`<6N63a^YKB
zTOC9RH*enL-1SM;A^mK_iHlV~T{^$+D6Wd$KXwP_?$25O%zn5x5Q)9TQ9?mb`n&#K
zjV$-x?D5rNg9H^7l_JN4&dJSvGbbtmk}SRc()n%YaL_wsAor?*6Dy$q(I``Dtgh)P
z3guNtTJd)r{eE=*|NIfw;{Pw}U}X>WFzZK<f`PJ%RM!5FY5gdRV!9Sa_J}{Jt`e*b
zx`9pD`5Nq}B#KMjx3Dq%hwq1azJ4__)0Eg3`Hmk$Sj<=J5QVCLb!^a7P@TzRf_4$6
za+39@lMzi&aIY3KadYFS$#^%-i)74KMl`=MUJ*|_?;9`7holvHOeUyG^*D$AJmphg
z?8$oJ@%qLFaV2hp)=iJX(YZifv6PI!2g72x4OA%9%c|XKwvJGd&O__so0@(5Jb^+Y
z-78!ulrQQ3$I{&YTd5Y(=c`d>W@cN^()dgU!Ic55o*iBxAkNh#=sUkUL`hS-DU0pZ
zT%|Sn2j{4{eP@w*edm*6>-Ng1&}5NZx7jzhZk`(!|HTWy%$5U$OrHRdFpuDofce;U
z9nTEodZ*njDz{ZMofnejw4mJm`H`<Dt-yi7+$_3<P+9hnrYU1<A$F2-vcLZIRpEWg
zC7tLL<k1&08g7^W`oF)w-21K`3}Q`sL4h)~x=K<~-lH>r{Tu2IPL@0N*a5=%hT8T%
zQEj>P?nNOYWF2qkc=Uq!n+08ifq?-N80W!*Z>l^DoPRIKBz0}F4<2+ul6t&)Q1hbC
zgUhOXlxuQX#CFPTMT~63!og_h*1L}*jf?k^OGYi!i#P`+MWhs90>HLb)V85x*k5z*
z?zT<mjCI3NDQU*=)40*5knX}+8_o#ty}Tv2;)6M}^5CSWxE9onT~<S6V__&%uAf7W
z;D?9^6S$GkF9GajX@h7tc-F<|h1yzi@U)u1JT^Sxu6hMJb<Cz+cz{z8NMZ!`V+N6$
zDd1B3G`1aFqQObP3WjVctA&N4?N$5oa0|UtHfXQzLJhEw#Crm+grY5S)49Y$FPj=x
zj_{n06i?O{q~7Q`XH$L>rN@t#*=ZJ+`Gbb)f3o~q|K6}-)48<$V`;m{u{xKK2WrFM
ziSSUT$Aj<VlWtP7nhCGb{CtUEhW=bLMMwHBu3r^(XW2JSA&~eGY$`Wp4CCw~Ko04i
zwukx%v!ZlhSY-h*!CNN{b_*s?yI_Vl0}Kd@;7jfS%^{PS@~^GC!*ch#x)1WexzG<W
zF~gbm;5jfAiGZ37Tzoq6+)8+m2awRVzrUZc0}7(e9+><znAP~U3B-9S{EPF_EjjMI
zS_NJAYVFsUMSNsmvlF@Hl-xa*H3AhI04RWD5K4%3UisNGV;jl7fPjF@$I5w$iM0_d
zw{+1OwsDT^jUA;r(J$WQWOaToB_)SqKA$>aPa&PVC2GeN8vsD-()qJ&TK8Dv$f2MX
z2?yIJ1i&BI#taL3?ARN-4xueETH{Z5)<oc(_;>O~5NeelJ-Qy;lmt|_WL5(BsF=aq
z3~g{9$aYIOjya{X(SmdPuA)RFB>lsYc-YJ;QWI=!Y~f@BV@7G0904f2IUM!_RVzFq
zf_`C-`UdO?ud03}=(NB;KL~|kA+w8jG&l*@s9@-%uXO&aaEe6CVs5l?bW!XQRWQR=
zQ_}dVSXF}F&z6_$QtHU;HBAv^lOnWO0lU}O>?>F4>F9d+NzenSxrDK|XE#lFUb3sD
zW<%&|)g8NgylIlTw<~%E_5J^1?>(cc%(iV&EXxdrszMM{$`VBZK_v+aXj!5Q1SDq=
z5hWuaAlcFqBcMwGNurXo<P3%qB<CzaNdl6Sti^)YCsJqcbJ~6P{d=w5+qP}*RthV8
z-~8qrp^rZL*qAKzTfr@$ReLBLlFnD_tsh(gEkTY-Bn6^pmJ_63XEu*L`*ri$FA13)
zRBo^B;)e$r!yWlLR7o+n<ulr4@nas(x2D2{h|*G7`+?e1K=bdJ8Gu_~4XAw&#f>&~
z*UVt~#TPcM7+u$Kt(%2Xiwb;ubnm|<l?ftA(a~ohuJICThH)G>r|GUzy}i8y8EsaN
zxkKD$S_VI6r><&PzA_W}tP*XJQ>RX~=Gez^o1$3krOm_6-00+QS0|hf4&ecVg1pLU
z^!$ebUlUF^zkCG=94o9vka<7m*1PuU){9&hvD1X`SrwWDteFTC5e`CCzY2<DAhAj7
zLHV?_y!?qFd&S7I#X3F&i&X&<mIyjb?lj^^Dk&%^giJ!(F&g=?8Fq8UflbR_XLg=h
zfBCOh_WkgG*<YiN<YG-;Q*__FJIuT3j|8{f>`~YG2EKXByJs^BqNj~>lvy7PEoCrR
zgPPN?H!vOlQMaJ#$+;BaShsI+@-AV`iV=>_OI19!5I0U<RJ568=`a8H#8Ll=ipc}*
zI)`}jdY=&UNyrdR3;cZW!PeNlG&E0L6n>RoQ~#7P{D+OrqLcWQY+WRH0!<zTb@%Sy
ze{32E16_Tvej;Dybe3JsofnzDIhFSQZI&lRY7CpQM-nqOwOcu!E$C3aTs))ZA@3-W
z+u<0;73C1Z*U*_6T$yOs9zC<|sa>+upsPX8kyT8u9g-*ShOydZ>&nW>#W|u_>VIQ7
z)7+Q%=df2~(O2uJ$!o_Zt{o!|7o3!gDHm?fA38u9_~7<7`@A35<7KZa(`=W2_+j+D
z-#FLbyMZ>=J#F*O@NgF9iwE9#&p!A?s2WbTtYoZUXxh1V+F2*BQ*-^A$Nwan*(rJN
z=@0yJDo-7)^fDR0c8p~w&ztk{^J=S@?(FgNFq@(?4skJuSloE?95nRWuEWVMv$8a0
zR<?1n3=9lV!;k;^9ILl=RJs-adGfFHETDo_OAv}cBx?)@yC()^t@q}4((ROZg6}7m
zUU*+>FBoa+>1Ju~@B6+yGYsxELNxE0x-=U?COFfU&mw>5z_tIbab0w3=MENl%Z8^_
z;x%2!BMny5T&3n*3$`Zh@~iZ!tyR`}R$O0SU(FinFlC^uTuXuqc#GUkB&>+Y%gaMp
zAtI7~D|Q9b^Kt$~&U93~1CKeSKr-Pu=#iT3$KD>4PkkfJ#9XHu&s@8C?T_3+i(Pjk
zq_bjG7uOjXdGF_m-I=)hhZTs>9Nho@=F$IV9lcsjYn#dn>t-YF&36K>HznHT)3Vu_
zR~t=CM@k&c5Zrtxgzwt0ghJe=n)6LI9C8NhTc-<j+>Y$6eBZYrbCc1)5lfa)3e)xG
zi%yMQ04_i5_K8O0D~FV87CK(pRIF*s_UB5<t7BauqL$hwcRWEJFacRd0{X=wC@1eg
zoAThu^6Q>Aq~LX+(O|!lZf`RP7*NAO_%MSKJ2qJz`qHAo=+J3D@ckXrAMC6C;Q}zF
zNhodGSld1R<+L>I5s@~8wwwm&u#R8$vU<?!LZA5hMnJLw63S<hF>&l!{?g;+_h}4w
zRPZCAM|%!#5KRWYQ0@MW(h7nh&~C&RkuD?Ir&q07RU@!!`CS)e@3&V=LaiC{5%qX8
z8cn4nr>akyfiCPS6}l4@ClEljh;#rxcC(#h(DFO4o0ueD&&)xPV-A7=#0NDlUApvT
zypS<$&4E_Y)(p!yknC+#M3=uP`t<!Zd6A}O=)|MVRe2<W<I<4zsTDM}9@}hHUETQH
zksLY5urzEC%&V^5^AG&AJ5BSo3#4Ko(P2lteWiH+b^;hv>mlF9fTC|EdRODYm5m7x
z{{vLELhk+S%w{O=lSWGI*t5&ue&s>*>O7z{P;z*IrD#C+qI~cXCDQK4jpn%D3CVB$
zh7BTUj09a>j{4KjKih*BWP<ji7Y@*o`qRX3-SS6y|H9@avuTG!IQmn*fbG}_Ev?^u
zT=MnXp+BF~>!*X#W0Z4S7Bp2(*5TCLlL=UUXP1fJnmnVT2Q|Q<&5t=0ZM*yhEo1dy
z5XAz-kx5r^%RYb5#MEo)<$p`m2@earpfJCCmHQ*pm0!wMb`&m5C%qWZvCdaR^)eT7
zbnW38?$dDwT54OnTyJkGZ7vius|d2n<fT5|Ls?O1;913%U!YtUVI}#pQHn{pr|#Vv
z)`}z_6<$}{Y;T{x>`{~4H^Xflk*;>5Y)69>6H}#8V5F?9>{o#>3SxXCs19`P!zF_Q
zQit^qi%ZOrEm>sSD$)SP?Sa_!SEP*k3or5q??|7yNxc_0%a;C(+vs)n`i{f9?@2Np
zXYA~pi;?ByBS51EyRhnxpMDw^SjlvS%DZWGT4s3Gr&WqSw5uqyj+!OAyjgj?9A}I1
z`M%&c@$Wl~=g%a$d-iQbv$!0kt-42)hx1)c*D*axnU6d47V-DeACql<25<N%Ncdh1
z{QWnr(Cz+3t>s_6_Rw@+HH!HB$&)9@BDmYHyklVzkT!h#SM>xs>5_A{FTe1P7VeQx
zJ$|ixr)}8*76Fesd9hoOXvj9R7hitK=2ZSo*PvxMTs?(}4J}<0mxFC&OlWAF_#Ynw
zk}SnU0)XY4UZVRPxPT6Z1c5cnA9KO4cf}~k(u;Ljai98F-mT3QSpLm(cU%K>R@Ta6
z1cX?YD~_*;1jGXdZI@s0>m{w7V`vmSB8(2n&aMfxFBzNAAcv9p-|qwL)fWResjuGj
zayj)ps-8k0e*AyG0MaZl?YxJAPKTUlZZlc|k=q<vK*rSv@#qORddP=(r04E7^e%t%
z@p2e5dF0OM7XOL12p+bGN%gN#06#<Ykf))UXg>b=JqNTqQQYRz9>I22h8n{1`>(6>
zvNOkme8nMg)uee285X1+1IQ7~w!4h{h8Hy->4kBhY21oAxMS&!hvZez;gnb@F~kqD
z#NMXHCk0zNR<Y@l9Lu_3F0zC4#_hpBZrr%R;WInjrm@rKG}dd&te!^Dak+c<Wy0X&
z8o(b<fIyBOH4Sp!jL&R^(ROpiJ6+B;>AP48%dS|uxrBP^9=SBIdT=+<jd{dC!&0~A
zyEu9k<8;MA+}jN7N@_TO(rAb(yqNPe`~p6EBQ$&n1Tt94m-HYjF>=Oh3@E;ul2T2V
zEz(6pfAg=jM``QQxXY5d5^UQnGY5z|>Y=@(cX8ujRS7U=G7G)bw=G@7b5`V>fl$MN
zMqWf}r9@oOu{vQ9NbZxYij=vK{URSewCd&wHC5FwxTgjXt>O$8Cj4!)%}(>+s!1=6
zJ-+6YE335~ef9O9PxY?v;6*LHp3^#SS7=ofoNUbV=f@*WW3e<Sf$EsVFDD*+`ID7L
zdHMP4QK!GiF5~5d?qHHX9oy-HjQa`dv4L!CY*9pl^xN1cxWsGC87%Lrcl#WB2~l@_
z)Kt}x>VyrMS#_G^fCJs8_rqq^0~2ZwQEtOZCBmAYLw6p!Z1UGSF0K0U`to3L73$R%
zWB-Q0S*)ZS6<$ht_j#|su~)e%#MAvTBoZ}x{@^~J;@v*QD*cDyG#(ds*FZMUtYX8D
zKR5%@vM<@=|K*8_f|9LmO68GmX8BMlJ(6#wR=|mvLLhdqF3JaHydmuLCSvN*G!kPM
z0*#}yP<%RvzT78IpGG5R9{?QIgzwbomuM({OSFkZP#h#a58P)Cl*bX}sT$3JUQyCL
zZUW{9HJr3@%=qAX$o?{#D|+DH_SAiM!xOg^f7~!<e80)F`}ggu#zKog0tE?uLtrS0
z$FQ>IkFB5h1qQ}~mEs0DZIB4hg*BO5b+UNPaR9ndQrcA~Y%VE<AM4pwh!7u692^2G
z%k~^Kl!CLscQ#+;4PORDG$jmL5e8)fD484pR3xg6P=|{G`LsSKIlwd!(u{$$+0i6W
zw9T-gC+1w?mxIt}C}j_xAV7+ZIxY>GSvdHrID6A?HwZ>!o4kMjUK=e0AmN6i-zTMU
z;fPIJ+(Jm!CXQCeM^kO=6)kMXxz}D>zEQ1As}OEe_!7f@w5|qF!vSu|2MWh@@JS2_
zIegWmkien(i+%jyQDu)sdS^Pst<dab23fHQI%ECno8+nxp+Fl}-F)ADaz@dB=!fOK
zv1)^=s%kj)L1AYSq=wre<dt5u4m8XRqVtCjA*El~etZak&RR6!_0G~ouynppp44j%
z!BpA5XVowPfrq@}zyT6l{7Ru!Si6TQN;6ccR}oEo5S9moB}XMfFLlBv7u~1AxK5?{
zaI{$)X(bC@FZ#9XyE{76i{1h%H@@vwNa(X?iJ<GqQTr-G;t&DFk!VrcJx|}MmvtB_
z*Gi13(M|*E6ruys9ML$Mw`#+|mDs~77!tzD#U=Re;1jUI;F<=Zr+JP5Hf3zAtfmkT
zp@yR@J(x3;r~(tp{O%lh!^A8gk{WR7r0bi6P^mx3gXW0`UI!A62nOXi<?C<rd;uYS
zyi)-((Q2qE;zE2S%qvPuYe6nTJi>!lpfc8ewN5?|eOBD2&|(qsOIjZg<f4*uhMwS~
zM*qR&LG-G;KaU?rCukyRU_w|!WL42usc{o!9r7p82#k7aID|$e`~!qdsiIaNqe0(4
zLxp*WA|3?BH38ZKi3m^K(BeEjGYS0}1Lcb=lGNNz$AURdRUOUBZeTzL?;*Dyl&Clk
zo<APeh}-i*QbYQ2;1WFb`ii9&lDSRMl4qYG1b)>k!(kS&kH1pTAd&z$j6+R@JN|1^
ztlJ7<8Py*;urm<<$%CWk47e&Cs!(odX{I5R;F$mJd8@ZC(wYjnDOj~Q$85Vw@zaan
zilxck0_Xh;JUZYSZ*EiUNYeQT3|6I|hVa}!{EkPC^{LKBr4<!wcoJyy2!|BH0Q&rz
z3ZbkcQM@8?{nNPLKNLmh$bJKNE8ZqGb#O8EMjzhX)S}REa4Qew$*Z?r4&FtPxYBhT
zW|E(eo(}BGYs+Q(n{ZkZn7Ki~&T1p*Sdn&5--i#++dF<=Zjo=UQWRxZSJyB<MS1xK
z?EWYiYek&<8a$k-y3hqx^03?|9*0JQVEENHL~R(xS}eOUa%M-2ih}qlVi17Ara?6v
zak<B1ZfurzgfYb3!18R?UVsVn<qmNkjPV)?<DF+-eUqi*xWMMM_2Nmec=nY;2)Q#X
zT7L4iF;Dou;+_a$;46OrWCa_VhO>;zuNk!Ze#LJmobz#<W22)@YH4JTLIQ<2dPFv7
zkai5BLnEO#wlztR{egO?eT*RgSM=aO@#Z-w^X6#PZ-uTl(HobfeF<~M2J#IJO#<Wh
zufc~4KTnCuG_V&W<<A6?8y0rX{~4!zpQNPZ*Sy#+VR7;7lHtrh3e|@<JU_(G_3{(I
zL>74rUm5x<cj?-mpHlPM=7NlEZ>AEX&oCk)!q8tFs+-|C9<A2)ot}n&Me_0Sy-Uo+
zURvEY>zpq!SuP}E`1VJ)i4q<nBlg3e*LQ-lQhgXy?hI~IWCmV|E>Q5IhG%v-$JGq|
zrP6fd4H7cp?J~j>dzT-xq0{T2&`_?x#GD}RrOP-jCq9}t$xT57YHG{56fWkN#n=B#
z8sDkmFy5IZj6Z5K4cW2JslT{?Y<&6hw|q2mZ<%#O<v=brU|@diw)7KjlKsKmnW!E`
z;)Z`>E&>S&|F&1n23q@Wqxq|A;}<Bkh?|}x_^s|%(`XlUZ1();tTQ8>095T6*>08D
zQ1Kl=ttUEr)WL#45m;4{YovpdM6~Ec5<MC484y`KOmZbEsVMQmsaYIV8$SKhm}urG
z2}Y>lLy5TxsJ8fCbq#L9^_c*HwcjX5L<P*z<4c1i-bpuzIgxah=<`p7p3{)n87KwS
zMXGn_<`E>tYxU%}ul+F%eI$}lI1%x@f_~&C*j?U%Z)){;^x9tIcip5y(U<yy)UF_y
zA8p4)9#GCqtNBSwI>VjhhGHMD+~gjBtgxF`Z(QPYm>K_|WBjL8Xy5G|xp$;8yu$6l
zCyKrYf`Ol3-WQ;910X*3b$(*PmI1D46UhAWXlUGrs)DEChl9)r+!PRA^9l$w;-F4L
zz^<XC3Ojla1=5>_Vdik+WGA4zARKqU4}mS-HtawYTVxd!8qo!?Gouq_tpT>eZ?}+Y
zZyl@~cHqOya2w}{`wi-xk1UcvH$gq-a>XN38;FfdR7QLQM}R^UF|5enqZ(P0z>;c0
z5eAci4l$;pq}9yK%uMJAc6<W@Vt{Sei+{bvEs35=ZM12UHihpF2Rzq)YB(DF4ElrZ
z6uf#DhYA<us8d}xH<fme?*j&aKpJvJ4(}At2+=I>u)-&hwOaS{-oGLt$zW_h`Tdkn
z;~d{dYf>1x2X0&aXwZ%ZVG`AXPpgOpD*f<*_$@?`2vv}tGGyT%8-V-|%~KXoiAzU7
z&a(Z;tD>jbET&eSckkW>dH^17dqkbWQF*IO9a3X@1{DlI{iH5rNDq<rC?LHV?bo`!
zXm~V&nQf%Zhap)e=-os}qm#gSCtFuQ63X1Ic}}TTdE+wn!>wxgtf&x&K&yheOK1wG
zL_Kph^Rh>^5?v85;5A4Tu7=>C*CDqdQl1lGz>xRR5mKW-_&p{uTSFcLi5R69=ZGxf
zQBZdDY)kgguwqw%4%BN)K}{M@ubNdu_XQXfyqniXzkK<7q^W4%epA~*mgEb|mY!tL
zX#cYB_0=$&|27VlEzLg!uYO<63L;tydYn3zJ|hu@4vtAYZ|G(<KwG9A8L7P4A}-`f
zIEUC?b7nr80)<ioX_@%jT<%$odFT-}!#IKTov>IruoL#!?596=k_`$6D3hjuz<a9M
z$Y%g5{`a$_DKIOwFHha)$e)KhY3gG6gcg?_Gvh>tR5--XZ>v(FeY%#O-R!FB%}8Dj
zAc%$V))bEL5Tqw0hSTb%zNS{0p%j9C>caKw*AFt5U92veI@RGpMbKA%C#lEcj%gsm
znB000^r|b;m|hDYQ4f0bNXh89IT8{mR){&f#Ti`v9z+gxJLFD7_E#U<AOxNMKa=8)
zUpi^gCf34ZuFRmvR?B^OJJTP3Y3y2Bx6O~;XHr9Bpc=eI?^@&LI$`^vgiKyM59~m{
z^bJh%#&fKBpd)AIP?lbQ{Z4kfl^a&aFT<4GlTZ@n+J3y=2hc$hry<_zr|(bWP$I3O
zl1X^y|C%JACH2S2BM1SBTr(n0ZVDQgOzv8JHzG{GMxHv*B_gjvhL?~S8;M9lV;(PQ
z@54%QQ#MIXJ^cJxo7;{|4<&z!@5=@bPR<EK13VvPc@_a3ZSB}j#)u;|oM0{BS=Q|P
z2X?xkpjy<G-xp~9`==3NnMhZufX25(nFs7!0`<{3D_pvK8P}g)bb3v%@>oy-sDUu+
zYRxGc0;(4%K%W6k>gxp3+Kuu*|HJS)-i^C@hk-%tf$o@8G?-_b2@FWqgoZ+H>N2>X
zo$%m^D#Pi+zT7gCCs!43wC`cMz8io<qJF-FaEPxjYoI9eZr2qJR<ScMbJ}_vmu@dX
zJ-94F<)=Ic{}oJkek;3e@(og?zt^o>_sET|uCH%pBeL`|P|tGt3vY;_ioAr!f#X@t
znq$w>&^1dxSqHwby8n`cIzD8qUb=Q?dhR`$LwcsvEei{hOxJU870;l5Pf&&M?vn!O
ze~RhojWZZ`d9#X5^PdaRIQ8ep&hiwzGv@2mt6!QL!3242gD_#2n3$L_ac-hn^IQvK
zx2vZ_nskl+JMM$c&X+X%jcJ4=T*&dqz27p|yh1aZB(Ps<IL<wxoGxD^?&wh2#C0)o
zaV<=Be8i3Uq?#lV#~`+NaMY_06LW#Y#GJY0)x{F(9qZD6HbX){uhE!nEpp(g+tTwV
zGC$hh2i_CSX_fV&X#VHoRua^T*!Fxdd9sX+G9BXM1tk`Ph4lUPonuI&Cj-dp#@w^e
z9^L6NZ!`U+X1BJna49T47YFZ40WwLzee5QeaJCPE1`{9~G#u=c8lsB`De7UwdmZfj
z&n-WbNB6Zs1tEqDG2Arvj}r77FtLS>0{d06PtxiXRG{)HoNFyyC(OF`M-JV{7>rDe
zfs&QVoAU>!nz1lxmTjW>7|a9FgRSpWmOuH9rs~QLUicccWNRQR)p?n_Hy1CmM>Df_
z>m?hTWHh?IIDasjoB{9!(g}+m7VyiX(>rT0_2dQYxUA5n@>gm9)R2>_CW7L{1ImmU
zBNzo1fk+LA!V*5ApQHh10$iyx8FvTvguj0j^3yW<@~UrcTYD`GDIh$*?owRS6c`F>
zV9tea5H7Q>X<feLBcZ(~h0(H6@Mhd%XcCK2^VaOEJz!~ko*WhG-5d@9h}|@-^QZj?
zV$VQ&VO28m&%1*WGlwor@c_ROc5*wqvI9LE1K>i(5q)Oh%?Lc@kiMXaIF$L_oveK5
z*ifkGcZHwZ;Sd0%oI+h7Q1`&Ce@flHz$2kO{v769qJIrys?I_K^!PJN`k}Mkr`X7T
z{}wvz|L=9TZrvj24jw>#p@&C1WP`ReT|?&zWpSO&JIdL!Pq@JY#EFx**@dMgD9O5`
z)`)6p1BxEn)(48gy7b!Lx&lfay$bT_2_F7s5vrWDq%mi;j?XB-AuP!g{rYaOUqe}$
znx}7lzv)W(!3%x5XEB9k=<og-kF$S{evK(MLwGIOBrl}&=BIxuV`;T^$@>jHK8uI4
z#FN0My4}eaQwItoC0d$Mn_+Y?qgZwCzWF1UpPSB~4y~a+eQH9VG&#LU@lBo>NEop@
z1i3y>vzPC>$D3_~nV70|Q6KvVg`6-yKHM%uX{Qlx4m}X6tj6}Wq;C!a2n=C=qC1CL
z*<t3BNC1FwKPCVuK~~VB9gc0cMJZQJ<8agLizpgb_M7vsDz7`@_!Ec!wi5D$mIqx0
zyk49mL=zhxi=+WTjoul>uvY8oq1rHx4O#8?Mnm<6421*)F8!eIUuI1AmvG4M_`5ax
zi`2NTa5pPoGS!gX(5Fr|Q|AQd;9bu~SAilbpnvTuBLqzH4Vp=bcFB9P+eaj%!%nFP
zTQOPqqdT}Q^z>1hHB0}?9sM$CS?wdMeIB1bs4lfCZ6`n=kS_<TU)%%(Kc8dL9e#2U
zhCfx`d0>y8%W(ROV)?py^eY1vnI=CkNh$^c>x@p@bfko&GeH6(frt>F1CAo04TrdG
zIFy$H)7=u@liUGG-~cqZG8JI!B>Xus=@;2?7%^yB&51yDX~4^-Ve$d49C%qUWL77@
zqa%4lym>}L4K3<Zm>g*eT_+NT5*;&y$7OzeW+|!Ww~3kx!l0*ww2uIDA5sn&|8rPu
z&UUK?t^~F2F;5k@yw94n&7HO7FGp49KM3cRz546X)A$U*q!dC}2FWiEQZ^>Y8zKCs
zl;DJ0L*^OH`jOM~1!<NAB2j>;EHMDC4}W&w1Y?S3Wm8xjk)54rXN5X;9HJ9h3HL)9
zXb%6)sxUpqIgvwINok8ic2Oc1CCEX%W$;bOo5r$B2pWZ|j)ei&Ab2TRK!PkaYbhT<
zK(FTDkRfW_5s$WLnP0}#-S|LZ`cuMxKOf)YtH+oVN83iBMqxhDQ)1W;Xl(%68q<vz
zimJsb5WM)>e4g3L+M;sym#2~27UuX5Sk7H|8IA(&8zWH;MD!=6R|)RlpWG{dsIl@?
z$k?$QUZ2v16^`*+m{-5s88uR?{JwNr69wC1iz93(B>!G$l{~B~Y}Bjm90Tf1$3!4~
z%w$|CC^lJNum3_C0@yt~TwJu5@$qM+LX%Jgj*#v^2ll11$)^bZa_+qKUI1-MC$Gw6
z-H$&Q&Kfp!3MD`PUbU^|TRfkH5;Lk>vEb-P48^DZrtOFvZxE;o(T&%$dC$5<@1b#E
zB$0@QagW1nC;~TC9-BN%pni~s5Ja}77(*IX`|n%&O#FuQ(ch3U?g(4}m~pjsRUY*!
zARKd2BOJ;7u-XrT^C8>=z$g;zT-yQu%TVhPW;(agI%W$r`i~)yq*7^X{t`Ybl=E(V
z+H)X<tIQVGeq6iS=n_3VP{TpvZiJ<<yvmz{ZOuP1)Yq-PX*g?__P(|_^Z2jlhIEB3
zo1<z}o3h#Y&eo;%9*cJOeSk&cUq?#9Jb&}NvoX^^t^3qpdi~DRZYHxfzifCre9~Jb
zxV*!ue+S|3RR)W1S=4Sm$SetMz8qi;=}-_yRwC@&j`V_<6?i#F7`+f=j_77HNr#9p
zqzId^0d~i(W6{ZXPH*4yhn{(}%pa9A@h@o2E*|u@PK)?(D>dmDWWGKo`GmpEu0Yf{
zbIS>LYR96(<|VJ9X@sgPHM~q9H=Z)&vrR~bdKEp2r_(x!stN@a*fU;NG7kY%1II1D
z`$QE|Gh7P-!e|jeJO`j74SW1`UlNcACsCsNfsn8p?JAiDS1Fol*%k@W4N~!tJ$qIa
zIFGQ+=f5SaX9Orj!<&@q0m|$HqX5LlIyg#$(f2keG58jDBR+amFh3zjut*#Sb)c9)
zHC2WR6i^AJ7I&{R<{{(#hPhC`Aw|rh2~?KxbEOj}PK1aEM|PeOF*XoZ(`(ao>l}ek
zBOw>)pKw3+fIo&j7WzFnERIsUpF4X1L8(C?Meu!W>wKUdeir83bbE+#fvHhiQnHTT
z+YOuy1k6Z?HxPiuFmpFYv5Z`@WLLt1UFPHQwoagKf9aq3Z3kQNL1wR=lG@ZM|HAo*
z#`%<#l<NC6(8y^n|6VJ)&dvJEFD77#5u*j_V_>}u?hO>i>6+7A1Bjix^ac0qIYmta
ze6&}uq_dhm4p=IRLFtE|;LzYY1_R9#YaoPq_1l;sO7Uj+tR;vmAV7<ceZ%UTfrpLn
z5u)SUp+Hcmer2NkaY7X!GjeA=f{<eX5vD7Nv#=lhb*ikJ7nO9lFTx%r7&H!BS^uOd
zkmMX7f&}oj?8p<h7*;d-1nKv4#9jmXetzS|4`3mh16RdRj)$21QKC)n{@XH%=J;94
z#H907t+kQxWi0>6ZstNvtRU&$Qgs0-7~Ca6Uj_1~l)+l%vj~zG-xy2NNi%swU<p8C
z>cAS+foP_Bu+%(Z5M2(EcsOHIf*uj9-R1~;`30p%A_IW8=$jvnmlD+u99}URWL>Z=
zPztHLPTnFxyS{zC54K;D*+=Gz{>lAt3`DwwdjNT-I!wKJ0+Hz_`7$yf>*g5@s*(k|
z4gA3jWk^&MsNS2hq|jETrmhYq=FykQZcBO5@pe>WcqlLKCk7He9yOed>a7_H{%<9V
zI7-44a3eB59CU`r4C_t-lpbJKU}=Eqnq$e+J!8q}bh0kkb_93BO_M4Hok3s>l!Ud+
zgS+tH2*+9(AA&NPs=9h4;#u5W1vEEctr6fQIPNP!7Cdy1FR^-oL1$%Nt%l?eNYV-F
z6o4QZBKE;O5NucZ$TYCDveNJBd{YfbItvWYLhhMxiDYyZrCc!39F7dKs>OiaX#~jo
zP-%r>C+djY1BtmKwNO<yOQ?I&{k#+bk^MUsF;d+@(16VYuUygU8TnP?-N7l$CW(fl
zCo~+Wj-Q+)BVjC?U!5SN4|%cnUosTqe7yWF&9Ucs%{jylUfKpTQhgxv<4?|cw5xKc
z*Fx*53Gl{{ZX`@b;2|TKW+%07VLSlOkWidHwDO)Is1_vQ(yxtw=9m;Zd>GbdD$@FU
zfE@klpVEp*TnN@ejw%IByy}N6ESQ<q1jPkH%OFFsNp%Btc~Uwwg~muu*NqKN9zCjw
z&v_d|+dr4*G+B?at<~KK9S7Solb&7se3ye1R&J$u!ycIdhkM}!su`(hB=AXr+azp1
zs7g>h6vEVz%9!XAbLGVug#$}i7T8+%NKG58>?OJl|1TU+_Z-BED;pa}5atD`qi91J
z$7C+HH}JnoDR0fB=`B@L7}RuYVRYKro-y5I4iOz^pg#mKCv}e(bL9MB5LFFHyvL6;
z*_&3n%Yzw&!w>^L^Z-)87kC&107vk^2LUVse}>p=J6SZpnc%yIRwQuN^B2JzZg_EW
z4en3im$e30-mF5cB0VQ~HL?nGSi&3}V}wO$n&JjNMyi^G=K~eKsoYM6z}3vr$S*J<
zBwQrckJLQMdX<4aiGbNHiFq`@^S4@US$a$fZO{Vej%hhDNRU-Z@yd=fXECz@^DXKz
zhK`#8?5A$=@@VC|9^N6>KRx1R6?k^>r<k)}Wcl^iEj=<?o@zTzuUs2_{n&%o*Gun2
z$9La*cw*~l)<?ebJ6*r7W&80L$E_!hZ#~uWX5UY&4<&V?>U7$rFJumcOUOk@_^L4G
zjwwEivK<Vbo$rXXi;XRuZgdgI?xE(P4pVh0$kGopc?1Y(tr3&4$1<%(hLTe5WXCp3
znoubd-i~=jXQDIt5rCT0=yW^euN&8OY<0v~0Uip_!Xm>qaS)97JktRyU#0edU|fdk
zqk43tuTlj=eJD9PL#Xmd*yo8q1@~c=fcV9^Gu}7fyY&f$#%N~+#nZZTKw0R6$W$ek
z*0x^hG>iA%f`Q1O!|<DivpR7`DcZ48Hyn*cgoS%L7a5&)eF2vfC&UizPXFY|c?nSJ
zP3e5iZE#_u>-FUA+U=Vv0M`W>`A9l&FEOF`zGN0MEUBkF?kC+trYuO2nVS46mOmgr
z*HY!~2PrfR%H3Il(MbNIZlf+sKg5j)=YCC*v&!!E`-%pGIMwd!H#bV`<9T-Ng4Fz{
zP5n6a<>Xu19oh~AS)@?j=NbQce&^aU^}k>5`vfG`a_lz;IS%#@v(P@<*jD*kc&nV5
zrDX+Y$CxLXA?O6j&ebbt%wqc7<EAxLbs`2%J-!HeVg>3|2ZxPXQq=DAHaht7I~kk`
zB0HN9HAA!M%_dv8VIGQtc<+;5eR`~V@x^pi$*%k!s+`}^09zv?qegv~VT+8GODzs<
zmUZY^8Ai(v)Ad7UT{lMJ0>zjuq#Z{-Zpnlu?cZNPOK-s~+tpHddO<oVHN3sU564G*
z)Tzp40(24$<JWD7QXc~@;6*y%Trizr-3w|9g?g$mZ2@L95ChS~!D3kGqCr{cck0EP
zETWp9+{nNr%GOmDWkE0Pv|6e$cE~f|y4y>v5*jl{cqbdx?Ft*5`C}8HZ2NiQlT3?u
z&k;>sIk~dsJB^Ht%p10}E(6H?=wi*WH)SeIkALTwd)G>?Xb1F#FfsAPCnOxu)6+vL
zsukg|I>SKyTWU%pAaK_qoz7A`nP4C~`vE;$h_+mO%-Z3uW%y}tkOdVe(lMmzHb8gA
zKiW6x;Q+`T2BXY%{Juds?j`_U!1a}P&*+NId+hA12v4G466>+vIy2m9vd<XwL)KBw
zwcjKpBxF7=3^rS*@4T<gSz_}l%S@r_p=!SkHj2w+grJ-0*dp-dYZw?ZV(k7%ncRVy
z5Dx*KUhmuXRBj6EtsUV2Ve<Ff?-pHM!u2Nuo0vQF=XoaY-!^`ySI&5Gd#AP)-O|#s
z#WX6!_VZnyzCYLO{D9!9cWQdN7ruO_Zoikcs}Z%wlhaD`Q;s8SI(G>y3xl;IlKQD{
z-N?wu&ftwka9X+%ZVzV2CaZ77sjEqg^%;Vxx9$3K&0Z~G#^2J5lhXaje=9uZi#_=5
za<z9nZuo7fXg^qr1NDsC2`Pm}ObB5aQK;DMmTGbwpK&$pd6HLb(k?2!&czS+#2hR9
z0#{>)>qwYdSlu^A%1Ueek=wdcsH5()xkPGPJv!vi4eG>z&!MngSZmLG1ZfcO!u$f5
zKxe5K26O-n5l&sHgDJLZ9={c)(DnSxB)6uE;xq^+ly++i_}d}r?)TNuFF{>41*f7P
z0l)|3$ssT>MpvvxWMiEy>TfJHo=5Y66KPIm<s8o8U)h#)_O%P5seU6ax+c=C3^K*?
zq|9eT+hubjj)g%$%y}rb=&8Si3d6|^>At5%RBjCAx`_F7z87m;Nx*ZyW4=#p)6wC-
z*$T#P=^|g7gMQVqtg3~Dg>po77~rl;Exhei^0Io$E|jiIJ;nA{E-GtlPX9cMrv^Sx
zWj|^myQJGgg-w+KmtceM8;DCPaQ-m=H?}ibBC;LH)_f-=v+3B_@s7oX*-aVk?d>5X
z3;%9iQETH!S$Ms65ZQ}`Z)Ce>3qznR_Sja$2xlQNrmd~5J~W@_`vB)^i@%<O7Jgv+
zdc~UftCG9jbPMMytoz`Cm!7Sn>*t>-)bZ}(eqeGo)Kj0vVYPWj9Jz~&&d`r=Eb;7*
z>$Sj;Qn}F^#^6mS#&r*+W5O>peoPd+m-16=dQY(HkHQYP2L=VH@cJtA`lbL(?R$Q6
zV=0J^skq7>6#Mkd=sznQ*~Mv@rUX{(8%$CVk5Jz4<>l1^-e+A+%_ARBM_6a+P%`bg
zv)lWFxdFZPjhm>b=p|(S*(imiyx*$RLk0jov_wW+?g}Kf9F!Hi54&!MhlTCNhsn#!
zw^*cBOvEU<nZ+o1h0gcz*p=>&x(G)u{`J*6;!WNxq-SQ5`Pbs`&bQA>CWnJsCas}o
zuhxkBdm<Z=mXXm1A7DW3f$OvAn2Ho*(emhWRwTtgH1|?rp3>6Nda#1p3qPl<r|Ug?
zB(=5IRvd-&xMQcUrju2FR!~r|W!8q<X9!nYyxp$+kg2(O*=)gV{;tX4f&qE!QuO=z
zBGYs~_)4$#jJKECfKj>{<2Q3`x1Lv%f5+eZb5{+qb7d(ddxI_pS!R?VE)GDE-{;Hc
zGbj+1AZZNMb7(RC5S(3>N_;1XFdxuS6iDuQt|7_Y_wqBRy;u?*i{w+7_UyCW{o5xe
z7)R~#mx{x~!%jGycJM%bBqN-FS=nQK?H#8rK;HhpepdK^0*S6oA+eMjkA8in3nYvT
zxIYXk$=Rm|cY%jep>%qO@4Jmh`GH%V%rG@E`G9Pg>cMF8sL&{+HO!-^x+CvP8@OSi
zQ2LVMw0QOAp`~nua;5a*{B{^$Vt6OQxET9@shi-$`{UXPmqT%*)VHoT*{XbVU2CMD
zuH1C&ASt!F&v$cO9{HF(R}32!Y~QFA08*LqkYd2$J!r|)AO{J<QFfD5hDM_)3;1jF
z?B6!-?teMl=l$z_H9%Y<lap;CW_2ugi*J;vE0s#b(>fU$$ewlO<7cVKfT|wi%#qWF
zoZKjWF=j)S8ogPwUAEU(lG86NBEndeI>hU*rIC}9<9BqE{D4Yv)YNAK4V6=2af|hn
zk4tozwbY&G=AKkaB+9U=X@R<I@NGC^tN%!Wc!afE_Uct$H+DG6EsjSe&9pmQy&9gM
zFIn<(lQ|A`T)wAgowq_?UQX-mQ4ZQ7qpU<GX-7)@i`gVo4<{Kf-q=LjcYj??oXw8#
z6SMDl)acaI@ciZAkY(GR4Lm!OTW53k2Hzhoq+cG`eoHXi{L@hGNWipC+iiY2BTszp
z+sb!~{3&M|tu3pX9NX>EM>EsY?Z@s7goK2YZNDX~Ekz$=%vJCSCeARtJ$)=wjXG$7
zUVeoad=v6#T5ozB*`&MA)ZW>ml25!lb2gs5sL7i%bn&C{z?KJ{0U|;gl9H*bS=Bn*
z#N73~-55`d6G6Wm;-BRGYEHGy&igX@_lR`FKvI8WPB3e2ZVekXGJTDKheiYVKcNW9
z`;T_cDQpB;-nV0c81XHqw?85^q?>G(7aA-Oc3~9eCCmhBh)-|SP8W!}1h!c)$v3mR
zy^pG!T|LsEAy|B|;q(}=kpp<3T3J^NM<5~lrnjP^q6H=%h6uQl5A&Rn70e(-M@J`J
zj^wgM(T)X^AzCOl*dmVXgM(L;ji_C3tpo1@QaQ8t`M=DF?q>?f=vyyb-1q+6^z~WH
z!>9lhJ_%QGwB+U8ibZ<iqRYXL@>}!IN#THr<NtQ3zLF4Oj+EGY2<<KQWpeT%HodD#
z-k3f?#$!5lb9QFEZ6k0qWJ1*w(cJj<L2D%YRsr3o28=<Cxi_2KUnv_%yiQSNsKq8F
z30gaq=`0--5{r)HO;%bIGG<P0Y*miI(3Hp?j7_Loq!}#oA#SmAfNd7JpnL&e{9Gz;
zHh;7Ny%W!TTzhu&<;iF3R<7d-en{Q~!_4u1ko~^4j*g0e|71pHenMl!xzn*b#}GWG
z-&7HrZ}MPh1O|F?aer)Xekv!B%}tr8KDjCTA?Z6lCjy&I+;=D{Rd%;B_xsAup?5e0
zw(IXNde1k<F31vWm${<@kH9aN3=)yN23$^`_Es5hXZod+L<3LCdVe=4zeiIhk8ov2
zn^msm*i4Gd=tlOeSWD_Wn9)fN-`V5#af2(Y?d&GIh7qUpJ2DW5v2<90r1x*XAC-bc
z^ffvVBCQb`9F^O9%O%i4Gb46Q9#8O<w_U?awuu;&uQ`~HbH!V{@9CRGk1r>=V*mYf
z01-j!zTZQEO2RA>U;!ze?Iqrk#EfKS*BmN4fnnYfys(TXot>Sj+-NmEzc9|VnB4lE
z^If0NYp}|4kFRWmE-0u0Qf|(qYRFAVgz~U1n1%1GQh_q{p}?XTqx$b(Z=Lexf+@-c
zvy&vO0QEu}Ci>aEAutw2VC1bZnTzWap}ec=Os*biA1l|^NWQ?q)0p$a_1`&suZw93
zQyr1!mI|WaUmheTh|2ld1u*yaQiyZ2K%N$G(D1FvFlh|jm(E=gRG+h+Nu~z8#rvGn
z?yecFW^oqj4eD`5gg|$&Z!UuWb@MCjr5?-k)4cv=6c3NuHkBb|^|N+%%CWTct`&nF
zX6vr8@oPSWuQ((rdA~zd6sp1v$vXG|_PSCJ+DT{W*+<&`$-f5{Tyv5g%8P6?X8NTP
z-w2mUsN_r57s;2_V8Mz-y2s+^F?azA6}9uD^75-$m+E!#ca$h9xj8v0sPXrMVWK&b
z0~0pqv-r)2yJXkx?Nk_eZ<+5j1^dgYdo#rWf2?`ePQnsGJ*lfc=cIv;t?C*k`)s+;
zA1$Z0>6!;Xh+ag{XQ&9hb}l7;k~7V_vBO!LI?dt|R$FiQ&eK)b#V)mW?dH6hiCpKc
z;pvf$?9%S@UBiS%@?Xy#yA5^cvaVrxORF&lJl(8?&unTgpKh_mey^n;Z;y3;O(*+l
z%)#7zNY*m<%BQ!y8cX+it`J8Ma~w^_91ILPI~|3~RaxL9Zf5c*KGPkx#gZ~H4XWch
zFo4NzZ06qC>{s9JbYdR7TJBOW#`PRE7%FnQ^W0Ej5p-mrMmlse6Z?J4>^leGTqgO7
zP$@}d78L4Z1&RIO3hSqZtyd9V%7+jWKjt=1%6Gt43;p-^z;E?{O(jbu>e($9?jNv4
zpbwDb@|6$-*?e4cA5MFoEx7_kfrP13ElZbLl!E1;Oe8wyn_n5cMK%b+*Qtls9f1PQ
zm1-0m{<cU=3##jj-)r&@lo$*KNnA-pBVgZosCYYOkNfRfgrQ1pxfIk3qvHWGQXh=R
zy<xwy-mh5Y7VvQDIy<F5)6^q88wReg+;|2tFv^lGYG#-5P54t{0QxGgn!2R9t>vp@
zVLy%SEkVfX57uli0!?;@5R%suLh2-}@0GMta`i=|dd>^J_%(r?FE1lThK7eB^IrAP
z0}q5GM3bO{9tGfFy%A;|w&Fm#WsGX6f5B7)GK9hsaPf|OzarD;8EPWcogy`Xx}5w~
z*!Yx*)}{i{PFiDT3LgD2a3JR13%Wld@ud?en{_QM0eAqS$i(MN{K%y|IN|2zCXVqP
zsqjaa(3Z8+A>Z14GHAbl8U8mgI$8xj=>&mADAc9!uSl#w$tG6}k$wvZ%ahiNGuCm!
z_Z-#Cn9j)T>T-L>+3_$Tqm`GS3tcH#V1gGQqS7XDq6O#%rq<R;Gl|52Qn?A{K)?Wm
zVU?5^RSt*z_k!yUczq9}4~PV|bEvD&a+lyCXxQY!DS;f!1Bv5Oz?&(u>E3Cc4Y+74
z3cQaDr=@D0#Pw2azy&zbGU5f$$&lJAPd0!2L(~U_C=;NRx&6^rIHWf!OT};Pnz!Vv
z93mm#?$*;HW@Idq9g^Wo*xrgRE-o49DRKpksRD_;t_=fWw=1HG5+tWj@fMyPJ9apc
zyHy0^mE=~uRze@Q=kc{R5~K$={t<4NLX`(b@FDM=7}(uiIfx^c3_OWibu81>km(<G
zn&?@&RXp<C@?vu|iU4VpgHm${39@-Yt}5?Ud=~8O?M-l^xf&n$w0coK70&pWz}WUK
z;(`g5RPvHi>_2^g^sF3DmxMKPLaLbp6KITPyReK<5Ya9H4TZgTA+vpr;mdcVyB&zp
z73fFKb4>t7XIB|%Ng{Ks5D3}XVkeMy+(c?QZ45eHIdTN?i$Oq(_kk&OuHf5y!9IfP
z0K*aLS&+3GOSI6rZ{69{Bz95%(|bW~N2EqQWuTru6upcT7iw_AKut_5(W(~g&}z!c
z_4rSxt8gc++Z{jJoSeZFz|39(?BUBFqdUkgW+CB><*Ue8+S(4sLORS8CaS6o_mO?$
zH-NxIk(xIdGP>P{6MWK>)Qk8ej>EU!Qh{+l<0f1F&}TQVa8Kf^vSD_Fj7y(<#B<TY
z9B4p{LMTt^0v;I{8L1>rnRtDxNmMz7K~}mPc{YGb78QpP`(IH*Lt`hJL4s{d_wyY)
zXKmc-To=^w#5VgFyQnl+$T#ga0+yYxlV;SN&s`kbwD@oo+k^mc7z-rS9EH?X3R{I_
zgp<G^1yVCS=KMXDYy?3>$q7SRObp&03R-0zkO5schZ4_{n};zMT$~WRnaN^Ah<xgq
zV3_hyQh<mgOl1%+6}mVV>I#`_MQVhMi-76v9S&!x^T@b#2i&~UJaJpS1oi~gF9?QE
znw;yp#zsG!By5O29LS2rIq5|n@<})vl3DF!>vGQ(F3v7qA{)`p&JJl6F_3UMeK@F9
zz(VqW7x4~H`17H&)}M2b5eYuINm3Xo*onZvz}56A7-T+aAefK2VUx!Xo4bs<bOt54
z4V!CuFs6~e*W&w0e1jj;sTX6Eiuts$mt)=QiRrzm;d~5sf^-#_jS@WTye3J(s27o>
zm+8$$hk0`ZTc;!HB@2q3N>XO?#O%Gq${)^LyzTMrHh0`sY(<uT$C6#P&<Qw5u?0-H
zQGhe+Oe^{4XYJEr39RWz{r6JdXJ2J=L~yRETktKo=$s;~2x7ieam3UlW)DwI6?)90
zSc#fZAP%({>GPd{#}}9+N;<D&*|XT2?p5ABda(HGXSTyf2cyG0&z=+yUc!PWP%gUk
zqpx?o=btsH2)bGyZn$w^!TQ(hJF*m%soSqspD?wuszgEI)AlQ`SIi+sS?pEB7rbJ-
zqT^?lkseNVB1!a9k(Utg=%(A?moI0i#}>YImwj}{EhT2Tp8vz<^5uTsrnTl&ctvul
z1doKZynKatFn@v~C%Lo_rJ&#-xO~xH1_x!S!eVgb?POovoJ^RRnYo0zH34KM3>K#i
z#Csl|^mBQX@TXfjs>Od#V_9JcbgAKmg&qgl^(Q)C-+H^X@O<+>FfM_!2>ZW><UIPb
zIZZ5;mUL{Iq$?E-7UnjgjIWndaV5PsHVAyc6L<>5K`>R(S6Eb3UAN%n<c5<qPc?tU
zf=2U^_+hKpJ-uh|`*kfXEXpB#OPdEKTnaP)1e`m=>?Ssj=3;=qGP<LsjRz0@G>w|%
zPTLc&WEY=d%*(DeD_|Sh0N+y2zqvtDqq#wi!a&Q6mc5{+eIO7n*`s{Ajv{TcRJ2hY
z4xKB%Ot$6{mIyB2n$sdU)mI%IlT=*}stc>-zLEV|ZON4g01Pl9k#in;h@C3T^;2ux
zGBY##fiadN!|_G6OoA*^U?A`5eNacaK)py}?TA2)NKb$&dngkKG^7a(E>rK0tP#dA
z>MQ7a;)wnyeUn`GkZ8vi5mG;A=QkPo)*Ku?DmO{<-pB0M2e|1QyP@bF9<w#<P;5JC
zTzI{{jX>@A)*gZaindt@y%Z(>mV7P{Irqm)@E#t|`{@Oc-}X`v24z0-%dHh**1b9m
zmQYoc)O1W9%#lH_+c5kLR{!OWgbei5JmC%yv(^`FK7TVJ1C=;^>Zhp2ER^=tvok*N
zCy8o44tPmy@-iCQ17w-Qh7-^b@U<o?H{4h4t2#;MuSUW@bbK;<`d`4LN$(Ocx&8J3
z048si6^`8!>>(LDiCLy*W&k7mjFQ65N1Nh36j=U3)>un!OO^+=1z%Ijc%`1LtZL(t
z8ox^-qiuQT+u&)Qpg?lKVE!|kMs&s_O?}H(dvzkV<iTd#T;mqubZ)i6827t{Co9e$
z9}rZGI_5IvZd=#X==ZF#k(-j8DzvRa3=K{4jaqY|H{Jw3e=#e#f7)yQw_RmwW^x{#
zFYS_F#0hzYT@#pL{*BAxA1;8H<x|`4a3%B5Rwu(xH|DeL7NXNTVV!1H-Iv*NKsW2k
z*agWj!*;*MyhNj(Nzor(rIAd4VvFh{@xb5iw2^Ab5}FB!OKqtd^3=#%%Pa&2N`L}H
zG|%{N`Zfv+iNPk(3fMzV5sE6JUuUEl1Bz>`rLlj+fp>fAIo>~zq4}xUoZHe3MU4-{
z0DxXLIft}2w5X^^7-cTh7p*$Ry&W$ih9&3S7L6pA;qvukE0Rz;ovXermG=8|LVRz-
zYV#B|S-mxh7mfsz=|3HXd2tFq^zbxAeky1?oiI02$lyE}C~dfp%h{sTWp4BFL+Ry<
z6b?42BhE*khVaj~RTSYB2ikw$q^M{#eY)<Pi(|o<u7JznU2T6QR=U+IQ>nCunRJ1b
z&l4?hWD$p3E`=TdRR_L&VYksC`zQs8RWI;76YnFR?*JcV?hpfO;iJbQ171^4M!0D-
zw*DNiuPx4P|F>?RDuNbrp%m`~#8JrVV-^WJk#V@$qN2E|vMGqTrvESO(c;XapE0+k
z{QnvSTrCs^CravB<f95^(vy0T;gu2dgbBxjT=eGR!w5N)HeB=9;EXR}jw5)@U?r?Z
z5W%JhLXMM?Q_sK%7bk&2OrOa}#e?3+IRSLIQ;r&p3}bg}GKUC3sz^_gEm%zlGD9*l
zGV;22jA{;Si9sqT*U;KZ4nE2-%q|`(Dk=(q5Q0%yXB{<A@1l{196;|tI_ICwm@vC3
z;ww(l&FeXnm5v2MAmr;4q+4m*Ev@g+)Wyg0qTBMmY&(8nseSZ%P95+W@Y44I`Y#8#
zac+7BN(>#O7GXfE^{=h|Reyz9F(08ko%8RYcg&K&x$3mk#G}{@f_A-K$8(Wj;a{HL
zjBOQtU3z$p;L=MuI5{WLPEas7>?a#B4+J66IhSd-;g96EOvg{G`<@f8k2O;f4h9QG
zPM)`;t}eBl&X*0l6RY;q(#!PXv|rOnEzEa>Yq<+l^H8J-`5n`~Xf=*EbQ}`ZxbIwd
z^;+IldFBb}>gj_^FF3zP&2l5@u|8j++<N}8u>1T+pa>WCvj4IA;7>nvNK{u>zxr5H
zKgq$NtS>+PAOkgwD4V+relA-t>U#~wWGKg|j?aBHwN}(%8QjK#!hmT~b@8f-ih+a^
zl3_1UJv_ilJ5u;!7egfavM>R)S>$GtbA|E+n<Af?agL;?7k+%UAmQ`|u3tgtmefpz
zwXBmN%X*Gg0MECH*$hZwVK`KUBtYhRnBIESlb_Q-EJ^rX=zUAUjE3idH8tosPWAy1
zDH46@>H|n6?=4&~H~B{2bJ{F<(Ys)zp`fbf@W<@t)>iGCW-_PE&CT5wWYZHQS=vTa
zhj^LTPvIVw1e)Yrb6tCFonnBZE%HrY=ATOLsHabA4(OiEpH14>;KEhbYpb`z-}gbo
z?0aXSy`4g4=iX^H_nX9KF7TVmA5n;lQRR&Y3p1MI`{CR*vTU85p-z*2lELxyouSU#
z;)FdG<}X$T?<$j>>}!x;EbX=J<FA1Rh`oJ-JQoryzS-ocC@WfXLxb-sR$BA9(;mVA
zmaJo7VjcBc%~ciCSsVpK4`@EbXVur$J-&q(HMRv6anuWO04}e696{Gq^mq;WoS>aE
z&67V!+QZ!|Hl_dtvwQs)G&J((6Zas*Pi!vk=y0CXlS@G`aL;YKC4nF~IN9=FHe6Ul
z>>KA68fKhjhY4R2b0w!erP<grfi%ZQn#CH8D}80E7fd3Gy-ZB2_I&R)-nVbX|JbUs
zP|CuebLYM*hF!n$)A#<3U!Hu|sDA!@SybvjAHnp$SqqVd9IWDb3)(QBVC1oT!-`+@
zo_czoGSVh<i|)C|W`w3G1j-gwF)_u3l7)@Fo65zocc#<L^<>m^X?-5$;eCGE=x2Fw
zqTs?(55fO2kIF38eueoB^izlsjh;#L<?;GR&S;WWaZ;dYfu|=`){BVScG$EbWj;-E
zcH=}L6m*Gu+ugC$>2*-lYwo%uI56T!Lj1HBNT2|Oqy&Ho6$Azm!nA<-)yQGkW(Nb%
z=uKS024AHVxxUr`?t(@<0y8)<h73YM$d7pOVi&UDY(S%S#JQ3}zM6)fFJS`g?x-S=
ze`fx$rCR(FLdZT;G`~F$0uCQYqC~z9F3k#GN7BC5)<9s1z457)vQ%A^UCMUc&E;od
zF<N-B^q^wT4rXmXI-I6LJyo|4=|*Nz136Jl^C8zA#gVbEvChoMXneI~`>&(rW_w=Q
zBwRfYMHIkFT4ttCRtQOw=00umcuG)NG{7qZPRRWVHI5eaRO1r-av4B@9wRAM%S(cd
z#soOsz8MDJE1#Dp78Yk?VoXbJ4J73`?-DuCQpnxw=~-Xa`wi*ud#T1cn^t&)DVxCh
zPMe%RxHt#DSQ=+_U*%@|sH?)#RLtsRz<bXVgP+{uc>wOpP22EqkD3-{G+Ze*m7Gql
zwb8a7Waysd^^i%3>b}aKcbVy2Xr9Y~la7v#q-4omLg5KXleMe>A~iOHig754w^Ag7
zb!axc?TTbm0R)!AnT20o-nWI@R?iy+SmYu#n_YFlj;_JUF}PO~@|?d1?v>|Ai7&i-
zLO(%CvOyh>)Aa7bo|3mV-G>WTPe1VN>+5>}Kjn1bz(6ALQ>YYLveGoI`!96=<i;=f
z^=#9m2yb+$=TSBndRv`PE-5Vn$AT%H{n$5U-|+mo(m_O15#Dv-1;2kKkWy{d_(?zc
z=pc>2Q-8`u*d;S=J@ObYhy*Gc(_HMlJ`lB6jbxwciwWtCrQwYkyUL1Y8_IZsP8r%q
z^uJ0-NO;@&zP`0J=VSNPaf53=ybgTnE+wL_<>BE$ig5%Bb^_oQfVAz>AH#5S0;GT^
z1aS;d+x8>JfbGsktz&{@O$3w@Kw=V%^iOSsq$Et|Mt{s>1)3X-X^2ZjB(ec-a?N+$
zX+w+spmG`Dw<U}W@$v_hxSwLL93`on=*KJX4BM8N10Xq#7oe<l5kmpX;=^<k{kdiZ
zIkeg{|L`blpB9q%jq<)f73tq<-v{2r?YPB9ZP}8apU+As>-vuB79VDptSg}5zOndq
zwq|0gU9zM2=%Z6jpj_PY+TgFPE{NqG8gNTaN=h2(WOH$L_M<zcf62+sO+9$hV@Gve
zol2NltTlh={rj9F)`yv*_pBKG367MUek3@BAHXCHBt(m_8KFwhL4`^AoYe0zX3sTX
z#VE?2hrYTY$qK-L?ZdNbgQJT1^X%yWG_+n8M$AnPMSAqwW`Ca48XSxbk^%w{nXAm@
z;yf7X1Ny~TjayQp^-8ag!jDH(zW3~yi}B}@-vpm*Zy%vAklpexpX?kUk`fokC#jX=
zGd*Qo2IhoNrlZN`=9U&MTz^twVd3X&O+`aPMMXuNCzUH1zlCbtx56-4Q?s44-crpY
zScDA`pcVD*j6xDqX%@ShNnMvwe#c<d%1<LCeZl-RV!mk62Etva2)a>wdi@P{HZaJO
zW|}_znl5(#b}lIzlAyN1wxNwM%P4~jFMlR4I6fWla@YO0?HdHLeg$dB#R1u*>!3=g
z_JL0$NSyr%tV5A;-6BR_eEuuP<nC*1n;ad<`2&woE9S~9F{7BiH;DqBa}1r}v!q}%
zhve<Z#b;TGiHQVXoXhl}LvtIMdl4y!#dR#H2%SH>4WG9NT#l4f6{*KiK_cLar`{~#
zM@<5<cleNk?E|P^EpM$1@N81WCZv~U(M;q-WK-{sCxta8_A;32+^p~B+qk3fFv-zD
zQ$0eId5Bgq!TbjX2TM_RdJ2A&6z<HCAezcCX|WMY*50W4>85K0kp75{?cE*j7|&4w
zD%sJm3<9kX1Qj`o&ozQ=F){f*m|Mykn3(SUC(8L(a`K<G_}5y*#x$;aH3&}`80g1P
zo$;5AiwUf0Lhe{gWmsCA^{_16#_Id5%J$^RQjut5VP?nj{}+dv2GZv0h6moCXqvI9
zb~aALP&PMbsd5#+g#$h(-TjN_QH7xxA7Ae}XO+?Z3(9jsW|BLij6O-OyiV(BkJ|`n
z<02>$WxD+ho}M}D{7C*3xrf%)nfhx|WArmKK3`eZ(5hSAMaJ$VaYF*N&ESR4q_%dg
z-c4tjlSoNgd65KFzAxHC)6{PcNbEOPJ?D!gWM(Ea>aqjL&_GFIt$II_T`@2ed*Vdc
zdDK_rc0^I!ZZ#vGC=a*kzRe$WjwEXym8Kpq^2zNG9}2(l+!U`ZW8`UHC7I~n^b8g)
z(Sjs>sTThAFr2r6d9pSNL^d`y!UQrH)04FI#~IO~ie5&VaESVD_ATR=B;7W{j$6sf
z%^=2W{Y7X!JOTXA#}+M;<}KOQ8ONq7t`i%!@}JR&kZNqne!7R&TzyomLL@e-6|RPp
zjXyd_tO|zqs`?irJ4MCz@=x_B3Kpz4Sm9STJrQ&V37fd-=*W%~3vi-DVk2pu^k9Ob
zl6(|yiEkzu?v6O_mX_>~RdJ`pP)iX`V`ey5z?zfS;}5UYZ9P5v39s|iTW}+@M?#L+
zeEx3H^XI)JB7Mtxdly!m<Mme&H1fsEse5QTgF|sa3wwMQpYu)6Hp!FBlg2xp?Co6#
zv-pH>Dp~*c)E?{NfuGzgz6>YCUC;b%Zf;=Ep69f&sW?(%y)>OpAL8YX3r|Y2j**&~
zo^FT^9ON81k`N_W{=eBXs)|}m7V-7d@0?Vo|6}rn|0J0I|0LTDszX0Cawiqg%n)cn
z!o$C?99TN_Kt}U#fJ_Z$&_;s#MPl;z2~|dqug_W&Di0}fqO!-OI|GW?68)c~Ou;DM
znOa)vb+<0%JVgR3!rbIMD@Ze@re<gRAD-M;LU@93!@CHs2p~TdWojYx9ev_xTGDDg
zn6d8KW8$|5hK9;?Q0L!nU#|rIryTeJ#N$I>CLHxK5)449X-*_|Y4F#13(m<E*2`f9
zw>T7J{c}8Ejhx!rI{df^<E`;&#!}3T;DU})$|Z!ms9lS-r+t<Hn^}k2gEFa>(f8|S
zpIk9YP<C!Ya;t4_*0c%n5^dc_nLriG(Chn5u;=gz^{q%~sPmF0351kLsYT3-l+*BC
zYmEh+)EuCbg5Vk@vHiEV+kQA~9?)6+aN^!CdQ3p-O@oivd`Pi%yxiCH-EIeD{jo?5
zk4iYO!Tem6iHVt+EPyiy5wWODlG*?l71sFDJ+B;NRr#Kvb%rHU4poYsW$7_XX0NPw
zW~wR<_WF5tws-<yB<aHay##>6%DLS72W~?<QRv;_9Vh+lpU}-z<)_gQqI!(2t+mzc
zT*ViYrKBb@k~SCJ(b1t#(wet_tqW+bmAKfF)gj?@sA8=D0+6;_>Gw^u?6xp9UY(wd
zvH6Q|xcKQeOFq9a_wZO;zb3E2f)41n6qHo$@d8{o@NE86zmbFt#W@j9fux?_O#-si
zIsCs^FJ_IKc3AausHqw!48v{JIuAt^D$ttKTDG#%n`c`qR9X|IYTxuVS!gEvw72E$
z>UFX+JEt_A{(vjg6aGuUKKx^{C1H6gpFG{$oo1Oo)kxvuY!j1L0p$3=MUR&H90W@P
zl{RO&{L2Vh8}+31@~6jrl}``VhOXS3fr`MiXtdI)hIA{P-r4tFMT*fuQdSv_;rHA=
z+SW=8@HG`T-AEc>H%Rt5nvi*dMx&dPug^>qXedrEwy&?S$@!F3TJ>q~&yC0}tVC^w
zdl&Q?40ww9n5K=(*W|6-y?b?EX+n6?=xEq^4i0r!_4_pzF2-Kc19f4IGl9<yYb654
z1_YaO9w;^BO^%p$IL&JNABt_=v~b*ZY>k;-Q8b4~DVv$z+h|^qB>%>e%k|`yM-0t4
zc+Mu+Zwrf^6Is)g^Ouri$A}C0?{v=i&y%6n?MaQs4`SU!+xh7hIa&#2>}ot#i$&W0
z2L}ZLGZYi#*wum*gH>nyKey>;I(5wCRG90nW_n<1B-Nz{$4%u%hWPQ-5WnUtlV1S2
z*RA*INN{M23bvJ1IP&SQd$Z}_Z=-`i>r;-*I}|8auY~5{<)&V-Hi9}*PraCbJ;ga4
zj%^5)Eya{Ca(62$D>*b&-2naGlpY8Xv+n5HM|WOglQ&TbAk-_dZBFF5DlZ{_Qi_VQ
z0hfw=lTuPdaxM04$S?VdKqF1k8KI9bvwP*h@Nfla@#eaC5NT=QVMaCeY)ip_hK3hk
zY{C1g_fVUR%v6v`7qmiF&awRuB?MB49QrEa9*h6AwGKE9>O6b2geu?vS<K1Q$W87W
zMfk_ugqilrt_wv%4|%FZ!Ca}EkX}Uw=ojAxSb@fxEf`vjZ;nA8E3mTT{Q;?o9DRPN
z8+p0{O~zbo(h9^u7x;i{WWQ}}V{{WT{@HuZ!`+`pw@f<=2qf;LXMQs;(>$Uul<s3R
z?w0(lVaP_Ju|tUIJhzOs!u1<Zg$G9Mo=Jjl!P=CKHkilUq-fprFRKOgYJ!wCD7F<M
zP5kzYgi`^j;N@<DL*MQ5gSqQslnzMvM5*#Fu}$!x0uT#vmmq_BN;pDD-2)`-&EA{-
zL|U`Xg3E^1_ul(Y6J*<#(xI}$ef?eXTjig4kP2<NP*pkk2)G2>4-8UwM+Z~My|(|S
zx-XB$@@?0?4Mjpk5<-#&LzJ0h%1|f`D)Sf_Gi1z=A|+2n(I6_7%9Lc*XiB6MA@fwq
zT$wWM<LZ6)yZ8RSZ?Co2`quZyXSLRE{T|PAd+zJLu5&of^E?jr8V0%69UD#V{k6Vf
z4xFR&@6hXh5mr+cZM+s_a;F5<Y0~OO`VLW>nt14c@~PbIf0*mR<)czZ(4+IOL9>Yb
zW|(x+*h%~MFJk+*fI<C#gEc?6Z@Tj3BNsHxnYv^g_{2v#-)7OOwJWyfs!Yn)@%?59
zb7`oGidK>O{9mqJw11;G8Be-+^kdO0B#!nYC$f);ha@9IaiRq0Ajv#ALZr5j-l3bK
zq)_w`M!M(RqqMX%mr?UX)c`NTl0n|}7huIE-lk6w)n1E{z`sX>^pOzEEw;B*X#N;7
z=Dqmz2pO~i+u1eIs)NseUXhipZ}j_t5s6i*EAyLdqfZRl38i`NXuz|X>_cCbVJztb
z+Sfimk>6Nw=SOk(IdM|W6w_Cm$iCnJnCSs|^XJ2keVBnTu(Rvw{FWF<8nW1uqJivM
zsQtdZtbu$GqetFoa~9Pn**GeSS@*~5TLzS<aj5h#@$vCF&ub8hfdGtI#fkz6Bc-5>
z)Gq`mogo2v(SX(8NxuotYRUZcYBkc$aw<3x^?QqnF*NPepiAMp=uubK^kTJ%-@jSa
z&LB=tF9uo&<c4Xc0K*sc4nhR6V4cX$$;pwLnL;~3-?Gptt=PNY7xMi^Y>cnW2)=w#
z>QHb^Gi<Nv)#WG8oqPB|^lfdu8=JGQQoMu8Hi!6B$#2H{2lFI7?mg^NySsj*v4`t^
z?DytAHm*g6^#%sq`_#2B-#Gt9I;U1hHK$g1YiVnv#B4~CW0K?5+#?ws9?zoXQ2WfB
z9RC@fnwnb1sT8kyo1>aTJzApl#}8+jO10oWPu~bEUAnYf0G`*(o(4j?9iE<^(X$4O
ze2vDiHh2vawzf1-3AkQTf>3;O^!Dxe&p&^vxcU7dNBhqo`*Un1|I&2U-cr8U@?Y}F
zBG+sgHvjvlI4>Cl=&z=7S;3vGv41~zSmY{gMuG4D-V1QT?9M^UfJGFCfPjRjXzdu9
z+s`TyJ?3tw#1wqP4^FaF7`O}d1Y*2dh1AR?Uc@U_KLxe8e^O*bgg##N>HnZnBZIye
z@(Iwqj{g45_~F>l(X)hP015+i6gNvmS)F1MtgU}iUj4vPfB%B3b_NFL@git{1ZL;J
zbPR^GD~f#Ic$K_FL4e)WRkvj(_o;KkPSxGurrh8rZS)c3pE8yG{Z407)`*CB?Pk&7
zV0?H=_&;uXAh#y@$$zZRFoSF7x_|vPcBB00f4h=zcm1H=1TP5>J9mx+-4Aq1N=k}e
zz0CE_a?qb11NH9p6UjO$uV4`E>(mITXbC3SXe`Z}^1eDw&4bvQh+V%H3Y|T9{W>3r
zs(3&FBnmdQE}pHWDw`_7nLq^Mm2a|x1FIAh&K&&w1Nha!{rfvXuz+-v5zN15Ko$ui
z#>Fx+GQ1#$*pVZN-wSWswi?tfI|2ffa4VxQ;nrZ$!wUOo@Vu*ZbfHZoF=Sfr4xw!b
zJ@|ONQHDu02HV%DsPL!mPS)}ClmXw$2dtZ@s3`dy+76^@Din1S$n^j>P#PQ`9vu)*
zw|dd8f61&iNnq>Nt=S;^%xOv99;$|+_a(Y#++=jG*c?5&(RZXh26qYy3=vsbZp^;>
zV_@V%o}1ZvN1LP!rMLIo8JetUUJp7HH|hFibSQQgjSL^>5AwRK)fNfG=?3m_VdlL|
z8pBXiV}eaO`ZEx8f}@~(zwvi%!Br;lMc3JR6ZA%0+3KY7zB0-7f*m6q>?d&pfY6t$
z)j9uI!JxP)&d$POH4x1hNGhdNgU{pwShoEA_59YVA2_fAafYMsO>m3Z!s3X%sOjzN
zvjJ_$hwnay@jJA&t&#SRj*WThmE=8E&<(1k!>Vo9z5GJFiT@kA8_t3x{l*Cob#;p9
z$Q>=cmQt<B!H6D$r8o(TDC|cTkdJujrRP0Ss08yu7d)}_%sg-dkxiT4W-7Y%MQ!0`
zq=SK74T=ayFMdqrj{c0jA9`{dSs^<QrMI_3>^#UaH3hZ#O%}0PThWFs^Rc00>4QdN
z^^=m4qApw@hvH63is$!_>ri6OYMu-T3bF%Xg;)Ywb#1}XxMu@*R7}iq`!^=pa@(n<
zl7Ouk@b4NKxqwFQHOk6-mX?+e9zD|3(OI15-gfTkQ`z4?8kXAF*cjE8$p(4NB4@go
zkZ|V1hdt!hz({{u^R_y;iZFT;1%3io2vvQw+{qF_kH@TMV4#gZ&B@CPh3;kb4GmqO
zhybs~=8HFP#(<o(6FH#U&v{rlB!67k<A}WI2@3$os5(JHa#h7o3SW+FE4*P}_#j`?
zjg)QQ!o-{6lAl#p92kt*iv1KPW~d2r7!E!@77q^(r(9ER9-eMM$`#~LAx-Qg<ihEV
zcpf5Kw{qcOls_;|8Aa)!SoqXerA?LSgk?g5zm)}Vp@YDs4)DbhRz3fKfUc3y1r<!i
zGgw(!)i*Y#6cq4+22iT@kqoxxuC|JHY|y!ic7eV<FS5TCZ9em#pU7!-``aj~Xx!Vo
z1l{_VELxhuW`Aq%!_xrxlW9w8G}5a^0PFMY-*n{|=u=aw9c!+xd9Z(z4c3dD_E9bV
z>mf@^DZD_g09ACSCnfjq>*(#=xg4L^vu|I|U8f<x*(p!*Ye)fU!UM$hfz*!!zkZB{
zc(H1bv7QD^^pZ_ib{2%p6^Xyj)%R9vuQpz&%(Qh2p#^4%H*-0<xn)R$_K{o#WJ<^P
zo}S1NtHkrL0g?J^kjh4gAIxON6?mcf7A=x%;jJX7>c3~t!~L&8e=ZtT8}hEt56x0z
z-aQZlU#{)6J!pPqTduoz)>p5~E^`$Z3kA$H=k6B{gzS3Ia%iSmEWd~i`s7=W#maeY
z)Zn<7l*CTCH=n9n9xkk&VZuNu`whGLte(rb1_BM=!{#&}>|#nCn)Z;e|7c(M;>GsG
zth^Kjt9N>rU5*Zl<#e)_4<A2%JjEegIBFY1OiWt;$jIiTlxx?n8PV!}zk0bY8LZkZ
zRaudw7<}Rc_;Hvhbwk6LQaf*M7ccWC_Df2hJsZPlmA)GLeYsw*=gp4_XR!em-?EuK
zym7=l`%H$zhSVZM@8oDn96qVa_&qOV&_=rPIDg@ZJb1}MF8R^eT{pLf3OeWVy?XTu
z`#eEe;Kl}Smfyo|{OFU|V~zVs2zPHQDwL4Y1uqyyse)Bntnp~a^KDco&V$t1DxOHY
z_iZYl&3EirG~E7dDb<}274-eZ8wl;y=yQH~Z%&guxox_tuc?VERVPIQhmGpQtdFOY
zInDkk4dl||AOMna`yxB_=TDJ{?3qi|(?`U;r{R;5L+4jf#PS!&O6fkyW$oLaFOy)m
z>tToNWb?8&#UXaXMIC#8$a_t@u7z7%FK{ZsoRF$gVT1t2WLwTKVV(v+K@U>rbr>mS
zAkDb&Ag4_;DlpU$g&G{p4?W1ot|;%nLE2BLFz_124REhq$v{b2mh{J7b-kUeeh>C;
z^w%$!r-NXNr@}5?<cQ;1{N0hIadOI2_dgy|yy7B_mtgbFwk>DDImT%EVt}Hxte>Rf
zdk>kJMMLUF7_ow@eJt0mYESTkOu+{jA37BE@};`Z(3ce?O38O`YyIP0XC8uR=s{W<
z19dtgq6^>kB_8XsAN3m<85wWED`E?a1w|AsHOpG%^{7P_i}Il3YPE6gIRCE98LzCC
zH$yowa6LC4pG+{<$uQ}Gfv2P33K7Z#gA(xq*mA8wrg9ZiX(<kLbaYb~;P5*STI!Fh
zt`3bMgruWjxr{9>X}50O(y1P4Ej$Iv6KZN<v*z&q%uJ)i?D_5UJ=?EKWIcLBKe}M$
zbG|NACOs|flu4T0w1fVpcYPcDCVJN=*x4B-CTzB<fUjS}9Nwgrft8#>t9zMQY}=)K
zO84*GyPcQE1MaL$mUlMk8!O@R#sc7SA@7tOx)4%UR@R9KIDGFzALsNuC#mhhS#PlV
zvuIYH!r)xFLvC(vQ@)1~nDft|7B5IRV|Ur+#b-3KDmA!CR2dJC-@s8eo2&RT@LXU(
z-fQmfPEi;>tdhd>9Xv{qt7<xw%@mOcA~L?AIasDs_zL-g$(c$}2gMthC~Nr80<S(6
zMvX9G<_#M+YQR%7YETqR>aJCfjA8Z>caW%J|E3jSoV%W$Qs+cneZkf^y6ZxhWb*LK
zSb@Hpq}g6CSJ$)q(i-HIS7L1BQbNM|r1!%W9|eB>{P`GaQ5b|@g{BTq&&w0pvbxBy
zN{~Ds$~nUJd|^HaOiK*tx6<+Q+H!g4I^h1Nc0}_OD*pL;I5v4ce$*`Ay^A;u_=U&d
z`X%p1wQ}JUvYnL_j1&zCZ;@=st|sqy7h(6W3-p=3$QIRA`s|TjI9#3UzBd28JVGqL
zGQpDGEx6dC57Knet)L8E2}&Ye&|zhR=Fn%jbrpQ;p0ozVW1SRc0Ql`49Bjbf&dkbs
zS=sysl0yb`_p1>;bbR%!x-HwcZ#THMWl?p!q<69B790&gr6)0gxB{;As-owey77_{
zKB>?@kxT5Jnr2M3-V78!d_BdyIWF0p79;5idU8(G!Vyoq^Q>HHH~yo6ID5!=oOh^>
z*Kg;lzIac`S(fhv@_U%AN*G5AfqTK}8+GkUjc`h=!KsII;U&n;E?v120mX;>Yj<lt
z&i?*((<oI7QAYcTUxuF7FkA~lo=60**^d1{ar$DXByC3IwAhdHyZrS5SgrjpvL}i*
zR~E&zs<q&ftyg|0|M0QeuxZn4&>jKsX$!Pv-&Ozlv+eZHJ}Qiy@=cpqfIT8qVP<B2
zi7CctlFL5G$S4PMnJDeEFFA1-2^s~nhRBvJPmDw0r??%HKX(lGvomVE1jUml_|1L~
zwXFPf-*Bsv;GO3Q*82KqtA^SNy_wWYSBm*fd)XM$yzF;}cqz%#qCS24#C1XSCo{2e
z+B!PjAZJnpC1spQUXq-W`<JInU`?(<NzE)iVIxhogOHiaQv;RDu3VR!hSY=UiKNSy
z?R{=S+=6-uqx)U{+0Q{qEv0(5^eW@baekL-W7;_X%O?ZR%?IQIhv%Dhw6%L{n_zBj
zVZ~5;-r47SMc-u(GBgxl#lxlS?D9pm`*v@O07q009?Vq;C8YC7vIYbO+JbQkG5(=A
z2QW-iVZ&|3#mW*XzpaQWQ&ju2Ad)Q~3A*23J&bTxtnyZI$ec2D3x>LO$(Nm<ziv?Y
zYgKys6<4q<B1x8nWeIQP1))mtYYXsZxTU`{4y~TYGruA6spCn|%!J!{!EJohC}Nd6
z;IE}EYh|fb?0}y>4^*!QfhzmCkG`M3Ix41DVA8?K=Vn+KH5Py<8;*7L%ExR1#*v6o
zv*pQQah23nYVa6#82-B#WQM2yF2iu^NXzD2Mbiu(1Xq-Baf?Sx-K6T4$$L64YAh<`
zy=9S@APb@>xPD<z*_aod<rNho5btZ=+;Z~d$!>d{`(k^mYHJr$zXuxMXVbO3j2GxD
z`)e-}SEQ;~I1W}B!)gTdSH6}tmy&b5VyB@&N9l#M6iZG|PfgtdV<0sj`Z_o0VZ#rd
zfn8Oc4*f_z%>Ej^RSVI>CNE^l>&Ftz+A0M66f3cPdrz~K^!gwuLD01gzQDuR#H#Sa
zg_uK!4mtgdP7l!;8XY}W6_l@73j4v7YYIQYoZqjX?^Guupz6;7uI`tZc6F*-sT!e4
zcV*`y3KYd=aeZJEdW8{F>elVb*vqc(jHUh&xE%Im2^XeWY#5j9&ih(mU$uO&weVBF
zR%o%mx_a2u^~;R{xFc-srhbvWyI~W(sY0hrI{E{$YHAE9b#3j_ibfB(1r9_Go9AcC
zk>yVuf6;F=&cCn5Vv<Ayl{fz$$=+~sPbBxCIx*}PtGK+9dC3w^50_o~`mf5PSC(56
zPeH_VBft;O6ISA_7BnFNv6>4A7Cj@sBmaaJHa!B+Fr))Jcb+pl@k#~C9-uQLd79by
zB9Hn9#yHQ!_JO4Id{L1yrGEJEs=fR68CzLhz{cIB+;!=hQjto~&$#OB?De&{q-)b-
zI)tSw-tWG3JGHH^I)UWV-3UuZ$H#YQYKG_Rb&Ah<s~H;Xc}D2%U{&^$C(F}JGG612
z{DvX>5ilSjDFNz8g;_oe?80Y#ch0`c&Szy}<2wy~;VwA!S1?{>o@#P@Y~*GjrbVxw
zvZTrU-KB32i$A+z>qw3B2UXX#4Jb@lPWJ7|zf|qWj|et#17qoo&{<YuwBRnfJCtu4
zl~{a!EOIk3@%%{!CcZWu2Zwc-l98j>wi?b%Pb<mT4m8DCA;JUT77jt;xE3K<Ck`-5
zu5-Kkczq?(<C<$*EBk#J=;@_B+EuZCkj9+r@!iDDlG8JcWQi5_NiVJuQ>v$+vXe5h
z1U4HkTk5;Pmx-_aQ44mCPyU&Xayf#IH;2s@M)FX>vD^03bhYA3=X}j_og=`nrzC1(
z)*NStZ(<obcRZ)%!nt#=QN9x8fBurw;gGrc`QF}Mol+TIJ_Yy0yKJXu%DdYNk$wKn
zJ8z^EH+l$olI$}E&nk$d=XxxO_AxbE<l1j{4SWq*tB9u_7TNN$_50F`Ng30N7B99%
zob;xqrVNyNmy(m0;Xoi16A=?z3=&dZd;7bey7MA4lt8y1HwBKb$57pvMe|TW(4-Xd
zSQ+Tmh(do_EwcL=qlUDKTI$1JviK|%zjOivl|hhsY~Zsbuq<n6s`z7~xb2bdgAAoF
zGogb7VHpXssQRL)rKx!_K0Xqhts+1Wpa4_g{f%8yOKS{`m21`11i>E~_d9+vsOp}?
zpp6?1R^?E(?IK*8<f&k*#8VcLkYEJ@qh)I=Qsnc!n`he<x|ZruY^e&kiZK-UI>B#T
z5IDXGa&V)F&uqbL7(37GxjCqIpI=r>e{*a~;tz!|H!Rv*1!rS0UQ-m&Ev$EgK{1cm
z_Fu3$yZ;_-`k&)=*(Vcl>3{s_-EVri>tA@E=)V6kxb=Vda|*}$-mchiWhee6ss(?!
z-J%xo^KQWqNF~p>x0pbf>D1nbWtby&^(nkpsg?T42KfZiR?TAHp|VMWeZ_oEgC7S^
z<5>N^-M6tJy8}Vq=i_8K;q^5@cqL6);ei99S8^G&DT*K85DQN%jj#<Ig9w4^yEu?{
zUAi$Et4xWA6h6qz)H!zSah=1?d&{BpqlNIFWA*A7IAVB%We5nfom!Awrqn5JF;8jn
zO;}79qr(uRbL!8Zz?5o9CBblEYp~`w75jO_IYhQR3!ae|S-aK~R}vfe?xQTe)=$bY
ziOH)g%DX4TZ+<oa@ig=)aRk9%r`a7PkaFh^Jw`<YpPEVnqz~0wO%o-WV91^sbZG(_
z08x;S*RLUX>Lq^bWq?`5`}vcHgP~2^2|LzQ=)E4gLtzkOTq`ZjfkeK*=X-3uO}-wm
zDTOZuL0M?xUFFug&iUc*m`=W!3b&AY&(jzu)JkjU2FaT2v|Y;_T2>ZOVL&k|heGcD
z1uc(0X4=eoi3@kJ6wCe^MVH8HTOBoT@8PxBtg2eFL$0i;I<{vF{0C>>Ul33VhxILO
zX(_G>1fwQJL3>s3%rc=0<P9v#mQBtE$9>cI<$r#7_kjZkrjR($Q5!dIT&}w|<JBpq
zwW6YoARcYXaa;z>a?No)vdcteCTAmeY4?7`JU6QbN7)&~i--t75`PSo?<h5`22|Dk
z2f}uTK-_woGhY>;2Ln}BS(y^LFt5Y!muo7CH&aw7blU2&?R4|80wSNFNh03wqfCOj
zOP4O~J+pQrK&2Bn@>Cf5R!5nZk$WfL^(9GGeh)hJ+k0NNwD2P-kBW{iMU`xivGGYX
zErxP6VoIM6QW)l|oudyNJjg^jf#136I4i#z(V0!H)YhgQw6fYvD^x*FeKy5&8CuWj
zD02`7BiBwVtNXhF>&cPo70ocseGSPtX{f!8$pvSn1z8k&Ey7;JKftK+MKoZ)NUmg{
zKnG2TC2DGFPM_|x5oj2wgAGV^A3S`Rt<?VRhC;Lq=4a?kGd|xRVh#^Onr8#Y!pz3D
z1E}G}%a>1QnB|+681Ad=fxPTWs|5sP6a|Q|+htPiL~wBVq|sIB+jUQ{@jaUn`IR@`
zh?aqA!}kCK!u_h_u9D)rY8jrvnt?Tp2}_~{q!PwT+O<h~7Cb51;1X?~Oi~Tl+q#<|
z47VOUV8#=QI)DB&l%pUhe-b=^tY8v8lJCIgn8Y6mq&+)m(p_}@dOJ{>B3d|!_u=r!
z05TAOep$2Sg$kN+NQl}zQYo&R%EvX~n=f9x7zSS^yNW3n&YGYKgmn@zqxOy+UFd6P
zLK#c)Ehh!|9I9?nGIkZ6=phKlm5~`rgMSsyi8uH?FoN6rH`V33930C<z9c{WLeJ3f
zc$3<@)mgZ~yZ7(k0bely2Q43;r{&Z9%3i%uQ>SVn@|%9{RkMRwLjbfKM2zmH3Pr+!
zMMVwU(Q|ShzWeCWPD+^KfP=Ojn}PO7*<^sfKQrPU!_S2$rk@nlf*|F(#AYcep4YEm
zmmN3Q28(o~WD=yd-9O=v_+mWKNY<^4RSP#t%~9O0g@VQkoeP3)Tb?6HMxJg0K$M$e
zhRRJz-OyEKL2wY6<y5}>c&01c6!L{rfx*GgngzgMDo$~b*de8QXk*&fS_V`rGEBrl
z>-TOj4|UrJ)H|-#bhUe9wM%>RPy&hQ2wi9!l*vAw^Pa7A?$F+Ol!i3%K+7wgV3Ec0
ziTvYOiPG^OmpcarTzsB&c3R&qC@5&QL;fZ8s-vUBbrv8WQq!1rNB<0&Nj-gi&nC5)
z)ma4o!%_mIg&<;jb88II+oRNxFDeu#aXX0h1#CgELKlW4Zc&Db%QPLSn%v<zLmXOS
zI&dL9Jw1xTsWmV0uX{APG(2pLRwSgr%DS>)UQ1B$LsrE@S%V9D3L3SBMvEI0k>zr>
z<a;O-&LRyVt^**YVGAY*B*dSAqdzLn@Y<a^r%}Mjwn(ktxqHCQ`D0GmK<Z#x1Fx#T
z^pAmo3|hm*;+y>NJ*Du=l6)_GkZAKFqN(Zw(R(>zG3o+hNmysA1kjzm<jPJ5Un^|T
z@SXR<O*7<+i7eSDzLCQB70yd_N482zvLWH|_*s;Z4J>a3m44lz<_dO_8ssmiTQ&vP
zk?EUpuO)^)*MRs}ualrXKKK6DuU{E~9{T!ALKf!yD0Kq^R+`Xu8iHo53I3sjfr-F7
z0K4m;_ZppV277d6=eqWG@K{?KBr4WDYGQ@Lq%ABY#V1Aw$EXz$P^d^};qET=XJRNC
zbR`QrOibbpXZpc^ub6%Fxu+exPmEZ}))TGIf)1V-fyy(#qzaPRcZJux)1Q1)3H-eQ
z>IrM-=P*Kj=jl^!WL|tRi_y!ao8s+;Ua5EO!^pQ>pSrj40n31OU*w~_T9%IuqOl}k
zVxS1N1jHq+r@_0t&_?gxUK?y#68m8LuN7Rt%?YUZvZ4^l_~FBYS=6cJil2dWN*KNZ
zDPBSFOx~e*JUOoQL&z>P_U>Jd(+(>B(+C5i`n-Pj>Jx;N3PYPIN?-5$Lm*fKz9mLV
z-P&6CbEZXfcekyRET3ocdZR>+Gb^j!zICpT>VPg5%qD1f&Sl+%OkUMOISRb1^LE>T
zn0zVVAQY-BB^)(4T%Av+r>AQJZlVs2Z|`}uCC)3ktE;Qqn^sCfB%fq&w=*^(BGXTf
zM=jRiI1Qz4jT0kq)i)#*{92|_ttoR@x!Llmvb&v@?EV7>mSCy4Jzgou353ZlW*68n
zyk146rC%9bBASr&Dj#=!#%9IAxJFH_eNdiJgF}`trr;(rsL#EDG`>5p+MhT}dCZM2
zqE3T%fUhmmD3wdhC~-q({)y?IVgtNMT<A3hVkT3R0V?q50mCHK<{u}1n+Lng>ZUcE
zo7s{pRBG1?z#Em)RTdsVPh#A&762l|WTMFGm}h{$*zVuMMi<7x%`JL4A>l?$D8giV
zstZFPPETgB`?{Z_eCX(<LTgR!8lxA6ldBpGkvvX7gbNwjyygdv>>b><8!7leZPK=u
z!6M?{>z`zk_+shQ%uMuqAe(j=pbY5c^bZWAr+`Ec(fY{I`gm_Cb_^#EPvoy}?_26`
z)L%5s&^dBMjOqlRy*iXG&S!r6{{8I2{K!I{i`8&Vor8m3hKU_kjS52sb-g6Q?Dih(
zs@Qe57hb`IRSf!md!Ky4cj(J1i_b`yA#Ex|UA%F_#k+Ob*WKUW-x?FRR2UwIY%yl4
ze{l*3xcXePx$qq-w4Layh^`xlxvYcoE#*{SYO*_}&KmJN6$WE0fPnu<&jLh<=qUV+
z`~a^Vjze3OlvZId?W&<nTiVm7`%lc{*JiV72QOc%pZWdk7uUjjC2a<r_PV(kQcIVz
zR?NGXoSb}MWPEJw6^=6<1-N7fZu7Cv_r2sipx^1Kvl)(c1t@t>0wqUrV-b!uJpJnQ
zFOf3P<mmoIV6G#LF*I5v3f^rqKDsoCw4hajJs45DW}zS~tnNyYR)FU0-T0KVuNnHt
zDZUWi#}WI#kuE8c<-qLR0o()chY*@u=uh_E24}e+KYrA>58%Md7uG!1AeATi!X#ws
z2$v~Y_owv;6R#gKLJI;rJw1Krw{Pp!p!eC?)3dfBOQ?~bLNr!W;c+f!Lg`2i?~QI5
zo$aa&)@`}0p)ofpiY1<C1bkZI@Rfb0USY9{cdG}9#JoZ+_cEz#`)rpM*i{#w<VKbQ
z1sei|rkmxn19MuLu!xDt4%|Bg%&bbf8c3w)re+};cO+==Fk!Im{smQ#>lO!Z@3{rD
z%|6ogLrUO)$>bK*ia*(&@6N`=l;ii5>~Fqh%*@h7NzYHd;SN5PW|EdGTd#9hQ31Pn
z?Sy>pX`PgldmqXc&VtZLy3nBG)N)EwQ?q8cva*sXY^<t+mHz|se;mzbw^h!WC%<qz
z4&?w9k}vPk%iOTluW3E8>YOhH$zw+3xke3pKF6>nibf;JpYg$iXApE?(q-AzEe<Ts
zE-o^#o~Sz5z%L!imw}$e*fb_@9KCq_T$Y4eH~e9Vc)e!kO8B}MDcT0{pjGl4uJsH)
zIW}IH4WY|4K0isDqXrXY?k)^P+Y)nk&q=SNk<+V|f5e|3yH8~>=g}h;ii3ye>ti?4
zm2hbmHHxS=Rj|tC+55Jss=B&&HvIbiTN}<##<q-E0?@&~B6r~XixK%6Jom$Cv6X<d
z8!wT*?ON-)f{$;L+5%?s-ern2(<8;Pn^ap}_4w7j!o{sf?xOQub{}r5e%#mrt(xl=
z9mi_SUCu1>6g`;459mX{tg*3iCCF@E0edmFuwctDS$flmNrOX}nI7aoZtWMgqP4-~
zM965ThK$iJ!T8_(z6rr!xKc7oPe0vHxU{nlz8j~lC&5Gf-}7W7%B-2<soMeTH8x?U
zJ2pD14h71oNd&(hzH=n=5Eox6C@82H@KcodK?DpfD^7Lw>i*HcVNiIiD_o233ch|q
zC&FffOut`W4U74%rAvxyMdm+KP*`;sF@>V47Wzoap9S1}b>*85uqecybQB=0m#E5d
z3JTHy5xpucO)=JbUd+a6tH?8f;??Rr3-9fR6kU>O_Pjkv#MYfJo8zU`wLt!3$y(IH
zL_{8v%=?W#d>R4B2ce#RLQ+ny;m6P{3NC=Z!kW$6*!asOuDPaKy$E?zRdq5obv=x0
zG%%{O`G3BM2n$naC19dlo1ZKK%DAN{$tblHK_*r)hnE&z5<KIZG5}wPBEkDX-$Y}R
zKorTkMGvC4nu}dP(b}sGO$}ocGwAWE?haNwC61U-#{Qk*XW#K32P90NzR3($x(?W$
z6xu0uQ&T?ZW4g7+V{&b(wN={!o*;*D@#1rK5ink+-A=91HW#K)?cK?{gd79?zmJFg
z&ZqAXeL9O0H;4<?GqbZ-K-;UV50W`r_|EC=?&zn0Zctt7ZXtVn`&-b1a8)k>qyy5t
z7=(aP17=_15Hnl8dG{^?nLhmL2j~InZ2fFD0JxtkdGh-wCP<TT1ub^THBD6UV~4)H
zZ?(?dH*fSjTR)J(3^c+xE7xacW$9X43KDuoTNFx;<o6)NxuN2hIi)Hi!--e45Wj>)
zF78JpOWF;}p+rqls4e(|kR;>FnXLssTBQZpZNWDLjEcL`Z3QD8EOi-1=b|4x*pMd1
z?R^Db?%aE><;$g^(~yW#FA3|d8l7_?@}zEL#Et~s`sr<DMa4rWo?E}7&w2K7b?$D*
zKnTUqQRq<f{2tO~VPe7)7P;VUCN?7acaNVUK0}@e5h8LfKcjg&44Z(Sfq|2sKN`i#
zf;z?{=w6V4DB0kbZx}#D=K%T}HnqDC4-7;@^`^5a+m7Np;wc0vtn!w@&l&tUu?z@v
zX5sy#&YsmceE6E-7@MFfFF-b?4<F*ZaD)D6qPw+cLuX%K1hnIY(F_5J3S@#gM}=y;
z@gh2p7}d43=5Xz<Z{L`JPNSbfTUXZ(H41-lFOny8B>&s_C-h-cQ~u|neKI?PHVWN6
zdpNjR7OASLa^TPa&^cR;2OERhfLZP`$72nJNY!K@<Up~N9z_N9Wk-jVsv3%eu=*kh
z)n%}%XL`TtAqa~J;f6-n5~O-aQ<g?(t`W?-bxW8e{AhSoM@|zrgu;|C;6HV(6sKdy
zq|qGa|7=T1+)LvbAF(+lt37*!=Wl6ngdQW{rPlg9RJ9Sx*$_-12KIeQ+F@$6OEGj_
zm8j$0a5NH9JSP0{<43+WUpE6pq@G~kI?`Bt(B57Ylhg{No{b`vCbD$Txj)C#diHPN
zo&p#17#Ojxo2TwdkOJ;;p+TnTHO3{&*KnL)rR=i-;I`|;=TOH4<Om2k6ZLAi(R7Tq
z7*5PqJZfn1TMUyKy>c@Tze3CyU~I(kP%VDZlq=-8YI}$(<{wjadNf;Xkr5eMezMB?
zUFxS8m|Xi4V423l{q$jq`n_|*Sle#xbAN>7NcP%4|F?gS2_0Gp?ihf>ob2tzp|kJA
z+en3+bJeP&jd`Ht8w0M|10&Y-#9=A&(iki*0w+=&@`otMW4lUrV9s|?jg=qj0n1A%
z`nfr|xle<GgH+d%37B{gpGhz!bV-c@3rc~`5Wx6y)TpwZ8#eWR{rdX-`}g;GZ@vTc
z0t`zR7;Pl`>T}5O5iQyQ6m5DQtP+A;1N}AE+9Ji@zD4B?$FvlOOxmu3wXCAzHC};5
zL#|wgxSMeCkO66Vw~oW+$K9u<r@N3Al1e7b3)+^J-P?N>x=N&s)tF_ai-ezHi&qln
zXZauv-SjL-fuP(7TB95UusZ>T@t^@z+t*kA>-%I6AAW4A0{<})D9xY4nE+ta@_ojK
z2m79MH>sCi<YK&=n#w?d7ta_S9cJB5WX1KISzfgoNRwDtdwE)VI`?`r@Q>1CuZhz1
zlu^*ej5Y_!lCYwp>(=S7{@XpMfBqjZ@0*NVwCX`SndyVSZNUr?QQu=~dJfX*Ii*Pi
zTdJEo_0M+>LD+FETAYxg5u=0(+lJ$MvHgu%LWp}J(aDG6oav8a<U74#BcLCC3ZLPz
zN*sQ|NJ%3OIh%5+0_dVAdW=X%3X!=|79gj>i)--Jf5wFW>;L`VAjIVA<mZ2BB>jH{
hNdM!H{cnG6PUxEWoJv_iFbipC*WY2JovCRZ{%@;HOX~mt

literal 60264
zcmeEubyQSsyZ4}?sHlLHNC=1^DIpz-gh~qtNJ>d}!vHEugS3)LBi#)mATc5$B`qzT
zL&taRr{4Acan4%b`Qv=C&aCx3D9-G?XW#dIUB9}wzoNYKg>w|=P$<-e2lpi&p-{Lk
zDAY-PywmWTq392*@Z+?;jI;#&gxBu9YA^V4*5<y3JqktTi~NTZI=yCuLR~{WkhrVj
z62Ca&>_XgoB)018)Runl#0LpQAM?An@LpU~rPKQL!drdk1l<X`b1y=@&oOJU+7vzy
zU{jHdB_qL;%ldK3RQ%2xx`aD^k#7>&>^;4v%4fgb&@?Wt9$1_?bW21h4t#ybog}~=
zA+kTf%}pzgheP+z>m0wJ*O@yK|Gd%~PjL|b{ZjJQe-Y#N_lxNcXM@Z?ZzO*I_7}sy
z4x=c$O7!=~2yvNixcLA5F~1YUmuvp{m;_G1S)G4AmW*otub&S0`RC8#<oKs|iNt@h
z@wsi9Xc%{=%ZY|&%fztTp?xn>t?%x=P|{f8{_Aw-SfdW8P=v0&ZH9IlU-S!G?Lxvo
zjT&;j<7t)*iNcD~|M|Y;PoD|@)9Y2j<udq!k4JmW=T3%v!U5IAzrVMu-~Yx}yP8TY
z*|4=V#ObuCUScsoWNT}ScmDi64-es3Udw=tM7ML?&%QOSO}5bb*l6fJeM<Z2(W6_2
ztpvlavk7o(@AQ6D?J6}_t2sW}udo?^kzP^dFn8i*1L|di_dVZ>IH<42-0+Fj#p2Fn
ztC1@B#3!+%wM#WS6M_70o0%hS^6~u7{Vy{nyKc<*g@j1Kd8yHxN;x{U6amR5)|vW_
zb^FWJzkj5i|M)mJz|RkF*s)*RWw~;;z_k0u+m4w>y$e~G7>>yi=dtFKmxR4DGntxN
zT2j>Vb?(c{f9Z(h8~JFy`May=kk4{(swAg8lvWL{Xm5LkW3u{iYshx)2SezBs{4Fa
zhT)<8>^DdA0bd$Vb@sUM@QH%>WTv0x)-*Td<Ne?Ur96yc_6rF3T)RCQNPF+K9(KN$
ziHT{|LD>S2*3jbgOPt-^UG|u^j*e0dDHiKQMk934Uju^6aQE(Pe*F<q`G`{->it})
z!`!0`Y+7on-|DJO3>KgK)}5)D8NpnZn>QasE6B-_kg;nKIL`OjK3UmWn|zhbB#uWL
zB$YzWWAdhZolUchcwk^4c8G<IO|HbOw|#o6H&0tLH9g%duU|PJ+3)yZR!rXD$D9Kl
z1B2w$?Cj9N$e>PhFvabM(E|lfUp6|rFOW0W)l;>#wG|GW3BJll%4a?5kl(<wgYDOy
z7M$<R&Ty?{QOo;T;_B(?>Ca_ctih^Nqu!IJJ!IrADJkh68A&X+&{xz+ELoT(#=+5P
zR4X7Lz>BGvSlVbBI?niOy)iR~{JOx^pQn9q@=LJo(Sd7z58u<zr#9z%WxD+uU!5nL
zNOWKB68`AW`1(Qx46*Ingk;9_sQZe@*90Ll(m>o$;dQ*a-I=Qy)6OfS!OJ7n<^fM<
zza>;)`!oy9``KfRx_|tz8AESB-I(c!o!{#D{^8!*m#fzNY$;6V+_3YnIO=vjn=fFu
zIHw*;Nu4ITe0f^*cVF(+t>uxx_9zy2v^LzyNNl2j{b@$|`0pjd&SPlCj58M~d{$Ri
ziHxGzG+!Sb?$fiebr_*5D=UBZW|P$&?U@IM?Z=dNMihyD`RjYWxN^DXuJL?NRx1W`
z64lhyq}P=!S>duq7bHbbEcrZ{skO7SB~2y<nXfem>lnYFpc2jgD#wLeM(xDAbxuoz
z>cz$vii(QfGo6Evgir~+_B`Bc<C^VEk}kIz;n9{qY-h>8#=&vPa<HT!QN;aCOw1Wc
zB}GL=Zz>_D_wosXMT>rf^tUX2-pbLfr&dx@dMoB7nx|8HF0lDTVDpRfWbD7@GLoXF
z%Pogqk5oGgX&*NSl6B>23%SbpR#fnRjpMr%!=d|nz~VJrYez?OndQ*;3R{CpEx6gX
zhkK4OL$m(Vm>7f68rOAn#0|LugO-Z}QZU(n*1GdODKWu$pUGC+9M|%Or6q)_F~ND%
z9|{JFX|OZ<ajxA$G|uw(1uVIcQ;vt&vu~6!7(Cirj`J$Jo>Q%1@)$mgp9FA$O#{Wx
z;2WVNmGpSykaOl}mS2>M<q0!_>KS0(pBJSs(;mX{Euu*HpWB{T?@JKj+?%bYp!37y
z_xBHv`VDv}Bt1N8a<VmwjpaG^<>lqeD&zMul01tv<<V`TC{DYn`@ZFuTU%S1Y~MMF
zi@&JwJggd7hb04(3scl7vK`;(LLx~B_c_c+v)pRQVV{*W&}G7((Qp?QJU{Eg&6F;t
zkE+=>`r))3PkH00zTDRP<W00R)y76q<A{HD{DAbrRX!_X!36nl$G=lTg`dPchPxQC
zz(bDz*@Q=sEh#pZ8PlJs)cADBYOv&i#XwQ>xJUNH{vzBq+b5$*<tD4Wt`i*wL4QuJ
z%;ZVD(Q6S232YfFH5=a^^=~ZX*H6F-T|b2XL3J4~IN0bE3x&ekwhiwH!8rDOR6S4Q
zb-*buF249JU#C{^^aTpTjdB{#9UQ3aP8eSwAHDUdw)MjzBMfEUKGYXv+2f!-eE6^f
z(<I(1Nj{^oe9V)Dqa%sWI<cQ7(x?=@?<DY%TUeMfkc{KCPQEjFf4qQwjhoO(YpQ$S
zz9x!XO%QYpPZ3vs^vFW0Z|{d?`N$3FSc+gNi?O;9)7_~lLje)b0~g<xg~m%_LB7;(
zt>Ydn3G%-Ab$bu%j@L09rLT2nJRB1vn>nTa?9VDnw;HY*QhV+^6*%lw(~a6|+kjI?
zcPoe;@|g90oVI(nJ?gF?zcN^QUPD7e9#g;G&XTE;72eGSOCqwnu&~gi_hVG|`n(~u
zrhb@HOVOGoCSmcO*j}~U4ELeIBFkm>=+pC)>@hblU=MY4(OA`(m~`J)uf}uBN7o1U
zY8~+h=zTs_*iIS>z4rArI6gZ3Jy4v{-}m9w-sl(T@Am3>1qB4f#vS1+``arkQCMkd
z=@eMGDDTZp2VzoEw=^B=4H!(!zXtQ^3%*v_czz}M&@0@hk9S(ckd?4FRNiUyS+zsZ
z>=No|<@iXUn+s}nk?YF*xM|;hi=c9f)CGYNSgtWC^ns!5Jnv+kFfc<O$Y)DaSFiti
zd8vKQpe4jq=Nr{LVVa2B=JyiW7<TQNGEW$sX6<UH3+TPghBGuCsP`sgiTt)Fw#U5c
zTR5v_M)F`?HADGdj2^16rDr8Qk9sc`$D659oQ|nFfBt--)repaT<6kIxpYVDZ9-Dg
z(=QwJHU!aoC0bwlw;kBobQ2is1pi)-%yc(gUVcS-qvJv!Ei>~ul+Dc7D>YD&U4A`3
zqnP%Pq^Y@ie0Mgf1u83Lh!u(WuisrM>$Q%lA6NtvE?%JEd9%`)AT(Q26W!%4IC6Zk
zYiGwr&eo*xStqr?;cDY0dN?K&H&_^e5>I{p{CP?yA@b@iJ#VdVy<1VQ2|nMTP51mB
z&s{!j7uc`A`i0X_0h-SmOgR*ag2%-BGNb&jK22-vli_8g1iLbgg^C@Sln?K()N^F$
zdL24uIG&@uWj`aUlBabA?pt#UjaLwSd3t*KlTs;!HzL|*(u{d1M1E3?t--Kn|1+#~
z`Y-tK9G8c$!08&{?uJ4&8M^*}o12@{ypQ!d1H*?WrDp1d`jY<Bfx7HeR8+aHBTzqL
zx?Npe<uO$h!3(CY(!`QsDO}H!r=IBOP_M7ABh~%IixcF*QoD~Br*pQ0aDPR(uaa83
zKid{kg~8b{qfDf}m!~hQpwI#sr<S6)S}K@=A>()>={WLjQdk(RwDJPh&0)T$%@hsa
zyy-kgjiOaV>GC?(bt3TJF8%&1nDhROiJzdNJmq?418q1*qxkg8Gnb+-Ew;C}i?58<
zt_?g(8er8F4x49I$~H#xlcQp`L$GAl3<2m5E*8)VVxu=CA9Y0-v?i)%KmGvV3l%`*
za^Wg(Q(FXMEp_>0m_QMi7KQ9*@B!{U<<>=WOwA^FjhYf2PPIk6<uVHJ7^_3O=V(`x
zB5hAz{vu3AXr2V(b<nqKYb2L#9mj@>_!VmY!Bnaeva63#FmqsmOF^H!Wj!hkU}rKV
zR5W};*KLj=t}Bw3mX^2t3BUbJP&$?=K;PcncgU)`<9wI9?*03I9fvPMX~e7|T)Ouj
ziK%>xD;z;@kF3{b?~`k4*Le&q6lPYs;}2XWBl9)FM7LqK?d|PtS8BH>R>vDfgGUIV
ze%VfbY1_7+`5M+OjwhQSs9HxO>QT{MHPe~IkpU$k6!66_-Qztnt6J=12}#Mj8QoF$
z-|AF3v~|nb+{fI*Xa(QlSlDQ~+Cu&(PE?X?I%SPZOhFM)XwZ@jO>EuTeS5j|W_OvT
zrohtgqts>+ueae)6ZRTmR<mY6L!W86|In;EJvjadYBvvC@zciBx~~jkq77Q9;R3De
z4@m)ComAvmuQ2USkB!FSV$m*Qhnvc-*29gym*_=As8EX4PD@0W2w3IO__arS3+&SM
z#>U2jK~CE$_wVC<Zfu<1UtF|^!BC2M4jWzZN@PqE`De*f+;Cwj_!4{-GZz$uALn|w
zhmGil`_UB{6w7a$9B<W?jQdd~YvL6X+F<opgWVo98tJD`KhUHU_Lf`g3LGBouT)c-
znsU9nW%}c~uCGx{u|aFtn>Pg485s>0e-@%p0AEPip6D&~<%;@uz0<U+#ASO@f>MmM
zC>yos6A;jd?g}qZ4Q0Jej?dmNWbh^EhtEQ!n=YlW%XcGJES`~cd!y^4*niD-sK0(t
zne{*WoQ(}HglVf@=TYgQd*F0#X|VKOsafyTzD}*1KyBy3=U3+oEe5!DbrKfYLSuPM
zU)+$3MWFyx1poju^E4R*2(>n7p1;dl2K@{#ibd_TqQmhdpoE{oB@gBBo|EZ%5b{|K
zueMj!y5b95CL($fD!PYfU5kxA&;(4w6RN(I-mmMWEw2#AdjVG9+uP>R2XTVq%YK4q
zv+8<Zid^NjSbZQ~2NS?<4_aWU>zyaI0CXE;Z<}lN%>{0x=7ezm#S;TwoE^Q!4QLV}
zJ_<>q>h+!?d0LexiU;uDGhKNb74=Hja~BtCyQH!44b(O0<4`invlw}JdS>d9>yP$W
zfKE|cjMiv~I>;#z$BVclv!=MRr&cnMl(#K?o$31ZQ$PrO<kcL`AsF-Q*|R|3IBd2S
zHR24*x=bXVJfV1;r{&MCU4@j`<`=l<JxJpk^gaRIpWbJXiGB|~0hm-$5XO*;_V#TO
zDnW-&ivz{ARY;S1A{)!oHY1unQsvnABTc5Zn3lqEX;78=4AP%^vXqVNN|ZC?dBq&~
ze`F}o2po?4(&&GE%ZZRp*{^Z3Hz<Xi@H<8KSp}V!7hmQwRc+_@jyL*tWhpbloYk@l
zY%~1al_K4huPY|JzgQeQzuD{hDEgl@_x&<4&$)BwPNB+mv(@sQ)MtMd8jO5op{2d6
zRqfRA6#d}AgCYwvvyaeofjqH_0rQQSW+M%h6aJ%oLvhsfdHuR-#2BWJ-IYK_Vo5^u
z{#Gw#ClofzQI-Jx@=>J}sWvVcPbSW>p_Vs&ha%8q;`U~~zEiGx&64)vp6}pD9^4?)
zC!-d;flW;k9ZR=8c5J#bmFRbm;4WQUa4PNWN%Y(^?cjv(-8fUrJu0l=vE~JA7<pyV
zAs~^6l=QXO(ay<Gk*&As>wth@7SwikmC>L#3G0rJ+~u}Ombx=8A#58EtXasw7(n;u
zB_%uvA=}^rs@@D!+k;%StF;Y={VA*wb_}~lk;G_cpK=}l>k@Ka3-V}I4Z5*QRD#4w
zo_kr+j#ZP*WV)<td_eV6Q?JWmpyODKRFU}m`?F&_R~ybClZ1eRV%{k}4>(=_bX{Oj
zP!n`VPPmC3Sho$~wf?^mA8`)zt`KTH54zYmG~Om?kcCc5PYyP_6@HeQ10SyN2JX*e
z@*S1?q*NSmEwH^aDj%oz2oQX}+Dada@NxA@I|iUHuK_I5ad8nrvAFZ-5z%RUa&O>U
z(G8?-y6~n#{pNF8bskqhHE8L})m$60uBT;Vy8r;VVm8tJdyT8T12pplA?N7r7VK>(
zqd*MFI>nBKjlaeF!*xxzhSB3)x+D!%sVhx}4A?sge!D&2nD1eWZ`uWw4M^axFllNW
z6vFpf+u9o9tn22Y_7W;r>h=}0AG4)qWMJxpDFuGyUboQFqP$2g><2h(0<irT&>3uZ
z*Np%xeSifEwC3dA)>5F4&naDt6W+<1*#|q5p+BpgIbim@gjGBVD=bs1k}Ets9JvSY
zVfBMVA#P=rr|IbaZ}&Gy+=t-v*j<9ZH6p^WeV4$~|5X8O{ZkmdGyxIO#9FrHP}$LF
zq+Ot;>|fg26#0K5vyssG5FA~6@#oA%{y*IEpNm}oT<`yF8eqn^B(Y2X=v=!(lu1g&
zxHoU!1eU;P)|Yd$d|=f4=wSEgBHuNYNCQSi>|FO55Q%^)tvfTNZNWG;>fL$|^<6Y;
zOw)2|f&XMwDH?jQnR8vnQgnPgKCCL}B3%);{Qa0yPoBBh>Z$eQNo;-NhevobbvvLF
z=-nd!o-TJ4j~0>slqx>~c(B=+kz>`WAVBINQq%!<&<YAtz)WdDcp?eQ<7<3;{mVt-
zcyDEIyp>Jo3aMJeqy3(%$tS$`J4L<JjD(AeYl?TsU4xa>%a@Yi#oEtGAj#cR^(p<>
zh5kb-x3f@{UqMNIc}ev5isC~_JlgVcsK{Gwf;C-IMugAa7nRBEZ!O7u-6$F10tgJ8
z8%_n}Rmcfr`0QECyhokgbby=)+$p<J04V7<>*DyV+l{0`sdHVcg?ELWrvMhj6yjgy
z{hDi(dvvg&Py_0a$ylApUjpFu>(}4Iq;r`|i&-5zJ|Y8xUA#+VRjB^3_o!JHsehV<
zPgqHz_)~7ocD58eZA8{I^hTh4C!9wd$&gZ-qgDB1sgqq&sQ{|X=E@k&ZSy_}6%}G?
zkJY<?<$9-|d6yehxo+IxwHo&GI^G}KnH4+s7j&Gz1_wdj3mj_|s^2d_%~qC482}BK
zx8J#q05h}QnXD}TrerPXc~GI%3BoD5EOGtLNa*?KQ%@uZz3%93Hp^_(<Ddr1ECb=2
zT@rF$Zi1s=n6>DV*X0zyx3{$sB3cCM*Lqt2LbK1=%M!4j<*1%@CPo=OQdT}JVW=ww
zF-gwS5ZwV~b&U3mkDjm-2uag@h5G250TV0VEr%;??|mur*tPTWRc4->o3p#>v^+eA
z-K*`Dste&(%o=hARgPiCeb5SiUPd*&p}j{L-PYdD+`Ai}m}sTkxju~<3AxS9t<27C
z*25GtUAyLL)j8LlVHw>E$|47cLN_m-QqOB3O1;BQ2sy{Zf*4!swWbzIEyB~n;zMxn
zHGY?hwS`5_&oaxnbisVxdUgt4i{B-6PzCd3_3dUW61j{XzC(*19&V4>)p{KtmLIqW
zlCn{5<<>ZcxOFFa9o_8T0GhC8Au`<nTHJJ*PK8aw*tm-%+(z+JQo1!f$5(T5#>UP1
z^VnEPWi)(bBvkTLPvE8@T<`fA>N}4gN2jlYlo-LN@EYc<`${e2u?~=h_q)BLVW5&I
z$*=q@Jc+ak6O&B2D=A%m1qB5~7unw9P8JqBujrJ=nEpsT3sQ;)f!thzu<IlFG5byt
z&!E&zSmATslbF92ZuS;7bp#~5RaglpP=JWls!$k0YjolexAU?V%DX|&yUWBUnF&$)
z5?v<F#w>7B1e*XnhE<$?Gw)8syLbLj`3*V~MG&@38I9@el-Acz#>o7HwR9dT!Dy}f
zzVMCQQI8EdbyiXk5xOQpOK*PySdxH-CKUSk0VqMBZ^qT%qoZ4A8BM0e)2_5bP+#dT
z7bxu}fQkt~EKwxJ4M@HwCPo9(sTtr_*GE+rF{wpeucKW??_`<hgC(ZQbd~2(f>a75
zC(0}a=rW+$o}=}_^glm&=76l=Wc~3`b$lI&u!rOicAI(E*&O%#KB{7}9lf>&p25-K
zZhq2s9!WEOM@TPaBAE1w0Fe~bCLO=TB+VKZUqQqvl-`fj2fv3axlwxUkvC(b73Afk
z+eW^BSM*qKW8`<=%8@13WF>`iF$t>qXHcpT!@<Yb7c-4@tL=75<TQBA>c04s({~zR
zXVy)fY|sjVs6{9c#0XMiSX|tB5I-fA-XtUh!(#nO8By!L4f5v2g*YD5aM|S>*RLa_
zf{B$C2BpGbj_CD8DrO9fK>souPYsna2$`H9R#J+3kb^ev^M^DFT0heCRsoS<ijI%A
z>(_wmo0|)ZG3U=R5$=Y9YzB}I2bCffJaK7WLS@|cl=T}zaik^xp$sF~1++m4pc*of
z7XUv1-c>5M(iYyGihyPyR^WAfRCIA*1vFsP>Uo^X65w*EJF>F)0M}+_O&(gq1*5z{
z{i5aK`q9}HsbE1rR#b-kws!Ai?0`k|_AnZew>UET7ivjgmhbFscnp<U7A?cLB?F>E
zSOJ1Gz~uM<dZBzwk|??=eze|83@Enc4CsQ_pi^{pb%8E*G``iUinSf@Fqne1>CDdo
z|AN4CSd#vLr07^!&YPN=!a%Xk_vh;-dw^(>E*E$Dy$KE25Xm6E+j)wMpD^xB3<l4H
zRzTni2q;h^zQE#{Eq<%7uU}w0DOK$VyywHy#@9PrL)L_sFTdodKOjOCxNaC9j2#~)
zHIlc#Tz3f}3Og1#+L?42e??}qw`qC+&B3HE=iMK({OUy+d@g92p8@lsh#l9r7Y7Ws
zG4Dk{9OOuJow*WMU+K2@v6gX>8YlwL!-l2uF?Jd*#5Ie#<XNcJ|Bn0F>P(z<G9Yw-
zA_eX@SKv3dY$qfTBMcFn0t4?Mg?>6lcVea^&Z*yV^S4rE9;ihmr~(jgi7w4EEuHLr
zc)1%Y<DUxIul2w_0E~<VB|7@qPSB1KQ3K9>7Y53bx^EqPFnvrko7G`^LQ+!bO8qf4
z(B?+Cu#kd+Tf=r3ww?a^qgMbohnLf`(gd7-{P=-f;zVBcvN-5o%hfBQvhTQX-@SVm
zYk-aV`c)QI$w^d|)6%>x8tVKG9REAmambMn8xMq+u*gUs1j1%zWywpM;7Fc*4+Ler
zgKta%YKsyGa{v-PfIQ%`RN7zQzHL?Qh?Ei(0_in6p+F(l9i6BsH7GmH5elMNyZ#th
z#_)9-p|#pTr$SmHxK$0m8eZNW=|hJ97CFA%lpc76b%$G;4s$<nVOqiO_&s(CMvUSa
zq}uOYHT74<d)}R)Fo~dE?oQ#CP&Mh<*)PIfuYd-zy0?%&xjEM@-#?l6qbyL>%8DJx
z#|seiDL~*m54hC^#F#&rd}5iE<rrM&U_!p*F=@H|to&|@HW;GAPs(c6wq{*z`^qf)
za;|_w^AfoF{g8q~ICcWNIXDf=!vd%~z^Y}gJD=3HuwVt2e|=1|ytD!cvt%Npa=L8c
zgXNECpfXMwXu^oc^S=9l-`Z2EPCU;*hX5u11P~4;&$=~8w#;4D9vdBeRA0cL@r4C9
zzScq`4a)$o_51s~Cxgg2KTkh1(%1@1xM@j4vuZuYL$IAxK7=7rD7%D`y>U63LpKEU
z;}7s3GICMOs^6g&0}jGRjFf%a_fG)BU^?G}Fyvb6Ntf(Bk><YpNT93U%gZ%o4+Q>(
z$vv%+c-=$PYrs8$@HPRD-;&eqQO!_t$8EHm7h=V{JpB=aj}Zgqb!|R7Ul9FW3R>R|
ztSXtD1rS8J9IGK1@Mjj_`{w(L#RIIs8w|PH>IJF?>KQrD(l!lO+8b2@a_bux?V58q
z;r$zWlmD4i5LV0v%Rt->06Zr+OqvZgA>oxPb47LW@$p2qW+F~N;;uc;*I6o9Zf(79
z3o9w6aPJ3@PD5ctea~7bRA<!(t2TD43o5}xK_YZ<KNyjU90Oo&yqGMSC)}2UJYh*m
z9Du{7Y<5BU6C6p*)hrJHfmI;*Y3m!>i!`1eJb(58cxeXUB?+ATVGqc37pgH9gC$wR
zWnq2%&%VWH&AGgV7E-MJLBn*YdA|PmFdt^8d`trOvyet?_E5jhkf|k~yN=5^F3z2v
zT+MzX4M)l+)DAO#`P4--OP%(mAQcSe4(-`gf!(~j`z*9sKMYfD+WjTFPy>CmJ8MS;
zmf~ugV7x}PQ?3?R2m4FWR|M=*J7(W<g9R63R9sw)CXILu*AnIkOBD08@(Q=HA2dKh
z3JPXGa;o!K*S$Bl6}~Ca3a{8_(j5F=ylpkiU%jAPY8Hj5Qvr-cDLPfay5AboH}`;M
zYz*CfXUJ>Rj3f7w#)iW_2r*Md9KU~We2(zV(U1de5=C(O<?~CL^vUOfK`cr>`kdB1
zCvo@g^A^Xdh}5fMo;8?DTX1^B4zFH#)Q<%t^{2MZVHz5yZmggfstKTiW#4r6seEJ(
zK-WwLQ?}?U0tjcW220ttP&wnFl3^U2>aUwE5uYFc;NiG;$`VUvk(*$`;vA9yN^o^f
zJ@9*&O#(r^{oxT2Myx=8*c?UrPDMud?{3U4^%Z&^x^*}nfKbSbp%!*g{D#;zi+6<F
znwy&VWj8a2ntIIvp*Srl(wss3F1!mDO833)q2oWsJdlxLDYr~zkf-$_=hXL^Q5Qka
zmoqK_-@JT7_Iu3%)bFJ)@2$q5|3$scFCAI*4-5q03v#Y@G+XCc9bJ#aY&1ZaB2O&Y
z768WfdTVg!OsVE+&Mq!VE9?q22ti}0JQ^O>sy2d_iHrKszo31Cq-V%}s+BOQIkgvr
z1hxg3nvsomNgnI|LoMYZ(>EJ5V?})#&x&J1xASyByC`yJjWVJc7!?#*n%u9prZCy>
z)LcqqXjqurx*0$R06EjtRhPj9Kq~Ws&DL>{AJb3HhN}`iG;c;JlqTiFFEZ_gBcL+u
z-MgEhH9-s22kCrWh5&_#5!DOKI%<Z7sp<uK&%wHq@(;n}X;)jc%O?tJ7#bSl1vb;k
z-iYA0o$vuaJ0{-x7P$mq<PmI{)yNYR0e~c@pBJAH)r<z*<l|F2I;WGbBci0P&NK^B
zzAU@fuTMDDwy@3vs72iTz!DAw>(=k!z~ci`fFiLZG@?M5+<`EC+uZu{b*;-2p}r4U
zD1b&L2?|<HC&*iX(ENdo2G*hX-6DVC21t1wY}X%0wS=@85}mn3P3%1Cn!#EO)|2mk
zYd=UIER)~4s+tl3-^PU;fc<wJ_$lDA=%IM<I6U{}7`uSGKINg1>9K`Th;v#Pt*r)M
zy1cc$ohTp~`My9xn*rG&t?TCc#)f4yVr$CbyVdH*CW&f;6a!F^?yGU}eaKxvor#zZ
zp%K%egMQ-Fic}a<_>RP&KfM_WkBVaO+?^&vKpPG*Lsmcw%&p>Qon-@{wKIzD{5p9h
zRAk<0G3q9V<rK4gcEigvfzZdb01^d2pB{KEm7)Z`sT7!MQhggp@y%%=Q5cd;AjZFN
zfrQ)mD*<4t&lbGM_5A4Wp2kx|C4;-~RPXR{sDCt+_sLK$zvB-T4AcI4{4Z?tY-Ln3
z6CLQ3C<5?m*g1~@8j#-UGGTiLnAp@_IOr;${Nh;s*kn^ml-T7Oh-lC;Ge1NTfLZ9M
z{!K8v7LXVFg358l#Ns@@f`Q!^7#-*vL)YKF#=kP*aK)OzMW#{htd7DS<@|BV6Cx}b
zhfd_Qfq_Bu!R|&g1R8u`$iO>?@h|i`_5_r19Rm)EGy5b*plA`_{?2roH3i;&XWY{C
zM~>y-rD}({@c5XwZ<$~)7>kOFjcP!Ep8$PRzu^ULtfvRt1+0_c)hKxh;>EYWF3<t7
z38%5*sND=#n|I)Q;QaeLZ8aABur7!uXnY%OZJKZ-p{f0tzHEX}d8;duN=+aM8dV~x
z535RE%RxNUL}3fHeT697Ssm*ysnky`4%gG_;#a|<v?tFdB@L8B!ZUMD+jHI0dDeTI
zb5{9{2?i~CxBS4rt;T9q+DS%Vg+el9>k{Z__#P`0f(=)FmOs&!4UD2y5a!`<2(6fy
z?hRv<AGEsi1#)4R0<AhIffuFkFArBLM-LSm_;%EQd!kfnXN2?VDc#^aG$#3olQ54<
z;%P2lPBEQ&<@oM#?vtpuYC40hs`1P7U@@bv4T6UFaazQZ3`<r)aodcaQk&g}02H7|
zqa|$w>;Tpt)UGR8a@tvaTB*ZU?xe^ViZT(**x#H}PNvqq?t%ug?g*<@qr4V#P!AYP
z;CK(aAiv<YI~`*=_<ir;{rkxAh|iO<+PD*!Mb#74!2JN(Qv)R_rXxThV#yn)eWSDo
zhH91vXDaw@0H&=itR)ZICqLgWv{Ey<E*-fZ{tG)rN%Gkp9U^vop!}`hPV$xGbxSR+
z_>SDy0m>_r)hl&FrUyp7(p$zfx`9Q%<4%7*>V7EWUO6A=Zm{2@=DB0OX`!Q&7=&oe
zp8n7MGKKZPpjjNLR`aZY@JAa9(wmC;h+36Aww60H^UOj0zPJDZtElP0QuA4d1enw7
zGIAhbEJd$Lz33m}Jgej|CS;xf;H0d^p%!4SPGx=1L~|gnB2U2k3NTbFmjO`l*Q$IY
zbPl0XOK>nhQwGYrRm=E==6XXj*Ab^7ONMzRUGc}UG7e*|vs9G_d}AKUA1##_cQ4Y2
zE*5A>Nxf!OJBTLk>(T|8JJu==ngA}U&}RHI(S})1CT;%)bOF=B(+gZ%f@{-h!CK98
z><eUT7pVjz&0LYij%S0#U50!`gwfJSara<de(hH5$h6FmRUEIS-9n(d={Ixtq|0^p
zSNA41hRp!dobdJw4K3fV*B)Vq#1{L)P`wwRqm|zf`Vz8TpL$R}u_^sRO^Mj`Y67Im
zY{7K*H42tW3F;gmx$}5F^$;;*h?H&NUO8?1>g1C_IA?RgGPGp1Pk`BHdyjJYIv%`z
zaYdk-qcUo*{p|$3!eQ4RW5%MpEr;1ZvX3d=BKqvAJFGdRrl@#r-&H1>b<N_Xu--^C
z&dJj|Y#<_gbw7V0rLO)qp1mW&R^+zLuR9qVKy5q)4#8a)V%rCPD-`bx*h1j&^LY;b
zTt@KnA*z5vQ3^V=&MA+Jj0e7z3ZeA>mLPNz1tWASqRj`8RcdPLoeX>Q{1K2)Nb^a7
z5}{b{i3Xkdr|?aePu@7FfW%mH^n6Y2(iIUm9?)sD@;Wc~EjgqDok4knz`xIPQ5sB5
z4iIvHzxvmMZU%W69+cwd=FGDsG$}yUFJoTEOGTo^13`dLPNEPc{VR881(h;PO^e1B
zYIl5~llhHBh=^7NYt?ShkZT<L*Hb7;0lQPGA64#3Ncea_AEsqxm8E_Lu>D1|K8`n0
ziyLqjs1hg?G(~BkeA-qo-H^pT@RbE=5Z@*shX?}83pE{AT+lNiy5!-{=-&B``wYrv
zHo>{7RxYtj1|XW&F&GX?@Vzspvmtww5qO;cs64;czA3?pWf@qLogf|E=UgMqRb6!A
z?CdO((?WRz`W-qPqXt$jirzz^ZYU%!uHRe&#X<}$4_~NBz+Q2IGtjziE&9W))-VP#
zSOF^S8tBCCmYOwS1mc6y15*yXjuy}mZ1$JRg;?{gs%vV5W3b@62~dHx6Y;jBNS)OM
zFfY)c3yY(*Lz8<a<TS7X?7DTVV%x7T(S*R!Gm>H;NfCnqceVFv?a^;~_v}!b`)Fma
z#rg?h)BK{rIv~HS>IEp&cf_V!{d95%WR^l8qlgKY`&JmKpr|r5H(heTYXrZ_-vT+S
z;A_9vkY%|*LVow04FFIS3fkp#i3KPuRNs^t8%wSW<xn}k|BcDz*r7`YUzznuN$ZR?
z%j29oph{x)AUmktlc{tPobVP%1R_g^n3(tzke!$+`uwAbvnaJZt+4JspHvPFA)^q`
zpH6zq5}QTE$7|6krF)vn?PS;Iu<F)PCjXv4laUWTXJn>yem^}JoB#`?0PG>51c$|+
zgtyIl?f`LI1x6?&I};B=qSNS@+X7qpV8c9gKs&c4KSFs7HNVXr`RwCz<lm_|K&6As
zA1*HLx)BcrAe?F2^wDQvG?i<#fJia5$&z1_Y<jf^!(-OdYOLdGO0Q=7F;~6t1mtn=
zf$0T3;x(dEKq#eHK&^N78PMEPtAHufYQQ7ybKsCxv*yc}fD-oet8%$DXfpmrrDbvD
z!Q`Cc-L8iqNJXnZPB6R`*Ad&7A<D7HaMJ_pT4&bFEO6MPtRTRO<eX8Tzy?Qfmz*Pn
zpC&)}Qh$c4dI8-Kk?xxOFY~s1r4JR>xA%x71Ia)^boV--a9?15a1B)li`gn!MEIvq
zdM7*CO+$aOs{(c)>Yr5sGsbn1Ip757v4GacVMa>3xbRyIS3HLt6M=LnwPUu9*Xk4{
z+c!0I$VAUs{fM@y^^Hd(jJDA^8`3lbSJ4M|7UK2{j<Z|`b&M*}h{6IEFsI4)Ylwd2
z0$p$of>Vg7v9`8`#7{boz#JGu+E1pmoF&B8mUQhz(xzh^a_h^$)l_H<D+zL9Gk;>R
z2e{2*G#?<xG#BD70C!w@)NGVST{yi!NK~ig+pK>xjHx(=$KD9UH@hboCgo@F6+vGN
zfO3QcE<gip0^#IMVqyrV!Ix{iyrl3C9EI_M?=p|XXD&J>|AxS`{gbU2T~8dvNW~n8
zwT%c4t9nemt76Cgrg_$bAwCXQ`2#LI27E0I=sGMaDovp7$8{GG%NUr<m^qMODp~76
zz!uOhHkONXop-7P!B&PfJ8gs#Xir4<^3mJbwrBDV4khtUyk;_TwVf~)kM!&EMLTop
zD{+ui=|!&tv!SwF)@=~KqRq4+OY=v0fWNh4!Cqn2uIeccUM+&Aip+4RZ}&i}geDnO
zy<8>JT?C2~AL<vF>Fm;iN&L3r@?>HhHCH0&;W$9rnjs_j8p%k2(i*X$6}-uMsb+wK
zHPG|IFV`183B5MMfF7OEAL~il@4UJ+9Z+7r0%_p}NK9OSNdYZP4_Gt#Z8Ku9?r%Y=
z7)d?CaBR;D_U0sIH+$JI(tW(&t~(R>P%fjp4a3YvdLHVYTKa;R*HKOPZb0%c&}zY8
zA<}&|F*!L6LQ{rzv>GG6hvPsVhyGx-bN=G6M*SipY5%OSO=)x^k!;9RN<k3^-`NE<
z4oN=16mQfoyrV=c&~7wPWOQ*s$!k{_EFa9$&3qk?HOUNmbcttSq0VDKl1BMXl;(Xo
z{=h2q!MSby`ZY49US#X10f@>4H?!;HeD;1kbB4&vNfg1AE2a^5ik<-#L3smr8w|de
z-w!7;p-?hHlhD`}Ll_e!IUAZysM;L!KvtGDOoi<azVY7V`U2c;9O8sAxIaxG)!vj|
z`PivyPIrZAgpESjG6vRCn<+N;<RYQ?FIeA@SP{Zoz>{V%-{&>?{%P50K5ww&3O2K~
zs3t)Ms?}79;Jf7@w<PrlAG5k0VKL|Bc)6m{flGSx97k{l*0tkS?U$!7gm?EDycY((
zB@Pv)&~k{k>C2Z1=vY=*g&cpIz-E2>-3pO;w8=`#1?%QiRcOOq)y~%N6%zWoW>XXP
zynevQjO^90b{xBXaz+~}?Cm@s<ioZcovi8CIT{qL)d>YiMTN-Ez?|Ly3A0|h4J$d0
z#j%C~@OQ|qtuK}qetsuNkQ_FoitYB^Jxjc6Wo7j)w|ZykRDh7f?>F)6OTPs_IjO{8
z>)wPwk-x^kfO|*j^Cz_w*IFuNpujcKT9A1`V(J2RB~W25u9!Mv=T-j@S4r8m{GKJc
zo(7~S>EOWQAehkM=PTmAWipruNWN#fHB~RYj`26#(&b_$dXPF@pjA%qPs9{uNmH6v
zj-`n7NjO3sPOy@OGg12g1mp&5GR!Bj?A7gYgus@^?1H_hq5o$YKXJV8mDRssJKu8?
zQU3t1Ae$_dvK}#1T1hIcLIpa(w8uqy`SRozuj?TDyhKiRSRw2+iE5K^FCQr=%TICF
zt1{zz*5lt-bnx^cdTzbOb%SSQZyKc5=<Y2mRUDV&jp5#B5%dAcE*>jdj_U;_v+3oC
zT?8`bXT;eDODRP<R8X#FFy+X07Is9~sIxONO6xpV1{~r$Tgazxha|b;w|Y&Q^=9U^
z)Q%O3oQH1cuBy@uM2LF2F2zN+9G-E>uhqJlz0^JDK+bI(<WUd7i~Rh?L4`9~x@buW
z;;+E-xB&S?UCOJpojeVgK4Uk$3sgV7wTb359Z>U&E!lUKx#K#>1syUyR_1ZYzDY<)
zwTFcuH57@FMpN-w#UK3OviMo>D8HE1fJpooBDzA%Y12)F*W#x)v>GJLI6giOEA0-5
zDvM6Cz5bL7B2$Z%HsdFtNzT}Ks;iSHdaOS{eF9Me34r<<y-aXB6(r<;zZ7(bukixi
zb9gstPtANwU>Q@(ebQ@OhaGi7t-_9zN~(Ks7btI!GrOkChmd%+zI815(5&A8T~0f1
z8?sA3EA5~8*OL(ww}yRy(36dZ!kW>dh<A2PNmw>NPV1Sg4A6gTC1`fWUcLdcb>{u=
zyAEmNrqw(aKNaPze-<=6t%M*-GFU^XfI2@int>dvG2ph5>N+yj0++8|Wm!?mljq%e
z0ub~-Rz^mqSRiys0OtEa4meECF8NQbJrm<Pj6vz3y3>Ux#+I@IF-D`$&k)zGZ^6%P
z*SI6rRV9WmUKC9M^9<?dO4Ux5lp<~=O^3;)E<5AC)0<bVYo6LCX_q2YNM2KVd7t@4
zE(l8gppL*q6q_=05GC;s3X1HWPYoA=TWxpU)6TA_M!X@$1IS-$sBYq3`ZW3&XVjcS
zt)>(W2BHw)-U*)ZyrZX`WS8zL>zekYKDeCUuOLBu>lAJR-1Min$WNhg?)-KT<b>*u
zXvJYMF^rfipfm!DC<GYNRcIiMa|c9@Z|Qk}w%Xmm8JEd12d&jk6a^n|0@^1jFCRi6
zjt3ex@UQE!eAet3=v7@-!}C?3MgMZR3MKbx@hB`}6~r&cLv+mvkhhWhZRZKPc!ldm
z`bZTBj`S!$U;<yq_dq?%F$Z=p8axMS^eAMTh%vV{ln%Sm)CUm{Sv3*0OfCDdoW)R?
z+_F8W5&+qnLH3aVRZcFa?=Wo(n%F?(+*o}*vj6E8MQ{-kfCvD+$q(X#k(r1H(R)M<
z3kG$~s(OhxW7^{><Va#C9i{*h5TYbtjW)rgZi2CTHDEz1jt4s@uEDpL1SBx+2K)si
zZ*a=u4%fP*Y5W0PAcB$)lVCrHO3jFb22hJ}JE9jT!gLYjRGleSfC}9};nnJJER;iv
zF23V98XDq|1<2ZjP`54vlDQ1vEfsYQHqU74Q_Z_9QqgQ>#t1;FztR$b=Q-M@_jg}f
zOne2^O`JGE4U&%p5b&4?5n3jIY-IE_WMcEP5^6@+uUxspdpxO@qfTpQZ$DiGHVqLN
zrbQYE!+6;A!72viDS?EQ%n>bD$giA2DS-kBa(PkF)E<CC)sI*WBsI!AB&OelhZIOi
zEc*~h&PuwuRqaegDEP={L`>K1&9h`+-4-^6_r&nL--69Kvs6@;QG4tK-ZTM_o)1VO
z0c_uJ*qTEIDj{t^$Yf3qD-t#Ds&#jcD-*Dr@=JFFOMKSu9n>R}kb$V49+iP~=l~>O
zcy4Fb!BA*?(a4aeLbf7Rj&XBw0feWw5Z%t5t;*M@073n&h<h17-~q^g749BKf@=Yw
zia~VmH$9^9Pr~N9*v38MuWxC^@7<Gxxdt&ewX8|z3)YD#qx1GtQ&X*O;Oa301ehsd
zchtr6Mqq0*B!dyzp4EUi4$1B}fql}1#2S#}hE8lh_Uh3@AFTKAUGO=a?bx#GCENb8
zr6HyZsnHuiJsFOU_N{j{^p+-J0Y1tzfSpS6<@G$srYl!g&HWS!2@*o+qVv$cK`d;a
zxu=Q2M4VcIpuTorK4ei4jsDA`{H!z{Eli>e*J@~uzYC~ADy${!21+q~0HWuRc8d&0
z?R5;0!Rn?1G98zO>8cB-Uv?Z7_1`2G_XBEfI+~~_w+d^rXJ)6=v&(S{EU6eds`=E~
zY&92`iu+K<t_I$IIzO+9%nyv`@!__er;}4zx8?F5`WR^JhR#P|H;(MphlhnJC5M0<
z*}mIn_<?2Ks7F_{=MR4oiH<m3F@PAwTkv%}jv-cPV#2U5Y)VEe;eRTxxF2rHV&4a=
zv4ewyN2PHrunMCc)h|<oWJXA%coK#n5U39*%xU2&I5(QJk^pav=)PaE_DFOFgXZWE
zC}Zpp_wC!N?0l~4hH<XTUrye^zrrQ4QH75BW%(o3e`x;@f`7K^x=@BPRkNehmvoB^
z{W}fjfs6QKKYFP!9hnVR6haEYp#qIK2CUU!UT#wi-5V8_t{s@Y59qEdq|dP)Hc>FN
zdx5uTmB0vA@*0yTZA~{K(bU&$%Duu1I-Je@YpAol?l@or{QbLzXYGBlUYRK?@-U{j
z)?EatU@aiZCO##<qOvQb8vAwJFMu<`gcSt16Aa6{Y5d^rTXIA)Hs6lW1OveX?>W6&
zEj2`PJ}WDTOh-BHais(KN8tXQu88@Fh=?V2F>vHu+Z@0ow@CbQ5e0<A*+oJ6{qOJ`
z5TysTp3Jv`(^g>EMpS+2-bbdM?L>zyMLKNu&h%4=2Q`@I*Us2!I}h8Q385AVfP8oJ
z@&WF7()W<}WnU=NNz5{?l>=4hL~=?B6+4U1Czy0N#8kRl(36#Q`etQo-LR_|j{5h&
zcP>t5X;+tZFhqP)kI7QYPi(~aV-T0Q7*H<~5v9yafz^vx106=eiNYpr5l%P(M=Rhr
zHqI3x{2UyLM$LEXLun9>aN-c^{b7|?Vmb6!^rzgRjUZb|{1EH_88^xVo&HVvT^l0^
zv;*4qO+VA-y5|a(t>dm2>&=^OGYx`y*Kgct-`7C`u+$?Njt4ty5bl1Q6|+skwm&@J
zBrPd94o0iZu3{SKl%yb1vPHZNuy@Gs0{Ed1<(7f^wYrY0TZkJ501(v|W9xe1jj!B5
zL+B?aCOjx&V$1hi6Svkd%3vUGCGjB!a(O=Fus|Vm8i>hON;!RzlK*{WXGSk1_NE(8
z=z@)z-zO(QY;zE&n_gtIG<cg6bO}yS=@3;3sYdvxU&3S~h9M&b99in#_MRZ^>MK`J
zkU1fV43Ye6n{Ks8?4pCAHKbyHp{y6QvS!#jCJ7s%UV*3>78XVan^!&d<}za0?Kfxe
zMt^)sWeVtE*n<^p_XOE-h`bFji1<R9;$vh>DMX85Z;U@6H0ahy6rmYd*tC%gVs;BW
z{LIY4A_H3!6*d<Kl)?8t56uafnj{>qsl>Fq2_%q*te5fWT{hO&hbemX&}b+UP79c?
zUx|U65NlUC5OQ*I((&-9fIe~`pGH&*1h2HFw4)FxOPa{v5s390>qbaI!T=ik_171!
z-at$%=S>4>NT*Po4!;#M9J4uEz+J2EE|LzRB($zsdu=jWQwiILh`~OBVGaPS8~_G1
z*wFC#bEt{R*~IRWj(H>a+e}f8*wB6Rc(6zis|;MRvo9O2!4M*?vI%DLw7}9gwlgJ6
za@CoZ14WXX3;hZ}V`bo)kfw*+_(GW1MCDFQ-v;YK2AdNO7rts**D|2|0CWhlsd+fO
z@9T$fP<)>|GNiEAs0(%_DnY*Z19-cjaQ*&Sn6lI9bwn=@M%TP0OULXkP;OcST}Xxx
zEgS%kSfpM7U3dcI=*vmyt#`ixl$d}|?iU<P0$UhQ(Z>4!odp2#D-as6Kg@K0SfLe^
za+V4j!M}Rff2>zpLoW&-VQE4fHhlvFq?sW{L0Hg|PV=wqw?NXHfciE?-c*)=w5B8o
zbko0^z#q^Q1`e#hwKy;V_HyiCws0`wDPWzFCILGmS%73Psd&q)nLHWdp8(QU2Rogd
z-{yVAWQdUAaRIE<WN?rtAd5=izO`7mU*AK6HniSad&NY9R?fcj(`(d(gN@#$wh%2&
zJaS?N&UhI-PEyziDW1{o4`UDr)_fr7RWWFvAF<G|g>_4}QvCg1sQM9z9=(fuF$gL)
zJI!0W@z1z%j79KZfv8ew6Yaja5C;4)Lu(IGG%+Qy<XxyyAoacp4}Sp-c~9s%9bNqd
z8wF&Fm`p%dksKwOhHG54=YjW6od-B|4|XI&l<O6Q;rE3o;oSy*Jm26&JFF>i6gb_t
zEVSx9N5;k>uN@W>Oe02(LPAL5`ThF>6m)dQf4%)7xGf&n-Y{Oe1W!OnUVjE^3RE~g
zK)NB)H0U1W<5A~p&TzQ;Z2#*S3F|Z=|N0BvIYr8Udzb?M|HThl_$Leg-|$=q<Pi<7
z-%NNQ>e`vl3A7XL{YjLao!vE`RdeAwg3Fgrq9E-EJHtIgzU(ure)NI;2`I@H6_}<;
z^y`SA4UdFC-SPAk1#=nsjh-0UJ#ldYDyj>p0(Dl;<FVQE!elS+LFm|_DtW96A|Z#A
z!P}}n_MALC#6Y7^-pFGmf~CZyr_k_MP|a{rQ`6H8`h`0JbSvk#?3FUQ|JY~=`&%It
zvoh;flV6h%h&U3ky-n8uVp+@~AeO&p3K*-WgnVCXYIw6gY)JPEZXwGVI`e5|Y3ckt
znw*G;C~}CLl=Mb<p>RB3-TUsjDPG9(zL#BnQm%kUxVcY&_wAUEWK9d7cYf)7A3C#t
z;pLImDT2*?&Yzu<SX;9~^%r(mS>U{RqWiB1kmqkGRW@q!{A;Vhf5UM7_doUHzcLwB
z+s1HQ0trq)dXL|A{W|g}0-*OW#V$cyrWv$kYdJ?lV`F{L6L%mIXamWmVTjiJkv&KD
znYq=DrtElV@cnacRoS}{zeeC06{BFi6M3l-OLng#yYQimShmxEKLvfO0njxkY&7<H
z^(u;)R~L}L-!Bri@59M=^ZQfa?4Z!#BY{n1Q`$xmJYd4*5ZnKO=^PSQ2CbJE=p4Wp
zQmgtyCnTN&tjaBGV%=!u?_)`*6ynOg*amXe0>KkxcTP`^#$~_-u`wVlF~ELK(06=+
z<<Y_ZGZ29R5R)Rj1KQmpY~u!AXw@=}sru(>Et&e~V^3L|o~11S)d+P5!m1CyhQjut
z`Xej{>Jw-s7`cC)TY^NiAr3OsuwYP#(XtqAI#9%b(%W7hnNGKY0N$Uk<a0^CYMquS
zP}e~DhkI%D4mQ@HJ^|PLw}Zjb_pUcGGQ!!>P3}!ZmNk;zZq~Pbn}anPfgRtUf#ocH
zbBCn%Exk`DInLdOX%CNuBEdsBAopLHa}b1@-#qrzMH|*(JTE!^-OAwk4HigWBziSS
z)CL11-ZHpe2RYrB>-@cQN83Z@;GK3H_v?KSm51%dFW0qHR$BCg<$Iy55JwKFA80{B
z1Z%)kr*vSigg4$|;z189&+(Xzhlhu$T&ea_V`C$Lr~~o~goH}F#2#Z5Bk(jCNW>0P
zDLH$RNOnP9#CU{JArYaAi{Ao6LpjuR1>2Qb>tG8H(dJNQ_nd`bcf<Ysesj^7ld1(Y
z8AxHeb)z8g{MKZT?(9VKutj`FmB!2h_El_{QbqAYE&uvs+e2%^qvIhoZdJ8GcCwq#
zmhZLw6P?YU)TsBY1Cn<f4`FI-ojNXOCd<)fJ)hu^$VM6%?=%}&#<6e%#^YmanQoOg
z$MMbx+^J@I$CXbN`!uX;+xekXUlLhj4hX`w<=o!03HDaP8<1Lpn+C&Is`Di>F>$#-
z7V4^ZvQMs1J-K`FGT*4nNe~W7g%mQn$DysqPOiTKdWP|Fg1xMYM@8DDW~wO32fJ~6
zFAbXBJOO}GQ!91%E)MLpKA;TSmXwl0W+m!PM8wIIuHR5d70~RX0Jwj%mtQ!0_O6yz
z7ZV?-NY<||%YvB$Yy?Lo$evS3NK<ExNEWuZ(uwsS9$r~G58IJp=N;<6nf-KkB?PBT
zw)xMd+tV{Lws6df10ti8AY7bHZZi}IGW%DPfbcC2li<qvbLS2yLQ895{<lZK_so5q
zOM&_Y=)=k7(l7)~Q7A}SuasBip1t4>a0;$;^0CEex;)*@0HVmVI;DEUbrVCxK||_<
zq1}42s~>SEMOCaL2<TvXptVjf@P5SI-`X{&Io*BCCvB-tSXRi|XHF6{o4jYwseYXF
zxTF&w0K|{X)$LON{-XHi>mAyT7;O67$3%DZ+GJ)ARR2|TlwK*UzV`8X53~{G4Odv#
z1O(axky%mSA&70K{O@l)@d>tM2Y~yEIL;6q2-}`M@LC*k^&rL`wybS08}n7472pqu
ze@&-&A0<gQ@%&oqFC~db4=r<#O3poIPb*`ZXgp7{a2M-~hnr0j@LrWY^vHyS;MKMF
zW!K&-dEt?~!0~zWhSmM(=vT~b&g!Ybwp*BmU6)lCd?D_!m1oA!ocLA}4!^;Yy$xIy
z_XN)L%*;n9c(?nsYu!pAb&v&{a5$DNPyX?MEYzpA>tD%hbn?~ecbYlqj`Jzw=HU8&
zfFeR4dc0L$-|LNY@?&mXSQxmSZuf!ybc4`X>asRrwgD;?k5PMM=HY8`THKd+aZfzY
z+_>7!`{!{{@v@&!)r1q{pEgGe_jND>v`+(V<Sy_nRS<NLptR%M4@uaE*$aDdl1H!Q
z<^@??{p*_MSe~BHDIdy(%E9|F7~sDdL}4hnjb#)1+b|d;sX+U$>%VoH<F8Yy6XX2P
zAK_m7&wcoFLi`8!K_z?u7v6BKE(G^r&~~%n>C%t|Z`y7<!-EDrML-kWIr)aw_iDK{
zDf&d=eR__kYT(CN<N9*(P>bW%MXrft$0BL3dA{$9kaj_NE0f;ifuG3BTas|~#Q*U>
zGQ879QFL0Bv(VEw_E3a}*9k?!whGytCYOxQN4zgOj00t>weoz1rF<I+-^t6#MYT+F
z7E&0fjC=vX>@o{yV&nMz*2x|#h*<W6Fl&~d@1Hzx8w*nVn_2zd*{-0;l6T^AqZvy2
zwJTMZs^l`ikQa_RC&*`<?IovYU}%oCZ&<YNGEo()7fAT#GyLUa=ZB%CXv{QMAq8Ys
z<ovhQCQX@%xwbD=+VE5EdSAKdOLEayF1N+Rc*UAt<#3hqqVHwTtM@)Aidz||`q!Z!
zO;{Niq1*)H1O2OlRo@k&{mT?mEEVX`eSVJA*MIOPo?EFl-nRDnh+~~xmzZcaq>hz&
z2xlTWRde<l@wu#=oUc6;z*p(K3Sy$7-W@ey@F3?CF2A_)<Hw_jZ2$U?YUrIq&+gEh
z^eVMx<;lt7qDh@o0TUeXOcT52XRwP*{ME@H{$lIQoy~f;-oBYl#!LrjmCkPLf;Pw*
z!4gp4J+1}M6L7tL>eeJb8wZE=x@a)b0(u)@=?xVF{Q5B($Ezowd&BK%T)@WKvA;#L
zQ*?~mNaUGZICbJ|Ww2GbCi27(hl!f}%*<P*iSQ7Jw}bE;m#+V*ZGSAL|6n2iKV0tr
ztU-DVTi?rzb_4Ov$4F%#eUG-A5ED(JY^i;XC2=xjj_j%XGU;^eWq6+Ic8>1A@VV1k
zj5k=?r}0kVFTg*_Kb5v`96aM4aTB|b)tDn}94sGNGIOfY3Ma-prJ|!_rLJq`dryS>
z2{}^6O!!ae0H|tM9?6?3N_)LEF0?sPGW5yw#B!Zr;!!q5Xm$Ho^yJRtNkR57k7KcS
z*A2vic7EiCjoH>c7eU>z&{d0^8(*S{)t1t7kTp#|UUn}WfTw7BGm$6b{8{i&tO4F+
z|KI+Rxoz>p*O~48+ish`1=-?~lJXbws`GS~>LD!|D_}qKAjWmGOB$O(bC{{K^j8b`
zWJ<I8@X;;uoNhaab7VUYjoVc8Z8eVD)D9sH2wvXZqQ4vk6P`e1=Id^Ab8{7>ZH$Ts
zdNLG}PH)>U<d)CF<HD?0A<_89YZV>Tpcx20^uuuW>P4<ft-RCa2Ia-se~myBS=QtO
zpT?(b+Epsy6?>$Ae(2LpU+?YfqWg>7Q86*u@Nge^$PrgH<zI))oGGDsxo!9u9Lfiv
z3rAY;YNiZAs5|NiwtOx?g5t5UaXQFAkufnDz^3}LmLLi88yqsFS$LphChYqP{+M6*
z(ds4ij2+sW;o(FpJ+}OmVWx2%(Qlv-S2*xfLWNW9Y=(5JPqn{6?-KU{f391ex^@ow
z=g;jLW&aT?zC*29e#?0E7Slg!di;$ays_B2{lo3%^yvGu$%fxVbKI!o&*jgdQ1JmS
z&Osk)YP1&m^JA`S+qE$STdf3g)cMJhk&$IrRH#Gl2=wGy2S_{i3f8RvPA>t|KXW6m
zV&a_0&aaojd7L`X3%;^9C|>lv2g-zT%+pMGW*Y+|W1RNu4`3{Jus7Jm2Y$ix5nhks
zaV>vsQ>kS}YDBa5FyWk=98)U_*~<U+AlCAWZEU(?#I`AM^$4{ijoU>RS!jgpF)Z`E
zHy*u-3J=%cn`PwQvnJiV?V2PdKq7k5U6|TccqVcGgW^O@-3b(t^n{05P%+q=55pmk
zpHfm%a=JVsJUsdA<r_?}9|f+hXuyJ*gQFkv6<7%Ant+n(20Mmt!|p?usEdD&;4hxL
zpRP87E5!Psy|dF=MF=Fi+tf00a=oCt_rM+*IKDi@DhDA)4bJ}M;so$rm>C!t=4)U)
z=b=Rfe=IJHmwz@jt1OztG44sqe$!6UoS!nfGlpNyK&5DC$xbjKJXP;j=ACPLc9Lr`
zpdasMrH-vAw6fgNVpp4*%`~oOCvRS~=ZwEIE5$?{;1=(5`S&p1sT1ek+918|#*cj!
ze#(%lOPA-+%;fk|BVQ!klGgVR)8o$@Ck1-e*SGESJj_85gYP!-snajzbndA}CB@*N
z_@f9`r3xY0M?yl<Hf@&$ySQItyFqOMR$~SsygkUxDp0KI*mvCKoOP|$iHeS<P|{B@
zGBlJpF$z1>efD0aJ~GNAKKDv?Y)r~VFd-~ybN|pY3SfYi(m`5!x|C<1nvznOBF_D^
zuV25m(%c0h<m|RlAb_O!zy`hVk@lQz3#!TZdK26Gj>7Gt4rfk0j8vkp)yY3F+nY><
zr?`f3M1JL$|6lCAcTm;Y)-8(KYPW*4Z9ovE)r|=SC4&S*D{hb^QG%EN$&xc@bHr{H
z(2al+RdPn6WQ=5xoIxe$48kV7vCuy6e6Q~P_f>uOeqE>QbSZoD3v10e=9puSRa#ma
zkRu=Z-l0P_jOW#Yi)*dPg|J^+;wobUAB<nG*S~^t2J4V2YTmA<tjurEr1H#pbPEH+
zOKRW=q4$_dkEKwdWfWTY__KbHwJ)U~A~1nejvu@9Z){=5Ki@oWx9eo0R)cL~8Z|@f
zj8>M}|D%p$FxvNdF_<_vZ1`!lVMksr>=uk}>Ok+%0M!qL3Q@LD>Oa-d%GX_X92THf
zV<!)$E`Tx<fO7>TKF$I?K2M(X!MLtLO~+ZqBv3f9Ar_)XGAcj|!$7kdx)~47O#iqr
zxdW{?;_qYEL)RortIc;^7LDJs;V#O)(DtA9v2QY(o9NcabFwd+&bA#*h*pXejJxvV
zqmcru+YdtvcYvA&<DBksJGQpVh&5R2k@$VY@(-iVw1z<yObXPd=*w$6^oN&JRQQ(b
z8CmK4^Xnyl%26TREj+r!22ofPm6Vn~5Xncq%Z4sCs@<l>i61}vobBW1A7Pi_!QTJV
z{!^Mao8fnEr$Sx&!j?1HW08?Ew9_vh8SCo0Pj5+Y3LlbiPjfw-;+!jUc&<Ka=8S2^
z0=FOKkk(~x&EK!6>ln5ue%T)HVYIwecO!>i+j9QLG_#aL>c0D_8`ip6eDh?j_v=#l
zR99Xwvx*@p@8x}q;n-s%i;ewOB}zSyDcE;&P7?3t=T9YT>PJMd61;J4&*!tJlYD%N
z1Rb+pI6qMxt}Jjd9@%<VFYm?`=Ex*hZf}!G(RcomOUuiLS?9#urMw=m(>B0g?bjUp
zt5vFpwfWxYm~D;e8u~9SHOlP+r`9+!Fci$!?`n+X%+5cS{qXhcmu=73ELGD93{fy+
ztoreq8MdD-*!bhW|K~ryKZyU%&%x)0&8nMX0p<H6YYZHikXC71H{A<Jza2(d8;~w_
z=GnN@eop6E3&dC1=sh<wV*mDHf7du{(<Iw?Po?r~WKC07@5`fi+EuURrZ?}}v?b>F
zxj4(*0rMNy$o{-@;?+Y9R<RGPxkcsNEUQfA1C5TO`aIP8cD92hpt=7Rw?*J;hAVmR
z=iZyMrL+u}6cu6VL02-hf0VC_tKsqf(YhUi0p(M@HQg4-PXhM&x^j_DvhA_Q#ddqO
z?w9n^3w-dUv#{%^>4YiM=0PpSpTsBpo+><A6(|>ZG1NG8G)n1I;xB7i3p&S(u1;j$
z{AlEma_g(pFzcYDXUnkQ3%2X8wGyIQo}T}5MOylPe~4LGpQOO893;$=ekX3#Z5!_T
zN1>=5;2sHKbCw7>^yX9lk*yp0i#A(!mLuJqUHTbl#bBPx&TF?x^AaN-ovghCFb0|x
zeAH@4;#3qBtGWdK<@EP&qteK+6aHgj5#ixy6Abw}wNlqh`y6b@eJ{$gYumP8CWoUc
zdzI{;m{`p({=(<3WN)AO=YFTP>eE{ModUT4FDqHRQbaFDp7lfT<k_~5zze4ZJ|5)Z
z5$e`DFE8(<b=C0PGV&{-4*q;|rPjG`dVi77pQ7WeuhTo<KjiteiXlq2b@`kL%xqyo
z9)(HvXy`@8O?N8-)z&OCM6vB%>-J{<vR=z}#>3_=3dR*2Rw}M<Z`L2z-FR<Hk3Hkv
z(;EVkm}7$<+&NkBuiHY86jqtlPX4~*!qeMA^@95arv0qeZMb_nB45hC%JtY&VYRW3
zqH<gM4`&hNX>$G84*qT}cmrHhk|{4J<@-kq5N4@(KM&YNSbSSM{_y5suXUbxPCHD)
zfGi69B^xGlkjDHJ%>IGtw_^TsEO5kx&L8eKVS(cTfl3r#h42~ZHtVZ?nr9XN5uTnN
z9a%LBk+-sjMhxjg5kpDJE{uUgr=)<%JJX!;bHk0V_O-YDyPzn1<QVl@i+i!_fMgYh
zL!J{bj!6MUEDlOPkL%n60@b~}Ly3o{-$@~rf5@na8{cG;PuL7aq4^UV7WM~JC)Hqf
zDOCKKf{RNY5U~{uCSszZiu!P<!pc9tzu2*ROjTN1ngEV8%qS0IRFPSH>JiAKu4xAW
z=XooXB61Le)?n-{<d$u~Kk0QJ|D^&=6{K%|P|(}9Z$EtE#EFS-#0Q@V-tU-)Zopl?
zKJ<g`WeEJkdwtHATk4wW##OinnKXcjW~K)C*!`Z}mH>{1Sd|(=iGn-LjNsn<<NA)g
zC|=#ZPs+0oZmr~(S+(2u8bC@-3j<>emjimfH3mi@SCPY-b3pfsLVcld9)hwe$V%#z
zb^m!bx;xm$Kw-l}N)aQv<oA5^;z(Si&&tp+8vGKNNDZYKvG~MnH79kqmlT<hL&db&
zpchWV{K*%7Tu@RNWgnY3<jC8Ps6QvR`WWiHFE(P6ZH@W10bfm1MF4pD=8sS|?0yEi
zC}s1%uOa+{_~%`izQru_rQug_m@q;`kOe!vZY$BjTDiLJ(=QaXjD8}&==Z9-@2;lE
z-24?nXPaLbt{gIFwj=&LQW3D*RBdyi4-a5Dx;4+2o4IUBOkTM*VH0d10(!O2xrK#?
zKhs{#pf26IrX4Dk0dytnFzfNe-@iF39n%_HB4fUPW|5tFZCYep?GW!km{N+B$gr#p
zj~&DL9r%6u-@a~iky|M{3l4oAOupEb-mjff8s)u~g;|9wqY%VsfyLLQx*cD*SH6pZ
zf#2h`&KoW&Ld;_WuimbXygL0$KmMw~_N&WUh<P0m++U)&XuI`}DUDka^G`(+K#iWZ
zB0qlcQPS7lV+rc7_VPNH9?huDT96fL+xiR+vj#b(KT6=+|JpWtdlqId4=;pRR*0l=
zci<a}rtreIjavNpn%}SDVJ~7vb6*YGuxZ>OzP{U^Ey0H7|MMf_PT$6CJz9*++VxV-
zYX3uR+_J#^!!3Q+GYQCn<4fs_OGQ>_yZzTo$bh8!aX(y}HT;O<_r4nRKCb@P9}$w2
zl;jnMTeXItYu96`y*2yJJ^bsx-Ydq7w7?D0rl4IKz?6p~siKB3kDhkx%V{dDroXn*
zWhTYW@1Gjr7p{?2W(WnB0hNoE;mHvN{gMGxuk$dJo`yV{Y>xq`o<4f++IeZ@F3V*F
z2M33LE-Nxx;b0i74sL!fxTDy+e)TFC`d%PAq9Gv&*$maxR87c|GUkpNpyM%$+p_7N
zH6+8aaC|~gWlD(O<Ge5H0e9AUZa<k0Si?~LU;_G7i`(!1{rfTE?V(c4nOfW^E^4z!
z)lF8*9xDFp9NP8KIC4W8imYsk0jcNe5?!`g&Ov;B0fNo=&EYHW>~>8pjZ<a{p1+2;
z8G#-WBM~fXAQp}h&x4G4xG6=7OoUK|F^G;*$Ez%($=CshTv)+J$q=3}Sr}v+)K)t~
zKWBC4<VlpA_1&aK>mJB*ac=&}|6*(6=Xfg&SN3E0E>Up*=xldy?_f%$M9rw{vSVn;
z_mva*0!1*CfU$6O7=ZLM%^pDuBEouHM=i8y@jCgJ(LYc+xRus1O8mp7Do%{{VPT~W
z&G=VQq1CTL<w`24>Ad?HubkHu%(7anH4BG*NncvSW$%W;I_v@!W#zD2!f_aTh(eF|
z@hgY#OZ#g-jM^`no4<l;;b=g)s>x+2Z_t0-?^ha*dWn>>J*euVVOvBp(mLkd>}e(e
z<q=Z5?f*b>$%PY5d`?b|XhU6NUezrax(H_fRPd|pgvz6q;rdJZLE#`#)Yq(IR6t$%
zi|xwqynPj=<9=$^*ZW+CWptP%aZ>REQQMR13nBidP-j~$<#X$eE_5KQ;+=!~=;4L@
z)Tgw&8u~cHvW@H9$OOK1ldfMhh$3lOSru$^0XX;KHSy^Mv}BkyV2Yp?8gzTH-LMqD
z)LH1&fZ%mKVY2u@$W06>476sZVC`XQH+ubUyOSL*bdxgc7|*+O#8A={Bx@5oe*wtj
zO%S-7Cy1&Uzkj9X#SE_35C@(QCOJ}?C#+d{GPoD()y~YURp8JXWL|lxze%I|$775*
z<+kn+CujJ_1yZHc@F(@3cZP>YMsk6P;cyze1NpXc?=)MBt;zMgb#ABT7G3VX`@-hM
z41IBfmZb;O=HVzpW5G-z$k$*vDq(17i0#hxgInSK=1mG%Q2a7Pc8G2()REyBLQuuO
zHXb_z5YXG8dm*xL1mIAJTZ;#$4WSXu!YMO#=_Z;GOmZ3y`&|EmISvJgJ*!ZNN4|Wy
z7jy?%-RPBvV5Fy*jowLbxjZ*Yr$0}3Rts?%e0iL{I*OSvzxd~o!b-KxCgEgPJJ%a%
z=}CzOUk0op+`ld803YgqYBdtMPOtUXI$P&OSgY4UTi_EE6i>`SdM6KU-9Ro2D?AH&
zL_<d1@y6?32K8~5EHbq;pq~a(0MF7jIj5|y9)+pM*pI?*pdP7)XDj&<EK)4(gXqTq
ze?VOBUD;cM0|``ARkx?q<3yZ+nAn7Ba8vjWu-6Ijrj?^jWp^yfEzdXPQ>aLx!Ee7L
zVT^*4iYKFlP`E%ZmUp?B=&an}GI)45?sVh7@08e+P%q+0jM_QSXi&cUQVc2rMaXgy
zwd#A0v4VkczsuE#V}rHwpbIHf%vi-)nv4%*=N-Ps`t*ct0*Kx{ku`^~g}@F@_f?&d
z7aRW<q=vgM<>3Y(+4i9jlA=M|maIUqwp~`Dbr@<We|r3k<hM7>dk3HR_!!{q2}3Ax
zF@Nw9+RM^m8<>e55*ODdFa%>V<WBuAI~>xBhcHzm-S`=38!i+(UWK6nrdd?7n<%ou
zVp!Nddn~V;Nvn0Z$nU>QZuQ;jc0?oi$4J4vrydWo4uf?tl1{64U3uFK9<Si2N4==3
zs#>PI|H4yp*nt836=-3Qgc@SPCTphIs1B~t-CbQcx<f+LKvHty3o~aO&u0&{j%l*$
zsG|L^GgWeNY!EFBgU%PpY7MG~(Uu3Og^`>|IeW3-sM3Y0NJ$?`JY0G8PCzaG7h8Qh
z5;!dk*f8eh%a?Kjby8w<kA^&kfwzxtXCx&lU^U^kgM%Fg4iRvM$<53>FhCm1-vb^0
zM<NWPUPLyK&myNTMIt+BBD4`hYT{+u-Q7*4*$g$lz+y4PraInWhJt}oM~|eB_hsk7
z^ah|w0CQBt#RrFO#4KSczh__nn)gZq<DS8n6NkT$)!AHL5wjG_=#QbO%_%`8SC8cJ
zz+xQ_$ComU6j@<ir}E>v_a16z@&zf@YFSx_m9y7tdbmXmg^3Elc24&9<Qw&Ia9WxM
z8a2pQUS7UoEsNIkjohzK;{2FuI9^ZT3bcrU6Vi1EcAfBmPms^BpRcdr>w5PYZ5<5i
zd_of8hivUAb~rgJgF;2-CwUQG265Q9QofS6KHsMFKjf>3vgGjjqJMB}%=9{oH&>4J
z<e>O^I!WdU@jTghO-=r}Zj=|$)bIOZ3Keo1tfq*KYJ1!F<?%<iO*ZT$yJzV;Q(f?^
z38Wx!9+>qaLL%{Gxt>`vBfp)It<LtyfsGpxtrH=Ih9caau&!xXUF9K?I`Daq+U>ev
z4wxT<<@#7OZ1m6etSI((vp#B<s|Y?3O`LTG0xu~mdnlU7D=aH3YjTS&TV<AmILu<s
z@$w5|x#xv^WaxiouUy-6s){G8Bg~-JWhhSfT*doV^b@fgdmh#(IqYTIAsBF!ch}E9
zy-dhPouJWTo82agXvPn}Fz1EwLbBJvl<s!udr`!TAd)~afLhx+A=|7Yj}`{yRA|NE
zA6tw9uY^>xux@(ySI(i}#g9fh4Az@x$?81Dt8FhtUAc_g5f*ROvYe;cjJCIZ7L<T1
zW06(2dUVGu{OPH*3b??mn^g+QgLe$p0p%=5MjGM*{CKYA(zB<kWjIwTty~s5Ib>)k
zM<Sqmtd9=DQjn&R>Hv;!$?ew4S-8NLn~aj|#;;MU&$4KTJ$dq^sMANSP0*&%eSXG~
zcf6V9r1pF9&%-FeZZv<n^*-DF`?YzKm><@tma={8*1nAV#dv^_Sa{qhXS2axGQSQc
zqWmxsT_Jeu&;*GZR!-Lmxee4o!l^F>mZjFsDf(VvTRL-~D_23e<0W$B1B^c6kgEI9
z`0<=#-EcP*;L2my3Ds)b7YSB-`H3|kXVB|Ct(aulp6M^VgV7M0Lw|@P)Aq2VI+{i#
zkrp-IHci5XiPJT3$GGtUAkR9C2wWQp#UzM2fSpO0Nl0ZRrX&X4cbYz;ERK7*#-%%T
zZ>uGb&+T6K&GY#Owj?^Qsy&M$1x_1hBX;r?39sDw$unAL`q08KWFQsfy70}Me7F|D
zJO-Pj6pW-^O0ix_H0g6yR;n2oM8j1p7U+AAp5zKb)OH&GV4V>E${B_f@nqs3USKhp
zhHEkw6k*>K1WPpFlsc$Ucc*p0RvPQFF0mH7{(J}sBS-;A6vB~jFFO`TZ0qWuVXVzG
zZ%z*}?E>Z~E@G<2SP+M)(IDtQ(1uAhfdsoC7%q&w?%YIvMsj(s(kD20casDnJZwqe
zRVUDs#6p{TzeWCLF^$u(Eh~+Dfw%W`sm{_x+El?B=03Pd$EGj1E;hJQX%X@v+(0xi
zs|1+NMY}Lfa*sz}hTwSP_l1H9VXYeun4bn~vhut>hhZxx62K0`{CMXZHDXsojNkl`
z;}9!R6>=iY;X83Ti#<a5RR|;q6cJA)izP9!Nir7#t?Ev`3_*|Kmd^1ggmBcv85Lra
z7x2X3^DRXTo}ki0x5+PRu>w;pWORhE7mH)}B%i$dq5xhW+QZZ<Z&t%>BKITH`yrcb
zG9!r!qYu`5$_P4Lw}h>Fp6L(3ZlE4L0PBye`pTMxdMW)lt3?4&*9EN&pw`P4!tByA
zGD@(BCK-riU?6bMOfT{loY5Sc@8)rh#*QoLw&+hT<tEfbBHlyB#|emh$;(T~8aOOq
z)~*Gyk4wG|TC&{oR5yCOtQZML)Nxas_vML}MP@?YydhconE8)s*tqNa;=4|=J2;10
zOu9v5u1zgKAMIL_c?aud;nXSP*6a!)F=uqMNAbYk-OfTr^%s~W7m{$c(-*c$!YhDt
zYp`n_wi(1JXSE2CmqwS!3dQhV-sM3nOxh7g@7<0=uN-C%PzfkVy{Q=Fm;d?CjfS~<
zS^*J5B~WyQNJU@<bD+*FPB7MRD38H#%Pg1OgZuYu;U^=$F!oLq6P@G*iDQ(iIny!{
z0%G9Bm)=X?v(?QwOfSM+M-^k48o7yGak9ef_2~Y@<L9JIBi1tL{Y~GNgVkQQE_KEs
z7u?6Spa!qm2j8oC;YDHgO)J|DXA?$}EsZc{+S=TnYT%9GmeBmgVYB3xPM2YJhT4sZ
zr&pfb?|T-%vg9iu5e-^@xnu$)M~;cn)z{}j8EiVd`sA4piyPW8!Bv?3Bl<d63bW6n
zcJ6GBDA%jOe8<)X!Bx+Xg8~Q!>KQlvZRL9zen}W8uVAR%^}p6&eQ^HE{pR7rhpomw
z6Ua`zhzkgKsrmh*4CimUuO-A2F{K)Tf78n1%#kL`88s_~O2{r6&7WdPP8TKrl=4wz
zVad#f`FzkOxf*^D`!sMYjjg9v6AC3D!03<fR}Zv(i;>lB*54@M?|)?j;YCBSuH7_T
zmCeR`<NOT=;Iwm3{#~{oLZSv=6a~Nf^EB?vuHOje%Ic!YU{k#0?m$*A;{Ug_u~xhF
z-c8~_w66xEUk~lOO~VP(8zBi}<sVc)fdN<SrK}~<D^HtrYpD@jO+&vi6wnBHf&<cc
zgS@@BJWFpfuG$!7p_GQ4@&1PzKu(itldVC?F6UmuaOL@H=BhDdy0a19-h;ElEm*gm
zT1?*`0|O-iYz`FY(JL$&xZJJ{ZR%$PzB%O;SpjhVU=%)2ZQE3|UDVKKoAqz$`sdU8
zW=B^syx$Ij=Fg4XpOB#_K|3s7Ac-H)&Kua4&!TOm`0r14^Uyj2SoPEt58O#-mZ6rD
zYdUgZ<P1L!J(7xmS6L9470?k|pXK%V8-3OO(G6#y#JyghgAMwm#rYb;EY*8Is*SEc
zq!it6He&DNNQ*;M!tjCE`|ZCHLVzW8AQ>psd(0fFnD3mzVIC!;vr;RENP>-4H#zSP
z%cEL)gz=Aua7*+7eMb(BAGPOVzr1Vp(TvuYh(!ZPX;Fg9^y|YoF+m3evB1l%KR$6@
z-5r`(ew;k24QItH2yv&u4hRhJf@N_4cBzAXLTE{pH!*bPRsQ1$!B#F)p&SCQy>Lr6
z`>Vs7VF%b%(!&9S$cDi%_Dv(dk+X2|&+{e5jdq;TBzV2P^<oK!Vj5wTk660^4OIwt
zG6vcAJe#l+J7=S#k0t-+N>aTp+W?08>xg@~!ojWf#we?rG5trH%m{OCUk-i$W`^2;
z7pu%Tks&bAjTWw)Y?ZcK8?q<iCp(+<kYgj{f?_}(AyXS*icMNht{x?KD4`y7oW}3s
zsYl~;AJhJ6+y2cnB6KN*H(Puu`#Q>3H2m3cuvtR>l0#;jDH1W#&pC4MXvH9{C~Il4
z&*uFg_$Y#_FRwwpd{ie-i^h){jcpR=@a<J&BtjfnjejWR!3XqTneZ&)t;Jfr=YYQ1
z3k}PxVcoO~XXMaofy1Io12ORK(CHyWj*IJylw34W@?HP^jtjnGuPh;vJnk5U`bKYE
z4&lo?mX}Z#Pd5O_Dr3YM35`_sO~&aC8N{xUdJ#XgvSG;qR@CZcoNgzWbL+=5j10%R
zL;;n)`p(I+IS?jz*fkJo@AE<oEbX@Vkp0AgjwQFZ23+%DZti;ESi&ihmi%<WINJ2I
zyp86e<91>+3ZN6t!r=Sa1wkMUi6?53WRMUXggPB_wEZ8wCux(VE+-{w75b-2bv8oc
z8W|~MpRZE*+(tAgq87TNZS=Z#8m*=of%}Kv#U$>59j9!2zEk1Wh4lVxfzxr8$Ju8N
zD(js-wGO%oY0qSj7uTs9ID}sOzUFnCsr>~PS%J=Jeu3689i<?{Asa`ub8NDS7IZ}d
zqq^v%YrWg6aH>*BZz|-s^N(1RK1i@&hI&OUT5%TgOl$MyS~yPhV760BD-Q8x5aEU}
zM5Gj{k$4{w7IqU>i6!ObD!>mwMd<CP!7hCzr@_$xj0Y9?DedyG>g8gmWM*-dcTcg7
z`98NH;kCOiFGfwGFqi-Pb`F?Q;|Yl~`=<;jDIxMuy-uq0(<*Q8+~xn_%dMwNV(115
z4=KNVFOAY-K!Q=2eZw;aug&D1Os1NKf0-29&&gSA&KCSYFNb9(XFb|<^e0f6JUG|N
z(0BwVCMjl^*9g&TQ~lQN=xyZ)+!L8QZ(f{v^QiUQ!uAcEC6Tvlqz-Nc8{w&uVH!`c
zQ*#auN^6)O)LD)pqIiOcyT(EK5sIL9vL7hicx=%8_g_B&hbQ3dSVNdTio#8td>)3p
z+BQwPBnN?#WlM98d_YK75MTAKOOqTy<{w#)uQ0D*W<tS*s%1AOV0M9|QsR*bX-sV_
z2w4Aa<=FL+>uR_+-UQ~nY~Q_&2@Dqo7ODVih#xZl&4d5?h=(6Pt`1ZS8^JK}l!TKz
zeDL5WTxcCQHqi`CR&_PccROY4#_w<GKO=_Kt`KlSC_&}-Q>#<9sXe^-pzHlFC+__5
z3M28(n3g7J#>4(Q1$*w6SxVCs+|~MFl30uR6>IqUH)MAphQ?rK7cf*Utq`s_p~QO#
zv5zLFRaS@k+#$m?E7~c+uyd9k%B_ZTT52LL$)!2mGCVeIt0Nd7OVT$gvMC(R)sNAw
z1{F1JT|KIRRo|ym&_*>U>pqZJoT_nHo8!~50hPXF4==hBw58<@q25k(aj=uJfx^cJ
zasaV+;efjeYgQbWbo95I&^_@l3bk-@%C>~u0e(4D8Clb>41$Xi^zGtHP^-m8Khx3r
zja0-sRk`|h@+ok>{ZV*pocCt-yT;637d`3t=f_{}o>r_pD`lVgTergz?Bwf;=~dmc
zT#9)XM|gPDem5Q=P2l;e>d+N|b<hK!kQcwb!R81xy}oyG9JI&r8p%Yqc$Aug`qQ1s
zsj0PLhF^JhFb1dhUQ?OQonY=kIQ18^PllUD49pN~joR?CVv&ckhSOWD>QvO~2LFu1
zT0`&wkFC8zI#vY5qFW+`))r7PNck$%QfW7<_Hs|H{*@(yc?jH6M$wec(uAK9_h+B5
zlnPN9(yYtqs5iS(46rRt!wmkfmQHPEd2{DzwV9T^-s#hB91Fkqh@k{b=Cxe^uek5M
zTV^|x7mxgxn$0?ubCa{9l^3sUMo&8W&Gt<P$|iR}3C??!!(~~^cw#(Bwoz~2*%Qph
zzgUDn+e+6m8|eIa{)Ix(lZKQSbip6HoMs;s7T=Zr*s@9kDelji0kp{rqqi<jKkVEZ
znViw?AVb=A;*K`Cu1Afr($KH%gB5-Q?ifl)wO(r+EQwa5APT8%Telt*5D?(y!B9VZ
z46BK?zunkphq`|?$6wBE9{P>ZR@8XkK;=~8{TR<|x|i$1yZ6%9Vi%}5Vooo&9%J|q
zAAU&%!w^M`*QwQio2_5TS0|k=1PIE~tUhd0<G;Cx147QzVDl2}5I+kDFGE9zQ{iZ9
z`~!u96u(mI>hb45r(DRhz=5?-Ss2$%W6utvh*5$k1^RO_q%R07BLI5+GQkY9js${l
zFcFTlPKa`p7aWs%qqM32C3*fijHQs!m|*Hbrjc+UpjWnoP(HBwWIKNC4-dL1IP^;h
z-?Sjyi$t<3dN(+MrQEQTnfPJ*$>22X<3>?3`O|feoU0UGrN0`;!!TwD98AMdL8ApR
zP%Xn#x4clfT-$8p?CBP>I1KjzILavF#=0#sNk8PhuI6BhPH%a@0o*8dO_YwrZc(fA
z=)@7&0L|Gp>k|)!1^w^$uYv`O$Aysl9ZMtFr4y$wc9@$Z3&3NOn>ydpv7~z41sBxy
zp$}=(^v3s2(McMBJA5nz;qG)E(g!9(co1r1+t0%Q5fR{pXrh|PR2%4hHY-k7lk0IS
zRQ?hhtMsuQwIETg%LN*6maYm3wj|x)o5c5fg)RT11po;I4O+tHhj&l!foCrjp$Sro
zlqs~J2{(@Y<bcxJ8vjbU*lQhP-!uqMAO#T2{5W3)pkD@)R;29#hu1`01d%W*8YQxC
zwEgn-?m%_;19Z4W5RIZ!Q<X6Vr=0JyI0c)Z`bJpZ&y=`!bgWMs&YqvrWbtTXVNz)k
zen%=7Qs<H00xSU(A<7^`5agg*go|y~JLBnAvV?hpFkrWSv=DI+c6JvyzeS_J+=m+A
z$k<1d;jV(maVQRwX=u!gO~{f)wx|Pd2y!@*t!4A*_0Z^NyT@=C_~7GyoU58tOvg5<
zB8@Uat~Qr{LPG;EjkM1|i;2%_VNE4`oJX-<k1;Z92yxGvETe-7<z5Zp7C?5*ExA6d
zLYy~*A{BZ?l4bC_SD@1G#G#mavFl~5JCX2F`pKi3Bn;nw^egJZ#;4%)pac=H8fFU-
z755;NVjCgafM)5#LytPH{NcKncm(Kb2y+Zzy8RexS0@SZlmj?iB<8r>7+xGd{LcWu
zVxK=hiN}sM=3(3#?BmeI;J@{`z<U#?2wd>B!3Bdw2?6bmMKKK?m`Zb*&F<(&7Wbp{
zgP%b=LKT!m*p__HP50hCc9onfmLEv-8fZ4t{%~FAeG59(VWjB*+CULd;OyDE#50jv
zO=@mJC#Xm6DLfTUFc7K-1@N!<Rmd^$(QJ+~xmo0rhIS^-D|zDU8-Z3y4B;t9vCC`0
zs=?UfSesRitRns4?5nEUuM+FbwDW>uaxQ&D6V3{a@;`Pbinb}^+i>DW0%)^Btq8;n
ze^G10T6>>Pj0j?G4`jmBr!&k3cpn-$cCWVcT-GviA;(Ewcve03qw1@30{ce`3k(cA
zmsSI4O06bmn4A%;-H&6lNTUZ1r~+8#<<Iz}(0Xgwrl$1*UA6v~54cGeB6Q$>#6)Bl
zIARqX=djo7z<~p?SZNM0tBE<P0}Iz;QS>~+&~!b)TiI_FgGD5e@a+f}R}H)?!Dq@4
z*O1*_2Y7kDw@(zYW6=_gL2*q5()Z9X0>DBp+WrfZ_--%Ltp5ENeq156@cA^9$VC%A
zr2;b%R@1|?H@>Fvd>v-V|8_SRR9Mk+n`s8AJ`nn)i3K~1Q4+ym^77{6t*Ag%>HwrY
zC|+TG#G@$@k;(y80P$p81W&IDZfe8EjijpyfISFC=kH7UVqQ#q?-w^<hVkgVcjncu
za*;~Np!M(u+Iwe=@VpoywewSog-<@zF!F?z24Z<aqwR&@1<gSNL@IwA*|7xv?4XE9
z<{M{nY$Z?5*q?}g3FtKzn`Xp9gy4Vd>Yt>KlG!#QZ2Dan6Pbaw$N7t)ZnfYg?$P9a
z%9$pfa)c;@1<_*)5%G`~J{r!ka?6G7LHCMP>gQH3tD5y!RP4=O_gLWe?Vld))MZt*
z-occ;q3GSuj8PB&DiOW^=c7~GN1wNImHqYk_n%pQ`f2TB?URCvGn^a#So_mkT3U@}
zn<Q_WUUWruv|P8ct3;)DZ*-t&uXujOKw!)Oq$$DE4V57Gf(db2s?zed9s*>_rJ<b6
zN&0l6YY=6wu}~uYWK@PQ{E?&}OR&Jg;QlqI9Iin#GPG9olor!15m?n2jliglQHpVf
z{4-2m98r-ied$k+ufMKn5*cVA{h>QoM*(LB>E7vHBeJQ-7N}G0XsfL6vUwX=151?I
z`Zya$WGLy~{JAY1*f{Xgw#ui+*B!rfRE6VBb{rcU+l1NjjG4%Et&$%-;OQL=qxdvl
zoQ#$ia{0&uQjkR_L}WG_#&!cyl;Pk@kw?EF+ushKx+l4?r#L77gI^)ST`*<My!{NC
z{eA@NKLbZjTC`%=W^;3uuEFFF?V(Ue1}f_TWnYP)l%*vw(~R%89C}sQgQC^R^z|2a
zcKfc!O;_*jN*>7?R4vnVSku#<WNa|y`I|(4nD;#QwTz_pJ5KWA)5`h7Q*zf|!E4vO
zdvVm&m2ikTXsS4`a!gaU%&&7e5))rN^^%%gau`~3s0eZkChoAWpKQ4q>ai|W+cjqV
zbud8S+y@?)$H)SQsTOa4IdEjWSuIU;e{A@`s!<&Om-D{($<CcSQ}7ixum>vj0!(~+
zpu~q#pMil<F_*<kr#<I|Eu9ta3a>p-Na#ZaSB7Z(6dhx3Ob?fyTEFWFPNhD&p8eKI
zEP%8aMmtSJ*GZHBUC4A5fL5Ig18VuwyrX{gItyP}R$iEgKz*O)GQhk%EiSXiUnbWJ
zKtZ55=e7w|$%>nMCni^v^Jn9MOYQOK*6Fr48;)dXc%4lX5EN9^YVSDMLhjVaz<~9W
z&0_S&&gR<MCtgln`|PovyvqU(*ij9wFR04Yt!b}x>fx(f;TEq%r=_Py<mQS81qGGX
z$&JoD*y!q8XBZprOV}4ECaig}sd6StxGtfW;^#pBaQ7}|f=n@)ldNS`bry7sz6FwB
zH{eMHLeS)}nd=E0AEhPOc*QMGl$O8VNhZ{GYahxJpEa9=uS92PHlkI|hh%ntc6yx?
z<Ul)a(YdaXH)m+_d*Pdf7!mO^7lR6?!B>>o=1m6WEkl^_CKDO~7W!G~c+X<P=<>jy
zNfvbrv*oS=hksP<1gQ9Pv?bR(afMNbhU|IM%4P4lxp&D++&#cDUOcZB>+9={bEoS?
zU`;fM_Dv7o#06VhTcC=vJZp)uJIoX+V&i;xf+G^VEUne9(?cd#mUFRuODnuzasCt8
z9$NGrLyM|7L_7@-+Reb8gPb*{2SuSAui5<HuJe(uC2$0K>bgAZsxxFmR@8TucCF7Y
z+t%aM3E)OtE9w5wZrs~tqux&2K8A~#X&cEE*5Kv&+y)rxtsJZ~g-u_KW=~cbeK4qZ
z7Fc>vGaYIn3YBzLyP*)e9Ao3aqfRs@-DEBCHdGC!*2u+vo1}9kyxvRYHUIh^0|Pl`
z_M=55-HU%OB#+pd$4sl&HlOr#dy^{WWt6E^fd?Fj`hg3b^$NijV{gDIvDmm|X*rSZ
zCwZBfT+uzR6u)Z?o+lV1JW+j3QwP6JndTo^H43;A!sFa8lL>SANt7Er$9^Mvv@+5O
z9ZGL8rP9_giycGG*SbRA=PA6GYSH)No*p6+wWnNa9*T-?)J<3NYvh~;H9Jbs8V$~0
z=;m>z5B2vKqp4(Bt0=`e=lImGY@=-cT)El)XSSza8zp2$nDLwAtr6u{AQ*s+3s$c1
zSAHgDZ$YyU4NJ@#h*{fnz|_pF5{w@?o2VDA+k>;m9Wb;u3Gy;4aoE$-Q_K9rHW73W
zyeTYNx8AHCob`O3hfb!SQL~3`+vwLLmDi7B;2+&)Xb5%hR#a*h<DrF{<7853hB>jD
z8+aS|J{3+&NzN-G0Y?nMxN5FMEssXQX;3#zGrN5$lW#i_vz1#%PkMUXnK3{1vmFVY
zS;;kWCpf7j1a=$t;-GO^np58y4vhg5xX=cmk^VdW#;9KXx(C0IP^9wVaOK0gX?3cl
zxQPJ7nj2V|<(z(6$wf1T?3NcuW+6^ntmuVFhhJnvz^YT#)P-Z^9C{Gy=K1LB8E+El
zdXz(N8y#uU$0093GJeDtVW;Wqt9f|dJW1scrumbX_6%-zGdMH>2b%2-zjz2d@g=l)
zCesp~O&%+H=qwJXSt3y00NWzc_uP#alw{sgT3VVejPOK2$+J9lxOSEFM-mV!;$D4Z
zJLDtHDrl@OF&!<>NzEJgFuRdGoUVCgv3*dSa5rG#s7gvo8X6iFHL^lS=YF~5xyW67
zlH-9zW?)4{MR)MNERA&*>_@1!wzIeC!jB545tqw0OR+!3MqU8zt8)c*?&&SukwG>Z
zB*Nwu*j!yBBIq0GRi`@rAZ`~L<BJtrQp6nY{XrTKM=G)g69Rxw`9YwX0VWANf3QzB
zL_!=%_EtK?YHNGSv}xFOBIK!|kLgLma|wLzX)py9!4o0R%%<5Ok{!Wl4uC~#{H45!
zi3u?@wLpCBB}&{$!gE7QN4JE<Ztv6Up2a{x%H}Ja|0dDC=UN!xB0Gi;qiqk_1P()$
zRD_)8>$XYsAlR3U#x<WlJ%U0_czJ?nnKtS?>PQ^k>D=?_c(gr;6cy;SWEKeGa>%px
zgg>Yj8oillkWPYO()~8}rRj#ZY%)@hE;j67fJ_>Vyxb;qaV#N!>xMeF7^36}=H)SF
zk>S)D5+}~qb4a>4;hdqo3&CO{MD5lN*4xU~Hd2e5r(B8m^Y<6iuJ{s{d;a?MPTtXs
zX1p852wi6_NS;}q8or!;Yd(1bbd%*fCZR4UoJ!yrlwY*AHOARE?q>Kzaxjz3r|N3n
zgT>Z9rEy%t!^5TG%;gir>%u=Ez`KlIT%7EZl9D5Bmgie-AQk_~d6R$rmwu7u16)oS
z<>lpA{1<4=yOhFZD&+fLWUGC1S$}4(V4qTZ_jN9(e86H^rJ9`45m0sMZ*@-W%v$nI
z7dnuLZ}GKw7gct7G2p=wGk<4!_wt<db=y>Q)CSX^otS@g);A;1lFHVrnpbqP%Icwn
za8P_*5TBVr9I%5kN4+CYMe^Q7^PVZExvC}C?UyJ<j7Fu*=^gfuyBij}?`qAzO%aHS
znzzxh%ylyDJi7hTlE;DF6{8&*<As%1zEwwPcE20daD3dozLg$p75}-?!>yafymaQH
zAkU6KG4aLtz=?b74V4wGa~;1GEfl6d@Vv6nUwbHXBK#2fT8XR*M~g1{M;@CXq)zKs
zYFBlhJt^(B59vR#6c~{oq&;ZiE8WoCA?>ePjHX;nbvwG0eqc(v3CF()r)}uAFM<82
z+;fkeMkp<37L#jp#;H~3fElIRp`Xsnm%R59Sb_`ER71ko=(6pSo31N8erbAh4%mzf
z`uh4LE}>=j+RPpf(}Iw@K9LS5N1OKd_4Q?&kxXf1WRxaDaFKfyYK#N1iFpok;?uS4
zw+dDv1jyu?AkiF(46GLVb8}2TLSFf~ohHhEJ$|wE3}DEKk=OfBDU~0P82_7sQ<;|&
zm**4nZV5e`Jv|5UO)o*75XJkUrtAS%yLEzy*;T3<8=sP339FWyyPc=1oIbDNP#-WM
zDB%Kx2Ea_#SI?O!F_Ld48<^m$(y8z@N95{s!n3loCtol-A4KQIlI;6dY>lRC4~}jZ
zz9|!a5!_M;Ho}SJLD$8^R-oD@JSesM)W1pio*#n{k9VhW(y6?$yF5px1-mUoNFXL3
z!d2S-8hia9{8-h|lb4uy>{Xk!b>KX1awTmUx)Z)=C}3kU?c>LfWYbb<u&!;rTZ=oo
z9b;f@Tt@H~xf;NZXyuYoRNmxCpBr6$S7BN$2FW>RbOx10-ur1GZ1TJo6yNvrlUAB;
zZlfUD<iYSweTa+VnYOr~D*ibaafWWnV>)G;*v@s~Kr#AXD#PE!;RC9;ECAZP7RY;b
zzRsh;I+Wdp355Urdpw)?(HZnAa^FuUA}`%<S(zkAvQuHU^AhQKn%A-tq|mWW?XV7k
z87sT_`_0bF%dNc<YXA8?B&IG;$uB>l^pA{GtOwL`8c+%;K^u_<`~CeS+@eQb&u-Ir
zwF$?Y)-*LeqmXztNn%tT_e!tox#`il!#7K#ln-~K<fwqsbmD=|0zd^A$FBQa34OK!
zC|JZHQq)E!O#3dAkJ7}IdfT(eWW#CK5`Whn<}FtOSfXH_i`nBY9DrH@EAIWRnCG}{
zXmpuR+bBq5@UX93XP<@|gTi^Y-^TV&o|_l}GdTxP8vv-+1E5mTu{4uGDP}W@du&~Q
z=>X&~Y;kQQ#f1BY*|%w^tt$APE%mmo3$RZa)@+~$%HFd>q;cymmj7h!4mQ>EE0cS1
z>R3Mg{nRW`BkhuE15-I^mHh2uC)+gltQv)6asr<XOR;PZC|QRUx7UUY`<>B1NhRGN
zl&>(z>nmN8!6Rc(XIq!Wc>67D>g^0I?p|bvVt~Wo10hZpkLeDJqsxo=lTct#s08Qo
z3$Q9P;%gX^_}6jDngAt%hkJ%$6aBvjD}u#OkXBwWr3o!1J{;cl=HM^nXP=KSmUIbX
z2<Mj0>udo39-tTLGiSQBk$g>^GobdXcysXkOX5}$|LhEnzXfWh15}bL7a+o_kUKH_
z1Z)2OATatz=Zv+)W`==5CK0t3v+K8ac`Xoyxa9A%b@X5tNgkcqA}^-+pX&!QuiTY|
z-)@_&d{{-xb?HG&E?ByMFAefr$3L*pO+nupKTC%a%hcz)S3r~wRse*&5YJ_2(??GX
z=*PGN|K+uy&lGF(ecr-47T1B&t<l!eAVTY@>-$mgJF(vz?I+t}910K)=re73xLx1Z
zOUy1~Zh0-_Y5eCRJ0R=-1h9ls40W>~R2%y3o`)|~-6B;H<m4#kkWiX%HcHHYeC>&A
z@Bh-W@Aje62m~kKf;)Gr+RDaeCf<^KaHGz*x2u(S$&ZveDX!Q}-F^`_u*!_)1hw60
zfWsFotRkFDo_DeUM_9gh0#P?iUj|AF6h{s}CjC7o2L}fmr#I;}ffHcK9Q|vbCv3P&
zS1*H5^F^I08L~7LX^Ai%jtO^TtRQ_<B}Jqbz~@#qWQYmGod>p31585!n0Q3(1j!3H
zkpNpDUnM$u;L<ZAH%`_kw}q&lq61MR`rc!=IG;Z_gr_#w$+7&Dpbs|{A}l%fhc@nI
zrCfyG@Q8(unwpv@(8ITes;b9C98Ac>WS~=hQcnT)?QQXu4tMH3(NRng6=6o!B$(M7
zsy}ux(!89zsLdK*miC6ms8J9=A;EGIzfPEhe2}+~Iu?3_GO=1G;3secVjWb|M#*p1
z?PSy=i@M;~+P1bJh}R`{nxrb?@dUUu(5t*$o&bE}f4FxXtsuTUt+VVynZu}t58;@$
zWzavbmwXC5#)BlaoTU|N=NGx|_Kjaxx8tnK#U>PtDOKloP9H`6U5tt-1PrDg&}qs3
z0ApHudb;5#CKwrhr>;aU5ZW3QqJwcrr!EZv%P+@(=nzRt2*7ovXSmAH0zp!2%i!{n
z78#67;RRl7MJ6g$89ZsEt_Zuv8DN39+?i&9_%%^j6{Fy(L$Hq|aJX@_O$~k#gG@IN
zAqor#D|UUFA<4y?g1_yz{g>&eD6Xsd_Kczj8;l_TW(S4Rbj6LSy1w4`XnNz`j&C1@
zCXi`VweqvGPe?-bkpj@HkDgjN1|lXfge6Jpfk|X&wbqDy5j(&1c;sS&VC})3dQ<S=
zM^;a6BCM11ppA`94|<g4P)7z~$qJ+3FM^k(#rw@tU1a}X<47zQY&k+c4DtH}AWJXM
zcPV~^7h7Zqu${nLmL|PRhHgG8N@+V=aj6ryH_m55{olUkagx{@m5%e)IHA$9L(kX`
zm9NvM89vk~7oHy5!z?y-63EaO#gWOUnY1LlVT|9EiYzZovZybCjp+f!Tn47Y2U$l5
z7`HDw5%-N|f#nE}m0X){4>D~MEJ823F*f9z2q3#d=PCz_W>TK-jJ3LYazmx-p3E(B
z!h6u^{<7KxeFy<REek4SPP9M(BN4Dzhb8vsPWBNK2a$oO(Cv+dgxbTe;RDH{Dkm}5
zb#^|3KkZFP%)mZ7$??DCr`m#v<TQXpSM@joOQ^|6m!O=`FPESqFJ&J&cK7XDG^U<V
zP$awXeRgIFbI1BWMnWoqB|zblmOW)uh_x{`7(@sgK(`_^r~;ABW=x3rN)I$%<uJ$~
z6-0PwC^|{v2FMLChxft#T#a5oa8yPUF+v--MyC;C7@pa35%kO^y_UauIS=Q}=8l%j
zyUy1T7Oo%DAcHjoiJ}X*!gOJTBcri-X)a`0L~56{G6@_YkPqOZ2*Ke`0oR~30b+1A
zKKsbZy{{UTT%MGiZ4-P*#E9xuyX`*Tp%fF~Q`WMQr4~DugD_{hkGh?8I5P=2f|oi$
z-psREflq7@;XET7J2pODgQyXm#@skA0HXpHob%cp1USgM+`GIGQigm-0?vH8BZvta
zWqEdase?pH15;Buz%THN$%qTxXo8d<1}UN}uqiCQ{gH$DojIIE6GF%h;5t2jfB*UU
zDM)k02z>!ULEEb8nh_mytItT#x^&$`;NoGQ>gHzucbiTe37M~rq}_O|SSX{pGcGR1
zrPeg*%{pf_2eFBweWWBb93>Fzxel<T4($ds2r^o;N$)ld2AHM2dUc#^9M}{3`}+E7
zIZxL$;mNXOFFJP6+bN|iW86ke|G_BFIBupxT*pJTKtVQjbTIdPGrB~ApEcHp#)qfj
zZw^pDWLovwmK=&y){<Bb{HP!wgXR~4{7R{aKPFL=Fbi^G<L|{_C_~gmf!Jiauh1oA
zVDOb1{qWk?My|D8M6Wmh5n*oA3;3TbUc!}mQxFeF*DLfF820TFcdVu!IC|?;(%ka&
zx}@9&sx%E)SqMel9IE&bgfOmU0#g<MZANBSn?6t~Pv1U+rPvK+uthrf=ZAm=iSoGn
z^E7}Mz6WhCLDu!ruXAkw7)SfgFZ-POGi8jG;#%v0aRUL$oJ%W2dILdyZ?dm+Z1={W
z=>K!oj*gDBu*^*RzXzB4q$JU(N{fx<LRc&&D1|`uGE%v|e_%X4g4q?;H3_ztjO-xT
z@TOo}IKLTtyX?TTRd9LjJ5_Yca>m|n6amvc8LP8+m{7g#lfko7<tB90IY+I_>x_+D
zNGwf8{OScxD#a`CMWgO$y1WlRVQlkKCpVs{da6zaUZc3;<KwA49{06rbG=$7s0Zln
zvw5u`1TP^2TLMz?jL)CWrQ>+C+S`NL^=uAOFE4{q-l+9!Ty;M3r@<W)VB9t>1VC(U
zxs}3b)Ul&9BEZxiOc-KJeKAS*;R)IPCY;4sJ$@j`#PM6_nkrp<$sD4YvSp^0oRm}{
zZ`%4ZXAx@S_!R|kZ3Xv}5j?x^tlwP(m;Q+a+gzh(8D>UCrRaS<hDwJMC8FJBym@{B
z6;A~S>pUpM2>H2M0z5KI;37<DT|l#CtJbq0dp@J7shNT#s*lK1funo_Nba6{%aZqq
zuTqSU%;p$X*OS0V$%GjAu$=5Twm!F#_AzC=<Tl{-m^KQzq>C*8Ta(a@cOi_F;3h?%
z3lhjlNZvU#20{oAN-D$;)968dpcHR9F?8&cg<qz7`-23cEShU>$n!<)axTqH#B&49
zu9)gpSC8W#yeTM%$_Ud^WP|^@P`zBshSbz<(NG`4S+|ahPie&n(OMT)`0_L{5X@^#
zLs6R0JC<E&T}WPowpsVuof*#+wCE6ZUO8xS@3NioPRO7~kFCFjHHZP9{uQ<bJH2DV
z%z02V6M=QoFMokkTU#3(Y3B*V19t0LMJkP)C>L$G^SowmXdv2l)%U&hM<-`|5}i27
zHluSlqMw)=XjM&}Lu}1ePGfpcL#Ax9@F1hse{`JEG1&d2eTx`1UsCtVhl8^Ahr2y9
z-RfN${@P3(wG_L4?&958y_vz>od-<ztxI&?aiLQDvA8lr_di+yE2j2)cM~4(QyR&A
zUNF^P{2TYrue91TF7d`$k2SAnxV;JwJ%~mjwHKijnO_GHuA98|-Dv47pm$gSRP(Ax
zl*qqmU1vDvKi!_?d&HK9iGv<s&`RVbKM-J19m8m!)#tU4iV(Xo)un-qNc+u?vz^xr
znJd$`z^3lPTu>3Ke&Ayi6tGF|(wC%Pl9+krrTg{$-%Yp<3xrPn0F(4fVdaE9n?o_0
zp*(#wmb|u<ZfR}D#A}Tq5QWdS<%h+}@fLB@I}GeP&D`o20`D$KK0Wi!QBF*VHRH?g
zBdgZQzhkbHjmQ_jIb0b$YSZt!#N-?7ntkjNz0K`4Qu2<cNOM9MUo#*mq0C=^Yu`CW
zr$?U%7$l*Gkg!kBY!aaW**MSsC)K#Bd^%f90MIWGwU@|zU#KO@mtHLM3X*eib^6}2
zg2h<sz6jKbW_j(D%)XZoEIyrD-%ScUvN@nx(8D38&yHO^jdh*GZuRNZ#wj!Nb=CDq
z<fMMk#Y7$`9mfgm!*EnTJbZ_Jq%%>(SX7;5Sy`!M2@(X^gs3zT&f|OtQ>h#%T&c*y
zWl0*?W6wtja(TvYaFMXw?8w6pT3t!U0mwu1+6fe9&iQ*fAW)d36hm_%@|-PJ@g3-$
zCxEU8pk%1{t;N8^q`VL>NO$*R2kaKL_;|87FsSRCKPx04(2YhwIf8)Z_|k{92{=}L
z;o592A*gNW<c)DzuGoxPLr^+XOnQxYKGq87u442x(4yoJX-Ltz9o8xQXi|(IsP;f}
zU4#=UCP0>|<}RXAI|eClIq4(u^Z%uGyLo&AiMF-j@-7qJ1oTea<#BouLwq~gEz6Fx
zf(e}gl@OE8loVI?h7{ek;aF=6WOT<N8z?;=WO__HcfrXWD}T$NpzwVf^Y))H&0Cp3
z=`8H^zHe(|!#%Sk4VaZ<=(A#HpwJMrear>L%%udn<1eHefE?<1h?E2j3>yqH&#fp=
z>S9*J^0#$UwN{OyjUJ=2Qdu*UMj3v86kLAQ{(}+YrTPt755xINe&@Y?=hw0NJMQl8
zoSqTEO`jf3Y>3fMLLtrTv2i-1RpbDlk+OW6k94^ILr)$bY3B@04GqJK?*e;ztm4OV
zH+iCJO;xsI)ED3ni(EI&=niRf_|-2GHf3@R0cp{Z&GU{X!-q1$BjqmN0#RF|@xrSu
z#s6ZkrrV$EP9c!HdtDw+)MsLH%`ZE3^s>BEeL0uxa__alo$&%udbg%#^I9O|bTeF6
zIQHe;;8KGtDRG`&-MHItU)BjjW!dRzTkcIe1>@G$F?yn(Ti0ve-0bUKo}OU6je&9R
zDzl)2euJEO+Q}A~Ip<vm<CoL=Di>Cng%>{6RqH<<7g?D0)%@b<V(gje_CgHjPIe}5
z_cG5FPn@m~k#_HLJhy9tHLY=ASokPf5r~BGr3_crlBb3Gt}HO~+m{>R!NE_qXP+}C
zn-`tyJG$Y|_y!|f!OPUJ40PQ8)Y{rAbnQh-isj#hwO0;W+tim&Hnz71i);%nbQhfu
zy^1Mj*rzdR^VO(^I>f~1TfV~dZEdM~I64?*@t+Fo6B79Ap9jDBgb!83eQT&yQN6_J
z;W4@UgGNq7C8W=HM=g`!vZL(8*wWHsq)`4Dv}#miVM2n#oVE<Fenp^v;6|=$5cbuL
zIiD*z(9PBkHH=Bjw=^GV6`d=VnwMnh%^(xS&kbX)FX=pCP{_||URgq0pL}<zeExjJ
z)|Y`{E$3(OTnr2c4}Kr9IC<*FD%>Fb-AL;HUC6(EH~0OYuVA?S=l}Lu)F}_Hh8AKb
zwaZ76S!1$0BErgZ$767uedlrFbgHw@IS;CR?(8NFI)lu_w-&RA>{h;;!(Ida0q*Yh
z9ijPPXWi06D6Eotb*cQ{Dlo<7es~qn)<63DJ0hn#H?EIg45sk)(3hW|TdO(zXrk%>
zbI)#u?M4$d`K3b%2^xNi+aw!ZdgG4A#n<cNwF+D#If;Sczs+k^R*K)K&YFV^H}{TX
z=@Et~C{Q$8k=7H|`+l4nri;n#L{gHiF{Oj3EC$7$-)N0F;XqlzrW*xxa=Q%f5qmhs
za1xR@h^xlEr8paVnh+(K_}&!!+l%sEj<l>Ybh!O$+S+p8Td#b~x9$<M9r%7sygVh~
z4?vmD3rPYnMHot8(iDWmuay)&nJ9`#@7SS$4%YiRh<&H9`#vk1>`dFE5I0-oVUh9o
zTgb1|N4Hi6eO6SPgkb{%)Pt_P3{49_U55SdY+)v*GfOX4#;js447~mc7}=U;Uvm%^
z51}9HR<3Lz=(g=I&;`vCIy27QXXOu%+$^emlTE3F1d?_GAW243v*mUR8qJy$sFHZW
zX6TTsH_n^uX+m|C@?pDXnDXJH=5*4#1cZ&V(?Jc40ywvE*wObI$63-sxu{h0c4Li_
z{q$)=jn}T!5+<f-ztVB!iu=xSk)5e8UR<r}<d4qpw0a>SSX<R1?OND>lKXXL@<5`O
z%w(SORwgE<ZFF^c^qpDyI~9cG1`Xz8mpp462Cj#9rncA8GoLCiJ=Bmd>{qa>WOy!N
zKdbQ&<SW5ER2p<LrkTzD=tOl40~a18d>CoE8snbkKpF?5$o*jI3Zk%!h#c)Y&}*-9
znhfaB8k#W^;;)rW!U14W{2sfq3zCB?MC`NAO)G1nY;U{^rK06M=E^v>TD~1fS$xT-
zIrP(0!$L`R0}Gd`+IKxS=mE0hD=}M}W}R5YQ!4cxnT>Tdz7@8H3ZH(zi8&uTZkw{3
za<-048`yq=+l5Y-u<QWWdw9lJecZ&6S&*z~Jt@t{b1T}4J3UuojHg{q5Ezd<i&Hg7
zbX+s9g2@uTelLwX$@qqCu2b?e7F9KeO-PtraoQK)B&GS)Ty&X7Y}FNAMadAh*f<a(
zgaaarA6*HFCJ<-qYb)?wlc<U%NS7F8iPB*oUWi{;q*Wuf`2oNhVkk>46Xxjf?8-Wc
zvR&Q20W=L+y9@Z4QgGRmsP1eEOk|%PyZTs{a0pQD(3pf;k3mmjjB&sv!rNSexMki%
zAFPQq8hsz=ggb(~ojcvrBJ;9KtEAZt>)F$bM{R2>`~ISA3#m*<NMKXu6~;O}jJrMe
zhn2Op$`E<5OdDrkc~*QtI-V0@U)_5xv!dcd0%wlYc5R#`-y>L|edAJY->6nx<m`u$
zk&!IOWQK;czV_C=90V=ZdgR;SZRAci6(#30b6SBquCCszP9FWmj^!e^pC!bJy+ZkO
zvgxo#aq8yMVNDG&PXKvNKro?tR>%P+J7y>zTt<z+VBsZf&rZB3FF(*-7zI}{x$OgH
zF!IRi*MX(*Hz(ibmca-wlw18#wS^TGS^b?jB(~=A3oj<?tz#9)&W|l$y)aT8=$5Kk
z40<3tJRI#OV+uA?&-AWDx{Y=CWjzmLGc$Sp?Zel_ott>!$XnK@aqQ<nNbx;lqVs?<
z?)P33zk3(LDzY}-G*vrYKAMHvJR;iu4Ud}p+e~$NorBg^R!7E#iS$-YL4YA?YC!+o
z!r?w9+ffjoguO&7XX3dI|7TP)I<ziGY}jiTSKjrsQ~_PGhuJm*G*uu>fVjG1G9aBi
zO{-lzdw~4J?E${CXL{~tlQl8|35hX+6DfMrPrq#=x-w=Q0zzZ_w%3&WaKR^mE~&8!
zju9eX5kV*4?@%il&*Xz3&}nf{N2e<fjg14OKA(iri~E?o7Rp$%d=>`tmyBp#YdF50
zBnCU(qbn_@V_}SQn{!<8NlTPu2dIQ+9>PyACeS&S$2siLt;ogJ4GxCRKrQFzbLbKi
z))35bLWiIbgua1L5w3|_8SdUu;_g&%A|JdhmduCt{3Gyn3cxVX!PySz|Iqa%JPe^M
zA<^<71U6C8kQIC}IyEQ!d!+t^c;A3<yV6EzcfmDPLh;Z(x`>6ZN(fYR&YA2u$fIEJ
zVVo2Bj?nM_4>tO511SD~1-voQ^=p$BkmmjUw=XNtJdx&#@_*BSq+0!U(Zu+Iyh#b`
zR-fQXi!*0R1fq10P1%;!*O)hd`jpgX?qAzso~6LCbD+;8Jijv)Ckv7CXK7wNCdJa6
zI{Nr5zUK9Lj(czM^9qukTjx*64MuzUAh9Zq_FlU<$7d+MEAq@2@in*SyV_zm0kPbG
z1nz-$@36aj)-S$jNBczFoo}fTIbhZh6>NwO65@3xNCj1<0aivLrn&UOi%(OdTG7p6
z-5KO04X&wI?G+zB-knzuK5=3=u`cTQ;lWX@p#cM#-V*``Z~h7;oU$0p2|fPE?A&#0
znf?Sr!DA7)e|p<BDBMM_-}BPC(iD|tAKt%gDc3S+LP|gKk)iA2Yh_9LB;kE06g(d{
z8EIc>DjIQ^yTQj3moUYSe5|UT{S;6s{e5R@WTeYwomEjB)05HWKMz6&*kZ;b(H<Ac
zycaVb=MBCr$%R}FxVG-p_u#ExnQ%(LEfnYLUE^>R9q9FX4#8bm?nRElA+560&c=QI
zk9*kaluQu1JuG`_%LDY0fb`V*u3PHsx0QY2M<%sW`?euTE5Wbh4+?Y;@|sc&t4nco
zv*JpLRf(@FizdGU|6Jq9&dNHB+>I=9ncR*ef@<}r<j)z`3A$1gsB36z(;gQwHcRw2
z`^mcuJ?Os+`!UV>Bc^wo)$WOhnrfW+_QlLV2i929e>_s#anaVcZHgy)VZC;AWTff5
zIxwnsw3oG2;LOxinAQug>EHZ0mn{RiYYW{I60$bfapk4k*f!u^T%C<$LdQ}fBO?<|
zCrd-q!qR^ox=50D2n3sd8;7M8Jg?p_94|EO!oA-|G879?bSs=aqvWTb5K<RP=0pEb
z7ysKT=YNXE|0@e<*);Aex}+;dM5^UxuJo21Q1(rrV^e?{Z(#A0qDs1DMV4@k{};RE
zLa_JV-g2Ta{StRBT^Rv@cqx-8Zy=7;hbsY>b_-c_1)p^9yHl{Ei_mo?t#*P6GQnXG
z)krUfXi7-${E$*a7o-z9{mm=IlDi;u570>LX=`gE+RH%FO+)7b3NwOSdF>~<2=|B9
zO(iUQ(l4g1%<?droRRo^)UPxXBLy@HjP*Y6on_{b5aXCDlA=){lBksaevVfGzyUzz
zF!rqwH5n*eMT-a_&~F+mSi>c|a@`F0t{_1Z4cZNKgXB@;eXw2`?O5rl_(YhHd%GQ>
z!J=S9V#&np^EA@*r%(}fus!KaM(b0CNy8TQYJYs7r{kB98-Eb5tBM>Uqhz&;2yZ<?
zrFfF!T92PdG5k*3J-i&{O08~c_RkcE(m%#8AP}B*nxh3woC*ovsOkK<rp#5Nm4$l0
zVhF7!=@!Qcy>NPYbL~pt>$cEz(5rv`{1lk1z^mKl8~I`nb&J}+0V^x3c~=7R3N~dv
zVc5q%t1{WQU0v{H>k7SuBhtTSHa0hFfVq=`*yDqQpg>)v)HLn~r6Ex~mGt>u-FReY
zyUGEci2h|Qc8K2Yrag$xx7^I2YD41pZ74guk1V+rFT604m#@6IKBvx@j>c&v=wHS0
zJVHCBrXET6_4LR%Kc{VUG`j2bO;+*!gtXNER>W_XC+l%ICi$ufQdjkWQCOthd}Hd8
z`!;&x;t12l<{XLYH$ArTDzChD1>{L~KdUiPP@GPC$o|Y7IgH!my#0GUftaW)X{LRL
zIU?g@BV+OL#`Menho@dyesiq$79j#le+@OAF7bWT_WWixgV7kT#3$z%HK!~K`qI;l
zQ|A527pNM=FXX+RDN?kqDV#2R7~fdv@iD=F#KPI>=(mK;`?zMz2kROx*viW@9gfLM
z_r3aH%G~G6zJVTLZ6mwx-dk;o*9RiJ2E8up{_UzZU0Xc9;iNO`#45AVqQBZREx7te
zmgoncy1R#m)LlE)a#?ks7k&PDS}yO+9odHGoEPD#dOXTMqv?2g%aHmY?>dzhmk{a<
z{X%@{4&{CF-&Eseb|{DU)bSa&9!lfBt{~v)|LCLQ7DMfi&kbD~T++C&ehNzIOu+vu
z<mz|zEfn6Hjk0ESGt8K-uQVNu*TFZ|z0!1gUeC#xwQ>K5>fmqd>ehu8)-Ps$dpv9W
z$SX(RO#|~TXPU7$3Lmn1Q|z(s>Du5V?L(iU?d|I@Wt!F^@#SAzEXs}-D(56Z8c+H+
z7aIh20E5)A+$0p2^<4Q3+UK+zmk)NFSUe|=?s^3lDipsL$pO&<2~qRS&CT;J)hCc`
z^Au;MZ@^f?Igu07P&Osjyc4l%q^L(rj?u&NRc<c2buWX-T*GL?(KBDP;-(#^uH3wN
zvyUEFQ&;B$m2mCrmjgt9^VjCdh`gt0gEoU#)l!@;cjc~ldVHU@HE)cw3mUuqJO5Aw
znu5YruOiM{<X=5}#*In%VzJ^9bM6_5c7`Ngys1$#21Bt&V4?@DnMD}Xq}_01@*NK7
zH%!m1*I1NK1had2lG$?d-g!hOYu2#}v!%xk)7O?ZcVP~gn=ZU;-dt0oH0wMxGLlew
zJ3}pZcjRQgTCEXBud7#7gQdC4bYuRUj;8zD|EIbyfu^#5+kFfbA}S$dD1>B)GG&Sk
zC6%O<$WTe9y^%5{l`)D2rI6IyU<jFID07r#&JeaSGL%{JT~F`(|DW$W-#TZVwa!{+
ztz*4wwO+PuKhN{~{qFm|?(4oT%CT2I{2^nZQ%M##>f@(l=?Dh73$FB>r7dZf;(m^R
zcbJm%k(@0NnF9+c=X1uN6Hd05q0{)qZIXCvK*jTsp)P?sNHXe04b&&&*^?FiHV|Z;
z149cO+x<X=`p}iAAse><V??U>oZm#CtFj$sFj+^Jon1qciwvU`BrKOa`$F$+-T(LL
z7b@R7(6z6B-|b77_vuG_{sZ|I=&@Q~UZ}uS!7JaX#C6%<V^+K@3N@Dc*Dt}WLy$!B
zNEVDa+Sud}&rdYEPJ@+DLy`|kl4;Mf&!z!cWJkm=es8gA99qp=0SDTKgBJ0$^YlNZ
zn!5Pkmm(GZPi^-94Q?H6e}bqcw+GhUi?eK)Inj!RCW}ZF+QR27_GeB_PWFQ6Y#5mY
zGB!!dx5t82LkXUXvi6n;=B)OTbZ*`wCO%~2?9Y5j70ISb&ieAQeBI%%c%Pq`ED|*!
z`%M{{nT^;4_Rh{@s@&w00iQCBd=<%I`-QcmA31`d$T{?7EodPx$4EXc4&?}uKXeR?
z<~HNE7a)*!pTrIgesqlaD#E6ADh5j3^$Q=%NaV<J{&Uoy4+-uAp6p)GXJ&6K-Max1
zA96=a7ybG{5}6Gm$q6AcYaq8E7-wfMLWkya3;d9jpT$BlAbHH3Bsl!ct@TMEKO*|5
z56B@T19A)z6K(?Alb>~3)Y3uQwNvHOr%wldvh$K$4d{4Aiy-#nG)XzD*4R{EU*B8=
zc;eY8&_AT;qr)_kWgg|@-FN56{Jav*mb?)f$#?+OUD5!b;HZZWfTt0!RhZa6?y<Nx
z610q|DGJ7jld9E!V^H(^Fb(9A;q)6MC=J$xhNuA!J^beEQd|B^>ht$L>|I~Gx3wDz
zYf5Z24A$ITW@57D)R}Kt%eUwASEt9sZmX`p`%_%<eB0gZ&ka<;&$niVCzuaaO^*Bs
z2=*7V7ZVGS5Zd^DpRVmavjO&4vokmETaBLM^3|x7ua&Pk_PS{-yR$4B*VMeQhd;sw
zqO@tObQzV~l2TH*Tt|>%gw~`%p{^zkL3wxoh3OG9PHyh(%37UAlnsr|&Dp#AkD8fD
z^ebVyo9q52*v%Kr{=`53d_@?hU5<i}{oh~uf3+0v{kP+@$RUUSWF#LcvF}7I04E{B
zKu61D2VB7a^;LB{s)6o6wJhWpNw-_OdvCKvJ#oJY;_chFFn!`DWyS8<)W)ibhL<Zt
z+L$>^mw~{<@0HRzC9)w{_TyaeYeTP#7lQ}R!YLWw)eBs;p_CuF)t}#(8D#@)jbLkz
zy*r3jHdG1M6Pl0|;t!+E&CiDnoZVXx8xR;6s$5=Lif_7>mX*Ez`c;s$t34i^Knai7
zr1v;IeT{&?*4LJMAzD=WGXrFd&&z2dD9^ZD0Y=|d;)P}L%o7eqt(z)L|JX_6pLC1r
zUlI@gb$fya)r*%~{EwgBw`P#}*G*XcKi|nWPgeQ0M+q@KfBw7}iZ*ccy#gY*X0qv~
z9Xq%LeP`pVX3i?`zQMQ|*LNB^8WqS_`=8%Twm#<@FjiYt^_FzP2H#Nd`HL4?kXW8T
z0|PY3)lPEqZxX2Oug|bUkO^k)pOTL&B4eZne1IQ$@7}!(jKgbS=XL?agcpS#?1bQE
ze+6w-Gle!Peh!f!%H-fdIGk<TxRD;M1XhUMFyan#+tzt|XZ72SDR;3<_YMU8@L1;6
z(_o*e<T1*h{`xg#e)25VR!MdBO58WyUAySX-+O92^v>bBtD70#&j#{E9E@}rD}M(i
z<rvP~zL6?9dHJ=W+%h}z7w3Ks&>(K;;?~0DYO)@<zBRNoE32xs&YqRnrLDbMQ1Axk
z0G2}^&7I2C-x{4@{%wcfVxM^6d{_e2t$99dQ_8}qMRwX|J>>>RJQMSM!c6``?i{_b
zm?f7*J|gyN=3C=RG~Uzx+*_c&<&S-y{PovHY3Ywm9N%$gHMPSbl2KV(tC&m94#+~v
z%&R0LR86_5vC#@?NM&`keBPT&OTYCE3@!%-x+&6|96oFX{@v;3p|)%pa0<`<s5Z2)
z5CA$r7h}|HYo?>a2=&Glf`Wp9rRP?9xn>OTyDani;i@y2?_WTH>RD6^jL52HmNPP*
zBsG0@JXFq;V$>>z7K01+Dxrz>m<pNNz*74yUamXc205S&yz=#!WsZw51Pn()>P^GN
zwY50L!9}owtDabPL~QIT8JV;5+%31rJ2ZVaf&ZK0bU%5U3YvX9UbPX^eS@R!d|0Ov
zsVije_YMsO&(2=JrVv(9;f2!MJLo+``^05uE1n;1KTExc+mG%S)3sffaUALeIIKJV
zlfe&T`kOQ~1b_ejP1?f461%&v@7l|klD4+CCx@GoK_1tJ$_ED*SAVae;p@#6kj7wv
zZ~zk<TNy6xQ<@DFpjbH9t+RsAN9FtX?=WyE`u*vX1L7Qb%%!C|Zlmq%FJHbqK0dB_
z;>3n<L5*+>!_q^tvL*f0N|He)Hufqdr5ppo@%2Lpe==QQclqDA!JcAS6pn_(MtnFd
zEAj=Rl_{4Cl)t#H%JEm5YxQz4FY8p&*4CCg`&k4zBm;_=@#*OR)5P6YR>Fn9=g*Ow
z3Y3cFq$hKgXAGVPlw2uz7#PH3!Qf-T%58SB2~=+sx8oS&YrGb5ynOW>mL-G+9Q>l9
zxJj$P1h@c<PfhLi@lgv24ULG1=<Z44eZ5&Px505_D;B8p$cKZgrue*47O2nv(-SdZ
z(&2*o$2t?qJL8Dlt(h^<NnimZXLdkiY3N=te`)kD3*Wg@T2GCcF|Qz>=MC<m|DLC7
z@Ti8m#J}5|4Tn51`3<~-3#J#us?XWmUxjuP`5Tb*F$!t&1$~DbKfZbUwn#&Bv!Yoc
zq>ITPujH0?NKb-rKB$AY9~r;63OSMdjph#&qG$#DQ<p|0oB9Wy&*Vj_QBan#;I!f-
z|H#z0!<%>8X`G{y5eJ>atx~XC%%S#$knezbtUPh!w(U_g$SW<ovqu!MQqO;Iq}kk8
z(W#w7LlN>TVp><HUk_ZWJ2AXt#$H-4x5=6Hes!8?gL8ELX!g~-u*^6|YGNBK;FTFR
zn{A3(!$=2#9-`~6+C3>@Ci({Z?ZhLn`E{G?zD8eMn0AaR>(j?7rYGFKH82Yc3-7zT
zu&`iL>_<oV;nd4f*l})hq;;*P_Wi?0jwl`zWAbm2|J0Hbf|cB(9DV=(P_Mq@8R?3<
z#j(G7sSYE-^%WH>HmJCP-qX;av~!1Enpg#74~yH{_)x^$?WLVOrY$}2AUV3)8HX^z
zTON1k$H#SEEyn_=FWVGma$DAXQ|zZYFi&ByXz{Lbc0~0VJ_K1+D+Za|2Ap{DrirWh
z`LB<UySyk#3*Hs43B5Nu0{(=!o=XygP00_cd#F!BLRbjWN8O$p14U8>0{dY!@}7g0
z(sX-)%?HTHcR|pW5b_PqiTWKnf}8%3Gk&qAuVqcktvK`WFg1$30xvcj9Ra4@>cH|o
z{k#3MpoVW$Rpo;S!If99UTxXs%9H!hIcr-~oDkD2;td^vlmGf&8dc<Y^#LPyK3C<e
z+w=Qe;}-qi3@i4hENp%1v?;1~2Tl=>cXAi1o}#NvM_hotgl(hzumw7i#b|1eqa5i$
zF;Wbs^(sV==Z*Hiz>imQ|Hg@5`~*d_7uuOs;7EgRA|<7eYeb>Q5nXUpmkzGtnrAkF
zHa(%CQKVb#)cJ;)U}9#LQ?|P4`<IK)4M7jve?KIL;6ogY=0(06wwNaQnI@KD^LCAm
zjn&A0w>AjJ=<(6ilXTX=VRXfm(=zxGR6-F0R$sezjfs_22NFec$V}XZn?59KaDV7K
zVrCYcm#0E#9zDtf!X~=|_VqKPm|dEh1jYpD?(8|0e&l(6>QuDwv%Gz{DOcyia{2qr
zi_&`YWM=Zh+SS_-dfJ)!E>xYu(!>GJO=#}g<zHIY@(dE`us>phT*)=q4k;ev;^6Qj
z6#)LYN5oui6@b(&4<Uq;3wxM}g~dMO*T)!DC2B8|-_p{?UZ-#0zLl;YUZQFIPN@2U
zvzAy|+Fil3$X$ZZYnOTKmgU_ipUuOmXZHJ2m3K6iUYz@J3WGN5{Dp$l8gM85_N&k7
zyb*2PU>0q2>=>{8xpNT@ACA2I#9tmwt*Nc$j)VUC<BSZ>g_$uuLqk@K3~v&@v*%g4
zeQ5uW`k4?`X6Cmb-d%rH^!u2(>G9*CxEw1;YLe2cK<}J$L{6V;_55)2P)B(rf1gFs
zS!`wK@fM6$r#WqE{(Gg+Vl(CKBf<sLScssouytMsD$O7lQ`gCklL=ARV|ZdHdhMDu
z{^+0+1Y+oAh(2j@Ff#f3!BP>CdKU*Hq3PpOa66v&Y5kC_$EZ273hk%H!jh6Nt5kTy
zmix`Mxv&*Y@^t<fS|wF=@Z(6;e(8$xyGohI91~pAaB+x1J#!;YQXk}GWZ0oOS%R9V
z?yr4(+<IpP_qtBb%v7Kr3EY0JsSE6GoUp2?P*@b%yjlB&Ne^Pl(G0tY&;;jL(YYaK
z<|6lDE;3+{aRGJ-rFiu?Ig)Szl_(v7LrUr5k0*O+*;mz?S#j<sRUJ&3;@eBTZL?S&
zJzQu$yo0*nfE0_WH}ZC`^lSHFQ-W=6YGnsh&C<N*s8H?V!ilVd!UM5&S>~}YNYi#<
zx<LyL5pY|&TwJXAq9l`?(jEt%NrRNs1q?*-ZvFZ5X9<XnsSd5%viUY+Yk<|vJqrRY
znL%Mw@}8)zPVD~i%=w<IZen9dZO4o3$fwHh@2JgH^|iFDIfJWCa~&`u*al8xJHjU=
z48fj;Jp3s97<$#xSk0mnbQ9i!YZ-uJi*<uaL)pPE7130M9V0r}C2(4~9u&j`((-x;
zCW}i*P#ze(m%`vj=a8EUvW#xDra7aKm|FwuMe@4ZE5H|sgmm!NHPaUIy$7<>#K;(c
zyJa-Rcka~!HL0!g`|Gfvpvn^lOD~LOuEFQSJpVk~o~NIaH}|LNI67{HkkcxH+~vrK
zy1svBL#pEEN9<e}c|D7Hj{pFaNl-HCv2*0cj)NrMa!l$;Z``zruP>Y0w}&!)cJX`o
zDAt*j#Ty>T-Rfdh%fYGZ7ESn@7c-m=?zML!qa_r)t*B8l+)cgWdegm|qikqFN&vAZ
zP?AGTO<mL0Rs?#&A!uZ&N>~q@-J)Rv=m1U=RE)W&F5d5{{Fw~ZL2HIo-(Lq_nBB5O
znp8FbwM^Y+14Bbb;uj8Gr}{0mo!f3}ca%r-_Z=%r%i`}lT0|$6KU$sXlrS(F=ZNBK
z^t#%GD$(iDj+MlL7bZ?e?q@W?GJ^S$)x*55u9FGFJ0a6^w7_#a3|g*&{rmo9-MgEh
zji7fHfE<6j+1&>ZRuY4q1^J03+x9Fi8-uprU`M_pnY(742`L<b&y*FFH4m|$flzV(
z*8A{LvaMq{)#lHs5*~gi{y-OstP&j36sXO!@+wqbuKQB{g?}o0WPj|=+^TJhTl1mC
zSB|C@9bxG^C#rIOWN@}Px$FARWW;GD-(N})*zbEJ?s&%0H=x9xq)zEJGcz%9#5FfI
zz6HOLLf2Nj`V^U?o^vHPOB40>p~Zd+1Rbme9$NU{x8dVr;i7S9LZdyBD>@=Qf3b<j
zr*@O8rTt`e`zZ%`y?Xo17F0pRNre72diAp_EgT%AU}yYbF?|CMkGRokTl<Z7pz%&<
zLRF5RFO|&!EAH>}!i<tv?_M6uk5l7Tps$o+NuRN+sWO}e2*2L!g0{#w<eAVvWA9qp
zY@Q!=EZHfe@HYR!0J8WHB@exa6GekgQ+$UEH(bhXbgZESx?)G@8=-hM^7z&ufut(|
zhv(@tQ+HM_wge#%i4|3TJJYa=ojvM}N2t6v&W{6$jff*Ip|NhzI(&!Z3F1NQ-Vlr6
z@_7qGB|Le;4=Lw%Gv9%a8z51_PVjn48B{g#_r3d*xIA3<Oo*wc<(l!=jZWvnA!GU$
zp(IRwGCOnyBV*7jgNq+1rpj2_L1EIyMl5#wedp!urov)pAm+<RV3h^YuI_nz@YLW(
zcfRAUxc%j3OQEACMRH~B?v`XW&^|<NNquSZn(%SI(h-na5e?o%xqkB|$3#{Ze9O$=
zTsSy#n2Z!8yJHqjBDzY|0iZ6{V0S!6vUuj6w2VvzRM{{?wH)OD9I<oCxZhxt2#bj=
zC+oDsmg9hkmtjBS>^ljQoac#&3<P{+gtu>BH#Iedg`53ktw8I8g#6c=HFEj+yn3J-
zT7k7rrlk|V7<oOvoGA2Ok9t}dYORcxmX;zSB1&7XomNhinr!Cwn2oQA<=?VpOPYP7
zq&Y<H>w5K&Fpvcaj(>JyurZMvj3KLMrC<5Is$D)k(t;S`Ix?sO1=ivpIFwL#5*Kqc
zmnupqMEiYc<i8l3cdi3Vr_Z=3Et|iiz}IHX-Ex&XU#@o+pn$62U9xYMA}Td;Xn~uu
z<#%%ZP-)vKwk@Bls;YP(U|r{EVrtq0*p1<nY<)&tT-@4CS>bAP$nZ`==<<0|l0xnx
zG~|Tg%<?&a*^62OAOJw_btgq3A%Zj!dgHMd5mm|Q*{*n5)zMCfijU)bQn<K?;HAHB
z-}d=foY~$8*^wWgI68-i&uW|(`==aLqujl7XM39vnU(;N*mGB~>rMJ6=WNL0w?N5W
z+dA`(nzAW!O@tD~{Ke<b#qpkc>D?=_^r0-OZ)D`8z<(tkE|msfWCx%BY={}&@lV~U
zoaePzzWPt~*@9=)OqS<QiVd17r4G?H@D@6aD*EH8)7j;))x;DML#@%4Cr=8Ic@hNS
zM!psaLJu4p0)F3Ht*xzPy=Pr?bahp>I-P5Yrrp}F9t>@4Cba3_q8ShXf4hySW3gUz
ztgRpS+K6}V<zReGbB$KnZ)0tp>N#cq<k_=LTehsscIw!L>Ku}SA<yD`za)EJ{Suu}
zuvC!(9ALVz`A$-pyod-R<Y$yTC&hsv95`^mPt{{oNI`)U<D*JFG+G_Z1q_@o7DbuA
ze<#q?&%?tL1f^0Tyf^ApadC0-DZ}!PL|{b3#2&+L!>R7j`qTneEJv#(ivnKAwlS;{
z*ck+A$c^ISOvukf&|{Mry1_A%KCsgr>16Cy<@Yaq8Cve*raALdIy~^bP1M_aI|2|7
z54T+T&)A&O^8Yp3^uNRH{tH&e$#)?b59jYM+Yq)l|AzZ?ww3)KgIoXo&runUq?lhu
zMQHq@6v^*~yzdZ`3Tcy;l%x)3Ic|q$BXZ3*7;VyxI_|mCO=Sb>iOVQkGoZ)nP4;S=
z><SRar7R2iq3y;AUB*o**%X>?VE-wZGqEeE-OpXG9Q!LHLxFr742Bp7ZVW~wM%uPf
zU0uLmNPojB5{k=ZmU+P-v@tUD+c$g06)X5o{AD5u2~bs}|7=k2(l<2JK{WV;gfKK~
zD-*o}Z`h`i?RkzKXE{FEDd4~7uU;wUwMWuWctVcsGSruMn4Qk)fQ0F$ZQItMmN=Il
zYxL~(HtGPi|Dq6gxRT<i$3SfynNJg4*e)QqZ59cyl!FDsIHqZ}5*vFka2?ptt=|KE
zrCp%OxImO0Ad2QwV}lKUFw|VSX~3-M{)H1moqGHBvEdxMifqIh3Z6Xb-b(Zg3=y|)
zU&Z1w*l<2H<I<9ELRkI`e7R!l%1=DLyXPxYN}XMMzupGDdm5M2GmdSAg6+Or8d)9e
z@ZBMZ3qUd2NA)C0)G~6p7hqduQW7=wT(h({`tkRaUBzHPRyXyoq~y4X_VofOWdRHg
z#mxRfbB;_K-V_u(NXoV@V<9w=O;cbnx)XCsIAdzRDqa*&xKT0q8xYww6b}3ZydySJ
z6h6a_Pj=U?T?b4|u0k&MBrr?kz(1>OjccuQj*gC#6GoDNw0aM^sWdb;#wR7QLKm|P
zU4`zUA&Hc@=;#nw@)+<?s$EChl?hF6@165AW6l|rBZ-ZV@nT{elT{lad+OF!eq!Uc
z?@*fYH+rUxq;B>4^%Cw%kw3=Ai?QWdh<n>kv0NG#BCnm?1g-B<C%6bsmSwmovMA5V
zQIa~EN<av8Hv7_oA|(b2a`BHI6*tCb9d&niC;XsHdEdMuwVMWkG&fH}o!$$GNj>-S
zQB&Vi;+>I&o-k1i6PR(-+I#rf$OUK+Ms6vi&o2*efif~PPQ7)O^)q>S&3CpOd1N9P
zZz4&;s0qV%3cL<q3pW6V-i_+Bi$=SG;XNTh!$2Ll@OQ68NAo_Qb-*;0r{pU0+DDD{
zj$gusn3MnzBCv#_1%d(0<;X5u^4t^4+7#S(`YtVcOiUpmTSnZBjg>AuKUUJ*e8hVU
zzs<XKuc0Al6zTZtU69K|qtWm=$Z^VO^NObQ^xmDHwup-tV?@diw}g?AQ3!_>$jK#e
z@7a%d740*IxzV!4KL`W#hHv5tRaWZH&dtT+!OeoG3<X!K^T(T9`g5%y(!K^~v@Vsp
z4RX(Z$QJNAVQd}}GC-J*{yt?l!UMZW8d*x!!8RzvzLABQnHj*}-dmId2-Xuo6&?d$
zV`5^`G%?})jghNId3j$R^V0!7w*lzI1Gz;QUI~-$EGfx?lSUU>sC6?WMgV9GeuC>P
zhfy!xS_*AwFbVu#-#!5mcTIbHNr;5Az<@<ZPw)7{qIf`DK^lXs>$jD@<av1Fv~kR4
z;7L6?p7U0A<N-OTF2=DsV)x6JFBu7X!U_SJtLPPk$i3qN0|O6$d2bt29VHVw{7}XH
zWz~<K9&2GKDMRy$hE3|~g;ierA1Vn;7kN&-tm8q6w<YCPbhIQ^eCSVkx0%rsR)8kO
zXrU;QQKw%6AWG*q4xS5NNB9fc1P1E1d$CIS@CYPA960z@b93{94{4CwrYE|QJILp?
z$EMql?!+GYWS{vC3!%mdY4!0Im;_YukE(5kyRo5D_1CYKvry;zAh33?(n7mz`wh`P
zL2mBNc%a9;ukUbZIY~W^hrG7-0p{YE3kwT%n^&${#RW9;fto4oh1dLAT>Mb}%J>Ci
z=CO`JpMYjw*Bi+Ca5q~f@&Ny(VwH-GT|##*`a{P(ux6Qwt2h^44;vgjZ5+E9$;w)S
z3B*Wz#JhqA$USIJAp=@v6`oAs)vJtzpV6}tcw!}c76~!&Y_ny8p(4M)1AG(LABL)m
zj(|OIF#2u{y|ykNE~3g!^I155s~TlUFhWN|Q`46WACG~W^v@5V3#-(6TLK6HVC0QZ
znr<4IhA?y}1|L86Qc+tY!%iqH?EH3=Ew?R~0#$idft@XnC-xpep^c2@zIma?n8mN=
zON-n%9Rr{s|DdKDz&`Tu(=(Y!9-%k{aO?FjG_6;Xoxocb@mVgD>h+&L%liJB6?@gj
zi*oNcUGdARQggG@#@}5@K$15gG1q%8eYYHW&>yTWCF3hM4^*CKcWP*SeK~{tybILj
z6+~%iX&2@-ku(PwJ-dQCfB@hEcqhrSNT4I%Bl|)=&Le!5p6`fAqh%oTCx?81%aj;k
zVN`}|9w9ISDG6Qssjt8PF&4_~*QG^E+f%1nGj8B|Le{no@hJkk?XY{NT2g^`ESpI&
z&rPpOM+j|um?-g4a!Wyi-VJN0;q`ot^PQJ~<y3s5eSDqWIED)pRGI<bH;;VpL~^f-
z>r4ZdVPayU`K(?mp-xCqk(*4I2z&@m#E91_7HUTx@&%YkuFG=%8jjqj2c=2R1PSK@
zxs-(+7r_?xUSo0c(CfAb@@G~dR@{t;xQrMZdMXzJ<dn~aSZ5-lqFUJP@V|usNWf9)
z&HH0ZfsnEgN|YW(w7#y7K70P035N<nFfLzijprgREghhnuzKcVPbo9yLPSzhl5W^6
zzJCSBrq^`R>=!70Klo)?6Q}XjYC+-jXnXEB;2<=r%BWn7Z~xLoRj>y)k-_iql6;D!
zyu{3U0Rd_C0i{1F(u{KRc^<Kikut{v?Xd4qs0o(NAXH~mQC+iR(5=xjKR?m4JCU|y
zhmQ8)!*@QxDrJJp`%nqpQ5*OR_xPfL7M60z(o$w}?5m}DZByI$GA#}ynID|A@qP()
zL2YxWp!TH<JQwC2*_zn@qlw*;&}@OVN1Y>%4J*tw_juyCaZV5KR>tvt4TxDnTb}P?
zUQQ=4uH=t;wT%P1t{KOYb7T%J$nDex;JwSt?0(%8(38%=>3M(%t%IJx$4*!4a4?dh
zK^z2=mX_8oXsEZ&qTwn@aFFDDvFh3w)y<>BXdjAY(<*&<1!RbK2y!)}&Bkc5ot<vs
z4Sjp~lvtVi6ncX>qd@#Wr2Od`Z{;KNi(J2=qM}(6*)%Pffm-lofD?p}s%qQlQF3eH
zo{=lY%F0UV((gC>3Jlm@G}_yqK21+RE@c^#fQms+kO61MmVP_uPxpP~KVcdGpmloe
zD?33}P*uls*0C(RD`mEEn&%fnSdEhh%suU3bXs=`1@mr{Yjgwz@OM;vcN#!fcm*WA
z=?NeO`(F>&#%TzmC#@T`e)^t(>`4M*ctt!XWDuOtg<0;WtL8AJz{`exyn>j;VZ54$
z$8N`OjDT?vF28?We6ab5Ta!90T}U6;R?hkYY?878{5Z92%ysvDgM$5tEE7u)wH46I
zSl#iJOp<%)18k1Z&&`=lS)fHoZf8Okc1XC)70BqyupeB0_U}u~%JiCXg1*LDpyWDe
zZnz-vfqMVjmySG#%+00LmjH8}KHo7oXg51Q|D|BRM{i|%UR1YStisvIR(A$^`qJfH
zpaMMDus~}5pUz&#$G7rPcOczyeRse;6b4i|ICew;_b}o!B88ra7u<3<*)$;~<>N>e
zX)=1Nnw4*o>^$8X2E~9(mo;g+dkY{+9^6OT7zm;M^!4@aDM7nLU0pqFuqy-O${8dV
z!1~ubrGV~HQc{h>ufLSe#ev<DCLv%mSt3s~olre-@bqZvejl4u`HDp`G78KLh`IxT
zm|-YM<8&Nq-9g?CG;9Ecmgcz+y8v^dn7O#f!eS`|U}p44@QWF2jL=#*51-7ch~170
z5>_8;NlR#T$3n>S7#kfINEv;1@RYkuiA|CqU-Lt>F+qf9(A0i40hlmhc!)2^uud_@
zKJ(aKZAYCDR~tCvT00-wiyREM;#Rfbg)X{V)st%CbV4E`r6a!H-rfXHoI_12-HC)c
z&4jvp9e3mVd7tm2G^yi|az3;(yf<W<YoO1c^qmgiu1@b7sHFG&#F}r*2*Xn%i$9#$
zs1b{?KKwvpeUz2OMV_AMIv_5cJI{y5#T};s?y1-rO7>kLCe!3s2gLc)EhUbA9_$H9
zPmi*?5~=)AdgYNgK0JiXmz3r;cgt<}k7Vt5)K+4)(7=AEhupnAuIB=>DZB6ImZ&Gb
zgpob=)v7izG83(0CriDDNg&|!t2UODl=LsRP(zQ#4gRl~{wfA^aNu8=`#A9ZN6378
zy`CA~-&h_Q6B9<quv~#x{hcYv0sV=2{Q8kRe<8BOIX_k(!OCYIeXe+`q-6Sq{OQ|`
z1*~3+C-U50v`(>A)MTDN%ArT?%SGR|G|k<|$0zOd2VP(=>l+&zkqdkpasx%cUr0zz
zt)qWYNgG|YvVj%s0-UsrUMqteP9Ck#)a1UX80znJ2l6%H*=Rm?fm}BBt$+5Dj=zus
zug4^LdN~)lbF#Cu1H2EtbZKdE=5(_lDi3%Bb#-@NMovh^!5p83tP^rPM!rwtGOBK4
zjVV1j*c!p=#oU?aUft`d=-io+7*X{leVgrtHZRv(5sghvAtmw%$4A>AG_N<m=mab(
zJ}-}#AR(;lf*Jzd-Q6Tav~%Z9-tE2lZ5+Y@HdfA^Z(6O|w$EI-_Ii3qKR_;4<C4WY
z{q`H(JOyf=lQ(78ke@%2_s`?dNj9YcATLEfudEqpWpd|7G+aUeXWT1j>_|OW6Ipd0
zAt~_TL;ee%o;^5Rfl)<WNPM-Go}T_w(WOg7F*N^OfHGoTyQvsHRI0;W4DHUT#L`sE
zLy15(32K}w)}MMhFg7M*M4f`TOE)ravF3dS2CJQmFh8Fy)fp(C^P^psU%RQ@))6j~
zWsi4<j(mwq0BqlbMms$L#baT4`TBmxxrK$$+7J60`YL@?_MSZXNXc{-^*wkw1w7M1
zW38{BU5y}*yNHHAk4H!)W)&PZD?fZF!@Qb50Bf)Tbj{VBJ9na8TlMX`>EXk|a&oJE
z<|nmub(g<?|Gsp2DawT&oQOyPgK`R=s^xl4eY*y^0PIck92YudE6xfw1)1s#Bk~g$
z<J5gpojO#~&ZMFd2p)j!h4GWw4mu&%uX9$gk=mPSr>eOq-aYg4p1pm2-LEb!Zh@nx
z^?6k?#lh=CYS*q^n}*g2@)yF+&aaT##{#;+VX5O;sNlbU4+FEV<E4+%mBf#1CEY?8
z9=idIiqdYX#X+9b-q5ZH3JFQK-z9g%48C)-JB5YmkUy89)k{HIKjEHi3R_B+-rnAm
zDByMZUS7F!rEFj&my~SVmX3o9ZhvuGdaou@VKW;w+S~z~U^=^5jqFoDjQ*@~W%4H4
zULna8$eg`_nPL%al$GTGyJr@SRla>Er@>y>Bq~~#;uEVxp_jbFLT=FS{m9<|$ge<)
zYWX88t{KQTn7#P`9KIVnmP~UH{e$n_<H2%n<@}14fE++Jf=x{VT!9-P#1kyn=q)TP
z20uSt2{J^)&6{K;_+YwQ3g4Us^N3JTXt=RKl?x#NmSf;k=(@Qnphpo3k+VNf=)BiQ
z@hwEqjT;pp>6R6NM&ZM_Y?)U4f#Q-925cG<Va)NK`qoIgZyjE6S$-sj52YIXqUk@$
zJ|xLRC^yPzWOERw{^P<z0o+x0eW!3v0fEjzy?FHGg;46@GV%9&Cg8uai|pdql(T}8
zq}XbN#uT7Ke9VyLD^_U79|&sRQliStpSl0c=eVC}xA(B~GES^`hE!kK&06)(h7|mR
z3FssBVm}0%iEy|?(2!xKU+kYmow{-B*5o?J=5n%4huqWQe1X?)UELlyRF|Ni?T026
z%uClbzp~6$F#>))R<tAly_%#dcbMh+1NS((Az=%|15fbNzr6?hJ!{lMr=1r)&YRH{
zlPPC#so})rWGV9M2@o%j75JQS>B~)nRZJSCo(C{?Z-0Le#OXp$EKu%(i%A2XVXq*a
zu@x0jYwz~OsXZFrfeAB1QY2V*dygm)bW5jm49{EC=*jb?gICD%b<~pN?@@`T#=+<n
zkvI$>TwUxsyzCm;AxC57yklE^{aWCvJJ5j-hCS^`)TL-m`y+8ewr~|XU&~a_k7$BA
zqvA2@P_GV`1vw11av}6U=5IF;v4bWiwo~U3Gf7Noq%E5rSkO)wx3S?W&^+V>!D*M1
zlQbMa975_^0llOF!vaQYgG>b#KC(p(fTF>0P@$pLxEd7|)#{k@IBBPv8o5o$696+J
zn&|D|JKbYrBzXu76i(1fis4yeT|XUDc6cBlE>6%0Wg$sh!tsLK_kiRbeSQ66vW>Mk
z?~a0BRcO<|MVMn8G&c6fnL-1~t*4`N^=LU(=LJAvX>j4QJ#)qe*`#jvYHsd8(2K00
zQ%~**eCn9O{bweor^|7k5elrzTAH4oDnu7JJiEc@5Df=j3&{H~j+zgQ+9cQ|l7?!6
zlAei>1N>=MPtcPD@R(pZ6OSs1yrag(kqyFRoQ^2{2j*=*2CAN{&%7HEwmBeQ5Tq2Q
zr%#_Utz0RuI>Jo8vP0uvue7*$Q5lpulxP$jN@R!4I8Y?mcX7TOyH9`r{tBE0Pgez!
zpQ)W7lXCo-wv{X_@8I}J5XeJ7cs$Fm?#+W<ur@xwoPC4%ixOdSTPVI2_%E37fBnDz
u3xt?_bMh-v;{ORC{r4aHZ-1^~iO%!_Lu6j7ZZ|ooW3byuCuP@3|Nj6HL5F+*

diff --git a/reference/plot-13.png b/reference/plot-13.png
index 2b0de7cbcc2887104dda79090cefac1cb4e0c574..a4ebe0dee02bc6b3ec3ac340858ee6ebdf4b8276 100644
GIT binary patch
literal 26160
zcmeIb2|Uz$`#(JDRHqb2g(8#|m2m9YbyAX0gzP6%D2D9o;69xqYoS!Kq$Jt0jb$t?
zmdc)GFvf^N7(*D#7@q4hI`{89zx#DR_kI8W_w##R|L1jkopW@U`ObIwe6H(#z2EQa
z>eg9(?Jb)GHeoQBEjp*P3^AB>J{Zhe+aK4%PssNkPr|qLwz}F{@EyJujuV66+s13B
z%-k^;p%C;hNA#De%NWdVjE>f^^FEKpDgO73Ju5UOo0FL9N^fo2WEhsza8`Ni)zGjV
zecUylZXa6k(XFpGdLXuO*7QM&uZnMed$~v6%+r`_KZ!_eK6boS@~VL0p+o$~ZYTWQ
zfTL4Is-4%{-M?tZoghE$r`D|6>`kAwqRyHXbT;&p>B~9i55&IzfqizjjBeA{u>a&B
z$RUOPp!v&29}e_~t*2jcqVKoQuCYengMZ&T$$sg+xL|(tgWZpRC`aEh8~69GL*Gvw
z{3j0a#<{=VX4x$+E>25FN2ei4O@;bpUlLsm-rr)p{R<JZJGHV>{i>tmUQyB5_n%82
zex7Ebb1mB>`?u%SwWZgKjJxyx61=qfmRQn#x*PjMe*F6<(x-Yly-nJsue@aGTm9g>
zz0ncY6@DkKT)8sywM|TYu{FB3+df&yyU*9Ye0gyU4s9-P=jrKrCRtsrNn>T{=t#=-
z)beswzqtt^vE%@^-fM5SOfkqV#aAE2>MI{Qefo6j)2I8a0~fx8hCa{C4FB4eUbl|l
z@~5>MxFWuN`*w8l)!M;un<d+FMvtRjRDnZlYRTegCU&Idacj0k@km>`LBO}z<cJ5d
z_9vASohgb}6Q*)17(GY7K8;QFruUt*wiezX5^ybG!aX^>yTD%C%8H^N-ECc35@oae
zExv$ChaW2XXAR}l4@PSUZxhpxIL@WC@36S|6LeZWpKd%#9E1x@RGUzn>n$*1CS7~8
zu^XRmklg1~<)JJjRXJ~KiJ$Fo9nY&f87-`qnx8MD{_Re5^}=wnh^&26L}P+t@!a`*
znFnVGIWaLY&tJT_>Y5xWsHo7BUgYLW^{S?CfhVy0wan)lo0UaU!!nbed@Ixh&l=#b
za&T>c+hPCV!w2(Sj&FY7_yFEFaV_i4UTr&O)yzlT2gTGtmA0y&6`?5qFLzDtK7RFp
zg?j1g)vGeYN&XXDOcCGDcOGmJR45!}7I}RA*wCsiLK!Hf!P_3hTl-}gV~2jp$r-G^
zwXFajIGbrB?_Z9~tskffmu1q5ZDQ;ECQ66hypo#LnND84+<}wVv-@0X!g#w`n>KBd
zF*Y_P&t_z1)-Np$Yq&5}1p<vtOzfV)jTvyYm}Z?xZ;Pz$E4*S9K3KW3%#37Q(aE8c
zqvk8nUfGwhv)uQk-|Xno@t1E0vWxpZbl*3%@-&XvDf`S=b{D;BW%=mm+sADwWuDmr
zOw+1G<E8qlCEX)t`IkaiIaSMxC2)Oc+3RbtX69R?0}J0SH*2f}Jo4(fA|bp|T*bm|
z-tqJ9q2^?&Ej3LyzV=gyh_Hd$?D=p!IdgB$VAFkhN0VIvldtpOt{f<~cRT7ojaK#q
zg{ZAno#0ZrGn`N2YDXZq`uwj1JlutoW@g=9_-A?veD;Z+CI!Q-Ewzz?*oOPku2lS7
zQU153_p|r}Iukw0F13w5)4z_kXXIUJdlnkng^gAlu-9Ok!O8D~CnK7uudSRI_2R{g
z3j`-Kw4Ezw(xgtnSHYm#RP{DjiYMu^4ILiQ6uh-&advDkyhBe<?}%l|&+5|;nlCZS
zXFDxiM>En1hksZaHLVgzP{4(KdK*&r+d59$wr6^G^wY7^Lyc1nvdvm6%d7&tRO#oK
zl9AN#f%js`5wMu%r=o*)WtwD%EiNuT&&`cSt4N*M-E7BfPE=7$3R+&Ui>|ozK4yos
zt(KQk2yFM7in%_)`gp16va+&M>1}zggSGcnyq$M>bzSUc!Lk>kSIk+54|fWeRz|5f
zetfEP&d6xn1|g5LstccE^PDN@P2YpgIc=f41z27_zS=MGHc5E=9L2l1#Na^etqh}d
zI~yCnuW2gdE1$T7j!wWLxKQrn>eN@R1Pi;pqoc;3UcqmmI;FLFr_4U`+#61*h>lFt
z;!kJ1BBM%Nrtwv1X%REZn?xp3)#qPx+iCp5`c^Mh?3nfN;X{Z=`20a3*J>V>rRNJm
z-o33OdwZibmVIJb0+Q=~;(4GeRUt=QdP6YDu`H!%zh%yj#rwi#JseNgPnS(k^fD*V
zht=mw?U-9c)K$s!L6MbtuI6l5gR;)OB~B~T%^FqRm8G}q$GYzA7Z4S7hX-T_QJL&H
z-nq!6t|X~BL->C7?3rB%3tgqt3fxm-H`AUm-u*zyeMpXo&xGgE@cYJX`PjfQ3+h#Z
zi+a4n$aAJb{G1?MSc~ud*9jX06`bA?xRpmzydr1E3z}<R7-#0^+BZGg`Q^)(Mn={A
zK=@$0kwIhSa%XX6$AI)AgC<zq=jZs;FylZmy@+J(3X566j}H4G3ZAQcx5nMmHLzW1
z<72Q|<-6&#B%5gaWW0i?Xjpjo;8dLm^;0SD_U+XMf!|`sWiWGn{x13=ABwUqt?LK}
zB_*Ra2>a{~3k&mlKgFO1t&BUh!L7cp=+ei<q`KA#bu;doT6d4L+}~^ztA_t4jWe<#
zb_{ILm_NptovT`5b>6Gg^=$vY?VbMLx9Ptl1WjHSDRj(kPhMFX?q<LS>K-8%yM!|>
zgIKfOjHQKXENgKVmP$u%U{TFauI<myb$c}@sSS=iiSE0&a+9NDv?KFLPL4BC9p2iU
zPZQ565hbo4kBW<ruRFnS6^CbqXe>Rh!S@`FC*?^<mQTJh!5g@J9kW^SBjBeS6&1<3
zWo60}Wn($xV+Bph;$n65&TNZa*Ise(@JuzS(6u1P>DM-<MV0+^<%w~)2%}uk%`mO}
zb{AzXEQEonfp96p4uZ4e*qPU%p$0*VZP`7;EyuZWGi7NZ=@6W=YNX+|(w+RPLwO{c
zYJ0s-VsSW{7v8QpDG{gm$g?-*rem|DWy#A9CM^ryca+<dmv)eq2P0(jX=Vj>H4o(-
zACAw#xhs#3jqMe+ouB-a2T{B5s27~2Dnym3ySV}TqtqB(UGQ*I3k&5S?+V~6rfLPd
z@ydRQUkGTOQQ7P4rT^B+q1W1w#C!&BiJ^QZD%uw?-8jEUsuC7qcGab7M+>)Rn&y_k
zV#78)QqpU3q8tV|;h0--4`1PFht)-o__)a8S;j1NWqD4B;nKLd`nTVL`cC5)XRa6v
zNtnI-^%wyUYeMVt<;&+yOiudx`oeS322jDsyofn^@y+k!ff9)p{+~yc2_1n<TKu%M
zh2gn#ts{GUqgB71ie)*Me7fo64}nHymn?jAqIcg;3eh_{)<9j!xni~xR-kp3WvL9r
z+f)L9&}w>IUS6JdG$E?>+#Ifwt_a)d*SUTDjj^pywCiA@3_cDK4r|j_$v&r9=0)o%
z8OtHsetvf+53)?{lW6sx&pXJ0T11Ot=WO+5Yk%5ZoJ&vPl@Pqe!$@-9vYt+QTldZs
z9*f_~^%4|nAW7Il_|5a39(taU@jg&~Gp6mUhgVgU<#k0-MN(ci+<G@<Ye&6DN2SS0
zGM7Jikxg?fA6O+9zkK<#K0GX;DDT-0)4E}el?94S7R5{mPaT>LTnp#yWw54C^{|9Q
zB*fpEh6l1w2n2<DN6fZHt1&J-0t9yM!i60?JY%HXXaoGH(Uiu@v<8`0g}dEjc$VA(
zmsvoAt*Vf8tnU4ZQ{|b3bIJ!Ad9-)k!3X#Kk@5;zuQ!M^5CGK-9F&m2d1X~CeUVyC
zNh1E^dD4!ZSw3Ug#RIH4YBWTv)v%s#bA1DM)6~u)Yu&NFi7+N1li~tr`zmUqkN389
z>(=E_1DQ1$=}7`8dYm^luF+GZ#8CpfFUQxEd5#ItVo#?DNEA&DCAbhqFMRbVYyHT@
zq*n#0WHwivKiuJv-A}R!syO$K)RTLYJC4lT<0cgQEG1=kC8H?!vJK>4O1IY^`6K8P
zr%l(V3mVH)(FHWLZHF2jY)gOSb{fF}1nN>!ekHbebmw2DsogkGH1zngMB|UtjyarZ
z2^CH`SZLei#4{B6Aa6wPjXEmI?BkRwe`!^PjZ!*5c6L^4$KjZm^ko*aIPXK0CoJJh
zWcT5wM(lnZsb}oI+!oR&)`WLef|cMLM7~?_OvFS9Sv=*mwJbu%WSRa$xr~~fFC9ra
zI5}pynqkKIxVv{R+qiVSgh*r!k&Tb}dAgF8wHokMIq1$4FW++26JUXqQW(tvL$cir
ziZ_+=VsdOWGq;HlvPF<j&2PrR{;tqq`E*k~K}_%A_KuDY->heN^Rg(N0(vF0_T|O5
zqPF$Q1s0^<ioqzAe7yO#E`>){JrCt?xDaUsLL52kCh1Fb&=NMBCD^3z+%$2G>P28<
z8Hc1`Z~n}joT$v4vKPk3oB;DJeT%PZsC2%1=myW;lhuGSAK=yO8tx0JXN>}MjfO08
z*+y}*zqjJ?o11U#Iet@1s~U3D0Cky0HD|RH?$^Cn9mFC9d0xN)R?v~V^6{w?6P|q3
z=;$cgcKP%~-=PN*QLR6RI+e5^H$t$qW`!BFlDp-u0==+Jzn+j=G7xr>`xLnoffBUz
z!?9k~_%ZeldAPT~@j4tva&hMCw!ymSXec>kOtLQUKpf=ByDZc)J4UK=37k%tAJNtj
zfaeey8k(P<zeCw$#0$&I&#!1+Hv?!t64d}nexn)V$4P)aj#!r;l`I=cJt>kcqTqDl
zNwA|gtXbRIh@I!ozWkzq|Mp(&iA`0YE1>2&K+l5fx*^;kXn9{6d-9RTM;+;3Uj@&9
zH3;f?Jp1LF@_|+WVvi~m3hWxg5b9a`ahri^Uh9gFL(}}!U;$0grCb^kNNzRRrAp|l
zhq9<B;0WxeH#b~guj8*CX?@}z$c11if%QQ=7gcdPa;uj87^?89K}hl8Xq}*!7G7<D
z>_l8-p+Z|O`yh3l$7OD{nX{@hEyuG*nUin170smzA4A-ekgQtzR<baZuvc^-Qn4@e
z+>?{EYvqBg#TqDz`UbQ|rg5)=Ii!P@myQnI$3jR|=*+Pagc9P4B|w=4NeOXNlJpgT
zmIF)E&CN~atRcm!2NbUwUem&>5k>}T^6R;UcjcCUJFTVF>o=C|3L!*+yuxWS|4RcP
zPHV`SUgwt$uCLz`)*Q$v=sxQSX(dwFvLu`AOq7r$j_32$(cVE^=|gcvv#p_@Qgd2Y
zcMy`PQG}2R!I<UG7~g52zHljA7Uxp-^_kDe;}G+rsIPE4v8|~mT8Z9tlaAXiZ*Oi1
zN7Vp47W0bc8~oNgIQfo@ynLuU3Xzb%YQBcnCw}PAEvT;uS6AM=dE>MAH61(I|5}2<
zYVxk!1~}P&J3l|&BT>bzS#7S+M@ane;Q=W0q7nG(7##Xm>B+{RjndZ@xw+J4T6{j=
z(YEb)Va$%3OH*~&<)sBw{AxqO3m7>r5(ddd8J5wuI+37&?Yu=3GEm`3qN3)$k~{1w
z>AgcuiGsTKj@*L4y#tnB#4aV71844|>Ug|gwxI$lU2$RjOzVZp0AI8-Bhr%tm0?qs
zUMTap0_R${CuG;w;~`v;DAB>ndEyUcxV4eCRoy<NrOCHXT&R@@%v_=pp`7$S{5uT*
z^%9f^5vUeLwM|XA@3ig0w=XE+?@$l$*%h=j<vR4h{$y1kOL<^>YDvjaVJI@8PUat&
zAkw4Kll%@Nv@p_F;g3!hUCR!QzyQedwNNs7UmdsXE1p0JIOR~r8K_phdEn0&%($wq
z9QzOdk7S9}o6OuwfsFGnC^mJw+|N`Rq#2j>EFhA_!}!0GF6RN0T>56UO53P3Z~k|3
zGXH?qd7c`;-*VTfw?rDE+Bd89EhqQy-+#{1au*K|1n0mc4u{zDhk4<Phv%G)jYxu`
zOOyKv%syi?Gqde{d?&TEAkIE*`>JaCL!mK(Msab4S8O=wlIKlL`HPB*79`g?JOM7@
z;NhNcjs-7Xd}x-%;0({6z0LBEd4Fk1jU%P;Lw$WgkP&=3Dw3OdIdI12<~>7riB|SK
z{x>sJ|KM)?GbGnPI0U+(x5Z~?XQu!aDe)b-@RL;4lG_mr!V%aP1FzO{M?jUW5T!Cv
za`4ch?oT^VV24ap<KC>s50K^7vFqz=#MGy59i1o{Onv$C8h_$ObR#s!o&qm(fO_Kd
z8{i-Wsumhu9#X8$%yv>CiDRMob9Q6CxxqOIVK%};xq?DK$7#<ndQw<eXeFOxQ7nlH
zJo)vvHSXTMi<l-J9;nYk;kLu0)7%PmlNP<NVy`GWU}jsy4~Jn+y#S8EUaTP7tl$7(
z^=V6<7877O!XXJM(8yQT^a1kGKwMdfNTAO+M02!k;KK8u9-r@<tK<h_B1|2>SPg8{
zHrNQQmFN@Qo0P=hXxQEoSLwi|d4O(_yy7O+afgg-rv_?VXIi!Ep6bN*7(WT-kI{R@
zsVVuR563%R4k-^|+D0FhMuGBe2hN0|I1RC>3U1*oSf0R0O#OvBV0R92qU_+ogQ3Qm
zCN&?^bc@N}DDT0t-Fq^E{lO6SMem+IL9N6OE{^3!L)^Tt5fq5vH@7{?JeZb3eSlS`
z5M@ZGYKV#|IQMorSO8?x7QxGjiq=MpR8cBnH@JQ{ln!tKB93wiu*3kzI>)whxZrR&
zuc3)xj<o_f3!)c}f$g+7-j5T4r~{Q4<Qwl_@1hrDRMJs~dwS-;HgS`zP&jLGxFv(s
z6&CeLYHI4aWC(5AT3SE@zzTTz-W8Y?ejXl|@~M+`e4EkxUv5*jdPVe^8kmPbx(n6?
zWPUBD2M?HyE`hQ)H}A~1S^@N+U9EAv_n8y^xgl^e?Ie<-;18ts_Uh=snJ0F2N>FN{
zn3}D|`>UeS`I%;$2e9)Smn8vNUYUPI{q48kXa{y<G#%xaTw;LTbmCgG&*0cGGQatV
z2ZxK6(_H(zyW*y`Xw5-|*r;yhmf6$O6WW!p@8T}7{02C+#+%9z0bEH|-tsC<xAz<$
z2wuzOkhk0<n)tr@X3xWbcn&3_1Rd>mC$o?H4SCM(>rZn(b>cA8sB=7B@|!q0*Og;F
zYeAf;)W!5%bUfM3mnhlKdbL6QCt16O&`^nE^!&^91C`8)a8%eQ0Te5t5y%4T3N7qE
z<E%o84{cdH$awnIs<G$c=nmPjPw;t~oQ%V{p;yGB8@+eJT`WA7&gmm~_{fn6j<xHn
zJ4|haOf6kc95F9Eh>nIfU_Rb&W`u`_hh9FdUoZ&uz=`U6Kl^BlyqAP!d&uY+Fd>k5
zBB3%KAHTA%lPTXAGK3jdhn*U~$oyg9-s!YdsKfxxiN1zf>zgGHM#tFNIw`S);5v9x
zS5EUc9C7qj(?~l9-6Z4uVU&PMzTC4IsNuJ9+CB;NM-nU>NL32N%84*uaZyooOY6ta
zpC1Goi!#pe9P6ZAdv?v<2<y8(SnJ&-8LLtHw$B!C4Sr8+yP^?<DKvZg<v_8z;*Y1E
z4Gat@(8-rO1sx_<Y-h*2WfN{oSrD?`s{^PA4Gnz~rThXLhRQaK!*xlXNjsSb<7MR{
z3-7cAaJp%RuC+VuVX+_QRx6}bLcB+8-wCa1>AGEQukW(MVWD;Uk3OU<vu$(KprCRV
zgM7|ty7@8(!h&d1H_DT$0V(o83602S=i(JHjfMB8(hQ}64)W>$%Z7YxmU+?P0|rUE
z0Q^dOjI;oOLYGuQ!T~1u-uK8?NJ8iwb`kYrGNJ|%&RM&Dvx6H`JhyynteAnb8~N2=
z)(+MRx<o*X^6DrBHt%_MHjcDIC!at#(BwanvAPJ3NQ4tZgp%npDfK8^K>pXRzPEAX
zSZ8)UJGsqGPzvhxbIb~A1<G&#3Ng>oozZnjIKLCP^b)8`2H?qr^IQAuLGS@+@-T#L
zxPa+m!w%2(bKPRP3YP(mv0+K!l{Q-hrhqu9NjY}ic4nki`fB_010t2|kaOE0(RRIy
z;r-W~uO{AYw(crw=wG+nw4wh-(-SpWAC($`RUyXe&i{@w^AD2Nzaj7a=MevzR`#DK
z^50SL|CaRlcW$R~9DPk$3?Q$A2S=12_&xJ;hisC<3#?zNX|6S()3T?jsiLdFGU72*
z9KKmUIOKXLQ-<*C%86(3cs%6IVPb^Uvz#1B>V8biCP=mr)~2o2d+dae4QXocn`RLV
z&B)U75i7wEP%pdyq1cWjoyTkXte%4mq03quh{fr#KN(U(IcQAilOrI-BKQ+3u?@4d
z!~x)n=-s@&93+xa!`Kfg1z&;0O_-x8kFf<<jEBts#0~pb<let?&;N6XzpCfhl=6dr
zcme(;CH+6Q_TRYJ4}b9HD9s15+~;dr{D5~A(`sp<IVeiru~Q3Va9fn8KkA6p0RQmu
zg)|aQfibbnf~sfz7J==_^VMPlko3c$Sg^XrhY$%Sn{;SRAEM`!h94DBn7~{*tIoC?
zO@W+M0~Dq$ko}G8i#YiuBLJDm05)e-nA+gw5qX5jk~4|Q^1unHjPGI}&C3M9lTpU`
z%^=pDjM}vm;Owt6fYDHpdiLyqb-+|j$=7EIwy%F%H`RZg8+jNiD9fmp(uqBA0t8w_
zu>*YsrJfw(4~?FkxmC41o9kLHQCtnR)VFJ1?9b5~+wM3ykyj@?Rg{aDxCwkLkT3SA
zia&fe?BmCe2)tN&b{GTZ1Q&xSR3ydZpntZ2ax+a9K%b&npnT#LIfl)qYC+uu&{(eL
z-up`fpyvaDIW<`4QtSft+9S6Qhj@69SA#2OJ9=ZvY?;<8@--_8j?`)ewG$8sPy&G{
zoY1?1^zP8m;pCvo>xbDSNtdscXunm-wToI#8?V*Z*9+n-D*QaPw7}<)-o4=^JQ^ub
zf_w%exw5vRga1105f+j~I|X%6uSc66505J_`nX~|=yE($%|8_%vN-+;)Id;}T@-%I
z0TNlzx#V%Tt2*neK^;I!3oHMR9i8Wgi9cY>_dxwv>`Vcwgq;A$l1GlrOmEOkkZ%w>
zeE2Z3wd4t;sBPNRqt0Dyw4oe0(Pw8KY(<s5Y}3)b>NAhIainx$B}EA~Wse`<toj)g
zmjVtKD=RDD(4KU=>Z>n&J}55kh+DX2`tWKgH)bLJbXr+ERjey<`}Xbg3u||adBr(m
zh*jA;FI#x@>=%HHg05)_gj@BS+#<q&OE1Q<!omobdM!wwaHk=M5hzJ=Lg+s1j#vJ&
zxev<D;$Amh)#(OVX{WB2z)_0};J4>I7FP2En6E-FZyWakn9q~9f3veQ!&wUzVZ9(|
zH#$>6l4Cm^ly2x>tk^Gr*}5lD-#eqq;I<1APwIsIwW{wK1ml2Cf*g4JJXFf7ToSBS
zhnd^HyUlv0GjjC#C!M!=CkHNm1v1;=%tN^=AqCEJz3$DSp+2CoCs^5dgLMNQ(kqS)
z<C9%CZrpI~x)>r)!~+tiJ#<c#gCb!;k|Wq;^mI`2ZmPgCSuXZH+?kX{ft*qcQ&ZB&
zws)HOh4v5b>KFBCzP&d0M1<vVCP~#dMAmt)s2}jJQ0emU<T-WcA7CE2@N&1PXq58k
z^WN3kx`iw8K!4*U8COtN>)H%7*z|^_d!xL>O(Ktl@DcYvRYMH$M)*KQBPu#@9~<=^
zx5`=3S|B`Rf12nmbw`x!RHRG56XQ=4Cdo^m#rA@T{Yc4u;4d45Lt({*hF&PTY7FP5
z1Wb%Aatal@P@|OD89zAj-Um6$m-`<h-7MbHO$RFcJH#jQ-bf1B@$;{*yy#WmK8Q>E
ze*1i2Zs*Z!zxKj{r<so*O&!aAP5j3N&up&R=bwTJqfq{Mb-#ybc;Pz#)-0%3aZSQ1
zUygTr&2VOys`~$fGA8jM&nhJ$?h&@LT<79?DGYJ1tj|S~%ys@?OX=(U%F&F5ej0=x
zc24s+@ox<5e`8t;`(}lf`S{kczVP;X&Tm%hKA*vvnOzE7=Ak+DY|rpeu3C32XJ&lc
zgV)EWtxnhiw}ior2$&oRZ(r;EnYiiQgVCS>+}#hRdu}_1Z04Dq_~D<SdeKMzuUPBu
zP6ro5fF~Vi-<g)*A8jZH)%n)@Sg|@_uiVvES>`RDRr@LBzV2rp$n0O7ql<_=y2h3w
zAg$zZvupO2FJDyqE5P!Gd|;xYAQ@4SgMptpUSQA*l5`%ZBbFju=rmL6Klr{<@f>aM
zdU^5q5)e){n*|k)h>DtIoAV-#1Zva0K(d$n(27WI_zUPQ!IEObK=>d>-y_(vh{me3
zaH-%$CaCl0&qKsOgwjzEY7h|=iL7eEA6Ch<lR{f3734rrDgj3v7IrUiP6l8d0Q=tn
zQh)NWY$MvhL&uz3U+Uw%Jr1D{#3Y^$*-&l)v7-iL04!`Hq)*K=imQY`*VRTQ8)Td7
z@}}YkpT_Ek*(doTe$w06jQ#FgMfGd?ZX)gRzN)VqVsq4&K5^SqgFyPi!iOVy;J&iw
zi;#S_6>|KQB)5+&pmMMXxOSS^p8&N$K<ye1UU}~9*`H9F%?)CWv0Vhr38J^>t<_I?
z37=BrNUBB75ca8HMVJ><!AJpt1!?iPVhSIM`lar}wLp}LP?v@j;kpy`>yR}EOkJHh
zhqs|a?Uq)S>(@(fn}PsX)0KyP0)@#`jZ~m?pYL#DXy^|4tLIf&Gi`ZpUq0MF>S~LS
zfv6~u=7Z?2A<tdi0y5Ctxx9)Q4bN$BE(5#T{Ck7It-!rmWm#7!GXo)n2hbdC!o#I1
zj{==o{8n0D=}Vjw6O^TqP#N}}*eWs1K?l7FiG`7UD#-5E<Cq(1q_4CeWM&I_{kMUT
z&C=yp_wjlqZXXd)Se%$h8w>cVYZG>zTfIaNi(j5jZvOr(67`$b_91djo@BXgur@Lr
zcpGp7T^Cu}t!>QyP!DxD4hT3^075>@zGUB9rzg8fYd3kbfA1CfsJTBf6iAj21W5Gm
zoost*aL|W^&3&McodhTWi?A998M5t)u4L&x@(Mj@#XqN>{qB)MH{nTruy>NdB@k{g
z9t2Y3B{CS#>OGa%AC+hVCkScl2w7O>(y4G3H(a)pe`l6ispJOqf|NGcFHErC>>*Ep
zE)~w21MbU-i4^ERe*{oPL&?|q3snOS-@WQ)!#=dv5xAYe`6S{@O-+ltsvzf+C&0n^
z=l2OCu85I(BHVl&IRPce%ky;_Nn>oID@HjR0s^)!s*#Wj%bkyD00Dq3ENge>&qqiC
za_*do2_Fv+x5m=70J=Xl{LF#2OH`5N$&jhG<YgeOkw-vdc~Ha6iX8$p(^1@ksy7fG
zl)a4scsmdlMAoBKY`i>I6^$rWloOE`FP{YzWF=C`{`$dT%C;?R-v*+rr_!YGheSmY
zPQCy0#a{|oRnv*|YCx$Jo;3EI@Gw_OeogEMS{@FnMd9J3<2Pr=@vgin;Na_cO)Xo`
zUH<+$A(`vmnMk?V8?Cw(w5BK|fqwf^zc=>17LfK>P<FeE{Me7S%U4KX(iwc5d<ZY^
z0v3@taE*N!t6xICi$|Juf0H4UowoFW8|1#My?(<ZCE36gCe>c#WoHaBj|dsF!J=mR
z$2}Z$cqjw&gVE6_E>$DnHwL4%;fHeI2*Z&nAQIG1JF3b+=wA3IDiq-JpChb4$m{lR
zDQmfo?oPrp*mki0iq%Vd99NiCD+xFakTu`16IL)+%yh6f$iLl;{@2&t)l}J^w{jaE
zRM5(Dx7Wps7jr_`3$kAXR&*37ujD?6;D7$1wcGps-3*ZD?~Hw7-=X;sSU*6JhOTNC
z$Q6L#l{@}N_R&RC50chO+El8dr;h>!LLtZ*R{~ZmESS__y+;E_tI}HqhJjK^whWbG
zg!0F={#U@OHdgkbwDjm`UI)`&=7D8A8=AKq`Vw%99sV)wy$hFb@2<9w{gHa_U^C@K
z<B$B2^8Plxx>af&Hv&|Jr08=N7J@uH8^FkxbMy#0fM$B<MGhCF#t8fFs{~wC4H|11
zg^*pzq|n*xM09{mUH00}ON(hh3_oV~l=413lszMa)s%-C6G*mHWPIA9*!vR?4+=<+
zkB4|^>7bWl67@r}Ui*V!Z1<J|x_T|be(Tohi2zp%n-oBsaw@xE3)VjbJDVOjN4&3&
z2gJM!veFbXv?0^l!6QeU@>jr_^a#hf>S;HZf>(q8BLPQstCwhO>ZQB@jMdFZUY<#}
zj9*RQS0I7o!G)Pm1Cm{ite<A6$8ecHTLdXik@SUqcCd-KefXuUAK~Kf-J58y1a(#^
zhkc(@rs0{83+$Vnh?SMg$s4ZPW!wa4>Ez%4>Mwr|WB#D6*^{%)+#Y|5pn?f^xa|Ao
zh{Fj&;whj(bSmyzf&AY<Xol+G&u7PKPgmy!{(iM|LS`t!uIIP*3j{LB)LMz+-mN@5
z6@If};Kum!r^C9yJ5=ZUuR~6>Ykasv&f)A51h^;=iky<E%Zy@a*MaJjx-#e`Dy*{#
z=t6^YvYzi<F9cz(7xJI$R1JR}qWvK`;tKKPBCnwNw<5KG3sh3sWn-|u_^_-`F|6%y
zWX=xTxb1)(*&YQK6i<;?GU_n<9;dX8-riYmu?5Ukfs1X)wMdk#zbAoA7AK)Z0)wi0
z9uRZL?w~C!jLsy*XsjjJJ2NI`rMa)7&diMDi&;r3m2L<QF4-rso@dX^ep|kWVf>pN
zz0IT^Sae^_j+ZNzcMuP!^it$pa!*kj%YTn=XgO%QU4kZRclz+}4p}7v0tY^@p2<Sw
z8Sd7I*!He>xM5e)gE{8xL(<p--@s=2g1UX+@S_LKOG9(#Yo4EbVhd|e%~hFwW7Hp$
z?>V49VN(3R_i-4$7}1q`1d{5V`Y+1(C0L_j7m%?Vqe(ZO37)%W;e5+Uf&6t4q%`zJ
zGlCzcT;~b{Z!h26@btn3sG75EswyXo=E{fF&Oy;PZpb(asvRiA)}u0hXP#a3&j7+y
z2=X?nPSPaTE<XM6;dHac$g%DUMi@yKObfUdSy>Sr_;LHEo!{CC+tvD!$%x$HOzSCf
zU0xWjL+}tG7u3#i?%cT<H^#xp$jCeUbTaBkq-P_yq(Ld?bt+~bR6wV#*3axj3@ez=
zMMdN5YZ$cUgIsF+sr}yDgXP|Ral=n|$K`xZ-LHEd`N=c@*kQrm2w>N{8rV`SP$b~p
zK5cARTyIxkWnL+YBiIY-TCKs*Ztv3pCO5up^N1{5%O(yN_2jvw>q+e`{rZELLoNJ4
zCk1}~RWm(W;;TQBdm*lXmkIg0OTY=JYqpv>U&Jj%$3sSza6(%-O`IL@c3(a%JULNb
zSq<>#!kg3V8~riuGY7p%@$QJO;%@fp8+j44p<E@G$OM@><@Z$)_^Iv*vYzsR<mTec
zM&!gq8qtI1fM=7liN)*y?qJorwh!*gJG%lQ^$GAmA=7AS*J?bNG2d^K`Zz1=2s^HW
zi6}KI>+|Zbo}O&ApG52jtO|3S5s3ZB;09Z>hGWC7pFsbwkzCypByqo2{!q?tC+f0K
z`{6}B_9X?!eb1Xu|KQ})zPAG%GM-7uV!hQ^IxZ(K&r2jDYkez|{iag|sT;~sZ_TBG
zZd-5h{&nb(KH=~~<PU%!Iyg9l)`au(S0dX2GQ0r~fsFdKwY6_5=PKta&H)KB&Q{Yg
zens#;WB`+slVjV)K!XihVImLjjcbacqB=1?kXHs_*G3`qhCEv!W&ZSS6`N|peY2G0
z*WTr-h5T@R%)U9~jTPYKB_@H7r`o@7I%yu<*u01;vsoJ8p90LIfy!+5%~{QKriaD7
zLXbO?Q&%=%j!30Q^is?qI-n{WC^BeSxM0r)qbX~{geUu;&bmPZdaJlQaD_D|<+Rej
zLF1s;D$1-dffKsH$<IfyNT?tlhl5;Xf~{Th&%aJ2wIHD}fo22V$~RuH74yDh*djQ*
zexh>GOEqB26v)U$+9=F!-_jg|UnRjkzd-~0h~hm-s~%f2GK5S;ugpi^XofzMCEM>Q
z#*tU8Uv2dHlL7kj1buaLL6+LZrZ*88flL_4gTlsvGi?UZ5J|NZ5`eSg#*Z9FvMnER
zHD6d~(ugUZXJMCsZ@5NBEO9$heYLcZKMp)#<N;~ej+VV}ny?qczJtzO<C&f1JT84l
zaEQi)Lb2xa`&bJn?{|Abs*#b-XY%z1WK5)-L(O*>P4??mF3CJNx;XM=P_b%354CLN
zL1A;Dz)nv~3ze;XkU<bVM;x0$@4-}97q2T<5rlmVPCJF#C?WE;^wr(Bt?cr5Oj2eO
z-O!&6og~N~!M;3zIdj!l*c;BT$eGh8cHb@Nm#CYfuPoE^0v5he7_@81<c#cGJUsk1
z0j}rI&zu@!lODJIC^65Rsg-JoHAZ&t1l-u@=m2u$hK8cTiA(A8?TLe{3ySphp1h9+
zBQKiw4F!30g#+h59Taz5K5vw+FX1Ewu6dOY?9&*z3y)P2dT^jANQ8&Y=c3jE>B}GD
z4oXS65Ll3`ImzF76b9PEZF-8l1pDSB%0L4}X#M6LXS<du^gz?`)k4BA8#WUHxSV9z
zc2Z6N*6hgi0HY#s5b*x>p^k9Sp|9k8*90loCL{{y;Be4Qe)YDR!s>l}eFHziy|g#k
zp#6>E7Kq<BXn>LaYe~SnFNO%2bU8Q2aH=%Vs{ynP|F1_MJoh=)K+<UiHHsCmpmD>d
zYq_gV-Q>?FD3<79$R>TOkr}-1o`gl8>N#0mIaNj%RvJu`Pzpnd2HbhkUnAfNZXZW5
zl7g8EObiIuLG?P&Y2gy-q+sDNje3$$t!;Jh5)$o|1B_MyMrBs2in7|L-?t4Q4-j&o
zB7$Td08UZ;a&b<P|6rtIEr|AIPgW6zb>@hykN45xNA3uePC;FrU!#Ef1D8;x1kUYD
z;s^GP8YwEfyq?a58q!d%L*@pQV9SA|gGfApbMdz*9~7d8f+gE~`0Mu-2ag7I^3lk}
z+A;eOxdN;=Z2kxV`NJn81&qhr*sq&0gKJHOcBigNynh~%9b5Y)!YbF(0Sa-Fbz-&?
z{n_WR{1l!9_-rq9^+7brBd!(=1}IK|r{x9>D2&`L3VT;fic#{l-U4O-HU<S!a82<t
z*_VvbQtZ7%14B$C+WS)wDdpK{``w+rgSs}0h5?`VirfMOseR(aSo$uI4f1r^ftS5-
zqrgg;-#spALj+zK{>gD5O*uEL5<==HY*N|R6P$Do;z6|fqC*~#>hP7a6E2VnS%CMi
zxoCCA2FHzl+}sC<@y(VF0YO1;GPS*ZW_X+Nc6|QN=#84mooVgz35|Sf`i9!u+qW~s
zbUxlSVt>da-*>!p>d^O|(bIq6ra$^$4ZrWAzfE|D!#Y}_$1gR@iBx_xp^;ElrGzM)
z@b5KZu1R6=+>6Yd(T~eQ-*Y1k`{y%8dn=O-8!*bxbvfwZ{0K8<KMjn~r9WQychl@2
z8tVVTiT{zQ|3?q;;-Du-P0T(WM9!hEJRg97gP;e5gZzD8?usGeo*Ye)eKZQW-PjZ*
z!aSgPOugm~{26f#5ZmZO;^>)TM($g0o0NpqT?slCoiQ7rPR$uK1#Jt&jZc!acqtoU
zgdEm!@>QqBTMK}UBW_;7ey3H3<C;Fyj0*t>`|b04q#;y)xGO&RQCA8_$|DnS;kf}{
z#P2i@55^hk-$F)0<gP<ZolC%=;1qP~RgZTU42s(<oL019?=ww+=fQKJyhR~`YjOda
zhdL*)bfBC7kh+3xy7p^&vi1BTSkVOIt-Q=oXJ;NX|6TwU3({29b5Hhm^Ds%SdT$=H
z&lPHL=-h-RlQQx$)M@jRAsXPeMcE^|b9I|MR2P|ahC3-B0Nr`Ez(h6@9srlArKP$&
zKd5HHMje_Kivh2Q7vJ85zD?99RbF1+t?)L48xjxr@)#G`yaERIRo-#Zs}r#C-ht*l
z&hCgn=HWr`8=&Fl-^Rd*32i4540i<7p(%oT<7(S}(#wi=usU!75C;YH{HRPyGq}rc
zKu69d1pM`&Jg^xR#W)xN`8GqxYV6&w^YJT|8SpBh(GDD>ki{hk-AXNu!_yM$%NGLF
zlLEfI=VL<uEu#PQO&+S>U9e<V6(C(D`vv``c57H<=4F`riuli(hi7V%?|5B`OKcsE
zcX&SECb$&k>5~v!&r*7RM0{+Tn_bnv3>ldYs_dPTas_70JRCPVMqLt10+$D$J^f^P
zY1~`r`JoOhg9)sw#1$(hu9w`{U27PR(Ny_7y`Vn3{6m@v@J7%H6cwsyf7OXh$gbu+
zCNi+F{`RJBCqgqT%FxKD?W<WmUcFxk6e=w(P(^ZTW<8O^jfV$l5$B>@)QSNv_i$#h
zQ}@TTubo$7n}^wz#%FoK({<C_erMI`)C(MjZ<<fhNW)a}`d>w)um0f$0Jw?-HXej$
z?8vC=4eDyZ@c;GLrrA6P(7Uiyz-8)1wgDR}tQ;HQ8=x^%%^dYvf;$ryfgq17bibVh
zJO&W67DT5fc~gx=SV-d{sBHskJ$8dK;6@48H1;M5>U8Yvr$8nNSQ-?O3aG>;MxkmP
zso=bo>h41gN58$>>#gL~l5&h)fP)rp3pBj6_e8H1BO2m6>I3$~&aw|CM1ma`8fqy?
z8Y0E+yFzG$w$K68CWAIQpM?2#K}F{XX>4sZ^1(rqVPjtayF^Wi^V54}VPz!*%AY~}
zB9a0h0a@e<xSl`bFgkSb1a`}+75;C-^ZzGN?*BL8`ac(P`|oG||I?S&`4btOBf1b<
z<L=HTo~e+s64&hUZM>N@-8q~9fp&MZVsQJUtRZuwkN;UPL_jt3IHVkS9`Wxy50I6T
zo;zE_fLr%lKF0llYu66)=|6a$zaX9maY|U`*`5o2TefTgf4vP*MGa&B#pDoh(_@k|
zaZukQ`WeLrevs_<=lr-BRX!}Jc^J5Z*;48`pm~+?S;55{pasqIP^3XZtrM1=7h?9^
zy170m?s`)Gbd})Hv@c{Go$f5bR?JgPn<b$%2)%7}elF~+`o9A0{!f3ve?!Xrk3zlw
z#EySU?BGq4gsMm4a%mCILYZMa#|fn3Ei3}HbVpA9gc)OYtLYClu~IAHq4_=VYYaXN
z{NG)J#+89V=jD!wd+t0dw?%NdoKod+S+f-<;$ioBQRu>`3?{`OPm8@XH2;NS=U?9u
z2=M#Iz{Hq3D{l%fYDYrzJfxu!a2|-S9oj;k=b=qLzD$*!$gO_#S-AwB9#`nSJE@*5
z6@Y_2Jw&ctfcARSm)V+YQ?=EEElj->*Ok+%Cg8{e2!jJ)JNhNgXaFSGiw(&d8mOBd
zN#a24b0Sv2FrH1WyyOIn0%!zksL)1sF%WOmUSwv1a9I5ZE=S`OK-IGaUOG*Z7oAs1
zO7n(totSFy3JCG=KtJH9Gt=?v)q%Gn%hv#G0|@#v&LRTcg*ePmFfg2yBiliB0X!gR
zgmZrd+tVC$P}2ajC*v8npbD`gBU2yPD{?Hmv7YRrv@k8TeO+A7-$mp=A3^J|V_tH&
zWZ6OJpKL*h{7RSg=vItF2m+lzf@IV?7#rb@KMS6=T8F{y1i~wBOP4#<=;3;<2*uS<
z{rq`okJ^%VoNfK^bfP)|uCv<-CS^daL$XasTuXt9%Jx;lKAm!~jYu%sy1Kds@+!{h
z85=+Pmg5REf3U*l=UKhe)a3Qc#-GKRo!mZ{E9b3QgoET*su9E2;(XBjleC;Q!3uFz
zEFu48`oR+i`f0y~g(-^sCNwswAQoTGOQ`=~Zqt#P&BE*8PF%M9dFnQ-V@;{E>OQAu
zvPI~a5w0(9wY3`4r4D-T#GE~kz%~d(T3Tp)k^~_GIJ!{9xBZ>VerG`kM6*=bSbHnX
z44_|1)6D4T?Bu)W)J?!yWCrb66MPTa(`!I(7vgbQR_q4$=rnZOTmhGf{|SKFT3Sw^
z6U1-4s#*5Ey<WxBDLL60nyFjBgH`No6)yEVDgr}85g+*H2MU5LXAsRCh@1kPSHlNr
zl|AmC1$!6pDPd+ISP@nj*bZTm1*oA!S}vS6a4cXyj5<0HDxAFvb>-pWB14Y?&PNLp
zdZv)8rM0W;`v?S>i*drp$Y___yFGbeCcXE)rA(uKpH56%S*Y4;*=E&kfaR}xQ4s}$
zdi+2ZkW9pafOV(Qj@Z88tg>_ddQIt)_X3!jfdh^${&1BkZQa-tcN}%Hfyu3IRHW|m
zrg)knKMXXr?ZDPX+(K=Ks6`M;$M*ef0%!cq=04#|>!qH;&?C(D>*zn>JIh=AJua`$
zm0y)J4gWE9@~=me|B0NE5oXBW65rn0c`{}nEbx)1;C>ZDQs7A~WL6N0J)n2k^#tZ=
zEASF*J}fj8%18~#lK$Ty6l<DW0WXg%gQ21GJ)}xLc*rid#MdZi!rg1D+XPtZ#ElTa
zkEjoltto0%kL6YggPs7eC%CX@D$vl{dV=*su)aT-kT=l}0rKiIN{P<@r-L<ub(mA(
z*SVArLVKtI_^u!hownS9xnc}!$48^vrHaM=Uj^WaeVAk_w0J_B`e9dj&5TPx&7in|
zzJ%=mA^T)}ZgdTXajDc&G6@E&TmYYK)`dK_pB72Dz=|E<QlIZfybj_w+ApuUEkm+&
z*F(+v?7n~S`dW>;VNTr=qKl~1Uy{iK4XqAoPe>R5!+<n^(_4psIDPCDXUlIe8m*SU
za`Fc7`i+t{Hn0_;+LlCxBM%SkUR=Zu>D2A}*W5OQ({U@c+^1tWYKS>?2M+C01fFih
zvZw;tBMsvjkXt1+t3d;I+-6VzItO^rsGA)L2B^Oqjd=4JR-x{Mc!2mHG(7-3Z-@9K
zEk|+(p_q<dgz;b^peH$Ll`jzM88q@BaxFXNlpX+SMS!i0K)9E3y{l=!Gr2|%J3HDj
zg}gyv{2lndDc@8=*zW?0$abLm7LvEOnm~e@IiX@8^Zt@dD-1wGeTBA-59M8l6TNF8
zdG7DOp<W$;54J-_t<@G&-4c*@UZy`}b3WxTL+&2OZ|Yibvm%5ZPXI~(dBa|KkiSL)
z?sGs|I0PSv1ZegN8tH-pAM^_ggE=cS6dXCf`QOkKvE5=FYzK?K)*c3Pm4K8o2rYOz
zU`W-t1r~@})O*kN(ZJ9k<Oyb{)t>;}BO<G0x=975EdXD3$n}J#0c_U7ewXDT7h5E=
zSJ3fN5N-L8Q4YC=gu#geloTvRTVR%^a=a@tt5K66{(W)fY<9`eLq`ywlAHPO-OYp?
zB^B_A6FoV@dmCnc=8)19?*sL!j#RZgiw5kWK3<qtAcBHF>YzrW_kiL*X$e*rX_&lY
z7!P96%$`z9=i*%C4MY^my%d3zIEX;BvoO~SIXsX%9s)ELs6SVj@=%SsM)T_>!;zlY
zSpsL~SB>V^90i$S??B_ua@3HG<X>T!xz?3`SvLU2ddxC6&g_8*+p-bO`a=W!*a*cI
zjt|=#_)2jvE|+@H$Wb>aNN+2lV1TbiQ~JXBr6ezUY8q^tT;uT(=J6PJIfJrtaX&mE
z8&hLrUZg(4y#*%us@KL1Dc*2%Jw5_o$w>yowf9DGzzm^iC;{@X04CzATI|R@ZN=4c
z94@i(LZ0mjn5`p*sC_uqddVPGALK(6<CkY`R^(Tc&~s>AGR}Yn!!tGXaSM18!O@LI
z8`@3%BJ$~gBc>7bWi&4XwL)jo(1-_q%WKEDje-1ZDMNP;sVd+T3=I+XJBS+B4dZ|C
zkcEdO%LRT%b{i(RMa~?tDw9KYsyc*#Aj?gDdOL{P-NDKw1AWu<i9(pOBm4zGT4c~d
ze9$d4IwVScapd{)pH?(o?&-wrJLI`KV8P?j|J%8y@FKDo!R?1oFa=^~eA%PWE?LLX
z7vKir%lQiPaK0MasLjsL)Y8}Dujg@MDnbB#kGP`ojC(F1DadbW33Z8lV*fXR2BM*p
zf*_EG8(ugjtK~h*=~;LP7i4Zj$=ETBFv>;MCb0L1cF5;G8BC56@4)unnP$|Ie!`4o
z!jsgD_5-5}_Up&A8^2xzjT>re>J$0xvZsEx?UELDFSRUAuAip|rujwST{u!)5ctMu
zQe{A3f=QIne+m-%yWweex9OquM}`9*%oW@|zf+Su(!I^GC)Ot*#WQIyro|t6OffU}
zfe@Sr$0!)=`K>(8ok>uT5EYe#xqF89;ECTh2Sp^M4h`!I1MWUF^eA{w*;6e65ThO!
zh<A_U(qLIWjX}3WI^HJG7ckpg1w9f?*@2mPykJcNIuq>k#lA~mWad=-$qz<8g35&E
z451ld$mI&YHrJM88*28)TOy}lA8j7><G}NX5YYfX3MlFPi@j4Qep^L3jyX>^cFZDf
z9%YIhYJMIF#i7Ou?_O7Js1dB9C}^v~EV?i_6FhQ`p}oJd)VvabBNK`iFBBTLHGxYQ
z3n*<0riQ43xW)^*K{%QyH-(1yErTbIc-Gy5Gy_x6Y7ynRG(W`-*!{t4y;LTkDLW#F
zSsWA&{%mb%^hcBZqBNH0+1_RdzuBGLkC2EC8xpza&?&%%aX_5|V5oB`^t1FAPL{AB
zR0FDDkK5!9=wFLQpP?ofEQC@ts|5Aa&)pBYNFVKr9$A{FK8C&PT*YF{ztdLVir9V7
z4(m!>2L}q0Sx#tTp-@g$(Nw!7vnlFEQ`DCxOBYk&!JLS?rboqlbN5*JTJvMa*I;m`
zAj-W0>>nF?N>p?KhESfLUyKJuc%NttC~HqMGBP^U!Tw-4G6<6n^<ikm+4-ex5ujz>
zg9yGd;kJY{4_juUMkT1obhlo%Pz=O(zQWidh@ddJ5>18qq#Q3{aSt?HMY1!Ph2Fy$
zFN=U9Juj|=EKCoJ!O?Km(*TP42IRA$+q)D-Lwc(WCDH@zALAwCbt~s<qzHadAQ4=G
zXGB|E&OtBwbW_lL96p_K8vJ%t&&8=LP=b*zAZs@SynB*CBIX4kwf!QJ%<LV0h->S}
zOWv0#vI5ZrP4G$>bb#qfI^Ziwm@T$f<#j*eG1F^R+SjCBYb6w&sN~{b>ztQNN)#eO
z!i2tS$OXWkbCQw6)2d5-EUN%kEy4+C#tdzOj<n?}7P$)N8U)AO1=DO=L36xyRfT?2
z&^Z(h9+a2}f`0Y^73zXMO5CV%1x!rj1Y9vlHOMa*e{}&l*^!I?qFF>!8I0{4h>&gS
zqK&CEpHzpI90c#3ijpCmh_FVq(R#%tO(P>V*u2tfSl}IugubsDNKrj@Hdd(qtOJx%
zQP=P}a%3Sd2&@1o-H~@`qZ<Hnxo5H2s7puERAGT_)D6ZXVi(EcjGCHoIIFzR$flTM
z_w4D!pVwk4Y}AttpnWJwOY0j<fATA4p}A%kPg**9C2iyjdRXcWaOD7ORQ+!Y0Wpns
zv1odZFFX<}hiQoN<DJc5)8%A>GS_Fv5_<4jkmtQRb37Jx*Z6|q9~+>a4E`%Im^P=2
z{8t6-$Qc344-L3_4{pL3OUZcf2wj2@_+F)ND(V*-G|Dyos(Go~7fJa<7)<pZo_7pb
zclRYXivf6k4{0U_xryJ`sWb9S>?bFlFA&Jix(c|SI1~_iB+UyQ;t9yFfm;9G$g@zl
zgBy%N+s=T*yC3zrOuHVDk}?38Vl=<3ASbsElC?gR+-Du!VE~f%C|YZ$U`lKfM3E(f
zZ{X_S7mkMu0l`SZ7hVwysV%>y4phOejOjvcZTm^D%j04?b+s$VK=KP2JQHGQqGdj)
zJo`YQu4TmR*Np+|$Fr6R$fFJg%bm4G?lhtdpR(J8t(A+m4K_?(SjDZ6Q0wfimP?fJ
z;s_oB#X8uz7#bknNKL?WrEc5`vP!*K2LCaktGVgeF$kI8V0p-sodHzd@JeQl+k2B&
zK+q@bOqs~q%i#hyD<2FFc1*PJDs)>=cVB9^7fc>GScrZ^$#|%*HrxJY8QDL5BeSiH
zwDyUc<~Zfinl6&SCfVkc02lqpv(qfwnq<+u0QpjpY>LHVWdPp(LIjXT>wLE3wIl3d
zF0jwTLq#^VZiTcTeTXuQ*r(XR6{F<E^ILh|!w`@duxS)^NygXj2f9dCRJ1#2g=LXl
zm5pLpPQ;C<N5l&xax3XR!=c${{GBgZfJm7tpBU|0hk1zVmJ~AVOqQvZ7V>K%;7DO4
zLy2;OhWzfd(P;$iiYsWi#tFcq7tqjGplF~i)3dNwvUEJZ9+?;$Cse@vv571)|7ybk
zm_%>{3M3>f#F((qP-OQmxd~ry2IozKW=*G{Q?&9ee%uLxS(rFc(2WNw8yT2j0>&L}
z;qHO_t)P?Ul#RYPDDFR9*9-x!vcnzKl~KrxN(7-$hP}d;z;5L<Euj1aJbU0uX@>Ws
z37gJJ+V(T;UCnj{Y$z=%ngf239jZRp!f^m72OMpf=8$<oBg^ia&w^0@2zGI26UB;T
zaYRi?m^kKJkzVP%R<nn_PK-e$RHS$qX=z!)m@BYfc{$30S^Fp?ZUC8(*PKa<F>vD@
zJy)W3ml8=1_)M7UMksL^?w><`Ckv-|Xw^ihEi@FGTf9nc!Xz4gf_=mN)^Thf51I(l
z<Cz0ATX9Dqbis2TYXzQ-GTM^{v%LH=@j?KZZ_tpGm`%Vn!mb{GnWW*{^pEaAgLhfX
zxoDvL-_%9T#(_&JH7_r(ZTQf$Bmrh_<>J*({vOcqasm=7Ox~%QM1<f7NSL4U9?D!k
z{izFhtKu#f$H5Gxnq8dPi9}*f@m@yURF_rrnA)tTAm*b!Of>h%1khak65n$ry{O9>
zkSsj>I*sK#s{#}6hHE$-cyd}=pWR9sab|J=<eglpQD)9OE=_jMdeHSFn)*f1pa^`*
z@;7Jzdp+mpix**(b3NZ2l&Co`p)jc;HdcBiy|w~$W=r%2FHbhZ2v}RhrG$o}`JuR^
zVkML+u+U!R3YSxZT*hebwi9^7^1GcrVTbE95*ocmRcktputCGOB@!A>LMDM<S4<*4
z2$cCob(8EH(b<vdL)+bt6nafel=U%sVr#Jw5$4)SILs*(FtfB|OF~D>8wOASUJoJ(
z({(T$^PGb>bZrs3T}<uMyy6+19f`Aa)wN|ih7W<U!c7F)jopf$sd;*T=|OWx-&A#_
z>ls9{CN^~|D<_31@tLc5A39zm7hM-Rpq|SS90e5%hBCw*G=Cg5ir#1j^Os~ntJiXu
zl4U8>k60#ymUghzw97ZL9%_mli{_%~GoQhFK<Ew}E|O=?(cvCJY$)n6P*0AA-d%N3
zQ8c$^$d~Fj9#wQEGc$7#`IAuvjtU#7?jf`6bLopXiYD1|%~BE&x$u$&zE^(XSVlmh
zeW$&@=kSFtvy2>XgI&>-+4A+6GBz$(K%PexPfPV=dR~ILr`^Wi)hJCXHv_@8SL~=a
zX!|HLn?46w7|pbUremlaP3&0|aBOBXW+9sK6B3B7HjbSjm?M8kXecTT(a@V}VB1j7
z2~hKd`Xg_}l6+E+<J0vV2ZMlnxj}>0E{2v;<7iXf%K~^x1Z!-WqpH`JqHJOk38OhT
zIRPs&9MJZCHGeOyawWkd@%2SYoD+a2B=2u5fxlZI3;i7d2Y+9Snmda-On}DenrP5#
zV0a*4ifq&15P%_13UntbFTkf833@DGD562;Cv-8;r2UJ}7|?>=cC|{e&j$+}O=>qD
zqCIM3<5hPVtJQNUn(j$c>0EMZs+X$Th5Sn@?cM@~-b>Ju#P1yFLjlGBlGsODk$k#J
z-J7N`-wGk$$U{RuM>-uXV8L+&PXLjEDXT#(aHt{=4b2Ul&7?i-0M2O}G~U^1mG3+Q
z9@(Jrdp$R~gv`4tfltN)^rpBa8J5AIK29>=25V%AnSzkb_vYz79b4$3nS$BJwGZr*
zcA}wH>CLmKMfN4WLAs9JDF!e@@{v81Xs)o6Yf$eP4-YVDt}x@UxSM`#gV1&uWGc~q
z54e6f#;vzpDo2oc7p#)$`jKEu^m^V`H6LDe&?y<7;uOdM+nqPx1#?o@%RLZ^5i3B(
zI%k=j5!$(eCO%#C{}6Ww910OWFmfsN@#Dvnv!GZauM!%`iRMi@Z>%9Y9w|4@gQyej
zDqV+_wjq~|`EJ2%Pew%$A1Z<_G7)b-=ejauuyVeu63}h9c^ema>Ct)v(hyLU02*(2
z=73SjCo<F*WnaqGzFN`!NRqGHxC1Az`!xIaZG&tMVH>>MN6_#A<X}e4-B!vZG!`Dg
z3eIGkYp>TyUwU`@jW3!OpaIjvH!)o*7lsh)WKMI3u!kZEGWOcS+C@i$X0HSKk9GjO
z0mF;SeNQOOyi58Z79WMY0^|`4n?&YPm;rpKod~$A+RQ$Y7Zwt_CtPz?>0|HaMU>nw
z^@8dBMsqHtx*dwnmdLAlAofsz4a^(cigwK4_bKQ6MST;yI9-gh#%S-bmJL%E8c2i*
zAM3Rk%5jL%+bJ+xk<)QR*u6oT*jxTw0_M3L%v$jQYQ}?{NQ3oIOR4Pa-9r6{QZBNb
zJkQD+XSh?qQF%k~MA6goa#d6jd97PUbHLEF*BTaLWze`**wUm==6gk99<|kvZyp+f
zy9ABWM;$teqd^9wJ5xbuTQ>(SPa_@0;BImFgz$emRa=eKWzSk+OzcRz^E21rq{`+W
zB5(r7Ugou){AH8M9tW`t;WzLveogP^&^q6;wrg}_`ES2o<l1e!|Df&Qy8N1a6N??*
zmaKl?7guDan;R63mUisGq_~0{Mko9G`)^|rUc4Bm)5Uc9`ug1Pc*=-V5a7_^<z;mh
z6%{i}%OxqE85JjI=b_nI`QhQ=?yfEvUaVt9g6>N*3yb=xDOX`(VHq_wmO)*{n6kaS
zeJPPxKQZB4MsXf3(<f5&tEkOizg~eBj{Ksc*~ctn<GY`(-EVb>d;RO}2M->!dnQ`v
z#y@%TL{(kg?#h*~ZPWUCdY9z{z6=bAvH~?|UMkrp;_#)Ur1XAz+MSP&PrYI#+4<va
zRax1?^z?K~E33=?{wmYc({z%zv$G_a$dpu78yXw+xVX3k5@$0>Z>VxpL1IdEdO39m
zl5u)e66O`@d9BTY*<}z-Hd6Zfo{`4(FR)zmy1Tnwl77VW4}XKUarn}F{KKl@=a-GE
z9V<7suBr~3_OG^&Z+{^WT{lP<W~&v!?|rYYI`Ge~`6u6g8lo9|9gJK3`%lgu{L4Ri
bfLTt5ru?>O{&Q{U{W>S~wQ_#>^|${E9X;#b

literal 26123
zcmeIb2UJw)wk-^8tKDWyC<t@|W`$M+BpXn~gb|e3f=Db7$(h!ppa`g_DA9~a5|APk
zL0f`|B#~G^DLEIh2nALD{EF^#``$C&efPZkzcJo@?=nU=h`aXw_7~QgYp%JL*N+|5
zSU68;9v>gyLd^r}C;0egyYTVNGX7x>enY?icmUt#7$4G5$9H^9?4^0*+m9CyoVDlU
z6Y(Yg@dvdHn)31e#HXpg`;<#~Z=?Hz(~j9{17(rt`akSD_Vex|6~U`5HH2i<)fWsq
zZCK=Ma!|!^+d}1ggNOEPk=QrvH1sSc#vy6s*`0x(R8K6qJm-nQryY_fPe@(<X~Fli
z?CaQLbA3fZR$o}fdTk(+STp=-*kjC)F=`N%S`)fDQl2r9aB5v}(f7RZT|aq?`5o^k
z^VR&)<cD2*estj{KP)}?c0T!j>)3aO<h#$`mJaYPmAvaCNPh9t<L|S`cfKFjew|Ie
zAK368xW(^t-|FNH-P<4~71-R|{6N-9JEvGvIchyVUvB8|!I^)Tx~He-m+I>M>gpJ#
zy=+m(Fd1w8e3^e7*EnB#PXbLmxCobCmnq#T!Du4`xwYiq8M5o$qtPZ!S*~^^vNd_v
z*GH~0pg9HHxpSwpDcjv+szoelzI4_v{WNBd%k*fCL~zj#t4~|?sCi#vPPW`RBz0XP
zNO}9mj~|V>(_=we-jkJf#aq+zl$cqSadxH3!=L_mukp$#&T;_(M@PqPj@@6<M?N2F
zDc`#|AUQdCXS1I~^C6?j6IrZ!tA&9nE`!YvemS>iq^Cw&(W!S;Bh5Le%%fdqo9E=n
zf#}K-_9G`D+vdFMcI^+%3OH=0n5n6$-IwDU`E4OaqA3?jZe(U=s)*X#wlynywQV*k
z@@m2_El17-7=%Pec_|j$T)j*~%+ul2sZ;89c6M3g&Hf5Xh2#z08|I36Tnu8VOpjEi
zu#ZM4rdD`$IVc*sbv-BJ33uy`smQSJc=__BUyx_7iCxj!ki3zuN|WLC5_k7aXCEG3
zr>BEEp3>2g&nPPh5O%JO&bZ{tpca%Z#0vcMZhLFKux-|Dh1Oepw+mFnB^pgN3myqn
zz-%10u&@X=30R}BJ;ko2pfOO^y!??hH^j(GVYpO@dERZbJC$yin9Zs`tQb*xe*442
zM~)n6f8sAyPvI`pQr?zw?)|k3o`%Kuw-$$c3>6)Tq`#Xh7VbLH77`|VL8`S|F>tla
zI(mmr?&RsGh4hKcEoYM@?FZh?m3N7oJ%43uuX2<(XQDS<j5FXHmd*HhZ*vNJ#q#Cn
zU&Y2ccb(MH37MEE^KR;NwdS5ae*E@BMMulvLP}}J!=us8tK2JJ961um4)UHd@%0S7
z)5E69dfAW-S9D>Ht!UiYly!kBtXda#Uz)vKyJL-k;9lQFl<EbAzG7ZG163zGU%q-}
zSzVhuS(Vsq#A!1!?)v<k*5^E2B42oAvFfVP(NVkpjE;gJuaT#XEbnhu1)Ia|OD%@R
zbEo@kpE@&}ws~`=*0H7Zs9OS+`qLk%xH|=Vj%i!z7piPCDY&__`{h&Px@6OZHf60B
zS?y$JM<_bp@bgP+a@)L8+OQ#RTGVr-!kUe#yz%+-XM@SZ54W$CO8<I!f0>E9u)NL9
z$9sJXT8o0i&bo41Cv!^1rio4S_~l2J;a@*L(@e6ceBtL;YnwaWY^KIOyTu@Lhoz;Z
zc*Ic+#fZS}?(Q==9<HB4WjE(cwMlEZy1G7XQ5pUuW7iTOf5Ax1W8mH3my=rglcPNn
z_yksTi|(5Zs$&^trtGX~7Tu0jXC}Y-`>FbxoRwj6lpCCBcH!?==bK={Z{xUP(+xJ*
zm+eb5%ue=9%<O)(O4jn!r*H+CO`A3~7pigW;taDDyL)<`=H#e0xpqX1Pj#q8^{F}c
zH@ILLg;ZFdpC2-L>%(8ywkFf5PiV2q$g#LHN#Yna#$SltYtrA4Vb_~fcwtO<`}qS|
zTn-!is_Db1W5daof&l-}(9oSjA0$lLKRrBt`t%B%6KqRt&7ED(f-gD6{r>y!>FuFb
z$%$evUw>OSR&;**HjB^CdV4CKYA~r>4YkP%m+^1DCPjDFCb<r`<5(Zp(Gg_ka=l<M
zWbCE}3(eSuPIab97fM1`h8N{j2#ry4nDUtXud_BK4*Sw$7JHAsuTy)>fmL~<V^a0y
z%lS-aW~2!7Z_AF>_C<v<+?m-*Q$LPXYN<8;n%fuhouA*Q&Wfig#nQdjRj*FdrYu~_
zB2{F_6R%8Hb~&_TUlp;kr-q7!`)YE%xz@Q_UPHk&m9)vp$xj2{-lt4UXSCkg&u-r%
zm1uJ4zBz7JXBHI^6Cd9gYa(C6;Bv-#?fm`yWn^V#X?@(GVrerrdG&xMHCUgIpB~CY
zSM<@p)YO>m4_xz5WSGw2I6d_o&QRZL)tHu%#lV~SP~R_TQm%b{ZZFld^|nTT)tNFi
zt_N90w%g=L*Kuv_YIE-9gmm#zRd#Q0kF7VGenj-CMv$moM}+hKP4_G>viaw(St4lQ
zP_c-q!WnO=NVTe&!;U@?GhXVP6YQyC`tj}@b~b0SXK-q`OhaA$`0?Y)Fm-sz%a<>Y
z7ioFjF_`o9v9IXIuDuyf_ltXKm+srQZ}5Xekh<?85mwRQB3Q0o>q@Nr19{sf8#bBa
zI#z0pA-!|jezGgN@a=E2gd1~??B2b5>g!y!4>>s<k?x^-ld4s^e_y>Tns3AA!VJ4n
zr>}@j{hC;Pa2m!XlH$!7jtUZ0aodqmRwB;`r<6shsZ}Q$<<dDu?E0F}1p;#yi-uF2
zyFRZysyfly$YQ8I(oMN=<V#gm-{%f;_^2vQy|ukUuW2W>wO2mfAMi1U^InWo-m1Fw
z9(j`x1E;eos=@k`1@bw!DGV=mUjU1qY<j4{n>$@sBdC9I_t8K`l1bstse$(r;?0fD
zi~zW}*7FVemho&k#+Wb`XyBdX*ODtgE?ak=ZZzGk)8Z!)=%*RHF8`2p_LuXunJE;R
z*%IQMVKr{bS@s(HPurBTY`VW$+`D)0NTBQ2-{xMR&ziHq{E}RIsf>`7m6dasj8%1<
zwLAN4;gLG8zPf$>D`nktRd}N{i*&sZD`sMbg*BwAz>=MzIZ>`mdrkKkX#fhStB)`l
zYKs&*AFV5F!b^}dGgy`AC3{q5^vll9M^~OadnS3q-`{!M4VE;8{f4DjytU%rEP@!j
zwy^o3S>*K_H((K6dg9ZXg&E`fJ(`zA`ISZ}Y1AYbwA|R7;HO~UzGG*KW7y$!%iw`@
zS)<g40Y^4&*x(oB(JE0_<IQC=oawm=GE3&q<G5~iWHx1=b=+b4?f0v7tgh%N(G@FJ
zV4n%VM`Hu8T)TE{U1Hy?x3?uUE{s`qH?a06B$yl$fh|-JOWD}g)~2K^$%LIYf6dsM
z)^LXs1S6bga$)<!bY>3w$;s%WgI!wQiv3x`rPzXoak{CSU~r#MsZPF$eXs$&`E_ps
zt21qfJ#z_Q6FdA}lk#d<FXD$p-FuUEc7)qI{cxZrUQck0;qARZXvc<3$DWm1suSn0
znDkDquNP5rvStk~-uczXns_!Ej;wV<b|2%GgJMJkzfh9w2l^X}lh*ZbHp<yHtqGI2
z4a8~atzq&13dh`E6KXZEzqMVFdI~V$XrWaCoX~l?e4|696kR7?_ntvi+UwW9l<sV~
z*%&B)?)SfqHw)&D|F*zt?35KFZDPg<EMCWND|+wl-8>i_6LReJ^n?$ENCiYkx^0rm
z8E@XOWy?)JztNtWLhSmw@hI*@lmUyOaDcMuWn)vQtW|Q~(9qDz$lX0f`wnC!_Vzg*
z4qbM1=g(}u>_41d)_WG<&klM{^Nr2eC(kO9IFmKG1p)F+4?I1)W1@kNWSaZZ7=hkE
ze#ecB#AZx8E7q2gqoDe<-O93&?rF!Mrm+dCsa4@;&Q-^WUxPIyE~{|wVwL4pRaJI$
zN}6e!k>_X?+(YVn)tFP3zRy=Sx%I?f5ZN4Yl==G2`=5Q+B!^UW7;d^(e=h!Ojghyf
z@2$iPBf8o#8zb`~44jxHj4jf0`t7X6=ji}g>yCup_4I7Jr_=8gC&nEwtYgg<)L&xX
z{^@#3sp4k0v3?P<-;N!7eaJl1V`3;<<?0;Sj>o1)N`7F}j8WXvd3LsO*?`wBT$e9f
zqrYr@_{BY}Y(XOryAZGF@Jk1&jkRtsmFN@vN3-2VEzjMH?-{AoDx-FkMuZg})J@2!
z*=9jC@*Q($z&}wMbPQeBHhGRU`qgIcA1GZsLa$@hI5wo)9VzuONV92Nlu|YZ%S^CE
zZ_h$uIas%z#N4LyQ65cclu@r)m_VWRU#ESPShXf8eWzo!`;w>aquBaX^1?edPlW<@
zQVq_YT^*t1+*G_tuP|TGD4ZM$DXMkdokC02(UTaD>d528`*6V2tqsha=%KW+CU;w^
zmi>41KC=)xvm@VV{h6_A+vF_SJwpHnNktrb*-@KMbHC+Ktax^<d?!Csm1<giKfOP*
zCv-)gwvJArwD&}DZ_l;+CszIP>n?|$>aA?I&hp1J&ofo;7ptu~`QmU<PLpcy^dklM
znU?XEpddfLs#u-E+-Z8QEc>wq*|FR)F2^Xg>V8kp19c#VVk&SFtM*TuJ65X3omy8|
ztLoR_72|tts#_=bhOO6d*w&YC>~Knt^6a^Y=q+lBuc+IGNMP@Eh1LP=Y90dHu^(PU
zzRAVJf!U<8|K=(I8MD%x_crV7H!v{3EX;M7o*W$vV@$16<qQTD;)sijKY8<J2cTA3
z@fkQZr8tvG_`t#n4Ka}vv$9fEhphlTch(*<{_^s;(v*d#scG9H{+hW*uy(+B`*y#p
zraJ)XRN&%tc0Y~a7Nzg&;OmpD>yjVHQFOyy+aFfYoSEaWPxY@x+lCa^RYa>zclD+|
z+-9=>aOjSW!nY#r3RWvk749{`h~a~96nI9Ppfdm<0ReK<{a0?i0e`aN+Je=0hV6kw
z=O_1(O-76ciM~=g$W_D;d-U5!`Qy?zXHBTpp7bUjI(l?YWXNEbJGN6$zDbZubzDx6
z8fSb_&s7^H0M2!*gp74ISOwU7I0ln}ukUCgiDqSygV^Kp6YEDNs__aB)V#gIFMZrd
z9@JAE?+?gU2<S9ypsNjI{rT?w`vq%5^zTHty1!p2e?BchM2ULVB;1o#SCMARaKYIW
z7jH(?auX3?N(BdlH~kc$ByR{yko|Hh3MOq?6nCm4<#Bhog1wW=#c!q9bqVQ{-LYY7
z(qx%*mK)D@FO<3BEVg;$#_MX_iL!0hwXZLXG67kEa8rsNQqvlp^px?ICAx(Ud42*(
z2pMbP$-_?X%{i^DU65>AY>e$%($tY$EaiW7z93ciNW7y+a66*R&r-}4f`WyQc3%li
z^*(p*oHDyNg^YY1+iQ%*2tbsp1dqr{FKu*1h!mC;2(L8w`+Vs<y!7W@J9}3~CeMl#
zJa`v2w{VU9VdcTRWl0Fp9$=vfo+2@c3q1FexVSB<L$`zSiq=Q1LD&;ec~ohuqJw#p
zsgSCNOSpS~28n3N<{|TQU&=rTPdbjner3+W8*q>XfMWF@%^tYVU2}1l@<eNJl5zfx
z@iuAi)n%$|>n&$q|8#$=-b(CdyO9bFja;v3;}5rf-gf@Otxg2yu{B?GYqn{Us0eGP
zk%diLH?}`WG+X0mYvS2Sz%~Wj8dwy(zA(#m7`I+Q@M0$;*E`&)-olPE(V+o^tk<&q
z$;+1;B3+uVyA0+Fw){3%)UPhZtWeK3dj*N5c3+v3pV1L1EV2KlAEvD~-L6G6Afous
zb1Hcc$bUkLN0Rucm|Z8~)4xxfJU4I|t<sUWcd&1?W@B{t9p*`k`z)s%^0~fy|CVLB
z_Yo0?haiufc^fHn<v&5f{IfjHtGzk%&9&rg8dibPn2?%f{utcNhE1D*r2rhqz2-dH
zcOvH0-H;FfZ9q2brYwcB75ukOJ%9fE_?a`S1O(tC5yuVd3zVNA;7DJ;;fd$$EcP*&
zFi<`1v;~i@0>VAr>l?2o7aJSvsWz)zeD>_wb&T~74;L_(@#)54f3cOGCnQKA%;Vh$
z=xS?I%~Il*|49n!pMCrPJw(`ldKBHD?#yYQA0D=jODuL?eM@FERrdJF^igg;=eXW7
zS;th@gdZ+RE=w{xemo}@bM$ET_gQa3M;%k`1~1oP5q;J<Y~C0!cas)AjK6;4vsQ>q
z<^L|???3&%|G&_W|HVf^99{wf>p~;WP{^6|i|3}`f^W1HhiD*b1+y6FcT!75cFv+T
ztMc>nX(A?#nU32K37>iW>+U!MnjFs`O@4_s{`&Se9L)5fQXUQb$n^x^W-wFy(l_GE
z7*p-?0XB(X{+OOjcRNILa_OEGPxjgMrPl0MjA$~|i&FJ81|@X-#EE1bS?$xO_faT)
zh5@%Fdn~P*n`fsC8_eN5(y4tyXz{7Y`^@c+8aE(<njT}QiHloWSv`IAY7@^oz@&aW
zdO;7-9^R-UL;TTm!hphNY^_f#`SexgtIw=vf1@emHO>uoc5nITk{F)6J3M0xo5A(k
zVwklv0X(4#To<vSubI~t3L68E63S?N;M+FiJiiTas=!JGN8}rq+hz^!Z?G#A*=Ey_
zt{fpk_WIj|`cFbx5*mRwQ_5787O9SZB?RuNO)Eqc9sJ%eQrcpZHCRAKPw*Zbty7(I
z{TmAsC?LG`llJ&F|G2KMP|T@KZX=xn@(d6A<ttYfOic`%fOc`+DMKFNb1n6ukF)c^
zg9o4JBpM0`B!To4p_*=g=rUBaUP2sW6rlC$85)Y3ML24!tA7fUPrSF8$ETa;I0+0?
zS65#!>u2>hy8BWHK!LqgS3ke^YeWRRlz;$n)^CjOkq7a)zbJ`&N56&Gb+b;wO*S)E
z4Ysm{!JTG2&C2rdRYe4T-OsPTk){D8aHVCWw@yY}yvbv@q)}8Fh!B9$nU?+Nk-_<H
z0fD}5ef~S8W!AOMP8_e{>AAb4mM>pU2zlq#yF}k)_&5U;4&F=jSPLTTS!I`Bt)k4>
zHDMdSmLN5+5DxvjI?B&Bd%=?fi@tcV4&xABco~Mh<)%!jxdVX%>o;xQ9Ofx?J<ZfU
zXEWcU)$``fBb<7f^XhUbgew!ASwF2;bnXvlF7?Sirl&`5_N2Eu{04%`I$c;G$+GIz
znK$OguZVr2j$vknL~Pl+oY{6f-#8RgQu0-$_w{>YER{f#Y^MR3$-|M#3Zky9w_U)n
zHYd>f>wkXTSAMQX<26O3=^A0G=rs1SOvGS20kni04YjqYIu;(E-$dKbK9GEPU5lAq
z?_Ml3+qN9j3nvKM>ho;VmMx#!m*Y?O&3(S7hcOv)f&(^*h<ZtEwrt&DKX5Ds&H4T<
z01f`X&6<0GZOvYG&6(kuR$&LeS;E+ARYUF20s*~Q4Z!^`EBa2KK1~A1=;+&Lge|Rx
z48G$lxm9$&zKXg2Lt0<RzL?lpvK0Ox(Ko_fV)MMewovKtu~~PXn-m6aZAv>LRq;9n
zyBAcHjk<ak(hf@2*fe_h8F5ko=O_J)OQ!1TYrMw4#h+PA_RQNz3qgKc^%koRC(upo
zT!Z2eY1`Lj;9fxtJqtc+8PQBG<I{a)@O>7H1Wk%rsLDzbp*}wmvy+)M4EUQ=C`vbF
zfJ=2jK#f1&J$clk?I3R{LKBaCpJi2(5I`7k0{vgcOmt)>rlzJQoiDnR4tA~u1lUbP
zj5|laoOG_^%0x{Zes&_}fsENvWe~1)+q4r6MOq4jf{^CutVuK)>8vn`idaTQwCm)F
zhjIgc`Q&5*ttQh|v26w$nO^dk{l>2>5=ayTi7~!X5v1?1$sVoVuyt!=tYYk|SM^mV
zKh{)L=jG48&_P?k7g`?wY=`Je<*f|UU9_a>z11*MD(4satfX^(w!-K~Z^<5b9SG#V
z)4lgri|)`9_pyFEX7*UY<=OL_5v1NEWCPZ-#b?$cKawAs9Ak>Dk8n8Q&{;m-#p<KW
zuhWVoVdV9R;f_L_OEEy<>p$<kmiP7Dm8Y*?UlfZJ;Js00ecpnmXTMw#^jRz}<8qF%
z9Y5)ByCPU0nEmh_eO>iTn$0}ue<knsU)=HiI|)59b^lxr^1tWe|IFz;<?A6>9*I-)
z^SpWWS}zR4KRVCm+o1to0{K~w5dOaXn?}pLC6TTh=gfMPPc2(TtYE~^jwGR9ZLPVs
zc`9qaHu@4zyVsXBe7nYG<45No`F8BZ4Ul7!Cdd2KnKPa2#XjN4YkqM4@vY&@_;|`-
zPTzXU6kjdKPEhP^w40t63@Ar(4#6vZ?s6c>AfV;?062$EKo526Y2PUTjUjyjKFumv
zc!C)G{9e6!^)fNB%zodlL-!B|9Y0>Y?$A7!vB!tkRVHxspQuD^-t~Fn;lha)JY63Q
zh*$J3r7EwL^UqnY`BMw<w;8W*^54h%KPNHx@8kV{?(x30nSfZ5>?Z_NJ}-<x0N~Ky
zpfJhm6DGN!HONwz2$_7myD{n4cb7>r2)cvys-rPl`N+BP{AD=rr0g0_z3$^o*w3rd
z9_K>FNQ#bR#Xm)=C;?rXAQA|elS)WNtHf+M9A#Fvj*+J<A?F^bdb$yw61GCO4+jum
ztW#k>xSWhRMY2tE778ubSYxN|089j6N+2u|Mx^&q>BdO*40b*ZD!3|Wm|n~gmb3N)
zpx=ia__z1Ig?<7OBps!lpuZZKkdp`=gb{E!=Yc62y6D7v&cfmep)8Q88KTLWPzo4%
zAkP&9q}&OdhUkGX=tPYIYD(X^(GdtAA&@u#v~!*Je>n7DPk!M&OX%LPPa4q5Hm|Ai
zGgRxd3BEQ-K<KLC;(+a-8PEArfaxM3qww=vg>XrVeLp0m@T^(H3IPG++^znEanc?9
z%=VJ7mKR|*Hw?4g@=31CzN579oB`dVmNTxD_>^YTm`UhvWb3-CV&6?`fhYv=itM{?
z%pNipAC-_{pRrJ@gzKP={QSmCY`vDvn!D)cl|_mh`g7R*XL7wf6BgHOA<N|#&u??~
zY~3Pyb==uy0(xem@+>}Tq>B4~^}I0KoD6ykh_H3Jyf?hGwqx<xupJgB8ZN$HxDC0H
z2RrSIx9F$;LVzRk#esy--6v?U8~{0hC@V5l9j+ja)=Z__`1|?ACg!+>^wIZpkZ^^1
z^>?fbf;)!!k}NRct9D-e^=S)q2Pz;c>w4>h6Mijj_Xy$JB|~B%fK0@>Zw`Tz%;EBV
zxSn7@Yo~qxt2Fp5stY5$>}!A@@eKI1ydma+s6tx6{rV$gh1ahL@>)*#RFXO!EH>gK
zpBDRg|7bLChQUl004ocMO?=RB>PqQ*Pj~IQ)<+TRY<JK2kvEr=OR86#&9dFc0<SMy
zUm%}5Q9|~;M~4#QIs^v!*zPM48TbQNuR@Mb-?i-?En*)n8jIfGM5hcQcvdACmE?)9
zOe;FnP6}A-`KJ901xlUM?%gd9U9bre{l-=Ffn`&=Syi1%;vhw}lyi)Fx!-;YE_$$w
zZ<jy(usSaMfg5>+6<0N@srNSo%>>ro`gyPM#8BHd*P+(*w)=V{O4(-DoJW>s!Rqaj
z>kdCyvOdb=($nIfc4$Dj1=?035+UKsxVW3}rx(1x_Qhd$Ve3PNC8ubJ0E@N8Nk<2w
z5@lpyKux@Qbpw+m*Rj0*Z?EdB!y*>$>PHajG|K*QDE`r-pI8iUuAkq_*jRsfWo4c3
zcby;+6MM}wWD@7Q{``*63|KbM45#m#n}760PHB~zmk0O+7vEGDv(oU@B)Z3f7Ch4o
z)*U%UrF`oLKOhWK<B!AM=TuEWz6+sF_E?kE$lPxb5g}R{c)ys@;>O#Ry2YxCU1S~}
zCJ~t3yV-*3AeUR99VoUDMurKyCl3IEmw|TZtCtmpbV5bQT`!$I804)?VwTp!U!@E(
z&vVnPFpUdYXF>F+tA{&$)+|ZjS-wZ9eJMW9Ab0G_RL1hi_{a0ss|o}u4;@gqj@a#9
zW8j=$pK3)^Ex6^BgiZRlYkswp>z~5#x_)}F?P<{<M%Msgz+i7mSpiIxAjwFk+;=_j
z{Dd6>wSDS&$f!_@S^qk$V@>dU>l>=}LKo;r2rZ8nSxFRh(w<$1>QZf>Tv$UGdy=gt
z;xmIx2XGG1y^&zgr29)uom~#(Oib2J`tOa<m+fcdc(&*<CaXo`zp)>dd8`AE4E@M@
z605(AsmpZgg*h>q8XqK*88ZKnP4sqY@MgK(l1E$h?5=^CP@5>yYAz7Ay(w!|eV!q`
zb!$MP_jHe4{t6v3`OZrFDHMuR*WUo*8l{%!z_6!xzls$%?z!2}7@t8VY#);O&aT{h
zI)>K=TMFgV%aD-}LS$!!98f>C{l%&OIn7mzcoDI!X!4w0{uJ)5RP0f=4?>b>3Ij#-
z!d09;*lpbgaEl~J{UW9aGW6t7?Cgn437y`N-p77k*>yooEx4$cHCNZf)d>@0GSpf`
z=sS|6A~3WfS}ST5GW-ifm0f}^d`~i}e7*A(7P~}wO?FwcKW<LENm7|aADC($C+#)p
zU(#I@tCMK3r|mv*FZx~+ZFUgKF-OnvCNO?$2fr;|yJfN`F$nHUW8c1f!+=p}ResVq
z%?se)sNn7lmzDtRiwx0~<WoKoJ2*R%S1#@6ZG~Q8g{XJJ0>=#uA}AsE?zP47M5KJ&
zyThxqAkib%ZiBRRkiS&s%OZ}Bf#Yc^m&k|oLwJKPztE=q6*QnF)e{S~qmKLekaP((
z`tZX$J(qYRtd9$yo28{PdWz}^nUs7T7rDL?s@bOpxBq$p)KOx|eb8~p*Lfr1S4>5+
zT&+Em=huDYHp6-0FmM%Ic2~ZKKxobw?+pDqc1d)A9KAXgQcIz|MtXW-Jk3o_8}HF?
z&f?Xz6uX{TyAL+!A7OE!V!BW99tw{7<fy>Af}&kIs|jjEQ~I*?N3D~&;27^x7K<qE
z`0E|YkQoH=5a8!mDCVu4!<}Zr<XOCC5=j@yp9%>Gz!VLNaR<dRq~?<QJ}UX>`j!xa
zBpH~JScp>uS<PTd)bth}W@18x;sVVSp=WL!f4bikS~X<_MaS;Df1i^@@!L9iiU}r{
z!yF$9$z3BLAe}vO;^rEK<O&XBy5G44yrFdf<jeTNB$s_{Y|rKN<TjH-v&d?wpMZlw
zY>UF$Bon>Z1>`axx!BVe25c&^2|+!sHwHi;3E?wI#{1xpN#bp!zfp;%J+lMc6Hd=+
zq8vPQh^HKq2;6CGv}O^x%_9Tz)ITx}<_7;3sGf?Af2Rj_m53a=Dx(X@yY|ki4jrn=
z<)o$_<ZU_gle%la8f3ZHk<=YY6IQPh<&Dq$s!tlYWzMJ(R~EUd2cAS9=SKt~fO(fZ
zck(WBBHICqr-+5li`AwET#))Bva$?S-aGE1g$(<~O=H?@snf{x&v)h>16@t5biTi!
z8Qqi3Dj6jrZ$7@MDC|w>CO%T<&!2CvqRjgxcRg-TBy@dHK(x9+8;&oI7Cs)EkhzKO
z1sU(9zg)^YeMp%?<${&W2Y?Of*~|OP5&@A*DqnA`*!(0pc{^dDiHBNq>BG-<5HA%F
z;s`GC_QB^)G;i29dxNFqQ@g_M0%Q@TaeaM#Z%;{>d?6CGq1oObZXGpw_obXD4Ke5a
z^ef}T#Db=dC~wu8y}XGn_rVKA!INB4k>p*m@U=ScnEs0<d{f7v9Xp<7W-1bR1yd&=
zU<YL*)BpX9U$BT@IRO0<*&fX>>e^XIbknTq?L<IA1efCCIl~c5DD2ph_Pp%qfh=v!
zlhD_#*?VmPfiALj&QKx|O|`l@5d}Rp%Jqsr{>QsjZn7GPgm|~^qI3D9-NbY4-@W_2
zuaL6KMXCzCcJ+pt^K%5}hfQNlOT9Q6f#U@+ktHwtOHw2A=Fb=I^@f2orT?+_+KX{D
zrp%O%G=s)d8RxVv?0M>RhZ<MmuLl{*fe4Bi;98v%<cF`hti`9XTw|9Ec)NO67@#&G
z#p|3jHH$?1Xi%7}(U~!FPB3zPU=FATMn>GSS+dEe_Cf$l_3V7?`>%`?_CYvB-mK_q
zpV<@(QOIAP?z>R<KBX*FHnxZ}*63_R^~ApI_^X8@$9=}m0MjZ0_fSP&{<c=gi%n+~
zh&VS02nY}_{qD-#zkbRVrbl_Q^C_|tPJQ*AINDRNoPpRp)=>-&D{b3_PY*=ypLapd
zeQf-(HE&b&o%O9=1vmECj^Q~5tF12p4-*IjZ|>Wtl$gqf5b+lXIz9_M=Uomn8ugA=
z^78mop3VxXez|%xVqDU4r?*PcdXuX+@n-2!iRHc3S#)ATye2E73Wy>aTyzlTaUW93
zYaGgV+kN=iSN-hSvsR+K^Zw|FRh5r(7{ZC2Ap4tNzh6Wb^yS^lTu_%XlYQsY4f!*T
zekn)&Jx1{)CnqPLtv3GkibC8qZdp@y;aP*4iCNxJ56@kAz|U`|sk>Za&w_XPTVz*A
zuWIj4Hr$?;T%{{*L9r^=7Mi}~T$!aJ?kGRJ)NuRWrwzyA-uOS*_;|^aq-IO=2OFHP
z2|S=HFc@M57Pm6W9x8a4MV1w|D2p-_7-eH@Pw(7eg^o{I?J`k36It%OLiuZUOQ6z|
zq@<)``uv&l2E`X&`I!)lEyWgzPbh1<L{z~<C(Frj&Sf_qH#9WtoH5&zZ!XXHM&J5)
zy)-G+;fG~;#H&0Y(Gb+<kRT3R;;q4C+>^%h6eOAkiEBTK&@3Gp0z%YZm(;v*dS(N6
zFW-ug<-_^yc2M4EsOJ$$Fudd38)s+!{?&3>{QW9<8=wd%EjAeb%)9r-lOsNm^U+{T
zFA8ktS<3?<Pv&M3p@6CVOvQ+Ue#NY&_Thfw{iKc8@;=vfqS`CCw5aWph-C^6K7lwM
z-6x|ysxdrBHW~t9%uYNaf`WpAqqfeZ<N;BqVdvl9NUP%5i4#1jE6Eff6*)5VS0|o+
zm2j~I-T&c23Wd56t`{ykpNNI$iZ~owu<Dmb<)*w5jJVET>{6!C5x$4Hl=sAy50>%9
z|I_n77`Tmp7Pf5sqp;=Rim7#0+z~DAa;(nPCJMAHX<ymA9O{7-tOgOI?U0jO>6Z3~
zaY@_Y^41+=-)CZCLQ<DdT@95jUiHgQBnCq0tsQqpVBx}r6OK&RGIv<|<nozQGh%_U
zi4hE(m>5Lt77}%1Ib-=y5e<DqYUhq5(ZlCYbVP|E(pFyp(6MsMnV(u`lNagy8-`M6
zEk{ZY&Lp4L%<52KUMJ||<3}eI<aw`?jWpO}#8rH8_~FuA==EEW=QSdV{y7U)9qYWq
zo1V#MclIy`AYMQ2WqFN>*phaYR&oz|sQ9!dSXF@hI9Dg$d?`N0mL)FLC=wDrTHj*j
zfwxmbW4YYvJ%7MFGpwZnf*7xi_zId5Zqgni2A1cqe@#*K?FU4qQ@8-}ts%w5#q<BL
ztWl>2e4G(EOtfL2q|!M=@9$5a@mN&6DKy~6y(WNXZo*ci*0b<n&nXC#?KwT9lIH3k
zsH$uemI4z00=234YU=9ha$x}^F@$7RP(G1ylAI}VhF%b77eekX0#HXbAA)u0yH^qE
z<l|!A`*x;m*SsTnwaH@d-n|5a3<gI{J=@zyC=ZfoN3G5B%)2TdQT`%OXHBg&9SJR2
zI6A16>zZC`%2yuvZN)vij^kn*oK~0H-i7uRzFb25NleT}Duu4ydV}u(GE}dovTq>^
zB@t4*0(tRRuvxV5fcq=b??cOW!D9Z(Eq+ASDg~AP82};wzC}vCuhWM<ZYB}d?V|H;
zPwuK5gwW>hz0nVA84{*FJvWd`6d?x&j&>uSEDcEMI?aD<i4xc4hi8?gXBTBYDg0c3
z<R<7U{V7&jE;O<(5`=&u`8z)lTl@2XfLUH+?J3|=%q=Z9Ijzoq@gnJQ?OMKa{tSI7
zh=lcfw?AT-b2RR~c<}<v`qoSP_?}fkd^RNEf`GX`kz=)LG8AtoWlP`uUUM#3Hd0-I
zkb1P>8|zGleu5D+_JnFsV%C+rhnC&CFL}L2jL)EYN@i_X$>ibRmiLry$()^AG^13>
zeyX#7puaXrLe9GOR@5P5M7G&>ywJu+HNNn>CP~d^vVW)8xm<U^592gPB?Is2(ZPDF
zL<G2~plV9A*~7yj<svF~UQ}0A<-f70BneHcv_9nRTDoHsLG6>mGBUxD8+h!?qq45s
z3NC68yS2V=By1WftJ$#8V&WJi+a=P{`suINf1KGU!Sh515XkRnFN+cr5Fi;qQU*mr
zI=E^|(lnt3i~lecft#^Ep(Ab!0n%vf{?Hf!7V@qUXPW74^mnU=?M}*vhKiA0{vd6(
z{ozWwd~QrVQulv7En|4KQfZx2ryMNl$e4fk2M!_r2D%1wFNLrYPTQ{d!&T+w<+qFw
zQ9Jy7XJf!5^qy!7Je*PPk;go)A#(b!Pbbw3UK1g?Q>L^&+nkB^sUZ!>{{DK9Rr=mk
z63i+t&I97$+M1M)%|E$=C<nK>vwFca%2}T|jDoZBHlCpwISX4yv;p9Nz!ebC3INtZ
zlqfiU9%1VvUBp${b!GkA*S=R7YpCoBOXkh)h?%KMQsv2`3TtgJknu0ksw)T^s|8U%
zEzY|~pGJcB!(npq>t~YDohkEFFFQximrDPMpjV=()}!!BIY=!SNoKpfnhA7NV>Pg^
z-k@+I-@v(X1qnvLD&zrJ3Bjy3v8ZYJq!bVIWpB}Y9^&BH>-9%Mi5TFvq-NH@@ADyX
zMGQyejB75w<-e**qEUwf?OM|N+BMPyX$v+y5P>=_clZVC$dTiBB=fiaFi*0M8*w|_
z>1a~%fdH(ubk@KgB&1fjrQxIHTZ>qf+pe64#v%N(gQtgQO@<!U)Eu|pSGdAsQ4_TH
zts%?26?j&$a~mcULGzcXAz0M;+3vh)=ET8DLx>P&<ORLVzx-C?g*i#LgI@x)CeqG5
zV!XTfbp93=2X&3r;Jd|MzmTe~LIh!>tFT)<z)9a+?Af_@+ojeI8#Y_9Z$Uw`#bGAE
z@I3KuRuh6j4HJ_VsWJom5?NAd>0s_Ie}OkuoewT+Rq#f%i@9O^hbB0tta93n4rw+9
z&R{o<t#k|FUGe7a@)>>K#;SOMEQkmvgkQ<-*wIVPiHYfwp1W!D_^;>4g+5;KdNEe;
zv8rDwnh;TVdESw1R9McthwZr=GeXR}e+bB){aL^IKX&tfl)#>N6?aT26iU~-cY8wx
z<>ln4898ei;|22bPV*4Mt|;x-K4Tztd#WcR{=f_(CVxJzyZVPF5RQVcc=(A=F-DJ{
z31)QOX<n4V7jgd23;+3X{i#Si@rUjBUwF0uhi?&+Y%F+egXWz)@IK8*<M@$s6C(Pp
z&GB?6LZKX(SSNEaJ*xF}NQtF+F%;4tCcU*ugTF2I_=!|`!hrXE;|aX@I>S|$4M?HP
zG>4U%*vIma3h60l5jb&3D&u?zYqNHo--^xJc|<KjC^{&IFIVwOtIG{M*L~dzg!(j4
zanmBO5u}{S7}7C22+N(!>4?Kf=`c`0{%W&u0g@a8xX1&U)dKY9Myh#<MJ3m`k8k1a
zcEyCU@3SB#GGF7?`FJAH2<~o?tviHO8o)ayfePzLJ>AqWUdvYBvFfy4^;xt0C;R-3
ziBJ$yw9D1J^HzR(nzPOdGX(Ou#m{diO0Jysj8OSVXnS>aB1X^FB~@a4yYwbK-;!XL
z0977|ky_ghCLBM5FB#J{;Ek%UYmA?XGD|3yqC8pGk(XFW2Nwk&p&hSVW#!1T=Wn9R
zKE0!#)=RSq@XP&M`qtG*zo@J4*s%jpjUxL)6P2QOI7&6UYFgjDFO~LbSwoF!_rKtp
z_r8mz39ebV>X)}Yi`|a57`o>R=SzL(<AmS*G-os=>NcvEGP5YTi(Q0;F_I_vs*)?2
z)u=C3WBFEiFyw9eT3C1FNCaOUj^;Qd@L6=I2;V5g&4Ox#ce%Ng^{0D@4cxz%?fRor
z>3jMeYq>BZsf`w%+WtUINO4#>dZMzq^-_k^sfS0WyY%@#m%7W1Bt)p4T&KBI^Um@N
zp-#1|Cce)<Yc9=w>aYjn@LjNasenLYF2^mFf`UFviC^#ksRa-au)X$<63`TnR0pXb
zh)+n2iMf6xOwKXs^GMz2+GD*-`8s_t^hDLDN44O$jKM_&`pI)wBm#SVSg~6z*)dkQ
zg74X`lP6E+vPDe6m~8v?t&bFi&Qzm2mAX>BrY8;dcgv+A@P|v5xo|kKiWfbUN8&9P
zA)xqJT#WT936<SSb>&GFiN3TmHIuiZ^_9$7O?Yom23TqU^FFE<XzszUR~92nhf<Tq
z*wmR-ea@JhHABLHB_xtnQUD+%)D*7dMDjuqB?3#5Y||DNVhC{^(prIUK+v;&ofVwv
zAQELexTB>?eIPA!{Yg~;cM1uDmIo$*b*6NZZThnk9@&I~h75b>vuY4yl4`TsD^)y?
z<6s!|ql?6os<3CW-m02+R?-iL@7zWzLr9&SF@&w8w3#Hz02a0q&Il4zl@q+Py-R2G
zp-&pnOn)wyW79`erKDJ(5P_G`qgT9{a0^tJiQG^8KWtL}9w7heIsbbub{+h`W-jin
zp!sl6Jt_I|<7C-d=@L_a5>0-InlU|`?FHz-;WE8j`ryGs5*wWa_};{-ac<r%IuC2h
z%{;MB?&8<CUR4VAqmKDf{633_Bpa;;R?-%9hCug_#d#zsyGUqxD$iIWEY1Jw#gaJz
zFc*^6r)JS2`UpiI$#oo=SB_SDJ(Hlynsq5d|I3#z%W(sFV);<nB_L3NU(l(P&6oC-
zdrKPH^2L<n5~i0%Ok7;=5cRtz!@DIHX!i=DkH8+1n_QW!>{zy3Z+N2+xRl8&O<%sW
z?%%<;v0Ti1|ErfTJBO&Mbl&m)?^Dh9&+O!nH{kJixo>Jpmp<4Xwb^O)v0og+4oZlN
zYu;J)0Az;HhVX0e!~(4+(h<k-P2T`JTRUu;wqMg}Z&Iw!WzA&ovn*-Hk&T*rlQsj^
z7nTL{R^cC+xc_So*MHR)Ij!CM9ZT|mhLN{Ax$+i}wq$@)2b~13hmO8fh_?-D@JRBG
zFlI!cg4&MbdGmOX!O8T-H9yct0i-q*XFVLHq&()D&?spQmkyDDda&7Fx}di%#ou5Z
zdB9t#@3RE#{teD0f)ZeqKfx|3)gbaB$DZmHv{U3$K3TvzKxb;GoP~nplT29y#S`%(
zX)FWz36jmDmK1U!AB7-T#0y9tKs~iTK&Z+%48_69;^L&W%HI(zuny^@;^&77(e+4v
z>RHPmME3JLlWKYT@A!&@AJ+dvn2Q)B+_Cy>d_S+86Z#&fr}Yk+e?_|27UEn{EbEVp
zW=tCE@b7Xba<#QrSPf-&U%b0wqL<{4Br7Si%l734o-hZkR!nEHebSPWwuy_QV~Q@N
zQn|dO?Na=lEI|CtiCLe!y@Z5>bQ`JCuQo{s$uueC@QBLYv*z?pL}^{&uDdjAvSzUh
zpT#NN3F?NrV6~GhSG`k_Q{Twbl@2m;xRG3?gTMXgLVFz7TXN{Ny=c?@^C@*xmozo$
zDY|dca@1m^gqH|vDaMie&b_-RV(D~Gp%S0lo~{qakB=7l`=elJ`Kf<I#4{*(dh`)8
zjzp!VT~qVr3(?MGp4oWJn(zS|^+1!-r`|8&d-DPgiq+)K1Vx%#1ls8a1iBUw-MH_A
za7bMpMTxE<{Fb{iNh1XC+~v<H?CFb@ME0Pb2LescQ$inFLv?ku1b9)?&Er7KwaAzj
ziG*^kYS3;#*E+@iRKI}6nu@<JIB5JAT2Z1r#T=b7jY5HDbR@zacp9Xdsbe*hkvfDM
zNr;n{709q8UHta@10`o065FVg*D(gqN*tK4h|`%~6y@$q;BgmUr3ulTFJ-JXKx8^-
z?4YYJ4{)0fR^};^1$u=q5fp$Vv+0|)K~(<itVc^4P`9whE?Blx37g_eTW?a_+tg>(
zdDoY<u;{iyB${2hRd+dQu|sN8z;+sgF~-5r?Oi%^_Rf88E4}@);OCMb4Eg50Bmc|)
z!LU;)Q|5Bbnk?Q?_%CaVPn}Jg->F3kZ#3_~`#4mfBuxxy(=sTzP#_<K>R`bPalX&y
zP&V*Ng4Od-0lbQ|OSrxHyPx6-%}BZ&sjGX8#u^2n6GGaD75oOLb#?38D6_M&U&eiE
zySBp}=`$1bVS@cYCL_;>f7Z-K93Fo5=FOLmANdSeXqeNXb`s&J$BSKRrvG6Yi0~GC
zw^sVDxpcsz*F+5RMQ8@#Qu#iEQs2aNO$K4f`=d3qvxA`URm8=Si9OM3>0?|Zi3pVT
z18)Hp%l>55{vp5hr!#sLS38@pw<x*JnpcaB{dxwKL)LWaR@`tY(NdD!2`OtqE%XMb
zhkVaIEtGFqLIh~~9)tOID6?BZTf=w4xT8bE9tVV+OG0im=PlE^i8eC()&*}oadp?W
zA6)o#lVFcY!zM8#8XDUsNVg9hM-#+lP#2(mQ~|QKH;#+*ZM;`<K_aYVKRFSzeU{fv
zObluhF)==#kE13Oyk0A?Sc<mg;K8X%xe>wd41HEgYAz)`(uklE`ji$@U6j=#!t{eG
z=>syW6gt%fawro>dO3<<=|*0adG_1e{r=;62u*Wo;9m2=?~BSeT->__UAK6dA<gv>
zr&>4t)+GLQw)s2Qgl1A)eix_}mPQ!pb;1$RV-l=RKD2|RNOE5sxWdk$(8b$HUnoG~
zogl(Z`s!18B}CXn>%U(0%5(AbSxFkVh>IgL`1(*f_@UR)iZlIraA%o?OGYHf8}%-5
zH1e7-Ra-?)tNRvy>0R@Q%uYZ6%2m6{Bl7v+sr+I1_;%6Zm>~N>7Rw=VNvFoAck|0H
zL(RwI$w{T+)JU{iKon;rdYi}i055}%*gcTQanZ5ic<**RfH*s1E=XDv{4m_`zJwn-
zk6;|yQ;m#igPZ8hk;86e77Vo&n;?@j2pI)0mnI-Ul+C0;SJOnP^U919ko~Alw9DtN
zCUdE-4w7W*%_X>~LG(jX#WvFaG~{Er4paJ`)-;+hPTGLc(`<uCZ5Nu=Eg?N?wix9o
z8v-bh&I9_;^%3!$aR}Ig5s5l$r?ifvJHP#nG}pUvY~iuP7~&#yTe|@zU*Y&*%b&ev
zYV_@okozQ{h1fU}s%C+kF)|%qcL|~s5^q}bjwqRVm1*C6#Yn>&BA@CVTh^%o)B0%s
z9OxTK{|!GskaMJB1eSr1Z_>a08%0x7lh|&al#BFmAelKdpL^z4#h-oT?fmk6psjSg
zGN3>3BlVio25?I#AT{#y^FtHEg#3+bd~!6&b+Ov<AEiO(H0_H6E&h}7?|=J|2r&pE
zGU86w<V(%%Tub&soPNf3^yyee^p*&AdWUM57!;|C^gt^>q*8Dy6p=dgKfMY2K|M>s
z@jcj1RO16f`5(_J*GexE+BIKDSlEO!H9;CV$kEZ?LXI@vF?@xIeMBLZcf?QlOXsX2
zXFD7uX};8to#)8oNfVsGK3i|4NPr!pE}#0g*xN5aSbjfJ%%r6Qyx<z5aX$Rumv@a8
z{E&cuB+uuvY`H5*ZidwL9@k#S`7LKlu%zaRUU8mv>Qw$iHSkaH^#2^&U|tAKDN16M
z!Em*pDbz+M1%V{9>MsVQr(jei(meI?o}<;HUH*)@lXE=<`6d@3|6ks-NM)ql<Ml=8
zc}FG(Nf*H>-v6EG>ou1K1O!yn*4ECia{Lr|=A^T%TC}9*JZfU(DZdud;DFr%R59<2
zL9rs8+m&T(&{Dc4O*+SyZ<jYQ!ERMRy35Ja=gud|CNyRmv0Qj{!P3V|oLGg{a`cwM
z&s=xIq=Y2qR=yD}(|jpd6eF_q0s?5GQBhvL1o>A{tOpvW$lTCB+e4aP6kc8HZhXXj
zY0cdFcG>UvJbeYS_;w{)02tY#^$culooNWkcf$Lj_0@(8XEFUTyy27(6(AwQap<wO
zq`cy1aq&oqOvhWqxMx0n#+4_qRry9jiP#9*gyG1sJecWbAm#wX^nQNnD7d0GdchA)
z2>*}`85rClvZ(~akP2;{9YkwL@?_FNqL(@iR-3E|%-=0<8*KRr&wdZ|6SNBHtEbUD
z4moCn?dF*aV(&=Tkc}r_<RKrpEJNQWgkl9rKYHL?$QsLL2Efb_wcJ#PH^+>~@m;sp
z$Hrt}R`byOqxq@Wv^j|&NT)8+SFWWsSSt`ZNp5LPLz6-_$aF$Vk|vl7*6jR+G#+cD
zx_3PfY35Gnas^Sz>Vl@1g|E-O=Ls;8<=+OdSs$0OmQel=Wy}hQ)&$tpE>A!sk9bAU
z5?1!KK>a9UP0)F6DKQ{K9+8BTn}?bfQmMjg{fM&Lu?ODgm^O@{PB$*Yg3ao)gWRi-
zr%+e8be;?d`fxE~wp(JNBfA}@DWlQ%>zz}j9a5#8+dOFY0kldn+NFllEMJEXM+3i{
z`Fw9A`1v|#BQ<(JT>QFUtFPMBInD%;Yip`YVwjpBM8eo-G94_u#1gZH4kn}MW~#>4
z6ME;hTx8B^#Vj5}-4~{oGxA)zFS)T;Iyc-X4IQVPdM05+Yr1oYOm@mu3YHagi*-^X
zlzYL*n7(nt#9Kt8>UBMWs=ml!Ojbp=LOi<sWuVumGre7e>7tS2DHnDmXW**z8`qos
zv*t(wef5-&IgfT%6`?KK=k6S!-NaU+>Ig~Hf)vbm@^_f#NLV}vHoN>jv`_70gLP$I
z)O@+dKDOrPQBIVk<`8;Hy`cvIm`7SOa<UsIl|Dz!s}odo@9ht_)k(B-W}D~)(r@SJ
zb=ElQu%-CQ<s@AAeGIpuW#rTlgVV=o4<X83G`vLG0sRniy9PL<6WcgugsokJ_FyWc
z-3+Mf?mejn&ZG$}bSZ<{HEJW}dwEvT5aymVjeMGrkdW_0qCyB-on!CYdiLnqecTw`
z8lB(yD$FKxV<{v^jghvhLKBem1fX_<S~3k0Q2`d}8)^JTD<ow^Etls@nLAD+p-WWZ
zM8c3tOYA2;hL}<JPY0U(nwV_TDu^UW8ofp^0i<cIv+#3{wn&#&$%<rYht}Gws*F9|
zeVj9$HKUP}ISct>bb9}`ny-_MFnc>9RY~zXyjGC$Y6EX)4NElFfA)|(Gv(Ua1aA}u
zvF}<4wlqP_4WWn9dZt?qZHOX@L{lRx1)==ZB+^`O^t{dX5A0V@#@?2MP~|`q)Z>yN
z#cP$EdJh1cgh2Arsu3gNRC^Blk0ev?0P(Fy=EcI=`Y<Bw`@NvrCp>Yo?G0X8koJ{d
z9O^Bi*Fp@ag(S&sU<dR+-dLAzXEGFPG`$H$GOC~3e`t!3WkB>+ghvGHOQjv6qs3s~
zX9BggFlQlZ(PS?lI;zlicWM&w?>vxWpJ5>~QRhp;6(*Rr*jUM^Nk5NXyAyJxcsnG4
zY_{zv)o%6Xba>w(_2k|Ln5PpEij&q83hfU}hS;RDC(R)|u(S7vrv8y>$u4~^7i~K4
z+A2XK*$NTEVZ?U9SkHDg=|UPz1S5=oNF6NFIMm6Lhx%xhf{OONdm?Pj7$)<o#FXqC
zSd%td*$K2tXMUgi0OUTRX_)yb>3;MXYotsQ@z&HNGC`@CQSG~T14Il#r=3=714udH
zL@a}|9v3?)Rrrxy4U!iK4eB5t^k+|0r{$y`D(6)j<vYPxeM%$0s(Kzu8W&GZqSH~D
zdmts^q`D*!h-E5e_;n9t4z@{0q(TniMY80|dg2Q`pkUT*(1G-6J9?aY&;Zex)i0L3
zv%(<`gc1)s2yIBS^X2k?aG_O>1wXT&<}QmGS6?bn1e19O>W;K}sj!lDkog`G7gxYG
zqsQfDbH1El_m5mHT|+z8L9ZKo5ZHOZyZ4hFy@#eUnAvZ`n9Zkq0ddl!!s#0CX&rTS
zgu|+&yr{7{3eD0c+QiU<Q>4D*w2r|FTX#AgVKS1L<n)2nfo!nfI-~JY)oGo47$!Y^
zXL9;NNEt%mkL$zBjcE`d<>R=b=nc8rPKSl4gPQ0Wm-il)AIvE8OsHg=+rZfl20CXw
z&37cYR72ENQd~R`J3u@79+6BoI@avdEDBTeBJDINqQg*xokx}_Dc_M)5}~VPYc6^&
z_|jE;6A1lX=EX{ZLxiM1P9v~lMRwH~Qj#r@EJNByvYVt#M4Nse;xM!^u0kCha!&ND
z!LxePF>9~(&ZmgbqD#|`xuvMFzVx_hc{1ksyjcdRrHUm|aHJO@)J<rKu=L^K3u$s!
z<}MDDDN{zP52@U#p+zOq3Y7Xcay85acii0qtJ6YJeG%IeIyaX)9>lG+q=7FZjiJ@m
z<Mh%JDrHGblB<lSoGTMu%{)5yiglMKNkQHsH+B*wAB%59q7|H6hm;EXiqRWtP%;y0
z#6b^+nhSJloKdc-O=j1Noo#nd5@{Ck`KSdcQ*$bf&Vr8K+}yl#$ZlRt+9U@??><))
zy<GR9kVFK!nonxrsgR%*_l}1~@eYHe3gi3J;yGXL>|tG{1~#7Tps#j6=-nIhhaIz6
zPjJ+iS&Nl#8L6VAMwcRwK|{NTv-sul@mM8|bm|M9DhV;I#>XuWpoC7#wCUK8<|nTq
zllB~s64%WgJf&BiU=U^`3iJ@2<{e8)B5WKR5snTQ6KhxBs530k083_`+K8jUX!j!d
z2gm}jm@wCm8(XNYRz_^yo2#~oOJ#OGNk`F6%kvOJ(xuzn+WM1Ps1X-*jg&}qxmzq#
zesLWE-*ECMZVK@xY!Hr>f6c4-fuw)+rpMh+{KK0f>ltYzDr@;g>S;x&8j*T>(BNK^
z8mFC{Pg!bpnH#8_^&S=|zq<1%veIc|NhgPI(YA`j(3Coy(66nXo3Bd27{*S0sIKFj
z`jAszo#elI(~AtX{mWU73;7%(!OVAVBW*ETAcidQjwrd{ygHzIszO;mW1>Bbc(qk)
z$9^p7NcX_zbRRiWq9LBBoBFDeRnk$t2C4hlKB%l3+r3~hXXH>MhH(mFS5urzO?A=v
zjmYh@(1Vz?-h=tab&|GCH+Q1uFXw0<O?ve<V|A|yWRgpaf&-Nmw|A#JbxW4sp%XXU
zxQuNA1Ih0b`7>GcPtGVRkfMYXx4HGb&^CDZ-8rqADMmR!%jBXG3w=M6)UoLV)B*yA
zfawDjMqe}P3{;htkqAY_y4;4(0qpu@h=fUi<u}vw2tGLvjDZQV=A_9{@rL(MJ|v%H
z9}S~UashOQicx*h7jQ}UOR(qTj7UWWtI5Rem6}sWc6_v94d1S^fi9q9KcwPGVJ1Q^
zp)Fx0RHb<~z=9JOzXJ7kCe?$r87el&xLy~TfbFM>@CoCCLbyIV#q-@B9n_A@_9g#D
zfHR<2o2}d^DFj|ArC)1FC+KbBc0K9j8^(szS<j^H<Grm30KqqatE9P(?(#NnvF-LD
z{o%tYYYD3JXc1;~e#xnP^mIBG$H05|J%JviO{{2Q{92Vd!O+dE6He_aFK$gI9b-~d
zMl<K|xm_cBKmgUHw#L|`*yHHOm$CP0CI;S2u!^7jl_Io#)KWP?hhZHqMs>9#+Qr6b
z1fD!hS|NH(kY3}YSt%*+^muSrQq#IIQ&!Y_YH%mqy9p-4K((5v%BF?}3&q3;IY2;$
zcQ^_BNg47CT>#PcPeixkH0F*HfIKAmhf_^c>Rix)wFMoUZ$3X10?`4$jtTn1M7$bH
z<a9$KNg8VsrOJfx-nE)b2>^o~&I21T_CPpx#R;=XE?S}n8%1t6JqYV0K`Ki~2M4vN
zk_#q?#d!V7t8|PSB_6wIjb2UL^ab-HB$%wWa_Emm{>F38cIe>O5mKhu6Sv5AOqx3+
zJ<&#*K|Yf0I|W-tM8tl6QPaI<gl2)Om=Ml^q#{-$kRC?3k^ZI1o@go@^oO=z6kHiG
zDQIp;8XXo=(i*GdO+*~O9REWIfp#-%U<-@I%<!s%(1xnzatN~vp<Zl>XDQwTOn$S8
z82b#NxglmD)D&T&P?{nz*uF4@=pK&Nkn%D?Ll;z)S~sL`BQkIR^a-~y9CaduBx+=w
z){3raW|v|m7xhsnA+gio^nj?4A^`}9F360B7$HPk55Znedh4A-L@P2oK5aewcfA$e
z9!mBB^XtUsPc&68OSIy9WCp6w=Q3$1?6rufX1zkf1XIlj+L{nc%xDi4rKL<mS)NI^
z-`Y5^wd4YdaB;)}5D<)vCy}O^QvpwOPa=bZ_DRmZ-*H#VT*w37tLdJEB_}PxOz>ho
z4)Fp4q{r#pb6XLK+$8@50^x<E`n<Jj9&)_Nu_k4aM1g91FU>!)f4=SMm&1LZWTnn~
zZ)tVcdu&7w?-KaZ+0H1fZ4E)y+~=c|u%h$Ln~f$8V!E(@L5ZCzuXh4F$7M8$Jl(N$
z_Qf?EU;dwyj!66<Czo{lmN9Kt`O()a{YSKoI|L*pFN^*ZFF&wk<DFT4XJ#*WCv)Ea
zYCM%RkG#TEdK)G$WBX!!@ay!4o*pY38=FfG4kJe5opSp6`mHT35-o!}J32Zn<icWJ
zZ7R7S6CEABI$*!6yL)kef4`bRG|HP*RomOz)~2PUS)|c%QNVtQY(weAix;1j3dzXI
znu;B?Ow~<DOq2;Lx#Z}0FFQN?)aiRiql3R)d@$h=+0xRYRhDL$XeJld*44EUpTy^t
zl$5%hXc@V=WyVPdVyh-c#u<&7J>1q-NhXu&>g9Dbmr?fZ+c)Nz`^Ag5Qc_YD%(;T9
zf`-OMiUS1PD>>(vjnPVFnp)i&T5~Ct3|YPbS<Bc&Wo^`T{CcFaYe&S<^+k#A^I2nK
zY046OKFs1pGylNIo*!ot3|E)V2x{i7ohct#eh_9JUmH$QQg!30Dm)c7U$xoXnSZe4
y@%R71kKdj72ThJ`_>I>(^U<;I{)0E@ULP!+vG-E&kRBO^=Dwrq344CM{Qm*e@CMxg

diff --git a/reference/plot-2.png b/reference/plot-2.png
index 92f9d6d1c17ccb70c96f4ceb4cbd46f09ed61052..ad75d2e6b3039adfb1d1db85e1370e9609515b29 100644
GIT binary patch
literal 26421
zcmeFZ2UL?;`!5>DJ`SSeC`uhi$}lvM5<25pFe0Ex?}~sJnqUYJnhnR1qM>&c5lA4R
z7!X2;io%4dLI@;~pp<AJgq8#nlAJg3`~KhezjvK=*1hYVwaz*B|E^^c_WSJpKKm)Z
zr|frm|GcA}-1my#gFqlT$eEMQAkfx05NPw|9ov8rTHcdI;A`7ud%KgsH*l?=z^4FT
zJEP8c#(+R7_oV+eWz8%Ffj~clASaKz#FflX#J698`!Xf&aJL`xejojj=lkaEPK%Rk
zH#cqBbbQbLlZUQ%?|+wcNagyL-0SCm+N#iX_rj}GvtO4VI&6LOOZCZ3H|w>3PAdg%
z`U7qu5>B}|xfglEIBXmKh;>r=V0VS8s4ixJkFaq~`mI&e!4Q;vC*$nDKK|!Oz$}4y
zp6$KpF>9c(8o!k1YEhREtHiMd)mN0hWC5ZNcu;b_^u|vjN1ykct6hqnj_8n<CGlpE
zC6(@~AY<v(hrNq39i7Wbm5l^E-O?_*vu{Qd3IavJ<UL@UK%n{y-}!+8v@D)j5ZfYQ
z6c&%{*~BidwecV{wLMFS;f{>0I7KX5wbXV*$6{8{#cqh;DM=Dnp1dn$O3f;b67$TO
zyp;@h<<~f3#jOc$+;rCR)}#eb>$WyHY=Y%W_ID?`6P6CLS|QxWEc@WBVsJY}a)E$q
zt4>JfHUye=d+=P~*p4THztZMDe`E!b)m&02FH_u$alRD&PFxfrjlZ@FB{3GRJSS(M
zjZivD7#bLyc-qgI&@AZto;Bg<y<Qlbu^HH#z4g<>H_YOG%0h%MDl^?98E;>zh3(h}
z{v3%;f?D}m(NYBS1*e#TFm9z?di-sc+}!ZmI4ehPB<}B4^1HcoOG%s~tkj__@EkZ|
zM|vK4t!Fo7?X5<)-b%in#M0*q8Z)7w_hkiaFYu}Or0^4zkeNiDqQvXjP1R~J@pId{
z*GOAMwjcNWJRoe&Tp-wtE()*>uZv8DVvgscBG1*fWuR3b&=^^%7KXMRqAf8(0Imbx
zB)3(_hnfEPbBTvYY?v<jJR*PB5BHeEaH%rw8=m;Pb>Wy2XB)X2Jj%=spI6<5K>OSF
z@F$1ljIOrORDzlGGZ`G{j=D_KN$aBDVLI4}{*joBynBhZxM`nm7J1jedb-@|#L%8;
z3|T_NUf*cg(!L#?61kRG$GrL-{S?#3z$1lzD;euu#fVLO-Xb6l0$ja%n{>SUfNHJ6
z&-390h4Hn_xD&8t<cjY1asET~<DI7mS01Hd%ZZ3=uJsK_xXB$Sh0H~pM3M#b&=3yo
z6xP@QHq{-gzbZLaEVtS$piZ2#rd(YQ<8ZtMJy{c*6-xNOcN&=Gt-sltByXx~9`!6x
zE>7Mp5-RflYkoBqZfP^*odoE#JK%_s6$sSa8K@AJk^n=eorgts>W&+sCF6VCeA{0y
z5i2e^^aR3~nG17lICKZml+~98(}~29RyG6{LSloUZL84FZ=%Mc=F{9_OSlI*M$e$e
zk#;5AZq8lt4|=~@iy&{w-gC&!AFl+v*2$ohD$Glsvuci%ubeS}dN@y>L7)x1t2izC
zqvAG}$X<~2>{9I4b+Vl0MsoT4s0p9<pHo+N%gvcb=5O{sIt$1T-ea;Gkl%cOgw7mX
z6J({0h0d9y1r2F?V0x7B9c~BubrH$1;xy2e$j-3WRR-qb0NC7CrkC+;{`)D{rWkg(
zhY`PFPC2Fi)9DNHr7Fah!&aWA72MOiuHkuMwKze0JuYQE<?@4&J2vsJ<GXHTZ)Mu7
z#`$&jFYn%(&=R@h5C!3sKQ^D<U)LTB_8P2_18?(LO;BsR>D9ITY^#Z`cjww$R?IFz
z(#$Ozf6obQhJpR^$E{Puz|xMufl1LAYydZQsGY_wq_d#Pz)lKYp1S!r?X;o<p^%<Z
zpAh`u#9xxuXN>FvtDXlIJ1Q<|PCOC^LCSBFttnqt{?zF9uYcUk@%b1s=JgEj`99=`
zKi<~O_QmA*cBPKIjuFNLeMCM!f9%}j7BXewG|Rkl=F>ugQAl?zO#`Rf_JoqJZ<g%e
zn}2dFJtl7$_a*-qlZaQsp}FCgV=}X#U6;9f_svsH{0?H<tYM}oa#xqlX#D{DmnzdO
z=b&7n4|Fio`@mxIyIU)|_(P*g2Uc5`kTzLf@0q^4GE+X>nvysF^sC>H2fcZEy!Ve*
zn%-A%@GEa`1RLW%s4e%2@w7g|l5)0;KeE*%Y3kNgIc{>z|4_s8iw8=k0$^DhqOKp}
zDEQYC!JFx9!MR8-pl;>OPylQ#-=P63J<F|0`*|u$G57`@`w1;nTKAmIVIA1TBRm(3
zB0Jn&27T)}(`h(O@Ko-uX2~4V#>u;0RADbDY3ORc=+$2P)?fD!I_>cyVHa;AskEc}
z^V>8NXUg(()@^%1N5u^1LqRi(>O1I3)=?ZOv;NqThKtLeD1qvTHg2(PJGz>oFXB|j
z8LCw!D4xMs%NP!mHR5_C6IWXP)zx=O>ezRz8fT3375blwCRh*h(h(0dMBwPA7aXsT
z5`CyL@#;2C<In4t><5<K?GnuUehjHo`b7_+w%d5itzOj!f{q)cISV(iPc*$M?6D*!
zf7L&@$;h+?U<q4xDyvs1_$c;cs2+x+X{Ril+}cfm-DXm~ODXj~^%|jx&>a$68X5cP
z6qKx5E2bfrx=UUQ1LxZF`uip)1_oE%{HH*#3>Up!K><~9j&qFpFK4vevoPX@!(rx0
z8G#4t);&8jZjZ!#MJDy;!OcE4Qko-49r6aF9b@HFc?5Zc(#SSHp2w9BKfE#26|(q3
z)AuPyy3;QKz+C~OR8)tAmDh*^UUy(EKd*ZcA6CC>N|hg@E$%AFa#L`33g7Vy@!6;R
z%~i2n=-M5PK;OX981_lpdLccOVg@>D_i7ht{wH_SL~`N>_nVX$4r`D$>N^1r919!l
zFPSs4=jjQlJm!HghPG{YX*27)Jdeod3m&mlobYYly+T^h2i-ZfuZvLYinhU9Q{qI@
zC<4s{>wc;qL8@Dl6H*L%Z&BEdBOwV@fSz8JlrGC}2Bo5U6(eGA<g>oYK_?@+l29>O
zqdbyEx7xbv60QVyh0z$Kkom&*($p{I9!u+4T{HH)Qo>C!SPA`5ZuBoG|EhJhAlpsS
zB(`<u_y-CdP_#j+yyts9-&<_|hy(SYUb^P!7a|EaH5`dec@F;w*m9pIS3U^@g0A0V
zSt<o1Cte<QS>v%NsFu1RzD`!0_@+iT9l9Ae9!Too0~X-eFrTzmlvW6{Tk$xMs)i2Q
zB*+hcEV1tX?aIEc_E^eF-q`xcay{!%U2NC%YnFn-yfAO9+_o)1(rA*$cO$l6kvO@v
zgcZo)TRJJM`}Tsc{5kzGxHD`(v&)Mdl3<Cw4jApE8SHi%z8MP(EN9kU-w+;9M%2&v
zb8IRv-M@nqu%T@KJcBk8^{LV^@}2};6zB9VK}q|w!KWq;lJM6{PQK1G6rtiBc12(>
zm`H#Sxxp7BdC$Fy8^)k;LV_c72l)VDpsI!Xkl(h8)z*IL0LM!xCd`;H!j2QU5tm~H
z$mHILmvP3F&Zor%A~Mp(zvEd4E~3%Ra>Xdg7%+~gANnS@f}Vfr)a%$ZR09q3Hl=ag
z^XG+y2U02pHG2M*a4oM@8>_Z{2I%W5YiGCHK-$ItGD!k`&9f#)h8@=n9G%DE%La|d
zUBc=r_n<l|1Y9?T<>dB+*NQYJtT_63d3TG|9oraEM?N4OzJ{=iXRxc8fyGf?TH_9<
zGipoM3RQ)Q+d(6T0Y+l~QoCC4y7@!#4->Jkc`eumwx@m9`Uq=GuIW4Zq*Lps<u-j9
z)cwl$R;s>U74HVU_fe~#7e3wPLkU~^exn=BQNg~rtMAju@kU98>Qy;Uh<I<~@x-~C
z-hN}3yyMvsZ?#Yc5gtcidJIO?6wcN2r?-rV*Sw$<CN432RucJ|2X#$(G4L`lq5l>e
zq4dX>6LTr$=O<bPRcTOoQ}dh=;UA$TmBCv4xSe=0=@e`w%`@m;u-ZcCnt%hxt)ct)
z{zt`ud8=ddQzU!8Zb!eTPttJ56u1cJi9ciNb`zOeCU+R}S9kn9o5kpJn9Y1=lsBCC
zA}h#Ln7dKeA&q=`s6qWh66Q$!g!jyyv^@z&QVdd>Z!S4vcI$p2Vn40i$QWy=a^ctM
z`!d@D4L(G1<T~gXHV5=RoTsh)Gx(Y9R{nlZ=FIib2O+iWIzOv7=JfGBih7Mho9Wha
zhp7jj48?A34>9hej-LyUoLlKcuXi7_Q(BB5<M4$E3ogE`(^j?(15n0ql5mK_O4cc(
zsI1j#xlFKR?Jl6h-EmIU;=pXKDCmJi38y<bk@-&ytUtLK6xBXT*|S^4qh5ka`<Xk|
z9hK>BS_>Dn1|AF~?{<^NCdZKPhP??Q`znbu!Jkja&DH69<;PZea;OvODK!94i?yUV
z*hE>w%|IJJ(ZsfHVNF_bL-=qLjee+aJIXHX^IE=(|HSf))`7w|AqjmzxHr26R10RR
z?e(ALzDn8#t_#t2rgu{2R4h3aWOr9S$|mV2F57s!;vXHBu2|c@#F1fZioB;LE-i10
z(cbR)wK3yLN`4f(miDLD!&U)4D9wp-pR0@$>ryNU#3z`b<eCh|%f-J~<7xhFwM-JZ
zf+~($dYV>@@@*Kn?u1nxUSw2N;IeIpyrpqI#?ePwqZnm{u6@r{=u>5yZb@jl>Oomr
zO~dk&YZL5FO!6C9`^nqE@fb>9R|o7w6e2&ZuoWD>*0!=;=?`Se)ivsBv9|;&y11H`
zMoH>S!x?f$qM9(4rtJ)O<2%P9BX^|w(H6^Uf3r%G)HWmB81MI7E+a3JIr(mbwc_TC
zdv^lI2B7c?G3xNJ&yBzaiEq;Lz~T=bo=#VH2oH?SJG|G2zC6(JQqaY+A)Ct?%`?0R
zk3VT9-n;eA^;|@JXs5{{j7%}^yG#h?VpxM{M&!6sRyY7`T7J@K{NKG^Ilc%XZw2bK
zaWk{e9?6aq3E~bAD04Hy-S^uZJ83)uuA|Zd?*F7K08VXUeRtIfb`9nQ`vdk^a1M3~
z7Ijx@2!J^5Sd)EG<#poL!*6|8;{v%c<a`%|`*|Ii1dQY{bjtF1_=hQ5)V-h)6Y1cq
zX6i-J@!rkjPHt)?qSpDByNrsz6~wd+XLtEfgSzCGZ5Lq)<+eKT!7!mYIZ!E>Gr*US
z!)p|!^8@nn5O?_yHA<8bu7A{9G`x3e{PJ5x88;rCgRy2gNmRp3E;=UA`qRN~S;-xs
zl0x}(o;M$;Jhv?-knpeJe7IST%GB)DdtFrU8xI+GcQW>xHI66l!H?nKS;i7R?44mI
zpmq@GTuCZ?ju4GWssfhDjA1{>ysj%92aPO+`zB3{XJ<v1WTPnk8C>5Am+jIUul}fV
zD`!U?MO^1@Emof5kr{9<7w)9ga9Vmd;1;j8E!Fq4ADN-r!1hOnxM8t2=Y8GSSjXCd
z>Ik1w|D+Qz+7W@x<AkM=T$yRTYTxeW9vgRH;e*v~7kS#k8`-9-=iF@llQ8{T@j{e8
z3PTiWNNaD9Wv+&z+tjl(7>sjaqNdFO)|AFgz*f`!2^9(6T+~>((9Ky}N?0GbGj8n9
z6_lMT!tUVQDl3oN;?7aJ1eAFmm4+#9Wq*mt9M}BXwml$_>Fk@#aP}llm58sy0#lp2
z9w<pdX0)xuap_=rkXfu2rVXU|sT=L?u9bdt<OF(`V%}pAD7D72j`c)7>1s%M5)TC(
zi{>Qr@rVx({9KwSa-iC>)clM%CvMK8uD19SuMjh5rZ$0I^-Kuq{DtwDL0{M*?}eui
z^eujpru27G;Ry@WPq=J<e9s?J?o-J>E;<hCptFO+!6fO%Q+>n06JPgB?@hk^1qi;t
zRq*R31L^mxlb*NXbEPKBaeu^1|9<@AV#GcEjkV`jrGJ4c43&P8eg`}S(qF$wG{&OK
zaNtk{uyJ5au%+Fb6Habz;wiY6TX!w-f=Dtg|7>p@b}xSr7uk&ZMM{`Dl_fQl2uxCU
zuB%Kz%5dpahZwe`vGKQWB$)t-h~^`_<+}eHNy_+@@VTT$L7S(rXK$A!?2h3#!qika
z!V3Pj0i*cM+5+C_evsbz512{4|KoqJucYyR-Tclt*~2u?{%556-){+N)2-kR)Qx|&
z#DB;5H6F8UZx{8NmT~*Skh|~Sridi&BUc4rulTnnD1&ggNVxGk7sVPv>rb0qKig??
zV;Qw;?6kWpw#KHF^+$l#sDotlr!S&duD_k?%J`#h+V>1^>mc%}+E=#j%(}K6Ake5r
zG9h6V^}yq%Ek+f``XzIpQ+4tUDa9_-z1p@NqdT{>hrLyEr-T-KNT^-#m}pEG+z}X#
zYeGjP3>C-n#zL@dd3r4=60v^A+UFud1Yl6!CB85rjnQE=U=~Qmg|#{*e%|LBP12ZQ
zs03QzStBhSFbYx3b@%Pf!*4A2B6_{)&X~h%DbgoB2+lIJY8AX@?K_guGNZbfx_$L;
zJDX^7P*`iPKE4*aUWE089Rhq?Cus$o5(CH6Cft0x@(`U$^!HifFA*hSZQKs=9I*p%
zvJSL<V6XJSNrU{vK~!(5$~%V<nT`GLZz%t(SUkM>A{y$3aPtLh3DC;Qj<^;|A=%yC
z*2Vg1n^>{C<h*s8fX`|oSD|#!3a$!aPK8p!S3i{imLB^^4U-&{!Op5HI|eQH@pUOp
zd25J33kOwGT$1cz<UE(1QsQ%IK$mC=;cYoyC*ZOUH}NhCH=_7wvQXmgGv9*sKF!IA
z1WIjk;KDirrB>ZROkjr5j8NY03`0cn1sFbUM$xf4;-ZK<e$W<ZD$2r0{ezuxEp&I-
zT2k^wm@w@XAB>JcQCve@2FopHdlla>(7{=7u<qbuR?L)*x|ELq_kD{I1Z)x1_Bj>I
zPfY1NM@Pkkq=<j-bRcKm2v`_B(k%n!*u!8?S^~Tb>fhca2CFP>aPrel7?OcqSyaV9
zU!CL{P`x>VmLy?YomUxuyM$*m3vbJD_JTl4O;wk6_DxK`Z+SDeHI`=kr`xCJG3;K-
zXv(|2gSJgqYS#lJqlfp0)2D*7tdC^I_wMcf&de|AIu}gF$>1RfhBZB~Y(waTj&cr^
zqg6dcW2jx5Kn1DUih1o;aKh#+Bej~2XP3sDGGo4@*R>EWU?cP)rX-O2_xl@lfg={N
zmLUuC&^VRAjb><M=iH*VdMdY+p*uc-hr`c(Ca_Rwlzz42*Oio=ZFxdh_E=MuHKJ?7
zdV1i-;^gbys#bAlI}z#tI(slOitpm_^C7G`aH7!r6@C|B7<8IaaDZ#6XVTiAB8oZC
z80oHs&C@aoFXV)kV}U<7VUp_;I6#b4=BsW@Zs(t{T|EJKu=lt`%#W0aMa2vJ+7N5B
z(!`h3-se9IEVnbQu~jzQ8npgqP(XA<)NQ33pYAGP+S0?9UUpk!9_Y7SB?r}K#CBu#
z-R5vHY^+D1Bq=ja{7@s%`YJ{wsKCf<)=Xc@+R7}I=sFR)u+By^%5f<O9`m9XB56XY
zU!e9GDl^&oUa7t4p!tK;D%Z#e56QF&Swjx0)6pULl!l9NDrQUWv-F#~vs@%@zEIPF
zAaZYWWrECZx*NA~M)0VF0NC|=o4MZoY}@nJPl3Y$J#rH&8?B{(Xk|_*B0+j22b#Ye
zycm%Mc7xZ7uV=8C#(e|8feg38zH5w9znk;G4kIt6D)`wbu4RyhmN=JQ@Wl#FazB`4
z3UgUbK&r#9cEr-X2<-{SJJ0z`X+fJf-}{&%bXUZA2HzrVz<t@HQfvNy7-0Wr;-<xQ
z^46m}<uR%>Gvc0??D;{X(+78fJQfnjLntLo+rx04fTiW`y9ERa+rG&_X=#yVb0jOi
z=~4G)kPRc>J<9{OS0{?Yf{^WGa2jSJB`O7SRB6h|ck}lq#NTNErOr53TX5P}5{4^)
zfbG@%Zc3H^QlGf7tjJ^@00R1wG>q;i>zr1cd);I!pJ}WLs?VJB<s?k355WCkhiISQ
z?i0oB0=-)JEIpnBu6_S&<x|xS-_b){nOSD4GVbOM>O4&H;I^?ovV`cl3}b!S=&p3@
zDP9`+A}Y&m5WCR4l_?v2y!3`bbIroq7`3p`D2j_3O4Ym6{9~)^=GFF8zBPH!jZ(94
zkq{QaC?sQD)oR3zZQXw|Ww<?x>)P#wtrb5PECT@}`Q_nJ>*?d<Q%&Kv&JzLCE3&9D
zujp-_&Fds*qm^8l?t^7C_yh|g*9&g1ZVQ3EIWQtC%c91|GAn<?pu`;sS72<GHwr$;
zCZ=$gS%G9H7cf1oNXCz5B%bm{<N};yHp?C7v_svUYW3^<Tz${8#|A#jD4t)zZ>DEk
zSOxOs^OfOzp#M;e@(z!8mEGL;!gmv=PAQn_N3Wk<*cv1B^PRw2RJL8@3S)!|E`$mT
zD53|jH2^I#>wbvCUa)=&oU&rlxG<+Ow(uO+7FF!VulDm|qv_5`2-pKwMpK|ufy%df
ztfT>GJCPOKoFWVAgj#Y<QS$K)+*Fo@+TfoD|M@NedC-;Pe{=wWzSn0hVLh7r_N?}O
zc8bjOw6QkZp`$u*`hIdYicv>zxo^m@q;!Z4U1g<3l(M4Er09I7-0&;`b1C$0zN;|n
za^LN4FIkoHYALKXmH#kFt_hY>g&urm?UffS+p<RhFwQ~y*ZO_@&_Ciky?*3d1smma
zNN18DJToPusz3g()<sC|p>{2otU{atx~$=KvIN&At`!ZldLFT41%uf_#{R|R3tVUu
zZ!qjBBLn4Mh?6`?;GW^iR#N77c|HB~I)+W8o+K&^;?#~g>&b55-*_0saZD)5Oih7k
z&WDyk#VlCJ8I|x|)T(V7sL@iQ)Y4$!;qU@zO2R&ojU~k<<eAs=FefgKj8_~v*6}QX
zRx4^|-9+oiNvTIeyS|SbEP(ps?k8_&QYnXO@=Af@oKsERk-Fv!cUTg)6t9Vd3=zEA
zZ!~H%zClcvJ!7Ym&aWHqeS|p<tCpb{5$VyjvCsv#i4<Pk3UVsBMu0lfOsEv(A8Agy
z$lX`*P59?VJT`HlOlk}5B+<JcCy99G6<f3(wBN5`vY^<-)|9^yO8!bNMM+#r-9*%J
zv48D=@;@tkfJuJ~in!sfT~dcyA(VJUIu|j3W8fzXu;ozVV?hVYD28U^EjrH~Zw;4M
zs<e>u8PEt!EC*APU6g=xvV2#DIe%m92)S`+JQ*Vz+uCgvfDv7^?-G1%<(<fClU19O
zFQY!6nM$lpNaQj?2K_y(8xowkAC6=a{J5LRQ?t#Yv)r4~-Of@D(TB*9y70eCnN*I)
zu@O9zDw*;4i~5Y_<}U-{-uc07yMw!8+n{a?A72E}%4~9X0jh|-p!Ca;om(=SWT}%q
zmx<Q@4is<%;1s$ej^kfyix6iwbyt#Y-Q8U!t(OR6@siPHPxM7ThP5k%ZG@J`Uhk`k
zVT<g$ERIq-77RylhluBsCjJgAGg^+T-v>y%7_EPKs|^4i<Wr!df8pIzUb231o1RZg
z=>-D))xd4-LbO3$GFs5w$`0vQg|6RbA-q(tIZoJHDlgKBf+>^C_;47*Q#cKrqIMOP
z%e9x<hKUn9`#$eGeQ?f3t|^XT@y4Tm$QRGbgtrGWOW|~B4`Tan<uB%g@s8|{L%pEx
zCa8NUY{YY7&X4bPU7I$w)gbP8zJGE&87GbY`;Gz!k~pk@msWYG12JMF`ZLhRSLAnM
z&qnj3LE=6UV>jd60*SnW?#(irJ*2Wbj^jeO^x6Zk>PV);S8=7F@KIMn^odvaU*D!p
z4a74`DHUp>FRu9wGdvC><=4rrlV&k1#f(Jv4Pq_ppS_NAZ#=-BmLI**-KMp+@%?F7
zaH%zaqk)|n)shDLm0=0>Ckp1$#y~6jwc@0a<mgT>f*o~ePl?*{_+F?CWi+p%zoW;O
zN32~Ns%+v##MCD&j<2jJ(I0fXh4eMEBVUG6x1)BUm@eR=xtQCmLOFY@;XT_TnLo%H
zm;u|VnW^~+ft~$%yUhs#9BWFhsWheaaC_1pCq{+!Y4!XjxhHG)P>}`P2}V@Y19Wpm
zl+22miU>Ks1Hy{5!j?e}3;eU-)>xokmFm(4Ln*?UM>qL~MzM#0<cz{_1J@?M6nxPX
z5b&4WICZF&$m6nx>Z>i`<R9owD4Hvny%O?7e?{nhaUgTKHBG5T<OM5cwX-tG;b>?d
z&!4)7)lt#*QZRb<w{LaD3jmc3(8*PYYpoRVhbOJ41GocYval2|)aCQ^Q#XpZ@TK!x
z+t&0FfIcnQ<oJd4dnhdz$@wJIrDj=^26RLUz8$T+IY2~7M>1si^GZ<)b>bLyr2R`-
z2&kU{93j$>k}NzEC<|)!Zvogy>BGsV8*56X0kTiuzbJm^BP=?SAuH2f{TR5leXs=T
zfAnL4j6m1W?UhiTtTsTE;4Q+RU6(<w6vJCsmMB|vgb(3~1<(QdhQx2~AIXUM6jnPF
zGRQekXb{A41*{<@WhT@PB?}poFTucSWHJOAMvQ<-9u3H*y(%%x|JLFPP|7g5&G$MY
zJG2MA+9n0|h-cSjjEn-UEZL8Ly`m)D?HQ(vJa)me)Y~_(SyU9$J^s`pek3^r7WLrT
zH=gt}R2IuTt<i0aZJ7iFx6e~}n7r#WMy8C(m!`8n%7McQ?pvxY_%%<jF<sP3aVZs4
zqs!l7L^lD*W31(jq6Q1BQ?A2eVH4LRw`|<qvdVFh7fNma6-hR6d@YsV`}tT`x82w*
z5?{lJ$VbTEM6LsfIC;oNXh^;($3rlDgsT63hYrOgE$tQV*8j>MYvslNr&~Zf_uYle
z>#Q9x*~aG;0o?$-Jg3o<#EY|3&Y>D0Rg|_AF98u1sEv%WM&V>+ati?I!Oy&8Q)2G|
z{O9-K)L!0C&MJcvqyBw-7n4f#@{L(oe|o5$>0)aJPyffeU;6;7vGozXsZBb~O=D&5
z%Cv3}2v`|0G{6^z&)~i>^F6?ft1aad>_((J5S{{H)Nw-tG^V<2;!|-0p<U3wcTPq)
zx*vE00$aP_D(V_<R#Ls*mt>(l?`B=s<_`;aD+?^3*t>?C=_nCd-lYYT+#py}>`r`s
zc$5&xO=_}{tr7O|zPIrL(aqKxQ9`ZP=g`w&v2{3^i66aH_kA4G_d-&D(!jTt06Luj
z9EV9S+R1;m5(D)<L@}=o?wD%7^sRRZJQI)%#Z@cg=)l3lhEnQRn19w(_Z6YrI2BY6
zkHl--yFkC0QWP%qC%-#Fem!E=V9|0t`EXR{-mU=VOE;L%RiU9@A590kZqhZb0qN-*
z2UW=w8S9o+(}5F|lkMzKcb7zt+;E^w82EdtK{2vA0uO|c$tUD;th9RvN(DISr}~S2
z@9ZNAU9s4O^S-T56Fe3)<c#8+4qmD)HtqW?n%oiCkkC@sHo=Z)VOcc4yJx%cNgqGo
zh@#cDNzEYD%lDNxtX}Yl<v^}O-*ZiAe3RfY|HjnT9`Q)a-L~Kmtvi<?W-}rN%cdl%
z_ni>bA;FNXXvj5R>L!blT7f21YA7Awn!q5<tK>}_aVsy$-A(2yueQ`#Oj?Ixk_0^{
zU;>bkK6c&S4lf1zBmXfYcN0es%>)w3ru}4@^Bsj~Wf~8_-L=9dUs{v7D1<w|h}MA>
zls@|AoPccrDaA)JWoROOGdT&BmEj^&>MKlrkA|W?I=Qv&0vEM^<R6O#_D@<@U0T1J
zG3uVwE^vWGJ^M#KZvogMM>$mb{t<8ta5eWIND=kJKVM?}zZFLruxuRO4RKl#`iK&-
zv?iOFeGCAn9@eu*U0zqvo3BX6QXviBZ0XfWDKdSMGYaFliPpw>4^Rp&oVwj^V63Xg
z1Y)7Uzf?traIZ9_fEo{LVoCbSh;0ccxnh&dBxjf#w=eB6ACInb<EZ}2Z8a1fYxFS~
zS3=F<m%cjV*3%zZz37x$N3^)cBI(~<)y^ekfSQHjMLr(9Io4pZrITUy@7P1>!%0H-
ziRi|Z<S+m3p9+0Ae)v;HZVcN%<zG)tg^Pp3i#_f6!>v5XzXMK3Fea(oVHMBx5kCD-
z8Zx1XJ6Q=zUt&sGg)xH?AYqgVOy)zjH8#f3@(YBfgea&1KI$@ee^cN<Nw_eM+x;JF
zm%J1oR%*nyA-H*I=lN}9e;1&=jn=Q(?FO%2pptLTY$jWybWWpWmKNZCP-n94(&+^@
zO2G|Y6i)M=2<Lh9TB5cO2)-fePN_TpjX=eq5#ZWhL_kL1ZkNHDj`mBZu~D+ay2)SD
z&2RG`-X`~kWJT6GVll4l8XH$%8_RD!lfe4@n>g*a<!&y_cqhsV)KuS)c@0I`rbacr
z0Zo^%K9Q}JMy3f}VL}sTX6q@~a;<1UmnI{r*ACp?9%4G~!%kK*<1LVlLfH3Q3=Xz_
zgX1Y`fxYo~{UyzX#FvOM<W%{^q#GmRjI`o<nHkp~2aFY=7(7{Kh!B5f&9mCb;;l)T
zT2)&p<Ecf;zl#<hYxbD%GT}Vz>o$Ri>;s&x|C@wF&s%wcsLe|Ad>{+3{wBLgNeE}4
zGpLo7@F|5~7=27j{mQL9+jibOy6e}9A8vhj?AJSo_MhK|jcWZF`-8&CpI)y0;`8>C
zYtHd29gj~2KZ5-J2okKGy0ehE^l*B*q=}=3=HsD}u^h|-hPA*FCze%}`x5vlS9G!~
z9>K-B*L~-)!$BjC<LfDAwGs6q8|mcS85(%_n;h=e*F8yy@bT;jerpkH`aUy$1qx~A
z<uT0q(_~U?s;b$-arJa~AK}n?(@RC@nm2g0T5n}q4${oe(~unorJ58KJ^kdb(`#2N
zq;)uq;5~!ZSLATt%;d1K$t`{$MD?S0@_8hf?85=;^s0qb>ov`OF&wGWoXqNX((D~n
zs{l?jfU~rzqO*$~I;1&7FBY;~mJ_Ok6d#F;%$**~J+@LbX>Eia(oLT#kxOnF{+%G5
z@VlLf$*In}5I?%NjV{R@Io{|r77kI(o}SBPSZk4T|D6jk#II=D2C|ShJqzh5oxY{M
z-K8hdibS{=@wWN%MTq#t>)1Zj9J0i6+5(KyEzj*|8p#atz`5+5bZc!A&DL?bkIu>T
za-U6}E0e_c7|S7)!n$i)%}9pYL&h6KZJ`hN0^SlmWU0(Z9Fnq^st-N(x;W<oYVhnb
zRh{@~`VeHdzx!-AgkEwigacQii*?&V&wdmh(iUzEtIInrcZQl?I|C3Sjg2b1d;VOQ
zQmo$frNh>Kc{CJ!`3>gm7<148fB1Lg^eOG3yLul9au|nYZ-_XVDtGCyGIDwd;#d%S
zLUVpQBw2rE#Q^jI^>qv9FUi@jy2M{e13B^WEzkOz@K<Blex)C8CI=!@&O{gIfQ9+$
z@j*R*Gd<zA)$NRZm|{7~_8UV}*~%8|)8|G%K~(qb^efn(<rqx>nD|~*ai^c=R5K*P
z7P$Mm6j@R-{a`3!2WSNg7|JG36>dFrf`NfV`<<YDZ8Xqmg&^}a;*&YdY^0Sg3H*E%
zW04HjI5hEg2R&aaKH)4<>1W7ta|=OlyULCCFN#03!P}=);+d8Vb9OLDDGn&6@|75C
zT|*J4=WN7FSxhcAMf9Th^>-FupJgwj+zHpH{RMwAZTPM0Vm{Fpm{b2dU_*X+<krgy
zUzeLN`Q}e=h0u$R74ZSBmra{M{0gF5$7|bapCn*Ht5pLsl@k|V?|L*j5lIw1pV;y^
zYqPx7(d}x9i&9=heE8n|Lk;1y=DTZZ_Q*Q#mfiYWKyueEpA0_607IaiE!pG8g483O
zXOk?gNxoI&pQT!Vi~u+&^Yb*l)lx=)_~jl8?@UoQ*Ca8s+qFqIFNVp$@BLoDCvAN^
zZJs@`P<Zzhl57#=M@@nxTa(PS!IPn^Z^166x@OB~yiPAaw0huFqXEfZv-MJsFIgV4
z{6SqaQiijFYrnk~Mnc35EgKZWzu+!R&DIcPch&eqS+VgLt62q#JBiHFGXG^p^D^I|
zvF^1%;axPcrK*o4eAV-<q{(>xV?BpO#$ovz+aO}|nbVj3ot7);%Rgsbt+rm7M5?P5
z#vYMwEp?3Sa*Lmz9mbgCduNY7;Y$ZWkB>Vze(QI?+F}B@`()a;p3mdw&wp&En7YUs
z=^&E~<YZE!TUBfv^me!mWCM0osK+sK$Vw=#{o>j0WW2nnt<hE-L1%A~`Ch;e-rJ;y
zus~nq0OZ>l^^Ke@vUB*~?f%CD;Yu;0^0SSRw;7Rj7lM!2DmPh6*8=Sd9N)qTmCN;P
zFury|HgX_C`&(eKxR$fG-CybYH$p|@vrl;g87(qNr#>lsD&yrbz_#p%#o1djW&STb
z(CmQUbmPsSM!Z+i`X7_K3FX^O&P#b-3D+jsXOCa*VHdn;Lbm@*f&or^RHz<TGxZgs
zIuun3hQ5@h^|OQbA%4dIS0~j#Y>e2aK}}A#@-4*PNWR7x^><%l(ezD7$A{mgm2h8+
z3M5)I=Wi=bTc1zl-Cx5Kh4_+W!B<EmW7D=rla-nF$o8ryZONJ%ukqL?0$@%-4B+?Y
z7Z41gBmU67CFu(u8n1Vqe^6X26Y&2NW&e-O;lC&ViGywoH-~~#pQw0v(oJw~(h<<b
zkS&}m6URsl_12@LXe3r6{y4HTt$OOCx;s~vdA<5Bd$P2Isj(i5)Y39<<l||Y@u!fx
zkXFHG*)rb%JOO@8wRk$qbX~|(=bTyYGSif<{JR74KAUDhQu=Lh&~bT}c12v|!)f<y
zpK&2A8`*A@DJvw--#&R+sQQqsRN?#Zm^`Q38QI2EkB1_svJ>mwXLE8sv}ek|tiC3E
zDB{(3Yq>qkL)kv9#|zQfR_Y|+LyJ^_oQEvGRTokt0_5zp;sc5x7Uko@R@}`tDHj1x
zAdAhMf!rCVFy{z!QcU)|L+fGM6w;@7TAM(07@9*`wSBUPoQO)2QrAbR$I;-~J<Iz|
zdL$7@QT9e`Pf0sPbG_~ym?Uh=dDQKY-bS$R`IXKA%X|r>YrK=rW2-c7L?c5q%#DNo
zN~_Xp)eK^#g=)l0aQv%ppcj8^$19>_KzJZeDIUjZ?^)&*&FN~bbJeSOWww?i9mTMH
z(x)HY4m_RD?pc0vqPE3=bOf1*;%}@&k_|F}cQHVq_eVTOI}F_Ovecp{tRcyUBzMH1
ze^K!4YgxKAo4O@Z*x}*XA=)wLUp!p~afCiMmri@t{%|s!LHVthc=WOg9yxAK@|*(8
zYM}xJ+VhFjtx<l+Zf+ht>%!m+WZ~g7yend_bly<~fSZ<*X%#s#>_Sifh;$5e?i<*@
zcm1(l_O}Y)veF;#FZ|blR1W`d<beyP%01qfhg8lF?$~4Y@}`A!q0}>r&Vv%i<=V8l
z_V`Y&6cS&(k>Y5ihWQcF$TpURF6lS~6ZTkkQs;5?$M)eRcY@tMzvAgJ$N;oj)|c_{
zh-j1r)BjS>|A*!()z{DVkXTLgpGhO?i0lnBk_jZQYE-6)fG)_QBi$KksSzK5v{P?o
zYX$+ye-gw7$dw^H99^c|$42ULT62dXVk?pyG>_=OSwcVt9F}X<IT?=#QtkiV_u5k1
z9zxHV9$s}?@GG3YMIX#iE}piA&<}OX0P)Y0pPse<Lc+S2mFG{lLLy41ALmR(ok3P0
zY+F{7QvfuoN~ilSRjXqo2=AmzzUs>cU_cIOMO@yf1i;uod)gZkJEK+Q<FOzT(;$8n
z9m$&>hQtoJj~Xi5%KQC4wzCmugVqg@8_7t*iID~al1_Ge^D>^H+$4?qpx?Wt;CeiF
z21u_k?z|dVM&$9ky%efn{zke<n5omQxZd4xth5sbiB0!(mnIGHp+1H0>@-^a&Nd3}
zZtiiU>110;-k*@~e{xUNg5CTgf<q^m3+J1%r{CmExpYS(l~XpBJiUq}`Pudc1Y>u<
z_#o%m_sI?!u4U!Zxj?=iwcB=M(T!%hi^rzrHw{?mKw`DVu(Q|lHOyDj!0nfs!wMxF
zWdooW=X3qte#mm8I?+8aH$;7XMVj#2DPKQ{4lmwsEBKJ*c7X2`4J<rxCc0uO(^8+-
z91gAXZTkF|4(9KmeBZnr?UFLzz}#+)c-p4z$`6lZSv8WI`~NZ;v@+`L?S(|2L#{w;
zm$~XK=CcKXje*_I+T4a>$gb`)NPB$<608;PoZ+fkGQHUnzR-8Uj?7lQK{IETWLqP6
z>Lfj6BDU){336K1P6g%?yD&a>jTZbG<BN}8dZ(a8VrZ@BDx$}akcji64M)0_W6nvL
zW|ko$F!+Tw^;UbN!HCDqW{7Iy^dr03)XlrE;=-FIi=Y_NRt2Aj>g8vmiNJJC5~U@a
zmIv$&BV~ML(AJjXhX_ZG+mgBg&YRV<EI<EwBzp4Z_T9SUFzKxUTgis{dNk6K>@l;Z
zZcA+pvX6-COy(2H4HW$1E@3(ZEcN&+NWNpM`MFmcDL)o<OoZyc_U=y}UVl%^!E3a-
zcEZFjKMAOdrRuG~dx$aXGx<(<z3kP}!@cz7KBK`fv|bYn-<gA7?$L4ovGc*@(U>nG
zi2I*TIfGA23a`XdFuwNqSoXCh>!#Uz+MIG&q82G*01(@OVxw$U*$IYyBd6O|yp(r#
zIdN}E{_<956$T`_cxAv6`);=HSsU{`KYPA)wg}p+H)?((euVxd&IlmL@_Tdb(XjPB
zu?`7`hjZwWuLqu>FLvCiow5XE_~DcSZnUJ(g|BR(Lvls%=FP7H_verskAufJ>ZErH
z{_V?K`<_-KcKhZ`KTQ(yR9XX=@9FAQ4*0$2x;Erni#t-zEZ5VQRgfFq2Vtcp)5U}t
z`O5U_f;)PU<li)oPu!Uqc6#VQ%V;cm?Ee$P>Fc@S1|yP#S6HuZ+b^WC6FJ07$lKo6
zdTW(Z$KC(=E`Z<?5(>?mcr+cX*Bo@fH}^_G<f%-DXb=1FLf4RIVuhPD<aqnL=Dw$D
zRmmTJhGaUnCImk!DO@JHb&ur4Tzw>XM|s}vMuA;H&b18ZdBS3Q5XmYI{9Js1#?lLM
z@CT8P!3^%|qmumTk3~WG&I3<QDW>9(aVW%YmAsEyUg`?!HN|zlXQPMCE&RsttT&iZ
zMp^#3FqtzL{9yWX&dQFfud*-FU*u&cYE%#>Bb=7+p7nKtKKphyvWhdBSuV+aJMW%d
zVthqK>v`YA3+L_y^%YxlS?*I`bE@s9@S=s%>Fl{=zQ6XqClZ7k^FCgGrm>{cIpT-t
zeDy4cR-Y=CLU!T{!mKRwJkNcsj%c*_9-h-p&$0YjHg@#VJ*{}po(#P-$ozuia!-zu
zj})mu>V~H}UjXk#y7N^bj*hLjH<q+lxPIBu6#5V{5ljj}*utGED`lkrXMGq2g;M2G
z&0%MdN?NTBNW6M09O>;QPR>>jq14U|Ky1L5E&WQSU5{XB&6<HPF54p!8tXIaaHr_Y
zNH%2P>@r!sN;5tMsbd$(d;4N<x%ybqG!toMN}5298<Q|OG4q=tbUMPO7f4`Ar(?An
z*1xK^utX8w$f4{+Z4x3oQI}+LVuWa<VIyxJokd@X)K~}Fftuzq<0@A@i<d&@+Dl$M
z4;<#1o6CjI5Akv+GXRfJ>_YML68HSdjdIb&p1IW)C0cguIY9v@f0~F?_FSLX2^mmn
zqMPFOR`}a5ryZs)wyN_FJ>5`u_^j-8cHJKlhZJhH#v|vDi3X&q>_ju51><8WhydDR
z2m8It+t@KzRR!emF5q(*BINY@a@@7pZ?F0ZhCwmxPmsLrRuN_=li-*O{>1L=E_!jS
zu}X!yxq0R7qt$c*q^xjEd<&hire0|j-IbcaSC5~L>yMn!u94W*>yW1IRSW$f7}fJk
z^f4OK$y>I?G+>Lf=H`0978TN}S5~yuUWJgKWv!Qoo#!+Jj*K;%US6F(%<^$u9_!*g
zj}I?Dp;e`h-9{GOUy7}8b?ClI7Yv7&omlyZjnFy0^0CU1niN@fFB$eRF@3hiN$w~m
zSo<|%bQCd^`8@DOQ_k+$qYdj5+jtdv;q6};m8VO|-H{pieR_(|n>v=`E@dRB9X**8
z**_n9n)cVA73_3z<^^xV^5>?!&;t20y%2qe=##gF!_Oe9D>Qf3sIO;P)t1PKgL>C)
z!2x)_eqz|%U?<(eu&Bib=FNO>#YNNp{9whc$>!0EnU(&f#%dMutc#obaQD>L&mnEk
zwf-~%6lhxOUeuJ=;=&$NI_;xr(fb?esK_?=s`N&)-)25GR;q8W^!d@HF2$V0?Plb<
z8LO0UM*8@9@@v)Q&qha`O>E||5b+~j%(qMM%Kh^8%L_U30{A)eOv}cEc6rEGv(jRm
zFlJd3rkaPlpN-|R=yWuHbYpr)+dVMeJ0tXn4e5qIt!U&%I`yx0V_bt12NGY}nu1iS
zEV0$$vh$<Y*Lp|DU^FKv+hUg1WN~($sAXQ~OsUn1qy2fo<Tc`6v!c!6@X`}}FL&`%
zb*FhU(V4#q67gud1|oj?I_VnK*xwufzA8e;W@Q)~VR3q81W;0ZWLedJD5?HoM-k7z
zo^fHU%h#KFy7=_{4~IR4!3i{n9sn(GpB=Ny82|Y7AXgcc{IiV?>1J_GU2?_7>kmcd
zM!S2cy-a!|WK6-mC&{|9sriwDYfowR$JRO2rO!=;koIN=`-_Ix&>f!dyKL&zxI-LC
z!>+E3!Jh41k(KJZ+j1uBpalnA8H1(>V!2l5+Yq~+zZ-HJ{m(+I`thcI_uA-H7)4E+
z2g6tlN~z)$`=VAS0c>!YXmN-rIX0MdEw%L{Zx`=LuE5|dnDMjS{SP~HVXSi>C97xG
znHlf9LKV5nf-^ycdq1?6Vzl-w6qA!$UpN(pds^C#1|RWmes<%Fdyn{Bpk8UF#nPcG
zbju2dx7GcE;Nwa{7YAn*%K6fvW7iTLYinDM381R`+99HYdY5m3Yt+g^+<JUVKZ1RJ
za_HFIf1*5r(6;3HESZ}CyZqwsB-S%JV&xY;I(tcAutKkT9<;I;y!EFnj4tsQsUy_n
z${T&GE@g~4)3a<Y*AnFBYH9C}x*%5jt=E<k<*muq$(>1FVNO`hWOhK7D{>x1i{|Z@
z?Q)s&s)SIZCyt2+e1{B+x(iwwoDjeDx|_H#U!?z7UZ)M8azrwmmI<f5jS;g;lBW>Z
z=cb%#ja;V02r4|Yf*}FeA{>TIHT^AOSr~dv+rXSTg!o57GAWVk|K%XiQJ0^y<VKdd
zop?UksP4a@$M}_`2iC^xktEetlB#A!lfdTPo*mRDSR;!|dF)!<6ueU*-aU`uS22*~
znx|%X<3X@}>6&ia^FZS)!^nhkplJV_zD(3=eR|WPDl})!%8~@pR?GCK$2|xhv=aLp
z*El9dl#nIG>hUq&8HE=dN>8j%t1wFCrxx5P_doo?ubJxr8$=Tm=mLWq>01o??pdn0
z9*<lv&-OX`uK8SN#l;0)agNf766Ew^Ej<Gkg0!qW7o75d&PmpKu&)a1mKW(>v2jAD
z7asOfFFTgevy2zyw7LeZ?=oR+d}xD(tYe6`hwl$Qg#uo2a7B|B3hkLeJ2@;z5*R>}
z-Z1b5)a?hQawUy;SmgR8?dt{bijosfPKRa^FCcxjE8YTSP`-17n~@XiZIwVQ7O5+=
zP)Z7ihoN)ryqbH!x;*sU+p8Z0!ximd121Aina}{V`eC-jA`(5EL`<72dv^WujX!#d
zS<9*2GmzxgCp<@L^-_F95J4@Nz2h!-p&YkFZGwz>wiuD_dQN1`akcs4AHaNy^GMg1
z)CO-071)%~-&TVPBQEJwROEs5Hm5A7*3MY2K8@TRVx-0#s$UvIMvrl#dsMn8#ogv{
z_wHYS?WXsu;*nN|C+vR;dlr%C_mC_~ap1sd7sV8tqUf5%Q`!}}DOzYqU;O)NU?;0J
z=+pNHe_2JNSzTA@!eNJY@F0TN)-Ue0;@51NJ?m{SO2$ttoNe_%p}R6Zh#`LE8H}*}
z>_np=ziN!4iH+T}^tT?K{1@u{0oIPHN_9Kn?8VLUG5KY<8u9DCmYidpu^t<%OWBS7
zs2uz|%^8$_O>K<mL$SFD00$4_+3BW-Dok0$Tc_rS_6<pJf<Ysp+9K<qh7Iw94&suj
zqjH}&fc^)`4vQEoKgL<KR!G5G`xEB{k~YU7<jTrgyXM3KShK&QdEjDs!aK92LVhy$
zuS=JWIY}%{AS@94CxRtGenKF?R@EuYGiry0joao*)2?4${ZdN^oD0qZYmQ+HHJygd
zmg4_ly$hbXeTf^d5XeQ<HcKL#)_=uBy7kas1dGU);#fn{v2%&r3Mm6uvkk9Rba);e
zunFgF3N;V9(wAKvHvbb~O%+W=W-mj^kFDfQs+gXxQeW>>heIGQ!shCl^ng>lBj|yC
zYRuOsN+ZK^oo|`!+{VFkt)E;=`5W>-RKrJ1)~OawNv!=9T_Jk~WSz8(4}cbm<08xW
zz<>54%8zFQ(XG_fuRgz#C%1H3j%=w{(e=eMmy2=y`dBSDx@OK9xjB1WheY{prXyz|
zqwgSJIpGEp+7w?ky;B>!y;_WcUKjX#h6{zq*3u68*W_R=KTbaFv1qdS7(lorwYFGk
zOdjI)-_qoVY6rbw-JfImIv(0eKGoS3qD|6jDhl`#QubqexX6G0%<`wR6U^tue3e6{
zHj$_t|JEC8P1-S)3;EHR>@$T^jTT<l%}EtSk>ZoaA`4?+UsqPwbk73dZhiW}`zlo~
zfKCFk1K)qP0mvlssd2A6PK?CXwngL|RPpu<fg`5?bTT1n{Weo>6K!8raX;qE<DA{h
z?Tyqz3!}=5(SKn3*uT+QucZTi<&2~R<JcSsAyZJ33tMoGu_W&vzgb}%`~B&R!Jk)#
z4~1Kt4u6+eq}kxq(oeMyz#F@N0esN~yRlyzJ0$8maSQ$<^Zh2z;>4QbD+OCCx|q41
zLi%b)SlMW@Z{_^guOq+GO$VL;=|$GQ@>5^k!Pj^9$CM`=fia2|D*XwMt_uxThO4#}
zK*QSuh&bnve1qdYhG)}JU`rtDSq9hS*uw9aIu1Rfua<j_4QP3fJ7zauVSlNFSBn3v
zqqvM9JTjJrA>no}cB%c<eIMZXqm1LOL@&)&Cj)dC6G-7ILIJkwYc-FknLQ4p>}$`q
zx`&GS7(1hRe(G(N1>yJ7l|T_<?9!1Ja7C)cz1d19>xjeyPyDS(lqdSU8t_FXAs#TQ
z;zL!EV+PXDN?F~-dz~xbUEwirpaOFa60nT}zR(;FwbTo($$c&<?1}Xo{v2D>s0SUq
zP3(_S;j-?1&ZqpE^5E<`XEzy08NfET`%Z+s_<9hhvJzUbYBPartb@OJS^7MEUwJvU
zwnYmKXVSkkj91M^8oy8rFIqKUwag8-u6)Nf4jNw@$2Tr46wSH#6C+LDHb1Ydi;*V6
z4sS!~LUMMp3d^fJ;h_bme}#@!y;1_;(Q3`61Ahz|j5^f}^Qqea$f$Vxr6L!W+<bbS
ztMcJQTIB5Txz7%gwc3n9J#VVxMP4tpcQfPRC*rzoYoqup&{~hJ+)&%yS_gstVsrZj
z!H9YzU32dWq`1pwqN0%iZ)vZ)o}p9@B%8zC7nMe{OdY0_1-(5y)v@K~F7nC5-RN3>
z@N4f<otTk*?b4(e$hv;BSsBNct<IgN2!>(9K-$GjhN(=g`U8p0Hjjf29RUU!<`vG2
z`+*~19U$*2cNwWMDirPXq>ZsYR<}XgzY@`n8AHct_iHvI6AjVqq@>eO4zIQpz4Q#o
z&PY@V0iRJ%fQ0@AFx)C*c>fBdXvBul*4VFbE&x$+?H8MsvdPje^A|=lv*&%!_?aFm
z=eV)V6Mi2n^+fFMU;at%AFLj;S)pRfy5`R31FVt(Rw>CaUn#5O0IR<Oth#H>m*2DZ
zbW8uS!XHx`6K+}_6JbA+d2a3P-#`Lo%2>^Jwpb=R3J8ky)7iU8RQbk~oF$8al=C%n
zI@+Z}8CF>5g^j+2?S;>$nhG&eAo-vkXrnzutzyk&^g@yozN0C>Gyc4C`^KCkT+z@g
zWhYOgaH3erRHgi$D=UGY+9s}c=Djd%9&2tMe1ZXhFLTg}ht?8V4C8EIzMVj1@+P)n
z09W$`a8S2I6Ub?uYvz^$M@;Mi*$OULxU0usMs_w)<7tJ{A;`qTP{s|;kV~i5m6Dh*
zz*)H2%<aD8o=U}*!QtDJ%t<t*tC8NBe^VoclE*hKu=&S0^s}Y@=c4CoHMe5QacXJ#
z*;dbQvYVe|tc5*)Q2GJLd%hMp{V^oGakeMJZlP?tTHUfyVDTa+nleudVh3@1wc$<W
z?@q42$c)dAXarJz)x2o908y3hIAMThcID+zmhNe-Um7Vpk);xY4-cJErshRMR?n77
za;8l^9WY@I%NMm*{%{L4MuYbkzm<6A3x~r2cK|BJw^z%6LW}?&yhuzyit+(}-ZV>f
zPQx4a^h~Lm%-LS`_1$hd%7cVjoR;@!i74&Xg*Q{p?x~gu8rIMB+<WWks<QvR!2yx#
z_DJVQ*!0ljZiG=Wr0N-A?S+VH3UTPJt}Af5I3!#h4%;BU0N#Mk&-N);K79Rv!(0Je
z`!(%GdRf7z!6)G6#*qH2bINU=BYvvZUzVDK;phwDl5!ZqX~HdWw#I+rw6rdqm_@9#
zul}PtD?IG0_MLpwHQ}}_RGrcWVWF6+ojU{88Z&V`QB_Skn50{2ln~k<PW)x&_Gew_
ziZUz2AIa2=4>()(RLAtMQ^f9Idik^%f>HW!-JSVcQ`gqVQG21@D!kgkqD87C;1SUZ
z0y0SI^%i>q0Sy8oqaq+fh=7nX1zUTa5ji;!AwaYwAR!o;4O8p@m<U7#!Vn-<6CzQF
zCO`;D$a`r2g7<mepKg9UdCv3feb%%0T6?X}ckN?y3xM&;(@NLOUrMk0C$I;5GTqkW
zdhX{V5q+p`h_i251pj*&y}Ij9f4JjHfaK2}pm-IjYW9P3yS*^qTQak~^9d`$K62iD
z*34hKjeRL{4qdDb_Ej{u)gq2}HT+5BWpzK=UpnID9B|;N#C8oP9^fkjD|pGLHkKSV
z!m0de1Ej|an9+7s9hMd4QVdj9`eN+SIV_>9Du6PvgITG)2>V<!2ep^A6o5)G&@UK&
z?|<Y~ky=?|#|Uf4;=n$0T}9GM;EMag+{YW(3nQRckt1fZ6Pj?5t$TJ4sKopEsPa+P
z*vvz`sNHpEi#r|oJm$@)wzweSy(9R&fH4T#XQ$Pn=%8hH+x-FG)Ir#X?+>bO05)Cw
ze7Gl;4S-U6W7PW8J^xNOgJNaXCX3l>&-vPgzjcHEUCE~Z=h-0nZ&W)ar;fQynpa55
zrMV7-=rD_cgl)>_)qUTjxV!UjQ1qeDQ~UJ1luHe*AVOM2a+0D+5LJ|HxX_e4bs=z2
zzWGKXz^@+6AGrj2?BO>9_1sBVyw)Uo%w-m-jn(PGAP<dJ><sS3s`^n9oJximGj;B^
zcyubeX{>d0O6OK<pfcGlDK-8jZ7FRe$bV5QUQUmMr4~AP(ml#!(B+!C>PD$2bqVwV
zu5~hUPN!;s7AC>MH<g1LL1!+&V#mb2J}Lo<N0pW%*~bPV%VF)sO@xvRq$QY$@HD~k
zoZ<CLlq;yZjS~83Z)f{KjYx(KIy(695Z^4ZxD#BqBmKDY#g4;W&V0#yab%H{1+>Hj
z89=2R1h*eRc)iC55iaFo{>0aOF-0GT-CW4$hC}PJo*GM2_q0^qbPvO(Ucrt<vi2Ci
zgMJTKCfUbCPGm&?ShDaQ)jMBoOe)=2a+=EM7(w&tJ@RP8jIl#opgo%HttU0DOn6h$
zQ3lE!nelH}tqk@lA-dP}EY{o9&bp-w2s3hO!+1~jt>Z!)`vuRaZ^tkk_)zS(A#Ig4
zY1i@{Vw}4o@1%x~H=tz+H$%w%WuLbWD75cy1SEfAa`O*Y*yQ0!k#OImva|ls4N8tP
z|0xBjb6FNSdf=yJll!lks*J+UQWVvES9@ekX7DG3HElC<07nL@uE)DD)^(Lgo2FFO
zzKuJOF+cN9|47+?<)o4v;sI%R1bjxYp!^BX<?g^+JMApW8ng`0AYeDEYr!c<x;_Ml
zJ+aa0C(S4-ZhpMcgKuLBbE1zzcDCB{kbM?`oCn#V$Hwdf;)Y^)2Pf~pm`do=1d(~q
zD^Fzz+jxPkDHMT{9ul2reH@S|O>^zN4^@E%wJlJ&R|!kgJ5}xY<o{Xv6V7%2#oNlG
zp)FEbt6#YVM$jNB*PXAyPfv$oUUjY~(0u~MlnF0cp2iC7qDWO!Z4zG<6#>CmPH!qB
z-^~>|Li%}lG}np$3G`LNVJoEg1VS7}pILS+#KV>tFMN5F@orE@DU*ojNZGnH4Q^5X
zI6|j6J8K;9Hijw_PV4NKyH#<JJ_RB!C?+No)w}%q6<r%k<9;aZ%?KK(jf-8#Ie7Z&
zsv0k9r#J4st?Y<O&?c`&jkz>HNqUiO`k{S^l-;yL=Hac$lOHLs0bpa1^i8KL5ITaR
zl(mDQ`#|vHAyBYzV{kof2&1TM%cljAjgrK7YM@9(L;uk0Rt0vdZNXc<D(zPBlJ!j9
zEn7_Hb^^#<KOCFh74@z7^8RaSH3?|G>zfY?FlsDT)QcpLHOYAS12*n9hVbpS3a=>L
z3_;CwUX=TQx2B>D>k`@Rsvn4-h$HKd?=jx<4>u#={V~Ia9G`sEpl3_t`3Q7kqzyeF
z%?g><ojWO;J9-fcSVm6!V2*VO{Pc4Ab_C^8mJSzY2HSD_tFnWH9{?M?S+j<P;;=m<
zfBs1MsP{k(mEpNN22Pxu?Kn+oW67WQ26)GdYSb&Vp^y>A0F%|Weo6*^NxX0~h+QzL
zOB~7s1^1<PqNLwvUB*^g;F>dvIiU=wd|Hc?g<a|mpAVlT?~FM>;$@jnp==FE;JpGk
z3;flie!7lR_2^n-fTZU5Dt2R=Q1%fKU*&N!#Q0;bU<1~rZsJ{>o2)_nDdipAaXb}}
z{l1LUa#8$tr<|WOr;B)zcN}HEJ}SoV&Ewc6k=@XLHo%U?*>F)&{7yi#ggu1uxSNk0
z4JY@wjd=e=nusplaH|(Dx-~ILLGqUmsKeVns(|UfXBXR#zqqBB*1PjYdX9*OLnh~D
zauv0Sr{pgXP{vhedi_eZ@7O~a>O=5@OGr7ap^i7oc#waFlm+QfZeQ2->VE`4MUkUD
zIFF>si=g=l=xn0k?Q!P;`si6*UKl`%hpHJ9C(nlx#ky)eFQ*<q_c~WGjtA7;xRx=|
zqU&`T8j$~SZ!G7tEUV#tA14P@>JJpmHVJE41t&z7=?NQ<uXC>Z(fX;@Ad#`$E5^mg
zWtPo07H(>xCsn*O)CnZy+gWM#q7)&vlUn8b>BB+-=vCgl{(1_OT2MFSbZ$kgm|siF
zpHFoERs6?(oN4dt<f7SkpixZ%_=&q?g#A70yAS+r%Zpd8mCc5Dl>fHTMN)*ry$xeU
z9~IdaTZ}Ton!_c6ahg7GGBJEihO*ND?M{iGlD1hDe7ZZ&c-W=lN_uxO%9;)H7EEJ`
zkw?GTrZ+bm7V14+X?cg8*qk86l-u2R-m@jr`ts?Pk!+BupG}0#E|wgZU;d}b<1@O9
z7_Q{AjkK}QkQL5;F}@j=%D@%2M(+bA)1<F1D~$O96(u0~O>^NPgxGuL(*8?bbX&y2
zm!2NAq_#++8{bGlkO6fxLPdKUsu_JV+(M*yNtS7MMc!iHk)_3x1XPs$+=;Z#UWTt*
zePJG$8Y9!%zrbwD#X*=lPszD?L%{B*7Ct3$?ett=|AUsaFapsyg7;2$Jq^_I`PS8c
zkWNyNW!5yS7}}5?fia1S{T`t-FP-=_N>}i3B^~q{4kc#^HZocY#;S^F(VY)9dK-Ym
zQBv94zH{EM{&h`^5U1jswjO#rez0m=W>SonXa-MGp!Xpu^ffZh_m<qYlD}Z5V;N43
z<h{AA4LLWum_gx%QW{N<92$7R6&CIph({w{NNcEGAV+vcwc8H}6E+(noCOvyrWZBL
zvDe{NHSPlHK8jaMW5X=Ujnk0xdaG<rRBRG$)%BJH<Gu_#bjAvS60!km|4K9#|N0W>
zRqPlibH#hphEtM=lSP?9k}}8Nk|kFO;wL`>RC7Bt_^`dl-TKK(#w*Ww)tzMNMRr64
zIm5-;nfuBK!RyU;>16QF&to63_7tdO-j0T}cmVx{#oTZvUXcHmrS1qa%N;6dZ1#J{
zvY8QD9&2&WN;kl3b<8GLV35Pkxz-pnDLgytRhRSUF}Z0dEk0W(!D@eF0K+41)yu^S
zBGj8v+|t>luXh%#d)#;X<v&bJSCg$*Y(y!s&}*pUAfH%`!fbz$&0abwK!2cH9}8aC
ztU?@a+wj>`SJ0fbCyz7j9(Q2ka>$5u|K#`Set))+nt68{JkR|x8@O(zVuPQ{@c#a^
z8Pip2#pMWlkZ3-|a<8vl@t?CimeF{SL#MU)=~&*lKK}$=oOy`k>tP|O(pUHmKib1b
z91$sbB;RP_3nN(V#ltMu%&qdvepK5`Kf%Y1VkRn9LK)taZ}Gi~3%6GiMMt&xplY*&
zJNX^~b7fP-ZT#0&l)mt1L;I~5$w{^A%b8uwsyImgVh2!p{|Y!a@JqaPZ%ZqWbiVN3
z2@0I<UDv=W{mObF;d7Ha|6Gr>K6gU+=fmXgJeOng^i}G6Be=cDap&A#M1c6=I9O57
zUzS&)%#B=)J{an7?YGUWwc-1*xdZvzCdf3!ym5K5yNjMuFyqbYeHoPNQ<r`shC5SO
zSXnCK&bNc+N(XwOv=DGDb>Ah+i}As#ke&Dj6cU&(;hk;nPq+4PtcWM(ogB`5Qc&`~
zrTu~XDcwX(^I8+{uMtizY5G4!vKx-$xNOrgnqwT&u*-dm%*TwK77m$J`L4MQdx(v>
zr8IIvFw=pA?c8?N@apBxfPnJX>MUnuTkCr$tk)qo3DmFN5ipyd>N_GX8ZLb7+BXht
zTjm~((Y`&B%yAfWr;;l*>QYpzpLVmfFCu#}X7-%?cc=CYG`FEOM{^+0iFcTOWqgV}
z$Wlj0&L-5osY>{lem>qA8RZUSl=EpElt0JQXgJ_x7mvXVD6EDG`d$_iN0<`_6*ir6
zgg{?8K$YhDE9J!~-ZyvOJqIeU#|<-!WI7~BJH#itG)<l^n8{MI_h69ZM;%xZnYmK4
zqjk6nr2B+6x|eDjEj?RjUiVByh^K~hTy)HVrAI7Ibo%ruLY9s&PL*P^o(^yO+;J2v
zk}3V_J~#Ph?W$!v>p-#tzuKPX;lYp0Ke>uBxm`NiWd%!HI%T<*IqS;j1&Bj5%NAP0
z(sl8+eBEV%ibnZLR6QX=<%u6JjyKuYlIXlph_(Q<!qNi#G;z|G*4w1u*gxg?Tk^$1
zrDnVcem?!WZ_8v8GFV-Md+Xcq25m~)5s2#iu;0ZHAER}sapk-h!TbJ^JT6zpX~-4u
zuk>r<qhy-bsSvN^e~Oi?_P}C)+9`XJsE-gP@^LZDHA7@9TtJ=P#Z_sLZ8$7wAJtma
zg36;nQ8|a9(Ae4XHx_=}Lh+Uc_kuYLV<o?8k>UINe1cND52Zcd!-JOx!LG?rubq60
zfU5tTwEWQOAnQ3p7bX=<;RIK^jU1I;4cIgVpJ_V8=Yw9bbj(?isJruEDJe+)VX_3e
zkQo+Koj3|X-)09xTLMMdFFqD03^IgF8Jj$l@;y{gjT#TWr4hl}=Ezd1{l+8?p&G4~
ztkPS9L_H`-3`*~MW1viU@*ANts^i)Y$^8$)Kt;B<i#1=|vnZag7pSbN9~$<a!lv@3
zRPmOciq)XYR`Ch2o-Tur=eFAy$kr4L4(1_EFtOp=@=HDM^<=OLCXsU)%c_V+FU=?5
zR9#qZL7X0?#06}Aa%ns#9SRPvkTm+|(z@fjqwN<aGQZWWRN*t8ebs!WdTKwSYB6qo
zcG>n;JbvHyUR&;&tAtKxbVy^NyZrbtSfui=J5F|@cu|u>qeJZrwgeU%8UDGy_sm%p
zsV-F=UeaR6$Z89skCwiDx+8R8{OMdRxyD~xG}1$Aeq=Zu?Z&>edx2Nut&QI<j;9CP
zCx-#Lj;*tZ&p3l<5cU*10HCnwC`OLXJR>;QWPOv{Z_T`k)eE=A2z@M5GNwNkkI|$s
z%2=(@lL4Et9%RjYBQp)$Ld3@g3(#Wv)@TpP`FPWUKfG+vI~HVX(s7DAwzA|!SAP|A
zYYd(4*!e_t^=~6_>@u<WsTMd@+^S)+@dMmA=G2>a*67S1mg$w&Sj+vLQ-Z|T%kdGV
zQ)C(<6yh;%{vc@sL#~?z7E1<OKQrmug{+^GNh2sXdzp6(2d7$sGyMv1DiSd0bTiS7
zk70)O<n_YO?-{3Rvq=z<(Wlg1oND25Q=FUM;Yu{B$xgY(PbIWeI?gDo`ev75cvH>@
z1N922W5sKpgIySJ17)5A%;u7@zg-{--i&IPsGpK69DgF+M#2-68zyG&7#*kD0`#6G
zLiPc-_-O&jyBAq~qM;3Mam!&@(O7+Ch2sknI@TJeDnpI<s|rf`e_rxlWcGFEoD&$e
zp#hua*#WPXw6GgPSt_*!T*KeB5_Nd*qnHEedn9jDx(6<qN8l(76NOU0fP^Dd&qj?J
zgMSeAmzLm8sRK|cp$2U3pC>(K`w8E_1X}-srV#<s6SnQ%f{N78*s)OjnG3ODf39BR
zV$yfDF2t%^*8m`8W2@p})Y6gaiN95I&fCkNj)`*VW8|+dIuD|~B>B(VS6_L2GX%@k
z(vEjvW*)-Zqz=^+dp55=|L^!M@n(Nrv)X~+;$0(0yy^b`VOC~q8rS#6_5Zr-<?2h|
NcYgnV@UN(!{uduLzvln|

literal 26396
zcmeFZcT|(vzc-3q$H6krSST_MO{IxQZ<Y~_G?gwTAW{QC!4Mz>P)8W0X+U~FMMM%v
z=p6#0VCV`VkdOqWL_-P1B#;ntpTyreXWl=~d)K<_zU!Xz-uGDx6ZW&8@BZ#@`FzWM
z<k3}|OVU5d{3InMB@Md#=XEKmZ4pvZTW;>$4vbJUUMvD1+izN4`V;sBuC)tIvB1Z!
z+m~I!q@?5%#6O!-r<Z)Bq<)tI{dvwlB5#fsS$)gToh^dH{cPgSJ~?v07xXjv&F|}H
zeb1e{y6<Akm(cUq_WyeOZ2d%B$MpT<e?NBHfjbRu`MvU%{iaRVtj_#yC*||=gR_SZ
z7+nx;jFQwYhGF^*X@WeXrh(ZS$Fi6}w$Mc23JZrB6|4+b?UwNMfByMjBY}xU;f<Qu
zcJFM0#~^ds)$v<3Yb>}~B-NG@-irZ5AE!}?H{zb+%U3;Td`{AhM>`HcCxttt!mC@b
zni7`cIus|iiN9qX-=rn}Z2NOp#3u0<m2T@1DX9rd<cfq+@-K^<rHntevh+waEY(pT
zk29ujWU~rMO<1^NNEzJ>v5{?^&rD*u`Y{`=VGjZag4SbkbhnnfdK>o?BTN%0cC-q9
z1q(_dk_Zk1CHW3W=(z%8hrPIk>+UU4wj35K6|<81Jb%`ShimoXu6D=s_#Gs3dpy>|
z9=W`;UpvE;G>>JMv=)7CtS^sp;HZ+!^%e#Kt7>N{KJ^E29~Y0tI&er;<{?+zi;Zd;
zOFuUvAsv2Gzma6ITBXnF^VQD^0>}oy&dP_k$evHwlCV_2vVGuAF3UalFt1SB>x;#@
zdrfR|SC5~-HebH#tJ@(bM#P6K@HKRkO|n*L3d1vb2ihH7*I&PqddS|1I%U{-7aiZ`
z?~;t4@0N)TBi`v*hxNq9CkByf1eM#68{wdW1F|vaDn@2U!9tc1e)_f>oVW&UYS)hO
zIwukZ?%c-|UQOV6DELPRRiWjt`&vbagU#_ml2%z5j;ZyP$Cn;yBo%y{Ulkky*v=~N
zy1RO>&tmgm-WYSU`%7Rt|HYuW`d2|6X_vGPOg|LoI?yS2%(9c6TcPNMr#@^UvE^nE
zi)-;ms;Tn_tHMHFb?!a$mt%tL;!&IDjnBvnzO#lE)5jQvnR&C9TVmP`3u^AQB4qB5
z*YHKDx+&(UINn)Kd;9=7YLDuK``v!deGb8SkZnEtotiT6-BBd`8Q{ul+zEE;b9>aL
zxAgez>`PH8Vc5xxJilvgg82C9qk2*KqQF)D8baTNQKwZ>7QgsyO|f||qr5+|^Dc=s
zlv~PPk5`sGI1l!%x<_{eH|3Tv-(?JNH;6^}^$#>id!$y#>b9=abvFiiXFE_^5{y2H
znMxTE6@9ST8&Nl@(?SAtdV8zh>5eT@p2jB8?OZ=c{lJ|LJiXqxre*vPX)jWwteohQ
z%*ZQ~>&&|m;1GiQh1HsC#k!@gk%fT8!biqLND^FQl*c2vkSe6X350--8)!)6Mc5}5
zh_Rb3bZ4ag<Q!{9r<RYkD{>w>yi3{~A9ip~w88qOSg>*xALx92;wl2$gd5`RI206X
z$9bVF+`xK;e+$MyVAFyY{O867@5C*iT+fs;o82`T_qD}*+u`m2;`7`g@C2Jp&UQ#w
z4UL4+SbnnShilwgjyb!WshYb;vX<HWq(!=FTS3Fr>K0P+p*+aPpXm=$W3$kZkdB;&
zy<q0*7HR#*eD2s1LiTN%kIoHz%kx@nf9DPCfTgP-Q`A)*Ve!@OV4j~98GL);K`Oz&
z-i6+NmOUeVY%+@d?Q|}>q5-f3i&?`LpF86cJ=jS7t3A&7)z9O7Acqy~kax}zLfi+L
zu2@9%k`KtE!E=50$T#DepXptIjrR3V&~ShyfG2^Sw7t{#=})@$r&aR`&-E>&Dp$Gd
z>jTO{fYd-`9PfU7_T2<NPZ_z#9)6>6U`wW4Y}d9L;a8Md&&VGwocm_7YZT#Ae)cw|
z+2CT$_nMe+gsb_KyRVMKDr8HTs~HJRvweB;RO`&)%CGSrjQWXHp$7ODADw=;>*SZ<
z$HVoT*xn;0cd9nA!=qo<P0H*5m`d54=*t$`!@BpU2n%+1nhlos&;Ox%W~&v1Be)3}
zNO3*57}IlqMZM|BXv1#4n;)oPhsM{H{B2!?p!TI5^TtW!GZ}DoLtf?81z$^k@{z@n
z5s#)>Qdsnb)w<)Q)czV2vjut1no&2g%2YSlAS#aBUB4eD_hzB|6(egCJ4(Nv8r0V0
zUhu7~wR2OPqNlWnlxJ~-@cz7QnHhE8t#y}<q<+MiXjrVGD8sPT2-)7MZ(rc{iHBl6
z+|89Imyb;OHix%!Mb4j?yn{iacPO)BT*dtAK`pf<C(BOEvE$LA`^Y+QG5e{(Yp>+t
zn&u+@S5)hv>UC3@AxHP7*#;;lC%(pSZw<jqgYKixq+B6XdI|>0D(2rE#6J-1m`t}L
z6<KnF!^>tnVDJC-4rcmAIY>bA)G-Ei`ABA>@5c=<PK{-!DH0?~9#^05%J@#;$aHDt
zv^S|=j%h=MMbqw^Au_+a%*J2tp(pgnZf^HUy@&)iOu^(!AR2~jJN-e?LrcC^)!30-
zSJ*txY2alSQ^xlc{u4+8$L1%<Aquv2wIClbDXvKCj_c|JxQ&Wq>x6o(miR+u4v6Rc
z_sWy0h7Wjt<VcO&?Di(~zX@}q4kOiOvvhxiF!u393iha*(DK4)=(0?g`i+&Uc-b9g
z%(u#d3=Q_}H$M1a5elgQE=N|0Qe3%abHUEC5o6v@FY4^<DxbqL6h_aAlI3*b%NoP?
zl7sga=2swJ@YA*lT4$dyA;R-74-RZL^AG9J@-(}Kb~0@__V;>xJa|vuV5UchAR`I(
z#fV9HRJ)?nG6cA^XG+{5snHXfkX6juOZ;X$Q<IcP9d)PadyfST^ySU&w&ZFEDBMGb
zf*2=6DaHY$ZR!<tD_X@|HH4?{dej%1d9kWgVeA>6Gt7eSPC+c8DtL}sfzEHe%9KIj
z7*334&wcWI<%nNYvHPS{TONAZ9LNP74(X21eH|g`$d0tplA0X+6e{O+EK|(CW4}Ne
z7Es8Z40nU!@Q2BX(NXW2=5htDmFy_ludy8!KaIte)dRb3!7^+3v9lsq&I{dIFrjg+
z6v^0z6xB}?eqAHR7s<b#sfTeHw+?s*^ZcSzB`Z6K3inERYJ}ios=ZXkU^3?)D&S(8
zqPmQb1kMwtf=lQe8o(0yj5!2jJD>e4U>pMd-csYdu1cx(%Nn}}*^T!$;4;`J)`wRp
z?gLa`rfbg@j9(4qNZJkz7+lWY;Tvlf*uLQdQD>Ls*WVIp+54U8ji*tcky+Z7{Gd#%
z))+hpHlxycgX9-wj2ENn#Bt7V((qvLY^Rx)^W^GYbp!6%9s(U1-SNvV2#3m`5Y)kU
z(`qj2-@+XG?rhJfIrQBS3M#k=$=P7upDbv7RqN37{5^gp$C6Kr01-I8b|Ku?H*zb+
zAn=wbo4}o<gDw4jwUj43)@~NxtwWt&!$p=)>oRViBXFQM!<SJpJ<$3H19Hd9+$>=q
z%8XI{s<j4M>1e#7gEWxDsa_!4di`}$qjl5JE=Z889+h1O2q-9#a9#L41CMSxpb@2T
zbbKZx?rtdOFGV%K3;7kd7@`~HNLx;|#v7lXb6dj6D-YvyAD7kqdY+VOPZ(mIr`tMc
zxp)hRB(T=k6|BbI&$fVipILHgF~rsiq2T^J&wpMQYU{sqQXirk^SVn6MX;R_o8r)4
zyrgwT^`g>V<Z6y?;B;$E{fGwU&Zc!<juY+p<KU)azemrZVhDtLGVmN`b@*}QToA(o
z+6%kV;WT0HAbb~5r15=SSrGG_Z!SH%baZ_TO~1WTh6s-IaiVlX;EHDMNcRqz63ALf
zyez(~`{nN&zPoF+1li=}nrsna8x?<DIA%1N@=bboEjxJUi@i8e3*M-hPmYJcfwWGi
zg$0oVw3-#iV0Rbo&A*1U<UEtE>fY0DHa@h-VJJYBoU++KNJ`NBRyEdTm(iSpZvOLv
zG{i@f#pA~BW1e(h%U7W8i-`@pa8@sN-4}Wqu9RQ%YNi9JUNwfF3gOf=eT=ffYkHjC
z&?;MLtm|%#-xsB5o=F&Qi|BI5)cQ&wd|tWxWo~p5+vIcz7e=qh^sn#P4rW^@Bxd-(
znP0OC;ppxGIF#GAXZy}e%k|~&bw;?3S_+@#LV|ZD9~}F(iJc$6FF0v1plLJRPS!UV
z`nneWefl$L-8;KOI--t6p;?*wIb>=W4h-N|D0SUhnk4}n75>{7UgTG{FU=?rYHu+{
zH?dn;9YCgpcU^}MSH{7O&4yf&$y=l@wf!P3)%Njb>;;3nYxjjtXSS9MnBEU-uJvje
z(mL9H7#sea-_L?Oxqnb`v!~U5_G{j+0C5h1eQc~Y?u!D_zjXvB*^(>r?LbLW=UrqC
zX6#7j(A&+I^v9Ee3-hVme$UqCs0o#n*o!bihL(B7hhNQxygdNaIkvSKSa_mH8f+^{
zg{3rA^Xc)6AuZla<V$`ntDnT&TiA-8S&o0gE@c($DHN?qgF8H`<5`Xo5ZELOJu3%~
z>6qoe%WyiAlI+oxyVRM1Kes*{kLQFG@6{cy=?$q~l0D>c9Zg%b%;cC9MO~#TXtv@$
zzYfeS4I9_NBJ|gVck9EK{i4*wh}*9%E@nJuUPyyqY+%MF=i<+@zsJMmZK11CY*p-T
znau<z2YiWHBQdPSOOKl@d!Op6-`m@Au7iv=r|d&DH++T+g#-)n9NUjn84q5K%t&Xm
zdq-a*bZjvO{ApizGxnHNly?WB60`XILKS7f9%*~w??_raK88>k70!VNn<&ZkgnKl}
za`dHN%)FFM6tY>n<;Rk^eNpExjMjbd4Y?nFt)pfUM&dcZvHd|=ULr0_Sz7<I@nLn`
z&Cnh-S=>D~W`S`kk(16cB_(3bq2sct!2qnO0@D7UTx~Yhek7oLyFF}h>WlLRx{<Tq
zEKYh5PVPURO1RV}slZQN+wAc_q4VGGmM5H2#oFv|3p)?HUW0FJ=AVawV4e?QlG63|
z-=?H8Y?p&*WZxIfs64RnI!BsB|7}>@Mv-dkMPl|=n+&^uU%kzV>unTwQlv&55g7A;
z$T$H|(N>vf%JbHHXC!6us2<^CW_<M_$J96d7@LJ2zv+eSVDqY|h>khh+6JLU!(of~
z`MFRs+p=S<OMl!dScmJFYIZAc=u+u^@vS&r6P((GF^2KmiWmsAR8{V?vv^plbW)+|
zbb1SYe+uWfFmE*CFK-F&<tYO&pX0`zeINIv)WrT(A)XqyOIGprt%kMV!>b_dqk$Vt
z7;%4!ggvXgIQVQ!I1V|oKLys)z@1IGtuB5-YGmmG@>^p=m;8)Dmt#nd>};1si5RJ*
z;vU+((Ol^E<TkUgdWM~S*w_V2GQ%|Bos4+-xLsnghCecPiiLlu6}`!K7us>2vR<-2
zv9eghEF)4dhFt=Uy37c{d9=c&H;XBrVtbH$+zog0ZXeBb+B>@NR<e&bSSP}DWJTY$
zCIOc52G<}|mI%uDnX-)I<Y+t$#=eSnpuKAJ>WFH|v{%4rzM)?ecFYY3)k=O9Qys0?
z+8p$bY`+)7O*u6}Bpo5`by7I&Wdb-llVWGuY=k5|465SU+lH)K%tS$5gjYL+&f$qY
zSU-ml7uYeWa#s_AqN{O4S(gHIRt+HN_!%NA5-Go4Hh?@}f7~5@whSB5eCq*wuhi_Q
zHR0IQqR@@Q4`l50Mj~Kc@yf>mVH8q94&;V@SffbDCa`YQzTXXAeQUO{luDkf326g4
z@EXZ2Mw3Z8wU|=*$t?=2XSE`UmVuLElKU1b_p`D2MxUI)WBV38^gaP?yS6>>K8Q!v
z4wH#ZSWR!&W<CjTc6brmJs%P)fWn)o!RIa-_cSo`5vL}$c`9wDe`^SK!lBnY9S%z5
zT$&LFkMeMpH-<EUj*3Mzck@5>^SDQq+u0*c3m+zTO-uB8rL9SxPJX6;QKE&z$z#vO
z6r>LLY-e+$F2UaKPq{O5_@QKMNOgi1#eUPDUqh&ET;174El^ev--uKHST4U2gF|*W
z{3cPc9CB;3E89A3-W69Wajl6riol~G*^pRaH%3A$#b`6Vj8_mgZ;Zq26pu`Nom7Fi
zvUN!CK1yctDf%wOq#v}lZEdDbK2%H*S0R#-1HO+Wt{L}8+<5{nt8^f<1}@K+K=ct`
z2bBM_*435&5%vE2l3-;y9BNa_yH0y?C+67g!m$E`#bE%@9fNyg)okAvk!CTLj_y09
zexLC1-gwSuE2c(rKTK|tYCE}pjpL5hytysk!}KocqLkET551WIEY?9_ad<WZa3<hW
zOlLmIp{{vaqp8~gR|y#Sc(EC$M6-V4eohNi%9A7Zz#iXJZIdGYH`b71GEh;g@t!1@
zys8>7;}c$S0rSUonQts%HMdA*RppL6lIY0*1LT%s{iytJqlW*FHp1=?KM%W7<45}8
zn8vAba0J?shUaIq?vZrK2EJg<1nU9mJ;@K-gUNJ&kLRBj3qJ0IfiG#>+#jiL@|!!k
z2~M`?npCWZ6K&>+H`kGuXSz|!zsGv4QSyseNm)PZMAUWJR)^QSa*RnxzxiU`^yh^a
zN9~2nmw!al11f-17u(zBA~srCUL;!wc78Cz5y;~%`3``cXr*2a@ko+2#%UmVX%uMr
ziHXL!TeUTDTFcZJ%sPAhke$)7bw*WDcCbLs%LeZS%DxQ`ZA5dOS7dx_wk)qzVk}KL
zKqNy_-2^X48x71%z22!ZBLtV(Ap^yPzv*G7nBH|0Q}#R*npRT=?{^b4>v9RxPne?S
zTcv9oOm&k$ND{1UwP>?c5WZ!;RkY#ZAwT+VscD|^ZiftAQ7YxA0AmVxL~fa`;hyj!
z8aH*!p}yTs5C)keOg&+OollRfSCGx;9_z7}s=Bg76$ak#Dx~6SXfLBKz>*yfRrd8`
zp|sBPy{b;Bqm<C_JWK2j{qY@}<Gu-=vUpVFk%17$R1etx@&j|VdqpSRZlF&=*x!2m
z(&S`vaIoGRT~KCeqEH&|4Mdpv;0SrAq*Di_%E!sNyWzYAntWOWzW&Q&nbvQ2`&fmt
zUze>N5Zt+df&9YQFGKgX>@d1L(N;l1A6VQhr?GHdzN%-#)v+VCV_WkRpd4t%evZXq
zY_MiAA2zV^%h6@tBSu{LOCFSXVdtE7e6hW`drR!AC8;>rI0pxxuSiACVR}Xn@(3bf
z)7O`YdNg+SK(w!Z>r!&v^OxvpA1#E7ZS<D|PVU>KTqi!J#@vMy1T-i2&I|<8m@$?r
zT1~|_{ycguyvsg?6gBB4a2*+qt-FxVfD@g0`4G{Nm(y0Us^oqXhX<`03IUAP<*m<<
z6b2}<rvxh{Wi71>HGaaq{GN)RBr{)w0BiAZMf#7Jl|(@}bk=<qn&cqsJ8AaPtBjiF
zD~N6VJo9@U?x>5<5B6-#;E)5JlEIRgx_pleu*hSUhcrCQ?C=|gKU6eL+Ja5QD!j?J
zlPBNS|8TLj>GNwpF+**dzCg4yAT;`Lv>-v|RbJsxQW*A<#u2~R4x?r*g$6V6EYJJB
z6Erh6rnMC~M6lp|V;z9V@upajqz4#ddsU|p{6=dOsKW{@o-4J9{gc}ng!;;h46Ton
z0ndL0t&;yA2d)1VXlVo>+NoW<lB0A3!oK=%a|3pl4kwZXYpi^+PrKU-?{YRA4uz&i
z_`^gFhbsEaoJ=uMWXx{EE9;(|=i-sptHPVG4b1LMKtI8hl!gg&bpN0y@aH^X4PiC^
zDpQ^CQEaVhq<-+G-=11>P6lJSWsQIT7GcdX#%g{!zSKj~b##CCQ*#6VnZuzw>8lE)
zWXbPUQB#;iagSowG1VEiUu}7vbrGw1UqUaYnqSztYdPj6KZRAMnDm}CkB$12mOq?t
zig0xA$v`Y6g2y3X0fmBo!F3#S;{2s*^5TH4$sJNnP(vnReY)k#Z}{Wn!7Q$W>5O*j
zHyN@4Kh|k6hur*{z{e6!lJ`lqS<GV_DJ0y;<WXL|1G!RT<GZb>S{%}+8A(m?ahb{W
zue>_H9A3~7th8O8tk{wl)^FJK#{_axmQm08CFMq4BdN@2I^E6@aBpK#G~*59Bv?oS
z(`xX@d56r3X}W;U!!>S|iUS!f)K3~Q^Jo=UIubo@eOiD5iJZjb|BIzzZz?v^nf7%@
z?f|6%aE)j?{w|1PS(6?Dk&ApdKoUiy4jx8y&evdy9Sh!g*wdi9`z0~x?BqkdJ~pO?
z|8~4K1WUh)ERBkQQ7$tjY0dMc&2*kaDi-HRD~lqHw+>+#xD70SmbFJQ|3_X*mNE8k
zDdkgHxiT4$h(9lsBL-Wr<i1+dO8(=g1AE2XltY51tthEZwk0@s+Uux=@m8#pZJ=}O
zXCD$8gLHKUaxws?uK5{fT&aJ_XR;7jtfTqW7C2O7iY4I9T!hgubnu&9;#r={wFr&z
z0mlIhl(O%8e^VeQ7uch@J-n0~<R@;1N=eyO(l)Ux+zq{XwwZEp0ZW&(fecRjw|4<t
zF|xst4w8uns@*rSlVyFGF*vV2nsNU;0triHt+Gm4sZ1`~!TqW`9MLU->a3KqpCfEb
zk2<&1MA>%m8@u|rK(Bn7wVfo7adH%3>m1D8&>m=W<8a2@k2-fINq%vm1nOW~<yE_|
zBIEn{==XS5E|7{6?2-QM25kI-F>gIaku2HNsmQLRGyNsfRWJqWDK$7<8E{ndQnT_$
z+6;4Y>&$)!@u7~??O$coCWOu%(!(<?C0+p!og6Zcc4qBJsRoKRaoINJyor5IAnQA8
zR;Y!(hp1QY6OT)kw*lkkP6UsZmmRrfO&=01<RtQ*cyrpZ4Iy~iDKP@i2V7jEWWLm%
z0cV{Ep2J>rjTR=qraeZ~(l}Ep{LZR;DhWPP2CU@Y7Gr`r)GDsG{zo>2l;$_D?}S$<
z`H=(_(w!~OjQin)`ng|u%MD!QC5)tK_jEc8+0I5YDQx<m1W^{G=^8b&@kYnbOjFV&
zj4i%aPQ1Ej@eyE>#tfx^@h2gXN%2du?@6C8X)OfAexs9JO4o)E3LD9+E0TgPJO{9P
zJ0`^`=q2M`1D9lh@hLWJjT)O<s#flr$8LZ*u~=Drh3Fv3QWol|KQbUKUL&i#;{Myz
zzM52E32!E);7CaZdM2D?>X%xlX}F+|2{FD!rD9^uZ&7(p=H@qG;%C}!0efz}gF{wX
ze{cHeCQ!HpER_v*{H(h>r6Gn>6*dryH)=4#@>#4`tU4xF6a}OFtt;`M17{rD1l<kX
z0ZkKDk7+1Q`1onySir&=a*XJ{cPj!XM6pz{TD`$d^+1CAFpPTi)Rq5Oa^t<5;9T&W
zuP3}L=I+8;n0VQSH8;VRZC%kTZi0`Nol(7R0_xtTKg3*4yt&<bc`~|^Ph!RP<`b&<
zeJl`3b&nxehSY)S8VE*&a-ND8pCKQi4`tGw7oLaBP!6ddnQaN1$D5;G>V11nf}iQv
z+6pxltkm!C6!RbF9$Xq%7azyVCk6Ws#Cmb-%lUPz$1H+wslG8Ae8UU^W6|wsfg@&R
z{8gE*yi?@XyF};Aff{_wU$Ed#yaPjbIm<7_<VWpp<h{K)(~;pUjNx1eqvDMbrPH<;
zg^<WMhA{kGw|J2P{GDD|Y|{Xx%K_|mYGgfVV)vyIsCP_}m+w;R{?0~M;QXO3704oi
zYc3EV=ODl;K%H|wAFJXL6XaIbSgA7iC*VoZ8|beh*mU1i{`HnO{PAO5CRnvC^eRl)
zp(I#h12>$WzQDkCrJNbDli5tacuY@Bul$@h?biI&5d2XO^l5}!2-jeB{quav?K+k;
zN#HjRam3?rp&zsd@MZ=O^CGV@`i1#`o&9^GKL4Als~tA6n+NpORd0VTZJ`1OqB^{F
zJ!$s)4y>IYRWrCgfN}|A?5OnyXG&(OVf^&fm#$to+>F+dRE}}-{>Ga<SnDhSGrz!1
z7|tn)AM3Xb+8I(XxQ}c0{McMe{jpy~&K%RK)=bB`koo|53D1eiE!(vcg)SYv)I$zV
zXzGSTRo4w0gQ>M0ve4_?4c#9tv+yH8!9!*^kTFNEA#WZK(y6DgJPT4&?Q_W+F`oBp
z1Glqb*jxwwuRVS*G*`M@?fO%e-^a_A2*Y7%EC|NcQNPS4Z=O(oY`EpQ5uQJF<)p-|
z!>cQ{(3>0w{CBGD?Sk%%*lTpP1;G__CN)~Eo$0?i#v;kb)c=5Zl_cE<ycyB~W`1mG
z)OpSft4g}cL4rSrKLRLa+h4=5r&UnQKaENVlzNpa%Pbg@H(vbJBpGmpmd5eEH*lLQ
zWDbZ2;*>joiZC}Jw?@CxmXLA22~5DxP>Pezs7Ri$O!{rn41q&-J?)erx#uGQGW4Bd
zjUXa6>($fFAJB9U7{n-)Y*aSdz@RYh-`)~<x&61dg$#48s=_AUlyDIi!zp1EU<N{N
z=dlm}2i9$33o+j0(HIUZUlA>NEY8qd^G90TrY8fSnS+JoP+lrdT8!UcE>B7rD?gXv
zFZuExuOKDY{MqrMzZA&F?0^@^z61$QgmvY4$m>H0O-AJxYb3Mfo^|etu*K%v(Y&{G
z(OaXqz=<VGS&=M(n%nlWVUOY=y1nnvd*NC)Lf3a=Y<nrTSZ>n4h1ihNIJj-c&*OG}
z17Ht#d}9>s+kLZ42Z9l#z=P)d>V{;E@P~jnsNp_m5gsr$>#g*6L2d(AXz}kb4(wv1
zfqMblqd&^t0yMdj?0;w%cM1q7%m!{`!kjViCct|DRF0HL95v3}3(!ka16Mi0uydE<
zutZno5I{FEP+UvM0bxo3gb?!2<@Nh(Y>jU*eglpqZ{dXTPXi^7jQ$4b8>~%kC9CjF
zBXGh`{~RwcCvTLo(i@nGzKi)T!tdkSKf3>M)`tO(BRG&MZV9P@q;W_NVa1`5)&Quz
zh7AAbq8Ko%8+JFuv89af5BoH(jmKz~<WryNdkX|C_@z>bc7ZUaGtpAo7{;k?2qqg%
zWi`Qk%3V1Kk}0O=pHs>jtD~HOb^#e{TXv`Skj#CWtE0K$tb|6JihR|@$&E{#tg*?z
z;$e|Iz0xX3ndI;yZhw$Nn=u>3Ql#GgS5v`fPFY5xmr<2}ZRn8<<WYPW##AvWY+<Lj
z<lfv*-rJf(Fe&n0dD`c1iihId+kt~J@pV9V<Y)Shnu_w(zgUhyL)j@iSO6wPDw5hq
z3|<>GiF<5i`#XV&p1gWTSckZ5&@44;X8jQt07wqGp^_?UVAWv~9L=4D1FQpzq_?a2
z*Mx%~yz>p0fd;RX9MT3$C!g{P(r<X#c#*T<aA@;fG{8<SEPzwangJve8@QS{TlpKQ
zBNXqS)!r2HR|Rx%kxEWYk>L2Hx>*NRImBEA`cCH&EY8O@dhvn6+?PZxx-0r=+@aAp
zhi%OcJ200VLrS1-rq843Kh1hoFzeP_D2d`8UGFH+G4*@^z5PJ;dy=1tH_0a`@scU)
z8XB?C0QBgxo;WaW&7;HS*={bsiNzsxpqdrO`_#atIXOz)zin%o(S#{*oUr__{%zKC
zhaJszXh=a_<w?mCD*n$lv5$~;&*Y1yAG0JwS+B(4aE#O{Emv3EXaUP)^@jloL(^n<
zmgK(T5kPc-;Kuv~UpEh08Gq{X4+oz63G4Rq^GntvQVllN*-L`qQfX%Z4e7{cfJgrp
zl8P|{jyo)j%*ABda5_Ntu<-9mnq$MC5(|>LboM`M_6|GiyEFyY_`Rw)7&&<`Rg!?8
zIsvEyUzQ>tP<pY}=&Yo}ob{{a840IW!$7`AsPc-RpT`vBQa2h|$|M1H3(3imhgmoc
z?+xio`C<77#LnkNE9aY6G4c4L&Pe}c?*e=Gx~T9AgMuflZzRM&QoTp<lh_pu);=U)
zCzSjXd~ahp|1}GYNyEgJ*&O{RD%>LlRB<0*E%gVP%VQO~TRRx~*EJ-JHN6F5f{;lO
zH9m?Fx7P*#E<#{8@H_|`NZ@*AC=7#t&R_4N=v8Gb><XLr{ts>dRdl!spt)b+#lUIN
zKamUx^weyzkf7%SB#hsHX)OJpYYbq`^dJQ{TACVdAzDqhtvDK0-Zr_x0(%Zqkt9Lf
zay7ZCjbpNvghnK%HvTG)MFKlG*@s~0F7j`n*J!m`(ivL#g{$tT|G{F2v`#31G1W$p
z+Taja)0nr$A$wj9=SL(u(0SwK=jS{JKPPyAb0%+DH>IID>Kw;fS|{q^j~JNX)qA9Q
zhy0t3818B-Yl?M}l+UUnF@3uvGP}LC_KyiP{Q0p#;rPJ<N!{Q{K7#nspOLC1I`&PN
zRsrYZHqwygd;@As_qcYkQ&%5_-Cy|wEOJkWw;^tH^pO@S?s<oRO%WrlAsp0Ghy;HP
zFv$&ncGSrDe(6tWI;3H^v5lnaJ726j{+HxoNra;fo!Y`19$U$DB4cqL?v2j}d`S3y
zO0iSj{}2pn-H#0BJJFgNZ*LK9_~MX+e?mDS7AG8JElYC+ZEk`+0N{uQ=;(x=P!i{@
zXhCQCiJ{W1KfHGCo4Iy2`@Gb-?fZXyo3!)zeW&`J%iXv4|M~kN@L!34w?01o;kT`t
z$M@gbx9PX5N^x5^U*39PbM}WkIaFR=7+2`ciGk1eEzn3b2bw#rU|{8Y6_)2w6G*}b
zI^j1sEC;bX+s^x+Y)6Fptf|dz)HV~N$o3iRPgh+fqCWVxHlEes;<HBCKRY^h)_A6G
z2o*H1K?u%~mI%w&)@~OaTb1*imJ_<A#)ykmFBrP&Izx0#3u0?n{y5!%oYB45L9ox*
z3+B}!b4%fg-OEhbz#rlCyno<Uwkpb`vTl7*DFoj@$J=L^1B?6iUGpw=wQf^LQ?Shd
z8(7f9+??&weKvs%LFw}{7*D2zCx-NtgqIfSn#2_~pBdeD1k|zE<#1P|>^ijMUX+-<
zxRk)a$MFg(k=7w=gGykVP^y_vopR)Pl+H;4;v5Pq^A9dUP|c@_TTtW1#4=R0BJm=K
zi%(y>g-Wx7zN`JB1KKEW2VT6MP0KT%GSV+mk2TL7@DslzE2W;La-bP;9aTptm}+QY
z$T#46AB>oa3ey8*xO$^0E@2>4q223%)mS@j1N3D$+_4yL(7N_oB0cw!Tle)(6xtVL
zP9=k442fYVxmsLrDymu0r=K38+bndsd;ZENf#C_kdZ|*X%`z=OFYxk!<uWzE_e$!s
z{j@H<t4`E~f~i<lLr;oyb~)h#YkjosE=`jd0D>CK$6Jk>D;fGwk^wfq-hX|dS#3j&
z*b|kCKC!Y41!eoeKh98ERET)xNLLgA6xyZ40D-ddb51Wuj?fH<pQXu>58`Jb9T(E%
z^ye+C+SC4M-(B@(0|eC;|K@GS-%GY>ItI|L!j)`gZ-6qQdYVRr^Ax2+XR6Vf#-_x+
zwC0n<M1*-k{?s*4tKR!?(vs^5;#U=m#a0z!l^tpvy_jBXqFkJJTC|0zNmyStnGdkd
z4cCd2Hu?TyZ(mscN?w4z_a8@Bvbt^!<ZDKAfkwh9*_<nj9{Hq(o0)oQ^%FSTWt0;8
z%8@<<R>@B(@&YPTr2!gc4Yb+gS>ML}OVP&gndcAo<b><osDrajY#&O~YLNo&!}>kS
z#7`(FkS@2K$~pWryO(GEiL#iiRA+sJn5(39IW!!Fu*RMfEBGw$%(Ds8rplnN)~1NG
zW=$VTe5`HgUDSYTB;p|ZwTF04+fIP1nCi;t+ZSM(kE_zw|LktA7NFLzqgGHRN`3N@
zQ&!JCy3fu=gMHRENs}5bbCuW9l)9AH+^(S3CqcC~Ro5hhf*>c`1$fX#ZQZ(`_G7p`
zQ?&t%QY{{TA_1T0c$H$Q2rHDWilNE0%FVr2m1XVxL+?XT@Zw&gpuGD>3`>6fOF4U<
zRD<WOcY-#KO<UabQ(E%~iG0#^W;j3cb^t2Xx@o^;wYflGwG~ip$fBSOv_L_K2c-s|
zs}%o8+dc2+$h0G_Uf@W^ui62=J}ZNf0B&541!&RC5NijVQMA`e?w|VW+Aqx^13(?g
zSB_A=C$aDwBg4N+&yql!C7hj#*LaZlV{W3-kCQ~BEB7TWw&#Wa_XD-b4I3J1bJPi*
zD<pmF1H3Cp68PKR9@cM1I9~8^R8OYt6O)g-=>1p{|Kn0*e73TkF1bl2#%)hVy=l2L
zSten@)Bp94U4A{jOQOWM6x5GX=l{9`Z-R%QQs~<vB|?s`*}i%Rn|V)qcw5!Ns1n{P
zbTOT(9iU*n+#jI7InH9vYMGKwuD%%Ri*izR$q&B({CA?K6GbV0s*&eAVDy)t`JM+&
zj}h@!P5bQ@|GvDqxi?Xuyp|lGfAb3c+0-0BL&|gCE)Ujf2*@n!7Gw)dcoUVNymn&?
zti6=_R7w1T#PahUrv8-R02;jo5ymPhR%sD#_tc!bjB=$fXGFd|kh9SotJgBE5kmCI
zbkB$|-6Cal{(h#BruFhhJd00E-^y-{XFbS<SLM>w_eM`_DW)ZjxW@`PYJfgwdOs&9
zwC=<EfR<C>&ECrGyQKzqK`SL%9R%!5qC$D`FmU~!f&UleL48e=iVr{Q6G<MvA@N-+
z^#0~F7*#1Ye<}f#kru5>v_#1n{VAWR+|bCA$p2v0R`yi$aiTuTM3K0^g+V((grvEp
zPwlb`lsL6mFKJ8fln8Z1MxAP;EvoMQj}}HC$|{Hv1ESxE+<2g5o_6{%uM-rbO;m0n
zn3@v7@9#*~4}5Yth;gAZ%DX!8`pjqHpI5H)ngOg2jYg$@@6JP=&LLMw=IPZ0Q>2}x
z2v=QV!ckbg<6+jQODd5_)FGuvH<T$TKj#*hDqZ^djnZCA9T1bsi-89JXb3s`|I$3)
zbjfGUE;rrPX8ElW!Vda@`$~zd9O=sr8T$TODK(_N-}sE11isbfcG0=C=E`WF<YmIM
zsqLT`Qy&}pENcD6>)a`%UH6Ac6$=_&$LCjetlm}0_SMe-Pt40#u7{$S%7#ilkK_GR
z8}xiw@d3(_q9UIETUonbnww-W5^y@0Asbo4zTdsf&6!nKT`N*5=H#236Ctuek}lAx
z9ZC^YUUK*Hu?wYjT10PD^lRSwdyvQeE=d;h^{ILgBlBcW*WQMOe5JkC%g^X5POq~g
zZ;K@qXZU+NwN=Y8gMIofpR5c7l&YKn$tz>0w8T_N-OB?>&RsYq-sNTCQmzE5dlLbu
zY1>@?J24Fi>s@b{kCouo%emflJH?vM+6_n!5LtdatI=TE7b3BSe}mJL|83y(zdrC&
z^yaq@?y>}>SnDY4lp8)PDW%UUMU*gOl_Na>9LId-i-qs``SRlRu53_0sIul>;~wi!
zPawN}BLT;QIa{@0>jCVKf9b14#-MipyS?R>O5Epe`c^G5iL;kLKl1heGw}ah9>KZc
zTRvPzp+Q{RN?jsd$^1%aFlzcltwa-9cK|r7OP^W*9nF~11}#*B#_-B(o~TrnwVNnp
zS)Ifszy7?7PK>&R0-kj1TZYe>dOV$4R|1m$dxys)RNl!or&Xp;&4X+#MN<n7AWfEV
z{DU@#UND9KQZLcy)8l#RWR<m|?v>rB=2Jv@lw4M*+#Z`ydp0wJ&JHP<T02n)cQVl=
z&bo$Zi&cMW1%Ltgq>>iSB-W?<I9*@KP=|<HUrpcL)53^VB<4m<gKw+}LX;W;jQb=t
zeB=51b;yBIb<rIZ15^qBV8m56)FsNQ5d_b)sgfCY^R|Wvj(WJnaI#=%O0guv^4z3h
z-Lz@<1WReh_l&yNNuBat-FNqpRmZe_-NpF@<BHhIv!~)u5LuafjvEITt3)mxOOQid
z*kgF>Ux~)b1BLb9K`~jOc`Roe)MTo;nWg1QtYO1kB~Us$I4S$=nb#k7o_-IqG5z}d
zLFlMgLJg(Xr96bu8Z8nqY*9227u!IY2JJBfPQ5FAde&oSBG#rencZr5E0mLR<i%u%
z{IsdmJq8nTaxtr)%W8uiquB_Ct4s4a6a-QlYJ<uZxADemgIyu>Ilno1MEZzJmseJw
zzpu++O->NO3k1=I;g`P^Zv<0I4S3^WRn5(LN|#g{$~#Z`DK$)fyrh7ZH&!9l<m6-V
ztwxz1Q2{`~M;wBbF8{5hKyL^G4w5RNyyw(@V|Mf0Sd}UWik_nCI<7I5c&5uA6sy9R
z{f<<<-Qn|;XO@}`N}vrRq86~@R?tRdSBktL|7)SEcxBQ0N|=yCRwbIIMOSp5RIpk8
zTQSAp4*{DHiw(}*M)p7iYc_s;skCN~+Cg%jP673g0((j^<&FZ>)ij$I-@3tRFwpXZ
zQbB1&H&`X|6OfVRZ`otu>G>Jgw9q&k`uX7@6=Ei8yruJGm+BflL&G(6{Elz|VGfR2
z?NFG>f#5)>E|s;m^xTfEy&~fm->B%BUz$mCm(cnx)(vnJRo9J~DucsIGptZ*xcWbw
z-0r-urk(~d<SOTa2TXb6wUpD?R$Z$GEUqz7S-bgoylFxVCiAd|63qL%0w@DMPpPSf
zka2oc9Y|~LGT)_3KW%)fd8HpxVW?BGVEBDrNf7hBP$9TPY2Z-%wi61!la~6F;LQ{F
zci#x=x56Oy2>no|fFR~;w;3nkK2T)kLuPV83sg&I4OAm@Fjlz%3cSH3&=@tmYu%mj
z1QxABRMc!;{c<o@CykYVfngiU0bcmn<5nK_m9NAoapC5ILW#<mrO{GV$ASBYR2w1~
zt;kF2qlVS`M3*7x)--L=g@xDb5R?g~E}E<)GP7{6vt8!Uml;J#Z?gW<070`+&!KNa
zS*jmi&#Smt4{ALYS$(q_uFxU&xd!#9H=ZW$jXxgihI()#u&0y{Ma>3g(4I~m)Ts42
z=#IXXWqUCN6zXqj-4zFP%6|M^fZ%Kg=Btez_o%3G%=_BjmkLEOpMD3WTv_Ax{$Z#;
z9b1-Cc7i$h2PYA4+2BZq{fUyT8P0IwJV+<68|5sh!P~EV2@v+Z6f}5s*Z4nmdoop-
z<CA&4|HVa_xEfRhAVc|_PpUVR4k?%9mbqUEJ+H#kV7QcPP0L}7|6X{W&ht&2LZz?l
z^l#I-?okb7k%oof+jXjI=ZkRQ;8!p)`bd4R(V3n#RC_drS8C{c0?W%f4IjE9-;~xX
znz9n#Z+m_48`gj5ZYatYFQCO1S^c#U;(}IP;HTrK<#Iy3pCD8sZ+4Smn4>vUlk>Wg
z+$#=~pgNXDx>jFk!hJoyNmA~{?J`y;Vy{Dkw5@K!;V3&hs3bj-dh+g9>7hzf9}k*9
zr2*M3Kb2~mjptF)o4EPUrd&a0R6zscq=p35#O(a<b7h^d1nSA&k_Tzy>O}dpaUCKN
z1a(~#^rfvo_jL!d!G`_RrDY|&>>XOI?vcXZTv2)HQ_Qq+RpKL1t?F6~3a@0SK}<lo
z=|)wlzD{0MB918WY?t3D@oYlRquft0M-$STn|RR~8p?(ziD<i<D+fSOvVc~r<fm$A
zO5A~p{)1R^VT7QgY?gW@ltEw7Nn3YE)hQdo$L0N9a+d;ToAat)dk=FB4W)zUhPdgo
z%cg1LSSQ}D6HL<d36WoHK`45|!W;1^1*EVdM6Ela6i*VG0#GYmrFGW9@4g0r=th6a
znq^LHx{zM*3Y2%j@`MNiG}0!N>aK($Q6Z{pT}nX3yrx7>Zj~(yG_qJ3DTWH<auQ--
zx!O4iX0T5ikT9}<1Txmn$u!e#b3@!kO>at_jDN#Es>_<?Mes)vLn*Jl)0OIMzy1C*
z1|EW9`qr9H1S*<=z9r@9Jg1w2jv9NcxWccnM0@A1EKeO{nVlf&Zec~c`wG?r)Xaj>
znL~qjlPD&t#YM?T{@Z=97_({V)zX~r+M);2EK}cJBkG7nQoi<|=pMilN9kwq^I>1P
zCy3Z{u-L$6n!m%4c21ocESEXh{2fbu{IU6aNm@8Km-zyWKBG_=$+FwhTjO$N@u(4(
zUS0U@Fzt_`2ZmNbG1z-Te@j^{KCkqeA7=?Nx$tLV{I^oPiN}pXdcx1w7)_^}&>w-u
zr!D#;pa93-;*w;f%#8G>P*e{}jy_?g8-VIs*L-Fa?v%ki(eA2mk(e_f&y|b%v=7#A
zdB4skX{OAUHez_jR>;!3dBzsWdeuUniTviI+Slcu&mWcwn|})00Cr*5`Z)9^N>*tt
zkV5bj-SUxD@DGr+_BXO19il<5Te!$&FOw>Zly<z1R(^9s6pE5fCa9J98<`1I8(vl}
z@X}07i04i$jZv^{T^HGc16_<~jwUCGg6s6J42@}ng%7MHGlQbZyD=jR1M~VND}$xh
zdC@gaJg!Ge*HqV~LDR-DTA-Ee66iG|7;<AsJ5X(AXt>j?#eCXmWkN|GWEktnqH0ag
zPU3GkF-luAtXWw=hwFHSD1WN-ihEW5M6jX-X)D{E8o2sbHEa*M8V3|*`4nty(Sk5-
z+=y6(TEFu^FkBtr;o3A<3{^8*8OB2;W@bZ)49+i(fqjZjnkliBE`QRB;%beuOM)gn
zG=dAQ3*UX6C`D?w45|14XlYY4H)#R=b~h<%j{{?0tT}O0ZhBeF%k`%pa}0I%^iuEx
zEn#VuTKIHh#iFX(r<o4j=*&+Ivly>0RoNgn>g%GC!!-^Jb${=IkyQRSo@jL2WT)S>
z^knvlM-McWBW7h(EJ)<4J`XmNcxq|v>FLm*qPcG;9u-HDJ7I5Tkt{Kx%R&~WQTxWQ
z8SL~pWaSSoHf>3uwK873JxeVZT)wwtIQCBC+GfKmNzfZE#r;ac0bF|Q$DP4>udfZf
zNOc%NOg*7~i`xA8o(nCrho3%{QM2+OSg+E@@=8KX(VVT`$fs{_4iuc5Y0;tlnM5fK
zIapm`OHu;<Y6Hnoqp*|tt~*MtkT9d&o;#fS@eh@VH2zW~v}j!S1hV7e$0?ZqQU35O
zDS6-z4wj5PP<U~{PL<=4d#C}7P9n!<^UQQLauW`zJ&%a?SsPXoye2)_)%3E`&95L+
zt%s>Qp5fv!ua5R8TxS4BAB@|sD)T+hreju^w3E{*t)S(W^rxZ1Im>0lGiK`HmH?d>
zHqi&&0k`YQs;jU?oa}MZw`W!-{ZdeN0zPR2<ipGJ7OrOyGfSeEAlR3x^r<?v0nw!q
z?$a6fH0GcQw`8`{@2P+UyIK7Ka0@yhOCC0DjYSyL{-ybp!U&wj&@GF%YM%zR4!gF3
zP*mvka90%LjfqH5E2HaOyT_%K@&d18<drdBsvcWtn=P=<V7M3d+uCO+=-f^8wM4I~
zH@@~hld2sORj`mV^*4Q)pxjV-&!{*cebvOA7<`>BM|iz-urEJyo#1~Ry*jRr73l+Q
zwptmT!W^T7nHpPneY3=IJWe7<GBVg>Du&a{0e?Nb%Oh(q+&XlUzHF}4;Qu%{JHD%U
zd|v;Ae!PJ)(aWVpcD7#$Zkye^G19^i`W{rZ0hg#V8Z6cdu<DGm&$U*REowdNdAphL
zXVY54Z&nbqBk1X-jXl>@%@T3n4)=NY=8hJZodAQ8j;Ppq;R0XD_Hdzl)y>h>aV6p`
zq9^srqmE|TX}PHI?&HkFw7Dxsl(}3b^W4y?oJfEMiOdsOg@O=$*&u6oS7wc^sa5pB
z8nC;nMdC>D(dnAVk9^X~c<iR<sd~)ewVo@qy@B-$AQ@E)ee1|;?nf8v{~!+N57R-i
zx@KCXr94`x{EKq>gDO1_gx(3`*>IILFGt>FJhB^kyrWN8lU}1uiJi-^xkOw2`Sifb
zL*3sBYgxJ53Kxdb>=w%1TYADdOJluVYiEL@YhOjr!wskROwRUA$~AYqdt<bN?VIWj
z%113I|D<<a-%N|h+915(HO&LjNx^mvv-ti*p?a(i7StO#I8_7l;14?#zVqA9Cpo~p
z&#;j!6^~qI3z+6B^2rwfP+C;BJDdZpYr5X5NphI%{JI@P&r1deWu`>~In5iKjILQi
zand_y7hb87svqpHq3bBcv}OaW{9xdCZ7SpYBiAe0ydWQi{7TT+klHk~1=N7CRGS_5
z|L$wkcWUX#Ef3>D>&`dHeBX8h!S~_s1V_;|Uy-zjeBnE0Cc-B+EBU~Vm5d2_Jqs9c
z#FVOE_lZS3qGA?OmX5IKh71{wnV5xKt`j7Ks>mZiJi;GVHCv(MJ+!ToW?F1%?;tD9
zJS=sN&<iP$@m#2ewQoGT(t-~xrw1HXwMZSH7}r9%5w0j<PQdb?$p;FGUwH&M(hoZp
zz_lImJ%IwzAGxVt_)$f3yL4yq^rgB)P=?nRoe$077P(2+WI28J?{EXE^e^|EQ1s6K
zc+0!kb97bi8R=cSke{Aix0PM4ZKO=Z0;!Mk$I7z0{#vC=#Yv?`=8%D+0&4kd=wTHz
zHZC0r?Uu%~nrC7o&bCzHh>%$mPKi^JOu@ykPWZ!m761rBztlw11*Bju7;qoyQ*cVX
zr<TwB?l0Cd82Al~<c}*SXWi32oijf@jm24l_FN9Vj>0>3qw^P%dk^#EqM}i=m663$
zCr;?^c(Z|n+~F6v1PgZPti~S(<I?fQpC-O^8&#Qo@@(;%)cIfnY%S&55U@2BUcd>T
zYSyD!mb=Z7ZKR7Goqi{X#Z@_;U;XlbX$}^G=PoaQyh39Z=knx_=$VD!)4^-3ML)Zl
zD>NWMr#?O1<pZn!5{l~5WvoB)=NpOR^sVo!>Q@LxM@%ge2aAt#Y9gQVN!C2@Oa-v5
zOki6KU5K%xyZ)7=l#<daW8zY7IxT0)?%DFd(T1X-kM9icdR|Fb>2R61Lg#)u(GY;|
zMXOO8qLR2al<O#c-<o;0ZA%J2;2CVeCCr$#XM9uPned-1k_UcY89owhWD)#6I!C3#
z7T!(4w}l7b2Cq%7)O)O{+2MwSy%+Aoe_;-8xa3Bc<lf5K1_XuSQe-gSQ_nVtP;#9+
zcNg+&h%5lGoRY3b124a0a}5$^8f;shHsbpdKjG;g>V=s<(jh&~Q8s3HY#RSN?y#~2
zDObL^+F~u&sk^}Zylip6buIn?CWtwt0WAi(R)&B7gs_W!HzIp-))Zs0CVMORQuXeT
z$G{4|uY8hl+!=jCrNI^tJhBZ4+yMYvgQY5p3VP3j$OoF!OcF3*pTehAu1>xyHflMG
zUhx(}#$G5^!(}J{)E3#ALZc790GkqLuO1Q%=a#Hcns*9WeMv*H+2VY29Kyekyjs+1
znk_U$vjAolN=w7=GSf}r#UmI{%U6+|kFgfyj<GcmPMPiCTlFbX9P1@WU^)R-v|bk<
z2xJVhKumq1Ljh9El(jQfKtymD8$sL{x3TMdG+^sALQNwzg@t@~Ua;e4%2HDGtod^M
zzV6!mu`l5{r=ig{xYDMk86(mxZ$5r>q#8(Z`Ya0?AFcKx2vMKNFL0ca9RSZJY$5Ah
z=Ql#{1;B-mok6z8_!hP5>s@82i&@5(uGr~z<Jo^7HHp?K_WebR-{lt^4W)hx&#45D
z#F#FY9oa||QJqH2n=IDA`1DxL6KYPi$`_r>WB_=&{D09ds1BGLLaI>_;2x?Bxqhic
zCE^m&M4ZA7o#}sJUJ}Hv&<KIJS-)crcJl!cA~l@$Gi|5N7@paSWWD~PGazJvzzK~b
z#kj(L$chOMJA1fT^oauq_^to){g(8pyeml66c<J*z;5hSAbYvR)$gP9J7!vttiN0o
zwLA;Xdp2fcHD555=n~A%)wzMF^P6`|t{Zzbmr*KYH1T#)bJKx!E@w&@zOG&HDqwzS
zR*f1>+*+%T0k*>4MGwfkU~C87LwY#nleV<xSd?G5J)M?a!yHt%{W$o8Rh3B{ypR>(
zme4p{jKcz~HgmDFG61X70ITN%mhZcx%VUqWc30j^KP-&)LC;pM<%Iz@dh2bmI6JIf
zdr3(D^b{Zd$u=-T*4oGb!uYV^(K%}%f)?SlitpO!FTcFfuv@1o<W^3~fNvv3Y`<wq
zYp<*z1sHIMo6Yip1EVv?U+*tqzj76Y4E_C)kM{)UjI}g)4J}357vXr)Z)SNXeFkK_
zbAurQIaX9b<G0mb?|(CeS(zCC1^FacGBx&FAYclg&qTJz_!Z$)Q(egFN0cpQFMV^%
zo+<(D=?P>8`=JV~mLHZPKO)cCNf$i1)BmEtW_dYX|MU*u;Gl(!8u{<jN^I!!mS95@
zi21yTww=}N)!mFxg8>Ic@|8Ki)?XWGJ>p@r{MYtcYP=qh=xT~_@#8CRvt8EgAplB`
zHrN7zZtepM=6_-uHwN#g;7MA=<9#s)4@?gk&A2xbfrLNGzMJ;V*5(Mh5$rgStZ{<8
zbV+p$I+B0ERxYe5IABtalCk{k>oX&f8Bx9}$89WUD&xe;c}MRvSpDB}--%o@1;fGb
z{D4gdzZ2Ir!7X|Nc7!ODFjJFTvT25LJ-$i1yL+-#ch>y2yZeqaqg+sck;Pt@Iw-nJ
z9bQW@NH~h-SFAaA=e~1skpFE^>!9%Hj^OJdu&JTvT{}+af{I_YtX3m~b6@?d3j2nP
za@5-ZQ>aW{56}f&R=IP~dNvDwqMdp-DL?1)z>B(?O20n;S-HlGfNMn!PHN@S$}DPx
zu_GKanCntDP;tm==M)14Bff9t6CSE)u`*oj8-EwM)1((OFKdFSg)Z_MwE~NQr1I%4
zvVk=ErHc3WN9p}veRS)Dlyv>aRi%AlZwingkE|)L6X?gd(c=KmxocNZJ5(ED&Nk>5
zuPbP#(x)B;o-`||u=&5bJF~E+&V7wz+f(alm8u=22&obR5k&zZia@m8MHiz638oA|
z5Rf6H1OtX4v30l9RzO!0=D`XC86-#~G)$>m1y=x}GKPd;OJX2Q5+*~Yz3@EGxjz@@
z;+&gYWvwUQu)gp6zwd9oF;^;(N6ji+;G+>~K0w$x-G<v0n4j}v3|-J2DSHT%ecc9Q
z5~4m{>)o0+88%U@h2zeA4c1Ys|E-qO{~K*4fP49X$MP6_UZ{zu25E=vimsB%M(L}+
zBt;(OX2x+Y=V5){p-XXK(D^c9?6=zsPch2X?7pqmnjcx*oYDey#GC6#xLkY4dfxRk
zAa3etX`Z|RZPAxinZcfZ$?`A2ma3^uM&^Q0U^Z$Kka%>N#p2eNvDEA~CuRl`DzB1R
zeE)s3;UE-)dsTe`4VB_#CKjOM0YyEM=&epgk1qepdEpf;CZaT>termVJ*wkZz7qwB
z`hC<@NJgo=9OwDKzBZXza!Vbk4glSbMm<?qpowh5Sy8-U|5_G)I)LmaY!$+L6WqaW
zrusPjTLtU?kIeMH(2{F}MmuN$NhH-6EIKw)Z8YKDl0BnVP2*kKHMEmYNsgCVjWia?
zfAN|iO7F~BFxwp<Gd5|?Pc0h1U!KG7FK&q22#H8KWUcAGGL?2DHVY-Nw{}S_qXe6T
zAKbLUP3}<3y(zgKj5ZhODbQI43xS<1c{vCJU@rW1OVZjQ{yP16oG}VrM+#8|a1u-W
z{M0iZj9}qH57^%7FTVdS--*OLMsB?aCEq}_u)r~I(N@%YVMOrwS|ifNTHpcAR>P%d
zG7Su1w(p0C)`CnhGk~<y<mTm`hvp_G4-<p&%u}<7t=0nCrlWmMR4iQvGqBKZE85ZR
z5@tqaw79sMLHT@|ymOa4n5m<YHB_LXEFw617_mZ^p`gdr;*U-CWWoMk<85;rS}JHf
z1MGJ^p6b`cAZv)YoTnqKb6MWN!nbHGkFCJ^Q7(AFTg6<DB=O)8o@J?gTBZ)>h;Nyt
z#ERlWLEXjn%|Nnn&&+)gWie@%$_Vx{dz5n3>+jc50Q~}5SfUPmwHq}&=8<x#*}d9V
za7eF9z)P+Q9aOZ0s<Nz)Y_+G^O{Yz5y;F};?H@d-s_$C=Fd)%87_N`ZBlEb0yuWu4
z+!2<p3ckr7XEMIpX7X#4UiU)}u!a;Mp&+mw1vvm^0cf68RG7?ydHGADQCbI3#Mhd%
zfqALT1E1X~USLuV`Gcc=ynPn0SCKKRd5qMzpyD-pt<_bs^OH1F(fI9lBZ=A6hFkcD
zVR#EwW(#p`;PrR9X%+_-5CQdbq(2mgyBk-qv%MR6yn3{30<d(tlVNcJbaRGIGegAj
z%UPhCJM=uP(6z!|`*a4yUc*Orwy3q)mcvcyBl`QT*~LM0qx1`{w!lk6$4m!1s2qG%
z<ru&Wrr?oe6?>9Rx&B8kXKwH>XVuv4Vf{w9Vm;Mc-Ni!oI|H4iju_>}&PCtL+K}4u
zGPyn4*tQ6$`;b8^Bl<KD3kbFZm7YTABT^?>x8*U_PZnl@Fi`$-yXu$=1PhuJQAVQ@
zr5R*t{Iim-iaauHp&vWu>2)pGb@3&}j_GEG|A=8R0gn2Uea08wB_3orLW?%Xc|ppB
zu;SB6-{RLv18$E8-S|98m^-FU`Pet>!X}`AiWqv2jFTa`_30PAK$4Xm)a8V!nf-=P
zd|o`4^sJRSi@NIX0~Fmyp7xhjCWP7`k29G3up;o|dra2s=helDIODxsm5bj!ok2J{
zrVKcsz<9sTN%mo;DSg4cx8=*u{3Y`lMh*%~w<H0AZNt&j&PyjHKRMh;u8FgTFw;q%
zUSNXLY-GzJXsb(2a?l9s8H0M#yv#jZJ40G#JFh8Mkf%wFhAqw`p|p%UKhAT<Z1Wg{
z)eY}rYO2ds^iH0qCe~6yIK~B=N1uH@6tW4jp`uIPTU;V&qXm;8?bkuy;eTE%?Y7+}
zOOK*nk~sqlzt5DxnUJENhfd9RxI#5-<*&D1TC5kKRzcQj&P~eww$heFT+pi9#wC$$
zpDWelD${kgXh?8A5c8<5WY<)7&Y!=mKG2@cc0KHC#?}oLk$zuBNhb3g1AQj+g8flf
zXv4?0$x!e>e9_}yb@SCiQ2(joCza()yV-JFh3;tX{KNjbPlKB_u&aGvZ05a$-|Yfk
zqoP``vsV!#)xCEv4T}2cc>$d%>9C4gd6OUFMdQ(@khwnU&X3ApW}a6pVtoqL;UV5=
zB7=)cpnF*V)x>f%&byDYWA~%i<zDVs=R4VOWjJy0_^9U(snW=zk8-+kBRSFurknsI
z77UxeCqrS&p3S9JR9a}3BB>X-*;~1Fyw(Yxb|`4{;qxktHSMBk`zNN}g-sXH)`ir|
zU>2-cMg=q4fl%|O3v)=*GffP9uI4cg9I}hTgVX6ux5L&m0&8f5HG9tadHMJyt&oUL
zC4ey;>4oot$r8*N4MP)ACAHBGHCN$Y{aT#ByFX9<q{lnp`HioeG`1_QwTO?Uci#?Y
zY<W;1qORUaHM9pF&VgT&cDFCq)*Fj6MG1Kc;{<{+(kcNfc6+3aXA3iJR$oQjpQ5wV
z4tp1(!mT8rE^-8A+XaZ+AK?1$XvlSgA11iG;Lf~9-rarFY;SIQ5T9qG@1LwflJBwH
zvF82PTRjS|;5Te2{FwPG|A~=h1i3ihPp-+aAQWGaF_@I$l+waxjHb>tHdL%Ywk!su
z#EhU8TwE<N_jURc;;WV;u!)jUi8JglXAx?3`VWhThDX*$yVbe`!<N3rPFzj8@>-L5
za}3y>QX~mmY_U@;;ws!sdT+X7&YPHgCczx&(Q_`3^~ywQMz%PwwhGAw)OoVyL8cT9
zS#KXU!pdb#rF0<PDjjtD?Ke`&H^JRkI#do<>G;JfLKy`#+CblQC}BIAl)d2(6b>d^
zjoN5RMa$<r_7WU=lrI)orT|{r@hZ*#Od%!0%2*<=6)lf)82dNIk&Fwp&OfKMG59<L
z+p#|dtHnixkd(iV%M61!Pb%gjx(<R6RZmhcFyZCn;k;_-BWeS}<cJfDc3wMGyRFgu
zW}{&hfHUssDWdI_{9G}IT^!l*a9tOPPUSA<*PDOi?67=$OH{wVdglEW_u27d9CKPk
zl;$v8DKWv<gr#d+6kH!OL2KdRz`R_<*%T)p-9EFfDPy*?3Ljc1pDos99*2a1Wu~qv
z)j@ff*5hLC8a>Q0yv@$OH*S}{@(ab**krBt#oSUu&Up*P<9q-iA&67Jkh6qNLbe+I
zseBkvfP=*9P#-mzJ;)*YsOMSSJ_E@MH)REQ+;)snjN&|thm(BB6KooO^0wo@5+{Eq
zNuF&%6D|C_!0^;;sz=Eq;3@5e++pIGfuK**m^NwdL7y7W6FLX4H4(jlJ)~DEk=hoK
zh#bV`=iwrjehw>b0R%~*)Ur|4om|&s{>Y6`DpF5-SA;*!Z79cu3t4NZs0}vTG>KoY
zEv%oYW}EI+NyFovNMvI#%haa`*AYe>XgSaLuWnye<kp<CG>QHR-uI6@n?KMqY4IQ@
znYp)Eu7H^}Yt7O#S>#h2T<0#MW(ze?_!S)x`L-1KETEw8*$VP#h_d7-$&uM2H;#GD
zb9Bgh!`kP8d0Vr8?@0S&lKBYYxN>OrFdlxIDNXvBy3zM2IM60#*a*45D@y!KxtcsU
z5=$JucS8e^jM9Cx=9L?UMAnQMik1))E18`&UvJ-X?S><qRdwXdD$0LKIxy1&<JM*u
zQ<SVHb6TG3-KBkiM?NKWrEkNwI)<8d?J-jayBUKL9EK*n0t!AXa^+kf$|gDg#v5uG
zTJcebxE_o8Wc3I%K{z@;HMX|4zQf*;6$dswzZZxPWKLDkgW*`tuVBb{tak~WdW*P(
z*ggcqflgq%Gc<+sd@|uwmu%-@4<)-0j5^knzpyK1#;-urN->g5MT&1Yj|vFaCFFoo
z15wyEw(hjxFmYyekF<eY)0|U(5aaGhIX~!{Pq%|Eb|{iTHy4Fxz*dfqn*ab|_Ei~Y
z9qVeBw90zEU?K8!m(%>bX`QR7=Fi=-l+nP`@#WZNJdBQSJ{^7SFg%{**%sUXWqQD?
zgv^t8<=qU$H7d-vH?q(sASFgC^h+M}pRawWSg1*T_dyoiOy}X_9^?xZy3j7E%ocvi
zLkSk!GD1fD!~^}Ac0Dm42|D;bv-s~CWia|%u=yVWAsY%=e>_vVPx1Aw<n(!Qese{s
zY0um^TV5+uXTH$&>E7=^#HDL}>z=1)F4r{QEw0_R#H$|uz+}&l@MIe32Y|9rn&Cuj
zwBHLloi%*o9^&>RtH}m`LDEonT0f#t{&lQ9q&3L;4*~S(li~`T=coO(JqLPB_nu8j
z2Z^zl+a6mEt{Q5abZ@pqwV-H83Qf#UQhYHg)1K6)y8Nm?`n8nf|3z{-DLoyAl~{ki
zoT?4$R`oWIudGQP9)^VLd3Bfrml`GK^Mua--Fd5g6zyA(QLkIr3K*_D*+?vro0++(
z=l?*astzsoyfmM#40?4N@OY7Kc#TB(IRUa3JY3z%2Rsyfl;+p>`J<drLWfq@!Ia+h
z7gQmI`Hqa)=y5krAOlaJCyUO===AE*hRT~Rh?vf4j_*duc>b^;bg{56cQBOMUi&?X
zKVu3z8$KPhA6qyXw%>hKE;Qa*Uv@FmmEFspZ<(25D<Z2K^$o45QJVUi=66gyzl6c%
z1fVeebqZWZI}(*A4lslske;_e)m$WbtCi?&UHM1>X+(CzaEE0s$lO=AJZ<n*O<s=k
z7k#oFMnjE0U~aUIi!(-o<dr;AfAs__b|2nTNAeat7*je)DpHw_5lhB8H)DqAgL(EI
zl0QX@WaP^=&Z>BALBpE_ymD!x5WGUe;j81vK=5Qb8O(=Wx{VJcaf6lpRf>B0bkcXA
zKL<G;l(Rm<(liqb<vrUIxTF|s4Y5OUA3aFcof0|Mxe`~lgVF=gG8l5p@tyX`drSR+
zs&qdW1SIKNldRkjsV6v%di!s^)OTcRf|;9Y6JbQDK16RTSFp}yw%O&vhB|MM0mof>
zg_>s<wZ~@9pGUL>fc}ASr(h46ON!*z*_86$H-Kz6Zr8Kx;~Bo-Xi(WC*FUrPY3$S8
zcB|6JlUhSNE@fbEb5@niL3j)@?j86El><w;mfc7J<!4fdoi%}X+f{jPm~=o<9$2A^
zhG*PFO&1?;dARPN@l^T7PuUac2NYX<ufHB-H|bv?WO-_=doQh}q?y>p-4J^yf*P_H
zNlDtrU6c!3@hV1owi-So4|;I%(t6z5H_qc*Oq`y65N2}WU@#6Wv&*I1pHUC7{=7VC
zmRvmPZN`3sa2AE#a{PNvx|7etKxn|=zo@y6^K8my%Js0lP-`!)?1i=d1V;4Dd7pnp
znYr!KmazUgl>h#ab4_Fst4%z40Ge?YbsmdKyRF#SGRy(Wz2P@rqO&R3eEFfxfl0?b
z7bvS_({qc~v@09s6^wkxh_nuN{M1HlSdolg+~6k`R^Gfi*$NPT$qLGwoNW2Tq<0g6
zy^sLYf^<=~NLC<+(90#3OgW?4r~gQU0tss=&W5(Cn>+L^VH0i4j6+fM$*dK=el@$U
zCW<U-&tIH%#~z2Um)f}i!!9auG=_8YWt)rj&h;f0HwD3#W4rG$K!bHv$6-I9V8PeS
zLi>pIkAeF*&(}K~p~xcS=bT<^Z2(|6rYM;6r38u`;IpLf)3AJTQPIT?xQS%%8%CG|
z*l2h%HwA><l6wLM7hd7Vlg~O?f&x=OgJlGH{hdMf+KVlB%v$LHrEUt&ulh9-Q8YbW
z?hQ<rG9M}Zm=dyNHVvDK6lhuF1J?cXdfhsU+yGBRID4=UUS<}1&^Mwt+DQ27n5$1%
zlDDrt-a^t6@VoO*!*wigt`zOyis}68Z5Cqh&-V!yPqnq^mMFYM(J=B&Km3)q_lkGh
zZ%q@NfVKoA_8d&3d1Z8dT9LYmWL`7lu_()!imMFiZ)nW3U_Su?`Syzc>mRQflI!oj
qO`AJqB(_+ceaoNz4;r=Oo&U&szsB^J=)}#7fN%W%(ePFH5B~;})xz2U

diff --git a/reference/plot-3.png b/reference/plot-3.png
index d0cc8ea45deb84a386fd4d7e9481aa8e2e4ccb4b..7480233b17ff2ba32dcb3a0751dbe6e48ceb970b 100644
GIT binary patch
literal 28142
zcmeFZcUV*F)-Q@nmt`xmEK8(`Wd=m5ARrwpOducx1w@L{yMUnwuq?39oC&?zs8Xa$
z2Md@;Z=r>t1dtjCB|;!MGr_gLefIhG^W6K#x##|I=JROCJLi1Mct`z>@#e`jeJ$>P
z@c)C0i;El7zHG$B^}8Pz*Y-QRcfu`0aW5C(kDYgPwJyV-@U?cSIt2dMb64BUn~O^*
zoc(WG^!TD37uRtv@bU#?zm!>0V1ny)jOd0(`d-1dZI6yC8XkXo;Mx<yWB$X2uU@@+
zK8i@PzG{3f`>Y>$ZsJalUPR%yh>6~s2fkJ(ViFGEPG9UX1S}tCHy&uby!#&yk1L*N
zcz&r}_-%l*Z|dOnUQ%^#fNzL;dr5kUbKzGenqCmb>>YGLaHjWv|NGxWfv@B&tm~9_
ziel@F1@CQKH^R}~`{AK(x?4aim^QjfGv#L`7uVYlNlv>sGidm(z3?0RuTtC&Y4)e4
zF8Fr?vX6_)+9@i{2X4-@JXpikpi`!RzYPY<<6mPpjG$VTNe`r#1tbit1w*>npu(l~
zzTYh!SU~f=0<<Z_5J{YhU_}DFqDmEV%-Mr=X_j(els2sx&_WF0C_|vW0qr?Z(Yx#)
z1jUQ^UxX~M8|F}m4(&cbHoK_G0Q>_WEp!wHRYxQOl`{BvY>+HIQuqDULn3IabK@OA
zeJTYMqYsvWma$$2WdnMUmG&A_IDv8^HH4aMTL@GGl`7-GmFLDsHf}*7`m}V^V_&Fg
z&C+>jlZU+|qjp^|xJBywd9P0{f^ihu?-79(P*&9WRbGfqLShcN#|h<|uL7wSIJA(k
zLEhRCA@R1112!p39YFj(zKVd9Sc>J|nyaWLv{jJu!w}17QzbUglFJ?c&1|cM-)1ci
zwa4KukDea6*3AmT?YPss$85O$wgTQ9J1cWhH9dcdwYCjo?_3uq367{z0v!658HC+M
zB4Z)a$nW%%@Dv-MTCryaf|1Y{(|F25-J`6UeFpj#rrq=<P~lBWkI?wd=-G&hKq&d@
z>{3QVxxvRIfO?ZcA6*5)qwhwFL@w2t>YenwmM+wlx=5lP)q0>P_T)=2k@00W#=+=d
z<>oreGkq1_@HUY(Ljp$a%bi0tXkySz%nasLCYczvoct-!X}F$u8$1<$PwTk2Pe<dv
zm||s@ymE*??V-7yx3Z)f0}TnU*IrvrhT)n=S(y8aYA%|tAow74pn?9)@KQlH&XdfG
z6!DWLECkN-TxAk?C!FMoLrJv8568pzJ@!pkpNmf#O+|W{?F>2E8McE8jOdQlv9^VZ
z3E27U<kB>+shZeu0)x*Ya;ik^j?BA*!Af{>qhrsGebzPVYr2&i?%-FPo!<Ng_;$6-
zoUoo)|Gr_S#zlM61L5zEB=KTZ&*Rr|%`_fuGr7|AcErH@hW;Zq!}KfIW7u!>{+5IS
z%mo7UTWufU|2?KoI#-ER2521XpoZZFGQtr9r%36a2LY9=JUNT_<CtsEB+rw#pn0>*
z1UyZ*Di%zc(ChPB13nW2@_dpf%6$p1snIz(WO+m*O7Y_y7CaUMJvPJ2x-6D%n{&Vp
z*H7F;%lS=4?NuS(#1`AnzYQ-<M-IH3HcMS~F@nxl>~pMlK)R~<Hcb;JhnLee-$|{^
zu-pqO^|4L<RzPdpX#O#(PO()Ly{AA-<0F~>#Qf&~2c*QqzC)Jrl!Bz-=ALCG5j<9I
z-Rn4d=781N7gpVJek@z<l)b5qDmnD7_f)o32)){au$OXi+`u|Fr6XyJJ)*iyFU@-2
zzGXh#XzfY6t5${7{XgWADT|nORS!+ZDd#&Xo|@?wTwbXcXSSb?W=Vg3Pkm;qx@W{e
z@hJJCpv>{iD(>22Cf&ihJEgA_|FMhb@!R>2k24R|c<>(_yLhMu>j!f*ldW+a^+*yQ
zOb`Q}Pi(Br<W^k?$`xpZH$c_;O11c=!xXp9f_mA6vWIZTWU@SFmA_;1XOcSWMmMuW
zer__);uEn4=Q+X)F6|kqcR8M!kv%k;?>@0GB|pc?ZA>UHtZZ_Hn){ZWlp7gMC(cTp
z`{$ai33-isx$*$393|E~prH$92T9=ZV$tDLY$r3sk%7}1XH=fXM{ZvqNnC$K@)2{f
zl=>9!+{U`AnNIIZVNHdqS1&77V5Z|^&pR}jf{7MCh2_!VJX6)iV^z{kc4ueg6sIKR
zCqwGnD}{{e_s_G4dgnnXohRG;2Y>f7w829gB(1imoFNR;!1S|a%zC05y4eEMN75c|
z8=F^r!8(n9AJ6@R^+kPd#(rVH4xka(AiqC<s>EJ1Jy<}FdtKcXnV6&cBYP&08Cv*u
z4?OC`Ikktrq>FX$&-N#*_peSlF(Q(D$O4NuT_9BnyFIvQ(KTmi1{!|8M%O3ZEGq$-
z@9bwY9Tw_>zYvRK_-e30(vb{!Q!-mbOJLRRbz`STRZqJQXqiN@GCHV{yk44P9(Ybn
z;3&8ETxivWuPV#@%VlnS*L*RpYkx88M_*7sCgIBN?-D{!+si80-J*X!e=8PhNy%jC
z(OxE1O|I6=(=TWmY3%iDOTzJ}2PaV=fC_?_)z9FMiRmUAQ&Y>fHx1tYQWp0btcxGK
z$J);I>^Ntad_|QcR-yr(rAL5+Ln&#T_l}Py>0-f3`u6FaYU;rzt!v{n)&qfNRi;<1
z^g7@8_J^RKZ|fQ{lY!|+;0@*H+K$PrCIRVsW*=UXjgEiqeF1pOo?lcJ+uRb`&?}}=
zEEEq{2QC?<U=d&obsP0HSUi3Wsg_O-=WidRs~3;7kHCXTR(H5ud*ye~J(0E&e@75r
z;l89NY9LvkHl{vF2Wh&gG&z^`V3~&+5q?()UHjciNHOcXB&_qm`uIVwe=f)vHi2jD
zt`1p>;UjrP*QlVBKo>nBjmQer96F`-c)V!)bOHZMme<)IjD;C7xs4;<`*cDT^YM&3
zh-4&<>^#kMnIsc?dx)(}B!MhUJ$j851~9T(!Yr`!YO-_aCs<qJHuMZuuqL!9bH$Q9
zFc+72)4W)9GkwVVN1pmzVS$rMa$|I&M|$z)i}k_gU99c&DrDbIxBcjr(4VV$QzfKO
zb+pS{OS5jK+V(px*uP*YD@o4do|bc}&J2mD$Y}XKXF`9yFBr!Pmfs9>yHoC@Szi^o
zpO>LlF!X6wZu`2blV;GSf|+jIWT4Yox(1hwojvSGyzO1dA@6p+l<xh|UMNlW#eB38
z%aI-^>o+$%o;}Z4=#mIJm5BsL%1iI^S9HJ&t`@^*t*4N2lx}&<NK&KjlUaoq^Bdb>
zS<7}A^mS>|P}(HEC&d)cHeQr$rbDnm-PC@-%Vi1%E82;WvXg~`u6Z+Q>XZ7yGHuVG
zl;^T4p7VxNYS<05&V!0Ja>O5FUfu!gq8UWr=`gp;v4BdZ>eCPugiYF;-*=w+EOwp+
ztB8iAC5Qcd)befUdL!@PEI?R5)*UOAt5h$7*rQf4zd8u;7|`+ohU9l;Y=Cqn#|-rS
zOuiSd?q>yMi3}*(`3w!)jLC^bpQwj9*;pgPe#2z{kCG0XrO-kfCF#x>oV?1+Iz}3i
zSc<A5FRh5-sjpE7(Y>pC2W;JonlM&*JqUgXvaLb9CUvu9KKKnG25xqT=n^nBm$K$o
z*)CYwrGla5gWLv37iWZ4vdv$eNNj<PHB5NehqIYD$fv7RfZS}Uln2{fM)~72fx>AC
z`VOj|j%?u*3K2L|18a~QUJn#Orp#+h^tAox>Y(a(zuvL-T|M%tqZY}Oq~Dw9Q#~yA
zqqxyjwj03N(l|#ht{;OC^k(oTD#SqN<VpnM)|f7MEVg*@rg^J~zdpqB9lWpi<sIWr
z2JB_Hx$Dq+4>Skb5bRxFE1$wA^L7O1l#BR}{5BXOg)ic(*|?mlP!oTH0Xt-0(_jZw
z)6<=xoTMSxH9rYj#uQI=zbn74fuy;BQF>mCli$tV;g_#NyF}O4Pp*fEj(aXtmEDSe
zW(e=wY%|F8*G{hg@<U+fNFwE>n6S^j5+(d%VvyJ3SFz+=lF}QJ6PT7p3%p$vw-ktn
zN0fZ$J_sA|b3bCmqP>Lsm5wD*m|}aLl2Cmqw~}e6m;MyfMcj&^gosqS-zQdO%dKvr
z4pv0#B_;-vam@vVx?ta}6xz?#46%}x8%eBK8k+jN`0At$B$z~bfuqt>#qck`q7E9e
zQfO*xQ#))@D3~InT1mu!iNnj}7o-%*q8OdvflxYwZxoXos*k6jK=mZ|JD&W9P;Ca4
z8fnQCBFesKK7m4pQc@`ooqWcMJZs~JS?~t`$E$(TqNf7i%}#K;n-{yms;V>K;sU@v
zP9xlYL<^P`_<E56__4jZ|9k!ahyn=(iB7vT#Z^z@_hR#$tn-Ahv+<NifQh`!G0rTv
zrzF~ze;X@(68t;+`@7%rlYO{KKg>WuV)((GP09-R=b)5_wFKv35l3&rz7cGe&~EnE
zQj0?_xtusp=65)dV_%X8*w(^Vhtb~+Ww1EBi%bM|0ZOOw1)#gWml-4u4a<Fg&weh{
z?&<?yfB%~h3On1KH6I2PYJ2rvKE>%YPEz+=1lb={CoAn#Lf|W>(ua{yps11@-bt(^
zh{I?{OC&*i6|K}slff3B^`O&u0VqfYZ=tr*9DR1gb0~!-pQ1Xa$sUL+j4y7K6hm3n
zNnXObPCJ!cf=a~Q+;xjL&p$}z+GELn#PKnLKa-jb2&jyw14<%@Z{}z%Xbig5>t8E|
z9}_t!#D3_VE4ZLf_Kc;?7{%HAn4AX+^J~<7Tn}*OlYhKX1rwc%lRnzD`Ye|AvNe1w
z41ThMx;$qkW~K3F4jD^p{MTlZp_qqI*J@hZbJywv0-O;D+VBWhVrhdX+#I?#6G_Tx
zciHWs<)(^rTsIOg70*eK-inW>Vu#PaV6E${g(H9G>|H5#2h|o6sJ*!jtW$pxO#D59
z)0}rV&e7B|iq9y-I%8wpb0=p?5+U{rQDl%$?K!jL#EO;)fmcdOkUffZM$D*CJzep9
z-)GQrSd8_e(UZNj&?C^OTfBkZR8_O++UB{EH_eu*A-=!(!n7Cg<NIubRekgAtgnlo
zD!U(3G`o!o45Zk)ZMWIHK!_Oq%72u@95>|mjEvh@K#2ey7*HNJ4Ip7B4i;;;IdUdo
zx&ruR*|Y;%0L&Jbu!0G;m<;9wj=NV%o2CKXdT#6U;Vfqf*E}_$5)@RnS4m3O>*wL`
zLgB4qR^9mS{X5v>54yLOZa2N6ub23Q(Q;)Oj<WV*gU;Dou)P>Ap@TKp#XNxewub2r
zRUrIbAP;n#8<O@NH!iRrNqY)d&G1eB#PK!AjqN+aej)UZ4OHE2;r>ktGpJf-#1c?L
zGZ1T+C!_}!s>lA}^P5B``zfx{CfzP%OC3hDS|hKt+A4ir@%nT{CJ;5m_9o!VG19>P
z?suQIv{;{z&a7Y&{93F+v!cMnr!Blh^HN@;N^o>Ou8^-|UNn}rcfF@w#NTForBing
z8>8cL)Yvn!IAknFmU}^R&8YD}ChLlii%Kb<WZ$&D!rr-%LY!i6xsHM{L9w^AvczZv
z)49c);tzIEe`3mvf?eI-QaWR48pP7X9Gr*6Jr20n@Dyg<U<YFYRkygvAbAd(a?^ja
zLf+=O0~c%N;24MCH}xO*Zqy}EG?d3N*dPr&EVGHW(SU)DnK}J_s)9MR5uH>tZzYya
zy!V1VA<gPMSeLwQAc1MIl;l(*9Z8GcK3IPm?<S9K&&Fw#EO)RU3su05rbg&Of@zcz
z(1*Wc{t=MHlU;uv@2xWKp?HX&{S4RZejFtOl+yMJgsPz+1YQ^zz&!Otc1-{!pnjUq
zarQGEKK2XxeRH7x42O}pbnjs0+3lfm&?rX|9L03c$>geax9s{Yng1w<6G9K9Ld9oq
zVw(d+*Z*Wc-0%<Eozk!0pTtLF^W3cSQjP2vNBSB}*|)g#Iwo*@HKGP8f3UwfswC%e
zr2fA_x&9Z}>;G`WmQt_9sw%Yx9lvWmX(cShUILe110QHFpsucp=iqc)H%`jIQX2@l
zg2m<ci}oWEKQlbIx70|(4mbV8#z0!>c8)$MFWG;F-NU~?d;f>m_)m!O-{;X&#j$`Q
zc-<~|=flxVQqQjq-M3ir-z@(vcEbiL<BnL_fO*@?X}b+)mt`&_qwE*be%X^3bFRf~
zko;dP-9JsAf|+@+JtP=Ic>z?5R4U=^zyfWr_QP936vqcEzi}rxNG~=>uM&W2PU6iD
zMRM$^X}LtFOz<RPEoky61qV!KhIlE0^Qn{;AS<JV#^GS=mU19*P7s}D<s>&Wwe%A3
z(55v4zAHV#8clx9Zr%jq-CrBTb=2PUf$t?~NZhT>pg1S1bwAivP+BR8Ume@+^V^oA
z_TogGV?y0)$aNMM0#Oohk34#Xz$jg>>ys;)Ky<S9J@x_{iV?6;Yrak>)HREGnO=>p
zDW|RX7hU0K*-}`kEUV=j)mdu~N#7uZD+qJ+RENmHn(m5_jG)b!a>eU}U5fwG?5mYg
zgVr7jRMsk<yXwjr;mj#`1m5cem+;b5knZ3!<M)9>^1(YKSclu~$CsDfD=fhI>U)ia
zGH1lcg%$oh%zm*$AJ*e5b0=1I0c-1_>lVB+9A*9PgW0Rw{z~@RoA+aB@y&c3;=bER
zgcY2q-SXx2_OKpd3o)zr@3+iq>_u3ujgUu8c}4v_vo;>?Z03*}jsPnx)X&bb2jU7H
zzw{v9FeQP4bh2-quux{r_inM!yIgQV_USIsfeP64Isa|?>@$V9@@25LRKP{x=!^lX
z??5mTYUPgLz~gV@_-e-JNq`3Er4N~B34XoPDpHyGvM9K(<2-w;chmAknP(o>s^OcV
zVTcj|L`P8B?l)Hl*OMmvdL&?F0OR8(qLuV}AGgS)_rO;f=97BSN_D&gRxr`NuY$9L
z8wd7`;FVSJWO_=V4kQg!is93N0f(*&oYy+YaX2KwUVPFA^fH26#L>NDZ{b+Rw))9c
zWxixoUDMxwUW3f$!5VLSBLxc;@y$cE=1`3se%qlZ_hh%YD6|ib=JE#-_^&99xAbbU
z<We_5^EcQNTb^KPQ}&(_B{`6(3(0k9bgCp%N`dfm$EIw5r3mtiI(B&U)<Q6Cla>vZ
z%b#ya?>Sr8d|SUThf<Pk(}D2JmTAt4v`;n;9Vr3p5-Gn=wM}m>8gIFZnfebZFz!gR
zhxFiI=08Dd&&aCt41W!qaGIBX1tvG0*$nl2l(wwg+;R4u&{!?DO;i50%dz-_-0bH6
z#*ek*w!C^4OWEc$vHiY<z~9DW6A2B3MbnYX;_SCV|NOGg^6Fw4pe159)L>dII)K1O
z{wMW`=NPvCC+ag_p!@wUQ8)~47WH?8gp?<J^GXM#@jj_E9;hWsY|BCUGiPcC)t#OG
zpd1)HmPQ$aQ6y|o?zb(=b+q#s=&qCaM8$WxuHa@oC9G{t9NVII=31t+H=%ul;<Srn
z;oXQm{55i7a(8{FYSg9i7-%+$GAWOl*QfRA`_}{E@`pJNTtjY$@dJA2l!&7=NLvPo
zQ5{sdSXvSHXHF;Y&YX)K#oW%=AOPlCv%ctzjsCDZ9F8;nGf8LRn3<SW7m8TD>fS39
zOZ&+GbW5>acRbTr4Qq_alg<4dnu1*7cs+=fXsZh;8Ymv-FV=;sje;}lRma)uG2I6D
zGjc<$&_c6*PJH|=+=r>UJ+z?%`MznHub0Fl!MZ*r`lA+>n#`9lKw5`}2Ndr$t>1(+
zkY4q_f4av4vNH1y1-lS#jv7pTQg#+3W&%Ib-iOXeHKDMAonrOgEySB*eWLz%3AJ9%
z1T`;=j?qdLkZwS00(ME?nH8CDOvY|p5pAnSdb5OJI}nOc615Wx8vp`Vdq{>kGw8b+
z^EPAqV(e^S@-krZe&|;_kB^<IYo3W}kCy2>kjScjLtc#MOM8Q93uR_Gl-dqeE6=qb
zdpT#6hXeuG?VCv`EQ5k1KT<0z$6mQI$a%_0(nFB8H&(ql!Vd#e;pkhFOh)tcvw-QH
z2tP%W>0q0bZN;wI3kCb7zx)8pZ|@lqmA1}{&QNdksn@Bl5FI#ycoLvDzVS8e6D>x+
zT2c(G@>DK3NVK%6>(eQY*+F$BM9@fK5d%GHrYnp33~`$yY^UIB!+avEnHCjWH6)I{
z-aAkYCz{SNWz2R0ekOINWHr8_FF|9dV1qmm40h9_y#Pc-){?i`%t4S|s=&+;c<edN
zkC6lfiu(JuucTd<>)WNgV%1u{=RNV>^t^U)K_+VuFu6BadsnjsX^N#JwUxE;EtTC!
zs@J7Wf@wHgGVU^S7*m0Pc8U1wPLSo&kIGnL>DIl&;lG!FQI-uvwo<iOD!<1vtyRTe
zNL{MkGlCW?K~oD-LRK!HvP**JSw3f^PDu>(uu93m_$@WZ7S&<bR|3e!PQJ+U)B}|w
zB!INEzDzFZVx5pDCg}baa_V&<vD!5H?Wd(5XRTbyrc-0heLdvSJgeURRdlxP(IZs>
zR`?B#gu)qr%!LV29|vsEiM>M!kv`Z_ivi2n`9MY#K)?CHbVfGhN&GGz$7&IL^cgqd
zUR1qqMge)^rd;fLaEUZMr-!OYGkNFdJ(J`CZbp261N2nfiwen33As!NoO_C+ej67n
z(GxxwWo@3fe)|(<y+_fXXBCC1*ZW=vOIzs6K8GQ#>$`-gQ_HtkVn!`0?V(%g^9)9r
zt|0XV!PF$OgXt?iNA5yZm}Iz<lb99g>5%Hl4LxYD3fhAov49xFx#tP<b@^2540#(t
z5yD?a$<-c$x;=BowN}3KC^AK@zkO4D&PwiLAiV7UrWc8~pDixoH0~;j`hyS|EOs$S
zhOzl?Ku9X3xc*yg+~2hTVTJ8}v)JMT*AEG76Y5&4m<o)Z8q&bmiWh3sd|KRJh%*p=
zxjA`ed<@zE$5&EgX_@L6hclBNQ#pb9qH!#3TKuU`MaI#2IE~uSU;%8(b4ul<!OaR>
zhFqJK^)2z%kKMIa^=Xtob+@FNU5rZE@%rQ$Y|S~m*r{7=&tAm+5@EKI@#(6w+e!r=
z=EiDv^Si_+rR^nMn`@Sx4Sre1O#*h^xsiz>(btK181tsdw9j`}R2foE#4gcw=m$10
z@g8Bm+Ps2@+qKWhJhjo>f`5`vOwP!`NL^1!27e`1!9?Nkj7yir$yZOtzJz_xEm5#T
zl^((l$Ii<htvBti!w0@=a}b|nV1B~W;o6S8b7VXfWYTvOeEbO;P;Mvte2NtazQEMq
zFEX{<pNrGzUGv2dBD#1pTf*Q65MY~bqTBk|wz2EjWMb6aO_cykY7Fg@CNzwd-M;h_
zm$i>smo?{^8WNI53Ax^uo<uv*>jX0G9vw}(doM%v2cx^4hn=+X{;K4gr7F4j6KD1%
zFe8s%)qb*xS|;)}Ffo-3Csp(-tx~MOf#oGF`OV~-T{E|E6tPs=c5IN#qZ#(Z^&;i{
zObWWYQlI!;B;@tKEuP!gJ?*J8t>IYpzRSI*wb4Cqd7w6HK2(P!oYfMX3MR@cD^}Bc
zV^_8AwUHoMIqx=%(fpV=>R^tQwpr85CkVaV<XV#Y8#lP8k#KG&7ENd96t9pSaYpv%
zWOc{uCkeSx!gurO_RRLO^h|1Fa2m>qwQ-=3Z9-(^57vkir=~=5En_K35pg|?d+Qp6
zV5f}MO|6J;TCwVZ^qdfU==nC#H*S4(%DrKwM~8I^>zY3mav_ds{3$ASQA+3~M!qpn
zH%xlZ!WSLJ7_YFs%RZfjHp%w8A3~X9?{@{N6?Bd4^n*u}Fg+yK22nb}V<lh4I>k!b
zRzd%iKOM8G&y3gWfbJLsBIo^yaeC`2cr1l7t2309)pBRHEW_v+nlF?)mPW!%lha|)
zct(dMVK)^fExNV>)W<<JNT?#ywP9rdNS{g>l1Ds&Rf86)Z_*;Uc=k&w7^Op-I53un
zvUrGosXMfxs{`GQ89FoGyTDkr5d*1-xb&g@Vsf673SZ6BDceD~3?M|1Ze2K?8T=Y}
z1k=b`qWn#z);+k6;Z0r?<)gFhPAcI#NzhdRuVK3ou1(D?t(6^WQ&-C}3hCcXER^z)
zN&f6%ioTY`B7r_{Q&`<?Yr=LzE7@<O2dMAA%!rIXESPSYxScEK*UtA6_NZ4yrDU68
zzsIzsN7fpoD`4O6;wdQAaC@}M-W~&#oL}-iZHNxQaZh)q;9i-k5UT&?QygD0I=4iG
zuA6wkeC45zKQ1v+Ai$DmXgD{$Ozg=N;BN+{-<Y->T%#9ur%tJ4B}HH;Cks@vl*dZw
zGcV1^AZ;?1*gn&3e~@mCHj5rX4#J~ZXJ;O)X$QSrA>ZW`)*XnLwefAE2FL9$^~0$|
z$&Po4Y(UE(tj$3w$&@|ZpLa(P|BR)zV{fUv*%SinuKQ!s)7Gii39nnyhAWJi!C}%S
z9qpi4$*uZW%B9$)nXc4tYmw@k^V;+oHOB+QR20e7tq>6*CsqjhKX{!p_T|Y{vPJNz
zsq}>OPim%{);9ft?1-?*nG959Ru6Q%pnRGalN+%|9pvwAb(zN&yH5_EdRy^XuRQkC
zL2hxPRARk<@98?TDet=mq=({YlPH-!aadO?d|!FHTChwdU2{QY6u5`YBU0BDCBF}P
z_Zb&>Gh)i|%khqW78BCoyPmNxJMY|l+<c<|b0d8n9Ddk4u9l{8hn)`oS)Y{DSbYLv
zSc1}l#>Dt)E_AGbX^GGmQvXy*5y}@y$Ri1(Ej@H-5n<nVG)+$6%$|V*G0yEL<`e;c
zzws4$wUzj+<kKV42qiNIB%%8^Z>Hy$vcOhAsTRd7ajhP{EPvagqG`TXg*9S?(s&x!
zmD*c#R(+Vi!r?k0emRFt6QQNz0}OaFnq$E#9~~-ImO5ZOEAVQ@{PH`agvRRP&6^YL
zk7r5e^QVPk%T0Pp{arFz6;TJ9H*4y;=Bp$&KEAc-CTZr+^AmjrYYGfVVT6_Fo1<lc
zwY9Q0sCwTmyVDwn?4$7;10TctN(Ja@KNEH8Gh92^4uP(hPgTeZ_xjM_nKPShb2)F-
z=P~H*XNy%~|HyU5=$Ku4AT=$WcA@Q-k*gcD9nR^k;maKQBItX?1fpBZ7+0b2drct0
zOS<>+qAMY3WN#6Q;I+GiucmE!NZGA`P>!#5xL#1-{aYb<=1FW(n%bNE9n_$9kQN7o
ziw>L`XUNe{eAFWxI#Ca2Xc9x(Z>6mBoNDstX?xT1M;bCwavwZLl($V)NdNw%BBB90
zpOl-ZV?m8tDUIJjCD20wU%O?riH5pu&2--A?Pk&91@Z-9>hfksj8ApOhEx&~c1sWb
zLk{+?+#mdu=JlHk_R*uy@Ew66;2_aV7N)3dV#AdHU8t9_9rh$WWSFei2H!=&gB~rc
zZ0$X={@7`yXrda%7keX%2mY8l-dL?XEWPAF>l5-II7Sz<oU7!7W}<wf&)?`Re8bq*
zeY(CnO?GtMN@sJ7u@~)LRnJS@?<m@+VrB85V*aNil&U2dC`ca#W9t~wFW3xtZocLo
zOIyu;+h$bmG9$>qLf&(cwDXRwKkTHa!F4fSaDF__tWpKfE#?wcr%FQNua=!f+^0|B
zcw)=nxJIm$OGqh|TU5<VOWB@k#$am{?NIL}K`9+tvXgr^0Z=+gB8Notcfgoroje#U
zW@qt}=Z!?xT^l$-p#lYo+BL0uWJ-K{cMVB?NY-65TOJx^hjt><kM3c2Dgu?n-54J|
z#mX+}fp@+W?ZtQOVn+Y^*7t_NSezAW{@^Ewn-3XFQ|E*m$A=u4$~D9cRRYX}LuF#g
zyDco7LUbXafI+o<qmT{LSN8mX-|chl`gGsXG*r}I6BuCJArPIEy2&2$na|w`>N)ix
zf6Q&d)eFLUX0MAYT3rbR6SZx5Jyf-c{zSn&mPcC$?>Q{i*%695@+`VGsY7yEg6vpJ
zvZ8}D5$>-^SWn^+EZXEj9YbzfrdJ_ji;;adqFTXVX}pWF-I0khF@c9g%CoVwr8-eN
z60tf<U_ZB#%=o3wbVTKGL}&FdoWii{-p_U!@d@C7>RiNfdVaM$o}3}^ZM3MTw|U=4
zhLZSDU6$bfaGT-gZ)`(iHeGRb-l$b%Evs$u0WR^qN(K-d5=SZ5=cFmP;;aStjN}gB
z9?iSi)UoRUxf*h9p6qZ8PXyqw@CO`H=IIn7y}pz_gl}1!kEJz!B`kab>aq6fe<bMs
zF$;rmZ}EFT{bZj%8gkKajSb*`jg5z}m;L9#nuzZRFpad3P~X4cC!U(<-B3CgOBs32
zMsiJWG{v%$IS0V~dLYG3u!!u^e3(ZU_FqdNHfYOAq9g$E3Nn4puwN=|P)x#&aos3f
zf^I2sqW+_Wsc`0=FyZ`6$#hL2Ya`D929PC7&7r*|Kk4xwzulEs_47|!i{XKZ6(+Cr
z(3c93k;N_#ZGN+W<HuHbJvTB!kj3c13-C<e<%=k^CJ9WIOo{l`!^KVD>X!|T-E{4N
z7vt?`CYh)0?7zGJYhbJfv-*qR1WE^BrQ?Nx0?*797<l<ZilRz+-t%tYu~bUae?`kT
zUwx}MGj4*Yba|vs)&}R9hLAx<%lxL(rNvUXG;up5h?70FJ^{odDo-w9$Mf{F03~mD
zT5sFvS)6Ru7}}{yplB9$|1*pfyHOP$8S}k1QAONoaTKko`o>n+CaBbQ@Y`$w<)-%T
z28ss;s%Jl1Y@hDCF|DpLaWreZ21ut0K1_i+ePlHxheQZNm$#A`af#+e8oCwAr_iYM
zI&XAElw5;YZku(%(ngEHcXyDs1x~gYK<y$*!4Zp5$y>;dgdAB{aqWFw*8Bq<(q_PP
zpUL$6G4F_OSqa!)EjehWr@I~crUEEccTJQ7L(!-D_r!3}`_Qmusl<1t<XoKZY!2gM
z#)ehE^!_}Yw9DzZayZ@Hq`xK#MuFhZ8>Huy_kan<w!MFHnFF~=m?IH>^+sY;GRoII
zW5Z-hM2-l@qZ#uk?$2Ec{{h>~JHun(yB11Z?r8-oON~MTp?0A1>=H3{&Z6#i#?kW2
zAAQAF0(tmcDs+Yl75*H3WcG^fqH^&U7rgY8LV8(dZG@&(4FFoAYeb;>P0OfSRaWa)
z=oaakgj>n~f{kj1&~<20_Z;Tkk%@T5g40g<Z6ECyMy}<g^WCn=p9*FT!q^hKd&kNZ
zKFP$&gZp$B(oi|V3a<k)yH+z#xW$YA%3^Axbk9ljv}fHTIjI0cZ8iB)$RapxJE{CX
zA*V1wG;Z+CtR0#iQXn5Q3ekiLd9umqZChY>gl)g<j<M;XoxOz~{fluYY}D$yRv(@H
ztm4it)?mwpp39T4UdkK5<*F;=-!q7&`}d4gqV1@O&WFdAm^+)R&f}k{j0>MT&q+FT
zT<q3gaL>jS7Px&*G5A$PV7-=L|G-xS?_pxeGk7avYP}W+fjZ-pj@nA))EjI;@C~`;
z2|K8~T^2WP>#wYs$ohROO#}q6s_(PNtop~M2@z`8*};VbfiOkRJ|vYR5ch{Di@oRM
zRB(#}N_Z)5K`L@BF<o_Ff&GB>c;duCZVfPTj?oFE69^<*x0hS6J<hB|QM6J5|7YSn
z{YPI|Q{~&2p6pdy_nPZHs35;yF^BT<Ba|GUguPMmj$ea?sb|?Lef59NUgv)X{1~k#
zYeh*rsC}QqOHQW7&)@uLAlNaF+h<umIs&%o(cS=}S08YaRgPcuV;`>Nx*Q?;2Tv4&
zH3)n-RC5Bq3l%4y-S-RJXIC4-SLvsBJE&nmg~?z_x-H5eSqMt8aPl^X=9&-8?)W>A
z$S%(CGKL~kX$FuW7Fx)!I)(QH3#MyEa1_r&PdMt@wERfPPU`7ZeOd!RN3xIu(?NGt
zze%|Kt2`*+$-b7t$&?3Yn>aXKo~(hM*OiGv-s%_|1pj}QfiB8H?m`2a0l44sZcDE<
zOJ{BUgque$j>lb{1KqzsCc(-r9;$gwTNedK;waw%jjx2%)S*WzT^0|0XJX3f_oCzn
zd`>BF2p{WorjhYfXAQ2|(Kbs8{RX=M5&Hgagqzi9`>s?pX6F1u4)z#V4~GN>IXHvI
z6!+BsoG3@3_Ka+v8UHH4?~PbrjalCg1z|Kd3-)g1r%k^l4cg8n>G=067x$?Adm2Gr
z{vkaDs3(e16N-!YKeJQl>az@=dl^CBzHg;1Zajr2A#9-77<d`-7U1_q_=P;Qu}GnO
zh5=iCK~5srbX*;0tAH;974(&k#1*!R(eeLGTo>fX`(Y^vka+@c$ws<gaz}AMY0c=&
zILGrPa($U?GrJIL`cN_yq=uh673F>F(juEmTzP)5R7WF=>)O{Wu&Nm^T{x}RI*lL1
z$GTY%AOWPrRFT$p8&0#6RAV*j(%3uIT50?q?r8tJAII2I(U98>W4i@mO}G8mZ^6D4
zoCiabab`6{`K{y%*P&7+yesr~Zs5PG0qKQ7<l6oEv=kuPz^ob_-nJ$r?zXC1Yyl@%
zaLub6;XG>Cy=R03rKC~tr%KgS@y#mZ4kByDAB#A1lX;~E4dY}F`Og2qSd>ZOxl@-+
zL=qE?U+BfM&kt~My*Po)Z&ZPC5a0N=nI&jg6fP|Zw(N$>F+ApQbX_mgOIsb~{R5_m
zaH{)*RR}OJ@S5Absg4p89=ayM8RqRpm=Abw5DN8*(Vv1%ObF3#r;o7jgkFD{vxE9P
zmWEJ6*Sb(|z(pDwc`vu1^c#P{yUQ>k_)2+2aC3j!wh+=^y(BYGQ02Z%R!8aYyeRTz
z$0M<r`(__fj$SzM$GM|B4M(rrRvmZz?H}2@FJ4=?a5DSn?u$}>2Tpzvue8?m;=7aZ
z{KSLHuM#wmYsSRH-2DRTuQQ8$>&vQ)dduDhn9LOvv`_l4qTV_emaTf2%;nBAS8~hP
zvpjOue9wr>Q(E8I!MW==X}4dQfWJC!JfI)CvNl?WQ7q`3-+yIoO!5~p|Hq)t0@K?t
z_|wt3v>P}w(P)bf5#NsF96_1ud)YyoPcg(J44vjbqp6@*$y7Lg=Uw|;;znt~cJ{V~
z`V;$hkBCDxucU-yDBfU{kdd!B1UoXf7yGBc;Qqh+b25jbGZfGGD&c+C=hNYILtV3a
z%Hk1wn>xNN9VY4Ivctm&;clQ$oBV83G5dBYCQpB&P91*<h3sUnjjQ44px%NA9(GWY
zDXSp!bZ=12W-#tdQJ^d2Ky>3g(jV$0q9lPoP!z+Txn-65T$3Bm2B%Pl<j!w7q!$yf
zJcwWOWx#lUs+iim1$o}_<yNNq>Uovk(to(Qdn8Wr!M(lXlSHgJLL|^~(Cu_7B@`fL
z1R$v+o)R*=gZz!KqwLW$F8s!yHg5t68pX3@@!XQ1FIBf`)5=Qa9?%<et}a^kT(p8a
zTCxv>R1lR05F<;+nxu|r<}lNkUJ#FnzX22?{F*EY>g&*c06cloMQU(nSUsJRQq?Z3
zFiTwpEfXk);3|?<1i)T)I*NOc&&~m2>3iLU!VzoNK8{7re9LKLtOJE<U1t?uZ(@fP
z@D?bGXQHA?<^3Vm09ml4Js%8~#UFxHkN3*obIWy^tOc0`bs7X7Xu}hV2mIl3Cb($G
zUZP=#zP5+$1U0gAe)$z?d)6Pk0?}7s!3w>ynsbgdG1iO~9DLC}h7Eds0d{*-MSHV}
zWvV`r>P)HDovfPj0#@2iv}l<;etgB#QvLIot3sCTa+%lLvy7TRNLOpw{}R-EZkGDu
z73oxS#P10mU%LXwNG=gR&AfA#@_)VW_hYByLa!*mv6~V;Hs@;Ne(!MOCqtg=L5=*I
zq^fK>0SL^}d81zJx50O+ZBYtcOUgNj?)Gv%(0v=F+GgMF^MJ0-y)ItiAU{iYLvQMK
zwJAQmf1u5P8d=3UI=3n|Xd&}o!>qQ!Rb@0e&>ef`eifoOd$1M~OznM`9fz%PiDVKU
zEf&D293}2bIs*X*AkwQV{?ATq*5-_`@Wc1sPze=xeeHhYqRp|lZV>}%{GNrxIDniR
z?cH`bx`a>d%=KfF5kP&6vvPE8$vVGN(kMw3fBj1JR|u}SYc;aaFrTTjeBMhWr}8h?
z(xM-X8+QkBp;l9uNA8(7J60H%GMI<I-x=eCHAm-Onx+0R<f56n$(SB?i)5CIt`)wT
zAKTe806F)}c|pxQ{UMH>OsPvzCD0D0-c^I&i;jsNDH`^8+H3}-xBuKTl4Xnb@+Hep
z$DIl%6W_54Gkw*zp?oX?Ji0tJ8cYV~oK#LL0Bk=o)V@HE>M?O@=d4C<KPz(Qrdmlh
zYiRqb@U>YD_Xeg{OrIG1#SFfCL3PGcKcHJP;-+S>mW78e<jWgP7Vjlz^JekKZuA0$
zx?Ypz4^t0e0Y9=;seeyG^ZA+!o0GOa#KSH5<r6Y%?uN&ftv=r_=N*4*&PnzuJQR3J
zli$8-bsgv7+Cla*WcGz_s%Qy%DB;V{YZDko9PH7=hiLRZFz+t*2*7`;a9CM7*RmQg
z6nBP~mI=V&T?I6JwzpC^^~_5@Lx{IbfR>3SzuFksxxy+s&9Z&CKCVqh30&E+HycJ}
zM4!?+ZAz;x44JmNY^%^vQ|$m<nx<vR`CaSbF+zx8oz=-U#2+5!H$t$qhufz&-k;`K
z&)qay{COS2IF?Mg57wnkNxurdXc$aQaHE5<hl2@^143}lX|{a|ZRb+)(;=77@ht{|
z(98_2yGiwkOlDSwqAfGurj#-BDz$J{dMD}}Jn+79`CejQdfJHb<LZ)tyT<h@Z}=Ol
zQqJtz=m&OXDq_C30d#%Z;xdyNJQ7n^ei)->-@r6KjKM8!j;Qt?-t`p>z7uIWL~Fhx
z=<9ebM9VP5j-j8sV~m-T_SOxI!cR|5K}YVWsnt%M80^<~KaqJ`B=Oa^*H@B7_P)eq
zX0u|$nonolH+6}!vQFJknC2m9opW3oty2g`N-8TH<ZuO{@J;;t83Roz(aPhk2i>$X
z)^E(dP$$gg0@24hiRLp^lRW(uvf&=AnhFPEbp6W<d2Xi<Jy+4{LAoc6)}H@(=&vE{
zEz2G?uhb%Sjn`K3p5<5~U;9lFa6!G$aj7hCGhjpSM!GIGM;@Z^JXN4O_V0w%L-Bg#
zdNa8H*fr`4Kzi;iV|w7xm!0fW8(i1?jlx7}mg&yQ_cLKXb<$;-o^dYOXff(aGH;en
zlvIX}L_x&-X6q{^IE9G*>KCoOLMJ0~CgwKp=5~IbNj+11>&M*S0kp}osGk;M&)3gC
z^wUlg(pu9-;dE@;E7IqjJHyo@^*Bl#tbHwwDpb?w_~wePY>J+X*qhL?3Wo(qji9<%
z%CPYv8!H8CmP7X}ck9H~)Xc;it7VORdTtegw4ckF+DFT3m0t5|g-r9$wSyiba81K4
zLk;G_t4+LF>auvnQgOgC_5G^&`nu6a1-l3I+~v%|qZ5x_F|i_hy`_-QC=wFQ94M<u
znGTSmMpqrdQa-(^`%Jao>GS61lj13XYCO*o1zyfBCEwIpSy@#<_}krJ&24;;A5^Qz
zt`P6#SE|sHH}bJFEX6`WMcMR$2>s#_MDHD2lPsOSv~=fya9O2wQFO@Uk!YcWk<+ae
zZ<5A*bYVjk|47~DQxVb2(ev1(yemq?Pow7r6Q)9z!5?EaN#`1qjajy)iHZUJtDlVb
z?uv_+2Up4Y*{rIwW54x%>jz+v2kE{ZR+l;AtLM<!iQvq!b3U&3we_yn-vj-MLVO|f
z!cKCZF}^i+z5SL$kT}>OP#h4@6yFnV6*r<y{&U=9j-I^mUh4>^rs8(8Y-6DQ!}aFF
zD+s^jA?fP}>9t^;34JNjnDy>_Zh<#FuZ>6E^H%3X<e^W$pDQ)UW&LA+(wiW%vBtgq
z!o}&IPqI0uL$9*0b!z)+{N7ue?hl#nCO!2*LY{DqXKYov6?LdWR(@>n&eEak^GpMz
z-|R}%XzZwvJDfTeNwX9j`^Y_Xv~lmLJYTP+KQ**h(&I0%FqI0uJW*e}gMPaQi?Zpx
z@t$3x`}$c8uZ96LAJ9-;VodV;jer}uzz<$1<+^H}WbN~&biNLElYl4VHlnINi6-Y=
zKd9SJEgQdnJ2a@D=CnK)c*oE^=|#Q2p=G;<h;{eP_D>TTYDL%Bbs?orGcmNMH1p!G
zhn6)!{n3TxtA=Swl%_jYl}&9t;iW8nQuF;of+}FJG0sErws1))f`$-xPjzlA{gdk6
zU!X^^>biOE-IG%J2XyF_s<XTf+^Kab>M3n<*DNdgRgf1<l5XGnDZy;<IB3Jdg831y
zc^#^+KFyPCLyuWs%{?1k`@PBdR5;do2d!-n<Myhh1&$?$zrsBf>vCS+ZM)v6{8VeW
z*U|yBnd<}_pgB6nG^@2<GuW5C^y3Z-opZHme+dG=>*>01((%mLF^jlwuZ^S&XH-KP
z>QX;13d=Swn$#5km|W$aPJURC=+xR+jidZ!hEYpDca`D&%m*vgw(H#8`sESVtcdKd
zcd+asj`Z4~BvV!!fC{}>zgufZW{q;ic<0k7g(b>-s{{L(<+E+ZOM?YH)yjCR5i;bR
zaZjZOWU#WLG=I`TQCFS?n@~ocAvL0^Eu(U(BFsQ6Dx~CWv{k^}&UPFAp7>EW`>q{S
z1Gow-*TuI!uWofdwwGmUs_${w?~rcJRdxbz`siHfo>c5KH@y~M_zLb7h-ZUZ@3Pa<
zdY=@;y0ynN$763jr@W9~5BkCH7Vb6%^xvq-cMt4)9I%Ubaw*KX`?N%c&T>BR*h;=}
zQJ0b7EA{$22m=NA0FqHb<FCBkz}&?>Ma7+~UmZzboCvOS>s+m9cHE`s7zpuX($SH-
zvTCmk_p5GsQ1TLuDz1SpN!%~i69KYE|D?FAUt@Wh>I?TkWD@2I(}8%Nx#r~1SwzoJ
zl>eN)^nBF9O>B&XvQ~P_t1T`MF3f{o0ZmpFQ)X9$D}+iUB_hfXzH=aA^7GRCZ6TI)
zufWd_fK}nw*C~TWs--grU}xOt$au9kCQx%P1`Y^Xu=ODAx$)9W8jj))&c`@QCX+xZ
zeVPZz6tii?So~cJ(94xeRhXqe1otE53+JRWlb~<<IR|sddS#~vl)w?}w0Xr?|8t53
zdTozWxy;ZmNHErUq!(o*nYTIsgd|etrpEX8ja|=SvcbKPoXi(51SWw>2Piy?-!PbN
z%u0akb;D5>JggY!9rX8UD01)#JYt6Xu&Fug8WU7h$gD@I6na$F`e!=8a?oI3jHvyc
zG}7%Uq1RkAHU39%!I-J_B>`W@4VmihHWP`#r{O+x*lCTHvEWFD7>CT8{ww~VW%tDp
z_+^Pq7^ilyP?x~_Do6aaT8qGAQONB9#)Euo+<CVP;}jG$yxC{nJHqm(BEMtInu|IL
zws#5CGM0pze-p4Y_g=6h8fm~Cy*s7_R9X-($jB9fG*QqJM|lkf54)W&rI^eN6^d5M
z;<s1t%_!1EMJj|Px`=b)CV@s9sHS}8ctWKc*fyiQ`=)O)FzG9F`I59=*-zn?;kW`$
zQ8Smd?9~N#Rb6mC!MW_qcI?J+{1s3tYR_j*4ELe*U`;c^-xW&N^CAL)#AvEX4-BA2
zRc)l_h_qSWi`J%v0b2X}VHR)Kr_tru#i`c85*bMkd9C}Rv{#V+50?@G;p0-80pdcd
z9vN0r$3lAc*`$>50BhH3^T*}+m^HCO0;tU{*}^tG^sy{7`?$!voY_>0Rrf6QOmeGz
zGMt<-Q+t14qD>N&?`mq<ze(2}GK$o((Dwo-RGcc~NPI!e3{l&arBW5VS&qn$U~gz<
zeO1Hh>7qUdMwa2u%4ATYTJ1~J&dOxPK|yC~pRH|z`wQv$v7;NUcgUD5VSCZ52tQwu
z%JS@`Mu4%}JD`FOe5y<Y23+_X+d|oLE5j}NX5IN75igY7THZhO@He^gjEvy<c#1lD
zZ3zsP!jE$grkdk~Nk$S&6DPpcoUddg?Thgqa#w*dE3&S%uHFMW<iI``(h&3${-pr0
zOk2Jo4@P=Su(vuq1tngT!(fyYq5iq8!ABqRX~MI0;V9HI+7b%cEc1mMOfwm_>Une?
zep|-FfM#N`Yn#LyMTEaD@xr?VI8aZ!h0`Ecc=$*9R6nJu-F#{{f<vKF6kpA2J*63x
zZdKIE7{CvXl%Hn#eNgu)|D##8%FVFu&SjGLGn(hz*5Q*DtefPDH!@()V17>KA;(v@
zPS19|EK{hkm}+fQadv(tFyp4{UiAk%r_&oCBTQA^ZT&5%WQNPSwhi<sYq-Q%ojbF-
zqEgULujt<Q?ozef#s;>xos|8EVPr@r96&4P%Ar1o+$7$IJAi#_7ni4DAyS0sqwpES
zr!)pU?Nim}&={lK>t*GpRw>yM$Eqf6)%{;VKg&C2R%KO09o%=U@n+Nr2}`l><|#Qe
zzI()Q2y=BA?Py5P6Z@><c9rw%5t_PiC?0qPGU*wI0Xz7p-=%4HU79W^C2>qe4bNl#
z)GkfU>l4%FDXm^lj#Hv<J?7%_ybSPng3@(pp=>0c4ySTFwiN+N(s+E%kvry#&w-H6
zJAwkOC0(k|;X}lm?_LEp+J0lN1@_m~@H1UOIuGhwrv2^AhH}6(BM-O8x-@x?JI~dy
z)3@3fr4Xc{h@7@ehM|d;jy_lWt%FZ8T3gt}K+WD9Hy>#@)sy>qp|6^haHcVELa&|E
zzrxG%iBS?{Nm~uif&9eTxP_!pxVx)?ya&zb8K!)I0~N~L4~ON-U77piTF#Lj*iS>M
z<>jTDW_nvNNq)k;9n^9F=AVQU3>cbqhkxtBODDIGjv9c?5J2o1Iv$=C;BKHDdwBCA
z#M4({G0<(K@l|q#qbT%#^j1z~lWq@L`5qp->U|9=DOs;vGlgESPpfHsO#f<tP_n+d
zsQ-+O4Zxt=rEq(6*Ww3&N5{)Lmom1A4Jt2e%U5}FZ|waQh-!DYYqbi{>RU-ueWPEA
z5nZY^P2EcQ-1{*#g%GI6Id3ZnHiz|@MGie#?wPwy)$zC3Qn--0_&uu=U;qOas;f_p
zpDbbF67#}MGr7n*i!EYyyrjR0cT{zX3=loMqJIA2dxcz^5@jXE<K-jiDko~+%+EYk
zrVDnoKf!Dc-^n#Ji2i$6Xc>xtExt$Ls}Xvpa9`NUhvl?zxt+5iS8s^Avuq>lMofDc
z<31l#_}D%xd<wM#J%!+lzuH4W^;j5BX3zzPof?=QOOzV}C*{|x91N*$b74wyzh(tV
z9x4^XR=qx05h(H_q(rIibO@$_NnIt?-;1oPyNRR9dTyPq$&>*f#4idO;|c}d)obig
z&u)7v{O9<E*Y`R4`1g}=^}7l0_-)k9TUIzC|DUxfl%}d08?~)6?In*P<stIf+DBu@
z&6~560V{I{Re??fxIgD>AYz~_sxGcemta*E70g~hDAF4CVV{{7s5p0)`!D|m&<no8
z?9&vgZ18p3_NUCyGg?T#948H9-IlM|_|-98o7PhmG`UZIp)TXLm7+9(w?OnXyDMuZ
z9FuH*e>8WbH|rS<54M?WS!wZ(@9pd4Vc&kYM-R$Yd%1F2p@O9?kGup8+ofXEN>7Kl
zrm{T)E-vpj?cu3vb-bGS^vo-u-Y}k3lA+F7uo8Trz-puu&3=ljp{XcihVvI!Ld4d-
zHKhDHuakh97|nQ+3eIDFm?!SOUH8fPtB&*N(Qo-+ocYLzYPTHwdnoeDZp);f;c(sc
zz~DtIUv2rP^x`AyoQ!~VSeqW~1pgDjba=jXCJ3Gc93{XuAnR9c_{6W@s`;1Ef1P3U
z*4avUTI>HhV$veE#V(=$yE_@|LvywK1y@KK4X%YZY@Z&gz4?PP>(B#j`o+Vv)wsko
z4zXP?UR~(=L=|1T2<1yIuPhk9_G8ulB38mnIaK7RTK<eCd%e~pX11Jk!vFWH`RA_w
zzo2V5T8PX0TMv+xl^U{J@H+X}7Rt)?t^@FM`fdmw>+nhsuNY=moN!6NbreZ{HzA@Z
zn+xg_;beT>Fb9<V_4qoF?w(WFT-L+kU<uV{Tj#g_o9%3AtX`BrRN6o`@Bu{1F1YN(
z%M%h%iQo{w-x}~!jDyc7UV={Tnq^o*4x(#}#H*YGF|MYW@R6FoJt1%;z7bCXlmPqy
zIFjIugMAM8LY1rFy=u9a9=&SwB2DB0(t5NBfVeUj(sg(&wOkIB`q9Z^s~%s5kiCQ2
z1`v;+)e~&Vb;@uNwSnOzTuhBT)z9}UEgqNf>SZ7gSZrVcpJmj_)cG0bgHV#kXRK^u
zuqo%UKiDINa_c^*2+TS%KADvibvs4?QZ0PX`jNABhNtECJtOm^iqq-;?pgqT)xJZo
zf<8a4GC49nHps(C049hYtRW3u$I&CQ18r&%iWZMK++->PH#7VUN0eX98wC^&^mgyz
zWZn~ahpP5lHx5fG3S}@NU)6*_)WXi-Ktm?A`~gQkC*X=DSB&Q;ri#egGCzYy@HK}>
z)>yeKADd`j4lts{2rSuFP0lhR+joyl1>Q&!?Fu=6hxPF(?RV^797E=M1U_&?SCm|Z
zt19a)VO8@vcuUm)Y*o|yt%$B)+;dT*K#9{0Uw}U}9#=tZU1pbIaakXdO@c%Ubi9yk
z;VDz=Mf+kn;SH=EE;(evKF2@38@UI*%wg%;%DWn!cE{Q8YE}S#lkICcnM&G&W?S7Q
zpv<c{*$24P=GP&Y|Kc#a{UcK<HRDWRoHIY*U`@$gr+{Z$l>?<sj@Q_ohAP>?M6tk8
zn$WABw*>C9+qjkt9{8@qd`PScf`5goZ-6|XK;y{%V=;dFZ>O%T^;!JR`R=ODrbW)H
zAXf)N&YQ_m@FA^DbHh5{e{HZZ!Wm4Tfjx7W082$*2A3^TDW2%Hup{e30~a~+)F1+%
z2^FBa78MGZ&vH0F!DcJb!j*vf&ocZhuk#ZV&T^=*{&<hURR0oKu84(l;H#vE!+#O|
zLA(f)%C`Hkv^Hn)e+~ry1Jz5uaL&s7n`Z84F@mB+22v>*q_E<5`?b9+a*3n!aFoUM
z?gX3e9X&?SM`*(pf`1*Q8L&cnU5EZhdtVyWRMK`EoM>MgaYh+!37|}(7^a{&0$~(H
zL6J#NW)Kh}W9X)pRutrfd5R(kK}MNmMo}=(66OQ~L?H+yKp+q>gd|^u_x88$dVk!z
z?(h5MN7f<<=Tz0HQ?>Vg_VXNXY=kZW<f%qL*{&<$T#!8ntT%75S@v?Sco%MVFl7uS
ztKS~b!Cr(b4NlowF6m@`<)ao-Xtq$J7D3`#(+w3$qU#Df29C*c3y093s9^um2s(6g
zYcz*VwlV0)^dY@5nJFnCwwP9K8~0|cGd-h}T`3vLPa3mmQ^MFqkcpv_O08riG3IGa
zf{z*~x%YA}*iF)$CWUzlREO6s_7ZHzbT_8WlL4bc2?nm0&CqzpyhZZQ%#>?!X}dEg
z0<<zc;UGfn8%#01a{$~ee+zf6zssDAS!<{qh~|j$OLQ|T+D5T1Dx1JT<Ho?T^7FqV
z8hVfW8IcW;k5pfjHx$PRv&^CWDLDes?AZa`>EPI}jTCeZ2uJXxIAp#F4ReZNx>yQd
zO~vXLCmVyHjUm`AotG&m3Hh@M-^NeB>`t+H?FYV%YUN*wTSXrU%u0qYy7xsikE62e
zJ_WX%kabUtI0p`*IJiLs)_*Wu@?h$eAC}E>+`{Y>;ffz$mD6?>SXT&=M7Rm6t8#-E
z8@+Xvnmms_ey-?PA>Z`5ZQMaWs?4TE)FI2uhGmOwR3~Ipc86G0C4P&vxQ7-lKL{{_
zcE8@;f(`{}?P+A~k-iQDn=cq#`)LjBqDQun49mUA+TID57iwP@Vl0XhJN-N9)47;%
zD~6SSgfIM|ztc)Um?_9~|669)KF~%nWE`tSoGh5kZDdCCOg~TBvgbkMQB;u=2HBcR
zI$u&cFtrvx9D)PL8{ximi+`Lzd^W5-xW$`9nkooBEDAfni7V0`i%LvYiq5s@5pwGF
zJ@(dIddea_Wqs|JLUH3zerab~A4k<3$N2qSBaULd2i%0UjK4#Lz}#}hM&uT9+k-#7
zpIBLW6YJrMtb3I9X0{zJSJc(vwrBwIETdD>!MdT<k`3}p*_xnwkEj@aZK%GRvvu{X
zX3TWc*@mbMG2iXTi@M3J-Ft(vgV)Ze7GwmW?Ir@kp{}@kqf*VGY4>^j(e^P5M|XX1
zPuHi9;hdOUTzJ(U30UjsDFAz-wbT?sjl$&bV-SS3*n!BKk>Pisf24Q*l_q9wzf^v>
zb<y4mTP03OE>7N>x1R&SNs65NMV`__+y<~4pxWf_va9x)pRI}ae8IqOxC97hcCI@;
za5~Z15poFZDyn<r!K?}jzaA#9OwCY6)u$BP`}s>>5ad(Bm`@%r{1z)QZ)t%TY<syn
zeV08$4VbrGVdp>g?cCdFdv}F{yE=gBb93p=uQ6QAv0JJ9D{fE(2J~Cld2h#b8k)`$
zSg^eD`~1B*nQ*i@g1#1u|8bp{3S=w(#jxd-X8pm!Z)>2QVANcz4Qf;k4-vF2llMfO
zFX|OumA>FCOWo>^wMrg>vLlgv(t)WaC7t$M6^U=i#!7&coqrdeDwAj(W-ytGZPaW^
zto7B=YT`tQ16>5g`+0Y3{2KXK4o^{kA@PbF)0g~4`X6~4ckaTo<)V_kjU5MhRTWod
z{uy&N8KQli{rZHvhLXjNf1$8PGH7GLoFgcH0eP)zeU&IGU1s(rN8@{i|M5=qLw2sK
zohNicg27Opj}@ZXw1FBpkiveehI1%zOJl^>i}pFmUP%m~Ob?>x<QXh?pnNCOSoQj|
z6ZTUR9<k=+4%O@&H*_3@p!q!NG++U#;w9T*<xfi7dv^lemE?&3@sWpL=Tq}C?l=!*
zRZi;Y6Bu+B{${q$_}k-x`^H@6K%}(J_a)4K>(PYO(wMZ0(@Rk(+Y2z*1Hgb37t(12
zPxnu2D0az}%Rp0_#{$g@1my*BaH4qwVJshxu}u|d5J>B*O!dyTALO%n%}rik_*Q;q
zh`^t+`0h~mqR$kvPW*{Z31M$xKKI=VY9C&)r`X>M6PEQ`wM;g)=cMzM@KA+s_=B)r
zg<?|4VWAGw;Fg$Iy%X_8Z{p$IUgEseK<$@Xn!al9Li~D(d2zGgzDLLdJXwp~Fdexm
z#_bh`(Nl$3JFO%J3#0~LI@cW@x^y5us1hy!Nb574B#vKu0|y0mbWx7r&eG8@K8^{K
zsn`gDBnGC!r{2gvneDa?BXHxexBFdy&7xcb@Q{xan>?g_l~v3)>=yHEb|!so*XgFv
z`9w)8<~Mjaz24s#@%*E8(dD^n_GFpl<olSp)NxMyQ>9bDcxJuBN&I!`Yf;1ux8{mm
z%UIwj;7@zNa;nj?4r(v?nD1{CFJw4j?>W^8h7s|hQ6o1W%tZ!cNOxDXg=uj1oo=MA
zCt~YSH>?CTY8_%Q4eb%TGhk;QDVHZS&{_NY=2X_?jDc^g#RQ?mWTVa{(w?9!x)*0<
z+AB`~>!DHDuCCsu>491&<3Lnw066(Y;Tn;qZQp0#Gk;d5<Kh%~#gNIr-2vjP4?2GV
zoZ61^eD?Vv9tR#9a(Gv=#87Rjf^Kcsc^Ir-QOLhu^MJRuw2QcKdQaeaNQpG%USQI?
z6{cG3>j2=RMn{bT=TT{l^Sy2p7T}PXv|O9G(o^9Hc>k7UFGt!n<g<JgQeFl|5_TT6
z!&xRokrNAk{sR<rvL{`5ArCcHGLGu82I1%hy<cF?t{PW1(c*sq3G7Xiz|_(h*rC2C
zlwW`F9>7V35k8G{0~G7Y=I47+>gKFJg&vrCrOj{LLu6p(i#b9Ldwvbz<k}>V>n*BO
zcQ>*D&C`LE2RM*}VXYsRb+^Hc7eky+0G~7eC_w@{nnp7Mb?InG_Iv`p0|5U%_}X*Q
zzhGJ!K#?^kU4ZSH&48aYW_=W%%kE5q3F|q$YrhZGgq8NU;LAAR2E<KnphiB4<=w6w
z_Nig}2=>CFGpt@!RwU7i;XoE2e4%Gx#u{4>dC>-zBLZ?Fc(c>TtaMZ`Ni^FyF4cn7
z2uGt3EO%3ghn2oX^~1{<oFn)v)HQl=-vfI|*e4+>Y<*l?!hjuWO}u$ld&m?d;JFbc
zeN(WvGig-DJn`Ds3bqlO-!BA4`Dz{if<1G$gwSZ2ZRPiy2Zhu=jwarhsls40<`{@8
zPVU8(Wu<8`n~GMN6wd7AiSSQ)-1;lMr0JnyTibtg@Rtfg^3-r(%D=3#Oy%(k($~_A
zh4ggq{?SIK*jM#z{<rFxo#V5VY&U9sBZBapG4s}}oc~aU_l%JTNBh<hZ?=mI8=u){
zO%hR!#+b2o1#@yx&E@gU@gN(he!~(K<tG#Q+|?997MNVE9i%=|V{Z~04TL3u9vXx;
zBNIW~S9v|mP^EVhPiqWx!yEcc|BgCGIOVz;PyolBSSM$@;SRZOGQJ-#0REn&!Q~dh
z=UT9hYJ`GoYR#)La8f~r1Si@iE)f_)_Cuo;W%F%Ngmc*UP1bWY>@p<PC-n$Ps8J%g
z-je|$FnAGuM>(d@>Yt^O$FgY&Sn1Q;7s3`qKkqysHb&0zavT#?&^<?xfkyQPBbp2>
zSYJUMTu@<hJ9~b?;@D;{Hjnc^Z~t#2@bcXNn6YM`Q-sQ<3B*;<)gpAm0iX$k%HW&_
zz`)L_g_A<|E4F@Iw}bZ6Spg`1Wt$!B5Q@X00N2Vve3UK!Dij1ogc<@2*(z9Sg{S7P
zZ?B%MK`SM0Q7yI1Upo@<0IZ6t5No-Xuv!5ZMejRt{*sU=M&CGljIbRQKc@#IYw|YP
z7qqxjN4Jn$!7ZVcuoc>9`OIS~f^->qXwI0O-hv>UL-{4sl&~8XMOJ{mmV3mb5W!QO
zH44{{t(Bt2tp<>%3A36B;J}ZTvsRi-LIR-31~JiSL|U%MOR32Sese`(_+ZlHmbA3J
zjj+rPz@mfhd<Xs%y9CXrEdHtJE##M*=eC!aD*N{?yPL{rXsN^=hZzFRfvn%`n3OQ9
z3IHfsdiQJt#$3q?_6#hT<)ItbtfH<#$dH48Rr+jcu}w0_rq!2j5rH|MnoISv`4Cv)
zkVd<SS^&X44VcyrAiq3t9hhdWt}nHGrBmNB4c@}Qs^^0?t$shkdNKbL?ydmBkdb;%
z_ks#t(WObamT+iJz2@wZ4dlu-<dXxSi`7m=2Qg(`i<g<5C#z1P5GEqg!$4=6G30QN
zUPvS%aqLe3U<frf*EIDF2;rI$Zp>8IGbdY%ft2^aHESCafwGuUGy~!5xo>a-9BuL@
zuZuyc|Mg$aBEnz1-q>ggqBa9}jR|X{nD4+L-`oK@XP%uh*z#Vs&sPVw>&hPzFQv>4
zFgV+R2KaRrSfjL(^5IkyrUWVWfz<BSi@@78W7tom=ELPw=;yrGUx}FAGbM?@deW!h
zIMoHYk?TjnW&EJ3#HpU(;}nO7tyE#&pbvJpK_%(j_X3`91f#`f-lDqkT}Z5cHJEv@
zZ;CldgF%b3!@?$fJ|G4gJG{2MW^sk*jh#a@^}rNex1DVLu>{P5C=d=vjuJO}cXrTt
zY@n8_SZ52>4pE*igWA*g61NCLS6<K>X(|%h2#W>#b~zy%J18HR>1V5;`#$EO$f0yF
zO~%q_%WLG^uZUAmCCJXtK;wLvmVvQwL4UBY@fRW$2nOH~*bOiAEAV%^uO-5WJc(^Q
zO{fF$V8lhVWsz>1x>;ZdQ~<BLuI;i#i1_PI&rUO!t~9*G&UqMs3HU#+|MLhqmpWv+
zp`nu@GGF#coo%j}GY-xH<#-zM&$;tP)jGYm$Gqo@ILDCtz+%=qO3t8lu0VlKvsziI
z2P-0co`p~tzLYb6czrNTC^PrPF^X{wS_x|vE2Y!OnjWY9*ydOdTT)ruJ)Y|J=tsip
zEoXx7KwJB`ulZ>JKb<+YbB9)f@2msSR8zlnt*5<*&FEn-i9`nrb;yjc@6jDOy32qM
z!G0QS#2$iqOo02zK9!n9`w3s;jX%)>j~MV0J|n=KH3tw5dvH9o;xz+iNS;~pb<07f
z$uPBH<fC9_!7_eM<!+3?-3O-TQL=j6`<sK}apweK3I))|4JtL!YhWP|O3<Ej&;Pmr
zXR6D#*g~4v+gWb5P?=(=!($?|!GEknjJ;%AGq$y+QnX60n#C>GkxQbnQR?`~4Qoh3
zVHk5>&IpiQIL;Kve*QqqEeee@I)c8t4#MSOP)k`tkORbo11Y2nJE}CeC6xC#dm7Ad
zPv~TU9&rqxv)pG50h8(~rXrpbj_NjNW!cC#8x1Fpd!sFSnFJJHkB}eSg0o~n@LHc9
zckHxZI24{I8OdL@iQDdYy_RPkIfw1@Ir^zH{9Bh8J)VVM$w_z}J1~5l`Ks>&KBsFf
zuYMs!q3-zL^3T>{p1wtx*lw@d9B=ot%tvmlY3HW0wy?4(PfSY65I4Pds9K16cFj7@
z^1eHD*Kso@mgJ>Y`f)uJh-Q8>+ir&Hc;+Q8(x~c0En3XOK=}koZn~x(=hE?B>n(OC
z@$laM!qB)(MX}m^6Gud-Yz}o%Ul3r-<vk6F%`>4V=>71#8D3G&p9FD6{@%!Qo}YsH
zXm!Yf3v*p4b+hC`C+1Ry7qGtm!hDri@8~%4k_XBVjVwlFIL7!oA+hZh<NsTOOFbqv
zUzd0bCB{R2_I=zKPV~eQ@?e6%FD9zfpoeIRI980h^WA-YJssHO8f{*)3}vRdsE{`%
z#}H2#3*P#c=8z(?Pu1n!yXPj0QO5_3>s{vkZ`9V^g;Y9p7Nm@_E`@Gb?W|9pCk@S1
ze%;0lj4ai5nqJC~)c!!33iq%t+B;#+eXmSVGt9E=_r8C%f$fOIe(G%x(s;X#S?(Qr
zXy<s%In9xJVWxuf_oSWCYrm2)R$+v=3;nPp&_df9COp)p$a8m!wSqaYUDBo$F0E6n
z2RLSJ%cyqK#gtPUHc%v82dx<TuxM7|2E7<Ay$TQYD{=#B8L_K3a(d-e@TXKbW>A7k
zfkxE`<G~U3)@KXW%#tH8UTw=8QIJuA$u@REo;@8I3&i@fgbO!$BW$H?SuL9M4jGJX
z5husCG**VK8}Y7eynOE~^PC2fVyNR7^LM-<rc~cu6drmR5-7*BZM|0p@BUHSwbp|>
zG+%0lxbnq?U6E?-5WDu4tBMUY?{+WozF!i;s0&ei^15XDX!=I52jf=Gkh^Cl?uf6G
zFnPONBTAvkAjwj?LU2KToi5nQoA@<{cpAU&V5&s0sJTdm&75H1FW1y|^F>fZ-|-SX
zCn-}=CSJ}O?S4b)&)95k;m^**{x1AJY3z&vv8!^`V^H)!u>Y4uIL-QoB{d*)2T^1M
zv*B>$++fELoT)<RvtNYyD%?irWhgzEddM~7JS4d799JK|WOUG%uM#u;(y>C}ty68o
z%Dmi~r1X$q6RG=sKov(Cj>V)PdTIaJdc)^EYDPc#lYcd@3(mK6{IyP0rRV573ySwO
zq8(nB<-=_yf}KuHkjqr*vPhSRsV^UF6LiLJtrM4D$<tfv3b|79z-EA`_bbUm3zjgE
zmvN#8gZ&E=ddt54$=Dh<nSCbDg0$JblPV*>n%r1LRuy6lsm%dpA6({#!snk!8D$bL
zoF3cee|r5t+O{X&f+<d`{y~<}GV*U>iLbnUx4PvvD)6Eere_PP(s)tp$#P>Zzc861
zhwVkhS}i$TUpeLB*#D+;c@*n4K)DrikWG@Tct4>Xw;lr_c{?+CI|gQL)kDyu(L7(p
z+v4gVpZvGrdb7n4w(Kp`$H+1l9=tBsFDQySIq4V^Gg2M!PxE-&n1$Y<w{ZEsQTM7M
z=eM@LIX%M&guzg(XSo|G2zpb^af4D9svv_d&aA$DEose<>`%$~#4)(Sv6rViUI)e5
z_7KNs6-=5_l|2W5Rjw}TIggts!PLI?{FZFKncJA}<uPFNWy}5;&5f{$S#ML#zcsUf
z#)5jS+R*J(WtZxAkp+dA*kEbzTlEjc=aap9i}Oq*9qs)a==1e=RB&CF=vv-t>N<2u
z={NJ<?l#i>5xW&DtiT!=b?@&kqBtxq{eqM4Rv0<a>s2|NbGgW%CzcZd`4`-Bp81Tw
zaY1f6f$3t%KDEnyqOsyQRU>yHjEI-szV;}1_4<1IUp#G<PA5<DG&+t|Vl>cH=z?=P
z*%_<t)6e{3LujskGd`p3vwK`UcuAIyOAQ&FzFTscQs{jjJRcz2D!^=6_b|z@Ii-M;
ze(I!w0nGUxK+w?_qV$@|PEZX7kC#^$4>Vmb(mf&YH3>K+6W(4JJ~^X|x}jue7?~X=
z8M*r%rpSA_b?3<G;h^yW9g;KtpB}cKvLvyJ$EmRLa-rknS5|oBT5ST^<n^I`m%u4f
zmXUdQ&zrzcqc7|HMeFWb$s&|lX^$hv-yFv@xMcS2>NEYzA;ZgpM#;-)3-&IeK2N33
zf7`iEo$F+LbLtB(&G19M+0$QKntf1eN|!uCVpSPv!lM5G@q`}cB3#_gh+A-PHAq&y
zlX(VEVAlBQO4HHXnd8S7?C-KkWwD`Gt(xy&9;3jAYcJr$)j9=ZFQZxthH4Eg5PcgC
z_<bnS8_BzN@kg^_6BB*CShDq61aSsyYk!1Qm1o9ab>*EA;)Q<VJ5Pr$-^dgTuI5(Q
zan(+_OR)1ng12R(58Os}F@$ABM}M7A-G4Z)>aT9<fvJA?hok5NQymF~4TvVjZ>V#9
zF9l9uaf-HieE?*51P8>N<GS8D^4r4;hQvuum+PuQf?}w6_I0WsYWd2+0^bpmMN17X
zCcr7S2F#uRby|S`olPN?YrGFFS<0vS21?>WaB}4sqY6RR4xY@l0nGXfz0^Ujzwt8W
zHnWrx5&DH6Fc}eUg|P{w6~xtxXKFMf4@RoGv%h=_cov?cbcl`4mrU0F`^c3smD4|~
zPb7fDqJn2$@N3|qXkcpkjXe~$kYeqi)P*YT9FafC9G0Lk6m`bmpSv#2ocS$S>tp!L
zOfFHX&*%5D68yf1T4s2}=<<<)(&ygpqdFrIyKg*O)-#*DmD9etQun`K6>;v@02<iw
zF$3U-2}MO|7*hQ6YIAvKA?d+8x7#9$o5wO}g3de7Xgn`S`@ovP3)*x1K1r43aCL=Y
zrNPnE0)^1PT^&tF2?Y#Rx6SLL7~Q5aM}JguqaO0@^nHHy8GAQRDqN6E>z$39it;WB
z`gx!C^C?x%U_(_Y)X8DoV2~YIy8^k|U*lmX@=@osF9?>sM%Q{iqxi)hK`upk-vdyq
z%Jlhc<-O~pHbLXf^8M*C+Ufl2et${g2c5FEGo@3W5&xbA;Bvf_p4B3p*=2cRq@~fH
zddATC?CG~g1tmTWjQKec-yf0Nm?qC~gl|D|RcA5Aa+C2n`uYM)_!?32%AR(SpSB*Z
zt_r{0h!Qca*ykPRJS|#YzUOk))$$t}H;&y=`XfZ^>Cs>KBHL9|c6~7HG4#njs?~cr
zPFpm!?e?lxgHooxxU_0Vxh4{c{M=B<wAuab4SU>ld!}~%W2?GUl;auw>GTuxlw#)Y
zuRW_>t{s%n?f#kW9=6Jb<f@ZKEd;UNdb<yl&t%X3_|kP}w;^s1+it;C-Ml_iv-sQg
z=o8=#X1IEfXHq+@*1dFp?q`JZFMS0d4R&KvFy`IT%n&%GT$B2))?GJ#68~+Ivg7W_
z{6HOn=hams<!`(&Y+wO))a!op^G4Iaq6>A8)P^+SzB#X`_<^JD{XY3q({Lpy!ehAI
z^9LeCF?dY0ay+)QBhV?x(<x{NOv5l*0(STaRdpuH3%z<1M0ct~8kGpWC0btl-{SQX
zcA;e9oTTh!Pi7fh0y}3JsDAJzC#Rn8kGCu>D_Ea4jAcie2kH>OLzb?2>fW&ay~Z7-
zo~IREpEA7gIS_J1#VgfiuIB+l$c5WtrN68X9Pk}OO>Jp{ZiVW2ar*~MpG2Z$-KV#u
zkDR;o7UlQG@z-p#lE#fRIuVC={(eV!zUu=#q0b4|?LB8ke<waN8mrzBi2HZr0UXgd
z?|Tgi<OS0ko1Q?}uSqZv0tJNU{LcT&2f|<d|M99?mWY~Fls|m0WmEY@U9q@adeQlh
F{{rnMc1{2Q

literal 27742
zcmeFZ2UJtp+c%m~N5)aGp(qNBdH_MBNCyEG5e|W%fPi!X>4NkULJ&t$s+`cPBE5H{
z!zeJJO7973q(d;22qAD!g7g0W?|tug*S%|f-}>&l>t!t`nRCwG`#H~k>hF1WUfo5h
zvi{EbI|6}V1=VhABM`rOBM|#7eme+X8IO6p0)HH|K&sw`KjFVk<ytKKap;NK12+VM
zD~R#$mxyoQA0ZHbAi&!<b-j}oC(%h(9vJ>@Eox^|6XM!Ur?i0J-?IVryOALW(k`Y&
z{=pgcF>LsynDuLb)64BU<>NXSXUpvoeY-CIm|ExW?Z4mpbpM9vfr}xBe-Syx9us2B
z@-#Bue@AApNN!SRM)y87C%15(N?VsJ+8o?8>8`5LI{KjsyRLAEN#TG0=YI_cn);~G
zz&ZKz9HG}P`<WpEzCHYwy&r)H5UaX|&t;pDPqy@CTz>S>ci<N$1;wH#>X=_F)dM*g
zU(7YYm3@pap#n!A>_-H&$TA--_G4Ve*{eV^z~&&(gQN@qwN>53RMG}O)S{5VLQPkb
zg>^c{)P@YSRTi!%0TEi)1`T)3Ameya-O!eJh_}+JCa>&uyb<nvw3Q}A=99gIx4}7P
z$E^ke&M3+iaENd7CWJ*%yuiW-x38lCA3JEKGF(B&ufSQ;6@;95H?^T!4wo>#BMS|-
z?N33|RMK;S<%)5r?ox|_WOpXCeJi+Vw*gG)-CF$)dLm0LAyu6Tk>Sku(v2~sanN5X
z&^yTPwG#5H=Pt|?M8`m$4xnUx`pNm;uaYS^(hJ3*dIdbv*O;~dN_ol%+pZVch2_)?
zDT8c@Rny&I;rxlOO2BAM+Zh`Lar~gk&{ZBAejkn0VcYgM%OOSSJG6?|vg5qf#TR3g
zX4hG%52?Z4QkiN8Hoi=rS|zJT-Vj17pWeX4fs`F!eC=4z=BZVY!g%rf-?^puHp_&E
z<?}2E844d0j_lajoxzGQc0p(iUAFS^p2(}vXi%~K<+_L(l{CP6%1s1SD%XOVG%fVF
z>rkJJo9O?V<eXVGBcZ{+`S-rYgGc#f(}Nr&>|Rq_Zk>4L;Z_Nv+q|+Yj~ybUtKIgj
zA=sef<fAV}j@#{E=S2O*4iVx3C6tTV@B`bxs`&&m*|lCoud_C-qwe9h5bG9M1*Rn6
z&SGK<uUHN5wM?DGI^#vjLiN)7T^_k7>S8l~*!mX@S6^vqo5GIR_!VWpx+(yf+eof>
zcss=|NId#PRlyx1bo9}=Y41{qKG^V__k>LLMuTj(8=7@!_cM(n2gn-Gg3fNoOBb~j
zwy!pJpQuU*QH%P;ZUo{C2RUB`YY6dYs^y}xMxNK|KxP`04R)T8<(5S4fmY*un+$Ym
zHtv}Y@Y%OQCwMDfYjIVMXRdFz1}Ms%rQLzD_#Z@nvD~DkXP+Iz4?~XQHjanIW@%|4
zOBPK*O7e|^SyWnjlV3j=zIcsVb*0-ZlEg2$x~yJOyCw^If;4o=MLe6pyoNrnT51eQ
z(&USMU*<3QvZ1LNE;YNb76m+7ZX0!-87-K*dZ7{>9MQmI+}kLn5}0KlG#(3GmsPoF
zE!R@m(r@3|1NdmSROEWDbaTg+7fL3lSue}ihNgnwn{xHbTeY&(zD($g`ws_?c-j^W
z+I9QP)jc(Tl)JE7So9u+JLqEubS+O^<>3KK^~b0KW9-Ba!|TVWx9E>lggn;&)aUa_
zw;+_9atqg_q^+5<rRQ>g50hL?AT-nXO};F8v5<@XV-8}Nk}r`aDCUMyUP#p~+0lSR
z(&Zjd%p9iak6Myy4v@nHJlDFY^$D`#py{WF-FXhJJf_FTjqIlf25&25TcAXEcFjU>
z5;^eZC+dnEMH~DVbx!IG?yqOdJJTq(o}so?g4Hy>-xz8u*GwY_i)|!MakcjJi7qo-
zQb4Qj(nUX2Xz6tIhUO`Up7d-tqemXWxAn7pBGOld>*3{*f`zMZ6J+)8ca9cVcfUI2
z=6_dgXFK?NnV!&;g5;yuRFyuzKq?RCOFOY#?iIZXw;!Rw-?4+{8b<oYmVEfPr`!@y
zD-Bu5P&%J?(0ZA~ER6?oMoiNCmjKY!u4OZV&*u-7Ti;f}p^P8iKe*?Xh{{G;KeGM!
zV|lA<t(QOKphg}?r%gXnYIxwXqblj9XzH?)CF$n5_{3>JH#S<P%DNzZ#N-FhDNK-i
ze67K=8j*vKF098Vt~CU&Vn;qtNaYkhqY)Dh@q?7rga&^H4UvKAgTtKDJ$>&dcVP}R
zUE3JbB|GNHaf6f`pr`PGS5d-dJ@A!aD8BoK?Q5Rr$`CJP_P{M{|CMQt;m)}=^URu}
z51H)p(F@_Z)f_{0MSMr*8{!7n*a*?|y6Rs>qy|ft#GDs91Y1ppzgA@8wzY##TsBj4
z^?}B5*Q2VbsQU$D@c`Di#Is-Cd-qKTdd`N0EQ;L|g-g@YWr!v~R(}1c$-Gq_!wwe-
zoKSL*3(d(@NMJuIIzL^R&`?8RTOF*g3X{Zm=_Qe=#6S<TpgH!6rwieN_vOzfUdyIg
z9CJcNcAn2j<&~{ZBi~;&_1aRop<RF2d&oW5%x_x)KL{$uTs*!x0o0~neyrW#r8kgY
zp&9FNRprC>1I{dXiwqElOcCbL^#qb^&W@U^bR$GcTHA2n^zR&4TX!o+9b6m4_-#i9
zuOhz@1IO9C=^4Ie?tL91M_zn>cWQ<hy)pc-4N$!wDxYP&GzXZe-}~#Wj=<}TTw#3D
z0KaKaMt@JA47>qN`-Gyg1N#vY-w5nSP2?`)?F8(nHoj&9JxN~6I|<)cl}ZWtWl4<3
zBaZ1jB>T}1m`e>i7Dv7`ocXgRdE~>|B%n0+#6JSidR$><Dn{+k*?shJTqnx&jI@!*
z9M+Y`ft6-sXAH}_`7Z!N<UAfhRp}$Wl}5p&)^oUR4``vbQ^Aw4uxli~@sis@wmg$O
z%{?u03g)PpR>=qF3#umj>g+TL95_cl{H{(fHPsNCqTOp&L6a8P+5P%Kl1@gIdIe7w
z_uCOKy(>f}0S*pZdc32J5fyjV%Sbycu!!-sb_=9%6X$+Ww(&ZHMYfE@4`4g}7T9@D
z$aRg!bvHgK^8MRz^26Kkf>+MN!Lmk!ojJJ$zB(;GA_hN7p|<KpAZwmdJ!zYfp-i{X
zbJ~kvmIkO_2e47Qhfb~5D13C(?5fLU;SyeRFkU}aRy5S<^H9?-nY%Pbs4F$&`2@`4
zm!r*x5kmT2nOisS=E*1JjeB_8YZ9@jb=6v>n60sD<h{Y+&s}r7H#Hm}KXaGQ5`{#X
z1vH5xb#QeTft2WAS+W*JATrs0OE`Z{K0UL@j+iI2o915VQY)u2`po}oODZqyAwn-O
zMP}PHIN7~kanUw4cvae6#_LX(Tw^k+lfCw&jgnx!1JnpcP8CpIaa$PMcdN~$ujT5Q
zRk7UT#d``U24l4#T*BdkzCXwzAaz$S39H_+Syq!@^){2dd1|Igi?S)aAT?JSyxK1S
zeJb;)(g%F>AS|+UcweD-K9dLpLR3mI(sS;a7M~9YDd;RZCDKQJ=mtkzXp|h}$?<n-
z*=jTr&mvl4x@ZRNyqr*)wasANf;NDw)S@{w2&zzMv+S>GC_ntUuT|ZB)*!-Haa&m%
zI7>!4Q`eN%iwSz5)csCk+!sYcrljWr(3n7~6drzDbNfTl+7ExULaw?5@L}@-=NkPe
zy!D^rMsK%TgpX_2yE)0>e`PiPI>wIA6GSRB!~Q;g_%_8M?hxVTBnK{ON)u8-Ih(!9
z<$AwbKz6C8d~Z%@$UIqh1)3PM@iPP0iL4%n2u%ME3)LNsR|gRRm0<#qZld6pbe39r
zO7%+zKXJU9&UNp{#$ABy1^lk`{EZjs;~23y#qkeuXvmoG)t3x@7p*i+jwkhOooO^k
zgz9P{zMCOkjpsYGbk7E*W_<JC-QNW9!{TyRMnr|Hi^t<EbrmSm{QfD+$+E*gfiw)A
zk0dq!`mt)jQJoSFXlYTBf9-7KYiVoFv#%CDRSzr5B0tzL`KZ$@oyu}hF^Lo};6K{i
zwFU57|G>96XLx6ieK2WFjv-|T?3fy4XKXW+BtWd#l@S0-%zEkgMWKm9-#Y;<^~g6P
zvMOEpQv!<ZewM-7hd6jQ%|Ives$p!!kiT~D$EBMBp3{noEp3I)T9NtH*IL@DiZn}%
zocp#@*~#cwc1gLTC|7fcuI*RPgNi&~b3x_e{J1~^pAwfv=!ZHHzCjjkfR?*PJ64Xy
z)&0>@PpJV^c}JC6x;GXeKUw@tA4(|P!m=HXX(RM`H`m!~4XTioX8;^<yC+~)M&9_x
zz{>T1^VhVZ5he%UzZ`G(qyXHhDl9sQK!DKPJ%&+$J<nfi|3vn%-u(C9|F<}B$H9K!
z7tzxx$)qYsL%Mj#6Wmazya1%7i@BMEN_MMX>tAshyGvvyZW?c*-?0UhJW+?7`5yE(
z8Mg7tUc|Gq=KsogxanAdR>?E3$?W}%X9HY@Z)@IXPIBkQe@XK8L?J+|0#=CP=Q#ik
z7!|>f0aU(fMSK`26(K6j7-{}0a>~RooDs61-DhjB;Ky-E3Ii`0w*q*PuU<6Q{()~P
zc-Nf+b|Ys<PSABJ`~wh$Ea_n8lmWkt$5~~1==x~db!s?t&w+$wQa{KYKaBl?_{vqj
zE<pHDbB~3Yb$-&UIzXNWj>fba$60mNiH!rYSrXRDPyx&F#bbN&dqX+O_DuadIFPp|
zMv_L@4-rd)p!2Du>7fNlknNnhJA<kwh__kZw=H%oEB$iYC;U5*aNid*(R7iyNNeB$
z@~lGDKtp!Nh;!5!ixdw7SwV=R0B==ivNo+-*pe(zL3LJSXIwT<Qd<cpC}(VB3R_B7
zg<Ey7lrXoTBNU#+M9;ox_L`^Q5_y#V>=0AY&Tw2&<!ZX-cB6741C7k7SB7dGri%nC
zumiex8S^!HLsMLZWY0MwKi<oJYj~sNG~=@9=O|kP!|<iPM&}%Q!OeqAN`)|<D{KUC
zP9bvVM(oZz<P!4qX*TB50)RIQ+4-FDLObB>SkXwFs$pDiIs?xwVs^gQLra4aWS5!V
z=2W2M<z#-9anoQ3@SbaV3+Klx3G$M4Ot})0KT)^#<uN$K&yXT)l!fZb`cEn9NH{?5
zE4hZ(g}OkgFqxQ5sjTWh7q!kAGT$;r;SiyBL=yiQ3<qJOhr#6wt`if#Jhg|dCJ*SO
zaZQ|bhRLSOqx^VT0jPq0ewbg@>cDrlzqZ)PcK;!p(hBp4m`FHAdPSrB+KDlQkdU9S
zrwJoJM_fHo_u+dmkSbKaV+tJwe6SGKA$dHC6eiFveLjw~1Qy0sb-xnuL};;IGhhrr
z{65a-BeCRqnBV=(Dx|&Z+_^A*1<xT~W%Yr~DmZfCZW@!MGv}Pu<;7@k6I`&yoYX(r
z=;W*G&gzje+^TzENYls<?fH{cWu8~VXd!HL9Z*;(OtxfHH0aKr`f#QlBu{Y}+7k`M
zm&3Jb6<k|3P+ivA)@;iGrv8O09U?Rv>DW6Lo|V?ICqEFI9@D1%{j2H$28kL6;G49E
zIrW*Yx}`M(4OteQA2K$&4~PH8xEuG|iMnTAJ~<fMZ`mZDjE$-*ts2o|R1ukrjJxI+
z;ry5opRvJ@a~yxzIRq-+_oY)$IYfIQcF8KOdyC65=D!2&+E!3CU|_}+vKj_z0iTD#
zV>McKVWV**tZx2#x;8B^b3b!HYe)M%mf|CD<b4V(w6FK~gLWT??^MCa%q;pr4t=mC
zh=DR9Ld6deKEId4UjVzcN-<sUr2*M@nY-;kk$pwuxP~p`5yYB>CM3;YeHs4@<c6~?
zNJ+hP^|!B_nd_i6lz-0S55_a1%Zsr1v*4137`_UDINhQ=#Qf44bwGsir72E3?WyTf
zg0=sJ;>@R#%zLxFIRf0FL!|HD-!?EsBkqRhN?!;ZQ3LqkgF|DAEy9HVk@cIo;J}>Y
z{{rFq$N2HW0SNpjnhD43j4%Hp1)5^==#0+)7$*ClItjxJi=O6AB&pa<bL^v4?yPk%
z2?)3#BaYYj&}Q>ulert_Cq|o*B8o9qme48Id=K+rlR72aJvB$Xu*C#()h*oNVoFq~
zyvN=D%`f%;g1z=IUTH3LK8d8sc>f+ddUfX03dWWoX1|beof3Y2)^X0v|L2S3f-P4v
zxMRcMz<L58qT%{@zXS7CMQ<<YEKZrg0V(5A^9i57IT>G?;{MBawkr?Cr2Mzn`~Qd6
z^I&W;qN(o!tQrJ}RBxAQy&4YtKZk$!fBm?{(UpX8>3{s64kL~djkcl!S2zt8v~&iJ
zE6U=x4h}KQOu(ZKHbS$!ES?MO)^y!{Y55>K9;lKkAKK4az%)Z0rY9xu=TP4QsamdP
zkT~u<yO|e*hBv>Q2>Y;p4s5`|_&8aEGSd5k`8>mj_If))jVM<eXcc!p(t3WJ>F7IN
z9w0yAf=E0w$!Hg-h`*X2p9+jxj@)F*^QX~P_M-xc0?FfIco1~DvK}4Lfvc0i>nVr1
z%M4|CNBpDgH>8tK)RkpwUw2<xbVsc@X;5OpjkD_MA1>@!xX`eg1J0x2pg;d$t#4!O
zmkW=_*@XH&zn*$d(1f!cfgSG6vfdZBlsz>lYQf|=k#HGCZ1RSsp3mC`e&8Emlj)Y`
z#fP^8*=(;f#+jIALf4h>K{(@NE9GF{e*N-px;j)4R7b!;eu6C`I?Gy=LH3H`EZ@+F
z9uCW`fa=C^+O6}r4X2%@gMI~Ii6Fiox9u5BUOgqiZM&%kgI2~#Bn5DTr`H#Z%ufYf
zO$Ppg1C9XRA|gz=B~-_YFPKZ3<$_&xM_WH`B5K}Dx09RWs7w7x`|0FJ0`!>2E=(z_
z?LCLR%9#8XQEAFS6S@Lp&+!Vv$kK(^!$`>Na+hr7j2FX>hJLhQ@C&*wud+`)FxF*d
zY%PU-fwy`5qp#NGu+6*BV{PDG#MNZ1L##|n<VN_I+3bbo7MTY2VW<5KL%<yW1$mqW
z%MX;YvGWjz;a6wa48yY)r%OgoT^+xGPsDA%f4lxdGti5|l~R4Es1{0!vzlRlm7l|2
zk)9f35wyksqMSgim^Mqf+98%SF^|5CZx7ytbMO&Ps*#C>Y5rWf*K(>n{0<3NHA*vV
z<4SFUZ|^K(N<E+~z{hNwpksFai*K1V_JJslG^<G9ftMGx(&YEHSvM4M&xxE$b~MTu
zX_i6vn8jPsc#8?{V@e@K%byw@<~Vz@4J@S&ZHt`U?jb(R@J^AQa=5B5(ms*ys8sPL
z(UAP2oGbuOXa0&)H_*!%TtQWOrDGbEqSC_Gv<;lw`s9+iUr64c$EpPFD5%rXS4Cg=
zCnb6cyv*fl0V5H_5xJcl_nlDz{~;%h?;f=m@TxkCu9*ef9nadr%<Cz1g-{wJ?1q6q
zLN(ycKdcMM_csuU6@Lzm7nFCWoU)WE-!da!2uB)X$R@Xs)EnYF^HZ0udFJ(vxalnF
z?$q{GquS61Ppo?j63yg8&FlH2TT>^R)?tka_{G~4dS=9Oq7JwCDn3zd@!L(E4~9i>
zWYx$<_mKBC8<fMk&3pAreS4`b#P5FA2(@AwMam!G_p*uLVEbd)Wi`LNky}61w%&E_
z^#bQhh7y`jF^Ig<hk7#k{^L+0iel?fpzk?LtadNhuRg<rOZ(WY0qy5Ft6a2`#@>Oe
z4ZR9)Lc}<(PN;>gFNYnR!{ChdINfxVOHz_NU%liDzr-1w*B%ViW_h0>Ub2gA_WttQ
zu)NX|xg+7%R>p#&5-|A-3U>UtsK?1h(Ijb{@$0k$f0=;78m{WWHI*NX$!-M|4Wn<#
zUVYWmON-$nS{@H-LH#UxgTB{Ud8KBwWiIZ|JZM6Xb9`xGFhr;HQJ{}l+i}*9x*Lgh
z{1NgdWC)1h`Rof)_%k*e2WE`nMFZaArjm`bGLo}>m4i)6q_Y&?dfDf8a7)w^6f<0Q
zc~xB91y!_NJiW;B$gfA8lWRrrl^LrMNEaSr6sZ|7)4Heru;xn*{c>!ucX817)suCT
zC;n{I;Y5E7eCKF1e~56>p>X|sC%qxuqCWk-m-VoB*S=|nX*?2rfJ~CRv~+t=9-2rX
zK)z;R<jDCon7GUYpP9@be`6rW<c<AhrrHVE+mI$)I&1&pu;!$1NFL*EjXhaM&Dg^9
z$Yo3He0&}xD>Z+jR9wLGOT)uI7kKdxGd8&c7)i^KkGjE=5n7L@erUjZIXD-eIGaHK
zdrKrsEe@5Trna0)LdGYaK@T1qN?Z>GZ1}2`R7y7z!SId=(rl2?f?tKZ)8jFDj8bA+
z>ympg%jNE&Ir@z*4|IvcoPzPdhaawi)GyqvWPQ%vm|t6E3Ny8%>b&itqmP#9l;z`v
zUN`IrsvO4{gxt_iRV(B9kOETha6J3s?e>%6cwjebtMpeMfv|c3qh-a%zV)VEd^dSC
zOLgY9Q!Bf7WtNf~%G!C#h)z$3HG^y3e@p&*GU{aA%!a5xvG2#Xn5krPK<8vw{Yb*c
z!zRwVeM$YN&ocPhardO;rFcJmpjxqIf6{66#JJ(=vi6Y+aI3&xFTm&LJ+HT)5EiQV
zM~%fB>N;GCfQYjzzV!x?7bBAZXH81KHZjo%yQ3L=+w_6#S@frUh4Kr%+O^~RhYXTn
z{I+;)Yw(g`k@3*iw<@jhf(X$k_A98uOV_H>Z;2ENca3737S_LT6;$inEQ>Vh#SAHw
zS}Z$;P2o;InS2nE(<~3is{C0$2ofIWdv9paWn`9$+Tt%6YOyjfs?(23jvm+-x9)s6
zPF(!sdd;Z95@&Xs&9c+y0^|~l;53n7Xt{LJ;s>0kd{oI77wQM~!sK467~JdSp7yPc
z9+Q?l#$-b13Dz4r%bFB&!A36o(Ia_fa`-a!B4@4@Uyn>Esn>qjr&8$XJ;F`n`<B#_
z0wwao*P7MGDEX55$5D1?%Jrkh^doHRG03Hbb9=VnO_Xh%V`I!lTUQT?{;XTkm=pc=
z^-!Lx@z~M3d1H~jMh=|<Tw8jvg&5t`;S{oRMn%fNqs45bL};0-?DKelCET{YC}vt{
z`WFm?Wy%+t<wx~0f5dG%{x$y>(4!%5S91}sC@_zU!95h=>GMr?OeHlB)y)iq(r!3(
zc8yZ{j}~1vLDLiqC-IRP6{e$)@(+-W`C*R$DMO3Umj!Ve@@Wp>0Bu`9TGq>S*GF=@
zls9wh(~0UO@_u}HR)t0+yl%j8Xe!xH3q?_z=Bt|z7dhfnzuE99!OJ(+MV@zbfRmQP
z&dbj5j!Or=W0zLPlB0OahpzI$2AcqNQ$J<~uZn}q8kEDzhRzN3$km-U!_tiYjt|9H
zNJ-PxC3tsq@{?RU3}i(O^>(26`JNphQ&#@$ob)mBsFSWA+~@3D|DeaYYy34qk$d+`
zf&*5(OX104-B3bA`p!d>6LoI)d9Cuodw545zFg??(zaVBU^?Ob40t*!fd4gTCzV(_
z9Mj$C_)_eeAtM}chd6j;ui~$Q+?uZX!3pmgaU06`I~4>yyWzp58S)zioZs~p<q0Xi
zY9YLH=4g-}cghAaqNm_2f3y6sJCw!egMk{66cKP4Rq}P?EijxuQbJD@P!~ReuHP&~
zey_Lv@(UyRD7LKys`=ec)~uSv7HC8!#e*f{J;H=<Hv>6p5>fJ-e%&uEABt`uX^mkb
zTrNWJh}SuQemY$#Z-#%gey~a``D7hP>0YZuhT-ABtlsC*7*=Un?u&sf!A8f=*%>UW
zZG=65`As&I{Bf82(?g;8$Q8wk0qWad+WzkQ10L}pazmIv4U;*YP+>W?yM*3+X~`JS
zS>R}!OoX8(X%)<0rW4xsJY#o-MDg6IR<wI8&7Zb?AT7X03lho5rnUb-8!hzk@NV7=
zc4b9QcEQ~d^MAmUj%e!~B3x2m+B_OESMKJeC;Q+#lC@<YZgBwM50(4vh0`Ml%jI!Q
zV)+$V2!ATbN#HZyvB5EHz*WinR7M{0`Pagdnx``GTnI!=SmuekbLvZ{qTf}Pd*oo;
zluMY`g$`@-dN-QblO>9r-MZ@dW5;48*$->v&H>|La=)2B@a<%@tsvf%!Nbk2R7A{K
z5)zS1tDM2V^7W<&aJQ~OFkUExG(%B_|5-eGN+EARQPR!ptlrYpq!OMS+p4y5tHR6L
zslXxNVFKzq^;60_HW-2ls7_E{A;goO6>Nlqa2JDF%Sbfne+e&{;l|JJ^X9FA`H4xf
zc{uhbHxI!~ZTizNp4113XMEl8DH2hq%+|S?X<?6ATh<k5eaD!=Ic968o<M^q)q1%n
z;=|`BN4~xn#3Nzqo1p#yHn`zpf;*DrtUluzFFl>zsX6U`9UlTY+KaNq-9D!Sv@{Mq
z{-X&|cS&6UUP}nPa?X@I1Nw`!pgu_R`WQhKtF7BYcpGgXu*mtx){$0jFkDl-@tDk;
zb(?<bPXzCodMW)f9&OY%&+g%qr86*k-@$8p`#z*ET{kVxY09VzA!3prE~fd(;CH$H
z@;H;!sNt%gkQ2SV&0g!hTmQ5o=3VN?ZZGp6+?>o5s;O^k4>S4nnvbWHi8nMIlf0ky
z*$2!#E#t+fayCdFH^IfThab&?u*AS9^x!AMVPMk=>c2F~L{skC3B=gS{DE(SCK5?;
z$J>~BPO+6_#WU)k?7W<8C`wSqDpAeV6o#tm{cl0<aNG8Fom+rDFe;8O0ok0BiMf*0
zzX3Dn%P6MM$c>2O=>qoircqW^2_^FUo9YgW&%hMjd9*QvG{T63QosX+0PnM5>jDi$
z|71jW4U$QP?5}F`p#0f~-R=<5&}|u6`Z&iq52#8{CMmqdo1YcfgK%usbR@malC84z
zXa%6ux~PpCR8=u15BQkmI9ozE^`ZzP#{ecJxXhdb<f7QI;WaUGjW%s~dL0AHUOxFH
z^Ddt;%n;Xoo|ZhT+*d2iM2pA`+B6r;9{eZz8J506p$d3t(e_ID{nagN^`#)%9{Okg
zmt^!5-7^Psn`f0xph6^hxaximMkn<<b6$-)SQ~7j$5KH`9QU+iEU7WpbeCSYldQgk
zH8I!&Kyi66WgO5%zdbpM^a2p+p6FvO(*tC$wd^1AbV#mM!q3cvq4jbw50zb*8=?`4
zIyCa!8^FH>3&F^yNnZRWD8*ZS9UlgBdKpLBED(DX#P4HEs2+|T<B(+MXOeO%CCP%i
ztP#<66t}2fGL+EQOfpFQf^Kvgc*?ln^xh%&_J@2Pz{e0W)4ZqSr4u9u=R%gyJDtVZ
z`a+8W79=AKLLgQp?x($lE8R*};lr6DPirwy4saA0{lv-8eniv7BNv;Eq;MDC);??+
zhV=u^7CaM#dl_F4O|OsmvWVb5{|z`J_cUT=<0o=`gTp5Ma#b(MW$QnxB{!F*Op-~X
z;D#pUBQU-pLpIqT9MmE!fFA|*B#MWy>aLEE!#M+{;M#s-19$ii69@4mBlxB2|MgO?
zEaYXdA=c{gGY2N~?!J8$WZhNh=0Ap?B=h>*giIhu!LV^&d_T19GdF=nmFg@oi8cS5
z4O!dy*HN6K$vj+w=RLzFn5<;|n^>vD<-NCIwX<g-FQ_kh0Y2$Lvbzvzf5AqqB?~z}
z2haj36rf0p;vnFuAiZACTsZFQSh?5NWtk#IPGfIMOvz~QVv}igd7=UFWUcO>g`IM4
z@AIKE@=QF_{FRUB(rwaqa%RN9TEXS@WTmMys^0<uj;e6EtV}lxuN@b|N{y(9lY4q#
zV6lu`7oMbq4@h!NcW+E8{f$t~&OPWd>Tpf^Rjn9)uYMDs2*BAnqktpv&#*$I{N{U@
z8kLL&{>}xX`}1h`Cb4jJMn$J{@)yj*N{v6AaipZaIM#UDAGIzQ_b>@oyZL65T|Ike
z=_2w%#)e4G!Z=)-X?SbU+I0`Cd|W|Rb9D^<(|J@G4A*jXTY!<iAXZ)`c5IHf7i>n`
z<<wK_8dlYp8r9|Fs|E3@NQ{}&<ro(?zZ{Huu<Maz7}++-7;a+~y2#iI^9Me9W8e0p
zQii3YNn_f(&%lNR(sF0P75PTL59zadX`j&<JCn8f6*_i{-!y-fu~L^{47;JMCsp2E
z0Senzq+Ty_=EL`bJ6;eM3^OYD65^`(sxue_6EZfEO<D;s=uh;!%ov79R^Y=2^%JUV
z1zJ!9o3Lxy7iKG;Q46(qb&oDQR;MjUhc7ZAX1GODu%1_(|0c|>>Wm#H`P?jZOY+<`
z&zwEXGT;S6Rm_xldOcK^0vxMXD~%H@g4p5iiu`{sEx`<-ZT1-!o&7T5vN|bDrvaNN
z&D}A@A*R^Of>qZJJWG&gYbO_te{T?WsDC4|!MZ=#>r<31gV=x<p|IY%x@tpn+--`V
zGInRRY0&fMOl8yv&UihQTyUk<C;i@$Zlcutp=gjWVvlWX_F!mcfSANQywBRSnD%>}
zTVI&&8+JS4_xQZCpt^>&n&r<Hp+jW;P28HPeOkApHa6lQlc<14k1re|6lIy{J=EJ=
z&&a`=kLf~>an~_~y4{8VgEQeKxctZ7U4xTB3<?pN<8c?Cj+0z`5=Ob&rxssZwH*wP
z$oi2|bk6Aq<G!fRod;>3!`W*wO0lGl_=%wWbq7JbBDjGpao8XL!~E4^@93P9#7}Y&
z66MTNnh^RhxZ=5GOK%0Ps5=Zr+p3Mpd&Mur0jcUUTjgH7K4_>ci1RpO@?xv2K=ZgA
z$J})J$-2bT+}*cp{#Y{npgI&PFS-8cFbv!0!w?N6M(s6|WvrtO?xOnD)-KV*R!*8<
z!a$T3Wf@3C-5Yu{@w4=K?-{c~dgkQhiMl-8bpuYfhmhoItK*)UfR)_0i<RMkkFhvw
zJhumelnsnmj(GvHQYwE{ExrZX_^YeeeKnyEFHUg%q%i%Uvxuh_B?@l9oe3(vzqw}q
z*Mq)rkqRyFUie#_Dz5GdUa8WQCzh1<%U;DuNL-b#TFh;sWy4J)&54(@L4TfD-(e^p
z-D*nE4N%U!&3qY&bo>cc>}yggZn1wVp`-b~h<cNZ9V|RLIKTI6Tj2nYn(HHo<yv*E
zTjI*yF-dP}kF&Ma9vU|PQ9adwon%$)m_8tTUBzx+O|1~V!*J=8fc=tQygGV#o&M|y
zvnXq@1^df}_XGaQfLGr6*H^vf2-^1K*QDiy%{=ub9?4V88m(vw{M7P&d*Y{03Aou>
zlPxMpNlw_Q!0Z*oHQr}(mwGznOcsO-4gQxBtLD;_f;667H8!5%UCj9$*N47XKR&$l
zpJbmAhpNdpqrIV3PD8H~+pq^Pi6?~>Y*5b`Gk4Hn8l`m&N2&zIVazn9a9v;-=fMm&
z;cAiz99Q5-n&6HA-U*I4DV+z0nL(9#N~XF*(x9P(=E8bEWQ-%F07Q^t9<gwqc=tME
zHxVa`V0Z2U>UqdCO5sD!)i8%4c;VnV-tme~)P!wXdYdq7KGQX(S05m6uoisiTf+m+
zw%%RzbN_^XF!2mwNA?eE!&MQu&Ksds5<=bB(?5jy%OU=Fu=HPEE-h1#x%j1@f+w1p
zklQRz+yPN)ltA_hqbj>(Vxfd}5nxm?M%4OV!c1Wiz60)oURi(*;B)V&<x!CGT#xn*
zY?ye$NPpnUplvKbg^RrM=uWIj<u$xX!KyTGJg459(5N6L`8Sjqi$q(nEQc2ia{xZZ
zPzC63DmK##kew^2>Xf7x7JC7}X?Uj<MV1aM4T7d{FBJDQreZ*`@a<ms6Z0H?*T(zy
zyH1d%LU(P(fMWT--%*7V`JRPDGc>F^+O$|WonX*r{=inSxz;O(zOrxFVDiR!#x+F8
z9auhDGd6-1ryrZx6DRKOy+_9_Mz9+@Cf~I%ZY)m5XKXxzvm?fUVj&4Qb~DVuXz;8O
zjO|m0e~wstIKzHaClPejf|k@VM`O0yH$;+&QC76(ApD79#TKgHf7HT<;e0_J=T|&P
z5X0XGH}cNZUc);B)tT<gB;cq7JJSjvFpPAh-0NF6P>mrhgzR89mal76<~DHbRP}s(
z`t0vF`_kTI|NZ9UA)7<JA1}KEa0HzTn?A@=$GH~h{jT@+Nll6BSGV0P2`}64o9V8-
z(pNjed5BN&_Q1KQmnYON9{BZC)Xi|!@R=n4-I2kZK^4~8T(A2c9vH=*vQ$$ObU_6c
zJ-j?$XjWu+1s@*XrkMEow6$gB*?(NQ{Q+lfUZ)5oC9B1fI-9+kE~GWxa0QRk(fk>;
z9ZS$y;lpd;>aM{Nr@zvmUtO|)l3)q?Hl$xZ#^58O`1i*s*BcPJ^_G3yUcH{P?BTw)
zA0R|VP3@(XZjVkJGPNeY1ySnaI=<J_);)CgW0>M0+EksBdEeF*y?eWdIor^i%sVTE
zT9kzX%GI3II<!AjThWe<u~Jq3{wco-*rZB`2U1_<-1%-gdc%w{;>P}P5N$QR5M=A+
z+q>*y`7&g%?V<zY4xA0$vEsw`hfsc1E~mNQ^S|>mo9$#sUE8T|Sgy3ms^HS&j2Zw8
z>cCP(Yf7v0c<DJFOo{v{CZZe+OJx$}#oFssa+z51Q#_{`az*C&Hu=J~3cft_Q+V9o
z!uqdt`WwrU-sJ|B&5(be#6R<LqxDPABH?!I_|M0WJS6I_&Ivzm8@65j7%s&UdR0yj
zC$6JF%+i!U#-J(ALo;lO{t%MpAY!k<CovX>GnN}bLKKM{+_ocw_d2(y5djw-Yq;9Q
zM6{b|xMHBg+Ps*?$w#~TrG6!nLsFOaV#?^TC{lg3+li{-jRv4rlUF-a<!5(9-vB!+
zvXbBD9&`y;CypNy_Nc7f;EE)X056I2OkaXf9=U{%85*)fLi>%3s_2~@dy~GoaE^0+
zCZ>F**JO62N^(+_vZq@6G&OsR%ij7kUICGv1M@^urP^(;T>xSENjH!hm{zB+jA1_g
zNfl;g#OZIT4%u4F0b7iXXaTT;f{pxlzB8W*HG!W2;PC%B92ii-04<Hux6M0D3A!v1
z9iUJ(I9xbHW#Qed1>|wYDI_JsZ#Pl)m$?%>{2=|zc<gW2i`PBK2e+7$aTY7!E%u+w
z>u2fjTs3kcC(fQ_tlPwC_4}{$GQ9X*RB6N02GbbQM>v;#Bf_{B=Kz~<a^GF0e3TMO
z^4p!0DPvpqAEg+#^6$c=$#c##6QqEI1Oo}C-UkT0X2yw#xO=A`7-3FB6H55w;0EZ5
zgsw1(Z^P_gnZ)SDyYPBPXiydbr9@IQ=;^G`U~_NgHIQid4I8FX1(ALLWR&aOSPTp=
zs6Y`YiHnRy&7BrwN&Puc#bjmQYH(YsV)GgPrG8M@{xQ8yp}xqdIyu@tikvSeKIoGq
zwRD3qB%l%vRLgeYB9n=+Qt6!0HF2bbPS3v5)TKH9oeZ;X%0jsUoL!kFk}%-%?8t7Q
zSgX9#UKd4iyAV(<v2=ix1)U!m8y0Ea%(WoCYvq~NJ)*wigetAbUX2Hg<mMMsNOfu5
zOQ7dD)#8&sol#z=Wgkj$M+aJOY4AVP{UI3Re9kSj|Jvnd8nR`FoZ<DwQ#m)X8|i^<
z=)KIZ`couJ3*Y*i8bo0g@ZAYZg;&}moG-^baMQl0cxll6$mF6LW&R88WZP+GkSBT)
z)=+N!D3OJcVdv%1$10l_SX-_+wC4y2pTA2bhfqEw9%cHCIEEi4>qKF%S@0yDdue%k
zGV3x?=FM`K%I4qG%1mLhW?&=ZA(X9@isk$y=Z&!!7V(nbE>GXoF<P{Lj8EpUVCus(
zBN)O8wbMKn$D;|$@Y;4TeL5-BH>4l8#(G8{f8A>>i#r9cxLbLdN9lHLEc-VtDfSL~
zPj~2@T&3&V>iUD_M1!ctK}TjN-&9wF&TSbut<!a~)~3YSp`^&&>idsw!0RkFPNzg+
zY4v=$g6j=48iz*@w@%$xPwU(tT8W;2Fm_gSA%PPv9zjHhXex@L1%}Vw^HPT3oRspV
z1TNNQzcsKP?LWAvRCgKcpY)V50+C02aggS#1EnG<BfwELSfhwqrK<O4{sP(_Z5o$h
zQ%P9KGi~;(lmV^uqw20o8JDpS%bzfZ){MuIl5>=Jn^PZc)|IY#X+t<8Bb|3e{9Wv<
zj0cL_C#46YSd}`HaWzIdyq-tNqX`und;4nr_cXu)RND+K-?Dd@T!6ME1}(g=OnPRh
zucXNKiZg}>NM`3mY!)G1hlhqnXS5(cY5aQ0>#_W?IC}!ggVphHfg%S(ajulTCh<Uy
zS`|{q%ACo7SGq3>Yu#SbWbCaCg}Bbxol%UnzbOjVRajz_$5THtXlwE_R9inZY^Fgm
z0u>`kxj<EBLib8AK-Qv!>ZZQnYOT=?Noc%;*M}xztc;*MoSy`~$m+e;Aa??35_Ga%
z_qcgwxrV$dT{Y&UMfrFkTt5G+PJP=%idByow@tz56*$LCNu5vGG|Y8)U(Pk~$;A^X
zIaRbsYvNoYM9urqt*yOSFB+^%0h<N~Mbb9#2Dc0PGj$t3(db`fMe#YH;%ib805{aX
z{vITCe41fCrU%P(u=gkhIgqo*`DjuNCuUh=-PG+aRB4vPqh9~#xV+(dPo59kJA+ig
zS9NiuF3aoPhHkeG60cD0XAQ`UeO}22$PFs=EEui*^f^L%H@44AL!fognh4BTZ&p;x
z;4g5kcdL;deC%=~1Us)WkO6a3R@{`yn7`YNR4Ow1Jy&KAakigWAL8qbEJf$up6?HC
z(>i;s|5+dR{aSKB%@r5z<Nd!d))j~CymIhPCC4{8_~07nN+oPz&a@m38&~SU&WdPJ
zegL#|bwx$K!M2<2a?}*{^t`r7xFKL!B*814-W$ZRk*(uI>~Iv(WAv9JI`eEY6NEp*
zkrZ;>)J11(A$l=daI;cE(8EqXS1UF4^m--lW(7dKyfq;{h~6mjSo5KB)9b4$c0;OH
z)rkEeUma(A{V}<A7f!>Q#1-q2qqx-=N7{zB>Z-4AZjK}onoqedbpK*wovL2K)MOlK
zOwX)HotAQjueo+DBCtibKVToLuicoExtSJK;LMQCrbxWLrclk`+~Eg1A#0TY?38zv
zA%)UyCBw$p`PdZ~J8bzQJ9%_kuQZY&?{69v`cdImGZSK~66;I1>%a73wkG08#Ne8q
zIc*!0F)}iB{1H}mYT>3rcv;ke9oN=c;hvMh2HXm*UpsLfea3-QFC)5f<Tr5l;fXp>
zQgywLen4Wjt{i(gVo8S_mqxJRJ2q_ZaXV)BF9pRVGuO6sdPiAO?DSicEdi|0I?Z{(
zp2<qlk(2%YyIyV)azX4Kg?YU*9l={gbER0a#crg@h6p=NYqoUvjm7Q-$uAFHt@iKC
zVvBk|BBe6wn}*v$+IV8-73K$X?(mvL&)0A6Ph|7F14qsZ`Li>{LzeIOytTSW%K~v%
zhSTXEDpXT_Yfr5^abgYfh8+4E*qrA|ccX-~CUzdCHgpTlD-?~2@-v$<&5N&%))>6&
zu8`akB4Ry_nwA1AcAFKp-q%;`>dSMRsnPoLii(bRs5|1Pdb=_-Rp_faVY>d#F=oW>
zC66%G`YpI+!z7iISfEJIy{>2<Nd1&HId`e`fV+$8n3C}`MW=hHHN_&~&g5OU`*NZk
z*jH;bt<TCKzB3Z%rav+plaJ`}85Pay6dCWId8r61Wp!ybIzrf^{ZE&whOMwE3~KDv
z^vz55`t$EEhT9BhoAG&zbS87$AXm_DeQ+uA)C6Gcc>{A7H79EySd}D2N@T2lulwY<
z)nr9((s`^~EPbJDn?tQi4%xn|?O*V(SD(qC?~to#1GQ!lSAlO;hPwAMZX+PS3xq2V
zP+Frtb=JY8cet!(u@T*?DoZ4r=xDkW$B{nMwepTTU;XmlENtshpqCxZG`FR7L*1i5
z3eL$piuxs4Kl=KP^(#x1a1GzksLz*ZMnCD@xxhucqX~!bS;3V?cU~Tzy%p#s<8kM@
z$WS&;XxDkJ@}OlBz)<^u9Vr-V8ANAsfg4<RcV7c$om0nis`zAGZkX}d4$_TdF-W%`
zp55qLiHjSP`?s9b<6kaHtJ#A#fz<gm6uVO=<*6Y{f#{I<wI4obt>vAbe_tzu4v=fg
zRdJ1F9Xrv0H@4PxJYMGD9iUl0DZ{KiB*%26=IpfGRZVnBS>!CA7E@`eo&%|BG=(ok
z1CCW_xGYKjI5mvB{^pJnRb*^iFPAiXEBE7<zFC6)sr3j=c^~7#ezzW7@@^qmDv7tz
z2@_skZ+KtmI@Ldv@L29--81OVZx-%tVeF5_>);KWu=ld8;h)rprZAqp8?ix-hu7}%
zT_UI2|FvK`-hE-7TMmzldn1yCVf7^6%W?{=+E&^4khw2Ol*u8&&L2xu4A0+F70wW;
z+D<D{)D~@)tI|t*pg!a=-mN7$Xl_UsVzu|R@7;MX)WZ@r{`|q{`Fiwe45r>X@me$5
z;h<T+W$>2BnX8@g683>!yXC25{<Uldn@$E-uD<`l6{->+>ga0}(ydP0UY>l{UBo9_
zU9qmSqrK&-^9w2t4xweL+rR(Q3xK%c<&2~$5Zn4u=^Lx<X<e0o2v`v?R-^l>^J>nW
zQU8K$YV;9Qsp4gPkWSY^tvKwM`K^6i*Nz&|P3~T=)Ec{lA*R}gk9&aJIL{MXS0%gi
zV(B5P?~APyDfMeAlJh+G2C#F#iFN1RJ5hHy=Z=jCT{$fl-pUB!G0()EU(P&*+u%?r
z+P1&{b4>Kfu|}`w7|jqypx@+r*sf9EP=iu3fL!J_C?8EBJPYodC#R+Dbm$#(?n4DG
ztCgC4|8dSJ0xR#O7({uufe!bccowX<Q^Vwyf7n(K$2zY!4UNj%c|5n9R0uSqt$H`|
zFO2_XeCC_wakFzJlXY8gOenm*`(Z>=)~t_1@6%B@G|2l<qr2aG$6<jMk(prQt{+y}
zbEVx`-PI(uMjlsp8Q)mdfVG5ny<W;77j(izQIHO_{bqy;4Gk-GB@c%SoGfii=iHRd
z=TfQe-sce7LjV;=1eK)mOO|q|_-6@&zs@|&zXJ|%_j|KDj5h+C_IcZHPHn7{()_;v
zPJ?3;^BSpi_1Tn>t(|8%C+a<*T9EQS&6xjzujW0)KL*oml?r|G`#N;OT<P{uSxB37
zI_JYg_f)+nM$kF!u&L`8-|>2PJRf8`^Ol~ToMJy8mU&$kPm~@Zs$ai2I6Dh$=B+h4
zOP~I>5L^R0)s$b_svYh%Jd8s;G(Gc5-d{<R^6AA^%{6>cRr~JP-Bg$#8U&}D=|Xz-
z5S0u{ay5zeb2tgKztcd=sH3Ku%4cpW2z20lzkPYEG~c^}E>PS!`*{RU`Ew1Ux@ZXv
zJ@RpND=<v;?GJVzGSPqMAOAO;#KxIBsjYO!L}<vv(<Ag8BY@r4_6Mw>?V@rH<hj<y
zbUN7c74mbrL4PLSz_W2_{V=FFAh?Sf>sJsOHH|`dwfR;J4I2er-+)YV?yxBVgB}w}
zB>qbqEsipEm(64=E+c+uorZtT05Lskngi(~L+;QY=6;;Jj1t<azC`CJGK8}q6?%x?
zlRD=tYWBc6Lyzv+QfD`pZT_$)+q}+Kwq(oiU_$Z-#oI4Bc+kgx-)eYcD?k6m4<Vt(
zFfwuaG~Q~dC9A<i{So4{?&_HEx|b2ek)E(){x*9x8-Rn0)BP!&X@#}v3A>(R_)D|_
zG%~NdqI$KBtaUjY8+#$b%-5o~r_6pJcB;T8=@20=q=81il-7SMH}Z1`+?FrHhk(r-
zGmMe8@qs*4b4mA;3-oWT;khtFqlyC+Sv%c!6$<DZ^>k>_aS-E`YsY>Xx-JAaZt#RY
z+3riROYL~$ec})Sg#`P%AAhRf{Bi2M+rR-bCfKv+LG$;An&K=X9*(GWo-sLG>e13Q
zv!;7WfpE3FSno)g=zS4vq97jaG=J52g9iGe|F(n=_^fL4+G=s#rLw?v8Ted~h~49i
z9)6z>uiMD(XsFSSYsLpOO%lw}z&tzczu@VN`ij~9uCi&VBcs@q@crqLU7pW3$7y`N
z3`nzoST@ir?(wIngXeJ9c?Yvci!u^w9;)W%oajbU(#knx&b4Ni&kSO=jxE7u<3eCK
zi<1tYww%d|f2s_+I*tub-`hXW*<3qu>rO38YH^@txLhDLu|O)eU}~gz`m74b)uTqq
z&EHn)09_k(-+2?0KJPjlffoj&FYlnB{o1^bQ)MTgoOyN$@_TpR-w@J5iJeCL&J3B~
z9O_X=>(1`zgg>9IR~mFfn<SjuWOaae7=ViTX?iL^d>m~i)^)>nf&iM12sU2C7YelQ
z%S%yV9BVK@rbLsX``H3tZQjha<0QN$fh8kjhAvMQ7AgUo^2)(w5hzJ~>5n)qW@?<z
zOYLkw{meq@D&VtN{_KrQt&OLDO4squfuB&A`JJCX&(V3AS#=u<p5c>yG<Fl=Hb(*)
zuQ4WKQf+?K`!;kmD<R~Rc^?}R?i?kmyMB+deAs=4Aw*4vPg2#E_w{?tmhU&;VMZ^-
zr?)ksB9#1Z=7pF`c>YYT1FOvBIiO;y4dhNCVNvqO%!TQ85DWiiZ~6X*KS${;s#BKi
zSbpiuAgBAX2EM}m15*4vFClLKp`4N81{|b65~@6?L8!{)LaZ{w*?<a?d;u$BI#7Vw
zoR6^n=ln<L&o2Eq*hj!a5eV^rew*aZ<2X-5=bx8w+p!X>N1wJ;HY^YAp>~KPPn@8Z
z7gbcPHO9Uo%vrAm#>&C$&I5;}KMm{Xgngmk`3EDsX_E;jxMSse$KxK@L^2PV1f2cn
zoWi@G$3Xs{Q1qX-1M$Cnk}=UGMBE{MURi~%ZRM%gL&KPJvrK?!I^lqb$8`m~<+<h6
zrWZ6O)2lBvBe_ICSYsB>%`V&rQxZwRd!SW7H6MHnHtrcOwtP{_bOz6wCdz)EllZSY
z(0;xyujdv-<A;wKDuPZRT-?dSEhbnPGY1M@YObw;kKj#$Z1bh$OrS`O();QT^SD#K
zVW#bwMlJs8?27f(4Iov63Lg)gxQed^yVWJznWd8evGEkEl8SXohe~pbUSb)pP6|(h
zCVESDf3*ztn&{T>VEYHZ47hoa*ISx@)No)^1G>uP;i=_ndT(X}s13U`a%c}A3wRNi
z_HZjD$|^!{lLn^52!1viiy@^0!<7w0h;-=WD`wHppaSsDBB!VM-N~IhQ!^S#8f>BR
zhhN<T!}!!zP{en^l;`{BcQE(-y;t8d-V#xEJJ4%uStrUFhJmKj{H*@{a^<OS$@}M*
z{~gl_JwRSF(T*BEnqB*P!_9Nt`>#DnsO5zni>OU_ce3i7seTk(+W4Tj1iN47e@a=2
zaBu2@!~PAT5<Yfok!bU>fw6diWH&o&UdR6#cS${7_m#>(Rd#KgcCy&NiG=Q*4=mP$
z4=obWoQn%rKvURNIy|E@P!5E+TA)|u_BZy3dNlO>;Fe1dlO~N%i8vvtx_Y-X8^%Kj
zhu6JP11=75Gk(7Fo1(Cs<=RxKPt3hSoOHL&Jzn?Ee#Cj)S?Nwu%~If4Rzit0A!hR$
zz}u#0MqAVEwM5w-5-R#`NR#Q=5r{jN81@IonEs_>vfgcESYUBcOvDp{dH+|1ok%dV
z)b;7iJNN{|o0R8pKwyQ2q&20Zsw=!8d2Iz2CGl@?%}4LoOE9<Efm*$hwn)tb`(Tj%
zIS>K5-h#~3C;<T6g?K;LYW7!97>tfu`1T1tT25I5j3D@offls=7;51Zb3{=J02;54
zIuvq8CPFSHfi$8!LshkuYc(tr!#{+Nsm?2I#_RS_O&MaZ0`&m0B6w;0H7_Jo#ph!Q
zMF6sbS9WQz4cn@@K6nn-0@O->B5tHLK7ctj!o<{?g1Mqe3FW&|a~12z_lmVzwMR1u
z{N04!TrA<YnY+VVfOL8I>NErNsg`&~j>uSo0NU`Z8!@e`KjA10CdZ&{#;^h0LpQLn
zMQpNPsT<ImScjX~YO_}>b$5@Lr3%_rt^E+#|Hb=m9$`cGRile+&da#~#)M~K^dXKk
zt$V42MK<gHZbF<V8(gbL`;TFZ70aIPFn*-MPF|`5=L+M&H=)`r^~$Tj=q3Dq#_^I=
z>g-H?!G@V*g+MZ4tm<lg)+wV@(!5?hN4Te8MLyCMvmi4Q@m<EDw+-4h&7G5HLq&fy
zt5Jnvs*E<W^(K8SR&vWkeNiMcAqKWbt<$sRyFVPieD!0zg~Vf6|6S+Ekwrjt$8Yp}
zB#oRf<2l-hbqQrzpS2V?wEb;D_U1%7KYPh_-_W*r8?9~%Kz5QGGmhR^g~M{oGW{`Z
z_Kxy?I>Q9_yW5XpLmXk4?>KF=b~bDLV(H<Qos%_-O4%dWzh8<Cc=(U?-#B8J?yUoJ
z2;dRn#v0@{J}X3dOctt3Mh`P!h!@ejGVg7|w!YQNETdtKlcg>Wd0Onp6kMr%z=*xd
z94_F)?Dz*w>jN9uE^o=cS4|!_qG2nL=gn-F(DdIevMrHgE)9)$=uCKl-nBvaQsOke
zz!~8y6-^y7g|i>S0VPbR3#@a$!rnX1gxW><XqDVj>?@$U!rl7_+jzXL$4<fj@xh2}
zlbo-?GASl-fIL+%im#qaoq!><x1ZnJbSd{YoQ@rG)IqI&?-FZ)K|Oca@CA%|+!_Pl
z=jo3O*8uW{7=^r!om!4Ixf<{7!kkPQ0}aZ`sn)w~Mj<lGjG!CF;~HW~b2)c1gwxg4
zc~W4Qp!+|k`}BM*SM%KGKbF<doS`(f-nOj1h8IQ&Y_N{M7iSV*gp`lp*ch^eLpCLR
zmX2o56CH+fTpGMyP4{~<I6PCch<;GhvzOmrN^V{eM$(d`hqH<B8_Ct5j5vpzxI4h*
zUVl*bk{3$2$cmBSyZgeeR9~IiXty9V>*$O##}ZGUK{{NNV}wVdwcT)skEd(yy~^QC
zEVQyTrx5lRJ;b*$T2Qv;wP4A6i5lvC6}qKS#AJe1#Fy{r%&l`@696NepVXlH?^jI9
z-X!r^ICM!R!#{xmw#8;t|2c23wbJmWr-Tswt;f!!M|kVUH|xJ(x3v|?;OP3>a-3Zj
zd=P&9#B*%2;DfQQ5L&ss(qP*UoK;mh$7g0*4#=a+8%3`qzVqryD=Hdx)Zz1X)+Dc*
zx@z3p{q@~*S@A7i@7VvNz3U8Xa@*43>aiT<U_nI@&|@eT2q*#RHcF^MKm;U2;eddE
zfB`WOuyT|V`9c$<9Dz_YfV4;riW(!*n}!mFPy^%;gqTn=JDhuG?wud=%slgRCO`7<
z<m1`d``h2zYrXG!*NTa6mcB=pjSAD{#V@c_ixEuXoyyB4_J=$#=>Du!ctOv41MQ0h
z>_V2_Bc4Kj+Lf=vaFGGSDBrx@&k}i@&nM}ZJMdHN;wqM}rWmqF>=+b=RYc9}8GizJ
z=OckBplweO)jqfw(|+q(mOW|!S4hU6MD}QICz=P@LduF^Xh=KuX2WRr&n&q=Rzy~M
zs~CU8$6Dg`SSQ}=-emj8Ewjn&ulA@xmKly)r_C^c>L{LB)ZL@&(l>(^61|zhozwJ{
z-Js-O%&DiHS33!)*pW$*)YnjHTnS@B^wr>54?P~fw2Mf4x$uM9B|ll4yF9U)c}Sk0
z{ftN}{zBgW8vhpnchfx+>-(<5$#6&QZ1`K#-F~h6Y(T=|dHVLc?l~H|<zcVjy|=V!
z-oAHuD{;I=THwmD#4)wuf?GN}iNZUGnoVOTA0$>AI{d1GvPSXlUSU}{yNNU%f=ca`
zJcuYHf?FCQJ*vCqg+@&?37kvSsU*OzAlQ920)nSYm$;-Z)bkgy{ek2}cp>i6g$kXZ
zd+wP`AG}jCare;q?@Mpx{Zhs@5d@p)Ru>F6a&t}p%*>n$^Y#%xqgy!|-fl5ceb?N<
z7}!~H>id+do7^8Q**aI30Pn8nGD<27ulUp7SYT?vj4bJ)8y=Du(TxQShZZ$Z9J>~G
zz307r6@Kt3+Xiy=c^J&obZ3mG1WaAQAH_pZc<<p$QUx>}#@QHdiNf&3zeC+weS>p4
zcV6}FuMPE$sIR22o({>&=pH&m_LiaUe8l?=*}or?2-<OhdcEw8XiZq-HM}QN;i2fN
z5b<;|+IaAJw0`SDZ$x&lul<xHMc3=b7i#s%MbZQPkV!vdqv+m(fP~79#I6&^l)`Sd
zgzS;_GjX*9r>x8!K)!sck}Nd*nU-k3WC?A|&$tJ8($&JT^Qt>T+GK`hLQ!RvJ+lcB
zQ}uZc<IDj8$y-^P-q5^_KKuRcvn;Qk0>tb7#c4P#ogIjpO_*XHTRDq8$Vi48k=Tf`
zWsJaFC>p^s0MH!}1Ks1-!2-0!2TTOtBFHpZc~s~(zV(YphZ5o?RX}xvGAK1%Yh>J;
zGf;G1#(~wl2Z@7)m#+Gq*MGP^U@~u#U^G2F_u~pAOPY2m7cEK8V7jma-~dqo2S};b
zfL^mHZ;^?~`qmq9^V;*lnL>WTlG}9fbX(pNW4hs&DQ^5$%(BHDD#h^*NNnGR<0U&p
z%`sJc?<3$L%{Bt_x~SKy7#0MTLzhxKOrdnUmKb4K#U7pKzG&wBlwrj0Asdy$9E*$x
zs+6nYjQOsCJ8$UZ472d=sG1#hejI3jGTV^^Y%GPetlqO}$PcAvgLGaJ6q2vY^lGDB
zsWn^5!1F07j%7VtYASZNja%vLK4O}iAtTB=3^dg04pwnKz&Ta=I6`QbQ7`zH$VV^F
zhxu_hRCJuoMp&u~SSU>Wvk1i-ydc=swe{X~2KzpRchpYts|0MU7X+W+a+4N3j9GCP
zdu52kmP8RwFtnj%kD70vDB~3kpf>bbsIsL-vUjxs<9-*=_Ft3}10oZuPR|GHv1TD$
zAv0tfI~%T$6|xN-<N$31s1b(6sT+$i2dwd5!9$LdS<u^xyRfm&Ow=rL<^U|J2b4*`
zu$loDyu3Bwh*zpsV+7}eYIXGp@!WI51OPIQ4MnDhfYH8-y?g8!rwbgMc#-}LSo)+G
zWXiMzLv`+Jz;_W&d%~{l0^0smL$E<n9hXwrl3?Dc7JbTLz7S>YN(1;L>dBw&K%aL)
zwC!Zo0*?RBNT|eWL8DvAp!D0Scn#QDvp?<Vpt#8pR00ciH5hI0-)O>KNa~TEYR?>q
zst#TegIX*h<B5j`!&^K)czI^5A%5`3e*?(=Z(ySsQXklY(ozO-nh@v{mQONlpkvk=
zYHL$Z0tI|^AO<!A9XbI_tg#scEj8!hd*Ff~$km;=hJK=oGc(yW_J@L_2Z1!^h5hDX
zCy)T1*YRWC7LU>R;>1R-$1%_r4v4{LdvaQdP&jR&GJPE>{20S@IBad7Q&Xv)f~y0v
z(9=vHN^}Vtx2@hnp#$YXk$p~R^I|}jS@+20za$#!Cw}?JV^7PXRmobhI25L@qwS#N
z`;#c~ZUb4(HiZQR+MZpbMm9V>RLC!1=*9LqQEkWM@r<AU(llVB5M+a?#muAAoLOTx
zQN^1E^1DqS#Q=j{{U8QY8|Cd2X|-<}{HHs(uR!^+D8bVtT%lcf>z}q*kI;`+O-;5B
z=FU2wcKhwlg7VP`uZwi0u<K0`*2f9r!7h?^ljR!s|2PMERehAQ;`QwMj<bgV?-@yA
zr(9_7QwN5Rec-mg*ex<eyL$}|HGY2jbaZ~qf8WcjIFy>IP&Y4OLOc{~!aEr9^H%mm
zSOooe9082J*;t{UG~I!X6<=-Kdfd3lV}A;}T#guVl%a!aNc5SjbP?yT>ptM~onxmH
zPy`*uiXPKYk&#c|v$nIayb;p08D4r;rN0&4mUiQ-cqABT*drK(hkCqvJG%&~^@6C3
z=rsc5#Rc-!)cCl<G0-*-%7F$DjCyoS-x3KZ41;h$G7|pB0&dCv*EJ!-+CcyR_CLD=
z*=2x<fyu;xR7V#39-L?$bPhDi@web9pc<aAEl>>*kjh@muug>qbB5vcijSsBf<8bD
zs@r_*V_C<57C8%4o-t^q67=rg@ZVrjqVKQZEbE1hK7)Sz!gD{X)6}e%kUu2Q@jqcR
z6H92gsu<F=c9NNdkFz%T-mBensD{VvY;SSfB;Q7NQn>1dbFk+iq0`8G84A&kG>8b$
z9;Iq8$f^)C-zPpQ>Y?Rpz%+cMf%$$)Z$ewSR##9gl0HOShltV(znBLJ-{!0XN8i)5
zMN>wxb=hw<-(c<0fSe<{r}nYRAJtIfb=0B~K)h2LqMxUdwQozl1gz1#E>DHKYn@7z
z10d<JGp|BzSNVm;z=ioXS;?^iGR>u7>j1&{LQZ6xD3#?5)Vjb6Q%08h^LPBz2m>d8
zFP$Yl%2%ouRs2l?upD0(j0F~`+0+XMz$~K;&~ZmD9;*V)4)hsKb}h0S8mF)c$f!w~
z@4{LTX>KfE=1$QqkDLO;et$>dZbz5tmmu*z4Y#o<qbFWj587Q}ox$zSKvnJOZ@nRs
z6~041lm-=Gm0m@=BDrSVbKFjaiU3QbxkMBLu#TzmyoJ#A5P#?{d8ZPvEm=FOmg^><
zTLU+t-tp~KOcV;Zp451dJ*O(`c<NsM8%US|>c9lDC|f4>1(WwmH}3!YF#OwcOCXO5
zXXuqJ2aFz4+6&gxjdM;GeY-`Se6UX45x7)x&Mb%fJmywkuKDWUV2wp(kJ-Ws(e;SV
z8%+|aN5Q@6vgI#?9%Q^T9!wz`X-0Lt5W7UnmMaiqH;ZdT0n5fFIa%_2-|-`#ddw$Y
zk(dC?DwY7q<C}9UQu(_!0Xf+jM@1c)!_ZUqJ9I3t_F48%P9UN^-6gpZTMS5LtnPz|
z6@wQAG$g}+WQ!zL(?q;kTyx$cTshZyGBgV=i;nBI;&76IaDsQcw~8H%YB=%IOag}0
zx|GC5Q+V<yjOIH)#atX|ZR2gP#!-Q+Q2YK(iLoa@2-&6ak;C0?oZjy)jVS#H$Y*ty
zVG^5wBB;--&0gpU(fpEt4zr|d%TFMxx#nJ^^9InAATBZs!WC_=(L=ZF4GDvSNl%}$
zF<qN?Mv2Ez&1qPbcc`dUWst|tu6ZNa|G)l=JK)Leh4+)ffqVa6${g|iNVXpbu^nA@
zD!8-HACq}>;yvgd`TT8-I5zwgyMXey);r8^nS4OH|7&tgG*F~g`pKA{m%KPLhb){7
z&iG#G%e%QJKf;JbyS+Lkv*7i+r`1x}#pfk?Fm-J)qg0dm*n?Za>ITHZZFHR3o!gb}
zXPNx@H|vQUudBG!#d-Cak#(@6Vo6{30#l7xe{I+nO#jcU<>=k80(Tmm2J$EfhAm1y
z2{ff59O^N}d%YtAPv7W63_u3YV1t(HBpQ#1!;ACu`A0lGs4mZ8D3#41Nv)BHQMy1M
z`544zR2No&InpOogQ19Aa`Gt$ixvw|*q`8z%3qR!WQ%Lb6+}=ggr7TfS39ffn?w9l
z7VlmlaCeQ5hzli<l#{efHV(Bo)k!x1eM%ruD29hPP@P^+-MhP6YmcuOqCn?}9LmT*
z0vnGqOko4j#S@>~b=LK*^R_4;sohoB8jLUU#W<Vsez#|=mQ-G80JX~~Yb)e1{;`#}
zQRw)d75Nl40=m>c#V>;gSOyXIMf6sO09FOwGchlf?|kgf-V6OD)cA;NQ<C>C3irNx
zW-@phIEP#M*`^siyc70JL{FtYq9=l?%1A0K9vSHr>BirZ3h|{vcrQhxEsTjiY+hTF
zId?RWmYjS+nAXu<50D+m7+v<J%^}7IBJguRP9uermJ`s%=OC2b!W92T0L6kgbj;@1
z4gV5OXIrv_-^Uq5ZG}Q&A}2QZ?DH%P{d3wV;~sj$Tg|IO!!@WT$wJ5xSKPE0**3q^
zfXQ{LsC6mIUCnmSOkRz-kCO)pavRBs`JdJmqcVI$32~qI09ox3TUzHxkAwE>+I@rv
z&=esI9k=L)DvNBHFT_h}MQ~&H(alR<n`cEQ4K<GtR9!pY`kJ8mhG6cW^7#2H55l;^
zb4`!vk>w^vqq5((pyM_@Ctdt;Kc1i@Ce3;Z>Q2)Im!XzI$P70ud}7#FXx-_7o>H_-
zt~$MWD8?=>m(#PVM$zJ%1<h}x8~8TrB+?ZUvt5`3icOZ4(#<_C<G1>D$(Iyg-dWeR
zyqEdd=ibdhLw~CvzQj11FF)PupT}=i3DgXfR7(0fgG1Q-`ntdS-)ER{T;e(=WyO)4
zQ8%F_d8#n{ZbL-`g3*Px3NC$o#5?F_vNED%vV&FgtTn|q)8_c4JJi^3<Ir@+qu^jD
zpPWXxT-a*&=CT*i8=H^4u)w*>w+CA9p&V#{3+Cg0w4#D;K!YxG6bqb{LfR3*>Tue)
zpb4%_HeZj6&vltN87&9OBRp8gB4>3;);5!YzYNCHRC~hPEci|2UY+olIhc~|f8dG-
zf$7xra19GhBGp{W>q#Jq!+PYt5|-6Ke)2VAtgT&P&y2q{()fb}#T}7bBc(7nRc`sQ
zM<<KG$I?QMR)?BA%qjGJ9J{Koi9--Ml@*M>STa{!5t1s~X0sH>+qq#nayhC&mzC|7
zF+1<1<nV0x!83pA;RCxLt)%>NGQ=A+f8T`);n?y&59mx@w<p%5m+K!K;0$bh=NSJK
zW0~|-#&|qz=<#ZfgMv%<V8RgU!~J7f4I>Y3WUZ^1|Iy!xFCs3W44t}HV&oUn%wPGN
zBW)%+=N4t==b(-qbzML9>gHaZ512IExB%gtR_hE3+(o5)Y)Qz``p^e)ISldKoiCVX
z;KMGJ<XJU%YDRE@Lunk4(L6)l|841^L<FwkbmWI9A>!zieBW=P+#PjNvt4~>p#DcS
zH0`gS1Hi``SHed>yf)f*y^f&UHqTf$MJp*-;;ftUH$&DGyLHyi|3K_(A8#-ol%u%c
z@HzfX_-gnjeX-!g%zCaS;Q@hqwM*6Mi?`_6s*CQTBwlLKhuVqv`g}62ZUf`y&N{}|
z|1`HtH%~DG9xt`L?DvfyvIbASOIM^sF<ggS!1BADi-O<eybZf-8`=JGrtRa`+@|aw
z{;I+WtrBn7(Xd%d<P;^#&v=D!Xm?6oQ2|S}y6Z{lmvV~?>(MKrrXgQg9&d28;fsh#
zTd%auL*6S}RTaPa7daWB{I69CF40GthVBkBk%8Q;vZkcI1A8Oygtph~7x#54xt&OB
z;7eewCKm&BU{P?LJQW(vran}9bi8|dNkqOiGT1g_9L7&u(V|#CHB$Kq(qedD+adik
z`nCBPD%FP_Itw;C!Uf)0EBIVFZm3<HvwW_lhnzw7EVX=DFgtqZS-4r8u>M%C^Tez+
z3AC`lp=QmJN}ZVvj@>lY&+$k#u+>b}o@Y*|B+UUPv#PR6?-8XgIn=julKSdvHoi24
z%*{9*7(2f&_DUr<Pr_SLlUNEcb&Wwo<_1{l;hr;>Q9NSdP&Plv3F4BSu@h7~${oW`
z>0Hc)1&z(P;Q@!l(5(fvoxw*;Uq|48JgHc=RHSAsNJj#>l5GP&s>ZQ*dfw;}?>4gk
zhOUZ3SqZCKHse;yYdtG4wW+5mA)h&&hz_j@`RTk#yw~~tBXt{^oLl(3lWr$GNBJ9J
zma)K}wYRk8uV{<{#n4Z^ZFYnByfhLoi!Qxq<Yn;hHMJWYQDyqCk57zrk4%)O*V_%I
zFU7VecNB-YHVxd(`^N&ex+l+$qE^D!H4YpHq)dAYN3Vf(5_?qA*)qB#Ng-HXbLKSZ
z<M!aev4)v3dE>D5f)7o;i(%JoSN&VG@>phi2SfadbP}Yjk<lEXeWWa+qH4utB9d~H
zd$%n=f->cw{(f6m?retQ^U-}T7V%*NC3|6~#H`N54J<@2&R&T*A<&T;9}P$Y5IBsR
zz)<wBA{#tf!Z@wYaWL|3i3o-B{I75qpYH<pvt`P@1-<%L3k%AHCTXJIiq3WR+UIO-
z-hQuK{dgG)M|e=BB(QzH{Hohc+V80nj(cn`A6c;EtW}J!F6`?Jq<O+%_rt<YtcP{;
zpUaKb9O}aK`;@CO5LO3TkKfj?iJ7R&R{`}5BZOQPqtH`>3*Ia#6pxP-iXt~mv-EmY
z-sPHOU0rk9T2qrvHW3cq9iIyydc4y1%$D$=)jz^DMHq7=zW)_B!`jO2?&Yj#GH|2y
z$?;1_ao9O=#T80N*MD=fOp+<&3>nup*G#kQr;TVPkw9za#>{D@x-5m}t{N3nsmx0q
z;N%G;yecqK(c#S-59Y_pur3=b#5frq50q)j9N+m!*l!p2ku$1lJSf|>Qhk_6OFe?B
zj3lo1Dhh7e@~7n&gs!#s?fHx=5U_am=vloh`0-72+1|W&bMoyutv)~Y-a6GW9^dWq
z)i<JOJ?jrSq7>fnKg<=&P;YBGIXfCym#ig7(}>3_z<RfW!keg0P*O34Nd>Gr6O*#|
zXi*9kWOg@jAt;c;5yC~P^BNEzL`rH6q8m`TouX#)82^%4nQ2iq!p4I4W<&haeYl%X
zl6L;Sp9FOkMz`wK9C%X$$FiCPeRcr}d8(*YRmMZ_NH)+;2miRnnNtjqF<d^TB=C4%
z=UB9~G*Llv=b%?e!=OIW``)ToPpQ#aM^GmGtYx$idSI&bg;GETGVzI{=$5dhgjla%
zhCG(}*7<>#?q1G}4jgD6xv470_Z4T0+l_TPLBF2e)@n>zwL9yi3D!Wh*)(6(uivG=
z+Lif9xM?U)hW2!Je8^8P$5f#yK2u-4XMZD(xb)hdF@_m0wda<j)QY~09~HjUo?yxu
zoSQB|eDn=l&s8xxD{H5?i-T#-qXeI>P%`;bE07zz!xY_zWt6s^?E*H8u^^3MPQ-$|
zks)}4B@ZB*O>0&LH${X$Q_0FaviZ0*V38m-V!$=O5yRCAP-w7iNWuEYD{UZQC*+w7
z_L0+?!d{vTDggCvB;iy&&=5Mst=<BS7-D?7@$Y{op0F?x>}4kS_&jzb51BQK%s`Gi
za<9gF0<ei2KEi!lm-ICw#4h;8rGJ>?MLV_K-$i-O84(waEP2%z!iaEsv1WlpNM73b
z++OgD3~c77HdB0L8e1FM|Cmh!38w*QYB|7qA%P=$G9|3b0cL|>93tuKGP3g1lHI+E
z&h_6%VKRIq3XDEnZ+o{0rz?tp-0~Xw^wx8>-?dJkJEy04%6Qvj=#cZNbDC$&_S`#g
zXS>9`UN?A?&aR8)Rd>g~OqSicO7M;>!4#!W)p2tUUl`ObKUI?6+Eh|$q)pu6)q@r(
zdpH}MRp@b@SHID71Jj_j5bpctO~2=Kv+=^`K;#Og9<<~4E#=8sSEmr`EQmFK6?#Zg
z;sHATLAvo*m-B1pjcZDS(!&x6@NmEy$?MnXMp*iefBgsX#s3>GUqgwkR1DOJj|GZf
PAbvBpK39AOd*|N(uFPD=

diff --git a/reference/plot-4.png b/reference/plot-4.png
index 5ab0954d6357af85a996ea09d8d60e015c3c6180..455a17f91d8a9a32325cc097b230947b434ff3fe 100644
GIT binary patch
literal 41063
zcmeFZXH-*L`!9+W5f!(HZ0XWF5)h<|4FU*Aksef<bm=`Hx<x>Ugl6a>MS3Tcgd!>(
zBy>ViX@<~45;}Lr{l52{|EGJ${dmV&LuyvST+e*wugw)B^>sDQpJh8sM@M%as(IId
zj_#B{9o>niXHJ9P;FCVAgD<C_YHQpDU%|0+r!f?KVR)`-=0!)x8A1CvhM3>5qod=Y
zgWgp)@=sfy3V6?a@34JCPtfZoM_WwR@$fg{p7n3&MHfXQ8ZyEvMY|9_Ci;)8yiFb{
ze<y#lV70ub{5X+pKJfL#!@u<JUes3y|1R8%^Rj&`*sNBFfAxI|s;JU=js#cH%jn5z
zY$#NkGVYWgHoo*{NdM>4|EU0|z^!>XV}r=H*%+5AXM=u*9H*m8eB@TkNUP0qo2S6M
z0%moKtulhjKu2d}t}rh7-=ei}AIJRXdwu9%KTiGoeVA4hoz#DRZDgQRNm~sGnbZG1
zjpBOJ{}|mZyGuz!W(HEwb}c7?f(r8P6(~D@hyXzwnh7<3deAKJHTXi3cVVK51C$<m
z)(n4Sic^M`nc)#R6>7S-k-A9lf}9Lwp|-iOBQhrvSzDh&5Ow^Nvx;2OG{5H9kkc+8
zq4iOjUTaB+@HcV5Xu-cMaqC463JFWvGGq%#Ku7uN9DEY+n&y@w2tzfr3a>fF=P(Ii
zMF=cVBE+(E^9s_)WWij`(!QA~xn7VRS}5U&?%AQDdiFN+aukrhwjVGkql{7eye0EL
z7pniyxpxdkcY~E11W+0d&~AgV)zi=rgRzLKNG%R7chl}73u4{8i8$d)ovYkCSv7Tq
zd`(f!4{6AuN4W5l_?Qr7Ddtx_)lqPrd_iGr-UqgD9ZgDz|HB3Ps7;({-<}~a^BJ9Q
z)>b(81O}dO4R;Sqci!YkBhSW*56ULHVrDDh9d_JW{8R6F$`&ngy;_yU^C7PG*`@!^
zh;fhb4$~$-vv)`0)?GGy=$+0}NY&@iqu87S+rUJ@_?dwb3R}n44;{hYJuSDxe>kAj
zJ>?5{0UszYg2#BvCdKK;_bW(mE9Cr}&;<T5-v;~oIVI`H>T9T5rG~pi&JjJK7WTO6
z`wJ=$zUYOGO7u%JYzDRpOldIS?0!v%uQgoxQzRqx2r+Efo$RbTu{u)S-lX?ZB5JKo
zb*K0o#A;wE>~?Mz)~Kgf`!)5O-s!A;_C|aKFT-GPPc09A0yAqMuCe?Pa{?1$wJDLv
zP#EO6h?L`%nY)GYK7siQs?8G2qdItOTE{YV-f9%|FCAT&=+Z+4`b*!sC!;cinD@_g
zpZq-mZO{ptemc`@vlse&yeYGtlY#P8;%~y@&&4ZC?w1nQjw7mN_-o<_D`|X$izu#Q
zU7}l(k%T%-z2JBIkX{&dS(Zv0!w%B7VQxxZeIm#T7xKHZiSx&SdY%Sp0HWf#LR8f-
zcz}8$r|LcKdl#2=a{er&CCDZmvwzZG#=|wq&+5;rqAUD#V?yai`6I#a?=)?od;*+{
zm6%wavODOO&TdF6vRvoK+$|^~pqM*u=<`SA$nZFB)1CQ_w`+>Lt-F7r81nkcNBZN2
zV1jREYYz`4?Zhf;=MxrgC{V+C68B7NsDT_2JjQc2qPbi*i?30C6de35P=LxlhCU9B
z=t?8vjSr8(pbhw37nlv>VpqtFt`F3!Feg57f1TI8-X%03yjuOVzlKnU*f5=YUTN=U
zvGXxnNpG>tU%6|>_MIZ-H4eXvtMX)Wnfw{I>W0|IgvWK;9rxW(rD`(A4GmxGr9+VQ
zJVjDX^z6%|br8PL_@0$r^-mq`Mp`P*f8XG5wMVs-COaYBw99obns;H-+?WH?e^<gI
zFEP0zi{-dL)h@2F{NPN?dRuV%e-`dKnkzK9d&^g+eN+-3+c6owY_EpON-%6l_gr)R
z&GL467P;VykR@5+WH&XvUTCMVND}qD%x11oD)iI~{~bZyBwuC**N$!5C52=X6+G@~
zc9cQaZS(`FaTmwHMF>B)ekh@S)$shf{nCVZKdpZgKH9qZfX3>`Gm|V+<8ds~S)X-;
znsiNeg=8W3-Wu{yr{7iCeY5c)Mdye}c5ey{Yx5rYuQg(ty7+qeB+HI|$K}<wIiK?6
zHviSkg-7bRx42s+`9R;*V(duocjKf6Wkxy!1v1`C47&LS9I!=<o)t)6tUOeEDH6-?
znA~2uaBN{VSG>j5ZgWDs?9Yf~*lg+O^al>aS~ssiR8F^dy*;YGZ&1<m<(5W3j`LiF
zIO<l4#kxL(`BLuF_@1!>UCi-lMoX9!S#x%4r=Mu5EIep2P8=8#Sdr$!OSq4#=D}qt
zrWA~hdsoyPOY$hb>*Uc>vZt9}!6gJj3}rK^Zf~0CU?*sV=kY3O&qTPT@W<(01humN
zUqhntK~{DUtn*aZ(|K(+Q35mSv8zvn&*V3TM4cbJaXwt#nAd*Kf7OJ-jumlR!t}^8
z8KV^)aaY?%h(EtMf3Dw5TCrJ*7~L!9Ke3bV6p_Oq*}dC-p!V5L1r2Md<$xx4ot$UY
z3^c{<K#q02hP35=$%uY}aAgtfDB|s!t1*N5NYK#<JNzM;duR&-hx!@SG)+HL#_P_W
zlbL7BrV|PlYT9g2Chjfil4T|d{r<z1@2O3JI$|mGmat~?0b*9|WIK~D@sv2q=CHEo
z;sy2dFXI?s>h_&V>nS3-qxSboz-qX4JXjxqYhr6ZgqfdHf*_5Oer`BmxKO)cYN0%W
zXvR)Pj;?IMW~qvuloD!cRa$632Su9PMhv+rZj-CodmB2VfPrV_JQ|tFZ_Ej~J^f<)
z2~wA2U0vL%SHA!5a&02Pxk{PFF8Y0E){oPLb=hX*sC8_qI`cEX`J0!ZimFh?rm<0v
z0uerq0uPgzm6Jl^@9su@A)2NM4D$8&w~3vJ#Rg!R77L$J-oDk`jp(jy9U)m;CN+`|
z6MLu$N-9Z5#`eWTWoN6sv~IFyyoir-(md2D{&Q`mkLzhO0HR@@_rq8L<KG8>lVsny
zbsS@0;Ea-1FlopkT%;aJd2%~b?*`0Vg7G+pbyr$>a<|uHPl!;ns_}ECXQp-QGn?tP
z6h&UzLBHB2k4hyIz7mH%l9Zs)%C!MmJ=+bsrE%U7JqnE$L6i|+ZRYAXJvFOy&k^2J
z20cRfQTvPkYa&~k=IHD7Cgv`0Z!BHAIbt(DGY@B{v~6<D)?|AsUAdFE`vp2IFolG9
z6c*!ElCU{n+9Kwl4RUqA`0e6zOgLd>l8%MiDbkx<_djN|SE7}Zd^Sw4RQFrK)Xe&9
zCRc@ImemVIf7PIzXz1Ma*D)AvU0Gctptaynvy&vtq`drHk?p>cNd(l`-2r?GHF+yb
zCv3F$BQ&9cH-&$(#XF-$eBjGNq|u#)k(~!fNygG=#$8@WEvuyAI6`kgV$O-n2jZDx
z%-dK~92(L(sMFXzPrhRX3zcl8)CPzvP0y24kQut=Q#`%C0V*1TXI$xL+mMP{yG8~w
z+&i}a1tEaJMo$o3TU%mrvbqM`&<A#b+2c0I*Zeo$c^A}_4$fXqUSXnY#xKoIpWNGg
zq21u8j7wXJn>mzp+*HB6UGH#yqtPG$Y0XTD;-V^}==|MLE#}L+1-i0}CV*!LgA?4d
zG+>>|TFSLnu+I?YZ~?<6*7z}4lTH@i^+-3-=vj#ckBnt&!^xe%Q%wgLy{Y)g8C!1d
zfVsp-=PC^GKVs<KxO#*)7jbU7TS5cJer#?7rIXdEeUIt>Yuj*l^^-+s*4g04Bd$T2
zRW`M^I_`&=AWO9?gWXh~&rYSzQJ!+61H~B?TiBDT<-Tq{NDK;@W%<$jrN(UfPe%xy
z8vtdX_y|Cb383-Q-L}W!Zp`-tW<#Lu9XfA}Q1|%L#Rwl<SaYJHJI<ls#eBGela`M3
zk%R7D?_4=}u*!T*5hYIec6mh4q+n9W!W#%WnR%7{SE*gkT?GqiOIzVu3upt1U0LVb
zfO^r^z()RbDH(OcY#*BgVS*pB0XnhzF_`6HK~A>-=QYPt#8$Me+l()J5KE^>ysWta
zBXrPEllnS`uRHf{WQdTXZn%$|;b#2BL*9at9wy<)Nio84JJq_IhV}+E)|ZN@?{nY~
zpBQVFoe7;<nqZsfk;l_?o95%X9@o(b3q&nMz04kBqy8wlUSL}Y+HLn*%ijVQgp|v9
zKkDL)m#{sK*67~qQPEtuLO2h3kr)6$n}-SxR=HVk{nOJVpK#TXbQ9VfH)Ou>{)m^k
z$4rp<W(uE|CA{bU6Y7&9+i%GwV$7P#u!WGD2KK*-A3)HQW(hCGBl&TWg)+A|21-5D
z=Tdd1fKTH1;*9o^NI{H_`;Gt-mbjIW{o<>O_u~NVJjo>~f;fu$>2(eZ5*D}hDm>l4
z7*pD-Gg1RC<p1qKF$U8{JK6X)e~atJRk<4+K|sfanCkv(MQ9JLNBnQrf(G*cUyrOV
zXMut*fyycKl{wCs9=NyI;!42$te@GvM?3@$XutOEeVi_o$d45uNFih2UG6d`?AKc_
zU~dX&?_{L?41Uq|LmK)O>GTz=sx{y%I`GlLazbE)c!2B({ZO;IcBwd-uOK>yo?qf!
z^#hz9G7s8t+u(qI6S<FWZZ{=LB`Uz!1wwcTa;O8T@)1U{o!Ki#e`nqfSnA-Y(h!h9
zR^MCj$6O<1zu)v9*TX%Ba+~c{TjExbY)OVqPoU}iF-coZiEfQK0VB>aNZ~4Lzb=m1
zq>-Zs^YEUjzJnO#bdonJhfQ*4%#oWStld<TehDhZKkQ>BWwNkcd@jpHm+(~0WRkBS
zKj${W6K|l9B-M@SA4ZPRterk#QbgpbidNzzYem$O2kPMWqdq*Tt9`99Y6*>$6AO&3
zQ;GG|RC=@!{b=oNg6xfMyRIp5?H#$!=S{cn`Mv6?k)C)m{!F0s<+294u9|Yg&*_rg
z_M|!W9Kz>9GQ%qY(f0EYb!n-B+U6aI`rO@s(8nd*x=*dp?XwJBPsMbeTBwPzg?Zuh
zacs%exyV?lh~RjI{66Zm&&+B?ZRjU*s7yUK$+pW(ya20Vj^<`M%imi&VkS~htARq`
zrbqK<Dg}a6){?wyFl8T&V(l26$2qz1C&ILZglG%P=uACQ5`7308dTkv3;L>lYS03g
zij1-iG~g5$(OXdNO!j`K(?3mssC51!SC3dHy{OE~zBM3q;9PX8>6YDk7J2_&yCR}<
zQsM{MAH5&-sT86TZlSq*JI~}fC+e0{tWManKU7Mq;h}*P!ga?nlsl=O_&6fx1q3V6
zHuCYc&b#2cY#+CRNyMPCbNWo9I5#p&ykhzt!)q>HX8#)eo0*R4^FI8&iC+Dk<vC`^
z3keKza#grd<WAgmjl77J!()qkA9INOKJSbMMKwwjk4`$LI|$T37wBwn?bl~Y5uQP<
zJGbB&1w-&GsRV`9_D+r>3KC8+J#r!L<kEFt$&@r%NK=I_u$|Rzu)uL7Zxxt}IKBpo
zk+X+p)kV`+A=s-|x+WL?j@#iq<-TVwWhFFh8#r^HKYf8O#HGI?YD5f^s8gR7jze`&
zKb~7;H1hUcn~Id|W+va=x@5#O-2hQJ%LhS+Mlib+VSDt?x*52JI*Xa-`)a-OgQ!;4
z`oQS$n+AppBZqHt7&((N^Pag|<Xdd!n|5PgUtN!P=XE!A6yi4@pjT7z2km@6DuXoy
zsbg`+UXyWezbGRMa}D)`HLAuzL%P{cn-Zw1zMv8HCR>f}tM<I97OrAs6Mt#$JYC{x
z6#x)O?_6?lW{<xk(mOln^ti|c(RWP{tb23ZRv}``*S*LGVna0MUnyQBm~~N0{#?w*
z;>Eox^Rw>Mj*~vg+Pfchesp%<4mf%r@3e>S3Y1LTf@Xhy#ACs7zShUf6kb9Oj@O}+
z&QRfR;kFBWeLB&d*LS_52vLoyhT)P&W;l_i&6;^sUklW9Z+q9t&sYxef{dJe{=`I^
z@n0c4j^$+Pa};cvkRm4Yh;##jjXsuz;{y!zDfd}_kBOb?5Bv+vF)l)0*VeL&5b55N
z>K@kd6-a#VxnzcO6Ac}c3v~~1g3x$dH}?njdo{LhURM01ukLX<e|fI`mGbOT)i(&*
zwr?aO{?|DqiT*(6!Niru{$X1|$<+MccV%pB1NrO>9EbtXC<0r@a<>!l=-WgPByntG
zE2UHC3&hxdEc_`7AH5bwO|B=FxQ?S)sYzSmJ^rmk+orU}2YrKI12b%LR|LIc5BUl7
zVy@|RvNcb;)^2QQ-?%44jGVde(xf}l@%H?7eUFFfH5u9a5Vg9YBv~EYk!@*>D+E0Q
z%fkv4cRhj^<d_M06;E{;eER)GSvS+!!ffxUUKnWWk;$K2c%3i(^(lSuNmmIXpP`zS
zC_x3~<s41P7+b%EB0jCH={t=I)3HE=6rO3X*Uw{hEYPtq_=`o9?OB6m!KwPRZO70R
z-(fD~2guGqU@W)x4ZVTDjZjI)<gPS!g{af@gT3-s8%rF@o-m3=y&<PPs~ns_swR5B
z?<r>x>Qwl|)t`~|+*2W`x3?<lbk^S9<o#j@swFhG=Q_xI>2Brv#r4pyA#7ko8*J_U
zRqk&q46xF747i&Q-fq<Puzr`8?B?_29_^bxOoX%^&#$)b4#;yT`}vn7RapD3z1)^9
znstlcnWAbfNR70Y>r#3Ch-<ks6cetd8yiRUfc;gq`jBBHrbFc>xd}j8k7<amBf2|a
z^8TLOat08+&_>-L>J^LBEFC1_#ouuR*M%has=CoOB$tPX#$Cbh-v{{mKSWI{u5S5D
zcJJh-<aGqP=1&qo@Rj)3D~LC?@nyc*k6~C@w0z*FwoIC5g0xPIVPMf_QX04thAv9z
z1$>!s`_5hGg90w1ZP1(7vzMo&@;59Eed20Lj3#Er@Ka^JW<o3|wrq8X8r_(yWwD^v
zYTX3$i(IxJUfuFn<4dYd>cq>6ek0($hLgVU?#RmOZ~gr(bGE{f*tx&X7FQq5uh+TP
zqs4l3r*5V)H&6w?k~laeRd8W|2UStZ2WhndRnEd$h?0&`snTU%`?S)9>DVg{RZRt3
z-igWNWpRE%NszueWc9G3-}SJ*Wi6g(M2;OoP_PnTL@wXugX4!4l*4f1m0O$_t9qp&
zSS_z&4J*_JQ`yiog!4yC#}?c<_jis;gvc&zidf#e`4qwAsFr~ox+5r@@HT%A&f!Aw
zP_tZ^Wb9m~wt_fGS7q1pGH0@TjLtgY49e@eNgj1WZ)9^@T7~qK;#i}T)uTH71a+%V
z*LkPY!;w27s9Yy1wxZE8h*}=qD_<*bW;#&8Wzz9hp)NCfwEL#>xS+yJMsQWOdczY4
zHfhDHv8iV)e$if9P0dkJ#G_^HsdIYjEDYUF+BX+`ZhB{cRQN(CL>6b$=(e9$L|E2X
z-nkUF6`M2Sxf*$M+T5W@2cg{9RB}z`SYk+t?Ah{Qb*{ytV%L~WQz87ZY3<8c$?zZO
zgLJkkuxe&Kkv%o3g*vDF5hm3=8}rM5SM`WJJ}Az9vBI-SHqaa3&Nt~gh>dBG##0-_
zM6BEv;id~30()swrPyw#`~&cV_FkY~u0|C$Q&*+<AW34F81Sa%FeyiSrKG+F8*|K+
z3Cvzc%9SX%bmSU?v);2`S9Gdh#$|Tz$PKXD)N*q3#P{f*pm^q+y<J&h?N)dXE-=zC
zHpXJ%@~vJI*~iqNTtN`+33wj<8oobZgLEq^vHgdCDqH2H4#g%;r=`vLQ#g0A4{@(Y
z&3d54C|K{~H)*>7v%bYA*3R`R(HIHJWT<{$``{{;V}^0`f|JVHJ3)Tt$I%`2)S~pY
z{kqXI<1Koo{8`k;XJxBy9^HYaW$y6=hUB+8i;F_N+$FMn$|C_2=0b3T4_8Ns5dtWa
zi<5oB-V{EHa=I{;c<Ak%?&#U<dZg%_u00C_OJ#gjBp|O{(f5{cHZ~$SGV}{~1kZ6y
z#fwVs+`+>uTD$H#yJPK4kQbe{YjquW!_b-o+;~Yp(`n-Y?of>K;}SpZTRYzhCB3G<
zOg||%w69|>W4J8?+Ej%)yY_laVv*tCzVjN1f0+A3Uj<HqoI$auH9CRuU8B4Szv@(J
z-b2BtwgXUV`5Qy0Ut{H>(jT{T>9TVgIT(d;|Ji8P7vF<%RB=xxt$1751xi7KsKU`c
z$!S5(E{0p$FbLY2ONu#cN0;Tu7sJlNNtH?p!B7mZqv?hCcnNC|>!%u;yS(;h7O2N$
zrYK#GD@B@u<&Ad+q%RuQXb+IYRO@`|$otVf+nu_LKj}xjVk!H_9g_X9I*6V(cg%x&
zzHXEHFZ5A-ga_$REvtr}tLdw<t>))Z7DNG=ZyyFt-`GLaxgGmAgh;l5u@jCaVimM$
zh_Ma-j6|JlkKrud6JI6Uk5tWYU)g`IWHLC;6jT@S0Mcf0Gd+I?nGsju+@G+QV@+8<
zyU{up<(an9nLHv`4~K<o1eDK=dbdv};+#XH?ZS6U>`Ld2rMs89hsKF^;hy7<cm_$B
z*q-grOt}>jd;59|^tx(03y%HVr#J1~X4O9P_3AwexD8PWk!e+JZ-cDNXh3Wzqsi1)
za4)+)#!ik@qwtiMC5w0Ck-HKWa$jmgP%dJ0S(X=noC+%xEB^ibr0#5AU&79%RH?P`
zkI9V~H~O#38Tz`*Qfx4pr~T`ulqPa8rd?64QGTApHD4e+8S9A3ZcVjyFA2WWhsDE_
zpQ7YC(}XlS-X5FO&O>L}2C8B|V%1E>UhoGA)5#Cpy3J>Z%W!CT=qUJg2AfSf9dLKA
zp7)NPbU2!3A^Ev{pHy{li}$*26{UNf^$1>^xbw^0DhMu8e2sZsGA_tnT4`piLT9hb
z5)nVoIXUq}<>(U0Tpq=>US%gZt#v<-M2f!;VP1d^aLfG~epF2j-G`60rZL$HiV(9N
zhr{8tCDE4Q@AQ_UdgMA~xBBLjq*swg1$oR-WqA{8Q_?IU8FEjdk8%<j8R(&fvxz!A
zJjvzuV%0YMrQ1wgQ&k3&k6A`>*2GLMq;>KNWYIndB^~G2x!iUcDlsVTtmtXyMmUPe
z@e@dwSgd_qL{5`hqqeUSIJhrTQ{9VKqB4}8ptv(4IxSyjn&Lm5`PF}L8`b!J_2I9`
z9}C@Sd}>|$W7|!bAR+u&TX>p<=o+{4poj(JLU{&S^gXxP)BecK-G^uBAg_VHG_nFZ
z?Ii!A^sukiE}D!U_4jjYmEL<vl1!ejAo_2x5x>Jmu1Btny{NkZElzcH1F`mjc8tYZ
zt@msHG2X%Gd_<2#60&}(%5gH#mu{q}y`KQF-WTjX*>@q4@PPx*s0mU3<v&%jXu_E^
zzT7!H@~oe3Tx5jQC`kYvhR(qXMOL;;B~a4ziIOOuZtgkN`&sGkE_xlX5fbC3J(V{)
zJ*!W5uk56wjh$Gt5pF1sVcRt>i1RV^$Kl-|deUm5_lq8crb+i!o@5+@zfY1HZF;C}
zs`Tkg&nyt{n~TisCiDbNlei64_q>jfblt3T9<?unJ5Q!fhNEH2L^TvF`bP2McEgye
zuMcuEHWkc^=~qbG!oG2yWGx<s?riO{FwJvYl^pb{Mo+3S?O!DM?Vp5IEML2bSv{^P
zU@dP4v0+!^CB1yl{{Trv44{53P9n%yoknt1&o+KdcmqD}8j$_rGETyRSHrAdoqRI(
zfFof^;W!=31(3geYIxebbs)S5%UHDN4(bj_o77qFLMM^;LoVSfg3>wO!#qUOxja{=
zD4w_Q2+jyO)$2C&l*y*IoO}cpR6n0d=ebk*h6{`hR=SNs%XkmRJ5i_ovEdZoTja0r
zM?G0MMSqGVd;8dh+okfm`kEd#kb{xr$@})=gZ*~j#C$HLy1RsZKE&<DXe%m}s2ma&
zrOPrOtF+H3t_ck8>_Bap7^Qn{IF+`qhPr7Ao;iA}gD4Y3_5o)Tn~gq~|9OojR_8<&
zU0Ox!s}O5{X-yX0;AhUdkR5DL-X0J~4h~y1KO9%tNDU~po1T4H+&ubKVo&zQ)AxWY
z|GgJrVNG(g41=E`Be`l1DNDyJVawBPSi9*_x6M@)_S%9wTDsv5GPd^6m*|?|)YMtt
zzZZ<^yi7esw4KN<f%qk2uB~wup8%RR^6zeo*qrF%W-NSN>)tX#R;T*(zYfFL@cfE`
zo2zML`}LlYl}Mog@v0N-om&g#?RQi(P^nqPqHoTB`*PYG)Xro!sAXccGD8nAW1Zy!
z#iKFV)6^bv-Ej>Xvjh@>h6kdFLNX^`@*l`7_$c|4O0wH7eyMUO^V{Q9Xb)61ULYlK
z{$7=$!bW_XGM)RUZ~N-`mSs3Qbf5ftdME?kFrjiDi~H8?4Ts~#un`W8;{C}C)qy2^
zs1PeYf&eOvOc)``=jmVRJfc;0OznP%z#5P7d4yf0QO@lC2lC`MFNve&Cl4`#9`TfO
zzrNCspu4x`5_;y|!ke`13J+>Rw|l(%zi^;DR{ImSa&_ni9MjfJ1)^$ab>l0w^4c*W
zmQCk`XKa^?GFcyWpBpW|BY5$!Ff9UJd7>zF?%<AL0DIQ6GQWHV-GlPA^__>UX<~Hm
zj6ndE<)qjfI4v;vvb6|3dVZuKwD<W{*HO{k52}xDpvFsbf7|NA?7NrU4UN+(wnQ6`
z-)sMomaWp>T2lJ#R=;aqICIIO6h{)!GX>A|W#Jr?W0Bdq-=}XwW)DGf{E$7!VmEGt
zMBIpcI)=_^pouAqb#^G9-Eq641X9zNMRv8xS1R?xBAz2gxcHHo&{zw5s<4iIcB{+$
zqR@jA#BmY-t4M{lWmrtK;aI84>td5NJ0W;K4Xe2h`5wC8*xQD^d#obFLb%yqm4Vo>
z>~2mjUYxZR6!Nk`i;?D(M~Inr$M+(%8}uTZ^=ckfFBT-ADDoyafsvUt|B}DU-}T&_
zJ7E|8B7I&}H4vjzyvRPMtkWJdLb93IqepmeKKR0w{XwT-l$~Wt6t%;kWjpbbwT8-+
z7ttL&9jG&&+Wh0$y|uRiMy=;-eBO|kW&gSk)S|wKc*0H=T<;Q*>v0MDKt_CxP8G~~
zF+o%C2TL3(xV=}23tx`ruIR#R4M<@lx^%8Qm5N?_LaM7NwwNTcesl54>8Y#mo!~{`
z6{Wm{qw$nU7`0J+KsvF9wc<^7n_)t-p+HO`LETDkKq}-K%4XOEH~Pp-&S}l!4I_)n
z2<QAHV^>DlTK)U2Cn&Dqqj-kWyVX=z91@i!%<JX6CWw-j@G1^YF~z^_IGIxTH5c1!
z?wf({6H}D7q(}JJi!>nciFHD`>F)bX=Qy9%dKG44hnzIo_?dg&XtMn*?=6*fj1UWi
zy|RrZh#(J>=J)q=2<|1#W(&b*Ix)==9g~9l4|?Qmf|R(Vyr#MMWL|}bOdBU(h0M{5
z_1?<;wpPF7r^ZisL?qvRu`z2?E3GB?%z4ePw1<p0#QCp;a6ZbJbs4?dI6o~GT3w@?
zU2$tlrxxcP$51`tb4N3;B*<vsz?E2iOndD;`AWyRe+*^p1J0wkLo;ee2%J^oQxtSe
zPtdqUUb5fvcEKsgN_&tr;aB^81U2r`Xhi23k-+^&V&o?Ro-8Ue_L@#ESlELB=W9Uh
znrR8m@ItBG7?T^9RS<2E_QS&W!5T`gx9o3n2bsn!h|HxIdiNdNb;Jwrjz5i}><|1>
zB$7XJ;&LtI(B9Wj#Dc1v{OU!AR@-mCbw^Z2eD(CId1?Ld*Nul;ZJyC5i4)6G(dciO
zqyoCN8i@5bz6*#N&y2^tVs*Rn$*$u}`B!_3o`z8w0=R|tq%8(uVH?wLEYNp@V!cLt
zZ5k2U4L29o{L4(c6+5@`d?vm@?zy+VC0Eq!Uyrau?sz(KJCtRubiBu$_Ah45EE_DQ
z_;u{v%v^i-jq*vO;Z2&5S(i7wAg@4D=4AL_Ls`e$P_}{T*<IKoQjp?NWB~p|5Nxq<
z=HOlSqPAFs-Nq$s=3fekMyr9?D$cN#-<6XW&Ww{IM`z6=woXBg1(bQNJ^J;8gWpiE
zOt-w`O%9&rd@TLIVXZc=UXa>kAff%NXbb+PWM~;x5Z=LwKL$~os9!NehmlI=<4l%q
z(k6qPMq|vR#;HSy&2#Ho_I0nPMgsdq8=7ZK%YtGCQr*JjDIxe0EF!c(Cw<oVwb6XX
z{!Na7X|>}gKur_F555LJIqMi`=2$B>Q6*}4Y}yy1@_jXlVQeJ{_1G?F(m|Sfwp7a=
znU1#=d@?RUx_0D>vM^77{qj@Pv1unr{QD`<!BnT0Jrxs@GLIHYAl7HFm%Aq28jThh
zQ#fU#e5!lkYY7a8sztxwG0Iyw!q>`IRz@n7Q8pfEotEaixIM@*`TUKOZBcMXZz97`
zhj~-0UHyuzGd4g=S=Yv;$xS9HlgBH1*&a13t-V0ckk8|Tv9SwOEQwPXaR$tt-+`64
z%_C{wcwmF#dY5V!{;t1!=-xFHm&so!SWd-R+dxBA9<LC0G{yBpjzt2Dj<#+b)Q}2#
zvo-N3Q)g7q#uRVW48nA$-Zh_>)1h(7|Fd0y%Prvnz9tj@;yy^LaHHFVAhRatHRf{d
zN2YlZyEj2lCoobw9q0Iw?Q045-92`sW1D1x>_frrrrrs}FvMqBV4_N+B6;)ddZ5ac
zI+r^T*M8#>#YVg1Z*5ps2H3ru9@wr<20X~?lS(AS9X0hRc$ZV%)?|*sI)^-bEe`5*
z+zD^8XLaeV<noF2MtSfMy*#*rSzl&1Zg{*)3YD=1rj8ExPN$sN*pL(D#Rs>X);TK@
zSX5((u%tU2Q`;Zz^i>ZN?Q4?xpjXRiaA7)W9340=c?LYIybQ#O^1l748)o8d0-mem
zXLwXJx-2a|R$5)mimGZy>D9{n+j*4##?tOlu|0PTR#&gHFj;q#F<?<N5Wj0)&%}JZ
zrI`VOUQDLy%hDBQ9Ce*@HsHFWMjO+cJ7KJD8Sx78@c33`rom~OQ?-4M4{4A5=x~+O
z2OBw2OSZ%SyVE2W8<ito$EY4#eQn75KhLF|_5ZWu)~Ua`e2?jq@AAf-;J?x1tMKpj
zz4MuQb^F7Eaml-ONvSKxv$ULq3Ey2<|J<=eM`woa3~b3y`*#<N_Jkiyw7uv*$EO_H
zeE@%sTLSlJ8}ok-QIY?73*PaOQmaO7FE6i5s|Ih!8~O9pNw5A7oJzhN=0%T6_B_D@
z62ij%7?0U+HVxhc&%+1uQ{G>`#julp4OTxJcxtPz-umi>^T_2K3stopzK|6>x>=Uv
zf?g9LFE#d!weNZ>{m~8ZGkm$CJ>^ur72WvH;R{<!!^Gm}H<fojeT-$h)cm%g$+Jj{
z9DGllS%09e=9I6}#Uo*1;rHL`JZ4jO$wa@U&;Kww`@1T=q42)(v_0!{fsP;CYH;Rz
zyq@Z~?Z&UKDtd~9oa75?`|F$)-EyuI3HWNq!H1zc-PUdgTZ4L`jdtBhC$zM+Nm^Q3
zPNmk(hvnl{4w)XaG4j*jYhCdJ8+dTR%Z3610>gXMwa{1)D#oi}Y8(d_Hff_QZCq6x
za!s$StV{zR<=vI#_{zJOT1FlcX5yGgVDEk2IIQZ+hodHzL!_X>H+IKGFO}7{G{Jvy
z(_V43%z9>*JZjxsZqpu%KwOo{tHD!VK9dR;*2p_)w#aXo>7ad2l07U6K__)F>*2@y
zR)7+xgVw6I?W`JoIV%InOO&6_{TGi8W{#%N&jzxi{Z=Ox9xwK#pJ>`yNM+0j-s~*u
zyE*%|oj6>CmQ!81>X26PGwj5L?clyNh336q(NcwbA{L4}Z5ED>1)Sv!c8%h6mEA?0
zny(s~er0FSzl*Y+2=`r?c!0Red$7{J(3{%Nt_I)lQ1n>}W{$jjTYlPqmOKOBE3sjk
z;+cr%>3Vq8cy1TNn|%J(R}C-?9e<`_Wuiu-+HGnVOIg9YM_W<8vRWHiWBfhzplqPF
zF$fjP*fOZKTYrB)Vs3F!BSp@&fs`FBX->jvOJO(09Wqsxst?qTcKYM_9e;oS(5zkK
zqCSV%5{F^72Q>L{N3Yr4ww3XA$MjV(6EAE*wPD5u8D&~~XLCfyYk7o(^2I4f+1c5+
z4|gKqG9kNLsR9MX#p2YApjAg=l4?4Ix5Lx+IIVSP3yBWin<`(nQ2wN?Pgqh?muN!#
zJFco<b?-zoH-}sE0i}wcP}waRqyirYoy4YXAbeSrUCw1pOAXI0{q&2;$NMp&88Qxi
zYv6a+?T!wpGl?z}Je%}kQO~x14`?*)Z+2VppTgmD%gdkdZEwt4Z!%|*o#>JGteEHu
zL_|aw$SZhi@mljyle#|MWwdk(=a3PwV5R0b=*p<*yIfcE>C>~7BngWP+hF!mG*cF<
zVTvpF-pzyE$a7mi?&bR6UD2FspbrMGNWmugvNZZ5xB`Du;a!z(Qw{r4&HI(wv=KvW
zJLEUI!D&~May0pohL)Dg#D;D#0p81F8|j+-ml|ecl?--SRNm}U@$Sv-pMS2;1&|ho
zz0I4z>jVdRzQ*BjD#s;qb8|m9mtkuUK7c=CM_w8}x_Ti(X!z5^*`JR}%&iojetDy7
zQS0KtVMqj}C?nx#4k<n0yQ3rw29wWt_N<_+>xB0ZXo=uoug|kv3Y`fAH6wyj&8D3`
z&o-Hnn!4v*<23w@+jr~<pBrdvIbCAAF1*T{fODE^@KHj8;rJ~sET~$5?;7>g4z`+f
znJ=kqcJTGKfiZ*S?Gu;Nd`K>{c`8(J^GDbTxAM^kzMgN4HuWAAwx9T<!v){|$;b+7
zCG)BF@#<uK{*-68M0>7z?A?`Hm6c|r4j(g;_lL>wr5kEKDr8KRtx$qRmHh+tOw|zG
zsxGq2!-oSWgwnlnVJu>gE(=a%+V)3|?Slz=<iFV|lsOLiD0B`C4|{aDH*@LBVV-BO
z6m|<YODkx~4QJIk-@}RaGUK90Cf}q;d#D^yc6m2T5@9f(>CC#4IszImcreiKaiLJ6
ze12~3?qpK#Anc@BtPzo0ZRH=6^!R0pK|#MjL0#R=#XhAq7oYu&cBVp;vT4qMfB@z{
z<#oi(?J`PX%8M)-7xFLY)VR?7c*TYJR1M#mPgaH&POY99R{J?tU2D8KtG7$3Pc||)
zPgxT>VAfOic*B3gbz*Dq^XJc%+w($c&p)5uTRV2*^wz;z=#dhD?nb!2=U|mX|0%D<
zz7^^&u`Kht@ABAAXC5Q(5ooH^XPzhW=V={6YXz+5d{<vZnfpu=wsmv9n_b+xX}?ZD
zJ4pvl>Xo;SvVwoR>aZ0spJ*Z(i??iD8ZN@C?aalmuu7N@Z2}lUUCgvTIy?w^fP&03
zRg|%?va*U>)E0N2m!JG@_v=d}cL-(Nf%>gUkCJn#471r4*Y^6nlEY}JRWs%6Otquo
zpow*e51<_GN%xUrv(ayqB{4BEyiF!woTl)l$S;SP>{V``JFgk^s$GT0((9uVl9Cpn
zIC3)DWOg@0`s1sN&;w`e-az2WWWCJ16ghI@g^_JhX}`CM)J_rcV8j`|6u1=N(5WLV
z?>bT4cC^2!cI4U~%jvUX2O85=)@z}s3Q&*NCg3Y6zmzxEhrM_yA)Kng+X^%$ut~~2
z09)g;N^X;N3IIL!dyxX#x{5zvF~61qb($Wl2A4y`0Eo}{=bwL40S90sW=Vhg1z+oG
zk-^Vt8KQPjPgZwycc0s5nJ~W638vTD;P8X^o7%^%;iqn(UB*4b_<dF;d<sSz;P~jQ
zuDB)gPc-&<k(a?ZOq5*DfBx_5S=6GkCdHeo{eZTsLv{)I_$B}|2Y^t59;C{-*0v|!
zm{+_$TzM>vIdDB2S2SH5$wppE8u>U~p^<O=8c-)UFI2*!)^eeG{|mF;e!W?hJxPE-
zqqr<UR<l7{zdiciOWT-BnU=Ry<R`@2#46Zh`Af`e3PVKa8AU_YRxzZ$M`9+W%W28U
zCc%3vXzOP1L`L7WX_d#sk&?v_Fok!dz^Lfi<XpB5T~Yw1bGe7sD{XvXw)S7cQYCAI
z7<K`(OVtCUU2~eO+n{a)#QZItJ(wV3c=#?k8GW$XE!FJYi4=J0M|)M$Lhqu={(9T!
zNP*!ZK2gksYp~3^Sth+}cfz^Msv5AH>s1=(0UdB|vcjglg@IYn6439;4LsW2{d<u~
z*~1F#$ZIC<CecfgTWVY1?<{KKO3>4aioVDwAL58rW><yQjib9b6|P%$o(r81ZfvI|
zIjT%>nB)BpX>DXw+c2&7r;T2I8Z{_zE?7JW+W5*EF;r;mJT=!DudzH<Ve_LeU5Q?K
zZ@DT|QdHF3!oa|Q6wWAlQ(iS@hGGqAuLZ1d09HxB@JNY8e>7n0(CwcQ&3RQ!aKu`%
zS=FH#;HC4{A>?6?m#5D!xiuetV|O14+8WR(L*|4Y5Uu5JfX@3Ov6j`JSeW$_9?+1r
zbLgn{`Pxy$jr7_q051+;jnM;qC$1rluz}<l6HAY;o@KJ2lX$v*F7B@5VRcZupn{P_
za1K6jwSF-+6R3ppO!zJ#IA|s#aH&9yN%42qKeVDfUn<DI;3Z}u>ht)!>-n(4NbB%0
zx?u9KNrWcuNd#x@H`jCpuZ6cOxZLhUAT@V;!NoiGzL)-Z^!BQZY~a>2mdlr2z(ORY
z`ptw;j}-k@F<)KTn|FHM<+Kf;P-rhG`+QJ{v@l}-;Ju84E+cezY7I{9sTqas<sJma
zu-(vV^d?l+YbQ%`e<^Kt9L$>(%J6?$$8Y8T^B*SNw3{CG%bPT@67=H*y=rW~LwAzI
zPYtdB+gJS9-KC;Xh4}<Scy%%h3rix*cigT>SA{(EP{O?0|JRh?%u4f<w%14B5XM(7
zMEC(Iu~r7!TeQ}iUD<zq2`Flq-|r6*s<l8h-}x5HiT9lAhzkntIRDRWJ7hv?YU-mk
z;pQDBhF|`4nlK1pAW;jeOs9Xlqi}S8sAh;%P-I**;s=J*n0r5lEtlfFm>GIF0XlL@
z#ee-r1>+U*oAuSzTOy6g^)r;2NBa8Z0ifAG5=YCpJ5=+$f{8-~ReL*&nau^(&2T2Y
z6j_2^e!A}jt|?>>9!i_I*t@(6Cr+RH+wX8^v5efM*5P#u6|ZHnPRP$Zl;gNSeFtBu
z*l}Jg5`2&%o(42^NW~~3-g_e2yXz-)cYP4%zfpy?C6d58>3JJk^6As3Ezqjlt7%?+
z^(E>rPcxN*<vtjGiqQ#>g@^NgL&3<4f`s#QY2RP6o@oIh;6hT)6$x_}Pm6p|Bmn>h
zm|2uVM*Ze1W?laZaLLV-TjTX!4!tNDhl~(l9#kpqYKH?pn8-0E*XSz}S_SftF7E6N
ze0umXQ^0Y&(oO<tcJzWCyng|RKD2p_lbQ+u3utIZ5SWMyQ`jIsb<kw?U*R2Zhk>L^
zkKl%H)5mG@%i47%U{`+<K^KPFG;5Bh$h#kEBe+yx2{|-)Zx^!2GdhI|z1V)@)+W-C
z9%($jv};5R=(M)tHo_+LHZ+M2lNN!-yqy1IC}1(2nr{2rWM{Q;4bI8d?Y`$u9L(<z
z4L`f8Mc|%M4cT?!Q1t#Cf|RxGc*h>Ay8HVljW#B;??u-G?YXQJx!F%~xU?PbWbt^W
zkDJ}7x4(bpu;m2PzS!YNs1Tu=m5|W3M*f+PUB9$NK^mKynvMXhV&ZpP8Y+nOaW3@X
zq05Qp!rY<*YTj5FI$UV1z5Vs+_bm09^K7!eXOM?jnmjBp%$I`$*`l|--o~USAtuI%
zV3%Jlgkb}%Jo*$CixGP4dyMWL9u;Lo`SksR?Y)^J)gz#CCYxx?1Xi8vIKV@BV9d~4
z0E4Zq^L5jwHi)4>mC$SB;xxDvgc2+G0G;41kx5e4@L)GLHz6~%$M<;u+EZ<OcbQYo
zb7ym&%wG~Ubsxw%RWCf8x;oYDPb@)KI}H~;>P!+bR$t)$vO)I_V>I#N?*CXEK&zSL
z27)f85Yu2ZF9GavC6FOiz+73kzu&?i;$xLo>uymc-u%n6RrbA0>tG@HS=YLlB?Fc7
zfIE1-^+e~gb*)-_OQzAk*2lW;`Oll3Zv`3wjk59Ge|yuFvOMmP2IK~nX0qCXD9Z!|
zOZAH@|73b@V^#O7Sz+PRFbuFpAE&MSriOih3*`(2OX(#bF>EHbXBrLE-ms5WsuSlK
zIzl$R)WtBMg`YwvDr_{gm`Sn#h;4z(ndwQnK^&(6oSuS$LN3x92z#=Jxtecr4H#;*
zT6IoJGdE!6shy2>ZtLlKuf+njqkXS<5yQOwC+k02Z2(;=6#xPrjRD5eXL&bO-dz%z
z1Lj7Fa{4)G7NIth)I;A5WPCXIz|cN`g|#I96`PJ}ahz-b3$31#KvT@M+6EpT9wkOy
ziBUWxCK@tj%=orI?TUzoC5r-~J|X&Kb2;K}p;15U<;!)`PdU#1Lpl3=*Y(I12=@ts
zP<m=sERcO6i({dF1e~3DCHlL=o(#i2G*_advvY^c#t_jKr<<<mnhs<~4voOX#l*T*
zj`m4qI^I*K2VmPG5i`U#y`Y~0zgNDp$v)GkPGf`A6oHd_F%x=p@PwwIRt}=@^6fmX
z|LycoTkw=-eSy?LcCF{s+E;$J0-VOmf1*JVRG-gxCHm~U&C(PKEoVrPbDze*0R}kD
zR`)gd5aHOEd;q6GG310;+s)*h%1TH(Qwm^w&jY#%&|j|HAzK56Kz9ZCg4K0CUoUfd
z9{|6K{nsd#_E;{JhBhEDlvikUd>a`R6;-Q+1E~mS=y^Xwz``pdaLoGQ-kMsnmN}j}
z6KXx>Iu1au5Nhz`$xnMVe4qEBItNo0<f;!fc(@QXogSMRydYM}FY?~EC26Ih3G=@}
zOh({zI>7j(cxIR8M4s3+8mPu{Dp8#Fj?h53U}((Hc6hL-L|KkbT<Pae9Efsl{*B<y
zRq4;w>e^=tWxH#!4rGqXNTpr(Y%{PZZEjfd$jC<x#NlVxsZAFtOjleVInF5vyps)z
z>p{sl|KSUy_lTJTaKLuSA(NmWB|b|-<F3?{)q#)g3NrRRzcWE#m8JX_u%pJ!@B;~?
z(f*VoOK9@Cm#4v5M#hkPQ5H2$H7;POw}$|kN)JsT${zu>;1ZYf{O0n_51)lHgAKHi
zn<Nr?7j^eq`aWrMGfG=atMCZG(kWfX#s199=es*>H=GhKaNK-8)$-!RC^SU{Xlxn{
za>|Du?oR_*mx=>;R+6shV@?L%gh=XFQ>z8UNU(JuTplVIECjsoIFNm>U2BtkZy8IS
zc9tM`<=cVr-?T&mYeq@5*nN-31w-YJO#jS?u(<eSB{0D!j;PaqFI)%RtPTD$QsS{I
zEA2N@kwPho4mYTN3Y181ND6G)-*!Ffu2Lp&%>4TGD~;9)Mvjh->NEs!94Q{B&VGw&
zTmlZBYpTY1RP53Nf7ZGB$};C^%+3|)uE(O}gB?o5ty&Zi!Inlw37&{Co{4a9MMpSr
zI;(xp`vEXlYt6JsVYH#dA_I6qh44?INLQ&Y=O7q)N2XtS&>RUEWqkp7zxCIHiq@eA
zJ|v^A<S1`E8=W!pF_@7#lj`=5VxX>Fj{t%covOg5rEypVZ+tDCO%G65t($$+M450l
zulJn)04L1=m$BW(q_!+c)A!!u?UVIh)a0C(Osf8IGr{`{Gr;EpSSu+!$^_)N4nNpk
zArp3J!HiX5K!*RXNHx|gR}QNI6bD+vckCVlVyJ}y3)<~gKc9pH(cmaI2u!4D#Hyxa
zZvk-DbQcl2Z$kwa&4l?CB^FCbam4q6Yd&gp2Pw>O-h<RtkJLplPiLp$dn;7}gdUmx
zH0DfNL?f1zbDmwUj#4Yqzn^~6LpiMLF)jy61uD%@BPn-aR`d~2u(dP6q+UkD2dsCM
z9(O;o$}9+kA@_&K>chawnJ?{ZE}{>lqzGIyTq=QS%|M7;q33wsvS^sE8v>lK1}NRj
zt6+5nP?>daKG_C_c8#pXO`AjZo6mnVfGl9L+-1CSSKYNLh`MUc%EF@HTep76$l^JO
zH;&3cn;wJ$f3<)BRNor9JDAzzYhI>|XDstf&^VYKft|!+ZI?)(cY>i^y}dsB*A{?y
zbuI@a_;FAqGd^pAHi<UCgNcHJ7EuZ@3+bj^{bKX^6@v`OJo&AwGOay31;qFXa;I<p
z^WS>`h-J;Irk0kNQJ)(gzrMVjK)Z($Zj4$EKH1$O_yFg+MMQ{!0HP;|mdOJ2cG?1P
z%nrr<9{8BAFi)c3-Te}B0QmN6<p!(I%>d}5@)LkK+i3=BqgEWmSI`AY8*p_CpYKIq
zA$xW;0*#q}0XWM=fQgtRfP<l&2p0k_cNqLduU!CEOaB0x1_~oY3Q8u9&fIwRJu6fT
z2lCz0C7_T9K+b-0L`>CyVt#fmt<P%%abSp+i|U_qfmos!AOKU(LDL<8&r^)MbxkZS
z(_}!hl?}XX!|b&Pj!oA5%A};EE8f4q|4a%!+M9{d*4Dm5%OwClHhJt(R@}iQ`8@$h
z(NqswzWJ@*D8NH^egd%2tK=2*j3Omi`}NF!dMOJ`XsV#j`-_@N*Fh0cpr_N<WQjh!
z|IGvd7=TRR<sZ4e_f)0%`OnX6Ag1-a$&s<&B~~W8)u^Ywfj<=TJRpyvYp8>&`SB8n
znb{!Pj|Vj21;opYs?E2zN-NVHAPfjD1{X$b2F_y^L}EizVxek_Q8OV^tZxAVLiJZ#
zzC<!RT;NjnugUa0Ieews74I7qae;FhIJ^B>?w6AzBs?s~;zILiL?-2PbmoeHVJ7wW
zfK4uc2#|UqM-+5(4H`cPR?J)she9PyYX>+$=CzIW|9CT+Xk-JK6t2*?$PBm+BIL>E
zSN6$PzT;d~!pR8<uV@L47r;@KwFaW(=n74B#qnuOkAUWg+2{tj`fV_Wza^o38nKpX
zT07w#M?eH;Q1?mwWn*BPuYq8OtXA5zyENt<_u>ToA!+95kjgzfSYqAW)VjYz+EWHf
zpmYc3@s-8z>vZAvkf9zH4FIOW9Z*%kVBUkh10dVs+?fl2%Xh8!ooCw-nN<MX*+60(
z`dSwRbw9QUgY=5?4tm!wug%5l!71r}(+WfoH8dW9Pk0+MK)rdR03xsCTih>eSSn<)
z1~_;P;Mvj;aL@U7KIrpbKAX4#7*ZQVN<s0&So(zKRBr@**qi_eZxeePRO88*xw2BG
zY#7OKbhU_#QF*9;uby+veQzzdxLBuxnK$=y|3<o!pWA>ddWvEKZ2;cD%?$|g3T!bD
zKaH6|tM#dSWFmDv2O59qJ>i%yQR5~w6P@WLkREdHdbkrKKLM=2&<c=RG+TiLaxd8$
z9&jYnchlz1@+Pof|F;jP*{>y1>r48wrj=T}J;3YYZqUT4OW^4<HmSh=n&W^ux<T{i
z?(rfHmElg#bVd)hew;eKiEchvxd#oLi_-{fmuj3tYBzyZu~g0|S@+{7fP!WdAcKbM
z>+6+3Npfgj3TUU`X+Xf-R!zY>U;QRMB4$fknN+sEUjt_r{*k5hoZk9cGWY1?LZ3W&
zG9JYuc1X+mlwp7a+*hhO6}|mQ0NmK+fE{E?!(k67c)cSKF>-qiJ=AzmWYtJXNlQzc
z0xQS_1isCxgq(PoKgbM^yn!|jxs<uuD?bxV^Eqv?YKPm&CHq~Qo13-V{y$zs(_i4&
z{tfmJWIIKgfiA|70nsrL@aQ!|S`m<`sf{vVcc2<@pjtJ^$NK(Zg>C=91dtAYfIH^A
z`gXiQ9^9tnx4Q4tn{s2622@}hp#~d7h10ZNGB<$9*mx9(qU}hupytme2Y~2<zro&!
zot@oJ;68AOXK)e~*#|5OTbVD9*?2Kg#;RZYaB0_@2omL$Ce!{h;D;Od|7rVa)z0ZR
zsaLRI0o$)Vnioi%wE$2y2-Z<^GDsuIt5bV~pKTFqER!_v_zfgq(?ZZYug}$A^F<b$
z&U=*d$b>%wU6Np^yHf#lN~R{xXVl8iZJ*X1An$WJh}RAJ`K6zNEa?Y<_kPQ}Yj;-N
zfhta>84NI~Umv8%n%x8;0=-G8Wq=__iYQAL<4gaLjp0ArHC)Ep{FewkA}i8o#j;N@
zDX+f<YJ{tTw($hiaHC3xE4TM|_0dr3l%FfmXlgkC8ejAwErvmi>^($_bP?QY@VW0F
z?lgD$2GAM72(>4M$ny~ma6MKl?*XVDVCeNhU8R*yfB?;94vos!Z|YIc&+nwS+z&eT
zm|I5v^4Wpa+!p|n!oIz|N_xd_1p;G{7^dV7fb|nU8rPbf@iZe89CjisN-W(stbq$9
z3k;erxWLBt7<Q^}ZTInI=Pshsix|prx{H7I)wCt+ZOMOWaBQq9tzia3Y1|Y(vHIpO
zKFQAB-YEr;0xX742-5NR6RN>zI}(sc<oB*bF(oh`L7qUzX;)ftJ+?N4%`muBv(kn6
zznwpRavAIx1>ME*sk?arv*CBLUWM+~&IbO1{wSXzkhOe(d=?!VIa->Em7AEyo`L`4
zWaD@1zyF^DxS!&3eDSqe$~<)B-;LRKmjunUnQ8Ap`g3F<foFtBUWyz-8mZvH#t-ed
zHR5{qpW9!@B~SkQ^Z)Z_9-g0IceuGLF+lA$lGjY~zkj{u>mjl)QC1zt3l)H<TkTaq
zaTdtZ#33!g-~T<OBRdHR8^~y#wB^zk@aeoA@ZUF2A`i2MPBd;H@@-#x{&xhn9akr0
z^uf-CG}0}px+TyRoukcvBf&c%r<*_I6ZR#hEb<!re@^D%E9^Y!zCyQBe}T9aX{LJJ
z%}fR=^{k&~jb+~0^gk!v8afhTdyk#6!4jX2IR7dSHtN|lK($E!=iw|4toT|>iR;mO
zvYVGF7jo-Sk0R?y3!VQwoOs@*KB+kdxnvf)*tON<a|vl=e{fLJ^Zne0B6N3bHwc-d
ze(e8Z@6E%Z?*D)Bv6SS-U2a67#Zt)@A?vM@RF=YweM!hp8v7Wc+*wOmA`}&7FqW)?
znH0*J7~7Z`5|gnEX$-@R^PX{kKEL~WopY{pe*gS_*E!#BS6%X+*ZN!@&+YYoO{Urt
zq%7g+DTP@72XeEf&6Z!kgX;N!g9qJZ^}u^{xt{2wToDdm?at=2XRQ)G1%5Z`Ulbd;
zsEsyXPqu4QF6EY=1~2K4J2a>BsoTZ?jjZ*YM7O<q{S5bW>t6tVH~rKX%7uu{%$Tr+
zL$crXd9*Tbv54Pm`c>56T32cv%MQuS%j|#4M_tNLetMq$GG*zvBq2j<6GymNee#nm
zVU_LT<8K1AC;;uBsc^T<QZE|wv?yH3ovR^MtHB3??G)<yR<bmDG7(E${w1<6mw+K$
zWq$AGS9EN2`6jAZ^ZN-B7btd-zsN+sr6WyU*Zk(WMCg9m)b!0Ak}@74y+;40(F45Y
z$o}&(;{?`$Ma+DzVRlyJ%C=!tn6%fp%_x&KG~cf$Ec;P4{ouo%Gn0RUp4iDt>@oV?
zTKV|Vlax%88_yE?cw3H6#qK*^%X2^W<jMVeC3yHPZf8VXk4;GU?e)++IX#52ZtmbT
z?*_>y+ED2WP-hz63)M==UN#pd+1HJfAfs#)th*p#NCP`3xWU|4N?_R8QRrS8x-;Mp
z_S$m995l*XdJufpGI=g86_H(MXJqjG^!Zlm%NR1Tf&C&P==$^6Fz5^C{I7mHgb&#g
zbamW9R@2TC%BwYo$jrOlCYiBev=`ufTm7O)Tj-XjAxox=Xy00+L%up!rNlnx1X|e<
zS*ffPKpN&_*uR3-lP>P6sQgv-d)^?B>J4sZ_RrWy2X{|cTz|HEyu!7JclUZD5xL#F
zjdD(MaV|E(UgF=?>;D^vAj0rQI5*rwV=c~clf@|B7UaShB@U-zBaBRQxYgT9@j{}&
z;t+A`Y?~d+{AL}0PD86I??J)x6BbD4s*hV1WzKpi;Zh=m5SpmAWkb)wI9utzZH_i>
z=0KhNhy6s{>c=dhFeD2@iCJU3VbX)bOwtiCQLo!Lyx<3z*75<7{aYo*TEB_WK?zx-
zJ3Bd*L~d_0U^rTy|7cZpa{#U2{gDB`dIE>hRcZX;5F!)Lnu+#=#(Tz5Wxxs$nesFQ
z@$8|s;=XoL4;eL}g)f*cj*p;n&d*d;a(eL5o+7UCjppT5e6MJp-=2AzCxxglR*J9I
z?dJ$c`9&c+hlqwl!zC)gJH1kshAq90AIO2#LpNsdsPU`=?$Jh5IS1xIuHJdM%$QcO
z7Y?wHK{HUwVyq_~Rk4Ru(N_0E9t7f|`ZP|wWU&s?RmP_oMILjKY5Lyjx)U%JWK7KW
z^YHNRo1oG#>oA9VA}7Y}^X*&VgQ=jD9|W9~IN{?JWmLfEPXTLCtj*CWr-(dc{2c9}
z=Z>q?MAb)HntPTc+JowV_b9OmyAwKvC+f6~pC(dMRK-D%d&P+O%E;)$n&$NMjcWt?
z4NBrpEw7s0xSd`7n{MEssNnEC&E<`O*!c~{xOmw62YGbg(6+w-6%g}l-eKgW4Olni
zj&^~`i$Ob|q$;x|Ylp`ymSDc<MU^>}T1#o9kM2$&ud52-R~U;Vq_;d~+yG?vx4uGt
zRnWH=itr#?wVj_6QTHa*_?$MeuwnPYRTgn$T}8QB5ail_%vX`h6VGtIHi=mf8l^jU
zR&M=95nc*5VM_rvm%UfGeyNKyAv_2cPsv<kq|eE?On%*3C{TsR<Y6R`M(l>Jxx6ik
zR1yN!w{Q_*0WL%tIKY~9ipG5=OKh_HyQ13|IEv=l=nzr`oH#%}j~eF^<czQ5DNM`^
zi-1xozv}rYqtcV4oBHL=jgi-B&6HS5Q%~>aif3X2$51Zhh_)bSQalyrV(vYnTLjeW
zG*D<b6&3Y?H+}2Vu+dLUPBW3)d#IGn>(B*CDJ6#zg$v6%M88iF#1$iStrNjJcC8JO
z4z-ya83%C__h<*Q?E6RUW0E;&26E6~FAH=%^FK2H{jb5;DZ&C7gV4DJ=m-0cZ*pwd
ztbRkJ8LbsyiT}?TA@<XhKfg3KOFP3#8DQEoN@GVo2PD`wMJ)}<dZ|;wNTl@RI*m0s
zh;%(2ur>KNnK&UTp_q|PnWShTpMue+*dwu}3%T%)att?!Q?Vu;nbd7zEK!g+xHY{@
z$aZNjoAI&pWAA?K9VuHwWZS5q0-TsvYp25UWXf1=6OOHojABX%a&qR&Y9ClKeRWUV
zQ?iV|DHoDDMd?Cb6eE1vSFeHpHiu6R;047(XR17ttA*_Eoj5V(*jj97z;*paY*=+Y
zSf*0iUJAGPdp!wJse{45X}>lX$|>VG2V^q1X+Rtzd^n;%FGWu#XtuynKWxw}<p&LS
zLW}@xZ=o~L8dLv6{_?M%lt;+{++$JPL|9iuADF@lh9W&*Eq~wxBwEbVy0s|M6xw;3
zw*JlY)z3r7fIurkX-DOihcrqf;zMOfg-}x`PK?kajsAruhY}@r(3I23%X@$|&o&|)
zWTL`H^%tj%=`~$#cL(M2L;E*psY6ws`@-q^4#;$CLp%Lf<K5$4HVQ(WdNHhts;m0>
zn()+V<34VpfJv_Km{}KLI@D>2i*9%CaMWUpMf6<<wuMG6KN;0|W#U3kU?U4_8A&3F
zt#JD$TpU^VCm~s{b;`yO#8DN)BWzyTTJW|ERn;+UYg^^h%VR1T6Bl$j=1I&PtE*3j
z!0nx2e~V4B+*E#LMREYU8_4#|zf8No{XL!qY#G}Cax}VJpPQITm-$z_JU{mRGylJN
z4XL6lZI#VSf30!W79`iX|F(=Htcr1&mW)$7(Z{h@So4(DZ|fVB4qO<b!MedEZh+JH
z&zT3PQB;AGlSVj|h4aRZ<E#+wCJaZ;b~K!^TW<iFKY>JpsnV0W^^AB59!G+N9a>%E
z(68LVov#8g_>P2%@lZhvU$Mo2kU|P<jb7mF<neug{gN-JDxm!P)rk;<IN<v=R5%+p
zhTWrW_0<P%BvQ<AouZ2zztpwQF16yIGRnYFzeuMDfZEOX%qVMWMVkZD2a2=#0bDW(
zsN7F*l**(40s<NJ75gP5bwj@a*dmKE?DN4#6Y%K)`pY4uR+dTYqG*)8=j16I2J8hM
zbzZGxe+H7jQ^!qQ&~V7#VtPg`7w9z7sW<|X2YDERIx>k4rj1iR;=D!JEnRiMXQ%WV
zBW-D)DVW?PR1|ibXp9SlK#<FpOe?526ijQR<l-dOst=RhIb=L^NOy{0NhoIwc_N{t
zFMguSnk6>$^0e{-8<@YsT69x0X^Ihdf_+&gu*!(M%emxIpk?8>pUv_YIUE%NzX|iu
zn%n;Q(jWlU%mF3a_|E;|0|BA26JPl9{3}+Leb%IVo1lSt9Ln4F0G~EAy&Lx-7%nF%
z2GO5YNU+BSh2AQUrGLPMAmDxj;DzO0hs!TG{C72zP@K~DcpbrMHD<h})XZ5qXua1~
zF<c95kvmGZK@mI1(soi>C>MJt&6|e4@1NlKaSthZIJy{~gP&C>3Ew?bC8S{vrn3I<
zK)!1DvdLwNN4KW6FoK1&vt-RvtkxJ-xK{B|N%llQd&Mb^`yQZ2uQY;To<xq19<gP9
z*rsK8bo!UG>KRCi4q)fqXzQNHV*d)xno`96AVyfi1GEaZ+Fv>&`RWL5&0X8H&mNu3
z0SZgEB@9f%goRTFaj3KrDfTp~cJ6yxw2lmmFL#EB3sat&P&{1JC&AM$VQ1%FQy_as
zRggjz0gi;%I<Lny#qfG1I-wt>*;B>xO9E8!v2<xWJm4=iY?3zI(>7-w&*;&WSX(h-
z)>Fh0$$P0zj?A01x%W}R(r<Lx)4A5LStC8n2TZpmOj%=37ZPs-TQ%>T46)o{oDE@i
zzKp?2hjFY(IX|1-Jh+Q<G#XMpY_-W(n6h)IEF0FR`jPlW6rvOul5E5r_n2dBzigus
zHR|#LY?$|E0RK^FEuAl2ReZ4mZ7pmMIDnf0v~9|LW33wD@h^*LnJk^gyGg8KMh_d5
zRD)0ygXDl)vLv5%`usI9!anRWus3E&Jzopv>*CQWr`2CZ%=8nq8q_9=;<?5cqr8yo
zJfcymrJ_EXWd!@>yk4hUa6|vz_w#|_vlm@ETHiGf7UdlpBqK1k_e8cH=Yds#pC=+O
z(6pR_1!O?6#*A9ZM`JCuA`9z4r+XqGv@aleD5T-@(R2jSvUTQa!R8=^ycamE!rrT4
zo&Hp-$u(3P<4>CV8Y7m%r~BNuQF7yO@x2D$0_WC-3GH@A!$CjXcLkFif%TF1HlyKV
zJb0i^C=S>bT{2VV$F>EmzC49A_}+$3I?N9eg?%TU6+MkZE&S}u_Fqhb0}^^>;=K(9
zTI&n_)L9)G>j*<J+Z%LdS_FDXC2^0~<OrZGQsBKPk~4rEOp2~<ivHWkyEKP*y0axL
z<hAqqIXHejvs(p0ya|IRs-Xg+uu(G<clJ^#&~p$ycwOY?l@O-}J8abWm9_WIx~%@N
z#g`(LH9s`h#~!$_y6a=J<&FgXx{(iD^QhFE+WNC&w6#fd%U=XErlrrAP`EeD<=!EH
zr1Fpq;D8}hHFb}9v^0)O+&hW^!_bUKTEFKjDkN`D+lL@}u@by$Y&ahbi*S~Kw3bW+
zmrWhcV~!1<;U!jm)H|HJXAa*I@oza+pCULCsv~EGsD-wK+U79mS0Os9Md!^dJwLy>
z0te$8ZSb#eZMQ-c&r$sYLhnpyA+3;FHVVHLG+m`lcsl3}pZU!h870O6AM~9?v%AU_
z(vW<>QL(2)9Z`NkLf@dNFGXX<>%ON{hObP%!pT^Z0q0d8Wf#7M?rSIVX$eHB1gy_M
zLupUfytK3gI#gUzJ2_eg8@=kEu~ygzE;V>#mlN@Eo4Mwo^UF!tdB|^e$zg&ubA$F_
zZS<zl#54nmbHcaH{vEqjbgE^GYvMDe4p)$Z!t&r{X{dq`xM6g_`NF_@1bqVM1%Am1
zzFqIXjq07f$9y?%<O8>KYG{tAT{>L}k<!ELqynyt7jg(mReo*7QQkuNDW?||IPx5)
zXDp#!@^25jiW}^+hx!OdLn3J;;r$N%ijcP)c!{m^T8yQeAPocR3l|H1-%SWC7O6Gd
z*~p~mFGV!&TG)U?05%FD$byvvl`##BD^R0ao(}euphJVUTvQbxh5?s=v(q%RI1jHK
z@=bmi^&6$obhm%iHvm;o=SvOMzrNr!IO7f|)Ixk@&y^4k=2xXua}yhcC?788a?Dh$
zM+#v0LoEw+{@LLK4mn3tL|nX_%L!*=^A9ObRYXS9fcw3X={8zY!MnaqXqETt2%>i3
z!k0~&AW|I>f)ut9U;d^iELkSQo;~(lATWCzj&-dCeh-@d#DHU9K|JmOyRHuQv?Q?G
z=@We%R$-Od%Ju%r8fW2E$^V@{Bnj+QD&JB6b+Z+8h%&Xgl_=QhSgYge46mGMzR=3)
zR&MIf*U|+>A(z_(+<|0)E{Y0i_5aRs{Xrw|0CH=$jbEK~Ym6eEogrDnyqqzwCsc27
z*a-3^0wjLusTh==>w-ZtUQvGdteF=yl}E0(w0T1DPa-*%#a0}kap35B?D{W?0ZuD)
zx+zSE!k0F@%fMz;Fm~&Q5%aZAkFo(h(+HOM&u&z`QVY^Fam1ql9d1dAe*O2l9so@7
z6{K8S4@!%_{DVUcR{}tDChH}!@$i6@b}2^V=I$C7+JQCl?WI-gPNJiqlPh&gEe@CM
zatZ1NP+NwN)*3d`R46qZDD%(fsqu^lIJfwP{E8|4rbvow%HNTo7Qp`kD<-kU0Ku?@
z7U!oBto2)T+*gQ02!YF#UB}9a!+tQHKG+%q_$?Kb$M18(AlcTaz%4gFY6UCkqXCEb
zBM;Pgh&>2hh>Ao@*tU@S^2EAp1H}XIkd#iWt3d&@`A$d1G&jScA3B*$9C;jxt-ko7
z5BC(gU_DUQ?{urzxbHsUunk5P{_uGe#HGx@is?i{P?T^bxfKs%ezfawh_ik^4}6EH
zVvSLQOAY(E5Sh74wP@n{1PoHQ)503`=xnWGyzy9;8C_IGqiqZ!{!;%ky5Q4OQq8X!
zbEl&VU^u4<>6PVPi_ngsun^h=We6vTFah)RQuW&y4U}^;^&C&@dMeCGocfX?8ov+*
zF@dC46gV)i*NT1#luC(+fKK_GW4{6iTFm2ZNe&yOJdS{c(q?cEbW@rdBQ0rfHfgVL
zQjouAFFZFQCfHD_nW8$-bveh~M_Qv4-o$us-suF^Z)G4iX*_uq>l~7glmQYU96Gzz
zyPTwU;al0}6C;>M+)@-Dt)er~qeS}a8AoaN4*Iz{nRE#^<<7b?MlvNbIpDUeOld(4
zkQ2Im&$U*6164O=(D{={k${|?JJr{J+*Y8C;I~jQ+yA!FAMgUE%Dj3a|8*9?X8w+c
z6Bw{<X~tUZ7YLP+&b9vYZv#STx_v@}4{6`3JmGY0@4?iR@A<eurDlFxeq`_`$sY8`
zO9S%N&H_LTr7@ZL%@D|MMO{I-CuDaj;oW!~+_%FY;RWs32?V`e%<60V(G2$6f&n?I
zPQ7BrE1P*w=w!vLa5wBWlyaI@BN=1BMUAAFj*N`lHO!99rp>P7c*F=F{|d;A%|7P?
zNcWAU2}*xFBF`orDO8A|Z&Q9fVdMbyrZzrmmZC(|=60P+Ei4?;Ghm}qp_!&S3ZxcM
z{gP}7x!~R_<c|p!Y_BCRd!V9!+70;kbYwq+b;i|sU=SBk5wuGB$1bJj?AE(;>hOjh
zEf+9^MR3@%A#6OyOuqdf?6BGf?#?D{G~{npTMTFJG`(}dnbOgauKcs>qa%=k&7IBp
zwiU!^wqfG)9m(*CnSe>2<<L~c!{`A<gD|Ytt8L}iPv2ac7Y5-V6%szs+5u%py!eTc
zWmbWD_s)lMH^2c6oa|d2D0Q~gFN^e}VGz`m;hy0aU_zL4px^qEpKk4|CCssWOorLg
zR1Ky0L|@%xgIta97*O4mieWA&1I|K?TXCQ{XrwZgPex;tRDutrvtZC4bwFmyM5gXS
zUwI2NU3<HmPw+_icJNcyd7xbD!#bCFs&P(OYN8@Aa<K?M=I*jHa+h=m<V!lR6}Xty
zb)7Cy;UG=`mESjhb2JaB0MRO42GSqRZas;4zVG`OROCI(+0yjVHgxaW2Ur?L70fY`
zSPiAS&+#Lkd|~-^Kz=od_HfU7t)xM)933@!W|8>3ILuv{tvar$4!>9@3Z;jA=f6DG
zr_Dg_?j-Wjw!k$9IG>Ulex#n(5~j#F*orGIJX(r;9P>VN5<`9&vr|yVL4$J9WTEiH
zsBY=zDrNA9;^h|$Cvf%dwT4>XI9YZquaeGGE8(R9%+2X~my617%;HL_{-hs{vT5}W
zVrmIcb}=`j)BgQdg3pX{3f@x6itwg+ZHjpfX(;_qAnkApHY5<L-!%Ku2>*t5u_ogW
z5x6yk2!gIsCCpxTtSQ^JeeSDw857M0lfmNF4knCk_9vi)egMMBmvcGRFxKhaCnZjt
z0~!<fpVkXHb#4Rq%sqTEH}PD!d}Vi-r)L366NqC0QlPFVb(FJ%>q&o)CGS%%+9K>-
z-@d+%Q{PL)pduY;vp&AiR0<L2wAbBPTZS_`Hh^tq&=d;Jr5Cl+3Ph`Qa-FOg=q<pT
zGop$hvcZM_up&y5LgnTtAymJg*rc-Pa==<DB>-oFyogALB>o5cS4A0%VAkU1oWebO
zqW${rPZvu5lY|O;X<dMgQs<-b{L{qfLoHpJ4-YtLmQuC<Y3)|3>Ivi5=r4_0cdq<x
z<pls$DG{Wgf#4}ja+vxbEFd(VVS`gcuk=2_`SOhh9{T6Zg1GTDMkyr(2XlzTKQP0!
z&bg=MW5NPx*(Yeb{*kg@^Uq`G?_Vj;DCL0HHwa|GdXU24!peHER(ZgvX#A`aP6~1D
zpX;s|PnukS1fl};;DH#*I^cvFI1r*(XE@A8yOk?R)^FYfJQn4?^;tsA#~dYG9s+&N
zVNh|BgY{!P?v+ynPhT2wbD;!r5%~crh}(mslifM}#{?7)tO)u0i~Wd!KQQEo7PeV^
zU5t}w29;-I+P{ywOM6T)?_2+_AI@UA#jA~$3+0R!_SEk-L3=F5(#Jd#kp!4{XTi<Z
zP-hY<694IMLV5$fSWt9rW+M<Qw0Lx4${77EF{ZT_$95+-2Izb^w*ss06T_^eBqEBD
zeYz2IH57n~xj285fA|NjPI#V)A>@`dY3u=+c)nETA1sVoF^IG@ESsoF9)NjRlH6W9
zk7N{9YW6Qcl>epV>R@aX*$BoEqSq_0O?JdH=71<OYug*J=Ri)5y%>lpU_3`9hqD)_
zkMN~_*%jYrwUPZqYJ>hu!q_6H&MA26CF|>z)<s|Qv1G4AuI)#-S>4H<5$9eGDXvel
zT_?m=|AIUE)=H!M$@BLJMz9HX`?jy^{$lH$quJqsANK9$r9{}qdPlpI+Io0^x%e_>
z>;I9xiYb^oEhB>2PU-8b_b<&y?YdOG_QK&>5YkwmSEX7hX5uIE6(?|kdIE;)1%-|v
zm8KyKzyu6qAV0dF4+1|QPW8s^;Y8EsP@E@TLk|;ahn608o>*U!sx*l4Wp5k%EVa(~
zBCd9#Eq$4Wtj%5Oi2CH(i$-2UjB4qoR8U%RGHGL7ZF4%f9y0A3B7K0Am*J%;@TMVc
zWuo3^D;GHm_-5E7-L`RwGw$1HZ<p{^MRHpetEIYQ2ctEz;DD>^Votlcob$v+*+I$m
zfjG-r!>NZH*Ub}YM|Jj>s7{CVb>KvhcxxAAfISmJGn5S6L%WJ!&tqfGHMBRhjvd0I
zwE{drR#m#>t4j-MsDP#)^0rd$d?{>$p@NWhVH)BF0k5bu(x&$4hSdo;3pQ?&3!D8O
zhH%M)NBh-QoauLTbwPilbBaGel}RO41BfE91~X}0GUE=*_nrb-T1e$j$PRHfYojU+
zHXqlpP-S))6+LA@DY4kHhVRr-&om2y2d~1;EerIm&*;C^Y1FCKDbp#^dB=D#0%=6|
z%~3{yTwS5R*hayP)|Wa#aWIZcFQNg6-<8^_^)GVEPB&q-j7VSl(K-SY3@!FljPMQp
zG%1e6t00cC8R8nrPTU~d4B^$j_0~H`V5R90h=1r6LSe%_OHPI>Xb+$^zMGHVWv+Kn
zz&H^^53)<cVae|zaP2fO0FY?<%jL-M;mf%p`b{m28Bc{uR#a_k63NCa%p{Q0q=TJ~
z4Y8C_`}$&GPz#|8PWKBv$~hN-HD`b1ZAtQYeSQ>Kfm_I3`W%JYJuNb|Uo%x>0B#+K
zTy|(JVvs4@Pv)FaK>qVXQ676((7Kbm^f%f|3ISJ*v%{rpI>-LTevB3B-0w;P;<%ri
zG*es@LJ22<%O=w@BfeZ=vOfovM*tfVE?;-~#!SU2?FPM|%Nle4LZPB+@QSp-S3OSS
z#Uj9Wl}XXyU=;{`$AX(^QV;V@98DSHgh(K?8}P_5a4XJ7S0MZWmeWbm4}i(gcZ$c@
z*YtouQEnJT8IIax+phe-uBMlo0iGZY(UTnx2TZwLH<@0u8;?TW19^x8X&Rn+f>9G3
zza1OtzFQ)TeKhe@w54$D1DUgcLxTmpbqx}n8!Lc>Xx_if>i;LDeNCn2IOva9pw71i
z7!&hyX-C4_30qhTqh!;By}H<|Safb$nX|M0y%nN2?gT<bkhnEV+0@nB`fIaG*oI!(
z@9^sH++?-!2TJ2r+Vjdo%U~vHjc$?aV%WSRy(~niXWWBsj0tk6Jal5pNeb>DrQ6u{
zatRqET<Q#wy4iY5b?!V)3W>H>k#2!+3yrep#_GrcrBf}0xr8IBM5jil88Ci*xH4P_
zl1X+Cuw$~5>8&`cIrsXA>$NuTce?=Dk_fuYFb2|EJfVYO1<{h%BJo{hNQsKtz*h6O
z{Umhi*a>!1RU>vq+)%T0=hmaGzzWu#Af{JOFRg~My2dc*s}Hpm%tPd9^^5aX>a-t0
zdR&Cwh5jfyEb%^zB7*Z^SGfS<tnLG<^{ITc?CeJPy3nYLy=UdXZ<M^nU=uXe0@0PG
zNB(?bpy8l2X%Pdf2=|~h?$%?l;p|dxjuA6nZaHv#I<ia+@2F84Sk)!9)E1_RzZO2T
zFT|->=M`gaUr5Ef1#79G-a+<1Ae>0|Z`$~#%$m^hVUgI7h@ChFY?ut`>RS->C&9z+
zgy4m39VjvJQQ2Df)6=oll9PJ^3oxB4MU*mJm`QG62lGqo@(51)9*JFvYNLOy)mhWp
zAs6A<G;!UdecOK)7Hlcgm>uGOXKP@bzJ7PtbN*Hi%p-#e&_|^s+W^m^(0{bva13s_
zGwQq>N3tt?v#^)rZca8@e)F+(99nzyNjxK^)tmk0a-bXo_F)-eL5ePQ)^Sv3m`dtt
zVn2_4RC^!j*F3H%XrndW)3j_*x&u=ob3{en9E<*`328XwU1?zhWc&*cdz-WSun)Bi
z{RE0z>|a2Ox{JG*^k0WyfU42|6g7t58f#$#bnjWZG*)p~8v6jCJd{uh<i%h3_|hH$
z_NqA4JuL)D9dKdO6{`d2l0N2Uz76v_baJ~j&jv`FR7$tKY-4}QK50~$R#;xp(92{Z
zM<J-*0ZtC1DmmeiA{9%q;2U*8E)V*NQcZEiNGg!&sE@oD!%0HLIvu^@7!ek@_5O~R
z7Cw<KllP$61?6nz)Ec=J-Fq>&VvZKVmTk}(-=U(zwtO+SEDc{ZyFOAAhtMlxFp>7O
zFO(X}4cx3EVM@8tzLwRwFMkKMX@Xe?jX9VAr5zzVpzz#S&OlT0IEtDhb`0N*?t951
z14W#Q0%w2k(4+Vh2pQ}2Jd(p}aSeg|u6s|Pka=905k54af_>{!2{_#ms1bm$@7Nk%
z$4A>ltFj=-c%;HP{8Ks~PQ=0Q(^d<`0oQq8PUj6^mo0$82WSC5Xk)UyLeu?aKk9f-
zC2En691Xm7`D@0e>r69{ICG5z^gggBAdO_N-=tGBE+%MEs_b14{ZMp)nBMQ=9)6=X
zmj3O@9FJ?h?)rMe((5f^$e{mXb%^2mBY$>n7aI`rc(nWQ@|%VoWrNx}pzt`dNwY$d
zQ^q(M)~dbi)!M}~J`uu~e|qwDwS>hmeQ|-aWRC645fQ6wnI72-A@zBwAeZg(4KOj_
znZ<aHsVwJWmx!m0U~7PD&j&#9K-v(;!>9tY2L<9NeIR8h!1l}tTcI2YsJq2sPn(_o
zEoYZz@wI4FvoWnFiogk`tK`|;MkurseY7Rd#NA5SSP{nuaz*@SJe0O8`~T|$dZlK`
z0kMQow9khY7WVjI4#mW-0hRw9@RA`)>x|a#Z0g(M|GT91zmNXu1C)PsVnE>;CvrIr
z9NOBri5E7`Ls%iYw9Ff$^ESIK0#z&aFeB%<RM6kVk%_5#WZLBdjw3Z9#YF|)D28r2
z_Xqzxl)Cs<0RngNKjKztVjYGuIN$Eb?znsItOSwQzu*8Cr_M%a*E1XpUN|vluY?d<
z?67E$*PHBiu7ta;quo)|mu-xqT=oOdh%^_qdu!@t+hBY1?t>%11KkiyQly&`=Yek;
z+mr-b7B-gxXU+husq12&vD#*znyfi6r+X*iJm>MbKe1>XqTV;h>~w8TURu?y{tjn^
z{}iVj|91uI-zPrV$3ot{(kaz>Jz?u#ri@T7t5>c9U<KfkjKY)?>@dOsLPtnj6a1c6
zdx7Crt+k9G=;n?$wf}=`S@)$>j17*Uk^Cjr@BY!#f4yu>DXJMi9qGg+J{jzCNHh$E
zw>fZWc;Rf(w?;>a+2-SEQzyT1r^Tn8ynl4x{X0AthGNeUGzd-vr*rTZy$a?YUbipZ
z?#y^jV-+YABq4Mn`X8mJ99PGrXqWsXnr6td|4K^8hcD26oBG4G;l-@Xq`d*?3?Fgd
zi#W*o)!hc|YT*lKxnAslVH|lkOf)ED!AlDbD&ZluGv#-O;1_uS-Bewv`9aT%ZQv5-
zu0Ot*SP_?RZ>Zn^CkcnEPg4l>WNsN$OV{pz84nBiiIk=NEs6}LcDqeWA+ItYxOV=r
zKy3wPhkxT9ayzPM2eW6aICuH)B}j_Oha>6TcbIEfzPQvq3prfo+y<h|B^=NCwcRnl
z{-yrjp|#}lf{bKU<<py#OwQgiG=!DzH1n)s<BMo{d&?KHo<$<p4^HJjTYG>_t0>fc
zW(iRW*myr)zR|I17FHanzAz$s8cwId$@H$a+9Kkx`v(f{LAOijq)CpgYIDg4S_A1u
z>9!zg=a^y)t0yVas`YuDuRx|t>*B<d^!ZfZB>y!T(vF+_+sj&SO9C;imYbNuU@<M`
z_ZD{BA~o3Pxt_}gZ@os$#81QLqbzbDYB1)!SIzcfA!Im@;_q1d)^gJZzPa$J>(}B#
z!3W)j%f{p?SD&IHqBXTMFL`N3#G>h$6>BF`l=e0fxvX3dhttz-^cY>@Hp6+5z4wHU
z=S)8`#5_A7<Q?E>wFsU6knPfQ3PP~F5r}B;xmcZ-1P@v0ev&qSfBHA<OibkGmL%`g
zwn12lp3d7iC5Hv8glRE`sDf=WO!7!?9JWnvG$QOX&1QOkEKLzo9W#q~yLI!S;#n44
zDu~|85OgSSn(owLSk)bl%arh_1TEKwcJ!UQS8}%&Td3aM+mPeDxBR&s#%)dQ(fk7c
zOfCKHo+|uAUdsG|Owl1Y&q!an3uZj$<FB(dqya~+MLl^neICcm8}c82<ty63y+*E$
zUHOzWkKrCt9~w&Tlzofh8u5D8#I5~3ccJs!i%i|Ofvhh30G3I<jLYe#qC=ygh0%Z)
zcHuO{Ez^}_P|@*17ZgZ(Z4}*kD@uzS!tOeiRkzUO;Xg8MaPV%&uiVu~^W{ta*%9CM
z06IK6z%saXJ6cA&sE(yNCCoEiClh4n%QV3b?JY(AWje)5;Dt(f#@*Q54-d=WWuEjc
z9II_>nG_pqVk{4T)2VstCJ@`|^W&P-GAN6DvVpJ4xC2%euSOtfcl=uVj$ZR{ycNl_
zB3|Pjsq0!oL0cvJerC0Y9h=twr2Xme5S>dCRj*hF{15+ha{3%4i_1<TE;wl(A1ivk
zdx`%{Wupqz*5XR2#88M5_c8<q@mUo<I-r%bru3Hjc^@GzZ#wWcyh%^S-TtN!>gWqq
zuejuW3du2Nc$NR6MEaj<eY&d>vTy9KZ;aID=d>OgD{+;I;=v?Say<1a!G9~K1}O1P
zY1hq{A?w)~E*HL-YmH7+p?0N%0<m%l4oSiwXYAXCV0{VglIDMDXT~iHxfEkfU2M&P
zaC3rG_TTe7_zc{&G;-riwXWccONS;M{~jM0{!aIW_QqyDo~;9(cUrXET8MR6!G!T?
z6mNeZ^F_exlD}L2J<i`a^<NMF_bA}qtF+fyC|agbv{9fDv;hjD9`J1_*J;?h&gaY<
zE!oJMla}SQwOCklPBh)4-haDS7~19YJop^f;&0n3Q?La8#{G>Vwt|jA(W2Tfs`4XR
z-W40#^+fDp=qgiEB<Ni$$Zy(5Jo31-&T1)#dA~E6ea@S8u%95#tr&L>i^GP+iu2I#
zAVMfU$EsweUoef^n;2rQQdQjA;^A6VfoaXaRng}bso4{HDpxYDLQhUVxLx1%^G16z
za8t7bIN!u`SWRFN{L-ZxRTh4x)_=w;rc@W)lZGt5J3Q(P7BP_Zh%k=R@rqj$_!V|!
zSW104eHzjctle$yIkY65$V-jE(r!~{QnA(6YtkkiPJgmy)<&`B`jnrxsDYC$F5CkG
z8Lu<+A8o7HmfRo;ToYZ&vMb?l_S67~(cr(Gf|0*f_gVPN(yYD&G}rR_=%wC9!-p=S
zUDKtJCqHYNn=BL;Ots1!GqSoZgQ<N|fUtVPOFa{$TLru7B)veBctF2aATDeA#=d=0
z>q|CBSYa|v!!-ONAwsU>$;F9)ai{^$hTO`rseP`2u4~{{W{a9-{o8NVN%g6Pt0UIs
zSyB*_-vf4{jFCIKpk#Zzs!?O>>J``XAq^fFX=o>ZN22JDo<`r(o|7^kP)RdEe<<-*
z%K72*_BAM^U%Rhmez5Y`<TB>OX9MDX?VKj^PrD0eO6^K`6u2#TLls))>gjk-tTT4C
zmk{XndU&?lx$s6f=6i%u#^p(7oJg#R7U4-2{Yi_h<JIuuuYFO)ceM^Whd-<!G}q7G
zVnn&W^fT*l&-XuUIP>bW?b2IH<3y6|>e*|Dyp+veSWaT@x1}K7-s)tk86+J^EH2HL
zMIXtz6@9S9L0#+o(1Nz(K1Y#ESio$nm7Nat%N5-&vf6T2<kN?<%nb4#Q^^~;+$f%`
zCogicHOC6;_w-8fRgG$;JjIfVYtkiaLKh~*MDF44oR(e)z;8&v%sPE?b-qznZyau*
zpY*yAY-QG&vgO|LU0=wgk0^_;3tI@B$(SO<-`1@hxWURP-PS$5nB5(w`?(E08VgNb
zs3yL1i(qd($r`#R#X3tV&P!x3-i0eVE$Dcl1O8Hp&Z6<z9x^mRQT}T$NG3T3KJmB?
zKU`m`XSRHC#>BXHG`#7gy~|fymCHlXsS=riN76r>n(7^4@bRk;r9agzraeD0>s3rU
zd~G=yQFqK9<BM(XT(vD!`MIb&AaL^|FY4hS_f&PlYQ!>kn}D7?W2YlQ;#@WeQ`Q=m
zr*qPN=W^Ol!$%{9`nQ)f>y%DgJl*=TapS<+IcG7G1-MVSc(Hd|XG*tLTGw=59BiLU
z%!{!g7eo->%GL;D8Osy(%qe_v5J>&VjGA6DvIe!tF_<Mb(5r8v4h0R2FaqN;c}9S~
zjOZs`qzLE7$K}J%oso~_<0|9@^I(ql){KWLt?YI6YL8tcxh`vk5?V4<4_veB`T8t$
zJ#|Fo(+`mZE#j*CRz3lp6q+|7mRa%{c4xcvuT4pn^z;!-{+e3X@0Gpi%oWLq^FgOq
z?zP`mSnH_z2r%duE#!z$Eg>$wVI&<iqBWL%T3>i<%pMcFV&~?N;U1;7vakKMrSKi*
z(h|1s-1?0UvjY!>BLqKp=q|*Ovn}b3QVVOQL>L(m*@qtk5=0jF&j~#?5SYB2bA7fA
zHo{z2h6>f3G4m$(mR>JVW6A4K2t_|0+KdI{<G&q3{73OY%=oAUT|=@2v7!3io$hVx
z_Fx_r&wC<7V+{!aRaS3&*FLi6DfU6a()p&Ioi2~xi1qsWOtIzetL9VB1!*^0TmSmb
zog1*V9NiruDBa)=%lU0+OKzp*sA=l>UhQ1fEhyPEOlsv+JFk3vj5b2?eUzK^9M>ri
z9fFv?XW|718-Fx3Aeq0}2Y?<Z8+zL0-Orh20ze#Xh}h|nm|c_q1K;Cyb(yN_a~4qe
znO8r8wf8$4k7YUJL|qHPH_C+{b3C;p@iiUOv(G)$S6j-A(Wd+0ZW~srYj-hkGy$-)
zuFd|!Oo)<n^K0GH2dxOqe&T0BM5fL>Z{32`IvM|u*FYOAT}C}&%FW%tCIWBUF7cz%
zMF4kMq%ziC8h2jci1XdTM6;)AGps|x#_1p28bqc?zS~Vbes*>yYYHY^^)TAyUG$!@
z^5+4FL}z}YU3+FL>QGf`u<l4=pK2=ygNOg>NBRuNr94sZRz5yn&;t2`AgTYBq?m-b
z)6nm)W&}wEU~w6M#lg|r<!{SQR~j6|ZRxj%$9vfFzL3JTzE~}AaXaKLuSr_(DlMaE
z`*rgMNt+&&?bDVECtayg@{NV?=pIa~<#)X{WjU2mI{6@FZV&K<cf8ZxYpu_tQu?{6
zl-hpwiUxrd{RFrs=uNsTrs5lG!6WRZVYn~fxJHTZ`$^Jy`Khdn!+-$Yx?d>?;2#0N
zzhcy9TQjRrN3-7_O8JaN=*+^uOx{;*xiR>Wd{VMAXI`TaSXAkZwqJV)Kfd<h7{a6}
z*mPz2LXFfAwl(Tm{&B}kUzOq4*-*Hy&~zFmHdcpq@1ohtT>kJg=JVQp*>iqT7mBoE
zK8!mD@EL$6G1Wv-x@iVW=1KAPTrXiRCQt%VM9^vI;B`8eo7B~e*NnOB^0YelZEs^P
zuX!qEOA`lswE3nqKe#~e_96GTd7CwcmyPnf-14&?M?YD4mQn+NBkb_!&=v)gP5t7Z
zJ&J5_)cf2P<=fwtvQTYMzKhDIzL{E>ZSbvlv>zqY_Yj3A2N|S;Q`+r$g9~w5Hx~2o
zV-BXCca+@ia=-SA4n`IzCWWSt>|76zn>P{tCvMFgQ4xrxa;x|<V<cC&UEdb1vyN1x
zubr7LKB&It0sjr%oc=*a|Guj1KCJK$ChJ_=oo|dER1^YPD|h~+y`hT9rpF+o|CxGl
zHYGUp9qZGP*{)4nnXvffzp*rTU2BTB-KKSicUy6~{>VPkUY3B{{bxO~**6#Czsgos
zeU$4%71P9Le~gM0)2MSNUA)j{{>j}#-wiZktUoq(_IP3~u2fELpPi;5yM=RC%fEzy
z$IOQ=%X!ZAunu{e9Z58Metc(Zv~jT%CX|wBJ5TPB?7|<BvO2Xu=q+)FO+Krp0{+a`
z#}1b^EZ1fHbP_(CS-I9OU^HSInJ>~T*bHEj*RhZ;Jif%FP(IGLxQ5Tu@1ly$I|p)o
zcrxriFwyf%;{^AHlXaI@X^d@bM{oZ5NKz8s_p55>^^(nZh7WB-y4(mt?kn^<R9Wk2
z{Q2jvsS)y|iTEbGt`Gmx=HiU())GO#yD!llW-HjUB?n-D#)bO5&YvdjRA%G`{B@z)
zajz}JpTu-fb&KvQ9@;j3lN7oz!8WhnfO+);%&Rb9UbW&s;zqEUgENMgX)+q(zk5Cp
zXoxsGWB97Ks^hw!!E(CW(8ply#Y#6lXO%+J8mWaJ`UNYpLFU%1$?m9<8oh^2g*4Ww
zH!r#(YyWNH(FZ$mPUd%SIZxb9JdWo&e1$>NoidV@Sqwee65bJLTR<vqbHCDgWy>*j
z32ft~CI164WUU7t43(Vm($kPY5`G<q;MM6qgCdMOx}m%)2ikon%@gp4@G>#-`~&zW
zSeNHplP#{;P!l5&HaH1-$Ry6(a+J4QXwiw03(}}k`n=kBDEyBgu8{POE*!zGFZd6)
zZ|~w`rTD{q>*?dX)0Mr6AQ#u=81&=iM~3ONxqlJxoBX~8y_gqGJV>pjeRFMhe%bfG
zq-$lO(z|2YIzy4AA@`S`#(aeO{rI^PAM(p;9?;4fKr1_4M5^sW9rGUp-(lFJ-#i?4
zuX{c4`sUV|l}u^8ZU7|>@Tc#b+|oWG4k+ZF@PnJ_%=MqCmm?8lz9(aZS|9vsf&lKa
zI^Zs20aPeEo>)Df>`cCUcDx};%&asM7a$5Xzdih<{>aUjC`#pjbeQ{v&lqPN=O+gA
zI3<{x&fzP{pllyW;~DHoT5|B!l?yo~Gnzk_Mn!^ip1D2Fnlf`}yWE&+nA#Nyd&p$o
z%p>pN_Y!>8y{_)czjA9TXv(tP|5!GUp=_^Ev)`o&e;)_y0(ttY)#BqR*-dRi+Yf{G
z^d7^qu*`ij;iJD7WRrMy%yYu;IjIx;tn&e8Jbu_^!GH0M9#^>7^vFUh|4c9mw9Tb0
zpe@o^_sYPhQU&llQczJRKcByMxLq6y;eqqUTpGh@X??wxdKQr3{|W~G52HYt0W!{e
zDnl*M>VwyTV?uA^sF}8bs12VYGt;$!Xnlq2pdHh(^_kqIZd-fq@)LR(Vedwj42`ey
zdCRe~-iyp6S*~+}lY6g=hHo25r5El^`*9=2&DZXWh1b^brMzyjE+W&{r#8v<6QcH!
zroxp1yi4ZTa&)!e%7v*AYzH<UE9oi`B){T3_2ds-2qLC9{Kzjnd6S_-|6}UQ2%{@+
z@k+crKe^O<Du0mF6Q0`2qy6Z22U7cX0zhYtcgi47-7FpfJ=QPfbi*2x&3W+io~?Pm
z!rVJn<KM>c$cO7<#!LA&w_01jAs=ZTlYKO?<ZO8hJrfYsTsZVK%OP&`z<TY&fs@jF
z%=HL^wth3N9)3LR!Uvf5SC*{W8uka(qqQ?Y9Jlt1i}K`kL|@=1SyQuctNy!mP=S?)
ziENqt058~b@kPD<^l6e7`0EMN)+79jllD7~Po=zVw_eok-w4@5BkheYu4CEt-neqp
zMK9uN2IUuFWPr@=Fnban{w^l6UsLQrrR4<bPt0E2rMz6jl=(qAcl)8wtB(a-xTG&$
z`KEldMa5-|!F0p!$UEwv6pY^tHP7KS7_#5t3bKj^*2=pwP(f4SQ5UTXxw$}=#y&mm
za}iL*ub^)Z8$xL#g}|4yW;b-R9tYt93yw*?t`C+davPgS7;HJ$dW!D3TqThGj+NCV
zTlFlOd<_@)M|%8?#SjspV-W??gdmsPcM6}^g8uB<>5fTlMc9l|ua>g2xi>$569(ym
z$QiQdBbzLKH7Yzde4lr2h<-f7)Tm9I$(+WP#3K2hN;&O$@tfY!;==fM5voD^-EO@(
zhxNds<0{a@!75WstTEOJOT&uC!Ok-3y@uDTpF405q;(tK{Z#*^v|PB*qw9iw4fOHY
zec~#vqL2T=%@qM`T}7A6Z_%K!V60|?9}~tC@-!=7<d``R$-FFzJDhiA9?V4YyZ05C
z{WZ80@=L{^q`!ZIce>}*xr&`Y{%tSo81xYu&!U*VJXMO9u$}ZhqWPe$-k(kc>&<8(
zNwhzn8^zmpQJXk=;30BD&dqO#=abf}$#v`kzsO+49oqcW3*|DG)(MY1{Jk(;MuUy1
zO1#n&6RbdP$nWxP_Ga3om^f7l+ldg>AJus@9r42&^2oF<z5e47F_C6xw9cR&<#DO(
ziTSOZH$dTtf{Ma<vYrxU;r;mqe++%%&%fT=j`I`Uiy7-DUm37dYH~@2ph=PNyT77P
z$gwovOgobe%O_d@r4L;9y5M(k1K!KT;+tB~WHGV85U!xh>FZ#1I?sZyIwS0QdK*FO
zbhg*xOmWdqZ+_xifTVi-#Ix?Nl7O#)ep;?18;zyn<_3A*HF;bntlqtx9ZBWi*6DI5
zUAS`a&AeD5$)e0xajqcDIoo~fn5^SOdc^hqu6q;6VwJ6RJBwUI!9dNeFs|w32YRbN
z*Ha?XdTw+*z?z@)`<pKLnKvs#nw~vYJ%b$Zk5I5vJRy%v>T3B$rXVFe5~t6tawYIa
z+DkPYIQZ5gofUc|E%I0sV%^t?1qr>c6@&RXa*)DKhFTZ(^HPszDE>u!`ZMQLyXeW<
z1Z3{ZTwdzHKFI0WP3+dkgLpl4lc<IRmCfgMymq8BzJ_M<6Pp{}lmd43>@D_gxA}=7
zpUs!+v$N^rb4N{!r1&A3v#{;#$X}=s^uY3s0U&0~##N94Ux(F*w4eV`3Ep&=hZB=T
zbQpGTclUW<^B`<VPU$^5Ht0<tF>+aUQt9SwXx5XdK9Pp%79f1O;OJ*{vk?DNHoPb4
zGot@Zgi<O+?{u->5b9A>&AiPxAq%~4X0jeW@N|lB>NMOodnY@8Dqr?dt>V>E3${W%
z(h+I-U0&H6{%Jl-`@Cx8^)0cE(+69YJoc>o)?V>cBq5>Qpx4a&jAB!t2X-b_$=+Xi
zZ^4BO>s+}_OZ0*86UV&OOs^O$;RZ49zTfyCP++}gZ@Am=-0}9;PMG<VqYGEN%5w@*
zD4*4xzww4NMDVOgwg>UAu6=#}G4k$J7@>YkbLx4*M{oo0^6HK8M9*Jg-q!0%<9XSX
zn$m*uOE(Tc^$O!Vm&v-6EuAXEst9D;XD~tdMD#cv@hsBd04`Y9tu!&;f%*Inj(+FD
zk<Lf;I`y?bg1=Zy*25&j4^MASWChj5Y(+S7>xnkXBj{^#v?is67nYva!BFKsMe_ur
zO0nNl)FTnu5Ycz;^Fi%6l1PHvVd?ji?!~R&O;<Yke<(F?W0HcGXH-t<CZ2i%$jb1`
z0DdB$349^zk{^7fDeF5=pbb9}X}a@!^<&j!7<#c%t}f#IbUZ{Wv1XpH`kh^mIG`==
z4NIju?*S+O4;zj5x_8c4A8@cd^B(=y#K|gwUjXlc`Qnvl8C~>t<655JYXu(pSc}UZ
z*+a~0FZ-9A``<#&gia`(hWlI|-{|hva~}&8QTXb4D(uA<MB#vudl)s1QShYB=+ydO
zl*Wh4GuicKXSLd7hTmKt$J_nV{-F?%Y<ua@T0P`uCCPN8rk~%V^R^B7Ym0IkrAJa7
z{dx4`o57vHW3nvtbMF56J6aa^>D%COnG#^*yn%We<|E?4fb3-W3@g3YHEJ}A%rfh0
zz@I+7Gx39JBNtw4@aGYcOeg!BdBV$sMBdLUf7D_hNiesrSg*%9v0R|{fhhhI;?DiB
zFSez}>tEX5r)Ns|2Z(19{$zYN@=yrJ9L$7Yyga@q0ZEIL`%|Z3%k(@0WZHgQ$&LHi
zE4=cW%=v{S&%87PWr>%S<~D|aJOo7Jmb%m_6uCA^vqO8mRrIyZM1V_+OGh-`=V=8b
zYoA2yd{N#(oqq4I%jgp^F<|mZ(s$o)-h(@ZSvN+7q?o-|SBNK0=Y`UZMEQvu`kw@@
zBCImz9>jEs0N+0Idu@WSGDzjv<So3I%RG&cv{jw4DDfUHQ$f%Dqw-2#Hf>|)%nJSo
zJwPPyqjJhf6ydJRgJ66XfP0U|?g!DLR2rW2{xUFb{8}Tl%10~L9s@;dGnl~=vtF@l
zch_e&?lAv)c2*^2#4zKge0u-)&0ioW6!CNO%!$yMX)%M`8xWNxl}(lrHN*_Rzdh&k
z7yTv)U&<kW#e)sTjuI8w5xDRy#I2fT<{$9YwTAoY6<g<4n<@&`u@+}3%@2OPy4D=w
z_l~v3oql-OY>)x-@q=VsF5q`Iy=m1ZTG8dc(%L{<^GXn=*)a5{c_1ob#tV5RS*IKN
z!qI`fC!@UH6+jie$LH|%e>{31e`|p%1$}#7*Pc|I5L)YJRAU%;RMx0w`>Z|5uy}D6
z%Xk4hFW!i*W3?&o6drxPy(-kMox_~?vbt5oMM<(@vJGC|)8F0%DITjHPVd|IG}NdO
z5efCR8?qlU?z#8w9rcgTx#iW5+%>?m0eJa+c$)N736|#9%v3D;X*biA^`<+gJj$?x
zpO|;^8WckI{gx$5ekSl){F-6+jkgv02gow}Tz(soBhESfW1j(gV{rCx2;iy6HuwnL
zbqI8h$Z)fh8A03$k+-K_KL|aIh8y=b_@DX;<oOr67mtG+#VbZvxfs%ek;LbNfoUVP
zm7maUf4XRbWPB^AAKo^I8op4C6HmaGc>N$D(nw*8_J~G~`e{ik(1p)at`42oToRLg
z^syu1J2=YjkdnXMqc-`pLE9&d4^-<H<r2uhE={SnH-62>8;a_`#UE<1DLpH{K(tuR
zr27gO)c22mtSF)(@4gcV8DO^h?bSop6=>SI*nA_uUR1G>^)7!tl_`6!47Rn#m2he3
znga$^lIs9@6P%?bA)z2BfAgA6agss6nXyC_lLge$hKrs#l)N!z+y{au#Zb>_l8^d-
zAN+1W9WDJ;>tg&Z)ORY~TI8a~VOma#ND7o>db6%&YcBRq>n9tG$6q3gzCHmC8JBa}
zGV?U%>d&|t8WwNAuyeI(#^&^=wdO+2t%iprgY!L0(iP&ZdzxfcZTz?9qOV8S`3@>d
z)lU9-H)^9=w&8r>o+AW%!$el1*k3S1d&9R04A+f<0rS<RO}h_9zYYeH;RiBejk9VV
zemu07%3JSW;Ub`y7<44ixbaq4#%**%;7j-i>8HjYMUYD`>=6;GAq8RgYYDZ<pwHUh
z;ekrfB-Esfa|j*?7Ot;c&B%S1_T8tMo-sMG^}w@PJ?mTWW_`~M1K;|)f+)gs-I&|C
z3dRe@3I{0i3(W6687h9E?#oM)-hlh6vQ%OvVSb0{c&QO5HTCJojzZ?Jx!$9%ZqL-q
z2&x8!)#qeQiBj{1(~Y^ebCTn8bhq185kEt0qP{lQVdG=f_n8jj5>!lQkZuifm>P!O
zqMF@D!XTp{m3ACYkVR3`Zx5MDp(E~w9(Ws@T{mKJidYfXVM_B5e5Yc1qK6kHTjrfF
zb2Pwp*}qS+cWIC@10+Lvot@lVtXsW2w&DEK6vKUwULm*q6wFV~N+_rmSg%G%c$8e}
zoZY)&+;1SdDv+T_n-I}$!_Y$Hf?`aU9QSN%GMG`%LOkZH^b7O9xv3I${?sq2k1^*S
zka}5w_(NZap=NxyZejCw`WmA>We0*IyQP0_@x0QRmZp$?ShF_al3~Kk3;<aTCtU7_
z8D{vi=&WYV#~bF)T4uVhWvISaH1#3A>CN|>J)xmHKGJAscGj03_j&+fV3ZT>v+!)k
zS0h?_?9?p$DHd{txC2X#R!5aIy`qK4)R3$?`7r~(SN^=VX4IZ=6V$++4IE%40;=X;
zA#>%>4jPuXX8MQBs8-*Zkf%;t2jsJRb(;vhD|a$pN8>r$L8}r!Od>mzHvpKk`I%~@
zYaHHB#e3x$i=GGeb@5<2-<PecbBSDC<kH$C1L3IaV!`I`Vo;rtPn2T%F;^LxLrWoW
zX_LB`N?z8a7iMp;>pLP<{Beec3P;QQvmQV8Ntf9eCwAC;IpcPHrrw1oF+-ML>|lnu
zAve)6aMU_ZDb6?6E919ORpUeAYb_%rJZm7<pLrU%8G9afZfa8&Qll&%9RxpSdf`^a
z<HuW?pGE6HDhfLv|1|FS`~;pnV)4eYCl+!EW60fjKwBbQd-_`Dx~!E*1qsNn{yCPt
zBRE-7-0v1`;11z(Nr^G!+c<da)mYp8i2FegzPDi=Vzcj+k$t9I?16(CD!di}`ts5$
z(IFS^vr-d+-(ZYk?ki$!QV48U)wP{lQEwmXTngFvBPei6d#dOK6g<f0_VJj56^1R<
z&bM`iH?H}@vPT#qrj=NyKM#GK5yu`lPsuwfp}5^MyyLOOyOJ=%z}<GKPzC~qvdiMX
z8_4!@(~rC?&@%pS`zmvD@tu;@KDR_2yBAfRJ9Y7$UEt5_SFSuWU;HUjeh+{5sbzQT
zcII9=9(Q`JN_fC|p<`dAx8-Uo_3h1@d|`?#cjytZFMaEGoc-KTeDdD;%`Kk1)hD|$
z8<sD>VtY4g-`&7E@k61BmA}ln=eSM!@n-AY%6YF%o|kU<xta0RsZUIYrp~C`ZXb8=
z-qL+)^UvFC+|BXhZo_R3MbEFBnZ9nBo;R5<Y~x(lzUI2eW&fv`?b3bUd(B^OA+WCi
zYy}+!w}Q5Hy|OliG@&QI-?NGBhs&4W2V-CD%K!2|Z~gzdkFMY7Rrxr1tIYJE`?ZUH
z-J7_$)-&jcc7>{^RQ-15e$|;Q%V%eE%1^3~_wEmA4Q)|5P@g)bTKB_Fp<BNNA9j84
z<Bs-zzQq1CXXiKW;wyjk4sGDv++1SNE^Jk2zwywveV!f-e+^bAq^KtH+|DaE<9Ym9
za<<x?yuTY6U*B|)c=WLM$?i*^tqx6ZD7_mO2W-q8UwS~hc#8F$n&V}mwQ}EPoO!Kx
zsF>xU){~WNzb8G_I`m{k`4^4NT?TtR_ng08qWQh&+WFoCM_GRxdJ5S`df)o|^Ge3w
zFHLW~FYQTh3ZJy(=fnM+&WU@vKRm2fR;>JDdx-tUUXJh&SzCOWy*cHl{O>K;{mbpp
zkyd%Np6Z`RryLhEWtMUB(3<P_W0Hb4<Mt+lz88x*AG5~ae05*zQ0T0DWgm^N7iCJP
zD*CmpXR3=VtTq>3H}B3ogW$i@Il^7;`Buo~G1U1;EdFvJY^OhCcJM3dZO=Dfu6i){
zz_}L1ggO@wm$L%<Dt?K6sOK#F@>1^W8G&P^ciRm1y~{ih&v|>-tO+}QNqmq<@PFX*
z%3J1JvCNmp+AW2-{wha&s_o}H{}4)Gf586a_B*ZLz#@2Ncy-S^*Oo-!jaBlT$*lX?
zepo&%O)z~JQ^a%f18=oU|IV4dTi?I=!S=9w>Mq&azkaeGx?l!s>8g0n{Fe4^?b}H#
z51m##Z7DuHllS{losR+P(ItQKm%pfQzj{Z%f!*z`{o2NFsvzZ;I)2#zFLt=z_FE})
zhfVvFKO2G%*GgVFr*ivJuFTWLp8o%?JzTwZ+xp|Xd%)_0{JyJsX2e`5h*#mAuQ_RT
zMSuPd{pJV{iMnOA`>eNl&-@(y|94^U->hh-OWO}iXT*bD)jNaJsR%guvE<MtHEWg3
z4BOs<z8P%Wa(3w7&EczQzi>X5Bbm+F;(V^N#OL2plZx+}oQ+v{`}A*)+TB9y??KkV
z+1kuiIVq?gE;LDT(v{36gU<UK;*RdNyn8-7^R)4-muah1TReZHHMX1I5eIo}lF5@@
zsuku8-?KLbPcmr$_Cc?j7+1~$hfz@GBbN7T4@@seGWA@XpZ>u0!1adH94GJews4-_
u$7$0)fnyq29oX$)!$!H(4S|rk{~2%odi&(b_q>Imb)}xJelF{r5}E+Ojak?L

literal 40843
zcmeFZcUaQz|2Ax8+0@F)%G3sG?o!hnNLHq}bL7BLIm(@T;L1K_(}9D~)CTTB?i@&E
z<-k#zpyDVOA#Md!1n-MJ-|z1}?!TVrc>a8jn<EiH-(2t4IA7;^UT=D0ZmPd;_wn6)
ze0=-h23IZk__l}f@ol@ka|d{alKy4^eA{u`NdGGM4sL5#8e+h=UH1&^0{QsF;(1?N
z5+@hk`1nrp!LRCCg}s^?4u2)g;Iu4;sv$p|c=jqzVCU)G?QREzto9cM6!aCv+8>_O
z_t~E7Sy*G$NPkg#q-3kO`5Wt!yNj2s6!rMG90va;%nxbU90^%wk8Ab*b4~>=mEwwy
zf=VQ6qdGfSF9$I5keo;=JMHh1{_oxYjQ}qKMKv<0_7mcM6BQQydp4!{_{`lVsr6gI
zD<5^_oh|M+vrXqug|5Q3@Wsl?DUlEVH)^X_VnzP*xajQG#{WDt*E{fQ`@fH4WuL-H
z;9+bB4MXQO%FWShyg#v$yamsaWF!SOVsD@_Z0G%wt8{zNsHUWqLaZcd6k#DxHHY7^
zcMDDrdXG&?+tf!*!6Q1`VW@A+i@V^OdOc|<(-bSqp3CrY$rmYAosuRt@CT4A*}^jH
zVN@PG!oKIN>U3ZdRtJ?(DQ@D%WZA=2Bs-H-?RpYDQj<N>d~(*W>rZ`vUGF)MN=Uad
z>XCv=7(`h1$e~DXbxxQgC@Zrm2bhI>Lt?6iw5ZXbbB`}(eBKhXNz6n|84M;rSa-LS
z>hAQeuKs(TuaDEDg$o|9h6Nc_JnurK=O)eXfa5LtAJP7X)t2aGPydi+BTJk3pe<^c
zk@Gq`1#&po+zjq1=b;-H=^`QQ7MC<|V}M0V<!2a8?8it5qd2fe$nrM5Jk{i7W@;P%
z))fDDM(Zz>w|ud+X&u#&NDgYijzQ?EeUgcx4kV#E?<*KA;$SM{tx>%loOGd=>aI3u
z|95fboqN%nC(V>~<0IkS*yK%7L&YT3UP+D?ikU*Q&M*#dv0uB8O8bN{&S0etZR`n0
zi8JdYYK*2)SC8oy&9GJbKE6?a(3Ie$Gu-RxC+CAY17t!S4tiWszVDE~qOMVrV;ZY|
z+T%sXrs@IM7N=TeKQnU@!LcXm<w-}iu1@3H|Hequg0}s{!R=y8t`T;5h;Nbbhr4RN
zDjd~76f$~-&IVG}+YiyhOykxcx<-iPH7F$*nVTlbvUM1uzr@mRLvKr91Q(bgGPMp;
z1#`JsMuU$#lC~}O>|3}7QLtXPzv?XzR_X8SUSIle>Gl0g2@r%lPp><Sza+JKAbr>O
z>Xe`~X5-hUOnojPxNWq1Je!{~cI5fW@<QMq26@+X=o9@x8fvh0C8R4Q=%umHjr?GB
zqoU;3C9kcRQPa{Nkxm(uWE!8}8*G-M8_P6_sf!Mh_)y{}5Jo`LengfSCpxejyHl(X
z?k`^mIV>d@)L+!a$L(DhLA{oTZ5i=?m5K57+QLi8r3=;9kOC)2?fl0NdYtxni4}H8
zC#TVZ{~Wo9kr$TVMdUXXM6IN@tRH|YXiuk{J*v?wt7PULNq)17l-O!~E;yk&IvdMW
z);&?n5V}{0c#?*;%pgw;ZBurhbLrFhQ!Wu_g<%z@M*Mo8KJ*};>GA>V{&dICQ;3*i
zV2Pj2)V4-c5W#4;@!OFuG`l8?hMF>l=gY1A)*A_EiwTdb(%liJ4>>E>$LCiA@>lbp
z{AC2}c`Lu~>}IjUQldeG?%e$sNbR2C`Rmy>=K|`CKPryg5%)Nas@KHQsO4N4#o*=X
zwwVi$Xyu9?s5(d3&b06x!$JW;{3A%eyl_9m;!2O>F4C^cMs?&~pUb!uN0O4|*l!b5
z-?w*8x;w`3TN|5&f1U?R_&-W0kiHmNZS2<)6gi0sy*hQoRh`|&pZlt3HWlf21$Wa`
z39TB6#A4=AdFA>UMh-nC_v~Vdi0d`)6Mc4DxQ*mnOy6&w6rX%Gb=_1^J~<E?GhV?B
zLo+0HzBkoVbh`@4nn+e`<(tp>_pSeX_9q{p>iTwpF^zVwYutQ{Emi)MNQF4c1<J8>
z4d+x$lZTL3y3&s&_H}lnr0V45QmtT5md5e(OI+DRRkII`n-BCeIASAmDE9Y*n~SYR
z%J?k=e>;+-?w~PoQit)*Jnv3P3d2zQJfdcJYKfb3Vni-I=nZi_Mkr4cg>@7sW(e3O
zpr7(Bw@=k!3Tuw=@wpxqaRn$nz&Z12>4-9PhkpC>&AMwAg&kimwS-~4X3FI;mog9w
z=E}kcb8n|~^cR{oZhf}LVMK{#Fut<Z?d7a3+lv^W|M)36>o30MZePYKlrYgwH%X<L
z*kKySmZA}uU(O}sigZQmu|<E$%0#W!y~Rs$9shWWx^^*E$A5I5f~N}qb8=hbfVR3y
zA89LJ?A|jU_wez_Hasy@<U3Ss-g6QzLFD)H(kvNQGc9o4D)OXc*>>te;{##b?DB5g
zsQIPnqMXG>re1c(#Tq9ebtmC2x4+A9+KiX9I}5Ycg1i7RQlFcDqe!n~Y^|Au*IP)P
zC0yzmM=7FqnX93WDM~VJ_aY~(U%f7AKbuK)&*K4J(k=e5Eqs^UY7rK9>PB^5wH7Kv
z_oZNV=^S>#%UCLS#kVNqMq;%bM{x2vi=GqjXQG00Zm1=GXpF(XrGDZDNWl6_Mc%tI
z*IY5DPQ)GGaYnQ;t^*bN>jL6cS7-Ym?@akuUxQIr6qn;FWuSSZ$5PqlSKgQ^hVsqD
zXVDhMWd=w))%GG^jM5?G+7Z7l|20e!J!(3hy!rcaN#E%UdnBXi3tU~4y&bMQ1kQfm
zxNY%)WY#3XQ^WG*&9z%H6KS)7D#AVre0;6vcr*32EJm7EtBmhHPUJtm!!W|YUEWOT
z&NmKz#AVnQ{&NzN=;kb)+@!oXC_+f5B~z)t5A(N-G;)PR3IdqVpBt=a5-YZlmL(k1
z8yMWQ4pu4>nm&3~g%o_hx-6<=5{Yd~uNNoohE&5(;T*Cub$R`MvU%qMIMbrDr&T`n
z$YY#h$%dW^DpFKKw5r$`muolYGMg<wkNEry!U(jhF`66yaN~y7q<Csb=7~L@C^D>Z
z)=7B2Yq?_K%U?^}K4pAZRzy&=oJX>Lwm{o+9DTwJHJ7a69_rOSGU)3OcO+SZoOObh
zT!wf$Ik7&p_bm^@kNqVIGe%8lHrB-Sz64G@?hG`YV?9@U3#K@K-bqfbHtM`Vq1h`y
z1H@?7`GfTomjR-ocBH|5tY}=Zsf8LmqVG2+1C{pyTjw~^eD^2ImYLpCp(sv_V`7Nx
zd}8=>3di*k)WF(JTiwzJK=Y*?e~C-l@C8(K;e@o5CvAzV9MAfOoCvHl?*4M+5WaMc
z51!c0A2zIwxhf|2ZZSa6Sj3ii(By<9yxP?_YZO8&`c-tJwDD}=kei(&X5<wau4(?G
zdudJi{wPs0>Wr{<XARq71U59u5fPrK?Q|J<qBu`ZUvY?we4t91<Y;3~R*5|qGMYY1
z6NYz2rlynzs-*Br0o)3{Wv9|;Hc}p`SSM6Q`^weI7RWxh=UJMemt%g*F~v2ZMT3;-
z@lX5|<zC5QEpd;om9z7FXw`?}9*-hYFMK~Ud=T@?%tw!fyl>3cm~VVjA%tTkov~b8
zG2dILGff_SSK=z}CLVsr(ANF(d)H}}#lf}1?fmt_HS@;sucUfC<>tdBM&@NUzbM_C
zw4ab#^?{anaW}W|p^hK~Xnk-ilF|B;)a8;jgshu{N1TaO)=Ar?8eTZ_sWr)XLE-Vu
z!&{u1&3>JOc)ZdNe<EJDbHT938C8;l5E=>-lEOI8jGHF9=?a83qeywS1epLozs9YN
z0~gg*hW`T0t;axEy~oL=>^fUF%yc>>ybzdqR`%Guk*2BpWo(iBY1&qJ;$;2lK8LIB
zA09gFtxihthnG~sdj#N9tUGmP|NL={!y+tfVUCjlIo?0oGErS=tOa4;qR(}hg1;qq
zna53Mh)yZTmIJKg7$NX_1NXSRtMqT{x;;2Za7xv%K1cRl0Nln%)3C=uf+o^7)HJKc
zlto#d^7sdP1eKSYG`A0~e+PT*NtB$2X<YD}#Zt<Cu6$u>htSz4+GlA8-C!RsUU9dm
z&JiwQrDAoIeO@@HtqmF1{C)eRoWC3ya_;I>4gE~1J_7UFMg>!T(yM*w;Kn=br1BNX
zMg<xaE@~m~Rg)IF2lH{ZTp1cDlwPM7@xb_|TXsh&DW`p<gd!59SULN`_1jNv-5w=K
zb@~7mM&u7nZ{tsxYMnt#1Y948>X*eVy3yPC51OT}!A>Zs_>-hC2=-`t-NQ}#kkxX`
zT+%LP9X$A8RSqOLZD4BDK-0uR6f?491i{PF?DV!htNZhDDcu<H)20XK#?`NelqRh_
z#ojqI^Th?Dsa`n%GWP%a)^GDLz>&v{to9`HEsv>qd-k)yPwe-N4mSTi$S<KSPXBpa
z@RkQo|6gyHs>y~H7)F?KOE8>#7c=`O{r5&=`(i_hvK%U?3Ht=qlwM`oqY3weufM~-
zgFFnlp%o*ZzUKTY`CWincR<}G>xERT1w43cM563Zu<Va_2!}yQ5<3cYHs($B9PVHV
zTDtHVRv)5}RAthWh{`jpzh?0O(vI42j!CQ}Im=cO)C@CZXo{Fwr3%s>xi!v_&7)bD
zqo&(uDy1bAP*r;FF^#8a6))Gr2F!Z)!zJt~RSrGFsN-%0L+`PssA~ohqlUGPJ=%2q
zH~7y19MX;HZ$zKZ3mB#Pws5b({fz1jEfmlJGgXPmpSj=L^No9U!x;`FB6<KOx2+)4
zvcA9jun;es<@$J^!Wm+l@+S2-e1g}&RvIkBr?!a!k}SQFiU@mmt$MeMFkHZfVsS~d
zj7R0$mdBLwqw!T|+?wt(It)!LD;@GiIs>fcPT47n6wD-`G}NzMlox&T#V3jL=Lnjl
zL+vttE0|+P&mJ%DN>;xA{wm{xoz&SW$v(Q@8{_>N*9TNe8O`t#Ix|dD*U|ece1h@r
z?UswuFCeA<aD*c5%ih$?j`P9EtUNX|Hf{4aX|!{qsXL|cP9c^l?lBy#eHNukvA>gx
z<v^z+pEGq}9FoGoa(FG4J{cvRTcL!oCC)9j3h@=a`ddflG80DigAsVssr3cokhMUX
zu^HUYP&Szc4=%uN#f)Rkdp;>ck@;;akY}M8)!YjCpBE16J&mS0Blfj#FN)JIrM763
z!VRZ<x8PbDFvV}{+gEn_o}vX}oIVA+Z7mE}-clhfiHFdRz@gepOM#pyhpDm$VyJcE
zO3hwGqP?>2(y6C4b&~<3X3HhP(c=}7H{<HRL|l<N8u8NjS6|b$jmuMm<~BVJ8mw1)
zjWQ%@Cowl&ROF(>FcQ0#T_gI%_us378LU6U3Zgs_jMmzA37694IILRROsxE47;}%L
zm>kUrGt#Y+Iw-XARhiA<$3*ox^_Yp%bM8P1CvFap^-dB`^yas|x^fH7GRhc7=~&B2
znxI#t!v!iq_r=OmtuP~d_qh26K5vaoj;oC-MVF-<SH}CDL3J%9>(o$f-ROr$4D*~!
zb!pzI>|5!??Xp@8thtNm$tMw~BX$)D_(3t^4-a4{gyvmKo=PgIahX;)0$Xk{FT~J1
zL|Lb5ss3DSW8=^Cx@y>InnnEN9hpH2iI%<rCk5bc?bt}oY!$sRc>c+al}~0uPPHzC
z^0zH|I7lxEb*$&J4%btvz|X+F|Dna~!1WHlVH7mS47HYg`t2W~ON$l*XjD*R^+p-L
z+qO$-x<XETu@FZuC=8<FlmW?-FTCYj^uQ2XYdlZEN#du^;D;%mSsncmX>cuixP3x<
zlpJw#YufNV`r7YTq|cOX%DTgosAy^7u%!y~5!e(qg5%=`MH{VOyzVBKT>5kerCvjx
z@F}9nS}OKvxA?(dp~}!J4`kI3Xs!15=?{9_E7A7QgX1A2rDPg@_(opn@}lzndrI+!
zMM^X~_~BzEh(-)*hb=C?b!Ewv?0_lH*o5>p9OWz0281tG^C`0<(t2uJlUOUAn(X`3
zxVtMv+4;~lkd$FXm4S}^n2)bZpLO^oh)VjPRGp@nB-bU}nZQi?X^c|zGswO)Wa9U$
zDK9T1*GUG}Fkc;_-qwiu-c7>#lv5n8xQ|tYKQ&biY%uo2dQW>pmEh+cuV8%ad^p2b
zk!E=ZwX&U5Fi&jrTK6K%Wdo6m+W-X$O-VyXA^pwZo9V}t!m>KhfwgVn^|sxCB~`<&
z2|pwxqx^D9MbUYt#vHs<bm5ynuF%EsFX?pbi;n)-oVJ<x!^&4&Aj=PkT+|MubzDbX
zjmZ>~Qe4IQ$GT2dDNU8`%B)5>K0TcEvV|7tBj<&f+4WwnZ74(Q%bD)08z+is-Ypzb
zg_H7ZZyIVOMWd$X?);N|LMRO-cLd!#X;*6%qd%|xea<tth~#vlgOTjJ+j`qfY~-p>
zqJ?2(XLh}(P@8$$#!YBTVy^w`yA7+(=qe+(gcsU+4~A&zna_twmE`x^)AYVXn5quY
zxG0_Epnp2v@5*o0C=l<?etqxq4*Jy8cD-%iqoSq`6knEF$dOigehA>D`J|)N@+bDY
zm#hf8`O;)|ygxDy?d2M$YAWo~sIa_!B?LWrpf1tt4mGCK`29zjS(|`t+JINsKgH1L
zgUOhE(1DG(Y}d|_sp1KpA05}~1iF+zWpA{|t&UGz44<bSK2H}<xjFoJ=z--tf`}wO
z@1v%MG`wo@sq$;qG3k+0^dNi8kKFbW{s~ux?mwf9?;L9PIl_vz?GaXFJZ|Mbt{IW4
zVekXdk|nM*^`X(KH`R6?2xYCHs_HKJcsEv9s&F#l{2LAkuy1@+<HijsTTukb>pUGu
zEfw8iZuhCp_KF;{8dA<!xoKJa-HW<EW&@6QU$92oh35uvUf_p05$Irx<kC~efm0P5
z_2OCZV-y9)yn3^6$LdhK$v1wv*$aw9y3G1J=jB7of&q^oJ$gFVyy~K@<`adHa5|$L
zntbSNc#b`7EM_MQQESzGhaE8)#4;+nqH5q?LVs+$a1=zjU@5&l@*~Hj16f{aA-((K
zwGwiCJO9xow{M549$c(+Yg%1FuZ<l+t;H?+d9B4kmYk(}i~HuvgHqo*e}wgnINuxI
zTEMxz)aaOJ<B`oMkXGUTt{z0xY->D#8SyJUlvPFDskdn(q&b0+VXh`I$L-+5ZgH2}
z!fwp5Ke^Ad&bTGulR82qivIYI{d}in_QQXT)XJX&#@&u(w+M;Ny9$XCy_F~3Gj9<2
z+h;gp>ANOos?u}B(ah_00z~YeqYcFBP@#tY02gG+!^2{9|A!qfAFyR2nx0pr`=FL*
z+iGQwV@2hAy#{n@L|dOj!*vCT%PDy&2L(11Ye{tqb5Tg`Zk+4Z`07K@vg7Sl`pw!)
z!Q)h0lRH|s6VZ>cp@858ntk~}(ge$y7?|}@!0alR3C#76W$X!nIp3MQFyxZuAP|N#
zUuoPKH%gyReHp9<XrQHQ*RYkF+(+cwx4{XxJ1sBWoVL(K4(;<6xL$@{f6Bdxx0nm{
z{`stJlwO7nsuN@1!bqgexP&w?%Zjs@XD|{9v&+yl@lIxlCgVMmmp<x!(mDcFR$V)F
zBU*G@yAViTE9S`4=<`qJcX^rJ9Xn!N^&mN?ZB6Y5;`UO4P!im|U<<OYSN>ZFH#a~~
ziewQqmO4J3uCd*G&*6RjoNI+`A+e!PUJ=5ouLtE|DPD4Rr{JM?yHBQYY$3u63&)Fo
zA-#ro=n$2xRL>nA?Ja;CJ4zAPF3$KU)ZR(1{VL8*Y!8na#G}WNzXx?op9p$fR^C$c
z_!W{cIN6nm+}xoTvv6-PC;TaMdhSSV2~fvsnCyFPts&vjJsVwyDnDJas3pN;(uXsA
z@iA9XZreZ2{u}gO{{Yy0cMS<6%c3whZ<hwT4YAYQU=*liH^a6?aHn1sD@kcFah0?G
zYOK1Nd3sq;8R096E1aUkT*MpSR36Y$tT^yX;rdETO|e*dsP#&Vgi@j{efWTRUAbrb
z3H9>Oqcf?;=Dv8veFv_lOCo2r;Zz7Q{4(LdAp0Vm(Gt5{8f1#k2Xv>84>#NZeS7m!
zM#+Jc95VGNU%|nn4@CHiYKf&(4Q1!J+b=4zaVVBTBtUEwM!l3e8_<;HTWmZp=`k<v
zRz8;!==Z|sX>^;>R7j_=+tzcVMN6Xzq0ioqbH7%w$kDr}n7>@FLO9k_Ml<H{oECX6
zIpGqn(~l?3x6@krLqgAL?zmkPA`=n$RiHMZDmy0q7;J!c@s$UZ>d~!qFC>agTxorN
z5nJ8j@EpdS`Q_~wugOT+HKQX(9if>Dm-M0Ps-2WwPH4V(XnRU{C7F35rs7f_wIC~`
z`XG^)1_P<H@|d=JL;auX&BjOk+@WmFtxrO=E*G+9-^U~SAu5dzd;RUt(!wz{OouwN
zbvGg0eu?L$^6Y`^NDm(rT?7*58pnw-37v72!PET;jm-(w9d(Ve?snTSYDm1Xxgz<g
zYeY6V?U?gwx$06y;-+i#uN6z*`^=47ug#~_xbL)kHtzOGLwbAFMQ2>J<pVivbN3GJ
zj-~AHeq|m0r!Y||hUS5>j8}IpEC|8Ghy7m8YPiFENe6%n^?H4`oaTx9y&o!7T${ce
zX-6{uOe)S(bk8?PrwX&5Jt9Bv?}eu0{h&F2$`d;0Of-JH(@A?s(J3`o4NI9RP4jE5
z3#H&JN_wR*;}d5w=VOtrD@Rg|yYoY<Sk&EGNv=ZMf8{;uC>;`ZeKdadMIluYqx1|>
zN^lv#-=9lj&x|(Qc52ztIqNc|s3Fjs`-eYpaWk=6+1bI%vs>w5wd>Xqw}5#CceJQh
zx`!WT**~uF0Ya;Un&UD;m+IXXUDNRe@ozI2xPR7`S#Q49$T>XAksyeLoV%0P0dW&M
zWc<CmyF8;Sq+i%<xD1~dLn%}az4|k?uJ6Ooh?y$!5Y9+BY4ixXsbc01a%<aF{6$Hc
zjNSuJU!mGdp~|0hYv&C{?YrX}<jroFlSgxvKMfsGZgEFq&L@Yqc{fAg3cV#>noDlJ
zw9SXuP{}N5s@JC*^g8pAO^Js5z{pEOes$$FauGBCxJ8GF%LfWZdrw|J_C<pIRXOxL
z^z|3vPn~FuYtcz-<%?0_EYq69Z5tPBtwvy^Jj+_w6L+D0*5_aqU&Mtm>(_VkRXpNT
z;<IvYA@cJ>sOS4a4A=7+gI<kVwZC3+K82%+OZ~x(VK0L3DtV<|)d@N2YjTi2l|j)-
zv)Hs4Cp9Q!of5BUT^XZE20qJm^<{Y<ugw;VWlS#y)^vyvb}8SlY<7z{+rr$6GETZU
zQb<}Z79@HzCp^8X?!?aQrLQ@ngHB<x5j`U*R|m#L43VY<!8yiBleGa7XTB?{*GRhx
z(bq<o+*zleWxeh)Whe4`xJ(u`TJ`))+qLSGj1;}e`o*<H3M`AK20iIWGyq+fr<k%U
zEI1t&ozNFl+pM!AYw#|D^^$y{L~~YJSYGaVVGgG*2L5+KP;{GIx0?EXLMSoUa*T0b
z?MZ==2=DoZdid11Q~}+u!~%0G&^OiN*{m0iGI0}gB;6y^ERCiyXgQZ7(3BW12D@(~
zkKD<RYv8kxA)7qx=>KNcx^YqpK0e8D394DuV7f#}2M({p-8W`KQ^Oi&yu#nUIn+~%
zINf5~t;^s~;G9UEKEIVOO%N#jf~MPq70yYjQ?`g55!oL8YRG#*wl;yW;dpSZA8#sX
zh|{z-4b@=zW}YzTmXCk`Cg48vF`I%eABW&iFNaTPV&3q15+ikM=^m_qqO=r!41Vz8
zY`%{O7Bn4Nc4~TiMKY3xD$6#0@126}6eK#=W}B5~Q(B+9tz$aEa!qd&U+gpQceW>l
z!0+T&x4<~^Q=F6%>MDYYcWuyydCc58l^0RsA^;mt+}FncI|of$41SDtMucbK{wOpT
zE+9+tD#g~G3L@Sc&dqR;sRvn};P(n16yzM5iFR^@O8luyj|nl`aL@L_Y%YEx(S!Yo
zI#0rf^|)^9>wO2pPyhE?fCi_YagrBK{3GTzOMFgdIc8RBK6^m)wo}84A7ZGW62@R@
zZFte7RI|?99yck=A(4`lx+6Ymb;T3=!dW+G{h&ybFE2g9QHqsLNY1acr~MKhFYoTV
zl%@|HuA+SV!N8%x_XiViy)32(n=RV93#g%JowpL>BbqAIb?t)pi#~s{uj<jEeP!QR
zvDL$OpgEGg!uRSrX2(O6Cf|3s-UO9;{DS$)(cnz0qD$^Ocfi+?9qf>WDN()nQ;+X-
zJIyoo0umUJc^y<4t?$D&bNG_k09bD}{^3maj1!dHm9pzxV<FTpxEgspBKHM)c4-4!
zjuh$+v2!{re3xObuS}`G)<aa@La!`k`N7{PqBj3Re$4ZAixJ%-dvW5yLSy#YHhSF)
zsy%n0`E8iVnAGgCFVBQZ{Lr*E8BE(TP9(aW#2|Bjt5BfA%dX$*U^YD>%3H4H`!~6y
z$wY{SW~5pxL#JjrOL~_krCgjjjqmN<KRgmszQ15IslAdSW;+a-m&i~q&3TI-$&$Lr
zXl`nz3TzK7;k#rCT;kYinmj!mZ7j3{H{zs@ppU$q)Ex9RU%3)ha$LC*Vf3Tzbz?{R
zz(`(^whPTVSnp}VCk;3<;Q_+(M{G9b-gaq3caE0)9(cXYkJooME`BH%*2}`;AG&_y
z6wCF#Q)AYwyep-W$A!PX_k#zEZW|P@`6YsS)W+Y=RiyenQBsfounQSbtTItjh%G14
zVP6y~k%y#Ie%vf;2h{NmW9Crf-nJ8=A-Hc}WhT55Ptp6315ZZ(9h;j5KM|cH1T@69
z8}2sPy<9>a*CsE`yV2M9F>P%_{p7Up0C74)NcleJj3gBMWi;PjZgAPEy^Epl+;f;j
z4{f}$*Ve%O1Q3RwK6V92NI*xRq~*>4@x1XP^fj4T&I5hTNvYdHKg{3!q`YYNP3zd;
zcT*-mKv%JZ^7PvyM<NXCG6}zI_2!?4TYTDA9sH6pqk7;RPftn8r>?!`UOGq@zgsH$
zBRi$qxO&8knra~R>u@qAvZYf?JiLERzhu3}V#R3ww{ptEe&QIiU)x84euTx%Js&QZ
za!j3MiZ~sJpjr!p*6N;(A`lPGTDC<Vcy?A(TQBdGj0e_OXnGyEI(yn?ibqgQ71I^h
zk)xL#O8zv>ouxXNGx;NxzF~!kE5Fz{<9m{C@qQ`k9sGI+FbJf2+j9@howUHmX?Lzl
zeYZNJIO|BN*fXpK=+JM?uo4xcA3j56U!XFx0pl?@|Ljlc#yn+O6id=>V{Tr%+G8HC
z-S7=7_V$ne0n|YL9fS421Rl-DRdd}j<EJ3Yn^5B^wcj|GrRTG=VQ)M9k*_-T?`Hbj
zSh}|+&6lcj(vA~aibiLXuv(=o;qBkdp0~MtF#b*&nTy0MvsB^ls#)Q-7-r?L`%F3#
zw{d`^`Q+QyV3kjX1U;$mp0)c<VZ4S@p8EPB^7qO^T}X(NDu+yGUrtuLMsI|7+2`Ns
zs0hLld|bI`s9QvMOG1u4B#;xPB;~l#|7TIhYK(dA*eN~MsAsND$<?N}EtDX*NeRpA
zY=q*~D%Lq8S}Ni+?G|R!T0t*y{C!6gK{0RWfa$T;y;K*W;vd=%D=gEx%5%>8i0Z)*
z#y#0E`;D&=o3`;mG*YL6qrp_<v^>pjcFpb_=6rn$BQ|Y*;K>bsP!F>2t{mdLA3FW1
zUX4v0%Q`>LQ!5WT(2I|oT}od`+b-`e;|@qFv45++3lHAApmS!P;|g<^8{BhlMAV<2
z?$PZ(S06sXuEG*soLZ1zu)*`xOD)BThw$4~NcyCuGbhsFQ^Z_+4nD4?`F8s>wcIJP
z&a`rH6dWir`O0xhia70bK(?z4WimG-FRx?&PE&f*X%()=Rb(QaErM1pPt;1Q_Z43Y
z7b}jgz4BCCsq{qpSEH59=dIyGv+XrGvwYtA=*f^vt-fZB;_4T%N~m3}$UbE4L80-J
zn2?CzxW?0{xf`k%#lJ9W9S^z<Qht++U^mRSj7a%UA~}9z@k8ZeXP4h)QPhI>d>GOc
zcl)Yytqq?af`7?CadOs^+xT#<kG%SQN?CDlDeP)S6Zt5fwj<1C)32eL#Z^Lk1U615
z6=`E6;?E4S^>2cZzBYVMc&TK3%0x)6Gei%014NW!nkO*GQQp!ptSnrzH-#-f<@CwA
z&A1{E-2straMPI}C<Gc6_ukvzcm5;diu=JzEw>0upQHgBircsLM$4%^Z_u|f6d!bZ
zq;BPdYvl=P19Mr*pGHLvi3R*tlk4<5u{7eVAeS^C!Xol-5FvOa;d_C{&CZqlL0_xQ
zzVxDWQq&=x(p5pq%>Od3`YoNV=CTG?u5I;AACRbYjZV6^+oDCwDKEeGI3@}YEQ*jv
zV7J}90oWO;$+RtXc2YYvuIDN2Zd>{Av~}h53u{Pk=F$PoGP0rCXOHCYo0YBSq7$uQ
zl;jTo<G8GjFZM)N=KK-NZ1iW@ZiSros+?K+Hx)&q%eKo;#24|OU4qkhT^sP}Rl{7G
zHpj#|R(w{5{?u<<SyHTAiqM}r-gnWLbFz#8x~V@G^{tJ6wAPMNZlCu8e;LU<%S09O
z;m9ucjl@xPOouzAl?bVi7cG8><8{%<#~oQ(xVEabNoDBLC|cu@AY)`EO6sE=My;#q
z227zBq51aDhhANBCoNJD?cqF{;^AzHUZz$A1=qA2<w-+9?qXFT!ZTMHRyI@pk0sFR
znU|lAc+UPjBN7W^3dIBmIv#kPQ8{n?CyoBp^&>@@asx&_%-d992J?sg{x%n`q8T6j
z;c^#LbPJYx7Zd0eRwlRADv)_`xsAC4Co6auAIdX<OHO^<qi)l7LKIDXJrk+thj5l-
z-MY-Ts(x*XxnV4%x9#=%T4Kk0Da_Gz-oY=O*xGn+`E5t};xqa5@PC-%a_@tW5Cy<1
zeEPnX_rW%e)s1{aw_K&IyC{9_4m`zG>VZA#>k{v8|NnTCePUiF7dtI|@X3j@h1bjI
z|DI>;E4JpL-9GKXl0_98U{9;h;tV{VT7U502(;FjkhbYePX4{%?(b7E|Fgk$>9F4a
z^CfnCO8ei3%nmwYa&k65_v9LRT~vtqbzeYmzQ26@wPx_Nb}D$`^7ZRKdQCD^>+nWs
zMUmkMda1t8(1-GqIwsGKC_FlR`0xw6N_Ug%^^9dpogQ*i6kD$O_rgC2KRlyg)h%8;
z;?IlU_rg9~nnC;ITO+qjJR%3b)=v~C2*x)HCumH)yuV1iOE0dO{@G>oO>;}y3rh9J
zCc!VTr%#`LQ}yVXAn8P{zpA_U`?I}MxPU)?=UefC%bDBwo&YMfRW)R$|H=_1r^rE>
z{8vj$$@=1TXF>+@Ofr`;)qFHJ5@R;TvvMMr+LUU&2Lf(}hllIh+1br9=wylWNajqS
zZ{1{iCZ)zl>$J{j@)>;{#Ely_D*Z;AsO(aN=43Mh9Hfl5%DIO5rsiQ%VL?G_aY4b(
z2KhY=3PxY#ocRW*!D2~osos!-19X+6K2ei;T7>*|Tj|%zi}gqPsb(ARN=izm)){mJ
zfxAvegViq5D1HQdHG-?_pcGf2U&B8z1?kPE8oHH<#<nMdbiDrE9PfMzE~K;i>*uPQ
zV?&78hX}?r6GV`@$r;^*R^9#amKZuWG~h?=%HlME!y0`{5JL`YP+u(van>2+Z~0K_
zt3b6AVZZL@bYfJfHaRiea1&UtQGR5YoRU)QHnlen<n5Py!yN?<zA42u-lcBAp2m3T
zHiXRP>77n)V!A9(b&4d2LnCa5>w^giS5Ln|fI#K>HLZOskN4eAes*&D`eExSW}`BB
z-=#?)3wE~lm%9krfdgDGMbt*)qS#EzZFCfOje0=@<R%01;)KRhdVY3<X9x&(t;mrh
z*LLsObH*f7Z8&_u4P6?(Hi;fxUFkK;32NUIidk(AgCAGD`yFRdir7DS2Kr1yDO&m9
zlNJ2hR3?lV?8xT|D(IwL6~o#l*pph^TeG#$kZGTUM%x@<hr;3Twb!~2nzGe=27ORg
z*RLmUe%vNh6>OB##W>v>;kwO7kPnj1+bxgo%v8rQe|>t;Eu)}dLbstGO<wNO^2-|)
z5)2)UV&NodMa5+i(CC4mg_fBH7H1C?9n}t7FodlMiD|A7L6*>6yy%LmdA|($xJ6*b
zP|aum<hDz0s<*#9ekW~~J)~!G67Tr**|XY2ap<}mY>PdPeyQRveap!UZf<UGM)1hZ
zbx>DcN3F7F2dfe58_V4Uy0lL{IW9p=%U3~0#;(DQn7$3Rxv_i&(iv#4lttG|VgOVW
zT=}J!7EE>w&AJHcpc0h-5cGA6M<SHj@3-;O4J7TMbL65P+Wf55`Fp=U;>OYQLv<?h
z@MHrBuBz%MIo!t9_9zWBxE6ROs2j?{uE<5~pPa89jW&DO{CgKjfAMTpB=0!PiM^AP
zlSqF1b|UeL`p~^A>}Da@#$d(;FD~?iU?R@Cm^5B$UtJ&a=lh$i8t;Lzi+LpWJi(cT
zCTvXB37ok1QEdD6?cF?x@EEM3EfAFm8x#9P)jpW+U*WRn$+IJP_8R!PQ40p!tUG?e
z^SRF0<1>#&LMT2yuSkhm8}788+za)=^blQq1Y@qdyF2EM6F!2K6U7|BXGuXK#-2!L
z#Dg^x(-r8a;p!t{<Fn-{R=nC*r_iOeG_J}{O3U?ewpd9f!nmuspp<kJ3cNlI)dhCf
zk0)rE8dQrvDcjc8?uN*D7UHx-Cb2!p8&!;|<t|i~xo>C21wHV-n#3aV9!W{b#8(0B
zGMpPFw%1?XMWUcmw~2@vZ@WcWeonOSyK67F=>RK-wt%zN-%rVG=i-;z<;->h`0yl`
zab8juz2*rwO6?s*2_5ryP}AN&3$AzOn`DxdKF;$Gw<JnzYQfkV1zz_Rl$4alYUKZB
z+n}1W7>Hl+3`BJ*r7;~)hDJuqb9mOs4e9K#v9AEf2>GvH-*f=@{PK#R#zdZx627XK
z65MGf`qFcl@8d3L*mXA!D6H+Ms+fj%KYwAzd)td=;w2cJ&y<QA8ymlyt>4KGpE?Kk
z1lf`P`t|GKMWUG|4ZDv?j|N!6?T+E9NS+XEh&B%h9Q4Rnc&QcYn?rVv;a~w=cA2R;
z?w`~@oxE{1Mf4(X*+SKxebeh{ayB6eQ88@h>^`)T^T@rmrAcXdy9(FOnE*lpW|jiG
zH1f%ExW<5RhY-DD8=Nl;;35-@63?~87Ky|sN;m+)P$`p)6-qw~obj`C1q5kwD${RH
z*>~uJ5R9|ZE8wze#>5`PH7>RPcpxC86GJX@+@v3F^61UW!!@l>$6gVFR&|%yRr)@j
z?X$Uly}U&YtkWEF@Cio?=05yS$`!1AwDQJ`&}&&4u<j&mW|=QeYM-{twOuBW963Du
zcf>5{nMoxEsE)!`YedfMzt!RM-|E1a`%eEX)aU*Nc(n`~c6xwV<2yVU_U-vGYPCo2
z)XKv>B2Kfa2W}O$T%FkrZcRExMW?0pJ-16|hGto@<_oUBo9cLZeq6*TU0!1nn__yr
zB64j>z@=AET>HcFTfJwda*B#0D<jBxsJL_XXg8#9pvH%?8$$b@!KMk73YlsVZY|r~
zwb6sjQ*&ZW*WxQqmTlj2M7{ve0}aVSFLId?p2R@ncNU2kW=lllM0|ZrW!G|hvW+YV
z(VPenq~dL3L<caqDb#Fa*(@lXp8!tkVmy2EI+-8p^M-ch*{k#2yDa2n6@DR8?Cm#b
zeJ-OQW~1Dr?-p}rARcZ3nhvd@09%_#iSZ<r3`D{sI1I8<p<U(FpSj_NoEY|OZLQP$
zoA+JJWTacfLWd&Y_SK%&Y^!MztF`DM<UGFgMw!Dv-Rsw{S9lZFgE8HQo*BV6jFL(b
zO0P5mr(W~$BKg{j<D8?J?%!YQN)I~End+=f#1L2`NSnbF{-~ukX&Z+qMjz_}U<wVZ
z6^{hP20ePPaDu|wv;EPGKAX{6*EZCahS2%B+VJJ6JrXe+i=xw{Qg&y1np`5iH@~~i
zG&pLunyd)MGN<M_n0oyno9lr8yxV+v-lc3bW|Kqq9&N(k0x15dg=sIU?Alsn0O?^Z
zGz;aNfrpNSQy=%kWx<j|EoIrVeM`dd&=@}Jwz_=vzSw0%9^-D{Dz}Mf79?h%XYEDj
zR4t<jH9C(&6w+$aLx7j!6yb3*OEZ{c^77oRKY!nPsIGE!k5c18JPL)X2BV@{scr(b
zXRLP>&oy!%O;1fNk<bd62}acV;?IGx$N@m=XM9I=V{R1CFb*DIjA+xw5>Z&OX?4tU
zmNgnS{$kw>L@gM#!Xrc^&{?T<X<{qo7GkP{xJzhN*vVP_ixV-md4URN6%;x})qRIP
zK_gaHiy?g_c7fv;$PSf5a^Rv=z5R@Z!01)H(+9XD!HTjBc#R+aSKOX!W*B9+1pu&N
z$#|b`9rjGMfJEfN=UGavy7qF1ia<l3LYmsSKVJJ{r=K{-#qvdhc^?n&IiP={{F_e}
z$n*1=o?K#e@r}~;uU%PMbmJTy{QL^7_rmQjJHhDi0j;>4$pV>((F&bw{Hl=XyfG${
zDOm`(6%)*c-T0=Xu5Tk@QyrMz%cy8Zu`@KLd7paZErK&R<dULez4shAen&q;-Dfa$
zvqPnOZ!#8?`^pdh@o$}v9Y+9T^-0uTYSmwy^d6{?-1s7Fwl@uH;~d3Eyz|Qcn-H@C
zWMZkftlqN^b0=G)QSaH2?nd@pLzKFA|3c1cdHd(z3uU0XhWm@Gi!IprE!%dK%ner4
zodsbV3;AlkLt*``$!8FPfGQ*k)x9w<xQtp_UhbV1o2-qL)}0aJ1AAziNhL)jQvP6<
z5qEI?_231VkHT!^G=P}p*$Ow}T!D4*jXKVVo`_>r2*5g)(9v@@C*rp6F$L%qLz;7&
zW3Bti$;u8kf@(jo@3`ttSJB=9LNJ{)4D}!Tf?%)EmRDC7$75nhc6StJhZ|U-A`bUH
zJvM-#$T(-hbrHv)ANIJV2iiB1poI>5K(VAaKWhFx*RvVn&BZPEor*tEaStp;=xa92
zw5QGqYbiva#^=x<ion(+DcOm~RNSv^Ai;>7L^}ku)dED~I)bqt8Pr!~opnUs*1W-`
zB>^!`HmTlcoa@W?>mTvPOMGhH{r3tW)pvi~PN8T5c_D+S3z&p<1Hw}+ofXt$1cu4F
zvruZ5Rwya^yvw)n7SOzw1orT}Lrdmk&i5KXgi|A2wqI|Ocp<9Jg`;?5;xvXK5k8T8
zMpjmK$?Ipq;<!q;cFIb8V~aR)W?{G?R0JH<^TtekhwWeXIgza2^&Ajfc#70)u#hua
z(UB|6)@vs<hA-y;%;2IR9J=$S=J^55T-ny82mKO!D0Bzlp4O$MX4P5fjcSH&wO5@t
zz1UJK=F@f&<AB|!j$ywajvBg$r2#x)#$0h(!*eNsz%y{K;~Fg1*|jz}CsBcY$5)ad
z`%YY3m~u&khO`3k$OOFp_8hP6P2YkGz%cw=Y=pvlKWgH>q9aF+_{M;hau3AN2`NEk
zBD9~)^J~AU5x*t>Y@n<i92|a>yR=|Xs&4IvT52xzsqN?M(Gzky$p;9zY&sUOD#*rd
z|HZE&F-UN?Y1soKmm+K~y0xcDfviAj4}^3sR=$0ETHEty0X<^nkALSHHHV<1<1?ew
zn46p1)HNE^&Kk$XaOfN0eSDq4N_kN2HSkV9QGI=dR=F}$+3n<9>pMKWNRL_hp&xJ9
z^Fv<(DxFq-251zD11ep~Ir?^VbhNY`?55u718kj!q(fq`06+{FlBG{O-vKDgsh^CQ
zF3gYSk`R6#plV7{rItl;^03F8EhobZ9Rv;*diq2a%{ETpN`;CXiEaqztid(rECm-B
zL3scE+`-)sWltv?i~s=Ts(-_d(h0Nw2%GK<qQvzR05!`{@gkK7@>pXbk11L>I5c_y
zxJZOlM%8hRx5il19jWpn`wo77xJMuEE<XS!>CfYuL4WKj?{w=>8<)F~3!UntCjtNm
z3Kz85|Aq0{47vfPYMF>rT$j>_s9)pt%_jC7y*_MlEP+Q*K7r~P>2`AWqX0;l*2o2g
z&mSA~<AKP|OS-zcg^=0)@-w+l!ZTDoEY$9Pe~r7-nZfuBW+0mlSXltPo?g}vp;0QK
zMY8wz_aBt7w@?^O1hRH+<IlUqGBBl=Q*)x%X-S}NIUyfox16E(6{|M^_>ZaMFe!+Z
zG6I03gK*F9ukX9~@u>qCYUZNWZrV9HIaz@(bKVz_Mt;!G<tZW;Um(aI{9VY|2L;5#
zeucng6%<NS4Dl8RT-W}B(=t-*o<-l_x)mk7%^4wK)sRlCY0|7-*3;7~t34Wj`3-*S
zt7zP24X_JlR#Li5%-OZz<O65Oj`cy9j2VTM|Au&Ca*{KDVpn`~vT2s4Pu2x5(kiI@
z?gAjI34h)`D>Dfi+2N00_%F7gUoSrk1{Iq#2(lo}qsp5gLRxPD?C0Sf{V7vNvHufR
z>ygA|ks|NFh%3A1M$#7q&zikBeqr4mNEv}rhq{WgQEeV?$l=)yh2TkgrDtD}nkl^m
zQ9Bn?yU5Eut~|RZ*?5g78Go<p1_F>Rlek2w^&`vz$rH&Fu1dxj<=h`t9=#a^0RAnd
zw&i212hLmzdZ``0^phtqj=^PRWCmp6V1Nc~To^I|%BTiGS)AvMuCG=-gP=khGJJ^u
z_sp-LfG`A~K+>X23Y5cQi(FSZ%hvfJ5uuvV8-=(JDD2I$Z8Z+H42&;TGR7y$eAZr(
z5-}Ja%8PSj<4txce|d}uz*Xc#YciP<1E7W?V*2Xh;sY?tG?_Ec(&*g~>mN^jVDwOl
zJZN`ryX=Ng%<(VPzpBYj$Ra=yrK4HHq54DN)T~nd=SLUoDD{{|r?9>=S<7eo=z2BF
zDOiB%GqC85)ls7!Uhn7Q>Zgkm2SGrpeTVBCW}SKgoWf#2|3_K2iSQ{u;ZrIq*|G1q
zcQUrek6gGy`6nkO1vJ`>oW&660XHIA3HJM$Qqx7-9tXlCK%mfPkcZ8;T6P`Q;jWHr
zqO5FfKlF7&cGFUw&CSj2ffKU~l%!ICp;ioc<DcGcGB`+M%qEw<&YCBf_RR2>H(KpQ
zzP?QDp4adjX@mn7H8@yknQx6FaO(jJsT?N~i8)40wN6lOr5T#RMMo6ugBl^u&R+nn
z#*<Jwf%a-q>nmp@0cCY}uFp-ZQ|Lqo5fcx8;eyZTjE#*cWUBk={2|OoiP~l}8Ip%}
z9R;vM)r<j8s$}oA+(D8+Us-N1si*5s!YDmPik{NNRRH`+3-o_H8jTiV%nU5Axp!tn
zsq>Ikk+89J0Qfn0sUQD@>Wa&;>!&(1{gSZ~A#*)gza}9_cU)Xt4`3Rp$S*tm74D8S
zu62W7=db`1V(WkoM!z_Eer!i=uu3?7p-6xKtXT#5neimGFPmo(lu8y}i5K+avPU<g
zn?16C2r3GfWf<wzlaKTD32$+-vP;xi|5L)FY@5I1B|Q0)ot>Pr!I}cdi(QSc?tR`R
z=Zu`a0Geg#9%0gtQlqf(NkHP2EL;Im6I9Xy1hY^Q7O1MA<k5hQ^id#R03#{&T;P>P
zD{$y$7&Ktd_;Ia^+_Hh;20JVwviSjwmvx>M0nazhs&N7|ojCVlJYk7P40&FPf6OL(
z6lNfWdvhgTkwY2f)*yKe>loO1p(UcTYIcgl8r|$uAB|iFn#6?lp)-&t$a!-$92GQI
zN9}rI20ROPnqkkQeWDIXifhGRI`vxiD5%>jJR<tzum>>rt%2Z~tsM@be75S{?l1O!
zvZ%EChL#dM`hPR6DKMt*7}m3Ek*qlpp1Jw;zJU1ZK&p(T(>{P#7?nGwn_$YBLTT8P
zNAk{18@jlazd7V`*f>P_?XR6+u6XUY$_6giv<)Ca31W(k{J{!gtH0wnDP@igO#mNU
zmZgCi`F(Vg%W~EzMYqu(+CY_ma6dRIfgyrnHda+tne&*TSj^*aAW4pw*;h~Z2TfBU
z1%IjYc_8M~Qd3hCftxnYqpmy0&bxQKJeL0|;3;mP8~0)C>0#7LpXjdf!v9_iVB;Jt
zub?nYZ#>T9^dRnpC9{}y%XpxZ`zQd(HlrN|0Q7(2d9x)Tp=Wp+))sZ``O))zg|9$x
z8{R=c6$jpsW$byo0h|}SG@M7!Hi4s8;Rd`^1Lax?@z5cEw4!}gLA>PvXeW(IXF_zo
zNw)S{ERUD@X;<VLp-F%Gipc{&wiHN!NexKGVix4W-S0QaUqzfZLg9d;{0!zfEyz!C
z+pA~G8vvKPv5^ns6gC5d3ccp70XS{{1>k1sOVR+h5F-KZ;oO~Zp*SI{tg1@Y4~#@f
z;7dfcGz)6YB$@{E+=%J#Z<@t6H>j}8R5UuC=U@p-n|k*EgrtPbk2pF2!h-Yr@cWU{
zSZ}rWz>*g{LpwYm@vqHEMJR8i&qDzDu-eYcXdd97`~hM|K@^|!oR829hGz0r@_nm`
ze@ki2i~hh9SOeAU+gb#kRSoRg%NI{%FSN_yRF@h|bQh5vvx&S+Ur#T;{3v5-gam=j
z;{C|xHJKxTJ`gw>YY?A&r{m?K74QSeF+e0qJ47yi&5uZ%r`j$yl(OT1>8P}8TJs#R
zL5^+PvDbDsUtHI5&tQ#D1D@CX@SagFv-a6AX&H$(s1=Xy7J`xYC`B(B<w56x(Pju=
zUmXV*FRpdR`1(1U0r6sf&b$A80+9c6__m))*1C>fd{$;Fz*ql{g{M>iA=zmU3@$V{
zdDVvLqGIMty}FVT`9?szdwEbg7{wZ<W|;AuQ83dSLH-7h62Nqpue$ju&Qj8&|NX6J
zmW9syCwU@zS<3X)huh=dUc@`ba2PQSz_@nl)dOI@$%^4F=MbKm)dI`u5f9BT_>WyB
z2jJ?aGT68MtRtBNcSaF}=qP;cNCf>e0C=uDkRpU9W`P~&TQ=0W_#f}|y9cT~EK30v
zIX?h2i4-$f<-y5nuarLeG<2PoPgv;En%`LeyhkZ=72FNE*!lY}=J9+i!F<FuT*F+w
zkW&OL_dY<h>7T$gqF#Pu4mq^j8u*u6i~tir7SkUIST$t^u>@i#B<lqsa%i6@wSVc*
z)rP73(qS7)IP&f5_5iK-fO?{zKHdG*^l|EL7@Oy)NQMpvlV6}#fDb)H;weO=9q?8|
z39DnzY8kX%>f+*f%O<y^vdi9c*tAIm)?%nhd3qCoM-rCD2y?TcNNxZ?zFA-@M}7o8
zf90u=a7~^K|C_q8fW+S$mnOIlF7s4p6X1>R0I^Gm!=a<l4dAMboaz4dLR86P=)+<L
z&jdiX{Zz60--%wL!Zz0=;XEHT;+s;#*i*SCpyk_t87bCHECO;ysNLkUab4ZrqsMkX
zJPo>Z^$lQ_%e=HrEKLG?1{BW;zkSg&<t>7gYA>5kpx@0jR)0OP%#hK1y!7nZGdbXw
zE|P)gN2LL+9_^Ap3)(Uhhi4A{;&oDxA&W`zgR#g2E`#Zkpy56Z><ogGl9FHO#S<ge
zI43;LRAVwVh5)7uA?DAUc%1|@78o>5+Caa2;@P0C#f?wF<eU`{3;I3=;25^HL{v%)
zmGuKQkY5y#BijI`bG$+2p(m)+`3@pc4hJkM76{X;Kw2(ZM-z~TQO#T^n4&NYa2S%n
zIS}lDcnStm{Vf*j>eHPK&2a`G-v=aU<G8C7AhrYAw?6;#=I?d~@TXu`fx8I!@Wq2q
zuq$UWMwjr|N*EvZ8y(1chZ@GfzbvkPHY2Ad#Mzr_Q)+K*iQ>5i!loC0w^$K74-v?l
z#&EaI^z`&8psnaQV7qUJBAJoCz&S*2X8{FS?GGF<A$ZTn``i2yvLJFdq!JFh>hC9S
zg=>yKSBd8Fg%;Eb&v=hpouq-?r6T>JSu;)Y9%+C8HBjfqu*Eam1Oy{LTz_}X8Bp;w
zDwT>B4`@BLM_}*Kg+IU#^YiI{|JzCIcF*CT)jKXRtYo4~^%)1BT-7u%L_YuA{^#`r
z!TNB3P5KJ<RezR%9qB8iJP*|4Y97x!o84Gj764A}`R{fC#SNef1)cE`!EDf+LZV<d
zhg?@C6k8-WOy~27n)*(fr&SOYwI&Dd<cI|TPvQj!Sk223W%O+i#)C$a+UEJy%0&2>
z=L4_2*-yr+eh0$o`)utf_c_E7Ia@9BLI~YjjB6TP0Ir=6r7n;v6B)ZLwp}g@935KW
z0qn`FrKP2i#N!vHXTfGyKiPF!>;4P+cD|G1Tbxc_vT^YLI{hv|b9xxqclq<YeNX<h
z`R{M8;95bYYXAdL<UzYI-~Ps+vTaq2*7S;sitivkTD(zsIYr8(J-|`orgKW+zpIQ5
zdp^09f2P+A7InEtSa%Az812Zo4k5lIjcDB8zyJLCvq5<HnqFO*z<VX)m(Z=E<UM?s
zz$bPFY|a*DV|_@?aA7#rNb`n;ZI3i+q$2x2d)$`}rj)o@Lg8kH)1u2G-f$84Q!#_}
zMATehn@bs11;R;vvfG{5q}_J>-|b+F&k!$<(;qX%_DL!vTaDhb!+=kgHl+m>V*Ph5
zt(#gH!L38iMg8}GTY10#mx=I}k(rk=g+?8t2+1^~!O?Yg6ddmwF?Gato@3Gz<M!ys
zMVp=r=V>j5_1b}boh1ek3w?`D2lBRm-%z7g=Fsl6srrMlXcFr5`NWZ@J4lIXv`?bR
zD+V2!Vq!}g8j3lOj+I{c_t*I@smmTu#W&wtOB+<Vhs5bSnSCv3>1g@St4aThy*H1C
z`g{L}EtRCGgp{R)6d@t&kT!Wo$i9<Mc8S3-B+_CjB}FJo%rIje>o8I#dzLX6GecrB
zmQfnZjG4J#Grd2b-~0Q!fA{15-M{7gc-;5no`-*&*EwgdbDe8>Ue~!^XS_mG4m=N<
zsc3$<x^r1>>Zt?vCEa~HD(CO#2~qR=c6BXLDh$g&g3VXwNBuj>>Q0UN|Lcv;Z;Q5C
z$LG(Kx+y%2xS?8&_g=pBXXKll?(T@Ku)C@nNMd3gP}^*3`0LSSScSV(vCWCUC%2R5
zC-pFuXCth0qLcTr3WBa`s{iYeD-@w(wJY{W=`(uc-FG<A`JI{4k9D3VzQn5xmI3em
z@;-;|s_c{1sWZ)q)=ur5pVM0Tn=Gr(8W+yb_NGM>RePI7GonG7z5lxI#x`u{z>{c?
zFFS`0nVEigg`d+v`n`Yk_k;<`YR~6)98zQ{;_BRuPm~@>iTJNd9}?t&lfcu6XLqU-
zrK|N$mYyAapLuM{6L}sv+uhHbX)4G*pMSLFyY?%CBK|kdjW*X5Cb30M#6Ofhd8D~7
z_T(YFm}kK!g@DT)p=R+mr$AA(7k9GWW$}Oia~aC%AKx!U{Ep2YX+Yc$dHUeakMCl)
z-F6)++P-WL!m8-kbi6+Kq6zdSwo9Ok!WnB?In}+?NLr&^1<#%p5KcP#(foPRq2r-b
zr;oombm&lG!f}~>dWDA$$@pa?M4f$Vw#QIZ>U&|TsLb=nhhE=1a{g@E?(HiI=g++q
zcr|3U#`2~aj%g9|U`rl!gdIHw)DNXLjkwPf$hO+T^imHNaGeETMF%QqY|xs3QiG(%
z`HrdO-c10<up7qYP(ETlTuzJ*U)t>A7QyiGT{-mUcA=NY`FVSbxiEGQyqI^YzQ9V3
z`@CdJv(QJsChFUp-2eF69d~gr9^yM?ZNDt%C|?@6;Fn=@X(x|+<^>7h#khC+LGJP3
zh@a5arjss4+`fEcg;QsvnCIdKfx2++=ZqXbZQLO4ZOhDH@t+16ZbSIn7UDk}m_6s>
z>U?(bb~w;bt_ro<;sm<m?qT$X>?Ih(8+b4wVGR>S#JC%5TjkMktVePe?}@nLf4weY
z;3f;;bL=nf%^KVl2=HEkPI$QY&->5j&;QhhcouH?BaHtuQ=n8Ke8;BsaU=Y3=`vq=
zrr|QCyUP$6(>0l|BR|1?n}_d=EWf(S&ra&&G^aL58ubD7BR`tk?r9^wT(wvIRqLfF
z6Z&`Rf(eV)I`r!a&uIMbq4<BnqW@E;AiMzaJqx`V(bTbdc5>TFhRe3!TnR|9u)uWx
zwP{8#X#-ix{*%vjxHMR~U<|RQ5lT<~C!>)Ce=~ai9<K@uQ#^AxfXi{d{}~DRA7{oe
zpr&VCVC}G@uyULP?jlYR*QeOWvpr?u0wlqZD-lgpBiuc>3vFgx6W&@%vPC1uD`$sz
z^8M`aB&cG^1N}}vEE!*LCLUC--xPD5j&~a4iTw>RlAzBOdKGooqTU3kRUga_179t1
zcR=fV=x~>O$Ykp7uQKX@2$o`NSsrXBi(8lv?WL}7VDHUREX(sJ0_jUxa?T)duM%*7
z1RviB2vFT76S|5B)-nY3Xl(<R0uy1bs!B!n^%vN&*qaOwmne=D>|l9sJ-0u`c6$__
z<Sj`xhUHtkRd&f4!2BH49d*}6S+98nzSQMTNIS<OFvsS{AU}AGf#(EBwP1KM<27r6
zJ_GvAW5$`o+@67NNsR?)$yWF51wWnz^Zd4@Ie=CmMYUP0#;ZzE!T+Ismk9j|{QPQV
zf7=QZjOY24(IoDGEVtUck3sMuIL*~3;~5-m2Yx9Zf^^roq0c4TvkK7Y1dGW`fbpDH
z^5kMhD1DBq4qrP1Ss#z?O(X>2G;s8st&PU^#MtcoHKdyJd>eN^66h8fh-Wjp(W5#@
z0ulRl+yX{+^eK?VwZnA8a(8m)etFVGOGcOD5s^oIh4X<ogCTSS+)tdxuKYg?Ij#~V
zR)%zF9Xt6Q)X;b(!at&#`i@%R>!Kg~mJvmNO7)nPcR09%XJih(P5|qn9OQFaizj1x
zSNF(Oeq#=AV9OzBp4qN<7I3%!mfX9_mYuV|$K(=-DLy*sqv-A!SFHgL<{*9HQN|i?
zczg-;=XM6I;J2&alw5Y#E#Z05KxYBcT!VM5k9#ZR)jcl#eBFwZdHL}O(R{qiYUE*V
zOGTw@%>qvx`9^jAPj(grNPzG?rtl1pL4RQ&hhkB1xko&CI+coh!<~Uo+-EE8F8!%L
z7P_kI^XU+`3G-VR7-<R{nx%ssq-cJv-BmNZRX<TZAevOCKCGk~u$|k{b9&D*0zD)}
zbx{lAt=qGm+n^~$C8++hq5tGC*kxolZ|;ui!vO)bT-<pIeMXQWd*%(V?6QJ?dz4rz
ze%C^+x91<}`WQIC?f(z|aX@0ZrK+(V1Ld{5rdi)zI1d|wSHlaC2v7-c6d&YN%;&`j
zxN%8~GrFa5=)k5lud|j~Eczn7hgz5%E>=de%X0yh>PLPp;EDIyX#rBdeXEnK8?hP)
z@*!Az^|F?0thk8myYXt=(U@Xw{hR1!1{o)&ML<lQ=uIT55Roz1%?TU${7&`?kN;=V
zYF*8kGt{ksM9IP&RGY!&)P`PE$a)GD0xu8H<%t;Pe)u8OU!&G&WXy$#Ug`kugCwaH
zOkbr2;M5)P27H>lsmw`tNg`{T)EQiF?pE|;gcIv(L?m4uM}(;g-PYw&lyE}tD?hEx
zt@ip_|JrS09y}h_R~B3~Yx8IP*SkBSLdF9lmbUh;PM`@NA+)k3J6Y#EXY`#Zv<*DN
zK}qJN2Cr+ex2hni9iCz*UuHBG7*9MuOLMaY73o(tUZH2;d~i-UZ`eUQ`IjC|5zs&P
z`G{N+YIcFSIK+<!)-J(w%RizAn_%)~>4<#yI4`j{c6AKq<Ij8B`hknbfW3<*Y@_QH
zkNFeHH&M#q0YzLm+_==J?omFc!Aic)<Buf`JX<6{3eN?d4H}s1McGFM)W*rjEKQK#
z?w_^C9X(L}jj6Vhk+SQ@R=&VMaQRZ*!r^Tjf6>2Dzxjv>@L@VmD^H>aeR}QZ*<s{H
z0kFqdj#u0|4R>B>ZL_b9#g~vq@xk@J0DU@JP|EfnJI_^`)E!Y|2M|2w3KQiFwsbeH
zkYX<3Vevt$m?jqeJT5O6WF^Mu8IoS6|Fdl^gfNw&4tWu`ngz}$Z7pC2_Hv>D(SK*n
zPs<acd&Mzs&>{C?WXTJ(ZXd2=J2*k7L6D*iBO2Xu-UV*IH`Lvw0px=TJ-TYk6F5)d
zOXZV9XONeBfde`=d5sknG0_YSi`aMr%nN+ZGy_wE9n@drTPq(i(~<c)NKS1G<-a_2
z(VB>V3msBkf~=flC74w6Mg{HwxJYv;F$TsMp{*cR2f57{WnitX-fvq%CDz=UtqM-x
z<N{Z~dwbbpAU)^M-V$qzt|b|AT7TrT_>}S_2>jOQj>MX5vsHZf*OT1de7PS>64r<!
zbXEH>vV|mdZD-D(h1l$uSY}V3V?Bn~(@I6?F57x};IQHZ@THuXx6m6AWQmFJ3~H;I
zOsoGt=L*1SN~Mz>K~4IRI(YY{m>4=LIlLd<C;cxvDuO(be)emKrBlZ2td02vDhroR
zZGs(KxJ_=gYt;`G<&p)gVt5A$wup0sXD{7mCsBoRg9sG?^~@@2-wK2$;Eu^p`9lCf
z_J-NOj7!uedbOqc$W1<U8{GSQe%v|uM)zF`9;o=yj(awXJPHr!>yNJ{rnK-Ll?^&?
zZGNm~UczI}sfiz|e}Y}zv;Q^dD5)L4x0Xbe#684$0lau0pvfZ;)1j$9i8iHUalLCl
zp??Ga9cdGVQ&RA{TG|xPyzs$K`ER9l;=(1<`^%wC3_NwypYq0-XBHD008yBhq7K2E
z?6P-Jd5{|uoH|}OMG~P~Do)7GR8UL&yQ_qFiybKddZ$TJGj>rM_Q<+`N*KvhFI-qt
zgKq$Rl)8fZ+>5HOZ)T+Usuc11!@^v&Vk;Zxnp>`0^{36Az^z1vv(0ewJ@e;bU|aC$
zOwPTW^2`=6{SjcQ(zRdlD!*T*TB}5nE3D+TODLCqg)bUSlrvaV{uRdOI7odH+|<5*
zzZTD}o$>+%vemUD=;(5JI?TN$baRaQ5ZCWe@Uj3BT`lp|7-S4Jjd<)!&<-pQwnqoO
z#fE4RmUx7Xe!8@^nd8jbXl7SaADG1HBnn01C>uT0@4k0w^7bEWKR94*kskH4a=jnP
zvMcxxIYIr)AtG_|JQq_@nzkb<0LAzn0-LwvxaXazSbGi%eG5GoG4}E2s;^4)Jggrt
zo(r-Ka&S25+7#fy-E2?s1N0}g_#TL?o-sR}Du~0w*X*Sb75(>wdaLs%BHtQ$>(B}s
zx_>fP2Lpbx2}?g=KbNdof!@$V!2K)OfSo_KVrC<6)5(PWt$KH&t5Xmy;bUC4Jp|aN
zIID=|;64vHaKBXU*h3Ne)~0AqrdzuD_!Z>mfRKuQ@p6)-gP8Qh$}s>dRBUYCp^JCR
zU4PSFy;U4ASAP`=xUU96B1W4@*A{_0*i@VJNc0?{8{{)P19jcl0gW0HB(LI&;PNmd
zT>w(#658zWXr^1!>Z0g(byRA^)CZ#>3Y)R3mMQ-N@Z$@(k_0@GKpVrtXs_^Cdk@kK
zs|c(|B5F4^tsZ3>k3Y)8+lk#Jcz|EUPiO#WBzNMePKy_oIJe5RXhERgPkb_fm6?y0
zq?Ebo|ES%AhP-X*4<@xz)UXgHu7{~QOe0bra@m@&3ota@`7HW5mMN}O1gqqro`>gz
zvww<>v)GN;ep>4wIrRW<+g=_6U?!776<J|C@P+h4VF2bDpyu@2PaC~&421T6fO#N-
zjpISJ40DH|<S=*oh~Q~nY+VstjypecwG>*zAW>_4Et3)Y{!=*z?Hq#j*B-8JdRCSa
ztci8{MQ{On?#br(i}VWWH(#+s5TEM)ZrZ|oN_#G-nWtHR*dZrDsF-m2Tk0H+2%m->
zr64bYgID>gfR}N!qSEQJf;d%pJfU~PCa1iwJjRYrULB-V`7)PVqL*X2>&0wIb8UkV
zeO*LlszaBW-O{Ci-GNpEm;%ObNo#sk=WDXMm=kA&aEmKqP=gT=cRH2&6z7(+by^-l
za%Z_!XZ^7~?E)m#-j7(7_6Z#aW2Zb~Aa1U$se$qffUY9BLHN)V>Ti>kGCxYM2V^CA
zwdEoYpJ6Ig4=jzId{aK=UcD9|x#XUwqdF{h;9o`+Fz#`~=U7R;Vxle&`&Ea*|2DV4
z6IM$yBh`&WAha|-W_FT%|7or}MCgjl(di0);0kcy6L<j7Y!OLnIbiC(CNrw<`To(`
zeSp@|+)FZuGiX^z1I<^2-t<q8K(4YPgC*8Lk#C{XZ=85Dok<2PR)8dx;$W&OL%X$=
zb^`ZI3bE%;qM(fHb-Hn!mw2o#$DMdv3?B@abC2_!ny`Vb2>rv=!1GCV%paseeK_M<
z#HWwiA?0eN?l?O!ZwQ2*B1qwV6JX}IHD^1J9nd$YCL+KMv-xfk3-;)DjSdl?F?xR!
z!}S%bfRVC*``LMr0bijl5*gwIf<nc39pE)VX$)v1gT66e;miCbHyl_1q=b1cZ)b%)
zKdtZzEL;>*z}t$7(o|9*k1Yv;7ygwADMH<>532P!TM#e*Cs*Og^`QCgvW9Q^A3Wh+
z8w2DdD*oQf3u(@6%rEwmlJfS%#u)R6w2cNbyl=l2&oN+%HGs|RRYr2&Q#Wwpu!1sZ
z9fSIBuKENGRG@)q^aZt^icS;B<q>iw*=MP(sXB(*4Ai?mQS9RpeoC(!`eocyiYo6m
z&b=K72ncEx62Q!JEY_7=UJuX27iijIn+W~smE1)5$^&1qj)Ol1?~2dLXB_-0!guCb
zI}C5H<Niygs2M$`DGlg6t70KxEnS2z%^eWFMs3=**4#G<<*OKo14nRt#5BI%!Xa&?
zR3U)gzpV1v)~pkZ+A;H2eDyoM`WRyybj7&-8@#%`v2|S3S#`cqbCj)Y^z^~VU(e*~
z7MxbQnelVC7DrpqvmA3k;aUz#EE^%RlH*-q0Xi;w7;z#LbvQff>&eu*BF5J@?jF*^
zLVRc5m#4^%Kh@E{HRiogaPNB~fK9sq=@Mg%u<kfaKceR2@c%_rqi&A0=p$pUM#R$9
zue|1Mr)MUB*+B2JXm2B(==47xnir;d4oOQaL$6!?$$Z-1eC49W<6?#Ue~l6To%l<O
z`7$mGSG^ar*x7`yH|BQ7yyGIK2K^{2B*<x=4`3o73D^D|0TtOVvu{>QpujNbt*K1|
zSSy@eJ?=!TXbg#1OAg34Zv6v??6`0Y3B;_NJo(n@7)N?C(UmztO`@s;aYP2(-8JqX
zAPflQ%KJ(X)_G$m>xpjlkoVPa`4V=yejy`+`sJUPGQ|$tH;=U^IE3~~tONu|?&N&>
zx10gmn{jfO13=C`*OGXE>0HA9z{Oko2Y@8opWg@w7NPq9w&GVZ1MyE=@mMUtrPsYN
zMRiz6>>r4-CE#rfMR|!yN@mFZvpdqYgwx!(x?@SNk<pzz?eHJQH0p!h<!#~$8{?bE
zkzOtl1smGfzWz8g<MF@OLO`%HB^t^a!z1jgt=61z2Vt_fIh-jx4+nsq1($m>DzGNo
z?0;h0k-&>fsxTe<4}JZzw!~ZP@w^O&wLJ14tgOt1VSg2rUxhD`0zF77k>R}(xB$5P
zO;n*nJ+DiJ*d+uIoAgl^SMZj$ruO&}lr6<Ust4&sOGl)KtVSDrV07HZ@|1HVcR(cy
zTwCq*1`<jC?8}b~1^1(D#SlhLdH6uuw~U_o3@ySRm3`yPOvu)+VWe!#UxYLS2hi(q
z9(Fp!UM6`UFn&6lGS3U}YtFc6+2&ni-K3*?SHFW|q3n|6fM8qM(@5ii4!1GL2P$cW
z5duVy)o`Ju+e>A>1=%)IeIVGFKgloSZ;xC&pkk6yPI8SaiHWIBDVN0u%9D<Q8&>;p
z*jc3f0Yhv|RekwK<3+&R+P2n*Zf5!xAZ_3-cBZ!Ku2CGPUL!nL95BbuoSsAnS7+6)
zIqQN+?uhsxIxd=3=*#R6zCdoW;!xxe(d*|}VB9qPv1Ff{E9)G+5eU?!Ps?(LpK=1c
zjcRXVr}r#21cC5ygO9hQK-7@=ddLu4qSx1x^SzxS^v{s`Ss^K^2k{Eh!vhe-0wtdD
zI+Fy5sBDw+8&FZBM+6`{kfC#|Hzw6(IzsBh5a;)?gk;7vWR6~%%!p(f?%{r4#o<&y
zL5n1*O12JV%PoKWWOvL;Cv{|nF-q-jYM8A^j<Hn9pLa1v;>Y_N?5DGH7E2Ak2?;o`
z^9;hys*if8mp~)O&hPwH>+AA8HgxP>*-?DSRb+7Eg2}dM(`w|;OL516!=ICcZg`3+
z<bg?xEwL69BGt7Gz>5F&!#ahJT_h&}KRn-S9BGGv(5UKwh&91!1^5)(?4`H(Z~l{6
zWl!X0zEGFjK&C9>J=V{5+*<%RJ`v9WHDAL(vTR^3YLEwaTUr}^>1o_(neX6mMon0Y
zX|q=Zb4xQ2);(GLmv4u+-pFL50OtTTp);t7Ziwh;hpo*Y>^Utd?&M0VR-fSnmE{_$
zQ(E+H5>4A>hV_ac?fHNfJwS{65rM2;&{!Q);(VWpn+xL9ENG0B7X?Q?3ucvZT7W0r
za~bRgM17P8X9Y+Tm35fPho&GZgXXKSqkb9D{icv1saDj2hHe4?Af)ctoo%We6CAd`
z{`13;0$DA9^$1^M)dsH!S31hqYiUCS-&`CMSsf(e{x$I8Z-hz#aEX_jE|fT)+GGlG
zwrh4)VKwm!^}GlY0)K52@?a&;55FicLfMdCqGaxTeb?Gz7bN|21xWFGDvA<ftWvJ7
zGt0_b;z#dXVg<#mor_ygqb-hh$LY9~khO2Vp!@noaFVADv<U7~W;l(Na6&FfYw<NT
z7|``;silUi^z2?#R0$(iC~_+=2UQ^ogdbAv9ZbZx7OdKEhzziT-*;Q+CU;D^nI-qQ
z`k1TL%d2&{$!M`#lB@2nZjN~_tkF%<V(;qMgv+1MHc$Y-3Q-pH!f4i}FG0w)p?@Ji
z@rw;iN!_*jsUQjFpyd+HWcZ48HVlIMJesQKTRs&amtO6v;u@2Lj`B&?ZMyL+U87fS
zFY1F*b_iUK6lgb}l|NXwWY%QODxfy@vZGrYfm8u5(2iyS%P4Pi<qAK5U!B`r=)b8;
zIF1_;KvY1;VtUebWyDu(w@d84DFWPBMi(0czEv$$imn)w`&4&J@;<p)KbFlN>pe%z
zaPYI0i{s@6V{@PK(`I@<5-4`Y#>QZx8=?US57cj=EZQXmDK8)8;gn{1{7SGpSbMQO
z3*}&07VHdykDdP%8DU-Rx%L7#O<Cj81Vlhb9hTJ?cj)sgc(2~o8BocXQB$rL(vABt
zL0bpNSrm?+MygNDpaBH7jr9l4^#I$@5C2E9l+QbL{2o}-NP!3TJxc*$D{v|)|8st&
z%Ln`$;#`8%D^cQ3%Lm~Cti^M2YbGF`fgXJfSP}@VH5VWM!tT$(6<|&=F7!!iIj$1O
zl!0JDg3LeI&(oHVnG=naU70>;8v;j^7+J9>%`N(3a#}nfiMbDu{3j+1DGxS?=goiW
z>z99}7ZL!hf$n%?#G|9^D}Yl5P2za}IKGV&j*4%Fqavd|W1RjpB?C<!BX(l81RIRX
z7M|^Q^aiA76!+ITPhOYRb`ytqUP6v<{^|eo!#Bd}3Om=qATk^}zXLIrXSql*EK5_x
z(GRW&-UZ^`wsE~eeXg~;9UL)NmExF^4j1)U-7V;99TzrszU;h^osQ$`?t8C2@_@{p
zr!c}s76oMR>YMc;W9KW>T<i4j0AnlA`hMKbF1c~Y`aQ8ljM=IbzELUW!DVRdS8a#!
zD&)P2Vs}SSSZKe?&Lu3{0L8cw@w-Y$`uh;Bv$v(~#w-@z3Ve+q=mTTMN3621xuua)
zm9qZ^B%+QA>XzFg`UYzo&;8;UUwUr0T;hAiD{4H~IOf{}w{)gB`mrk;nZSVWgVa^r
zD`0?U?W)eCPx!hOeXJ$IXSGGYSRXdlb#f&8po3}1SYz$^^jDD`kU4ydy8)Q=&dHI5
z^XacnpD6a9-;+|exICx!jaV56A+$zAabO^Q81VyTmm(8>#|4$X?y!10uyryq(lutR
z4Oc20HRc*@Q@i-!=y=Tf9jTx0VPigSQ1(`B4tQ_wcn~bTrQ@({#HTy0^yVL#-Asm#
zno9f!Fa>3YH`sh)M&qpGy47fdDv>hId6@}PFW<y%AQzZxKaGm#EoYH?+^s-*t)!Y@
z+32EYjlJOo4P0{!k)v0;LEE+h;XF})?y%UxC;1jK*BDdKoO^N0(ydK^1+S&W^l9q<
zuAf^%{rV}zaq@^E7^^~dI_4ZS`qt*gX<2h#-OpSIpcxcZ=d(6_GTQWixmdPXWW=p^
za?=Fpsq%vPDa&8v_vmN}I+&o5Vy<&6ss~~;K5x91<&(i*(Z4!F#?;9h@p6fL@G~>y
zLY}ye4*y?5=l|5R<^ORixF?GkcUHFld^2?J^xw%jd43vW^#iVL#raP-7%TF3s9^qF
z0yr02XVrr>5{%!DS9UW!#xqD<R$xAH4`L{d{<+DlpPz}Z38&xiOy((T)Sohem)tX}
z*NeC@cr)d`2%Q2P2=Y>t2^Qv<rqvc_H{{rvU9Ar2cUeNU*%7!^QKdBbpwFW4rO}rX
zWCwP*xfkT&Js82kR1NZk9|1rVHK)}>e0(Co1GhQxOPA%lG*9y-DDDQ%baL(c;ZRhJ
zHQfU)k1(q3d22ss2{RR4iiWqbMm|o%q|^aja<>NYGVDFXX2vL>Oh2yRcIB`<W%(u(
zxaUHT;RSGa;9N$(9LSmH;;P}@**fEo&<Opv+<>n)w|sDYBEdai@g1x^z4dB}J4L@c
z->1M0I+g6UKG#3e;Up`_7r0ZWWIXcI9@*%jgX+=GSu1H``F0Vp)iflqT^N4FNl+-e
zw$Y64f<yKgvv1Hb)E>pY+P(2Q@oCG6)J|OVJw|!Md>n^p0HpRkxDtF&-Vm-CzcSV>
zC&=enm)`tym%(BkGy(_|v%yBcl?c%r`$mEQ<TUUAWaredh(p?cu$K>!T!+3HlN3!+
z3*#kmJrlPycZb<WbOY%{c>cAx;>Mw>Z9D{8Y>H_zbg*qD1WM52ZSioD9lA{a_QP<V
z(HB2F>DJP3OSS<a#;rk(jO!7|Se~U%K<_?;5Ho-oBd(T_oZPBo!fPX5o#&02&rJZw
z=`}S5(5HLlzVPgQg(>%xp6u&K@1n}X3{FP#lKh^s;>!pRj{wPEJVF*;{Ow=<b1F0b
z$F=AGzCI+#WtGqTBOT7vo)r@K&}$ql3nU`=Jkg`qh#!Cu|K84DBx<UU7(WKbE%4|^
z%AKjF+~T=F%#DfSm}#zPHd9Cau|VS7leVR~?9&PysqKqf%*#n`lILbMfXV73aX(XJ
z2gTeu<A4~pHgDQcY-NW6?N0N}zfQ=`ilS|5e-z090P8L)kdmngejVG}4q>lN1ac+E
zA(rtva=~?VA79z?lrrAF{V%P1`QORi|9&IOt-i5e{F2Q0_}+rI#*=vx%|aupaLxzD
z?b_;?csd-X0srxnPEFk4MZ}|@fHJ2(b$dTMV>?+LWAHrH3D<yY&;I+wz#6E!n8v~=
z3p1>Ha0ff}UOe|gEk4J?Uf;$IY8XSw)9SWs!9exHHe3-5*+*t}QgItVE(iFi9J=fZ
zS);;)fP1HaD78*YB%;+3U8r9gGrgH3AB4)m<9Vtw3J>a*^Aulyn#>5pZ2+;{H!2%A
zmHc2UR<4GWb6Rwq=XaQFf)(5}{}Efafo<njyCj@wFpX#h@>XB6;ZpY5Cn39a6M`LC
z<_D*lV~e-~y8_46Ll`!oY-ob|FbATJUo6Xab{kl@8<<g|RxYO*AMy%X?)1ZO5G=X`
zh{`;%J7?>KgP%>|t!}HkL-|0pG2gfnnM7>`Zlv_&aBV(t$`*o{WOyJ%k@;T(ElF44
z1py6*Y3dF%1WNP=VAT+~ls}~D6;ko?@$>2W-zI(ftNPtpYXwUzdA2ifO6e&MJmgoB
zBP^z)JF5J~{NxR3h3?F;@NUk1uXe6gSFw*~_4yj_F>D-@+?5fq8enO8EBY|aFNejz
zZC5%Y5>+S074B!hG{uY?rV$__d6}J2-4f%25y>4SX0+O8G|v6xdV3bKKYpewL2C4n
z;g^8LylN}tZ|rXaxT!<POL^mlm=Vk??j|MdIt#d^KIf7Q_s?+#Fd>JK0(s6~^qSaS
z-cvoVsc~jqGUJ^c2@*}ehm&5J{-EP4&kIaE1-bODsSekTr7+clP=1S8Y$^|J2krxE
zR@T_b3=)u{{s^Eq%38b(cRtUeQPcwenY-aI)1LY6)uMhK7{Q(s)cSX3U<+{EKyX6)
zTOpBJkkLPG^KT1T{GU?0_Mf4{{)8$a&%Mw$F|pi(D(dF6w$MMyw+tI&CV?_Jxqp^#
z)l;*8YC5!>4d{)2IFNn?K3qUn_GtrmP>9~h+??+F|6ZLO*L?<+io(-NQ1%YCQg+$4
z2HCp2Q_PA!;V7|YRoo(s31s7f(b)n0W$fY@8+waBHpdds4d5K?K2RLl7*Efnev)vH
zAK#?cP#1th1P?zS9`~MrqQsu7;<{lvHIz`GtYcyWOVoQC<3^{(61l?@wj>L6Rl`ix
zWeRLSwT*TWw$;dXZTK`rUM#1;xHjf?HLl4a<|chYyMT8>;JC&uquPc;o|G|0k13NV
z4)QRy1wx(csGf1WJ)OBSA0ITu@+b+eIWx_3{5Al{fMht8a7tn>(K&TsidUwsPhkfc
zMh(unc=PNTDM^V7p;poteX6a0Hwvhgl>DeaTvRxH8ovnJ`QQ!S=Tnd?BK5Z09-+{E
zVf<JV)8`j=Z`(3(DG9~z)Of40sj<nQt^3OIbywe)2VYVZHRo9%OG62}h+nV8r9CeA
zLZ!p{Iuav#_&x~8XRuet`I(AlnF_$)txvBe%SoC=bg`X|_|g;fzj&=?IVqA_#Jkax
zPX`zt+kjtLc^(5_y*(-Wd?gu^US44E+{!^Ucw=d!Y<=#t1-K|gbACiZIiB1bKU*bE
zvbGMPR(jE?=Vp*bKediYrY_BevfRK@%JJQLWbe<GP<!hTm`-(M(A!_|Y@*gL4Rn}A
zWe|IjY4qjO@Q;q)zumwR${b2{|FpVsb4ek|7J1X044JNanlbk(Vko)GgP#A4VrNc{
z9cege%u$k{7snjLmz}VM;%J;xzcfHiUgzKbN<se?kTkcj9nPf^V_YiZV!Qkh-!wU#
z#z!$WXuCBDLTf8~UO9jHUbhO>0_3YNw5MlzxWM)E_7tr;#EI3$-)#ZG-!$>$YXGfv
z{^u)mAH_#wyPl@cB~I<cco{d|cK=*+iTtC*MoxAJ^P-%7;$x@?>ydcbXQN%Bvu8aE
zGUuC--x;t_RSY8w?$~<UZYb_Vd#yBwBOXfbVeE1#YnU0)XV}!b+|82qL2QlmX!?da
z&}h@(GzpgWnRPK&7aZv+BUfy^{Pepw>E{@vK{D#=c<cV5qsl79ob6?~POZ>tjfZnS
zH(CoCBuAsoug7ewH!3oJFbA=(V&|7-S3G?pF%-{N>{t9jD$UHCyuGadc(`ilm$di;
zkHhVkUbZYE0~!{Q5zyFDb9?S%kESr+sa^0#ift*w^c(R|v}Dvb`6b!Mi^S~{PpoI1
z69{+&Wwq8Rg?agl#85zoQgJn^&TTeq<#@67u}kaB2T#L9LvIAIG%z9CN&-A4sUwWF
zyMDW@9QHiJv~L%tp6(GS`X#|eC<rsGM~o`{;-VW=E5hl-($587uHIMWTP=I59#Idx
zXInH8jFFUBdoS8?2RyxR2>g`RmmT(SZWo5unLf9ekO!k(RSiDE6d@-ktsR;^6k<*j
z4RjB3@PDdPT&L8~9x$vbSan)}J5oU9_H%-uqidK4wIct?!s%j)^A^Jz24eUd@@#&{
zK8lMuxnNJDpiaAdC;N7^QAf=?8TX62UVQ@D;#VbEj@w=L;^>XnVwC=9irHVaFPF>x
z3^X8HE5L2~s{&A2oJMbyYdKeqxSGT#I$)LY{w!rdJ$_5ej?G7*O(bHHi-IKImF;!=
z<oRUUi|fKbvh{Bom;P3=%<KIMsTa=}V5;^+nGj{O|DGth6M9u(#QJ2V!LHYT?VoV{
zqmh|>DK~F+o%>kdC?v?->w7cylvgvU7NeRpHiZ;a3ue6ze)r+;o`28tH`)Jx<9{Cq
zZuO{ca~FOsIKe+5;wsQAAGQ3T*2O;0MUcs_zSW4|RoL9LTadX>P^B%A;e!g=>=BE=
z`o9X(=3m&ksXmEL3aXc@7joR?Di|xRn^=(--CS5??9_d6E5ksInkv19U50<tmGQ~t
zuRE=y2EJ8eI`eAFi@klsYyp+K+L&FKx`b^0wE$Q+)&FpX`qXPy^v4E9q^E3!K>hA0
zsS3;VM#GVL?FCx)xS{&x%)cUzPbJ?#VSgR|_?S>p2asv%Gpr$ESaP=YxrYsKDL0{`
zf71$F_CnUdlv093mmQ0}-1Q445YZ<fCG~@*TVmilA>WRCG#&apudkQ@5)~%J-OcSJ
zVGrIFFPR0UKWa+a#8T7~@?wT(D%}gLqguD*ODUKsLJn+rRP?=TmU^K)u3CLn{7&yp
zWx4KP!n$<4#V7w~@{Z%(O`24#w=WS7Uxvl+Y*kKgx2E6B40@eDVhU<wTAfQQ{$d0*
zJqoh>a738oG^ysve9m2%<_=mmZ(X%X4IyukTUM1O(zVQ@&J&{*KU<$44<3s!5?YsD
z=AXRmsqVRAk7hM%SfPr)RX#$c75p5r<*jpXFb84>=NRxkb|#!MsYm}j?x`1E=R=l@
z=n(#l-S?xrK0x_jtKO%&CHP1nAy>FgDdS3_&egq$!xKxbM>~y3J9J+H^&L*<PL??N
z@qgg6+#0FaJd0W*_+s)gW<A6ZzYqW7$oFWI%u5sP3D52-X%U~kSj%j7biEi=G}sF%
zyj{1~J?efru@N=1g8}h=6KL_-J1^*<@$}nH$Hii5{rDq=pQo?v_fxZYZ8d>RY)OR`
zU+-XP7(J3nDJscRXxjGjdhFh27fl_#p?O``?GyX6;=E?iHctAqua^z56pbZp%(MG5
ztW3&Qvjf%!0!X13PhY>xJ~LW?+S(&4RDst?eTFG0s>+bBikzR26u*N5c4?lwOK}z*
z{yF;JsGZga!Sv&P=fZ3(I#QY5&EG+yroHY8gj$FBkm<}xV%iOZ;67{idda4-{iu2t
zO)WESErEaj0NxvYtNj<Wb0u+KDRsyG(vvO%{YpDgtpe*>Qx`}m)WfS0qt9-<=r`Vj
zczIb<FjkkraymZ}Y(psgrPUyoCNH7kF+KC^W}KNfdcVbrwy$kVn4##0gm9O6&V`q+
zpsy#y%lp3{H$GTScE0UlTfS?6ffgLmPRBB0pZbxb-Jcve7Nwmk$!B>awLe^aIcW0X
z00()fr=(%ay3Zn-Fl;iwz~6q(@v32O9uBB04YH2TnMmo#c=Qtavr5;QL+gmq$e9i3
zDT$qql3@#0=uCBI*c@28BC)hhEufsX!?4hFY%9j4kQ{Go_rbOi%eNL%&e`Fp=NUI0
zRn{*Ry_!eadg6MbL;H1sD1~JWPwi!!k0*13bofKIL>}Aug{?gc^~U$Q2#Z%fuRUpT
z%dg-jD>K!|Bx+jL7)2gK!*Bjal74-^$;#D}4>z3ZYBt!8e2VAu<Lf_W4*Xu%&MQ6j
z#+N*du~VlA66==(SeHHH?!}c3K2At0r(fA#Ac0cULrDo{D4Oq}FOQH(q7(Ms@^%yZ
zyw;ygUsfYIi8a?JHDVVX3oL&v8T5-}YAMKF&}|!;&cyQZ$uZ3?nAjpbm-yzYF?mUy
zwsM1h#1Psw%AxMT=aGGBUEigrLkf#Xs*i#llNW*C5K4dRoCc`c5};}W_#0)#rTPd-
z!MZvsnJ*Au6lgdbfb`K`K7C1N%I(54jcN9N`HO(kpAvTcYczkTX*1rnV={=4_GC+s
zz9fx5pI{}ABU~|4dozcksTIG*Plg$cq;#G_dk6qz4!;aa@D{H20O#Mq^UEs%zjpH@
zrKoXlbM_@4x%_LzLwN(<k;iN7a3~iF>g5~wCBV>u0YmpA;I#dat+azUM$ek1-hI44
z=k2Jwk<;z?6l)$qViz;ot(OmcL{0Qfxa1HzRNF+R{@Sq2gl@W7?5}odpK}~sBl;bu
zh)jeT{vpPHrTLJVHbjqTS?{;EW5ngFE1nUl-5L2EJ+7NS)<o7J*wy^EX^?+JW$zgC
zxGuLVmQR;&G=G_)JHTkroZ3+`W1Uxx*`v4?J*p+Gxs>tK>bI^;q`{tGi~OykW+ya*
zg-KHU0bkZGc4sH_=FAnx{K%TjQ*<3;!uSdM>%8MecMdTXmjSZUel){Y&wbplSF~_D
zbFmY0aX@y!n}IGYdGz1Xb=Ws*O$mv%yW`-=_9K&<B}+4@YnwaT_iI*epo}kB*kQeF
zjwL+4oO$S0$B8!PvCJSR!Dh+Pr=hPc_*?{11RO5SsQGBAA9|&JY`a4~?em299my!a
zf{1iNrBUkrIi3abzsx*S73CxtD-gA>Xv5@(VP=rtA=8aM4gNZr!v7XcRnz{7rxvW5
z`l^p}?#)UjO{G0LW@I{Ws>A%lMvd{Mi?UcRareuk`B@lyP*>WOM6gum;>&Gv6GaRA
zrjBGrZcMAt^17<zJM91g78Egzbj_f9X`SRbkZbKIC54!2o!5&qYLhHX`+<44F!`f%
zoTqwSA$=1;OKUIQDvy_kyO@8qD=LjDYYLk7`>ibvztxorH=9f?X$iST%zo+dU|gEg
zdH%kJZ{sU<ruV+#Xqk~)!ktpW{a=n7s0owmo(5u@!k?3mDz`R&gqFrCJzZ$3n~m+M
zmz!_*cO<EE?Nt8n;iOgp4~3n#*}Es>FvN0>D*si5W@)r^l;-D_hBS26jr${~U%WrD
z=z6f6(olF4f0G4Hl6ncX9r&lU?E6{8w|-_BTu$!alFsj|>`wy^Fm(I(8YydNdaPnA
z;7r}JA#dUHiF~oyB$~(c<72_;Y<5ngzDwr4Li*#|&pjl|Y36$3;8Hn?>3l}FHS~1`
zJxu3E3n1VZ&6i=f;<sEq=89w1VOn<wci#mps<HU7%MAyjwthA`ZXqB7{0#c$5$E_@
z=}?fiGbb|rR|JYMj4Cm-SUNv#dNDPiIdI$NlkZ)<>U-T=VViHH`**}yCNF9Q*<SZ;
zS;+7j`4l$#{KT);__XfyaSA5tK^C;7EUdYAKqIS))Elcmg0+xl_N^o;>}t4}B<92E
zD?YE=BVc^AdH0R9_jtXxgg0E9BzYR#j7CF#-`ZP$LK))Oapw26i%k*1kLyMq{=+72
zOM|N>c$h}SW>QnOI`&MwDq1V8M<3he-E$|$lIiWJ9VRof7`iRq1LC@fmg|{Qi0jwf
z7zyqnlB}=X?DvyDJQhNuS_d1+{CgYU?WxjU-aFeDBds1COek&dRETSQ{aXdf(NL~S
zZ1b>%-K;|dX_z?V0rs(v3q`)(D2vp*>>9MoRSLvzx8<0G^LxFRi_8U|W{Y?2bm{pt
z3mC{V7BVUAOP4yP>AxBj2$kuiX<<tNsS7jeWzAsaZt+)4Ct8S2S9F<9z#n0Hw}*Zf
zfc@QOS9`AHE?C_#@Dwy_h3+!U{C<xpnk0o>-_`vXG7L8C+<&V8QAvbR7dp1MMdUof
zDvw|F-b_t7=3Sc$o%V;!x~s<AmJh<FZ@GH3xnuD{eOe`J$}e-dS$6B@-G=^N4MCK_
zj+_SoRK0nesVgHjeY(?`g8Qj**ZXdeSvvMJX8P`xmynZk_FzA4>_F1PAyI)yg^BVp
zlAm}|(`oswvFQ!W#cPd9Y{QXUoj>|G_#pSt#IN-|v!Z3Bnxn6ng#!V4xg@e}%VA~a
z*a1R$VV`iLC1Vf1@@+RqtZ%^beZW89<XBk5?R0Fh>e~D@a^(p^tHuni8j~FpNUJa9
z!q=j=3)7*lsaF!ECU1Q=UL|zn56qQb$69a7#2CzlBcsajexqddy8uSIc2U%CNDAFI
zbEhgqLN&)q1x{3^zW!2jG)V8=Sr44se+0~V@{W&AUfkPEd;t-Dmf2J1Vl&T$?|<{;
z`DF4bOKjeE0`kM;sT1voRNHb<vB{wa;}2xe89SNG_h<S=_Vyh(p0$X@?49(%?7_TE
zBAi3;x0t?^y)*Wp<{#>_Up+idVqI}+$IJ5xtb~-kM$3r9hU(e;oJ%jG?zm|Z18wsF
zfBNJ;He>tw_l8@e<fcaE(c7oP$O4<&bj5U~>WkhQ`6JW?j1^OnkmH?tcMg7(iqPR}
z-P&R~+FGYQcqQ#L;8g$rVDSHC94Iw{-@P^2r4eG&?ziW#Xz^XzbH@<mx__aC*-C#b
zNXeX!V=}rbp1auP=qyln)UY+fzg|64Yw%d^Qi8&*1=ec?e(hZo!sZfDn<lau1>4dW
ztf5{3PG2qkn8O!xyCkusc7OlsM~+`$)ep}oUCt*IWREVzR_<EfJ2`}rz<@9ZJf%a`
zmgOeH_8B<9phZzKzX_BLhW^^l$*&^})0~CN_l^luN^VWc4v@Q}($EKWvv#|XF`G#M
zpS5b#_<apt0A870rj}t0Gu@c8e-*%(3oOXFX*2e1lu&(tRr2Cup2KxC`WyV=nZpVX
z#~0nNU2mEWhBOuo4Zbi)z>BX|-|s&zCj?ybWz^DV!QU-Rh`Ze$cWV%;pt17c2h9iF
z5iEsUS>UHWeHYyu@<qYSA}YM^_L`u(m5-@H>9PK;_Ern8QJ^VhvX0&0Q8Tp6)`baY
z&Yfqnw;Y+TYj>=>ZKaciW6NvVCwi<eU-K}G{wtIEdvT;+O2B32c>IMLsD0lV$vp_G
z@w&6E+i(|hbBt5x2EaSLQol!iNml2#R8R<$+D*z<4`i|TPsWQQ<euMei#1R(7bplU
zkC$4%au1+&%ko+|U0py^qBh)ro)U~1QuaR&sNx{sH<xwM^bwEO)0Oy|E@(z5E+qf(
zfp@4d=|Zp3@uY!fZS)B--=zu>2Ne4SR-xi~EaeI=WM9TT>xFP}(ZkXC`Cmgla%zM+
zS3=KXIbF~+bc-Dxd8OnEF!{C4Z(@7~d{UVTdNE_5m<gLN1(N`jgzV{jcZmFu-D12P
zHo5KN4O6_}2SKs^ACyBuayw)1pQ;=@FzG+(h8c0AM)55hP2Qe#pY)wv!H}fm2uJ;y
zNj<7}DW)^>IyVT{Y)4Z1tGo}Zj_F?xG@-3iM`pJiKC!MhSv)e0<RghK=z8Y5px7;U
z8_c@<9`62dH>7wN#BV6MegV$LB~|+ND}DO0s>jGi(d5KppD)jR%^Bg;ZD9jA$}OG2
zFSieLj3&E<xcT~fbfc)F;aNAL?MAHVfu<mW0CHQ)MO_kp&wcp1v{&Ge;1`{@6RVhm
z+r<aUZ_?-V&y~qvSS3F63G!>jnheyZscw-Yj<b=1_`SzkoGo<8(DYN%j^p8{epKet
z^`#DK9fPN14f~EpLt`vX>YPMA%;iV&LHWwIEFF<iLMrG{3{|M}ZF=)Tt!v|FAGA4+
z36sPN8IdY)jkqZ_#Ye_`6Y^0pzadEY=+(e1C)0JSr#fx9+I!6X&IRsWkMCh&2o23}
zilk&nIDcql#;U#Mn&ABWX$CkpqXnd(ob9(TT~zezmN2Op^}fVVm~`6v?W57e>(9zm
z6=E?o+-#*_O@q%R;?LWW*)cS+%_CTE^0~`<-_J>=P~HvKN`&_tAp0v>yZ5RsVhuGY
z-ujDU1KDwmG9?SUem|$mriA6s=klYXA(uc(vnA8lJU<*jRg<UR+??p>yK(~G;Ck`$
z{uek3s`*)2)}Ci}pS|6GhSO(KtJ;@y)<e$;fXdSigoTIvOydm{OwmtV?U-3=92JAM
zgRLIX0m5`H2b8V-62W^qB`(meM)&NS+VR}!qn(A7MdBJZNhRx7>|~t(!=U`!BD8_9
z15qZoIzHpCTTNYCLWqC>J>3!_U1@bO(1g8(mV84AOz2i&28KUT7)Vcocl|PAi+~vT
z1|nsxgaS=^*G(Ov)eRbq2~Ww~34w3Q{x%Nz+$qaCY4c~D4`Ls-tY*HLbURx=s1pKO
z+r1=NdfTA;^78CMf+J}Q?c`xK)QQsmpy<8LKA&&cQ3jh|T#As(vqsI~KY>Pn!xn<O
z(Me&!gjR~#^3uL6<K}Ko_toXAnV-gL6^FS=-_i?t!tUF}vuljM-?{QyW<RJn?(uaA
zVbW@qK$PgzNT=|*b6z2{$CO@VP0XI2JmvY=KKJomzb6V=zGg<l!COcdE<2m=&9sC>
z{#I9ww)IJ@TM9Y>yPQ~;>R56Fbtpcu4qe9YzPGmF!Kqt#b%QQ?<DiC0u~JdYuheYa
zwuvATpN#2#wZl+%{b!DrmZeBSBw5%n`^hNv1qO6NnfW7?t(Ml+q6k^J<|d1KmUUdY
zFr5<qb(9ro8t1WfRq#v*PG=0p)JRfix<9+tZ(5|kbYv&J0(kdVyquAL_CdSufIT)+
z5+uraNI+Tgg&Z~XAnZkqgcxUBN$?ZUxkQJe-Wk>T<cW8scEbAJM}L0!HO2RN&@b(<
z#2bjj$dj@r^$bYjslaycSCGJ(Y(S26`Oz1%ISv|qahr-hlv9d;sAL`(R&8VI43HZY
z_YmY`gO6-rT<j9pwhbCzIH}Ncovg;hWR5rAUv7ivo*(;t&vCqL=J6}$-AY2FZQdjh
zs6A57nUvL%ggsC_4s7&L5#q$hq~yh_g86ET<3`pto9}zK4L;)+$6yPqy64!6R+LC}
zzabS#1JH5h_)C|@*1Nh4y+<R(l?HuJfM0)w74(~UgOTYB;OF5?PDDPY)<0dE&PG|B
z)@hL+dv88QaQdzLLn-*N<AwbzD2HnZve`&gpRi8{Pk0aysNDxNn>z8I-Vbm>loZ%a
zuLRV7+|;p5T>EA}CjV};_0g6PL#?YLQeoVM`vVI%FsZkQd0~OABPc6#8h_C26r-w*
z&dSJVxjA3UjUOsP3ujK9@V#pTm<DIt)je)(kBG#&K0rurCW60mh2)K6;u=mmC|D|s
zc$V?q#78NrbzfGz_ocC|NpO0M;#vJV<}E!2pV`MFs$K$z-xAbTXnOODzPaf}YSM2I
zSM7`o?sWvm;ucL$Q>ltA)JB(&D4W<T8$ykntqY4XT5Y*H!H;#1!#H2nI7sC-7hiMx
z2({j$P^dlSIQe_Pw^sw4f-Gyis4T1JuVs?Da&F|B2_XOLpf4JVxD#e8=US&90#5&d
z(?=nxglzHP%Am){-;Bu5EJw4|)XD6N^XOyADuc5)5ZhQ}Q<;G#d^%QnrHJlcH{swl
zfqLKLcoWbAR~D%|PeBuHxd%~Gc7D?Yq#}BTPd*iDe6nbBXQ*`_^!2`S`B&Fw6$co%
zPWb%#VV-X(X65ifxuE~_bsf3jsq@ki^g(te4f)#$UsHZ4_0>+|v)sz!^a0e7Fqcme
z12L~0()>!5kIj=Te@Yq0pIjep^bq$LW#|cRo+?5R%;X#2<tkmAH$JgkZ^BY^CZjtd
z6>oiiHJPQLJ#%m%#&8=M;%ppmzHa65zI(fTeN2OnGug1#wjrp45EpQ13{prhb1~~a
zLq@&7d{S$VOZ-xaX79!TBUrH}*GD9i6)kdDICI8tfB}{ELGq7K7P^x$apm;2$PLbx
z&hHsu@s73y{QY#Y6mV2lM&&9n@%4@4$p+gWzJ~^0dRZg#(GCZ9>CPFcQgbdYdLQ@U
z^2m)e3z?$IBVmLyCZh<jps_%(@6e*ho~-@@#;B8a9XE)~6IYkninX@jh?Zl)X1A|4
zJddsX&64pM9~Tc}-(UXh>Gmy%^!^{~JTpltr-M<@rAxQrvxn;Fvsp^gFX&$7kIwiN
z()$#wKU6UtKE)DV9AO0<K_R-;1TY1KbbX_<WOe6$15HNIK+K3lw2st|?roFp<#hDe
zZ`3!Iuy~Y7eO%0ab~$xD4vTpI2+r7$(Tbc7yB+_m2%(kdJrrz`dD+$ca&T>S^1$GW
ztRDg2_NPfablL9R?QRxz)skOSDOH8oz_J<eZcJ-)Nmll*T4L?<h;Btqde6sNOej0I
z8kshoTALtu`(jp{=Wjiu+bKe?cCoKr%<RY!b~n5BY`o4`{_JHfbRnG?18az|JKz`n
z>}lAb=aE`lZP<_Lj>Ne2>ox4gbk65#TX4FV`|!?X@&FCpF)Q;f%`j(z!a9%zU4>ts
z2;Q~%!vA_nV3FLju*AwG12fnQFj;1Ixl{wSZf96xoJDWYmBGuJZ7OE|gBh2NLYw)c
zTmo1g$HC2iZH;zc8}j9hEE5d;8r+KLg>{G4j<&#p3SMVCL`JH{y!U?LayeMc`0n>J
zl=1{`mwBnLoUa0v;f3_+D<xw0X&Vds$oU1@G8fc;MzWO-3p?I85pX%xYgyEjFv76F
zh-}CHv`<orR5C+BnA;dS3mQ%r3@gX`O5f*58B?N_bP3u`+n=PKXH!P5eOT{e&1l!D
znjP)kf>bCqo4<@GhKO~RqYDCx4ZKgtWrsA$%vAfT;Frg<8m3K)qn&Hq@9cdWTt%`(
z9NYLfYtn<xGw5v_VA!$lD7SZ(7d+lx*C;3>!Qu>Ojz3hg)_;BbSna_MiYDs5LTA%a
zmXeIavz_5#yTemF_FuAtBK$3#>m;KTLeCsN(U`NWcq>+W0rlO%z90p3>~X!iGr6eJ
z{YF#9cRDL9P`}h$$Fg7T<31qwMepB78^L^g$Eeyip8f8rPohKSa7~}s?z&TJDBoSP
zh?}fVX&)&8WO;=;jI?r^-I^3imrwfibyplpT<Q6*)MxuGVe5sdRW=IKw*@z~{0bhe
zpXkhrS$rr<#xolB*4Mv_L<m78SZvv@hN?TG`TbXap4uNG`zBx0*;whWl&F=Z-6J3z
zd3mH^oPNCu8y#oX!GTj(DzG=)H5I%~vCpu(a-Gf<%76SB=6Gpi@k{n3G5ba?r@R%h
zOQA(vkyigJQ^9=sugn~<M7WZRUYf5G;eZa@%y-e~$!{l|()MSTL3zL8)9&{R7`)#M
z*}J~Cli7-V5;9BIs;hx6S*$|3bf?zLKH5QVu^*m#yALCa(T<}d>ULpLoacr1c5s5p
zv$4vbv2+W~?ui@tmZVO_m8-!z{DyZGPLR(8_WONUS4#zJuEBmDF_I{ce--Qf_3EDd
zm6YFOzSbAh??HQ>e46I}-IBSXficsgmKFB5%RRg3y1U%?4X_=L3s%&hL1*qTCkn6w
z<pnBt$@DR#iOl};HRjfL3cXj3MJbwD<SHt>8=n?}!oXn?T37ZD)h|k6-YS?SCMUfc
zTDu!xQWpTjyuhSk!p-gnygZWnINExi7R2$xprCR%&f4#m1IyPlRh*>6jp|#I6b@^c
zUH%gH?E53<1c%J{>u(CbegD#WY2MEY;e&w}lI2AvcDxo{uw1|P9@IH}VSX(xL1*vk
zOg!)lFT;AN{eBBElPd!>DL#)w3i}8Sr!03Po=gZui7fw8qfhW{h6ltwT27vvE60AC
zP7b+x`D^RLQ;6R>B_TUWN3bTwz`X{wBX!%L=KBftaiRqRaywsdcg?TvQ)U<fdi6=H
zh|W3)+>QP?fm}BNzEy6V^?k?0)}+HA=nk1zMJ>Xal^<I#vpfJWWqGTde0CK_<pF^^
z!c*rzo($#iQ=`Le`>jD;7Ij_k-1gKQhf$hk7MEnRj1k3WTII6(8Iy5Qwr3VsyMv01
zR-Km)H6+HoIE5fr1YOw^wh)n<RJ_2$1mM`uLGD{zFRw!L70vxeuh^Rl8XCM`nj87m
zoBpe5H)2QkP^;6VdFCfS<YM27>xYJtqF#BxWxnv<?r?pU$Ob}#Y(`G3(aNW#Ba?W+
z;G;<jxD1iuA@@cLeaFv{l6s<yGy?0|_zzUGlJoyD`@Jo8+n-vhPZ6CQI@kO2Ev;4W
zdwMqc?pJ%~y6^KTsRxTMzGp9A`C9Sf|J{54m3PG_n@>98f2}CVt9G~h*T2bq-&8#X
z*8}U)%s+QoZ8}qg=H%rm)p+b%Gfi$K*J=*WhW}?YzFR%Gth(iU?IF>JbH%nzJLg$H
zO<3sd^k*x6&uvK*PGf(Wz^7jRr_QT2w|4FXhTm~v2Tyn%lGt|lU5*5>tTFT5arf6@
z=4+c94UQb1eWHBn^Z6X{OxH@QD=$7?ARo@S{#@WYj!&|;L%&wuNH$tu&3Rt1W%Y#R
zQg6Jcz2=;}_}<;cXS5D|Q2S^Xe|hPh;wXD_rf`ur$5lN4r08aze-2z2wV7+1zUSw8
z!1`p;v-bZ=Jgp!74z<7UZFzFBoI^h0yGqQ#)fdfpbyRBt{)HcU`%0QqSiEk6!Mmr=
zgXN;H@Dy+;t}GFI=-sr2<qp>&i$(qlJ>oU7tLs;DglF#SHCTA1z3574n}y^)t{;&{
z-`;iGQB_oZ$meUkLX2Rg{KLu}tUnkNdoD3YKegjq<GV5+R64zGEN8G5Zfp2yrzmLZ
z{NvHf=Li0)9=WJqb;a0O;(Dp{p&z@_nd%jCUS~NydUg1KaHDNQ@k;%|8|Moz^y>>A
z+iKe?VEp!P4dX-a2HA%9lk#`3egiJd-Wu)_7i<faOxv&8BU&T#LHN+?16hZ5KB<`W
zP`a#Ju2_0@*8Q6wO%I7{ms&6U`q{i?VJ>87&FJmXJJGjQIS;i4oaTJqRxJI_JMz&&
zzilsm-1ocqpKtxnc!vItTlHbgZ$ZNplYrydk&~7jVwQf_z2xC1rb(X<`|*BH4X~b^
z<GsCbO24Y@&**mj?YH(xzBif%w!qSMhRVW1&%?D7O=^~Ttb1g;^5H(7B*nxZGr#?O
z*P@pST>bK%-Spe)Tu#sXeHjaC!3$qZlGHd(-aYL(i3>PrK4HniuR4b;lca8JdbqD-
zv*ia}C;M{69!Z{uHrsg<&%B@Qajx{TNpWkQ{(IFgZ>{!Jg@YWb^7TW`Bo+5PG4391
z9zn}F52@50t`dF!ti*o(Qr+{J%Vw{fCZP6kHtV;1-~b#*eddW$?+<wla<@{J1D69;
z3h!B+nfA#9?3^W+j&R<yZqR>lBx{n}{c{Jr8`m?03s2e!OfJ)EWh(>~1$Dscz#ax0
jHp-=L2&}aF&wN?cZu6IlJ-|y885lfW{an^LB{Ts5%+8l)

diff --git a/reference/plot-5.png b/reference/plot-5.png
index de5116d7786d042fa0cde0f705cfb3358a976aee..801d9b876aba8f6dbb4fbe2ceab733de10dee525 100644
GIT binary patch
delta 32920
zcmZ6y2UJtr^9CAhSivhI7K();T|q@^;Hn@Ur4tAuU7FHKAO|Z7DiQ=lnuXp%k8~6P
z>C#IGy(WavlF$Nghx_}l_traWA#&Q@XXcx4zL|Z_Xz8BS(mfyi_jjK@iT<4*q`ikN
zs<3fSA=@!S*D!&1R!xe-w)~J+<ePox*diiR_BnGQESnBLO1%|ptDM}5Jv1q-4Iv6I
z?9bl|#nh7JgaUF@+a^Z0W#dVAvA(4e+J4zC)mpb&vLxVLQ$BC^!oU`+HTkfV@xz;R
zNAZ5=J*o8@UYb<<f+BINPJuYPpfvsp_otIbESqi)UeGPk{%pr}v6K>WA~yI7p;nYq
zE=Axr+8g63!1#c9rj3%DqxZLT=f`EL=!HXWGRmKt4UOgds5LKq+P%#@@*4^Yg!n{d
zPd0~PRj`M)d7RsA`<=4~)03;qCj}fhhtC<hYAY!FH!sU3%-Qn7VEbxtnK0OwZ~mGY
zB8plcMV!AR>)St*?7?PFT$@g_6XwvePiU6GkMW%-bwlJg7YOho^%)9nZg9Kix5uD)
z`s4BL=U0xp<1x7955vt0Hh;SF$$d*IB(4%Zth%BEIdTyy5D*lcLDodO#tJ;Ze7t7e
zX(;pb&x9R@H~pJ(_}FW0<bKhn%-2oJvQ{1MpTJ-bZUyteU`8j)?cJ~Ym2uyU%EcmM
z@aDv>v0j2~LV@oO39oWDbXqN$dkRv>eH36?b0;&g%qGLBVc|+CyKHl)p)~p2$&p?Q
zY?)#<qfC2v0E>(~^XtOE2SIM*Oh%pLqJ;@IC{=v$DhCYaD%Qlq@{s0+xkJHsTcLN@
zGkfLuh(g7>{%&E-8*lKTB3=FVv4p%MZ<@sVXYIV?W2g-`V(i)d??MJ<5ckq~u7+Ie
zN+Bj<3d*Tg?ffi=6S$n=Gfyq#?NkwG2;4c)c$8u=FqvY8xqCJsiso2$k#7P9t4Xbp
z-T=d5W5wsfWt1DwHtyrj{ji#zEF{{Q7_qp6it#D;PjA+gYObPvsQUJpz&X>nn!U2}
z_((AO)G1Z!^o5|AJTdI$S69M`MpbX4Vp{fN>oWt<I_g5qTAP93{uOJ!=<;{@@se|F
zF;h@vq|cE~o*d7rlnv3+@C3v^@|2LLTF{>4N7#i!k;tPmY{bZjqk3#sk)ub4*e*p@
z9t%3<73$XhvAVpwar-gpY255T;p+nejIu}rj>l{fg+DoNvF%~}DLinT!p0^Qx%$ui
z9!0^(@e?&{<&odHM4)QT>8)b`_MS!>o$j&LqZ&<tYF=H)X?kyv8ha^Y)-{ERXZNu8
zdftMjc?OvNzT*<BASFr-{TRV?LwDIzlEQEOX(m-F_S%&8JinS?b9DaK?9X$)h_36=
zeS2bU4t$e&sv}Z-lA02r6IxKYaWi`4&V{KMonx^!oZqlgx=_KEP92(XO3+;8LdjOf
zhi2M1#cZP^!({cRJ}0ueq1t-Fb}G@Je(F_<3~e-Le$yf|UfEx$%SAy<_V+QE|G%6_
z*q^r{_Ek>Ok*gxH!&>gAF2@JozGEOn-!d%i-D7i8h^|y_=`z-~fmKSebh+nC$5V7;
zilO0S7jPHyXP}0E|A-s@a(BrMQ-+JpRrZgFXlGLMFywx(H_7^j#wB|OGW{Q<Xk(}3
z8WO1=&!FWZuG=*v*`a1$8Rznts1_HtufoKt<YgxOMv~WjuETy@tT_T36E_Ha{mex$
zYNMsW?cu>qLB8wu?&VgOSMGR!P5$Z0FV~S+V$dlmW4#99n}<Rl`DN=!fAw$+7|EH8
ztcX9=+;`S-Nr@ugsHK39Of_71nZITFXZ>RTV9buDzw*T{*(r?X%E_doGgh&~#-;qE
zL9=P!LGwYc``@=UNfR{X9I-i^3|<VuwK*i&wV5U16Rs@17+Y1`^5S;y3;j4dl&`hq
zOmKu9YCi{rU4_SvT_|ZBk_w8VC(dRX&IgpVxFuWJxm#~*c5=%6{4gj=3iy&tup6$W
zU~k+->D@=fT;mi~u`%V=G}YR;5{(HP#}`?U6Ba(`D-gu?6NZg^oKK1SeQQ}fa7PB;
z1D7Zae|FbTQOK+9hjl9BQAEq>!rDp9d_N=HD$@<pH#*<-yy*6J_>RtztepJrk8`DB
znjTHO%^vmJRPu*<1r3W^GDgDi`b4e<!8YTqn;S{D28CoA)Jw+I7QRfOiWTWwzz5t<
zljym(By_9$cVvrZuD|NCy7kU}&$5^O`#B#OWy6F$lXI4rH0P{vf?A9X-NP8-_|)f=
zY<e*?=>~;7$(gR#uPn67MO$OfWTDfNJ?wI2+Yf)$OOb&euouTj9smI%<NItJpM4;f
zt&dV7Lq1hawtME#D13cgeVT5CFM<mt=hv&WL~q`97Q(ja{=ug;Z`_L|BnUAsoZb2;
z9{a%3X!>8pj?#R4mtsav$#cW*^>d}+kT0Ygx*9H$O`TG%)N13}bp7acMZ%Siz;V(e
zwlSsDxD<}L17euwfC7z&ABM|(D$A4m_&T!<myx~$*ah8t?eLr3U1E%I88#dAXHCss
zms7t=4iJ&}GTy2CoaC`ni@uQyzUqR?l!9&dSlDeDJ;H1Wr7@lt0ex#$m{Pud3o>(i
zH^s=WP|2_C6{inA5)ue0jL6}TyO|xCTX6cDnvB~ApSEJfyTf#HcE3W<zhZsj<dSul
zNQtcP{>O7m!z-E}y*>b^SY>*B3_1aC7BEZ>Fh}oP8Jjw*xeQgFm;|==C63crw)Pp{
zempp$RwCbX{1}FmAS9bwiYS8+CpwcAZ$G!Ihle+9y*|QXqQ|8z`v+2k*ZmJ2P`BS>
zPdQ0d^La7;Gg!Zf!Q+k|)VD)53eg42gi0#RQbSzTB$75{Y&k6E2!a^|&3t>@P1hJi
zaGytMKxq4UD3(Hdev(sy?{jj+whRxW^<jWr<7%1H_qCfLI7L?tYX}jv5Ix6+<wJKz
zoT*+edvOgp(v=}o`72u!%Mmqq^;FdA>svv;e2a2a+$-N;^XqY2>cR#lRt_WTcEimW
zth)|&A=j)dL8emq+~q2La^q@u>E9AeZqEBGyFFRGm_pRn{98%*b<KcFw$0OxM>XzU
zH)$uyL<&azjuxT+E`VarMXgq~T#d{Y`BJPmUf_+tK1(m_xf3dY3lZHe4g0f?Vf{X3
zr7Q9}k@EKP_5ec4OC{q(mcEtdrIYY#;RE>zC0Bb&M7)ArnHQwUAN_*mvP<e8CfH&e
z(K)J1K{MVw^|uKM=j`asqB7JN0sDVOmG3Pni1ZddcZ)^TY}7&h3UV)u+{JwC2S2B`
z@QF9pAIP<BuD<hmJx%;A43|V-falwbq+FhmsDleQHt!3|xl#38$73M9g?LWS$E!>o
zyJd~rSBi<;vc|8KIyA0UMORR$=ldSH_gP`cL{a*NLu`AI59Pxp*`@J18ThCDJxV5R
z^~aa@08exF#z2Ys_j5iyi+S)MqD9i}O{Ta?MDr4dv)tu?(cH3e9zp^iiJm=f-EzXb
zvMFA8+Vk+@0ZDwuI9qA249~2#?T#Ytmp50$Ap_yfi-~Fe=P7>6ZO!O1v&L#2{@gV*
z@|3K_j@lB#7EiBGmR)-C_}{?{PmWB*xl`JOy24kecaA^^z9fTX=CZ2mdeR9bzN%k0
zdL?HH3eWLM(Jr;+YbwUY_-Ahp^#1njA|lhu{3FuV9K{m|dQ8`u*3dp#bC@_{i662$
z5p7Pm**-?|E+LYHVzb#3>lFGBYW#SO0$T|umQU0NSNgT)<(+gdPp?kgf0``kI!6f?
zuzLn+y4OAuZ1N7q8!RP0>k~8|HeV7D8dBm%P!m5i=^E)y_vbS(Bcfcco*NxGN_#3W
z?|05q5@Oy|d)l`t-jh#4XVxuCcO=$|rT||&vts!|yKb(mj(S}?l~D}Xt^7&3Nn|{(
z{8{&=g;4d4g9zig@IpJv1=#%4tNv`z`M}<oh%XZtP)lE0%<SHjh;FBJ&Cxr`KW-h#
zzD^&NiM@I!G9M-D9kDKR;?<WaOxl|N6KeBO!e6b<EYIrwZJKY!@pXI=dGohprFC3R
zZs&OUT~?|8M#T1ealiCZ^c52!Y`^~#c*YZzt$CLqLAPwTH7^a_IsX@t>X*sW3%#%)
z+oSmJL}uH*Gj!gGpVUODO>3`~dM18)<S;LP)xhnTah|5b+Ecr!!9H{Y{qu-h)RqU%
z(5hlEWld#yW8zs7C0k0??p2iVz@$lv44Z~CHD5nL*leYcqEVmNhq4@@to-9Gp)O)m
za=kZ!v#^-4FZR;Ic2m*yl9P>F51>x<X(qxMVWQueBhxUy|3bHHdSER5Ye~!6FZ~05
znm4ur)^*k-<xJ5BG*;}~33{%P67V`wq)=BgK6_1hn`+=NSUzF*%xfolrPQ%p2PTVX
zM5ifQUD6!06xm;j@f!CgVjEqeuh5GptE!1Vtg=cE-KWy>XTEn`Zs$awgJx-{!!?+<
zFlpF}_QSr#o>t<#ji>^P)Vf4{Z?nYy=GKS%Zy~)J(eHF{yrOH9+BVUOY7ROTd+nc9
zlI`3nUWaQmQ)lXr$37U=A1v(a7~yLwsNV?`y)%rF@ba%TS~?#WLueT>Sxz)^L}xd6
z*tW-r@n3n2n%BDPvR~Ta52WMnlRQ*C=@)SZ?hzedEg@pdM_`Bsd{D(I)F)Cli?dqx
zsr1;n2T6n~`_}XKR-xb}+a)iQa-<o4;us}i+Z@NFPibLWZdsEMI)U`i)OcD}7MVJO
zju_vxiD}uy8!r%C`tHnm%*@IB%wF>3%wBk{yn|gVD~23|D-s_;gvfkh|K$^pPp5Pz
z{gWo}kDv$JFT1{Zo8s&L)Oob(@yc0ZQ=8+V<w=#fzb9fJxF3m~$f#^F8!ERW!PV|J
z2m2hmsNv9A^j#?E{9K?A9P)UljKDur%ttAR9Qe(Qj&mv>Z_vN(kp4+creP&5KTz!1
z`3A_0Vs$a=-NL0p0Vttush9JdE_QOv`<>uRFILKuB$A_bkcBVuaY78H<a)VDvr(l;
z<JKny1Bcmf@TPsik?(>8UN9@0x;bCM=oj|+vz-qNvu!VqT1R9K)gz0duRk`csvYwo
zpTg5PnkC5(+uz7qZE~BG(%<wZ8Z9G(qnqte+ryVu?qrrgScZbu)-z{XvRT5nT+Ge=
z3(2b}k~}SW`Gk(xhcij~GEZN4D_@*D5zAOU5Q~@yv)`k{7nwbBiM|CnM|)Y!ukQ_+
zG7n|Xq(qka6f&)hQ)A4yW2GGfLaE|p1?POa-PC&ZNm^kq!VcX^Ez#P>_hm>$7ur>2
zzmGYzT!MuL1bQ)fsN+=$>Q+qGw5WP#f1a=&ZI^&w<lgOdr7<78o8QCz>lH&X^QS6&
zZ1+PIMO-GFs+;Q<vP-7@V+9nGCmUAvVv+7x#*#yr9Y~LHEsOHK;i`?o2gHy36Ps9Q
zCDa-bd-B>+h-fosNi~;;2)^)@>SJ<3qF$-HmObQd16fRm9M?RXwzyXd10pJAaj^V$
z?~l0wPk2LfN$Iq!2n==}rg>Y<2zfEhMNl-J%w65*fyqV41hk&0uZkXG;744Fb$Za{
z$+rZL#W;tn|2g`-&~Ta;rFE9}*tQzE;x!D*OS-_4<(n;ZKaqywUt`x?@=hiNZJ<M-
zDZR>@*~52_8Gdw=)SoBLnx3BO?MeLk6+Jano&&UM0CtYM=l1lt3StSVfmcjsUZwMz
zP^WZeQzw-(hETlb93@Q3%#D%smg5K6|E!kD>pR3(9<i(M+j9A}(YsIT%~N?f`Op#U
zPI0Y*k6qOdH*D-tq5O(VAIoOUqjg51E@ZCHW);(9)ebnRkUe<IH7hD<gyNGKX6J4g
zWHtSx8tKsg{H)AVQJ2k}rGH1XzgJ;OR?_=d@KWT-yCm+-&9E1JIm|+_F`qOyzrri@
zK-II(o%S@O>vUOkfJgSQE!h<p($m}jJQpq&(7qF9+l+3iBx*WTwj>*3(GcgTPCkm0
z@iD-<{7<DL{QzM^&(_O9Q{e`kaU#U0QxfPsuTN=}4e$_~W5!<L6$ulO#bNHsR<%En
zOJ2-rMww7#pM=f4OJe}UUTV^8R|#R)e`bZYT+w6NsZi6JhKiS8gewRY>^yF`?E<^M
zTOARkx(d~0c>H6P`k*q}iM%}$0%@^n+#lqd?Gw~P8LnEqP!I0Ql)4@2v`LhAWJ`80
zwzO5nTw22A?SP{l>Q2tRz!N8$@4b2RFuU=yl)-?Gze#Vl>VOs{=v<(}HyPzu$f!e2
z=2QybA+mWT7ZKGgRImp*(pWkZe%%525cm81ggV0dLv=JduO70X9-$1Z)VG&t{VV~Y
zfjz^1AKUhEE}&9J__PfzG3<{{pm2&@Y)lfRNCoZg+!k}lwetHKkGE(QbOPVuMj!^B
zMEjnVmO0vMaKfFdR1<w*X&x^2qNCTSFL5GJ;T_&>#?c8TZeZM7`M1Enen!c((^RH%
zXJ~=+K}VJF2$IwHF-waY-<-<&&_sJ=?dr7!S_r+A?bx#n`L|?2IDk41@eEn`ai)sH
zGiO>j8%2#SwVj5`%yjOxA8gO!4ji&1vp*By9$w#Dqr%D~cJ<bFQi~jstLgWXJP4PE
z&t2w|6UVsZSQ*!etbO`mR~mOFf2MX)X`Ar3e+81L^RVX-b1G#tzI}4j*kPz|r|E42
z;kLuC;@c+e2h+}5$;yYDDryTGbQp0v^8UVJF6)<e_St!>-XEE7l&|F^zMj*$yb{;H
zf{-Hf@}Tv2$$!PD*lSINX13C)gK~OY==A{;(SCZFu8c!az_v_dfrKXKWqu`}v(mC>
z-~@R{gdX_0b~zPW|9g8|`<IN%jv&XSF+b1J()^hT%f7?YJ^d?(WZZ(e$d)Rm=y1l_
zq8xcydWH6M{A};@R+10{f6`vCDZ)Eop2{Ga95s(_*%!yO+cUvu|7;vNB!I56Y7CkC
zafz;^g~ne{Yoh%+`|YdILM4%5-22<E*s2$by}*?Y*BNd|(APNKr-{iO*+SyiDjhNo
zZYumK{u~E*rE<JaL2YkE$nQih5sv*ZnCcm_Xjkodu`cwtI67}gPMMDC@_N65GFRu0
zI2{!u7@%J=RjE2%@`$YMFCLqCsJGyEB9F<Sucxo6Yr@6Yf%%+7t?TwxYZmq*%@fdz
zewnl<*ASvr`oF~R=y_U_Aon1$J@Sy&9qy#dwVt=<X(wWl7QJS#jO|cy?gddX636{B
z+}7X8)ZQ}NEHjv9R)}4A7sKeL1c|=SG3>HWxDv=Op62m+PVeKH$c3A+w!}e!9=m!|
z=7ngF>KEP#+#@lj;#EQBGWh&N;kjt&#WhA<Z}p!P6_18qd6_X9m)q2_2%`~`KHgYH
zL3wh-;O2^#hlEvTO-eCxJjNN$s_=mjzMJakVwB2=@M(U0J9UP%W2a(xk0O69m<N#4
zUBqYElRI0cF|iUIq~hy0H3NFLHHXweCNVs&jYlk9ea&_3UCxTm=K#VxD10jaj&Q%l
zbvi1#W$}1BsG$ahn^k`fe%_zo$4@ut+v_@I7Ma2)wIV+!+U3=%BJ5h#vOa$cQI^Wz
zv~HFWPBSVvIpUY~aXasXyv)z8^cGIx-cOUp-0;+IgPYNeFH@(qP&(7h%-pqgk&A^p
zIDC}yHO&o$P|mClrPhKjh<kF4vvb&r8_o0);{0H-;ncnoc#BdBCpYhFwqCj?*SJH|
z`as5??~}yjn#)w;Qo1)4XNx}*d}2mW*3z;+Esjvu_ZsNN2DudT%?)Qn&W@QO-IeRu
zU@!yq!N$PGuvGt}UBmdFs6z*aCAd!Cjh?X(O8QdU924_;yiigRQpU`DxN$eQPrsCA
zU-jTl)Ygs4R2l1|-(;+l=d(w(3L37}Ri`V`Rx5<=SaCTf<BqZo^VX5AzfDd&85E#j
zJ-8#qVEz+z=)K^IPN?^rNZFthXr@8P7BGr+<14#7h|LcrcuxGUnB0<?r-k&u>A!RC
zS;}xYfou{ZB}H3<Vp=#a^^VLf7bav3$Am>DM~oBtJ3%hV|3NgH3E|*q&ViX4Wxz$`
z6elC3x$gQArioh0Z=WZXOrIx^B`4o;XBVq9P?SD8yFE8-pTBLh&;OjrM=E{z<}qc@
z;1>T1Jwmt8-l<-1(e~}rUneVz$^E@c8;KkLnf-b<l(_lpQCg~S<Aheqa7^7H=ZXXV
zOe9M=M?SN{C!<@jb3tw+0`8lNe{Pgp`K{+LCipw!GaeLhb0ST3^QAy%w(0KeRJpk8
z&F=Xvr(>wiTb(0+E+q6lcwiNK;ZSr_;2Y$L=00Z=ADlH(Q1(fCm`o)@%g({A+Ik65
z5_t$ZF{+Cnx+{^CPEY*Nyzr}EA**->(-=Lct6(@CG;pyH+GtoVsXAIpY#Px?WsLqC
z%dFOPI1%4m>=z!>bbQoJ*n{l=xj9k!&j;x)^x~6PX1-Nw-_)MiH#+zC^S+Ncqi5?Q
zK`oZCp4aP2th{`wL_x4T$U-J5E1e#HMf9E`6x)~-UEf4Fkwxy3QGU<0Lci}mj?ya}
zLK97JQwnalm@-~*uPw^Q*gsQu>KcNJP#N;fFOgab1ZR28)Q0Y9vjm5xz*7mr*E|Q+
zr3nl5BzbyvttUEIyyucR&b9o#j9RFW%%*dk&J~|Loxg2i-h{Jc_SpUWD={rI7sXbf
zffC_y7vV0&$Jsj69k+ZpGoh{(swZXO`^_kwef~s#cei`SvBRhp`!?!G(@@IaejDbM
zCgr=SGn0a`?Otzm^f#Pk8ajST(Ijy&kFo~nFFm)oWyxXZo~=5TCg>HnMU&a|KuC;u
z*gY$(jx2qpg?IOz`=5iY@yU=^>qUECYwcmW-v>86?opZ{RVlVSF!6(Y-@?Z^rz_T}
zt={G8^i13AVdO$C@V2T@^a9UunCixGjC^0&JGDbF)lb8b&S>m@R?OvbvUFZ7<6Nv4
z<->CBLD+-p$9c1rpskcQavmJ9w=HVPX^Bl7It~k#vwIc#m@BRytS!Jc5ALY|zkt%5
zo6m~)NibGDVLoNWHV=L4m}SGB{phE>dlY7{P}SPhG-xy&mEPjnRKBNQ`ud0N%nETZ
zG}wYY@uKu#{Q+?0f2YHO;h;MB@Bd%07bCmH1)+oH<-U~Mmd3__CyiJkV+xKj+c8mb
zS5QV}DIzHeUiM_7-jBXO+<d$L$SEVg=JGpXr{tz*X2Ow`D4(j4jhFYr3p%TUHpMSR
z{8>|^R;;{U&OHM2pX%ngyX3WaPlM0r&UnF<wx#G00n88M5DvZrz1ij~9r5w;JS!_J
zJ3Y{kCllq0J|yp@1i#^c-}Ud#c|DJQs$5!Z&MpwRIC2r(_1<^&>qlfqmhb;V-e+{p
z-R<e*j*>3oUMWT2ZGUjVonW4W9iOg(j$4t_J%*v4{8UdZhoWmpkh!z7voH7&T)Jmz
zY3at?pt%jB$T`}I-m{Cia%xriJHzeT5Qr1X{qf_6MUqO8)FPtc3hACncG;_lzA-Rg
zUBMG)c1E9bmK?rReZ_dPjO}hXLh*cEyGw_YH_jywJXPEcw<hPoqPY*%+cX>lLmzOg
zCKlK?vk3_amDbhOUGK$NL$lfDg>9u1?-kuW2qhGQYWw-{iug3*phQ*<&CnP6<Yr?g
z_Anth^mmTrK~!RDPWlfgao9(ZJr%02UmZQ;St_W!{O3|+La%dQF7cz&wZR6I>u|;T
zw}?v`!)CyB9^X5hdinf@o)G*-f?`&A*1g)~9c$Uxc^B*E2MXJ_mz$-t_Gh?!KpCln
zS(z9JWmS-|=@<KssiG>1_Ub{uZZ`NW46TP6XPFd--@d50vls$bDtkQktJ!y9Xuxl#
zEg`6yNhfI6zdhr{l%I)T!Ik-tb7_$~+o3Dhg1vtf?^kQ@D&pqm0nS*y4V=-q>kKfn
zf7}L(*7Jfk>4f;+=C(Ge;vH)<Gc%dTqct9BI<cNHf>F`Y(~#2EVy#<3=gyQgbVfvT
zFFS{l;@zO<z27AyEQ)4h!Sl}02_8G&l*nu_q`w>c&_4MFXniWC=ai&Hw-xpkrvRoQ
z{oZ$nVmE>>8i_<+la`hqEOPEjsNP{xO{+IZSsu{#`b_&;hCn<3Oz(D4j%wM^;Iq6o
zFzG0s8Au-%i!~hPhHZUT^mBiw_WVGCp^?$abdfCG)vcBGq%nLx-woIP0IYY9v1#R$
zNDzQ<$*EJPa(De~jai>k!%hn$JQhcPT63O}Np_;o4>)@C=h^&Q`|6nAx~R01GVII6
z(+b|R9!NB$SP%Q}oTQ<FL0-`0p9tK>p})`pH}>QQGi!~JB688m+1c5hU<x^l2zFSy
zPH0$Ip&p#Mx>B{Xy}4mCHIQpnE+k#C_=_FWSysH?CLzsP90q$c`!h_7T(!BN_@pgf
z-cib;C>?P(d_iAjeUiG>ZBH!o39t^L8K<%CEuQ;hobZJZoshuiV-Xz_!$X1oey!c?
zaJoj`-lVoWaU#-CI&n90eDUr?sdVe=?K}{uGg&4%3d?_Qaq<QBS^4oTd^FVrt^HQH
zk)bL8nm+JDNs72@XGtUVBt0UQ(R`k>bE-HrM@ypLote63uBs5A%v|kiWf4S*c8o7J
zxtdAw@FR%CD2@j2h1~JIl44>_n9--~7D@i&Z<yrX95m$azR<t5wbjAS=l$0$28nW>
zvGXbF7rA2wSO<ecR<^yRB=H<Mazp?=_0e!c%DSqeCQ&#6MCd|BT4I@Z$G1Dg+DAvG
zTVlPYl^H``|CZ4XX7L^Ld_Pj^<#-$Wt;5Op-o1N+eg4K!rqLPhH@EBC6BP041yf+~
z=E6385Gm+=-OTt2$*xr0EGA)On?CgF*ty}zxVX(g3}$;aRX2{KbiSZjZ#d89R_7ti
zR?#i`$|_b!JJWZrbLy9Z4@v1`WaP@j1O*>{1#)(wo_@T+<%E@@zC-R6(Fq*z%}WPy
zk^2JNIH5?l@IKpL$l=Zu?IU{1v~I6+*PIr^S!O?X!_{B^G$F2SyK=cnT>tg$ixV#0
z=}XC7OD>e^-W-H=%#W7#CuP;=u0FiIB5IhrdTdEi4G;L5+1QTJ>p8VgSs&pf?j*i-
znOw3*b>JJjv{()RtdJ>IK`uMRi!+nwGOid>^d&u&HqA&*7Hrqwa7+rKAJ=>Y7|n72
z{{1N#z4{K@TkF?LTn9XZ1$`EV0^ShHh~<7saL2azVUJ2$uV!rsXVOfXQZ;S1D@{NA
z5SQ>)$F>Eq?KXHGMIvUCl2HGiZ*yrT3EF-g1poN)qg}B(apM)YxQTaB8lfr(LNo1~
zqkF8u5QoBer1^K+KoWUmo#<K+?7bo;DH)|4?sV-l_d9jU&JK!yo`Cmgmm3N6QXRhm
zP*6r$X@`%{qfC)=vS3DW9Pq70${Vs-<VBrCrS-h(ttn~ze1HD;_r<QjvF}p#lQ7#X
zVkbS@e<U|wm~c1E9fD!SMMWE<k_oxaxO$qUt-5ICYOmbJ=mc#LU}exc@s|_31g3pS
zB*DA$0GSF>UE+5AIS-w;w=cejpB4^P|1O^)?4(&TM3qNk+W<i#an;)!_J^cra2Cax
zOhAT%(w0B+rqYry3WHUD^%d=UDvOG)EpL!<I}U%pKx#bmgT>@N#o!xmgWanjs(2qS
z{$yt)%6m<>ns6?7w{;<fe`uz;;<gsT&sXT2_^5|=idnd716kv$Hb_SRM`R5|?|0Ye
zB355gfBQ)}Xk+y|k2Er{NfXSN29gnfej493CxeEUM|gatFJJCo1r|_=GD}RF@P_bi
z5TV3UGqW@A>F0RJVRS1x{PcGxO-;IV^%mB5QPvo7d{xO!1EGc(i`MwI%t}m>sKsxc
ziWLsVR-F|#Um9X{pY4C`*!tP$wO}zoiF28ASZHWL!+SnLvPL*RUh97Gqn;Zq=4&46
z<ONxZEaW`Z6h&cn_jxEE;N%a4T3#ID+Wr(5SI+$Gk{j?Ctjg^5r4AGQR;F8dKsXr6
z_2pV^mIJ)-_Lcily(n}1{-aNO#Z9tj!9|E`uKjrglN<}5#sy;eraNGtZ)ap3j#hxs
z<oEdd<)=Q96zh&O1q9dxQb`)%szRZ;VKkNRQIaJ%+XoMs17sll$S--2H$BIHzj=<?
zm`e-GaYHYZ&g8muCO1B#5b%BQiMO%`N_(gPX{FyBT0fKP!v)~};3=7`B+01bvEI_Q
zu^Ag2A0Iy(W9ixW5<@yrLo7|Z(Gz^c;sA<wI5V2M{Jd!Io2|qN7BMHqU3a;mj|F-J
z%F)g=&bm>6z`OGd1u=+j)9pz~MQO&T!I|tFy#C*4FPW2(`k3`vPIyJ<=gFDEc9rri
zmZ|bUq>m7~*4#qog>^r#Zi*Rax}K5uoP4L~J3ouJZFrCQt`T1K6&NQA?=ga`2JX0(
zjdZk0O6w9Ogk7O)q$YRqTR>a9T+y15!py3?yvHoG=8k~;ey78!sGSo;6aGA5)Ncpm
zm6fEPEYp!iTAy{&-)d+lx0mcu&GaQYCI(ESG&ibuHmeoS$+=t38~OG(G;N>9`RZOL
zbqFn95+6T{_KM1sJh%ch8-UUE>d+w**g?+9+Ti1XOj;ila<}tq2!EMEPZ{8z0gp=&
zcje~#to<P7*QqbhIV`ROEKh1=nc|S+(G49M0+r@ifOwZ|1YWA0a^zn#T7QAP_T*ez
z62F9|N7eeYwZ0;yNFc}l<;#~srdB>ZO|oq_ZSRJil1yX*A-4h0GIq>GDQ}?A5&t0>
zm~s<(83(Qt7q<f0OB>L3QK?~?o@_-g!2@Y-Zk~b#EIzT8wL07JJx!#_1n3vfMxmJO
z($Qx}7P3Ho<PtZ{Rodl9km2*LoAY0H>9s8Mp6f)<Z!V07Nh8Z$`f@`K96fy{5D;lv
z^v91%j#SaH6nB_diTB`Bqi0b3gkyW6as?Q*FUPWUc{V}OuQy)7=TTCIVcMhFzh9f$
z+eZfTZ5#afQL|rneCGR~X#YhGHtB3uR5*8~#<Ljvk!IOyr*_h5;CTL-5jut7f!<nb
zfah1AO6DsFyM3{Ix-DT74`9w?jyrbx$^&B{>UM?!?CzbDgWQ$u))vPkJt~&!t)a=I
zWF<<PtB4IHTt^eHyy~UXKQ%MsfvoUf`ir8GUKYGlqkB*Wi~Xv>fV*Wu1}opJx5&0z
zdV;`#ps|-c9PiY>4*=Q6_hhM#2j?RWZ?WBP-=K{+-W%|_<XOo>Yd|9}Lij6G4+3EY
zJ<by>cItQ-8AHoKK=W!n6%`efaQODxj!-!h9>Lt>KKAQn738d+#t#C=E(>Iz86Zwj
zsmxzBxxfI5oQm^#XMlil`Ss$EZg$_f>yK6o1tqP@ie8SCIb6hfpFDZ;qwnlrn^%Im
z@i+6|bfI{V{$nrf*fzqX%ADTWLGB{fg%0m<UsYj7mUAV0-Jiuzuz+0gur74d+JEdN
zSx;du*)-D>x9x&UWjW`QO3=o7g8;OZ4rk8kH?ZfV)wKt#FEt3jivX{uJ?hDXD{syh
zCSoR!baD*%kh<rNZN@lg13rW1dUFWk*wvApZN_aJ0sH<+GY8}X*=dzgz9!lJ$^|Ij
zIVS!?eE&#wC5kq96DVa3p}9~X+L-;e;gu*>4nq_<U7Z1Z69_k6p9+Zo1($YHGn;W9
z44rfmryF$;4#UR2vP&O-W>xOXefNG(rm-O=--%T@fb#9_hZZxVm6>*&ivQxMEQ_?;
z)}fiCoj)MudigLGh*lxud5modF|lQuydIw4Yr4f*N)re<#j7GBGpFk?=RvmXrKp+T
z4{XU{W$mb<`&f6N0s?I7-wsrPnqdJvuJZuk9Xy(`P&t`h*k)l3DgokLw_-~LHei#2
zC#$TDK2P8Q;(ZKg0`~$4oG8{S<3cdWwKQ)K1+by?389sNivJuHWqkIXdZFKZ|Jcqj
z9L9ZLfubsA6F;%ZpjM|sJKH<at5hFy?-a|gOMtobHfFzvw_TQ$eE0VB)$t8}Wt1iQ
z=YF1SP77=J%oK}uA82m0dSGM;<o<BA!3*V5R%L<Ef{{ANFx7z!qF=G+R8udK^f4Fs
z<^Ofd!V50FgFb{`{r}}xV3bSMYvW<L+A&fTBcSA+L1h$<9;jMY(i^i@a8vuca?S0I
zXAw6#a~70Di+`W<98`06caIRH^ygoi`mHoDFi<^Q!1d<#w+3FX7N|E?*73bdS8D5O
zXJ==8m~_=znUSs7Kn%~xlWXABQA9XET7I;h)B!Cmtz~U3EzjK_ZEdfI;je>!!O(+1
ziY{z{6p=gEpFi;%=&bcGEE)EsCYY_3+?^qtYhLI8Rc|ho6Z~`hJ_QJoMyjE{L}j#!
ztbNl*3^W4mFu#IYwI`dR)qkS_=7zykO-KUnGp`cvfEg^Mba5n~sFxqk1%X}%%CjwB
zpsJ-YK%+oyps0&T26x7p{;d##W~N%><ao611MFhy!dk+2747=c?_Fw8wCA`v8ce|&
zfvwA>vhyE@f&Z6Zp#Gk0bFZ`{Z@piG)j%U{xRR-Q2@1M&)VZJl{<Ot*55(vUNF5Qp
zZgq)bC<NPirPa|pDU5|O-N7~OJPt!R^{}sQ_ZPM2)Egedfuo_^;*U04Z99`Szgv{J
z28=UCAZWIdF-&j(TxzY5Qx3AewTH7S@OfwcgW6n30}wx6+rjfWAQPqtF%T%sYRW`7
z9OF(Xag){q*!uZV)bKL|9=Ji$x@v3f>#Y#}^+6Y$?)GYzev%})LINmX+;#V%A8_r?
zfSCjaE-SZ;6v?9D9IJA=dsc%gtD5@6;&g93pX&6n0H?%9y$5e>G^QnOLZRZChtv4-
z?3Q_<<wlX6!0vk*x{06^rACRH@+Z0`08}JfFZfahr`A%Wi7TE!W|(LW{&@0zg72R<
z&q{7ZQ>%BXK6e!yJ9on<D*4&7XRVkO>8yg<K>_ZUfK;Ru6^Ah5HuEFvD>HqkRRY(J
z_3luCJ?=<Xu6Ak!Z9uD<gXIIcO5t5inNAc`5N%fW>53?EY6pGiwA<5|m7?2-u@@x|
z^*1dLY&B=tkVY23UWAL9)*h0beF(<cZ+~K*J=<f=b*KoC@*I$xdTXRC+~>QWCq%-h
zTjO>*OTA`d()1DrXYw2PH<^FG-tzFZV07ujb3`=*0s;^aQzvgajh__?O`ryL0c_u(
zy&Uw!fQ2Fqb0de`hZ|AQECkvendLRpb_v|H&ud|*l<U3x^yzk<bj)%A@qJMUVD+!>
z1o;9cBd8Ty%gr7lh#8RBofnGTF*kdT1s4LtNXE%|s4PMPxrZe6vw}1=PE&$~BB2?7
z%}n5?Q<|Z-FQVUue+Ht`2=t36piKCqAMHL|5m0=8ibAUhY`NNy9Z1f>uLAbvwZ4CU
zxgrMPQTr%F1vD^jV5{zyxRSavLNxCeI<!{ll_<>yDBK-*^Xor1T<oG9Du7tZSRZKG
z3!uDQS4PtVznT|94kO_#wrZ9tcXa}J8K-;w&Am?zO;x!8E7J%;=f3Ei4V%GigXNai
zR!K1l37vBGyJl`ry8;H^mt2$|3{|GMxb25I?(@Irti1G_OBUpaawf=5*=N-;fN9Er
z5aw-A^z{05NF@8yHD|0zwpoYRT|R#FeEv=`&?e&T`fz6VR;**9{8~G-y&~7x8z<|i
zcS}<N@HEc>QRxKKQGO-=M)>wpc;o?9lYQUi-_J)rP(8EPr_U-fQ1vPoXwSeEiOcbA
z&3%?$t(`zPr$yGNK4S}tyrpIXF((9eW~oI$a#!}LVNvvX`7RIhx!&)feOL~f5uqZ2
zb8kcvf8+yoI@202UzmDb&k&T4^muu%!yZ4zUU7u*dj7GdtkYO6NKkzFLX_gZvmn@(
z`mQ!cOPJp;23gY)Q0sSh6B85XaDL?~)5DmRM*{^@c<kz+gA0zfwop!`lb-y|LEyQu
zt3WFzVwxrKMI%MdhVKR4`tvR@Ks`7A7zloqQJ<X@abqIvCK=RZsU6=nLOMC#@+tam
zERNOA7~_!26lt1G@m}nX*H(YNZCsJZ$aYhld)Zv%n}3}lIih!01gp?>reDJlE|g=j
z*sV+9O3R7R&gRHFK-&d?!V8e7xs-ZPQ&hWvgu5TOV4FT2=ds<CD4e}glvI$gk0@~D
zltel|=t*c?j93E2Bx-Lp;p8cJ!wVif(HDVmQOvO&bP3v87$LZq=TI~Rg1k+T$YDAJ
z1f$9WnnFC#C+TOeKg!+#C8qu?V0%;iGLu&vgZHt%V)y4m9e4mx9VDncs0lX4kRpIK
zM<teg&-s$Gh&46a+^brr!HGP-@7*~$DONqz!s^+aRYrLi0$OWgw0sABEp!UdtkqVa
z7qi)iMCXiws<-ebR8{fg$4wU?bFGBtfJH4&7A?$m+k!q<p`6>G5tG%Bt6{Y%;$-Z#
zs_mR}G|xzY;Kso{hAWVOjj*->;AWKP8SbV>v3;P>*y#|*Re07lMTv(y_ht{QfRxn1
zYGQ##j1;SBov`=7Q8S;BAV#ZYnYVKqWCLb)21rW&fE$mxot}JGe`f-*I<mUP4c;l4
zi2f<IgVtsg)el~QPo6xn14KjQk+SS({$v;Mwv-kZ&sqhOuD}5}oulC3tAB5KSp!#1
z0WqjF`RbheTEj89p$Gb06u?Jt@F>uZ=v8?2%+yRGdZCs^?(2gBfP|3#tQ4?J71mGO
zD*x>bn%~_!^E;cMLSuDycvz`+MXGG}TLbGK<!J7cCtX2D8o!`faqQ-<_ldy!y}uQ>
z?O&gVH{2FWNl7WtKmQT5i~K+d_7dEGWJGWd)bCYUK=@R@zhuG^S3B$N&;p4Fl`{+{
z>fddyfLJaS^bOlUZt;q%m2i*U-E9RCmcQoYea(5yQ@;(edIwM?jgt7G<xoLZb58mB
zq}QNx8neG5U`1bO4m{lE%QwwQfy<4zK;|*d0eInxyXJg?^tJ;@x@z<5g&%x<GE$t?
zoSXqrE4nEn;s`nV`ub{uav$Raf^S|MxbzJm|Lu;%J`ntjtsML?iUAe`u9vb((!#<*
zKY(v5AQ;!DKN{)<tb_jl`pWlkm5q!T)Ou?cD2U=3K$ZPNH|~Z2xPsOI`as4O7H8k6
zf3M#D3u=aM4LCp`4QD~W%oq#}Y3k|eb*QoP6#-zg>1UhekGE~m$Rd@0IGVJHFrz@5
zqP=QuOEI7Y=Eo(X$LkFWIi((;o;Y24GK<B)6F33}DPrk&mXdI<#xN7jL0ifmNQfO|
ztpTf$0FB12UeI8lv1SEso0i{XghrLDb7!&|Yb7BmB75lk%o<q7nCW=~7UvZJAzOz<
znS-2C&aBzXY1xglu69HQ(R!Y|aRwbjUmnZHKOKRfEG+{D_UKK&XKTeGF;*u52(+ra
z!1ZO9_n;Q46wW=wD$M4MKobb)0+Yz(yMhH|AWYXpiJr1`?N=i-=b-tKZ{NPn17amw
z+`o?{6gsxguv$cwnAy3WtiJlEd)ZaQG)*m^)KTbf3g%^=WPfq}uX}%L@bF-$_wo^2
z2$sGk_8Uj*ufF}g<C4&R{_lAg6&7puday+NKSy|^Dze=OB?Q@f>XE{_?ubGMT>{8b
zRvkbR88|{M`(gXIiugu(ljMQV_zHj}>6bL#S3DW5c{&p97t};x;mQ!iOKDcS(n`x!
zR?Uf+vJyDWo3{B%2NL;`^M_aExoT-_WK>k0qZACaxaUF{ke5kO$>iK}shp*;3e-xg
z92sX`nCb?aFCv~-*<sxJAAEzol$tsru+H+nv8`=b`Qh)^jHYs_nkwPQkwc)^gf$)?
z-T$vQST(>YjK995;=jXWY)Y6HBtPn3=j2yfTL#_M1TJBnHU0j8y|8DXY<v7(!(3EK
zs?8$*Vck`bsd-1hGS&EUE@vLDanf(k;9;1m%v-A2c7dRAgpu+6+xOoj^Fdfl#ALvz
zw-GN(gLh#Q+inkoHq{R}*ZLmVuL(RbOn3nn^xP#yMN5BFklE&&WoQY(C3|2Wz#@7<
zjL_U^%}T)p>5PQtvM|BQi*pcGCBo?Q(%}CV?9#7&vdeKz5M2>>=Mr%4(=0RTY|fli
zMl+@~ds$0*5DfO=l(6}c*H`_U)uWR;u)`m`9{Xh_Q*GNDuSH)|>#VNZ1RoBtHr1mY
z1}57VoP#MlG%Wwe)e`W9LH$rMqp1~pCK4Z;Jfc-*E?gt786=n!;T<F_9&aOV|7_sD
zYx}F+#Hfd18N8RdDvJ5>%{r;k?_D+Vf|F2}#BT-g8ujjmJ)P-qaAQ^?v(_zu$aO{f
zf4fx=3yAW6R6hRzNplNmJ@N^BuC=?Jf9V#=ZeRO}x3wwj=8ON9KEan<4{i4Gj<xZ>
z*7chw-lj9iF~I@<XN<{%iw?ur2qPN%TC(0zsB>&9(Ep8{{J50PD<{|Ps8WhmDVg%I
zzg)cgDIFN;X6D6=yGu3mftn#Hu^KL#Nlxucep#=~H2yb_C<BuK(+BX5JDuGx`b@M?
z_d0ns$wEvC=a^((&0@jp*zS9scd!=1$rRB!N{jLTP5*&kSuzm{W&EP8T#oQoGdBgF
z$L((Cd!oH#k`uI~^AHkqDw?#Ws;2)P5fgjmbXlNOW`dL`ICoGp<gUV9hl*iqVDKWH
z&J&ul58Lae?i>E^<p2BiB8<OcaU7+I*KXptfP1aOIjt@9j`IQ>9RH#Oy$r~)G@r)~
zEL@$k<OEB{qG=W4!CGH)lYf0!QM$Lg@N2N1khO@55`SI@R_b8Eho8cvD^6dW6F8h^
z`s<3Osiwt0|02Q7R9`;+{Ws3Y-zjUyk=iyPSWZUmR0~H2h3xy+%w9yT;>#b-egE8M
zQ&TnCCx?0c%~H*-^oJ!>8G7zu!J*LcU*9gq9sYIal$z>Esq<mCPn^H-RQ0@|-K9+(
zhw1HmQHoJE4zr2!Jw0jU^*|-ky(BLZx{4X=osu>~9NA91gTLy>gIW6?wDA%un7siD
zKC@?dsT8e<?_H}oiy3NW|J=|z4ukbSz9A6!IF3`%yTh%8$i^;E0nMI*1zR2TZ<w);
zo3}yG4On0gPASom`wtlX2K)X4)+M1)@Y)m%c3Kp@*f#QJZ@b7xcGllwY*dT2Kt+$x
z-6*+oI!kRAZ0b#ihl^<$k2W!FPnM6rW1R_`5E)IV);IS1+&f}nRhqQo))>tR;tyD^
z#L4UkH8#VeBwd*Gcknxn8bwGTK11KaY!@vxuP%X04IN13QrX}&0PxXb?LS}^GY%xQ
zbmpPm9TQI?i`O?TXqUP+KC?|LdI!t1b_X9*ttr2OgpXPh*yDGvJbe{$jdcX}_9N@N
z$H*|(9>{0(+qnlPbFGOu8_fB~<#Q{iF+UzZ_Sqr5z9UyqKWrUj)1eDKBE;TI-AIfk
zkIWD<{fv1e%OZ}A{~@8{w$AyQyW)nef_ysO)u8fppr?7%?evX~r!y5@H7l*P?WWRZ
z-dsl(t=~<ofRH-(b0rH%nK%3WeQ35}?hl`@K@^fG_z&B523RGC3#O^oG;39OZ&U;X
zmo5z1aZ_XWwp{lRF-q`m86$4b*76XWhpdX-&>oweFRwGbz%5<IUu1sQFSBB|-a&;e
z*e{eI12tm;mkp;z_--kDC8ZOHWw+cPZ+%0@mb*w*W{1I-8khN>?P##Z@!L3U_v^^#
zeHAer*G7hzi>)4iBdL$OSb(ywM7iLhu8>ni;9H55IF+r<@@*?Bz}NJ`xPD-Pw#xcu
zn7i$*vZdz>BgYn)eBh4-sm*!wpsl&;{Mj21tj^S`r0*Jg{#E3YM>UbEkCBko2uIHQ
z>>HA0|FDicVE(h1n=_FaVyd6PZ_Za8cQr1n!Pq)GH(M$vH)Gg@h;|#?$2zfowC0Xn
z-t9ySWe>R-C?C~Pr1Ar0?Sr?f=QU7%u(3uaUG1Tldhdea?JMpImx&od&<_MHq~}%|
zO@KigTgb%}Y~(zJHg<unt^*LJVIrL<za2<RCh=s-rp_SEiJ2R*b4#0<gc}37HeNnt
zl9jC27Alg$&zeE7kT3vYNbySbif9kz^S&y`D8Vu)PoV@g(ejDveq!4_Nu|xTK)F`K
zVLHf(bw$m6E`ZWn2a<>h!r?vshIu2x2TGe;iTpfd<Yr26s9#Xn<@mf8`zluaVq!OJ
z%LAAnt=_{cVoDuw)y@1GyRoD_#(MO9?h1z1C2@T?(9;O5Y`LyT7v3D|AStIeHjQ5Z
zu-%+n$=}<uKd*zBj<WG#?jqMp+2y6JfEDvMFWNjj*Qr2Y2ZC6J5B{g$h>2;&8!U+T
zaAo4au4#Q>4@4HQRqCS#DV)b(>r$?*ubeMo8(;DeK4`}|dV%OVx(>VXEyN_;k_Bhw
zo|UXD(<}5dJ(4qntsCCzH25jvK3L7ccr#|_w>Yy8)mht5I&`l<dxqCHb!cu~8$U=}
zR!9i$5Hmr1H$d#yr$8>^bgv%q%+|dvv^CP0xzE156LDD!&^T>lfH3UeQJMWlqmtU4
zJCJW{XuY%dukwH;ji-I%y}SRG&5Da_!de5QQeGWMoSQ=5gat<pTKfF$(D2z5*<sva
z6bQFri03v3(^L$r94*hPbgVF*`W~nlflQe_Qn(S1n28V*s5GRMU&#=1+Cd=l#WVem
zUF&1$CNYY%&IDTxO)lOlqaJF`FKnIGjVp6#Ank<y=(EzFot9I1$ibH*oun{<y@Q9r
zu4u9IdK)whuj`HYj~-Z@^4n_uhIsI<v~h5Y$<$$r^zb>p_b{z=wb+?kUB5$tFc6U|
zwa=O?@aoZV7D!Huuu->|E<SBVDiw{r`y5TuOp|ioyBC~gWb{POeUi<x&aDdC@VB6q
zlG5jtcuc95kqazX1p73fI<|tZL>bWXeGiZd<(?1pP99J&H3`qyE>ZZ=$j8r^yr=6v
zyvxfHSH=DN>P0dmGarV}Pj5Bqn#`C~mQ{}YgVv4=@*h!oyvxHi2hD$$`>Gfcixo=B
zFg}_-Ki%t<aVT9{WKU)0rOd8OhLMMRLFX<d173yk=nJ5|^w1;Uc41m`3VebLHrvRj
zkMbgq8(e@gzU~>W&RMuI8n}BPaPO`C1#l2D8=kZyJv8>uyEh6}K8>oQ9LwS{<}vcf
zUbq?(zI!*-+Lt(d4koSa;q*{#Cc|{bxY~?Q?r+0x^nMxJGu&=keUHzOh(pb^xHK3`
zXIEJ;sU+lHj3sNV;FRm$e<3h)=^h4%<*^LiXG9m}AE=Slp{~qxN4J)C$B3>6FLEbZ
zSAJ3WT85yJV*7GUmGO=AV;)&mS%XRqtfUTu-H0#l7qdar#z+mB-?JKw9NQ|kgGtIg
zFMFg-<t&~j?w+LXk;C{^S%k9pi4#_%W`EBb!Bts8OoR{hQJ2My-9&js*oAMAzq^gQ
z9M|9c18qXh(aUg`XRuy}gTd5iYp!qH#AjkkP+C5VQaC1>hS1$ZBwQ<9AGA%w5%U!)
z%giIADkbVuNy5ig_CEc7;L~21S*dMmxczbziF8K}Jdle`A{*7F=T_EEk6?O&&mD-y
z-jifH(-IFa-k1}9N{!3%aQ0j&9(i=)c;RWt46Frt4ZDMw4#xS-kZ$Q>!XCxOTM<hJ
z3ue^;F%Q4u@Ru|8dvxM}N(s(-X?>f7&+;?wo3~FajC)a=6vq!&VxwM6=$!P*2Z`#v
zTR|uT%Qn3SMqijDeW~Rj4n2Vy_zPx{y}#wHOTY0cmIe4!3X20N`ry_%u~Fp`lpT%S
zqw5zy$t0EbBpF+q)x|rucQ;T?{pNA-B$Zc-&q$5y>^!jS*pNU*E2>Ul$fd(A`wb#+
zX2fUvrKw~YK%1HDt^E}<G+Olr<EJn}yrOWhjMHnOe<XF+sL+jquXv~aPP^g-?ec}g
zLqW7fw&~p?{U2T(_J><47os#aODz2Khi{JV!UlG($$*Wj%>H@TW5L`C4?6zCbe_j6
zi=XACsPUqzAE>FrxXmTfnlrPZ=lWjSu1W?Aq}+=b7uqjpCOPZJ!V6MB*9IKQgapzB
zSOW$h^GKIG%P(VC?Ze$y8I+2dRju5eh{Q_}BAYGUj@jd7g$|G89_`-EjJ220-W`J*
zFB5kpMY5B2rPl+|quh)9iiRH6bv}v?B-;c3pWTUah5`klWRA}n)i(_OD;%^ToAhAD
zURj!(=@X))BWXwW{|>PBCpnVR!0ppoyQ!zs{|m&FD@HY&3|X4K38%%+>mydo`Ie=u
z|L@pHGiF=M5+c*lxPjs@Ps?&NqJEZVRFH<AqIToS>@YjW%|5CTXZpeo<?SeFZlbvd
zG9hH`m=Nt^$SU`lq~M_cdu)?M6SthILezlNjD9wMza8O~RV^{blo5~Im1#9%N6&yW
zGB9NdrKo#8>+bcIDnXQFQhra*UdHY{@=m@Q`exJX+#{_gpkbt;-wx#vL#lQUWp`HW
z88(lBM7sZztV4hQr}^^K|Nm+>BY^S~{m_R%O6~ay9B04cyzBZ06K51gnG2+p9yj_D
zifz}RHHVLt-l^@%@++o}v0hn*!LqJ&A08$cx5vt>;5LXPc&2t1-`*hJ-D9b*4nzJ)
zR}7)NGQVQ`FQvN%>VNmO#L2%#huQz4eD^FbIinhMSnP)E-qu~^QiB98#yF=A-}oE9
zd%}Dj0A!p4=@R>AF4m6V%F|+_K|MM_)Th(Cy6Ay4fb!0FR9(?_7-{k8^nXa<1Us`J
zOzK7mNxerSOYzuEx!v29-+cTf0I8Tjem%ED$<jCCF&4;_vK~`{Uo(K8`+2qJP){mp
zh=i>x*~MYD90)H?9LHemI@y@>^sKGKbKM?JllBbv;%1km+(ZjzmxbK!fZ6)D+&gL(
z`0iJ_ZTK%C>)mpP4VaLXwZKBJo$)H-|0(Rv<C4nv|8bk9oS7DH(=97gXUg>2qPgcj
zjiZH%jw|kFYHp#EiYAJua;B!yw7?ye%3M(~Hxf~FDh=0Ma6_<iK|v%zL_y?xw0rO8
z{{H&$j}H%r_j$eFuh;AOdcL0TbD$D7FW9L=`cvOAi}&V-oF-i{h3QZNv}cOk%x~tz
zl)0N$n)VkY#tJJIZ*N<A?cssO?^oG(Rg?x(Zm>nm0lm=5vVoJ77u4j=_^P&M6EgB+
zFK3`CU8(lU&iTJ@R?0R}WA>;ul}@wp(&QN7?*RL+ycn3fV}+k(%<=>I#qE29<&Iqd
z%Pk|av@Kd8Q00<~jO{lsZp;(QuQi|E8OQ2axB}C+(8v5al}$5sdjK6!HP)LyO6wRF
zr>Q6QMgAh+H-ep%;sjcH!o|cqPH&#LMlw8dmhlW3^m_BB4=TeftG&7VMq7&UKo=C3
zLv6u}&Xc~C+xxOvoX74=jSPBJ=+zpx-B47$dB2Nyi#F{p^6wZ=bSLDGD!}UuI&4-a
z*xseWTeOXq<1q{NtqAQHIJSa7#G$G?queZYKNn~mFjK9%l2KFCvY>wS!?(1R)X1;y
zT%N*<hpmlX{=mbxH7uyh$+ZDbe%#GeatGN&p>h0*R#&|rthF4nu<?lv5OrWHTQR1)
znewhcJyu7xye-5_b*d=pl2OZv^`4YiqAta$a42X`*B0^`LZpsAb*<R6jx96YT3C+x
zyQuA-ktfG)P7ewkBtO%fQkVDK={V_$Sa_%r`#NztW}CKYx6Ud10x=W82*$L<*K!_2
zFdmHzJntsL@2#w{%kqDJ>`jWim{1*2IbGp59@KKb#ZC2OIZilxfHqtz+*fJjp2O9h
z&*JXC&GSITD#S37OOv~PJy6qLhM!grtqH__P4?cKuQ1x$q6)-PV)=bX8J$zUttd$&
zn5#;?0UFG5tR=wz2zDgU0vUlvF$BBUV}Cr0tcw$=?}F31D&>iFM@pj#D~#CjX*@!I
z(P$Tp$L7F@txe)j4at`=?MW~!X_<heB)6+<=kA?DC&0v8wU@rBX@M;<d$ok5v7cJ2
zj7%#Z^q!oqhOA3dxjrl8Mk;i;zj~PwB>C$waHFRAGDiBB>P7Rd>k%#Yc2L&i0k{-A
zJs03+&e&QLVL3}rSSZg9HW&XgcAz68w9eqIH7$%5lm9!BLRVXoepC<OWzptn3A79c
z0%LM^FM>Yy9Q@Xv?at{07^zYD6@<wy<AcgiGlWY7Y!w+5h*<62Ftt~0sRuX!M%!H!
z!O8}O4X_*eUMYMJZ^`!?LlF0^Zm<UwMtYEI%k29!7yLf=TZ}xYyO3EY?mhYM4k@(!
z2w63hWl<0IV|fYiDDQ8lkfq5oaMJNGqvj`*`>>lEJfA{WMybh7u{a0&JE_cQg8%97
zKVrYzzFbuV0HOykG^Abfls$KjynIVf=)IR6d_K-K#D1qL%Ha)1jvgQsR^a@Vjbq4V
zN5~p5$D-r)H~)>d$;UySKoPfwp>7HX)#(S9`tQ<;D;;|a62lRT|64#SLmFaK8FcqD
z!--VU=R6%=RZXQIqaVP&Y%LQP?~-hrBj@p*)n4+LtEj@yEN1zBOIowPX$qq<$5ljV
zIxxNi`t8ogbU##2dYM;{+=RXC=Zv)G=aAY&XW{RQ20KGk4c(2&7ge#xN;9mmOFfA{
zVNh}0#+`a~@lL`Xa(zu@zi&#$7e?TEA7FkV#&--({aZV!0O9|YU1Qjt1Phb)lXAw^
zu3i6mE_V9@cH4zHg?4#&uih_L_irDm{pZcW?Z0vtovo@rNU82ekE(j1?1aNKr{Ngj
zSJcg?7CUcecY<Xi0%p9GQ^|rkRwY*=;}9Ldf7i$wK3Lm`bQY$w*V$GF6b*MQJpYIF
zaz{%sa<ZY#P564>(wE8+dlZFfbQ~DT@c1IV$enIjxD(H@mzwY6>~QaEzaIs8tiFrt
z%i1qh&?a<n7mpFb&baVJo4IqyE2v!prE!28>9T;!Un+ju(B0jAg7t90cb)J`P-!8O
z26{Ix(Cq$~UbK~SmdjdB&?_ZwiU&u&&;VzT*`o&(VFMKo<y-K({~7DobGIEM>!g3%
z-mK87)T)l#zxr=N-w!Us@!fqp^K0M#-G%7Xow6ghmICSz$H0bU8vw$Ge~RH!<>$>T
zx7V({94xt85s3HO2g$pGwvXL$dTIS-jH?G4?}-jU-N%{5;*vZ856|=|^yQXE`k9xL
zc)htS$F7~iUi%#w>oN~Ul^DOcLV;ye^-uJTv)nb@3E|(bTC|C0*u5Y}+G(=DID`-W
zc2)S2UCz!_@8B-~&4@rXi<#`xw2!nd{OSK=b>Nks&~6QjZ_!4N_|(Hq){3F`KH|##
zOn$Z8&sm`n40;v5PS3NL4!zUtLAJ!JkQdZP$KOKR=A~oLmA1_Sse-g4Pdoz>n@q68
zeGh!DfyI|cntU#}0#=aJl~<swAZJvwjN4Yi<UHiJk;&X8%JVH^taoJE_*d=yJWX1?
z(v3Yly-Hk#gX%S{(T6T=GCCcsFQe`CmI-kHY819356QL5^jzCbO$QFqg27yWpC^8L
z7a4HdG=B+e_Ip;F(Xl6o6-<HKnqM(bD@>9>(Gh&r1eRl~T7lBG>9aMGnmjRTw{R!I
zKxPM0nz+(rYlQTOPmH~=GH+&V|9=8voBu!&%m#}llVar6mg%^U?QBVDU^=#_rCf~t
zFB7%mcC?g`LJ;AYUqwh7wvtPe#{-T=6J5eJ8lxq$mRwDOA!z^25Rh=^SG{Y63$xkj
z!!h!%qFjqO)++n*aKw&(K}HT{?kEdy>L!BMY4e&GX%p_PHDadvIpiN-sGQ{5yCdYW
zNvGBj@%lsdpX~B*7ep9hB6cT_<s<<ryLdpAGT7Ah%x~@hxLkJs|MAj!(Al%>L$r8<
z35=+AapQDI=hSnP+g`XQaHZ|Fl5-DUxFdR{ZUk$Qeb>pXN75RSLxuBq6L-uTe-Cou
zkN2VOpZf2&DDr|gIhFfLg{j!7-*?)VJ4e9U#TW?!_AI{~o*}}FYHDC`#%qHySBhSB
zloJE~^h>Lo{Wq9C4Epi+$yLbTgmXuHM5B4)kN@ixRdW~1my=C@bJ{MtOh^3h$51!e
z8C?EkdaF<ToIQ&5e}33Qp4NBqZ_J1}X#)K81^4X&L7c10LkiB;m<E+@ZT_zp6naOx
zz;V{VaX5Km>Hl7QTF1pQ^vIZbWNC8G|9)hwe`J7=USP}|{*H;kYYe0v`n(fIRom0q
zpz)xbKzxR~1~&j$`u{5+?*0`AamNMy>`FF`OFm9dth78)yHh>ylw1sIPlO_H?6-QX
z$X7kNl19DpY9s_93#b3Ls{#2|Wi<^`>FqX9r8U`?doKU$77aJYT)!`10-RO8RF*JM
znG6?SYM{1}<@F~*#{9m7&u%cT&B>Gc)!Nf@jrhtKdGVzkzuSY2rJs+oezg%7{=?3Z
z*C0dnGlJ|4=@AIx4h~yU{iVQofC>v4BsCc-p$<Feu3ZOL8lfjd?X(Xd{_OM*QcD`P
zOz0nU<V#;O1zy5k`m3;?{fa%pZo%)CFWB+Zo#R$}Y6*q?jVNRihV4shVjreW42zXE
zp4}CH3A@i=qN@5SH(&)=e2cwm6;n1nO9`O$9G&=P`|`D0<39(WB!alTtgp5s43PU^
z0)8UnM5xM+b?%{{b?m$I6Ms64XGDZV-HX3&D!SA*3R??|+&<yj?An<R(v`Tq8G!jU
zZ6%uGq;%^IURc5?pzF7jx6f$5%C&2k@Z<JoCw_PPkM`|DaxsP=52{xC+RxA%uT9|R
zV+Th3mpDXFk=@4M>~Zo}esQFyBqvO}M4>XLf&Vnl&NPLOJup4_m$DNB&sqMKOlS)e
z-%R}WDaeh87f25c;yi_0;dBsGD@{sCS9*l4R2q$9u;F)j^l#;s<xg3`pPShYpa#<(
z{SQ#^t*&Ib$y$-Og!ME9*AT6`3O8gJ25xje7AU*R+TFRxpi#Hw{^S~QJ@^?{VLtor
z&i@3l(AucO`4vLwzx{ulD={?M@u*zxIk02#^^_(F>)4N=_xc9hDNg2i(v4fy)#KBy
zUG7-<T+TtL`$}^1o#s0$Ef893)a{${%C#$s1BD3_S%ruRx9x2L&fid=aygl$6g%?7
zYDzI4Wy(4Si)Ak=<`Ar^3J>ECntl45PGZ21GJ5jYC(9h8=tp3Y;e2cX=(>4__LjJ<
zVp6b>P9xakfJ&qXqWdd}*Fz#y)#~<T;Vc_{cYe3w9avHq*L42rBN=Qc#zFe?z}D<U
zYUJ+mM~c#UhIpP1_(pB-JNdVMtT*2pv#eb(^!WL~^inBf1u#c#i#r*XQX|q*eaN=B
zll|@=iIsKH-gtj_ojh$3`qi|f{>0xOAMaV)Au4H)@5JjkR6B&%p^5AYsNqLS*YguQ
zY2SIJz2y?UGQ`O9tz<=AqSs16CLrE?CHZQ{|2EeFBQr9z5oN?OB3$cTE@;L{(*{|U
zQFs+t>kFj4Fokyd`}UpWN_<J##arC{sCl%L@`rdCPVn<;tw5p>18nX-?oNPvh^qeK
z>)+;s3K7mgBj4?CjoK@cx_cr^(Ta)tjVnSTjb7Pu;nfcHzjj2Kp7V5#F3-EiXs(64
z+hI7h-k2xQPne4iRsb86`P=_BMHKV46+W3E$|C$uGZ?bt<A!b&Z$|RqqpFwM?i5$J
zXYA>AvBrnsGb%9i>mQ7x3_d4;r`<Fo61G^|%Da&(&o}n)ms674kf=sbpi^-%e`ON<
z@9_>+n#``xkmekv<IfX44~J0J4*1?OTvLn?hsdvk8cEfgx6tJ1OTQ;N;D`$P+hCwH
zHUC<Hvz|Bw-}Y0?^p^J6Ub5V|PwEkvfLmi2RR`BE=2t4~xcIkyVTO19sJ|BvFLad#
zBa$17#RF^@J`kqUWlakn-r?J|Z@wl+uoh_*v_Sk!8LO=Xc?01mEZ-yi;^53X@$k)1
zVL>Z7`Bx#E9lY^H5jm%3UqG>{w8R-(lp%k@xRH%2RD69eq5y7L<=Cv?3-aIj;nvpj
zbiYz@;aHF=GhY8&1jSW~K-9A@4C{bRR{j`k$5-R{-HMF%@7F<+v_dNZ+wEZCV^EEB
z!K+r(R3z6;TJZM578dS*Tc~jS+Kzhg!}f0HTAtW{O*it2up!(Vqf{sLKp=#}!@$_l
zMG@X#<#xZfg4qs!o>$(M8lp?LCco>HMI$_fZ`2d{e!kMA)B_0v)ml}C3Kz$ofr;y9
z(Cz~+v0Uv4xD7;vXU)XBI##BK)mguu!T$ct5q|4O-oCPoAIVRV(HLys4)RJm9+BsH
zQy|Ma;#Xe`Ms4XLu!6yd930<KiE4L!bDPB*$rI<g`4C&h&)DX}GyWCHdN{r9>s+(-
zxCE$fZ?|?w5GqtWP&bjAY4;~UtrdKY%YjCSKIV!0CBI2&w0QS2h2fp4*E(&FX&J(n
zUz$0Y1Dp0k-ICFFj4}Lcdle17zeE(j3~LRxoNs<XvJ+aawVd9$n$<QYrn70g`-C)g
zBk!Hms{J>;_Nadr_0L^k^dGuz^4s8ni{DMVnv=mz*w64`RthMiY!8r~e<vsFcAW6^
z?tz3Z5J160cS=r!(O=|=YMV4`_YqaJHHt^%Vz!Oh-g2w^US^Ei|8WoWD<&Qk33kTF
z##ZgZINf4_k!rfL-}@9wdj=Bj`EYN9HNvei@Gfd+E}Vip@mzT2vKPu<XhkC~1utpR
zEM23jEqvg(#Q5!@+C#s&JvM3uc8*5AAJ<KxsuWLDGuT^+JSTR{4N_n>)>R-Agr#dI
zm;Jm7jZjPwUB_SqDr<*~UHuh!<zD1zeq(-piXBEXf?YA(Fgxj6coAiB4Y`$0!(gkJ
zt)m&P1BHoTa|m9Kw7qSPb*1RWY^JH>Z{z$?btE!jIy#_hb+dw|>d++{$Q#2sGp<{3
zcY;6gNAP@HQHAV<xH_DEmNo8Yjz@O%Hg|DD{C5Tf-hB&t^0J<gx6JYxn0)=El77GK
zfb(~wiYf*e1I^&JC0Vexz|K*cm;cJBz+(E^o#xRub{2vXKS8r#M{TJO94q|UeR(-b
z5{lis6x?beV^>RJ8UBR{k+RtSJ-g%Uw{A{nL{M0TxKP3O<cC`0U848XI8-tqi({Sv
z-?jh+n`tIQ0l8#qy3qbercU~dz5tHkJ)P(fzp-U>U<%w;?05^~4I?sce@Zl6{qTsg
z_VJUQh|76D<{))d-T1>_<rP-ASSje2b*9A43~O&<4qlple7D6DX!1>%v+#|d2pRCo
z7rm#kxO9qg|N3waaibO(+YgRhFJ+@7CaAfbe%~U|`wf3a8K)mPhc}f7WtV<4;tZ@^
zkgQ4HXQyiUfuGObr{xp7uSy3@Xm~|>?kJW{*&np#uMFdMnc;=XG%6UIZ?`x5!wkL+
zS~JKzUNP)$y!Jv*i$RQ9y|UzjKOR4r9YG51F^IVt0x&$ei3`gieoe8wXIWcD;@HNS
z2yY@>JULn-j*7j3p;*E`HL&MHl0Oa^H`QxZubQvsg-c3uqRc-Ghg_gd2eaH23BN6c
z(F!eq{$OxKU$4c)g*0|P9OjGqki|+0E0R_j<-{_SlzDw&wAPv}cBd?0S<79lVuB|j
z0m*3fgYy%K3*BtNxs!=r!Veb|{ak(ZDe0rQthNVj9==U8`4Q<pb)xs9?E399pkMWB
zc`f<_N`Ko<TlrVFkpd-aGdWA<4b>*7L1?X^;g)mG+r&U&Z5S$tJtNT>Q^w2kM`f<&
zw=L(r9__BA>wjDRptd+mJ}5O#5C`mC?fI0ZK5bw1<{7woJl=y`ZDbd?rBT8VBLzrL
zLVuMzNq^=8do*S#*ql@){`+hv&O_O>%x@GwK;)}_t>VFmOK+{Z0&C#Ef6D%yEskLa
zw~Bwp$j02S$6!S7by5CF2n>OC4v}<P%*Ed=7zUr|&N?PeoYQ$_c-w0cAl&h#P{*DJ
zM@OD>j4(1tP`EJsRaHbv894cLINy9w*O=h7=$LNkMhxe-3hTz1v*ry$*0EO%Hi#3t
z%}=S4Z;rJGVmC{`q82LI+9P_(PSJ~zPW`~xfsooO4-7Yi1raF|8E)|&b0>*-3p2g0
zZ1(3}i93qD4G@}_d<6bv*`+lxCnm&=P-!vJkNVVPxY1Y;h*xrp$0s*X37lT!HR0OE
z7j>y!1_$pb;<n7U+y$1SuAX}Mjm{t6e>(KzH{s=)CAJa!tB?Iv=IBJTD5?Ia>2|?3
zD~JEIx{P)L=bUL+k(d9M`<{}ArOs#X9sAjya^GGAYy=hx3J`?E{s~q=>p<)@Z;V4;
zwh+~{j!sUu4lp@YEo+mFiE7PiDNe73$y(58S;Ci_v+m6P1u1&Mzm^IyiPN^_FJ;Q_
z6fQY69qZT6mmKq8qJt2Js|oiu8=dDl(&TQJ!~utBceKJ<vR}jF{2g=x5$MUg9^ib|
zBWsx4=*S^R4Q5sy5?MIw0EzUFN=cBmk&6WHGaK(Sg}1}eEc@ojvB@;2=^{0aGtK^J
zx`we9bxY0Pp8rs-?2NGiHNK3nHWhmar#69|?ZTK{qH@tEv9mvQ>#WVI5f)qhZzw@(
zvS;69j&Fvdm4wMeZ7K^>P&5lXf-v)DC5PvMtF}oF4$%*o5<LvrfZBsDFy-uxt?F5i
zMfmi4({<S5T%X^yn!<qYgpE;1B)vy6o;kzz*2LAvmlTv)GA6TQ1DTS|4{EZ!*=`yy
z9TLq<+lZ@3Jtj>;=Q#ZOK;cZY;Oj+Jm`c}so9+;98)P|uRt(Vs9Qb)^JV$<$nyLdo
zWzneE1>P0Pm>ls{wc@<5S=P1Tyf1YiQ49&l7L!GF8_rPaUXKV7w9a^5c>3^cJbv%S
zfkf%wz23u*ngTP`G+yZcqjN4X4BZvfEbE|IC!vRq8Hd=n<)}5wZqj&DnqgbVFFl_9
z@$f9l8NIJ-cB23|FIpLPOCp(QDL&&KLnPR1*(%akpM1;T{H<H@bFcvqwe?(?Z;tFV
zYZ<jpQ?a;QgoRj)PmVSaIDrelXZGW7B~U_|`W9U2I$lIWL{8UxQUku_uR&5n1tyZv
z2hPD^GGoxBv0~Tgq%PvKim_lcwwQa&SG(k{G2~`)J@5y2!B$aG>TUF{K3Upbr8M{q
za)Kiri!yj8iab0kVxNx+Y{0Kqxdb+V<{c4Ov#(>-H|{m$&E8YH()bDr&u3XsJhMP9
z$u~iKHheS~$^B5cdQ9B>E#D{SAX9zEDp=QhlSX{YpWAj|mTUSrL;tl37@cgzB>h0u
zbBVMDBWezvq|3n7M!96Ibf$NQTG4eXwMmMlX?3r{yQ_nhAO{_r&y@EAdQ`U?)0qwp
z$~Y@bQj3=;6|($fman-oLGR&tPMpBaY`<GjFe}vbUyXWFr>f9>4N!eD^qYycH<9)G
zp#+t@YGn=F(p)PA$l(ocd^{8@0>ACgYZ!Fc+amhpXj~fUuZ>fPm(ZxI#sgCZ)JB(d
zTynB}*_6dr8hqmdr}3trBnjAC{3iJVb;8ZBhuJ~5PGcWrV&mObj2GF*Ov1dy0*qNz
z4q(Z%!c6@@Ek=`HyoHC;PYqP#-<K|*wcw1@M4H-gMgcIonhgmqEhb@>Hk^^tz2jLH
zPnI3W3Dw=N;8%iU!}5<QKdDw6#-?9wv80CJ8x(R$rKvhx9arGTt?TI5HZQ7W#hclS
zmCgDG2S({EKjTwe7kn(vHmr&}tLaA4F4JFjr>U(R4_09{S%;mo@tq^?CykeKi<9r8
zRA3!I)yg6eV5V0xb1lo{Ha1_)Mku^FB1|r{{~nj(Y?Ce=E-D&YX;>y*Qm95^NrsT5
zIF}aEsJ=~0Bec*y4$geUWe2UT^_URB2(um2SUTsrfy2?rKRFyO4y;7v+YiYW&PorL
zmCu}lChl)-_2MxPXV<fpzp;GM(d2Vc4d`pTS*Ut*<XyJ8<^gf?B)!3X_-*L-xLc#G
zI+HeOq~56Gu`i(r4Vr;hcZ^f%qk&sAq;$}qGv0yK&lrqc`T))EV(GY%_PI>nZ?VR*
zo*_bAqQ>+sS}J^7eKE(l^M;6_rAacDPNUxB<{+Y6hF>CHdF*<-8$TDK(R|YhIFeT%
z1?zz}CDeOuMfV^)-4TI{t9ut^O<r3o*&N*{RYLZIK3u#sQ$wsyRdlxLJ`m%HX1Vit
z#wc3$&zSY4I1n(D1DS6PeVs1=gc38w;QaL^Xu^qjw3#_I#H)Ic@7XnAjm>`(a+=C>
z<pHMN^sAhjzQ9mr`UrFe-1MW<pw}C)^btsXgdj2!roo{l2fSVQ@Q1Lr)k(&-b_?3o
zmNgsLIv1hQ{PE8Q33}5;xaI5X7EWCPsVd`Xl&(oYo-%lC<kfsStcMWxm}QEe!xg@=
z=_vud)zTE$kggMzh>a|o?QWd|@viVF-{(cxCjkeN?zjO&Z&r;)G$f6##0-|IuQWUC
z#bMPFtYKN6&TwTb&SYui2^?dpCi44Y##Ct}63*=7vJnxK+Uv2O|3<z_Ku4@)%Pgp4
zM?G?^Z(r*kPR8)B{po`C(Mc(Lse+!(*2GLqKNYoT1c}}(9;Wdh9vGS~^J=s1LF5r}
z<=Os5p#L9L&SmJo6YQgp`p%bar4OrFwD^Kx)|E?q2~Bi~^hdd(0k(AclbS;QGryke
zL+d`KQ~|O_qwGxp0~)j6+$1<tumL&vBJKOc1axAFS=Eg~{fCYylq^GSdZEU|uA(}I
zLu#tfcsLLVhZ#K_jErz`jDa)Z;5;HJa=^u3a$?w<!C4|-ul~fM8xIF{G!<!mI)o^V
zW`8?jAu((Q+$?Dw2<CkA2X{HYdW979%Vco;RWu=_RvC5zy*y%XC>pbA>RfoA7eP7(
zozd$z8BiQ`Pa}0l9k*|RDnJtcj+16`#lnuNt`+^Tq_r1QYb|;F-&eS!w+=EPz;)Mw
z4pTPb2dY&)QhASQ*a@6QNUd~$CyfNaGNr6Z{0@iH?dE@bJob7`is3*tSb=JwhjEPe
zH8lp021&#U`YS_1VhgF~KRF1~SQ3X3<((sQyxi(L7os741%=v8SV;fXJjHM~s1mN&
z+2;D{ohiowIk}TT!1h<cj+vWM3w4iuY<MzT2`S3Fyt^TtzBPfjh*#1S6bXqRpPe2m
z@+)cS_%pUK%AMmAEDqP|1tBHGSE(4@S)RI?-Ep42TD#v8CWB<l_W2zie#3gw+TcIX
zuPo0Q?Jx~X%a8lK=C8f{VpO09N%He5Nxa?NxLA;uJ|F<<C%K(37%?2MG)Qhd93T<#
z67`4w-+6)F+coL{&da}wkFrVK(E9tl4)R))#Y@BpCm3=tvaCDCz%EH8|HjKjK&NV&
zaJ{Msb6M)2HCX(rzkEoAeo-cVNWqU&bs<Teo@1S%SRM8$O7O)V!$;zKKtrCc?)*o%
zd)zMdM*}q7g-fqy9_xRx+5y7vRU<kqhE9xmhxnuMWocDl0m&$lEaeV|=c-u{Duyi7
z=kCIwH5*YX7+zh3V2b1bXSapT)GJNWP?*lpLiz~Y2Rqj4%LtYP`_f0`BFa(^uh}&D
z{e!@8ZcEjLrvWP7)$swgyQP(yL{g-h@p&o)V&Pe0YK}M34IB+zxC=>sJopIxpw#T-
z(=A5j+eHQ4atT&<>+UgC!jO@2*l)>={dF1o<B>z_Y4z!k2Acv$;*xuw_Q5Bw9<N4D
zH6<Cj*j%oD4w<QcGTVAQ_Tv6}2W7G^O6#C4zv(PbPzU4>DCg;0XcLA+QOaA_x_v6-
z%T5N$m-OE9%y6Hw=igbwTPnhF4@N6MxL3t7z{*_;;%=ymV<Mah<+7XT+MIEa6WRF|
z)J7FmAHcgALDSM}Mw|adZM1p8z|NYxiLW^Nf|x#Y%*3puV`F~*2^UtWzHRzqiSf&`
zEFe*DB%W!ACjXE^qWsu*f?ejunIZ)kBX%#>hMjGZ8}1A#K!m3?DHqpVZ@VfOFU8*A
zT&TtC!xkD=5`NN_#yj^9zpk>9=b5EHv#M_tl!R%#*3C7v80a637{=;?{m~z8lLH}*
zHY;(r_E)=Lk=g4@*>MOZkdY88;)hm&NwW7O%`~r3TIT={ztcGS_FD<ftq7mA@A|a9
zPNizxXYT_;{zO}V`A>w;iv-_QRkthw^I%WdaM4ZW)>V?e>Pz8fcq>Nf1;wYy@E6!e
zyOs0XnQ@{R-c{a{tKDrzTs-b(oWUtiYXWocwBAA*S(4j;XLgtl1eO<JfH*gmufXVK
zFYd`dsY)jVUvY4nwq9T#^U5(4R;^4N;Ib2n9=N}Rn%iQ)@(>Ek(6s|YX%Y-20{8IR
z9DY-a${$!9d84m`bZP8|E4}b<)My-?M-0k-a_e_7IbU``QYXB0+f6;9e9ZqZ9PJ0`
zfy&hosO#|CTmy@lD!^)o*eMIFb47-Xrn8(z<?P{X&rbpj4Ve@4Id-|w4{J4KQCGrU
z7$Rpp({K$ofBrgpQa2)L*|j>+={15a5Is`=SA-ME<o;+0lwp%J+Ujf06TGi%tmq1h
zhWm-)gqP|Y{70Wb57eG(K>eNkOV;#0uXi9g9xje|<p7%=DXVm)=U43g`u@o4vX*&)
z^i3(w)xrB<|0sR8e?C<v0Df-VVky0l(auWsG|opbC;uF{gl8Uuo7xEe+#p}Lj$QsW
zOC(P3$){DVRG8i_Lz!2Ctaj*B`ZLSw?iQdcxUcfj`bp2~>4p?y(Ycrj)7vd(_d$ag
z?n$FS^M9<xhJx0z;t+>HYu#HxYa<#G{$^f{cF3B(r8iGJU;1z`b(_%K0PLF#o7}@(
z8D?5y0>1l_X%9mO5sJ>Z@$0l=2uZv=Fxq?~L-k3VV^i{4q>*)kV8|WBow?3Xx=uc<
zmlftjUnN)eY79IhhczAoa0uQ9-#0q3SG!+7bAKkN>sv2yn~Q1G2~(hs28QO9g)Z=q
zc|9;hU{^fH;)JLAt?(nIk(JcO1YeLh^bI9@&KVlC*9^^n+fq~uC`Se_V%2R#_|c8a
zHUq{)4eu-J<PdhgmQ9YL^ik&hB*s<CiaFiM*k78GpP(XdL|g)&4a^$raye0Ens7a9
z8Slx|)`fS4WV*q(Q!b%1$?s?*RLFgVg(Sxw40Z@Sw6by2og--a^BA16M}qZaK2T~>
z#$oVeKTCB2%ROwM=vi+K0=0jeD{|MX0$Y$w)-$&V7bB2ECom>KizkykY1<YL&wne)
za`OWe@^lEny3n+yjG+w&^n)|a*Y`dmTA`=ZsaNWR&l6cqXDu}9Pg+^#%~ojo2Z0=S
zeCm|9(gGIZH~cfICEB%VizpL$y%X4K3YLX}?dCG%z2<LOH>JAdyxGeaGU<P4ebVbb
zYiL7HA0!7_D8QqJH8mAfno(%Hy79kK00Gzzy3{-Nj?&piE=4yeY07h{lPvt|<Hn}I
z*y!%zMP8<^iSWbz^hf5JtQCp+hEJ1<l|s?WshZeZ`?+?_XGl0!3wlZ|r9Z0`vgO@P
zevC-V7CyT!STCrK#*$LktGruCsmDO*A>ktgU>n4mNcMaSu7DT)U66teut-^ce^K_D
zIEU~^Mc{J4ixS}+!-NYlf%w$Gg|;viEU_#J;^QtXzZ3BC*cxf`&m_(h#~Ht<-sm`!
zGmN)ooEW-U;}>`Y=T=-N2w5EZkuq)17iMZd><M*}B!R>pocwFu4V~3Gho&lJUuCPY
zo>6*nSL;?Cz~~Ei8^hJRMY+Tu!6Cw7@rQ~L;V$tBaKD^5grbgDnW2GQ5bZ(x>$*wG
zYkX7u1By{=d7<+K$|>qy&$FbT;GGMFnvVweoye9-H2wdX;|aE)7jw=eB%xc%^v}A#
zbpVY%F*l+X4~<^Qjn#J8I2q~SKi3?7&LXVVL9q*%Gix|rti|`<>kA*QZCb#)m{hhI
zLxUBsDT@;5^9lQvuktOORf-zVPX09z;$pK{H2k!8x}Q&c5Do`h^r6z_h;tU4@tS4q
zSToX%K7!h=w~f9GqlmQ8Mrhwx7X$$2j%w2ruvHgn`?y+Xzi)AloFLnwagb)j(yL4$
z^<xft(+)lCd_sw(U7nsA>xIUa>Fe{dqt6W<(v$@xM|E?9fI^xqS`QyzW)?dCOp{nu
zlo{Q_$b&G^vjf9R58K<_fudQIreL@*bGB5||4z+g-LrE`YO?&<YE71aaN$Mv`Fe^y
z-%;~)?s9I2PV6t;7v@6LH$2c3h(7;rrc+f5tr?OIZO+=vRI@D9{Uq_$B--Ngqx+^G
zai3Yh*@F%E2i|3dh_SJGL?|qR^0MR9F5rC1ZitpGpPJ6MEYFcyLzp8@$LmQUdlBT<
zkM%Kc7Q-P$`Lj55QcJg_*z`p!1g)sH;p*0D4JjIZwU#<4yj5R0X^}nN3f5jr{4I-I
z;%Ut04`&QDv|ee+UBsYLYvknKH-oAP?J)&qN!<YXBPbwcoZ0vZEl@un2mm}<F#S5f
zW7ReJ8|s$NbUxOH&cv3f@s#;QF2~{wRqw$IhG}VmHMZ+k{a=%2+3NvHn#zfgjR#b&
zqDM{*!yIHfS`K{jp0VJGq;u4I%w{B-VjKN4y633ZX}R2?B2bo+TbOFwZ<5kP(ZbD=
zAj^fb4>kRxDFw5s5EbBL;g!@{ORD#Y($>iP@ylPm=H5TZ2UGAlakD^e+WVSsHZ2ut
znc*0nVeS64+>K``igM#r0}NT6N-2vyrE967ZrniJ%+QovkLoUe{YcG-FvP@rB}35X
zN*j3MFx(^}_-+YeS`Eg#AX_<guI^1+UUan`+VU^n_HMv=pdv$UVyQK<mK&v{d3rqE
zcJWe@Et&vZG;)aywutk~QvURGKRagf9!Iyg6<i<Xkaw&tH;__e)*LGvV5*pG+TiHR
zH~ogXfAX#z^~tGOf;1}y<_+ZaeCj-YQo*wI14*vkb@RdZa&b9a!M;fKW;Lo2I_VU(
z9(}TKHU|&rgCwmd@x9sEcQRa}0YvK<X6-}x#!vEyUGBqNm;N~jUy@q&?z>B;sS~Yp
z)80YWpw-uMkzezK8Xi2Y2@|vkNWYr+!yIH5cM`*LLvMzoiDhY8lFPOl^BQQfF4anJ
zA+sE+dr!2W2Ht=Z<Ala3W7;03pv}%x>F6ZyG8oGRv-vaH3K^E4Mml-4ECMF14onUc
zN(%iDlXgwgYONU(ji)+%EfHw4wOrbTLJcnuzu35i$#lvg+VlVR6Ythk_$JA_+fbSI
zLq>}3c#V-8g<_3Xe1X}Oegv@FSZXt*QqF9|MKScyn_=C%U*+!?A3NG&`XWtDmSnrs
zf0aM0c4g^tmjQay0i99SE1wvNwzSKW#eRRK<mPY%q;2w?sEWBZmHcMy<S1TtW<oqM
zu~;u89qi^Q9qpm3ZM;?sS>uz-VXUx%?2e{K*pS;#BypbU_^zPe%z%@69Y}D=E-DVk
zeO8~div}2k(%3rgi|L8m^M8_ya;po^7W=`*n<9q~GH0Ak`ox9B@h6@%RRm%RjGos_
z+b{kjbV$?ayhB&oJ75y#+xe2l1FAa=an2>Gv<}lA3uwNw5hQv}O*Qh@&ZwYuo&3$u
zw;D9&esnG7C-HMh0Q9;_mvi+r)o58*k?Bz9)YKK}k2XqwC-|u;?y+nr*Tf8Izp<FL
z(Xf{ugGA%GwOauPM!6K`_~i|h>05r(gIUD-pGo&Ch=QyGL)y{PTCZ<q-88>qQS;Gc
zds;^Iv7i0Uc~j0SU{m9LLR=UD<Ff`(c^+KvSYCVp8VjUN)!nGc(mjRnK~ZvMCukDJ
z`wmkMXg0#x&mcH6VmR8*<P<@C2^ru0uRiqzCgPVOXdlO;(t!JcVgGQ&K~f%-xa0tT
z%mO=*GnQ2W$_^);w%Rf)uIEP%5M69?#%?TrfERDJnPl@I$NS9B<9agwq+X1#9JMUD
zEVf<-=+>$*RHE*>z0^Bpb=zVXXt8=QDP>C>uZq3_nk);j#A*^5Dmk;!n#C84oZG7W
z^{A~FvIQpobCHi!%F%RD=?y<Uw<aRJo=?_P)&kqvX(rM;-_T@E#d%H;7|34dpXHYK
zH*1;P4q=p0*Kgkp;=E(nq0P=xfh^tIKYdu)_?X@spkQnAFH7)Vlu7)|Av(XRN4oiV
zGv90E<?7#R8IB2eJ{@r>y*Yk$vDZYGYK~3<N%Wc1{7KC?oMSXg?ZJ)O0bbD%gyA^M
z`-P%U4RkRv7l9&qv_<92X3?QJSwPnEJ++Lh3AG>E{3dejHpod0wxZ=9fdp>FP*UtK
zdA?Z`m-CdoXnUU@i?2L0a+0NdKz?zk_*Eq+sD(^c=zz=f^l*LL=Qfg;>|3j$H1<uC
z7hW>1OC~OD`GYiz9fgC|H+x`T1!nQ~Xbyk96y%Q<g+$1|Jzp5DP{TiNB!io`wwFIi
zhR1(ydg+K+S$X9Pxb>eop1c{UmXMsaMqV;gV6XMRsMjN(4gs1R%6{t`#Oe+`PcKnF
z*gcv)XLaQTo`bm2WHC#K8)|C6LL82v*B;;GzM_W#Ctak{`iTVa$BxV)Oy?2`_<LlA
zl@uW87e8=j=Gm;^Rm)Iy;lr~$D^Lfhun<fUQ_m;1Oam1|(^Z#|avmq3D&=x=OOd#m
z><7@ni`^wOKG5%xGiiekIkPdtlni60^r=Q(-LfY%{%aSNg+>hMzw#1Ym^Dli+k#Zx
zMDBGYO-JfcWw!iArUb9i9FVf<2+13c(NNS#Knj!df%Mf2f|s#Q-Ifq><iu~S@+J1h
zF98g2mCs;Hc_Fv2tcoz3PQv7}K~Y9|wxdH{NMde}^L3xG7<b<snLx9cM6Z2vnKcWP
zyoq-P?eB4oGdHlp2;}9?A5bRCwO>NWkI$8Q$Udp@I2SBU=5{$tLaJP3A3Z3vMA=$q
z*_Z;S3TwmA5oGIx4cPISM|=F@e1WCmLbF$1$+X;toENd{tBeGDG4Dm1kMdj~jo;Y$
zLOFOMqolraPH8{X;B%o`NZPvO1{$bgX3*oyU_GYh1xq<dONvhER2$*O`-jQSDXTNF
zo?F3T@xa(=x3UdZ4_U;m%Pds$ugE40|6ne6{?Sg!uU%yyOxah~I|EQOHtf6M^+3*0
z#Y(ms&pxl7@9$!RHMkWU#n>{#6RsW)h@Z$r(5V5M#Df|om=DkQa>&LrrbosgSV^qL
zgB!mkZsh19M}ns1E9bE35XsBTgT+p5unUfYfk#0rT|+ju=a(mX1nZgdV>AJIG{qvn
zcVbd4PIy(UodNL%GW-}8CG8!WLTLBuIVtdIX<|)pf&`TUm-G|bNB`xU1KjAgw4Zdw
z5h%L)wBL5teQj;^iqTaCIXf$PZMlp6ptLvu&FgB0)I6E3fFv7~2xgeAuxV45$cM7e
zxn#EYJIWLO`)Whec+BkQ@D05KW5#83Cg)oWMT-sq+xQQx1#K&W#(#B)IIScQ1to>J
z98NXMuF0E)Ln3uMOkU4_s5W@$E6pPZ*-tv%Vp<r%F=ARNHx~!nU^(PYUlNL1!b(GO
zS#7?hbwrjC=hkkym|*bv{A&f`A06w>-Y<fQZ+AQ(?z(N)l9KMR!P`VNRoes;ak*jl
zB5>SZ;Wt;15hX+;Vfk??L<W`o-Q;B1jQ%4y<5|6qKJ8Plz{BKJ@IQrWg)KdygOZmV
zzP2(h=oCUD7~_QBKT<d|%&h)OZr?WZIX35|>G+r@5gp}|FHy_TWPR#xXPY++la%Ho
zs*h)Lu2cbjwp|~mMNFAJ+NhM_zyy$L6SoeGH9Mrv#aBQiQR6O45AqPW<rhqOqZ9ez
zc)WWLJR_}O6vTjO`{=h!zA<DO$A4P!+Qs5rqO*y%k@J>QT@`y>YPA;;DK`06y=UwO
zrjqp|MO@J1hY*Hx1xMZY$>rWV=qGnAbGkBnU?5h3dz>|tY3$7_=<vw|9F^+(-$Is)
zbocz^Ty#@r6c#0}*zM*UnRi3sACkot$kEcG8G$V*DBtYlmrj(?mET=c`<xNP$vp6S
z>Y3Np$nW4+XE=2Yoldl*;AT5DWnUW;VYr?;M1$Z+J^7&8Mr72+S{TUhXWA4HzR7fU
z0)fy{>s3`svf31+R(=i0+3y5VF9f3E+X^5j^DwcwvhEMxvSIz}Pofmd%r3FHts;>_
zboeA^7G=d_6{%SosB>w~Hb=Da`zq&f$1rgK+W%(76hyQb2e@6Z{LOx{6;kBd3QYeV
zj0j_P4KerI=m8gJSccN(<2{_oo<vL0i?qEsH<U_*E-)5OpNn6tQ<DvO*QmO_yWIur
z3As>pRPN%QZA^(v#B3&^MKFrQ8EuKxr9`MF-0sXsGBu+@6MJ%kH+RPl*~ReGq83{z
zSI|afg$~Rd_a4I4=-J1yi5PO_AAC~m4=H5>(^^#F{TKAB{He?h?<<SZxX~@LSMsgy
zt4tuEh>3?e+h7ND{bT9#sPn$r9aA;L{sn2QKy4uOS+ADVsd2Mg9}Bx3)or;aU70V^
z7AC9UR;C}4{zb9lm1h%P-r>V&+{(Fn#s7NRv3n}T$d<pJxnbh(B$a+rvn=Z1@-dS+
zv)axk$8ZI+_b0?^;+`_IcL4YF@!nzyF>LE;g`IP0=%s4W78th|>Zfg`TWYxF3#wVO
zF`C0auU^^Y+9lMEKd7H7j>qU;Uy-@;EDLmhZc#0kf?MGHQ8fzl2TF}7Y+7C~w@dUm
z(;)}fbB%Q0TvIueV8_o4Z}rc8UZvG1@tExWfK$1mmJn}yB@qDo8)%-tBn)D~{5iqb
z)RZb)z5pIcp0U<sRUz9Jdd@T_qr22MY`eet98lb8={hIsIc|<7rt(`>`@=Vm?)w*T
z1g_R>7qzjJrYhXNZ!1RlB=aDyFVhL7=tclH7zRStLkGMp357OLmEoraVuX_|%Z2-t
z%_|EI0bK-CfGnVwNlm<IE6;yr;-5R4K$Dmrq(-?Gh*f;%V(>abN$OedE8}oN?hVQ2
zrW$au^dNj`_=TrgI=IL4Z8s8O8`9^HfAUJ>41P5u{Bd$-f~uVn{8x9GFf)9^+KP|W
zk0VqMWnlMy)WA2qo>yh&@bijhU(rD+WH~|voItk$kx9M}<{VG-K$QLFWc<C0ip^1q
zC?BD3nXTl_ULkn**Y&Tp<(9O-d^eX60@~2C0h+~w7N&uD{#TD@t@92HC7%g?Gtuml
z854$fL2n{>QeS7Ay1pT*q&ZXG6&3hP{%%d;O-j`L==qQL(o`R!9Bdzqbal=e5KusV
zX4!<lC3W$@kcDP2@=2d&4;xe<OB~N6{E`wYg7CX8WR5l3Ij1*wl};=yX{$9y-B&G+
zf*#jt^_5M%LgnW)_3(y7&Q02yr<tPkt9G|C!Z!|Bx$J634|VL5YoUuUNjh_vgSD^n
zA7>^wWH|5v7Z9syH_-7VQ=LF6kU98FHq|3}4z}^S6dnT^jC&bl_{b+;mp8{r<nc0R
zd3tpCP)JEHSNvz@p`>ZY#Xl3TbXPNAc6?R6SxZM*uJg1;No=lPzOKm{R8#r#nB(yd
z(|$8i7&^%ySZhMnj*renMTenzrAwHuGBejDW6zq3<4su6%hkHfb-?mm@QWzR8@#Lh
zS86Z_Ki;$d;7XxSwyvV!h_eNY;IMd!qJJh`)gjMH+ytq?^54G=pvQT3FDyu2#4Z$K
zR=OOpbMai`R-F8q=DB<9T3eN2$%eLOJlNNRuk*b{nS(s6rgGo@_}BYOLte4&eR-<r
zv05{*S2+jvtM_ld*%RknuE$1BecXAD?EJ&l<7uBzo%c9(Rq4G?4{uuR1Ai+8e6=4H
z*)NjG|Dp!$tLf22hTA6f|24RCGkrUH;%dS=)`@Jn^sc@KyKC1!%YP*vL_ZzddFUEq
M?eI&@&)4q#KhA8hC;$Ke

delta 32897
zcmY(r2UHVX7d0GgSRTMfP*j?QA|jw5A|-$Y6={m}D$+!%^gdWo5s=UfU8P9xy^2yp
zkrqlIL0TjsgqBc3lK+P1{nz(>vsS{&%;es=r|h%$nGEYSd!}mkeDd4>@yZ3C){<b&
zJ#5isoqNjIIP{#ug(57vl(05}kZ;WUeSB=GF`4@uxsxoq4nNDfg|vZZ^dJt+ifTem
zWoP%7?1i#_5+Ti1(-+C@Kj;qEhogl_9wzZi#v>tdZ$BHEgk+(JC)r`FEv}>0gZzA9
zDBLF_<D2=a%RB;$vUN17%}~A8xvc~Do7~gMTOIqn{8plK!qraE+a4G$5gE1{Rbh|D
zeJv^_z8{d9Q&~Do-<PoNIzTEx=Ge#^1SbnZ7~6o$3E8<Gg}4`oh^Y(3*e6kTcAAQM
z_7~!LMS_L7ccceW+lvQ{D;{bv-8)sg1?%OrM&rE#mgDCf6bIX{!(azPtqj;;YU(Qp
zJzbyJ3%Ubp8fjho%yLCc+8w)fJGJ;@yA%`bTz@JFdtfS`B-p85uTNd_PR4|E#Gm4W
zMoE8u<*-|yoN%RE&Yf*Y@NW5(V>41T6Fem5tRx)aV~iI2!?abscdxf0RB}v4wRz`)
zV`E0P?VjlN&YkNI)B^>7{+m#LSTEM#-?%x4+OGmLFj(_jm^2Lb{ell_;@Ogyg!@Gc
zkp?NZvfAvQ1yv4;b|iO`c^?c_Cu~Hb3-UG}UM&m_*N1!m)a#F&wD#b#k3XJmiI%_6
zSWqGC?Q2Z-PN**vX%LlteXI1W#>HHHa=6)4MZPdi%c$Ij9|jxMkCi?E{DW98>$bhy
ze*d(H>;YHBTFqxk%i|{!DBNzxb+bxPGF8^R$Kni%`#a}7q;OF#QYg;lPmJ_p=;HJZ
z(MXG?0L|^HE~Y8;B12BYQuTozaTMpEkP&RSr263>#HsNwrJb53JD;wuUL^K;sVlaz
z!9unDt=8FKFkxAv0U^5pgdF0~Mf0-@4>dIPGT`U`Fey?*pOps*G3N2X#IwQbe=3D~
zV-`&C_&+CLTwRi(*q&S4z19Z87dWg1>vDolNk}7)2uB!fQ9pmYUprzw7CO3SB@ka1
zQIagVz?LuvCB%3i8Q_)lXw2LeuZc=A`&WS!_ToOYC*v7ni8IFX9~m}_nCO3W*vw<5
z{+VEd$24#RpLq1ar7sq$9_nPc6J8|!{WofBOlYGz<^iWWTYT9M&RcAId{Jcwj*{5e
zL}S+fUEG5aiJ3mu%oY~&jav+2CUtM|0kh71kIa~qX=;v&-Fi8n)zD2UpuObuAoq{m
zr@3)qyXFrdHwbg9h3~@AGT!ET*yG~oX&7o2YFt$)>{G&Upl|+9k34o8#h)if-}Jwd
zU#!e>x>U4Z=I1}R?e-vbo{3{M*FN*u1yY`0Y6`#EC5=2S^HcD)h(#h~yM_F)Q7L(^
zH0Y-D)}ylt#8ynn`nN{~e_y2u)`vtvO>OO6@Gl=x9GZ57A7*1475WN+%lETJy!7qg
z@xhi~3)aIvo#H|T^;?QA=*l$2O<a0#o$dXH^!tlj*&YS^Tiow2`uo(NTT&<@{`YIp
zUl$kMFiBEQ%zD0?mbaetK~Zl$8D+hmM4R&lhUgCjmNe<c>*K8H=o0)fjkK&>=l!Jy
zE6w*4gi&rE;JCB0RM&G(9g(V?>%AO#=F<70&m7Vg*z|)uZQIC<r0J1i*whD*6R(TZ
zR?Z$SjMgW}3RS&7yBOm&6k9{``O?kX{|0dY<v(#daQvo*JSyl9gp=}=$ek>aX?WdO
zF&CVG&|UQ~y=3K-+`3kd7nWF<&`H#(;cZ*BI*Si~>}16d`$KuB)fY<>>3I|+lk963
zU^Ex--N^=taddF((}>s>%ZVD<D9_Dk>YB)P)?V~X&>|O=4#BDQ%?{gW8SY~y+$t`J
zg|Xm@<h^$g3&*cR_Xe1@Z^K4KJ?qvD#sk0R>6GBdWU8WlqHBV;#57bp_T74x{T=@T
z;Tq@9Ri!z8W<f#2#bQ7?WAn9zfv|=6!cXIE$q%@JX9d2sw^SCK^LU&C9}Q=k<WIbp
z`N_xAIbp~w?p8T`Zjuz+tuPaU#OA5X*mQHo&`NA@sy;cD5d7f_Ev|uMTdE9M$^6PT
zy+>L2T;T?B&r40GXiY`8P80bZIXWL8cTNWC@W)&?TABF^aXKb+@bis*V6S-<PaIwv
zUF%aBG};$3UdLH-mHvd;_d9juq>e9#T>PPqzBq-fk<N83@57(6JtKrOeH#!LCW-Rm
zo8I9GPVuGVBM_pBF3?ty$fru_Mbs<>YuM*6r63xkYkHL>UMt3R?VOA)*sM4S5~OSN
z&<9-UY1;$lRhX`Pvz@}2Ly00uL$iEHjPK+CBlc6<Y1(D8CGO0)dy4XBaj8<oyLPU=
z@e^TBmmd|{zI^@=d4zNu-xgmmjIA2y)Om<^n^j?qL2}{)s;R||2^;Gd6?7NN9`^ny
zE*^7hP#L|uO`B_Ui&xCx`@SiO?-YE0BY=peEFmm?@0ICv29DWj){3danNtsR^Cncg
z_Pr)Kii(&NnH<EN_6nRNc*JVL^TW)VG1|45Q_<_?sO>ttJx4pQyL`h`ig|iC)89)G
zV>ga+K-Lv<@woQY?O#;WqEV;3xFG)CJC%wPQ^h*9T~FQ}=q$g1Rv1}2<BusSdqP}4
zlw)3$kC`vhF3pdezEM4|vMQB#_ViA<Z%*$+s>aSGNGp2Yjzs_YI>AY}lS#S%3D|nn
zJA7o<*6(RQ9U=CydHq{gH*<XByFbsnE<mn2_hOcgav)AIl`pQHmtq<E$D9nWZPK3k
z8IhAG;||4EJgR3O{`Uj>UX^~VRW->`HF`lLVKnCN{OIZvq+sd(k{|-=qr%fnx0no9
zvDWy8>0%34Hp`~Dd2y9jJ9d>LTzbmIUQZFL+bHu1`S7`DkwN0ui0K*r#-(C`V9141
zqnX8i&u8)BUHhFb;;{k0VBPGl^}}VN8jAW^?Z)~8=Q$~dF#VO=)fCbDZKs=7`H*TR
z-pF9SXn$#%M(q`iS|ytWpRRSPbNZ%Af!N?9{7uYuO{HQX;!wURt%tBuCR@2PBs>@K
zQ8GzPmS26?7@mjHLg*heOC9A#L6)xi12ma-t<uy`wBmczz$7{yLp0<S52+W-+Qdtp
z<DP&N;(K*<(Oy-uUpF1xYPz>F?&qs`i$pxw*-1S4%V)ezACuZmDWQZ_=Rd--VT;F4
zhkHkErI0H2v~HvfTAbu|Av%8=lg{p_w+e|>58b%kN=!EH<E*rEWh3%JHpGdedNnyi
zagI)3Wqk96`S_|MC7CB2=hF%=@2Lk##rzeY68rUI84oikS(5uw<@hG2OoQDw8DF>S
z!#xY9HCCa9W3$`ZtI*^$K2c<lX1ZG0^vXQ1#=>{2z&Cr8m}I9ih_1L5+5Ib=7q#>F
zg#Mbb0^hBUBf!%t##T^(iblng!<H{!sLNE9i{ut=-j-@t%9u=_eNjjFVCC%V^Bq(6
z0lK1TOS$No_!%YM%vU~<^?`lX6lI<+9YH4#9U0WVEvklG4EIZ&eo>C=RFWnyJP~2a
z_*YMp{Y|=`(%D;aQIq3q)b3yHKMpx&cPV^d$}qdsa}C*C&npQzdk5X@+(7LunS<{8
zO>RGtH|ed4XP&R@jgy*-^u93CXgOW7<Y2~)()JBZAxx{MPNx-kxhhf0Qsl)g`)t|r
zom&HUyfuvNU1pTSMh3@x@)B%I1^rGC&E59<PY8BKmZb0c#Iestq6D8V_vQ0ol;(Z}
zwm7p&HRRJFNVJ9{FZ#kD8}?>v;9<)cTYraEDAs+=&$*%Q)<yY;a!8wYEG;-&tGILF
zZq@3+<!uGVt8!mUQASss0{Qk_&{EC`RMYK1&g@viX^q1^EMG_-&nBK|8o7kTjsSEc
zwUJD<JsOt|>ye$}cE-;*8yr{dMO1{rirL$y_Cuk|CRTS|Muz=ueI;WPEi#_9nV^%6
zxl7hxl`ck)zCziy@o2XZdVZ_Vqwbx28Ba@@o@s56mUX@*p;6RDKXF}5<OA-6@9maS
zZo9_I1rIl?dM67vPlo$wKIt>y=_QTkBs<fT&aK&XER=|zbtxP5gh$!UU1Ypcb3<Zt
zER7-VX9Woh{yf^Hk#ahjbWeoX!hWQ5KfcTVNiMPk|4XexPXC7}@((*A&dG`_fN=_`
zGFWYD(D7b*mM~50AVwVtsVuAQ{&dPP(Kf~`u4^BEW<$G*cjDuozDl7}ftJMQ!Bu^C
zf0vw0c|C?U_EqUdW4Uc3-@&aiXEA!hYp);;KIDd5ri%$&qpE|yjQ90}zVubQ@vw23
znIo#F&PWs2@N=?%swT`yr}%qfYFv~`v`cq*@^s_1q;aLw8=FlQNJnyH%%NVoSuZ}7
zB*WJ2EJ99M@`@0C<C(%UyWR7C&|6cq$fRX-1VLl?%=i?{i{5J(X-|#P`KGM8=mMpW
z5XE7%D|_l;M^z+7hr7HB+=Ypq&yXhqRr;;{pGeDxAOFX{M}#aV)8LUY6(jw|WN9Dr
z$_i)fA!=bvO+}wLObnUIcgyyrzp7)O<z1%x1=MNgS|*P_t)%-8MX&Xhv!$ynWY!s|
zmqIELY>I$e<@z-@FO*1}EjV8P`79(`GaT)~MBvozcj`?%7^TS{y`6E{vp4o=5KabG
zl1B<<xHt7<YA7WasH3L>jcEk>=r-oqYI)-XE^~9`^T&$R;3uh4t5pRECdoTD+`<6a
z*X@T)C98iRhgrywafUT+MHal7O)1%^ePwMZcyaA{>W*d$5zRm%mjiMk7u~kRsZPyi
z>sUSLPIAcYPxbo`##oev4Wn=KG|4GFpK#Yml=5VB#?UfuY1+Q5k+hA>rpnknCnIv0
z`!po?qUGN4BQ*vi<JVtgti|o@G{{k%Cl4?EA}l1IJi1}#`jzxSNcCUjmnR20`TNq^
zR&x%%<Y!t}_`c7-RvXnF0(mL6o2fcq)j)&|${~1qStYB=tH?~ozvoz6WBkNM)hyXm
z@)0Ffc+G~OhWrxFxWEn5xke2>O5M2lNR?;LXxP!I{h`Z}k?;}2dAA%7zQBG{bhGsq
zXV^%A*#uhYQdNF9T&&lNWMn)yE@m32*BQuWDiyP^6<F2Hn==mG#}Y5B9LXAvdE)({
zcKZ)cb<6-U2R{_krFl6=%|1w_bKkK6%DW6rTP%vt)-^&p{?M|QR%uAo=o&NAq}Q<o
z&&40_ATId5QkQ`4q52viEuR?JHYU%BHx}g+Gg`-E;_ytpp*~Z$Xp&qL=HeF4GP~Be
z7qL~UXeru(xewVx#<U-V8=F3-y#b~fp3eSbami4+VD3<n84bVodStS4^8NAIH4jv=
z*)lrf_|D3JjxiiY!iN=)C(hV~EPg@=4Jz3M)M#MalOz$RnpVj&Kk>$@dqk2>=3lyP
z8_??$clEBl-tS*RS(j8t89MbT6TPSR{3!4ZP@Jn3kArI7&>fLNH-#OOTevQ4{n^+|
zXmKJRGnvV9K+*Z7F_ymP24S!>Fpb+c^aI`}7e;5e7aHwYHf`0ocFxLao}`(2;BX;D
z!!ibB5`r`Os(BXt^jX=Dy*fzGzv7NVKO^V?J$?Rrn?qPq^)*=*gG=EbIUF{a|3fc`
zx<D3y;*}`V#R2E#v>sMgD3|ztv$BpgGYUKqwiNA2uDl~-Gpmthq|c2ErvaQ7n4JGa
zZ6EbCZb*>*l7!gE@E|_7RT!mKrD1};$=o`_e`b(4uZ(<7mHKqbYOUxH(TuxV_`v+c
z?1d0b#t0{ID0*wUQo~**_Sb6U1462UiOdT~H?RgxqEXHIV}|=o`vhUDWd(*IAyp$e
zQivLDiZ^nG5rDMjde*ToE1y)=b|hFjVtae6_uG>`Ty1{L`5dD~vh*>_Dr#Tu>yebo
z$)1+cH8#f#4WP(MkVMn@hLV7&g0codLo*w$TE_QTI8!=Ok}SUABZj2N#OP=&LGV!7
zRswwD(R?uJ+(zmcpEINs(SsCO7#+oF@}o7X0N?AgPNfPm`QOUv_e`$(MCziYYAIeE
zGXbrAlq4K6UdyuVbxDy3o-QzJDj7pzXw0)0U6lEGof5NEpLWr6q>EdH5Zl4{DZpL8
zcJDpQ4Q_Q>eWyFkr|eeEAtG9{`EYSivVE$d+We&i@6+PgGV$j-22C&H`JxxBjSU8H
zw)PeA-7eJ50BaMQZk6+c63-&0V?G&2-q+%>(MEf{=(2AqE4AO>W%!JYnHu~zZsE#$
zuGWLz05>;X<32*lU$mK2^zr3wonAPUe`W1hpvZw!^5fetcPb`c$wBA<tEYXb@*$N5
zhbuu@iSEAs3E`Ec)kn0G`Ef9B!~C}Ww5)hTXZ?<IHf79H)3Z_QF;SqkDP41g9#b9R
zidmZfHd;357j0eq7U7=Mp*=49&Vl*x?8nNiS}H$&(6Qb{N%huV;}^N5OK;*OWmWJs
zh(^Q$9q(flX1izH5UTUf{i#^MgOj#;qhDx5dy+I0h6yQl-RK*t?z1)*+o}m!3!FT*
zo|vh;;r+=;PTqPL0Ed7qGg=`*@WNn-Wx>?Uys7HZo$t%NLNKcJK0mfIvW#<{FMK~|
z+>P4~!rWQGsx4G&te82Gaxbd<Xn!q?n(TfD7ZSy<;NG^SLJr~~5}prkpc-`TKmN>-
znQ3Gg*(+m3Ww;APN(1g(-@lO9>0dt%BK1R7?)8~7Z#xrfl!lM)TPSTa&|duFNV%=$
za1)99m5TE)!B>xZP8)|A`X^*OOl>_KcxAZpdEzLluk!wn$`KdD6(VXaK_lKyBS39s
zDre9S*||B^3n`S~2l?aW<F1NQH>{4uo6y9bpFGqQgX)QZ3*l=GFBi3?>V9(En{13<
ztr-ujA4zxV0ldIM27kLs(AiMBrYZZv`G=7iC>yAWT=}G<)K+wKQH=W}`2si3#x!Ge
z&E>v|Ry(N#|8TsH<B(n<FZw7)rU#kSSX3%1nnx;ydjCPH(XCfimVy*ps&!U9>GK=4
ztz<b(_0f?Vg~JoJxme=yCiJ1xQ*dEw^7ZEzSOUdZ7b$q)Ari|yS&qN%JZ!fI(btY%
zKz?Fam5kI0cLtV9lK0D08JJnMm;vos94|txFCM6jDO0i`@^p6FyHWIW#n)O#E-IM_
zLSnz-6(YWk-CqelW>_<6Q^XX``ArQ-)p~F-ck}kC`14PS5<1ogO50@e&kbU{VhZYQ
zUo>mFpxk0l3AT)nypbW}4GuZh*JrzuHJ>}DBuWuq{{aL$cu>ptYxifb_D+6KX}^og
zHWIi>f@mTc#Qe?1deapW3Q-e5xvC)#iDlGCS2EP4DE>;7z0rq?(w<7Vz026Htex+u
zPnZ;m7{aEd1wL8O+b>l>Io6~_8#5Fir78}q%fQXP$uvaeo4q|R12<xdtsy@=>-)l1
zzg(7bcYGlE%O<|5w%hi-VNt;kjjl4P?)0@<m%5~v#<i0lqHZKVed?UzFwl*0g@P_c
zNy_{{=a!6=fj)%aot5B$YbFN{qtTb%HBgJgY6Txlv?pF7xg+~Dbdjk})GO9&cD)Wl
z;{$S(2fgk0gY&ZynVXGfnd@P39nptU@7RT0E@=}k%H6zj90=`tcFC#-12!^bBk$He
zKm+JdL&4$~je(JeLB<jDj`1l_;hA>m6Ll$flvpI;A38K}H!u~?_pFa@VkK(K_z+dA
z!6-6%Y2LKI#&yNB2eA+=nTf^SC>ow9tlL4;0+5H$rce8XLZ5Xln&px1ZRn)j=4xN{
zAc}LsV4*StI`f*AS6gj9QyV$ZnwOVFk<DtQ$|D!I_6b-v*5Bqya~XpcR+PfZZvds$
zRUo~#Um|z5YC$e;>&el$t#ft``;8FrEOCbVWKK4|N1Tv&{d}<9!R7f2mds-!LQ#W^
z;;0*&Ts7pV?^^D{NC&+P&E({Tm{DSw;%q|ZCgc7!;aBt{)r059j9(bmyl+Pg26VQC
z>iC+GwzkGBt7^TT=d?pjwjn1=7&-}QBrQs#d9rW>53Vhqvq&kTmDHxF>g8_iy1)AC
zeY!$T=h>(rq*^WJ8rhIw&A(GF$3AB1cGfNbaREia75_!ci7z?8UhMGsX3X_3K~)=V
zj(?-~+P2Mjk7r4px=@v+=}&DxC_*l<Xs?VM*%&I?HGAO~h&sV*s3a$+A!u}w(s)Vd
z-i%x`aYIqOgObFvSSvE=6t6{gqA}btxEqyYq*|0)gV4sOd9D311%Au5&pvpA&g_Fb
z=VThj@=big?b@n?tmYFKcQ_a759}p&{i9K=Kd-rtFmBW-%MGNjo<qJ5w2WP7Fghpe
zM5BXlPKp-v3FcNicjw90?TU?G7CyXfXp;i?hGwOf+W=CAj5>j&530sjQS3TMh75a|
z26o=AMW(h)%#?XyLtwrBTbP@y|B=haR@B8CZ-bE7S5c_ND3VNr6mN%?E#l&odwsY2
z={y_%BH&TqMCzjYsgmKdW28aEmbrQb{<X&Yai~*^QGV4B8*glp7QgU3`Eyl*2lt3)
z;vAM(86Y|07}zj0XN;V2%kI7{C4EulK*q3U&jNdj{=k?HN<#*ob*-In_0fFU->YAu
z2d@Q;`N$jgl{g5*WA>Wo2zCb8VZ37x>0OE*)5X}n%z0(^(#J9bgEm&}xU~Ga$ySB;
zArk7Vl>6687KwdfV;8`53wQZ>5t#}lIQ0p(=GdNVCQ{v<!gIUU(Mi&tAxy)0sW)w$
z#IT1;H)P;qVx2*$nzjK0x-klooIW{j{`>s~;fz<6z6ExX^0cVavWR>!BxbfKVGP1N
z*$O$&olH;Zmea5GF4O#<gLi60pcJPQ;${Ce4V*o5VMS>H_|A#Bwb$J>KG|A#Uv0^_
zWMSk=jRb5cR1e?%0DUi(cr}H>U;`VC-8E>Jf9}IzE$gXTn3zi!SUDGbkj)a-QIG3L
zb_$F2RfoZ7Z(gaBEx-}_0dV93-#0r~yWaKLw=h`!Sy}zpJeXfTV9?RKfW?EqFqnpe
z)&A%o7!5?nMe^atOzwy~T%U&0>xm%Hz!vJU;bLfr&RQ+JZ5RItd%QU$=S7C$oW-Ht
zqp;XI?GpB&E-CWi|31TDJ7;5X62j2`K3){xQ?J%BUg6|xr=!ywt{HzR=NZ&+H%bt%
zelB|Debmm*PJaZSeB(D?GS=aeadCRB8CKG?y!wi&Dzr`*&Re6Sr9TG5#!*Qat5ff9
zx8a_*3*3$MwSYBCWc#RnNP88fNcb*sEaA=b$SUQX<(~zLUVkqa+xI|;QS-C2^`Fzy
zm_=<7e7ND9N7>=z>Y2#%*IXw)^OgUz*jdn{xH<KTDF{Z5IxUxL@_8#h;e)YQ`q$@9
zM}(Pp$MFwcF3>XxSga&i!n?MQr#yduRS&OC^XL*U67(6hR$4^e*VHW2)YLrxK5BY;
z`WbPg$ZZX6=4LVe<rWkMO?r=5xFoL%sRV6$wzjqoZ?7+SzQ8HIeHc~xWLWNFJ*Vo<
zveUWfj!kx<pkjlQTW@mCjc}+1OpLRMhggQ+`4(vL3akoO?)XS&FiSgZhf)MJ;LG|{
zXry>aGtLX*;+>LGQiUZU^zB;?J&E4Eq$*s98K%-DY+KR>f@bHGNoac$mx8p?pUy{q
z>itXHr!Va4a^v^fRTuSEbu+ef<$tI4-@Izu@d+ie5Peq=xmd1K=R?Z6{jFaGZL>Dt
z!yCb`u-XP(2k}MRDRXV2Q_Y%aOLZAd9ciAu3VfW`r$VLe`%;6c305H{lJ_iL4Skcj
zC@x;|@}kL{1(b06c#SJ;U?x+*{aBkqqbI)CDBrXqH(2=C*jAY8&Y%7_M|dqO!R|&S
z-=#$wxOht$`|i)gJ_UR-G+bcjTadQ1VU(QBmhrPd@?bz}R>Ak`r@$Fvz!~>;odK>a
z>gixsPZ$YPg_|$8^zre@p)*@rS`aVxvO^(Cc3rVbB01&dSMh(sgj|WS3~6W!bzS51
zb@!>U8w9>`K|AaIuj+lQ^;S*#V0h<DxM0gIPD)dv9SRJTbW?ui)BdkZh^s-H%b2?n
zr{(foFqN3K)zz>5{{H=0+DS`0bTZVmJ#1tZOk3+yh1zuKQ_j${GHSNV{sCwXed4t@
z>#FOoLwh=6lUEC2QzH{Ko=*RqzWO)$>({Spa1)oW_%w*|Pg4Y`*!pL0Oh#T|EkROs
zLCNcHaYb>l`>vliS5eYjh^z8r0k7D(7y%uh&Z?I=^r-~?e9OUo`kHe!O4t7eNY&#%
zeuJP}8+R)5M)2HsLqacKz9gN$8yPgL8+z@j(wY5A?YgPTH6_A!c6M=`r>{PJgHwd5
z+3A^^Cwp%%WT$P=H*h0d5`8|TaY_F_Z+KOfW6;3vL}hI=HrTz}T$cK#8<W1c_n!0J
z3ZB4hlQ#prpw&(>2%jxQL>HiFguEg}*4UQMTaV)oOhT25i;Ld}OY7_FpN4S?V59DQ
zGugmF(4D|h#bKNAa>t4~Z=%l)`ZgtOZgy7Ls^ZyjbeLek-zg<>{hE7r{r&s*OI>Rh
zvZw{76}5$?70;L-Z=ajVH7a80gXxO+l>L^km{mHDIdxyi?%XrbNd#+fWu>yTMQwSE
zFdbs~2BlIn!N$(-o>bxb@Zm%1UM}U|+rPMV7p<yXCuyFZp6&B^e3Z)H^jEG8>jRof
zVtL<MRm59Zs1rybc-uQO>y3?#H#fzzlLvgaR_FFpuPzL6sCnqZDMOjzCnO0#WL|dY
zO_n7$(w4Xf3ho+{)INFc^rk4CM?gFvXTm>x7-($E)64Aoeot5?WND&C4j~C9w@CF4
z0huy(K79$#?}ohQVoJg>s8#rD&ZEmq6B85owY9ao@-E|E9d-#8KC5pz_z*hFGuZ;A
zY1s*vO!O)=6Qp?2IgjvPl9Q8pi9<PSzfBxcyaT3R-&{z}ZqRX`saS-(Hphf+_@xMU
zI0b%o4y+gCrmn_IS(v&La<Wq~={H^ldU|-Ix{Vh9nY4CxQ>1NlNfarcJ@)=%(Zg?T
zF*@x<6-?7A*I<ij8P)a$hU>3)|Mp~SAqV7tk@rmkJlzmer70Mg9bH#;`BIQxu>36_
za~Nh9E?ED)JY;Jw!CV6(OcclW!oORl*Q1w4bJ!crGZ6@cvg$%wFm>#faE{|(R@!kM
zp4co?Rpizheq^-N4#kETs`12&xDtKH*kDGof@eSz1#KqA0T47bZFL9zO*c&iE~@o;
z`7X^#U0r<(n1SSlajEh?t>m-V4l-%$JeB@HPY*F%>kSe44D*fq`%_(MR1zgY(##MV
zE4355WLfXG!wRw7VE<AOH$8df<@!|Rul-LAub!@8GKv1hZn#D`E@-WX?LDSGfI_J3
zQ)LEWj4b^#`@j`3d^Z1tx#^m>C-PXY&US{;=cJ(;|Mo}$?|eJ6Dp!}Q!Q1O=Tmaai
zN4%Ge-6Q12?kIZi{ba78p;0J28K4~~SBO6?dw(Ibs<EG4sAvSl${=Hv=Z@0jmB2{V
zg^ZYM#?S@{v#~CZ^|N^*eC+wokIN6Qp6=0*yP;V+7t*(a@ak2-uLsa6Bhs3F3ea$_
z0jd4tslkvxEpH@M>Fb02)HfmHXY-bz%*@Q_5i5u;|LOoo?c<R`izX~vIk0+`TjSPz
z=qoOj>WyxstXpY-d$wmPRpy7bqFvyeFSPHE9U|Rp{8pV;)FB!zfCA9%fw{SPPvs?d
zrNkwMz?CPz78eGyg&O=;rXT4jE@b2Z+fv`bNg$*YrOc~~18>~;k;SD7?JVc~THWE#
zrqu!1oV%|V_m*R5c1LzEzAEU~Np;Qpr4Wdd@>gj&<1f?kj!NT)#}8L2AB(#S3X^mH
zE8aNlAGtgNVxD6!yw~~IB{gLybUp|hJ5;>ppthO5ek|N)aa0r@uwJc>$Ti5TTmi^u
zF&{q<D!iGrhJ+DR=uFOT3?W|eTb{CE#Y{WME7FL$XV0r}UTMMNz>THGBzd>SzaSlI
za{%h$3XDsptFaA%X->Ku&3n1ZElZxLv%d7z`sdrp+GoSLnjkDEeF6IL_I$o#GX+wU
z>IaM)vZ~kTwN|@L$TPYFN(sQLL`jVeXu`Aa0d3X;oL)B!GHJ5bOuBx#C7N4Ihkt!g
zSJlGV1>@NV_qB)-)jr{jYiwYga!9_?Q4ms8zuI+N&IxTUAF{Lg0|fTpDprw{Zrq%<
zI(+2F$O&HFk?L85rquc)dDv1fydvj-`EQxuGs}h*K?y<`0{3P&_yI6OV$6wrlQMtA
zI*X9#fvbw1to5#&Dy&<2CJQlkGT;R*QycuM?o-X&1=5RRX+f(fp^(i#acE&xR*KH;
znhG#81~#?LP03c?aUcU{l%r>d>WDt?Hc?`8w@E<BXPpmtfCFH+X+toLx(z;@d_qw?
zLQ_tGbjB=Sm#E5!q)>lP<3AT`C0<?Fc=P5B5k#ve9bI51{o&5HU@&1=6Z_bKCYtYj
zH<Foe*|7b#sEM)tnn9=eR#-xS77$g|x@AW+2bgpmbF#ns`OK5-(w{1ZRCfOgr`(58
zQZIx|{v8=j%F}nb@fmP75Jp8?2D6pW9yPze0ta4C5sR8sZX-D1>%WI{(DcQ!z7W3m
zw`(<;7_~Gamn6G@*YB5|F=DEw4FQy6Rmzbn+kbly`XJ(MfVuo#t&X<jwgb9#P8THC
zV~v2d9(iWKxMMc`siefEOP6X0xp{`E&)ZJjGA)0)vIvOWZwFAA-Hd0T7d@%)x;Mfp
zz?RYzUW3|JCJUf?Sq=am+LNN_o%p8$8?x5b)pap0BRQGB-6|7sVmN%Ig2i7nrD|+@
z_Vb#%TY>tRW?bVjcV!LVSJeOIR#-vj>UWc}`NT}k1gs0rm)9z2W!$392Y&T&bLby%
zl4?ap#;ii1E_6uRd+&pi%t6kKW3&9Kfz-u6VAfjKxvsdo<cXTH`JtRgG_4dxgIi&o
z1|cB~bfkb%<J#|AT=XKF_DHjr=s`pKar~Ti(zQ8d+G3fJ=0IkG<@T@95}S?d%t|+0
z2xcZqmGPclafbBC0&qGx-=&ErR+x7LxHN~d<w1(ROFL191*Q?>z-MQFhH<(1&qT;G
zw$D2l;~iD)lnyyTw)bmEU(#$^qyX1nn{EvcL+2Su$GZGe3OS7wHqgL?wJcen3!-qT
z&mq-+9sv@EPCFu9@BS2E;u5Bc{4QL&(XYU4%UV$4f|BL<%i`X_^5f;lX*VG=wlm7>
zfZzgV_4buWj(Yfsr22ff$!#01-1)gV7iedtohdy=V!mJGbw9!@wNBRzRHXO;Mq@_8
zHYhcD6Vb9H-UB|s0F(Zs)?q#G0D<8a)q47*0sH;ow=@yQg@H`T9kd*`f9ALJ^z?FI
zM_FOV>0j@)lhJj{Ezl7$;UmATy!zFhgguqE66KtNIQH*JpT1uu;LbhEj{`=TJ}zN6
z!E8AqZSFguA=aP=BG4*Ojs%?3w!Sdfr^?uzxl3Qrg%nku!X-m`QNZ4tUjVEt7~~n!
z7sedYDC_5~KKXPMKFZc{8*RF3mMHJW&;&lPjLu*}jEyF-fXR5T*Csy3wDXS!T)61`
z+&*BAZ8zf|7UG)>(s15L8(_*R7d4E`pSQZS3kEI@vufA`P&o=LyrJcEN%zh0F#H{l
zv?%Hh8VcVvE6M(VHH01Hg8RKVx;S2ur-sNiEJ$1pS7#{)Am1Lpwr%^Gs0xKZlpn9y
z(-Z(|d-kd_ckW{S@hN$AzT_oE^A`;p4i6tj7xg`_rF@ok(DBqT1w`(>f`UT(1?pZ<
z?J8Wkx)9Sgu99Yz`GpyA2rramRX3lkeK!?o2A?9ZT$CU@pnehzCjxj6dK)MKvQ9SE
zdOofj<JM%c@q=9mTC4}`ofAy$83Rr<Dv3A2RC>&HDe5d>s@$5S#)vEypP!#U@bqe8
zgH1apbA!S5{nLOWyWXK0eq0m8l%vx&m{l22ky(|L8}Mrc3%LgF3I%)JaddRF1&nB9
z<8S}W_z|h<$HBk1w-uyWe3)Hxz%67gX$-P#5BX<+bIf~TV9qmJrq+kFJy%C=%!`vS
zs-)^=d%gL8+#;6je)Bu#MB9J$6)Ec=xYf#Q<z-Rp<(LhKyf#?7-vo*phkt&UIpBEY
zWoovg1mau4=p%JE3E1l}!FsiW9-f}z!j6E6h>Oj4_Vz*yN_@&a;bZXm&o-64fVr3_
zU31Awh>xE)4_av*$vpk2v#~xsz1ir2>}0^s=1f$Du;&#Pcd`O6GuF%0G?6>-_9FLz
z(ZOqgisC_u@X!Y1i!pZA`q;HUU;Y!d_rO0VEwgn}p7$im(b_G1Cx_dd8h&<5JY+H;
zdL4CfYz-;?C~KX@ppm&)TGiggsHIZjz*9hP1>8CT_^rylKM?vsI>qNq+~wXCACTx$
zf<T^C$vX4~tpJX)=?O&Ldto3vf*W4WOKvGiyJ72V9v}ckPbimvD05@{Y~7uR(+XH|
z&t^7u=Y$NPs}e1VagmoH6zBf|m_qxWL?wEL=P@(`4mgs78RSbCax5Le{6(!@e*=YJ
z_NcPt{_Il;s(wEpvVXp?OJ8j89dQ|GvwG?MdE4Kp*y-y5j?+O^{%iAljq;5jV%!8l
z;XXYKSSxoElR>*huED2X0~kObaGV6#L5Bh~Z9q;SBKPz8zya_#TFAukX(e4FsV>Z8
z8iQ#rLI8n0fm#oI4q}TV3vlZlu;A;iKxedb2`H|2j93P`tY)W$Y&)Ca?T{+L0R_)z
z+UO>TUO-<V3-4irv~mE$PNeY)Uo-fDTA-$WurNKNjs%l&>ws>p>6hIp?$=eJPZ*hN
z=v^m;ip1bIV{}!v@ZTa8pREEZ8dzjrv$||cjE#>MrF&F4jYzdi2QQ>19655N>iz&=
z`2ys3_jt+$N`sj6jp5{Ym(k)cxx#?8J5!S&lQ;p;X1}llKxJ&~57q3D4YM~*wW)3S
z&ri2v1_Y5CHlzX;G80q?gW0+R*dCub|71f$b*B+x-rclA$+`<tOq@i^(<AlC*&^eI
z#mbKdj*pBVhV>p?{yG5cg*~CM9pDt*h^-wts@}-M!?T%qFC7?G6VOec79hEYZx8UR
z`d@X!?j!&b-K6YI7*dIXQ%A`_Z3e^)Lbbqhj@lYsC=`a^@KR2davfxXp-!r@2|#;g
zO9VOm6)TF?i6d?nO(Ar&Mxq?x?xrb9zJgakkfj3BmEzugrQNSljS`bU^2vY$`gX)c
zc%kEbR1kg+^gNWt6oF3A_AK&V`<dekOJ*5ntlod^1(W8k4L$WYC21<QJY~OxRBqSQ
z18%Cirw0n9P0$dFRI>U-KqlM@DfI%amb|!&ZbkSxn@9!wIWxD0OcwfX0=<N9m<ZrA
zR9XiOjLPVB^-?>4eQ+%Vs3uhJ(zzRbgEzW#c>d=GWTEaSI*S<x1Bu58iG%(Sd8`b7
zgq52iEVQ!n8MC_unk_*txf&l^lntrgJxTJoa~gG(&cj@T_HPgluCA^Rt*x!c;c6|<
zjwJUE`jj8<>^EF=v~rSwT|eI(exLYWEcLSqC@~~Zi)@B3ucCT;(SQigU&lr#aqC>S
zZq3k$DSy)X!SLD*!t$t16uk(Lli(20VVLhzUhDn=G1k(WG%B$GkWSwp061NL`JVs6
zkj?Vc56z{94Q$K-HN$<592K8q9;=<&3u;D8klGdQN{p+L(&TD#FN(!V2LO)N#WbtE
zWOIqRb;BB(3?w(uQ_$djL-C$oUn<;TTT)^&U?|VXZREI-`O0fy_{uMCc`5+wdH}8H
z@;1kd7cX|Dg3|McLw@4-pHH_qxANdkEbiZ5<%U}-YM`3_WKmy5(J_RGW-gU5Ow0#t
zcc=%@HB$e#_Ko{L+$=$L+A%~}2HKM<1aZL|la_E!kc5>t5h?&;23abL3Lv*DI)j7n
z+AP*@CPJVWzA?dGztrTFnac-r0EU^o8nFKN!T5_3n|2*mo3I{4WHvohCErJF6^ay6
z@^Ow~^*y($n&_mM_ct9Oh=##MDl>Q1bjz|=Fq~s?v9Ys4{<G1(Od5{qY7SuA0K6yz
zn$mKvxJG&ksE|_ME$XOM@{?YJM%1g;DcST`lJH*!sMI$opeLaM6h8#SB;V#_R`I5S
zXni$M+!#}gqrjjgamJU44Imb{sBZuLR<^pjS_$c<C{oz+M*aYPa(DRqLmN6!n87!h
z40>H{92sxCQU`uQefHMdOPqNECxLWXXWM%qfAvum;O4*A$^~5ZySz!LZy6VYT~rBJ
z-gEZpMD_FMKp;6tfu7Ca-~Mk&ELdxF5EKB75D9o7b%z8Df@z|TJ8`huf*cTa4m46t
z0L>*|)Bp;3=4-mQZnhw3b0s$bX-i;&rVqOgQYI4@EB<I(MAJ#T>#<2wNH871HiZR7
z{HQuGn2?rt-IGuCg+ORTS^Ce#Nf;KI`)ifi_oVi9b$zf0O)N0Kdnb!koV@(FITtiN
z-R1`}HI*RXO6Ly1D5)J~55L_B=aYL@zRl{$y5uTmNf=DFNO7rcwmf5hnLHC79)2DG
z&0?ebpOz(1$kJy5OiWBNSr;7)NZ9yOi2fx^Fp&maCCMU#XXPi{e>Q-(S8(letELd<
z&PEfHp8acQCp950aH)oCq{zw&!h=fK@Dwy2nv#_LSINf)1-yqIGRv6cDuGfZ%FrV#
z7E<@s{rws)$a<q18$NhYyiibutyNmNHU-FWNS6<{7i7#$)Q>&;5@2@@!2T|k7Y`J7
z7I6xm^E_BkX`zISFdzkcfk<O2MjEpU3=^-)tSR$_{vR?vDoF@j@yy>hX88L2f2|-?
zSY^xG$`*^0w7qJrceyOj2+IS6lt8Ql#k=tjqR%lqh05wItHxzOJ2OJOqEal%#5Gpo
zWK;q4hmP|=hQ@c<JApavuMcr!>9aAqh1Wqcr8y0K@1F*}E+}PeY)l07JZN7)BcMzg
zbnV3EdsAq411&-FW7n!CJWv*QflX#X!?2*Yw-+n${<aTj{o|7!=_t(2`2rT&7X>6<
zwk2UszDOrU(M3=-WM>NSn;>JLVuikeJ`f;x!lxq`+qXsl4;E~i{vaXf^gI2B5tteh
z0VGv@S2KdhRxSZRr$iVjN?Sjtbb%$eR^Ie;?o(*D0xd9{Hn?^ETO`4JRUe>$O1?oJ
zIv1=U1q4Aq=vOdCL7}Jt>Mqb8w51XzAtpVL<JQ9m4<6J8ZLOMt9uH`O7h2GiBql*?
zeybP2>H!WRNG0e_smH7285}@HBQc%i>wiHzGf2|V-qF={iPhh9e+X(>sy}Gkw2Ne|
z404WHxc}In)`OJA6IcSz<lCJHZ&n-vCGG??;a1D)9I8kGD2<K8YSH|)^0d-ND|M<M
zA563n;MPPT!1O=aUZ@@()%M$>nNS%7TAHP-=BOs<Xt@HcO=|*;kB#jw*+AR!v2*fo
zeiya?9pI`lv940ouacLgf6yoP!FA1^N<5Vq=#>6QXG@>XJKBV{VIGUP6BWzj_p#-}
zU(jx;cL8yB_mX+_a%rUt#<lPEDec6oHQu0jx!`+}eAPi!znKDE;e{R5`t1|y88m;D
z)gA?w9FJn%r2sl6#0*OjcLPa7*7aYi!56an2JQ7LZG3KVzQK>(lb85p=qaI<=R7z6
z*)wOaV`z9Htp@h@$C1~6K~E&W5CPt6%iF@%2tD$+{3gH{D*@yuceL`u^p$Unv-PfA
z(1ZX+D2gg61#Z0i_dHgDTnnQmYK4fU7l4OlZiVt5><6@<k?`((<mMD;r<2QUI}y3o
zcvaAXLV<3pj}G9>s@8Z8*g>g#O3ey_cA3@H*PU8F9B*G=9P<pCX^RL))u}W$ipJm$
zfm<E0&dZH9TPTFSx}Jf?zZxP4AL!n;Is*S<@Zs|y#ui>zGeD8szX-Ig7+SMOU<adc
zz%a^P_bn|g%Rv)hdbN<fSQ<6ty9dxJHJN>WD9iW^8(|8<t1-hziYSFx|B%7d*uJgj
z*yi-#b6~Lafyao7QtzzpqH67+3=6RZQw`}n#Kz)ef|$u(a6=E=)KfG4Y%6)DZAy~Q
z1aU_*;8yRC$pS=U6kKf3cwnCf^8}goWDRS(a{HD^jnM!M!z7BpFD$rd;5t6mIFaL$
zL}W$XWHp-NykfLMjS$=SOwTWDYZ+euY1p-|?7v%%#SlgxdmN^x@Ichh3GNz$)RGh!
zL%8a6K-nhmy!ZV#K1}$E{z|&@Zxj76%xbODW6S^T4&_nNQ@#+W)?81(tS0ha29Im)
zZfb`yJX~FGuP7wO89hJf|KCio@4s3OUT;Z(A5B^st$V*n9+75S+w<QS9(4XY)l=_S
z*%Wo~vk@N#H@J5m`tOD*40i922T8%zHMK9uLnPQ4MeKU;?0+VNxdV4aPrn%3LU3xz
zMxhZZ{i!o#LynTj|6Rw$FZrv5%^_dIGja|U6~wN8%}_v~HMdO$q%;%|&orJ1=X}cu
z7p~B{hj}!BLFTa@4`6N0OI{jv&~fq`BL3mIg_os;;PJTKO)Xb)K}SO=&hl}U@jx(w
z5EmTxzqRS>|I`0K#lx;`7_Kq!FZijT*Qaj3h9+QeM`VTw!v0rkI4U#if5ZI$e-84(
z{r<F45Uw$?0yi!HMGAH&+&?XJGlX^gB2<`&u)qJz5IhTIgA(yZlu!Pw-75k=Bm0NS
z_<DPw0Fiou`~e0lZmPcfLo`g2ntqmL6>6}U_zwA4+}!j0_TZ}}dxk2M1s^Fd3BlA3
z(*~tqFM}W71RmGn!dh=b)Foxh(z3!0diG$-dtuursSfwf+<&n8a__khd*1GS2Yrm>
zeW?8I-NB0il#BezXWoT|Uf?`)A;z5N&zV<KKMwu#?WlGO&q14iUUv&GtS}zNVdApt
z#_9rx3mbeIa79K9)FMMTaja>{qG4mn4Nl1qc;dYT^91%f@7^uoim?bG8u3oHC0<{X
z52-_`24Zvj>IMf>hgN)PR0Xf{MjP{0AqQWyr)BZqYp`oPY~;;}Z<A(D^$>?D`99XP
z{Fu}UuwYI5!Lv!2pxJ-JU2R<WJsuOEa4PH+XKpGf&MyBvF}bJt?fEHzH0W7rn9JC$
zQ!K;Aj~{ib*$tq_Y%!r)$Ds)JRNV_)tV8;Huodo>b<!VL4+6q2pUk_l>)ktDhp`pj
zIu&bY@Yns{-C-RIUEyBt#VHJn4{o~-$FVEm(9T<|{V?0`w-w7+<o0~{v)#}h_1jm!
z=G&$fJkV0Rd%`t-Hu6r2!XpU(za1*OdxBlF8vDK`{&$KXuSrsqX5L+GB57~8?=P6=
z?ry%&P+yn&=gl=foY#3-doDkzd<u*C;w~1m{M<a|x4t>_4E2@oF^|sorIE|h2Pdqz
z`@_L=5YGE<F$$GWU~-#^q|Cg|!$Lz7;`|*cKlk?ecQvoE!J5zQJf|$H%a_0)efr|p
zBp7U8eSSA8TS%Vk@`y!J{xsY-v<3!y-0y1ay=bAc^xG!N#7o+<iqcZ#yP%MkyO72Q
zGgfAkmRPwOHHbNCIVwCw@d2rH?W38>d|2&Lr?0sepQrDirr__IeOq+(DmGZRaaQox
z|JKSAbJjike+%G)>KX+e3Rt+9g3AP3^Y-~E3o|i8uDp<v059)`hL2<B@W$;Y-ge$5
z6%7G*z0)cjxmhQkd?gv~`uM$a#dbpjKl2Ttb#S|wkd`+{J?VLm61qT=&TThT!VieB
z&b@rvaJeu-ta8J>im;IvrbSf!oAh%MPWPj9zks%Popwj7SqZvpB#o?rEGe@De4egu
zR6}m9Hf3;-#x$R7?DXMC%EK6vk;obOMCT3GsIW^{fM4O|lTe?7q3_dUn~mj)HHzO9
z3j@}=Ka*XKF_x0Cy)LEjpJ5L3!S`5SXx3we&@Bgon1PwYlABX0qKW{;F0_lu*L)vO
z_38dN>A4HDj}IO4!;^=Q+us8Hce>$Akh`j9Pbx%Exw{*jn%2PQ#(U(Ga-?yn86(nj
zCLayy<g=*FRsC&PsiCuL`mE*A>Y4SK=X<;N!xnKX+Ldo>e|+ULu@QRe(epCS?>1%g
z+2GE&iEp8+2h@+{lC1p~cHnuGsOknMh!&@(4wSnj?9#FL#86*GrNLb-qV{khToAXW
z${om8R#l?J*;sL$_D3`j#<eiNw~rkbR7NvWt^F%{Vv3f>YhY#eW7Mi>NfGCR&RpYO
z-s8-pn=e=njy|Ra+$kOc$+6K%P|BjbA2!<X+kr;mOJtXBY+I`5azVi;^E;z{T)Ej?
z_q(7Wy`6;}w5l3{if@y3)4HrL2tudn_S9`%i-568(=D=OQl8~bQnq(ql8|gwBRbrd
ziAo4u$?KJ3a=}LLT{D_owB{mIKU%8)YdOEvC7p(IV5|?%Ih@*I?$~^8+Ne^RqC+nT
z!-2{nP4FevjP0`EZaLbA3HIFu-l3$vnBRV}&>mc%cK~+4=NnOIY=d6KCtVvM_k8n9
zQd0ekXWmQC36wgNK%`LP7w>r#v!ZFT8FsSlo2!3#WF1i)7ckY~qA>qgy$VK*4A1Yu
zCD{{H^X;v4>TXd!IMVM9MoB}iWEv6c9>9=e1bjch@^9$#V<1XY4XhH;BaWqVKWc`v
zb9MDcTuM)G<P4)1K6~b68~LVTQw@U)Yv+Ss@fwjWW0rQm=#4tTu~t>4*s%SyZZE~G
zPr30M$RRt*D4{~TFlbAOhFrTM&=JVBdoo8WfVh<vdqM``XP`oeN6c`IK~Y|aqw1Qo
zODPvwiG67oAWxo^@EhqB%OmGU=IMNw%8{BtRpat3t>}K<WWsmCBB5c}EGd6|w{jm{
zmTGPt#4v(#=x+V2fQLN}-^JC?l9+!Ve2GbSzYz1-=^!-pfQ{^wH+Ud<cZN_m>+fpR
ze}_*E8|9`I>70D}AJw2*|2<wM|J~Bu!nmQP7SFHp9|PdvZNxSP7*ZmTHe3Z*BZ~+0
zo8HrEX{?98V4-i1`1R$5lqpu$T2Ype?K=6E`oRU;xZwu&oqE;@Fioew>E2Kvg==_2
zeys3Mj`qt*C*|+k_=S>egAt+4H5LnlU5fB8%%DG`v=H77I}Q`=1(g~DLSjgK*oVQs
z75<S8taD(_*PTiXT6zhuhNJXH3Ixqkl-4%)=582T6{T8EKII75MU|5c71$#ir38av
zQsTabvO+Vu4keBNS>@97lhCCKtZ_oGm=HrZe)|-S(%0x8>x?K-!^q(RJm6<wYd+{=
z@~zJ^iQtfHmCuQpDS--7$Ao`3Sj2=E!CkjUQ}T2T<lRb36Iw_0C(ErZk6yPARWMfN
zEO4#=D~cYS)2@uQKADsh1eu89{*LpWnVaI*n{<Zywew5z63yGe>~RWT0W>5T+wdAC
zNYx%fw<+(%p3e)@XX?hJ`=I4>aRrQw?Sbx<vfQ+10O2aOD@B=GkbO+(=hjU+0neVg
z#<{nz{Ymn}6p;Vu(O$xfVVrbrG{^Mha4_%WuhSR<cxk}kSH7_`jM{*|V4~1=1E|Q{
z3t;$p_uCXCK{VXeHp0SUvOojfc0k^%xh_XBQ{&`jb)$bPo|elrX~-ULO!HU43G*{1
z))_yKElUj`wHr?)v4193i{|-BpL#5Hf;^O)#P4qmgy4>3nKZVxL86j;J)?6&fsK4w
zr=WHvI?TJ{m~xoZWMu=Yk~9hp&F<Iw?@&J_${95;;>T%GQlKe~F{F^fm-fRR3l~~V
zW@slV?)>6DF^NhX_36wcO;nPW2^st9HLbsgZd4QchuzkD++yl;141b6!@P!9@OTFT
zQ9$39KVKDzeY@O35Fds(t_l&G+nKb<gy|3OnpR5ss9|Y4tI$`>=qCn+E!$`Di&KTu
z?`dUnzV!M8b7!YYup_r&qk?PkguKGDewXe^O{-v?rqv06&>XPPeubUaGj%kfrl<Wb
z`R~ipHfR=`ZXpilqPOU7+b=}}pZ6+$>qH+W7gh>1C)aUSitol(g!A7yJ&>a4VE+-z
z7lG-5*Pv$b&kw-HHvsaGJ<q|2OlUSFE}@dxlXuhT@vT!Ary54o)^^V-z8Q9@cDL@p
zIJ+N6_{kgTCZc(bicJ6btD0K4nKExw1gG!vSJ<`sFMF_c2GWV>rjie9uEun1IpYDP
zXfI_qt&0C}RH*-a<v@U{il*y*dxyAAecvTDp_!NUn*(vk?ZLe3<TBZcBNe;T<+p&#
z6L26%7(U0w$x=G^Qg(BynCI<bzo?`f-@@s73t39Z|GiuKa}JkPtAAMUPy*-B?pm7l
zPF|b}W@-h~43d3`$ZgyK!v5XgPCgd@=lC*%u0=<INgZV=b>IMNy}SDx9kUoKg!SXe
zYYoGF-4oEH+J8xZ=?z$5kx^e_4YQJ<F#JC7RNzg@M*>{7J)}qxO8*bI;#=a`@BpDg
z3FQlPs}yXbUU2@nNpPU|AiS66PI1}`QFqq|7GnCttfFAMkZ?SZzv(%pSrl_^@zhdd
zY3<0m`L68^tvf=io;gt-4R;QQ=Sfgy=07ahh#5rX6$)1G!XYr-><Nyt-og;;0Hxg8
zrS;8B68t|fejGP{e7U#w+?QR6(>(PS`*nBPP&w$_f4@vUDbBX5-kR_AIB{Y5jH-%U
z4F!PXEU%=oe!&XpT<Vv-SaN}-<>V5qk9T?3{Q18>nO)uSprmptPyA=F<6s$qVFZ8K
z4e9S8b=waBQFBV~+jV&}3+elMh8*-aaw<8m%I}UE2wfRE!NK)8M<~I4*Z+@~fJfZ`
zKlVsh*uBKjC}3vkz9FRpiMw1cl+JP@N-KC>&GWw)5W2O$z6hB^mi?W)JLY0Lm?$h)
zH4P1!S~#TtCqGBSz%aRK|6f&K9+yP^|8Lut+gA3eK4oU=wwbP#ns?sYx|w8Zsd$T)
zd5e~c1`5-$o7-wy;7vkho+#duBBC7{UgUu!DpuYoQYw-l`Wx)?`F!`sA3mHJX6E&N
zozK@X19`o%@v`s{FMZ4OW-#&x_IPj4o!;HwkebV-2ME9Lp7FM_4xD^E{c)?XX73U2
z(MpbMo}X{^`%Ny~5(a?=e}(lF43de$Teq#+_20WnFA1A?#t9ZH_jx6(v2uiqjEWYq
zAt;Rb(T^<iLQ2+r@W>P|s&s=Ar4S!peL@Upkrok~TmcS8ZWHKj?XKB7S~E(@-UzZD
zC%9O~rUe8;sio%3Ba-{P$FnzOSM<NXyKdhy?{=he@2r8)dLf|8f#gSM*r?W!D2+8V
z+9UFK=M2)Usb)7?RArpIwRh2NV^y+{BVLjmTFTB5m$R7Av24@0Kw&aVIXKnw>cfT2
zGQ+K28OBfv;++)fP2DySR`7$#e#8>s^DXmD1{?qJbmhZnKZA7)Uk0-*FpEa5&E)7&
z$`tT}#K>y)ylw2vMOq1M!_+CS#JY~y(34j}U-HlODlmtpMP@60zhEl8`E@h%13|B{
z*$2xi6dbW}Acf{G%nb#k4>i2^NBmAKX@+|dJ<tf<fYj^~3P^&Af$)p`5IfAG1~t$U
zLoocDDC0t9?7dw#qb{MJnMF>$f0Hmm6uPsL@qZ9>OV;C+V-5($KpcnuRhX5aw6xBX
z6bi5KC3j+KNVn<wcJU{d@L)Wh+A@XmN2Kw}woHM`1LSTM7`uXzhVkP__{))aH|rtw
zKSnrpcNyUk6$B`)7zF!8(BNGIOw^(YtG`gVlVswaCD5CK@)d3he9+g_Vn%NiV%V2<
zlUp=P$5cbe<GJ5AdCTW+!kNs$ONyS<^SE2}ynNPOuc06NDa?UA%Eej2DKu-kNGL1?
zM6T`gdQ-buibcYm`0u9@IIfn!)Vi|#{9f5|4?P9dmY7{a9H<lhJxbK=8W5?1X9u|p
zKjcV?jCRn0r=uqc6r+X^&MFI?8{v23RZa|f>>B-({6zs!Rp;Bf>G{fRKpPQ(;%RIT
zkPN$vI&#G1t-@+mbQ2Js^x?eGQ0}K^AbdkQyc`~j+_z%&4Dvo95OEH1R`{wv2E6*O
zqdj#Yn}NcEevflk<Ypj*%AaZ9yLGvWAU%i6phS_$<}quI{lnb()%e9XJPe<^xM{u~
zS8d8a<k3RjPk}!7>qRv8S6W%{Mp5Hg;Q;R?xC@BHL1`4Sm1$D5Y`wSk9uC_kkPOq9
z^Y>Xmw?#d@{QF3A7kbna*dUdJ_p#%jb68)CFLx}BbXPG&ubp)GZPdwVXz|nB&e?y<
zF5tli{l^$<;g)EbmvPcU@Wntlyqy<#Dh^5{aAjYRbAuOaALgERp+7N0O|1jXZl4K0
zmVrTC)!UjjkIM&wCQf2)<PNq&)k3TJE!>C_RTp&P)O#0F=RX63n`Qo}WQN;pMq<F4
z*f~l|3%pVug5P{-IDwR<@P5-LgCR%%!zgT&f?{-4DT9K>@D<`nFt<@UN6%jkydM~U
z>^B{|H<)P}vua4mpkr4Cp(w7Dh$nn=oG0Kp{Y}Jj3>szr44foLG8_tK!>F6YZq0UY
zF`p~ttr>l_*xX?sIHP@d^FNPc-)+XATaONhCEs%)Ic)7(dbDNg+p!-(eF%0<QaBAT
z|8-PPwy(vxG8?rA``@Qw&wabK-R`<OqIPdjkHp`FaF!Fa{bHIlQrLT2{OvM_!OKK>
zOn&t%fnBmJ5i@uVtUv!3gAalczMjWmQA_8bY9TKwH_I>nTBoA-aD8Q#1TQ^*suK7X
z^?N;+&kb7XxmZVE0iUJ-KPm5?ZRNrZgd**-Z*YFES8Pkd%tiFTf9fWOo}QC$5@mxb
zUHPqUXB+Zpi=e!e4#s2lPTnT@{^|9*44L~&yH6gXl$bpg6L`%A?G!g%wg1B!uolTD
zGL5(pHebBpjz<44Z2wFNdGP`TL%ibo9sG|df06NGLStCB?DZfRCaS~OzY=94l=Ghj
z;R6Qs1S#8)^LTnL@>{~wa`s=iE8&L#qk(gS3zy-CL&#|4#^dQt#W<7!8a7a>paSm9
ziNCc=cIN35U_nRrIaKo0?Xy)i<Hz9yH@bez8j(bCBUZ#gDaDQz&A~xkI}67*zvm%?
zAoTJ`7!8Hm@FSgf_v3WFpLRDP!}`@{(7y@gSq-2xHX#lOFgVXXeg@r;9xKP;&M@sy
zD*NjNp_G+GnseQNsTf__4F8}q79VIzBRf7GOsl9b|L|C)ze+81->wZ0;o@!a6%VIk
z3{93=#EHMy$_ff{Ep2fg!c<<W47sDL8UY03nevuFI!KpVZpjAvz;vD|;IiTW9aqS6
zWOZ*REM?!G24&+v-VnfUPGwfVfFMM?(d5YQJvKnY6&Scg`MyC82lE7nixuyQl36jz
zLE8idVBoegYViRVed_kHDAc$ka;idGmU{Or`Ed%S6Qg_Ka>w*tTg=Uwmsyjb+5eRz
zes>#n<t8<E9uHXjkzpl+8&66d@Fwx^ceHP2?QgGq+Ut+F*Z7tr3F1l0KQ##mB^${9
zDFBEe!h?C%6^<UQrtNMOISm`)+o*3YO=dh56*}3VgCLSsN4ON<_s-Im*slBi0g6z-
z^#3?%d6VKMDg}DN_VHBxXayY4yvyik3@dVc*+OsK-ANS|jYRsVM;W|!Zh=ptK;sFj
z=Fc0am+MW4rH(|xH}HC57idpLJtW7H<|5Dwn6Kyuod<)T$i%bv<TvznM$U%(f!0Rm
z(fWtepy#3OuK3c<(oUc#U{hPp{IK-H&0v1mz8RR+^fycfM$`WxLH&vYO`7g$WzAFu
zq3OJ1nwq7-R3__6zqo&>eskUa(0d9uog$)d)JZD*=V_(vi*re?Q7SLKS@3T7TkXS<
z@2(wRh<K6k1=F?xMSQHqKyb;Gz~<SNzkyuC=a#itMxr+ISPrG*B)H^EaDuQb6y3?I
z{U>ilA@RRTdU<DA?rzt)pe`is{s(@S-p>(3Sg8jHqCa!Q-2XWnnev#s0I_N9as26~
zlfgXd|3E(g0!A9^BAT_O=f-O}^d>J(JpeVdGnm&60{+iA;--JzUO+5y8X2$3lb8zX
z4TO?^3aB}}&<fOPOH3hV_@8NjN!NgGaVnvKzR5$U9HnF5Q1zyP0u^o>;a+O)=;vmk
zY5^42v=S?;nE4V`;6I5c5qcNUu~B|`P^mj2FGo*6CYQq43&~Y93TpP&e~A9`=vFSD
zg(hWn9ig_W1}v}_{3*OZmXxHj^?zx^uA8KKs1rHT#gf_>q8rfMs}5GxO2iGbn-vDR
zvc62yZpjdjT|_ySJKvdLheOBl>ozs)^evDlbFTP8;eiB;I4ENMSK_z4q6FNZOtL8w
z7D{(-c7*&7+3;KEeh%GaWq1S_FJlbq4txur;r`$z-$00v|1Vyb@n1^H5hwf;!AmK;
z@_w;X&ENlr`bnxV8r7`Ij3nlUb>8|`emNabEVlxp^EgMrSS`w_E?<08xEd-gw@CbF
znwbPmTA2Jg#T%G?z>?5&*HqkSS06ptq;4)v8~FDi#X2<py3RM5kY({b`}Wi~o&FP4
zoC?(t!h@N6@;4Ud&wY*@S+Gp4_~Iw8CvzEcr=#WfeO?ct-fgEJs?2)8+sQ`*pIG8W
zSStLm|7Lvkf3BeZ0Ub!)+KJxQ^qarzq+6zV_PIf~ec$Qg;VgH+4cVg|#M52r_BNCq
zm9vk4ZPBIrdW;rBK1ivoPA}voYTfAB)$XJy_?y(yh0!|E_g0rExtJrbR`%gZ2}Y|+
zy0|@Ve%`VBe>GZ#A-xbUWd+<|W8@VuCAt-L7A#j&N-!1TA>MHo)79D|kNURIZ5FKf
zj2`OK+6pjD_OKJXGrQ^TMPOOyNvXU$K$R6}!yRBpMyqCI9gRVAuzc<72}1L8!YPf`
zso>JFGJ*@-&nNukN*3#egIld?nYoeYn*^?0O!u$@DKcjH(WrRl7+4V*@ukHCZ$!5q
zH<HF|oqGu$i=McW7ds!_l=DHYhhFRpAZQu_|Gl!5F>xo@Vp)318)R*iVG!aknxrIS
z<VhZCC3P15gKinY*;oHvVvH!C(tOsbp}+g!Xxj*y&x4PC(N^<_Rc?V<e9+KbUZal2
z$%-EMrdg>ktv^#%nmB3nnowFcQqN<Tn!C%YQ<x^kL4_pUN-(N{@z4K{@0*fuxFMWz
z8BI{9ax3@In9I926<{1}F+5gb#ejVdsYpW+hx8M6gqa$9ZR)u~N9#gs0yQj3c@hq5
z&8C@(ev&BVgIUyN%Vv}(SV~PQJ+E<V(@0jv{kL+Rb{kF|#w*l5dNk}wYeLxz+juaR
zxuYUy6U$dFcphzRPAmX({PTF9hr4B};7?SzwoTBk@Y%u*#Pl4V3M+IcSabqE9_<S1
z#WcwW-%1m|sC|o@nb>>=i0x`_hwa^hy|fqLRL^Wb^rxT%Qx8VuuPd|d0o${84pQ-M
z%u+IS#&+=iFv%KJ8M$4b{^B874hKiy9+oh9N-Xj=(H6PS+B<G~-fwqLJ94vUc$bA*
z+4-~*Omwfqsg|vqWqMHw1|!>gMY682qJFTFlWRUM>+!tiJ=n~kB80#bUe}hXJHmx`
zrD`u2J1lvbCLXfB5PZ4^GnL{rxXj$$a_@QwtC!LoXp->SMpisKBXWX`4kqhrSu^Es
zJ^I(@>V1mtfWbQ-Q7&f0^j%`*!sy3F_Uus;^Bb~)7t1_;P$_xgyIaUJ%U{PEx!`C+
ztaA9LCVFCk!*{Ula<?vDe`HkiC~OPJjuSv#VDIkAu=Cvm?x(&%M*ojxvi1=aa6{K|
zpy)Dj9q$FS>&9io>hfmpFKgLwRwsF7yc;ZGVS95qDh^C|vbPC0`Ja*ww%gXO3!9`C
zm=Iwj@4)J}?%YZBi;w9bS3p2ez}w>B_Jvz3zR!C(3j|nu(yaWxxwCEG-(QN~i1Rzd
zzoQ}f+)RQLOD1``rFi!!d@Ax9j@8s`ER%5y2JRWV)&4_L5Kq%JiyzpP!MaPDc6TaQ
zg~BT^O&n9fcK4aa-TTedtiF}$ewB47MLA-%6@y53VQtuX+|FvL9ReYA=?B^m&c>G(
z?Xj4K%M-pOpQ&KWCeqTyukp?or;5}v!oW2rE{UXB|Nfz9jW;I~quDdY2`&j#8yPj5
zEuOft=ruA3+N&JuLvH?tJH1?@tuuk{T{iwh(ak9|x@t3ueE8|bL-typwToKg%a}N5
zS~*vr25hABhPJM3+D(MqcidHTFjmGLnC6#(Zap5g;Z7RsU~j_Q?0pA&#*%`0_=?35
zK5Hu}%~4>Te`Te0lkLWGpd&(GFKnLM@)$q}eOX2KLN^WU{V%d+QB{t33z&EwV2zS=
zRTIrOnP^)DO8%7Q?In_0&Y<_6s|5Q}7m}aZOseeYt=%&3NG)aWHe|DNeIj<_Zn<)C
zKV5gd@~v4K3AHSfnNR9$yN&$+_>h%xGg4T5d%9^!)*%H2)wdVaRVKNIWDGV%(IhH#
zde`YqQJ*rBSw)r1eMRj13^RI{D7$NAt<SaHxQ`B(ag|}cKnd8Lyk!hM@5(MQb~i7L
za&?DN55C_#y|QP=O*XfXXHHTpR)wL5F>M4-DMGjcwi0?8;x`wbat~BQ%NHn5M9qOz
z>p+yGoOhCiBbfvSZX))FmN(fQg}e-wHz)o@f+wPNd7L})0u>+>CyvTUx%EBpXZYk0
zD<F7*=x13D)`?<q6|WE^Wk+jOeM`(VPs_ng02{x|TAJo=CW4g<vW8<JtD<KFu*E@}
zbc@$(8lOdRqpz@+zk0F+PVF0G25B2t5uK~Gn|hP-C#Ywbu)_6Myc||4w5O^D<tTia
z6Od;8wpm62aO;t*)d?1@qSPjQm_zNGK%r%y%ztIJ_y+No_C%8_76Q0Pu-G-bd+AQg
z?n1Q+>lUBzPl3d>=!%VzZD4nQp<cj=DSDVeZ8x{-E8LRcS<#FV)^9Zvu%-!PW9ZqJ
zx2c{$oa*a_G0{RA1-XOpyQ1ocm>CD+1`sBTPFv$^tbA!pEd)rB^}?u1W8k>;{bmef
z5WE6^<7<JVR1l@~0xz6>nOtG@WJz`PIWYg%E8}%eQnIdjrNAbOfN)e>UoF}*(k+f<
zZS#{3-d*{$UswDJoHzKqd21i!<d(en)nF^CA@eCo_e|x6c_KLTI5Y(u^RvEgGRN#8
z?U|P>XZTqGNP>-@l(QvIG~`YrGwENV<QIaZLk+?fj2Z{k`^J9h2nY3%tvLE!xU3WH
zP0BA6PATYY&5`=GL*@0DVIX*#xIMkr*0R+7YRG8EH3h?TrIH$V)0O8=esTWvnY8z&
zB&GP?LB4M-LUIHc_KbIv9BFKC$kH8+2`K>16N}?~8{zGR_7rW_SZ{*^219;5#M17O
zz6^|s2=?`wjhVr*dOFGP<KAjoQC+P&dn$BbAK%xITzVwnBNqSpN<V8w#od=S*`bR@
z5~O7&S+V?`Dgx#Zi&nADX#@1)_nbs4pz9j})HL{@yzWrUOb4&&K&mAb^=`1j&(+tE
zo;pa(uu5w7@NEv4tmJ3So7R?#yMAmZ_~PVhlHJ7PQ8peRDd8-39Df8!JD#AxFSe?i
z(eO}K21+_V(Hc-QwN`av(cSF19u{zMj!sgI{Z~wR+fiAEZNLV6-QCvebJTMHb*nh=
z%d7_dV%h7xU_0%7Cn`2nSO86-dm_3XqdY5m%H0|b$18dDG4p|zjZNa0C)&ndm_<6$
z%G{^x78SpvxKfA#x^ulIUdWJ3U`5~$Q(-j>{ggqk%XmLBVOo2UuIM*9ks!R_Td|iW
z3H!+}Y|#KXesA3uxr@Am;0A~fR_@PD)b-MsrYzRRx!|VX>1($%YTT6Z`Gp33zcy}X
zX-0E&ZAqg8Zmc}(O^*iiX@<@-^Fqc(cP8Uc2g3OlIaT&V(jb|4Oy#S|hz-dgXiBVh
zH+IuMv{<S1PVi0H-`A*>FVuFJE?h!i4@xa(A-4agga|whM4jaPJW3H;CmfMP2y=N`
z`CQFNZyaUvi2G=<S-JS5Mie_o*uu+O3g7ffw>*p7hlgoDihhkWFFn5o@JEqNXm+B>
zXQp&FM1Al5?B8~L`RnKp7Y5R8GX^i7y!UC=#kI~UH2mfTXtMKUfknt%XkA;;zY7Y2
z!cKS{0Mh?1bUtzC&~F#%_x$cj)~b4tJ&kP3>G_x*Zg8VOG>3r26BF35a6b523ISr!
zH-V3IKeFk&CbHF?9FUc7)K#eRIE!PGTQ<p)gIw&b{6*q2#iGHnIA_97fG~X(MJFx}
zaBFanGbXmui1Lcgn`L;}RUR=qfLjICRWm=f<eHse7~w{KVQ4`d?W3hU0sF#Y!9C>Y
z&kXsZvUVKSBBldD9Slkluc(_EF=)6E4aR-QistH7Y?>xA0BfSYy0FY5;B`LX)Dv)P
zH?i{S4G8Rh>=8TDHg5Vv22Io%ag33Hd;EAGUd<u;E?2N}d^HhkfCZX#80OeS;N{vL
z`}<5w+(^A>y-Pg`viM1Tx~FlA%ACHHuS(X^U|dS0m4@QeWH^Ty4YNAVC`TcQ52Jn1
zCnsZ8deWDci@b-ikzENywcH77oX=D7=od&2UT;hXu{WRSwD@cX?{lj<h4_=yqI>)h
z$8NQ3^dv(C=cCC0&Oxkj6KDuGcftmOIV}x}E#)67_r^EQ1%#+@SiWuG-vNk-Jq3+&
z*BpHv;>#J33+KZi#_dg*Yj@Tndw-%|sqo82hBx-crC=wXr*D7xB2gEz`1X{@`bFbk
zPR@sqO{XwKpVUP;<>EYZ%Wmtf@>q-Ocbc3WqCaL^5P_UYD@^0xPVViKKi8_{>!dNx
z&0fV@S)3)PU0D6DxonEYB+_k~+q<)a*^Pho+GxCuF<{7h4d<>P(7W=|`;bODf_P@V
zQeK$3?c8AD!r~#XtT=?bo>!JJqK{vP1lItO=y??x8?|_O7b<Y`gSokp;ElqI3{Pv`
z?~VfMD^+}FKRp93+_eDT9MaXeS0(p(6&H}Y-Axo2p2bwWT{O0({>?NqvK>yn?5^kc
zT&F%1t80gc=-5R^Bo7Z1A{UnMuE-;0ne39gl+cv8aCyP=_IFH&htrL*pN;SLHrAC;
z&F&4B_>P`S87y(gi;aiFKoa{fvaQ`f<=_HgLJA>dMxX4s57EI*?4Mo#1KnAg!bZ5-
zDsxgVqkCzm1JmDRI7ey^+eKgN&_0V;$3w_xB!#ih_+O6@CWcv!eix&&)UzD!Gr2H~
z6LawEW7*M91PBbbcy1#|OY-I6t`*T`t=&aS)rbVg!;hwWhd4JD@58tfAYoNCOHK7`
zd_|lI$;kJ$rssS^IUL?zOv=`ERvcdDq%U=WigIl7>+E!VNC$u1X*6@J8rC^g#}oyh
zzA(?mKK7lgn^$S_eO<ipffnk1fQTcfK6RlKSX(phCaj8<tSlG^oP`Brr}s!<z4qcr
z<BxD2`BRM}pI0AH8wtR9&+*|)W_2XoN<gh6JnWY5>^R5)X=!PSH^_mk!u!3KhfsR7
zFMriaaEbL762?lx8xQrX+6Wat>1vo>{hcGW?ua6gbM439M$pg;fyer+N-l^P>1`ZG
zf=7WQ`oef5)L|d-^3;t;@AUes15ixo6VQyTc){<dZyW%+4incE+YDUH6x=}hl|Zh#
z`Y~;N1#E`;7gP1X2@h+&l9r0o965RET`8p`9QQidq>dEs5K!wqJixfdo*I2mb_|N5
zm&M=@`<KU}UpD(>Y^-{A@0>8yGi&xLVyH{giu<f1|2EU<t8ITbTq<0!x?)P=-u}E_
zDWY?m4bV5=Ik?(t`ygTn*Y`wFL<<Zz@=ekBq7*!;oDY9p+_{!{R&m%h-r2XVz(O&Q
z^YyVuIa^H%4(c~SmJadT6u8fkiK?Cj)YAp<7v8l<Rj(vtVVuKw4W7dqHoD&%8R??g
zP41>P_~vyDVmw90Mk?M($p<ctRCr-*vG$4p^pU9~u5RQS-h?!%OYzAHgqmdbggji2
zB2B7AHRvp@RD~hmuOZnG<g+Mj;?cn8Bd4=8kZ!_ta=$xV%kVxkk{y>as5j*s(f@|E
zDDvuvxm*b9VS3RGO@<3Lv5<9Gn}}HRsV#vd8e`WSh3k}>x(Brj9E*E{RSu(ufE7sE
zsJbn+!6DC(YAl$FLsY}~if$<iFpoXM%EAf{*K-Fyctfl>-p#hyximYfE+fiSFg64&
z$n4_o=x6U1Yvk|G9J<0<EJ}Lb;83^Lu^ri*@3lU3KfKl__jNEO9CQqamg*_9g%I*P
z@AYZ*vCyN;bGaH;bixEm3-C)Na#0U5nPwOr&VafVyX!*?PF<H@s&8Lf9uVR?JRsxc
zFw5a;Egt;8LdHp2<bMe?afq(tE{<uga=x3Z{y}7q<U~2E?z(}(2I${BJa+{WNzl)c
zA`<8c5phnBtzg>3k)NcNrIYwNpDYm4hOM$d`HwvDTEBkRf?f$94aD#SXB<A&4A-gT
zBT|sc!?h-pE372V)grDy=R)*`60h!*#sj7i)`Ch&WTu}fqlTtFJ^wr<+RB=7JN@!i
zT}U(~J&9F@)A&+SA=r2{p*#u=)~Z89`m&9M>R*P+`8*9h4jj%41vR4Fm)65q1my0`
zL_6lu%NCn$I+4u&Q+j4rlg}DWr-XcUQDjN;k}*6{)umk25TWw4a#W?p2&M?iU|zv~
zQMjvqe9@+lb`;kwt$S3iXO=jXt7F^$HKnF!>Cd_Y`Q%0K?fsGcTU`j1zSe<M4Em?J
z8x{_SiI!RvX=I_rl|(Ay@!fa;dx#bDITfX1xA>$CcD~gyw{3cdQp5)M){$Z2qGv)v
zNBPU?qLk{yi0ygVb5FWs<{E2D!uvrhtlhN21vt-`+&#x7FH}pbdqDS5*T>pH#c=qy
z(}xS(SY>*R7qN=hrjz+WSf_%nopq&!513V2$MnQ=guIE`UADj(xO#&#wmtZrT={rC
z6R!VNPVUGh*;`+Ml~U%YZAVkE6n^vFU$w!mvt;w1effBnnfv4B+?@uH#5PZMTL@l{
z_cJ~4bf>Z9nO95}fSug9!+ve}1?i#7XV=8Ew>Ba^J08w2RB4j3cbb(5*DmfODne&f
zvC(id&HV8t;Bi?TRWDRD(&z~1HG6)lS>W@UAy;evzjcvi>*{CikSLN8iJYs=$*1j8
z7P88nB2<K^8(5p+l0~2HeMI>J&j_*Kt~n9u5Ggq5(nePB@-Jj3XsA<2HXwaH2A#i_
zqi!``79LRBeF*VrOg}piZ;J-baxN+<Ej=_oNtMI3O?N289vO+W%SFt6$TPGp(nNY;
zU7XFlR)m8?SraF;j*)sg2LonR&8KP)Yusm^B-N%}M1Fwc!q@R_6V!Q*1%>Q_Z;ax6
zoxc}{7<;bgHEoPgW=|oa+~bEZo@_7(b7nvNL3>oL_&U5cUGVIpGLU~BI_I0+_bc7G
zqnxY^Nwtd(!#cUn_Ppgi=*kuzYV^aZU5m}1IIFcsz8Ii{-4Ng1#akDr06(?0mo7V;
zKm6FQyk`E`?s~i-xVC_RTK=KByfOWd7`(BwEs5|#d#pSC@+DG8-`5Crq|<hlU9ZYq
z<fGh)$u`)gx(-PbP&O5F9<(X@(q*Z4lJ!G<civO2_V-7%VtfYk518OaC{iBt-0<{M
zM3{?ae|s>cmV}!Ps8!|+^uzG+F4RW;N~c+U$(BK9Ma_A9Q247bmc#!1@lu3>MuYgT
z+5}t^^}@zqvXBKoVspOG@XT2H*#O>1x-H`i%}~>Q5=e}?C+-=x#ZCuZ60r+CZKMxa
zW<`dliXvN7^c(^e{W+jPIX>vE4;X-UDTb;J$BswPw#8*k{g`KLCE^!sICdaW*@&4B
zdF^_!H|yh?4f)qRw)eM9D-EIGD70;SgmuLQ$!gi)FI=5e*&>+xh@*Yv$nis<Y=n-v
z0JsW!DoV4a-cJTcDhC_ST%kYfh{=dLJCE0}6~0oykj{$3p|ec<v(#k6isbcIq-bvc
z6;n6|SDv2*%bPk@PjD%ZXKtU>#g*^wg?@Fmum>$8t1V<$kEcJCtj~m_!k|6*<|p#u
zAXziJ%#H~xH29_QpL>=Ll}-cCeKV4N1+^E2x~<XPz7jsv>oO3(w-H241rC<siE%bm
z6k+-)G{PmVZ-UdL%wgqs^<g}-WU3S{`2#(4f}UKn7Gq|l=a2&PD~};<pLOR$%IlE6
z%VT~SORp6=Lpz%zEEQk`#$lc?>r;`59WZ)A>baj4s~7Zuc?Ot!cL;87>)W2VcZ%jf
zDeXW(lO}5eGrz7~tK+^=vdBv)VoyD+-4h;a-e@QYn5lw#?%`KHt_NX5e<@;*u}_f|
zFy?=!`d|SGj24^$`0L=&Vb+q>o^mfd>aoVA?xz{pO9DX{El2%rSaUwfZPk+U=UQf~
zp&lxs4(LfUvZK?Eq6Q{dKG{g0<qAUimj=xF$=b<x5j4xs=W^DpH#DJhK=WC#UwNN3
z{AKl2`2$cVcru+ZOv`2t|6D&?4@FkYz9{qTK1^)98_!|C@_wJHqy6cSxWBYX66-Tq
z#`%lc%Mv@7-<)gE&!XL>bjr~E0Q^lbkoR9|8}G}gjdVBB1F2;VgVfsMHRm6wPrI(>
zoe98nUd=sZNe2PVwUzeZ@PP0PTUPh@vkhKpN=?m(iVGN|?6wQhXA4P!^@u#u7u2!7
z1;!wgTVC`_6pZsX^9*T3#^<HKS1q)|@C4tC)jv$rP0Eb{(Br8zC7-#cV2?aAa3U5~
zuSBdVO(cUVa%Bj$OMB_xyM0w{=H3-X8d%M7$gtPQkPOa%gHeNVML4yw#FL{-8Y$fV
z9?G$}!rs49@v8h3y8rbImt|)CcE)si<=WiaKjNKy``^qgKBGVCdsv}7TU763i%;zs
z)U9TAMC=Bp$4HA}j|=0mnrWmyv{9$zTp|B!N_FWbdzsnH&S&){&9oZ^jI^GoU7@Fb
zwjy?ip}7b5=JP53fI+9^`m6f!80Luzb_!$86UwOx{-q||SIvgREsQj`ntM+sh>#&H
zkYa~uIE)g9Z~+a?tqwG_xmx5)jAsVG-WuE$o^x@3K0iOU88^`iNo73w66b;S)>yq>
z4~vZ&b9*y=Ei+(76KA0{ncVeg=rA517`_#4z&Pn$9Oa(cJ-D&50o#d$Sp{-w7=juk
z`qeJ8ZRG57zk8tfqW>99eRnR=*7J?b9&_Wnrl0cRW|ot!$0(5K0`;V8z;#uQ9Wrc%
zF@*A#26gn$XI14VUX~<gs8)kH=R(e6thnFVwn#O%wW>;U(gIVVP6Q*s%oc;p;SA8B
zptUH!i<T0DvC4t(8mdhgfPIc)b!u<5m1~45u?8CH62F&-2zQU)n-994cmy0)6eqM8
z)+N0!-0l&soHO4F{32z;m8XD-`li&7s)eOK@TRr!QH_eg$Oy!ZU85(v1D5L3b?<kU
zsvbCf>fkF_mdc|j%f--<IfQ4AW6^W!*I0BUzUZ5KEaAgZH{;e%`RY%I@Q`N0;_(|w
z+F`^BXZfgMJ=Ap#>wB;fMrK_;DPV>(gi2U`0X{fgPxUP4iDT+{|IJEtu7YU@Pp6`L
zu8+L%v+dkKVbJ|D@xN|*xJlw~vQ`#8iwYI%`bm>n8m6c1&nXsnAEF&A&_kVlRSj5A
zCV$eOTRe1ls!u%)5<Q)6`=<207D#k{(@|NvZKyH2)7dHs-|vnjbN+TZT-N^vxM8{n
z-d3Cp!hH8(!!m90-|4BPQI0M{+dSIQ+<K&{jXZWOEj=l?{O|ftOGD|G8>bC_0jKxZ
z_)oot+uwm;NCH}ySv^TIg&@ON#t1)IyoI}qx)0|g;4U@4cCL-7e@KF>ua5GhO9!Ek
zjxYD5dm9TLP6Rtgs+j8mlUKhMsBd5=9%wyyu%zhPehw9Y#TObH3euxb_FmT7xPo!&
z;9mpsSR2s>q<FA`Gj&#rQbx#)?i|g5a34=F`{(bsw73C;37XbYe_rlHo0i`#oh<zm
zlk@5uxf9x2(@PcF&ofWe&@V1JX&uXs%xcw(b?wle4Axi;!P1Q(i|gr^%c!gdh@)LY
z#@e#Fb)NnwiMJNz0{!S@E?s%p2F)4B4|U%|0i{9)rSgtEh}lxrdUOlmv0WZ}<l^G}
z)J5y(SsShD6@uav&rzehX52WtT*#-`Np+_Si%yVc-%05sl|Jq-j2K*E^@(wRtfmQB
zdNme3QdU}@c08`)fx4hG?D3u{{*1b{-TiLHPV!E<u4`k@PRhx}F5HDg>P<wAIYncz
zPRC78GS1_gtQpAtnN@T^#H)r^(M2(y!@9~!12+Qfatm*mX)_Y;t-&RYP8SzzkA{j;
zwSs%6Zj6*YGfq2>d2f569p=1#B^tkNupm6%5rjI)Sc`;Kgs1V?g~b$gI)0)8`#61~
z@6W*><O*wpd%^L3Gm7rro+tc@PK^dPEL9Wf(~&izE2}%n1HVuA{ygurICU?yBI9Eo
zwdFeX>D|OAEPY1X+JGTC=#dpDT;nq5jyH7veDXWFns?yD-~2=v1eP-~=3=%thls-+
z;d-yPr=zP(0tY_YV)1UF0yk~?X@;H_AB51F2wzYCP_R%0MRfvAZ~s136;<dnhn-0K
zB$2fP>;c{@h;c&2T%=^!w&RET05F9nW>3sP#og)NIZoN`fk|i7?~0plv?7$IB9#Wd
z#2HM)5Kj=zA3!9UP!1^t(#Z>%^SBzUtn^y+YA4y=edYPcgQ~9SuD~0wcBLi}Vb^`-
z{7<ag?2w~&uB>h|!TCI%P=;6$CT1b2_KTfRAlJruYSQwW>Vb(3CM{Q8F$IzkQkVGT
zZlfG&)>vzLQ5}4y%<bB?#1o7m+T@t`wR0fV{@JMS1qclffx?g(Rs^KlCHyoAnIIm*
zO&`Fmg<&ZLDLRs~7c{5ZaZRTfclBq|iyiduh+e2qFY{tfGnlE6vDZf-s*fkiA<aNP
zX-OHo_8ayUDl|8x@yNlVa93e@00ttI<iQY}WQw$0XWWY=P#f}30O~JT&u>Aa`xpA3
zuin6=U(PadT5N)ew`%SAAu+teSY`O9G#mZ*WVSn<?unf)j9v7}o-ojgiHTJE!o5dM
zE8wiFn?P3Qn@=$4$5-Q`MxBnY0{!Xo*Nv2x^-jf69F!pItLg92Cm3MOvCc---C@xj
zVwI_{Hs1hQKNYy%4>^Lbc8IcDWWgGOh4s_M$Lgp5f=P9I2AMsM#B%reOPH}r53W8#
zO~bS%Yq}MdSM;TAS3P{h;#Kkpr8C;cQvdGw$FDmE!3nKf4cYZ~O|<*~tYi*jRDgqP
zN_*N2&QAzmMDSgegpDKh2z=aULUv#1px}rl{C7^Fqg^!juSSePfz1<0(MaVP73-fj
zLj4d*ny&PXy6wY>s0@s&yx^3?Ff?vNopAu0SbSEA{^(<S$l|N^UP5}(r3W2LI|6vd
z_mM~J>oA&QFW8zcW(>e~NR^6)z)XH-ZXM#BUAGV5ef~>tvdDc3ryRjlf{>eDiO(B)
zP&QD$@{=O|zdPwFSoWi1i%Nyuo-7b_y+u=ZwL`0gM^<O<&4H@XGSb!8njnmjR$D~q
zaBt)}Ad;@&*oG=iln(!E+&>|ahS8n54XBR425tzYFl;d3Du}m{7nFfy4?>Uy>6gh4
zoygw5nt0<gzFPT)_z7hDUZk;6ML@}5?cI2T<89{r{APYtf(@AL=jfkUZQVV0eethF
zQNhIn{jmwndHRh_#`f~yM1hD^l^EX<vT$Y9C+_!)_e{&ZHkP-~DmFwZqJ4yX#s{!n
zw!3dDIK9x~)7{sKPoGr~&y6ANazT=%Nz#bIk+_7b*v{uE^&@tx)tEZ<c&EkOi`>=(
zCQsn!l(XpXrmRJwvsH{mj<eT?>~c0%(&3?;ne+tz3})Jcl5zXe+I+L=Mlhv75H-ff
zs~(jfi`R8r{H&hE{IKOVyK_(cHHCyreMf=Sk7ywarjo|V4E9}^PMTm3JWDN@K90QD
zvGw0+keuo(#|jMQX4K;$i$kSlim7kIi{7KH7<}9jLaP-$=7J`|+JU}bid9d$FuOd0
zyB5{4TAaE>J)gdj1Z~`jwJ@A)y(2*d`e{rqEj~IiO1(ExS9D?N(cZu~=(=OoS`g5o
zccURi^-u2a(mi*=O*REOGhVt<AC?zd)CLOlmSY#ERq}V3v|RyWP+Buv3AV=^E@Si?
z?~2?@>1gBbsjKi$anNSP=NI=^;=mesR=LW*YhpVrpL7I|tWku^-{sqi>qc9L+pHYg
z+Q$c>ri#H|zfd1dm_j*A)k}ay@vu3rr{{C(W9g7t-b<EkREJ%%S!V7Tb#L>`J!+50
zWOmMw_#vHBGTaxbVH#@3c~xCE9$6&4kth@~gQk%lk`;AiOvd^>^XEgw>+2u)A`#H_
z6%RsI@5Ymyfp${U9%Woyk+>^nOs9IDk14tU;I_|h@eegbKKV|r5}=|Ry;P0Qz4|4~
zwR6KXz&A`8LLf|}B?y>y(c|i4AzC0KJNH14PuPw+j?o*L9X<zq`Tcwz`tzKU#Vm%}
zt-f)R!A1!xuoL3li`JHbj?6rt^a(ANBy`=hkJ8oN*P0Gsmq(jEqC`$P(N{G$9!^kM
zk}>Az)ER;1Dv-~UI_3-1ytKy*0tx&pIu7@5_%b~azU$1Q6kg;j+wFO{jf{O`kZzEE
zmm%T6E(h?ZLMjbo!FiwY0TutOoU77gY!ijD_E);R9iLd`C`}5Gx(TGi&IFisSxw|&
zM0*#${kf$sBf;xb!Vsd$DSq)**y?0JAMA18$9^DQqBl#&=|>~d)7(1U)o^j?f!3Kl
zrI{7xhOCx&Y+~5!F%#Tcj1V)@Y@YAMo&9)?J|J0rpf|aKHoEJ$?4tZ!t*CQR*MTab
zs+O2_Tl-DXqOuC+)mJanC3bHzdfq#B8~7N2d;B4vGl8lbmJARy;ic4c#|I{wNZ@?O
z<BW}5eNNf^an#((v?n^W;|HpJQQ5F#s$B!(j8@+F^2%jPg((78A5Hi|e;%m!{7Ju#
znaen|pdvF0OmWV;n5EdR!s;x#txd3R)a;eDs$PWaGUo~`UXTgXb*js~*Lnsusz~Si
z0OUmuYMHm9wN1gHB+9waZ*~LNAIz*LG6vzijaN?@TkCCS8`lh6*01zsiW8+7PfPCM
zr<;8Xt0_g+<g+L_kHcm+x7@lBc}W|H?6Bx@h-S&q5lJggEnYwqb00<9uO+ojK*xSt
ze~_LOSvS;xS3mF8Neq3_W2z1QUTj^1Q@k@%Yh<}(#gCNP{@{g_3uMzZ$IbD%9urlu
zb?Q+L(fK<Dm5k-1$|3VS=ER$TzSO^$RvJR_0~N;b_O+%C>-d*zZ6)+E2LsDj79zfW
z2F;T0nWik4ebNP-ej6XygUp@0?Wm}ZjP3ZL2IpcH<cDwltIhoQs((j+@A<mRJ#YO{
zw*qi$&<<QbwXb#wc)@p^g1#HHQYi|Jm{Z7m@U-M$FrmHgvQ~bv=In^CZ8A5_Hw$2V
zRo`D?u7ywW3VgC~gt!uUK+fX_l_t5CU)-ILcM`|wl!wz`6D()q9Ap8n|MNont|_;s
zD6F4q)jCp3B?%Zy*f4;A9UF7x+@oun#y-=z+L>bo8hko%#i>dyXb|0qL#d7Uhm@9+
zU9~Be`q5sX55O2O^k|bY*b&Xivo7Zg6Wvyx$HF3?ujOC!WPOn&?c$9rBkY1|W)?M}
zOdCvwX%>yg>*Y*qmmQQly={{?I#ioToK@u*^c>fUspneVZTN*~5tNsV9Wf33@z!&T
zp>-gtkGt$5U)nfg(XIN^O8+?S)J4no=}G11bVkagLyZ%~{9&uz2Q|i^mh!y@c-<Of
z2l$Q*&_yQ+F&JycZkP2P(UBEaSwV9l(*e~@3hJwoQLDzGAmSfy-h=SP4D*l`6?K(f
zWrdA4YfH(c-^*RJk=K<w-Bkiny-Wix0Gu3LZ1zUShaEikw_hJcDHd3q;lXJlFx=d$
zWiuokF4Gc%3A@NSS(I`D=BDV{X|1QhUstEUcW%Hf^z+wCy_(0e!4jE8Girmoqm$Ry
zlvY4fN(oy3wT%fE#~5U?>#~+H3lOE<RwS{-;59O<zEjXw09h}f_*fS|1ip~nwTU2s
zaxMRs`IKS4!{QQz>n>fehpb0*tdTP(jw)F%^53ePENJ`Xe%bR5qT4-+Yiba!>p-~a
zab`$AS8ci>qt~VnJah42$6Vj-T?8v&PMxiajXcJvah{4&Dd<FdNtVJ^w`m<iCLu3W
zz#vL+EAUr;;dl{H6&t!ehDyNpO2FO%f%99AuQ)$!H89k;;xjiL`0?@S34tF^+5~F@
zG<na~bZ9iMX5M!leozWIlL_<Cmh=BnW|Ed#F?+Z_F#?>Y<`P4NqGasa&Fz#<r!YS(
z-EP)D@i**T@EmSN7r*!uWl!+ww1u(4)w_i{jycbOj}K@f>x_x=9H|>mbbK{7YW2N3
z(LU$kGwV-LejRt;Sfx*@((vhflQ)V`8&A{sa-@g&PSMU-iu$T>`znIwB(ZwfYSElj
z5Zk!sv%TMF9y0rD0s^b)HYa`g??6N-x4kcD`^NH_6AY@e8Al>Xi0WA5I5{p>fvn;T
zL4fu6C}dQ}vNF@jG?=w?&QF7Zz*?MOxI5e4+M{f=-g<)W*E92|$v1thAl|jp#QWD(
zQphoe3aT?Wc&#`d4A9x6Jw>r!BMMV^<{mV#qijX|m~R;_ZHhZ=giO2}&11Kw_Y`rC
zvc|@;^FR@aK@gyypefj)rSf*1mk3<Jf)AxenW)n*-{2+un!(g=8xZK0){ajVTWWN#
ztgb@V{V%F;-WNe?ZXU^+FyEgp{5!oTH_y;CHH=N_vu-*ozC49KHTvSLr<A>)G&#M8
z5!LseR(ioY^6($TvOWboaMQ0N7=;~=H1Lk1R*b^!#On=TSSp#;p{Fzv*MN)-`@_%u
zCYpVhZ?NLKPw>YN4{Vjo{CjVcTqPCc<?=lJ-s%Ocd{K(6WIiPjb2QNP6Wzp4l99eQ
zc-rxD&vA&O(;^$R7ds_}vAw2X+FA9I@WI|th36yHQm#@*PSQ?&jQWbHmWs2Q3Xa{h
z66#C{0`k;@4ecaf(`U~C#zm({_DfcopznIRyKnRuLyd9?#@2B(ol?yR%CDh6oPa4|
zQkstkzFv-##*D9o!Z<o~`!V~W=I1Fl$IO4$e&bs@$yRXKCTD*EH0=4vF{i~h>GBzt
zCXB5vQ>K<Wj17kz!WB0>gqstsywUA!o3tLIE4T|;HwwgznFGR(^?{xt!)QoHQ&z<+
z&03o=7F<94oOvN<Sol=y*w<!a-(Jy5x;M4r@q~5ITY#{xSJ0g1G<%w+9vC}6=VEq;
zjMKjp{}{Q@uf7`A;Ztp$Iq|2KWCE)`<}jq020<3TH>mM(F^Dh`1kAqC`U0Rj?8Y!%
zT*5>zgq%GA2&x~ed@^q^4AhzGEJ*C}d(?LW1G#X-I$b|$xg6_ra`I{Nt9pK4dT+1D
zZuXtI=6G73ai@S|kYK$B61}vIbtS{1TD0Wnyx_Ar@avInsg*xw;$oJov>;Zx$h^+p
z#@?Jf{0S0G+-0Uy(e$D5R83aF>zR=kufWXwEI5k0FTvo@8_B>ncA%$9-dWABmIr;l
z&B<sl<n|$vrx|;9k~{TL&KDAU{_fp;FslVSWQY8UzuaU!KT|V?-?HW3*Pol(<K2xo
Rph3t%&N=>0{>}f+{{!h7%kTgI

diff --git a/reference/plot-6.png b/reference/plot-6.png
index c264b4e8ca1afef7976df88c64914b63d5ffe458..0701f933bcd57589aa12d0e85351cab2bca30285 100644
GIT binary patch
literal 38499
zcmeFZcTiJn|1XM_Z9%r8vK1+60HtoD^d=%3Lhrq(5HM8fHDK9_fKoz<p(+HB8afjC
zMx}%zp@a@n4WUVBNeG-LzWe>n+&g#X&YVBboHYYs2rKI;-|wfbgh&HD4UUtSPqMMG
zalkb18?mt+3uI$E^60nY;5TE*AJ)Oc@kiPk_rW7LXlixg;Nip*O-mm(Hr@!<*P+<?
zpKvxd0XEouRg=K9<%xhaW7GYEp8-Ox&EHhXC$$o?1d5_>MBNA#cU6NQtDDIxIc6qS
zlx5NUDk|y%v-qF#lbr>hvZ8HO@5Zb1s*j$$Sa9w7#3d)8CY7Fs)Ze{5S9Lv5mjade
zV-=wZA^NrAJCpi7N*afM4e0-V`@bsyRzP#Y7Nx~&=iwxa4H9*bDz@4h87tjC#CA6)
zIg`RFZnjJ3l1;>4`qIvjjXFesODa|l;Tck4WBWL;;ca{9-!;7J^4t2qWp($0`cv+I
ze_e3naQ(kO8mQlDzYKmZE#&g~by~A1>$&&PliEvH^E0s-s8MYfCuE}~F&K7`RB1va
zp)Oh<cy3$v975@pbuL{+sKBm@9`r)Q<2GL<g+t!XM{=VEtcmj|x>kW8SsNy)nF2u+
zs--wUIO2Vau*Xr2Nt+qiCYXp;wz4=RU5xyMGHB#vi;LeZ!;YYGwMXCmbxHLEY)O!O
z1t#3F%<U@JAWD{lW!@NepEs}Gwta{E@S#h7Re=0}f>eR&3J#)8ges=O#C_h>iz70y
zD{>z_rQsXzocP&)&9LNn=TY8kH|yzvV%ZT`xblXrB$v+S%n?}52|G{AepgGXY*LjP
zvCk_=8ddoxj5iNfuv!*Vtp-76K&FOC+NdR(*mX@^_~E}i{L*qB^RD?}db>~8da4Oy
zMgyGXu|ZRUx*xhY&qpR_n-G6M2&gD0iTSO~cWw5(M9;Vr0$(o|4Gj6+ccPtA8tPc1
zbIOUS<zvSZ61TF{@N#^V6taUn&s6WPp}s%KlRt1o+V1dgG*pJ}^@Ey3#T%PtYNI=X
zgK>y_N>0&~HBAa-vV5ysEQrV9sy^}h&5{@HQ1QnVD*}aXl74%JLXVSNp2{RWbrIUJ
zZQ8n-7!tQ8gf1sawSDM<@eyA+9<GffPNh89_dQ&H7FOsOgL5=Gu`Zv45i8v<SHtTk
zU<qGurIXj3e+|dri=RPo%L~Ute=A~=G34Yj1EXi!a(ag*YoQZLf#S|Dx@H$eLx*rp
z+7l42(BNB7x1I!|yq}wx6NNIjL{a4O76hKB=}?2tT3y(Z@r|9KN%LBXAo~8afk$l@
z=M@jUEcR`?uiWpF6KtOMiRT0l46)_xffZgl&cUp`L#z}3IRZeK=apRO{u}HO`167w
zXz`Ps_yJQPi>=zWy#q+UFdUD+S=BZc8+Rfss47;cDT$pfwiplT??wAow^nir`X!s_
zE_m%$Ly>+$8`anpWlC}ZJN$Wl*p+MKndw55LGF$%&ZXTA=W(WAGI2Ao8U8iIJXiZ!
zU=S)s8?}8dqE{uUa#V2QIA$nE2_j|Yi;RG8r%rmGiQ|LxXZlnpIt%o=UFlDP|66TI
z!y+q_Gp=uSD3y*WFubbJ>lDo+nC2*BLOB^V+J5xzhNIOf<ymO>eIwV1@-(aUQCw_s
z`GzCCi#OSE|8>`0LuN6J>iAsPL)<D=cA&T<$vo?h=Z&AS2Q+Vwv-CJMIaQbM2Gz`9
zOQ%vrh4&;oq4sxf>G+>xk8US0N=Wp9xJ!S0Lj>tl4bblurw!O^l;2PVlVq_9U#@oF
zM8%+YJYbVv?cqVFO4aIV-XyG8lXzf--@hvkcHK11_^u=DF3p}C7k-~e^*&=XzKB{q
z6miGn(BwQ+oIE4$4daUU)p>sBi0#7Kia`y*hWKeqogNZ#$BwT~pHy`QuHne1tzWsa
zk*L^C`Ko_cvY%0crkkKcp2%1dM&4KX<wy5FwbM}OvKQHrFx>kwXC#@&ua13ir2cxs
zK%eJR8J;#@>20rH6<Est#HAtNEp`;Kz)V-0Jc|kQNFRth@u#4cdDiFR+3~~m!g7hT
z(tix!i@2j}OSGAeTd<CxIm>r>p2{oLQMH^gs<t*LN!P{;9H}>Q+L>_N%9r5U^Eo*L
zufWY&MDs5BHn#3b%61(*c<NWMYVg;6qM-!&`RR2bp6MPi66-eK+#N8Ixa0KcV{B|m
zU#>*-wrP)E<7$5=)}KWU#Axg^*bq$61Dy{?ZD2gM#C;(|N^9j|OrsF<-TXRD3w=jl
zHgrE)JB5Bixlm%!LvVi{g?2prQbksWk_2&=-+<FKb>~E36kDPMnjwGED0h1Tlnq@o
zcWopv2a5cO=jcg-1;~enIXv$@IX}+1YTRO?fvW6>r%W}L7@MAbU^k+e>r)jA`h<<`
zsmPp7ZOtcN7j;G@c2$B_?G0OlmsS#Ni3iFpdwSx56NfvF*mUlutEDKS??fKI?`_RF
z4?Uz>gEk%wut^rReQVpvyLOd%2fgXBS3TS{(3o1JtcNRxiof&8&rPF{u6axv^DbB~
zmlY|(B07D)Y}8}*9iJa4R8o|e2h82?6T2m7>?6;2UO@;mdB;4ZW63&ev!H`H_6h)D
ziA19lLrb#*kD*>^(qnZ>bIhIxrP22iyP8!yiu^DOLk((*HE>Zs%yvcVVi<0Ad`K-r
zXR6slQdm#?w-?jfKXzjIh#?r^-vI6!58i%BKk8nGUA&T;&bt$y#Seijnl)D|V)!3~
z#c$SP+u(=eBM5u425NPytq;-lCleA)FTttrO57N-Y;3PpSxjhh5e|(_QBXRW)>VeG
zOQ<oNU_NnOA?FpXNc4~MjY)Xz)|cC~4R-k*vw^tg^CWgwuT6^XJaDR(ttxNS>w_0d
z=HpsJakX>l!U(($_nAmMB)<sd&Rx^&S`CrU6&+JP!^ZZ=B$<b`J21MbQ)%{;cJN=h
zoHi<SEqTalMYjaCO+_R_^EMjcF(L7pU{>1Kk$VN58C5LNpAw?k6mD?grvMkjqd+hD
zj%jVv(18(g>1bw6+`uHAaVMg(x09(6U+9SU|LAfOrQ<n!9p}d1pM+o`(JgOeA9#ps
zKMX^6R6<-uxJ*8o5L;2n5Lk%T_yNUcVP*D%ghs6nV^WRY`H#C&+RgrQi6pc&o1(49
zq0L!EtcuXbsNLV^8dJMu%z`3M$K8R|y5G7K?_1Ma8C|?X;mxhvDoYba6cIL?wB+yR
zO1WiZF<ct8Q``{K(Bc%Vp7KWL;H~s-<dt8Iy!_#E$D7fN#}6{m=eiEETkFr(s!ZD2
z=0^X0pRAR%+vc68I+S+Y5vB93H9t)E3i13Wtk+Wira}abKOo<_L>x`^UZtQ)s<iQr
z#re!L1@jS8NEu6{OXtzSv}Ed~dFl4C<_~t(aI2p}Db-r5ev?yIZOc`=TxzjCJWp;V
zF<TJ7SA^GLX)0*C;AKbMg0wbY5mq5+9uW@&lFD?mB8~k)LHzXXnxU{+o7{<Oiy8&|
z=#Z(}r1<{eKdv1d5g0_>5o%Zpgtov(nP)O1AWA9E@JCzL!20-ORKJ|_dno`5M{U0L
z=AIKz-VA=;>k3Qx9_Lg!{$ic+NhhQYZY7CEs9Mjh(|&4=x4Cm*9@N_>{d{-k7t_Ih
z{|~$vU@p4Zv0rkbtkcSmW;RTtdIeFr?Ug4h`3*g;aS7Ca5u8ZiTe29bfB#vP>@svm
z@`%r1a?sI63u01>X?I_=M88DxW&mbCaK)XZ<+NU`;#luwh#3!P7>1Aoph&Ec_uH;!
z*V>u$uRRynYsaL~;|wQd;&-Y%N=UdvM$?B^xi{g&y)kBZgrHx-=F6@|ig|Sm*@o4_
z;CO#B(5SD3Q%F-DW~7ZVgZooj<GN;>&;**{0@sy|rcuGSQzSJBf+42vah#4lMvr`v
zaMWw*2EM1T<7kt9;)%E~J!{T!wZ1yu`91}bGGY83y_=1cD&=Cx%9D<RZ3rR#=0l7?
zC3<BeDq`;>BuN1)nIxBSvN)(vdr27Kj`Du7n)TfxmO$B>A5edhnC>9o&)P$93?CUj
z^tZ2_>-_j5aeboop~Rn6I^Wu1aL)Btj?W*f9!)R5FkLL!@6xY7k^FdP+H{$qTX1Mn
zX9ehzeoG^aw%m1;SV->pvQgeJ+W6FXAZJjQ<hCE0Q2G8y{X>{*=#b~Dw5Du8HzTsS
zw;KT3KE3lz9dz}uaboAuh)&*I^Y)`gd04}*VwinBZDRX%(lyln4RV={`R53G!L7uR
zuXcNo{;2}vrphGUawI8M9Tnq`3B&22IYy*AS{1*blz+aP(uKY#3zAYFac{AaK)jEo
zO(pxj8n4TIVss&fEWqNS-y_&0!9}_1uTlf)oe=6uqHl@Ly5vZGSy;R;9;=M1&M|1}
z%(W3}Cs`8r&J(#i6kn|jM7Ririsx5hb6^XiD9aLis9pAhz=;6nJK-JW*KGvFp-kOE
z_|p6WUl9MNbs1Ph``!;!ml{gKVEaXM1^l=vL%bujS5w|{fpOC`iT1AN^DkJNTg0<`
zMRFmW@5AbYhBNUeepI2zq_X6$*#;g#arB8@pxUp)D9B_fQfFP2&h+*URNQALA!t0P
zK_|}fS(ih<y(rCrY1$_PB}`c4dUjoRJWg4<QPCKlaH3Nz;RIWEf~9cn$&8ZfCI2^A
zAIInKx*hqLVH!j#N;z&v;Gscz?OO1bC+2-_fAgaQeWHI3DQg0E3)-SA9LBE7Fm<xu
zO~+CNw~_{6xi6WIU^v3*f1KGb`t*I8=fxnW-C)k0m5++W$<{C&EZD5&jAC-W10K5i
zpIZM_RsX-=M6wM^M8RJ@Rr(9kT>nY0Td^9@N;98;B<aF`PsYAt?b)xx#PBEpoB!QI
zKs*Q)47)5z_JLUox+mT-CK|vP^4Jh7qkOC~2KQ+CACyG=W@Oh!vQQv0xngu)&vY_3
zYz{X1;q#c%epPph3O8Tb(vy2Jo%#!^7<&47gD|p~=QqFnH_KZ-q0bTdVh6XOZK@4N
z`~T`3`x*a(@u+Nz*1@;5o+GtJhAn7sDPAZ<>ZE6G{uy4>T6{;_acJZ?j9(kC;Y2I6
zroNm<HhgKy*tS3)hf*jom^w<DP!wGQu|02$c68CiTM_eypyFs>9QNxa5d|^wpRjC5
zcRA5Lp|Y7?l;0Md_o%2#-ouqSWz<7aOh<-$%BVRt_;&cIZ=Xw=PwDRcDP_AbjKicC
zfCXGulGmi089_QI(lDAA;=oCll8}QxPL>n2U+DEQs~gYn92<xg(rRR`kl5u4vK<dg
zoP`y8Td>QVa$F(_z275RPQIrYT^gY{9~|x9lL&Wq-8VebuhGG{MQr<!kr()}?V9W4
zz62HPlMy8)?KJ5fbn5Sm?MLGil9**u;lr)<dbp;bkNO`XLZwA6KlUP85Je(3=TR|T
zcSr2D^L-Kq6ihj-Xf+l4o`Q>h89j}eF&$9VvW!Cu829ZmqYH=YL;j(ch>_gFVsJx4
zm5g#@DK=B}603f;=8Ty?Fv(`cg2?OrqXo~pI9xKYbf`=aP7`ly)RZ;=4crp6bz>q+
zyEa}uvg7qO7I%zqcq{Xm<evG%&rL$)23KhL5kXgP6Ly&(RG2ffLH_A~CtF*bX_9<k
zNMV*is7l#$ZS4_!1RZsQ#r#kh$s!zAMcmJf*x*Ma3>$5U0x*u^@>Y}!;b<o=xw0#z
z$)<;bcqY|L10HG1fWM<MS;<`Jo&95R?Pp!erT3kfU1*<EwWU}&l$B<RDA${g1Vo2Y
z4?QHgi}RssGBX;RD#$C6A7dIDTDZ4Z6qJD;xH)pN9p$#hprHyZY1gP0Q?+IH79yxT
z@Wv*v!Ew2-P@Rrp>fu%ke-U)eF&ZX2M<?bcp%v-dlNaklqA8BmKMA!ZucNo2el{pd
zlw-$ab$!?fWwD=aquhE;&l}U`@#RO2=#iVBF4n7N<i%8N(2R1UpS{-ky<ZopZtcRC
z>}V&cT(u!D{Iou8AfCJ}aE*K~B1q4)vdaD8b8>$XIoA2Tn~bL=B2!!1;iM6wCAAam
z;!U$!4aeGX0n;2X3b2Jy;(>zwF7ns<zi_Z=W*w7c!_KN=Qs?JUC2<J9xdrWGToxZs
zyI8F8_*k8qGxDS$?etu~_cW@8N#<3xXEeA6G0RE43Ww&Th{55Ir}faA?bLy)Ax0}|
zvT9%1%IG?JZ`68i9<`rPRG#$tqWTLF<dua>4;!Yg_V0Ko){Vu8I$A9rx2)z-cpt2D
z5^>0cmkOt^H5&H0db5Sdfe<Gy<(_)ojZc3WsYlUdIaYsHX#YzI)8-WI=mCj_a3q9G
zMU;^#T6ow(E;6-i7F!l3*+06-c(<n^`tyOfumx=tDVULO%dY*CYVk4C`{40>)4d+<
z@@;(RtyOBN4h6-4h@1G{O$~6g(ln(Sker~gUwQ^hj&P-;-z(cyBXnUTf})2TXV-13
z+^u9Z{!Ka2BO%BnII28cZHg3p0DY@CN;s|Q$hm7G)-ST>BFFxANgJ<4Y=Yw>hDE3!
zH<Lz@?+fjQ7>XDTO-8$7W4$4<7uphcoNsC59<dlY7M_aTLhVbCe+M$rz_+yIGD-ry
z8K2}|kri7pN@cd>EW3$&mg6hSJJYWAH?*9=Xe{w3TB?xOE8bM=LEAR36-rEXE^B+V
zd>!86>LpZ{Ug)e85H}BIgy$af%RqJc%MQ{Rei$Upk8<!#g1o$DUZ?%4V1}EbWElQ=
z@caaNe<dy~vTHv+?{S>M>+*z~6_eq*SKZ<9owenuNltNF2W_KUqo2u$Y?Gcm48Bq8
z^V*hubxDAc<rKz-q$*aEV6w!Es<jJ)<z}v)gH3hVWM(8gA>A%ORgTZ!3|0bx_0#iL
zKKyXVBfoh>*?jzt`FgSns*1aRBq3Coy6b+ej4bmtr-JckR|4ZMVjk7=ny_;pO}h?_
z71v(1y@NF>aXjpD7ISJL(AlDY=<!Y)JVG#^8sNRwx|{+bx3zU=x~1CPTznQJ-7fcD
z5QY}aClEUIeiz&>qZ~2nnpLiqB$OXTyt+aE6qIEi`eZ81w*iMGT_F}F`JAd#garuZ
zFJ_*@RuCF=g#NgqaKLk7!FApsOd3rpmYA?zdeD^ie4=C5_G>9t*k(a8;e;||rut1s
zwR%x@agZ|vBT2Hoa}+EyHo7QOIhwW%p;VZ9ufC{2=<t^KV0sJPeES42K4>PA*E2lG
z8s}3S)N`hE*>PE8AwuZgkU<~Y__LdH>BSP4jpWRQ7%_B<I_<5{lzd9zeHx=-pD?r)
zGqsi8;g`PZUFX*qr`4baWn$`b>Tar9hOq=H|Btg6es31qWVJxKO}`)(62$)aBmRW1
zpJC-O6K8eLA1cN(JPX5{nH`n>(ViT>2~rIvxi8&5B$;2wgdJJg<mH+RPn24@5Hat4
zYCYG=<5^aAv;8?LZ9nSx>oZyglR;2!a&eL3J!rA8yvV5ich@t7TYu&%XTz<$=PYrr
zpiBch9`sj&H1lq*DO#z$PZcWOcn!tp{gHr(TJ^~zjE|2Jh`PCO{rkB&h+HNxoL;W5
z)z7O<8w!{QmhKq%3Boe_3&_Op=mkAd-AEnR-mg&}(MY%zdGD6fzRN9E99Zgt^Qo)S
zkCb8tMg%Ln>)PMw7gX0R%&3ANhl$kWwpD+P5|_C~Mq-fS`@7NUl7`w1ZW5qnh8aQZ
z>anC!ohJuyQCkslOQwW_MW4gAHD>L@Wi#Y<{2kRk2Z-|P6|kYnDw9uKC*xrY1iz;;
zAE}AwnPKuO3l;a&NvUJ~JhvY$SW8ye1rywEH<h2ARx;|N3l%ujyd!%YqAV5%m7FSd
z6K1rhWnY8p;~`7jpC0N=79;1<qXZ7u=ckF<SLT9{>WwdGpT<V5MY`k|Nx^O?AeZEa
zC#?8(C%ZI+SY~>n<T6$$uR<}Z+`UP(7O~&ht>Esz-2hSe(*_ihx?x#<DM9n%>G7*n
z3#<Cks5765w_AJ_n8AW)ng`7`vuJ-r;FSu0aF{99!Qb_>S%ZMnm*AfN*r2$f>*Y3-
za&TY#X2J==(MwztI@~ud|E^#^DL9rJowR#ACn0N0aX+^>r7IhC7W35qT2)cyd}nC5
zTey+0LNbD+aeLOD9in;gEbYif<Q3=cCet1QKh95wm)~^eZkL5C{lbad5t)G7^W!3P
zC)W%DZ)f_PfyZ)aWZ2P;?G5~(Am*@oEKDUfJ-7VL_4CEfS!`w*S>pTj&$<<8uZPcl
z!_ZS6g5-h2y-u0J%XeePM8o(`U2EnU2omw1it?0t%qg_6*_!BBJ=pYU<28~g#--y;
zvAS-Sd!*j)g8R+Dx2=k8H|VW@r~VuYbvP_GW~S}b6zR^iyT;|_P45%uJ~U~v&GhX_
zvOCVy+6he|Qwn`?2WgHo!NMc-o=yDrKkl_TpGj`yfsl{>O%m;iDN(Dgkv)-Y+tAIH
zQ*8NVo+3nY|F>ucoPI94#<VZ>ubMWMHItcCgSvy}c_{LQ(4!iyAJ=`<&i?I7V5;^h
zPfeo9)gRKa=JWf1q$YT)^Se{7$gF5$QZoimZvU(qyRagBYv>%M;gx^*^3D@XYGgjN
z_%_#wkWWRDa5(a6-pr48O|4KFH9g$m31Ut8<j$6XnUs3CrmQ7c_Fg^%*L0o2Y|ZCE
z%V8q3v1Ny02XU5p&h0@D3gm`D+}@9_7QDB}Wjwn-|Fvhwe|)jl!Y&UVSZ3U}HM{kb
zVUk={trKv`Kjr=rYt#0lS8^s8N`5>Ky4N=NT^evdWt-;0-W=`YcetjSp0_M4w1C)o
zCM9gKX5JYj=ik2ffN1E%Q}PB=jl)_yZjT*84BfUJg!hPYIMTVp#JcyE9ljB~;<ZKw
zD_x&d-d%`a+4aG=XPctqbVR4zV183gw)?$Fc1Qh`yEnH?@ucvd-d+s}WWSY{W8Mm|
zyh%#Y&J?ZCN$XMX3%im$w&m~Op3hyE<@crvq*@~SqhEx3@>Up@pcrXY2<v<L)fZh^
z2)80>k~y=^JJ$9)(yLvsaxL46QUf!L1$3d}fBw~ZbWYlZG^8e@kO<$Ir5T~>nO3Xo
zK`*nOmG7Eq``h*;*X9pDY|tt~H;DNQI5k210<|NpjnY>UCv<*Sg~oo1K-K#TX?n6t
zDkonWUow!J`mqyFR`4uI^GxO{i;WIRl+_e~DHZMPx_cLO2`E=N@*#b#rq-;fo^N5U
zQ{%B_zAAd3GbF<dGL$>_H{=Jum>Dlvq+aWP6c&+1d63stJ$7wp{_UW~KKdsow58~#
zH)oMY&185qW86-~r?SqGwcpbnuFc;s399I&h2392cf}W<!8xON!ECc^d3XP-Afmb}
z!2B7CbMw=Ep?70ePMJ$0{oFfx82CG(eD}+Kx*CmLPZ3QCn;)<hb9)U<UxI7+j|Duc
z@sHt*$}_HZPt9WJS>&d^D|FnRKd=ZgIZj??0K97WZRuB|nBWdX`nU7|P8FFXD{yUv
zsV%e(T!q?l+X`{9`7cpac+18_j8)Db{&?@#UR|_a<47E7%NnQaRpv?gW1Q<+7q=Z;
zC|7dFhOE|pUN_4V`RMJyZWW>vX-yrGh)2*A{#tPQ`0QK_UsRJ}rL|g-mXSH&TThga
zwnAMjNfFbm$w4uF&@h7*9HC9*3nrl-7=6&03PqiOU}vtvxZ9W6Ngaxp?M1kz$!BFe
zRVd5@n%L}73R5kDd{zC(;Ljs?UT$j57`>WAoq0!FQ9MY;hHZ!1e?ewM&-)cbJ6hT8
zi*Q-+Lb$|=KKzg`wf?<vKD#7eEIa*czV#oe6Za@%pL$oesD97ycn~sQ^G;Lg?WM+S
zgtxqVKfS}stl7SX*BtfASh`77i%1O21T&f!qETG^BXsza!;{FtZTnYm^i4m&Tueqt
zg%n|F&=-+H^oO0>K3!8D6W@m?1U^+6>{$C>y8w+XRk=!4dktxL-*q%`PYXU(ljB{o
zdF>+=k&D9_)$SL%krXg?q-qm1*oR4>{I^&y=wkK>=){l2_Rxs!Z3}kEN4Sx_Fi(&o
zot-}RbRhIBH9)ehmbfTATBh`TQ$JUIy7<5E1(2Z1R(&qDJ}(6`YE(iNQfwxV8Lldi
zO11l-)2u&AC6Ot5(BjnXgj&sB<I1gX?q?9;Ws@&l8q&@%`slZ`CNGOXc9PXlCm!UY
z^hT@nCrtRM;1<}kZ#KDu-54lxg(HsaK&8J6T4}L9cWfbTPL!^>AI`ggxZyKvCU*M|
zEwQ?;o{j3pyw8cQDvkV%KZ3L9Bh)YK;pGkW9)5B*P7hKPG0(n?5{_Q~CrYAj!u&^_
z@=|Ii)W2r3DF7pEPlOFkMb9YLko5PUc=4u$Ym|oCHk$Fn8auandGv_1Kc%e7tN*#I
z{+Hu6;f6EG2a8ty`iTrVLWex&Ssr$-_~{F$nk&kNPIcRc8_6RShkgI0%-az1&d`+h
zl?7N1P-nBHcOw}p8c!Dcb?ohF4f};}el5RIFd;oZ*fWvVQus=TTknNq%+@SC!zyUN
z8JYHZ|4G|jS2k87L{)cG#>IENw90MNp9mw@yntlp#^HD^<htP*xlz@6S_82zB1CZ+
zv3{e{Tr3a#ue{{0?2Sk(qY>m6<(vI@L{-+2$YFe&aQW~N368j(M)liY!W3_jkS1Dv
zcSp2V_W!82Ka07jhU+}Ku*{s<eiyVn6d86rrs9a5N_*{m?1ZyjmRV%}ww(Lal>_tD
zXO}}7)khl!b*ovLcE@@xud@<wC&Bfm)VlX2{J3($9>-i#Cv>!}$U3FM@vyb0Xz&XO
zb&`G-+}AoxIMZScvrHLX)w(uv*Mt4@9`8Z+J^W(+=x9}}e%^kyL(ZtwQ{IoAPNJ2H
zm`1tYxJr}U`OL9?fodc5-nur0u715fzluA}<p~{IwFTu*tT{2tnaBN*^E-4&G)X}Z
zQSP;Z>hV@MQ7{RI>UY)OpxTb!Ti6S#dQQGg+K->+K1O98dLMPY8E`Qp8muWhj8l{r
z;G&^!y%{KVudmI)VbH`kE(UQTSD0G1HL2Qz+>I%pToj>edgF=WJA9b1lPYoKx~)to
z^XIDY?P)8;I=>gjrNv2UjP!I-F1N?|<2%l~WjY2QUexa-q~6^AIMIO@(0n;j<KiZ&
zTX!KVv&HdD_2iT<X7LHIOezun81CI0#!=f1@7yLnaV#aR%=;BaBWL$?pChiq94>HV
z$Zw%Bnj!?*sbH0shr!w~uHvSHb28m_Dv|hjP59H=V?&JZB|bTerWEFH7@r6(h9axl
zf^`=a{R*}kjTh<$ijM*8S;fIN^da`d2b8s)38&KrnobXTRH4s)@<9)I;Z`dqJ&k8d
z<N;q~_N^{v`V#yd0{RTD?hLJlh=drFAD5sy(nBu~Y$Z)mnrv*=Al6eOpZsCYwdsxp
zla%ufJtwX;k9`f+zY}-^M0ug|hPg?1@8=%->@`}YQZ))a)X_1dDEu|&AayG!^HZ<$
z*EvQ`S9ag_TVDmt(6Q~x`{KgOo3`h4Dkc!!6>3f<!F5%JN|;phy1n~BS03v6EOAz(
zXHg&@y%|=So{1(h3)I7yX`}mnha}z7i+c%hE2|OYxuH13EvqZWc3BT5U*78Xj3e{?
zV>jgouU<^@LZIA@Yut@ox{W1UDk~h8phmg#uWZq!*uS7MJGf7C{D0$$<xlHeU5SS>
ziM$d%X~SDM!Tz?TUbD#;kBC>Ye|Xy$|G=N<Q2ZzOIW~2sv$iee;0hXF5AXJQ{2Za-
zLh_rqky}E8GF0th`mUteH3?DO^5z^R`z#)^n<UQK_M&CKoyn%x_fj3gU^wAQ^x2h3
zsk_j8vDpu|5PF82A=%Yk0@41_#Sc$(R{Xb9UYeyq)fobMj)_uJ3CbHWQeU*1EQN-x
z`ss?0ex=U|Q6&j#=im=uTZ&sbTL&-rmPQqG-wZz^%6D857VfOHi(cWb#&_*}*SLkS
z7P`4;EI36<IB^rd8fM}$p*2T1YH%H@k}}LhkxYCuqaN>QO!nWbE5T|wR-3x7(3w*<
zxjYx@#tlypWlSoq-yT6GrbhYxQoiO^nN7K$TMVt`f5BN!FcTe3;gzH)^i$*~=Kb+b
z+EtUS@Z%P)#kS#l`N9=;D?SmK!WL<YkLMSMtXQ|clzroC4Wl7J6NMA}&bk**U%Bez
zlX;^_%A4;&H$mOYqVH@%y4|7zR#&(=Rjc-`qetR3t-X)7+L&m@>#lACF$GQ34=L@w
zRQJ506dA#M*S+=)%*k~v;yi5WoT2ZNT%GpVn|U^ojM+EHFFUG%6%q8Qr1#^(3D`*f
zcjVg?>WeGM*RenF{!1}ew}r;Z7pB%SY(4gr|LD!=&0qWBCIL2#{%PR5CsgI*a5!T=
z;8M=q*xdM01oj8C8r+ku6i*wT5}mMFD0^YE?M2?c94~<M!`#J>LD4sZWX_-tr~~`A
zGOP6B?63L1m)`zkb>@Rkh+LYLbK6PKC1NFRH7heiTbe%Lo>Y2rzJ4>D<a1MWTS_(3
z+C}CKri~)4YbwtOlV9u<8uhasUd-n_Hy6J|yVFlHC)*sr8NqE7de0nQFQ7KPWW=a=
zCy*1NXG-5V&tXT>fwE%t(BBA^S```u@g#EL1`=bxoGOo{o;y9Rk*n|=4wY%qE>fKM
zOMNHUPz=)&kby?LG?%!&K}SX@2d8VwAF^wje^nfMZPahQI550&!7|5|q?@4={Y?%!
z2CwL%%k_4SHGc34xzd!RBieio<8B~!`(5{gl}Q)DghO^6ibFCs3%T&Zqy+^`Y6tZK
zZ;Jw#RfqM{md3tlPN)eQp(JbPWb2WD2%_b5JrMFxjmUsbJSi&Qu)FSK9Pr%VUHwu_
zzFL+>o}iE7UzL;xut?wdOM3{6>4H;XiGF*fY)to*cI{7U?OX56xAR*@=qbW-ognnZ
zCq>-jgIIr2sl2=DP&@RkAw#HmuC%3J%5|U93LKB;s^8evs9*ChEOWM9h~)4V4Ea)H
z5i_VJbhqrjlahPViQF>@Cq{$j7Zu?Le^7H`_ZXIl>YI4xJ?In<w^j;-dbHWhRW2+M
zL6fnltG?l%5XpucBdlBz!Zde2aczWR%o=9TyZ7<<jxY$7DWPf$ff8s0Ohwy?U%ok~
zr1s=GePX9y$-EjpLV6VbZCAD>)X3#O-Q&YVuTXe97{9jExpo3L{DHc=3nwx);Em4O
zj+bwQO8E5nj%|l+mM*Q#pEw2fcP=iI=Qp;y+YORbrIn#6VKz3^5c7q-gbw~Kklg&v
z20~7QT3#`#k`k13;Sk%h)S2Il1Hdx{r&-U?l!U2&R9M%Du(2JTw}(!sf8v1r22ug7
z=l|Z5(G=<Xtq@%1|J38|;SV<EY$2JN5TcFItPP3vMg(xk?I3TQ7wPd6EGXY&J)iRx
zH9C(!_n#-*YadZwo}Zupb$gHXtEU6MZcqREQ6%dB>u%QjsB1uE{O2$|f5;AGYErg%
z-&#c#l>?ffcJjcR%psh8)9u#p>xruqb@rE4iv1`<GA1}XX02(VnJH%$UrtWW2d~+$
zRZ4s7t&%R|pMFjZ7MP~m6kAnwyuB=iUtDkHZ2GZUx3+3vWp(*vR#8EbE2#t>(fa*n
zz_^!&vt*|8=ppti>!c^Lit`xf48*)lv30snl_Dmw2j6L08#3eH{?cT}Z%Ad&TOn_Z
zFLb@de**8vV}!G7h@eubnc$H=l_KRTjW@^HElzs1@uthT4I79nb1d$Se{@?6W!wLK
z`U_v7ah@LOZ*bL}sqpYR8`3d00o^oNuLE2oy})yAvcZ1k+lSWeUGjws7lOdocTGt7
z*jC3Uui1S&D=RC?az!s+oQIN-e#ZEtDdY}v7f&gv3)$bJ2_NiHd)*10odxKmB-5$V
zhia)b>SCs%_guRIkF?wF?nlGyGkP`k1znRub}GI1kpJW-Q_L1^#Px-8DoVUfJ>X+!
zF!<!IO(~ZGMljs^;Nv(dy+1lSnrdC`CbvPS;F>7Wl8w7f-KjD%l9G}*GcX)yumbGe
z=?XOmD>YMLV^<@tKt+_r|DW@%uOB~ietE0u>*2fbKf!2PP@CV>IJ-AftHTfWDXB7^
zjgKemgYv?u-7-~1#jUJiHm?73ihr~mu3aJTw^Y=ykm9;}046Rm(THTcFE1~DMj0wr
zSQ|9AZ&)a*=n2j(Dk>WN(U+}JTcvZjbg^@w3PSog%mq=%<M-*hh2GztjkRt4{-h&w
ze={#4Ix0%y=FOXzER2kx>x{LgrUx^hBU>3eYX>#R?Gb0)P^%sq+q8YyuiLWQp<Kdx
z<Kf*YQt1l*%e#i865E<wRGa<&2AbQW{^!@aSEny|)6?X9?ABBFsl6(=+fH5YtRZAD
zL8SBQcx||-m{`&4^gLLCkOH_wQzMx2n%e|^i>WP9JNK!+zFt#gYa#);HE6D{6$~Y9
zt+y8Un0f_0eeZRpxzKTO)3cG5J7jK;95hFLeajQT`1V1q>23OCIh=rsmcTzAROhy>
z?94yiLrY?x;<!6_7rZIysnWna+CJ=aeGnCYJjtF(--@;mnoly{o(Pyq59^-{S!;0C
zA|{#J(VPnAIeDa)o33lbjNkH{tnVk`>{W`vSR0qTCW97juSDM8SQ;#>q4(>nFvE`>
zJ60m&Rpm;O$dq;;RmdL#mwKP(H)JU+X5YBi{C>l=FYC+FC9nd&MYJ{^N$Kbz-*)bC
z6M}s(_&8HgSYj2g#gtt<@>IpQ?`aX}f}T?}8ZE(fsED?y%FcUdxkq7bdG3zq@?G9S
zv*Lm<B1|}a6cj_quimn*S>2s%2;tn@9`iiq(69LOGZ*_S&_sk}af%=;oJq0QV0H3#
zVGwnp1|EFr(0gd$YVD%L!BQbE=G2l@$o6stSU^&rr_xY~t#zp{nP|rv7g1Z1sXRD;
z^Tv(WzLcRArY7VZeyx#l=4XOGK#xIgNxRTWh^^nCX|GUSANA2tktIir!s@3(LqQ!|
z_0<nsU%7xu^s+8(kK9U{oz0z}pZ@?rKo6{e@jk6LT>5S1KB$H&x!vg(xFXxSZ7$Xc
z02tPvvF4Me6ynbmprq=QA2Qd{)>Z=>M>`8Ttm(RnO1L+`u8if;Dhgb(VbiE$W@aXo
zkyh`}7L$lmM~hh+Rp@6bhr11zem}B}mtCps-;aCToZ;xVt?*RGVC;61`?(PJO1F{n
ze`aG<_TPZnPo-yOs?>u0bvT#vGnnJTou;w&Xy2GaI0Vy9I~k3R`0LFdeO0z~{-mJ-
zQ%e35*}Z!!qgDK#Q;p23)>j+~Zq$YD?<N~n$AXseUh-#*YWZT(Z`b}-8hL)~)KdLM
zrzrY(kY1*o&%&|(JblmQ8VS1|vY?>gLs-=PYeD%Q#yzw&>Y-h=Q+AFML&eq#TErM$
zg#t<I>LQYR&BTx^5ev%<V{VZ+zo<w%a1Pw^S?D?wP9xZRj5Z=e)Ivs+#jR6%GL#ra
zdg=1K2cX9790FGD4GljG>rG#e*Sv8q^7DRg80Q0=ft{V5E{d<VDxzRMFF=>MOVzG?
zJc0u+Q@7UKRne<F_7}>k+Kr~Zw^MJAWNb4-S@_vRTS(P-%Om6Qf#jPYtgqa>&j7_^
zm!iiDAp`JPm2n@fBs@~_QNXR#tu-nr1g!2Ef}`n{!`IEwpI^AAv=i6<KEZWnxXeN0
z1q-)${(R6Z&!7^CM1!5xiG%vbeObz7RBmqW8rH589Ae}ax&XWNaw-FaqQ3a(3H3Nq
z;x<_?H=b{tmkLOP*~GK4GFD?QDI&5Y^JKa??8R}8V3COyE4z@Dsuwu?Qo$kCD5l=}
zczJmdNcQ2(nImUpr{};9ym=k&CfnYUL!8JkS{nP@C>^{~MLN?+Un*ozOimt(!c>n*
z#bmmE6v!!mJC5}^y{k)Ev?{N!R~!HkOED|5=({ex<~~*}Wz`rOls!*C^OptwoVn@Q
z#M~~-x5++^1Ql@ULC4NkV3Av>@G`mDZ3HC=`=0VretpKLzh^DtretB|uDSb2c}}yZ
zmE_`Pc8t7B19PYO*^z^FTsTuSC;6W=s@&p{^%#4DI6r>CrhRZY{0LZN-yTq}%2}!}
zj;j<|l+COs=quAs=iR}90?XN28njk`cP2g@tMTMFVIeqeG4nthun>tgt=j45r}iKB
zh5$Ily}5tQxyF^`kpY?TBiUwOi*Fy<b_L~J<K!TB=zaO8={M5&z`J9WcH*fyieDoa
zdeT8vj8?nXiA})efP^5yh@DBINIxY*6O(~^sFNp8R!ae}x|1${8enEAqCeNx4D_cv
z$;PLf8O<%(I7|py0R<DLjhYOceS4cgAaI>2*&Hy|=aq!zWdtu3mStvociv=ji(3r{
z1OD7ACrE@m2Gp_$2%BSXvtR#UpxC;m=32bwk4WK+PhlH#9pcXmJPY*X$=GLw<~U`n
zRh5f^M<Z>a4^*jQ4HoM-4Jt82eXQDjQ8!(FE6SGut}G)_V3_@NAultG{t8qNCn$Ty
z`^AiqRXlx*f&(+O&JJVJ-}+j$sluE?j+g1Oh8w*!R9xr5V!|4?5pO-nIXT8+X86AA
zUT9d!hv(-vrL`9SSWg}5t(pVnJ%+O=%hITF8T20qWKzT8SMEeGhjBnla{S?YKjQ$q
zIfl-DeJexnHt*aoOVuf07)dvBZD0JpG&opkpIK>7tGXScr$Yac9qv9@FnA9Z4Cr6-
zqiNw#e;aSW_)|dU0ee8uPy)rHeUyQAnmjwLKD0&dcXpM%62n@jy-rb_4eblJ{hQMf
ztqLk&$0`7QWtPG%8iKdX0IIlky;prPbn)Uvv-9WA-|tb{8JC)Eiw(bvQU*$Cc2++P
z;Qu|;29?_H(Z~9%O!xs~N*d++O_g2lOzzC!TG&pVF3N-@azNE&So0OzH#L5M3>KPu
z7WXS2IevP#X}B$Cg>*tV?Fn9~ccDAARUgpOpRiy6VK{!3usvs9dEepH`FiQV^=bZ-
zr%pxJxQ`7zUK+^n<{Bs)1?`GVgG~63xn~Y%;%sWmzbdTN`^0R~ew^SE>H?K!i2yU*
z{L$w?{paPWgP~fVMNSCf^Xs$WQQf!G>A5pwo=xIu3IQJBuwXhl&$AZjz($vE?^P{T
zRaLP7vv~kTY&_}D)$RAty>KQXK*FMQ8mIQ^G_A3;v@{ER&CEng?t6Xz=cNqbFBAul
zbmb)(51TPxf>*-Q_Gy<?Ag+=9Ghd=LY<>IG3tfQrSvBc30p;-f9IG5Og^SUy9e{7E
zlCdm-z<>R(Hx~G*Fh&wkx0+zh2q9ZbRpb%pOy(~C!ET#I7_}R`EkIp_VN~776{lp^
zO9p(x6EOXZeP08mrKPomTyJ4_j=e0k6ayswLGfR2FNaem>UNrejwSgKUi?oT^i%;>
z!>UD`;n%np|E&_yzpZ_zmvsq5q=#LP++x}wD(G|&z=}rxTZh>kCqdnF#$f^Fa06I%
z(@x9Ey4G9#6e7s+APJ3L9ErWw)(GI?pajhqvRvj5U}zA)i^}5PTzb2LhRS-2RZnEE
z$bl-)(<bizoMn8?s{?C&=I#A!AHZw&vgq*)gUV`g0ayXhHKT^ve<2e-c4_bh&8fqz
z>ipdolSIvje#>gifA{CZe^gNeK+WfH+lT&){n-z6spF4t9}YD0PQ!rKA~iiC#ji9U
z;oMvKG#eA5j$&yDK8%N)htWias(V5L0E0}s<BDB_QYLX@H_{yh(kt!mxQ}j{!vLdM
zx~h;-9sjRI1_&c+TlVM+5-gBPiPF-?Y`c7*L-e;RQ7Dvrg>$cRp)<&GY`nf)P@32%
zJy=NNuc}F>9tu>m$F}2v2*}S;k@y?J#Ho}69$34MfQP1(*wzW{ZB@O0FL3VUOA&xr
zliMp}<S}+Okq_w#0T;E5z(tX7pQdZN@rME7ydKKypmMDNV5X2BwH}a=CoGt--^B7?
z+!rpacpFQH@9!NEM9?<oH5L{Z`GqK<7bEX4r@~0o(QcV3#l<R;U7cgg^JBsull3$j
z-jA{`X#w?H8rVnO0nH`zh#)2+;&KxPuD#!tzX~XQ;vE`oZf0m0uL5-KD|WuX=@fw_
z3MorHdK-<Eu*J)6Ck2A8!Qlbv8;jpV4dm%(;?i?@K><wFQ+pJwYQ5o%%9B3@l2$Ga
zNXyN<zOLw=XJe~&toAKY?l=)f`66C3Uj76C=2%oz6kQsqF!!b~=A=ArE&-k9KZ>AS
zzVp~%K`JwFMA^SV6K>x*6mYn_ZB32|UfmiA)U|HX9Y4?{$ADgbEbTS(Ia?W=YAhlm
z@(-7g&L5M&?0)#kOw>-JrY|-AXFGsyvQ<}CR|Q{<TPNH6@vED!AAl<7<$rkU<vm1f
z)JeAXG?&4GmAFJAoBPj4y<9I}zRY+b41BbuI4z{>wxpzIjZd#+)1Hw%R7M*p)j>c~
zg`fw_slj%XzU;OIfMSvbA3wG??M#Lo$XwnkvaGnwg5ebalL4psl|#{Omt;IAZ8T7q
zfu$_8MxFw&CWrvy@PMaVd4f)_0(dZrs_a!}xcR5#0XvS^insyOO_iQlxKj+C8-pO)
zVtBWR;=&k{YG;EntX)H}pxx5DLLfzI4CUMI$|D*ckCekZoHLRBy0LspPJ6(4GW1iW
zSIU9=47354vVRO1qdQw8cJ+s_igk6Yt)$Tix_m4B=`w6FZ4wNbcvbCHsETt>+9Hb<
zR2JvwleCEd|9y<IXmX1RW2wO5)qgl9SuSxd(l=p1g0uzR=J92|rps7XvLWO^@}j4X
zoI7Bz@Zm~?ar6aIvpbrF=TMJ42k^e+@-MD<VD!T0+vBeb!t|B4OzMCia4-~^2lyD0
z3gJa=_DS}pNKw9+Y{&wl$&2MxsHV<!B<MFWR_mf6WI)7nTBzWyC8_ISCKOQ09!c1l
znMb4t!$4e_*`t65&768(gyRQJYf=OTnBI5U+zU(_!A=jSlZD&PJ_kvNy)g<ficF4m
z40l)!SVxE?&by}nH*cOCpVQccp+eB09!!>`Xaq6IqI1`~zTJkMNd=IBs3`6M=2($)
z8ThCMie8z@lAsk~8(rIFsW(UHdc)R<?i0B=oGKW9Wx=Mo{j{*nK8=IXhL9c1sf%K=
zLGztS>F2sz<)%g^eS6rab>|zQM-Tr_)jY|zYzkhp%o3H`Kugc<vA_(uuIT56e7Y!Z
zC0k8=LIg4dj;aL4tCI10uQz<(SpS!(+P!qC4o*lG+-~2BRi^(C%&)eRTP|%i(=r0x
z>yY`Z<INSh4GM6|8zAT5-2i%zbW>cMgT)n0LGmjekKI4R_Fr%+?`pv43#p!Gd^&x$
zmq)f(l?^lM%}_cBSme8pWXb~nWey{<SodSUBCctl?vWkF*y_{Zm#dE2n|{G5uW1B)
z=YFb(oZaq3#>k{l?YaNbTsNi4k;P~19G5G%K{SM^3#DyDfWc-H)IJzE0tOWZ<ocjy
zUu|Mz!gxEu4G9!6%V1dB*-iFDr7LVvi)s-~oi%H5SWOUJ)X<By;}$oMb6j>y#y*iv
zd-7hhhZecw&XPIbo^yv3U6yiLj>B$IND9>ghn^~pU||WXfk0Lq33-$S)~e|Yzw&s%
zx8>K@V=R7t<?(#{CF%l#1P3NY7ean|<jjqY)a^AOXDR>#IrakEn9QQ0V*u(iYzz#h
zDQCBUaTTux^+*6te&7+{pC%w!>K*ul^nebu4jX<L(VeCezTe^S^^M%dutTh^nURrE
z9!rv^g>H1HwDpF6xnM391gIOZZ|=n%5G|+z`X6?`zw`m{a$MJ4Vc?c%QxC*d2S~Bn
zk`2D(Bb<uwK}q|k$@{rffLB@}Iy(mi1^tNE;JcLQAy?tlEzgYL4-0Gn6Rh(!E(Jig
z1f2M)CCc~fCFd^fnk$lf9=&d!2{tGGt#auJ*N$vZGo*k1`R6TQ>?vn5F~^TnVYyV?
zj`~0QFj|SIm<8M1Fb_luhufbG0R4-Atf*OQ(SISI=Q)9w+UpR`ydnw%c1L&ES!OYm
z1rON(9z1|nV+=QCtqzx6_XMl!hpSt~dw_}6Dz|$BA<bxj=w>Q6OmV9087Q^aA9^qH
z2e5p2V6eN-EJ+1#{=hwASN!R+KHV~<MGTlilH39IJs!1K`R{uH5Vj%pU>7DJ<j-9C
zX|po!pk8Hc!<kz<n#4UCRUa{0Ngh6X{V!kJaz|MAFqODGQbEZCF%=Uum9I%CA2@lf
z2Q2TVAuJfQspk(8ZVCf@bN=E^y|nL-Zvldcg)R(}AJCeyAi3*yZ>UsXJ($2qUtPc&
zS`abU9-sY+lYbHkl-NU<73(V3V-+{*LC!Z52)hip>40z_*|5ZlB>9wryW+5{)Ts+>
z_e9I&0(G$$HwN}p)C?#^P5gW!U=kG&?zQQp%U4-~&HV@r<tB(&q$nW7NC(2|CCBAE
zyUKmKUb(v?tRBn%v^8YSc>-i5|A1f@IORmE#{Z$%d8>^%VE*9K&CieksU{)YhI6ba
zSdolP&>I7Aq6`##mVHy03az6?WLenGk)rU>^9hK$N=|R2g#p_quAjpPGKd5r-BBD%
zY)k>C6VV8`jRWMG2c%Lr0IP!;K?@i&V3hc)K;`A4?f?+OL~eXf>y_hw8c+#bN4igs
z+*$_sR)_QmDCbZ7-Wm}O!Va7b-g~~Y6?if!x$`I0?|m>(2Ny~P{Id`vKsd}M`%A+!
zwpSL#SP_R$uM%kqoTY>KyZ|Qp4cM!&BFMy(Ru20dR2y(rO^-<>6CAA(oJ}7-efl)W
ziV;9QGUT-ntn<<!D5@3cKU<)Nm2lwcZ5C=@T?3f@9!y8_A?17xbu?%(y@nnoTC$4(
zAoCV2VH=zX0nxY1v;IAh#H9`8XrmVnm}}v&fj>S(?9)LkwIbd7lG|?gN8DAl#@GYJ
z*K|57b>Rl-#efqq)w~)pSAy~;IJ1gDZrQ7ssf;YGr2Iw>y8=93Z_~j(!<n@#v7haY
z&jX*u3Q!!3x)m+}Myjrfv<K;R6z^-{3#y*kzA?{C`h#neZE8GhvtOR#&R|uA+z=8V
z&yEa&=$Mxon$y{o8JP5T?Wkic+9w!+Kz#c6@uMdQSf)ToJ9P_ZaKpr_l|yFS2Lyxe
znsMyE+BEb%Xwwpp3H&LL5^8U3Y;?K~7Hs*j46$wC_m{CO&r>cyAOm5Yd($}gG-bGK
zRs-1hJ;8I4f3vh!yyoBh(rf|*4?H>796|=ZTlUtVBFhIzgJ`8_5U7kxjFlRD*v9!t
zj!V!v25{AgA4mXr+N?}xqV!>F5-4;ys}jL5T;=_jWf>@KiAzm@=%SrKKMNW*g#!~7
zvIuggx=qgT-PWm8X?MJA5_R-uUfM<!GXQ{sbDK&hUm@^hkAMW`oB*$1!v~xwKljCp
z`<G9?yvE8ske|<L)Nh0Q^B@Q$Wfkc}c0+p{OJ!~|cc!MoIt0;*M3%8&4W+bnoz#=A
z5H1UZ<#!f3YoN{p3rI)W3`sv|ek!@pn;Gs0)Ri3$_!X;=vGl;1$b((*+Bot^g|pdl
zi|Bdh5Ur63q1xYnS$|C_me&Ii7O(+yFBBiLvwE9C!ZnG297F8NnKf1j82}_g7!BM9
z*rx)OvB%#;8{Ph!3E`PK(Ov?Q?3Neo2Y&)SDU|p${k-j0nhv7r@UGro$rCT{#e>Xr
z-y86W#2<Gae@h^sC7b2|_%on}eE*4wB3km|{Ngp|3}DZ;PMl!-c-gA*vDX+lOCC4~
zTkrO(5pLAfFc44G`GLiX5VQHTP6Qhg#HY)XL;$XBjFsAC<m#qM*wy*3xYw4%LO_v|
zm%c=E?=%9z?^y?sh<g#Q+&qbeh6xMQ`u37Lp7c!JeiGUmLHl6vxW<gU9&-`=TMGfv
zzhe2AMG#vM!U^x{GJrmnu>zUHRi)s~cP(%222Jhk_%4G9)}nj?CM#4ccUA)`uSvv$
z0JGflkfn)b6IYy#{Nkpg?Bd!b*~VwfzwUjNKLSLd;=ejhYX!H44whL^x0&EST>9_@
ztGL0?w;$y$MLamq&Ak|~GP+d`a)L^$rZA*b$LZyb4T7W$0szQcR!{^1TXF?>`5w>h
zP69y<NLkl9pv8|s$RMgYxCuMQrfizCb~F&(Bd2t!hi1y&CSwrvO<isEC1fzp#MsEf
zs200|3QMTe*fvz-<^Z3Us*M6Sf>^hap7(D)k*%lhGuHV1K%^L!+8z9*ZZ<|i9vJ_X
zgAkiaATkb8fJadAwiJG#Z(`cW#kSk_^DSGSx?0NPmQ>_bSQGk=77>jSaXgIYffUzF
z)>vn9!|=Am?~kkxflr`=)aE!G1jtSFcMdU_YM=p1K$xyT1<ehxta6V<l{O|OzLs!&
zpX;~xEu2T#&!nvbhF8j@;Mqd53bxbPniXO^-*3D>=INg<LOuaw<iz<GV?Utm%r1D2
zXrBUi7g>h|7><zDOD7FYq7Q0caB+I|B22|{E%Hvn9bKYH*~EYTicPb#*YDW&xd3gv
z?81Q`Y8Abij9qiYJHB>L;z5xUYLc)A33u)ZHH*TWwXMVvfkL8b4@Ke6NB(pAaNPy)
zF~_@5amVL1+Z1U`%fMcHg0HcSa`EA!>;GOs;F)36ad@WYZJgJwptrEw|NXSz-E)~Y
zE+@KYIN7~@v|;tm>ECg)J%v<@Tu$V8^yH2Lj>xw0?>}cTczNvXlfUbR?-orp2F!l(
zz)cam{=KDo_kzY#8LHEly$QMQdik$=N($YekpFYQP7jqZ7cOGo=}W;fzs~su2~_=i
zkC#oh;3^^_>)A-Jpx-)1BLtR-AngaZ9=Fi=_f%7Z8>9=v+MDs4s^iY0f=ZHzMywwy
zEUD7is1Zv?rD!c(1^<A>eU_MUgIwHY`rrQ?^E-tT)p_3iWol=k4e_a)`F}p&4H`=-
z&NmNx?tiuS-ce0;U%O~%A_AiDDj-diVxg$?uA(3!B2psKML>EDB@hU&SWpBN=}kl_
z2~B!QAYzHss1Q1#DS<%fArMG%H{t!-8267e#vR{1zweBb!GMIl_F8k#HP@WaeCFOO
zTf7UY*PD!gwG35U*c%Am@l5GhP(kL6)+S>Uk^*r5CEblSPdEGJWzs3D2yFU7am-B4
z{6`J{$Obxor)J`j@MqaExzoyUR2#4Zf$|$wS@;fc9BzML^=>0#@O1U4P_VrKF!E`G
zfg=}FDwEzfu@NKQv-Z1rOVTm?<a8FyRTRHluufrTa|}O!``6fkLhCm4>+&}>C*Zp}
zgkR5JD;Be+z71_GH~Qwg<CCANzjMDF3EV+&@6bdU6nbX3_8UqewOR3Pl!yZGG(RXZ
zJK$1m+at9ghZhN*XHR{H01tXz6lX_S;}kOVePn63b~l*<UK?JKy%$;UfM1oy(*LzS
zzR6q|i^zWO;jl!<-FJWeQtyypo7a`spf<Kk_k}bh_fG_ADm^{%&X~(qSAqN1q{Z^_
z2I*qrr$^H*@OOW&zMVL1@fVEe#I^lAoPzsh;yAxwf1S}4#3=5_c$XpS1zjYOnz!lE
zXp%J;4;yP`bcb|@Q0vQE`~Xqlud2A0rgHQH^zwCg#k<85KG2384zp0*{RIDXho7~z
z(7D?-%jIRf2)!q^(~^d&tl3G(gNLms6)&nu#9W0;yY!D_C`z$T^7&bLlo9P7Rph1y
zC=LYj(Pbxm-5$tT8B6O2w#@*pe{z4g@X~0;3jM$Qqd;Z)i6W9`w}ta`;H%oN&2_ZI
zx$prHB~kJ%OOKUo`@;vm&F?F%^}yWSZHIX}^o})#^Lx(S2?T*o%I-cp4+4ptW@D{9
z%r&09hir|m@9F&9ZFbhugTE5o+X*S*&E7Uh|EgV-_;&iiEgB#ce*U$`n2g@-^Qgvv
z`;@1oK{t)6Z;VFTW>pq2Rc2FPNRz(hdAKlu(=NXodQ$95AfDO#+hef%o;jtC)Z6LD
z+5fP6ohuH%orj<1E{~{)omXtLM1mEX<7LBRrZ<7rgfUuvj;AW`{=<yo(CK&Y;bsZR
z7Wm7<KtQ{yvfz}9m`>ej7_=1vzf1M3dPaT6=A}67X&q*;lHQ{|neIld3eX@Edb5<b
zeax7nK#!T4Wu&;~yrEb1`Vm8Dh0$)XQ?DA);a2pb$S+T8Y+l4bViWmv=c3zWs6!YG
z+`5EXJ@z-#6c~o#0u^gJo5_h9;J5OhY$5m5lQTTV1BZMSMvKBmM{Q%adfe)P+0XhV
zA+`NtjK-ikgo^QoQMP;LGuNWshkSjm)yJ%#Y$AV_L@lTvU8E)1k4ENg3w8Q&^rxI(
zc*wGBO!WT$;r8+0Yv=?Dd6v}R)mbIRT4()IY)ivUW|TpaEq)qdxuN3WO?ilyM~ETk
z5f1n)<dZTpz!-sP7xA$R<;eP8jZ9l2ZZU1>t07VMDM@$^n=Y+)(BJDD+xTRJ9`79M
zCL<C4v%s>p9EoYLqnP3~%&|--Cy~YSbv^uZZW1f%iJWI!nvww1JG!QmtPO7K70{N~
z8^_C9YG|=@(=nxs{r8(6m*E|d^*w#zF3DucT@m1BH!#iN<G)&3iNQ#fEY22Yb(1lr
z0&jvBbDxQVB`=5J)<Z3l$)W^3OKKJ?OscB^!@xSbHs|m?8CVSs+jcuh#T-3JwXt~k
zdb7t&c_~X8pUZ$lYhLQw)Z5mIUVMgK(!N$Uw6+w9v1MI)VWsa7zYlv@wy0BU-n2$p
zc+}@hUfn(D&j=mukR-pVs_>^8oIRO@Jk^Ve@E41r__Ihm<>psv8+9bN;2zjLN*vcl
zmE_ou`vwePDINyF4-ej^-^lXMA1y)@Sq~^RA3kahT$u*g*c@wzK%Y(YAHq$i1aD}B
zQ77xw>#-U`9O2i(jx|VPjMlsH)2U-$1a(oguU=U3U?;1bk3anSiv++-*pL|rlz^GT
zuK^8S?|4##JN^FtDzvObc!JnA!`7FS+XwD^Ig&)x@Q4Xk*4*iz+4xNgLzHp!L9bH=
zOSRe4vHb|+?C2nakopnj<}|gH9Oh<hD0!^;gK`wd`o%gMdPSOEgQa;Re(IQ=JYy=x
z>6bsF{n4LDO%Oc7w`@4NZ6$}&CFK~Z>^=3`Ai{9KaXEigZ@O?&2p!J+)dHtl;FroY
z8T@A@V}dDJ+{Kjij1?V$*4oCgTWHAJyGfY@sRt<+KxaoM(+g-#z3SK=b-Q{W&jErV
zQ*h<Sc3yvuf6M~A_FX5X&1JPtKQ@r_o5aBubn;?wg|dDi$L8<xov|uA=NFCNho|U6
zU7KKJWRI?XHLYxJbD)sgI?A99==nk!ij1h@eY>^~4-^EHGlDcNns&|fF$=JOxKKWk
z;^dHlc@E_4p2lKmG$o~XBp8uogEgPGcc7CqLjX7e0+rg;J-CC?!xtc_HOja0&>~%n
za>73vKSFO)jPb9`%9q2Bj}QHx@?{y1oIBUjaJUnMN*ddaU`U$T5N*(f51kH2dL+Zb
z$1eoz?oA28?6>{Y9I9_M)~a371c8`QGU#2A5tLz4B;tt;)`kd<aYBD=zwWf#7R0tB
zXs?Hs==vZL^?VPF*s+2;C?VxvJMjJYI=25+5U>}12XBw%yATd(M#GPRFgrK13F^t_
z);g(UoWS&Qu_>=@ejtf*4Cn?=KtUMcCUZ)U*LIbdt!tW97%~)Cut0vI3bfL!r#+|z
zBB#8X?bpa|P%Q}lu-UklZe#3ER+pEkU2yFG@*%G&A062o3<*k30T4}PK2LZC7Fkp;
zjd}-lkDg$MtF4(i#S}#Yg|(&y_5LpoEj07y)oKmJW+1b>qoP#Bn&N*|kkeOZn9VG=
z&JI4c*o+xwnWtDs{^MF^$#Bb4Bi3h4)w&0)HzFm54xb`_Uv6T3ns{joJVXk-{ueH-
z*7p)5sl4T#cC&VYwtLT7ZuQ$}RwwG&`5XixL|%DrOUD}59$*YUc40yftzDrVfyrbw
z635(%T$Z#zH^L?tBk%)OMK*xv&$*jpjSin_6t=0)Ah4RH_ugV#GDJk0V}-#~B*D?a
zmYk6h=0?dUUBz=0;?VD*&Xl28C@X3X{`L4kc-}dcXro%!#9#tmX0#0IYO@DF#a~N<
zmkCo6+!^(5U#G#i^_952{zriRMOCJ~!Qvx2DvqMdj@l~GeHYQJgFXigxlqmSz}|%7
z%|Y%_$Rk*HWz5DzEOhA9&C(v9Ad4oUv{9D2Di87>S$0%cA=FI`a;Z$)RcO}1+@rcK
zq7#NvNp{F2D==8fXO$X8S!ew0^agAQup)mz+HoBfeZQ|C>$?+{TBinwRHk`{(-RQ{
zT{OMp(u{AJqZ8ee@&O2cHk3kAnAqYP%j^;cZ(W*Pc!Y1c|MjCrcY+h4C8cQu>6ELr
z@hui=>n~}g7X*!DUYbFd(o&D$U~jekzXqF^<{I1y+Z`A4K=H%`z3B*+Q2>+vBWkws
zn?6sn#1HgzMdB}_OEs!T&g^_<n;FfAE_O|Wt+6)L(iW7-DMm-ev~roBigOaHll{Q;
zwzNV!*Fy)6U^i@tszb(gaxQ<g@a-zWx%RNAw~qZ;6&a#VL2#{cEtP0jN)7382GOrH
z*Bb3N)95euUT3!(=Ndo<{(gePxxF&k_H#mJb$**$B*a{BWafhD_7S{4Umq44GSjvB
zjiXD;!+pn<yM*$0#!8GMgq2TY9#{JE8}zH;*9z;&_;NX9q(&22Wm=KU#6^u~tUTf_
zf&A<W|HCzZbZ&tZxQpu_7E+D*Z}Ijw3n8Sk3I1~aNJcaeq0c=n!wXo|JB6O~Uf{q>
zDL3-~!L!s2qccAv3mbLYyarjC+f+B(IPN$%o3ZkV;TMWaFxQ_bokTa#5@WpyunB0T
zq~1r`BR7qYoaV@zwxL2GzNvhCg(86Qx3HutHw`w>@>jki%M8YiGHk_Uqw8|(i14z0
z740FtYb%X3*}L)zpUBad>A=6!eyJ%7XKqDQxBV2@&{HBgc;A}SCj@H^!=IL4uPw@{
zS#Uh+H;{`)@9ZRjC0Z98K_Xp&*2a0!cZ>8FqydO;X1`I`>N!9e7+A+01XU7%snRcp
zO-|=Ou6Yu?D1vH%_Jr&_Bk|)YXJ>Z&w%G}hHq2=FU61YrrhD-|;_@ewEC*h@#01kE
z{1#fdsJHw^2ujdftO3xyA2wI79>@WG&edzvL&vrPlYnHvd#a<JxuytYf|yf$kXLF4
z7xIv%h0~UZ`i)1(5k^D%qggLOcK;#s97PzT0@~__kz_^s!*>PPmK2d;RE65-Ux(~q
zy<OAXszZ*Gt!lR2cavdZOHK}sdZn~vQcXs%pB>#LSy9qUa9_or1FWFpEO9p!qZ1kU
z@x@LgddWNJ;|Du&J9JeOLc5jLC^tzq&5T2j_scAxKho?WSGVgb8XBS3#54{88}xxd
zGpc=(DCEZvG(rY;$=>Fq_it)eDGFu?Y3o|^F!$dMUVnzoD{je$+3D{#2W1U<u@mZR
z{p}vvb?HYNC;Rdoyl)D*k^DTqJD19hoOSad*yP;RtA$>IWNG1=i=mDbVSM@5n!WYf
z4V?UB{Sn(zDR;%YnN}Mn^GuIefrOdT`;87#IUa^+20Ph|c|muQ%`T7eBB@q+4%drz
zhs8A2btxVze^50{r6M%ywl?03*}=l0EEKJIAITFAUgWi_tsd64TywTlRn=pBo!LDN
z6mSSQMy>x9Y7Wfq7}B40&F~2R+N+;Y!n~~0osR>)4M_R+4rxhzTwuuxx+C##8U1eS
z>qX!&<l-F(*nT3t|K9MPn+<<_Fub*eMj>_DM6~<^Zf+X-{ahX;m6hp_vWS6+LwvP=
zQ@cn+cS$D;^m|%8smW{U;@)<x@REFXLJ?qXwa`1Y!Eg5h)B^UFovLn-`wpFF_fZlM
z=%`;YoT{WUEx>OjwEG$UP~^-6+Q%Ig`Sza(@&$0+PCW@pV#RX14M5!ItuWh90D9v;
zpFyuDg%wUSttC`$Ij~wCbS{S1leiHE6@FlPB&34Po28G+wz^o-&Pq-H$6l<5^%Ali
zL9m7S^=OZ4-vYL@0A6AQU^rJK8NiFbNv}9ye60Vv^EK{nNe8^V9~2)!VFmt8p}?YB
z6e->%uRx&Ss=YS<FD!{6`BU?+pLnp92iU_3+Z90wKHA5EvzW&!A(+U^lwN!<_&;fR
zAF2pUw3ZjRv!@dd5ibG4NrXnd@~B<~EtAyeMeRaaBC8R?I9+NRNxfHWPk>`*CSWWl
zHbsiYk08$?^;Fi%OZBbL#Zb(8W15vfslLuF7Lx<YY6GrQ$o(6xIJ7IUr#UmO+1kHr
zZ^^+8FZ@10utQBBU#lw(-yvn_=N{VYU+m0;o~wHl`I&2^$G5W_1XGW1Nymy=>?PdH
zr5GMI&=tP)%oW^|;9om6xWF|isr#RYb5oi2J)7uoPNjwN%yq=$8<ww+6;?5Yl@}5m
z%BVIT=2)yXxLZ8Zj>6x&LPsXy#C0Owb;UTVTXa7_FMUQy2TZLqLOJ^GZABOCQYd#^
z!Qdn$p>@G%SHRs!YXKvM?8w5RG24YC+1`~-WWAM!sx5lEQ)P{yc}tmZX@QgoV9V$U
zr~+0%sX11*0Y-VsqPsBK=k8-7pI0CWDr=hth&4o{dZWll3ZTad#Le6cDk2u@5{^Se
z*SXj%w-nQ2No{OiA8%%Fzi`^(0^b^a8ohxsrO}@o)Gny+3d5)X%%7@Zn_&mpi=9?P
z8+o-MZ)*0D;M`H7{}_f=RZshUv9?^Q_~88axq9s&OHLHlcf4ug=<fOYIADUhVJ;7J
z=63^T_@!igmOMBT(OMFX!a1$%DNY=?WMZHh1vTdzS&MA%xV3vG$i_vHgJ7$nJU7ka
z7JWnv?Nz<^+aW6)fPl68a;;i`Q+x2wGkwOF>;BZSTAfqs9DFxfOtQf9BDWPMr&;Fi
zv)}v3vR}!fqh1*_C|Oa|hL)yAVy%2<!#ga)CF6M!DjUH&Z6qRc0G#I-uxDOk9VgfU
zi>*N9I>1KQW>9ZwpGmH~o13iRv)W#=EzRMDkVLS2g=Xkg2&rp2?cio0%OR+LIC*ew
zXY*GefGGFABY<qCl)hn(cf;e>w<Z^*tOXtjG6+%WQY;m~MUeoot(OxvjWmM%e&#A{
zt?XA-u?Ru>v)~u}p{*HayM^R<Hh07U?FDU~2*_ex?!<FDGfd=0*X9_WSxm4ei@ZO{
zl5HlaA#%B;nRcx<7|senAWcrd%lc=FGH*frAb}JPe7vD5O9dF=`b;=g9se0%N_4~0
zfctmx_&jR?M>kgT37V<NJQNB}4xmorOYqf5bDTI+8LxxDS>Y;f8g#K@BWN(`vx8ft
z8|4|PhQx;Vc2CT)qThhnQZ~G{U7{6O0=e_mzG%xOIpD*r<*mknxG=K`u1IH&^+vD`
zm;O4j(T*;sp-F!DA%OfC1A_e}Ac-BoYvC<;XIZo8Y-qRs=!gCtJ*0LWZHAO{HZiYP
z<~KT@M&_cjlu?<lpgn>9nn-$q2U~}gM!##nK0c(MTVwPOK;E^2o~9a_6uuolpvn`?
znkdaHBdMDo0fID-aI;*p{>z@Wj66iohEs_M(f});1A<$6Y1&@&fE&vEf2Wgc0Xit)
z*0`N-PGXFA#tW7X=9A!Iut-QPO#A3w8jy!Q%9?%9NjENlkPj6h_Uv0O0!P}2Gy7cn
zSP{_ZCcydt4{)gSKAEnf1A8OGS*a1`+P`z)r4tT18sI$LFTsntDvT>m4SU|bGM)$E
z+g0%7OrPH6H9RuYpm=Pkg)FhC8s#0VQwM$Uxpb59dJavNg(ED1mDC)s77C-_@QZF5
z>{gI_l(*^wR5=|)jMpk!uJlpl#@2JN`okm};w~Ch=ia5vJSnTBCrGD~=wccUf3?$x
zbchb6jByO01~3$OAk+?$#KNED(XIfdwMN%zdkmQ=d@=_s+lLDI90)4|+@V~mvp*zF
zZs0x}{LwDa@&a#(97hm-$PDgWqX^+U5e}W+zJ4*ca;$tM-u4ff*{nqS!0%!<$MQ)o
zMY(+&C)F8KjnB(lKR&N$Eh&ugb9QJj#B(eKk0cuSJ)NH__fLx^J=PDH#Q$MR_xH?M
zk0k09PUCi}0LEIboIdf0+%ox1(JVUI62psW1F9fBt=1k^2G;c1b;dTrzof_Ep3_?R
z+PZoi*a}x#E?O*E$+pw;5J&n#7Gs+J!oen{=?f8Q3eEh^2GLvcQa{r2B37$L?lg=?
z(v6`VyiB(t-%1{d;3Az+9FyAE>E~*OqBa`*q)0V}5BY*B<M}=oomp#cq<xf&yyY+`
zvkY@eRuoUXdyhrpVD|Hbr%iL<$FfR)mqA05%{HZKN+0i95`5+GvJ!5Q<dIc61@lj)
zX8;8OGHFnDgSFlh;^1Lh`gg}Fpx0}Qptpf#`ZOw<b=t#2$IAsG5l$70chCje`B2kZ
zNQN0}k<4cSrnz;Wg2#txG#+P#X$+e$z~)B9qVZDJ{^gRDzgY_h4gm<(Xjq-e_q|Nh
z>E9Cfe+}a1UsMXOkt#9gzgo&^laC_|Ck)J%lBC}5Mo+PkRFW(nsI}aG0ec5^tPLJ|
z0h-T)r$y{IQ^*_xJHKM*f$6DS$+o5g1)n>w2f)nA1vxmy9H^A!-bI%WYhNsrw@xbQ
zk@%;(4Mf4sMl+A7@i0YzoegZ4YHbly+PY%KTmU&G0R7sJ{m%I;t$QEZ>hTr|2;dW7
zm(*xoq&v&DYn=9s)YfL8mX`kfc`3^*{?*H5{~FWHVhasJ7rHisP3KvG>W(&0+~^-e
zj+d5~ZrCs_=yxE$r285WP0@SVk~&b@7>@9x#FDm=mW8;nW~e9RDkaDCpTfs!;AnUh
zC74^Q1r0T$*llI+E?-cvpqvuH0*~~Nbh@io)qaMcV`W~ducEgB!+WnquqJHg_%(Z!
zam`ZO1F-7Q6RLk^TvM8LtJ7Esv^S)uUc<I~KA071#rx{qG%9&^(lS;o5%U`=#BZc5
zgi|qqf{bgCv|KOTJF)Cu9)2NhDYc8N5Othw>F#Dn#>%+FeFA}`g$LN|``hJS#uTR_
zZ0ei`f+%3WR?fkCxEyX~48$ytu7rvlSH>FB&f46+N`5JkRHCTy8;zp*?2@6z8UPvE
z3y`6}%h__b0I_J8-C9q3MfxHKBwim(L}&}C!mBMoyVXF1hkE0n_-@kx5&Q4nD@u8y
z{&{QJXrP+Znynx0PWi<a$x^#Iz<W=@oZX|Qq_gXbX#J$6qNJD_T6<K_?#b}eE|XK}
zNT5b-s2c``+@$2rO$)1a={M{i#Oymh;eZMQn6K(*YhhMS4i1_(SfmifNE$f6Ocyl>
zN>S4X!`vwGpZ7A@!BcgL&9P<^tEA?_>6|AP#7t5PKE_}nku@0qB|r(0$N@g9`Cc=n
zI#w`u-s9roAOD9`q5t<TuU!ar?ts&6y*hw|Zx~;X-u@4Jxsa4s*5W#mNfmF9+*P-a
zHoN1@$}f^DzMstt=1Vz(=i=zwwF$VH*=|+Y^`EM;2EP?6pH){Bwq-4Z|8s5``0tpk
z-MIBu5&si^9^da31~G(K)`rEy_7FohJdEsHvgo9T02Ug7J0sGMLc}|Pmk=8Ys69GW
zl@sMXpM?ORdUh}aU09!2s&7lbM)9y6Vqu5-0)*PC4*+mc)(L2cbbx$akB+%n;Rr^n
z=V|QD>G4aGcOyJ0?=!GzU|dNHO1r$BI2lK(nWG=>B`(?ORkxGI?4pb*yu3>yyY25#
zIZ@BUo{)?^BwLFC@90$8gVY}o_G_#RqwR1boHBcuD;d)`#9~!?=<X%JKOO@7PE3v{
z(~C0YMV0O!v_zLezjg&1gs1Oa<4Jj7=^Nt`oPyG89Yg;?-f}gpO0Sc?O3ThUZ-)qY
z)ANllq^(^(x1@J_<lrrDGZe`%jWV3%_;Ht`93Z6VJnCAC^=DA)_y#+nkUxo8U<hH!
zAtWLOGUu^x(VadNEJD=Z@gE=PH(NeI>pJ6P7!%r_Lhn5?z&F}Wt8vAV98HoKj8@<6
zx);n7v;{RCpaUo7=MQ64F=o&NP}{}Xqg}0uu<c%EenHacrneMurw@cF;IJLb3#F>$
z8U$znUI+|4Z7EO2ayxAyB?9{_IKrwK3oR?(taHNX#!3X$ihK!h>7vII0p_rOJ;kO%
zq9KdoflI{G4Peu5Y5utbKT`4>@e#tzMenBHkeKs8pSau{<aYD}qBYn*bKpl0E-G|L
zvGN-*4fWO9q1uGuNw?$G29VJZ5y(OOLu4eM{o3Q*`%kvic<$SXbtLp{J<+5tN&%vd
z&n69WJ-M)i$Z2l!B9?R2RWFK@yk}#D9a`rc4c8bFu*%^`a(8pRedJmpO{b~2eE=7c
z?KHOaF3G7L6WaeF!pTqyv0+bb%u3pv_?kuX9{PjF2lwc;HVt5v(9yBvQq#4T$NKi<
zVOVdXuZAOZ=)|_IfBD>+a!AxUB3NK-W$p#jeenR$NU0pS^K)yaKz+!2_8ZA{w-abr
z56;orp|&BhKMxowuef}?*?S-`Ye6j}nDR=CnCp}bsz%PxSL>y_wvW+5D0Aq@jqIv+
zN5f_$Z`v5Ax9hYr#I{AlYW#x4cs+E<rLW0@!kF8vKw!m<$_xXqP~6PeNzx0x47ktG
z;%Eyzx_UDp*<WrZFa+|AJ<>L*yi7?iZY!)5c-C5Z9)A^&GF3?|U2MWQ_7}_}lcd^q
zdXdRT`#PbNk25hR1MDF6dv_D;<X^&j;>(10kF3Zs`)5AZwtGKNfjOZH+L`z@?GK0P
z8~BS#zN>06l7=xAy8G%m&DR(Q2FtQ`oN6-c2|J$TRJn@$?i$d1>Vzj@;D?D>i~R77
z^5Pj7@qsDjgIP;GL19BRg1@`@>EfoGL6)yvp69AI`@UtmS<3@*83T=HK50hN1~TrN
zOYRj}KnG`M<yv!tP0wxTk`@e01Kla*8OL;xuKN~%iYagpSAO?8@<Hc%Eq=sCH!P6y
z1g|qvZ$8>>7OhBpPYT6zAg~%+kQ`g0mA|ch^X?g-E@((C5{3)_2af<O&Rq=_&3Q4$
zo^F-QYbSoavbh>+o6Kv^YHJ0Q<x|OK8+h0Q2u=?Cvehz~4)ym6!V`Fryoi`g!jk<S
z+0APGKmFHo%y2XHfeC{4V|qNer|aCR$fduoaB^1Y9^pKEHSo)R;Jx6h;+OVU8)d-V
zl-d*T9IwWATsS8EQ|$aHK`H0smv0?DECohMC-~pG&(3CH{9Cpo4y)TkcMYo`jD>_*
zLzYO0az=DNNe$97R7)*)0)v@Q+@iy48I%9?gYU4%OT0Yh#kI^iaT>#h`Gk@DIUcqz
zI-|P0hy$E7m{iZPBo#Pb^&!DxM7L5<^Jutw<=M)=Ho2Bz6SpS>C#VHsk@q^D3Siim
z?I%89s^Jds+PL6_3ll13dGkI82agUuIh0kz7Ficv&)L9rhs&PRo=x{wCF4)rrZ~TN
zA8iXdVTx(Q_)be+Z0|@gw<Z30{~^(0h<N`<hi2i2xcOB!Ol(<Gm~=L{RTkCLa=qau
zQ)cMZ8`odBEjdgKds1R_y-=ll7qrvLD1pJJS#(jVd|xj3?T(Dulz8SzRDUAdL9SfR
zQVu`P7M>{4&4bGmvrjA#edoRI-gMZ|>Wf1)b&04+7d>7b&nnWZcpP-E?@jjI*T={r
z&gZ9BHrXk9dd9Gak`*G`4?13H8-XGZ3&xc~H9!>~2+U8`Mp4IQPS(5kHA{xCgpVnY
z1^$lVFXeJ{e7l%}p^HLV@}PMJZeNQ^*QySbxLVC<*`{1boXcc~L_b+(LzT<Xv-9vp
z1oF4iY9F^RvngC8d(M`eg$KXRZi^2;KNp^2O|_nuFe<r?E%wxcWDsvzu9B*D<vZ2^
z^Y^>#$Uo=W_kLH{vYQ+zyF{4kpKB5Y@`8e0Cq8Z=Ik6V07YOSD-z_|LHlGIyyt{Js
zHDzPEOpByfdJ#sn9KT;vx~Y0v)J&qF>Yn3=3yCG)x!+%%kaV7p!@l_?lebo61kwG=
zSHlyo;h*3Rf!EJ!tyi%_)ZO43<_VdpmT8cKYNsodP8ykYl;oqRGljqLFZFjn$d>#}
zWN+a@Jz2JX;}B~MGI+nkyP@(<UFl58i@$DTPeu2l^UihN6Kex^&cM5QoOqtiJ8?>?
zF!JA=fJCWtExy>Gb1h$X=Di$s^e!dmQdm@|1oPJQK-B>0$%l;SIAWk^4l$BTI`FO`
zb!9>AeUIXtq~?|ja`8hMw0?S0Wj4_y@=p7k!&1c?!>M;kA}L4K&ZGq1$dkACILH+$
zlWjnzsY^$}cGh!gB$}go2{Gz)j_th|p3+1TVjJWydj1)AI8_ljEp)qItEQ@GCkGQ>
zMjL_nA6VFz%@?IQdH#dVpIXSVAX3#-?|YOsWLFo4T1(w#9Y|?dLgpJw(_c4e`hT1%
z4a8~#olSh-c)k%Sd%N8ovLx-6#;$SI&FMmrIr2P7>#vyO46e$dmsKmwQ!#-xe*NEO
zLSkTxC~3EG$&~HG(zf%&n&Q!l{q18lX9V+H<N|$>*AXHq)=m;{0&k$JqxYdDi#|21
zc*Z!Ra?b^vGvh!-9ErEsiB?~rx0pQp;vwoXbEkRXOdH0sHziOab0b^ZMn=E-4H&!n
z?SW0O*QpJH?(+z>guGT&t)kTTMyI#`rhhMH=w$S0e7Cq)V_9RKdzEG$-_aTor*Grg
z7d*TAptayYoeG&_f6PVVokh`G?mT1j5Bal(E|_1N9dXNhRa%7qE|jNWW^QEprMQbm
zdYj<-jJrfKt+)q~a4gTEfIeK^nyHE5-EviZ&VhPqpkMK#W6b<JzsJ>%`CCFkB*%zp
z71Ibvh3@&xKiS7t(HgiQNB-$Q%4WC{>nb^vt;vDJq`GllpHjq0p-}&t9Adg~0oh4*
z(bVB<`!9s}pVmXZ1{#4*a^%RL8*eFVxmOwrlrcCSl|TE2_GgRIqrS{KARp<jWI#$>
z`@W>71>Tr1Aa4ueZBs0!sxLN(X<(Y+V#yoD(V3e~*G`*9&A`L17_`1v6fc>(ooi^;
zRkHTYBXy1RU!?2qa8h*>pGe8f*C;V)MI>5H)MLWzMs(OC`A#=;hQrp^z?~$kZiT-T
zq~zBl+~xa*Rv&E3b9CtH3^Q&dYF9k1V>SdNkz~!I+#N_FYUgK4l02OzewLAAxTDkF
z_MCYkn8)N=xVae+Wwa`ZHy3geKK&;iCp3%l1L@n^czB(`YxVVIYx_sJYgP0J71;Ka
zthMZ_ZLDVJs^vE}#}d&Et~!6;82(MON3fE%mAGd5<%aWL0x>ori{F~W2lFb^>3(J7
z==h=dUTOgu7W5{@q6Y0W)pBE4>dJ^P->GVk8kkw4b_}7~KWB-S^&f(sPV|gjrr?uf
zW4!5p63g<g>qw<%-#m;bILgQcJb@3IEPC=S_g7~xarO(k$HUk)Uk6lY%C!sSZPMum
z@aB&Bn~EQr>eSVWlmzo^yOC>O9`py1EC^X1iWPoAj1wlu-gc=$^d<20oi7mcUz+EK
z<ChDY(pMUUHPtO*<bC@#QRj!3+?za3t!uwo3@?+qUALIZ5%xnX(U|{K7|+*!MrMJI
zQC8wuuh;Jv<^W6*PCaAfh6i&ONcMK}gbgMxRY@4Mn5py>y<5qUk0OV?3V5>OXlDPg
z{#wG6TKE&aes=kDFb*#I?-#g|kVNLgvT;RORDa9mO~i6eoNcQAJC&zHoTn?w%-Ty_
z3%+<9T_xQih^Xlm-CQAzt^+Y;=R><zt&G}rTj7OGzj|*Tr?gC)>2I@#F+=My)efBH
zCrf$NfGs$1{c#y0WEhx(yEkJ}mD`5{_2?%1F|6<0rbAwXR$G$?*;Ui!+K{zH<JjXj
z&&MRlYw16mZ8J`+wEyb6uj-c2QCxBGn^vKaXjB<vKZOZT!=74+utqLOtV?M=7zX+O
z6!{}D;cL*N^kE>`QbwP6<&UFDxPMd1<jU6Z-W-j~!`|M>L8Q9Csa`<nvTF&Mxt2x1
zT{cih2Y1G=zTMbTRLZ-7EP$Gu-|;{4E&k_9$oNOd;IzGSvZ_OFao|)i`_{c)hO-D$
z_r*u3K@KD4*<^J?`SXs&*VT;8#MWNT@3&?2Z8I|y3KJ|uDfmyCSk(oNUngALyUtpM
z*0Eomu2#&6eMsax)my_xcz0g=V7Nd4$mOk@iFckX#+63}+5ZEkgf8|s>&?Fwr7x5M
zQ=EJZjv%ZlcNW(#-gdOhdShAt;#i*TcjU;kZ^i9ekY_+BBI4a<l+C(<P!!nF;Eel8
zf85~u4vM_smEb5$4l|IxH2!$mdO*ur#aVXwbMfezLVx#f4a6TSn1_7k*KZyl6+g}x
ztNA(y3!z1yAJX`;>1d_DbmO5;0gR;I`xg9j^S<Z33z^zRz&DvZe1larPA_ZnHb*~?
zv%S?@`~dJ96}_U)BJWK&P;O|YR5qtXlH=zPLfHEUQTm5d{9_=SBXPC2HKem$JNJ9r
zf2%$s9GmujiE-t93lLC?9{-$HISaUD$pYK&CB`uS+z&vEVf^~CI~64fyc;bc@;1GG
zZ$x~B$!muyKfkQv#aLTxSsE>Rs@={u%n}mp>713ZtUvtvb<SP4jrie4{{+U(Vew^Y
zZ5UH`OORq(06X}7=w>#?SN*!j*>he|RsVqjOvHN$Au(K`nf(VO)by1-Q<xHy+IrW7
z_Jzv_9`wCVoiHU_daNy?roZ%I8$EaLv4BsE=9x&|UbW}-3BkoCS`^Kdj1-jiSFOI^
z*-d$<rS9t2V5*8A<JTTY0i@ru$m!iV@}<t=-e2Pj6J=T*OX4X`Dwm~@swEoq<bu!^
z$9p=byL<H_=h#<qgEZ%w@t;~927fPJ2|1?!SJG~9F)^~l7I&qC-CkoupnW}J*S${G
zT1;2V=6w48S*7FVHfTu(mmh96?AgfEr@0cJCj31(dc7shSL<>N@>ahV0G^<un_ZV3
z_1fousLu3#{vF|;`v?V14_r+v{N4AuFT6x~(C!MQtcc;Q{pbAk1f|3ct#ig(p3Rot
zKDYg8<Ije*%D#l&w!KV^SU&wsmbCI@N+sycxZ;@@!~9$u*HOSWF7Mig8y5IRQ~g4Y
zf;xS>dpacvLV1So=>5HoI$^EYDR@=I^$>Bl%yq5x!s4>z>pt=+fz!bOHA{wC)cA_=
z$fFOCXWViO0pDWzrD!gK^1i~bH`UUkeQnpbXm@IEnF<AYnPl<+vdUkjz)oF)LC#%I
zXb~i@b&3Cp6#9AOE8rtcP;*Nc!=g4$mE>5d=Wox)5^wFqFsRQqT4jy52Cn=W2Q~^f
zF+H58(3_i3nD|rXp1!BGc<&{PNa57c{)TTa%ylt3r0L%Pjx0RWlOOYRI5M!^_r=+(
zQFRF$I30{wMT#wzjc@^%FZa8zWUBW!3i@jx-?Ou1?K>dmZ<KZtpi)m<iL9sldz3Sd
zt=@=2)^>+M0G;+c+i0&&r{4^bx9e3!`U(2egs=FP77{otv)BiI%`FWj^pAfCslYdE
zzpPpdY2C<GyBwMP`i#D9q1G;3HTfF#8lhb%aK$%QJ9fh;<%GVO)I`~o_}!ZgT79Z~
zj)e(0eha-3_csnVsajS<*<&5LPckQwwn-q+kzSK4m#kh|7A2Tlu941^8}`1jB&HD2
zjU^Ef$LZh7z1h3)%_QPeqVR%$8`yT(DE{ZipZWq9qPiO+nmpv})d1{fF_Xm*mwuI$
zyq*$2a?O)#OzJ-FKI+TXn1#dmNtq%#O6yFR4uyU^SzG_}4ewoJNKV*#Zw0xrG9V@#
zTV(dO+rsK_URHYffZdVvB>I7ku|Dd|<C5v%5UYVlqL3WX6bC({EH@wt*adBnsLXV*
zIZa#DR}12Jm5`N{3MQ)gF)ow=;iWtKjqt+SyCz3{cL&IY>j_Ygxt)WWhX<eCT&)ui
z-xt6?=HG-f#jo&)62-MS!*wPfcFlxf-3^+A)~`;e@X+j1%jOv4j4=7)HH$b1br|Bv
ztDDjD7UMe0IEUK5WAb-s=PjeC#~I)5t3ndwB-Tu>!ERs=`{}OwIEy)p=FI;PpDZ8R
z{z0#$U2Pj%nAg1Jh!55`)37y_C@A0wD>6twrvnkvCVtbM)Q;v0P#GisFva@RE4XDF
zn(lXs_Z!%<o;V${4}28PG`b^vA<@FY|BQP4z>q4j&+SW*X9Ps_yw~lU4*ikA$*Uxj
z8Rgz|OVis0-4+YqPLZ*fwC2ZAMdjUs8?f8Av9p!Aur*y$wogjnK?y^~p#9Cwmv_J`
zrSMB1Mb-!JT|=*Q6_sMI_67go>3D8FG*D-9eJ}6n!-ia-JDG~@+lfBh@NilBV<H0D
zQv@VEkc6A>H?>0iP8+8h*%E&U%`u=M{nNdk0%uFAoIHASEz^$V$^F5*eE}RFBm=<z
z?a0oj7Rf<g`0q95v1ueP&pczFeBAh2ONoTAZ;ap_otwsI-&`;PAA0>WGN_I|5GU{q
zVl$(q`rT;a1!gkt{k^;E&%eIKo=XEgVV}Rykz*Dvp_EAY6My}PRM3Luw+Jns)2>!V
zFvzX`^ZXrGaq@gtV|suK|M7*WTWgYQ=pD<v*$~lRbL~i8*8b`c|L&hNBgHZLf0@D#
zs>v4lnF7+z;C+*uY|OoHj-^hqOn#){yqDL_vIF?7m}D8fQSs&0f;<CzYh$ljWL$-h
zzSQQ)HZV3+7A49M+++(lEj`pw{Z8Lz?zXej<#!PxlgKM)_cK55K-vb{85wiT0G0By
zJQ|r=vCAp=l(>pf2Z5-=o0?mT`xQz?u(AG!`ity%7R)w5>(|;!-{3JxvC4<biLE8?
z(-76>989D1PwV2^#2*LRz#SFbExbFt%ZhCZ{k9OXO5RE2Z!DYg@vcpV;w17{+9AT4
z$9X9)l_JR=6tq(APoG<>hK6*7dd#c;!fgC!mgE1v{}<K3?fP1bAw~eX__T2NaNOFg
z^u${>H@U!a#pkrZz<Bbh9Ic70IO0MMKJ-Iq9jBzs7x%np+b2wJyLN%c*i2rl2A3DA
zIODfBB?43eP6x;Z=vF#>qNUYInhH|FQoDXeD3zBtC|hS01S@s(pHe9a?hb}Fi61QW
z@r`-gk=XacKb9)kL&QxIEm-;~{H2Du_X(>EPkZeTpU>~tA1GSf<V!CQTJt`$Xo^pK
zFZ3LMsw(lpzP5cyrlvl|o(drE{kVbVvp^QA_pC<?cK<8vl}yRu9VtsdIQ}|reU2Tz
z!N#eUYrl?TK}o*8OjwFU$;W1%fna#+XWV-h5oeYYzHiA-FDC0`*RQc-gTk8p4G$NG
zPy+1DHxSh--$YRo5$?-3|5gbv!s;%cjVfOazmT&=<2pjU$~yhXUjober;WE{F{gE-
z+8?{QK>{9YVMZoF70;jtbBLsjBUHCzqt~9|YpuhsyngRV9B`2_xjk0K%@7E<w%$yR
z1q+;NO|4WX2|Vd*{44y-vOz44-GFP+mmDdvED)C^^QS!}^=ISxp|3xe1laZ2N7+Zm
z5mYPZt9jaz+$fGs$pEPUvE}p5RB<x4PENd67DFxz^?x9Nt9~Mmb1!35JdX|o(f#AX
zF7c4-`Jeq!fwxMRLLy`4m$KaYQt&<Tm^2=Zjpiae5e(|&?Mo)wbA<CPADakj<HK>=
z`9@1=3TVboX$UQN=uFsXpWm6Dr9SlY(Od{zepxpIr^gBiCB`TQ$V(Si$Y%4}?$aCJ
zW|LyK5z1W>i>(gdOkgTZ=O$f6H&T|go$)V&tesR4ey!w%{?=^{og;(hEEQUp&hilk
zvF~91@_ZL49l_H`)4ANky0a@M+b(`PhgMNY0}1n&1S^hlSTj!+2o&9~F-LZX&1mLY
zW9ncV;|kaoGWM!$&3M0Zs*}jAff+-RsJpPX#wQZBA?w`W8<L#2zvM5QrRi5N06IMk
z1GhO-PE^J&ei|YlSeK6e0{J4zA0>}vR1`72Lw<bh=3u5<5K9b6-lh=!qla4`_jI1J
zqo@^-Hbz3@HzEvsU@Sj(n?FGX)66l(!9(w#GSk8|UrYBK@}DSElvHSZea`M2vZt9x
z<*Iqt&=c6>npVzDo7m~bv~vZj>OCO;XB#6^cO0*#^J^F9Dn9;l|E0wKYL9AKxYROX
zmT18pp49o%W=8}9r(SHkLA~16)mgN@1M6cWz_x2@`6tz0)h7n;TW_QemdkV<#90#a
z4M{g5^p6GxSON5$8?wdSYAkw0H)r67dGMPwmauB)dqB!@6>saQ1}GpX@-0V;gr9T)
zd7o{2%^w`c_CzsO7$C>UQ6bkHR-8??19_KqoqKK2opvy^@|j-Au3-==@!WS+0fj?T
zg1DC`uWQ>eLMhGMnmpkOE^oaLhI6{;{qQab0v{~))<2UR=Fs(qOnv@m4D&JW6V9dj
zQgzqiRKcg*8;P=ff8p4&4yAs0+F2u2aB#WKzoGWpCTY7oF1+ifmU10;BD>OkAQder
zXdP!;(0r>Lv2eo!v?Bpy=o?cb8YMMF!~e<=lUeNf%;a$UVOB%a8?P6d`uMJ>r(w+Q
zvAAK!+Q+Ax2AsL+7Ih?VBRm<al(Zr0F?59GUQ{nlA6(9rA~Os1V{j^%EbN6Q?rn96
z+e(tB8gtLx8N^+e)UT`>fLC?SN!rh8Y<{#{r)JHz+PIJCE`KfFN#1^Q8k$_&N2Y!Y
z;{WVnE_`?#G%wk9frGBV)-u$aWU((i&3M~MKd9n~z%qktYz0@nNIsq<!&j~(m?|=>
zk<KVBMpO8r={;;x@3$is>IuF}Yv{~R52H-y9Pher>$}~~OS&3mfH><dK|E6-3VC3#
zkyyNA`R{XhZV|Lp@{=Jow??(%Kppb?(&y*>2YvzlP!{c!l+^vxRvvO#^JD>?dex0O
zyGHFTXeEli9acZX%w?mWy@1JTq7&{&rWW*|dY+T?)C+u;Uhga5TUL6pA*IftYud_2
z&}wV!WzcIAG`0hU+MvD9<35oevHj_JF&t%Plw&%}Cn9j{uLW33^}fxU@g0(*#HA7(
z7Bw}{JuLnQiE{7Dr{6&(w444HdFS8J4So`rPIHIXCk{g?@Cz%>?dbc;Cj;E9qU#f?
zloMI~klAW<R7mk-KZl8SFosPCfd94w6*La*GqH>xAULEJ>;PG|McRW+s1^2ALZ~j?
z@5)hun&^xutjaarWQdvz&%;ZLNy8HAI(1~_f>rvyxaTdQxsoXcA93TBc}Wqzl9c(T
ze-~1>S|7*14me>cQ^X=1_<L#P#@B6ZvB$wdG0nd#H-0b1iCz2pP-11Eqq-l@xvAvx
z_Fj7PJF=788??soB7K-D7st3P^3)F}p1rgs+>}(J_dT5`ckT0=F$M(#Y#uH$l+bg0
z7T|t;Qakq(ziKl6YIwCgN<Ulq>RdXM%cY;36w5qwYdo}!KH@zb{krno!ngCZaCMjN
zxp6_ie)U<k+S56~%V}}8Ikr=mdeJH!GZExbVPEROE8s2Y(+ZPTrG_OLz(9Nqlv*7U
zS6|DN2ylnXn*6F6QW<WE_@Z4W-xVdkf&cCJuz%vkSenCo?{j`Iqaha#Wwdwi%$$?Y
z;EitG4nJny-xU7ph#Ht{56sW($D%K08eT8tUa2HIdU2t+xa$a}dA~++M^#J>On7pi
z$28w!OKxT2(!L2%zPzobt2OlAYzk9um>^x?lRI$J!AW4n1HpY@QK#M8NMa;uUW}S4
zWAdwu6jse)^7_5@^HwE;jthKe_p8W1=UeQz=pR57q3Qgx_^hytA=$CLgL)XT{nffu
zX}*r=+#ZFg&v0Zc@AsZ=Fx&V8Uw{6n#82~o+_hc@oK%Gyem7X=-*Oca*hqo3SOF^G
zKE2j;*V<TDQ@=;!f{aO~=fUOcK&Y+asI**P!x<G%SHR2ej?@eGCyc&2*BKo9QmVUF
z(k^MVclE2v5<@vhp9o8+_Vbn)ma462o*5<b#BWz72Ar%8hNm5JPnQJkM9wcMvoUlF
z64m0^CM#;A87C)BJQG-5G`w?xeIg}f--{>lt_?)Y?XkM}I?JB#i$xu4=UYAwy8=da
z^s7q6fK@-Q$?L*w4%BNI&rFosaEs6;?l39bdVZW6E-$q2yWdH^Uq0_6`zLMs2JTK!
z%g7$d8;|e5%#lwWi@xqXS4wO)*rL(?UO3@8bn(s;YD5TaqepzZjN_oZ0vQybx~%-$
z3F;fE-Faz8jqYzXh`Xy@ohW5aea-(+{4ZbTDG!|j#A;N+fCVAqZ?gI&-Q&@w);TX_
zJcqS_jeTAhe!V{NW|kOc5rxKbX;&X;HQS&iE1$i?d|%5=oZ(Tqh&_T;!9mkYMwEMr
zcFZ1*m~@L!yS5ucCY#C=P5m1M?Hb7;5|cCVwEAfM0}XKtmk5e*@BP(JlsO4Dl@)o<
z>ymJ%nn5U8+LjpC$g!)|#A%}m?lSVGdq_uJWDU&F4GqZB!IFjK$F`|5k@a&ankUL9
zu1Wou?Fa?*7`HkitO<)#mz!I9M_&70U%t%l65yA{7Q=L#A0KP)Inw!{Re^YAB#*OB
z{1(uI)!!R+@(M98)sjPkotOGIjH>+a;p=kGRc?Rn$0>6vxh@}Hx;-&p7EL#p`12Q2
z;B}Jmh=-8->cQ1=8@og9BS}?`p%&J;Nlx)~QX1|K)xT|jZjd^l(k8#&1jE6vW$@j)
zE)L)HVl@Q&+v)e$sSr%?T$BUq=~ZoxduQQic^-O`FEwzrX}2zMwrE_YC)XX@9O>U+
zpY&e#IDD_(#A@*{CK*1!)+*QaMO@W#omAXn;IVmwaA8=vQzJI7rmlXUpPT~ZhklD}
zT&-uTVdhY?DLjUAk^Lsu#__I2pOL=<K4ZLROY^t6o{f$A>bb-}HnlTgN-w)f>KE%8
zH^h(h?yo2^Y&Uo*zOA04_ApH-mznm{w%jH6+$|_uz}=ehv)96Un_LjOQa}xaDpp-?
zd!AdjwHBSZ;BR$p7m~ftY<PU`N7J=*v+Y~!ce=ty*>%qsFhcbny7MjO6y$77*F4xR
zWuquQ@37Z?dt;&6Vyo2c(82hQgLj2Od0gIVFP*QIYNjIjG=nZ~Z3Ks0iYE)daI<JW
zv`@X77HY5@J4+<;75fi1)>$ToLZ_Pz^PsC=?z1&$o(Yp3dx(;!c%H>gPw4yla_^L5
z0&~nozRwrUuAw8X7{A%vZnsLV4(lT3h5jAt2)(QF@A)YJKz_Ii;0JG~%<ynnkg>;G
z_uZ7~YHIfEQjfVJvfQzHVqyvF<1N@P<euG<7*#D3b5NXP#|JasdW9%w|8XO@ubJ~A
zX`?elW%3&|jQ@0X>N1|;pD@&q;|XjJ&IV?ShVuPVrw+ohF-K`zTXCYm4(WaBdVkVp
zqSmV)FKgSU6gc@yD^yga6ORj)kfKmY%fqqfkP$A|Y`K&Z>I@7j-^iGB{03FXm>`n5
z;ZBGABq`Eu+WATHUB`Ch+gABbXOAGLd~FMz{Fo)4`3Eg*01~gb5$fzEF2>{iPXBdf
z?(A0Fr!M|Dw$3Gx2Rk>rj*#DXwb?;+LHE*@r{`va&JJ<7ogPsh;g73zF+b6@9`zQF
ztvHnWe(>mK5+_-K-APuVyHaXdzioBj{Qhd!m>YMDY3JG+!yTAjK_eHR(jS%O3G8bz
z(lT`!y?N&|EdyiBbKbZ0ak-2_Srz|sv|P2SVFjWGORZ-(>|4lt+fo?mJ3lUUs3k}G
zp9D80L%u7(QHJlBcctE}0xsanZbG(q4CHvjV<Kqe+4gM@;^BKCV$nz|7Wy~2ZKm)e
z<7*`JE35>mILG&QYcY_p{c|Yo<o<_g0&H2<qiNbu9cg`yi3Nv$hEh{8Zg`bd9WLc(
w+Sa{zlt)7f(C*%8|M&O*KN>hQ!{o3&VnuH>$k5uoAIRkD%`4@Xo&WxS04TF_3jhEB

literal 38474
zcmeFZXH-*L_&126azz0fR*FjR8U&=PfDk}HdPjwzfPnNGP*IU0CDc$A0~mUbKp-}1
zC=wt@2Wf@?5kiqb$n1FU|6Ma5X3fmkS?^hk7LuKPp7JZtIS^%Jpv}R4o}Gn-g#)Ui
zY0ScMFp!0X^}#=fz$2rn?|y?XhaTu@Yl5%f*w$za2VV|9(XsMjVd0Hre(Z~%TZOT(
zTx5Z2-ZTx&S{e_?G99EdR=1WUkLa=Glk={LvEMqH_fOt$t;47DG%F+y$weQ#8xj3r
z-vx2al3z07m)LbpV;@yW{3CkeyBX?Wg%P(EX{D->x-&DDdG1wxz)E(*k@AI^tMS9x
zA%^wtD^0nITReyV?&p7h`ri=%5xD5Jr6cYYSdW}bm|w0LtnnUPxc8OD#=^pF?NKMe
z3=s-yWSnw<FsFpl6D&9V@MuHvN5$SgmK!fqla`sq|Mxhd@$}4p--?Uv$Nu-N5%l=f
z!GFJhsvCWi2)=y^&(8VxW%hr6$9>nx*a8X{^h+AJZG4hUT}H_3(aKTWi00IqE?RhF
zuXkL6?<+HN3uq*i-Euoz+9eUCP`axDl{Bn*ipoKhAqH%WIiL=@!FLI#pvt_2EJ>FS
zC=<cKq#A3&4q{IjeI<%^Eos1kuu3ex;cVxbupWh~M>y+_WN4m4pGX+6BWxpR9ZP2}
z2sMe26rs7|wWFQo`pb=1c5cCueq-i@KH|=O#Cg3U3ACv*j&41FHb|SWAKF-LG^ww<
zaJ5^F^Y01Tav%;Xy*LrLv1WPO0xAKYZL|_j|J4$S(0zcNs4Ez%nD-KQi%0R@4U$i&
zxo*JwP^W0IGOo^1P%0|PbCwK)`h}(@>b`;PzwP0d_5KMjB>>smEm`lWAwn7nc&Lop
z{v>p#xr^&mW$JrP!jJ2C#A{*cIX`N8TQ?^O(|+KX0LR@2WF^7s-EBVdYyh2&C-t2s
zA=|4nxZ~2eO1$5lt*hBll!t$}Y83Ux{O7(4GW%ZGLVJZ18Kcqsr)b|UEwdqtI%)<s
zjESD5yXSSp*V3Kiux~081W1VohCWLpPuC#*lg;YVl=d#3tkJ#pDO2Z$T=q`cByAS2
z+8z+2XVsChU^IV<%Y_y4Y|N768Gkh#vTq^*<p*iAP>DJanB5tB#xlI+()dF!0_<nH
z!Pb`S-|c*u9TBgex`}1E6{nBya>83miuLC66tYl4kt=-I)M~ByRjQmCS>aQ@2jZ|=
zQ}XkTC!ty1#^^efyBgU-v>z7H@2e2T8cdo9pbv;RtdusD``N9{(tF;GL6H2{$9#Qt
zE+9zW7p<$9yP1n-yw}m<`t8iz0W)`qsr;9q+;0fSKKZkI4yxUGz|6<VdYbPtI!V96
zHL7iwA=)njt5TAvY#TL9I2;z__gWv9qD_-rNW9)JgYeDyS`#nmmtd+j@AbzV0{3fO
zi$g7{kyAfJ!k%NpcmzmQ0~SIBzLZ9Lv!r4BY~BKg%cRv)Y3G(zk<thA=5&<g3`X$0
zY7BisaGI};lXaSZ%7D@I25<7PXK<(Djo@Vc@E)Xb>MIplFMW$-IM3ghncgGUgvXR~
zKuY_6x?Cm+BQLs2ZaD3iFp$DK>$-}aWa&tBk@^#h-@OEt;_38u{p^NQtL*GdB*_M7
zW^HRgShYAR=6e$l=X|Vv<UVVJ88Nr7Bw*1$)b|!T>$QGqMuPYvHHJ6I+{n*sOHb`D
zATK&%{T5q2W4e7;8tus@cbxCtD4bKpYuFjjU5ZrtxN$#A0{!FC_&H9Oo`eC5iAJHI
zp-T)iFG4}pK}z~{*tYxUOthoY_|EnJa-OySGTtPaA;-BLDcE#z;bIG`G>Ok!XXf;j
zolrkQdc-*G+9jw+n~rQdY%hn?uaO}1#>N4K^V6A`tK4z2qZs+?%vY{kj!J8<MqoX5
z53CcKD8Xn$vwF6GncA#7wdT@(Ugy6TM}FNE@}`_<iQkmo@TT3>)OCLv;G3mH<6ss*
z&$s`Ymy^YybgL}MRJH?)^J`ZY?C&j2a)s<Gup~uuH~MASJjLW{D0)OZlk(T;{~^@f
z!s%kJ+@(HxKg{OJGnj75oHVTQ;GA_MN)PtbLYLBfp<K-j_B`C+bA$e73Xcm?BU{=#
zouL)E&2Ml&yGSYdIKmg9*I;@maE9Z_g2Jc&6&RiU@ZbAco<={&`MQuKxSVj<Nk7G<
zZ`fkK4=ytVF%yytNSAjYn5Wg?bTg)}NEsHCL)GW6hc*+1x=(WEGL{ep9<jYeo%J?R
z;q|jZ$Q#pJoc#@L(9Bp{q?)ngwl2Xrt)_UkInG$jZpTCAjv!pJf9rA031oBN_@M>e
z!bVVqda=E0b7Nd9CTMh=pi4Ra8V+tJQl#=S2)35qL;NpX;)zFQCw>yp+{^ulhL5wO
zV?s@7p9y8&P{!z4s&eg=-w{DBU$GelP8V-m!ngCsI^0#0I}RKew>NN^^s_2+lJgY{
zKt!&~+7cR9M<2+7&0uNQoBTU#?)4NbdY3s=e+cUJP4`5$Re1~YO7wH%k08`i4)0O=
zns+Kp9BMB?F%7f1ou{!1Emo)1r2=Jx^b14yK@UTxEX-I~jP7Df{zUL<;ghbJ62hIH
zOUXUxni*IqbUMTt{qQ~h8fJwzuU^U~3SX1UqqY^oyHLmH?L<4|AEM~xdNZBfBR-5V
zb*U*W$oLZ2SYCsWQLgEz6iG4CWkHM1Pi8yZQO{i=r9ID<o+~7kxc*W9;#we*pRLGc
z5`O&`!XhlIN<2@Wub<nUxufI%{7q<{>HJ-vLhWZWidqEnntV|szelMmJ-K!imNDu$
z+QOM@kxc%$I9&ELCQ!=HBtLJtaSN}SjubN}E%7u8v?A*+E?=1>O9w6JWQg^@)F_dL
ziWdcrwn+WX5UY$MMc2i%vON8Vd#)Aq)(u2h;<=??7(`AYiFGJ`0udKQd)$cM?Te3Y
zBYcBp*kF$ILAW3LR!QOKL>?64YYO-X{5eI@feCSNg}Zd2I~JSe8@S{ewbq4h!P?3_
zHT<RJYH!BfS!-k&uXrZ*dcO^=U829uS8JpVh=3Q59oF};uzai1A*4X0I?m${aF->l
zTR`zMC{=+)#vN(u=9*Djl5GKe;SyYHx{j|W(ZwpQ9WTX_BZ*<5q4@~8Sw+!*5<1}u
zIi4kJ56+3YL|2VIfC!T8BVE$0C<;pY0c1x-Z9-rgNkLEPR)O5(xGy`dWqKL<=mkh=
zG-^|At&?~)yW>A#<av9(J$8i8?Otx0V;3XhEA~>o`h=ZXLCgzFlJ2RUHt)il#H>S3
z2>tJ0(P3|R2($Gluf_g#8_=5pXq&oH1l5~w5mEM1uOYA$P3J6{i=2cj*u#tP2L`iJ
zspnt{?KZvd?3H2GtF0Mz1}lDQW0&l(H@jRvqI@nqxtd7->~gF+yb-mno<?gq?{udq
zz0LPB6Z3TTO9g^(!Pb+q*zOgbo{_~6!%o^3j0-Pl7xAZsOxANJ_6MI8XRuxzOt>ap
zzZeJ^hK)pU=CTQ@X1r=ZJqNqU8HYqfw=A;XMC^%@j>BZyZ3Ibg&gKp!T*F`LwybBH
z=JG;rmf>qwr2CEC(O(zf+>P<0u8_}xIPsrXvlp2G`gW74(o9c%g;`ew@j87-`MMzC
z7`mCKr<SIdA>1$iJxsGcQUB?0`7~d{;17;k)Vv35`Nfv~*rQ!$CiBPtT!s#z%3R~@
zy^;P%6b`@M=H}Tr4~g)kOE1eE(OYOHx5z_ri6jBJRI<|{d;ICb8=-EIz2kNDvl9x`
zD9+p=#5FmCX7shmX(|u$V$C=sz{vO^)S-Q;F$}ht-n02X`0`AIdxdtd3KfQ6cd||y
zF6Cl{#53%L7POIuKJDS@dgpQFQPTLkiH(mE^c|1}q|}ZBUan%;p5pccIK#BV30ggr
zhY9sg`d(<CJQ;;B{tm;fW^%1sx6raz2czdYK3n=Gc>A>FwW7$~HNg}`luV*x&RS_u
zvF@UPi({6z$+*^6F}}1RG9DdsNKCfPw_(fcFK<0zsbKx511sNI-(nqy!k8Gf4nb}T
zh;XZZx{z=<FaPqdWHgJX9nw5AY_ZOEhpqgwgU1Pu-vw@y8z#A&2wl6*bKDPy`{&Hl
zl(d?&tfy{g{<?~5A5sMw<DKHr9hs^QtDfEEojX0%{q1V@3NvMb&!o?@uzY%!rzw2h
zbpA?{t#E>pR1{>seLcWBOOtQ-G-90%8V_r{thRd@o!KS7`BNLYXP!;VFMafLxiL&K
zW#B7}Mu*tRJB^&;qMm}%1{}jM?MGYZ2d}~E4*DQbH`V6DwF#8D3Hpnq&L{eX-b~!O
z`xm!ZKW9SX3(McgPTE7yv>#yn?7l#H2z{uxDCA<Xf1~(Or_=LLUO_4Si|}_{jz0|G
z9}2z>A*^+*k3Vd_g`A@)F3ah1l<$=B1GZpW6-2htWL=u{o5sdOD6D7p#7Sq7{B}Q)
zqNE2fwU5^=oQ-N7X1cawQG1X{!>FS6HeaPp5cFf06h^s0>?@8Y1-)n}-B7wM{C3E;
zz@hCtB!0^yrAAkHYAXj7FXX;yMGTdooQ6Cbo$RdXQq`aLTIhz{L})?c?}V5BiQlle
zJ=rc9pn+_@YM5}Yq&T~awr3_RT5T-MQgg#O+esp@VWMcX1JRRq_<?+4(E5%YR7Qtp
zG!gLvjp%?66^G*w$Ymcoy%Ub`m#9Cz9QG#Z@TTIb@;@Uokex4{f_8#v3~Cc1wUh=8
z)^I+ird*N!rd5j4xx~5vRnjitcDn;D7HX0vS>^jrRr>Pwb9*tv{QveaI)1Rq4!|r6
z&y`zZT`}Z=b;l6!BM*!BzYjClx$%IRv%kj;A+7)A#{PZe|I=p#LIB-}f+A9NM#Nl-
zQNxH=dP)PA#`_Vvf*ZQw+$PeZByVVLr=%w|c4)z>*lyEw-hEL!4y791H2&V9v1Ge?
z`gk#CAq>46$d`(Cz@3^)>sl*lkmgQo3Zu2Ar?#)D3;6I1L|t{S!SeD~Hp`@1<!)^m
zMaq;0^~T9M<Sn3e+%GTgq9V5ics=v2q^o=CbZD9cI0Wx7m?0xdAxqIVYT6nGlB^sI
zb;Xlw?40eeQY6CbhHoZ!2v?xFR`?oS0v&O_0KN1UH6!>Y@miZMMvrirT;0k?&VIcL
zA%9HS2!EC}6a?AX`|Oe06--KBj^+^*@082TopCGZs&r75HDhIAxgJ=PQe$l_+C%1q
z*57quINu?sylW^R_(bEn^s3Aom(X3K1M#<`arEV(NUNgvPWz=VLA$Fh*%V@&7Kb2O
z+d|b?beW!(Q+e)JM}2DJE^o<p+jV_@0ZN;WOk{A*^gBMi(jc~vT}fR*;)Fl1gYdIb
z$1+4tYQ|Y?iV$~ia#59<_U7NIZS$=S7d?5~0@Z+$c?fAxrK=pUkl{k<*CrHA)yH#_
zBQ#w7-?IsW6m%YFX~@6Qt6v?T(s^u3^%yZgtKOOF6IxqQpqhXz+V?@SYB1vv3+E{j
zd`*q(CDH{X(R{<n!^lT)Fr~gD#Eg)QpgHBIqAe^4Fyf>(Uefd8&p~<R!CQxhM+0Lv
z$2TkO3!UCShkO=MHmbU$=l3K@fTTd2B%35SK=7H14W^nNWRb+V@PXiP$)3<&(Y>o{
ziEWURiA7Y?G8DN4r(o^E=+)Sc$JdBXO1V^^Xy(H=p>nYOb6*8ZYvyH=sKM4w-9Cw5
z*CkX2v>~N1nS{gh3pRL!NHv#anw9#Ixv^W)qAocoXT&`IfTOpQwYOo#UXB1&!%`f-
zXGJJQ#HE<xi?qwk_I!vcGCHpOo(G+D3byFBP_vTI#IO>|lBEFk>$ZbaGY)i60*Z#v
zg@tK_ThHId-w{D-n{pk<o|}MF*K$sr#<Im+aH?CC7%qDqBM<Sj%~_6iYX4k^4I3se
zRG*rKVJ5PFLgL4x`#dcU882G@E?86Xi%#iHgV-H?;q*M+V^Ms}nELTLHFy=7GPEt$
zj)zJiJ-BWmq`lG?^k3SH&bOtjea+YlJm2Y0txMUg50n+Yn0Oi+wY1Zb?wk^G>lOY^
ze&@jRts9n;DU}DyoviOfIyHRU#{#XHI{4H{Mbf7lt4kP!l*WyPq6ZF~7Ju$a+#!|_
z_YV*B2RkGTT7RteOqlF-m4YnXv$4l5ZBRYf*Uq~)sUx0a31TS=XmaTmPB*xh))<L8
zB)wf*Ie{0Xi`c^?Uh}fEnFT9$$#w?g^a?2BkNh#b7WkT;JFsDorKgJ>hfxQOf+Ukk
zxE&dGha}eP?O>4e^xLe&nAT@MMCZw_HkT)n?KkBY+g0RkM12ITL|tBt?Wo1-JJ=AU
zAPUQkPJAM7)@_hPwMng6b@NI#H^uJGp%sZ$o@Hgr=-jqj^yD2j35J|^b3*OVoZj7B
zHkfsq%0-vTFRUY2iT+zf(UA=Rp(XvBH+j>4eL1_aT6L#bui@^zmt)AioJvS320H+Y
zP4Q3bdQa$2-o8MaAnv3=XZvnD-?$v|sqiw=eERDw&$S{6k~Nf(O5%G^2%b3fi1Dnf
z!G?0giSN>~5cSWRIaIylfW~6bhljFI4E{00jyacVL^<-zhwDs#ZYY8_=n~q@N0!;u
zdt7_Bl+&tYYq6_~gfVfSRQ;_(Ax?bga7$;p;MZQr$OxXNU$3;GBy~7#?D?BSvPedn
z`+a<x%T4Qo*-eJ8-e8a}WOQ1eOi^&!oKq-au+^igaSqGU=fq=jOSdfP$1Uy#I?ym0
zv^mI*cHEbnE2hc3hUkqwz5Ol{(V9Ie*`?PaN*U^X%|pTlh8Kjq)$R;!m`R3}Ga!CV
zN$oU)g$9EbZ)GTIVjq$`mj<Jeq*K>RyEaHxP-2_8G}X~4?#qxPU<jR0huPKrGrC0U
z4B}r6$N#dEIk_G!cclXZiJxpefedpqRchq=%2UskY%WaNy1SjT0tp8u9M;hk^GdTb
zMgNABpwDNX<t<_j#|Xn~__){NZt2+0l}2d;8uTu;ZM0yhJ?LJJlsD%iC?UmbaV;8h
zQZp)aiT3&cHSop?z6x?X;~l%Y2OpW6cMQXCN0B1=MP9owH}*wZo1p5oxZQS^wB%i$
zeE5)d55qZjJ3~1+w`0z)Sno`l(hr%!djTq8X;(!_htsz8V9&WKKT0`b-H)Rq<tQcB
zsj$YFSEkv8f+S;!++G1S@<Vu?K@a?ieB6hx9MaUFlql)*f<7x%xbpYB(w=0$X1U%V
zF-c{<oy6_ws|AA=*I5Fb_6I#I0_WM2vfBJ!t#9vyYHk6UCT_m>Z=7cW;DtVRB8_7n
zcGm=rDNpso=RHUsQFl`(!OHz?!8-GiwjjiUk6w3ancYQfe>-yMmD7GZUT%+lgEyxy
z(0aLdUMWK8z|xH8*|3g3xFeJ3aw*euZtzCI<PBwb<Js+8r#m5o_v^>@W1~_t3xA9L
zPSXwz`}k#F-unm10q?rBf_)_7pX!sN{MolG{xp2`J{tX@*Akh^qev`n)>ls+(#{8c
zqMqzB2;=hhP1cVoPe4U+`YGy7!&e?7Gi^ewl<+l6hdXMn-N8(XVkJGKI<kMzEy5&D
zN*0S%OWs7%?^U2O^9W<^`C_Rq%fm*~?p`1CC9>5uyBvh_;?&y@m@oTzU^d0-3by<{
zEc6rCTOfAErkF)56XygJ#4Xs#t)$&s1EH2MJd_uYYu|oQJAj{EYb}mx%Eg%d3FPQJ
zu;oxm5F#K3DUd_lYR(q;P%g9TMW5HG4t9QlzMGFRuvpA)-)(DZ91sb0tXQ;?A#|Ej
z7;{+-ZxPs3uiA7~;z4sagq&a4B~O(&xo_emcbR92l7C$K%IS)*&lwA^PNgo-9z*(=
z>dZsaEb04);@cZv#$!(=MzeTcUg%6KaS>?|6F^d38p_KN^?UgBjkvUp>=nQ4C2wWB
zY9VXN3UOi7DemD;!zByn?=451!mjAO6ez8#Ly4+`!FB`!PF)Mao|}1n=IEFJ)zZ2q
zgPrReL8{ePneN=e`DxJfPu{juWP@t)Zw~Wojj;5778`?o6Ha(3qw61tx94w#<ZpzR
zq$aN(fIW|`^eCX7;(To<NcrjB4~vFJ5bUSj`QZKLTJv{167+8<r=B+-f#l=V>(s@u
zyn3~i_sG6-fd?ptQO~|@OM}|^NwI~wc;e-}xOj2@i_Ta%`r_ICrOd`*Zx3#Gv0Sei
z=bV9;sDgL1rTTNH;Gprv6NWio+EBS|1}Cjr@3IfcnzKk~PoEBN$}1Sgf4qKM35WN@
zAbee)J*@6M>HoHEnY$@I|MI6yK<l&3+~cm_`SiO;kzyT>i^H{Mk8jhU!s)Zb+S@2!
zwm>(DG2`D#kwFILe<BYnB$aHp=2twn;Xu-bt2RO^^(#6^+L*@&0#6QaB`I12pIet0
z{x<_ndp?Lba&{nDd7M0Q1T&ZVz@b{mgfczCXt-!sB!Mk&Q4-mIz5P3z42xiEGg_y>
zP(y|<nWQH<V(`-Q{5T&2Yg6fYYk7~=qGO%oe9XA`yR<G2Epb7cl`ZzI?agQU7>-h*
zCgH8NA6dy_yN_%lKe&i=j;)tixblZLnoNhC5Rk%56uU6m!s65Fk?GR?O0ubT5Ct2>
zwV^ZKmxDETd6VnUc?XxqCpOn_gnfvtDiYn*xD~D_6Mik^M(G{E%eSu1SMH!=)mqmC
z=^D=2d-gr7Cf(H&0<44n76-K;<6PgwK?VZy)OuUjlm=U3n%K+v%i||Ss$%6}4fu-w
zZ|8p$*$PjIE>BX>qbzP=X$(s;Jfdw|Xv<NMn|`$wQAACqfBUw7S-oQ3iA+DQDAUG=
z<kuhRc>J~Yct2-~Z)R7R@|BKY;o_pMp!^(t#+P^@WG?(@u%h${vBv04j=1>_l`3Ai
zbhUf!;)w*8xlFOBm7Rc2Mm-_MG~2Kr4o_LyaHx4~OF@O$g-q9t86ePG!eqL_UJ){(
zo%%M7EXKli9bD~6IIH|<aKx`{w#9Y)ex_qn?k|X83DMQ~`rE7hLd(HOqMY$sglK-%
z*f|J%R1P>NkK`bgUa<sW<mt2TVf(#pYCXyCY<N4Xc2+|b4JN`$z5ifsr*ByK77H~>
zFLaEVE>+sM5b^e>m5=#YW+`Dye@LNwHfs(DzrKjn{>AELAL59@M4ZWOKkDIElI6J`
z(y^Yc;LxLhul&*00*~euOhA<pL)PULwVaRT0^_KrR7&A0*$%jks`XrH6UXwmJ?K57
zDrzVL3k%9#d{8XyQ*tNC<tk2l+ONXGd2~5pXYKZ)5Ci{58F`O*t)9(1z=ndm4fiO|
z4H|L4&c!=({hpPZ`W^d|U&KKDa^PW3CA;3jpDNs@e=3)L?X?QIlywDI<ox7X|7a_m
zK6=YJcTuqa%$7Y8mVOCscEaz@?Xs??F1W<?M3m3mZWHcnFoyqNzypkb9A~tMNu7Jz
zmmLG6g3NR`*ybF=GRX8m$I?zzmPC`z;P$ogA&V$`h8c>S?ORUPiINOZTBH|r#iKP~
z4blz3+%63ag+nf5%o@T(4;V!qV=6Vt>It$~Q@mFFM)o5^w_lK6kI3HTwgGuXeT;j}
z#Cz=$1Jk0;H$0D)ueNw}KWwxun4?@rX1)uK9uF3!-R|??sFXIaqkFSALwlr_m(=Wi
zoc0S0mRRI$MBAqZE(8>&n%X~yZbRTysBl8U>nMbD>M)l_LeRBlP2o@da;_6<#2p6J
zs?~+Kqs`gR%Fb5wp3PTT?x!ba!@Ya~cAPpIw5M!9U)pYtrm0-OlTFWb)L?hDge#OR
zg)J@bO#9u#Tnru6wmWHcTltL%=8AmdOXA{(-sKIdU+gsxJbw)D6qVLqVZwrc!@Ilc
z9ad?>ayuM@`l&K_XFifsY*WonvcL1r+R)GvA5u-yGnEwFws%RCICYP7I@v`oBpUS+
z5)su(yWhEK@@>*%eEP@u#gDZ{TQ>fZbCCE7^-ENZ*MdzS)(<4_>N-tgguKhvFPkm&
z`#LxXdDAWW$^r;YW$sbZAP-G?#(L0`PCeR5u!cUd)NRgXQYELorhWA`eS+l)%jHi;
zSfaKs|D>$M`3Uv8*R*@TDFN4G|K1DW7;=EbZ#VDd8g(Bd>Uyd$G=yd_RFBous*ij$
zd9hd3+v6S4vY=eKqsBu?fBv$n_B2r=;5v#v9BgPto^$#UPmDL&B^vFk$M7KZS!5G^
z3C-|DVOqMUPQj5E1&~sYo<x$H=UaXLOoiKBZd?;5B(5w7T1;!NG00a8;m8Y^%gE10
zmxp2g378F4sXW`AReF2dcgMlr92uoQNhF}U@T(Bk{@M1UlEmA>XOEi(4lg&~nxx*H
z)J(06+1Sg*tJEWCR^c*lXk+&3efVE>y9Vr~TLpXzhA+m0E9=R&&Z9p{+vIQG(Q643
zhfL9`A7FNIpEAN4#(J#`<U5Y42(@?~%yRX|Pxr&>++<ojkxhy}Jqz~~SrK7w^ro@<
zIhvhuZEBp<Q;iHC)JkZ^3Rwa-6wbXGEHX~E*7d0GH=~q7)C0|uNMBqkx+*6u=FutI
zaAF&DmMh?B&7r3(fHfYa=^r`QVf!JUyfd(z7N)GTX0?BN<XK9!?Yv9L6Eez@{z3Ti
zuy+JR>R_=REvD<&03yg#&0J5svHXV!%vzz;BJkpIo3vk=r$avFKU<r?JZkt6FXG%7
zuPeqQYEw+-kJyqB?0@h}4*4mo$zkl=)%bCxzTQt>{-4MpikRKXovxTc@vD7mh0B>U
zj80V}$6k!-4yK`FeXU_|?k1V8k2D`Dc6xr?pIra8>(fI7?iu!k)$N^hKRur^{d;8I
zm!9AAf77CBV?FScSrf8qHr#I14*Kmv`||78xcA|vI-4qHCOey}q=89)lxE|h3RW}Z
z<Cm!d{R#^dq0$7G?S;*2uS+$jsU8lILDmvj4|7z-QeYLltVnc>sL{|{h;C?J3!$fV
zlX(lTAwT*<o$IqvMHBfD!!E2K)ow(8epgW+O_CkjOPqR`Kfl9zLr5D8o=$8%L>{TF
zL+lm(nM5z2^5~lx6n)7b|HjmA`lgcew{*T<5n_mq;?}fsYjAG)Z-I^u-MX<ICz{x<
zQ2~V*-nrfulHT>yt#66QVeq+b-g_m$zgfTH-Hl;N@9&eyx3_G9C?X1WOLrS*{B9=v
z)95+aZl2*Up{X9x!Q<Y9n}hVO5<^PDIF}(Zn|BLNt%O{iXz;o)AkmK}o1fWf-OfDQ
zzaP;m!K320C?lJynd<d!BP`?V`hIAyO*li*HKQr)fuBHPJBv?D$<~bBf^Lr+apy2(
zfm38N=w9N3t&NPXphbN$yu1rsFgOChXRO<6EndiiaEwgh8gtfv_q6L%Rs?MSj2&$M
zPkYsSO_T3_*!hjQG^xL*_o{cKF1N1ay|Y~@9p^ILeCxDfI}hF3zGgOL0enQ2SJO#P
z_`{zgLdZFPL|L$>@gF4-?tK%f;Cw!7fN{lCKM7KL;1)#cqQPJ@9X*(QSO-WwMOAV7
zWJzy`7F<pF@}vxm?@gU2$_8H}T~XNLs(8PUa@H|p*~2YI74n`iTKZ<OeGEG?*VZ|6
zz%9chJiV0LUIZ<U!E9`sLu&=pdXo-IzdXhF`wQ_i4Xa;}pE53TUk-Wtz1H!$7WR3&
z6IbcN$dyY=8+JC%A@(OzgeT<vMumllDt-_2m1Xjz-@~jue)t{Mb-n20!zj|*L;tQd
zi@bRlrFyr(p<xCVcd~I$&WUD8@8}G9qM4DzZNGA#y*%@2mq1A&v_YsSYo}zpHvo+v
zC91Twru*k@+6{4BUd)iF=3h@;uh~@HTd5>`J7PORZD~mCVYUejEw?Q6lK%{6XN@f#
z(^YwvuxH2mEvX&CcOh~9=u4b}Yv<9bN0=YykH1$5p?|Z!jhPETc;<_ledPLS*=k8u
zB?(Y;`b*YFVb4nkOM6)%;~FaH-%5(Y=7&->drcgy8R|>#%I*kszUC1vZR+y!ZqO&!
zwujv>?hNG*<4LT$XrP!Yw%5#Ocw^dcE8+y%{IZ4c)ANY!zHcjO*oH4VraC+7YdQME
zwL+?wt4m#7Cn<~q$oPjJb8%6L&COrNU3p0F$20}2DIz{)*jC!EUkhj42tqNpV238&
zN5%o^6=*d{vLEV@#`MeC<ZbYY$$qjvvb-~6$cx-?%~jr2`TTSCzJxl4_!HR6-|yac
zy|D)K%CKd|bvNw2DN$tRj^(y{0+q$hUOGC0JwAPDy`lY*JGe1Jv(x>e&-+uxOWMoq
zPSPtH5Cx6IrHjy#A)c3>b=lT#6DJDG`ULQW5}zcX7A<H!F!ewZE-B<?L0VSgW!JdJ
z*``bR7Q+o=y>nxegoE!D^p^I<dt1gm{X!l=E5B;bd{SuPiwp6u@7uX|Ekv!b`MqpC
z*29l9XB>&^*nRtev79yuOaAGZbmRdl@eBS(CoH4C?#YAja$kRB8>jbhTmGn<Y3#Zg
zY(L2ZZ(H<d{JPFwuSukKDDm>`u*x6LM%o=ezK?_~&pmO?<`n~7B77It=za!Mrkw#0
zgj}QFWNhotn^tw%A&I*&-=F?E2<y?WDfBnek5TZUm+&0gmD_w#Ie&F-*M>tuc>uPI
z;#)>`N*g6*Nc9C9t|iRt3(06tPx)<Jr-Iq~r^vVZ#n(b=#=s?WarK1lmgY(gC7I8k
zG@Kd)EX90IV#hlB-A#SV&$J-Ug)SUKng_xl0_Ey&f4?896g)Y#VdL?=Qv?NPn~dIM
zbIb7?2^gPkulXliq}r|yUy^&I6uW%kJf>|m6JN7mGI{ur79?W$Xy0C`IpN$?N|z&C
z0On!sZ|%~YQjf&yOdrSgzpKX{JiBzE>DNUKGbAx0$%!~8jZ95*=sfED^R(|T;eL)b
zB?$@#M8U7nWzszP^J}GV!M@gzHb<Qu&eT(S+Q`qj=GHI|%p&cDe#}h!Zd39F)!b3h
zEu^KrjNf)?SF??z>}&iH_ASOEjIj^9>9-lba{}3b(Ofgl3WFeR75z_dd*FZh*?fC|
zIUhoV<+NY4z*_jm%;^B^JB5c4`wfuy7XLxM&9(^ZG1-j2>OgD9#iux@eHoRxNNw_p
zLWw~PS(a*2XV{Rb;TkobF%J|-|NL%f8tb`_I9w82GcS?iGN^oW`g9Xodglq!Jjt_@
zrxym1A}zEpX=r~Okh9rmkJ|~WTIiIA>{!ooozMN2Zm7G&R0G9MWef<sNg&8!l4Lv7
zA&Iehs*o7G(4jI-{m`}bxKf|>r4VERC|-)TMe&4vP6cS68>hGHpRuqwO|U^G`#tFJ
z{VWNtl#Z-$!uESL@M~kHXfS=OjSn@9@A21SVPShCph>m^)?YV*`J2$*4wcp=Vkx*O
z4!tIWT)8}U=)?iA>cPKPUBLq1xZPfW8x;P3dt7{VqfX*!grVpuS8??HIx=7^a6B#9
zkv{g%DOlr)6f1B`;nN6X4QOe#-G6tXI@u*|+<N~1d>!lm_O3z@d6xF{I9_G@l+4VX
zFKcu7Vr@fme_^lU=X+1A)1>XnUgr?=v|=)g%u8uw4SwXJf;>JUA)#l7PYTy4?fsdN
zasN><-DFl|inPwxO+j;<zxI*xXC@xEJ`qB3w=p(8p`&G$VpV96UB-SWqJO1<T*hgy
zW{44pyZ&?j-j(ai$cH&DD+;AH+10fwq~xxKPV;)UpZ@KyG`IYS>bu^`g`<37f2N7q
z$^m@VU=`f_{Cu-ft?M98&V}Igcxz=`<|DbRQR(q;Rlct8`JV0JyL~;|EW4LJ#ju+1
zY%Z4{0q@Ee`uj6+xa<hajo`iQ1un*7ae468(lE8Q&V6(xF2~;9KF7q~p6}?(TRV2S
z`sr7uDc}s{dXy>HXB|Yx#Q}6Tr|SB5+{BZw99z=1A1fG!>hyu48gK>3#l>Y%%BHTy
z_T!WLjK25UlXkc;dRtrUIUnDuIWy-?`Bhrbp~aK>HMSU~hi*XxF{EEgskg+4zs^vt
zQ$zXPljB1YBQ))n)08ZGQ`2M{Bcm&{)(f84g&3LUKe**ljDnPeM6oeA-1-adXm{tT
zhkwcrm<VdS+QuU*tR%ItyY%7FLMY4Lv8k4Q#U&+OoTqr!ajpArXk7_j|8)s*&Fkm4
z;jn(rvsdo<fCbW|Ei0y$Je#)*Zs+-(m38jwOSARv_{OX3e+_L`Ol$>_T>0Fq&e)9}
zZw%m)mXhMFap^BO;t;Z2OYTwK8hfa#r&k;co*rw17i3{8^R?6SeiO}l6YE3k5XlW~
z;ZooIE_U|lX~t@N_^#Xi`_pqREiDnzF)?iLt?_^}U}35N+tIVXF&dnTP8z<}AX8Hg
z|K2Fe8OnU$=?n6Ye|;~rYm8U+Tda{yRN32IP{;jA!|l%HywixxQT3d_o}m{wR5?LQ
zy=Gd+g`sVHAxn+ag<ghDSXfxltje47awE$3K0WPbJ#;iMV4)|Q-!^P}u5f;K_QeS_
z<=Ikkd2_HaA!)cQlHbFxEEdm-I^VVr8y#T4n9uZd4c!>DXcBVy^5Sp+C%;;#-|YJH
z=g)uY$u#+n=cm~>^q`OSY;&_sa@?4^0i^m<)&VmMJHIc_4<1p<7sr9_INd}8#l;u=
z>s<)Fpoy`!)0>ELE32O`)x`5(eef+@JD#uU;pgWEAA<7;y8Zt4o6y0TFGpmslBd|`
z2GAxC=?`<^n~b%!HCxZ0uEY{6dB@h+fRm-5NzvXb^$R(5u7gDaCMgcp&fUb`3^_OR
zD`(evzsCj`<f?}FpM<g>*grR9AF`aGx!9)2&$9b2=8%(ZsdcS=S9f=|byM(0VZ74f
zhvNa0@PLrQAOW^TON@uzU>Kd2J^c1&#Ey@TkHZJM#!Xa?Y6vHIx?h-6hDc7}&vwwL
z?sJ_f<3c15B)$tr4;2oV7u?C5NL+y(cp5zgHr5DEyID!jti0Z5AxHWCmlytF<Vuhs
zEVqQYFBjOBg{@ahK$Ns0XtobN3qAZVZoT`&%BAuoHn)aMEG=#T4a|q|U9KHcyFj_!
z8>8qmKcKuZV8Uj-AT&fBNtLoGngyMI47@cQ<R~saGc_gUV39?+n&R1`20IJtjN$9S
zv~N)-ZTR5peM18!R@A7mv9U9YL*)#k-W*l7?rdd@hdB9yA$FgIuCDHWkhj6ZcQWOh
zBoq_^TtU_3EDfOhQ)F<v-bs0Rz_VvzANJ<FDA!7paj<W)D7O#);DFoH*lf5?E~ySX
z<Mt^h&~H=useV!2!^@+BYa@?9e74~`n{@csR~(dX5bu!>ci($e5(1lE>B`|o+hd&K
z_LaNP?e&-&zy0R?Ali>i92)v?cLsg$%AW__8FKud{RQ0{0=HwYdjRD6thrX3zxKm#
z#+OIXTa{1h<m6$tDy^8)gTdhZ%XQ`y;hRgtXL_>~tKUsF1S%GuIdg_hNDS1GV8b27
z1)m<aDK4qNL@(ulmB%8`kj*7@gZEtLO2C+x_Li7QLFnejkb_}t*yC2d4~}3VcLr#*
z$*Pd8720ME6AyC03+n*@QFi9i9PA$ktT@=)m$e&AX}mMKc#2(XuQ!Zq>aCHvxw#gC
zufD0cXjVAj4*ky&e68=C%t%uRhVrSax>s$~7{SiY-u!wmXRA1XGNE@`+V%&d6f>d3
zY_XW8f=ng9#p9ckAY?X)`n-Z9EVYL$3u<0MK;1{VoR`^0%D%sTd)>c(KNnP!UkE8b
zZ6ZE}GTV_9|D*b$VUu;OtNZ!uPo{)-s0ak&1|Y1x8ebB@K9PEco141}Bz<Z&$xv;<
zFr3!^HC}!1&ll4H1wPC^i#pFqzQ>cz^indYkV%g1mGQ>n)2B~wp1bzQDED&&n|jL9
zJ*jfs9<7hrP@kGYw)QBk^&dWbn0liy@p0+3K-@2N(8PE9;yDVvpP-#Eg~@;9E6d;5
zhtoISM{-<{uRx(tx4+fU%e;1NHS&!wkG9`|v^|}y2`dxAcXsY~AE~Lo50Y*tK%J;{
zvxqP&HkY#mymlC$DwVm(<et=aIoG0iI|XT0$?ayo_~a<c3)XSK)ZRj3mZ}cg_Wm~s
z+i>+|?vzvXJdm;3?K|YTtS|yp#J!HeckR)4%HBqKxX^%cx{n%Qh>6uB?2whOT<S}A
zt*w<Fjn=s@fN0FTUmPsnq5~v}qfFu$lxzYP0GI_6ie>=x-(H$%3VGt?wfgpqec*I&
zt=rIq5#S>7&ls7#2vthO{jbDVx86~$F&<4f5KMf!`p)Q=%1$jv)u?P`7Py(zc6TSW
z;qkp<l>NawC-`PG0aAO4K>;LLBbTap!gp6Q1zna$YMUE!L?tC33qyl~f*Lcfxfmn#
zd2h#_^%{#kCtFrxoZngYmzrSsRJ-{(to-926&N8>x5A`KVM&SP{FJVt>V~NUREgRp
z>kzZM8!Ov$wt`s&ii(Ob3;UqCZy+DGrr^APftuWL1gtR0`SR8?CbqSrO$z1p%-|;n
z%N=kxK{2Qo=%$E=cr<MgD{a7ez5Vzx@OucQDZsDTy!5nwt{Q#O2_H!9R*)$*FCEz|
z%nhRzFv$`GYHMmCCuHRnpn<YcW=hrIv{--ZHZ-DZQ@=$%=<jO)x)v<thKJ39MD}pK
zytP1I?>AJ??p;_oFP>uD^ai1LXt!X)M@lhl_m8|Av4q4_RjON);f!VV-QQnZ^7Hc}
z{Fi=s?E$)ltgqo^atA;bO2eorknp9}q+iX<GKtWta5a-#>6ai`cyzDl^oOkfTGgY@
zcc0NC*mySic`?yWjR~$`6LsSVAwAb1shEGO<oJ|U>&D`s+rukoj}8JJUdnJQv)XAn
z;}FYvC?Z6i_Uk=W-gAPd8fKZT<VP&Gt@j?A`W&G!zp#+DFgN#7`s37djwInb!8(Xk
zNh^W^?c2G34s;5MDJUqAPIIt4{YKxyG2F$V+Pr?rGxx;p>GKM>VPPncTeb2YW54R>
z`sTYc$$G|Mae1X~*(Xz<7%`!?BVpP8BQ6g|si0w1|9m@X7@L4nm<XI{g(eBZCxD0>
zx9@S&cy)AJO<f)LXsFDNqrfnC;@WNA;!yg=P@yh?H(+cPP2V1=bt`V#S!rCb$&Jre
z30iwDd?zFMK*VPw@DhxSJ@$_$uafT{BLqO_jkb1`B(&#c^K$!a_3*vjeTa3Cs7e1`
z)vZvoa{DF%7K=5aOf+Gn9h!H`-&{~IXpl3za(1loQD0tWBquM;jwwgL$2Kg@Zu9HU
zmKaZtLlG@Db&p*X0Xv#^q|ONUEhCHEjX}@5gPy<O1E8&}*s9tYX`*_v{*~@aQ;+vf
zanGCx!S$)O758o)fhz4RRWq)-4t(H_<n_1oVrHnss=5SV_Gk`#bJ%bXsIJf@g-Lz>
zMti|38Q7xoKIF_sR`b2j&kt+!1<i&FqCbB8*b~Vg7C4c9)p2E!kdL1%GArK1{N;Nf
zVcjt**Z)!n?>>7cyPG#}dI6kJKMY#soUAj?X}28p-Dxh_2jkeR@V#xa5cTXDZG#L~
ze{g_B2iKjczz_aj!n#IOSH8SG_4><~FLP}6L8C8x5|@y`SMqDcg}%J?j$tRGy7|Mg
zR1XNrI+uRu375j`O`np`tRA<|_pS_JJSUNeYc2%TL?4NXCysz_-U8A)GFszO-L|)j
zF3eHVMF0_0>(!Nhb@H1yH4&T{6ak9o$9;e!gT`QnuAif_Eedr7z9bqi-MAF_iv*Uw
z7W(IRxS+`^K|jg_-64_O?mwHTy%)TQE^HQuGRe<Szm7*C%A?oRJqZXRdO!YY<Z#0)
z-8p&P`LjC0rDs5n_p}h^Wwyg#3>yL=@TK6y_U2?X<$zP%kWVnEd0n8_sGrn97RZK5
zB$Nf{-tG6Qr6A<Qv3j50Wn$^5tc3&IPXVK{gi~CI<bBM9<?m<2Akt@#h86sdKe7+X
zAjdx*EHaDKvA&R9U{YvsH5}fJ#~(Sk|Jj<6XmWMPmIrNf-&6KSpz^j6ra+-N2;6Hs
zF&sGwGN|Iv_9I@$bF{8Bv0tUUX>(*0F;T~$099iKa<Ma(EM@!gw+<9A=3rEPeSKSp
zQtr!Jmv)>98(4pGDW)d`^ooo_&7(fjD8@7Vo&WL(<47t1Jor;Vj&qRN?smu#a0sF`
zA~|!F2b%z$+xU2U>*DcvpEHt{MvQiW__2g&8m$%}?^hwTV_RGtT!>kiWG0wu#v}*=
zU?IB|x)f%4cPdbtcgl!mcB8`RTPiwEAqB3PSx>$k&Z#r0D|=^L&%{9ll-%Nu8L#`Z
zy^uR;W@-7O5#*(mwoan@Gxq3dxqd{eOu$|ii*mV-kZS%-I!+Fq0|EOXZ4^a1(A2i!
z(=X2$q+Ns0w7v-fdf9btm)e`VPAbgJxeLr*7U=(WfY3+dR|;)^W>HMHB9^nbK3%Z>
zeR-x6kEenUpHhn}RsDX2+6MsiFaG(*=HfA(U{h0;8#Gh2F)SYS;?Sd*(?L1yy}esJ
zDx@EkGr=8`jyZsHcCtglfYEF~)LVhtFSZ6MDZiwoBzlNy%|sdn_Yq$gKy)kgtw{#1
z@54eD(u4wt#5FrJv+?0<d*Y5t&qp97tr4ccO*)J%X6EKP2tomNY)?Mg>>v)616?+h
zjAELo#(ovRZTdy(no!rVkE_y!E<n?c3;h**Nskp*1*=p|YVQCGYqFFd_kXG9g9i_`
z<bpQ#vupIv-oVS?c+{A}OQvzD>hK16D7-R=I*(xzM0W1(<NtGfMUelN`zwHQ<Jcgo
z{hwd&<M)!2l1zl5z~wpMEU7*{0sdFAsrv#oj@Ucdik@1OMEfxZ8f-De9yV|98F+8)
zIY^FXJQ;b#aUl%nK3eAtngyL^DkP{i@xc$!TjY@M89U#;5n!qH5ILYri2>t&Lj=vv
za;xfjY3O3#`!)%m`K}<bL>1|VUwO!xws^`Fzr}%SO4HWFEocL%E&~J#Xu95LzQG1i
z%F06M4<Fj)&Tf*+al{m6=w^pZi9_)~`K%<PN^LMoo5df2yg;$ifh>AI^Uuvp1<x{R
z==S#ZC_<|32yRr?Y*bLB@ByY@=YJr<0<hG`_VB}8IvgkpnXU}E;^`d<E__y-AMU#g
zO0)6JIobIiNZE%yd%fX%)RJ5kS@=?cUP1Fo&eQqI0WK{Lx%x`EKXa?Y4JhD@Wb7Jz
zkxhU=_S}Zb-1*)3dbWEw3w+dAJjFLtE+y<g9*<)AbTc0`@TibWtIeb<t)v#(B)|8l
zArgcS=_tV=0#v1y7*xW%M5iIXor!9Gf?_f<o{_nUl}Fgwqn8uO{J<XJPrBE7k9oC~
zrl7ujxz(n|I&)4nczuYemVg&icJ4q3)iOO2KbX9*KpRK}nQGBF3}%*ZBAU17GiS2a
zvXlcn#;dBT#1R7^c#QG-!o21%9<Vl9kOV@kMs&#m4DE=!i=)%PIEdFq3#=9CE`$bE
z#kf<)BeUz#tM8fg82UfK>?YSb0Xd96#KnhQ1w$bu4xoG#KTwEf&u(0-30(cze+H0?
z1LK{?HlOGYlo>={P{TOj?3bh_8!gnkk-6)|kGJ@s?6r^j9u!VCpw_bN0aY&}P8~hu
z$a$(mQN8Yedi>{8GoV3L5j8<n=eE;=EKe?GLSUVCp4ipfojK{EqN0CbpbwiPoJ9aC
zWhhLBnt%vH8>Ek%k$x-))#mZid^cGW&e)mERbBmdvJqGf?ySI{QD=I=oXQo9lghOR
zv}4a!?t&qh)Oek{P4)EGNQOTF1&xExDxyKh4STimDuhgbc}Z`3b0OfTSYcLG6<`W;
z;L)3zO5CuI>Hv%`o<I24CD0$G!%VL{037A!g~Z@Lzo%J+WWPg!Y+xu@>WMFs+qf-k
zD}TO=<Ri4cxiESsSFNc7$g(X`bv4k(zX38IHK@x1o;r&TFb8+n$f&|l;ULp@)4&e1
zC7?J4opMIdChkla=3xN_T8Hw!AmnpYFiC#ct%FZBco&hIi}M4+w#X98%Dhdmcg$C}
zX12~S1R9tUJFt%}6(9{U0PG)%UC=KF*q<4^xkL*2-EW%RAFwiZZWpL0!$g#MsddXz
zb>_cXv+wzta|&?Uy2g^jT`-PIs(v#4Wkg}@iN*r3`xEJ%-k{PqvIV<=#q9+O*g-BO
zi1h~S3J`^NObYaonMFfCDjjjrJioscU($G0l8W-g(d(#y3AKM2^!?Ah0EmXoKmZC#
zdIW&{YQ3H#9^(z}jjOE}C`qW7N6zH*ee{}H1%sHyn$YdF#l(etFu)+HQm|B-q+P>0
z>X!<0^w-3kUc5)FgBF+=C^XFV0V>s^jS}PzGC_e7mWHd@RDh~^s7p{>$f8=81OcqB
zF9h?0avj1i(Alv(idFc{<<SVl-*;j9j+YLht6C1w^9mkg3_Wdllj~^R@(K{#JqQ$Y
z+%yJoY?L@L4+^ei99Wd%p>l_C{i?a-loT_jfNEpr7;0r@^_H1n-~$v$fau48J=g{;
zyCY4!<JItII0rc)9QBdDIU0W$j6t7&o~i*R`Gs|J{(tEu;%l7%m-e0ork4V6N<KRD
zRM7U?+~CdzIe-Y@lj|@T-GCHr_Zc>)+*e<{Yyzeb5n!U_>@MC*0=fEHq`5dfm!$^l
zO#TYIyzijH-ddU?uq$+$mX;R7&9V-Fs(7yoRi7&FJ`y`z>8R}pJr5?2OP1SDdKLiV
zDl-|k2h9Gqn4jos80BHZltcmTFy<u9z7k;fOooD|n%uE}UhIE%BM77_)e}g&GSk$9
zEKi6iXc7V}++|bm4ch|tt880t$TsQw>u%<ZT1YQ7>oQbf+_&E%YW6B{R5HM+!u#73
z1w6&Y#a-3FBYl850<svWB!gi0&SbHj*e6UwCL<~?UbGD`+vgVmboD==Y2t^KT9&c?
zKvR_Az|O*fq<i=k47aibp@256X0I_o6+HA^`cX+@;=)N`gRDR*m`818E+F)zgM5E|
ze>bTWo&)sJRWOV>10>d@H<-vS-;U>lW5&7i+&?|t*V_Yn^mPs8A5z%*^61t}F10^X
zSyV6=P5k8+y}P@chnWa=v&{<<`9j(3T9;of#$s&~Syu%w4~~i=9WVCP*p~?baWQaj
z^=Av+9$1gI1}=WJ-Os?!EN>*0o33u1EQhRSKkZueHjoh$JItJSNJ~mC(WfG~l;*jC
zQ}YC1zXLQqM(8$Q7FBV9_4l*W*!GXWILY*McT4qjbchw-ecx)xH1JP4gwj+R09{F#
zSQ!9pzyRgpuG#3ntX5{<MC*NLBx>~UP<^*zAImv^yc7m24mDI+{YHwKU;W`2Upv+q
z;Q5AG@=8k;U!!LVU%NAdL<e;7l|OLm*=U0=X^jFlQ{D~UmID+bbN;{_4%eqSgl(fh
z@NJa<wzyQ%=$Tg?TN{~bh8wptPT89PO%0368tS<|6Kk=I18!VFfp&LO2eA1Z&~h0}
zM3@L0xT_5Zz{b$Kc<i4+=J55fNY-x}n7mL6!Gx^KPd7K37W~)j0XebK10>RJA`mQP
z|A4K4m2e=Z)9?S9c>vD8D+Lcmd~OQkPpjqNx&Au_U<)UCq?UXE$nB9wo{jzBngM^Y
zMR{`>m@1q*cTWnDyDSFt&veo#!rX<l-N<ukvej$eng}uRY+SCZWJ&=$jK{biAmw~;
zDaMmd+rTXkS1(9_?e*mD(S~q)ZJy0_toPMBz5&rIF`)==Pq;J*hQj0#=EZ|m&^T~g
zumZogk53Hivl}0RHx||TgJu(B0LAXk0^cVcKJ|>PIW~oDA(VN^<o<6lrdCrjH}0gp
zIKX1c3Fx@d5sc<|*^eBNQJ+tD^xwV{{^Ne+cry^<j;CepL$!eJQH?Kn6Ac5@CIuMJ
z%K@nEG<Tpx;~o!bBStWu>R_-7Q)LEF*#k%4gbkN*J-LE@J$d-G%&<?eN*0ytD9_~Y
z`}Z#%`%!MDgnfUIc|q!RrDL0#1a!zDobD3V-we>2gUG}`pq?Lky|)NT0VCAbc>eHb
zf*Zg3zE*DBEpVT}*56>x8h>D(9xeL7Tpq~O|CYbu{P&+_oV)7Op1`jJ=81i5{Gm@K
zm>0Wtpvn)vy}ktePzwO1Kry=w4cAlL+;jem1EkK5BoT#4Y>*Fk$I+#=PKoksuHy|G
z4u5w7tUhzfT{5}E+>5Y&`gd1Ar`kHklg;q(*rWqb4O2+U`zC~Z4B{hw!sb4&a+vb?
z_9ejgK?eoR(oQ9t3cFxX4nn*M@PY4I;E$!)%D~Wh3=})=*EQ9%M_1s#KeM&jf>~m*
ztX26VAbu?(nS~i1ALCCZE;}4KE%`@Vo_v7kwKzUtKMUh}bK)(qyrA`OXZsrjPoX!7
z>-i{e<kOYY(9F`UapJ;D#7eR+_J=tO%i9wlz%-lGg_MoV4qfYzW%8RGv%(Av3|t}(
zahPX^(MTr1>6Mr0D1e$7+zI>s_GS`uSa_z&?#$E;)8XcmEP-vzGpd>DE|C>dy+o=w
zK=4SuW=oJLnw1K1`F!Nxi?m0i0+Te0zjytk6Kp`h&GL+mUm|IfRa1o#&N>;=br=49
z77Mg~xa`x-pZ3r+tz8_H5P_Hge_^4Yb~Lfnn-v0IlTTX@N8Ii>5GX`afs%#Z=s$ef
z|F~5D%6~6s!{j}-V0m4L)c;Kk8^0u*p24q(@QCUx3}Pq$^WQtXz90ML=CBB&%k7q?
z{S2b(zx}ZsYb#`DgJlXMEh1iHPm3V`=bx}#jLQa(n_Rt~k!=BGUjsKUFb|`s{L54S
z1SCGQ#K9@IW5(=p!vFJ%r@C=0p0w_$KaCoQpxT%}As&dJf3M%Nu>3rm2jA)L*1M7`
zj!=tfCl@6T{QFN&z#T~+GEl1QY|d#qu4LJGd4$?fGo4~?c%S#b*P7mX_Sj<TVxJx%
z@@{Tv%KCHEON2a>rbV!a)*}{mT_w@QD5_9x^#9S`djK`neSM<=lp+c$4+7Gxpn`xR
zy{RaO3J6j~+Jp2ih8~K5h*A_mkY1!EK<FJ3v7oew5PA{>AsR{om;@4%+(Uf+|M#6c
z^WC{K_q+Fd-#gzKW=PJ?-e>K#SNW~A%efiR@wRo;8kkIHUzgwFjLuoM3HzOZUo_%U
zw*RyH7MM%6F#U!}f3~PtXLsV4u2G^}PyatP&YkP<_?&v~B7;zHsw(d7>vVyvUvR25
z!0wv#ru*N36=z0PUjII>=1--Vv68)tU7}}y2^eXd{VaE8^zrs#qfzr0(m-*{Gg#`|
zu=E`F&J$uS_sm0#I<*J2EWU-l;b;{+;+pQ4_1mLGjuV7@H!pQsP5pkgi0nh4)*Lb<
z-8@>+%+>)xZcBBujOlP&Aoui<xj;`BoM;ILlf=*eawsd`OJt<izHE}&%Vu%q9<OH4
zs#0Q`#ti!NQ@EdaMZPy;GrxBAz<Ue$t%^%ym48oB$QL0&xOg9HE|9>PJ)v@ie)~dd
z>W{@OhKDtM=I4G6C?H7SVr*W~<85?<eahfxqiXxxCg`rXza<wfh6aL#L31(sF|8YK
zPL3wlT$BEXYsf3-h0|uawpc5eu)M_(dpzB`qO<WbuTjk9V=){Fb$brv@;=iHKRw4=
zdTc&5Rqk0_QAwO2j{&zZ`SH(>34!my3L<K?C(jA=e^&Q7aqM6V=<3zMtE$Hyg>=xF
zfhK+e*DI=OnfaRJns8m|v`uwfP!tYVp^E``KxvR3GGkXrko$Ono$<iMefrN?L82Kt
zp;^S0q-DOv-TN^&k)2|?lS3lAMg|1RPDU5){s+Z1JPrx6w~*8vK1$6mIkbDrDUuVr
z*jcF=tsCV8ejhkKQ+R%NC_ynkBXwfTVCTpQOBME`eDr`HMqqlkbmf-WsxcY7r)z-P
zlCU0UJ<dL`sBV=PAcBj3!LSy}Ua8<j4}96Y*jXDs9~rr@hip<%w0q1KsO|u7s9N8E
zOf}e}_ulReB+>^!?!cvVh68W}z>T6kUAzB5afkox&)t6$SojNE|4n}|-ALdT?R|sJ
zQW+BGq~_ovf;d1it4{renTCNyv}FR#QjHVD8){qV2<?fQ&%vAC7X*Q<zE42=;2lZA
zTbYCiYs?<du{l>r6gUbI@%UchgpV-sqtF;|@`+`q3)+_$jiX>Gs<1WJUld33NNwj&
z9Tip(7YHUh=c>c>i^}cg&)M4*mCp}~@m*LmrRA7(61%N2pT60X5x7aP2%{G|0|Fgc
zc?&7oWizNn`2U{+@jpYH{Cs7tE37)LFl;|lM+Ss>VigLnjjE${5GaP(jh(b%!h7@q
zG#Q<OmV+7kndI)W6v<1e?ueEA@e7TT{=Ut+)9s<FTTd%Q>rV`76o-%PP_)pmVEVUy
z9p3%1=o8_t+=a7}8nZRD1Va7hItyKq*y#-kBIhO1*xJV}GiTp(7v^|Bbmtv`2U=l8
z%-5sNpmm_cY&~Wn8h>e~);R=>BKRNi+6ir3mD+7JM)EeYGcs*?34w35q<S6MVw!_-
z1Tgv+x=?;_TWzQpr@uM#aj3;Tbo45wkFBdK9=lpU+q*@FG5FI2FWG&u12cD+JUR9j
zzBO_DK@)#eKlm62al^h&DQfdjD#CYYK~17;e12PGc3yt>xlV?YfC2=^1ReP#t<}43
zS{t^LR62GqFCyze$fFghQ6Ja!zx;V;Bch^ddzwKprCOy^<9FO^I$j!6Xl@hoHfgdJ
zINQ~G4qA4@J#HeakOinXhtpv}ZwiF?!BVXuNx%~EZD_{6H+7dMT<9;G>$L<@&Vn~f
zIq<E9VNL{T%g(M9?%%>g%2?A3)B%<RL#~rkV19q^Wu_4l(3#8G^lHN9>zU!#k8C*|
zVpiwRqOEHI#k1)uIP`}BA>^X%tzfjUcPcJ;XyN3L#<$t$sfcDUeT~ox#R!ej3J5S*
zbV1P(P{5;3nAG+QEs8!4%(A35obCDb3;wHPS#XRN?KfplE-4K4C<IT;$k)JY<}IV6
zJr!xIA+K3HARpOI*zh(1zVdsM*h+ACz4qjm;xdFqE?bNc+hOgv*z`pIT#BKNpl?k`
zuo_g-r0p2A^JDiS4cEwhxakf^2`x9tOD8-k`W7sMBPI#+OQ7QSqM%*`+SljVE6SQ5
zh2NrY$ysE!n2uXc{v|&Bw*q6Lbd{SM`PmOb?F7^C`K&jx0)8l86MHa!-D2D4>5cA4
z=bZuc{ER0R8s(zP%-m)?*W6%sgya=QztYQrUSW^z?Og#e27if+gt^%X-M`;m5pE5>
z*NAR2g;P-Y`}cJk!$0p(k_lXBV}x3$F&S$7i!F?k4S<J<OoaAX_^&NpX3Qom@W9|O
zzsiF9jHpIAN9J!cFWst?oweh;PpPZ1dDp3dy@#m?A3aon@NY!7TSnhud^IDi__qiW
z9ZhE0PD|;ST&NnV6UJj*VeM`;VP!4pj73>T+W>l#fUbdsSVh})mrUJs+-(xXLELv=
z86i1zS`zY{E$bk=OYDN=|K85#e};`?o0$Kx)BWSmY6%L)n@^CwGL`Sf>svJ=(8fLu
zX_nIVV4<1MYi^E?!Re+JyL#mNBc70FsaF3**Vev=AOoP5hY!Ky(A>!f6BZLS9ek>Z
zF<IoT1VWY2MAyUzvQ5f_rAT#0l5uclM;&b$P;lceC@p=r0Rd}AyY5JIijPpVg^nNE
zxEk4Njc8{#8Ss$YLl}nnJ$SVWld|b{L8sJ3aypDJ_@a4oYMQhhD%9*gMy&2>w&~`F
zu0{4I0n>)w=x%w83f+HYK}|}#SW}OKXrWN4NzV|{a^~rJQmYl<G9j(r1`EH;iLJVS
z2V9|*_pJb<HK+1>oy$Ii5er4xg8{S>*QO7~2d@&|&g8cf98^EXdc&8$#&XG8QjL(E
zCjqCPmNn>qH}nd*iQp&;?K?smkEXw9iLN%+W4@+R2Z%k701dqbhm7sT1QRK&*S~i{
zA}b5S=!oi$6yIO|(X8IJV7wi?Bc+TEEJhnk+ZhzM#&Cm>0VbN8$x<8Miub|dx%2rb
zRzfWe7v(l<bpOGDT`_jL!|Aa5KjsSG{~0U(Iq2kn$q$<GqIiTe7hd8y|7#m|EazmW
zF<wUrf^`pg01JNsdR<)psAJ3($>n`mp{#Fga0%)g-1mRCKdZLpm{%|!E4(G%s`VZ|
z3}s`kx7##mW_XN)=!uT5>WPlS^Z#MXxMzP1b%n5s784q06EJt=b~6u=4=vZJF$;+x
ze?;4&-z9{(TT*Ka8cDNh)~&fXc4IabRh(Gh@Qds;oTE-<xH0%T*uxnsjP8WFTMGit
zc$VOdHk2i>sdqBWJ~ziGPguVTgDk7#4-EBUpw<s|vmO92xbWuTUZSx>L`GQWxOUW+
ztJ1i?*bi7%wM0g&^=@^j^a&^?PjGi-$tO$en6WE?w0bQ#h$;LF@4Zv)>y>m}hw4A_
z*Xcx-IJ7*Uv7&kfn{~|8wy`5yC;pssbKQ@eyWEHBpC9?;UaQd(;v5M}3KyHIC9I?A
zP;Y363Z35R7RAa&H?u9FkILP>U92W0UGPx7wPLb#*V1Gd|HAX>0u!i%27)nfwzOKy
zY@<a(BP70Wb;I=FQJueZ{n?#CmhamqqF7C_^-g7M_io`k!^+mMgzNl$4j0T6?S@1w
z(sFRj?K7KE(D2zDJl3omG8BX}*9mG^+pQkseE#Gd<C{t!_S?|$#r7e}55{wXBTUgY
zn>wEqKIL@;*!JFOmmQQvNEDGmv~_^N2m*8H>bi)6M6?6AlP`ijg4~xZk#rQ=aoC@F
zKWHTY?A!=>;C;l(!6v_63Ev45G#(qU+Wg4B5Z#`jVR}y#Q9|n>2=SvZcNk9Oh6H?p
zl2Hh|FLmYnLIp4J`zrbzF{vegce7L5@d1-}+VzJhWe_K_w-C8r7~}06`TqQ1dw}zf
zi(wVHI$<3#6JW@$-csi-q&MD-P8;-(fQjeUtYOwD3dtehA9|a6`4^}FL;`4N7OA!?
z--N=wbJX_qI7LN&0}v2!IT_9j#)OQ-3PWZsLmkO60QwlB9CKLyk+43(^b=4>Mzwvk
z9gQ#{e@ODiy3dF2)~Q>#Elgq#U#)@TJ_H$(CNL5tT__E6a&SA5Fo0hWf<apEcn7MG
z3}t@#L83~+?V5o|Lc~fChP`m*{B8r2BS41{wYl&6Tn>1q;b#x+1d@}txAvk7Eu`&~
z@vWU2bKQ>QwAMSzSpEe>4)h&3)S5h-<du(=pTp>x>9E_VFYow#v~?xgl66rU*F(!C
zsCqB$w7Q9uz-)QDmU+R!mU^wEEmt858$R#%klWLxR)f`P`lj$2A3(O`Pm`YZiAZ35
z^-=L2qF^8aU|aHM0;_hdEnrv@it}#ys6W&@H+26`w#nxTOdwQ5QGrsN39gUyujz69
zzFq?pv9h)%Ru|ypyC8mzbyU8gUaUAY%{nV&lOoImHEe=#v2!C%`Q_7bAt!?^aUG;B
zGR*72s~wZZ%+_f0{1(zq*yha3ysoA7M4008(mx@wq|ZqXBCIpaE+JQ5H_Ersm(TA@
zEx~F?L8c`XOY|$s8tLlLgoWQER}+GlwTbftL#SSj-+dGBUB)0iR1cGMOH2hrMRX#$
zR2;1t2yu1)YxErZ8><Ou9T*C>b{7&1?%&xm%t`1VR$=c$9-Yo-J7T-(bFCh2T~l7T
zEGxJNe)VibTR3rtt*HTnK&!PFkruD$=7{2@kroe+s2$}YcEmn+1}KyAEL$!169^Ub
z>yq3h12papJ%4mOqF#ypP39tCV;I-K>@PMILRJ{|Cy@Bbe-aC07Iv4a!NFt=v<CW1
zUjWu$zJlFO%&I#fhKjR5+=EcKQ6hiB^y<+JyGuI(WHn#yS+?Dq6TRL1nCgJu6p?at
z&}j{K0Yku{CBV^(z=fOzEEx5CvZJ+5!?d26FZ;o%P5^aM%}Pv4d?)+HEj55ZYQlP_
z?4O(h(hLxChYlrObu;5XHU5UiP*F!dRhU=^?cRtf-8Hq{3F{~SbB&&QTFv$r!m2f=
zQh{Bq)d?EYO3;GpLDwu8E?{PSGn;2NE~sl@=1kXk4L*%%-`(8N5e^}*gU7>mt})p4
zd?EnT=xD#H)4S;o*s`B4?7MYN{pW!r=YVVS+TEJD?xBBk6z~ziDnoYLPWk`CG)In?
zAhy}0R>~&dI^PcCm5<kPG5Y6uT6cN6DlA!LH0FT<#Xo~$Q*o9H+X!p5cE4@&r_Xju
zXsIq)3;4=X0Cg2DV(o6pBeIlliRla<?hVp&!1zV6Sq@12zEEj19VgwWXmSxj1ucv&
zP6)HMAl4Sx>pWu42Eh_7rSmJ<ZX-t08DMPg5n$SfQnQe!c8Na(=I~PZPhO_)LBDX9
zTG%S!2<~leo#ux`-evc`dIlKNcK(IsaS5$1B?obmLxWP(?`z%c!WCYCb!rO9f^d!C
zkG)$NDt$*3krYx=IGa0@hXC3X?L~>UK`SK*>u<bMSM5b{UL|a&4+@TNc3v@H+y!GJ
zBfY@n-g;|mM^$OPHrm(T`pqMso?lV28=zXCyZ|fer-eX_vaRSO)}-3O783yg#s)!a
zM>G3Qf<a7;KhUja^sA)>6yYuxRu5jFCC!Q?XQ5?tYOs)z-Y)CgWBj99a8395oYpsO
zyRpg)svD*Vz*@NgJmEMR21NdKWMeTB_Qx%lnxb~pEi#1=HKgz<_&Pa<unAK>veX?~
z;*gCMw9*^BBo|s}4>2Sc@Oc5ftmDdinANDo0@frDR8>D-*Q4ID4;kqM@SM^MKv1In
zS}wK{3cOP#YZa=oR{2;TIEH^>aC=P`yv*J5`aRlE21>3^A^;@S>90(8cM74j7nDtB
zxlPmqiS7k^>HwLhUM`fHR-gqm)`%CNeaK5<=wbpK%9^aG-TH$>Bmp*0EKCy&ms-2g
zDMMCI*e0fqBUg;1pjlUv#?G_V`Q8p3sXn_>+w%bZN6nHSpgD*bsJq%s&v!J;_fK2$
zu*s6|ZwVvlG;6_o@A}xM^GzO6m7Bv@Vk!Wq+L`P7rEXl$$NUdwndxod!j3}WH3gRW
z5%CMes;Cy)0-^sj^$!U(@+aqXifKE9UuYpe=ix>u4yM}{pC-5~Jas2;i;)WKA-Bkb
zgk+UI1^Dp2(48(mJ+?9<Qy=vP79d)u_Kq@pI+|#|bkptdfO-6LE`Z7`gFz?;k{euo
z+x5F`fo$qVgu4nySN`@ituX-*!aPA^h=T5pTBu=5V!$PFzEIpfVAoN59QrxF^?7RR
zS#~|$#*{r>`18p)3&sO-zZdS*pNxH-LSH8jF&9d;q|5$fGbYBR?qv-1Ny2)Fr9TYo
z2r&fDoXuf(a!z^X1~Q_PFaSf?hOVulZvp5l%UTfU^vC>-d!9VLk^-PZMffnZz`?#f
zV8<H@#IcsB>S5hsDK>fPkAZE{5!cC&38`p(n4XP#a5#AsU556CcK^GBN@&%6iBL5Q
z+?{+Yh&LIVu;AF_##9@|vs<XUGB7Dir4h=|cniFxVzof+o6SyiGuqKY#7g#jF`MBr
zzP?i;S0k*+aX{eQX9af*+Ua#WPLze>?mzrzH2Z<k%w2&wm;qTy6SB~d6-|M%{=@hm
z#A`QM4FH(aXj3b93&&acKhifRx@!BqPuHf~ElR7D)*(z`Ges%SvU*M@9R_o>1+(oL
z=;YM7KM6=s-;MvrnO`pyRD7<or+HdOn)T_NIGY76QXIr_O1c*@QvQ!k)lK-vfOEU<
zC2nBW)ral(&!K7sYBZSeJ~MWtBAOQyvaLHdG$SvOb}NlETgyjalONacC(Vv5s-DAW
z^FY%i7BoWaM5EhGaV6}BO}+y%<sp{f3b&a{J3i$3ap|!&;?k<*nkK}1Ie3}CpX>IK
z>OMp?oOJj*g#6SknwkIO06G)q$64v-C?|an2rLa$r)F}x>w&a7()}2a<%+P-bA<Sl
zpQA%8S+yOZnZ}Z(jx{}j?A)=xm>(@~^%Bd%zK$Rh1MPv*MuFg&(^@qm==U6uIs=>(
zVV59%at1*Ss^@vcrw2|;B2+-dttX~C7=GaJM&X*i<t{f^sGj<j8rRNSXdM%svLI8P
zu-%BcM?aB9AAr|v7m80FUNZ;d@0zUWwqnE(w@G>mogMGNbT`Di6T4j9+P|kO7oUP|
zM@QF2J&I!aq8D-)=LlAm{%zlVij@Uc_F87$W%N%$D1s+GzmcH&daWPA(?Koljzar_
z3`vz*g;OXUQUT1*<?ihEBI|39eoG!;WNhGPDHg5_0Si4V3_RSl5lO8XY6(#HOn|W2
z%>^K$Pg+p=ZnBd68%R8fR6~#NDzK9_Cl;+?!?@jO*~J=L`THUZMjP^S_i187Q3(+a
zA^}nqeuu0B*vv10Mp^&ZWV=m?1W=&2x&^}v5c6q56G0J%f%&lK#U`isblD9ULKUGs
z^;3JN*jB=Cq`*=QS)l>svB>>|V!1W8Nto;femP;7oAAeh8b0_}ko#XLI{)`<(*ME?
z!M|u2+vVcHo+cW$JWPKs2v2Q&Q*^`!98wk4py|8o(GitV5WqPpCB1^-twS8ZCv#U;
z(T%Y8a%)_mIpxEo1}U5^Wc7mz@j{@^<g)?`$P6^sCYE1_#OWiFR$b6Rkg=LuNBdh6
z9Rq>nVc|<42^D&i+~7ss(_C;L;!1Ae^Ll61+DaG+(QTB)GOB@O?js%nNkXRsbuEY0
z&M|p}VwE4hhS9?Fr2(0s-1+&et|rFBeY>7Q4p7`d;Q134(b{G;hJ!>wV++BSu890#
zm{i2@sueKrva0o{(xH{00J3?~?Vg8SMa&iNN_iwH#L=n|;Jy>DLIWG%|2LiS`u`va
z`L9Q?&3iT`+<9j`6U;UM)p@||$+9jQZf$Lm2x|q*M1ItMmD*Ze)!p+;K_IQ{n2=2%
zOi3loz-CZ|5tqqbgbjj`9D$8}L^EPSf+8X0ol*&$9pegFd^?Y=*UP7F-|u+^&63yd
zc^hs4n2?aFp0W<FsFfM0-XNZ><TgB4<~JbM)f`1vH{9qXre)(D>Ni3Nw9_hDw{Ww0
z$ZC@|%WliC*&Tgg1?1SM#4I4tP7byg)1U-Aw8Gl7+ON8&U9)M0d*WA9IbXhMP7zxS
zUc`hbYd>(_(M!EZ3?L^HoErHXAo|U48g<hJ_InhqEL$6fN@DLGN2Z<x97K@Fr->RD
zrZVg<l<40gOY9+>tx^5aT}|`YjrwJQY$*K;8<T>N>}iv@K6O?AnG5Zk>pMcy8S4F7
z(c1BXny@+Zm%|;MnyHUVyL87a%w2UD2$|{)C2FEH?(4ME8n+iJEK#|2vb)7j;s;3z
zIISa}T6Lam3RtGWnZe90Ll`RqCAM3vBrAWvN9}wvF+3TMwXwJ94r6Xj88-^DG2HH?
za2D|Z{{m_TxlIQQDpuK8Yqu6M_&Cs=Rv`7&{O+hUT*@4>wKc;Y#+K(gFh3`rwrbjw
z?NC<WSbIxH5v_P(jcv^*kL<d*Hd<Vgm;Kbr-|P?^)Z($*B1kmzzkq$q_^(XEu6QcN
zqo6$<p4fw-Uw5L2|4K^<0d`IAt`2}SsJZOm2(+TUn`aM0^T2i|!qN$`=<B-^(}1){
z8Y2h7yIlX7eJ;$a+zqBcD>nbzw6SsH|MmQehc);i80HlMuA^Nee<6_e(b<(+)H#St
zGnP7S;g85$1P;SD^cF%I6j6!2BY7|UPufxmKwZl)sE9SbI*7TwITH=lHD_EVZ&a_a
zjZ^~<(QV3-;YN1HJZy`AfVX#;H->8+tQgLJQn;5N_YQ4z5x(%JDZ&cOFbK`&Dg5^)
zLh2@6XbFXbhXZqaooZo!lY?#%MR&(@a${H1vIvGEQhJ?J=di5C@Cfp{E@U9#{U#n~
z#^{Y?M&DOw#-TA;qij`I+6PP)Gw2EHiq@zq0V$UheYB~iqD|<UR$3!Fv~UX8ol_j_
z-D>Z%U9Rve{$n7xR&BvtT1bY-rz}bL8acn>wT9hra2Fj^Z!;uiDx1$b?5y3QzZbmU
zvkN6Zz4s2;XNySe-jJSAaaOcL3)2OBes*O!MB1_#=`fi!w`Gk-68NZX7<PJ1l60cO
zD-K?q#TpQ1Fcv3FW{1X`h+jWB7^Q7fteZnnl;}f{-_s0nO`;-mEnQ)&HCi=L&2H3k
zVrAxQm?NDLJ@G}>kJT(VaUW3+*PrNKD8c?7bKh;|_m=_q*5IquIeW#OMHBU3Z<9Kw
z!Uce1C7uk^C+S#(i-SI00XNB##M0#uQ)_pb0rP$>1^RSV3YZ4W0ncc_i{)x(zlITk
z&7eOW9Vit-A&q2-X+(WtBE=Chlp1zm6+tM4`NbOVPJc7dHTVULq$79379!PD$}r!B
zOd|9MP3(O5Kz{u_UxojH>e2rFg#12rsxGlR%0=&|;7W<hn-DTnyfd7Ya?8hkKx2I3
z>w5*}WNO5L8Y=OpJ^K5yfW#~q6Dvs$c@)@4EL>h6Cisa&|D>I6X*^v+Rr$R}_)3=6
z3+U~{B1(I*?5*q@huewO!>qZ}(sqAq*?kLl)F;;XX>WajMEJG+_~uG!MBksOaQM09
zfwhHv?~!X|<+giQ0}U~~aK&6aCezOFVsWToZP1z)3_i;mfx%!|6C>k|*om)+urx_v
zY3SXMN$;PR$vxm<i2D%dV66kO72)gL4X_F(CkU+eq)SqcYT9(WqmAn{T3F^;X!<Kc
z;O6>Xw4S744c(HZEzP15<kCvV>(z^CD{dU*zXV#DF+S+E@gWX|$vW0e?{;<H7j*lr
z6<4{A+KwNhONDnaZbsccmLk^6_BnxngHfj`4Z@IW+Sdz%dYzh(-6ClphX0<mDqZ)a
zZlCLg9*p8EXi1oI7BJgDE_P#$kGq64PJA_?wTC)3!qLlMIyY?<mUREyN(3|fHluNa
z*Kc3JI9<-kU6p>X>DB#l+UT_Xkb}OKC&#$)JK%V7{R%bu8xPs`u6sl+!%k?#CHho&
zh5FcvN)z__qgGE!bYuPxs2lvG6DJP4k~{vLAU(4sGTV(_KH>$Tp2DzwX25e3KgzO(
z+TKS0+F`9vN2yS3-5r-`FzjDbCrz)SgncssghK{qXB-(0WSSpVcI|Sb<A6qY$6fMQ
z!g4O7W4MGLhPdzWCqpYAejDtf2JgjuUUeeJ5BW*#8ig3mQW|PV!6#7u-5Qfnkz+r{
zwq8XI*WW`|d|TjaB34xI;Jw?OsxxrvtD)LydI6z?K*Uq&*5CdlTXtLd?|va!6WDIU
zTjuCQDON#{+mn}lyd}p@91C5nI`;I;vy%qE=NC_}CHy$of4beP7A|~<eD0>j>k-~0
z?9(eRw?JHn9zT1!)i!^?IWq2q5bsmX6N+Xs+Dn0J(w!0Hwen6FG{V{%Or|zk!K;FX
zYr8`j9UZiZh+ty+DWm`RdwcH<tb@y3zXntQ(%!2MQWu>(Nuv)V6@$jfkLC!6=<C6A
zjFW42Eg2WQa(S#c`#4eD!FykGB0xQY_xIiB+==?_XL?&~ox=uHz~A*(=#e`9y4L}z
z_S})cmi7E0ju}q+!M`^zIibdhuR*OKSj_Vnxfq^(<A}V2g{84^_w*bEBl*-t+I(lZ
z&e*<LKfBzO*EDwF96hi4s#srBMu5F8NWIRIst~_PXpNtkvgVukRd`A({o!qz25!%m
z>D<T&F_?2a+1CF}|I?8|4*Xv~-=mS6`!WjO8s+H+#ZOFMKo%ok#ek71NVQl+E*yuH
z1Via&rlx`MK-GY5V$KWeEU{9VA7giz5t~hT^c|Drob<c5%u6M?<lVHT9E*A?@Rj~w
z7ld7;jWx{sc%2llK7L_I<eqCDqwBTxKNXCB@MAnVe1c0+3QjN6O5K6SHX{f4$GQ)<
z>6H#s?-DmFs<$%04pzs($^F{0F!N6r?C%y06A$XfOrYNuZZ^`!Ix*Sc*EPwa-3^!X
zpCemeNg9W{7V1YF;)XX6dc3;cpRpu}s+_M-*p%?P>0ocQOP_u_ame^H>^LMjCu5gb
zv}a7nKz&-dM3u|$YgZrJo>Gm@oKx>N|CGz*&>$pBM_*XGl2Li10UOn&V%VK1TuF>1
z<R-rsE|fW4TIFJ4H3+l#bU}DKripi!%O{_dZJzTZh&fi7npm<bbAYszGEyx-I--;4
zU{y3+zMr&|WH$Pc&@fyqvxs*6P-&#0l0n7JW^^6>)(3oPKBY2khHQ<Y9_)G6AV=bz
zOU#IV7iBQ^>H6vBmsbg*AN@P1LT<J``deO85_KBi<*o!Wo+Dj8>1CL|O+}R>yGMEu
zAO~KbD^F2ZVLi$}Q&F`G(l^hhax0vQ{EAB>X)Ba&M#7JsGV%VYThRSvto<~GOIFVZ
z-Q8qK4Aze1#$50elzVe=o(t_TV}47rwBc`uvWuy|HshBEi;=1En-%srnRs|wA@;7L
zJ#IjToTpf(d}~E97Hwt^mYzF*(qJ7#>~RH0UhUURw4!7ngw*RTqdV7(V=*zWV$)=_
z%g3NyEhhRV_EwQO8P*l40581G;Je$6!c^X-nZ0`GWt3xttJvYExR<I5$XBIYOgUUC
z{0Q-8P3P^3`CNJB$}NL6PZa~5#CgFF2#~6{xhhk7Q89IVXnHbl4UWoJd%H4I`cUu0
zDQ`yJkxaVq@%CJ+>+{Nkr)qsIjf6`Z2(Y+|sZ}B4sG^C5OQLGJtAfcBLfiu7o-u0h
z6BnN0P*1vXYDO=&<WCMtcxTyP7Y+65xLB({T5z}T*$4O68w4Ay=#ZDmfsfc+_Y3eR
z>zi3J__6e{Gp>$x(qian+0kuojho5A&nRCSZ?MjqZL6hy?K_teE?_UU8e;J-xEuod
zF8EVoK!>YezBmF>=d5hE#K#BeTv1LZHysU|e^vJDi^nt3Oj}3EjKJIL$IuIh(YBdp
z3yt1Qju8}VA)e6V(aCOsF(Mm%_-3<qI$!(u>KkE_URO;pa{iHSXW?s^vIdQLclCRy
zOSZ!>=dPWyUmDFf$K%!HjNYw}P$FVeEWr<Y*HT(GB{u(VV!%E?s$lB2_cBX&L`qii
zGHU~GA&S#dO;Olaydl5$ZfQ7|&mI$xRW2<|6YchHej%&dr~ppR5#79)>IrFrIF@L_
zF5Zkh!W*){9V!yw@=XeDD&#0~b}<PhK}!9JZym`$sbb$BRGFj!#%>KhbjbhD)D_!q
zKQB+_>M~dUEknjzqhIdZIkM&F9bxE{cHA`!1W$`%dfDbf-y}R<T!`02t;B2EN%^uK
zTz=zjI8*q=3@<!Fkrr7q>;931ss=<==94!0!YaRZM>B(nBpx2KEi;?nM8shDq*GBh
zu%$H5`Q;O3+UsGQ`4b0_vIB(M#$}p`IXQRnDJXe7I_&RD0|1e1w&lJ?c=W3y<y7AH
zASpGga1KOOouZ|BK|re`ui6uJfyrmX7@I6Ey3mnETn)KX?SfVyEvl*8G<8GqSqt^n
zY?cu{&V&A#!<CZU0tX01MNn!s?V}t2gV2u`&L8aQ3BO44uff%DF&`vHp|6i@t?)(`
zHA5~P7cf@q*Lbwg`SoT#@?Niey(!4>u%FfFsjzt=1cJpAU>%03DzlgvFJBr1WE&O+
zE)X5MXQI;|v01%{HXa6cwsU2)mk(Ezb`%!2tr;DaHPAYQwGdI3qH^}h5QuM6juxsJ
z&@s2E`x6`zze&Q$`u*dWhjRj&ee0DRiweb)(~d!p`aDMKBkrYit$+FUgWPNWv9g**
za2j(7*Z3|lvu@wA(AVnuY1`tG?q)okp>TLj2@6Z|CLKvK>*6NX_1&A>Q|@0>RM@qy
zkvY5`T}SRL1DuI9ucL6!E2V)A<44>Va*r)GH=or2`($hlUUiTh5HBsqkDbNl<`!6I
zy+K^ksCfO>;Oz2aN!)7i`R~i-qh7P3I-KgcO#hbWNN_y3xEx)%K9!>+RN77Snsd@r
z&RdG{jcNUAba|oeyd+{-NX^iOS`FDRyZ>zGxpS>EFCNx+mO`YI!`0;Op3=3=$w@9x
zHWMSG->Cy{#EXNYrHh%;qz%#2x^S<|?l>kFkFogN`J<&>TwSYuQ)R!yA3pnl&#Ro3
z<0BfkUewl=e$cULa3jLk*W3i7Y7&`KVBIPV81^8-Ux05x!3G8$0m-^$zK{Wa7X`K;
z{^DD%=~cg=S+J(nA`p?s?~`6WAf#dvX>!cbXIgsyLSe-PD`Lec%k)CzkqNNbl>NhT
zY213qQ6Af8A7pRceKnGC;aq8trYJu93FPv<Q-jH2uKmMSMIMnYWy%4hJPy|!Rixb|
zgFX%-hrqG@P4b?rjeA1no!!t!J5XgJ<Qh-SzmibyB@OV?v!rvV2&StTdECC=+xyi+
zLW|jgGJhD@ZbVc?qXIzZv%kAK26xJ;c3qK)n8=Do2kv0VZ3}-(gOriw62_YBsh=|k
zdkC65KHSxNqC_+8KK>pW6s1aNN`=1oZuEsOK9}nQBDY1oJIw^AC_{fDihJIV&-V;)
zZ!*QWAN_sqXra9EZG*dgXMp7jFE+B5cpEK4R;xl@D)D<A6;1){23V78f7~U{nAYlv
zj~C|#%rM^B!}Qd@Qc$ub8>?`E=-fIhf=qmnkC=1HKxoVS2~zrROySe93BaL_DTPP6
zorXUdak<2=L9QeYH3B|n*fA#?7>bjbf$y+cYahI(0hh8`0+Hej7Bvs%XghZ<y8p9I
zjlr`ZkD#l)VPg3Y2&z}j8}7meb<MP>DI++eTrS;f6~sbW$C4<|CKQLpOp;LBkijn>
z;LI`8*Z;-<my%qjbJyTVlhJ#{rJwMpYc>!k?^p16_JW9~hKqrPxshMAKHuR1in<>m
z)a(aIv8#fHuKvy)ZtJfD1ys&fNCCz*-MT_OF(>aX^%b={%xZ%W^=ZiQ4@d#1*VTgt
z<scqUs@_bV&y@tQ$aa3|hPS~_mrNOQdGy1v;fY&5vSnc%?uz!8nPPU*$rLS3!)?Q_
z`A<#WC(Z`~&gJYE4}xf>*e3M$Mz%@XbeO)8?^{)`$d<RtqG6#1Q=Zau`9C!Nf*Wa_
zSBpI=tGm9z88)1Tr8b;5{OK<Ycys_frHL2Xeul}0N#=aY3(wXDSp33TP}zj}EhFz<
z`cXyl-iYUwJ>;=y;|gtqq>+y!x;#r~^n8Ky;EV~zEITu;ip)uqbGLr#3hG|3zox^y
z6KRmT(RqE*|IFB^SF$)D8#e&is9edmcK+7)$Z2fc9q3bL;C#@HCS15?2!4%fBXNgf
znUq?3`;~4Y_OE8JdD80Ji++&;sT9Z=yzV+V0>$ZTSymlX3D}lrTbFd;1;4=_(+>&q
zVQkytE+!l8dDY~+k@9i!Ui#b09>LR<Z~=f+>k>%8o6<=C6CEX?YI4d+Y79vLJxQHz
ze$_AYC|ZXk@r;FHIgBt{f1F|ERPIFVYf<q$JO9dc`qjwTg`8`HvNg|KG|zYud(&r*
zPPDBj@q`*e^6~&2Y4K6xx1d>sqC$I3VA)gD>k(!cPv}DP#bsTj-IB&we4eIdWKN#7
zDNXZ|uLf9Zm&`nC`~?%{#Z^EDhmtb@9h@7l?6L2whARMs+K%Xk0gfkW?SXfW8`^l@
znDw|a1w9tw<aKxMo+d*lcZAlNvDSPvMA|&KU~K~1XyD;*ZvIhr;F2r=S1(m%o0?np
z{f@01x-0t?TWS8G;ulw!WuE>3pITzhlXn9d>+Q#(?lZ{^1HW_=30>Fp-{9{EJtSnT
zdwMz8e*}BvJS22&bg8MSz=yr_vTl?{0<UK8FuHcdV#hTn2!L;A&nix?KV}$jox3%2
z=7Of`H2%sHxQ3c3bub>bi{1JoM+;A$+6YYVxfm#Zwx)<4xAG=AOC6yFU5<$V(Nw5r
zxae2WBmKa<&pq4I7!U@J^b3U<(A2Zc@P!kkCcY+(XXoaFght8XuoDK|j_-PMY1mmM
zaAayKp+l*(R=KnR*5}da0|Ws8Xsbvy71|^iIVP5$uK?c084)zPLFfW>s$2zdFK=FL
zJcc+}Obql@!%Pg4%!u;&I1X7(yAdPoYc3S1wwP{A4Xe`BJ-(2qp9u1ojeIecw>G(A
z81lG>H&W(G^gx3OhBd=l+XL2pR=4KRIqV%PJ1X{9=zfg!6OmXwqpyBq{ZCO!quI&j
zsUvS)bor{%eyR7S7#1Ax;gT;6lFT_ds}dpIfv`f{Xv>92^7O;Phbw-`cvrbRy7YbR
zH;<58h2K^DY_{zEzBCTMa=OIsdjVgUB?o!%eU}?q)kN&mP8go+=Op2Hm#==h09`iz
zD6_7ayOh_Fymje3T;ZgFaevi7xWAvdgU**7*Q3DbcmdqQh?X(b&$u&ksbkHyy*;Kk
z38IqYjs69O`LAj&4i~$8md+jElRmtBu0UAn%#k=LyZu#a1=|_qwx{6&npeWs&(fz=
zjsC<v8MJJZa+bH}`xu-eXUIX_Q{!fqsO?L@8%9${lK#?596rDJ`P-RgiSQE`$F$+_
zNX=E})0UKFpXne%!~E#mu8w3;!0;(-cDJ7m1B+eoGB>F?`i63rWNv?cb@S=@-`f#b
zwdRGARZ~vyzF!^*=AVA<yLvI)bhw3;L%LEw+^?O;J;mE~NIma`#qH-&^`gH;mk!U}
zak4jPZ{&l&|2WdAZB{j}p9<+$SCl>On%oa)dcS7*mG+<8@v+cOL%@XRY~@v`ehKn8
zPr~u3p>mE?0ixyndaXudI#QgBO8mPDRb2jK%(rTO^g~yNulWSQbEs;@oGt!-;%WxQ
zLsgc=nk@hHw~4u2%#h!ki7>*A1CUHo<`AAM`q~y@vNm4ry(6a{Na6G;@er%AzH|2b
zR&L$6BzQ71(_`=V=xl_^mgbb6GDF($6ZrIZ17+ERJq;b{q|DCcSVc~0S)63-X)s-#
zbfA~^NG`UUPdY}*C_A&^=+*_&>0a`VTIO$aFvW8Y$E%!OppTchcV+A5HW?Wb8_m&k
zJ^Q03%#SPhcx2{|`rO`lF&yG_q$TZeZv<0*N#A#A?EM>S!ST0w9V^r&nIGsS#s5fa
z{O1(M|Ni`6m;;2CBcZ%o&S=RI3$4ciQaXDiE)@-)8g6zq(^F8Wmn>7}iF`%M>u#K`
z9UFd)jEkRKxf=+C2VTkBP_1J|>2Aa5N8Hun(g-d;ZiT(Wds2B?<ZM{q#$Aj`n8WEB
zny8OxVQQII(4*n9b`Dn=aBY~0Y1aGL%$ij}i>a4KY3E4}z1uq)JKE=Vb7J2tI=F4*
zx3>0@%r_1M=!{L{X=9Rw96^u%jxzoTshWQI=<js^s>%e`o_|gY^h{bkld{+8!1wL&
z5TF!hIHFOJR^0|jp~_>H3(#QsI||Z0rjSF+dRD~f=nv7k`kGY)yy2uuu-f-<0aD1;
z8;f0ueOo1jn#0uK{e$9dJM6gVoE01)Uv+Y+SDdnpz`>u?<62Lm_G66#xQV~Bd7=_f
zmQtqWK@Nzb`JNl>>W}9+u(FG15PQ@U5*4TFs;dQtm3Tt6B<3v5P{o_T7OVN5+i~K2
z^&KsQ)EnuSR(CX$PZ>cw$w(H5KvGnz4vE0AIB|H~fbso>W)`REUZyO`cCG^1T?2u0
zF=GQ>z1432c2QGJrc<59xPml{v5TSGkqh=!|Ge9`5EBBzajkLrap`ie6qGV1$Kzc(
z_Ox}Q*ACPygi&5BOjV;<;1Qxg5i5nHaHg2ia-}|!o1_-s>__xxKpK-f8m2W4ZC(8S
zDT>-e+u7$778|~i=M75ZaNxRclKp+#2{uIJ!gI*p*>QW!@Zp)gDf=W^f)VvK<v^m2
zfYi5fi8?)!bMHEQ@7k#kznkNOjt>;!BlPC<kzT!<kdpyN1Pj*-U1vAzE7e+=_B_=`
z&1(tX2I$q+d<-ol+Npw*&Q68CeI}g$PMmJjL|l!(QtlAu7YRO#QGU#$6Ln4WvuJt9
zlf}bxCC9<FWf4V$=m)>QXCzZRo41E0`Wp95wxF1~i<7O4w#s<Q4DI6vKNn#-@`ld)
zm8*Ce8-`Sf5O=OQ=E3Ta{xkui-GE|-xs%pzidH-9QT?i;-f17J`ys6@b+R;;_+yeq
z{vDknpxF{OC#_7-2H$}ARt@)^n4kC>Cguw3akIcg%_xlM`hRXuV=FW?Hjq0sq$uK8
zG45X1S)Nc4KIQZD16|D@<4#*2cH?(Lhp9Q2|4h=L*P&ievOu_V0ov5VZW*6GiOm_0
zMA=CUz<3+4@Sqz%zR2F+z~xNwv63coVZ|BR1Q0P$WBQqNsQ^jpM)t3s8`)vo83l+m
zO=PEkVN1*LE}4z-z~LBOs?<zo{M7d_g7%{u`DammCj?RnKzhyxJ&_EZ@z1T;JWBIS
z-C!H#DjDFZ2_)g)6lNlIJEDLCE6C-4YQK~3b(;p~RHeDP)1<3Fax6bhph`|k-c!$y
zqkzX}8X6J?gHx{q?`^jgkC`~c{bnh4@74SBV(HcgJX!uF52+woj<nPJ9}RJ&9joRE
z_qAB-=Kv2Gx^C1yf78H-s_zn4f338o;iKyDTBFyTa~+ATkB^<#a1!cm_zQToyUQ2{
zmU5u=2%jSamw7NvLlggzf;2-WB&#AL9{r|j#Ypn}KAo2TYkv%)6ote8)K#3|lI2P(
z35xrG3EPS;+TW36g}~E;uWDQU9usSg7<l1wBTV<mAyy(%NV(q%GE~{Ym|PsuU6AiQ
z!Im=_k68}vW+?+Vh9`z`hi=*!P@Qn)`u!idP6IhSe!A_K_dHgVqj!QeFmZAWZoS}V
z%qStoM88iiPmgG3^uxl7*Zmt_cQCLvwa!~BzfR1HJ7M%~+=>cMBh?~k7y3cO4G-1;
zu;-J+fkhN)F~i2YC4an(e}}%_dzg#CJz6N-+*<Gsj?<Wnd!(1f81Gz#^jIs0zVJVb
zx#jJHtGG?rtba;01iRKO++S2F6<F=V9?FdN`qvn8f0CTjQJH3)gfI)dVnljVvZsA(
zB<_@l)V-xHuh(m6F27aabbrvq>Zx3jf-s}Pe^a%&EM^QAamq!}*SSOQw&KJG-aj0B
zGZALi-RG9EYX8kCg9BSO!hG)*<Niwxsb#1hGL1mIUU$>;yK3Vf{!*!}Td8Qa-7FXW
zr80;wpJU-l+~aOk$T$dh)@&+{zN16l&bHp^u+1%v%$!KpY?6MSbs_BEGPg@F#J-6o
zOWV;|1QEy0?BG&&WTfQVvsPU9)F$^6Uz<@rvfJs_i3xT$(5ctDM~{S|aL)ut<$ijp
zi`>1(JkPC9cMd$vK%6_LM>u@IF^!;}K9X92OZhw>JFfVO7|NaG-cTze@2vEhVb=NH
zt*?dQYSDv>2^g6e9xdIFrEEtKHZQ=3N~7$v{!OI&IKDl$;FJ-;#Rx6=;*`<3GX+(l
zEK%|xM+BGQ!SRCG`~a@1(7Kb%dtu`pIUwBW{TBO!tbt@_as$<1rrVK@jsNP%%LN=3
z8@H*$x=Ov!%p<lryT>!TVFn>(qh`W$GIvC550LxK9(pMDy-T#OkW=k&L1*+{)x+F(
z;l~C$7#B#6dkHhO&CwCYGp2{fvoY!h(kPi~4TfFs&@^{<xoTOE05e?)rhH4=qa(y%
zN*kU}FImOL{ygU>xUTYRVlOrShe~PcYTsh(fSE)3De`v^{FG6SYJ|azTAEECCH_s?
z`<|Rx@2^L@>gf&7A07BqgF2(-GglVkJFU%lejvB%jp5WP&Z@4*Sv9T2f3O`@!#CZ1
zAXI`B+`E6gEMEMnq;O_CXQ;?`ZcAHAS_|>S?3_>DJIsX8_&Nrp9rs<?vGr+7bWS4^
zcWp-NLB`PLbk*|maprCE<#!O-u%Sv9dLU%M;T1rWEyFJuKnfP_>l2HHwojb*IxjtV
z`3|GmPrFBMYDE;k4c`=anyMUdw38>4%lS24H~z{LYV^(ZNYMb50FJ5l?Q?VDHLEqT
zs0*Rpv+_V5GoCwucbli@c(l*_yPgJA)-Fqqxfa;M2v2$X`nC<8oH7FT*f<!aUos8`
z1wV^oPVs_wgzGH5=~lftFSQQ4g()1|C~Y5iS$-LDuP1Z~!zvracclf6`_|~wODW)q
zJi7MrK<V*Aeh-2LNQZm(k8;IC`!g7)k%jJK^-q+J?KF?0qW9dtwtfW*;k{)5>PjCj
zT2~6(PnEEN&Zv$A_lr$2EW&g@saZv=076v~x<t<D4?k}7Eh8w#I{lRKt5$=V5KflH
zRY|xPgAO7+=mkyr#$U-vd4$rOeMY;-zthvq@cD*)vA<#SL&J=z=lOhcBvsbY(eZ`-
z19@@YWs)wC8pFPEL4c9p<I5yLsoczjom+5UQ+JK0h;mlSKDzBK_wn_GUk(k<FXBp8
zr5AnNEi4Pt);0UJOzSW*PkBE0S3!jUQnYwNPaAz}vf;}+%-B(Q2B)@NcDMT8`o$r7
z!*%a+VH0I;R@<5TXWM+}lC7XJnC?o8T*igX2`cyd-O4_z#mCxve_}f7(zaWA?a5gQ
z(#AN-kR$7b&gag(0SBBZqy*&5J_<Q#gt9K?DiYIrO)t9C$*#1L5IM`cQ`9VpOE#Gb
z2E?zjS_Gg)W0w|l%z02Wccd~6qZpf(a%aLo)4Nj&NwSRsU|HJNPp266O%SQ=abbsJ
z`-fLA&Jhtlj83ho_P@nevljC_=&z8kx%$t83U7YJQsNf{8{E?U1XeK9*c%k%>gjPI
z?qI|uZ94(!x?k#t{l3U<G@QlLl{e42va#+#_vyWjy@Ex1@%Q@B&Bg~5p#7WcawC+5
zPu1S9wdJNA>}?eRaegR3x|#mA<Be?fK$|SdpjWfKFqzpS5UP+5e=bsg!~L?)-hk82
z0W~_jt(R@^)O;k>pfdWK6<74kw8OE;15Ej=E2>HTI-H@0Nt$yL@o%!@+@3i|qN(`(
zg<tFp=&su`q*vpVapX{ZnZR^kYkZyRYRC=ch!8$f8qywFg_Me)%J;Q9=qDO_AYbJX
z;&Od%XN|1<NPy=uUT;s&;Cr4-Jg-_{#L@5MM`yNq(p_-njc%ley_>9yngc3>LfkhM
zK_<^B5qp36-BY`riC+NbK%!j6Rpgdp@U1P@uNW!&*sjt-XyPFEJrTY}Pw8h;&&AU0
zVr1RMaJBAVlizg*HH}8)9q}zxDe7@Gdvl8?BMG8<uCRH3?{TxKE8e{d$M;*{b;>C&
z)Tm4jLxD56s8oeB^^B5wc`uP(q?;9PjYsz&V<(S;d_Zd=xPQdNKQiR!UDJYWd60<V
zV(r_A7u2`9zKdZJvlTU~F*srM+D$&LFveeyo{+TuLhYj4gb96zTthJA#J`|U#8nH*
zkOu9zQ95@3H(&>$-?W{NK^dYyCXh+-wjpXMQ%h;QTn@Gldv+MvzQ(=by~(2!Fa2Nt
zK*hSm6du^3XZ@Uwmm7~t4bKAIz(l?AlbN+4)*g2qKio6TO1UsY(>SgWmby6+Z(G=!
zGC9sYr#@F`r>9q_RNBPInJ%x``IeU!HGa?`5%%!+^7;5h%Ojf-*G^dGr%?|%XdQG@
zQQUtPHkU9$!SlQZ(AXP^xy(~W;0+Lozjvrq_nM9D@AY`wIMy|rg5wE60JOi74Qc1t
z(s7m?QUj1UGxLkU?G%@j&c<VghsT9<3YD_kW0jn?CFgF>U3PNsJ<!fAIb+b9j21b>
zZSQwFqDbDA?%Ek!r@CSk|E55&iMA!&oBr1fC+lTV{`EOne3=-93u|bDb&_-rUP<Fy
zz9DU(E&^Y6H1-qctw+?6H{T&A&bXgVp5gPh(G%J_bG&?Y;%jDnnPm$H7WLq@+fPgM
z_7-y-F%ZKL8k=|o_hrzzdRvwMiE*ngZpMoZKaaoSa-fALe^D&8ayKAji_{+E^I!Y#
z``HCL5>~XYCi}hj&%F7k2%5B4voX{Z-lhm{KAnGsBw`5!Y=4Rf5BE5eHjX<rKBJo(
z`pbU6bLVx-{VA(*TjEuF7<7dA-q)yY3UlK7QrtgJ`iGW(n)u&0|KH=l$DL?N&k4C}
UER&zR%Ylrpn_R2C>h$>k0QCnR0ssI2

diff --git a/reference/plot-8.png b/reference/plot-8.png
index 68f52b39381546e62af0183afd5ed0b9c4fbd7bb..e59cc9852316ed4ce5cdbeb7980e41877f409d06 100644
GIT binary patch
literal 27081
zcmeIb2T+ySwl#`vYPFT7RS*?u0TU8h5fBhCpfsSM<g`ISM2V7fXl(^WKv6&>iHb-T
z5RkOdCP_x3WCW3%lduVItgYvqzIE&U@2~prsaLPwv8v0!-d|X2t~tjXbIjSd&nd`k
zT)%xi0|UcG+25s=7#LPNFfgpt|8Wier6n+O48N_>mzR;o@Ax%$GTR-$t-JQSrZoct
zn<x2ig<sFOAp^s&46@S4RUATx+8k{{4RhCy>Fp`uzM6GTJ5ocg?CGy3He{$gJX>1x
z^!E*|*S$I~_R4*V?Yvc5&t-M|?S{3|hW9J3pI{15-A8%Osr2&-0o{FfcnuGDx7EA|
zzVv13JniwZ<`*S@_HUMqu(qlcbc}Fp*V|*UN3g@Oy*Gg;KrV{@biayb%CXS@bZquV
zhZW>czwGW@O@9CVp$GjZzn=d=m;BvBdforP2R2U@3b-Adm~;6&Irm7^`f^io#1Tt<
z;iYarDczaz{+Ai<5vJRO|LmCDVUfe+<KuI`s<+!yYT;tEt6Y@Q(Cb<S@(<k{%U{KH
zcHyC_*?wp*c%7DZD)OAyiO8R$lwyXuli0J*?R)5I+Mf8&i}gy*W@=Df91jZ%7p>bH
zT75MlHa7OnRE_+zbNdP`+$KXFNjT4l>+HrAUr|@(Fu1njdDv^C75%)tyalL7j7>}$
zKiW4^?0v%Q+eJA!eI@7nu9%oO%NVc1SF&W@w#cwK4<$G4|CC2-8|9&FI(IzIXh;}$
z`{c<%tz7C@UYh0N?3(M1sJea;-?)&^-FddWkVS82l+u3kV%eiqlcC1uX037Mzy46o
znzwJ+!S49^{v#0!U1{l1$1lMqVY%7OBiW<uA?8_ErKMe#CL=_=$^Hmm_(9i0a%e}#
z*V7Gns%P0Y5jy-Vddu^TT%7u!{<_J}@FPC8<F^$8$2g`3;*yHU>w0;8Gz_BXKR7Gg
zKjF$KF;yMEIBemb-5NzKz>u?Zb>B1dobjS~3EP3X>8#N`A*R2ym}O9;rH_uiT{%8c
zCJ>Y+j88r}x+<sb+VbM85zBAduH#RIM7!?_rcmcxB*We2`-NuA%*`w3H|{+0+nvI$
zmU)+PZ@u#Qoaxv7>FMdFEsE~*wL>)L;WTy6?P~5mtb#A(r7V&)?mCa=iYQ3Veg0@1
zm+&+)a@T3!ovaaa<wp8NcUi+4nv@Im4}Pf(+Yue%<}{R2#(&UZtn+1DoOk3&&msl!
z;VtB&UHEtJ=y0dlGLxT{7f0RwHt@gsb&q2B&Qa$MTFSUxlVQ*!F<aw>`Nlk7kq(zs
z`80`zv3$!f!A3>l&LgZh)@-~}R8;iQjW*o<(ZJ8A#UfAQaY%^s{Lk_A3v-j*@u`(}
zZg1XO<lH_Pe&xy)FGiQnm6m_4+gnf`Ech%fElquvgX#RfEnO!$=Fiz3b;VZc^}60E
z)E~;|aBFhibM|pvoQbqz=#hghjl|5D;u@ltrjy*+irMUS?(P&lw0-+_{kVkmJ2A@9
zMS4pUK?=peQtocV3X&%)#buV477hBUBQ2|suPq$T=xC4Xb~kg+FsZFInDLkAFKO;B
z_8XmWQy2g8$RK5g&Av6ceReH2t9`}phL#k=GC8L6cf6Dg3=Cp4coUOHUxb)Hp7!yo
zfjy)bQ5hw_UT7E{%V!*YT@@1@{&+h0puuTRvxI~MwwbiVq~Y|I5wmVT4k^i&C9F&U
zGrx{Tvt5f>qgZ97_^6K7>nrD3IC5=igL=za%`TG_*$dXKHnnF>*eu>%UmYbe^YK|(
z_ty0{6^gH#nenw<%ikFFDc>{6`Yu+xCdqwq&~_=_gyw4Z1t){ob?O-hrFY4bkwe#M
zD7m;nR98`Ad0{e2hd*d|mfX!`*}6(XWKjIl`+GGtQS#1d)C~42ft;}uS5qs5wFeDm
z(~?~IXd&iTukL4ao%&!hoL9!59Y5Kt(a~Wo>f3~k7i`hh*3NF|jS&jQK@%?eI^Nw#
zTf$l$8x_2fv19v=9R^c<ADEav)YWaNl3I3&UetA8oX!YqSeT4hcJb+Mw{4_o((awy
zwEtw3grM`x5V6^=lD!c+CV8G5ZbxQXt*cVXKf8AOi0I=&CN5_h8$(5{_3?b(nby5C
z4)NONnY(?Dba^?97qT=qWyHknCuXXy=W6RWo83I>zD(P8P@jJ428U=}-U3CB4jSk;
z#A_P1xEr0myY0h8R*#Ygdy5Yw<<wm5@ML$|N1aJaGR!clc)WFyHal8B-yDJsG{p^*
zFTA^+HX}aUYTdn8YEBkDvk<?Ry!rCjq@j8F-CB;jD>of^Vrm;W_y~(7lRDGxJj$l3
zsyZl9B-~>9AkC;MobBd`NEspGB6{|A4!_{?J9@~Z!@k2cY-TQ>W9eR+3{EOj+zJ1{
zz(56>Vu&!G$!L0u`5;Bv*4Fl+T-3e*Iktfi31_>R#Th-tqvHiy#k9Xzs6WdG@%wyu
zCR04rlxk4ozxzBdGY*kZIL!SBPGu_9Pio1b)pfRQkTR(qW?d<o>@qju<+bg%3&(V@
z8|1yNT)lc*KY1HGgIc@>lR)m2QhBJTAa(4ffOMYQq8QsCS;!Uz`Q=h|>Mx3tbC=>Z
zk~wi8$s*uHboEC^_+G7jQ7B0pO0C?U(PEzcNZ3s4DE{`LwKY^?j|Fw^GI@gJZeQ`s
zu6UlW4%kwsF|YU(+#Lr&BIRe-vCemv^{VQHWdeCSabC!4U_0TmsYCgmUS8`q@3Fbs
zbUXafAwxMk9(TkmTyWM*UYb`kg?rDLNB7#OeNVl`>gE)_)&x<vlm!X+wb`}seW?gx
zA%Di}Ra8`rXLl^}V>?pgM`2|<_l7&@!^9*$eEhhrQP<flXK8-$Lqh|znVH%6UmFAp
z&daa`Oy`YKX<gXgAxod0qnCRtLW0ty7CtfB$A$l~a_wn+)Q%Pvq&v`XgxI}6Z0FFY
z=Msn`O)+I_*&QxKyZYjFm82iW#>L6ox7n&UEZ>)5chv4U<;`r^vX+rOr>Hh2+bMRI
zelNT%Eo;F#O)5%)Q?7WY^_iH21T&vSM)9Fn@uMr&?yT-vj4xr<HofmN`?cH8-Z@l1
zS)<vqm`(iX3wK&ehm%SRT&TSDmvYVCm=Qc$6tS_ES$pban9$5{>1e73Zy?-FMtnzN
z_K21i)n!g=R?4vSVf=D(F?&lI$E&q!i8`9iPJ;=luHjAtF>C`(CP}#|W}Ek%>1(iB
z(Kg=DJ@-&qL9?66!msl+J&IAd&8D^{#Ddy%bs3K$*6Fb_g}OKN+F#oT`vyicLyJvm
z2A#KdnD@;b5g*O57Ou>lZEqR2#7cRzUtD7$l0eOJDTwuE%(X4jk5MUAph$h~ykSr!
zF<YK)D_lDr>B(N&9I-SNooy>-o4Juq>@a`!P|EwB2A4;n&Lf$cG+*(NzVX$mf&7%m
zX#^u+MT*&-<Ma0qq+YJL!<W&TB9k_ZdyXgjxtfOMg&?v^&42JqX(2o4{JH}%j%Orq
zdCAM`WkP~a_DI$N#Eyon<-x>k{(-?kK#thB#DQsVY7fwskcpaIypC1L?k0b(-UxR$
zEcISa&RajR{sLEMead(AV*ahdn=HC-id^$%b4Pui`AKHw&ze)`zHB?{a4mW<E;hCc
z_GfNK3)D96KkwQjdAYYLA|g$s-J#puFLmxqh>@8ffruCKx!@j}o0~tvIdE}4)!;2s
zR+Ol}NV~pzL&~d(j^)LUXL@;V2knMFdiA~8l5d>R?qt#se)y`AML@1Db-q5y?ubOT
zU|dkRMnX(Xj0~+6m>_k=&u!|N`Pw3&FiJx>K%=MO=Js{)a#eq2s_Q!L#?ChlCnx4D
z&BrhH2)K)`*|hUbCGGIx!v(kZhCgass*PBx8(y?mH5we(b(`y9YiPs5Ivc$8SdptM
zZ$zDn5;%4CY=su>wcf~uXFkGr$n8vqJC;s*kry=Mn2Pta;z%8COlCiP<y=N_)1?Pz
zQ)V7Fs1w-F0|dA-V!kdqWrkSDMQxg`<1GAwmlu3H7bj;I9>L47EYP;kJ%6LfrQwmp
zAWd^h3|CNDVRhum)E0?#hqyQ^k4~1yyEb2qE>zPt+u9pyt#B0nFZoi5KffOdF7d84
zvU{grzR1wZv@#^_$dY`P0G1Hb6xq7^`csk-qhr(Mf>oDh`%A>$2pUzEH%ELe*qh~&
z=jZa7OVgRcQhFdLBeA$;DH+C=x9q!ic-Xeb!Cb1=)=y$aqxVdAM&<L7l2h+XUCR@7
z%fvVv=8ji1<I0A^?cwV-;YH)6R#-3&r8VkmX1gBS!30FNz{#mr@q<eTt(}F5i8h>}
zm%ixdzF^RRB@Rm6VCl0{)XFFN&%D|!vB9Lp`1j%bA6_lav=q15cd!B8GbIl^RVHB7
zp!JRA!7J5Ih^d9z)W{w*stgsX4CF=3P_XT|9Af6AqD-?C!1393<l2QH*u3SZn=D1G
znbrmXxgTn3enD``S9dnadZnxWI!~j4gfHBHxW<heH-4zD{uxoINuB1Axp;F1i<W6V
z>h8MuwOGnGA)_63K7%Lv+QpLh)wjMnII-NtJa4Bgjgj9;+hS+xuz=*p2c4OVp2BfK
zi&zB);hjIUvwyTI<Kv2`k|~J&*?jtLCIPi2H)jU>&9jHon_NfVbo<8VI=R++c|G$L
zi)|bniIq!OdGz96f0EdtKR?xP`F6!xn;+Yl`LwnK>A5-H1*#&4F%S0NZnvCqtGq|+
zmrm^F-Q}--%YElHvZ>!fmSKjrmX`k9L=OpsSIo_QzkK<E2v|aGUR#b3nB5^RI+^m~
z#f$u_i%d)eQx-M`=yojTJ(!*;I@sbi+*;_(0!IUoA!Ne1Lpc6lA^i{d7tftmzuVE^
zPQAUnfU(6k^Z=2BnkB)%EY5aFaf&78w=8v?K?YXroc2Wp#!>iizjD`1i-jK&uno+-
zUg9HfxO_#wJbIa!=<DURTRyM=(Pw9nj+Lw@jVkoW#QjtHh|_$3boWqdRi*i8W^cG<
zZ<u}2WR(<0Ss<S`)^Q)R_j#Uxlq5F0kCDZ+1e<-$Y~(n2GV*b9O46FDwDN5EoVp*G
zNqIYA^V`^?7qzHyRj2>CGsYZa?T)=+OiTw&8sdkXVKnldOF&0gq~o;$Kf!vv^^+g*
z_5RGXmGaEyWI$Pu{@1Zj<F^%EI1{r5Vl|TWPw8BHx4LO&d7)}~=(m%}NEY+EkGL-_
zus2>CPHVh8%;kH8^GkW~X0i$f1zt?V$})9E%yzZO3*^>yFRH1k7J5ofMGnmn$G1y*
zWS|kR+0w0*rQe?8I9_4lCYYT&&uMt`#4d#X>Sx~frVVo~%YAo<@S7~dKk8)^Yc9T1
zmCiBi--x$*GO;R0;x%m~+h~|X6DFo{;LoF+YJ8tweK@_twy(pjt(`=Kahz!h@gDb~
zkI8He6}&;=iQd_f2*EzJic%6qzHA|`qX<a@)kX_n$2DmrG>l8O=p7!tD_E(~Ti#4U
z`XJA29ga@hRkj_EiJfQ|&C5v5pC_>*z16DB%j+v{y^|zgQKc=N0klVE9Anh!(Pwdi
zyqYx^7j^#?03s1i3|UU$r1f?I+nbb!iHmTG=_%URy*%G^^N!nGnO+efU#b~-r82d~
z0q=*Wq|=43=?OYb^~EnXC|65it1k}&jqxD~%A2~PH*EA{lCHg@89()F7yEEhu7R)U
z9+L>mB33qjr?f~|&B3cR&oxpm$x2J_bm;uU68;iC(N8n=vf<!9J(ux9%h@)2^_04e
zYMFjbVRp^?O{yLX#c6X)9i6UI)^tYPzKf{EUQd$xhY=nbp4j*<!HBstb~K=dlIF04
z=nxo7>BId@OqR80MGA!)bsm_RNYRp`Py1{SNaH}tp5oIIf>6ZTvG7GC`|zY~;hnAH
zWqM18vfGxfD={${6y5FrnPD`wN^%@<-Yz?R#(?736JRndj-PyO$e`8_yXMvd7d#51
zF3%4p6+f4Kkmd5>_391G1?tp^fRy@_Jvc48!r8=)_(m)a88z|t-sh4jnr_sKm<~e_
z_1s{eEQc4I&FPgmX!ZHMmxiq+b%d9-#;xcplg!Q!!&-Pfk9Mw3z8kU+_81P&q;4+0
z%UXMNxqfg)SV+kB&9?frQeWSt)YmOd4UM{vI*${qcBJ0vr0q<TQ85Bm_pAI(-q_f^
zx~|jprfm&MF<ayVd2ctR8Woci+?$#2j+fW`{QO58Lh-xGs;c!D&ALg97VdDFJTe%c
z+SOzfF+8RbuakL*xZ*d!4hA*PWy2CTJAb_BX*djDAcZTvc7Trud^lYYD|9)+WuoNB
z$eV8czy4g;<9uvsF0~anuZi~~c@To*2%po93s(ew_0rh-IrnRqx8-0$Mp41-&E$+W
z)$Gdcf%)GNtUSV%Edh|ZN$@=v=O(FzG1Jq|^77u(4&vuW41_tm>Xa2Lnh6LUecYTR
zu~?$$W|ce2h((){Tw%fbno(Ju)+ZzNn6I$9DLvyckcg(DJ4ppo7X^UjK~ro70|LBE
zf`ep)o&iJdu1AutPG^wu-`co?84SUBRn@JTI{a%kZr=iEWACY4j1=RG0tX(qT@3t8
z$E6JKLj8qC_eFKn<U(d}Ue97<d0>rCsZ>dr+E(JsT7iVa8YYR`vcswk%>cV?1D17E
zQCF8i<_3oCYv)?2M+sr`nN_Zd`Gp^xjIj>mZ&xy2Dt`a;ly?`5`ZizsN6vwPfs}}-
z#6)^(@#v_d6-g=#Tb3UqH+g>Yi7S%RVmSDYs~rat-*BdX)JJS^SQszvxF1{?v%mM?
zjt`a%!$=~sbklNXnoY)grItlG)e;ybUA~S@FI*+&W`Ery<0s+fE6;{bD8(h1Nwg8L
z2~!ex8Ld9a$Y*kPdV1P&vO*+pn1&d({^p4->u+u}Ut>t2P!>j)mn?+y9Vz7NZ+gR>
zyF6BI(lCf16pUci(b|g=H6tX}bS%weu$^cZ5*^K%?5dF$;G;Q3aV(8_c7sj0i!9lG
zPO_N8-K9K4Bq`oDUX3T9>&cBxrI(dP0Vdc2_c_L8Wo2ywIE2w1#9`Ep;WO+JyUfK&
zY>U^!z4MQ??dfXM&95WQE`*J`L>H&wy%=&&zSZ7ik^7{X<c<wXNR2Lewh&S)Q~nMT
z>R)wnxQ~FgFi6UsxwGr!t%t50Ju{JvuKV18oeGegFq6b>r`y+0<fy*yu_B?wpxD<(
zSwVF7<?=^N_?}i!17EwoH(wV4R>+$-k0)5T9w_qT;8?llCojb89cHi3-(?dQCaKz+
z@uD5w_3C;)j~_o)C}stmI*M$LIKE<Vk?i@NjDGmW$y-0&#^Em{5v~9+YYX9-Qm2i|
zhI2<<EfxmU^X>%cI_>ArovJx9^eR6kxB&Z;T&o~n2>SGALX09f6mM16Vd6S{w=Y18
z|4rxhA<jR@W&3NRlO}uFg^X3sfC9*tfJ=5j&~%ETJ`5Oq<fD-`RANjBj-VMm#CY09
z)<e&S%6!RM_9&|kR7TaW_`T?jWp@{z))ziR1Dp~-MnW2B#tsf7n9kDDxNt)LO>U$b
z9vi(`30+H`goz0mvL(D?V(Kz7dUhrz^0JDq1IoEtJ8=1GLBf=p0}+)sq)QIJu6=rL
zUwrcD4Mxdrg!Yn_*1^7+P?iY6@$%>jfw$RjdxG@X>+9>Wq!tBO{B8vrcnso%IQ8qr
zx(2o6chWy=GWG+0XW%aVN!NkJ5n%r*j%u-9oWR@q#zFF<kJbN;I~V#+rW>viAm2Uq
z6tpw>b^Dj^7n6Itf&Bg6z7_vtA28;j95W6S&|?Qyt}&cq#h#w0P}rE_*jtohR6gDL
z{Wx-NxD78a;K^|j*`Uu>nS|i<@&YIW(oDQ|>iJV-Zp3{tF`Yeoc5C7-6?~o~pI%-B
zhMJhf;WJONvc%}==Ti_lBqPVAPl0iimd5fC9{y(BuC2ozeo5bGn41SUSGW7khTpn%
zYbcqb_e1-1T|>i{PQGVui}R<>R>bDNN=T>(;mCUa;>8|ub1<=sXKQw2TbeADd70Tn
zY&7|U%Gwu>nD@oHNREnnP6K@fu!-C}@y_yOP@219&zs0Nq{S%)(P?S*G|Q~#i?rB)
z1L{td`=8;&n|Cz+LNM09cjf=*H#hc0&FZOJH#e{pp(Mcy4*wjl&YR!bY3;K`NI;lL
zCT0o>KY*VEE`}$(^D1w(;qs8+oYDc0!$!>mbwzg;Fz&mAhcq^xuk)iXMUMP*%yVm`
zC<o1&U%!lrxh>%GX~RMD);BMclKdJP8XWqgf(YVIvbjwz&R|$;^h0~2p1Z`0vmwk(
zOazJ=e0+7WDII8Po2b>LGht$BN<HLUF+8M1WWH~n>aPVi0phg(=MnnRnNoG-?BolO
zJBYB0;)F%sB99LJQQnF_Q{?^d%3-{VkbGWVBy50#H^9S<Bf%zCb(d9e&n6Tm2>HsR
zv!C4M*xb}@5Bm>KPay@c>8mD%rUit;_vu0+BE|8Vsf0qce80=H;DZcDZsmAl&O|BD
zW0-jhvT6h1)1G}HUhjLBgl^{`-l9IU8ultAwCprc+B?bS%cb!E=Q|#uiEVLyI_`qd
z^XH4!ce`&T#==gUk!G?j*}r>?C~P#0icXbie1;Bx@0-Z7W~p8WDFyX3_p{2{+AW`4
znI&o;m_Ad?snJ0~FD;z|6f}N=W8v3LVtr4JOjVzJ=Dxg?Y}Hfx6}6ciaK~2x!1)d$
zD15vc9o8V$9-ywv3Z^m4d<UGZX-1o!;WDci&g)B?b=>>t*dxh?mHYHtd3#awK3~|n
zT+p%W#>U1jzrA$=nKO}h$9&w)Ws#i8bW_c!vX|tT^Ljdq40hzXH{}nXh^!u_&D>W1
zDNf3khuJ$B^mzdi!w|DLm;s{iX=P<)9qS4qI_<eTNJl9~iR|(38mQttP!Nl$F+6u1
zo_>1G^e(rEA05_i+W9DLFRC9`BgVUni-Dat199sh&~J*a$gS5j3QH?e-Yhxx2V()D
z^VNO}hC?vDd_q~dc6(5f#|(;wGt$kit*y@#GaY8Xhe{?UtU$7U;hpvB(mWvSQ){Dg
z);x(Lh(_=X*x=-%)chy8h=r!i6kt47C7ULX6)QRQRr~7d>Qp`%|9*3}yREUIL9PFn
zm#@Ik7&fjv5ELF7*1GqH_Tx9!7!DH@9X6x6_(D>@-a9I7bq-Ee$My_9N><&5ghR1O
zNmb2!e0<^D{r)IStj@vHEbZy{$Nh)gk0)!`v{oAqE85z&R;vyxCJ1a<Z|J`ASceJ%
zpGV{VSMh%JD=cDEtjLFqIGm)YZb#LCiBpCK)spz)bb=%;4WDA$wn@4;NG}XtH?c@V
z#=mKievp(J@q}JpCpU?5b8?#JEzT%pRFMpeGaY0a%K>r<80P<!+&tzsWWeFQv`8x^
z@w4{D*|G*T-Yx47f>3(Xb>{#lr<jfh{iOYB=z5vQ1nCkfmJ#s%rvF7q7s^9U+7X0?
zP494{tydwHB!+Wtu6sItyc`JWLqd5gDBSUujwNA&od|<Nzz8X1035vhr;msx&x<W3
z4EUMgLs2!~O3+iWlBwTf;M;7Ue@DrgzFhy_s`mfMEhM=-O8%h*0HFCFsj~l{JkNig
z;eS@~@NeIXdX+4XIzs=V*2P0P9dW;GaZvCUiaTnreo;~a7D}T{53+y+-5P&uOMOE_
zQRllG2PkW9BWJ2?_T7;cyJ!*${~!~i5{ppw6n_Kke&0JuKCY^+PSZOT<S5nd3TPO>
z|Ht~4E6;qMCMG7v8!&qpns=zb%?<}G>BoLe46(6kLla!7g<Bup0T%wQtsUZ?Xd{bc
zl(`O)lYLv(Z%y=40jeBw5BMH0!>zTnwR7E`%UfmYFb^<$pQ;kI32r=M;|agqE3uET
zx}MxU)epv3<qXC^tx+0q-~e8daK%Fmx>T3Uy|P))ArpW^1A>R1(g2j~bO@A19G+Qc
zf?vGa>G6MUy?<xsco^<!Y-(Cw`P)^{rYKV)XskzAVIE6={aPB)3`le<KrvY98!MLM
z`eLz&m#akE{gOERwgT=NE+0!kOm3ql{R6%KTeUL(cFXy1Ny+~Y-S2<g-M`b_sho}4
z(03Lrxl|YGA>JSgQLytPjBfz{Iszr0n9iHh6O)|DN^hhVNImH}B>5RbPrXsll}F<C
z=7jVhdKcBKE#%)lbU*A}*$%W;c#4Ix1-!Tgk=6w8)4|CT^{En%*{9z8caVJ?!52ZD
z#n$wY?=ei>kz^T@oOsW5(4sx(D@f&?IPL%VUgK1ZM@D9*C>6!eLc(K`l5bQQ{S@}8
z3I*^#33XfO=8&<rwhpyxzACZ3msCTyco!;UoPB(Fe>%$b>^@a_blgue*MIm^OJ#L+
z79k<mftb6xHEt#00Hb{X*vKh%XSU`^GTH+if)mutMy>&8L~@Pc2#blioSrWl7MVJg
z%RE`}i?n^C#f^&E+S*a_PyPbVeMWb|>F#W=sPkudCO$t<EFyW$KwS*+jO;$PfT=QY
ze$g$G6ZicJKq!BFHr}t3x%``hJ<|2zu|3<fID^VGi#*LOTdUdrqGXX|IN!6mdMmv3
zlKKZe;`LStV0{div-fXV-$u2F(0CdN4-ATxjZ4P&E))$aS%d%qyC)s-=$$i4n+08k
z-rd;B17%mwpr|#sJ%d!enFVxPyIo(&Az8E2dJk~l_~!7M*Xru(K^136wdM8e*D7x~
zmF|ICM>!UHo95>2bauf@<)>}G{VGl=HDSn#g_94-Y^kez{SQcg#nkk!mlxbPxdw6F
zaaH<!$Wv_kYu2lu<e8ry3<}%WzxZ|j;Ef=Zp&l;sCWMFn@d3D5YO!At6#hiX-aNCG
zfxRz@DUl)-6BCQB!^OMXg^Yc=eb^<Od|FVN-+n+nap-v3>qa-92t+qUe;Zj^+EW;p
zlg;*(qwK?q*0?F+X#C<bPw_@d{nF~ZWmOwhS_u#S@tFz_lCMLf#o1K`5Gn4XY0XJ!
zPDCoM%85%DWGv_Sm8@TN5Ykm*vRd8gcz|6m6vDQnb~&C3dY>(xC`}oj*iYZ~j!&o<
z=&$Js7p4RDb5~s@JLyu*zPNwz3j>=?#u6(~E@dV)^{6uPRfc`y^s3CiTiW@baP>bE
zOX^khqe)^+`2F$H14#v&#D{)<u;=U@<W6!A>2%4x_>{FdL>8pzHt~RoDVbE%!E(1=
zq`kgc+x7C9@4#3tuuBmn9NV=`lTT%S_hPA~ROx);CuWA4NCvn~#9YVIYy%6wi}srx
zOhjt%5p@irVQl^GFf|Ps9V}4Lj?V@0ZuilyCqVtQbb)AZ=w_04gO(vR9&kC}B5p%}
z68HD-(X~b*5K3%n;m;ZSiyYS^m!1QJa$LD~Q~8MwFE6NBWB&e?2&lf|DR2yF9jI?P
zEKWCeKQi#WtuvyR-3F^pnITFptDu#{tgDRzBsw869hssL@en7cJ}BbRu|=TQ_(jx~
z1GB$5C&{>>6{BOH@>A`A@EVpNRr{HViO5+!m!Q5iVcdD-)!!e%9_r9|2^4$dU2%IE
zX_QR3#HQR)n@F80mYhlc``yEk$~H)X5AH&F*Bi`tagO6;7mlqd*ae%uJg3VR(go3y
z(=q@2Qhj`O*}N!Jv<9SnN9a4UN6$|Cl%~5*$LYChFI(Vcn39e8+Cfae`PYL*ybFQy
z8QQDcDDo~YH_P&~Knc^<($En`0wk0oC<{aDnwP)2k*p4bMs`Y7r;mnhWExQk>7x2c
zm{(KOnZj;mz^#)S2V`_oZY~zzjH=pbTJG%*CPMCpn58#q(eCaLNzAaL^M}>F0j{@+
z(giwFR>O#QG*D9FaDkAvKn**PMx*~Irv*E*h;q#+kUrCg=~wCEvvjZEkdZ1)-?L*+
zg}prNr-$0`-Sz6<Y}xnD+8lxqQf6o!%S-FkzWr+;d%`<fW8RVw=r_;N1geIXbODO#
zD4X@NT7h6f*@O=&(3~M>BJPqEr9Z$ZkHmThqTe%hyjf`qvmF#_uZvN3RwCLJhPT;r
zg+W(P8(g(KVxQ+tbRXY22<~Sxf({(eJ!bHI_zmS}kbziD!Y?9=h@MS|jlF}2UU+ZU
zNfMwW_S4^`lM+)be>g`KA7LKzWtYg>n@|7uSIl?_@k!Ls0-S%RKbF|}84r8Zi}_~n
z(f@9vf8)*j?esqmCJhNXs3TB&i&}pv0JX&KcXZ<SoP~)pi|k*ujfjrAo#z`;QtS`#
zuwPo3OUA1cm0|0SZ}<B-_vbNw-JBy!5G(NkT%?TpfPO)_e+Qndr=5-5=WVuH)5>-f
zdJb=}&j5}1W_gK5DB7O(Z!BxJZj(TY9Zd4_Fe-?eZ|J3mYWY(3vC0T3DU(qYe0$j0
z$(20Zd7R&7H(d@f90ti{#Yi`gw^G=6`eD$`vbmb+Ne5z`5%_vIs<xUyXruhIq%nel
zVL=%Wf~rwLlG}6~^icq4-B4s4nB}Mc*vsp&72{VZ%!{G=|8xXJwQeG9CuVE8G}l{<
zGTUaN&jzMTYj-DWYA;LwqN@_Fi#qt}$aC+_ML_dHzTDhH3zCbn3L^D<hU&fl)r6hl
zErR~OK~Ny|u>V3n>Y<?Vly0~G_t02{tQRUhrvb?_MwcH%EIhwk|4`4>)pcsLBaakU
z0HZXWAtZ~vu={yza`HidHj;Ih^iCdxVp%TeIA`;65~x4bVZV?SE7z#J@;_XGwMI6K
z{PxsqDLcYDABYKs3h)q$DfmZ!)RY&Ko|ffsj8ehWZUgHZRrcii8b)@VnPpW8$6@6e
zkJ-m2iMQ#*twrAo3)mIGR#|r?p8;Hwk~Al%(ygP-EXnZ^ID*LXnTS-~X(}{7TZTW#
z8zII^WBPi0W@aWyneo^6J!)Z7ufaPKNCkL`(hLGXX~1$-d)TcK)dt6vOR55zSu5B5
za)|fhh2KA}`|;0;eN2~_F8?L$@vF{HT<cTby#0x@^VdyWoIh}SA9~rj`={*pl^*wA
zZNItdr%m4PsSj5D^1h*8!9JJ6L3z);DDfBt%dAmaZGUX9%3}6_sQltUOm8_QamCNM
zA5h?a1W92=gW)+YB0*`;GD*-1I=^pn?NJ*Wn@g3UqC$zco@$IuI>^h*i;0OD>zucI
zosrRt(sZmXYh5+8*Rj-!!-sjD&uCA3*n9J(Yv^~ie)w=73O_F8XoZB<^pZUxDt|rp
z%XXanVmQ*8na2ODxDVyd9*?ekM#@3=2tZSwm(P^3AuQ`1T)#%9GA~@#&5KOMS>Pe-
z9k=2K%4hlIb~Ai=CWCTVs*^9MOjleiAvxJH(xb6=ymC_O1DoC*-m1GRR2k_$M<Fc)
zC3jz_<6EJKZ-Q*zx^SyHueuWbXjZq>4RM;Fuv&u197M5e+TEyQeHQ9$i;g<FyRkW5
z@k9IP8|!l2Cmuv_%OV*05@AFd@3(B|obmRtJtb>)n4Z&Is!wuL%|1aaoMHWoFDpi`
zPmZ*s;vp1IKOj#ujM)hhq7qo~?%Vly6k>E>3ALY9j+j^3cz}K)_66RG66t^J>+7qK
z77URzk%#WItvesk!lFZu4;M;e*~exE8wJ1@QFJYc*ay{|SPI<(AM;b4+`k=bHPJTe
zCemHxBTu1Je8VRD?#B9cF3wfChtJH+>`spXvCb10?~j)jK`W3YPEQ<#euHCv>eC*x
zdP_Zev5Vi_DerYrrf)Cxy=HpuoNC0=i<YH5R$5wG{@f_i$j7K?omc#}pj@_tKeh)8
z8hwFApn_=IVdaN~grcC<hQnk<y^25cvw!)cgU`g}>6h(G&f@FL1sk5yY#i4EbXEH2
zy3I8oJ|tH!Zr-a<jv7+fyBlkJn@y626A#h9;S_Hk{PYvJ^HM5&U$q$|Dn{w}GD<BA
z{fh3T@blqV!q@5PU!Xyd^GA72ug}-&+jpPGuFZia!Hv|kN;}X;;6Tt55}^x`cBw{H
zm3hmwyj~+Inx_9}RM1M`Tf<SQP|fBhU-w{-8Bji~V_UC!)x<>JpVY>P(a8MO#(mY;
zxRf;Al=ih@iFF`MsQ;cZ7sooi1%g^iPjRUf?R~Do!<W2>a*V1b6_xL}e{LO?9rz=L
zHwn3gyjdQU9s*Vr`ZEW{573>BVJ4O8*1x)LG2u_7Wyarent`fBXB*_QJY_gz6k{52
zl6s)b-EHTGzrS%j-k4PO4d+mvEL3U{aJZPZx{;pR(9OxMUtTYkH^QoPe<m7WSXyR~
ztCcsR)!|ek-B929;|>&y+-WO_Qq-5)p<YSF2Ya&y6U!jZ4}|RFs7ULzlQW3uLpUD+
zc0`*l{PDt#e?Fp+P4SH-sCuC7Qs$`@0QBL9d1{-|h=>Dy5{g;RzEKbCaRXcq51zxF
zs_R&uVs{yPN7BXQVIsk4g>%Xr$r`MwuIAGeBr9{wuRbAWAR77RQ55uoQHO+<*yl|6
zQPCbNQF5r^!2;m@ZF+0zhk5GCflD8UP@zhBw`Rv919}7AlRH_4cfMh-EWM1ooneAT
z3Wk~BAyrMjy?uv@>#EVgq`Yv5K4Hi`Xy4H@ra>I{qGkyShD5t>;7CHPeq}qNduEDD
z-FX?lv_*))&j0r<CUsq2;!AXlX%!+JIVmnsOsIUv(s=t7qoh|)ll{oGPNkNl*EMnh
zJtUp}yDf>MY@8?I&{o4ig)RNfMn-|4c*?AMD`5qQV5JjJj^Fr~Yk*4zYmvk&g{UuY
z2GLBFdZ5&FjH2q_Z;qxcbxle1nFTg4K^z;;I|{}1f4Mmdq*L%evR2?DJ|n4?%hb`m
z)Dp40(3^>Zmp?Z$Yf%XO|9)T6c%x3SRqYu~ave5lLgF<kwLB>m3~d)X`ucPrb8Etl
ztv}zFQER3^+N_Dw^Us4(U(Lpth0cdBc;YINytxYQ5;zE}zDQ60^hK~GVJOl)7nh^^
z&%WeX$?Nu13zzO4bLf)Fst(QJAeA*J*5~9Ez;li8&<){9@jxI`&g4c8$)mWSMNLhO
zKPj-2P87H`HB?pOb7z_ZGM&C%HOqXXL+@Z?k{$$RfvAxuctz7M)3-It>>suh^o`wp
z^5i>Nu=+cBz3(3iljGm{;PwA>qu)C}uYcF1Z28_2`kzZMF#KEAqW;|A53z>OHUvY*
zxkn-N5m8{1n(f;^RtoADcnu+(G{J$GP&nz{MRnTCV-|JYs*9d%GxuAf&jkA?8x&uL
z{+HOWHTbP)DwVosD?O!B(ewM!K|V@}NL7jWHUHMeGMqmpWZNKK&pi5V7ZF*Lwo_8n
z7e#O}rqDt8G1VzAQ3rLct5HOmPp+Ig5F7UbE)e;%D{ZFY<@sk5ki_kkwQ93%jLOZ;
zEvo-^M;Y8P=nKH{CI9C-{6>&Q3MJA#*%L9RQ*T>7d^oQ;(vo5E9b3E%*JT=$oGcfV
z^+z}73wXGHeY)UOL*R3$iO3`Rf5ypiif#}E)?pS`AB9pZJR!X~)_8i_ZsSJx!RrSS
zUMu00Y>bQCH!!d?)24!!NuTm)Wgu$9Oh9q45Ahd;wv=3_?OeQ7i5h+quZWNk>!s>b
z8%9$5UDy1y?SgyuUBHwaJDcX~#XJ$k*+tV~8syA}pW>|&{`hQVJHj{V$RrpDul2x3
zxcQ~>;8`r<<d+KV&#r`&CYVKY&vM80ivl)kuC9wG_U@@>jZ!M1h8jNYc*PsqZPP}&
zKT3Jx>au<^MiJ{`@Jxn4-h$POGr=_*x1L{Rl8~m>pkd$xjH}_RN=fZxU~q~?uVk#d
zFj#W!FSgD@%upSmC$9H1gc)UIAK*wb8+OAhgpENy45@H?j4kVya*5g22auZ5q4Qb}
zmuq-t<-tP?#!qF|<RBCgNin-qUnI$yMUj$aPYEVL6J(e?3!PODS|bup2Y<d1@kjY`
z5s}_(p#03p91SHHFom3d`D392@aYMbr?wnN0@SnD8`zIDj`|#Ux({tQ8go$|)(m2C
zaIyr75ol$QdHT}S>B@D^8R)?gfNqbXTP1pNRM>gOw78#NYi#l>9@eP&5B@PNqCtXQ
zC_2`!3EXe_v+K?}Z^flrbCJOcLNXbO1e3xpCWV};uwBHU=%>A#+F)zq!O_ahp+Tns
z&6yXTD&}3(>=&^KG}y|UZP<3XoTE9rsnM?OddnFZ8PCR~4LNEqt?9w+LRa5k|1^T`
zreGr5Zw>t(!3|E21(x-Yjx9%sKvN2twLhZ~ee*Zvl7yDuws+YV-iYIPh9X=4i;jq%
z6b~|SIq0E3k#tPU`UA2pmo3po^FF4t=v+JApzA!mRh7;F9Lwlu0unFnnfN8?vO#Ab
zQ2V#J09U$P$^D;1_u=QT6YDa*4A=?zH;=8cGUeYf`i}oN?f(QY%%J#BF#q2`C9#nW
ztGAquQIVsB>EwtznDxgi#k{UgaXV?m`%!UfM_>T{B&zl<MZ3Z5^1ET*_F`)KAam<w
zL;@xLAQO$V$qosaH9*rCwqBJ(sY}w!Lss@A)=v~Is7Dv4YE@m&+%c$ra-8JwCX~|`
zp*D;LlLETpH-OH$g1wUG2M_K0b@7&Aa*f=9`m8_ynmgipLBDVZu=Jr}3);+Z%f|Y1
zTTuG)tu4FQ`Sy=A{>XF9EiERLAB55oCR7&R)HXDvmk+LY$bN}n2(e_$fB|MDJjg`v
zL)`qMEyWOXhPr?Cbbo2fdb1{4F{0G>bySpLrrq@^b|kj}0xQu_tUm23Dxr=t=5Eu+
z#7iSg&d&^oNV?8*%c8640l1I)H&68s9*VUO5i)7OlZ9=bF^7k@4IOz3rATz-ryCkq
zAffE<>w9T=s&H4QA-;x|{)7R8N-29C<{#SmjV&yy(42>7QuykefnXUs8xxM2W#TVm
zl_4TkgEG-|jg9I*=(ey?&|R;h*<jHZKgaRt0pp2iV|RwW{#7}-VAfi2@74UdAyum>
z8J&IksJ^wFV{u3zCE@xib;Q>oM@pV69c%o@3H-+ikZ}2*?B;(bH4&=I@-W7UcOqk6
zAlG((kHX<0wCfCkGhvl*vH`(qLZK7>$LgnEddvnJg_mUHEe?ZcRWWx)VlfHH!555o
zC}VURyD>G~l5y*MmZ9hu{PZH=r6}A|Ex1+EHA;H62~()!)E}h@^*Jey^gt#MXB$lL
z41;Q|!4K_3gFZYwjQK5+5ArvO^M0f4N-?u0Jhf8n07_P@C{{sMsxwH}88gH_m03l=
zt4N!Tvj1naH7&efgzszu<>SwdmX6N#33SHqN%j<qP9&>>-|)YL*(u(psZeX|mR&;G
zDe+Yn{TZ^9k*su}n#o-Pft<3QUfz3QllbJ*{+~fRYh1LYC)zgAx3pN!Z6x%Zf8NbE
z-Zgpd%iOY{+2N&GcC6Z4f*}X@;6M2$s9;GXQP<P*HI|A+5Qz1YNk%jJr2thm0P>qk
zJOMmqc-Vm99b$3G$sg=wqWK&upLA4f+S<DBkU4nat;fT%p6zPNM>kO2rX(@oW8!ZS
z0wNlF_~O;GY50NF#sm8aX{r@;%xB~F!!HMl!{BcXbu+L!0OGQNF30r7@-<~-iho(z
zPUO1DD=g&Y%YJF3Fq<T1UY1;*t5TmnxZhW(LF3tgYplrlihG7=zcsbz-Hb@}Nu%EO
znT%g0S~X25YlKoJN)HIsF4>{-ayCNJH7k<y&IQzw5U<{uGzvv2$>L8!!oq5<+5OWi
zEKcUHT`pR1bYL-jXQ3Ww=a0&6#tFrOMv#j;L6{vFnqy$->^4OuCdqc)mTDBb?~$J0
z21AD2V-!l*PEWr8cBZjSm>?uO(V%U?mtFx1-{0luH3EDWmr1enBMofNeGJOsKN-MV
ziY&SI<HtwbNXi~Zh*s~%G9{fcJA^QaDoEWM&O1+#4Ns)zBHb;Wl6-YIE<Q!Br}dpE
zLs&gTgTW{IV5^GQOyn&;!d#T+f~Ro5Luex2t}cE4C-$R9O4hCnL6d`8xADF^D0ycd
z8ffda6pKS9#EN+&{+AFG19E(NM6i8wryEoyx@Ps`60?l`FM(I|ZrhcMLcOg2G+yOq
zJ)PdH4s()zD-Oj@X@-IYjv{BvQK*w-{Xr1B{rw|o-8h6d<t{;&=kk48FQPP=N+df0
zHreTB13jm%rr=s3+#t^sk?4}aBZRjKaIA!!<S`N&A!Mdy@9%S^&JV;jzEL7Z?=jHF
z#G)xL`9=e+hK=q$r1KHIV_|VgRHsN9*(R4k?T*;<w)7KH--uJn&5D6E{CNu#t>7WZ
zfg}}#pFmR~bJWI|tt!>TZ+i0T&F~u{vT79DdKCTjN=WyrRuDZ_S0^}?@BG&8l>1%s
z{r^3`y)ljd$rY4aYXAeaf<74N*nBwUI1m%<&utWHd78X7jE0F2$sq1r{XdgtL&&f+
zy6^0}zF+ypXYG!8HUCRc7l@)i&qRmm6dC7WIPtk;e}G&jdRI*-n^s$rX%S#^Wc@LD
zhM$aiAf`zsn~=N7`V$&w?aph*NTTg4Ht>>E!QS87B?>G`|9Z*ppwAdR@w-3COj!?I
z`kj28<oTpPAnQ-KZ3z99eGUwW!r=o<t*~IJZ)_B3+)(*fxck!F(WS55vi|TU7@XFC
z^V3P_gsBkt&a-)u$^=UGiy(QQ-xxLn)9Hcf09@Bt^u5L_=cZHz3z-B`PezxAOJvtr
zfNo#8cuT<<6g>$GV0Z>ZY??7$O{1w7MY=(8n^KspcE1Ro=z-hh!HeH`OOO9&^6@2n
z^0Co$F#bll(+%?e1e6y6NAECybCLSBP+-qo=la@eR>BHJb(D9E9y20k5mfaQA<?tl
zzvVs7V^~%eW06vP=9kCx5~6{R5KnO|8Nh-IaTCG>d=lDqRR)#z-NUdVBLopy7xw7M
z2ITmq$;PD4YbjK&&J$GDqs2lG6~`KbzZH@H7p9Fc%;1y`a-$cxy___wlAcMlEmmQ^
z&12k$(065i>ij{1uJcn17!p<ixyWP4BxZv#NQrdh6Dgv6f+4!630qC{0^|}zUjyAr
z3TP-JRP}X-vPg>~(M#->T*9#q*eKHas%%-=;@q%XXD)Y)TV9RGBMV1gp())mYt!qD
zA)vL$Tq?vMtmdzz)R7?HW$+S>Oel9%jJnNclkrH{U^UFF@F#7{q{j7)tonVU%HYw<
zu;ffw?L^-*Kk{cyv$Xm{#&s|000WjNvb1(;6~etECP7TT=kg<V`W}_O`>4kKv_qH|
zQzQ&d`>}^1E8(@G^s1Dhk0}F$o6!W3p0Kg81+)-t!BLX@U@X}LX`BKVIb36)01^&I
zut>&SZ%mZA=YL6$3LM&oExT%Aq1R=Nca2i%5LeCSa1fBqGFpCW@mog<yGdVe;Oxg$
z8}Knvys;pJquiPU$+z1vSAlPDn2i!?V83bZjuzJmQ~-OvgjiInV}{2N6hT6<_p~(I
zCm&g5hLUjrbJ)M!YRoN|(Mkk)Pufj0QDCdB{#8MLoOIB_S&VRHC_+pMxmPJBvIRyg
zeBR43m-1G$J|*>34Ic@14-_Ru@%l~f%Zt{aqh45aP#sY=vBuJT!9B}>V1n?OOnE<T
zJ-vwY&rTb{GnbLPkVp&B*v}Z4rHf%jAPZB_&})e3&5DUO$Z>0$n;%2e`8m|OvIHGJ
z{y+zNFb?FsK1VM$u@aS#4^fYuQ%CyNw##RkEFX%XzN4>CtgY$w&o}fQlD$B=!_^Al
zpgwD-<cmb={J>F693z#-{n4TRJ|gW$(1+lNcyA2>Ru9&z0)1otAa8j{$Vn(OT&;!m
zLM=-Y;{puPwK+j%WnkddS)%3yu<F4z-kW8#HTcxmD#xRyI)`56$E1lJ6Z5o?5!SZ1
zhZAiO3&rMqxa&-l3ksW+D6W*D_FC#x850;L>d?;&4U9Y>+hlN+%N|cQI}LJmrI?3s
zU=l`QwrX&Of^G|6QK!Q)X4k7}_dI`~P>Pys(?VOvA_ZPwEUjJcK*B?`x$TGC$c(sq
zB(%{gJW<#WRwy$tvTBrD7RNYix3B-pxYQDxQ}$CUM|ZcSxd$tDT+sBxQL7gdeuv2#
z))2NBp-NjqhFT3;echIgbK}pAIaDXezy$c-7b-l4paTdepMpB{9$9v1m9zq7H}Y0F
zmP6oV4O!kES-1Jxh|4I}odMmg%o9)pC}i89xtC0o`jE|AkJ$&0K?4gCRvGB#0p1s9
zEgnxehhabwkpH4jQz!`oluitaMr+wg7S8-a>dh+|kKWT;=wfMG(&@3vJk;FzPoy32
z!IHR@jK*44VyF*X&e7mmjdjjeor{B88EU%o#(l*#)2FGGQp*c?jW0OoI#;AO^TLl9
zL4qjjk5Uz1N(CwkL7#63Y<!ZMh`V!_pwwpcdA7JUpHV85boB>gSYv{Ge+t!=c9}R{
ze@yhSy$Yw`jX8Ahpkp~?*;PP7oM_*Z8&-r3hZu5brWek`39}+YxC4A<^1nn}TNw9q
zADVFzGk7=Wl_h4e*IVtsJf<M2^%49<81PzpSkEukz)=BcdT=Q@Vw6;5xSKFdPfdfe
zMH|v=lpaI4-HxAQAhVWGY7G}~?U%>s8v`o{IJfK>Gld}JZYu=`J^OW{b!?$vBGnV5
z$$O0d2!SsaL|49E&-G(OqDhJfFk}7ugKYp*b4^ul4L!XrAd)6YM}A}r8E8Y#OjyMG
zrxy-nQOEw;Kyj-a8AB12d+yiY20HSV^;=MXp^zH+B#xFLVWKd7F%^BAmoNcPu(a!>
zaXn*BkVMO!KK1!E=gh(+gg-c^y?*_fOjsqsMDE&Jd8+~@nM+*-x96}}p=9*Xd(;sk
zAgV+d7q@SBG{OMOhZx(a{=(QJ$`RF9;x@Q70W~Kfy8MC<S7ByJ4FGk?7G@FS(5)uO
z{wk1GhLHJW7;R9onnmXxH~q<QPz^zA57h+StS|qQIU3;w$hQ)DoySOM7Epw>4vIcu
zt&rw`c}q|5Q(*2Avgn8o&r+1x(HM!5BoP>zX%(~MtWhF+-oDACk?be49qv*k9VY@$
z1?TNTo%sbyQZLk{OwG(Hpj>^xJ%_=c)PnR4x>0tBOu<m!6#483f++1XQS{?%)Fz}c
z%ks_tF`Lq>&9o@j!4MmBpm>i{l01JVH@ZDVKmEDcn6iF#G6YCHaQ3QXtQ$efT76V>
z!PDU`!a{%Z4>rxX*aMf}?sZ|PP6fIZR5FvIqIQ#2_?T>vN(#TI)+e10C?{%o1m)id
zXd%;+2%kugpZIw%hW(xKht7b91X*DyYNOot#9rR>`Ux4*K!#cXofu9Hqe?)mCFxMx
z2_u}~Ln|!)I18x(<I%BW=2`tO<oZZ6G&V`H2`%D>u64dukZK_E5!82bd2FHj-K{xs
zZEm$aKjMqf1_yC5F)Kus%gwK^vI<^4L&k5MN;iQoN4sYbq;{8KU;wKGi4cE8An6>Y
z1BF6<A+i_aTGlTyN>bHqq{eh-HnlY+O>KPAEaq(b)ZQ5vJ%r(JJ8|47V(y->0<-&h
z8jk@cNHE3hM&*d7wO<r(%;4d^pn_it)!zdGRx$75Jy=C+1dv{g&#6Q(ctm_ZDOLF#
zxJ-pVySwcmC)!SbM;b>Al}ry}lv=pPjtLuZ&_?KzE+g2AIGzdh+ps#xq5OB_pGzK~
z?pSpRk!yIWleJexes3q=OFT^Up;hjf>?n#cZUns`2Ygc$5M_0$Vs5S1Da~sulj<|D
z_OL2cPVqWqkH{zumKMA7QUNBIe@lV}Nsox)1S2~lpq(kZG_~AuXJ~DES^G0_S^#y0
zin6-z*(qHl>vT^{oHog(63QZvK~0jV1MMyW@0C~v0*w$t$(oZ4Z(-Jb*`8dMNIRj6
zjJ3_&@^;}k69EX=<B2E%*BC2gr;BtXNV3!W-K51KLC$a;i2>00?jh0ja#eU3I=a6Q
z^a6|@Oq`RpS(>VX)Z)}uwJLIY2|ZJ?HlmgYSacwP$V3`BiP<eZ*EjI@)%EFP&3b3I
zuTJYMs6k4gc{M6PQh4*3s^!41K!;kUA<(zNPsPU+_O$^I!AA*@p#`LCCe)$pcG=O4
zFEHN&@2VH(E@)UTzIATOt}_M(W1yU`FxV{7qN+{_Ahp8iP^&wVC!&ehf(0n}FN}Fg
zm7m^W_7HVZg!scEBE1~T;~bMo%kxQBF%=Cc3fcO8dbXaDLy$pA1)6&vayw!`-GRLL
z?(R4=mf(pu<Ytr|+ITXJSP8&frTap$`=uKjh2I02JSOvYZVw)b9yrS7u)&x(G^o)7
zExU_)kULahu5$!;6B;8)j&6z^gz`8(>+xpeBuo>N^+&SDYqwN}j9y$erQK=Jc1|>?
zae=J>z%&A<=nIH6-I4PJq#O^~$ON`kEvB>!eg|tntFt0&5cYu$?kADxm}ITW4;H(8
zrp~PtyIdI>j94KeJAr}NM=<koWPO~UUo`q|L`&9Aj#WyD%)cUnuS}?kClRziV~!Jx
z*DI9`3cY05nf`q<%+fee1|k1dQ`4cDtUls&$tbM34vc6#icyxb{s0NE2T`J)Nrfbd
z*D80b<^8ld5DZKG5lj0?-Ixq7MXDl@5@sEn$TY^Rf`|zfYA|U{C%9n}qewsFtt#PW
z!Z;TDWMq{xO9@V<*2V2rvpz8l^;(o-gd_?G6(j+G8kojqMsx_=C1mPQPp=O9hLK<C
z$qq7KRn||l*!exTBc%AAWYgfI?PGhEPIWK@zR1QYa*-EjOzQDEAnEqSFH}eAiuf{h
zwfo#U`I-!ij`*|&Ek}s@Nis$L%yE^>RlM0eIbMy7G!H|V=b%drBF8?Z%_6N{jZ6<c
zgu-srt?@S7`)RxDb6z`zD%<*aWUR$BTUKFH4KfLQCmxk#{ygf6q0nvkUjm|h4{vb)
zESSmrJM4t^*74)haG_+h(`Q7;<@fa-|KM;+)^_kF?IS)U+}WHRW6Q|ICsB;2iA}lT
zfPqjvfB4^~F24{bZ#o-W|F1VSopisMu`h`_Z%nkK#PC6t>i(fRZYi>?#Xz!*7m5+o
z_W}_d@O|7RVuSGpQ~Y6cu@QEp93VOn4>-j&TgAa72I{vo?chrv98DHImxDNI7R2>H
zp@{4`m!SGXD7N!2#%7BPq|96-Ku};5XCVDOElk-qEV)AB3`+kPNqnC`V#0VRi1s`*
zMbsf25g8}2RXBKt_;FYivQhy;L>d|iV!(JPHkHg@#-Q)a_Zzkn@j6jYP%%keEg^ih
z{m*dc?=jG)y`QZBs(>KC8D7lztvSJBXN&`mq0N&%7A{;^N8beG@2YW`d;5F=Kc9=w
z!?*JK^@s|;>ndmL`P16vBWZnBJ|6}diz7hy0j^O9L?;mRL1^^|Jm4l1Zz~3#JCMW^
zs=UuomQ(_AXlACLLN$tj1ac5$Q>s^geCY%CD^AinYi!bG(7Gig?ISbAD@h7Q25-xM
zGEX~;Y@FmWWK>$IQQGGlZYb3W^guvRwm~509@+{F$&AJ;B*jfs!6chB{_7*qG2A_4
zA^Zma-+)u({}DidYdMK4BxD}=EQ*Ja>ILIM`Rs~mY6_TC%E4zf;043oOXLM)8%U$f
zhPFoN#z-H@guRT6%oqDlt3~EFD;(KHhNSbj69s2LW`$f-%`d%XnVr`+FwZy50<Tzq
zyy(f-uUA35Du$)ySE(>ZHp3gqDlNK2S-g5;DA7>u%vY7Xj@ig9Z##HTGBr^vD8INI
zU!EjWw}~cCzvV8wgcy>T>jLYXf_o(V96>}_VN5#jVYdA1o8bHPcDnpT<PnbOtsz7I
zPHi%ci6UI?w7dskR4B4H4$-|<3TZ=kV@7wYL4QOBzN0;dqW00+7jd49%_Id3GCFgV
zN;AL3$Ptcs!WThn?G2pMepFboA+>W@e!gA*s&$!*LmM1T4-x$mlOdD?vV=2GiW4=R
zX?oM;CX7`{fdKr7rPke%fwi}1Q$*tIK#Y8S)kEIccKYM0SG*vB9uf;Ai4QRdoLYX8
zB|+IpJjHI^Gq=Mn94iePAflp&Nl?CYyn@`af+(8lTnUso$A%5M^11Y+6rkS^CBV}C
zYd!~IOUU$dU?6LvHa3K}sv?;>t|DOjn6X!dVzBDlJgDQrH@?Y3|7{g7l@e4J{6WAx
zhj%7pt&E8jIQQ7e1n5pZ!k_B!M<aHCtY@h9-u6w7a_(Fd`uG!Vzv;1U!kE6Xy#H05
zAkl!@Nccali#L40P5RyF{VO<2y3k+U!|^#tgzixBGi%MloXW`jEi%X)t^1Y4GRf$&
za-^HgG#3;{<a*}90VARsL@+p(%mo)k_)3KfBm9Ua^lYg}gNYh6wr=4f0a{>@LU1yU
zkQ<WfH)-`pDpC%GbFj8Wt`G(=5BzmtHN&y<WSu?O2Z_Ln5QW^1)Aj1~9R>KITxvm7
zlR#{D24^r?uvzgEkQ;2OOr|*jr-F`$QCR8bn$1NNCbh2z+=SdA;qPO!d1g=GPDqFW
z=VpFc_Jk<?1xiH|v-n{!Aw?i*Zuq5<jQ1g=?e6py3{Sq^MbGCGbu#RbWyi>#67+xt
zkSUPl5T%l`*`e06{z%jI!i8uiYLQWyLs&lfjrLOU$htu@Xd0uv=h)~j`gM*#NqY$Y
z3kA|_NaV>_Zysgtx_Z}ZS}qu$X^1dViqelYrg&aP4FgOWX+maqUl?Cl>{xd7Cz&E)
z<pN`TN!%c=tY?&f67rHTV#wt06WRtKq68U_HJZ5jUXNgb14vhzEIBenc_?_f{C9Y$
zo@Gajma-!450+EEF)&DY?uSUKFPS;?X(ZHRWS<Gs;`f?{y2oSG`O+tdZG_<Jb6mq@
zO6Vmpv2qjd_O(L(d^wUPE_f=R1%u$p$SYZY0y;^y6ulfljxA^=H~-J%f69S=iN=0Z
z`1*pstuC2S$;^t?Mb<{qT&hD0-*R)dLG??22H*VU3Z4yet3E}Z__@I2*B_f#nerB>
zT;Ij~^!C}eoEkqKOi$O}wclSZWEIoXsOvLVdAIod`to`6Y3bc9y!%eS%kH{%e#AdJ
z_s_H8b5vvhFh{|brETp?2G}%kwwH>1*<}6IlXTNuZ$(8#skKU2ef}N5wplCBezaXB
zuAaLDv~^262ZKk4ALhfoeQ^!zZzx3*T?j`;(Bv2(x7%|Q8yX50z9tU~<~YwL3SmgP
z75;xO+Jh!(T4+jnz|FdSyPTe$-k!av;g&o&yxk|gY9%ISt;10@UEkK$R^~R-&6Xpp
zw59M)RZP9HtUnr>GF;(XC0`dw=}uQCMn*>FV5~kD7gumZMAgTS8fbod{B)nvmQ#t8
z?3pr2G<mhJ(#4eIo6Anx>22C+!(Zom1=Lcj&?O-#DEI)($_w43USP&dn|>ucpMRWs
z5;72pOc1zQJ6SQ+PXWg1v8gH7+1@1Q)vH$ny{d8bLoFEsdlkdWAs2aEaT(Ld?sK!U
zvdZS_>P@3iZEE@h{G!e8ii(ORM@2>F%6!_>s_W|m#>dASI%Y>Z^lE!oGQ1l95pP+&
zD@}_JZbH`osPhbOhIyN@$>PF-3qp?CPx%p2?&dyd&tG2Nw4UMERr=?TLs)I$BcPk}
zXtK8oU9BQ>GsD{cdF{0=roxj5COU7f?LslsxWYm>MAU7;0c6Pp8cuon`P21BYig7*
zrX+Z2X{liW%)Wh}_6mkqy6N$xGxCC9|L|}{L4kClPNqM1a9CKy(9lrIWb}&{OEY;G
zuHFl7@4!fHTU*$>ckk-in6WZd8?mx|ukc!}xy~Zm8RljtZP5H$p}1$-l5SS$m~A&)
z>dhjsWQ#@V*^6O<Z^e$-kLYaoIsGC!TJX2u?qR1A)RecJny1kO4<5YllpBufeMSSU
z>#ncwiw{>mx92*Gy3X6!jkE?surt(=CzYR{?>N?Zyq)SA(%#-~yI_Zvmh16LTk6Nd
z;Fs86<f#x8{D1j2)=*9k8;D=~_W$uMNIfS8@&D2XVx(3$4qaG3#5vSVo<a7Mf^_1E
H3)lZIigLtZ

literal 25035
zcmeHwXIPcz*5+2DCK@Hg7DXT;Rs>=J1Suw>29%-*NH?G$T|hb;Fh<>ifTD=f1p$#J
zZPUR5A|RmB*)-{WQ#T#vezrOJGBelA_vd_bX68D%t{jQ%cfarZJnLEOUiZ4!I=7V-
zPp(<DWfg@&StIwW>{$wBxdVl=O#kZ@_#4K<C&Tz-h5o6NviKAKOdrd3!yn&V`BlS)
zLgBte{$1kJIdX|Y`H><g`|~-6pn)dGN9wk@D~BuI|HO6u$6r<@XFcdz>U3>idcpF2
z>#95rqojtKWG$o2)tX%n@G1=URX@`{uqkzYvUS;w9mS^)sbt$NFO{`2@Z`SP@}6nG
z_@m;%1K<8M#^vf#RNL>^<(QW*-ZCZ?`dIwi;f3Ha@k=*;dGa0m+4!S#s+O~V`XIVg
zm;B%^`;EgA^27H#TG_w(^?~~)^4~w6`6qm!xxl6Ol%B`)=g*%#f9|nIC2Bo&{$s>R
zqyGG<HqYaFp<NO)-NiM(nNEodYiIn_*Vh;R{P|wjsW-gz!_msS3-V@%xKveDM{X;*
z=&x4)8c&sa!(D8@mzUSQ(oR1A(&JB*chAi9*OYf!AJ+?<upO_sAOEIi*k<ANro8V2
z)D!MxWn~@ik4w=EQc+XexNhfZnzqoU{OK}F{f1Oytqgg5;RPGKzeMIHJ(vAE6#~|C
za@OkD>zq?lv$!&}Fw5k9bBn`=MpIFe64}DVmHw+2t~kp0+Tln{rIL(P_KrFG{8^ot
z(k1ejn>J+I+S*#R-QHz!bzw4pah^tVn4hRkx$pxnsgwA|VVzBrZ7m*{hP}AO>pHcf
zTjbE82XhavT!>egw_$LGI1gn$61OpwmDP3`DU8?6%`U&TlII8NEX#{V6LuZ(OlZUY
zxW_+o-xRiPwo5Gkv6qt6M3q8AbWV<B-iVi_tZYG!<Cr9c>@;0}sjj<XUz2@{SIb<N
z7$;{^i|cIR+9Lxmif4J;=0@tLd`}7Ou`?#GbMI>`Kuk6>?+BL<uZ(L7tLe8}-1<IJ
zEE<g#9pbdN*>!d>yoT8N`djYlYeHN`iwBk%_VehbZrh}D^^9mbwoKQt({D|=POg)!
zXrlAv(7lT8+Q6|$Vd|d1v1UdT7Z+EGxqYM6N!IkSwc<}T*7Qr?T+5v$r)Mc0>Qlt)
zCRr}t9aMM8UniVS7hSPt%lgpy0i*V$A|BJEa#l(A*nWezcQS@wuQte^XL*K5IiJ8v
zj}J8+Vd4Fz28?95#2T+0dmkWTdf)lm#d)TiPi$hI#9MD}t|LPk26XcLo7#(feDoG)
zYLhO13|DwQSZ@^S<tdzZc&+qQYYz;vs=B&R*LiS5bf~M7<nYU7Nlp`eEZ5bVBF?OV
zjPXyW490stCN<eHj+>jCyYw96^++j~5QsYc`1>_$)^Iz`(TH7P|BURUsUx0xjXl!S
zF4EqI+h}H?c=oH8eaJnGikwCYH*ME9ck3fhV)b^jQ9;<x*U3BE)nD@eLB`c(u2a~}
z*ebX_#i*!olipThd=^bv$C=H!?T-w+Z?EKWxNyIGv1Xx>9+lfYFj1=)>UGk4Yi4ng
zPIqdhV(D8BypAE016yUbWVt9~w6k<I=NTI97abj)ZJ`yGS1elcs41D8XEHwD<PtqR
z*&7{N-WBZ7c3ZGqoS8G0ERIb-pJI-8D)UZD!hh5m<WArQkImga-`KQjtgB3507o)D
z;h@XZ@awihPqC5gbSt;{e!bPpR<3RANNk4@8{6h)+H`MRE723CpeyWOnwFt>?AWo-
z3E4w?$NOt7BTTW@8(eXX<+!Q&V0XQ*Sc^-_aFP$zETV{6o2Z97>DKi)4NlDTCvGY!
zElp{c9{+GGzC6FpLm)Xb*J)z!J}r9Ov3_Uf^xRu6%k?vp%$B~%p?<wZCzg>@S%An5
zoYu7J>Dj^bmJ|8jW%J^j+JkgbFQ1|3CMPFXx2SL(u<3el=5=rTNek8jIRHgA%PqNx
z1>D_sjE_&h&|||0?wx+v^40Il`<rU!#zGfeuJ=pN4Zo?M%4qi%_$6}ipm{?oG1#_F
ze?1G^=9CI)u9dtlSI#7K=ww{^?c2=<E?#e`EEA#5jQ6zUyS;z3KcdOB$(fmJ^+|45
zVW4GR=EqC{wK!UmLD5?!x0y;iUi;vd#f8cJ7hbwM^u<JrN=%lCwcL4f?AF_=7!^;u
zxrg8W@Kcxumpi%O2s5X$t<fmNacADbM50k*v|{j<rYzh34>JKy14%{t=}mT?*a5l(
z(?cME7CP6l>tkKr51}(v(QP$amgcq#a9!LdxoyvV?Auo(K07y7Vbp$%L+VgTNr|Yl
z7mQqxODRxP&^l8)+hi%$$3y8Q<HW8|7i&>hcsI?Uv0E~8+qgKk9NNuSbCch+CeGMu
zq2Ttql#wMXx4x(sQ9XA~mhI&}#N`=RMqLWEoEiCaN;oa6dAd84xAdW)=eA=v3sQ|M
z^?N@>!cjy#d9qEHI#GRiq+nf2rf}YrzU1h;?SVRD&N{Bsok`Z6B{W){VW4?i`4Q$F
zyw_WtbH31ulW-lXv1)}{mbn{LqE6o)tB{e&vKwq@({x*$ZK=%`oR8I;H_4eBElo11
ze*Ug|j4VQ_zp#(GuH%lKia|fz;<l?hVcKZmBc<O~aC@Y|G_kO+IyQw_9Ddxh?cPbA
z+f-Klu0ZFm$3|~o-&oBU;npx6YRt6y@bk(`+ng#wq}m^9S=>%<aXqqS%N9IOk;euB
zEKkV0W)~Xm2N9F1ooT_BeXQEwUP>SBw$3~Z>!~&6OuE|p3BNqYn!)|d)w&-J<HU=r
zC1`J6x%JA?_d((Z$<J^eYKgks`Lj*TaJR*U`bL{_@m=N8vjYy}A5TUkBnVk#57N)S
zxbX8KQPC3y1_n7JZ@H{W_C#MC3&mk}nCXpX=osv;sDFIW$gthVrDMDK^JsYGGa4F?
zb6xx6(;HIPVqZU8lXsZ7@+rh^VS1pZGr~tYt56(PWw?!R)rJilZXiY-4qR-ssfZV|
zA89MBXTkAW4BPc7<W|o<P*E)IlEQ`s&e@9OMoRImiVn8Ze3F!8b$v|f@aI3whR&Si
zPAe{|zZ-1Vut$ptXJ$4gG3}D~qC$H1&}_)UprxCsx;gf3&*@NUYD9XLn5gJRMd|5|
zfx4FIa(mIk_A0I+`23ZZOIBVSW3{*~zAo_-(2`onuxd}9hL@g+qFx(R*3;9gv#r%h
zF$l45b}Z`?@3?y`**xNemx)TYJKcdf?J|_vZ82O2$EB4x@iUy?YR`>=CRVP}p~72j
ztIpIn!i}3=w>9gl$b8goR%z1;FJ&?&0l!;O<2W}`#K>VU)PC((j1&DKLcB?=&&Qa#
z`bo;WFQzB(Iu9gS4Zm7x#L&$8H1XIdr1)*<^n0U*t4C~;CJ>!0;AM9Uw+tIw7j96$
z*be{jqKV_ZCf!U!bfI;nOkJ0)uEXaWhUw3gf+a-knjO3Cs_lB7+<IFivoPYdU<TLM
zeo|)U3~L7G?YC_2BRFXeqs6<n^MxlTAK-DF`ur?D-h5&TCg@yMRfXs;nBMs5GY2&m
zNGnDq+OWHX1c<b)nSIY=^=HO=IXOSZswX~@bhMT$XG&9N3PftPQ(hUkwJzf&04J@!
zd#6&!Rw-wenU$-ChK9>zc1xLV3!K*A^TfLDfr0vzlo1xgvZc#*#oDcpymVfzh7oD;
zF0tfNRa1NW`4%q^Cnqe|uq>e3Y_>7eCgsIw%i?@XglTQUezUq4w9CyNzXi_@Weaop
zY(5wjQPR9mZ?>T}pedxu*EW7vjYg9G@oaBLtbk!V?|jcm^R;&@5%0|;oyL0%&DYWY
zKqS2Mxe?xP-NeL%xo$uF!TYy*(<SQRux+lUXv9%+Mg}HA7sjJSdEHdfs(dC9A<Sy*
zCr65Seaz|gQEyC*i~aNGI(t4vo{F^zpdYETW0<!a@Xnr(9Sh9+Ch5xO*AvY>^;-+2
zjRILoLx|dewe^5Q$s;#s1eW1_Be%Dj-y$x%ehS}dNsl+yYrei#VmBY3qD1dgV7}f+
zfBosv&a$o+7-oG&{QLn=bCtf`!oohyxz0w3jCB`JumN8E)bFc>XgYTFJB0G*Mjcr7
zMh5VQfjQ;yjL9S2GK<r8)#JK&(~)=EeF~Ou(*BMFn1L5>)}GPP;RYx!lO|r%ER;3=
zD)_)9dC`TAT{4-)v()hj-m<q`&dRz;IqAHXmCShpMg@6N#nSe4+f)kZa3C;y=^D>9
z3&b$SzDw2*P3d>iI9YI_<(aVpaFys#kH9M=1ny<K@TP!k{9IydE^p@p0M(N%Xo#f%
z#}(0Nz&6{H2i@k{c~eFhmifU_G}8}ZKXc{ma5=@Oid_iM-B91WagFm}nmY03XVld<
zahmc<On6zkY(H`52Tm!caT@%|Tk@WdV2djfAMRVfs?5T+#XP;KK~<&F))J?u2TQiq
zvE!aqnMiHHoy`aJn{ynKU|;V#t;?hX8a`Z~5ItUb>k_w9&l7S7JpCChEv~e*G%Qgt
z_0XC}fzp>P%Y($tr5CzpMtt11W)EeVrc`j_?2G`M8hciM@Kwkh%``sZ8)d0|HP!pD
zQJ}QT3#nH&m15>KY4g}`3Y}+Zvt6BIcW?w?E&|?_{)9)SW@hg-g$@lyXTzM0W&ygc
z*E{IARU=VQ6pwt<-+V5zZ<(Y?)wA;e1E&KH?eUiAduI5A0mRe}gS-RiaUxMSw@p2N
zrrK#XU+kig+9`JeM;F><79w<h8|(t;MZ9{HtD9n|5T~7afJ6lxUhnEy^&-T-WS^)d
zHqxOLEQYVTt{v>A-h7^u(<sR5MDeV=<<Lm_#awQya?!!y?2E;|qKRWe*-XQBEOTn?
zPP|gs)K@u!%DTEdMcnq<tQp`fAbk=RS9bz52bw3w#ogKNBYA)?VDhv1%#|o5m$r4>
znl^i%$Hp2oW>}59T*e_Q<WDfh+v^<CKM>b{4nbeG0Eeo*&cMg3KQTYAK99MkD?mCU
zKAz+T83W0s$+g6SB?0CLyea}XjYvw`KRWHQe$|Zu17_}|#iZ3KZV!4PFjez_f+vku
zK^?N0NvDTzFRzbMKT{DXmRQz*Xs|PT$l@!!#)bZEt>)2DOPByJ$lFw+70aaI3};0G
zk>C`aENGyAJJwy{<02iJW<#U-vUWFV`uY2x%y*q{yB|7tvC+CzkU)csnZDSR!Nuv&
z#rlC&g@A~LEkyv}>Df0K7TJPap;tf2?FWvVD%d5>xopL^G^CU`&3eL5ok=96HsXpp
zn+mCv+7!bwlZ||qeMKA8wLcDNiWa_qe6U@sWk!wV)O4%_aO%z$k>@%b+xp*2v;}eR
z*OwJt$X}ez7fnnV@`|OMPn;krma-GfO#&H6$_Tf8BPS;(4oC%ZhSj%lFzEGjQB>!}
z;af5V1T79cT>S0LO_BaUjeUcIp4n+J*tgZfIX~T8!}X9lgn(o$YSi)WUi}o1it*(B
zK<Puwa8IFz9=|ltO~U0fd(&&5+~R4_=hh0`aq8ir{)6)oyz{%GltOvnV{K$*=cX&%
zyzzZ6&UKnkcu>nl*EqDyb{nV&>SU#AEb6Kc1024rIr);Ih~+8f@enmC51IlD-*V9K
zJ&pEows$R$<Y8y!kHT)#rH@3-HR*!~L#I!F?9-gQn@oG>w9H=g*v)T?-!gNT&)bB~
zYK!JAF3h&$!>6*ntH)P;wx#-Ok(4>D-$4N=!nl444{yTy5F39S&<|-9F}Lk}Z9Ne_
z-W6^OV`o%VHxL_`*{x^aWJ|o}gXF4swWNxA>SU7`7lLe>g5@}}L;)^dm*H0cL-h#(
zSaJ8Zb;EQO67X0lp=G+ENaN}=B3-jOJ3IGVG-l9f8Qmey1UZ+{MRI47a#wKh1bLB9
zg~v1Q_4W6YEw0(AGo1xnngW`8oNF<2G=@e4co$vm&ViKMVXRDyp{-S!W|24DrPeKt
zv}yzZD7&o4DUpE8Y^obFjs1r2A1SzAZb0O0C(!kyN&Q`c#GC`!Sq?~Ld`L=MuUdY6
z#TpN6&%wYW__!g*kbXQIIKw<$=UX-&mkvr46g(E7yWbUwMq1`Za@yO!cJRD_6r?_*
zxBB+6TWig&7P9hkk$zle{q&r}ZR*YO*qGkI`j*+|$u<Ji>|!Ts`jSh9JmGzdzS$M@
zosaa)<s5iAjg}g47pMG%ynyQq2{!{f@}|)O0s?-Jc5z6tMFtTtA?n&4Y_H$?@|qPw
z;ak(h+?oOBGkSWwNV;wp-``dAXupBxz&hvG-|V{B9-EN0S16<PcZYT_F@os}8hBEK
z^XIICkR0!a|7@ckTbNF3v~Qu-tG*zQF+Ru;+j;uYcZs<y&6+lxi@TFuftGKv!<!;b
z`gku{w&G&^F^Q6c6_V=+H^Fi$d9bG};YS}DJG&5QsEyNz;I&*0Yd#?x1k70P0ALiT
zqf*&iNfIPcXTsctwSVK_8D{(_tGq~>l@zz(*eOP{DF}e$x$};T^OF_c`2@vFT)liw
zRrNUjA%Cc<R<Bsr6aVU|+<kp~^kcMA=rbHgI0q8?d8C{Q$XOex8Nm{yPvtfP9JGt&
zPb;t_kUF@{*SZxd9vNJZuzm(a)ul_9&ZwxYyS|dcyWK}ddMJN!VPpR^;`?TxF?-fb
zfN8?*Ys*%iz`Y;%`QhHGE}+cHouXYouhzTFx{}J;^3#Q*k`q-b_2S76BW;H#8*L2W
zraeHg4VcCxBzP~(&lc4s8MJ>q$t&0}B6blWq-^P6e?qnp3yI0b?&hnN3Q3iUM~=%D
zATDJbPuQoZfP{k9rQW3Y=%?ROOdiX)xw0JuEp7hQ>_mV1W4$$4x)(c$vB4h8c=*#4
zP3N!9j`yz1b)K|fW#Ucpo{3afRTXp;->;p8TlN8IlBsPGoBexoR+hxVq}!rc_G?E2
zI#N)Q=(InJ@NQ@g0tPDrpVJV>YpjJZQ();f%P`!=ys3?_vj4Z&e}qp8%)dJ+{k>)0
zwBa4@BX;*E#mC+sH0XN&_;|cQFbuY6tIcV<9TQ0D>f??VJFBXzi}e>d6G(yFuV!0X
zWZd~~Gg(<ejZhY1qC+$j)oo1!bQR`;nJ&>yX?QCN_pz5+{7h<C1E63{-`ssA>CF0m
zLS@zMC`6{S0py9Iwr#bl)w^xCWD(gtT<_>J5ER-s<xd@dA1Wh55;kIj1^`KC5P1Pr
z2qq>QLwGLF>9KChvn_2TTUwYZl6iYdICp)^0xR7B{yV8mlp*Wr=xB!TU|)pSYez))
z#<38~f+@C10JPuf4t0~}0azlpiO|b;CAR@v;z2y6+v22K5y_MZTuxp{DP&#aaQV@7
zB36%`KNDhfo#Nqb*}fT3Q9B8%Mo3frPfz8NfFCk?qXP9`x-a2ATUxEI7c9m9LOoG;
zzwxK1Uan2VL~pO#WtdoWXY)ElTjt%FvubfRqWb2H`?jt#m7x}&MpobWg|0^yrP+>z
z?cE{Czi9EquRVHz072w~X2@O&0F5FLS*xd&q^B=#b!ffD$+_REt-z4Oms~>^N@!i)
z(_xX-8%3j?2g}%q6VDY)Sg8}TvLvEchAv#a#U?3JeL#@jXBdT2lbdTcX<zxd!#Z<K
zITcB9JNPcRD}?p<Y8D~Mj`o84ZC+`C{Q23lXS5ruH@)=%q}|vJL~^+O&OttXbKViJ
ziSJk9v+_zDfAZy-zi(<jsq54mxm!TMbh0RG<rQ027Cu3t+x?G>dE`oA@{6P4+U#VB
za(DNVKmX#Z9P$gw?e9qxrBL|gUy^L<pYefbLXkp0iV*T!TS&d{K-e4NB5D(Kw(}8|
z;<;+9yf)`iD=^$A;5TGt<1|v<wsaqjCJqN_KW7qaQ2^zFf(=VjLl*-_T~)NS0_WW>
zfh8AHF))Z&{O(f>K~erfrfwn~*5chqJ{aRvUd(SnD4cg)CgGK!lWmya@`V)f&){=s
z31FF6*=6}nJp(|HjGV-c#8dp2y!(IQ@;*Nmv%2SNFbSTwc${{JICedLBxb4WLXziY
zYY+cO$lf{-&8KvD$h#dq^I*fO8^6SO?tTalls{D{+(uI8)*v`=oLOV%gj}=w<Y$SA
zmSYPq(gYA92I&kTK0ctteHzJ6vHI&zDkvyi?XMB!<5P<}&l#&0zt`$Gr4ZiGhXfWH
z?YQSg03l)g<)AuZ207|9+Rd9c&*wN;-4^)WyE<Mg{aH+mab;d`->PQ0`#TFk(nTP8
zTP;j?7tuJkhvTeco7&>jn?1lRhgI!ho8o$f=x-c0?K}V7x?PI64e!G6EfUSlv=EgD
z$aw3+tM`1Ayd=dVp;GdqIot#%rxmh7k=6F>N|HpZBIoeNRW}|XEtQqsYE{6wBY|LB
z%7xON^@3X-?$h`lT*hr0jgSza6%6nVBd=Er%Yvr<mRsVGxAaU8Ay?(K_mHcOj$HL{
zw{N<?9%()|q562|T36Q7Nh$<#)S#~_hTOamo;&N~1c6v<q#aqvQSQ9DzH(E9S>0Yg
zKfg=u9v&V&4QZy|#4gz#BBZl0Q4s>PVwlgQoK@3ZNaQ+bYHAvxy!*k6E$(gDj!8Ea
ziQFi+P|F0oN=<F;N!bEI3$B!z`gyC(`~6lV!AQ4g3eU@vcAaIky#3>wb#2YgOfSON
z0vIv+oWN?^96EF;HN$+k^%Y#a^ZI#H_xOAHve(K{4cm)<<#kpZ{a9U7^DyhHx@VoG
z{?6NfS4f6cob^AdzgIvYA}T7X>QbtlkiY3zUNB#4JUkSQ<|54tG^iKkI@4QEC%{X}
z>1y2hBtDDWiJFYghgxk&t%Ox$ux!J5ERt<++;?8N?|{2v4_OiLh|yJ-%&hQrgFttp
zJO>R=d)l1v-nu!x*~t|7$}SinyZDuh2z2#2bEhkc9iwZ%57q=g{$h=&gBf&|`0Yse
zI_z*jULYvA&<W<sUv*F!skvzg@|?>|Y^B!f?%f~G;LDOTb)1KO8d$%?5+Epf1Z+!f
zTM!(*Sq}1ewPnrQy$|bOXFL?tdYJZyd-*ft3V#=ArprnRuP7;dt}R>Uh)0P^562d)
zuAn%y$cq<U7L09tM7L))d7M*MC)pfdn;wU@b7GYGHZ^s1%X@5BOp!Pn{k6k(K5tZj
zK+3dUTdCP7jVcnTWFW6VspDM4vVK$z-q|{jc*_I++!GY{0YVJpsCm%Ra>b$)0yl~^
zT~(2lO<DPZZP-uaDz!^Ak!PsuQEj!ayjpHmAvIYbHQAWb90W`tPO#JW=SZ}mD7)aR
zy=Zk-McnPtGV{ig;0Q(-EeoQ}z`8xiFTdpy-A77PNGW&Q-X@n&j#tJkZKly08XA5O
zv(Uc7C6-VX$^OOFXNr~A*+tpb`4j)NK=EJbEdP1Y?ElAy++bt^VZawH!2fGu@c+%7
z&3cgKUPasmaL_586=s||Syyj^e|(trt$R3NT<Kdv8mg$Myf&eDMCZ)u)ea(wi}=Iu
zWt6c~`O@_&fJ({YC!|9y$MTjomp!QHW=yQP6|V!Q$Y#Ds!3$`s&4c0*5gyJr%W(<W
z4Px>4>R%~u#kKW;=TTHWvwJ<TS8BQVs;du>{+M}x3=zx!DkpFcgbY+FHKx>ktqMSU
zb#*nU`Y+Wg65b05G(!yI<RoeI-+{n@%nhXRSAJflCACEO!x;o~-?IIv1lDv>n>MjM
z*8d5>`Tye){r9m9|F6qX{;x6^;u^fik@jsbN6NQ*{i=_^&8YGpk=Oa21mB|{uQs@+
zLyP36uDcnBn0++Q<D`>+JYp;9mqBQq$bm-0>izzr2U+XM-@C8<xMx{2FkMkvtu7ZT
zsSX4MM+&i_LD;dYCGL}e3SK~<hi3;;BI4rWsyfMal&dtwO~cY)1@?n3n<`Py`(8vu
z<R90)x%%AQexk2>B$r7QCRsXR-5@?97xpOY$m<;4PrwYjNPvRThXmU^RFH|+vSQ#N
z^(UL`Eo*09h?q9iJcXBnqfle}JN(S04pzi@&E4dv{0gGNVk6<Y!}05{d4py3zi4x3
zbd;`li($O9*<a69JPMKnqNo9{m1-J~<l#s79?eG9M<3TisAPrO#dE}f_p0sts^<Y1
zitpiW1Kd_DxrqGll+IE!D9n0xBiStO`XN(0p={R9W8<1kZ6TrTf#e?!T~;PM`Jd}y
z#u?o9$q!{40g^SKMsev(^Eag2EtU~)&uk|2gdOiPx*BqmkD!@ow9A(-(>4gGJwW2w
zMkHqdQ?&Dp^!&WMJa$rr6vIyab@HWB(K?H@^o`|b!Xm&AT(rq%X*)LVS=uZ!->Zad
z27;uxbn_;?twb>Znn;w7FCnul;0H;E;T5%92QI!_kuIo3fA98nZ&d)xWzYImgIUKZ
z7fe5;p1g!4xyABm^vBe03nBk?Axnny{3^Qd&)d5?H|(=TA`|@f?OQu@wk7;32apOX
zvtf;eGZ;|)xv3FS$C8<?ub^L0CEH5jj#ZJo7%$TAH0mV3i~Plh`l~Zv1bSg9{~!;Z
ztBkjrD=aR|w<Gruu-!~PM>*cT>i>Yi@87q;QvWlgk~;EMAH;3C3g#zUcnO_2kUQB-
zN&+bI3m#|Z!Ia{?PA{{ehncF1mfpb0Ns6o5D0P(4m4XiMR}}AjU>MqutWI)qZj5lZ
z8?v_&J8)Nw?GfBUPHbhAf+u0_k^2Xj2Z~uXJAJlb7wrtdY7@Ak?@vnHln=%mq*`-P
zjY%1KwUW2di~%juR!&YFd`D!o1-?5@=}!%R=Hwhf1-{@7hm7`sHMxu;5g)!X=b%yf
z`?n~197{2;)wWR}vMN&MB>dV?Q?}6)OIeaIsr>zt#Jm|3(S(GA^0%uu3Ef$~dSiic
zbnwQ`&P9m<@TL#Fh@fZFf4s~RCUiPlb900rMIoAqg{XPpGPv@Qe--}eV=J*%j6}VB
zvmEHT%;tz>iH{G37azkh@R6cJSf`{vJ!h&1&+rfmhxHDuf%^I>A~T}VP)Rrxm=Cd_
z*&GVTIdy-!XU*dr4n<yE{$3Rb7LN`ZDU#~0VR_Kz(AmcBaF8oyU5VMrVti~q@~<0Y
znYom7iO=*z7?4Vi<5*WA)JnYIPRr@kK~p^~%Y_~O+TU}UI*;%`Q}Q3LFtZ^C;Bf>R
zoI)@UuiUtw=15vV6EQKvEPu3rdkBh0G};u@rY!^4OxatIn$V*@d?Jqep#i*J8_|&Y
z38=q)d2N~2x0a?RCT)akrqNJ;wv+v0Q^9JtzRKa`B>pqsZBecJ&JX*;Bi0gfl=7i>
zL+)|5K~S_#d`xW3?eIMnj*2ZGpHS}jr?m{W3u~QEh*RFZ-Xrq@o)n%G33gQ^BUvd1
zBE5Z}1cA1%J{lbzt;0_8@j<d%2=iexkVLZr3gwI7juc|qV3`O55EjAmLb2B|HjTfG
z&&G}QKZYNV6k68v2O*84gRJFaN-y7sA|!3<+Z2T-Umo*lIGqqh)!F^cwqN+Bc6pxR
zg?8S>cW()2bn5&!<h_n+HcZ@2%ZGNZf8xE)<cm}qO(VtdxT9m~W^2H9Q1L~G^^d6*
zRnN{0?)mbT)JUBrOl;1*H1*5H;NJ5z`n`B%@}$0)MNZ;_oa4s|8h)H2a^Qii{)9-O
z<>XdBZXV3~eG<Aiy~N{hh`JCpF4+RqRKG*?6CBb4Z>N)O=cSwLR4B?Ys|-0nE}}I1
zH#CKR&fU9rZ?e1nySv*4HQ1b@yIQJ1t9fydxQ$0HKl_7%yJ6(%mM!|iUtVlA-Q!jq
z8xFjf_HbR!lz>p0)UFBt(U#dA;z_hZxhs>?I`D$M&wSZmBH_388waC`5bj%Bt$)p#
zXEC^wB-y|CxMT?vo7L8`FxWywk@ejuEN^^sbB!kuf<7-%Mp0U+u{}$eV84>D*rV*{
zO*j-()KmuEu)iXQxnX{rDe4sVq9Kxw$yeAMM%eZBQmw&K&UT_;%yK<*zuax{+_-x>
z5xZrChlkgdvh8kI=^w|<Ur<YJpp*S-Q_=7;-9a{rqOKkHPI~<9a(fghMo>E4%g0BA
zK#x$OO5$K&b5!=~9@g)W6WKuw$9ua}D0^QXRN&-bv_Fqc@OZQK$Uex7$T5f?Y9TA?
zPs$FI4}0JVxdBOF*#kgcvF$ly<&2JbxR9h%Y__!Lfk$&V`yTFs5jcC0u-z}zWg|!T
zjd)K|=TG|bWns%T53yI}e@*u9ojF!R9{sYJnc3Ka%yhAm$9aVj)Xd090bgjwi9GNl
zgdk`Am&9vkT3^b6%Zn7cE=xFmS!AKRt#&*mwbzBgA~nk<CY?r9HV<EXUw4Q|p|3(n
zTy=>Otc+?5GDTpvh8)X}%bHOc5&>5iIN8#$teH^AP@S-|(Le2zMgwLcWf$<uHMyfD
z0#==V>d2kL?n@xn7=Q%ZXS?~sbI_5*&C_VmWje0&jBkc8h#M{)zdX9D3LNrzJTW=9
zLOKmRl^d}*sG-K*hLFVv^rHZn%DYqxqB{pUs_*}48=emYYhKl*qqp`53s)>{2Ko6V
z{rw=3fB%~IIXyil^B1}kJdei9Y$&#&@&qlZ*{$$(SOlbzJ7+D^S&brU<L;9XL)1To
z<1O<XX)h{50nz~H3f!E1sz40eRXF7DsXtajhkS%x?jL@`;S;(z*S+6v;5@U9y}xER
zeV6CJM^hIn7Uz^?Wr>i*3W3u<d28!pefHR4!G5R3cBZr8`HP&ZWKT<4Q`V?xe_h4D
zW3prG=~5A@gQP}4KyYg4o(BD*Tc<os)eLt8-|7z2cpOmr@${NQ-)o%pG2PuM6#irX
zPjBAO&%Rl?Zl~u<sYY$Fif+By_f(z9iOswYEjIaGS@~kbjBcE*ei_J*VwO3BMf4AS
zYuE;<{F5q2PHAE`j@G1_)fYq5k8x@>he<K$E7;|ymn+pLUS`i_Wh2_0+^~!Ndb=Oq
ztY#(~YHDf8e2_-+Q4GDw!~2R7F@v+Emh98`!ej$SPVf<XMH`#3Excm-1vH|Vh#x=9
zHfoXBzEg(~?K_XOOe^xh<5X3vQojI@*UzRl<Pc;2^yyP_2^CRc><I^T_x$W+1^8h9
zwcHZtoW3Pr;vS$r@^aR4k!9(ZcZ&V3YA*vw*9SUoc6=$6tv^@jfBF1l>&zgSB)?pD
zd9cb}zna<@_B)uI&DMRDec{#b1Qu;Zas&$^=?iM4J5X`mENXT>0caZq*?g&!?5};M
zF}Tse65P2Nz1~o~_v!rumr5R_(&>5KEJ!H3I_@he;>?VB>dl4czWv7)$+B-yGYNp~
zXk4m=3u<08GX$KCc|fE(nPcTgDjOOCK;9FDshlr_RLN3r*^l>34hskyEag18^&@Kn
z`oT~puuX4GJj0o#g?P8nvN-EDjU?&;zj)Up18cbLD0pT+9=3ZA+o|eu`zGu+Ix|yT
z?g0Fiw%hy!(=%I+yyF~HL1x)b>5-8;Ai7-C>9t}%u8CUVGNz^LSRj26+VQA!d*iJa
zNk!j$7NVe-zxJ-}n*C@;u{i^5^!dRBE%UVMRX*$pcA<JNk3(QUKm)zMzh7PEXj98c
zIH$P3T{ub9lMFzo6!q*yh*uc&Ti<Rz`}f<Ujiru0Pb#!&v%-t7OiFp#wa%B%RV9bn
ziUo!={JAyK)5NPSs3v!zmqF`u68rh%H1YiIOO{!qew$8}f+l1DV*AYLu0SHehj>^D
z;4;ygsIrkrnJ}0{noMU{X#Mp7$DA7>M9~c(Z3gDkH%PL|fff(>Nf}78ZK_q((~5Sp
zZzU4jrart=4on<T?w$swhE(x<uWKlzU!$$pIl7=UK`TRcsv-Hfdq8!9>I4yz@j!mK
z@BE7&35-Qi!w<HJ!q&x7!SvJc+6`mlU%uTuS6h?;Zh@5XYwFmG`z*{(<Imv1sufZn
z7ShehFn7MMAR1Gb$G&Jzr!cXV3TOKdD@L=-oXMn2=L3}n)*{LYYJa^ZmI)`ehE9d>
zC*E!m<@_iA(;iO(+nO<;G!DjAp)^uQr=r_Q)#=MI4Nx6<c?4*IK8UnF&TbKKJ>488
z?UwMj-L{9E(k*{1tGb06b58Sbn`8+ccLUuY)yszYz`#HT9i1~8e}6ilre^v)(UaRP
zLaUHx?hIG|>~CK^nG!m0#$W+0<u}X==M2B54_3Hz9rWX2L$EMISc^B0lmziWzH@!*
z`6bXV-?z@pc};a2y&u|flgr{)->W?zwh_7|vgpf+)4+)X`A|GH($2_}9!#xFhQ_7K
zZK2!kfNfvZX|y?rH9HvCwNNL6pqw|K_2Lo}3be>F{_FZRgI3Wf_+C!P87*-ERzKx?
z`tgAsDCZ5xPv!%cT7&dS{ks7Pjqxsy@J^TNEsTbUI4*uPLN+f6l81;=7^Z)^xoWc6
z2QPmJo13Z4SA>F}Xe3E22Nm24L`QEhiKVAGvESjm&EOUX|6ZIdBD;p{C&8zZ{R^}8
z4d%Dm!Hd%1|D`IVti1G#zToF?a@Yp+m)m^s?+=g#S^uwC2a$8m?(&b1*jte7?li>y
zN;30Tp-;Mxb9NCpM4ZSyA$n4EmL<>V?(zKg%OjS}Ia?vMX3j34UFh*FGhb%1#m&tQ
zf=CFVcS0Ag`iR}q88&LdH~dgO5{VlhQHK#D0p^QBmk0y|k5PgrnqH{Rx}yRuO7<6R
zRT;Emo6p|xoi*$#aJfW(9LZUFQBg;1Mm+t5*hjvPv<}|qCzWhXP0i<$g5>FrUaFF9
z)d>!^Zx)w3E98IBZDDS&5*r^VZ{|AQiYl-<V+eKmKVikQATPdQY1qD;uj~BGCF0vg
zzo1~hGwXPh>3i{zn~&I;`E~%f_pOxZ5`aWWzP3+lVXFNgPP0eZO0Hx;Qc{j-FnGDw
zcCaC>VLL@64qXKytLs}u4$aM9YQn1p0KgU+Uzm`OJezisVsh@*TJBY|*^Denx7w@Z
zsmdU8eguN$BED%eXuOpY7SIN2fSp`rc?3|d5~X{2?{B!`&c<}K9O26G=00KwAYIL~
z?Ae)M3ESrlHlI-ve*kw8vA27Li57@NRblP#9j>v?f;+AA{@B=<Q&Uu9)+*><N?r)n
z<A<EvPjmn}=%<}r8paJ&(FNa@$ok=^mu7u-R?RKQp?@N|QFS>-Rf1x$#3hKUaX<G}
zF~u*ng#2x$JN>(%8Z1FmN&pm%HeL(A5GiQlI2{FalBxOquuk+>0GnRalYNPX?=d7P
ze!G5G9kX12SqXBi#0I7-azIsaVddG9PHR=YTA{_CdnkU$z0cKI)B-l7<;`StEoCNW
z%o2(O$z3U!;<M`MhM_xZf!O_;MX|wf>KAp2{`h6Lf2r%V{&l0^pTnfm=ybKg%n$8c
zA429rII6<E(hTA>rW;=h^4i;@n=uFi*_gz{MAms?G?c+`!vwN2nb3hBfM{<3A%2>b
zo-a7W2CX9TI}=)Uq=(jSKERJOG9-fy5UO$lGJc;#s#=T$)0ZC_wncwrYG#qMr1PQP
zBsRXL_VSmnkJ`DEIf^%govJw=A%AyN@Y!uP1Jnpr4px=?-G$uew?)2mJByz9x2Gga
z^6!Z8uMLdT7sYh`LksXP3Q+$kSRfHBe2<@eOl)Ogj85s|EQ=Y;x7$y?nrF$g-{62$
zorT7yR;2FA?_QR6*<kR8rOYjT04$`!G`PQ}gm-bOXdk);8Vo2RmpujP??85`_}N%O
z&I#A_!ix0yNY8!dQ15I6x=Sc=yw;$;*Y7#2?jf!8=xi^9%Bs9AHOG;6YVryUCp<xN
zwGU9a!643$G+2IqeS^5^p9vv8!y44`vk}%zeYUrIx#(Kb=rA`u7Hd6(Yn}&fdPeQE
z&NT5cm;2PvpqXZ~EG<4WGxNERWq-Iqk+&%|?q&8(xR7L&l}vn=y!bh$%b-1L&TriX
zTW}lCSyMHiKArU&TgtUE(e#>T6~3abC>`l*+(c6ImwNlZkJG>QI1!ijFTr^K=@I((
z2gu$W{h1Ah0|ACDGCLp6^(wiQfY3gGRA>NQ4x7cTF9L$bO0fy=qlH!HTB&Hjwnj}k
z8Va33XQB=#11KQ)<j(8~_asPluOgcwQWh6Uk$r>1j`pKR1Z<&IvJ@4NAkxM~hI(L7
zK<MU!h8?4&!ilU$<Y_1yR1RL7gfLg>)TvV}q^1OMlS~UB5{@L0wB!M4KR#r97Ky#;
z;F*5_=cSgJ+U0};;ul9BWV5}qzmN{$Aa=+>K$xLx#6}yJg+(|WKv0qHDt@^c^hms>
zg6~S_FD9H&a~B)=Tvoz|TAcdh`Q<<~sp#qDzoydb^ga2DN$1sHA6iq*6ZU;qS)hLh
zlhgjVX#K_e{Bn>h5xBGE_xl~7D=R<|s5>wqaSs9PjC+$>4IuwqYN>LBgl*pg4r<Ra
zRdM^tp{7`C-3#p>P%D2fm1m2FcDP9sy_j>)8w|93LEjam8NOWc49-xM>Y}wNK0e-*
zYNqg2b5~avKXb)RKMm;~I-Q<yU^owg2Z1#?0GF|ncE{+<yji;7r6tO846_u4Y921<
zi9SElC95hfraVgu0%=9*P&g;B?}l*@K%(AT#SefSj%%2{)-VA|wc^I09T&CL{IbJj
z(RtMzGh<_8hHjJI>i)hylTl%hlRS<W0qmb;U`r)Q<6>G7{^RqTTSllw!u^M}Nl#{0
zs5*b;9&mQuwe*udJaWFZdT!LnS%iwi#Vcn|b&@dj1<<}nh9nsEhyJtJ*izu$&-_eX
z?nRIz=N$RmOM1RUu7~^@=-y;7S5S`ELUc`-1-Vh!N@C@PqZBR=@O>!T?2vmXq!WNn
zNHs^k%X(0enmSACsfbuT?n_?$+~P7;HmMzxor9!dyW9cHc9;-lS3r(BBg12)7*$*y
zsM{|$+83Lca3vHe+AcZ5MEw0jT6_Hn(Z^p*n&>8*^3crF2>M3OSIKqse!s0KF=BTX
zURX?SExO+y$VU>PX(j{`l~q*__#dOnQjT&fg6|<<`%~y~iIv<#E;@SY0pN0G!?ANE
z>y#w#p;yKJM!GqO&`fM>C0@u@q6lXx1(DD;i=*!D^j(oKwZHynBYqMU#p`PIThuFm
z)_87$G<GvYtK?9Vk|-&)V35lLB8^Aqm?isA^RaJSSXH$lr~kTuLyRq&I!yco9Ik10
zuF?(LnEAWES6P<!E3Jd3U$|Z~v#-9$3x5Cp3us>@|7Aqc?%mhERFcB}Z~VaXCk@Ni
zE5IXs!ZHFz3>u<sdHR!o-UI&4C?ye@(O#vmlsAJuR)&~9lwTZO-N|T#VOw8DSAgkg
zm5S01xpJAsxpCB-O4coj%Z3k0%@v)GcAJR~LfKa}<m(1?Ds}z~9-5h%v7O;q%asMA
z?5zY(+Jt0wui`Rgak3ALb_NP<gClF6ZLW}6bYZk>WoomgR#g3g^p7q@7u3zEGvJE*
zea9BEJ(T}^jmi5_xE_`VAL%BOQGB1G*+?~HMVK`x^gw!1I%N0ij$7PIpaY^3qvw}{
z>M~Yx4VZ)4+VuG$I>=jmpzej=7Yk(H_C?pekL(}**9hv|v9y(xnnWyW7r5(U(&bMS
z>*$Cq!K9{86t$!B@?7JGQnZDP;MfQS=X;WudWhYmj!HgKPa%?X<~BI~CqzvAcpagk
z5h(OJ=k+j+X}ag6OqQC=Cgngv+mKeNPHS<Pf9V>rM&-eKl$!I12biD{&@w+{?}(uj
z$rvOs-dnkgxSWF^t<91OH&*j-hBc+7rgo6AJ19YoO||=UqoYaz{*bUXs7wfKKk0qC
z$eUZ;VIV7K<Bc^6G1@}@u~J{XaFqmwA`Ti6wS}2H202=uLRq^(OlY>F<^SVJ8#Hy~
zGiTA>SqA@*{;8b=3ka9&{3dfjc_Jk47r0Y_Mz423)9Q|M5FGeoqY|xoFSMTl*8ss&
zNAa%E)4oLH4{2B8=sAjM6bcPS!M19+I_VQN57UW^P!sRB+Uq_+X3M-=#v%FQ6C>Mh
zFn~02f;x7rn5>;h+ii9k{Z3rBQAsz#eT_lBpl|LWr2c$(;1Vv&oO`5A8FBg@TD?Qi
zpcXemt*xy!Dxh)JgTzh-7uE^7=H2m4I;joq2*xP---90J2&N33#^@Clb#;H>dBVgZ
zQk7vXZ7LPSIx!+iqjx?C@IbsL?0SuZ#fA2R=pmCtNtR3ou|~hPn)6ooG;3(B4By__
z48r6TnLK5Ni8;oXFTca|ENeuT4$`zo>O`a$1>?8kSs9c;!;~`7QZhM$Fc<Y^DP^B<
z#8R=Dc!P36M59Sq2nKKGikevDxxH!J>M^z9FP^+uA#BD^G#mKrMHEWk1GxC(=tQ5}
z8)?~+mx(cG2>PLD&$(0+r(x>SLUvHYDA-Sl+NV;F08)ul%5ha2k=v$5!{`!#i~_2+
zXK@V<J0Xp*XwzgG7(XeBRHDN6X*MQyOnF6I^Y6=7TcdVhh`X2Kgy;=gQED1=?3?`f
z#nIfB42@$H?uN9k7hOf(s5qJ7lXA^e{|!e4k3cKKHe5J#l7L|oNHGl>5z_Y%gSd%;
z&sYf>l)E$Jb787C_AXUT#VI@e=%;N&M8}wrM0^>PPs6siQPb_Q*n_Lcv7-0yGt~#l
zqc}P=M<}Y%Lq|XAfd20N@Z^}H<VeA!_Nyhz{5W42)gtfv?bgFrZp;p#q9fNl|7PRM
zET^f@x5ffn=5``{Ca`v!K1I)WFUCgsUPKybdNr;#e7)Dp)%_YaHa5^a1)v-!OlBPs
z5)|Wb=>6h<Au`6gMj1`kFRf?{HLlgDDHt_fo><oKl2S^_0{3?*m7{3<7+a-IY(@=%
z7!8~J8okWBxTAtEnZ(g=E)-@bh|*8{Td&ehdBZn?tV{M}0t>C;lp-~dgvKU9QuUlq
zJicd(>F{f(52y?hLm{jm51P>T&6>R-J)giA<>>!Sece6;GoORE(QF?ZgbH<f$UdLJ
znm~cxkH5Dmds2QAO^+p4N~$AULKAa|SCDJ=`V?rETr5ACjjtwEv~Jj>B92K4C_*E$
z`I!6Gx>xE)fVJu7$vl4y9f=h5pscS)bN5|R<|Jo1J#Qi&W-SWRt^%!2?C*hEs1y^*
z8XlTZ&Tj$*?M5Iph`n8eicZLz>Eka3APU70h{KR>mabe_<`k*ho*)lrNDevtj*y_B
z;B;fR^yD>vPVKJidBjj&t{!}<E!2%x^Y^6u*@%W5V-pi>HJZza1_de;dc!W1cyrYH
z=Yd?trSvfRLAbbO`;f;z=T9d1EZ};z%%0Skm_0cE3cfCAWYy`+D|P#Q9I~eaXf7i&
z+%Sae*SxV{=2LuTP(t9vZ`mlYRAQ>e!Scfq4H1-c1qdULNtHiyVWxi!%~I`B>EM?|
z^kzQ!A26xf9jZb(M|#qvXFmFLVK%~LqOp^lAFs^Bn~ef<ce6bvVahRcsDw0&tL8n7
zt1@kL8?Vh@5Fx|l5aJls>GLxarH}?S(K|OvKNE2jT*bBO+NQl`IPs5v`t29eyG4J|
z0JYjy>v!9T2mAzs;Be6+^WN6tRcu3I;E9W$w`eBM-M@lPHPvO7_+KGd0f~UbdRkvy
zSI^J`=os}&blE|$9`TcV{gQDcC80cnc-3^YV4YG2f*_%IYsQo)X6!b`Mz_UzakPrp
z_Ka-0aBPxfN5jGDK<4jJXh^>BYN?O&(L#(WB$W!Xq+miup`+_W{qP;ce?eqU6p|$I
zR`+Ge&<q-ZG-o=WyhIG)oy^U4NUJ)@Y$O=U;x*x!>9EA(za85?{L6Cam#OBK>ac2i
zMU0FhtB9;5p-+3w8s<V`o*8%Z291T{7Xj+d4@x`H*eweAVkxAHqlLn*R*|y_J@GX<
z*>+{o(i1Vna)>4j0~{X!^Gm^Ln^z=VTcZpoK*kH0VS&l>J(pU0v=C{Y)buM3*Sjdv
zqJSq<3=vS}hYxqrPuU5%<9pHpOdu>t8J$qW9JIQ&r5d&KZ+WF9$zUiP4pKX8L@JjF
zXlsK5XVmlI2^pYQQ@W3c8ZdY+q@$^pP71<rAr{10*iXP7IWg6`1*tp960}PlQU1A9
zxS%=Bv=+J?>UD@;>Z`dD^A~31CpFN+>1%IrB`u+ukmeX-Hum8d2XX#oSj)^B(;ilT
zZK5DWSWD)cx{N!#GSWmcideR_-)fl%8LI-hAcrqMndxxAVYrnfFOg^WkoC%+E6wj9
zbD79VAcI;;DwlG3qQY_&mQPBbjL90qJvZgin_8i!W$tH5?boWuyN+RoTPLa!nw|Uo
zTm)BDx31EC76!4Z4AFWLy~iA&RT;?o$NVU-Tsh&x6HHdpG&RFHa$^`1wI$yUq;A^1
zGg+27BPV<>Lab%#k|%$j^#>Vn^^ej+&9r<QYMh3s%{ZdgA#iAiGJj>Xl9DfRt;}rm
zdfpDA6C(5K<j7pDr;v>Gn@hW=jil$#3l?Yh4-3t5GkK|~2*1ayFj8wEy^?&(KL{aN
zCcwIrG{X~Iz%Lhbt^{JM`|NQ}c*Pd`_cK#>(=H*_EllVwiV(poK!z`nWx|zwmV@A`
z{=k>3H=Nt+H+s*<g`dn7kVbvHn?Mz~?LgAo>#yr%?0XTj7D`aCN<oNCVBJ3<kVc{6
zI99@n4vC0#0`rw&X(#dwFmNk~pUfwzopR|nnch`J=0U-WTO3c|3&AW<Uk6h@vktY%
z!<zB2rzkaVlPlEDtETGMF-Dm}%bxvFQ&U4GMiw(&C$q_r40E4@1U|rugSH*J`4m43
zNaf{9Uo=-8OOSU)l%7y%y<vA2q3lZQOEylztbj4BNgDN255VF>{%K6Pxku(%LAo`+
zD)cMz<X9!V%NQoRNC5u`w@k{75?U1%z{ed;0@5|n5Ihh?9RZgJVh_lGh7FFtP%-_K
z5uSEq<hS<J0iE>%qr`x4F9l=+Hb7trMKP3`vgC>l4ybb-v>$5Ru&`|wQ^XW~$#G3+
zUS6YtI0FARHrujDS|XI-d3^Z^+~0O<t1l+sP+RSv)4OlQ%|}>gHqwQdWW4EX2BSS~
z_qpqU06#;?!Pw;`q>)T4sDkL32+TsWF>zzWFW3ctzI$wKG3KWEl3JXWS<-PrB19r!
zkj$C**FQ67t<SgaHNjC%Lu{L`X6dQn*BPn|or^E<1#_9qlRW%6J(1`ZOy;?e?_p>b
zA>2NeraEcWZHCNE8YXkj%rH!J-)f%ME0qty)s;imGK#k6j<}Ta5;0709cQ-4z>^wd
zx)fTq#~_E;Cud#2dGeg2X4u9?SeLn}8>U=ziKASZ0tv#o`Cwn7)FL|p8#Tn386_kS
z_j6_yZ;^3Mq>q5y;Hgb_c|8WPG~0a1D$e|wRhY$fRfL9uSm7sA7!)x~)=sA7e`W*J
zGt3HLqW&1d;S{+o3(f^u)m26<$RQ2Z2SlWf%|=1@NEhs2KaNbMx%vCEF{vfC-o)Qc
z?i1XPy(Pq&K$DE)WJwdG&u-VJ!j4>clRPjIt(9)E8G}#Lik6(;j>jy6^7|p?vKU3q
z-T^;^iB>mdE-vXed;P~Zj)Q5n2dq2ZZP5MhoFKqoIRRX%LzVEK+SbK8m|9U+baXvm
z-+Z?rNBKB{N(JhE{y;#Mxj~lydv<~;F<)qXseiZN;=Jm-l(DVp{`vMKjMR7!Wa3Xu
z2GXd<IF0Chu7oy}AisL-#u#RnzW3Zh3F{gks6R-!CXj!Rh`xwqHV6}+P(UT4KqGlA
zzIq|LFw$4MhKzEx!Haak=cw=?aU&xi$!WrX%ZC_Hw-b`1cTmcN+`Oq8ohU*DQs83`
zS^}+IRg(BKTMBdG`p!SaaQ|dA!?|~0JRhO*9fcD><{!pNLFLPJ?WPG4gr|jT3qg98
zi7F1|1*g^WKb0o`eHQ=cpo{-Li?50UD-n(FB$)(}y6_hRTa4noNI`J+D^ya+wYtuo
zsN&0;n)W+;Q;{C9hbDsB>qXN(8My?4aPd7DDRrkj%xX>=bn%f{m@?_sLZoy>s^5h%
zP>Se)sRW)S8sFldS8_ivO~{Ng?{ceZYz!h&2BN?}2twj&U)3Jy%-^Bk$QS0mKYxDE
zG6e~{$dvGMOm5IXtF?Q82;^lh7;mTKOM1Je-|#NnCF%5ySrY4r%8#UNWOgy+Tw1V<
zmUJ@Xgg~r|sBk&7n8ORUkbH^1n0QtU^RkLeX@X6Xr$_-RQ(gFW^~Qa_!W%O3T#0QX
zNap~E#!6Zsr6Y3?)iSZ{h3qyS(N&23P4ev)zHInHB!XBmK_X34`@#Nx2e@eBq-%{I
z5}`Y$P)esXWX$>iec}M*SHDG#QIv;Nyt!_WL8-+1;EC>`*WxL7b@SVQWM$dA2})z?
zi#3^O39&t?)Q>{(76n0JSy>tJWDC8a3wz`U_#t_RsYnrV&VZYdgZ(5Pli|S|g>{1v
zm5A4bs#1+iE+R5WrZ@|;C@U~!(vP1wUlLWp4zM}>?rB7p04|G+-~7}$mQj?C5F+Sk
znWpRQn?gES9Y_;SV_}=qTga%YAg8_<b3M#H%ZJMv7|WX{JrLhw&RB7_`sWY=5i;km
z@B4TK%5b(^&sQ5WWW@p5WriybqW_;{IC8#ZR1OSp>SfVVigF+^*_S6iw|NTd{?@4A
zOEhQZ3@CMqR$;k`TCX0%knZ!N^w6=T;spp%dPX+(zuOe!E4OgxSF347Vhn`b0msn}
z5osBmlapX6FGRdR9+)sr?|j2Vf|>bg9;m{u?zda~#Yj<V9ByKSK$`jNc%_mObaWBz
zM?lhJ4YtWygUlh6HksOq))pVKwVLgYn42RF($n4R7cc?pPK;y`hx<>_0T-JS3KC$4
z9CW+24mXK9#oW36EQe_y&O<p_Z!$oNAm`dVRvH=JOz^hT<WTTx1VE|oC6q8asZ5!z
zm+q-fHg!@R3^>2gE?m<oNoI->@Cu%V_$cC&Nrnnpbr-U4LsA}DoJm}KUhocAw-O%q
zypkvwZ~7o%L4uiySQ7$<pPh-Yo~Twg0tZT{<@@|NGcM?u$t>@NAma`3WG|hPF}Z?n
zu7?Rq%*^og@6lgijp^8_R4Sot#ut`Q>W06@Q@#ixN0;dPd@lkZPLs}@6GdcLF&TWl
z_suYzZUbEps@OEH^%VEl-+?y1AeDtWQ3IW99~s@@2q9@D970Evty4ERGLpXR3cB*k
zMj`Dw`XVTx<jH1d_67jF9e{|DUPp!~N<xoR4@}g+YC(vqsZ@feNIP`S8Gf<u5lD)k
zkX37tdCa@y2&W5`ZsOHNM9dj#OQ3|U3E*&U$ry0H6f)d*S!-Z%D=IdyUnv-~&e(+g
zlaE1K?i9+BljQ#+;Mgq@2@Ap(p@B3I=OE5HpNL9PN{MaaAT#UmYGJF$T*;h?8ZDB*
z+Th?u!y=HtD<R6HK+4`pl06(!5+msb;NWc_4(%kqLP4zQ0Xeh0Gaqh2zvPd0rw&5q
zk~vjmX7c94S1xRDlb}#){Lf~7;%)jO*me5+XG`I~j*wq)J7RZ~RL*|vS?ss_O5zyh
q(f3$J{G;+ayC!f!{!45;z5IsHhg$~4Q)C7nMeewwY{D-We*bU64=L&Z

diff --git a/reference/plot-9.png b/reference/plot-9.png
index 408509b1b3f74352eeb4d158bc065b0541400aec..5c90f2ab331d78fae798c8306d1e4a8ac43395ad 100644
GIT binary patch
literal 30546
zcmeFa2T+u0w=E1hqhkVF1q1{X#Q*}Ll9P@o0uq#*6+|Sq<V-W80umL4R!IU1k_9A(
zR-r*aKyn5J2~BERGEKwXPcz?l=G0&3o?G>wuj;?`e^$+i*xkJE^FGhsYp=ET_K}8~
z;+~xxI~f=l_9$IdxWT~it2+b3R<qx>!FPJ25@zAIZDuNp3h+DpTDeq;gWq=CyQ=TR
zz`zxN{$mUup;$059A;2bxTxhGGu7|){E$Vg@T_T*=+hk{B7MAvPx3xd@!Psh#Lw^f
zVZ4I=?PAXZPmEnI|KY+YB2-d8MERw&!9;;+;Aqx|i7ku|F8t}Y>%rk)6!seZdBlkC
z`Q+`h=lWgNKR@l6xh*7nuAh|JI5sCU_gaePx`0g^NXuGRYd0)DrW8THz{5@)*2eU&
z7%m<C_AC18^GQZw^!tS?JKP!37iX0}>_WdkzP`l-{q7gCZ*~j%;`TE?HuU@9q+iO=
z?+omG|NTb{gcN`Kb64V<H+%*L217HY<gDej6!rMH#tljhPxkWKDedWn+Rtb9b<72^
z2qc!5%c7SJu1^no+C4p{bjbPZ#pafleLQR5wx{a^)2@w#K9JMNt}`y^fNRwd*fJ1O
zJ2uzc-_O<G-~Z&85^;=RH2`0-#EToIEw0|aeQ?L*ie~((mb0_7f|r+<_s^&CQ~9bw
z_+T8Z6Bjx&GqX$-_QmXC6UtB)Ts#*kzHe2poZqe6U^^^G=5Owd6Me(s^4#Km_bw?Y
ze0g#1#LMVtsx5V3sY0Ad^2g)Az~!zQIo1_#-}JWMU_o|=`W33hIyFj?-Xty!;&7Su
zjMjHs*-NLVryY8%QucJMr=@2F1qNEwN4@!Yh9NAQf#KkbU&`3eSvJO8jW`x@?2&o6
zY(bN&tgNgM%R@|J-*f?oFKyDaCh>AGFRxighCVH>ytMXv01x;66KWxWflWR$PK`qi
zeg=yoT~QJhTNrL0gNN0YQ!i9{I(8kdO{c?#gM(uz*mTWIL7{B1$Hx93dg@1?`?0-J
zD_@;Rr@&wF^71mt&VTK)N{OxWomQ0l`NVnkXT5N?vlYDb!b7wSZR)tyR_E1o@he|m
znUuDwNfYI$Q~NO(W|`RwWotj{Su*#)Yv1l>B=23D&?P8i-{p`-FZ&eFx2Uuh2nq^P
zrt+E=y9@I2&e@5(PIa9;u+#88+88Zj(WYXtO=GQNzVk^uZ#Hmk*A*k%-jT-qwlbZZ
zToOF6)1lMYjhG@&BXv17$tGoH3c3#ZEJ@6LIcL7Uwo=|9Y4q^-Be1P1ipCRzhrV87
z4y6p&DB9TA*b-=q4qq>BC%&+ZV;@{!EF5+tP>s9IzX=+boWIi)#|4WXQdl-3NLoWT
zzx>5gZohcHvt}8kn$x1az@0MmT+~MAkjIZFlw|SpQ$4-C=J1KZ@NiFzHbk?<#>Q3}
z|3dQ|ePKC7UY{YGt<IAuqv8a~o}QkeShBYz+G-(iUDBSOo@uM=7z}L79C8IUnMQJo
zr}p_}pE|IU@&~i`VV{N&_H$`c{@>?Gt*uL*Ym}O!X2Z*m(6x9E?T-)acN>(24>fC&
zT?%RP9z93t9b7NyT#<n#5V7gVkofWVlG&H%B003h-kJcp`Gn`<SQC93fiPVlUpbw(
zvDUN^qsHSi-)uo3ALrRvu4yG1V#{5=W@kT$i;I&O4msB{Qdq<7Riz?a-eyU)w&RR^
zj>Xb)($fX(VJ(&iH>kK8`_gGvJ3BiOtCrVA@YIGLorpVGTH&~=hb5ba(x^X2>W5*&
zgu@E=HxCSO_hJb?Ut%QBWmT+B=;d2$D=64@y-WBp_cnt1$CG#AS^j%C#i~@r`wxu8
zIH^tLcbYVJbg*K5=c0#;hQoq4=5XukWwW1lSu~`)q`lL7Q$Va(94;L6_3~cKiw|`x
zpEO241nsKXO6Dc0@b8O{lzB|Gos608bZZUk?PBobP&)WN-=_08=81wr%fLX~ft?TK
zdeveiMC>>*wS@%Qv|O*;{MSoXXeVcOipA5WEL+9q#+rmZvQkn~ew1%p?Y4~fDR{oo
ztjH59N4Q-Z@dK_><+$q0HnKN5`L{>vA}xf<|Ddfe5!44(I|`#{UPd_D0<96d@^hpX
zqj>20;#x^*saLEz{BrDf>EtV&%B}aCyNkuD+n11%8F8KK9hO)VZwm3pyGka`S(;n7
z1s;A7-_}NKQIw!z)cN!0Uz5Fh?Ftuq*KJIx20gBy9_*-nDQc5uFgQwEdB|pR)fGP1
z7vIl^H;S-tH8wV`t&v;I@$KdD98cJ~&VApwDRMjyTSmFE>h0v@M4+ZtoHlrWGPVY8
zN_M<+$zpDy1rMtfHNRB*^u&pDoy>`RyZnMs<wm!;K`LQ`N_aY5I9M^b66aWVO3Bi4
z%+DpNwI;0`*EBX)Yk@cJAg^(7az2g5R!Hg-e0^#+nBU;+vpA^Z$4=p>TvM)c2hkor
zYu)yi5tp6UC?%j@Xm3oIOS>a~Eusz^9NkG>n`>Gs4&(8j{TR`ylcugUv~2y<I;;NU
z>1bsh8%72V!^Ur1e=aUAK9})uR8Yt;EEIx;>98s7G{yM@*T3AL4Q`Y-tqk~5r%Cg*
zC*qC%P!6#C{^?=PSe#dNtGawYwv-smR`W!WTmQ*958Vts%+nL<Gfpp<I%Jpn_Lt6-
z*z~y#`c%esHu)~*p_4E~WHUYVZo0%}(ron03racJ2irw1twp(lh>ie6yAOVLrtr)p
zzWHr6`}pUtEM6M}Qw6>De3cAn!<%@FM4I%<t<6?C)bin9hqIm5+iPAOl4|Wk9Bqo{
z@gkSIoi?o~8+0|L_H+(O53Z>Fm|KycRP7(84sQ4orqPp=`2Ju&zmDab)hS2@QEEI+
zUqqhNk8G0-FJBsvjx{N|cgxw$jX6E}qx*Dzr`ZN=Nu5fCT*|{NzjiN#OWHNKpuI;d
zt+=7zb21YhSa;e&C)LhvI8?m)*(uG$^z`(Mm8kR<>e4Z#q4`$zaDl9%#6i~;Z@D{P
zo?|u^u^VFJFSp;A^l{GjCaIVPy*OtX<<ddvD|Q+-CVT37CeL5s?`KY@NxHz+B*HB;
z%5^&AcIzh}%~aRgJc6s}2=?rsWop*9jEz(qgxfAJ@R<F$y_JN58Lyac>dfS>7Wl|k
zQmSSOQ%Q1lbu~Tzol3>R^CNArZOE+rcuH#|&&6+eXXxi0Kdv5k2(olYd%CV-f<_=j
zF87(@v7z^^YAU^Q>&wFqHC%4HOGX=v;Q=%^H)AjuxH{vt<r?|0k$SwMuC6YG0LVHy
zzO+?$(i=QNB4JIq^*IsG`Eir6d}mUNppeQ&G+`y$eU+4APFr2Le)0fsd{a$NzRfTM
zgV189v1)io>x?#i5E{&8`id#AeHAS&ElaKqxDSUy<dPoxu$B2*Z=BamagG@==+@~c
zp;5x7+=_=B6gYB2c;w|OsQu*XdSSvLNbvCSX8k2D4&?HsWSn<k;BzrM1N}<1-8H9D
zVj@iH1_9GRRGwnR4`i8?OC_sDN6D>2e$l@%)-l@&I}+WcW{O(q<zUv!ii(PGd*f46
zAyhI!9z})s(<o(1kjv(V8l}l0aE6vYGW+h$CainHj{7_|NBOi%xC-towt%oS;5D2O
zz|G+r8ab60%qpmyl5*|E+1H-cVO*RI4GpB>b^j}GU#G<TN&$Yz)+=$gQm{4RDeQAp
zdH?>sZF7M^@0I+*VqsrOFalx-n0U;MzM*zzWo5bltUWPY$+Ld1XA!n&_%5NMn;$%w
zuDn?yLsmCN0h2U*N;A3YKC|3&d&|kr>~M5oA$r0CWHWZYei;{UmfxkQThHazcYWVW
za&Enl!(?V*)I1(tj+#7e{(VEPMg7q7aJcYf#H#XQWhO<^1)lE}c;YCrIE=-*Ri|mE
zCu*F0T_Z~NX!7#(>Ay!+;0w!6D;L_HXUp%A@hiiJ-*Q-*ShEDeWf%24yL*&Q#J1T)
z1KbcVKen*gi&gL4SnVVXBu>{w3bc;Zg!2rJjiim3Z1G&|woI9xfpeLQ%O0wy@$#;~
zhKqv*mg|l8oJh6m-LEE*FttRg?;ThyaP|$CUtfMarg`!}#8zgRceh`xFX1+p-dUGY
zXG)*Kp0WI(@@Q_w+p$C6@(r2Rp6#38R4|g!^n1eNYq3(Ic1s9#lE%l!muTveBhTK`
zB*$Vj2ZSYdbBfts9iW7{y(X`8H~AL4j!O!Y;`g!dd2hv1e5mwL^wJn%HJvaqS8nPx
zoi8Y)6f5Ci_OqH3JKs~Gaq`%Sn0vqXEIy52yB+WHtTaxb+cKE?!KKq!{Kv{6$InMd
z-ZLeWSx1^u^l+EX_rdr1Dt*7i$(A;)Xxa3>?eG>Shf}BW)N@^@d*sHb=!W>F@ergf
zdp|NYYt+)Hkn1C+3wnj@ysFtP=OqWd<>F)920RRHZ6do)XqofG9?R!UOn|6#d;9@x
zgQ7uS_l0&{{OMKz5#^n(?c)LMYcV}#1AxOTni8Ig>4wUQJR45W%onOR7K@bX0L=Si
z?<~`Gc;KG*`o4aBM`UzK(6~yNMOmvBLV3%gE`(*)xoCNu@9G*l7IK4bHrvV&NNH(r
zZ^u$0U1TMiwgeLxGEE7-pXWU0n-x_iJmLQV+MPEuA^iA|W8DUdn_S^>q{Mmh+qL%f
z^^F~TO_Y+7Qm$U+Y|K5aYdTD^mz$ZHXirZ!sXtDndBD!&o((3C4o|NGBAzJpagdKb
zbL;o`={o(U$eBDyMOjzRc)<ZlnOphJjFZTCDMk|bRzCXDMmg%^1@qTBo9oMLzHR61
zu7|(Zdd2`Ajknv*IjiHi;R~OhYzLrpd*7h5iwocW<JaE0e~>;nGL!W5wJRg})5BdV
zQOyu)WWL#i!M1x(yA{CIVH0gVUuo^D#?omxQx=fex4Duz^mE{X&cWwff&NFV?d`)L
z^@#C`4Ln>%pt*Lg9xog&YSW>hP!&(>jwg;cCk=nQ&m0!ml5kM0cx5!YmBfDTj#m2A
z#p30mU=^XJnTF(q6Jn6>``-=BJvR?iN+B#{V)v|MHOQ}hXKp2tT2t^wzQv+|e2)(R
zHdj=r638yw2Rr%T&obY&VV;y$)8$WWo?Q2xod9<_40w#a2}dCI2mx2@$r--%(kAN^
zyW3J$=}Zu8uB?gt`O)l+)$F9y)YL&SVkK7yr??Q4^wc}wUNS@h(mWKGbrk~MSpI&?
z;2It8*64xrKUSt7eUO|@hXt5*BaS&xh-@i7u5E9xQ$8OJ-(4h}(NT~iT2Q=1*nl0A
zC|qnLRI$F0of1yJ6FtRZ{$iXkx8eFwuyEM*q$>;3gQKIP%XPXHH}1S?zg`RJ)5xvg
zg)|Q_mP;DquwV8{PiIAxn*8jCtvQ|x6TZE=X*~9sO{oxP3Nt*IFN;#EI`r`O_5Zf1
zE8{m-;!|4Tb98H+h{HeaIh<xg&NT)G2A+ubKHHIHY}it<{0;YZ*h0rZ(ggrx6-29e
zIqn&@ME$%wZ(7+*+;2@4kHtBRMCw)#5rem{IcnOX1UqlKMl{`>p4?#9_3p?Fq^=@>
z+rx0*A+fGKrwj}Ylis{}^J5I&s$zOB#^D~+Ei=xmD9Tb@h{Mx+oR7Xy?)H`$7_z{%
zYwOxAyeij|q<KINzLpX$F%-y%z1Gpuq4U#SLi|X&bA12#3NwY>2Y)Q2<mZdV3XN4?
zxStRCQDUJ@vz5UL-W6gQ=W%?!b7R$XYXzi}>W;2aF(2;TKcJjbO)-Tmp$GV~dtp8$
zg1Tf%o6sfAL%!GTpyJOBtZ3Lx!&)B@?i?BpU7v!2i!Xe8N$Tx4tKU0TV_wSncy`YT
z=}gMyh<1$Jj8c6QmJc<z<f?pZs4tgG+jY`Bgfup&$HrP}HYhwB7z}6A7Vmn)fqfVZ
z&+>=uEn_@T0lE)T2iPzeVZxfD=cRCjVXDJ<Os8AwZ_Mc9h?bX`mQFv0@=F-4qyzvL
zb4ctt04bJ-*a!zr*M``jqWdWWw>yjgS0%H2S?9U4Pmeu<1b^D64Dv6)xuF#krv_1n
z^_4N^)v(3;cBUE2cl2uxIbB<o#+E!$rSwk+<8gBF>M0wu0rI<7Xxk?z(zC-LbqM=z
zPzO^s+@^ABbk>evzpvf+LUqrcjXCk`p_}PFY0Z|3;oPx{rQd&PA|?!6u)M=WgvG;m
zNFrzmn5=$a2_ktHN3Rj3dyv-ct)HQnlla5m?~-;Z%`4&<pYFS7hP_pf_SajBAI$Gj
z)V&mO3{{|FJ~ChR3!>4Ud#&`e<=s3pZut@v`Eou5*;?{_2JxCu@2Kc6uo84LVPhN6
zH?{<d*0&wDk<XOTrm^beiHf_;iSr@FkQ11S?*Z;gv1C+xdH$5obY|heOEtFmZU;!Q
zy5%#Ddz=@|@~=gU3F-ifM|4cgy-z#%5_%yc4?`??loT!}b^O}%y>MPOiiwlr%t!b|
zk9`2-I3&FBQ@GH2ug1v($`uT5E-nZxAvOqro3pDc!U?Ob{r$1)2XF~l)i3ZqR@T=1
z#ToLvP<k&bG_1BBzwfh=sgD(1oiZF;?Qr&NPt(2>*OZ=J@lp-%q))lY5`Sc^P9Xc4
zu?>_?nfVQorD0CRMyrzhMb^@f1&tZ*h+gV%XtE}&P{>-R=vZd7-UaG)3xaC^m+RG(
z;HDpBRX*6QYcU+Q1oj<1W;U7iABCd7b3+AmCige@fP{pEBWiLhKjtYBd@8)WLl99^
z2~()t@Tn*+mMr(qcRw_MN?6kTcKPLxX&o^vnVFdmb*ECQ$~@*0OG~8@3N0M)wDE?>
z8V*=HFi@}D)5Uwak|zweK86$Wm>&H|OwM^6Ku5Yz5b*kV|9GNUZCBXOS|24C3`OD<
zx%lwk6ZY0tbFy8DIF_@qPGzS0*l-0g5hHa?^nWS_2L^@%g`>?$$miBdYN{CKB415G
zfCHd|`{_Yd-J@D9f`xq%GbePjjMx!mkb(bgt;j;hy=PR6Cpkq}DdO1!&Gz4}1eni_
zXwU537s=FTogbbR)qtP8@ZBUwRri<6wlYr%@{!{KM;Dx|U(Y`R6cAu0QDeZs5OR9U
zz%70zcWmi&0o>lZn-^Dc#Nm$13f@1C@;nL*tvtI%3CD#1m|_~+ixK5Ya(-=n_imuE
zYd0UtkIOS|1WtrMyYnnln&NTmD}0u;5y#?)k>xMvns%4^BL<Q*&u$rmS?Y6A_ny7a
z6VmV6D+n1lDIwuju&{%0`FzW}L2-P2WJu*w=^!oZNB!Mmd7`*YYBE4{BEDk&%{(PJ
z`k9NHTg`i`R)3fMxt=<qa#Z-i(y_i1sVV+4@v69@pp$z-1uWK<DU`f;+UZ8A$&A1*
z_oLY;y2QB;J@Pfx&9pPOv2z-E(1pn%x<5B`8h`Bg11_|Ft-hbk63=e31D#S8A-q8G
zge9rvjYWBu#>r>9w`+JG$6@)KnH49-+OCrJ8`>B%b>0j|(NO=FvP$RW?*S>T@~h3^
zyhFH|laP`LveluSx-Av%eFNKhyw5}l8NZBUae{!MJF+{}vsBs<C<P<u=F1N7t(1)=
zU}Es^C0ECjT1jNzrP4~@rlIIjV^7kXt#LW;9Y^XS&SCwNmhC%<E+OZEY2atKT!r(b
z(+Am)<bGdWhN6HzET11+1qnlt$z6O<t}Y+aWCX=4-C-NHs`}>l0Qt3B2s&bNC{vC1
znWXN1C`r$^vkkSI0Z?b9KOKbsxJ((Y=iYM5=B1R2wUiHFq^O0LL-G%FeivM7ZolE}
zeSIz9+q$i9Qtlts3Rzn6haSai8YZbf^YYFY?XY+g=|smsN4{|-1Kg7Y(vN3-Hei`X
z#_=rmyO}D?%f-z=N%mym?f97dhR126qBA}-5Ye;sjDg`L&EFK-{v`sal$6$J(?8dn
z!+p=kR*Xa$DmVVg9yFAvQ&+6u*%%{P=}9o#<l%sEdv9ibPv-_yw1A&pTYAJgw<uBA
za1=H~dhcRAeYIvW`kbX&A4Kf@(=JtM&hfJ2ukN>ye%-~oo5U<j6mOFH`T1y4aj}P<
z=)wtnJ+WQaG<F?x80uE2^@s3;HH-M{)J-5GJyUel#Qpn2-`-<N`|u{7R;$a187EG@
zeW0l>D;itzvrb^UkqMNhltLV}kR>xAiaKt6O73_OlzsI(c`O@VMyjd5IBW4Cnxvh^
z6IRufYQ69%;gG3asEfWJjSGb<M+jG^;4*-7eId7=4t0lfNg>6V6Da6snre>%11GZy
zaupjia!M0RJWayLYbr-k3JC6v60^C?WW)-hbCO%JoMB3k^Ie~dPYD+%I8zWAymI3w
zaBcF4FgwzzBa>~DU0yI)0GwU+U3Z6Xe{4_2lL_mqlOjO3<YHeIc(43iM%8>!dka1r
zN1G&d-+%=$U;A-FUa2Xn$7iX3xJG{6p~tm4gq?*TQ_8JX(p0h1fDPMZDB>{E&s4&L
zoBX7K7V<YzDRwM2g0e?ih_Lo^ej`k3;%yNeNiRFo&C>{6@|0V};M%CTIc11Q8deA9
z)}q19MtbzQc}u!F5cm7zSKCsQ?c=mhD@7o%CBS{4-I2#w5Rn7do}W45BjYss#kWQ%
zPTI}B_W3%I@fHpp)}b-2{M9Oru<U}4lGBQO^z(XgpZ_!z_1AdKg5wt^JC}OaE#;~D
z+0&&ZCEL8S5tn5}U6~tO>Yf3TL#;QIOIl1n_qLM$oIE<7or3U(4~t3>#{iW&t-h3B
zr-WOSCyN82P4WinKm3?mpMxUoh3sR%Si{SdpqkRjG>AN{_wH~vP&usyNmoKAT=eB3
ze)H8ALULMx`sU?n<C6Ec^&zDmZ<62ecC6eZe(Xf7^Nm$sqJ726XoQFHy^p`vLaiPF
z$sXh=RJ5qeE@(M6N{-E!*rSMRf*W(}978Yh_xFE4UF0wvfm^}jlu+ma{`iPp;jK#l
z+lXET>i8J~1CIi4!x<OC{<^yv6cT0M&J%I&iJh*%&$#y7`T3)^Txd+0(-s#8m0!<#
zj8h}~J0H1t;gx=ADJf4o-5JlU+wrOq$6m;Kxk_aNzr(alwY5itnkfOc&qYFo@JI%J
zsOKo}3P~N;V^a#;c>8lKg<MXugm|Ei3gh?W`T6-{P$H0@o@<mydfk74mJ=hV(?FxT
zRm{akcp^0X1Nc@|svRJWpMaoK>hI3Q|DK?sBkPmeAt$9&H|Se$Cw_2lv_T+sZW@d8
za)i%5G1-x+5^;N2?k+^|+7t3CwGpnG$=66uFLcs*bZiU{DHBv9CgAjOn`(O*N0r+0
z`_Mzz;cuCKP~fT?dW%R*y{lZc-2hr1+w2SCghjb-vFYK?Y}5D<4pHmiC9Au4uY#KO
z6snfnuvCU+_OE{_yT9@R)PwlBHBiMw##a_tqZa1ppFJ?%O7ou3j8|TS!@)>6$#i_L
z`*=b?1iba)hFtq@cU{;XsN(X;mUJ4Or^`GZy}biEKP^goa#?4OD~+t$MjWe!c&F7N
zmnxoJzG$%81I50(MmE>g;>i)fldiR758yKwdjJqzc{66X=4I-;GNNQ|ZmvJq3%hRn
zZXIi`S=OBof4qLG#_z>$>_BAU3lD%|*alGud)z@S6e<}{ytqJkwEx6$0iBF-C*Ss|
zfc7rd0FGImboJc}%lt}dr9uJwW|>A)2<Cze4AxD1_pqF??<<<WawB?PJZ=U~RT(k%
zt2~?nt@p`L)e%F*2a1RrY(TEf%!GklI8Gv^j(aibg1`iao$%<Xx?FL_;&63nFue7g
z8gRFAz1QO9WDy4#LAe@`n3E&oyHamjy-p>|-|R(^h=K}L7M^V9x@V^-P(4<Iu2}Ee
z0JRn;kZ);sxuPLH^Q?XiFawPNgK>c1AG!N_ZZT9;5fpb+q=*2QY#P~<n{mTOjMr?a
z>XC?D&-=hYcZh=9T=cyup?(#s?b%GFQ3-Sc5Vnuf>M~^7P~2(?t|!qLfU32<6UuGF
z;)E4dAdL$KT@eo?0|A26HGNbY1k(78wK0e9580Ty7FQ>;!;#WR8ips%i1ibFj6|F$
zfJ!xc#QQWlByxciu||RvQoxEcJakKV!d}zU!}mi-mx)T}Kp3sfL^E^OTQ+n&y+;I0
zm3Yv(As5nsC1ydfv$aN%%SDT5BLxij5WvbuJhX8|#P?P2SC<hPB6Czg9a@cBtHD)4
z9^o$RcQxz-?w<mzYt=5{vb}#PRA}cT{@ww!#LJqkAB>mnuwI5TkSHl_n(D-bPE#Hz
zxv-~A$|O264cW|V!a_k0IL9P4{t7BSxvyjyp!j$Y7l`;Sqyh*dfzf?s1aA(&OvGiP
zO=9A05+a|2VXx_U=Dc?8x)re81X>$<(J%rBYd`UX5Coi1!nzBL3X-iH{=EE1^dDY;
z&qo5P00^1^Pq)?AiYE*qPw^cwKoRxbtK75I|2#^LUt*=jxtd%cN#)pf=fY3nO4G4q
z)lO@=qF@9k0MVOQ6sndgmdcBOdD-s00i=GAKvrO2=%lOWRij-gv91QTuuu*l*})6p
z%2xDBUG0oqyDj*p)AH@KoT8)8TiqxFCD;Ke1W<Qz0PYB65zwhZ^^OFTv_oZcjVd*w
z4Sz!AP&AR2J_NjXxJ_2^aioK)%dOlF;g*#OgFyEjs_ht$(HA1OK0S!w^kNB*lsa_u
z79_cp7)Rw8K(8X$fwCf?i-rfh$f3Z?*8q`Z4#&U&sJ0rU6@-<_9*mp`I7)I#Gw=am
zuiI{fA4#juVP#;*k5q{`(r3Ws_r)FHHCHejopvEY0hpAZpFg-rfGs}+C^S4URGfOM
z;>oc~ZeK)dUgHj++(-Pg;dBWIyR8Dcjqt3OaU0}~_M$|)0AG;p7i#wIVQ{h6bnocy
zW=GL;yDhHyGTUW^DxuP;eW*Y(Y47P;1crUNmq6>Cry!M;LuV^kBz(4*_({1T^f4@m
zuoV#C{g%A}+@2@7LCFDo0FdtAT8#MlA?g2!P#FU0(N4kkP{(&4vX^OSI@l^ci!lm+
z5c`H|SXf@Y9J9+tJ`3&T1(0~<D38<?s;V8TwsWW)3W7?-f)a}LJ{omee;**|Aa6My
zAS=3ziybY##LLSw$t{0dCTuKiBxe^x_BMwSGoPQGf#H>qgy^oDoM+&f$dRWil;CKi
zvu2peJJA5(n{8v3c|*gMinfr$$6wjBa(Kb=VR!$W`z_9&Kc1>z5yEpWHYIc3Yxgp~
z;9}%h%jW<>^uaB^z$b6<&fCJ^KaHfT7|7prrJk;pookZWk0?X9L?j1PGD)8flU?ju
z2Ik@rkaCwY>%oNZmJ|FXT{~G%>uF5xOG^W42oZMZ@LC)1q`h52kES=#2mnZCFn9|X
z7;esneEc7Dru=v2mUgYlExE}_Nk>7(wK1*^1Ra?hgHei500JR2{Z<hmLbUODd3Qei
z9pGw3HH(PRo;`bzl#Rgvip~MF!l7GrPgGR&GNcOy1(5BK5EUw5Rs@L`7&bm$-jb4%
zLpE)HL`6lR7!erQ*w_f-jA&NicSzD&=f%X}(5@~_CTW~(E8_J}2m1pDhgjTP8;Czj
z5$Ibst!}M>fi9CB?eF&Se{^eI)m-a8A5Yh%LIpVV!8p)D{^dU3H?G&-a%7dxJD}Je
z#n)d>(0FG%apBUXOXo%H`b6Q%3{zZQrKX-TPFcLQkl-%OvaKUvX!?C#UaM5*R#4hW
zQZ6A{Jm}M0dRkkLzR%tY6%Suc`IdpH-ueFXO8cn&QjhCLlov}otQJMZ#H8dVYLcJ2
z`+tI?g?{~?ScU$(2l9X5Hu|6Y2<tQ##>j}KvEkH|xFA%(BOegd_&3griV`8avnz6Y
z+%6;gkb~jb8Q^!Io750-*+1I|E+JMSBR~zp`nku@VI8E+xDCgzlj6;Q>dd(4f?moH
zsVmM%h7G?LL0tCjhwfc~jnCBF*LMh%Sj5)=ZMCtlNoMXEI&&0ol$?MKM?W}FvKN%F
z-(SkeYYc*`l7qm2ziVan$ViAyRaG?yc{x~gjWDnmDUi8qf`u#eCU4NKUHpmmTL#wO
z7Zg+jan6Ck0Ln~vfP{puCr9;{GKgNJ7p1_U7>1_P-|#jBeo&#fQbZLHUL2i9PO2bo
zE$7Ko^A5^-I%n3?ePM3{OLn;<0FSI8yh;%uvtlqglUb$Jx)nboC4ld)e^~=8d~}7Y
zQvi>zI}nxJk^F^1wk1}^Liqi!I{z4o-YX_1W&m3-;X6*K<DJ^%B>;X>{e6A5u^oRT
zCvQj|v$3)|)oov!3<$)z1OonTuBWO*>Oet3fgC~jF&B~A17C9J{4OdXmnICi-eJWx
z+y5R{xx%D@SA4nRUGakg6(g&<Jk1%A#86+K{Wb|ev<DC!X`vXR(@~%r<Ad7tvA>ip
z)CAn60TH<GY}*U?ff0uvO`)GZ>Hcu)uAN<`%MPusx7U-bjkmCjJ}<1Wtshhc-3~~_
zwvvz_Al_9oRo>ENiKzlu(dO=MeWG<i90Wu7Q@&8r5Q-d7?|03yH|D06VpmY2@AIB(
zz~tl%mw{u74mo_mKU76q8svex@YKqrG(4jVvPgXO1~{4J!akpbpe2~5BKz0D;qVBM
zH^gp#8_4)U2qI-z?Qvn7Wwo)jr4v<m8eXy>No5F1^G8S}1~qYMfm%YKmJ))GDS7(L
zAk$L+`ma`R#b_g`8YinjI<q#~Q>nd%Pw}Kqk_*z7TkxK%@I?m1iSlRf!5}1N+odra
zbTA8!!2IbZh+QQzdAAH(dwO2RJt_yKT`rsHs5A;1={EssSqAz`eFZa)2~#Oj3kh2z
zxS&`^+ZOg0$m}-8YO2fYQG4#!Ms{SIni7@YK7cyuNya@1T}r#lhf<Wf5D=_@G+2ey
zjNR_?vwtwpuk?W_#{0>@mt^MVKor&}&xIfU{t*Hf90#ipHnAP?adF&}@0pOJhB4d4
zfF~%Rk_31j1ZobQOMC~I^PAh-^NH{7-Bvd=H1wP*y;Zr55zBS5Lc!CJc6!0%>7M(f
z`1PwYKlzkyJn?C~=ReSUXXe{?;VE=*E|hm4-!gDasl~x#2_!`hUBCqp3fNsRv+Gbc
z1|i=7fKv@e$=}`F++;S^<|re=I7w6>9E1>$3}&Jk2he+7IZl}E9)N9v>@7$e_bIT#
zV#Dd|g(|Y*F)-w232hkwQ;r;RRw4F&7`%XCZ&dKrkR;53PY#3~Es(4h%YmeBWqVip
zDSqtY^3=UzL=cP06-h)T8{p|D_nmed@!+PHvzPZ@Y6JbB3(Ws-%&7mqivLk>@4uHb
zEw$s<+p2)>011|KScIGem6(?o9JgjrD2itJD_dJz=c-M9NVYrA3m#}9#3bV6dq8m@
zvD)bEiPR`l19grI3Rlq#f91P(m*eM7LanzSu-!Wj{si~fFsRM_BQ;JDC!PC>97Ho7
zD0k0$ysRH7=r$h^A8=3V;M4H`INt>R9)6wQ`2a8K+>Qr>iT|1bBl$4P&WE=IXFc1x
zx(?ETE5x5o;2Ps%U<ntAT-m&0GnE;wQ;;UE5uT0CtssKK;qq=>O-;>OsfJA`3WfkR
zd37&O&qMMZe>8NaewCwwOS|R5SITvtyC9+%2^Fg1!X1pBqc|D1OR++E+9_ybRzh#f
zz{LrOv%u>@X2iS!h)>&V4CQBL{jKNl^IP&5=T^#G47R^~`Le~YFeRl1)(503gn}>_
z@H2wP={m|UklOEB_qgj70Hg+&QwF*~FE208<E}`>b8%@$KzOPv$Ld3GbMyCIZd?b&
z?TuC~JVA#2ohqf_t=(GS`DL=`4Cl61c`mjv?}xu9@KR%dD&D=4U;W>E*#EuH{~b2|
zMePy)mu@Z5_7Xp3ePA9=Lyg0Y91<X44ndEAW*JEp8w9XbK3P4Uo9x+HYHbXJ0>Grp
z?Uz54f}poNt|(tZ+}J>BEXY_@fBw453{gPHv<IMn4?i#qKn|3Z{N@fiI&!{+$*qoe
z0aalR5wQ8|*S!!Ja*#bAXnW*mUI!PjY$LFb26#F<pn-7_*+zElwPRy5^>zR!BOfvb
zgocKKi#8EL5JE?$t1Wm?(h;%gEoVAxy;&V40l50(5YVU?4D^zKc_6vE9bF8AR<#%g
zanJP1!#{HKdyVitIof>dX03AtkmG+8O+~c^I4JGS7N@#_!39qTe&HoDR0~T^7Ip#l
zH*0yV#FC!P4Z`jGAjF}zA`YnJS3?5t+S<NXo4jzTa$`*}J2kZy0wP>F$yr53rB>>A
z^vjp)V~;bS{P`i$-PYF^qZZB~$`7&Fnx0F@Tu_u9Q@T-U{r1676G{rGAhJ!bDN9le
z&vM!E?A95Y6BwMdA&o%Xe*0O@Qetp&Vzu`Y2#~nt09+tqzZ_bWPDFBjX^a*ZhSmir
z3}ne3>Qc$VZybwbxYT1oH-P%=8a%36HmzK^Mx}##mcVDIe7cy6HDq$bL+whhbME_2
zfgR@lPH`v=J-#pgwiH!ZW)aQv@}A+keTg7$^mgsd)s*2+PqJ@NW4<3c*FG)&>6j8)
zF$(B`Qs^mxFV8+xWed<zb=tbdEZG&S)|vIMc*f#!zGY*w<Wr?Ict+sS)rGoeGToGB
zT?Vd$w=V1qwwGPlL3P+SxLhqh1k8Il@|dz!%sz~$Z`|(KAleBv130C4fJRGGc;Pav
ziuKe~u$3|QY<MXOS^In4h@%K}sRRZ>HhhG*6~w)`zeW8*;J<u}(hsQLo-6%eZcNO}
z%R|%;<SmjZ4fGWr*q?~Nv9_^MX-YHz&wIRXKznJ0zpVKWc=s$VxiJHhV6<@xVxBjv
zA8-w#>Ic4GK+ov5Nfm_9%{0~jJ?2*<H%4;uy(J{)B2OkdD@>ldgF$Luq8C4AmHvAw
z1@p2%SKBr&^s&BTXqr!HA5=UCOt0vlJdn{UrBN0;I_H7D!<=_37PuXi+l3=86F1hM
zU=3&D7t&Df4YgY;B^9i0CuXo;+i-5L`mCEebz7oxvaQ`%Kd7=F6!()rh@Lf9rPmHF
z>GqA^Kt0PUnMf-_7Ge--1BG!b^`GbsX0Ni^iR@*>?=!IcJa|*z;(PSovA13>VE+M1
z;vk~n6%^<qil&<6o2CM&b02}F!p<fVHP<m{h(ZO$suJ;fknwH*_$C{{2H!%eo5Ng#
zcxTCO^fJH9G@-vzco*xxq=W7M(h&Xs@wKo{BSx=-?KRNQzL*cSO?2TlXg~+B1AnR$
z42_oX>umZEBNo_JBppDM9rUFVV<erF<KjUhkMUXT=G#EMWQb21LJH5D`psQp$ikC&
z3wkqh?tTBH2kjDN;JO#`m;nZ=m$xsJZhb0SDkq0R6M-^*vsy3Y=bjyKtQREBqh^tx
zeehaN3<i9|79j6dfq#7H@g*j(FTi*B?4b?p@9z^rJv9TQjXbxp>#ol%)<4Ae78W{-
zD;?gu^_R~_p2$``xbXFG(ZlB<%?}uz{qCRKdU8DEo<Hrx(5+t-e$l#j@z?N^*Q0*>
zeD>ApeXOdVMtARIyuR(Xy<57aV+Ur);n%Vrkxs{E^%f5H#y81pt?FpY&ubQzO!Sg{
zCXLAk<b3eWo&{2O7_ouC9c<msblSfHgF&q~9V7H*)XMm>bpQ+>sYOOWKO2E!R%i}-
zIpsx@_wlr}^>sUNG&K$VMBOGXlJs}52+a8vf&;WZdjq%%xNl_bn)E5JsHiAf0}VrP
z(vki;ew|T9GKi)|Mkas-99F<9>!YB6T!53ArY8>s)x*_%DLI5*>lYO1@j)6yh@qcr
zD|5m1AVf7C<u$__yXs?UVCqRJ@&<Lw!k7uY_yVy`=9?@CcER<a=$oMiLv-Y8;OQf)
zfB3}2#6pLubgnq>5Hha+^FAjUvRh<ZlU0KgE(KJB1$vm25+8;+dgXwrG?*2{^){gC
zAtX@LiPHF7<mi9g!%=sr;D(`rC<NTj{kV<e+S-)X_;u&|D{hw1C<xL#xZ6;B+NA6~
zI-TvOTJQY}w(8~fOj*R|Kv%?N$OlspnT-7l7Zw&M(SvKpd3ohP{)tMYb31-P+7W)@
zyu9Fznf+cVUflS{c5b~iEHk=W4a(X0MEchC8+fwv+W@ddXTVqv_#`n{h`(D5y@r8i
z3`>pLlb}|_<c!>!?y#kA-LHc$Hs5>rx)1L+V`<OA<mBWpn-Jx7(i9^Zh_c{%WXY=6
zqm$GZ3_Q<or$^w2zuzj)&h*hUP^g+~lIPLZHn#i>E)YtGA(ja3T{>$$KrRhD-F(!m
zk=(zQA={pV=ff=2S<&A!2>qfR|NQG*_2#`f$A9w$-=G;_j1eJvcky9%S&olX@JM0L
zT&_f^0Db@lLjg~nf?Pb=u@}-GkhLPWKR@oe6K&^tgC6=X5ZZ=Majahlug11A;3{$I
zuJncaaD4vcx9K`?wZ|+7(HCs|cUYA$>6z#M{BykfXAb&;_2XeF^I;u_ptmImsq=B(
zKWkAll(_q0LtUiS-H4=PO^xfy`(_(KLX!hF5h=?iLH+c#-OKlzJ6_0`1Ihc50=ldO
z*AI-_s~EQ*+Uw%tqK`wAEQ6mfoE5qq-M<ugWiKS5i-21tgFxRY7YO*3OC87oz>3Ef
zkFb6hLT|e8=r%X{iG(5<Xg|tu+q}DtTA6QLa^CI^lJQNJ<zDE84D0G}gD55$nN)at
zbd*A0G6Tc64S0JTG?)-az!@Pvxp}p-xU<aesEM!$9PzMuK)eeB)2F~BmcCRM6tpbM
zD}h>+r3>BQMQ24o3T;}ttSAS@{F+x6@52_gbEHDQjmYF?&p7`LxP-qNK#91*_wTFN
z2+Ksl0cak%ZM+X%&xOl%-a+&y$Ka_rdej{m^)uWycN-gA6l$CM=bx`Nqqi_T{zuz+
z;$O|=kNzU|j`7b0WW>Uwg5p1?y%!o*<s09<oNDjvR0K|<xPDv{=yL@HEp2V$<0F^q
zjP~fKrKUEw?$p%PWdmS5B6NSZD43G{7`N6LZ)3Lx`WtjrNBl+R%yPM)K(x#1^preC
z=2jf>AMSvdQ#_D<=Ki)l6NR$IUNa3Nj<iL~cTiox-?EN3Qcm5Up#EI(K0X!$XgYI1
z44yTzQ@qd5w_8~Bk^N=|1T7dd9P!O58Ra{mT^YCwqfzVe)Xj~J!JsxR(9bNg0^`!s
z()hRefo_KE|7Ueu8WLdjHs=B;sB7!$Dgtm+P=JqKDc52I;wT8`ZDnUS-Av^2xdf-a
zt*s6EF90ceooC>+fO9k=^hX285iWa7Z$SyLYNrl1J9v5vlU|xjSSz@Ak^zA{IW_g=
zM~tR%`GSZYJjdrE=3*Y2Eg*Cwl^=917D7Rj9QD|_#A_0{2`;!tSqAxOeIs8lX~X*Z
z73Sq>sta6z-7fNNdVKtA>rM?_-9nQO=_!%7rt>un4Wpy;c51*&ce_LRL!EBWPtZ8&
zw!`*{jZOOCQI<Ok6YaEu6dSTRZ{pzk?jJ1ZqPk79kKBmR=I(gf-1B04nbTNff8qiN
zW(Sc~p$~E}@)jds$=9UU^ppIGaH{uGhd&b+2fZa4*?Ezr9>j6)Po~TCy<L)E%Lw7P
zSPu1OfNl>qB`yLqYt-v+rjr|W9J5yxknThri3bbdhyeZI9~QA-cJGXJTY~Ne0dqe3
zzO*{!^`Ue|GqGV)YJN9T$32@RO`btL|7UgR$oFv+J<q@sFnr$#;dK~FiExBrmls^{
z^y}$*4;goLb@i@|&E%phlE9CKN$~zN;dH0meVdjRlno{r3bfH&(bn$Xgf3UF!0Ew(
zE&Gb1V$JbR7;TUPXSq5MbJPsl1jB$P%c!Sc<y&`Hy7=|^l)Qn>%Qil9f_`lm{&}3>
zNcx9Dk2!LZVla@Dp#1fmG^7VMt=AD@-;9yh01Vp5Jb48kF{mR7APA#_1vXlGLn`W3
zeYW`nv@#-oEklkQBvby=Tb`HaRNfg6@N9W)Or8gIR3YEXX>ITwz%Q2cW18pt0Ma8<
z=0b%S7-tYM2SqRHu^dLtJD?fC77kVSnb3r^P^<2ndtikAUai-#gfJ(82<AECwWS>=
zV)jC-sas-ds#W;{l$*oy>%;OxUV~H<1%*Q(pow_={Bcmm{ayzj{RzO^Q|=al;{#0$
z;N^w(Nml4M?BDj?UIE>tH&{pn!v<ha2O{IV{$DUK2Z9xDA>EV(53L?QKaV&@8*Q|x
z2nz}|y%ZJr=i`H81VALH9>P8KPUj$`%91`Xs6Z}It(W-*Csj~rY;L82O=Dqc9~{hF
zA$rh$WoE%WI|I;&I0DDc&XvZe5_JHNdNck8y4=8tM;t-6r)Xnb|Mv9EQn0DPujnX`
z4^-6LSL6+?P(-k><Tbk>Y=TZg>EC{OKQgvLx)omcnrKU<BryA&MBV1}K1`%!Beb1-
z?ZrN@sN8iZN50?1F7xn=zmT(D`<pJ3IOuEcV7dxw6_#24b|39fduJNzDXf6g75NW5
z6A9@xFc1(Y>c&9U*yCz3Ma>HXa8Rl*G0Pr6hgC}#f6RuS7~bmfscJJr<Gee<2gD$j
zoSPs2MBp4;H3y}f?@ocosJX38tL`sI)a+@|Wef2ho!QZ@U(p(T)=)~mi@soZHLra3
zPd8}9AdWC@-FCZxo>LicDB*XSc!5KLBN4SsfElkL3kSp$YbOqDS$ThZ4ya|EI09$Z
zB5x9HU*ZVJwtoko<se@N)guv2taKf4i>VRBh|<4*gdDW<5JxWF-v*D;uAn=)XbqO#
z@^ARnxXhh0=w_0?(m&={EL2Fphnuai!vdJN42(?5Js0kDVr^^$gqXQyB>uj+K5pkR
zG6K))`kl^=deikAM!wK~g#X(vSqeNFc2dX`{PNMp8{i-m62d{Z%ICiWZ7>AB&CU)F
zq?cDnECf;BIExCcxM%E(zWWCTzD-Sigr4c&elQjeMjv!C6LC103KD|qePqF|0nHVI
zY+k721i6e*hg0yRF$k0*_r87jPkRTbtsykxMA(O<g25oW7c!8Em8`=dVFhD??I~sY
z@qDZIBK({2<REnL55ph>J*?UQ&bbPsNC%?O40b2Aj{c-!+pvEu?EtZT2vBJ#=*+0;
zeF!Xks2K%Vk6^Gxy^+urroXyhx5dvb6XNbcNypnEha_{wE4a{KTp(!Vp4vo(Ck6kK
zIsX|a{{cJxL2>_=xa8k2pl6t4|H5@h6LMqJhn$rIb1(p(v(dZkAwHq%3S6pULf|lQ
z#M8s+<Kmzl?qgs;gf{W!ZKmX@sneG>oh)y3MHsQrtpwK0>IXaa(=#Vjzo&$Xe?&Oq
z>BmsxEX)4cFfc5m7MHapPHfrrYHRuyQf|7`LH}akUv8tT{}_67o#otiY!q;a!pH!I
z&yIx#w8@4A6$R+DS$F&T>Y_!K|7k#RlUBd#QMp6W0RvR3p}nv~XzBp)3SI>E^dq|_
z7o9)&F`{neYU|IxLxm$G{uf%+Q(ejMvTYRy^jakL_6)opu+xlc=X`ucwsegp9L^mA
zM|J_&dJ*Ha^Wi5QtiZSFPpHAGyu7@=6NWc54|9^>ge;?`QSRycEu7M>Q=a0MwCiB9
zIi&p@(5HAW%N2D2b*Q`w3Ai|L?C3#JQCxp>zdth%H=L$kI9ar#M}0Kq;@!%1Z>6QD
zFPulnEiIiWb7~X2?b3|Uc5UhCC}{8X9K(NXd80B6e$$g~?6%clmK{^r$xuEXWoZ8h
zjZf*bc((&}FnUj!^9Z;BCdjz-q4yzb!$`_T_!5e;)+CxRG!7{Cwo0=KoAe3G^BQVv
zv)tbfy~Tlmxb+Hb^<M}irofe$fBOKud~;6^2Y9<rI$fUEf=o*pc0*Q3?R1^UVqVWQ
z;3t+TrBJF>15B<ry6!Q5yKPH3w8s=1c76i9#+TuL8nrjpEQ!wxLSx%xp)6t?Hw|%t
zFnR!A8k2i(Bg%iKOtGJ^;KRkm^&-X%V0D4#v3GujyDkMV^^e<tvyn_u#qa7r58V!n
zR1=U{eK*d%{bh1dLtUV<#OWN#%Hpf$jpqL0yc15f&C)SW{NdE+qN|fluL$e7lxlKI
zo#xC^oD%hav(bR>U#lmqsZa)O#wt+uM${NFJKmw1odRtzgw9Diko#;v{G|+I6K7CZ
zoE;rQ4{>h~z}|)p2VS$j>oaHRKf^Ora&xxV|8rOWHRDU+FS_vGFZlOD!Its+|LcCx
zaw*<*70riBON$#BlI@QhxUQ|;F6@;*v*E;ABR!Im5(#Z~nS~iCq*nS7cj`BfuBeqv
z`8nT7eU580#ciW4Y)Fgx;&tL22}@w9)G{^YfmklvqX;GPm39r~Ui(tJw$4s`X-IBb
zR#q4qU(jdqIH9M<KsNKt)@{230n<J!-F+6>HbDCu5lT7@WI&zqmS_+^zh({|9jWNP
zH3zkz5vSJkKeldPq{fXp7W-1qxkEDmyv*<wWDE6kCzXYPMsjQ<DVYFimJB2Xn#2{p
zm;=q2A!!NEppm*+xN_M@NO$em79c5L(hDLf>R_B6Xsj3;-&-)<<r#{qAY>k#01L)u
zP{qh<8ya##y$Hd$rKLp}x1o@+QhsT$07wl;Co8PkgJ5|4Wc~RQ(B>uWnyVGyp!Dr+
z1zHjcDm3*5(Eo@~%IQABn*?ccbKD(`XC`3dI6l@7PQlCpoe#aL5O-xOp4i*jHN53e
zP9<zRJ{k6&G&MC<C?BAF16Y#X?hEgKzAr3<TnmLX|F$w(amuF;71^aVmOwz<61|-u
zAMeDkjawfkmmKAyPBvW_c<euOC?0v1Gaj6Vn&Vodr0mN1=%#CQIQp-|H2=nz^c3_@
zdhcHdAOF47|Ei^aJK@OoI3E~`Dp1^qkj#U+r>=x@usl7k8ie{gfFv!h-^3)>F8^8v
zJt9WXa4Ct*&ucxvdT;{EXxj%vanN8i1vta(n;hI{HR84AchGJr0V-4y%!dqvMy(=J
za`X^l)xfeXb|vH>Mz~_x4D~yW2<=C=c=#M(oB?QxM(ipXw?Q37st^=@5MdDcUIZrU
zqtFA5Jo_*;?6g}dePLd_4fHDnXEPdWfWasNO}#A}p>JrFhUuE%JSaEdL@&IMs-^Iu
z6lxGOB|$-9H;-Idy$dptU@(ZB^US0Z4t_V%0{xc2j0;c|S#n_o@RP{$07Mp$Mhga;
zgI3>Y8P9CVeFxoJfy-Wjy^#>z6%-Uea00)$?fKur0#Ps7CWc4zte(SgCgwC_&8<Fz
z-#*??0z}(q@k|@Stu5H{f!yW>Zvt_Io-7#tQ!VkSMAq)^-IFGT^WyF8EDlx%&D(Y#
zMCl^BWHcEIz7l6Uja%E84s~I@N*0%_gz%Gv^7oPO*2nkm*AgdhFyKeM+n4*eY3!Wj
z7yw)Qg#s1_mVVUDl2gms=OQd5EiEnXk+TAw!M5kW*;!eUC=acHqVWa~`gF70$#Mwa
zzBtW@`jJ&y)2o(otC8l`I!$eD49~kB{_R}O8c1q@J5WW{d10d@qM#7L_<*HZQ+oQn
zW}&APaCqGPem6WJMP}OEA1B~$_5eH|+2dmb<w9Xt6AY(OQl}OH-E#b?lNtfY+ptAF
zX=bc0$UH&y(WMTEVkb4UfB0i7+7ZIt>x>LH&}ajE=`Ux+075(BFI0nEROE<fu>Azx
z7l*NL&m6rH0tD+7NV%&;LBF|g-@|bLgMq1u)mJ+8(d^2M9rwlYY4X66ar)Rn_i+x)
zEXjPE>$r#g>@kocV5wFmuA)4IEdN5h9U)<+;L`s`)iV3-BPM5*`PP*iZ?dzpgbKRv
zL<a_<ULVMcXLS|S_Z&R)3ThVUw^IPjkKP4hZm_1^X~Tjq2aH*6Le+B}@+}4f4R0n`
zh8<QMKyo^~`wCnu7uXoTz~b4ERbf69MPK%{0LY6VitzHHLAua-a$_`TJk=%TEKsb1
zYQ=RNfJ3fpYUXM8>Qq@WbXI&Hgb{(ECU?uBB@gs<Ah?$PxoI(sWY~YfI8{>^rGcMB
zvu|NaEU-T&>2#OXt7rVY-#{+eY&}Iyed6vuK*^ydFks-11RQEf&!;EXXUdnMM!~eE
zsE30n!Yn(l0Yp-sL{m4kY|~o_q0T!f@9Pb7%rd@2(m96PNxa;-tU03>ZO}XycBE4w
z^65@vO!2@ZYEOxA>Cm@Gqm%oGy&x7`Q9!L&Sug+q4Q-Wyfq)c{rK=Ecgjxb&HcjP|
zrm_0JtY~-m`M-q*9z1*dY7ER<p!=OlH&xn*wC8_SN~@gzR}w{tK}iLRRv-jJBud4&
z_E@2gv`hZG9|a4QyhYLun&|RWP3jacFACt6F%DPIwljftYcy6DM0uDqN`cme612Hc
zkNz6;?ZL2NWTJwG)jA`LtP6C>*Pvce3<eeW$drV@V+l;V7y@M*22=@qf8Uh@F&`nw
z2^dRr5w!<{7Y<qstKQwJq<;uZ+^Ruizc!r~ZSC!-h@U_MuF!FS(1O}jM}z{n4ImCv
z2ApAQg+fkALDz*Q8KPYPEF(}K5+lzv>x`}sLc>;}ry(O&9P~YI+6RGVm;%ZL&McG^
zfRH)mCONMG;2zqQ)0VNas-_Tzb5aIC%>qnHj03Z^j=I1HI9kS2BuEPL@3)|l$;7xU
zqv9Lz7ifyGnR;(;@51{n^hOS7gjKA1NtKaR9Yhm7P<PI5j`LUL1ulR)nNn72-OewY
z31Pa8dfxCa?)pFYr2ne&MkUB$A2cokonnaCdNVgZU-0qqIRukH;dZ^3XV2<hVuhij
zrDp2jq9UU|0fA#OMFMpX38Gz)HiKljpu6M+FixnqOiMeHT_p#G-9{F3<lckceO52Y
zyI)_ScOo?ay+lC)TD#CUPy$A!!fuF6eQzHB0S3u3IWTe#XpaJ@2Zh*ZF6t`GlsQmm
zOjpqu`Ykub-~Ua~*ghiUHwA+yj-fUr0(5<&F&QE-8TL6eA0LA5X~PBQE?{(3qE0`8
zp=C!#8)G?91NM1w@tUq2^Wi^t33b^DqHZ`D7?w(bHfBkniw=Qq8`PYnSo5RysW}K_
z1LFmGmQ7(WzsT$<l>ivP9AL=Q;)W()ZONd`QYzL*w?now@BI7^qich0Pn`s5tw_GB
z+OH0Cf%ZahbbzT9&q`nWDl)g|LZO0Yd?F_Y8r>?On-zjwB}n~8V>f|a$?4<!$(&oX
z%>ZPKKG0o6+-CZaq=6JN5r?5FbZ@)4q~2F;Y|luCKGJp2$mkZ9aNL?Xnx=tRtskGC
zIKcC$hR$wQkgtvHCwo$H_d;xAji8cwR{C4+9GG*6FzRtX@K>B=Vz6eYlQ<E2mkg;f
zV1z<rb2z<yz#EIY_3fQYk!S|csIb<D2d1Yxz?f85c4b*Kk`_b|tRof828Ugpt(v7-
ztK5wBne0_=c9K=8?1&2{MSFT?z1aW;7ERnEBG`t(pml-P@TlUn^~=mHoe)e=1AiH`
zn{yhE!6<9w(amQ|H*@lI+@p2!0G+5u(j=#`CCoj!ZfrMYh(#UA!=P*QjZZ>JOUcCg
z*xditpRc*`{b09LqZtdD>VdBG3KW8<BMLQyBRRYb@(LQvhng4<>KZv*j)AtR{+|De
z-fghQ*N>oq2o&hMK654P5IbTL0TdvO5r&cyNARZH$Y+RTGY8O??49M&oCDN@-k(-1
ze^MI+WHP^YQ)f$Fg`G_kab8tF)`ldH#)TvNiY7aN6QaNo-zrSHxC;4*9U)UR1Pzca
z8<;WNv~+cA&_q&5KOv^;Kk*cp9wZMmGaJyW0&c}?@o`35`=Qs5KHdp7z_5zx>WxFr
z)=c0uW^JVZ8v)<`xDK2F@(F-ORy5Zrtzsm*aThgrp&<+?+@U!DiOI>wk-G&Z{)9qv
zrWD$wXq+~E<}Fn9eT{!D@94M8!)T-n;LR#%)n1o?><GA04zLTP+|xoZw+hXJ1-w)P
zmZ{SGL5JaLBAQx==HCsWnZIy<w#}}+wt1?^bw?knj!*`48AB`ub^u}4HPm_zu9M_w
z`=GF}u+W*5w7Oe8kvA4#ia^$l1#dyw$=-rRICsz5i=)<2ymf%dfkZTH9-WiodJ=yA
z)me0mAW6Wa&?;mWg4StJt<8X3M-QtSBsflt&6SWAH`{5^uLwS%!8ZyDNX-oe&G9@S
z-o4O)19XWQNY%j*?;6JYO<ZAu2C*$w(;WDbFaW8DhA=kfLbxy6XW9k`9u^q95d_kq
zUHP<;^a4-VlgwfJir;}I7`pl=OqTP!-w7OW(9t>A2mLT|>WkOFTwI`@CYpO*Z=`Ba
z*NycN2tP15{Gr1sYdRXK)2J;E$++dKKG|T9MQR)L-g%Ti;h*Q0y=n@6Yrubjfd~rY
zC(-;L-i}{ZUz10pu~1EbS>RLPlma9~WEO*j41+-Wdphzpmyo-N0#0XsRzU-;nHwcU
zkz9u@19ZJ)vsR(p+)%U;)`QU=^7979(YZ5+68mG%y(b7*!p@et;T^u!u&KC!490|D
zaMtk(!I<^swiG!QaOirhEo9@S^T``v9I8f6J1}X50E{r9R;<NxYxx6s8?Fft59}KE
zT88=WkmF$jQg-J=;(2I*?*sQH26LtV#B0d568ui!>mzj(WW|<Pq9+Zes~UJz-Du+D
zFq(m|FCoSJ_OLf=!tZ79A%H#M|5)eT@qG0MK45P4%*;~tfN;QTrm81;^MhY#mK%ik
zYLLD6BNO;;5KPf1n&r=ma{Iu|XiDI^c)ihg($Hi6O4Gvb)i6QX&Kn#MFGX<cYenE9
zr(3}Cn~rgxyK@cA4FWDb@1q!+>E%uK8k}5Q>VeB2Mh<d(c0L%KI>9`0P}<d2<aweS
z>7s=s8qkey8F?)$)<%SDV7}W792(T9gvP<qXYoU19@5(L+6E@}Ae91xLGmQbVXA2m
zA3TIo7j$Q_HjQy|HmH*MVfH^f>uTgP1+t5RMu+srg?)iXd=2IwqLLT+qro?|l>#$?
zq<MMi_X}PqZZM7a56N3c4RMU~NL~?B|6q6ohN81#aLn!{xr+=ynuWso%as#-2fYqx
zTop{AK=Zs*!ET{x&jv;tG)EgvCb<HgXQnH4x{1K;z0O~}JTg&O5vh)yH)?u?9d9g-
znt}rW<Ns-cMc~L8h7l-2+qS~!ryv;KS_2LdagU?G<`r1W6#{?{tyn~X4(tv?fZxU!
zeQW)>wW81zHF_U28nS5_>(b$w4YL8NVZQPK$e{~Hf9`w-Rd%4>ISIN;AHrD4vH_Gq
z^!r9rUC<<GG??Z^+7tp`1R@wZxuNWl$aP$v{Z2tMl9#Wq3Xb%oQX$II|A_=N%0*3f
zp=}vPhaZCR?r!um1D;fvqyS?q&A}FdaH;Ljf!qU~7cgN@X7<hM1i}&ocFmuea~XUL
z`^I9II~2RaXtXPuM~$YmK&$wKV>t|_f*$IMz33o(yuTgAKRCzFQ5uG+9Tc=!lm@Kt
zIW%O%WL^Fw!ExSjurB?XZ0EUg>4JG96<6eRLX+B&EruwcyoX0l26f87{N6z1C200V
zlg?o_aAQUmY+NC3=thy4e&1S9v@Q3|_DB*$`{|#pY6y)$cZ&#+xCw(SH>ca~z=T;f
zbPXattMhb50nn~awh0#)TFFCbx(FB?h=32xpiRyqs|dGRqXG8lhZKY^khOt$Uj;&+
z5fBZil0sVp4l?p-R>4DL50_iLi`Ef}s9{u4DLp+-{QTso__DKQN9Tn}#}HddM(jg9
z>W`*;!sn9Q8O|glT%-`d2!I?l5ct@U2MUb_=^rmK_Su~Ut?P8NB`VNhCQoP$4^9RR
zQTG+M^2IK}l4aAEjI+cki;oljxdv=7j}jX7L&s`J*oAOm+ED_*d<gy12xtPo11j_L
zqt=y~`C9=Ba-v}cfWFQ{%R(*+vyhk-K}ME=fA)cLpJ(kbH*SrBCfk`qg+3yL=hl<{
zngH|@qIr-dC^#D&hjd^G!Iy~XN9LnCZEJ$4Sydo#Jkq>o4L)C>cZuFZ1T*rhkA}<a
zLwi(Q`X(`7a}l~fhG7SXz@+apC?TNm0fmJuM71~+lFa@BY>Uc*umvgIPj}`lW`Frl
zv_nvxh$o_}&@hK^nV(nDKZfG~|K5oGLRnm80JE_F4V$s+Bz4a`nA0QvY*IGM@~d>N
zOlR&$hff!3rLxUy5c3&I;DkO=bC}L{9w3QK&BYhv7e}mMS_B&4!^?|C8rPV3O|xvK
zS~RW-234$|lAC+cga`>V!`LD#Dvm?q-c5&<VV(_l{3Lz&Wnds`>R7gttV2W_8kB?3
z4tU5H=_Yy96Q*iE+><&F7zZX5IFv87c3dMq{<Y@wDEmjlBmTSDq3pw!FLYcP8vr;Y
z4p|Q3F}b~mcYVEjwV&)uPc~>aa!r!5A|N^B!UnqPL=t~%vmvdcup%jj>>zu;g`Zd)
z^}kxX`nQ<#H9XdJ?6Fo|cAd>`Z`YDtv~q~j3zb?7DkZ1asZcDvOqyC!Qw|56aHXlF
zx@tm1dKsZ;Ou3>oMRl;fF-6<^)S%3aOwFA88TLQ$Ti4X}?K|J^=kq-GbARsVewLcU
z%(k6fJ=Ft@c`5*I?F24BL(n%PJ@|B;)qy9CU#9d;Ur)N(#6-$OmR<>Ai$pk%z_wWL
z<m87Ygynd<;R$RD7u_Yz<kLSVr2iEwmL*T7nIE@dFQ`rIC@Mu4jvn4rAzP_WV+Jy$
z6xuUzzH<?$9KF%k_X31q5=U|^y*j#aFr-C|azC4N7ib42$rb<}V45UYKB0cBiW_t^
zP9ljtd3S^goY}TPVM`y@*Uda=`hZBl@^T9wU*BES(g5%tDu9V32T%*bEZHBOt=wJY
z@YiIgncT8o53e)fbmCx!F@N|p5@NS|lk#@1!%*6=U*lPEUnG@)wfHakRJD`U#C{CZ
zsRiiLqgJW;^OIbCx{(SkCsq!*+Xe=4RHki<veZtxOCEO4$;$pJtgWhCHW>c|dr1$6
z_Z+H@NvqKg^lRx=02@k?d`RCyspgEPi6HGG4Mz`Ij7=hTFI5C$&A&z8pDn_RNNjgF
zZTQEK?7{=v;MzF<WicT)(&>>=ExBRn+YXV&OG_v{$g19=Jc}ZYtc$eo0FuH7f7A0j
zBi5I~qixY~?{sSi#pSe0>;<KEFKo5GWZhO4O<o%YRx8$`-QXb<{stIh#7}ZfjmJL1
zBZRjXn>qw3yUq@eDvP^U-<k4&hDdm7?Y$r!K4-<*{Zn`H>>-CfZ<WEnJp;I_z^py#
zGcF;Sb7EpU*yK}P#SAiXj2p1&{x3<*wb%0xu(93`DSYh!YpXvct$|^Lu(m#FNlqCu
zXAg`WLI^j}2nZ-<L!63Y?5M(kw{&$af9H}u$5wR;VA7J<xJWy5#B~7Ix3I*n1sQje
z-9n}`69arI6qd(NzBe=Gy`z*#5-ihziA}rZMXI_V&=BZ{x8OTj=L2$LQlpTqTG=?3
zJpKIvS@(BWFWq!mztHr)jx;fdxydT&$clip5|bl&oP6oAt^Ygel;pHv$Y62sxw%p9
z0Ul}I;)YFoQx^VazVz1p2FJ&L>{C_$-FRiR1uHyR73VKe%A|*6-J|mN5wC^PJ2Lp2
z1mW#HU*k~-k7A^iI|!P*$tk@{Ai$%19@q9(|G@sUl{-3guEMgb?(53BuDI5*Zqubp
zox$}&OL&enJLG)sf?)@M!hK*mBztuAIx>sTr@px=op-dy!_Cdjt8O3aJ!(bWQRj{L
z-UUjN;24v4t)#NBaX5ec)V$^M@$kf`7sf!?=;eCi+>(dZ(_VlH7v8ARy}AS`f|(di
z5PX>2&yCMus*O^iDWQM)7>fBE3gU>k-6f^51x<1Qx7}#(&qUV$pnT2G4zFBH#~n1H
z<JJ8W8fiqujhN(-J3L^D@)ENnP2+e=1AtFmL)Y;P%;4kLG1NAzV(qV$V5^bpyJTLt
zaTG0IsgQF!K9qMT>^4K(LR-2<jk!m@UKK_8`S}vL3!p-k#IwmVwEYY?o^sLV$A}LU
z{EVievot&Jp7vFi|GcZK>!ALPp6dnt+9=o9aqrD-9d~0mXrjFcy4{wxSH?4eTm(Xv
zPm>%r?;9LC<?yysu)~pBn?kWkGs1vCQ*g*IE#9)sQfVL3R^kjL`tOa)LqqWEFQ&MI
zn7`2(me(ab@R~%THZN4S4-ETZA^XGVB?EN&NR+#m=Ubx=PSptk|7u}j0~YN#%qR;$
zg5!H}NS#U4#rIZ`y+#`OPEYC8P}R6X{V#!VmAASBm_9xR(oJ_y&kR7Y0_Zt$Ky8JV
zAPzq52Om_A-TdND_FeV0w_OdlqwR9m^w$;L9vR)UT(WJ0UC59%SEX>qx<mdER?ZF7
zeJ5>OX1@DTJ^D%bc=<u7OW^R+fI^Lg`K-0}i6s>UN_!l@*W*^rQ8|#mF9vKeHR>r2
zyn*&$E2~)do=UcRc=RHj*Tc)`97cMqMgvPnS@33ZF9Z9x2(Z7g<-FqoVbJl|0+F)0
zqf~NCS)XfuRNDZVx6%8rA3jNLl0GTS@-}|15AsrrFft}MIC!zC>1Ggzo{%@&>%OTj
zp>r#R9+nmWz|b(XL4yvDKH48Fa$ks#!&sjvJ;OaSw315uluu`>a~BgGt}2Gr%;ln@
zUvTXStZ1GIv{>~3h?xxy4H#ecGd4*xlGBsU&PrDIX?@Fn^xiapD|mu+LRA};TozD;
zHjWqjiz*|kP+m$}E5KM$F2d{vF-~r_`TOT0Mu6lp69ZJaUzy;etRO?$d{7?g>FJA?
zEo;X40uL^8<|IA_S(uI9z~bo|cLrmwX$Ynj3c7vZ`qzXRbtW`1s|-*V{-ogrVW4Yz
z$Sn`eZC6W6o58D*0jgoAA?*4l5FI%1j2w*^!AvW<l~fZxSK;k~(<xRfxnLIH20(|6
z`rilroYf_Y)l;-#xzlVbc!#qqy1{IZ0fO9oK7(<%NBa!d)PO?2)fi#PBz#LJ6Ep>j
z>Vur)Lgth9_H&fdOZU;t5p$G~lH)?nv)0zLXmP`j48}VH;)xIa2Z;O9g$wfmCK~B~
zL(Wn2%}mjuDXy<As761K_q3Y9a*X59vmbx53FwWSa{<XLm}3Za{V1<GFIqz#9SP9#
z4+TljQ3&CnDU^xqqsF%fjOoYQ6oYcDkP2bogkS!`bl<)`9r|CBY)iHa{_xcyasx&=
zamyxl9^>%TiG|RewX?&7PWK(YG)%*UrU4`=$$1Gi6k^oeErAATeE_X@FV0KAn3x}Y
z^U@@txkiM;f^r)*`|NM?P?nQTrimDp)(Z3pCUDo#!UETf73Nv8B{;aRS~iPuPc8mh
z`!K#VIv7~+Dx3!=+d=^sRORT(7tTELhC*E%!RrNEI2bXzTBDBr@x>Y#IIlo{)REi4
zjM?4CC(Fe9?!o;hnssr#4H$K4pZw3r-g=hNbBp(e2oPq^)6>#=QIt6Iz?teAmh2eq
zymf4J=C?l~1m`<WH8wVyBPy6Mu@Hb_d58k5fdt%eY-i(#My^^{?KN8}*xk5h<qS5@
z!^7jI%DS(wkIBPp(`kDHdj!4m3Nv|&8&J15B#tP}E6GU#Wo0u$$2K>&l%%AjExEQa
z!f^&eknk;*bjyOt=N)B+IoqGl!NjYb>?RKS^n+n@I)NAdq^0EysD=SNXjkMCDl&*h
zjX~J5xfsM?R9eyY9a%siWk)Q?*4U{-*aF`J)f4|ms3Y4V3@qq2o(WB6!Xi<8K;LE*
zn^s@@2C`tNQPAKDV4bFxYo4W8qu(h4P;k0%xwyXZ$Lr%u64i@+6F0`Bemfm`05)P5
z5a#bkUjZD~C}32jEZWLoBps(|qKfl~$JGKZDu4l0sbb{eOlVk55=x&6_ddN${to1Z
zMLnzQRL<EJeH8f-P*xy_kl_B9K#4({t_X67^Rq@_@Ai%xwH-H}u8EGu@ncJ>0qVR=
z*ul%tVPGO2>9}DYs~BOG^Vf1e=-5$bA_pyVw_<avxT=0AS=)s>r!fW<6c!J2i-5eB
z-F>r82jkv!<TmK_9LHfnZ{AUzeH9YQ90-T9a%jx8FP*^(BoCU<&_}>vk*ItNuwg0C
zT)DCli3pAcS>O-e$t1#-f}1y2Qdx#YL$g1r<epL~T}%B%?%v*+MCX!6aurxHx_#Ec
zXuLerg<&~UgW7I%hGM4qN1fv5Paa4SpXH*@G}8pdyS~x+qE>4##{AXAsbpQ?k^cjp
C4=$nr

literal 28236
zcmeFa2UOMRmOY4Nsb!=vU_e9>MI{LcD9FVCBBG$8NTvh<$&zy@eM(YLQ4kSOkRVZV
z1}zCn79?ISAV|)V^X%_hRsH_ctJlA0rh9tUn&;|X)PsBPFMZ!R`|Q2XxgHngq}Q+6
zv4(+xVZF>*$;%83D{L7UmTUd862GDPy&A!fm0GgWlK6>#GpDm%@MHC@v&t3>46JVC
z|Cjl6j_NQl>}HUWJbA_T#Q@FDar4B2#E8wtjXz!5JQ`|{nyQ#{F{V%@C-pb+mntbO
z`Sz1nV$!Y}8V_C{tmm72=|5p;nzAKMQ7Z0sTWz%as;Jt~EmFtVGJjhmW8FQqiZNpK
zw+4>v#`CrmHlO3Wk{c@>CzAz30)m_d%k&OC=8*Y`oSAFc8*ZEB<i8ktb}Zu~KTn)l
zZM%&8hrqdaYst@tmwwV9KV9!{9HGDI#uL}A<QKcce$F938Mg0hTS0z)Jn*0R2;DNf
z?x3wgLPD1m6gEw@IGBF!XA`%#R9PBmO!m@{;CX8AR4q1sZzAW`*FU0SV!WO|e?C1^
z>C$2lz#*eGMYHiCznCtT&`3&asNAe++N14s^;Kz|D2`tu^)8S<Q;l~A3rkpHBL8EK
zgZ2wC{U%06-1fBCxG(*_Ew4mkyDOcBED{}OzLO6ZtUR7vE3jqDmeJ&p1#Y~rBf(|C
z@O%g*m%}(Q<X~P)Rq<@}DgQVteEJhF*R3yo0>vGyn{7KDFfs98|KlG~@$n|f1JV++
zpBpSH#rLwe*MuxuelyS9rN0;Ns1o|Ah0>Q&BPYwZ#i{3&TM2FF%lnEUiK^GGZ7pWC
zQFH4|$m)xBcYh{gs<C6;^0w(U46ZFQxQ0#p&%1NTu**C?`t9NAChqz#U%p+w)U$65
z%|!OA2WPPRc0pZ9$%1fyji#Z-?3lsiN(#SaPnEm-(Wz#Opu)gqodeo$dCeSGFq|;u
zXj{?qfj9HpW6N1`ZYRgCC@L1!=S`-%yW7qUrqd3SMQqQ%jPC^7M4hT>q2|(Ty}NsZ
zVM4A}a=9><ZHu<|vHd}L14*Txv?F-w%d>dsPG8oGG(VJc`MOrK=SxnYahmc#Mu#^O
z)9A<t#$p!JoJ(KQ$Py~t(>8cJe__zHcfGJ;Yfco;R!v7Um9&V6{TjAk?+TaJ)z?RM
zu3W!k^Ngc&oIrPwjfRSDd1-`2rrK;X+v14Z_|mGKw@!Eo$1YN>>ebiU=g&A&BIJU0
zge=W>Yg3KWA1qtGvH&N|${B2{bv{5Kyw=+K8YRSM6`SK>-i)@}^s5+Yd;dA#WvKzr
zOq(p9P4dV2oo7llizJq2DsLCR3ly_4xz8p+De`7z8Mdo*sGgl{GEA8MV{6{8=`Hq#
zIb#)KD^iWS$^y)K!~Kh#=LWa37`;9DrM9-PLTq?vWRSJW`QYQn`uh8`vwvw4(yMqe
zU15{ADNNeO`+;!NftBmF{mM4qwyL8{u=2jcaIOee@44AoCZ-1u9wgStiG?rpHd|B<
z-U@c4u(8cG6j$37-QTWI&A(3CXID^O9rnatt=ef1awadFXL}RzjEY^CE!QGf&*%7c
z?Wm8-d|0hLPH&UHhQ0nkW_NI6i+z7#5sT^0km&+Wt?%DHt(aJvOruU(rdKY0<CKtz
zWIR6d?zW~)s)D?@P*7fnx0tB73;z)zp~BUiFCOyd8>cny*Kk>w5H9sMpdL7r8#hsW
z@7}$P-iW~VrG@;Zt*XYUSJ!Pjy!+mI7H0CyjxUVYtVmCINU3Y;_t#pYxGc_RV7at^
zU|YOJZWB2b?PBi-F$sC1%zPR<nV7h)#T$*<T`5~QVcH<=|3}GFt`0oK2e=(PX;hQ!
zZHIq5VU+Uuj2X5}(b9b7QUTtoa#1{dz^#0Xp{kLggmb;RO<|*62&>q@+tWCP)P)Nd
zj!!md$JKqNQ7#R1#9WRqt>6y4ywh+Q!^QopgVHQ}t2x*oyPxgr>wD}-Y1M6fb=oaL
zXD|CqC!b5BZlg4t(-9t<X0zf{m9By0O1V<9m<lcMsXQ7nVfn21Q`{`MZR0b_$-4VY
z^B1fIj2hlr_cvnK`$~B+Z^;-Joox5w+du3y?6B6E&3FF=gY>+av2M<}p={Il6GFnm
zX)5vx3JT^66ZM>`Mu}2xRygl{<hi;Hr{+#HKCjns;AzAP5p!JNNo!ya5HeJBo3N_S
zu^W3_Qc^N*HH?*LUFxzl*D7DSVY>i_a?%YM8EU$&i}OV7HD8|e<|LYq&1b2t;Z)1h
zr%!+Hm3PUaeRmj4z2-I?mfWAY&1o{orbQ+s0TIhaqSg-~<h9jK+qU0>g7bL{<NedP
z)w5EhT-Yd%wn?_5?TV#-c=)=xhIzv-3;mj(@DQ6#ayyD1$YQ078R_kDiv2P-rN-;8
zX8vj4VBNK=SD!XpQ;k1)ZsAgy&*xgOxDehT%_;MhYO0q$R*`{a>)X`Sw4t?LhBN31
zw`y7}E$dt(qwni!YWwp+r<qQFs#>wn@$YB7?7V6@J<S|bRSwqc==ohp^hsr4IKZbj
zVtrCwU40!J<v2ULACD^YSXa4WYV++CYwNONXXIUG6#^$(5F>iWKTETz|HIF^a=f>O
z9wNF!T=Hn<f_fGA7bd9*2lT%4s%0F$$H>;wB`_F^hr^fFY+h<R@_tpedVRS_>rT!W
zw@&3C0<ik2rx_{VXB8869t&)>oU^(%H`ltfi!A=*^CKTv{hS6<>$R{BAL!RaY#E$T
zO|Df`P2INgw}FPl(XK$#G?mH3;$2=|yjcgFjvF_nD4FI?oNLPKiZ~yzk!qUnR2CwU
zPcv^_TAb>*mOpzHIpb)%``-5S`~~CePBy3U&qr|-ueu7YiN?ra`$IvLo|kt*@vp|p
zD1TwHcO2J;9G9qS+;=~{)mhBCQO7?epUt}A`e=i;PvOgxcj^wQMjMAcp*CanGJUwW
z-fP8L<`mVTh(HrhroCq*u|`f#Fh@7cO>xMyPj~qEl=9{0(9DrN5r8(4*eFKp@_ypL
zrhbT|1Z`Z_iXf}wX6*$H*WFmF1;fQI4>&9JRlX4$&Jm<czh_L#^!6315EwLe3VqU_
zBv=~JP^^Na8RDAmB`4bZ+H&^yRl9DcmhJU9{#Xw8qK+xCPu5CR^WlEE7tiE;b{<Qr
zF^-Oo_N1ph=D|RUll|;cmrEsO&M14rsaIChbDykV!N}JS+Oy-mqeI!lXAtNkA|h-N
zmD`bgWJ@c8#l?p5=7!|b?JitXGn3-h%)?@-({x$L7|QC`TAZKxTvhceQpooi$5^FA
zb`s+<06;oqHD@oc+j;bDZB|!BP<EY8ujWGEwT_wY5T7kvijp{t8t+zFf8GaMHEj3b
zY_}o3+;THUv|!b4FYp-sUdku!<m8lfz6HQ014~Lfon|dt8f;dyJrNkjg!|FeuF4QL
z5(suAt=o0{s53Sc$&yTnFOMp_wHuRkM&B)Gd@Vtdl)S$~DEj4f&MfWb4CI@6WC^p6
zd)#8JdIz(I^H{>pJUGX;)FUln@VO#*1hMOwAD`x~z0%(Iv7HOPcb4uAsU+uee7x#(
zVyB;KgbsO-#mag?TYwDYNi-gIp2}8w``bw@xR0IYqkS<6MV~lBUii7R{l2Oln`h9P
z#j8<Iuk6_)IiGF(p|XS9kKDX?<=VAKA1cd^SI>f77H1kvvIn(>nlqUa4V;>h5CP<p
z6&o)bvxK*F0azqRX=`h*V--EJUq1N7kS5EH9qkC!55l}f5AldOOb#4HBomEI7WB+@
z$zPo9*RFmo)7yb1SwNPCi5{0?tb2f<UJ;%o3lq~;wOc1IwaQy`B2kpkY+7mTM_RSK
zc-p<ihHie=BxiGUak`^M0P(=<!v?X~m$H3cjf@WM7JZ1u-pE_zWZQ!shCB+rwn|e%
z<b4b&-yiv;h-%u*N%DIqoXh1EOE&Vfiv?EV`+P@l?bJbe@rf(;jZKfFcQqOW2v!{3
zo69i~pFMQ0RARJXli^eKl$)1k=iA=jq2{QiR>_`_r_6jyX<p8F{Nl)*fpey&BnS4Z
z!Q;}-CT^F3#Nr4YMv3{6+nSL)!w#);)w8;l!L&|$EWspXx>ThI?5+H)+B5<UGKcf#
zM01`T^)KDW8Dx2RwyAY-GM&n;`f;|bd+gK4M|*3`*%o@FeLGA2)8}SMEG`YPZqmP-
z``$z;(_H8K?~IPSoyq|Qv!xt19Bp$Q&%6|NW=p5}z^T~Ako@UFzO<Ppq^?O7|JNJv
zc(7V+RCq!vyL~?}N*w2L=nBwFYxF)<Z^1ujG)B3FL}im1EI!pN7^m0w<&AoLWai|(
zo6kh847{#O6#1~V>WYUq37@+<{CojB!REXluX>YiZtKF3jp~yS0NOwuF)_*0{8Pio
z_UFkiOc?;asM5_vh&k3Dn_Sma=b0#fppYUeQzAmb(P^(&n~=kOtmnSgUely$1(1Lq
z=f@=_!QI*Y<@uoaz}pW^JJ0D~2=CdXek`x=jg@jw(!sh!W(I~_H$<5oJ9bpx=|HYb
z-4Hc4<2ZZQ27BmJl1^#$cZ7pNm5B=h0{fg6$3m7I0~4se%Z8EDUK}}cBt>-*NcvEp
z|9F?Ct75Fu=T7@z$M2t*2kpOq;>@m%uTVjzooxy<&9e*aT%#OPDL$p95|NdaRnC7l
zP&Lod&WnFBE17$3ZJlk7p?5a+`LJIOPmIuTC3%aRrepn1(<xJzIo0gWz^UXtr|+dG
z$3L7<8=#0ePLG8CoDA?`sZu$N%x;pZpPKeli(!0bM4>>>&HG=-o{F|9KO7OpAQ4iT
zuH-{79&Rkl2(!hKGfw2S4iJrKb1N=`<Mh3C2kqZcx8EHK^L_2OY#13rl{-wN#Ww7N
z-39=ql=&8iVVXg}<?#=fo#li~O<!5rNWF3j<tn}=o=D#XR%90pUw6O98yz3-eR{(&
z4t7P_-D$vWJ+JO=#4uSuZVz{NdFSuG{I}ZP;aQ2Zhjw0E7ehnp?;U@}uYIm|KrVCS
zkF~05!L;YKr{!bQj2e?na=T8hVtpdF)V?*p=ev3!!7!0EK*%O$Gdl7lF}5!ZySPY7
z=FD^jCYpDa@YY+=tm?ySG-pb#sj%L$ys&)bI``xXQ7)(XZ@%)@GZSlTn{0FH)hsS}
znb}05Am~rXcm71Je$MAIQ~EhUGvB)O2;0zaAJ&W9&z)+?rhNxmRu|1hFupy!ruDhj
zRAZMmm&x1rE7jxmP~^x)Qg>)(W<TceSj(4lcrZCNHFXpj(I$hByEg6fCTjEhRctSY
z_BLI<bcyMjhMmsr^w#`oDYwA`SFpOvi~aI(!C`4>X*Bzon3xXL)>*~hd3YXZ4TZ^e
z&h{tBN0JE9;5b$;_M#gQ!dY~IR7nWuRFfI4i4VcEFGM@;3$BUI<~rBNX|JVjt=qV{
zUui8$!PD=k>qBP0L{|5{QHV^lji1TLi7DQB{Odn9v9r@-YPhU_+JV;jw)~h59cI-%
zgHf@u_aBI~9Cd1@@kx@kBI>j_(_IWu%|bF_*^6VzANWMdFN9FUgBHv5C^d1yar&Jl
zPt8ym6(ixXp(4~YN^8_56;srkH~OPf)v|oyW0rb5n@NNUqF@iM2~L=?bzr5PeWO&j
z(<gl<DZei|#{T$C)A)yrIj9mMYDlVc6WPxK9Pd?3%UrFTI5*HYx@B+ZhusO{3v*NQ
zk!v&gFY56CiFu1pHXJ$8WLa}TyW+*M(N~O4Q6KDNQ;o+9gwtooUhX9TRy6mw_qUZ2
zwWQt;>dWJnyUbBrt9wr~il@A+=ev<J7T?$FWclWZW_-DiNK-PZh*aC=&lmQ2)iOHt
zSs0A>d|pvcQ_Gx_l#eAvffg%@f+~j;7g3f$(KgrR)xB6flnMm8@ljG(x+_^Rw>h+o
z1-rXXHxyI+C`(ui#fx*pY)nk1IiqLnPy58Bw}`G}+@;nRvGsLT^5XN5EseC~6V}N8
zB>i{vg!y>ul=>AmXIgX^YR;)R4jO8DxnC_E!-jd#;UhtrL_Sok{CNH`N3eb0oAJ>}
zs%dMSuu#I(bHjKKi;82BH8v^3Ml)T{Te<sR-q~=jb*zu2@koBQ(^zLnX=imW7GZd4
zLaXvMO((14(`|P~k?SIs#K0AJI{QXd9QsxkQBtzB*j0PH;ZW-3@y6emuZmcR7kh6p
zb}=DLP%yi_>NMj8>M>L>w%;B}uW6ZjVnFTjFpi&TY#mg#Q07+4cw4xBS$4D%0f#DT
z?=Rn^-rK;caxt^wKs_aEGd7CLd<R=^hj&GidR$VH04hPhd*KN=-_H>mz}SyZQDWdt
z<r|bSp)V(eUE<8AJc3pHgOr5}kJkOf@Y2R?rKw2H(!#L(a34jx=>GV9qy+yBnYZRg
zizD@xZD0}R4w^!OKN}es(rAVt?4Lus+t)KtczL^!!4;g{VUwKEB7%=q|6$_A%;SlG
zxqkzyvuy%5&dviBDH|>dop#q+zxoy_+4Usm8h5S#2`Sf$@7?<K>k0Rxgs}akzMfzu
zgwEG4z*)VTV|-u2kB%krEX+<Ankl<T7W77jXrwKssOV|tb5d13DRV7Tc_Kj?Iksm0
z{dWCv>D9fbc)5mRyYzm^N90<Oo_d9PM&8)sZhVKQr{}oE{?NH=GDi~9x77_~8wS_5
z_;n4xQ7TFF;e9IP-*4X^cWopm&?repls12Uv2Jr1+vK<ID|&MmOnWwor}Bzr8)Z4r
z<1u^N;5}ruqq99>HO0xg<(}^D1c@#r30e;{jL#qejDN4~>KJK3i0K_KmRKBpoOX<B
zc50Xn5W(w{FQ+u{7|IQaAv~O;6JHd<qN5M7v$r@+(T?^-g|%}^%*c4z)vx1K^ts#2
z%9k^8O3ituv$q5F65kMtPQp&1I6Q*d;M>^DSw&jLVXOdk>&9?ug6h!46t4J;c28yr
z6-mh#<|POBvE--n9gqrHc(;o3P!4E=3gj@Oloyk5=0F4rA90#IuS&`RYrto4KwCv$
zB>cE(h8t>X{IJ^op%DJdnQZOy&Z|AYZY<o;T#`z^Uxw0OH7sHhe(_YY+VA4v`I*t7
zzx#@5FS%3Mp<<-c80Y<|#l&FO2tnZ>=TLHuyObBldAT3$dZx{;Rr_`wYOQ<#<j4=l
zBqf!L4-Cc~;^NZw;z@G{aO9?ga#XKc^V6ffW8E(-<h+s>4;?zB!d-mKqRTRSef}J6
zxVm?f+|fNq@OgxRq!)vPebCYARMBiF^@K^jiIkL-m)Z0pD9f~I0hJ!jvB1<P)xhWN
z{hCYmv$hi#N<cGee~G$8%UZ3hsfbnemC@yBkn>Pxca~aZA6q=T%DtjO0o#$b3@oaS
z*S>O|?pH;=H{r56upmur@_-7&Q_Ht()RHbx>9{`mt!mHRsHmu$W&tj*=5o9A?rlAt
zIr%vp+3It9bV#N}H}erqN{A`{&J$yTGpY*rof}joBJE{Edfr$yst1YFcO=t@bq!^1
zz*lYwC*$tE)N*N_q-3LvulIOLMA*`&fQCKrQ4xBDz_h#e9XlPCKx56!$`YmXYybk)
zONiv!JSBuu=p6@(+HaDtI~Lfb?l@Y=8l0GN^K&S(UW=vlShl)|skCV_I0oj9^8^yO
zBWCiknvUS&yw~z(yi_aipYWvV1+L05G-11IkJO}kzg5*!&~~rlinaL&8y&I96}TsQ
zHl*5PP0J<=$jCyZ`PS)^ozMC1yuY(*>+zpY$?rI3enZp=AZhav`7yRrUawxv^<36>
zcKkf=);1Yy7nbB*B*}|N^&mt+{4?ho@w`3{D`lg*gfpsg_{BC`f2>?Y-qdrlge#%I
zi!W~*BW3zG$rMWPkTBKAw<W5_Pu0Yuw<a>OlGSB?RLbxg_z6*3K<TpN8`C3Sd&i&q
zzYM{C=p7$SZ*6Jkeinbzx$fK^|FiPX4!=@OsBeA~8T?#M6L&|7<xcW5L?^6~{o<wl
zANTs)uXOAZB=mSq#0A;n$-u+cPc>t=b{ZKZTpgeggc(_bKlN8LikF)Pr?xuJNm*H0
zr8j*EM%>Am?Te{lTZY|we;YshQ2x@QaQDF<I0p3-YeL?vLADMyFfQq+(|l&7Q+&_h
zfqQs@!a;fMo?N188YoLg%T1k=ql^!9Q?jl+XWOKPG`!+29es+5YZbU}Fpant>)gO1
z5qYkbGt1nuc2j@Sl_oK=l%{?iu~xtycwyV_(c8i2JnGeJY1q-L2-Z2ntb?6h3+3J@
zwRKV03F*d%mRc@MeO)7ed{pvtA5ZvGu4_g0`HK^`^QK9KZj#yQH$M8@pBMFeJWt+}
zosJ{#M?ub7Wg3_FXMv=l@`&k_JPEzGHG42k)qZ9Xe2`YzGyWB=M~qSKkJctQr`|Ny
zFMWDVBOsV371*SRZE3oA+~^I-;Hc@cQU(B8J)P~WH80}Eem=ueNa$pym<sp7lJMg4
zN>?#}t*mChQ&+ECDVQGZ=twH%m0hxVw`^5`x(%(?VCKhOe84IO^ye}j##UGsAXrlz
z8RBeza$+*&&IZu~xwAJuJ>EA;ZggRJ@^jh892F8v^P#JxI-60_k}4O#U53qvd_<xb
zr`(GMt@BG=%dXRdTu1knH?xx0yc6sV)R{w{(c>KZYMM1xH3w5-T`Qs6cwDeh(^c|b
zy?XVbUUY6Cx#qh<#CiSXW?<hyKNghl0)}<5xBDbU-Yu`0(Jg;odwqsO<}t{w<3ZgZ
zplnW#r9|ZAQR2FvX?x3%J@pNLJC7*ki<dc4Lbx?@js^%Dt0W<qN9q_F8mg+-ku>4e
zsj93T;0QD(2jYaPrHqWq$(KKGZr<@gWWV$-yBo9i<)k#==@UiG|JZJ<YacSb%wrDy
zn>Wv*CeQo^ZN$)umN);sG%m0ERDJiKY-h^AJr<K^^W%{c7x2k}v1*wDJn1M5Zs4hY
z!n1o0n&gm&`wd(;pV11-&)7ODIrX^fS+(YzA&%xMy4Zqoyxm&;MlQhuI_FdtaiIRU
zj=cVcqb>e?nk@#IKkD4a9G~!1YqCJ=G<=;?uHnrdcw8X?23xMO)$dfD>{yz#q&ez{
z6qR^HnS*`Ci+R_UjKN-g`tnAhCa>npmnS*X?OvZqWgDs{y^#2jqFAypm*6}R!#s%X
z=H!dh5g1Gd?DzyOzeXpe3$d6lO5FHhEehW?t<GzC>bLuH%A0(ym_ls(M4lPwQEr*X
zSLa?L;O^H8w$t@8-+Si%O^X1(Xf{6s)q!*Q7amtnc*O70d}^=GoMq(cM8^rc+jwpR
zze*T7@izdhQ@NK=;Kp8cCIC2B@s;N8Yq3g?zSPylPl!H!`cwuH<FdMX5GYm0xEXud
zchrD<V1;z$ZbJ9T)z_;i)qVN$g?*r}XV@ZSPAv(z+G<)VQSkS7VLp`SV1#UD7oG^T
zsRIoqeZ9vLkst&;jIQZ$5~f4Nv5_}5c14cN<s3>Nhs<|WLR(Yh4axxlf-16$cR5%Z
zw0LjM(jZbynr#T!Z{wKGbE(Fgvs=1`=g2k#8@MLg6cx$vVWopM?*c(ofFVk1)3F3y
zoNn7pb4|Z8*TZqp^c_Cm{QZipmeEV=!D}dI+`1k(@id9ePfK;c;GJqT<#TOE49CI2
ztbudnDkr@Or7Us!$maKCm9?BTNS!RIp#eSBXKkA0FW#x}MYy)EuIPdYEv0S=Ou`Xi
z;WL0xP)N2+2xZ5(EG>>g%_$&E^TinmVTX~rg9#j^atqpz_Yg)><-T;&maA8<nk_9%
z6=P9VUvK0}$PsX!Ob*%Nlv7REE+}lwkVSZGg9*o^TlVbI7wcFcAjkGiA?}bAq$CSo
zx$SDwa(^r|G2EJ8XqzK3|CLd@<jKK(`l^0gVk*x_5|ZuAgB@F#n7}dJisbb&b>90D
zOam1<;k6f5b;_eagZxg8McefXpc8eZmOKTi-zjYK<&tzEq|Z%vR<1WlZ~p4WwhJ}t
z4N+?c)2pa))0@p@Uw~O-^$R+pfB6OWX>SKm<_1&O+D9#K0mnu=ii=TZDpeg#!h<wI
z$m0}lx*2x6kJnma;6jK(c;6=4vAjc_S=?6+(r<RYbEnA(O#<bM)AJ_c#z#?6H=E4(
z%84FyM$ioRVFX<jn%;}T*hi$*Q4rdnL;>Ng<s}v@6Y>qG<-`UzyMVgUet&y~8F;HA
zMBgpsqH~b#_<wunx=q+fX#kstAD9@QwLDZa-#dGR0Crnc6&-j$UNXM>3qjNUO0)n{
zo`EB$%V^l9;*?1U4XWK>^*WH3JyC8KnS%Exx&UHXi#sqyF$lO0*X0}BA(-)!a%iCN
zanlXo3+*5sWKoZ>n3eLR7c5M*LT>+lmPiPf)Ya(%6%pn54ZcKeL-pdO*_5qKAm)Xi
z+}3U12zEx>ZBO|mlceWgUI3trO`AC7&KFy-3hJIO3l!lKZoJ|9I91TgvsP~}J8%B{
z_l)jf2QUC4`7;;P0HRs*oF8+TVN*yKL|bKxa`3v5QzVodkrePkLU0oVPh?<bvdNy9
z2eROX<v^A(tGOUjNNzO%h1cD+TZ^!FTaJQ1CMfG%u~7MK?86LH`0Xz&Dm+M~1zL9}
zq2)oDAD+a`+XnTXWPAP_A0IM{Od3!W4Q$5mp(4M}BEsv9Q+RgV&YXxtgyb%mpBd9`
zd-pp(P{dIrB!X|x71M_9YfXJ<89#Ovn>C0o-<^8ePB@V7(Ptm?Hmk6@mQ7<#>?F?)
zI+7AN*l}zM+IPYQXoWaUdH^HO={xV0$r>URv&&|ppMAbv&Ygv<U(I!`;%LYH>HMX+
ze1Y_vv{!6DF`Q`Cc3<gAF|o2$)YN1n;L)v<51KKwZ^ADtgv?%%p%!IvhEx{gK9`S=
za#BV;1kzye*gZ!S@MiGJe%|S};8#d}6{tfr8(YXvFag-)&jra^9E^st66(e4Ns47~
z<w|Z?9QkE&@KAgdUdH!Mw2hSexVuUSSZYoawQ+t(jdBtT<L*jA-nP=(9;YoJugO3?
zmT-scSlC|Zwb#E?svug>dMl60W+ZtJVD7z(Q!Yy)LEWBQ@$Pt@32GN`jy;=RJYVs4
z11X3DZLBrpLPObu?YaVX88A7O%kt%J#xtH;!@Km^$N30U?3|5hzc4WTMr&KAq2zWP
z2;vfIhr#?>k+#16!Jp1`kc$B5to(I1M185Yz%2|_#zfZdAWMSm6(VCj6V#I<RVIKZ
ziA$3p8;N_NJu1xG^MY{W>*jKJM0x~q@2ZwZTnJPg+W+Lr(&B>laPE{hVd<JLykd3A
z#gloL6$R(R>*fbI82kof_uv0_*iilhCrfQY^ZJj^!LXBuni+VKkuagq?A_gO-n{vC
z@s$^zS7=(AFgrVbsRimx$AsyIf{u<3QN-EVaq$2_{+Ut_)sbJBm>zM+K!(aGi~D_G
z$I)BAUAb}vz70^)CCxiN#$zEzLGf~T{~<H+S$OrxfayFt7Fn@z#QHL?|E7^RZE`%#
z{HuW_P|4$5dhXRyFKcUu8L!TYn(4kw2}$QUDH7Y_x3lB)6pS4aub$v9ChuIA_3A-v
zR)EMOknSG*1#BK~rJ*}<C;eC1c>aU?_kYC+^q>8R2`-!Ep8Nbu3n8{|71S+rZ%o!J
z25GkoM2(kmI!ze-S$>1d(n1*Yz~=Iw$yFYBPT4df_w4YV2>Fn_x+T5x=UWJMLa@zf
z2iww7VVY%)K*|R4(wEb-=ig<2+8<azn4$)V4%)sKggub)Adv9`3r<5z-(k^J=0U_<
zAUjQzH&U5_^i^49?4-M{g(NaUv6Ijd3VRE913Wb6L=-m#o&K7;fVUFCVcJ46E}>l^
z2XzhY5nN{KKv)`8lS~~+Nur6D#nFvd2fVG=+l-Bj&Q~_m3}WK2R_O0&?-A4<N!u^;
z*cQ}NJBc08B}3-Faguo2X#yDp=B-QPnXXefq&I9Ue5mqZD`8?HM{w*{ZS@2gk9#Q^
zpF7!P*&;-2L*xuM$7J3(k?*`jJ0BerLwn7bg++NNC_X-Z@(zpTrLoFa_@C5`n@{TK
zgky2NO8TC^brsd`W&d**z38~O2OKgb4Lj^r#+>x^CNf<`*xCI{Q*Gz*NQ%cQCD_CU
zQxpIrEsrjx)@!h!vW%mole+?XPw9#EV{!PvR!r=9=|niU(T>${OlchO_Vipsz05|`
zT4+byHiC7>Aiwh1Y^u|*t*yPJs;c@$lm!;Ayu7?9tLj8-R{!kP{VC>bsv^R|)^;tF
zsX=>qk~|==@vtkd$W6#!w4d)1$@c&?<}(WJv+y6QH>u3CWBr|IJ#5wY<=Ig)t<1n>
z;>gjLMu~AE%8H1|@i1qF)~(K^L3JiE@Q{sQ*6Yd@IpqL$g^H@iTL{DM*vX~o0fwjm
zeDvnI;e40uF{f^^R{gOf(~Qm|cM?(jT|VNk5j~Ue3Kw%9iljjLFG+8(ItXlWzh%AN
zQX1id&`6S!*s`d5L(6z>52%1mLheTT`4KKW-;b{74=pMx3hmzP&8I{RbWerG`YX0{
zIEl8YlK@XrEp&Ki0&oTnw=ICYuIsXZWMfwK)?1%acU?ijcY;y<No9A9tB8MTqu)ys
z%&J{QLMrh%Srk|0$UJWFn{`jS*K{W@c5`tZE%VCGr0C}2I^1>j;Pn2mDc$p`A{Xd(
z!H>~cqZb}?u=?8oj`3O;lGVrH87BX84isk>Eh3CVTTa`?mI@~xP=4ohkw_jBU+(y2
zlpw@Lb*;#c9soAWhitop^C^5S<4ec^W4|{r<`J~ay2M02ba(%(<hyJDtYXN5tOQ&U
zs}H};P&3g4jFLX>PsF+|B=Br6+sxVV{(8<9Uh>8hlH-rCAlt=<?c{eNJDVXp`)3~{
ze_{|1mH#uZyyg8bu{Hi@gV}$tx$<9U@n7my{nzdM4{zt})9hu+H2@adfNSVLDJtr0
z<SN)5A)o3Pt-o<?%=is}l7WE%@<R^`a*H;sn4KmoZ)m_QX8gv7*!(gB8N}mJP)oJC
zf!48?iepTAiGVZXm{&Ts##ZcLNTrS8@4n&)xR;bO`)<H+wlz~N9`VsAIyUzFX&TZS
zq#HU0Lr%VO^(unsV~(_aJu9h~TRXCM^oL&FsVaiPJ(cqDwmBPPY(;R^sdrQv;=Tl*
zWG-@N*A-P&76dpZCVIY6j6GoCwL1`JTjQGk>eU{2%@n(sPYUA<q<~go#`I5s>gq=j
zUQ9w+N%vS<IOPu;p*ksiB(9>izJBYEW2xG7m)U9mS2(8swF>{kRrr6$fc~%B`Tz0T
zNkU6u5(J}XsNs*p-Zct~bRnUC8h*R8UdvzI+T(MWbTI(;#i9N5r@8rR$S=07M9}Pf
zqEI)xL{y?@V64vv3iF(~zm19b&Ji4*zPd|K{)P3NKea$A*#?D(=v9e>Y1ATcf!l!7
zNfaO~1L3%cYDc`Kgx%;g`GF}ejeGFh))P}9Tp!v6?o5RCAY~l>^|a=@+~0XjAa8LZ
zL0Yl^fCgarX(s4K{44y7gL)6Zi}9l@R8D&R&v?JQd#u+I5vH_ZBYCxJyE0x5^D2d%
zoj6oUunbV8L$8ZFo!a*s4uV5>$4TNi$GTrAR2`=~uU<@0o1tiEHN3qpWyqGRiLfQA
zuAorU&VMn3SOOxHpemu=;m-gaNs1KM#h)e$Rf(W}Z1S7^<(g3vvH$SSQexq0iJCac
z{A8{nYsRLlqOWF|Dcq36;!K<poZiR3f4qICktoant}&HP-}bV}6p!oB6hM3-r2ZA_
z*<@$k39AQh)ukcb6FyBw$>mz8S)}4DK<5o@c`vFL_CVNky>P?JD67f-`pEqDQ!#+1
zp1VGRg5FHRDfI88#f;Al4Go=ka|4l@k2(0A=cn;lIJLfgd<3VZ-ji$@hN|xQ$@KgO
zC=dO!w^xPb!3$9AMj3w<T4x6bP$4LLFBQ&lYhte338yU1kKu+!$U0kJ4iDndr<Vrq
zOnck8G-u95@~+71dT!X{XaWMz8|J~SVF5QJQNV~=Tfj8{Ng|1)p}zVAO@iGEqQ6?a
zm}nG9u_|0Uu2bsI$2U9r8$4~7`tNW@B_#08rd`q0)ci8Ldo65go_RbxU<Js%1*uGj
z@r@sxc-z!jhgwj^CW788D%`5s+{@2RcPbMQ&jc>ux#pSR(#|ZalH+eZjo%EP1xa$j
zbr-rru8cb=sZP1L64<^R36gv=wE9p}S?2;h^(%O>V3we_W4-k7;N9nL+0d^Yh;O^d
zmR>8*#!@U+nu0>Cy?Tw#kU|(UbnIYh{dI`GyPQ-)j^0VP%g8R}%`VXKSKr@E@AjF^
zp_r{4CNwsj@h2oDkB^lJ@|!eg2$zz&JFUqeCfI;pxt{nm?%|rA$syCks?=Kk8-01_
zg`!d*btxeR$%DYZ6Fz;L{!Ey$UjK^h|Iga2{$I>;6I{h!dEZO8%%Ig2!}GX3dmu@c
zsM%<Kx#%519xcNS#m~!IP^lL)NuvRT37n(Fc?jW!W1zb4`^r-?3ztYE5b@sTAi!{0
zqfrk3W@AGcKOcsd;~D(mXv-3(Km$p)9fGE=!PWf0-4^bEjxe@`n?#(+K1P3!6FC%d
zngq4L?W%Iz2CT~&$$~A{)QI6a5ypc*d$pi)-p5jpX(*@DE<MSfyGAOW@$U_4L4Z;@
zK7|IB!(f5hV>A~GkUfZCP+cWLSFj6B=+x1PkgMaaQ*e)-i=3Mp4w2)7H;I{B=~v<a
zzM{A|r2}jJoaHRMoeqhC^x$2kw?(`yE+N5mVu?6^LC%AZFdkh37ic*P-iG(zE__uE
z=@Svng@e*^miRB+-NDl*N<qA>?mc;D<^OWMpmex`f<eKGI>9;#?FjrtZRkAiv#;E6
z{?g(<pD^*o_Kw4Dv5Q!?NHZHhTv#M^64HvNB!QBQs+ln0f1dQb%jA%FMM+l0^69Cv
zfbI7JP9FaC<R0aBEI+Nht@Jv|PR9CRqNSiyrg6ODv+S5`ojzT^{(VA1HRY*aG%~q#
z&KPJYCLexuAl`s)>`X=%tIWmUw&^~zm}K<Z^l{zck+Pl6g2PjqmB+fL<}0>3IqOx7
z9n0^Ys?xb?k}ETZPMm#&B_jrWw0W`LTetnP{UB^f)Yt{Y$em36_3c40CD(P*AHJn>
zKxJDTI9noRfP!A|7kLxqPMXOA<MPFzgFlmSah8-MY*^E`dp}qUBjqlbOX^y04icFN
zraB?SXu2ge=BqK5%servxV!K5J{r3B+>fP_B+AkDz@6}eH>+Oqq-~dQc9hN)CueZN
z-+yM~`XA@wU8Xmp0f)!u>gtV5OoYpK8~m{fXga$_G|;p0xw&~K6LnXeNhO8;7Fy6g
zddOthn5yt|4vX%vB2Y`_4@6-3M7&KMzBPS_yHmW{`{-}bZ+s<WyA@qkg<595-NMb>
zJnZe{Dm*>w1izhn^()M^ifVS9Ps6*tk99p`J-CXz>!oVrD_0v}m+;OlD#=))sWJJm
z*BVNf{J1*Bl03=N1+%-SM~m5fzJ74?&)!Vl=6YkGYF!KIwM*W&<BZQP*1M<nu(K1d
z^vCY8xS_z(PQr}OP7bo<+D|k+5+Ltk$O>oG2>qH1f6Ku(R&o1rZ%an<>vzlp7vE8z
zgF!pk&r3h*O{uPB!|#4yo;>v9auo+Qizw6Y*2@KfkoVaqWo2cL!vkjqoLBhA-L)MP
z^_r~%_l)A~h>a|ogM6whZ}{mSsA&UC_qMRGi-kInKQM$Y|Ea~M#g3D7`NdpI;1##2
zI=QM4w&cw?r;oFP^cQ8tl`U^UV@BfUA!3k+Ri71n{Kr}2AAij)GfkQVd7P2rgub8E
zHuTdLJ&V%@)2Z3C3-Vqajef=>O9G6xtJd$h(p1qsrfl=$ikC`{w=JLnPq|40ZvB6L
zWhCT~CTDcJB@OK&hjLx=w|gf>9AHpgpeK>uK~%?F{ps`*+0A|^d1wTe?{kNda+EiB
zyqaLq4r16+v#Py1`pCzT!+vUrHB-&RYmN~rUH{`B7w{UFM*_w3z(83aFsL2qb!+Kb
z@G8`*+eg0`OPK(^&-B-x=>F)qau}9{zaPhqT_fITll<#x-TiB!{>k{C{i`YXuWn&U
z9cSNEh|Gd2Dk^CXOUU*6J{Tlj1OA+tnCRraBk%^~F~Xj?|FTd0fNWripJcBSxCqEP
zwDD!lL|-izmQtX82xU{Rw}GLdAe(GnObu)8moGOce)bc6PxW0M-bQ#XE57UytD0_-
zJynkTOZd<7@^WhKQpK*3c-3@X$TTJDD}xB9jiP=pduV2+otw95PX$?#?{1oyOe!}?
z4Se*;dKjH*SbqkPEy8Km`J~O4=4K=AlPg8FxZ~8bg=-dfui)X~3GfM@jEZeud?q~R
zIt4YeS*u;`z~**P+g?zVp9Zt=Ye^nFL2ZmvO|Mv`E~3EAEVA0RK2}+w%X9YyTv)iP
z4K2&6=<?P}2R4_e|74j3!J<s!!@SZdn=eeQaVv5xrRhi6=gZ&YjYFpvd&h2jh_zta
zl4ER;jxNTd=$vSWbIFrbK*)l5r)7l513O`(kQNvVqksglY|?$5=t{oi2<g#BD^V%l
zoc>fFE2RBk=nK(jHi!;j()80Uv1E6wl>YqBo>N}l;_mJ=*%+C!aEERFrQ?r+i6deF
z?GpV%6qp=zV(Fl9H;)dZE!L1MwXTg02OF&M3lR;Qsncw;7q@f$_&nLpy1*3qR~H*1
z&`rwc11kY1Wh%4sX>hA83ppcK9&C!ZH*Y+;y1J}~MbKS#6o#uL-XCGA^w8YK^`tpF
zHNlBTx8lWi6fS)9KkeR*FuwqGe=F{ns*x|cs7Nawnkx#3QyaHh*`Iy}Rh@{mi?r4S
z^*VJrB&u3t$WFf(9DlX9N0s^CyeXTgbRXopyE|O(&{gV{P3bYbYM)ilx|XXkiiM;%
zu%p<=2bD<FkA(}*rMp>2Q6YRHn?dale5A)MhOF2PYi-fVGt>ZSs-0M8h$_0~?jQRT
z_>|u6&KtSQCz<cO&<<;cne@{iC#hrgapQWpg>23TiQ21iy8h(_u=4FDYCGwk1@lE*
za?Pj@uy{h>yg5jjcq5NQ-?}*1YAyK#`lw$<Fd~g&q{QhR)uW6B(7Tbb;{sC*Q82nJ
z)Vn+&EnrMcW+ewB3*hBsahe_fl=Wx$JUF&*2eAobUwFH_A5@4a04<lLoWa5b*@b0k
zlx^wK^IR&@+0umO(o?HHkE)2HR>Ip-JxLgP5_Sd?;0dmt1PJ{5S(Y4c3qq^4zC%yd
zD^+6&L^s7L`oj6G6u~kOZP8S;f%mVtG9UgAJc-yJE3zjEf0Vwz?NXBlT1gbC36Mup
zh*0y-fA@h5UOh?tQ*mjn(?td>!*p%#ogbb(vE1+&Yu<XlVzliJV_KtiU97Sh4EX6;
z^l1kSs(#pBXr4a`%kMcKPTXx`Tx*8gmNf4Xqd_wiK{MEu3(@zeXdnVG+MpNW^!If|
z54g*bHh!YYoh#)-kA)zXYNPHPX*AD+N^T?hLvm18Brt;=afdX`@e?cp-6?R8G*1@6
zQ?&#6N$-lH_1U_0WPw)cSlx`CCEnm&@Rus38eAowl0?LS1I%8eKZBrHqJiygr6-M-
zvi(o=lkubzVa`y}{xPB9G;5M1JtHx&CY;tCiC0dJNTDU`RhBi;H^azQY$G@iuAzNJ
z7%{d(Fl44okt!c39QTDZMU`ajB&Xua%Y}Fc!`?w-mm!M~s=-{1yhLt_I8ft1KL&y`
zt0z}cMt+Y=V}mI~e-165AOGniY(9rdg%7smI+&(9C1~WHqmmW<Z@_I;4r0WTZ1)z`
zVG2aVe|kVvTyFJbQ>tNlQ(k6fW(qA*KIET|%y{hE!|F+7;r!Z3;<k{bvdvbx{r!lr
z^u!TX&QVFXvLaOzz$<)^7^0N@={KiJ2Ma|GKoev&5IG62EG8LyJPWmAjPs{*hYJyC
zcQG*`s+fUs&{h9|{r#WXy@M9Xg644P-cizxP8vh!;l{Ax#ztr6Rohk*Deptts47q!
zU(P0aB<a)r`^*oCh1L-J1QyuVCG@$YG8*fy^g&;KYWXDTG$Mw2)=OkU190WjnRPAe
z7kfh%50dE#7wWz_B@j0{@lj4!`{#YRq(ffEV5!V1aL48jU|TV`tD2ieM)wJ%CGK=A
z9J*KPxa}?dIQmQvhNgtm%Ujmtx(Q{(kB*QETQTw}t|qTO_}wJ`#qd)5Pt9uMf0*k3
z^#XFETqXYp?!y(G%(WlSA$!3@Qh@%oUr8ryE97oMWMYF7(?V2pCq2*Hh%fy)hpaGJ
z-pm`x-D~fj+8#~+LyxWc{x&d>{7Ash;5Hw?PA1BA28_9->OY(XZoLA_h8q&KNhf%C
zG3kWb9`om$O_J#R&BMROzl8ot-gxv07>?-ndTGvUrQqLe6G=!H>Sn|HW1^-@hj)`9
zFmTNm8SGXEX_Z1lOJG#&IA;{s$kL-Bj8@?ViVe~a$<wU*6ZT=93Jfw{z6jsyj1u5n
zX^O<zlh~GB+Hb@?3)`)S`(tCbkPEcxuS=b60^x^VmuO$ESuc!Y@E^>5rM5w|=jDYz
z-g6&EAIxVUPW_O#$t<9OyEyZdeJ-3mNeQW(pKB|UIB?qLGm|#-C%q=N*Dpgu|4_QU
z4sMzxU1vJFOmG$9C;f++vaWJ;o?uM*Hksu>Ty`~->v}eXO2f`n9umO9`1sH@eG3_N
z6PQ4J<lrV~9<>Z)ibU5k(^nTi;ws*wQr{%*Fj<yT$vX3N`{h)qQ)T&#@qc1Z_=kZ#
zVIDX?cV{#FsJawC#W)o@1O{@#SwVE8ir-OsNA+A>m`k5&f?LRGnVX#Afz1MpH*9Vg
zvz)yueScq%w`XB!zU;$g+N+a%CzQSi;WqDG<h5e<a-u)VzcJC@Pco})M-G%NdKcC%
zQzb#|uYcU-hh?IA3?!w=PWzr$K^kmi@t>&6I{%J^38ijQZ8uLAz2vdnf@MX}9V*uJ
z=0C&J4#OXs*uOv4U#sR%J^o)W_}7ghE6d~mr|TiA#C+?+=fM42wwRmsoJSoL;FFC;
zNmBQEZ<DQJwJMl8>EhqxeShCaxjOAnHfOTJo(M`TGZjOPED4Pwv((k36Jm|#6-C`S
zv!{J-!UOIAU&X`~eSR)BI=X~}PXI9iHd(@gkpE?DY>Z$EG)O95#ODB?eVMChm;Q{g
zAP&+UG;sv{rSQ3sAu4N2F4R6{$J=-|uL+o3bx<kS-iowq)KKc<ROzQGWLSrS-TK>F
z@z(}}O{oZDsIzrRRL#shHkQaS^-@eF-9(*)a76SUAD^0%GF{COWQ5lvpgh(ODb(U8
zCj>GsU}?ReUynD=1cZR{QqY~vDE@iJ62G}gXN7rxJUBmc*5(ST>*q6rA8Q}QU!rHG
ze@{;Q4`k|pN09vM9LV<mA3;t3dKUE3```A((@x&qhGg^#86-nmNJxh?8Mow3neBOo
zHW0{(=Kgf<<zWEtmJu|ICXYT%aNM`%?y25PbY6)DV?0>~c#n3>!|~ZAZvQ0l6#elI
zSO)Cdp%|~W9b&fg<~4VxL4j^3r7lE$aWiyq6+hf_T5Ghu(5zgj{z>+E`kTru16%=^
zh9CI(`O%vZ(7#0{0TRX?wn>l+T*Mmr;+Tb2Z*_Pl?-uf|-J8_e+jyA0$TS1U;+Qm*
z(%27RM2dG1C9%rMqN&34kN2+L_frc2%qS`MvTIenJQ<f30Ng7nIX`5R-=C62Ud14K
z+5Z<?(orQXNf;X(Fuh3^x-}CM!Nzhibdura-m8AuuG~3bYMDm;e@zzxh`aCZ4r1@7
zzP`TTFW&A)F*ZgUv|n7Fb;Ysnf&!0CHZN6P=2}CiiHa?o>@biGSypv*5G_xM{qfnl
zO?q|On=W6})C`$X>I2Icv2b#Y{p3Kvj8fCBmQ^$v+*`zJ)cGXbg4Qf?<W7|a!#3oU
zU7)ONVPPR@G~+c|bX^X=)5;jb(@5VwA}lQIC$w!AlY>;!rtX=)iH$9DY-WW9o@h`T
zeSYpAK{V3&HlwryLI!D937A|hmQ5OC#uAU1fBQ%dlOo^HNZPCL8_S-H-RCXEaLJ}!
z+WK&t7j2|f>~a{mR9BWjT?N^K(OAhXT?o`%_@JjJPr`Zh6C~COcbYfpM?TH|xo4vs
zSUz)q2K7HKNwX5RSC$kVy}?ZHCJBRzzSYoXofF?NVu9Pm^&gfHk8OrqFkKM+Y$=Us
zU=(6XG!|Uug1KBNbfilrC{Zu%dlL;-Og%UW<ZyN3&nfaqTD@cTczGgzBXI3!eYmy4
zWs9+l&dFX~HVEZD-(5*!`;D3JojUO=t5b3J{VI}!_&!Whp>l_!xf!#@_%!oA-H^)4
zo1L8<%nLQ_ei6|m=QKC@z}+2qKc@W75>XM|C;SKKOU7~qmf#@D{@$(LXlaY96}ITE
zz-vJQysqh^b1pq+@se_^V|1=!#Kb3g=cLAdkU<_W4@<DK6M@sl;D_SC8ZP^D4ii%`
zn~NiA57JyhhF_s<*$mZdiOg9c>xyXZbbAOFeNTTp<Dc<KrC^pNZecEe$$>No@q^-n
zfI7QBVHi~PC?+Iip0E8;5hkD_WMaZxDNn*qpbL$O7~RQ4F*px)74Nh-?+91NL0au5
za(yqkdM+<Bm6UAFbHd1y3U_p_g*Au&xbi68<dt+v?S#$VAB^z7MVCJfNB<M`@qa$j
zlMpC`aDm2ag&~0+NzyNX{%%APjNIHNz0*uGfa~@Vakv~tgN7YB+3ALBvbjsum|p8+
z2@)F$+B=r5+WD(qkd-1{ZE3tsmY6Nb1Q0F@p@;FLg8+k)b^y$op{?{u_Rh0)=r7ww
z`rM#O!gJy7yfFJTlU-DS64E;4v?6B^er=l`iKD%CP}j9Vpn<u?(2g@$+~jnGjvRSX
zRE=n?&Hdi`(B?JaiC>cS<h55@zq^+>j(G>IO1D|jI5oeASg^rZANKog)jWnqaxWMk
zV)iQ9L@oPMto+}j>}YPAWp9(IsO37yBvDDp0bCl{I6{~ORIRiF-D&|bzgxlq>0eZ>
zxlpV5G;_yU>b=Aivad2Xo~1rHsGwEox#dx5XNme6jzI{j2b}UTG6#3&HODn>X0J|O
z`VMiOO#VSyp7@(c_&=8b1RFC9rsCt^b~;0M{Sb2jG3t})D`@qT!u^Y<K-!{@dc!6l
zV=U-h0H~t3;at@$KTD~`bz}J1eN4tHAnlMuq96b{>QY!cnbku2waT4H?+9xBa^|^w
zq#Z3{GmCfGYH3AY4IUK4gBE&zh!~?W^!R1X(Z%a?o4pF@ha<u<@o*_7Xs}}LjSuMu
zCxrg;RU5c%Jl-lGMR=gWcR!k(S?JaW(lw~8yq%2eBSR_nUwFP5MyfcdfZb<LQ18n&
zJ?3KeDm34^jM_(LswJTN0BV4+gE9J@=^cQ`4o$RSv{AP|mgQ|6m`T(wrdx@+oROMM
zUSQme>cAX`aBn(UIx<6M6k@ujz1N&3RN_Qv!N!T`NC4g@gB@&Vdm|y4p*ft00s_}o
z1brA~q(%a`lddr(wEhMfQ^<t5Z=Za{5)U;TQ^%kHenb#q%><bE363C4^@ZTp&lVgn
zjf?gf^WX8mLFxn21Ak!ino)#50Wu!22bqqHe=-9<N*didNPi1CN19Xj*bE`cVHepm
z+3K>SglSx4K#3}i4^0EerX5&F?$<xulM10YVL&3iF$;a-W}vl;$p{{zgApRtcyjD6
zpW_~Mo@7|puctKv544k+6J#O}>3TQ!UmjF~%hN-QB`y&n^I#suZgNmESPA-!2z~Si
zw$Dxxnxz664oNI7L*}ny;bKak22@~MwafN5V)8@Dv;_%pluVC6kMOUR5=)NsUJ)XM
zqD@RcKepnLYx#Enl)@c393B5@tTQ;|00PboQ%(v=-!6$RXs9r$afasZNt%8(P^Q~@
zsOhEvY(Pu6tv=-L%~SIiR3#;eu7QfGT32GrmV4|e#nD$|+A&+fiwx%@Nbj$EFbw7%
zmIoj-5hC;>*kIR&A`8=1BNE}5`V)q34-I`$kbeLJU@^F^?O3VgRb(I#T3bnT4Eo(j
zw_7`z=XT@o3zmwO+=xx1f3Ny)h!(#6Ey8Wzm!`CD%h{$+NX90(klwoZl}YuWF`@4$
zIbvwo@RzAvjDiS-mP;7*3(f)ehTf3{sl-i$TS>`vEW)w1!en}~`-H@62IGEQ{<<AU
z4zROh2h!r5g7zjRlL5~219}p4#WwqiH9v2*$TB_|(n}Hxwv9b>{PzhBmb`~cKagOW
zZb$0vbUowW8nz3Gh!kVOL_3)*DDTv#Kwn`PqtT#0oDZ$&8PqLDGN=*&L(W07VLOQg
zo#vU_L`>8qB?Dd3i8F|a3DX(}QnGeSfkzDAe*4ZUMw9xyiboBNaVn{Y&{LW=jdNom
z&A8B^I6SIqG6q^=-^*@X{2PZhsf{>%VD<6mR<72!GA8r>SeW`x_XdkQq%{tqW!#gD
zKi0r*?g0+jdF$OWGt%yj_L%Ytt;maztI1?b@|s=fiD|gyQh}!E!(=QeS-LTE;?Q_U
zX~WLxv|$l+vrogNl-D>04v@?Q5hT-zMP=NE{upHx8yc^X_aU*9w04o1ne<*xSn|PE
z60xlazOL;b+!*<IY8i~8mAH$DHw;El3vj^&q@C7Sqs8QovAFbY^J6e}Xn%aThi<_n
z(-Sf<84}J1(r@kKG@#87WMaKg6+Ch5v%_|O$zN<`3W&_f<g(gKHTmmlh_(k|GbnNH
zE(Y)>6HxG8HprFf4b~lE$N0d?lB`R7y%>pDNXB1-ULLAac_oBMw}?3tg=BObk;93y
z%Fa#)KY(3mVK#};+)23QxUVTDv@FGtd^8k?qNj;W46~kE{l;ScgEti$rDK)E^vPz7
zbt3r6&bJ=cgm!|FusgCPelq&zvNYvFZ<vJ~|GZNKz9DjJHL_wIR8zZmt}Y2j=pyaZ
z4Tv+^O)2{H83UwW9deyFDX`et$?#Fkitxtx03#uXkFzfh{E>0P;wO#ARm4W5ns?>5
z4#wJ3uP)ZPre8cjoSMueM-delf@8fwHry7|NJ-nsTqwXnNnU?1Btw#^ns0qrKZ@aW
z6m;jC7Gi<4ep>n|D;b>*MVJi3Or+`F;-@g36~Yq4Mm88x@hmh+`~Oadfy96>6Y!E0
zktatS3Qbp+TdweS`B;HAuIj!mnsD3@3oQvZ_XTZzrtT>aI~P<`VKS%EIQV{2T=&_x
zf+$Pq&j<OJJO<({A{C|9<Uk`o28Y6BWLCysOa?EoB10NZtZC-1^bponOTzeanW$Jb
zmnaibcd)o*JhiBkf+j*AISn0-m<(N}-CS-a8yosJpr4H%JFzr<Fub=ARb)G9^2aoB
z%Nvc@qNK;HcjIdyu|ndKBkv}tEHxfnd&YRRtad`~q$lR0N5rO{(6g97ZM_RTcm~Ro
zFtoCmbw0gT^!EA(5(kNo3}^d*h<o=*lOS;s0E{2B$$YConzPANJ*LFP7yZOSLJ&CG
zRd@MFEZV9;uA?3a>M{HkUor5Jd`G|?Gu6sRzv*3tx0?76i1iyWk0^~~`~-bc0j4V4
z|104iOx@<6<8SD5_K$nYn~7QzXbk0h6I1_(HuLfpQYE9@E<o<vMN~@?x0;58EQx1n
zjjKqk+j3Upj>3ulzP`|F2^wOB>~lorgtJ7<41+e5{GXc?k1{qAZ4q`Ues=b}nQ~M5
zZ1g`f1+<&R*@Pw0#l_(z3MR>r`QAJhsc$V%g7HK8pYFSszXKG4d=3;QGHlV*EQFg}
z9zj3ooKA$*_(?D|SZ7<(Z)zK_ujKO}DGd`M(cx|!pD>33%dm7-y*e$Hnzmlp@x&sw
zk~cA9VCm+65cF`vY;vLlkwPZy(DhQ1+lbqrsPoQfBq%15D-#nSwI(qb5>?2Wry-<v
z{R(Du)-#b|2-MiYdl&6%qpuRV!`(dt(2PW=L1#qvV~cxONGqdctfrgI2b<EVSu&ge
zV}DgiYoZy-{G*L`g;BFj2j<TNX23^I#t-xBSHD)IS`{4I_yk@ySrmFM?n^TvOCD(M
zmnxV|yaileME@=U><krJLYQe%+V5tKxvOnuP7!Q_f)uHR?l!_a3e!4WGJ}_i%m=o#
zwhMGT;SI|zZ_dac=XII#F_eahiI?eh4Nij0>1=<ZAm>5!U_xEkPJeal0DAC_iO<3$
zI5Il#8K&gi2Wt~!<5NC+?<dv%7`6Q8r}b_jxHaCAcjLL6#-c%;_GEr(rOLv|YUTZN
z&>MspqBoMkvhA>^9%P*&J{5YjhCc(8L6a66tC?>qPi=Op%Kg%KnRl`q0er~)cE)!R
z6p=Ba<wLvkP|#;AEza%LS}60XxWdg$pP)&<j|j&_Qd`xZd|LGl%k0vcI37#6W4ItP
z>;tp!HQkB5jLZsoGPG+BlR>d05QN_!1z07S<K~3d1x1m~4TpU*gveMTG7;qxzIp*^
zvGwcJ?Vtx8Y)Oe<omUbUENQ!~WvkMI<&TL8AlAPz`|XNCt;CeYZe&hXr`)|2k6sD_
z*V~R3Ztd{TpVuKXmg%K96Nw`PL>4_@sgIA@A=bCwa9Bsp+7yA=48uq0Z6qWLU<_js
zNqa~=^!bn^K=;?hd12kA+6yL;3`#1?NSgmqqw*rQhomY8ys!<F*xd@;MsGtW<`gok
zI;BD%0}8lRQlczRNnqv)dBy@U+tV=On2#oI4dOfz4K!p>h!*B*^5bg>$n+64``&P!
zH^v`UXKXUd+FLAPlucqBwvjeQRM9aduB?*;CZI>MyUExIjFyLcl{kt5V(xxd0WU~Q
zNMtcLpcD!tG)Z0MKU@H}JIF{hjOiwA5~RT*aDdb;Zw1MElQm2urYN$RrmNg2S0=d(
zAIPTtRraOTlKHp9#;EFKOrk4RW^}D=5u4q&M+6E`*S#W91kadPoOkn@#OpVW=3m($
z@D%~@sMyZ~Wk0EtEbBB^RZH)}sG-&y`W}Se(~k_Q)17ml5VYaL?31AO{nx6VlObco
z0%eQ0w`0i3A=&jTMc1L1fF@+sd9$pfaFlT{8Kvb>1gH)S&DT#ZM=MV|_{xb7t7%OH
z(Bj)d2AHX6{3yh1vCPKrc4Fq<Ka1T#I^-rs3qqL2Z2DMIm~lh4tZm=H{~f>5_)7C-
zlgcK&j_&kvhmUo2wRXdKNE03aOvlJqI}j2HJxu(#2il<9_(92$(Jq*w!X(Gr@P>FL
zy+!+?i0u!hHG|Prgo}2B+y~_OVLXpE=vCqeAT=lekn!mLV^u+Dl;$(Rc$DcuQx_KE
z8X!j?If4vRqtDVIc07#m2u(6CeRRr!gy)f##ssmo9glhV@BA<rT{5oxM3-28i`@U;
z!}@&u7y^Js_6!h}ZQq;765_w1oq+EF;z4{Uus=x8%u=vZEPtO>CO{Uq4ZVGwAthw{
zkFTluUnq_f{;Aj?e|;<#FL+?wrBS}6?|kITRM52JL!S$Y`9xBnQg5^p`!E@uunin3
zdGf$iTj4enAyYt%^IFMSR?msUSD+ea3es`ZH)Nhe4x@j`cESv<y=1mEJv5=y>_F|j
z1_}WBx6aUCBWih*yoT6ziD`obHhP}wtBs+KYT|ogt|;S<yYd=Be>a158GJSd_V2{3
zyo3(aV8*F~#L73->f(Z(OYV}^AOBd5eyI%Q>^!HrBC@;CsN1HS0JpF#X<38$6XTe)
zaZuwrYZ%JO6!@J0?yB=5v8Y6Fuh03Q%WYzt*yK`Ew*RlJt&|OItpRpIQeod#@7u^E
zHSTBr@u%d=9d94LKcoEd%}!=v>y6=;o^*hdx*?VLb~nC>=-19GM;>o;(-W+?H_AKi
z(q4GjWyh|unb`Qb%!K|t=c%DyYC`{_DUiFUnuy)^ZvbKuAK)o`BEgC$X1Ug3YG^?>
zBaj0_@jk){@g4Ne`4>k=W4@X^MkK~FSh&I?W<Br`m3$t{&|*F#8|8Ic+GO@uUMvtd
zd`Xe333!3^2RE-dW<8)3Ea4J7IXPL)wEF2&wk75S$c88g$^emTs;VBs`S%UmL*LM_
z6ikvJFbc;fKvoNE4413YSYf1lEsJzm#H(dK1}F;8%@rSR$%S#W3?8n_nwm?EO;9V;
z@}0$H1W>N1W!&0R87waA;NZ~fG}MwC2vg&RY`0|$(+)>Nmod2J*F{Pn86RjAoEoCZ
zf>6zWmX2*sro~t~;Y(Z4x;W@%mu^BULH_Y|9QXrYN71M^<?_w_9_HwE;#SB!hRs+P
zdtYLnl5W~sfnWJ@U@F2V#B&#09tH*%&L6iiP{ef60Jjkp_Mr!y7oLyT-V%{PTjV2P
zcupDI*t*^wEje}yYix=iu=vC5YGts1UYE;gJ>WRzvgjB&wThu_n2@?JRSAtOGVHWt
zz9LX$IB*PuP6SXSa(n_L6;uC!YW7@5ZOpYu@GuJfp!RAS8lG_kpydJs<3o(8X`LWs
z4l;WM#jnC>Cr<h&qIR3<%5|y0G>KEMs=iu41oq_^92^Xa|BC88d{jbO+_DZ+qbx8Y
z1o`fOKd*WT>IeZ$n}eE!|1)aN7)^F!V3^J(f@%-5bBbzOvU+OJXm2;4^Z(J@xxF$N
z#&LXcEB9*UN=?ZHA*PfZn!_yA$|)Bt$2qMuEktoAMs8ee;k6d+LfN7gH+F?BWz@={
zmB^frvpIa8<&W^zd-L}6_B_w;`99C@`F(!}orV3bmAFb{<gFXhc!1nn!v2Kq|KRiW
zCKEDA4lcBF39_AJwJ3PixQr4nJu<h$pGMT#B;boUXKn*7+r~y#y|YVY7X1Le1#Sqj
z5pp@PSZ6rh=RE;ucVunpU5mYF4}QGtxO7S$mb}cLSIpmNx=6Vv()y_=Loy9jWa#+f
zlGTsNDRKOQ%!KnEj7C2W4aKY`M3t>AAGwmsg0#_r{LQ{#370stDc|OzFK8?K*vg)H
z52Jp+AMfET1mWcl@I?0a>XXRj{z8L$NLaO{FxuJeqAN~zJ{b1igAm!)Ea;F3a_>BL
zRGNvPlI-^Dg=f~%_ihg{B^CH)O-As`Z{Y$~piN_}`C>)+b~z<xk@bowQdE}%kr6s|
zD;Z6ww+W)GA428+AYNvT3W1#Gu$5MU6851COoK>_U@A$Y0IJz+IPH49S~!Dt;;FTc
zatgBLc?Jb7Rh(kgWSFE@kB&gm+@Y7YyR&Tzo&NH;`2A|F>Ep<97h^rF<yXuvkD}&}
gY{>ZYR{TF7e&SP<dpmCrsWB2#Rje*@6gH3j0`-FhV*mgE

diff --git a/reference/plot.html b/reference/plot.html
index 653e22c0..b61d51d5 100644
--- a/reference/plot.html
+++ b/reference/plot.html
@@ -12,7 +12,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
     
     <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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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">
@@ -168,13 +168,13 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">scale_x_mic</span><span class="op">(</span>keep_operators <span class="op">=</span> <span class="st">"edges"</span>, mic_range <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">scale_x_mic</span><span class="op">(</span>keep_operators <span class="op">=</span> <span class="st">"edges"</span>, mic_range <span class="op">=</span> <span class="cn">NULL</span>, drop <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="fu">scale_y_mic</span><span class="op">(</span>keep_operators <span class="op">=</span> <span class="st">"edges"</span>, mic_range <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span>
+<span><span class="fu">scale_y_mic</span><span class="op">(</span>keep_operators <span class="op">=</span> <span class="st">"edges"</span>, mic_range <span class="op">=</span> <span class="cn">NULL</span>, drop <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="fu">scale_colour_mic</span><span class="op">(</span>keep_operators <span class="op">=</span> <span class="st">"edges"</span>, mic_range <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span>
+<span><span class="fu">scale_colour_mic</span><span class="op">(</span>keep_operators <span class="op">=</span> <span class="st">"edges"</span>, mic_range <span class="op">=</span> <span class="cn">NULL</span>, drop <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
-<span><span class="fu">scale_fill_mic</span><span class="op">(</span>keep_operators <span class="op">=</span> <span class="st">"edges"</span>, mic_range <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span>
+<span><span class="fu">scale_fill_mic</span><span class="op">(</span>keep_operators <span class="op">=</span> <span class="st">"edges"</span>, mic_range <span class="op">=</span> <span class="cn">NULL</span>, drop <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># S3 method for mic</span></span>
 <span><span class="fu">plot</span><span class="op">(</span></span>
@@ -285,6 +285,10 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
 <dd><p>a manual range to plot the MIC values, e.g., <code>mic_range = c(0.001, 32)</code>. Use <code>NA</code> to set no limit on one side, e.g., <code>mic_range = c(NA, 32)</code>.</p></dd>
 
 
+<dt>drop</dt>
+<dd><p>a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to remove intermediate MIC values, defaults to <code>FALSE</code></p></dd>
+
+
 <dt>...</dt>
 <dd><p>arguments passed on to methods</p></dd>
 
diff --git a/reference/proportion.html b/reference/proportion.html
index da0ec68d..ca13bb40 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 9d782f7d..35e1b1d8 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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">
@@ -210,42 +210,42 @@
     <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     &lt;=0.001 0.25    &lt;=0.001 2       0.0625  64      1       8      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [10] 0.25    128     &lt;=0.001 128     64      0.025   &lt;=0.001 0.005   4      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [19] &lt;=0.001 0.125   &lt;=0.001 16      0.0625  &lt;=0.001 4      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 64      1       1       1       0.025   0.025   0.5     &lt;=0.002 0.25   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [10] &gt;=256   8       0.005   64      64      128     4       4       16     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [19] 2       0.005   &lt;=0.002 4       0.25    64      1      </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] 49 37 48 24 40 25 46 28 29 48 32 22 42 41 42 27 36 11 42 28 50 16 41  8 43</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 19 13  8 13 40 16  7 20 14 47 19 26 41 25 14 41 47 40 49 40 46 30 46 32 11</span>
 <span class="r-in"><span><span class="fu">random_sir</span><span class="op">(</span><span class="fl">25</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'sir'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] R S S I S S S R R S S R S R R R S I S I R S R S R</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] S I S I I R I S I S S R I I R S S I R I R R I I R</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># \donttest{</span></span></span>
 <span class="r-in"><span><span class="co"># make the random generation more realistic by setting a bug and/or drug:</span></span></span>
 <span class="r-in"><span><span class="fu">random_mic</span><span class="op">(</span><span class="fl">25</span>, <span class="st">"Klebsiella pneumoniae"</span><span class="op">)</span> <span class="co"># range 0.0625-64</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'mic'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 0.001  4      0.5    16     0.002  32     0.01   64     1      0.002 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [11] 32     0.025  0.0625 0.25   16     2      0.002  8      0.5    0.001 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [21] 8      16     0.25   0.002  0.001 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 0.025  4      0.25   0.001  4      0.125  0.002  0.125  1      0.025 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [11] 0.002  2      0.002  0.0625 0.001  1      0.001  8      4      2     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [21] 0.5    0.25   128    64     0.25  </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] 0.5  2    0.5  4    1    1    1    16   1    0.5  0.25 16   1    4    0.25</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [16] 2    2    0.5  16   1    0.5  2    4    1    1   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 4   8   16  64  4   1   4   64  0.5 16  32  16  32  4   8   1   2   32  4  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [20] 8   0.5 32  64  32  32 </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] 8      0.025  0.5    2      0.125  0.25   4      0.5    0.125  0.0625</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [11] 0.0625 1      0.5    &gt;=16   0.025  0.0625 0.5    1      4      4     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [21] 8      0.125  1      0.25   0.025 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 1       1       2       0.5     0.5     2       &gt;=4     1       0.5    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [10] 0.25    0.25    1       0.5     &gt;=4     2       0.25    &lt;=0.125 0.25   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [19] 1       &lt;=0.125 0.25    &gt;=4     0.25    0.25    2      </span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu">random_disk</span><span class="op">(</span><span class="fl">25</span>, <span class="st">"Klebsiella pneumoniae"</span><span class="op">)</span> <span class="co"># range 8-50</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'disk'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 20 36 50 50 46 42 47 48 43 31 42 50 44 49 39 29 46 44 47 20 48 12 10 47 12</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 49 43 10 16 28 42 41 41 48 21 20 46 23 24 41 13 46 46 17 27 48 47 41 20 44</span>
 <span class="r-in"><span><span class="fu">random_disk</span><span class="op">(</span><span class="fl">25</span>, <span class="st">"Klebsiella pneumoniae"</span>, <span class="st">"ampicillin"</span><span class="op">)</span> <span class="co"># range 11-17</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'disk'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 14 13 12 14 11 12 15 16 13 14 13 17 15 14 15 13 15 16 16 15 11 11 16 11 15</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 14 16 13 13 17 11 17 13 15 17 17 11 15 14 14 17 16 17 15 16 13 14 16 11 12</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] 17 16 22 26 23 26 20 19 15 15 24 23 23 18 16 17 23 27 21 26 22 24 23 26 23</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 24 23 23 15 26 16 21 25 23 22 22 21 16 19 27 23 25 18 26 17 24 25 22 18 15</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 bbbb0b2b..01265907 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 fc2015b0..1bb6a197 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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">
@@ -198,7 +198,7 @@ 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] -0.007908431</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.04515028</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 4f228853..1a077e2b 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="">2.1.1.9002</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9003</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 55670868..a2d5b9f5 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     #> # ℹ 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) #> 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), Enterobacter cloacae complex (0.571), and Ehrlichia #>               canis (0.567) #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #> 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) #> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus #>               argenteus (0.625), Staphylococcus aureus anaerobius (0.625), Salmonella #>               Aurelianis (0.595), Salmonella Aarhus (0.588), Salmonella Amounderness #>               (0.587), Selenomonas artemidis (0.571), Salmonella choleraesuis #>               arizonae (0.562), Streptococcus anginosus anginosus (0.561), and #>               Salmonella Abaetetuba (0.548) #> -------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #> 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     #> # ℹ 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: 90% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 712 isolates analysis. Now data looks like: Time analysis.","code":"our_data <- our_data %>%   mutate(first = first_isolate(info = TRUE)) #> ℹ 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. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #>   of 2 #> => Found 2,712 'phenotype-based' first isolates (90.4% 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: 2,712 × 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 M3         A        2015-10-25 B_ESCHR_COLI R     S     S     S     TRUE  #>  7 J3         A        2019-06-19 B_ESCHR_COLI S     S     S     S     TRUE  #>  8 G6         A        2015-04-27 B_STPHY_AURS S     S     S     S     TRUE  #>  9 P4         A        2011-06-21 B_ESCHR_COLI S     S     S     S     TRUE  #> 10 Z1         A        2014-09-05 B_ESCHR_COLI S     S     S     S     TRUE  #> # ℹ 2,702 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:2712        Length:2712        Min.   :2011-01-01   #>  Class :character   Class :character   1st Qu.:2013-05-03   #>  Mode  :character   Mode  :character   Median :2015-06-16   #>                                        Mean   :2015-06-21   #>                                        3rd Qu.:2017-08-24   #>                                        Max.   :2019-12-27   #>    bacteria               AMX                    AMC                 #>  Class :mo             Class:sir              Class:sir              #>  <NA>  :0              %R   :42.9% (n=1163)   %R   :36.1% (n=978)    #>  Unique:4              %SI  :57.1% (n=1549)   %SI  :63.9% (n=1734)   #>  #1    :B_ESCHR_COLI   - %S :41.0% (n=1112)   - %S :52.0% (n=1409)   #>  #2    :B_STPHY_AURS   - %I :16.1% (n=437)    - %I :12.0% (n=325)    #>  #3    :B_STRPT_PNMN                                                 #>     CIP                    GEN                  first         #>  Class:sir              Class:sir              Mode:logical   #>  %R   :42.0% (n=1138)   %R   :37.3% (n=1011)   TRUE:2712      #>  %SI  :58.0% (n=1574)   %SI  :62.7% (n=1701)                  #>  - %S :51.5% (n=1396)   - %S :59.6% (n=1616)                  #>  - %I : 6.6% (n=178)    - %I : 3.1% (n=85)                    #>   glimpse(our_data_1st) #> Rows: 2,712 #> Columns: 9 #> $ patient_id <chr> \"J3\", \"R7\", \"P10\", \"B7\", \"W3\", \"M3\", \"J3\", \"G6\", \"P4\", \"Z1\"… #> $ 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, S, S, S, S, R, S, S, S, R, R, R, R, S, R,… #> $ AMC        <sir> I, I, I, S, S, S, S, S, S, S, S, S, I, S, S, S, S, R, S, S,… #> $ 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, R, S, 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       1852          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          1319 #> 2 Staphylococcus aureus      676 #> 3 Streptococcus pneumoniae   400 #> 4 Klebsiella pneumoniae      317"},{"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: 2,712 × 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 2015-10-25 S     #>  7 2019-06-19 S     #>  8 2015-04-27 S     #>  9 2011-06-21 S     #> 10 2014-09-05 S     #> # ℹ 2,702 more rows  our_data_1st %>%   select(bacteria, betalactams()) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 2,712 × 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 S     S     #>  8 B_STPHY_AURS S     S     #>  9 B_ESCHR_COLI S     S     #> 10 B_ESCHR_COLI S     S     #> # ℹ 2,702 more rows  our_data_1st %>%   select(bacteria, where(is.sir)) #> # A tibble: 2,712 × 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 S     S     S     S     #>  8 B_STPHY_AURS S     S     S     S     #>  9 B_ESCHR_COLI S     S     S     S     #> 10 B_ESCHR_COLI S     S     S     S     #> # ℹ 2,702 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: 1,011 × 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  #> # ℹ 1,001 more rows  our_data_1st %>%   filter(all(betalactams() == \"R\")) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 483 × 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 B6         A        2016-11-20 B_ESCHR_COLI R     R     R     R     TRUE  #>  6 I7         A        2015-08-19 B_ESCHR_COLI R     R     S     S     TRUE  #>  7 N3         A        2014-12-29 B_STRPT_PNMN R     R     R     S     TRUE  #>  8 Q2         A        2019-09-22 B_ESCHR_COLI R     R     S     S     TRUE  #>  9 X7         A        2011-03-20 B_ESCHR_COLI R     R     S     R     TRUE  #> 10 V1         A        2018-08-07 B_STPHY_AURS R     R     S     S     TRUE  #> # ℹ 473 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: 483 × 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 B6         A        2016-11-20 B_ESCHR_COLI R     R     R     R     TRUE  #>  6 I7         A        2015-08-19 B_ESCHR_COLI R     R     S     S     TRUE  #>  7 N3         A        2014-12-29 B_STRPT_PNMN R     R     R     S     TRUE  #>  8 Q2         A        2019-09-22 B_ESCHR_COLI R     R     S     S     TRUE  #>  9 X7         A        2011-03-20 B_ESCHR_COLI R     R     S     R     TRUE  #> 10 V1         A        2018-08-07 B_STPHY_AURS R     R     S     S     TRUE  #> # ℹ 473 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":"Since AMR v2.0 (March 2023), easy create different types antibiograms, support 20 different languages. four antibiogram types, proposed Klinker et al. (2021, DOI 10.1177/20499361211011373), supported new antibiogram() function: Traditional Antibiogram (TA) e.g, susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Combination Antibiogram (CA) e.g, sdditional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Syndromic Antibiogram (SA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Weighted-Incidence Syndromic Combination Antibiogram (WISCA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) male patients age >=65 years heart failure section, show use antibiogram() function create antibiogram types. starters, included example_isolates data set looks like:","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    #> # ℹ 1,990 more rows #> # ℹ 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>, …"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"traditional-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Traditional Antibiogram","title":"How to conduct AMR data analysis","text":"create traditional antibiogram, simply state antibiotics used. antibiotics argument antibiogram() function supports (combination) previously mentioned antibiotic class selectors: Notice antibiogram() function automatically prints right format using Quarto R Markdown (page), even applies italics taxonomic names (using italicise_taxonomy() internally). also uses language OS either English, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, Ukrainian. next example, force language Spanish using language argument:","code":"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) antibiogram(example_isolates,             mo_transform = \"gramstain\",             antibiotics = aminoglycosides(),             ab_transform = \"name\",             language = \"es\") #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"combined-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Combined Antibiogram","title":"How to conduct AMR data analysis","text":"create combined antibiogram, use antibiotic codes names plus + character like :","code":"antibiogram(example_isolates,             antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"syndromic-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Syndromic Antibiogram","title":"How to conduct AMR data analysis","text":"create syndromic antibiogram, syndromic_group argument must used. can column data, e.g. ifelse() calculations based certain columns:","code":"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)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"weighted-incidence-syndromic-combination-antibiogram-wisca","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Weighted-Incidence Syndromic Combination Antibiogram (WISCA)","title":"How to conduct AMR data analysis","text":"create WISCA, must state combination therapy antibiotics argument (similar Combination Antibiogram), define syndromic group syndromic_group argument (similar Syndromic Antibiogram) cases predefined based clinical demographic characteristics (e.g., endocarditis 75+ females). next example simplification without clinical characteristics, just gives idea WISCA can created:","code":"wisca <- 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\")) wisca"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plotting-antibiograms","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Plotting antibiograms","title":"How to conduct AMR data analysis","text":"Antibiograms can plotted using autoplot() ggplot2 packages, since AMR package provides extension function:  calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"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] 0.4288348 our_data_1st %>%   group_by(hospital) %>%   summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #>   hospital amoxicillin #>   <chr>          <dbl> #> 1 A              0.342 #> 2 B              0.564 #> 3 C              0.372"},{"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 (v3.1, 2016). 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: 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            S          R            R            I #> 2          I         S            S          S            I            S #> 3          S         R            I          I            S            S #> 4          I         I            I          S            R            R #> 5          R         S            R          I            I            R #> 6          I         R            S          S            S            R #>   kanamycin #> 1         S #> 2         R #> 3         S #> 4         R #> 5         S #> 6         I mdro(my_TB_data, guideline = \"TB\") my_TB_data$mdr <- mdr_tb(my_TB_data) #> ℹ No column found as input for col_mo, assuming all rows contain #>   Mycobacterium tuberculosis. freq(my_TB_data$mdr)"},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"transforming","dir":"Articles","previous_headings":"","what":"Transforming","title":"How to conduct principal component analysis (PCA) for AMR","text":"PCA, need transform AMR data first. example_isolates data set package looks like: Now transform data set resistance percentages per taxonomic order genus:","code":"library(AMR) library(dplyr) glimpse(example_isolates) #> Rows: 2,000 #> Columns: 46 #> $ date    <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2… #> $ patient <chr> \"A77334\", \"A77334\", \"067927\", \"067927\", \"067927\", \"067927\", \"4… #> $ age     <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50… #> $ gender  <chr> \"F\", \"F\", \"F\", \"F\", \"F\", \"F\", \"M\", \"M\", \"F\", \"F\", \"M\", \"M\", \"M… #> $ ward    <chr> \"Clinical\", \"Clinical\", \"ICU\", \"ICU\", \"ICU\", \"ICU\", \"Clinical\"… #> $ mo      <mo> \"B_ESCHR_COLI\", \"B_ESCHR_COLI\", \"B_STPHY_EPDR\", \"B_STPHY_EPDR\",… #> $ PEN     <sir> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,… #> $ OXA     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ FLC     <sir> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R… #> $ AMX     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ AMC     <sir> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N… #> $ AMP     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ TZP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CZO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ FEP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CXM     <sir> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S… #> $ FOX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ CTX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ CAZ     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, … #> $ CRO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ GEN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TOB     <sir> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA… #> $ AMK     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ KAN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TMP     <sir> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, … #> $ SXT     <sir> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N… #> $ NIT     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,… #> $ FOS     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ LNZ     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ CIP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S… #> $ MFX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ VAN     <sir> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, … #> $ TEC     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ TCY     <sir> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, … #> $ TGC     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ DOX     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ ERY     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ CLI     <sir> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA… #> $ AZM     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ IPM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ MEM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ MTR     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CHL     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ COL     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, … #> $ MUP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ RIF     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… resistance_data <- example_isolates %>%   group_by(     order = mo_order(mo), # group on anything, like order     genus = mo_genus(mo)   ) %>% #  and genus as we do here   summarise_if(is.sir, resistance) %>% # then get resistance of all drugs   select(     order, genus, AMC, CXM, CTX,     CAZ, GEN, TOB, TMP, SXT   ) # and select only relevant columns  head(resistance_data) #> # A tibble: 6 × 10 #> # Groups:   order [5] #>   order             genus          AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT #>   <chr>             <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 (unknown order)   (unknown ge…    NA    NA    NA    NA    NA    NA    NA    NA #> 2 Actinomycetales   Schaalia        NA    NA    NA    NA    NA    NA    NA    NA #> 3 Bacteroidales     Bacteroides     NA    NA    NA    NA    NA    NA    NA    NA #> 4 Campylobacterales Campylobact…    NA    NA    NA    NA    NA    NA    NA    NA #> 5 Caryophanales     Gemella         NA    NA    NA    NA    NA    NA    NA    NA #> 6 Caryophanales     Listeria        NA    NA    NA    NA    NA    NA    NA    NA"},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"perform-principal-component-analysis","dir":"Articles","previous_headings":"","what":"Perform principal component analysis","title":"How to conduct principal component analysis (PCA) for AMR","text":"new pca() function automatically filter rows contain numeric values selected variables, now need : result can reviewed good old summary() function: Good news. first two components explain total 93.3% variance (see PC1 PC2 values Proportion Variance. can create -called biplot base R biplot() function, see antimicrobial resistance per drug explain difference per microorganism.","code":"pca_result <- pca(resistance_data) #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #>   \"TMP\", and \"TOB\". Total observations available: 7. summary(pca_result) #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\" #> Importance of components: #>                           PC1    PC2    PC3     PC4     PC5     PC6       PC7 #> Standard deviation     2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16 #> 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/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 52 171 rows 23 columns, containing following column names:mo, fullname, status, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, oxygen_tolerance, 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 14 July 2023 08:49:06 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (1.2 MB) Download tab-separated text file (11.7 MB) Download Microsoft Excel workbook (5.2 MB) Download Apache Feather file (5.5 MB) Download Apache Parquet file (2.6 MB) Download SAS transport (XPT) file (48.6 MB) Download IBM SPSS Statistics data file (17.8 MB) Download Stata DTA file (48.6 MB) NOTE: exported files SAS, SPSS Stata contain first 50 SNOMED codes per record, file size otherwise exceed 100 MB; file size limit GitHub. file structures compression techniques inefficient. Advice? Use R instead. ’s free much better many ways. 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 December 11th, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset . Accessed https://www.gbif.org December 11th, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name ‘Microorganism’, 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 20 October 2023 12:51:48 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (44 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (72 kB) Download Apache Feather file (0.1 MB) Download Apache Parquet file (0.1 MB) Download SAS transport (XPT) file (1.5 MB) Download IBM SPSS Statistics data file (0.4 MB) Download Stata DTA file (0.5 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 20 October 2023 12:51:48 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (6 kB) Download tab-separated text file (17 kB) Download Microsoft Excel workbook (16 kB) Download Apache Feather file (16 kB) Download Apache Parquet file (13 kB) Download SAS transport (XPT) file (73 kB) Download IBM SPSS Statistics data file (32 kB) Download Stata DTA file (78 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 29 747 rows 12 columns, containing following column names:guideline, type, 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 20 October 2023 13:07:11 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (59 kB) Download tab-separated text file (2.3 MB) Download Microsoft Excel workbook (1.3 MB) Download Apache Feather file (1.2 MB) Download Apache Parquet file (96 kB) Download SAS transport (XPT) file (7.9 MB) Download IBM SPSS Statistics data file (4.6 MB) Download Stata DTA file (7.8 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 (2011-2023) EUCAST (2011-2023). Clinical breakpoints package validated imported WHONET, free desktop Windows application developed supported Collaborating Centre Surveillance Antimicrobial Resistance. can read website. developers WHONET AMR package contact sharing work. highly appreciate development WHONET software. CEO CLSI chairman EUCAST endorsed work public use AMR package (consequently use breakpoints) June 2023, future development distributing clinical breakpoints discussed meeting CLSI, EUCAST, , developers WHONET AMR package. NOTE: AMR package (WHONET software well) contains internal methods apply guidelines, rather complex. example, breakpoints must applied certain species groups (case package available microorganisms.groups data set). important considered using breakpoints use.","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 transport (XPT) file (9.5 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 503 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 22 June 2023 13:10:59 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (3 kB) Download tab-separated text file (43 kB) Download Microsoft Excel workbook (25 kB) Download Apache Feather file (21 kB) Download Apache Parquet file (9 kB) Download SAS transport (XPT) file (0.1 MB) Download IBM SPSS Statistics data file (64 kB) Download Stata DTA file (0.1 MB)","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/datasets.html","id":"microorganisms-groups-species-groups-and-microbiological-complexes","dir":"Articles","previous_headings":"","what":"microorganisms.groups: Species Groups and Microbiological Complexes","title":"Data sets for download / own use","text":"data set 521 rows 4 columns, containing following column names:mo_group, mo, mo_group_name, mo_name. data set R available microorganisms.groups, load AMR package. last updated 14 July 2023 08:49:06 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (6 kB) Download tab-separated text file (49 kB) Download Microsoft Excel workbook (20 kB) Download Apache Feather file (19 kB) Download Apache Parquet file (13 kB) Download SAS transport (XPT) file (0 kB) Download IBM SPSS Statistics data file (64 kB) Download Stata DTA file (81 kB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-8","dir":"Articles","previous_headings":"microorganisms.groups: Species Groups and Microbiological Complexes","what":"Source","title":"Data sets for download / own use","text":"data set contains species groups microbiological complexes, used clinical_breakpoints data set.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-codes-common-laboratory-codes","dir":"Articles","previous_headings":"","what":"microorganisms.codes: Common Laboratory Codes","title":"Data sets for download / own use","text":"data set 4 957 rows 2 columns, containing following column names:code mo. data set R available microorganisms.codes, load AMR package. last updated 8 July 2023 15:30:05 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (22 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (91 kB) Download Apache Feather file (85 kB) Download Apache Parquet file (57 kB) Download SAS transport (XPT) file (0 kB) Download IBM SPSS Statistics data file (0.1 MB) Download Stata DTA file (0.1 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-9","dir":"Articles","previous_headings":"microorganisms.codes: Common Laboratory Codes","what":"Source","title":"Data sets for download / own use","text":"data set contains commonly used codes microorganisms, laboratory systems WHONET.","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  #> # ℹ 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 clinical breakpoint guidelines CLSI EUCAST included, even epidemiological cut-(ECOFF) values. 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":"One powerful functions package, aside calculating plotting AMR, selecting filtering based antibiotic columns. can done using -called antibiotic class selectors work base R, dplyr data.table: 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 code work version R since April 2013 (R-3.0). Moreover, code works identically data.table package, starting :","code":"# AMR works great with dplyr, but it's not required or neccesary library(AMR) library(dplyr)  example_isolates %>%   mutate(bacteria = mo_fullname()) %>%   # filtering functions for microorganisms:   filter(mo_is_gram_negative(),          mo_is_intrinsic_resistant(ab = \"cefotax\")) %>%   # antibiotic selectors:   select(bacteria,          aminoglycosides(),          carbapenems()) library(AMR) 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())] example_isolates <- data.table::as.data.table(example_isolates)"},{"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: use antibiotic class selectors select series antibiotic columns:","code":"example_isolates %>%   # group by ward:   group_by(ward) %>%   # calculate AMR using resistance() for gentamicin and tobramycin   # and get their 95% confidence intervals using sir_confidence_interval():   summarise(across(c(GEN, TOB),                    list(total_R = resistance,                         conf_int = function(x) sir_confidence_interval(x, collapse = \"-\")))) 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 (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-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 2023\". Supported guideline currently EUCAST (2011-2023) CLSI (2011-2023). 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_ecoff  logical use .sir(), indicate ECOFF (Epidemiological Cut-) values must used - default FALSE. 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 microorganisms (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 clinical breakpoint guidelines CLSI EUCAST included, even epidemiological cut-(ECOFF) values. 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 public University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen. AMR package available English, Chinese, Czech, 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":"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.s.berends@umcg.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 #>    `Identification number` `Specimen number` Organism Country         Laboratory #>    <chr>                               <int> <chr>    <chr>           <chr>      #>  1 fe41d7bafa                           1748 SPN      Belgium         National … #>  2 91f175ec37                           1767 eco      The Netherlands National … #>  3 cc4015056e                           1343 eco      The Netherlands National … #>  4 e864b692f5                           1894 MAP      Denmark         National … #>  5 3d051fe345                           1739 PVU      Belgium         National … #>  6 c80762a08d                           1846 103      The Netherlands National … #>  7 8022d3727c                           1628 103      Denmark         National … #>  8 f3dc5f553d                           1493 eco      The Netherlands National … #>  9 15add38f6c                           1847 eco      France          National … #> 10 fd41248def                           1458 eco      Germany         National … #> # ℹ 490 more rows #> # ℹ 48 more variables: `Last name` <chr>, `First name` <chr>, Sex <chr>, #> #   Age <dbl>, `Age category` <chr>, `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>, …"},{"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_codes abx_doses abx_admin abx_coll abx_coll_names #>   <chr>                    <list>    <list>    <list>    <chr>    <chr>          #> 1 given 400mg cipro and 5… <ab [2]>  <dbl [2]> <chr [1]> CIP|AMX  Ciprofloxacin… #> 2 started on doxy iv today <ab [1]>  <dbl [1]> <chr [1]> DOX      Doxycycline    #> # ℹ 2 more variables: abx_coll_doses <chr>, abx_coll_admin <chr> # }"},{"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 transformation 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] \"101498-4\" \"15-8\"     \"16-6\"     \"16365-9\"  \"17-4\"     \"18-2\"     #>  [7] \"18861-5\"  \"18862-3\"  \"19-0\"     \"20-8\"     \"21-6\"     \"22-4\"     #> [13] \"25274-2\"  \"25310-4\"  \"3344-9\"   \"55614-2\"  \"55615-9\"  \"55616-7\"  #> [19] \"6976-5\"   \"6977-3\"   \"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] \"101477-8\" \"101478-6\" \"18864-9\"  \"18865-6\"  \"20374-5\"  \"21066-6\"  #>  [7] \"23618-2\"  \"27-3\"     \"28-1\"     \"29-9\"     \"30-7\"     \"31-5\"     #> [13] \"32-3\"     \"33-1\"     \"3355-5\"   \"33562-0\"  \"33919-2\"  \"34-9\"     #> [19] \"43883-8\"  \"43884-6\"  \"6979-9\"   \"6980-7\"   \"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 circumvent automate process adding antimicrobials: 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. Note definitions stored user-specific R file, suboptimal workflow. 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 circumvent automate process adding microorganisms: 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. Note definitions stored user-specific R file, suboptimal workflow. use method: Edit .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"). Add text like save file:   Use clear_custom_microorganisms() clear previously added microorganisms.","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 \"Enterobacter cloacae cloacae\", # since it resembles the input best: mo_name(\"Enterobacter asburiae/cloacae\") #> [1] \"Enterobacter cloacae cloacae\"  # 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_ENTRB_ASB/  # 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_ENTRB_ASB/\" #>  #> $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\" #>  #> $oxygen_tolerance #> [1] NA #>  #> $url #> [1] \"\" #>  #> $ref #> [1] \"Self-added, 2023\" #>  #> $snomed #> [1] NA #>  #> $lpsn #> [1] NA #>  #> $gbif #> [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, even though 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  1996-05-18  27  27.54521          3 #> 2  1993-07-19  30  30.37534          6 #> 3  1988-03-07  35  35.74247         11 #> 4  1967-08-23  56  56.27945         32 #> 5  1997-05-21  26  26.53699          2 #> 6  1991-09-02  32  32.25205          8 #> 7  1994-03-18  29  29.71233          5 #> 8  1985-08-18  38  38.29315         14 #> 9  1952-01-29  71  71.84384         47 #> 10 1988-05-05  35  35.58082         11"},{"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\", \"oxygen_tolerance\", \"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    #> # ℹ 1,990 more rows #> # ℹ 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>, …  # \\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\")) {   cat(knitr::knit_print(ureido)) } #>  #>  #> |Pathogen (N)          | Piperacillin/tazobactam| #> |:---------------------|-----------------------:| #> |CoNS (33)             |                      30| #> |*E. coli* (416)       |                      94| #> |*K. pneumoniae* (53)  |                      89| #> |*S. pneumoniae* (112) |                     100|   # 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 (according antibiotics data set), without need define columns antibiotic abbreviations. short, column name resembles antimicrobial drug, picked functions matches pharmaceutical class: \"cefazolin\", \"kefzol\", \"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 \"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. work base R, Tidyverse, data.table. heavily inspired Tidyverse selection helpers everything(), limited 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    #> # ℹ 1,990 more rows #> # ℹ 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>, …   # Examples sections below are split into 'base R', 'dplyr', and 'data.table':   # 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    #> # ℹ 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    #> # ℹ 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    #> # ℹ 1,990 more rows #> # ℹ 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    #> # ℹ 45 more rows #> # ℹ 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>, … 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    #> # ℹ 45 more rows #> # ℹ 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>, …  # 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     #> # ℹ 952 more rows #> # ℹ 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>, … 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    #> # ℹ 746 more rows #> # ℹ 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>, …  # 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     #> # ℹ 16 more rows #> # ℹ 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>, …  # filter + select in one go: get penicillins in carbapenem-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     #> # ℹ 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    #> # ℹ 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    #> # ℹ 1,990 more rows  # \\donttest{ # dplyr -------------------------------------------------------------------  if (require(\"dplyr\")) {   tibble(kefzol = random_sir(5)) %>%     select(cephalosporins()) } #> ℹ For cephalosporins() using column 'kefzol' (cefazolin) #> # A tibble: 5 × 1 #>   kefzol #>   <sir>  #> 1 R      #> 2 R      #> 3 I      #> 4 I      #> 5 I       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    #> # ℹ 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      #> # ℹ 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 0.807 #> 2 ICU        0.551 0.780 #> 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    #> # ℹ 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    #> # ℹ 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    #> # ℹ 102 more rows #> # ℹ 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>, … 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     #> # ℹ 521 more rows #> # ℹ 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>, … 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     #> # ℹ 417 more rows #> # ℹ 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>, … 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    #> # ℹ 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. faecalis` `S. aureus` `S. epidermidis` `S. hominis` #>   <chr>      <chr> <chr> <chr>         <chr>       <chr>            <chr>        #> 1 \"Glycopep… Teic… \"\"    \"\"            \" 0.0% (0/… \"64.1% (25/39)\"  \" 6.8% (4/5… #> 2 \"\"         Vanc… \" 0.… \" 0.0% (0/39… \" 0.0% (0/… \" 0.0% (0/171)\"  \" 0.0% (0/8… #> # ℹ 1 more variable: `S. pneumoniae` <chr> 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   # data.table --------------------------------------------------------------  # data.table is supported as well, just use it in the same way as with # base R, but add `with = FALSE` if using a single AB selector.  if (require(\"data.table\")) {   dt <- as.data.table(example_isolates)    # this does not work, it returns column *names*   dt[, carbapenems()] } #> Loading required package: data.table #>  #> Attaching package: ‘data.table’ #> The following objects are masked from ‘package:dplyr’: #>  #>     between, first, last #> The following objects are masked from ‘package:AMR’: #>  #>     %like%, like #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> [1] \"IPM\" \"MEM\" #> attr(,\"class\") #> [1] \"ab_selector\" \"character\"   if (require(\"data.table\")) {   # so `with = FALSE` is required   dt[, carbapenems(), with = FALSE] } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #>        IPM  MEM #>    1: <NA> <NA> #>    2: <NA> <NA> #>    3: <NA> <NA> #>    4: <NA> <NA> #>    5: <NA> <NA> #>   ---           #> 1996: <NA> <NA> #> 1997:    S    S #> 1998:    S    S #> 1999:    S    S #> 2000:    S    S  # for multiple selections or AB selectors, `with = FALSE` is not needed: if (require(\"data.table\")) {   dt[, c(\"mo\", aminoglycosides())] } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #>                 mo  GEN  TOB  AMK  KAN #>    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> #>   ---                                  #> 1996: B_STRPT_PNMN    R    R    R    R #> 1997: B_ESCHR_COLI    S    S    S <NA> #> 1998: B_STPHY_CONS    S <NA> <NA> <NA> #> 1999: B_ESCHR_COLI    S    S <NA> <NA> #> 2000: B_KLBSL_PNMN    S    S <NA> <NA> if (require(\"data.table\")) {   dt[, c(carbapenems(), aminoglycosides())] } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #>        IPM  MEM  GEN  TOB  AMK  KAN #>    1: <NA> <NA> <NA> <NA> <NA> <NA> #>    2: <NA> <NA> <NA> <NA> <NA> <NA> #>    3: <NA> <NA> <NA> <NA> <NA> <NA> #>    4: <NA> <NA> <NA> <NA> <NA> <NA> #>    5: <NA> <NA> <NA> <NA> <NA> <NA> #>   ---                               #> 1996: <NA> <NA>    R    R    R    R #> 1997:    S    S    S    S    S <NA> #> 1998:    S    S    S <NA> <NA> <NA> #> 1999:    S    S    S    S <NA> <NA> #> 2000:    S    S    S    S <NA> <NA>  # row filters are also supported: if (require(\"data.table\")) {   dt[any(carbapenems() == \"S\"), ] } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #>            date patient age gender       ward           mo PEN  OXA  FLC  AMX #>   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 #>  ---                                                                          #> 905: 2005-04-12  D71461  70      M        ICU B_ESCHR_COLI   R <NA> <NA>    S #> 906: 2009-11-12  650870  69      F Outpatient B_ESCHR_COLI   R <NA> <NA>    S #> 907: 2012-06-14  8CBCF2  41      F   Clinical B_STPHY_CONS   R    S    S <NA> #> 908: 2012-10-11  175532  78      M   Clinical B_ESCHR_COLI   R <NA> <NA>    R #> 909: 2013-11-23  A97263  77      M   Clinical B_KLBSL_PNMN   R <NA> <NA>    R #>      AMC  AMP  TZP  CZO  FEP CXM  FOX  CTX  CAZ  CRO  GEN  TOB  AMK  KAN  TMP #>   1:   I <NA> <NA> <NA> <NA>   S <NA>    S <NA>    S <NA>    S <NA> <NA>    S #>   2:   I <NA> <NA> <NA> <NA>   S <NA>    S <NA>    S <NA>    S <NA> <NA>    S #>   3:   I <NA> <NA> <NA> <NA>   S <NA>    S    S    S <NA> <NA> <NA> <NA>    S #>   4:   I <NA> <NA> <NA> <NA>   S <NA>    S    S    S <NA> <NA> <NA> <NA>    S #>   5:   R    R <NA>    R <NA>   R    R <NA> <NA> <NA> <NA> <NA> <NA> <NA>    S #>  ---                                                                          #> 905:   S    S    S <NA>    S   S    S    S    S    S    S    S    S <NA> <NA> #> 906:   S    S    S    S    S   S    S    S    S    S    S    S    S <NA>    S #> 907:   S <NA> <NA>    S    S   S    S    S    R    S    S <NA> <NA> <NA>    S #> 908:   S    R    S <NA>    S   R    R    S    S    S    S    S <NA> <NA>    R #> 909:   S    R    S <NA>    S   S    S    S    S    S    S    S <NA> <NA>    S #>      SXT  NIT  FOS  LNZ  CIP  MFX VAN  TEC  TCY  TGC  DOX ERY CLI AZM IPM  MEM #>   1:   S <NA> <NA>    R <NA> <NA>   R    R <NA> <NA> <NA>   R   R   R   S <NA> #>   2:   S <NA> <NA>    R <NA> <NA>   R    R <NA> <NA> <NA>   R   R   R   S <NA> #>   3:   S    R <NA>    R    S <NA>   R    R    R    R    R   R   R   R   S <NA> #>   4:   S    R <NA>    R    S <NA>   R    R    R    R    R   R   R   R   S <NA> #>   5:   S    R <NA>    R    S <NA>   R    R    R    R    R   R   R   R   S <NA> #>  ---                                                                           #> 905:   S    S <NA>    R    S <NA>   R    R <NA> <NA> <NA>   R   R   R   S    S #> 906:   S    S <NA>    R    S <NA>   R    R <NA> <NA> <NA>   R   R   R   S    S #> 907:   S <NA> <NA> <NA>    S <NA>   S <NA> <NA> <NA> <NA>   S   S   S   S    S #> 908:   R    R <NA>    R    R    R   R    R <NA> <NA> <NA>   R   R   R   S    S #> 909:   S    S <NA>    R    S <NA>   R    R <NA> <NA> <NA>   R   R   R   S    S #>       MTR  CHL  COL  MUP  RIF #>   1: <NA> <NA> <NA> <NA>    R #>   2: <NA> <NA> <NA> <NA>    R #>   3: <NA> <NA>    R <NA>    R #>   4: <NA> <NA>    R <NA>    R #>   5: <NA> <NA>    R <NA>    R #>  ---                          #> 905: <NA> <NA> <NA> <NA>    R #> 906: <NA> <NA> <NA> <NA>    R #> 907: <NA> <NA>    R <NA> <NA> #> 908: <NA> <NA>    S <NA>    R #> 909: <NA> <NA>    S <NA>    R if (require(\"data.table\")) {   dt[any(carbapenems() == \"S\"), penicillins(), with = FALSE] } #> 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) #>      PEN  OXA  FLC  AMX AMC  AMP  TZP #>   1:   R <NA> <NA> <NA>   I <NA> <NA> #>   2:   R <NA> <NA> <NA>   I <NA> <NA> #>   3:   R <NA> <NA> <NA>   I <NA> <NA> #>   4:   R <NA> <NA> <NA>   I <NA> <NA> #>   5:   R <NA> <NA>    R   R    R <NA> #>  ---                                  #> 905:   R <NA> <NA>    S   S    S    S #> 906:   R <NA> <NA>    S   S    S    S #> 907:   R    S    S <NA>   S <NA> <NA> #> 908:   R <NA> <NA>    R   S    R    S #> 909:   R <NA> <NA>    R   S    R    S # }"},{"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. unique identifier. cid Compound ID found PubChem. unique identifier. name Official name used WHONET/EARS-Net . unique identifier. 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 codes associated name antimicrobial drug Logical Observation Identifiers Names Codes (LOINC), Version 2.76 (18 September, 2023). 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 Antiviral ID used package (ACI), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available. unique identifier. Combinations codes contain + indicate , ATA+COBI atazanavir/cobicistat. name Official name used WHONET/EARS-Net . unique identifier. atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC cid Compound ID found PubChem. unique identifier. 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 codes associated name antiviral drug Logical Observation Identifiers Names Codes (LOINC), Version 2.76 (18 September, 2023). Use av_loinc() retrieve quickly, see av_property().","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 2.76 (18 September, 2023). Accessed https://loinc.org October 19th, 2023. 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_group1 atc_group2 abbreviations synonyms #>    <ab>    <dbl> <chr>  <chr> <lis> <chr>      <chr>      <list>        <named > #>  1 AMA      4649 4-ami… Anti… <chr> Drugs for… Aminosali… <chr [1]>     <chr>    #>  2 ACM   6450012 Acety… Macr… <chr> NA         NA         <chr [1]>     <chr>    #>  3 ASP  49787020 Acety… Macr… <chr> NA         NA         <chr [1]>     <chr>    #>  4 ALS      8954 Aldes… Othe… <chr> Drugs for… Drugs for… <chr [1]>     <chr>    #>  5 AMK     37768 Amika… Amin… <chr> Aminoglyc… Other ami… <chr [5]>     <chr>    #>  6 AKF        NA Amika… Amin… <chr> NA         NA         <chr [1]>     <chr>    #>  7 AMX     33613 Amoxi… Beta… <chr> Beta-lact… Penicilli… <chr [3]>     <chr>    #>  8 AMC  23665637 Amoxi… Beta… <chr> Beta-lact… Combinati… <chr [5]>     <chr>    #>  9 AXS    465441 Amoxi… Beta… <chr> NA         NA         <chr [1]>     <chr>    #> 10 AMB   5280965 Ampho… Anti… <chr> Antimycot… Antibioti… <chr [3]>     <chr>    #> # ℹ 473 more rows #> # ℹ 5 more variables: oral_ddd <dbl>, oral_units <chr>, iv_ddd <dbl>, #> #   iv_units <chr>, loinc <list> antivirals #> # A tibble: 120 × 11 #>    av       name      atc      cid atc_group synonyms oral_ddd oral_units iv_ddd #>    <av>     <chr>     <chr>  <dbl> <chr>     <list>      <dbl> <chr>       <dbl> #>  1 ABA      Abacavir  J05A… 4.41e5 Nucleosi… <chr>         0.6 g              NA #>  2 ACI      Aciclovir J05A… 1.35e8 Nucleosi… <chr>         4   g               4 #>  3 ADD      Adefovir… J05A… 6.09e4 Nucleosi… <chr>        10   mg             NA #>  4 AME      Amenamev… J05A… 1.14e7 Other an… <chr>         0.4 g              NA #>  5 AMP      Amprenav… J05A… 6.50e4 Protease… <chr>         1.2 g              NA #>  6 ASU      Asunapre… J05A… 1.61e7 Antivira… <chr>         0.2 g              NA #>  7 ATA      Atazanav… J05A… 1.48e5 Protease… <chr>         0.3 g              NA #>  8 ATA+COBI Atazanav… J05A… 8.66e7 Antivira… <chr>        NA   NA             NA #>  9 ATA+RIT  Atazanav… J05A… 2.51e7 Antivira… <chr>         0.3 g              NA #> 10 BAM      Baloxavi… J05A… 1.24e8 Other an… <chr>        40   mg             NA #> # ℹ 110 more rows #> # ℹ 2 more variables: iv_units <chr>, loinc <list>"},{"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       #> # ℹ 1,990 more rows #> # ℹ 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>, … # }"},{"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\" ) #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting disk diffusion zones: 'ampicillin' (AMP), EUCAST 2023... #>  OK  #> Class 'sir' #> [1] R  # interpret whole data set, pretend to be all from urinary tract infections: as.sir(df, uti = TRUE) #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting disk diffusion zones: column 'AMP' (ampicillin), EUCAST #> 2023... #>  OK  #> Interpreting disk diffusion zones: column 'CIP' (ciprofloxacin), EUCAST #> 2023... #>  OK  #> Interpreting disk diffusion zones: column 'GEN' (gentamicin), EUCAST #> 2023... #>  OK  #> Interpreting disk diffusion zones: column 'TOB' (tobramycin), EUCAST #> 2023... #>  OK  #>      microorganism AMP  CIP GEN TOB #> 1 Escherichia coli   S <NA>   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, keep_operators = \"all\")  is.mic(x)  NA_mic_  rescale_mic(x, mic_range, keep_operators = \"edges\", as.mic = TRUE)  # 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 keep_operators character specifying handle operators (> <=) input. Accepts one three values: \"\" (TRUE) keep operators, \"none\" (FALSE) remove operators, \"edges\" keep operators ends range. mic_range manual range plot MIC values, e.g., mic_range = c(0.001, 32). Use NA set limit one side, e.g., mic_range = c(NA, 32). .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 (2011-2023) CLSI (2011-2023). 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. rescale_mic(), existing MIC ranges can rescaled defined range MIC values. can useful better compare MIC distributions. 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  # rescale MICs using rescale_mic() rescale_mic(mic_data, mic_range = c(4, 16)) #> Class 'mic' #> [1] >=16 <=4  <=4  <=4  8    <=4  8    >=16 >=16  # interpret MIC values as.sir(   x = as.mic(2),   mo = as.mo(\"Streptococcus pneumoniae\"),   ab = \"AMX\",   guideline = \"EUCAST\" ) #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMX' (amoxicillin), EUCAST 2023... #>  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\" ) #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMX' (amoxicillin), EUCAST 2023... #>  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 }"},{"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/ Micro.rganisms 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 December 11th, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org December 11th, 2022. Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed https://bacdive.dsmz.de May 12th, 2023. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microorganism', 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 = 1.25, Protozoa = 1.5, Archaea = 2, others = 3. 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 = 1.25 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, Penicillium, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Talaromyces, 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 = 0.381\\), 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  # input will get cleaned up with the input given in the `cleaning_regex` argument, # which defaults to `mo_cleaning_regex()`: cat(mo_cleaning_regex(), \"\\n\") #> ([^A-Za-z- \\(\\)\\[\\]{}]+|([({]|\\[).+([})]|\\])|(^| )(e?spp([^a-z]+|$)|e?ssp([^a-z]+|$)|serogr.?up[a-z]*|e?ss([^a-z]+|$)|e?sp([^a-z]+|$)|var([^a-z]+|$)|serovar[a-z]*|sube?species|biovar[a-z]*|e?species|Ig[ADEGM]|e?subsp|biotype|titer|dummy))   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. Currently available breakpoint guidelines EUCAST 2011-2023 CLSI 2011-2023, available breakpoint types \"ECOFF\", \"animal\", \"human\". 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),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"),   ... )  # 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),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"),   ... )  # 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),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\") )  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, 2011-2023, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. M100 Performance Standard Antimicrobial Susceptibility Testing, 2011-2023, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/. Breakpoint tables interpretation MICs zone diameters, 2011-2023, 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 2023 (latest implemented EUCAST guideline clinical_breakpoints data set), can set package option AMR_guideline. Currently supports EUCAST (2011-2023) CLSI (2011-2023), 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. breakpoint_type type breakpoints use, either \"ECOFF\", \"animal\", \"human\". ECOFF stands Epidemiological Cut-values. default \"human\", can also set package option AMR_breakpoint_type. 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":"Note: clinical breakpoints package validated imported WHONET public use AMR package endorsed CLSI EUCAST, please see clinical_breakpoints information. 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 (2011-2023) CLSI (2011-2023). Thus, guideline argument must set e.g., \"EUCAST 2023\" \"CLSI 2023\". 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 29 747 rows 12 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    #> # ℹ 1,990 more rows #> # ℹ 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>, … 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) #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: column 'AMP' (ampicillin), EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for ampicillin (AMP) in #>      Escherichia coli - assuming body site 'Intravenous'. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: column 'CIP' (ciprofloxacin), EUCAST 2023... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli #>      - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: column 'GEN' (gentamicin), EUCAST #> 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming an unspecified body site. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting disk diffusion zones: column 'TOB' (tobramycin), EUCAST #> 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming an unspecified body site. 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: 13 × 13 #>    datetime            index ab_user    mo_user    ab   mo                input #>    <dttm>              <int> <chr>      <chr>      <ab> <mo>              <dbl> #>  1 2023-12-03 10:39:01     1 TOB        Escherich… TOB  B_[ORD]_ENTRBCTR 16     #>  2 2023-12-03 10:39:01     1 GEN        Escherich… GEN  B_[ORD]_ENTRBCTR 18     #>  3 2023-12-03 10:39:01     1 CIP        Escherich… CIP  B_[ORD]_ENTRBCTR  0.256 #>  4 2023-12-03 10:39:01     1 AMP        Escherich… AMP  B_[ORD]_ENTRBCTR  8     #>  5 2023-12-03 10:38:56     1 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      0.01  #>  6 2023-12-03 10:38:56     2 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      2     #>  7 2023-12-03 10:38:56     3 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      4     #>  8 2023-12-03 10:38:56     4 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      8     #>  9 2023-12-03 10:38:56     1 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      2     #> 10 2023-12-03 10:38:56     1 TOB        Escherich… TOB  B_[ORD]_ENTRBCTR 16     #> 11 2023-12-03 10:38:56     1 GEN        Escherich… GEN  B_[ORD]_ENTRBCTR 18     #> 12 2023-12-03 10:38:56     1 AMP        Escherich… AMP  B_[ORD]_ENTRBCTR 20     #> 13 2023-12-03 10:38:56     1 ampicillin Strep pneu AMP  B_STRPT_PNMN     18     #> # ℹ 6 more variables: outcome <sir>, method <chr>, breakpoint_S_R <chr>, #> #   guideline <chr>, ref_table <chr>, uti <lgl>  # for single values as.sir(   x = as.mic(2),   mo = as.mo(\"S. pneumoniae\"),   ab = \"AMP\",   guideline = \"EUCAST\" ) #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMP' (ampicillin), EUCAST 2023... #>  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\" ) #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting disk diffusion zones: 'ampicillin' (AMP), EUCAST 2023... #>  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) } #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMP' (ampicillin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for ampicillin (AMP) in #>      Escherichia coli - assuming body site 'Intravenous'. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'CIP' (ciprofloxacin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli #>      - assuming body site 'Non-meningitis'. #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMP' (ampicillin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for ampicillin (AMP) in #>      Escherichia coli - assuming body site 'Intravenous'. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'CIP' (ciprofloxacin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli #>      - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'GEN' (gentamicin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming an unspecified body site. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting disk diffusion zones: 'TOB' (tobramycin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming an unspecified body site. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMP' (ampicillin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for ampicillin (AMP) in #>      Escherichia coli - assuming body site 'Intravenous'. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'CIP' (ciprofloxacin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli #>      - assuming body site 'Non-meningitis'. #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMP' (ampicillin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for ampicillin (AMP) in #>      Escherichia coli - assuming body site 'Intravenous'. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'CIP' (ciprofloxacin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli #>      - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'GEN' (gentamicin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming an unspecified body site. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting disk diffusion zones: 'TOB' (tobramycin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming an unspecified body site. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMP' (ampicillin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for ampicillin (AMP) in #>      Escherichia coli - assuming body site 'Intravenous'. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'CIP' (ciprofloxacin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli #>      - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'GEN' (gentamicin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming an unspecified body site. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting disk diffusion zones: 'TOB' (tobramycin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming an unspecified body site. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMP' (ampicillin), EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for ampicillin (AMP) in #>      Escherichia coli - assuming body site 'Intravenous'. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'CIP' (ciprofloxacin), EUCAST 2023... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli #>      - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'GEN' (gentamicin), EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming an unspecified body site. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting disk diffusion zones: 'TOB' (tobramycin), EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming an unspecified body site. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: column 'NIT' (nitrofurantoin), EUCAST 2023... #>  OK  #> ℹ Assuming value \"urine\" in column 'specimen' reflects a urinary tract #>   infection. #>   Use as.sir(uti = FALSE) to prevent this. #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: column 'NIT' (nitrofurantoin), EUCAST 2023... #>  OK  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMP' (ampicillin), EUCAST 2023... #>  OK  #> Interpreting MIC values: 'CIP' (ciprofloxacin), EUCAST 2023... #>  OK  #> Interpreting disk diffusion zones: 'GEN' (gentamicin), EUCAST 2023... #>  OK  #> Interpreting disk diffusion zones: 'TOB' (tobramycin), EUCAST 2023... #>  OK  #>      microorganism AMP  CIP GEN TOB ERY #> 1 Escherichia coli   S <NA>   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    #> # ℹ 1,990 more rows #> # ℹ 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>, … # }"},{"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\"   # lowercase transformation 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\" \"30273-7\" \"30287-7\" \"30303-2\" \"78772-1\" \"78773-9\" \"79134-3\" #> [8] \"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    #> # ℹ 1,990 more rows #> # ℹ 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>, …  # \\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. coli` `E. faecalis` `K. pneumoniae` `P. aeruginosa` #>    <chr>     <chr> <chr> <chr>     <chr>         <chr>           <chr>           #>  1 \"Aminogl… Amik… \"100… \"  0.0% … \"100.0% (39/… \"\"              \"\"              #>  2 \"\"        Gent… \" 13… \"  2.0% … \"100.0% (39/… \" 10.3% (6/58)\" \"  0.0% (0/30)\" #>  3 \"\"        Kana… \"100… \"\"        \"100.0% (39/… \"\"              \"100.0% (30/30… #>  4 \"\"        Tobr… \" 78… \"  2.6% … \"100.0% (39/… \" 10.3% (6/58)\" \"  0.0% (0/30)\" #>  5 \"Ampheni… Chlo… \"\"    \"\"        \"\"            \"\"              \"100.0% (30/30… #>  6 \"Antimyc… Rifa… \"\"    \"100.0% … \"\"            \"100.0% (58/58… \"100.0% (30/30… #>  7 \"Beta-la… Amox… \" 93… \" 50.0% … \"\"            \"100.0% (58/58… \"100.0% (30/30… #>  8 \"\"        Amox… \" 42… \" 13.1% … \"\"            \" 10.3% (6/58)\" \"100.0% (30/30… #>  9 \"\"        Ampi… \" 93… \" 50.0% … \"\"            \"100.0% (58/58… \"100.0% (30/30… #> 10 \"\"        Benz… \" 77… \"100.0% … \"\"            \"100.0% (58/58… \"100.0% (30/30… #> # ℹ 29 more rows #> # ℹ 5 more variables: `P. mirabilis` <chr>, `S. aureus` <chr>, #> #   `S. epidermidis` <chr>, `S. hominis` <chr>, `S. pneumoniae` <chr>  # 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 #> # ℹ 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 #> # ℹ 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 (2011-2023) CLSI (2011-2023). 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 29 747 observations 12 variables: guideline Name guideline type Breakpoint type, either \"ECOFF\", \"animal\", \"human\" method Testing method, either \"DISK\" \"MIC\" site Body site breakpoint must applied, e.g. \"Oral\" \"Respiratory\" mo Microbial ID, see .mo() rank_index Taxonomic rank index mo 1 (subspecies/infraspecies) 5 (unknown microorganism) ab Antibiotic code used package, EARS-Net WHONET, 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":[]},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"different-types-of-breakpoints","dir":"Reference","previous_headings":"","what":"Different types of breakpoints","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Supported types breakpoints ECOFF, animal, human. ECOFF (Epidemiological cut-) values used antimicrobial susceptibility testing differentiate wild-type non-wild-type strains bacteria fungi. default \"human\", can also set package option AMR_breakpoint_type. Use .sir(..., breakpoint_type = ...) interpret raw data using specific breakpoint type, e.g. .sir(..., breakpoint_type = \"ECOFF\") use ECOFFs.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"imported-from-whonet","dir":"Reference","previous_headings":"","what":"Imported from WHONET","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Clinical breakpoints package validated imported WHONET, free desktop Windows application developed supported Collaborating Centre Surveillance Antimicrobial Resistance. can read website. developers WHONET AMR package contact sharing work. highly appreciate development WHONET software.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"response-from-clsi-and-eucast","dir":"Reference","previous_headings":"","what":"Response from CLSI and EUCAST","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"CEO CLSI chairman EUCAST endorsed work public use AMR package (consequently use breakpoints) June 2023, future development distributing clinical breakpoints discussed meeting CLSI, EUCAST, , developers WHONET AMR package.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"download","dir":"Reference","previous_headings":"","what":"Download","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, though initiatives started overcome burdens. NOTE: AMR package (WHONET software well) contains internal methods apply guidelines, rather complex. example, breakpoints must applied certain species groups (case package available microorganisms.groups data set). important considered using breakpoints use.","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: 29,747 × 12 #>    guideline type  method site  mo            rank_index ab   ref_tbl  disk_dose #>    <chr>     <chr> <chr>  <chr> <mo>               <dbl> <ab> <chr>    <chr>     #>  1 EUCAST 2… human DISK   NA    B_ACHRMB_XYLS          2 MEM  A. xylo… 10ug      #>  2 EUCAST 2… human MIC    NA    B_ACHRMB_XYLS          2 MEM  A. xylo… NA        #>  3 EUCAST 2… human DISK   NA    B_ACHRMB_XYLS          2 SXT  A. xylo… 1.25ug/2… #>  4 EUCAST 2… human MIC    NA    B_ACHRMB_XYLS          2 SXT  A. xylo… NA        #>  5 EUCAST 2… human DISK   NA    B_ACHRMB_XYLS          2 TZP  A. xylo… 30ug/6ug  #>  6 EUCAST 2… human MIC    NA    B_ACHRMB_XYLS          2 TZP  A. xylo… NA        #>  7 EUCAST 2… human DISK   NA    B_ACNTB                3 AMK  Acineto… 30ug      #>  8 EUCAST 2… human DISK   Unco… B_ACNTB                3 AMK  Acineto… 30ug      #>  9 EUCAST 2… human MIC    NA    B_ACNTB                3 AMK  Acineto… NA        #> 10 EUCAST 2… human MIC    Unco… B_ACNTB                3 AMK  Acineto… NA        #> # ℹ 29,737 more rows #> # ℹ 3 more variables: breakpoint_S <dbl>, breakpoint_R <dbl>, uti <lgl>"},{"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\", \"oxygen_tolerance\", \"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_group rule_name rule_source #>   <int> <chr> <chr>           <ord> <chr> <chr> <chr>      <chr>     <chr>       #> 1    33 AMP   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 2    33 AMX   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 3    34 AMP   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 4    34 AMX   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 5   531 AMP   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 6   531 AMX   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 7  1485 AMP   Klebsiella oxy… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 8  1485 AMX   Klebsiella oxy… R     I     \"rep… Custom EU… Custom E… Object 'x'…  # 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 503 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, either 13, 12, 11","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":"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: 503 × 9 #>    ab   name            type  dose  dose_times administration notes original_txt #>    <ab> <chr>           <chr> <chr>      <int> <chr>          <chr> <chr>        #>  1 AMK  Amikacin        stan… 25-3…          1 iv             \"\"    25-30 mg/kg… #>  2 AMX  Amoxicillin     high… 2 g            6 iv             \"\"    2 g x 6 iv   #>  3 AMX  Amoxicillin     stan… 1 g            3 iv             \"\"    1 g x 3-4 iv #>  4 AMX  Amoxicillin     high… 0.75…          3 oral           \"\"    0.75-1 g x … #>  5 AMX  Amoxicillin     stan… 0.5 g          3 oral           \"\"    0.5 g x 3 o… #>  6 AMX  Amoxicillin     unco… 0.5 g          3 oral           \"\"    0.5 g x 3 o… #>  7 AMC  Amoxicillin/cl… high… 2 g …          3 iv             \"\"    (2 g amoxic… #>  8 AMC  Amoxicillin/cl… stan… 1 g …          3 iv             \"\"    (1 g amoxic… #>  9 AMC  Amoxicillin/cl… high… 0.87…          3 oral           \"\"    (0.875 g am… #> 10 AMC  Amoxicillin/cl… stan… 0.5 …          3 oral           \"\"    (0.5 g amox… #> # ℹ 493 more rows #> # ℹ 1 more variable: eucast_version <dbl>"},{"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 \"12.0\", \"11.0\", \"10.0\". version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can \"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 December 11th, 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, J01GB06), amoxicillin (AMX, J01CA04), amoxicillin/clavulanic acid (AMC, J01CR02), ampicillin (AMP, J01CA01), ampicillin/sulbactam (SAM, J01CR01), arbekacin (ARB, J01GB12), aspoxicillin (APX, J01CA19), azidocillin (AZD, J01CE04), azithromycin (AZM, J01FA10), azlocillin (AZL, J01CA09), aztreonam (ATM, J01DF01), bacampicillin (BAM, J01CA06), bekanamycin (BEK, J01GB13), benzathine benzylpenicillin (BNB, J01CE08), benzathine phenoxymethylpenicillin (BNP, J01CE10), benzylpenicillin (PEN, J01CE01), besifloxacin (BES, S01AE08), biapenem (BIA, J01DH05), carbenicillin (CRB, J01CA03), carindacillin (CRN, J01CA05), cefacetrile (CAC, J01DB10), cefaclor (CEC, J01DC04), cefadroxil (CFR, J01DB05), cefalexin (LEX, J01DB01), cefaloridine (RID, J01DB02), cefalotin (CEP, J01DB03), cefamandole (MAN, J01DC03), cefapirin (HAP, J01DB08), cefatrizine (CTZ, J01DB07), cefazedone (CZD, J01DB06), cefazolin (CZO, J01DB04), cefcapene (CCP, J01DD17), cefdinir (CDR, J01DD15), cefditoren (DIT, J01DD16), cefepime (FEP, J01DE01), cefetamet (CAT, J01DD10), cefixime (CFM, J01DD08), cefmenoxime (CMX, J01DD05), cefmetazole (CMZ, J01DC09), cefodizime (DIZ, J01DD09), cefonicid (CID, J01DC06), cefoperazone (CFP, J01DD12), cefoperazone/sulbactam (CSL, J01DD62), ceforanide (CND, J01DC11), cefotaxime (CTX, J01DD01), cefotaxime/clavulanic acid (CTC, J01DD51), cefotetan (CTT, J01DC05), cefotiam (CTF, J01DC07), cefoxitin (FOX, J01DC01), cefozopran (ZOP, J01DE03), cefpiramide (CPM, J01DD11), cefpirome (CPO, J01DE02), cefpodoxime (CPD, J01DD13), cefprozil (CPR, J01DC10), cefroxadine (CRD, J01DB11), cefsulodin (CFS, J01DD03), ceftaroline (CPT, J01DI02), ceftazidime (CAZ, J01DD02), ceftazidime/clavulanic acid (CCV, J01DD52), cefteram (CEM, J01DD18), ceftezole (CTL, J01DB12), ceftibuten (CTB, J01DD14), ceftizoxime (CZX, J01DD07), ceftobiprole medocaril (CFM1, J01DI01), ceftolozane/tazobactam (CZT, J01DI54), ceftriaxone (CRO, J01DD04), ceftriaxone/beta-lactamase inhibitor (CEB, J01DD63), cefuroxime (CXM, J01DC02), cephradine (CED, J01DB09), chloramphenicol (CHL, J01BA01), ciprofloxacin (CIP, J01MA02), clarithromycin (CLR, J01FA09), clindamycin (CLI, J01FF01), clometocillin (CLM, J01CE07), cloxacillin (CLO, J01CF02), colistin (COL, J01XB01), cycloserine (CYC, J04AB01), dalbavancin (DAL, J01XA04), daptomycin (DAP, J01XX09), delafloxacin (DFX, J01MA23), dibekacin (DKB, J01GB09), dicloxacillin (DIC, J01CF01), dirithromycin (DIR, J01FA13), doripenem (DOR, J01DH04), doxycycline (DOX, J01AA02), enoxacin (ENX, J01MA04), epicillin (EPC, J01CA07), ertapenem (ETP, J01DH03), erythromycin (ERY, J01FA01), fleroxacin (FLE, J01MA08), flucloxacillin (FLC, J01CF05), flurithromycin (FLR1, J01FA14), fosfomycin (FOS, J01XX01), framycetin (FRM, D09AA01), fusidic acid (FUS, J01XC01), garenoxacin (GRN, J01MA19), gatifloxacin (GAT, J01MA16), gemifloxacin (GEM, J01MA15), gentamicin (GEN, J01GB03), grepafloxacin (GRX, J01MA11), hetacillin (HET, J01CA18), imipenem (IPM, J01DH51), imipenem/relebactam (IMR, J01DH56), isepamicin (ISE, J01GB11), josamycin (JOS, J01FA07), kanamycin (KAN, J01GB04), lascufloxacin (LSC, J01MA25), latamoxef (LTM, J01DD06), levofloxacin (LVX, J01MA12), levonadifloxacin (LND, J01MA24), lincomycin (LIN, J01FF02), linezolid (LNZ, J01XX08), lomefloxacin (LOM, J01MA07), loracarbef (LOR, J01DC08), mecillinam (MEC, J01CA11), meropenem (MEM, J01DH02), meropenem/vaborbactam (MEV, J01DH52), metampicillin (MTM, J01CA14), meticillin (MET, J01CF03), mezlocillin (MEZ, J01CA10), micronomicin (MCR, S01AA22), midecamycin (MID, J01FA03), minocycline (MNO, J01AA08), miocamycin (MCM, J01FA11), moxifloxacin (MFX, J01MA14), nadifloxacin (NAD, D10AF05), nafcillin (NAF, J01CF06), nalidixic acid (NAL, J01MB02), neomycin (NEO, J01GB05), netilmicin (NET, J01GB07), nitrofurantoin (NIT, J01XE01), norfloxacin (, J01MA06), ofloxacin (OFX, J01MA01), oleandomycin (OLE, J01FA05), oritavancin (ORI, J01XA05), oxacillin (OXA, J01CF04), panipenem (PAN, J01DH55), pazufloxacin (PAZ, J01MA18), pefloxacin (PEF, J01MA03), penamecillin (PNM, J01CE06), pheneticillin (PHE, J01CE05), phenoxymethylpenicillin (PHN, J01CE02), piperacillin (PIP, J01CA12), piperacillin/tazobactam (TZP, J01CR05), pivampicillin (PVM, J01CA02), pivmecillinam (PME, J01CA08), plazomicin (PLZ, J01GB14), polymyxin B (PLB, J01XB02), pristinamycin (PRI, J01FG01), procaine benzylpenicillin (PRB, J01CE09), propicillin (PRP, J01CE03), prulifloxacin (PRU, J01MA17), quinupristin/dalfopristin (QDA, J01FG02), ribostamycin (RST, J01GB10), rifampicin (RIF, J04AB02), rokitamycin (ROK, J01FA12), roxithromycin (RXT, J01FA06), rufloxacin (RFL, J01MA10), sisomicin (SIS, J01GB08), sitafloxacin (SIT, J01MA21), solithromycin (SOL, J01FA16), sparfloxacin (SPX, J01MA09), spiramycin (SPI, J01FA02), streptoduocin (STR, J01GA02), streptomycin (STR1, J01GA01), sulbactam (SUL, J01CG01), sulbenicillin (SBC, J01CA16), sulfadiazine (SDI, J01EC02), sulfadiazine/trimethoprim (SLT1, J01EE02), sulfadimethoxine (SUD, J01ED01), sulfadimidine (SDM, J01EB03), sulfadimidine/trimethoprim (SLT2, J01EE05), sulfafurazole (SLF, J01EB05), sulfaisodimidine (SLF1, J01EB01), sulfalene (SLF2, J01ED02), sulfamazone (SZO, J01ED09), sulfamerazine (SLF3, J01ED07), sulfamerazine/trimethoprim (SLT3, J01EE07), sulfamethizole (SLF4, J01EB02), sulfamethoxazole (SMX, J01EC01), sulfamethoxypyridazine (SLF5, J01ED05), sulfametomidine (SLF6, J01ED03), sulfametoxydiazine (SLF7, J01ED04), sulfametrole/trimethoprim (SLT4, J01EE03), sulfamoxole (SLF8, J01EC03), sulfamoxole/trimethoprim (SLT5, J01EE04), sulfanilamide (SLF9, J01EB06), sulfaperin (SLF10, J01ED06), sulfaphenazole (SLF11, J01ED08), sulfapyridine (SLF12, J01EB04), sulfathiazole (SUT, J01EB07), sulfathiourea (SLF13, J01EB08), sultamicillin (SLT6, J01CR04), talampicillin (TAL, J01CA15), tazobactam (TAZ, J01CG02), tebipenem (TBP, J01DH06), tedizolid (TZD, J01XX11), teicoplanin (TEC, J01XA02), telavancin (TLV, J01XA03), telithromycin (TLT, J01FA15), temafloxacin (TMX, J01MA05), temocillin (TEM, J01CA17), tetracycline (TCY, J01AA07), ticarcillin (TIC, J01CA13), ticarcillin/clavulanic acid (TCC, J01CR03), tigecycline (TGC, J01AA12), tilbroquinol (TBQ, P01AA05), tobramycin (TOB, J01GB01), tosufloxacin (TFX, J01MA22), trimethoprim (TMP, J01EA01), trimethoprim/sulfamethoxazole (SXT, J01EE01), troleandomycin (TRL, J01FA08), trovafloxacin (TVA, J01MA13), vancomycin (VAN, J01XA01)","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    #> # ℹ 1,990 more rows #> # ℹ 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>, …"},{"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     #> # ℹ 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), ] #> ℹ 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. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #>   of 2 #> ℹ Excluding 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,373 'phenotype-based' first isolates (68.7% of total where a #>    microbial ID was available) #> # A tibble: 1,373 × 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    #> # ℹ 1,363 more rows #> # ℹ 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>, … # \\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: 434 × 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    #> # ℹ 424 more rows #> # ℹ 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>, …  if (require(\"dplyr\")) {   # filter on first isolates using dplyr:   example_isolates %>%     filter(first_isolate(info = TRUE)) } #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #>   of 2 #> ℹ Excluding 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,373 'phenotype-based' first isolates (68.7% of total where a #>    microbial ID was available) #> # A tibble: 1,373 × 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    #> # ℹ 1,363 more rows #> # ℹ 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>, … if (require(\"dplyr\")) {   # short-hand version:   example_isolates %>%     filter_first_isolate(info = FALSE) } #> # A tibble: 1,373 × 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    #> # ℹ 1,363 more rows #> # ℹ 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>, … 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) } #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #>   of 2 #>  #> Group: ward = \"Clinical\" #> ℹ Excluding 9 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 857 'phenotype-based' first isolates (69.4% of total where a #>    microbial ID was available) #>  #> Group: ward = \"ICU\" #> ℹ Excluding 6 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 447 'phenotype-based' first isolates (69.2% of total where a #>    microbial ID was available) #>  #> Group: ward = \"Outpatient\" #> ℹ Excluding 1 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 98 'phenotype-based' first isolates (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  #> # ℹ 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 hand, returns TRUE every new get_episode() index. 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$date, episode_days = 60) # indices #>   [1] 47 43 22 23 10 50 15 34 13 20 19 47 16  2 26 37  8 44  7 14 10 10 34 29  4 #>  [26] 52  3 48  3 24 48 28 28 14 17  1 50  6 51 53 42 17 45  1  1 30  9 27 39 12 #>  [51] 21 33 45  3 18 24 35 52 25 37 40 35 52 49 48 37 44 42 31 24 50 52 19  8 16 #>  [76] 31 22 46 21 14 16 14 10  5 41  3 13 52 33 19 11 38 45 43 41 32  2 12 36 52 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE #>   [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE #>  [13]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE #>  [25]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE #>  [37] FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE #>  [49]  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE #>  [61]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE #>  [73] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE #>  [85]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE #>  [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: 4 × 46 #>   date       patient   age gender ward      mo           PEN   OXA   FLC   AMX   #>   <date>     <chr>   <dbl> <chr>  <chr>     <mo>         <sir> <sir> <sir> <sir> #> 1 2002-10-22 718963     82 M      ICU       B_STPHY_EPDR R     NA    R     R     #> 2 2002-10-11 974319     78 M      Outpatie… B_STPHY_EPDR S     NA    S     NA    #> 3 2002-10-20 F35553     51 M      ICU       B_STPHY_AURS S     NA    S     NA    #> 4 2002-10-20 F35553     51 M      ICU       B_STPHY_AURS S     NA    S     NA    #> # ℹ 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 [96] #>    patient date       condition new_episode #>    <chr>   <date>     <chr>     <lgl>       #>  1 006827  2009-07-24 A         TRUE        #>  2 008268  2007-02-20 A         TRUE        #>  3 023456  2002-02-05 B         TRUE        #>  4 074321  2015-09-20 C         TRUE        #>  5 09B453  2010-03-21 A         TRUE        #>  6 105248  2005-06-16 A         TRUE        #>  7 1435C8  2004-03-03 B         TRUE        #>  8 1710B8  2008-01-22 A         TRUE        #>  9 197007  2012-07-17 C         TRUE        #> 10 207325  2011-05-08 B         TRUE        #> # ℹ 90 more rows  if (require(\"dplyr\")) {   df %>%     group_by(ward, patient) %>%     transmute(date,       patient,       new_index = get_episode(date, 60),       new_logical = is_new_episode(date, 60)     ) %>%     arrange(patient, ward, date) } #> # A tibble: 100 × 5 #> # Groups:   ward, patient [92] #>    ward     date       patient new_index new_logical #>    <chr>    <date>     <chr>       <int> <lgl>       #>  1 Clinical 2009-07-24 006827          1 TRUE        #>  2 ICU      2007-02-20 008268          1 TRUE        #>  3 Clinical 2002-02-05 023456          1 TRUE        #>  4 ICU      2015-09-20 074321          1 TRUE        #>  5 Clinical 2010-03-21 09B453          1 TRUE        #>  6 Clinical 2005-06-16 105248          1 TRUE        #>  7 Clinical 2004-03-03 1435C8          1 TRUE        #>  8 Clinical 2008-01-22 1710B8          1 TRUE        #>  9 Clinical 2012-07-17 197007          1 TRUE        #> 10 ICU      2011-05-08 207325          1 TRUE        #> # ℹ 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           54             12            36            44 #> 2 ICU                28              9            21            22 #> 3 Outpatient         10              7            10            10  # 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 [96] #>    patient mo           ward       flag_episode #>    <chr>   <mo>         <chr>      <lgl>        #>  1 557266  B_STPHY_HMLY Clinical   TRUE         #>  2 280834  B_STPHY_EPDR Clinical   TRUE         #>  3 1710B8  B_PROTS_MRBL Clinical   TRUE         #>  4 800264  B_ESCHR_COLI Outpatient TRUE         #>  5 696587  B_PROTS_PNNR ICU        TRUE         #>  6 B46416  B_STRPT_AGLC Clinical   TRUE         #>  7 904485  F_CANDD_ALBC Outpatient TRUE         #>  8 807228  B_KLBSL_PNMN ICU        TRUE         #>  9 838434  B_STPHY_CONS Clinical   TRUE         #> 10 008268  B_ESCHR_COLI ICU        TRUE         #> # ℹ 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, main = \"Base R biplot\")    # new ggplot2 plotting method using this package:   if (require(\"ggplot2\")) {     ggplot_pca(pca_result) +       labs(title = \"ggplot2 biplot\")   }   if (require(\"ggplot2\")) {     # still extendible with any ggplot2 function     ggplot_pca(pca_result) +       scale_colour_viridis_d() +       labs(title = \"ggplot2 biplot\")   } } #> 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   #> # ℹ 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 × 23 #>   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… #> # ℹ 13 more variables: subspecies <chr>, rank <chr>, ref <chr>, #> #   oxygen_tolerance <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 × 23 #>   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… #> # ℹ 13 more variables: subspecies <chr>, rank <chr>, ref <chr>, #> #   oxygen_tolerance <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\"              \"oxygen_tolerance\" \"source\"           #> [17] \"lpsn\"             \"lpsn_parent\"      \"lpsn_renamed_to\"  \"gbif\"             #> [21] \"gbif_parent\"      \"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\"              \"oxygen_tolerance\" \"source\"           #> [61] \"lpsn\"             \"lpsn_parent\"      \"lpsn_renamed_to\"  \"gbif\"             #> [65] \"gbif_parent\"      \"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] 1376 # }"},{"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] 2.989289 kurtosis(rnorm(10000), excess = TRUE) #> [1] 0.0300932"},{"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":"# data.table has a more limited version of %like%, so unload it: try(detach(\"package:data.table\", unload = TRUE), silent = TRUE)  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     #> # ℹ 96 more rows #> # ℹ 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>, …  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     #> # ℹ 96 more rows #> # ℹ 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>, … # }"},{"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) (doi:10.1111/j.1469-0691.2011.03570.x ) 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, J01GB06), amoxicillin (AMX, J01CA04), amoxicillin/clavulanic acid (AMC, J01CR02), ampicillin (AMP, J01CA01), ampicillin/sulbactam (SAM, J01CR01), arbekacin (ARB, J01GB12), aspoxicillin (APX, J01CA19), azidocillin (AZD, J01CE04), azithromycin (AZM, J01FA10), azlocillin (AZL, J01CA09), aztreonam (ATM, J01DF01), bacampicillin (BAM, J01CA06), bekanamycin (BEK, J01GB13), benzathine benzylpenicillin (BNB, J01CE08), benzathine phenoxymethylpenicillin (BNP, J01CE10), benzylpenicillin (PEN, J01CE01), besifloxacin (BES, S01AE08), biapenem (BIA, J01DH05), carbenicillin (CRB, J01CA03), carindacillin (CRN, J01CA05), cefacetrile (CAC, J01DB10), cefaclor (CEC, J01DC04), cefadroxil (CFR, J01DB05), cefalexin (LEX, J01DB01), cefaloridine (RID, J01DB02), cefalotin (CEP, J01DB03), cefamandole (MAN, J01DC03), cefapirin (HAP, J01DB08), cefatrizine (CTZ, J01DB07), cefazedone (CZD, J01DB06), cefazolin (CZO, J01DB04), cefcapene (CCP, J01DD17), cefdinir (CDR, J01DD15), cefditoren (DIT, J01DD16), cefepime (FEP, J01DE01), cefetamet (CAT, J01DD10), cefixime (CFM, J01DD08), cefmenoxime (CMX, J01DD05), cefmetazole (CMZ, J01DC09), cefodizime (DIZ, J01DD09), cefonicid (CID, J01DC06), cefoperazone (CFP, J01DD12), cefoperazone/sulbactam (CSL, J01DD62), ceforanide (CND, J01DC11), cefotaxime (CTX, J01DD01), cefotaxime/clavulanic acid (CTC, J01DD51), cefotetan (CTT, J01DC05), cefotiam (CTF, J01DC07), cefoxitin (FOX, J01DC01), cefozopran (ZOP, J01DE03), cefpiramide (CPM, J01DD11), cefpirome (CPO, J01DE02), cefpodoxime (CPD, J01DD13), cefprozil (CPR, J01DC10), cefroxadine (CRD, J01DB11), cefsulodin (CFS, J01DD03), ceftaroline (CPT, J01DI02), ceftazidime (CAZ, J01DD02), ceftazidime/clavulanic acid (CCV, J01DD52), cefteram (CEM, J01DD18), ceftezole (CTL, J01DB12), ceftibuten (CTB, J01DD14), ceftizoxime (CZX, J01DD07), ceftobiprole medocaril (CFM1, J01DI01), ceftolozane/tazobactam (CZT, J01DI54), ceftriaxone (CRO, J01DD04), ceftriaxone/beta-lactamase inhibitor (CEB, J01DD63), cefuroxime (CXM, J01DC02), cephradine (CED, J01DB09), chloramphenicol (CHL, J01BA01), ciprofloxacin (CIP, J01MA02), clarithromycin (CLR, J01FA09), clindamycin (CLI, J01FF01), clometocillin (CLM, J01CE07), cloxacillin (CLO, J01CF02), colistin (COL, J01XB01), cycloserine (CYC, J04AB01), dalbavancin (DAL, J01XA04), daptomycin (DAP, J01XX09), delafloxacin (DFX, J01MA23), dibekacin (DKB, J01GB09), dicloxacillin (DIC, J01CF01), dirithromycin (DIR, J01FA13), doripenem (DOR, J01DH04), doxycycline (DOX, J01AA02), enoxacin (ENX, J01MA04), epicillin (EPC, J01CA07), ertapenem (ETP, J01DH03), erythromycin (ERY, J01FA01), fleroxacin (FLE, J01MA08), flucloxacillin (FLC, J01CF05), flurithromycin (FLR1, J01FA14), fosfomycin (FOS, J01XX01), framycetin (FRM, D09AA01), fusidic acid (FUS, J01XC01), garenoxacin (GRN, J01MA19), gatifloxacin (GAT, J01MA16), gemifloxacin (GEM, J01MA15), gentamicin (GEN, J01GB03), grepafloxacin (GRX, J01MA11), hetacillin (HET, J01CA18), imipenem (IPM, J01DH51), imipenem/relebactam (IMR, J01DH56), isepamicin (ISE, J01GB11), josamycin (JOS, J01FA07), kanamycin (KAN, J01GB04), lascufloxacin (LSC, J01MA25), latamoxef (LTM, J01DD06), levofloxacin (LVX, J01MA12), levonadifloxacin (LND, J01MA24), lincomycin (LIN, J01FF02), linezolid (LNZ, J01XX08), lomefloxacin (LOM, J01MA07), loracarbef (LOR, J01DC08), mecillinam (MEC, J01CA11), meropenem (MEM, J01DH02), meropenem/vaborbactam (MEV, J01DH52), metampicillin (MTM, J01CA14), meticillin (MET, J01CF03), mezlocillin (MEZ, J01CA10), micronomicin (MCR, S01AA22), midecamycin (MID, J01FA03), minocycline (MNO, J01AA08), miocamycin (MCM, J01FA11), moxifloxacin (MFX, J01MA14), nadifloxacin (NAD, D10AF05), nafcillin (NAF, J01CF06), nalidixic acid (NAL, J01MB02), neomycin (NEO, J01GB05), netilmicin (NET, J01GB07), nitrofurantoin (NIT, J01XE01), norfloxacin (, J01MA06), ofloxacin (OFX, J01MA01), oleandomycin (OLE, J01FA05), oritavancin (ORI, J01XA05), oxacillin (OXA, J01CF04), panipenem (PAN, J01DH55), pazufloxacin (PAZ, J01MA18), pefloxacin (PEF, J01MA03), penamecillin (PNM, J01CE06), pheneticillin (PHE, J01CE05), phenoxymethylpenicillin (PHN, J01CE02), piperacillin (PIP, J01CA12), piperacillin/tazobactam (TZP, J01CR05), pivampicillin (PVM, J01CA02), pivmecillinam (PME, J01CA08), plazomicin (PLZ, J01GB14), polymyxin B (PLB, J01XB02), pristinamycin (PRI, J01FG01), procaine benzylpenicillin (PRB, J01CE09), propicillin (PRP, J01CE03), prulifloxacin (PRU, J01MA17), quinupristin/dalfopristin (QDA, J01FG02), ribostamycin (RST, J01GB10), rifampicin (RIF, J04AB02), rokitamycin (ROK, J01FA12), roxithromycin (RXT, J01FA06), rufloxacin (RFL, J01MA10), sisomicin (SIS, J01GB08), sitafloxacin (SIT, J01MA21), solithromycin (SOL, J01FA16), sparfloxacin (SPX, J01MA09), spiramycin (SPI, J01FA02), streptoduocin (STR, J01GA02), streptomycin (STR1, J01GA01), sulbactam (SUL, J01CG01), sulbenicillin (SBC, J01CA16), sulfadiazine (SDI, J01EC02), sulfadiazine/trimethoprim (SLT1, J01EE02), sulfadimethoxine (SUD, J01ED01), sulfadimidine (SDM, J01EB03), sulfadimidine/trimethoprim (SLT2, J01EE05), sulfafurazole (SLF, J01EB05), sulfaisodimidine (SLF1, J01EB01), sulfalene (SLF2, J01ED02), sulfamazone (SZO, J01ED09), sulfamerazine (SLF3, J01ED07), sulfamerazine/trimethoprim (SLT3, J01EE07), sulfamethizole (SLF4, J01EB02), sulfamethoxazole (SMX, J01EC01), sulfamethoxypyridazine (SLF5, J01ED05), sulfametomidine (SLF6, J01ED03), sulfametoxydiazine (SLF7, J01ED04), sulfametrole/trimethoprim (SLT4, J01EE03), sulfamoxole (SLF8, J01EC03), sulfamoxole/trimethoprim (SLT5, J01EE04), sulfanilamide (SLF9, J01EB06), sulfaperin (SLF10, J01ED06), sulfaphenazole (SLF11, J01ED08), sulfapyridine (SLF12, J01EB04), sulfathiazole (SUT, J01EB07), sulfathiourea (SLF13, J01EB08), sultamicillin (SLT6, J01CR04), talampicillin (TAL, J01CA15), tazobactam (TAZ, J01CG02), tebipenem (TBP, J01DH06), tedizolid (TZD, J01XX11), teicoplanin (TEC, J01XA02), telavancin (TLV, J01XA03), telithromycin (TLT, J01FA15), temafloxacin (TMX, J01MA05), temocillin (TEM, J01CA17), tetracycline (TCY, J01AA07), ticarcillin (TIC, J01CA13), ticarcillin/clavulanic acid (TCC, J01CR03), tigecycline (TGC, J01AA12), tilbroquinol (TBQ, P01AA05), tobramycin (TOB, J01GB01), tosufloxacin (TFX, J01MA22), trimethoprim (TMP, J01EA01), trimethoprim/sulfamethoxazole (SXT, J01EE01), troleandomycin (TRL, J01FA08), trovafloxacin (TVA, J01MA13), vancomycin (VAN, J01XA01)","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\") 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() } #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> 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 S R S I I R I I S mean_amr_distance(sir) #>  [1]  1.449138 -0.621059  1.449138 -0.621059 -0.621059 -0.621059  1.449138 #>  [8] -0.621059 -0.621059 -0.621059  mic <- random_mic(10) mic #> Class 'mic' #>  [1] 0.125  0.0625 1      8      4      0.25   >=32   0.5    0.005  0.25   mean_amr_distance(mic) #>  [1] -0.554873825 -0.827456801  0.262875102  1.080624029  0.808041053 #>  [6] -0.282290850  1.625789980 -0.009707874 -1.820709964 -0.282290850 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) #>  [1] -0.554873825 -0.827456801  0.262875102  1.080624029  0.808041053 #>  [6] -0.282290850  1.625789980 -0.009707874 -1.820709964 -0.282290850  disk <- random_disk(10) disk #> Class 'disk' #>  [1] 26 32 50 20 28 49 12 20  9 42 mean_amr_distance(disk) #>  [1] -0.19375217  0.22143105  1.46698069 -0.60893538 -0.05535776  1.39778349 #>  [7] -1.16251300 -0.60893538 -1.37010461  0.91340307  y <- data.frame(   id = LETTERS[1:10],   amox = random_sir(10, ab = \"amox\", mo = \"Escherichia coli\"),   cipr = random_disk(10, ab = \"cipr\", mo = \"Escherichia coli\"),   gent = random_mic(10, ab = \"gent\", mo = \"Escherichia coli\"),   tobr = random_mic(10, ab = \"tobr\", mo = \"Escherichia coli\") ) y #>    id amox cipr gent tobr #> 1   A    S   23  >=4  <=2 #> 2   B    S   27    1    4 #> 3   C    I   26  >=4  <=2 #> 4   D    I   19    1 >=16 #> 5   E    S   20    2    4 #> 6   F    I   25  0.5 >=16 #> 7   G    S   18  0.5  <=2 #> 8   H    R   19    2 >=16 #> 9   I    R   22    1  <=2 #> 10  J    R   20  0.5    8 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\", #>   and \"tobr\" #>  [1]  0.05387469  0.12082470  0.28731436 -0.12784750 -0.20825803  0.12342200 #>  [7] -0.98202095  0.60531151  0.06239096  0.06498826 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   26  >=4  <=2   0.28731436  0.0000000 #> 2   F    I   25  0.5 >=16   0.12342200  0.1638924 #> 3   B    S   27    1    4   0.12082470  0.1664897 #> 4   J    R   20  0.5    8   0.06498826  0.2223261 #> 5   I    R   22    1  <=2   0.06239096  0.2249234 #> 6   A    S   23  >=4  <=2   0.05387469  0.2334397 #> 7   H    R   19    2 >=16   0.60531151  0.3179971 #> 8   D    I   19    1 >=16  -0.12784750  0.4151619 #> 9   E    S   20    2    4  -0.20825803  0.4955724 #> 10  G    S   18  0.5  <=2  -0.98202095  1.2693353 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 #> # ℹ 53 more rows"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 4 957 Common Microorganism Codes — microorganisms.codes","title":"Data Set with 4 957 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 4 957 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 4 957 Common Microorganism Codes — microorganisms.codes","text":"tibble 4 957 observations 2 variables: code Commonly used code microorganism. unique identifier. 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 4 957 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 4 957 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes #> # A tibble: 4,957 × 2 #>    code  mo                #>    <chr> <mo>              #>  1 1011  B_GRAMP           #>  2 1012  B_GRAMP           #>  3 1013  B_GRAMN           #>  4 1014  B_GRAMN           #>  5 1015  F_YEAST           #>  6 103   B_ESCHR_COLI      #>  7 104   B_SLMNL_ENTR_ENTR #>  8 1100  B_STRPT           #>  9 1101  B_STRPT_VIRI      #> 10 1102  B_STRPT_HAEM      #> # ℹ 4,947 more rows  # 'ECO' or 'eco' is the WHONET code for E. coli: microorganisms.codes[microorganisms.codes$code == \"ECO\", ] #> # A tibble: 1 × 2 #>   code  mo           #>   <chr> <mo>         #> 1 ECO   B_ESCHR_COLI  # and therefore, 'eco' will be understood as E. coli in this package: mo_info(\"eco\") #> $mo #> [1] \"B_ESCHR_COLI\" #>  #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Escherichia\" #>  #> $species #> [1] \"coli\" #>  #> $subspecies #> [1] \"\" #>  #> $status #> [1] \"accepted\" #>  #> $synonyms #> NULL #>  #> $gramstain #> [1] \"Gram-negative\" #>  #> $oxygen_tolerance #> [1] \"facultative anaerobe\" #>  #> $url #> [1] \"https://lpsn.dsmz.de/species/escherichia-coli\" #>  #> $ref #> [1] \"Castellani et al., 1919\" #>  #> $snomed #>  [1] \"1095001000112106\" \"715307006\"        \"737528008\"        \"416989002\"        #>  [5] \"116397003\"        \"414097009\"        \"414098004\"        \"414099007\"        #>  [9] \"414100004\"        \"116395006\"        \"735270003\"        \"116396007\"        #> [13] \"83285000\"         \"116394005\"        \"112283007\"        \"710886005\"        #> [17] \"710887001\"        \"710888006\"        \"710889003\"        \"414132004\"        #> [21] \"721892009\"        \"416812001\"        \"416740004\"        \"417216001\"        #> [25] \"457541006\"        \"710253004\"        \"416530004\"        \"417189006\"        #> [29] \"409800005\"        \"713925008\"        \"444771000124108\"  \"838549008\"        #>  #> $lpsn #> [1] \"776057\" #>  #> $gbif #> [1] \"11286021\" #>   # works for all AMR functions: mo_is_intrinsic_resistant(\"eco\", ab = \"vancomycin\") #> [1] TRUE"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","text":"data set containing species groups microbiological complexes, used clinical breakpoints table.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","text":"","code":"microorganisms.groups"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","text":"tibble 521 observations 4 variables: mo_group ID species group / microbiological complex mo ID microorganism belonging species group / microbiological complex mo_group_name Name species group / microbiological complex, retrieved mo_name() mo_name Name microorganism belonging species group / microbiological complex, retrieved mo_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","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.groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","text":"","code":"microorganisms.groups #> # A tibble: 521 × 4 #>    mo_group       mo           mo_group_name                   mo_name           #>    <mo>           <mo>         <chr>                           <chr>             #>  1 B_ACNTB_BMNN-C B_ACNTB_BMNN Acinetobacter baumannii complex Acinetobacter ba… #>  2 B_ACNTB_BMNN-C B_ACNTB_CLCC Acinetobacter baumannii complex Acinetobacter ca… #>  3 B_ACNTB_BMNN-C B_ACNTB_DJKS Acinetobacter baumannii complex Acinetobacter di… #>  4 B_ACNTB_BMNN-C B_ACNTB_NSCM Acinetobacter baumannii complex Acinetobacter no… #>  5 B_ACNTB_BMNN-C B_ACNTB_PITT Acinetobacter baumannii complex Acinetobacter pi… #>  6 B_ACNTB_BMNN-C B_ACNTB_SFRT Acinetobacter baumannii complex Acinetobacter se… #>  7 B_BCTRD_FRGL-C B_BCTRD_FRGL Bacteroides fragilis complex    Bacteroides frag… #>  8 B_BCTRD_FRGL-C B_BCTRD_OVTS Bacteroides fragilis complex    Bacteroides ovat… #>  9 B_BCTRD_FRGL-C B_BCTRD_THTT Bacteroides fragilis complex    Bacteroides thet… #> 10 B_BCTRD_FRGL-C B_BCTRD_VLGT Bacteroides fragilis complex    Bacteroides vulg… #> # ℹ 511 more rows  # these are all species in the Bacteroides fragilis group, as per WHONET: microorganisms.groups[microorganisms.groups$mo_group == \"B_BCTRD_FRGL-C\", ] #> # A tibble: 5 × 4 #>   mo_group       mo           mo_group_name                mo_name               #>   <mo>           <mo>         <chr>                        <chr>                 #> 1 B_BCTRD_FRGL-C B_BCTRD_FRGL Bacteroides fragilis complex Bacteroides fragilis  #> 2 B_BCTRD_FRGL-C B_BCTRD_OVTS Bacteroides fragilis complex Bacteroides ovatus    #> 3 B_BCTRD_FRGL-C B_BCTRD_THTT Bacteroides fragilis complex Bacteroides thetaiot… #> 4 B_BCTRD_FRGL-C B_BCTRD_VLGT Bacteroides fragilis complex Bacteroides vulgatus  #> 5 B_BCTRD_FRGL-C B_PRBCT_DSTS Bacteroides fragilis complex Parabacteroides dist…"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 52 171 Microorganisms — microorganisms","title":"Data Set with 52 171 Microorganisms — microorganisms","text":"data set containing full microbial taxonomy (last updated: December 11th, 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 52 171 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 52 171 Microorganisms — microorganisms","text":"tibble 52 171 observations 23 variables: mo ID microorganism used package. unique identifier. fullname Full name, like \"Escherichia coli\". taxonomic ranks genus, species subspecies, 'pasted' text genus, species, subspecies. taxonomic ranks higher genus, name taxon. unique identifier. 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. 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. oxygen_tolerance  Oxygen tolerance, either \"aerobe\", \"anaerobe\", \"anaerobe/microaerophile\", \"facultative anaerobe\", \"likely facultative anaerobe\", \"microaerophile\". data retrieved BacDive (see Source). Items contain \"likely\" missing BacDive extrapolated species within genus guess oxygen tolerance. Currently 73.4% ~37 000 bacteria data set contain oxygen tolerance. 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 July 1st, 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 52 171 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 December 11th, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org December 11th, 2022. Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed https://bacdive.dsmz.de May 12th, 2023. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microorganism', 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 52 171 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 52 171 Microorganisms — microorganisms","text":"Included taxonomic data : ~37 000 (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 43 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 52 171 Microorganisms — microorganisms","text":"convenience, entries added manually: ~1 500 entries Salmonella, city-like serovars groups H 36 species groups (beta-haemolytic Streptococcus groups K, coagulase-negative Staphylococcus (CoNS), Mycobacterium tuberculosis complex, etc.), group compositions stored microorganisms.groups data set 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 8 'undefined' entries (unknown, unknown Gram-negatives, unknown Gram-positives, unknown yeast, unknown fungus, unknown anaerobic Gram-pos/Gram-neg 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 52 171 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 52 171 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 52 171 Microorganisms — microorganisms","text":"","code":"microorganisms #> # A tibble: 52,171 × 23 #>    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… (unk… (unk… \"(unk… \"(un… #>  2 B_GRAMP          (unknown Gra… accep… Bacter… (unkn… (unk… (unk… \"(unk… \"(un… #>  3 B_ANAER-NEG      (unknown ana… accep… Bacter… (unkn… (unk… (unk… \"(unk… \"(un… #>  4 B_ANAER-POS      (unknown ana… accep… Bacter… (unkn… (unk… (unk… \"(unk… \"(un… #>  5 B_ANAER          (unknown ana… accep… Bacter… (unkn… (unk… (unk… \"(unk… \"(un… #>  6 F_FUNGUS         (unknown fun… accep… Fungi   (unkn… (unk… (unk… \"(unk… \"(un… #>  7   UNKNOWN        (unknown nam… accep… (unkno… (unkn… (unk… (unk… \"(unk… \"(un… #>  8 F_YEAST          (unknown yea… accep… Fungi   (unkn… (unk… (unk… \"(unk… \"(un… #>  9 B_[FAM]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… Abdi… Abdi… \"Abdi… \"\"    #> 10 B_[ORD]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… Abdi… Abdi… \"\"     \"\"    #> # ℹ 52,161 more rows #> # ℹ 14 more variables: species <chr>, subspecies <chr>, rank <chr>, ref <chr>, #> #   oxygen_tolerance <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 = 1.25, Protozoa = 1.5, Archaea = 2, others = 3. 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 = 1.25 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, Penicillium, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Talaromyces, 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 = 0.381\\), 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) #> 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), Enterobacter cloacae complex (0.571), and Ehrlichia #>               canis (0.567) #>  #> 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 0.3809524"},{"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_oxygen_tolerance(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_is_anaerobic(   x,   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\", \"oxygen_tolerance\", \"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). Determination bacterial oxygen tolerance (mo_oxygen_tolerance()) based BacDive, see Source. function mo_is_anaerobic() returns TRUE oxygen tolerance \"anaerobe\", indicting obligate anaerobic species genus. always returns FALSE species outside taxonomic kingdom Bacteria. function mo_url() return direct URL online database entry, also shows scientific reference concerned species. SNOMED codes (mo_snomed()) version July 1st, 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/ Micro.rganisms 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 December 11th, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org December 11th, 2022. Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed https://bacdive.dsmz.de May 12th, 2023. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microorganism', 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\" #>  #> $oxygen_tolerance #> [1] \"aerobe\" #>  #> $url #> [1] \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\" #>  #> $ref #> [1] \"Trevisan, 1887\" #>  #> $snomed #> [1] \"1098101000112102\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\"        #>  #> $lpsn #> [1] \"777151\" #>  #> $gbif #> [1] \"3221874\" #>  # }"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":null,"dir":"Reference","previous_headings":"","what":"User-Defined Reference Data Set for Microorganisms — mo_source","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"functions can used predefine reference used .mo() consequently mo_* functions (mo_genus() mo_gramstain()). fastest way organisation (analysis) specific codes picked translated package, since bother setting .","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"","code":"set_mo_source(   path,   destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\") )  get_mo_source(destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\"))"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"path location reference file, can text file (comma-, tab- pipe-separated) Excel file (see Details). Can also \"\", NULL FALSE delete reference file. destination destination compressed data file - default user's home directory.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"reference file can text file separated commas (CSV) tabs pipes, Excel file (either 'xls' 'xlsx' format) R object file (extension '.rds'). use Excel file, need readxl package installed. set_mo_source() check file validity: must data.frame, must column named \"mo\" contains values microorganisms$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. Especially scale_*_mic() functions relevant wrappers plot MIC values ggplot2. allows custom MIC ranges plot intermediate log2 levels missing MIC values.","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":"scale_x_mic(keep_operators = \"edges\", mic_range = NULL, ...)  scale_y_mic(keep_operators = \"edges\", mic_range = NULL, ...)  scale_colour_mic(keep_operators = \"edges\", mic_range = NULL, ...)  scale_fill_mic(keep_operators = \"edges\", mic_range = NULL, ...)  # 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,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"),   ... )  # 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,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"),   ... )  # 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,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"),   ... )  # 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,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"),   ... )  # 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":"keep_operators character specifying handle operators (> <=) input. Accepts one three values: \"\" (TRUE) keep operators, \"none\" (FALSE) remove operators, \"edges\" keep operators ends range. mic_range manual range plot MIC values, e.g., mic_range = c(0.001, 32). Use NA set limit one side, e.g., mic_range = c(NA, 32). ... arguments passed methods 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. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort - default TRUE. Can also set package option AMR_include_PKPD. breakpoint_type type breakpoints use, either \"ECOFF\", \"animal\", \"human\". ECOFF stands Epidemiological Cut-values. default \"human\", can also set package option AMR_breakpoint_type.","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 2023\", \"EUCAST 2022\", \"EUCAST 2021\", \"EUCAST 2020\", \"EUCAST 2019\", \"EUCAST 2018\", \"EUCAST 2017\", \"EUCAST 2016\", \"EUCAST 2015\", \"EUCAST 2014\", \"EUCAST 2013\", \"EUCAST 2012\", \"EUCAST 2011\", \"CLSI 2023\", \"CLSI 2022\", \"CLSI 2021\", \"CLSI 2020\", \"CLSI 2019\", \"CLSI 2018\", \"CLSI 2017\", \"CLSI 2016\", \"CLSI 2015\", \"CLSI 2014\", \"CLSI 2013\", \"CLSI 2012\", \"CLSI 2011\". 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 = \"nl\")    # Plotting using scale_x_mic() # \\donttest{ if (require(\"ggplot2\")) {   mic_plot <- ggplot(data.frame(mics = as.mic(c(0.125, \"<=4\", 4, 8, 32, \">=32\")),                                 counts = c(1, 1, 2, 2, 3, 3)),                      aes(mics, counts)) +     geom_col()   mic_plot +     labs(title = \"without scale_x_mic()\") }  if (require(\"ggplot2\")) {   mic_plot +     scale_x_mic() +     labs(title = \"with scale_x_mic()\") }  if (require(\"ggplot2\")) {   mic_plot +     scale_x_mic(keep_operators = \"all\") +     labs(title = \"with scale_x_mic() keeping all operators\") }  if (require(\"ggplot2\")) {   mic_plot +     scale_x_mic(mic_range = c(1, 128)) +     labs(title = \"with scale_x_mic() using a manual range\") }   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\",   collapse = FALSE )  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\". collapse logical indicate whether output values 'collapsed', .e. merged together one value, character value use collapsing 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":"Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine data set one four available algorithms. 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. 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    #> # ℹ 1,990 more rows #> # ℹ 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>, …   # 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$AMX,   confidence_level = 0.975 ) #> [1] 0.5650148 0.6255670 sir_confidence_interval(example_isolates$AMX,   confidence_level = 0.975,   collapse = \", \" ) #> [1] \"0.565, 0.626\"  # 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 2023 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     <=0.001 0.25    <=0.001 2       0.0625  64      1       8       #> [10] 0.25    128     <=0.001 128     64      0.025   <=0.001 0.005   4       #> [19] <=0.001 0.125   <=0.001 16      0.0625  <=0.001 4       random_disk(25) #> Class 'disk' #>  [1] 49 37 48 24 40 25 46 28 29 48 32 22 42 41 42 27 36 11 42 28 50 16 41  8 43 random_sir(25) #> Class 'sir' #>  [1] R S S I S S S R R S S R S R R R S I S I R S R S R  # \\donttest{ # make the random generation more realistic by setting a bug and/or drug: random_mic(25, \"Klebsiella pneumoniae\") # range 0.0625-64 #> Class 'mic' #>  [1] 0.001  4      0.5    16     0.002  32     0.01   64     1      0.002  #> [11] 32     0.025  0.0625 0.25   16     2      0.002  8      0.5    0.001  #> [21] 8      16     0.25   0.002  0.001  random_mic(25, \"Klebsiella pneumoniae\", \"meropenem\") # range 0.0625-16 #> Class 'mic' #>  [1] 0.5  2    0.5  4    1    1    1    16   1    0.5  0.25 16   1    4    0.25 #> [16] 2    2    0.5  16   1    0.5  2    4    1    1    random_mic(25, \"Streptococcus pneumoniae\", \"meropenem\") # range 0.0625-4 #> Class 'mic' #>  [1] 8      0.025  0.5    2      0.125  0.25   4      0.5    0.125  0.0625 #> [11] 0.0625 1      0.5    >=16   0.025  0.0625 0.5    1      4      4      #> [21] 8      0.125  1      0.25   0.025   random_disk(25, \"Klebsiella pneumoniae\") # range 8-50 #> Class 'disk' #>  [1] 20 36 50 50 46 42 47 48 43 31 42 50 44 49 39 29 46 44 47 20 48 12 10 47 12 random_disk(25, \"Klebsiella pneumoniae\", \"ampicillin\") # range 11-17 #> Class 'disk' #>  [1] 14 13 12 14 11 12 15 16 13 14 13 17 15 14 15 13 15 16 16 15 11 11 16 11 15 random_disk(25, \"Streptococcus pneumoniae\", \"ampicillin\") # range 12-27 #> Class 'disk' #>  [1] 17 16 22 26 23 26 20 19 15 15 24 23 23 18 16 17 23 27 21 26 22 24 23 26 23 # }"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict Antimicrobial Resistance — resistance_predict","title":"Predict Antimicrobial Resistance — resistance_predict","text":"Create prediction model predict antimicrobial resistance next years statistical solid ground. Standard errors (SE) returned columns se_min se_max. See Examples real live example.","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict Antimicrobial Resistance — resistance_predict","text":"","code":"resistance_predict(   x,   col_ab,   col_date = NULL,   year_min = NULL,   year_max = NULL,   year_every = 1,   minimum = 30,   model = NULL,   I_as_S = TRUE,   preserve_measurements = TRUE,   info = interactive(),   ... )  sir_predict(   x,   col_ab,   col_date = NULL,   year_min = NULL,   year_max = NULL,   year_every = 1,   minimum = 30,   model = NULL,   I_as_S = TRUE,   preserve_measurements = TRUE,   info = interactive(),   ... )  # S3 method for resistance_predict plot(x, main = paste(\"Resistance Prediction of\", x_name), ...)  ggplot_sir_predict(   x,   main = paste(\"Resistance Prediction of\", x_name),   ribbon = TRUE,   ... )  # S3 method for resistance_predict autoplot(   object,   main = paste(\"Resistance Prediction of\", x_name),   ribbon = TRUE,   ... )"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict Antimicrobial Resistance — resistance_predict","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_ab column name x containing antimicrobial interpretations (\"R\", \"\" \"S\") col_date column name date, used calculate years column consist years already - 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) #>  #> Coefficients: #>             Estimate Std. Error z value Pr(>|z|) #> (Intercept) 27.05312   72.24652   0.374    0.708 #> year        -0.01286    0.03601  -0.357    0.721 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance: 5.7883  on 11  degrees of freedom #> Residual deviance: 5.6608  on 10  degrees of freedom #> AIC: 50.745 #>  #> 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] -0.007908431"},{"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(\"Dutch\") #> ℹ Using Dutch (Nederlands) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacine\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Coagulase-negatieve Staphylococcus (CNS)\"  # setting yet another language set_AMR_locale(\"German\") #> ℹ Using German (Deutsch) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacin\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Koagulase-negative Staphylococcus (KNS)\"  # 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":[]},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-2-1-1-9002","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 2.1.1.9002","text":"Function scale_x_mic(), advanced function use ggplot, allow plotting MIC values x axis. allow manual range definition plotting missing intermediate log2 levels.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-2-1-1-9002","dir":"Changelog","previous_headings":"New","what":"Changed","title":"AMR 2.1.1.9002","text":"Added 4096 5 powers 192 valid levels (192, 384, 576, 768, 960) Added new argument keep_operators .mic(). can \"\" (default), \"none\", \"edges\".","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-211","dir":"Changelog","previous_headings":"","what":"AMR 2.1.1","title":"AMR 2.1.1","text":"CRAN release: 2023-10-21 included many isolates patients altering antibiograms within bacterial species See info issue #122 Added 1,366 LOINC codes antibiotics data set updated latest version (LOINC v2.76) MICs can now used complex number calculations allow scientific number format input (e.g., .mic(\"1.28e-2\")) Fix rounding MICs latest R beta (‘R-devel’) Removed unneeded note used language option AMR_locale set Fixed non-ASCII characters documentation, according CRAN maintainers","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-210","dir":"Changelog","previous_headings":"","what":"AMR 2.1.0","title":"AMR 2.1.0","text":"CRAN release: 2023-07-16","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-2-1-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 2.1.0","text":"Clinical breakpoints intrinsic resistance EUCAST 2023 CLSI 2023 added clinical_breakpoints data set usage .sir(). EUCAST 2023 (v13.0) now new default guideline MIC disks diffusion interpretations EUCAST dosage guideline v13.0 added dosage data set clinical_breakpoints data set now also contains epidemiological cut-(ECOFF) values CLSI animal breakpoints. two new breakpoint types can used MIC/disk interpretation using .sir(..., breakpoint_type = \"ECOFF\") oras.sir(..., breakpoint_type = \"animal\"), important new addition veterinary microbiology. Added support 30 species groups / complexes. gathered new data set microorganisms.groups used clinical breakpoint interpretation. example, CLSI 2023 contains breakpoints RGM group (Rapidly Growing Mycobacterium, containing 80 species) now supported package. Added mo_oxygen_tolerance() retrieve values Added mo_is_anaerobic() determine genera/species obligate anaerobic bacteria Added LPSN GBIF identifiers, oxygen tolerance mo_info() Added SAS Transport files (file extension .xpt) download page use SAS software Added microbial codes Gram-negative/positive anaerobic bacteria","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-2-1-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 2.1.0","text":"Updated algorithm .mo() giving weight fungi Fixed clinical breakpoints errors introduced source import rules mo_rank() now returns NA ‘unknown’ microorganisms (B_ANAER, B_ANAER-NEG, B_ANAER-POS, B_GRAMN, B_GRAMP, F_FUNGUS, F_YEAST, UNKNOWN) printing microorganism antibiotic codes tibble, mouse-hover now shows full name code Now settable arguments breakpoint type PK/PD, like .sir() now contain name guideline table subtitle plot Fixed formatting sir_interpretation_history() Fixed WHONET codes microorganisms consequently couple entries clinical_breakpoints Fixed bug .mo() led coercion NA values using custom microorganism codes Fixed usage icu_exclude first_isolates() Now allows searching species names Fix using keep_synonyms argument using MO codes input Fix using minimum_matching_score argument Updated code table microorganisms.codes Fixed endless loop using reference_df .mo() Fixed bug indicating UTIs .sir() Greatly improved speed .sir()","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-200","dir":"Changelog","previous_headings":"","what":"AMR 2.0.0","title":"AMR 2.0.0","text":"CRAN release: 2023-03-12 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-2-0-0","dir":"Changelog","previous_headings":"New","what":"SIR vs. RSI","title":"AMR 2.0.0","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-2-0-0","dir":"Changelog","previous_headings":"New","what":"New antibiogram function","title":"AMR 2.0.0","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-2-0-0","dir":"Changelog","previous_headings":"New","what":"Interpretation of MIC and disk diffusion values","title":"AMR 2.0.0","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-2-0-0","dir":"Changelog","previous_headings":"New","what":"Supported languages","title":"AMR 2.0.0","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":"outbreak-management-2-0-0","dir":"Changelog","previous_headings":"New","what":"Outbreak management","title":"AMR 2.0.0","text":"analysis outbreak management, updated get_episode() is_new_episode() functions: now contain argument case_free_days. argument can used quantify duration case-free days (inter-epidemic interval), new episode start. common requirement outbreak management, e.g. determining number norovirus outbreaks hospital. case-free period 14 28 days, new norovirus cases time considered different (new) episode.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"microbiological-taxonomy-2-0-0","dir":"Changelog","previous_headings":"New","what":"Microbiological taxonomy","title":"AMR 2.0.0","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) Added function mo_current() get currently valid taxonomic name microorganism 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-2-0-0","dir":"Changelog","previous_headings":"New","what":"Antibiotic agents and selectors","title":"AMR 2.0.0","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-2-0-0","dir":"Changelog","previous_headings":"New","what":"Antiviral agents","title":"AMR 2.0.0","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-2-0-0","dir":"Changelog","previous_headings":"New","what":"Other new functions","title":"AMR 2.0.0","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.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changes-2-0-0","dir":"Changelog","previous_headings":"","what":"Changes","title":"AMR 2.0.0","text":"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-2-0-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 2.0.0","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 (January 2023) 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     #> # ℹ 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) #> 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), Enterobacter cloacae complex (0.571), and Ehrlichia #>               canis (0.567) #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #> 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) #> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus #>               argenteus (0.625), Staphylococcus aureus anaerobius (0.625), Salmonella #>               Aurelianis (0.595), Salmonella Aarhus (0.588), Salmonella Amounderness #>               (0.587), Selenomonas artemidis (0.571), Salmonella choleraesuis #>               arizonae (0.562), Streptococcus anginosus anginosus (0.561), and #>               Salmonella Abaetetuba (0.548) #> -------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #> 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     #> # ℹ 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: 90% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 712 isolates analysis. Now data looks like: Time analysis.","code":"our_data <- our_data %>%   mutate(first = first_isolate(info = TRUE)) #> ℹ 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. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #>   of 2 #> => Found 2,712 'phenotype-based' first isolates (90.4% 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: 2,712 × 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 M3         A        2015-10-25 B_ESCHR_COLI R     S     S     S     TRUE  #>  7 J3         A        2019-06-19 B_ESCHR_COLI S     S     S     S     TRUE  #>  8 G6         A        2015-04-27 B_STPHY_AURS S     S     S     S     TRUE  #>  9 P4         A        2011-06-21 B_ESCHR_COLI S     S     S     S     TRUE  #> 10 Z1         A        2014-09-05 B_ESCHR_COLI S     S     S     S     TRUE  #> # ℹ 2,702 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:2712        Length:2712        Min.   :2011-01-01   #>  Class :character   Class :character   1st Qu.:2013-05-03   #>  Mode  :character   Mode  :character   Median :2015-06-16   #>                                        Mean   :2015-06-21   #>                                        3rd Qu.:2017-08-24   #>                                        Max.   :2019-12-27   #>    bacteria               AMX                    AMC                 #>  Class :mo             Class:sir              Class:sir              #>  <NA>  :0              %R   :42.9% (n=1163)   %R   :36.1% (n=978)    #>  Unique:4              %SI  :57.1% (n=1549)   %SI  :63.9% (n=1734)   #>  #1    :B_ESCHR_COLI   - %S :41.0% (n=1112)   - %S :52.0% (n=1409)   #>  #2    :B_STPHY_AURS   - %I :16.1% (n=437)    - %I :12.0% (n=325)    #>  #3    :B_STRPT_PNMN                                                 #>     CIP                    GEN                  first         #>  Class:sir              Class:sir              Mode:logical   #>  %R   :42.0% (n=1138)   %R   :37.3% (n=1011)   TRUE:2712      #>  %SI  :58.0% (n=1574)   %SI  :62.7% (n=1701)                  #>  - %S :51.5% (n=1396)   - %S :59.6% (n=1616)                  #>  - %I : 6.6% (n=178)    - %I : 3.1% (n=85)                    #>   glimpse(our_data_1st) #> Rows: 2,712 #> Columns: 9 #> $ patient_id <chr> \"J3\", \"R7\", \"P10\", \"B7\", \"W3\", \"M3\", \"J3\", \"G6\", \"P4\", \"Z1\"… #> $ 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, S, S, S, S, R, S, S, S, R, R, R, R, S, R,… #> $ AMC        <sir> I, I, I, S, S, S, S, S, S, S, S, S, I, S, S, S, S, R, S, S,… #> $ 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, R, S, 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       1852          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          1319 #> 2 Staphylococcus aureus      676 #> 3 Streptococcus pneumoniae   400 #> 4 Klebsiella pneumoniae      317"},{"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: 2,712 × 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 2015-10-25 S     #>  7 2019-06-19 S     #>  8 2015-04-27 S     #>  9 2011-06-21 S     #> 10 2014-09-05 S     #> # ℹ 2,702 more rows  our_data_1st %>%   select(bacteria, betalactams()) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 2,712 × 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 S     S     #>  8 B_STPHY_AURS S     S     #>  9 B_ESCHR_COLI S     S     #> 10 B_ESCHR_COLI S     S     #> # ℹ 2,702 more rows  our_data_1st %>%   select(bacteria, where(is.sir)) #> # A tibble: 2,712 × 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 S     S     S     S     #>  8 B_STPHY_AURS S     S     S     S     #>  9 B_ESCHR_COLI S     S     S     S     #> 10 B_ESCHR_COLI S     S     S     S     #> # ℹ 2,702 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: 1,011 × 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  #> # ℹ 1,001 more rows  our_data_1st %>%   filter(all(betalactams() == \"R\")) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 483 × 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 B6         A        2016-11-20 B_ESCHR_COLI R     R     R     R     TRUE  #>  6 I7         A        2015-08-19 B_ESCHR_COLI R     R     S     S     TRUE  #>  7 N3         A        2014-12-29 B_STRPT_PNMN R     R     R     S     TRUE  #>  8 Q2         A        2019-09-22 B_ESCHR_COLI R     R     S     S     TRUE  #>  9 X7         A        2011-03-20 B_ESCHR_COLI R     R     S     R     TRUE  #> 10 V1         A        2018-08-07 B_STPHY_AURS R     R     S     S     TRUE  #> # ℹ 473 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: 483 × 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 B6         A        2016-11-20 B_ESCHR_COLI R     R     R     R     TRUE  #>  6 I7         A        2015-08-19 B_ESCHR_COLI R     R     S     S     TRUE  #>  7 N3         A        2014-12-29 B_STRPT_PNMN R     R     R     S     TRUE  #>  8 Q2         A        2019-09-22 B_ESCHR_COLI R     R     S     S     TRUE  #>  9 X7         A        2011-03-20 B_ESCHR_COLI R     R     S     R     TRUE  #> 10 V1         A        2018-08-07 B_STPHY_AURS R     R     S     S     TRUE  #> # ℹ 473 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":"Since AMR v2.0 (March 2023), easy create different types antibiograms, support 20 different languages. four antibiogram types, proposed Klinker et al. (2021, DOI 10.1177/20499361211011373), supported new antibiogram() function: Traditional Antibiogram (TA) e.g, susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Combination Antibiogram (CA) e.g, sdditional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Syndromic Antibiogram (SA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Weighted-Incidence Syndromic Combination Antibiogram (WISCA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) male patients age >=65 years heart failure section, show use antibiogram() function create antibiogram types. starters, included example_isolates data set looks like:","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    #> # ℹ 1,990 more rows #> # ℹ 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>, …"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"traditional-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Traditional Antibiogram","title":"How to conduct AMR data analysis","text":"create traditional antibiogram, simply state antibiotics used. antibiotics argument antibiogram() function supports (combination) previously mentioned antibiotic class selectors: Notice antibiogram() function automatically prints right format using Quarto R Markdown (page), even applies italics taxonomic names (using italicise_taxonomy() internally). also uses language OS either English, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, Ukrainian. next example, force language Spanish using language argument:","code":"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) antibiogram(example_isolates,             mo_transform = \"gramstain\",             antibiotics = aminoglycosides(),             ab_transform = \"name\",             language = \"es\") #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"combined-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Combined Antibiogram","title":"How to conduct AMR data analysis","text":"create combined antibiogram, use antibiotic codes names plus + character like :","code":"antibiogram(example_isolates,             antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"syndromic-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Syndromic Antibiogram","title":"How to conduct AMR data analysis","text":"create syndromic antibiogram, syndromic_group argument must used. can column data, e.g. ifelse() calculations based certain columns:","code":"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)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"weighted-incidence-syndromic-combination-antibiogram-wisca","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Weighted-Incidence Syndromic Combination Antibiogram (WISCA)","title":"How to conduct AMR data analysis","text":"create WISCA, must state combination therapy antibiotics argument (similar Combination Antibiogram), define syndromic group syndromic_group argument (similar Syndromic Antibiogram) cases predefined based clinical demographic characteristics (e.g., endocarditis 75+ females). next example simplification without clinical characteristics, just gives idea WISCA can created:","code":"wisca <- 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\")) wisca"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plotting-antibiograms","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Plotting antibiograms","title":"How to conduct AMR data analysis","text":"Antibiograms can plotted using autoplot() ggplot2 packages, since AMR package provides extension function:  calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"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] 0.4288348 our_data_1st %>%   group_by(hospital) %>%   summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #>   hospital amoxicillin #>   <chr>          <dbl> #> 1 A              0.342 #> 2 B              0.564 #> 3 C              0.372"},{"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 (v3.1, 2016). 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: 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         S            S          R            R            I #> 2          I         R            S          R            S            I #> 3          R         R            I          S            R            S #> 4          R         I            S          R            I            I #> 5          S         R            I          I            I            R #> 6          S         S            R          S            R            S #>   kanamycin #> 1         R #> 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 1.232e-16 #> 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/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 52 171 rows 23 columns, containing following column names:mo, fullname, status, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, oxygen_tolerance, 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 14 July 2023 08:49:06 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (1.2 MB) Download tab-separated text file (11.7 MB) Download Microsoft Excel workbook (5.2 MB) Download Apache Feather file (5.5 MB) Download Apache Parquet file (2.6 MB) Download SAS transport (XPT) file (48.6 MB) Download IBM SPSS Statistics data file (17.8 MB) Download Stata DTA file (48.6 MB) NOTE: exported files SAS, SPSS Stata contain first 50 SNOMED codes per record, file size otherwise exceed 100 MB; file size limit GitHub. file structures compression techniques inefficient. Advice? Use R instead. ’s free much better many ways. 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 December 11th, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset . Accessed https://www.gbif.org December 11th, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name ‘Microorganism’, 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 20 October 2023 12:51:48 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (44 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (72 kB) Download Apache Feather file (0.1 MB) Download Apache Parquet file (0.1 MB) Download SAS transport (XPT) file (1.5 MB) Download IBM SPSS Statistics data file (0.4 MB) Download Stata DTA file (0.5 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 20 October 2023 12:51:48 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (6 kB) Download tab-separated text file (17 kB) Download Microsoft Excel workbook (16 kB) Download Apache Feather file (16 kB) Download Apache Parquet file (13 kB) Download SAS transport (XPT) file (73 kB) Download IBM SPSS Statistics data file (32 kB) Download Stata DTA file (78 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 29 747 rows 12 columns, containing following column names:guideline, type, 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 20 October 2023 13:07:11 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (59 kB) Download tab-separated text file (2.3 MB) Download Microsoft Excel workbook (1.3 MB) Download Apache Feather file (1.2 MB) Download Apache Parquet file (96 kB) Download SAS transport (XPT) file (7.9 MB) Download IBM SPSS Statistics data file (4.6 MB) Download Stata DTA file (7.8 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 (2011-2023) EUCAST (2011-2023). Clinical breakpoints package validated imported WHONET, free desktop Windows application developed supported Collaborating Centre Surveillance Antimicrobial Resistance. can read website. developers WHONET AMR package contact sharing work. highly appreciate development WHONET software. CEO CLSI chairman EUCAST endorsed work public use AMR package (consequently use breakpoints) June 2023, future development distributing clinical breakpoints discussed meeting CLSI, EUCAST, , developers WHONET AMR package. NOTE: AMR package (WHONET software well) contains internal methods apply guidelines, rather complex. example, breakpoints must applied certain species groups (case package available microorganisms.groups data set). important considered using breakpoints use.","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 transport (XPT) file (9.5 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 503 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 22 June 2023 13:10:59 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (3 kB) Download tab-separated text file (43 kB) Download Microsoft Excel workbook (25 kB) Download Apache Feather file (21 kB) Download Apache Parquet file (9 kB) Download SAS transport (XPT) file (0.1 MB) Download IBM SPSS Statistics data file (64 kB) Download Stata DTA file (0.1 MB)","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/datasets.html","id":"microorganisms-groups-species-groups-and-microbiological-complexes","dir":"Articles","previous_headings":"","what":"microorganisms.groups: Species Groups and Microbiological Complexes","title":"Data sets for download / own use","text":"data set 521 rows 4 columns, containing following column names:mo_group, mo, mo_group_name, mo_name. data set R available microorganisms.groups, load AMR package. last updated 14 July 2023 08:49:06 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (6 kB) Download tab-separated text file (49 kB) Download Microsoft Excel workbook (20 kB) Download Apache Feather file (19 kB) Download Apache Parquet file (13 kB) Download SAS transport (XPT) file (0 kB) Download IBM SPSS Statistics data file (64 kB) Download Stata DTA file (81 kB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-8","dir":"Articles","previous_headings":"microorganisms.groups: Species Groups and Microbiological Complexes","what":"Source","title":"Data sets for download / own use","text":"data set contains species groups microbiological complexes, used clinical_breakpoints data set.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-codes-common-laboratory-codes","dir":"Articles","previous_headings":"","what":"microorganisms.codes: Common Laboratory Codes","title":"Data sets for download / own use","text":"data set 4 957 rows 2 columns, containing following column names:code mo. data set R available microorganisms.codes, load AMR package. last updated 8 July 2023 15:30:05 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (22 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (91 kB) Download Apache Feather file (85 kB) Download Apache Parquet file (57 kB) Download SAS transport (XPT) file (0 kB) Download IBM SPSS Statistics data file (0.1 MB) Download Stata DTA file (0.1 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-9","dir":"Articles","previous_headings":"microorganisms.codes: Common Laboratory Codes","what":"Source","title":"Data sets for download / own use","text":"data set contains commonly used codes microorganisms, laboratory systems WHONET.","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  #> # ℹ 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 clinical breakpoint guidelines CLSI EUCAST included, even epidemiological cut-(ECOFF) values. 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":"One powerful functions package, aside calculating plotting AMR, selecting filtering based antibiotic columns. can done using -called antibiotic class selectors work base R, dplyr data.table: 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 code work version R since April 2013 (R-3.0). Moreover, code works identically data.table package, starting :","code":"# AMR works great with dplyr, but it's not required or neccesary library(AMR) library(dplyr)  example_isolates %>%   mutate(bacteria = mo_fullname()) %>%   # filtering functions for microorganisms:   filter(mo_is_gram_negative(),          mo_is_intrinsic_resistant(ab = \"cefotax\")) %>%   # antibiotic selectors:   select(bacteria,          aminoglycosides(),          carbapenems()) library(AMR) 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())] example_isolates <- data.table::as.data.table(example_isolates)"},{"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: use antibiotic class selectors select series antibiotic columns:","code":"example_isolates %>%   # group by ward:   group_by(ward) %>%   # calculate AMR using resistance() for gentamicin and tobramycin   # and get their 95% confidence intervals using sir_confidence_interval():   summarise(across(c(GEN, TOB),                    list(total_R = resistance,                         conf_int = function(x) sir_confidence_interval(x, collapse = \"-\")))) 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 (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-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 2023\". Supported guideline currently EUCAST (2011-2023) CLSI (2011-2023). 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_ecoff  logical use .sir(), indicate ECOFF (Epidemiological Cut-) values must used - default FALSE. 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 microorganisms (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 clinical breakpoint guidelines CLSI EUCAST included, even epidemiological cut-(ECOFF) values. 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 public University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen. AMR package available English, Chinese, Czech, 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":"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.s.berends@umcg.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 #>    `Identification number` `Specimen number` Organism Country         Laboratory #>    <chr>                               <int> <chr>    <chr>           <chr>      #>  1 fe41d7bafa                           1748 SPN      Belgium         National … #>  2 91f175ec37                           1767 eco      The Netherlands National … #>  3 cc4015056e                           1343 eco      The Netherlands National … #>  4 e864b692f5                           1894 MAP      Denmark         National … #>  5 3d051fe345                           1739 PVU      Belgium         National … #>  6 c80762a08d                           1846 103      The Netherlands National … #>  7 8022d3727c                           1628 103      Denmark         National … #>  8 f3dc5f553d                           1493 eco      The Netherlands National … #>  9 15add38f6c                           1847 eco      France          National … #> 10 fd41248def                           1458 eco      Germany         National … #> # ℹ 490 more rows #> # ℹ 48 more variables: `Last name` <chr>, `First name` <chr>, Sex <chr>, #> #   Age <dbl>, `Age category` <chr>, `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>, …"},{"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_codes abx_doses abx_admin abx_coll abx_coll_names #>   <chr>                    <list>    <list>    <list>    <chr>    <chr>          #> 1 given 400mg cipro and 5… <ab [2]>  <dbl [2]> <chr [1]> CIP|AMX  Ciprofloxacin… #> 2 started on doxy iv today <ab [1]>  <dbl [1]> <chr [1]> DOX      Doxycycline    #> # ℹ 2 more variables: abx_coll_doses <chr>, abx_coll_admin <chr> # }"},{"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 transformation 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] \"101498-4\" \"15-8\"     \"16-6\"     \"16365-9\"  \"17-4\"     \"18-2\"     #>  [7] \"18861-5\"  \"18862-3\"  \"19-0\"     \"20-8\"     \"21-6\"     \"22-4\"     #> [13] \"25274-2\"  \"25310-4\"  \"3344-9\"   \"55614-2\"  \"55615-9\"  \"55616-7\"  #> [19] \"6976-5\"   \"6977-3\"   \"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] \"101477-8\" \"101478-6\" \"18864-9\"  \"18865-6\"  \"20374-5\"  \"21066-6\"  #>  [7] \"23618-2\"  \"27-3\"     \"28-1\"     \"29-9\"     \"30-7\"     \"31-5\"     #> [13] \"32-3\"     \"33-1\"     \"3355-5\"   \"33562-0\"  \"33919-2\"  \"34-9\"     #> [19] \"43883-8\"  \"43884-6\"  \"6979-9\"   \"6980-7\"   \"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 circumvent automate process adding antimicrobials: 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. Note definitions stored user-specific R file, suboptimal workflow. 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 circumvent automate process adding microorganisms: 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. Note definitions stored user-specific R file, suboptimal workflow. use method: Edit .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"). Add text like save file:   Use clear_custom_microorganisms() clear previously added microorganisms.","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 \"Enterobacter cloacae cloacae\", # since it resembles the input best: mo_name(\"Enterobacter asburiae/cloacae\") #> [1] \"Enterobacter cloacae cloacae\"  # 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_ENTRB_ASB/  # 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_ENTRB_ASB/\" #>  #> $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\" #>  #> $oxygen_tolerance #> [1] NA #>  #> $url #> [1] \"\" #>  #> $ref #> [1] \"Self-added, 2023\" #>  #> $snomed #> [1] NA #>  #> $lpsn #> [1] NA #>  #> $gbif #> [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, even though 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  1984-04-27  39  39.60274         15 #> 2  1935-07-26  88  88.35616         64 #> 3  1975-06-27  48  48.43562         24 #> 4  1961-11-22  62  62.03014         38 #> 5  1951-05-07  72  72.57534         48 #> 6  1946-04-17  77  77.63014         53 #> 7  1972-02-03  51  51.83014         27 #> 8  1981-04-25  42  42.60822         18 #> 9  1957-03-29  66  66.68219         42 #> 10 1934-06-02  89  89.50411         65"},{"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\", \"oxygen_tolerance\", \"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    #> # ℹ 1,990 more rows #> # ℹ 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>, …  # \\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\")) {   cat(knitr::knit_print(ureido)) } #>  #>  #> |Pathogen (N)          | Piperacillin/tazobactam| #> |:---------------------|-----------------------:| #> |CoNS (33)             |                      30| #> |*E. coli* (416)       |                      94| #> |*K. pneumoniae* (53)  |                      89| #> |*S. pneumoniae* (112) |                     100|   # 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 (according antibiotics data set), without need define columns antibiotic abbreviations. short, column name resembles antimicrobial drug, picked functions matches pharmaceutical class: \"cefazolin\", \"kefzol\", \"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 \"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. work base R, Tidyverse, data.table. heavily inspired Tidyverse selection helpers everything(), limited 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    #> # ℹ 1,990 more rows #> # ℹ 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>, …   # Examples sections below are split into 'base R', 'dplyr', and 'data.table':   # 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    #> # ℹ 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    #> # ℹ 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    #> # ℹ 1,990 more rows #> # ℹ 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    #> # ℹ 45 more rows #> # ℹ 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>, … 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    #> # ℹ 45 more rows #> # ℹ 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>, …  # 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     #> # ℹ 952 more rows #> # ℹ 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>, … 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    #> # ℹ 746 more rows #> # ℹ 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>, …  # 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     #> # ℹ 16 more rows #> # ℹ 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>, …  # filter + select in one go: get penicillins in carbapenem-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     #> # ℹ 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    #> # ℹ 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    #> # ℹ 1,990 more rows  # \\donttest{ # dplyr -------------------------------------------------------------------  if (require(\"dplyr\")) {   tibble(kefzol = random_sir(5)) %>%     select(cephalosporins()) } #> ℹ For cephalosporins() using column 'kefzol' (cefazolin) #> # A tibble: 5 × 1 #>   kefzol #>   <sir>  #> 1 S      #> 2 I      #> 3 R      #> 4 S      #> 5 S       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    #> # ℹ 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      #> # ℹ 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 0.807 #> 2 ICU        0.551 0.780 #> 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    #> # ℹ 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    #> # ℹ 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    #> # ℹ 102 more rows #> # ℹ 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>, … 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     #> # ℹ 521 more rows #> # ℹ 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>, … 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     #> # ℹ 417 more rows #> # ℹ 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>, … 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    #> # ℹ 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. faecalis` `S. aureus` `S. epidermidis` `S. hominis` #>   <chr>      <chr> <chr> <chr>         <chr>       <chr>            <chr>        #> 1 \"Glycopep… Teic… \"\"    \"\"            \" 0.0% (0/… \"64.1% (25/39)\"  \" 6.8% (4/5… #> 2 \"\"         Vanc… \" 0.… \" 0.0% (0/39… \" 0.0% (0/… \" 0.0% (0/171)\"  \" 0.0% (0/8… #> # ℹ 1 more variable: `S. pneumoniae` <chr> 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   # data.table --------------------------------------------------------------  # data.table is supported as well, just use it in the same way as with # base R, but add `with = FALSE` if using a single AB selector.  if (require(\"data.table\")) {   dt <- as.data.table(example_isolates)    # this does not work, it returns column *names*   dt[, carbapenems()] } #> Loading required package: data.table #>  #> Attaching package: ‘data.table’ #> The following objects are masked from ‘package:dplyr’: #>  #>     between, first, last #> The following objects are masked from ‘package:AMR’: #>  #>     %like%, like #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> [1] \"IPM\" \"MEM\" #> attr(,\"class\") #> [1] \"ab_selector\" \"character\"   if (require(\"data.table\")) {   # so `with = FALSE` is required   dt[, carbapenems(), with = FALSE] } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #>        IPM  MEM #>    1: <NA> <NA> #>    2: <NA> <NA> #>    3: <NA> <NA> #>    4: <NA> <NA> #>    5: <NA> <NA> #>   ---           #> 1996: <NA> <NA> #> 1997:    S    S #> 1998:    S    S #> 1999:    S    S #> 2000:    S    S  # for multiple selections or AB selectors, `with = FALSE` is not needed: if (require(\"data.table\")) {   dt[, c(\"mo\", aminoglycosides())] } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #>                 mo  GEN  TOB  AMK  KAN #>    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> #>   ---                                  #> 1996: B_STRPT_PNMN    R    R    R    R #> 1997: B_ESCHR_COLI    S    S    S <NA> #> 1998: B_STPHY_CONS    S <NA> <NA> <NA> #> 1999: B_ESCHR_COLI    S    S <NA> <NA> #> 2000: B_KLBSL_PNMN    S    S <NA> <NA> if (require(\"data.table\")) {   dt[, c(carbapenems(), aminoglycosides())] } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #>        IPM  MEM  GEN  TOB  AMK  KAN #>    1: <NA> <NA> <NA> <NA> <NA> <NA> #>    2: <NA> <NA> <NA> <NA> <NA> <NA> #>    3: <NA> <NA> <NA> <NA> <NA> <NA> #>    4: <NA> <NA> <NA> <NA> <NA> <NA> #>    5: <NA> <NA> <NA> <NA> <NA> <NA> #>   ---                               #> 1996: <NA> <NA>    R    R    R    R #> 1997:    S    S    S    S    S <NA> #> 1998:    S    S    S <NA> <NA> <NA> #> 1999:    S    S    S    S <NA> <NA> #> 2000:    S    S    S    S <NA> <NA>  # row filters are also supported: if (require(\"data.table\")) {   dt[any(carbapenems() == \"S\"), ] } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #>            date patient age gender       ward           mo PEN  OXA  FLC  AMX #>   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 #>  ---                                                                          #> 905: 2005-04-12  D71461  70      M        ICU B_ESCHR_COLI   R <NA> <NA>    S #> 906: 2009-11-12  650870  69      F Outpatient B_ESCHR_COLI   R <NA> <NA>    S #> 907: 2012-06-14  8CBCF2  41      F   Clinical B_STPHY_CONS   R    S    S <NA> #> 908: 2012-10-11  175532  78      M   Clinical B_ESCHR_COLI   R <NA> <NA>    R #> 909: 2013-11-23  A97263  77      M   Clinical B_KLBSL_PNMN   R <NA> <NA>    R #>      AMC  AMP  TZP  CZO  FEP CXM  FOX  CTX  CAZ  CRO  GEN  TOB  AMK  KAN  TMP #>   1:   I <NA> <NA> <NA> <NA>   S <NA>    S <NA>    S <NA>    S <NA> <NA>    S #>   2:   I <NA> <NA> <NA> <NA>   S <NA>    S <NA>    S <NA>    S <NA> <NA>    S #>   3:   I <NA> <NA> <NA> <NA>   S <NA>    S    S    S <NA> <NA> <NA> <NA>    S #>   4:   I <NA> <NA> <NA> <NA>   S <NA>    S    S    S <NA> <NA> <NA> <NA>    S #>   5:   R    R <NA>    R <NA>   R    R <NA> <NA> <NA> <NA> <NA> <NA> <NA>    S #>  ---                                                                          #> 905:   S    S    S <NA>    S   S    S    S    S    S    S    S    S <NA> <NA> #> 906:   S    S    S    S    S   S    S    S    S    S    S    S    S <NA>    S #> 907:   S <NA> <NA>    S    S   S    S    S    R    S    S <NA> <NA> <NA>    S #> 908:   S    R    S <NA>    S   R    R    S    S    S    S    S <NA> <NA>    R #> 909:   S    R    S <NA>    S   S    S    S    S    S    S    S <NA> <NA>    S #>      SXT  NIT  FOS  LNZ  CIP  MFX VAN  TEC  TCY  TGC  DOX ERY CLI AZM IPM  MEM #>   1:   S <NA> <NA>    R <NA> <NA>   R    R <NA> <NA> <NA>   R   R   R   S <NA> #>   2:   S <NA> <NA>    R <NA> <NA>   R    R <NA> <NA> <NA>   R   R   R   S <NA> #>   3:   S    R <NA>    R    S <NA>   R    R    R    R    R   R   R   R   S <NA> #>   4:   S    R <NA>    R    S <NA>   R    R    R    R    R   R   R   R   S <NA> #>   5:   S    R <NA>    R    S <NA>   R    R    R    R    R   R   R   R   S <NA> #>  ---                                                                           #> 905:   S    S <NA>    R    S <NA>   R    R <NA> <NA> <NA>   R   R   R   S    S #> 906:   S    S <NA>    R    S <NA>   R    R <NA> <NA> <NA>   R   R   R   S    S #> 907:   S <NA> <NA> <NA>    S <NA>   S <NA> <NA> <NA> <NA>   S   S   S   S    S #> 908:   R    R <NA>    R    R    R   R    R <NA> <NA> <NA>   R   R   R   S    S #> 909:   S    S <NA>    R    S <NA>   R    R <NA> <NA> <NA>   R   R   R   S    S #>       MTR  CHL  COL  MUP  RIF #>   1: <NA> <NA> <NA> <NA>    R #>   2: <NA> <NA> <NA> <NA>    R #>   3: <NA> <NA>    R <NA>    R #>   4: <NA> <NA>    R <NA>    R #>   5: <NA> <NA>    R <NA>    R #>  ---                          #> 905: <NA> <NA> <NA> <NA>    R #> 906: <NA> <NA> <NA> <NA>    R #> 907: <NA> <NA>    R <NA> <NA> #> 908: <NA> <NA>    S <NA>    R #> 909: <NA> <NA>    S <NA>    R if (require(\"data.table\")) {   dt[any(carbapenems() == \"S\"), penicillins(), with = FALSE] } #> 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) #>      PEN  OXA  FLC  AMX AMC  AMP  TZP #>   1:   R <NA> <NA> <NA>   I <NA> <NA> #>   2:   R <NA> <NA> <NA>   I <NA> <NA> #>   3:   R <NA> <NA> <NA>   I <NA> <NA> #>   4:   R <NA> <NA> <NA>   I <NA> <NA> #>   5:   R <NA> <NA>    R   R    R <NA> #>  ---                                  #> 905:   R <NA> <NA>    S   S    S    S #> 906:   R <NA> <NA>    S   S    S    S #> 907:   R    S    S <NA>   S <NA> <NA> #> 908:   R <NA> <NA>    R   S    R    S #> 909:   R <NA> <NA>    R   S    R    S # }"},{"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. unique identifier. cid Compound ID found PubChem. unique identifier. name Official name used WHONET/EARS-Net . unique identifier. 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 codes associated name antimicrobial drug Logical Observation Identifiers Names Codes (LOINC), Version 2.76 (18 September, 2023). 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 Antiviral ID used package (ACI), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available. unique identifier. Combinations codes contain + indicate , ATA+COBI atazanavir/cobicistat. name Official name used WHONET/EARS-Net . unique identifier. atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC cid Compound ID found PubChem. unique identifier. 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 codes associated name antiviral drug Logical Observation Identifiers Names Codes (LOINC), Version 2.76 (18 September, 2023). Use av_loinc() retrieve quickly, see av_property().","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 2.76 (18 September, 2023). Accessed https://loinc.org October 19th, 2023. 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_group1 atc_group2 abbreviations synonyms #>    <ab>    <dbl> <chr>  <chr> <lis> <chr>      <chr>      <list>        <named > #>  1 AMA      4649 4-ami… Anti… <chr> Drugs for… Aminosali… <chr [1]>     <chr>    #>  2 ACM   6450012 Acety… Macr… <chr> NA         NA         <chr [1]>     <chr>    #>  3 ASP  49787020 Acety… Macr… <chr> NA         NA         <chr [1]>     <chr>    #>  4 ALS      8954 Aldes… Othe… <chr> Drugs for… Drugs for… <chr [1]>     <chr>    #>  5 AMK     37768 Amika… Amin… <chr> Aminoglyc… Other ami… <chr [5]>     <chr>    #>  6 AKF        NA Amika… Amin… <chr> NA         NA         <chr [1]>     <chr>    #>  7 AMX     33613 Amoxi… Beta… <chr> Beta-lact… Penicilli… <chr [3]>     <chr>    #>  8 AMC  23665637 Amoxi… Beta… <chr> Beta-lact… Combinati… <chr [5]>     <chr>    #>  9 AXS    465441 Amoxi… Beta… <chr> NA         NA         <chr [1]>     <chr>    #> 10 AMB   5280965 Ampho… Anti… <chr> Antimycot… Antibioti… <chr [3]>     <chr>    #> # ℹ 473 more rows #> # ℹ 5 more variables: oral_ddd <dbl>, oral_units <chr>, iv_ddd <dbl>, #> #   iv_units <chr>, loinc <list> antivirals #> # A tibble: 120 × 11 #>    av       name      atc      cid atc_group synonyms oral_ddd oral_units iv_ddd #>    <av>     <chr>     <chr>  <dbl> <chr>     <list>      <dbl> <chr>       <dbl> #>  1 ABA      Abacavir  J05A… 4.41e5 Nucleosi… <chr>         0.6 g              NA #>  2 ACI      Aciclovir J05A… 1.35e8 Nucleosi… <chr>         4   g               4 #>  3 ADD      Adefovir… J05A… 6.09e4 Nucleosi… <chr>        10   mg             NA #>  4 AME      Amenamev… J05A… 1.14e7 Other an… <chr>         0.4 g              NA #>  5 AMP      Amprenav… J05A… 6.50e4 Protease… <chr>         1.2 g              NA #>  6 ASU      Asunapre… J05A… 1.61e7 Antivira… <chr>         0.2 g              NA #>  7 ATA      Atazanav… J05A… 1.48e5 Protease… <chr>         0.3 g              NA #>  8 ATA+COBI Atazanav… J05A… 8.66e7 Antivira… <chr>        NA   NA             NA #>  9 ATA+RIT  Atazanav… J05A… 2.51e7 Antivira… <chr>         0.3 g              NA #> 10 BAM      Baloxavi… J05A… 1.24e8 Other an… <chr>        40   mg             NA #> # ℹ 110 more rows #> # ℹ 2 more variables: iv_units <chr>, loinc <list>"},{"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       #> # ℹ 1,990 more rows #> # ℹ 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>, … # }"},{"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\" ) #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting disk diffusion zones: 'ampicillin' (AMP), EUCAST 2023... #>  OK  #> Class 'sir' #> [1] R  # interpret whole data set, pretend to be all from urinary tract infections: as.sir(df, uti = TRUE) #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting disk diffusion zones: column 'AMP' (ampicillin), EUCAST #> 2023... #>  OK  #> Interpreting disk diffusion zones: column 'CIP' (ciprofloxacin), EUCAST #> 2023... #>  OK  #> Interpreting disk diffusion zones: column 'GEN' (gentamicin), EUCAST #> 2023... #>  OK  #> Interpreting disk diffusion zones: column 'TOB' (tobramycin), EUCAST #> 2023... #>  OK  #>      microorganism AMP  CIP GEN TOB #> 1 Escherichia coli   S <NA>   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, keep_operators = \"all\")  is.mic(x)  NA_mic_  rescale_mic(x, mic_range, keep_operators = \"edges\", as.mic = TRUE)  # 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 keep_operators character specifying handle operators (> <=) input. Accepts one three values: \"\" (TRUE) keep operators, \"none\" (FALSE) remove operators, \"edges\" keep operators ends range. mic_range manual range plot MIC values, e.g., mic_range = c(0.001, 32). Use NA set limit one side, e.g., mic_range = c(NA, 32). .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 (2011-2023) CLSI (2011-2023). 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. rescale_mic(), existing MIC ranges can rescaled defined range MIC values. can useful better compare MIC distributions. 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  # rescale MICs using rescale_mic() rescale_mic(mic_data, mic_range = c(4, 16)) #> Class 'mic' #> [1] >=16 <=4  <=4  <=4  8    <=4  8    >=16 >=16  # interpret MIC values as.sir(   x = as.mic(2),   mo = as.mo(\"Streptococcus pneumoniae\"),   ab = \"AMX\",   guideline = \"EUCAST\" ) #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMX' (amoxicillin), EUCAST 2023... #>  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\" ) #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMX' (amoxicillin), EUCAST 2023... #>  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 }"},{"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/ Micro.rganisms 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 December 11th, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org December 11th, 2022. Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed https://bacdive.dsmz.de May 12th, 2023. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microorganism', 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 = 1.25, Protozoa = 1.5, Archaea = 2, others = 3. 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 = 1.25 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, Penicillium, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Talaromyces, 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 = 0.381\\), 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  # input will get cleaned up with the input given in the `cleaning_regex` argument, # which defaults to `mo_cleaning_regex()`: cat(mo_cleaning_regex(), \"\\n\") #> ([^A-Za-z- \\(\\)\\[\\]{}]+|([({]|\\[).+([})]|\\])|(^| )(e?spp([^a-z]+|$)|e?ssp([^a-z]+|$)|serogr.?up[a-z]*|e?ss([^a-z]+|$)|e?sp([^a-z]+|$)|var([^a-z]+|$)|serovar[a-z]*|sube?species|biovar[a-z]*|e?species|Ig[ADEGM]|e?subsp|biotype|titer|dummy))   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. Currently available breakpoint guidelines EUCAST 2011-2023 CLSI 2011-2023, available breakpoint types \"ECOFF\", \"animal\", \"human\". 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),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"),   ... )  # 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),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"),   ... )  # 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),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\") )  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, 2011-2023, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. M100 Performance Standard Antimicrobial Susceptibility Testing, 2011-2023, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/. Breakpoint tables interpretation MICs zone diameters, 2011-2023, 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 2023 (latest implemented EUCAST guideline clinical_breakpoints data set), can set package option AMR_guideline. Currently supports EUCAST (2011-2023) CLSI (2011-2023), 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. breakpoint_type type breakpoints use, either \"ECOFF\", \"animal\", \"human\". ECOFF stands Epidemiological Cut-values. default \"human\", can also set package option AMR_breakpoint_type. 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":"Note: clinical breakpoints package validated imported WHONET public use AMR package endorsed CLSI EUCAST, please see clinical_breakpoints information. 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 (2011-2023) CLSI (2011-2023). Thus, guideline argument must set e.g., \"EUCAST 2023\" \"CLSI 2023\". 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 29 747 rows 12 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    #> # ℹ 1,990 more rows #> # ℹ 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>, … 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) #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: column 'AMP' (ampicillin), EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for ampicillin (AMP) in #>      Escherichia coli - assuming body site 'Intravenous'. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: column 'CIP' (ciprofloxacin), EUCAST 2023... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli #>      - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: column 'GEN' (gentamicin), EUCAST #> 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming an unspecified body site. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting disk diffusion zones: column 'TOB' (tobramycin), EUCAST #> 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming an unspecified body site. 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: 13 × 13 #>    datetime            index ab_user    mo_user    ab   mo                input #>    <dttm>              <int> <chr>      <chr>      <ab> <mo>              <dbl> #>  1 2023-12-03 15:55:17     1 TOB        Escherich… TOB  B_[ORD]_ENTRBCTR 16     #>  2 2023-12-03 15:55:17     1 GEN        Escherich… GEN  B_[ORD]_ENTRBCTR 18     #>  3 2023-12-03 15:55:17     1 CIP        Escherich… CIP  B_[ORD]_ENTRBCTR  0.256 #>  4 2023-12-03 15:55:17     1 AMP        Escherich… AMP  B_[ORD]_ENTRBCTR  8     #>  5 2023-12-03 15:55:12     1 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      0.01  #>  6 2023-12-03 15:55:12     2 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      2     #>  7 2023-12-03 15:55:12     3 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      4     #>  8 2023-12-03 15:55:12     4 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      8     #>  9 2023-12-03 15:55:12     1 AMX        B_STRPT_P… AMX  B_STRPT_PNMN      2     #> 10 2023-12-03 15:55:12     1 TOB        Escherich… TOB  B_[ORD]_ENTRBCTR 16     #> 11 2023-12-03 15:55:12     1 GEN        Escherich… GEN  B_[ORD]_ENTRBCTR 18     #> 12 2023-12-03 15:55:12     1 AMP        Escherich… AMP  B_[ORD]_ENTRBCTR 20     #> 13 2023-12-03 15:55:11     1 ampicillin Strep pneu AMP  B_STRPT_PNMN     18     #> # ℹ 6 more variables: outcome <sir>, method <chr>, breakpoint_S_R <chr>, #> #   guideline <chr>, ref_table <chr>, uti <lgl>  # for single values as.sir(   x = as.mic(2),   mo = as.mo(\"S. pneumoniae\"),   ab = \"AMP\",   guideline = \"EUCAST\" ) #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMP' (ampicillin), EUCAST 2023... #>  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\" ) #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting disk diffusion zones: 'ampicillin' (AMP), EUCAST 2023... #>  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) } #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMP' (ampicillin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for ampicillin (AMP) in #>      Escherichia coli - assuming body site 'Intravenous'. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'CIP' (ciprofloxacin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli #>      - assuming body site 'Non-meningitis'. #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMP' (ampicillin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for ampicillin (AMP) in #>      Escherichia coli - assuming body site 'Intravenous'. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'CIP' (ciprofloxacin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli #>      - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'GEN' (gentamicin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming an unspecified body site. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting disk diffusion zones: 'TOB' (tobramycin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming an unspecified body site. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMP' (ampicillin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for ampicillin (AMP) in #>      Escherichia coli - assuming body site 'Intravenous'. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'CIP' (ciprofloxacin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli #>      - assuming body site 'Non-meningitis'. #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMP' (ampicillin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for ampicillin (AMP) in #>      Escherichia coli - assuming body site 'Intravenous'. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'CIP' (ciprofloxacin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli #>      - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'GEN' (gentamicin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming an unspecified body site. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting disk diffusion zones: 'TOB' (tobramycin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming an unspecified body site. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMP' (ampicillin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for ampicillin (AMP) in #>      Escherichia coli - assuming body site 'Intravenous'. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'CIP' (ciprofloxacin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli #>      - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'GEN' (gentamicin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming an unspecified body site. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting disk diffusion zones: 'TOB' (tobramycin) based on column #> 'microorganism', EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming an unspecified body site. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMP' (ampicillin), EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for ampicillin (AMP) in #>      Escherichia coli - assuming body site 'Intravenous'. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'CIP' (ciprofloxacin), EUCAST 2023... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli #>      - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'GEN' (gentamicin), EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming an unspecified body site. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> Interpreting disk diffusion zones: 'TOB' (tobramycin), EUCAST 2023... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming an unspecified body site. Use argument uti to #>      set which isolates are from urine. See ?as.sir. #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: column 'NIT' (nitrofurantoin), EUCAST 2023... #>  OK  #> ℹ Assuming value \"urine\" in column 'specimen' reflects a urinary tract #>   infection. #>   Use as.sir(uti = FALSE) to prevent this. #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: column 'NIT' (nitrofurantoin), EUCAST 2023... #>  OK  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. Note that some #>   microorganisms might not have breakpoints for each antimicrobial drug in #>   EUCAST 2023. #> Interpreting MIC values: 'AMP' (ampicillin), EUCAST 2023... #>  OK  #> Interpreting MIC values: 'CIP' (ciprofloxacin), EUCAST 2023... #>  OK  #> Interpreting disk diffusion zones: 'GEN' (gentamicin), EUCAST 2023... #>  OK  #> Interpreting disk diffusion zones: 'TOB' (tobramycin), EUCAST 2023... #>  OK  #>      microorganism AMP  CIP GEN TOB ERY #> 1 Escherichia coli   S <NA>   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    #> # ℹ 1,990 more rows #> # ℹ 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>, … # }"},{"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\"   # lowercase transformation 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\" \"30273-7\" \"30287-7\" \"30303-2\" \"78772-1\" \"78773-9\" \"79134-3\" #> [8] \"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    #> # ℹ 1,990 more rows #> # ℹ 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>, …  # \\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. coli` `E. faecalis` `K. pneumoniae` `P. aeruginosa` #>    <chr>     <chr> <chr> <chr>     <chr>         <chr>           <chr>           #>  1 \"Aminogl… Amik… \"100… \"  0.0% … \"100.0% (39/… \"\"              \"\"              #>  2 \"\"        Gent… \" 13… \"  2.0% … \"100.0% (39/… \" 10.3% (6/58)\" \"  0.0% (0/30)\" #>  3 \"\"        Kana… \"100… \"\"        \"100.0% (39/… \"\"              \"100.0% (30/30… #>  4 \"\"        Tobr… \" 78… \"  2.6% … \"100.0% (39/… \" 10.3% (6/58)\" \"  0.0% (0/30)\" #>  5 \"Ampheni… Chlo… \"\"    \"\"        \"\"            \"\"              \"100.0% (30/30… #>  6 \"Antimyc… Rifa… \"\"    \"100.0% … \"\"            \"100.0% (58/58… \"100.0% (30/30… #>  7 \"Beta-la… Amox… \" 93… \" 50.0% … \"\"            \"100.0% (58/58… \"100.0% (30/30… #>  8 \"\"        Amox… \" 42… \" 13.1% … \"\"            \" 10.3% (6/58)\" \"100.0% (30/30… #>  9 \"\"        Ampi… \" 93… \" 50.0% … \"\"            \"100.0% (58/58… \"100.0% (30/30… #> 10 \"\"        Benz… \" 77… \"100.0% … \"\"            \"100.0% (58/58… \"100.0% (30/30… #> # ℹ 29 more rows #> # ℹ 5 more variables: `P. mirabilis` <chr>, `S. aureus` <chr>, #> #   `S. epidermidis` <chr>, `S. hominis` <chr>, `S. pneumoniae` <chr>  # 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 #> # ℹ 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 #> # ℹ 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 (2011-2023) CLSI (2011-2023). 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 29 747 observations 12 variables: guideline Name guideline type Breakpoint type, either \"ECOFF\", \"animal\", \"human\" method Testing method, either \"DISK\" \"MIC\" site Body site breakpoint must applied, e.g. \"Oral\" \"Respiratory\" mo Microbial ID, see .mo() rank_index Taxonomic rank index mo 1 (subspecies/infraspecies) 5 (unknown microorganism) ab Antibiotic code used package, EARS-Net WHONET, 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":[]},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"different-types-of-breakpoints","dir":"Reference","previous_headings":"","what":"Different types of breakpoints","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Supported types breakpoints ECOFF, animal, human. ECOFF (Epidemiological cut-) values used antimicrobial susceptibility testing differentiate wild-type non-wild-type strains bacteria fungi. default \"human\", can also set package option AMR_breakpoint_type. Use .sir(..., breakpoint_type = ...) interpret raw data using specific breakpoint type, e.g. .sir(..., breakpoint_type = \"ECOFF\") use ECOFFs.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"imported-from-whonet","dir":"Reference","previous_headings":"","what":"Imported from WHONET","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Clinical breakpoints package validated imported WHONET, free desktop Windows application developed supported Collaborating Centre Surveillance Antimicrobial Resistance. can read website. developers WHONET AMR package contact sharing work. highly appreciate development WHONET software.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"response-from-clsi-and-eucast","dir":"Reference","previous_headings":"","what":"Response from CLSI and EUCAST","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"CEO CLSI chairman EUCAST endorsed work public use AMR package (consequently use breakpoints) June 2023, future development distributing clinical breakpoints discussed meeting CLSI, EUCAST, , developers WHONET AMR package.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"download","dir":"Reference","previous_headings":"","what":"Download","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, though initiatives started overcome burdens. NOTE: AMR package (WHONET software well) contains internal methods apply guidelines, rather complex. example, breakpoints must applied certain species groups (case package available microorganisms.groups data set). important considered using breakpoints use.","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: 29,747 × 12 #>    guideline type  method site  mo            rank_index ab   ref_tbl  disk_dose #>    <chr>     <chr> <chr>  <chr> <mo>               <dbl> <ab> <chr>    <chr>     #>  1 EUCAST 2… human DISK   NA    B_ACHRMB_XYLS          2 MEM  A. xylo… 10ug      #>  2 EUCAST 2… human MIC    NA    B_ACHRMB_XYLS          2 MEM  A. xylo… NA        #>  3 EUCAST 2… human DISK   NA    B_ACHRMB_XYLS          2 SXT  A. xylo… 1.25ug/2… #>  4 EUCAST 2… human MIC    NA    B_ACHRMB_XYLS          2 SXT  A. xylo… NA        #>  5 EUCAST 2… human DISK   NA    B_ACHRMB_XYLS          2 TZP  A. xylo… 30ug/6ug  #>  6 EUCAST 2… human MIC    NA    B_ACHRMB_XYLS          2 TZP  A. xylo… NA        #>  7 EUCAST 2… human DISK   NA    B_ACNTB                3 AMK  Acineto… 30ug      #>  8 EUCAST 2… human DISK   Unco… B_ACNTB                3 AMK  Acineto… 30ug      #>  9 EUCAST 2… human MIC    NA    B_ACNTB                3 AMK  Acineto… NA        #> 10 EUCAST 2… human MIC    Unco… B_ACNTB                3 AMK  Acineto… NA        #> # ℹ 29,737 more rows #> # ℹ 3 more variables: breakpoint_S <dbl>, breakpoint_R <dbl>, uti <lgl>"},{"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\", \"oxygen_tolerance\", \"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_group rule_name rule_source #>   <int> <chr> <chr>           <ord> <chr> <chr> <chr>      <chr>     <chr>       #> 1    33 AMP   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 2    33 AMX   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 3    34 AMP   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 4    34 AMX   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 5   531 AMP   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 6   531 AMX   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 7  1485 AMP   Klebsiella oxy… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 8  1485 AMX   Klebsiella oxy… R     I     \"rep… Custom EU… Custom E… Object 'x'…  # 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 503 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, either 13, 12, 11","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":"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: 503 × 9 #>    ab   name            type  dose  dose_times administration notes original_txt #>    <ab> <chr>           <chr> <chr>      <int> <chr>          <chr> <chr>        #>  1 AMK  Amikacin        stan… 25-3…          1 iv             \"\"    25-30 mg/kg… #>  2 AMX  Amoxicillin     high… 2 g            6 iv             \"\"    2 g x 6 iv   #>  3 AMX  Amoxicillin     stan… 1 g            3 iv             \"\"    1 g x 3-4 iv #>  4 AMX  Amoxicillin     high… 0.75…          3 oral           \"\"    0.75-1 g x … #>  5 AMX  Amoxicillin     stan… 0.5 g          3 oral           \"\"    0.5 g x 3 o… #>  6 AMX  Amoxicillin     unco… 0.5 g          3 oral           \"\"    0.5 g x 3 o… #>  7 AMC  Amoxicillin/cl… high… 2 g …          3 iv             \"\"    (2 g amoxic… #>  8 AMC  Amoxicillin/cl… stan… 1 g …          3 iv             \"\"    (1 g amoxic… #>  9 AMC  Amoxicillin/cl… high… 0.87…          3 oral           \"\"    (0.875 g am… #> 10 AMC  Amoxicillin/cl… stan… 0.5 …          3 oral           \"\"    (0.5 g amox… #> # ℹ 493 more rows #> # ℹ 1 more variable: eucast_version <dbl>"},{"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 \"12.0\", \"11.0\", \"10.0\". version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can \"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 December 11th, 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, J01GB06), amoxicillin (AMX, J01CA04), amoxicillin/clavulanic acid (AMC, J01CR02), ampicillin (AMP, J01CA01), ampicillin/sulbactam (SAM, J01CR01), arbekacin (ARB, J01GB12), aspoxicillin (APX, J01CA19), azidocillin (AZD, J01CE04), azithromycin (AZM, J01FA10), azlocillin (AZL, J01CA09), aztreonam (ATM, J01DF01), bacampicillin (BAM, J01CA06), bekanamycin (BEK, J01GB13), benzathine benzylpenicillin (BNB, J01CE08), benzathine phenoxymethylpenicillin (BNP, J01CE10), benzylpenicillin (PEN, J01CE01), besifloxacin (BES, S01AE08), biapenem (BIA, J01DH05), carbenicillin (CRB, J01CA03), carindacillin (CRN, J01CA05), cefacetrile (CAC, J01DB10), cefaclor (CEC, J01DC04), cefadroxil (CFR, J01DB05), cefalexin (LEX, J01DB01), cefaloridine (RID, J01DB02), cefalotin (CEP, J01DB03), cefamandole (MAN, J01DC03), cefapirin (HAP, J01DB08), cefatrizine (CTZ, J01DB07), cefazedone (CZD, J01DB06), cefazolin (CZO, J01DB04), cefcapene (CCP, J01DD17), cefdinir (CDR, J01DD15), cefditoren (DIT, J01DD16), cefepime (FEP, J01DE01), cefetamet (CAT, J01DD10), cefixime (CFM, J01DD08), cefmenoxime (CMX, J01DD05), cefmetazole (CMZ, J01DC09), cefodizime (DIZ, J01DD09), cefonicid (CID, J01DC06), cefoperazone (CFP, J01DD12), cefoperazone/sulbactam (CSL, J01DD62), ceforanide (CND, J01DC11), cefotaxime (CTX, J01DD01), cefotaxime/clavulanic acid (CTC, J01DD51), cefotetan (CTT, J01DC05), cefotiam (CTF, J01DC07), cefoxitin (FOX, J01DC01), cefozopran (ZOP, J01DE03), cefpiramide (CPM, J01DD11), cefpirome (CPO, J01DE02), cefpodoxime (CPD, J01DD13), cefprozil (CPR, J01DC10), cefroxadine (CRD, J01DB11), cefsulodin (CFS, J01DD03), ceftaroline (CPT, J01DI02), ceftazidime (CAZ, J01DD02), ceftazidime/clavulanic acid (CCV, J01DD52), cefteram (CEM, J01DD18), ceftezole (CTL, J01DB12), ceftibuten (CTB, J01DD14), ceftizoxime (CZX, J01DD07), ceftobiprole medocaril (CFM1, J01DI01), ceftolozane/tazobactam (CZT, J01DI54), ceftriaxone (CRO, J01DD04), ceftriaxone/beta-lactamase inhibitor (CEB, J01DD63), cefuroxime (CXM, J01DC02), cephradine (CED, J01DB09), chloramphenicol (CHL, J01BA01), ciprofloxacin (CIP, J01MA02), clarithromycin (CLR, J01FA09), clindamycin (CLI, J01FF01), clometocillin (CLM, J01CE07), cloxacillin (CLO, J01CF02), colistin (COL, J01XB01), cycloserine (CYC, J04AB01), dalbavancin (DAL, J01XA04), daptomycin (DAP, J01XX09), delafloxacin (DFX, J01MA23), dibekacin (DKB, J01GB09), dicloxacillin (DIC, J01CF01), dirithromycin (DIR, J01FA13), doripenem (DOR, J01DH04), doxycycline (DOX, J01AA02), enoxacin (ENX, J01MA04), epicillin (EPC, J01CA07), ertapenem (ETP, J01DH03), erythromycin (ERY, J01FA01), fleroxacin (FLE, J01MA08), flucloxacillin (FLC, J01CF05), flurithromycin (FLR1, J01FA14), fosfomycin (FOS, J01XX01), framycetin (FRM, D09AA01), fusidic acid (FUS, J01XC01), garenoxacin (GRN, J01MA19), gatifloxacin (GAT, J01MA16), gemifloxacin (GEM, J01MA15), gentamicin (GEN, J01GB03), grepafloxacin (GRX, J01MA11), hetacillin (HET, J01CA18), imipenem (IPM, J01DH51), imipenem/relebactam (IMR, J01DH56), isepamicin (ISE, J01GB11), josamycin (JOS, J01FA07), kanamycin (KAN, J01GB04), lascufloxacin (LSC, J01MA25), latamoxef (LTM, J01DD06), levofloxacin (LVX, J01MA12), levonadifloxacin (LND, J01MA24), lincomycin (LIN, J01FF02), linezolid (LNZ, J01XX08), lomefloxacin (LOM, J01MA07), loracarbef (LOR, J01DC08), mecillinam (MEC, J01CA11), meropenem (MEM, J01DH02), meropenem/vaborbactam (MEV, J01DH52), metampicillin (MTM, J01CA14), meticillin (MET, J01CF03), mezlocillin (MEZ, J01CA10), micronomicin (MCR, S01AA22), midecamycin (MID, J01FA03), minocycline (MNO, J01AA08), miocamycin (MCM, J01FA11), moxifloxacin (MFX, J01MA14), nadifloxacin (NAD, D10AF05), nafcillin (NAF, J01CF06), nalidixic acid (NAL, J01MB02), neomycin (NEO, J01GB05), netilmicin (NET, J01GB07), nitrofurantoin (NIT, J01XE01), norfloxacin (, J01MA06), ofloxacin (OFX, J01MA01), oleandomycin (OLE, J01FA05), oritavancin (ORI, J01XA05), oxacillin (OXA, J01CF04), panipenem (PAN, J01DH55), pazufloxacin (PAZ, J01MA18), pefloxacin (PEF, J01MA03), penamecillin (PNM, J01CE06), pheneticillin (PHE, J01CE05), phenoxymethylpenicillin (PHN, J01CE02), piperacillin (PIP, J01CA12), piperacillin/tazobactam (TZP, J01CR05), pivampicillin (PVM, J01CA02), pivmecillinam (PME, J01CA08), plazomicin (PLZ, J01GB14), polymyxin B (PLB, J01XB02), pristinamycin (PRI, J01FG01), procaine benzylpenicillin (PRB, J01CE09), propicillin (PRP, J01CE03), prulifloxacin (PRU, J01MA17), quinupristin/dalfopristin (QDA, J01FG02), ribostamycin (RST, J01GB10), rifampicin (RIF, J04AB02), rokitamycin (ROK, J01FA12), roxithromycin (RXT, J01FA06), rufloxacin (RFL, J01MA10), sisomicin (SIS, J01GB08), sitafloxacin (SIT, J01MA21), solithromycin (SOL, J01FA16), sparfloxacin (SPX, J01MA09), spiramycin (SPI, J01FA02), streptoduocin (STR, J01GA02), streptomycin (STR1, J01GA01), sulbactam (SUL, J01CG01), sulbenicillin (SBC, J01CA16), sulfadiazine (SDI, J01EC02), sulfadiazine/trimethoprim (SLT1, J01EE02), sulfadimethoxine (SUD, J01ED01), sulfadimidine (SDM, J01EB03), sulfadimidine/trimethoprim (SLT2, J01EE05), sulfafurazole (SLF, J01EB05), sulfaisodimidine (SLF1, J01EB01), sulfalene (SLF2, J01ED02), sulfamazone (SZO, J01ED09), sulfamerazine (SLF3, J01ED07), sulfamerazine/trimethoprim (SLT3, J01EE07), sulfamethizole (SLF4, J01EB02), sulfamethoxazole (SMX, J01EC01), sulfamethoxypyridazine (SLF5, J01ED05), sulfametomidine (SLF6, J01ED03), sulfametoxydiazine (SLF7, J01ED04), sulfametrole/trimethoprim (SLT4, J01EE03), sulfamoxole (SLF8, J01EC03), sulfamoxole/trimethoprim (SLT5, J01EE04), sulfanilamide (SLF9, J01EB06), sulfaperin (SLF10, J01ED06), sulfaphenazole (SLF11, J01ED08), sulfapyridine (SLF12, J01EB04), sulfathiazole (SUT, J01EB07), sulfathiourea (SLF13, J01EB08), sultamicillin (SLT6, J01CR04), talampicillin (TAL, J01CA15), tazobactam (TAZ, J01CG02), tebipenem (TBP, J01DH06), tedizolid (TZD, J01XX11), teicoplanin (TEC, J01XA02), telavancin (TLV, J01XA03), telithromycin (TLT, J01FA15), temafloxacin (TMX, J01MA05), temocillin (TEM, J01CA17), tetracycline (TCY, J01AA07), ticarcillin (TIC, J01CA13), ticarcillin/clavulanic acid (TCC, J01CR03), tigecycline (TGC, J01AA12), tilbroquinol (TBQ, P01AA05), tobramycin (TOB, J01GB01), tosufloxacin (TFX, J01MA22), trimethoprim (TMP, J01EA01), trimethoprim/sulfamethoxazole (SXT, J01EE01), troleandomycin (TRL, J01FA08), trovafloxacin (TVA, J01MA13), vancomycin (VAN, J01XA01)","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    #> # ℹ 1,990 more rows #> # ℹ 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>, …"},{"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     #> # ℹ 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), ] #> ℹ 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. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #>   of 2 #> ℹ Excluding 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,373 'phenotype-based' first isolates (68.7% of total where a #>    microbial ID was available) #> # A tibble: 1,373 × 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    #> # ℹ 1,363 more rows #> # ℹ 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>, … # \\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: 434 × 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    #> # ℹ 424 more rows #> # ℹ 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>, …  if (require(\"dplyr\")) {   # filter on first isolates using dplyr:   example_isolates %>%     filter(first_isolate(info = TRUE)) } #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #>   of 2 #> ℹ Excluding 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,373 'phenotype-based' first isolates (68.7% of total where a #>    microbial ID was available) #> # A tibble: 1,373 × 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    #> # ℹ 1,363 more rows #> # ℹ 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>, … if (require(\"dplyr\")) {   # short-hand version:   example_isolates %>%     filter_first_isolate(info = FALSE) } #> # A tibble: 1,373 × 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    #> # ℹ 1,363 more rows #> # ℹ 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>, … 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) } #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #>   of 2 #>  #> Group: ward = \"Clinical\" #> ℹ Excluding 9 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 857 'phenotype-based' first isolates (69.4% of total where a #>    microbial ID was available) #>  #> Group: ward = \"ICU\" #> ℹ Excluding 6 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 447 'phenotype-based' first isolates (69.2% of total where a #>    microbial ID was available) #>  #> Group: ward = \"Outpatient\" #> ℹ Excluding 1 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 98 'phenotype-based' first isolates (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  #> # ℹ 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 hand, returns TRUE every new get_episode() index. 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$date, episode_days = 60) # indices #>   [1] 16 46 29 25 10 45 28  8 21 31 26 42 16 10 47 22 10 39 17 23 44  6 42 40 43 #>  [26] 37 11 26 14  5 35 47 22 47 30  4  5 30  5 29 29  2 41 46 42 22 46 12  8 13 #>  [51] 11 35 18 24  2 11 46  3 39 17 18 23  8 13 27 18 31 47 43 12 26  8 37 25 36 #>  [76] 46 24 23 32  7 28 26 19  1 33  9 15 34  7  7  3  6 24  1  2 46 20 10 21 38 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE #>   [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE #>  [13] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE #>  [25]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE #>  [37] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE #>  [49] FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE #>  [61] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [73] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE #>  [85]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [97]  TRUE FALSE FALSE  TRUE  # filter on results from the third 60-day episode only, using base R df[which(get_episode(df$date, 60) == 3), ] #> # A tibble: 2 × 46 #>   date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>   <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #> 1 2002-11-07 430011     82 F      Clinical B_STPHY_CONS R     NA    R     R     #> 2 2002-10-14 FCC668     54 F      ICU      B_STRPT_PNMN S     NA    NA    S     #> # ℹ 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 [96] #>    patient date       condition new_episode #>    <chr>   <date>     <chr>     <lgl>       #>  1 001213  2009-08-03 B         TRUE        #>  2 011307  2011-09-20 C         TRUE        #>  3 022060  2004-05-04 A         TRUE        #>  4 023456  2012-11-24 C         TRUE        #>  5 066895  2002-02-27 B         TRUE        #>  6 083080  2012-04-16 A         TRUE        #>  7 097186  2015-10-28 B         TRUE        #>  8 0D7D34  2011-03-16 C         FALSE       #>  9 0D7D34  2011-03-19 C         TRUE        #> 10 125492  2017-09-15 A         TRUE        #> # ℹ 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 Clinical 2009-08-03 001213          1 TRUE        #>  2 Clinical 2011-09-20 011307          1 TRUE        #>  3 ICU      2004-05-04 022060          1 TRUE        #>  4 Clinical 2012-11-24 023456          1 TRUE        #>  5 Clinical 2002-02-27 066895          1 TRUE        #>  6 Clinical 2012-04-16 083080          1 TRUE        #>  7 Clinical 2015-10-28 097186          1 TRUE        #>  8 ICU      2011-03-16 0D7D34          1 FALSE       #>  9 ICU      2011-03-19 0D7D34          1 TRUE        #> 10 Clinical 2017-09-15 125492          1 TRUE        #> # ℹ 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           62             13            39            50 #> 2 ICU                27             10            21            26 #> 3 Outpatient          5              4             5             5  # 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] FALSE  # 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 [97] #>    patient mo            ward       flag_episode #>    <chr>   <mo>          <chr>      <lgl>        #>  1 550406  B_ESCHR_COLI  Outpatient TRUE         #>  2 987C84  B_ESCHR_COLI  Clinical   TRUE         #>  3 F8DB34  B_STPHY_CONS  Clinical   TRUE         #>  4 AB0003  B_ENTRC_FCLS  Clinical   TRUE         #>  5 C56827  B_ESCHR_COLI  Clinical   TRUE         #>  6 CD8C33  B_STPHY_HMNS  Clinical   TRUE         #>  7 0D7D34  B_STPHY_EPDR  ICU        TRUE         #>  8 022060  B_ENTRBC_CLOC ICU        TRUE         #>  9 F462F5  B_STRPT_PNMN  Clinical   TRUE         #> 10 083080  B_KLBSL_OXYT  Clinical   TRUE         #> # ℹ 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, main = \"Base R biplot\")    # new ggplot2 plotting method using this package:   if (require(\"ggplot2\")) {     ggplot_pca(pca_result) +       labs(title = \"ggplot2 biplot\")   }   if (require(\"ggplot2\")) {     # still extendible with any ggplot2 function     ggplot_pca(pca_result) +       scale_colour_viridis_d() +       labs(title = \"ggplot2 biplot\")   } } #> 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   #> # ℹ 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 × 23 #>   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… #> # ℹ 13 more variables: subspecies <chr>, rank <chr>, ref <chr>, #> #   oxygen_tolerance <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 × 23 #>   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… #> # ℹ 13 more variables: subspecies <chr>, rank <chr>, ref <chr>, #> #   oxygen_tolerance <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\"              \"oxygen_tolerance\" \"source\"           #> [17] \"lpsn\"             \"lpsn_parent\"      \"lpsn_renamed_to\"  \"gbif\"             #> [21] \"gbif_parent\"      \"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\"              \"oxygen_tolerance\" \"source\"           #> [61] \"lpsn\"             \"lpsn_parent\"      \"lpsn_renamed_to\"  \"gbif\"             #> [65] \"gbif_parent\"      \"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] 1376 # }"},{"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] 3.080482 kurtosis(rnorm(10000), excess = TRUE) #> [1] 0.03543362"},{"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":"# data.table has a more limited version of %like%, so unload it: try(detach(\"package:data.table\", unload = TRUE), silent = TRUE)  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     #> # ℹ 96 more rows #> # ℹ 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>, …  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     #> # ℹ 96 more rows #> # ℹ 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>, … # }"},{"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) (doi:10.1111/j.1469-0691.2011.03570.x ) 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, J01GB06), amoxicillin (AMX, J01CA04), amoxicillin/clavulanic acid (AMC, J01CR02), ampicillin (AMP, J01CA01), ampicillin/sulbactam (SAM, J01CR01), arbekacin (ARB, J01GB12), aspoxicillin (APX, J01CA19), azidocillin (AZD, J01CE04), azithromycin (AZM, J01FA10), azlocillin (AZL, J01CA09), aztreonam (ATM, J01DF01), bacampicillin (BAM, J01CA06), bekanamycin (BEK, J01GB13), benzathine benzylpenicillin (BNB, J01CE08), benzathine phenoxymethylpenicillin (BNP, J01CE10), benzylpenicillin (PEN, J01CE01), besifloxacin (BES, S01AE08), biapenem (BIA, J01DH05), carbenicillin (CRB, J01CA03), carindacillin (CRN, J01CA05), cefacetrile (CAC, J01DB10), cefaclor (CEC, J01DC04), cefadroxil (CFR, J01DB05), cefalexin (LEX, J01DB01), cefaloridine (RID, J01DB02), cefalotin (CEP, J01DB03), cefamandole (MAN, J01DC03), cefapirin (HAP, J01DB08), cefatrizine (CTZ, J01DB07), cefazedone (CZD, J01DB06), cefazolin (CZO, J01DB04), cefcapene (CCP, J01DD17), cefdinir (CDR, J01DD15), cefditoren (DIT, J01DD16), cefepime (FEP, J01DE01), cefetamet (CAT, J01DD10), cefixime (CFM, J01DD08), cefmenoxime (CMX, J01DD05), cefmetazole (CMZ, J01DC09), cefodizime (DIZ, J01DD09), cefonicid (CID, J01DC06), cefoperazone (CFP, J01DD12), cefoperazone/sulbactam (CSL, J01DD62), ceforanide (CND, J01DC11), cefotaxime (CTX, J01DD01), cefotaxime/clavulanic acid (CTC, J01DD51), cefotetan (CTT, J01DC05), cefotiam (CTF, J01DC07), cefoxitin (FOX, J01DC01), cefozopran (ZOP, J01DE03), cefpiramide (CPM, J01DD11), cefpirome (CPO, J01DE02), cefpodoxime (CPD, J01DD13), cefprozil (CPR, J01DC10), cefroxadine (CRD, J01DB11), cefsulodin (CFS, J01DD03), ceftaroline (CPT, J01DI02), ceftazidime (CAZ, J01DD02), ceftazidime/clavulanic acid (CCV, J01DD52), cefteram (CEM, J01DD18), ceftezole (CTL, J01DB12), ceftibuten (CTB, J01DD14), ceftizoxime (CZX, J01DD07), ceftobiprole medocaril (CFM1, J01DI01), ceftolozane/tazobactam (CZT, J01DI54), ceftriaxone (CRO, J01DD04), ceftriaxone/beta-lactamase inhibitor (CEB, J01DD63), cefuroxime (CXM, J01DC02), cephradine (CED, J01DB09), chloramphenicol (CHL, J01BA01), ciprofloxacin (CIP, J01MA02), clarithromycin (CLR, J01FA09), clindamycin (CLI, J01FF01), clometocillin (CLM, J01CE07), cloxacillin (CLO, J01CF02), colistin (COL, J01XB01), cycloserine (CYC, J04AB01), dalbavancin (DAL, J01XA04), daptomycin (DAP, J01XX09), delafloxacin (DFX, J01MA23), dibekacin (DKB, J01GB09), dicloxacillin (DIC, J01CF01), dirithromycin (DIR, J01FA13), doripenem (DOR, J01DH04), doxycycline (DOX, J01AA02), enoxacin (ENX, J01MA04), epicillin (EPC, J01CA07), ertapenem (ETP, J01DH03), erythromycin (ERY, J01FA01), fleroxacin (FLE, J01MA08), flucloxacillin (FLC, J01CF05), flurithromycin (FLR1, J01FA14), fosfomycin (FOS, J01XX01), framycetin (FRM, D09AA01), fusidic acid (FUS, J01XC01), garenoxacin (GRN, J01MA19), gatifloxacin (GAT, J01MA16), gemifloxacin (GEM, J01MA15), gentamicin (GEN, J01GB03), grepafloxacin (GRX, J01MA11), hetacillin (HET, J01CA18), imipenem (IPM, J01DH51), imipenem/relebactam (IMR, J01DH56), isepamicin (ISE, J01GB11), josamycin (JOS, J01FA07), kanamycin (KAN, J01GB04), lascufloxacin (LSC, J01MA25), latamoxef (LTM, J01DD06), levofloxacin (LVX, J01MA12), levonadifloxacin (LND, J01MA24), lincomycin (LIN, J01FF02), linezolid (LNZ, J01XX08), lomefloxacin (LOM, J01MA07), loracarbef (LOR, J01DC08), mecillinam (MEC, J01CA11), meropenem (MEM, J01DH02), meropenem/vaborbactam (MEV, J01DH52), metampicillin (MTM, J01CA14), meticillin (MET, J01CF03), mezlocillin (MEZ, J01CA10), micronomicin (MCR, S01AA22), midecamycin (MID, J01FA03), minocycline (MNO, J01AA08), miocamycin (MCM, J01FA11), moxifloxacin (MFX, J01MA14), nadifloxacin (NAD, D10AF05), nafcillin (NAF, J01CF06), nalidixic acid (NAL, J01MB02), neomycin (NEO, J01GB05), netilmicin (NET, J01GB07), nitrofurantoin (NIT, J01XE01), norfloxacin (, J01MA06), ofloxacin (OFX, J01MA01), oleandomycin (OLE, J01FA05), oritavancin (ORI, J01XA05), oxacillin (OXA, J01CF04), panipenem (PAN, J01DH55), pazufloxacin (PAZ, J01MA18), pefloxacin (PEF, J01MA03), penamecillin (PNM, J01CE06), pheneticillin (PHE, J01CE05), phenoxymethylpenicillin (PHN, J01CE02), piperacillin (PIP, J01CA12), piperacillin/tazobactam (TZP, J01CR05), pivampicillin (PVM, J01CA02), pivmecillinam (PME, J01CA08), plazomicin (PLZ, J01GB14), polymyxin B (PLB, J01XB02), pristinamycin (PRI, J01FG01), procaine benzylpenicillin (PRB, J01CE09), propicillin (PRP, J01CE03), prulifloxacin (PRU, J01MA17), quinupristin/dalfopristin (QDA, J01FG02), ribostamycin (RST, J01GB10), rifampicin (RIF, J04AB02), rokitamycin (ROK, J01FA12), roxithromycin (RXT, J01FA06), rufloxacin (RFL, J01MA10), sisomicin (SIS, J01GB08), sitafloxacin (SIT, J01MA21), solithromycin (SOL, J01FA16), sparfloxacin (SPX, J01MA09), spiramycin (SPI, J01FA02), streptoduocin (STR, J01GA02), streptomycin (STR1, J01GA01), sulbactam (SUL, J01CG01), sulbenicillin (SBC, J01CA16), sulfadiazine (SDI, J01EC02), sulfadiazine/trimethoprim (SLT1, J01EE02), sulfadimethoxine (SUD, J01ED01), sulfadimidine (SDM, J01EB03), sulfadimidine/trimethoprim (SLT2, J01EE05), sulfafurazole (SLF, J01EB05), sulfaisodimidine (SLF1, J01EB01), sulfalene (SLF2, J01ED02), sulfamazone (SZO, J01ED09), sulfamerazine (SLF3, J01ED07), sulfamerazine/trimethoprim (SLT3, J01EE07), sulfamethizole (SLF4, J01EB02), sulfamethoxazole (SMX, J01EC01), sulfamethoxypyridazine (SLF5, J01ED05), sulfametomidine (SLF6, J01ED03), sulfametoxydiazine (SLF7, J01ED04), sulfametrole/trimethoprim (SLT4, J01EE03), sulfamoxole (SLF8, J01EC03), sulfamoxole/trimethoprim (SLT5, J01EE04), sulfanilamide (SLF9, J01EB06), sulfaperin (SLF10, J01ED06), sulfaphenazole (SLF11, J01ED08), sulfapyridine (SLF12, J01EB04), sulfathiazole (SUT, J01EB07), sulfathiourea (SLF13, J01EB08), sultamicillin (SLT6, J01CR04), talampicillin (TAL, J01CA15), tazobactam (TAZ, J01CG02), tebipenem (TBP, J01DH06), tedizolid (TZD, J01XX11), teicoplanin (TEC, J01XA02), telavancin (TLV, J01XA03), telithromycin (TLT, J01FA15), temafloxacin (TMX, J01MA05), temocillin (TEM, J01CA17), tetracycline (TCY, J01AA07), ticarcillin (TIC, J01CA13), ticarcillin/clavulanic acid (TCC, J01CR03), tigecycline (TGC, J01AA12), tilbroquinol (TBQ, P01AA05), tobramycin (TOB, J01GB01), tosufloxacin (TFX, J01MA22), trimethoprim (TMP, J01EA01), trimethoprim/sulfamethoxazole (SXT, J01EE01), troleandomycin (TRL, J01FA08), trovafloxacin (TVA, J01MA13), vancomycin (VAN, J01XA01)","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\") 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() } #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> 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 S I I S R I S I I mean_amr_distance(sir) #>  [1]  1.8973666 -0.4743416 -0.4743416 -0.4743416 -0.4743416  1.8973666 #>  [7] -0.4743416 -0.4743416 -0.4743416 -0.4743416  mic <- random_mic(10) mic #> Class 'mic' #>  [1] 16     8      32     0.0625 0.5    >=256  0.002  0.001  1      0.005  mean_amr_distance(mic) #>  [1]  0.82035688  0.66129723  0.97941652 -0.45212029  0.02505865  1.45659546 #>  [7] -1.24197617 -1.40103582  0.18411830 -1.03171076 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) #>  [1]  0.82035688  0.66129723  0.97941652 -0.45212029  0.02505865  1.45659546 #>  [7] -1.24197617 -1.40103582  0.18411830 -1.03171076  disk <- random_disk(10) disk #> Class 'disk' #>  [1]  7 15 31 28 22 45 13 21 18 41 mean_amr_distance(disk) #>  [1] -1.4040837 -0.7472024  0.5665601  0.3202296 -0.1724313  1.7161023 #>  [7] -0.9114228 -0.2545415 -0.5008720  1.3876617  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   28  >=16    0.5 #> 2   B    I   31     1    >=8 #> 3   C    R   23     8    0.5 #> 4   D    R   25  >=16      1 #> 5   E    I   17  >=16      2 #> 6   F    I   22     8      4 #> 7   G    S   17     8 <=0.25 #> 8   H    S   19     1      4 #> 9   I    S   19     8 <=0.25 #> 10  J    R   19 <=0.5      4 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\", #>   and \"tobr\" #>  [1]  0.64013819  0.32815059  0.24520983  0.61863348 -0.14954667  0.11665680 #>  [7] -0.69000290 -0.43800225 -0.58498190 -0.08625517 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   23     8    0.5   0.24520983 0.00000000 #> 2   B    I   31     1    >=8   0.32815059 0.08294076 #> 3   F    I   22     8      4   0.11665680 0.12855303 #> 4   J    R   19 <=0.5      4  -0.08625517 0.33146500 #> 5   D    R   25  >=16      1   0.61863348 0.37342365 #> 6   E    I   17  >=16      2  -0.14954667 0.39475650 #> 7   A    R   28  >=16    0.5   0.64013819 0.39492836 #> 8   H    S   19     1      4  -0.43800225 0.68321208 #> 9   I    S   19     8 <=0.25  -0.58498190 0.83019173 #> 10  G    S   17     8 <=0.25  -0.69000290 0.93521273 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 #> # ℹ 53 more rows"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 4 957 Common Microorganism Codes — microorganisms.codes","title":"Data Set with 4 957 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 4 957 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 4 957 Common Microorganism Codes — microorganisms.codes","text":"tibble 4 957 observations 2 variables: code Commonly used code microorganism. unique identifier. 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 4 957 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 4 957 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes #> # A tibble: 4,957 × 2 #>    code  mo                #>    <chr> <mo>              #>  1 1011  B_GRAMP           #>  2 1012  B_GRAMP           #>  3 1013  B_GRAMN           #>  4 1014  B_GRAMN           #>  5 1015  F_YEAST           #>  6 103   B_ESCHR_COLI      #>  7 104   B_SLMNL_ENTR_ENTR #>  8 1100  B_STRPT           #>  9 1101  B_STRPT_VIRI      #> 10 1102  B_STRPT_HAEM      #> # ℹ 4,947 more rows  # 'ECO' or 'eco' is the WHONET code for E. coli: microorganisms.codes[microorganisms.codes$code == \"ECO\", ] #> # A tibble: 1 × 2 #>   code  mo           #>   <chr> <mo>         #> 1 ECO   B_ESCHR_COLI  # and therefore, 'eco' will be understood as E. coli in this package: mo_info(\"eco\") #> $mo #> [1] \"B_ESCHR_COLI\" #>  #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Escherichia\" #>  #> $species #> [1] \"coli\" #>  #> $subspecies #> [1] \"\" #>  #> $status #> [1] \"accepted\" #>  #> $synonyms #> NULL #>  #> $gramstain #> [1] \"Gram-negative\" #>  #> $oxygen_tolerance #> [1] \"facultative anaerobe\" #>  #> $url #> [1] \"https://lpsn.dsmz.de/species/escherichia-coli\" #>  #> $ref #> [1] \"Castellani et al., 1919\" #>  #> $snomed #>  [1] \"1095001000112106\" \"715307006\"        \"737528008\"        \"416989002\"        #>  [5] \"116397003\"        \"414097009\"        \"414098004\"        \"414099007\"        #>  [9] \"414100004\"        \"116395006\"        \"735270003\"        \"116396007\"        #> [13] \"83285000\"         \"116394005\"        \"112283007\"        \"710886005\"        #> [17] \"710887001\"        \"710888006\"        \"710889003\"        \"414132004\"        #> [21] \"721892009\"        \"416812001\"        \"416740004\"        \"417216001\"        #> [25] \"457541006\"        \"710253004\"        \"416530004\"        \"417189006\"        #> [29] \"409800005\"        \"713925008\"        \"444771000124108\"  \"838549008\"        #>  #> $lpsn #> [1] \"776057\" #>  #> $gbif #> [1] \"11286021\" #>   # works for all AMR functions: mo_is_intrinsic_resistant(\"eco\", ab = \"vancomycin\") #> [1] TRUE"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","text":"data set containing species groups microbiological complexes, used clinical breakpoints table.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","text":"","code":"microorganisms.groups"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","text":"tibble 521 observations 4 variables: mo_group ID species group / microbiological complex mo ID microorganism belonging species group / microbiological complex mo_group_name Name species group / microbiological complex, retrieved mo_name() mo_name Name microorganism belonging species group / microbiological complex, retrieved mo_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","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.groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","text":"","code":"microorganisms.groups #> # A tibble: 521 × 4 #>    mo_group       mo           mo_group_name                   mo_name           #>    <mo>           <mo>         <chr>                           <chr>             #>  1 B_ACNTB_BMNN-C B_ACNTB_BMNN Acinetobacter baumannii complex Acinetobacter ba… #>  2 B_ACNTB_BMNN-C B_ACNTB_CLCC Acinetobacter baumannii complex Acinetobacter ca… #>  3 B_ACNTB_BMNN-C B_ACNTB_DJKS Acinetobacter baumannii complex Acinetobacter di… #>  4 B_ACNTB_BMNN-C B_ACNTB_NSCM Acinetobacter baumannii complex Acinetobacter no… #>  5 B_ACNTB_BMNN-C B_ACNTB_PITT Acinetobacter baumannii complex Acinetobacter pi… #>  6 B_ACNTB_BMNN-C B_ACNTB_SFRT Acinetobacter baumannii complex Acinetobacter se… #>  7 B_BCTRD_FRGL-C B_BCTRD_FRGL Bacteroides fragilis complex    Bacteroides frag… #>  8 B_BCTRD_FRGL-C B_BCTRD_OVTS Bacteroides fragilis complex    Bacteroides ovat… #>  9 B_BCTRD_FRGL-C B_BCTRD_THTT Bacteroides fragilis complex    Bacteroides thet… #> 10 B_BCTRD_FRGL-C B_BCTRD_VLGT Bacteroides fragilis complex    Bacteroides vulg… #> # ℹ 511 more rows  # these are all species in the Bacteroides fragilis group, as per WHONET: microorganisms.groups[microorganisms.groups$mo_group == \"B_BCTRD_FRGL-C\", ] #> # A tibble: 5 × 4 #>   mo_group       mo           mo_group_name                mo_name               #>   <mo>           <mo>         <chr>                        <chr>                 #> 1 B_BCTRD_FRGL-C B_BCTRD_FRGL Bacteroides fragilis complex Bacteroides fragilis  #> 2 B_BCTRD_FRGL-C B_BCTRD_OVTS Bacteroides fragilis complex Bacteroides ovatus    #> 3 B_BCTRD_FRGL-C B_BCTRD_THTT Bacteroides fragilis complex Bacteroides thetaiot… #> 4 B_BCTRD_FRGL-C B_BCTRD_VLGT Bacteroides fragilis complex Bacteroides vulgatus  #> 5 B_BCTRD_FRGL-C B_PRBCT_DSTS Bacteroides fragilis complex Parabacteroides dist…"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 52 171 Microorganisms — microorganisms","title":"Data Set with 52 171 Microorganisms — microorganisms","text":"data set containing full microbial taxonomy (last updated: December 11th, 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 52 171 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 52 171 Microorganisms — microorganisms","text":"tibble 52 171 observations 23 variables: mo ID microorganism used package. unique identifier. fullname Full name, like \"Escherichia coli\". taxonomic ranks genus, species subspecies, 'pasted' text genus, species, subspecies. taxonomic ranks higher genus, name taxon. unique identifier. 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. 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. oxygen_tolerance  Oxygen tolerance, either \"aerobe\", \"anaerobe\", \"anaerobe/microaerophile\", \"facultative anaerobe\", \"likely facultative anaerobe\", \"microaerophile\". data retrieved BacDive (see Source). Items contain \"likely\" missing BacDive extrapolated species within genus guess oxygen tolerance. Currently 73.4% ~37 000 bacteria data set contain oxygen tolerance. 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 July 1st, 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 52 171 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 December 11th, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org December 11th, 2022. Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed https://bacdive.dsmz.de May 12th, 2023. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microorganism', 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 52 171 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 52 171 Microorganisms — microorganisms","text":"Included taxonomic data : ~37 000 (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 43 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 52 171 Microorganisms — microorganisms","text":"convenience, entries added manually: ~1 500 entries Salmonella, city-like serovars groups H 36 species groups (beta-haemolytic Streptococcus groups K, coagulase-negative Staphylococcus (CoNS), Mycobacterium tuberculosis complex, etc.), group compositions stored microorganisms.groups data set 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 8 'undefined' entries (unknown, unknown Gram-negatives, unknown Gram-positives, unknown yeast, unknown fungus, unknown anaerobic Gram-pos/Gram-neg 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 52 171 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 52 171 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 52 171 Microorganisms — microorganisms","text":"","code":"microorganisms #> # A tibble: 52,171 × 23 #>    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… (unk… (unk… \"(unk… \"(un… #>  2 B_GRAMP          (unknown Gra… accep… Bacter… (unkn… (unk… (unk… \"(unk… \"(un… #>  3 B_ANAER-NEG      (unknown ana… accep… Bacter… (unkn… (unk… (unk… \"(unk… \"(un… #>  4 B_ANAER-POS      (unknown ana… accep… Bacter… (unkn… (unk… (unk… \"(unk… \"(un… #>  5 B_ANAER          (unknown ana… accep… Bacter… (unkn… (unk… (unk… \"(unk… \"(un… #>  6 F_FUNGUS         (unknown fun… accep… Fungi   (unkn… (unk… (unk… \"(unk… \"(un… #>  7   UNKNOWN        (unknown nam… accep… (unkno… (unkn… (unk… (unk… \"(unk… \"(un… #>  8 F_YEAST          (unknown yea… accep… Fungi   (unkn… (unk… (unk… \"(unk… \"(un… #>  9 B_[FAM]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… Abdi… Abdi… \"Abdi… \"\"    #> 10 B_[ORD]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… Abdi… Abdi… \"\"     \"\"    #> # ℹ 52,161 more rows #> # ℹ 14 more variables: species <chr>, subspecies <chr>, rank <chr>, ref <chr>, #> #   oxygen_tolerance <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 = 1.25, Protozoa = 1.5, Archaea = 2, others = 3. 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 = 1.25 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, Penicillium, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Talaromyces, 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 = 0.381\\), 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) #> 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), Enterobacter cloacae complex (0.571), and Ehrlichia #>               canis (0.567) #>  #> 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 0.3809524"},{"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_oxygen_tolerance(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_is_anaerobic(   x,   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\", \"oxygen_tolerance\", \"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). Determination bacterial oxygen tolerance (mo_oxygen_tolerance()) based BacDive, see Source. function mo_is_anaerobic() returns TRUE oxygen tolerance \"anaerobe\", indicting obligate anaerobic species genus. always returns FALSE species outside taxonomic kingdom Bacteria. function mo_url() return direct URL online database entry, also shows scientific reference concerned species. SNOMED codes (mo_snomed()) version July 1st, 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/ Micro.rganisms 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 December 11th, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org December 11th, 2022. Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed https://bacdive.dsmz.de May 12th, 2023. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microorganism', 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\" #>  #> $oxygen_tolerance #> [1] \"aerobe\" #>  #> $url #> [1] \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\" #>  #> $ref #> [1] \"Trevisan, 1887\" #>  #> $snomed #> [1] \"1098101000112102\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\"        #>  #> $lpsn #> [1] \"777151\" #>  #> $gbif #> [1] \"3221874\" #>  # }"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":null,"dir":"Reference","previous_headings":"","what":"User-Defined Reference Data Set for Microorganisms — mo_source","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"functions can used predefine reference used .mo() consequently mo_* functions (mo_genus() mo_gramstain()). fastest way organisation (analysis) specific codes picked translated package, since bother setting .","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"","code":"set_mo_source(   path,   destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\") )  get_mo_source(destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\"))"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"path location reference file, can text file (comma-, tab- pipe-separated) Excel file (see Details). Can also \"\", NULL FALSE delete reference file. destination destination compressed data file - default user's home directory.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"reference file can text file separated commas (CSV) tabs pipes, Excel file (either 'xls' 'xlsx' format) R object file (extension '.rds'). use Excel file, need readxl package installed. set_mo_source() check file validity: must data.frame, must column named \"mo\" contains values microorganisms$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. Especially scale_*_mic() functions relevant wrappers plot MIC values ggplot2. allows custom MIC ranges plot intermediate log2 levels missing MIC values.","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":"scale_x_mic(keep_operators = \"edges\", mic_range = NULL, drop = FALSE, ...)  scale_y_mic(keep_operators = \"edges\", mic_range = NULL, drop = FALSE, ...)  scale_colour_mic(keep_operators = \"edges\", mic_range = NULL, drop = FALSE, ...)  scale_fill_mic(keep_operators = \"edges\", mic_range = NULL, drop = FALSE, ...)  # 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,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"),   ... )  # 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,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"),   ... )  # 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,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"),   ... )  # 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,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"),   ... )  # 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":"keep_operators character specifying handle operators (> <=) input. Accepts one three values: \"\" (TRUE) keep operators, \"none\" (FALSE) remove operators, \"edges\" keep operators ends range. mic_range manual range plot MIC values, e.g., mic_range = c(0.001, 32). Use NA set limit one side, e.g., mic_range = c(NA, 32). drop logical remove intermediate MIC values, defaults FALSE ... arguments passed methods 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. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort - default TRUE. Can also set package option AMR_include_PKPD. breakpoint_type type breakpoints use, either \"ECOFF\", \"animal\", \"human\". ECOFF stands Epidemiological Cut-values. default \"human\", can also set package option AMR_breakpoint_type.","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 2023\", \"EUCAST 2022\", \"EUCAST 2021\", \"EUCAST 2020\", \"EUCAST 2019\", \"EUCAST 2018\", \"EUCAST 2017\", \"EUCAST 2016\", \"EUCAST 2015\", \"EUCAST 2014\", \"EUCAST 2013\", \"EUCAST 2012\", \"EUCAST 2011\", \"CLSI 2023\", \"CLSI 2022\", \"CLSI 2021\", \"CLSI 2020\", \"CLSI 2019\", \"CLSI 2018\", \"CLSI 2017\", \"CLSI 2016\", \"CLSI 2015\", \"CLSI 2014\", \"CLSI 2013\", \"CLSI 2012\", \"CLSI 2011\". 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 = \"nl\")    # Plotting using scale_x_mic() # \\donttest{ if (require(\"ggplot2\")) {   mic_plot <- ggplot(data.frame(mics = as.mic(c(0.125, \"<=4\", 4, 8, 32, \">=32\")),                                 counts = c(1, 1, 2, 2, 3, 3)),                      aes(mics, counts)) +     geom_col()   mic_plot +     labs(title = \"without scale_x_mic()\") }  if (require(\"ggplot2\")) {   mic_plot +     scale_x_mic() +     labs(title = \"with scale_x_mic()\") }  if (require(\"ggplot2\")) {   mic_plot +     scale_x_mic(keep_operators = \"all\") +     labs(title = \"with scale_x_mic() keeping all operators\") }  if (require(\"ggplot2\")) {   mic_plot +     scale_x_mic(mic_range = c(1, 128)) +     labs(title = \"with scale_x_mic() using a manual range\") }   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\",   collapse = FALSE )  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\". collapse logical indicate whether output values 'collapsed', .e. merged together one value, character value use collapsing 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":"Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine data set one four available algorithms. 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. 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    #> # ℹ 1,990 more rows #> # ℹ 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>, …   # 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$AMX,   confidence_level = 0.975 ) #> [1] 0.5650148 0.6255670 sir_confidence_interval(example_isolates$AMX,   confidence_level = 0.975,   collapse = \", \" ) #> [1] \"0.565, 0.626\"  # 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 2023 guideline implemented clinical_breakpoints data set. create specific generated values per bug drug, set mo /ab argument.","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"","code":"random_mic(25) #> Class 'mic' #>  [1] 64      1       1       1       0.025   0.025   0.5     <=0.002 0.25    #> [10] >=256   8       0.005   64      64      128     4       4       16      #> [19] 2       0.005   <=0.002 4       0.25    64      1       random_disk(25) #> Class 'disk' #>  [1] 19 13  8 13 40 16  7 20 14 47 19 26 41 25 14 41 47 40 49 40 46 30 46 32 11 random_sir(25) #> Class 'sir' #>  [1] S I S I I R I S I S S R I I R S S I R I R R I I R  # \\donttest{ # make the random generation more realistic by setting a bug and/or drug: random_mic(25, \"Klebsiella pneumoniae\") # range 0.0625-64 #> Class 'mic' #>  [1] 0.025  4      0.25   0.001  4      0.125  0.002  0.125  1      0.025  #> [11] 0.002  2      0.002  0.0625 0.001  1      0.001  8      4      2      #> [21] 0.5    0.25   128    64     0.25   random_mic(25, \"Klebsiella pneumoniae\", \"meropenem\") # range 0.0625-16 #> Class 'mic' #>  [1] 4   8   16  64  4   1   4   64  0.5 16  32  16  32  4   8   1   2   32  4   #> [20] 8   0.5 32  64  32  32  random_mic(25, \"Streptococcus pneumoniae\", \"meropenem\") # range 0.0625-4 #> Class 'mic' #>  [1] 1       1       2       0.5     0.5     2       >=4     1       0.5     #> [10] 0.25    0.25    1       0.5     >=4     2       0.25    <=0.125 0.25    #> [19] 1       <=0.125 0.25    >=4     0.25    0.25    2        random_disk(25, \"Klebsiella pneumoniae\") # range 8-50 #> Class 'disk' #>  [1] 49 43 10 16 28 42 41 41 48 21 20 46 23 24 41 13 46 46 17 27 48 47 41 20 44 random_disk(25, \"Klebsiella pneumoniae\", \"ampicillin\") # range 11-17 #> Class 'disk' #>  [1] 14 16 13 13 17 11 17 13 15 17 17 11 15 14 14 17 16 17 15 16 13 14 16 11 12 random_disk(25, \"Streptococcus pneumoniae\", \"ampicillin\") # range 12-27 #> Class 'disk' #>  [1] 24 23 23 15 26 16 21 25 23 22 22 21 16 19 27 23 25 18 26 17 24 25 22 18 15 # }"},{"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) #>  #> Coefficients: #>             Estimate Std. Error z value Pr(>|z|) #> (Intercept) 27.05312   72.24652   0.374    0.708 #> year        -0.01286    0.03601  -0.357    0.721 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance: 5.7883  on 11  degrees of freedom #> Residual deviance: 5.6608  on 10  degrees of freedom #> AIC: 50.745 #>  #> 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] 0.04515028"},{"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(\"Dutch\") #> ℹ Using Dutch (Nederlands) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacine\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Coagulase-negatieve Staphylococcus (CNS)\"  # setting yet another language set_AMR_locale(\"German\") #> ℹ Using German (Deutsch) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacin\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Koagulase-negative Staphylococcus (KNS)\"  # 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":[]},{"path":"https://msberends.github.io/AMR/news/index.html","id":"breaking-2-1-1-9003","dir":"Changelog","previous_headings":"","what":"Breaking","title":"AMR 2.1.1.9003","text":"Removed functions references used deprecated rsi class, replaced sir equivalents year ago","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-2-1-1-9003","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 2.1.1.9003","text":"Function group scale_*_mic(), namely: scale_x_mic(), scale_y_mic(), scale_colour_mic() scale_fill_mic(). advanced ggplot2 extensions allow plotting MIC values. allow manual range definition plotting missing intermediate log2 levels. Function rescale_mic(), allows rescale MIC values manually set range. powerhouse behind scale_*_mic() functions, can used users directly e.g. compare equality MIC distributions rescaling range first.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-2-1-1-9003","dir":"Changelog","previous_headings":"New","what":"Changed","title":"AMR 2.1.1.9003","text":"Added 4096 5 powers 192 valid levels (192, 384, 576, 768, 960) Added new argument keep_operators .mic(). can \"\" (default), \"none\", \"edges\".","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-211","dir":"Changelog","previous_headings":"","what":"AMR 2.1.1","title":"AMR 2.1.1","text":"CRAN release: 2023-10-21 included many isolates patients altering antibiograms within bacterial species See info issue #122 Added 1,366 LOINC codes antibiotics data set updated latest version (LOINC v2.76) MICs can now used complex number calculations allow scientific number format input (e.g., .mic(\"1.28e-2\")) Fix rounding MICs latest R beta (‘R-devel’) Removed unneeded note used language option AMR_locale set Fixed non-ASCII characters documentation, according CRAN maintainers","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-210","dir":"Changelog","previous_headings":"","what":"AMR 2.1.0","title":"AMR 2.1.0","text":"CRAN release: 2023-07-16","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-2-1-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 2.1.0","text":"Clinical breakpoints intrinsic resistance EUCAST 2023 CLSI 2023 added clinical_breakpoints data set usage .sir(). EUCAST 2023 (v13.0) now new default guideline MIC disks diffusion interpretations EUCAST dosage guideline v13.0 added dosage data set clinical_breakpoints data set now also contains epidemiological cut-(ECOFF) values CLSI animal breakpoints. two new breakpoint types can used MIC/disk interpretation using .sir(..., breakpoint_type = \"ECOFF\") oras.sir(..., breakpoint_type = \"animal\"), important new addition veterinary microbiology. Added support 30 species groups / complexes. gathered new data set microorganisms.groups used clinical breakpoint interpretation. example, CLSI 2023 contains breakpoints RGM group (Rapidly Growing Mycobacterium, containing 80 species) now supported package. Added mo_oxygen_tolerance() retrieve values Added mo_is_anaerobic() determine genera/species obligate anaerobic bacteria Added LPSN GBIF identifiers, oxygen tolerance mo_info() Added SAS Transport files (file extension .xpt) download page use SAS software Added microbial codes Gram-negative/positive anaerobic bacteria","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-2-1-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 2.1.0","text":"Updated algorithm .mo() giving weight fungi Fixed clinical breakpoints errors introduced source import rules mo_rank() now returns NA ‘unknown’ microorganisms (B_ANAER, B_ANAER-NEG, B_ANAER-POS, B_GRAMN, B_GRAMP, F_FUNGUS, F_YEAST, UNKNOWN) printing microorganism antibiotic codes tibble, mouse-hover now shows full name code Now settable arguments breakpoint type PK/PD, like .sir() now contain name guideline table subtitle plot Fixed formatting sir_interpretation_history() Fixed WHONET codes microorganisms consequently couple entries clinical_breakpoints Fixed bug .mo() led coercion NA values using custom microorganism codes Fixed usage icu_exclude first_isolates() Now allows searching species names Fix using keep_synonyms argument using MO codes input Fix using minimum_matching_score argument Updated code table microorganisms.codes Fixed endless loop using reference_df .mo() Fixed bug indicating UTIs .sir() Greatly improved speed .sir()","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-200","dir":"Changelog","previous_headings":"","what":"AMR 2.0.0","title":"AMR 2.0.0","text":"CRAN release: 2023-03-12 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-2-0-0","dir":"Changelog","previous_headings":"New","what":"SIR vs. RSI","title":"AMR 2.0.0","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-2-0-0","dir":"Changelog","previous_headings":"New","what":"New antibiogram function","title":"AMR 2.0.0","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-2-0-0","dir":"Changelog","previous_headings":"New","what":"Interpretation of MIC and disk diffusion values","title":"AMR 2.0.0","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-2-0-0","dir":"Changelog","previous_headings":"New","what":"Supported languages","title":"AMR 2.0.0","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":"outbreak-management-2-0-0","dir":"Changelog","previous_headings":"New","what":"Outbreak management","title":"AMR 2.0.0","text":"analysis outbreak management, updated get_episode() is_new_episode() functions: now contain argument case_free_days. argument can used quantify duration case-free days (inter-epidemic interval), new episode start. common requirement outbreak management, e.g. determining number norovirus outbreaks hospital. case-free period 14 28 days, new norovirus cases time considered different (new) episode.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"microbiological-taxonomy-2-0-0","dir":"Changelog","previous_headings":"New","what":"Microbiological taxonomy","title":"AMR 2.0.0","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) Added function mo_current() get currently valid taxonomic name microorganism 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-2-0-0","dir":"Changelog","previous_headings":"New","what":"Antibiotic agents and selectors","title":"AMR 2.0.0","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-2-0-0","dir":"Changelog","previous_headings":"New","what":"Antiviral agents","title":"AMR 2.0.0","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-2-0-0","dir":"Changelog","previous_headings":"New","what":"Other new functions","title":"AMR 2.0.0","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.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changes-2-0-0","dir":"Changelog","previous_headings":"","what":"Changes","title":"AMR 2.0.0","text":"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-2-0-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 2.0.0","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 (January 2023) later. prior versions, please see archive.","code":""}]