From cb279602b6ef67faf33ca77bec8292c4def1051d Mon Sep 17 00:00:00 2001
From: github-actions <41898282+github-actions[bot]@users.noreply.github.com>
Date: Sun, 12 Mar 2023 13:14:27 +0000
Subject: [PATCH] Built site for AMR: 2.0.0@dee675e

---
 404.html                                      |   2 +-
 LICENSE-text.html                             |   2 +-
 articles/AMR.html                             | 198 +++++++++---------
 .../figure-html/unnamed-chunk-13-1.png        | Bin 42758 -> 42726 bytes
 articles/EUCAST.html                          |   2 +-
 articles/MDR.html                             |  54 ++---
 articles/PCA.html                             |   2 +-
 articles/SPSS.html                            |   2 +-
 articles/WHONET.html                          |   2 +-
 articles/datasets.html                        |   2 +-
 articles/index.html                           |   2 +-
 articles/resistance_predict.html              |   2 +-
 articles/welcome_to_AMR.html                  |   4 +-
 authors.html                                  |   2 +-
 index.html                                    |   2 +-
 news/index.html                               |  28 +--
 pkgdown.yml                                   |   2 +-
 reference/AMR-deprecated.html                 |   2 +-
 reference/AMR-options.html                    |   2 +-
 reference/AMR.html                            |   2 +-
 reference/Rplot005.png                        | Bin 13224 -> 13279 bytes
 reference/Rplot006.png                        | Bin 13245 -> 13276 bytes
 reference/Rplot007.png                        | Bin 13122 -> 14563 bytes
 reference/Rplot008.png                        | Bin 18935 -> 20423 bytes
 reference/Rplot009.png                        | Bin 8637 -> 8075 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     |   4 +-
 reference/antibiotics.html                    |   2 +-
 reference/as.ab.html                          |   2 +-
 reference/as.av.html                          |   2 +-
 reference/as.disk.html                        |   2 +-
 reference/as.mic.html                         |   2 +-
 reference/as.mo.html                          |   2 +-
 reference/as.sir.html                         |  22 +-
 reference/atc_online.html                     |   2 +-
 reference/av_from_text.html                   |   2 +-
 reference/av_property.html                    |   2 +-
 reference/availability.html                   |   2 +-
 reference/bug_drug_combinations.html          |   2 +-
 reference/clinical_breakpoints.html           |   2 +-
 reference/count.html                          |   2 +-
 reference/custom_eucast_rules.html            |   2 +-
 reference/dosage.html                         |   2 +-
 reference/eucast_rules.html                   |   2 +-
 reference/example_isolates.html               |   2 +-
 reference/example_isolates_unclean.html       |   2 +-
 reference/first_isolate.html                  |   2 +-
 reference/g.test.html                         |   2 +-
 reference/get_episode.html                    | 118 +++++------
 reference/ggplot_pca.html                     |   2 +-
 reference/ggplot_sir.html                     |   2 +-
 reference/guess_ab_col.html                   |   2 +-
 reference/index.html                          |   2 +-
 reference/intrinsic_resistant.html            |   2 +-
 reference/italicise_taxonomy.html             |   2 +-
 reference/join.html                           |   2 +-
 reference/key_antimicrobials.html             |   2 +-
 reference/kurtosis.html                       |   6 +-
 reference/like.html                           |   2 +-
 reference/mdro.html                           |   2 +-
 reference/mean_amr_distance.html              |  73 ++++---
 reference/microorganisms.codes.html           |   2 +-
 reference/microorganisms.html                 |   2 +-
 reference/mo_matching_score.html              |   2 +-
 reference/mo_property.html                    |   2 +-
 reference/mo_source.html                      |   2 +-
 reference/pca.html                            |   2 +-
 reference/plot-1.png                          | Bin 26673 -> 25830 bytes
 reference/plot-2.png                          | Bin 26483 -> 27048 bytes
 reference/plot-3.png                          | Bin 28123 -> 28297 bytes
 reference/plot-4.png                          | Bin 39541 -> 38715 bytes
 reference/plot-5.png                          | Bin 39068 -> 39587 bytes
 reference/plot-6.png                          | Bin 38228 -> 38761 bytes
 reference/plot-7.png                          | Bin 37663 -> 38635 bytes
 reference/plot-8.png                          | Bin 56299 -> 60111 bytes
 reference/plot-9.png                          | Bin 26372 -> 24737 bytes
 reference/plot.html                           |   2 +-
 reference/proportion.html                     |   2 +-
 reference/random.html                         |  34 +--
 reference/resistance_predict.html             |   2 +-
 reference/skewness.html                       |   4 +-
 reference/translate.html                      |   2 +-
 search.json                                   |   2 +-
 91 files changed, 347 insertions(+), 348 deletions(-)

diff --git a/404.html b/404.html
index 8a8fc8cd..326d9975 100644
--- a/404.html
+++ b/404.html
@@ -36,7 +36,7 @@
     
     <a class="navbar-brand me-2" href="https://msberends.github.io/AMR/index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 9efa168f..278e2da1 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 495c0bf8..33000078 100644
--- a/articles/AMR.html
+++ b/articles/AMR.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -962,110 +962,110 @@ antibiograms:</p>
 <tr class="odd">
 <td align="left">Cardial</td>
 <td align="left">
-<em>E. coli</em> (416-416)</td>
+<em>E. coli</em> (415-415)</td>
+<td align="right">64</td>
+<td align="right">58</td>
+<td align="right">56</td>
 <td align="right">63</td>
-<td align="right">59</td>
-<td align="right">57</td>
-<td align="right">61</td>
 </tr>
 <tr class="even">
 <td align="left">Respiratory</td>
 <td align="left">
-<em>E. coli</em> (406-406)</td>
-<td align="right">65</td>
-<td align="right">60</td>
-<td align="right">55</td>
-<td align="right">65</td>
+<em>E. coli</em> (432-432)</td>
+<td align="right">64</td>
+<td align="right">59</td>
+<td align="right">58</td>
+<td align="right">62</td>
 </tr>
 <tr class="odd">
 <td align="left">Rheumatic</td>
 <td align="left">
-<em>E. coli</em> (428-428)</td>
-<td align="right">64</td>
-<td align="right">56</td>
-<td align="right">60</td>
+<em>E. coli</em> (403-403)</td>
 <td align="right">63</td>
-</tr>
-<tr class="even">
-<td align="left">Cardial</td>
-<td align="left">
-<em>K. pneumoniae</em> (106-106)</td>
-<td align="right">57</td>
-<td align="right">52</td>
 <td align="right">58</td>
 <td align="right">59</td>
-</tr>
-<tr class="odd">
-<td align="left">Respiratory</td>
-<td align="left">
-<em>K. pneumoniae</em> (101-101)</td>
-<td align="right">60</td>
-<td align="right">52</td>
-<td align="right">57</td>
-<td align="right">53</td>
+<td align="right">63</td>
 </tr>
 <tr class="even">
-<td align="left">Rheumatic</td>
-<td align="left">
-<em>K. pneumoniae</em> (109-109)</td>
-<td align="right">72</td>
-<td align="right">53</td>
-<td align="right">61</td>
-<td align="right">67</td>
-</tr>
-<tr class="odd">
 <td align="left">Cardial</td>
 <td align="left">
-<em>S. aureus</em> (221-221)</td>
+<em>K. pneumoniae</em> (98-98)</td>
 <td align="right">66</td>
-<td align="right">54</td>
-<td align="right">57</td>
+<td align="right">44</td>
+<td align="right">64</td>
+<td align="right">62</td>
+</tr>
+<tr class="odd">
+<td align="left">Respiratory</td>
+<td align="left">
+<em>K. pneumoniae</em> (110-110)</td>
+<td align="right">65</td>
 <td align="right">61</td>
+<td align="right">59</td>
+<td align="right">59</td>
+</tr>
+<tr class="even">
+<td align="left">Rheumatic</td>
+<td align="left">
+<em>K. pneumoniae</em> (108-108)</td>
+<td align="right">57</td>
+<td align="right">52</td>
+<td align="right">54</td>
+<td align="right">59</td>
+</tr>
+<tr class="odd">
+<td align="left">Cardial</td>
+<td align="left">
+<em>S. aureus</em> (212-212)</td>
+<td align="right">61</td>
+<td align="right">53</td>
+<td align="right">51</td>
+<td align="right">59</td>
 </tr>
 <tr class="even">
 <td align="left">Respiratory</td>
 <td align="left">
-<em>S. aureus</em> (217-217)</td>
+<em>S. aureus</em> (236-236)</td>
+<td align="right">68</td>
+<td align="right">62</td>
 <td align="right">64</td>
-<td align="right">60</td>
-<td align="right">58</td>
 <td align="right">65</td>
 </tr>
 <tr class="odd">
 <td align="left">Rheumatic</td>
 <td align="left">
-<em>S. aureus</em> (223-223)</td>
-<td align="right">64</td>
-<td align="right">56</td>
-<td align="right">57</td>
+<em>S. aureus</em> (213-213)</td>
 <td align="right">63</td>
+<td align="right">55</td>
+<td align="right">56</td>
+<td align="right">64</td>
 </tr>
 <tr class="even">
 <td align="left">Cardial</td>
 <td align="left">
-<em>S. pneumoniae</em> (134-134)</td>
+<em>S. pneumoniae</em> (131-131)</td>
 <td align="right">69</td>
 <td align="right">56</td>
-<td align="right">58</td>
-<td align="right">68</td>
+<td align="right">66</td>
+<td align="right">71</td>
 </tr>
 <tr class="odd">
 <td align="left">Respiratory</td>
 <td align="left">
-<em>S. pneumoniae</em> (138-138)</td>
-<td align="right">64</td>
+<em>S. pneumoniae</em> (146-146)</td>
+<td align="right">62</td>
+<td align="right">55</td>
 <td align="right">57</td>
-<td align="right">61</td>
 <td align="right">67</td>
 </tr>
 <tr class="even">
 <td align="left">Rheumatic</td>
 <td align="left">
-<em>S. pneumoniae</em> (127-127)</td>
-<td align="right">59</td>
-<td align="right">54</td>
-<td align="right">60</td>
-<td align="right">62</td>
+<em>S. pneumoniae</em> (122-122)</td>
+<td align="right">61</td>
+<td align="right">57</td>
+<td align="right">57</td>
+<td align="right">58</td>
 </tr>
 </tbody>
 </table>
@@ -1090,45 +1090,45 @@ antibiograms:</p>
 <tbody>
 <tr class="odd">
 <td align="left">Cardial</td>
-<td align="left">Gram-negative (522-522)</td>
-<td align="right">61</td>
-<td align="right">57</td>
-<td align="right">61</td>
+<td align="left">Gram-negative (513-513)</td>
+<td align="right">65</td>
+<td align="right">55</td>
+<td align="right">63</td>
 </tr>
 <tr class="even">
 <td align="left">Respiratory</td>
-<td align="left">Gram-negative (507-507)</td>
+<td align="left">Gram-negative (542-542)</td>
 <td align="right">64</td>
-<td align="right">58</td>
+<td align="right">60</td>
 <td align="right">62</td>
 </tr>
 <tr class="odd">
 <td align="left">Rheumatic</td>
-<td align="left">Gram-negative (537-537)</td>
-<td align="right">66</td>
-<td align="right">56</td>
-<td align="right">64</td>
+<td align="left">Gram-negative (511-511)</td>
+<td align="right">62</td>
+<td align="right">57</td>
+<td align="right">62</td>
 </tr>
 <tr class="even">
 <td align="left">Cardial</td>
-<td align="left">Gram-positive (355-355)</td>
-<td align="right">67</td>
-<td align="right">55</td>
+<td align="left">Gram-positive (343-343)</td>
+<td align="right">64</td>
+<td align="right">54</td>
 <td align="right">64</td>
 </tr>
 <tr class="odd">
 <td align="left">Respiratory</td>
-<td align="left">Gram-positive (355-355)</td>
-<td align="right">64</td>
+<td align="left">Gram-positive (382-382)</td>
+<td align="right">66</td>
 <td align="right">59</td>
-<td align="right">65</td>
+<td align="right">66</td>
 </tr>
 <tr class="even">
 <td align="left">Rheumatic</td>
-<td align="left">Gram-positive (350-350)</td>
-<td align="right">62</td>
-<td align="right">55</td>
+<td align="left">Gram-positive (335-335)</td>
 <td align="right">63</td>
+<td align="right">56</td>
+<td align="right">62</td>
 </tr>
 </tbody>
 </table>
@@ -1152,45 +1152,45 @@ antibiograms:</p>
 <tbody>
 <tr class="odd">
 <td align="left">Cardial</td>
-<td align="left">Gram-negative (522-522)</td>
-<td align="right">61</td>
+<td align="left">Gram-negative (513-513)</td>
+<td align="right">65</td>
 <td align="right">76</td>
-<td align="right">74</td>
+<td align="right">75</td>
 </tr>
 <tr class="even">
 <td align="left">Respiratory</td>
-<td align="left">Gram-negative (507-507)</td>
+<td align="left">Gram-negative (542-542)</td>
 <td align="right">64</td>
-<td align="right">75</td>
-<td align="right">74</td>
+<td align="right">76</td>
+<td align="right">76</td>
 </tr>
 <tr class="odd">
 <td align="left">Rheumatic</td>
-<td align="left">Gram-negative (537-537)</td>
-<td align="right">66</td>
-<td align="right">77</td>
+<td align="left">Gram-negative (511-511)</td>
+<td align="right">62</td>
 <td align="right">76</td>
+<td align="right">74</td>
 </tr>
 <tr class="even">
 <td align="left">Cardial</td>
-<td align="left">Gram-positive (355-355)</td>
-<td align="right">67</td>
-<td align="right">80</td>
-<td align="right">79</td>
+<td align="left">Gram-positive (343-343)</td>
+<td align="right">64</td>
+<td align="right">75</td>
+<td align="right">75</td>
 </tr>
 <tr class="odd">
 <td align="left">Respiratory</td>
-<td align="left">Gram-positive (355-355)</td>
-<td align="right">64</td>
-<td align="right">76</td>
-<td align="right">75</td>
+<td align="left">Gram-positive (382-382)</td>
+<td align="right">66</td>
+<td align="right">80</td>
+<td align="right">79</td>
 </tr>
 <tr class="even">
 <td align="left">Rheumatic</td>
-<td align="left">Gram-positive (350-350)</td>
-<td align="right">62</td>
+<td align="left">Gram-positive (335-335)</td>
+<td align="right">63</td>
 <td align="right">76</td>
-<td align="right">72</td>
+<td align="right">73</td>
 </tr>
 </tbody>
 </table>
diff --git a/articles/AMR_files/figure-html/unnamed-chunk-13-1.png b/articles/AMR_files/figure-html/unnamed-chunk-13-1.png
index b9ba916c1ac3e525f3dde210a2fc339fad7d3c39..8f6ec5946e878981d62fe55599c6bb4795baf5f0 100644
GIT binary patch
literal 42726
zcmeFZ2T)X9*CyKL0GI<34S+}%Bu67QS)ydgk`-ujGJ%Q$Hc4Wm<kT&hCaNeoNzRCb
z2AUu_!>og^-~Zo#Zq>arx4t_yRnvtpyxphI*=O$+p7pF}pI1r>(o~0O4x>;gD)il3
z$|%&nSQKi{=Y#v<o$}}X$?$8xp{(>R_zkbsn?+vm>(HaSn)WCZy)W`_SHQ@u2?}){
zg}!xN)j4Xi&m~yLBwFyd?P7$-CBuud$3A8qqTD-JeE#6Ui%(fE#uZR<-FtLy7yT(=
zhG-|=!)#xLyw4wgV|S4Ht@kdS`_Cqye!C$34dug3bN<4i_@Wl=60hMYmgwo{4$b-O
zZL97L)xyz*i@j@o+W8X_%i7Bl`KnxDwY!kfdGAtJ8Q+ckg`$qw+qCn?sT(LN<PWI}
z|CTo<*mOUK#_MpIeSdvAgli9qS}IODKtW!9+pERa*0!-Ggq<S!ASyXH)2LFtaIbg0
zKg#tO#?jGq_Sg46hnM>vRlIrgW?4^eZtnP5)aPTDy!VgQAEW#KWAJ}x<RiX$A1X2|
zG&Ho=ZKi}s#uxE=ZEbL9W--Oxd0S&sG!?qJJRo_1(f!dKe>%-^-YZQciDjbL+F0Cb
zSEFa>p_42Nt5JLnLYIz;FEqL0_Iz%NCn)u`=N`@9K7e|C(R+VkpZk2hFe%Euxxc@E
zXf{DMY_vgirY_p4KYYfWB)ZyJDG_?qW9(*1Mn;BG!l{(FxJ&f(^kfeOZS9^ki38pr
zr1d|nWmq!WY#&9Px{ab@LUR#^u{s`$ZD{Nzf1!()qaNMJ>oSeJCLoZg!$lei({Bh;
zln75Fl!}Dud%7gbg=3!`l_>bCwg>g>w*D@Z_uC8J`+05qi>LS0*f)v`!d*KBj3+2x
zF{*e|9pg3~F23B$7a+OitmnBpA`@I=`m1=pA<f|sit?hziw{OBOM^?>sLvIwA+;lR
z34Wc*4h{~(vz3elhjUFgi8>7jBv-z0=oJewZm(3%zYB0Tjw3B{Z_jbNEOnXL_hC9l
zkOA^{=mf!a1slbebxJp<Y|Um$mKCI>rMFfpB~7P$i7sOwe6x(Jv2W-_1(5OeFPAQx
zBt<)R$F7zwG|LhvQq%0<yYVS0SJtPCY$euy1ejLRJIP^-;nQ$_OLd!)#dc&3i>3=L
zCAMY*%<}sjdxlLc&CRRTG;~AYZT8wQ?#9Ueg~TZ5U$0zhwfnRBUBp(0gNRGrmh+$Y
z9EGbh!>n4{{r+-*R50XUJN6@P-KwR(KL$*MOR)FR&8@BFGPCum{Ki3F#u%djae*?p
z6Z?J_(nxxCL(K(Wy5_jPigc1RqsKMedM~MvJ41IpYy2FasqyyavSPCvWfd45oyWot
zb1SR5#SZ<l23b6#`|mIAtF`(K$EE4sfmInvwI5gQJ|a39=;Pz#nQv)h)6k+QsVjAx
z=hMRJwK6NXi5br+IL~Bmhu&(Hv;-FH$|ZUV)@Hh7xnIX?bJ1n^owPDnST+oMbSy5Q
z;mHAp_Yo2O%K<ZEV^zIGd;1o7!I4I<E&J}C9zK`3;mP~~PvN<$OME6Z!Fty1{l2y}
zTavFNQ0XIYZQYF$WKo@yLVIZP9{HW&toilbFNg^yzL%vw&2sW{q;07%3FqZWFh|?{
zxP8`SX|hW^F*he?I5E<uMqaQlc)TTv^zEdg&di1_EKro!*6R4=<YbH^oMPBtbh_&M
zS^cQTLlq<OK?*Xmva-tyz6`Fc;8;ox66=%5z-UXX>`4vvL{dzT(MGw;{HRHPvCHc4
zWyx^Fg-W_ZYezPI9+0d`*UWYm+Zqm19Id{Ls|(T43#Z^JIB)CP66q>u3zL{6xjD|T
zs|{8yw35M%L$Kf3C{aE#)ZgEqf&8Ji0#D)(vz)rh(6LcsH+A*AFBNU*o+Co%kxO3+
z5W|9pWOoz?4U;6dT|`7g?CP#oE<`L>q+de4P9ijwnXFIQdPUB7ZLf<b_IWJ#5y3|7
zYpx^>KR>B3)|OXNBCfyI9EP|cLxAM(&wE^cJ~=R#C|Gg@nL8gU+V-JE1@XmJF<1og
zZigvRLh0HBvXUiVdrB)F_jhi3eLRXn6^rb;W;Mv*Hkrwk*7%b)S+pGo^SQHf9%~D4
z7$qeVlvCWghpU6;7YSbCaGZ37KYe6GM8q|I{t>X*hBxQ<?8XzqMq{5HsmbfHDQZ~t
znsFPbJs|P>3E~vz_$^c;A|vO^8Mk;kbe(idw-yOW85x`h=pIQ)%!fA`Z>-FkTU*z2
zd(QglxQ-{RjtZ8A>u=44dBoh#xq!Gesc**9NkL>h{tc}lm$kJuadW9>KEu|7T@oDD
zZZfmdv^Dvjcz0clxM;R%eGOvfUhDZ@wXG>CD*F1k#Kgp2#~y3p<z9RHiIgN$FsE}8
z?#@m{_6@?96(u(v@&-KDH`dFx-H_44EU0cxXq0u6rL5&?vtz&yNIPE#r=AZh+h8tR
z`OdwD@m+^lQW7reWP>kR>XtYvp@G*4M`F>XJ8#A?$B3EagWkW!*%um@%eFT+aNF}<
zbKki=Gu`_0d+lUP=c8waA;Mi>>UW!wix$c4SbHyMtH&buJ`nuc0e5{hzig9Jf4#e7
z4!I(qX<Zdea$%3y%FkzrA7+&w5gn!-Sh<r2;i$E>H6CmM@uRnb{hdZ7_}awj?UNoT
z)Pqd{yv9DtbBYqH#@Ht%s#Z9UrRsBLQSXp!Bl25#Yh`FYFTaHIB#Yc#m{yqtWynq8
zh=oF$QNRvsl&!V7lDy!8qb;~ChdTib(aI^o$1mL(p2uywTUc0RInVx*ixCqxi^ixg
zlLZ9?M!_rVEMeg3_cJd-<dzh0?Skd_6y?}$-glsuii%43*UL+DOZjEuXxHc$t-ZZb
zeq4pDNXkHbR@bSIACBp(g=i*C%4tym=asc7T^*fvlnv#mh5+%dySrP*;m7SR17CV&
zZS6?--$__C;?JiP?b@WobP|>D{i)i>fsN5?*RBmO_1MllzM~oDHch;Yo`dxwR9@e#
zz+Xb`_bt7#T?~fU=(SPlweWuVo-}<3L_`?XRCySeZUn7O!3DJ4?Dw-~VY-gv{iGQf
z45Uk0<eEs-Z<JWKaw;m@7~w`^TNFe&RTAaK^)wb27sGi?WE6x)g7qX{6B;Z2uEXA@
zPcI<oDqvw5O`{uK)_Vr}$@=<X0gl4s@j(I;=S|@<=&Uunmc><)*A_`Hpsl^#G(ddm
zF4ldeoN;=7@u<jHEK~J-qvXI$ci{s6w>wz6N1u10n}<I0ppkT!n4QhX2r;Xrv$NBz
z2VXc{x<1V?g>5rCwTQ{<0`t%RVWes^<~mJ)DWd}~Mq?qI5IPnt@kAE%oWE0XT;&jL
zkw1!>Ztd{qe6^xyR%3Qj00a-FG~a2hTnn}3AtZughnsF5t+&*hkI<V!3<B{2G!_g$
ze|;989gG8qe~ZV&j_pCA$VLZ`bbiI?))bVAwh_eJ2MQU;S@i-v>C61vU;!gYG-urU
z?aQH+!wTjno+!E4szSDweEjR11S6xMjSkkhIUenyFeCCvZ$6e#*1p)SYet>n(cTyr
zAQ8btgd9B|$GG{hWL~e}k-_qS?tvgOjr3J?|6FLk@z%yFIjS^;>|#WRX}yQ!ay)S~
zf)4UxUjR2`bc!<^RS?tMtQr4x)7eYEX>=#Z<m@4T(a~3zW2%}HWRXn9M3|iK5S(7f
zF>jdz!!LFd3O2^@xFH$3Y>QlGx7_DMLNX%2v8Y!kFWW5qz7TnX5rw*c7KyBd)BgRK
zb7uMg0rRHIc5d8=alO-(q^S?7+I)Lnayy6}Utm}=)#3CxB7ObD5B#$X4821}S|8+`
zsC^V?7F;(9$#^nm>9H4S^Oqkt3mPYQSf?kV(FLL|*0?0B!IA#fvdQ`O`HJ^oeMl6M
z++J;T0qf=S+*n~ESlvV{!!5U~6SMd8&nAsAeSL#(FOQScEq{G`<?`#*W!H@dC5GQ$
zGgWtH8g+Ex$#<S2fMB+>O+!Cgmb>H{f=G%Q58FO`SOpLbHxwXQ>Lw{hy#Oc>b6l$Y
z4Bk?#@-ohyS6*J8EdEfY-Q#m!B8I+Le7=s4`0F)$b5m0w!DGs@3z8V0>%tfku~gX-
z;^N}mI7Ys-Vmj6?I_#?&Sg8<njiSo=Fr|*yAvTcGU5*td%HT;5a*lfben$NLm4eS{
zx)-tCD`B<a!uF<E4X!xdmVf#4+Tr^8dh*6Dm+|HVbhA;xtJ1F{xtM5=QpdaEU{g^p
zqv5N+&X&DTig9I|Cp9)k3ESJXscDQvSmMFl=Yoo-F@&DjLctIT6zUUKg|`o9FNBsJ
zkMIdVRr+I=n75W2fYk*KFgQP`cuU*iQ66Qyc|h-WAsC`PIN0J^M@MJvW*a7HdCOlL
zu%0N(xQ9wP|K{LPud(@A83O6%QSoc&!lOcGxpdwGfJYpBY<?2|R#zHDdBo$z*5c`Q
zeinN-H@9WYR^1`N%V2m2lEajGcDTBf)Al!!7689N!r%wAVZ~Ll(7Zt|+p<Kk!$^pR
zP3P_-#~|#-a_@fF`87l)0l8x|R+fusTw-+CHWgnhPo7vST`_D=*L+dpm>?G^z=@vg
z8Q2nl+|m8h(QPVM1G@u8mg{ltIS+q)+!$B%n#i6?#FOxRnQMi9n?CP(3~uzpLLtQ$
z0>$Sc)Uu!l1fR<Yc+&6U5dt(xs+8P%j4Qw0YgfyO#`ZZ4h?o0Nkv-iZ`Jl60B|9-b
z3lXrwT_wHiGiBTR+nU+}oY%&a1Pi9+`?fEyL76$Qw@C`gjMJiFUeortS{G7u*b9oY
zO9g|9+S=M@xO6%_?$lOv!rMViP$nGBdhq4x^t0^K?CQh5k{bqiz&Y~wU)Q%J-;0||
zQS@L#&%IJ~zX-6;4NyyAQhr{Z>EPEF2>N%WYbqP%Csgk`Ko_{QwB$ZWp<ng!wjbc!
zmaeXF1k&NQH%J|;5s`&4PTl57Ow|OhP4ZiYh~YhqD^l1tr;;+j`9Z~Y-X(cEtH|K-
zf@-;=Y>R(;6}KI3QX9&w=`lLbncs`eZj=zMp2#lhxMuM6Ii2UqAR61~KFWu<Uo%|G
zju@4M^Ay3Y)|oZ``0)-rMG(M<wNnv0p_o^2z}-FuP}%e*#>~v@47XksQgKzY@0#(U
ze)$K+Etpb1Jdd7x!R>y**14S44v9`{WP<(-a~=d9?b5jr!ooVg<ABHVB%t1KKpTmQ
zxPHsEl%$vl$SP$6^L@P@Kld<JWtr5@W#cwEwzp=sOB+ZY%S8iIv)csE#k6n<57(`=
zj<V64jB82(=3w!<{Y!cI2s|&T=<aP@+<+`{NkiWQ<GI#?8wG4^ZeeK|#02oA^Cusn
zFlISTf0GSu_wBg!0`-iY^-kawTRTiU8myvFjre%4D><Vi(PPqxk$j}9V<Cat<4U90
zyxi6XNs@FDw{nXxj!eJX6eG@aZF~c=o}L+fk5zWPNTb+vt<>ZW=!q5FbuG|%aU^%f
zwWG+c>Vog^bkR%$f}-nil9IAvT=l;P%lR-9t$-UDTzY!?+r`xfD~c;%Rw9M%M~5NQ
z)_#3K!3V2^ILJ;}4EV5qE7r~^7<|gE=86l<Ni=dS<k(sjMe&e|iV7>J47(r=+BaQ4
zfEgH0seO6;lHK>y8p8nKOh)U%gKqns0YIPM2*4#2!GwKbL;mOZ5<AG&HLm^errno)
zXNObM@HM!tg`{a(MyQLa^hO8EeMtzeh5{=nem$M|L=Qp*Vr27prxJFgXvG&!r1pAk
z!(3a`fX_q+xDQ`K0xcAo5O$Gr#i<XY0n4X9;<df(h1j*OV;3`&lMY9OhXbdj3Tn>+
zq@{zYLeMM&Sf&^tWdtEZRVz61@{)hm2VaK5h7WRUyvNqJZ$V}o%TS%R4S4#t9W1qy
z(W|)G1a<S9c~W$!FQcarlthYdI#>!yJiTM*1%Pv#HXXgay@XlKC-ptxpMzrSliA^X
zW)0rGRn*A}o*cWz>ZzMhYGYkyxcc&=l2L_)g#apv%_9hZvw%iXeG1pgv(n@$n#=;I
zki2dx1r!Hydu!c(uKMz2w8UbH;$k7wSvdHCy#eJz?|M~mlI6GFYgw>vBM^sahf|SL
z2}Gf29`NzQ!Xl>AKqc542ouN+mNCF*(VC3-h4AQmBVOz799U?XutV^jPYO0SgkJVO
zKZf#Q5WKJ#RakK7f|PBAH$v^9Vvp^9i2Oz!1}X&k{Xg=?J&p0*c}&2U0QydLIHw&9
zxD2Vb(9PZ55fI8$3ohpEMUXip)tV8zP!3Jyal6jlZy3Zy-oAZ{#zJgKC@CrVdfHkO
zyi71vS64UM^UHZl$kFew8ey3r7itpiOkS`XCbNggbXlZjV40?-rZizD;XPR%o8E%f
zwhW0@{VZcNoc{&;?gz3Y%K!QM`Jb4A|9!rKEY^N4pr!U46$>2LoOW`7=#gbNQey@D
z1K6#HUF$t*leyt)vUQnNR~Ax<F890H>bcK;N3u1zh4T@Km7lHc?MIq{ABLA|*@e?m
z`MJ3xwfb9jXE^RR4b7UxxUeAM4i5!Jl>1z@@I-Rln$<Qy%TadyRT&Xc(IUo3NiWaQ
zMjQ@_KH39o>NK>p6uLXM=l#L(?!G2)tGcKAD0mIOy>thcuLq~Wf<GbUnEhy7HA0yA
zahD1qWEhKlL~Gm>z`J0+MlrtV=xC%KwEueOa(rgyRll>`4T5E>^&Iy<ogY~5U$z?!
zH&cH&zBBaH+CZ8;YZbJ%a@Z({!(G6BC_|YII1L!uo#cR&8%)(OS50YCfSqu_hZHYn
z*w#x!!A?mlzyVet#3Ek<6Bi9u-wVmiWtvFZj|u;C{<c8jJ%u`4NkGe?v!J?KT#bT?
zpBuq!wcKUBX;VN_MKTEwK0oQ2OMBJuR{v^)lW9YgFmb*?bROyvTko_3;O+;?G(Ela
zrmYwPT>uQdv7ilM8Xp@Qlh8YNP=!V>()^MW!>83<s8c3Ydz)ZHEs65ANI8PAcZj>V
zij%$^V3fm7LX;P0&<DR!&(I2CLTah?sBO>nsjz=iz)7h+z&S52nsL_-rNv}8oFhzp
z%joBVkdXQ{@K9{fFp|0$0LMiEm$2I1Q5q^(JV*sRt-OkgicR5oVqK&lw@Q-26vOMd
zS}e0Hmg(eW1v#jQaPu)@E_T>qs73DEQgIjmz7gX(ks^`Q*4jD@T#Sd;SwDFtmH8T0
zJ{UgKo~P4&INw?Oe^Fu!Dcr`VrIBa71Z+z$?bp!lW7i#sh@c_kB-Co1lC9rkH0^2e
z1cD+_2mgQ{#q(|Vm~~zki-ykQ+enm+dR%c%Jn=1^FmFqeVk4BsZI^=Oun?1&T$qig
zYaxFIF+m81$-Hl-w7EX8EdeQh%)Yze@sQnoy`b&yIi62YNAWpMcFKf(ITLS5=}IjJ
zG<d4RA?+xzg|`+Y(T`bRx=^g%ALs3)%<xG6M6`JL@HhR}G#WlrIba99KVVLNWdZ<e
zwk9U)GEx(w`RT!R!YnQ*9;hR9yW6oW)1SRvUBal4on{54_00*&F(~^5kvbuJ5y$`l
zwvIqMB(=7+C4eO%@DE|U5o=ooME)KKLQFonkS^PjZmcxj#j$0r;dLjHh<hJbE(%+B
zeljpOHy@ud%sd3d$nsGN@DQM>G9KNwIs`*6;`UewH{)sT=y2Lr0wbRAZN8p&CG7LM
zKeY}&Kfn3Ihj+0#`T0pZAv#kyHFWSWz;t)trX)b{(?;!TY3geA0^+LR4(ZLzC@V)F
zIaFVBGnRt8m<MgwAWT{)I5b%G?$S=4NAlgqT&;bVN!Z9ji{ea7KW4mJ68LCLLmAO!
zG5QmEVxnDm;|>;~Z@VlBC2LXbkmsSquvxj0Sq=H2XwMknTZu+*-zV(=3&2<Hh*ptC
zN{~(p-3Fi_V^XCx9;yr&NOlB+(T8q|6RNMhJv4+}L%ralfOOm3B|V@%W{$00gTyh7
zS_At1jMP!yUDu>+Z<-$T2YAk+DSl)jVsJj<h}iGXOx1u`+$NtF#cXfPHxlgWD!==k
zy^MB*x!sj^4sr7bthBtl96E#=$VHZzj-1>^3u-9&m%DhN7=fAtx+OTKF2FT{M0MnF
zZ_Y2^OPA#%f{hpD4~B7Sx0CFr0jn`p2VYTf-P;BpP)TPmhc$-crlgx+P>|$9Yjbz5
zMza<9HY$Ap`L;q-r?^-JAhHoLh;@DC<~cwrJC&{LdTtCcUPdbj4L;YK8kmn+hVpa-
z$fL%(aK0j2_2sV=+`~Y|%RprY^lm%71F#!_4Lom-vOGu60iS&dDHG-a)`>%RK?~O?
z3`E_!n4imJXJ+_SqdN^E<n+2IVLph?lUF@zg0Bb<z2Q5YLW`W`0V|F2t_8$(kkv(6
zI@bApp-}cCT?uX55==iBHL2=SKphZ%6_`9PD7m5_42or&H8uc)9E22-6Rjx-v1SMM
zn~<01cr5G>94VwqL5ZNj744xBG77{^U-FW+b*>sf{}|xVk3NE)BfPXjFYxWZw)1Bs
za27j%yf_9?4PHtI|Gq(OhX5dyx5aGS+0kLa+8M@At8L(0s%*??aDe?*rGymPFu%ko
zi3jt09@Oq6ckjb4>8s<trN06~xQ5iMJ3e8+wPR(@J>5CC_l<+GJ55K{=O`NFV*ktE
zxTTvwf%5SwynH6UwO@UlS5J-9`FPxUYFH)=8R)0mFe`fuJL?4#bKEr(YMs@r-Q;B}
z2X|ng_m>9;k05t}Iy?v{lx=D3myg(zjt6qsVs5pllP<LV;8XAGEZ+N*4ZXCr!UiO-
z0(LG=ttiaenSeYgpP$GH07y0<h?08uw5y$SjLYMB$#1lhA1_Hy_Nu};ysu}iE-S!c
z&T5LqN@3f&tq?`^+VY0F_cftD?K>{DN4r>2!bQPGZ`OBLq<)I(#8#0G*U?V*q_@4o
zlDIX-Jpz(!q%4wc)wZs~ckg;avNac)p~uLSMy8MgPHROc9sKzWA)z-SB$^XhIDRKQ
z#BR#KB?r~-0+3o!ku~xM|I5lzBWp@6L{ERt<vVi--tbv^_l$SWOsQEyExg|heNxqg
zh>==l3!7BPka#>7{mFd1!)esBRMT;a5H5pft?9fBk|?PkJ4gDsjV5k+G2-FSRQnBe
z&DH8g^+HNGj|@FMny3S}s|z8_+BzW(W`h}-tJqYkveC;a9!ujgVY8{-POe4~0qat5
zaqsIREYD%gDAd2FkG<3RMYYAnWd|qbDJRr+p1(99szOe3gr$Cc0=$TJ(pHviSFLNr
zu3NExb>n(%R^Q6fjzN98ZLfE1XK4S~-EQ6EhWGwx0sgxW{~z9XStx@PLMT<-QbT-P
zHdu?m-J80?ZTGHxZi22je6lumRFSH7(j|@!8BN)Y7S;eW0;BnKn|Cj=v?$bn9?SnG
zZy>k*DFGUw=>Rc;f#qTF(%uHNz0`fAy;niN%)`1IgK3-RmuSM3L9E&pt$z=ax>}hn
zl&9wDDKlGM9v+zV<>Bp){sNZ{%d%?)?HiMexsngO9UN2(pHiu7XoNLu^;}O;O;zIG
zZ0Qitsb8;<SF7J7TDS~g``0rpCR&ijc_toA6P0(jEjyq3BGBWy<0ta&V=$O@uP0iT
z7Rpq8#I?0GN_|Vw<WPTmKzIb}f)5{Vd|yyWQKk9asPE?Ck$T}*a!Sg0?&Mo;b;U7$
zU`rWxExZh4zb_~Gmip?&XS2BjnePm=kK-r=bRbL9q>jkubUQd0t0h==N<m?mqD!{F
z&w1U{!^49x6Ua8+ywH`@rD_er2)ZWWGjVu4UPC{pwA7QnvA=ksE$N&<`i<{a&+o<E
zF>%JETX<hck||`pSqUhVP50{=$Rz?p6>nv+IeB@b31NDvk8a=WsA_0XEnmIC$;qji
z+fMofnu{B43yP2$;d-N^$I(6-iLsunVYQlB#^c@%_MNF}YMCd=0K6AVGjxbqaPR;S
z>n%#ICh~;)_i)B?BZnt^M{2{&(DH7BgqB>~IH3(kvHA2%Yg1DbeatmMK_~=LzMiJK
z2eCzMoJODt6d^5n?W9LWUaB-!Y3dmhi}cOiA|lnJ+vPPA67_StqDe28mQtzpvvPC!
z+1p(Qw6wI?ROPoF937W_B%KR2Ha0mtk%Z*U&ht^bO4`)5PjAtn+7fB}`+NZx(KH;V
z7irfUXqQAU+8h|MBc|q$Kfz;U^_ISXOh!d-XwAn$E3{OCFEX;er1V2#MA^+WnUIVQ
zi@qZZ6tAQ8-=(Is=c=s>Xvi<_retpWed+3s7pdL-x2kCV3Q_*OTAlwzR{@C-%;7=;
z(OzwoqsZ;^s0KLXAiM8nw3fDZwnba&uE=H2ILG=WDgr%H(|uM#;KzgPob;g)chkXT
zB-Rm~eYp<--iST=uO$2bk-njJPB>l5XV@MR!e#U<U1YK<XXcX%lJ4Mj_`Em6Q<>=K
z6wSi;W)rJGWlKwdK0BWn{wJ6qAbMGRwLX2qigu?+g>vFD8VY^The({Cm&XoOFa|Fx
z3K@SM-3iS%-=ZSw8#34#Wb8?)NSa%sxta^r1H3+)%7c0FJP(ObPh4X30vQc$yiw$o
zvJHepz}ufuNG75*$o1Qf87wPEMTV3Z5FO=Tk-?7e2Rz;g#|5u{GX&cKSWw)J{rFC0
zK9lZnnqc#*%3qGA<sO2Ks6Qh1rR1H)NE4md4!P7>&A0f2@Juhp1ew+cFaKYpD8UQQ
zceS?PlL3&x1|WeYg^=>}27*<REp|VomXa)XvpU1ow;`!sq-awwJ&^Sg$V+(feEYY6
zQsjPF5q(q`t!?quqhi0MKpI6X{@Zf^x>F1={-Q_^r*<9(g6KhQ6M0tO{VMPQ^@tz{
zG*?0pVxJWN^oCcPb=AJ65j$Z-gfJI`+la3m+M$e~x2a=a&mS)C<~T9o%DskkND1yE
z?1(PL(+ZlQ;RtaAG_<V;SsU^shx<+uhVprO;jC0+1V6imo{P!E#DpCpkbovZ{U?KR
z5SqmxtTiwz9(iuiYeYyOq|XeLuRBsJLB>P+Ks$mBC|HKw2G;c&#OCX+>s>^~>OFBS
zzQWsc@WolAJH>+LDjfSr=g@Q=p_U>0A`Ag&gWQ+0aZ~e?Br>5)0t9O=WbJ{70MCWE
za{^ZP;V59Kf|1q<%mMJTZgWt+Q!N?pU{BQqpwN)9qmh!`1bW4^y?BtWbz2hTu~Cn{
z9x6!;fRu@-<ARtF83#%g(P*N07O28ez%P+PwQbN?q!qJiQ3R}Ax>hb5I%_JgXXy{$
zgtmi;u)Yz3d<UiN4>i)l+(>N;(6db8*UXg>$xIzweSWmQ77F!j7#*<3{?!ViQ>|%J
z|5qLd_m{_EeTcBiD3nj=g_Ba;u;$eh$5vq+)%(usG>K;<BrxaqyILZ$Gdic<$A~z<
zHAgqo@HSTC#%L)eqlDNFJq#QQW`A25=ojp_R!Pgk)^+c%64xfu1f9bL)QP>}OJDU(
z9=xI9XG=JA(p5~IEd<Ga$hS|nr<UOHB3x|A1j3BkdZGm(1??h5y8i|`7;o-dc9C&-
z!VbV$7jSpRxa-LzG<)0H)4zTD2R-EF<#l8Q+!y|cB=MEmp_cpeHD<%B?Gv4&*tYe)
zUHy}5s$90c`TECkex<?s)VlnePp=wPER3DKMwm!`z`u#`?0Pe1xeMl?PXf^+%>f%9
zdU|u(0^(^4=t!T-ZC}sBdv1*;W@ct~2y`?l(f8=+>h^tJrkEh7ebiS1nsLyQ({0Fp
z&&n#h5-{V0W$WSjw#<6wwDoJ{X>mPAd4?%raV-zNh4eo9l!B|ut$#kIczbiE5xRj{
zkSV}a=y!U_ZcCF0<-eNbwU#uUXKkHURQ2{Pxqp_>b$n`aQpc<^2UY_i-Eb`+X#=Xx
zT}qSTc?UIXg0i_%JNkHP+0w)~$C3Eb{(Or=2eAwH++G~+I=E-kWjy`k3&N7MP5oM0
z^1`;h+bp5wN9OE>@duOAsZ-g?T1L^TEuQLS52$EY<K&oCx7QQ(n-a7<@y%y5qP|R=
zMK$#RS5dTku%V#=(W<RNv!Y)d;iFtCMf=|YBQyG%9baV<rW=a%=%!=zH1b{f9bR;L
zXwLt%^N3CWzV{9FkL7S{(8UgnWyh}>@#q=F2G@?HraD=dSQ26w?(-9t0ln7RM5ZZN
za=Y<o_~n)*X>4oK_w-G4v+I6e8$Y`MP{-|gSets$%-D1|Rd4a(L~bqNw&H$4HGXcl
z83}o1&Yr#pixjEj2ljb?;Wgep1`F-54b*s9Q6mZICLx{wtVe@ib8>SNN<9}B1&u2G
z<*`7tY~LqsL`Fsir5}0U;iTFBLz*!f!c}3TIWTN}L-J=+OuMq)aBQgUuK=1ODlfMk
zo$F8qnBKJnH#Ie+OvRYi-j1j;4W&rJsoB|Vg)x9d%{9p$WEx3N`wsZfGSbq5=AH38
zW5WlidsmhRK81#=ma7Fre9`<t_OL`ie7?hY^R-~TRXy#@j}q0LHK$vTB2rFl#IFN5
zYDv$HJGtz??0<w6hUwM(M%;-*zf_EVk)ZQ#$*{-obb6u}qH6_PCKjuuUU+W~Vn8f2
z`;IxE6MtmSt_lXD)!{lIFp}!?rM0=a*)mc)*TMl??<M35OdJf~3WonNgR_mtS;o@R
zGL*1b3KB=v<cO_tva{nzjRE?O8USgnLI-o7w$one!XRPzs=p_;n=+h2sqn@y6PZrY
zYPd?nu#10BQbt}kbh#0xExViuqAaFrkU<ty=k9Q$R)-L7v9t3%!tqwK3o?Or#4IX9
zxPKm4Cq$dUq?z;Z9?lxN@moyQTkA8D=NJEws+gNB-U?U{GH{`J%^IR4k_MpXj>e{_
zBsm7GN2N2wjYGggV<8HrX%vl)j`lt(r$S?a_kSd1iPV}X>Qo<9lm|#(%|M}th@z29
z;26-;3)CXCw>iV~T+Oiy1=kJ2mWxT^S3+bEX&)bSDyA=p=s6F*h{CjMnu7)$q&K|g
z&GBEf&m(8`X3JY0;LKp8p`n=rX`mo9&Rsnrjf|K{u4@uVh+?V%n!f$yS!hU@R=hbU
zOis_{g|h+&mtDDVMI~|g4POImI#lRzvJa-vH$l%8c9d`-LT8(TK_4VWxPT@Bk<Uy*
z_7Ps{G|C34T}Jj4A}!N_l}9oy^ekdHbPKN`xf&7Lf+~+AH@EGP{tlw>t}hRyhz&Ac
z`8~MPS=pGaTrQeY*R~D;>dkCzVSJzJ&K!Ljp;k8fL$-Zd{qI<}|MLd2NdLh?{-62y
z-)BCy14`Tif{4@Gl3P*XeO=}0n1&Rs=j=toxU!2ftsyrLpM9sq`m5%H7KYfYc46ev
zJuLPjlP)@e&aU9Z#jhehFY)O&({nAI>c{8Tjl4vsy1VAz@J|hS7ERTgk><CG2MCQM
zJ|l_sh~;9CY#}di{k^B1P|Wf#4xiqT@G-zndNO_&lnG&Do>!~<X3Tq9<=VcJ3=A<-
z?)&#uvDaASu5Az>3Pzf&<VbHHq@q&$rnYXgiOYTe@yh$3KYv=0IK_(JKfQ83sCsyw
z6vcN<l?%5t#(2h$xu>*8+3Nc#`7a^d`g#{eA2iOCiN3!2em9#+;v4*z9=s0ztN6*y
z*}~ry9$V?VM{+{0sN6}Es~oNgk#aUeYow~CSdsXoxXe$TIg@z!^jG(e548#GrQ~{o
zl|bZ0f{5)IzXW6qz<*dZ5(5Jb4<~v*efrdWYa4e-=)Mgy2=z3zytF0p1)YaiZ>#W_
zW()VY_^p({V4nj-W#<r@f!H=c5$Mo`leV+V8mQz+h~HcHN?ukrXq|@cq}QjOsjh~k
z+S4rKYuj<(#~s*&h3k_%_R~Ij$Mi^uPNrz0SU9@D@O9z?9WISLQdFN!h{NXPCaQ8J
zyEzlwlKEqy-M=iwZ1P2WT#D?Nm6Z9$XSDt04vn>{`Y|!Gq;9Y>GvBVR-cpo){^Ins
zb9@;W{tM>1g~ex`v+?oqRaFjhTs<D^+dh&B2?-{mI3I%`hfT8&Ed+CSA&)O3%CW=F
zAOOTpX|+xrd4A1wjlaIFQ8<ZD^~vwcdTVY*uNBuR?TEX7wuO53xs0UFk3QO4QTe>T
zKOe7iDD)IH&RwU}T@!zPd%OO`@#BNbS5fDibhb4vd-0l^m&&iX7?s*FKTt?79}FBy
zH8R|L_hQSqgDDK+ICb{fUYq<zoZoey<Hyc!a)q(?lpd&IjOcHOY%y0q&lJl%u)TeD
z!fxNK-Q7Ht+$2sOiG{%CAU>b*fODmeZl88D@3PMC2?-tDHq!0fo8w@Or+7-|?@i=_
z$+j~QVbP#_GH>v<eDJrO#Gav^BmMofew_xNnPQ_Ak|uY#a!yPY6Lr2Eel9t?@tBil
z+VQ~WYsY5R^*U{vbu9bw=FRq`C-1yL5FK<^YL8Ur!RKXtJ4Fx}{jQgL|MYC2%;$zr
zcRT@w+IaeZ^LFela%)OZx_a6cC~!|Zv3{Qb5p0%L2K>DC^JAZW^!xJT&4G)5ibGV#
zw=d5QEP@R6;Z%2ScfKu7Sw%%HQSMwHNc${$3o^CxZ8AbRwI@a+Z9}V#*`^<PR17|<
zf}e4RGjQ%29!g<4|6fPS(}stQGqiGLXa%gA@~nFtR(=}@(;ZPuQNB$@E1(JuKTzsl
zZX@XHoATM)5H2$TOM|>*3gY9L7AM+df#a*mn_d`i5%E~d5wPutZ5gI$Ea>3UB|O%2
z$UOIUCnP3LKsIgxeRVM&&|85&qa+?kMRoxJbwJGRfF#qmHrLsOgjQ&zDQy4FQt$)E
zBHj8(!Hy3%eP4^7I+dNA%zDn}Uax^Oov5Rne58QdjT<*g^M&EEQ0`p~mt&!#xmFeM
z<3ic{fIqz+rKk1be3e1&M~)n6hl?-3g{o}wYo{Dx>&%raSJ2V{3~hi<Qm;STf92Y>
zocD1RxIPd+sS@-`II2k?npN(}x7E0D<7t#a5||&|5#I)!Bq3VV$pY%xRHY9$uHK=t
zeZOmG{~b!{DGaGTyV#zj(3YmbUF#ZD9sly>!P~cPjX@20Z+W_pfO*MkKv}Ld(_az-
z(G;DkL5Km_LPDxqnyDr2sZ;TwSxM0$+99hK<1%{A1N!w@JyRVbR>R)s{_OTcF$cGd
zw}OzF35{GW)V<+oIge_R0=;I2dLIZ!R=Z7M#|C0BFJCsn+-9Zs<XVP-Nx=`OA_URR
zwq=`%um?pCM0WS?-+xbY?O`m?3%Ad5>(yzvgC;8jz)#%k)9k4L!6&+N(?Yp)*}`f=
z2+k$n9X3`Ti$lx!^{ZFNS0~ytBCU&{FKr2zwD|Gy1q8^NP|h@kXc2Re9m>ebar}Od
zOfg8FRI^RxVJ4ETd-7mcUBIwoQMOg&`}fI!w$i{}vbwAaK^6eIiA`BF8cp;EWjDI<
z{o}`viFr^+wt?=kHA#_Cji9G{;lhPft=v@D`J`7eTR|K1p2CRSo&oZPIHnZf3Azh0
zX&uAaf~MJ;^rDWcq=xA14E9Nvx`!H__QZ!jE;bgph?BrIIMdoDa@}9%uN<WpiT@EN
zo#whY0i&?!%1QvWN(xjPB$0;PZ~thnyie3vZ5i1Z-o1PG!x~Sx9Mm$!Ys)hP5DJV#
zFFo~srlJ27{SjY1uT2a%&s(VOdkTCEz}uWdk-aCJ;nZi(CKVJ2$wvv@6Lp-dx@!I~
zzo)Ip$(9o;m)aqH_t_VvE59iCEn4qt=iAiVGxAl!9OXXvvRfQ>2x)d3E5sM|*?h04
zm;ez$Z8HnRaLe}e9})bP3#}@O=0NmJz)i#$(fE7-RwzZk%u}=5syhcADPY|UN?rn(
zLXBZ2D;Jjnr&f-VmR3YkA8f9PKda|_9s1s_{UvT{2{IvKm38%C8+dSBXH!<QmjMA9
z?(W5iiNd}TR$*aHfk@8AE&LM5i=0Cxp^biHaZvtxqtTtBk1@4xlrO9fI*5!s5*l_8
znSZklo78>;UbvZ+)0Jl(34tYXcBs;_Hk1>rr@EtH>pxGeK$=0h4>lfroeUB0#RQ`5
zSzyXWuAtN730@F0JcjIdwCp)5vGSQL*G>uEYtey^?l6vKTAb|S)1DK~)-7^OvFyNW
zVsl-<hZXYT6XH3t0~ukriq)aoG*}flPzA1EYhOpSCz;AhnB}=HS^$}&{Tl8QqL^JT
z4pdBOph9)=4~qm@uS#DbrZ&`l>2!Wi2D5DFT`b2LKM~BtaM<HHdj{qKoUA@lXygss
zo5HRXWzf)^Vr2BHH>^2y=#Ns*`^2RF_F9-35oYl9+qZ47;qrd$UL$Ac0-vLdT<8d)
z$A)=KdZyfE8(B;#;Le~ninN<rhFz7u7cX7Pbr@IHEq4CF7vuo3BUQI3k7<0eD_g6~
z(<9GX(7HRmQF2>H+4Z9bP=GRb?*{fvh4$HWO!pPF6}#AHXlAqMXUFo%fxQh54r&!V
z`j8}9&Vxk2yV%n0Ew>VoXlgDk!wKz(I(9;IBJL}n3NhqS%>jabnV75*vdI!6+|$yH
zIa%h*mmy}l$viM-+5(}tCT6Nz4Hh6m1e4kjElN6P-+|XHTpf*MPUu)|C>S@sPA};x
zR_nXJugIw*M%)crer)+YoJlPyFuj(WYl{=`!G{VVudQM;;W)Y%lEWAV=Itn$O)-}&
znBvj;NRo>+#KGI(DZhi89}CgZ2tE4A|LU^fzmm@<F5SX0kVj*KV?MbqwrRA*qfd5O
z38-agslm3Gml7MlZ$^t?*g8Cs4V<#r6DLmC$&GD+YNR{M#E)ql_A}M-38b~90t=I-
zLC|B<EzFT)2?89~8hd>Y2y~rd9NE>=tMaBmem$eP1$8&S!<Z5R6<LDNU%q^?Xo`K3
zXVvu)R(9lEW!utJPm<^6n#)=(!dlI9fE=9U3jfrA9SpBG-M4RFisMwbR;fD?$yDfZ
zlN%tEe@}LXr4ex$3xs-D0!9xR?1hg{Duhh%>(KaxuAeXgO|UB0To)dyB`ba5GX=GN
zu4z4uqF4WkrPbrKv}DZ~0qY2f%>@-CLx1@2fj3+(>plmY20jWP0RgP`RH{P-L|O6L
zbJ_FubD;QlvAF2AUQNF9oER-#ElFw)gOMdb26WM*1>h<laqR~zsv1}sdOUjwb|iLK
zo(cMoi7$Y2Swd8ufZZsel|w^AJ27hGcX_IMMnrd>bsB6BcbytSG9EYt6B_nj&32o@
z22O0VEk_500kNS7SY|f0)L~2?sC<8-86X{s))oc_p8{<!8rvEteU#s_z0MM_{Oy_2
zb(@&S#sYBvZOQk_$va1Ne!8x_MmZBzqx)WtrT+cZaz3}!sr-QkOkH)*<uv!zIlT^(
zm?!Iqpt=x5hb4JwDoOs}P|WD{<(Wo|m@NR<kBBcSyBZ1&-K5a3)p<sQ)IrRvPkt5E
zqG)3$8i3tyNFY$s3Gq}$`s_b&z)jB*o8`WuME4h)NWG;MY|jdyZuzSPm}pB22@Z~j
zw~+{~0v?fFXV{lx9t2@31JZ=c)DZM&vB47B3m{<EY!{AJP&9P?`uq^AmP>^L=EjD_
zxkEE)<X+R5jA)HRPhr*BV3%CueO_c=p)kHyY#Mg<#8^rSMAiqjzVY*`&9$nAEqNhB
zg>|pSkv%}dd3@n=g}{(AC4_NaNv>NLuxMo^@cDRLn3unD^5n@)UZ3D~m*5R2E{_DS
z?JXkS7GQ?iD)b8$oP>xM|3NXR0KhDLK(Ww>I;N|q-~Rw8k6yz4zM|Od^CWp#_>jQB
zK<gfVdQmnpF&!+j5ru=i1v0}4sBN&ri=(#v#&w<b=sUkUN9;^oHXu93jKJiOx_zsS
z=YrX*0)XZnDd3tfILDuAAM{QtEFJ`IbUh(#YHXF5b9}bUDK}mmCi;iH>*bVtx&Rul
zm=S2g<bLiUX@JmBycU?@>RfBcYsS=fLUFtH>>sv_>TQS{F0&tJKgVBUZzvE6)6I!)
zhVVGK>}>Zo;lA`A>BMJDy)iH1hY{j#d8GYM;i(EL{ui=;{(PVq{S(+FVcC!Ku#5Oz
zuxDHzG{H%3lV5o#<D{l2cojTNZAcoU!;fv|XmMqx%(9vRDUy}dVsYz5Go`iJ!4++D
zZgL(j3>3UJdUNZ8Ql1S@BpX$(2SS?@c}bNT$LL0^vUrD6F^GZHh5G_DxhqqasdGoX
z4}`@PW!Gf@LVPq^{r2i4F^`|}rrYxLVs$~d*?C4$M~=p&a7zu}7cW+s_Ip+9_^ktC
zo$C<-fFtg@5E$&KQuno=Co|dIg^Ta<(|H-5ZHRKKuV)~U67fz&eIj<lFPTzI>mybt
zTJ-;#?|FSdv}VH0%-<~|p<xdWqJWL>(D%~CE<mW0#li;PvSH_;Yx)S(GIROzbRRKK
z+-~@;|GKhLc&*4V#=bl!=UT@7%o|t$%i_5D*u}8A^(ENIQS>>xjsgVE$74niNYBYx
zZ)-I2EsVrFUw?{#e;d=aI#0@O<7=#LW46JcIeV6**`UJ^FQhZjXtgs}C>=rGnum9l
zL4s^&CFr@~jBW<-2Z@z{4q`#C4Y9n~GR^L4O;uw@H^VlGU?WZg$j-1CsLpK2$JaLv
z046rxwsiFlt)NX^<`nRQ<P=?T<Emr0??1T#J5F?ECo!2<`qR6@eveH7buc6`(hQ`*
zLSRxC04l9pi`l|lTDB$+YU)A2?vS7?Z$iMw%nEd==;=iqs>mWqE#V_B(H-?-n{WTA
z<D?F?f4ogmvb9F;2B-fuG!zmVIvU<*LG6RZVi%y5m<$`sT}jbSVm*BzoD^Q_f?MbV
zN){V`gkEF}><vIWg15b#Sc2-R=U~CiVcKOB6hedb%GZ8=d4}K}0qgGJmd7BK9MiNJ
zC@n#dDu}^lWF%uQE5@h}lzQ;nkIJFPG1(C5W?HB>n1`P2MZ3Z-rq+C0eR?tHCY7Wv
z9!lLp2Xv&MZ95SA6GTYYkas*_w+0LV8$Z>PpQfB3;|klya?KhUn3<VPT>KN`<Hwpl
z+>E$pj)ihS>s!gjGeUz2Bv>*2B;TWq_yCEuD!#Oyafb;wsu0BWi_kYX&04yjism6a
zVj*T|(vFzpG^3Ym(IzA6G#$(Y&xp`{{rWZA&zSWy{{l-^>q}FvK<u{VS!*MS+`zzq
zH?kfQ8vOV5n>VV^%gePJk!ciowx0@lB89(4M7?PVD2lQ1dk!4Ai-n!<X@P;qwx{J-
zpdxMo`Q@c6C857zGyg}pqiSQ`E3B+{fBg7?ED(~Z&Twk2SoOANXs0RSBw_}(0V$+G
z8i*M>clvY!C}Q2(8*!5Q@7}$WpdG;35J%3)M~k%0ufy{YPSevP^+Q(~EE^fztph4A
zjrdGRfdJDSQ&dxwvCU>_is8IFCA6_|e4S}F02}x(lt@rEr9nGVA&gr;w9TRo`pJXn
z2WLZJLY0&PVN=5GMo%cFrIEW2mpgpqh&rIqTtKReV?S;#k~<nTtRS&s0qVhqMWn!N
zoG%3E5z|eEd<g)HyXM00Lnr>J7OV0>#9bdPD%j`<xT|(<9XiJwr?DCUVgSg61MVoL
z>$xQ@ZP}KpcKGPg<?&96vs`t`m7TPH-(joWnH1ZBQVIH=0Z>aJ2$)q!sB^~XwzM?6
zgoH%p3FdyAu?~X1WVE=OIuylAXi9h-33R;QB*ps*LWpaHKI<tOng~L{owqazu(^7;
zpcLk&n`obeDC^x9Ns9jg=zxXrfM;eP$cMpo>;h5=K?C|-mzDa%hslP9hDiO#sZ*eI
z=+Gg=4wIs!Y#^tD9XX+8xb*{96Ky&G1;0e9s0{rwJ?Q3p!{?`IXi_0pO+e?q1)%Zi
zGiOHUA9J4ZLp<k_v_C5_5KydPzZ>2IRE{7fWW$k&({vOv23&v#=fZTKHmMPZtZ))u
zw<r~o6J%@z83S;Og=WhHv|6EXyv<N+nmrCMq#Z0L*Ll_u3H4X6UOh}j6#{36i(Gs3
zQwl-dB5q6Rfc}!fcGe$ox2#U+0Kl*WV7CV9@wQAOX+VN6Ad*2HhycdYtu;%)k0YX7
zz$cVJue2Kq`WWY5hY*GamK*_W(4NOb=`Iia2kXT2`mL?yonAT$<s~8zDUVcqK;s;C
zIGIx%pOD~U)4K$Z0^u9z;kW7SR2kUW-cYZ6;wvp&0Dx?o$NG|Ro@SY6Nuyy+Isjal
zZspMK>eY~es~PH+rAD=ul5rnD&h=@eC?_B{MFlWPf68x%Fn~P?p;+pDYMt|}r>AsG
zc_Qm4`b&xmon~T;A|W)NPHu$WvIVdMkb@mR49y9e{zHj<CV}o5GIGN2-;Z+5TM`S(
zcPYvJrB+b5$0E^M*`{wnpCODM=Z~9L$=dT7carZPE=tK5)-t%-SozVTN1?RausI@&
zI&Qa8@85LynY-VW%!%tic0}sug}*=tcQvveVJ#4g5eLQjclsmVhIgp`ly0a~T>w2G
zn%4a~vS+0QVPq)FS*LjpzxhkVpipJ!|DFo<o8qs>{Ttr)i~aS7&O)I&Vroa&rjH%2
zGv=+fGL>LHcK7cs2J_hDYeP}?o&L@fi08nyoN}T@9rpg4p3oR!&Cvl*&S}=U@494e
z?04l}0~G7|zr$t@UY#`G=doe%*!LaxQs-H;)Sg{0KMMaz$^V9X`X87t?-T66JV0Dh
zZHy7e480X(<KU13_Ugw;MX?W1ASn14oN{`pSq$3+A%h_;**pxSCTyF98ju%_74y<K
zR?VlcMgO^%SRoFGB9Jhk=Uy|n^6rwf!*JDc;0dJ>su5uW0id2%3DzX`*#K!k?3=bC
z1Mvqxij_O3`fHTlCrGeIN^N^<6I6)ToPR%{7kVTGyHf0m`hc`U_FyakPcjCX+Z7&|
z;U}td{=wBfJ4$*Kin+#Vz#|EIop-U2f09IJiur2S0EphcecK#bl2Go(LWsCeXK<!-
zZISFJy7xToUq^}k#zT1%f_V^={Cl1Y+H<QGP(;#*I6MrkgfX_`b<=<@O$81Pf)b?I
z=Gg1ca?BcI=+;xU^YOp}o;Z0D^XuCo(5v2nGAkG|5R@8O3)?^wr$Qb<II!-Vl&#Gf
zFESuQVnu{@$>`S#%2TKR<!pus{R$W)kZBW+;#OsZCxrX});CMc5m>u=$eECaI^xkm
zr)X&-twobsuK1QCtQ2x*n(Zd79~98&dqBkG0wQQEMA%d)D-dV|L~|0|P)&xOy@ApG
z*RTJ28tYZqt8@X45QM;Qhu>I)%94sxw@@`n5x3NPjOXgrdq^^a)CwE_lc1s}6>P#b
zDE<eZQQs>o5w@3y5;NI(4~s0)R9}$>z>wxrk9DA_O!9kj4a)a#!e;CU0c)+yZ<MG0
z;t^CZHGrrvCYYr3CJ)f-$dtG)0*(kdb?OuoM9QkFyBKo2yuR0FE|WPt&Pv#3;KR)W
zkZ0UdK}ZW^0%>+-#hFvD_&x;bo;+(6Ik`800iHg4)`r&=g;F=A({TS`zrSq&nKw{q
zp_NzXMmC$h0O>HJ5nesB{`sMkSxhl}R#5a}7vKRd$-s+rwL{j`2f}FaUc6V8Vx5%1
zyT6sjt9z-cSJ2I{<L7m2<C8rHt~pNL=YQBFb^Q2oa1P|B%L*HR)Az_ofi-}n5($R0
zu=9u5Pn`+y^!o`urTe!F4-LYe!=WpI)9g?dESe+F!-JQU&)C>Qv{&m>t~UT#?!o5Q
zs$6*+VLre?i?eUy3-Y@f&#|eezjyN1JH>Ar`{{MCcMR;V@po$H;P@M5euu#d$;#lU
z+{c@lZ{N9d=HWUlmnBrekS5yzEwmP5EJHZ8)nUp2q8#zPRJqrFzwL<N4z-JofByVA
zUH&HQKB729ARx1M;?HB`P^?#xG8<HU_pl3K$Y-3_p%9nBLQ25{q}PYtaSOezifmfu
z0YLMju}Hl-+Bpi3Ph)#jw)N&*mF&9{O<;sAkh?wB{^`-I_*EM>!*)$3G(3eY3K|<x
zmg`XTAcBdQfuNuuW`qaQ>p45pvk9O%EI5X`3>dq-#REqfT7l;{+)I!&+CQfpzZZz~
z1dpx1XH@}|%Wu`mgKh?}fCp0^2gY?NI094D2Rvj)vCF(FJkNzwGwWNn^^P3?Pn+X7
z)6WSM8d?N6GeaYzMNrY57QsWHN&&J6*t@l|?R80dVY^$vIZruSg1K41j>!*ei)M9F
zdaPgmbZj7C!83@O;K>@s0+IYi6_fz_WP$R5nTGj>q8WKqRw6W6N<ilkGllez;6B(c
zT(}PCWv=xe9zc&CJd*B4$YK?uT0|(xrI`T&P@+{<0=0&j$*}DiVV;nO=>3$~q~{By
z{k!t4kHI5+j?kw<9DfLG5YjtfQ%ydf-IWd(1O%Z(?jUpkDU%E@j-Cwdd<7`t5&`$Y
zSfLo;r=sSOf_4BB=-&N8nj1h_^ILac+Z^HcdItatyi3n>)nuwGTN+T;i*60C3d&)Y
zF|8w^&}D#enM1&lk(XBkoYj`0E!YPSGw9%@{KvGFU0-zp17_Y7dwsQ^1*!x%YCd?j
zAi{uJk5Llp-qN+BhYtM!k=ixT$XStnD9?SoaMQ=R&jtdq@RDZddwcT9s(_2ku~g+v
z&^Ee=276WmQrOO_$Z<*wNH>odEfFlF0mdtUIB)t;{s2ycIXnDN*Pjufew*Eke=|cU
z+e+0daZLe65}==p{}e#iMEGZlmM{}=n%3EbC){wzu>SKMZ?YViEx^P@jsduWa2_qB
z4YSo3XZ4zZ2wMc^(#&N82#tF%i}$|%ZAj`!gJ;4DWSbFzbl2<oR#{2zu`LS;){8R*
zM?^Siq*(*WP!S&Ipf7G+wlP=x@%i7zWr6>xE4xbEmTemCAQl$qb$v2;X66DCN6mJC
z9Y{46Gf)O<$N%z;=+%a3$WwYf>AFSgSmW$2RhSqnGIVapn*G^kivMMNrOvY-&AN<v
zU97XA@<BHP(KV7Qf9uwBvsLgGE*YVJO~GB50w|SYOrRvU_OP%>h0>cD4a&Tt4hi1N
z2|!!_*HZ&SCRTgy)o2}TkG|Y!2M;AHUNWHeF%#zH&0-z==fo)drR%r53LsD-)gqHQ
zJpGaeHZzAVLU&-11K9UgOGaqy%yPg|m9P#v11lB_bcCvp0qez!H;^JKcN2oi;&>8n
z0wNI7Lj-S4fO<<Cq@%u&2pWg}nNn}bw2SrL(?8;Tcji&#(NAyEGT?kKgkO(exP=+j
zY7A#)VsZ}U8whMV3v2{zs)XDHsAJfUXlLy;)^q{!4kQOE;3P8GHSiAsltf%L@`-_4
z<fyj$=bogdVdX)cst)W2GX&S0;3yV-g}Kwk^9p?qE%FdYu#mM`MmnWOX^-Z?cv%OH
zv&~yrrUuINmj}GOVyuI(SlD7J1kebnMj5>}G7*Xp+9|0JS4CVGl3@qtXv@l<3%-u7
zK1D5w+O7Of=Fwe*#c9nkS3!alHU}J%gN%c1^mMhhcbgGbxK_CB71tXm0)f!I2_#Ds
zI86pT$cv+62_AVz)Zq$)g@^-6$&G;k`ISX6CLL0^1sD)gPk}CMKkoY<^{$dmFTX|g
z)<*4j`u*OLnJ~AZ&9-y}05+h0SA&>0FUCMmp8@@6OVC^sZQz*zyp48{++Wl9S3;Ng
z6dheUICoWWWs<Rvf%A!0_^2jN0@zN-?F1$l3%x%J_p`f(y>idbHI?DLIZQ8}KYzF|
z_G1lLgv&*JTuCkCS3KTzcu@{`ZI@7%2O3aA-Yq>SExEN4muEYG2bie^WW7sX85nv}
z={&^cv0Qm5(wI!5Gyk-cy)7$w-uybUOKR6?;@-+OzzrUKCQqP#8;1u!sR0FG$GQ0q
zVbhq=r`c2kzZ=eWCxVy;8xKm(RTib4CGa*39V<+~dFAqJ(KqhqXC9|<u*|s7qB^*7
zmt*~UAQ1$Ih9*MakmME#c8y-UR6QvVja{Z{Xp)cEjl56p2U!6;;f<C+zH#&BBJ6V=
z7RBEv!j5iVb!TE?y49X$3Mz~a^K^B~Jk52}Y{gR5pH64|zMBmQRUKm`ak8?q_LPzq
zTub|%MQznBXFbQl^jU(cNsYqxSG|6Q>w`3A-9<L%3@CrjGBDte%`;YtTodNm!UZ)C
z6j=*6K{0a4tC*|UKPbTVnJ)L&uV0OgMOX%xS`FVMK7i&wzIt`R7hv;1#kAcjo?9|o
zE93ByCbF<Ca%rBe;`yci`@<4@R$RH18Y1(TGXeuYztoi}OFTsJrnb^@x^fuTI`zN`
zDS1c{gy7pxW?c{b_WkUVU*!vqZw|4YM#8;7CX6e3W(u%%{DLH%9ai}Ih+~nx+<kg+
zS5-#Ol@A>lpecSJ-26<z;o5^#A$>D(D?V)&A*<V$m-om6r$c)7igMT7@@tMWvU#0;
zH8#9gj+Guf4ekC|5D6)rXxb47Q3RH0Fd;LO^!=mM-N*0c60O$tdI!$1z5Mj)6UU)3
zoqjfgDDansXKb(HFEaJGZ-RiyI=5zU`JVeFSPD=uF}2bORokbvMSy~;$}D?(JEFqZ
zmX#|Ezk(Mm10pGuxyOa#%}LUfD;LO{iuGDfg_K*hFRs5EVnl#xwr=CTiha?`mJuLn
zItFtMrKQwWqUnsot5>hIavr`z3T5CBm3$16ZTbo)dWqRkn<Vj?)SOE30)&sK2=3jx
zhct$s<kdTuHzx0yk{^}i+GC>rXL|(f@l3MBdh2BxzF=_vxr^ARDH$Z;G8eB-(93}S
z@)*paI+S=6;uS!#g+SVd`skE%`;7`K=s78pI$8MnH`!yaU%yUl2(^O39f>s<U6|mc
zUSG*@Y|v>|{b_hIaR@op=me|asRG~%^XA9xy5dnQB7vZqNRYiEG1W5R$bM{;qHD@A
zK?l<xrC^3gv=BLw{d7&pF?7dvuxdFE;5aM@nH+`O?Jw&}+86b8HE3&T{S1~Vgik`x
zHC?%K9sAXfJ9c-0`)U@~ixsaJcZ0G!F<XVdw=qwInQuSg9Yku@JBdbE<K?Ms+AU<)
z<CHtiohw&R{q#FpDzmPU!`@Db4p1<5;cpYRB1c@IbDkwmqVwmug3_BE@3<D`(Vf%=
zM`>uUVaz7fNcA>H0V24TC1%PTeq)=)#LWEP+WXF^sM2;z4BfW*Q`;6K*tP|jKqLwX
zXsc)uL<K>Dppvst1j&XLyH!97BnK4)0RhPwv{4X=k_sq-N|LOELh|ev^qV^~>(1P}
zX4d^P-&+1sDNdd9o+s>QKYN#M=$tPU+p%NEtn80$%eNNtKU~js;327a>2+vENLhs*
z<pE_D$kNpN8|%}|>UR^$4hku7Pjnv@Hvvncn1a3D)L^J{l#ic39+L-p6k%l`pwpu;
z(@~^j92lVQ)&riv3A$OsvmkS>vvF#r$^L1~!3H!MBgqoS^c@Z?<y$g{r1aCh)rs*?
zuCUm02dphBM<#P{bam??r%EU0#G%O}wb}!M8W&s*RYzvGX&43U0RVv*D`|c0)ImxZ
zXkVoU22(OZvJ}4>{Y>WU{1VmEeSx}nqK@3F<zVZ5YIib5H4z6rQc3IA%Ti%We2?Y?
zS0$}px^v0ktg|5E#^^7(Om6n$ZA=&a(?4XlE)iNwd7gCa$-uDoN#kF2yq=^o2ZeK&
zb&RiN^ALCU$h?BR848sUAONb3V+B7J^*AcL`_2<X2`I+2&=fr&efFa_9ppR71?~Gy
zgs1G8Qjl<pR&A|u%*n8k=3!uFyi~FxWPA5praiUsXH&c%v>vtOORsg9*j-Ve+vZQU
z0dS8)YB9kxxan<3xX+2xkYEvW{dyUoA8LLIldy)!Y#SnhK45Q==xnSR5gZ%`28ROO
zs&JO|d&y7Vehn}6zR{s}C{H^B6|tS&ZBtzSv86!WFWK#Rgw>;v)0?C+o2!wy3sHNE
zTeUm|f_tSviwrsXQAP#S_Nt*tI13%3!dKrv(nI{1!l3|+xuKL=4XuM{03}x}Yf&*%
zYoLDdK0O>YF^CI~DY3{H$5zIiKzK~R@Jy4QW>C73R0Tl5>j660jeR-|IHNbnIDA*e
zw~7F4KU9xypb>fT$Y|YK$9)E6JHeJA^|05n*k{{5^aIc+J4@glXsbO2HR25FnL3mu
zYMEB&RR)dq>aGgj#@RGQ1JYOX<j%Jy0zJqlP~qxG{@%Kk-ErcF#d~zM|L}dYL$^FV
z&7i{I3|*ix$Ud8Z;-vNb!ZmV#@$eMsK%@Bx_>A$z4R0QqI1emBG)*NpS)h<jLN#3R
z_z&Mm(p{nyaVlu`2eI_ViuDbEc4jxO{`N5Ze#V_MehQEU9~lGn8I#U8_sRUZKHtTW
zRMV(=&f=r&hFT;OPz2+-d13NHS)^|f31LLeNF+7a4d1|9w)aw}qc(B{Y3}`H?KW8*
z95zx1KSAL_CN$&=pt1mdgCC8-8`}>QxB*l$QH+4>duwiXiY#7PVh~!JQnPfUNkHeP
zarEjj3qpfc*`Hs&^cX(XAYE%SA+D4ls;2`X0-uCdQBpi!YP|B(8q8&DK!u6iSMc?B
zR4mTWo2`4MDFTK^3**SQp5lk@1=>%_Oyi(K*a4g0F_Kcp!?i=-{HDuxv*Xj7^kkLS
z+dCSUa=3<Ht*p+b83UN<hs0Ygp&Fpu$p8BB&cP*%A9wlu6+8+<c29geL>R0JiCVW|
zwTf_^prN*z_Q#?3quNWg6biH712~Jspy3mG{stf=Y5<0SGFL<tEv!heu{jBc%%AW=
zV}h@tfU!{xz<OT7N!XQGecJ#DUU<zB1hZX3r`XyTnm63X&5KN}Md(P84)psm9=j%F
zf9eQ#j!-J^ALluJ<_tZ_b8)cD=K5H>=PJbC*cNpGGOg%Qga$F9tpWaE1Q73NTs(m-
zp+gr!Mm*=UF26K)0osjk*9TDhbhd35X^OzQAiA7V0ID)rBy<DBymaZY``n~4nJ1t}
zKZ|J+0J~_E9@|U4(&CqdWw+zfOfYec(@c%V;{%|`zbpTb5`;9uS*edzJ&qv&GBc=t
z_kg^2*q7sn52NwN#RX8vuw2n=5aGqh)SBIs@O|uuS~M|hR|Rf?pXMjCK}nv`yh|$R
zpD|*=ScR7G_jBYDkPc<OyxJt0ac%f#$t8mhx#Jjy#RH*=JM-i=VHRQl_!NBvp<WYs
z9~7X64}S$Ta}w|&nMtI9;C41qM-z&u9!qnce#2-L))fhf8kMtyMyZ1ROMgNa1@c1L
z5Wr5S@GR3%<nCo==?D_00_a4Uq=xw8kL6FIqD(+1afS%h&)nQ$0ybf%6`dd_iPyrv
z&28d59_k+OK1oLrEMc7pywTZff9BR|j#)viv=jJ!47}x!Kxr%kbI(lIheJf3%&`#4
z?Ew<w(?4OX9xUfkuvOgRDVpq_16JK`l{)&;w7Dv)%zg;OsG;jmL*;>njSO|k(88HD
z5Q|wOc-K!}Bx8<m`r_&m1qFo~R8RTEJE?ejenlUCUE=12*huan6e`aG3CR-F&D%*O
zchcC|1E-C|N$d?3jV?z;9+Ga~uTH>4WJJ&aPNAE=-IW-+_{P7eM_0C;3P|j{zc4pl
z)VyIYz(Qp*`UE=?G+|V*q`pFedW);ZHqt5Z(2YaV%^7BZoNjx2Hvl>`$YhfLhYkTb
zzyuvW#*w=E9Q!Z*k>fBJ2WYT#&|wdFh=AY|DW(w9X-1%X#Ihmywa|sDF}JT>WG5kw
z5{u~{VEI@yrvkT%J~S1gty990gm^(MBotG}7zijFo{vz^DbN92cf}@IvU`#toZ$e@
zN*q|RQ(z@55)0D-J%9>ygfOEp09AY1c-)(7*DjgTfeO64m>lsDD~5&Y#+!Rp^of<9
z2=a`mM@Dwy7L5;akT4xkAkh?O9-Zhftyem{2rZ0^_p2O$hCy2WT~n%YJ#Yy(umlJP
z0;gBEllxm#S+xUSo7Dcg&b_?U8Z2t1JX1M&vH|#z8ep5j#^fU?2h=dPMB>HUB8JzQ
z@C+5G)ORRtWtfJ3Wq~5b-eeMt?gilq?IWqFox!#`3oWyx{jXoWG65D$o<2SU#rPOl
zvu23Q^eB`lo6Yp3F?~cos)FxO=|96GX6A>a2t;N#hTLybJuow_1)oEJ8Z7OY$Z!|g
z$kN~+5<y@u_B}L@)#W;n6);sS1!#xt(44GKAPLgTBX0e2fCZ?x{+XsKp#$T0`dTtk
z=PA&Upd<8awiTR>T0VyNWIMGPFcQK{45sc?Qee|zSlA1Q!`E-Fx4NDb283lrK7{n=
zNclPc#8)O83Ozhjye{2&{aZ*yG$i=WAszvu8!e9`+s!`$VUU3m<*L=IN1J=FX1$X-
zg#|hpA49d(e}W2s`7Zb&Bf;g|IA|1893(({0+$OB9d@cM&46V9y{3QX7J2!wUciL?
z7{ioj6d){xYnJP|jXz+>+TLk35H<zeMV~t%ZhSenm5h`r1b4f6d)p5hz-)`}M1v_L
zneo@{+jI&hJHM8Idy)1I@BC&?PHn%--^z=nrB6?XP8K`PPERn1DprDJu~IiwzV}Kr
zmd*Iyx2OzV?E>Y0f++wOtf58bV_+aw5;ADm@ciPDB%fP&@R+*LYswUUR<oKsxJzaZ
zYjK2-4vh2jKtzR+F9m4G(--;*9-W)upwY&~wEI+jn4Sijc~bxA?O9ko-1F1*+%dX;
z4$svOI269}9$;CAY$87c@JR&ujna#=P?4-$y!s({CE2Xa8#jKOT>kAef6hdS*sdDc
z2>J&6FU*)32_#gal-p6}H5w&1qJv$Mvb8MXd4rY}0v&q0+|-9b<sJWAwBE%k%@OQe
zm%d#QGA`mJ?1~R|gO7$_t8m8OxpvYpU5lnO0a5JTe0=f<7-w~KLIw2-Li^`7zxobb
zyh&C@?xRq#-so~oKY)VMfG@ubpfj@5#6ej=ywu05ip11$=0Gmtr2=c$(9zNHSNR!?
z{B>w|Ajqgl&x)sft9JGTDQchF`wQ8EfH3+4tn)ku{BJ@;>Y9z~Ofl&7No~1M#jFB}
zKE*;C(6Yd}=Z7F-LgwmiuA|BX8YUD)!sbAlaYBOVw3Yw;puW28;ZC)`KVq~LEOhZY
z0FiXpNn3Cq8bpq?2(4)vWG(f1peb)Fxnsj%1+YI}Dk`;orv`#x^Pq}zCZz}34S~$a
z%OHk@$wT`iP4Iwtgbn^AMFxIXEWSEsG^7LBDq;1ky80C2z^512mLVjw>a4z%TZtL~
zJhf5`{)!rtDmxidn<}`&W|Bz0<;>$JFk3hU__`Kk1rqpyoaF<+)n)dmNi<t!nuAJA
zkHTPYi&S{LWlI)@Vs2A*gpouDXg%_O(Cv^ee;d~WtBXnsKVSV2kWLioTVWLO$p$*<
z;$%kIlwychn2thNkm?McD=qKvZ!?i6j!OeO*Uqqbhg!@a`5fTMG=yu+nG}IGg0P0g
zagaZZ!&N&LzY3~zj4JB!CWOg(wE5ok@66uy0V9_&VAs<I&q!^q5aBdX0X4n#N$HP8
zJ`%X{@1i2Njd#XM?)D>gCiNqb2lump!Iaii)eFb*LKO25)?rICWHA?n^x-`Uf!`-r
zH?Kx#MfmP#rjntPJjYAz!wGvdee}c6lC9S1TI0}>+bJNROcoZ9iN-LU>BFPWvF*AI
znS&_YS-n@^3iGdFm<)k3#!Dr%19Yu^;W}YBF%<Irrf|T(1nq1R#D|et5GsarWSOdt
zq`Z;ar~{%1wj0h-k9j>vTXmqwQ>eJYOvX7v#098HI!Ew_WTAwAblHz<?)k~8HU*$u
z!L0^6iX=dlk?EPqQ9>z)oS=U{i$g+r2JQX95`k0@YoCJsAD%4yb_&Y?&|)U7DK7F9
zZq&hvh&|81$X1|(fS!m0M<kogy$xmF%gKgV^8k|4w#q(6z;gn&;)U3QWTMgf4X9=)
zVI+xGY!g`NrvY(~9h@r{sUnp@4qy<nv$RWC_zdzV{@{d?G6Jd5k52#C)D6J%OriTM
zP-cl;2M(ND966yTC3%+s;|O}rULEL#89>paVDNAl{{?WDGqmw2PullB$C8L_>o6f}
zp}+^x=M)f795DmoAhk!o|B#PH%^8u1);fpU{5}QZ>x56YYSk*x04mO?Ok)}0H0n(|
z45-OC6!R_|N2oc(qXml!3}h!Y?y<H2(5c3U+uLbHuOJ(n@y6}(`<++5g~==T_pc^`
z+#@ZnRR+Sn<Wf<*j00JT$H<0ouFuB#1&dq8;V0E`vKaW4y}ibm3{Q=e?Gpn}hLkP%
z#W<>`(H)V}7Zt5s3Fk^#Y%r4rF2Xq?ON&T_5P9|~m>!ZQ6Q8k3*$^eZ|LsUDkqKiZ
zvRpwJgxJB^6Hvx`t7v<X&_PQQr*Qn-aCAsDhABDzhK!&=3pxQZ?P*L^R9MF7ALwMJ
zoo?0I%<uO2Lg3@Y;n>xUhM*y$**qLRh%*?@YZI)A7g`LcpK_2eZ<RYf%^p%5n{%RV
z(9Gj}`winfei`kC?1q!oHk>JpnFz$B%jmIF5(WgNK$!LEUSM`17@<3!IM`8oFQPHY
z0f+(h3}nIplwQj`{_dY_i5C&x%t2Q9#;TobubcaPrv<Rhse#1$jmY&BXawpsGn-p#
zm%b0f**?cW;ot|{QQ0zmr`7`-=8moWPNebJjpvV4!oYNm9Lmj(2Y9!BXE<Q6cF`(p
z1zE*%YNKpm{PO)D*x1|;Fa1X0V0*viJ9+It{ns1RTE4~dhgZ@B&#DxP&PHVwz1b2U
zB>Kxg{=Cw)c;x&}snedn4Z3j$;JwJ2$gCr5rw;kOUrcj{4q*%FHTTl1OD}yBhdi{>
z&@sqvKd%ryXY-Dh-{#jW=lMQ-<*obDE)YE1{`}**9eUBSffSMUt`$qy?bBZTEG*`J
zXDYL?ef{AZACv6s{#XCC-%j-C<c%HKv~lBOu*T!TKg1k5Y)#P{n`yMY*b3ggfB%=Q
z^0PfPpbDV>gw#7<iTFSHJ~Lu>*A&Qqe@48wYkLv;-i!cDB%~M8Fp=33p@w3ot)I0s
zZ-%SrF73vP|NbLnU20#EkO8|V>I)!oQK%-VbO@J5lJQUAj^#_YeZNC~CyYk7#qLL5
z0Ukq!$7C&xVhRb-*PFLe2n!KVIZE=^+^cSXCt|VPqh?yQQpoDz1Lk05`XG8xQB0VT
zRZERhW5K;6b}r%i1I$bnq;b`XU@?#uqoK#33<&12pib7kgao_|b#axYWRjn*l@4<d
zi+*9ovpLiNqUiWZKXV&=SxhE>2Ep3Y1uucuHaT5@YWFLQR7vasw7^wje}GFX*C7E@
z`MxH;-Mbg?5hRC{bkZP@4|bb&pq>F)CZ+D9Zno`{7)y7vL&Ipok@e~lGTY*wKNlih
z0w^3+DJna455MOAcA)<8=N#ge$(wuIHO-D;&EPc1*mdBxgK(H-!DRP*gm4d*k*Ow6
zd!a`m;Siu0BdsU-OaF1F`rjQEmXdU>!vjxS6Uz|7VNRjcLs~W(I9e|nhEo{HZr-#h
z5;c}fLlXD`7zNe>JA4ABri6C;|BjQ?QIMsRxVK~<Z6>?^szYuobem&Rya+5=5iX+*
zp{EA4lAu@eCNVpjFMfN!@ZgH~eng5k+*NCnPG-GijE_w-?CCuKK8ww?L-a8yAoZff
zYs!GBjX%nWXmnDRtvP2=UxF8gl}LgmfOTXKH#98NHT6srjK+&wS{)?QiMl)<af?7o
zFV^4G5X|e-%{54`y2$KXXi<9cXjoF5dH+*O9q>Q4)G~u1V&;5^{wrB1yPf(jb9z1k
z($-_H2maJ!q*B0vW`td?q~y1_HilG&z9ytUyC}U#^V?IT5WEA-^*YFqQ>ePeLdFf?
z;F4PeXS^F|OMjo==Hkf1tD)Jm{;TTwe53U%S1A~*Ubkxd<DftJ_b&Ts=bPw1jqP3;
zUeF1QJg|FX(3ejtIx8kl8~Jf8sbXL8v)?jdsi!;5mIp)%o$&rmaA%SK&4K)eVV^)g
zs-yqOzQMje_i&o+I<@-ziRp&vwzy2WiH3YAp%3Scj$l9GAa&%%wwVj@jHL6q;?{8Y
z69!Xj1~ze}#4if^JZifi5R-^4I+qV8W_`T<Os`N@Z|38c{<Z?*oi$l~OV$tWVCJ8L
z8N|o17W&TcKUgmbvo;*w$%rNtWsrWXsjq(kmlg`bf(Po|@OBhBM;a#mGxK=C;<gIM
zZ;VM#w|_2xMGhmd#QiCyP&#SaUS_%OmLosK9m*mrrWf2Gp%ed>T+jmrUZ_jL-r>l|
zwe_yQ;=E4$$xxmp@^@@(f%gC+<-IgtuMuBQ+`EWQ*mpxhbdV$K0z7)8+a(7&v~`a-
zE4D*iU06uyo?~7IcW+<cR)?m5yiQ&!9o@N2R7F!PoXQ4|@Ib+)3^-Z9f-4igQigUd
zWMG6I4}woEM0My5ZrowhpODE%g*sLmj5rDt`=jR;#RQekg(QjOb#?KQt0ICg(&t&b
zRGi*E_klx?NNKc4T;>==gAPz_OL+q<>^t%W<_CcQQ0c@YF1J3MyD4wT(o$CSD`0mn
z)+CM|FBPMR0|TSPo@>G23}nG7FdBYj3KKi0Qt$*=X=4d91IBWSBS(cQ8zIPx`*PuT
zSZT!nm?YD}U<43n2x796m^bn^6h?7HZPnI_^=9^sCAXyS9H63(dPE7adYCEswT;!@
z%{gKTVFBl+wk|;j2g$pG>HJj4PEB+^FIrc2Tz2Gs3rOUem@a(DAda8Zk3Zf}UesxP
z@kg5Bd#wa@8nK52!>TY9SXZ~X-n166J<GAO9u0cO&~+J9muTZ0YRB@+^oE4oqZjf;
zaJ-U6Ahsk8(WLATo}5_W^;r|HtQ%inQ(LQQkQ-9<rj6^UNXFtLe_sVrDB^^PYi?#R
z7#f-Oay^n&RC)_4lO0-CWg_=7HLB3X<WJumB$)ulK*>ICdx^0dapHN&xq+_~8{4-n
zCg6bNK<^}1cz;|~Z&5+9@oxa_A;vQOnwkZ!lNZ>}uMe>{yHX*>=p-Hjg8MXL)rd4T
zFUMSSHSn4%#Gz;EXUV15rBzPpmG>+nmXj&1hQ{TA$@Nn5ABk@xy#<>q*+_`T^A1KT
zE=p%=KULcegB)33ic@V!n_pX7Th22_%(y1u)fh-25_iOmkEjEDo&>DdjVrGpMxryH
zZ^^ZoH8BcU=~TJ~yl^IAMtIMlHa%(>65_-l%s$?RLiQyo#F{OfsCnOzu!WF>6OoLc
zfk)S<Lx5XDYWM>R(OEmLGmR<G$oyRUydr(3OK;AQ`0K1``<z@@C`SnNqi`B!@Jbbb
z6VJBjaH?|A4Id5dF#tJa^6xDLr!vQhImCk69WrH?;XW5l#BzUyCv^OQO6x&5BbFn~
zuUJ-J(e~L>!hneKk!3tUKyrA!>&?W-sYd5VgGFY2Lh<=@?8y;xEKN+?yy4rX7GIC>
zM64*bO;z6-ft8bUZdpO!*(3#e-bDVevWtrg#53{Y7nCtt3((CFEjeSLdc<UwyeRl-
zqWgMv*}6BmZR3eA4}}&J5lN^_2#H7h;6{G!pr<pgg~%qk6N4G}Rk~ocHoz=B7&V6x
z!vBI~8Q~toN9S+#GDzWeAu^sNjt$+WDlS@a^=9N+T|aJ$cmph(7<}c_Nz`PxLI&_H
zao6N>Xe2uPrK%BAb$K}RmE;~U@`vo1=UG8ju14+<d+LUo(;IN<Yuno+a3<=^($6<L
zz&Q{GyH#EMa+6fJ)iDe(*Hlc`s;CWTr4h%_$uB-0q4>wrakJen57XZ*O!Hot<a7s{
z+v4O7jM=0A<P!hKEEEIsi@Km=-!m+FLWYD?xCa~{P7#h;B}N$Zl)@jQAaB@hm~*5B
z%(y@dF)ufEIj9eVZR&6(p&vkvF}CN6WEqeSlqmS(<<!C1j_@Syye%BESA(DBuak(M
zIo)DZ<y1)<**5=Wo3tCZSI0MHfZN|SkG`v<|Mp#lbwOht(%0WL+K+wLQ~xdkyWyL{
z>Hqo>TAV~tm5t5I=Ka8&&eY%bUV{1oYGLv#m0`GpzZW{3u3_#}Q>PVK##X$4K?DB9
za+1Knm=Mibb7AS?MJXMEwZ=PMwoO;#dlo+L)c^S+)5LSG{zW9~yCiCus_vZj#Q&<&
zvdz~hd$slP<0bdzEKE(k^`+o?Fuv`Ma*Z`onJF`RFd?Dh%jt6o>bJ(Uj~}Pi=|b0B
zt{ct^xdp#N;MbGI?DPKdtcWe12=#c`rs46Z@ro~!0TkjiQWu*pACP?h80*dlJNpOi
z@?lEBqPv<t%4pWsw!Scvho~j(gok)pXf%v8Rbv`#+J}ad+H+PxwfMww-^w0HMtk1P
z&fuH0;<7radh0=hqub3j66>w~-8qL`;!{&4+a?TZL!;v*+sv-o#7NTb*chnsr3nad
z>@0kcV`6Gb4K|<`CMY}+j*<)MT3-=0r=P4@tn=gy^>v{6pUytnT2JW7oB~z}P1BZT
zQz1#LWEll{ecfui>9@ZOQ&X2;E3I~W60~MwHsv_p`s=4pO}KL!ZnD=EY6&#Y7;X>{
z(;igLD`OKCTQauO1;xM}>qx_jU?stV2UF)T?sLv`Qo(FCP>K&VGFMH+?5s%5n%uK`
zdU`w_uJ-2{l26nXMU>=RoSjo_LfrfN`^`^@?MRJn&q#FZD5g^R#G)T=c#70FKhr<Z
zOBD%yMaluOBU6_8H{v+5G0}?2A9>Y-xsN3^S|ah`hI6?NmOR#b3O$I!UZ($^WW6;p
z_Onx>Ws=n-;Cu9J8ZPZ?n{wB9kOo|O`>bYsg+lOqW^!(>G>iok-S-R(SM3aC>TSX0
zDpiTkiVIb9eQ{Dzsk*nH!ITy&ZJd9g?BU_jDmTwJKK_KG@-=LYDm*Ql+iGjM3NLUE
zxnR?&lA^j*wfd(7-FzvTgs%C9Q0xMIv1AkFESX<8raS-7Z^-|Hm+W63@gE2W|M8gh
zuiN?0+qwKoQptBNz(3Q$yXH?z`%FoN0juHr#7B<QPpKGgDn-c3p!Y4?6%$G~KG?yL
zo90dQc8Py7aj`1L`X+yv-5DYpiL&9e$zn46DrB|v6W;G0IJa|-d<C^A*xF;7ISCjx
z4AVNI{mWcSN5kw-x|e0kmfi4ah14<RDO_xc8#wVugz3%g^aUj9!a)v+C3a8Flw#}C
z7E!7Pa5C|ZhBhWWJe)K+a;1P5!b!D2jIBp*Yvf&eGlGt*6m^gYVh-_ZCJhlja|ENI
z9)R2)WWUsPEOJ`~{tsuh6nVL?c8gdgE|CEiftF?X4Kee>hb)#u*y%NR_xRXk34DJk
zrNl>SZf<USSv&BI-r?b@{Uuz!;M43wQ5~zcn;3hIyj<@tgklTeAMxxKZdP7^s!bF+
z_^!4Ek&>56*Gllm3XjFOPJ((F>H8?9xCPP%O@!zSL&l4AVnn=8XBWA+7hi=gF8w5s
z)SowKAF2UEsiC1EN-5Dz8W^c^8=^s}xW(r9qg4yHtT@1iw)RsP@ob|jFQI8@Fx0Dv
znh#YNsOn(b!^<cNmFdL&Pr9WNGqDlOtJh8=v6Mm@x^qK)zVvRUY0){zkJ1|&y16_n
zbX*`_+uS@+ciA#YZTH$ZBLmO)eC%s_?`Uu-7gJ=0<hP?D)6&+;oSHW}ouDq#AlE0~
zk($*s^(^JJON|z12Y1#@<&FfWzSc9XJ)+7Z`N3=2Sw=xy)@Zn-x)%Bc-Vs+U6dTnM
z<(SH^SHd!9LV~Gff>_R)t1DwAjZ1gb)z<n#9|nCje9BGVj6xXY<jB!C2SY0!*{i6i
zOifQ`P9JlDdstUWnII=Cn=JG;Xj^li9?XqwXb%Ygs2%Hr`DhG?Z3#T){wvSH>CebQ
z)g^VNwW*;&Jc=b#Ut2>r3aqrpa~=`5v_@ZwWCqP<si<}-X->8BpU%P@%O|^l#afg9
zc)k%8jd0Z9`S{$7(Y|D3v=Y50PM5X?r98U!`ffGx5AV4s@$AEpK=K}ifh|QGfd?h?
zUuT}#2FOF%@Xmz6dB;?>f?m2MlV$9yWJ-)XIYgu!=Kelk-yShEm5}fhmAqPJZ>G^o
z8zz%^>uxq*P)(FpLc{E6Qf9i?GHDOZA_!X>)`(lSv$o{u*FUEPcL&uNfIQY~Mt56&
zA<xvish)?Oo&62giRPZ^WD8@;;k-^}ta^J=?_ld#NriX8*UUAcnR%g$JfR|U`ly(r
z3JxN9l_F*bjES~urQH0?MZL^P<D(%-%Va_Ys;rvdX>I`TK#y2eUyKM5HLY2fd0IlI
z!WO7n(fqqBDyu#X4{xi9Shmd4)U!hh?{;tEgr;G-(Sdm<)xz=hy_wwyo~Dr+A-}Lt
zj7pmsOCE~w$aQMyHHoh`JJQ^SCi9`0d?IqXDQ;-*pe412D`x%p*jQ9<?u>D5_{?2b
zweBDCD`>yiAhWBFUJvIBsa-Qz)qNn|2BnmDgrf4uWT?f|!69aJyP2|RjA=vmr|98g
zso{2InFw=lH>0nT%3>88_t-f)T1!6J6|GrmvVvW?;)`-Jr&+p|EFxPs?wLDj>Ig)^
z%ug%A&bzNcE(12)ke!?D&H|=#i;+zR4=~^p8ld4@YJyFfD1I?B{h4+^DS6VQ1{n?(
z)0S!2>pO>c^;uIi>)JIAQ%|ago?iPsyh?sr0u`SXJJ5Jov_ub9Z2XZmF@gItH1uyu
zr){*b{CwpNH`c9LW6*lf-8_7mH7SKuy8i0@dusf9-Go}L;n}YLv2H5z6fN5qg=M^m
zIe|9c^n7#9a}{qaTC!|Tcr=&|Qa8Cuv&O~@DuV6DwJ&;jL^oJl9<z6Jj4^)Wnkad_
z92d1+KQYtMEcnBVlFPJU8L`;H!d>f1C&tGOi*Oy@%Wf9ASzER=1{o;muiE+u^qgqx
zpA(k|T>X*VY=GY|_ag1<43XF+cG-xqj#nB+&|cFikYo7d;3F#sRURt+#yY8|b3ZJ^
z#k)s31h2LUyUGHOpn>{2G*+313F!$moAgd#j$AA;D6t#UJD)~+$63E55s|H<E^F08
zb3C(=`#zny?Xy<I;LtI@|Gf0t2w!E#M{?j6`%<ul-+He{!lLnBh;@M^)fSshjLd~i
z>Z>+pEz7Gzih9t3xdg%4whb!O!)LzcHE9}$p*^7!JOlpm9C^JJ`aUxL#L*h|D8x_>
z5)=_<lozgoU8A@{`KS4@7`ZN@V^8lS;y%bYJr)z`2&Vj?@P;Pvgvh!%6N@|@=iVnU
zIHAIpGX;VT0PY0AU7y;rF#HiZ=@r7}jAqj^S7KAk%*<ud6JsBKG#lC-so2$9uw(40
ztRM#8STVLaYOJ<!_mu2NRsjXj7Hr$0FV0|6JRlCP1YhKiTNv2{W))Y6Ex>qe?Are-
zi2hj+{j&)AzvzkobvyrSZ|7s6E=r>4-d<;(HZ|W<;N=X1MP<XE+k7|1)%V?-d$w4j
zpt+Yf-0OX;C>^DWr1$H!8ZLOXkk&PZ_cQjALPoiD0VA+BWqzCwG488|x;TrKobTaN
zY;2=wK02m}G9XSnkKfWXQBpb8J+r}0x4K=g(0+46`DpeBGi!gzip%X@E^(U8ZRNCg
z2A?L>%{RG~EZ8@uH+&Li<BRH^%wXpSK&S4?ncfyY_H(;}o1MM=6T^^+C{4au=*l0N
zO4Ss80`2)6pV#63{-Q;8iNF$kGfZ7ll|r$=e9yiAlgX+5JJf($$aYUPH`lD$a9mNb
z>|TarVxI-R3>N;<>?}4MQqf|(xFl}^FIkS8Nv&hUMvdy13fU`PL{WPM(9j188!GI^
zS254YC7A#hv9`AgRmF_cN!5~lesgo8idFUw4$(Qvg<^{AjVOu?%Eii@b{18bvec~8
zgRkWzCf~(n+F91Jj3lp!4TMHJMT-VW*_-7A8*E#`ZXLYCp5r)ZdZ%4EADoRBj{U0M
zr;llTFSA=Fwmx`!u@V@LLQ!(lQ_)X29O6;9Bba3*_J@iJ-81?sor{DYkAkPIt@}?S
zcwGTDS<9J2xBG@6AiWx%XEeQQZC&odjJ|Bkbk|1GBQOF8R2G{LC27|B8<=&h+IkIb
zhw-uWq^}O3MHXn6SvK!`TeUjQydg?+<QnwE4lWy1QOk{UJi9EOBM4b*U2m0^rBTiD
zc5fu?^4pv3XLS08t2#*u25wP|V~9Jm^@c)ZLT;|ho523wAiBZ8t)P%@(fIzH%EW?#
z=&S+*YisKUi(2beW!vYL7cN|Q69}ID4pX{}J9u49LaU4q4WnInow=miBKU*#gpJ{a
zhch;l{(LJh`zm|5`V_mI`CWt&ez)=**B#IPOe4up@5<Bp$G6ccT3AMLZcH17QUo^&
z4E+M#(d!9fqgTwkoesB}TR)z-m^ZF1;#R0s=k)RlelXtc!b7LNAJ|obKm59A9Hso-
zIO^X=^ZpgN{%a%GztAJL{~Xr*;v8}5(t+0F<U1dCKe7ry1TEliL%e&QXeEf}93*Af
z)#&Y4+y2IYjtu(@@EU$jb{Wd%2UkgDLC@*^vTY~8Lt3_HVc*Y$NATqr<NpaEz_`p3
zgPx?W)QDM&bqC&GZ6V~mPsin&KM5~%)xn-@Q&m=ZUy8>jCVGft4@@lr7KgdS>|tS;
zgb4;QkO9-n)Gl_RK2$!;78a!*-xaSnYew(H5T1Kv20KF|80NsR7rm5^ndQQNtWaPJ
zQ<mTv7#ccBC;lx(1>M<IZdUY8peyX@ISc<MIEYQE6q|3p6i@+9rfYGt#4vEQrgwsb
zzU}+}(KL@-&Au9I2VtFm-E%qsgLf(cN^sTI5>E?2_NKkwtg&Xg1?CvUR%7U5y0ix8
zCl!w`33Vly3#kT4(&9^YKTtq;4MHDU-V)6+OPBQSg(1t@ST~jN%(N(VhL}G%`}Y%p
zo+2rrV~a*pbfVDSUaTZAUTwGiHrg_{?mg|_TJ}^u{>ZeYr3MMsXv?D_yZ&-BFmt52
z%JYX5I2u=S<whv+0$;D=39Atuy$*dPUZko1-07`IQy4QlEduPYj}kG?U|8Rroi+6j
zhi~!Q&h5y-J(K#%1p434Xlvsdz5bzqb3)C-++4N%7!#>7TbU`%(`!OHdD5@9^U2h3
zMbJv4C6zU3O4Cb<xtG|{(dtk__-vS|T}SP3ZgMx(A}w^ol6Urb6&Akr0m!G9`ug^8
ztGY^$pK6^hjkSO=kX2!7jffCy-ok0<Zjt9$&-G=#%UD*Y4(|{$HhMCopUfJ#SvqBJ
zf8KbdzzUvFf6L~%$N4^$bLW|IY+~wMb>n{9XB|HK(l>=SJ-jaeFhOfx?$aLH{BI93
zq<-YPsTQroUBfa~c5xOArJcO7duvceLxb8LmAXQPUpG6ADy(m-D8J))-*Efv6-RW#
zE){0MPKV1nW3?+*N2jZp2D^3biyn}RlJjwLuBlz#JKw_3aMC>$8`ZE^e<+sbS-x5~
zd3cvol(FHB-Oct@A?{!Axl3O<DeQLU1~RCoZ@=7@iag^dEWZ!?Vh;GLv6>uDFz3yZ
zjANRGS?gT~>cuTj3{DT$9~`~T%IJGqz8ZEdXRzD*;gHQ~Ox{#5z|P|*s3DneUDU&3
zS#aLR%}FN~CbP^iAva<$4;v!!0UBDoH=yfiMLb?|b^pcVg?F3p7ZenvWk?Y-Qug+l
zdS8NRQ0cdLoK13*An0&}t}J)0{oS@8GGQk6$QL6xfpVZd%UJP1_>A^kUQ$eq@n_pT
zr)n_$#)gPLrg|^U%a2e2y>QQLt^(Rs!;oNk91{*77^-?>o*wM<a;Leqb-P`Vk3}%l
zJ@9YYM`$?1pvkDyl~;sebD;kH?ZBUmz$U$Nm4?*^o64q7L?jI3Wp-L{$8Q&JT896O
z#x{~U%P|O6N+?D0)59C`34-LEy=ugFwK96gtHHsL#2sYQb=`C@fhwuCb*GPeX)R9E
zDwOc<%XvxvdmZb)i4n8L<g4TL1mQUYT_i<DMutiU<zO!{#X;$ma^dB55|7p<{!D)V
zLrEBdM<@fx4LI+hDG2&0`60BmO?{JOCJe<Kn;UV9;G`@NMq(;Y=GZhWd>Dh?gwpF+
zcV$=zbS5SF!>?Qd&KIe;7)7_j{+&3Pkv3B>>-}~4fQ44Gx=v?NOzP_YN6pnjPeOsA
zR1iA-$g;pwJn)B`DUzExLN~xZC~*$s)Y-7<C9{o{LGU<qFOvZtlL7JpMawVOpVjEP
zB{u`Wm#!#SdUyqQ-TT{oIJ>RV;qA@MX1YRe-M`$?>#U8Kv!i!*`l<k32(5j8n|;{X
z*?A<Gk;k`SvoupHH>-MM&!ykKzE~l957>g-A{oP%U&ZS3F*YT;rJqhCST}sy$me(M
z+O-xeXHw~4i8;@6YzC#P3@|mlvht+9_vtT~pqqY9O^k^t$ui&gK;f94R|?AE^+S8W
z9hjI?)$#C{e;?RCG$h{5z8NbT^Y63FO-=U?v6&nQcux-9e`K_5VrjSJR1L7w(KwC#
z-BerbqKId0<+~kFF!n?fqd+Zfw^zba-gvLb8Kb6PBq5Qtl|2{A7$OJu9RN_osk3&|
zVI)B=nrsAzKuox^SvY`8ekyF&@K0=%2O!9p+ih65pu2K{i&{g6{{3p0%(mC`kVH+0
zCX~_=koyM;=jWtdXUV`Het_*Tt%{J78+M<_&75uqevfScIdFzg&}RnQ=PY%{9&fRn
z7$>a+NC`CPhQX4|5VR6yqpI*Q?3MInb$FMFgnzjUj>LwHf4J!s4`mpRPEU7B6iRxG
zYj}ta(6r+adJtUJ0V4~A42k4e^$|sJ^P_hYgmZFpM+u{eApO|&^e5F2`aSYLQt{I7
z)k@omp*5IRABw9luLFyVI9jz0rGQD_PkXy7W=-w@YKH~728f`9P9pSo!~TY*q!Kwd
z9Z#og$!BjnEn(ZS{i;!UY`xheh-{&la!^XKk}#h4VXbxJ2jSV-*&9jo;PUu`ePW1T
zUR1#H@tD!)ftGzjMy3p5eU1F3;`$p}7Hye1gu8B_cjW?^Ep)=l(9FAHgLIItICx}R
zKnlNQ)6Cqu30=#{HtXI;w|L%SYt9eqLt-UdG$p(znPM|OeERWs$?cwe#V<>8Ljuom
zZ*M8?F2TcG`&1*|mVwWeH}+(oa=KP)pFNZF2(y`$-|WzYC2e7Je6BbRxAGFz;|E6^
zOaxi|UsSdQT-3c_@!*m!+lvz`A9B!!@Q9yb;H%;>RXf_RH!s!xwOGOyq?VY31k0E*
zuoESqx~>O#+29`RzN0Q9W?t$^t>N*BIlHmG<VNrGQ$1PAVH|qjjn%}D;ROeB5^nXv
z(_-k*lx4?bRR%u~{FD*x({C->Yzq!d*B_fso=7o0QwR?F0&ArQw38MLbTqRrUnB!H
zXL0jacN3<VDFWcY{7(Ea4|eTR;h534Mq51za5VtC2-^v5kU)%h_$H0^mXe<Gac5$I
zl}lqmhE^#%J<w{tM!D2oX0EqmN@QQ-tHwCF(WPt-x6BWTbhls~kxCB$-HjB~WI9hi
zEjMj|d=r?>y`;X&z-Wbr?FD1(J+}`X-AQ$yEYn+;*nu%i8R4v8nLH{Xf$#<~>r!#9
z@wII>jp+(DPb5r7vA@+%T5QVpSpY|ok}7cw&IF9}Ml$>bsxFap4*AZLfN2SPk4neF
zh(i@;>MeX}?*vru#?J)^GD~<$UEt^`p?*i&!=GsE)qwikb$-}cL-xyGDh8nDGS9b%
zhpwDpc+4{JNr~Bf@Qi639Itle8*!I8t<)(izHet)qwqeq3l{%4BoaL~HrA=#K1K2<
zKvsz>%1Ql+xmE%G5s=zDiLIy^uzbqN+^%zy&;@(@`^yw{N9bv3Yvg7K<cu)^Uz2FE
z<i|gEIAm2oxhU`b5BnOQOjVbJc&t0nqO!`-(ann|i5z5lC-&ePKd7iEie$X?O3*+p
z1jgCi$pAv<6tQm0%dzm#UO0Ox0r`9Jf-%=FWB1^ja+lelG)6AFWZ|+0$Ysv}W>&_K
z<}d#6K$vQh`76r%g=sC?r1M%ORQ@nO@Y6~&>@i5?rDBSa)!K!9&<`)E-FT||YxtgT
zn#w9D{#|>kn_Ec*ZZ*+oD}33nMFg4FCK~w?mS(Ja{AAMnR1zTj=yzR0s|FYy5weL%
z$<@D%Ugy7ibMEODoFUd9Y{B~tM>$r8_{?bRe(-W-CH9gE6#E;2LleF$;o)hA*(c+C
z6SF(8qFRQygM2$lG<olw;aIf6Vev!#2|bZYkHJf_b~duMF)j(OmjsoLvo-L%A~F`G
zKO(6v9W+GVwTYhZE)??2kJ-3+-OLEwa`58bvE%uHTiBlSFR#AKL7W(o)<{=|d*k@|
z3xr!_sUr|#a|&=T2Ay!^<Cek$7@<^<f%MA~4|w5HnkF|)6M8L`eq^1b!7IxPC1W<T
zVkrV7j1$_`%Lti*PTqYS<6Ff=GWwE^YRj^ceY$X-+lC6yuUt-;sTdD;Mu%9v#r9gi
zI;XzRtn*B5$+)rZ^S_eMdUXH5Hn4JusD3d-EBq;vSh>s0@orWPAMqn3J7`FhlK^*K
zi$NSMDU-z#1^d4?61E{m&%|X`TE}5jc`pvV)b+lm%~M%(@~HyabN4S7a~P9;vVsIw
zN{G8VA)^11bkT(bjTV3Ktt=+Qie{6)HZuwwZuJii54V%+U50o_QUE{oh9>PS8{3Zx
z)T4*hWU=P%^YEA`=k)m37@z*CybGK3<L6M?H*QMs5@e$*OQe5&t5HCw9N~+{%Z>rJ
z!vaeV;nG<!@AKbbiPp%=LDG&^xM9|v#6T505;R1Op0wJN7-_VANLgq^(OeA~nX^|O
z?eDO59}OX9pPmPyi4JXe>v?~~Y}+@Z$GL2d5*qBlh=aUK9vO?xO(5yhKn3mkVc(U*
zcw-sQBbv5qhxl#P2DhF$WS+sbUfS6r^^Rc9PQn-zWIUH4gA*G3H+nLdR%Puo744l=
z1Sp;)-(<tbgN#k@j`r)1UVo==VM!9fxsPj1ivJ+wU2-_^%Tlo$yw=HmG-w4mjJ373
zsfzusD8fR&RZf%WZ2~Aa91Gj-$H)wtQp2;1ILG(Do4PU@R^n*KS8}-uvobFe=}1Z4
zeef@C0a4(_JSi)5K;I-`W2>{*asBx6#$Xu2(3OXWSr(E!;t}H+JI1&2BbJh!?Moqp
z*#4B0#r}U~=&2mtl;BgbTHYtw5EtACmS6E73nMMU#K?oMGtbCO_ewQH2lCCi+bo?^
zVXjPRvh&JB9pgjlQWi?JFhR%a!z*~H&^fvW-nn~ui$P?Qa+<7cm!GQVY!L}oZ%AY>
zoX#yQyMu~GrmfP%n~`j*F>T4A&Cy~cQ__Hx9!i-6jUoaA(KX?dX)=*AvZPX*nJ%1n
zfoNOhLBuxc1fOo<cdY)uOdgLXJH7n0KSMZ68G)l1m}u?g*49=+2$A_ZU&Q^WeP&aN
zLG3GR4Lb)1Bk(M;@gxZUBoz5HS9C+Ot7R5t604E2e>Vtmxy}4hF<x(eM)qAveDe4o
z8}$)vkUp><_(ZV(Sq$ptnc4PvBxSw%aXrUgsr#ZU7EX1<1>IA6#SNRvSL0k4Z(Jg}
zHgi(2M#Zg9bu>N6%`!g1%5rj8QE%!D-2hcvuIsH10Uy>jFk^Gmlt7KpZW{rCUW}pR
z3tGJ6tt8YTO=geFPEBQneyQ_sy=d_O{B@Gqd8vsYgzwePc=mGhT6mCL9{|^cd?Omr
z9*G>TzE=zB*tS?z+u>I7S#Q)_F2A!W7hzCok93?rU?_d?cR6g~l7fy(oJt)78r*RB
z*PaxWh;Vzphl~YPRb<4dBi@dAJBR}}p_RW3F+s0OdW(7Rj(rKC<Obuq{*|e$XSlxm
z!WnO&4l#E@n+zOvY+i46Lcv|bWJ-Sx1_VN$*9{5Rt;!D+3_n}MAb}dyWvXy2mP=yc
z&BAAj)ip^V6J%VCenc#+344S}kMx!@i%F7$Fvuu29)2ack_NZ$U-B~=`*kOBxGuj~
zLSFG7`9q3GPdtL)mVA!8;^a0TZ(P)QLkll&LdsqEH_&qVGFxoR)_FL%V7Icn0s+Dy
zV!p9N)?dYa#8<kdjReKCw1KHFU(jVAT)}Nu{r=p;?1lREpcJ{C50b^CNYLV5YO=Kt
z@4aG5Z}bL`a-`j6^=4k{8K7X*%y+gY72nH{i+-n&5Uy{@`;`#(7YM<qyWS8db<lDQ
zu=`L++*wHq!BWrH!2FeE=N|eTFS%k?mMEMde;j9m3}W@YCHY82K7`sH4}C9b2Q)M^
z*2#W-zd*(&w51nIRt(s=g;afwGj_wG@5Q#MhRg>0d{NJZL=mZfFx^mtLp!o4kL_yf
z>DX*3xrLtPhpcZ4KK}{K^uGwvpP6U9zeg_eQ1v_`@%aKi`uwN3*@KFV0;VGQ2kNnt
LM-zX)aQ(jlA<?zP

literal 42758
zcmeFZcT`jB)-E0k7A$PH1*LA4u7Gqwnhgj@kt!Vpq}R|PfE5)14NYoLdY3L;ML=rk
zp+}?>dhgu1aC^RS@A<|(cl^dV_mAJW8E$2VWUaT%^2}#G?|P}EAbpIIh7yHB9Yf39
zQbwUDVo<1kpAQ{?cPgItC&51l9>_`Gf`8$)cC*+E{yA(bqlrPG=zWm?_5=*i8KY2t
zq0qOktGYx^_PGY>7)MF`P;^|viJkw8C42vQ%0rKzT)(>S&+FH3{c+pqRH&WrPr=6`
zZ1$oeCTA<38H=+0mHpcO&>w!@d!&Onj}~8G6TWcZ%HA)i!)Te7sOgLh662a<pn|i{
zWo)r>)Tpoz=0=~J4R$6yrEF%a&7Gv3qMq8mJHF3<StL2`+5PdT;$G9;A1KdzBa=bJ
z{P7=o!!M7^z(P4)J;!5hUMqAD>gb0o!|H_h_XoXNXC^1>YlANxNjii|3Z5IRCKm1Y
z=JZFo9ddMZG;aQIJCAE0>g=tH(*Acam<6c<^4M4tH_H3I6zW)0DErmj!LvO@(I7u~
z-#8SrJFoxD8@CM8kD&4|TUl8ZZcG<rs_C7L`b%6jOI+-#UY`prUGBbgkio?uK@lI_
z94k#Mx<k3xMer1)lGwD(C|jSn6L~d@G#cqPkzBa5y+PcdLp^83K3f-T;_01ZM6ta>
z(TvUYkhTR$+sg&5U0q$|IcB+#q2(SM!c+k%dd735c<sBiIBunUXNx_wP9P=df*MD#
zDo5~=cw%hqATxg7=h@gyrJVyJ&#!IoL)|_hxetvy!d-GjL(kP1H}vA7|M$bJk#Fdo
zI?r=*awe;%YGysS&ET~;r{g*tT)5aG$7fs{bO$e9TBWuRb@R6V9+bB{*u?ygCsfOk
zO3{uTnq4;KTW*}(!LsLstaX<AoJo_#Gi9!e&C)p!Dqm>@)t>noiD^uBJd8Se9{X&-
z5aU1Kzk~Xmz^0bkZl3PjsRd&dT_1~u{d9xdyOK8gLvQ<@8IEP#G8JF_LS;Q)w7sOi
zBcLd@q?!SXnRYMhf`+c6+H~2*wCF-T-&&({S>V=k4-A0WYqf%r*Lk|P*K22Mcl`qx
z@yM@`Rm$nHF1Bh~*LN9srejxmx{#bvoNeBoGFiT}z1HL-9;EBsCxD*+dW74kBTch#
zZ6qScW$tImh(KnIw8VPzWFAp3q7=5+s_^HlOZLbfcV!!QVORPvKVPzr9J6t$a3jvG
z4hD>dTV&M0+o6@d)FwXkPTv>DTQR+M)y9*ZSzGzzVARN!$eHEhiV|B6PW`T<be^jN
z!poh8m_a|G5m>64@kD&|O)9ZmjbiyO0%9iO8(oBD9j)SN<5rVMJE`NMv!9u;qu!Pl
z7In?ij8P#WAv(@|j{EfNw>C&S;`-b3p_p$cq=(v66zPMo`}6YaxQgQn3teM-^78XX
zUb05B#auY6vrb7{ZdDh;Np_ve&(ANJcFe3hD6#rwGOL<lUw)=~>aH#KITDB8`!LAn
zX*F2rErioy7n_Awe>`bzYFgQzg9XQoHa28>E~Rd+5xf*kOiWl5qa*hnzxtOngR4=(
zov@a~hN9}(2H|bi=7ZjjCF|{(8DIz6bQfdpUn;O2_<C6@mkn(Xt7FwsR5W^gbIQgm
z0z(ihAY?PG4N!T#>&h`%(dy{v2+`k~&{%8F^b9`7_i*UjNkyHRb(^xakx2LH!tsfT
z2~DRStAgSD9_u;^YMnQSPqGYEFmBs+cXzvmkiZlW$HI(+nT&YvJKi~D6>-hkx>1Cj
zrnkT-4wG@6{c><&F2GB4#%pI|BR5?=Rd{D}fzP5NO)o%Vdv(xtbFLb*HXJ%yvd~yJ
zT|Coo;c8)FA--0lxJ;SE;5zhHbpHFr!hz?syjI=0V9a_O?|TxaNAn6S`h*#X+okq>
z!hcD>KKu5`aS=9Ty<lsDz(5KJMS(*m5*wNv!Lt6sBk#n?{jgb*bBm`1t$VCX=Bhk)
zHb^shla1GA>Z6?d!xXU-D#8O#Pp;=qff0q4Eyjz&;7b|GXUaD0N!S(Rl|E-e;b?Tk
ziHlN;Zl!^W;=)v7i%OY-ebtSZGD~=gy3V@z+5B?XNTH=w{MbGeid!CaOuG=aim4|2
zgvS%d>UCIqA!O?w*1vmemEXk@ubgbsHB=M0u$1W~wlSHbSOpIC{Q2`Vtvn7Y(OJpH
ztAnp~9GY(n552jWJ?MY!_^0h0g7~!=?2J3&7H}CJGb^ix0MEr(9rvlcwRK{7w7<VU
zokP?0wZ%k<DC8bfQ&VeTJ<+~gMQo(a5upWN{ml=gySHe6UL&n3^w0S_?`|f`op*2G
ztG#aI^tFzHvS7xY)oR3R_~0gbRu2i$QJOgqjYD;vYRY#u%1tKPlFiJ`Yg5!x?dv!T
zhSG8xYin5Xkzi0JL*L(uO_gm;*^GiKe23+Y+*u9S`L1wnD#)loW*9MNtU_ByKKq7i
zfY|bP&VpWCpn?dy*IJ$a`~rT*BX-($C&Rh)60(;9M4i@|no-D87+8LjwxqjQg$qKC
zjC+(svCsS#wr0E}b~Xt+k=&khzGCFE@q~!^z8O!UQM{KYMDX%EQ6hPSZ5J>=KD(da
zBJHZqiCtvdSuQ9aw$R_u0zWPvdd=7P{hUc;bL$91cr1h4*!#)sI<B=Up|ZF1&b>Ux
zjvYf{%GYN{Fcrsy7P~X|b@dK<eV{_2(ntwQDc<)7=c*YMc|V1|D&1bnBqS$Bhrc|@
zx-2{=43@o+AXLhJl2zeOp^iebYVBBaET-xD!C}ZYPPn1a^37p4qSw${>5)MErcsqY
zeS~sy(A`+6VGMplhlYkG+qC(^ooG?vM7*aPfn1QAi#TR|9fY6D`emZ9JraVhJ^iqw
zqd_VP<HDNO*4B+Dr0L!klnisCTTD(VgBN*1z<${K$*%Cn<0JFo#1fW<h6b@7h%(a1
zm(beU+MXLdq~%;~NT$}sQ~5!4!<(}{67^iglQ{drNce-K*HQ2fH*#;}R>^tR)GgO3
z3;l?ZkrBjHN~MAviy_kEn2;3U9@S<uA->nZbnKF4q(<3%@EQSjM~^H%krX@sjN9Wp
zdY!H!^s*)n3D0QzFBH^91hJJ#;gJ~#ErqjRkEn2kBI$hymT|&t1y+a|ZC}ftXjB`-
zF1C@9oXoD0aHl3wd?{I6;#DRgfnnz)7KJj{+TZjs*uu(69`~A7$olgr%-?2wLEkGh
zz-a>}Q-WsYfjDM@69|pSAe3*`l`p!c>2R6gvCC+j$B%uCV=GHk;9*QP;9JGbd5&b8
zSAI;wV=+%~am1JRahnh+Wi&K2`d7+U;^9Tg?{n;P9gQmOX10(zwItMSnHc5NJtWK_
z<RD>_21{lbASQ^mhnX#EdbX=+Fr)j|I|`QFJXBUrpl;sOzdz`)ImkHUytw#Qnjy@n
zp-necBXinyb9-Y(5l83LdEd3GPOl@euQSVV0r3O0J(&KAXup{38sv_iA+3YH;waQd
z@q{1V={hU%ugS$KTSb_yV~h6+0A<`S&0H%e-{LHXWH4V$+TuDw&2>jwS~_zWqA!Wq
z;|aS=UYUa!&V*)>4{w{9pFTO7r?@WIA(`1H_SLlSt^|@2aYG+`xoQKvHV2n7+`l}g
z5Zjp+52G_>sAO`jpzVL-x-fF=lLuyHA>k_QEic4!9mjUHFmbFKVbGVFyK!@EfzNe*
zNE{y^-zSYadJLR3XL^yQFFZUv8_Z<{CQ^rEmg9xbFmW?15QSzIbIn@kbMRYfCL%#b
z@>kp&g-3^wNP_|68ySlYXk>7od^-hp5|~KUMYoPZeU?PFom_U!ZN)fpt8h3(+iiOb
znK>iiwT|M(goK1Wp<7^pieSvyOZobw!xp@=%pdw#wf99i9BZ473sk6uac+ouPUR7o
z&tXfl$e!D4qk(ZGT8a#dsnHTJNOX3;oJq0bhYh~78?GvM!g!g%o$K*Co1>zT!@3?u
zSm8=m&$$7nA|^H@OkYGje_r;rYU#9;AB}*4E+kD|7<QJP#650sSv0Tb=IWjvN0N*!
zV4G5g;`!Q3nC9Em!&S64ljlzN_x45(21tlkGkSRt>|EH86teGxWQBT$yLdIcDVnOD
zt~G;J;hjC)(|th1)qTE(71L!L8edo_tiRDs%wFktHvtr6W@;*vtSTF?JRT<J%H~MQ
zD?8M;XI`jm&QCA)kzj5g1k-~ajoqy8EmDU)xq@YP5h3H|kAq>5xBbPIJ5^KL>m!Bv
zYJ5YRGpzn$wCj}V>FN8ny-SR0a7^O@eYd;@=8KA?@tL&BHU)i7!c)1e^OfA5Jcya`
z+4Psxhw-s$=-A!PD4Iyw9AcHIxuiGu>R5_9W*nIp&q036VJAQ)(RHes#nVMM^i$pS
z1_>S`d<g#GyZ`lRH0=lzSz?Z(<u<i$khXPUJi%+Lp|m8cXlAi2YvIgv5jg|B-rr_x
zRld<TvyLt4*jW2maa;};eX^0_$Qc+Ua~HVyI(=VW^2Q<W_wO@5+F_qP8ZnxloSZy&
zyHCsuGyjuk2Z8n8nWR#Ey`>b5rQ#A;OO-^02C&ZvBw_9>+w6qM#^dU^%RMRtZQme!
zvbDP>svN_)tqO8D9l*QfC>0lsqrP=_XXj2Ob<V>Ej>$X=<L+EDG>+D$ga<sZ_lHxa
zdmkCcBmq#<&3ZdG|Jf1h5pXZVaw{Qc3Fm-s6Ej@Pv=4rMd+EBAsvm^_bO?gAnO<9D
zrY+JR99@gp{hynfG&-+R>wDOhx8Ck`Xq89fN>}=?U6gt~=DG<80*e!<{xW@{#X`H#
z-k9LT=<?$ct?Er@2|x<boj;0%R(`>ft&@`bc58}hoo%F08jZv#oVk3jiTqrGo}OOw
zqrr8;ix7EO0e2>JBc#`QyhQovkmcI^@LF?#XAoc=d%pY!b^HJfJbs?U%iugF4H@gw
z3;M&=Qq`9(+t}C;9#b5#uRN`>DDvJ5Vp#}+%%Y;A);32a@G$QLt@MP{)Jp-P^D^Qq
zg@j|P;St4nXK)s#nw{;L9qaivCIsOGrWKO6000CIRi2!5v_cZg7$lZY<&bFCGI!Q9
zFY6ZZ*Mz?%2=zIrG(xGuWFh68X$4Ctyg6GD$b{csOkC(R42Xw&wVMkliq72<-x^m;
zR8BV9*;*m^Uz_f1ou04bCfImw^oRob600U>WMm+i*(AzQ9Y?*d{}|8N>YZDRDAZUT
zU6Ukk2!SVvp$sEsgd<V6pkK~%rQ0IYWB8JO*BG(WeWo-A;Nr-agN*gB&+)BamRK7K
z#7q~CS}(d4I3SbCaTssW!7jD01pp}8gI_6~!DbS7ykO58;0yD_@@)YGCcAU?m?cKd
zBlsc5gfPuV92uD&Eht+`FNBz}(2C#Ymw>52x(sB3ilyp6K*@w>KQ}-qrW$|%%@$>K
z|FvN6c9AWFE6hTJ(2C~r+W?O^RwMnMU5E?gTZ36e24E)*u_5L2!I_g74-c>y`?*SL
zs(aPPuT5V0@qKXnXw{rV^Fb79{ZFWGJ9P9m07QxTFu0jc18UzS?QH3fG(?0#<!l7-
zFHFSId>Op2WL(~6A}rNbMQ;!Rh>^k=dkD1;A)SO>wR?~t9}#j=;p+KoGbIaWyp#*s
zA!I>nR~)9FagiRW4-15(&5A^m)|M6=WLWl%sRI4ukf@_0L>*}b%vF)NzWI~7{5xDY
z8>*d27>esc1SxW*WK>KnbkLtxD1$(@YV_;}+-eV%;P9(U8Q&3L8%@gy)$y_Zk+&D6
zN8a2#UejBM35+vo>ou5x5bj_0^|>p+qUfy^k^~aMroqh+NRA8uDVZP6l&(w{$YU@V
zK({3j#OT+CxxK=WErDvOHrjnUEH*$~1X&)%zj~(ta6dRMw%Al3bxjCC_0g^)VXx_g
zd7+GV_rLn{1(NgkDHyllzg*pBh0G?YA3ssG%+_fH1z>>qx@meH>iJ6*1z~37%bx44
z_~jxd>a$ufd%5t42%#cf3k$MT1A)@AOMLbb6#|}+r9Ln$RV9g?H#{t<KjM8L=_XR&
zGeAh-7ZiLrKU9;)1-prg(cf$8&DquxFNdjN4mBG1`b<n{pE-Ci%s2u;<>>jBiXI;b
z%!LTgqfLpC+-Lc|sHn)zdVd=pDMSIS_5Ptj0l)k>u)hhVodoE2<fkup;}TIHk8B`P
zg(EZ#RAfTcbeKEHt}7?2l6eur=)e%m)0^Mr5A88nwL5tO6%!Hh$KFNb<NpR~>=*IH
z5zGs?Un?|A4XB&lY|RAakY<^OtKX#u7FXbR{QUfJTImnxepcpj`J{ga%!ODRlV*{F
znaH(k7COxx@j6^K%EeVxQd^JDcR-4^8?0hRYiVoe*bmpzXneEP;0VqJT*88e&m-Oq
z?{ye_eR|l;QU{Y#4jc!hQyPu^pF0{4{Tt@>A7KgqqZe(yN8Wot8wd`Bv_T3`sOY7$
zVeu@>x0YSE$olwLe-VyARz*FVjd;1xhs%>)2sN^>xj;awOeB``Vb>R-GCALkFu^GA
zb)mGqV^FJlK*fk4O?R>Fp+LnEs6ThPAH-~>=^a(erx=0xK|(Vc&^y6!VWgq1#C3t!
zq%qQitWA=YlaqVjgIo}GMdxTL$_+wJn1q)nK(}C|M(u)Py|uIR-Edtfd_%Z!A|-XV
zVu4ZD#i0Y;@~{KXsO`JW5|R9ZU^a!T15a~vbB%yIT^kW94_jLp9i7Y}G(sR(k4*>Q
zfI=m4SC}6kqqY8Y9=%%+AtgURVr8&XfQBVITQdYb_o-l}LP#R(xut+{z*Xj<7Uf}F
zZ}Q=N@a;vAZ2X;Yes+^+*N3&k_6P_<IJ%of^2p^;OXi*Xn;_Q#Qk2C_0$AP>9|cZE
z1_z|x2gM$Xj6xQ){nx{+i)KRRZAm3lRyK0DUeArGC+d92*FW0|TJ9Z#D%7ksff<dE
zl%Dtngz`co*mA|qW4t9^v((KIX9R9PSzzVtf`uyQsB9_nWmHAq$HOs!LxzkS_aKr3
zevcG|y-QZ*K;r-y43A3exH5RmenDz-6ApiCC=(QLmVg|=HMcU{Ata%3fH~4WbM9X4
ztoZYG`9sc~_kDDnx{M2laB}t&$%)Haf^LgrKzm$7c9@37ZFIUWlv{EB!S`M~#BvsN
zcTbN>vg(1ODWi>15x~o(k&I)d1CYVwypO2(i!E4IY{wdii>HTAn%_I7?K@r06R%uj
z;(tDCna2#kGccqAY0r-h+k9)%F4k{?SO9edje12#f)3Y4BOg=8H1%sniL&RxsLwPN
z9Nr2zR=h_x>2B>Mjf{FQMTC+SKR5u!$z-8$>p=GM^^(+7i_a)GfTL@Jj60n(*-&}K
zW!ZI3%@^I}2%hwM9!a3PX%#bgvU0rar@6{1?cHm@IbaVkUy&&)YFVn@u+XhMz&`a+
zMV8xrt*IoWj7{JN-@(rz2KVUwJi&WLfmFPnPx9RG7`;ZSff_!fs;UY}Rt5cT7F<xn
z^a4&nfLoiz;`Np5h3*Vv+i6()aU+P?2b9ti3`Z2r;SOe^BcFnN*q|BFsdX#@Rg|92
z31lS|jtQZ~b0EGgU#1mvbLfZvODruF3)OQo<AP1`7&xLorr9rcVI4`U-eeo7!n0Y@
z^hjgbh*u*Su)Hp%-JIajdekqE+tCtI3y|YVgReSiEOQzJky-^p1C)a1y2rI2@fcPu
z>Qv@fc4a#_Idy#ckY1Odr<|#OWfAfmpVMR~lf}9d*mjED?F!X+98X@x{e9^=g}n3>
zo>X%2$}9?z@0bv_Cfm4<(w%cm5PT~7y1HD*WgY?IAKKS^Q3ihZPfBv%`}kK)xNFS8
z^(S7y42`zPaW_Ez#{ki=Sii4xYZJ-_r_XCq)SOyDOkJ>gL~UtE^)Q4D-H!Hl=GQb2
z$WHkVWoOOBF-P;v^CmDF(j^)<<;o3Sv~_vX*AgDVj>?y#yg9#o43qod&M=y1!<CaU
zZLF)STgrgHa{q>+X$im;&z^q3i)h?FDpA`U3nYc2I%$qZpxj#G(CCidr?%j2;J<)V
zWpGe=#%n0)as=Dg^HejTA2x)yCqmk{$SB`l2iSXeH@eKWJeNn6_Br(|-3gcjKq8EQ
z2ZG<ep6>4X2+}4CQaRv;o}N?~Yu%2T@h|}|wJu%iKx#e6ifG(uv^y6<HbEle1FAJT
zh87u<_Y$ojMv}D#DIh(jG5ESxMTsatBk`nBq-{aETR&JGKmZEuE&M`^&!n*)DnEBl
zJg>2=g4j~qjfvg<)>;4OYA7)%>vZgEm<yq2fC1XjXnS)_GtXQNG2fEqyvzoK5YYhU
zVqd-&LNdv5m+}MrZG_fAFaj-+bnx@V#d(OXg`|j+dy(NamES)t$%<A{Q30s>5rI+>
zZJtm|AaI)3u<G^ojHAfM49L8e0E2dath?~*hySMMRx!=;6h33At7W}T3c|Z5bQqV!
zHEK-0TNYwaC-b5VWDX;pt|W`$+lq;sZ71`%2AS0&!Yy?7qK*oy*dmulDIMCwzx%_p
zKO}ee<v;v}X&N*YEuj*(s3nE)w->z*RpnA!NYKdZC_<)g@B)TB(W#HOs%;2(o#J?o
zJ30O?>KOb+`{KVIvF*tl21-HQS}N)47P?h(90Gy~-EBIs67b5W+-qu}^m@DIHan-b
z7MWL1xtg31KQ$X`u8S=6XZ7pIZDx1+%IDwas`VT+){R$YAEW{J1eCmb+C3gNvOI2z
zwxFi%#I>W{0DNB|8zb1ckUreIvl0TZ?fNplCB*Jx5nNEoM7;FJq-fE4vR3FEPN8?1
zb8RL{LLV849uC@&od$C1f<r{A0GJ`|P<v0o5oDvx)82RNdKT)6=(#;Gnkz?j9CteF
z-HQs5oz-|fQ;$B#o$7cF_4yCz+@VlM_e}-IrRM-X)l+siuFE`uIxD&hkCNVEc6crk
zIk(EMYwII?ZZ69Zv{31Cm&!`a9z2|0Ub?Q$h|;Fo-TSuhU;Rht6FaKSVa>F(H1CrC
zL?x+M-#COUo8)-_%E;@~jood4H|qBsvt05lS|zSw$d9jUlU|HUE%vU)=jAyzbzF-#
zRu*5&cHUhfSR)qi1Nm;{dQLW;rIY>0TJ&FWQ{26)lE$4cyKUJ2<_^Da`4zx+lAOGk
zC)+Va2{o8ycT%$^_Q#jJ48)tvVMLO;9NaFK<-OcukQK_X%0G>)%g1(J3Z!5_p`KmY
zOM`lTPdbk;s=YQQJyXXF;)pyh*C}1WL}N52-Tl^0j4b;xWIs`zMbH>Fc%i%AdrWw%
z#urxOfa`=~xeNyF3<LF+H>5nid(HoF7yLKA4Vq}2`K<-`r@lLaQ(-XP>kHM-I19(Q
zFM_=vSgWT6ek}`(8<j7+dl`e+baU?E@!_QtmmJ!QP~Pe9{_i%!|39?ck^XsSH&mS2
z9n6+$G&Fjfeo)l(>`;Ik=5gJBvbZA5hIF<c4nj4wm|U8ioGjIDS@LCjJD8R{_=h@2
z@WeWnmgm7OEgHSFg7|pxGH!GWAfvYz3wp7AB$Fo((zjO1va*`Dxxp03l!Esl8rPCN
zj!(oZwvtRfMQ`vw$!c^{B4C@hH~5#D8Ra?_Z%gw-KsoSPJ=xR$OvN{7jacY@RZra=
zmN|E@GF4w805ATG1>vMn1urlBfLjfZTzpH6*NzA+tqRo%>6<riN~PsYK|cc-xF3U9
zkp@ca!L4JyT{56qs8`zT%CwZ~9QmYrO<rE!ami3gH(52is6)Uq23i+E#5INvxi?*%
zot>jLGEtj?5zutIzOzBR%av%MM7;OfzOXk@Uy0kxcb!bg%WX?~Cw~P1v*nj^xqU}1
z99wRm6Ne(dP%HG~e^Xe}lvO!6#xqnM$;-=&w~Cs%@9ysIm(q0=$TjRXwDtrzYLdE7
z<(>do6zy(37n6axrL8?NK=(m8dC`@mo}r@|YcAOTv(mR!$1SV)qot+_ZZ7YV=|zO@
zA_R*Z@rs%rtgq5qC~e|CD+}{SWC6H1xLHP-uOoojPA%fcM{t?2Wydm~h=te|C37GM
z7KDtJCbH1^o3}pZ<nTcc*g=UXoDzF+=}KG-0J^#kR}%Dd%~cz!kcRI<n0D5KTY#|G
zO)qllmAJ@ANg^cgv+@IyrO5&KmEACtbnUle{gX5O|MCss6d*qQ<iD&RQdKSvNl;Lx
z+F}{)IP@Kn)}W4FfGGU=O2^05uH4>DGLpVHRP@3?V1hzDKmD&$?LVOfB(YzK++6BP
z$aS(@D<{9EZ4TCDacbL5>!|0#d-#ujpk-u;qL9nq%xv!u?8&L4{T1g=S=2G697aN`
zq%IJ4$PfQv5b{rh|ED*0VG61VzntN}nYGjmSyaVcMM^`O6|J7B7X>1O>)I<B7xmu0
z9JYIs4-KxfT*zE3-=5J38MO-7bWgAA{EK>WKEA{~VjR&Xq4KXL{6J;~+9@f?oJ!XQ
zy!gA0i3zxHvg*W|bDkD`?vm&B@Jvk&w-+HM9a^@q543yuwIfgKrkhRCl%R^(Qj5D0
z>;^B(=6{_$|B<hMXVZVj8y~U|g!0+eVRZ1K@;C>A-*N|^HkR1;_#<ZZ`3f_DBJaJu
ztFvQi=A`+3V>k8NIpD3BEB~1OKe^$EH^Xz;^mMbbjb+3PMOONSf<sG7I*gH-qA325
zq`Dd3Ze_OJ*O$j-_{3rAW8g0#*K_uNZKh}Qh0c3`*ux(B;X|Mis^cSw28NbS8Y3N3
z`_%y-6QD^MKlTBq$mhO1*$cFfE40=494FckB@WUftR0^J!+JM_NMdQ7_BcQVNbuTT
zK)OWZORJ!)2usZ<;y^@5i0+`Osw`rAwt{N(lKz@3!aXuX2-)Z%bh9vKWJsH|uCAq}
zMM>Jl_TB>qofJ*bfvFu1)sKcsIMQRjrps$*ix0RHs3{);!x_eBqEJ4i2Ys7Jr*1QZ
zu!R;r9$}(Eprx5>dbez?!R8^9Cc;3^V1VE`Hg{%sux*L=1r9C(EffmvAW`%@Xkf1R
z(AmpK`O}2~X#)8QX%OPq>P^<^w>&`F1M-d`KN}B2@x}L4#@}9?U=NlJ<2O@AdKExt
zA%!_G!6wh$a(1_6qU9rHj7dXyFq81~r$Jt`mbhcq8tLj75J51Z5pqeqP8ztWg3>7~
zG!DcK*grUSq3zjiDEoBbgRSyH%;@71o3noA#Xv-+>6MD05!J}@#;#^)w<T>U6})>h
z?ST7y2N$7GZt3rjW<o}VC~fM)1ujvEuNp`NFhu<(Y!T^?xD_?GUL;c&&<o03p&Pn8
zpjioI0`<nd>)N}QyHbJ_u28U|JDz^u?Z*Hy9&L=sxj^ZKG#*Aj4f@ylNNf)1S%q@z
zM-NrLDU?9Og}B^;0%*w`J*!HH5Xv-RS}ua!oPSS+HGNWG7MN~*X<Og`8mPK(>452_
zd&#OPcLit@p-UG_OfG%@UYv^qpbOTVq_Z?%CLk!d+_n6ujG!I9-Cr;=l0c)DV<(G%
zh7p?Z3Bw**8k+Soo4RldNODO30N_k7@uGTLHL1|3A<KPnEJG`lC+0#K^;TKqixbbv
zs|?Nw+Z&}=Oa<PZN*73bI0)?C(V7j-r?Ass(o2=kl{lVGgQ3VfcH_x2Sn`aXkoJdd
zf4_**Mv-Ykd)-C{$u1S(lDT5XqM1~O^vVf?&SZRSrQBHb4MQM=grV^<59&I;8CQZk
zFSOH;9fK^Z?K%5|(Fl0ZE@u*UL2+kMQOxR^hzKrC*zWB2?mcQ68bt3$?fN_G#9fbZ
z?gBDvzDCj0KDNNhP)+)^jQc<)?^WO{T4#4Ew%mZk>|Ch0RUjg)k1o`iR@oRq02Y0l
zz?s;rEFM3yrF-fACWLe9-~9;g`cA!LNL{kei&#~TrLN@!k;8;QftjA}J&SI~gS8_;
z4R^Hid_wlQ`#lC;-0Q9Fy^;Q3^^I*|apH12#LqP=Nes9GUxHuBQSgk|+QEij7mZQn
z&=hmAxTJ~zL%7en%OC)&c64Y0NxWfX!qoJ8$VT9xFEE<n>x-iku{Vic_ynm+%g4a!
zQO4{sk5l-W9oj+v7f4fwfzSMoC<b5CUlW9S61tz?AqI3rza@m~*rPM^`wnhS|4}Sq
zf!FD%18pn3ytPsS@LEEjKiLg}M8My<B$hNv2ST4hQ2JyWe0l8P;IQ~wl2*dg12^=W
z#?r+cHZ?RHbd#22u>HITi3A(E>Rh4j2^(z6W|gnn>MHx79iE()qnOg}c2>H0<e;x3
zi6Cq2faCNULxWN;gj-)AiEKGNAuINp8tJH{IQADbHZ~$Q3Ah_i=$&Zi1UPm+8Ti_|
z(DEzr;fb*%U;VqqMMdi+K%_14skO`x*SY(7>*b~J0A@U$djf%V!<>}?fuy#&??eyo
zS2NW7X<ET<=VtSOc~m8KvvTKBO_TgCfD&=pRU)-L3r$Z)Ph2!RU{`M+uY6ECK>$$R
z`r_^4qKW%-Q{kysiCguK4zKS_{GcsQP|UGcB6jH{w|yzFk}uD3j9N+G(38?G0@U7s
zo)|O_rq**gPw!WWx5$W+M}4dUzPI@15EIhOD<^1f?9VDKEoJrYhlml#L=fMY^j^5e
zjVKTi$wx+ke@!4O)zwf3Amc+WYi9Q^KY9N?Hr9IY7_5pz8`{T0H&`}a(IiVNJwOTi
z!xl}~ik9ZX$TmIX;afT7-Is#b*Z9Oc@rBQ2<GJ>ZndfyAHq@yCebSnno8KxY7j^6q
zs{@|agaZTYyYt8lGr~jvs_A`JPR{Y4NdiD=sc2f@9TWrAso-Za%PlQp%*v4oHt*?j
z2@OWC(e>2Q)Ku|h53lN~hW5^OwSyIs6UE%&i(=M}P8~OGEd7?3m-!vqt5;|ZKJEIj
zshL>~FivlVDrx`BuxL+FD~%>uA#ERPy_0xOolhT0`M{am;D$c$qoNCP+f5+Pi-CS*
zt$}`FDAR5hssm~7GE+@kvWk1(*n5x^yfgGi;~)yBDi#AG((&3_LgSuLiMuFS{9@5E
z5Ec!(AwX`iIQQB~bCqw-N4rKqbl8>Qfo4Y*=dl>eIMurRKQnweqb(o)7k&@=2xen~
zyB-qb-%c2MPa6Zl45Tb8yS)hweXJv*pB5*qeLg7u1{#9Jgu(wU)%VqWmmxT74TFqg
z%5<r`WIrAsI<ijABv=*ng|J4uT<EdPk=KL3H41(C22cd@8}ty%mh*OX&<I$7l&G0)
zgicV13cu)o^@5G(Lc~U?@cG>wvxPtf9MU*Nx-I@79rZ&bYdsI4y%@%0D77becabf{
zp10HeB3LYi|D8bX-vRdi-!+h}{NM0X|8If*#R4Jaf~6G=tuJXf-o9{_c=j@+?wi?)
zuapRRp|bHs=~&EC!4x1*pl~9$&3C-u$V+5{<Ic|9sOCt$eN|`9!#}tlX3yxKV4kSO
zeZ7rlJ7s5_ETx;_peCcqC3}ILiz!&O7B?*W;?PC87h|>e9y~mn6E(F`y*a{f*qAm-
zH^?8bF(oL37sjuQZh2xGHzLCVh^_>(A^(BK(HCW&4HUJ72iHr&?y!XUjaZTnQU=z<
z(QZliEv;(OnPMJ(pO1W}E3~@*;oCX4BatpS^J@a~-kW;CqImZh9cq_XVoILntAkqn
zN+X<aGH=I6mKW?DcYfVv8PYY$F?|IUbBnS*bqJ-NaOYfkz<_;c@bt=Ej|%g=T9*dQ
zpKR0y=g*%P9^q5kCY?KVYG{G@pr|=X@>ArI<Htjm!ZB2olpG1Bl+~7kqwfmdym>&G
z@aE`&Cj@<rlke^0_~kK1saT%sLLa^3obahk0P~cB+j9(u71^r{D3eu_m5Yv-I}O{O
znhG8ZSb4^(kg6tF9`KVkUN#taz7AtAz^Qa0^y}BJ_Vp&-IxTEhuii7R-M^7>)90pH
z8Mz_z#?70?MGyP;GQYIQx@TW9vUdn4#S&NpZBfgIIg#R1%@U6vKR)HuP<Zg*#_ZOp
zG()D@-cg(sw8Yg-y-TD~KL{+6Eyi99hYuh2WUR1V%ihbx8V}0RTDu0!PkV|RG%B+4
z@(nzAC-+Z#2ds>9x`N)=wkKkrUyY)Cx?_?UA3t6zu<$U4Lh5mab-}1hLwv$>nLV@q
z&V@R%GBS&1_>e^him&0ciLGIV{mROFS$!kkym`a92Rr;qQ`u#XLiG?Djg~z$zf*k*
zCd|=dcW&m$jz0Cf487pWnb=h+Wo2CCc`s4cP^(a1p75_|1%*Z)JeP(UJIeD~LVP^?
z`7b^ap>)S7WED7=nQt#!j5LfNeafE@6dfHsC}5JK$=tVkPSB^~{nMvR_~mn!1qW|o
z(Gd*^sl<A1EiD5NqrtUsmV1<MoIM^q_|TG3K{-aA8{kFl52uw-S!OjX=-Co~p7r8V
z)~hG2<0V{#mlFH!C%Hl|85pjFBx0z7sKi7o8Q6BvAs#ymgh_`3xAuxOjW!I}exK$y
zTHCHtOfa^;N++~QC?fx<kJ6>SN_Sj1Ta;C3ebe~#q0EBb)y;4gZ#_(OqnJ|05#&nc
z`kQu~=7-<79vQN@%RWjl_PJ=QOzBjtyX?IhA&**JIQ*3A<!(9xci!`2|F4D+l=<8;
zzvvivd-L)C`P(tK$lZCVm(Tb@|F!(IGh5{Z9DB*WcNzY?^7G@Iww-0Y&ozyN4tUub
zUiDc0D84>+(W>*~b;y&k%F4>B;4ysw63jc&!n*S;5(TZglR)agd-iB0eFmY^Qm_mD
zOfCOB9)GV;hpWpn|L5_FSm<h-PIc#Xm$>FQP4^~4C%yJzPBLu2c}v_&tpe-x4Bg@s
zkgW64Xky$io<|xi@KXB9%*>o@Ln_lMcCzxvnh>JfyK+qOta=0+!uX_dxt3kLOEaV$
zH=mb(vVkptgdh?cn!|QG01zGLhtTO-d9pk_JSjSb*?OL9#>eQcDqX*RohYp3asyfq
z$SY>QoT8!zbb=i~ZT9mxMdf8)UMNgcKULUXv-|l}E7$b>>gwuVM$Gc0CJ0&$VU{$!
zMlTp+Z(h%0p#S?4z#>FnTcbO5Dh^IIjECxb-gR>;id0CXJMM$S;T$$s?XaM*MZS0A
z#*J6P=9^tn*PIoO(F)dwczD8Z@+^p4mDSaC)&+Ir4zMocK-qTZm;?X|@PYC)$0@_M
z*{7#}0TC2)Cm^=o9)LJ^E1n<AnG!mCl#+@{!^P#3b>Z-trHOX3o6n0stA_29^~*hF
z6%<041S~t1EQlFufGjb&0uc=y8X1wG+)ad)8V@sxZYJt`r7tcnlIaEQzfk@X7J2_!
zYh6GFFo}yh!>KhSBToIaZdke0ZOJI>o&&7%sj+vU&Z>Qo#U?7MV`gU7wLH~VtZ~No
zHv9n`8o@MPK=N!|AV_4oElsfV^Q-#N3Uz?o^TX4pPxn@?GzgKBymq!zKuRJT%9YL_
z=4M*?>NNNb#Vex99ahEYHsq^gW2VoZKTo;-<bbgMHP`t#Xv!ybq-)oi=cPbfe*&y{
zq%kV>+qZ8w0$gE<;T8(8km|4!A`W9QKt4z+B=YrSy?psHTo{v}m2b(O+Mcq$L?%PM
z8w&d}PqzT3Kh0xkXt-b*fx~?WzNC?Y3`;X7;qj9vYS2#Z1B%8C4tw?TP*?iXU&}SF
zJA+mR#dV%x_3?lj6UEn3u|*E!AFTU|hC4OdT<a}0+6r}w9FpC8H}x=J?tzsL=I)7q
zQ`waEr)vg9m|D(5dChe7JhN6Q=tg(6r)n(Fjk-Oc_~q1+hmHBSvjlmrUi~D4J$v+k
zeB{-;`wkv&11EDF_)4J$eNN)CV1#^T`F4?|U<9ItGt>#KV~%JT>DPlR`x`y5v`l+o
zXei)1Z#30kdW{%&9}E$uoCvZRV|UO&Nf6;cO9m+C%&YzB*P;zO-hr8(;nqt-&K7A$
zq`h=VY)yXm?kt?uu;}V$Vr8}1T3^yC_biQen}F7A{+&|yl}6_~p>^?)P0T-g@9FBc
z>?=~!F0gI~1wx{LMaOp|UUor2^>pn5b#wES;yzIA#X+%m9hvW=eFs$`<a<=t*MTx3
zVS8(%ScA>vm9MXgj!tAphHfeZfN?O*Y_0UQW#@FEewT{UEjY|TolK=jc`YDpswAqe
zUim!5{em9IN=5}e3w~Z80i_YNyq7O{&3U@5G3uIt-OpQAq%vKo<tS)~_<O%2$&GV<
zQUYczw{W=-J)*?1MKW7(IB(yy(elPjIo_lP7PUKy4)N0k%MG}vyG~6wyRfh({WYh>
zcWHrf;@EZY1Cu=G8NH>k=8M|df)a=f>}_sPkk#pV5NU>Ayzj~hc?LdcI$RsP<}d8E
zy-8Oyng%wT2of4OJ3Bk`o_wo}QOw-W+m18+Vx+nG`H={l6bNg=I6~P^2a!CFHG4F!
z#z>i%p}WPaaDC+N{FcsoEutx5!sirutYwD_TBUsY^oe{%#rk*r4>Eu<sJyoaXwkcO
z?z{$O)aM5C*@~}U`R#{f&@Di)VVLH!-QZjtGkU70fC%0fX;O}y`Z;yt#QW2i)Y;CT
z|5A!`AeCWLv<otr#zB1qigOiz!vgER6sW%MC`*07!a3Pj0Rf?{<Emg%%TqmxaD>1N
zjDtdpBFYRTSCmJNw1W7icMD?v7HFYyaiBa?0C}rk{(_j-b_v8nuvvcwv6N^r_o5Z5
zvseBiwVdTHUBq#yCdF`<t=`bc(0SWB?hyL<`<+b*ph+g0V|{Refdbfd2i;G$U&KcG
zOWoCCrH-M`IhOI+h-lt}KvBQ^{(7cfX|C6fXM9}TcZi-4w~*+WYZeH0uMx+?rB^c6
z-*{PifNHHFb_id%7@Vw<SZl;<2K?>xij<aCxYJDk6j!`qP8Yw+><{vvihBRGuzkhO
zL={CbX7-EsmnKp(Q{b=)*k+1WUJ7uWC$<1_HAD41KRF6Y>tZH$adC0t(`l&06JVX?
zaC#ndl^%vU_b!XLEheomPo)BpU1xCRyQN?%NI%!%SXti`(y<uq1gGpT-GT$TW~;LU
z94ZN~0ZamhYx`tph}g1KPkw`tzr_|DTBW&S;9Xpv20cUJm1V?<;H!GDhe6GzL0y*W
zeMOFW=ItCvro!>oJ_T}wi7*F960+NgKJZW&J!CM-!-wzA^p^^lG}58ZrG9aq=}(8~
zX%3>b6s7l%-Rt2rk11^J^tLGT<dF+3ELEjqhE+!q-+<QTTgYKXSI!#ohN))g2!qj~
zK{v;(JpLLyyr_UrV{~)`C}l_Dt>wjFlHZ|)aOoNe%($HU%5?ji^EZ2Ej`>!80LtBh
z5G{sWE$(&u97vDIj=nV~|NNcfdO`S;XF_0b9B*On83f1CDsjnT@LatQaZH|G#GwgJ
znTsvkf$%2@1ctac_{-HY$9CWb`kYV*x#|7QGCTPw5w&0rwfRpo2@%%ztN|-iJ)u_m
z_V(HN)_ol4iN4|j$Oz+ER8Uhm_q(!^u4dC*b4r6_#=~~Vrvz>KFS8pybG9Dlg1taM
zg<k0<?AWOA2&6?d(ESE%WM_QdD&N^i4fzAkj{SNrfJ7fqG7LQs*cDHlIDw0Ma*%37
zIGPnw<T~^nn2nj9^GW2+=A{X|gY&!*CO!4x-wUzB%$cr1n?j*wykLMjHZ(L)aCl~-
zYnKOZ+Wqx+{6ax!y+iIuR{?aKZ`t*MsOK&(=C=4f|CJ-Wi})?XPjXfO$m!6^Rp!ld
zp<Kp2CIvlxFUQe=EHy^F!?dk=hvzJeSxPd~F%gb=PM*w%X!uQI0GfLk)+_?IG*rY*
z{siY}U81+{rK>wD^9c$H60(_LbxblgvRM*#Pdj!u8eAFc(6$K<x|<*OF*~~F9gm@C
z#@Xd(X+rUL?%Y|dJFDEkz_w<?g*E>1`H}0i4S+=V4If6&S?8;+7k*EsV0PBF57eSG
z5}qaC0qJpZO!Q)Is&KI4X|X&+ol}gA_`yyt-6FQjmx0%Z76cNLNjno0?Wwq-xe=Ra
zqm0f5`G-F{2W*YpH{nd>^i;Tj#YmUW7X#bSOKdNYeMF`cCXlj#3zH0u3knL-=>dD-
zAY#KV*)S*Hct+{!9~#!nDf@U4!t3-DL<{Qm)yD`_ba&s`-g3<c&nwm8(#X#Q1ajIm
zvIK%LNoQU5q5MqmSwE@vWEE-LB4pGJv1n04_KxOX7~S?J-`S(cb^taZmpzK97_bdB
z{LaC~_Fb2C6*@_cA!8oH!5U1)y-DdXEeeKWHn_+8Zt91TkJEa&lG17hOHT2Nd!@^U
zWJ@G&)3K6YApu2@q)bEP7{g4|xn8dSw8lQ>vyUQdpI4*G{Ws9SLtc`PjC6Iwmsq?t
zkI{>$Docg=K=6{Q{gkEiP^=F`zE$PdWf8#Tz5({@n(tVsBVzl*Td}I4Xz$v$mnYZq
z30lP>{QPaKJgagYTD=lmi-Dvn9vVNyQ{rAnJUFxT{AK_Au+BsI{iC4<+wepK%2TBA
z*2IP083z%^$xa@$G60?<ZHeXoHKNWbrxhn~rfNZeR_c(`n9&G!VHyVtFpy~rgoQOr
zRc2+XfV5VYA!;vJx<d@oA;f>o4srZGGx8ktBU+_yc}&UoE1rvMUng^G5~fHy+s^Mb
z!l<i~?#A}^06tO$oI$Fp_2VApB{!VPO!td!hZFsnHa<c_9<z1EX<Axxf1?&d+|}OB
zOv~N1LiJwZtuvKTh6IkW5(2=0ZUNMBnwmNhj)lss53)Slewe4#o^Pd%ZaK*4@y3Gl
zKENW76xW*!K7aNs`KHftTpY|qOFQ4PCT|K3dy+GB#Vk9o;46P{fBEudqBSvq$!ul1
z&kY)wTY~Bv0FM`(w>Mo8n_q{sAX|dbtYwjPT6yM$1sdsE!&y{-s1W_{=Q+-MA0+`T
z&V3?S7nr!~jym%xdR_btVjkyN_h}(Z2tf>Bz}5JKgd&ZCA{{O$lg0tZXn^nJ8hqKi
zVh0C3^)s;fRz2#_Lkna|h9l4je1N(SRL=vXz?usd*(JtxAxL1&&?&sY9{YT0pu(p;
z%TU^?uc$q7P$62htF$j!g|#7COdFU29B(wCHBpfa&lqT#?8;#_Zfjt;Y8#_yI2&3|
z^(WIfoD6G%^YU>OK2*hI{$d?2-7*hNfIF=KjV8**jqaa2dH^{s40R?hPPf=8Svg+T
z4UYEYLF$Il8@v0lu(FcBzr7gFYy1i%eFMXq+g~&=nwffH&JE+xy_o>^+BS0{zh_Fs
ze&_|9gkr#g=JwgEOvt5LWgdkau1;6!5S#AKw@MXrUzWkafg*+5Qc`E;>#@Fn7s!%_
z^P$!f+gfa$XnOx71q8rNO-g_aVQ}#G?OV5!Aj4?oJoMXmcmC#cHVzIBqPU+_EVOtj
z&@Jz8`Y@q^x7M(={ph90;tLhQ1UzLRB<QZ(D(Fq`!jzE2C>U}2^yzp2z$suS=6ywZ
zgeogyM%4Ps%*^&S0u*ZDQ4XHs7cXASgZg!48&2r4U%2qqWN@PuPOyIfpDHfb&$kTw
z@ZkfLTfL(iaDoJ+xK?hkr=>Kh5Zbznoim|yW@b0cTZhB0ZE@(6F!b-H{D5m};BTmq
zAZEG28aXUYsA*n4gN3&F^nRziYvx=H4|4#Q++J~AoFVDIfBznV1C*4?PYxW*weAyH
zn(otv1E(BHmLPaVs#wt3Uw`J~Q>;&fx9y2~LSc8HCVnfV_(<`;{JiqpH&f_xujSKT
zF_6rkfk!9|WWEuFE3YrMDmnrN9O-7^;E+X%(0ljZDOT$NF`q9|75(lG3#V?8Dm?t)
zjbin30X$H~9L@&9aOOAe-MxDk3WK`5TQ3u7*N>gOA~{OHBj)Y#=abBld8k|MLKkP_
zAk86r50HfN6cJJpKXQI}C6q%=77FBMK)O0H+nX={vH4EJwWpyQ?HtZ$;*ZQsz^Ypn
z9-8vD*;G|V=EaBD*!FTSFHX(uTXS=Bh#eM3O6py>aKQmi4$IcE>AzV@uqMjE?u+ki
zVnEl7ULkpv!|zZ1`R4~nR$~C*+RLyTr)X#<7VP9%0zq?-ZdmQl&cY%kA0ZeFxE`i2
z;y4k9oJA|L9e9k40Uv-BTO<<|p)cMB=eSa#8cu@j#2a=80b?T0(`jH4WI*wN-Sh&C
z^R$)#hFm&-{yHFtR)9LE>FLwoy!iuIQYi4{0C035mDs{4!2%k?9J)ohRE@l_WV~!_
zGQd-GK$Jno`rSN_19B_)NgF(QVX<99zs6FK`YP9xFEQnCcm%PqXqTUd)smHOvjn0W
zB7}%=(mn&!)$)K8mzNi@AAXqplEZpS&_i7fMNTRpk!MARwLFzoRQiOhHvk=L{uo5k
za>h&q9wuRO>F~hIF9u493Q>@Q5_NhkyK`$yc#PE&73k$b@1Q+3^q6Dp-5KBb&soFH
z(Qn_rrI=0yvXn;D<>LV=2A!DYeq=u2NceOnsr#oL8JK5AcmrYcpaf_G0#m1FvbQit
z2`dP79t6C0NbJ6Jq2*bBb3G_=A<W1fVY=hbAckW%mMiIxdq4R4H|2p^ecfkG1Qf8;
ziy@$Elg{bB!O^3(BYzi=s7z1>Y8`k2{L%F;>rEjDqYqwFO4|L7&8Tu0a(aP>+s&(}
zM?SylIFBk*{uEf2btt`;0KHQ?Uq!t-CxGaQ;q@aA$Se!!&R5bOm`!X?)8s4E$@qCZ
zw&@@DM9m)gjfqjH@_%5#|G?W<sGWbMM%1Tmy5rJzK1!xASE-9+_D7Qc;L4ubi9`XV
z#5H||vccdAa6cuVDnJg#^!I#ZQ&KgIHm8U`{Xa-XI&NPGy8{#RzVRFNDcv|nzz45Y
z?3{8wOMzopxNz9?FVtQBUs_;T4eH<5L7_aa|K76yOK&SZ&@HsT4d*-V;>EG5a4-pB
zp7cuH+~EA`B48cR6P$K_(FMmsuR#LSf(I}8V<CwJS3bB4)CHkxK^o7dSzq~!5Cq<m
z4*Jp`zC1Ztw~J4K`4MrNd<RtzQZ<i*Ea@&(X|LF8CxLx|syI@?#2*X9j$lMX;^11W
zV$kKZ->B~80v!A@2Ph5f7>$TS(s6o`xVAP`;V6ap#6&Vsjm>Z_{5>RHGCW@h*xN_m
zmA?%-hz;B|9k?Di%k;@E7L-8#mK#u3o;r6f8S<(5?2j*65Qy!bYMMp}T5(MO;Z673
z@Vx)57`8V9dItyR(^!UO#Pu&huz<S|mftlzY<+%=mfvI57O8U7Q`Nvv5o&XJhJ?4!
zKSsm<1{MXVb%iLAc;IuAou;~B$zTMA`P(Fq1$hDUHW~O9r&ewgL>e1-494&|3%#Xq
zUmCt2oa%pb+A-g*0{aS=U`DS4?$?VGukxc!1eKPN`y`NdND2aCG7jJx8w(3dwH@%~
zuzrvY+5ti2Ox2~`jEam*17>>c^P_#!SR@$$?SMcd$Ej`s;-~o(qE6epJvRRc4a`;3
zeZ|b^B{ESX3<7_fG*HL64=O36#N0cuH$Q*-b_=^co(Lg0{L1~uz+!}x=RkAR7IXc{
zDQarg#8x<Z77t-n2KW3peF}imBpCZTsKJa2dV-^)Ga!JHfoE+8?)DY)!<eMs6a&oD
zWj2N>8IaiRX+t=THqld%$)T3Ql+$$?Lh*|ihn6P0RP?b+!Axe5VG9{Zg&31kIMj&L
zDNvYh%mE5ScsU5tK!B+_IOM{Q2zBra$mMlDIPmJ#?{Fmh8VW76GUV8;7BAqunb44u
z8}b@ple3wl`K<a&G;xdYXqY6OLVMw5IM^oxyEJ<@&WlZ^Uh?kSf7s;R6jgRKkQt#R
z6QhuUkSdNw$T|&*I=~vppLBI}3z(R_7~s?*2F3tCzu-7go7bHHiXOZ?$HtLqePgwr
zkMQY8+(s>IC$<|7#v`vc>;My581rI$e^yfK1Fi|2w+z&1tbdT@;vve%K5e#yNqBYo
z(g<VB!TzeFr-C&Oj{)O8>}JyN;dhild6_}U39Q0r5QnMXx^?TlzXZ@PKxxE5mgLkg
z(}6NY1vp$|-Y^8M<#gH0Uon|i^}LA#Ty_Z!Bo|?+6P`s?sx-IG0b9lP>m&rq`_EXI
z3<L$JUjSHKUYrB;|CXsCQdk8L1)f7aZLBOAKcs6G0PZ1+L(0{W?h(-UU3MtnetqtH
z01OtM-++*9iTb~$>s<~kFNmV=;T9q3m;qXL%~zC{ZwDj~P!UQJTwF}dUjb94YvgUP
zCv>gMYyC10;p%PhL<=~U_-vYp=h?S5$FWBjFe2XT?>5mkWLH+dvphF&4xqR*1FKqJ
z8^!m<Ka5gH{Q2`|F7PC3=~`+)+c-1!q^Mm&x4?O;bV!Ib;|{VoAk32fW`J+|r$UK>
zRDckq;CTw$iohBN)(zuY#}TR(!8#X~$b@%~HkBUlN`p}-=0+qQ0Z#@v6dFXDDJqHn
zP;`K)f?Xnb8>*-~5HXO-sIMCMdIg|!lsr62fY_YKh}C(2gQ9eOBK0;0`EC1$+rFv=
z*1GhfE=thXfIJ@r2ns?K!aG4SE^5NAUwil$_wTf!^b=O|7^ApOD3@*vPy+2aCW>lF
zO4p@g?;;@zKm^2tI5^3B9sak=zXxJsKg6Tk($aO1rD38#hrB`9a|msjKcT5Y1iqRd
zh%cm2eG9h+=R&$jQ>1czV3%);Xn0i}9jf#YJ1BSOP8;l29(KbCSjZ#@nG;<(i4#P4
z6j#NsoklSk2fd?*DEa*8IprUQ^$&BT03>q_+Hr=~4bRO1NIadi^W%qs-#I=hK;AW3
zHk}{u1F!wAdJqO3WP!i!C!8t%=<zJao+<l6D5DtBH~_tEK*#I!SVM0P6hfKBA^uEA
zP4w#J%jS>wD;9yKsWCOWF8KElHZ@&>=eFQ9eS3k8{?b$r()Fs%>w`v48@RG1Fx4Xj
zd?u)8@%>MCh0O51PhVVuz5BBaecU(Th+#us-;Xa(`g*uaW*;ly#nbU#+rmAAnI-eD
z{ycfI^ziRYZo3aGvJ`0c6{kQJdC~lrzP_zOB#(>iu~JB=aR3cxDr5z%Pqy#3fw?Z~
z@q{Nt^gsREf>56xA@?k>!MhhjjmXer*<aFCDrVjL$#MhC2r0!@R`fmB;)MqOo*2sd
zFFod%oc=<-%a`j7BcQJa$CTAmCpyw2485SR>FwEAnOR|>`}^ack9|ws?{MSBWA`3|
zD=wBfQ1+l(;JH-eS@O_3a^C<(b@_k!u&HU1dk^%wJMqvaf{2T50U6RrRzMnhd~wmi
z=Ih^%hw7EA@2B$s9B2-t9FEso@LsHxpXG+v`Z()~6X12R<pDSk(gW0B|I@*{VD${x
z#k)Y5LxqK)b?|-}nI;6GBiuREAc*(C$!so!H-LgbJYZLP4)fSfE79812f1Kn87MK#
zq<+?eXYR#NNiuB6$jM=Bh-sRw5w`ZWg*xC@;ge3qK)(a5i=GOg6aE0T;W&6a(n<{u
z2}yvG0r6Bf$Zt87yXWHJ2HztfTmj);Tu=>(7Yf<J@*QAa5&_1`)yk-vq%TFP!T8;;
zk&hk5-iO(WKpul)xXh!6g_+q5<ZJdKQAp$v-<-Xfu2-rB1&KyOxPT*c>KfkhR3y&*
zy=I^BVQ{CUBsSoBZ{r{wpMrV@tYaKbt2QBVHll$A4YinT2G*!_&H!R$>0#Cgq@4_7
zo&dOyoLu+l@d7FVAd)$7b_b5rB|)f%l3oo?z`!$jpzvyeTHOH%7<}cfN)4qndGz_`
z)xCebDM{JGiH57G`kvu*Kv=96=xyXoc>&jr;VeIoBvl8SQFYcLHsFBcAsT~q-6`MM
zc8|8w<q8$wT2w(RL*oQ9Nd^56v6bFfq?3WP!{99LkDRN&Ci%)YN1Z-iV#nUjXU`AC
z`qa}+%2j?K=DQjL%ZY^8>FU@IA98`X2eRcFVGueY<Ne^(?+XTGaL~^=2q{Yr8fm%U
z^7B^sH*bDq%;*3}4X}dFIVZdO*S}4h81N{#N%HfY8$~V?#leFXK&Bz2HUw{IRNi4x
zdE~zZTjX)#u{Ka<de|j3sDI%g5K&XN#N{qd_5F=!I)fR?@voT1ff00FMhG!z)VuYX
z2u5H-j0nEx*}l&huD${Z&6fx4(^p=71G>Rpe)7@0Z<8ti$cxgrS%+z<SR%esmpb|Z
z_ZXp@_EDxj=8^sMfhXr<MmeDZJ!7a8G6QGEZFTokP}Dpq%p#G9gC<$cbXPGC1tW}W
z=Og#Cf6Or3yX2fo$UHJ%IJe@*^d@r@5lRsGqdkS(E-5vN{jKJy-?MnF>Ot1`2TrG*
z&(7`%^>y99*N@<(z5g_39s}(v5V5oa@;>5G4-Z~o-g;}a%mkfi9Oy*2d@9~r%o{)k
zbz3tgcoivwq5%t$k-|SUNhjYSegk`obm-WzV4e#XdJG@gH#5@FrM<9tKot~;>#}S-
zQC!gCCq?nq*c*Uur@$x!8+9W~AbWKQ#X)M7E6)H_cx;E~9He0XLz(WE42WrHhbS!c
z3eX&d30l%emlIS#HJ~)A=Drp{cl%EM+8}n|CEw*cyCR^0fdO74R|VYmj(!rw<EEx2
zx`lIph4Sz0gje~LD}nB%w*J}Er{5hKN+@J`uduPPRa<Z=b-Z^eN|+02Fv7Jaimm%V
zCuj^A7^9wRNy6bsIT~QTW#8|aXDICdlU~D-X-(D6=k8ye734ajLvMCm_SqhJohG-3
zUfBK?q}LXJ&}z`u-pd&3n)B_4D6g>Q$wp|U$EauiGG~|hieP-(g6$pTBGg*xZ_z`B
z-jWN6XvrQ+!BTjv(5u(4RUrveI$DlQROgDScNyvS1rbwT6giTSQifp#CIEVkc%`h<
zX!Den%F@h02&07=)tH|Lor=Y(Jw&3u%wIQiEXQ1>lSR)>iMSeiZ=MV4vksjKcfB+*
zQAutXcNlSL=$g7t8N@hTw!LO@^o=W~P)E2?Y~cw(@(m7mA+dRSTb#3jjr~eUKS-9?
znJah510|Y;PGT8Hj*gF0zIpZup4(C9r28g3TI+T5DFrt-H~%$<!W(fYmu0>TGmlkF
z&A=h=)b~fBuoxE};k=Sz#+xykQJRn?v**pbnokMHxMKPKM|<BL6?N8aiQ0CzZR)R$
z1f|syR9cZNNHDcn1Ti2eQ6*;q$rRWox|JXWk|Y?AAVNu&tfFKAK`5aJw&W~1!|YpV
zzqi&~Ju`3S&G*)<^||~<gyI+OJ@=fw_c{B=S~;V#doqQip)!HB66-6NqJ}#Ulx#;H
zaE$q9Z23%&3C9cG1kKv5LP94=eM0zmq;I7VXdn)gGwRzF8}ADnlsMMRqlv;lD2Py6
z>oTt&Za3`$r3UD1ctbpvo$aVC5)fciGX1tvVR%2M9$XaoK~Le<Q#al>66m7T&kC6x
z$XL?$P)<fhTb=PoW7mpP_bK-oUX7Z6?osEM*|5K5u^iE^`G1oV?WCx(`t;9~`v_uC
zt!)w4JTT)>*mc=CDE+`rf68`#{!+_{7O{X3uE+z_1JRL@o!w{V=T(wpmKnQe3rY(`
zN9{T>ClHF~EFaK8sSD5|PY_(H$K(pnJmvH{^IY0)DV|+zSxqy$a5Y_$2_1{K`F5E!
z_1ZsLynRt$PlWr0Rq`C6lk@W*f5y%^zIdwMNl|h6NnEg>pI=+{DzW?214|AZXKpu3
zbRIQrMB|O*o_3OS08TN%^iIO3)kLdAZ1qV5B~PvT{U6(GdP{l6k%!?#45XM~2n$hi
zP+~E#njVSFyH%rk5{M8z5_*aal1IN}13RcQxvImcVlr{&wqBO1!J$e(Ni@G)BcYd6
z3}~K6rw|zuftQ&rl04o#y64p28_EWpL9y5n7l^4YEs4zl1gLZ{&n<SIUq1ZAPZPAI
zBN#qFp@zt~q>+Iz7P{3@$~6pKIRf-U!BdTlXat4g29r=y%480ZG7&ovKp)fqLp;+f
zzrB{}FpvOZ?kvu!_lRi8eJWJyI{<4GD(Ug?{q!Ysx<W6&(XwHWR#Ey}+~amCA@dR}
zNRC0R!rseX#;+bM_F(wQN*bon7N6g1jFg1Hr&+Qc19|fagi1ysjhgQS1Prr3ZToT;
zGj&MF5>7t4+2q*bC2jTb?z#G{jC)S{n|ar&x=IM{047KBVk+q|sL67$f<EH#;hQVo
zfuvmrtO4Xx)Q7*W@j%O&#hDO9a*3JxV`hXJ(*Gj8dnnEo=%kU#JT(X?*dFC@>2q{s
zw+>dX1~u@MQjOnjg=9i|-7eKT!26&?8o{6*lD2@$_S_%*<q5p-dJ)G}`BgxK3B)_3
zq%@FEzXN=lyxo)^3L;F6>jv6c1LgNqY`|Es2LP%eeIr#DBf@QpN*6V$qA-s0NdVtq
zf?`*@pN0d3i6tU-x${Kc>#w=-_8aN~_YWUBBnp87?&27*115OuGdP%0N!K;DL)%e{
zr4x;~ze&zbizqJ0EnxvG6Q!CPvO-kKvltwB1_ykO?3ORj`-Rs_yvJa|&hom?oCHf%
zjjm=pOv;H-XXuc$8I@{Wz;4ZMR5|s}E?mo<n=&K8drzPu3Z-fcxgb~sbfX_OJoxgN
z9F{-o#9t{V!UqW5q-@(g2|GgcaX1>Pz`eEEWDs!|z<N{c9F$;;6#G66LjHh9<ey}Z
zzTmjhV_0-&3*el(T%wSI<_N>c4ij{#&?R{3tpx2Xq6yoxr$5uDF+qDjpa2Nn@DDF;
zzlxtKg8^)?X*1Lf1hO9~+U64j<U<PLE{4aK$NIxtsNf8f4W<=TRG2`CiN?>azu_F&
zgKS_@gA&}K$|D@1`Lkcen#y3T>nLR8MDai*bEeFMcuXhp-SG3mjaz<?I||p@6NtY6
z+xF<@KE(=9pw}{V8RR2_wtcfWqWPyvx<2+B;33GSG=Wf6VppohsqOA-$f%CU|8i-Q
z!d&ymypUIt)08Z5WJ%kp*v<k8mDx!q)qELRYFA>CzG?yX06c&E{O}^zz$^A(TV2M8
zGz)BG94;659hK0JqacvYpq`lm#`r!;nOg<}sudIzhH5@9T>cf65#T>;388~qj|Nd-
zqHuhG-7SDz2CAYsLgt|QFV=H`<P}1YPGop$88&UWpPHDH+hj1)NeTt6_t?K7bC^wP
zL`Y&#U1yFQx$ZpH8;k8P%bXZ~vM4OI85uJPuurtJ(ongJTQ=<2`RID#-(h|O)@Ar%
z9SHxj?EB~xeCBRVuRf?1^vwoPsRG7{K{`w{heYa!719ktuw+!SLU7FAe=V|90T3t^
zyH)^+0-E}6jf%9d8TW)YbD1$HSEvXz5sFUZffFr^m}SFRa?uF7qCoQN`s{<>8Y&0v
zJqSkO6l#!E$H9vv=f|$GKCr=Vp&mU@jG9Z-vSA<5L0aXE27uX&1%ygq1(c_!@YyL4
zr<fN)s!QrDl<g5jvNbW`h-0t^_@OgK4X$0c?(R10uycw|sMAPUh9Q8P-n@CUbLUPf
z#{UK*iGPe_-o;ah!BrVNi|wb1XYihYAs9#oO=pIgjt>-ycvbJ9uOfsw`SdSXCi-U{
zVy(AJS=QQ?Yv}Dan-j#TiMAXM68iB$J7i^bfM?Z$pTMEVOK9NY;^G_LGFSzuaG}bj
z11D$|I!X<Zc1kiA=U&#?gDN)#Kn>K6>M|H14Q=)v3YO%XoNPH<G$4l>EV_QTMa-C!
zhxgm}RwDa=q>cn|TwUVX{t|ZEHwTB78G;Hu5*3u8K=l@?ZF|`}z?gU-kD-ktvfJa)
zr-{f?&ONuPy4nuFP7e;@em!yX5BH&5B9NZPs!gXBB(dOz>^dvA5V7XB*Iw%Vg8KNB
z&t{p+?PZ+U6XcPg68EKe@ycN^WS>Blq&JTwAt!Crg;30PS3SN>i3NrZ1pY>OxQqa#
zkFt5Fj8uUrui;awgl$9eC^N}G5oZX_3?N|mU<aB7&{=?Y2HY+<@d$K^ZsYxInX)~U
zS}FkJs{sFw!YN_>&M|xjN?c(V8%Tb^g`Yt<IDsSR_DI8nOW6ts1o3LI)=uB}VO@&T
zupub;ZbW?&QIUhC00KJ0(9ve4TQM=Zw4o5u0t#haLSBMd7(qcB0|J)_QkneUEbQ^M
zzd|Ab)MdW&ai9*Jg7v(GHAf0u0C&4|bF-mOi^t2|L7^N0B?Ih$@Bo;iy&F%<3_xao
zi*Tty**ysr?C=vOPLM_f1m~Lj2bbhPSG7ws?P*~U(ng{SBXk}<1GKL*FeEXnCTVIF
zSkqgV6A-6s3HyjQ*Ky3v2QU=yz%vkQg3fO2U;;|Rs8Mx#Bu3G>^h$_aaJTp*Tb*Is
z(XecUY<~c|0VoRrlC>0grMiq;f{r9rf1wYr;N@O$xg?sv27nvCXHQ`wh!$T8<gG%?
zNIBQixNk<VPqt7&+}{IF5(6A;Tox)Zs292+CA;fC*j1TIq6fxFp8$p6WtrK<=e22n
z?Y&E1qU2(3ND;D*CKl_{YfNkMSIhl^ABW~7Q@zHJNX`-Y!)asZK<YU64*&Mu<~tM`
z@PhtA;kc!*3kudt6@W-^RBB(vC3=ibM83%0*Jd^Y$6L2^5gQ@pwDZ5cHM#t9p4%)*
zK6s`}@tkzt%s|oO+`EnO0ZAGVW=fZjzAYc(7Lk$Aq4?8`oH*$SY)YhA6sP{%RAqhr
zNJ<wniHtN8AQdE?1=RW%e)Plk<fGQL+|5L%4oq3sJ1tlsSnuM+q{j1tJ?e%@l4g5)
zAx%}sOAdD(qK<owxuI&+vPug)Lx48f&8Sn6V^T|Y2%J3Y)HWRHIe{0z0CgfDE!{wK
z!zQ5<o%<o5owL3w+34c4vA|0-qE;dQlI}-HMh>KV>L{o8(~3cG+YH1&WHm&}cH<WJ
zuN}zTz=_c#b=@&dS3KD23eKU9IwFUdUbBF!aJ*;+PAKExbup_k0aA%LAV<z%&);V`
z446xl`^^>EN`{r2OMEDuy6eQ7Qs(~H^GpuO*YM7IbKmJeWY93`UlAb}Vdja&S7N6N
zQhWPO&77{Q$T~9mU4Rr4!W*Gh04igoLvM#?erQ4dLXU(}zO_CI)CE0~!1tAjiexcB
za|ksV1G#wyZ-kKEP0Gm|e@YGSS|o!*k$MUT$69Xgdwpw`;;rFYyD)~;pVGf;A@p70
zR;aC74{9a7!R9v6CnJF|MlggxL^*9N*e(P`LOyP0cmy)YSo1<Ps1q@aP)$MM)>waI
z95Bz*Bq;<OPsHS5`>bE-c{*(m6j}2rIF7!2Tl!Dy<$X5SAk`oO0vv2adE3A46VkY@
zuC5!K1>(wDZ49^8`(3~O4V$%DzRpDr!&m(y<>=+)G=Quw=ALCHq2~ov%RYMpNP|!+
z7U;Va*fUTvWE+40L>**EG@qy+34aK%&7p7o!q)kCbR8Gh6XdswB{9k7&<joruL;ye
zPT@Pf8b5($C3^Ao_e9-~xbK_)XC(-Y-b9+E-J|O2>Qt*{S?Vbq(6@OiQlU!RN+p2}
z35YDT>hEaV4pC92($!}raPrbefIN598zNnzN8*~+JB4am<q+~vBg5t~$;<EnsPu66
zxk)XnJKxWL?ydtJTAxeHA{)!En1b{MA;|=~)ucLeVJ<I%a%0&JX>)x6MH{{+x_^9k
zCzeCMehOt=c^O0%Pmyib@#_(x>ZyVfJ(W2ah&Vo77sjcMgk|me_3eIVBjwbzzFWG~
zsX-16vC=+*@CHb8OSpS(_XSLIg#ts%dzKt<VrBu9E-dfeXDc^@YRJJ<cc`M`Fj!lc
z9*F^kF-5e8#@WZmHGf%8{*T~KD1qPtphg4aO+>&rAw&&I)&l;R*s8Yh0$vOtUPCM>
zK;@EgP61RG>Dh|Kbb31#XC(U#gR>jAh7t~G+%vSZKEa8V#p@0cA#{<a;9D_o%ZRa;
z3X`Hur65V;qXG}jk}gO>RZNm|A~T8fyjuabhY7ZlV(?UT>@iMIJZiTo6MR-h*i5eD
z;KPd-FOFdTbeofFyYd?BAKLhE01?oNo`1nHem2;E2Bd&gETne<hzRzRU9)>a%+yHN
zHY%5-P2};=eGkYy?TmoYEAtcPIT>~4wM5PYFCS(67Ii3-4FDt7$SmCUrAFxR)v$2Y
z0mOqW|Kv79dQs3=Pb5pQ{BS>w)D({ff6?zo6jc~^xkFO2j#frm3KlK-wiET0+QNAU
zZfPszR5*NzEE92Od}ATetB@uTqJLXB63O|lQ1blTc_aZe=O)4fA~7M+0qc=Sr_S#s
z${S}Kbffs}cBh$bKGIvM&S*VGX@rB&An|NcO|{Om^S_afC8*(ujvKf<R^t=hyj-Lz
z57D$jRBk#Eir^A}M0e(1_(b%9b`d+Ie+XZ=caF{a30K`PgS0`S8ER2y#BjTj&XYuy
zW(v?3H-n<=G5GCHOunu`%6u86-}7mVl+DeZ__TcGX6dsy%t@O765;m*uK<Pk-RjL{
zqerh|l17l*%%HpsX5W+K0U12(mn><>%BCR5=^C~b-?)MlY=1|6eU|p|xpj-6;5Cor
zSKr;OskX}FFkbkcz|uxOq#z`-`F9#3d<9t`8T(9xeBd#rTU~JkZKne5Jb?}7TE+i^
z$M%wi8{ct~w3<Mi*}+P{Zm;_(`dm%CraD4GHSuid<Hx6wR(D@BF8EBCVF<`l%Lu16
zaw^C@0|o{Slh0vBLnV+NoK=02#qS}|M3Ny@Ar6WUr}8PtfQG#IPanLPSBs6eor=~k
zyqz1eo165$wAgS!trT6|rL#T!9M?BYUV$&l6^@x$XjMtE3xe}Bgqw)hHJ?_+zW$3<
z{;2XN?h#MaHLY+c%X;g~?VrB#Npq?|*NLhNg>nzp&7S^C3Zh?y3>*?c4;wD6K81>G
zD2KW=LGZ8t_)m51{e6$$E3JkFRF>nuV~Rk{iM*7FnCJOBe!T4~y8g%C9Xl3kSN{|1
z_3o5#Uc0s5b1`tzH=7n;IrQ+GFNEFVt1r**=>L&p?N48yheFI{kAusKfAf7Gzp;Pq
zq|wixzl!~KJUBjQS=BIqb3H^n!n40oI43pTe*XA5Z9xpi!7;IP*+LH)re)}U{i*+k
zFCX|rSik7UbH4<b>q3!BkU%){Y_Vu~eiF@`%oc7@<DYRjqI;}o<7k8;Rw_Y1`WrH|
zSeN_i&!C(|*8x*Vc!KNe?fCCti%fR2)KSS0aSM<OwyYLvjJ8YrhNL(Ds(z<qudU^e
zAO2Ez9B~Hv&}x%G{z4@^Ib?u&4kzd-ZnMrr#PNn_%a0TP_Raqjc;RPY!pb%oT2H`;
zT6-)O{OiFcb%JT8bWIGlfNyh3lR+7Rkvs84MaKXwku#4dJRCd;j)c8)0x^%A_vGXS
zQALD+{*U6(bi;g7{uUFh@eESXvBxu=RQq%exh+xoka7kIAQ7ejzi6~T{Syh!n5d&5
zhan6c#_9U@JV%V+7B{c_{AYe+;gO?9O#u|uLrv*DMv^gvA#i%zsZaoDq-iujrD>hM
z``F4(!aa_3z9Z2SQT`0NJiRcLfhGCCG5GfWFVXVnA8GLQVmEndyo2WA9gr_kbhwd1
zsZa>X65>4*6={-;+vkr5*=0SyN-c_q_Ye@6PY=i4=)JTL#G-VQv`})>5cDHN<D#$l
zgrSvjia@F))5TYJ0V_m8X*bY}@Ceu=Mlf&Y)OdrfigzyIK>tpB#I&{E$5R)c_jIu1
z%#!-9%ZTgYB|>B9Kovvg@HT9}I{LdvB_+Mtr^n-K8|Q)b!EwK%>JExmDoiB^E#VbG
zNco|`3h84L4q$2(x(}n#G09Vi0#hFLStnFg{qe9L05~DVG9RBQG(iL>Lm{7lJDHft
zzPXw=0kMztK=p?(@yhQ|WYFejS{>yD#o_}D)kTxkF7&k_$ML1eAb;JgevJv?sDRqj
zN)W>6t83^Og0CG!#HL_zN!F4@!+{BWuwmI4e*SsSC8)bm&cq++5C(rpI&raGNZt$8
z7b-0R0^m4<D7aP9;kjE7i3~GxWBt;{hLUo5C$geD8tVMC*ZSWz6WjGm&q1}PZNKmM
zxb*7Q0Irj3em{BY)=A&Hud8?+3$}&q_=CQq_3V-#6n;B$?bbuf7mpr(Q+Mk15;3jP
zS53AfBLhCx9(7@LdiK-7;o(j>PttzK%q#5Z>X;mpp6$1C&*R{r)MY!_vONje6N^1v
zz8y9u-WJ1{aU|^H{QeP!qE|IGh7l_?GGzoqs$o)?rs;+%mhJxltgszI9yen9cG<y!
zIDQ~TNMcpkA}cTif;TztK0kgleL#%iq)bc`^wIyyozXtpJlEG8h1u66C$jB);L%RL
zVDs^}@1jn{Mn*^TqbKshDwx_m)y@*nn|LEwU_SiT%FULz-APZq^EvL~GJQ-_XXN3A
zwwv6R$LOxzrz5zMo6;^hS{?d?yx%d>icDP2?mojP0jYsW<y9D7Qz$n#$Ts0YkAeFu
zR@C6a)RMAefU(DlQa#|~=<MP${6sxx7hF`>gXlqY57^8)WVdW;k(Hg|s(M6g;chO1
zDxN){J388^!A^IG9!Q}kpeOVXaijF6v9xqAr>Bn|ZQ~EUbcpTlgjt<+Cdz<Q$FGm2
zgS4R3Ju<tf^4d$HDTw0qa=FeOgi5i?EYWcGY`80&W-Pm#^13>oAZ;wiQp;u8@eT(S
zdQMLOGNG@CI4M&PgRC4%@r?~S&l|nQkIwJM6859W5hK4RGFeF$to`-&QJ>L@Ak{w%
zW=DyRCRDqI4)-Cyh}L*3ni(QQ02Z@P%H#BO32G9kd)jW=evAxT?I-<{r${P$^fE_~
zFR^R%)^c&YbZIYXb0U*q$c%VNfTo2x51Nf@Ozj#93-{mNM}?L?oRTk5$y)C^Fk!`7
zIeD}>h;h>1KI3+y_9{Ii%)EBUn{H&!l!nRIKD-B?WW?-G7R1re@Ab-WH5bOMUg0#k
z{80lV*3hMid~8_G7E7XCQFrPT8NhwB--;Fd4t}3)_7Y9p1I@%eV3|eMk@GqGi6zJ|
zG4X0_Y|Lm6F(XETUZKi53}$t6v#x#~tGuXb{gJP?_Hi6^CNa&#HTQAz@R)x{i-3s@
zKQ#dkNz39TrdG6u)!0HE)<?VG0<SyEg<(H$iJ>J~JFQ2rQ;Bit`nrMY@Uaw1F|l~r
z%8E{0SWtV}gYKfG+jgGA$$4yJ<M%8}Q%CspwBnv7H#5{@Rc+vFN#UHYuOuE|WVlel
zTFFH)RZ|jn-IhP-3OBi?u1ttRnk~84SXWW`w^I{vTf_lnjZQ={nN)}b0(0#Zh<zg;
zwP~V2!tnh0`&(FHa8_uVpBv|=lDCYea$y`h8=c$`kTNi$ZfI^iPp)~k-yvOdTxG6R
zg-&5O*TfB>>07G!b7~}P$ap(28_DFX*P2AKREt?y&@ld+sVMT|L5tEo;<)NQEa9G9
zZ5J`*A5Ec>i(kf+MK|wE-u(DHo#H;>;T|N2f%V%YT%XpMvfVCk3+8^<E*14<V0Z7w
z;0jm6a6Iu2EYcF<6ent)wL$CB!KR^rbbXxV#F;Gs_!)@?3jsmJZOD%}f%qP<y9e{0
zQAw=%VGR1;hYqAmcn%oyN^ZERsVNjS^2#`Cm3a88k^x3Qs{@IZ9UKSXkwE4SvfW$O
zL`=Ls&ZH3&C`!h}r`xck4aV##%r5L7v`ZJv?0g9CA;~&)`jRn%BV%LaS}?$s3?gho
z81(+;lW5?=G{=I(zF#^)Rpececps;_i|xWn){_&9)f52n7<`xv!GTJ`9Cn!4`4Go|
z<I;n#`)Yf-x{6iYr>YTO28voXk3WZpi8QQv-WJM^C6Xz1fC5d9(bia$lIcyv1*(WR
z55nhEdMwe0ZDl$G-mJVwXFM}3VH$HCCCSSr!xRnB`i+rioEtYD%h73mL`zRiZG)fD
z<y`bHbfhw97OSK>?~`BGZ|zeG+Xg@4n&gC$R*f-HNvrGcX|wYSr6_@f=Ud^;^Tskz
zGuL(U`EQ*SZez*X3FDz1(njeAyYUJpTrozkjCig#)+WA1_tS9rcs4(owO1gGxY5xD
zT{yQxPGe0LcAU0Ok24^VbIACgSEQz=cffJ>aMS0T5}wmD2|6A|vcthy*}fmiD-M%Q
ze~T*~nHfeTunc_U6uQvpb>JUg=5XHnI{WnNtmds>$54G8i~Ds#{I6eqlfRBV`VSv*
z#3gw-x#RnitF%>1f30A>?-d?9R%7B{NniL-5mlqqaQU~f5?L$GbHUjQ-&?j9a~FeU
zT<nueISe`2qrqRSP>od$(O$fF$#dt0k=uX&t1qp2MNNbE<frKvb^6>V7ZXn;QIQrN
z%*ZpASN9?b6q>DT+A6P#A$R$O7u_+tcxB^7P6W8%oWrdB?$vdXrhHM49_cCN!KEPb
zB45m@SW(xqmG%zNwUxUAPR6ShrsO!-TKPQ*evX4WDlT5Hb)I*l@kOCEqS&oUuOb@X
zo4QE+q{y?Og;T>tSK~1Y3de|&7-l4cv2HGD#XY1mL38H5JC#YaG(M@)B7QdRF~eF@
z;0ewRTZhhc%!VV^kd@_97Sz*idP-=`du+hZ<9WY%d#@0?eA@uKDayek&7rxuk$hu(
zyag`S?LPHQ?+tQE(R$;hiz*#={}iaqOGNg$KA>(*yi9DXn9RTjR7Yinjrda?arm%|
z$qnB1?@d$D6O;OYWzI-HdTj6By?1gqO<7pwKPnb)uwpS~_f`h$cUMIAj5>o5XsEPM
z`|i8yAL_~>5(;wb8L+y`SmrTrxzhWG`+z6-OuSZ?kOsvl<+hgJ`}=d;W%vwqp4pj|
zmMsTXN1uve>f5^OHA^UT_w;aAM~qHAja=Fkd$U7yrK|3f8jbeDN8R1$gEBfRPYApY
zD*pKKV~~)<z7KcRCVrQgNw-!!oPI98M&qL~cX-99PjE$Q0rPtPa7LfmK#!S96<4z>
zWSf$Q``YA^N5<01|M-@}&!D^FcvSFiO|Jd#xKvoR@g{fWoMvZdIY0a<7Mp(JPKKWQ
zs+D$+&n)gd>hq><)VWk?D!spQOl{eHkDE6OMp!QnY>)r(0Kb>}e9vYc^FIQZJ)_oB
z@nW*Ze8mWWBC4D`lk>T`-YE(kjd-@ZHNSPp`zTw_;&9GRJbB2)-JQ$P((ELSMvL`w
zR!&<bI#yampPHU7?5e1p2rdETpjUV?K0wlorEzk}k|kYM?=m_aXVv0Tqu0rP@{)+I
zQ%}BmQ{z)<+F<*i@xmK6xgW0%!=<I;L1uDr-QOAIc8cAHdqDB)l;r<{E&H#J;P|hM
zfdAyjd2VEt?N==Tdz{h#ld%3zxv3P1tSd(`=hR~eUhg}Ioe$er9Z7dE1}F#Y(GDS|
zaw^VMeq+YmWZdK2bl&`aWMsse)dtyJ8@ys*&l7fi0rgf|U5zPNd6@Zz*`7i^KnP~p
zj?=`Xl6b0NIB7=p&rZ+_6E9y8m<bGhK#dy?Hc*DRW3vr(<Kp7ZvVCPZqWnzO9DsWo
zF`Tb(u1#jojz(#0VrHi3?(WV`2oCd3ogftx+js`^pG$;#pt0YAs<{ls4#|K*?P|}F
z8P3G8j?^C+X!aV0#UTaXV}7WIoh@ioq-1<M0#CdllI8$YuYd#xjwYo3s;RjP{Y5Kd
zuA4!pAcr@=;Sn^-K-q+_&I2-^%zmv#wFwtcVt7Z~8S$gZ09+bSJNY%TrvP0E5Vcs`
z+2~YqI-E|4W%krT$!}j#(=+Nicw1MAPA*cfHcZS+4Jk;+cB&;v)mL}fAfM7|5xy&%
zY1$o|7%emC#eU3S2vTie6YZ5XClWsBNht;%e=6n>xsfYf2K8D?oE;#b^3&gUzQ3!h
zu0I3a7<^Q~&MDR0Yl*)fm5yS;EO5GOV4woMYlXA~9Y%C~ylXqgqTd>Lgyg&=GMjIE
zicKF?BXf)<q?4CgB2-<H%J0Q%8{aUd_Jmew%~TlJgdE~@uFmk)zSJQ{vo@!L`DP3z
zcY6G*D9yaoszKS)9ZRd@n>FIc($Z?3+!qHOH$1}8*xVekn$~%kN+)JPdf}L5MWf3f
zY9BOT9zQ)jeLAkjo=cVZe3x$z)DrHuMIljva&zB<`thSVvi|H`0|j8TMc!<h%hNM_
zLNYQkbygnrEX~&6yWzERdOEk;%1lu}sRLuctFn-7xHY~b2Y5~nO)0PMGp*kLOsp!G
zZ2PxanZjeY=1!|%2z>R}Ic_7d*pNT43}>rq#)^b&bamI<4!%$F_?P*XS}l#57ghZC
z?I{qsJRiDZZL^{tXnDcACmF>7xoZ1LJOD7eD7mK<`ST&ifpKbofyFa5#klM<WLkC&
zPI{x^nOHKg7Rg4hIg$<0rcguOl_}{)#^blew1J5m_EnB0xMbNUjl8d_trd@IcGm1%
zPhGzLmt!hsO4Ey?!fIzS-50}xVhCGAf^0ybQ6c=YgMa7+AW@z>o7VDJ@+h~=KQ_@M
z7ihBr=GG3g{xIj9aPqt-QM?9`RgBVr(;;K5IU+5yPpYzolV)F?;I@a@E49Tu^5z=P
zAm#P-#$Q*i3TT$Uh6VnfuSIO<d1GT^CHLB%K5Eol*YW&RiLs|i&bMb8?6cj>?h4-R
z9-Z2ks$RXTdAs=3ETiewY+AZenRxz-7YB~c*KzYajXV>z`r5T?oEvw{H>C*5ZHw@9
zI+$~#eJQ?HjBMrE4O2#qY0HR_S*R)Vv(v%lIfXT*sVlsYd<=y~iM21ycjxAIaZOjq
z^8$a|CaJ7RubrrQfj^OD=K=1u<nW#sPA%Xs$~nPcvUfJyk_=TK@vmcbJ>En{!BbG_
zyVz&upsS9;xhl?KG&%c=m-5W>3QWjsgC%Q2HD7OP+Dh&!O;<s$MDVREq-Q?c`=pk%
zTH~2JIeOHarB0gM$TtPBuv#uV_W>(Zn~{ECMAnrvEPnrpX52SC`Lk)XKF#!U1>sS_
z4qQ*+`&>rm^t9&IdQL8`lAA)aX{pp2r3MIos?RyK(k!Z@yeY#W>XqM^H#`?w?cgt$
zyWy9!&(wSFV!2i4I@xNpn#$BcBW2p?lg-!j9oKaFlSt5OK3x^gv<n)4-tNlH&BaAV
zktJ6eT3`_$1I<R1y9Zv(-e-Av?qxyT8-x7xyLHPSz_@N^)~fd7dBxZFkqgx&9cJOe
zS{C1GnQg33dc425w|%YX!5$_<#ocAHA_SciyK+}wQ4<->+uXdI^{Ru++9jnRsb;mq
z!_aT`ic$<zA+I{QiWs0$iZ6I<s-3vX<}IQgm~1%UvnnED>V0%OrViKlc^x5HdU<nm
zVhh1ko|h7Pcq(1bTQVwfDO>2Nbh>)il>}zbBs|$e$!|NCW`2&8to`q_p9<V%yM<AT
z&!}sQwNK(TwqkBD{Lo>a?X{ad_3wO0i1?zBw9k`d<UcvR9CCWJUDnE4*`fyq7+)&@
zkrK4UA5F*~C4+H@fjKfnZwx}@!3^)mA?&kYZ@kiM*5kehWD!l)rsv+JOg7KYHiu#o
z|9<T;l#PyHyRsYEZjp#mbm9U{TCVWHX)bdBSIU=e(5jqRXda4+#S!MC#$Y-)i@da`
z@G%1Uh4KHgesPRkDYUkiWBNr9oXc7vdo72#dGE5YlkB8oZ?*D0j;Z_CR+XeOb+Rg0
z=bm4>P>x2FP`JvhjA6I)oNo^iXD&2vo;PKCQ=voopOQEHFN@NDUB&;;8SZ~)R!dUS
zU_-R|v^kvOE;})qBl`O3xn%k#9Y6Rqe5u)$+|}Z0_|Aw3!IW?%@9o38tE>Qfchx_@
zzvBnh_UYu=Iiwc^0nu8?YdczA{+>JYjWDL#7mTrP_T%TQF+@UHU9+%ge%#W;OHYPM
zOhlZ9qxyI3wAS*BmNipT<Pj*o6yX)V!MvN-_@dYvkp)m=mu(3Azsv41U*z3L)hI3d
zH7hHtI{9!Otd$BRrEm>;1zI4u%a#MwYw1|p*SC96hY9??d{cdsla&zwjiA9f%h8dP
zf<gL<3KM53`#c^FFD6%Psi?51=(6g;;t%rIR;*l^s+#i%siDy9IR?`&rNHAYmGk!L
z6FE96Ilr=G=Yq__sP6fNS~=@v4Oel`=R6d{j~E!M`oA~TvowyGG0PFWP!pNGA^n3R
zw~V0i<GeHZE%A2Un=G>P1g9f!=y-IA3m((hcICKXfA;xoA*Si@)py;awG+L5<}MMI
z_UkxA{5YKHm<;=1LbhJqLcwY<N3?*{;?MHUs&=h;h7(6<R#H?{F8-3iJ&>?B*>%0*
zkpXh3WFBUH_uY5*RNuZv@*p%@%ajl(eu{Ht(WrCIpLz){=YNjB^iwHPb8)Y(^jz{X
z2zcz?=G*nE{p}_y-=9qBmCtbULRvI80ZGP7op|+6&o`<}2kh?JnHyN}0nCr-BOO1l
zCEWMUzI{yy0+N}8ncNVJLI#<9A^+bUg}BlYf?*szBnHsMux#auGHO*#=vBV+*^+N_
z;YOc&WJY%1zM$QDKpfoqgqoV;<2AGV$}Eo`T$2I~$cnVU`KNU?Vxv9>uM~@q`ms$7
zIW&lyAW63WFHpOG1;~E|NDO!T4`}W`bzMBnsavWfhA~Yih>gPx7*7YnIgoTD((ic5
zGXBszsC9dX*lCf*O%7*&F|*p%SIY%aT2~h7LB;Q430hRkq0x@bP9iq)sj&s4l~P()
zTgIUjzXP?dp|SA}Vn>~jJyhb_rKnp%NDUsFqAg!5@yI#@cBPxLV$sNewo6|s;D6$9
zWCJm8bXL}WxZHoobLjwKhoZqqK&eUWuS2wVkqbMZ2=ovI_2j1wc|jD(o~OwL(sBsb
z(!lZIUF|yj)=z^CCme|T>44Eh!8Krou*_H`W!zwZ>e+}w#en4iYTN>deisJSgu10B
z0pAA6U>!*K8b1ndkr`RT7Q<AQfMa-}`pj*1isEy<tOc4*Dt%;pJX$TTPc$ZXuz;Iz
z$llWp;BRf~3<CS~QnS=~pFCDWP4SM!x>s^_DN9BTU^`+Y<}>TRT$QRG7e5$wTwcB~
zsDAtm$mP+SmWiHtnj^iHV{nT8a?~M6MMsMjXgxk<G7-cLMB~Ka^s`4%z_P5kUC5`s
zjgXVmw(#NEW;><PA|u5)mz8z?<$IdZE^?~5&G!ZbZzv1fFTpt)V&2bfj$^Hw#o8Yw
zr)hAUz!yuGOL3>a)Yu(gWs9o1Ym-O>w|lr;&1E|~FHYOAuy`TCjRRIKxlh1-yw*wq
zPu#daC8sAhfm|J+0KF-TT3Y;|Y#=$<H1z6M@9q2kzTb)!D=3`dulve6|LDY`InE!2
za9>9FPwR+#a_)1HO2$1oImIGA0=W@KWBTViRqY*>bsQ?Yx_CpTcs0X?=6@HObsBk4
zyk%M<_WrW7+4rkHC!Te$AJQL=x@nnwJ?-#n71k;X_dn+f+dqbfxi1ns!&5sPBs6P3
z@rb@Aq9O7s-~C7R6RSt=Zg;<<AirdZ-=ST}9iArBr`u(>bnKQ*rmj;~l6P@=a#y~)
z*~;F`GlLS`%nB$j@-<D(@p0e>vflg(&n;!Nyv|Z*Z@R60DUX2r-sz9Hs)jZ6x%J29
z1En)x4qsK+KB~rP>!U4gsPnmGiAphTwkJgOUARko_O`+i*2&v`PRSvvmEZi*YcZoG
zsu)3^(ksXd{iJ$I^5Y93i}^>h;Q^+s_w%3h^1L(jOy!L8YJ)diwpYdP=Xi1Uu5(V)
z54vkNj9%lN`FX%?JS!O^CbK(a1~?spp)YB@%WyTIw}zM{)OB6&x~)5Nkr;2YpRb!`
z_FzH5+Lati%d(~n-Y2YBwJIv7F$3G?#Yp>U6*>;u?Q9M_Y)>-Cd`iCDZ_PCxWyGf$
zKF?DNg)xNnA3PL-W8TRP^b8E}W;FcZp8~+(F<Ge2v{tU+6IDRYj=H@W20CSM=~DOn
zEq$pvR)jCJ$8Pv4wYIKK(lyv;MMWP*7EI+eJV-sW>+UaF&-WI84m?hRkDtIrz96iH
zq79)?HF?>V^D&|D0T=bcznW{qG68c|<Y&m^o4YEEA-h^AGsR6{X^cN9!iz57%-ho6
z%aiNfNgj=LV8&>zrXQt{$F}UR4f{<c=bT9cflMew98n;hhnrpS&e8Lcu|H+TE<cUI
zQ8e#|S`(<BO|`Ow0W|8s$W-WLpmav(!`s4070~4*MS_u^06Q2Muf)h37wAPw|GpHA
z>?T*rq2Yr73R1OQBw|vD1YAOs5-&B#Wa*e4B}w>60EhRqw~}u^Q2OJ6f%BMV%vPm9
zry!oSmVEt6Ks(pMJl(bbd9v`Idc6ECW%++_6@72_oaY1XTM9K>h<1Ue4OT+C;QWxF
z9|a<&Qm2kEX6vn>WldAH>4r!NiN`xB+9k4h46l&qI@#}#caK6)XWI`KJj5~{20xCs
z7|5-#va*s84AO^Nn9tB3Dz00pHW1Eql!;WB+x)8J02<4Q*o;%oWjk8WO)=&(i`NOQ
z$p+zEN*u>2lb!N%As`O<)W3P5#D67R9}cc>_gpwipdMmqL^%gdF*Aai2&D?ag1h?r
z#oL3{=0OK7-_!$|$}4OA$yn8q8VU>dRg{>7rbvd|8=zmlLQE#ps7z=l8H3?#&8)38
zY*<uzdCyDss{h}}8{RebF1{ZLUbeM{;buo~#n!a25i46ONb6EeMrxaq?sD6}hJYso
zS)l9B&yHK!i0!o_UatB&pWjEzrWV)iU4tXgSEyq)xOix$LrzY`rM{X@L7k(V@GS>j
zAxDQHk+sku62%+%k*rRR`+v_|;~jDo48IX{6T*BmCwi;Z{Fs$3=GqL{O!`2-F<Bsy
z>q}unKBEZ^eV6NiT|&sfPqqlXm6+TOzuJ&{DZ13EF;k2{C-Lb`(C*jjqlt@%BZ!KE
zcr+5a0{Sn$4~IsqIbB!*7L0*=O(Rh=?|5h5J+JV>$xF_59+(+QPPY0Dzvh=s)KBJn
z{bAQ50PDouiWp_-gQGZyAw@)1h3^oO=cQf7^ip4RTzF;mJL(xrlS>|cx!UbgbkE#m
z#JtmS$u@s$CiL?b<q##5Vc-W==e+RCTA>=rglIXM)=j7163^UnTEwwt%N4^ClCj{C
zMrgzbP$q{0^FoPmAWI8Lgx4?g#5WSOvVxV>!1K~MnAx*ow#GU8cJAhhqfl7*7yyyY
zDs&&uYHkCVrfvEB&zsVJE>kH&pBLt3zmhTKoqFz1Gbv4DI%G3VN6h5vfgSk)JL1Rm
zeLc)zd7eEQZ)Gv%!KWAJNuajL4~4P;DCYV$4}caxt3BZ_W*VDkwRHw>skc5Bq?tUa
z>9#NJGhfkXT*x#nv=hwgO_J_8SD|2J)DhGM40!W^NA0KQP{&wvwl6JE+u6R9L&={b
zWM>D&NhJV0hp+QXZ;^vEYX}F{_Kr~7<5E&m_F4UeOF}OZ$?KfTZNo+Sg>`4U)zdZY
zjfjq`9WBep>7h0yJ0<&4s%>5@`i_WMVD1cwk_G6Tfwo%$2}RAbjm>>Sz2B!r`*2$c
z*V|w)Y6sMyO2X2y&1f5h28F>Ekq}<x7u{TGqH)`HPwYa0T8o!uDTdqzZOG~T)u~Kx
zv&hf|E64;VF&rw`EFpt$9s{z%p=~#Ul|_rC*~rF_7!W<EEXnyH+2ScOmLQZuDI6Wa
zZ?4EbACy?*h#T)+$;3;}K%<`Dg3lSqGQ6`LB+!f*H&!3tzZ=_Vo8QAt*zD|2g0SB~
z>K_X92v|05V?A%BE80MUVt`JSuFlS5k55tIf%<6kXiWCUrGiE4)U|v8oElz@53Mt_
z;Uy!y-*Os-`f^)7f3St6$j8|IsN&)i)m_0Ll#a`cz9&uTR60aeHqG40KA!Of-tizi
zXpy^#ezII{eulUmf+U0Dj|zim9Op?S#TX77C?kR%WQ`8dELNb%pE`3xfl!-0|8xsp
z9R`f_!#%;pg`-MBe$yYa@BgA#n$2+~_8`Yw|0)nY3|gNV5iX%ws7Nd)A+!65jD|a8
zmQHAjjA))|mmpbuf%Lpz#0&*gfK#!e4m~Yx(8E1Pr$SzK$d{78=H4T3Dbv8|UL?D=
z5z`GaPgc{i`#P?)%tl_aN@w;CIzw271O{!N+H)f*qIz)+ADr6+bc1oc_t}SGZm}q4
zb66D3IgF6CC+JdFdWuj?-|n3)8)<Ro3+keo>zwdTBw5yb>`H}6Z0I|PJ4RThc<e=3
znvlKvVY_TYrc$}tq6LMKg0QPa(k2`Lc2qh#tOhR6Vlu0PKHUl8CFPW5y*FhopT<@O
z80t1#bL+(=k^yU0t(gJQ;m)RhahzlHz-$T}9p?vm=Y5gAiDvQe{np|<RbA*Mwyyr7
z&gb7_D|x*x;)-&AJRtJeeE+r!EQ$HM3I_UDiA(ChJ8`0aBKZ?j%l56kkv3k$b`lOT
zrNO~jzMV2zSK%g}6c*Na>3R;<q}i^NHdgbTJ-s#2hh~0S?lPCA*C@JDrKw4dgP*k1
zoP_Nh96BJ`JWhWK0|W$pP9A1;1p6M=4_}p6x-n<Gc6c$z*bQuDJqXQ7)kf+Cl!>0w
zb5D(A1gJxk74B|6ZUxW>4xtR&M{;~Yx53-Kg?SKd@z>2tj3K%)U<iw#$=~p;y)v(a
zjtDCWYGYTtF3!oi7Db&QI+bw&4{T5&r44knarwR$&#*a`-vKOm$AjSWY*2$4;Ba3V
z=M`IMEi>EIQXnF@(a>Bh96@#Z5LTTvvbOEi@cEezD~C;~h0^Up<X|8*tuEHBOG-Sl
zJ#i`tKL)LdMCk`<og`ZG9WwgU?wy4i+h}o3GXIEmi?X5SQTJo}NWiH*XVeDC-4Xhq
z5fLOF?a|3E->B)89={t?T!t?7#&K|bYf3$GP_?N`xP^9h#!qh2Punsb#o$PD`1M8D
z<v0#(=@jDn%U8Ko!)xJP8|myV`(P?f9_+7Ba9z=#h+3~Z@oEEIzf3<BRE4KVc~LaP
zp{|4sMS7FY>ceD_pCPL&R?i)}OK5;|AWunpKq3N2_{O$O)NNEc`pfza%r;gi^|X*W
zi#R71O6T?+-8(<wGappCC(FESSbme8Y7_K!1A}gSF4?unH|Bx09rWJ!>38YzjEcAD
ziMcl>qN!(gHZ~ERVNqdwM4Y92yAv%)tO#}d^oNU9ez|mqWJNK$3Bu8<xYK>6&^=UM
zru*b{wsKTJu)S8r5s`SGx6Ao_62;0$RCvu<skDr!aUsDETDp9FE4U4=;h8j%)wxqO
zj3`i5K53+e--pni0n^4I2z~{qNBiA!1bkc?TIkX?Z^gOvqDBYP_WKNUD|ruwV|-I+
zr5m4ZHfb)*fU}MKoVMF!TgJz$%WdADlouQ2@+nXYPQ>(pPn3MdK)L7U)vR)7*4?9C
zex|7*M5L?2)?b?zi3e*K)?3T0d7C0DE*K<T5P$`B+DGJm@J~d&ODV=M3bL*p%Z6Ee
zCq|uR@T2p|rCG{Ww&)4t=gci&kt39l!Vq<Swq)+_eU?wBAmMX>--NV*WzxHDLk->t
zoMA0)do$1>VoTJ86?456LnNh%7#9g{v3t@`<0fCAc66++DA5iwVty*#o-QKU?rlc0
zeP#MSK4kSG4Vw2ypNANcWn1<<N-ax4APX@;AlvKY8SmL!O&<0u&Pq{jU0vN=>^h&B
zdB^5TtLbIugrx^rYd}$-!<<`UjQLbEeUDM7b-`xk>uK8Dd#JfHZGtM##60DB<t}Q{
zyh8jdMg~pIWgfo%fZ-$;=&ew!8yAUl$D;6aAKc*XOPTb5W>zNiI3@lidq2ttZ4eDe
z9Q}TH@$lj+u}3(Rt1Yr_kLdAEe;~L|eSfs(qj_4tZG<8nn7*OUz$NJ^)*M(^H=kM@
z6l`TaCF9dA0ir_bwV9loo9pAt^nC178hqTMj=Mlj5=aoH*|^bLL6w<9<WSvut3V2r
zBwKBr#<DC$BYs1#jq#7{^Z2hHBM0*GMWV_MHD}bfQ+39Y0{e!zr*H`dIMk_h^733#
z`rs#eQU75^U+*=UM4W!o^OF@S^laqn6$XLY0oZUxt$bW~`-FaXpJlqYp*P@dQR>Hy
z5wd&Cq*4tYdd4Rq2^FsjSia}^&v5K8AqNu>5YJykAo)WZzDw&(6~EY1v7VuQm!Yaw
znws00&wg-8QKvQeN<Qo&=VwkESEVSKGj|6v4FRf6nNVS`{Uk|Gz=Y=d5g&A?ceiy^
zN9r#!TpD|Tqj|JeDGF{1Huvs$mcOc$8=};Ub}3RcX-RzQn17;}Bt*8%Hu8V`(Aqa#
z%3ql^Q`bjy#MxcSR`p%OytAG1!|0!N4V<3Fg1nQKCNu9ggE1~;V06WW*Be`S_jIZ7
zs(5$350Ei1S6)TJ)=iF(s7z?g=_{H=A3{l)UONAhcjh26=43!)#He+fME;Hkh-DcS
zG)tnvcSXAT7Gf1TIZvVPE<#lwffIfgTiny?D}TK1-ZQ18Q;s%D(P_Oq^~`Kkcikoa
z>})U)S$S)Kre!g5ej;IyLbC;Fm<UI{L~=99);ovovvMqqT*Z=daYPAPIhAU1Wom~k
zJmF%Yu==EE&-mgCI9Z!w-fM^Pl5*wBVyz|r-C+KUd8<NiHk$D5q^8eB-3u5X@ynOl
lzRMSs!!Hgk3{ZadJ?omUf8iNBS#t5zUymP2ICTE%{{VyL{Y3x(

diff --git a/articles/EUCAST.html b/articles/EUCAST.html
index 42d4ca36..62c16cb1 100644
--- a/articles/EUCAST.html
+++ b/articles/EUCAST.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 56191e8c..07dcc7f9 100644
--- a/articles/MDR.html
+++ b/articles/MDR.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -385,17 +385,17 @@ 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         R            I          I            S            I</span></span>
-<span><span class="co">#&gt; 2          I         I            R          I            R            I</span></span>
-<span><span class="co">#&gt; 3          S         I            R          I            R            R</span></span>
-<span><span class="co">#&gt; 4          I         S            R          I            I            S</span></span>
-<span><span class="co">#&gt; 5          S         S            I          S            I            I</span></span>
-<span><span class="co">#&gt; 6          I         R            I          R            R            I</span></span>
+<span><span class="co">#&gt; 1          R         R            I          R            R            I</span></span>
+<span><span class="co">#&gt; 2          S         R            S          S            I            I</span></span>
+<span><span class="co">#&gt; 3          S         S            I          R            S            S</span></span>
+<span><span class="co">#&gt; 4          S         I            S          S            R            I</span></span>
+<span><span class="co">#&gt; 5          R         I            I          S            S            I</span></span>
+<span><span class="co">#&gt; 6          I         R            R          I            I            I</span></span>
 <span><span class="co">#&gt;   kanamycin</span></span>
 <span><span class="co">#&gt; 1         S</span></span>
 <span><span class="co">#&gt; 2         I</span></span>
-<span><span class="co">#&gt; 3         S</span></span>
-<span><span class="co">#&gt; 4         S</span></span>
+<span><span class="co">#&gt; 3         I</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>
 <p>We can now add the interpretation of MDR-TB to our data set. You can
@@ -438,40 +438,40 @@ Unique: 5</p>
 <tr class="odd">
 <td align="left">1</td>
 <td align="left">Mono-resistant</td>
-<td align="right">3216</td>
-<td align="right">64.32%</td>
-<td align="right">3216</td>
-<td align="right">64.32%</td>
+<td align="right">3189</td>
+<td align="right">63.78%</td>
+<td align="right">3189</td>
+<td align="right">63.78%</td>
 </tr>
 <tr class="even">
 <td align="left">2</td>
 <td align="left">Negative</td>
-<td align="right">959</td>
-<td align="right">19.18%</td>
-<td align="right">4175</td>
-<td align="right">83.50%</td>
+<td align="right">1007</td>
+<td align="right">20.14%</td>
+<td align="right">4196</td>
+<td align="right">83.92%</td>
 </tr>
 <tr class="odd">
 <td align="left">3</td>
 <td align="left">Multi-drug-resistant</td>
-<td align="right">462</td>
-<td align="right">9.24%</td>
-<td align="right">4637</td>
-<td align="right">92.74%</td>
+<td align="right">464</td>
+<td align="right">9.28%</td>
+<td align="right">4660</td>
+<td align="right">93.20%</td>
 </tr>
 <tr class="even">
 <td align="left">4</td>
 <td align="left">Poly-resistant</td>
-<td align="right">247</td>
-<td align="right">4.94%</td>
-<td align="right">4884</td>
-<td align="right">97.68%</td>
+<td align="right">242</td>
+<td align="right">4.84%</td>
+<td align="right">4902</td>
+<td align="right">98.04%</td>
 </tr>
 <tr class="odd">
 <td align="left">5</td>
 <td align="left">Extensively drug-resistant</td>
-<td align="right">116</td>
-<td align="right">2.32%</td>
+<td align="right">98</td>
+<td align="right">1.96%</td>
 <td align="right">5000</td>
 <td align="right">100.00%</td>
 </tr>
diff --git a/articles/PCA.html b/articles/PCA.html
index 54ae466e..9d21b7fd 100644
--- a/articles/PCA.html
+++ b/articles/PCA.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/SPSS.html b/articles/SPSS.html
index d1490d1e..508d374c 100644
--- a/articles/SPSS.html
+++ b/articles/SPSS.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 1f54e2ad..a249f55d 100644
--- a/articles/WHONET.html
+++ b/articles/WHONET.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 6c901b9d..49e62d74 100644
--- a/articles/datasets.html
+++ b/articles/datasets.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 163c9390..546ac2a3 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 5c6e8636..1e81f2f4 100644
--- a/articles/resistance_predict.html
+++ b/articles/resistance_predict.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 f4759077..fd0f462e 100644
--- a/articles/welcome_to_AMR.html
+++ b/articles/welcome_to_AMR.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -267,7 +267,7 @@ every code change.</p>
 academic research at the Faculty of Medical Sciences of the <a href="https://www.rug.nl" class="external-link">University of Groningen</a>, in collaboration
 with non-profit organisations <a href="https://www.certe.nl" class="external-link">Certe
 Medical Diagnostics and Advice Foundation</a> and <a href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a>, and
-is being <a href="https://msberends.github.io/AMR/news">actively and
+is being <a href="https://msberends.github.io/AMR/news/">actively and
 durably maintained</a> by two public healthcare organisations in the
 Netherlands.</p>
 <hr>
diff --git a/authors.html b/authors.html
index a148829f..a29d75b9 100644
--- a/authors.html
+++ b/authors.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 422d68c8..f76b9dcd 100644
--- a/index.html
+++ b/index.html
@@ -42,7 +42,7 @@
     
     <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 424b379b..8685e658 100644
--- a/news/index.html
+++ b/news/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -142,7 +142,7 @@
     </div>
 
     <div class="section level2">
-<h2 class="pkg-version" data-toc-text="1.8.2.9152" id="amr-1829152">AMR 1.8.2.9152<a class="anchor" aria-label="anchor" href="#amr-1829152"></a></h2>
+<h2 class="pkg-version" data-toc-text="2.0.0" id="amr-200">AMR 2.0.0<a class="anchor" aria-label="anchor" href="#amr-200"></a></h2>
 <p>This is a new major release of the AMR package, with great new additions but also some breaking changes for current users. These are all listed below.</p>
 <p><strong><a href="https://en.wikipedia.org/wiki/TL;DR" class="external-link">TL;DR</a></strong></p>
 <ul><li>All functions and arguments with ‘rsi’ were replaced with ‘sir’, such as the interpretation of MIC values (now <code><a href="../reference/as.sir.html">as.sir()</a></code> instead of <code><a href="../reference/AMR-deprecated.html">as.rsi()</a></code>) - all old functions still work for now</li>
@@ -155,37 +155,37 @@
 <li>Now available in 20 languages</li>
 <li>Many small bug fixes</li>
 </ul><div class="section level3">
-<h3 id="new-1-8-2-9152">New<a class="anchor" aria-label="anchor" href="#new-1-8-2-9152"></a></h3>
+<h3 id="new-2-0-0">New<a class="anchor" aria-label="anchor" href="#new-2-0-0"></a></h3>
 <div class="section level4">
-<h4 id="sir-vs-rsi-1-8-2-9152">SIR vs. RSI<a class="anchor" aria-label="anchor" href="#sir-vs-rsi-1-8-2-9152"></a></h4>
+<h4 id="sir-vs-rsi-2-0-0">SIR vs. RSI<a class="anchor" aria-label="anchor" href="#sir-vs-rsi-2-0-0"></a></h4>
 <p>For this milestone version, we replaced all mentions of RSI with SIR, to comply with what is actually being commonly used in the field of clinical microbiology when it comes to this tri-form regarding AMR.</p>
 <p>While existing functions such as <code><a href="../reference/AMR-deprecated.html">as.rsi()</a></code>, <code><a href="../reference/AMR-deprecated.html">rsi_df()</a></code> and <code><a href="../reference/AMR-deprecated.html">ggplot_rsi()</a></code> still work, their replacements <code><a href="../reference/as.sir.html">as.sir()</a></code>, <code><a href="../reference/proportion.html">sir_df()</a></code>, <code><a href="../reference/ggplot_sir.html">ggplot_sir()</a></code> are now the current functions for AMR data analysis. A warning will be thrown once a session to remind users about this. The data set <code>rsi_translation</code> is now called <code>clinical_breakpoints</code> to better reflect its content.</p>
 <p>The ‘RSI functions’ will be removed in a future version, but not before late 2023 / early 2024.</p>
 </div>
 <div class="section level4">
-<h4 id="new-antibiogram-function-1-8-2-9152">New antibiogram function<a class="anchor" aria-label="anchor" href="#new-antibiogram-function-1-8-2-9152"></a></h4>
+<h4 id="new-antibiogram-function-2-0-0">New antibiogram function<a class="anchor" aria-label="anchor" href="#new-antibiogram-function-2-0-0"></a></h4>
 <p>With the new <code><a href="../reference/antibiogram.html">antibiogram()</a></code> function, users can now generate traditional, combined, syndromic, and even weighted-incidence syndromic combination antibiograms (WISCA). With this, we follow the logic in the previously described work of Klinker <em>et al.</em> (2021, DOI <a href="https://doi.org/10.1177/20499361211011373" class="external-link">10.1177/20499361211011373</a>) and Barbieri <em>et al.</em> (2021, DOI <a href="https://doi.org/10.1186/s13756-021-00939-2" class="external-link">10.1186/s13756-021-00939-2</a>).</p>
 <p>The help page for <code><a href="../reference/antibiogram.html">antibiogram()</a></code> extensively elaborates on use cases, and <code><a href="../reference/antibiogram.html">antibiogram()</a></code> also supports printing in R Markdown and Quarto, with support for 20 languages.</p>
 <p>Furthermore, different plotting methods were implemented to allow for graphical visualisations as well.</p>
 </div>
 <div class="section level4">
-<h4 id="interpretation-of-mic-and-disk-diffusion-values-1-8-2-9152">Interpretation of MIC and disk diffusion values<a class="anchor" aria-label="anchor" href="#interpretation-of-mic-and-disk-diffusion-values-1-8-2-9152"></a></h4>
+<h4 id="interpretation-of-mic-and-disk-diffusion-values-2-0-0">Interpretation of MIC and disk diffusion values<a class="anchor" aria-label="anchor" href="#interpretation-of-mic-and-disk-diffusion-values-2-0-0"></a></h4>
 <p>The clinical breakpoints and intrinsic resistance of EUCAST 2022 and CLSI 2022 have been added for <code><a href="../reference/as.sir.html">as.sir()</a></code>. EUCAST 2022 (v12.0) is now the new default guideline for all MIC and disks diffusion interpretations, and for <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> to apply EUCAST Expert Rules. The default guideline (EUCAST) can now be changed with the new <code>AMR_guideline</code> option, such as: <code>options(AMR_guideline = "CLSI 2020")</code>.</p>
 <p>With the new arguments <code>include_PKPD</code> (default: <code>TRUE</code>) and <code>include_screening</code> (default: <code>FALSE</code>), users can now specify whether breakpoints for screening and from the PK/PD table should be included when interpreting MICs and disks diffusion values. These options can be set globally, which can be read in <a href="https://msberends.github.io/AMR/reference/AMR-options.html">our new manual</a>.</p>
 <p>Interpretation guidelines older than 10 years were removed, the oldest now included guidelines of EUCAST and CLSI are from 2013.</p>
 </div>
 <div class="section level4">
-<h4 id="supported-languages-1-8-2-9152">Supported languages<a class="anchor" aria-label="anchor" href="#supported-languages-1-8-2-9152"></a></h4>
+<h4 id="supported-languages-2-0-0">Supported languages<a class="anchor" aria-label="anchor" href="#supported-languages-2-0-0"></a></h4>
 <p>We added support for the following ten languages: Chinese (simplified), Czech, Finnish, Greek, Japanese, Norwegian (bokmål), Polish, Romanian, Turkish and Ukrainian. All antibiotic names are now available in these languages, and the AMR package will automatically determine a supported language based on the user’s system language.</p>
 <p>We are very grateful for the valuable input by our colleagues from other countries. The <code>AMR</code> package is now available in 20 languages in total, and according to download stats used in almost all countries in the world!</p>
 </div>
 <div class="section level4">
-<h4 id="outbreak-management-1-8-2-9152">Outbreak management<a class="anchor" aria-label="anchor" href="#outbreak-management-1-8-2-9152"></a></h4>
+<h4 id="outbreak-management-2-0-0">Outbreak management<a class="anchor" aria-label="anchor" href="#outbreak-management-2-0-0"></a></h4>
 <p>For analysis in outbreak management, we updated the <code><a href="../reference/get_episode.html">get_episode()</a></code> and <code><a href="../reference/get_episode.html">is_new_episode()</a></code> functions: they now contain an argument <code>case_free_days</code>. This argument can be used to quantify the duration of case-free days (the inter-epidemic interval), after which a new episode will start.</p>
 <p>This is common requirement in outbreak management, e.g. when determining the number of norovirus outbreaks in a hospital. The case-free period could then be 14 or 28 days, so that new norovirus cases after that time will be considered a different (or new) episode.</p>
 </div>
 <div class="section level4">
-<h4 id="microbiological-taxonomy-1-8-2-9152">Microbiological taxonomy<a class="anchor" aria-label="anchor" href="#microbiological-taxonomy-1-8-2-9152"></a></h4>
+<h4 id="microbiological-taxonomy-2-0-0">Microbiological taxonomy<a class="anchor" aria-label="anchor" href="#microbiological-taxonomy-2-0-0"></a></h4>
 <p>The <code>microorganisms</code> data set no longer relies on the Catalogue of Life, but on the List of Prokaryotic names with Standing in Nomenclature (LPSN) and is supplemented with the ‘backbone taxonomy’ from the Global Biodiversity Information Facility (GBIF). The structure of this data set has changed to include separate LPSN and GBIF identifiers. Almost all previous MO codes were retained. It contains over 1,400 taxonomic names from 2022.</p>
 <p>We previously relied on our own experience to categorise species into pathogenic groups, but we were very happy to encounter the very recent work of Bartlett <em>et al.</em> (2022, DOI <a href="https://doi.org/10.1099/mic.0.001269" class="external-link">10.1099/mic.0.001269</a>) who extensively studied medical-scientific literature to categorise all bacterial species into groups. See <code><a href="../reference/mo_matching_score.html">mo_matching_score()</a></code> on how their work was incorporated into the <code>prevalence</code> column of the <code>microorganisms</code> data set. Using their results, the <code><a href="../reference/as.mo.html">as.mo()</a></code> and all <code>mo_*()</code> functions are now much better capable of converting user input to valid taxonomic records.</p>
 <p>The new function <code><a href="../reference/add_custom_microorganisms.html">add_custom_microorganisms()</a></code> allows users to add custom microorganisms to the <code>AMR</code> package.</p>
@@ -210,7 +210,7 @@
 <li>The <code>microorganisms.old</code> data set was removed, and all previously accepted names are now included in the <code>microorganisms</code> data set. A new column <code>status</code> contains <code>"accepted"</code> for currently accepted names and <code>"synonym"</code> for taxonomic synonyms; currently invalid names. All previously accepted names now have a microorganisms ID and - if available - an LPSN, GBIF and SNOMED CT identifier.</li>
 </ul></div>
 <div class="section level4">
-<h4 id="antibiotic-agents-and-selectors-1-8-2-9152">Antibiotic agents and selectors<a class="anchor" aria-label="anchor" href="#antibiotic-agents-and-selectors-1-8-2-9152"></a></h4>
+<h4 id="antibiotic-agents-and-selectors-2-0-0">Antibiotic agents and selectors<a class="anchor" aria-label="anchor" href="#antibiotic-agents-and-selectors-2-0-0"></a></h4>
 <p>The new function <code><a href="../reference/add_custom_antimicrobials.html">add_custom_antimicrobials()</a></code> allows users to add custom antimicrobial codes and names to the <code>AMR</code> package.</p>
 <p>The <code>antibiotics</code> data set was greatly updated:</p>
 <ul><li>The following 20 antibiotics have been added (also includes the <a href="https://www.whocc.no/atc_ddd_index/?code=J01RA&amp;showdescription=no" class="external-link">new J01RA ATC group</a>): azithromycin/fluconazole/secnidazole (AFC), cefepime/amikacin (CFA), cefixime/ornidazole (CEO), ceftriaxone/beta-lactamase inhibitor (CEB), ciprofloxacin/metronidazole (CIM), ciprofloxacin/ornidazole (CIO), ciprofloxacin/tinidazole (CIT), furazidin (FUR), isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), lascufloxacin (LSC), levofloxacin/ornidazole (LEO), nemonoxacin (NEM), norfloxacin/metronidazole (NME), norfloxacin/tinidazole (NTI), ofloxacin/ornidazole (OOR), oteseconazole (OTE), rifampicin/ethambutol/isoniazid (REI), sarecycline (SRC), tetracycline/oleandomycin (TOL), and thioacetazone (TAT)</li>
@@ -222,21 +222,21 @@
 </ul><p>Also, we added support for using antibiotic selectors in scoped <code>dplyr</code> verbs (with or without using <code><a href="https://dplyr.tidyverse.org/reference/vars.html" class="external-link">vars()</a></code>), such as in: <code>... %&gt;% summarise_at(aminoglycosides(), resistance)</code>, please see <code><a href="../reference/proportion.html">resistance()</a></code> for examples.</p>
 </div>
 <div class="section level4">
-<h4 id="antiviral-agents-1-8-2-9152">Antiviral agents<a class="anchor" aria-label="anchor" href="#antiviral-agents-1-8-2-9152"></a></h4>
+<h4 id="antiviral-agents-2-0-0">Antiviral agents<a class="anchor" aria-label="anchor" href="#antiviral-agents-2-0-0"></a></h4>
 <p>We now added extensive support for antiviral agents! For the first time, the <code>AMR</code> package has extensive support for antiviral drugs and to work with their names, codes and other data in any way.</p>
 <ul><li>The <code>antivirals</code> data set has been extended with 18 new drugs (also from the <a href="https://www.whocc.no/atc_ddd_index/?code=J05AJ&amp;showdescription=no" class="external-link">new J05AJ ATC group</a>) and now also contains antiviral identifiers and LOINC codes</li>
 <li>A new data type <code>av</code> (<em>antivirals</em>) has been added, which is functionally similar to <code>ab</code> for antibiotics</li>
 <li>Functions <code><a href="../reference/as.av.html">as.av()</a></code>, <code><a href="../reference/av_property.html">av_name()</a></code>, <code><a href="../reference/av_property.html">av_atc()</a></code>, <code><a href="../reference/av_property.html">av_synonyms()</a></code>, <code><a href="../reference/av_from_text.html">av_from_text()</a></code> have all been added as siblings to their <code>ab_*()</code> equivalents</li>
 </ul></div>
 <div class="section level4">
-<h4 id="other-new-functions-1-8-2-9152">Other new functions<a class="anchor" aria-label="anchor" href="#other-new-functions-1-8-2-9152"></a></h4>
+<h4 id="other-new-functions-2-0-0">Other new functions<a class="anchor" aria-label="anchor" href="#other-new-functions-2-0-0"></a></h4>
 <ul><li>Function <code><a href="../reference/proportion.html">sir_confidence_interval()</a></code> to add confidence intervals in AMR calculation. This is now also included in <code><a href="../reference/proportion.html">sir_df()</a></code> and <code><a href="../reference/proportion.html">proportion_df()</a></code>.</li>
 <li>Function <code><a href="../reference/mean_amr_distance.html">mean_amr_distance()</a></code> to calculate the mean AMR distance. The mean AMR distance is a normalised numeric value to compare AMR test results and can help to identify similar isolates, without comparing antibiograms by hand.</li>
 <li>Function <code><a href="../reference/as.sir.html">sir_interpretation_history()</a></code> to view the history of previous runs of <code><a href="../reference/as.sir.html">as.sir()</a></code> (previously <code><a href="../reference/AMR-deprecated.html">as.rsi()</a></code>). This returns a ‘logbook’ with the selected guideline, reference table and specific interpretation of each row in a data set on which <code><a href="../reference/as.sir.html">as.sir()</a></code> was run.</li>
 </ul></div>
 </div>
 <div class="section level3">
-<h3 id="changes-1-8-2-9152">Changes<a class="anchor" aria-label="anchor" href="#changes-1-8-2-9152"></a></h3>
+<h3 id="changes-2-0-0">Changes<a class="anchor" aria-label="anchor" href="#changes-2-0-0"></a></h3>
 <ul><li>
 <code><a href="../reference/get_episode.html">get_episode()</a></code> (and its wrapper <code><a href="../reference/get_episode.html">is_new_episode()</a></code>):
 <ul><li>Fix for working with <code>NA</code> values</li>
@@ -284,7 +284,7 @@
 <code>mo_synonyns()</code> now contains the scientific reference as names</li>
 </ul></div>
 <div class="section level3">
-<h3 id="other-1-8-2-9152">Other<a class="anchor" aria-label="anchor" href="#other-1-8-2-9152"></a></h3>
+<h3 id="other-2-0-0">Other<a class="anchor" aria-label="anchor" href="#other-2-0-0"></a></h3>
 <ul><li>Added Peter Dutey-Magni, Dmytro Mykhailenko, Anton Mymrikov, Andrew Norgan, Jonas Salm, and Anita Williams as contributors, to thank them for their valuable input</li>
 <li>New website to make use of the new Bootstrap 5 and pkgdown 2.0. The website now contains results for all examples and will be automatically regenerated with every change to our repository, using GitHub Actions</li>
 <li>All R and Rmd files in this project are now styled using the <code>styler</code> package</li>
diff --git a/pkgdown.yml b/pkgdown.yml
index 543f1d3b..d745ce81 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -11,7 +11,7 @@ articles:
   datasets: datasets.html
   resistance_predict: resistance_predict.html
   welcome_to_AMR: welcome_to_AMR.html
-last_built: 2023-03-12T12:06Z
+last_built: 2023-03-12T13:11Z
 urls:
   reference: https://msberends.github.io/AMR/reference
   article: https://msberends.github.io/AMR/articles
diff --git a/reference/AMR-deprecated.html b/reference/AMR-deprecated.html
index 2b6d4499..d22d29be 100644
--- a/reference/AMR-deprecated.html
+++ b/reference/AMR-deprecated.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/AMR-options.html b/reference/AMR-options.html
index b4b382b0..99e74492 100644
--- a/reference/AMR-options.html
+++ b/reference/AMR-options.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 af957088..1472b873 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="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 d01d6a9727d9f989c3824dc0baec9f37ab9587ca..67a0d8bb037be102651112ede4aa1facd3e43fef 100644
GIT binary patch
literal 13279
zcmch82UJsC*JcVG=~9#?N=J&IfV3!r5UTWE6cJELl-`3PSZIbOU8EVh^iBi?rS}dA
zAT>bfgg%$|n}5ohS+mx^=9^&=as&6CyYJr5e)e<rIlO<Qqeeq@l?ns`(ZJPJ^g$pn
z?&3v32K**)s3{(JBYUE$rUJYJUxyFsgMl|ngu0nK2t>_z@d6J_2c3XG*FbQU`-VQL
z>(l<JmOH1!tx;=KH-Acj${b6z@O=Z7Rec-EVPW%%%fnv{EwEo#N2tsVPaP{O-Wz_<
zzbv{s@I3yxQ<JLR07)i1R`9wBWo^eZ@1=wGr3gj&q@=XzuCAo%Kt=9o->$C1)Wg(+
zyYA0smUiyS#PGsV|F_@hfFAJGkaoLPuJ>Vq^=~{q_uX^!own3fP0~C-D7m7m%gl-c
z0|fb_$&IxojhunGPale<S3tbX!y$0;YiJ2qtNi-oYY6hv+kR6NYp7d2KQML}VOuZ)
zTI$;-8_iUw5=>!vIr4xXp;StWVBZwmq}r^#`I19@+1Lu)K&nL+=4q?+97zCaBF`a9
z3&en`;z{L%`?*0FO+70ah|zk)d8FeM%>Gg{|J9%eyJum`fj3HNeVA(?Y<)VX^a(CE
z5YL^*a{5b;CEG-w`g%XBRYy_vrECf`Q?aR!;ifohIF`91-N7*sE|OK``q$pu;2U<H
zr;1nII7^vJ;k!ztHw8{=-B{UK&ez8KOWD7EuHxg39M)5OOLQ`957bMQZ_nL381o5^
z4AWl5Nd7*t<hC{6ZHoCzEpeTt7vpFuS}J|DN-d#x2qK`iQ1Vnuz+DOSOs%zvk=@Jp
z_L}$i$8T>EloLPlqM05tp1m7|OqM^6)`b?q=Ucze%BZvkF|jA;V5-~0L?gpaWu*&m
zUEN<<4s0H~UG*-~+xzyKKWUd*L)5fgEBDt^lBpTMfT=x<!S~U(yx0nXtX@vmMy!Be
zGq0}VmhVTmioco|n<$!2EzDc7i<=rs7JIDruz&~*<{szZWJ>s)tg`YLkMsun_tEo+
zbh=*srGoFHglAV0KBT|+x<tHvrupLFMO})YmcP_}b^7tSfGT8fRrMvww1?`c!6Z#O
z_>;?X`OAkX{X6LIvnzf|>W)Uu_f2UD1J56Y4E|v?$n31o>Qoh$V=ApH-wO@&lVRK^
zg{)Cek#Z<c!X^x(Mw86T8e9PjPj;f3v7vo?<MLOnNjVZ6%*QWZbujnuyf!G`zw-n0
zjkaxH@Nv=_X#hx8=xUJKu0l-TaDXsR?Wms5K;%d9!7eDA+G>W=N=&~$2{}C7P1>YE
z+~KCsiVPz>SRT-t6yBGc?mm9Rkb9mnhEJ0>Yo8j!i`tbOtYMbiMc-(_AYwcfxD063
z#=TTLGUe+iovNn->CK3+z?hNqR2YNwwTB*G&UV9euw$kDSLQtcU_Gr>S9O3Gdym|c
zz3L;FxmO<6w;j?qU4ueg7RCGu-O;JO#=$nE{89|^T{55D_Ii<M(|ycVsIiH^F@yPY
z5G$j3Aon$YR^Qxx0NpGtLBs46?>B5Pn~>bt{qkks_T|HT)A7PF{4~YROI7mKpxb^*
zuQ#!kQV7R3Ss<EGVyx(8EzD|<F@uik;jPloh|A7*W}{^T>$(mo-aD(@KJ&nhVx%iv
zd|e|%D5jGMpdHF%MZz`Sj}BCY^LRbQjEGg)Plyk?{#0gUN&1*`NV5QMvD4)lBZW63
zEN)UoaHf_3?tAL1x{S0HNA@9m%IbyHU~axOtYRqQi)pTMg11A$$A7VnTvzLQPDc#k
z-J%9D&DbyCf`o=n1CtS44mW1MByTKVvnA*R?b}XRD;C7mO&)7GaH`093R|3}fNTVR
z+^+v%PE{|Q%**V|Hj9c9K{ogMSvv)r>w+iPqWk@Z(fa(j$jF1+<|Q3N6sf}@z3FQ0
zJ?-o7^heTM%xhimy(FD&;qa`m(uoWe+nB9Tv`5PnEjcZjw-59{T;DgAT(_KiMLFxZ
zwo6PJbje<Sc%OMTm#GsO?&Qx&fKtc=`tX0aGg@=FD$B}-s<q`sOFgGt3fj0Y`^yf5
zQ2vz1&}{mUFGJ~-fJ9AfMq@Z)=DAQ@*@jj#&oMgMNRNY_anr3VLMpXt>GA=80Cv5|
z&M1`n!$}BY98R%Bhh-e@k4DURPd#lp{E<{eb|#SAbH5^eXDS7eZ*0?&Bf;Gz<yt%o
zk_X##Nm_Xtq>~rwPjJ>;@d-hMgfmynbX}YN%AqWqxT707dA!aYPsej|_cR32gf|OK
z;Xa8Ru^^pMtEsTP@hIcgde@c=lwZ}}j#6}BrecPLP+ig3GVs2_TGNLR;UmCs_7r>F
z((60DKjTJn+)9C-VkE|=g)^Qj`pJ>%?H)DC;1OyH)<pHmD@3*187nV1Vd^y&ePxbk
z8<~68(8~Cz;KgmhDpHI<+0Trkp8ilm07H>X4iZs`PKY?-Nwqs6j2#mflqO@{INu=d
z$6%R?`}V#&j>#zx=wNw-X5_*UXRoT*&Ztjp4EM!bYJbv;eRw4``-H2UlAYS^FPn@B
zC;iA9<zgB!__`blG5gN>i0N!9oZj_WUvwX1iq5h=mvW6O0;b$)%a~bNey7N)+vuqq
zc<Ji7BTh@1S|lBmPa*nk*JVTLo;tth-MKWbU&9%Fq%-MFp;_++r<2_twvt1t2PWPs
zAxnS6G|4gPT`Hh_pG`(|zik7}I&^#&hSjb?jZ)c~n3n_w*pTBleuW*3Xh$C}MLZr~
z<xn=AIi+L=^)g8B;wi9j1^y=)wF=T{#Ij^An%-eZPjwVS;NpYOnegVeu;4hvpKb!9
zs|lOTrxU9m!=WOvId1hty_ULoFS??l^{$uh!{EnB+A4}+qPFn^7NJl3((5VT+cIa8
zCT`?6id=1Cnx#_L4rNjCYc~6^CK_)!kEIL9C>6h@N@Qw|MZC>+u=~D2rNvVbxBaUg
zRVDKTiO33hIN{0`K_d2buMM<<zgu$6Ip74<hl0h8B={dyWO#I*V*4uw4uq9Rh?gG2
z7R{BZeb_QbaXHI7DfGp^w8&Sv*p9V0Gv&RUZkHr=%2}jkko3H7bBl;~d05kFsE}LX
z?bztS7rKyQZM-t6BHb@-w#Pr!Er^vfeo*5%tEEh6<*z8V(1j#2FrJHlJ)+XnHhq@Z
z=O>W2P|=vXXB_I=EA3h=_r>!HI$Q!=5SPnPXX?#U5pV=)e|CjX9tt~F!sI!w=WE-f
zcpLB5K5wZ^HhF3s!!t@`r7yk_aCf%X*9J$athsTcqH69L<39DX87BeJ^mmfI(T(pn
z9nR2WrB+!hPc^_M4+rbw`toP4+of_Mn^j6)P*`=+*G<86OmkQ2Pn|U<Sq`H@SseXN
zlve>n^R|%nKMPG*%({}%YwSSt7@GB=FGoo#xtH_n=+@DoInuX+|A#ZXQ6~kt9L@Vx
zr4I;tgC<Yso0*<X2{H6)Ok(nKANqz<xPql7_Pze1Tr1CFVu*u_pga{%XPO31ntvYp
znsbAK<vk9RVGIKX8VdW7M{#^9(<O1EJG8h<6%(R&NIx+X5eVV=r1W4i%QBeN|1?iw
zo<Nz(5WjxUky3^p_2ZSYrr6Bs!LVRDQ3>bKuvJ5KcN0$@%JQtv;!a>nQ+U=v2?L`g
zH1oLXeP2uQb&f0bX1C8qD!3$u?v24_E)Ty%+@@Q3`F@5jI?_gcdaF0$L&6UjMc(hH
zGkk&;X{QwI-~!5`E$jZ8B}Vh*t)uxchA7#dwb!oMckV{<SJ00#MzO04tXbm6iYgj&
z=B~9g&0STF{#hT256t+?pjj-o+<)c!ujo41=h8y!YtC_hugZ*WM$LRyMoF7LQ;yB%
z`XT1{e-x(KTv_?2dUer;!k#2W2iEbtPEJNf!$Z9Msd%p!(?&G>ws8G|pRZoa*v0S)
zfq_pbMq5b2e?Qy^bsEgwgB|{jJr}^r78n=hUy*U0`o8c1zL4mmxcmWoyt^{UPOC)3
z-H;6LO_ItBIw5XlXkO1P&Tc%1j#4fR3`V`~S<Ovxy;|$DHP<xWp`)<%)7QeacA<_~
z>J~81o>6yxc1$2Ot@CgynQaZnlJ!-)VRQ0QUv(70PFHl#$NT#FruzCypB?S4&~Tq^
zhMVNTQeK=W+oef4=$s}tpDX}^>?hNK$204Ai>5u?N1KzmhEF%t7Om*^vBBqq&w5ht
z3QZo+_DxM$dj94K=eX;+I;2a(A-h?W;@0Fl_c)xhD%-<7&SKFoP9vwxHKMwr!o%@K
zqkLfodpXzNqbUDvxy&go490#b&3((Eieo^;ok;CglAjI>KI`SzO+V4{Gj(@&A8vU|
zEyFJ7v3#^!x?YVv8#gO|W_5goFBx5L*y$Oy^c*%49~vF4<_JF761Q~kzEh9&!xx>i
zmdK0}-~OyS-JdM!-doyVEhyPnoR5%FIvlf$o;D#)xt%!Fe<WDFI{T%@E&1xr9`2Ae
z(YSf5jU85hc7)$4ChzX<9tPDm9oU?%npiaKjaa0JnwH{Th?o=?CP_SQX$t4`8k^5a
z&8s1Lr@ARlu;t`er}i`wzkUo4@F<}fc@tm0G}HhL1xt~%?|==oN{+s<#J<$E7Klr+
zki>jZ^3+b;Xs~bYYR{AAHk=$7kQN6#T26S!aAW2JT|s{SbMW$HwVS4=r>B9owl<aR
z+SvC_!0EMCi@UEPg9qRO>(~;TW}y`4vOjbLz!lx##qN|*hk~N2BOPt+?zGCv%Ifxd
zq-1B5!uD^2^X<fD4if_d_t7-(ncBnch0Yf*o<HBnN26=qkJqbV2i-E*!W5-Izt+#U
z>~zLm>(`rx0l;LO6Wd`k;A=bOsy4r^Fu0Jw<YHL|55VeKa+h(Dc#X<hNA0S;YoenE
zGXR)^cDiM3Bz!h;xd2j%>*u57xiaKDS8-X<N=Nf6FRR9%rBK_p5~Qty=H3fLy>N6S
zngOO-nb-vG(U#8oNd+G5hE028PepF1MKyf0X$~?FYd+c5>)V%a+Ni@<MSAmznO8Ut
zc5Tg<6{U>-9PPEln^zsad!po-;!>4VI%@5o7FV><#g?m+Cg;=j`Bomt(to{jGB44t
z_)y%C5Ehmz+U%nRSdVKwD0FdC{Jqclp!XsoqBz>;PEP3j{CtgH;oZr1@L9vd!^heB
z=v7Ve#@(`+M*lp}^XJdYyhfk0pNEihu_<|POm(x0ntL3f_%!`E5g4!oH-JDBog_*7
z_EtLB$*1>B0zu8Wx%_t=dQy)((vs}On}|=ErFs?I?+1uf)z%*F00=W0wFy3zUP|}h
z2x7JNnU&gE>}KC92|mTo?cqmFqS<9O8r*vo54Qa`YL{4(GK^fuN8VIeVvqIII4d1T
z3eBYG6gDxvV03wp+>2G*k2j-v7Cm~6cS>iysS$+6gQ(BaE)zW^jr$X3D=a2edy7BF
zxl?ZgrdwGDZ2fYRk&!Wpydt%@z0)I4nA{@x00`Xk#s(f74+gGRjy|-H$`I?UbX_!)
zCXA-ISanKs59aB*eU@>X5tDk>RdcQUqm0R=i_zol+N08Z|9(>Al5}6}O0)J|h|kDw
z@0C7(yD#afvgjAHF0VBA?gq=juBY;N5>noA=$l>v>rK<XXD{4<Jo51a9%b}wzq*<8
ztjH}8AF3%<`_~=`xW%<ktbR>Wraa1d9=1PbcO%1Z6@7z@=IZW>p|H{Pz<_?a*ZRcj
zPA@EYkwJ~)WfyRE*};8s&fVQ{hm2GGy7aCxsUF$+h1J#7gY!!X;x^Xxy-LTaN<d5$
zQ&Lj=FP@$Om!!BqHovl)VOe))rog1c@T9y$e9|u_0q;-#8A$bJ0j<xQgvG_hd;rx!
z2LS0NYptP5S594%u<M?qRtHl|x(eTih4NCj9UCU)C;iG@r|VXyy(ZUB3B(87M}07x
zpu>eA8XB7J+NOEuc{(nI8llnKBgIx0R_e-~RmJux(T-CB<$TzHTNilkr3F|t;ZLkU
zuuNLqtL|O^9kpeRKJvnVO^2L>=JPX?8XZCQ`Y-a|ya_Yt$E{(a)=dG2$L!)Ou!O8A
zx##^X7S3C3jR!OG5F8NMsr3k{p3ezpYE@eYlWxs2?UC%>1bIS(`xAHfoh1YG(5=0_
zJ<$i@v{vVTV|8b?wmZdj{eT2*YuX*qJRRJ7n$VrDSdRs+kdm-cdO7!#Q!Se7bbEEU
zKsQzP<y@$Tx8Di5i4k+q5<pg>TfM@~&iMQ0#RPzR-LOvvvf-0=0HAeez?u($YsiNI
zwr<$&5RsP#eyg~Nfwr0yncrcPK5@+m+#d%6Y=ljz2D}Fb%A()-fbaN7N?l!D!WF4!
z0Z&&3avE7g&4=(giPjT#iB?`_X$1Km>7XX3%SUSef+z?ezV6!9Kg_LpK%u)KUbp;}
z`vQ>d#a8wEr|0DXI|f=w`1@4^pYbyLMoWRXNJ=)-*ty1+1~!56JGbmNP<L9xXeM#t
z$FUhFe-D;Y-FuYASCel$4RDJ!5$EgxV~xrK4!7@fAO2P|{+r_EcLMO7>yORZ)<ha^
zrGQtSo_i(XH0)k{7?Y;$_`nOAT^-Epbpf11h`f4d^)yRRs*_f6w=V~%B^)s`(f)w1
zdwfr}+F>JoeTW@^ER(%qV^x!`$JQt1Z$)c2?Dr;yCK{uH*4De)vnQXGh<PafK4|0)
z-0i343fTNHy^6;N*LI54pAa`&>4I$msn97s?J@WkxMQ_;x`Hn@EG%pR-~{<+U7zFI
zIlZS|1GQ_mGV?*W4xB*ftekQF{)^@OilX1{FAKNEy<Q#I>Fy*RkGGh^ZUJ1TaGlGd
zYHQHGHWw^`z+p1_?vF3tuo?jh|DC75V+zUx`K7`DwsXZc2cO?bw0!la)5OYics+yo
zQy}V(46Jsw$8fkNXk7$*v@w|!{i`|nX?I`d0~7lwZX3F?!1mT~I=3}AK9p*7a3gHj
ztl`R=y4sb&JekzsvqSFS-F`KK_=1OZ;NEZ(5btqo?`cny@u&$=mD<H-k(Wt-D=YHz
zg+F<Yv6N=(j63C}jToA5$Ja4hU{KlVT`Z-%aK-nOrl@R^&qzMF2J)Z7-3pP%jZx%}
z;VAQkM&mdCv;<alRzs#k?wfVACCXfeqx{13?%mj=v%Q1xdgyC6egnmeCMR;1n(Wrm
z){uTy$H&)cVNksB7^;bMMkCS_ike$~E`Y#8lW>uRJ-re{iV%s}QOX3a+Ia{~ToODM
zi|NqghohwRn?AnY6|uMsTzlWUo^y5<BjX;oM;=6}C1!COl}$4=GR$c?*hiRnGGeAW
z?1uF}UME~G@6pE#Cw^66A!>u?Pq};q)y9{QnW`VvQA0wa!^85f!E-OO!q%(3^;Ta5
zc#}$r)i?*}35#SEzmf`@q~;JOt@!Jcjj$=>H44pN75*mNla-(U!fVl3gPM4I8_Sz%
zQVaRSi{=o{AyxQ<07p%5(7{o4@fsSgqko0!>zwt}QHEsk0}xlj@4vB~jBk12_WDAU
zvO9~^NgINi7motZ;RPhF-(@Di-INkwO;jA{-+%Z`TMl`Bl6efKHqM24u|Tbg%ATsT
zfLKFscI#C!Itts}MDZ>RO*3vzEr06ldqsL3uCKHK{U1d4zx}WhPEIeyAxk#K%0_h)
znhx0j&uB=9!BHQI_02KIpb}7E2zv-6=JFR-3FsKCwvQJs+@#?I#!{@2HKgA<d5NRN
zxnamVIpcWYIr>7PI0j4$hyBN_SAh2;HQ<<0wdmQ3HbYWpDy<J4yzo_NVLq@8RaX>O
zH%9IY>1XPjATh0*l5mv97yW&(4Aew5`s^OJGNX0>4?jQXn^$o(**okU(666A@R8n(
zOy`3uKFZ<6)x`H_Nhy(~re-;dwEVE-l+G?5jj$r$FTy|7V)yY}NIv@YVw9Vr;ZdO+
zAKXflk00k8MH*y)@u5?Tc8=mnwV8Sheepv7AbPUWk^FTU%w=Oz=G@`FiP=6XoAFy*
zAZEQ*5ooT%g>yBW-lmZa{S_h@72g9shXYTpOR`GAPBPAg;`J1XkDDs~W8mdE{xyad
zedz*wwQ1XyYSNmhAwDAwh*EvpwtUBbfDK4su3scK;rft^wBy74Umtz}Rsf!mfx@B3
zkOv`eF&QD@A-N>;K)x;mmH>5*b&S)LG=Ret!a?43o!8-?hyalz2?&h|1qV@0fjGzl
zsA^b<uOu_!D8qnw6w;gYjNSj71%cQ-ipHc}PC(L;_x#b5zC?WB0FC2Ce+GhgPA1Dh
zwBJ2rGmo`bl*q#P1+glY-3=>sGec$nym)4AqKE{a-??0A@a_=^=ME>~gX^OWH6TIc
zV{iJjEop9kh*ykLR{=8aB9s+;!1@JT|E^(&lRAobPw;=8X8zj|A0LN2Ak%?XMa^)7
zr=Y3eA%5ly@IDfvBtFHld;$A_h*vl5aSoU>jOw4a{BZp(BaO~ikWB_0C(h57ndeFg
zSvq79l*s(3*Aa*0u&-C!GfWMeS)2Gc(fYy6!GKz~*!lU9|1G@M*t?L%7rMfWEOrL&
ztHbf#-E=J9HP9B{&?T_C{3@^Gx#2P8!H!`WsOAR~hfxzDN*~!$Nxw#ZzaVixR}yKV
z(sX7F{%%Lrht+)9(|mvK;1;h|fe5al62U!D%gNuK<xA(T3;C|`NZ9uK?yvyDpv9#X
zt3oRA&(d!D9hLmHLIy6)N8GLl3rtm_#(|o-N{wDCgS|RXE>YvO>HC29>qFd;lgM81
zAW|1f2gv|&0k<6eswUlt%w%VJDzskt2Sm$p>zklww^?lh{#mcMhE@F2A<WTYYn7mf
zMPVRQZnq|LktA9ko$osHv_$cv@${;1iOiNqe?DGX*q(c{788WARMzyg!PVcR-W0(#
z{8f(SMH4UZ`5H0gKE;Lk@~tpujRC-)GJrpKUx9z}?ybhkYoHLR{YgaXg2UoO=mBCz
z1yI&-Z%S^O;2wP`z6o401O?VvVv*e;Zj3sV=HRJ&va%h;FM#@_?En5YX&2~>b&Nrl
z!iURLL)LB7_&YB=t<M@#OxngI_HK&=!6(5+yw=A}G@!HtESl^{L^jhk&;%qPiWXf$
z*eUbKMENXJd;{Lk1An#X@aEbyz${?ubb_n_f{qanIs1<6m0|s_hFSH=Lj>$E$<vNi
zddva^P3rFp@Od)AAhhNf$tppqiK@`su0&QpZ53N&v3<JI0u(BT--$>!fz)#gW_B;(
zO#7P`W;cl~e?+PwsY8}1OfY^lM3t3RAesF;6Op5kW9SW}b_gHl^{b0~@=g>7n}Gcx
z_0UGi4*tZO*8%?^11O30nD&qiXlEx=6d)NMG?AQW+e+aYC4T{BB>EFUaQUwNM~Ds&
zuRm~lC~*2K+v~IGm{TdPU(%#L%tUeoZ(knCAV6hL{^DOZ@e9LbHOEKz-q|zRUfQI=
zS>udbepnAu9Po*$a7x@(8>gRWKK&|J0+aai+fxjZ{K3)zbwoe1^Thn2tlj;$>>4i#
z_A{4m-h2|c%CpUfTkm91?ITfu27rme2)0e?7UtR@(8)KXTpLtiQIMTdp|)R7xPT$h
z@APqANFdDAz{qO3P2YNFw74MSbR)Suxi!qRTH=Z6DP8r|{sZa<WW#q@vYHkM#e}`K
zmf-_2(}u;0CqttKL%T&8cMy4;+tUk04~)B#C##)?^Eq!(7Q7MO^LkwnQlHhHfT1J}
z+W55msr%MnSI!qV_VSC-=L<?%VFv^wgSexh(}^cdTT^RuhQ>U82gT^)SyF%6@fX#P
zRndgEiP%MAd0t(8?weKVE88<YtQj^Hb~?|3&R5@dT7T{@8ZnAj^S3IKhhV|35i<nn
zu9%YwyB||cS=S)IpxJ;UbQ$Z;)30;nT*|cfdrUbexOm0}M+sXukpzg&f(o1a{7kZ0
zLk2bJ)%5n+HBduF0Loh_MXepRV>Xt6*iZmNScHhHmRL}XhAC0i)c(8$<ih%bJe410
zlYaJEp%SJU86S#&poz+kj1dPnlXIxlFwaupTx^5xz6JnQ4*)9qx;N{dBHueee{kKv
zD&Zc7j<_9d@!-g8ov+LR=U8-(Lef$8W8xGClnK0W_RK690irm<Qs_ct3H%v_^xQ1q
zvE+PV{@=hn3Tk{pNTHOGBL=|#QJMh2I|Bzv0jOPadn682Js<_5b4p19O+GyV25JAD
zXbU5mqKzOA106FI(>2QX!S;crbk4XE(uTXG<PtZe*YYs-m}-qH06R}xHL+~=V>GX{
zK7(e6TFC;X;?g1G=3po<hqvHn^uXI2aQ)tx66zx#TvOm669~lQez6zeB~G~Imy8#5
z1UlbAp9l<N4&;QiL^498<)36?{G~_gOxnQJc*cQvW$49(;EoaQ2TGD~{l1RW1Ot%@
z!OwHJ@xV!}?<JRng5PN~#w0`pNr2`(Y`xGj{{&M<-j7O_?`qZ6no^RNmJ|xyjp-l`
z$`ei>MxxgHTp_srlWouYHq>i04VpEyw=o|vHb~8n9POQ$gix{N%Fw`Wr|x?h5d2*Z
zSpl_i2^?J-P3gxMY{sMbmAci{uY1-FiO<cx?>6quu$y;0lJVI*95M`Mb`;W&9~RY)
zJa;>l7jYBP56q_r+<{R3$ZCGjJQ61u<p!Yx+gwkz3jq4lcU2u3Y^-b*n?G`mb3iXm
ze-o@PZptFt=kz<Tu~NHS&=RdA=`uTTFj5WsI@D^oSIk+z6Y8$i=lwSUK>T##Gszco
zy=tsEl16a>R(;isjMqa&YlU%wxJAaFTe>(BTSf#7ab4mg;_`kuhE7vL8|W0rmXIoh
z^a-ERW>OPvFdDddz7O_tn|dvx^!O0Z{s*XRR{<MH5S{Eh1l0gVixYgOlYY=|;zH`8
zCjr)XfXahDW`zTi3l(sT#F8E*{6`HS3gebA(il(!X(lgRH5Ldtx!xhREY8I$83DxP
zN*N!H2P0{FEZP1K=@n7?J#IS=?_hWbFe2Pv#tuYO`I7~tQMeE<pz*V>cY4x~Y*fr3
zGx_HgF%kM?|FEkl7zVH5cb^!%LICfJm;qvC4>ZoW3AT?`1`?(%@IRs7wx{W2;SWOI
zgb3sGY+sfE<-yPrn1`e@`8SGel9kFxun@&JXL&CY)9|7!8m;X`Bi~SrW|P%06TzE*
zAgkk?MAN7;I%v5(`<EfoAv&(H<F(}ERqJ2*rL}0^KikasVB^9g!^_a;<hKK|fHp&}
zDRXXe%8X_6t8VcRMj2EFz(mIt)<Sb_AQwDOb+R^zDY)Aa1|hhpSnB6Yo<J?=WY1p@
zD&78O%AD?bSk8~@C=YC{vdCyYd`wO!X#CdQwZ;?7k1H$4CQ(R0bha|z({%O`eOqm5
z6U{Zg%g6f|JvkBJgY^uLnEdLvr%lvzvFjH$=-<5}UHI~MdOd4VlW+CZ%0Vg;m=DN6
z@>EK=y9AG~Z+35o0a@#Gp&1o*$4iiV3sfqo>{s97vcBxxI@Cbbr4`2j^W9aif0JDP
z-wW<915BG=v!MVYE<cZ@z4l7<QLH^N7xVPX72-p)3nB{zL~jO2;nIn;9dQLSf6pLf
zZwT7z9!Hb4ZE>P8f@qO5yeW!dznA~clix)`qV%_C6MVM$C3^B(9Ek!WkpT!WSE<<5
znM+fXA%6SNrkE^ecHD|iTOOBfAF^)cc)ixCf7Rc$Z9wT`Lh&huM1f;Gf8pqtkYLWz
zR<9I2qg0piN;lE9^2P2$0q6m1jxw4W9Y@kYMwFAd`kBfSa)M^sJGtxk)cuk~g+V`B
zjjal9H9sBO=3-QM)U>-Uz=}QXYjCbNt}9$kPFBhill7N1G6M3tD4feF)qkg?ZdejU
zW#nX4CWQ<P(Zkr?D|y-q@bhf|j2xp{vVwRW<?9z&iT+8FT1BDz0GRg}#E+1Q0+Ql?
zS`PYuq0_^m<Pd1qfj@{9JReJ?jslw5aU>@&3<pjV(}{VE5f4$tcw+S6q<~5n8sg`?
z_gBN#1b0VS(}zdmbqq3?Zi?na8jS;Jqzb@ZR>=9o5>~J%W^U}bk-BgRflKAEZ$-j9
zr0>D)Zxy>P<5JN1PD199=!E8Ci@LQJss*~jDB!{aXbN;E>i|ol-;cM(bV2ZkR=d9!
z*=!wwS&<=&g^OpGL-m!HeZ;S8TrZrQ{~k&;H`cLv!habH9V44*0&eXG>96hG!i+D_
zsG+h4^5a-Gm2u>EJ2uEh5^u2pbKi@p_TlFMKKQhbi8{b7p7Q^Hra}1sv0J23pwGA|
zh_eFbI7&dn=rGGZ8gMvcKG3oFO@}-Or-N6KLKqd?&1=IUer+J`UOnln#Dj-F1>ySr
z!1Rubj^j$BHF+w;l4c4NPzl|;2|0_;at4*=#%=-X-Vjjt8N6%9RxFLW;3cpLsF~zB
zRpC-Mpus@4j!O>!ZRB$?VW=KQG>c<YF&x4IIzSWRt3Ko8C0rSYJr;VkgALgD!UxQf
z*uDtqj<1%f^1$`480ueitlIxy^=tka7g>x#Mr+G~0DI5{ytv6&+Iy(%C;vFc>7I7_
zwl(R<Al=wawefefs=RQ0N`TSQDJ(&^-JU8QgW4AaCs};FZr~Gn(SiV#KrLhQ)>a{S
zpPT<m*#{SMpX>2ubUv@-7X%XdsNkrMY#<Wj>v34U1VI40u>b=9KTVDQz0Dv>ACgiD
zweh)c4fNsArN4sgmriouyabn)p?!bN1pCc)_XJ3%*hk{=NTZblya8I(0d}78Ywyl*
z<gahRfx<?ELtQ6hUp&vvoAT>E@02=P*`>_H<{EagbNf3VEe;!1T+TW((iROwdOvwH
zlO~BT8usqCIPto*O75spSutMu-TAQY!0UL3@Ubc);ii8Ht!Zq~`Al6dsg2zEA5~A!
zJNS=!!FG1;C3}%*qC(;6q}0d!j|y}yBYzTroAd_HPo9Pt-dc4tFR!B8ban2NcRM+1
zvftr8nfL$oE#tBgIyYBl?#+m(#mw$W5>|LZ()_%q-V<}YC0$|zEat?I-Ym>7auI%b
zir&Nf%api9ox_UFkGfz6gr;r6*X{>{z)Hd#!6G@C1vtC<=yK7SQNb}tWPnhhkG9`&
zJ>5f{pB*)MswUlY*VNW*J1A_iqCI(C$}M}^ZVfclPA(DeE58J^f6*|AmA)!QA5*vN
z%e<NP%@R;oS;8xzvSqqm9^P5z^$3yw<3@mgZ6~|fJ$C1m-Z6s9;hB(I*EaF__*W6F
zCSNv+lT?IEelVy$L8jvMI+cRTyB!|eB~vqhtRCNQR#@n<9ag#K@o6lE_=?Qr%l_i5
z`NSEm@K)~1diMhpqT*6Yc=4vctD6;ps@WlaJPBjA9$^5zNnRxwD4T|I;YtVV@cnF}
zDZR{QI8!=d1hoV^LkyvIA^cz5+I-#ysKEt!&vuZal+`rT$(u_RzP_zp1u?##Db}pc
z`lyVT{VsDi4+Qo8>t3vN#U`r+GfLUCeUq^77+5F0rh|P?D}76JwVd>tFz)XwvSGME
zh@w;&^ERi^2j{K!QwUKd>B&lAm+xnFB8>;%VN<xqxnf?<Qf9$fx_c4cYUN<6cy(W`
z|KX9`<F^Fi0lbQ^bm5gTnGjRCuQH|V5;80I<j{>o8NL%9MT@F;a$=(j3KWHqvr7?T
z-j6tkW)Aw8gL=1omp?Qw6_*wlKq{*580(&G6l%n{a9NW^33!mZklloG3A$Ty>Zy)B
zQG#ijnT$Q@VLf*Wg>_v<sN8+C`Xyio#KW*gQ^<Gn!}#m|Vy4JRdO6_RLq*oIV(+(D
z_aDyS#F0!4Cnh-?y7J{Erau&xVu9p_QZq&hHj9LKVxm#B*|FQdV5Hypc#AaAQyWwJ
zO>bCQ!1qR>88=NX%vA6I#;NjM76!~LD`t(Ptf>$K6>G}9(=jChnj(E|0wHvuXx^)O
zuGkwK_kHDD>c{IoD9`DFHIW&h@OYu(sO8_@eafaK!rDALk5-zOEO+|igL>neido|+
zYdoaMtH{%J<zjSXFE6W^!jvdEKqox?@5)l{nYP*FzTn3OY>h$$K#gAyQXot8w8d!N
z_3l=5Xduc66CQF-dS=)IqJH$-&kz_m;Gf|EBMX!{G-bI(nH8^wh(C>RSDIDRLvMGt
zB3e3{scMoCT#s|m0zap<CU>M@Eb*+{oP0o-!29IZ*F1!DNTY<@Bi4nUa<xGRDUXBB
zFRkg#&`%ouR=8(>o<8{>h%MR0mjAcp_|F{Z^ZAjojs59OCkfzDGlSvo+j1f#9??xP
zHNx^^2SxG%c1Ab@Xl1jt2J4_>z)yXdN5DRBeR(eM)vR`l(7#^y+GKmd4<^xhUN@e6
z%UpNkQ)=vmMuP*tn!>2HKF*wb-N-fjt-$=C$yX$Ah3~T~4dU*m<0pLgf(CBVJ~^Qd
z%v36DC63Fw=Vr^h$%&)F9e;n>2GQ(fGYIgL2lqnJ%coJHya^rKwLLmnsi@?QHs@;#
zlIeZIE@Jnm#~UsMd@9`nTIVx1A&Qb=djh;QkNb~(3f6saM#_vy_wWxFoz3gNgfp01
zPA-04q{ltB1s02V#xv+ebamZcT~?DSD%vF;XJVeWv^|(WW4?bssGaL}eosr{N^T77
zm9(=j0cE@oYo-pM`!@007g{5p96gL7AZM?5HYzXAoeGq4$jW@!sn$!4r7U`cl*1)E
zD%p_IejIiueO!A-b&ws~L)>zLk)O!4H(1lHP5<$cmr}w2+sd+dJ$=U+5q`uU{dlL)
z>|1f04$~PizuO>_D)ZS)n+c%_9W1`NcO&~MEos&?)TsoX#^uEhRbqm^mx(-i$q*==
zj%>a<tS!IQmIvO1AS7q$g5{N!*%(r)Qz(x!jzQ6b(pw&0GXwdQ@U8fW+42w@mBBT-
z2Cx?28hH_4g%Vw4+bPp#r6Uo9g98+4RnjN@qgv+78K?8bE$T3org<sq=Va0rxCs)V
zsH-(_y!P7I<W4jVlPZ-dw1K>W-dwHZSI2?CS{^5PSFau+H<V@DN{>?<4C`f5qjss6
zmBKC!S~ob|pm_f`;oC+nh7Nh-$-E_`FmkmH%odxI9@S%EPQ6Kz$)_NgwKm|GEE0mH
zH%GcLnU~8zkLejlmIaIXmGQ^daWxT6*@+fB^H57I)1X@CE5D}tePo`UEL7}wsrv$3
zxs6=9!;XcqWI6&B5kU{sA8i$n;VFW@9PYn#)o^raYpl`xv^WA-l0t8u`%=8WiaJnr
zZO^=nV6eX2z^UT61vn$Fo!&g_WzwJ(qd!)CEWFul*Yts`Tmu7<dRD$Gw{I(P;XNl#
zTiTh2m66aE0ekm!KeK>Z5?mm%N*)kF6U&Gh3_bN~XWIYutKQ@wdd6WxH^KrEAZs`F
zO-GFi?^K_GP<iOv<TIFH|K3Rew@2GH@X41QG)2atZ93CP*(pETv=A`E?@@Wcw>G)1
zZOSiTA7L{5V{1~rZ=DcojJO*~_AJgfdG#x>n_DBF@)&lmzWmxMRj&#9*v6h_wzu|E
zDM4(V6L+uW(-3*=^z|07^vcLGt@D&pBW$oP10jM-;e03K!owpJsPw@}1gG3`*v{Gi
z$rrCZ=3~nI%j;UHKY3na6;gsOL!hFo07=)sZ<{mf=rYS98N{;@H7AzWOHVt2ny`l$
zn{0CM+!f{LxZ;0z_T8+OJ4GJ2ks)%DyXKZ%s%L2SL7=jTTnZFfPH}uze2v3#*ep-Y
zvxuJ~E#Lw9{M#7N0XeOJN9P%jU)l?pm(tswNqyXNWfA{dv!L(i_@BE2v^>21q@f8u
zN6T{+1p26L9d6R;8RA=?*L_)!iqC7)Li{PMh5>=;*sk9s@^~69$$IP4e}?S-_0KO>
c6X)c_Ppzd?fA`lf{!a(4s-se@Z2S7Z0I&2sI{*Lx

literal 13224
zcmch82UJsCyJkX=-cdlh5<ogqq!&?y2+|?+A|fbVdJmrwJ^=%Q^se+8q!UU+1*P}U
z2_n4(>3t60%w6}+U9;|*S^t{LO32}yefHV=-Ouy9Wrw$VIvUgztP~&+h#IE(&;SGi
z<FDRiB*2-Fk=8`ugXF1}#zWu}_&I)n2?IXJJv7a{Kp;wnt2cOPCiDyh;s(JU-Z%2k
z*qrf8v2<v`?rgjKEO61hb?X*#AkY5JEsDQ$|Gu4rFjzDYRTqP+=Dj4o_K;IgH0SMm
zO+!^xFg@`Nx@(Qh*W(x-|7E)Z&5J`m8@`88IE+1{3i3@!&7A4!Nu4<l&2r32_38_n
z-BoZ{lG!fihQ&kw)tA9y2Duc3w(~@ZZvWJTd3|lmf6>5oN0S$x;0=mzDkmw5wZ56U
zRS{Z`QLt__vQRYmIm*j{$cvv;SOr0EbcQ9bZPtO@z)X)Sox@$LyEZ5liKW0^;m(M=
zW)7Tu8w}_49@a)%9C&tH2%ZlQv1RfwMadrUZJ1b5^neS%JQ#7VTC89AHEcR|BA}zc
zcD*jeA<mVK@YD!bt6GxqXYTK=+3eBFuA)n6{X=PeKS?yJ|IG$s)53(4Tf$S%@p-0g
zl%GQV91=yI2lzQQYQ{$m#1eSsh^cO!&9g%+si;t0!EBn4Ll8B#U{F>B+hA&S$((3G
z?Lt<@=2IB{9m#e0#Qyx(LKgi#hA-*`quP)u=cr*!_CufP8{ZqBxR_ru3mxK$=6;I`
zzuwq6f%HF``3|)~3|9F^JUm}RB<i<izKSs<W#^*KPL$5vI0pGkuN{t>wncvmZHwI(
zdLF=xv4p~A#8HF?%?#r)v0FPY@|0e<aIJ?u8QGSp3QZ*r_9~leFPghi722!cCiZCN
z_0!>TZj-HpqPfH9lvsZbg^rUsjb-wNYKJ(b*-`95RsVPBTukT39aKz{-@7K}>qY#^
z?mH?XZx{$~t3oHp8^#x{HeS8&>UgKqGGS?ZBUk;<Jtr{^ih@Fsz4i`ssE`ZCy--_}
zybW-~t<mPaXU*2qS-8WuiLsHy6!UxEV?+|a7wY4WW~m3Bx%o`(*BKANXL7laC0TuY
zE@oCYYQKM^`dY5>`8_+iU{y@6x=x{<_IJ_k3Q^|b-`d!<-Z47Vs+E>36B(M#gAL8a
z5#}@;pzP?}z0)W?CU)AXFxw((hjVNz3ZszC!PdMIxYrlcmvq05ckr|oJ7yKLdB0ua
z7)rQ{Zz#GH89b$HuUJYT``HryE5Xe&IQ4n9ruoL7t+iM0ANLWL&2CBKosnFZx3Hh|
zFRjQiQj+&#Li#qAS}KW$Fa0)l@6D7g)Q&G#RrM?Q*_{P<b5C7@nCZ+3x?nb+TYaNg
zpTXtRkMf+Pv(YE6_fOsX&XFo~pD=o1OR;@e)3zNmho;ZXn9r7Z@si+D8E0q)wc=j`
zCuaAZj#bBJ8e%+{M6k!mbsZV*43?2cFWiW#giF|LDK<q~bFunJ6S!j<nTRxEo6bCh
zi|0`Ek4vV=YZq7KLJ5wJVa5wvbh{v7r+Ou%d*%G|I2!&m+mE}alh>-~^vlXQce^|8
z9`?e_A<Amj%1YGWXDVVeU7B80y^$zISC+Cag76@t(Q@e&`S6~+pbF$j8hw|;BFlM!
zq1~;Q7mGnIWYyEfe`i!5+u1M5>eujL*J}7yDEVFZwaW{9TX+!p+jX<3IpZ_MO_5oX
z$pk*+@oaS52p3O=(|-5u^*3>*?nKJ!vaJ}d`G-|5--Wlggqi=R)cB+pPHBzi4&3Y=
zB`B3299=0;@!{$1dHKHQ?J((<L1nDy47;C={H#)GN|ORxA?DzgrF>%Pg-`h9ov%_s
zQSwn3p)}|XSLl2aN-Ml=Fw|<*c2W#%&PF>JiYtD_hp&Bi_-t~!YlJFe_KjzMfirHw
zS6yzsPr5vqHm3pUVUHq_7cG3=F1n@f-TbVWYZLckd8$J~hcuLaU5DPyvk~EO&$l4>
zz&m~QsE}LTf92b4ysme1?4dO>QdyDyds?MSu6!R+grgV>Yz-yfUYo5zFS=t&DqHYv
z`0qL4PibOYjW<}vdOf$fE$hu`7RwZ-+<%0u+6zWO)QB#gpd>!6_&@PfX+PO=Vbh{_
zyYq*%-If2ArOH`!5$Zm2y>+30zJj)|j-IgUB!4;(+Uup#o^K8zuWxTIRGkPu2iFmy
z`tDlo8(5Q;y_%$N0Q*OHypGJTQR(3x{>ZM=khB}xzHrW`{xy;@G~+_GVRqlU{VVOo
zqtj1B+qb99(KlJ*q&GMItV0Yx*)fuf429OHFm2a+H-8<fs+rUBPcHK>WC(q>Oy`C%
zjmad}ebRMMTa@+OR=1rokrVZ_Nm+1Lpgnfqvb~pAq7HmP-|M(!-FZlt#Z+@Atxds6
z1y(ZNw6I{w{{0^FktOQLYg8@^-M2h=p+`aHm;SQ5gsWY;<1)&p)BSKFRc(p|$@bT@
zR>K3F<Hyiaa+ty{GS7dVrG3A<@u@%K(VWw`7t;Cdt?BEFB<;P5*1TtC{tCJi!9J?v
zG*OK*4N#vo+xB-nuWo7|d2;Far+nd7({drPOa9~09`c%$-kijcygc)r)7)srZ9hs`
zo8t%J!(<|5{zRJe=6b1}(;+c&+N^)RDey!&i`y~#JCZQ;-R|2M3+7)_A#QkoV70PN
zcOm|4tMB3Uudmc@7hk(z@5RMADYJgMsAt^zU3+~Tbr8fgH~PJ4{dlF?N7CUkV^j>D
z`IWpoNPM0aYlu`ip+@>J#J(MuW$r9sv@wzVcyVlweN%V$qo=aY_$dKrdZ|nA)_APc
zzCf{1pyv<wpq}r0mf9&A8ijUsn7$w6tblQ8mFE_&7AWdRUn_f+_CS*)voSM{3=oBe
zo{Gt-j~5p7TZj=gr~A)P>G?lr41U5d{IIlSoeaZBt{n?<Kg~c1rgdMex0+V5UUT=#
z5jP1LEvr+(0@rI#Y(R|)3%8fbaz0Xps|{N7`>?QOF{^QdfB4lUT&ldMI_eP8`cM|t
z7(YP72^)7NJe9g=LI&L(e$y2Bt>Xpv2Fi-1*ZJUcpYif?xa6lly;XMWj9C&lVg>BU
zH|r~q=wQii!QGS}O!9oCp~%9UHzqq?lE;c>z{XF}qMK7=_E>e~x3CZ+o4YT`x;Ka>
zvwzp5pbl$h<HWZcyOv5JlgaN-$QaRGt7h$@W3J>P+2L4vxvr2@g8dxV5z!{*rR3pK
z2rp}8gh%C4{=|)Ez|^OZr(`w&7G|makpuCk#GB;}smO)DCUhr%{>}e!W8J=z=PV`c
z)|flSNq%Aon?G113au%T`5{;>M@U}r60yTcAg#8td4-32hC}b=+D8X#sZmN9wx$4x
zX4g1+P@|lie{4H;)VNm0d-g?>-nY0vX31-J#jE)u<{sW>GQqY&#W<RbU!FR4O&RII
zV3_Obay!?jH9n56lhI9~N!kwXUT}8eJw5eaw_x;yN9|XC3UvnEXcavnbVLPy1?;2U
zKZUn{#pQjBA=%<PBU9FqeNM*Vw>qmjo+w8<uE46Cfugm~*4E6^9oP}Z&2=iz+2ow{
zVT%XRz2qYEkJKbgwVi%`_qz-ciQ*5Xl4W#Ptaz$(c5*BCP3lB>T7dRk_Qx$5GV+S|
z=+nA41MNEWeKA%lf^GDh{8Z8msMz|4J{xSk4+@0!4~tBXpRS=|t9@#yF77ux<U19(
z-?+y9@Q4weF~C|G+m)s6l-s!;%LbEOkPTw?mfZa5shInwA>LzFmCp_kRJXhZo*R)2
zs2ezj1I=L7hPlHkv#Q@y*OZin>W<6~PZ^R%C6Ow;$6ZV7bEBk&nnUK@I!^H;{0)@m
z|7BEK+^^|lop-2NI7~Wtv0)60XDTo#)<{V4XzeWDH>piQ@WM73y2+Je?#Jg5U;XtM
zL2ml*hYf65AqNv*I=RBm3Mms`ynI<QV;`%8Dl*b8X||lYuWPw2`Mk$z+HWp|wrfiz
z=wJgJwAHw52m3aX;C&E;zn2?)GHEs-XXQ8Jv#`{kts;&?1NR^7SJ`(~pbk$vWLkIL
z%$;xf`4I_QHlRaCiu7f`RP3@I^W^*)9t*mme-gs(-Md!@yem@f+CN%2m~x-{a8t(S
z_^ExSQqTc{QCD$?GFI`=5005s_#)g%LH1<jlMR=Qo8^~H+o3!de}9IY*UO51nt{p5
zv`P22bDGe-L5()?XFuZPWTrj7et^)(+TK52y3@9?c_&k6`Al+lY-}uFWwpib)G~dO
zdrp3<dkXnS5)5I^3|Tt1VO3~W##~Bn%ZxUkZedi?@3~GgriPZ6mlq$-2DzWp^E@j1
zxi{rf-J2D>m<X>3TraO@t($nBE+mH8`0PkR#WLVKqOYOk1zg<nUw9{T;odwOycqq~
zr@Hr^Me26v=3=r9r~g`UVdzC}Bxh6UQKQYNXHfLEjYr>c<d)19&(xP;G2ya+)6M!Y
z-~CZI^E8dQg@rTdPx+zE`5K(9Csoq%ywPj<0{7XW`z60_R`V2?l%3!qw=5vqilTJY
zaQ<YuZ!%=Iob7i(&<*(m^!FL=h-=*+vrtJ_4mnYgIR9M#I(WT7%$>2TKTHBUa6v7?
z>?Y}}cd$-rA2C*2>(di0>Db3E8@RV3w-Z`yR6=cw$A-H~OG}H?*4Dm2yk+DUO_TFl
zDwr7VhF>%+pwgwB269%{D==HJZY}#`cR`hH7lD`5GSdVMa6|pr=;#dl+_eu%8~E3>
z%Bx~w$KCI*&d&h0vu6^swuD<)1X;McZkDIJ)=ijgH}C3vjkzhw%CDW`(O!`57Py5C
zhlGWGzX5lliBod>$^42|+(q*;Nvn@t<<6-OUfF(qc-1>HeeCxnjRat(j*bp#`;M4Y
z1($JjlIKE4dFx`51?LqiA~^zAa<h9sOaKehM$KhapE}>k8MMLq?%g|;((>|_%|G2@
zbL`4ZGd(GHKY4a?_?@jacrNs@0rT677Hjo)UTHvuoeXEYG0Y{(y3aQ6f9~#FoV@4t
z);;^UuGVsw{(JCH2b;U%aYFXlZ_mcHKGgi61{cRYyLKcasG@1Dq*tQ*?%Wx3sg1pT
z_xLATt`Fy|b=`eFnXaUbV|Vv9UDb{!h_*h~)zujZCO+;wZTZtBlwDF>JRzN_gR}IX
zf5W`LjuWHSv#?0_@_SP82O#BP5zacuwWm2?SH}!(d;i=zXw#=dUxUMEb#HaJUXg%>
zaHU8%bkV+ab{?*F?B|RtmJAxF6l)e=sTtNuXS-ycnw&gI1Q;<g3b$$5F>}nUJ@sqd
z&E&TZJKrzQ2DrY9D>E~!2JDm(GG^vz6%3fgGFz$QPj@ZHv)>lmk?gO2G{da}9F}H-
zwnJFrl+X8ufhoI;r)hV~mDDpSH@Shgns@sK`;|{O?zuPbbgZT_!7DbGvVyi!PbQr!
zT~FI7#ez#~Yo(<DJ4IhLbN`Op*_fy%oen+S<kC5OCo?-Pj|qNm+Ui=3{d|m`wVX1q
zIQ~a1)+^guH+TD0E;l!<nvb7e5l%Ey*7H{nfHeO$fGTd%&SPa<j$O-XmroBReAEwo
z<D5CTya(>@o}S#xoz})$)d0*8%U1Aniwp}&cW*tOvk6#CEDb%KnnTGSPJ6{R;{T$w
zCc0v5gd1kFuF?VbzVz$?t&-H%PA8X&)5<`ujdtO%w5zLYtq{DTOl~>D>p3UzfJk?s
zHD;r_UpY)=Ax6@=XCy&sW5iH5q`079=_}Xk@6pV-&8GE=!k#Rppk-hMPRJSg6hVqr
z!#WD*O1nJCFW%vBxWa{n5;UO0k6QO>!~_2P4ttWNcx-beM*y-zwt@T8A8jO|Qy1JO
z)(;9*&Q}euv@!zF*7nysGqa6#>7%7^+y?q94OhiNY}nbtW$<c#GKn-Gxl0zEja{^C
z_sV$Zq)aL-%?!2Gx@*f9(_)J@>>K0Vb6Kts%O6Q~^H21P;erscgb#mu`}-8Oo6D;G
zF@iYXxE2)ulQi$QQ^;4I%-;V%Q|Z<3AIH(mVi$ydt)<AtxCb<;^<PNXnnRW|{ZJ$g
zfTjIp_eTt~dn<V0$1{F&=b1_e;|qPfT1o8x;Luw><Ol(VOg@TXsamS)<isrICtIhT
z{NRbz4&M3BOzODylk;G$w35dB^Ru6EJp=*60vEZ^v!moBF`Lkh4+4gKj=qJRZ63u&
zM_DpbQZ?T@jqMuiUdIHlpD(4m&)Tky6sH61Dwqv9oC)nt7UPn)#Ukp=s-L?~0N$es
z5PS_l=BQuCR!s3{2fxbvXNc);O)n}73JPf5Gn1V$FPxm5f-a7iRjNGZ+fM>^mqy!;
zenl&|0*l>ES-xPoNcrT`IM?Jq5Cs*LmOA&sbz50lkG_j>2G0g8pRWvQi77o@>`LUi
z$hwi1ZO}DK^QwG7J(_70^W+XQqd59@Z;29T$np6GZV$&)#v=FXw?=D&%EeCXw$g5o
zq+hQwZlu`o(gPR**Wk#A5ypS#S8n#vH~u81G~a@=_ZpmXet?sWe)Nu2x*R|njz<I4
zd>vm3I1jfg)!pBoyDW%^h%mNrZ`qG@)PGJp6?S>fENW5blG)=q4ft-~gRitIXQ_aU
zvT+6zzWmks<=(`z1mhjgXp2`Z-G35S6SEcfau2I*4BZ2L0;;C&=kABD<VN<jEN2Dz
zvWVMa#p-8#r-Wz#@xcHD>Q@I`<ZKW%HMPtWz?;?s{eCA5`X~TochKMr_&wb#Gk5k5
z@T=PW?AzV-GcM>aB$u2jp=ZZzt~G=GI&zC2(n$fg%4E}drM-_j?YoVYC57jtKoI~m
z?VIl4f}cgy0*e|!%M}_>;rZGAO7Nb)CZ)CZ{Bn)C*?YnvdFHIKKV*xbwwqA7<x;#P
zE##Pa+5*gAcRR%P```3DX@HQ|zqVZW2Q1ec@*-Vt=VWp?t~G|%UQbL0@Fg8yeR9jy
zw8}^L?Jl{@WSh_cygIYh-p?sFO!-sCO{Px<yK@N*+7#v~kMkKOB3!{*Yf0sGlP}e4
zd2NdMtF8&d*1BE5TJa;v|2Y6twZSlsS@<&~L5r=v1W9XNg?f3dzy8jH#6gS2Uh2q$
zVDXQO&2`%4Q``A9M&W{Vuz0~-bI}KBL{fAYbiV_E<`OF|%+;72URcZHT1!R+cK;kK
z0f%4~ydM2eEkS0@HI_$_?&x`z-Rp20qG3U%cX<_)<#sdkxWt-{>xY%r4J$qIV*-h)
zx^iDu$?!7jA27@fV*Ti^2zuUVO#?X>`2+Opddth|prqf#gJYw;ie4x6N|6tZi_QD~
z+Un_5Ptt<823W7I@D@kqzch#w>=iN|p7Q-uEK{7sgvJ5$KKbzS1vrQU4K3C!F8J;1
z5N?+Y7p4Gal4EX;&CE;<4p#pN%vY_17o0u#lVBmp@CFLIVIXo9s49Oa7KRxRX=CG|
zyx4!`a4nvO`}uVbSu~qCktwkv%`~_NCN2t?w*nXhhNv15j;+N$vz6DQRixE^EGh+D
zsRXXzh!jBX4*Ov&4cCs?(CYcGf%1%HN&YoOR)4@o$AccQ_`r8BVOY3ErG8E42cj`Z
z!K1HG*qSsj85^=@))w>dQB^hqt2pzK&eQj>cq5Y0S5yr*8iWI>NlqB!2}7b|h7T?R
zN_c8Yc#AVA&S`vbtlU<=<P*_Ar=Hy(;ePC(j3meLJNR(7F(H`x3l{mk<=WRI?3Tj=
z0|rqi=J1LQTvPC3%~HNr8$9a7NU7v5c=;7EA^mpO3|q@9xeSkxDrn-9lHAZACd<M0
z->{zcS_X5ZVtv081yr~!<GYNEOBPu?9Nqp6Z01;gNbR)<+`oa%9BYUmY3KUOQVf`C
z;>*Wwa3&VtfbUl;Ms#I}@`U+LIP_{c^UN6tH#4c)@^9;F#v9!ljR(?=HN$^>>Hdoz
zazs}_PshMC#J-S6plxs%$pvu@lqDGo<AsZe;9U_o1V=a@z8Xt}*TmmYdj;?eSCqs=
z@WJwv!C3J8SZOR6%Zw;RFh&<SvZF(?>=+S;IMJTZ6QJ?pZ$7P5&E~)4d11T&7X20Q
zy{3okwsR}HT;Uc1qF`Fmx*w7*A+`a25aB}o^V7`FxD`7`x4R8t&H`%|4Ndt=vwlpo
zmj_X!HiGIQ@p(g~NyKMtcvF0`{^2~9>tjCzu^e5t+_;k1J&y!bZ8eTsLux}Q&<VDd
z*5bl@*lT7g63GmO$r))8DU%)vi7TyzQl?xFnWdB!ewzb@2_^#w(IV?q)nL$}N@r&C
zwYg0X;Oh}!c1b2E2LcL9#Cxj2;u&m-lAr=$KHLAEL%k>D{>=Diwx5blx$!i3qa4xV
ztHDMBgYAU0fmp*a;g>`@#GxeGM15NSMzf@Za9xlVqBNXOO~RT+k*0@4RPbX~3*x~p
z)hP?1zZO;F_>x5lJtuvxNiA&rrgX|bY_?>SKe<G?*nl;HI`vUV+x}x?o2FU^<K5Hf
zOG;Dy68VHN_ZgqIgp%@!Dx>}Uq18HLN~SEzW^&b#^KTJ$5Bf!n&q2jWW2bNtbMFv=
zb>Vtogr5)&gUdaSngs*hNybDzXBCDY2SjXA*7yoFWx4-Ixu8DP%{;22&o3dYbUqh>
zvP;ATelU9Q)ow~#+3Q*H+Uen_jIc{+*p7IhK9NrT5Wic{tXX<eNvm8~u*Y!_?b&|d
zM`BAuc>Q|H`GK7GxXx5^-i35`($3lk=SL7eyjuf@EMo(?ER{dg(QOY}f*d~l_PQBG
z^9RTAI@O;Hdb-1=@Zry+^1RNVVLiiPgU?-g6eY!#?l&9eNq9>u-E1#s53MvuYZ71G
z+-H->6T#Sg{d@kyOIBxPQ}g&h!E$pHIQ!ZLO*4rL*#=24trH~lT1KX&Q#>@$y0!+9
z%+MEznj&Xmr9S=9#t)GFGhmTpVc3-u>dH0uy$^V>!vE~S{<mtve<&>ctB0z&#KA=3
z&1#+*pfa1>7qIvb4Qk~6bOZ(h+%_yHH^x(&)SBc1%ucl@won6wJ+{mxR=mLi31&RH
zjlSMMGG3gp)8uT0_r{CokT)o&nUqis$3q45GkJ%k2#SO`Y!X%(JB(GsYd@Y6bOUFB
zE+8yKimICJyW!Q*WeckK_llL2h<94oOQA65{EENlF%U=46eJns_wZa7yhVB-_`tF|
zeCiX<6rXat^E70yYth5@+himkY-mzwgy;fHP$oFoN}$O-M9|0qqE@o;>Yg2NGsMz7
zd==2fyKl^~u@GiRDMl_7{$ZCGkk%5NT;h|?+a2yA`s}L=qdYK!SUo4^y(Fxs**S{1
zPRK?!9=cBr+}B3p+r8$;OrYYkgT;?M&SfH)DmO(pQnSEYJpjkUM!}0uC&yiOxFbCo
zavg+$Hfp(61IzSo5jz}sfp)9nS&%aP0u3~s0h%Uap|(<JPBbXR;!0H3Hi2{3(UNE$
zy=-Y0^NHJlyQ97H0ntVp%ycA&ZjhMQj31G?{unz4Rs7^<{O0L<XmS4?Z>GJCDD_(~
z11`fmU>hp!TZ)YS9%EYZ(DFBGn4HR(i0yRy@kfi~8bEN9t4B0j_7rdsBc0bLuZaNA
z%Bv=B<|SC+CyRpMIW}fI|Lc(|w9{mYG#PaJ7CH^m`B0cSC9orBjATS#sGS<Vw+875
z_~`*A@deJ_QHy5g=4ins$*{xXPa@RFj~?MwKJ~jIgC1}ZP3tYC02AA%u!@kn*%0f&
zU=oIxv%NWgF%S=ZAp%UL6cIuedz+oQnYQ`gZSdenTl5Kvq4Pb_J>uCYWiAq6M-oHR
z5Ph%!+_vxk1_h3V)`Ut{#zJr)#%{XK31mVm=z{Ht)q|V>!RHlaLt!nCfVJ6UKd<0d
z<Zl>hg;>&uWKp?LOR3M;5M5B8mHiz^LeH6-(!Ekku9wNm+46W*B&YG4&%tv6Tl>dB
zwe1wPI%Pq`;x)*9NHckvfM;fl8O69%pjhG}cA8<V@HQ~?#%6MM@(j~overZ$451$I
zp`GsW$Fk&QEEtAg-42ZSpIanF?T@IPj?PhENnB^e5(R(e{v`u1<0Sn0Rp4F|QN3fF
z@eXI<hpomuF|B;oPYC_ZwLL0o))e@x028_r7a8GF``yVP|9I2pcq`+gdoFO<AMeLo
zjCN<4bN@!r7@Ih=FV!-hk7stQs}Xi)wJ13;%1|Sh4Kfv(-!kn_x`lFQ=xeyp#ap&l
zY;n<{PIX8`#Xe_6euRrgGG4TlQk~=*P!FqAeGh!St~Gpgvd^}A++kQ5I1_*y*=X7o
zm%j-DF)Cht7hw2@alQ5as(vQ3NFR+&^WhAy)>PQC^Gj#8I{D=q{rtyRudSe*<65@R
z#lWq!w0VUX_v3M#uJA-)=*b7KSr@ahk%0S&M!CezL`AVa^+dEF8-WMxm&B-B8_#D&
z*8GGCG`Hw&3?p7Vj{lINM&2+)Yn1{Hj*8nO1(kjR;)xa=cq|y+&Z>CpLHtPfjmKCG
zz3JV*+=cXo0XW=z!?4jAo*u45o}9d)Bg^BXVe7HM3yW_m(xddJLNgMA%jR?u(PWN-
zTJd>qf+IxA*ZuF7&Xr=5yO~i?SjdB1;xj)r2GkVSk;ZX5Kq>b-HIyfJ@OF5K_FfrZ
z?u^W4jcdOEUciMuAm&Ir7`Jkp0m^RQ5QG`PynRy#ldy1LGv}utTrli44WQha00nlD
z-6&2{oxe{+_!WVD{3|Y!9|_vp;Kh)qs*q+-hQZdq5ndn3B!(<xxc?$s66i78_zrkZ
z(_fKWm2G~$KN2MKL5>gTvqjDEzAm5Qr$dbDKLjF7P<-Qs8C06w0ap?Mo05M?rFobQ
zVG_w6$LGyf=n;k97-vu6YJtKG-d>j{IRlHM4Z;D|Cj+oe2CI<XBN_XmLH9V`NaBs}
z{Y!Dyi+c92_XKtSQMpD2f`=_V+U$+}mAw3@r95dV+d37b{~NFs%#qd}bYwa435Y*q
z1-z+bVLW7qxjJClUk-ZldDeV3l>Qp+)rd~UtxNW8PMZkX=bg&#q|MYDMB2o&#C2j{
zrGOf^b_El`9y?-sI9J^S_7(GFXX{YDq@Aa+j8&hJVd(pD5lp`y%$@@HT^%gjOpI;_
zHs_*N12;AnBWxVvDqx<THfBj#CTyG;9~f&%?q3<DF*vw|%*);pMr*uZ5aqwzTBAis
zJJTlzHs(HHt2ml(WE>MR4gpf;<JbII76eCIlj5}xBSA*PckyqL%QRxViXj4@P#zXN
zPn(%qUcu;5B3g3P?0WSl-KR-g42N@<`<bJ<iuF0q_F-#QSE&VM23<%izw`JYRz`>o
zz|f+x2>H<mqOYNEFeo4u)V_-Y^Y{K1g01-kssZs|G43vqh<yMt0=iEw{{Qn3@lqGN
z8&9JepZBUrOCIsVSk)DEu?`+anBduNywe2o)siIQU@8JXzTOOQrJN+@f}HQ6Fb5+b
z#AYX`U?1X7v1>(PLLQFHYjPl2xqkFrtW6W+GzsKn#wej=qCjC`X(-LjLekM;cOE;H
zUt$<PQVoR2*5F_c7FtCLOVIS(yTGm3j{e1NReQg`N-K*~_v-sM(fWQJy^EOsma3Hk
z+?)j$V2ORRt5`XU)R%{STZye```1x9_Os$w6Qb8mj7Ahg?|d3*^!;?LTm`K@cR^uF
z2DQAlWUxI!yov2P@(P*4K&s`~^R+t;kO!k$8&V)qt$g<19&BGn%ZNkQX31q~$0dt_
zsQ;h5Ae-bl<dVFZm>qP+kV1NPT^3r5y^6w{@*sa(Qn-*d-*+N!BaPvM;)BAc!sqyI
zWI$p0CcqLX5}xk`a1N04#QJO-JX0bia$LH3kKyQnAH?X_0q3CJ<&+QG8a%0_{d=$T
zY)0G5-irJZ-T0=DK<c-RxQoT;AIgM#?=^C!m_1W!i?wcKr-@l{Lp-)zz}2%4dz>WM
zM*{cm9G-ib6|cqTRg@&zW9NuaC?DN-A<FEspFRWeg^~5>5K$=YkzJNuJ;Yg%1*l^<
zTxD}=+N}V=3;CfyCKJf<{~yW!fh>rZkFgLSmCTik*fg8a11wny#Ksm7E)X2DP2~8D
zIt=~mpXAF2MoM>li|_(Zo&!#y(FW^~4p|jBhNwyOJ1E$$nt!`fUVayA!dtOxgH$Px
z4@?1i2_hoZ$c97sLCHWUVY9BL#72Z!zFBueygGdc=Ir`<5Yndb;#ZM<M}<mJ{`LLd
z)V9wD@56dp7G@4jGW4QOpvES(P<$s@XDGA!%nQ5=o&le%>iT|P@g|V{v$G7I)8*G1
zT38tF*7DS|?EU0;l$6)%XdRe+8M4;$QZ5Es99^>5eJY|HIL(Y#w7oaVSO{d#`XX;P
z1MnXezqZm!nTRt1QCfvLgScw$!qaXcrmN}<kmg-gXTJU`C7J&sum3-ndU6V(c%FZ>
z#b8Z<{AlT)GTDc#%FZhZJqug^koWt+6td#|=yJp|hT#LCidTs{1;I7PM`S(VrqfT!
z4M}WCZJAAcUVKp!F_p(vDT>*w^K?)s`5l7ZD(Xs}iUCLt%ij$B?<h+B-&x?FBF7_e
zBm@KIL8xOFupHP7;sqca_zl!wnuWwES;#CYwMhyf``BG<-fy)8Xz?C!C0GOvA()t4
zMuuA;+Q1e@@^^q`%m;|-gl|#&x}@<n+LPM@C<}f?%z57x1trzih|VltIL@y2H8cvp
zv>paYIW*z&hIjk?Pv&+>N5-U*KVC?$db?@~ZFdBghq`$hJ`q^9n_XL?3DPwma&-?|
z%Drj-(x4{s<3&MI-32z@w34=<O~UcmwX#2~?fO2XCT~ifebThXf4_vI)x-T~(%@g}
zrL2e>sawXS7E2e)a<RS6PDNIRQ77r?7V9{v!i8P*A9vlJQh^;s?{U5<;2A3C@7$?L
zhVqNr9KLs5U`ea2wPr8&t)gdJmSamF>#MvlSWQZ*UuZEAGoSvm_hLE8<z~6rpZgGk
zU30nfShK?JkkFjg=xFVSj~9Ds(&y&5nvO88bSCAU{yoy_3**lLPG+sm0<$w?LlT#(
z!+o1JpHI@04oQ=qHyN((eZ;w+?f)ec7J^%g*UqUeuM=@rD)ncE;22R5$Ix*@Y#_ED
z!Nqs|DqJ)dA-J}y<Vm6B1Ko*Q*{G8N7p%$4p-S^Q`~nGWu1Wa(UvBIF`}a_lhGP*+
zD?a9^`Cd6|n^IHn?*dY^4-namS&#N<Z82RDCN-OgQ!>w4QWr+Hu?hJSk~hC13BemS
zbeyJ_ENDmG*Fpx4yB;?<+DJ!_2KM@va`}U^DSIF29x40Pl^-Ivs|7ct@OwI4BSg{R
z)LmRU^}o|1`4I_ZRqj;6#1SU<%5+AFl0oZ4(%6SN*H}`FD^h$uAfNtHVPnzqcUgIa
z<SM+INL`)tbjgkat+n_-$h>Mwzd5)zBwHapGK&x*>T6Lswq4EOL6PZB#TXuD!kx6$
zl@&RU6mx%4Hs^rdNu(-C=e*|Iw+8Q#FJWr@6^r3KvV8M&wPGV6MeT@Y5Bg^8L2X#J
zq{QxkVO#%k-G0_y$d=KQW%Zh*R-JKtP*l&o_o#j6Yrb0CNfj%k8=&SSZ|<29v$J*j
z(73EJ(}z~c?wniTG=vIdL5RbM*{6;(u>DX@nO14%begfw$2h$2;vIDkVXN$AoHM62
z3F`I+jX9So@wC-^L(^^bqt0L*l%3K%msjqQF5j7LLHKLIn?}qx(n!hP+A`bn&Xd-$
z`rM;cnm^5O<_zpQY)o-&0W2K-oU)<$fYc3a0_D(T-UPEhKB_vt=5bA2X7Fzc%ym=h
zGu@$ZZ^PW9LWmDC&*#=CMxnmiXW)@6c;zL2H<6+wTK*sN^-0qzEuKyr*6qjN1t;U`
z!?XD~9?JAUTD4_gEbL-C5u^<7?zC+)_T?zPL188Wqn`fKg-{~6Af!fjDk@gG<ehk2
z;G+d80YwMfUabKuu({=zb7NXv#FonTh+aft0!<#<0VxT6U2H!j2ppy<^>LbyF=^-R
zUe9lfI~VeJmK^0ZYg*X^k5^BmS6w)DZnGz7gQp;u)YCRULD70WzDApK%WAuN!0)Wj
z<)x3=?>BIYaI~a_U;YsEqM6syFQ@4JdZ)c(j(A$N0fnBV;{G=CxN}`ng^^n9IR|fX
z^%0m4+)w8#piIGTJ<p<G*rT{X-owp)dM=4Jba=2~baxjtdLFXna<yoU)vhg*ZT<h3
z%=u5vMxB_%^U@bMNzXKZtq?>IVieno1>sRer*UpNmGKn47VaJ_Xqz_$K9p#;HwF3D
zIee?2JRL=llt!f-@^>BFB()GhpE+V!&vbWWXa~cPJqt%fA};jHY)G=+HHBfAhJ~MJ
zJ<c6yHXe$R(5`1{VDU$P+h6h$Ae2))(P{xIWuq0GN7n~vSxAGK8;FnYm-RffwYz2?
zrRy$)f5FY+;||k&y2`%b<Hwr#VZ<A^L~EcN#9*FZ?ogzVoQ(KH9@Yvi?mQJj8=ACz
zK@!VItfA^BQAE~!(Dy%UE`J6m9#{?Vkc(72`J?S6OW?N^@Zh;M!Ntgyl{#B<2W2v8
zvAVNGkA2mi9T}5=;CVW8{HxdPEe+U>B<-Ueqy5+CV4J9uFi<d;X8B?>c=|?f=PUdn
zwy3P>Ilb@B1*0id#*OdCQ+N7aAWiP5LOdL#vc({z-_{zD=xOthtD6(B<D;5vqS+qF
zN;m}LEjhdOfi&gu(bqdLCF=^}jMX;xy<;=AA(s0`Rh-(I&ltnseUeBeR!kZ&n8?9O
zW%F*BSAgqypl|FyM>xih7-P4p?2^Czy3Hz+g=k|MeZs%fQ3xhbc__}4h8e1<v2di<
zvypXXpF(Jd@9uc}%}Tr^gLwkZ4j#V#=>-kRED0wsJ83yjjq+h^*DUQBd?btmf7dqM
z)T(+V1+O4<Dd-_2F5Vy9u7UG{|Ju|EKvWXhh|wkN-SWF%S{Rra%NHeuCNjNNLvOC(
z{w+fHHhbX=#9v0YWu4yqN4>gFGuc_QD!Z%q^!L;mD^16p=RhbOZ}ZTnjpU3@_KwLC
zk*$LfP8y43-}m;Jm6$|mkcrIantc_GDQKiRqRzc`q?{`q1VIbkvU?Z%>*cMQ)_w@8
zC+*Sm#v6v5_~7<G-HtBTz2FO0>A>#$oFvbUXGA8twe(y{CdbMi6uAz2Y-)c+ZAYC8
z%#+p(-0j;ZEAMS!9upiolaH3Mw^Hkt+ESO_QnO(_ZH}MrG{VTcpVFBJ3z3T~k|W2Q
zEDyYyzcr<kcM@>1)<R^Ckus35TV-D@qeR+@R5w7|gh8fPcXukD9Opv4sH~LY$W4_7
zx$%<0QUc(!OpocUJMC2sM_Z#XeZPa9Zw=S52WIDWTfTYd2vjGzh9kAub@z^s>~~$S
zHIIHbZIW<b2g!gV8It@|+W++VxirQdl4B-G8Bm)CJi-YJpS$v#Ok1lNXD(s~C@S)H
zu)8<13Igviq66bu+jsg^7<|9<^|Q9h&6_%i%y%}XdTfZ=lcQzkwbw#^x^m7Fr)xWV
zdyQ?S%x1q((P_9v=oS5zzQcJ-AcQyS#RI$oay=IFQD}E8mav>F5asG_+bOCeW-n^T
z*{bFQusL>k=UKjtllwfAVJl0$h9`U|nT+2RmMH-)<@b+@%UELUi)l&m=<ukUKz1W$
zQC&176}^JhX>og@s!r<5pP4=6=f~%Gk&NX{>mQ0L7LnBnm8(yi=@{@(ylT7Kgv!J0
zU5-xCyzu}FGD{sNhkpJH?t`|O6zh&qBvd|mfT2IbDr3j0-E20sI!8gsCPsrS=(^y(
zLOrzyza6|5sCG=!8*S;B*2nie(Z1Tv{eOEI#2#GMoYvx<zxXm={lg7RUFTtin(bfz
E19$5gT>t<8

diff --git a/reference/Rplot006.png b/reference/Rplot006.png
index fc31e5bc497597247c12854ebe92c6fba85231df..a7c4f9b0e09a1bb0b975ff1441a8e2e333f96279 100644
GIT binary patch
literal 13276
zcmcJ02UJsCmv)K}nsfxDDP54FNR=jt7*J49dQ}mmORphlM5PJ{2vS6P@4ZD7R9ZlK
zXaS^23oQf!WG?Ufee0k3*Q{ByX6DNx$<1x&oW1w+>}Q{QczaJz`z+0Q8W0F{7JmDt
z0SE-foxZ6kfjz-PtqH&v<s)6~o4_~lI?!wi0lui+Z<~68K(x%KZ}6`v#1RN|0R+E!
z<AGo5`jr0%?uQM?tvY*Q85G^PriO{#p!$>e3lQp$FBmD9SmZ9Uv!1aSyID(1V^MaQ
zpN7Wb-US;7g#Si;rPK{|^*6H`>T=_8jLgiHOjncB8$wbY^Kw^QgjFn6WHvYb50f_x
zhpj^%4h~yq7(TST0FM*=-~Jgaq|#jjGknKxL}B$-ZHoI;%ZeVDs~U_C3#K8n^@_lU
zH|rvM=A&IF(t@pw3u=S1D~oiMz;L@7?O)Wl#iC{;hX-Bg?|{$UtaCz2R4tBk`GHI*
zdnigNZ$0hZk+!|P%a1E|X!Q*>4D}A>LUy2a(I{^JwHq8pmXIDW0!p`v_85ZLLw7>!
zkbH~&h|<gngOMAx2sG{eVkhK-*n<$p-avgLlGJBHQPy{3?U;|q1avoAKlDUvPSd3B
zF1qfqi`rYPD<-Ax{oYja9zR^{hRUoaf>Roaf8{CYu3Bz%4YQ&yE`8PkBp01NNaxHL
zu{Y|_nt(n;zgvDTUj7C@+xom%;$$-Zb8S&ps;ZXg`R*)*9O6UPd4ecL;!)Y&=vWYg
zmW#cY4bKxbx3f04cI)5I(Mt0tVvzYI^d?L-E=lu>-ssqY-K0t@kw8U|RTMTlx+aaU
zW{T5E+%7n&(Mk}WRt}zh=|@pcox#2JEW5<Pna8P3$&sX4EMbUri>#4&gOz++Bdg_N
z9mTsJ(bCyiap3wE+b3?TICrHss%A!O3u566G{32XdpmY)5boAFKd?g|%^PwtK)Fpl
zl~~!)R?RAgo(Zq9W5*ec`WCEe{kht@G8ErSp(?z)Yz6&=;(sokdBOSI$NjXi<2LP(
z{cfh6g|XufMXXs?Tg|`>(WzN$0`K(n!=Y!#xfk~?cq|-vQDY=(=_`43MN`SN-1-yS
z@8@1DZ@wLvar=&yc`hfiTR3yE_;p7$r^(zZYgfV0-qgSze?l6CiORTGyK0LQ6$t|N
zfw)2lna3~}r*zV|na8C5cWk9v^L)%}#xT{@ECQ=^E81znz%iY(18_P+R$Cx6OX<Tz
zFv@$|-^S3!U`SQy@H$d@=*UImx!b4R&s^-*dE>CS#KzX^jaJ!}RiL80K&V0(epU_i
zfwv|kR60Ak0<ng8`eDEHYV(Ox1<Bm&YA06(X|-d+Z6<VS$s9J0pg;Q1|1yMftQ_iJ
zdIrHY)g~Ukbp~%WRIBKcp}5c3svfzle$>OBCi~dM+^%;pc5f>7AHy;gn03HqqNXw0
zsB{L@Y-bKC%K>~6?>@cKh=RT}6UD@+UygbNwYX&-<95MaG;{P}&s7cmv$#7NWp0KZ
z46*j&fH`Xz2}M?SMZL(Nuq3<^r}51YHJ;;^3v%lTW5+I3-CKk7-&&;(o785joM>%k
ziv4vgXLu~748~27!|@UVso@^fc-2|avGeV)=Zjv+J5;ZnZ(bwc!9BGR!0sy>f7hq}
zn!*REq9&>2F@|>-3Uw-|hVLwhb~2$1gTgP_@jP*&h=!ir5w(yJ4bCU}V*{F>VZ(({
z3)5-$hWJFd9lV3zKEKF1!i7wuCi(7P<Q7dV`3J5}xh9)_$33t>iB;)y!};1lM~wdI
z&VY2c!4vlAUAXlbpS9nf`?<$uE(y#{W1dQ@5h%kFj}ATv_XtJVxaH7&Y*~ImxIb&P
z(&tcuS_jo^{;W=6rZ1g8ukJ*df-G3X3)e8gh*&9**%0F1n~WXnNFOwvV2t+ZV<SJo
z$+ZVoURq7$PgP=5>nKPkEO|2s91Sfq<L+ZKHiIDUmTS+$@$nyasI}ef7kd#?!q|{Y
zx0xgjDj12h>mmDrzU^B4>ERx|@@}n!r|kn%FJp(quQ4JniEebzEV<LIheUqPOQE6r
z^V`!+D0K<T)_B#YR_$F={JZdNBtHE}Gul6F@Pzw!09H?`P#`tF*^;$Q&E4-ZK?XgA
z$|yj&w{Cggk3^t$zRe>v6*3ei!q8*OvaPo#6o2iZq*$Gp5lWtBq5eBH&>%|U)9BHM
z^0o{n3s*nX@|BLI)~5fkQ61OKkHVG-28p)PMn-ffg}P_H)TTDhhl7)#Zp?izRg(I-
z-U4<wXId63Wu%PReA2SPRDF!VN23_JdhB*iZWGawYJ52C*!FMp6N?@8DwSC=-SOy)
zyQwM2DJo-|0EMnC-vQ<8EULvz%!o+-$Fw*%)E-mYleuf{Y+>habtiM$ipmm7Dwb0D
z7UbB+#I;Wv9=TE|!+NsT`Oh&T(h7ydRZ*UCWX?ctvVXsUvo29hnO((~+C|`chHxtR
z$lL~#?;@0GV_%xjy2+!dFr#Qp?*@+18a%n7u+PyRlW(C*+=?-#wmvugQ^u{8gK-*`
zbCg)0&HL#dgP*kR2T3jBz{@}fadrES#kSbm7kgJTwFho<bd?GP6}6@I>yE#ZKB{M%
ztqk=>F{}q?S)AN5zcO)7;X-2ur(+MBll1Ipc64?yc;;I8)Vs$GhN$UZ0~HHB^%rZE
z4-(Zzk6snS75*sf*v(Dl2ZN*)2}dU=sdLHJp8Q=Qika_*Ap6?gYRG;==SZyETdW}p
zaxDe_>WDz!x$#w`tm9+y{z-AI#T6LKiIfvlD_)spVL+WYljHG??~dqh^61;Lq8`au
z4HaA&Q|pff4=~=eefg_q1tE=EZNQ&?c025O(tSVC`Y^oi)8y`J8+1Vea>qv4fQj*)
z#R#x%nq^p{L?BwM?l_e8*-T)>KK)!?)uaYd-TKz@tF!#9y<AkJ`f2{P9W1JxT$Egn
zS@YK@`whmbz8lG1*MKCCcC*oHlo6e(+n6y9xI$6aTPn7qewSvuG_xWkgEtn)ZdjUc
zv!**C*BxUvGbWMv93jt(p*`<N%<V~utJLglV@b8Vh^H;3nxYnD!QGy#qjJ-)@f3Ed
zv&=y(R9aqCwkA6i8chED;f9WD;EJVdjA4)28AnWg%A}2Zq*w?`l2Id?Pq9j<B7?JU
zJ21p|wHb{lylPi*QIDO_$7j~Xf2fVsUi5qR?6nmOS^RN7H%pdB)lPw8SZ-PMi3Hs*
z!fYK`I8v>yeXnkrIX7@3Up^{wIk41!rvq8*o$cBMDfYmoN;~fdz1;~(!)LZo;@R3^
zgW~8LsQtx8)aM;x+p(&!{=s9q%zZ+KLx6-Wk+@e(Y<P5oSn#uq3E^5{JtP;=FCJ}e
zX)l6f(!jXD!ugI8+z!a`wU8}R@w2ulY#Xx^7f*|8tr5=te!ALQ4~!~*kPPn2GD5=E
zg!7XWUPxn8$<zB&0!o9RehrgHt<v9fl=CfG&7OHSVn_+K7ic}Q<JnOx6!hgbQ`nHb
zR(FG`mm05H76p;rMN4`Vzp2q4s`oN$49490?Zy~6fNs|D_N8_uMJf)><+QR3tlvtg
z)C`rvrH4Ky`|^0iynoWYYbM3ddK@_#n)R833&MAEy<xGJ9on9h)OMl3_y_Msw?)v&
zH7sH8DME%QOvCVnUU&@__`ZD8EoMyFo-usE5XF$6;xOHSN}prZ(7zz_J4kMQziOJZ
zDT7Bm9b<NXGV<|fi>o9g7d78W1eHo%&%MV%Jk`tr!912H8j^y$(lTYS4aGCopTD~?
z(^v@D5^IyC<K98^F0xX6Ug+hP9=feip05~Q;X8!Ta6VyiXu@k0RLo;IwM;@n1XB6q
zgCAyETp=;Ff-Z~fek0x7N!L&?P~=r}<(PdlZ?XCF&hyFeeoc!T3ey*+op`{LOm_oc
zl)D97U6=Zz@UA?gK2e&#;sc(9(|)oL=SCG!R6My6b$8-pd1<;p(50WJEFNw3L&Jb!
zNSy>REL>}UQ0S;kb9gjYb=x42{)Urxi6C7B;i<hy8uGtX$vKyNypwoyYHGYgbecFC
z1q*yl-s$Azifpxeci6AocdP*E#$g4GSeiLr3c{(&fnV=IwB`S9voV2F=4r9_?^x~V
zs5%Mr+G3wkOJ5byg4ArsSSQjOPxAuFmUU5whsjNwt$|}b$uh5hr_^>!HuZ4HyH0-g
zn_xiTUzQ!M=!Z0(wfXXbBDgb(%Xh8Vv`W&=+4;y}u_rmo+^uD=l6W+jr_blrr??cf
z6|2qLz-Dtq+_F|&{O~w^I)r>Yvsq~O=Z&sp!;`8jk2^0DSFOoPL2F|bn^8R1313Tt
zT1KryS~lxi_e#p1jA;Az4-LJ?I%GxlDeg~tmWANIz~oyF7C-XcQOfK4QA{FjG;P)z
zB<2+qT=HJYNpels%Ts&a=#Zfr{7WPwXxpi#rlw?z&_jsT%QGacRbfVR^)pTqz);Gb
z1_IVeic1Y~bky0|=~e2IgRNu9KdLpLK4&BJ=evEH`{dFsS?9?zYs)5vxP3K~`<^q_
zt|XE`jEb*+u0)z%-gMw*i+313Povm~5!8J-^R?=Jg!R#*iJ$QzYKh0^SG*2q@;Ixj
ztFz6_&5IU~_TlluEe+#OKUzxLppg`LX+BsA%j1BL&XqsIHy#9m|E#W>FZCpQN}>m}
z`Hqc#9e5c9O(j(-`%e2W;mId^c6kibvz{3r<j8OH3?6@vR<Zrg<7<)nEW(X1UPRx@
ztuIw^wI}WR{&n3X$r6?MNEx5=z~H{V3o$QPTt6e$R`TG5X1~sZ!Z55%%*><SjP&M*
zZNzmH{NsKJyrwoVq%TF*sh3IMR?km1`<@S1XzhDbYM%R$8dpn6TTW#ywRVPvhD{9d
zz$l{9uG)1qG6tOIa%<iyIQ6-&qmz?c*W{l+e_VTd)cm)9@68Q9?Mof6-v^Au;J(u<
zUuI}-9%!hPrq`RQSan4P^X{UiWQuC=;U|q9UY{{rlB8Mn@_SmzdfXxKDrg9}9;0?b
zL=v5(Ov*nopb@0?IyX(tuba1`x&1f3GN>twlKi(UuGX1WJ>8pdshbjJ4WAmY`%yjP
zGvOi-t$Mhe29j(g&h_@G>`zKOUJbNpLmZc$zxw2dmSLVA4`&E3dU+r##=PQ@*4@^H
zfaASUQs+(|yL{DT@)(Qo9pbNpr3^J1+#7-Dv6_mCt#B+B`|+>)d6Z-XDJm0jFJC$V
zBQ7i}%TV@N8`G?`7MW3cD`g(Uxh<hzd-BnBvUyrwgwWl(XB~2!>!md1I!z=DjE&Vh
z{{9i`2|Ig!sQC*eS8*y}$FIo;9xmbsweb!-NzEls+(bG?3>)eaHaa22u30%bLJ2xK
zW}bJ8N=kBaau!%Nm+s1qKj{EEpRqbow>fm$v5khM^snGPHS$qj$U(RD1_i#z`q*)h
znnie0-gB{s49^>U?OZ)@3nM2h%Y&Vq4Du81+g|7<aR;tdB;^jSXpR7#Oa_&dl*q1l
zDec8)96f12^}5E>=_%it;}I{V;2+uW_!IYTi8^O9hv}g8=X@bY&(~E+YdfUVgCpi{
z#~}3kONJw9DuIMI45~!j2dC18#ZhhU=ijy((m(zbO4H-p28iSEt%J%4;HqT1A4efq
z`NFrR2YGxauN+0mPje``O*e0z9B+oGE%nG?QdvW+b|<lqx)YAcz3lQbo3^icYDu@a
zT&i&ZiV-Wd6RunUeMCx32krE-{94()n3o>#r|4irzm%IW>4|v-!0#Oe?K$qf#gA^T
zjn3nhSc4IG(0FCi#Zi*C_qVzjD1);5G{7FJPBAKk59Qx^1A`u8tDia9FLu|I{GQPJ
z^msz2bX~AYRji`KOa7dVH&@oJC^J>xjmg-CT$P<38ED<4N4LDk-6rx0iTwWH-4uY5
zT#$457fNw+Gc$vo#okrVDeuwsV8XclclqU!wO}UzDn{f?&DWB!{DOjm{c09H1zVD#
zo}!)EOlvT?MQ!~Hj8C~fH+ON-ntV7qa~+R{T8m$E%OpgG9L@7-0)}p302vfKphd^)
zt151PoH<&|ii(O_*_!{3Art3gM!WVV{Yudyt<AqrjV$^*6vjJ-dn=MY7J@^E$~!l%
z<c_xNjhIiAS~OHHo{%=x1P7r30B?4BKWW(R=-sv91A-zLAkLpxG+)ynQ{eytHznt1
zMK?;dlK5v1e<po>mX}t$hXg}i&ntTT{s^>A-az;~P4jxSwhoX^A8|>OLB(tJcz=fc
zeI5XDy;j7z;TtC|LZdzqo(ZFE7m<6(E;&sVprVQVvNAUwIp;A_GDBQ_fnlEOPRnMn
z+pP)?Sy@@K<kh@Xk1mx{JBSq5O$SuX^W~2l6`S0baluww`c6Lo?!QG)^js|7`5mv1
zkPB3sP7mDh_te$x{vto={xw-7-ER)1v9sueF{`b3&4(cBZq)(-({q|Ud>tJfbyQAK
zyt=w-%xX?Bc<k%N89r{8WH}9Z%+ig7|H=?^8^BF<ZLOZOgM-7kCq~7sF9;WNUiME#
zw4`xqeK3jO0w%86tC7AHm9++J{r(xh9{|u4mk)3oU$^?%uqhdpQH|{d_t{tMeHM*g
zD;EJ`<{|D5>hD{pdH-UYoA?fdef`Ell6C9zQ({b4X+j)qFF@HPEyiFv6Gn3r)bDHm
zibRhI9pCvICOQuuKPK;g=Q|$5r=$=xyNgm%oa7F6mnp_~$lFP6M>{`7GLBxYmNsq7
zz}{Ux&F=%BwV=LZwy%YF{MS$2(&yVonp)p%*`Ll}O>qO#GRzt1sM~rKMs<DL|1=Ix
zW`GP`SG5$=I;_7oS~l~7R;n2v_m>?%TzmFwYzeS(_k3p*i5WH9GvzZ;vI(5v-T{C=
zac^~W^YF$?%HGzE##Kp)=M@p1=>Wl<<uMJ*7r9;mWa8YK8d+H&J8as8*@Wz7@$q;7
z=^T?L=YnmY{rZ~xv?H7i`kIb&y9~zbhs$dS$cPud8&Eg2(8cC^a&%x1qC0<W`71zq
zBWmOWOdCK>!7%}VbxWI%2@^BiN?ywki;PMfn{h8_`^Et<E-so%W&D^0*kEeAd8dzr
z0WQr2k_F)l50G+x0s@CCq9nd2V9I|f&3H0kwZu^OWC^4hMrYbUe3fKoeK?@V_e9^k
z!2!Mwq$?x4I{Vrf@zVOatun-3k@fY3_lAhc{{He^B$U41JN~Ym=|IASPAiaWt4h4I
zrw}4O+Ccaqc%(W%{g`a<Tm`zjKW~LNSg;mK(^UnAcHZ}=P?9nypVHLh7?swoeoclc
zquvTIy#fx|lBbsz5Rz+*{TRh1zwv2hFpqq3MOmrny0dtZ?G3SnfO?|yf<4%%%nJ|!
zbQ#jN?OK3rI!=BKjBG7?C${jR)R@s_bb<{yQP2)v3<_jS{C7g|pPR^OA71=~A8@ST
z?(4p{z$w5q$&{(Jjk%3Y^s}aia%{k)$?N;-EO>bvMR$Xd-P~|{LAZgGy9oYZ83A?Y
zap!bt8?po1D4@NZd3@>qCEOBPb|Cmvbz0)2&^YdOH40&<f7!4YJWk0?9CxHV*mtqf
zC|?jR)~2CG^Q-KU>Fu}&V3l<b6bUS{m|T0#T?PL!+)e;~h-wTAYjNTtS(^2IxMUD=
zM10B6Cu!ePI%0_k-@brj5bD{t$CWA%3WGGe6JrGn49y%JRa<^C!D7zWc1PR29j*4G
z#$+HiT<xfhZcHsaqLWWluomx+TN()`#R(ONWjh+Y6T?U({7HY=F#HRun%K?`Up6eb
zDF`n_Ij(@MZxO-Kjmq+X0p2@`xweT)ug`_sU4$D@JkVvRAB+8|Z6rtwj}yx_qj4v6
zuvgwKxP2~8@Zs$mRvX|Fw*R2J|7q_3sK)>2d-HXbLa%b2jqH|jxsL1Y%Hf9_AXG{~
zA!jcCka4-xZ}G)K5Z+ak$A**tY_6&)p72Hx{;HnABrf}m2~r5IgQWOB-y9N3Q}1!N
z;V);AW;LNw22axTz@??&ab2Zm0cdNG6=dX0DYy?*3ONK}p!0FMcjL0RixPS9vS>N<
zgy}j1)DWp1+AE&+9*CP9;1U7k{`Q?fwG>IXLCk##3S!r?TxxBq0UC>57`O)5H`4JE
zS`BO!<@(G<s2|b{?xE<rb6Ex+*Qj2z2GxVI->j=a%2H|h!C~`5Q0UGbF7>50_uUu|
zu_Pm#8@tAFL$nWd!O`aDr<5M+<9;X)j)-vg4BNisI#gB*K&PBd>sev3vO2s!vu|9C
zh5D6RToHm#>B^qNS<pjH?zIPnymf83BbaJEX$$@1XilzPU9RMz^7#`fuUb#2X_&H`
z(*3(9l8c?$EbjyhQVVm~eJ<ZKw+YD55dyp=1jG>W>Zefrs|%xmDR^^I!Y8C??ljf^
z4F@0`PwvL997H<<z%nmPgajGHt{95Ny%L188AzOZ&#D7*8Eyc%jE)l&LjSKfKS{2f
zd2|RRfv<x#L-&!S&^Ayn^`a2mVEmyjgC8yKEWihxcp)2AcWFFLrh;zV(Bsnq)S>B7
z{B(G6oTH60o(*S!Gyj<_3^zCcOv`(=nJrKmHg>&{+=hOjBaQ~py_76RSKm}ctrPb^
zZ`c4y262Ne_*Bwwn^&ySDqBBFy`E(*$wEy^1R~U;h81TR`WjgT6!g&f&!i?OOWbXp
zxa?A3^h8aY>+RCd&q_<lt(V@s8V67h&|u$(3@-?PKaHByFo2=T9s!(m349K~BJ(K)
z{wp@`-KFr;#+lfBUEVrDm!S8NjoEQPZ^R$yf>oL8=^f5&fLftJwDk}USZacWW1L`u
zb<OT&49EwhdZzSDB2)O52aX!&jhkeibEa5Jx5w7rPc0lY!7Qr|MM>HBES}@F#!eoM
zdUW0ydjsC{j_*2%s!*Id-2UYiV8*GbqoUz5(>il|c-Y0D^^L?eDNBTebX(AsrZxZ?
zUqf>Sj$=Nz@o9&AL|zo?G=(&$*<%my7Z(ne<(~9^mFGot8bL`3LeqibDa5^^s!fZ=
zVr@1Y55deH?{97C>mJ+yNmre>E|avh1t(Sx@2v(7bj~Op87_p&haAR>pHn{ArNgy)
zbgWSq=744(sdST|JD{f^Yl>WOAb3BKW2tvJQJ2qV`H$duf~s1!T3{rHNuo)al;xK)
z>om(mli?>r$kvsu)x<-~;(D2<M@2QxU$WB$AI9@;AFOwub3JIAc3u2g`D*n_p&cLo
z5?T_?g-ZXUhgMXl#RuYE|IGP2&=~xvXY-fp*O8$E)ro)QCUyWWshHPrkqdAG<NU;n
z4*wlCMjG@%HyDa3vLCr@`rK8D6WpVJMT5tpp##Slh!n&eqR)&FGl&!PHpqko$-497
z8E3^bvO=YpBo9R3aYmY%!Dwp;@7s;MEPp-_VyHtA9yc`4CWa1nXrgMbLOE@hGS%qD
zWe?wTpi)B2{@$S)U3P)=t^bM?g#V$SVZ(iYw7)69jjW-RF4L6(Vu)Sz+L>U)$b5%M
zkPuuf;*r|7Sh1^?K>HdMd~*&z2VHFw1)s^Xile>NorIWy<R^bg?o68S?u#n71s~RO
zJ@#)2f(2qg2gsHcmvLe^JDeMOluFzD6uE5YU!1^m;@;4KRbfY<`m-PCj>6neo)>(@
zokjO64^qw#XEgpYL(%%aA5{L-&~qiensPSSN{=>_%`SR;@T^df(R<zVer1tcL%OUJ
zux7b(zVeHBId^vHx(JVFl<5$9Xf(JJ+6EngN}fSjX+%yT1-9=@fc%1LsvhVGj4XTZ
z+fx=d_`4M5Uj9zkV^vYPSrnnBbdjg^%^sCN*j|gnF_ROooS%)Rq2io`S#u%g`ldvi
z%La*$$2{7vMQc4J*qRfc+-sC5J{@N~ZBpewUa9|>$p{9Fb^^=*WoJT!d$3B=jwQKk
zWtUcS*9$e4z__jEb~d6;IXh(2q^LmuuFK47o3ta*EZDJ|d2>;IW(kIAHcPj8__?*U
zcJ}jN9pBM`OBetTPbmaCQ{=9%?_HQI9p};3IkLawc(h{jo;7=09ll{Ha47I$!u@=Z
zHMKKC^Ldq4=m(lfYUimA|Be>gqv983B5;Ew@oIf9U0hUgZCz+@(}ft1jTpdTe}L}#
zLS8?u2^MgalE=s<3hs%$3i}iT;}xPKtHPXVFGGC_)UAL>bO0jpHIOE6w->PRfjrCW
z#NfXi#ew)ZpE=e?nI3aub{Zouqb;KE0TlB#N#H%ec2>ZT>@$9f8UX*@D+c9>pXQF1
zJ2j^#Wn1M4FlSr?fmqy6*8<!Fw^Dz2KkOqd-MIP-=$Y?n?8%fH8cnRDtUD^HbmOYe
z)9fKf=i^C8bE?td1VMOFCU6mxLNp<dJNRS23?LheGW!{KO;HgO8Pv*9XIL}!+VI{p
z00dRQFvXG2(Ns1rHmcHf%y3}eAlr2Y4qVH!Fdz@I-$N$(Od<%R7c=WAUH}?+05sHu
z!akESD6G!Zo=N{5p9df;n_kq5b$1y37HxzUYj0=^1c$7>P&TMen|==wh5*?ZHrK%?
z2v@n22{{tN#V#Y1Que_(ZOeDx8A4|Vj^~k&kr@!Omcrgjy`<k=z$S(cRE-QSNdiIV
zI3SlJu-)r-aB<5goap@J8W26m3R=4SVl+YU5))wi;0n?bSOzJKo0R-9uhlN))^b<j
z_}N2ev-2id-xU5`Ui0yc`g;CQdHkNCp>-*eudep8DSoT)%0jrv9_!hclxaFHy;pv>
zr3YvaS6`Dq-Y;ZGvNF!~D`W=RF#dn|r`=Xys;4C~v@`Axqh^<kG-8Nz%=43+Qy$4w
zbe1KTo62Oh&zg8HXZY}_e6|4lw)%Dk(yCfL47yd21Roxa8lQ`dm$&qMx!1+)wnbaz
zw@^tQFTDKa!9x!=To6oI;;ri?4cp6kZP3>&VaYPuCVV#(rdHahC`<tvB47-*f^Qu+
zTRwlVeaB{LxrTJhzj*D%XsqC+=M6OYcg5c$KG%AzV?>{_t?z>YGMWD^hSsg(tpi1f
zzl#6@>{jLu@*<t=s1ZLCK)dl4D!4BLGpcBLq_%icG;mzA6mmqLNi2n&uq$ML<^+O)
z7P!N)So&YlSrH7#nF8AXBaNzF+RbG2G7~+mPE*g|r#22mmslAf?Av&o)Xw|C#+W#u
z%@odQH&1Cj0oY&0*F^TjMWE*g=6|AJp_6pqR7sTEr#~}?mifrtDBxPY?x21Q2MXBW
zE;i|@)buuopM~OGZ2EQsZKWae48V+nV>es5?9|alVGiGMsm0cV6d)5nZvyFe@&ZX;
zVyl4<Bn4<7R-&Kmc3k1qkGt|~A%9lBBYbRtT<d-^cn<fc3O=7Fr|kWk*g{GY$E_Oc
zr$<oK&lMJcV!*%9%%6ltth85DM(Y5f<is3aKJ++>yLGWE*vGQYd8?1;Z)rIGqQH5C
zL$3H4o-mOpG2K@B@ef%M42Z2VaCG>Fes>DtF78`bt^nL%6iAatQ;<OfLvi5){=bA1
zH-+V!4nP{ud%ZqYlrfM0MY`#4JvMBGToO8c;9;2ws89YRNR1+PloA2W4&{4rO#o2x
z_W%v}6;PrfJ&t5aymY_CqtsS3UU@L={;>)|$Vp*V<$<IG`ntLk=VM_PM(IyIS7vf{
zT`c}w0R)K~=!lyGnhGn9#15iec-~aex|^gw;{xmZTvgkHKuPjFlXj$<?sJ>TOYFs0
z+`feKAdS1uU9%0Qr1#4wKjkXZEL5JcGw$(si;9^}3l_xZRi1g@7z_B55Bwc!4jv5U
zgYJ9r_Z<&NB)vuV>)GX5*^a#n%Qcsr@jmwGAjJ&whL97RP7<6(b?P1(nm2Xk59*)S
z6MLBG=ef~Xw-V(PGLtnD3p@wniVNl)qvoJc2JOP-cz|Kws@X-(|3ba2%w?vj04Pll
zs5&D3{mui(cwJcgmz<&a*A|z#5M}N$C<mj;G`#}tm_z{zpk*;2hQ33}pk#0}RV1tw
zwg_&cq`xC}1?aGb<uB@+2+ByxX~A984D=Qw4L&2(9H}OpkSYjYFp{9~6UUV-pD2H#
z@KYoBeP^CugQce0W2JE$eZryz{5~!mO3P*jH4=6aW7zHfE(+Q<!&*OsWI#oqut+n1
z;89U!a){d42R}{VSn69&?7nH^V9)aY0n=)k+SV#p6mibgZd_&L*U-1B88sJw#Yd-Y
zl92k)R?fg6|Fx5wxa@xhJTE>F{mMUg3Q}BXBMG3s0YE+bqpbwNJ%y=E;Az>={QnX4
z{8t6`14HlzturlM&lQ)tu3VrNJVl~1jR~Cvbq}RpR|>+q<5U6C4g(eSZw;Hvc-DT9
z1%(A=DyZ7$Lf;ZnY9YCn4UZZAaX&7*vLF%C%!RL6KGDI6;vV4~aUbZvCBu#cFx4k$
z?X(_!;FMFGE7;C#wgXGZ@S3<h^q#uQ+vn4O7}|w}`KW3ENjT4p0M6(X*(X6#DVp{6
zeqVwcm_E?84dhjj$5ihiJ82eyZYC8aLg@M}Y_Qw^m<2He=%HZrKY7adKcCn5|8Iue
zFhDZs0BiW-x(b?Daa{3w>T%f~g~05`j|z2{(3N1F$Uz1Z5ll4`PuK0ZY{>^eb$Au$
zb)hBYc`Jw;2(mn6p0P};Alwd^L*T%%Q{-A&Jc@*U2I|bv%)c)&0AeM?@X28;+fR+S
zY#P9z|KeKvKd;vRs=&jbETL)0Otc1278=>;z3_*}J@7f53xMF6%Z1&~x>LHd^~3yl
zIg<WaO#-}L3!d(C4B_|y#E`SqO7=gnstwv)NG2ezwXpszP^gHSupNlaGtYpZ(fi8#
zuJ-94yW4Xgo<&#o%s(tC+0hFi)a;W+My@3YoD`0XU>C1dJ-#bWu5d8DtC(av-IeQ;
zCDL0WvAK4D?unJXWY)zLV(Qs_9{su^x5K#Qs{O%Tasc_I$2XXRtIM-Gr^mCO6ZqVA
z64a7hczUgg&!PDHi(B8tKQ5hoFNnc7IpkYDj3(Z_yP{f@Vm9AhJ~xoJIQ-t96WbzU
zZe~91Pk`l>P*f)hAa=CHM?mMkr>F)kEl#lar4!otk_1#svdunzoOvr>Vo{%eFuqC7
zcV2ZEejUMu3|abURpUq~&N!ZHd0=VjsW?|xYW6V0Dj_;VRni<kG-J-*wu`Bh(A)tY
zf6c|1r+l0VA&}I}vYfY)B&?w(asH`}E|$cypYoWH)x`<p#08395;YR0Wc!4TAe=Ai
z7@f>OU@ez(r*OB`0K7AlH4MpPX}wqj5zlJtMpk950D7IHAe-fft%S{44JIptCszNx
zWv|&AEBv1>H3qAF-_7N&dn}e{`HV*5D+m`tWt?@$CEZwAkl=jHo&6%F2`TYtF#I^C
z@J8m*MGHQXQai;eAIzudiHDF_EnU(t#P$5nh+KB_u#IvRcC)`e1Iv70ubZ+Ff^jJR
zogqjB(z{7DP48z7oj!O*_;)K)If%n#ygIy)0zpFuc0^uLmnnZb5|nck6L(MNMD5Io
zfQ6iwX8Y^GA6>bo_bzq2bCy3|Fcdc}*vujx4cDst-B!|}RoTe8DK$~DV}|ZAvd#EH
zQeT%<*cVh(o$!bc*!_u6{GaR<G{MvbxN)IY>Crp_wg#-aP!bR1loW7=PQf$Dnl)}1
z&Sbmg(h1Mj{QzHVdPLzR)d-`(EtGtaZ>+z5btm>0T95koFQz@99^!(F{DOCETYtod
z?s|1x{Zk**Eq&I6a+1OM;Yzkr{unv)x-(|WZaI3w)7P)8h=!FUH<Hx;)pYp6=+$OA
zZXFJKGnfh0<o(%ZC4MbpXOLs`v#D7w&&;D3p&*-D2+8<r0lio?9V5+`Xf#bb%vlz&
z@04v|Uk%-qU7+Mb%QR2zQrtLn!wgu{fsBdM%YNxY(Rzbe-aub}=EFyaTd$7kOsJ2P
zS8gg5+&eJ_d!s(@u@w3$)Qyopy>bWjC!mwW6CS|W{_@lk;HgR#-=zJ3aT8?I$O^hF
zUhgx@BG_7lyL!vc#t=7sQ>JTExt;0#OwO^ix$k|QlM-|gE(uMfSr}&GIpC{ioHicL
zeQ%dfSXZ36>Xo<NfSp;jvjcA^354E7+Q;i<vk)i_nWp)yDScoYde^ch`z{pEyA7yB
zxMd!#BEN>J4$Rkt##6&aAH+ZE#=Equ{$wqeb?0{17h<`5<I-l9<4iiO#HoX(7FtTh
zo-@t{RgWx>oZ(KC#T1~TKAW+bh0UVm$yae^?f$6YT&atj*VZwZNtqO@F0v@#CKv}y
zS)0QkmHtk#dvwS9O1dVn3^fU{39Bt~4%bSkq`NmY;aOp~Ob>kS2LSE-&lZdu$bJB=
z4N^SM18Fv1C>4CzVu^$y?a=e+FY00gkq=xPLzA-F7Ua&?=hM#V7(861O{Fe|i#EOt
z6+s@y-_dz!ua8@k3{QhBf}FhW2f_cy(SuLK34S1Vfy~!Obkf3d>p2bqo#)Us72oTi
zchMd=JsZE!(JA^5;2tRAmbqKey~wK1&&KPj9v$3`oRfs~eJb%KGnX5AwNd&p)n9WO
z=f!kwjfm<6rQeB+LM}ayiRi!>i5iO@g)GxrKzd+LnNJw*`5mvFwfK<mwE}%b(W8lZ
z?22}4PI1Pg9gqe6TI#&FP{HUjZ1PMoyHg;&Lfkz@dEp?x=r_47IwVN5kh^Ady)4=l
z#qc{w4|<+TCG!~ySPXx~5jnE#HdzF$|6KS3o758ZNPd<K@xRTwUs=4Qsx{Hb!Kz{f
zxY7(IMNtOQ^t{u}u4bNTk%=eIUc|9}O1~Ew(-WTNbUXx&IE@rmyz9u-C(TsF9|dL%
zM)WKELD7`an5{-YL0QsdQ}g{AWm5fiF$j(6PTBXuT^~3$pmpz@2@TcqD1hcdxO^_$
z3V#<!Xk9hVO1*ju9MhRwq9sZ!16lu$ry#21z7NcALSXoxkR8)@#y_2kR!#SgoL_lJ
z?1q?dnW)bm6Anf&IcVO2;{o)pGRC?`WYoW4HE&EAH1nocz*~n<=T^|9@b(_@kVQ2=
zgtLa>BHi207Eq8Ffe6Kh$z@<t_9IXXdu#){7_G8c%yo3(vRq#LmCD|zsSHig56|<!
zGq7`uej5U)m%q*<=c!bBof()$?zdhX(%91Nt?4J*a?I+&ov62}jv3rNyvWzl9sY}f
zN$#oYwc4Vs2%Tj63$wbYp=ZE?0s9iP0q{VK{d4Ur^oZ$BFs0vhf}cyf`%{&2m*N(+
zO!=d;?e82_GT*TNDJbFV-Iucsd1C+Q30Jyzh|x&p8gw#gsi7C`Mn$s9V{8RIAt_}V
z`$}eVbh1TQJC?pWVW6e5)=~2Oeb|jkC*)B9Xu`<MSU+ns=g)e-XV|4|zV%be4M<XF
zh1W8A)?m(p+&8@qb``XipYbqHS!dj%yGnx6p1r|9G5vX+YE^|wOLeq=+XlBY5EP80
zr6OI?jk@nZbr^%1HipiEK7D;OXeG#cfeIFC$8dw;0_*UI!#F7}kWU&V#mCU$A$smK
zVj_<DPH2K+q{9bb3fIJaTKAytta!?GhV<@Q5!)Zvum2q`7b1gpwam^Eki%1#&!|Fp
zp+?LBjtu>BXQpT(p@fLVrt6JrO{g(iD$Nu(=q5`vHuy58IRfcIrM=b4*0K@B6B&!S
zhT}tJZnIOvlI@grXQ5x;c<3;N-@GXEc~mj~<(BTxnlZAiTYFN*tAQQsWwg^tY(KQM
z60IBqdL2)aDFy7~kS>9oplad(YOI?+3@s@_39B~kH;g)z7-0jlZ7A7<No_0OG0qiQ
z_viNDJYjsJfhnNLk%9^NVeWs%AET|Pm-zpC$^Gv|ec3cbVDawPmV~pHX;1%22d<@e
KvsA<8#lHcecV=w>

literal 13245
zcmch8cUV(jn{A5prc$M<fJjGrM^QmQK<T~yq&Eemh9nvrRgvDSi1gkeQkC8bJ%IF*
z&_ln6-}lWtGxNthcb@Og<soFB<dk>6d#&}Zz0cv(Gc8pra%OT62t=j!<dF^t1jb&z
zNQr?@V8e~kz#FlRhUz2W9r!tVSO*8*$UL4HdxAg|*Dqh-!Req=5a=dI?a>20-=xiH
zzc|(xHO1R?_M&2BH-3?msTon9Tpt%_twd=)`1nS^n*P=O2UqBcz(^>0Ap3(ybdRp;
zk;WkqPmH2q^jD2rkXK&FTzR7W<jF4;(XXrVxQMEWT=9mUteiairE_yb%OP|9vpro2
z^ZmV(QQ}B0wSW0nzt{1;7~a7Jb2d<1Cd-sphFj+wiHXpAEuqG9Gk3@dQM>Zxi{JEs
zciq2#1B=wUuVcI!?FETcUntVp_eunRMIH?-$Xc!EJ01|RLt-GF2xsIq;~{zVmFs7`
z9&X~(C+HjKCs-A$Ya-CN&zunrq|-k_kmG25^aNX6gb7yOid}@35R`JbyjCmt;>KU=
zH1*7FwbW1O<Im*=$`4;qUHFNgPOR)#46NIwk@>O_?iCw+AzY1d6~pgiia8#)Z2d~8
zj9Q$IU%aix_dvm#vx$!BKH;Yfq?*)7EMbY{K8pyw14uG>W(bnc5wc(9P<zKp!RpCg
zEtSy|Otm{53j-W?m|;DpB6CKP?jR5{A2lEQJX9`2zA6|tI%_keaGgr74%&6|1O`LK
zW{hRiSt9020*N2B9D}+tt>ymsYI9(#9Zo~@`+ld;Afz&^d0O9_(^1v&OXf!{ny<b3
z!sXq3jH23pWo4^4y)g77U;ra2c{<^M;jnEHB_e#o6dw%~ciFTgWLjH>TO3Bf+J9lZ
zs#IEp1gsV3?^Q-nHGP;Ts>X=^3Yr~350xxu)6ItFWE`?xYnYMoS17ulM5u3rm(%f~
zEkmffgwWYSIGxy#;TNZaxjc!`{F~(~Ggk&tw_jdKzv;r9c+h~^UCwMG`SbC}f8!{F
zwPhlssbXN3VBek9a3V3Jc{Xk%_TcW-nw?WsGMr!~Woh!GyGd}c);Cy?`pcJlhjjz9
zMIG<$=Ur4B^qW+Ts6!m2id*}5qjYn->N2|?^T|;Z`<F(eVE$v*4u~KdkSWTIjvp)&
zmGXUM6-&M;;Z$8Sz8#h{x4dAZsx7)`0wgtBBiGN4&XjmuG5mas!e#vD6#GqquNPM&
z!FN<zcvRseS8EfRwC-+EMOk<)%y)^O+#l45^-+xt(R<BcweRH)<+(0-4n^=}%*oN#
zii?egDD-*l&gj1HgNq#m=4rM2${hsiZ5%Dj;?D!SZcg?gIIf%E`@k$-H~a2js|I!h
zGMKcX9FJNgGhvCI9CIYCc<R=^(|%)4NteWYN7>SPxl*&N(lSsUk24o5m3&nH@e!(j
z2iZSejq;!qM8j!&<=E7uMZQPxm|d|^qhrC6PnZTmk(s0gg*GwQO`Jf>S507vpX);8
zO-o*UE{HC++&58954gc((7gQDsqAr`-`?RorW)Rs;;tGO!QpfB06H)WC7UcPK%A9*
z<MsCQ@v3?9!X9j(T^Z%U*ZBhd0hMXxOv*Cfy(-FhoqQbcaO{W{M8D6U*fC>My5r>>
zMA?0N<1p#zU536GriDLudl@sCI<9MN^L~p0u1`g{;~|yq(eP;1ysfBzW?pm1Li|?u
zVG1haoNn%Z9*cS?MSAfJ>4rL{sg7greu+!A0IpYn@gHL29rs~UV<f%*TF*#via|io
zr2-A_1}5tld1Psy$QSGK%+?9#S-9p!VcKSfj8l1%oTB;9uVPn(2;Ey5G}-2lHTAnQ
z(?+gQ<@@uhkg8G{>QFUgcI|Ra6wMD)CQTp@-Bhawb?v9LrPpdWOF|lQ$|^ZoH<S3!
z9nqU8++UufoR6iw`GSIV74lNX8YJg*GDDCZ#`}TGy2y3gB3+b*O)n>FLvp<2iD#nm
zzUqTukFUW*XiMY$+S!2XZr8V6qMREYcd6WOu}&*6sWm7==hPp(EYg?$Kx@2jL#Hr8
zXZ4I{Hh^VLeyp%Rx}@yv%TyRjFNy?(`@`Y+R4i1#??GYC*<OYs$}`gE<9rbjD{aUb
ztB3HMX!O-ysYb=%fmDwa-zk06?x!+WP-vLq!ftsgr@1!aK?(8PV7xBH(?oY^Gy~eu
zGqA9h%oNQR`?K7#+D2@`^!D2$FaC5%<{$~{OoF0_5W~7&a~L%{&s6#yA~Ul3htf0$
z5M^hspx@|%QbkV7eBYGWU&m9nj!i*$Efm#@rAj~eZ5NRR90>vD6139;FI){dVx1Gx
z7}w$|Pf9>4kos8pi+67O42aiVmmKvZWz77AAt%UL`?b2qOGkTrNGBQywDV_D4}+O*
zC@?;*=>Z@7{sJ$In*<3%JRO@p5ux&QE9}}ncxde?*%mFtk_fgPpe|ZM1_Bs|X3ENB
z=1!MqApxo_38X}Fo~qft>rBl@y*01m$mDN?1;l%7-9v?ZKTYx2j*uc%VwmX?b8Te0
z+DMXZreUT-^Xxm3d|ba~>}|6>tl|Vke@)4%dq#s*gO<Z{@$0pv&SXt%MO@`Nk~h7S
z+#XiDT5&Y11YF#WZ5p!Zt|XxjPO-0L_H1K*TAeWbGT#I(^~px+PaKXey~0b`MN!Nx
zv`4$~s80fR%lT@!Z0~8DuM)E&D2^`R!UV;^5T7S=ZLulz*%K!uN#+s9eFt7il@|A~
z_c^3Q4&rkpM{I1N<{de>w#4{@pZV4`Hq>`cEl0Udv3^3GnMiz-#^Oh%xMwS3skD{`
z@#eSRnV>Cioqq0Gw84s@W<IFewD@qFW$XqvZ&EE3rg<28P|c~^#ZQWFk+U+hz*#m6
zHa8r1+YrUwh4-gp1g)Yck!bGx3IC8P>iL|qDRoSmFn4(~@wMQkFci#8jO?Ud^3KME
z)2$7$fOq|Tejg9024%}<pWNK_tFd|DKo5VXk82fUuD%nT_*{F1+(Nl}w|^zWGd1A+
z;==*t<<rqU%ZT?KhuVeOgn`JKFZaK7!S2nec2S`c$v3nED3r>~#<=MU;nz*<;saQ4
zssyBHga<_<8S1tys=wffFa4uDTN|&$0Rj`iOgde%OV%Y&rys|^#7K7H8T+W~mYAD)
zvw80O)_MEDrzhD$w*n(XyZAg!e=`^e8tkL$I3762EFD{pUU&1BPR3ubD4)0?a!3_~
z8LtI>A6fQ=3rV0}96X$8a;GrfXU42wTT&P+KeD-hBB~?x0&F1Ts<KiUklngUbH}Cp
z1Y&vHCGMPvRg2&j&PA8(Lpq%zfr{B;K#fi=XKq)Zwic>N=a++t=5)SV-ZFD5fs4Dy
zrt7md)%6OYZBa3k;dDv_{@K$DsZFAJ8smi^7OD#!7tG3vXITYU>C?{X^QeG>YjcSZ
z8r42$vguCH8AAA~$yj_HRN@->_`?D5L|T)y_*IQhZ1BEE%`~~*WL<~I{KMs7V+{iG
z%Wbb*?T97Mp%p**AQgT*yqxi;*>n>HDTypqxDAIg;Tnnc#rh{y-K}iWu0I}Dl<D4$
zNzblU-7@>;rJ-!-Za3lmds;VAaLDP5H+{vvo`{vB4Fe+FWlnLP2)uXm)c1%#(*NDY
zR%3V1a$g3_(tRo6r<86r#cL63CCmZ}1`lZaCX@I~AeGxC%_+kXC_ceo$pn#4V|V@X
zYg6|NF}oW^+kvB@^L9y0i;?3bQ%^7iVXpEiQS-3v7yT*@``%vX6g8;+=I%h1rOF_9
zm_^gWyB=Yvtw{!DZDn%l<YzZOE=hT!pK;EiGh*(xHxA%+ZHH3TPr?}0!+?aupcW5(
znt4UXeds&s%arCw(4xv3^+E{;Ud>-Rw+SzuX~U&^_mjKnI$TKwTNhCaRPzO+bLixO
z>Kw9@Z#*Syf=ndW4`|1(`*Sf|E2If>F%UYcO{@Cdxp|JMhLLrxJ#LPO3>;cwnHgX)
zezJySHmX2J@zse$|6q=I)A*gh6<N+#msU}mKsue#FzyE!+5gTb>o^8aPiLz-Iy=jK
z9&~#zq_)Pkl%tc&6G^}-w(+b!cPasCL{4xU(AM90%cVwE3~WV#D2o3>vo<=`!r!rH
zAzaSQB@i>1t>Nr53p;H2+Nt(~p=LBfdMtWva5DUjL3GXGOu$%*r0c|IvxV1_u2nS)
z?O~=&90$p`z-0~i`EIH^Nt_ZqXuA64f>wee>^M3sEG+eDqGZ8KU}tmY)bmd~DNJ7H
zN6YZ@k`DGdDyvh%wx#TLEA7QgERrM$qrW?RE!O<?_&3}3(D;k0rZZOzro>i>N$&MV
zhxw78d&9m#QxR;_*|Jk0Vk@ntMy6u!JxdDwClTWm9^XRa{;Er`N<PU;EBTA@0gXH!
zy_wyu?dj>cw$mj%E0C-OU-KHMt*za2%t=15b9Hq!4fM4;dy+Ghdv-91<JXqoZjB|T
zWD+?!Y`Qq3U+9QP9m8PadMgU`PW&O!mWF&kA^DHa@Kd@?N8^Gll54X~eS9pDG)g8G
zyJ$;ypi4ZK^`c_Hb#fF5*tgyvsM%^oCG&}~-!sGgx0jVJjt;)w(exKR5gT0HYtj~T
zaCOzJsHo8IIl|HMXa(92X-YNtq7mTTKH2sBkh<et$r;~;#pzrEWO!_>swYL^1cIQ|
zlD4)RJn}LK9FG-M@|pG?E50}yp;uz6U$9q_aGM$G`_>-L7O<uzw^@EyOrSC7X@a=p
z@Wy!AShARXuT+eHu};9jxIIoZ=#-#(>!MbtXPrzh$UOH-U0U>aJ*tZ3r1}wNB}Lc7
z^X-mYTB&uzuTG|mCo-_(J&E@;9Ex&=N%w5q1>%6ART*zl4QH*H?~$6Fs;#N<iw^tU
z`iZtfD@ERq3l-C@!s6=HUODM@5;ie0kth}c95EFYA2gVymN~!hO*3({8rOKjPU-;+
zSiY&LX@I9}vX&+QlzU=5pKm=3I&P)arsNaf98l$GxM+Syq1{B74{`rqRFoQ-SFt;+
zt4;M0esFrUyAGn3#v;yGn3;p5Y+653u*3ES9*pBcmLvV1rYbgEum^11OMkWz-uuvw
zuvs;$c#5Lc!VqBxHH4@4-cSf(WGm}C_HnZp3AY};KlSR2;^myjL75c%_oQNlEiJyf
zH_rI47X)qB@3!@H``kG|k<m7K3_NCZCG*%Q>DZdVU<m7%{S$838`Sg^ObwKgKgGAa
zb#``kF9c#0f4za>EK}l{75&s_8pyuWqg`1cIsW~{up!NQ>Y0&q!+yfh>K@-v`&dMK
zdwVpeN@$#zLw{P#{+MNx<IbPo5q96YDhmIYYG=GK+o_FgNTD;;TC^~%WuYjp4I3RD
z)#HnH>XIm|tc(FRv|*`Imw?GsjR-2SYzoqK0-|UHvK$VsUdaeK-wv@nwc$Wmp6|pa
zK8+KZLplz9JG+yt#Zd!rsIh-)Do{^Dqw8M0bXSaEp4@8IS0U}3A=SdF8UG;=&_}1i
zRj)NwrQ-qA7f#j2g!tEe3WxQAz~M44=UbI7)09Y;AUTV#rsp>p-m01nf&;`-b*tN~
zqyaw#K=CKvxa>WAZ$OpD0)TCk>;cAByLW%)snDv|@h<>Gr>hP!qjbU3@qWKf;i4>U
zzU!;KayZt5gGu*@I60p!-ae&^)1&1i&)!zTNygzQLt@d!g?nRlykmso@rr`flt;_a
za<b1%=%jlSjG!UhP(C7utM_!0O;+3q73=looZa~y-7*t^Yq)tCH2nN@Vb*UsSu;k+
z+{Co=a)1ux{~R|Kh7*oo{<`&{d)l*4AqQ76W<h%};av1EM=QBj%dXaJUA=Mtifm=!
z9}{iSuFUJ}>OQt$gQ}9n7*nA{fMFla7E%JXYLKRYR87{jD*!qG?{)d>aNIuiH)y>G
z7!?2;PFqto-eZ>Vvqm6bi@<koT{BgZo=e>o?*YZ2O#Ak2X?8(NVSl(UIp}y*_hf(l
z5O?NCqjZYDCLtkFb-HHQbUb?N1M7}mx+4WupYPFP?1xZhi8W4y9^VK#zFVv>lyJF^
zudS`EKOnHX2Kh!Mc7=!So<xfK3*3eqz1<IQ(J1WoA8RX}?IEEg)b%gFYD(4wEp^9F
zN=ZnJ$OBQU+L_!Jv+HIW`*PIDC^hs3w)30k5WW;|-%iOUyGM)YU=MI<Scp(MM-sL|
z0=sxDsqK@!M=PPkl+}K%AFtuWB_+@6j>h9(4APg5MU?d^o}@;|@BDJ50i-ciOY!KB
zyO3FRG0h)K!&e17{UzSqvw*}J{}bBB$H$dG=1*F>-*dSEq@0AUo6h>@8igh#B)}Qt
zhk&>n-_j`hHh+JfY1bz8eLMyrtC;uN@U+i_V@}QXT$z2J#bLaC%F)w<p!$ZxnUhO`
zaJtoRw|h38Oyjifin}(>8ShFS#My?&U*nLp>yDGDntIhG)YH?G`++0K*8>JS+p*cT
zaJrO^djN<hPwD_FZF0a*w@WS&TUlAz!pzKU0u)4?sVK{-VAa&l@9<v3!s@f*eP<#<
zx4q)-9*cYZ%hfEkuLpjI(>^Sh1@q7yh(SS2a7=k;(c?FF>{HXyzkYwwh!MC!h=8Bk
z7K|9*F~8LqH03p<$yrpt(-{V^&S^B?cpo4oOSRR{H?ntK%|8|KJe#PEbXP!j7VuAp
zky!IS4-0dKNuQ&5K8y_KPgK;G)@0i)^r-grrJOT15!$A6l3!2sULvlE=B9D2m*XZN
zsU%y~3*jMhJL9Ts-oqQZS~82ChqLgD2y#s+pg6ph#8|zJKLIq8@2ETOp6$lPIqo7z
zA>eq$@XY$`<S-QuL~mx`FcH`yc1J5o`k;-`@^l`k`;DM<InM}%gO$UzyrOe|K*cmp
z4sr07v<GkP%;9JIv9Z#Vk8QP6<d53JyB`imD&!Xw6nrZv5R`Bn%yNHGYt<*e+szpv
zJ^xWgH%H4{#068F2Mn*G$FI+~E)M%2(ws>fu6xe6e!|7dTGq_>CQ9L45D(rG!^})g
zCtm}3zIc6ccBphdVrbcL94@!zYroQ;K9+A*Q@TALBCH5cg&o9ywXX@}`#>R7ZM9Az
zT#s)8IGG42*2xE-sh1sXL*F!=1uCD4c6pDP`<Mp-qo&DwlnSN1A)4hlkS8`_m*jV@
zI=+y=AXLaE`q!_{jk);!g|VjdecelKZF+UQnsZT{o^Jj70STMeWIF(Ydj7HKK%mgt
znXiV|o@s4_I{;pd$-O=8SCWk9+ToOEpL^r^@p{u!+1S9#WNwvq2?gAl@va@S2w*$@
z#ThYW(NhAZmR(7M<vfkwvHGSzkr>7zwT0(N@tu!QTOKf;wl+7W+SSb+&TQl1R|3|r
zvM<FC1JtV^{}w8#Bu_wi`_yGQa!vN%c(*~!$QZ2DbYfb`i%G22{;L}VjNyvwGPo(^
z^1BJ|l_ZD1-RH0_TMDpZ%C)c3b7@F*Wc0fp81XzOV87R@bKh3e;nwSNd}0WqLUCNF
zVUOg>rJ!54siZK89M071k|}wMJfR4Mvk+#<7jIvlZtau*#3m^Xs4>xgDgjM})~D}M
z(NWzT6!{D9-o<>c4W_xvm}D>-r*p$A7Mes&a&FY@<pVwZb3+QuWKtQ*<^3*9MU&9E
zv>s#~QObHV!Odx@O&5;6$Mr*f<Q1LQJ*jAEbC&*u<pBl-F8vZcO>&(@zbq$z6P{H6
zNGC^d0DCc4u3>HrL-8_LBho+GiO)?*SY&H3_{A+X9U?sqifYW4-yQl~6l#%tStjK8
z!{E?Vvo8W%YCk?Z@uGnw@Bs;oylw@K;o=4VE6skM)hjmd!5_eNpod5z^bpz=y>Vq1
zNZ-bfY@&KG5vUQgnp&Ern)o}^0^H6Wo4}>EXecO%T}Nype9GP{kv}D7Cy~ta(uw5q
zjs`kLL!$UgY^%kgo=Z(ZrGgHdm)RjQ;i(Q@2N|j?0BOJXBO7!<>dWb$g}jqS@5>Sg
zP5NqN2ONxa8Yf9277Ax~hQP=k`@SSu82$`TIlgK#5;+`A?)pL2nY-U$DNfx8IB7Na
zK&DtbbhcN_jmZPfv!Je{#!O6os_A+WWSQ&e;J~X|zqhlvzxtx9$jPFfaQdoMcmI1k
zB&ll-&7l#uwYBo4%Nn&C!kycd#k@xB^0M9MePPM@_tI9=y*00M?>6^Q0SyuTXdoi9
zZq0v!O@o5Kl~#!+{2SPSZSpiE{_V}Nhml$A*$KgueH5W}<u=AoBK5!{gOJ7_zX%I_
zk?*+F=ye2%2mz1VwF{pxL~`jrsh|Uf&Y%82&4B*HWa$4%bLFcS<eoE_1avv9Gzb!8
z6GV_~iCax)TwjC2mmlkD6=21SC$eJqxPli}ei_4++wnby0q>4vBYVt*jYU_WeX$zo
zMf8~Z$1=rY*OZs3M2aR2;&t6mtUic#1hV;NcHiSxdja&!S6oEwz+bZ1eeYBlO{dO}
zev9nc2{mOj8Xdn<H(1=bpR*wo*zcHFFfL^t?z@|4;<Ob6KZoI9gI>>M=G7uQivW0Y
zAZ?K>8Dk&7hbnz1;G!s|<;B0p2-c;)`g#Ao<c36`m9b4j#a}Xq-6@uWb&OnUGI|<d
zMX2wU>O1~tLLx%`jXWZ^K4k$2;~UXIDBanBnkRZ#VWu$E0oQ*&iOdqx6C{$hwHPD=
zhKFp6C+c6M&HtQuBYFrQh!#ZhqXh@)6mcNM%2&Nh&b$NY+yNeghM|+uQivjO6LC9u
z`Od%5tg8&khKxYwA+u37^_0HMz7*AIDCq#mhou`;WcX7j`w0aJO3Tdi34a~uD1Iwl
zevTgV0WY1Jd9~<45eKyX@JNwvp|9&|41EuW;3+X)t;Xg~LC~+yo;np0ld=33KMD%G
z+v<<(A!6X~c`QRB4%qm`V4BZPYO_@~Ki|woi`g0G#X#gKjjk*71%5Jo1W!cX;%@f?
z6GZx|VMwo>()DxV(3Q#Ji)dTP8zzMaOPEu8V6zZ3*HleH{3&K_V`x;!uwJU@{Ooio
zf`K}NgdVH+W7F9*HQ-ofF<9zEm`30Pd#)O!s)uks-W&I84N6t=>ZhKJ;Xd!=*6>{j
zcK!fmz-m>~OH)-}iv!hP3A_<Uz5$)Gv`K11JNvb}QbiSjyrsfmP9~K`c`j5|%uY@w
zoeWQZq$SkW#z>5^7+3njMpq_xN8jmbI?Kd9KSO{E##@-*Q-3o{EqOICU)q{m4$wSM
zwwMJkS<GHc;i1@1ehMZ8y~<UK)C&T5Z1GfjO~#GH%1V4ow<!g{S1+Jo%eyZxku*&I
zBO9nNSr%;&0W}?n03giV=>OFwaBU8n{6w$ofRLyIO6ZIjLpDJz!N+$J!@1PV^GwJ^
z&{wf~R*+&k0$VZkv$`m+T4b$qY8LHBM{o*JII-51cG_V4UF>zNJ$I`#NLsM3x&j$s
zsHCYDX<#WqkF9=zE*P+-^LY9RSI6)F=M|HP*S_voF2{X+T}CfO98^h7mJA?mZWIIF
z1f7syD|~P!x;*e~$rlKyLYc^y4V%7lWRDm_6d>&p=AB8Zrtu3vCP&EWvCc>wBm&ff
zcED1gCFy@@0J2%ouZR@}MLg~s8Yrg$+rNIP1(Y`5cjO*y{mcq*q(_R|tV1=InhDjr
z+BA-j3QbpiKlRhY9>m&lsp$wm`wmjN5pWKg1LZfOr-XU{mRqDfW88aeT*PR-9nF<1
z5N!UjWnDxga+nBcWlHpUP?T}0<)s^ex85@T{tVQ(V0x_A<5Hj%%Ue!Efb+!wa^3JW
z9deqf&J7e|cinsnf3{}LEh*3p(H3~g%NwBVz8A1WEeCQ3wUEYUu#!I|d0lwl0{GYg
z_}CDd%1LL9kVEpK&C!+SNwGi`G)ZT`kD@1yTV{wP*K5511hE0hs6%9yzGq{m0`|yD
z2ZH{MUO;Oj7?GREY_!`ylk*IG&V-?6dQi=XS4F$ji^c%^76AJiqip0Te7TREFFQ#o
z`$|q3$nc$!S8~mC#X%cK)@aMjj$8EIKwS#{9y56@h{HG3BVkAi`IaoU*#&x@=yC6}
zW$cj{x0?FXim7lsE4FmyXdE4b;w%JHLuRtdAyXViQhn8Pd0*3lnOPM&SIyIEy6**)
z(iPu&X4)Q?dX>7dn~9uKz%~#OkM0!h*!Zo3R*9;*RZb}^X7=(jjxoe@6xG%%1M?e0
z_h+v!Y*4a(v7ds2E>>LL@VOW@y|1g}7={lW>-`W=!q^234&z4NX*7L(Px*S19|WR(
ze)%kb+TzDC4VUWP&q2bQJLBF4QT$k2_(j^mLYt<uy~P+6E508gD9NJrLw@pnvCQt!
z{9x;(0}L0^K+jkw9;d(MFm)c%Oz5P?FBs-jl?xcl{xI9tOHd}Nj;!K56`E6_xgbg+
zGov_FQeHoC!46(ukf&34B@>EYH#bjHjCQKF>*yIZE-RD|D>9uq`izr5RFN@#)$pLF
zS1!N7rgPEuLo`43wPj<#Xh+Co)viQE1(j*i_NMhBAG!=z({!{sd-3zavtyWY#w{^M
zQ|sJh{Jc*%=~a7o47Wyz_V-dgudAay=+=B|RZwQ#&4^{BErOq{2^B$fP9(4HP;p*P
zp{N^~)k^0Co`q~gN^DR!>zCAKkZMC;6RjbF?(6OWpzYw*L#S@A!q5ffsY1j02J_$>
z0H+E8glM94-^|MeG-^5A0BAM>+#CuN@=%(iOJ7E?A$$<9T17#?f>Z*0l@ST|INy*(
z%gbv&jm#>&`<h(@O)~H>k}Fz`m&jMpO4DGP@ib^Gi&ISp2N164;mKn`#fQ}<Bco`s
zz~jBkdzavM<oF5{zn+3}H9PJ<k=yZ!<;|&d(&i(y1d3rh-aP3mzz2(Th?(liCaERp
z{Z+T%XJaF3z&VEK*WLu(JUCUxaw{9|5t%1^0f=}XAYwLVmxPqWYM*H7wWA8aGDr=%
zN8?GTU^qK(vnvEJG(14YHR)#iOj8n(>XE}5z~;kJ3*pfB6-#jjK!H^*gw%7bd-E56
zhtvRy?Mx?2MW$@%>B@cm3s<hx?-2+)>sbMmnuxu~OcpplbqzT_05xBCoj~F7*JClj
z2s8fDN1PbS15VsO-sS`WT$1tcC2WLpY{t{UXI7TY&oMM0j=u$XQ6I3zRW^O9wA5E7
z$(W$sS1L7cUn;PAMLA+DMB(BF-Vd9*vd;zRZwPnm5v7PwUo|iyvhk_@$a9!;lZmNm
z+3TA5if>PZ&1-jLt$@2y>T5E9>UV4s%hLv&T`vy!gfmw;-&%W&(#^|CBBpk!-iQ=a
zs?(~OH}_ju4@rUf&Adg2zG{ma_SDk_vqQ7Ue9VF^eQIg&DxdDngtOOVk)Fh4CylO*
z<*Z_1Qg==3>)8F8R>xzPqXkp4QnCRz=8fJS*v&OwXe1A;DqRJVRsjT%s$(_9QM_&x
zIozVx^}PR`Q6RUPf|k$S%gKoJzcK^)pIE79n?$~D|ERpjT*>$Uyh@Yoz<VR|5aRgc
z;LD=G_#*}qNQpPZY8$s`gV~8p_)-8QnSTd*b&T#q+(W;+uPZK3H8q2jUO;}`31tDx
zPsRUHIml}&xuajEZ$Fc$QCr}>xUtyt0gQ|0|HhRYkQ)OzCG!w1rovyrKg2d_wblsx
z!-YtoVaaNRh2QL^7EWBM9smb+`3X0bVcRcGtjqx;Fzl?a0q*rB!n`YPIy|{=PBNQH
z9P{T!73<1ue3nlCD28WDXsvfuH_?gTR76duO3#&uN%>j>YaGNS-a`n$?I$Mz^bOB#
zaiCK)`kR-t#9>Zj0DbBRBBRKxD@OnO%}e+YZxB&_w7`LqC^o+6DFb0e1Af&OS%tWO
zSU}sMQXo&HCHMnZbR3Z6bnQX1M?sKK;+b3JC?(K)T5|j}x&WOTsjd~7^$PHj^;9Ba
z{)&taM2eveGr6tOB%qwd8&#4X?)J2L^9Rfm&kl2LQF(WD(R*0$cE)oQ8r66LPF6=1
zaQf$BB9J)xhI65Q28AN%w^*j-!pjK)y1)0%_g0CN`lFBUb)`5Yip{`PHOoibi^1G#
zZ2E#Hh(1Uoa{%ncWl=oj`=>=RL34m&|J?B5FD`v^DG7_fOxNsVn_=Yeg4c&j_ZDIM
zFHfugN1)aus^%zG5E1F9R^Uqv{)dgj$DnUxovaw}4f)$Ndh(orhCI7;ab}QEq7))#
zgfoI4p^L1fZZ(d5*6n$NO28tFHG4z?g30K<ccDfY;LA+}fLl+3k6!Vk^^o`A^$_&1
zA(_u7*@&^9VnFX{E%6joerXw#cAtu~?g%xhf@{dk_ndq9ZQ8<9Jty;N6lEG&x4HBU
z0F#+RdJ@F=sAv)a*gs-7kdsE}Zp1&cpL4e$Wnv;Bfi?n@e*t%^dTl)4K$uXKKbj&L
zDP>X9A<^&Z3}8r|!BKb!;TcB0=5VRKcrN&ElOFp<enIF8Gk;tN(BDG9w%$)av+4TV
zfYCqGyo9(#%m1pA{;y^|t&R@c@>T0T0M#`i;EA#<TA;!v{jD<pnD+xVhMe{DW%Bsh
z)YK!h3NNSi<XDpX{t^rI)a3xTrHXAN7BnLD7=RflILN|kDx15VaM%A&DOjZZ;SHe5
zJ^t~61F!$Mox||omvUzLMT1`bZ9$a*kp)Pe_y2*}|KSpQYO=^UYqT|52>r|oj2HJ{
z5s~`|EZ*?d0gE{}%5Kmos1VvqTnJu67$RQ&&c36ja|A3Q5M-_7u+#Z-5s<IQ4rC8s
zauhI(m$kzK`&NEIF|<8)9)vBIUmym<H>@l`0*-3JGpmh^v$*a6PvidjQm|2iUqfku
z>(-%#?`}&_g+$Nd$EMPPf=ce5d`#N6SVo}&-evi2S0{_?!>sIr{e}jP1j&p$kDX=a
zg<TytDFRtp%2yKLJr=KbFJOe{GV{>#@`}PzXWO66-{>b{t6QalnSK-o;-J*OEQ%bQ
z;&(0W<t3`p(_g=knU|8vSvQcZYFoBGTIlO7;N6wa-${0Hb%x>I;gTxu^c#c|1XBfY
z6av9i$AkvcEh7=<HA{miPDR(O2@{i<5E>_w;^M`3?y1I=FUofo9#f&4b~TI4WSoz+
zgU%oP&`ync)#`3qQ>xe?$~LPglzg$V>l$KrTIRCbU+OYGNLU|@iJ`G19K^%A7~rd=
zjp+IAbv@I`Hy4RsUG?!*Qv-T2G*7BVEA7rw^=m^Av)2xw_L3XCR&3ZYG&k8IU_c@a
zC{7W0id-s(;Bz6O-jH?xH?~=kS~ovPARAG)>#Kj2|6e}WIz6O~Tw2-QWL-$~l-J5P
zdY#QHP6I{EUfojJ_p$PNq02w+fMr)sIRu%uwbP$)=)YAydRVP=u#aLHRU+DW{`-%*
zo(sE>{m6bAvwMPt3wv$Lv6;i)e>du8*hcO3F;z(2kf*v70#{UC!(@{qo!L`}8nu4G
z9$G1i6qh>E>r<REF+qkwwg?8Ru>4i^BM$ELsM98z(A!gzUeM-t$44)Q1inZ4RYNej
zJGs&M!?@>x$4;iHAw3U$j8Zku3Juqf^RFZ+Zwt3DVUNiF3>uH`io_M~i~<jkQnDKU
z&XQdv!+ILG04LTDF+1uwqsHVY*LWEm$eTSdBB}kVeDT4_GF41;x8HnhIdFD=t6Y&$
zUpMf2!MCqumV~X_yU7kAR2tAauFo2(!_Y~vJ5TZx9{MggTQl)02YuJ~g`J)jJzy>E
zX0c}W*R?d8iw|4HiXF@8a)o`<b9IG$&o;)4l*oPT_1~K9;$=PP(&Ze?t-v1k?{?Qd
z(#<jM-jHec{$SbtZb#`;@lO{hb^c#EY<dptH|gFaBNkCqE)sl_G}G3wUJ07%7cim4
z%cL$xjzhLgPM}|r+L2mWZ(oy8bGx_{k_bVUxDuXQD}fUbsoa-Ac3v>2(WE<^eO%k@
z6+BRK;ARDRN#NVmxknzfEv3ul_th+=*X8$3FV<N|GGGh$p;$vTo&hpXV4_Ik8h8zJ
zrCMnqbf@mK$CX9i{E41X^QcHTiwF&YNTgBO&-;<;IY|Kur(ePQg8c^!)+<Fb?FcJ8
z!1)^+BkFodt!%OHvS?RPXUKCDu7eS&fDEARxm#66)<tmk>YF#cd!dNFci()^pA9SC
z{8M5i3nL$xT&yih<U$chTU``M7Lyi*dtOo0_iibDVr+S9$J{VlI>|%P#B|SrIJ`Gu
ziLzP!c>#G(i%4@UoEIO^?1vip#&HWLo|lqZ<0IY}b@4nhc#%eg+V@L;HsOkg(K}Cp
zZ&7|2Xq*b>@Z@Ik!xy>&Kcf5)YZm{6QV47;4+aC~*#DQ*`A@t3_<6;uN$8{84W>b&
zm)yB)fLp&JK$HRr2kCsW%W~4CsAoSDtQtUT*WD}x7D|H9GH7O0h8&YGv$V+XEIsc7
z;@<TP46l`Be^to5hECN>F8qZ}5eZ1@n#r#|u&vF4-J=DuN=Oh#0F0!cQUJOIc~4K6
zVnf6tYS7va?ymH~FWtn2PF}hRG__<@Qy|M6-KYCNqoFBdpdxUreb1z>vCIrZ+b8X>
zpGt1w)i=CL2;=07<nJzM&JbM)KeTxB1(;ko6^7I(eI-pMeNWZR5GuF$t14<y*M%9u
zk2D7!w&dDpK0_uVpO;t*LprXJ&#R!qmhlc#EcqsgDq^?pYgNRUx5W>7mj%+0{nr!J
z9lWENGl?BS&f6QnQyOta=V%4g?bsW!yq{H7UKaiG$CZ@}hddZq&o&;)S0g;f3tKsa
zzdV|^=a1s2J6Dp2BvB9325-MKE&-lv@VC15jIlurjI-}~*zSz}#d;J|8W^kz^?-Q1
z3h#@~V#mF@sfl@Fsg3#g5QFaPNjM-uOJyu)GhjoPk;uodR-sS9k}LN^>24ivz{cDk
z$-HnOlV@kCx|5E*pdiSn3ZXxMltL|1A*`)A6y5fu=HFQs2^-4WW%QjD{TJ43R*LTq
zCznkXW1#_$`&6OYfBY_PD>ig%m@Bq@151lbh7I$MH#KH&%?F^Gs~D^wo`pCNt}4em
zOy!~BtrqY{0b@)2#zR3V#m_C-!!BI*ZXDH<<cG4C$=%?bOv3Drqu$)`VZgguJqSNk
zRFS@004Cf?&yL@`)-2jN_Ri#PP;L#wu~NP!;rXC}@~KM`SSut24y_jN^TN@F)BZYO
zYKzt89irqq{rFk72a>;)Ph|u-rNqcESAsB-&b5LFAKRj_TVD2~(8-SXMUNe*IMi2k
z3V7(gC<|8XXROX}Z$xZ74RvWLP0{X4h&@A)?9^8gRR<U2yhZ0o?pMAgE2yO)Z4)Up
zI@R$M+=Ity1A)YIj#=`QmtVXs#vT5YA36)u_tE$(sx(kN-i}4_Y(X>9zx*fa>}&iP
zA`JERdm}J<*Gx=QRr<y_>E3pqqm|A3A0p$+IjkEP(|;|mnXpry+KZdBdEB#Bvdg_k
z?)~86Q~$BkO+c}{T&A2+kSlXHi#LQ;z^IG~hMoYWS2*Z<{KCQ)BkHzG#5k8+w9F#h
zule6w1J>1SfRO@qK;6sMetM1{-0||WUpDRB;gzI9r9=ch$@|GrT$@OQ=0ShGPn({7
zaCOtX-xUo!#p6&Lkhxr@{!t;dKl?}se{1ghG|%qSx~rIQ4?0};il_UURcPQhh%jw!
zrzI!rUoqQ|cQllooxG3lUVm3l2kV_<3$;`)Y&)kpxgK;X@NwqCi9~T%8^wShA^PS=
zDgAzGvRWrbJKeDo;>NRK^O<Y^>bT&ZQ;c3AAh*W!&i7fh#>~i^$!j9&W4OF+8L;!c
zM7>Y2p4p{qz|EXUy$t5&TzhTE30D$eng5gc{P&xD*bC?zvXWB`3|R8=zi`wZYdtDd
IwtDk_0Glm5;Q#;t

diff --git a/reference/Rplot007.png b/reference/Rplot007.png
index dbe13310e811a6e2d7073b2d125b6db5f6b6c25f..3313a53d489ddc1fbe8f319232ca00ba6b46a4ee 100644
GIT binary patch
literal 14563
zcmdse3pA8#-}gwRvvx;0R$E9RA#&UuHX(;ZB11bxM$YHMnA+{K%c)30NX{lRyEM)-
zqus`qki(c5j1prE24hTy;r-w3=Xp=xyT0|j?|Rny-t}2iEbe>mx$o<`{{P?Ux@T)`
zwt0i}1{4al+5F6DI}}Q62>G{eE&K-CO-Y4sYp+<Eordr5wQ#By2jABFpK%I8p*C(q
z{)u%C#4Mpudr{`6PuPb)og9dGWIvL%GSgmG=FPrS;k4=Jtvhyp*cyND`X56__od&v
z<+1M9r}2$y+m(a<NYTt|*3(S?<MdN=57UH})3>W0m^x`UK0PhxZoOK|biJwKTK=#5
zEpK|T3D}4j+%(=ItjhsS>t{#~)VZ<z$4aBP0%Jk7&4A6orA+sD%~dFr_&d*9F%(KR
zVclgE>hRv19j!mF**Nz(PNUCvEoxK5>gn&koLNV8QCA+^g+dK({OF5XeqL7A8oC`$
z{Y48oM=DrsDb8)_q-S<S;gy$9M&GCB$r*$`DlF*o8t{L+Sy~74$awme<e1wE-z~M(
z!?MeSm)<_MyvU{ZR|h$`xe<A71~vv^Q{5W41<S0+@e=*fC+>5%6_%bcgtH5E%N**m
z<#wgBwn0JV%bygMPt1hZwTI4oqK!08?4}z9d_FA~^j=dqD4-Ob%(V?5Q-w6*TzY$S
zAp?8w^vQ>3PQ(9nbXeBO+z0R~Je&S}q$SPqg>J~WJ$Z4ewt&G7cFwguvk*-gUoLsw
zjvBhIQ@pAwFYi>)==+XR2Yh#LZ;66v%q(&=-P59L_u-4rPVdiEyHqh&Ryo+Y$fXhw
z5vrbR8>O|d6Ah+57zz6GM2iCNo)=CT+Fr@WeERbF9J+VQV>RI?NnA1AHLC3O`!wp(
zHS)q}rtq!8^0R^3FiyNy+lLoU<-!}@xE>mhPsbA}&KlIESFVxl1CxW~ORO9mWiXo8
zdY@y&dKxs>AJkq#CX<7i?J@IO=;)b-`=)uqH<B^W&MM^v>zBdNu9eJ~s0MmgRFqW}
zCnbX-EFxmpqRiB^ehm%{&1h^iEyt}apDL8o^RGX16dq_WbC4XBpqO$-F<!IreTGiU
z(&yb&6)PN<EQn`vb>ZOzGj*v8RzXaxAhk!iIp40tm!m)b(Y-ZiWr=m)rz`~BD5VuP
z<x(Cx+2_mX>gYHZU_`F|bXVp*JdyJ?BDndFbxX4?<hI54TJ2AW(Ibb*6>p^6m29$a
z3`VfBIw$Q_uPLUxs5?5W*!Ov&{IzB5J?mdyJj>}R>mt!z@xA4qoC=2Tp)5A3vhvWb
zxP#IOk4F`b-FTwt#iU>h7Vk<bxC#xVHkH17dCr*WR<ln!K{c(#JUNs*sAi72eq9A!
zvH8x)zL)N1m0IruF;0#)QS%e$w51)eSZof5&3<=?*sWICzu!H6Fp6vB!>F+8Dzr*b
zEOVo}mV35eIDb9`W{>egvkeBuQiph|lz>y6)I#ZHY_17AVUum>WU@SybiiDLJeO>3
zyl{20zxsS@YwP`{VT%I04Y6;N^I=w4CTC=12uXy|QI7>tOv7-%n%gJBjy<zzk`0RA
zcJG(ZGgVh!UbJe-_WynT{nPt}!wM_K4)_afZ@0w^*`{;)Sv|d;Ic1Kn5kGEMwzl89
z^+T79Nm(tIQhTH9hrKZN=W7P|`~l2jQNJ-qcy%=@@!0M0bye3|b{Q>y9WMA`-JO#b
zirZ9oY{1;O(G9aLnAfU%D6>^HrFpDT#;=<Ng~8Xefpi`IdLYf^d8TiTIV_#z;Gudk
zEX#m7vsC`7Aw<Kq?qXI}*3ri6Z|`o7<djvGIxlnFR%#v}c6xrm^6^-*rTW9*X?{P<
z#KJ<QHLJuX`$(3$gW>KC`sE(2o#K*)&raq(gGrj{M_G7q?811C#o{JZqQ`C3n@J|i
z;RSXFt+MnTVAZb7*{tO6b`3vqtNzXQ1sdM*P?oj#mp>mOlMX2w5(?4Nzx70{hy<Lf
z;lwU>Q4ZmZ;&ZBqL8TR$h`f#`4mT<}ybfmDSv@&&LOClq`R-<!LLKj(>q}o}9MlXv
z+ho<yHTFe0F*T;~Qv29LBSRGx$FhRjj^A&%QuF!#q2PrPs1iY4R=Qri(N-9a3}bAK
zdfoKzzFmvsIh6T5DA_XEVSb4$3Z?hQGkx*3sLREBZ!R9pvTg}@>bimz)H3A)Icrc=
zah4xYsEhS`j-XJxw${UsKcK`>m&L?3p>D3ZJ<iw~PL)Dk{_UWk!Rofs&GOzG?Ak2v
z@8TM+#vjBwJZEHNX3}6W&71Aq65Gx?4>fRpqIWpb#~kL=hPAiH$Hu1fV@{pA{i^qc
zUCAS5{v~JU*I`^&8%od7t?P<&b5BGGrb5US!PBvNvP~u9l?97=B_-y~S6?v%;k69`
zvZU9qAH~GK^??fb@Jzm2k^s|9ryt5`Ka_qp(vI2U{xg&72y@@l<oaj6W-hEPsFv*D
zz7*lxSV_mIs9NQwUoIahzIeE2vI$$dGr=@UWN}sf#J}F)>i_Dl|C_(8+Et%^*0ijw
z4CCWt^rcdY{=z9QPJ?n$(s24_yk_iS20a*sx+_gzrMF{)7k`{qGyVI<+5xI(yF4=Y
zk_=~mecn+#h!qTE6pEvc>{!#UKE?}apDVjmiGf}<yAYkB>3$a})B(ed4mtx;c~+Un
z%#+8*$GbZ_JBjmB?mO01QA&~(n*t2+=m|>=&SEs>#3eWVkGe+0swxLM2g|vJZ3_4z
zSTri(>Ln_28w;3Rh3+K$6=u{MPQY*oGwQf{T%Ep>tTl6of|gMPedCH7-SS=Hux#MH
z>ds*Ic;C7iQ}fPP<GFnUP~>%pRH^zmmwy|*hA&e{Fg2<Tp3+N{mX^jik3tDfaE%yA
zE#)|N&42g3>Ge;)-_aqxW$~K10z830C>$vr(MyCT>`B5JyR~B7EV0B>O55(K$Zm<U
zb4zAnhaE(>^pyU%^78xfC$^S8#Kl1vVnv&z^`gnlK$~@jl0%yAjbqKqIo<b^vx`aI
zbWGoCF9#dJLUiQPOw$4L-WwNSLX|~`8YE8cf2dct`CwjkwUeBj+`ExL0)2iV8sqCr
zh^S>o^Q_2ootA`(5oB=|8S_gr9(!HCZhI`#K{Yjyr1qnlnpz(fNA>iq-@JLVNIHo2
zt7%@Ktl3O>e~Qq`%F041<=xez1bV2f8626-w^G?*r?{<SSp?764OV}&G4bkAwmDWK
zX?+aU4Nl1BWA$V&9Fej&GmBN`%gD&2S{K2~sKg}ttABgq`Zt$tp_wesPvRqbuWR>l
zwi`P#*9`Ral_LEs!yt?>7_;bOE@6;Qp}6u`?a0(_OpqzES1l;8OnaQQ1yyg$8s9TL
zPQyo$af5Xc8M=P1>`*<~o@;673!(Zb)G4_fr~&n>R!ITK`x}-LL;ZFW_5a&1_g=0Q
zqrp(Uy!c{t<mWff;x$Fi71G8zHc+(hoP5LYOB|l_uD$){8OLX4AzG*M`>pH#{5C;r
z2j$t9S5D`h+vDy+oa8MCNv*UEt5=ESyJ*vkBB*EC@ptxXFs5J~O-50f8aeHmyrl1E
zp4gUscJFDyUUGCS`k}sd?D)2%^&^da&(>ddkN-Rq-rCY|MRSk)qce(z*Hd?Fxbx_&
zQfkY{w+q-iIy&+~J<FZw3_Fz_7+<7-{xt!~M#($2#HPYI$3H-iTC?g_OhPpVJ{q(Q
zClNGL827VEP-dF~cHBE{=pCi9WA=3n7E1;^n%tbP%qJTveHF#rIk~T0Sok%+6)%KY
z`1i{A@XR0O?kA5(Jz{dXj?B|}9j;u~HtqkS`u{iU;D2r&|Krn7)Q5@#%-x;TL~?IR
zG-mM@qM_+QD=99W`Z_Z_B3P8HjF?^r9qO@9@2elfnAl2Ah##4M;m7^o(K)}%x%~W3
zozv4xf}6|;mK-{4Af$k?B4VzTy>xGS>|11aP|c;v78xm0^g<vpb2aK_NVdc@d_+<t
zA7kDLCA|pBmF&^>f;89C<3Xe013OTtM5$~E|8uITc9xcLvQ5XYI$ye!dEv#y-0C0}
z1{Q9?;>J5aUm&;~G?zAxt}+&OB=yt{{;+qid;IU8{8wb$ZVl^|;_>JA4#ha`S}g6U
zSQN$38J~FziNA)<!GuzFkLQGlVyLoBGdKEIuM)F?ZeR@aU?F;n`mUQ9-~>?Y199ep
z+P4YJYOZ>?W!^?*M=ATs)=g`Tye#!(2_*`!IF@3lMZ-s>HqB><`_9Zh`2c551?XKC
zs|%fU#(v(OqxWU`^EDPgB|s$z7|k<FQnzxW#*h`ge*a~?{^CTrx2xaGtx*~(OtvVv
ze)@UEu%1rkK->_=#-MI2g>c_z_Vb(3Z;Q;#0=koh&HlQh7Z*LAC9WsMTWVz2@8<Np
z%4&Ep^NJg1V@y8!Byqj?w)#dX(Nq|<Yk`&H7MeY{r4UwiWq)9t;=#;T%*wJzhiI@X
zu30MY^!{^EZ}A`c`(K0`5?s_-kv=)?MF46;f`hwXyC|gukkx=OjD~7!YpVdjOtq*U
zlsDgS=j8argnh29KRI;oR@+mjPPH;z%C2jx!Wv6zGAdzqcjrS*Z@#GF%AYCq1GK&W
z;jf;24u_N4&PqM21V_LH;nH22Z}1gBV^nk69gT3`s{Y&y?!PPfZjO$uUd??{n^bqj
z@$r7oJFXK2l7H4d1MY&(ikhk}V3gQfSf;6`D*6o8IRUV6uJX_AWCvH4y8)0W6+132
zuj~61%D-cc+?Lpo-fL;*$x7BwFGi*S1vcAgEP+6`|MBYk3;F%q=|}cbB{s<(tneKu
zWv(m>ux%{8L;xR;EDD5A=~8)d>NS5RjD9*&_w{pJA2}*4hX<qq>Rro-@M+k@$CnxU
zA*C{{GM(8`Q^y;IXN*}J@0@(c;=v2s7WMg_^7y0k)&Y4DW`|cvnWe%^Vgvas!0X60
z?Yo(TqAm__=KwA@(=xh>Y?N=}e}m}<)TN7-kvP0*0X6!+u5SH>Z!WgBe0vH)(hZ$x
z;~bbM8}9tfA;~qd>7f$0PkdXj7+<r!TOYKuQkdK@vQg)o<$I=iM;q<QXVK^^WgoT;
zb1v+x<6Exa5MZODyYAQBsAY1A)s)@Chov_4^d~OMZij+UW`(ynyz9K;wvx!@&+Zi_
zjEM?cm2RTu1&(H}vvCg54SU|DLKlF0f+kQs9gW=xa6h;I;q)^H2?_I~>GtYrLF5YP
zJ7_ezm66rzf%db^wJolegOvqXs>J>sfBuYwr1QPg&kM!T`dRb&YNUB9ZRyluJ&|-5
zZQbfsKp+2(gpkHk5qmN>hStvF5h@R4w*K`w>muX-7@o<<S-1N3n~cidxa|*s3kDhN
ze|V;~=@$$<s0Gn-Ou`}pdqn?u?Y{gW`a3h5B%}E!gTV8QS%+f8&2<#sXB$SmQm+|%
z{?=DUs3D)k0nwuv0|oS1w6Pnla`5oQ-+mZwN-}xh(;K!pS?S0xI0~)t&dJ<TO7+ms
z8-pA7cvf}P?E{9vVM3|LXH-6>tW0>C;LTH%<a~KY&TQ^OffP|Wh+DxrddCcjEBKS-
zF^(!bT$se<Fy>924;6k)LR*`)qjwG3uXy%~OC*2uMJd^;)tmppMR}qDMHtO=L7{$_
zR9!p$r8$Lkqxa~6l;&F)Nm$)??u*s^^meU|t}dsJ*VbjS!ZV4Qb+~~DWU6cZRd#g#
zxEr0n+b#N>8z>|pe3~Po^hC}y?3O6SqEMU6v6S^KQh6ZxaAcTY?<%qjGkrS|nhuIi
zAFojib;RCbNhcq4C!5NF(9B)xF2xMrTB|1cs27TC;QLiDo2STvK`}2yfRt7ZD0$5*
zzbsG3ny5%^3aw=Z$j1B*#pkW<%(gjbgOcl+j&4|o<Q;Jhb>2JS1eXSs@o>CWX${;;
zci|P3#O*hyDDiItWNl<tqv|`JT|v3|zgZ&`adB=nWUnqI5zS4M%1T%T+!g;-=>@}&
ziuQaOa2IIN_fXLh?V*w?n*4zT&vqaF3MxrP*P2o3M~Bkrpt2*Fl@+GCwYX{>3RQeE
z$4h$MYVDBm?pxP4!edOhPZXmr3pp9bd@^8lj&nGcD=1X`r;_lshPdTuBjH#c$YY&9
zkR(ty^G-Hs4Ph7O*<Vs@<clj%DF5zasrs<~fLLhL5r2B#68m=I`pAywKZ1af+&y{d
zpjLW(Wf^?zU-44)GvU$C`rw#z1*J^!K`L(Lhu3T1T}>NM(C*8vjrjJPWsbcFW`2C>
zZsZ35;S{<7MH$WTo|`Cd4~-IqvD&h-bvK}H<`S-;h?gp@^Gp*|2aYxV$rVHmsz5`i
zxi4lIG<p_fr!-%`kH4LOhVi%KKi59v)^OBG6&0$Pt>gC(5k3qjnGisrx%R9NAkqT|
z&6`b$a(GsDl(memFVuoAz&HiAD)wC)fULvds~|MqnG(IQj|YN`sR_tSb8H)&GfohO
zQ*H{tI7^^Vw~<)@8tSXwiG6}F@?ms>r3Tp^#7TBYU$#-yYj3)`(d?h!J68u50Leus
z$mkLZg)kM5?Gz7(s~1oayz}B>2T}JY5{cy1aDOL4cSc(?={|iuWtVi|))u0TTc2p2
z$~@+yy2d}(_CqCwuV&r}6U@@HGudzf{TV3!-MeeeJ8y|glsXrJHd^gMC?#?>#lF=+
zpYWG8OB{aGXJ6(_w6lkTn%4JSQWNy-MHi)qXNuu&X}X{;eDeQTPBcm|%>ymYR&m=*
zeoWaSJmQuB*@b9q%nquI24M>gzWQTI=6$S-x-5#iux)A0S0A;r@X{O63JR`uCtio2
zE%vArnCZ#R2fOruP9dFe!HCM_rok~0%pUzjd<Wnt;D=oSiYd)1sTTa>ZUFc_Jw0h8
zzz2&@i>}-c!Ttu24iQbIjUr!<w&%Dxi04yXLe)VR^Y-ql@L_02&W&}>5oCt-+V;tW
zb($-iCxb+k>!MEQ1o|G_v7sR3uxs5%euzuV!iYZSMR{p9tjjgG-}o}n37|lGcL_ne
zAr1Fj@}os@$t;Y>socX0rlO9YHwpMdVD(c^iB4w*Iy!KKHsAA{Q$;kLsj~HE<irTQ
z%3P{n6<!l|0@49B?ab`x6I)q<YeFVS2@X`fDLjpd<?v^!oI^rF9CEhqB!kr9Tyevx
ztQD`aV*|U*fO~jAUGU|%!md9-f}dMVch)=`8T$T-ru!T$uJJ#%+jRGlymfr~UUzE4
z0=HDJKHd4oQvpqV*b*m}`3+{FiI*NVU&WMic?&>&Fup0fCHQbxnAqK7IV~f=uJDmc
zeG0&Jny+@2b2W(}3OmKCdPeH>K3psB&FK52mk4;P)rUJd^5X5a_&pB-*bYDJN?LFB
zu1dQRirAKS0SC>2iI>=y7QP(fS3R<yS3TXpS(~C{)%nN0?K&XmBZ8g6;>V`B>9>;f
z?gy=zx_A=FVdHLfdr&~fI`UF23J_h-ymR`~TSN(5;IZ&zauyvYhLms5dfeBy3N`Kh
zNp!Tb_0wWd4#R3SWVdi$wWuE&$kz26+(+CDI?yABv-TQ;W2;dp;ku#G(b4<#CIbTl
zwBJ^!UbpXyEg_X3)Z$pm*9HxX<j3mh((Br#KgR9ad}bJhk}8GP@wp+6bgkua=cQs|
z-K<4|?D1sa`a?$?qIU-a^?-?Vp@!_<v}Ln`aX-%#Du1_wp?J*n8wvi)B<$I^U24hg
ztghRV#>L7x(SiS3FHGigCA~ml?yCyOi`NuZ;udvbQEawnz(rpPm!6(GBGvuN`t9A_
z-P&6x#ZW`Vwlg|I@Q66w=WS@e{q)vCkRWH!^SA458uBJoo3<M-zWDjgWi}<tAT09l
z6|K&9KtDlRr!<%$HQa0yBZl&ST_RPl<=JK*6&1yv2eGufuWvje4=y{dN?Fe#)K1xX
zd*eZ7BvgIgfC$QwBdLySOMRfyIPDbwy0Xk&L3|0X;l>2ef@<IdAme}kyT%E(s7Vrt
z1`5ItvB5%i(RK<7Rh54Qg<Dx<(rsd9x9{9==U*G}(jATH6UYMs?NMcisz@A#iY@x=
z{qG7rU2BMl6LAWtZa5KN%zG1zS)8l*SHvF>Ayz|py|g&=yXSBcbRC&%Zb)CCLT?2U
z!sb4|d-txG7YMhf9Ef6um~NG<z;_3e^FdF0w5;Ao!PW}YK)_tb(YeWl1$@gFim=&E
z*Z+yS^KMy6{AQk|p*+$u0qcOc1md|vYgC_C(ZOx^WUagWpMd)zCt7I7O$AXdA5oz)
ziT0{ARvj#n^Mz*txbFlT&FAB9xup@ngmxx1U5jT24FXtlTKQ?;Dyf^_+>gky+*lXl
zB8iAiKvhhPq`CF*+D*8aRuD8dpFuSZ2iP=@fQRC3bhG*&hDmRpE$rU{)GV&Sq7wKn
zhXYu2pyyLXO-)V4xItpjpP`Q515oaJZ46Y;&LG-YkW&dz6U3MiUIg9S(hi6jAZG+@
z^SuYmZvF7gZLufdX~rng6tuj<yLb~VM*$gtRtZdxL#mz6-QBdB*mP~?$<{5gAoBGS
zZ-ewBn?{R1<cJH8I6JoRQJrq`ccfjwa5dZ;SnX07Ql*w<O}A&VcqO2Jjvj*|<eb@q
zIDr^5glfh>{Wp|%-2c#@T%ic=0+=rFKyAk9HA$cfgCrhNjiFp2UI2x8Cfo&T8ow_Y
ziX1%X)aI)$!~uTQP=jW!o;rEM&}ZPC5AHP$gM)H=7WruK0Y7LF5qilBSTFs*NcKY{
zx_aAL@MWA4*#;=%DhY*1=-KTNNd{AT*x8gE!-)zpy;oc7Z^j|>4_vN6S_ZHYS|loQ
z(?8A;{vmY}(RZNiz97c%@gS{qP4t$N{6B$@a=yf#P4U-z0e)=M0N~vhJj0uRE7^Ek
zl`$weoj?U>{r&wu*?+@4Gc^YRife*Sy&w7)I7~V^I&wz=ky#zO@Ztmb$vS?6b)BL+
zCr@nLb@+jzRr=8%bq9jadpRGG3MMDy+KxR?mhc6!ONS1!$>=p;cvd`w1g7<U5-g&L
z3R_~~a)&{Abo>z%_ikvz7Y~n`i>@$#Ik}JS4RG2Ht07e0cCYv^)tf+noDXIyu!Te0
z?p3)mSiDrB(pLW!{|g{if*|tOQotQo0#hZ*4)^i5pTMd8Yx21Au`tz**?0Csi48-t
z012jO;sCD-?JKiZ5%?q+;YCn`BmWf35AUtERVjv>WK{TcK?gh_*Z(VSAq{u1mEPOy
z<lGjQmE1B?Q})&e>8e7YmV7>6r}AJ*b2ltcsH}Dd2tDQlXx29{fT@ips(}S$2c-zu
zWp=m5ycI%ILydDZXu6G6uGEi$d#8U#7Qz`taKG6LRdb)8JZSZx!Ou?Xb|kHbWs)L<
z<pPhn_eKxDPdw!8G{yzGJUB!AzQ!lMO_PB(?~NpfK&FSm^`7dl-kIa2mza0?$j3?4
zuwG)Jb(Si7Nc<<zlnT6DC;W+PG3S6ym&D98Ta@|CKKF~)EC>z%ZdC(Cu)jY4_M0cI
z9!3Tab}cq?0@yz?cQ=h(=0<5W6*&56%#+iw+t;l&Ea8GZ^-<SVE=2<4VlhJe7`d!I
zhFG%BFR$F$rRJR9({sUxNkRWj`3au3a{f2rhdr?y;QxD`*3iN8g=64+fcn;=UVFCF
zZ7_cg^pRiM#r~;|R`_yMy<Rs!@DT9VFX#}5^+1@=;p$RmHq+>I1?w&(TY;*0W~-u-
zWnJr9+4pQM>SNP^>)!$z|IbYYq`Htmu`i&?H3g6<!+Ovbj^(r~|0UV8jDYXQP?<c+
z%qxnc$nC}CHKyfY64+)@M(Pq#%&4D0f^-#-ci+bj)AZLq=f=HDXg#sB{#LzP28VXL
zjHBN_Dp=eJL%KQrhrL71H^=*QUFXQ4c)a$jBXF-VkEplE6vH%_B=E_yX|5#PGvOfq
zrHSqEtMFP~u#vmi!EEly+@VO($=nY<a=tAd<-jH0d6Z{&w_iPq{0QO+eF}M3tZ|N`
ze%1v<u&nrkWk&Vtq8A6Hh9gBT>biS}kEXR$yE3G-_6~Q(H9dmA7j=07Q?lt}lDNo~
z<x=91j#Xg4Y#~Pc=T4A+n$-WF#m0Z<J^z~m`49ba(-{;IC@M%FFXh9B#P+yjvJq2k
zI0D7(EjOzTo$UNR$?!Q~WW+8gWCJYx5w!aMNHG1VqEK2_f+nzd<W<xaC|krZ(b#l)
z^3BKjH?FuN0y5o)TeC&OyGxKt|8&hqNQuT0?Yow5dU~&{)VSJ(%Ca6<zE3TbaSxuV
zW?3X7^8GmU2~{ftZXHiMV)m*dL*L`Q6u8=Fl3{(ZFh1rPN3Y#?c6QbVW`Ugw$_XMb
zzig7nL)+16H$0CdFYu(yj0_7b4<X)*0&W;-8Hfn2v-3Gp`k7C|Ki|W6d38KGc;2wY
zZ6b4zmd6;9O~RAR5@nrAO`_&+TsykQk&_eJ=LN?-WO4=hevQETp)Y`uY?W*KC#?^x
z^={qWF8;Tm@Qq_cfXhk>3Vv*qvJraBere8@-)@>9T>vDT-BslM$FJWJdVGC-)%hak
zQY*7KFYhf)2pr)R+yr7qu`}0iS1N|ni?Tv9fK1{MtGC1t(dg&(bKDJMIzj|f1Cg`M
z$|3B6G)!xT4kE%dCMmelg5A%SxWg%kZN^DzxKg0yrJ=2>i-$|_<y7SQ??GavuiP_<
zLppF!cFQ5DO*PB)2NAyh%{80Nj{#mYKQ(|yR@Wp*exM0V4%E`2Q|w4E9nrI<HU{fJ
z!UgaXTGE?6w0-39<6RJ6Vju@>xC0;F1()n-|6kd+7(c%-Sc9VfTg0FcG$LBv4T$V$
zOB;QOY1cR*sbK5^tuDg^N9kiQbbR~u0O3Kb<MqVcet@o<*6xJYy6~7+FjH^td0?Lj
zrfsEV%_skhY3NX=USyJnL~r@08n*SCjZ%z+V$;85ht`$@Ddxu$^ZfNDPT=zs6Ssha
zwr17T%IiQ-SwKwDzVY!0f|Zq0&cO))it}Rttb&UTeq;VJ1-sb&@uNykyNLC$+Qk~e
zI0*>}2;{I=J>VV>;8~e(psx@17~CeB=o8-oCm`<#CMHtC5c66njD+`>NLmk|05pT_
zZtmEaEy&A=CJz1H(bKaC2FwNU05JM$mqhoI8X!?X77584xE_<;B3Qntd#XSAfF+XS
z0&f&tv<#5b!tJ5}i+D7~@5$~y*7yXJ!gLOiI#@2GyWz-ABuuI&-}C*GpMV69noq*<
zPoVwUU}W5pcX5ZT&}S!+M)aqRqjc&rNtrY}Q?>A?nei21m&*Jc?7UMD2&++c3@LXC
zFN^y`VdZO6HjO&=Jnns=Ri<GH86r(VZ`SM%M~>+0>w6FH64E{n(WH+{;AZe&EYGpK
zTBinTjY>qbEgG3?Htwo&PWcwY8KOe$&aYx%oyLJ}>l5USoSPJIb;^7Jmq{!+UlbWw
z@(Oxx`q}Xp<!ymTY9{bJn7&F@8DGprpgCiwZ!2^|_28I`$WelE2(rz68My=5NcVW-
z*@sD0u9x(~_XK~P{-poum+ginI7m)eWoVltMTSF4&_n_cZO2)%HtHulkM04}Fbir1
zjOP~DwMJ7_!`h(}7j4G5j7l3wp7FkHi~QD|deS|!*^xf%A<OS!^z|3Mq<|3yk;LuR
zdM7}sglPg8SqCc&wowlNtJ}O2l2B95ttWWW5WP9IKJ6Lch0C^Ur!#cCgF&X|+>(&D
zt=|51aFjhsfshuA3&@wHpwc-|`{xFym>`*gV;Ta7dN0h55PED1><TJ0T&g~##*CMl
zY^n6{!|pBVUWPcHHzbjCxa~0FXxxebM})V6JmIq&xUk8h2;OL_53bsWi}%0@DJD8C
zC9es~J%@{kGXZ+<61G>8Aqps*ucS_zPlANv_2%a_HIM>rNgQ#0#NiG$#^5I4F(3f#
zx68LcyxiA3AhIpV67@=w3{4M&3C!G@US=n~ES-Od_hU<=YQwn41cOn;W){x_^v%(%
zu)z{cHsx{caH7Fk;~Z`fC9rGJSsh@#!Bo}TI)2Y30?kG##RI}Zg!%LB%l_paFD0RZ
zBxp7&rkL|SoRZ|@J&vYpJEB8U*<)j4A&a#H{6`zwYifT*8ooCZLqBiX8Ypyj#9=Pv
zxhPcUvh)JZE2U^6(bM-3BK#aViLMPzhdWC@nR_`q{PTL=Jj1W{LajGWpfkUiXRGoA
z8~`H2;8)2o6MGEn(^M=*K5t~+4Bi8@A1xw9h@lQPGZ_IdSne<Su-ap7H!Hy(OxN-R
z@ZtrlE)u*Id`1y*<bI9}#~W@B|II%Ym)KI}Oa-El<`Q^MK59bKQCQ-Mt3sETl$0<$
zrE=ht7=d-{Ou{%==^&naYr0m^gbX^8fQOUlE<p(phiaHlW6THYEHVTn7_p2Hnsd3d
zYjoQy@5$`pKh)YyR_fY}1TJ)rg@g&mY03B|G=l5e<Ia-=(E$A;mk0uZub-=aKQdsk
zwF;#X@sAk_5Whi!HagIoMckETB4WscYGtYab@b2Ay*Fl<j_~YWaa6_^r0}5%)hm<V
z$!nq2zt>Cb;#|8kI9lRSjTukNboDy!9-kdKeh{YY<IvYK{I{Qed#vF`_zyNY0b@{_
zRn)U^h(!G;(I8<18Jz^(!D0vpBFRbIGA{>DNN;CBwn-fw$u2-tV9Gp+m}<P28H8?#
zaR!gwHT=^W#B`6LQVT8ZMlkc0T<<cc5_D=0Ehpm`|Cn|EE6CVo0W!$03%ZmH8b;_;
ze+jZAAT1^AE;1fzj#Nz$Eq{c?M43$`j&0BRdivmz{*RI)eQa9HSnnuhJ{H8SBT|Ru
zY!pEVx(+kZfp}_Mq<LY|SUT9V-n}utW1~hLq!M}vXgoB0dmS5{7Kih&Mgs*bUWC!o
z^jm#^JRr}w5H;isr*}zoewt6&jb)ZgMz&Ew+=EyPdG_5Qoym3}VUS_MKvc}RHq?hG
z8Ypi=V#^?r(B^kJl=d<yM3TpG+SFTTOrKqeP5<-~uibm*a|6WfXf+`m3t~jgHK<5P
zoLL^X6d>Zet&jlMfw28N(49&JXS^7ys7uk)!26J*`_dPqLbukKKjr76UTFdqT|RpH
z#J8~X*CrDJl;SZkq#xQZY^oy0u!YEIZ>~YfLlgr%A|0@Xz-IC8uL?-sLfJ|QW_P~g
zJ-TF_3>Asb%UN*=VztqrZL0QYI2QGba7)7qq$>>@)SO?Rfm#6&KlVM{k<l{X7Ja_{
z_d6RQ|5cmSJ6D+#Id4X{%n+6JmP>+rsLmeJ;^GN47B<EU`SKB8z8m2!lzJc2(edgo
zK8F+>`N*+-S=jkr^1NVvl3^*DOIrDwG`lFOnX2JLPA&#ATuVG~i<F*>k+}*2fy1_G
zr96^Rh{gBo1++Xp9`xFyRl3ZT?ChBp@)-ODE=C07H-<#_M%%LRW(wGDQoE*Lf(sAN
zWH5YqGfZa8^3p;i^t=!->}x6%-jR6zf>WfVj=;sd@b$tuFc81+eg~I~umZi)E>zBr
zoAeSR`0iWc?B^3<Yc~NB{|_jB=w}RdX#meWaR==t>`d$K==kZ{06aYh^|bO=zOwSG
zqzHP@qqM;Z@D(5~0tll}ykZWRBLTi~_an46-L2MopH@#Ysnqs``_xCBnFPl9$>?Om
z#dC9nc+JR(mrZjWNip`2`T~Dz>L=Wb2A^1lq)}Q}R5U`gZ~ZXzn{nKwHX)zG2V?W1
z-NMVSBO16~nugPx`h7P)-3Nej-un2DIAK?gg#d}m(%{)yR04LrM2qF(v;6i}1$cBY
zCkZ=xd>5cql);m;w=v>B0(25HU!L>Ln*1)rG^9%OAt<7<i7pZ_i3q45&^Q>x<<jwf
zoImRx0M%0ZGgZ~#>UZ`2t$ey05pg+_5!(!u@(V@Tkr=N?x>k~{N`B7rT#>YvM?7K~
zlIQ!pudyzXBS~<69$<rUaAmuY#sV_&7+AxHvcCJmTR0VSsj|AddM=C<qFU1JVea6Q
zq-X>QWu&TDn}~wR;DM6cAlO6)c&@s29k?_lAZ<ZG`T!vtB5(wB2+d(YJ>u@kec=7r
zK^}{?S-1Zg=+2eeYLL3=%r}o+3O@-7G7`UsZ3kz+H;sQVtOuz4LkJ!1DBM7$^z<^0
zQKA237)4e5rKk1~-Q1i#AUZEZtaSi^=<rW>3Y2e*VE8?fMwOLN?7?t>P@EkEiV5U-
z=W9pPU$ulyvX*bS+4%VQ=(F=vY6cu9!=qqQ>cCD31S%bJBus=>_{Mj@bR1xLRzivi
zuw$w8TfN`ymkO^#j2*BP=%XIsh=cR@1PrVvz<|>IVtARO)vRo-Myc&_4QF$0k2Fes
z-+b7O(*K1Q!Gq}e6Hr|qVfat|VAPbLNyA2xM#$pvK)>um4yhNHeBy`YTxyg;$jfo?
z9%@Mkcg3YH6Dq9z7$iR?#hBsJGT#pmO)0G<9kxC&;$@G&n!7WNX2C_Nup0o97gj)C
zdZ8c#NEm%1p`Z;FW1xo$-hWCv%L*DVWa<yAEtcnq-uDBAjlIDD59GZvShB8_wS6J;
z{i81Go~DViUa%*lz?X65{TuAuOcJhtqL0+FW5b$qaC(B?9V~hRF^HEdHBkN-j37BA
zd|VA9)1vM+@$K0{K=RBu35!jrJgel6yj#VM-YalZ)qn@ApdHY{A(Y=Lpa{sFlLYsd
zMJ*cC{yJn1S-*W4hq`+x5i;_yN#lJ`yOHNq9nU(b$H{Y#``HH6ROAO_lf!_zuG`V{
ziCwP%oohjEU8~W4cRj6}?NeK`*1!4~{gn|KcWcDbO_<zU6)?Pyx5vFkE?)P>$IFDe
z*>|0jfL34!LdoNN-=F3t{cwyr$ne5bZ!=!Ju|26tvbO?(@?dB3YX)e9a@H}h!{4}s
z_Fg0RfW1SHSKPj7t>ubh`uzO-eG#3ca6i(QdrH#iDr~yB0!h)CMbT&}o}g;Qsg8b`
zvsjKbx?N$166g#bgAcGv!_}q6B(pWtn}b_w!aadK19`!<b|^Q9JFlMJ`^SOnRTx-`
z!Zh5%ZZ+4ss`iLl$`;4EFHT;@O#wBcnqS*loKPpy*E{3Mk)pCb3}>#gmE>zP<9B{U
z=I6*oyCp(PXjTl(!I2FvQP*R&OWIiBoGUKf6XJBp)*e+#V;@~V%C}sza3T$9u~<L~
zj!G#9MOuxITvdxJRw65WAV2(V8&3$Q2ZH__@KiX*c9oW#o1Eu=0aX+B7M<ST!Fm08
zg`A-4{(hf(F5XN&;xx>x7Z`(MzWv3L(L>@6t#RK=h;4F&9U#!_YL|^_X)koQt{a2$
zr9mzioZ*A*2@w*Ar;h{GF!;iM`~1_}*hBJ3mdQXw@>35vB0ErE0|u?sL4Re52jnjb
z9SS=mK+COxSi?KxJ*7k<9jSE4HX_*N0vj?~#<<li`H{ft4EjOy6lI&96dU6bhziIb
z^??e5NX>J=l<0HnUkIWPnXo+r;1axU9K&;C1ho!o%$!M1E9?+Cj&yPzwev13vm-5}
zAeQwWcf_VmD@69YfP462y!8rDRYVQ~GZNz~KC%C%=G7M$U!H^}K}dws`28rln+(BV
zX$ZDJEZhdkqh0G2U&bx7a2VK|0gVV+ZRGL-i*&tX|Brd+jAs7W9tpPTAp#3(h5ga=
zZU}O;v<0=ORgV3>QKK8Fqct`9NU#qnTRYaR2IBAS>IwT}{BVnx2EvCE3YckXl=Yxz
zU_?T`a9Kblf=&em|6HCazvI%tVs7Q3oe)~018t;13el^pP;RzCF!Gq&KF=RTHU`1i
zxbc9qpRqBV`NgB{DbfT~QkowP{=!FgzJb@EcQqZv4#+umg3U-o_N_3#HtJ$rO(uq$
zq;Br}Q!4M0AK3vmZzUoOkkPtl7V%L<LlP#o?(Xi?#~M2}M9y`X@d0~rVojEE!OAx@
z63xNxz56*@o{93<xsHtX{+|@YHf_=b>AvH-eVSWLb<A9zf?S8Acw(q-zyEDt@Opq%
y0<N|}L^x%)v0LA7|F+Mh4iLis5m|h*BHpk~d9O)$)^{lQGXK%~G~wjspZ^6XV+^PO

literal 13122
zcmd^m2T)UMyKb;yL9mMm!GdA~MClk}L5eh`DlIA~9YhQz5K!65Mw)<%fE1;7>CzIF
zEr>KFK%|3_mI$E-5)$sa-2XXe&N=tY+`0F^b7#(FaBvoDt*rHZZ+YJ5c?iC&ueo`n
z$VL<jwOLzB-4KQ18$|xCUk68s?WE`MW8F<1O?CJQUrQIN0^rAnJ6hK~P^eAYkbit_
zeg4ZR)IOB9`Z;6oR}+0c&y0r?R;CU5EKKy6a?OYNB#wMeUlS4hWKhWRqlC%>jWy4r
z-Fvr3?-|yO+M1$q@SA!>kGtK01DQuM?{B>udi&{~oiQojkL_DckobJ~t8+~Udzm+A
z+_kx{10Pb!Jv-~4Q2V-y-p`!%V|us|E3*k3gl$4msIygfoefZ^`!-wo1W@<auDQc6
zf9uPR8qQ=@Sq2}<el6dxz=Qjje@wsg_7z8kTb}FLKK7@z_t4rNBNR$>$a@`fQRk|~
zQmQp&JR>Rp$B&zOFHfH9DRa>o*d45Xj~1qwtr>w<nS4jP$H*%`wksr!NuK(+N#1wC
zS?`t7l|zRPiKu$!mbne&lerUaA_~qgEANfJ;V{YMF7IIV^FnHi$!r$0;K`GX+#kEi
zm#wU<;%A#~<p$y&>W5*jzenN6+b#wy)5M?a9zGq;8HsnJf7~SF>}f$H)@<Cg$pnWh
z7^h?$7ZrqWr?IcE<FEn#b%fd=Au5(P9Wgf=sii)2=W4L}0R}DFp&5nhzq(<N%V9Zv
z{9^-QX*kBj!XgK|veZg|_h@4o#Gbl1KR@67&OL>+_+yhxfAuvpYoA+ep7)vjAaVbz
z24|-KSeWd~ai`TeObvc8uky?@%`;C6+p9?brYYWGmqdKgKN>?`PE&CHo@{{S8moFu
zwmW1bq{9WJ%2dlbl|?SqDZfHP+<)=4+T!5qiQWpM(N`YfaQ;*G-YY2@4ofq`*;!fI
z17-Kd4fPUKOfFu$Sh>RE><rV`8LVD%^V3Gm+}8_x2E1IyF+=IN_7c2bxA|$*ea*Uu
z-QDI!rlz;y`FuYUISUh~qjX_0*=2oIUVF9ENu+*Zo2GZ}v?iy5nir%1O1fSh-YQy4
zqOH8opEEJiAep2aA=x0OprEif$F4OI7Mj$cWL99a$ENA8;O)(-%xX$*60Dq^XvQJM
ze(w~YAHkAsNdZjXfz$9dH$r+y({+1Cusj;p{8gZAoxq+`fBDUKn2opY-McqTVtYh=
zv5H!3rvPd&;kn)WPoF-SSKLj@C-Vql;#g}|uZICU)>=<5>`6$7iLvpsev-#%OH?Yx
zcOlh{(Ov4qrd>XgT2ypOxutw!$IhKc(GfaPo|C;twU0?j>5-lH3I>(B^pxFhJ!wIM
z(3$8i)q!<8cJ!!&$E!dqe1}QpY>Vox&~NE_@g=t!cZCVdo{uVV>^O#e_VDoV#-LjE
zkWfG->>~kv;mhQrEwZFuouo~K&rkOg=vf&O+Twmw5A;>WC0qtbRy%j@%+AR%vav}~
z!P6zQyQ{nkB(kcj&tw!HJ9g}6Wj>uw-?8!04CU+#B-Gh#LPtkO*1LD#DJ02PcOF92
z&M%E7F@|GsoqCFWsb}{kYe~F`Jg7-eiBN01&$oJqz`A)hqku78FYZ=qdQ#X}PDC%D
ze~#Pd9AV(()HxTj6?N7#_sm__-gj4YUYyY8Kd&Te3h5Blkox7z7dh8nr9RIdmnWZY
zKG8^tleKv)4Y7Q?^-0Z<<Hsp!G1l1@t`$g>N2NlPj0*)U1~Re7<3%`+_qtwoamfpn
z&Cbv7+>P^#?BF38i)ZzDF(#{+op9-~@%NmHZ}!H>nym}$F0=`{-SjfDum!?uZjNGb
zJXZJEw&1a{9#>V{@YryJbotdNZ@WYHAsBP&>NGZst64sJ^oZS2;Gb4L;BWC^@PXvd
zkX=%S65Fn@Ci^;y?F^njduHEX?Gq$F@~f+OK7r|{nUP4yh84qqySRH?6U&ZV_28_U
z`4{_^SIYDOGI^b#Xl6@-s?+q3PjqSl&Xl7MwaH_o>04GtVzz5vrMAk{-KkA7)ya@r
zume{io73{&{df|JcdZ!Sm4_o+!vm?%LQmi8_ZgM1BInU4opF4q>J7r=q*bd>_b*J1
zh@nusL}&R#gVlGeUt_-PMYZz6zjNn9X|A(=xDNIHp$%`(Nfb(9%X<{+>RHrk)OEh!
zb=K5BWYqv`ElSC>WMNZMH|D-g;Z?Sms_&3eI0Qu$ua?jM$kC%$4n$tQa3N4;UabDg
z#fy(TC%T7qh*>ssm(9(yz0XWq2<0=fArmx-La^i{-nr1WHp5RhW#ttV*oNeTb6W(~
znV6c6I0@albqh8dPJ?n6sr6S3`@{9iX=!QQHNNFC^J%q=*O{4_pKxhSI$!_&Q?7wW
z-}N6K{eS(S&*s4jk5R`j&yEs)MrzuIHrP3!P%6)=*G|Ev>|DQQZh5vf$8CVbrsovQ
zn24b63rfB$IG><`H$3T`YzdKl<;s;}4mJ1ML2bxEdUt8gC_9{%>csiPl<m&pw`sc4
zcVXX_r!=Lxrw?dK8`rENCF$fd*b0vsl@o1&NsPhD{L~bVrDFj)e8FS<4q^S8RZ;`4
zGjryJvKmNZ;c>_-76&y#zq*w7bvl2Yrq%02J>I-GePD&hQVtuc%-<HQu9n{O#@Ksl
zX~sty)85(XxbUm%n#<nxYl;#B&YgRZU$bCt;1L<#ho3q?MO*t$$mdy<OXs-M<j#LD
zw876bNczS#7FG*3zL288or}6V8&zI;_4|mNvi&oo;%9elTbj=Nr`<T)(!3aS3wFhC
zGy2STisiCej3F*nG<Y;h;!w2qqw}vXxVHAhY;vOJcMN_A#B=AV3@&Sc`sMj?`MW<q
z&v7BmBPC$tC;Dr8Jc#w~?P)qOuNIn3wY7t{1^*!;BI1|DR0@acbB6WSi)l@q`oQm#
zWRRhsAm=k<Yry&09k?69;YmG>lo!aTr1w_LZtbeA#54S^F_`|WDtcd4c?R*4j?T`I
zPwn5ozho_G@prziuI^>wKc@wiI#KDg2y6?-4@oy#xM~YU?eBn7o`-%t;gIC~>Ry{R
zy{kAQg9sI92c$}{`iz?R`1@iO1T3yKt#<r(1fp7ceBVP4p<1}rqy=x6bR7ElD3RQ~
z4mEf+ipsjrd`AoSd%hZ#e1SH4pAYr+J_-_S)hc0tQ|qszP$%~Nw>!ALjCKFP*|X3N
zl_w;RW)O>a3alHh%onXsD$s#Sr_ZgwDLXI{9oIP09A64uId@@GP%UKoHJ2W!mXP~|
zIJ1+&&uB`d{J@_p70+~pJ|tsfY$0%=ks^TMM(yL=(J0-+fS!7KJMmVVMdUtHUfr!h
zS8E}WkjR?7pd`A`TfnG<1Wfdusw9>fjz&={cI?>Ut(L!6(bd?+#U;LH?JB;tfgf_k
zkNdJF^JZr58hA96@@wa*36}Vx_SzVmnT`5jAu6+sGX8T^Zrr%>4C^uu$87YA%l}k#
zt8osx$p|qtM7mOIg}K7T8gP`tQ=r=QsH93uU|Z8HegA|&ZIav;oSC1muj={hE0NU~
z!(v0s#VLEFXAliP{9vj4^}7rbx?2nIe`=&8a`0AW-|X8xgo4#SQWm-{CFoec%g=$v
zswMHmfqXG!*Eq!hJ6~d;z#GQ#E@mWn#$^!iPMnK+^5ltmbwW(?{}<u**$Gu}tylxg
z>1s7LF-e2|KU^$_LWx~EiF+incklfB)k0$=&ZLpHwl?X@D%98g!&3LjZ2ECP{I5Hv
ztlr&uEs>=YBdx-nqhP2)YgR?2>L;iSt2HRR%RgT4ZW}tC=D%;t<$YVGdM^Mtl9`Pp
zVG^NU8)Ah82$PEPyUCs1?xHw`P6ca_s`AJA{dju2_S&^;N$st})t;h~8Qkj6^WN+l
zL2C$z%SV3t<hJ`Hx&=`#(A|3*{rx|u@l)|0^mC3i5!Gw&9EkiHE~a{~>sCybS%G!s
zczV3Cxp^EZpd;HPD%GFMc4*JMGTwvpU%FMpUFf+MwiQacU_D@g%Qp4l^Ispd4aGM$
zB!;zr^vPq6`WcpTRlXTXPE7Vy$pe1K9X)h3r&Rjbu{JTU-XRD5ESW#5(NwD{&+!&j
zW)nf+XchX>rAzlX6Xl9MG6RN;EdnDGQCM}lx7uve>2XGlly&~NcJ$%#rqiu+av7Gb
zPUM21g_j{X%$mS+kIwIR=_<O&>i2as<#<=1j~zdbr|2nas}yza-?ILQu)xT?NOMoe
z_bM;98aOHLv|YFwch0B=AgqX@t0kM)zS``>E=qrmFQuH>Rjes5FTWJ0QC!JkF<B)B
z|Jo6L*WU){utPr9rKP2$rh4J%M4c16W^aW;Rv;tL0EtM-^?;2UrNve*-o3_;B|s*P
zSNuhruBOJ2F#32rp49F}kY2*;F?-5eXXBo~cmV^RkN?hw0>=KB+0xux-QPLqpP;jO
z^JbDYC7f2$Ge3Co+O^mNk$=MX==AUW!nonApT)%Fz@Qj+>eiD)pOHObqROXmX?&=$
zJ2PxT``X=>$(y9#?MpfYN1iS!|M#r+|FM5PoWNX%d~tW5F5kYMefrTIrC#S(&JZFM
zkWSO1jHt8w?r5!a{rTx8saaA!1~Q^iI16v@>^zr7O1&o}9<W@f;L_bbvH&e4pK%Nw
zVP_KX{p}6*q!&5o@#Dv}adzoTse~y8;mDCA@F|6kPEN$Efkce=R_HQH;g;0->Ll^I
zK9+Z_%!clK_Ykr!Z#8uk1GG&9G@;KHUg`_n4FEJbd0&P{N~x{`3#Cv8C0Uy5QzrD#
z`eMqr+cevh)KqmSx#j%k_3p-(FAw@y!)QTj${06a+&PnlpUUCa9!K~4GBGtU#rhP8
z3#{v?@%0*L2}x%RQs=h>t3&gk?k7xrbQcAD(~3i(wIcxI9TN#7Ozw}=^6pl%@K4fd
zusuZ#U7q}~iHe7=2I%5Ugqg8%`;Uq0HJcGq<NplC{@0h=3S76*^=)1a^vR_<6dFL?
zNJ|2;whM7WC{+C)p?sEBR(Zf(go&w|t}G?3WHrYtmQ$-ysIjl7*4<GO?ZmFkVi|p2
z106u1<RGIztuBmG#U%Au&mus_aW?*KsH}OZ<8y#v1KLr@SW?4dGb%^oocxkRp*EYG
zq$C)qEu~IYjH0dXHXN!PP4sPt=C9p$%`V|ky}O)KXRc?g1e~c?hnnoni${tDAj;e2
z{HypnIy>1avVh5pLS@de7*yudDeli`0|{+F9K7a@0LZ0nP)VNM4%hmC*{W_3e*gON
z*i&2`6idH^E%1g9L;3vRlM(!<A3*Y)Fi!~JEs+f1%5Z6#;thn1P?;#9$ldt3Mmans
z2Iqf`S;MmSZH8SDJZ^)65S{L>#OIh6Sm)WE%F;ypsDHR#X9cX(u5Mk*^^=)HwL#_e
zmME0cZCgJf@8&pp5!su6AiR#DVHA%ag%UKVvwI&cY0&xc(I$ECslk6)_tyKYUtKBD
z`YKs9BdbvFWe?h*u**}mgz>Z(!KG_ZKJIv*BCWx=bjx*6sXB(mIPE=Q8r+1PuuPV9
zsQjE9rve{1Nw<N{<YNN*#r(@%3ScIzeZ1Ldv?YO3K0i*WXKC}HzJ9wUFv!TS(hpeS
z48+&0R1rCz1Cnb|XEkGF@0;aYW}z{JRIFm%XS~(F9<C4FlM)vfH#Z8ye|C2E3=v4g
zd-wK`H?a%r(MBdF&u<pK%&(rgBz3JwU7R>~<sWNCJ3XjIWZNS&5RBn6{?nU~W&xeL
z`RY`y5VpN3_VlcZHtc9zXzP97xv|$*Pk4Tw(8IlOh02^&R`!Aw46NRTI#hp|iv^no
zi@tW#E?WWW&f}(Iy&kO{z~gv)CYJYk#!F<uWEQ93V$NPjAGncV0F4;f6o5!4g>_eE
zVeUd2`I!Rs14BK2h91+tV$19ahly_&V5)#ln+>>1hntRB#80LXw79pc{G3{0M%uBl
z0dLjnp%O({zb^2uX#MP~WtA9YKjDfuZw}z-s^Jt@YT)iH{Q96;pLor>Nr1U*8!BEN
zPdA>Ycj&-gtJ(GdXT|tyyMc3=fzfJy!DkYHbfLUuxEUOdR@SSQYOpQpQtG;6rI^7j
zf-!T4U=W7Na#E{%dC!IHKj208Zo6>XpY>ErVn;+hk#kd@UB*w&A2${PNElD?*EAIR
zaDD2}UmprQvM>b>GIy;IDC<5yH~gv0rN{W{)$c!u$4zs;zEbvpk^h;NM9K9J4{x6%
zLTp`9nL3514CD?|57Ytvl#}!Bmc$nl`eZD^X_;@|e)Zr2&w(2`coegn{P9q!pr(fq
z&?QK3#H?1ROZA=N5T=~Ox{LW`9)3uM02n1jq@vHizO0W~-1%N?h@b_{^T-ZEIg#cp
zW~a5(XW{pO{6hNJ%tPBYKy{S5`ZoEKi=^($<PF;oYeh{~GV*sN9fIPt`qa9qj3obV
zKiUagM3StH#^mW$s6pYUvRAl-)uK?mtm^*A%gnT&u77F>O<-1SKMEDTcF*cXGLJzn
zap_U8@|`G+N>%n40iGbUD`G1j3dMwbZdU9~22w1Y{D_^MlWzzl%?3y<L8CexwD@aT
zQWj;Y*nu*a_)5<SIwuv1-q<O%)rpF3iT+c8;`ZGo4tGU0XGfYTIR&I{YY(V*wO@qa
zXS}-mM8UN;vm?i>Q8-0)^4$f9ya{<li0NE~fR-aWHf9B^@Q%sIxJ4`>Df)R4J;k19
z$UA>OiJ$CMCR!w^5&e>OKqgz&p-8X@b1d>oO&4zHJBe7uj9!IGPH7Z=f7~qJI0B7n
zOWJF*XhBAyex8H+efoB&qoZS6l9`!VPImT}f28*E;oN$(uda!S32<hWGhdFE+BR$>
z&VAGT$3unkE%P``CrC;28bT>B<!IkuvX5$q7UK58LzvdZBo_dW2_sU%-7iN~=G%=n
z;83U;2^-Xa*8q{gTN*`%FDEsi)3?K#0_~UYKuL!*fm_UB|E^U0me2*QXHlrZmu){z
z4-W7a%Eu#s7ooUD^TBZZ?Ivo#qZKnAL1xu3OM7ggUYbI^gu!?*rGs@J_66YgGu^`!
z@r-9LUX(TdgssRl7Z`-HWdAk!TvRGhVQE<XE~vySr(qjbJIUTx1mw{F`x8I<zW=AP
zLjQF;T?6Ak(49Yi{BV=pGH17O=*~OHW2918nT69@sfQk%9em0w-Wu}BFW$gW`t{|{
zF|vk?uiw9q=@7L^tjm`#!?iad?G_Em<0^}IOe!HWFK<50-*@4yFUu8ruS^b6v_4W+
zqwb|bxs;St2duXA_-Df35a((>gV5h9l!ZTX;sih_n$oz<PcNYXzuB-v=xVv6&nGGX
zFTYOW&1Ez%xM)}Lzm?Sh3_RXSn|>!lQ~zJZJ0#ZMux7N!=i5E8<w~@Qp-`0<(-dyi
z&+wY;?P8(-23>%iXLlF=J+RJjEH537{{27z=t-}1pai;=wE&GR<y!wwrtSXg4f@}w
z0{<EJ{3&%GNZf+#>|==XF+1AYYIY5(LMrfyK#P~ye>;Eyr2yoSZ!o6K{)10sK7`U+
zU2O>jX|pSN@khuZ?F)|uzU7to$B}9Px(*mUmDss!7j&QVg;#Zxhzr+DIcrw&X-CPX
zH@k?{f14VrJ)|A4;L-+^WwA)DyXc|lhTO7=j+_|6+*o8sSp|@a*_oM7f3B!VY}<gN
zmum{arofTQXV9$QL4^J|^j5|umGGzf0T`EB(ZA`qfqsl1r2e%D+OYf4zon_D)XPVn
zanw&Ak(M?EvUh<wz|<j9@f60Omk{tO5XKa=L@9eQh6*^_zN_dKz=kNAT)<u8Y_p<{
z%GADV78da&%&w4OkBtt{R$!{@E_X`;HicA86$+{)0PA;1JAli;&68I+WFK+bn-J+X
zpV5R2Hk(~deIb}@<|wsn9h6cL1hS7Sw49l_uVuEUx?C~NT}7W_>eHmHD#s8(K%#Sh
zbX{GYC*zzWcT?rx!}jvhti1aVb<jS<?gr4iZl2iXv|yrH*1FCk)&QnT7rrz%@hR@)
zhl1;oWBd04X0H<_d|pMp=V@44>{ItD#QRLY>u3}XQ!+Zguau{&=EmfMq#HGVd;I~(
zGU>4CE`62q{}>4u1iV4ZBMd(`n=ugtC>fTt`oBFD*e%?S@0+WCaQ1zSEYvVYWpmw^
za$^^Qz16-g_T^}V6O=S!44QG<WiJtb@soX@zC#y%OQhC*BS_;*V5@3`j`N8M_^ak7
z3B`T5{*}5N)4`7^X~A0ob$8n-(Jian-zrz^bx?c7PcxwhXOsJo(a^p-aDaNj*OKCo
z6N29@yh9r%mH%}KrFkH7t{zHLj4U)fnB?Dl%LFCCjguVInMSa$99bIi5-PmfM8%&3
zSfh0~niM+3s%Za47+X&*!7-jzq5<8=%=geN9b28u4Rdh0Q|SL&L)!n_qV;c|e$INe
zHwaO2D){U~f40LOo0)5f<Z;N2uUc7c^eqattrPJAI8mi1z879-#jsuhIWiX!uzc_z
zQNQawE`VEF7rN&ljCF)Ldm;$b&65QM1!kb`!5$z`ISJS8AwXt$J(FF|XHtdzTgjNZ
z>j@Fnvf&HtxjTzCYi8xIQes}Me(-+3%sHrbT~Vn<1{jm#+s&5-{6}K#vVlbgx~Ti>
z>kB3T;h>#b`AvWFDPUIS0}*vd`=DmHae;Nsj9T5>8?}BD`-KJ4eU%uw#qH3+sw3P%
z@W?4DG6dC(G`D5rqc%*ai2NmFI^hT-xDIa7k!2)_BLKP|M{7i&^MHvPCWaadAvE6{
zH1MaBL%)h!O;>o_Sv3thavmH8$ekOs-QbKY6UylNN9Reblee3MH;XErgac@czz@kX
zm>s)!D>7~Xn(-000aZ3TH}@H%60U_oaq?W$&BCi8J!^Fn`*Zs$JTk0_^CniFUAN4u
zy$dx$g}?oJAR+$%H2lg+l~zP*i;=b}0a?vYxBMdb6D~<?bF{agLTWjb^{@F>Reo7A
z_)s*Whrs;6_<*+p;JK8^1VzLWY0m92f^%DbD<xw(-0-g#K*$2ds{r%t`SYV9Vd!~`
z`hl$%4{XKJ`2)`ZW#$O@47sKt2qvK4_0zZC`r#y~UBDqC0!>GI``98a!p$=Pus?jC
zd|58p2XL`;Chl?cVZF9g?dX|n+aQ#J|H)-=y)dvFg2EB?r2#ActsIb|CVI*Z5>&kw
zZkY1R$;o|o7u~sI2Y{^_!kyn7Q4AXfKnO;lj$Fh*d4e7m(9XIHCJGP*aC99WYfD7E
z3CICXLV$KQ!cJNNA%7nJ50NpOMp75*(vsMg4H50&koC7RX_TRV?8J#PbAlevzQfij
zgBa^4W;4<hI|3T+OdfAxqNn+D<UwMNTc|9k##OPZUiohG<<4C<A-pCmh&G`W^PLvW
z#jR=#3oZch?$AA_>&n@6!XD5y<42PM`pem%W5@w*0@sDf-iG|T{o&zSQpd_nqf9d>
z@l50<OU-WnHr9mkLj!c60Q#55G_f;zB-y%Ux*7RY$Zna1JRay}O+Zu*BB#fy5i6G(
zal9WET$p3xla_x<k~$RYUIwhN0u+HFZ+6oAEf23^ff`=bydY5&se2d}CZo7TrLkNV
z90HI~ZV0~+!_Go$go}>r5Xa97K_+#0k!HnkbjfIKz`0`xft*IVmmCD7fM^FIP-f%E
z{&YYmGV|Mm|A3;PkLBKjl^<BR)9YTpWQdK_IwBIr?!N#ThA8SddOgh@W=HNIKNdo~
zT@CXW&7BA|24EM7i2*VWu_OS}8PefGf~X&OP0?3vj$yYwK(7-JP6nJcns>X3%r5|=
zhO|8jm8<mT-xayi1@)RVm$?2i0A~njE4FJj1#Sb73ydR}4z0d2e=Y$gD+hXe7Gllm
zoOPby1-iL8JL|SiN9jm6^vDKlq~tC5D`86Roe`YePql$kCBpq>hebFN*nBFz&`qTu
zQovj+r_|=Do{)8fnSzH)UOu%@FvHcUNEs(+=2U-VKy7*ILh~zSN;w-cyW`nWqq)xq
zBT5JuLge^&aX-DEj;)Cpc&s+~4b1baw3l!Zx*pAVy=u$aC{}61a3$uKd;_EW-mSN8
z18T*7OEbCT5NBY4Zg=KhI~=L?5@?i{1tj*d9C%v=yB1LoAi5N)R)&Ja6%cokh*VgD
zmP8X!G|n($cK4Q*ax5xqUcdfu!o1`NvtD9&u)jEuG3+fjv7cHAgun<$;tD|7V{P;G
z>Myxe*?)VJ8?Kjb^VM8Ps=+h2bA*0T!DiS{ac`bjj-Se+@UW4bh`bwVDZK)8fLZGv
zVH?k1-){gL=>)b99{@^CK)%J6ck_#LbgAn3$U&&p%>ZgC<-<0ZQ_b??PEpPBeo9W!
z6ZrTD`RVyRFx|M-%)fxy#|K1heY;ogkNv@DmBDngx7P}|70G7B4=ig$&COGBPE9Xm
zVU|zPjOc`q@zLVGD03iKfPetOUvQhH8cxg7(6LbsPk}P<JSL{R!o`VI?#V%%7dkRE
z#xkVEo2Fbs*T}yv!cfI%Vq?~%8IG6+Tt;SU>d&VY=rg8{)d<T5>q#qYxF=xF*iXfO
zmZNsk%OrXVt2>)Doa&DynX70l6@d{d0h*gnL3|tZo3(-Gv<?5Z6ZAWZ`~5m?wLK^$
zIXStjwRGfv_U_z`A@wu_j^F$I;P{CX?QbrI#Br;@5_c%8OJ^NGqfaqg%)3VhCk&HD
z&1r*NU}!7vWkK}(RML+}BKU<Xvpp+%q&2C)F40E=<_e24=Z5STRF~>W+V4F^M>y4o
zizPA+Z`*)0`xq<FSi~yB`y|C5ap)+fKTNSd6Y>oh6x%*81&nBmN{IsUo(kn<bzjmZ
zBa_o?p2<MV?F^Po%sjjADYa5>=kDE3;G_}3`d9aZAX@G3^$Q$mhwqj`h9O2K#FEFa
zMe0l`tzcIi=5NJNOQ-Mljo9e<Z61W7Th^w|9+D7jGD)VnS6|<1e38a%ofCNH#q1}5
zjs@;0paxTTT#mRQ`FBS9%-%)@k06tjaet`ct!kXyx^S@^7~XS7y1Kcs3!B!gf(jpW
z=KBaiw>!#7NJ=as9b;^2iujnmjDLq|sX6wvW{l<CU@+GtgxaJnbXEHlM>W`X6747#
zq#0j3EiH0-w#hKY)AC(h#8PTYVORU^)NV%76s!*vCVqqh<}cuVuOloeV(2TqJN%_O
zBC@Sb-dzbPP=|D0APfgM@m#E_$eWiQmTJbtlui?nH}9T^fBU7e5f5{QqVEP3%IQI9
z?%AMN0Ly3!ZnJPK&s&?ne@oJ7JN=v<xf&^uoIy++=VTZpfj4cwAWq5s4cdF~L9U~m
z7-zN>OMSR@lN^xBNJV3G=1IBsbc$M3VdnKv@S56$Nxfl$t^@?iu|CcR8Jm&r3p({F
z4v2{dl&?g_i~l5n6@4m9btAj~7~o9|fN?C{AgTYy)jYAlGQ#IuV%>30PI!x`=D1PA
za;eys7(e?7;o-(VHf%5Pp1vt%UZMrDmwwnF@fDRF)lLowsDVC+Jn$}Q%gks4HDwvR
zh021I)%FVWa@Pb{Qv_-dp(v<K|FwO#5}o(EBuvP^YV7u|ah)wZyex_!bkT3v$7_Ju
zzW8rmrZp#@ra7+&X5s>7cX)D_N3qyn;wuduU<;&R+k(RbQ5v24N<(YFR`;gM#f{7@
zgMql;Z&pv+5GG|)?7HX)&xxTww(EZd&9bLgdRIs(0Y~vK>eG*s!9MgjX%lMgH+>Q5
zNoF^0ylzqB{Ho>2zVK7+rQMn0Evb>qT>G-GpB!rx_rrlsl1QK*)y_)_AukWh%(8V%
z@7V6UA6W{ybS`k+_zI7?0)|FB)hh1fjq~hTAW`h*0~fknspy&MrUO*c=|^}{(<^18
zO}nMr!HQrE_EE6X&_QTbeUo@aY|>rHd&Msa2W_kqsH4Pkf*z2rXF;=xZf9300VWqa
z6rj8dz2*JXPb!&LiYAQh*KPwHx;?`{eDhv~6T|?D9U2U$0RRq=4iCqEa}GH@$cEMU
zvO0lpD0QG5#>{*^NC&kBQYzkG1E}hIA2&$K#T&qiv1!f>+bD;Ys3N*%6tXl3FOK-&
zpc7Gkcv3P9K>sy@atF4*Q?cy;OYXHOk7j}x3*{7F9CN02=q-X|09|MzPR-lRanDII
zyFw7lYS8kk6+pp=<ueX|6kMAQwE;{zdP!;+ZVtO!q&O`RsKHe5O67oI4qhe4H185~
z7McJ<61x<P$lj37Y2@opd8Rt`LxcV}FbjjL2{{$OZ6%0TU{<y0Bagecq>%$_zzZy*
zbmw|;PZ4H7ZRzKB#06MGm~Vr=(Eh8dQdR>YPvfywVjCYBTU#e~D2JwK36ilQ^_e6h
zCk~$VG#DC<a5%R~634X|6etg<R`J7)F?(%7k4j4`G}yL*&spMh0M8HHtW5|=gV}~s
zD%kG|C=cn}um-hY8jW26eNGle+B0BZ7(qB=*}9kBXI$9;GMx265F3vu&Z8LOd>4K>
zc(hovW96M*1lQ9>8fD0G5J!rISARX72$ijaA$=4kUnq2O^9*Xd`hgo@&as31iVMSf
zO<KZ3=t8Sry-q-jSp_EES<u_iZ5DWZ=@NKMzuz3_3ir9wI_=$ikJ;bn2b4`SL3ZT5
zNCLj|+HGhVhhuf)z{(m6(HyIOAaZ(99wR6<?>i-o%!}|yra7F9a1Uv&*ojzO5pAiK
zg1lKNk+3)bYVp9-$HDuc1D4&l!)PWCrsC_o#d>Ty7BqbHuP4XzPl8tHFs&w<u>QJj
z9;iQvEBFMC)O!YkaRi{IT;0o1zTUZWIVB}>Tpy<%K%jD)x}ZtD1T(`Po1#sF&h(OY
zGYZPp1r|tqZ9d>*C<lrF0@Q3(ZFGzIX>_`yM~8`78WyCQ^mp%+tU!D_iP(0Q1_Om)
z^+H9J+uA!i#D1^H9`G}S5bab)K45^7j}PEd16&IG!A5;sA{MwZo@7INd%Nu!f4ztc
zUp{aVZ^dn(g%0o7xQB{w?l8k8keGv9qk82So}gg2E?;lwlBt!Ii|f0g$8!^fS97~8
z5iznD$nUw)*j0-lVH~)xDs27mu^j?rniU6f_qx7&-Ru%i5{e--oThj}=kLPpb{Ea)
zYRm=@!gbdP$WiD~sc7kP!e&M2y%^Zi-X`<Gt<sIc0VwF$2b?_O6>m|Zh3;DUp#zY6
zzHYCZtzSfGo~+PhmoSAc!!VH8#vV5a$hMb@<JpC3lTDsZOsjgkT9mtXK%dNhe%uW4
z%)<EM<SA`ybg+jfxNH3yxqD-mLAWgU#?e<^v?$s%?H@gFghT-Jyr(AOu-={S^<ps{
zWlAbgikk3NFhVZQaPU1MioybpAi0b)e_;$UoL|MK>c$ua<jxQolJ{p$&{43km{?gQ
zNFcQU5lc(lX&QTM=3o+Hyz*$da%6|2`vAmJPGR8{FuGdh-~_;uJBF0H{E86~#8}<-
z0Q+zTw7Dbb2w${O)MJ4?Z=md$AR+LR^dXSn35ZKJNR=4k?z<G)*C;TXeEfJG7+BCJ
z$MdTuzc{W~vYi^h6PXLe5%e>89#D&=ps2!g05y=#>;WAIhP3Do#gH^!(N)0Um{vYb
z=~cp-&jEywA@l)Y`G}XyH?F}nC8UV*#*=v30i2!zbK?7e!3X^91vu=Z+8Y)5IUsu?
zIutk~+m07hzwwzJxg}}`H-~}$GU!O=eIsD#X-7Cxj}~Q3ASqN}&E)S4o&`t=Mx9oh
zb%E!8yKq2MV>27lNgIyu*ysQrqe>=;V{B}kdPgZ2RF)F=k~s087uH#00Iwh%1ONm(
z{q^V8pyeN&&V|-A@(W9Fp~qo-%SO4-9IbtZcZTgbt<hJB2UxEcu)PyZ`RPZE(nX65
zZrw`zICO`N1AkMG8#!-)JL#2JUmqEUtwV{Xh03xiNxW3#4+X&9RF***vI6X13Dycw
zNMBHR4Yr0zXM3{q_}o}ZgyXO8g#_@D788#8!{0lYgMVw(Zc?de>-+atGGD(w2fe29
z$CKl8qwp6HfBMT0UiE^%f}k4&u%qD_59`IH){6)9#^A;tqzL`?G*`nI_@B%cA6EEV
Y|G2txnZC9Z1z*}1_0{vwUw`;N0MN&W9smFU

diff --git a/reference/Rplot008.png b/reference/Rplot008.png
index 7029d7e2b98e4d5d73ddc8d0871ea1a28fe30077..10bee16358bd36e4fb606c786ecac2a1e424b77d 100644
GIT binary patch
literal 20423
zcmdtK1z1+?wl+Em5u_DKNdr_$K|lngQE3qYX%y*hr9?VK5R?uHK~lOA0g)0A5MCN-
z>FzV8-`abvb@o30-v7SV^`CPcuMhDf-gnOD8P6E^xbJ(+03`((;#0Jz5CkE<dq?s<
zf?$oIANY9i6}KNXZ{QQ2fvk)qe1<=}5|tkCiNN}fh8=<so<={gx`*8l5QGW2D=GHC
zIqvtcOC&|_u*gbAR(4kZ0JYw$8{|rqIAV>)xVlOd=TEiyOKv#fp5>po79&Ya8>}J6
zOMB*qukm-P3tmasYhHmdcT%`6iAhDu+vxlD)*+wl6`j-^hDRCK9dEYFamj7A8T!uH
zk<5HAc3l{WS#r<H&R)18j*rE{!eXkH^&5|joSZ(4x=vD3@-(+6=B^NfpFVXy`$A@a
z_0B|4fqsK8pRwxG(ZeOD)lQG2Ps63MlJ-J+Na^_=UKNHJTI(o7u_m?6!V>h$-=FZ0
zIhQ<XIXxiH#KLlShv2Qh{~a$9syeg2kM*TBn;f_=#pDu%X!f@j?WP+It{v<&-<H<U
zpivMQ#~rfk6uz9{##OUpAauCVlbM+*%0}_|XTjsusc7Aa;(ncm%NcQAoZ1y*PPO~m
z-?mN%;4--UB9C*P2w=N>xqiE5TiCl{{NrM<bZXhr-U9h4AM5!K851G=BgQ3iJu2Vd
z?e#{IVLJ`koU|;TqWoDB@3?Q)pWE0-r5ksh<8(mX^7i@D0SIDQxyVQDi`Br*Z9l8T
zu2UuOIMJ7sF*Gad%8U&=<x73)I~PtPh0EDm`*+}mXO*zD)YSZ1B3K^}6dGpc<uzBi
ztl#%qtQ<Y$WC_Q=a`o!!fKj6U_R?_K)}UGZZj|9TsmBgZg7XByj~_qu4t7?lg`Bxd
zEQbS$>G+Zg3r~9-OfnXj^jtEEkF~0yNp#=4EV4Iy{+xg{l9A{x*c8T?Se&Dhp&(Ui
zIc&W$CI&x!TnBF%!x3>1dPB4JwOo&+i_6V#-@dKPriO{D_8jj@htT@)kGh^YNy=cK
zo}*Ju8?Ebp6Z-{%pzBCKu_|CtKz($yT{~IO%+R_MZ!bGaz%N|aQj}-Ac%Hn=M#7$H
zb8|B#KVJzhm^W5UQ}Zg~Z!Pd!?w0-RD};$9+@DO`e|54s=!RLJz`E1Y&}AWpFj%9G
zqQt}BMiU=05^PIOib|$-MOtjmc1aw}+&;dG>3E`PQ1F%~r?BuoLY8hK*mLE|l`j<z
z3)i=<4lEfZx;>0@95gX#3_NR6`usiP@lNwZ0BeFZgU5R8D8+L1w$9+xsPiQ8Y6Ig@
zzQ+D*Um_Y7$KOB4g+irMu|4zIuP)Sl>yu$gnUwe2!#ziYx7u-Qd2E05Smcs?jQ2@K
z*YCUDPOx?mi!(j+2n7`t4no2x@>_0UusD0&L%UyDUH$Fa-Rord0=Lf!T8|+}Qc_ag
zO;s~$ysbq{^jgc>9au}rFa}{gYG0n5j~_p(9^Tt)QFQn6^1{}#ZMit-^qu@^M_hpJ
z;k+7|xZX@h{LfEDq|(9E<87n)`Z(xSq@=J=`@SILEJNPBd2_Sh>I$ajTUwZ;WOD?G
z)7gzz%n~c3$A_bc=jKBH<gW<DCfKup*RM}|H|R^ID(|?8Afoc}#7Ghx5>?EV%a>m?
zPl^eg^UYn5DITeD+t-Up|1@%RxX+Utk*nMC@?~QX)#IseZ^{nVS__<44V;A%J}%CV
z*4{pFzg=R1@ilCX8qcj<@=9StXlrW^w@Pb6wnse}aZZwWr<*t*ZuYNc+&;W)AuQiR
z7S<f!EMGg?Zm4Ux0MW7fUZQ$k_2I*bv}he$@j7lINfz>-B_W?biw85_X4oEi&IR!(
z#L)bE;FHaOVRV}76%kq=AD^;`z^mLw9p`2~rwtB18gd$Sqw!$WR9E*$7X;q=HhZf$
zFKuJI)5t)U`^CA6(EX)K((^K*U%Uy<#2lsz#8)oY(ADm?Tdl;79^k2I+4%4b7qWku
zw4peAR;-8j_h>ESC!@{|bu&0$>iK$D<``*q%7*Igk=X7Tx&15ABB#me6$Kw4+lv^g
zE@O%YKT^NZ<>oN=jq?Y~$46YeIjb)Z)}~s?gBrhAKX=-g?ZQb`!Y8ESv@dRlQ%GC=
zZ1Xe3^{Lv?Fj=l@iqYK;r#QvebL^TWM3_=58E)z?CK>U*fq{V!x3fQBtZM0_b)H>r
z3Z)M+=8Y*nj9^u4I@n#ef#j5;S!&s_p~vliQ8r5NN2W49-b*x1*tFm1fAaALERgwV
z4ZV9Uqfvs(rKS+N=<Bz8Y6N4@t0$!hpG!SQPEKANK(dKu+00LnlJ7%;N@3$T+TYCc
za2qOm;qU**FC!_y-~YlGs~)6#f`7~M=$G`6GvwrV>yS$<EU!JW;u;}txNU#kF|vL!
z-r(12*?q0CkbPr$bkreUo!wVOQMkAl^Ud(kHJ_dEB_8p&iuPE`3IYcQ2aAz}j?9#l
z&opE{nLG>DSnS?)gj`jg;pb&OFK<uA*>%*zxkh+N@Lw(Ksvbg~X`T$b9qKF;T2Uc*
z*@|d)sXsyZ`F&~j>qMyo78mV`otAab!=H=!wfSuIXn(o%nP&ONMAGNmOO3N#sXUf;
z%Olm_d5KS)IMGoAA<mDPfs4qup!3z|tz<3Hr|xk3#3MZOqUohqN=gc4bDU1CyD+-;
zN^IVNfv1d$vd?2)AM|%G-Z@?k^U(YCHGFok_^SKLOUCy@*u^C!l;~cGCzGWs#0T!}
z?Zx%A@-9!uS~bBvy_)6{{o!I?h8#_Cc?8TPdN6R+9$bek|1eJ{#N%*57na74g#O#J
zu<6udhlPhk)Z7TNrgKXT?y)aN(`F{#iG?BT*71n*WXQwby`9N0%v6NYbE~T=bR9zL
zZNaT**(=+d&sIs-i1KmU-%9(v{zSBtH($EP<XytL=;OETZ?EB?VSk_G2fQtMSGSLM
zJ2SJh8zK}%0;GZ{u{?Wxg`aOFDJF_^OrixNjegvknvUP{%V4otfz_x81tsOgQsuJa
zv|_yAS(S7-F$)WhZ>_C1y{c-4+FY9LU8yn|L>pp1Oj>As%N*tx7JQl~P55S?-?y<T
z$hXkMXZFrrnAn|3q+*S;_M*OK;Qh18<<qA)_@WP$4q5f{ZOTsJ?Kr3i7MPNNg2&B)
zkP@%iP0J}m*~qQgGsO~h7Gk)j{}~s~))z=~ae^E<=e@~p+_+)zGhd(ObO5}AdWreF
zw>(Cn@G4?2@d&j0rQprWrlK?@*(vSjRaI330?x1oiZU_TYf~-P9=it}J}S3Oo9)ex
zrWUl%+=03m`t0@yBo%|j!D5aMOu=KBdvqhu7C4x_$pR$Bg|>b^HmLK&67P3V*3seg
zCYyjLqw?5kVEnS#ugi7n)Tu8yr6cn{3fW~3_WEln;k2Lrb7La=A9<|CKl@D7>@S@;
zd-iNfS(!TQ8;?PI%#ECpzRx~{@tdP=+uF$s72h4SVfRI=U^6+kZVWqmlYQouUyVpS
zkhEVO(I%qhy;F6#g;AjE%hMe-kCk_uq#GrJFuvTgx3zc`YSEAZ9pW#^5`LZD)4$O`
zBXM0E-)7v0DiHS8A8>(&`Cw7YyBecThHD02{EHR{m?a$?imAEvM4Jp__72uN8&6Pa
zF?o~0diskpjS2BvU?^XRCN-AuX>&!p7{%F02ki%C^2{k=RoP576>%9bdqe8!h{Zxy
zq1c>iE&Kw}%~|2J%qiDnKP?RR*b$px5|dI?^Z+uF_Jz{~1=i!@Gwsfwy$KACj}FE0
z!%x?}>mwKU_c-1kHTV{J*`PU;enN9}FAA!0T27Q98k_;FiLTu)Ly%Dm+z+2+kk0KD
zLoo`!bKy%fqX)Y$3H`Gl`>R#!9cQw#1epz~eLr#e64PCRlH320n8&Dtf>d}z=@Wmy
zEN8i-miz8hN_x69r2N$oD_4mJYHEbc-fwT1MLBN|JNX!Du+WACu6Nv0JSS-X^#i}n
zgyf98<8<4*l&q|*UW{pl`)dnKN=eB>sQsm7jNKNj<vA$oP>;F@;8@DU<b!|n<iu-P
z&Xx*!q<u?ww8jn2fz6f+6B(KK6VfsB&fWQWQ=2{wBLZe`G~m8`)cACNTGwr}ccNk;
z_e@^ZTHw!OGqs=vMtNk^wv}VzjhYU*I4=WtD1)$|;SoT>;R(l>-00RSv)poN^MS&P
zGmhjAlJuOO&4BP6kHW>?5NR=cm(8We#wpgz)QnpUl_|Hy-RyBO$CSWUedcYC<R41M
ziZh9SpLl#QlaiDqiXtnU^h3x^<Pv@)XDf0E!<x@SSr>$`XLR35etYe)w8Ek3-n5RA
z()Zu(?!8&69dw6uR#oBGJ4-Ca*LP#+$u1B>Xb!n;4;!>baVQJ}78loY9``Z9^le0Z
zE3G}WASdx_L}LRHScDiy$SR$dDaG--DxIuYgMEyN*jfu873ZWc5Hf$6@6B$!ITLr@
zRU~o6S%?EqE`8B;t%cQK{>O*QJy6V8(UtDc)s8>xwvWC04qISX$n;7y(y<Zeaj@Fl
z2!-{Rd8O58O*;u2{A44PQ4SQhUo+``A6Y^#kKA7SdVV6h_8@J&tgx_90|7Wzp@A@#
z>u|0E_G&@W74jt9PPzhcL|cHbF5G+Rqp?>4U#}g$%}RGX{Mw8Z>g+_C&@<zXJYN>J
zU+5z)E-prBxjJ?MQu{lNxST9dCyM2>5N8Ruj<WioQ{rCCo3I1`ByM>F@MKk_%l`Iq
zGZcEfu;w0vMbg^`WHSlRIT@bKG6&P%IEAcq$uJfRB-lWLeYU+60)P+kw7_I$Wr;J9
z)uqeFuC(!VzIUCu>@C`vAl&u+yC?j_A1F#oON*io0BH{`vgh{T5<Wxq^?7qu4UV&3
zgD-K}m=|G8uWTtj^0Uy8u;ccz>r8>6#?K;?GeQic(5sM0Dc>&{h)iLFbjZdMe&<sK
z$D*C8D(vw6sIW!@B~^$GBi9Z>=9O&S!&6gJQ`|&2I(bx(Sk*s0mH%d|U147j5gH=H
z;DQVF>@(CZ&EHO!P+SHDL8T@e((2U_kK@Cqox8c(l~1)Gr^NDGH4#8f__26l{th53
zKXK+E_x(k*5mVOICQe#y-v<P5ygL62&KX3^@4;fIs4LhCf^+XF7f2X{s3F!^pj~42
zt}8TbwO%Tp5%1qDvS=ykfcQ2FW)P+Y+|PhQXJg(#b#}bkQBwF1T2N_r+Urm7ALcyr
zX^-Wv9FWq0%0v9PIaL0ZZofm^^QH2g-A<3tmnTWF5wt6s2;yR)3`%3KeB4YT_m;cf
zrRw>%B^u+1Cxh@ay^$&x4!o{V`dijrQdDU|3OYW+F%~zpJ=Uk&1s02XlpZ|r23V{I
zsYGD^_ovum=3z%938Hi-@pzXJ@*<$~gB8EqU%1>?2_HOsD5xgNKRg}IS^4|A>)u=j
zM~7L9L%+5k<hyfGhRor2(D&Q%I9@&$Q;rPo+zp}QpZNHx0}VM6TS!&bP)@Ln4+w@Q
z8iV4m20JZ}gpi8tAQvxQyje<&z#1_ib=pqDZ>|fI;g1FLbkqf&y~^Z`{>}ZgjoP_-
zBzVCv)<}bLYk73}ToSp%qx&LGm3o+xomSl=7BsmMHIM;HsMx`b9$oS_cF%Hz@`h%4
zB6`6Gt}FE?6+)sas-YXT*%~k+OzdRMt(d<u^T4PAy6HaXcY~<;E#rEk92WX+X7Dc8
z>|2uK<DymlOXAT6D~e(THfwe!2yI#}M&mbd-{<@+Sg0%iCNh%u*6_O)2~y!7zkjSi
zY+v*yL(>8jWQfr(@9#!@>4?7-?{@Sl@lY-@D(V#EF+5`03s9*cEYDtj_|e)lPpA6P
z0i-&czFok&94LP<Q`z~eQ7MRu%TXcr5@ViLdEuW-udN-AZk3e2e)7(oqQ@1>s@0}Q
z!id-178!F@(5S4!?`xxFT!9XPsMkDJ2B1<j3ODYj)9=#f=Pq=781(hUTYbrRAQ~AN
z=|3@HV6)!Ne~W76BBrO??x=~*%6!$Jg8aO+zjmb~0j8w!+ncNLn;n7+>;<m7YmtVZ
z;=OWna~(M)<w~9d$muwM^c+BN<`TYkf8)hyEf!N#)56^7oN0L9MgS2#B>|bAW{Vog
z8C<@-%M{jrzF537yzK4=*E%3SXMIUAAs8~S$RrJ0N{1(woTTIhF~2>M9b<0S0RLI_
zJ`)`124>Zp{)X$(?_;4~x`U0!O->$;OZ2A^h#9@JL;a?TLyyy5TZSdvnD6Uu*S(QS
zewW(mzWWp>V$!EvNf+eowfzh=ay_1=nb3NZ<?g3vT@hkX)Y*GVjVIS5l)jgqWg1j^
zQ}~guJ`+P&Wp1hotvRNn)lO5HqTfx0;zUL6Oq$iM>o9}-s8@Yb_9A)l!x3x&hOkML
z96`o_c(KQ${8{66YpHCK#JO5WE{2z&CtY4vgNrsqu*X$|k&;Vi$iNL#0@?AEie{A#
z7o<ilKggffmQ~BX=_V`BsymX1EO!pwq3!Iqic40S&D=5(tjIl2O;nMK!@IN$Jx0G(
z9rOwSLuPPF?BO+_9xc>d4)lVq3wqD~lT=btQc&$VMjG~vp|PH=SgNqf%7We{H9fsa
zm7b|KPg7OZZb@^^LE*>98fBfYzTWQIlrz*Qm2}c`0%WbxTz(dqx3`=)N$3TC{C>L9
zlcCuBt0iK+mx6^Q<+09)qF4sqeMMDO=uK}nwA?5Lc8p!S0{-vp+v|pbJi{dxaW+jf
z2--0jEDe?4EKN;YEQ%Uk?v#yBZzw7c?^HIxej%ze|0~-HGINV8eO^B_Gm+7ccIP!s
zOiXm<XDdR1d8qnvFI4N-!<sE+5#3UeM6Qe}=YFBrY1%b7l;UH+ph>&9xP0<dNN_B%
zpp%VauZOPd`zIqsBqU?$2s(Z&W1`7l*5(6Hg3g~KnOUT;7dYprD$~PDK|wM0Jy{}m
z9ud8Jm%!A_OxvTDh6r!n@suDL{;w&DC()eR);lZu{TWFT$x0&%Vl$K3I8w^r(`2d6
z2|0h?jCIr3XHMzLTv*m}6Jg|)mvD2d&KAtd0`&ZorlK1yjOgy=X+k=Og?1mFUPua?
zH7<5RSWgoG|3(c9MRVKYBG<I>9$p$-AWuxNNzBdK7DDDY4@>_`I3ble5n)^BG}hHK
zBl6sS6zPbV(#v`rr*8^kMss+U&!?0`^(QjFiH@EG3??QebwcF$zzzw)spEo8P}6a_
z2<=Y+G@X#S+Y)axicU&Bf|B@_&w>^hs`a^OXsvl#7g)!pTBG@6`G6Dr2$^MIgNElR
zvNO-Z0ed`2VD&}pKR;&w&;9n_&!v|m4RL@!o31(YtFPSO#f8E>ui&;3rJA}>oXAQk
zl3Mq-g|nPOVklEaa(gJhLp9`PMqH?h>>7{nHD$9^3f{jT>i^EW{ZkhB7yLp}i)CFG
z0KA(V8EmH>sJ6T8+CNbBNnvZ2i%;Jif?RTu%VN!0$YoKpFoACOV4oH9t3<d(P#j;a
z`1PXT!-R<4K^Y`&3+UJ$zTvOUA{T748*uYe*uFam&luj2w>y+haE<upQ2A~ivDQAh
zlVxQ&>b=%m`b2N~la-~!mEGx>u$m89G0`<xp6TTt<@9E5vJ79R7M2rb*`^=go_@@D
zy1Z8(TOI!ea4$WDB8jA{&gariDh8ike{VZ%DHxMpzpx3p0(l{dz;~hy%Pk`;Fu9ob
z3xf9AowAU)m<$Tk`xHgXi&}W)`z_X<Rm08ZF0ci90imGIKD{^gd0`WpyDtXU)Er(9
zZON;u;$pw3Iykg*8*ZAk>BpGv9ROg@T_7*UtHt??Yx(=$|EqW7-`Jf0Gw&ewC!MEO
zRh@$qlW%d$a0HNt_3l*k<9e?XWa7M<{1{g_A03yhu*|9PqPTQJq1#N8%gfDe0_LW`
zxQhiB8|#6pDt+&tAG_1)B&I6gz5!KKzwPZcz^SP+VNHNOyYh5v<EAksAs<vSun~ou
zQxt9Qcupd(epcgb)eM_pUMW%RAkICmF%KV{9rXWXyA-5Ptr6WWsZ5cX6$q>;Y>dSf
zc6NKWbWv4ReCDyuXWG5W6ej{xex+FjrT~fh)q!4BHF-hsz`b^13-T<Yq^0${!Z#}m
zb`BpGJ9qxq{&G~_jH8&CSl^7JK7$CKw|H{Zp?1KdtIJrf@6)+1Nh!xvmtAjHqd2iW
zWJikB&8#Qa)YRC-*XH;hu66hEe_Q#yzEk)yXcwh!d1);UOAsJJ;Px{PEg{1)oep3M
zzf+bqxxKHC;e*7J4Uw64U@^pY<%;-)(<WPIo4QWnVgtdn>O7=MuidXtjrVyxKU)wk
zKR=(FYoCpp2)LyNUU@YKq6W`&6Ce+A=i%<rLQ&%*&J5X2J?f3k%~A_IPXIF+?ZCpK
zF)1B;(i#Z00@L0rC|$MCm($=+e%>}AkL?uTLp+3A{tR$42+})6XJ4qpSCK0L#ofwL
z93P)qtN(5xQ0=h~_@H?xv6xAdhk{fXib0^j1W0~%h<$BDFw_U@EIK*bzW9!iSyE?j
zdeu28%T#BJ{1s;5G95E6d+WDHt&4VyqB>j5;hHYhrz#+rpnE<AdcX?MOgKn|<09OI
zH|+exSpCb{{yfH%F1G2;#6(9Ca&l#(=zhgIsK8b>dy$t_xDCHi%oKFaGz8P|ba$|_
zu+0D7x%FT0p8sb~%722u{)f5OxRCDERVigMB0MZ4Dz~9cB?nO2Nzb^bc1cy$%&hqe
zlJ7qqp_F1uw0paFiN*aN=UPE-gM912EB`Vp3(a2h5PHyFR&5X?*e^U6Xc@vz!WPC%
zFQvr}$yn~Ev)${+%P@yD-Z=w$7ftj!>rmAm-L7lRpO8oMGZ{9}ibzt-l8&dmnc-=C
z+$6Sp3aw95OlW!K?X*uKe_=H}ji_uvV9rY{Kv^rxN?j2A10Sc?cY%k~V${maI@QBV
z9wM#XyZWr!Wp|&IGzQvL9THAZr2d=%NY@p>V8p^@|M`K1icn+TQ&5g8lauQ=O8`~$
zJWcGJsbEkxTlxP$A@r2{R(~w&;wCk?8`W3#{6Xuz%3$HA*h+m@mo4lp6wm%B)ser2
z`Qd=ie0_V->r~QvGtHvVP>E&wp|EXI`a8Eqxrx-HPGCwj<MNQV10L3N#i5yp^!-@u
zrX&)(`>iH38DZ&{d=p*d@bYbqZ@)^mddhjDMwrA4OU`BO?y!DURYqa&f?3u#;68l*
zqW%Aw<Nx1`^`!RY6V=cr4>@8iIwoZp-6-7lHf$JVQkCUm`G}FR_1%2u)rk_z(||zi
zEg0%__Xqp)U3YDfI+U*%seaC{`E+k%?yZoEttP(@G$n7lEK`}KQk9{n6IYIWx*o-@
z;gu#E4Lm(IP|-H&kl}$?IfHz!_3+TLYrlbbmgzGsrEvU7a%2H|WW=0+Lb<8EGxE6?
zwjQWT=|@f3CbOM_BJE8^6D^C2?J^e|lhzLKeRB0zjuqg3js2()XcU}<xQQB6A<3_A
zIpKH$C*6MoxDt@Rs&w)e1Y7!F&_OZKYPN{Qf^<qI7H+N03gC8e`QDF5i<EU}R`fXR
z_qY+8Ahc+X;X|(*W!a576FLr*Q>C$x3i~-P=c;RtX9%3QM453xRF(8Cu(mUdxpaE9
z%@3#LRiKduCsjpm18;lL<Mc}dPdRZ&o<5Da6v=L}rS2Ue`tM!<AtlutTy8u>sjJHq
z*F9BLrC7o}=WH2`WAL>5*9ZAa;i}nNy{6X6vlpJ}Bt*bfSA`ALSYn|K*ij{RNax~{
zSO;w<?Xb)ngBLx0$WS`e28e=m?Mu7jmV49LSyzBV!A)8{p`xPFBOpx5^$Ska7lYh9
z@u22OQQjCE8u=b5kaVSU_nF8<g%~>b@IJo16q9HsAPD$~MHXJ&FL%LZPLrD&==j!W
z*5UGNOpO+?aN<GtU4VkbL~)9lGN@dn49+Z+j{@~J)%XjgZYrv0Ux+eg2wIZTdNyrM
zPl~3`t^euMWPZ>t+McynRXy8rbLQNP4G4cAk6|IGlm=2ZP-VXA>``lfkoYIlXvMn2
zy-kT)_rthmNDn+u+Yp54oWS?;J3$a(&h?{M|EOyHZ$|vz+UZJSnLFjmeYx0t)zOab
z)wvBN>})2|y~?Ko^%QH1b7E!!$zAA857vv;+x@ZfOw#GE-7(fCC)6H%*naEMLFJxo
zRfUt$;4bu`;dh>#yEfk}q2QCe_A_Qyy%`bd`~AVI&`@G<papBVo8*Ri_l;4GZ*+}A
z#EPz(gS!FF(<;|XhOZun2sZr=lPVbhj-__PJX(U`wDSI#{&f`V)8PDE92*C>#*e~a
zETJb~$x!mb82;pKp-v8}>v$_buPWca?*;nH1W=V<mWC@a+1l_M@sFAiJ|diGEn9*k
zV8teU=`8C&^9Td^eiEhGo)?EfyQA{$8F>^LHXWaNoubE{3Q`w$({5#NA#V~stk#=A
zbaZ$~<KesULq&1C{v_j<B9oq#v6rMNGGPpe=ci5)x~!~;7S=@bXlW2Cvr%_Ju@yf{
z)WAHg(e`WzG~Qu3DWOb{5hvhW8QnI>?X?x(<+7auZV^+YFDXgin0R}@n|`x6N9Q<g
ziS<{(jUUQSgiah5=egx*?6~iY`yrl8VSWA}ZgM<Ez#%brQ~o@_%+!c$o(|epjyxn%
zcb}h;1iAB@auXu5T(u5F7NbBII3@zuJ8##v(@9B6hV<oX2Z5@S9?uvcx$^r{XUXHB
zp%0KaVs4nl$d5wq0#T16S!(SG(O5IXb0R7&Yb=yYAF^&B7MO{%L){f6Y8Ze2(C5IE
zHxRJ$>by79phP7uuoZx`4|FDU?FgpCLIP@<MqZksp$ToSxId+pU7Y1Jqo`}1`<n}X
za0L^nx(E!lKl<-4XB5rzN?;fJf%Cp*{*!xVRnw~42VgV6Nd`E8y2pF{Sjgk1;Cc{K
zUKd(c7-D35zAyG>Q(cmK`#l%4j9)n7FVVwG#3*v^ymYW<y3K|-OUl-LZSBP&4=hAM
zWcP{C@xi+DX0MtJSRjm^!K&l~e;Mh}!?C~G%s9DNI*NyU)U6%aHEabI?wa!om>gc5
zAf&7-WFNBck;nHDM;$AkO^Usrp0<mFLuY4oQW-2TV327`5T-la-);ur1`ZHxq-?PS
z!wuR?<eN7)ZdD(^jq+)Vd`Ppfe@KJl49f=kAui-#@#I(Q5O;$*z$B~yQz01keCu}r
zkm)2qbe4fmhRQ`q-3y$PN)I1Kq4(SqKM%HxFW@RD@HlptOBA6;F(YTyiujM-UVTW{
zh^Lya+gZDr4k>|iO=fx3zWqAS+dmY$?K=vs)ZwTX8IvPp$$ku<foewIkq?uemDL`l
zXlg<iuiAtp2s&J$<ap!_K>0_1|E*&<6N?UugJ(fUm3s&1#d`Xk(Vw#edvhXbRXis`
zLGZ)C9tX$5`tHhHMxr?W3(yamH5@_GXXD~(hU2)p(m>i!VnOX2zSc^GDOv57<*F~S
zz-P9eON+)fK4=V1VFMPv!~zEah%^|!@#v8VhEFgx7|Ru?l{gSi7JDgKSOh<R2{;qq
z(+X~?7y8uj3r@ohR8QO9zJMzQ_?|SN=FP!Dh=6q57{pcMuT%YO9G0zP4cKc56O+p*
z4NU}8JC(LQdBXFho4~ovP;5w1X?yo(tDUDfRnJWXQq&1VNmn-v+yDlBA2m>?27(wG
z8aj`?I2q%1U9r^leuag-G)?V~LvY`CQt7&qL+u`4_6YF5fU?2s$B<0UGlcPLPG@E3
zn(}cB7iMJ{fY#KW-WhlE4Cr`r?@ILQvDwx0>s&sX(4tN#q17*EI*Pu1yQgJcf5I6K
zxJq9yFZiL%tr@^LMJGDhSPk-u^`|K?0W7lH{o)hMhJ~4Wqb8{Piog1zp{&OP^t5X?
zAtZhci@hWO{5|*Jbh#hywOC5~MQ%DtiR1t1>q`N<jPPJYR0>-MLKc;qx{2fArrNr-
zPi{BBnb>(!G3v!-0KHjRH;MJg`*|-Ea=uCE;ZN*z7=<8>6xt%zC$^-0v*7q|I67yP
znwE=Y703-b=L=-`Pqdr3gR9J&6Q@{Twz&N&f!6YU6(3KF3`!y#5b)<6NsA<g;e2~#
zPotX##SJA13k+K+XHKt)xb2}Xp~dd9bZ@c_R@+|tZESnf8@x}Bne8dTpz_r=U9^YS
zI3}ux=0s!R*feJA$!MhU4^?^u(!S@{?x))Q<EzfL^RfMfg^a6)dKf-h+n-=r<Cg!l
zYBlNAzW{_xZYS>LKy0wA+_~_2Q{ndR&NEgpy>-~G0Oy{xP_Y0k%Bj5mSe1C!4UTgp
z8P$bb#?Ulow2Qz3RIfkZYOGgZNVH;TT)YKBCCw<QQG1L}?a^l5ET%M%Cuh#88*-sj
zV9Ho`%^^~B#23iWK0H7>{}!qdV7drff^xFYn<m1*21w##IgH|XmRI)P_+1l*wK9{Z
z{Db+4(DF41<Vel8pmdT0onSG-hEfNdldNOqb~7o^7N(m_44UPk!k{-uQMJ{B(oh0T
z42i~I^~T!yN54$B$LecPPItt|Mqi}?hfAmvjiOlen>WeevZ3aAN(9Hy4>X=6*=Wwi
zUOwRMMo?1(h$@jL;dgL5LEe8)NnZ_l48+{@fsOMrcLhhD9N-SwJ72|=6xh$**YSSi
zyZ1fp#a;~M41ttbb(XmN`(vLxfyYE&q(&{Wr@L&@1t}Zj!j9q{-Pg7&Wk&F8KhWqh
zdh^_?ER|ZHWkU@@VIq5Mo=v*R?7B7Q_(z=2qy8%p`qvW7p?W}pL8)Q5Q}D=GZrgaq
z4nido*%`x$v;B6N;|P~)_^Vv`kKaU-ghoVT>DwM#&O)fEs|$!+xp=|icDlat-=img
z&|BI>A%!Da5+Cv$R?p+5JRo4sFJ*r1R!>BG`eDK&i5yjJ?GP|_rpd`3jJ`MkWq1V&
zwgApWr0#F!W*pL3l<o1+`Gx|uJRR^>P=PkLUJ~-8y<;rX{jeIWBs+&y4-)({g+Y4<
zo`!*=1M;rsH;sFU!0xYzRWKn;_NwX1@1AG{wJW%E)SV8=hdl23r+&xZaxz#MgM5QS
zNXhPLsDTRIWT$-M^h((Y|5s&YIhe*8V^OL<@g<<6Q!O^Zz#WvBSdPRW-F)45`#wVb
z2CQP;eV0{#sLUpxz4e2`6Gl`=0~GxDdy?4BMvr1_uer%S6qx$+VM5rDi4T`OM$5zH
zDr!oln-B&lZFnT-{GryMwfGB-QCwGYjS~~g{8xbZueYXUPLiB6=KG-wn(ZnWR+Hbq
z$Aw$F1wIZ^ro#^-ki*a-h$_Pyb3LoyuI7z>CghUT)}{xG=P3j&yVsnSw7|w!1wJq5
z#p3?BM`$X*Ar<mO?K=o6A48b5nb`F#W@QbU@4k;J30EjgC&NxRF#s@uE>ynD`cuTX
zfK(ixmzP%$ayx4L_!ProQ40+TwTP=g&3gRu7zC3or>H{zcHdX$Q+uglBKNcfS`Q6@
zY4$)l6_6TB1gt>GErUWEBfq`tDgZDtIqM2)lnFj}^E7~K)P0J&M^P~xAO{wL3fo|o
zYX)j)1zbca9>+)AMCYeAiY+kCP{^!7=M(6;di5$$X%tdWp*o6j8Nyf>bHp_tiS+Q<
z!U?AaP6|vt$t5Ly=-wb>5GRVyiB&2V)qsJ6v*_P=-^w8I1DDKsIQhSXgCo%D>n0y3
zP%!Xe`rpdj{u;nEJ!(9n22}p^LAiJYoHIdaHA2mKuzhh;Gwrc{-QD-K90v?h#KKuI
z%OV8i94Z>kVS+d-@UzwOq}eFE^r=x99Z3R!g%(aY{ML<U)ez%@sW#Y_@4q8lDyAYH
z4f;Zq!k&da^C<ptaxB{{=JPl=2v?yWF&>~yD=8^?x~xx+hs!YBEg#(tcQg6$l#?QS
z{B#lUk8f%4M8Fz+d!D!?$|NT9gGtplb?sfMv0kc2x@{|;j}A|d1t}(pIB(p_xx62h
zi1X4{tlCa`Feu;l`cv%RANs{KHe}eWz=Qc(^{(2_?e>?2yTWW?$0?f*i2neo#Kz5Q
zlh^-<UK1<hzumU`+WaWZc8x`-O8N+iwcR5f%29cUxCt8exEXCT(7s?<;M5vbPuOiH
z+-uu6&(2=tINI9HyfIm(e>ke$TfX^Ae<yEAn1?ep>&m@*_mn_Ht=4{nDJYu%^srP$
zMODaS>%>k~4J8N?xxEgEUdQE($sezC5l?t!H7a&+cK=kp?ci|rUf1?DT=B*no!V+}
zi5ftLC|mhVh%u+T<+SwWFT>$iK8&>4uTRS~xBJkZqzS$X7@HqKfaD7kW%9h5SJl1g
z3+cRjt|x;O_=NM7G!|DLBcsn2axy=DwC{*Tam8?@lM(nC>6`z(?)y^g{(j!H>?tcN
zTL4C>nN@h<@5N^QRpzKZ>KwItW(CAnP~MEj;Fx-6WvQ20GouQyj>UPFc{1-2;#|w;
zT}Rt(NsmP#G4PpFLk28>qleGD5~O>8t&>-ZmLU5!P&#MbBeHJ|e3(}rJ`*4q@UfPo
z+ku`n!I=dtpo6Kx(9#5wVXv}!;1qaRZTa+YFF7MCyK6tM%;Q6K1b@>tm$fGVyc&0R
zB@;#5@&UFwnw%q7mSM^gdHHiwYJsq<>>ZAS7ER~#wwN1)sM8QEW;`6j7G2N;u{qJ;
z?0{4DZh2||nO6fjurX6&aKSuJ<-YmT0%Qe0IM_1+{rzckv@0o}Z$CY9zj->qIw_8X
z?J?3Be~SurfXJT#gv^0f$o=KoKeg3<NVCNJk1-z!(Z!<m#RA#UuH$C&t|tXPZoUPE
z&*<aWd;8XztT}71NDsJ%(c>fj5!ZEjb12}W`y<==u<9F&n7>v@D;r4veEo*|>8QO}
z3VM8t(qZ3zU60{+B9IkOXHdK8drc84^!S3w=22Y#p!L&CkBg5H)KcaLW)|211>wyH
zMncfqnLh9B?FBoi%(o&<%W{yb$HBzY2!JYnZn(n1jL-Cb#grzkr#pZu^z1{P@H#<=
zxSoyTXs`TxQ-XqHC_zAl?{tOL&~m3`Wu3B{ZmYk`8t)A)SAF5f$V~tdI8d?}n_xO&
z#<WTT-a8&<5P0{eS|Fzae`&MYNR`oc%X&;SD7mkDje}TJS3@IU;FHl~Fvh(VaT7!Y
za7Igcm;a&N0L5qm+&^epaNoy_3O-&x#jF=|355cvsTRC63L4AK(bqilpq<omnR)~K
zRR?v%0fn6850Sr;w~F7&1J$#Dthas->?~S&EXe6rI&z?Hnx{9QcesgETVTk<$Dqw<
z%v4TQ($NV;y(MrT3Lm<<g~FO&LMlb-!2I<>_h>B|wjTxNG>&$E=`LXK1=Zp1Ul-9Z
zo^OXs0mg3+z*{1h6yknPe3(*V!v*R#K=L~nm#}c~M0>hNH9yRucYoVzL<k7JiNaP+
zS(>j5qCxAZ?WPWgH0k3<qEzXS&wvWzd(_^@_um>R-s!IZYqt$*b3sbX2fUaie2dId
zA>((I*-XYA19%&MKDwW<z7ytgkh*@Hom-JLQ1LFx16*AzP@e_D{D^40-ticve;fr1
z+6FWWNKAXr-+BiRFs-zgjM~rEBun@S_Qd8o37NsEe@QAbdvFZK@u!tHeE)Dp%mQ$Z
zF5KDo5d%{_bO08Zj^haMfexnGprnav5SG@%hrUp@z>8y{v37iSz%mHHr{ue0>bl)?
zmfW-<27^T=2$I<82TprnJz;Rc8A!L4_+{6VYT6E}lSn@#0Z-`h;OI~IV?-H<!#We2
zDV1fyZG^^L--)<A`$n%Bll6CDtO`bNz$<2(8*`pTu#d(UD^*5upgoBsQybg4<@H7N
z3j50-k$~V48R=5#xM=I1Dg121*nGI6mjL`dD8@iSwlC1%b{9hGx*gtf4yOL0NzH2+
zuTeQb^S+s&N2V<G;%klbqnLCtQatE)ijD*-?vA>O^t-L^A1Zm(2e)4E_{G>(cn)Fu
zSTCPyI~<yBqNLip`TZT}1yI&YYo?gcuICaXe+2<(Q*zr}7SKQxF9)Uwbe5a1b(QLI
z<9mE=&#n#coN2|3zR4^e6!MJ7h_}Ery}l^>Cq4Y$?d>w?xs}cazC3C~?$c`HQ;mn3
z{BHyU_dKxn6BTaV%Ft}w*u9bD(4rY3_$jvU@T~5S-lAuRU53@x+x)d}nkmU0NEX)!
zsEvu#wEJUEnEyvHE6xS@s6I`r6W^)7+_({9g`G9Ggb#wSwEz(>Z`l$3LtfewpY=08
zx6h0=;RHJi5TP9w-O4|9SonTW3IC`UlSgfz%`|cotV=9Xna}xB27O+bca@mj`S5wN
zy)5`oqjhtW<BXK@*!DhM>ANYH*I~EXrC7)%<+dGw2?NXkV@u{#omM@CWF&s;-KEw7
zanTrR=~U&J<@xnpt^V_-A$-U5XjK4na;Qm9I&i7Ufb0CFQ{Ue95b<n!9`7s83}F8(
z%%J?*u9cLu*D_vLtOHlt7Z_PkNe6`y4snBv5X8pQQDR_29iCR5q5OZ>CGcP9(!2x2
zNskjv8SmXvyld=v*2lCzUAxJbRO!&7i&xE@S&2R@J>JS6w~FOOg}TI`p;t_(rymy_
zjAyPhNgoe-x3O5l9cu`fRH~X@PU_=vwLPyK3LGHNI$o}=%)0jJSWj+tfJ>1Bb!sSz
z{Ws`g!M4s<6jEZ`)VPRx1qlynF?wvG7uR-OS8!kYUpeI+1nTTxhvxlpy`rPS-%pL0
zUTvkIuYMgq-iv%Z{*e2s#j7`&s+MSR;iw?^Wx0>3eb=knEMf;1%Fp8C>b+ai9R%sE
zUXLtmen~*PhZpx{ebojBHPPop>-Bl7U+cTJN0sEoZ%EG6iQ_}pfOwi#JjW6&1|L)+
zHiU$z0Fi20fG%)zUmEn;qE0kQ#s72Mj&E690bQ8LZ@idYO;G^6RAeL)L5*X4UgyK{
zSyswEcK<B>wSJxcPryPxXDSp@oLDVe;QObTHODxrHW#wPcpY5aIg-?5=ED$W+p{6k
z@wKs?Gj^!j;Cw3P|L!sSA1a*Op8s(=<-ft>!|0)BCJzcqH=_n7grt;}jAJu%a(u{A
z8V$u`LeEI}SPV$A2sXjXujw68VOF}vUjoBS*O<1m%P&1$ASG%4aq_b9|9VR1zi}PL
z>(9l2P^Cu)4M7w^9jkDP)7rwMQ#*M7st~0P5m0d6o2?@Z0@h66iy-+cWrJBAA1Keu
zttg`jV?ve-=_sSmGibqkK~+^5yr&?Q{im`S%61}3K>4dE+)T5nXnyJ4x&`bWeuL*!
zVQB7xQnikHpjXt%nAVW2tnbx27lasoa?Y~_cK_r@O)eC9{f1|OV9NZX;h^R$Kq%t!
zvwvoA!bRQprZ=A@XD!vV-`$e$8LGWdt;1E7HBkXC40>DhWQ*BoaM^f;LZOx9^<r5B
ze8p?6T;K|+8VI`SGB{9T!LCt=3mvx@G|ln5dDUCAh#t&epnh5ev5CftBl;l8ApVV;
z$hk_V-{G~@{cF)!L`->ZD%CpuCvQQqspu!vRMn<`FL08|rhQZ7IuQ=#P;EFXj=J*D
z37tZ#UHr=p+<Il5qg9(6F4p5)2$8X?<%06|Nj)MxLCT_kG?Iat!zB8TH8F!oJvZx$
zJ77y31ZLFa<>e<gz*-UOO$I>&^`KuZ*{4B2sf5xr=0zVW<QWtfD^1vxxm3z-$%A^`
zi$%MC$<&P8UW1h~sQzP)Pd~4=@Hiy*c{8(QDLO3=$DHhJi7$p=eWvvVqv3_qOC&1r
zZ7|TGig)RU+*}fe^vZsG1e$p8p`@F@JOU+%We&0MBXqiUoFM%*Z6iopTH0Sm4c!xf
z>G)87=)Z8SsejARMY*%A_S;yb^nw&u)pE`G+?s-EkeZ6WRI<<i0+sAvXKDB!9{7LM
zwnZa`J++{*6=gz@d|u**45kAjj1!!gDd0Bou>CLpEoBqp>Y37>=A0LBTYt9R%|CU#
zRZa(%7E;_K?;A{gdl2|6WcZ@wI^gr#KlFT^SaSj!D>JL|@xYT&dmtJvfv-Yky)1Wf
z6a8)Zp0&PGc*Df0t#^m2qyA6kxncNC^ulQ@Br_^@$Kw7~S_ijNOtc}`;rI{6FS%p2
z7LtWYCaF_WP&vGIQ$hV=o=r6YcW}j`9prkvX#(p09ax-x0WoEP%g@Wp^x7|@)+7e(
zJkWBf(ql>>N5gdI0^bc@-lTea#`^{8>|Ac2*sof-1xd1m<lT&Z?S^x-tQUL-JHhM!
zXKu4f+?9Xwmz~@EV;f6v$0@6*phY>i6saIGd|V|}Rfh-bP(%b%`AEn9*-Zw{>4bGx
zW?Zz{;Lt2VS$7yFglZ19rp0bvVurA02l}w)h-f|_%jJ8LvX+i7>@LM>W@SyMS+%%D
zhSgk5l(oFL@A&cXaJ{S4Rk(D0bNl4l@lZ#zJBulYRMG0qQPiRj6&*!n=-4idB}@WU
z*lto|fdMt>Btk^TM+Bp>R+Agrx=aO?*BALBtf);Y)1M`Uby(l#KoC+z^tk}%h?UiF
z%vyc>`mx4nc+Z(C6MWq67kj%}<N(~71!is`a?@+C_{AO9Rf6BgzwcJYWo40xzqg1m
za83>pAlyney&$s7W)ZKommY6R8>xr&yTS?U{eTXM14Sw;g%y|otP$CCeNiizCv6xn
zCoo0Uk>Rf+k{dxAtyAAG-4HlO$$PL|Gn_lW!No4Utc1n)@c5+P<AM!LYj>$Z9#w;E
z<zqEBdXa45-&yjh3bVvL3S+vuOAe|=N;XuHC%l}UjzzrXc@}L4_WIfx>t4OS|2s2V
z%uw4R8}pv6?k4MoSUgSh{(h~w13j`M9i2Nnv3_f@vsp~ItIKp95rL^|bfg0)pU2Ql
zp+aHJR&m~x&~0#G!W{aqqIR(110Z@!Gs0^NQwK?Gr~dfiV$ze7k`S0$u~j|!$CUx8
zS3PPfO96;J3(Ox^9N4A7``zHz<0qYaPgd4u_Iq-$)pR)<8=EH_H0e~ZM^)?WxYooa
zI?aL2^DnNfvZg6HGZP=Qm;xgGL>PrQ31i}FYBO1&vcS^^#9;g`TTs+pw-k)cH8mm`
zQ$Rb>)LoE@P4rrVX_>zbl)@BBif*m@Us&FKa2M?^wJTk*u7hz+?%nl9m*4BV+`zOJ
z{fXXNM<oBjxBoA|R7v=y=e<kM^zhm-=v6`$0+kw~zLzhxi_M#CW&bdD9-U)hAqQJx
zSr!fgQ&FgYb-(ZXcLaurQ14^`J0UDYveH)&pafiZxK9xh0z3#MY#8ST``0i(Fatr2
zTHEsYFMupg&CFy&9W6NE<jOh|)TpOHiB1CuLIVW_ud%TaoaG^Z+~4wc{P*U&YAl0-
zf|A+39a}B`emr<(l;0g|5$hZ7UH)k~ew(D^#{pt5Cn&#L&`Ld-WbY{~H+drJ>JiXl
zaLlb49NI3q5q9l#Tu>cf_jO6`OdcMRNj>ENjo+eQ%-Nq~VdG54UA0hL9%bAwx&3Ic
z+m0kSdMMG`J7GO;p(Y_Ab|fyN(<L<PEm10djNe<iOY^g{N;xVT8h05z4hl@;J317;
zb#|)dNJvJ;#>xr{3uie1`62;=fKpD+$cO^4^D6Mo4N!V}>gqgA=|2s~J<L`u?f@Ic
zERY_mIgd**b1zOZBo!1~>wSbO*7Ua=<%&Za4VXU-e0*f2r{@VVpZ3Vl-x!Ee`}rT2
zdj%8SwrCcC+&p#eTHD5FiSI-W+(bM$nRE;9(u9t7CV{g|HjRgon7TRptiHQ)a*~#o
zxu*2U*Va}ESJx`j_~78+%QSon_wId$DeMOeNF7jos-)XEq7NTZ65+ixrGHuP`TMtd
zuV8q1I82a5%#Rx(Ut3!0yVGP<7v86*vs}6)U94bk&Mv9^AO{Eahf3VP|Jsy3wab5@
zKTq++dEa+B(``<5f37QWjM>;%pW62w*0d{@qrf>bDe@Gy&x?-FOSiYSo|Oir_`?i6
zTQ`<xObjio?}G;qnBuTriasLo!vTev`<)z+<fPeFu(a@-z{<g3^ZLLxEL}2~vVic5
zcy=oNOjf6Dk@#-pf{DF71-$x`>-DC^SNc}JcXhph#4B3BS~<Mr7RF~a@-fRQK0Y4R
zC71$Pj&^4l8wG9cZbe274UxQ0y-+F<Z*;SqUQXt&B~KfN(Iq2K`HS#WlJj?8V=n=j
zhm;RnN(sJv`SO8^iiBlpY)owI9M}y4g$}_4V?XhQz_xPu$IqX4z_M^kQ|9%B!BQ(m
zVp7sG<S?s329tE*7w$eOe)OTVR4rVADe<1LPq*4c%c5fs)E@;235h_5b0jC@1#G+=
zmq+MJ7(H@;r&W6VIQ8S?%Q=`aLV4;3b35l*OAm{G{MvG+zfr#Ek?>~PG2d1BHHOG8
z`|Y|~9fOy=bpdAa`@*LF=*&#snKNf5tGSd@yF@d3F0Vx<B!osqQ3}E26E!`3BS@%E
z<}O^gz{Jdqg;=jnND1xDruqj5OFBC8btDL9-(h{Fn3f<ucXbsP>FVkt!xbw~Ff+Sq
zYis*?>$F^}L4h9FH3Xh*WE#YejOajSV2jzI{4M-C7IXB-)DDxHdZ`p^bp0c5>7hf@
z{*$)@->N!0N=o>IU`8`AEG#Ifp{@?$GymxcPcFHfkVZpC7X&&eK0H?hxHEkZ<J07i
z9z9|TOxbSmZX#|4dye-Fd+Iwfm+OUEdPyDS6ctI)Up_iI%0KmxDLy=$93u0Zj$Xa)
z)k~M~U~XE#ewHjwft6FY=HngM3?dQ|bR6Atz`O|-l434c#?o@%>~gz>2Yp&O{jAW{
z7K`%T&)lDUT(ms{@Zo_Mbf?dpIcsL`;81VOoATiUfuisx#UKWw>S8N!E?=D;E;6Fz
z%>@EKQpP3_o?IWYU#BJ#a9O+Hk8fdNp<d%!&O(0?xBGqy<NR9~I6Mi>#2r~#2{pB7
zmiWHDhgF+>nh?;7i;MbhiRZqlNJ`=sJ+e?g!Tc(;UeEKwU7-57P))C>xcI}`dHzxy
zlgllON(sMy@saHawYD$n2@>&8y`?H;4oJbj`<je~CSYyNcA0T;cYUU-zu)iUNA^ZR
z$4Yvb@ntsWqq~3qKB^$(M@4S*!)3y$@(&A>0rv+OnuI?z0a>cY7hBz`umlgufH@#6
z<ecy`3d*1piXyugK(7$jaq#XY*cxf+qGezp$>Qo)adP4xfvI+5UfLm;`E-V-7>JWd
z_|3=q6&7B9+dlou-OJ<<L<4wG&&ib3)bPZ_FaTC^&wQxbRAgld!N(LH7Z;4;oo=C1
zz5M0n^mndayQb`Dd9u4}VYATg)(@Ac2u!m5uW7!9A=7Cotf$nz`ftmdsu;Zf{A=J8
zmEK=Tr3~SLG}A^@cfvX0%#vHtB*Be{^4eO)ckkYj;bL({6OMWu3xhWkJEG-%({xd?
zW8b#<IJkRA_WLfTp{fLDwArJ_+(hfg)>sK?@87={CBv1Jk!djI{cO%R2}64#77z!l
z)g00-J#kHplZ=x+inn1p>;#-uURvT3tI<$c*m{G4uc2q|cw`auM$bLDLg=BZ%g9SB
zul{MdcLn?jEjs4j`qWeCJCtd0ZftKiSeB-yrs6<uLOcCX2wtmL_-lYr{%hlHX*D&f
z`zk6}L+q9ygez^TS88_oz4O0uHmqO+eY_7!1FNJ{1w)F4iCMG3Np2#$Gn{pw-a=6Y
z@Bl|$j6&-7&jK7sIU|P&R|Q!<!$dCFat-jkJlopZJfDHM^Am=&y%%As`sDTN*R5gy
zQoucJG`f@B1hZGX{QT!HUncNkV&dbY^f=mO#T65Uk!6UYOXmog-q)_~4G#~igSId+
zISHP^37%h9@Ud%-_ROe>@L&?^G+!)-9%I8|P?xbc6aF1Jxi9c|0s_B=@iW;41>|@y
zu|Roe<kTwjglBKCKPtjUp92MEqiPE-ohpBLxD7ff;{3ayc?G1Paqyn`_RM)pr@mmq
zAMwM&HC+hk2Ymr7!g%NI!Z|F1C~_<DSx{EcEqcUp`t)hnl50dfA#&RWJ;B$bI5eeU
zp+zw8XaFiG)Y~<oK|$wBG4gQz8=x{r^vuO2;lOcAfwzheJ$LiEer>~e();%p<`)+)
zMz>1AgLvNenzDXbc@-Tk1DRh^@<}P?G6zS9zrPkI6~uI#%q>pNP=B`fx5H|B=70rJ
zgi3Yc?&;pXzSnGlB8O0}pnPOjR>pSs^r&t(PVRpVKM&(AQ)gODi(g;3Yk#m~(>-So
z9)#Oa9v*0G%fr(vYPNsEdi0EqF+s(dUtZ2AD&l>8ApnRTR|6kYLQfbD#TMbMTXa0*
zNl??UNo{$H6hiXHz`)J=lMUP|q*P-X_wuphqN8Wr7B91bS0fFcjS~w}<s8h&h%ojc
z#$cD-06Pg@u-)E<$+O82{;Irq1*5MiZxRy~)pE6%SyBLxJSs3aLGHs@mwe!|0S^z6
z2V6F?2z75_Vxme4Q;R<g9`G}Puj6Wfqw<H_yPMs6|G@(anC0E6GD>jdw;WoT>xU6J
zpt|H?;XV!EBPPX<mPrxFB3l8sG&DPJ-+h6v>e;Z0-T}P2mM6_LWxd3AJAX89ja~Ox
zU0ux|FfUuktyEH0e$mz@m)Ih$c>9nyCME`3L6n7MxzNir{6fqA{*F<;!gQxZlAl1Z
z75~w`W~Ykl0mio{8elj2;2%h{)O2(&+n=tliikXfQqqZi4B%#@=I(267!9u<w|4gM
zXB*pVizp`ELclbP|2`vQviA6hu*~XunC&1Ie{@WoIgPTfemWj!ot-Bvj_dwn=}$1?
zK(~NX(luxQwY^;moXNhvzOEwqviI(-e3u|iE-JGBd@{Go_zOHe$7ZhkA{jYs9feD9
zV4#<D>gxPXXFBmz=VWSfGS*CI;z{!HR<2s~p)AS0rVU$Umby(%O=M6OfBsY#h*to^
zWXtl(@q`<=TV`ix->#6NgTXeillg-RO+1}KFX4JM!)0zlib0Fr7RD-ZOP#0zvQF>p
z<}-XR1n}8pb69Fd#>r>Z?&6h+9!E4@Oroh>dhi%1co<cuH%1RoyE3>Iax_c#v=#}x
zJYjyDL{VrJ7j-_)&Kftm=R8CKfORvy(~ee-@1$aOZ%T3TS$Mw7IeS5R07U3BU9P&9
zx!qMzkOnvj=kZl|_zTtShb_y$kA;rmsVdO!`YB|*WWraka=zvnTWWTZmGwERzK`iG
z8VCUa=anRuFk7^cIxY>}T(qI~Eho4g{6c>k^?o;l!PRSLXU8N6y~@44XBw#_L=9|y
zgxmmu8`=;d+;;fP&4c{M^6(ZgsQy!g^Dk72bHK>sp)8M;q<Rsa!-w3JQjp9Q*Yo<n
E0NnwNf&c&j

literal 18935
zcmdVC1yq&o);78j5fDKdq(cRz1qEpdQ3*vQrBUhb2Bq^w6p$8`Saf$tij;J>QVXP{
z>z|MB-s9irJ7@o6d}Dn3oN*i|ifgUsS@%8fIj?zL*IYr5l;jA{)0{`4P=xpI$vj4(
zu%?mUc<10J9{siN;M+L^c{v&Q4!`!LtGwVFzV$r~I~0n56#0$SJK=eRLNTH4%iMYD
zoUk(C;!eJOEU`Y+v9j`B5f9%t9?h;sz%q-Y{)n8z_AJGt!)<nBzk|Nf!BXSQK~pg^
zj8Cv^-I?o5-)7X%89Bw8-&0ux11U&znSB2;i<(GG>|Tp-cdFGWRJ$Q9PM5bZ7m2&l
zVKlk*zPsy}nUkZ_T@oogViFSNtj8~$yaNLlpEsedvar1I#u`_Mym!{JjMiUYz-@#h
zxBQIRP_Eedng>?1O+kA<q8@5`)X2!ZrNJ)8N$oLiST)D);=S;7@2mEpYfvEj&6pLU
z1UyP^E%qt`85#S884*1e78d+ZIP1X_9M-=o7FdY!^v26=*f}_g=KUJ=j}C_F?kX#j
z2s^F>PMz$O;Rkr+mCv59{@p82E#WC@YirBvzGuBMRmZqiv&&F>II)&$F;=?QmwfUz
zzgC^opDo#)G(E}n`sUWwMpW|A*^-iyONSb|x)IMTD{yY#zAc2wE?WPB%P3&dw?}MW
zxoWC_=c9YF8B>0|Go~N+^4oTvRt2e=mW^yf@gw%L$=w0)C#rm_OJv5e3ch;EtaLk)
zB_vjFZLdu+e*XM<aMfDqLfKfDI_Ej2E%g^wVXzc&Z&A6qxh>^3bNS922L1j0dOIs4
z_+M_>%*yymF~5ELc3R?e|04YCbz2nMvn|5Ldj4+F*BV7HKNJ>{vdV?atWDMwylAJ)
z)~=$wa^;Hi&WKTg&D>qH_hXh-gt}hGH*c8_d%woLIJ;8R-SRb(m4C;f!580nD3_jy
zUf9>`WGgvV%*`?C)<(BFzlEBX0FA7T4PS6@@OuA~hj&z-Oc6@^U-%Nh=ou&-K%~(Z
z&7&KBKkDk2wOwq~$?tRtX3`)gI-)to(>>fI&x57n<oj%g3gsIF3HFBXR&SA#D4&yh
z6&FVn86VGaor5F3BMXftMafuL<lM|jO_f3se|U5ckGTnMs~L7Ud%wzYl{79bNS*Vg
zB5al?n~wX2teFQ&#>2y-p;Fj>k*O9|{_Sz-?iD(sXMGPIJUELY3A>wPGS?bGm8F{V
zph|ngv+iIy%V4_R$6%@V^R*$jPr9cELk5G{8iT8_a8KM%_7|>=dBLvEE=ZiZM4dEm
zFZIc-j92tkYU}Fqpzs=bZ-lF)SzKmGUAgCiONVzh-*ww`+@_VDTEvl)!EJ>PLqGXJ
zI)E7OY_^jV>U)|ZEvkN|F(Ajp&(Cl0)InR$($ca)<bK_h)$!BC=`XKQ-g>;o^k|2w
z35Z&GXCs$Su#oTqMAlUkgT<+-sgxFK<5r$qv}a%631tuy-bT^ue|vjvuaWTt#eCLr
zb=0rMZP%dQ8*A{W#B9i5Ww_vLd;9CL)t1jOI>B$=yvfE0Mw?_8j@)Ho`KB;hmU8%e
z>GkQ!+UYq|wnou8F}IzjljFn4<m8CaQO*9Lp+?O185C><7RtrNg-cV-(^FhPQQE?S
z%U;`R(DucP7lC&_4%*@p(cP<3K(7YZ3B^iyiBGy>xD?_9@V6Ga1JX<`9omeVq<>l1
z(#H(us8>7l{i;2*WDh=*GP5;Cz+H~gb>F~Ik~p?6*W#`|JvnrS6@M~lSD_Us!!o$q
zlDzhkU-f-yWMJTpsLfD~$B$pVXph!lo_4GvIq#<zch%O;j?ebTy>X{054aV0POP|x
zaBl6vvaeHfC&W>RSzZk#|I3d2+VZC=Dt@?(?!2pOhIV##HcOvlW@iEz{q?Cc^72~q
zJh!u8X%liW&al~h#^0GjX>Ow1tb(6ZEF`(DFMf=e?U7?_E*a7>Qxv>_4Ot5o#JWOD
z$a)%;_AoiAbP%GF&!~$oHZ90nDD?1G6_0*=Uzp0Rv3oKy@9*PJTTWDdsI6r<KHLrH
zUTEa;+#oo@=$^i6A6WNyZ+mkEFQCzQs#Xkw);l4At|MNsdr(-{b?J)zuklMpi4K(R
z;*7b~n-7kraV7F6@23^Q;WwYErT39UVdD}t?QP6GtJV&ia2V1q-~7hG2Wbp8&uHZo
z^2GXPJI`EpQI`~)mh2^X0s$3w(_hTe5R$wuo6nCH<s=?FGiwRGG+gT;0+;C$SrZMV
z=3~+KtSm6?LJp;A@V#BizXAgT!_Rvf%gM-G{&JtOe#V+QIQ8<e+v0}+r8se75@pOR
zvoVr>l@6ThWn^S-@OlRY242=fk&%$xum4B7SNQW}vU&&{Y60WF9Bh0b+S-5h@-_2b
zNN^lADaH6U>L)l1YH;bO-D(&4!h7NO@&L!2aJo`r@bdEV$H`=}9(b=g1gXTzPkPU&
z<M{|qI%B^+NB9cX)Q?IhP$f$h%eb7$yRR<%U@Q4F^gX{33u#bFWu@jfve$iMB~ELT
zz8sVl6Thyezv~#R^*H3Uo|YW**mM@%n+@U7tj;x$c4|O=Y`^%u7&BgG#i+$i_3BH?
z&DG*QC9@mz0|NuRie#2$Rc*HmCM?SwCRgkaMZd47GV$>6EZs~W%GP)dr<;|H4Ht4u
zS+1kQbKP05HYK@(Zm$zExKAm4eP+*{qU0+HXG+lM^J+RSSCF!!-*6UE`STL3N|&+;
zN7_l}xiIow6v=&pX<P<pR0t(k1EeK2NbsDxwRFjz2iGn%O3KR0w%7>>4n=c4`~Fex
zBFehju{2>am+M*Sv|8N(1xsY$_AV5o;*7yki!sfgiAH959NNh4Ru^07KxB>E_bKA{
z!3$|<Xf(vOVhfM>-K2RaU)GIHsP8xzCWyqlqVosJ^L|KGJo?Gwb;rTsrjGLrPW8^H
z8A@+kA4<vMXi1jCA>@_^i*k?^26LVTyyH+y&Cb41QBhG2CntNK?5aY&ikh|`8hxFd
zifZlvlh<9Yl@<pnO|kqtE(!ge7TG{EQ&ZnG6GiR=h9St1`f+K*Qh3@!&8e~>GOgkC
z&E8npO=*hp*^ZUDA+X`n_Dj98oa*_KPz&IS&FeFbvb@yfPtp`fR8&-E)8Z{jQSfpG
z{Ta$EbVMQI9tY|UZ@5@}iQm0@m#SOm$wGb}pV=2uQ9&%9p+B)ypiYe|zV?u8{4I;7
z)v_t>hM9boyyD`gp_lmY?j3u6z*Hi^@y2rKnN`gdVW(f52Ro}AP$$2y*tkQoX#Vz&
zBdTkx!p=yFQT$+$mD!iLKwUxf_;+8bTm*yEkb|<87N;*Ul58luMYlrBe?~u(f%}y>
zUV50L>8~Fcz~Fih&bbEiwyL2V4OY8s%?ClMdKAhip;K(qcef)^L@isrAhj72Cuo&a
za%7b3an)X%3YLRG%ry{lpWsqynq~yCl*~uvjrggfyTw*hI&gI4-H$d}RT-h2^*v7v
z^H_76B7+FV@2hmcTqR-@Pbj{KlHsJ3k0p}yZJY@`tR|MyavXVK02@17I%Y{mD`;7q
zwl-0vc7d9g>8Gs}p0bV(x9ji4$WleaDfg|gazIXg#8MEKGrNa59(@9e@I7j$WW#o(
zLu4Vr?oBuq2|n``@1dM$F`IW_4SXc)U2mY6kRZ~h_TQV2w~{&BS(O!_DVgs~6d7{u
z4Y~U<H6=w7Qk!;@0Uw{$q$sA%8TN6u-UnL>FOs-^DVjto(ChSYY8IW9ODf{@iyVS1
zm-hnehdfxxAssix>h%VK!PTABv5=c)gX&{{nO4nrC-WE-#15BQ#4nDOSrxTzz;;ri
z(VEOZ9jkH*7KieZq1c-B23&iR7TC(@RoxbaS?u}P{ONv_%!?PFp{UHdO&un&^YBnf
z;qh6F3T%uRCF3{p0?w!@htPnN=%-@U5z99iwet4b6B!c|laQeS=Y63*0a+E5cgB5b
zioG`XN|N>}bv(9J;Ef}%goA?vnJKX6qnwczoA=c@SR=s?CUH0CJ?nJwB5!;1uWtSP
z`E&MYs~av_px<~7E?n%pFF0WzRlYwT-wfxN)p2by(SCpa>^c&)lP(MN%rcDxEr1rT
zb}))MTla1Y=y1ooNF4M;G|hL$7yY8@X_L`JMbO>8@$-oF^>v1FcsmZuPcho-kq@1r
zI+MW>DzJ=CyLQIM8>%V(!}2g}p@|zdyHj3uc;{Zvj#<@}q}4!yNwu1)g}{dUMeYSc
zAX~RiXB@JYhK1T3+pZq(@Q=0=xT(^lMTx!X*S;=kCg<~p#(v=3(aO`QX%gEVXT4=L
zX+AJfU>H!oQrP}@Cuc+C9iRcp{=vautJ?j$sQd#scPJ)6vT$%y<zu*2sR??BANlJS
z8*7KfP)P;;Y>x>c;HiG(T%+^BM6=iXepymp?ZE?uSYBrR(LfSb85frdc23UXmd_i)
zbG<m$?h>Y)@hGLT?(T%!PdJ~Ic4#`4Sx&@Nry#q*=yg<p!1U(f0z<Nc!331&W*b{G
z_C-;>3GQpxOi1e?U0IOU%W=naRzs9PZF~IJ=B4$_^rYa6;(JV(kRHL4@u$jfeY;zq
z-n1bLuO?)gqe||Dq*K3;>=h2U>;t5G$;KGFB##}9Y@He!sGo4W;Pe%ZvSr1XRK3xD
z=mdd2Xyi(z>u%Ff4P{*qVCi=OE|b-5%+OqF2zSF=ghV*Av_A?3$>*=%t}T%57JEMv
zY;JC%P-!Nq#l_^Fb&T+;P2&}IWa3-oQYlZ;l`^ujq;+%{MvG0bQOMunz`=Tj3_^g0
z(F)EsQ|UHAUh{jz?hAl%wpX6V;xW&FD@t!`zO($t8~N-pe|^2Vmay*vga~#?Wyi;u
zD=3ryULB+Q@F?T{#B;-3^z~{TE2%?hR#a3zU|dH%bntv6j%F^hb94KTt@^jl-7zt_
zrkbrD`8^l<DQZz?-X!<U`%j-f)oYLDx?d)e2>k-}&6_vzlLsg%DdB9^8W(m`QBtO9
zL-ayYmVz1(Kk+a%%I7VrtF7&ew$p?k(sIH1K+z;s8+FFL`#shMn}6Mxc(}$5D#RRN
zf=$b%`<R_y6SNRbR{{Dn`PR=ihuHMUQr#Kae(TXz+ySi_)dhNC@?@`*+b!YrAqNMp
zfIjxz#o<flV-Mg2!1}mWb;q}ES&Tk63cN8|>A>NCkuk}(HWyRAm>Obzdb~UL{ev{h
zJ2jQ18V&-0ID8AIU*kc_AMa|E?LrgQ&Rul@1rNdPND>n<AzmG?AdAuUbZJ=KE6aaY
znA_5BHCl`hTP*`SM8qH(2(hKNKK(`U;4f1#)OSb;N8P7K7Yz!imob<P05o)Ur^jo`
zj~^R&U{`JkN7!!<sLf&W>Xd|e-p5<jUg9_E^3|sfY&L)<?UpAr1yagyiw}<=-CF~!
zW~B_#;IVxmFTXMU`r;YmJ^YEI{jD4;>a~#~TwTxoE6~>#{h~!3?UdEwpvFCRC-D1@
zJtewsFVJynSCUKNA%8ad6TWVpyI-Z1=Hjmp@rgZ1AiyI0>h11C>J0A~M_x6{!8V3%
z+_FsQaHXhu>h#2kS*B0A1cHeSG9HpQX4`q{icK$LPFlifo1x?J8^TZw>3Y>TWbh9#
zFWR@8PgZLH{%lxoN2_FIOULbw+eNG&9;ZG`7N6Z;NTx#4S+=W);|lRdxd=+jGLad1
zbva8l!~OkzMIxuPt-ZMj!P^2y5Ju;jePem`UwwDbh8)4?xT0QQHpFe#;k8>e74+)W
z8OUCX16eUp|J8IoRN&3^qifvvZ8yGg<f*rd{{AkVYS<b+Xq3FcBi)%SF(qtHQ+GW0
z%&Np0@=soFE+urylz?((C*9U4$|haS8mOXXALpcRRF98q*?hZ}9#~<wpy}a!xLOvU
zy>+k3X^paM%G0f(m{kIM&|yOO=`%GoHUtX>9UZy%0PL|DvnbQhoetS$8xi-y<dj`K
zzA=FypI;MIekO6VIc1ZD(ITfuTg`8-$dit~6h5o1qhsko!Z*3*d3>O;#c}<5Q-<<K
z-&~Br@5P?f!onMKw(m`IwJIJ%H^K8FIZ(;5_2?&Fgy;{2{<y0TlR^>v@wKz_-Nd`O
zE`(1o?ny0{O9@nZdEpO@62AXL`G$K?;G&Fu2QDuONl#E97J}cguww%Q@0~dZKZ-p2
z_d}@>z)vb5hP9zgDZ!<8>#2%gnu$g-zi?Cwm0X$pkVBI}!Br|ftDwCKzqy5|QfU>H
z40PP@w@IC!+0WZ+JK1+!Xte&3{(?J(KnhQ;MWIiCJ$QCFwnB?rZg<33V0k$9eVU8H
zuc8}_3xFUZw@GiRW#Q(V#6dc1N9W{X@XnsmDC@pVjF*B&e~V<}k7#{eSt%@+Byh$D
z<x3oHp58j=GlX&Y6pco&@4IJYXBTS|prF-}`tTR)9jXiG^r`O-^33;cxYm4kn26+q
z=5yV(X3$S$W%KYjBRALavcT5nCUKC=n<T-iE62gQ9Sz)Uj0?f30*5vXQ<SNLEz;kS
z1^9Oz&%a%<|Hc!v3PmLCMfv<BIXnF({Y+R$LzqqDOfZYrbq`4sS6~A=*GE2BZG4|E
zDaCMJa($0o!<;&IN!S0sc3=M!_wx647aweUQ^@*sFTfV0nLJXtiP;%}Wee|DR-s^s
zz~zoPW)2;)HW1O~jxjV$V}DTeE6;q{MU-K&(y}T#%|!KdrPo0W!)I4DQ96YC+)I}u
z_)Ojq0kiL_160bgF+!D+(@GIfM&SZ0y7maW)Q_11*wF7C92{hiE>{w14;5J^-)AD0
z6rywd(bh4y!35M2P1tDeXA{k$mMe*SWZ7gTvk!xn!s@ygq%V{D=u?-PK|oRa&!|sn
zajU78B$U@-J)f|un~H$K)hA$)*SG4(3NJz$G@SbxdKFol=SL3O-L6Oc%H*F%BJ6;o
zajTKx>RHCRz=Xl|CvG^fvz<^HutN{_j4Gv@CR~`C+M6;T6)x#AA4~dr*iulNOhV6f
zbm8BemjA}y|GnP-`UC*uzA`_my<*nn*gH$2j|V6Ku9@VSxSJ|*SK_>q`{*WPfA$=2
z557THcwT;4?x1QIt<q8(>Z<7#(X)#Gr$hX&SNYej{eLcQ{@Yg&qO1qH<)YYc;;Tvt
zfwCc#w^gEnL-zflj>^-mW1!Nq#-ij@M`;fgIIZcVKY)l&wANChSRO0m`Bk&4Bf8bW
zPkqyr+;L^tM=4QQV{0f+_abWDS#%NVvSXrMMarKXgpU92Kre+i=0Gm%)Bdw#J=3%i
z!^Bqlq)(u=rK8QWxq0nL)t|f;9`+uM))=r|7PnHWbI=YjiMup*n=f}HJTvO>eN@EO
z9<`8z1PQ%j`08iz<cksF2kfZnzK0UBNT1~ehyfdQ0GylKT8&^<TyUzSGWm}mKc1VL
zsnW?ztVpC;Jjr4ACcd{T@Uv<u{SiUPjogPSDga328blfFl|)Shj5-Q)7M^mcs6eYl
zPs^{^2UM8FkZD(}i&-@sBqj`>3OSBvokUjY#_?C8vav*!saZ^~K<AEd;-rz!N^8u$
ztF5|M@3N3^YLycMF?KKZq=nWBg-Y#VzkdCCA^I~$7_uhP99C@jSbgv;K#v@YJ^q<t
zhT__aas@Q@=-IOnV*yjQM4*zS@Syh_D-mV5SgpeYG@-@NV-*T0#2Qn&?~5(QC;_1I
zUTAd8(c=v?76=A_HC6%-wAYq%aByfT1<3dJ9dog>O9CaNy5=3>-ZMrD|EaOvxwb2<
zOb+e9sE$z)!@?!@;8#$y>BQZIFjTGpB}#|n`$@xbtS^&(X$m4km_7s&!nKv&fwUkz
z15pFIf_yZm4Vn&$7k*eB_TQ4j|FMSsw}bOP`2@)M)*Uxe-!HVDK32Xd`O}Q<ff9;W
zf!`uN!-T~&j<>J4j~oY`Dl{&#5G%!WRVMCST1n#4B0A2dgIu=2G)MqrpLsoeg5+Zq
z4z>`^&tGkiOwo_KBD(KW<;_-_5WnVeDAu)~&EQKd_uQT`A9Be!by<8j*5boJPwzEV
zhs$4PRjk6g*vq$j6jH$U^!4fPerxIB@0Ag%)Q{a(RYZ~W?rwMgW4Yw}@({KvIgWTT
zPgZGM+8vdF>`{U|Dw3*1?CEXS4}0!-BGx{3+Cv{+^f<+2%~*bQ)VeY7&9UPKBx{Xb
zwOiW|llIzW+VXt@SzeAyl}X#N6%&?Y7;Ktf7~`jQpP~U0bT8PMa~56-*z*`pD0OVw
zmCiF6&czWB?MfWY3(`dYsW;X_2`bOvG8k9KRaT`Y9zX%!oQE+%`qaFI8GqLrdgilF
zRUAI4ioQ+AfM;vSv5)|FF;-%)9iUGg{##L*yj&|c+w_t@KNc}j07<xTS+{?qJ|46A
z&^Jc~;G|R8bHrbg;4&`g=gcg1ukDIFj>@Re6!XTa7wF6$!)Gouvx*C+dnl+##;eFK
zRsZV{pZTl|r3aPN^SVTh{xgM)bLK)C?QN?9ihYrqog>`|G$DJ!iPQbDx6)PZcKF#!
z``Qj^y+4)&Y6d4`{paw&fAS>!-z(t1txYC?5_&X`nIH#bej=O(@=x#Acm*Iuvz!x<
zAQQ=Z&#gm0^cP4Rz|_tmR4H%_eVWI>T?>EsOsEHf8;~+{Q4htxM0gz;j@!0#QQx+`
zEWZCEYH*nmNhR0jV)A{7)vFx2kotnE2cB^K<Zx{+`kCeJ<wRC^9p#GNPWO#ws*Pd8
zh?zLE+%JX^V*VhYflzyq0lMUk1_GYq2W&xs;VQrbBuB5_`5IVS-ZD+3O?=@j-3kT$
z#olw)z}M#k@3UB$#H$UNu)m*fAe@ARp66t9;O<Ixrs>9J`>2zBzMy^eJvY1qk*R&9
zsCDf+Pcalzy_y-|QLgA)-reGdE7@ty7L(PNphe*b4hd0K8Q4^N4gbwnSsai8^^5pS
zaH@JA&1qc$<maRPZm$-yhoU|pLKwN-2UYdeecF0pB=~BNHsyf^M4|FQJ=C&oyK;XF
z)|CvH0r^X{w71MrC}&XypdH1tx=iBE$tq9VeqOzbk~F4C5VDGC|9M7>JNBB6u|NPC
z{itxU3XLX~dR~&Kk;C84*Pcd+k~3{KiJQMiFRPql636Oc*5x_>^i-RhN<d%uF^4!&
zrVgDTJX0e~+HR+4&-1yQySCD5h|;P^j{eVY?-oaj*n}MwPHx+<lQ%$fWDPn<iEX!L
z8W1ASO3lB3GDDX3&nEY77|oSTqa2=9hP~f?59bh>6PlkC^mR%eozRC8#{wo}AoW5^
zKJ8CyDG9=Z_3nfdEA<7<tvLz9KbWFwtu|XYo&7wy?EQ?W2idn!Y_*j0yN4feUS~We
zAM^RO?K(qrPPy(kMeOOwn?mj1Udciw0_i9H2SnZrg;_Upi*6f66SA3QqJt%w{u;QZ
zWQaTEjhB9VL%!sU3f^9C4v!I2e>JnS(-4t+v6a*4_O;+QrMeTZE6P7RdF`2U@c$MS
z`JzYs^76_(E&#83yo>j~1FQx;kn?1o!`;)`>zm_7{zJi+HCtn|0qMKYUzbee?RhQ+
z@bhRwX@7P0?q1D5y#TknR)*xpfEd{T{T2&#p|QSGa4Hnqarz!(^088j#%t*buX3K1
zu~3Fwsg_h2Q1x$_x$b+fa_Uo@CyP97{X~^hE664JCVf{y_yV;h&3$0_vpkji!BV>O
z{?8bMxXjipxjsYD1sB0W!K+eGP-r+P4LO`WgN0OcyE*iAxm-?V6&0MPPbK*jhdm@f
z*?aU%8Os6Vs;9k4#XS;SIgXd;X4B#bZG}!A)w_2)uccH}ek?2)d3e3(Ewy+C%H9{=
z&`^sSmG~4{r(sRq;SRZ`l@JTu+UB}%y|~s_am~h3Ql$l3Ymar6m3>TUC7zjNS60$c
z1p9+_T&1e5O%K>7e?#)pNy*gY`!xg$oflyyKR@y-2D;3Q3`v!*?>F+$XerrZeF;u>
zcI!{`J&$yB!jZ<iOaeH6AS}-C)ck(jKoX(f=s%N{S1Sc1I)AAJSD18}-9pzQFmhSD
zGwLyXMIb00?5;&%VW0aX_B<*kCIHAIg#;mzyz0&K+~w0}<U}6G*cV>o;VJ8?L!l5w
z7G#k1UsY?ffL8~9BUEB`HmCl`Q;RZ-G5-jO6KYic%T5|h3>PE}<f>pLK!iwYf1k`J
z=`3t)t>+yZd3k}h2B4|;<7*^zw0D7!1JyFTPf3I@M$0y6`xOR+p(c<+9zA{P3)j@!
z-~6e{D1Nlwcy)E|P0pOmyIbCZR+FJ{*Yyiq%<!TI@-Kj{bUxl5+5mA>*m3v`*g6Ja
zGghWfy%=%qm;3PvC^;oTF#c7E;YP$|l=b@b9oYUWobhN2v~&&F4?wJ~+*Y0iF8dEt
z2HM1M;pZ9FVo)@wy2W<sOwG(TfL@uY*_|vhwE(>{LUa>P$8Gf{svhA&n?tA!Pfv~&
z2Q$&8Q9CwmgEpxTXzLNd2~=Nypa39gs(V2a`%OVfX)sagfZqr36A?I%_F5%c<QP59
zrp$zB8YS4=jrti?vh7;42l5oNZ~gK>R!%868Sa3&0nxPBlAO<^nKZ0AveDJvQX~V~
z^0i4EvD7qBMQ6XS+k8l62YH^i)4zM^rlP#dm73?P{IF(kZ+Ty26c0@jb1PNVg^mT!
zuqCvBh$m0I;0!=h_(Fsm%j%80{R0CZKq^ucXHgiWL_u}(7o2h@OB8Wx`SKb!XQ#-h
z`|h1PSfFZ~{sux52*le+!xPbJNdF4ba(e-=R<O7AtB!`iv!T4fL1B*>Bm~)^5%g*T
z6af1M)(FwwABisL9h=NS8<q!$SF(+gT+SifXl~{5^=pi?WF(0zcSEW5kG&v7K=M!=
z)$%&tR!sYBnuWeT>3O(%wJ%-a{VO0SFDu0J8xhTSyf>V1tk8<RX-4_7Gwy{V$F*x;
z5R(C{6^p(`OuHY@$(m(V_Di7jx7R!r-H?ge(XO;F7^SNSCsP?k)k0ikU;Ij&{N~0;
z;sYp1BalzPQj;@q5h^H=!`dW0WS5wUu(`*oL)eb9f}i0rzrA{Eu_r;O2J$eVGhXnS
zF7w)G2?rdCf$LCtg&DMZGiizZFwd&1(}Zkjn1v?+1DN!s%~*LI(!#>NR5Uw^7hp+^
zN=#)hFMQ@y-_mAwGlqYGI(<%l6a_k<Lxb8^KjQGj&?F$fn0&o8ckqZU_A^B~Egxc=
zbek)&r53k}`4iGcTc0m-zzlvUFW<{>3Wm*<iP@oFx9b)Qn4L8M6;2^Z^b&+gBb3e_
zO%DVU=zk`h&eg7(ZM-Ofh?#oS=aSrh-7cT_HMmTRpbCyv=0!*a#b!f9K(aQ1f`RbH
z&_$J&9Nd+$?|oKbdj(Xj7+x;&^D_f#y6^fw&U0{aAx@Y$1&Ph;_u1l$v9wfG{Wmvl
z-^Rqu0XG|a+wN+T*U3I|g`WFX8?()e!v(C~{<<GTfky;Vb-2>OEXquh*54=IvJxK}
zItWS;$Kk59Qtb_IMF*U;r+r14E-d$G>j5Kp#ii-@e!5>i7cQciWd#7D1g`WOGsL5>
zDQX`;Nb8R<@cNT*s(eXU@1~@sorAVLJ;C-t);5JPO*j(Hz)#X?xAtUQlP~f-)>c(L
zmmHz{j*Z2X5U6~l9q)Bn9W5!!eM3G}Wb}|zvlwp;ydn)e>Y!l%?@R|;69XzAs9fYQ
zzzy5YcL2K+E9`I`r1WeKrc3@lu;Y-Ua1p-~Xx6}Z^LZTDBL;&%iiX*_ahHWeYW|m@
z+gk%IK1U$T-ZEi~F{jULGK-FmHUKY#&|D}#uR#+YwZHy=uq`dTf(_)O6tz4ZBtyja
zE0msq&t|rbE!h_|Fj$dnGp0|YJJGOG@bi|XbjYm_CFl{i81Ux_bIW$PEU9xEP*I;G
z9U(p+MegmP%qMK1VxypEEyv_mAxRvSd2u`X?OQ!y2oPh5W%<mxYVe0464@hd?J&^x
zQK(6eU2Rby#WQkpnt??K*qwCiMwfUTI*M+7Wp!RH8A73;C<p<ZG2gD(a8*&U=oRN4
z5q=yrL@osknE~j0$XbD>aTayI><*p}<VkZ-A;esN8$aeBIS_GJ_61kQ9InJ37Gl-G
z5PZ!nt+~?cN#F``85E{%5>G}F`T9HoK>>JLK5ePyY6T%~I>j`%8l42ENtZ_|6pS9f
zNzM_{oXr~w$*Vh|K|EoQQu)k>c~SLXJ^5be<%Jl~l09}VDT%BKz#;H$?*=Cqczg7L
zydM1h<_+m$B@v)V$143efRTLykRz|GS{3dr4Mv7RiM^wu0{S)q5Dcn0tYugz?us98
z-m<-UP;%1S`xu0DoEd8&jD<;f@tU_bw^{?>_L5va!xn+t{aR{j0ic=-xajB<7~qUs
z3c?riJ`wp6<qdCX1|X2kLRX><l&h@cm60OlM~}RrqSGKd`!hy29EX6ier`iOz>25t
z$QE&AfcFpb|8TVnKMDzZC^U_r+FQe3fXec-<A3Mgwn2UBbHKfiuWURhB}8TG<-jKL
zc<r}`V&Fh_lUz8*=eA=m3atjd8m_4Avo`}B{w%%jI9w7Ej6KEzKO2BD>D18|py@nV
zeo&dqN{lxR=7;yRjKjFwlbM~rx+6O-J;bK3lG^{d4b8s!0O^q8Yijpv{TdgsFybXc
z1zX&&T#i>S#RFgjO++qN513AneL&Z@tfKsGEtCTU`a$+pdaLFq-&1#Lzg*hZ^}W|+
z+%d615xsKKqMDQR<(Bha$JYAV=CQN!)@TR8^Ze(FFEVcZgF|rrn?pD!vA@E)l2hb_
z-vhj3kd3y|mp)yK`l;QrqyAu&wbC7uwB1YI7r!|g&hLbkn&M~+&nJ2DIR==npNgLj
zt8$^7cUA~`dKPd|<mBW68113Rh8f<DUN?)x;K%lEG$#qSxO8eu-oZ(M9E+Te@KIsJ
z`v#)N1sZ|HjGT{G62^Cmt;|k0W9m}CjUsWf5rKt*{kn1FDUn<COL&X}*q{fW!ETCJ
zSXM0QR7|k<(j|_sfmE11HxEMi9S}+{fv~|=00PWMx7}ZbRm^@-V@W&w(?CS6OjHe3
zLI(41XpoXq1y0HShWsIs^JKM)5Evhj4g?|;gqMq;A3B<ChXpQ{GyQfn-cSGPUE{ap
z3~=gj8xsIwjvPHHyrYxdx`5U>lbl-U@V3Zhu|^-tZYgEN-cyNpP`WI9=lPLEWG+T%
zyZ=dKOw16?F*A#BE*>+n%Zzd$_Tdddr7y?ra;UtaFFHLNOcp3m+32A$YXs3hr^w5G
zb(8|2q3OFWc<qZaOI}QlV|ybBVNtigZ*Z*IMEtWn0$q~!S(uB4goNY;j|B>Zdf}PS
zt5Sh)(>I|vPF;ce1qbhnma-uPIB2`#%ZaIjEGUwqQzHz9P3CPE_+SdGbPu{?q(2bz
zytSiyaCqt8R>Y4V6~dzH)i$1w-r7F&k@Oe-;CNAz{k<lMvOK3wHT6TWZE|n_2o&7+
zd0Az|%ZvCkjmJv4VE;-jCxX>EqpjcGP&qVT{ad{JiEnp2VW>FjRDTLfxdAC1!Fzjc
z4?SPOu}yteW|=b}2z6i6#pO0Alrg-k(A=|vs%o>4=rnu0J5`o;k@nUFT`*`w(PWMq
zeFV^ct+8yQ#54Zm=k~-vU=8S<#<66Tzvt;nAP%*`ZDP>I7CCr$<N}E3Nz*0(B7q;U
z94Z#C<+zZUE8KdV*3D}whtH`}rb^7HTz7vF0=ID6ZXUH)m#=VZ?OzZ#NQnS0Sp@~D
z>h_v>vK?anYZZ#jA8+HfKuK&gk*VkF`Q%{e!I#&LfP*%khe@u>*8p;8*)&t&Z-I&&
zE71R@<e?+h0fd)AvN%21GwTTj$5E(3zqP1NH?Ra1fPE10IYUDQZ=V63m9^0-ane(H
zE06dLv}mwe&Hb5A3Lr9ke=0e`$pBAP1DFeO75lQy6gIVa!R`m%iY7o*q-mMx^C~Jx
zfj!!Y@G4G2OwNB0HBH-SE03gA+*M!VKc>XzUx~p0h6IoEY!Dl%>E-xz6B=;!p-_zO
z>sSoV(^v>@dVd2aEYDU1N<{`b7yub`VOKbv3jfB2gB&UME*l(!lN<x(TcKCF9a1ky
zA9#LJh_xYtK@4bz6CI1Woi=Vs2}q@oKBkwm@|4Q|67&AFh4_@evPPt2WZ)qsNl|d(
z?SS07uo#h%D>Y>hV+^8TRzZ0KuKWmUbI#~B78X+k1z=9W;)BGJ!GtFmrgKigD7~#1
ztvKWf&ME-F(GFGc_}CjS9yDwM#Jv;4t&K`)6a6O!J_X@kK*MWJes_CyY$$HhxgtuV
z*Qq{2Y$ta66kNtd1H#gfKnka?2!YA+^1XeZJ1<^b0kOd(Zt!&O)fc@i`)~wdz7y~c
z2@d|ArN)CItm6tfmEi6(>49gk*L=e2XeT5kc_9Z}qt`y34Cml1P2A9eUc{nv<c=1I
zlW&v2h*2fYHZqYiM_lJC`(J6>oRdrWsqE|fg@+A3f5g13od%2_8C%<L*P2J};=UBk
zj$FWk?z{5}5OSTm-o*ypq0N8}gtS4!a|4D&YJ|{OrM%Qhh8J-b{q7)UH}>E#j}T#S
zl6h8__6sQ(pQLyB{o2|m4l{jE`|``&2K144Y>bn)ZwtJPO9OlG3kNSFQm@JNIR8~m
z$wu+`)h*(lp5A3FPG8XYx)7NcUbbq7DDk5(X%I?dhli|@SI@W6JIqS1rP+Y5sc<od
z_zW8T^5#oZf%9xELUcsfrt@qXV6(q>`IS25+ziPX$GOXtA<Q&HMkTchvxhnQLS!hQ
z-)1uG4$Xz=l!UN6<pDe9OB+&#G}AHir+3LR2FVZ)(j=W6dDgMs42qNgR+ds!Q>}mW
zfL^0SP*E;CJw6RT_9{8<8A&{>nMZZrD=l0nmlIZFZ!^~>j+9z-IS!NP#zd7QR0^a&
zIR>V-Z@%9aZPmKDw{;`yGOfgaM3J~xL8qT!Yb}w#mGySZFO7M5LlWQbF;$O1AQly!
z-y~#rda=jY>h3l8%j4;fy9BZNv6k}MF&7d3cCoziKQCqfH^|(7wHN$5e%Z$m%YAQM
zA4x4RG=dNkiPz=fI7!)ZdXE*GyuKnIt@q^>PDW<t9dLv_`3$aPC86Ap%KZ?6(9kQu
zW5`hOw~yLYjwsXz6Jnz5?w_`|H3_(>6l4pu<yBPJxv6|phkwPVvJ-l=zl4pY=o&wc
zOT#jCED=y_M<>pH->cH%&Ojdzz2D&LEIoY>R(9hC`%j>9Lq%v^iM_4C5q~U9yTQl$
zrmaWJ8i2(f@T}o$;A8yS)^<O37c86SP~Tzx<LH-D9X3j)TBm<RD?ODROm~3;=N=gr
zQKG<zYW#%>O^2rlD!CJH;9u>28J(Jyxs6m8SPn*715dUl-mWU3wu8?oe)?7OMi(`u
z=p@yDXt7Ixg0?8d_)F;OQx#bzV()u>W#z_x$5c<Db*k0NZwvVn4c;zb6l+HxjFq66
zs4p~x`qF3q+&u(1N{=rLW(qxCaN}AG*;Z)54S~jAYSzrS;3>3~dW9H|DK^R_`HFBH
z9U-D}(jz@D;#D>uDa7$O_+2p1rBQemMH~imH_AP{1*L;KeAdh|(ltQ(g{P~js9;f0
zJW?QYoO%dCV!!HB$*|Fg3OKI^!ROgUN~JZnB5Mp*MN(^1&zKgU7atSD29c4OoYk-j
zI>J=&3HV1=<P7oZ?bf{X@DO96pa3g>nFc=Z1#Dm$Gcs*vUobpE2*1Xl7zs(}1h115
z7N~bP=0M8>_A@5B($iU{6ZMOJQl)0^(}IG7&p-7`xO56m<)<>VJB<MI<V;C##u#E%
zTa4X8ric97e>P|sCHl#8S2ShzA|yIp>zQ(+?xevgEx<?*BL06VGD@<M@`vod^mqJr
zdIAc`Fm?Tp*!nVwhawV3T+??Nb+2!`NJw>TiOC(x%Gh)M!{+}#e2V|}7raufWB5~t
z+)>`KV}y-9aUh{LU}T$oG=caH?heQ|I89xz%$45S*E6OuxMc=XLqvB#Z0;BXKSumf
zpZ0q}H+bM9kMMrmstNCI+|RuyX3{b3IGqcx+h2GIs;N+jC+B_eA_5yw;N6>YrqBn0
zxXw<rS|lET_>SNoh(ATuJ$XA$Nz_narD$V;nMDKCI6q==_Yn$SFk(=Enh_{OQYU$t
z6l?alsEzjm^$ag@5pA6$C#5teo#XS4*<Bx+u!bN!qJTzkbYoCDe<Lq3LBPKhAlCW%
zz1G$aYwKh}xx_J}#u;8;;!?AJDLzGkuOqhd2iJcufXbhFzh5hdp&q3d_wq<0oDN%1
zv2Q~&7#j=dDeDYfN$IJCMYU?m+1=NQ8>{xOOyYP!2;?P^64Gh6w-!lUq+0j#uq%vy
z;vo{<I+dv@!`0mVZ$>lzKQwv#yX^k|+AYapJ41&ND_}wj%qBLoZ`7Yc!43`s=opCT
zguKC9P6+l|n9KWz!=OCjda>KC<ppRP&}ajouZ9a>5L7y?S$0k#OaM>>DJdx+v<MI?
zRX+r#G<@(?JI)hjbHpPN+>Y~sz4Q=lQH_phv|-89nU0A(6PgrZHF=wg6d{2Vu)?8D
zPaR&mcL*pJX%iR^cOz05sM+P=Gkma{?LWssBI8Ppe(WpV8e9kB9Ox+tFjF^N=OqE$
zj&BHe<<~d~Fg>E`o$3y?$H97nLT%NHH`R_rSK6H(`o1X*i6b}|k!D)P=w%-YSTKSV
zZIb5IYqw2%wwcO%qxpKEag*v)@mc~_Nd}pLu7$6L&39F<5^Y@L^c^X4t7{$FQ^V-b
z&d1i<61Xg?*d(KG3hDovh|Ro>jcUdza#d{6lnVUqv9;|z`z-*^!Y6RxlVwb&W2K1H
zhW(fD<p*z7)ke_oFgtpZ`fhaDd5vk(OKbA7$~VGCc10Xy`$P3rMkb3_3oo7W6O6HT
zuYRekn+OnYyf-GubnKY)CO*LOcEzyc9`QG+l2TKO1NX&v-9V2IH0}=N4XRu*Xannj
zu8f<hdn;pwZ&v}AnLj!tTCcLtsv+ai35;tmK#4oAGXbq_F>c*Rs9tG*?c4=w?=|-Y
z5yes2kIGL~RbQ$+`KLxT9K@ZPpHB=NWw^We1UNOpmBNB}%_#8BGWklflNV~Y1DL>U
zzZ7d(g02PpoIt>i<>H{AKZ6MXickUO$p0q~Ov$Py>@#>sh3psqTGd270-_0elu(~H
zVb#^5Fu}!2M>LiTmG}euc~i3|0rsT{?g<=-5$4rPu`Yu1=c(&2`xz!<fma%I9$ZBD
z0P{a7kp!sx$}DmcXDgP`S=Iw8YPU7sAdab~ssD)nPd?K$uoMppTsDt8Lr636c|YO{
z=PdHn@O&BrE`C&uzZI_KUIn}uWM*&$atIF&4BSyB2mWcS1jdhq=<YHR=WDkMD1wal
z0Uljyh9wD$22v#w`dOu=l(0w)5v`6nVAN~mrMpxF0xgUu{+OR1g*6Vi$avS%lKbhv
ziP=y1Y59cZp9~Ioz?T@({GYUnxIq~gU}8@r;{B6ue3{Ox;rc*6zK7%fhP4DnrKp9h
zQ=o|q??22tJ>V&T8BRG<v6H<y#BuEOY4C8Zjwekiars^jc;G`|PN6<hQwk6IZM8rU
zM};3_fXXL<xjOq#gAINJ&A*rW=rc<IGw_-Av&)&{GdBU_+YF>CedbNj;R+0z&&!#1
z#@&<`-5E9v?>~S+=cbEZd(v{IpnwJWVB-c`3$5G}OZ^xWkb(@+eFycg_VBqA3q>O@
zxYZz3r|;Fk+8TmYJe=+fkakkZ;vQgWu&PpJCr9k^JvDAlWAMiCfZVLztyvq%eGzvR
z)HItfI8=zoCG-@5F(C4fm6)2EeuqbcVAqDMa45K!!Q<2C$OY{c^*La1pI1?$04+_A
z+jTqqc;u$2NZ2JbcL8*Yu0KGE94z}F9+zly(1d|3o8U74zSIn#`H_Z35U5uow!=w_
z#-`hUN>1R^E5w5}8+(2lFdhgc0NNV`6jAkEi6S{B&CSijC1z?NX3a1QL`BA?)YS2w
zKe7|c`zi897+e>b5t$T*;l81DVT?x(w7IK_alZRo3v3<F-~=Q80Cwg1K;F~sUJExn
zv(FeSJ2Nc8fDa)dA$%|(U43$ZDK%~O04u!nv=6TNc1~^WBu$Z7Zbb`FjtG5gS+lJQ
z_w*51P}Q7h(R00F8g{#<PfIilPq8z1eWF)R+e}OBPuWZzNb6A1z0kX}8WgBUErCKu
znPxJIc9^cm;&b|?1ruz};NWfo8Lw|v(`+!CghMTtJk2B+nvp<|SD$NhP=*A70(X~A
z*d7^ahLviDVHPGBQ$jkJW>}D-+;`bJg(st#@60vjVL?G?K!!EQaW66yt8l>R5DeM{
zBV!hw%R`adz@s#Q^&w81RV4=|0W_Yfsg312e3}<O7oPpX=v1OGI`LRHu*swT*GaOu
zm+8{U!+y>1Os6~-O;7l&059Zo0qSkqYDJY{oE=For#fq2HN(8BFt!!byNA!TvVc=m
zuXVqDRUy_Ji9Cq0PV_rauOU&P>VY9e$bHCtkbh?3`fZh}g|2M}W@UqseVUpaU4MG5
zG4wW&ZrD^h&iX|MZ_$%*RBV7FpA8e7%vUi3#+_QwQY)m+JleEFbCX)AFnZ#+&W20m
z&`ZmmF~90mFn6H5PNubR?&U@85>tv=n1mS1C4-4;uT#%ZdJ)QG_f7oKGOMXs&42hV
zDY-O#Cu`h{j+@wh>o<S4!yKNu*!{q}0psMdZ3o0!XjiUxXe+MFWYdb(u`)xLm4-2}
zcHvKN2GAh+1DT|0PwK~tajDUPv3y2Ep9f<AjFgPcY8I$p_y?=zz0{X(<T3mH>DZ;A
z@m9$~O|FU4r~z2uivkn4D=Vj5Bgui`IIXfM0_JrX8@s(3l?*aY3JjvEmzrPV1_c48
zfkquW;XwEiP^H4pFO9Vr@F8=6)Z*^W4ew(r?U!D89b<HfrSt~SDjKKNtSU9p_f)e!
zZ})LIfzQHlr0@j{GEkM8kMz!Jo`SNy4j)-S4#!Cy^vuEJYv~whU4cBopx{|BNd@yE
zh&RKg^X3rpilv7GaN}rAu8~vzRqW1@)4A|*T+PZ;wk%ouYN7cv<>lqnYt>ssEfI`X
z^#0emxGZ|~Tb`FFiETec#tV^;PJoquyS?@zY9QEaWDchKTHdMYydQ{~1MPeR^s>21
zOs+H>SCB7pQNSC@C@B#kfdoTUFOK7<pKia|F1u3lc)-e}4pI6bF~AfT7G@|9L~$`q
zEi?EG2c$$tb85WW>Xu-HI%jM%HLEL7IUi>Rmb#)751T?Dc3lvm8_b}{1jqAZk!5?s
ziGRMaCj+K!Cl@bIA@3`FG#Sn_S;?&vBE*~h%J3IGGTzYU{eKy6SjSJfUH3Umem89X
zZ&d*g`ehh-0V{$u1V55bL?%Q{6UxKa3iOuyGdmC39?QeLCWF5|-GBqJvLkgpX(A|&
zAPga1<B2Lk#Bc$lTE4eH@Vn$iN)~oEzUEg;JILYikqzC5Rtsqk+#$$p2EB-*-b{mE
zr_RH|gVVYiH+&0&uvyeeMAW`ORNVD~oCi57cW9pc#qBPsoR7y+@)-x`4u^VFuKRRL
z&TK>L)m+mLA+N$zvt<fiRb~XH;z>mspwk>a>=`qgu1Z<lcuz;Q+x6X~x7yO+{O%Fc
z_l^Yj1$XTq&#y-9Z*mQ2oxheG=H>2Ar<!$>C!{ys78JPzOMwRJb_vURS@Mu(UOqmb
zr6rRKgoOIwH$Z%FSJ~LYV%yr<;9-$rBey{XF+56uDG&HO3ca3GSr^*6zfNA}YKiZ4
zd`?ftg^JxZ=4=N98SVVW`A?z8dDht6H}1dDD^Q2(rupod&2s-WdnYH_!);b!mIiPY
zK@*R~+?0UnJa8A+f{zf;Kg?3gdjvur<Ush4kJz0~QB!j(tJeB@)VKC_W?%2lmyJ*&
z;4f2atq4i2L%{UHgTECzePd%|@5UMLA<WH0TMDUZ$XqZF0v|EK1w3o-?U%w-&@D2b
zjpo)4h6)6FX`Jmutp_bk4iA^xu!?&g1$K2Qy?ysiA7UUjF7AebgvWvJ{QOJgogx{G
zp1v7Ac;f^5dbTQuFL8_FX;ky_K0q1O9L;yX6&hgTF5mOmwXamctm7qjA?L^9;{M_=
zKGn;9brS;dz%@}E?`<>|uY$7$yerQ|%f?p8BU-PKpYMUhdQWA**9P@hnu-0&Fds-4
z^23Z_cQdQ26DI4t9u}yFR#sNti~i|LSJ~2X_k2L3jEsPom?9{KGBU7f8Bo&%N+&GY
zSRQC3i`SyjKkS7~%*{gu6j|%09)b>;=Au>SnJA$6@e#X>W!a%bw`^WjUS33HRh44J
z!QtWkJ^>m!y34Z4i}3zFnb2rg*!p!PiGk_7wl)dWx#l^WxY*bf@SgMvgk8RW<L3hk
z0&iD0w<vBR;)qs!NvVnVgRv$FxivM3I&hswxP~ihv*)yA>~2oO?aoz9K1xeVGX_uu
zLa!_<D{Gpd8LEGLTmdq`gE%8+=h$#Y2}@Bmm76d60I?v0WPoumlYStiy+y3Nd@cbk
z?r}6TqYsn(Ej!MlN{&?%#-l~?-@$c87Wep;U4`rRbua+GdGY@8eU`i-vxDR%n>w%4
zTb-Ep7@lGn9AIHNf9~~J@8=q~7*LCcyK8CHO9tB)@3(z2t$*JSHx4Dt8vCU=!}OEy
zTOCjVp-?90Ps~^YUuLkqJg~U3g19;qsAv&E3T6QByJC@&+<Eh<x<B0{ZezEjM73`q
zUA{E7v7`L`)YNj;PDgm>bMCphxpMdY8%Yvg;ZOkJqd(9dFS$dS4Jw09U*QmhR+z;3
zmzLfFU1aW#%zdk>SZ>E$KTb|c0)TbkJ$q-bTS-$CgzX&}Ndn{HEGAr`1$6<m>!Kt<
zGS;Uj9tk2&MD+CZ_YKKqzd)eRZ0)aaW7PU+ONTJc$j1is^hB=vLxs0q9lhy(a&$??
zcXDzv!SwBAeATQ0YNMTj;>_hfWV&i^eFi%a55{Gsq@}T+Yjl<SD=d%($><f0z!!KY
zyOJf6k9|)&p?ZlyTDan#f)qE;lcV>_=E6XCHp29qEt-!O$|jH}V9LMd%-y1HI_@3P
zv1BiWhXqQ>moIlh!@|BS*<WoE^EiNML-ib0=(A=4B;ZTD9)Ye_Q&SUhkoEN>5udrp
zAX*P*nnxhOLe-qtvhwnhD7BgjhoRv3a8>@yhtV%YJ&&lNMti5SQ&Uqz9_YUAm1dU-
zpPd6mNm)}f7*0zll}xN+^owfueNuo7FpPoc0~{dg^hhhKvU0Q>k3+>=7$h<=F|peN
zv0*^-%>c5;>29Q!kDxNtGfxt-{ej1=U3oU3Q8}NEi;Jt=W$UG?5k0k`-xtDIz7}eZ
zGvYX%)P|uHSb};j)P~u3<yidiUAF!o|7%$v%GaV!a)ia57a)wjeN71Y{HFV|N;0`p
HdawQ$S1`8u

diff --git a/reference/Rplot009.png b/reference/Rplot009.png
index 4cfae799cb67147d857b6b84310be1f84b75f0a3..80e9e5caebe9b9770e1d75007d9acc1f708acf4c 100644
GIT binary patch
literal 8075
zcmdT}2UL@3y8a_77LY-Z4jEw-r87vA<|qS}5d>6%lpqKwEfkSX7#k=E<0_zZMVd6}
zgsun?LJ=vUg&;^V)DS{QzhB&)z4z>%xn=j9z4vlX$jSdF-~W~8ec$JK-Uk;f%mw#I
z?SUXj@XTpbYY2i(@m{-jf-8}O%}L;Gr^`8WQ}7P{=%*Uc;BB|xX}drO+Pk0kf_)-H
zu^<Spe8%*riy`UDgz(>zsF7RbKK+JpXZ2`l3_@Y5@A?k6AM|Sa5n9hi_UGr-7d*Y+
zS*9a3Fjl2Akao;9ZTLa&2bQ%~NqP0q-H#`8tu);J^t$s^4@*=;@w<zSPc05R-2NVh
zS?*fF7`B+O{JXjW5K~K0DV1e-0j+S~K1yh&Q3EP-KAq6xW|zDJ(kNap<cFgBPe;SP
zhrxU`S~!a>a(yP;2$CkGw+|+_?Wh7mDd`=K0VyB@NieB?*F>Y_u<ekUo_Q<uTWjo|
zH5*H#1Msa@{+CTnM!t2Q?~jzbnp0~gCnxI`OB-Ceo*pF!n$QBt^Jvbzm5NwVEi#x8
zL|({A>|oI;IXO8BrOgb|2qw_Oki9XW=`~y&(ic7{f@b5<1XSc~fPV8vu6ldue1>>N
zMh3Z|h1t+gAVhXm%7yM>)GwCCnahR#;M+*<SXfwy6-P#ppW#@E+^xjAmHI`7BYt2&
zq@<*zAG`?*XR((&axNS(eQ_Z+PQ6+0axuaxuy?f51DmXB|2ic+Je+qE;~+O^Tu_|>
zrQi0xJJNzi>)cuz@gA@7vCFmYytO=*q-tMN9LA*Lky-4?RkqXS<~YiOKp>1Nn$SC!
zM6$L_shgpcIk&*M?#{^wN>A}cB{v!&7&$yT`rK%(mCp`|MAo3WoFx{eug@v$Yc9z(
zidl;`V0L>LKan*I(NIo0JDQVdoaz`lo0M<32==IQJ~O=ZIR2iv(=#7pvr*ASCG8Y@
z6|u^ini_A~3SnsRa;d{}LI6(HoIqO(E32D+=)F8v%~xuGt5|v&r`8p<{v_+7I{f&}
zBXO}{I<moV8I%D`FI3@hE5DFjcSj;TYk9nOwP1X6m6-Lj=`exd*tAkoJJ}^KzxefQ
zXT?&+M<eQ>&CqzQFCkT1Tib0m024+)a%Pe4iH7T^gCd0=%O1a3K&<aQzF54}qf0dW
zYFD|m0GW{Ai)g0=ziL>iS7tI>xJ=S4t%b`T6+&;dxlVG&iJV)TG3c!r-b_9Dw?ddx
zA>+GSJEb-Av&YBBUBMds=QDykz*fW0C96Cz3g0v}eV=I*&coT**w|~D$xOVKyu8cm
zWYf^%VT*JGyDm5P5LgbJ&f<Ez*xTDbJ)0XFyEWPuB_=F9)A6V%^OLIRo^n@g0x8VW
zefZa3(sX>@G#vD8Sgxs~O@=GrDn{yqyu864&vieyX^LbssQzp!9(#AU$jb{y-j}hu
z%HGfQr13Yg6l78G^o3j7G(r?ejvvJxZ(2pnDkC4n#)><I98_yud&k4`&0Dw3f`fx)
zz{YnZ$Vv+fGdGvxVq<rU98MpsKW<=<Q&@PkzM&x}H#gU}QcjZ97J!+^PGL6Fn$)k@
z+s{<vo#GTTL*zM2eVk&(7Ko><DSi`)#o>~;y=ryer>3RNJom_a00N@LU~V0J%qc4?
z3!{I)2lH17SzI%KNi+&m9B)$QddOd%SYCFuIwJZqKVMNP>1;gQ#2BnYJd4Yk&q1h|
z0Z6JiHR|~`)90eh+ZA0{TVygh5pB5A%v>Q!rvq5Pkz4P?6PSTj)$S!ReUU4H{#-T<
z%cc?GaPAhi2%mB`cVTfc4lK<yFfedwtXf%2-N_-)4m><A_gsouuJr@n{8{rR$fo5G
zrgK59i1RUNO<1Om&lTSOxc6nsf;c6cqc_aSOO<_QffNJ&uSJ(j9)h1eGYkz#4;r@X
z@9!7JC94|b1{pIJa>$dkW?i+WjZfEod>%=o8Z}FsHpgb<%H>eQHbHHIy6<gU7(;a%
zpU$~{dUa-K5g=-&94i-xWaJ_rZf<VYQHSkko@BAE^|%otX@v3{19`lGaOy3FrI@+Y
zs9O9O|EZ=7D929CVDhsZbRk$_ZkGgPCIXW?fkL6a1YlrXLNGB>;npaWZk&F5XY_U%
z7~hF)+XSH9yELF5zW<gS8ISf%3hg|Rqd%-3ETsuE_M1AfvALNiH&dnSJ5hH;EY5%7
z<BP!@jgzw!`ohvuJfR^tU+$i8F+g~QN)adZiiMNC{djbX&gjwW(CHK3QZ#tdb_r<z
zch3Ltg<t0e`2>=zbN6%Ss|5?O61P-URjmpyzI0H_mvcj83=9j$^)`J^m4NOHzkww<
zyr(n-=Vji$`eV*WO4#;jk!jIme_mfS^fwFsKXsv}kYBGDgVA57GuY^C#8JGDU3>&R
z!WhJsO``vm53>P}akES6o}Ru@ib9!+`6~z?fJn6zTt6OfTRXdZZNVDsNo}nzRU-dh
z8`rM9r63nXY=sKNu<#B@BKtzvvAg|}-N4R~>J80O!i>>oe9X8j;NX}vHwH!OFDUpw
zwJHA*p8w*B4XU<+eEYDpv=mY2ceNN%{*vfZWLuVSZ?AaAr`&TO`-YY5hb-)a_O9&^
zcleGM{Ij?eEJ*m%pcp-jjRnDhEp=>gp|8*O4-bpk*LWA=m0(j<Lk_Du<|y=M8dp#L
z@=W8PynKz%xLDwP@1qD~xUFN*rA7v+NHdbL5gUtWiHc-_N)&Et>JYwMt?AkkU*BU@
z{B*HK#jF{8TUJ)K5UZyOf*YV?Q(RkHx8+Gp#H4akzETQKFeK8EFWt>9f|5pz2df|{
zE`PA;#W<7_XNQM}pZ(}|*3?vV&%J_+N<nG4nYihVsX$}Bm=?(q<Y2&7c=dc|p_iwp
z)Ec9HN1cNj_Td3#7Y>UK7IL|XlOEXnK-+8hJ$*h4-e*K}=zgx1A2uE@srA~tiGQC=
zY-VO=*dvkL?kDcY3c>J^oMRPWwxEtxw-gk9%N0CKDx`(Hs?V?wS+2&r%yuP}U+;gp
zKq5WVoeWWsmk0Gx0xYlG`R(nIN)Jowh87g>j`6<Rs7i1Bm9Kw%+`e`;sp9Me3!POu
zzAiE99={sTbVq|Mkbe(63vjRUaM}t;Rk!uGm1Y6z0ZY8yRKM@vlij}<O~2>ZKYr}7
zd^yPZ70w8w63`T5ov2y-uxItlL8Q{5%xfWTB|eWMz5Cx3pNobi%#V7=?_04nYJVf;
zILd}&dLOijIp=5d=<t6#*A_@lso?H=?SmvPdG6c-tm|P}<gynTkV7H7A*7)+D6n>4
zQ2J$Mr52CvZVi~Q2e!UiqK`WS!cLqjlAJpE;yXBV1Un57kZ6f!4EQsbYq5=?6y61R
zDILl_C`&t!uuE=dP#0&^In(O$^3y8~s|g;Np@)52*qaC8`yi=HfhE%L&@Yb>71z$N
zI#4V#6}yTbc%&>gwr;&kSs|+r+a_dUsB&uCiJU^osWVRwx1UpQ5(U|xUD`Jr;J-HY
z_Mof)BxToQ^AT{)6)SQixm{dA9uTg`g}iOiJ8iXEQyg!NW|#6o7QggBwN+IzRGc25
zEx+yC4(0sR1EIetz#qWb3+6+(vSx-SYh9hY{+rMa=uzoh+8fRsigQ@4VW|RW1ZjCN
z84*iEVIr49i}BKdt|PSh3_*~F%skR{0bC7a)#<nV@c6m8R3R9&^K+fHkFRe6VCzCy
z6-dJq?5A>B<Ni1?)rWYeaMje*)Yo{S_C9EyLa&!|^hgRIB)5NkJNo=>=R7fVF@~)j
zasXv4dx^xZ_Ak~|$2;I|@7Ozy<WiBYfa(Rwx_=Mi82QEWQac`3V7)qTsel6s0Kfb7
zE4k>aTB1m4#RF4K;j|9G!4deM5^^2X%mE1?B-?gHbjBaHOCF-q2R^LR8`(j(1MI{&
zmL|sq+VLIbx1A{1-#D8nu8tg<Mb3#5lYsQJwG*SoRj-H|85s!+v!f2VA(6z77CRup
zQupYFhK87L7}`mtMbnnYb!94{)&eW7Z;8{c(!E{MBi*XWmo!*-%2*USR-X!Mo=Gs6
zHXzb1TJY%Ll?nYXr(h>?0G_J7@EuQ&+PqGwRH0PffiCca-39l*h5kAZiA6<TA7Zve
zo7qwvKMEObT(}t&^m92P-X7JYof)y-i2z*RuV*JDSmYj^Z;>HIWUmCf@{UC*Y5GNs
z+{2SEG#n~nC)!?1P93$$Gc89Lk{Ak>=lCE+u#DiP5i53`b>aK2B;}^vdm&P`dvqde
z@4oZr&zJjsey7J=s&svOd&enb3FuhG4CipM!tzMD0;tO|o??)Mwx=AdFI`{jY_7Fw
zBV~SDT)RuL1112mlP}urvR12l(}p$IKo{R=0BM}oj#H|GG>&<S)l^mnw!%*xUz&P*
zCkVa^GP{!s<M6}<PmD$|2C@=jW6PqbMZCfRKqHm>8OpXA5?F(?-okmDzQ}|bKJHW7
z{-yKD4hhJ@st1fk>DF=ulzj+iaBC^{fr#Qh`UU9Y0+^k;eT_;KSsbk?2F?*M3ZNgw
zfNqs69fh*5f<X_ZIP6c;MI}6v)-_9Z^9LFI{WDG1``=KGW$Wi8CnxKzHd0*P-QC@$
zDFnsu(MIx3R^71aPSsZK)tiYY1=-|V$gaPDvirekvcVI=M0#)VOP~|N2)!8wF%d_g
z>o>Le8i5?;l^tq8vKvR{AT$U9urI?vcXMgfrm692+kKGzrT#ohgvjtIAtO;oM@OK*
zXNK)jj})^aejF~b*Zutz;jAfsGGR4JSI2ikBG>w;T-d^kBcglSw56eA#i{w(V7|Se
zDEfCEN_n4~;-u6JT~O5KW3{VCX#ye0Ii!@-<=4Xp<(%w+xWg#+WtA&cM?h2p-E@%)
zgYxs{yjp=uZqw$6;+6R1VCa>=tfDAZmvWiFxCW$gML=Te(7EJ`CQ)1_(ZstAYQ1hn
z(RPNMugg%hry`k~KO1Ky0zk00-uTaS2;%&pj|Dpg)&Rm?I^rdTg*9({dPTy<G?6=`
zPnk$U$KJsB8iN*xKWFkmW(UgtmY4r=&G@gY${&8?1Yz3*9xo2IxPNHqK+@UVKVmn2
zz<;<@bh%v{?=Y~+_|HiVJFVGgyylrfu<sWNy^i#{XnM#N`RMCFzPai9&E@eKpp6q_
zV=27`>OfB}>X?^WmyTCR&;u|IfQbycri%q&(m_r-i*X<cOPEnPV<ExfLPg=x#J^G(
zE@zdSa6tJokk+cT6Z<`~B5x6f$5mQ$&Vn5Wyyn{=T$96__$k@$*>`asIj7W8ikKj|
zIfcotLs@~n`sMCDsd`)%Wd<{!8K)mOcM-+>7-K97XMBtSRSK|+aul(-zNGf)r3AF1
zlo`mhi<JDwe^xwIEfyzAt#@Nvb513@1#!o&dwEH(71Wom-d+LJ1r@nyuZiXWJ(`G?
z5f&Cvw1`hgNI<-C{%L8nGCzzV37J*a%fZU7RC~5AqUyiq)qTxNBVm*vy%M_qKdz7d
z2X+6SP+|WIPs!G&LNV1<RY0V(JC0uN3bGedK!S4ydRPSQ+;=~AMVw^vahGjG!_hHZ
z&YIUXmbrWft4t(nWE?Z`oj}{^r?^>*mGCoybXF_29{5M?E}CKC;fpnm)|U%;zys#O
z&EVjywV5mR=>7Lr-~w+=6~>SJf-*JT&~X&a{q#j7%0Ufx^0s-53eJKQen!>D$A`~p
z_5X*U{MTtGfpgKeWlpkt#ns|@?n$y+TlrZ?qW`{OYeVpAf@8z2_Vrm@PDzPsjrZt}
zgGJk;h08(<Y~TD*sxU3Q_^`RTnS=$Q=eyYRGaGQD&(|_ds1d#XVQc#HA@>*cfBXV7
zUVj>>ma-U3mnLqRO@yyD2@8L#UBHO4ucanes`zH;H~8XQucb|li2>p{x7(!j({V+m
z64(3A@@6l~XFN=OyC0ZX?h9hCeqihqd}|ti3^Tkb5x$>z&hw~P9ABBT^t8Aedb2XC
zJyqLlS{F^xb)}MqG(CFL^=K1;fM{A50s|J5Y9M%A_jOKz0;=|~-9_g3m$V&xxOqzd
z=%^&v0Slu&3JBoWUb7PeEUM64f4RE4`e$I2SmA}}UafzZCiJH>P}>2^?f>*?KfO`+
z8lOg(09|;^Sys>QdjMbH28;!6?<VVQbJvi8;Vml^oS~`f|HNr)xNzkBfb+LOrRD;w
zhi5|f^z;D#%{uYju1kd%UAP>!e}|;UD`533E!Pqg9Mr^tm^*LObpy3DQr<r{7B)x)
z(KU#jZjYO(z^TRY{4MH{_zS3Jx;tQM?UjATX<?#fZ}{axzTb22<b*#~w$S_r!r~`j
z$N=9XyV-c{SE2VCUk7Xw-|iY78<PTMsSxXYx8cj5&b`Fg$>0Bz%}40B=x_fZ-Piz(
z43hCFWqK|Rl>n42&%7i(mUywbLXajhhHI_CAl1RElRPy^r_6tw6-eua=AH<PHQiWS
zr_e(@0}nX7EpUv38dWdeFX`+d>aKX_+Q~347O9NmX$a~AIS0-@Qd&=XJ*c4lgL$jq
zbkk}i$`Fvja$mx6uImnqPM}U?bSL~y0dqiJt%bEdICD$|=KI}hXkYj=&(!C23!2xH
z%HHRlPqR+ko}R~(K8U8UAl5r|*2yJ8gDzE1@LaU?X;5PKsyAvFGx`%dIvz>eB=V)_
zfuab6eI0!+jR(3y><xqypYL}(J2iX-qbM&As@MVax+3~dA@Wl<b%E=DB<U>B#nQke
zIAdhdMh*my-d}d?ueMjbGj*$>7l`A}C2|_;yEFuIFDkVIyLcMTCBrQj{Fj%P*=kwd
zRV3L4m-3LV#pA<20^5?--W<u?sf}%*S?*E%vF!wn3Kuq$g5|x4SPdt}+52J+t+a4C
zA`Ext%IPNHQz}xGjVw3)6=yEuFDWJMj3i#+3QMSc`=xAepP;~fXy8J!irs$DSF!C*
z(^Yo@t%IoA1AFdm)uNfTdh_XlF2luk<aL}npbZ`-%u!%G_D@X6+&g&?B<z-)T%bOA
zb_=NGioAogul0Sz50B6|d_Gmw78x1TlJh|UUJLkC9fz@)1N;U6xng8XbA2NgjyB8#
z@*vS@6VV86l0OTf^>_|^7Gp3!fpO_2lFPu)07Ewc2+b;@jx471xt50+IG*0#-a5cr
zfM>mWmqvK7lm(Jkdl`GI>}PXxbBDklZ8A@tv6BP8Y=VWj`nEN!N(=L;g=b49R1>iE
ztB;wB2qp``gzlT}6m$V?kkJFS4v#@c3pm2(MgeE;U)_xT0D3%+lYq+ycv7EQ^VT%U
z2WSw`$tiU5MWM=^5@Nl9QDTR2=xt^%qdY({8U?4*RP3PUaV|xzhXasHD!>9R=e_==
za}*%zBMK9fLaQ!|R8>yF*_9)@dkrbw-UE5(d1C+>kQ@{gL~U3FI8Fz@TEd!e{B=Ut
zzZ4@><g{n50$Od~91*&?)+QuWr)r)G%<mcyRw9!o;7$Wj=#?O~yMfRd2cks>;0{a#
zULA=YzaLDj9j+j<^<P&L_&}2<s-U{hYniV=7nXO`aBJ$$=o3rh=V0vs9o}PAH%4Ea
zig{gYZrXzC;&nZod~e+NT3Z~<zqN)%wXkpL_>5JN*fl6(CgJA5d2^$cc5!64FJ?9w
zHXk4B>8Im8@*CmSxX;iy0LL)!-_<X~nDkbtFf)ks3~w-?dC!(5cf)cN{45=iiECg>
zNAr!Tg|OP$(|kKZG???VDac-Vd9NEc3ORAH-hG)y<~~Gn&1V9wq<SeBo#5$1J3%vQ
zgnKSit}r1|&(Z5rCxK2{ZK9dk+S&r<Dxu~6i}z^7q0BH+VHC|^UAuK|?k)H5a9bjf
z<)95<)nuI2C$yE%u26}K;EW)h%qMj|vMUsvT8snc9Mpmby~a_EWU~l54w(Ryuo=+m
zjjVN?m0Aja`6YAdZqCsZ9QlD|ggk@6c+CZa05rP#+LZX{tvBDX+q{}zL8#uOcN&{d
zneNsQ{0x?ppj=uf-!&URm8OAXp56=kA=z>!{?qRlR#r;K4S5})V!O(@I<zRyP#|y3
zBw~T81fFKd^aI7|HaN$xs;cVZDy5Ih>H{bR4Mo*v4z&!+`%JUvO|Yt_#>~shOEj*%
zZnc?7s*#P6#h~4L(<O<lIo+8U6V~T!;=-*)AHCw=kx*@%b!T*5LuUK4`)jl6XnkYK
z^S+G+o;$!3dHE*nul{guE1%GNz{|&1&l}{w1a5dUm%YkM;I7@zG})7PgI)nKy2YCX
zqUyR;Rdsc{ZZr82qQ={<g$X3bvtu``BQ|p2kMgsk7zG#7L8kPl&!+G?VlKVu`sBAl
zQEN}<(X|RROB)*-kQ0oXx7yXsK<f>hB2Sz4VBsj~eE<pibAQ3_@3UrU?sPsacymm~
qZ@U0=W|xM>D;f5?*1zj)aNs35<Og>Nc>)mlJM*)JY3WJl+y4eU)WJId

literal 8637
zcmd^lXIN9|y7r=pg21Q<3K~XXP(ZMQbVL*aNbeYML;>j<LX!aMsDlV7Ri#Ul-a!Zi
z90w61y>}RdP)w*Hw7_{Xv-h{pch3HGuKi<Q*Upu!m1JeT>s`<Ll>5G)1RLmS@^A@p
zAqc{Q*3vLS5SB6KYxger2H!=DhTX1PI+_}=hsXLALLlt+cxYK-5rlg`^TpCJ9I%BT
zKO$(2%O*bYl;H;+CN##*Vy}Gv@NMra>QN!4ZjUY451)O}BPD8O?dilF^2p?~d!EnT
ztZs$!u&yGz+Asyt^JasR(s_c~+}hD0cRUIe%Jn#MRSl_D8G4mbA+Ha~?CJkLxxD$w
zb~W~8;U13&y8W{M*E6?I%aieXvc6OC!(xfpmOTjKDK+W`6Z8{h-HQaVu->@s_F;Ti
z9){54w--_4-0dM)_~NEQf`I-Uo7y<HC7;0y8^4*K=Jp{7N0{hM1lh}P5@eodmCk*-
z+F;S=M&t?oo{^dd*ovPb(Zhi|453RN$;6fP(=!gLn{SM=OsK2Q1vO@^%JldQ-8b)+
zmhNNj)gK(Fa8I%cWZ*&%pTCwFfB$*FVu)?2m92PWyRp7<`uL}3Ioa9Ts%szlOl)lf
zJ4(uxIPjFR-r0VKq;6a<CKE<+?JFFAP1+h9-OhmV%e!$rJUq{@r8+q}z7!kAwkki@
zI1Wz*ay+)wvHe{&HteXPnWESCFBm=XNI1}R`&_~R=X&`8#AE-BhnIX7lBJ}iumms4
z{eG7o%4qGVv_;{M-kWLY$RWPKwJYA=zU(u%wVijXoD}fxye>ECmUQXfyPW)ducUyD
zES=0wYEc6UYieprZ9I|WdHwoz>e$D}ZomJ!&u!v!XeE7pgia(=tC#h>*A{+`)yrf~
zkStTP(h-@6Yl@O^qs?|#R$dD`GUGKrLMiTd8JWy5k`N53zrh@-sy;-J5a&*;Y{tj+
zK3J?5BwTdvDJ8g(<As&|N;g{rw<8HdeSOnnm7WU2<0{jRiY&;9{F|))a4mB~!*Sjt
zm)fSvA9M04p!11D74KnkQXo~V^6ArE<RQTn>oO-(>S}_bcU45h{@BLb_j?&zWL580
zl}#5ne8deqJG&fQg4e)ZYNx)c^0;M!v+&qYIx*icHahgtz}iiUdte;Ff5ewu2_JV}
z(*q|$<%VkLBZJhc1d_;D*y?4?!9B>OdmLCU1ys0m^V^G6n^eNZyB%W9Z_aC$m6b)L
z4GdV-Pu#1&RW!udT%|~%vvYDtnBD^0+M&U54~v5b4=yowYJ-a`bZ0!RB38f!&=DIe
zGm(nv!oeD)-t!|ve)_k2R`(u0msUc!TruL8gTk*E%9Bg6WIf7Ajdgc-cjch!+8EpQ
zw-?aS(b2H@8ymf~`INy5Z@+bETq$3eH&`se+@RnQ3JjVUxb2JXcrag+`ASsNX_Sc1
z&dxT%<7<S3g-y-O#L)^KV?knB#l=g%^uT(jgVnlwd!>%>a>_cB#^jcj3OY&3%U{nQ
z482Bc*A%(i81d7_jDFzT2RpaVW$DONYRvWIby_+kq#K+}tKFs*Y&5$EV82d2Q9pL^
z;>Ags3jctL^P6Kn;FxW`9LuJjU=k~$zpsqID=iIh@h1C{?Dy~6eh)rCYrDcH5v`q-
zm4z;GXzl6BGPXW?P$c~MbFN{ZiBP|VkDS=k9tz;tDBo|dcmu;^M{_Uxj6LLBS(^x-
zTWk>N4V4>tO;AbD%YhNVE?iI42WkUr?OPKQye$u$(CgY-Uyg3JkJc4uUTM;8&QZjZ
z=EbV!Zgz$Rb?)}BV2_Jl-z~1D8Zy=zRC^TXt5Xf8LmscB>%>aF(RHc|Vo{)!cAD7O
z<a2Nf#CItqJ)p`b-1~f3NX{+(UHkid0#yNnZN7LERpfPHuJNssZ?TQfI7g_koE~G7
z-ii?kZ=iMPoSrEr$rvl4dcVFok0VJ}e8coS=2Lm^I#@0t6gvAZM5e>k9Me6!>c5$s
zQ*i+CJKu`){J@DItmbDl>kwo`oc+Vm#@j}T<g(3qh)}7%J8Xy_uij-g<Yv&Hx$FmK
zLi@*tzsucZjCK<Vx`oZPrJ9^!QSqF98PS+VJEUxrq=JvqPVegLb5!QyikjbK`o;C@
zp|q1HZ$cVusT?ZEst*pSzeU*|xc%1^5awn6#hV{*E31`wT*@^#`Qctie=Z8M(^?nP
zYrIB0O^%Pp_N02|d|Xu7pvJZqp;9#$l=KtH5~y(N@JFlyyw85ZFZz6JTx6_<<G97^
zvP`raZ=WliOvjk*^wCG_VxIlx<#jbWBjM~<zZa;FbbNG7Og51?;>XWl=%XY<ju#RV
z%GI1%yj5gD=*4mgAqX4$q5ss)Lyiym`Fg<`>T*LKj|EQK49%9`A;sK<#Pn7*kQk4>
zoVOndLP-@ObjqN)POMCK$Z6vHPtW*rGBU13m>3(Uf|EOs|Muj0{jGr_J57Teb|krh
z3s<kMqoZ>$SVIgArKsbVJqNyR5<iEF6y#Y~I8C>uM5IMana{@7Zl#OO5I3ov@B`Mk
zW6{m*<)`?GkWh^E<m+MWJ}oP)3N9@v=|`BDnb|9$TVoqbM^n|<s$B<Vhat07Z7q>Z
zZER%Fd3y2>5_@Nt?!;m;Alqp(6Kg6PCoj7!HwzWhG2@vWo^)$!lPD=E*=PGjN+?u2
zBJ7C#Ef~?<%q+v>($Lg~8k>KQrNiCzrAe#G`|0Iw!`bbrn$B&>m#vC!J-PPcP)c`>
zc}`W8g7*$&CQ}m=tq9(u3Td~`9qv128O!5hIej>ch7q@N-WGq&c;X+Az2CBUX-{2|
z%_e^zwoW8)t7w1lFB#Y4GI)e#9ig5^qy;cGvZ||ZbY@&Pc6D{7uAE1Znm1&wZ+99m
zi?C#3%#3#y{ce)3{H#cLryU+z)ApAyL#m#%Ik~6w1S*^)p9!^Z&d{&g;#7vksh%mn
z0N(#tKR!0c@-{ju>g4`l_Hw1uaeG`WPx>d4IX2cL>=VgFwcv6Q`{l_#@$7Z<LDe4V
z*N7rHs@a~++U6xmeVlt^(fF(Xs6$}Nl@@u_36^^;We@Nl*)PR4OD5=XmRG-+xZF9&
zz7AtGkA{}izSNhEi^hgEpph_~z(aBn3vx#7KXLPeMNoz7V1~x=l$p*<=k|A3^W3b<
zUEXBqC*`j%lS%S|U6L%wbP+4}0uv`dJ#~Uak)4;<4ZuwXot>4{zRlPy^B7mJzjc<<
zS0wu0loe^Sd$Ke<9Un>dZ75S4rJGt>W;HcwqdW33gbP3a#=cH0Se$5Z2J9iE;Q3zc
zF2I@%dhL!hTIhn~%Z*jagX4p3GXHP+CH4nfgtv?OBsOfTKhxIM{uV!RZ-0;-?t*)e
zW;iOZzr?|Lwkr$J_R;*`<uf5ANxsthLT3!&q@OwZ`Yum3`;9*l6hnIt-Fv2|%2?s;
zZMT$Q9m-iF7Fe9JSH2eh+qSw&v+XAxZS5B)X4V1k5_-uz3mde#GN*1yCOu##D8TvN
zGcV4!IMRu4bgzX7NOwd@7+c|Q@d;hfk}}I}`*ojasUOYB)ZCoxMp$Z+!c9h-YyX*C
zs;a6cMhflFgjKmi^%9Yd>`ZSa&I6@*&UjM&$Kr>0EYO5PRC)umS|_5vY##9t+utr%
zyM7&5kQsB@ZnX@r>+zQioqO|Lv7_NC8(JZPr+Z!;J|CqeR_f4-LL1cvFqqipZf8bT
zdAY3j#$hCA<SX`Q^{IaOOg_~u1AV2%U!0tsFFsgxsiE~)4^(0^r<&sg`+j`Jd(`OM
zo4qc+cVoM|0;^GdO9p){`R_aI>`s?L$#zs>&CkjzLUC}s5FH!){Z^4z(o*9Ct_>#j
ziI>N&0N}R9PJL~^o0%Yvu3G8PncNIWX4!=#%QXs*fnPWS;oy4qDKDR{r2mXhgxc{D
zK~CEHCb6MpT`c0(-1-VHcL-*lK#;m%IAOU}wbr<o1qqsnl&c%}_|RMsrZi&vl?6ct
znuNy+0%$oK3!nI~u@#Wl3-A<Yj0!)3)XU6=6v83fnXTp4-fxYOlbghtQ<m?%NAVQ|
zdp0E5agt}DE#*oX7|L~|+OprpOkb5zs-99k&x#-lDWBdxRX-MbRIyyqo&`zn1@kY&
zNL!m=FnNv_vX_>YhEx&6<NgwgML$mVdf@hQ(nY7P_tdwXNRZ93`F$Q3Oaf4)eoP61
zv^js{Sttl(IM;v!xCRJ78jp{!&P;ar`uf$T&LQc*pY9kN8~5sQAZo8j!ebj78`QUK
zFw9T$5{@$=wVeKAbq}epIp7;n_djFutlj7L?xV!Yxm|pyp~)*Oh{5-zT|PazmVV34
za@NW_1UM)o!Ugy+R<6#uC%3)sAsaR}0V^pXF|(8cmiF>xQ-XNw?&+D50SaRitnrTY
zj1@`lo5UYn8LGraYl#7fynvn3m9{0`fOwqC?}yW@;546wn}2#rwQotsOzBihLXMVL
z2c`t(PzrN^nQEsSx#qk3S^@GpQd`Y3LhyE{Qe0$nfFCX&KLw!orCePid2&QXLZW?T
zrW3a@lj%HAdKaStW~tBpH2O<-2M~22CIlw|p#P!OnsuqyLX8v>V#My8JDhGLA#GFr
z!Fm@g;A>dG&!NKBqw-O7DuN_OE(t`BL6W(+Bv1~pcUa_#lh@k9cpWF)ncNrQu~7!q
zdw}3wK>aYmi|znnAQ?*Q;q&&MBo74P&#7kFZVwMg1I9%TSf%$^yLSh%u<bsBASWc|
z!;dBS&1MbMJn*J2!R)%hXn!*>eYelYM;seX=EQd})E7D)D7hL${`B81=1;98I{`l$
z0E@+A{no~xnOa#5{ADqPE%{VH$xJ1oxVTtx3LMRx>1gGygBR?-9->hO<-CW1qZ9m3
zBFJ;h2-5oYqK-RpJ>$1=4{8;Da}~F408=Mav)Gi_hwkD%@|y5Hexh|BEAm57HA~`|
zpC3VRV`G7)7HAxA|C^@}cRN#uDz(M-!%DrC$7aNww#~=Q_27D4V5mH>0$Ft@3mB?S
zoSeIvLP8Jt17N)>XIYqn*@$r)Gk}y@J1fbFIrk<$@3Vy^jgYHjW)d6!#jAHp`u`RL
z?m@Mi!}Cs`UmOO%GZfq3$Y29oo|wnh(P*^ci@<+sv<aGZd`b&yREo59x#u9*dPRV;
z%cmt?zOtefxSE^rE=X9%;S^Y2=OfGVV*vZW#&D6L!DfJgozkDc^Ku7x7Qn@EtNji-
z5gYXRqz8olMCAun0q0=K76g`&O00u-YYLN(y#J)CKXf<CMZEA%$^z6IA0;<P^<%z?
zO09N*#|oxuACY|%7sZrqN$0;vbC_30LL5ZtiUX7wSR`HXi*%7Cg*W3O<~Jp!rSHHA
z|BG_`>Er*6tNnwu{R`jyk_%}@o$MN5E0a);O0~Yz)buSrG6FqkSQRZ8{3SG-8iU(+
z6Pf<9mHACX#Zmcch0{jh4!x=~&6M#CwT`dI&F-aGBM27@<m5W!-(#b8lf})+$>{hi
z19-~Dx#9+k?~pFwH$0n@RBc--lg;)ZYT*Yhf<#~a8Y6?r&dIOJ?xukp!PEl;WNy5N
zU_Pqo#a^ZjStHk=jeCXBbA7bTIaXZv^^c?Uive3cuESNPSDx`ki`9U-V^!suC3Y7O
zg3x(;-J967;x~0MPktkB62YzP{^1ixpZT?xpjd{}6jkL|`4-x(67R1n6?9KR5u1C&
zE%Q<g*uu$ZZH{sq)*-?rGAn+I|9tB*f3QZMZQWURM&&-PvZMhSzJ=t=k4taWbA=u{
ztDa+CV1~otsO1p%&XAxY(qK|qMMY-|X&IRE3B?r0zl}3WKNwQqXWQ@*t9OKr&Wl+r
z6B7sl@S0rkNKb6PA83si?lke^ml{v}bHZVntXbFGN=8vO<_dagy|yCXvJqsueNF!V
zmlOVlj`{CC6a<drly9QH>W6vG>RfInv3umd#a}YT_<dV}H_<_vrOMD7@5Zki1hCJ<
z%1WqjY3uK~LS%X(^R;i{iPmLUl4Ez3k*ZL#?{oI8%6qxVg1SMSGo=xYW2mXSOul&1
zAK-*$*pWZw4ZLl;V-y2-M2Y<SS&#X#c<wE%D03Z^$;rsSE4lcg7=;v?L=2@F+^?Y>
z8g5!vN)OX8d8=y`-a#>HCq?n_m7&;PY6P{D)@N$CE^dp=ovp%5Y0YnaYmEvsG-E&X
z<e_mJf*kr0GWWl7GaZARwl3H0%+&7%<!>^6E(~aeL0gql-T%r2o-`mRCw)@#Z9e{U
zej)LvswhD(C;L9k>oxWuskeF@4^R401Ur3r?RBc?FzwLH5>lZw`Q6thhg3k^$YyG`
z=#Ec(s=3bjmEwgo`}oeUhIVY$D6YXEN8QXszu9}Wwz5`vE~Ta1;_AmFb1qri%q*(v
z2_4uG?d|I0IFQoj)Bd4JZ_F93l4X+3z*Wiw7_FAHs&qa-D7BI>@2^RUkv7KR%AWEF
zi=#nBg$6)G+S8{`?RbM*<~lROjk2PIgx7C9T!qq=oh{kj)n&mH&B7Zd<BhLec}zR0
zY;#J$_>_P%79Xq1IaFY)8XX(UgjYb!niB$Ih->OU@IE`p`)qQv`bS};?Sbd|J`abZ
z3Z7bxw>v<C2p7HS`3t;uY1Vx_tH=tiBUh*+|6P0hKSntJ*HO0of&z16<4C4T33F9X
zd3pTRub@jBK&Wu?p-h17<!N4KQM~`{SEW{COUpORyMf)icE`9mPy%;<{@!1L+g>Nn
zRn6s>0QHy!vETIgt2O`%b|7~4Me3_$X^FkFEU~u?H!8iv!Gi=nd`Uxg_dF4_d7pCS
znN|2hR)NrK0B&;|)E+9*hp-2^Uj3T?yM!a$p~qiR;Rh`ceUxck#%~pH*&ynyQU)EA
z_j3X4Pq2AFxj1*$AbECn7Wfr2^EM|cTLV!lMrAqPdsM6C(qDYlt|6=&<30RS>vC?x
z<%cBuk{;3Ipyqga<I%Oxa;07rD?9)|A=Q9tn%31|(XrzoH9Jfvc#pJK;pW!D4%VUH
z$hjwq>nG-34H1|D<+vD?>9`Mm)+CWReL{6TxQRrf0iSUmEO+e@WtmQ4Qb52?x`H=6
z9>T<O8zijQKm4k3d|&7hc_VXMe3`8Kh>J3B5DFsLH!(BIIMb=W_)gCRzjPv-%zmcI
z58iwV4K?rul`yX|5sa*O-nnx}JF`<;JQ6TY87gynfeF73jqzWA1|ky=|9xXn_h^mK
z0-Dty=_s}FV|6Dbq4xnA{RU$`F!08_V0eQyMy1`6<0!#I64x=O&~vUuX<7Fubl!}y
zM`{939ckT58IE<=FmT*`=6Tbgdu<-{k1WGgx7Y6Bi<A|Nk^;Ba_b{|u58!JL+$)}g
zIVBL?iSF!Gq{XjoBY48H9h>JamTQJ@&O3xrYj*;eR2U?RyEtp$$+p`|f;>RN?LNO~
z2W3tS4a?08pTFD=*nCcEIyehcw*VPXNYN|Ti#n8*nG|3JnrH7p^Be86CyOUHDMOXU
zk>b~llu+g{D5r@3OY0G-g6el4A9K#7$J%r>hy>=sXCr6;RZ+?Z2eu^JV{HR0VVo+U
zCcRE^R{73d?+u}y1B|3&*n2cDBO_yC#Jiy+(2FvV?v9_cmOOpB7y1L~fs8rZyqM1W
zo5x>$Ia@J4y{YZw-A`%u$fc<C;H{|zc9dm13R|+yrDUzHuC5Ot)%MAghu|v1xt1kX
z!!nK1R;6EnBGI93Lcq4jpYXOuql3W(D@lt!O5T<c*3~{mq}%7H&{FXzviMrVSRIs!
zZ2SOf4q=Bey5qVWFylGEbLdPRbjBX!#SB(gt8R_+F@ItrIjN^iGXXtF7MgEEk2tt*
z9Dn7A8wC{w+IbX}^=}8{Zq^~O+UWyd`%We35uR`hR>8qCg0wWx;d8%3ck3v98x0Le
zXqMnM>GR&q{^c;CHvzxe4;LuyCx(^79Zy-Wwu_|t4-Svu<LFn2_(XRR<g#epNg*aP
zw<h{mVp}R*WapmlJ^TkWcCZyXPPD6)`bjF4Bv1vqi^F(5mItBg-n;U2Z{x5#ojObf
z<m0{^AS~<RAvfxeC1ACiI2;%oG%C=$=HZxTdyEpw{ownliUcfmb99F|SnZ3AJG=v}
zqAIXp!;?=xn;RMZ;YOg8dsU>Tud)ukVU*nM6hG1U<kR?&DmuTsyu5i8SIVO~DmPjj
zzI|wNkh)48tQJ|KEc<UFyM;274f%oet+Z=G)&WomkRlsEs$+MyX&$wANxshD{c>$^
z**XI<p0BSj)o+wO$mk4ckzd~~QmwHX=^0_%2q70(w?GMsBbU>^_oq4}`d$N0Z+Pxa
z7x;Bl<86f^Q&*flH0}-$dv;!Ta&l^7U|X`Xvu9rqM!5@HjZ~Z`xp|!%OmI(1nzm!V
zWW#>w>d&A{SFQaK5)GYH+YjE~`%*MfzG=quqfjuQPsl0TI^BLbPD>5`KVZoGFF<P9
zt@`J@J10NCH%wh=k}7ZnK6}4~*)^;JNdw9WmcHJZv@J%CXF4@A03iHgt))fZ=t>HO
zGM~}fGtk-Mc2Rs=HlzxCzN0BxinhHu@4fNF?WpUO5`U7)hFz39VHvNaSkn5CsupC(
zpXfVvc-WVmke4s4<WmT-yA4a^wYIIVvYrM7vS;?H;t5fHt{I4`ZYT{*T0MK!LK+s0
z0U4+lbTIyq`jy$70w-YTdDIDj_VoFJoC2a%LG89*oWWjUSzSSl=POAmDN~SjpzZlB
zjjU=#Vi+44z2j3^Q)6=4TSc#-Ofji7ouN4J)PCG!?e+hdYn_N4(w07;=*s(Nw@w#I
kB<6XlZ8H9!HJ)|^Q^w0=^1F|OA@D$7)zipVzxn9D0A4f3@c;k-

diff --git a/reference/WHOCC.html b/reference/WHOCC.html
index 065d909a..51a147ec 100644
--- a/reference/WHOCC.html
+++ b/reference/WHOCC.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 2d5701b5..b8178536 100644
--- a/reference/WHONET.html
+++ b/reference/WHONET.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 9992f4d4..9015261a 100644
--- a/reference/ab_from_text.html
+++ b/reference/ab_from_text.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 582b4d43..189cd690 100644
--- a/reference/ab_property.html
+++ b/reference/ab_property.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 6c9327c7..27aaf621 100644
--- a/reference/add_custom_antimicrobials.html
+++ b/reference/add_custom_antimicrobials.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 09b4ae23..02ae9dc5 100644
--- a/reference/add_custom_microorganisms.html
+++ b/reference/add_custom_microorganisms.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 b2dbdfd3..05bd6384 100644
--- a/reference/age.html
+++ b/reference/age.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -205,16 +205,16 @@
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">df</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    birth_date age age_exact age_at_y2k</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1  1968-02-06  55  55.09315         31</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2  1951-04-09  71  71.92329         48</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3  1972-01-24  51  51.12877         27</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4  1991-09-11  31  31.49863          8</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5  1984-05-14  38  38.82740         15</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6  1975-01-04  48  48.18356         24</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7  1983-11-09  39  39.33699         16</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8  1972-11-16  50  50.31781         27</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9  1958-08-26  64  64.54247         41</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1975-05-07  47  47.84658         24</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1  1967-01-30  56  56.11233         32</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2  1995-12-14  27  27.24110          4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3  1956-04-27  66  66.87397         43</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4  1996-08-19  26  26.56164          3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5  1980-07-28  42  42.62192         19</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6  1973-05-23  49  49.80274         26</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7  1988-08-12  34  34.58082         11</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8  1959-02-23  64  64.04658         40</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9  1974-04-16  48  48.90411         25</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1956-12-27  66  66.20548         43</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 f8827b48..bccd7b60 100644
--- a/reference/age_groups.html
+++ b/reference/age_groups.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 1124b180..1b83bb0f 100644
--- a/reference/antibiogram.html
+++ b/reference/antibiogram.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 7b91139c..78ca1753 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="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -611,7 +611,7 @@ 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; 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> I     </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> S     </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> R     </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>
diff --git a/reference/antibiotics.html b/reference/antibiotics.html
index f973a6e2..9d3fa407 100644
--- a/reference/antibiotics.html
+++ b/reference/antibiotics.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 83c1c710..5e8075eb 100644
--- a/reference/as.ab.html
+++ b/reference/as.ab.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 7ee25081..0e3c522f 100644
--- a/reference/as.av.html
+++ b/reference/as.av.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 db31d454..e80e16f9 100644
--- a/reference/as.disk.html
+++ b/reference/as.disk.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 d3b8bd68..09bc263d 100644
--- a/reference/as.mic.html
+++ b/reference/as.mic.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 4d5b2e00..5201d535 100644
--- a/reference/as.mo.html
+++ b/reference/as.mo.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 f4a51af4..d9311e22 100644
--- a/reference/as.sir.html
+++ b/reference/as.sir.html
@@ -12,7 +12,7 @@ All breakpoints used for interpretation are publicly available in the clinical_b
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -534,16 +534,16 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 50 × 17</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    datetime            index ab_input ab_guid…¹ mo_in…² mo_guideline     guide…³</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dttm&gt;</span>              <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;ab&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span>             <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2023-03-12 <span style="color: #949494;">12:07:26</span>     1 TOB      TOB       Escher… B_[ORD]_ENTRBCTR EUCAST…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2023-03-12 <span style="color: #949494;">12:07:26</span>     1 GEN      GEN       Escher… B_[ORD]_ENTRBCTR EUCAST…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2023-03-12 <span style="color: #949494;">12:07:26</span>     1 CIP      CIP       Escher… B_[ORD]_ENTRBCTR EUCAST…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2023-03-12 <span style="color: #949494;">12:07:25</span>     1 AMP      AMP       Escher… B_[ORD]_ENTRBCTR EUCAST…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2023-03-12 <span style="color: #949494;">12:07:20</span>     1 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2023-03-12 <span style="color: #949494;">12:07:20</span>     2 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2023-03-12 <span style="color: #949494;">12:07:20</span>     3 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2023-03-12 <span style="color: #949494;">12:07:20</span>     4 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2023-03-12 <span style="color: #949494;">12:07:20</span>     5 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2023-03-12 <span style="color: #949494;">12:07:20</span>     6 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2023-03-12 <span style="color: #949494;">13:11:56</span>     1 TOB      TOB       Escher… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2023-03-12 <span style="color: #949494;">13:11:55</span>     1 GEN      GEN       Escher… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2023-03-12 <span style="color: #949494;">13:11:55</span>     1 CIP      CIP       Escher… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2023-03-12 <span style="color: #949494;">13:11:55</span>     1 AMP      AMP       Escher… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2023-03-12 <span style="color: #949494;">13:11:50</span>     1 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2023-03-12 <span style="color: #949494;">13:11:50</span>     2 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2023-03-12 <span style="color: #949494;">13:11:50</span>     3 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2023-03-12 <span style="color: #949494;">13:11:50</span>     4 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2023-03-12 <span style="color: #949494;">13:11:50</span>     5 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2023-03-12 <span style="color: #949494;">13:11:50</span>     6 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST…</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># … with 40 more rows, 10 more variables: ref_table &lt;chr&gt;, method &lt;chr&gt;,</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   input &lt;dbl&gt;, outcome &lt;sir&gt;, breakpoint_S_R &lt;chr&gt;, ab_considered &lt;lgl&gt;,</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   mo_considered &lt;lgl&gt;, breakpoint_S &lt;lgl&gt;, breakpoint_R &lt;lgl&gt;,</span></span>
diff --git a/reference/atc_online.html b/reference/atc_online.html
index 50378747..7571f62f 100644
--- a/reference/atc_online.html
+++ b/reference/atc_online.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 12fde243..e20dd928 100644
--- a/reference/av_from_text.html
+++ b/reference/av_from_text.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 f00e3e0e..010be073 100644
--- a/reference/av_property.html
+++ b/reference/av_property.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 eeb8a428..51c3df4f 100644
--- a/reference/availability.html
+++ b/reference/availability.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 b5ddab75..4f88ca8f 100644
--- a/reference/bug_drug_combinations.html
+++ b/reference/bug_drug_combinations.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 d5309276..236e5f69 100644
--- a/reference/clinical_breakpoints.html
+++ b/reference/clinical_breakpoints.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 a2b2c2eb..9f84c33a 100644
--- a/reference/count.html
+++ b/reference/count.html
@@ -12,7 +12,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 cca41709..6c2f9f3e 100644
--- a/reference/custom_eucast_rules.html
+++ b/reference/custom_eucast_rules.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 712872e5..565641b6 100644
--- a/reference/dosage.html
+++ b/reference/dosage.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 59709032..7fa99eb9 100644
--- a/reference/eucast_rules.html
+++ b/reference/eucast_rules.html
@@ -12,7 +12,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 bbf54977..c437eb03 100644
--- a/reference/example_isolates.html
+++ b/reference/example_isolates.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 a68866aa..69501a89 100644
--- a/reference/example_isolates_unclean.html
+++ b/reference/example_isolates_unclean.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 cd9df859..211be3a3 100644
--- a/reference/first_isolate.html
+++ b/reference/first_isolate.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 a7486019..1172b237 100644
--- a/reference/g.test.html
+++ b/reference/g.test.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 2cc4ddf8..72d45e97 100644
--- a/reference/get_episode.html
+++ b/reference/get_episode.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -246,30 +246,30 @@
 <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">$</span><span class="va">date</span>, episode_days <span class="op">=</span> <span class="fl">60</span><span class="op">)</span> <span class="co"># indices</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [1] 46  5 14 31  1 26 46 34 23 26 16 39 10 16 37 45 43  4 39  7 26 23  6 48 42</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [26] 16 48 41 46 41 11  3 16 11  1 14 21 48 27 16 14 24 10 10 12 13 14 46 19 17</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [51] 13  2  8 33 23 34 17 25 40  8 42  3 32 36  1 41 49 50 35 20 17 11 42 38 49</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [76]  9 26 28  6 22 15  3 41 44 31  3 43 18 50 17 20  1 47 31 43  4 29 27 22 30</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [1]  8 47  8 11 43 23  7 20 28 46 43 18 13  4 36  8 24 49  8 41 21  9  2 49 37</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [26] 20 23  9 45  1  8  9 41  2 29  6 24 30 25 12 17  9 26  1  6 27 22 34 19 42</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [51] 38 24 48 43 33  3 14 19  8 24  5 15  3 44 46 39 42 35 23 11 48  7 26 25 31</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [76] 21 46 43 40 10  1 31 32  3 45 35  7 14 17 16 16 14 19 40 10 24 36 18  7  3</span>
 <span class="r-in"><span><span class="fu">is_new_episode</span><span class="op">(</span><span class="va">df</span><span class="op">$</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 FALSE  TRUE  TRUE FALSE  TRUE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [13]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [25]  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [37]  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [49]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [61] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [73] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [85] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [97]  TRUE FALSE FALSE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [1]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [13]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [25]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE 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 FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [61]  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE 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 FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [85] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [97] FALSE FALSE FALSE FALSE</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># filter on results from the third 60-day episode only, using base R</span></span></span>
 <span class="r-in"><span><span class="va">df</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/which.html" class="external-link">which</a></span><span class="op">(</span><span class="fu">get_episode</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">date</span>, <span class="fl">60</span><span class="op">)</span> <span class="op">==</span> <span class="fl">3</span><span class="op">)</span>, <span class="op">]</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 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> 2002-08-19 A49852     70 M      Clinical  B_ESCHR_COLI R     NA    NA    NA   </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> 2002-08-14 785317     51 F      ICU       B_ESCHR_COLI R     NA    NA    NA   </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> 2002-09-08 B8CB09     60 F      Outpatie… B_STPHY_CONS S     NA    S     NA   </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> 2002-07-30 218912     76 F      ICU       B_ESCHR_COLI R     NA    NA    NA   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   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> 2002-11-16 762305     87 F      Clinical B_BCTRD_FRGL R     NA    NA    R    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> 2002-11-16 762305     87 F      Clinical B_PROTS_MRBL R     NA    NA    NA   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> 2002-11-04 304347     62 M      Clinical B_STRPT_PNMN S     NA    NA    S    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> 2002-09-23 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;"># … with 36 more variables: AMC &lt;sir&gt;, AMP &lt;sir&gt;, TZP &lt;sir&gt;, CZO &lt;sir&gt;,</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   FEP &lt;sir&gt;, CXM &lt;sir&gt;, FOX &lt;sir&gt;, CTX &lt;sir&gt;, CAZ &lt;sir&gt;, CRO &lt;sir&gt;,</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   GEN &lt;sir&gt;, TOB &lt;sir&gt;, AMK &lt;sir&gt;, KAN &lt;sir&gt;, TMP &lt;sir&gt;, SXT &lt;sir&gt;,</span></span>
@@ -304,19 +304,19 @@
 <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">condition</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 × 4</span></span>
-<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> <span style="color: #949494;"># Groups:   patient, condition [97]</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> 008218  2009-05-05 C         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 036063  2010-01-28 A         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 047634  2004-06-28 B         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 05C73F  2006-01-12 C         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 067927  2002-02-05 A         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 071099  2005-01-11 A         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 105248  2005-06-16 C         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 141061  2014-10-22 A         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 1435C8  2004-03-03 B         TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 195736  2008-08-29 A         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 006827  2009-07-24 B         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 010257  2004-04-03 A         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 023456  2002-02-05 C         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 067927  2002-01-13 A         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 0E2483  2007-06-21 B         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 1435C8  2004-03-03 B         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 15D386  2004-08-01 A         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 15D386  2004-09-06 A         FALSE      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 167474  2008-02-15 B         TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 195736  2008-08-28 A         TRUE       </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># … with 90 more rows</span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://dplyr.tidyverse.org" class="external-link">"dplyr"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
@@ -331,18 +331,18 @@
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 100 × 5</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 2009-05-05 008218          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> Clinical 2010-01-28 036063          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> Clinical 2004-06-28 047634          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> Clinical 2006-01-12 05C73F          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> ICU      2002-02-05 067927          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> Clinical 2005-01-11 071099          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> Clinical 2005-06-16 105248          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> Clinical 2014-10-22 141061          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> Clinical 2004-03-03 1435C8          1 TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> Clinical 2008-08-29 195736          1 TRUE       </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> Clinical   2004-04-03 010257          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        2002-01-13 067927          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> ICU        2007-06-21 0E2483          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> Clinical   2004-03-03 1435C8          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> ICU        2004-08-01 15D386          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> ICU        2004-09-06 15D386          1 FALSE      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> Clinical   2008-02-15 167474          1 TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> Outpatient 2008-08-28 195736          1 TRUE       </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># … with 90 more rows</span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://dplyr.tidyverse.org" class="external-link">"dplyr"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
@@ -358,9 +358,9 @@
 <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           57             14            38            45</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ICU                33             10            23            26</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> Outpatient          4              3             4             4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Clinical           56             12            36            42</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> ICU                31             11            25            29</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> Outpatient          7              4             5             6</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>
@@ -389,19 +389,19 @@
 <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 [98]</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    patient mo            ward       flag_episode</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span>       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 527928  B_ESCHR_COLI  Clinical   TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> F35553  B_ENTRBC_CLOC ICU        TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 671180  B_ESCHR_COLI  Clinical   TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> B61944  B_STPHY_CONS  ICU        TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 614772  B_STPHY_EPDR  Clinical   TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> B13757  B_STPHY_CONS  Outpatient TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 758698  B_STPHY_HMNS  ICU        TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 960787  B_BCTRD_FRGL  Clinical   TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 551943  B_STPHY_AURS  Clinical   TRUE        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> B13757  B_STPHY_EPDR  Outpatient 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> 1435C8  B_ESCHR_COLI Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 7D1606  B_VLLNL_PRVL ICU      TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> C58921  B_BCTRD_FRGL Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 628820  B_ESCHR_COLI ICU      TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 3B8892  B_ESCHR_COLI Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> D22254  B_ESCHR_COLI Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 549150  B_STPHY_CONS Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 43F266  B_STPHY_AURS ICU      TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 3F562C  B_STPHY_CONS Clinical TRUE        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 658640  B_STPHY_AURS Clinical TRUE        </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># … with 90 more rows</span></span>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html
index 5e3e6b75..4b3faf27 100644
--- a/reference/ggplot_pca.html
+++ b/reference/ggplot_pca.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 d7fa2452..21be1693 100644
--- a/reference/ggplot_sir.html
+++ b/reference/ggplot_sir.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 771d614e..532b4af0 100644
--- a/reference/guess_ab_col.html
+++ b/reference/guess_ab_col.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 bab056fc..d5f84fab 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 bbc9c2a7..44b95275 100644
--- a/reference/intrinsic_resistant.html
+++ b/reference/intrinsic_resistant.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 2f5d1f05..4272c646 100644
--- a/reference/italicise_taxonomy.html
+++ b/reference/italicise_taxonomy.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 d2d7d582..795c01fc 100644
--- a/reference/join.html
+++ b/reference/join.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 a30765c6..e61eb3d7 100644
--- a/reference/key_antimicrobials.html
+++ b/reference/key_antimicrobials.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 3f2f811d..8c6691f2 100644
--- a/reference/kurtosis.html
+++ b/reference/kurtosis.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -182,9 +182,9 @@
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu">kurtosis</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="fl">10000</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 2.980382</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 3.001502</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] 0.03954956</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -0.00633944</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 ad7be2ec..d74b15df 100644
--- a/reference/like.html
+++ b/reference/like.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 88c4780b..fd79364a 100644
--- a/reference/mdro.html
+++ b/reference/mdro.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 17d2eb42..2046c6ed 100644
--- a/reference/mean_amr_distance.html
+++ b/reference/mean_amr_distance.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -201,31 +201,30 @@
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">sir</span> <span class="op">&lt;-</span> <span class="fu"><a href="random.html">random_sir</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">sir</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'sir'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] R R S R S S R I R R</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] R R S I R S R R S R</span>
 <span class="r-in"><span><span class="fu">mean_amr_distance</span><span class="op">(</span><span class="va">sir</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1]  0.7745967  0.7745967 -1.1618950  0.7745967 -1.1618950 -1.1618950</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7]  0.7745967 -1.1618950  0.7745967  0.7745967</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1]  0.7745967  0.7745967 -1.1618950 -1.1618950  0.7745967 -1.1618950</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7]  0.7745967  0.7745967 -1.1618950  0.7745967</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   0.005   16      32      0.01    &lt;=0.001 0.01    1       0.5    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [10] 4      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 0.002  0.002  256    0.0625 0.01   0.025  0.002  2      4      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] -0.1172241 -1.0092500  1.2273872  1.4194745 -0.8171627 -1.4552630</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -0.8171627  0.4590379  0.2669505  0.8432125</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -0.867240328 -0.867240328  2.066590798 -0.008525384 -0.465717781</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [6] -0.237121583 -0.867240328  0.856106358  1.029032707 -0.638644130</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] -0.1172241 -1.0092500  1.2273872  1.4194745 -0.8171627 -1.4552630</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -0.8171627  0.4590379  0.2669505  0.8432125</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -0.867240328 -0.867240328  2.066590798 -0.008525384 -0.465717781</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [6] -0.237121583 -0.867240328  0.856106358  1.029032707 -0.638644130</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] 38 45  6 20 43 49 29 31 39 33</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 42 21 42 39 44 36 46 41  9 48</span>
 <span class="r-in"><span><span class="fu">mean_amr_distance</span><span class="op">(</span><span class="va">disk</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1]  0.36674754  0.91296727 -2.13025696 -1.03781749  0.75690449  1.22509283</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] -0.33553498 -0.17947220  0.44477893 -0.02340942</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1]  0.42276578 -1.28455756  0.42276578  0.17886245  0.58536800 -0.06504089</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7]  0.74797023  0.34146467 -2.26017090  0.91057245</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>
@@ -235,22 +234,22 @@
 <span class="r-in"><span>  tobr <span class="op">=</span> <span class="fu"><a href="random.html">random_mic</a></span><span class="op">(</span><span class="fl">10</span>, ab <span class="op">=</span> <span class="st">"tobr"</span>, mo <span class="op">=</span> <span class="st">"Escherichia coli"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">y</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    id amox cipr gent tobr</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1   A    S   24    8  0.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   B    R   21    8   16</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3   C    I   28    1    4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4   D    S   24    4    1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5   E    S   17   16    8</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6   F    S   17    2    4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7   G    I   23   16  0.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8   H    R   19   16 0.25</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9   I    R   20  0.5    8</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10  J    I   27   16    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    S   27     4  &lt;=1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2   B    R   20     1  &lt;=1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3   C    R   31 &lt;=0.5    2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4   D    S   23     4    2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5   E    R   31     2  &lt;=1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6   F    R   24 &lt;=0.5    2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7   G    R   28     1    4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8   H    R   22     4    4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9   I    S   23     8  &lt;=1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10  J    S   26     4    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] -0.216474562  0.698891501 -0.010082051 -0.234063901 -0.060228857</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [6] -0.586746706 -0.145237897 -0.005286713 -0.028443276  0.587672462</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] -0.2706405 -0.6068529  0.2354050 -0.2518529  0.3034383 -0.2313769</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7]  0.4976524  0.4511070 -0.3605963  0.2337166</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>
@@ -266,17 +265,17 @@
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>   and "tobr"</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    id amox cipr gent tobr amr_distance  check_id_C</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1   C    I   28    1    4 -0.010082051 0.000000000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   H    R   19   16 0.25 -0.005286713 0.004795338</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3   I    R   20  0.5    8 -0.028443276 0.018361225</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4   E    S   17   16    8 -0.060228857 0.050146806</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5   G    I   23   16  0.5 -0.145237897 0.135155846</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6   A    S   24    8  0.5 -0.216474562 0.206392511</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7   D    S   24    4    1 -0.234063901 0.223981850</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8   F    S   17    2    4 -0.586746706 0.576664654</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9   J    I   27   16    8  0.587672462 0.597754514</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10  B    R   21    8   16  0.698891501 0.708973552</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    id amox cipr  gent tobr amr_distance check_id_C</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1   C    R   31 &lt;=0.5    2    0.2354050 0.00000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2   J    S   26     4    4    0.2337166 0.00168838</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3   E    R   31     2  &lt;=1    0.3034383 0.06803327</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4   H    R   22     4    4    0.4511070 0.21570200</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5   G    R   28     1    4    0.4976524 0.26224741</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6   F    R   24 &lt;=0.5    2   -0.2313769 0.46678194</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7   D    S   23     4    2   -0.2518529 0.48725790</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8   A    S   27     4  &lt;=1   -0.2706405 0.50604549</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9   I    S   23     8  &lt;=1   -0.3605963 0.59600133</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10  B    R   20     1  &lt;=1   -0.6068529 0.84225790</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 f9a6d58c..5235edaf 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 2ca64138..305de82f 100644
--- a/reference/microorganisms.html
+++ b/reference/microorganisms.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 2b9c950d..07575baa 100644
--- a/reference/mo_matching_score.html
+++ b/reference/mo_matching_score.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 07e73f19..3f397322 100644
--- a/reference/mo_property.html
+++ b/reference/mo_property.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 4d8295e5..d69671ab 100644
--- a/reference/mo_source.html
+++ b/reference/mo_source.html
@@ -12,7 +12,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 1d81095b..2d5bb810 100644
--- a/reference/pca.html
+++ b/reference/pca.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 755a0101c951fe8ad50e153caece684327de81af..8786c393fd3a7114981afc184e7788a4699d2136 100644
GIT binary patch
literal 25830
zcmeFZcT|&UyElq`d>v%eR}oMc#{fD?QKUB=95EuGNbiaiDT#m~^k4%W1T=JMDk_=)
zp-2KrASenGstBQlB$PlT0Rn*#AtX6Z;yZiKKI=PcpFh5{zJ2!E&sv&?`?<??U*&g|
z`$-;Nva^=|hw?w<<mBYR=g&IG$!&?2liPIdhpj*hHRt&P@UiupjrCdJ6S!8b2ngU~
z`_1!ik#ceeQ>6bkWK1pk%gOyL2R?hoF}iS;7DEh!dod(~U0r8)|NK+_q0TGUj#U5j
z{A}~NGq02Oe%y|5W?%aKa=AO&=}33>nK!PeI*04aE}wqUy8GfcxzgW$y7c(;=8gB>
zKh{mTC=j%b2y%-$hJ`=Ei)wrDtkrn4fyqRHJA?04Qrk0Zs>xlVF`Q(y{m*~@A90{>
zrHzsu5{hy2zZ|c`(3JD7N%w|rkdwQ)*%WUQkR}S5^Ml2g$R&|Yf+<^MG*szt^p@Tv
zJ>0#pQF?PRD@jrM>HA{zy1bkmw#Tp>5B!-P3iR{6u;LQv<l{oa;5}gIVR4V68iwv7
zvf@{=X|-)7!%W!xCG&`=idq!aSr{-Wf$<F~=zvKzqeqxPEg@=~GsC6mN|CE5j6cEN
zLSgMITzbZiLt!c)4vnO>J~qjPYxF1<a>e|`8hbb@xMfMy%O<kZD92Ft2V3>AR7jj1
zZlYD*QXqM;hGHK>XQDb#q;hW$(PbEU=U^Re#mO)%x`I(U&S|o43E|~X$P|jaC4w9L
zt%-<@wc+32+fT^EF4Z+$;no3LYF6caOMW)dybrq#x}Y5B#HD?#SHq}cA7aLPP{#&`
zy!A{IC(3(Xa1yH5o?5jM-S}7-dtJn?x9xX>orAqMne2w@Kz}#{AAun3B+*4{1CfMe
zOO4QF;leg~#Iqr7T<{!*)t>zG&BJ2Roh8ZJL#GP`w6HmS#Ix8~*j@R7W%d?7V&WM|
z!j7ioW9YKV$ckJoBd)DVIgH>_V7lnN10Zttge7gjsyOZ5PHPuZ<o-$0>$ELkmX?u(
zPg#_&y<iy8DC}+}xV8M)omRM%Y!D^6UyAZ!Oe`&|vNx#Ix8_9&Bk{vy3(pcLh#}vD
zDG--Ry_NCUzB`;I-%YIfkRKujO(d3;y^@<7>6<7iDotga;S0@#uTSlLSniUL_c1I?
zB;Af^fD<<E1E?9gJKsCLWhdwUPHX+n8ne+5VueXm5yt=W{?%y%Lz|@%WeaR#uSQ6G
zrwM;9B+o0lzUPw*CbH<n+zRU!dINXK(5-D9OWVM0QS=l%O}><nAE#v`INgHWlwvZP
zBsk44ZdI6on~c#O4o*jr2Biua*AK-;JziiaTl|rD(dt_@>CiKR3xAWB<nW4u_errp
zdq3hB6;r-ccl;lRaW!F6hx^q$%bGNg8w+|+eaZo*YEHC<t6Mql36@?(Ro!*1HPn*6
zip@mEtvsK$sBOeHXCL>Qb19g}`PzMjg4`WzYClF>4(pb#Hp%NnC^XGypddq4ovl9F
z`-P|ZbDVztfM#Jotg<dU39ji~Koxx#(q*;zj!h{U2AkzM4Nm+?ib(MQ3EWcN^80)8
zG35_Pk^Fmd7>eQkMRFQerp_-{JXdT;pkJ2f5G-Pf#M_i1y8JvhNhu03@YBR`HX?OP
z9WBUUz@0@z)dikbrA^cYm(($1_V+;&?LwB`kAt;#+ABaTy=YCGjkrk^!C<`{(KM$J
zjZO?Z6V@@X&jSC+hk97CaOqF>p|JRvCxcr1B_Ckj2ld88!vN{SN$@g&^wocnp88vs
z;L$0u!two*)BG3vwyBV6m_#3sh(`ab%=_}UY0t$Fvhg<5wu<oX$=qec*qxD_uIt{|
z<e3D;!rCVpoQ$i6mSuITV>gG9n@b=+MCBSkUwZ{xdo-@CRT<eU-`EszMsmyp5tEr_
z5(f72+q&4XkzV3m*4nj|Xu3EuIxlbsr0lEUacYvA0?11>9iT*by2bjjqH)uqEO!o3
zr!E>hUZcyqx}$q_N}WXvdvA>MjOgA2z=wE?$fxm{<ctuvpr3Z*I~@xj)CY|N$uQ?}
zT7?%2e*CLsVvDHc#5LPCt@_s6>{)eIn$m)}-$Wl`Fh+X|=vfn>XVjp>YOsxRuCENW
zh9rgTTKVy|9cZ<%Z$|`SBST!Hwz_BoW`W9ppBM8Fwm|WKDb~b))1YO)%D!1in>x!3
z`d)04TNuex7N&<Re%URFBfIK%Ze9O;$4|kPH9CiFXWcrM9+sy>$jPV^rwvDqAl$1Y
zngc`dyKE1|WeH|7>k7rmCY=`kk#jt9?t$MNGbEBg5803uHf{N*Tr)&K<s2YgX?gTM
z(4bthI?hg&H{800QfJX3$kl0^Z=`-`QHGp<r<AK-UWCu_e?PHh$BIuav#>5v;<4gs
zhP#U8)y(y8VI}gB;W#P^Fo1dr(oYAPXWLM)-g`$Bc<LTvsR?q1A$TWVtJgPc6F^*@
zr+$|=bQDIngor+uwEYgj-R3;H+6idAPXUz=s7|Qf1|7L7Y9ldi%ju=`il(yI5G&WX
z3FU-+6?@#RZdMoD<Q(w85?kDJ5)wK#qV2VZNW+Z99CJl${c=A-sK@nTc@|bii|a~4
zv|y(vqPFRhC}T@o)9T|kd+*QC-s59;(@{;BLs_I}YiVq%^2YW8!{fOpCAIYkva6vh
z%bn0`*JOa>0LU~HAoWf-0^S7uNlneWXV9SY)aai@V}^diq3vDXoE<jlS_SgoUTqP=
zJGjxB<qIR$>_|M-EbzTpje{S2PuB9bj0P4|E?#mub$ncR;N*VOt8RaKcHA8-su>80
z#7T98vyl3$obHxf!_l8-nkZ2H^4kO@)~Wc=czR&YvgPvRREXP3_?kaQQ0Y%mYm_Yi
zfgv4q78OE1)o@#A@bW*pR;ld6A+sb~7|u;Nx`idS>$v+(tz5N^|HXNF2$s%>+2c32
z91>|N6THUQ7P;3<D2f}Fmk`yF{PV;^O0CKO&vW`oikWx_*`z=}tre(ayz*YYkW_Fy
zPFw8M@`zXw+VR`Xrcq$Q$&n(x!wQb1`mrH+mskYfk*@Pp<k2E2TW2FDB~4c=IDP>M
z3gXU#jm*NLh)ngi^tX1@kFMz0uyiRf$fT?LI{-~p%deB&k}Tb6wO4JsQ}-1{a;;Q-
zdME0lNUx{Vwp&)luHW%<sf#{1NMqY*?R=$3JCM4V@b;4>3HMgD{%&1ps|{cy&y(GV
zAzfeB&-2sO{phc+CeZ5E&+?DSTh_Y3MZ+Vh-AdHk$%AX|^VLk^HYm>oK(chH|C5n%
z6SR6g6*}_Hd7(Bmc}y?&fG~d_;zyyOds8b1QunptTd@IR^5q_Mi6t>&rQF9mVphQe
zvHI8#RdN7RDBn;XJEbhNFBeC@H@1hao>VS7ZoGn5Z&R&e4h@MR@5L$aR+MpVhR5To
z+z5+x4Z{E)`KoM>-jGC_PeEg#lZG1jjyoc*mmecF_t6qYB|w8-$*~xSPh~gAZ{Yqy
zYPhWoFIB`v=_Yi}4#gU#qfFk8moDWxS?YS<h->dah3q>(0Jns8mpS6*@r<bq$PbA?
zVDvnB;Z~W5wMEFq`${?nl(>;L>7vvvLa1xw&7$zO<>@WLjNl)hD<m%p(Ryf{3y$#t
z7R9b>DEe3EvQi+gC#ozj_AxIvsk^E%@?IY-a56+UK?mR!bI*6QBEBFa4G88TMVKk#
z7FJ^-**L>9qS52H5o-v*s~B=!aXpg=H@s(QIE!bA@{;A&Rg~}4t%?g8dq<rCZ@o<M
zc*FI`@eDak&9-T!YYSB%KVYA%iG4Rs9;E(sOJ{9O)s5ymAgE8C`iHg~3t$nqBEF<4
z>aGv>9BwJsGvx2yahp|2%dqy1#pAeA)Zn9=X>k3`wjHx8x)DBB-we*Vs*fEERW}-S
zY4j!Y2dK`w7Mw*X@&mY_t@F=|>)tO;<9u8rLT8iU2!5~afS^q)k>s{-Vg4fkkqjwB
zUVFGzZtOhV-|E@BuYTM?gUyMlkbl<gg1%|Ok0+nM67?+Blkel~^`|QQ+0};oPRHwR
z8hKaUw#aotG`wF-M13;+&9lP>RR(z>8OOi=Wa#195rT$>uJZPzUL3D$pvP&+RoQMP
z$~8Zkb&?P97EhXpDp5@p<6CVx{37@b{#W*U3JZ6M0l8(%x1!8Ye8R@p;iux`<9bKX
zp`CS<9%nd8d}|M@@%37J*xR54wYT&dyAq$^Ny9_L7bd0S3P(6G&is2(7M}5bEw+?O
z*D?mwDI+KvGmN6GkBokmt}p*RjORo<w6s-nPf)vqerSX+s`fD-%ByV`JHoEEIBZ8a
zh<@iU+In-kYOA^ZKaJ9HR|Dq5R4jfGEhhU;L6t3nMU~0<FzXj2=(iClz3TO}2#xTj
z)s7pJUL5L-fm+>M&PirXeR29{+-6~0GT$FM@)Jm$n#^r+F&H)C)FfxDNWd-D-z@8&
zo|GUg89%LP)eILr^YS)-Gae#NXBV>HB@gqsmELZmp2{8jTu$7cg2{7#XLyRx$=qHr
zMToXUC6<Ll&9GjWuA+F%_xuC!{r{^g-BIZ51w4rRaC=u9&vbS=dVYgky03MGG#3Fb
z?73!XbS$TP`Fr{zz5j1<`9eo<?2SP_uz1*SbtL@jwclXB!+h^clQ<xadyqg$K$ldA
zv4=i-y=yFu4dzoQmyf;KD}9)hZ|$UrGwb(ym+&q&G{9?<v~l)L0!0!OlO)V%Pu3!G
z`EhOmOR_X?HqVE>c4$tWas}0g-Xr9>H3$QQ_UPhK-8i^-J#hX!MLJwAZZr&^Xe5l3
zH@rf9REjDwxn4-ka6K%07V>Sla)*BjJkQTO5>j4b*;y@Xc5U;C@;Va7X*s|Ni1cfm
z(`}P2=zFk)gTEB@sG#)V0Z#j_R#~G)=%eN*Q`2{cx+)vv-mG#5_of(2^9(uN@Qw62
zL2|>&Op5V<Ico%J_HVPp;UZXM25rKq&5_lU#!o19+$J5-{9Bug&n(_|<qli5;J_q=
z;sY1);r9Eh&q<&9-dU-@Cz(&Yr<5G5;i{n(oe)k5<#-|<Wr8<p!283T`4tqeoyGzc
z)L^JjgLH^oXIwN4HrVxn)*fZcA1cy5AZ<*-gZU*Wow^2JK|4Dag&A5!N=1`IYu_!8
z4!_uf#|OHUxxgzSgH_(#y%cNI4z$3L)g^9>P|ucanUIr|n^q6!-($bo)oN0Wx8i?f
zPwZ;lWH9ec$uv0$`4Vl*Cr}8^p3+@4R|;x&Lg&Wnuo7&k&xT9<cdZC5OoXsByhIgv
zoL5}x;w6{4u!mBB5*>cT&tu<~x49%|%DIOUG9==nnCX0UrGJNZ)3*ul)pnPGCnxqO
z0mF8t0wjF%<rh9_HK{u^))@P8G07G`*f>w|Qxgt3<yQMc6F43UTZus5D%SSsGFGA&
zE{f;j?DhlDB3I?Ukv<b+1?w!I(}<BZq#Uec%Qh!Bqzpubz}0*f@SxHgjdo=smFs*s
zLo-Vzre8O3?Y{XA(*6n5767rF3b-p9cri<fXv#6C)TJ1=-thb9_qLKGxUjLz#BV^1
zVyC7U)3e=pvN`|UEpIyB!y3VPIPH?o()`4_nIP&e31LL~e=jguAK1WMiDP*REsmjO
za~Vzi5RGGOn;uc{{A)~`?Esb$h=LZ^VN|aYC$0C(48~~V57F>eZidl7-Qjc@L|x2N
zSC+0Kr*YvwV$T1fWCFMe>9t242;P7z@5uk18~=Zu;*CtvZaToCCfCo5rPm?^X}U5)
zoxQQq+hBs^%&_3)1wJy9YNgz)v9wKqQgIPl@8=X#4x$v&|K6KY8}?%lD)Hso^7%=r
zK%0xFG(w~U{~I#y|C9z0sEZUZrO8QArv9@Q8|g)`3;Z-nhI0m{=~|kF5Y2sS;R#f>
zLSZmIn|<5#iiT7Gx?8L<08TQGQuGFfBuut(?MgL81zo#yCeo4rd3famn}7<zy}BUn
z>wB_ujHYDyd1M?y!ML!xM@YT7DTI{R-}vvIe1`*#i-29)p^8lf-|kWrTr3?Mp;+z~
zN)Za77q{NZu9WZ4pT*33ie^UlTlQ6r$_%h7WjkQ0rG)vI#6TD>xmiGREOTPHXu4qX
zI7>Pz9~dEl)ZgVh^oTIXXrye!$|<R_FMA5Xl<de1OmH6A1J*jrP=nOJ9eG}v&x!k5
zFD18Gm8lUz<#sl#YzaAlU>(Yk#FAZ?SS1ZrgDd+vfuGa>FPe0ia`~XKq$koA=1ciq
zuF3+KnK0)mj0`zInt0si<9c!+o<R+5c+n+n7{)?Bp5W?ExB_#dqf(Q>#yEG_p~I_v
zY|b9u#@SfFfx(?%>*b9Y9ZAOI^XD_}2?Ol7uJK&#RTqQ!gHqFYlZ=@hk-5Slt)?2c
z`ndkfeZ?Q+=IREWi~1HMP}5aOv0UhFDGcJ)ShXuhDVhq#iIY~U7)oG86W4hCi@|Je
z0Cj({ljuDE`IA<F9YgxvW=oyLi;n`!@sC;}x{X=4#*!Wq<gT2Uvpi<k;sGF_$K*HT
z6f+Ar*)=0CL_*0qCR360AEX|aG(AFcE3y!LgHX7gBNEsOH_|;Wf#%WKZTVH9_1B-I
zjLg%J^4p64#7%gQ?M3;Wa-U-9Xc(NACd^8%gV^UCl#tm?wR3GK1xLfew8$8~rm2Iu
zbYNA8+g-TO%fp;F4nb|(#5e6eNO~O`&;JA{m7+_VGl_V;6MCb5M(U&Fs#5ab6ZGhp
zX|=H&Ab$(VEArqY*$gcuprQ(k_R)(-koIcc4D^bmd(g@NFMfkR!~UYbW?mY518}di
z?2l|3rNjjjnl~eh6@MP{y4?{-J*jqMb+^p>$mIk{=a9?$^}h>D|DQq|85{r2>wR|?
z3vbxuw54sg37ul|n0wW7L)3XE(KXly)DETh6bho!8^``FDU2_o7-H}bp2XLz;V5O!
zUO&t}6FS?Y(|rI_sz<HeXUn6tHyg!E9ZERG_;5xH;Yr&jU+!u*dly>!+F2KkvHOQ<
zSI{2i_y@a0SNUI@6gQZDeMo6>_WJlh+opMk_2vyaDvOO21ZE{ecR<0B;n)}=BWA^=
zC~m&N{OcJ7cbJyP$~>GzV2I}!mEmI39kUQ2x_ISX8SD#KvjU%^C2V8}8@21l?(mvE
z(Q8TU@QgT50f8ZM_Ug!K6wN`o_jTg0#>ltqvTE))=XqK5>AatL@Cx@fV3EEVgt;&%
zH9xSpfqfD?i$Yhr!?ri?G@X2;XR^wisdQaIh<hY%u<cb2WyP`Vmk7bP&OnP=_U@!7
z4P;r|@$aG3e~sWFxI9VliQ;xR{d?GL()y3<Y92*CUJjy5u)Zg)vcSjpc6Hz=ofvBe
zyTSj1EvDZ5Zg0vu@r@z;W%iCpp{iG7!`fLslb!XXQa0rD<QAe@c_J1c4zuHHMz-0#
zE%p*!;kQwS4|{(<km^x34Qj;4Ch+-y-!OZ1XvqBQE{aN^FtHpz{O6#Injt-Gq{f^k
zI?xICrW@sb5<aUz!RQ}nEU?*UDB`ek*-B#%YWLWvdwPgq{4h!yii^^IYAXKB?m^o-
zS-R08wgt=ZFppT|y+cq-*?}UQ+T6GVaTG<sz%Xxqb1=hhpKLK*b<=|js(moMC<Gi#
z+UAJ3+*pgB#Jb29(LH0jcY*%)YScVSd0;eQY_!_YN#Sie+76Q#I6pK4U}i<s>x)vm
zNtzkzP7-XTOrqgVT&Sy$U}(5n$I%`Q@hI|Yti;!>3E2-Rb7*+y+I$KBJ9oQ>HgKER
z-arkqb0FbH%kj~>bk77U*d+Tc#d_bPrRVI?hBk&#M3KxaGvPp(RXD2aAqZ3snCd3H
zSgT``oD`wgIu*L=NE214f5p9NdlwR4jBgEI>V~?ykK3crSl#K{En3p^W}v)c;M^Sd
zIa|IKR>I&1jzO|t$v|3e-~MA`A`V*-J&3Iv#-|dmef*vTrXTU!LR8p)LzXki8GL=?
zB$J_B^S~qhyFel5rJIn)-lAg0rJ^x;b%;`Y?8{ggwdv8oKp!XbxB+p#Z6Q3BtyH_h
zaD*zql|`0Ghv}|1r_6#YMF_)iddTz$E8S6s?ajry13%KY_0%zN50}K@m2>C92H}&*
zO5H+KhEs`mn6Y&7W{4ipiO^GLNqV@{WHe|I_R{@zs1M(kB2*bTDNbRV*RI5&QZSXL
zq`iG{O2@{`V=5EoG`dZ72b&hp*UcKVIbn*!dc3|s5&RU2#DjJtZ4!?0rrg{_sBVJF
z*0`6p_n(Q^DicP5Q2~{r{3pS~Ve>xbx3G;NV3H3?HQ~=D5=K1#IDz3NVg^xU2~DQH
zbF(m~VZ{sB)Irt=!q`EJ(6wIk4@C8|o-mYJN!&USaAwQEk>?&%V9!CxCwA7Zkj>KN
zG=czYghkG&`3!M7L_?m!y2F$js##E_==qj}CjUxNJ9`^ir*lqtjbBRP_RPi29{Fp@
zXA?r}zupA^mIpk)eUP8wCSfi`&~2VbmhKi_5P}FlQvwCTgnndoVr-=-Ps>HN?txwD
zsy7Plo$N8<$=O9T%BM210>AvY$*>}^eyyP)q34Z-fou~dEke9FZEB}UWM#rQvReCT
z2!nO=kCB%IM@GL9;-`A<EW^)we+Z#yv}$_sAzJ(T=D|~=&D<A!lZ}~CT=rD}<UzRx
zqpr&~XzhF1Yp7)5{sY^^Z^r}I+JV^C^lJbG?%0OGKi=i()c9^~0kva8k`<uI13xWT
zyfX<|`NrtM+Mq}kl@(&Qme|@Q`UuYreMKN<d&{D!!LNFf9X(Dqt@wb-gv^%m5WU3b
zYtv8K%)*$9*cMWlEBD*^#iXY7bBREi;&<Cg5v)|3v(~YvT0nfsK_~~a#c$VIWG;u{
zWLeGr<=m}vd`UU}2RWnuybW9@%Ah03EkQ6U_UOXat!E_v6z2BE7>42ClLXHx+=^b7
z7G<z&qzxO5{jwf9W^?W>#7}&srE>zv<zaR$$LVmuWknG#QyS-C7&}YRveRIVznbJ*
zTbzjF<5P>-X7qyghTWTycuTYJ<_A>BaLrPWB`H-K|Aq)XAykU)^i%DVhupj?9R`i*
z@K|f!W^pO;^>7@-5mq7`EhErjdV&eMm$W|6ZOSF`l(+ikD(j6^j2{lP4|RPyE)1Nr
zvZRYw*(Afw@3~5=YlL34C|Vb@*s1Zw+UTJK@6WIe+!+dW-y@iyVa1M~HP1zrDBUOz
z)Za|ZT>W#-EwG4!OQ7N^@Q<|Ogj7bN?%0-<2T;)fbZ**^w2JB&s_D=nt@}3ba7qy3
z5OHON5$)1O8=b~wpfT>ta9|0ofmKY9x=!L!x9R?cjm?#(25bj*^5@xP3b=M|pmuI-
zQWIzRfrc`4QF~fPdI7vhkesp{<2G0^wOpQD7pqW*Ib>w{r?s=x)O<6jl}T_qOC@Jd
z{q+HQ$WF(Mr*ZMlEs=}N<br5Q)!9!8%8*lnX7&_CYr4C^tp)$bHt0#-cvRF@SzV)Q
z$K2Uwo5s?$?uIr^=N?FXOeX>D6ZQiX(R64wA=GL}_$m3)`W_0dySouJ9^f`Oe|B63
z3-kIb%EaR<ACmKNn6GQ8OAJ43#nd+|RYd)`hQFG7>G#>&^4=3_#pU=Ieir)+0DqTT
z%IT)gTw1cM?3uKEq*959L(RL>usS98@txdIdt*X-t}^81$}GE^(uj_8hQEdAnkIs+
z-o#yno#DUDc9h}wY_Q)pqNFA^(aN=9Z``3V^RK7*O~cX`hvcem_-XepOj1g){F^e2
zPO{w}vXx6i`4q*iKV-Aeq_-ASU61c2#$AAEiN`#J>+IW<?89j?uimWO>i%N<dE^||
zu3m=4RdcQXh=y7YCUVdF$y{mGN7oHpYu3o%qf@dZQ_kiQFb%h11$;PdyNo-#0npcZ
z8e)0#<p~)ex?Tfd4T@qE)gcHbS6_yIZ}crs!m$~Sh)cAZSRloF;3xB3Hy@N*E3^D-
ztL+NSo8tKC>>4%()dRg*_;;tVfotg8R$eX9pjcVsW&4(kZ1sF0n>Xp;DZnkLq@UU;
zrA1EV6o7`Wp0vuI`QAO=D{K9$(46#8IpkroRgtrKW$Y#3Es(pz;>_u_(I%6qfkYpe
zZ$YqZn9(n32N#MR8tY^K+`4A*EVj2i0m5&867v1wi?oKbEnO-B6Wu9<T%8#SkNw3>
z*g$F8^{?j>0GH=O@@~iRW*0+H9xjG&E8TB)Qz(6bb5xhWw14w>fJ-J-?g-L(qk7q^
z=Jt}9>nT*RMtu(v?4^#myJ0006=D!g{}ee_`}Zda3!cI|R_O;_Bj}2(5rOJkSuH2Y
z{3^iqoso0Lutu$mhx=vJRs{mWN`zE4BKEKj8V-ub&ezEpGP@m+88knnB;wENklaEz
zt6`<!KP&;H&Eo||+_>oxjPl>-_m7+lbk^)L_da!3@Q!Ugz4pWRN}+*t@l1+N=ZK~Y
z6g}VYjN41WIdb!;f-H9JVaj(+(gcWcHm~Ch^a6pTfYOHEGrhJBhW*Y*RuYt`B`y;G
zk0vtmR_>kHL`S2yx%3oK;Y(I)g?+I15XurN#c}YF>-WgDST~^A1LnkU0SXd6<=P&l
zJ&MYAXmQS#My9e3Nwy}j1+Ahsc3B{GQJ%w7rBroS)<wy<a<0m21DALVl`+>0`@UXQ
zb?*0U(l_N_8`20aS6A*XUrE#Lrk?v=UG$ZT#hhdka4)>|hljG6RyNzn!VS5L{jPe^
zC7)YAOiZHVob1aXMpQuwddE4`-MR2vuyfM7+w|c_?^harhf$prPasoK9nM~xkHI_G
z22v-<#c2D$cF7$kIKep5$2-tHQb)A2V>`v%#p~mFi_2a__Af(9QcI&=mz2CIb~Asw
zHb}uZ<^nO)WLzSj24q(we@=#fgIrE{V-iKV|A?6>f0dstb-WEQ{_R$`-+~Okxx#{B
z6u`~yCm%!!oP;i(ZLZpoZ%-?|!Vk)Y(h5*<KsL84!AZkuUaBAkGXBy4wj-QqK=A%M
zziKo@C14?au|p8160n#Uf3JG~U>Mpj!*ssh&Ya)xG#KGZ7c>cAW6ie<^y3|x)iPEp
zsZMPN&RF*0Lh~*!%s35h(nV5Tgc+9NZxA?v3M6PP5##q22WCm8lw4;IdAXEvZ6|os
zQjsb9%}izR8sH$`^&rGcPH^DOCe89+$0|%NiCp>J$!hg&&N^OK&5y6t7+BiGJqdKO
zM1Bq%G~xGOi4!lCkk}`8c9g1#zFG)}dn)liMm;p8FQ%JwV|qMbN-0xMGUBiMeEXp2
z?6tKSTTi9U57d^3W}R}wT~&FHQ*QAo(<r$EM-xSZh{ip3Mpr5Rp~F3*(H?%QVN*E8
zzv`~Di1h%G7h(zdSIzq3XVfP8Dr__9KQ^c^3hQfrmFbuU5H4~>(<A#5WC%8N&uJ?W
z{>B@q)+jjt>ktHV(EN)L!)K0hnv_7XMPn*d&2W--_Uhm5$D)jbDI+KiI<g!~{@pUO
zZBBW5i-KXPY~tPlnP%#O{kq1?E3U5IPphxNmjjhT-<KR2<jxucqUEam72O8`V`T*1
z-2eYr08fKu)(M@2=afU9;6j(a$+~Br1dukFo%f{G{W@XOb!l4?xcaPYiRQCu`gaB4
z@_M0MAJNBAv7s}j+`{qT8@mcs?}RzelECh@@9-qmGK3?oa;>Dc4T@6rNw}WYL~^E~
zVlP{L7K$iPN5y{!%z@qVZBB}ghUN6(pGGV9&a2)6^Z67HAgac>X#RU90Z{dgo#iHs
zL!k|BcI($R>2GdfoN&;R5gqpdaAu^ZA*2N0NH-FQh@X6i{GmO7*HbeZY>L>!@-iP3
zKmB_<3p#+!ue+5|5~8N}_jw9fBdpK~;<m0jhSA^KQ&}S<58ACwtkL?|-qA^^g-RC(
zvK)Z6FYG<FD`bigWnH&Yv%}g&p@47fkLA{|1?T<(f1_BDwn~IQKZ(s%`HfcKC?Vjn
zCZx3#_Gy@ao#<uh+Vt9lfWP+AUACo_d(|81$^lGzU91yqEtGMYA_}CsYnQ89nEqQt
z@G$wVg=^8}gs>Joaj?9X^%RJZ1`RvE?wywbbLjg#y=Vo%u2w^Z=|HZMOle}*Qdj}9
zBAuMkkJMcY-KL*^`kDwSz{uoV#d_5EUD9%<*9id&#WIK5@|0+pH)naHvS$wM7dR*I
z{~n_x*(#ZinZFvJ4ptaG&DiFs^R%v~!rM6FyF{$toyZNUk7>C$x_VQ<Qy2Modzpk-
z;>I;XWxEUg3<#_#S*c#LN0sy%4})d0QCnD^Ugik{O$o4pzq>O55Sat#hr1~1Dn?vM
zcO5DG?;PS5hFHGE2i#?OnGIUR{rw4$cj_21hi<F062dDJxc?@3F==Jqv^yK!q#_A~
zMvmjJHX9h^?cU@5!%sgyFQC7_xAV~R$$$QELt!rW^R?Kq=l9iapZ-DTw;h|$zd6*L
zDYv~*`Ny5_w@~W`Ih?qJ1i|WLkXl!z%a<Vd%ibUcchClNup+&?us1#slmMG9)RLz}
z-fidY4Jr3yY#liPP1Gb$(|M-UP}!lKZrsxg3*I@2ccLQOHwVLJuee#%J;!!t;xvax
zJaWDtSLNIYevm9FKdy$KsBW|>hEKNhT$F>qYhLnaM+JJ1M$CEW>TNLHX(`4Ya~$yk
znBi&5z~X4|M)3nrbGO756SfSROe)`Q=59k0-{RKsAhioh*h5OAcLmen;wwk>Fx%;U
zeQE($&AUCP5If}iW%J*1ykd2#uz-1w&U+7T%4P;^YKMS%Lm&ovotu&AIdaKECQE~R
zuVWW9$f=p5hsj0A1DVVea5uOH@v4)BJWz0Q7Zs_a5fgcLF3-tD!#Jdq<w>epU;0t4
zXTNmtknhur3-e$wUF?Jm<cr}&d;RC@Cd58vKla^p0><dGG>y&t^O6T&hDOrP&do2U
z!0Wx2Y-QStFDGw?I>f%UFeP&`&9ul|4dd&-=XYeTd$s2^YU&fkZnsLaUJAIcLzncB
z2XdK+k<k9U;v%Nbm(ItkHs*K?^|Y6q+!9j^78{Tg!wg?;h(nqg@-=<7;%B<Rs*I?^
z$`MgWp}KL!Typ2*5cSpHkcX*|^Hvs5m=D7YH)=ee*~~?jsK;F8VxjHz!~%!bVOo&i
z-26pe9GITZECna3lkKnUevLW5x?svlzZenJ0_$#3RFq57Fh8=yKl}}P?vCO#ZI43}
zx96@Ea`eP*q7x~HnG<IC;tXl;cJFD&E8FR!%;U>B@bc){KR~3QAak#kG?3v%-WO00
zN0bnOOi^D=Or1kbHXJ2?)regve$4!q1u~SAJ7y-DvG2L7wmya}s1EQEjtoY8*sl3V
z`-jdnz>PN!HzmYTyIgBsNNbc;UM^`gN-tob0ytkLGSzi%5R^i&_4Rp^>86!)0nKOi
zv&R~bh^ntCChr{`tGvit2XVnOU2zwYT=f_)q#|gT;@x)+|K@pYWzZW{xuk}7+$Q04
z(x#n3(s?9eVTShR&g?lV4!%aLg8?z(eS<135A3a2iMF;jmbN*sUL*Fp#*#UB1hlS8
z?sACbX^_=4jn$_jWak=5?tqCDJGc$p#TBEG#9#e~lDQybGqQ_A>rukF)HxZ@nY;ir
zyX@DFZ3d34bKw`-o2S||hYOfsaQDRs#wnciD;IJ}#{G^P7w=|I#hn5=)fkeg$OGBT
zj-y~+4`_Hapj6gpc);<FfP`F+gsvX!O@&u<Ux*k4*`LpUEK90<pJ}AXMW03Xs2iX1
z-(4N2G5p0>r1I$+5^-KBQ?MahMLI|GNn3Sa%g^W#C|CRhEY|Y>rJ4(<5YDXquySyZ
zS^dxCGAcw?uF)j`TeC1D4>*ZQ#^kVAKyJXL+|T?;wwm0<%U8^!6{Jj+Ucte?z9*oc
ze=o?rxuLb4uKCjPT}zyDsLWR7{9JAB2)t(O-xwTue{B1Y(jmH6ZRLBb9Ew1?(!b<5
zRX1+Qj^^b8o@|z-wx_MZ-@jjQE9*aP=+A*!NOF<0UQ?2SkSw<@NqMcgY+9Ap*gZr|
zL&pH}2X~e42|@4v?@8RV(t$5`1%#0J?^}>}w|TL$l7^FYN~%`+s}tyZu3>WFj?A+?
zEB!CqfPF`kGZ~);7v~2g{#0+v<*saHZ%j(N9*Nw89DOzwb!y5INqjYTPEPLhAb7!{
zZ4s<Wy;)=kske~JgX3&@qga*RXV&VgRFKO%8lc}_Pj2^Se-nY!z%$10yh%mO4d5mQ
zfJ#+QLT!+x=_bG0$9~ClhqSK|dD)oGV)HnISNT0aT3Gd5I{Ox|MgJu)QZaIjM!l2(
zG#<G1`Ah@LoI4z)H!!7-iLqUo9qx<L11c=qdD8EV$jSW|=Iwu3q5tQEN!6k?Dy^ND
zKHx43fZy3E&}Z;&NRn*?1o;WXumw<aTgK(K`(SMjO)7^Z`HOepvEt)og-kPba$T&1
z;DpT8G}a{hc?8Rh;Pd6(#J<IP5C-@@%m9dM!|NW>lny*?i+q@gvq$1w=1ZP16OT4V
zXI$h_!QEW*6()$AA(Giv|Ibg1Vg$B`Ywsc*TE|797aRng0BT1xf=a$-$2eYqdJAO{
z!Tw+ys{D{#Vr@-)JkrMHXssUE3Q05o@Hgfb(i~izcb)rE#kJ)sSoLuLvK#kRL>oQr
zRpo7XKB5Ou<i|UZ>o)BTQeQmuGbmb<yOKTCqi__L$FxU!W}iGnoy?rQ!0V^`g~eJS
z+fI41v{Wh;1}3b)kU_w;F8>KqgAk;6)VzFt!HzC}tRuJoh-4HbRA0TDbs`3?zS{V%
zfSF)*p=VMfmPSAHN|B2&yrft@SchZ+2ZM1dR455ctqHLF_FjRvR3b5(2~z-Os|uIS
z=H!>-bCEX-+V<+l-kb5rV-CrXpkriVNu$Re?-IaXdM_nZL3*NF^&&p*vVKQ<q#sA)
zc;m8UA(z4aJu$X)onJdVLM#GLkTa3-M7YEl>`nJA0OkiA$<OaiV=RG)>{RR0ibl*?
zq|VB`r4BjuT8T{d=E(b*g06NRzcqfo7$mUg&Cx}QW%=PZrBA;P_Q4!y#LD+DvYC}&
zJIL$r=ODz#V?{)+a}F2RY7zx%NZ4I_L^hwKCkLxiwPF1i@?=~GVeoR#Com+FX+KE&
zMM{mYwsgDKkOF<j!I-0&6|y0J!7uthSKa^VA?4!#3)dqxm1leQAw3Pd)_=Zvdeip`
z)044R#gZcCu>4@m#TLGl`OV3P-`gD2)G*d3Pj6+L7?D?iqmoN9n+^@yP3&JZl<6ST
z7@P<wp>*-8tNH%}&Hx|&_xdKOynfyRUa_q~AiKbo7b0Si$B@UdL)uc7`?i7?>=?fx
zsp>JOk=CFF%HtY_J?{yq<N}WmiqoXa%9YRbK+$EKLtwE9`Lce8<N|l)1t?HGCJL!w
z-Qz8t<>%J1qMZ96?gifBk9PCkPnm^u2IasLrWKg3CX~6`2WQ9M5zWY%zX}jf08|`3
zNnXhk-SkDa#3Bs&Vniya2RzeAVD^H&O&9mck{UhxJ8)k5MINyo0FC8|1TW;C%-KIF
zDW0ygB1sCE{`r{XPWvPxi}{s&N~Tm_|FNA;jtll*tpIx?nWz(-p#tV!u(!`Dqi=9n
zw%KQsQgFVVJw@^i^up}F^!-$0gVsA*lKfZ=Xz;5R)NUq?plZ`%?3sU^YQjNraG7Q@
zH7^1UUIaUne9sPJ8-F1$?KJuNOd4Z^;rzQQ>nHvwjO;cB^FDzD`yG}Ck@0?<fePb^
z2ZvwxzI$jaTJEa_vIx-Ng$VKE7nU{=cMqo<{o;Xx6kVWdjIS^C^rRm&MKZsEpehc_
z`e5<$*j<J0+uKx)vaz-wx{FLB=NniWWEOHX@O-b2J<lg{U*9&(hG^GbkhPZpKiHL{
z<?;Fp7Hq^NE=8<P<!X}qkn3}gUtsKcg~or(*lWf#KYjtX<Gl)_Vy6~b6iR?qr{4A1
zdAN@jpFB?aIZ)j=KK<Gnka?QF{91rqKQrjJ#_Q1OiD4Dxf+R@hA*(;==43?yaM*Pq
zYUt{f8`PJM*J2qULh?V&4as%sX1qT`>BN|cxu;ndE=rt{f}Ka#20(3U5q};s9!uIJ
zCGE|}W$R3{`18GS>Z_xLpO$leByHq>0Buu)^<N%-_m&`P>oymR&W<+dfmI8bo2>q5
zGAH-pRr^e%X8&Aem6U)adCbcI89L-Lf@)uCYO0i{x%27mcfFoo7asVud^j_{erj5G
z3r+NL^dO<n^A0XHs^Qu}{v(bZ7(%~Ulxyh80IeSZOOFyg;}@&qF9fa!eVi;)Z&+_X
zh8ZRCZdY~y84~%yNy`fncWb*%Le5s(xnAT=?($E}0x5dkx~z@=`htuL;+^}nd=wnz
z9W#|J>;hjbDIR}V^^&MzEq#~yN>KO5hcjrQ8`6sa=h6f1uB~M~5}PfRf>L_G7rC~)
z%w7Hl?iVHYNE#t(Zp1+Igs3meZhy=A5wcg0(d46}rvfK}+m`PudgtxXA>QO^JtbZS
zx`15fN9sa2g&&vgPp%0$8OYH^mZfC%pHKC|UDS<Z&MKXtg_UCAZ#(A<FJ08rTn2n+
zhDMAlbkzXdRGW^6JYjBYoU+%5`E0^gZhH+PWg(wIK3qd)Dx*TaRF)*(Y3j(an?0tv
z>@o8gM4R6?0Czv9*@6Wb2mB)}KRMMQ7M~Vz3n|hZ&AIVYAG)9{uSu7z6P#6$n@7)N
zlJrboWAa}Tyn=E}^-B>8w5L3!>#F@}Gb*5AXpL?DANUgqLG63Jm&UR{rq)^c!04aJ
z!}{$s-B*E;gI@W_+1-5|VKxzyWJCX7yX_<F(jru}USqPEBbm@{$Mxf-2v7Qh6Y^O5
zy=8yB3(&uim9=O38WQS1*YazqEst`+t0(m3cOUfQJ7_zm*yiCdRI_0w4>k%S4<3Wf
zcE>L}%Ywmy_dm(^g@+<_%7Jrt2My3S_x7n+#aEucRIB^%E^5gfJw%?2;CIx|moW7*
zLu7~{=cI5^DK#|wAP(&ia9WnE`nK-`17!!oN$+cLIMU_ikC2^G2z>nUC;719rzb1t
zy^y27_$Rk=UOgI&`yHw05RhVdl$;xjfaAUZzl!kR?s~RGs1Xx^%+N5_A^U@yo-%(2
zR}NP54V+lF!H{g`b};=hlK_r#fml~oCv@guuaU^GR7%>_k?N7_o?6lhh6uSH({8X1
zB7Xu$fs9X(37KZ6{7pIEiuTSa!Gs_7&lM^5JyH_-t=LW&&DJC!3-hPKW6q+rg1cXX
z+FlnDNKcqZkZERNA@eRc@fdkSn0~rbm~BLAR^$rfWg)u9w&oal7;I6(Tm?hCOSRk9
zZgs7(Ua5EQkW*=<IoQpoT0t-uVp{U~Qli5Tvt2Jn^zWa$W>B|RXj|z~4*9V!o$`8;
zeOaIFfuB3SiagbRrS(nZ336_05Z5d(s&h!K7P`CbYHs5R7sh<d`33qf<Qb*L49o>y
z`Ne6yos+^~wpxh~IAZ8A{s5=1?|zTX!=;VQ$u}AOh&?g(PfdsIuQl2Z9KB&68Tmu>
zdQi)m<)czPGOd{Nin`-m|Iz10t|}+|5S>ln11VlG&N40>?%%-KPFE=d1+@;h^@P28
zRes9u4*Uf9K6vNBOSygtvG|1;a?A$bO-4o?mi>u_<ghE+yGF8jhv5F)<jH_rjkbfU
zk3o`RrcS2Wak4FPb`TBMf(}T9gV@d4hhD0va1Ar5RLzN4Wn77!BSj&@8pB-AHxPj|
zEYN=yJ9_PW5#`=QSZh!RG2~dlY{szR!^x^tED-AXQ^mSdJZ21d!8!Au=@o4O$!<*;
z9fu@bUu}p#p}sF@Fa=rom}vp7Ea4Rs-Z56d5f_oU0jr6^g`~f@BNI$X8DgWvyFDd^
zLacI_@60m1>*m3Ihn=FjQ492IUsVj1OyfVX<Ad~^UG!$^M~V|C{7?87Hk?6i|Kwr&
zOv{Vn49I24uMnUs2YFpnW4Le?yr7ylMb5w4Ha-BdtjJ1O{^((S#o7D$iLVEI+K6hL
zu&3EQHnPQYj;j?whcJnNJ0v=+3<ica_KofX*UpC=TPbUVRF5QtwX%QidjP!9Fc3ce
zIAZr7E`tHm<zCH)59u3%5d_E=A%h5t&agw6+F>q>zg<|hEbt`+(eKZ237pE>${6zj
zs==|6+(N}?*Bs1!pM|x(v|O6LakO#&R-jx*$rdU+t>$Cvv~FD~Sp3CVsqI`^F4QSU
z0aCG-_U^J@@^sf^c)c;XEw)1koS&aq?CI%7_b6Sz=D*sRO#N_u?(K0OY&U*-(Uoz8
zJ!{I^^5*fFzc;O+W?ezQ9f7S`f1u<6^`QP{Sf`|OEjv84KYAEfv)(e`-fOF(9}q-0
zQw2O$L|>MgrCm^iR_4viNa#uqri2+_B`tefXA31JZ_oACQ$Y>R(A5yIwp(DrQgk`=
zpRkpprHzi~7fxaz-frUHG+tRn^t^lEXz|Bb>c~F)+pg$c=tq8~tsUmaI@I2*7EWG?
zoi56~ZhfJL0#}c@*?wJ9<x}~`N21<@5aXSJ!~9Gl7Pnpna={MefMYKr(Xkyqw!Al>
zl$VH5ZUHmuXy$LC2rdEvgqugPPZq=Mlq{G^U@=-iI+kgMy^UwhxiZeP=Yjnv84Ic(
z8P3yxd$oQIDGCgdigWU5l#fTX<s1M;^C5c9+wyOD&J{36y+v_I&&cz`>^w{h#1y&i
zj4xPFwJwkvU`BX)i}p|!Q{^aisntofB=<yg?tvoY<^!<f-i6F@t-2@U#gDJUx!92=
zp~KS{g<_mvLQdxw)~k;(8<1b$cdVP#)Wly-;~m=uCAI!ofvX66UNG`VvAW&Jz@2y;
z6oXjHEw(sH-Xh;mKp+Lz`5(K9Grx6)z7+NTo@ug^x|{_f<$Nh!N7Mnn*hrsjjwHsM
z&);(;c2?Bq=>D<ZKz(?GP_aA!64>U0BF$yeAE@CLD#}d{DSVN<nm?ysa-}FNuEP9)
zMm7BgB-Q%~ZP0nvK8|*+PB`+2az;bR1%v7r36y9pOP9goljMWQ15ZyzjsC{vjvqCD
z){W|K$YVa2$Hff1Q_@>$i{CFWDukuH@_b6nV^(fz@AOchw}FQ9W6jx&{OcuAPsu;o
zcP@yqrc)`Y{5*gc1OMWMZ)L(UQRX+Dj3Wdlu|2ZV+=^2)jXnPq_8;Zp=-F7~z_oU0
zRSEro1(l%s6P%}o>~gn9`KUCkB|gBR?W)H<Wc(o!=+B>*vc?jk)~Gdl<i1Q#%_$>~
z^E0myI1r;EOL(qb;Y8=_`&k`}P~=qG*K0^tMRj+K=|Eub@dWP(Z@IikHG9eC#1!Qz
zm-If|K!=g0j5#me1OWc`dE;E?nhgA4k6%v)A0l&(!4i*jA$mtG$(8LUYz+%S&_2I_
zS84Ao@z<2wuBxaO)tKjkhAm!>N{#<K!Zb>=f&RFrATz$nLHz9@F8%fBuOjc6R5y!L
zJx7*efnRTNTO~4ya(iR<>SDx%8c$ZsY}2uM9}6o^Scu2h*9Mcnm!C|J$;h=mn!CZG
z3@*9rX3-aUMZ1Sbk28eUi`~B5&l^53EZy-McEFDM{-~e1rhlAvNVQfU?^`=BHM7v-
z<*XC9*u6pW0(|Ph!*AZmJ--Unt}Mj>YeP44hT+{iE**N`L0r4+-(aUx!g0w#6!OsY
z^76mw=CrXl7PQ)y_3#W5`5?v<*|^7@k#VeJ$;>9&<yt`LY=)cR+~m!tkld4?^T2t*
zg`T=II3JW5ce!0sVo=d)!0gVR(&;~fxZuCKkVWU;Y5|TXaG1-9#Ton<96f;r?0z?~
z6OD1qDY;a-p73jj#K`YI>^?0w0U2*VZiy2xo-pAjhjpH|Ip*jTkHwA*WakYZ5&GrS
zg&)BBM<Oe%Aa~oN=Hh%|53=jlUHyn%^FwC7G5RVqt|f-HQXEW@_YKL(xDD?+<Zq~M
zT<5T2r-zkVLbxpf19+4b{HI_+of7(PxnbpsJ&%9kAhewsI8Qh12uv?#9oi6ZBEWg%
zywJ}XSkR8zSQ{TdQ#V?Cg71v!QR#!m6_Ii2-MqOu_ut|U$Azs4=T@!S!|wSdICr(P
z)TN70_|KFON!e(@HCJ9>c0^#HNOKKb4tJ;ia>GCk@clP{Xrns?&^CR}L-eN1LdO^}
zT5nb7sfR;AzPCGTOTJ{ge}Gm1%3wmAryq78rFOn7KSR)wQbsyRI2nIn-WF_e<)~y;
zre`NQ_S_^HJS+5T^s42_qJg{h0Z8+il8>ZfX3h*^7$4QHa)EF4(J!SR9=|r3)9C*P
zr^vAg{p3`ELf9uUXu$k|Ymoxr=z<57T~Y#KpuM>jS7}ZPHoSIFn}+f6sgtd*bSw)T
zym6Drb^9EWL52q_mhdbsui6s-d}i{dcVVG8#33-^gIiq<d|e%=p)wyYXS5|`uE!!V
z0jt%~88-}b{8h7=uZ~W>^49cU|N1Hc`#2(Bgwcrc0BV0w002w;8Wa9S{iYb#oRU9N
zXcmRzBvo<Zt67h-dOEnj;W0&X;>|#HV0IhVp|q$o8Hg`u07zdQJXdaT&_xHmIC=)R
z$@wk!;U=sEwL#)@G5>aKsVr!teO?YX(wSC&UjQJc$PY2IqEPWJ3l(^MJ@RXVk{S3F
z5J&}xmK-LCa*~V_)_HI>oBZoj?YpMvSv(GJ;ztZ_#-uI(f#1yZm$LbwV=J1~kZKdg
z$R8ANE9DJ1V{1iZ7R3TLcRoL1?XBMc#Xqc=fdWD$gSV59SK<xS^CPcz@pSB0Zzm6*
zI-<B(3^KG+ntr{rZS6V`odW9e^+d?eD(``l<3c75X?BWCbI_(3ogiyE1k9SfI{Y^a
z*|}IV8kf25azP=FpwZyeo=e+wMf-QN6J*28^(#n(y%HQ!nlBAcbWO$j{%^8?B)luj
z1_MPR<xCEf;mCNisg!3+?!kvU8V{2due1&}daSWN-*t!$C@y3Q!4~;7h@Km-vTx;o
z|7!Msy9DNc=Z196O_8&9JeskOW^JIyg#*C-ldYVC`+UO6_Y6fFu#K-CB1^!-D~dR3
zR>Q5ENUx(9E(SZgFUgMDAGJv?FV4r#azmXdc?;MeV{zRS6D^(xDX2IkhH$a|T4`RT
z1_R_E%8>BY(Td33)w-x|gMo&};t=7~)<MGM16j;PFdfOHg0<}cM|c!$k;A+XhLkLJ
zXJ7PD%(z>JjVW#FG5?uNi|xRK)gICs$%;rHDPlHcOAHrP<1Pca?8gD&VN0NPtH(iu
z_qmMD19g4NckN3V00nSN=fjX2v&wbVcIVKo^b=QH8QAtU3%YM(%LF<NrlAgg_l}M+
zsHexkgPbkP<ncwhOo79SUTs-w+k1B^aTkMSplN)}pWrhP;W2Uxz3ev~04&T`jqAE#
zuHbELE~oO;=TEty7PW!naG(@vS93`23U2`v7)Xp9d49ApzQp{=z6~wF+Y1YHp2tY&
z`Hq}!w_J_Y#m#Ke&t%Kx9L53Te<bOL^EUfF6;}%cy<qI%5y|IvUP^GKpT`A-S00ag
zEBV2GMWF|-;f1QXC(lK|{=5&@Xz;tR8*bCi;#D*XabO<FAy(nT@u48rJzFrn-$Tp-
zUN)%K3UfctBQ#FILBpoc`NS6j!B>!==rd3oO@h7g{s7Ny=lO-02|juc+rM01tI#{U
z>3DwbA?1LuBCw&3EwqDQ6F+pHyCQZ0>Mw1t>8r>Y&P4d<PX@U0${CU!saML7mSvw5
z)AH!@8NpD=GJ7p{lk_(g<k5*6>Z6c7WhKo0(_6Dyv}61|#L|E@dXD(z&uVYiC0MX`
z36q&cPESNK)4+)_XH5rdy9uCGPS(ThD2>%KNZ_rJo7pCn1xy`e{4pRux3lLNf*~d^
z!VJsHg>Hv&pqR6-*5?94JzDkI9f5t-x<`ib#X}d8+XjO<p69{;Xwo9TZDb)#$*D-s
zz#<Ou99I%oV|Jq<;+Ky!u!Zik`THQ84KLYSWa`lRROEQ!k*a%X!rc`bMspRNrIr%4
zqDnf4lw3KA=u+luk}qeo`Wx@~>igJ9U?xY&O`JC&`kC?OgDM0Yps3OhQoCqOhrjyK
zWs!T-uYi7tuaRYXip(v$corUovDJL_aji&ijs5X8HnO7aeSNq`oj$oDjw)#aSzgHh
z7Hr+Vx)OFY^R|4f{6O4ndTa^W9eUBuM3wU`dJWT4^>8X+$~?RTM9O6LfwjRASKGsR
z%;g?q1=Ksfwm!#eZMIHReJlE@-GT+$fzd1954>f2>do4gF?aC1PlIK-A~iM6Kk8Nd
z+cyPc2laP#w%;3i4MG%5`9!<Wi!E>MX!Bv^fYelLnhv^&@ALBykHFU-V`*<3D!Yf;
zGEiA+Sz|y**XpGCo_hWG#b2<<MGZIVQ{EQDkV#2>I?UVeER8}<ad|6n2RAuPHXyQ)
zAm!_IM(bab@YulB7?vP76ydO2+oq?%uZvZFtMd=+XnY#~$CGadYu^shki3@PilSHh
zcJYbz=MFN?M-2V*FDCI9H%vLxn{tu5p{On!wq4D@*^73+s&Y_G&Ro^YQTyn75XS1a
zE#9Q>aM=A?p>myDsn51uYYdhoX14*%aJIy(hi{S|=*YY0kI`}x6<#R*7~MUcrFtaw
zXya2P85C`q`{l~pZbiEARDE{_#g(z;Xd|T9wKjod;!DfCxL_9tD3{$G+(XyAv!UvM
z0+;lBWzK(@BZqjhVv%PD!81<If>o&6m!@p0=<Ol?fyc;O!@UGVK(eN3X2ec>xr>-s
zo<RR2w<0JoQ-dFCu$Mq}(CNk9Qpp;lA;0wDAm)l?o?0i#bD_Vy_t1o(*&5M`S0*N&
zC65qa0_(obE9`H0SLt^QX7PWOcjbRcrER}ndvhG8=^34B)Y6#=#4VlFv^1lsMg>$x
z6wSRfEi@%FazUWU9G}cGSv2>j6n8N<$ORZD)4;$++)=a;*OWqu+~7Tao<HL~zn<%t
z`##ropX<9^pF0|$@2B*!Pij^C(B<0!{F5w%Y6+CIzn02XS!6pFPft>bMcY+>Yyg$L
zx8CEUY2<q{-R4v+*lWc%SvUaBq_}^yo)mg19#avJLnytk9R-S+Zu5N&t5lH6y@`Y^
z2Dq@Z8!-B-HSS<2ZD!0I_Q2DeR~g~gg8f}_jjSWVVEbMGl8eK4x|Dy{mlN+ME->wQ
zQp=@@TwBZtsou=7u2Nd{MB-arDNMSr8^93&POPJmFYYZaHQC<BX?82?nUjY=$w~0=
znASZj2+K&U%QJSbA7A~?yufH)o0j4V&g?n<a3q1<xH)`4$#&$`@6bC~pN182`PQha
zWw&w*O@E~8^JmJ*&XJPzPy#ncFJtI}*q)g%A-!}gzGG$|ObwDLoj`P!dSEH(23gy5
z9YdF}90q5>WkMa#o|$qX9f#I`>S*lE6sWq>*XArPX3{e%*3_Zy2F!Q21BlnHCKo|T
zwL0_L{>iC&(g}Z3dGIL@V3y+Tvmw1sE^2)$8rDs>Ia67oNl!%SNGNt+5Z%MnfDd_m
z<FU6+NRRdRtpy#j#jx(Zf<O*7mvlKe*4eap8bFCzF%x^_DG`_T8fjU1)03yl83J?t
zgVr>H*l;923+nBobSrc*DEj82yP_RF*4(s_eb$n-Sr^RieGhj-^SLn>#-jWAP7$Mw
ziURi_k7nsTuMK4|_JicgZ-o5@t-op=4yl$_1`xIez=chxIoLLMeg*5lJLCK));vP;
zErMT<Be#0b-7}52iJBW|=Y&*a2cL|^av;V*$y*cDZcVfUWc^L1av1iJSndB=q-9)s
z;V#R`URY)F9>^>6z++t<K%cu7&N#!F{0v{e>ZgVRuB|GYfA#wFjm2-BNRh8M4i1=<
zgdgYW935Bj;bVpMiZA8*ouY}{_=knlA=v*OAbOq1BaOlsKmH1DnwJLDDyJEx-tS5U
ziXsSm@qez!ZcnjXjmX5m!+-x9WUezwx7DDUB}0c>o;}oVC}Yq}w^JC7w`<vPVb0GO
zmuuJzf5a%mmtDSP<DcBQupErN%Zi(LBo)#&<V?Pl0{`e>K%qG04SN#^w+GFpX7TUw
z7TM5i7eYot{}FoYNVF^>wL=KM+~0#$+P#(<*63e4e{m@(eAjd12j*eH<QbNabz=x>
zc|n=2AJ+>ua5?(L(X#KQX}ZzXV(1TwFX7|{^h$$6BW%dgqVJ`3Tj{-Iajt2^(}$yQ
zxLkGF+MQIP5}3Ojt9-ig8^QPR2be;^-^OQ|aUFSwqM1@UO_s6GY!$%0mfF<nh)6}M
z%4snWLvFaL-xzcx?j8E*qG8m68)wySE4X>)1NAtCGDImv#~vv|^xPN67x@OnHH~~R
zqh#_h^2){iP^i&rn}~=^y(O1RK&3~#M*vK4ix<5DFk7tSC9F_xmDSXx%%%UGKdlqG
zYh0n`-^6{xIrLdgw3Z>ZK?FOQ?#M5B=v<Z2#hNjkA8Yf?cab&>tL%1a3&tomsONsV
zL-^91<>KKPADXPp=%U<gvyuz#cZc20QpjntpoeDoaI$3#Q??4TmLJosTV&gmL)WFh
znM*G*hDr{oSoLL$8W^`^>EYz|fC>CvC$)PZ@B_sb<Bv)~%jYL=e~#<0U`^7*1GJyZ
zOk=J~f2cQv#N}C)UmpS7DlueHf^r$!fg%omY4ps$;^BpdqV+quKU|E)iBgfBt{Zby
zY<Z{^p~*ELjL?rKLSz|TnCiII?|g!7&dvd5xwp9q<kU9SR;OgDOvLp~aLH2+qag+k
zi+$su;Wk!Nr!A}nC0aJ>brt8vkB_)sFSc)YC(pF-751F7Ccg(Tob5JF(PZ8(3NMqg
zV|a3BJ1bQbyxsI(T;VZIqxMy*qL!^g;x$gSxrS5aVZdNQ9Ij|WK3Em=m=+;gDJA|K
zKSiAx9*B*RF|YVOYj_yZ5?%DdlRw%t<(ce>biXO5#?$@dG_(8!KclonJyQdUNP-+H
zQTb=BPhdEZxCSdtc~1&OHmAP)8Kx*?%YLOOJ>kj|m<_iBciK$5bk7#%T9Y{E;m;Nl
zQ&bIau2_epC;K@X1b7-+ePeaPfO1WWj$pn!LX%x(B-J^2DfyqedRrZN`z=)&4+U){
zxWRvB&xF-7S`zAk{L-SNLMiv3R^VJrYjt`sVK7?^P9->E(I;Koh&^|ri<7`#+B<K*
zlS*q13#4~|6^=9zq}GT&B+<UdNmSYbwctYsMsRba!73ZCqX_iHw@!A>Ce*M)dY2bY
z@(mWkjMa5(HVKlFnfq*6|CQzQy1s-Kz|N$l1UIWvfVw_S8ocqZm%gl3D2jYXI#FP#
z7jf{kBkw1+%!hK?R(4WSG7ypIvmqv2(_Jo9J~`}$3vYd?of{pdJkj9XHKues1RF5P
z*qURD?a}y^(i1YA)~y1-Eg3sbBVyYRRgf@p@i;~pT=S?eQD7)@6Zi=~v=ei)O-{Wg
zSoTT1er_r7E`=nPYtjVX0mM_P9}}ysNZ4Z!4c|qwxnh(x4oQ|;WOJWARr*4_+Y<}&
z2jdQg_16?HK$06Mt&qcs<<K{;iknlvqz3>^AsRs$4#}Cxg`FuPe?OU{IEra?{P0tC
zL$yPHipL|Vg0I*Y6!Yp@UZqA&+zck5dWR-Rw{os~Das{^u<WTiT&?L45_5TbS1}m)
z)M7v4A19CY*~CN<tm~|%5-yC@!%EI{F@H(so_hZ&%_l_z1|PqrFdXsmG#fI2G&)tk
zj{pwZBK3<2K=v<)HS9ekl2^0r8>L~uZ;(8L*jGOXfpPrWG&%O7=3B#|{j{2wm$p7R
zEhhWR<rSc80Ps+3SK^-^EELI?H|SZ$bIMOgjZ1wCuez8njTfp%{~6**qXgmbXHfIr
zUKdv?H);`_gw0VqXhh=$OzR;_@5M)!`_BTdrE62Frqm{yf5jw^IV*Uyc6FKKv$wy7
z^VoKM@DDBHN+U_Z%=6vZv2cZO4XW8DEDAc$yf$ES9R>6K)S-J*g{YMEeF`#Fhfbd9
z1VI?pxR=W?MU6yJjOx*Zw!c|FU_c6<`t@M*=`q{`Di6Hm6~}r!<2Zoi+vK;7B+wVp
zKiIrhgi57J36blQ)sB0YXQ?Awu0VJ9qUY4N!{OMCPe~cy$a$%SAe(ycInem&rQ@d_
z_50>FhyyKMYdN!W;dF@zF{gGi9@@mi$R-VqAxrU<d_^GX&Ysc7J1Z22!_P%yw*GS5
zZzFti(5J^fR6zqJBA~G6C%_A9JWzjroao7&7|=R~oKXo4emP5xKhAx-sUd5y#Y-DE
z>FO!LN6DQqI2W?SF%O$CDJ7l@_ko{V(OOVdd6FTO>h81B8dEnlqiKs_zeEKgt0%Z2
zWIIadaX>mF`UVtSiGG@>nT4r;j0{s>B^%XMCG4bTDQLZrzugZ?#>3CWS8K1HgYV}R
zLe|SbTsGVV0$HD8%LL}ouQ-jWqQ#_ln{?28X|XM1ZPSi@JgIkQYXCYYr4Q(@g88F8
zh`En|J11}E6^w&!7Q$MRha+wtF0HqkVz&BaeNG2yCWFpAW2Kg^;iuzZ1_ROuFk`<m
zPWoE!*Ce+JO~mFoGG3XB6wXT)0qfQGgcC9Zpz_Dmmz6;41`{S8-|WCg?p1y@?pHl0
zJu!?I0ey!DrdSSuH07&J39Tv3ntQr@pyx7cbwK_r`-v@MsxG$gWKAP$9Axj=`!^`u
z(!e-u2uJ*hH)oduwW;ukKKZ8Vi5FRWe-%Da{`rpsvTXN-1Adi+2QUeR_&#Cu_DFKQ
z_WIwMIAYup+(X3+YRMBEi<`13LNCUwD(mz+#N<>$vG@rUG&I*!b#;L`UY<r76~;eG
zYwcOC-&+^aD%Q2^!(RUn09uOl3o?1IV#LyTXi(c&?XF#W!guZnsFsHu<ukWSk|=}D
z4Iexd{<O`+06N16Z5+0|O`<R2KcE+S`>Vtcv<DN@-~^Ii#P%-V`7YgEtMb1=iAa)O
z(NIx=0=!=6gZYkf0|VEahPqPmQDm0=a}Kg%?W-^hz`NWWC~ZdREl*W>t5#8edR`1h
zYQ-dtWo|+|ZiL7Yuseis->ysqs^@vgGXKwjRF~S@mBDe_i@4!tNaVjWu2zO3`$Z_?
zo}yuyOlqth^N9^Y)_bo!txmj-tPwODQ*a@<3PUjckYe_r*p`n(nYyvBx#uH%{&cKf
zSuL)m;@#zPG>qdC<?KK=ftJOkh4yRv<wEUeH+DPhv>u~BsO=)nvgYWi>l_Pk@>&kn
z=6P@V+B98!8*<7)_CPU34`vY2{O)ucq=8C--FrGQ7*Clv0j^$qN;T1O1y2jT4BXZn
zQ9Zh6sz*2opA?K5#=nPZsPXpKui<?}P_{PjjU;-(<-GGgQ8gqyJo|N@PiTf6In^mL
zYrt8_XP@C6&{{0qIST;c&5`%c{QO_3w&3qt9c3AnHjrdb_+j>CcNe@*56;CbGIde~
zS}&!>PUetbq~OTTrfrwW*^w#tU$!0wc1K8!k43uQ&!=y1<x@>|Gm2+QeXt#nvDtuZ
zUVCoOyd>AQj&HR|<X`OScUNG|<yy`tA-J~^n)>n|+FTK(HmJQxYJwtO%x3~qG(<?`
zwVhdnJ2`P=SsqghL_azhU_SR}HXxx@_+~hbkzPe)pp83JR51<YSX-z0^;n$=5I<kV
zswnk6*(77`YL?|Jl$v}S({^kLmjozG<0tvZH`6n6`_2y5ib!e^0fhgjVu@b*_DghM
z?Cz-k*){wpPK%6L*E>vocQsd-*dIuLlb4cBNXc9dym@0I7Jp55X=j4hDe5P1+h+Y5
z<?CMN*pHW@T+C#L8=ZM40^|CkKO+=f)Z4~6wox}w!US!4);b}2W3GJ?Q<<^y3#nv)
zNqMH)F{u2~ohCxw^u<Exp!2)@0^Rewv3~eZD|tixF==u8F_rmRM~ZB9rozc$fMf*X
zFZI;tPES&1dSV^EzH|ft%8BL0JQ46>m!Xg6$CA96<>QTpZwVQY<a1;$QjtczvVu0(
p&t=>2B2ao=J8}O%p1)?c!s_Z;*VO5u&O0lH{pNLnaqiOH{{!C*u73ak

literal 26673
zcmeFZX;hQhwl*5<Eep_XDFq0WMSwySR0O2gvJ^wB(gdUv5l{j|z!16+EK9IT1PvfP
z*ddV62|{R+08)aX6)<!m2}mFknjjDY2}$mou<PtP=brQDj_-bVjPLGu41}zAHFM21
z*E63v*NVSivbU1`M(G<62qX)+_{$X#XnQ0GwDtO~9Y6{7>GMV4*N*EpR=)thfoJ_(
zBO3U%`}Re*a1cl(QSu{`GWFFD1o{C4`Q@xrWbPadNeDuEa>PRrt;2`+e%o^9%dxbF
zKh5v>$8qaN-)+DA-JS>AFK_$q@<Okg7a?NzyP6-ozDp00dD3oo{K1ufL}wg4b1UKc
z#mAWZWr1(^dfr53K!h=jGZMbKydGKFzTR_{!SyW2LitxMTQJtkw@AhM_doydXuvf@
ze94paE@Jh^ho6<#jAcMJvyaa!0AuZ}Jcm689d7EkIN0918{~R*=dp%O5ezRh?~(jf
z`Dz<MT4H1USHai~SrEvuS9s79c(Kz^Mh{eJD_6d71!aZ6v!7D9S^_bS@4@Y3w^9T+
zd3#mykV!@OBs+;*vv7s>Y}7ze9896&pbl*eqFQf&MkCe{S5RzUk&LA~5{skHa?=%A
zxi03l4M@a1+m`~vi5-bZOSbWw=xc}}a>SL^S)m$58E52FQB0~+8yXIx&KuAkzzjBs
zVh$w_OLZP9)y2|1h_9pl5G@CXYDOZQhi*kyFd8PJfFJXypAkU_zg9*Q&uGKL%P|j%
z3YOu96n4kLLnr~qBs0Ee_Tq5yF!AoZ7mU#6iA^c>9qV=GdAEm0dLq`pp^l_ZTG{ct
z8t57nbDrXI4i;H{h1H2`9l9Jqna3f^PA;shRwe8W!v*t(R@7Wd;08uPYwf?)hz^Nr
zC)9p2?Bu$yuY1t<ZYzK97?3M2kd^uT)(ZcIx3R!hq!<jk4!G&JtraN>k=>DU8BhoB
zsjGT<q4b<V*IEB2BmC#;gMf^~KKX-f=kjZQlS_2!Wu$!V4GBKmuMG6&K-ni-Z9No5
zG<9ebyiQQeg&DKBW%HsvJDr=FT81WY6m3{tTV|~Q)<7S>o#IK+t9X-)!goRyW6;D~
zZ3yqmgm9$6wtAgBvAFaj8oh;1qZBE}y~d4PY&M<E?sc8Iiq@Yb;7h~kE(9YZ!KO5K
zLujJya=80)I}N(3#uzX{wMkGq>!ePS8J^qSztNjeQHGAqGqAXk&W|wY(j8+AeJ;m&
z1lUrA2DF>af-%J431V<Wvmjn^h=@JxRKSwc@1fBuna!kCqj8!is`%@@wKBO#{AXhQ
zNI?xS|7JO^RoNl%2Ba0*Cm+1aQZ8iImxW8_<rU&Tgv9A2@uYqm<HM>u&Md>thk2p)
zQS5#Ge%vQsu`!EOZc^`4yL^_?t>j->{)uNpd4ltR9hFFOu~}d)ST{2?9XBs+2x_ZQ
ztL&%`=sC^l*5`9}M>c&t{&KxXRzHkK;K*Z>LtOnQyY<`Xp2FrMh)0Tx7N~SsFnFBy
z2XERRJt%=Q(@#Z$w&n>-rC$>$9!>oF>;?T~??#`&iPCZA0a+h%i);(|cgphg3d56j
z$KD)w{|P+TgYzr_fW=B{F-_JeGy20T@mUHQwky=1+eA+qP0mWH={K$jnNob5cq*oB
zRG&qV^BwJ}xK(C(n&4mEI$%y6z$(`m-8LmOoNr}tD{9we;1o_zaWH-KUE(Q0ecp-5
zTz#zI?dsHdQE1y5t0HV6Q-)Jf8dk>LL2&2N$GUp20+L@#%s%iU_U5avYDT^rG|HYF
zm7tBm+~qiA4|n?h6PD4o2myb{m^D{!?>>=h)ir}=Cyh>b-^j-&%;W{+RzIznP4O{p
zdR-$LzfH$%^A6k<!7{GmKXT?LO-y%}hY!jU7mQzur^-kK^3^=EqVf~>)}mxM11=?@
zL>bQC%&Nt|drgp)Fp?~oWCSD;8z*!=R3GZwYh8AS?$@0kd|EGwAWjO83-2-J<c9X_
z4)2NjloW1Z_)_c!tXSycj{Jel+GW_)S$VnMg7CZnmPf!oi{TE0Xgo=zm)$4kT_~Md
zR2&-ir1|zl`R*Vzy|sQcD;tSt6YH}8D{I!g+6m1Afey~t>91Ng^25%pea)ckS0;6!
zuEPW>tVRFc1T(yk`JkI_nU}@nun#2^Rwp8PS^A2bx{X=#0dq~adyL!5D>;k)ddI%<
z!rQPuhT~cRhfMv_!;PpvdJjB0UFzdMXKg`c8~IZM#o=wDqS8u^5{1|MMch7_=14gZ
zo=m&(=WJIv_knNlR7`la1xzO1Zrbt#s5ydHggex9t2L=5z_3)jKEuW&C~u|R>WR|o
zGOm64L$FJvYpd+<gOe{3Y8;^_^;~0zl7!H1<A6ejc$#>y_f}?^8Qk<%TRk#Qf5G2<
z1=g@{s33gKPqtbcp9<|6Mhg-a;iJf|FM{VT-fH-Sp|xb$PAs8vA|bE<7Fod&@Is#|
zmrt1Zey=}H>i}#X1=xI!E#M-8kb1CSnU8N<vF$FLo4a5sSD*KmB7TPL(sc=M$@N`+
zigYCtSVnr4{C7#hS)mG?4hy~1YErvg>o!fh9a4Ra51@IJWukmY3wLN1Mk(k%y-F0A
z#l1_4tM-@SgkIwAmwmp`h_x(M9oc(Hl~L)@vBG$NkClXY<Xm2(@-H=Mj&BFmHtOIS
zPYGsQarMYn+t!+~-ErpA-LE}3H6%mUFkvJ<qkFi?Gp^k<?Cy}oCGUax=@DS9>xrCu
zzB$CW#ZtFs1HyF0P1komrGCe2-w__yPw%X7_bcR0M+i%|gIp#QoI%$VJ;rX|du0mk
zg}H7;n%l$IM%Uhovj@<Oij0aI^r0D68KV(%9?ccm>>-K#T_w6+j+4WWRvj5+nNtE>
zhSAp%oe;rU{T@`@^1Zc!auRH^@K~1*u)ZTEZKG^JyRm<1_h>2}YDqsXnENf^htDS{
z)k=d)xvV6VDmucR5mWa3X-t>#>hA`*O?h20$4HGn7w8viqYj^D+@$wTOa%94J!!;w
z7lh7PX`)3NNt4z7;=x6+c9S`4%~5%<!QxIovX@bRZaTNxx!jyJ{G39b{T_Ci(|y8H
zVs;XJKJ8mbP(Dg5@i{oYA4fe^N?!k}$QlbRXwNW=DWk<0ighLup(&Z6bAR|(Og4CW
z4$fVQ9_)VeJ;SeCB#60=U>_W;Qq{|UBMwmV|5|?P<+45{hLsd`y_Mgf6J^bHmqiCu
z%*_VCdQAd?8kOm@I`!P#<%OD_*`s`-aU0;CE6D&IX}-(c(@nZHVBOM7Q2ezwH+&9#
zN&p*1f7~$e!sFa3-ut?XAEhQ^<ATK<BX0nFc+&*qPMwo0iwh-$4KiP_opt6cvHV0u
zmf6eo&YOpfSi_b*#t|L4zP^GXMFD4^12(4GS2@=b9o){Fj+V|IEVZVBGE#L7BlF9&
z{wn&$eUx6deou;Fmo333rfD#i$TpUh;T%Q3O;EzWRHz9%j{3qJ4m3)}Hu$$fS$bGi
zBd=Sg9X2?e|Dnc+)}S6OXT}(vF(rR%R~-5+|Hiq<21;B&o5;OuSlsX2CIG|{quN9-
zaZMh6V<+-VeZ^;G53l4O7mc&su}G-VXfvYrpHL-nh^tnz`a<1`e0N%tNr{i<Xubgi
z>CA8v{`rbyO+^JHiPwx-rAX88bk<UvB1<@s8{U%{PMqyN&8jqJnfdh8r~>Xp-tad|
zD?ATj`SviV$@^TTAq`D5%BB|2i?Z`;?(#|So|?fbPm6%L?y%dfvMqyG7>sfsLaWlR
z3vRufB(_p2DbeUC^mfaUp*)3HIFogUV_?Ck34v#Ymz71rLsmBl*17rJDyspF&qk`p
z<gh)e6G|NTuI%ZN1s`U4!eVezrFFCE@WtD8xagML*|OSWKaQ92rwj&-U^^B_Jsy*h
z15|N#c+gxtZ=7v8=*F--C0O?NYHmEh>FyQ*o6_)i4JA<gR@#*8-Tu-FqBm{tT6e?c
zPge<~23V`?lcysK><Nn4>&{`*v4w=Gg9qPydMAo(QQ~_z5u+YW3a?bdf2+eN0L05m
zEJQe0!)xm>Lr(I+qNV-SM_XgUYXBE{Ep=C<O#Idnobn)H1MkfDylh@IvBM6>&5Q8i
zj<dZeP@_wXh&#?)eM&G6A2PndGmJ&W436STyD5|kC!;d2#^mLm@({fCfwBdCN)&Eo
z{n23D{&zMws?o;Qj=-K$Rmx!EL-@_QlF>rkN{kn`IiWZE(P$5+yCzNo1>;isUinA6
zQS3Z_Fm--a_W95wrNLj#uoF5h-laWo*CP|TY;SCD+45DEeH-c4x!W<Ugsgg_n`?ip
zu#|pH#~Z~`_(ECzS-D@-ZbmM&8<mzs@!0Obo+eCZ82)Cgc57F14=mDC*W;HrJ1fyJ
zSMOC5*|VR~@HOMH@`*M#1%uccac)AzBKrCXKan`D?C@NUAJOP+W1UIagt7P<A{r6a
z8niC&Qh^n{Ahvw|QL}Qxw>6-T=fa>Z>Nogv->}mu%gVeq-yDE;bVz9jz&!r@XZ%bJ
ztZzHO%+=>qLytDKmZuD<g2q7+A74mA6wo!_{}QA8_uc&er?hkO0Xv&geqKQuR#y(F
zK+WGqU*|qxH|VSw*F^_$X99UQ8nGrg69lYdn?x_yMpH+Xhwb5(rW95s`<Om|enj-b
z<Yp&Ul0N_qPpA%Ud|W-78rSPy$jF~pbayqCls6|Mx)U@GuKrdn_7d81*Vr3m(nD;H
zJvR!4SWd`l*lCxXH!ze)Xb+wM(hR^e{3Pxf@%dbg@>8GT$&c)A$M)5Ys1M&v9`Ycf
z0!!6s1dNQNT5+^n%(;>D5KIJ%6z9JHFnR>9B(CE78f(ehYcq3K@5q=ougi8Gb5e0c
zMHNh1Ic3<Ill-|9MPECf@CM@LlR%Gd6`YkpPd6<Y8{%R9n*V9nqznuxOqlXX_PBFc
zy$;J?W()U}Zyokvc%g<~Jp7)Fb_sCtcE5AD@_xwhZA*;$LC+7n%8Nw~+==rFRo!Dn
z4Pr?x7B;{+L$_fssJ6gn)O0q&<ryhP7PUGbGv&Iyyv0V)rc9jYtZKQtS{`4cw4wF%
z!eXZC^yDjLQKj$EgGu$~v11uKYl#flS5D&zyh5^I>Cj$_fzN1N-;kXhmPfXjc3H!S
zC(Ct41$u*xN}`u1f*EI!g%&zoVxZW@y}HN=9_YF=5^es<r{s@itMB)?7O3ME;&_d`
zV~nL^56k^pEx08PTr8A`l}pO{&>U;!B3zUg-?mage0l*JsT?A$)}Yo})pQHo=gh2J
z@}k)#w1o(~`<-LTt6x*lgz6p>93c@Jc7yU#P534G^z|sMzyj3j2x`t(O~=yr8f15*
z5yJ`#&wHDKBUSc(h0QNVN~9Bh6lK)<7Zm54_q%EtY$LcEZ&l0;OEevOo@hSj^HA5f
zmHssuIk!9VuFB^m{hlv%lgaLP6qQ%|QsVA7H19F(&s(@tq`LA9NI)dX#!M<R>a@oF
z5_VdywWy$0`Xucw7VpXf?PZ?!e-Fc+WJEd6rw?kHpbFZT<Cdewodjjd<+~*2ar%c{
zfQSs+8gX&M{HIBGm)?T6;>aadHEuujdscMnGH;Fg<{PQiKTVBCE)z*U6YENY%TZ|V
z*93!`mL4ht$CY^y%2cX_T1DKOhUFVEVO$O+pF{BQ$g9?%Y?TZ`eQE#<LZNKMB5RB;
zHIYAk#bE&>bh5lcEH)|ScN_c0dAD{`B~{~9y1u2CeWkMvDW(9|3u~Pl!zw^6Edz2p
zl?NxxW0-ttEsYyHID_jKxxth-6a-e!S)u$RhlkB@O2IB%R&hpE@>y(WS$&jre{3V{
zx&8G<4okn=`-QZi`5!<^26#IEBT4gL5b?iB<NUjZL}UL-YyGd)N8@_Flkgg%>%|`F
z0?8fSLht6~P`q8dv0ffD;|eK$9^Z0ya*$7Amr+<)EvF&RAY+M0%_sLS>b4<p0nXmV
zJ=g_j6~c#KCFSu`>f^MlMNVj9FG=81fz!pt6`F+N9!f!8V#=^QcXL8R1b2WEP?W6V
zDGcP!u)8T+ajmrtXCxh5i#k;GWlKcyL!4!)sF+{NR#5ttVZLxxcn)!%JI7AJrG$h&
z8VGSgIZSTcYtGX`e^YU{Y>`N@Q}F!rBHe`;(RUt{!>(iR#KDd>Jl^NwMtsZv1WfQ+
zs~k4&*;ZCg8$37YcJsbP-NA(fv5%O}9!|)q6gE+Ir%ooiIOGSZ73W_TqPR83%9n?<
z43cM<PujN*4+Sf|yTA3lpDY7Zf1~d{C}$c%(bH>qu#fiUaOI9`#<Uv#!}AI!Ug#Oz
z(35l?qNZTf^$8WiEt7q|z_U}atdIWsN?1e*$MQlh2lw90JZskWXxtv2S+ntcU)z>U
zRwj1VgJnYFk$9eWjtQow^w3dIuc)aBSw`I6#dmjSGqI+$8G9Pr1%Fk7f-?^21|!0R
ztn(1?5j2<Ki?u&J@!JB56QkGgRF+DNLFz@3Z932yr{i}=;tOa%E|XIcR~7vWcZJP&
zM8ySF&5ypwrhA(Qzk1UXv@23i<(64|eX^x!O;s@5uG^3#T<U<5s&py{VSRoecV?QS
z{mgTD)Rz~X!<EZDrf>ian`Op#7}d305nGraSbQhbX*XDl*K<bqSJJ!jwa6WMlEsXV
zM?&Vx>23h?)Y)$7h4O!XKx%1ww8lLx#Lngw4HN&gy@NK*jf4oj&BX4tH-FhV8{m+Y
zTXV0@U%j(W(qZ#mtLDaaI^m1dp5nKr0yJ9kJWY$2q>4cj=$DT0?}GoW`cMJ(Z05{W
z3K#213Bc(ct34!Ljm8uAkTE09C<lN_e+E+RiJJ5B&hoAvw<K?yBLJv~O@s#6)v~%~
zkhi%=iZ|5Ab7HLw3x(=^=#_eli<jySEO^n7RYbIX6Qfv_&`Xkc5;ydc)Ua`(Z`qQ5
z;tMr^eg-;^Y;-1BXt^{U=dQ`)USg>(cwBO6z5!xm2se45RBAr1KLZGd9CZ?;>hoh6
zP6B6&1JpZb6ba$}$)0i)s2W-$q>}ajV=;u}ZeOF9)yIl+ocGSw9~^i+DnD}jy!5rq
zgw!k#nNaA?^Ip$;@wPR+1^R>SMWc*b%kyOeT&17CoDiKIuT8a-SM@3Fj(EgwRC?O!
z8UZyJ`@&g%sJm_0$*5$Z=F5*4!hBl0&sSW%bC}vi#3=)a@`O+w-TUI<wF&zQd%)<`
zqpY~}^9nLS*q1nc>?Ym2x#l_h0^rSamNJ}afh!^rBB*2U(OJn7rL-OhZ_5@HjKMeo
zp0i&C6!i<L%Mt1+ctusHZ~<V3ecn)c?>qaNU-PN#<cGS~e~~Zos{it1?GtyX_$?sL
z%2Wd5(X?=(_3nHv#aL3oJ8;c(UhVaGGq;oC2w?03Q60Yo5NLDo_SGE(UYRKHz6Hyf
zapIPmi>IM)>C8y8Ledr5=tdkjy)6q7lfh=PU#ch7jFh`09=eac$f3>oOGuzDVeuV(
zwH8ldu7h^Iops5J7`^2=sWjv&K{yhRSkW6htv?kG^KN%miJ5ytn-4((6i_f_P!VMZ
z^+s{~WSe-qB9Y~@gLl6o`E%91A-o0JP~e(aM@H{Z9WdAuSqs?@=@d-e+oN0ej7MOZ
zPa%(;z9yw!&JOu+drQ6g_CB?bIUBQ;PBo163)R492R8YaZW$UUkPQ>`mAB9<l26fJ
zo6f%-h-Rv*)qgo@I7j2e@$%yph7ZTFi{h}8DVA}4enG>WjUEm_{M=f8*UBGu%wI}7
zpYg`qi`OuO81btXndjt9ze=^g%5NHA<%Zi!ko`p`sP==9gBGi<HSYufS5}aLUe7R>
z(YqagJGG`88Nww)1Pv6J^5A^dYCn4*bu~*C#$A9xy_-CmJFWg^&-liP3!X7!g+5XD
zwVc}RS3Ub~54`PlDRR++)x8-}WjGNleSS|=dN*E{+aircgU)S{a=paYs%p;WxQUQ-
zwbHo8!vtYXLM6{f)`z1ZYoi!T;)}@jP^Tg%DE$0{)a+ONWa(wg8M69t@5?IsHR8e4
zU=6Fe<+TCI%)IB46n2MOx&ZfIflIE)ck--DUbdhg$MG~NR))`YC0$(Goz>FF)rHkP
z8Lh_VI3r6#9BG7xHs;!Pz)h}G8nJr1uYhA=prSiKFM1Z(FH@x`aE*4fPg4Bfo%H?X
zD*nwX{|5!NeG}>JMDzRet5k!o?_KZCwPNv33Qm@eo&m2yrR$dIGpC4ehxV51mU>;L
zq4t+cOV3Q+ufPLz2o)U2xQ7eJ^+Fwrla*&A$1fnzDX(V1?`$X*=3E42*{Yb9N4szf
zD3(sV@4s$(anr%!v$N?GOJ~MNrAKS3(yuggRac@<6^BeQdK8P2^bxP(s~^xG_uQnq
z!3&YWe(%^`?kNmuU6KuOE^^)pzb-lI1deZW%Yal7n;lG1$N6H_N-Qi88zhP)^vk7?
zy?@;W@GgyeW8$+XP|6Ejquq%I7cy`PPDb!u(M2kbLVv_rZX5ek;E>C~osupqZEH=&
z&EFFBBgC=xO^#S}2^~tiF@W1_{<HgmMfzKxhy|$9;FvjrJ)DCTI3+^yW%-tfxmwCP
zg^PdP73>2=JY=7x$V27JLx4<}RK3lH_^js>20)m_sjwKVBrW))`g-HyY8uWWQrK#^
zPMo|`N=b+l5!ufhhcNQSWhh116sL-y%&Cn(*yjL-&GB>v?l%C=u~t8#xe?cJj!Q@w
zY0$fsdB3r}o+1Ra?NDK%@0;sj*maFI`CNUdKE0=0bgQa+xr<Gq{0^Mn;iGT0!eGJI
zCA~a5cR`6Y40IrV>Ep8S)&|EY6TR|@U|&UrVNo<8Hss9$t#WnIjNxq_%KmzQvIi#*
z#g_u<kLm(+X*7zPG5K`2q@SZXZ?@2Td5;cHyDL3)IbC$P(xZYVaQ}ok{enMG(#~>{
zFCm%KaEg@u&vCLS8&;G~Y-jC@Gm@s)Vw7~oX_HHU5WVxW0plT`PU>}6x)edJ>_-bK
zdcG*%quO9OB^INV7GEz@%$46=leS6(TA|#zvlQ6qa1hPS>%?bQDuriTgC>nshgFp3
zdkDGAuji-@@w{wVqgG|#?&%y`(c9$XAi?b81hIum{nd7j+kV`(1m!+pO-n~yWjNaE
zkE-|65Ln{XYl2?KdALebT9&BJM`ip3EspsWG#r)hDqNNL$Yosn(FPz)zgC|_{AKGb
za<XJq!DS|<&q=`iYLhin0W8AKl;eZJO8LuL7rx&UpEsX_vp1P}C86NwhpK!OykkSv
z)<0MUF2w?K8T2icWP!h?uD+-28H}XMI}PQ%6+fGp{oS-%V_q0I<BVG&MpvnPs+8ln
z7{jU_O?0tYSiMK5R6(X=iUGNOt*Wb)fH(1hqh{S>*p3AaUrqu$N5h~k8|#e3<{ONJ
zR3<N%8zJbP(3zNLCP*xC^xF~7d101tXgW=r2sru>SUyg6pqPQkMXovt&Z<+Y@@UT+
z<}Ij2uaniPJJu8JYsA50{Swv3`{@t<Lzb5vh`yu3vP(=eowyLpahqD}#^HO6<K_`b
zK#~&#IyByv-4cT<S%nUT(ls3C%LuoIBM@yz+TrGuxK{sz(v1{^_U#@wkHDh1COuSh
z3Tg0SO^{IvZ2l}i18N-CRI;jU<RT~O?e??pb;gCUG4S$;NW_d%Z;qGa>KWp#;ezDT
zgH&9*?dYuAH`1A$20c2km=86N?I)e*k__^ZsU@p9R@kXCt40r{9tJdQ(RTG7PpxRQ
z3D8Qu86~d!qU10h1e)9ByPYtgZv(izYqG4^81AHFinrO7O^7jl2N;L~F2L424#8EX
zgzBsi?SW9K#!(Pg!JqqA4`nUx=bB>}Y~9p$hrA#KCr(LuxyT*y*@8uU$uHbDD;;Tg
zAD|m|#Cb$7dtETs6o+U^@KauuMT$#J{K7V+oIUQ}?2d@yW&+e`gaU<rS*DQRP@PiB
zb(QO-+01wqpDqoh*Qm<qcQ;J2CjM38rmMy)Qq^C}Gt?Q+Tc!NA39~PiV}6%50o6YE
zuR+i!|FV`CA^RXj7OOrON?$fAlJfHSt-ZrC96YqQEQHjuyqzGG8g^>>AF@rCh_#Or
z8>I!Mt*-i!a5v%Vr^#MulM9i^537lJwK>J(CtR8Z1CH}}ry`e(lv9S%20GuveoqkC
z*Qn*)-V8nlfW@NU#0sB0Y1>TTCIfaT&J6T6=+Bh4=#q59bDW1IpVE{TgeM(XB;CfM
z*cW}J7IeHFSQjeJ63C9dH3|C<XH^=HXSZXU%Iu5jZnU^R-)wS^_QUFrmix)BR(7fk
zY&0BknLEhF!49!vE?}jgKOS!c4EK@!@xZEgV*}^nkaWmv9ho+#q<>Z3m)P{m#mUW2
z7XW+7cyvl?pdi?dO(n-`XaI+7z~g$w_gG(LMJg^4TI7tcC_gcR?k=$~!ph~mwY)k{
zf3@i(<u3Z}B(yv0VHc_sNJUP1xo_2+h=I(1zP#zH+jl#*)pl&=Ht(~s(A&lH^exMx
zQG>ty9sy97r>y?%lsq$wM*cK=lyX47r@kt>?G@@JTUVVTRZ*onpfh?IQD(joyVFIX
zbb=r@rNn*tm(6Y`Se~N9#Udz5y@F3p0@eT24u%z$qmt+I!zG*8G}%c?5Sx>ayEMlD
zuve&jX!5~6X_N&TJ^>JkrjrzlNEMcf1x57d-$!8C)ud2a!<6uz#t|pM*ptA`71<}@
z9{QBHW|N+nIVT_-`J2e3J6K^CJKHfO0KkZPetn6>7B7in;}H@p_?g?JIq!=@y5O_=
zc#qivEYE&E<_dS}?>*!4>2$;$_+`{Hz6XNoR1l7}!71Q*{y}07A?IIv@!QCX(gkPp
zfI!Aq!HO}F*b+ifbK#YgE4`5}tz@SG=JW?U0GH#^<mLJH=Yk4@ZH@SnxahFWwV%E7
zLGzISP8rao?vtHeQvFofQ(hA>w2(+Oom^jkcvNcJ!*P;b8F7m{a;&cB?w2Ua^VHhS
z?YL&*tGCHsn-7lsp-3>LaaSkDW<Vl7g(5g49*awoa@?c!z-q%wA|4&@F}3>Mw{hJm
zCc|x5P5kB^>4cZ`Wa1hG*iWRw(4TGZYDhHItmO8>XV&AgXJt>gJ%HB@aF5MQ>u?L8
zcQwjr$Z*6jh(z`YN?$6f$&m<qWm5Zc4{pDb|7o#3H?hpTm`EV%zl5KTy9JB5h!C{Z
zYcsy8CrOu1!tVMJt7vO(7yBzE#ifa|_czrCAy<W7Tt^^_rB<PO1r_@Qh;_qpRw-uF
zWc3v37BlU6@$_ch3;qaj#}0+XyWp|q9+%8>W-#nf$}$dms_QqQD_4gCe|xBR-9Q$h
zX7<k2=w{}7K|PQJldQc;@DteLLV3OivBKx+Y%4b7Xk~u)>MC24A}-B0Szoro?KC3n
zppMwMi;vE`^_0eyZ$-9A(I+VD#Gn&RO)WprGS<gAC{G9LjSX?nW9(fc;eB=|wko(v
zF{$#mgDQ(&Cn%!i`_6B;7MEzReKg^eR4lq;fmHmfM62;r)nDRyA4^s*@4;O^Q65-V
zKV}CUWotRHT+o9(F;agKFY9`zIR;;{+IZ0A{dHjsw~XT80)<*$5n8o&vknTEr8=yA
zbAHDIqjE1p?iyUdsXgL0f;-0ELCL}TVykiY6x_a(3|C21IoW^Oa%A+~2TvfU9s}iN
zkD{Z_HP;V#UZPd=MeJSyU*o8xCWzDnfMGQ?^7|-fL^SvPlR&qKMx26kfHTtVwp6TU
zLqnJ5#(fG<^c7(QVq(kea5-ss?~)WIN^b%*gqbj<F!*_i{es;oTkX8LN8<srOG*9<
z=I;ED=av7;&Igv}l+wyZ4f<WHbMfTKO^T5YbTb4^Og=SuG)Lu!&9Vdk1lFS^Z~PZ6
z2tEn(G+dGGJo+3L%TA>%uwSudD8wSZS$My^WPZ|}kMcvbvh_4m7*6Id_+8(i(D;Tp
z?z@C%JT07Mv4zFI{I$)gLtaFjNkg>vx!a9b>Kfa(Q;;s4;^b1gr;5S9s>yKFv6Gtf
z?Jm&LiCml}3brc&efyv29Pt|<G$%i>DMp1wgkyz||BfyWd4b*}fLP@QGQ$5}e<r7<
zEZ`VT{q<<#e}c;eC&3CpuYlx|?0<qe!2hu*h{4-EHCP3E^EmR~VN<Wf!t$1yJL)aJ
z+{G(m;xSv4YSC`D>ggn;{71FKQo5g=ZiRh`#mBvDuSULr&1N5?B;sh4GHfH4+p^?L
zF~{0iVK=#f6#$-PJ{J~_0XZ~$-kfQ1gW!fcd7ZLVleOtB<RosZT?<Qv@<LoQc-RAc
z4`*U2zb8SF_)c<H78Ec0efSQJY9kJvA7tsO;;iC1k0p7a{Q4yud`&#uxO{x1yuoX%
zz`}9I&taG|JGT7R@<Zy)y+7)o-Ldnx_+4AR`Ekqiksr2r?T!29SD9ZD_WbnV2aoY5
zXLrbC`89kSc;iCC-e3N)Wp~zx$gnVb%s`W+3)ivH)F6W6*%-kUSoFq4I}c%N2FtI;
zKq>e^uRS2cXTO8slrZZvyrqQMO`E;L&fi43c*R?R+c;&ZW~a#Jj?+x@XFTz}BT>nA
zRWCC{oXlEj?Q2I6J0s;C+&t#v_A`kv40^6mEtBaT7_#Z>e#V9Wk)VF%!e^IzjS49r
z#10VMxkB&ss$I$wZGiS;b($N~PHsU4a8|8S$9^C`hm<QVW#UrJnlda5$-50|HWlw!
ztkWD*w6gWBHzZ69X4x**$wT|)%S$%-WzNr@GIT3wzk_X9_irhtI1I_%E_ib;-?6`0
z%y)&L{kl<|>Z<rgjHo_)baSn=KOGn+LNriQCY{4g2@z6xf|L;3BlRj?;qAZH36IR^
z!vZ&_@%j=pp2yIcX&oBF!~dKN@q&o|(-3i`7!gHVmA7WizxQ{U1<BTYKZqQ5Ox5sL
z-XJ9Z3R!*g4)yW-4O<8-E#txrdKR-25@G=6)Pm(5mbe|_{A^~`-(|GJuwGlHEQCTx
z>C7?kPM4)vGjckF{#0~5sK9>7$e>mV2iF{n^_yc5nEj~)>{65ZAhRCHze@v0Aus}s
z`qyp$oZdHx9g(R_EZD(5tQxF$F|04%4EkzY1U#K7ACt1Vp4L>QBRl~lpGIAE$jU(4
zbeJ_xk4jge_$;92t*J-3{f&cTNI#4ka4ANrHFJs5?5l_wUP|fvBqj0DpA2?ZoR>yC
zApg>G5Jxfn?Y>PWtbb|fJ6SdRuud=jI5^{z)54FVPneb93|{ToWcwv$$U^1~7^XsV
zeTeQY?F!6&G%hT%=6e~W*WZ-0SxN@SU~*HLRp0`Lu(|A52Zyi-3{*4931g)h6^f|<
z8yk>&6l<jJC^H-oOsr0BTIsKFP<(I0;HKv0GkstpbOv3VGDC~IgK1K|+_M`(N@X@6
zjDU0p2=pZ%SQyCCW3ZY-SR@9nvHt#((e_`4gy9{_RiCTC3cP+(48eeWSG(ow0YHJg
zw0(UQr5oZ026&13;_a~IpgGfMzxi33g(g`G<B`gog~WZdNm+iJJ*w{zlFlqor}7-K
zsw0j4l4-qK^;Q@KjS+fhxFAhi(IwV|+=z*NjTD@Kh>)0N<v@LjNFQZ@X{hDK-L49R
zVJ2g!{)j6YC~J(oa|rq{ITf?v$$?*aeY1E65*lmh_Zw$28qdi&xg^!V@GINr9|;1d
zez@vwvA}T)K}Y2Ci(7@gWpj+UOBdLB%rBqx3){~cqyf@<zSSVhj)weVr#l}EdUyk7
zz2Uph19S5HPdR5iWxO87$s%*C`8&5RSi-yK^{vtn(HbZ!0)I1F&tb{RAS247drAh>
zOc3102x@2#vghoU_CQFZ5$8aGE9A6WrxaT9)3nbtQ6kRW^aAY2+#tRBfgJADPICts
zOH-BW;dL3pxK2BkO~*U_I^YibY4&m2WkTHfEud@F^(f4th+&_Rs=^*_dBAMHjb1k$
zID~ukt^dC9?FY&8sm#Sz`QHBiE7V{&JUuG9DGl*_Tcpul?zTv@j?jk7xZ>-6kWvUK
zAF|OaJX&5m=seTwWA;Vlv_Q>R+Tax9?(UBahT~d0Q_VEUDX=4eRhvw4E6}Kjg|U$c
z{lR(QfR3zX@%8uG9GQ=qM#LFA<HhE!)R49YbCz*0IlxjdQk&EAZJAcHpl#*elh!SS
zUz<?@uhhnAdgKM!w3;;0ax{5Ihjf1(JmdJV)b1bK=toJ}OgZ8T7n5u7L+_;i$jVNt
z&I~8GQtBqov>O~4FXS*%&B`-GGvCTBl5V}Lk}G-DM0#>T+6`@pmL+VQwA7i|Z{#`{
zn!ds^bUi3Fme31}h8)wGilZ@9*{6smYyPtGQ7MRhIW0q<_DpB4Wp|hyBkzxy5to<P
zN}4=-xh=ABYChOV@&S~oHoX~xefdddGh|2}{qmixW%><9ycnMML+=mb>Xub$)gUEh
z*~K{gYcrjvgH-A6iSKUvPRh4IDmD502;T0jNomdEzUVDA$Wr0~=_lLnzba{LqT$EO
zTQ<Q#<=+Jo4gEjT2X!<A<k)DhLB!R#NYz}X|0}W<KpZtpZov&>@(^a@7ADUzE%Y*`
zGe+uuD}Ud;*wa%0UU$K?Yu0;l(0H4rU%@Pms7RZ6sgE?Vjz=1*K4HFBUtr`jgCX?2
z71E65@xv{DPS0mB5$RM*>m?iQmys!ur7vKG)dc|!3{M%7x{J*CGuM|FGDPogvI@Ie
z1#i-xEsQ|u80H0t#c=2H$Fx3zmsE;tuY!Y(uI*prTJxFU4A~FWrsS;{0+{TWYIc-N
zfvBe6@E(xO{a6dOF-TLtZO*6#kBmmB2ZkX!t|v?e!5a?iSLu-rf{g&ANTntf6v^Do
z0>PIT0H+`J3uy}MV6*$<1_ba9BdJVYzbJ<tjxobepM+ltj|v)Vfspc<!?ns+QuQ?J
z9WU%O7h8}g0|s*2JhPd9g1v9mtAIc`lqV2iuNF&cIcU~9jEXJ3Y5sl-p}X;x25M&8
zdchNBJlMfDD;4R0vAoy_$Hqjvt2F%rp9#L8)Kf6ozh@zYHadTH=a5IZ9cJt>xo3q{
zr^+Njs{A^i9z8}mafYZOSe@@&T3Tv>*gpX2fzG&x<apgip0#igUyns@{jv?%w{`;m
zx?Cn1A_vKOF~I~wU)$0?x;*BcCzi<p@S6q>pg5@-m@d>!E{zV6YUllfK0vhc(mHjO
zPbqg?gGY6`hpqHk84bViC(illb?yXiQUO>7I)edD*evu=Gd2L2C5#E^yq=}1yNw`E
zAB#`Z!a7X;o$}r(FtD~F`g{>M>g=OnsaYJ12NX<lhNzu<Ke?(AbrIvh3$fJq)AguO
zlo;OQUn*}OBsoDCH#^$B@|YO|1&+)0Zn+2Cq#jcCALGGvg(EWs?(LI2aD5DhGdLj4
zn_auS3qUSd`ifpzb&@B*1DJ`*aA}U9@~7GwTD;wOJ4AKFhC@4JoB=krAP*Rg{U{mk
z+F_s_+?Y%aJTm)h+WSFC8OU}sC-Bkv-@qbK$N%TJx(QB>m_jc;gBm2grT0-+hQ6?F
zi<u{`^cN>dq-?&fe_l#v&sgnVlv>)IG+;jf&l#(%d-y1;A&>lLW|Gg3e~CvP?6E~(
zmgW}YM|Ulr{9Q|Hefg7q^jb5Wq5SF6K}ofsz%7Jo#8nJR`RL>z;?xc6cCayF`lNhu
zy|kC%JKG|2kXJDUGllSi8@wHz)SMBA^~EHxo~^lhqyr=@48x)PtEX<k9li`*Sp&#&
zy$<^D-XJ~P&u&UGO!I4K`vcnxrZpInGptNf0y>xsY!ML<iJbd;K~M23PM?dqq(c0A
zUpc@XXES|>D-?q?JX+RIQViO227p|;o3EQ7@Skq`xZ`F5^<TR9zbWV61rpW$Z&J{w
z+FIo^Pcd@^%x18ADnRi607Et1Lf%M;TXXLI@jPN77<q9?2cn8$MyM}|pco!_{WeAq
zJTeqHD-krlXYY7k*-lAtrfi=n4v40POF|klgs!3<BL&IKdq|h37{Up%K;Nt`#tpL^
zjoxW&LN-k$n0rZS+7DaqR|CE}rDRMLiYd~l560ZP;+1$W_0Wi*v5+UGf}^ZEzR(WH
z+bm()+`p9!+GP&75tUSCRywuUPVv2<3e2#r(izGR^Qq^F;Tlor^~BXjc;ZluZ_X`U
zNrR6E03RnHTJsGcq94fyj!)m{k@ut$2un@v@V{8<ZUC{NIx|gU*WC0DU6D4c+<i<6
z)=S`_nL_;Qq&C`v^qE8D)>Xdu{Os6$ko+gnaeGhyNcPcgVFx@SQ)?A8qoxlh9>ScH
zGk!H+TS`rbdSwB(;PfCCc}y?$Dp4#(FN8rdw^Bo`9L*lg1g<CRbar+^!iZpn<PUry
z*vOdw<z+QWu$uL@Y`20}q)){BXgXL+D{B5hDLRiiV~SkpYT2n~mRf7z16*M-7H=%4
zGAAJmra#W=f!9;E&kmTTdYn80!zj~%J9i&ygQ@=gwVEEcTejDK{zC`D421wO(_n^H
zRL2(XObU2CeV-dAn;C8zG56uJm5Kq-?Udw_vh~ulv|A+!##-dddp;GZr84c*t4K%4
z`y1&+6?LEgkn{&xx(k;7{s}i9VnJlY38p@%r{qGY*;i%;1X?5dKhCa0%8^|s+HcGb
zmOaAbw9_qwREQ|_m%$;h@%qPe^4*Av7@A~~zzaH9UiAr=4zVCzLq)q^@V*;;8Ivcu
zvsmqCgKSU9LyOi&fDj2|W=1x>a0BIuX-5zpIHNT>5(QxhI^=U!H-v>?wVCJwFE6hi
zw}8BwuyBt<b5-D-$1W9!9)UR_^~POike1rWcKWA12<xD~?gA)`m*snwkDW0dXt2S!
z4|z-<FvuAC4X#Dj$ne0}=qes?m`Q@C)QfjQ%InxzoLp(_v)QWZR1Xu)UVQ2p6Zq1R
zVrtp`r>U_4w)sG-_X^&*5A{fO>jw?s9t9Ta<ON)L13P~E%s_zOoga>B_mX9MEd+Cl
zr;0-PD>W!fay~JJw}Afv#ZO8Sn=Q|St&UwRpl2{wPDB~H7l;bLF}%5vYTvW`72cH{
zK1oaSV5AthVSeh~^r?`~7O1}S3R5p0>IRa|cPkGSHDxfd<}s^5-j6L%(QRn7Fal#L
zUQD#iJsYlsw8L<c$uAv{)H<OjIlSHOQ<2+VNo+-g*s}{$9F{1uX^0#!<4d7s8dIeF
z6HLh+v*BbN-yqisvB#B^zNVcPf>0S{xx+)cU+{y^Ar0n7OF26g)xM=ZW!5Ra^*g9*
zuQ+4a^9CXpRClcUnS*@oMHK%9*fReI#*391T<Qk0jW^SZxr5x*9T?!hAy1+@THHJO
zD@3qjhdJ~y$kG3LzXd9?<AB%0@g#6#JJaozg)kM&vVY8Zt*xHZxPVQ&mSI60O$Hmm
zN!t3MKA2_A5BL#25OI1mtxtFT$<r6LPjwF$-Q8_wIQ3h7F0<23{Pr7aYVK9-Ssv^b
zz<hz{+<d_jj%9sqRR<Dc?5BvoxOCrIF{b+am+6wX@v_Iyw+7{AG96(eo-l3uZjt^}
z81bQ_?vvrCRS$YDL+mduJ@62mEcdow>YL7%hM{LD6|cL_*Ani|XQv(5D-C8d-<(S|
z(<A5GDh$(dnD@0)bEGT)eB%5$_u8T^TnrwJhqyiedO>@c>y^c{(#EwGJY|Lxn_}E=
zX-nkzKkTU-Uv*e&gwS)Czk)NgfP_{!W~|tkJLo*!0tv}r;xU9DfoN{7EU3M-8kIEU
zf-bEN5e9^3BD~A0y^m=aA0g{$tXHR@PMec8AQ2kt{um>TsB0Z@_Mef1Zv)0wOSDHD
zfASv*AgC@^{61OPxoQ&@;lk^D=^lI;RSPygF*PQ956;jW$zwJ`MA~GyR(**3Bj0E9
zkdV}{9+4m|oVyNL1sfZYwNinQ(%s?GQ(si=v~aoq8`gGEAR;Br2s5>h>Rm{v?~WlN
z($}Az@H?tiKk?oC^{`-WyUFYz+Az_LM`;jH&T0Jw_oJpSY23<fx4%3chUvBIqn|%M
zYca{qMB5!o^jz)mh-p$d+#Hhw4)EK6#oA%C3bT4o+j2hH#>I-<G3r)nQ?3Phy0Ko?
zxeB1-5#h;o<w5r=3}u`B+(Eb3AWzd#b_!ysOnj7N$Pdi%c6F%@c%<!kCbZ6wpETqv
zG6+2sLFp}QN{P{#Dc>0h&;Q4>tZ9R|3p&UA%5H5TbV78k!`2>G9h$8kxZi8jvj_Ak
zFu(;JFS?4M7I`GU)tK7K9ewhheD*-}tnRW5#3GfcmztZ#JP48dlHQmvzeBP*K`(&S
zc~e7y&MrW;3Oz);VJ|^N!3`_8+S4n#@YqY3@TzF<5d9ZjfoHz1X^&QY8D;BxrK}JA
z89IC(*p69MaCBN&)Y6M>k+`$(XxbS9wI?DN(5%sqTOVs&sQ0lPscEc1Mx~COo-#*H
zA*g!fP)L3TrGna+_?LFVyg*;-3apnlz{tSH!{+FxqMY|bjuyq=F3nk!svYT((`9tG
z`A%pRX;bE0KN`RH`pxPJx&Av%CO-+AJHtB6!dsoT{(*-*JLXAoDfk+FH2GF?tM1j^
z7?eJF@Tfg-52sYFC&@$(ueI)_!D&^3t03HB?zdHjZ-5U9UEu?SSTBhf#p;-ivl#t;
z%M%5|*JE6QI@wVmE8)934YIsW6xyeUoi|0=_0iyEndtOv1GiuS1_+`5Fpm2$-9%UE
ziNl$%qoZ?xe%_y)2^}`TUqm;B^a&V%a#HNjmOa@&h{@?2=>BVs#f<YaO%X=J*W4aP
zm*RuHEcA@^&YX8tzxGUb2*~fIkym@|mhjqIO9!B}eZ>aJHt6(TwO2`*-}`$5-~9|>
zpnKTCFz;fAoQmh7)>DQs^m>>EvAV|P&Vc{C{wMgpl-4o`eaJ?9zN2I1AroUA&Dge{
zp)*qr3)$1%(10Pu-t#;5`3=d<c4-tC_&!K)`KCK2=5<$ZcoYVyQ6D}fs(4jeT(3_?
zcM%Xn5kuDevi(f4tA_BgF%DmbdTtR+O5n>643`T1#MXTGvYHn;hOmO?aStO8S!j@t
zImT_AE}W`8HSrnj%`SxJGF{Y{LTr}W)2ZT7r<cXFuaa!1H~?c~<VychbvEZcxFT<P
zixUti={?1a8In6ObJj~UY0iew+$`qrkYwwu$HFo<;X~RF4t8!23d`sJJj@+yz*rz`
zb1+LSjr?J5`2y`1V(|PP?yY5mPw31x|9QP8S4flZyY}4NlP5X9rtv<&HKTkn-ZpM?
zH>xu;fIa+$#66U_Jl6*C>bEIKV`2|d`(H6^!|e1>fgQ;E=LRfmc@ANTY3HBEHSn5>
zw2a{i!8fAa9bd~w{mj3IEVo`-RO;}0>x<IQTB!;50%h(uvNdkW&!OyprKhzH>9~Hx
z$UR`hLG9A?h1&gzpO#71`H<-~_p6QzcLx?+?{Ry=yt|%{Ce`vMO99MjsOTn{*bWE-
zfdeF7N0SNJM}5hK6{}J+Z@omF32BZ|3SL#LX#bo!eR}TKV-Qkim1V`D+u|2pEsdXt
z(|#DQ^Zkke%ZHRaN?LWFk$<MRGK$jW5_am1zOitao>{)~nE?8jeGX%G#E&~$qeU}^
zc;#2clP@$K>ORsecyFCwpo$47D{xW`7nZDIN1_Aj^|svUzKOH!X$TP(2}b8|t*1sR
z0Md`YB7&!oEpMF(^^0-tp3k$Aigi766%ebvaISOR4Fy^L<NTgQO}|$gxRV=ef(sqF
zw5}p6zcm<-{b_mY0<hl8H}Wm?%5DQXdt()_xSMxOJA5`6R&i@~_0bB?x4*BAl1>~F
z_%#0hKgc1%URll27g=>BYOZ&JwZO(^Q^L>^9<MlU-zAn)hll-Ab2ilszPS^R&xfAo
zB-_@XiZUkC5}9sqK^!>XJ;-r4qt0Q3qqj8GlfGHhElQe0&Fz?V1=f?^HNYzTGfL*!
z0hkpf?xbIGuC5@AFVLTP@M6!A6gSR;7n=Va3Jf?kMPDu)<Gt!Ka!pi$Sa@g)a*wX^
zzgZp*z#39#`wZ+<5`*2Wmr7Rd{m^>!N<5D|!|7ZAr#OWD(h*vl#x#K}JYmApW4&#S
zc0LX|Xrry(3x7gs{}P4?D6j51Z#u~8Geb!*>L$6v?*=6piGhOGNA4^?{3sY;WbD`c
z&=g_cJll1dSUsj7)8PM>q88wZ7Y(3-S1CU11*nbofLvMHde0~E`Q_%cnMbc>V<Y;^
zR<mjh2WI;YRG*fkE>@-q>rau-sxKY0j^}*=RsxPue)OTyz;7?vT9m2`AZEQM|7Cwh
z>LLC2z6xB|0;}bvC^DcZzr><#(Kxl!(AtL>GcB@1&0xa}@u+aM;M7^s)~e`&p&0U|
z{St%U_9eT|1)Yna@f~<IBhbW>YVTmkQk!)Y=VG|!byE42-XHZ;Po|*!A9pWB-)T9T
z(&{#vC6eFWbW)Q8fM~%}QJfj<*A1gGre#q}#N{Sdp?K6B@vi+S`;A4#^wcs20Fl!(
zeF;D$XyJ!h&e2$31`fp*s9DT<ZM6%%)=O4d3mH^w(d(w4VJg($&#~7dG|b5lUMNHR
z8IY)ZZYi37ht-4iO+hwG<zUHSoDIebh>$O#7$vV1qS6=3SF!*JyBgip607edrfHwW
zFuhOmmtlyHf4ZfMEkj%$m61JlW*qDui<7MR#h<EgD^!1-u;wT4XL>VzQGLM@$cX<&
z#%pv{2<~-IqzINFBHgK~+*cb)Yv?NnFRov}Fvxej(|~-k*Q8-rlXHKQ!P&+IcSsX3
z|LW4eVX*5bQI$6)rO+~LnXAq5wu|d4E&m1uxyOB5ri1dY25402qshu+?)vMjp+I^$
zO1{+oO;x@ctP4M);-2_w=ZI}P|3%596vDzyuaMGEKMdu+^D!m|T%qr3>n_AcoW(y1
z(UL0(h;iw~BRls6k7Afz*7U}tk;Kc{#mJ|E&;JnoXLB<gt&p>oaPbQ3<$*181r!}C
z;J>!wkDdZnwyb+4x(jER-0IfoU?{3z-K`Jjwh>MWYaOcA2H&_5n>_d2g!}73#0bH_
zzIDUa|5(^FiHCBTPuz%XKSU0*D$gx4jvveqOGsH0rdpH2tG^nh1KW}!an416bM6G3
z^ZSqf-YPfLh3s)~k+Z7!^C|K9aQBWQ13Cy1_+ittE&?|T+r1Xul><l<#UmbV1HLUX
zz(_GyG%r|u3RWv)if>Goz0^hausP9|mf!+kqUS#i{$(;G3(Ct~ap=;j9eMUgE5;))
zu95VZSv!M%(-_y01Lbb*Y_&>5hVjE+3Fp6^4aRnz1#EvnLbn!tR{kj&QOI5_RXxVv
zKz#B<T)`+5653U=!W1VvG764q_fqd+HXK|mjZ%iM5j{U%04U9!bF}t}7p`gN2M3dr
zn@*8mVh&}CzSf~J-WS6HUDO`(vJt*+Q_r>c6wwUG`?R63a)Q5&Hq=iPgc<VZ&aS@y
zR&K+1kZPn?kO9z9lMwAGOnb%+;QLR(rrJ2hDd4Ve&lm3_;z|t9K))3K`^k)xk$owF
zictAFamfC+sS|%Et27~deZN`6a0E?e$~^Fc$XlN&?u5{fUq>r+mkN@GUMjOK<P_cm
zn>Ljhm$fnMJwhL~wke7J`L$(Ye!&E+;`CrZ0Nqkg17NMEm=^V`IrP!_bVYQ@m$x!C
z4ea0-U4<w7zNIc$oOf3&Efcv00E}<?_$=uvvgxYezF&55Hi+|V9{8>e#E@E8QZ3<J
zA;p5z9^XCuZrrZJM8%SPqheP3gk6W(_Xo#SYKg{ECq+I0QP$@aWVoExJ5#E(3n7Ta
z<U!(q1D(_{^{J}IaLx5=9qeZTcV5wJSMT%Rt}C%EZO@_I=G9U+tU7jOHfq&-VseUN
zCun&~gjj8|WzkWyhN3+G+Q6r%wpp3wNpHv3*7Zn?w?q93|Dg7AsQ4!`G?idU7GOL+
zUQu?N=K@KilT8ce{g#QUn5A<xWa<VKBVR}$v1<CQ8@i&T;pYEg(c=Hw>15`4LRK}`
zM}Hk6Z;U+YmtWP4a(FbA%=Qlcv^&TmhnriO#uOLM3Bswt9Y2p|-Einw7E~X@Z+|vW
z`<4dez3Hq0`7`r&OQ~S@qR0);A+tC*CiMc7L8sQ16}_4LbRjPZIv-<c@X{pb%bv-O
zpqD*`D+ScvAy<xn=tba^Zk-AS?)X43G)T|IB@(#TKCImqS$<=+A@WcuzB`ADT7Y84
zUS`BWa%6AyJxvS$L<jd4Z!{(Ih99O9ju*m-=AZY2K*#a+c2xxb#?J3j(5(1&%bUN5
zh(t4nhlB4>$s-fRicwy2&RLY{5iYT*EzF8rVaT?%etff#?g%SI_NnBzu-^Jmw?jlf
zOd<S5cfOu>h36Pe3dRP(tXFfqW%m%9oREyTf_mpEF@o5x>Y~jWGQ1?${CZm9BxrQe
zYX5X&2!?U9>M%%uX&lV*ZwMcq(0&<PFm~}_{AJZNX0aC^P7S))>C)qtp|NhibxI5-
zyT{Bl{r>&-=7!TzTaMds*g}*m=APyUakDi)Z~x#Mb-&;GgJ0hqx=?djD<{!{hDy1Z
zrCx6`^-dWxW~}j`@z_-O`YL^XGIfKK)_3o?ydl-t#|8uC_KPvh_G7h$b~X?ZR8PnU
z3zV>|`@|;TxYzrf<lC6amx#Y{Lef`SwIb(?VxlZFPqL91ZKZ4PU)c5?ha)s;gj2+0
zMjh;13Zy{+11!l7q~_^m5p`8}dIp6nFXW4s?-V_y_P@?~EjG)ooF+9ed>uIH-1|x$
zzyVI6i*U<rA-KTS^23P^yCW%$NKN_0WcQgEEB?b@2}Mg{s#a7KhV%Mp#GPp163_a~
ziTSaMc}SZjcG~^_tF<ftOEUZZnwI0Iy{4SBwE2W0Sy`5vrjXUxfZODnY1+oPqNy3V
zJ386tlO@U`?m8v7FPTX$K-;KbN#QOkR-g$XiXZ_Z-^ae+?|(4Q&*$~r=bn4+J?Fg7
z``m|c8w_}8m|N^Zx>ZyzkA+LNWANeq2#Fj(qR1%QhCy}ZlO(J|C03IY*Je3jDCa+&
zryV^s+L1>iJeUv86a*sl|3$N56jFQAOaTaG?~|AIO#>>;kpI5cz|L)-ra<DKJeNJO
zr$7n6G_72Q#Jx*BNx~RIom5%W+w@!>2?E<9nx9N71!<bFnr3~b2iC~f`1sr|-OOaD
zdUmcAq>8SncPdJT+K!|B_7?Hv*cC74A`Oh}xX-G5i%L9W7Vz8r0w}GZW!q#s)uuF`
zJt8hz^aBq+D;GL$A&V$J9NbD^w1%6{<3KhBix75Pnho+;^HtVMXUt@bIL^mNgmaVk
zz`J1T*^t3ku}hM&=fPMmT#^T%GwtU5lZr_x%TC_ZDvR2g-zgar3h>a2KIn8goFK_*
z_zS3URX7h>YK^!^`UsrI&QBaE#1IcA@jhIq>+|jmf1_<2EYGAxxlueW@;E>LQ5p{<
zkn`JRBBsEyo(?0YfH9$*cLx<_g{p6V5mo?N3rf`2YNo$1JZ4bzC4g}&yB_7C$bi-M
zpGQ^n3m-r_JE%#g+kxStcRDO%yfV(c-Xypu-sd^&<exI7$kbnkL9~g4o{5B4J@xSu
zMQsy_rTk3;gG>H?+{t{&>sKH(f9^UnVO|%Re2kHIe3T_K)*D#n)XKLqD7RIKh!@R`
zHcbV$@}Ai%Z+NL=JcnilZr3IKuaiD`RO!CIM_9z+)90FeR?%{OrknC{IM-pC)}hli
z#Io@CKW#+lbe&?GI7#b&#*7tNtwY)&y}j73>GIEz0|Yb0t0b<WFqx|>Q-hF$?;e{)
za#>qyIvHBxdO7mafbJdPmyK@Hh8q$sG59$2d1s4y-d;HIYwnl-;4dI?(AD?@<CNQU
zn8TwIAzAnGZCO-#>4%s7l_i-+#C-aGVKC*BB4XUXid@F(P|oML?*0-mmbLy4h4esL
z+FIh`*|<9_7AE{*!JQplXSNy0m!#Qe!^4;E!`F4i%NXQ-63+gK=giq29!ll7NN!)Q
z{_4cxLzX5QC(1xCl5Uu4QZ`HkqtnqYO7&NcH69?84u+!AZ6O<p^~sDqKb~xN;UBXg
zn%iujzC70NCP~M;fQg>#{zZ8!n0~XzdL+Ran`CO&>^Hj#Pc>Cnuw#|M;Z1M}4`1<j
zK7M*2CSu(7(4D~>>F8mV)fzMwFTHD~^tDPzM<Y3<ygY(Ey7JJt%p^kc&~WZ#qepZN
z{x~D~zb^h%mLE9b4D*)$&@2+QO-Q_lw+*GIVR<hhe>6C0Pt5vARwoLU%PulhJ)hCh
z)zRL}JqG4Q-G|Cp50@uL>LFqi+{6)vHdWFlWkR9+9$L?4$SjFqbp%l;_*fm3l%L3-
zgpf>pbx==7Sz(w=nUu4u=~qcBepU!@Oq3R^&;1~J5if}l8|>v(?DyalcyJ6{Y)^Er
zHT;39DCk1bz(+`Q-<OEs#4F4%ViG>gXg+tSr<-X5Pp25BLvOxNyS<GIR_DcvPM89f
z=<E&a@1&6>bSI}b1WRZLS#y_Cqa{WkUvYV}jQ9EKCo%T&D&@K=K3VrV#)>Sjz4krK
zh1JoIZW+grb-JET5X0PzrYA##Lmx$(4y?a}#+XM$Ff;C2Bm{T%+vDa8!fRJU<KnaQ
zbO0!z9xE@|w7NZ1lv<99z+4OzW)W7R_u?q_EwNa;NWGd3?0he=!(VQ&awAbE<)YV=
zPPgd&wjN*Rx1bHI;Ero9zH%{VORtwa)jer%eHGXJ`gvvv<#9%=@bV4Rgxz2L(6Pbx
zTb{u+c-yG{uQuGB9`zGxsfg*c+y*$bgFFS8Ub&F3?#)#d|FGa&)xAf47NBYzJ)60%
z%LPJ&Th5LzarSvLG%A9|cGvVi`T9>h#MI7++NBWZK?;6V`Xf*Z*)u>h@R3Y~18UT}
z1Ara_MK&)wKhWNv<08l#7gnbV?6=nOsP?Z7w$`3{p~^z6f^!(1hmoyQ)mpvdY8qD;
z^{Y}l`>HmsQumghB>Bp#SUtNNZbs;+iuz|oscUC%-Q%cWD{}pX+Q*H0>mx^MgFIJ>
zI`d6QFk8dv(pIkf=SG4=d~AA^3>fY5_l;Byg>{<sa%Yl01ss%f(_H;Wf(QCzw5No=
zk}aLeHAb}Tc<+JmgFCNyT@)u@(`w${2MG?u@;*R_qm9)$(nP=4qUR?*`{`;tO&!B8
zJP#ABbpJ<1#I1zzmn-hN)zU7)F&#Xbes-H>{PJBJrLU(m9V5X>{SvuFR_xf_wZVfu
zvdlVMS4FHU=(<GgBB{~mEOsH3l|LsK`@`v>zm~!4H<tla7yk%rMe^_mblt$=m7#(b
zXMaVW_|*gGNc~A$WIyl5&6`ha8?N8_`-J+oVU-U%KF83css?H&9H~*hX349Pzt(ei
znhdKtEg3!C`1sT;kIPoQjzH4vy7>H|GR8Cow`X|c`via^)5;eSZpCk*g~);YpYM`_
zqv0iYBk+9|b2?cwwUmBAYPqAC*pwiB?gAIKjV&XKXz!i5%8x}>_Y^lxQzAFZa^kKi
zn`SYd8T&scld-hPf-V5*8~PKaxPi4uoP!zN)OaehVL>HUTW1n4Q)iZQHsgJUq)Y7|
zXi2A)UXZ}t9TzWQ)SkOV2xhh5G8Ki6=6H=Q`?e7*e(63uJlcKilC>NC@)LYZFaR<%
z!TXud<eCY@YRpp4Fp2Sf%7)YG#rJ`k?<9_k_H*T4v;u~beb21-={4lf{Y~VRqbDtf
zQlN=hvURu009Fag&XO^kG#9eGYFxZZDl%%XM|<LQUw5bYM?C6JAxOvff4CG+i8d@o
z1(U;qjZ%cIi~d*;thzE}alNvGAg#1`T<!<Q80Ddde?+z0GqRKB0%pdEySPxKc2i{_
zlxitNj}>vwZ+dG@C6})qW&(%y;L?ivtHb?nNmrDAyit2)1XysEnQhksE1Di&Zw)U>
zEylMa>G_h9GAs`YSwb{TY8nVVn~E|i`fke%f4cinp4`t}G3rkP8+*gcp-xnWAWmbh
zGF8Ze5=XD4xYa<x@tW#&&F^*ZXon6k`7CgyZc%C#T2*@@Ztmcr7I<dfP{m93`@nah
zj~<S=j%AJbvFY>~n!sTwVpgwwm<HkkTy<;PS!Y4Px#A{8Vmzr-2<YAMBqNPf2=%5k
z${FG5F~fe?6Dan`%%6zFq6_r+>J6i4*7K-z_np)0E8H|is|GnRS4nG-QtT;CCj5v4
zu1gM37c0<x`b(1lscd-%Pgb21-YNSNQ<HPM^mgUvlQ4uvvH&Kf6)ri!jfY&)P6*2{
zRS2jADZ?61e9K*G_mxRjVg{Umi!WF#vaWX3n4oCKbz_GEjK8STLU`%{{z<4g^38m{
zRIXasJ#H&bi8UO$JCtKsRF^U|o6KI{ALRgb!LB;+U|&&Bz4x`J^*<19_;LbZxQ@4i
z)gNxL+%@c}k+y-7yWXWlakIVz6VOnii2qz)28%6F`LG9${QE@ja~7_Xt$8_<<)30F
zKZ>pc=(uIh4;NRaxceZ)4lpD0pq$=JPOQx~p9ISeh1+6SAw%=h%r2nRuHAEa&Lb{n
zq`oZms<E|#8jk$XVSxj1_)f2kN1dLjpoW|*#}l}^x_3)c;HsEkOj8DiFCClOHAkm&
zkBtNk#^eSYmzxIQ9LU5}ne^RwbNFxS2fGD&Uk<HJp>28N8%&F`<#(HzM+l`s4{x%J
zx460O?u=SlphA-$thykseCZU{R~i0RbffHz;L&}$otb!8I?H#*dU%YI!;<aQd5IPq
zPpu}1#Uwb?&&#n2tY9%k^2)M2($aErqfhCE&oxHovk$2UvsFM~sn@Xfj{tfA)*(`d
ze*!uF`HEv388$A9+XG_>5A;lMy^Ck59`516%DD4>2AE>oKFA-dm3vL(hB!^L>+Wf)
zn^LvH5=M+?c{iOaycLw7_3sxOPpfEUL)i^o=At8Q&q8mL=;89qR>?jx68`V1Eyl61
z$%P`&`7Q0yE?C=2jY9mcFp@93nmVXT>U*GZ_22xBdJu`{9!<+@sqn@I=JJY#;gg|$
zU`1V!@(5%pttoJ3<&`U}Nqn@=f8W;*H|8fS?no2`&vT#C<NLkktQpzC2O&sV^<vnH
zo2i=YYNIC_#?P+&F07BcG_Y4s_{37ZdgqKY<^~kXSTZwz`d=GXQh7=Dd5bxt*5Uh$
zwJb%kRWeV?Y){*<Rivh#V4#P`8XrD)F{MtK?ANw2{k~KsJ8~}-!WN>PLqZUJkT}Dp
zZxoRHSJ<!3j;3Fve}Oy-Q3Ihb;<3q=jLd=Z#;Xy`u7YxbI_+-_<z)vn7@|o-N<&|;
zNHA=?25&i!afR)FVrsfyo+CX!XvPr#F1h;$*=KXk3KyY#STnK*ZXD2_at{;&PCx<`
z%a!Vumfk^6@iNfUbBzCNMGms{_nL8lmiqw9tzwEWW(k)OK;1!i$&>j5BLJ-U48j=y
z)(@a4<w1!N%&i$sUmeNz!QZqY+DdTMTMPB_+o&y5VL6$yi|&$_YYiPH9)+Ac{Jk`5
z#zU-wErSR?^#5AwIXK6m09t|)c=V*X$4PT4GHe|>fQdIQcaLT3@(+_>e&2-0RVExF
zcA}HG8r6;Q1!CCWzYO9n_50wBls{rut3CTOop~Hi-|@(E?{g4#;_`~A0AC60s&hQ8
z0h=~qAXa$;9(+ae9$|DIWl#yrNVG!+(GxhtFmKgV#V?_P`R@_@ElusPq6Sp{_S;B;
z9rD8K3*E9gkmA|TNU;bdzKGL&!C$zFYXvx^0qm{GIpbn@neZy~8Mz+=Bz!T(c~(2(
z*y^Szpzst_%$fzIpwDA1BX64Ec2p|A{m8G6%-p>%@*1qlZp$zyI@-1}$BFaQV$N^)
z{md?0$xeX)=T_<hj{l9+Wt+p`EGK<BY&DjEL}Q7*QC!q%f7`kV*cWwLgCOav^_;vs
zh7)%WkH#Sq(yW}+THZeam&{~~AA#hqY1>Pj`dt;+6;fgAf6M~&cj$imT98aH=S4?U
zi_bz1yc!vw;tU^A#<?RZ)Y&`n>bLl?o%y!x7y1rq??hgP^+FUxyLoF)-^G|P=H9l<
z#>j%6*{9*rO#ZOt)D=6wD%ZsYo~sReNe=>B*DsJr)?({#iCAv&ey+~(u_u%NyTb(%
z{H!_TaW188G(X1O!+)>r)AocTE`?K-$|A9Gu2`E&9|0+tc+n^}RkMe(ISHEf44XQO
zm+3?%0&z#P-AWSE`zMyGLXzT!Soq=y3BH1H+4ItcB65qp#x&q-ieuZA){u<`wEf7b
zKDvoyG)&|IwB1y&PkE(+elkcuWs5ZaaEkQSAo)^L3X$}HoqUOz;hARb<PZ3z*Oo4l
z!Ytt_OQe+d5n?e!KY~2R+XZ30#$b~{$Q*X3R6+&1sm?NsDYJ16by&cmT!47JNH#a!
z_RNxhuZjg$2=S@X``h_nT?$8Jxp+l97t2fYT#olcObh;o&zJDX@G0Wh`Yj`Id+U3Q
zJZGLcnvz)cUHv9*itW=%_^K&RgvO!D<WRdBf?Wr?(zVWtwQ7NEoCbpw=a{M&jrceQ
zW6pznGm?L7mOt*>`1KC3(JY=WS59%8$gZe0Cd|>{!!1ho?exk`f7*lj6AQF_jweyz
z4c2RH7xyw#<b^SvSe`FIDdwJ01LCL8Q_a*BuZ{tzT!AG$Z&mTJH=omT;XH|MnK^SI
z)jLZsE##vcwS#rPZmk_TU4PPCo!(uuCJqZ2F!@{61&D9wc#+Hf?YNF%r^{Hfc~$Qw
z&a-PFN>tL7jk2|G+V>Q{V?rIPbh^8f6S?guWxhN%XSA4Sf0%;I)d7GI#3_t>;qjA@
z+4i%ZGb_;J>bS|N*5hZ;c$iBVC+t|F0q~BWK8~=r#(Gl*JTrdQia1O<^vSmA@tv)`
z(@W#cB5l!$xav|!_QGLsdrIH;cD|X33N7&vJZkFq$67X5*jnPo)~9z2*Jg~W)t=gj
z?K-mCSEoZ<4Wbix@8Gn&9dD^-$d@e?Q^h;{>7#($Kl`j`L@$5-PS>o^G+n;>owM}M
fj_?n??VB!2-nd%}|7OiPwBWzEpKCmGCF6epV)La+

diff --git a/reference/plot-2.png b/reference/plot-2.png
index 9bdb147da35685a2fdebcd9e176469a227a6ceee..f443525ee8f3a49ad49bb678304440f4fcff4b26 100644
GIT binary patch
literal 27048
zcmeFYc~q0h)-Q~+wv9Nn1Ea?_f{KF3JloQOQ2~`%gn-OLh=3tL2;k7rwhWrcJfv-u
zKoaIj2q7p6hEatug(N6LBoKl?h$MvEC-I!~_PuxA?|%Q>wZ8YhU)FMw)Kj&eUAy-F
z?O*LGA6~Vyk^e#I2RS)8dGN(wugl48iI$VwbZh%opoO0KYytSQ_10yZUxB~CXVr?7
z0Q}i;`=VQ<oSbU1^xuZ`sjq%=a{rJ6|N4t#bp9Lz*&K@SU`d7=C)LjXWApESzEj;>
zlz8dcxpS2_w+#Gze1Fx?b!FR6M!&7EE%|fH&#twBk1i;e(?0*!c|r4DV&eH@H#ctn
zsqw+1?Atp(fi&2Hj@%{FHt)VUa-S#Czi*_EJ)x7nR8Ti4AX(H1axoY|9GfK7|NsB|
zpR&OCu=qUOtKFy2GWP@9iS9fTOsk=PTiqmQI=b$65O^!y7Xp8+vrM-vVpXN+ClxsN
zg-x&(<mLjASsve&>acFzDg9RY*XCwfi{_<&HH`Vn%gMEjiDg|wpC%rZ8-BRrgeY&R
zYq|{2<K$CU`kb*PT4hXka!qXC<Z6InOkqV?3pAqK)WxuD<w|=(2;YUKsXlRd^!2Rq
zXd&kwCmAnt#5pB6kSk+<;jb#9=H1Neg_)dK+BUpp**cP+c6uNZ!}*0~nPb*c{3$?O
z#nHwOxoB6chwo$`>MG=f(FBePfiJbR2<y*<WgKl~0iw?BQB#y+`ERp^Ybh`n6l1b4
z&)KlNmslS3GK@8{pcVt?7jrs~4UjI<R|}(3HvyB3H2>?!9iuxxrX!&XDu{As!^3dP
z54+XF-e8@!4MsNyS4(QCIyJrgw;VN_)R-uKJv=zh4gPU!h}qm4hdY~MDvl0<Gi(ce
zt{8@GPno8DYf2mVwrt$dvt-jFQSGhv7@F4%SYD+>3iCfMY<PR4V&WWs0PfjUWfai6
zI$!-tCqC9%k`TCkH%@%6W2MGoU_od<;$@pBUghO{Rodlf@5a8Vo%4YwuS|rKSpAQ_
zi4*~?UA^t+hd1S=Zr&~;uwmY7E_Lz8mk;U+*IAs&q?{&Y;~_))c2iPQ+<lbiQ2TsU
zWkc+W&iaon@6X(i`VeMnEU*q<yKNN+CvM<XZN}4MxMB5$4!MUH#ohUlP@1xT{?ZoZ
zFC^TNcOC=@#eL#i+2-bCe7asUWiay81|Bi8{2);>pArxA#Ib&6_S3}zU8&oA4jUSL
zeV@Pa0a;#Gw;?vI!AYanxR019NR{t04_!8sykFoiDTGzGz_1SP@}K%ITYocN!@zrW
zi*@mRq17!2y;CD+LL_E+^2_z2QN|T>oL)k`K%oyc<t}JdWSb8!Z0In9SQhb~B^}$A
zR!>V5T!n*Y6OLE+Z(?3(#x;nPw#n6d9o03HlRNNfhi<*ZAHI}y6%K1VHCBU%1#TxW
zTV8!lsPwk&ENk-VSmCdb3?4v~;m&PM8HTLMm_l;zI<HL4!iN<?Si<j9YT1=QNUAd#
zTI6sBYzL!=g(JHc!`fGAf^<k{u2jJijB-2#kzXYd;n`_%y+vYh8+xM+-JO9fXpuy;
zuNb-<F6ieSR`SncHa2<N#4|-o<#{`ERUb3!z0KJdj9<$8Xni&(JCuh5*7MpXx(G3z
znDvpb=^u==<L{F{!nsjCw42zS>$bAU$&}ylj+r!ak7CUJW<@*gpC2;#BeN}?lARv{
zYs+o4cLkc|4gV0XP`@ITUt?l)Z@~15>UGt9%4qEUW#Uhgb^pHY<lF5r?RPMWY>TyT
zjHJ9N)m_VB{kzYW3v>$tYIF@6mPQ$Y<{E}$hi(tfqTX!{R_xOCA2<V(pAgfs{Fg=b
zil~;dCYUlLb5dWoJwalE>d1d3;td%O`3Ed$am>k#AtRX|_;2|+D@AXeQlPW`jPsX&
z@=p{!981@KIp^R)3&mTV@qww2>wCs23|WqOlRu%IeT`1Z?GIqyc+N5_>a%W%qBvO^
zOpGNfwPdyo^1@)V$<0fl>hJ0EA&8^={DHOG2y#x14~dq4!=LefR!uj}^8MPeYrqI&
zc#rVv^LF`YY_nZzo_zGduZ5m(^mhR|sciG_VTzt`mQSaN5xVUb1C_npQyPYwuY~YL
z*F*Z!@(wOoc(un&Gk%<d>I%oA?XWgIUzYAFc2FYPzb11{lZNy%FHh$qJ&z-1jm7Oh
zMu#9P7<`U#>5;X({e!0?$|i$&IoJ9Hy+-T5>82%2|E^!4H=&Y@C-u!3`nQsp^797D
z|29$Omcj8Ya#4R6b`P)Tb0Xz`Y3J#6e`$ubxd;pwVL2FlUeB<gopYGIOvcBKq@Xkm
zi299+6?s)+7<+lOPLXbDj_b<_2{kPp>AJM`D!(dt`wx=%_7+lEKSFcGkocr2^AW+m
zdq%WAElFvw1t2K4w{*SPn>$jZ8tD26!R%?Q?@!3B5^D?%M_sBJdL^!yc<Ml-MXo@j
z%4U7w+~0jpFPf8yAu537T#uxOuBUXl)dVIKHTCC*!whf|>#)oft4LwOl2S)s?kiL6
zi}Azxk#Q3#7D)E8+icRsrTAUjlsCSLNq0j*blGqyO`e@EFjoE|sWuj~N9Iy`xJ^+*
zgn+?`C3)TKteFA-Aq1u<4YiOm!K`@|$t|0IU-?tl1scTQfdn$wRq@F!gr<g%kLvyA
z(>pGH2%x9G|AH2vq14|hw^%qyjhR)y-jkLVb&EgDsZf5M**|FHKbVWvA2SLx&*~X5
zgvBHm^&p|!rsqThDcn3UVyqe4#!RZa%QpAV(Z7HD$E6mb_>=rH>z$6zaCwNpl`(aQ
zqe#!7!GN_`C@QSr-~F|xa7)}UGfBbA!h=kX4YS(4YOiFWV$`TjNfPuZ&OU|r*6ouT
zt{(_!uRo+Y@<e&iH~cEGVq*o{9nXUKAmFT&m>P4#$Z}2gT%MqgtKBo);;^oY;8gF-
z_95pq`HTb&&G+XW_Sfnpx((XTy$dm~Y3fUvh)?JpMiR0NPkpK!M1xFev6QTsSF?W+
z`Gw3NzzApjaBJxs<j&Fd%Nv>sr#XhogRP7dkt6$X9k(Vj%~DAC9AX^M+Y-~Ir2@U?
zJ#h-_wz!)9md&0M;pLYloT?v261gv<(rNec2OHKBW+k@#5Bl}2=S@A&#g7$HSk{`t
zh4`1=LCH>$pG`!t#)nl0#Dz?-?9C&RFPr3CyC6;yytn#Ds`B~YIvgq2J*w?kFjLNI
z2U`VMAwLiyFcw+V-d#6+3z!{Y&`mVW>}ZyYBTR4kB4D1AAk&=Qsi(wn%h?RfQrVO@
zZRd^o6si?XyLVV1##mzYd?d~KnF42uS%Ade1wucg90ju}r->tlr#ku5bEeNs5c_;i
z&zh6p$$Zwxu7GiSXr(S}Zt0_bnq(oxM?qgQ%h1q2^`z37vZ+k$y-Lg@_B0)Fkbl-e
z6o%;)^*xLfcHih<=J~5g#*FJu(?xk=6WAFX>-6-pJIsLZ55w0f=8*xfWe+4>g$~ZH
z^<*k9dcv$du(0g6q`{VSJ<4=N;RhHwFT211$7)vAQtRTMnN{~sFvIm(<~gu#7`hP(
zRtqgM0S$*Y>PBx!bXqR;XC=-|>eu(=aRo$UQGEFbq%=NkX{eX+&|j>gMxTZy-nqeF
za@P#7Dl`mWB@|Yn?D+)x)viMZNWfN|NqK*i)%|pyrT}ocm?OL?+TrplaV;m^e>o<2
z$?99p^c=D$ABD)PfravbaBnFiw{xlT-fRBcH-hD1Y4GgYpe<MFVAkTjm2M3?dxxM%
zXEn6fAM90RL!7mPv-d}T)9f@1-o8B{%#H$Kol7<C+39Zj)WW+OcGPg%g#1Z!A<IP)
z)%?*vmYR=ZLBvsQvCYW|2*Z$<tAm>w$kj*dft>PtpCfCy(wcK4{`sf&kgl_uzf<@r
z+@*N7egbql!Nf-d$Ftl5#&pQx?JH#YBg>!o6oC_hxDIKnBe^|_wbzy=b~!oc)(V90
z%zd_#LP<-pxAVxSkz}M&%eu&_XZ_@o*gr+`(imkRdU?Bdc9?ouZ=lk7ni$Uu+8$os
z{{*|bL;jOMth}*$X2LKM(^wY$DnB=!uhZvGK~oq*^VYMCZv3~#6PA7jLu8K?{Ri)r
zSIKM4t?i~&CPqb)yGern6Br+RJV19t3zpKzzqU1mF7U&abwVB{-RMTfk}9MreB9`^
z(YfKyiE95oSGqen_(5cI%@Ms^Kisxvq|*6Xo%6U0Tbct5x<|*ZK(m%JKGBwa3Vr0G
z8)l_khwU`fZQN&Hwlvz%)z@1ZB-9DnI{!gyY&^O@9g<xGn@dDM_*J%p0-K(7(_qh5
z((i)hD9D6d>jBrlHpm%ysswIN-qkyt0JVB7{Off71YCZ$AU<fP&nfyd4hb)*o;z>w
z=?{*UJY^#Fn{Qhzp=R&}BWREmWjNtMGoPMIh;2j5KU%pbuM!!<&r(+51t^8J*I&~P
zuF}m`a84h7^R=S9rD(OwzPtJd-i!_~-|^!cJ>|xu7yd}9z)83!9UdO$)koIcdBZ{2
za<v-_g7CG@KtsPu==RlK0yE-AG3&#E{+wG#Nmw$%xuJwf>J8@wMtfkHD}S_CeF#D5
zG6&Z5f5-MO)U96-23mP6nU$roUM}D{Ye@<ogOT3+k^_geT3S982fLI-j=`K2g1?Do
zT)C5g6>b7ncq;DF0!Y;7aCykH7RF?Fyw#tGnk2ZYeaN?1o~1JnwtWrQCM<FG<(UmB
zYxuDKI`UFHos`pP2>sTyq@i>kO$q)sx@_t$2@w;%Fyt#lN%A2+%(_qh9u2T>F%a7B
z&gP0Ea<L9=eZFp=W^6R-7&XGeq}@yTNfq&w;-(T|je*+UG`}Z+FNwpgN{naQZ%N9U
zs>^x;Swt*syU2fJ&h~wI;L8IoRYE4mg4QUnJI8Vmd$-ws+qLacSN7OqZc@njCmMi_
zx6H}Hb9@nPNm<bDLkAH4ArPtm51-0?T(_tV0LL-hm1>lkfD5yvXv-x|v@&JeJ~_G8
zsyz!ErQ?amfoT7~@1yD4SH07#-!3mW!8^!FHov*E4Hdu^vXpa`Z%FHN3<$fGvI*0Y
zY+WJcf^v!a;$Lm<jT9Z;hUYQIjQ}^6Hf|9X`X`h6)7dV_A;ru8-1z=O!wJi~cpPAk
zEi^Zpi&Hv&-8)!vIq&e|57B_hfsMxGE7cvome)K2Qhn@7*8@2(v-C_S_kTC|sdq3q
z=&?A>xPx0-XbhjbP^S7H;|hRr6{Ncp360ti45IIvXv_Ca_s+%N(l%GyB-e$EIEXop
zScoMa3vdbG<MoRLeR?BPy+gd>NZE-K{#ykUAj*F1R|=WNv*)8meM$f7@5y{}Ze`AP
zg#i(dI7%c}Juv@#a%A1ZX5)yds+9zTZ?H|(5liLEMOBfzw7d>R&pL{Bb-b5crkUdf
z=?*fR$tlC_Z2ZSse0NUVY!Ke||31NeqAZVAt%a|gCzzGHr&E@4IB7IY{mng6VYhD%
z*UW8n-GL}kqZwU}&38sY`PoT|ROvJN%m-C^bq--}AFXC|)Yod<Xc0ZwLZv&aQL5g%
zNPEm#2N=c|SDj4Xma?$qv&HqVyQ$x(9C=#Ho_661=iZo}RIA~54VQwb(I9i-hjh=V
z(>uR*y0la)#4p@;!fBBX9I$HbvW_c>?Y-!OvbAbO_FSBhE?GC6Q;J)uOg0Z4@jLbA
z?LkPK(YRmM?uGI~(j8y!q~YnB-BJa-8aFo+*l^koOnu&@bX==eJz`{w^pV`{(Eq7G
zLVQIM_bSGiJp(qMa&lL9L?8TQrP_ExN&2fbuzRa)pzAe&w+<eo)w!!ojOqL!b0BAI
zB0}VGy}&YvRVw^^pu_a>vBT0qiKjkPZm%c^PT*JnA^k0vX1S5c6-<||aI>6c(r%CK
zSqP&%!i}<OYfnmhBu=COvcwEW(LKNYGT|x-W4=%ksy_DHb5zgvi2}3Nhol`_!_T#n
z4rwcg6GLoNWF;S0@615y6FJvw{|txy$ACS`f3gO^8|5@VU8C6>^lsZ|ShyCmvfW3v
zbmi*fen;m$QH*y!h5Cy?@f4_5%x;e!`8~nG!<DP-O~eLg?}?-Sy>~gZ`P@qXQCX2l
zj#QXOlo|A25Ze03-v1s`|G(Ez0%XL*6-NyhoU;gjpO!&$qCGn{C|lFH+Z&m0=?*9d
zej*3!0<mNdKO(RQ>n~$N;M{r_Dc6!C{<wj6%lxIVj#I2`w!TK4uyo9FO0S4F)I~}S
zkqG$vaU`CFU;lID(>xn*Z?}H4%b3=MKgbqHtI~3y<2Ro|h%E`g4uGKH3~?!7E+CDH
znm$bSkuhAhljZ0cq@Xg{rEhrU51Bkw`&N=T?7Db)U<s$!{Tb!VFX!w;O`&%ttJ7H?
z=E#V(K0B_YisMgr0P;Mrd?z<I(|I}$Tu8;b5BxU9=cc=PS<~%cx=(|v3@j@@OFP_k
z;9^;yUG4L9zK`0uBf5b#WLsz1^0vxC-{+=Ol-I$%Kpbsy;G*1BLWBZ1?LdbtT{`Cj
z(L@fzqnHOen#7JGzWYC8C0rVa+Z?!T7Y(%uBaJJ<3jM!H-)UN%|5}w`=+Z!+Jk(Ii
zbY;XWu5maB>ryFX$+^18F!Qz(VEaDxKHgO_PDtnc#W^i+2q6KigaIjVW4t`_UR=m=
zwn<$b=X7$lr2BBW;gQYFkG_s_ev$7wsnYUcw1<GDPZ|w5r<Zu@-|&<6Xzi^2we9m7
zmNUmGrQNk%AveXw!izWwsW-n{<ge~=4`LS~fwg;)S=@w#d&0Xo1Oi4m^Uk+G{yGQE
z(N<3gGx$lierb^#nNu&DVuvQHept~X?l5al;q}`b@_AAYDX5<N`M_Lt($qQWONT#g
zZVr9(8*QJp+S1_B^afS6Yvht6<4jx6k=s799#KXcw3WXWa@tfIe;ISW*<ey89NX>#
zZ7_92+L`kQ?2M<rVc#IRbwg3BQ&}#kiRuukM|k;2<u0kG>)dn|2lBr@X{Se}$4wqZ
zP98nnICr+5vESw92Shv@p+)*ps)xZZt)$rbk57(mV%Bz_m$oEM?FB%6$VubMC<zL6
zl{R^G{x3;U>mSuyc`K#?lV2Y!O1qvrHtz920X|v$9T$sqlz);*xRCRT<eKF;6c(Nm
z*>QZcw9OR-I0OO=Uw@9maUQv=@Xlq%N!#Us<@mWd3NNO0os@Z$(fW44{qk_54UilA
zr5#$sZ2-5|Dhy8O|A?11$fcP|oeSZ>-I?hm6MlOw98<>?jBq|-R`idiAQrd#$U1gx
z00I=SP*u5V_$g_N*KcmXMnL-I!jA9U#93OW|4-Jyg+y&2K2UZN!15I~_vWoN!X8w~
z=HRk#p?XhWxth*mHpKAn5FX%yh2^_7Aj*5ov{b!irWmG4eJ4-qJHxvd;=P}Lm5CUN
z!01cAB>rd2ZU1M|GP8g?$yp8t2(-*~qSOGVDsV#C@O@}U@yf*T?iNlJ%?7XSj3tzp
zdb)X3W}lUIOmwPD7}+xSv!$nbWGdIbq);~q>q>5ETDsOTS2k}Lvu4K^)9UEgk4ep4
zc^1H(lmz<dr!ZO#A^FN^A6K)DQDlx6rXQur@)7aO1|amvLRu;NFkTHJz6hW3rSHVM
zJ6amWtP44V@?H9KgfKVp%)lYbp~^Aoxs6=qBPo<ynJz}Qylk?y8W%zEkpSohIBR1~
zrkV1ecy*YLZOWMe-)2k05AT(YAjn4GM$-wqu9#Drywzc}RgC3f)@_HnBGr78l`rA4
zxp};^9js@m;znT*AIV!A^Mu*_s$vl<eEQl1=}+Oa_SJLaw;8zfGY7f^*&O#Qk2m)`
zu9?3OzCq7=A&dw@dm2B><J~=R3?Gu^v01M5k8epy$|UT|x$_6UjB-{{s}Y%%AyQ3j
zaidt^4Nf-Zl!#u}{to73w&rMaf&)tM&W9N>`PDTI5`qvP{alIAKUK4l&ZR4d4K&it
znm-+$+cs#rTP`uaR@mZquxhiRee2GQM8ux*a=XG>fn##oC?z13ZF9J0uxeX_=V+37
zxsWAq7)Qt3I4OM54A82{wIjwZVz6U{<~ESd4y+4BNFz_3hLtiDgKJbYoEbBA2+m!F
zxbf-`x8HH*+#pr6=-aT&CmZF4OZE}egzuB1)>qjTh`CPDaEHa<5>~RdDDhj6>;uxM
zz=>rPgmpr3O2k9voYfm0MEKk>cWr1JP&2*rylQ@I_kySC$LKjZL5qNlxX_BE9?MeM
z747{jECQ2vhlgY#rL0_A{81aeF#fY;+h#c<i+lt#hV+@0!b9<p3fr1nAB^bGdA1JR
zZNw^x1+YHv@~hPJXqht4Y}GP!ZWX;w%E{R{QL#*t@`K*I*!a98{jIa_dVCA}M`vf5
zfgdH6EM>wusaJUEr|y4eL5z)ryYY{5TS%zBr6x`hW~G#7N>Fg-RWRKPKAce&bSQ_Z
zM{;~6_D(Fb7_%xNiDQBZFE3g5JwlVYqv_zVZWwI%%qD(6t&2bkYcqE4siSQYE$Ror
zJQ=|sEnLn265isAF;ER>d)W4P)^+ouqlUo*;e1kFgDmz)!>*rZ%Lq7U7dt32kl)V1
z(Do^Xsnxrai<(gUp^#9^2Jfga+9SM1k0SeAn@MwpQlo`z23u11FT|G!1vI#Fn34fG
zuGNiJ;V8K(z6KZU=_6fYx}Y53WKJec9uLb7#jbSQcY2Tf>n;GN7}y_Ilz7rBSLTo6
zbqSK1><C#D6og6MO?WtnS=5O+4w8U{Zr2UTMhXAqEOHLwS>+b`5%Q*t{g@83!%%4f
zN@~esZa!F4%6ZMPrWGh>9yXebm7nd5ua5PD_XV>I24yMm_L$q7>pO;=VP$G=1s`r7
ztx~Y#QXp+-H%nZ_j_`>s1G1!7xp;tl0HBoz9UFqT$Knb;RGxa}M}s&XbVMF-ld+-J
zlWuofk>_pv=hoAWI~Zj^Y3DO%n(p~Mt^RPsu@(6!X`~SgbBz?-D@DOd=G_Y%3tY?-
zf>|*%5h3z`gSu4bR?cv5qTnfKk94*5h!x1szN`#d0x|3cgB)uM|1%cB&*u2kOw~K)
z^S-mc)~8jQnC7%07s|*Pcfnivn%fFUAGn0mM03EW_a0*N0E9D_q&=DaP7Pv2HcIU7
zq>(1ep);C@Q74)%o>gMe{7^>{k4N1<?Ia`Hw^fuh$Hl&F@&m=>M(?2CEKAERl5`ZH
zb;qSTCQgjFZs2)uQ}Um+sL;mVMG{Z-NZaMkv;pnz1PIZSQPne8Fg0~FO$Mj8Zz^hz
zlj#>HWW6eXLH_%I-=C=1EW&r#j=DH%4|`D(t}(nm#)rXsF7jnvRRrxnMDrZQozKOQ
zd0!AkoE*#w#0hq&R89Igaf0c3S<fsQFYd_uBEXlfSXtv`@AGmw1iWv^QS{MvSwt_b
zSFV%!L+EQjJt%GzGW;t=+pexpwe)~YjE6UHB-~xPQU|;u5RYO!36^yq-VeOo(wJru
zv`=i89h?yL{Dw@asNbqKG7W9(VC0AKhS*@q)Wtek``iycTbe6!f+cU%h34USG3W1c
z5)gQC3Fofzmq;E5Vr!o8;qQYauI6ygswN)i6i^mE$0y(%6HMy3IkXO&f6Zsu64*18
z&AUjcJ5D3L3r{I6FKc<$M7<_nZxZJ0nU!e(Xq@}X9S(=5b5?Djmbj*bwrY8sWD>=R
z0sSarUWvAcbjQm~u9_E{G{oBRf74G}eeKKQ5)PNN=tTfGDu55^b@pYwD@c3(1L{OA
zCk41jQ6Ke1wkK~@hq|<ya(C{4%j{!qxU%>AkW;_^kGyUms_Yfg4n7M+GZk2yHSUSz
z)3I@Uo)UZ0T?dVXcp9*?CGZ+)2yN2uTSReubDDCN$9B@|A?JjfjN4TBsZn?QHm4yi
z((XTX@k7pr?h&ewR_<~<X&INxHzj&_%_Rz$v?aW|s!zfOUhkcL&eo&pF`e%wHV|eY
zjzb<%dv->NIq^H9-BJ>6Fv3PIoO&fD)DY}X0rDy;=3mbJoH;I}#n6Ccxm~V<qO*yK
zEs2kL^L5x5K!+O>-Q(g_4#Y6-FZrvq3kN+0f^Y@(QilHsvi8|??ejrx(vMNkkYf{S
z=3gJ$-;7e+`Spv9Wm8|=v~HKyCZkSn7=l!|zKqi*rTiBC5mPwuBFnRB?yTJK1;C+T
z{aMK*Z0PpIb-eH<Jn;EarCC<GUau3du~)Gvk1UwIBb<?rgd#qIHBOT@N0sigGkT`m
zYvd!%LxtwZOplF~Nn(+JmrF@r;nExC_R0<W0fG~NIG6^mv9w<F`OZg@bXp=HZ8z>E
zi_g85U8qTf0;$LpWiud1)sumZTx1FW)S-CP?dLkOCC&B$B0-^ev_s+%A#;YPM8MY{
z6^|ZF`P+zfC%&aRSK?e?^6{mtSN9p`R(Jk=8qdkFoatO(`FU8@tl}rRIMM8Tb}$IT
zZYX13^YA3U5We2pvC2OBJxARhQwCrrjq^ai9#YZ7y2zFd^Xdc~Q1-3zoKMd<x%c0=
z0mK(XDM$vlat81I>3sijC#e0#U!MZm&MYTJjS0oXuUfoZ>J#Q%U=#$op*mXpJ$TbD
z`)q3taMC8ulkWQPoIUh{ET0S8p2&81az^E;jlZ_ZEBk-8OD4H=8?V2h;WW6AvXvCq
z?*R}FBPJs3+kPC=)#!sbp`3s$#!*C-mlm1?v4~Aee~)Rz%*At}72vv%pYj($hXa>)
z=<jGFDwdMIuu4`kZWM9&?@v4(xDAKvc<=M$A^E{9@4sUXP<(^HQ+)Ex&a2AILuv=`
zjEx%=8GPV9FI`FgJNlve9Yw^V6%SM3#1J5kDt~_k4PYu^f3bXUqZR+1QpX&)0Zur3
zbFBV-!5GH|m-f#xy$m;Tcu00_ACc+Ih;OeF5qRZHojPs@p)b|(yJ^a0KJYY+ScIJA
zFlZWh2VB}JhJ)u^P;FGChY%!ZZ~as2*V!9*`ZO{gQZO3TR+QFgZ*z}<hY;r7RaL5F
z6^TR-@9x9~4u|uVb{Y>MYC$HlHI?r%=q{FKGcwn1Rp+vb!zJ~xi<99IZB5Nc<-w7<
z!TI)^4NceTaKdPQ&QzwX<D8yP-?&GYll+FMj=l5c0rvbpf`X%qBd<&=r&haE?Pc>v
zsU;H&{P%3&g(0r<JE;>cY?X!zA&VvncK!T0u%XQJ8u=d-Lut2v{POANvAQPK<kw!~
z4)e0uFW2Zy5AtjYl;t8G2&Bm(NHc_}AI%%CY;JWXLm%iw<<9u+Uyp0oF)WS0#aXSe
zErb}y#9wN&osGY<xOR{Lq+WB+9I5bDJdh(%k?_~sCsOw8<>?9XgC*h=pB5xuoJ><V
zTqk)%OV{80ilslYQpt(&`Dwd!5K;ZIUMPirrH*1Wku9>XOYrY?wFfruA{`f5wpTd_
zBm_~3!GQzVn<x~(9854!Nn@<&B7E-Ge%(BPnhzRHr#Wy1eTuX99k{U3OV{#hDJGO~
zi`_3jZTD$C*$9WXS3h#C6gXocQ9u=4^A@a40!!sf*o1K}_}58$x%xn&B(d2S(;@NY
z6FK2D3NgNP0%1O)p!Z<ZDZmMPV<H=a8+GYZ3=vw!57BRV<b7@CYy)aDV^2En7ku%i
zb!B?I+1pGqfsI@MCiHuNlf7%yp34i$G=<Zo?THhwsx6K*H^s)aEp8E%F|RY~oBYB2
zP~6ZR&^Q^ww;VE>wxu=LjRGaTT%Ez?(D5$o(l8%S#=^rzpfcYS)OLz#pK|8F+?@uz
z$x^qjsc5by%cHB_^v;*7S)Q>$ww}PsDn}6Tk-f&aetS#QNL?sNv!b~=J$|`pij^W)
z<TmYcZkLmN=(ii~r<MGh$k7k^p;;2ff(b#3=*e`6hp%o?-4WeKU>f)k>2`GxbLYFt
z3upt&%gD@~gu~o#1y%BpPa71X&!wa0e6l>+>q$Zmfy5HeHvkzy&iUhnn&9Pp><~!{
zfP6uH1G!AYd<G1pk@1(+b9qEEKpQ+R6K)~NRYDP1w3UX9@)dt-^SqAo{n%3JUB)Z|
z4svw(afb0+WvN4CR|ChgvE&h0LCip~siS!`XBA++F45=V)4p_?yrHcq8vYCaBgfhS
z)myFwc=)rVGtp0g!+&6GqH5sC2HtXRHSToTA6!8iM^s#hbrxTO$M6d|S<0{Jz_B6n
z@Zj#1_$uLfK9z%1M^%*1JA06Re=Qqc5RwzJ;LPyDi&tJaRUM-ApIKU&qA5DiVTLip
zsmY?kgy&86Hvg<dMOFC$S7%0(UTohbA|InwT}*IhR0_WuATU)52HhNT48H|hB10C}
zUw|Ya9-_6#t2E%6;`yg(<l_3nC5499Pn$Bbl)|2`Xwps-OdT64!kh}ujwHBQm5A1p
za(W~$pI-VtEhv!Gj8UtxO?ly!-XnJz2X3k1wF=Cn_L4Jz=3Lgh`QdhCm9kSp)f*F8
zqw_~s-RM53YrOZ-U5;5U*pTeEvb(~qIsmV%>z4Ity>~kDp{z0~SAXtbw^IM7Q<(N9
z>Zk|a{Aj9vS6+RLtO%EwaTeGRHgO(2Y0vc|ZSs{$OU$_ST~^|Lec&1$FF4y9ImFSx
z)l*u8Z5$rKiZ<k^Ky2~jWCz%$$&k%!pETuDgd9$TMjz`cu$TLy)L~I%f@>-{@&&L~
zBtaqV-^e_;S6;&jR#EQYF}8cGeE+;!lpDVfPbs%6TZh0$|DLtdOL}u_54(Gi?^<lr
z=osAnV*>B*k6FgF^#p(=e{~em|IJOMT-yte^4cdZ&k1Jv{5@mM<kdm_Pcr}j&`9~W
zHo)xLOIJ`p(kgV|4yFIS{dMVzq@<j3*J7wlD>rui^WPaX<R&WOqeV?qZS3t3ZBivA
zwskVQMxCxzvCjUtilQM)U#ne?2m+I_o8ws_yw4fs^w5xi{h$QW^3<!vSDbm8(@~mi
z=U1r-u%E#*M+Jj4P5hENeZ?73U^ck8&bf<eku5Qh_b%(HtUTkaaom*#r_5ir>ueKA
zhDl3CZskp3YuHA2tr5fOU$$@Fu|p^H{7>6;e*ZIZ_nFOZPUn8s`iH_hd*@$%+MTfd
z<R9q@owwA^ox8f*chAT#NAv!H1gV33?>-=AfeZ!ARdIN1yn}AeO!<5{XPy&7fmFoX
z^1DkNa4k;#C?OqZcMN8~nD}%L8BLODbu=(yGY|b1ba>WW4eN^qiH^BljDVtNWBp{0
z4_PViVB+6CeJ~F{4U=u=yxM|d|M>2y>d(veG$SrW_@l8`axj7eWERi;MC27TlsqL#
z3z8v-T++uo_XLlh<`K**tw%(lm`K>FH+a4DQ@u=lcbz&e(CCyOC``W*>4aT`*+*P}
zt?HjJ^p?5hpT)&{n|Gc#dRt-6T;hc;LaRKTEQSw`WK0T>T^%6f#%JHfgl@Mz7M<{O
z*N#zb^_Yw3%fUp!dgtO%rQq*mBI&*3J;6cEE=rCsqKB_VJca#u)m`>}g9pW7)loP6
zRv!`8>xTo#vzlE9Xvt|ko(($xiGR!PmjOI>{Z`IKxkE!cz;=D@O^4s4o-j0R2_2Q+
zP%@JO>aZp4*%x&UEy-Iq-k%8SITj6bcWd!}V^VMUe%I*NkD&D6m%XO?HxJ9T9{0?e
zwCE3e^OFi@vZ0u<Jy9tvZ<2>&{C;B9A1%p_I+{2#1gP0`N+Zw^H+HTpjeN4}px(fN
zTmc%yE>@w3Gc5gn;teSSFy?hrq(Qx}w*03`^!VNTol(pfc0m@9ax`YRhUd1;Wr4P-
z+&-taHoncCbb8;{C+TsZERQI+&vi;ArKOvBlw&c0u+_)J=CKfaEI$n5wtmG5`YZ-w
zyLc~&6AQm|Ptcyk*(7&}t{9O2#3$W6YP2VivCU%P?u*UZ=Wr@<3$J229SYBK{ja<+
zq^$NxTTk0Ee7RN`Q{FAt2`wg+ioY)l%h@g7360Y18&ZCS#DV%so<8}0@_OjJ!wx17
zH}<<Gc-NK1-=3Q8t?;{yw$WIPm_qbjiGW%`WfW+5ckZPHb?T!Uv-EjYblhn_Ehbsh
z&mw;^2?Xm|lqXqaPWCB>%3QNG>AmkggbTVK-CVp#YIDwLOvv0v0xyn&6s}a0UPZ~4
z9PkzOXv}0W$lg986rFXfF(UP><rVN;;p0ie*jZVZGtW<-rim=Ug)C&KeNGm_q0@{s
zJu0gO1fBzwCSy)5XCjC~x;$758fYpTv#ggfPCiK;WG<ek+>^EF6_pxCGeELEt=DT{
z5`_>qgL~gkEH(l^TPW4d3080!c)Yg(#5TBO@`kP!d>8it@C6oT%>lMr;q#O1TFbdv
znIROxz}z2$AeR>%!1a2rma^{qZI5}e1_>edywx)FS_ql}ZpoiCB~Sg*8(3m4>k(N2
zEd5b*sK<mL4>MLKkTw4Ki>=)U*|*ls`ug_}lq8PK=CZgmG;maqJ=0G`kE3p8;-ZX5
zufE6InFrVQHpAX6%F_lfFI?Ivi`80U=?Nug0%o%e8@ztZ+j>wY%j*F6`%&_pr<=de
z!{&eA09A|>9|l(kq_SY;*ZfbjQ%Am+XykpyHuD<gGu*1r1Y1oHX!{wvtqX$;Wb8EC
zd_p}sXlNE4$~}Gg`ztk5(BtBdzt74PdH?6Ny>)`n=KV|D1)ae%=4Rfzu{f=XuJ#fc
zQhhNx8cs)eXX4xrb-WE(>&<^FoqXk8sM1ZD_(V6~&AiF6r<3R-Lyfk6tACBfeTPA>
zO_*^gYd<*cVovcViS6R(fc?OH_u}#{8$*_C<N0S7D4LE4MO)e}hJZaWlSGh$eFRfa
zS^s(RvDGp?-4`9{%kgRh=RD*Vo`dWGBv7AA1{Y>cUhWUuuKc<Mynd2;tv~GD1C&0s
zAAQP>bVN?>{>6oQo)APSoaBSob*Nx)OTi=p(`-g9pV2R?w^3g$O`2_nxA8Yr>X83I
z<rV`d^UWF^0H9Uqb{r^Xu2xxR3+`A9{1QbKsB3~3?0VVPEfQNm)W}#%Z1&_bNTUBX
ziWcsSrr1?C-ffCMN>v26d;~PCGzNPv+&efqeqW(5%Ke7mYkGniFJ3^x_%kuj(t5xV
zYhOFq#G2mRD0lVGe%eSkEy~*B8;PRa`#pP`F>nVoTiw{yPmc#gvN4r#U8}A+Jl1x0
zHVK3cEGbXs$2X11C${HDe|~7Zw$O%Sqzs}&^sW2Os!9<}%4_XNiu<ZiqLm=S4RT(`
zZM#c57g{nWA3S~13|{O8u{9cpYL#Tmbtio@Z+Xsc@sFoL%Imt+JzxvWq&#>f=N3u2
ztchul#|v7PFl(-8ocai6G7EDvfAVhlcSdCa+8xkV2ujG990Xew_)0*BQ<T34IqKp<
zZ{}t6m?3rIde}ReqgR%n?hlhQtjV4{4YqhZIeDT%h7;M|dz))!x19ic%3i0R^Sy@r
zXA$W8*`>*)??~`pt)c!3X~UiNOwdmGfr-n2rKW8iP*dfuSE@rsQ|07-*x|{EzJ@+%
zc#{oPE(g%3_*rVpB9V@nR0MnKa+Bob_BIbh`tYg%pETgBk)+8`u9%C%_<DOE$usDY
zybVUK%uzpo{#*<aJObdRdov*MTN|@zM}^&Lz2vntu_cy@FqW&B{5v1P{bAO#7OPbI
z=?mJiA8PY9$T@aPT)q?X$p4}X^uP4a;I3Nb*7qx=r+C>nG-Xa*s(TMnZFh0e5Zr?G
z)uCpg%?zn`tp>vV0QM-$98zQ~$Pdt1y@A#^W?U;EnQFwip?Sw*mY;slra(&I`I@Oy
z5A#<=gCLmgqR>cPD*Bu)`XQIDNu52}N6DKExNiNGd6KHF-uRcGK|_6&WF)(ea!!8l
zLoU)Q6PB_<ei{>P`4l}sHlmJz)9e;oJacp%td~p-WWpfs#f(Jxakgfcy=??n$*<gg
z@ja;G3V;{sm#Q_X`_OYiq`Ufad6O+c>N9a9X5Vj8#s`3eyzRXPlz@0D;kkX|TTtJn
zYSXEt&~)1fAGC&G-N062N}W(DUwWQ2D@}lj@-{&42Er+bL(v)!)OZ2Sl9J+f0HkNs
zWBTl>6VN^K1Mu^guJpqGlucmxJ&+d~kna{BfeQ;K6Q7<?D$JcsdRnSnoG~f(;(d7F
zq7h@wIYyFZuSAQncLY?Hb7sTf%>W&$wnhw)>UGi0#8(Eg<Q91jcsYb{>hPI2u}FZs
zPWq79ec;8vjQnLLNcZ&?SwP$@V&B0L$5ux`kU8^&@Z6kjGB%REX>T)E6zaEHew3Kt
zgBGb9H{?u9hsiCSmrBK-l63zLpSu{*2RfL_lGRJSJVSG$HW?&d#+cPNG)JbymfJ-%
zfGFlskEFvA8~^c&-tra%gO-$cIfT`3l{R{P&z6bh=<oFJ%kdrGu`%txW<!ZIZxq}H
ztd<RIG>qy5O9$i!WVu5*ce6I!OoLhkI9Hndkc`?Yr%BsPsT!Ejus_RfK&w;`B<Mff
zsC2><yx#n*DJ%~?&Y)dINey1<-AI+)&ph}4f^hxsDcOJG9#Ftn-<?Nu!7Fyv#?*O`
z<&_94^xG5lK=J@;T;TvQX?Q%T23C7InF2!0JFHO9&6-pZCeDCbIOr}j|D7ZNsLb`K
zA(*iy|5Z$E`oUw3AZkCRS=V|+M&Na}@t&Uo#aN*QzT(OGI&jO7s4?>>ruit9^)_6_
zkAJ>qbVB>`Vpj$`OFhOJeb}}d))_u*N&Q5|=EYJhb*Q@6t$A88j%b&9<}ImWUejQJ
z<T-;29b(o$KfTsmacPkTGL2X|aqs|=BDsOK<YpvzPVpp~R2{CKERetau!Ff`-dMes
z1&-II&L5c_#!OCuJv~+#4(*|`9`Aw4a*?<FRu$Tbvs1GJG7E&B+u0x1y7jgP8Wr1>
z=U_dN<`ZO+oQDoot|@Pd*P-&B=V))<bvfeprD|c!rcOJ07sia8xM|nB4ck=CaO-Qs
z1oMp5c?2a7#IiTGgP&5Tey}-Q@Y`>z3C4|!otlxr)i-s&U0fHM>lDBqTUha;sXy=U
z3a*3Jc=Z*UH-7o+5!yEZO%5)PazbwkL(RGe^cSy6dcWcc<(>LNH!&b>0LYXGYd6qC
z^EG`I#*>e&Zb0e8Z@q|?n%d|`Aj1LIse7OXV@^p?wO?1};HbW4dE<2MO*qX7{1k0@
z=wEjMa$e6U>O04Tq78Xu0n>Q-y_Gv4=H<fcoi#6CW`k@%jl}ko5W9$vdu&vmK<VnV
zy4=Fqa*~$iw^s`2$y9Kmt4~HUD24|dlSK}D!%5p1QD5I43>Yf+07rCySoo?=4NQK-
zuy?YbXoXu=4?Qq9Ty@mtX7~m4niU#bQPp_@Y|#j%*Q<oT*#kUwz8OJ1);Lp~hn}1S
zVauvIkr=bd_f`r$OeBvHO!t2!Z#ec?dM5<D_yi;Xh660W8JM36eBhdFc+`kr<JWd&
z%;W~J#Z8|KLyefZBgrag)XGSJ*Qat4@F;S`FY6eUjTtLyKM8ECdy8%JB4*ty5G>K^
zqL`OpIRr^Sq0P=xO>tu?>R#)XJ8W6KdRgOT{+67$FVIdhW~}s+Ny6}L6!cpx`X-7`
zYXqvzI7TK|D17W^FSQ&!xQi%kKQmQMUKxGyX2Du#&Ukqr8TfMD(3*9n`lU}t+Hb0+
zQMDt5Zjqjyq5xHheMH_P?Ut8sr@NmwvEMhK=T<){B)lv|o3XZQ*{*IZO{>r_p80rL
z8(L+oTRLyN%mPO-Znw$18w?xYfjcw5yu<<>USw5{UgQ)4*ZU{i3rrF7b+3A#%{sI^
zAWqDJ)gpj<mJP%CIgSFArsGsNDT+IaxL;spcx*MQry639)+~Q{Yi4J5>R<E5cVf_8
zg1M2%FI!EMqcn^Wqp4nJrxV^Wmc>5keq<No@*>t}0DcQyv79L}_WSiMDD=|e(Gxp3
z>Zgk1?}d(rVWu{Vt-rEP6g)Jk*J$*31kJfH1X`WnJq0W9rDTqtCG=j7@ad_hqsLm_
z)_4Cki~ez`?A;UauF%rcHJp0`lAmv1Mw`|SXSyx74cIGzsGm-_^o4>B7Oz#%s*I;p
zMlXs#D?4}+W=-^{r`@Hd4(mk5tJ(@ONM0wt)(ZVU%o-a_)de1H)T3tTcWFJ(w^9z0
z8@&D*Fx(xlOW44ptDfklk<s5k9sUt6tG)NkPq42tez_xh5obWZSbotsJ#nkqi7DF<
z9qPfe!kzPjAhsh%NVC52y`0iikGok|^E6w03q6rcoOt=PF)x!doa4K$5g-FS;poMz
zzij)1z!ps$gamVTk1)YErtoFnb*(PrGgIyV(9npv(m7?~WSKX4pQ|qephWyOjggG0
zYcGq;qHtpyfHNR~F6=NpPQ83REL7*pEkpo13pX|{6aSBgCzOi)LOPq9a3Euysa=aU
zxb7jORi_8C&vr2tgyo3Gj59K2U8Q3F@A-5;{Vm8)ZhBxH43r3;gETaZPg8efP*_@2
zM93h#4}?{#Qq{h)_{W|hs5?gIM)e75DOl~vqy;$ba<xA70=Ol6(j3ezm>m6(XRZT%
zqX9P10Qf>WID!Z2P#<{`kw!M8y2thfjGRZeVaARfGqB8@v?H^i;D~s1-?5kgw8F7Q
z?{~rm+#B{X2)A6Apii~bh(V!ub};T{)+u7^d%M6PCe~0arvH7r1=Kdi!@`(Kj)fo2
zi}3WwSWH;aK2gyR2?@0y)tT{WaY0MK5rI>P4p5_ScPxFtIwk%Dbv7xm4Tl+n{l19N
zdl(sbWGaFC39R;bau+!4(qb9tu>ImX2n&vw_(J-CFZR)cp*Zz@&tkr~G$<Cud~wj=
zQVbDfNI~tsqL>+nP2F|L8g42JFZsLqj2dgyta+i)3bVFo=Fl<f!xO3xe&NsCE!K9g
z9J~qG;zYrdom&S;n$)8mA@Q@lSHzOLs$ez4Ul};{#`tpu74Jf^me9AjvZV;6+_Y<X
z?8(~NDtq>i*Vq9-fus))KD=b<Uc@}wL9Bs7-+TdE4K!v?I;3)Kmqsm;?XG}5<?*7U
z6I^RTZrX=tekt87dHMx};hZx4!oHwZ#57Y7bDpmr>T$p-d6oBEzb9y|5)`Z2c6?bm
zq1x?zYspl8&c=N-y5F+Vx`5VEkxtroTB*3EWjWe2HTJO4uc&~4DAOY7+kiroBHNCV
zvTS<?WszR@w&!m_F6ze5U!j75iu0<6CVY-~s?XtGLs*JI@IAzE<d-U|0b}hQOcR~l
z1`D`5`@I?P-)w*XV8$=StT6M~FCYbahl^r3GE1tj{))D|*!a`bOHjkb#TT`bd*X=<
zXmiaAsNS=gLr$za750jL;jW!8{C0v@Y#Tu(Hzc>v?r9XwvOp6Hq();?_51~}TD~vk
z>9^2%n&2Zbr7wJfcS+OEesSfgf)HVEoJOyXYZlng7<r&GM3J|!CJNo1Z>U+Za2S_{
z^bNDPH~*<;-q~mLcl)!9q55Kd)uQ@a7Pip2e$(ro0f3JfoM87|M<HL_v{*+w@q0MP
zk>_cvgQwE>ipoH2XHG7EGfY_ruO7`CbZ*mlUKaUfz-da`e(`~)_%fVVFqsDKy|kDm
zKkoprjM35G0DFAR!NtS-Ww!a)i(CSXgPHcxvquFd%FjFsl57!`WkAOWFQBzSPY-di
z8L(o2Bu0gxVJ@8(!IPPG9VP6m{7qjkfLCT5>V>I`BI}Up?qNesZ7k9OTdhreDvG!i
z06pDT@sworB67&7?F|kKZmzez?4T1&XmJBVs7#Gt!<6_OOi{+ZP)LE;f>rLEc;!BH
zMhD-5<s&IrjkY%d?w#+dUQ}Z*gUxzK2A;`HOkBB=x`lEn#KXzZTDi`?;70+od5?%j
z{U*+enFp3KqaISJP%@AZ)3mzP_)<z*DbXPSy^eG4v_w8Iu<I{jzu$Ip*8I*p?j7^H
z3l46~EX|=4HODmB+hz@`{TwbO$Cu8f8xDS)DcOhpWtyxz`fJK45q1EG1{#QVO`c|a
zx~QG~vNQH_5oKEERo+1Q2kR>f>imhN+DnbI4#Fhm3z!P4rS~|`vamRV>y+3!6P?j_
zD9&>xa>;t$MPq^wnI_xYc1?fk`Qdo{v+A`)5VDx|cn7Je`epzYIIKBnIF{+=#65*7
zw_Z&ko|5>sL4ivaj+sMwmb_gj5X-h`-t&~F?tMU^&M{{F@rOKveVjJ|MF1`>9a=J2
z33BmUZA#+gXI}eUV>}f$23|yhTWEog{QJz-A$p~DaR<<AVN<I+tE<!Xc)38BR@r+P
zS|~R{JT$D88|@1QkTB*uF~_$>sC_48vvzkftw30Vqdh+H@+6gw>}mi<oX1wXqt_}r
zmlA>q*L3}t9#0Weg(gM&{6|(g7l#H<L}EJ^-Gc{KpLH%OS~UdL4?OI%9=ek<tut-Q
z>t~=<M%~xbK|^|1*?Osau>l}si>bD+mz7~3=5jhDkJM=o`=B*z2^bJ8l%S)FFO3ZJ
z6b-;9YMzK_@LRP{fbXn4!w5<&^I!Urjx{>LRw0-;*dNfGB*?`^I_!GbkKz94`_*xV
zgIw_Zle0NuRU}2k4b3+~RaM7jDNP-Ac56XO?TW>pqCgAIBJiiM4n@5@;tfP5PiT6v
z;9KY&+m1jrALbhis9j?mp9qpLMGRZ8nz=Q}|83>hMq9MYN~XR2!?tGSDV5mZ&g1MZ
z%-luAV=GA@%dCi++?eYc7BqoCVi$1%*^>jdCD-?cU}Lktg$~5S3l2eRE%?WIJ<4Se
z^FHxGxxo+9`6jUP0hA<Zw(xoQtvgpb^EjUqJ7{3F4_A>I(1CAeglcAtoAG(?x^2FG
z%~AXlrnB`LtMQqFiE(%**|e=kT=zIbXB0V?X=lUu_T$;UrQh`T7u9p}wiM0xPh6j`
z^dNUd@xPXKcdY7##u>c0xkAd0Nd8Kj4&TM}zU5eOzk%grgYLi~AKt4QNbQTPX_i!Q
z#9<+wlF@ArTKikf{GLw10*oITE9AyO3$iTWwBuh#wu?FVCY@Y?1$-Z`XNg%6RwFsv
z2Y^T8$HARZ{ICYo>snbHe7Mh@N1zJoD6IYwxCMPy+tDRe32SxT`qrs3WEi@?+U#}E
z5vn)15HqzaP(RGQ115ZP`gu39zE&Zf=dco)5rLR<DCca;fQK2!>f-#ff!S3Qd{Flp
zutwE*<)dQH4<?kwL;WS2JXMR{uw7#PDA>Av+m<rNRSm4+`<gGO57mC;P!-iJDhN-J
z*E^B@TV@VpU0%?|?axPs1_AKbR@ATnwD&xFB?X8~#v9~Pvw%RKqjZM=$!flqe^31M
z5?MQ_k{NVJ!<zCq0};v<M5EB;+@Qr@Q}<yzaGs%=7Kfa%2p#8&cM&3qf!HP-_2T-<
za4z44_l^D*)OF^oqOYfEk!^cPs?h(Hk!XOo>Eo@=ci9^HBf}>S8CWC-7>D0x!8^O%
zfuwb)AZJnmou7{m9Qo)Ta2b8{y0tTqFxVg428pH0Ne}znPx@gqBiaZiyj7m+RUR>9
zu?}7+9Jp+q+GqX6MO)FpdT9(7_L&^R6w#n7km)*m`7?;o=vSWaIt|~@^&EM%DN02+
zmqC)ni~I3FJ}U?E88{*st?kr_(qbs*=m49Ko#3!55<BXIL)fmXVvnKE%gJtGBGTD!
zNrxaMPb*A6jxTna)L4A<s`H-EeP<SBotJj4pWky&T@ZA{ua*-|;CNh7+(Nt1)*f(#
zYEzT%<=SU<ESfH)3};S?Gn%x)hCI`=p>fQsCn$Gj7I4?6FEKQyROp#!JN)3apKqaU
z^jT{U1BLKFU|!TOUq_AufV6xF`oMx;0z^$IgmFjWrzRSZi!4n&I7jmH>%h-iKm70{
z10JPr++;t}1Gcz+LNf5g-9*(k-%3<`$B30T)jROpi2}4wtl#|gkzTk@E#l(=bhAkp
zG{J@gA7DASu_trsrBUN6iZ7}h&IHG8&B65s_Ui;|PWS6QL^VY^dv2t^XgqI|+E)}i
zRRNAE@a!`KMQeob;ud63vzow3ZonYV!VI1*?~>%r3f8%^5F3{I89Jeoi?iSx$>yNm
z`wYlX6))z2R50UG;WxJ41yGR<(>Ij0hXY(VbBo7*zij>R8sC3gAK}`1RK{Xf%KP@9
z+=0Axw+wDv?OU#1kb`rYXRJFXeK^+1kK7;flybp2>Q~ajv*>&vF0>0i6#ESLcjNrf
zqGfbvRN=@+gIt5)xLw^5XBU6j)EQ;|%9I*eVxj?c3EQY|7l-fPIbdw*OvzF;I*-IX
zD+91rI55#3aCDiVv1&5#)&gz`XXY86r!-;1h1yFWaa*&IwiGX(ODW97Kfw-X0vMfi
za(0Yhy;{yFtqZ;m6Jkq3*{i3+a3|cW7tOxWe;m425q~^BD0LA*d@5<bdb%JjT<S4*
zq#i@p!6t!u{p6GM*pNr`piZN&t23Rkr0O_eU#s?L_Nh1{X{E>(W04$BsxH%Lcv(;k
z@Pz)8v-8A8J3e1zXB8T9W7Gs7n)Zxe<EF_F^O>P%<^0lZrAtgRo|yAX`wXSq3W#B=
zK;ae<HACphI0rz+(?h7r>R$VS<CLbOO_GJ4{9JJJ%;AEJug1?_aP|Buh|OjTaK-gJ
z$v(|lETm`+=G-ZH^_ZSptNvN{;t&wU9f5mwu(d&R{U<WGWbza=HDv6Y<G$u1i{4-_
z%Tc}5$S)Nq=}6x$x{}Mjuf~<t@r=zdczL&*{X%`rt{y9FQ|Y(vNJ{L-3M-FUhoNh1
za!etR+fw=yW?}(l+X5H2AdgVDWC+ugYg*iNE~Ts!tL+M?bLrI1Ugs=pn(b#>2{Z$m
zpEqK6g<CLL;}*sXIeP=y;6HUUwL@@%bV5@im^XjDqW+@v+m9coq)wdc*sT$CL$uu4
zX@E7o%T2{U{ML=Uhfrd_UwN8m1wRrq;v4th+B@^Or1E}`+cb|p-KL!C>9&|OO-GF~
z9rq>Cnr18%G({zHD;3QJop8mCnx3X<aw$M@Lz>)j&t2T8sWd<%Ma4v9CP7kC>!=ti
za*sXt{(Jv<{<#0#^A|4;=XbuZ^V`1P&-eX3^mtUtKWxFbg~{wJc^J#)f0@ep)E~)C
zJ_wmQixGN5rUpQ!W<sVCH=k=b__t2`D;ftsZes{-KjLBItIQcE`9yimq5Kc`H)kKA
zXU4QNPFl@a!stypQ<Z$KZX=oY(f$=Xuu`!d&JeUXbb8J=_Rs6&mrn`Yv7etV#f(V{
zpTjJJy)OVK>l=iupo1S(2_4HC>&*SNJzuWr<ckaSto50K6Y<??r~S!Z@Ra%eq9|XH
z{DN?87~K~JTN|OK>(5?dcGO*gy!_fge_^|tUZ<?wVqIhy_*mjqskjzAY6VTT!C4P`
zAwc>b@)agXqGD+uwTPM%)3Fj+_17NrS9VJRWI!HxCR9?BF(7Vgya~mZCwDZroxB|)
zYg4l&rx`jG{he)I@-XW9)m-WqZT(!`XLpe>@skA6Yv<I}nV_)dnf++{Y3l1GM@Bwz
z>i!yFXUu6eX74;%I$^bDf+OM7L0s|sFf$Lw+Th8u!Wziu7hvYy$d|b9hPxY6qoZ6%
z`s5_aVk3Acxj#=taMhE)&ZW6Nq|dI?0VBK}3(|G7OZ&IuG6!oWy`pw&!u;nkZM&Ex
z1-5P1W@OX0Jn7)Bo<%AHJI0{t8@c_!eD5*WY)r%`d!edB8UA>VIuw@yYmX4yXXECh
zTCVLxI4lP*-k|B;X{xe6e!2T%&B|E4d-uqF_nWYV{wPBRMfp~sj1&R?9iBiw9S*lf
z4qvfn2ckgr=5<#u^>w*!n=B7r`sY{Vo!vb-h)8EB_L^$~!FOk}ZEQMyGb7eCH3}%U
zeQN<3XB`B^9JAJv&45hooH1SLKJeAd>eoqm-oC;lFG9f7LcC=7RgE#*jFWZkp#6{N
z-gjr=jKLF;(QA14;eUkT3gxepl>j6z>y*VE?+B`eY<?#fZ-&+7TVw%Lklb#YOf{;i
z{8ZX&Pbi{OUx#FjKZ0EP7I($+k9;?zm-;fT*X^kTY_S{-9vZ(drDLLtNA{v$$VKpy
zIMYWB1N%ahYiGUD%(KuU7%=3OI^7W19pYI(;y;Hm4TrW`Xj-`TW)5~KEAt=XHmtX`
zvu**VRSp_KxK)U@+LdcixZ(~j!s%K1bTIUj7|`TP5pYH(uVT12JlUPnSr%t`IFAS)
z+<;5IBU{RdjcoT7r-=;3Gq_CwDV1wbT~Y2wTp0ltTtQ)F;<?M0%h+yU15GvKh9=5s
zx~FwYGhFO!IAjBroF-gcK$qq8H`*AfR%p9WD+8ciGns23gFq)0pt&dA124H~Q?;FX
zsE`O(<s;Lr@y}Ph;dB@FDH(!%RCzo19cHB>u)Ak-^r$x@_Xx;E{r}^Ce*)KWkB@R*
z0f7;f8-(uFL_-qRRkUAtwsE2tGYYpr7438f{{S?_(peWV)4$|B5*fgd@^T38QQ+J0
zT4q)&zpyT1)h}r3(s}S>+|=^rp`9pVuuIguxvW*|$;C~2K<l)^ks&Qstk3-d(2Z0s
zlo&vjN^yt8rI&=LImGyT->5lt(Z~kx`&?1`W4HW{YxXBO=Y%TJY&?@G)g6U*lAa`#
z6L!nd`>aSN*J8KB?{t@@ULi8!tz`sV7svrDq;wIvgnTQk0f|J}XQ1i?j11pcjQr=8
zC%;;wKO4{*Vt1f<wIPrqf>=6ASVihfDDw=d9(y<?;bR5TG%MbuiNYxH#xmibU#g}7
zvcNa}M~b2x(s`G|x|w}Oqz{&QUuaF;gEFC`#A+Z02@cSJZ>h0#AY~<2b5eFO%I!dW
z`#ouNPNNeRx4{E+%9a*#xjm2PdcUwx{DsVVcT)4hQqTNZ`aLj!4v=9?=<Rq~c^+~A
z_Z~6dJ{toulVdxP$U}}!oRt%@eHatGV}fU<e_yL{n;e0O<|U__@CaQC`<ERWJ^5R8
zXlaD9_q(=ohxE}5*BZhR-bX|jmk`b?r&XJUs;pvtpmm*po5>*hYF!3;e*f}%CVqMS
z=ex)R1J<{kebg>X&V6Kht!Jn&x2sEJICy$P!@^Rf+%5Q(D&vK#zn?s9BQ}z?I3NS8
z2Y!irf~6FRd^gnp!nQ^+`cG4C9~tc=`(#PgnU46x(uq6ku3Klj@K6FByg?hp+mr)l
z>^vzHbuwk8Ka6~LG$|WJJX4Q2MJ+OWkg6}DI2?hpb$)`##ZIb&8AlnAZ(m{}yel9a
zpR4UnO_Z%fBC7C-wX44IdTrI~8{t;Ul?)fva`fxAkm2BhVW$|1`78)SZZu=l&vAEr
zAz?PU-yVJx?3=ry@XwJ*5S(fZ#X)GK^5IStRiEp!`F$?7G9r@FS-!r|W5p3t{lva2
zCm>2ortKH6omR826XWSjVKR4j)&rzyidy(+>Jb284BO$0PXqF#n$mu<ea2Z&<%}3<
zq9Cm3b*owKO!b=z;f6m>#cmqajEXLeZBUkjR8z<eTMjlQe*S$6=^qOmuxCd>SW-b>
zg3qh!3cM=SHuTrcy2i`jI-@@r%FcpA9rQBb&1fSnYCVG4QOw9e=A>tD^{Mr>d79$q
zG86^3OTNTxjMp!eQXC2DGcMmhaPy4rw$&F@Y<L_VAsq5msbfaQ<Q6AnYX(VucIC+K
zMMLCHG?;%-^eGUhIfRZ*hY?&@+q~KK%u^2t&+?wLyjo~dy`@h?7fKCtq(MZHGSX%z
z?&oX+1NC43Qn7O^Jz11`EmN)XqdXH{Br^{xOc0ZA{!r3u_VUPn6?JKA!%fAr={=Rs
zs&JSHXIoFnslU&!VQtp!p!#jMO_nr)&%or^k2_tgz4!bQ4}<Wjg&f&^lF-Pma-n!6
zHjex2@R_-86AteL-{$+D6DOelboGSgnsZy`69R-4b+e?Uzi1u>@w2I?nf8i;VV0M-
z&C|}j-BT@DgOrhml*CMX@;&q8s%=)HpXiPv3B}wSC04WS@o}UP=>CSS?9zk0?9z<s
zfcj$f>hlmph4-ja(`R$TnOIkAyox-lu;M})-X}j~QvXDA<8)jVPFES`Hz{aWn>8GY
z^<KFtq#hu!?txd-K1f)BbnbxT)0AY{g2X)^yzI9fPG@8HSZAL{wi`o3BL%E7M*}h#
zwo-1S-TZM!PNSL<O8}Dk5apadlZ-=TFU9_lfJyu*H|Fc4)ac@uehbb>5pgj%9#@~a
zR$tZBAzl<#*4KqIT*(De9NgxD!!bPfms_-u(HoE~D;4Ds)Ocpc=x<^ba-JTtV0?H{
zrwpj1bBzrK6OBm8E<>>x$^g9n+=wl?l&{pZd(LwKcQ;nUpShE*G50|Y_{N2$TaOWz
zyX5ngZ$f=VfhTgE&f6L7Jg@P17w^UXradha6gadB9P6tbFW|z4E6t$Ze6SaSGx>Ej
z8j*rFLysB{z-hJ~jdGe{{;6K(N_rT{u??fwBre_9a@3y3xe!WMoU`q>YZPL3<*>!o
z4bkk<7i#BKE5@IMw|Ry)r>-q<6B6+1#l$0=>f?A*&iLYkIyXZ`;qo3k-zK8{E#GBH
zPEUEf$w<S=6MJ|mD{w49h;0H5$=(%p$2;?+&*Qn#eC~7crM{^=u7u5KkzetLvM6iE
zW#qU_^X)lorl6v(wUuq3OE1;zvwXH1R5m}im0LGbjD(58qf=TWwKR5QCaCqD#q7j|
zEm~t^JwCXAnBRU$Pr&AC!ungiP6DKG3i};G<3~AV#CrJqD!Ht2y_BYxT#Lt2&e=2%
z)Lm8N)%hT_`7c4nJk}QlY5-Nhf@&Ai?U9EjA0C@rCo2coSdt@`e$+Ko<u{%W8gM8n
z!+r~caEx1xjEbzQbs$6UQ2jx2Ps)er>Vs(-DHoi9FGkgexw~Xk&mF>wM4B?1hZueF
zC6Nf;hJVx_1+wRi37z&<XBk3Wt-J%fyR@EsGxFuwN#~22a;OC0q?46Q+tar%N-#9g
zP%KQ@xIya_*3)bBezY=M?Ad~c67hrDZ3xA<ZFwXKg;&Ms(8M{E4>%%w3#0`cV2$%3
z$m*Q#F%iFp=_+|-EV(B3<kca;97i(b6d%r!-^>!rkMknMMengGPWAgAiw)>_yF2L-
znc&T4mKqVROv0Y4$^vD)((JjKs8dLjT4PD8GYK*kVz@Su2=6`G!t9HEriRdJjBnCF
z%vVw%MYpUUrwW9Z49wa#w#^SYRL7tWTxkn?-dc-i(p+y-DZm&mn6a|x_&11_-Ue*`
zM?mOLp4W;kNho1sVy>yFCVDlyzz@8`1sOUtb*jrBkC=+%ypmMbeU!%U()l|t&L+ph
zn8c}$S%><OGl%1+qiXkb&YoYrt3#{J3h+miJqdu%p}9*ZTm#=mU0SxlvD{q)ZHHM;
zeVhm}nre7po@Lo;a@tHs*{BUC_>`+Cz<=ZALD`neq30n``Q}7rW9sCu@qT&IM*x)9
zD}-~hZ!2N0jx-{2JxqJpyZfK(ztIY#t@0<(5J=CHRIy(DZAaUcQ7g!PPX?xa-hYmb
z#lbA!Dm%82-L;JhXY;06an-<rJ8|f1MU9P4j5lM<TVs^C<Cw_wRkgglR?t)Bv1gGw
zzH#g9sLlE1Me#9#1qvK{HPng~+RT%-YVE;8xYp%+v*_Yp*6)HGWO2UvAMZ`zcitq1
zkbaojN=>zw&A(Hx9%J&rC9T_ls*`rAqOB|%Y@Y#Q_gTiHbSm{k5#NVh5qk<OII-ax
zn#b@nATeyPIXhS+k|5e&UZ_HH?Mr-cKIp9M<d*-?LfwLJ&DZ=vc;dZ4a6o&u3(lca
zyG%GGp3cu1MKadj!#PsnY7H6b^7cbu3ZLxAIVFgqwAoYMw-+cTTA$9fRk~?nx;w|}
z!zO||a%auj1s!ER_BD+YOD|t~pcQ_r3_xc&*YrI2lBS2AD+Jk%a03#wDF4W*sp?&z
z;-X{4eXSW`<D$bp@i6eHVb@$;b=GK4NJOpH^<A6Yq;{xpOeHyYhV#Ga#7swZ2j<Ej
zrhdF04<#%g(BN7WV%Qq_e5}BI?oqQ0R++f9fvN8o5>XYK3iB&j_wSpSJk}T2qK|`Z
zOI6eqHC{nfc;&Jkq{?yg+SnOvvQx}?umm3Xv)@=GzqsbmT<V2TVHO}%<}OgDW2SB1
zEnX0bVlKr`Dn#hoy<#u)uS};}XQ-+K;x(@?7Zpe=bL4D#=rrKZ*KJDGSw3r#Bo(3I
zHd(8u7gT4dU8e>NNRq)d7M#QX>R3t=_t9HiJRYyA(58tCr0^Yt#IfLxafx>>`O0ZX
zHrzyx8jiG$lp~q0g-s3xD$D_gwwd6*i2%{$Yp4;t<)`+l`r!EKON8V#!Z=mxl)@X)
z+iO1dF-I1Is@<#g-4_ttce!N%38i}bPO#UaDH@BQv^E#8^edREHHLC(C3hv{!;ZM2
z6N|5b1qFiq6J@15ANq8h7lR`HX)l8^)*|gzVYxo8XKfwgP|_L1?Z}N|_>Ix@affnm
zvOg{z%{@Q4ydF^Rk32wde3d$gE?i<Y8}VX0i;Fh;W}=Y+Lr<(lOY$6<P1$7EABC8n
zf7m84*yOgA#ZO;tpoV*uqNS%?&;v7;juk^<+x+mQEm}u6U2^NPenb*fsp67fznBT7
z-Dy&zrc~DY<5oabNc(asmRbaf-w!3GGUsS{2ni-8qtwuda>;-A7ouD4M3t%!Xgp`L
zA?g*XvH|2}sO=4wZ)04!nJ*lvZ(!Ypv|V>Ko#46!{Ae>hsM(5h2f1O#v4|KJD$V@C
zN@M<1C&FL`95Z$oK873IP__d6`kIyJyIMc@#$DXTP%uQKyCIuR^%n-<i;-+w&c1_V
z0isuE(5PEB02jop<G7JN@jdT+0oGYcg%QgV|K+FTl3pkO$_3#N8Hmvn#RzU6=Or@#
zP<CED!cnL2<e?uPB|^J&-lJI0-HvzRC94TOu)AAf@-X#avA1h{tyKh=cTiO6m{1Nd
zt6IG`Hi~Nqv6MteDYu<6B4wkI-{?jxE+YeQq;yIAtYTk<0km<)^0&;b6}r4R6FUr}
zCHFxYKC<nD9q2=X(g24n6mj5dk^LzHTvM*S++*Zp4j49aOu{eamz@%hjSfwza%4`v
z6+KsiS4Le_X9VA@$CbTLUet|2s+h3pF5iAGZ@xM`ngP1jXU0TcTYJ_^ytw8wOFsKQ
zzDMpvtEcjf)%X@Iix+<n&>FjFT?QcNFa<=)?D2FiNlWP`q#NPA-r7Xcx-Id~-`qMH
z6_!Y`psuGasMHwe#!RXdVJg%1#xB8|j>;%=*_jOtWV`vzb3uXkDEC&^uXh;6qo_oi
zwNrq~FSC)4%nt~;_MFfXJ|rKaQyo~&EU38SE;NK&{o(Qn0VXC+nMiSPP%uk0Mdm@t
zEoH*q5kZ$_x7ymD-bh9s_}Ig8$Ni>*&6PAn!=X?P+L|9Xi0?@s()4n=?V);uX*+~D
zM}y8UzT$TmxnUqP-{`E!*s0DA5TBy1W4Y@+y6*{VCnB*J-aL(DDw#}4zeFJqB+-%N
zqxfi{QANg|okt9Itvsc<cIvu=lHg${q$YQ0QeFz65-T#Y(y%VyzS{{unxvC2HA7Q)
zp3@Zd9e|uqL?sxptYvu)CuHS85FLRYcsveulCa{rrVM^GjUJ?Y3h+@x9-yJULH$~f
zcjx*+Ji<DY$)MOu7`yxUrTs_J>VY;_*38@FwN@j1+|o18-O2kxKClE*rAYbPB%gqs
zH!HJ_;|P+SG!`=M=U3cZuY5x@UrjSJ80=qldkHRmrJD~0pPL5-$nGb;1wX*s@VVV^
z1Lu9cd+Rp8f08j)B=hJpg?<-0as%h~vr`oJUln8*|2hKa@EB0>2Xtz1JLc_T=Kk)_
z<YRx{Em|S_z>Abvo$`rqV$5Wr_cxpRH;yM7A?(n-%?AFys_D?b@ww3V-)Qgu=F*?O
Zes#>a<Z`45xOso%k8VHIeSi7q{{s0NQi}ip

literal 26483
zcmeFZcT|&EyEh!mSjItTbVj7B1du^RrFTb1Fe0EJp%W45O~6nRLa+?z(1rx*MMWtI
zJpw`kL2<y)B0?yEBq9hJ2%#krLdbU~KF>MNci!`@_5Jg{YrSVZ_gb2`?_IC*yMFuH
zJ3hQ(YbEo&{P!RbNCtB8cY6@%yBH8?+l^g2fRgd7CyT(xjvLlizXPAZvv!_{20nJ*
zy66%K0x2a){%lGAwB!Q<9R@*u|Ls~#!8{|j;AXT3S3J^tQ+3b%?YmB22XC!ERCCPo
z>z(8EkRP{pv-kZhJCt<nS9d3pJL&P$r~W_xd{y_?!*?HSb%I`ihHp9YHtW!ZdtMnA
zvVK(WnXs@;b@;)9#?UEiX(=tHc9kfLsv`RZNOKx0@O`YHsjW;pvw#2fzexgn8Psb?
zKhv4mFDd3V&d$&Aj#S$z;XY7skkttyFsAcgX0(B%cieJ0(O#d*)4@%MD0vndblq*d
zb$+XKPKLT$Jtbcfeo_1Io#c!2)qfUzQV#-w7T4Om#()>?mb+uNByjxg;I?o9JBrqa
zAJiDPa6y%~yhMcY`q>QZZ1Kc&Y}>Q-PD*osMd&q7UWz!H#&XimPa$(pc~W0RIY#5!
z%39pu=XiW}GwnNCncr%2B(I!ZO@rgn)t(L_TV7g6`D_Z?oHk;s44YVN-Arf8(B|;6
zPAVoTktse-@FcbutyQ5qxd-RuM0OL|Ol?-`2jQzd5`!i)6S+$#aN5Ojtyke4>}fVG
zagJXo!_9DlhE<=0oojUq$lwNy7y>IiHF945ToNj2@nz2rYPc=0%_c?oB=J<Z>L!j(
z?40@qE7G`}mJ6?@LSfPGaz<*HEw-uJ<!OxTslq~!){w&RG8MGDhaYzK(4dUM){<Dg
zw(4zq=05%5E2I}|79B1J;heTO%VKiD;H;|*`mysIe7>saMSqad&t+627DwN(GoAWb
zTr9KY;|uQRcWom=c~5bJzVo0jSxodLCAQKIj2Mlq_^l$Rb_98>A`x1fD2c;EN2g3y
z+kI8HyuL~!?DO7mdJ%tYHBk;Ft~ajsDqXPb=jmaWs$r<OdnnJ5&V{;K;yUg8hH_k~
z*A5O;SWjs-u%99u+=nK~FDhSp-10Ukn!t0%uN`68^%{*#BBoLzJ!|i@5#{erG^~qj
z^wKN}5~5-7fDU1hS<?Q+e&bohtM%gE&w?!YddS~M#DLg*8!#)ag@a0-F}0ovwL~x9
znaQH5D%$DDejTk3D2I-SeF|n&w0_*i2F7MMT5Qxn`_bGCeV^MgQXN@z+Jm@WV>2vB
zOH@<v<rMo7?l7O$F#3ift7fSD_|*xg_^<ti;Y_^3X8UQXQ}1VDjThz4J*CigVhzG>
zPJHzyaVN=bsmOCxZ!btOF|`)}!_n_8484T}T)yso98tk{vdC4Go><EcQBV{U<%ge{
zx;K4!XU>clCN2h**oHVaEvx67QfsOS;z`;)zj<T`e2)D&F};KJ!mFykfmClCk8M!G
z!2);18cfZzGp4fZOE18P3UK!h>YTm~A05P7&7j8CIvE_-yj9DfjnoVZ#{GiP*1S7L
zqD_KV)E6U~jPas~Ie3dPQ8BjVOSWF+EXlFjvpolxH}j;_7Hd;I=(7D^*V7Zn0_Kcb
z*E14-gr67**#-6OJB|$JJ(t<?b|G=>E;TQ5qm5lDH=Cz-FIqe$gN@{bI_n5_ju^$m
zI#Qo5SQy1epN}~cW;qw26G-IWtZY~(RW^vfqOTGB?wf~n#&+KBOo`;8Hy|B-%lw@P
zgRot4Bf_G7!kAosGAYWyl^QN%u%m#7;5OZH?OYy`)9ZEX_*%}6)LBPOrI@pv8H7}A
z>t&qW6zqj9>$KmZxmRIiNYoB4w6#nYl`1uaQ0U?1qKr;)o7@x^Uo<%X*T>n}RU|vb
zd}S*$S>E@((tPpE>X;KFHr0GzZsbOx?+~96x!&uRyc*=YI`+0}Vc*C;s-d7QG5vWB
zk1)<yKf*Q5-@SSe#R+{@<ejgwP;C$y6>L!G&{L^^KrvFL1o~)5X5UL%L5r{G>a$Ln
z7!|T@YQ9WN(?_$`U!>B#wLM1<$=!)NurG2RU3i+`mIx2^Gpasv-4)N8bwx%^kP-w;
zoqipz@Aa_4f!V~!b<-P`#pt5&jUGQmGOOr(jE#3JwIGtZ=ToSD87pT?*a<$}qo&AP
z;WgRCivg{EWXfUa1R}nsOck3<eTB)_m3sRXc*EdbMxxFiVn&Fs7{1$>RR}xnb@`p3
zz4!Da5c)Ayr?D7@!X0`0^<JV}p&<C{=S|D_Y@<fw$j&wcN0t0R!Jq8!bofexwZqfC
zq|i=2I(KkZPNxxl+qM`xG8z?3$bR&7#mJlX<IkJpDX3`YGVY`y&MS4O!QC0<Z_7%~
z6Pw8oKCNc(3Oq&d4mx4y_;~F>E-63q#CvgWoyZF4qsq7Do-ltZ(}DIVk{=RPHFqA~
z_rN>k6B6O_%`VTiJzvu~>DTvERK>>6q#$PsHWiv(=z`uaV!a?mi=Z7`(&4o`k^M_F
zo%Q*mLBA@6qkTeicgByq`jNeCA<n2~?iS;Ee=L=CRxI25#e(YV7krW}5uQuBok8z)
z&(f)U50>0cB_vC(s}*@~mhOS_>99Xet=KF*vLOyGM$1rL@lh}yHnLMCatAlPa>Z3&
z=U%iHYcRR3b4x<UhZ-EHJ!+||3QZ`vr{rLUTTe=?{d8Y1Ew^FDPsU)cE7d2G-{;1h
zyuRW&&NLF5pYay@qSNyQs8L3({vxUCIKlVFG%uzVS&Z5yGKlCs1mLzZZd~b{nKzNN
z&X5x-$Gux=*gSld;J1a_M4a4rN~MYkHETWcV1u4$u~%_q7S@5BNr6|v`x}%bk|TOd
zf!crGq7AC`aHlO^U2#$Q&Xekaw+Ql%hO<&)31$Y7)jhNG`GPtQwQsb|BR<_Ee(by6
zI_70YU2s#7K(lY3PE&WK%W(Q!dyrXk%XrGvW^n%~5ie_Se6n^J10JU#EV5%?%|Tt}
z%vu@M;h^i?QEMvOKnXZI-HG|nK{!9X$~#24Xneo9|G37iB6`v4?~#c00@O;Dhv6vn
zk5t0O2!@J2W^U%kk_~w*PVM^j=C*_3z^aAU&_m1U5j2u_N3XH?DkJWF8!M%a8oH@+
zL77qeq625-TPT9HJgjS6H)}1Fj$DuavJK?i!*XIVPGRR0WuCl_hLY`D{R-PyG4EFP
z^&9a|DwKapi{!VWw~3mPV_8XIfjKVG$I@fBe4WgbeT)|ESZBpnp40d04T?77<`smU
z*jIVdzgb6J;Ammu%~I8XiDy$pE5puA57Fxak%cD;(h)q$P<jbX&l~i^yGQr8mEhe%
zZdCfTmNhK{{sjW1T^R{`86<Qy;IB>TrHKV{T?;{*UBvVd=c8U#@klpe`X@qYz5ZO{
zWxe~#1hz?tWwH0nYHma67jnPh=9kDox5nf=k#UVd8OtSdy~-dTK`@}vQ~ErKbF|2~
z^X2+;aC7{pMAAs%)sZp<-=*pX?)#aMxG6c&F!ABo?#T5%;^fCC?)wO#H~6xV>l5C#
zs36A8s>WI5`V~NEA5P`G+5@V5nyxF{ak;CiE`%9Oh7}RGWqG0uc9{X1Gr{p67EE3Z
z+1zna<C>j0DMEibH<B;<n8G3AU~Xqmc#qCE5Q};gLVs->w%_H*aufyj?1{DTY`vMv
z(YU4!Kp3QN-=tk3f$JE_XL$9u)rkgEID%k~UTAvIt|@~cleV%hiTA*FNs;KZjLxV&
z0{bMte{{1=uhA^J-;m{LR^tWzu$g|Wc=*R3H-`%1$#r++oAPHHqmH3gZ?Y^yd*L@a
ztUdthV8uM|Tiq}cD!Yi{I}juxI?7LfB5rQt52vX1qcx1wuQhDE!th0po_)F#F(EF#
z-z^#B*P)nH(o9X?hPZDu-k!H}F2$tO0B{E4k^4;Yrl5$ePqWZ>C+0Uea=-Y%rU0F`
zfB!Xg?{U;(FPlk|HR%0y+(LNEsiO-`E*n$vX&XsdBBO1K`^<~<XW_xKYVv{3T9Ht4
zw84Et4fqFS{O$8!MKUs5##Wc|n<*2MFR$D7C9UW95mYF!)du1KqQBDI#BW2RdZk6y
z$L{>D){NqwWc^dj(=-6qxFInX9zhZFbu<RPXV{24YOOl~zgH7olI~)yJ2XuCXXLX=
z5N&xqX?ik<pVGjfX;~U}nJAnqacM-h6Y)oWS5Uo8$0Z1+6J7H2D>!B-uNXa1&g_+K
z%nKgG{EFFOnqy@DiuW9ee_@Scq-SH{+t+S3I;d~RT%GcpnO89Rz;F<Csm~z;2;$T{
z*~L%^^#UOJ)}iAAAdsRaU;qAjC+aBL=KNplqz=QbIr(7|(S%n@U9b?h(~;aZe@=h$
z0F5Ib->I-PnaG3sRap}mmC>BgP76nvfuGnxJ}Y=MP&W8OPP!AC@M<ak>7;M40*}-f
zwPS@wJFg$Up@rY6i(Y%^0-ZE7n~{*M-`n?q6nA?LFD|X3U1ncu;ll!UiHA+1=vMd@
z9PwJQ(f#$te7wTBHa90Ua+ioTuy}gjC8+%5YZE*HC#a5`Fp>8hv6A&1Fw{o?^T^L9
z?^s%sQJG_1Qt>YjDk=_|GB`<kYD}C=KRFlc%6rgg?jDEgu%T5u&%X3?6a;(~e7O=b
zD_A`$#i6S`Cqba=AB76$=TOO%srkgS+}=dElAZt87;YCXS$-?o$qDU&^2g3125-(2
z<nNBx1mxr*OA^yf(B((KYr@2I8!~}sF--oGNn30QdsnMJ!%+wwhW8|L1;5BV=@rUv
zeQaP5XP5O$QT%oL@j`r(-u;|1gSb_pBP|j~4xBnN>D;(XXOxrucj@^toadYHJtibM
zPCjosu{JELkGY+5KxX8%lheMM1t<gOl_(<Ovvnu#hoNg<rch4Q1isv%><+Ny<HHAl
zZO~>VB5H~m6MU8Xg%cwsR)7!u_s{r1wT~>|AU}Dt9(b>r!Iw5j00e@F*&g5OCs_iD
z0-oBZ+kqGVe*XVS;2JT@1Ee*ng}1s!v^q3*pKXofR&Jv1F9BAT+qvNp>*($R>{OXP
zvRgszou*{E!SYm7<LWPfB5shLE}@+~|L9UlTk6{e+RFlLGvU!<2~7cSrkVm5vu$Y?
zR8xS>bWc<qO~gfu+de0^`HwXmpHRzof8PV!T+Snfg08P^Ck4L|;#aKX=aLn+GVzx3
z`#_+qh3e?mAirW1zrM%$<p_C`C*0-<x}HA@nfrZ<WMzt1|Dk2AOaDdf|2Gz-xkhby
zfj6!;ZlgPRczAevT;;gkklJ;-zQzXzf`}W33aZ<h*Z(x?@^o%3Ur%OpaX|+?l_a95
zJ+qzUHrGbe!6}3+ekEw6i#<G`W~J-c-g#+?qaN6|j5&%zd9!_cqb(d!G0X51Wlq}V
zo5w5+B<&CXHVIuDV`n%}Eu77&*O%FE5{o+;@Pq2GXuC#M%lebVF{04yC4&80OR_Ns
zHJWYVek{By`Z~0tq2K#a^od=Jqptp8eR4iO812NvW2ev&f#Q8mekV)aCH1d6?uwD+
znv~JWS4FqsJoT=ZrR&Y?0Ecm9cn(~uOfB1XV$7^<LvdV=w`d|ITvC{zbP*_onbS5u
zc%|_&<ffzv9FxBiqL=u#4Fbg(=Ru1at-X=Qr9=F^8yEuaz;RQVac(LIByXPTlGcX;
zyZWWgIGkvnopWAs_A^A>&?yy4)L~!|W=-3AG$%?C*6?JTwD1+S4b2EU>jXugx&)W>
z+TLv(IQHE<$myD(dNXht2o2FrVtcc)_3^~~8r0JxFU@eq%`IN%9}8>Qr8`q&S?uKt
zvU?>hgTuBmtCr|&bD5rB=g9+f8(cAc+HmCBz6#INu0G%TuK&Gbc#TZs;WZqQ24jM5
zwk9orw#TYk3S!PzeHTNk(w*%5cg2QzGU{63yyvu3{FHpw?XPFyS9wKj8Ej3y%pX!o
zsRD~Ms;=A&SeEn*G6Zk+)c;sV6NeV4nWYuRnR(x@1B#)H5**CmPb6NW(Zsm;kg-32
zuom?meb(@H8FWNBw%2>aVmk=*;X$+~e7Qc^`L0mo!*_@NNXSl&iQ^Gcb?yl?X#&UW
zlv4*KAUwI-F6P{9L1e$gKby!Rj@jl?IxI>KWcBkFF1y*IA?SVl%E2Omj%w<ZZ#Xdg
zP9y%Px5?($scetV*GkCBeUV#JY}$zkPbVd_0B&CNVW1A^)=h;eRe|Ii==hxk0PO+m
zxd3G1BwwJJdVr2sGrA6ROE%!3M+wndB=R<c3(|ibN*#MFDFtbD|G=yQ<dfH-I0g0o
zS!xB9=76$W%Z!olH*su2k`a@6d`(*U=EKCQMS2B4HQNzbIxzn9Pm3p+Zl&}lcUJ)7
zBn!0ytWegrA&mIqS4lyFYMR8N;5by7&o9z>DaKCR3|TUFF^hTM7I=RtO~LC2NyQJl
z4u4?CYaa@)46;=Grc<vhf61GF$vOTnNyz^R1(IAW^tC39AwRost3h!buj>!hj%;X?
zN7PSl^9N`_IDD4JE<g+kfN6c@vAtB&WBG5V>J-B&ZQY`NvR+(d8}*!&=<RHKkELnk
zGMu{08`vx#XIfhxO8#V0#qN)8izG&IT%%6xiC+3!Hu}r+?BV_&8USr1gj@vLZ1AQ}
zCzk1LCl$*Fm^R+gFrD0Oa`XeLQ>SQP5A5q}pKOWV7Fm_|Bu5SZ$rt`_3>^Qbg5dag
zIPD<~AYf8|3bp{?4Iao#U?<^|u8p`&<(Sb*aMpM#v=~>7^Yml{1a(XO3Hl1)1lM=Q
z2J@_gg2k`c8xUa*n@2O+sYD>Qj7%@>q^aVA4%(|p^>C&aa2VDIeKM3*0~O@IC}zqg
z5_ckh=MAdTq?lcKN)mXSMf9N+fm3=#{Nt9Gdd^*TC7Y;Dg~1q=Ud@SSv}|ZiuKZM)
zd=^sDDJV+XX5<cc<quk)!d4cnCe*R@xp6aMY3MYJ_2Z_ePOSU;TGI}T%c)4Rq(Oob
zfJ!m5HVtGl-GsN%otR5<^s{R-FP$t6+(|^RNAGq0X8>XN;zW&ld>D`dAm{50-xiPe
zno5mo^e0J3H)&5Tr5Ag8i+|Z^Mvm)u9_%U#fBQ=avsp$bx$En__H3KBBGcE-wG0%5
zH^in}S9{}ID<^kgKp-n8YI(nV)DgG&Vg>V8vAp^{UREzXI$9BFE<oYuJhp*J?ZhWj
zBChPkih4Jy9C4dR@sWM~Q4$8b*)JhTrv~n05pg&se(`jW67doFm}#Y#%!kvAnCYeK
zCWtG%zMhjeb}#$|VC<oZ#%%^zCS>8N2pWX`^FSvbMMGtIyxD)5MfEQl#H)DRIx$Y$
zgSP;j^LP;eeoQA6?-?qPmvN5|n|(YRq(m65;~H(e*Pln?Sa^QQsbK+hUpdoAvM^3X
zOOMCG`{vmq;=Jp@g>J2y`K5Gp+*;Tk(5?0&oDS8LU}dpB&{OSWU4Y(P^FVxg{wexs
zV}B7Y$$a$*ispo()d#t}wAOBYu|l;V>u)NLJHQL)-2KGj8pWk}u>ehuZd)n*#CoFC
zu@AAtoVWv|IB8s!7vfMr<S?FzPG{a`@&rpcK0qNVmY0{&*&>*pt90qU^~OlFI!NS%
zgc0Y@NGIkVwHwr~4}TBQ;5=X@;cN{j@B3u8?}ZP$_;DTx#vmGnw=as)jAn9_mP8c@
zinV9`{Ql7!q4kY1=rgp<RQ9j~HO$it2r+$>krg*2iZQ4z5+DbhzmvKQ9s*cZ4c`1z
zfC{Tt_mvv$0u8AU+P2z619Xn{K3Nf8xt>Rv3a62wLh)tZLpIIL-b`YTwIMD~y_Tvg
z`a_)E(dftV*a{J_x2iCAGpk30b`)wCpYiKW(w`v7NGgxMlK2p}Jk&+hAwM2QJ7v<u
zj-`nUbo!Zz@EF+X2$ZB&`}b$=MbD9HT0-Cv@Jl=x&1%GOAsc6bQ@+ZmGk$`I=F!<|
zGCC+}c-v00UVKRLF50Jyeki4XAZuMoC!oq-f!rVPwEww(@mrKV_CKIN9yrPgh4(+0
zk}keBt#0esPH0%gNqqccW1nYIW(vyjnwM)bDDST^cWr&yBHG$7t$TLrj~WAf(KQeA
z1`Y|Jl1X@#N?WBbQVI;x^=ubzvUk(qIHF@s5j{GPr$s|UwW}5kKgLR1M^)C{8^+_v
zl?wy4?j7C=cNwAuht~LdPB@KcWo{uAbXwip+!rbpMbJWMp@UgiAPbH!(?8ByTxR#v
zioP3`D&f|fu|J>huddjvUe99F0eH18<+JdJ5MCX-3#SnJn~kI=kX9Ie<hAG;{5SYu
z;^Sy~1HSB9wqscN!Zi-P`=hi5lmgvJvWmXmxxno+TG&g2Imto|Tw)x*HUC_G!kFqp
zi*~YbSBiYsW&pKd{P>yqVsa*kb$Tp%j{TI~DWijQN%Nq|rLOF}gS9*)C29V3TeuB4
zp`#doq`VMt$7C7-x9@TYuxTiixx=-_)%d?$quUv5e{;O625NC?ImbXH)>Ja$Xn5z|
zm}o~ogZp<_1Hb`NoC6Py8@cAxCpXKIe?5sVG)14?%JgZeo%E~oyI*G;n)A017J2q(
zfDnJX3n1Nxf<Pv8=Pg_c?L4j+7kc|$n-;DNN_G?p)jKzuD)_v;c;mez1oiaOrd8`t
z*ymMKB}rD0$uQ6+Ng?#HKIeptwHB$RW%<X>iRhOXq<T}Nbb3DZ3hM}F`0>|tZtbIO
z`^Q>8JkW)<3!`aNXwCdP-?{$fk3W6ek{CWs`DKym^-MyFft5XGNHRLWlW<;V_y?vL
zO<+eaE<ZVXoh&A|FrSH5*|-e0bST4*slP17JMy%!e7lgRtN@y}4IK?@4S$A^woi-k
zJ~42MRl$b^KV@l^(gg|?08^I)G_T48!AS>BvcUisR!R>YlDcv2Ztw3%Fn!u)ulhPF
z3WvgH1WHF4{i*Z&7`viC7~x^Ou#`R`_piNlIB>jyUOrdwHLP&bFnHbS5a2N@*Mz?L
zpQEuAd{)XeDcNmLI|VEsSHVvQW;0bvuTlKhe(Xx=>E->5v&A(vZAkXoisip_CUWLI
ztC-cs$5$J}Q@!mfikw@01IM);RsN%g$?~7N5ccpIb^&EYrP%Z&p6Th~AWCPa+*=o&
z=N(WTmM)}LHAU}U6T6!|hx1c;0mxB7J}bn~a6-gj@5Q!BMHc)TIOC!**(P{5$62Rh
zm#G};Wj>>RJ&hfyIwGwBbkh8L#|yyF4Py(gcp&RV*ea!Usi)OKlE05BL#aQJs;~Vs
zU<cB16HXlcuU88i%JpLGYC@rma^uhgqN3KE2x;eOrrtYAFqj#k<;xMi>^Be%ewSmd
zyK#}1x7Tdqn}82==Dk9$uw_e&&@T}YJOQl?hf*z+nr6box~)try9Sm$qk?YoepK3P
zrB|hz{DK~BGwr_QP6P<F#{k-noVg}Msiy9h{JUKd(7gCGtqt+)v~<Qf-hhEn%jg<u
zy6Tb&ph&<%&HaEaPDZDc#$5+skV&qI4|~7KVSml|wu8Q&2o&PDIL~X0T0{_>|G-=7
z<G21Qv6P=(!`9j6n2PJ!O0+IKxyqdID!eWB86s`_NQEsv%2B0qGpPBKbgc0{z`eHN
zN=8QG8EjA0LT`;xN0b9^56xvK2N}j2Ws5`MS0y#u`MNXvWhyzN>`&OPnm(gYJ6;)W
zkMQ-arbL$kZT!i%RA~tGC&`gCQg!<?KJ%4w{@y5yOcOS<N%tCC@pLB!B5=G3%FR^-
zGE5-Q{B`fX?V^c`>8b<G4jgh(c&{8S{jwR_vIEiCRNY20Zd-p;A+m5QG`^84oxu0Y
zSH*u50b6JJDz3b%;9KLYoU-jggu%QH+Md!zvs;;LNxg(eY9BmE`F0H!CQ~_KIf3Fg
z1$D|6v^Mb(C#f_Qf9iC-C%E2(Qyw~n<ju2Pu^bIsrAS|R?=w_*hbzJAX-OHK=qiLW
zeQ~IPg1VErfh_*5Kk3ihuI&pbu-G*bzsv)?bSsplghw7HJ~+Yu!fwK+YZHHbjH@Yc
ztC$qLDEt^n<2s7#?syBXu^dqYNB7VZnT}@Yu?rwE$4x%ev<>U-Dzb(5)$iHTNUqz)
zbimI-1rzOc(d9)t0jwIYfV-*#76u#X=Ve&M>6PKV;SL<F*%UbMny@9wWyE*mHt$15
z7wOa6sV(>~>GH!zSOI)TJ79x%){#7R$Kos(Y*498xHGjdx@bm!=*j9lck6mx==(eK
z*UMt9cv(KOTY}NvJ4goQOt(VQ2F`(<X2tw2+M3Lh*&0V}&f9{&g}A-jNdnyFtyFKO
zI|KFJymZW<Aiqc^ZtNH>r1t=BkAm+l#p<x~(2-MGTR*(PC7I28DMj|4o>L+&GdKB7
zf^_lMbVrR7#DgEmh0bu6klEy5YvdzIKK?BWn^0hV?WBJtpVxpEhIh&Vp*9-E9aVju
z?78~XoL6B-n?<kx=v;IFZ)?6b-EYDr`Ha-7d9HOkMUDAnKVr$<kpXA|gLZ0LG&)E4
zCwpAs+}l=E{HbP^BMpm#1vpnv7Nqr(FN*?rFCoGJTC#$9kqPH6u%f1Frm=VBm-J_(
zh{~-7A6jWxAnQ4um=MjqIJLzpL9&X%n2#A^@qn!Rtyh}~vZKC|;ror}Dt)r+ZfT>}
zdIUR3`4J@wjPfvHW0>373**!v&y_sU7VX+pcR|Yt<Ft6MN6h3OK)N(RMs7|$s3~Zd
zKb{F?l%@8a5+zsgw^`zRh_Md5Ff5;k8(IAW{Rc9>W8GNqetS^WmL@z^Y{=bXS^1O1
zkez=6fP!UA+uSrqSB=JQqHw6LjKHx_zN4TJ5pN5eHQiInS&H-Nuo*OWW>k2s{W(~=
z?oRgVdxEqng3*Wjzu{ox696cPmm647@7iML*zr*~(IZt_y;5rlb>AyHf9Jf9Q#2wT
z=D#bfc;ZLCdF_#TQ7|u;-9hV6N7V$s{H@R=Kmxe!dku!=Nw71twvRt{8HV0BZX{Uv
z=fny>vgr=vVW(p&KYid*p3@k{wbN1{s=b#A+#Xpk^+M_oa7QcnLMxn)i4f1;0Wj|L
z(5Eal83H2AzBO#iKXQ=%m2#x8g?3{?8B`^YZV;w<N4qeP9PrGwkx12>xX5t=?kzb%
zn<_(HJ%Z}R^@&#mPZQO&%^Nc>NkB9qBm@A_M@qY5s8#XN_e9EiwD_@$4V8bQXn$j+
zVBKDt+VFeF3XleMFEr%E0kPiA(LxvAYrE%Lers<J0EAY!;@lT@)l7Qre7kEJPT959
zJ8;|^Z<qQnQPqw2W^ZK)lEC(m30hLNfcN6@TDV#G2jAeDs(92JV~OfOQTI=8W8#7H
zDYAY8XU9&Z$fmv9iH_9r1s4yq8`88f$V3|b9?`sU1YB{Id0Xe{Y=iB@rb=Zq;HVpT
z+SQdB9*2vcBQ~bP-8))HM1|q8hT(6~Zr8zI7Hx@7*FPLAkS^}Jl^pUP+k?gj|4iIT
z)*FKJP)%7G{;5;aBS=avSoTV0uMNHLzmXQ1+*Om8et`}->%^!GbiV#LC4n)5aX>vs
z7T=V5?VI${P6K>m!$w(LLeq9)!B9xv3eD<2NQm?Bei<iddWENpN6q?p=bn6NV2ir%
zQ~e6KKsE6U1?9D?ePhVA5m_i~gTS^y<;Ay@9tirbej8J(j2;d9mh=N@`HyYk4mfZQ
zWI?4dxcz(AsCFc=f<OMB1-|V<HJXZ@8F~OCMSYMn#0m&I0w_}d)<iWs6S(K+QQF9@
zDl)P^@ok~60sXF_HW4lP;c$2p+gp|PjYp^+k9?SDca40d5ZdbXP?~uLRX(fpU1Z|C
z*8WHxbwiQXuQh}{TajvI-Vks~*>qiLo*AU@qW2%-GyLJ77a9MT9HQQ^&2Xp_M?Rp^
zg-QCbBoz&`8<@=;uEa_{DC@$`x6&DE{k>lrLP1)AfG_L|%<)1B*;ZwZ<TeC^7e;Gi
zC*vdWL|dhm9_*B~!RUR!vn}wU?xM}<H3c9Qq~bg^S_Un1q6X|jked()IOSr4wC4OS
z2{{esHL{<vUnhQus=5_T?!h6iiLWTZMBeb%|5>f?1L`82{Y+HO?v`0?MnufJDcqe*
za-u3$D0lK&X;TV7QgI|Vu61&k_m=hp3h~9GkdrlS#C5zhH+7r^EX%;AK^%TSbh03j
zWwC`D1y5#M0y)O_K(MOS0qo|2B=ym6Ejcy<wq?k&3O>_jl^BQ<!BuImzsA2@U+c_$
zC49ps;*u>7?E&Es>x0EiUJY6#g<#}ZPs@k9d$K$L7G0YP5GC96$aB0eGJKOz9BTDq
z!>Klu%$$0<SINi@Q1GZ`S;2cAaFiu!s!<g4vECXGM`LbXXg$BQg3kbuso#M!lKwB=
zEu5&}a~;cqA5-DD*~@0)l7Hpp;_~&yXQ&8xIGi=?88unXW@5c<3xkC*yrloIrj&*q
zTqb)jjVOPPF$YBh)VTFIBCNx>8U|%lQ}<=nR4+Jtoc<T143p7`2!pm(w(xF_FR=O8
zF4vQ|{=(INvQO8iUSxrjpHp;*bGCr3m)6{}KE+m+(Lwz2jb0{Q@0BA7Ie1T#SW}Ym
z6XOcHBMP^^I(<m0_`MxRw=x5OLuH1Y_E+g%4rI~~TrH5kti5nh@16ikyN5F|sH7mP
zDEtFuX+{KN-}h~XJ^Ior!QG=<xD_%6PQ=+AX|3mZ1rT98&DyeBI=c@K_~KFGilIw?
zi|0nMk=x#}^XD#}J9qf4e24ANyN*0KjIaC+`pb{oYi|8C-F@`WUB7J{alf#HxO%Yp
zZu@s?caI-UKKkc85`6dm*@N}kNnnlHf@ZX-8M#8Pl!zzdTZjlIcLWb}Fk2aNutVWX
zg<rxfc-OEFx{A4H_BZ8Cw8ouXi&k%b*0j-*nO`e)t1Au;yP3~3e|nk3FG3%qoQCYR
zjzAT|`QSAMb9o738(RJi4D!zb^@@7~@ACIRlnbUe<mXV2>o*=G(!W81rtYH;qP6bj
z8uc-^$ks@$o-*B`Z<{%rCl%Yd0gpauR|?0M6L{4X$60kx8o!C^dmFWwFsMsgRIhQA
z{yQl^B-oSy^=XejRjF1s!F<*V{$C@EbT3cNZ8nCrBbv&OqjCS*kA=HS{JULqW>xhY
zoB!GdW#zM`2iaR`$~Cwvh=LS3XsQC2e1PZF-O~LeS+&>;VQcTe#dZ<n<~~hovB4N^
zr+C2x*h%kS<{yS3>Ec&DGtW>)u<kiG#g}r^!Oi-VaRNf8F<oPAYR9-#mbIF?xgQwV
z=@;M{{KmptxX(g9?M*0#WKI!b=R<101+n~d*xgKUvpI!;E%<9X1)LpnWhP1UA!A&9
zEv)q$mL3{t%wmgbz}gFDQKh{-L^Yoxk4TPG(7KNS>1;x>5Iw|U5r+<z)qU};TSVTa
zMbDy}$gtReE5Avln>R3|ND9SB_s*w$07;V_T=egLJEHm?s2%^fKJM0exU}0-X94X-
zL$kPpR|=(#e|UH8u7HKv@CaL@djBj<o1L8l8mUuCFmYhYB&4lqx(pK>R2wg?{%hW)
z_O=qIkv8T^K6tGQjKsznQKFGO9j3S^d})p2BtY8f7wb{?SYb91Wz2g6qH~viz)q(U
zf+a^Qpi}6}PfoFzf6?AFZ<h{NYY9y0o;>c`zA;a-ypbzr$)7t9RJyh9W)8R%PR0EF
z$AKYf0JuG2xSOeQQ*Y_(@Q(5N^8UWgl>_N)l1zNl(Aic=_h+qAvA~sQ=z%QJeiEi#
zQUp2?1h7pwraixp2kBP{FU*H#k+W3_zio;~-_S2D*0V8StLI>nm<+D56aCIvs8sJx
z4{8f&Q2J{+v$F6&jJv^{X#Vx;emCrdj*r*?O9eN_45vyuzYa`@U-;MbK8&djWe#I%
zM(M!DA+u8D2a2CxCJsEq%2(IOXmte>)^1=-`dXy)^#_et<848hjY}8<^|kTLH#1F`
z4JgJ*eN2Z~O5>w3ig{<A!tI+Na(^Rjx4|1Y>JZ@aQ`bz6H*g!?&9w<BScqFnhFwMo
zB{FH>z<ww!qN+E?VzSj@f8)+#v8Kl<PQIhn#^$3)-N^<>5qA2|M0OBkoAP3UDR3BC
z4A*guUi*|in~@$jT`46b?Yr`F93HJ#+bzEaYp5HUs7(q#Wojz&^Sz`{ny?r1p)j>Z
zqdBDeA`=`Nis6Erg_ZKsP2=tK?cDi!n`K<)RTAuS#5IhaTC4+RoA0QUk3CjZFT5w{
z!px6<5V!?pj=gQt2)2_&X;3^crY9(K*o_+)D|I8Y{gR*1fZ9P=^%!bsdGDXk!xi3#
z3o#om7#%E0i(-b2^DjLiwcweepPsIK+5ihKE#J>nsF(WVs$TytrgmzxGVi!XtQ%&Z
zx>0Ori1f|OsNq98QIGk(enh|}%r~DnU;6E^^YgLY-+pnChWWM~_;<m7kw<w5Arw@2
zs~AkZtOMIM@kFrlcn5o{^KC1zkt#*TkMWnL@OR9}H>37A{K{I%&N>3o0WW%Y3k6F4
zJgHgpD!)%vqC^HE6mb4_686LMG#F&L8tkS}J|~McHKaJnm+!eg5MqZRPJB8U`}Vx@
znYqTxe@m|5%rl75)|XX*aC#+qyEfdtXgW+DH&@hJER@IXyFMw|Q#T7*xn^^|m;aXY
zE68X?3i?~*04pl=x-C8DK5GT}mbto}`3-6QUGV=UkD*2vgM<vFD=thu0#xFOARWK$
z+}jL&3JlzQj1qy-R*Ut*oVO~Uss}p>lqD6~)zx2_m{IMPd0;z8#P8hbCc6krj0!k*
zK7}r&K!DV_w@pt{xcj~e`QR|S<xWF&NyD=?n1`HkZHmILRC4jOpS|T06HK9MG(QtG
zX=tp?8A@T}tjzw093mPTmcqZ685?6!fEhB=rnq4hv!_L^XUfV_X5hh+&Jyl4_O)!u
zZ&%gop<axj?eM9FEWZQS*e*|i(;@X56f9;wh<Ha&slXT0FVwrT6Lf_Y4+%XrXL)(d
z5yJ16!0{=W{Ui?2&1bD-TlWc#SkLaHj9HD?I1GU04T2_@^;R%>;8tOi|4Vbw2dl!I
z=|Pv)Jb!|r76qaS%h?8MKZpGg!s^rNaT-1*R?sX7j@R?CWzGTM?wdc|dYJMY(x`uB
zq(3v`_YwUovY=C=N*Kjj_}uXPM04bDNba8=#3(gN2u87J`t?{N5X0$`*l<;QpS$hy
zJUHpu<$=YN%$uJhb%<cDc-|~;kq)Lpn#UADr@j-k30hb3$!qz9(9Ub7%NgmtpR!@*
zbH0FXhF)ek2~U<E536bJkH7$^?8{w?#1sr%&zB1AyTia3HBMRk<h)u@5ambHQ?0Yw
z7njGwd!?_HDX;mc>iW2e2XhOM<peP7vl%)dFL$RDKAk~;xP+H|*3!Hy(H<D2nh`N~
zM$!zFeC}=&3Whr_?#+SEUy5h~v+_wsk{9hyNA>4+1aFlE$Hs-l3bymyGp21J@u-gn
zr5L^EH=tb)gvq?(AzL;PERFN+KTAJA9%|n>+`Fs$Ns_KWO8+j9h~dAH$G9&8WIX8$
zQO@vf`|+hUca{%eHBdFd#^!FevFD@d(=sEmH#&F{nYWj!0%+|AHqxLB?_e9BpcG;Y
zXlFenJ=a!17VW#T5$WnAeYID}kn{g&ApH>{@c!QlkZvme8wLE+LK{McWe!_IqSVJM
zD8m3l-Z({RfMof7<3rc`A&a&wdkj%M_BzH&tyz>%4+O*_u*${dT(HnJOiJ?K$?Yc%
z@&FvOiwN#p*5E0V3#T{0RNLi+Lu1m>-zbs>>occqApMuc(+iG}_;<qQj1mb`f^~&U
zlQBoH8lLb2ul?P<vIk?TO-a`<I`zp=v0%Ehwb%_owm40Rw729|$6UrZH8F2U^nQH-
zve?K~hj?Cz-Fy#Fxf;lF1DLp6;&Ae~AYRM^qer#GPrF&@6O<*6t`rGAe-AKUvxfSO
zGDy4zh56ImX!i6Yh^N~caK9i}TH{?ffECCqK5L&24D-6pc~SzG|L%RILhWw%!s!I&
z@^I$BORRHl|KvK@M7<JUU2M0k5HPdnjw*KIl$wj^=@B144$$YVw?~qu|2n@{N`Ird
z%s8=-Wrs<&PjgV}fkaG%cliVaEWvC~66fpP;WS4`?s=h}9yInhjFa;1cfT3U*LJvu
zjGK-&g~0&K<f>5?z(SfkXBnKNh@7t*tM^$VN0K&{dm=wQa#E-CU^b#s9gA|Oc{>$v
zo!x7(u?QX4*KQLsxYfs*hbawuP{3_$(t-OH!Q+ip4N-l`S~a9I*|7Ln`_fwo+f+y8
z;^`Vb3oTX*fAdpSV~A$~a-rG9)6*7Hf^m2Dex$8o^rgHctvgL=knP&nPjiA50=GzX
zA<Tkl3NYzg7y)OZS;tD*r3v{iWVSHclRrO(1vl9)_sQcoA~6nWrps@E=s}=0_T2-b
z$0v;SNeG4vS$->zFThL}N6(LDl%yHsfY-^u%@m6f7#f8sD45m(`i5b4ssNK3LyJXj
zfIwdI=|L;^R5lJn%=4z(fOTn7vYSH2v%J%cN+r$rXp>Hjctm?(CbTJ5ftAZn5M}h&
z$)wqYZF>*7zMQKjF0{A<8GXodGdQ}ct~(skM)c{)QbY!mg-q?N<dZGOM%*K9Fs8;7
zqThsK>GdUx-F?gj-Ruulz837b&mrArn2G91WAx}PRLECLjC=UszchdRQ_Gi;6|mm<
zz$Xll+iW8(D;7R{>{E3M8~5nVqVH+8(JHnT_+oEhDbBbVJ5udo4P3bkB$%ztjB9d>
z;c==I8!B)#aWG#en_YCCb-A9KWU(=sb!j<H=jGu~d!8A$WlTTYbMocmzh`=%x6Hmn
ztB*x;8b(6LZ_d8^#RXY1^&3zj)AMzpP6cwW$O$tS#bvg#)J>AAA#3~V)55^9i;|O?
z8<8{ijL6SONHvq-N4=9juh?QvX$s5|0Sk$ssA<94gAc-~Pm;0jq%c;Ss{WYK3j@j+
zA$0p--PiLAzT7aZX=PJf8TQWw!2iD81pvbe9#<d0#x*o;2sBlHE%ma-s8~a;7<d`+
z4%{R}4OXguHb@p<{&IP!PKVOx7Tjana)u(XI))C$+;a`=A+1MY<^r=A8Pf+(H2Nre
z6yD4XyO3rZ;cp$1>oogpQ})g{W}^P@#@-T@()jm>A)y5kyk6gk&~CA-lAA;SlfEJC
zwv(S4TB=ns6Xnkvd-2>d((K5<ZCLXj=zhDou!9V$jDu&gK6Ver9ZHzD?>2goyW@Hl
zxCjt)Qt9eLeXI(&BEQPxQpCB^=to@B(d>x5G^O98uXOtn`ld0NE4%#L9qgGevaoUH
zQ{)e!cFSKbd5|YA&PnhG^f}0{`;(E@!!d1ebKe8_#IHUJrH_oGG+Z_)xsvw@ck~CJ
z`x9=vV$!RG4D|EM_cysNg&GU%nLQDEZJJNNr9hOkBbse#+c0tEtus=rxx#x4M%EjD
zA6?MJ%rrSHEj#6tq!#PaoqEC&GQVKE+?}cL?IH~L^o5SfV7pu=K_*zEbmF(omzisb
z8k^=I?ii&1(lUifW#|NGN@nsu>uGm)$%AjZ{+m+4v@gW=QbYu15Hr!ZEXdbbi|=b$
z$eM<A26tIwNP)fZ@!^{(&|pYJAjS&Z?1ynv8!MVdLfZbC?gevgB4}Uxit_Y7V!=R?
z#Dp@1nNX$dhD1Ft@a9@CH!@dZ8#AXnAq#))&nJSLJ-E^1m^clJAC_c7Ny56D+z^>K
zd|asom#r27A%>+9>y3%jVBE=SkSlKN?+FMpwhSr865e&1huFn>m>W?TNO<vI5uP4J
z%V_>S&6+`2Sg;-aG{?IQh7n(i`2ACKmxdAgHDY|oG9~^9MZwa)gA8t_?)p>~AV2%g
zzFSQ1zyhFl0_$Epoewe3nBE34e>B~)6kXzI)8#*~J`_@XKKWfusE%dwyE0okI&1+!
zHV)9ROnzUs!`XkIUA^ga`1~#&qskH`Qno9K38N3(PQgsS$=v(879miNh0X}~R0Fce
z?0;)?9{4{QrU$TkWO!NGrntQtY<K9%f&<oF=UKm#PaCzk@#EldtkE8uuJXRf2q=BD
z%r5=PY?-}z0u>*ivSLAh%q-EUB!q;#`K+lr)Lbx)P373E(9M%<FGD<K@WK)-#}c2H
zHt>{JF>ac4@HvbDO*#17uJDDBN!lf1KU+I=!oFDk`jb9~yMlKQ1*J+hU7rtXD)nBy
zv7#U|Z{<dh8MJ7&ETnbSTy8mVLjF0SZ8_#@DuQcq4i(n75oJ04C-o@YGC%FA+v(!x
z`uu>*eHVKmC#@r5?g@vU1t_nWK-r@nE}qY}hcOO?3~%R_&%T?oDvtyRJN(8Ae-yj1
z|H%cmQ`1i+7uXIOoK^$vOMoQJEt7|&lC)X2gLoE2Xm(d&CJs}w-*4{W+g)p1TF7b~
zxL@jh4K>)R?vwd<hGurw2{nMiU>*mGdLW^?Yt=8Fe)&3IJpE^P3(I=>F?i?mH-14i
zZB92s;zIR+w;#PA%-rSQ#QmCG;a1Re!7ELrvK2xU+E($(>=T!K!!`Sw1x_m>y+1we
zF*1fgq~!qG>g6|71ps=e%AwjT2v@5!M{<Q~%>mtO1bbGzW_CI`*aNk)?x|np-DZX_
zd9#Vc5VYWZ$h%Fcha3-(q66`h4)2GJZnImKWi?{$Yc?OX2<#%}z;<@aWiks6gT8BY
z`XA<Sw<VWn@$kXZt}o2t@B5J}M}Z%v%y_@Jhzd-w`LZWK{GE{VC}g7bdB}@^f<sCL
z4Pm8CDp#VAm@vm~bKm)_{?3=PS9sf&1Q^9rkAry%p9JY6z0Kj*hds>&SzCNy_L_oE
znf-K2Pj}5|&r7q_c*}>>!-Fv)`HxC(3_(#Vk5f^u7VI*X*jx6peDR}wDKKk@;%&`W
zZX?G|<JyX45M&s*<c*(I_uINh^~E+2(-STFo=GjtvTAuX^ZW{!hogaIeuG_M3ZF@m
z6VfQtqU;L`4%?1rHo|K6RScoSTh?|M*`}n`R66)!HvO)3TcA_)``^EueYaC<&g}LZ
z&TX?hiw-V~?4I?^v8;xbdouE4^=i-0lkNE$@<CsMhaOT-B{Sthj#`Ec8|(wzArQmP
zNn{s13ZII^cO$e)$Oliq${$J}@V&gK&YN1Pywn_Izn++X0b2vOY?)_GXq>(S8PRHd
zn%;|IKVu?Sd>5QFusqnoRy&)XgOh#VAB(T9-{=MleQAGb5}($)`xR$v^~~x`WW_pi
zj-wha*T_}JS;Wc4%j2_JJ)#*Z(WhGpg`Fm9s(xA@%bpf!QB_Ri2WQR|ehrQS(Bda=
zW~Vu);y8?PrI2vOHm<RBBGp7fi47D&1~y#Yh?CW652^MT7S2@MSIJR5(V|X$a$(+K
zB$W!xY~<m9?}CE{kC|@}Nj7$=+}xuCoLeM&p~^O^2!U^Z0P<dut)KGMn>h*f<CqQC
zPiq!0Xyrx(_?u<gUR2@0?v-FapS3wu_XMGSqfs6n|FV9gJi|>F#(v{R0AMru&`PP`
zi!CJW^9eB$qoq(DnbrA_8i|=u|J1RhB_H;mGwiEhM`OWZq4?90xQa;stHL4p6d_kg
z1Kc<l_|B3{5HYZ>eLH2l_VGj$wPr}znhJK~dSXcKAH~+q<6-upKZGMN9rXx<;X=ql
z-kDrc6_PCE1s55jp47)B$$vsO*2m2zj-Ls!aCC{cDn8SMKaOs(EM$2!Ets-+(K*IT
zWAgx#9qlANMA7pi*4TsC4L5G|h(_%Ec21sbN#|@S#FOC`?|ez|Aj5F>osPYw5v<^*
zQ}?kt1GU(T-F<-vA%$PBu~=dqiW*1H@1!1&-cpWYU;RjMk*;HGeQ1hg5Y`yaJ1T&I
zK9}?v!8dXC0+d2EGEe>Cz>;PduIIe66FBzw^PIxQ@TnD2H-gnq|LOZVhIY_YgWuV)
z*d`sy1s6uT0$)?WOwkvN*bjI5iyyifE5N68dXG|Nhp(m3abe0SL_52S%^gv^ljF25
z4Z@+>!SbasOvD&BqFbqR#O;kyjMc-d@V(66(mfzWISXpv>)RWcYxlA@$)y4!M^hZ6
za;f>+*N>T3v&cm>@XS&F<p%iG^&!WpH-0*+cs#q)e?T~7Lo(1tkX!mTdyWb!wj8|V
zraej{G8Qg1yW{ZcsRJ??(n$sK*h!7Gz;4nN553U1y>@aB>*^QAAhtd1U1iU~R_d|8
zK}{ahUWOEtzBn{xp5cylo12E89iu|K8HlsZIBTuB3I7e>{@!Cta=uqhN^CmIQ`cu+
zfrYEjR?)znjv4VbPo=3hv)KqA?e5Np_bym3rpcGI>}EddnsW`{t)}cRHn`SpruM1z
zOTiCm)S&mAc<=Y~L32lW6;7A;7hm8y)f`f@OlFpaer$~$5Yj^Uu!9YDGPSqph*$Bh
z&zaeJ&fMdd_m>p+&`lcmB1oqx@qN0nIQ56r0}k5yS}H%_sl-EgC!YopI`>t{%Qja{
z<hj0eeDntA2F54z>kowT!U1*fAxqTCbqq>=bxVMe&&^(kyx{pCQsj#j{Pe<a)tLJn
zJn_mTFvZqxc^Rxx26k=a4Uc6_muF@;&iZkhR|yKkLoy|AW<ws;Q!h17Iu;5x1Tq!T
zOuaRF%S9CXm#I#PMx0XQO8^)&SYnbsi`e=??PNL--wZ=Y*R_2(3LkA0aM94t)B%Fl
zoCo&(Zx&}h*^fL2?*tp^QL=yiWS+l}+MBbgxZVsN@wrtt{ZP|jXZdDzQ22UlWuwSU
zF8+K9)v0?#Dd1MPPhqS|KrOQzP{&VzI=afgtk3+06~4$k<I021u{~_%zN1|z>Gad2
zbk*lw|Lm8spA;g3&0k!;bBQrqmM>6}(=!jlWx|?of5mD>K3~X=NaI}0ovJr>4L72A
z=Y?%v(-j#5v50+h<7;qMJp4?2ivIax#Drj`Z>T1;MO^;?2vdIyb?)I8);D_7;8Slt
zf9^IWVigS@&kuuDsxr}?qbCYe)%%}pb1*L8_5}K|Co*j*ORZ<3%Y$dxyXht&))3Ep
zS|eGDXUz744jVPkMB$WPZtDyHbfQq~b+AH*emjN!=h|@_@8ZznhV1sJfa-UEE9Q8v
zX*qb730>}Q=j!%95fSv)><uWV_I0LGwFvyyFJ^KDqnjO<#<<ViN@B(hlMYtcnL*<8
zt;sD9BmFakLkblDA#z!#DdfHo*Gq!!AH%>Z+_d0PYy02)GWDh$LAWPNJBD7lw6@aa
zKXu$@&PM#@5ht$lZ;oz&^>CUe!it?fim>r%O#*7&tK>Y0nmoS*miUFSL=k;C)NWnD
z7oSHQ)28Pkx*-!^74v8}r?(|#au9c;^JcHWZRyn$+XFFQua(J`N5)s8wM}lP@cYlX
z&AxlOTA=|%gU?QOEAaAU6#F+efKrL^yu;X47{KLZY8Uh3UzwNNvx<7f1-9^%10{lM
z!_COlWe=i55bXtXRa{YDDBJY1ur#=bzVbm^$JMHJ{mIu~^txFs_M4R%uEq-3PQRQB
zpHuXeN*cY*_Y5-2h)#_>{=uffOvuNT!ACs)P2R$Uf?1X@Q}jHMUjkg5ONwu;uPSQ<
zIt6&b;IDHF)y=j%o-j%^I0#BN2AHND<M+6OtuQmr$dhVbMN-9z)b_p9zmSig0~n`+
z|N3{ribw07<7WZ)D>#HIukW`TK1Oa+MJ#^qE65EXeNoA7SZWTj9uMe_cR|K}P&l)0
zAl85mi)~Yv$*!8><)51LLTujn<y1!l1av_6hIt5Kw9Mhxaim|*c;3*nr3&?!E|h8O
z9g+Xw@%|?k_+ak9iLfAhn>VxXy4T?cNVMiNvu3ZxXN+F#La|Ff65fi~0pz{#DmxFW
zLcz=hAo`w$h@a0%m{$o!cK$hd_8OZ=yU*)O4!^N#^$5<s?2C>Yj%sYt#f8d;t#iwR
zr{;U3Zv=|L#gsrKEPIJgGz*#yB=sfKyVdj2TxIXzFxR|4si!;^B$5vJJ1)<+!LFt{
zc;yd=QRd6M2h^t;F@E8DIhUJ_uE}DuZ8~QwFPRxA+ei5g7oA@*y>LE%_TACdiu@Gn
zQ$F5r$$03wIot!z%$IF|d5nNX*(U*<*MFoAtHmGSYrlSeT&wfpfa<Sv?%A&*`$H)7
zjNK|q#OH*8nfy)rOIB@kh(*I>i0+pUm7>E!VQ%Hr2JumgdGUKA#@iLGPbYgf;{OUT
zHotefLE>_yaw`G3G;>GoFn8OCY|Y?EMZyDW)B7rMlgk@_k=kgsZ+uwj=<PUVXvJLN
zdG@;K(ilNw)OQ@%i2&p<)Yv?Ew5EtZcV%RK2%fskB>9WjE$STdZ3&!z(OtVM%DUuX
zms49Ed&V|pIQ@O|8Q^e6F^kqe-Ve6O08UkerO4t^d2=;v!Fh|qX*Erin|pidqZ39!
zCZB=Bf#I{r@ran=_;E2&W+=8JGgbg)Z(PQxPy_8hSh@(kVgU3~7!6gRKcF7=n3oE_
zD^5O-SvKepY8pWWBHdU9ZRE-GOrkIANRT!jX4{-EGF9*PgZC0|V;f4nS8FqggT+~A
zjN#<UA_X^l&n}gs>2Vk2UbE2GoW|#=k^XMoY6DvE&GQ&+eV+SR-gKA!%paAV{+?mK
z>ZghcfM&)4_44rJ4+enfHjW7Md=}5s6>59T1LXL{^OPv>%tu|CdUF=HJUn*lj`9Nn
zj4cJQrqIGJ)96OJVUlvudfnRf?xZ)dj!M70(>^Ft-YHYwWV{?OI>pGPK67~Q8Ap!~
z$uBMS9)kAzReV7q5c-=i`uMB>*p6LV`^zvLxTj70f4V!<zoxErkJEat)>DCxcze7`
z1(g6sM8<$X2&F0*MhRnNw2(xGKm`K^2+`7F9T2;dfDi^NlRyAviVOi;l$=BXmDx;I
z6C!g>NMZ;{?xyGb3FpQA-2Gy|+@H19v(~eo^*rC-+SIprcb{PH2kA@G=($d87r{ty
zIMuGiO!=t=o6{14*VhnO!GehVe>BAld~Sh0QZSz<zrX(PR+n4EntyIAPtj~IX>K*$
zWUbQQEYKvb)^jlAN!$a@|Bz19JLRHB@io85yf0RF6O9y|b{shXx33R<^_A(f`=Z1<
zDy!ajVCfn)FP5wSL-r}Ky1C&UW&cvw#kio86o-`BLE_74gktIDAj~p^uyCTd>gs9u
zd>|cMqBb1620pyS23$W(l+QwSXd->6T)k!-4kH=a`qku~4*iIJTA;X*%!<hA4rblI
z4zA#p$o9L=KHy%9Et*&JAa8wbE!8VrxmRWQ$@!OFbuk@85u_xb_tJ6%G^0oqEPDpQ
zHC%EzUL9hJTX+G<@mshc^pc?N7lsa=*F|w9NqSd-{(GmUk~;o{`RmX!Ms-CdM=%2n
zcyVePg$J%)?bg9L?9ll}4;W`96k%`xa6BqmhJYD-6|`OIbr)$<jw92=B9xE4z@|m|
z@;7K16-;MHfz3dG?6>b(AG(Sz{*mg>0gr@NsJlgFjWI{HLaJ(Wbk_Xbufd*o3kYQJ
zV4SXD5qED)ZT!W@{Qt@}{ik~)PlyBr$iYdJ)+1N379^`K?Qv+@f$J`AT!%XORi;;p
zEs;7~&R4WPuTJkd3NYZpDYN0tR*CDoqrFV=HLkH!>%C9syQ$(m3nNRvbseY>_fcp0
z=Rtnt%Jnol0ld^}TI&z=E7ZJh<nbWc6a;6fjscCCsr#8XE6nSfW6laSZtdE#dPdL1
zrr`3vi(om8y|Ibc_Spr=WFTv1G&jZwd_S;o-)h{lU=ro`ogG*>=4Tb*lX=@Hl@;O*
zNPG3g==o6zGOk?Aq-+v^J(Oj7@=b$;fM~F$XK;_e>!{duPJiMEXOseW=Ey@4;_5Om
z@n*J{Q;VLh7HeV8g>WsU{~I#>koO=Ayiut%o+e)0*Mo`EhO9~P0Q^F!;b&KnXTb8m
zX)h9BTB=K^ki!#M*B}~IsU9YYN(<aacuW-s(K?Kqj_H$uVbn&B4JQ6|cH-4u#h(yR
z7@<!pH7rjoYZ#cZ<e0OiiJ%?lCmSipRA$UL6%*tN)2=l_jN&WB_x;oos(*R#-H1fZ
z>@QQwO1Jkt>+(B{o{w>RCgmUeJXPi>rGnPsYY6qvaV8vp^r)UOqMC3@_mA$Wiz#*(
zZ!ed>V|V5v;0aqcc=V~r#K5IYA^-BY^&CYEK;qBHZa{pkiP-uVx2Z&8PC)I|O?_6o
z6$f*YyBdiWPJJ7FCA|;FO(g=A4$q4&J&B}L3Ziuaw96K)w9$0?ElcvnlE5|9SEgfr
zhRLbtU*GgUxi}C4#0ScLqrSp`ak<0L9kDHVESsu3HFd<|a!F8TP`hV81)g$7?Vb%G
zq5xw^Z^z6$nCHs`s#uN$pIGJ1i9nl`){*=SiOSTo8KYuoq6wz1?VCC@OsNW?$+YP&
zncwjfD<-`%0LMjH8&dZ$vLKRc__^1ej^JxE4-$PNR7;*Tk;sjc8VbN?AZjd^U%BKv
z;QH%IR#QqI;L)(THt5N51!7)Bt6Gq{W0LJLgzcpDn*u1*f-^)h>2J3J8I3~M2k-Af
zhG=3rRQ_8vT}*C#L3Y0}9I9MkA&)QPRScnypY=rd^~+R!HA5?!`#@egV4FrhJrt!o
z1$dp2b;nip=i$xR<!Ax(ynNTVY<BE9QNdtT8?#FuZh;>G&mQj-*5#jxL#uwLLU=wA
zKyke3*%^7KOOxY=i~sB+ELmO7paIU&$lKK}_tIjmA#UVvqU{2aUrZvoiklN3w=J5h
zLIfi_X(GL9za~Goe)Hbn)*x(W@JI#W>_#HO6HSLob<zv6TIs7&D*dcsv;3!%Z(MF9
zSNezBAP3Kv*fl~p+tKFzc%;zt-dHS}eo!wtgugs;XQh!jS~fjf1F~=*w=bEWO%%}B
z{n4|!8^zzLnWRPc@4xZXhN<MXFz!~jhKV!=sW?A<bc@AINU=ixX;#*4UaA>==5cH;
zEbxD(iMJ?kS$Pq@^RX0h-POE^@rzIk)U|HaBBY%G7wy_9M3y9@?2yDH?va^39&z|e
zU$gX;FUlu*x=CKJZIO|BPKpW_{B14M@0P2(kIgiDl?7WIaK?nmC@DRE7;Wnrvc)vt
z?bBqP=6;YhBTWVZK|8=o%>(z13fij+w?D1hin?p!YB!IStT4t~zKZVH@KO}-mw*-g
zgl4e4R+zp*n!<dx@x{ZW3GQe?Y3T8ORybf<cdIAHPyMif0x=Ghjoqq#ONx@L?X3?W
z+BelxpNm>{(tYb7F{P;aAx~m#xfPHZ{9sZ}Ir-*OgWsGtr`N?~fMPb0bQxaSku6@W
z2Gf_B!wliUKV8(XPGN^kEm!_oV%G9y9v*%<wrXmnpfQ+47+s9n7>11d_g;N>9vbR$
ztRB*76bNQzMa^kT!8<0qLzV|AVgyn-zUV+s@B?z|U-+4jG5}m7#FGh`tB~b}70Z2h
zr0I>1N{;)>CaI=hrZ3?&+o5{9nkK5|Oif=;7&jmL#D(`p=ksX5SyFidQt12ZOo;Y}
zxBvK;H8%#DHxe-5mB1sQHLpYjRJ<jnVzlvVq1dw>$vBMG`1T`h()T~8B9D?a5sRbG
z&aqiX6>+7GflqvTEY+u`t&UhBxt?vP@rUY+mWeNS?O&L=^aJyHV#Tf!w&zja97QKx
zLjf~k-0GTQsdsEmfV+owj~lNu+x;i{E+OqImT-LPkwxmB=NjW9k@%UJ4y6@2ys^!u
zzf$}Zfyu$*-=){SznCvs6LCl658Gw|v6d$D8uTu&=Zd^M@3r;x3?WLTxIgzg*KT}U
zzgl&Yn?6~cY{o;U=dnc1)S<*;nSs6QBLD1X2DALZIP|LP>EVNUHqZ62^U=+vz`$Wn
z4OlsG;jB<%UZ2$tg`o@lxP~kj5P?cGeS?kcA_UB|m=#Ks6hW#bTWhsBhz#TvgaN$i
z2l{oYo2P3g2@o~G&85$O`AVLDd?iQ?Bt45wZBxxsq1G<4ab#OF+{;3{=hcjr`aM@(
z>?8653;z$P#2(FDnaTSU59=wVl6(jgmb0g{qoQ*2ykEXAY&6j$nAa9GdRD5~W-wm_
zLU{H8u+Op!9j1p69CjW^Z1rB9RPN+;Wo9w<Dyqr$l2wHkAiRHIg%x_$Evu1;>9;-7
zSX1^Ti;|XOddZ!RJd_E-X4R=~lLobj+@X%as2?A~>?l`dEikyUR!!+S0tRR|`lQJO
z$n9|R>}5nWTKHi;X^5{Oa_+3OJ+^N_u7{>l%^OBn*8Dr!{q872rCodjDNTyuAv3)(
zYAlSq?FoMiR|2)B?G^Tqu`k>U5FFRg<8*C5wQfX1$v=S^<F?n`a%p-0LD**_XAHVw
zeiRHk@OpmPhB|&A$PY-!DJBTZ{y;uE{aWlG_@@rd8ranAgQsLZ^9nD`06Qi{nfMgO
z#G=il&x@0k($^@zV`lsoxeL^_tz}Gp?!bBoZfx59QOoT*uZ%Nuiyx*Gp?_a4do8RB
z80wH&j+GL;DBYSa*-s~{Ow$ZsE0sf~vY(Kl&FwRL*KMan&Lt~wd>%<%n@ZhrrS}lX
zPwmR;=gOFD#(O@54Iv`IjSg7=?@~p5{DbQM&I?x8wrD9XtK(m4;fT|LucOd9>?mO@
z`LUtAjg#2-%VOp4gtZiXAy@WrH#5>$o#zOj3u7M}uLl9}Zvcu<RHNwYfpEfik=h!*
zQ{D2t6YVwho*+&0NZ^a$RE_j@l*oQ+Jm-DDxcy)AAue82Cp9z(ph5Q=TshzDN~{nU
z1%cplI`@lCk2(leM4Q@OtO}OdL34b~Cw<jv5sf=ez&Y`ad^eUYfx-^7F7LnBl;|EH
zn2p%u?oaX`9djP9FAVhwDFOIZzMY8M_zd#epNB1)te{J`&SAn~Ziv;XqXzbSKtAWm
zL3TagyWknw8Jv^I!Ah|q(v0GGcvZ|8YyET=GXC{Lof>+5n7r&sP6Bco=O7sb$Xshm
zTUO_Grl`SPJ5-SKXbtyzYg8N_$BIy?2hiyB+$8a1aQTCE|KZ%{QV8)9x46M5Kpj5^
z00W>OzMm8q{$4eX#%BrGw*#=#Jo9jZnXI;9V5P0)_B4WwZ8f;!uoapW4&)yFVjTRF
zz}nV70v>eaUmWRA9=Fd6olj#0M@aAIFWe!t1<RgLp$0%EPBVnenh0KL?vk`M<!pPj
zW0#$y)07(`BdT`ufHhxuf_;e<i$1V3KW#c_?&UZX)Mb=BX7#)P9d*5MJe9I}nOk;H
zs(jOFm+I?bUaIc4;e@j!c@1j1+E4?ZR4VBaRjQ47r_|G~;d|bR%EK)T621mU@Efm2
zE257?y;fK5hSaFfN37i|I8{`V-;a=tgqVZlb^%Z|^g`D9bG?`lb@?GoMx|r(<+#`g
zw65t8hG|VacanENakou1LzC2)qOwzgy<yel{HOTt<TujS(~R3#T~g6Fe&f?l=1h;P
zq97f)&k)hHxD-{@8$lNq(3O_z?=pIGYrie(55%Y$<=vkfj2zs$%#{h_*g@1_;TJsR
z4Y3&@$y5#eVIeXK$kYgBgS&>V2IXSRDnNQvYV0Q)bf$<&Co$?#q|r)fdMYrKmXL>k
zL!0U!7g=y@H>lBmG7VJDG<rSn(7GY#%9c<*GNs&um2HXXQbXjYUGZRJ2N`24xtZI|
z8$CdtU>h%O!aTB61mw#9o)M@<z5NkE))#=mx8tT$h$$~);@Z*Y>=y}|SV9R`Hs_T3
zUk10lL5;>N;HY}L0uox7!7<lJT>%Nm!znV=A$Wh;Ug*w|y;i;WY2kI<J*sIh$sJUt
z=<ti83n{_e|2D)atMvhZagxlx2~3|Xu_U1EzyovcXWDKu50!pQ+4Ms7K~Q%qYBQcD
zt?}FUE6b#I1sN0|Q-R8u+QFWZg&V<Gf|;laiY#N}2z^Di(4Ad55aLA)%ev%It4p9J
z=G@p~nb{6&^%=kWB+2EB!18!aJrW^$qI{E`4d~~hg%yt45%c;1Y`FC1pf?b0Ma%}i
zMEP7%nZk5-w2kWW-z<o{*#bHu20bX>)gwCXk6h(NBRqzESs<eJB#h*4`9~)>J2G-4
zkKp2uRqN&}=x)@EPpwr+`=hGG#2i0aQ;M%-4TnvPrVKQqRxQfmoV;)SJ|{-eK{{=b
zE6-CjWt^>yfP>q%pr~uj?kMKkXh>ev;sN<h%BJv+i}S?*O76!K?i`v$+EOc&Q&BwW
zR??gv3e~Ugm+UB4_FmHL=ba-CfF(0u(xNFWJENUy?qc|Ra3ohrCtr@{>er7*1(~3r
zEnaa4A&HDL6}@_=^evV=Fk(#V8}>_Z#55c!uJtjxep#N{1#WUTb`Kal>`g-=bKBq>
zy$3lIlus=w^>=)MD<<q6dGbh}H}~hxEcWb}5YlL^9%4yVf%`ZvNI=Ddx<2Sj`ldV<
zT9!u6|6wIy7_$#yqN$|*I4LcfgtaDCQ2XaZjvQPnmE_G`Km0r~y#>tCmVrL33C7i?
z0ba@A6y{Mf$2>h0J~*-~XLv&YwN^vjJ+ag};L=tzU%#-zTv()P{kcD@TDNgg@CO~6
zZG-g&2|*wx|71QJ53JSmA1s4%kvop#3$Kv6VD9=Z%DEt!F|_QX$PIJp!;orGSS7ZB
z$~D3v=1O~dOrS03oLKp|HFYvl=Rk?IB1LfdZNY*2e#^o89bP>NEz1_2;E;pP>YcTz
zetGeC0DA%5(Ou~Yy?!sc;nv#&*b25Y@_0bGztWnrnap*Vql=rViLwDI{RZo(90nWC
z1-mG`Yod>=yR{yBeeN0ncjXj=YNdInLxaDNpji&)fQ+o0ran~*sV54KVvH90t$T^w
zz`dFk-XQhWD=#|m(!+Xqyvm9x>WNeote(l7+1LH6fAy3`{fuf82?mP>23V{=_XAio
zKjowqxU@_-!7u+?J#q#_*IKSWHywX$D_^#SI=H*Geg7BRKK~T-RPRW0w|I{2Mx!Yv
zn>ZZ*Un)45StT&Q2ieAbOpF-|Vu`>#`|uZ6n=p2)p1(PIL}*U9z%`VWhNl1HuRAaP
yYQg`}*Pr?~h1*QF8t1K#XIg-<=YRgWQfRPNRADSS)ZVuBapdWsQ*GZS{P4do1`#3v

diff --git a/reference/plot-3.png b/reference/plot-3.png
index dcf36937f5c3ae9e0d9c8779a376f0cb92a3b342..d9c4c46000b09890007779d12b5d2cc198b99852 100644
GIT binary patch
literal 28297
zcmeFZcUV)|w>O>{+bE)rj3Od1jt4N(dj}mX9O?+tq$*9ifHbL!VgW%<LNBpUq)QhF
z1Vq5lA|N0Ef=U2s34{_MB>A0$neYAH=ic}Ez5m?jegAm#Jdos^ea_i??X}katj}6I
zkNz^g#JlhKJ_G{63+kOWMId&CAP_sQ|FRoCGMfBi34Yss-Qd!B_#J+2=r%;bZ+ma*
zSqC5xLJv59KP65ryC4w1Bf#_Lu7qSRjE7;J%|pevCT4jQ6@QoV=iBvL{6qaKKa<Ln
zLUhj`ySw|+E$p?s$M?uQN_ph>GVPdyLzv-3g~xYp*vmn3r_Q|k<<)^d>|-t8KYR9&
z|CfC+zyESqN^0DVWff@JU@}ZH59}>1G@A*>VF;|%Qle)G^GcRoegozz!+I~bi~sN6
z|BM8h`{`+b&22A!WBJotwutDYYdtwT5Qu1Gtr(stOrYORSY8hJ31M;19Cd)(gXsIu
zzU<<BN;N%<<sLE9zst}0;HYoy%!@!Aoe|(PjZQLv4_lR5h^wjNeZ@l!p$WZ;sF@87
zXbeqefGi|F7z(dl`H^sv5O5Qk)R<9M$1|bsTkUKNC~e|@&TVVt0ycZY6*_?;9|04L
zDzx!QV6PGFD-b-i8fprqh==+^ykO*0GERIW1DLQsuek>ZwSX=K*X%GO%?m=d>Etd@
zJB>VaA@ddQWW6-e-x=Z;4fW`(4swDl^KH!ryu|R0W+Zkiq*=T&!-N)6oCyFu$5v(n
ztW(y(kc+@bwL2jl?r2P|djsnyct}U)LXr5ykzHPbdo(-xz$TUVZ&gG?r=y*s<nYPD
zqxZ8W=QX_NdaKp&d^1n#f+64aN6RA6f~I49fN(bXvG$}z?FuyJ`5~an<;rlITmRO|
zRXxl&WP63LOK^8Yi|%KfShf`MQS&)$c^+E|x%Yixit{y{2P1&5Nktc8IlMHF&3}Be
z`x7=+3M389go~8T(vyC=Zgi1teY}Hu0iJ{C3t<LFvsI4+&&tK{BXP&r?^aEW?XC{c
zdBCCE?oUEw+e?A%qZ)Yc9qV;KOv%8TGx#w6AHriv<mDZiuZ&({rH?{-UUQWO^$Ja4
z-mmnQ#6*7`nGY8ceoi&i|D)EDtwZUHl-o-h0d$P(N;4!CTWo@RYw2ne;vSn3jV3t1
z@Z^ikuwUVxJU{Zl#Se^+d=zhvRFL5hOYjvNDfp@xxR=yr>eC~#s#%*ewwv90a#ExG
zWK9mxqD^|oSo*F#<KFeUEKZpZo07{`TEDadNBerhGN7ph=^&}7_VOfY$L%Tyue-s@
z_(6A;{Pp99v6DX}pKNaKL5O)h9T7kv2EHjN5im*QpOE07IkNI5U7wZ#+Nb&3tp0AF
zY$_N~E}X`~zqA+5fbI!?vPZ5dh{%#dNux7Acp%N%>CY*UrXrq*9;F>LDi9!c&7c<4
zoxOPE=H_1-B^t3ky<E@FG^yt1G=~>Knz<$u{KJ~;H9*Jxd$2+0LjNn&0*XSo9tdff
z&KVAi>WO5MKWY0_yU+j9exk$8J-w9UWF7A_{DLw=gLHQrJY2;1&oi7n6MXf+rB6LK
z8P1Td>0!AdQ&*|%!H<#3_#K|T<|;A4J`Gg?rRGSb0EdBmrdQ`(bMYWznLhY!jPx;b
zBK`CF_xQmg!me4ZJB~(k^K73&HIs~sp?1Jx@0SuEs*(R+^~0ZH(U`A1yB~(VAldO5
zm}z}g0x6}l!Qn=jNtC8kOv;XeqD$dMy0{v*rKW0=%+}s7Lc?zEpN4hd4xg2nJFH+?
ztH=^ink0yA+?Yd&M)iH9jw?l^AI1*nbbrsF7RG8<g4v&55mt^9Ma=j{XL%#iJzff%
z54b%YW%E0I8onT(zLQF=IyNDD!24l=&$%-3p{faJ+7xJ2#hxs!mpMf=UTVrUiEkAP
z=~#w!dF>$l;E(C`*7wScD@On5XsU5t7(uJo1`g!{^|#j=G#cGzl|(}@)&beU%Bvf$
zMi}Pt%}k4dnLSF2I+g>Gy>q*;c{TC0dOf1a5=)myPU_<L&%S{Q4+tghY<A7DfnKGV
zR3qBGbZu|(kcm&??brCQlbe4lk|D1>!clEQzh@;Xjm(eSn}jp)ckNvKy2trV)WO9}
z|Dtf26w$is7t}IS%$reU?(~$bbU`TV5^n^_VP%VOd~;!BvM$Ta!Qz`4ual@t&{v(|
z6Jg<)rZ}a-`RiAXnqumVqOPuNiL8hCq<k7~VG<Vap@5CR*79qfhBBXOj&>ZMadRhb
zE6c91$T_!~LGNJk4M*sbQheB7y6kh1U)hcCQCAYz0~JCt4&!kHHxo{IZWeodAMm`E
zmq)+m*=abHS5iv7z*|<t`>I}gx=Jf1xJOpTXJ?R6gx8*KPsT!e(1g&EEv&?U<nHXe
zgbJ6!8-dDh4A1;hi$yO;WjKC(eLqQMHB_p#`->3V9gZr<UTaF*g-AxN^kcu9%4mp&
zhC*x|(dEjfOBwaVSD<zCYmA^^f>Kg&YT0bHx4UXkK+j4m%jV5&gjJHM;9K`1GZj?1
z6x^SSU+dA%(*RQ*ucp+ItwHrGlld9iv~CWt$PHg!t^_QBPzU`#Xno*1i1apX46;y(
zuQtF0NL`rRk~A8Cbwe_Grwrmv<`|0n{nG?AMnIu5F)bD;BJEzV5PR3UVx5d?6DRCe
zwoEr_zm%R5jw-O{Z`@D2-=ezZ(z8-#K#%yWfG<~C?DahsW)7L;p>KXS-{)N5p)W7q
z^Jv&_cFj6HZ(amp6`e~<yp4#i+0u0B%Y6l9CYkOCKGq=?`U5*dxp6~>oxEnh?yEX<
zCFojH-uY$KG3~D_GVP~J<1XJwf23t>DYgM7&K%UE31%_*JT|klre#jqr<oG35f?tq
z{5Yu_uS6mASJ|==%|Gic{fy`uoC1wa()wx~^!+DuL6#F##8}meBu%mhJ_b!9$~9kn
z{T#Ss)!|G!=|NPIe5>zC8?&FW-rn~7T_au)`>%k7iCe7jh@R#85gfR|?r1okfRn?w
zAS<s~=7v2nte5tKyl$x0DC57vPc9%p@Bk4AE9G@#Yptf?pLJ)}ZbU^GjgBZzxHbjo
zKe}hntjVGCQv&0Pm81>?hU<nihE_)kUYOW<4|;kMab+iK+~pSnpH(${xN3&+8+}TW
zBc;j5)ee`7<ZoE87{jacwGT%&V<Q83grrxsN^RaRRPsh-6Aw9*T^NFLH>|IIfG5&0
zRRD`<)(;Gr<G*`#c(N|f`T^Blb)9M&<w<As+iC<O3&TY0v%m6RzltFX#qs0^wsMB+
z4Qh{1ZmfaYW&<;e?$V+amaXM+#}l-gzFLiz7KXCiXYD3~?DR)su1U3V2b%Ykt1w&V
zIt}a3@oT78O>a=wLMUUge)gGUCuAkxj876t1@Djxgv@WPA>^c$X7YkfZ+p*J;qJeN
zoNLiG%f1Hw7sFl*9-j&<A7kn$5p-TpZzR$MsJ*dHiAAB2bRcTjZA{tDVr8ok4XoOZ
zFAjpjX=EGlz}4o^_BCsNCXA{OW|L(DQV~mc0D_^vW9LFm-Dta;sgG{tg*o4X$lE<X
z$~?X+EYuWy4V0WZ%nKHbSPxShkocVOfe6BI9)QC<gI*Wah`2thyMKHwu-w)_5fH_P
zgzav~WBWxf2kBQ-=dqK~^C2n5qgkk}AV`prS-W_c*F<<}?Lhrt(ha@#73<?`e>>N@
z^X=Db1kH2;e9R#p8`X|rQ^+zf`S#slG10W)i!{&7y`;-_{9S4-8GvuEBlYt5{;>^Z
zURC=*MrEuM>^3dM>#lwHp^e&{;5FJy;w=0T@EG~>&hJ2+9Zp6AgG1?)qo#)oL~P!x
zNrSZS==;NYgI<ssw3QF;kr!i@#!XRLcU09$;fHshpc*0L%6aFUK$7x#MjJ7m9I9Kz
z4u??vwvjJ;6~mJ)HsM|yWW%#~*6*CYP>;x(JAED?m{_kCIE)rzL_>|CCm`kO*>HoN
zA)w5d_7Rw{I?MLWA3I=g*%erDY_;C1HxeQjMG&__cRR=~S%SjZG?z;{8Bk{OvN%63
zv*I<eY_m#30$*twDI@vGlqFfm`pI!$aoR0-+N5rqcb1Gj2&{P^9_pxn_c|4wjw&+q
zacL6Ez4ERNWL<(XVl{_dvdTqLp|@IRTwEzkQ=f(=q^Na3Xy@YS1ejJ*JGMnd4>y|V
zKk4ldQ<KN*nv$rnx;<!0fgWypbcWF?<=Rzy=dah@zNBAK*83*zIa3plGA&Kw2h?Db
zMJkNqGROm<Hso{N?=YX4w0@m-b_UrYxBL3I6#gKTi6JjkH7tF8t!=<vIN(nI_b<nA
zS1}>@)RpcU)ggaYZ7F>(A{xLKf_u2_o8NI<5&SYl?zQJ0`G5cZ?~#C*r)&RDvPXoI
z$oD~uYS|$l@PIxI0*0$)g}I&N)y{UVnOZC^Z{p6ORqT+VIwJ4gJjxbw4&-slLOVM_
z)N0wI+{R8-NSEEzG}M8eoUeIyf=cFaj04AYi0zq=MvAuZa1O}AUXgny?^8Osr}+RJ
zlBJVRg37=~C{%>9MLCb|oa8<)`Pg1k?$ldA#L)k2qEjsp>H@8_ALj%Eh&Rs2KH1y+
zVjF+DdD5~0{+wWc6_A1=PlCPrX;a+CG(YqP<wr;6GOxNTjLrraLn>lw8u;;*1dQ^#
zkW1cXot~-z{dYJwL#z(Sv@}TL?UI!FMXhzpdZfYLvZ^`-eDAAYg7$BB_PMCOq9Lgq
z3|d@ZE8>@tdnXEeKx1QnyP4%pz~qDh$E*>}kZ6lWsJP|21I9GrMM@ZiEv5a)d3J#-
z5yM@3N%*i^F6?jG-nc6xIu*>nIM+nG{{dgK$8z15GO%9L$@;qZVB28$;O3*YBNN2@
z#hTvag|~+UInCd|24zfgNn#F05PNeN3Jp$*UpdDS1J~g|+v-}vhlI*oWn~N0XXK$+
zmZt<m$3x;Y=e(mjxRfxQ6p<j3P8Q_tP}wiXowhgK$i6pKCnjcoTuWlgB+4J+ww+ao
z3102}@a55}!>Sr9zLS;9!^1UbD`_C)5>;0!Autq530lWZe0$5ep!qlGOO`Yy^<$`s
z0quc%eojYZmJZ$Lnk46>RUZ%{@SYEjil^l09-*4tV}6`tRPhlsN0FG9JLA6;*&O4}
zn}s4g>rO^c8ZZiZW!;oq@iqwCJA$cJ<njf@A&0T3a$m>^I*~&D06Y-2wozlPpmz;Q
z8`B&($`jNr$Cck-em9^MGpM@N%ThdaYv(ti`$ovJ*=}$T_w0brAS|tOFg|(|_U`9R
zZB=|OsC~Gyl%to?2lkSdRpsz|&Lo!U(-PodnO4#pV1vRc=7zUEgM&Zv{7So;l*{g=
z?$a?`>f-rlCA`*bvW;r)r3^QV1U3chv`2K~e2vV=(w<lQg7%|taK?<tYwzke>Tarh
zF`gl@NST=YVRNRVHV;V1e+I8Z@Jm)nJ8k;?VrGQIA`9EGoCffl6ElZ=Qk_n18;;av
zcviCi8o|AIF>WHkAW;3nmGK#85;5he&pFpe&x*Hb+@6<Uj2<WUSgao`c3;WrWSJd3
zv28MvdP@nUgA$B+PaDzsGmP57stt3ODC(|DT=kUXIgEWpukK{=dDw_17A!<n=k*x6
zuoM2%>GDq29%zC1s;3v!whG;%D><3FA=qUX+PPvdRNG7HW0*owlm(GgG6vFAz&F8|
zoAwD!tUowcH3E@wL-A|Povi!bdpJ}g!Z1Y%b-;1gTXFm&uxTVT90#yVUNGZOW7Yu+
zBS}nxw6-FrnPWPvx*bPAVGKC{4D!7}hkzO76F5zLfCj^|hVMA14I;)0L!JVqjr}d5
z22{8xUPN>x?5^LFfjpoR?9IIOJEx88A{>2`H9(nzBb={@qiuh9azFeB<%H~Oe!gy`
z;qXhb@3sBhbGuiw(f9j^V<ep+OYVWLrvljh-_ijcu%iBcd5}q3TbLV<6W623Ud%9c
zUCLIQJ<Dn7NaPY&@CSYHKd*xSjW0xR>p(La`d`vv|HC)gcJ2|7rqp8i$(vR)CJxfv
z@y54wK;1Q1=Ax<t_q*gJ*CqLfbkPy+A|M2#bow5tc&-LCAM@5vYYCgq*}*j=_xuBA
zIG6k%9MnHc&HsjYOJHnfcpnbOtce?ce)-;ung81(MQgLP@m^h{6A3HZqx5&)EfW)B
z1hJF3%V0XW0Bjm~XSFS@zC5%5*3DR*>q%3Cj#_ho`hU-Ede5eT{x_fpz{peb3|MOH
zZv=Vs#dE!&)pZ~wC3zAGm&O02l`syGVX{)tlgqC+L%HW$zuTpfd9q6IVUX=p5`G-0
zGl3Gt)g)Y!xt;`Zszwy=<yI8nk=x^yPf!C%spJJ9n4l3!{AE(88uo-=|M7$$O>jG@
z^+2#W(H8B@#40xcYPkOCUO*%9kI!;Xxw;sge7a9Iv_ov9x{^HrbmRl!Matr8YWTpc
zj(L=(pJX3b_YIPFQps7iBT*6hYO(edkXGM0iMn`{>w9F603qVkz9eQGql#(j!v3(X
z%e^M=5qyo1?Fg>UWOVcl<3X(m*DuH#!OeXGT%tTNHKXb_6_bB@ab(!M?-aaLrkJl=
z(%m&+W^5s$?;cmUhH)G}a$oHajo$vorV898$VPCptYm@xy5u?M!suC`MZ?~5T`lqk
z<g0jMLiRm(uA1fZ&$ssbhhrumn6KkBJI`|4<`VXk`ZP|(i1$bJ&HPX}!$^^qJi$5R
zSoidcP~*XB)uAcSo;bpejN67OW_ovn>%*`2b4P?Qc(79@MpX-M({J1}t)&q~8QxZm
zH!nfhY95HGy_u2=_WfrS<bY?hYhrj7Xi+OWgvt5zYos@1Bo)tHY$cKfV3$k;bc|>P
zK(L`|^8Iupe_aD7=F40^Zim`DU*(<YA*GS2&{#Iv2~2oQ=U{QfjXUQjLzq60FHncX
zdqQJw6<;}r5d(&IgXLkAq$5_RhM$4F3^mrgIlYcPa{g{G7Iv?=?l8(M*py1H0ep@8
zo8QK87t80hQph-Tk45&V0$u?0DLzr;<6uB51~i^1^)G)$(~cAV_OA%$s{T@M8fblM
z5xZ)<>I2d!z=H(Q{^e~wLgZaEyIbo)0B|Xr){U0AlKnOFAN~+JxQ!1TJ@%)6$x=X(
zYoTz-M!rVaZR`7=pMRDB3zXA&S*2K`C5v$h{4D6BM?Ftf+;)`F$;Iin5Q_C$_&ul-
zPstz9){UIj2oMO#Z}WnD#X=+ND{n{1_dVnsjedmslBFG-^XW??d)rAN#0>5R-!}4u
zNjmuTT=btLga-#s{geR8E6o1RITJl~>2C09lkzO*3;Ydy#L0cZiH-k)YSG8B`>%`P
zPrA?W+~Jjq=c+|>G)tZ1LiV(8ay8shKt3krT(}m#9e{C<Clv@2tE;|W&bcGv*efHX
zLdZWdm0gsq;6CX;VDtalmwEI&Y}eAnBf)Gm`70PStBJG8?KuYq%`HUf;1?cV=k~=a
z8D>W*N#rrWMDY3IBr3eBGU`vrr1k{Y@gQ~(#qr)q1;3e^jl`lMpe}c1s~U7|KXRI{
z8ad?_`7hr-Y{5%PLkqkO3lernB{y;Kd!P{p#=MP{QGY?NY#wucrQ?Mg5-p6>TYOb8
z3ugfd?)gq1>12UYZCuTI^G8+EmxyaFY;&;Yz-Q_vj6SaZ+jR})6;W+irxj1Cg?H(K
z&dc0w7i)2^<}emU%u&_NazoXJxVF;#xX<D)y|<G!cXD$Pb||I)-qxXHfvQCPToW7l
z;L5s;Cz6YNqmR5O*-4#|V07^IQKyoaJbynq`s6}ZMJmw?8if`t)PgLhtOma(&D4sG
zM66z`*y*Lek%sx#`d+ORL!SnshDIh7@G2<HoV>+!0FJ9ilV<EQ$ozi~via!{u%TaH
z>F<UbQ=~4pfs{1*7{9{UJ1;{O1#TqtT-3Ygi;?~}IFW=a)T||7l{;J+&J+gBrRT!*
z2j``0H9!l&8PoP4D-|7*$#j%g>hN?5$Wj*lH9W9u#!c~no_{!J-Fn!I@pcH~T+;kY
zu|b6c#AyH1V_=S28DafIx#nQv+7a$KdBp^4G)|uu)qYd>;><Yc18P)5A}R1%j=8#x
z!=vRYK5(H`RZh=^A<jjxX4dE{YKq~ze`fUNq5a;f;Y&c}+TZxNcdQtKCjbIGZH~{6
zg4$2XnR@HoIsSC@vFPyxJz5i>W8yz{ZyP9CRS?r#!u0hY#de5ow05q*9OXF}H)=LT
zL2kgtqJD-E*py9f20Fym@>aHtvzkmmT3hlrE>eB|U<YdKnW-858^BPi8rQ(Pse4oP
zY0aJG46zOUna^I32C7)_9M-Ts4~?1sCm=mNBw8mb>PPPZiuQc3JcG~8(ykRBsrL8E
z2Zr-A$XdBQj%la2J<$!&Mkxf7;#}6tl7PH~o)c;S+{DPlDLl`ajv7Gi6&%yG?fymf
zzALnqh@N=lo8fi33QqL}=WiKC1-#=JaWhU;XMCha<6xh!QSQX9m4roWwV((dxeCS9
zmIiF&X;D9=@5){gi)lcj0j(6U%t7C*lgdq?H$a+d%&0wDvamB7OEtBahSMM<fv`jK
z>Jha$pMDX^oix>!v4IwpzXeF`Q9w@J$<8JxqQd3zbW_UVItjcvsGUi^x8GsY%^c>h
zNEu`r+S4rn@`N%|Xrfb;9SLhSut<U{75Oz_>vY^zuQcww@EnBH2Cp`lD<nC^bpPV2
zuD2xZD+AI4G(H5)9&*xA#8>Z#-?$$uXAGyMe0CeuG}aEVwoYDmcsPiBM22&TBFlBj
zad{Z()?z8ZzTNC6lD^ord(ayWnADkUVQg?NVR=V8M@3F4)xITq#b&tFjF0faz;L@3
zptPS#5$-5r|LPX{^ot$oBJ2=Yhn$8)L#Ph!E4@HN4sbcKOq2as+fP$+T%ejREW3VR
zz3#B(z{{jtys$SPC5^dt!+EaS*7%J}JKk^h8IO889px+;2F>@aKYQgkNie6yU4jZv
zG!))HW_=vSRka>>>CdE|gc{bEWW&&^3JA*w+FebLx`*F{22fjpJ*rUyv+U{x)mB<{
zmQ#kAe#dHs`c%-?2q0to*+AfPA)s=rcdg)8jHg<tv3}jX6d?3Ur&5Jk{V-k_G&T%&
zT^(Eh7%PXfcdu2$$DtO?1OjRS6FvVatQ?)fH{W{38zc9r2v+9kUtfy_`hfmrKW#1~
zFS1b)o7hQ)WkN(wwO$$=22Yi2Hhg(_v>i?gwNu7YQ{0x+^UdLO-gs}G<UX~DrlbJP
zV%PXOV2RzmlzSs~?Y+KcqQG*U#Y;E`oI(lP`kEdT=FG_P9#Cpj#vg>As&V$jgaJJr
zw#(>8M77c3uZ>8TXBqYiUoG+_4X>2Ag@ko3_M3N@!yF0pI&CMX2a7wYMG}+Vy^apT
zYo>Np%S-TFDd9{O{I;Symt&3|Yn9bb@<BeV7ia^)Xo8d#JuzuUB#CC-AT|}ju!nGl
zX;X&F=vz^FaJ;GZy5IF_n|4%LKZ|!qRhj+7<l9nso}Tx81u6LT$}9?4P!2P%l_=Z(
zS3O?P#DW~DL!yH&;<pf=xRXj7a)rrcd{5G{eeJ{Y^`iIyN23f4jSh!TrxFY_I!F}{
zkL|A3X1XQ!79>|}T!E8TID7)msc1Cg;^DQ`0x_oBo*Mz<2&bl({gF?1jR{-1vxz?u
zVfWhnK?tHq<;QsG7}B1mmk(JgCm-Yty=NzNM+K7_*LYOULh17Ge6aDre$q@{#uv|Y
zqtWCiNmj<Q12ySi8dXQ5<c6xW0(bhEWO(%B3VIn+?oC0{#h`y+?xOaN_kT$V(Z;u*
zWYwJ`nY~`VnqAVMgx7)1{*C{S&Qq{YFz3TkP5dME?I;bV(2bcK$@i)_z(7XaqP^br
znO})Mo!&=XRc#8MvK@TC+GwhOqFYSO|C!wWIf6v_iT-?Vr4}2hXDbWn&2k0gqmEX{
z*1|o&+n~HjdHnFN-d=(>k3A0&Oz6xIPu&LH{>C7R9$4?IS9@Tk9>66nPE1^L&4?=P
zC-A4k(;=xAAQ4Z+keiTkr=HyQ`y*MIBaHMf-b9K%?XDtQJH#xP8KzDsF{)c*zC%O%
zYJs}b_<6HAvhs$WTd~3I6B$KW>bU8}vG~si*997kEOQw3!)xvuIxS&FbEtcsmCSd^
zOj6l7tac0&yJuXv=Fq_JL%BAr$!vg58DKpJ91Pe$d`<lOpO#~*``N$hu5+NoF%7sI
z-XC{T$JQ-*owCf`=@8nwW_V42s#PHK>gJ8yagluBlA9^?I6y(s!OT6%upVr%lORl}
zOsO|1i(Y27p{=vL$DaKqb;NHNu{a|h!VvwXC@$LD_9ohHAoWWYNW*ukImkNV>`Y6C
z>p$aA<a0a^XV9I;@}-}164%XEAnY0QfzV6PD>IMjQn1#rITBgvTU4p%hRXa)VW=)i
zxy;xfPQ)LM6kAh>aTxkYH3KbziHn(({RB6QO<zN#!axzb%{u%EX}luh-5U(-ht^kd
zy5C;wGX{<dPhVlg-I|C=bbiyIpPk}UkPtaKf*dA1&$gQN{E0)GIRe>`smcbQl-F<$
zAfN7GUx8gjlPhP%s(lQpBV{vJh}}}>1Ksr-2eQl#2WW_@X^s*;WExo?E9q@D^|&|j
z3@UB}rfN1lUQ+F{{IqRCeRS&;B6KoMCxgtPj=YSmuuBt`6J8UYo^L~<*9Uh}`FG!7
zKZX0oe{51Q%&oqREiz#AU1cLiAJYTcXIDfM3(=mg5uILC35i8%!WTU2)EOqrzgZjZ
zD?+!RH^<9U!>zb1I@fg=ULC`<G~Zszn7i&2u(vKx{ugRVRiiRb8C@%7XQQ~wQCg<X
ztR{PMA@x`>$rU&HGa&<A|8_9#1M><W7IXYL^IFon1hNGV`^I-vA|0%Fp|NpXzncNX
zP^q%Zf}Am)?64i}K_R&W>mBPb%#DsJ|2+C=zSw~F8twdUM@0m)Q>?XEv-d~lGctC6
z@}b$82|!IdW<0W&w$_Cnopcxq4G)2$(ub!XtO(=e@inn>6RGl}^2oUBaL7S;5bqhN
z|A!iA|BURG<RnAg+OR|_L=_d{Kff}aqe;KlN@7Ox6w9TWgDkPBU=!2Gl&th=Pxsyx
z2?m_#i_je{0qxT%fedkwwSt&=&C<e#Mz6y{k~Iy>3H>!aQm_ki9NS$W{c){dxI@L2
zF@lPD`?7y#Y1jMJIpYb53Z~arr6fO$=|FJl#U%kARcdD2r!QIW_mhV6w!DZ$*o4dm
z)6w@Ls;4QkabL|D+ClLAU7)!td``*m;BZfGPJ8!$8P~y^BLmO;v>E!B3wvF-16Lg_
z$G>u+k2O1VR!eP^uv9Faz806xQVDFg>ZUe3Sgeb@dFa>fVh&ZK4!msdMJk~&W1(9w
z&^ha}!yjO@{%2{)Iab1WSw+<1&%LnJoA;@78|Fn$hD(pXZ#RddkcvY_YZlWHrXJob
zMrQz9I=9!QdPwvQw#0z;u5*$-{;G#CAT|0%+2yB-NGI(I+K<?i-_pPWr7ZM>$x4^*
z6}LmfgP$8sW(fB9xpUqID&>uyXm_jEc4kM(83q(q{(j7uQH_jXg<tggp5CzV8AQZ^
zq6G)7vm5|??9rT5o=*3A<#^4N1al$tP}W<y_aFM7pWm_Kn}JhWTg$?jxd+>)^Bad*
z<}NDfw%;<X=XpKYqt{qIFukUgP*Rl1_<WL}HK8Hlr1i#R6im5c^>|$mME5rK2D^Oa
zq4e1CS0dwnrc8W<&4))~5hwdaP1|O+GIiG`{CLO9+D|D_e_@oUR(5w)KoTi5(zBZr
z;QG&R?gi~(p6~WeVurVt`(bR?aDXX<5;SuirDXbC_cn*Z#MR`c44!*r^_vN{+oJqO
zH*H7bAWhA2U||k;Ahz)tD`#@-56U=dY;ZRCmTp7vEuw9TkWQkwMQ<bH&<te?nN%K`
zZ(_5Tw78f=TS*Ih;_E;TnKwPEogqj~0P=IkO61MlSH1%dg!SPssF{O9t%mhcmm+<i
zmBYHy{4!j8oc)QSMzvCE(TF-acqhrJOu#RG;LBUIz`e$!&l}Y60pk2uJz*$SHqg%?
zj>F|AUQp$pWAvwJSaYR^e)OH3WZlT0P>UcgCQ&>$9Q6gFVXUjKa&C)XH@hKa?oUtV
zb?D1DX)Gl+U{p&W{7!BC`>g@_AJr<gS)&0tLPEB)E3xMs09cOk?`-{@lr?(28XCTS
zp6e_pJW*{2>tv@a#v^Ozo5W*85A=4v^Hs6m$LUS|PT#V{UbMhmjC~iKu&xj~UsLP+
zc7&2x^j_h^;<AaaDqYmwx22p%-HDX7Njn1bPvI+k*j6tig)&UKslM}5KbB;VPGzCO
z8Rh}uHwQV-f4^9O$+gLPR<Es*t`XF6dPnB-yu(KVjC<Di-jzZs&`GKCt$wV9wt<Vs
zsITEA!lB5tGY+ddRo|!y2hx@HRKvRaOePXvk>%O`U4=DZ+C+Y<?GiLCg;X+(k{Q3y
zySv(~J}?lYbso3F%)Wyci5cvIw&tsBE>5N1(xE2$+z@YRXuK>Mf`YJ77dsagCCv0y
z#!#k*zik0BJxjx1f;DEEu78Mx`}(oIj-Au%+9BY@Z<#P2MJlgOa-zq>@|sQgVwbY$
zcGdwc#P=13)Q1WyMfjoLZD66jz?sjAVF;L;lJV`j{md+FT#y!gtNq0#0&Ww93j|!x
zMqCneB0YHFO($zms!iTI!Kh#=4Q8eM!}Z_QdIUfyGuOwWqCaiDFF(J>@Ldg1Mp?i-
zt+*l7)g`DA#Zq-Sst(`J@KaHL)}z%Ap&i1P)z({Yq0&6fAY4EPD_rtff&&hwOf3`W
z5{)<ga~B!JeK)(izua#rHV!^Y#Tcs|C04uD<GM^zZ*me!hIL9(PgasTS*XegtriKC
zZ4&tfDEyQ>jc$7=oaL;WJI=6q>_WUU?`c1lznF4{aj8aRpV=2)Bu`AaylsXv4&62>
z{LGY)I!pi3iwPDB1@*`0aw$E?sZ{vxR@aI8OOfLE`&mrgX`u`w>(uF}-0rKy>Xo;1
zN04!1CFlQBuI}A$s%P+zPzUao+a!HGde=#PA(^a(I&l3gHJh6fT;)W?!y|I;HfxjU
z^V2pER?=mfQUhR2(vDO?mWL)XeMTNfR<6_p)|ij@om7T<0GIT+dp@f^2UB_nQDYi-
zV<djc&DBLb7)7pt`3-Gvw|-{YxLo>8PTOerzM=i3Ys}k!&SX`cs+g1<_i%r#1&t9u
zMtQriFM023yOIjhXh34tt>2p;uWq8#TP(M^>6&4<XVf!JS?cXg19=ls@du4)Phuwr
zFU)R<hq;8hLkA7je{pP6m%}@m9(@?F2d<lLKHLvq6FS=~Eu;)Kq&9W+EV~;o30sV?
zX%C&QxrhFMOMNY{OlVA;%c2={=IARH18&YsS37Xu1F-c^$KgEU17x5v9Ay<}a5}U$
z0t_3g{{j!BruMhP*nrC5S|i2BeH(wpIoW0V7ezwhxHdoQ&88%nVA9SaFY_hrvI@0-
zMx&4TGaF!b!qLM>y~QT$Nl1jZ#59M4mAzamT6eAg973Ys{Mi_c8?7L*G!C|V**Td<
ztTY_L8PSIJ+k3pk+nTbYkG69tis&QWC-;(M@@y=w8Lur{Lfy)%7_h<<deA#hXG{Hl
zwB2a}5WFgECS}c$xK&QtuGJAphz}dLwgx1|jq0RSX!ZB*C$XQxv<y}mpLev~#yufn
zgwXuM4kEju4sgOPA)|`(ttMu!j7Y%c>uLNrOc26K3(0$~ZxjDw4B_#<e%0Vw#a{hf
zG`B{beI4$VeeIAhoM)1EHG33$D}nA=c~~fg95bt>yOIo)8Tyl~PH{$nNG`q)goO3{
zF`n#mH+cSCt97!pVQo&E*>Pf{I6jTHgUU&bXQz`_^`3J1B&m_cit<LOSska1Px=`n
z#+U#ID~BIJy6_fum<V3OzN$e^DJhCpatXuady#udfgq)@3dZ@Hq258De`Ib?78oRa
z;f>Spf9EExm1Yqi{(tl1)lZLOV?OKPW1umaMt+x~fb|>itow~i7jW9SCv;`#K&TzW
z?hg$&h2kz9<5q#cd8a9gR~l)uUyvR3|5SFq)jVmEK^O2W(;qFXKJq0M*~b|_Ldw1J
ze5)X|MF$?#s7~wOyy?Q8*7o)-S`Z;cp5`=YR&*~q-|7p5T23vSps=U#Q()w?DGiNU
z-i(2gmWFDRQSU5o^B~)YaLGA5$=6^n&{R<YSL1f%pv9i!x**MlN}PJ`kWW_HEjPOf
zxz6XDcG0<IFo`%I!H{`G_Q~SfADn*;1K@H_HnEeHNZiMzdsB_QMMDl2E<}~(%B8%L
zV4Q$i6_w)&zQ?X}GvtnGp0L3So?$TOKA2K}Xm=beT8Iio-{Cv~Vc=k+5TcVro(NhG
zHn6sO@-2RUwjPz<r1h#IYWCf`?HP)L8Bq}<m6k9=i0A6@V6`7y10v^UF{!nx+%(>s
zcUdTBW&9H`2+pZX+GUuQq7=l1IF^SnY^d>A62rrbjuXU$!WHnXHYd2n2GOC!Gkr_T
zy;k!YiE#1pDClEAdjbUWs`lcg+PHf3W}!Oiix!eo+V87JTTSMGK)A&Bg3Vt0<XR=9
zs0#7)Y`F=K6Xz(x9j>wnGnjm%81Xya;HvP-QgR39CecUoVHu7~J!$P<-GFmvFd;q<
z$5kP|9^3T;i1r(1&<OA!DK+m;SGKw3pa043U4aR^8I5h?+q5AtrFW&loOlB5oZri$
zoB51#8Dht?aFH2Z-`@wy=l!_83~CccbWY;)Z4$WmivDR4MXo`oy$u-M>M)0MCjFr-
zBA_t25B=z0G-%0jtO8QuCVfG(NfsZ0^3tQtr)$G18|KD-4qU4Jjmw`mKmG{&NV!?D
z8)bTMjzs0aM`~pf6ZP-3Hsf6<s|Y3*-%P9}F~zI??Ydwm7xv@Oj_F}i2@%euoj2Id
z2o(RqQJ>NUw#OZ(V$YKYe&9IUXR3z7Rak^3le&OIYQH~vTJ;NfWA3+^9QSJdLw%~h
zwM`cPhTD0tP#-j4oIz&K6=~*#oS;>io8HsEoWD=_WYWdl!SjDUy#gG5ra{kQGMsuS
z#ad}20X^MHVe1RbNSytA^>BCfP<@j^K+i%Nwb+_#j$X&$u9OwbWnB7RYh#Xk`!~(5
z%%SAjwu5x)NYLayrjNrQ?xQJJw-JJW!Z0-I^;}2dN2-81^VC1A_%A+b{_@prfTh?-
z5i!WSx+KUskXZ_-WJIPG|CFDrIW123JBJz!v*R$95(%1P02?0AsohmDX{Un9w6qJ>
zhcJK34{?eJkymDWJM9c<G1*;i;v*vg<N5N$M<c1euH5YVSp%4J&OpCk^aDw@cELs2
z$_Kk?&J-I+^R&x$G6!+-qbm&6ee`HsK&=7o5^{cdebZh4I{hI)SX||*wj(c*!%NpD
z@D*I!zUW3zviau@0u&x~WPRHvUKC%vo3aty0Ep*iiE2?sHX2I)N$q@#N4p#$N>XhH
zZMY^e{}*iBD&iYi#>@`)Y;4${kj<^RsBC99gXNE<W~gutxMG2sgto>`cx@ZQ^Iezn
zH568+POWO>&IPlP@7suzfG6i(A0cH|XEA(h(WdpORYr)p&WnGan-24}D3KQvQ5JBG
zH+LWxIcjQb&LsZ2X=gc@==FC}gqM>P(Kctc|GFt<v6k_VQpa*yKQND|{dLp3WX|$G
z9mW)GUW1>wQOIgb-p(+6KE%OLFAH%Y_abGA=h*+KLcAjmy>=_5bD4K@OWy>uBmbz7
zC*B>INXJ)hSB*{E!QzNQla&Ui5`j=}h{>oLm%$gojwx__9u<!Jdql_jhJYF8jpDI3
zV8X!PLcgEhuRV%`ro?TWA{1h{dOGyl8zg0Ci23bF3I;~J#h%GaY-jd9LN&=ZtSV07
z2cQ#aWFxS2abTL*TXPE1RKl-Gb#P~;`ImLQ3b*`Kq5}S0R==-?U>sal%oVW|j65fA
z4fol79|`i_B#{61L*7z-Z;G~)vWOe6VF_L^Zw%heO<N+&IHD^qYzj7T;w;t0&L{CY
zxtXg>^Ge?P$qOj*Cc0yl-|YmC*J(Pa<~MX5;)B0<W}u4UX%ZY60)mZcXihfP80s=R
zd{sw}h6iK}{GE9iL@Ai|KkQ;K^*lDhclZ&t0<g?CT-aPwWDio-jVFY_$lTBmr!=&u
z6s8k^Fe5*m`5qFOpijF8_!`pIBz24r_^9^wX)!M%Wvs7_S>|e%^5-<-AD!LdmZ!N<
z`p_D?UyQT1dpXbKXq@w~+V~UNA@+GVV>5mPXkmOma{ab%y670`vpf66vsXSo0s4nS
z<?x<)Z!v4r@AvDh`wZMHekB;M^jg#9z8}4as8zfywL`2%2`_G%9iEaGu3PJp_iOo4
zZcU5e$mzW#t|y3!XO2}Lce-RQlKiF7dvHAv3^D<8w2SLm*1GL)4XP)Y?GGCZP=%=n
zGQUL*WmLm4q}zwj)Y+<r)2_?!9uxJOv-<?*+k2;CQ1D`A2lg7!vWz2AVfXtWNC-pT
zaG%+=W}Jm<wf_K1cjy%rfJ*}NQCl|kT?&$7Bgy2afQcdPu`wgUG+F=H)Cz6rmRm0?
z3WVbUq2ucj<}*|^q`?KrAn7PoJP!)1jfX3R!=S8)O<!YLaMmWAJGgQ0?WJ-gK0{*-
zXF_8l7u~doN#sgU8%6FycKvP(k>ikE%JJX2+?%{Q8Z{Vt&_Q5h`RRI@5$y*Y*oXcF
zB|bSODEo4Av>DvzInXfBJ86>=Ts$WQYfh@n!A=_CuZAZd>4RC?Gx>n6OmVxl`)+F9
zEu9IS8p8UHLO+VUW4xLA=p}IL$z<x8WQL)ijv&5HAYwQ5G|BQw)I@AVQxau)=YGe%
zyV64}Ae?@vlWc*Tc&IbvTg9p4Y`(pA5UFq?C6f6zs&nr^Y@JhmWt2#oD=yEjBDiy5
z0@z5=9*wdZq(04vEY(nTiS>mv&kSUJSo_vHAqa)L=#6@VrL&)OGRZ3HYYq9`$&pt2
zf3Cnp?aQca`G)lMF-^R0&>TBSUKjgFCBIlSf`CrL^4E$Sf8$)6yl)<(#BomPb(-S>
ziAKh*O=?p!>v6Nv)lU^MEn$v>l&3#=m(F@c`4iT7K<(j?b=vN7<LiW5li_YL>!XG1
zZh75TBEymiPbpF|o?3I&zUd0^o}MSncfW3qm(B(2cgg)EE5^z3MSrCv`&=)Y_3Gdc
zB06MJ499Xp?*?NE$A68ek-0KzvM{oq>M=3Es$TLlKauG3vB`KTJIi1Ag_r>k>uQ%@
zS>S;Fk~C0Pb(~>wYPBSVx9)jcDk7D$iv)2tD#PsX8-gMIy{87W6&};{ASdH&gD0Wp
z^3a#@5qJ7)?NMLrCuW%V`Dw6p-Zm-`9>QF;`H5Oz&Qqk5M1atc&hGE`(K)MM!KS~e
z>t5ftG66&PkDlqMhIw-M!KB9H^aZ)isNy-CTb$oNBl@;?R*bUQ^E=ZvU6iPtTddoV
zT4C>AmbaG#L*D#xw@t6Gpn7oTb~2?u{&TJJ!)o5ZKm5aD)*JVdcobAGzg5D!n2*MY
z$FE(OWq(evNyw1RPBMg3_t_p}dlLHC^deTHgFT`c<N*&D`2X3NelbzcKOY_SdHxP}
z)XVSp8<QJF@xr_vp||r1Q?r<*6QV6)4+<A0Orcji$VY9tBaJezi6WzQl)~%~-z?e&
z^(8PY2Ipz~_n{(dN>U!Zt+|FF?WDc|zE%#{{~7oSMr*ya4$sUf4Pm<{%Wspv{d`<i
z9xnl>JEugtm1eWc{qZl5kGfk;?Vp1Q1X`f`oPQ|XuN7m?_R;57$d(30v-JYqcGchU
zrN#`W-BkjjEg84?+L@hcNCOa<a-KC1zZ<4E$bNe!kK`XDX7ehz%%j*>aluSHq^xu4
z#QQ#ThIj)1v~BvuyL5rU)H30Kc&9=+<!^YZ<KQBOJSni1_<a4x+~$L_>+wa4lgLLD
zlLPMbSVcw+#XF69?=43#%|=o=-A#>7(6GzKOmj-<#~HIMW$M*y3zzw@pnnQLHSiDA
zuUJ=rH+M{7F0peIrmY798F?B5^em);s)}fxZ~gpgHc>V<^1uh0vmamJ-Qdq|D><2t
zJ_SsLj)#8s)mdK_wd#l`oe~1BC|$&f>O~(NKZ8%QW|_RE`+FNe66(Va=ndnmr8)Kn
z?oT^hHkehX@Uuwv`-x|iZ=3U91<!9AL_#Oc`VDjFZiJ!R=pa(TW{&_|OxkUF_?BCz
zbD3z_f^C#LoGVEAq*LvHBqg1&eX{&DHezlkHI0`jVqcx|MQCEYaJfmoMfH-!B(~)K
z+vuN;LYXKEAH`{wH2_b>#$Aum#(a@!!~F(JJ|@-=Tnl={{Ri4e!6vj9g*{40o%?B*
zxGQuIR(+gsCx6vzRD2wN_8`6!u2%6ehBxqH^Vhs=(#tm>ZDvr}w;ymZvDWN<`@N(V
z&St-c<J5b5+vQ^TV{gpAx1+-;Rb%EDaL`C?o-DgYP!%3Am0X#=Gb??b-3lD+GU9)X
zlg5LfG{u&r5HoB6FU*b+uXPke#w!=Ik&n874F(#2A*NHPFf!%Juueqx+iVoHlX<nX
z%p}DN49;6lVXQVtkK4BV>O^T^VZ*<qIu+-cRnVLiJk)&5Ux`fgKwB31O%g5}oqprk
zLKVDHOY8s)13+_TuxU2d$kudF1O`Pb(>aB#muO!{#z-3Z{@~P;bWSnL=hMmuU(-En
z1MkbfUQ|`P6Vsn{0p6f2SmeDXV-zL-oPAI|iZ0|d0n!EvSu0Kn<>|^UQys3K<Ynei
z?{R7|0YXNf9&N>m9osWh0;6&m`2~d4lvJ%R1e!BTVgAl{E*BZg#F7WhnZn#+^XB9J
z*UhQSVl5sXkThBVZwU0Idre#(+*48>7S}sb)E@t}9i7$+ZYV9R%XM74eH7Y?4BU_r
zJ3(D4WcBRr_}SvkEwS#lu~ldHwY~2)t%)@O9(_GSZ<Ipj*Me*J3wW9huFuu{Z0j<_
zDJMKeX|j&2SOmp3wk{E^ZPwa;(1!o11$Z`_eb)P<SAUnfxBVx#(e*P4dyEft%oqCk
z3_ovF3dwNy<Rv*2x4WM@8D^nGdMiE?6IfCrfE~WFmK3SnU&PvIZ`)y0;=3j}ea*mT
zER{lVxNY(tuAXWpu75}K<-_@p64S$p2B@5)rrI+w1C2AJ-7lPA8_)*#uG`4G`gjX$
zvsW>oMX9e%==rxs2l-OBBJ6$A143?^v-?y10KCqXwFVQ}0|S>ahnAH15~`n4Qhwrh
z4Q1`UF_JbLnc;tyP;)!Tx9n7rH#OTeT0zlkw{OG*Z$?YQU)7BjB?!wj4v=iVxYjL+
zNou$f#QQc<4kjYjGj8JeEhdE&r;kx*S9K!v8khr(TE0fs7q6WZ{k370XGlj_Ayzaj
zH!Li+4VBt5>UolB^2I_VScBo^>rctqEVu3^qXr^NkI%Qk4B5N|jH2g#2?;RZS18=P
zboH@|l*So+R+xUo%euw~t7TC?uV5BqIdX~Lzv;!I0JSExSLX4#7=~zQxc=SsP+cO+
zvOe^V+pW#9RC%7zL;zz%3*&1KFoF_;<`M}va6sO%Dd%v5++eUbQrK>geP(JIb3V&y
za~{@)*tagp1j&-&^994HC4-g0!MR&wPFi!!qh7iGWhb44p7u56+QxNrbV0e$2&ANv
z@kj%Ka5d4;28pTRvYFg&bi|*XhYxtCIq#;{+!`K?`0)$#07y%Q!TyHr)_wHo;L!O>
zr-4Qzx^O>CeooCf>`63DaF?ZAFlRK>9Gc&iRX+3%+<i?~RcTy&xfVLnSJ$M{(jbcW
z%`>Ds)U$RK9iN>$F51GFJL$wU9b7LkdmK#UQVFS_@^0}FMAH2v4Lg3Fojxn9K56A>
zBv>;&f7(8YoMIMP?`H)?8PQtN7)MDMI{I0&Rvwv%Z#YP`mk()*_uyV^;h!$9-Ce~9
z%5Z8%Q^Qp9B>9(zL1Vp*w)umu$A9$>bDyE+g1zZ3zrX48GNR=vV#+sX>Sont_6M1E
zc8}-hHDaoZmAOIMjn?T~Zp#N47bI6)Ft-9OiFd0WwOS!&!aL|xfY3<&!_-+YQUl+l
z<sF$`@o_x^6<#^TEDMg(n1>&Y7<wCHW^CBrzT^hlr;uHcxLeE-uhti$CX_9vF;rwW
zWNSaqQoxH=9{4%WB@BXVs?_J{=RM}fyG$W^!`0I&dEq;PiG?JNZ+sOJ*{*R1{ql4t
zFeQ3Nz}=GrI#IPcAoxvc!y2O2R*ASed3KsO9KCv?vWH7rIO?X~5*pqkw&C2_-7ON_
zSqfl4wUem;X>(}~4N#Mhb_!}Z*<){i<;2X*n~IeEBq@-TLcYACMg-*_+nOi_r@&^I
zzrj^_DdcqH*_r2P25iJ>m2m0lquvSYk->h|IH@P_)gR$R!e}?lRqLv&PAPqFzs7s5
z<i~)bLkwBQ?YGIRl@5?X#a7csIHVg!Y&;!9?FumM1=I+}26a-~6oaGP$SHhX8dE*<
z@k_5U|H#a!$7+6OlSQyIqM>J@2s0>=ZbVDUR<6wgb38kwP`1xlCaih0rYR5-=e=Q;
zBI4ZSAj#36X!npSu9&gQzg-Bu?wItXayRu<;pPTNc?R!WC^fW>p<_6FeoA2wPR=KK
z{{4jL2(Eq$g@*YV17)nr)aW$Zu6pAYI#)VU4WCgs?>iU({{lfY`6ZgXuURr8Z0M)6
zd?NaEn@3KTH-c{sQuvAv4POmBpB#k>2(K5%XsF{8de?jliDM-M)sEHpfUbgzT3TH0
zV}SM<{YbAeyCwg0XS>9NA<gEFn&0L6Jyc<ds?idU;I`G)tCetqnt9U$vq<Qq@!!+2
zzE)goz9>B5lUj6<1E^B-X57{v317IZY#$0bUTn91`Sp2W<oLeDQWJXj9Y)zlpGL-D
zA*)n-l(0~@wSJ`iwt;!n_s_p_$%grNs(VTI3!7AP7E9&Y9egRagSY`V#=$UY2kbkm
ziNd*H0gXR6O6zF%S$-a&9L+4;T#Kloi?uMSt>I-ZvZI{Cb6eq>QplCC!hXIfofU}i
zvOSC)2oB^>COoq`^Bb{%jmBD%>jjxS$uKwutEu@JF0rz@#L6>6IO?V<!f5o?@W`Uu
z$}iq1@+e<><xx8{%$C;{V||uod1j@H{04VUayo&SZ_a^Hv5ilIEEYeuaTN*+nyc0G
z2QkV77=!)z-KJ<W%5a;VmYtF8lZ{|!l4r9vv-)*f81VL8c)`c0fZWYp&0B)vEsQle
zhW8bR!nqDnwXnP82a4t}8IBz9fN&H#CVn~W*8JsSUv_eZp)NG$@~&L?%Ak(U*X;uJ
zOa;E+O&>k~+T`|4S7>7P6T2~2jw8`%HF&3)14)N#>4@lRr-5QF_k?iN+@?7Y0ss7p
zim`P}idc<!oh7F_R5=UB^Uq$5Lsb2W&;FquToZ={^`QGp?js!Ne-rAFiVtMKq*F@G
z-+TLtzE{D-?0*n)#($S2=l@N>PXAMqocYi06|?`+jlXxVbpO2(lrsXDU4Tm<{-<wZ
z<0^y*`1%3Br3Ez|dh0Ml_4$_s+yHB@I&6TsFIP*8bzPVG-%~~58HXjX_7s)?oA~#(
zj^dXCoSi+I^Jh7Ib|k6c;p`Laxc+Y(@P84#cUmB#`#uWFgkTA%R;>i%{!V=H8r@A1
zgMUgUQYta@udYds6q;jCwHWXb^x)+-1192kfx;Q&t$%M0v!l7m?Y9jLxVF+9JF`tc
z{+q)6Yo<MyO0{@E%2V<~04~k-gNyO&WbscF{@&r@1cVGHPol6&_?CzoyJzG&kVP%L
z$Sv_Lwobm0+LS21;hbMg=7o1Dvp8E^E<g$pR_dQEE@m~S@YnPA)7}>LT+b)W!u2&|
zA+VG>!g`Nv*YJi*9AZ&!+xd-V+1A6@QIxGs^~QNfD~!01P1ZHf@Px}~pEz1_!&d~N
z>iuEfN_sO8c54o<0(^tM=et-Bh>O;}4G1Lw9mV?F`#aNNs|yo1U*pe}?D$CN1!Rh)
zb^JZHeO$n~yTR)eyFr3*|N6h6H3HEM@8cX{)D=c<Jgu>`SUWX(a2FyvSrXXDN3Y0X
zJ!)Qf=`nJ)<b5td4w-&1V{XQI#IX6Aoi35|8HqRI;8)p4ji?hz<h`72Jo89=d9MG`
zPA7O<BAT-`_Cn`gCjv<81Zst@WsZu5dO}^0Z5Fv0z-2>*-p4y!khL?G7e7-~g(321
zW2Q+ywruSi-a4|IR|0eOTCs>Y%(Iq&BoPc;Gn3k!0K{eW6Ve9PrF7e`z9!nM<t$ze
zysE#&wf!%+tW-)^{JAM*r-TTvPJW`q6K01%c230VKITM*>t3u%5Hae;CqcQFJ#cAo
z8&H-;YZ^l3yZ-RAN+IwH_~Hr|Tl4tjVP5lG))y@fBJIRjj?x9?hiVEJE%Se)R>7*%
zOjLjg)R^P(4Od86EpR|$v1p&-Oh*HAt%Wc_1SB`yA})SdqT8a>3m(rmBdMNR?iw&8
zf=R$em<0S8A)BY(qEQV0ERxlpE8Zp;Q|thT$Mht$qQb@S1)%UVasm?kROYq?6W!J<
z?eFs-C7XuYFyd@}&XbIix%Dl&oJ1O7wT&t64cB)Y4HVe&eBA>|FI0u~Kdg1Sk^zO6
zRhB7m<d|o?L@I$(dWRWtpzv5fTtDC?`w9qg&~h$zC?skJ>Mt8-z$`#_Vt_*AUHTF8
zi4|Dso{x7G1x<7rxmo+@)0v-~7^`JC1g<MOLQ?b&E^~T*Enz<OOg6(X)M4HT7E@uH
zA1;Y9c@Xi2vfpMU39lvI6Lnx6b#ipxdXG^g1ZxM64YcJGxZwwuihNwJh9Th;;Il_m
zO?_&om%+Mm+FAy{?$2i*EZP);!e6;DT0r}W0-ZCWeJVyWl&!{-cuR{LwNg{g0U-Qi
zSh&7|2`xOISu*CW5o7@G#Bs4Mi}KX?%=@heS=mNSBYy<=3Ci&5rCfnBbye0>T4{*o
z{OE?PPJ32M*!jUSP=0YkU@I<fN0Ik~{okXE+!&~^QU|>a({MSLyrrteG@oMXiT)bx
z+IMM4>LSVBZnzBVb>?!}|JUAihBbY@>rgF7t<Y+#s32McNJRwMvZ_`k%oYe60t5&f
z*&~FYh}MdtKSBT*aWG_$FeMC&f<Xk7od8kUGZ083goN{k_WwWExz4w9UFXv|`NWlw
z-+IUMJoj_ocVzm2AfVBA)XG5`WE<0+%LtD56N(Pl%T&&`o2?i$&2wp@OOoX@5`!E-
z@}+-|+f=ruV~197faf_q@MVe?j8sZK_Muh?)lDXqWQ{ui+@hISKlG=MT-1W6fwFh7
z6PVyf%c<TN^+3z<>Y_SOi+0qDk84@;MYFX_Yp)VpES?Wh#|R#nna!*0IqchYi*LsD
zre68oMJ%gcTug}y`0?%1?dsA46q6}2MFk37cgkQGg0=(~2d1AA-Sy?<=0<FE$`aE7
zEaC{oo}&JUC^JKBk#efSUN57Q_?5nk`i_49{l#?WUCTi+fzRHlTLi7aaxk=m_cA~7
ze;Z(q@1qTouN|`5-&_+JUbbyYYVKL~r$3w`hoyPFY7W0yuYhEr=(nqv%vM~|U*72`
zMJN~3j*VU{IooM=peuzi;qXVH`AV5}nHkIR@Y2ob78JY1Z!aKqwchds3a5103HhiN
zOLn7Gn}K1`=4cg$E>jdxZiKk6oD2Uk&5PUo*cb0V^P;#)&pvj6!2Rh^9$efowTf`@
z=#1v`l>Q-2Z${=v8Ddy$1kEhJJM>0mAtm}|rK~d>>$G#0VW2K+&OVn6@&ebmBydkx
z2AC21cuo#%o##EA7-z<Kh!^xOJEBnzgkAonB0-1Th{ogXkqkhIZ36hw`^UKKk4-<f
zVV)gxuEBr);#TI9Y#!nZVdX{)r&R47iaxi(E#9y)y6z&(y|p>2ND@1)3ebxN>+=r$
zs?>e{fvRu^*^<(F36fD%-(iZaH7*9TqTFBtapfA$%h~e)4XukcIZFy_*Dih$$Oyx1
zHmiBwqnjzB($ALmVSU@Q#dMCpJ<#6PFFtOe>5cz&K#+lMuRrE*678S1eE01Ke^Jiw
zNZFYiAPdL`w;4ZhF7O7otBBEbq?+lR5ILISdPeR-WPTUSnbq0NP6JS0B!b_^Z)zV6
zvK)Nvd}O8#jIsE*+R<u5kgJ(+j6!f%c>6!SDdN-f4;_KE&ikR8RAI2^d@Pb#H+r#r
z`~bucOTq=3v#y)-?Gb`v8Yiinp;geeRXPCZryavgC>x0z79ULSvam9z)J?t-#m&oU
zm-%T)!{SGT`Pk!tL)L70wJ6EL`EBBAT;X~a!M+S-5)u>6ClW?#5UeQ!ewoQe2Hw&O
zmRxL5NxOa^iR@sG*zmM-oT^9OYnE0p;rR6mrjh)r>!o1&_U0<2xYo;)ZLXY9l!TRp
zqn^)O7_nF<mhh!T+wY`!x~Im%kru`w=$e4KB0-7>AK!Zz%~0hu<>rKMtqBjVDK~O{
z;kf)YJ7i1I1>X6&xZfL}fLIA6r0dRE8*yvj!c>Za^19|niIcsAj2eb?Z-f<e$TrRJ
z{O9I_5T(v)I|0sE9%+77B<X<W9?Whr+n<;KunO##GDW{vffg*Xcssgc36W#%EEp2S
zNtDkg+`kNqZ}89rWRQTy6I7CQ8YA;1Dyl_gOzucr`sCe{%L}gvQzFfolagYuSbTH-
zF<fns1$TGIq4g1iKa*|QjuzU3)qEf05K1X)4*4h6q;s{px_dR%zS*hmIJbEIP_4`l
zpmd<$59=AmUVzpVl-P&!XWsiLR+5BSWBikDGcK+p;8yig4XX1_2XPaxLv^+^zeR-C
znIgU#Vg|&8zQT<vc`ngkmrw?oh1r^iX7Ue6o0o9#<nOtR1>2&^V;Z?jAW~-RYd4_y
zZg+{hI~9Xy7OqI$++|4|w43#(6GHT|)6ZRMH1Ik$<hqy`#z?^zzk4hK8<y%e;qIGV
zK9gjtkTfIo8z|8?5w94xRQM3V84<*vm@s&XNlNWx<Y)H!&Fg137fAZl;UVYjT7W`b
zbUQ&E>ofaqd-V_AGNwE%sHY+wkS($9o3e)*MV(sGCuR0^2pWod?%D<dx{DS&KaBC)
zcxB?MaCy|8$&LQ0R=Go6if0?z=MfwfZFL28elae)dB|gh_M!%basv*H*csC=MwqUJ
zzU<ObAoU1I*iYeiY9Mm5rcrzJ3g&sC0iye0t?er}!I2G~{&QqAHPFsKOztrSam%)V
zLekOloV!UndU8Afp}g>FW#xMi@Fve7nuqRu+N;PMpy{1`T{$;y$aQ&=4Nj2>NfRd(
z71csppsMEOGQ!Pe9mV9a@;oJm%Ab2Jm+M05-yDR+-<x_K#z<<(-#I00u*?i_B@NXl
z?2PUkahujIfmSW7)w){8@wz}66nNb~1450>>wActp+eGwkiS&CX7aLI(&Tmgh}Qwn
z<}J})tU?4NXR6to-9Xke{+ASZw}&CO5eM)WJuND;M>A`m9|>+T-%kQ*h8WB)5*}sS
zfvVFcYURT!BK#2{TO|puW-*SCEqr4P&M;z;M{bc%=+~}s8x<E!Nh)o2fJ*`4v%<dv
zpr??VrP91q+5Pirde&r;y;$W-nXJhKvcywQhH*%Ny_g8BWZw=b3nJrxsn}d&<9^!L
z^_L4GP=Db#s4rh0p{Q3zLg>EkLtoV`Y&m-aHbQ!yp)sop9K_$K&Ri#Y6#ZdYJX<7Q
z2Tr|mfQevzA$DvP!!3NGO<{IshKP*IVk)P%36u50vvvVxv-+wnf@7q!cRhDG3&;+i
zf*K><{=6Zz2_v(+;t38t2&$2a)hwciDs_h*ARlG~Y2|OBtbVv`{|+eS(-cP3Kv>%;
zKPEQJjc<JIT7%P~%E~uH41$MER`_q_rLd)*6l4pvkix%fy|p%G55V>k8DX_F0o-52
z0j>bR_j;wj31$E5W$Ayb(*9L86`mnW#5Y_6iZNs0Lm^GyXvF@@UgFhZ#6d^p|74E<
zCkpk|&j6jRYDff5l#^*7!TX15Vt2V9uj`|JwD4Jgs*Vy0X$b%LUoX390Ka*25n2Lc
zTctxS6N=Z#@Vo!2`6j@0W?Y1@^3g_63M5@~k&Z{OuEQ~M8zIoqf@3@Cbr=pvnB~C2
zyn2QqlyB@_)zdvQ#mV}2?QNeB8LkA^(Who00@WVBvw*fL*6K(J&{jCa?U>jROIP^1
zWKNNT0a3yj(ghPmD?7EZw?Xg*e%2_X$&rh1EBaMm=5JaMEOg?dl0|p(xhW6dXTyX^
zPyGpL8ffSt{&t~t_*@|9<5m|54j}}fk@$T_y{hNI6`z6do^+MJ{V{HRaeqM$4ATE(
zS69-$Ie!!V?$3YIMaUAayXsAl+k%^DX9L$!A6_YZTWzW9i$TR2m-r71aTIu2t}hws
zxH(u3jyDD0sGQ%u&?7H}u?EuEL}G{2yDwlR0A$P|JsftlRV+pCsa1<3RBom+n4J>)
z%xW7LNSLb(p3;<Su)1QXGip4MJ@jD*K7v`T^(&#YBI)T9{(wKCF-Uf}Be63Ht^+&>
zNHi835E8P%NxJeIAsX#~BT_AOQuwjTZKd}W=kJ@a3Uh=AF&2tdoSO7A1k$F?7jJmE
zSxJj7h+}D!68bnm3Y_TM3-Wa^v1qG^h<x3>ky%xzsn2Y5ci71=2nST4RQBOBm5nE<
zekr5w>`Oc2Gx)QKdRO}(Av|36!DKfgkThAVxWE=DQ6p2F(kC-PbAO`|E&mWK$p3%+
zkDkD9N?jl=&Ud$h#NaxrR2(Gz&;twXwPF>hUn80clcx~^Icg1A{D5oQW-Rnfmwhs2
zi}x*BWY}nah~aV<SSCl8Z&BeGc^!P}=dWP#+Y#IFw0z2i0EY+?A{TaaNN28DR917s
z4yFlhvg;54Ugu->EB<mR6QFId((?wuUqX6k#k$JWqoDGRn|oY=TK8)^LtqHR9hj1p
z2Q5uUg%Kj_QHu^x%?74`&1<WNVE--H_1{Z_l4;6vgaEzaC&HQKG!V52$Ycpv{Jxp!
zIRRnuFF3Xv6rnb*cSPeyq^8*103^LQU;0q%FlZ~;86I;QARSUL-vx_DWH}6Q?~DJ?
z3fet;E8<3Li||;l--Qi_YnOYbmelTi<+?}QC7wN@i{!6lTm}nmq3ukJGD<Dw6zmpo
zt*|pRa2R0ZOiGZy0l?JY^)y@dS-FNd8?dQXs4k|SD|W5?8FUHEp~ixeqoS>}j{poK
zt1hx5{bn{JF&n&jUYSiiKn47-=4Ckr1pm^Y2ka}35j~O*zup7rBCRF;<t>fS;`7e3
zb-W~hJ>%DJKuD{x?L3Sx@A>#E^TSb^ZGM*}%rzVYhpsF9P2CcpU;8+66A;9yL_*$S
z7s6!QVLdW;EB)Qx3Ske+3r*O~)Ii+NF#}*kFGB!2Ppm{G{Mu%Yz3A5!BL5r)TaXjr
zD70O>XD@Ks_+#K(U{>?!yv|!j{kVJWCz$^!5S+;G*V)`eWaC{+s0G|@K7g!TUIaPG
zYSXX5G=BgOZNz(Xv+=>?8j?O^YN#H+>i;Mlr0`wt&{>-_$#mp#@TGPo`Do*C;H+8O
zbLwFHkKpY7P5==6-C6Ly<SWoG(U^5abT9VTDMW9Axd)5SjB+QIzr?Qiw}}k@1_H6l
z(Pzzq@k(EO05k@Qqt>XM;T?wnCTMP|)5mN^)aG~fo6RtD0ERQ^oLx1khG=f|1^Y&8
z>=zp-IeZ=qkERI^?V7Et_7*d#dB#?51TVV!mA?nr{kz(=%UiBm<I3*XQ(BLpMbEaj
zb1H?+P}B||aq#(GXzNlzGX~mLZ3K9}T~BR($@iRLcsU>)LVK;eW|xia)fOpm3_RKw
zw4mfoVea4<B}u$Z>`W1308v{byMq${k5=&inJ=jfm6-uP<`72a&8*;IA`B++weU9r
zV1D||wf-gz*}72d<2+ScUNikv@{j8iU&P`y+;`d?)9ur6+x$#IqigLak0*alt({z_
z(-a-^zqj5y7!w=d%d0Kp;D)9J2BhWH*xLu&{oF@qzFT_4o~Up^nTWWy30-L(otLK?
z%*8!7)T3s59eKEW*Fpy|EO{moIa)r`OH*0sa2w<SGjL%ht<L^nFr<$icm_u1W6Q>^
zgD`LRZFtLgVarKswy?1mnnHn36*R56(@S<qxtDg-5gVAU?5Jju)+26!4BGo@qImOY
zN*1j!$c*)PKyrtb9xs0oauVjadD(C2mR?KBz|ycSn7FxU#JWDe*{Gm%hMI^dGhvm&
zgE~L5aookEN-}vnuj-kF&NGW`H%y%D4>P+UiX8QFwsy<INlyf4S*^V^Wh)LUmv4}#
z$|1%dRe|=_vz&}L2HXNr8+3UM2EgDGK%s!-etK$mbd%^<H&!7U2i)=kXYLbtl-dWA
zIf_(4d8B*uPgHwEPTIK%Y<j?)K?KHT0)+juDNdt@xuZh$NwFwevw>MF-{9A|xW>)P
zQd%i8X9R_DaQ=_XZra62WHBBusm$bJ@zR=)-#J7}3~($O&KeRzMxE0pGZncLd@_r#
zy{tN1&VYAx!+R1vws@`|I@~^>qiN-NH3*a%?YvOS>w4)0_J!=$f<`3cH)s5{gy5J1
zF_{oW|LuXjO;et@_K{E{(u=JClAsL=X$lUUOH%We9rB7k_sRZ}`;pqf;J(~XM8YI8
zh4<<5zMvT~v2{lAeB@n1uAl;!la*?5aIkT^6k(w0(WM~KVDV@)5~KMCPL&$1cxMyA
z9kTA*&2)&8keiB%MkWYQh>%~}=zZ%_{=DJCTK5sm1cdBuMDmnR3sKo;LgQH}TIhnT
znH!nUyB<xN-_*4k+73(zo|l}(6m;$JgY3T4<OJKOy~IXzmQdVy8^1$KiilkKVE*TC
zxK=5-)LM0ubfr``06pb*<sR+d_5v;?#vxOCcsTk4`fp6U-HpIPK&Vxvo31EF1TMT&
z7)4n<laIn|NYfy|shpFtm1~%KAiTiEp`Z_FOqMpSUef0DOWj#JmwUT|9Lo;}$nd}w
zR($FgkC$YR2;POI9O4s2o&!EoTBjtCn0jU;0PZ36lYI__;@ujRS)_>yjKV_i_fx{Z
zZOQUTHqWA`=)pZV67w`o#BVzza!63u2qC0JdN4H8K<7d%f^|rL6^M?0M?^4R)hSLH
zG&vfx_;cXFCqsJY<i79MO$mH5qq%6x>Voq;H_$_wVKFQ5^xmNDp}Z0<B}~tMVf5Bn
zPW4gslhsrlw;g_VF{v@AbFn9n=T8EDFdNCfD17B#c4X6)ZkEpaW)UM*(d4G|@Cf%}
zXK)m$J3ZX8jJjk$C*@QqoK@Pv)#0-62}6khnYVp0X9CtVo-O@7Mo9{}ybBxYC+M;&
z9wp<FvxoAxV~7ZZwUbTFQr+%X7i;)gCwOJ|9(~j|qz3My9m~v=ms!?(yu?wm*dzI3
zsqJ$y#{6tPT)X`}R`=}NPBM~dTQipN67eQc3DrDNwT{|V9l1T^xnd~`h#9%QytG}k
zn$&A!#47XI4+2g)O8~9zr;wv0WE|KV$?K?u7%FH!j4pp5<NDFperrJ_Xa7Foy=Gw4
zjz8n~lv=4*I`lyktO$dh711W8d8sbv6Yu2-G&}B>mW2nLo-*38Aq>w~E(-6KSP3>2
z=Nf+730|D@0Ot?YiDHa|VD16GG?yD;;BCZ<v<|>yjOb?+$qP2dG2G>CpG)nd<-Uz%
za<WT+yb}|(#2gk0@XG#}{W^4Z07;6YcDjgJFIg;&XwIu@KWF52bUzQSj+TY@nXSmT
zf$85wtlfC@o>M6a=GM`_+~(qbaq+n~cEN9*Q)KuUm6~eNnhL*ayTr>r?xV4vV)j|D
z(bsUXYbm*NCAY2OOIcC-Y+ad;6?<tk_1r>$+z@if_EBa1<kv`E95s9V<~tW0ZN11M
zBGQAj1TK$T&S58k%LBT;flzNn@yN?<97ITTK@TRTOqLC1e)&B#reB*zk%Z<HqHh0}
zfgenbGfdkBGlDQkc4S%a@;xM96@XWGMOlh%h#8X6^r=3;Z~qJ)$!6~J!6K0igDZNC
z1(~NBev<R)zqOV!TJaZ0|HD0iwvdUp3lG>|1^?wkG#pRJLJmj<^u|3%Bpet6)@v5<
z1~`?iC|;b$N+{wlDGU!)cYW5WcI-ruAxNE4YgM*DSSYq+q}9!?+s{qjXuJRN0?i=e
z)@#*wJXdKywq4G&Upr-M8y1i3V-^H}U)ixV|A7eK&SHt+9)zaE8cyxLR1VOS%~P_?
zVuv-`eZBY3+(Y7K9@M6c=f@lz9g>$WjsRRFEdWfaORwEV@@p^A;>e%U8HMv=8jF1k
z9|;}|W0RouBh{izrFWlo`Wuy(o^#(C->G%$_;@@(R=4eQ`ZP|Zs3um)Vod|>BEZ91
zi-bSbvbiw;++1QA1bf%h_P*Fmf`;O@r*W{VOyd=A?8J&Zf~a73i=P#91kE8wc2+hp
zrErIt20^|Abj2X2QcdZ;&$!sL^a6BrO%4NA;b#9-1U5S}rJq(Op;+Ul(B~G*?#a4+
z2HTq-)9%z#6U1vkB4ssCvsP}Qk68}1Fe)sT<mTjGDVy6L*mywDaMCKEXt~T1_#IOC
zrsCv;o_s7y;|x9jyFOnTU6a91T*aVpclLt8egr(%$3qto%UTz<3oz^<z0p%lpE46I
zDz>=9<?}Qx3=_<de~XXPf3vG-6ENV8K^{G#XoKfP+kjl&lrz4;BNH;F@tKg(3r$a=
zn|{KzV_sKk3#p;Az@cxhpGlflxSW|ka6W=)$}{lZbkHnCH-I&}bMDl8EVn%{?`5h!
zJ72BOg*Hc4q+YT;(S81U<wNtQYc0i_ipBK|t}VMm!lzMZY~lnkq<7t7NLArT1p7Y`
z2tbQ*?HW%fdhLl*ZW~&S-TGsq=UH&hrGiL7pz&JD&*t>%<ZPD!hS)O$i2zyQMwiX;
z6BgRN9r++div5T(MI@<*(~B4l0M#z8C5^aJ+KM?n(!J9kx;fcCg4gW(=A<<-e_4%g
zE^_I%+|{@mU*1R6iAJ<WyF0PRRQmPvy0!Eoez+o;C8|^7i;dni)MN_|SwYrf-%15p
zL6V@=D%%^F#|u0$rEPB-g0#trj;_lY9#?M{TnRU?X=xL!w%^<Hq^(FbGJiz8S+fBC
zC2%N||GcgM)uMU-@4e3MT<3(!J#_Su^1Og%FVl%(vH0+|Z^5udFv3z^U=%i1_uzmt
zRT3xCEX4b6@IKZ?H{%`XcOFH{GwUEq&5Ny7c#sLgTd9=U@W3OPP;=J8oRxc!!)t(}
z=KJdkx^BeAzA$VWCqR_*C*KZTi!*+d?cvh{-+nmxa85887up%{#d>B_u0<>6i@ho9
zp*fC8nBYARO938G&ACxXsQkXFfO3Sb6XpgP7H?*yLF#(vsDa4FF{}MgfO-5Hho8}X
zghE{hgV9$nc_mspKUm3Pq9%-14v1W^Uu4;)b-_gYDBpo?4SjmP>aGPhdjs8o!o^vF
z+U9x&_b|moHBN^ggl%Cx6+tJR$Fd$1G~zRtxnqjd=?iskg<}O!Y}Kdq9_JIyZrrio
zPreecikfpUEBDtx{D-XlV^GSe;|HGBs3z2bEf56%7m~!JP3>x&@G8`Om>R;TX?GpK
zQ>{=YX2Y}&FO5!;v|(FWLdW~o-@{R}q5MJf42}TfdA0M-iohe4i?ugqt)AXTYgYH%
zH}hIF01rVcg2e|BJ;tn=eeFjlDR2wF-sPL(Ts4dKXo)3L`y)VQquIyO1r-`A2z1UM
zuq0w?aI*hg$3jh*19%$0Tn6x(oh3<Wb~}hE5l+7Km$t<Ax*io$47~C9;QDSN{rH8&
zZ;p%naN*;gue2`z2DfV50~g=FOHJ*;nLi@;sCZ2Ahy?xQqJ=qn-=FxNXkP7xE)R(-
zHCIlKEaKy3`cY2mxMNJ)s{=EE-Gk$@18y~r`u<-%>YLtd(QqsyX|ba#U#`U!E?Y+y
z+nPDvUz7LNEjy-R7#~9$Sboi&cM1Qk$)~oOWGh>&FyPWsmv1*8-nVx>n9N!k8a7my
zl?y~N8+A_feZb5<jTwoRis>z(Z1wlAm5A>pX#}7GZJ9I|vvR-XEuRz(yAWATowT|Q
z<*`BC9hR2ele|)Zk_b#}{<Z}!A;j-sXs&s?0T>Nlr{#p~SWs~)itLEUT8+j3R_zJj
e{6Fz>%^+>0v#00Qd`QCkMO-%jwd|tnjsE~&jsZXb

literal 28123
zcmeFZ2UJtp+cz3VN5`=+>I^CfjB)^x-lYhZQ8-j7QVm6#NRtvm3t+=W(UZ`dC`Eb)
zAwXbIn9!xy1SNpfKqxT;$UO-&|M&mC-}`-ct-J1b*Sc$cSxZT>_u1vyPyId5&hwl4
znn!--`5A#g909fdFhU@H^hY4}-uY=i{LN@=`ZD}y{~aC8Kj6Rci*XGf3jaBHU(3P=
zf#7?}{<kN3YQ+(O_#FZMamB<xd0{*#-tAU^)SOMxA#w9C@z?kt&RzIL=)xZP+rYUO
z&rUvk54^Q9iFhN^rt_BAdCNzhfAU43ye9bj9QUV>f0(@e$?)B?H*Pn7;N2(n%i(=j
ze%bdx=%spS#9VLL%S6;{>aG6%?fNdcG=7B^$B_O@uFq{Em2dpOS=|5m?|%;kioavj
ziOok_swWTs9C{LA8(^-m1kYsqXMuKUO%p?Z<`f_MYYTH3_Sf($Uf`fL`)}Amp~ge(
zH`3{V{~q=mVVvFo{G?qJ4;ua?ZgadA(O^^|OMC#Prc#I?Kh|MpfT4;VOQtZv`Z&r9
zz^uZWtUQ6*(DK%uw>%&m;1A^td?)LBgOHEqq=D4Lc6pMvw>e}cqI`}x0+98*ouFu-
zIxnls7+ZUR=m}WFQ4WAR5=62{b?}~*g+$6Qc=R40K69(kQ`h?nR3yUCg+{sWM+!&G
z)!%L10j*;xf8l}_vw$L<>TKa*6X<Ey)(%~t_F<swsmQP>u@YP|sJ1E8s#YT4C`n+5
zOt`<MOMF@;h-bkY40D{jE=r7^4LI|Z9uT<~dt0vIgOW&qX_|eg5Ql;Vn-v9$sKFW$
zV!FG%M;fJvUuMT1>R?8AHBVx157>{*t>ge)r<Zy3P7h#K7{08aj_fW|uru%ZRL6Gx
zmpu!&Pjofo{;<^`8TGOTaQp7`g9&7^oeFUja{H{QW|Ke5nv%q1-6J(M1EB+xfzJJF
z%o_qF!c8pk$gkw<FwMh$WWH<H@ai)-XMYRoJt@6YJ)4qT^CpWLhs_5Qaz?u<j^2%|
z!a$?Uh1H6+DubpM05xTXI<W*CpnF9MA6c!BH#k>fl_nvpI*IR<iP-09bo_NnA^o-b
zrhX>it-8*dJi2%inn=;7P2!j}Nep5C`9ek|{~*&>kMJ^RjrWtY%MeSFEwS=<sN&+!
zKa0ICIp;V%b+yRL1?s4|oh{iIWY_wRv28swfNPluy=WWUMLlk11lc+(EC*eD--1e%
zU0}W??xQ}L#YAskAyb2;H3x5d8n>Q2G`=t>L_(^T+uVJKRw4G!?l^t~0DsLD3!aT_
zE<}WdMwuMUgP-PIJJVt*uSLQB-$cvqSLx95Kz+Rc@fv|5T?<~H^wf9~@vVL;1U;S~
z^b?(TR`+h0udUMD)`4n^vzGF}5Fz3mu7yU|wva2wN(l{nHw;bN57Dn<Qy~(4sP9!F
zCZrYmVbK%_u#B#kDpC&hf>?7s)B)UZ<^kd1vt^+#W57z#!nsEB%H|Ltvi2+wObVBt
zKCiBW^=0ja3DY(fy*iQLtdvD~Jk%)~U9XXu6@t`$nC%fh>I|t}5IR<N&T-(Y3v$Fj
zWVmeF5+&#F9CgTY(DHSu)6#rUd7A!k@f@RIL(B-ei1u|M*z38CmN(D!%_RG#UM{|{
zKF{)aU#*V~53m6`IwuPFsd}YBPShNfXlPU1+A(@?9bP!rWBy=~wzrKQ!T#Rn87$@W
zY^d_9n)PQzi#<iNr)xud1Us)dFGtOl$uBSk;1$0)hI*cm$IHXQC9Y&+gVolF5CPuu
zxB{j|$iGN0;q%~9E6kTzwTf$LCs8h;=6Y@-=-FO(y}rHoHrH0av#uUy)@y}hCooqY
ztsJ>iBq`JH{tb0kj~O{z|10tLjm6_;<8jCIMEqIrH6tDgbB^>Ae543OcYKY-<075X
z{vTTE3!N5baVU@Zd=T_S&7o~nx_ahgCorZRDAI6dSEd5SZ?0$NW1_modl%NH#0@#q
z&dEy(j`8&CJ;4v@0=&;$&dw?N=OrlzJLxeA9@1GIu7xrwlvj!B@WkX1!R9L`YTs>&
zA7SnsKs2}WuvHf=GI5ovLtDi?Zf$|`4Fc4H2iNH2Yf0H^7s$jXpvQa<ozy3?pr~W9
z>ivsNo-*s5Bu3rbXD1uE={EXhY&yrr^lKTiS-W-NPgZ`8>IY5o6P^K#jQFZWS~VDI
z=(9stO&&eBK0mQT=y;3;7>;o@R|ex#iw^LdamD?I)>)`B(kLrf`XEJoidD;L`+!H!
zk_}{+`jMLg-RYR|!z7RAl#{;I%{vMhQ{UNM7yB#^!c#p_xfnbj3ez=Dt51~9XWqHd
zE9Y-^gf<;++t$}o>1s7P(^rnq?Q$*9qCLh5-BeGdyakj*szrRLDK!=Mvawrs9YcCu
z6BwUpy6j(vNroY@;TV{I?WdD05VoQWlN!&OKB5z79YYDt20{k>?iyEZ+%-~^o}0>&
zM2*Ly?+z~3xi1)@bvhVVSXRZORJ-e~BMswGDk!<rNwV;QP8Aw}PEoy5P_m&h{m;t5
zl=`2v=A8F*nZKreYB9jxj-~7seg4-YXyH|9Ik--;7<SB;2)yko!!zaV0xg9iZ24ic
z6P8KY0S;vCyrx)(2oi^9J<*ISy67BUiY4Kj(ct3B3iDp4R4j{o%=@oNsR8C&Y)HNI
zsVDUQ<WPI3P_9v7<+oYm!^mdq*AzKXor}JfKRcAn#X9KMh*)c8_Wm;ucUx%RUWELV
zvuKhe-z!QvPT~FmuRoxcsuX;g4s%0uKy|oU2U+EKas;LGYxJLd_Vqhg4^P&g3ipsJ
z(VWGDJ9$kEBvvi!R)~ilxwm(_?U}K0tY5r*()#<*K7@VbW|_PwT>~M_#W9#D5W*Te
zl`rC;G5_lj0rW;i*SD0{?NBA%xu}A%*J}EZWKGtmx1-cAO2$s#J7&JcB0Mdz^iM{L
zDHK+24pX2Y%`-QrC}}$h$6~D%O0Ii?rJAj7L%$`|X*SnMdO`W~MpY`r`n?efg<TLw
z!p&tX4Tdk(_wK)zHrKrqo><vCyA#it7S#cK=c;5<2=|Ld9STOLZ?^b8_iRa2PeRQa
zS5?lT6c#nQg(^YWZ<yEec%o&M`nyWW6#H5+M{dvT8i8!)yRGQ=vxBZD$|{hX{qDXO
z$z-o_VJDk!Ju`nvuAoL7BrKBCG8tnmL_>T6>`RE7Rb_+-zE|MuL?xlVr%Iw&a_iB4
zp}&t0b|NF@Lb!@rCwOHF^k^GTp7egWLVcOJumPma-w$zzx(3$*tZJl$N|A9jt2KRm
zW0{36W&_EgH&lwe@WPJ6kFH2GbG9n{YM&IM7`Nros#_DHq&ku}y#-H~GVGWfC&Kun
zb>U-zW+qyfwu9T}nL=0;Z2mOe?yIj|<k7!A^6UawsU7euYXiuZKWSNDM>?r9&O-X^
zd{&t0nv)?Rk#iw+(^~_eI&A2Lk>)-j*R_uaI*l*JfT{5mRnRZ31$ATH!kc3QPE2~r
z;ny0ULTX|}57DwwCw|^E?SR9K{+!T^%>;!>LZwdHmFOc#Qp+9dw`WCN{RY<Y7labZ
zQBI*VdBzY~GZUE0T|6-C;87oru@UbV;(;KCTEXq{Ue;KPf0EF!NpGlqE2j2(-r_Xd
z`O4xSbPs$UPX4yKy%E$W@%dH6^uLt5jNJ2xCufv0L#D@G05gjBBVo&8hT;x-h=ykz
z{DbC-J4mt{KdGbKm;{8z?}TWg<nE8UfAguGTZu!?CF(C1>UvLxtzgvGS&F_o%TZr<
zM?NK|Bac9|kHkY6BPPbI%i8DGPaOKtJP4h^m9C6rB<gzyLoDa92N7CD%sV+uEdh5A
z18>8$$vOo`xylgCIbzz8?vQjvp@7ebMc5k~^0f(h+X)26#xh~c@G=!X#>-y%ttyEc
zbDx*uzz{LV-fV04ypTjn0C?2Eo38fN$_;)P3E6<Of`B!G+LYIFO~vh#FGJXTLx6(I
zG?UO7tRs7=dbIC2Db>K?SCo$t>eb)ZaeDnri=UUufi8Up+OD2^)tLy-c*3^-18lwT
zcDow$Pm%B_HFaXD=)o6@#skT>6KLmGD@1jKlG7+;?3mg_g``_#owN4WjWAq7HJ3ra
z!say2p+iVC+SBH>td(aDLqn7r^c1I{k_I`j0A|KkJyj1pio`<CHl13jlx)EWFB~k+
z>l-)+o0Ka`SC|%1cGj4f6zKg2gfVuiw27hkfa;0EAHQA{&PtB+4y~0YdS<oX8NWc}
zf|64xYx4M&p<;C%SV{gbUbenj&3tgXiLTPc0izvHBtZm$fB-Shm+<$ig8#+6|6h#t
zf13V3rogG99IpcnfA||gal#g#8_cbQhA$C8u+Y<1jkA)Pi(*9IrWO}L%mMc2;(Y~h
zOs4x-Nr+(y#T<!1xj{|X+r16uoX!#5;keVa^DTTou|Mk82oIS2$bM&g_+Nn?EN<|t
z;W0G9X^Fp?2eS#6Y>Yz@z+{JE&S0Uww>xy8`P4~v$Kt=VmL(-MMHu%Xq-x)IBBc*V
z<8k4@3=MA`pND@@RU*b&X(~}cZ+#UJG?roqc4~WDwu;-ZpGJhc*+a7fhBJ?~lV(lR
z<e*w1;w-r2ny2CQY~|=^IF4Q%GWLMebFD|L5id*}k5QZvvKSq3dI>n2jf}-OtlI5U
z{Q)+o@T>b59r+0IF|g7tCKRH?;~snVUjn0ayfO3ZBf!%rK!x4O_A@lWN0_`YrIab&
z(oNh3N#+5Vh3<14>_Pb9KI(Tq6VWvCyG1jxsqG}LT_OO-YbUETGlO-aE33>}nTi5z
zpamff0{$)3RP6xc=t{2gC`u!52`4&ki-1Q#nu``KeD=Fg7FwqI^u$q4e^Jv&)i6gV
zf|`Hq(vcXNBHOVHMP+Wi%X+vwJ$4`UE(Q)!tik&J%#d+}J!d?fWFa2zIQzgpDpE8Q
z6TeH3muaVKo6y>u1{thc&27UN+K;P;I2>Sp0q}oHrShDahk&BHF$D6&2lnI*``}<s
zJdM8>WuQxAx;TN+DV7>lp0?ubM)Lu{f41c;$UnFoKdPX{&;Cwa^?2>tqynf2SPVCq
zhrW^({64q8nZ3?1N4<yW74sN}3&g#kVx)4cA6G{8;{^j=9?k+{FC8SU49O7fz$pFF
zP^b=@U)aYIcf3}yirvzIT(P>*7jLj9KwQ|+_V&{*^?Nlr2ONk#OR^h!MN1usR{e$D
z81D<~rv_PX?^fNhna+1qd+hC3ly@@oJnY81uP68-XG9n`r`BUYQYytAtnXE+5+#Zm
z#d;2D5OY9D58j!NxYG^BO^3PJ4}{%m1L)3v)lVi>%L7a^cRsnJ@FEP~Y02SbeACZ<
z`@}!9dm(fq%$yWun4OY~JH<Uq$N1)0?~MqeLSAamcjY7O`AUMX!C2=|NzW1=-&{77
zZ06!~r_3%YVpnYBKk~qbqLmqJALpl$gA2{Ow(Sd5nOX}}yn4ePKa42Ss2c1lHg%w;
zJgE?e&U5j(p1D;Xpt+CTDt_>IZ4<poJHXvN&p|IUI7lxkNNj3xhVK}=wHVM^K1#kG
zLpxhh0Ew8jlJi~UZi$kWsn6K0VM13PqQ9#fiiVyi#`2B0rBa$=2GjCS=c(-duED)4
ztI}WCoRJ6al-(jp5b11}BntG?@^)w)sfqTe7>=jd+-n{wGg7abz0793Fk<Pwu|48@
z=^_Ckkd9T6Kq|!sUmh9&;qT3*8jFV7eM#r=Csy|%T3%Ae`#vNkJY1uL$3n@&nL7|5
z<rYI2)xFDJ9%9Yj9WoPA)*z09OFFbjAoP_3Q>Ix#n-|g*vCvq$%<f~`lhCMYi55{V
zK(Rj}#GkQICio?c`$sF4{;_v^M5ZJ7Y;olS8BW``-XJ5z8?LnP6Ix`>YQ(QNE%!vb
zVz<oL`mX}jDo(=loZ{eDVaG;Z$v#4Ff7r{}9_Wl!V2|<N;amSq_%AiMt6~r*v>$Tx
zp6yj|nIp5h=l|y*+5fA9Si)#s{7+muv<UgtCk>>6wEEi|0-Sna03>*P|Nhjlp0j`R
z7wr?70s>m4GQ`&X%_AC)+*Gdr+5Y)V{{Oar1CZ9r-v5JwG*{lgPnBOi!J+2Mq`(aZ
z8|{W;R@p~!A+_p6f-qZii2%y7#NUoYa5Q<tPwrX?jrhNfN<&5&k)u!k&D{NS&aJ|(
zFS|^jI%omwfP~V7s#S;;Apa?Q4$&LV2{z(CoJpZHfqsiO(XNm&Huzq~F%JD-#tQL-
zQ^x=s55DnQ5u);J@|Yg<R7hEj=ptGc%;|EfG!o3R(o8vV!9B|g)2T&^1+qn|iwbr=
zo^4ij<252RZF(Kxa96iZ(LQP-fHoURj&pFgN|Jz*kuE-FP{-3_3qNUbIPmf{@z2sB
zn!s@BtsM}uH#%N_iw-DD64wg`U9o3Wpl_TbzOM5Sy%m**BjZZXToZvM+J0)i<l0M)
z9}xc&Y~Ii}GlN5-D{TsJbWinPIdp`ymZQl0F*J!wBP454>S*x!hI4W;6teLEZKrG{
z{OYwzy#OP7d&q{*hiyf~_ppaC$gIuL96eyENt=iqvK>3B1zL8@e1D|gY=fmtf$CVw
z0Ip-dls+vR<d?ae93%`o4oT2h0_6~xngHo8wj`R8Bf(U~2Gq=?1$fjohJ1<QD)Cz$
zc1ydpj`jr?M2W>^%yoeE$&`XwmQHD$V{s5e>{2uAIit(JZaHjGrP)gf<i+Y%Md_<s
z<=FJw=jUjTjs}$@^A$5b65yln{;*dzDD-GxZEefY)6C%0J?;?%Z7Q(1g4|3?*n!-s
z?iCvN=*mMSvO=%bQYh}%>IaOxy*3F{N!?QW=oNh)+uB*+<l3>xYhsJhv&<Csnc5rb
zEic8%mgNTqzcFHU^V_?dx&+O>`pTzxkd*0q_8`d}AHj^+)66iR!`4Q>FAs8tB#fZ<
zEhe3WnwFs8+j-;7Ydf`)eO@si{CH>;vCx&{wQ@GG?t_lCc7Q*YGFh-O_(_}%$D8-%
z9wb?}Jh;?6Qqr>#arTp{+woe!(WfyXh|!B2aZjpFOQ#A47`09=ia`s>6ifnBIWVNd
zJd~VEIM1d310nqu+F;JPr%2b^J#SnG|8eZ`Z=i=8a=whc&(&yfN0#^+^wR+;MOus`
z&s551aOa#4HNk^!tI){&xPLO4QEIC~G&~Z;)~WJtul7-G)=tgm#8iqL5hdASCxk%t
z*TX>!QRK+BSLXPX=)oHOo%cOU&1(KPC@<PWxNZ8<L)Y7|E&aFcg%55mt`V%M!F-m_
zXNd6p-u>!wwM(R2VSl>o#c)^IAOo1V4Yg~Qt1(Yhg|;<9n5+Ej{T9~S>iZAB?6?Ni
zez>*c(ki%~lCMpB8Z#)8;fCucszSBG#6r=sK(4#I=p>v40i+4=ZL%_qNMgj_+}H8K
zjCoVWkD_3;U7*kWj1=~EPv*FjXi|p|aR$5Pl(&@uh-8UttSkdc0OL+8Bu#cz89KP0
zi96Ee6nkJ;a;oitVPI(M;WZnb(jaJFGZ+KY!S-X6PJ*7FkubXBhMd2$a+%yY@=)fu
zdW_#TP#>bSUiJl@+qY=i%PKV8ZX4+|$P7-GO_4SB9~y8{9Ee`KYlVzsmWi??w=mP4
zy6zez72JD~UWtV5Iyd89r+7JkdVa)qcz6q2!JYc~AZg0|0x?=Hsc$4*XVlDer0!jG
z3B9nKP+H(Isy(i*;MHI7h52W@aDwJaU(=Tu^C;@)%EXCF8bpiP)Ug!hs54i`u+z^I
zwlRGj=M2J=CJH3^yl*wxvq9@EmcBmdG2Y#<vWjzPwuxNhD>hdX^*I5-%4{7R)URwG
z1DsGPW50n*o++VMp4N|M`y8j973D3`6B#B%pGsVg(iV@NW6zhO?4eIf#+}YF76oVP
zu(!=!e2~{l>dy*Y8SeA)zvk6%kx=@$g(%vF(XA?E{w0Zl8JkO3S(R7-F6lGQeTl$j
zN>|1^>)PZ;5d2c@;l&|**GX#x!`p<G1Dcyh&BfJIhl3`b@w;~wmS1`wU6NOJKie4A
ze**3)EX$|$1njVb4Jvl2Jz|K9?zNqDJ9d~}oTWLsv5#7jV{jafV~jHw3y*x<%o1N9
zKiHUD-hU@|bFp1GS`|rsh*Nlh&k^>=^Pxoo!2RSfs=)<&HM@p-_9`$5+NC;>ru_Xa
zr1<CJ+wRzEVzG4_-)$oo){uIUmsMuhhC3?g*4)lTA!U^q$Rs&ooVlsZY_gnL`fJQ#
zMN(8J^7de$fDLG&o&FqEk_XP=nRm&l6N@pwZ>4CDHqG!&<$p?@xKz?sp&oxA^k6qg
zI7mvE+}_*lADl@&xOanX%Wg~@5iP5kQ(U(2$Zy04*Ig)kp1+~jty2BAC!|Zg$Ph_J
z&m3LvM`1>zyRy{&Oaqdf{q`0*-`W&NbuiXrt(Gk-1Xkrnpsx!o<{c!d_5^Kw+Y_@P
z(y7t@{If)(YKm+bJ%iu=Od4eoJgQrYY$f|U&Av=Aayn?{e8WH_U<o0^Z#nC{mmP(g
zKhBNbA9_xxvFD>M0TgP)t5tXwR$2*QBn!%^=>61!81+RsxuGUGRp#%kUAo~4Ms<u)
zzTqwHgVVh|kWS8<A;6SQ(r;iYG>i%)lbn-Le(htAy`=j0QRA3*2?m+l#MKBdBfU)V
zfR%WF3b!p-abMhU5~a7c`zvL#LTBiCYeD26CfTPG{-$>%x++8M=bd8M!<0N;+tzzR
zE{J8*<DAxIx_M?ulewYbKZCdH*FC<rgFBrhw^NSVX-P<#ReiS^ziql&>tQtTDdqIc
zDyF<h*k5B*lUY|5e4=)7qit+>=nxX?-`4IirubLfai-~Ja%ICQHUYw8S9N>50?lC*
z7hDUGnJS@fJU%8a<oO=Qv`9S**4v1pEj`ka%y0+7w^fx}V%RO8ysbGvqoA)YcYSqR
z^m$|MJ=u-0fZOQi?HKQ(BLn8~ZP+cxsr8rK7fDh^inOE>&$R8_w!pEnzbAzAVe=CJ
ztIuyKK{QEzJhe0_@Pz7U)16XOt4bdTMx>596Y?%hg*Yvn#$mo&w?_PRB?XdgTUP};
z-zVF|40laesjlHgrb@Ol+7YQmbItcuC2W%o1Fp3i|E$Lsd+q}4H|*8|JdKv$tk$_N
z4#;Rs2F1{>!5PI@+Kj9bQ+VSyuRTPUOX>#Tf{?Hew1v*}m<K{rY2WGB!PFGmcLm*=
zlcudygMf%S0r(AJT$3p%HnkyTv{~ZgG<f-vByn#{O^4W2+Ta?YdfjJSt?<%2mHF?l
z51n#0F$FBfCuHu4xwfvZf750}sWVMZFa{NVVJj(igzdj2PCS5JfMn%X1V-n$v;!#2
zLV&7YI;i#T(X?)6yKdS_)!wx_VMkMEXdzX$tg`b3;8|F^Gq|S^i@Vt67Q<2_a*fU@
znYN-_7drEE#y#&8VGkHNafvwUnYGCu9$XchYU92*kO)W>)b40tHu6Y=W>V5@4Q%^U
zZc;v~%pD?w0e@6sj4NjWGvNR)?H*@n*ug!3(ceyLS_Q4^3VK*QeO<I*pUm-&(_3wM
z_Hy_Kmg5Re>Pen+H><L|w*~jt`r*;0aL}b`m6YE>HkPH6hqg~j59H!1lAN1sB(}SL
z+PRuZzN+|*z3uP}@;+)r4nT;doS04imNM*uAv6o}9f9q^lwd-gC)2_yef*lSB7f59
z$x)C$ZkRH>0g_^>)1psu`9~HQ57ejV(<?&Aq<cOX{^ZLcAyb=HT>jgB26++d5Bc4N
z{AX0u9ZwJ*VHaM}m<jj#^@J>vXqin<W$CHa89jw_0ip-#P1uF_Qj->4{BNT4nN?$_
zV6zt0^e+EwStjAw;My=70XS6xXV`9Wf#9EG@tw~}AONc;x0q`B{7695YOQZQOO-E8
zf94OQl7Q#>rFWY6MI*)MN&2vJH@s#Hrs9V8t=Js#U)0YfR9VY_%bh5174pfXdz}U%
z)k5pMZ&NP9E||dYq)QjJNn>@Q@O;vehv07U;JK89>gSI+)77eNi(Ci_2%HXyRpFzy
zP3XvN1?Ewav(%OEFbc3U*fVy+Zl=7sR%%>eJ#Hc;PqNVp9V|~&L^kK?Ec>Y}Y12v%
zZ~WLK8nY90xSO4yJe4kdy!O^85OAy2G99b+5FHS&?TvJzZi~oEPsfTLtnCu1mYXU=
zS+w>X3~INp?U2P6F8S>LR%O*H{9LK>&If5n#&cn%0yXh60{&4=Id&q&^*Yp&0a&us
z9l_{PBd0MEmO{gw9wnrdKE9?gc}Zoe?M1I~toMgH1ridJ)UQ4nrE5`dchf9$OM<(m
zk(a&kKj4g}^Wn`?n|IDO9;z$&%Z<iZkZJkz{sMNG<?g=`&&H2zA1WwMMtn}}6XFRY
z@oUj)Oc~Xpyul^qwVCUyEnvNL=Vf@Pc-V<VyzC_a45}%k&ujnH$x1z15BwJQSRHO|
zZ_D*MP<;4IED@>y;FELQTGi=OI!$T9jGIuEFvGW1rBt`x*is`DM@ht?9-YMVe%*l?
z)*QzM9NxnCB}7vKqXf{uM2zJE<kRS1<d<HH@)qblyaol^k6nQ$ShkJb1q$MC{u6=0
zk+yJ)2~;t8^au0U%G2yh6NInR5$jEv7>c3G^q~i+BM~KME~ya-BEvo~O4!yjV?2Ne
zo#aIKwvq>}H&+?~Lho{Xkdhk7h#vHM?8j#3>yQ!<;0dwLa6%RnEf^|zi9M6*;JDY`
zJ<VYhs8Wy~@b+$lGXSx5Hb8X{XK{$hinBytXnpdi!=G-kvOY^uIC5Ikm-`X458^}_
zHjO`*5P5XNBSGsl$`~BFiR%>USv-Y-g0Sm1{s+7uDZkO7T9nAym){cK?!JT@4`HX)
z3)oVQ2#=i@S0b)}sfm;ou<~vSIk(4&4kT5yk`0D4+2@W3Q&&it7?&g_fT^D~M;~E>
zYvGI|)TMBS19KoSBWH<=qH<o&Q5%D|@}RL1l~5#<WB6mMxjXFA7YU$$XAdGhYbr0_
zY0`Rl%-yP}OAq#FP+*8f@5~P1=^BGJ@V32H!tRC;Kcpw*pO!qlZ_yD}>Xa<?O-#Cx
zdS&`9q#XMs>v*kH0nR4L!8mQ(<2{bc<@_$>=9r$dkNPEs=ID-Ebfub(8;feB|NBEh
zCav^Np9qB`A7Ki@_?DY|%tT$Dk+t$=0&EiId0%iMc3X?P7aExz+5z~=ngca!K_;2O
zcK^OGf{CcWrOO1fTdgzlzwT~-?~uBqT*WL<B{H0Aghpo;-Xpg%^uS8~ka+PQc#U?Z
z{{sNea#|)q6e+7IckJy%hqT1XJgD{}aVo~gQ;W75euuL#+sk=SuwYr4cQ$rxY-4i^
zpjV|ZwfB$4M-rX(bI@}6jGWW8AUMXKO4*L_Q7x}fKIen27SN?d;Dk8%0^(FdZg>mR
z5hCj)WHMTTBCxcuphF7Ux~6^Z`&@WY*L}F8DV~jMgcPLdk%QRuX!s=`EH#FZcLXcI
zbz(cx^xgAG!GEFrk$1SK>cE|#eS>!Xq!h}4e3>b&D7*dZw)Ng!&{+Daq6M3q!ouaN
zcR>z>U|-+yBL@1i3SITRqHPz)AW9`bm;dzyXEtcS<RRS^G*yxxfWvegB|>Y1gFy6I
zM(D^d#8LKwA+eNiGA*1hi|;BZJfeSxcb5uL=wdU3L`pKi8V=lk251Te1ZH-G11Cj>
zC5SR`1fN3A@29(f^*uL}*%Kjri>)A4<AFXn8qr6Nz{OLm1(j&H0LG|F2l6bR%fiB-
ze#b9g9n};wwvdB=qrq>?*}%ca@l4Qk<1;M8hb%EmTBVMe9%X=%gCa2~7~3dox~n+4
zUu6wUU<{!}%PBLF;Xci@15j<&Es`)%=K_isn`{K5{a5#L@K*DCiQ3CMPMN_B#CBmW
zE^W}1zOK#aejv!%qxo03)$~L$?}2aV(9EsA${N)RQ^@^M9LU7>x$A{Se6EqT>Se;*
zv`gOTT$b2W(1Cwzgjtrcyl0c|wrJ?P0*^9nM$Mi{4m+9C;BixLiL$OiPuD&>=K-ud
z+9uY0ee_l<D=5T{(?=W$i=0L(Wy3T84-5hUPScX(()Nx}cBWf#rSbe&3Y>c$Sb82q
z3pn!WAe*Sg4!+tAoh=yfDCixa{R8?bV53_qO1ush>RA}0ivR-Yo-xWC3L5FG%QsRd
zq+jGr^M2($ZW!N-;n&bDHRv#R6+58Q5{@Hm|ARhZ3v+~coVI=^+GEu&IqHq;@r|gx
zqNG7*Vr1XZa}!IJ3eT`7N0htVdmf%U3$O-M;PmYNr2#Mt+EODT3lurc;tkTU?d%mj
zIF1hwW+x)y3_|woKa%m<u4}^4#orPd%t(;hx8b@yR`Xz2@qGKzj+;_URO>u7tIByw
z=&re(KTHlIIH{l}ON_;A1WmsBYG<bp(sBSX-c$3TfU@wvus->_NdUEL%NbR=2CF?7
zzWzt;B_=oGeIPMxzUzHm&I0erm$(eHU$kj=^QN}~270B}AiWqrRUp6((kM!1rh%2N
zoMj^-hSC#|PG6paibZ|yx-&wjj=oRU2;3)AdeQtXhoU?^eTPYLCapy}-Zlc5`V`7y
z0d3~aezwyVtxp>Rq;zPtIEya5?E=ok(;v>2m3dXU=jC-yq}<CJEij^Qo^82!Fbb}^
z^Cg8X#R5`iZ0<(NJmO%^+zEKOq-SnvE`c_2H8C+YQb(1Wy_EmSug!e)V0u~dC!bG)
zvsLWTi~kyXy=hU?e>%ribuA?a?-Rc#%5a?L<1G;fkK<^2XgAxIkOZXZdw>095=N@n
z@Djq@p|I7B=7q8vXT;QKmiHGF_Kj;$PPJs3Jw)qGcZR7;ap^zyd`(%<PQuHg1@Yjy
z-R$uZmwS_iRFhV#ihfL{(N0BWO`X_O9`6gso-?l<+DsWDU=#>`Hyy`PJ^+JN)<t^W
z-?ZLx>>YOjY(mKIarC=+s@X<6^X<ZNW+DMLnM*!LKFPZLPe4m^)71FkslB9bQ>7Tn
z3+?T3u>KXLvK?twiD86iz8yS%#LRRKy(BZ>7gKps;kSur7Jr{mX>Ph#p_aEt-q!od
znps$1F`|LdtoDgZxdk10*s<=L;#j9>ZAL7=yJr!O?!JqKhIR5o6l6k5HD1(Fn0lh+
zbl*>FiH$+7nIXB+D~gUAbfxF*oP=rE!77z)A-TAR)(1&lrgK3Z?^AQj>*_Q|KmDCJ
zfH~y&kw=?T@r9}$%&A5>&paJtH>_+^b?2NmLsRcZj?vT2f+-@97lHcoC#HTPb>lr*
zj969ESH!!Ut_!PQvkbR2$B0~6-(FYQdD~)cP*8r!MQi0~(ymm!IXh|j)=pVji1<Q%
zbmzb32f5*(%?UYtpqb~>@)q)uJrbh)RzRckp5bs={N+iw(jy1w`QH6ggh4b%@({Kw
zr<~z35n|hgBD}j~v;Vd=qye&Y^nB&8edLN=d9}4jGI&TAOF)|1-B@2&>cKRX=Kxm1
zjF`$0mMedWG>@vV1)N`W4OHTYN@Asq(1THmmtkT8U+c0P)?svi6j#<($6&N#FozEV
zo2&n5GB9#thR73VN_~|qc>8wocv;tt-7Iu7Q?;fn-D}D9E=LcUZ%kt+7T4}>Hv?i~
z_I_4x&%y`RcAK4?Rw*(F`p+o+K=AR}6sOnfuZH(gvlvgyfRv=+itx07%<-Te%<w}F
zVE-D_kJ_iqUQG9^0<i-=w+6)veXdRyg0(Wl(;)0Ptl=Dw<gSYxl_A-U{^Giu5IXUe
zEhrFP%)L{mw6dUs8|*ABuNEUtH~r+dZ#Rwj<}ADR0xorqEua}dXRy$60bEM#1t<0E
ztdl6+um9lWQN=A+Egn?~sEP#mLQk_-v)oYn0{RzUj(Ek;l8j>Qzrpozp1L%Hg25JH
z<<Mtd9HO?h_d>Ums^>o)^%lQg7kl?ZsS&{^x}-9%yzYGtLbLt&k=@-0$KCb!fkJgk
z%^ulH9)LZus<pd8L7BCltLf|x2y6zd`T4OFWXzxjNZSD707gLU(!!YV7<O2}Uupd=
z^YXm;>jR!2IT;smIoMno6yT)vz0F&D_O1EZK{Ms8V#Wg_1#&UN7K7-ca_8Bj+OEJ>
zli9A#$k19E7?$En%?!GhS*r())~wkeMOp82-mrUx5&KLYqUqX<#PH_ZFX~6%g%kng
zIU|Bc7{@Gx2^Hq(4S3|^f(it6>i%n1+{ReWUn7Y^x11_4QZ#*cQD|GFCuo}#m5Y;#
zx4Fn3zQO1-?CDG<=q>ZdQj)`)kw5PgHkL?&v=^of<}Rp>5chEg5@2``l4v<|=;dHI
zj_L5Pq~Ty6Pc58;0$9lo*+86dK=30DZi?v6f=SkSFS4R?eX|6VH1H0BJcX+9U0zYH
z=lMDN5-Nfn;u6pleUTtZl;f@8&<^n%OgoDa(@gQ|&mSRvRe27=fOs{1yw*xd{^-lz
zr%-a=ay(P<pR7FGoeDENceYe!`vekBaB&$Vd<ydUm)rNBGpY(DSr^!oBkX_wr^qMK
z@=?`zJs4#`>%&!8RsE;<BEXGZ2K@g#z8H4kFc_@lcxsJB<?dIx*QqNJCBXq9hNE5@
z6I)wwi*$+j;K-+4H&PsS`7z#3C(7f50{oz)&2RK4+Ki#U{cdw^*iou4H-fAF^e`KE
zIOfRs_weDVS!-NHe??K970MuU>oNPG36?#zv&kW^HE1Q2se6X&+2M1)pSyAFH16W-
zk9!^^o)WqzfANn<VXqyRzC($(BSHCNlgocpCw|;Ich1ZwvbR6)k4JlDtRL;2LtQp3
z{J8JqvloU}qW)S*?xjc9saDi{n;W@Rm*;0TIJ2t8%(+!DXvr)<;`8t%>k2sHkv=)1
zy-dnF&vIy2udMooBf<@1cf}fmkcK3+t9Z-{+HVrO`{r~FDLpSRR^>^(u;^ii)A($}
zPN5+XKIsMfs83(eas<nRv<*W-b#`4L>4(>hB(f^w;ad<JVjGL%8!=H*{A4U83(JK?
zP2`&tF=mmw8N;%|f@8I?`E)_5|4y8N&M3Wsy$Hf<%Ga+Ya?sZM%-yK%)aO`0z`)aF
z=?+9)8z=luT&i!`5q&4o-s~o~8?ajT4E#jO&>n*we5n^L9-qx-b}UlcF-Yh@M&hm|
z4LeqzFrf#f3ByM>D$FP|SzXy+dgXpx@&Z#WsyOP%mTx4LQVdi@Jn56Z&aw67PHn$N
ztzZFi>YrYm4NteqAFpk%hYNHi7IkO6=$n34hcKLnr9X}u>*aM%Z)D}DbDkXv8{uih
zD-)>$Qvz85G<Y5l>p0d0;!^C_bfmK|W3XKAAw4AL#)|dGRZe-)<wN?~o(|K8H1DEs
zd|t9X@$BV#%Q;u}wB3;64F_T3wHGvoU^&y)Fy!vvgl>OQdAv6J*QwCc>Endo90hmp
zyWHqDeMmogJz(ai#X_mpma5%Av*9-TbT1o{RIlPcy$vplg;@}O#UFAP@+Vl~=<pB&
zZ-d?cedILp)+&MY@vdK|3@ZL@5ayS+TS4%Ndl>HA{MU&;GU~|2m#2)p>Db;JIipze
z&D@|<dp$6Xg!-L5jr2TB4dyl=CAvh=AhtngYOjtzVn?qU+|hLxh*eg*@pITQcRg>z
z*yvWIF0BBV$=2Qs@p-bH)<OF;Ff<gg>$9NlSwTMDlfcrO#Yk{t7BH&$+ozLoaeWak
zgoo5bVK~A%$zkSal-~d|N{99lP%1yeITVE8&}#>B;Cz)Q)HLs&x!rzpmu7E-tSskd
zp3Zi>!*^GCI-BwnIl*}YPr1VNrqtL?4yoRx!_<yDIkj_)<FO)iFIycXt-hq~NUU?h
znuZIoOx3?TPp-;T=R9D39qwQt#9-EJg`fJw8By8`CO4K+4x9v(FA~k5F)3m!H<KfN
zZ@Nu?*jPl_1kE!w0Y(cD1l>-i@S+0Zdo5o)^-F<o5xL9={D6M%$#plK6Ue{$5f6!1
z7MG}RyN?85+7=^?F&rgbyeHx61L4$I3Jf)kU9~YVpe@7s{oQBcUE%pXM~pP`4K(x>
zrf%@^n~-@*IBL+f4`!S2f9+L~yMv?l-t7Gw;*x7U=Q`hY6?%s~6Gv$PG<3aPpf+e>
zQCgCHz_uv~u%Y@6MC;OCf=f7x@cJb4H&m2%X@Mk5N4zEm$QBO3K!T=2^g5tRmll<C
zgA>)ZN5XiJ^sQ3A%!0L{uPaY=Wtv@>wor86=soL{#+ib#PJM+|v}sDrTtM>QtaQ0c
z0^0h$4f}eVu482ssg2b24scR+sZK;%Fc@+C*r^#8Gsp+4ra|O|9poz3%83Yc&#o(D
zn_{8mZ5Vj)FD^j2mQ-c=a8>zJeOg*@e99>p#Y%N>Hk@e!Hx~1JumDwJ?sl9*8n{pO
z$G-!V&N0C{%H#~>Cb>}%JE4{}M5i5W8H%wxNV1XG2o-sBq6%J(uXQd{r{IS2rN6^_
zNKayY_3_&+Jh&n6hITZpN9&ZdC-%Oets3dI6hq@+s?}KN_m8^;eNI<leldPQn_+Gg
zDAE_!uQNI&KFPYa=7>t3GNQk`&hS`#$~lvE2<F5p(N>MqWzmZgA7x&87~_igjpzri
zGrn(J<M5sn2#+=Yf;Ro2!XrP~bK^zqodkuc^V4?5Rtp7Hl;%SvoNhi@um%@-No&L~
zH=K$`f15HW|Ftb&G>u+N`F=I9Xt)A<ki(quzrlQ?VS+Q@OCN_<$h3Olcu7@;co1V%
z-95H*ZNyWel{MhO0Yw{XRe=6G_g>~qKOZj|swx{YQ>ph4Hh_}!)!X$Kw5b6{M?*Zc
zY@TwXx>9b|V*N~WzZ+0hcFX8c?vWF9Dny-m+8$6nj`Cp-stRC40w+&Kr@W?U!o+Cf
zS94&MTxX<-oaR6q#XIgUB}FGisy(3~;c5(21TZ`xzUwK6pk!#urvrAzZ7NT-1g=KN
z8)R_lcxx1&x4CG|@vIPHqluIX7j>aV8%IWMX+B(8ZIEA8dBesaNt6SF*`_cPOpFzS
zHI(${mULr=F8SHID$4GLo$+T!v1ei_vBMt@<+%GwY0-L3UVbS+jV4`a3>q*=<B-<v
zvU^7(UbK#~7;Pl961xS!T2AHY_)y|K66Fau)2)mn#I}d&2DH~>{ygE)nU=rk9`~Fa
z8td60-+93i<-4Cg2>Mq60j`kd;h|7(2rf#5ftbwAK#dr8@F<Mw*A-BfO=dPSOu)}l
z0OLMpH0F}TdfAYX23sZ@;&;@P_$yTJGLxe2-1a=GP1_LHP!cfkHnFLjaa^vhJx|<Y
z`~qzxUdow_Per~B8wJx64ed1+5}>}OlwnX&3qa5M<ST&O)(VdDLT3yIgZ>Otnc7Hc
z-?)SNo5|C&v*O&gymNp2u<5R^Fyfm-^2y>J-}JSM6>%xjqdoe%!_qGmBo=-xiObSX
zPJ<{4n8mJ?duxlk^=e^9>y+yLn8cXopp6WvO~%h$>r^D$uw~g<Ro}<JSs|2-RbO@6
z&)dmkgI#A&L<V~Q9hY2-%Z_sxE77jQya*&G1)I+gtoEvMITseQTr5&QV5Tl$QjmQ?
zZg@NW$lcmD-*4e-P{g5Yb4#Lgoq30PR1dV|7qgbd4VFoEG4)GEeL*_^(Yf5A_mq2U
z#!~Ue#Z;M1@?HJ&tz>gM7Mi(aQ=)BSBi4#Oqk0Y|^3W-6qXO45G2%=7kteZdUfsL@
zr4Y~<^%5_(i#hb5!UFy7p6u6vk}D~F%-A_3`A$Ty1A8^nMcfd~L1xgFAw<vM`O8NS
zN&#WNJ`3Q|E5#gX`QV&8%TiCH)R?>swlUH0BbyjsIO;PB6d8Em%6;-@SJ5Q@h8{_1
zbiNV~jTHra7gSZ~cWy_58%Yyhiiz9U<aml#4!0lcPOi^~az2@>Hde+*JeVmagY7WP
zrXRz_(1-7=23&zAHNUNX%CR0Sa;j~7-tf!u+IQ6|bw1MH^jvVO<?@T4@}r_+*>-oj
zU$87Vtn1!rrwOsjF!|Ng{Pf`w_0sT*EXj`J9z{9x1oX!l=@%V2&|fb>YuUz0H+inM
zWmk%+;2PTS(N1io0iaxdCGEg(?)++3#V=oTl%;($VfFKtFP=_ca?fS}S>NPlQywK9
ze%~wKXcaZF9b&adKVg?rH^5~{_t5TQYD-l2%)Nso!Iyzj2J6axKDoeX&!hqG@ix9*
z-djO8o4&VrSsv@PJE7Kjqu22vTBiRQ?Gf)9Gqz@DO6J8$OEY1?QIg`@vfiK&&7<#E
zt-w@s&%9yNwe^I`ACfteZEL-4386DK7tn*i(~QmJD$)M1)NNxSa_o&vNq3R_K8r8v
zm~}!NWd+63!BDj--qYbk@;c0E#`Kk(F@KeCxjD?B$gu32?P(yce?3(iSgIJpQLNUz
zI@t&F_9?(HhvbZr*p>`!N~p}FkUu$rk_m<=6AR|+V+-*ZXiS}$=EP9gEZn~>x&6*o
zhPYSr*@3&4;i6RoT`FU{hli<>@ohd?VWa;ywk`_mUyQrAXDPEQ)YCF^snSI7T-;oU
zB2lkJZ7FL_eN`jy$@Ww3meb|Sbfe?n<y_Qls2BAm?=YSm<abUDR)^<L3cMUU6PDQi
z<90>+#_vk;FRrk>tU6A%xG5E>BXI_xcO%bc4aTtk{*i5%+lxbxarYW?L-VN`zA-fh
zI^LI&a2+30gz-7PWn82Gp#@0G{B0w&>byV-r47T3)}rOsY(3NU`+bnwU!|cJ(`VgL
zX)+@ZlHw?X^E9M!q;JUm+{e=~p!zFH5wt~Cd?W<Y(qZR<8hGhq|M`Xft?SUBaCH#$
z&S-mbukBoA)~3|ZjB&xM8g40lss^$pzoe+}6gyay9@}$zCvxzKjYix*wE$1VZHAcD
zu4>bE*KMl!Z_d;NvOEiZX?wC>f3i>}%5Foi>~n`s>+I1k9R(vsvbY9ed-RZHbl`T<
z+D{}0#(GWYqs=Obxip#dctqBm5~Nl}azZ`y;!yodtHZp|-nlK^SP`Kl@!J)NvN^$H
zKQr5&5CjIQsD|s4W)3v+=B3PMldq4=#T_Iaf@&}2iJ;>uZ9J5h);)R-lJLK7+w>Ld
zDZBq=Ci2S(g#9bFl$}~CnH6f<@xPq%^f#2G>zDW>XrYi9^bsBzWv4IBPq4^+GRTb`
zD|m7-Jiy{u%OX#o)QMW1QM8eSVr6LV$MsmCMVRr2;S7!Y&LzQH4@(Ocaol%!9F89(
zb-?$JDn5{ppvZWmZJ)O}T)%oPjvC!Y<Ue2wS;JgqHvQJdICzxjCiTZzp?YhiMx_FA
z5(<{?_x+iyL;C{2RMK3M_AF#Fhkj<JrdmHNJd=r+Kew@dWYhCn!m}~bn~>0l^wPun
zzk29--!iNsDqD4|dQVblmX*c>r@HQqXf3Nt);X+cuh?9?K@PoLPFRii+S7fGL(fK7
ziIPik-J2b^=2Bs;eJA%=rJ2L}zAt%Yo4@|7c@o7sf6LqV?5#UqF1}Qs7n}4P0r-AJ
z)YlcOtj#TS0IWNAMB#<Hrg#)53*T{WQrw1bhk<`)XV*QRs}=GWNNX%1NVXNd&f>RN
zTZxyM{Nc@Yj`4a_cdpK1Ajw!r{s062cBu>s7VCf>Q+a!+STmO(>)fJo5u*BRq<qND
z&pS9EXn#53ch{tMXu{hfDJ3^!VgDlHT-PN~|H7O`NUgMUPsWMbmpL~!)V)5c8%U0H
zO@2iq3WieW2&LKO#dUK1Nu~W#ux}6x=lY-LqqF0*_$XuI2FVhbA9t2=prS)LP*RIC
z<Nd@Zlb3G`rlUYoB4xycI1()s;2$fZX=6c0g{%xcn2lV+80RW`C<#?-2ClB9DpQ8?
zlws5TNe{jf7cKuj^tK@@$NuBRyphyL=(D%fzr>3ZDo<k7LLZ0di;vV*KKX!{?&&$B
z>+a#1JO|%RN0<}1dzh7zcXA)dG(mVG?Nf6s^k<&lgXP4xOH-%SA(Vm+*bTFWB!8&k
z_ID+p$(frW{UHRI=%7D7w=j7sMNibuD7+7KGqibFbxFjD@z+w;DzAV|xpL4CUXATm
zZG-3H1z2+Zyq;JM>F{b~uFbDxH+DD@W&z)VPzFQr^{gn9)rckc?hPB}l7^2Kenup~
z52=2E*a2-7yS#Y<TNNTmD+gGH3qjf#=E)o=AXD~ikWp?xfMx~$ypn}&<oe`SKuS-c
z$_G+Ysr$6Y%y_!6&{Oq1;)e54ww7#-HF@6S-8pW^eO5K|E<VpL$s0GlB6ucmEx%@a
zrc8|_ah*)(?e=p?op6o_Sg7c1R9!q)>zonjJ~E<jVXu~#0PBQ|#9aH?Q=aZa&1Y(*
zA9JEWX$&(X5x-HtzO9s@Ei4(;-Ey+|;?nu;BFggG^p#5zZJLLv#<)=VH{0kr^`mE2
zziUXgYREYW^p=Yf2MQmVYbtV`FPbiX8ypan-q0-58oD3mkFbi-*Y{+~Gbu|pPHPko
zBfX;K(0ST`$)Q&TPC*d<S2{uA>k2otg+%UP$NXPB^Zs?^jk2GoHQ8DYcCi-pq)O@p
zvs~g8xWro1Jcp)--Uhq8EI~*M8^bpr5vy)26EHWkD48JZYPGO1_@bU9lTl>GjgsP>
zgwZ+AJr!5p85QNut9d@bjJ}*Chek|F1q6KVa_8-<`pZ%doBB3AIn_>vIL=e4O1LYx
z^fee!uEj=S;>`|-RKwELuVeYmM<=(aKx&=w1wd;{?SWB{&pFeo5?EI*zLkqgdZlF^
zOrN|y)v`jL>mFMLU!MNfLYRHlA~jz0ZRXgKIh|!iIsJ$9*Wivku?`wL*I#YDDbjQm
z!eF=DAOev#Pc3uVh>m<>BG_~i8(d(gi_neVbrh_jDX?(drT)Xfy@Q`eg{sqwsQr~y
z5yzNTbCokkbI|vpOQ`X|?8F;(XNkyOonHudA|~TnpA3Y5!fGvVSZOD2cs3Pw1Qm4;
z8kf-&DxDi9U$QTd-E1=VG%jb>8KwdVEa&=Rd*04I>iN;)V?~-y3@y@?(L-{Zfok;p
zQR_G-o7b$ETT!f%^0IG3%=sOUzm`AM0RC}SfB6qi5XIIM&Nr&w4f4r#(M}A!@p(g0
zuzauWL$*g77N9_cf7n9EL*Nx<0WizrdBUhO@rhTzRQF@&jS`8v0F;32RlJo*05bQ<
zn4b^>zUiG=_lZQslByYuvnV0|I9EA1-hk8CA!4vp!Nu#$oEyt<jmKA$cLagBdp|!l
z#8jyJ_5L`Fi2s2T#thsu4tzdC_d9b4>vcPjIAybVk=uN5WV7ou%qI=y3wpHtnyqf&
zso$wX*p;o#jij6#>3jF?7V?*U3l(DcAw!(v9(332h1Aj|HRNtx6*y(9Gsup9F29V@
zRS-G9`b7$=l_ws8ZsRB-<KfqILdxpvWQoz(F+!7h;$~p(lcRQX9Cv<53$8~{Kptr9
za|dblg^%8#G~sId!7K7>TR_n_m7k`uyHOd!>7VO05uE73_T5l2<$d8JSfv%%UocVO
zU_lKxiajAIK>6ML<*@3t2sj}yOqjnMufb9IZu_{jy<6B%jKHKSnE(A>-?I`OJO{5J
ze5mut0}WfR*#_BG@cV5D&bk>5K0x@)iorh?Lpgyw@aNx+@BGamC11XM(s86@iaWgc
z;L;iaJ-7;0si#w8-;xD+sdpFKlO6>8UA|t=sh{i1nzUTvf+>WUj@l=sw`B*fla11F
zX)C;-i%G1(b9PgcK=mHfFAZbWZ|4z+tf_q9C8+<e5Sg1(DS?PL+>O2va{pZNf!*X?
zbl{&$KBxcrC7gG=5z~LA9W1l|n+JI>0aH@<q6G0Y8@tl%uR2iC&B@!fD;%^=^w)xd
zD#vENNYFVLgOBFJIAIvUMS+S{-GgjJP88x01O}S*;3oJIb+5$w`ukmoDy-W1_3nkJ
z|7M%Y{)1^x08J72X2}CEB%aa(#0d?L+TCW;y+H-O4pX0;NLdG?%<ahV%`!QnY8KBi
zc3ZJW!dp9<dOSsNy)qHTr9(~imxPHO*xF0P;hY-`4tb^RVAyS7^!I4dIq?DaPCeQd
zoZ0feM&4;LkS2kuV*NfB>*g=BM?-|4{=bElx{n*n`%-s6zrHD|Z~*KJd(LhG*+SKC
z>j|C^Hv2dym0#=uF8fi--)_~K^vdKrprILVx`2AgGxi)|3BNav)}=|co;;kx{QNe!
z>K_2;`94qje}fCLzlLGqpzywND^FtN$vYfibQ=Cm9lE+9PKhfA|BB@5x9IRE2gGpt
z6kE7AUQT4X`JHc(cq{Da?_5}{;+_h1728dcDT2%TgD0OZXs8gg&4xoKe%gh@d6PwX
z2I3BI4Z~i+&J4JpL%ICW8_W76xgNFGJMYdi1<Y|$XQVi}P}_H}CQ@pZ%7+G>;hz$L
z0mGNuoCoCK(#6W!ye4}xK)iZr`@=MQ!-%YJLLj#l$y&AFne&<V)n}Z%mmV|^xp|})
z7337+(<*nl3}NhRntQA^8@+plxLrli-?>XFL+Rv_2>vh!fBY7^kGjAx&viKp`Eg(3
zkiw}it8f=AwpIHdL@ePEns(H)S_2GVPyPGzeN_3|uSst?z!#!GhG+LK(v8!Ns>nFu
zVSKZCl&2sE+Opk%2}Q~TmrUZQiIh!+jViIMV9xUIya;#se%DaFH1Buz=dei4hiIDQ
zh1M1nH>?a`p8TQ(KN<IeHm>@TLzSWXfWHWp_BJ@J@FBYcLXrPvz8(p_@O3K=UDZwA
z;NIN*8gY;`>+k#$v*=y|dt0zF3s2ZhhS+jc#bvoe;0_u>g4Sd1=Y8hG0Hut;BO#TO
zaMESqN49|&DK&sRGqvj_+@C7lMgNUGtRLz$po!%i&((7)8eP)%CValiPkhv4B~R}X
z@lTGtbCzhAW8J2qO-luAbiDr!)mTLd!;<I-Rtg7L1*gLHE=N_2cn9jmAKizzJML<{
zohnrYgQ=Q@-A*n{yu8jtT~;BBRPxrt36Aq=cc3`k<%_(;dqwW7N;%>r&qmPA&r6Mq
z;fE2@LE@!1PB6213VND<dOkW(rKd?jAj-aUf~mLSLKE*jo|4uS4$wq~T!+g56n>^(
z0e94i_C|WomzDC?yjp;+!f1x7pyv2klTqXiJ&2Gv;WgbK1+WB(hTx8y);RV5YVSIu
zn#|U9Y%m@bX2uZ&5k?6hRS@Y_9E4D%gH#cv!vKcR5>OOS>U@zRU8;1XN=dK`U_^=t
zgrNl`5L(Eg#t=g84zupPYn>nWu66(3^Zg`WK6ZBYe)s#7_f5IczK$}%Rh$uTpKuli
zv)#_PDuVueUjtP+2tsH{4Wia?TVr1hA!pq9QzvBvshxg~y-81sAZ-h1Ub-C;R4tJt
z5P3Ss(p2kJ7P%83?_x%?_3=Fk@it%RI^<`J)aJK5bur97LbN!u<+R`yy0>Wm$Rv*t
z%q`=rKH_v&E?J;L`?s3W<3KIK15|t-=C;G~u2i$_g2;aYRadQj2GwVMyc3fH?ZIt=
zzpFr<xIp#`1l~E5_Ke&oy%lr=l(RDYpaSFc)tpCIY*e)zNz_I!duKJ%M|;MRNGIiz
zTLqh#x1h=L2$p#^rwmqCC*+@p-}D5VM2JeJR*3jsc}R~b8d#TJb-lEs<M)_l@C!3l
zP;sYhpenUd`hB_TWX8<l5SpBDmv3cPtemlu6vMY-K?Z+)^6kJ&W+^>lzwmaB=iLY*
zScf;Uv&UkR?}R{gWxLb+-@dJjAD?;uRQ+#=9?kX|cns$sW4fr`m?|Du!Kso)3in%z
z_QNY~NEKLVwhZq)94pS(y8Etc$#8X&Z+kH7CW!00oc3?ys`)v~^xZQK_?HteMZ;1Z
zZ?k<w!$fR^P{WB_1(tuVf8g5gJ4!oNQI_x3p#Ao8s}Q|f^7MY@Rli2bQNyKfYc^*i
zm+69ZRyoeGJTt8$goFo!Gev|Fk;1Ru?O%VY{Wl7i<N+w$|I@g^dc(z(Nu`&r;$fLB
zG<djSrG7V`rC4dI>Bf_yfqB_KBW^@|x!&|DZ&=U7R7)8bvfc|MJl)R$q~m{r-Ms~p
zjF`Uw%k<6`@a2C{iSV~M)`{M-Wvwv|OG^&nGn1u1y{Z#YTV9XS#!(t@*H|F$yep6!
zHkIvjsJ*n^pju&!GT%d(Z}uy%R>&`Fk!WkzUI1;=l81Iu6~BuBVdu?Jkg&a3jSQ|N
zQS3bDOFX|ErG3`N=@_L8IUA<iI|)uRbBp%T%e}RThT^cjjEeo9*P4U@IQyB8`eV~#
z&5AS6zWlc8H(sXmRzqg#`&Lut&AjfTG(Cv^OYdI)Pm?+gUd*h}>mXv<a&oUm>JU8A
zlBKN^uA5hOe5NE*#>HFX@~B<_qQN|(SX0EhNvAZSJi6mwU^F8AjZ;5DpVgbnzhDxn
zA2G=87r@|m^PHMm?ZFXf!XYU>)jqZ^zLN<B#e&q&6w(j}w79R8n6FL-`Kv?zYUJH%
z5~KSLcbUpC-Lzui_4G5c@KXPm$a}GKyun@>5{WsNPIroU-(<fo+PCUW2+F`|;wbxT
zTM(mI_0{5?(j`3*d^11}^=37~x{@?UEEAko|L_v<2pLde2Uf^QbH8YJJ#e9i@OGn$
zS=HU1E&5b@hFEX<d{(RO8taZ?^zoU;Tl?ZpCL04unV^hzE)g>jAf^tB5ojC>CxA%7
z%ME8tw(l*%iKa`hbF7lrOrUaL=yE4nEl+kCDs8k{i+h+0ZjN^(24jznoei*pRKyvV
zw^^u?_WWJfuCkAks<p1y!D3f51}ij1d@bMSz7MpAhMcSX4EU+FGQ!atXM52%DlKYV
zfoE*}dxo{BAC{~r-PEB?ut73jE2QMexY|?e6_{CNiVeFfSC&JK`qn|ht%KbQMY98~
zWRqG@r_eLimt2+V`CxCjS~Bz{z`)IOzU)bF-<`JL<`>GJ>8vcj8-DTkf}*|RL)0!S
zdeCe_uk*%S(VpU+9R<@I$d9U?aA*gi6up67VFns1iFJPyWXhhVW!8aXDCYSA{k`&8
z59+Gw1D!8inL9M8kFg8mxNvl2f0n|ksZy{iBJ|Izr{*U9Aivv!I-;M4LLCUD_DDjh
z>=wurPu>QJ-}rZY{>(eOR>8*9`1016i9(ACyHR9*>pMJE|0G>RF%JG;pmidcS#H!R
zr`2m$W;D|}zgLY`&;sMQ$H9#L(&d(CJ=d#q#2F$;1`)e=Ul$j$Jn1>#6HU~vzZ4yj
zbT%XE<s}mG6zfwlkSZk!=a-p4y=OHk2CG#3+-*!?_Fcg}W`iU1fQ}u@Ve5<%ciEA)
z<^%cZQqQASiu&~`e_M52R<HXss}_-SDV1I2yXYy*%CxTQhxkhvhXYmohu9WN$!ppj
zd1;4W_fk3IS|Mm*HI)ei)}kSHn<5Canm+T2NdU;AEyW_)Y6Ov5h5Qv@$aco~WnY%-
zP4+iSPxEh33i7eQct+kn4Ew~#Ilspe{Sh-ZnIhPGu5E0$%0F_W^L)%^3d9*65XmIi
zh|8QN|J3f6^=aJWeXrhO9T@N=Zt%>3UC`zZX4q}a&nWO(D+eB?&kZ{|^^XX^-Z&i?
zE^J#>aG#5KO@qDSU)>DHpqc$}3JM5vx&Y=Jg1NnBr;{g<jOWf=dOV<wTl?N8>*HbI
zsm()3*r!JwCA*^S$<aSdNf7d|phZwY^N<MazPABnjy!+VD&z~i6`>^i#_r;)@pQ)$
zL4EXfs17M)Y*%Zzkp@5;Y%Kl&Q-on4gXSs9c@CIrua_j`weviza5N0oBVJ2GU+L0`
zX5I&JqaFilCjO*}F(~jH$HkSepm!!A({b@YieJbE_$x{j2!ta<e@n42RO`rro#>Nn
zV)Y?d62%NL!vSGV>QV%tdkq60gD#z8K{z`Iv_B%}l?e#~c6qWF1qvC46gWnLo{&C}
zIxghXFTglwh?~h}lZ`JNq&e1%Z~+ekPvA^Y&!$}kAMdfO7HPn45DYX@j==1>4_-j|
z1N0oS6c=#>cEAUNy=U{EIjGM8vi?-R0%#BpO6}b`340UG0l{ZU`EY*Jd63Y`cbR<V
zWO^|9|Au^$g6*wo9+A*_gmU?AB_$OLlt}mZza4lhxkqVRvY>JV;ucerB|NDbVOc_;
zr-cpyl%eH?-9LC%_2LCYt%=eV)CkoPA^eBbHO)R??TGqsM7l7Wm+}}+5m&oRsNou6
zSU|L~R%ta&f(Euz)<uQ$jF^gWhm6!7;Pencm>D(%253s5USRgV!v_(duG5dbT=AQh
zz>UA*tKwXOjQIfm63~!~fpl^V%@P^~su}!mN0{uo4Vw^YX|WGV_aKkK1T=^j|M*`0
z?G-rx4vYw}xbM~x#Q}kBJ$N3;-Q6ic=JM0O4I4ucSUK|CYxd8&a6R~1;UQWH>mEch
z*AT_?Rt55U|EELZ=T2;^^youxgc5yiMrq4N+&;P?4s!!p07v<>4SM(Raz4?yVLjrZ
zPY>vDMss^qCfwEjx~l}75$wDl5ASM^!{2xXBE^;6Z@*V<qk@e8#zZEcPB%u)O!?p0
zX;dQU>${r!g2RDxaRJ}a?$*zZ1?Iu4324yyQc+)|16uV`xWReVjSFB>bu+|<Ip{p<
z2mwL-eq}ciq|)UT3;No>j=Fp3WRevSDQmvWH*k?$r`Mb}!Je6!HfZ21UeEbOLYnRq
z4-ODbnot(m96Ocv`R9%fs8fc}UoLWmb924R^P(5$9Ig7RB5805c$+gMYSm9<V&Uw3
zasx;(+v@StqlgmDt^dCMFEe1x*Ukfrc><R63<4NA`5~c9up@o3ApF>2qtpqWoReiB
z;N8(IM<CBzI%|V|AotInc%vyvie}1+x2cbkK8hfMT_EGKUjToyR~{+GxL2ujGbca=
z7@%SpNQ(&C+g6$?Ure|qo~`_sc0PI5?BX#mFDnpFhAOSFPFduH+9LpTotn)@;Ljep
z7=tu<$nQ3%`YVCNPw&bhA4dc{_i(JF@AV@>;cYgME9kpaaYF&*kDY4Dqa*3YlV#RV
zrZmLD9W7`Ha8DC0uyhv&8i$!uf87O^8D$}u?d_*rF#$;%`;nm*^z7rk$K;hkOjJ(c
z=9~BIe&-EV8J{QLJnIhZSiKp@viyV$0F6ME0nMq#&*F)w=tv0**hx?sSRWKsd`V=|
z0YK?`g{e;3J~HpOL-LP~UVhWF&a2PJg&UyG`~ZXb!7j^u1jPW??H+(|C$eL#RO$9=
zT+VDG363&V3Ooy2iPay2wJWT$P`<41q<aj~C_T$I22=;Op0<*$ZBcdWP$#q@gmXO%
z3=|Fnzv)o=1`A~lId`{6NV89|$YB0pZUI1D?IxYOqWZaGi<_EgnQRGj18nZ0b_+b4
zXL2T=oB+NwSd52pWraX~CK)ZD*+7TR_MedV2Vr~qtQhc9LfH1>plZoB^miy8Xmc6t
z_1z4)*uz=ZId@zlMbm)e%or*LZ6NyCw%P&NhYrJZt;#(aBDE?sWucfhkW~*Qm(jWg
zWtW}vr5NDsTKT94a5+7Gh1GG2+pQMl_xFpJm>?xj<wq7MFos|~jTL}6T>Rt?nV+Ha
zQl$U`VeiR@-7^bf+aOv{znpke2|mLDQ4i`kL+>Kl{0gR;2Q5noM}|j25-3imnyC+S
zjZr)^qV1gjkX3Z3@LVyTEo?PHdhSs`c+4d)eDK{8#ln%mk8*2?{s!)7l{{jK?g)fB
z#KS{yvOwpKF1Q6^hWt4^=$VQPH-IFNoF|`~3U|=H*t!&I3dL$9@Vn&;a6VwrOmwRB
zgcE;p*aKwG4=yo`od<BxBw6IZD7rENcF9XQS`N9j<HRvQk@)#ms=X7=13g*>%!3^&
ze`=4k>=y_Pf?6fQ-62QhX>pjsL;gbPIXpZ6W>beUp=E~yQ2u@W&ogjuVgTL&MZH?l
z<+7)(H{W%vq<~U@4#(w?2}*#Ai?scd6%p9j6H{l9gnvrvM_|%iEZ3^0<FJn}W~8PL
z1^D9ppZ|7Kmh;{UDn*?dLR?ta&Y#(ix_^SDq_8&lBXxNfIsTGX3X@j>>o2Cv%q&<p
zLS#ABG&tT&6moN$8;54LJLS9Qb_H&Uv}4Y<Yiq;mxxhZrwHkE&`l{Sqi_^+(AIly(
z3^NtVCEG1;fFijxG6f4b+-TG7(HVzVH7l?<FoGJ-f%7xxriH;z4A^mgBfuoJKm}$m
z!Aan<!~85TrjR0#)Zn~X7}bMGx%w)NTnTt~yaS1p;WGlP#^zU7JuCV_l|F~lfEppt
z`F9skS{oB5^GPUDd+PM|dS;8Wv|pg>3s!wPiMu<T1{$~rho7fpVmp5&>;u}R4<4Nh
zdSccZ2j7K&?9>{TWnovQBTv9WoyFP%rx187`3}MbB2z47(};mXn|lm+ol!7?Gwq{l
zmpOS^r3agTB?}l%_TYm?O5Y;0{FiW{lrt<qlLtah7IY>9e%UBEkqfP?hH}JCsf{jf
zwwB$J`Db=JHFM<-Nur-iPlk2UX}EArp1%~O9iUS<5Wx_TI-k4jgjX3wqR^1BRbFr8
z-R(`)HKo^<9nDS}z6=M$9vyC9^!c#}S@T_7Q_~ndsihVTrYXx9WlD>UR->#{CHAIz
z4Vg#l1zJHN4Vtfpx^QlfF+1mn%cf?U^_aI2ZTqUNBdn9^Jp(f4;+p=3Chr$y1G2A9
zK~mwPWKGSwubWYWF+0pN)PQ`c3Z2!ga?kDVce|-wN(An>Eyni?loCRlM%}g5L{Lng
z{?c`2J=-UH)|pr5Is%F7e0_~MD6ZD@b<;cIR12D#bguW-;M-fS@y#xMSj=VT;hxAF
zjzn!R;7_G0rSu1km~ZQMp7yc}uj$NlZ=JAA{=pZr|2~h$ls%ut{W|)x^L8%-15qm=
zL(^}&f7uLJVM_=tSB<k|{NIDZCWI0caFdxc<{$6}nOjtdzItKsh30<!(dpqGjjFA`
z?ma}k!qlob&aGz)D!rYY9~=Ux`_G&mKuBMY(<~?>&Kgn2BB)8H4bD;AZXKVwM%(}%
za(|p>6OsyI(wiyC(=eLzZFcFB*k#*nu0m^!uImI?7#Awa)a(t@uc|5cVKXy%7Xz&<
zD2k^F%&**f*qZbe4+Mjt0P~GP>o1pOMjeb)R}mYl)`VQsih({VGyjXh*pIHz9?~@`
z5E1MS<&3QR8zIeTkQ8H|95=YhL<r|g{WPKj1-ziCt(t9&z`5@-e2E43BW7bL^~39R
z=1Qc^;qCew3{Sywm5SGIUf2+NHhi##sU<(UI9M7Is>^92hEpALtsAxj_7=J5I2W5H
z=Cw*mJ)WvmT-vhw-sAScE0RPqqdN3d?O1>;aX>Yw3CRPkyT-TF&LeP{EsXrnJI$P2
zI%F<r34K#L{KI}E?@|@}$nIT-aP7VlBWov{5ohmzBs_L!>t9cC2D2N9fY4kcIP-wq
z`>MBfr>PG4KMHS6#dq7K<hq!o&z?14Z+1#mrP|^=El?;fi3|J<QM)Ns4I4s*tllo|
z*z#SV6|(fFy9935)jV2hvEF<ncRsN9QEFyV<}CG_=i<wveq!T>(1@|}UB*giFP>HG
z5*s^Fvppf8l%hO%?aF?n!)eoh9sQm0AEtvJ(IVrvM%jm01J{>za4#c2{)1T#3dFxi
zp}{V0dC45U(2EJTpubj_lQ!VlXQa*5l(ECL-3(bOe8^~&;k0bU4Ps5k)|$>fHf}5Y
zCKx_C3F9yBM6(g1X&+g>pLZ}m^F(E7F=^)cTO&G+fuxtj*}_m?T<DGD;{K2{6Xmx#
zC%ZBvwp}I`B5zFoz5E|fmeUV~H>-tm_kW(O+l#@N+PcO_bNniGO}fKi9h!dYEPScs
zH@{=x5jK3=K8pAYy83FJx7GaS6xIiruxl?b*D1RP5rE}JS`4*&TA5jMv)r|9d>RnJ
zfr?)veh;cH2bR6N4_m7WUX;S8=z?SBP}PdE$Vz0?i0|*Eq5e)9#XX-F)C|Q7X3vMp
znlIy;`aw9UL&L^z+U~Xs!E_<bnoC{Y=@E63A5(qC>1@RI+ejtinfaWntF8W~-}X|A
z_G&I5!K;(K2^8@qF(Z7Yl=n!SF|>C-oux6HNGDk(-x4k}@yJsxSyO4;+ZHD86-8Oc
z?#9ZL+*(}Yg`!jm>4*Ts^!i&9c&m}FX~7gFN$0(jRIJbE>JlTtwd&TD6>6Nj^x%B|
zigJ~#oa(BePzlx7(_RRx$vtOZN8Esu6z|Ogk<z5!$kqP{Y&^r~MLnG1QOUa)8>LNX
zwUU+JGN`b<CNlq?{_FeQtT^$YHQA3H@gvSFAyNi!(YKS)>9s-)>@s~u)hXs)fQ5Fj
z;k5C&9Y%6dR2Z--r5L8KI{GgsV`3L(sh0dY*IApxDG@HV#!aU>xVXKU69RldrFi>t
zY_nODKu>aDunxhjI6j1Ga^^5Idqc}I`LJWZ<+NjIHg?1K^wUJbxT4-P?tK;9A>U14
z2f2*cYJ*r-&N<vEpEK46!mWy(VoO`<@@@y2YzQT}XVlJnv!af?nYse1Ug{zKooSM~
zhx92&llb*?*AQW4>b0DYKZQv5qdOqSU*>NYUrhBMr_>~Fw4Q5`WD&Q1cDF0ZKFt`b
zSonQ9cR6tGafC)#(!NUjhWdBP)_~0y|H-57*<;O(9=P1tZ45Y3fXsG@V<KSjm<JCk
zHAM%P=}^9Q?wN+5BM>u=9uZG+@|Kdn;cYUaSrKWG{PT(NBRC|4KDWh)DcTD>wM>r;
zMl-S2qWz?FsXv+&M=!k(KKfNADap?YTks+efdL9z`a)un>2OnRo)}h}+mzHNj+AZP
zc2QrVpM(|4Ulxgj#VlZ|>Za~{Ol+<w1Jz{tmv1ts&u%s)44iwqAz-DsHu-d;IDTY0
zcV;^cE&}+uI-q3gR$4p-Jav2ZwE~ucZ1Rtt^6x3@QL728!J;5c+(1pBN-3l0DOP#O
zx7>A-;x<BjLG=x{q?KYZgPat@7O+&jmgremlk~IR%O8rSDR$#sHRI7PvHK~q%Q{Hi
zyudLgs+^%yiyHNp6R3|KQ6=3P5y=o@loqWW=}n?#6{U8|h&)$)YEeKT=(AR=l0Tj!
ze=1rVbzn40rgmjXA&`qP`=Glx%7Q+1Xxml;b)upc+YKjrZ2ysoT1uog-w_J5=0DTh
z!HQ8|`Rcj2qhHYR#qU8~f5p#HtyHjKDUFsL%C&hGb6%T5G6M6+tUsGIpU{VuNs8h@
z)FW@=ZUbuaX1hTIV)_KM_xqz3yRz0IRvs4cGfuHlo%rcoGP^G-i3~inM1HzX7J1eB
zcycL2iyvAwRHDx>A@Yab1p!KIsl8Kc^wNjkGWzI>X$52#IJlvf8!ge#=P;o{AYwH&
zadlIHX4^vh6__Z3NQy9q58K%#M=My~)mFff#}};GC1F9(VDe{nwcxhrVgsCA;}sHA
zS@Lxqu_L)W4Y#?t^;gA0C)!b5kvs4T!?&CSA9#DI?mZlY?#@gz&H{{K6KAG73{O1@
zg6*G~7`H$EG@Tt&YX7A9!)?#H$B%z4dib#PQkd;UcZr9}?!`YmoaMeW?sYfFP1H+N
zSt_A_Igc;o0q$;~N{+D^XzDKiW2h&V;Blq9BTyVB-}8H!Bla#|$|=_V@CTH$huisq
zkE>0UCo}q33SCQ)T!EuKtpd?&b06Dmb`*Sf$hFUuVpfFd0Y>%V%^@$BaV8r@3)<#p
zUZf`cq{j8h%RK&p<dZYv^r4Jrh3?b%>W_;jK2Lm@^}n-v)!V=kee?OYdXrX0N0u>m
zXW^tkCrtjqNf%gQgO;@7biT@X@B0^2MagSva$1Oud!Hzdna0Fkwkwz9<##!*lsOFI
zxHi7Zg0A;ba3I=ug!TOAe`2&sXP&@Sd{+aNXjq{-i@<>7-X$#f_XW>NubGv)l+9D%
z22hmOcn@OCcQwhUgZCbe$3Jzxc44@7;Uvs00R|R{0LEbm_pG&XaM=;E>zkZ}gzSBb
z%j}IDC_!?BSa?B0xG<HKM~(u<O|$Z&UYL?xHvO4FZC&Zs;5J_>Q;C3wGqF=f4jFe8
z#D;cc2!B1}*LCjqBf6)N-Jt7;wGz!?e3r)rLOQ&oG1&WQSwMO9u?RJ>&3Yf^muEU+
zq44nB{?i^l5xlZuZGUX^4t;+1sV`NM#gG=WS1o8*?aPO!4m%gr62}Eb?}=P(<MB_K
zm!lkvAMDh^_-r{UfW#LF04;!PQSuOA(*cW_|MUOy2hLspKQ6BY5zX+bf5Z!YKHxAB
M`ewh?>bX7q4}VZ65dZ)H

diff --git a/reference/plot-4.png b/reference/plot-4.png
index 281cfa4524eafdd9b5f3ecc304c256346d471dd0..bbd23ea0dd54f47f577b32fb7255d7425145f7d6 100644
GIT binary patch
literal 38715
zcmeFZcT`hd_bwWWVg(fi8$|&T5Ku(vM3fe)0)o_70O=?l0s*muQbIEzpb$W6s6ikU
zMWqGM&<RyafKY@GS_ru-zVCO=@7^))80VaS&K-B0Jw`~f+1Y!ox#oQ4GoQ8BetpwO
z=it7R`ydd=L71+l2?Vk$00QB-zh^htLrVPk2fXdRudkyC-odtcqb3x*?R}(c=?#Gh
zykx!DqNkP}AP`{)OjE-w;Qjn?;Co&gqiNayDx&yw+uIN9;jhBk?;qwd<NoCpaQF7L
zT)rtCcdaa!+zK;%zWI0S{Wx~Pn;&VKkz5&G5@+{(65bOa{4%4ts<b#|RA}@B?^l8-
z+5lx6pe7WpTAG4hx>Odo<WFt!uY>RX+ou2X=l@s$Sm1+8iexTfeA!Ay>Ur%Se|8Au
zwmGs$ie;Pm#uc$+iulFVlU^_Dc_ENbr7K>xZ2wv<49Xes@8=H@XB+nayZiQ0HoJd6
ze#*FGe-M0bes5P*#M-y!&icu<+eZ?+ElrGJP5Lg<xvyzke6UlZAu_X<VM6*=E{r;{
z?_S)oJ}+*YKZJ4V@3Y(t6?U$}SQKoFIG>J=i@}(R+Mb&=gIy7oM#<kIa>7V9M4tqC
zGZTN9uUKKcye+X_RN5$0oeMT6HLFPZWiTC#O_0|o8o*?Ax6ZtklPfGuHHEsg*{qA}
zDQgpNp<MI^MQ7DuSpA?|#Oe!uQeD?kb1nG3=oqC%5&Mz2w_-i9<<`?%^gGi_E7h1P
zl;7lsx`FtXbw}Kw)!&X53!M<={q$-hz(>FIO$RD5CuV6ktkzWV#R*jFX+f+d$@LEX
zbApBB?Br+FQ+if0AJR-kk7eC7hPjA4-wO|ZAQa~i9y4G*&^g=9Nz=#V5|Z||><FaA
zZHAvUO>W){Xtig5(y~#ezx7s*?c1HC)=`A|*;#YgcUZ4;vc8~a)x40G(hE75g4lVA
zWs*->T!Cx$Xv%<->)*j?KssW!xdKspl_&Ls&c5A-n|eLLSjx<fqRb2(&79??1YD>3
zZaQ*4lye@r?M0EDgatK_1N&ui^^Z7k=9Z>+nQ^?t#bD}1Eu%MdEW`0#S4deydtsP^
zEu3Dd#L=<hP-V7}SC}hup<SBWoB_kF_$wNgOkdbKGTdh%z|*n1aMxcW(LajB+cliI
z_4ACeW4uGP!-W{LcVl;k!ehPT2hX>^m8&eo>Xk$>j&#e4Uar14?ETm#+$-E{pfh(A
zvh?mK<uJY3C#*7BziLWO!&i2}mZ)u?efm|BN0`D{XLLBvw}&bXDR0*uvq}FBNqUe$
z$CGCza+MkU&79D$a%|OWD4D*r%6@-pGqX}=lO8yf@b~gaL8QmW9XSn#%!;nc4HIkh
zuZ}<zcfyG;bLC5pupZv{WAUv>BL-(cry*5QPGC$rzPu}IUVSfdgp68LOJXcQmi*;J
zlA{w&#JbimQAK-DXvYb)B)kY}x7a6(7`Bm=Bk@z>|BuF5W!7?VyE?ERd@Ua<f1S8j
zhD2$7qwKT)Jzkji!rnGcBW~2{=cb**Fln{9xbw%A+9YL-9g%(Gd)K0yFI?>D9E2L1
zaG`~xNIP7gy9>Xywn?;0c?q(iW!I}*mZ*w68hHe=nV#CCJEn_YOzD?QOuV%?JhV{U
zaaFyQQdBlOSanmDhn@F+avP(+;}OjJ1*R)ud-bg6=gIoB8iT?d_{<~m2`*s@)=TXM
zxQf5ycNaRvr)kM|b0?{0QYd!y%@D^a|3FMlQ>H5R?&Lmnm@VE|m8dD|>#KXY;Q`as
zW^VclYQUAd>wzs%)Vl_GNRq0QV8U}c$PiX+d9Ij}RgF<OGSB<W+zXpS3bzxlnTKb8
z>q-&LFVv|GKkyB)D?sPmrw4g#BW`$w%fr0@lpv5W#yirhdMO8OmF$WAz`}Bs!Zo?)
zc;mhFDoqzN57vgq*4h$>W@8<n=qLXWbe8dgl@T47&N6h;SM?|f-Gt5edbwQO76I|z
zc{)l}#bzC}?TWS?UfZ>Lg$CtgRq=;oeNZ~pp!a>^|6ICO;qm~ubl3sCX>)OKeVgQk
zm93{JTq`Fu4dqioC>-LFsA4{jm{dRM6-5Xx@Ia#EzdKle#B9Ohg_dlS`YaHb!0sv9
zk{Yr5aX_K&qNzT9GB(Lxn*%vjTBsjhj!tazp40hAXucy(NeH-ybF2A2MT08g%`eQd
zCo!0Z4SYpmHLAFZJ;kSGkFiZBv;z-kflkalpJo8urSym43(^kS2ci<uOz*cC6ZG>Z
zQOml0{SU0=p2$~@eYEEj)%$)9U1gdYu#1ns8gI84`(u^~#d<Ht5S$#aI+v5mn*t|O
zoU7Dxkn+p@S1ldo9UN9N+9(V5;h`l#)$=t5ugOgb1SNGVN2`Q~_Izd}VX9qpz2LCC
zVa9u5&J$zp(}>Ha?$Q9`FS6rVO-d=4WWQFjPwZgA>PLfO+>1ta59%|$bB|)1m80G5
zx>OXGmDRCfJ^nJ-oGoZtUsZD)x~Z2k{=@Z4l|dU#iPlJ2Dwf|rmE-af^H_$o)x=OA
za)4IeYQU+&l3F;Y`1OJ`iO=QOTD9f6vcB0x-iyXZ@W$Uh;XtiL=i>Mv5c}h-^mB7y
z0i>A4N*-~dts~~1oYa?`udebAc@7JI`lqj)e3a8hNtg=7&naC%z}0dR+Mmi`;rTQ4
z4^cy89=}t&E}*@4!U;#YH4LA|9<<YNY*G1>EN!&Z-a5!~2hI(hV-SehE@<n87@q=6
zlf!g_;rCp*%C}8}ze_^57p9Ex9y#mD^?k4+&)U)V6Dya`^pU%Hu=S1OmibyPe6Xz_
z<Fu!u6*#1No7e*H3imgjIG=20-ju<;@P(JzF&4Pd6f_IIue~@t2GH|%OE9cH;Y3L9
zopXmSX%ZQ7-hn|%B8%%ZLAsxQhKzWi8(r@8YQpQjs*MD~D^(~DR#<Uun&z(vPw=+4
z*CdYG;11%_m@}or?yFdP^_=bD2lRA@>1vtcwF+!iaJ2k@N#6tOCm5W>WJ3PBW6XaU
zPRWSo3&rx*jz`COP=n2cma0~<_Fs?dnWF1ee#VLG(&JjF-Jjh^V(Wo6Qu(P~JbE?K
zQP0N~;^{O5NYtfwlFSUe0vUSfTX-W8<@(t2ev!_n_S1d7E8;N!7Mrq~3bK=XrMyOD
zGrlkN^muHMb;Q)<R_zH*YrrPt-aup_kp84#@pY|~_v}zR5l$V18$Ex>vk|E|;*B>q
z_aNW@WCB;reSskn^E;3fqiFX|spZu(yHG{@i<DslqO%S!`&@OC=TOt;fySA)Mjwz)
z36iP<-1ZNO%V5)G$VwjhEe42~yw;5og9=unI-wcH4~Evy0;X)(B5TmyIz^k}#u~0R
zlaHWdUmG5)E!>2_cv?W7Q%%$qkji}R#cjZUhjc{xv>5D)gGcJ9=<Lc^9u6L8Zc^2T
ztCq`D!p8c1$Y8dS+c=T3v8xFNf8<Gveu0dONqy;tF{4+b-Nivz>Jv@(t{fzuPU@rD
zhyV1usD2?g6B8|ZW&M^BZX^Zm_McQfe+eCnbU^*kos-T*6LjSfM#LD*3zSURns3lH
zQH&|2O2lk^mi<yf@Pe%;>Lg!QZB=OyGyJHNJTB;Pa9Ov~?A(T_{x7PXFfU|DjQ39D
zFtq3S^mg7n*(W)A>)`5nDYOn+X9RsD(els*^p;wZCi1!56OYOXkQqxVJYxwyyC_HE
zDaSU}sQz<llgsDm0i&4(|2K$^7x2*~ml!Q(4y}mR@?AS2)W(^NH(R@FuX}ETPqR1z
zf*lusbQ~rBB@?e1ZNnKLHY*G3?R4k#z|&C#*|o<tO}Gyn_<Jx{(}Gd6s!9dB|DFQE
z4%~|X)m1T8DsJ!js^K-eGrWJit<OuISkhBAa@4@&T{t@%4C5hJpQTs5BKYjDdgpRR
zfEU&OFb=bhA}j?}$v&I0M`2P-`k}T{UU0WxEfiEo?D{INN8VS&<y(J!?%6*+S9|s{
zd1{W6zJT)iNMJX7z4n$?izyTHjVmA4VMu0r!7TKYO^B*Ov;1vCbqiP5B~hkloZ~Tx
zsO+4W#Var!2aNXX5D{mO@Q}ruE0KqGL~~OK{O4Z}iO-&OfPPjqb-Y!Peq?_AGp0_?
z{VhCUvli|4_wvHfJ^(Ec-LZXWQc-lVAi!a}c8BTkf-~Owe|S*l%?1g7vN8Um?z$xf
zO6DWd4(ydpt#1fwcc%Gf56V4B*p#cZ3cYZ-)C0bnODIu5p-riru=<#8?dkVSQC@<j
z46kCcl($`7r<TL|y?lr1q(X6CUDaFDOON6SUoBJYJr+Vur}=hdy-nNGhtKiOd&kY4
zb%N^6NqQ6N6Mgb9IJwNJB)uR@VqC;-KJQCmQwPzwPxTmh%l2CrJu8V>Etv87{^w0{
zv=M?Mt3&^z+g<pvR$mrTEQY`0e8<mh%zoj`1HSRJwf)`w_dGj?SZK)F!XzVD<Mh9}
z-4*JwGC|V<E;RYV`tG17W)h!o58*aprUS=d%Em-{7+h~q!Z{VAAj-H!j742n%1;gD
zY2H>tqMyncciu-|yS@AwV*;xXz`Zpq$Lg)l@Fn@>(t<`s=Zc#L*WIE+Rz!VcS3h8$
zqj0b-4Wc~E0_GB<sbmtwSbA)p>>x*W%fwis7Gsol&brnYqoAJ4(Iv3ntRli@kOA>N
z%-X)A$rXj<HRYG>5w$%t8$t;%*R3`+$vlvlYBb1S5RxzcNr=^V`|qP-S_L{)D7vt@
zB}0E&vD)Dbw9*|(G;_>>4HnE?`Msw2<II8+T37>H&@JyPWj{Bf%H?pioz;a$7y{x!
zeB0VBy&>*_XphIv3+uxtVBHhEuA@j9QglqY+-v2b^Ab=EOXSw*b@Vl_jFpF$^p|n{
zeGAK2(K*xin(9zpm#J7C5tyqDF;QRn5{E;M^-^N5x_{tl0HUCIT6F|n^eaq--VVnj
z2dyxLJGDm{9}q7MgyrFm<sMHkPeoIuW(}31?_1etxuf5<W`ENC^+2jw13i8rVXxUS
z&lbGGT$NiT2figr^6fFRu*&?5rEz0XTCAClqo${><K*&Q)Q;=I1Z^sDbZ7ms>*xY%
zck`R;?PY>jL@N^H4T!w3N!pWyz7L{nzNQy=AJtvUF2rU#2|a#8m~mFQGuLhW>==Vi
zNbDx)!OzaxQE0;`?meI9eJIje*a2l3603j`ma1+GR<soLPPwm1tWhew)iW;MV4Yx=
zbRy(btWS3IYDwk~$%#*YdgXkr6Q9bB{&kPL(Ics2CD8(7w95%M53!8|$vx3lX?{PW
zpUf@Sb^h~m7qj<Px$Q#y-BEkI=ajDoB(N%1jQ35HZgu}XIyz#vNu&VIxC949Jt^%n
zMBp>vVeAmeD!h#aaQy?-gF$t0$D`SzsTXqZdE`CS!$cEwvt`9<=S6DO23=AK?`snb
z5tP6n?$%(DNC)=YUvw#(Pu}-_CDlemU1g*ay4mdbTIg-3p$tz~N|g=q<=$581)QXE
z&kco_3w@TI3T<zkq8=I#EUTbrCBiSL43|^wrxzG;ggtj5*Tl3zhAhVB70xshk{q=2
z`k&z0_7aT}P7Dv&O<!%jB`4%q*>*wE`}rDvp*9i&IOCH6QRj>yPf~Py4GxoH&j*#L
zNPF29qUj_T`R+>WDnHD-ocvYsY56)=xtq)&A7ZG-floFCJ9GbXW$T;st%)7IRN~RD
zEAdKE*uv2ir={nJ-{dSenOQHicd_~OyQDTwGj1;l@@^?Cylu#wq(6<DX_wQOYMQK7
zH0GxS7A4fB5Uy-J#Vi%9`c4qLqm*{%#5&^U%+*N~E3c%x46V^h0?=3`9w`UDlX|OJ
z`T}TDDN~%PPCDmZL!I)nE0(mF%0hXh5~}?2^-C&3a^5o^db$fl*6!&&2fJ)PC=P|+
z->fpqT8)|zbhY+Gug%KAmYQv>t;^Sczu*EsJbj1m7PE@M_?^g0kKYNDNj#xpCDRpc
zru`kY+Zqa&$UWP>y}efNSAWvHCwY1bK~bt(kiA)MyPd8-=pfgpotDA2DBQ|@*8RD`
zAw^?iuKEfWaLllBhW2#tSDyQJ6!%)3HiC=${=j~`jTkTHvDN!YLp0Ba%4)ahX3nIc
zl{%|)H^<@^_bYXFy>kkBCZ#raB-Zk<+Q4>r+S5Mu#kQ%seu8!&S7*4r?9RlojmKW^
z@tw=9pBd4x;!w?wFS-7O?jfRTWRyqEd$N0cM&-S9$yu$WRZ98#t4oL?18mp;N3VLl
zw?tXhjpSmCj_usNPK|+IS@@3;Bg%`b0k|XaNiNk#C>5z*o{>?dYv_gI1(|5CvvN-c
z%r<I`y41Mo$(w}+#aD57i~DPBaXyIfEe3q|9RIgK^?^ZumvDxcl2+s12*Ef~a6#_I
z+XRa|hUdh)AR9^vytMR^)>Hd%rXQ0pKNqEf^)3k6;=1E^)7iLeE%1lV4hx53a1L@p
zdPo7P)qEb&=!=E*{JxPcQGv6(cbk*Y7C*Uv6Jqxs;*Q_^!-q11|Jvomcdi*xMR;Y7
zC@027AUAfdAwoaX_I==f)v|7mj?G&8Do?YYzJ^zpF_dA)A4dQ3EOdxcGA52k?9?uL
z>!$2o+m2bAfWg%4*{`<*m@GD(Y*Sy-X|@SHgPu^={6!H(n!(cuuVhq?%UMqkHdhL!
zI%xmYo>a|ws6lufBF%fgtkeI_xp!q<gUBG8!IzVQ7veIg^F=??MCR|ey%9Rdm$;f-
z;hu$wJ8JtUrw((e7iT!AG8P<}!B%}wE;Dg~QvHN56iX(5R)x+gOFaI$bi_5?`nO#r
zd3F2xxqv&SzbJVh4G{8QMd0GdqAs6kk+tO4%@=06wL3ERHd)a@O%su^;BE-xm)t@+
zpT5foSMKb)v-!rL7%szsPm<h9+jCu+vDE#Jakcq<lBxYjO>V1p*t(_`<rKUr>JkYb
z-qU&hWH)!-{0Abgcx~X_{eK4`F#`GMwa{KyS*PE}bjzQ{rZ;b1{grZmC7LG&=9tHZ
zKy1%-`Hf_Gai8lo^BIqun7~!-y1D<@!G4BSOPf}hf9`t@MfYQBva?ZxdfK`~cQ3(&
z$~))A#;+KD@$aoWNc?o;W5$!*ehFyux9+K3O7N#41q)My^x*GyOdch-qzsXF6FTFA
z8ig&~&e!GGaIe+`87@gx4QGaZBaq|cK1S4|0zJdh(=@?eSCYN<S1~El)Fy0QMXLQ#
zbVyq1+N{9()&UBw(e%>;<)I=|iQ0)*wMOF2jF8a83^=Z87{}yyO>bi_fZrqsq7pil
z_O6w2;14gTE$DBH?5JW+jRCEp;gXgMaUnEPtoCS}&isT5TJ~aGE#+(%+sF~Edbb+l
zJ%oi;{l@ajhl){mwnPE5!MTXEP^|i4mKU=>G)}qLbkEvY3;O6gmz0~|MWTDrHN<4w
z)haHrL@WB~ag_hIQ^}n^Yx<ig#ISVOV0)0-uWsIDuft=$Dmj4>$X5m9w`aq863`Ki
z+(F;eaT3*!?p&bbmKOA6j|-so_G*@sbxjaHk8_7QpWD8CwN^)~Os8!rS9KOTaeb3{
zgJ1|(!ATs)4*vLF)`pi_q$Nj01wemh$)NUbXxe-9BoPo@X<sq<I@JLKGM%2)E#}F*
zs%s_Gw>&4YrFKiD77>I;HGg5!jY1PTy;5<kmGHgBaO8`aN(|qqqxlP}+xo61v3}XB
z4Jk8+eZtnYDOa#sU8n>PrmM>Jb7Rg-f%`&Js18u_q7VGxYaxgT#qQNrq3vqf9kAf+
z>A_PBJnqF&wWZwP<z0|wIX+7c$h_;<2qUdSP;!B2d%UG0=H_b-IVl^v(fev7q4?B-
z%Cup2eo!QZ^aZK^dXRo+%}Gz?-7R!gYS%t33wLA3l6QTRs-+9_M=52nX=No7$DPqM
zxi3yFoED?R5vJ^b$kx0w1aFqK_l*$>jmnhIE~>E)KW$w&>h32za}_OI8fSY6x)eon
zge&+fx|Iz?623FldiAzo)BO*0#QWm+jwQ7OeoL}-+*ZvF0a|lh0e*PCI4!g^t)}G-
zGF_x>#=NxDHf_|Wwt(yaM8fmRThJXvL0sCymJ23+<wv;Mjx2kp>dgwg6i}?~IRm%$
zN2{E5X&yRj=c49k#ZwdIX-W6oUyRg9eTZ0Y*3<wgPfp_sR;NmT(4IZSD4khXlE$N7
z8s!l_ADRT)$|t-Un?or#2R|v-pFW<g>yqFY!e~vhlwaUSjU@UMy{Cs4_IiKBGq0}c
zl7-kWsC+6fA$7Sw{I_l{#wywUx)p7dZ@v`2>PaDMQirQ{HO=#=*4W^h7AJnV7TrKt
zXL1Ji&tDr#7G3WYtMr!4!g(Q{w27o1uCNw0Oscw#h^JYFz}kO<;3@9no%Bz{2swm1
zayW#G_uQXziT;X^ma+AxY&ukx@mpAD);@huU!FGV2RntbRMZ(n#D02}K~4A}ljmD$
z5HnR7f&4OVHr@0XyI!8gq?quNVtdPKbyHIp)Atg;x7`lR#pFxzu9D8K`!xt|GC!_y
zPY;6v4J{j61Y#t^;M`b`WiAzwnvFQUc4em9h!pf~+h#b=;tD#<@}7afLZ`ew2P@nM
zxL1yeRn=Sn>5UxTE$Y2L#E_zdlg4#g4~tSC@!B&_<(oOqy@$4yH~Ebs5~7u}r7m<`
z^-q#Fp4R8AWbUeq+^kC0Ns}%lV>$ksG{mLUfeqpn21=Z}a9;Y8C4^G+O1Y>0QdTXw
zxt$N|<!aqIdJf&U-b>Zk$v2%^jiEN<_}N>be%yl_4Q~Xxsnw~QiZI#MZLIHG!f*Qu
zdv$w~Z-vg+x)X6S7^kp4sk@$BX6ca#HTW-QF?D~Kweh(-I2h6K<pS2KGv`jHyTh~`
zL({R27*OKygX?@$Nm{*6Ze;368grGGvMEYYQos9N7uQCOE2^NWwm%S3+1Si(#J$4g
zIAnitlAN_Lw<EXM#DyAsltE%y2?p+{Ss+@trt6tLDv}Uj=Bj&6S3r}28S9|lrwq8Z
z#`p|~ET@R~%Ni5E^mbD2+cD2ilYO!=&DzWwJEBN$XRbly5l+P9BUyC4t3qHjVdN0C
zjI_G@46MltTJu(Z-4x{e2OK*W%6CH&Sn^*(9VQ#IbE*F2lxpkc-l~DzK|7=6en+k=
zs8pWEA8Em9rDfL!c7(4U=RM_gP~xP`TOr0tiU(!W03919W$z(WsUz{yfsZ3@&8Gj;
zlZ?Jkq;Qj6a$T~zMCfnaz?T&4(bN~hX|>_Cq~?~YUg`-srqtk<#T_p}2lu4S*K2~~
z4$=1OCu{wR2u_4P2W^zzuUjwdX5ZV_fP_5%tI^0iUN76DPqberqN9d^H)z<pK%u3W
ze2N^UnCUGk&dX-1C;5NK&2GYJO11?eJA*tZRTe?>xwno;Px|Q+I!m6=7EtG`pit4!
z^uuKX%t1KP(Oc6w4RGy_<%e=z#Q1Z%rrA!=#d6_%du;-VJXHp1<33w>wAOl(ezc1X
zQpyt6?!vvYX?T6d?$>>~*i(fe%=c5ugGF&vcB>w-;kyh~u@?Mv^J(wPQ!;mJWhG2}
zcG@Kdy#<F(M(n1(&z3AsbyEwSE0z|WU5ePP{eb*MO?8?aS6&%|K{$A1A@vX=C>fZJ
zWhb^ZRqgvTlIqX3@?G^Y^4HJZT;ov^(sx5ISN_qTHoq%z@@RCQ0Wp9=hJM3)M4z9>
zcK<cgNvlqyFKsb|iigo|HmK`$Yo|-?GLt4YYgzfDF>R;OHE6d)5K>mJI6bb_D~)X3
z`I^sCzlqvQ-HRFwn18q~O|4iD#)ii1ofkSYixk^=->>~EHpgUbYcv>^WCg9Cs(27~
zUCuj(Cw}i6|3v!s%<}wmanE?FynupbXLQ?UTa-_$k$B<4P0Hq5mdya)_KdXGwjNIT
zR>vh-E$6d+UAn8FkJKbydfM4NeJYk|Efd%us8g_oGhe;2bPvV%%`H;sEnU{tTH)o2
zOjL{iGr~}b-_{Yd^<`P(f2wrd*PXfc+A3N{Q!~!;{*nCZ`Ja0MI%4Fl965bMv~*ft
z*msx;!Qgjj{hw+*&&Zgss`|&%UpXV#>K;B=+0*vsH$C0pkywVBXIvtIUF$H~8llia
za7I5&=<05jJ<DsKw|H|=Ko9SNuT>oSB}gZjuPRoyAw;umv%8Y^>oiSzzsL~janU;|
z%KP(`?n%L<B3FZIsiU>$uFH|BNvI+$C*VMRql2Mk1ll3g<|!e`%gp7ZOMyS40)T2X
zsAlANau<Ct|IB6gW`FbKC)_ljiFIW|vZCm$ABCp*WSRHD%5e$a@``8`__SJZ9;RfF
zUVF!~qioo5K9T+@xP?F$h+Vyd+We7XpEY`x*R6u|GR7cu{ar>AsY8MTjr=<D@FT%Y
zvEn|iFMjhkS;95l?h+|{CY02=Esbb-vl3;CDc8+r28f??-v(niV)1+JVr)zXdC@gX
zy->3*qi^s4%&?#@UusMJRdZlO78umZ5gejWwL`C5gvF{jKjvl##ao^0?{f^jiC0^s
zxp4+y<2Ho_9|sg9P}yDW*vwiuQSVpEgqHJ@m!jE5#3w_d=9^Ay=|FxUq&-IE7St5(
zDMaI$Vza1s=?`k1YDZv>qJ^egQxPLqPc3n{rZ?K+ULIBJw9_CRzBG04ProeApz_$l
z5^BAw?x)!BgT2Hm>Eif>6%#{?8;&PAb3)+pXpSoxwPYGYKiup6V5w$x;N+BVYPR=j
zlhtJBg<@&DjQWR~9p0W6*;F?!xJuCc1FnFDtKTV)Q*UgYMSs9s)cQ*;%X2R{u!q0y
z_>vm2`^VF{P?GqqqL9-bnX3<W9UgN(Vu9-$6+@onB|VXY4^{~nS00EL(5$7}*Ezju
z-rTRnvdo_MjE}*$_BjVxIpbVAV{Z@a`GW(ZhhMZogGVKST}ouKE+Q}kg0<%xsJh{J
zCSh1ksAD9)D(ebT?-k=xgSD;W;WCngwyArJk}=6)x)pD0@{%*~XxorSje+BLgv#Bc
zOF()Xk>G_OwSPJDdz26v$};W1ZP?M&N8lp$2E-aUw#wk7py$UJ7ngjbibb#BaGs9_
zJXE>{J?rpf-ze#_pAYKe?D?LNE_kauwgsOV=lg^cke!90m2S2<bbJ>@k45!4HM=E&
z^7EWrrm#XA=Na*~a-B2iSg*tATbPmvdu=oI^VFRY4*}9ySf#{fgOw_@&sn)Cmf(95
zTTA$A1x~ZXhn_%oOLj`91Eg6{pV%86a-ReLTF@x7#XZ%b&Zn4^<1Y85dt7e+g>xbo
zE-Qz;A)H^5lS;3&-}?09F`cW2`NDpyI8w|oW^bKmS=59p+HcKdK`F0a{~SBsF8aAl
zpl<e3)sMl4m4r_tzapY$4pz?#&kYX@rV&25u|}dFE6O|bZ6~*gP;nTv4)^?M?eL(x
znM_!BKYXjF-B&A2v{u5qy|>f%@4$U1J=3sXVa+MGM5(AL%S&)zPeXmC{fLE#x9wr!
zy)R$=s$rdU3@E5WHZtDsHmJ-M>akk)kiwyyeQaZ$if9t`veLnWhn^+bXE(h{AN+f>
zkT5&`(Ru-)Vmb0c65{h(T3m@?B87FHhMd2$6a<38bQO|sTgp@x(zfF;5_0>mOSgLo
zunzhE;dU+IFwm=unXfg^a_PCmQ-c2KqcFMd!1L=VUbe6EE&wIJ0ht?%Y4o!F_jaqv
zp>q_r^#92_X!iX1JNFHNTsx)}#=qwdeQiOut+O-KaiG+pClxn}SgL*LJKz7<X<@Kp
zsT2Go`_rcxxM8w<RfT?%4F5<lwOz+!=yQp%x?x+Qw9e6E$KG0(IU3%orcq7RiG3e$
zylneA{s)udS-07|;r{ZpS95wJ_}(NGiVuw0WN$u*MSghLnO48=Wn)~F()7DC%OodC
zK8}%_ihbkDE_tf5u3?euw~&^W)<m+rvzPa1T}bDha;IB{Sp3r&+3-&~xS!LIYxg+=
zo<QCTLF;BwV){mEQ}L#0(BH2_{JwdR%AHM-WsdFIJIm3bct&Qly0SZHC|6=q5`TO`
zLXTBFk}>W*O_=rXGE^AuFS4z<wY(W6sJ7KoT3ULNL0^Y<dJYpMghmE&?g$6P(`i2I
z!~R0kIAhQvJMijvu<m!aK5GZMqK-vgi>P}j65DLYeEO@`!mgZ($YXVa7hBhWWPXFT
zwzj$y!0YSU^Rab7kDH<dn7)+4O5Ec%=&PNxuY&CLW897876OaRrmNXLZG(|vJE`O~
znT7Hh4Ousg9PO=e?K=YxW%#~)q==_7cb1roaD2eBn2P`6>0)nk2OAu@Ii)qx6kVSU
zuSYWT;PpF$8|92G8tKv46E38j|Kd;?l(E`%5;>P>wE4%}&d!6&?Rr(w{;;8yIj$og
z3$b`7G8^VBy6!^=eXUC*RyUMh5=foi|07C3CBtL58m@gd_9w8CPoo7qWJ>Y6!rx|_
zX(d#tVF|t*H#fm_GP@jddn4PmuSl)bFy-o!&&<zz*7G=HD5LxbYiJTMzcO`3S2rf2
zJDvNAhS$D+{i=KK-aU+EnWIW5HD$CQaA%VYr-T0sdABloLN^*UVJDS+W(IP5rC+VY
zritk%s`9<o+!_s-c$+?1Ed!Bj%ooz-a}VjMfL2iHT_x<fdN|qUwzm9Nj9Lr^6Fl?t
zBitxmoxumo`}C=~etiT%s>(9Xsozs7IB+e61M+%`T+JQ^r;<CdSKRuGky%+;&LOS>
zB3_GC<c<}3bYrUj$fgvmdV8e}{&w`L`(XJw)X{y<C0`sog=;n5X?)4U44!O`%>wpU
zLo~iPXv(cP7G~R$eJ~>!^68EC+mpWC`(^PzwM>LFZ6hKhE29Ne)&ikyR#>F4WjPHJ
zlMLMKX1aO^lD1Gedi8*0=h^~E7O^#(GrAUcb-3=sgQh5JeLcNikAc#u29_CfF`Gv8
ztS&gGB@g7<RKp%SGfV%W$4#%P)9otWEhsTH_MrD$e;Rq1gT}VKh1M1u0}s=E_ddUV
z?8b`&n{r5cN2*FA>ga3D6+M0ZklLkv`}d0osQ3j-9eMq1v6@m8U#G>BvF*~6rzXh#
zQoKDKTA$dp=1b{4HW+z1J}GJGu#<$WERU`Ku&)5O%6xIYYxk$+=tqjGC64XM(d8b)
zTXW-$FZ~Bg9U>>*TP@YmJ%+oCR2JTI0JGr2!^3kTA|ifVaqV@R6ic%4S^-wH;olPk
zLW;6B^J@YRzK@{O)0t+pxt=CL<i>cTXVp|9&WPS5gDi6!D4iRw_Eo93s|{M8B$J`0
zSi4$8hccx=-Dv+2wXK;<X%^H>wI%t-VxY{;&dMJ$CXa#Zk2o!*%>O==8=qca*2#4<
zL3O2BgkRRObh7)yRyc6*)IgGZWonFz+7TK@ORIx*dZeG-N?$MCxo^9iR!;3e+1~VM
zPrjm6i&$6h%Imu<CML#{=G7{}VP0fo`F<t<ReSD!n{{1q;PPxvI?^ap`|VC5=tMUg
z*Xc*rhniAaH*4oh+l=-Ahy#YD6_aSL>Gfc2-~#pSOH5IE*`)_R%HOGnY=uFfysPgN
zAOBw3eoy|e#cpk9XZOwBTA860L({EbEO)^JDwJBLlk^ZWFyN?xp-lRyb$Y<>*H7pA
zu%Q+j8eg7eWMn)Bfmb{JJ5qLWt{1!M%wbx13Zi(AQ#P+49l4cz4K-H|H92UctX_z3
ztz&Ay)#&)vwzmBYOz7{|;@k-sFpwc;Ha0eg>lv6hqrtmO)w>O0>aw+~#O_Cm^IOrV
z6jNh0-*J2(b*pY`E?@kupjwbd>1_CU=#kfx;#NZq&WOOkz)!$u`iiV7^1SCMq)JcE
z&-cGaypS?4P`o!w%!;!MWzfhED%UA}*!4)+x&WAspVnVwTb^k0zk?5WoX-Q@eq<9I
z2mp9V$)PoV^hce!m6cU_@aBq)!bre_q`kc7@XX~Vp;3Oie<^=L!18zg=#y97`U7kH
z7PbP-HnI&<l50WGKIKuGjHxSo*jXpzX9m~O`YzaX`nBfuSY6Sz_3d{dyfWOeE}JcX
zyw$_<FnB!v8p`8y>C<HSKW|Qm$#3?^uCCE36(F|bq21zhTqd#kICatjeiB=_PAb}T
zrZ|&R#mYVml{<GuZ8Qq)JYoQ(oEsqFJcb7YrU_^=&YiTjsujs+J6nF}US6N({3tC#
zsz?!AP*9*vz?DNOm>^`+n-iD)!NK)+s(of9#8Z8U$AFza%U)e5zSpQMYlq+H{{8#6
z;S!$OA+kKR$w;>k1*gTa!;~@FR~R4Lzc`Z>?tjr+$a^&jz};9P^cJ5cy!g@hS1W6r
zo6=2GwckQD!4+Q5(7H*}r7^}U>mRv#o3zL#tl6M;A2|M#GL-}uJPn^tzT!HlM(Z|R
zNJA`E?EpBTo04$ul}$ZYrmkM>sb&k~)&7&wn*R%*dvbj~u{U#PxhebhOC|5=(~_`4
zs|u^da>itw@y_3jAv)V*0k8c8;O_xqb>{I%Vdco6<M`L-Q6gFIPXoB3h&h7@7cj|%
z-<+Z^uh%ncX?eHvmHCezjUNdlrz!+9Xnp((BejSI5GJzk0AT#om#Lck0#3*{l;Q|L
zU3^ez(MWxBl37RXP)Qny<P2H;rITbAIF;b9;7pt(ZIr&?5gKuBjhCpGK7YO+Ih9~B
zQtLBI{8Hn;Bv{PKYJ!22v6Q*qLV6FlbQEyXB_$)@5O5sQ6m&-#0Ixs^`13@?Z~nL<
zpg69S?%Xm*z>f|h*5;Mu{JIB^S=f{)A9=kBoiA>j)*}J{SC{mQjR2lM9Mf8*SFx>0
zR1>;(+pA-?U48>^?yNQ_Xx`+%`FiV#nOOyH#8^dk)0Ohnsn5jO)tSryRwuvcF0w@o
zV_<PFmikK`i~+3XQl%0uhn|;^u(UQeH>YGo$|ytC-;VCU<;XY;#-6dUJb@jk_NDBK
z)x|LV0Z}0PM15J>OjSdyL0x(as>Z3EQ0Tp%A2fy+`rwF#imhn1wf-xp)y_~b#ODl?
z<Es(`Rx3)OPG*JI_$w5J7jzUSndRAC{PBs2(}wRZj*q3OA$D$~T)ID%Ea&AHS&b+B
z@C-eyskV)WZm;os07vRs!y_46)}2u1c8O_@k-%Cb57fp<h!kh69!UGbC4_fk8Ld-s
z;>|q|U;V6u2dc4b#i74bgoPpLp*x!&Q9c{vFR_nF4;P23GHZNhy?|@vhn9e-8@%jD
zn1Md{ap3LAD|&!zSp%xGy`{E3M6Cy=@qFAi!C$}e-4P}!C6!+R2+DOV&u;Yj(da5~
zGA<rd)~QaPr4-dHUZ&H4b0_-G5*@sgWNaP3FV;JEWp9h81^nIztKRq>DU^Ki-d95Z
zf`GD5|JvA79^@f-N20X#*J$;vnMbgo&8Z~jMjL!*1%a@J!C>Gkyl(5w<&NWkBe#i<
zAn6M`r>?pMNFa+!0W)*m+1?BVjq%N8432$yTE940L4mV!Ws`!(P{7R0uT1N=$1mNF
z4)wb6@#b^UDKa_digK&xrct<}5CoNE0F5cg>+Q`M?9ksC-1;zh1?n!n5IpD7SHx&v
znQHUQ2UMwwG5DO{?z3NCk?r~cLDhiOug~`z%gV}z5(OO=E18wYjvaHi18DMT_ra5#
zUT<dKj{*-_Oj8Y9X$Q<M#QyiU*Y!}~S4p(lE;0#tNT`|Uk(N^z?>4*xWaUwz#ioXx
zRCAiyRyfN7<&PAHZfpg9o9=vyO}^@0?KD{Kyxvq7`X18-$C&ceFY!Pb&-tO7Vx+X_
zQwkoA+pZu%PVw9N{8AtT_9Srd)YV`oFs7+|GB$VeS%hX{-LT@IL6!$(?Ac-bH3)$1
zN3TRH+y=bjNa~=pkg%?B>9GZ5XSIg~hu$Fkw*xs_!H~PQzgMCTO+-U?mPZ+j{zX*7
zZ#!MMfaTxu+7EW!suOy9N@3>~Dp3|bB8O%O1^@ZdC?zSml5c_CY2-Clja`KXg1qwQ
z9>4$@m*HyKe1E9i0~Q-0AJ#d2iA9034bY{1YQeVEK7kTzPvZ*#c0*}^nJB@wK*V_8
zFZI>C9W%xCSREoNiv|?#c447$MQYM^1x=&EwaSgMp5wQ(P3=toee`|xSor?BSi-90
zD5#M-O&@G7Fx>%z*fGBO#a4nrC4ok~z44N}7LIyx;DpyiNs}PrJ3zaXa$tv%%bluJ
z^Nba526XQ>tU|WflN^08kB^VfBlOP~F6N0)N`0KqR;16|_&Q@er~c0oA7qQ9j7-r?
zf62rJShBKj-UCvy%c(t(+54Qbhr(=Y{Eq3HfFa;T79W>)`l;}A_ra}7Oeog6vgpxV
zcdp@&XrWLdDE0icL6ij$-d*Zjogbj)0ciCM2_EeC4A5th#n3K^(dhae8g@T8zCL96
z_xGG$VB6cBX=-p5ewLPXD7%IL&-ZUyVuHXweHNx8;M+Z9HXZd|!P9O(%&O%6_t%@7
zo2m98PmaCToTtxU2g+|V$amdI<;qv1LDYoICx)&s4nspG-YS#_(dK$vdL?daBno}o
zgv8*@ww`F_Sy`vM{DYh>eO?2-OU4VqFS!MJm1^zo+&Ru7x*)fE0`P`*UYZ55koxHB
z-Y0_Ahsge*Ti&453|SRV4<?3eu1v8AxdI?|lrQ#CvVF|BhIHWZfPWjq%bk85Zy|`)
zfGnB}C_dD?<Gm820{{=5#ayI})M#d%v-`qlWVgE;m*|bgq2ppR+rMkq23=`@^DY+Y
zCtjEZH4Ib`k62%HS-VQ_H^<u9c1pPCxeG%uku0a*w{M@Gm@{angfyWGAbu$*m61vy
zwLD}2$|!kiDnXwp;6L<><jm}^>IF^h+Q|{?Sz$Q!77TRosBet10>YR9I8cfaT5hWH
zDHo5REnvj2+fx*nZLExY&W{N`C+V7i;XbXy@p*HHzC_WT7HV5rS;+veaMlYD@?gs0
zC|i7k^GNlNx2I~N;uZfia8&A>I6FHpn9U_b2)FV44u<HajUpRhw_Pcw?qso0_mG<l
zR^a|=P^ZgEN_y3PS3J6J;n>(JiAe%TRoi8(te}xP3Meh1B6N|M)mCor`LUD66#XNf
zP$w)ayF^~sRXiQ#GuQ1*nI9+<$2oU7Is(_*0k9U}o3ZYGi19o<Xd@(aV^$W=nF=q^
zc)HtnsVBehL068k&<4n~f_5MsHmVnWe&Bt8m_4|g{aW?$FOiZ<FLy<n;go7~IsHBL
z1GwUI0AmQL0O|gzd7;(qK2Jd~_AAj%r@aZ~n^f|;ZEcXRbxsHfyLGC&>H?{9)U_zb
zN7S@V^tPFq;!b9jYoAPC=rt9enc@Y;`e>*ZU__k;smc|<R%g4c@r*T6Q=v`O%Eqls
zZ2`vD{jvshP<f@tU{EMjA*%fGgm#l><8XI@rFV_*X=Vs0bJ7jP3KvM8^@Za&cTg?%
zh~<9%98i{yFSA7+B6EDw(;g|iR^giEpEL1Nh#nyY+m|g=JXKxT>GNMazN@2#3hWHV
zPN4G0w@us6kM0$Msv7Rf+?h`Cw7VF%GP%CBzO<xRDr;WE6wi$?_n^b-5zqx+ncL>$
zp<7+TTj3UfDJ=t&r2e%faJb6K6IWcib3EvdrS*UEbd;B(&FGPCWO3N#haCl%AOHR~
zs!zlsY5AeHKx7bNmG!QyKqqBp|0Q^U!*1oEWFhuP*g!#g@T7eISy<L+f$6NmUZ$~;
z-s8t5Sl!2z-ohm8os!?rKw_T13!`o)!Ye(7rBO$@UkdP>Z2;4`>YLoVH*OA%JdZI&
zYG`OUTUuJa1rb%$EU^&2YyS}$C>&@xD^EDMicTp$8dHXOJ9fOc%mH%41HKD)ezH=p
zsj}}}c?IAh`S1|h?U0og@usAtBxu6CdvdC>ua%nnpu9BT)B&(M^;fk3;M_$+ewVxd
zcymHW*94GWsK36Dw{^1YY@(3VD=M4WuJ7N!TVM4Y3I53(**sUQy09a5OoPT@vN*Wq
zRzXEMXPUTEp1SlASrqPalP5F1Weekt1aL_;0{JU;YHCWWHi#Bf4P0<E>cr*gWWZzX
z%|PPD>Xnb##`Lw;6@eUMEjmk2t&yL~Ut(`5`|#mIIR+y-b+|-hb(geN`9?12A2apR
z18pXkV8ECHs4uHK+(!-8`0Z$WG8iyieX|+TZeg>_7&@$tiUJwC57dRsy6N&xet7V&
zvk6;SXNT){H|)HIfEw_nV33m^5QQp3nT++Gr_F#i7=u#$H6{3=osr`Bv)fqwN(Ar>
z-*=-cmF^@y<WlW=wZ2KddEsfwCC?E&hv;m~4Q|W%%G32z3NGEO#GZd@n+#<tWq6L(
zAsFky0J+mrUC@6c4OkC4-(Qls^DsTcoS18xQ^@kX6_Q6yVK9pof`Z>~2xRl<bySP%
z#-H)FowdsPg%MC(YFOC%_LSI3xz_}Oxar)P%g5Sz0O%8}3@oxN%bsah7*(}EbtsYF
zS1r#125&A210?K`fO}laN-F1LP|w{3G-n;UIT@#O8*}Nmq&ch8qh4*&ktT-xn$Wv#
zx2@9){p?Vn#s|3@IcPPwNdR&6P_LR?sS_>8&r&s~o^T0nv~=g1!nt2I#scQmz5!~S
z>U19>%%T8zvO0DZFeQ35%OS0+yr$l^CCPdoGD{n-@n>dWFy9_GJWm1|18dkq0lF+P
z0W~fUm@jGK-efF=GM{%L(etwSx!BihO@;M)l<VyWsYD|2I;^#`(|`Nybb^JQ^JkC*
zA9UrW$9>p3389Nmk7*^`HZd_#4_ck!5jS{u@$##t&yOb@S$agBIkbM2sBfW1WQ}`C
zdASdEz316pUhc)89~&J&;HO8WsfPsB0)2q7J=F@pe!pPo_VRS0b){ehs9JuY<N?ih
zHk!h3PNxWn<yv6v%1m?Nho(S6rVGG=fGWlGT%Gw<2!zb*s4B*v&l6EKfP8&dOKn{Q
zy*t6N`+y=1K)B-Fi63vFojN#{WW!QTTtS|r0lKRNs6u@L2GG*^lYoanMV<e04RHE`
z=ll74OUa8?EEy$zXEi6Y6Qo=CkHoGI8c%J3b`_|L5)7L8co-Bm+dUv>xRPdpf(0Tr
z#lmZ$qR&rt_2&)dj9DKmw*i%R3&#>i41uz71H|CRmN;>RSzrZe7WFH#u3Sv&0~*%D
z<yor=7h0BR?Vqpv)#|tAq?%$qTmD$r#R+$C-8|;AG!oJUjLSaDbq-kC8+3jDd-GZ&
z(Ob}%2%8GpfXly`FgYFP2X`a$OIw*;KFw{+(z5M>m%nQxqb1HG=}b+J&*Lxr)*cV`
z`IOR@5lBzgR}fYsmd~F*Cp{#*TlPoDuMp6eg|N5td$(AH)H7DXTbCT|toXftXMLB5
zPL$0_GbpH1o}odW-x)Mk3!mFRSm9d9n8}Pr-axU!=Aaj;(=#;ILv6bby1mM8w>_C;
zfEdZtiN1|m9e*jbR<*N5QR!#Qf|%Q<{pR?u<@eqln+Q3Y{_UF~2g4Y=B0xizcP3&A
zfGd<G0S@gj^!ahSZ2k6%n)xjmDXIN`wTSPL{OK&gu4bAS$e`dnk9C$tY88HXN`WJ!
zZ9W&*h_cEXm?Uj~#x|&FAMQ-H#94q~TK?&ZtzGl%vqQMMRW~<0;b1O<5TN@n&-P;B
zqmRCX@2V9yPMfz{ybU7Xf4(rn7?k%aKq11e|2XAoa}h|daO0G#3w_JZtce2V_xh`T
z^ZivRSKaeA)0x{3XFy8k0#a|t2h+ec_8ZGA{CzkQL@9*ZKKinY#|HdCxJk*#jI07E
z8JYHJo4L|wLmlz%R8z2LRVT>`o;zmek48MZ{xJf`m3Ie#c|HBq{)Dyq$1SqhrzYcC
zDIlB=%l_f}<p47VLn=}$`S;~rsN|q8Fl)`s%+C91V9Yj@>VacQRZT>x>|B5W&3Q>0
z#F$J)Wly2C|2n8k5&B>rLkP`M`T)@<IP@+|B9^@iG2R7xv~Pwy5gi61NGE6%r5scz
zJ^|y;`MGEMu%j>f7iL%aHM$!+>5ZPD<3Eg%E#+jEKWs&*EWWE{iDUjiKWSE3!h1)n
zf?5m@ILHrIkP$MwP`M9F!{=Nf^XbRiod_rYb1%S`{hX6q6l|ywN)RY0cFQCgAoO9g
zjZ&58fz}X&VTt$v|Hzc>?d{Z$H_w}ZY_?=w?sO@1tl>#?A;3UX&0=-ia_`tnA>_tS
zP-*nc3{_FPpYo_HW+h}iJzxL%^XL0htqGwEpab<Ry(J-ajpeiqfNm$Q0vNM2nq^HR
z<?L+r)sF-bE;SkCo91*R12t}V{84}fxL}<kn9<pxgLX1^0EcB>R3D~b>vy(6!(OHW
z`BV1J)a|23kGer;K0Hl4a9Z(C2{}C!nPD1enr#p`0h)50m}8tS&N7HR6dL*v$f|)r
zQmAw7$?F*c`s2n&Kn9t;5(~iW>%TzqDPr}Z4OF34{J_cefd0M)24gls3S_VC<t7yw
zM`D(KQhVGv!;>RGY4pzn6=sO&k-dO5J_GY6dhm=*=}~=UGRHL+{MHkDuo|<|?=OSv
zatE4n1^vECJ%II-Eav0SDo~&pE064r1?FY31gf=_RdOGIse0BF)hf7bu2j+I``9r%
zR+w=MD6b-6C*|y$-<Cw$e{cO(5k9{sLpBBj)QP0gN~d4HZ@{*IRN;l(UIPW<hq+i6
zdYn<{_&4AxI5H?p0v|QUoZ$gDKT`$_yaGm*2s$8hQZ?Yu4UprjtL`<vKpcDpv-pb%
zrdb;}L3)y|s8>0roP56E4Y%^lhf{#PASQo*kCgp8g(FM_wa8P?4ZuAIrSBBq)dg73
zVJqez+E-v%7W-G#W$9?g!7NTvRS{2Ep42ch45#kxL|I==8Qq?aXAy-6Q1E(h>jRb9
zrHI1ul;SfQ)6&U#b!$I_{x0z2%~wd-gKxm(k`I_KQ$GU((@(y7)JCzDl{Q(0ZPtZ-
z7l#5RnGI~$rV@;x*Q}%6k}p3r%3-<CgCB1KFZcPftzAKH8QjgRhd{oGH0<Y`A?nLA
zwwgtvgXy4DnyY7JDvc?X;Eg}}R4Ub<RZ$ImSmA_l%BF(W3J#c<oAZHOaXSZ0J{Y<X
zF)&Ne&Usfk8oEI{lnwH#$wsD%AFO#d@rB)kO`-gBw_qm3NVg9w8?3pkgjJ`QK%j=t
z6$w*0AofwC|C|#PtY9{!{$d{uvgUx^_;1Zi2{{8x6{Ue&A~9=%beY)I_ZTK@>zawy
z4_DT$`C$9ddN;OWUfFcodgy5g##Ve*62`oJOlY7+R<@M&gb;8)QG9W;Rr0&13M$u8
zKVU(y#V^B!sJe=@RInsmi&+1Cl=b8U)^YDL&RFl9bACivqKFMk&t8GSn+a8?R3sHd
zFN=G6!*XENM&}x&U<|Pkk=bMebHzsUHqNrYYyTm|p`>`qKFI6SqP+MDA%)fp*@+RV
zu)e0LF4YF*f9I7Ur{9>!pHEqo%*%YRQZ3yMR`LF`?ZxLf-CS#sr@9=E>kF5s{^xkn
zV#8k_+zv%%4g@Cbiut?J2eeZ1!mBV5{P*4WO;ZsHcJrf@od5QVo8J$Qwd4ICAl2pC
z@q^#CRWJXSi`e9;k=vzL(Z)|O>)`qqVg~<dIs{^KJI*Z6>8{^LjED&#vjW3YPTsER
z;VAff-wx}F=Rx1*#j9b-ht~&PV4b?j;?A`gA+ZHiylie1<_AhyzxtX<{^#;q(b~kS
zx&Ji(J`Ft1;n%ItNI`Hfahu=w|6I<*7FMY{nA^=>;{cXsbqmvP|1(aIo0gZfZg?DR
zCgdYf7!ff6$=qB*a4i(JrA^!iYZGlRO>6TqG5wF#{(a{J8iMYRD=3fnc>Zg4_t1w4
z{QAj9uOT5Q|71*#*llpH2K!AJXADNi%l7Z>-4HJcaL2&4gqv^^SVLgid3O9YT&hg&
zts<$zACUh(U8BGA@R*jIw%%4s_=zuw988;7;XYwmh`N0Jn2kVyHrB?Xu~H~0I4{e8
zB#Gkkw<R|v4viHCMOpZ`ysfgl;E%YrS$yYo%!7~N?9U_fr>alZJ&)YyZTru%I?03V
z_}iT|a^C%VgpUXQ>BxLv_~$9Xe>)Y!@&EF~5AQ#Yc5z`;k@H~99&5XH1gzTJF!`nR
z>!0rs;frr}|M#_fhw%dy1}0j`f#nA?&i;Euo;o`oU1?yVLLom8O~^*W(eGCN9U4{y
zoEnMRta2`r{CC&g(Hmh3)RE}TD#H$!|LjX=?MwW&JtXn)pJx_;1%#bv8d3t`-?oGG
z{>yJ0&$^;3wM;@O<YBf+3+XV`f7>w6vFDFtQ#^#4x98%2mKWYWZYuxpzyIEW@t@{`
z0s8mjO|8GHv{~Q&%eurc=EJ3qi3>FpWoEtd@Br@~m_b<B{M{qr-^4hed>Yz`e;}Hg
z*Pd$}ayS~RrNI>%kues|sqy&u!)s>tr;gq`&Zlwf5a+W!zwQb@<J>c_XRkeFRXKDh
zMF*a2msj0cNXnVp3|3vtR9_!j+Uyn2jnnI)BPodE5^R4P-k`hppJ%gZ>;3a!mWw9r
zc7IzKd*q)RO5Vh?Ryso<cQKs*bVNk#pVgKBn@(}{LN%NAc>kQnLF$VJ+Z>_VDpu<t
zbXY^1$@V?kvnOO#_pwdZuDk7r|CAoZrXg*h4L0j;r4j4lY}2gh4!dH=I=FH5(zCK;
zPJG`1`M;|q3oO?({`%hkrk?$O!@mCmtw7vWDNxR{s+`egnK_IjI?Ufez(TD$$dC4U
zZo~GCC0wm=%ggtMuVF=2kiRc8!5KUf2m(3&^kAgN-GhNfG<uEV4z))rn|7v$EB~_J
zN$;DqnAwTWd~plXG*OIo?8TGje~R9uJNJ!y^az<nzwZt0u6$9Wg1AqkaM1n^(wlc-
z{yR~5c1qOkj5%~q=WViabZt@f16o`cS*T!<XkYV!){~mI-bq-NGQ=TRCx!H0WLw%+
z%M)Oxb>=P(ivD3V&c)AV_FU>AIsOO^jr-$IT79e$w{SxJ(&Z%)j=#T<XZ^w@8>wC4
zJiR%6PAV|pC<Php7?+Z_;6@?zr;`6RrdJpoma1-=UK`ohIWW8Vf3^1>P)(*?+bE1}
z91E}dDk=gqBf>BWh&1VzaWK+Uq)7=#2{jQg1QJ59q62~^NN52oNCJf3LI6cUYE%d<
zBta>WKnN`a5|W&U_`d%)-&yBd|2b=YYn}g;tmTrG+;#7J@9Wxo-+9s|JU%e9TELI~
z=lUR}nqh7?HiR~Z2XaFZ^P*NlF1RVNF0d`PlBTM|&a`*gr!;@BnVGMidca3_U7+uu
z(Md#xZvEq%YvBS^67+`z>YlkC_9VA7m|8O2*gHKPJs-!>ryQifWgZ^oT82X#V$PE$
z5&{34Kt#DZy5HEd0eMkS?y`(<R9L<V2+GO>Ev&REslQe*0Bh`B^m;f%ACwKs`qO6$
zNY=&uO2-F!Nj9!B<7Q7_y<7egI3uhlKX}KaGW2SnNEQb%Yr<;A=UqOA8ygmdkE1s>
zD3PRL1q14py^A(3vQU#K%eZ{$ptPpif54Tm9#oQOZ#@frass&Z7sUMfXouz8y9oaI
z8mL#eP(`IVD8>dVWtln2BH-MvMT!eKvtNc5&F|$QG0Z&$9QHW*UdTVz<+fi7kgCKF
zK3{3|yuE*;@|8SN4++}uZ!{Z4wC~kdzCt56B1ADzxG#L^MlOjYEIuz>@7Ju@xo^i}
zO|e;p8AbJ`NHx%#ebut0{uwe-fT#aLhw*)T+Dz(VRo2^<-gAFLIbr2R@I+rzZgUJS
zH>=_9pzF=}=GxmYe?illC34g_?7xZMu^J|@1P)6X9k?fq(4Q5OOk5m`W<6Bh(fOiN
zf)vf8XnEU>4co2@mddo0YdAVUcm}+}zQfybm;c3m>;DPcmw#9PKS1K(@1v4VZn7dr
zZY@4~_kQxn)QW=OAVCzpY(uxabu)B3_3QE-?q2#6I)jL!eE}Y92fRLB|1#}GRCXSq
z9YS3ZeBmx|Ii#`8qV)r)pkV0PjJhNs(Yr}oocY<Yyjt!A-IXMBVj`Mp@<B@jcc@lE
z3#BEZo0<$Givmz73YX}ro}-3>3c@#F;@1aBF!IYi8ccrOLv$$WdCfY$IIKg&8xJ*p
zVbX=p)11NYt0PwI_BawovhtP$!7I4!^i2k2>z9B&tGKIl9oMJ8^L&7jK6y-0uagVC
zU72;DkLJ^XbeJptiaK!?T<p<4x!yvd)&JE7)@?@FHKC*M_^cJ(`U>X?A_L_r|L}07
z?_DdJDk+bc>!n)W8!EdZLE=scd+ec&%V|@g1ul6cD8oNMFR84=tk9SBkCVs*Q-x7Y
ztt*K-^$!%o3LDG6X(rTpu*W%E`sj4;mLel9mqgw5j~se_AhlX-V6~6{L&V^oM)wQo
zd}qvMFCYpV;TGEez(!mV1}&WZNH*De-#yv-bv$&`ly5c{r%+_+SmI6jrnz|u%WcxB
z-|ADsa+Wkd&))xGl56b@(W~F(cG}uQ@&$P3<2ItQ9?(LzLv^l%<Pf@hv(+n@*Zk@i
zt_e|S;$B#84i4MB6MPaa{kE|o>4pQ2{9dFuX_nvqyElJ%LupfEMM%pon@Z{Z7G-9q
z6{9-XDZ44co<5Z{yD5XxOM=4RekryNYN%1ETi~ite8>84lbm+Xl~<2ca5K&t-x$C5
zOT49bvPyLrFEqP4Ezi=2zW?`8%Y3A#;3z#j8X4{<_{_yz0bgy+0p}E^vdOc9V9W{h
z_Af<TcY45v?k&v+5sWs=dYsz)$8$Ahv=Y&Hfk?=Xn|jVraQSG%7R{wtM>kM9*WN8!
z^<}eSzdUCo++D5eg|cz8F<+lE1|gWm3G#AUYEI2J;{?SWd*{4a<C{Y&tcLwV?+T4p
z7YP%?jZOA)<1Y$43;R@B*dD@k6?PxJE~LNMlJVU(oY!q72KTUz&1fv{XA)|5oRkx*
zX5xt}&8WPalk)yP4|ZO?A@86tll=?;L2|BsF}cLM=G|g^ky-pz*`=z0r}ERS;6i=&
z1B*}ewkpO19N-Y#B+^PLo}0IsD-SJw=qe#e_iVcQ_*YwF`I&|4rWe0c)zG&W75N(i
zxr_J+mU3W-Nr-$lw|l@~l<vL(ov{X$K(5nft{m8W?AIk7n9r(CUaY3|nW(>(uQ|cU
z_jQ7BAyf1*AO|sLCwX`iDPO(Yo_jyVSf6lW0}EpM=1Vz$e_s}zFMS`7BOJ*TQ8x(W
zVOFW;_PukB@cj5|@X6ght5R-3U~ZG|Q#k`w=)VIjiR`SG6)WxG9-&82_M^fCZ@ChK
zw)m#{8~>xxjtxD6=iFcD2YNY8hG@^rXyUL78Up|9z(GG3O(=@x1_&(d>Kl)$x_Yo6
zi;%)5B7<<@D$zR;U=Jn)31BYp>OAh>>`_&S41r$|{Kc*JO0I*7hTG{*M3pVG-&JG3
zKQif_dtPCDyy#?k{m{~i$uRuecPDb9`{*bE?sdj%eWZjpJE-Tr>+t9w|9e`UHK5i|
zL!7<L%r^@=z@Y|O@z%R26j`0!(!d5&>+2V-a`nD5Y`5pX)C8@(=Sp~}+J8;6z3P^*
zgwF4=Xlf93bHT(=B5wm4fPLVV4~QNEh&Tu?3+X)-C7-`^+u86>bB{7b+FKFwJxoFY
zy+s=cAX~EJoGif-(66M!jtYNjDsM1&8xVR8t<PqMq1^K+ppIW@7K<`P(hc46>*>vE
zfXbw{f`VQV#>Hm4;rByD(ZAB{A;8hW&NW3=?gZn}5&4>C{98^fHUyguJnrQnNLF56
z&gi0g=z~Rzz^Cypx3gWfT&9V(B(h$${Qm#wDFa}xZ`@I@Lrv!dk8WDET`esUJ>w!s
z=Cez~vGgdSjPm)rnQh4jMB_8CvzTRxZirMG>vzR?N%)OMBgO5+ZwME1=YTvb5>&gl
z!A8lD7IK3hU35)4ZJCS$30@OY2p-{N2ZHJHORjbP*!sQ`jKW?&d1|tA$?aiJPNB@#
z#Ls~kA2GQ~%9?@1g}{Z}``ifwa#O7tv{_Bwe%C^QbWw;l5GW%ICg%+V=ES_>l04Js
zvw|6}4sb$a9?h;xAmP()fVg=<xGK>Glg|tUdVJp!<*q*KTAHd{+@!Y`QFF61Y1}e<
z7jmwPs+DDYUsndhIni!<F?+^{a!}R6i5xm0%PFAs<uO`3fxhetYkQqu#Ff<043mws
zmXern3Q^sR2)a{t7+ycx10W@NUN;UmdlD{L#(lEiKHdSiMiph|kK5p6u5S|b+Rxzd
zNPBXBc)O1))LrrieDJx-f#ylB9&wxU$QFgTq-PsgW5lK+wT3%o!a{HBV)Y^a&|CCx
zi*(C;K?Hp-Wxs)V18vf>s|udq_&$EQ>QIxn<U?~*uEkv3;@lF~M49<6p|PMH6WYub
z)N8^ziud7NVBR<O9P7(-MF9VAsIL`~&Y374{Z>cUI|g7Fv#Q;mtT>u*!oL}H2JQ{-
z>MA24kXF;(^rKmKa>+$fM^e;YKD+wxDEd4OfkrGp=2|;WpB<KTqzuuU@!LN$Kd*(r
z9jf9J>ePn)je6=fbQ!^HoK>SzohU=?2#*_j;;^v^*Bh&BOey!<IpGgo%uCK%mS3M0
z179sTCoHFxtPN__&u&wG!uJ@tVQ;<A)vs>F+LYibU2LPwKG%B^npMSlO-zhugMi9^
z8!GkNqV5V(CiapxZEU_7`Z@vOh1*+ea<`5jyijlwe<fQq>9e5e0Z<z~Sh-ir0lLi=
zF6Y$3fIX|O#EgXWfb$&uya!Segne8bB{S4GAdY`=hwhu$9W_`%hNdDv|K?;i#m~XK
zieWLiD+%hChnBE=sM@2O`tN&Ke|@HkT#LaxZk#8%vi>=D2G|JKS2Z$;S$yE+TzJFG
zLiP{dZaB4Q84fu-#;+IN-154niEnGNsX^iTse@e2P+`dKk1hnJzpUw1(Q)*Eo#fk1
zr0K`FTG7`^t41V-E$ZQC6ZIUYfyj8$+{2Z6V{+AF^Nc+8wEr348-~#Lb3d8z_foVf
zY!A$uU)0-@#iP$G&<wKfu&voCduiF(GpWd6xJ;AbnsYL$r^aZAyz)yg2Mi5x{hm?S
z@)Q_mOIII+zcie?OsD$hH*!u<58bz7nk0_e7!PHAC2|eueB-S!Oa*9A++zQhr9zx0
zc9OL0Xv~rE&2Wax^N4lh8f`Y}1Dt7Ip~>vnp)qPy>*uvt+>;s@32Gd*W<D%Yr6Ht<
zg$eFy-r)BXky{IZ0_LdRQbFfQ{>MzD&dgPrkiHdgEJpz_++^<}U_BGf91_W~>Cd3o
zdmGe)+pH?uoblZa)dj^HXAPY&F;BgB-Y_w)dpz8ky|&1%FsrMNg0CzIY_smv=Y^I}
zydT*2S$lnT*H5YZK8l8!|G+XJ4d~7{6m<9T552~h4SEx-(d!KqjxjEJ7&Zjw$GgM(
zx)zzmqn59b{aqPV`@YL*1`o*TD3P7j-*zQ0ten?e682gRj7CllMiE2*j?cwNU-RWp
zR9iR{M~z9%II!3l;wQKlJ(A4ZKv&;6D5nF>Spd9IULj8jIx_vVW(+4!^L~sbc{^Ly
z4x9PN(;o_<Zyzbb=#xM&tx8gMb>&^_64>8j;z)UfTsfFDp9s(BZ2-UaK8`-r?+<3(
z&YGzRC4Wae#V!E<s><qbY_d{X-&%9liKTtt6VpZi_;;#lN7cEODc}O~<m$M>_ZXmP
z>)A~W>cu<Nd~$y|b8Wq2g3+6Q^*e0T_Y+I2y0Ubol&eN}BE2YRizl0*o?5LpQ|M0y
zgkz!;wYEsaJJVjrKNk_W9sh%E>;q<6-w28{MoKmE9F!+1OEr&0DzSL<r~-j1N!{D$
zJ953K_?BHnFmbYu_LXl%A^GF;4Xkbh0mj=+&UF+HSAI(nj%JEJ+=a`1_VF`GJ7f4p
zK(Jr>ojLp&^jMM4Bbm@FWTZ`k?`<xqY|i3L(M3m+JzP~iEK8BiUGw&I(w4u`D)c#a
zL*8s;Sh3m_bol?EGv}b6uEPZs`U866zJICiw;cfLr)e_uj=j(461iC+;!}BYmHBiZ
z0N5GkX{=l%ZqI?f^1qtn|M6cX?~MBF`)8etRf0;Gs~>VW^0Tr32_Q>M(M~ns%}hjs
zTv*3{uMU!rwZ9Nxp;p!HKQU0Qv}{x98?Gu{nZ%>lxI$c%at<t(Po0#vOJ8S%Fe1YR
zV~mzM(#MEN2>s<(^kU+G$yQ{Zx8xH1%_*w9C7|^1PuGLs(eN8^2i$11>z7%3-NN{%
zqCen&2*~uu|G^D`ZAZ`}dPTwX$s;)xaI<Bc{jD$RWN*uh*4wA2=-UoX?zoxlXw8aD
zsN3J~OGgpS^?;m7+{^Z5ea1TCMg4&b6{RFW{eM<3%6$}i(9JTXgvIbzsiBXe=@=kO
zqA-KUo2*(!8r2rH8yDhr>vv7UfNdlOaxq@5u^a%XGZKWS1giA#&AE%;ZgTu&gsV&p
zfU4it(*RGBpm*+?7T5FFL!>ccUP)skkP_Rx;6<nI13)nMisTA>?751fPo8C!S;#sT
z_tVkDnt#oLF#hLoCBUk?`h+U4mG22>xQK&+oA}#Ec|}$x&7NIvcCq8LXQd<>L)^S!
zP%CfMHXJDYpR0MJ`1ON1@L(D6+z1fNfUV|}IaTnn)^jzA$CYi^0tdocLmv2sig;qo
zS~61&W7dB&R@nc+MqT8kTIVZSk7j5h{343|%2lU+hs#GG7?`oJaas3JErHzITex?>
zgu}{V3;#$*hL`@lC4BJXFKw)ApTcMIZr>aB&Y}CjJ$tA3F%_Ff4pn{`oyX*PbHs+t
zA(5ReN>i_QZD~j>7MjmrHRy9)YcUYqPLE&-yZh7+{XJ~ZgK(|G%lQ+mkp`rsYtdw9
z-*<AGwazqKxeLK)J9xa{el(T$_8!-_SHI0Z)yrA({y$C+?+vA}ov_*yzMIQ>jzm=z
z0|^Ij9NLrLKkiUh@n)f46Jd%F?q5G_2>}4VEpGp7gmQ&n<Umj}e_bzJDmw}!l5%qY
zO{viy)Ffgraf|T~9x<)k8dl8ml$^rtrM~^{+R(?xe_~~ak*$uS*J9xQ@EcVn_4)PD
zX7Z{}g5)@m{p?@Xyg6GHCAE;B@6eu)9^Xp=rDrDBEtgwH7a9%WgmY8#F9KUfO3eJP
z=H-RU(@zTN0f2L<)iiNM&FyYw6(A**@&1C8n<%*#aiiZGdH&^&=Ftd93zH-~F72j2
z=@mV(hrX3h-n<(KMN2}<e-LI&Ene90Vc_3)0fbj-N}!j*S>2`p3$dSOI77DoO~xp4
z^u|hvs}fn){cS8it5DtY?~x%ckPErufsbL~1G(b=q?&|G(G0<x(U`ah%_!vmr*M)7
z;XV|fZ1df)Bwu%FAP^&Xn>&%gt+9zceabujHoN$7rl=MeraE!)UvMfHnmpJO_Vht?
z$Nq(se+xA24FsLU7lFBa=qA24y&|yn$L~c8xzG8FTaz!)r)!aL2m^qAuW)+}+H#$|
z4F0w$7=0U~EOpk47)9zN9u916AP>7ypQ)PoiBAa<H!Oc&v*z{#bI?Myi`2-;!L8;G
zV=tQD0-oXGtW;9{cL)VyNMs<qjr*SNOxYiW#`y@xxsU>md=I5|DwBqMWumg3dZN(~
zUief~JTn|B--nL^0CYB2rt<}Gb80n!lTT^iF|`k{KRW$QFOr$NSx!5SCqG*rh2!K0
zC}FQLkQ(@67rsf3Rn0#EhZ&%3=!%<@8`o#_Q37H<wuO`R^}k1Nm=czLu?*reFCVXq
zc@aQ$KcdjN&6(rE28Zvz{ylvF=%~372+gXe7I8$Os|U%^6`a$LTFkg9`hNHXz~Jg$
zstrkF)1=-4rF4Fi;VQySkaf;jKJcNKuxHnO#JK2<obPGP(QH8LYL*|rTfwnUz|J0S
z=&m?Os8#3mur~5tc!Ogrns{EDJT`3pz_V=@`|*BMlf^ZAyO8v%rX>&X(?=;MtDy<8
z>nlijJd95eR`Gs3hESgS>85;9lVRQ_?j9F@Z0HXw(RH6RWm`F6>R`<0-&fo+xp%I-
z<q2IUdn2pKl}dcIr7C-PR~kEd1xR#%;5MQcbDPE7<=)h4yn@0?pi0(S7kp(s{7L7Y
zr31x@jTs~usS&d7smr-DA>!vMrp+TS%!1e70DDN^n(yBPWTcs8A_=$2g<KR*6usfz
zI%iyEM~0U3S|2&6#8@dLl!@<hj{tGjDRN0}<9gaoB<A*=Gtqx4Hj0h<>p?HkaXqK!
z<;e{EWZ!J@V+@CXj|(9VT!Doy>{wW&lZ{su<2%1z^(b$8ol>}-nMWA9zejyih*DH2
z($sQRjsIM1K67VGo=IC1z;5t1Dfe^T=Qqu;K+ZzzFBv<nf*LR{SEop1-d_*1JB7sr
zW<7ThNTZ3$Z1n2!=xQoY!hGe5C+m@b63WDyEj$rO<AJl{*B)>$(M`9_glYpp_4nl2
zz?Li}1s~@+dW%|LF)eOx9X)_A^$rolSUi9pKAWh^EW~ui*;W-j3qc-jIvgzGK4+Zt
zL34CcxZOhY001{Cc=UPLY5{;xQAY1_iN~0eTzvN0)=o~=#>PBo`Q4Uh+h*jhc>gbo
z{A)QLgc1KEFo2S8Bcb$&zsQey9FSG4s1gx0Ninjj`T!tVTt$EU?_LiiObt#Cz1r&#
zxap2QCGt)DvcaFqE^IH$Vx+TRoSR5&(@c=_oMj)(1{Q6H;iTpml{YCJa-r5EQG#@?
z6&<y2SRUbAZ>~7A)Eo(dH*wptMjputtWV8yz$Sjd^1MZQAPOiTP)G**M&t{=uE4-Q
zT}Ex;-|||$iu-=U!3Y2EWdHx_h1CD+$^QSwig4e(b0*EdS(J*%>t$SogSDC^P<kp&
zFCxbm)JtxO_jha|S-SxQP2qU-58mltgXw--au-uJ(6%U$m<U&z7m!B)GDCEF>p2s^
z8~JID2586UBW-a{&Wfko=`EzF%^BMi`mwi3JDUVL6lUrUN#k^f8NbLHDo)p#J~#;h
zH0aj5vEkTzIvuQJIDpPquh{|Afs3@auxGxdGzkv?k#|$v@?J8~;oN%ijC4D@NbY9-
zSF4Es->S&}@^(NW;7=0*%p>y~q5lf3wn2aznpvF)Oy5gi<~q^!T-%QMrI3iB1@e&5
zXUCqok{obRPjT*LRcp)WQ~+Zq=wr%#asUNNPOK;Mcen`QoahZ*g*Y%CDo3GXkB>)4
zcNR<iCs*Io&t?^UwJbzlhEFV2<-KNQkmY7ty7AV#Rn&EJXmWHc4jsd0X!J=g2srd`
z62hU)qPjiA8!YEyKB>Unf}2L80Q)a+9hkY+wS#_`h<BkC#=jiCY*{vJ%-@Xv7Pw=N
znYvl?q2p<AdN+{jI%Vvz9*Q*|{B@Mj#3vNWph{!auC(H6<GNoMsV+c;kkD9tzRAlH
z1C(71=i*BBq(!2601h6h5Vu@W_qZ<a{nL7uP&JSXJLbE#NNOQL3<0{>%7t7F`+y1-
zRE0<sqE!3+_mZF7JM`nMN0qd+<tpN14C34na_nI^yIwHIU6P-I(tH3<VAChsVC3AP
zaawEMpub1rL;Gr|+m#REg(<%uA<ndr$O_YwKj*~vCz;Q@gLwdv!pxdXnx%{8v-RrN
zJ)VzZE5$$DLYGje<o(r@0aWE-c~%$~!B<6;T;wi_>&0$!Cp}J}1B2LhMLokx_KG{O
zU?gWW`FHLv3Lj*Osyi(Zwt|n`nW;r(<1VY(bw}R(`_F)sQfYm{k;Fw<(X1;Pni0MJ
zK?40uvpFUY;G*TJQJ$`7m|#KzllyEpo$pHd%(W>H<V!L*)AT8zJd3cAk4Ib|k4Fb$
z?P-!dv|7%JeUiR2^RoX&clBSM^8d$PqxrYfsPg@^<M>ZCWHsh-*pG3-rLi{~y5yCg
z(5wXfs}%y*28ew7Zp!Ps2}U-+y_tsE`qx*18#PL=8%d(nzu%~d3lZ&q>u&!83!r^+
z<%OpI|1R@CYUTgsd0f}Q$9irl_kIYvs;a(m*ex9F1C}3fMTcg}LccTH$+8G(2T!$3
zwk!~lxL))&Qg{@9=X#U<I~4gH$Ug)`uCeVQ%<$5DDE$cCfK(G0AdeXyS;n|_)B}8p
z!9drd?BT0RV<jU*q^+Pej=A1FVdLGS6YuFbNG|xQWlcQ_eg#Kv=_c+eP*=)Qv#vtA
z;OG>S)yLddQG9toeR6awz*t{$@j#)jwEcG$-dX1DHBbYsD@7xLg0oWon2gaiO)G(#
z<VCNeWkT6M0N3UOg#0D()ZzLr(`skE!k#SC<x0uFSfA;-H!*dRO~&^Ku*@#G%Pu;t
zvX9w(@VYQl^jVTK{TH*#9Og*OWdmn4=eT4gd_z~&ue$MVl=_(s>Q?~|7RGpJq8qv<
z0E;5?R_Zvk!t5ty6ZO;SMpCx-3Zs~DoCQnFCf6g~$MoVE=9C$U>|_CS62@qK*m)4c
zysP|Bn#Dr4EJ!nHt90cWs3e9wW$)@$RWuKdpJq^&s2J<@=5+%AtTbI;8d$1UG0_QK
zD*(?uH-g(^rgSj9NBBrtPJL!mvB^kj(fmLT=FK<q6wjmjSm@dY%#3BYTNBiQ*~NC%
z1+xZLYP<zg-U4NrJ}@djYZ@;UNLHQ<+#n+`kq*qF<ZyG?ueHk7b5ci>ueSB%k<td4
z9(9GS$92x5#;lKG<dH74c`sX|!;(c=-jfra^bN9Qr%-NI;99E~w-6IXOv&k=&JL?7
zB?oTkpO6^WVx&#nw40oU_|etLF9y!*<#*s;_jA;Nw~*xpkuqtzCY%-dZig}^;D{)*
zhJ1xR*qK4QQp1iJnmGEgXC$25!|2SYina&4F-yZ2(_38IjU=x$#TEQ^2$2!i&}U?M
z8AZu&y2z{zUz|J(e2Vr($zmrBfGZU3M$}@_jCZSD$rKs$q8rR+$lm_x3maJ4M9&Y;
zHdZaaP%=ex%@&>jT1%{C@15r@L)!<ICS0L23rAfS2Jm%53?s>E$YN_TW2bb)sB_yJ
zrHgst{HCV({CeT0fs(&4tYSK68R+lq8a6VrS4Fs-C;si&R92&}z_S-6=1_yXgeA{H
zvz)KJ<J%;+GKe<z)4e1NGquj4SQ>s+#^!zz>kmX8x&qwpIAwW<M|Werl`ThaT=I+V
zHCd)oV`G!J>5DMPxwtZFqOS^(Qgll1=oc;*dnB3S1%H==a^G4q<>g^Nu;i@6(*wJ7
z8c|=FHBrI6Z7y0pO@zG>mr$4%;i#td?V`=L)O;9N)r#%n^6oNuYHWC3+1n4{TUGOi
zkC9g|n566Rw$}VgVs{<>NogHua3V_@n0N(|g*kfkYe$GS|F@E=P|?btOUkvV`Mb-~
z$%U&8`8SWmT)@FH7$isI)xa{noh4Rg1_O>tpH<iGTY&<1!yznTKLyfC8vQb(Myf<B
zU5B!-s*;|pf0`iiNxJuBJ|TodX^MM>L<`C^F(n8%&ie7-Mb+MM$G>Q=b3fKd4-A|o
z3A?`M`V|5RL;iZ@Jn{VCRmt216S)^^9uKr`y2Kn@xW%-m+h&D9<?mh`dhUB^C$yYA
zizYc`k35^NwqKrRx_OO^&(6s%p@T#by=TniXtTbS|L#|UY=A{b?2LW_|3OMXMZx|&
zv-$k3?Y@uxaq7j-KcBsS`licGjn|5byA<!9O!)e)@5kMT&-yX!K5yUgLu%JkAA`fE
zHyZB#^TFm{jej|_{pFq?>pY|C+NZ2toCnOF9E>J7Ia7!|Vk<{W$UvcUXt9@=K_NB{
zqjdheckzFIysOtQVr{fmNg8;qI<xjzP;Tb<q;3CfCD+2D9RyN)Jbo8sh4%-;DN~x_
zwrjyhmkWlf-I!{Vs-)gzQk6#AkySlV?&I>E;0p^p4HVY%(RdZe!PcFosYV~ji>Qgx
z`o<?_K)MFil3@G9Urv}GZy%`8*M)r!NzI4YV4a*&m>jLT-`iOl%12^tvEcKOx2w(C
zPEoKU`V$$Fzta2!COA*rc)5nU!$O}1BIqI>jc(j0S-gxN`=QzR`h|`+X!JW?tTyaq
z5^H_Yes-$UAgJ-_CO5nA_fkW~J`$s)!4Ezh0S~+_s;{xs24h=eyEej0v{nZ<O1w5L
zrW|1S&39k3_6*eP5DcL9?ruQXMS@ykW1%>UUn*9UTgH2A7bS~(Kq;!1fVtz-zbsz<
zTB}g1Gh*G%{=++ON_7VEY1VK$&Cml4%vcG`NFqOU3t!x3D_pMBhhO+@=fI!;-hR30
z%Tj*?xT;}hD~s_`BS>?o)e-utU@2SU4JCKHL&HX?u(Q$1x^phJJwnk(<>ap_)_w81
z?F1*?UBizTeyNeo+%wo$$qXJ?eX5~uzhDD$aEv^Iy=^XlPIhO#|LihDdmdg^KRo;w
zJ-CfNrHz+a=J(1}w1$}OiB)NxDzx*F!Nw(>sy2P@TL0u5o(8*kdFcD0jy1Z^Bz6Y5
zVw0Tuc_vP}j^0l;!U!58*^zZJ{i*P=7Hm$=!?p!Mn%h$^n0=%JR(z!HTn7q@pr~ey
zSA%j14;eh(S#CL*MmrJb9qFksxH<PROL1*cTU~8)EYf*)S{t@GseiVif5KpBB-<$I
zRq<dRgmDdSRC$1OyUVO(r>)+6dG3jxk>l_)^Dhr91-Mu3HK=amX82os{>juB+60OY
zgzs)hg!n}U_Qls8ZO4<!l`>=b8R^oahWAv*vfTt|s(sSJzohoFO53E0fW%JGka<6m
zeA*O>$esQ*xO#2)FG*~Tjtd@~U1rv0@s-;~GA=oE$>GerXgC=-1yzPjC4BeRsp5gH
z1X=NgTf&v4{xR(42^z6O#@a4^zvPw4sunlu^j8tZNXEX2MeNuQR|4Uiw^#D2r@9+f
z>1h@F@73wHFOtMcZl%$TtIFw4+4H};WWI}jh<9}Dw5jY*nyB>hyEo<WUUD6RMMS5|
zjt2yaoELZ@tM!{F21pZR$2z^Perd=m7ntQQu_*eJKf_e_)VW)DXE-@zb;phl%+ssV
zp-rJR#c*HKssno0o`AvzVoYCOm2&!#WjWQMUB}U+T6L)lc8^p1+R1{6#;&(D*f?TE
zT#`nO`laOr4M*ZE=DG{v8h18r{7=FJ#x9b+Z`$KM8chi*=N(dh`s<rcV*S~wIPcpF
zU><5V5HgZ`juKwHt+TK@W*5os8l|-})wL4_UCF7q+|xy|XwOYEa0TPWZ))QRVaiFn
z0lh6Sy5eg;Kp&59B24t+;>hnCyFQ=Ac{1Fs^x88)DRwZn)o<-ymo@L!dvP#L+2blv
zGly}E%oA}R#;|K=IT*I>!lKh_6n$C1dpT7d<*DEn!SC`Pn=N*p7{DDWKgH{b@=6Y^
z|Ihj?8sk3IV9)e5S+`$0+|lpXm{QLJEo^gV-4SZY)eE;PSJ%C*3hHrm&r{wJ6X)DI
z$}_V%vh^esycUh!QqyPiC+ZI@n!tD(E-bU-SjGVJpDRC~hu__{>HOF2-Yx_7qzRbO
zZ*1!r@w07xP><=iks$`&?|#P(Lg&lV$L*)QkRn@9NyZs!qDCxwvsScA_lb91(Zgz;
zK`vt7Y&G1t&E=loWRi682_<E%I!7!%`M0Rk9S2YtQ@omfp(-dE4wZPPk4v?2{?PZo
z&rUPm2UZgRH}{kH46><@S^p)9J5+6gy4P}Sn82w&c{FmhYOi<^>QSWIWb6meaBrnA
z66?YEc}NW4#8a^&I_;md{o0D*QZ;<!=Dt_XglUjNc4VHieKuh9MfX6$mUD=(j#ur|
zpjpe8T|~kg@kb3*?@Y4DRm-%L06p>cumO_7vt9ji?WjvQ7;Kod6ssKpXd^h!f&X(n
zML&R|OU{iN+Z6JDWM4&JxpmG|{`mv2>j$H?Vqwn0SMEGg#XkMO5JouaShhPaG2GoY
z5{d*p@^#UEBYwBk9{|IGL6LSH@zv>(=~8WwmFp98>xsnu`+Bc-Osr`6k6G2{j%#NZ
zMXhV563`6rLr^Xwl+We~?Bdg_(g=|;>HtSBoAX&ZP1V;htneiI#6V!tP6J-;RojIE
zX9BEysVPVeJDx>+nE!wlP<da!7iJ!w8laylDczw2a?c}S4;eonA!@|Huq@<^ZRFyv
z0EFi1b!TZ&rZ6SgRGV7yd7vU9C^y0d4hH}6-R1n=^Sh6;B(!Q|yk+JzTG&K6#jCE;
zJyu{mcCP5e1Lst{sy1Fx>cWwFlt<)qf>)u&xl}}=_CVA5^j`w2dzaldFAghlc=yxF
za&hHWt~G=B*w~n=wIU7RfS|&$Z7#w*)1;mHd-5D39pJKOJMwCXfNLmy63(er*t^x+
zh2C_f)4pe+?p)Q{`!uj%-z#Qy|J{kcz=r$Inq?jHk*BaVuk5yC)Bfmc!Frm!SZ;LF
zAuk;J(z)A!5r8^YAInc%yn#>q<urDj?9}6_5-pZ;WARsHvWZxD^o3bxojk_VHCN)q
z*yWxQh-pL3)yB}<3#0}zxxN@`-nQxW`MGkU)g>M4=ciZ2&Ydui|K$3Jt(`YPR*%Nt
z5X9sQ3$MGDlP1FfGoqCes&#C;!tlJZ55t2>jB<nal3g{v0eBwD$MRiAV0@82YjR=G
z)IatSuaZp83JwEiN(lFHHBPyS_<FzG9P8=%gvz;9Wln?ZmrN~$9$n@ztKGcf!&|w>
zJsy(IV&lq*WA8I1UCb>3V_ly}78=dJw%RrEDlPXptBAieFilk2@T@LgL=<1p-|!o?
z(Ihi&dL_)09bg*-gXo=yQ7qwq)bUEmdc<?94dVdd+vgBiZ^XTIQupm%O|DNU$2_b*
z(w;M8i<4dsg#-cf1H8y<2QdH79uG(@I8QfA*=nXlxqHLqz|7g|*mEwJGnwa-GKZr_
z)ROWaO0IuGo08_^S4RPm$x2pBMA=}4t`pW4i!6N|;fL0ZBdOc9Az;|R^Y4hNnR21r
zntFRh3%7O(39g3bEZn<{xxccyOF($SstwR1_)#i|QU3r{&_AxT0G{tVZb*=U%a)-q
zPgTN0NK!8KL3L<>RxA|jggcZSdB9KDNlQr^;3aFw(jSt$TdD_VPXrwf3`?Cj&XZhs
z_Do}ie)f-idybBYUULSiWsZMXT5h|IT?@i4mPddaXbt9_n<1L9(H${`Z7-A6nasdY
zZTyhzA{GxBi#E5uy$7=_Sk~#_99dV}_)?g~qttq;<qM4Ent(XdIXO6cjO*Ds`rstj
zY*{I0ZG5xDU%zs%xEF9{C5^_{*7;iWZO%>#)8`aA^-#HVVCSj~*|EKk@xH;Yb=hra
zFH6bwJz%A{acOWP9|3~}y(;Ka!?&kCV|UGge&<auR(z9t?iE?G>02TS(xGvbt>QHM
zUb{(`taqK#TM~tRaI)S6Z>5t8IEsCMqoDV@6!n}aX|+ch7IjB#Mnf666XGR5t)kG(
zs@OQvkXll>i})Ab>ZuDSI34#UWG5%K9NgDi>iKROH=-Gkk*zs1T~P&03SekU(ao?e
z>K7vaSm(1>u}+tD>9s$%Q=9^4);h42jQ;x4oZ)2UvJQ=Roqu8;kE>tqNvK0$ahDrs
z)N&)Wzit9qJsH=-#Tf>S)bRpy0cYL(b;YOSqPF$pamZy|s?{$?6ql+r^6VE%($sJ1
z!U|KvmWUeapaWoG+EU6h6FXCb#pfEbv~f#qhESz=2^%mePo(}&f&vG0#R~<6eI(Ey
zr?31^n*P82<1SjRh8P<Meb4ZppK*9_8x3OVzen1<cU1}hfkBJ5)GRu(%1DD_Tqs`d
zARCQ3%?W|zS-SX#(!S_QrlNuitF71e6wfyD9G0-hLgtO8vGL<eyCx8D;E~Ju71@1%
zW+%J0v4ph3ZufU@Yt0xzQ99It<uRu)1f|v&o2V5VjHPL;Mq{a<M(j8Nl$$$Vk~!nn
zzNRx4Upiz38xYpzU5LB@S=|&K{qmgo<&;ZetVNwjX2|kudNOXRT_(u?;0Pqn-<s*Q
zBiX!MU=76^axty<7sO@(x`TEQlfs9_2goT@M$a=U(;3Cjx<-BRmGsaUht=R#qgelB
zc)@KTbNb<fyakP>^m2l`MQ5#gST&6?kA_09wod(G;0G2y=i@#nWini4dHtxFOU{-`
zBWElX7+K&C3PCL}<70^18rCMADGFWfw{f27?J2=F1vwMqcbQ8+?w?53XoIJNhXZY{
z_FD#`wc3>-7iD`vtdH*Tpz+@njNiZ6lMsva;PB645A{D?3#Ko4s8pAkPi%i>?(mUZ
z9P+}xT1lU}rj~x<&q&Mhw<O%i;Uf=Fr#d(bUIa1X!h%>EqV)FHL1^0@Rgm|HyL4JZ
z_URgUxH5g*)x48@A(ELq9P7a0&x2;IUl;@F44v0{U(D;SN>?}2s85}inc5JE@d4lm
z&s+lT-#Zs-;ND}~%ZOYII~4sTiXby8JKSCv9NpkAO^>j4YqhHR!kVHwAYgTR;q}f)
zatl+Wmc5Lc!JEO+kw2~9{d01`DAq<`l?w3V`zO-ri}Am9?`aqd6tM`bmp}7gOEDY`
zOxF1GwDy$7wDEHws7ZdFNATjf1}6PP*=Lpp_&gB!CMvxi6Jys-V;$F{^dlhe$K&tx
z(G+L;@}AZb&_adtDn)m&-Y#SWPYZ5)Tk1dUa_#5=Rq*X$Pg~GWjTY_i2^@@Oy8-S{
z#d~8V-WmpDtf?c`Ix~VF?>@`*wvEJV=mFGOE1+mdnE&qEryU}bFoq-uYYkeB2!e6u
z&*Ij?ou#ig3$)t1i5x@fEuTlJ1wu2gjPa*BeMTRSfoa99(1FJwwd`>nZL!!$yTuvs
zUSYkIS3`Fqf$V}H?ZG$Q>7}W+X>Luftzg2*V!_Bv{PEzC{^%pCUwTAM`t40G?c%Dm
zCSt@3Y3<GTsGB{Qh&C#XUVRW(k<zBgdcvAE2)R~wF4ocFc>4=%gI>EcsKjo_QH{XT
z=O{XZ9ieV8Va0$nZGVxy`5W;aV&hoBMW9M{{G32JLFLG5J6HO;VlbDk9cABv|0{nh
zD`L-^C9QYS8mr);mAf^v<-W(ECu6g>XvB)|FE9I#DfhRfPO;q&i=wBAp1879T2Y6R
z?HoYsa3R=HLg2BkU#qe0n(mAz3!5-kvQtOSWGAK+QW~k~Q&f`+3lBEa(3dY@gECG>
zthk=#njKR~HE-(ok>Ie`r>r<?JMwz@)o65Gi{|u!Ko>Hn@|rLqO?_Y1yH0*eA;*0O
z_oQ`uS(F>Cdv*)1HJs;lwnM<S%EKPdamk}(Ig}ee%*b)JFm?uRlc+jH?_%wN;jyRG
zUE20)Z|&XUBBLN~BXKPEDzeJlVC9|$0p|%j_Y4|v2mcgk#UKh0n?Lp5vw%Q44*WK)
z4KX@W0h){dZNl!ACOC#~^4^#|^ptw^a!-A%e~VP|@liD-bCe(iP8N0}-|(96zOpBV
zj&t!R$8Z!T<Aur4qzm?swwF21{Y$IlL!X+Yf_VOYB=iyX#!zpaOpxpltvRv(2E1Tx
zVrZ>lFGMJa$_f-99gapL3f!NQQ7Y-9mOoF}vGWCS<`W^0-Ag!zyBqAkR`(kM>5$#Z
zCOMaZ{c6M%?8Q}>r5nRToH?0(9o3TD7?P)~Xy$Yc$*HxD$AV!;EvtXWuoY7s&EHTC
zQ%{QV+I>JJ$?g*7mWlQr{EepZ?H6)JDv%Ds-S6EsVkkA8Sc~5#KDSq;-Ff?9C?jX3
zBaN|k0V~!pec3yC8WjIK_2p+I5*$LdUwD`H@_kC$cyG8^WO6LluyZ57VIbf#4p`H!
zZ58*6U&h|5K{2w&zc^=8oHTi&#Ky=MJvR<1_nH%tS;_d?^oP?8*6m}L@7cZv<E0w?
zjynrsACch4<8QPd6rbKY0&xw%#MogRi#x1>*SI?bcZs<X51xa%#sniFGmS;$#-{U;
zm)G+<yl;Kzf;4q;eC&Bk%w>8+K*vR;Hn^ywzmPwx9pR=mJPE`Ovs<zsFiMm)R|#nY
z(;w0*1QhbvE>OG~kS;-jjbB+I9-3>~8E8V_$HIvYcUXaTL&oeq1D~@=PgQISUgb!r
z=_kTkyvifk-&laur#CMBvOz8H{Yjx9HBHiz24KF<!L(7y8!qNH_C8b3O1VII7vZv%
zm2_gw_FYMu`wS%jX7<Wh@BHdlqYv73Qspw=igfj~hv5BNirb3zG%R}JLH1dr%#iQK
zv%3Lh(^q8>@b)cq2oEdo_Fs6A#Z?bqhQyrk(P_t!JG`^rSpOg(3t!bq4<t#TE>AGs
z8Rde^C-$wSsxKX07DMXC?(~S(C6)Wpm3_Ol_~Sp?T;Xx1x7+l1!;c@%OEzriutQmD
zF_nz>)ZhjQCnkBXUycN7xvL=TG6L;-xNgIJ5TdB{VJ34D^|g>b#<l~kDFKm)SUen#
z9hJBh%!uz<SS4+QSsM1#s(%H5!W0mImu;OL(sDqZJzGj#8zaeRzrQh`-S*DidB~}t
zZ&E2G2&O~*qTP?nG04ZziaS{G&0`e~b3n>M(<t~Bf>d@qY4C!k?VMG#UAB#LfDp1W
ze!ACTuCe}inTlVB+M~#7*6WSuWk`0@X?3jk+iPxbHMF-fp_wvfDeE+JaG>pQ^+nev
z_oiHrhfGlsLpixHG)+`AeR?Y+UT|arV>mh=Oy;=kX|NxQ-Yq0@tpkARFU&c7SNrpL
z^s?b^Y+$?7q&D^A`6ViVNqXKAZgw8>bye^B^^FH+bL?OmcjMqbWAoho3SsY&9m;r(
z)%R((VHB<bm1R5@@A+ij;1-5>Rn16yL?b-j_ubeuNLp5J9dr#hyh>kqZ95C`rS}J9
zm3{m(?8wB7@KVWnSiX>;4Y}HLR&oyQd1%IQp^YvPCu?K8uu&?@V?*?Xsk0px4Oio5
zADysY(xM5&pOwT{#90>Xq6n^Uqp3Iz{KiyYmBRj153Sd=Jz!vZAZMhgswy?wes>M7
zw)AT5r2E)d*YBDfDuJsWFQwg~5rn9(XU#BV+l8C@aI`^7qV9q5HsVB6=4h;kK%Ksz
z!x_|V{)`*l^i&5%OrO?Y$z`pIpQY&uDwYb<^gLg9dg-WJRQBCq6b*={{W{_)SJe>O
zQ<dR0@Jx+;!}p6Zd{R_~(517@@fkB>CkTd$*EzEFM2`(=0@s>G{kdIuaCxEy)W~p;
zZ%t?eENpGwLwn2z+AnPsQ)(`{hW7(1@)M3O-Md_CaipyBz?OWw;U{SoMBxo6%Dg6&
z$Y|vDQ!o3E_&*Gvc~J%WS{<qrK+R1bzYWrD7YxYCiEFj<egy&P`lfu|6|ESVUr^zj
z$<^GLAY@zGV(+nZR_{|gSH!P9wEVT>C-nqoZ2uJA563LGdMgYsJk(I<AG3b72&6Z4
z=_4HhRC^=Py@%p*<0NA;DP4^FLz2&I0)+uveTfNw*MD~`Ndwe)RfjgVZD|K_BH!Qz
z<^>>)KUqiiECpb&97MWfmq71mV^qgRZ%|#Fs6LE^5Y4I^l=h7WN{~>a=;??^YsS+O
zUr}4Wr3Uqnucx)!i)X`K;nN54-$MP=ivcBq4tOj_X_Kc+L9^K>N^ZzR{GwcgpzSR8
zi~zeHlsO&3l5qf~a!_A==?nOSmDyny%c+q**u1G@O4&s)7w&-nur*Pk$Z&mweblv8
zsP=<=8~7|nucGe8UKjUXe}96Zo66M?<G@dCp;|L7B;rJy(9$K<iD_-M^0LHsfku~H
z@{5ALdZ;-(GMQa<Ofpgg`no&^1W`lpK33|W?E1I1#)3g7LhWn1nZyZDkBD&3>`f!0
zCw#*(qxD&r-Yhj<UqfQqQ^#a9J4C~Uzchg-0|&|i=Lv(QBRW7Az0<5@QBk$IVVncH
zdio{AEM>83NmmJT28lOSPal6wm=Ho1&u;6J60cf?{v<e(Bt$Ng^l0iPhu-}rKuWzX
z?7og=RNi~%w$afc(jT(=Rl^!zuCAUQ**crD)Gz+FPgL_w-}C|UTGiOS^boKl%Y-mG
zvk%bT)Wfq<K7nCDhs=V_IMs^jSsV2f!s|7u6;rvntXEV$Sb_P`LNz@9_2!ATZkL{~
z_2-OJ)J?l2qzRDLu7my~oOa78iBD8TSVImf14o*m!SbE_a+s?W^NGts4bvCMHB+aM
zg4{o*8Z&!dMklQx`<L_a<CdD)jwfe9u#6;Bz}Vcjq@Z**sUFuafne3oFD$3M{Fa-h
z4w|m;O|^^a<t{#N8`o&`&|wp+ypx5@B^Dc~a658OesE0D!xBL8g%xWM93r%n`%>h3
zHAvkj7&Q08$7-qGKfs->omoR!M@bXv>Q2$FSa7B<ID?vZU~y%aHbi#hi2jXFnvB9d
z7wlU>MOw~r-!jV0dsXn}Agj-%j<}WcNC+3M6}@)5hfTu9g<asqX}4Ru2-%OPa@^~K
z$dD%F{$w`mj3s$$jrn-|7mwEv7fk?8{2)jOCH~l0TP%7dqYXmmoJ(!bFR_Kqd_ykO
zo~y{kesk=QM&#*Fq+Y1l$XD(4;53bR4phHv-2G~*c0u8fTgnN`Xe$*hk67q1AuN<W
z1A_I>q5@ZnxS`rOM^&rwi1e0D>>=~<G9Z2-usBTvuZFs&TjGirh3}GOcJ5kh-Z_!!
z?Bke>6VI+-J#)uz*$beR5>W1ddn2S{h0W~D$-}j`2~-0uRd8MiyG+mWsqyGg&}^>v
z<;KuY-n4ju^M1$9){0iGd2GmPRj~19UQgP<T7Py})Mebvmh1>G<a|s`vK4OC0hG&p
z+T*?y`^hhIH=mr|L<SV(+B8Y-o--(*v`IC4X4a^`w=0_rk&9~~RSiSxKPy%rS+|Jl
zT<jxmiuwxn)BDVi$c~Iwue|e-7m%P^UfQ4np~h<QYV^YcCk@;2IE$0iF9)OZlapeh
z9GRC4o)nI5Aj(_~L}AUTuMnEkr?tT&>bc%$uGw5TGuu5yxRkAt>ah6Wrlzx&jTC?l
z`~2ynAT=julP#946?+M5Ua<E1Gr>QkI7+q?6n{9tLyE?FEYs`mlOa4?koP4f27`6Y
z*Z3y|a-94s=a<+-4aW-$NgKFzzqQN09pl@ro}N2L;Pj2%RnNgb#a!#C{S50^YaJ!{
zTMs4+H2AZ})N{wrYWt}mQooqvMl9MZuyOUigo<zSLu_?Xhg}EA2m!@nc*oc;E8?1#
zdYUbN4mh8hs$oh!jSii9xK~Q-lC>>YX;^=9Z~SeH<^r(Q$!`s5dRu|oW#wIDd<Bt{
zylY|^-m%^Ma1a7`YSzI@)HP>mL{!M)L+4c>|Hk)tq8hzA@z`12hN<hw-_8}JMM(CA
zdud*3V?6~0<*Tl<Q~w)r^zhv5@s<NkgaIc_qeB6nc3SNl68os+C{z@9HTz}G-u{=E
zX^?lG_t&UCq)BXXqG=7eTM5A52(|P`&A?s(8P;U}w9yz$(=4Nyw`<kZrhq_~(LuM6
z^);t)JA1BKY7HqFtJG-i2=@EPo1!ly<$vUqXtv!6em`E6^2%P-g)Yp}4`9SQTKv+T
zaukJI1HonEig0E<GVY@o(<HK79$xwi8R5B~<x&Ei*?glQ^@P6+VibJ@UYX-<pR+eY
z9wh<K<Hm&OT3*}Nh{JANq3BPj<&D2`3gTO8L+%I}^g4l~C@T2EbIICfxb=~M!8?ND
z8>KfZ#Itz@E!C_HtO80{d+Q15GC&(|1ho9xm|KO`#(P*}k_0o32fluJ5FDM)adnE{
zm@_ajvCg6AS2a?@FV0pPGHU*9+77V~v61^0diIG=^x!XR+DQ8EpJ0OyuwJqzQCreb
z7diYYkd=Y2b@WSR>oI7HH!ZIuoo(hTR$$};zBWt=lXv2utxlI4<G|Ti3#3hl@*pEW
z=S^oM13%uErq_YVjjR(@Y41^%Ms(ET$@>>ZKwkK9mNw{BP5#>X<GP9n_l}d#5snA+
zR^B?u8gY^7ll+%<zX=*P!|l%!w#yu_sXet+*vlNt*c%;o0<xXy;RT8|xn%%|lLL}J
zqoO+`!C2X$%k1aWTuCE-yd9Kcd*FvjOYH}*r!U9Hh+?p^^xZakhc40J+GF*s;{kEZ
z0j<@VG&mE7twqLTQ5tp5SVUm_gO+Z<Im~m@BEo5)2br_c@tBTO1rvt_;AaC_6*Iv3
z_jh9I*CPtHlgd9xwF8Pqf$IU;X~LV9+HKg1&nk^yR=B3@4+1!OCgyC1($U4`J3VY#
z{`T<oJD|t=w+_Vwo1ePuaHrmIVsNepsFUdkE@~w%Sw@Em2GN`mvPK=6F5Lk5f1tVB
zWj$NH!t1?2*6mn&o-KdndRQlVh&cAy1Alt#H|rBExTeLqVFw2OopUSD{M1<!N4;ke
z-koU0eYBpXp0DU=p!r{3+AXvjsR+mY7bpD)Z7h?vc)03gZ?;3|Gaz7A@M$l4rGc;}
zcwhd~XKcRlSkac3hYl|vF|C|v+#Y!yTk~>hQB&J*v)05(L`#}}lb!?LtwxWR^US=%
z;Ib&%H;1}@EiHMjc?o++z<ZnK|Kq8qsx;8rKEW~8ZG)9s<C*2B(e<W%yR>n0#aeYy
zz}kTJ`$nE_<V!Vbf-9U9JU#-EuollQcl~neaU=LG;erM^`2js)0^y0Z(TMfJ>b=y6
wBHL${&O8LEWnq7mUt$H?{(s;V$dM^-GQl1Ed=2EjzFp8chd)Zr_}u#6034lMNdN!<

literal 39541
zcmeFZcT|(x8!i~IAaVp1yEN%Vh*Ctl9;Jldk*?ANq=q5^0^(5=rG;jw3ZY4p8i7FU
zv;Y!1p(7xK9tegIm>tjWH+Sw@Gi&brd)Itxg?yy${g&r_-e>PGFN_Vfj~%^m6as-9
zgX!Ehg+LBHf<O-3KXMp6LP~wR2JQ~u*VDcY?!iI3)ffTp*nM@Z;SdOKH1oz1KfCS%
zfe1igw{Mz1%3K-?$>iQ*w5$hT3w_Vi5j)8e^*o9-_c@!Sn{>3B#f{>l8+mnaOl$k<
z%vFE9vshREXvuk6vL@!VwB+;l{O1C1q68*c1Y-o}+dq3FBf{MDkH{rC%VsQnCet1f
zgc{K_KDmrAe1=gog`xXrO#lC%|K9>k3#5<&U?Q9(&*9Qj)Grh45Qw=={ct(+8DB_8
zO7i>~t{2|Qw`T}}ydk!?1s!5O!#wtmPyXjTQa1(y{r5qLx#_`2EdQRrLETSO0H<$u
zH(UR_&G)X*M-B)?GU+{r6V;%nY+=d)i!GhU!bXG_<X}0vJ37Rp<ZUmBR5%6$FRfD6
zQMNN}ZQl=vy%cV`Kg$oB3CsLPI7?)~12!gXkyK+&6ozpNFZ;rJVZ^&Id*S}n8ZF{w
z*nV=&<=7n_(PAvdthB6>u(fAHw1dHg4U#r*U2sXp#snEKiX<ov8ur8&sJJw9d*T_`
zsxHEwsE4}d`oUpVTDUll5X!iNrl?!asSO*OO2a~393uGJcI_R~YHkqAFZU@XwNskV
z#dT-?OqA#LPWwSgLG`*WSZdEr;=5BDW5UI5N9V8Dn!fl#O|QkL2fnfm`G)Fx)luDy
z`NqDeWMO*q=vFdj0F^4-su+|Z$o&tYCI)FxC|2YssNEWdPFR|lI)sLbx9bTh<ruxt
z7r1gh!XJjW7L7bdmZXJtYYWGs%wIcaxqkS$UeK|ADGe^37~mOIl^qOIgtj%LZimWo
zdrAKp-1XL13Ht&@vNxmcU`B<B`<}Wo3G#y?j11Jf)>o1S1-PWL-NlYqllr2aD!Sf`
zr#vt&8je#%Bv3#1m4L;jXS&Dak29#vG-2`ye2Q?Ziao`yJuBh%NP@n=pai3nv!#%G
zGFHb1N>McUY7{wGMlfM---*|2*glEr_!?S`BiZ~JhVOA@%Y{QcClB%ZJ8M}N*nYnc
zqp^2=lRl^9SR6|ny$mPqjGQ7rx*4_e)LH9nL8!DL%-AqNaz|n7;v{d<eYN}84bC;H
zzXaA?v}h$iR&V4vA^E^M@z|P{aGCkq<KOyhkL1?JFR%XBC7A34xWK(qLfNkn*-!EB
z^&`t;F_%WR&(OR0X#3wqA~Z;9f4+XfjRNP`I?KKoT(3PcjIwEa71Wg&kW4sYS?Gk-
zeV6{G^o{wZ=c3rhP?Pj|lW~^uFj%8~`$~$y@&TEJ@j#_<&NZZ%TrSNSeWIR9SJyT@
zhIYR;9@wTLSR4_BICOrpN#Uk7k`;%3<dYg~3xXdUoTlX3h>ugA=o_~LV}*~U1*CWE
zjmYM4j<JgMI!B4zLw{_$hsxf#t}a-N)($^;NNC<>cD9wUs(ks=`U&)*0-5)5-HCmc
zqR(f7b}z{UN>y$6>r!GewQtO|hnJ7Asg~bLklav6G3Da1ObcD>8`skD$bIm!>;|z%
zwqh$wavxG5JT`BtSW*8JK8y68|A0}_CJyT&5s8X+8;)55UI{GYiR|sV3Li6yw_QW(
zYX1y+QegJXZR_*Kd$;U1;&l*)E01GCRf%KsCfQb3{A=|;%8a-0xeKC{l`sorzg=sY
zK+1)-C57mPHW_zRy}ibf)3kKmVm8jE0^uP^?x8JH3!*)H9Q)t!`oUhWn-y~QaZr=#
z1p`!nd!ED1!_!TyIH8k$SNkD8qT?B}V*{+rF+@IjO?r;^bkR~Nn1QF^gk-S-jm3i8
z$^N=n+m*ayO;L$z_QcWUMAu5a^k;nTGQqIw@3X(p%e0U_t9ME0{AUDKp~=0Sd6^II
zT$)Ghjm-Xr$+S%H-_1H|*Zy2b`HPS}n7xKrk<&Ytas4L6j_NXiakJuR+bl4ED);OT
z3YXr94EOFME#j6e)}O!N=U$6M0{uZNO_oyGK!Xv@)52+}MNtzZF4!*HaXxX*FKr&b
ztz*_PRnJ;)B)#q!j}w-=u%?$5&dZIF_8G<%Ab)nedKI`4dlE`Wb;mZU?rTTS9~H4*
z^xsYTWUh^>X>oGwg><Z&p1JFQ4)aO1Vn$K>d0trvr0CaP;@t>2^ob{VE_3^K(BeC;
z_ug=;5fwIv()0y7r5}l-54|w0)O1}j7jt<0O+=<U4T167+3zOXs0zy2410Pmp{S?g
z!#Zs1DvJ4IRjCGv=MAhAaX&Z0JkgVmr7JQm<>u2gKM{DWQ3Bd>RtbHm|5_VvaK<s-
zJ5DDzQ{dqF$<Ci3$wxqv8+RR9V}(S<c*mePAm7^4<nIvKWIv5~aJGEt<d#H#)D*m^
zMh!7oo%~AP#j#pW)T`z!?mu!E6x4R*v#sci9gKp{N|OfrxGL~pV*X0bd1sIMUcW@<
z-lktkn;F$&A^8j2OLHeX3NBoQrL`Rtzu2muc9L%MSLjcR*KbN=#FJJ#H#|I9AUBl$
zEiw&FDCJfLb?SCQ>nnMESCQZ5k75h-YfJ6<=s2wdD?uU6xx4XJ3NKywiw0|QLxyH)
z7f*-t_o3*!pS<%TMc|!<ZTeHk`{d7|Cr>-4;to`xr#Ht-i<q#g6B%I+zQ|=QkH88O
z(?jUGa*mn@B-MMV4qS_$nel-JPY0?r4emA>R)v!p1DDer`KrPV7;8H!JV}){7}sLV
zG0tv&(c-6Dln@#BoYY_eWDy3=u#)!N;9k;@e2SB0j9-ptLRqpLD-IM;S!>}1McKUW
z3+)F555o|~t};f@`LB$<*p|wyq@s@VeDr1=!Env#Kxw3ioM3nN-06cYd$>K$w4(cQ
zuU_fwWOOvM49xJ`Q5$3TQV8?O_|T>OB!b;w$Ur64Q)W=eprDUBlqObeaWuq*+;rag
z-w<PQ0A4+>>3rr+x|rP2H3UrBW$vdP88!FMhlvOj-LBECou0bJ3JbTVE+^7e%TM=v
z*R$o1ooV@dEvSRB75W-eEqf=;Tt6se&jxLRGZa!*)UlaMoWCbaAuH4JFrm&4)uFYk
z54^DQH($2m2D5m`iRJK^S!a6F`P=_v2)+U|!<CoUlnx<rT1VojL)%vM6VDj0ar2ML
zibl)7-OX?IHC2EGW2~~C42<#79n7Z}ZfyvUow5r=RcR7CIJ>Qq<i|uh#Y2qpOa`4M
zF7nZp5lb8+Ei&CX((_8*C9V3Q3GZTlkL%a|$@S+<DpT~UJZY0d1b@W@o;Gi#q^j5^
zl=E0th&$eRvu^H|u$mzEWFY1C$-vU*qHRi7vfP&SYJ8K0Ez;@(kuNcqK3Ock*}!2;
z)52?AwBX|c3&xuJY27L8yPV0J`TA?}y(@tsdk@m)bBq!uIOD?$8&w@|5#`u7juFGs
z=Hbp!KZ35PUoOtVaEU0an-~@V1RVY^0uFJ@3NdtrV-x$#Or6k)52%@#ORy~wdoNyc
z4TC7M@2N_}G^iy^+RZg!LVJZemH1|Pr%|F4LY-IQs}6@?Y%g)|7Qg)!*qy{4xt+jX
z`i(DV@ozX16nf<^+Kio$<P?JCbYgG_d&lJ?srn2D9YK5;`UaLn%MvR7p<H*vwIM2&
zy?yIw7@ejP2-^rt8XKpGOL2qT^9H#Gj*mCnIh8ThUdeQEEDx;WknwkI*~g;L(`x6=
zqu84dohTLj==qzS=b5m<nmDv4H5L>jhDh5^eeB2B?HefCH}7EJ9OaJ}Q#S)+wlZY4
zso$kOX1oA(2n*`ao6IYk2OyB^KT=u`CXafDLX+5k*Gr#Dc@-?2C0H$B>T)JA-~~pz
z+CK4#-EG%~rw{dO6XMumJk^@SM%di8TRrrjU(Qt+TeIux_WAyf4}03kD4FE#O>S?=
zFQIZKe`1cE8TP&cp_;B;9f9m|B%uXa^r7^Vycfx#uz9`3%kG8oI~KR|QROKCnV6^Q
zL}*fdILT|pLby<$r~~U1?$o_B=81}frd<%JyGe{X*fN^`^X#Qvn5ickYJ4)}2647M
z%-1iDkn5o#a(zVt*5opB0WY}`DmVNm;h&5?v;t-nc<kxy-Kah=V$*T3DZQ0&$l72o
zb&_AWEEO$zuK<G;@zSj<rCE-5I<sQMI$jaf?B>oa7|1?O(Uis7L<q}Ot=!mrL8w<u
zK$}tIVPY^333z7&ihZsW#m*z0P%ks#mRH60K2Vm@F_6eEoGn8MzV6^URVpourFJLR
zKiz-1WKMAlZaqjJ#WWlliAO27Wl}0c0+af5U2Ara!k~KTMGx*?y?Efxc!K-O9i5#N
z!bDL6Mgir3+N_DPe^iPa&p<!?HzJr(@&A7C6#e~g)4_Q(5ki@c80zy~1_8x+SLxq}
znV;~HpNWQl4rADf|8^h$dF21o9nTvZ9m&c%u`0W?fY=nJ_^$J<-Cj94@~7<|V_u3F
zByK*%jG?aSB5o1+g?Ys!l50n3+X;k;nKWy0%F<t{?B-{TV3Mikz4s=LXwu)^ww?b_
zBUL&dDrOR$KTtUz%9A8ioQH`Pkxs0+MdU)c>NFUdHVL;U63G!pmBiBIde;n*&Xj;O
z0`2UbmZvGZfiEY%i(03R$PSX(VI9I=N%edcum;%z8HaZ|4O+y|0a|Hl02Xt2_D+zp
zd!coUrGc>qbH1(5@p>_LY>QEJ0ZS8x*_^7fn}7y397!~H#wXNR^b9^H1?Aov>5;t|
z#dp5gebKU`uL*XhT~+Xj3v0?JKiSpTL>aoaq@RkjPu2`^WVI2FDr#vNs66{&NyCzu
zF1+ab5;J<xg|-kuabazH(VpYU%WpV+2fLgD>nptSA`F?(H!2i=7&Xv{kxmIfC?#ik
zH)UA2jL!Ih9D+a+PSnc^xd@_~EjRchr6gH#pBCT#VjF98W_?>Pn-@qC<`rEKgvrgS
z#<&-!)LDy4X5_MJc1uG!T%O69iyB+wBq-xWGgT9uYoaRe<SmU^#tmbdqcu|d^h~AE
zCz>vy6I%*lJMn?92GeYj^ohb!r(oShDY>HxEhFz>9wP2Vn8Ku1a=28*9*l=vU@STr
zR;+L9sM(`FeH-1=8}z=fIOmAe&Nf=CxH6?#hsYS9go^N<^c`Lpu#IVU^{iiF1Cxk+
z^-GX4<y=hgia7yWb*|M)iOzxexR_eFZhGA&)~gd+;x3dt&--lK+UMnRracVGwC!Ux
zL~vBg)u711Zu7ry9(+V@J$PYR0G<nVA8FUmoBVFHZ>!f6R2v@Ul(e3mGMLZubGG1_
z{uO1xg`?4odC3bp(g8yn{UiH#m?j-DJI!D{1u_0K>)Z&4aB?2GtcA@*>z*zVjaqao
zR4%%KXnW<n^Ea_Wok+?nyVgz^w(Y5@<a2X*OHh=(BWis9)q6hvb`NRXSjDdgy%X*F
zypt})I6Ps!cfamEpPoo~!kgWg`;EC~Y5_q*5lLH*G6>J-9#NBd@hRwR!Pcr?gGOW5
z@o|mzSCGRH^SjIjpY2_GOdKK1+7%PS+e*$5SPnq<WO+Gz=kZ8T?gbSRqT~i2OpwM^
zbP<NGCtgdVMB5m3j3Jz-Zi9vEdeVKe-|JEBe?+UFTMNM=66GD~GOv0a=@B<Ne&%{r
z&fA06RB6V@T$9svy=_tu@Se{K^SZp55EACX7}udgN4YhiF5IwN&gGv5W#@6aqHp|P
z>nSPPhA?dA>PGlvv6?<9?2;OnR=<TP+r)E}Zf3bS<Z0;abm=;($QX3KhWlP{cP;Ul
zt(KFCACeubL7{usozUd>TCQ*3-ga#0L)A;uBV<>rd1bLGT#1w@i(yG<O29>z@>*N_
zqh*Z7*<FoL4zk^n=KDIGbm?Ao8tr69H`!cwRduDBx|QsnQ=Yn6NAUE_pgNAOT|sYu
z4<o-eK#A&l*c)yo5%FD5WrcL~Ty_#;ij0X6^1P==N<KPgVsCK8>dQO3UR;=u+IK7?
zB&P1=J{;C)I#-*R_aIW`dDRmgTT$+VU5S02+IxS}t4_+rTRKc*u0DGJ4qD1dfz+mt
z9f7hpFBE=*xweNvcxS6L1i9_}JLm}XjmKsU-LtAjPPq#w%D0O2Zo6u3I#W0hoDOtc
zbZdAex^sgw`%s(1Zj02~%+FIHjx!2=Ejh0ze?QNBVzzpRj({OPiHsR4b(6$3eZH)S
zE{LRUUaZ}Jel@gQzanz@*1%PKphWeHTj}pHE%uA|x^E75<>NlZj4Q9ys5!E<>nYeL
zZjE}^V>MfBTL|!>;COAN6X=f*C3l+*d)2t;3Y3fJyIv1ND!%fh2`zLT+@j<CiyV&+
z()7tg3+!<nbEl3KN+1lqDU}7&x>6P9^oeIzw?6dDd~kNTA9J|v;QsQS^uP(-m4+^C
z+yeLA;hXu4r-1~x`o^CWt|?2V8E=qZ&)>I72|C(5a^Gq#`_h6A8gi?gfQt$`?$)dy
z`96mbjZMHb#K_#J7QQ)N;;dCOl9v)^w)th7KC=I%%nDNLqu-q8td%;#(c71ZKCr&?
zL4NqinwGN`zBWN=o(I+<zO#+^c*!f|{d{x{_64e;{op^St0pV+7do0(TH0}Yf6LB?
zV{$8!ew&0V$qF?r-ECDx4{DXy1}2=pc`=canH_R<ek(=)4WW7b9{O&Vx99$_pF4kI
z=zIN`uIfe`dffY$(sD@oJ9mhl1I-GkF5`etISr`qcj$Ew_9H3FF^7LWYky@^=Qz0K
zJQs%(?wF;)1|42JK^`b`@{SK3&+etb(UbQAH65+CTt@Py_-M-O6lH^L)%%WeKv&4J
zX%CT~x<h^qlq%DjrZSlYS<MOPu>+-!#)GuOQaLAp8(br|B{e6wZ|W58XYs*DMbTvc
zR6?kt1^UQZcKL(w8S;>fYmU|X9VN{tBR5lc%|{9bu4b4K_?h8K9MJFKl35qJw|vL|
z$ND1Z%7&BDgebyOe{Y>dwoPB*?rOYa_()gdovu$`7h`0?{5?k1u@H~uT$V;hsSsf4
z@&q`fFeP_TWX#02?Fg*lR#)CeYTH3oT!F;zF6BCh3xTax@>fo)%xdSoZqQGnc)m}o
z#hcR|D~1O5g*fT?zmClPHu(@_>=|<_JwZQGI+vl1A90Zh(g{w=b$oQ)zL!T+J1{sd
zq`72kloOrk+twZsf&^7TSB^0A+O5<H@m)=WgxGt1YVkEEcCa{kJmhP$p;#*;JR&>S
zv95lsZvQO#b;%!lg;b?<KV^@rK7c#1tg)=OdnSSxXjLOF_|?~f3JX;HQ`FwYB<&??
z7UAscw(&rwuq3F7f7m#1FG5Tc|9gGoec&iBODZtnyBLRv3iT6A1G2gqWn7uK=NPQ1
z;roqE>S53oqF4Ei(4QTg^=u-RcUpMJsNmfPm3JcQ;?DDK<7^?d)mve?#bVPgLLbRN
z2Jlmi<JO%eM3xe1xzo|So6pCB#2b9?T&CBpX6ZuD@uJuVZr7~pn1%-URgP9XwU2&I
z#cyr1l4-I2Ikr8V&+X4_@80u|@Y*($KX<lUhLZhZzC=oSnqi#Qpd&iTomg+-D7$J9
zdNj$9Z(-V{K^4DHaL3({a{YUkqWS~e0HS#YDbXHH`h$~Q*zEmJmmF=I33fP@pS*Te
z2i(`aTs1#*1v&caM$OzmfO}E*<^T?Rr#nPCJ+(A|?ntVXWig_!9&b$1ZMgoTA=xbw
zd&8BF_FVtQNHJ9B+DA{LP`F#93tP%H%&$PB*_Tkrjc1ju7a)^2xGe)Dqkb#7iMYoR
zK3}5g@+4R<Z|q(p_w2n{`gxJuBkg;Yls6h&+S02Q_rfK<pytW>(`b@{v{^tu)%ncJ
z5Q8ouQkPMsx~;Q=SW6pWpJXq3P8vPAm@%@yFA{n?N9|Z-+B@0DJ#DgP{-;{{0^{`)
zoEo<pJ2?-IUE;nSR3$by-{ePM8tk;^q(7tz!q+3>D?}K=@RiEEq>_$<YkEIbr#oci
zkbJ!kva4-vmgC+**6|wF@mVff`0h0804Zy<Ot()1-@uG+8l(0UFM~gVFWx2|?s7&#
z<>regE-!wEWa%zsMe|<golU4Hdr9Cu(eLbi0$Nu$HsCtAZb1Ic%Q65hB0q%kp~YL>
zLUl!p=4w+KFb1MO<~M0RPqrQfpaKK9o%b6}iH<H>2dz^2Eax~dYO?cJEKDi#WU^H`
zA=h0DhR`Ew`mb=**~Zg0_DDlQSrJFxFTEjtUopaFu|SfA05^|3&{-T4hX}B#MPM-z
z)*52zt6kF9T_YxkXR0))=M){_yd(4r!mVFi*`^1?Lc*@q+nY($rjRIhbpcv^-bwO$
zbC%1A1Mol*g*cTi&W~NX2@@eGWHNjDRuX%*`HSKbwWF)G9|G)4TheB`9PGTBs9bRj
zA|k9q#7oC@ipPClGgmOC%j!a7(Lj^z&D06;okP)PX#Q_%6Jv<ocs=`vE!Oc=!N8z~
z2w|PGP}^D1#?|Tn@W~h@v4sBIXB*LYy&Z|^aB05XVB#tna}phC#g^Z-N|qm^ymx$J
zxLi}_N$9pHLs%rTzn*RB>)F$<7W}w#C@^bT!0{%krSXtY(~#E<S?muhET0qUKE0ve
zP?k&aCKX>9<5JK?OVSU0xZN~JD{^Z1Eb9e73*!La<V$y)Y<!(l^62L<0kJFjvFeHL
zMN*-t?HPyw2UAhwl&|Ff-JC`dH=@^V@{L5qjq0yUVhgr@d!a(Yebo5o@X9HMR!Z>?
zGtL<ARl<LKXLDV8lRdahDAYl~<|4{2I-e0MxkR}{Dlbtx8RV+pkdVvbP!ZY0YaD)Q
zg=VK&nDUz-;>C-;JA4La%S*+3sAapT)uGTGn@(t&?##E;^ZakF_aJsZ=gR~kLic*(
z%Ua}0TYg9S34*XRYB)16^H;pn(&W5;bodEacf0;gzke`})*<$nDe1enjs~vNnH9$0
zMeD0BQ8|8-x}|3LLc8{vb5!5WKBJ>6%Ft@Qrh>m<X=*3&u&5b-VP0<r?&=(k%L!@d
zrW>QbiJ#Cx5O$PO+)bkH#`COPq69BrUQHa%xQ`6V7#9#;a3;H96X%faoRVG-Kju6g
zfV^R4>VMZ0%+vbB=C<EwYKMGp5SOzcbzAe%m)>YqQ7*|;@<A<K&xos>`+C^v{(-Vf
znommB^BkSVJk_$L<WBn55|(GE;<N7w_wN#$IWG2_Z7M9*y)n-wY;vGBok!JG9#Qgh
zdyjvuwt63YQK|kTLREV%LQ`dEOuH+ubXitoU{46Ql(^)OHz<QTA4_W>=$J|<xBrmJ
zO43i87t2yZ47tadw)HJPJJ_Pr6M}V2X1|l;rWWD($5<&u6)t*5QTI>Y2k%xYp@}j6
zSrfH<wjq2ee4!r6w>j#0&r{$J?}is*U<2LptrKov^{IGjS`M1WBwc7h3GKOCOsJ9n
z#!gHYABk6yZk0rzpil#8@*u8sq%>F0(A2*LKqCM8*G8G1YAcE71E&>;<TK<U4VMpb
zbs0Z#k(L%PMOzIc1KA3i?EL7RVU9ceI!8}sMZn)tv)Hidxt~-Ei;q5Zvx(2SzQh6B
zY0Q1xGZf+*|AI#QYZ)rMfJECC=kzpVF?z)HfteD2H>0al*SKobygtED&pHmaXD3tY
zzowp#;J`b3A4<)=x&A0tBo>R@3nx&h&p)f{8WeO;lFFO6ex1Vo)rO-kCB}A`wGL+A
z8t~loSH)MDNi<R37w)g6XWxu*nCRf<naAYX&1()g<!TNP*K@ZXrDIb0T86_r1eY}?
z$g3`_^cG!&@e2*HI`e>^#mV)1idIC6fo>lJ@3}V#1fHJ^XVw|spXwS2)zGOn+n=fi
zEoB2UX8wCsLRnS4Fi(|sM3@k;88{|Mrsy8aZ85*4w=v~^@#2j;{vy+^t-rY3Voje%
z{M;|l;|m%;QPM1Zit!_~2J`JIeOh~FuXki{_$^NA{sym%TySYtv(DTA;gZD9`MPeS
z3li%JDTD`w&vUH~yiEJ=UH~)m<Y>5XZ0h-&_EtS6RhQ7@K0V^RrP?*mecR_8r<=N#
zf2!Xfafn={y;l7!oA-N9jN9%7=(aE-)i+FT&kc-A%ffBu?#%Msn?0MzE^84ezAYo-
zpfK*hvvM%rX-)!>tum{IBFgkGbUfw0zoDevljzYevX#dE;I{CwmwJN|qGJj%&7yzH
z4pP!kdv+}Cz#H*_yO!v|TRL+-VabHu7RRqY&#O$Y2cQR6BxHNVhbo#pWP6{5x#cg+
z8yhJi9SavSqlL%Z)pEqIAQAd}Le5cj)R6ghTy!JsLJ4Ey<>^^vlGk^K`uO%V0&e02
z%t#pRgKM+g@sj;y*%*GLZ8O+N9MN2sC7ZAyw&axBs{hnX$TcDm4Mk>G#x0zvhCbiJ
z0?DZ=njpS9q^Zl{yc3UgB7X0V=UY`1@#f-Ymq08xn=bb1{*>IY$&fBi+zPYMoM1bl
zWK@}K2>N?c5I&_P<OSzJ;MGV09-vC(yRygC4)dQ{eHE(C%`%?hto~e^r><fa_c7fH
zVj=rjxNvt#8fig!a`hv<23L{T)M*uP@Gfp}I!?N056&G#ZKda+4u9X}A&Zb{R9PYS
zEMkS}X5H|kRofOGQ2hWap&lLKjF+gWXz{CbX()RT5s$0ba*<ic)=B8{dYrGHmgdm_
ztSE4umyROv6anH`vFdm-d8RK_gg7h&tr&Dyp1-8&C@0NB{ie6M{i-!&WjQ7wYkAUB
z1pqLlVyxhim!c8|n!w&3+8<hR(rD&~mZ-q%Ul&Hp9^6$e+QS;0oE@2;x~8jPd7Ncj
z%g-g0VLx4XwUSqolAlGQ1;|8KJt+!~Bb<#rp%n4<2s7XxS8F6g4K5i3OLZD9t5it>
z!(0Q)5GzUSL(3`FcU?Y=T3t0dHJL&XR0(dojH$Ak`!cMLwzZil{O9_)12_wpGglkU
zT;=;gpHO4wT24?AsWnK@eoI&eovS4ddq8X5FjZ-FOqOMG{!~`bRFKqI#SC$&Cc8VS
zo^!kH;I5u<lMuIWM_{qlogOZVA(mCo^p@woXd0sqUN&>4E!1ItNS2}&r*g~n=7%!b
zL8olO*^}OnJxIkU(RRXP8%emdXve`|oYpPZ2Ap}X0_yysdJEAbN}2^~N84T{zifRa
z7;7OQm%DKfy}fNn9!=G4xYfosFii`|DuDB~;S6VL6Rq!frVzHqMB(G{wzKuKW+vs;
zV{WR;e)<n16?^97(?<tx&gro=hL2q0*5?(i{&-&&q-1YP>#Gf!%8d}Sf_RT}!C}S>
zADsfO#=nUN(UWvevT8HR-K~PyoEOuStQ|S%X*-RIcHYUpSn5P=f3?V4_Urlhn-0$0
zao$o!+pBiwj-tpXy0VWAc``jZaMz~il{;$^a<R6JvF(QvF3|PxBg5rK5-@1lis1%u
z#&n#y{J}?4-}$5!u61mE(Cres$nrZFN1%J$u9IRFp3=FBILmyM-HFMk8JA=$Vjo*e
zYFPD<2JoqL^GlH3^J4H&WQgId0|%$@vb|1Ujh2~t0y)m&@FCP;uGY`=S%;byy}NBl
z_Ddl1iAgiVIw|G}j5s_nGxAp&sU5KkmO{~o-dZ|;;AB4Yzv1zQc*LcV_4@DIf{C|8
z6k3DS{+(k&pyNM(zm*;?zwMHmv3zv$O&?fzA1?o6L9m4E{j&B`Vdfu=|Ih0uxtQw>
ze~uyylkfdGZir|`{&5lbPg3H4R=rsV3Jfwz;Ba`3ZN0yXMImE*B4WR1d8GEDUA1c`
z8}kV%aq-`7d0MerflEW{RV44m<qw@1iu2zJ-3LmC*P^1L{K$h9P#r81*{;1aQs)&K
zc%3IZjO2yoJ&Rv$6^h?A1OM;#nHedt%Q`}ar~0`9R>v7uQ@)QjUWjF17f14Yh0g|E
zsimB*G`yiXGjMi@abwrg*!W7hNuCyVYh{d{nXi{-x!tbvd{6K>!uhBdCq!5)^6Zfx
zzt?Bt?=**nV5_^7$={sf)q<>+nx$>)eqQq)ee{)oZ!KCyV|VR~<3m#2@(&`Bh(n=J
z7be0eomVOI-T27?Tc4)<K=Odl424$>vH2A|>k+=QSwG}6MwH+mx2g3A^kPnBwP~Ag
zIY{5y+S(A@hFlznyc`7`XsoTO704Sg&2B=8t;qc+Jp)#DH5B^0=Ygz}Co7siBKN=g
zh=_>vH`C@aQY)Hw7Ec4K(&vjz@@yJr`EN&@xUD8D(t1q{O;xp(^dEzmhaG(M7!rH#
z>a-*bG5MIy_3J9RuekROc<VbbjB!T9{&obHjBVY+$(6B2`pS4y<LX(*Fh3fwg3pJ^
zD2_dAtT%wXUI*V#hcY<THh;vQy?n=aPb??QwA1L%jCA$%mLm<sNr5a0_7032f;C&G
z&n~M*nhW=yt#R*lwG=+N;N)YA^==AS8mj!_Iaton6h6s5h9H+IM$l%{;)DX8{DMXm
zX}g#wk|;5DijdR%EDiz!GcEBN)8q=gdbvwSst?wS6r{b>Y@|-ZjBLzyG{T6#dh&e2
z7mEw$!1xeuNF-8NLqmi3!Ea{F${}QS2TWqUHeP+_7klAu#6Eq(2!;9dge@n>V=PLX
zf5EyrjK-a)gXxyWj&AtXky+n_J=!|ZmRnYV<17f;yQ^#^V9`cBq@<+eQi(r%`g95>
zpX%?I*43_KGB6hxmwFJ-5uVu1h0U`!2ZXpEz##&a%tf&^&z@XOg=d_+h?{<Xel`;}
zEzj)&dNkpTy&2!_vEY4O)U&_NZ+PrBpO<r$M`4V`&94Rg`caGA;J@~8$W2J=mAJ$E
zyPs$o*SyjZt(T&gVAs5k&mOp;DewQYb*9iDW5C2zAkY3N2S;!muL@ODgT<x-DPUc*
z1xZK;VP;(6(1hGt#!d`yNp@fJ8YvB<4!cckBwZV8eq-MlM0yz$(>z}1MS7bPw$XM1
zbw-Id%`oDGGXRCt2ZSD(xQ_)6+809YKCF$-cBBO#dqAfC%8TC{F|r4N|9XF+Fem7*
zr+>RVIectK5V;Sc<qPW6i`)J&FJFc?t!_{#LLd@D<xal%XQh5VJGohtD|c7Y;+^Vg
z&S){HO-<3W-o4n1igTowa>=QwE8Gtxq@|CI1@sztF`Yy%u_&QBgS0SjLTt6z)I9KG
zyubHl#D`SZ>1S9LJRVX=g(BBqa&RgAOi<a_*vOU;7e6-$oPFW|8>cF~x4@vB<6sk|
zOQp6kXc^(h*x6d`>g)_J@aTW%6t6V*`W|)y87fsc<8&5p4G^inD@(=Ew9qh%?hl}-
z#H_F@d_F7K0a|$I1z*JOdI1wf-dU6=>k@PJHVaKEUz+nEe!ayGwTL!ezb!c{Ce=@7
zF&B+jqYj<!NQ17PF?W3LJM|g9`e7!n*R)JK7dU13A-TGzOK)!kp|PBvQ<ohyZe7LE
zUgn5ze&g1iJ#VvxC@(7dH3cxHWHnvE^C)+gQB}o@p$*G28;eK4Z;nH6Za%@!&dsUA
zK_rZx_)07A4`(KP@8_wtpBEH7R2Qi`wH1yp&q$ZYPt0#epq>oBzUq|Tm99|Y6Pd%_
zV3;nyfJK(Wjl&;Hi;0O%*Fm%P1X~f9k-gswP~1qSGes|7Ee$h0J)Mc)XRJyaYA`nU
z0B$vjL7_f>-@N(hgHYV{={c$L54lOU__@Mk`nBGW$$w7ZifWKM3x%q%cl~qM`&DN8
zYBnTkdu-vf-ItQm(w*B$dmvKObx{`-y?5_{!+Lj5uIsu}5(7Tb93INp#sgWFGunRN
z#J{)97AwMWR%VH@SW=OF6|wp2TU!A~?yin7D&v9QfkH-pe*W7t(l(<Xf|f@(E4x=m
z>#O$``%B0m&Ub1~OIhk88C!#nuf`%i9Xh)iwveB8_`8eMTECTJEw2VLj0A8)VSn-o
z*Y|H}gM~&pD`tg;41*>3w{PDLl~|T7KI)Lh24r5j_c@e5e6#03WrSF$$%`}x#KP~l
zx1t?zz)LyWPkn}~^4B|{c-2WzbC%S6#&H?<wYO4IQkLOei8{g{0q#e1$0}W$nm%5f
zn)8>&;DI820EiUfg*i{sI}^3;*Cv0PIWX*enzmzuR>!k8qIYgHZb(-354bgCrR~;k
zAxR1vUTVv@YUeY_8CRXYS2CTkj{`8NOl@8!VHGpw+`2T&Y-*K57l`?T%F?%AFM0}6
zR}q~GgLZbNrl!)ajw3mNrcKRG-(Oi@aqG%VvMjyhH*nr9)2|a4i~qTS3cwi7j#O!~
z<()epW&u7RFE^{=_mg-CK}$o4VlZvXlH3Wbs_OFYGpIwJnoDA44su6g7TO;5izD^2
zwW$-S&MR7%nJ$(HUNRPc<Q-pev&Js<thbk;BrTtqt!K(~EpB(C<G^%>9Ud7@cFPVa
z*y4cVez;A2ej4$x|6PCAhlYaD!v!{q5Z}*79Ly2@={#?rjj0sY_N#6EtXaGgvA3Hi
zTpU7~E6Jqhgv_BeuDKJPD}|eO7E98sN-Vt|4wl<P`4l|NJ4EzSug4|(`?pIJh6%yQ
z17)5Wx{N5viTFQZJWy^=x&(z*{9b8XZKjn}G&h8;jyHv4eI}3#qxEn$E}rXaQ-_aT
z*gc-%x;FW#ki@^zrAi^};}}$u73t-{@ZVF>R`#LGwcFP``hJmqS3Ns+aR9m4SB!7#
z=pcnG*A9+1-v1WE61u&z+z8S|k3V#&GQ$H@wb_wcPossg#_gwuf#I_$R^_Fp`R#R;
zPA!}hwVqaZfWTW96}?AeYd!jH@bLMrow3GX{u3%5%jurw)@NVb-r)gZ$RWJYrLxL%
zzES7f_t=X*hO;*LHb}t*2d+1=^=nJtFZlXVYU(Vt2RH1I0A)_6l$CAwGErtnV5HW3
z#AYO9-{H~J)0+yO1EbeNHh$g%aJYyCV{PtM+a6tIRsb0ZiDExccO6gWeD~RhqCIWR
z%{M!l<$kC+oSx4tiop}%+l2vLpH{cB6n(~%`b(@PHizAEvIg)G`!ZBYMHAI#Of37+
z^kLXei^iUxEue9_qwSnKwf<Kg7>gLCDXIxCOY>|*Sobb)fbswr)qm~#XnSRBUlP{;
z&Vm#M3QZYRcLqdKE+2B7A4%s>EdH715$XU+`Er__f7{SPZy^_BDw<!Wy4!2|>x)5t
z<nJlH;FVG9{Y-e*`9({t`N+pN^a~I+v1WEw)-^CVUJ+Oe51fF&I-R+E=SqW9OWa_E
zEp`P1j69gB78;Z@T<zxhvo%ptTzqI8B>3Peh>!iiUq9|;*z$>G1rRM^lE5;Rely?0
zD!2~Mnl$rsoVol!6gHm|u~)4KDpets0xSVt9;%!f0OhwURt*V~s@@3TrUEM9+i5FR
z*?XV=QL3babZMLcgGXvKXL^z9p7Mi|au2n+(E;6`O2hBYZd03cb90H8Zaphina_l4
z$$JhgEnvqHCEb8E?_JcevIfMg`aw^wMlJX=g!KLx$>JEe*tY=H21sR=q3EOTvH7c~
zGn$LPxm%<Dt#Oe)3e>UQ3%s+~FtM9<T#z=s0)8D!7f-U8*StuCSGX~=1f>S42QuU0
zJt&GQ@2n~uJT!ubUDCpv0MgM9|8?#Pq75bDmX?}oSfh=mFYkLDI&{d^s`Sp)Du*V-
z<4h;zK`g-G&_aE*zb!~9{tS6{;%XNNs}xYPcc>^mne*oXAK0NTtISn($k0bhb_E=z
zS~JaeqPPHIK0D?6p=6WtyfcT*k=O6*2|YK$>-D=!^cI*hLsNNcjlQ?m>-cCr$+2pg
z1`7M!R}Cw3b8`fURG$yOr(dqVbHwinF|!@-+=jXUxCV(Y;^8~5yOMziAY!j353#;?
z?=27#DF!6Xb~MhtugIhgB;;xcsHGJeGq2<via^!q7BU7XZ12>SsdP3Au~=-xpkncX
zSw4c)oH|f0^5ri`J>OtF7<xYykE#K7a&6<;U;DzvZ)RzkY6}vVKO59O%zj(wW{-CK
zcw?~7O;CU5zU?KQF9Ctm$Yccdpe)D(Z!uP;$RJcAoHTTX)315$m;nHDOu#U?XrY*G
zq4^<e<nh4+H#SfUY$%K2ksWhVNSmWAzj<S7XWiaik9lbQPgJqtr(!*5Q@|7F0;^#_
zh`P<3XuvUQq14@A<4^Vy3^J+Ot-JFH8yC_T1wbvJqYV)APU<r0BJlLXDLBY_*W8V2
zh6ejCtQPv-XFmSeySe%K)i8i^`zIpE_@scHmjR1Y+l;9~{MyL?WSivW%a=j%8MA|>
ztA{r@k<vXb9)T>}KL|Y@3BLA}t|`c1Zshx46ZrjSdkp%%9ud!^hZ>;8(PC2enX%vr
zwZhWU%c`z~*B2XtSCM1i&N}WIgpCaOJbKCGiV?jM0IT=5Dqgn4^W)*eRW4%yUVCu>
zr7A#Q7CIX;pS5iO9K8LVe}9j@$}a(H0Vxz^J{M>A!S|jS%m&2ULVbPxvP<~m(=To>
z(U<-<4cKc1$vK!(BXcbtkh_SVsS#;1_Ua)sv5Ga`V-0881|%$Qrt*IUR2gT!^Z2%;
zt!<8n3Y6wae&R7$H9WwtzZ?egR#90kCntxW9q?S>MmOLxjqa-p*nMnP(1`ovY}@05
z!GzaWIp#Vuf<J>njk*A9@N#f)U<p|&+f&Y6P||+V#~cvxfPNEf8Uhy1wGl)bLBKA4
zd3H=CcyE_>me~++;r4dOg8}4$PIyC<zz&DTf5V?$8UN@HyoRneVS7QNLt^}%iH}|#
zYsAJhb*p1R!RhzY&G;Wa!QpWCVIa<}bU7&U9(_g5pr$zzG89+q0ickBK?9SI3K+Bx
z-U?7w4=S(y{P}auzB{ZoOass}93AxK__|xsoV@^{T7(J=vvx9A4jg_}4hS(u)F6G=
z1!R%5Q~X3jpl6~muy41Yd}PKde!n@@d9U)aw6wH1V0=={o0L>XZJqDZ^dUpafWpI}
zAAy*!Z=?Ad%uoRTJqt`L%n$x2+;^!54FB(pfAi*Jvh?@2#t-n2!il{JOP~{sA&K?u
zi#ycCe$U4YcuO3wn{X&cc-_9(V~yfXW^bl}*Zw@^iwUPwN%KHFC<Yk}Z*On+hc+%%
zk-3|EKwr7r_hps-#VBG*<k#-~t<?#<UOb;#_G<-COS})BL4D2a&Z!~avND_!uJvC#
z4k3PHk`s-~4)0K93kD(|g0(-Zyirwn@ME$AuvNY~fPf=N%5@4sE<$zvE4SNa(AjJ^
z1d?r(c&h;d<oL`06zW96)$)bV8zRM}rG$0<PLMbs_6`6MgTxEE{_(H8DnY7>AYl%J
zNqX!6!62*3wKLsJJB}yhGqYt5Tl>Ujq-Sbk(h~$5!G%=MV5htRU^n+F$M54nlha*@
z8lVCmX9*`^!PmV*nFC_iZ#0gU(Rj{mOoUc!OVbsIjlJtn;={hB2p{U@8mbx_NDKbI
z1blB-BZah2^7oJ8#h(1SWnbkCi|xtNo_MgY4xc0F0hHhH&QTBN(WZWDr|t`<^rZ7@
zEMQmjo4WFK+jos3F5GrpYnQ4}?Z<^sdVwWLPAgz&sY2RuyI+@wHkP2c#l$YUQ2(A_
zFSz6&Qwi>202-2D)MkkzV=?E}v%f;{pf-{K)HDt9DU8*64!#6+XiO|6jOC5$iAl|T
z3&8TdQ0BOMc`=H@yBbOoPoF+*7W^h{Q*);lv9;K%&e-hzA|kzMiv&=Cg_&OG39>Vu
zdcvh%Bmtw*Xe7qv+yp~fNJz-HKY9s$rIEF;=H*$a;fLL@t;K#l`W6{m>4-;O6zR;7
zb2BV6%u<fE#y#?j*L-=#z-ywJK?N{c5xUiHwYA>DpJQWVv!h>yb*}?Na_rT4xtfQ=
zRc0cz9RVtp<bzG#tn6(D41-?^2Bt|PNQV==+?z4B$WnoFHskZB&YbZkfhf7R9~ye=
zZpa8jPcmwe4YO3ch4z>i>D9^~8DQ%#&+i=|cBS#-84)1^Vp(KcJG)Fcs9y0P$)thW
z(NfL)=pcYUihuE3_jLw*yj-tl(mtUg<$yitfUz**cH{Pg#k7e-C^rWO2R{=NlXju|
zPkwRyDL>%ldvc-FC3P+!;-mmS;oAP=rxUm7Ne?4T9B3i6^WEz7hk7vOseiZ~_PZnY
zDCxad0sjf<HD64N3HU>}SV05G7xFVP1Ypq^4IqfsF+qH0CeJaR>pudU<)DQCDo20>
zqX}HAHsV8530gXW2iCX$!|vm@?tJi_x`Yb&nW!y8v5lM9xG4YNJwWKdtv$wS1V5=U
zc$Kji+B}<T+htTskn2{Zv;ffJ2YF|)-D{*F?ZNbnXGK#`KX34;Ndw@~2moMev&_P$
zh{gf7Ubo>BK_`tf(=`Y%&8($bVqFH15axHG_}z_JK&w~b8S^_^WDeJ^%#cK3dq9{u
zrh6@lP4n-d0B-+O9w|`m2nI<3k$eJ65>>nIH~n>{gLuyGbO{)!3KKW-i$SBx|K6pf
ztlL}#3PX<Uycq#~ob+-S4P;eXT0n&lvtJ?MLg3Z6fWc<~p~drXMhk)QW-&lK1uFqc
zQf>_@*#U=zCt+ngzrcX7ex2zG*ixI?cMirOK?}rLN^Y)T3(HxDs6`GwAVBQbf^6>w
zWa`HzQ{&tS#?!~<4O>#&BBm_jHy%Wc?!0_M(JN4c%E)x3VMa&oTA=rV7M8LDXp@Rk
z){YYUy9yRx-}{^n<~Ev|n)!L!@q2ea|HBTC8Uj{PoG-2J+Nj;3Sy<WEYN6EkcPT-j
zSEe^r{NeAQI#%q>@b5qRTxwmt2%>*>9%^;XXFPM8DZ;w<<bG>qwyVjQL?P`iUrDz)
z0(x1vSUaANVp{BYe0F<jnBoOPfOy{bVUkxpUERM?^^CP-GsF7(Ob(FM4kV>*B>?8j
zzzH?Cz(?;=!knc*A2k9<4uu&3il>gI1Ol1Y76#yOJR9&24G9?Nd+XmaH5F;=D$Fyr
z;6b3=9NqujqLJ;}qj{DgTbBvoV)?xMgC9buP0+>v1;{JC0+ev8UPCL8W7R;q$-U@<
zs~j`Y=G|f|Y)DN_J(KDCnLT)y?6~h?Zew!+2ph)?(6ad^zz9AQfcD)3n1lbYP>`V>
zYcvQD5Gw3GQsX`b;!Ce3(Wh<AwmIof;s-1cT{-}pWnEwcM!ez%X8tnEvdK``L?a>Q
z;lJtsUw%%#@o*k+78^AlP9x6fFG-Gwh5W<FZV7QDegC#M5PS!yAtM!FH^I#I&Nz^p
z%RrYlMghWGa96e(eU<v)rxJWl)dDnQKI-(P>fW90)roH<VHy>u@$(3Qb64+wjd`>4
zn4MdcaQ@2Ok5<UV6XuRT04QaNfyrJ(ZDR3^GCau-Nq-qY{tqFX7Q;J_P|EEa%Tq91
z>c8I%=Irmz*9Ap1fH=fWeTl|4u6^d%&(>#{o;G(pFgf}4h=Twm_JV>WGaPUhC6<9a
zir#ifpwn5Zj`4@VV2_xdPL;MDSRM~u4eS&H{yfI7zFVHH7U~^0VP$H1(gqZwv#<;5
zVZS%b7th|N0oimSSHi5IbQqY~L~;Dl%!D^?{rgZrB9gJAMyrTmkg@9vOgVf9fcuJ)
zOZ%xo|BA+yhEOLTi%gKZBp}TjWHH}J1gh&U;`^)f_&6i@^b4^)=w3Z&&t-<HT=ce>
zy!?q<XL`TSK&dq&v%UK4xtL%Ots2{N89sZ9L_KKuFYsr~&GFv7sZ=Y+@GZ_lTP>dt
zQ_q4OnH@K?Gnv4^lHC4&Yb*?=#p~aCNeT~W9?8x~_07f@CR7a~3<Yj4eOR}Je)y*?
zJI-v&s5iXkm!C#Swl9A~(j%ZiU5>FoE^!)JzWVto$IF!la=8YK$Xq}P1+7NRayY1z
z<Mdrh_XKD^R$Bl|+iV0hKR7>7g@)Y4SrV134hj`D-$1Q2(^t*{O>O<<7I_ex1#;@r
zSv%iP1w#y`YTfsxoBqWoV(PmQvaM$Rz~R4sFP1m1q+>zP6US`-pM&l=G)MK~wTB;a
z?|pej{<GpCY}W03sDBW0{rdDPnWq2l1-Ke@_DCx;FwP1u0AyP|fnR-BQoiHAv$=4K
ziMTB`bq{GlK*4Hm@9Z4oJ^EA-1O}rSgwRWXdD~xsNX0e|rVxictg-VePkVW|y3i(?
zled7`^h=bT;=N4+rQgMKpp-^Fbk=UI8_?Yd_d+#hjfwy|%d<E>HAJ~t16s~aoid2u
z3+!WpV;&Gv$#8#v>a#CXYM>pX?E-Y$=>`i5dyQ=TVQWu*2Y}O^m%W8X{0N|D8dbMT
zbubm4p?lHNtAiC2+|cIV)53)tOji7zzKYi<0xdH)(;$M1iowB#hKBB-A#C;>$I+sI
zNIIPecmw0R#4@wn0iqX2#lnxbUpf>#`i9OOecB!al$RA|4F}Vxrvk(bv?E}ENMS2x
zl&x9{h^voPnavhmNBE?qloW@yU}AFfZHjGkT0l8lWFK>BqTSUtXCcU8V{o95R8DjE
z<oX(f1XZ(NSb~RhRcX1V7pYS_x-pI2heC_(CYY)&$f>>Bni}~EfWh5%@J<E#p=|_p
zsXhd>2wb{EATN8ruosrH48d7~&Apq4P@5f&dmD71K_CTS5ws3RnHLi|V1d9~&D1M`
zxB$)k-3A?|hS4@=xwtOK|9T7h&9L;OB(wC44BN1;C$ul~0$34Z61p;wn)GAOVOGZB
z$0DDYJ^fwB$Cjoe>>q`xnHMj8&Z)BW-nJQ}#tcA$YwrP%P<cY2gEku<0=lHKvrq@s
zBA}K%E=PsF_Z<DO{+zj31!NbeXfEa|%)>#p8?!0i8aHemkq^k*fgl936+nshWNH!X
zKs}9c><kLOyQZvmC3)VB^&;a?<N?BGWAWSgw8L6!tSmq~@9RYc0O`0;Uc*ihgs4B`
z*u`r#E5Kg?dyfx9#z<u$BkTCk@<5ZMf;J<x1@JvNu)f79Kf8tgs1?Bid9KY|DFlUb
ztKYAMcY7eir+L_Iwmrq-Nqg|n`+HVEw_nLt4qT+%oMnd`aZqMJywqg#F&N<*P{ycW
zS@88JSh7Qbsa*e&Josmj5CJ}TJ%oi0)?E0~HXkK~0NABw3s4R~k^v=TzF!0^aGBcJ
zAgt@iEIh64Gjfk(_gEpv<4^pLD~`Z+QrVRo4$jB^U89w_msl$g^7G&Sz%s4Yn&6CJ
zqK*NqzsjEL-$$LtQo%Y@=JNmgFK+l67YP<OV2r^+{eM1~t+4@Iq!!SGX+|w7@o=Yt
z3u_Uu6P{8j^0$cA=wi|Tyi8!=Sj^#vPFV8&xVE&-><&NbH4{_A?;igdErhN9(gx>9
zwj1xZdDP63m-%7xe;4N2$`)nKE)RVg?iR6^TDSl!DVwj;@i+q~sG!n2UiEcWQak&a
zZ;m&dS7mOdXCBwZx2)vF8}(f;9JbBI1UpfO<LfzT|A<J(w0Yj8S)Bj(RS*G~*{Ngg
z%F?KyDk?9UG%Qrndp|DY%meBF{6GYRURikWxz4+(_xKp<u{BFds}ywz8ddh6DMhx6
zCNCf5IT+)@T8%;*DNg(9FTuTAzVh2j&KL8ve>sNoPAs+sTPe-V>@K<cLuf2I<4|EW
zsbY^hsgseWFd<>J)a4k6xD^-qBRbWi8dL4kyvxRUW*)Xvn41$VS*?chPL`J;oi4un
zU8mtPDdkA3C~WNOT}iOr!Yd}OA@FFzvzpZ-4iy3(OO=gJ$_ffqYJGU04Dq$yWw4yv
zmi)c#e+{b`)ualMd=uM_wB`O8<nlg^t!ORVm^u-B(;Ip?G=%erZ88I|mtZ?_7s>^3
z)+*47<Re(Zl))8`<(SoJ>fkH-*uuF&C#wR-T1;%hrs5&FsBLf9E#b0b0#Jy$eEp^%
zFTybXq16{Ge_BND>0ph7We-T}n<TzQD_X6GOXG#+aO%(A9q+EFhgJt#(?h+LVFzS9
zvX?!6Qx|P7%CE)4tL<ZtdbzJs-=#Q)eBrotrhGu^Vsm$p0^}pMoy2Oz$8qY|8Z}w|
zs&@qT!^AS_b>?OqE#%&gg<e}%<c0*hI6p-F>Bhz>nNnP^?lX>!YWqWw^JmIU^G&g~
zZ+)+mEFe|ybdh%y-pw)xbNb90pCJa}eWxETwHnyoagVACNwGaHBs;aNaaBX((!z(=
z6Sg{tksTV0ynYWBt@IQAJY}5vxAE!!Y>+TkIx#67v8pq2mtki?)Ryh#E=}Dm#t5Q&
zJ}9=n@-FjO#>0qh13`J1Zjt`?8#EIl;Z+~szX5mS>9<qIW-Ihfp)kEu$BGnVhaDy5
z5<@F7EGSV}dm6fAz(50SVfUZt<N12-Q^xrY;o9WQ$6qLA&RX-jL}ysUT5I8LSHAH<
z*784BEr7i@y3bGG(AB!8p|D!v{=^y!RIhB-w%$$}rb8s_QgJ%wG-@2RSw+xs{BPKQ
z%s8ZBlaY38BRluMk^cJsJd>3ApSN@T`=<ZLpHLBG{$k3XBjwcp%1)8r5!4k`LREF?
z_IBaI3gHr*RkmCBub96+v*#U|iY#gsh!TwPy&e@g`RKaFw<oNUtQtK6?Vnf@O*D_A
zPbZ#tKB9B(^Kk)TVP^q&L+{vpd=q@jkaWvp8!SItjBN)lmt;2$HjX2RwWjurF`uSC
zgF7d|^5^GYKI*Xl``a&Cy8n*)iYe=#%`K6MC;#2q@+$e?Z7p{(2mi%YA(4MC<NV*@
zBeL*rmKqlJ!9B5m{8jQYGI-Cyxar-F_g4C1ISu{?Jr4^!u(OUYSGNgoL?8l}?vwsR
z$anpZjpte{p~YP#;|^n38+_|C=4V8%pI9p|O)61bSsRh)$6KDY#9NQu&CmE#8ot9m
zHdd+MQL9pZhtLh|B2<1UnJ90z4*!7A3H+$5_-7y3tLVt!t1|Bl%bE)tY`32A#_iwO
zGV0Io=P0o)e-O<4ijU$C?*tt19Iab)>d$*8lrj3xGWH$WAl|1mTOmBG9O1n+x43b7
zg|gQVl_y;MKiYfmsHXCMZxqGRaV(7bSP+5nF~A@qDkai&9Mnj&5JC@%fYb;GAwU2t
zIy6mCN+=d;D$*i^0E&XNph9SYfD(uVNN6DtNOCuE&U2r8-?P@e|D3hnb<Vs0V3D0)
z{g%(S{j&FZWJYW)Um<MW6hq7t5pa4nPj1INZ^6Yj@WQVNWzbE)jdgDB^fxVt(sjq|
zHaW)i@%k^!<uKd!3~gw?8;_KQToM*B+X$xM&Ee|iVXOH(!!Ge|rZu6<v6!mmU}-Uv
z4OWe`h*w1;gzZ+~<ImLt=0xuc5`#Bn;IteqtG{b*tf>1M_h5tPSoYxc=oaR;gPS9r
z3pUiQ<OMOCajQ3F9G(`Q>(HC0JU`#nT(g@u5$8w=nmZ$|6Ib&R2%X!H?QP%(5>38~
zwQiL+n@8Dui2{JiNZUY--<56i=zV>0K}3_U3H+|lNNJswsI2#>@u&`ZUckQGB~w#4
z8{oy9PBp85n@^lg<a<+s2(jaL^Yr5MUjMZ@QiK>LNr~clo*h$<ngLZE4k|Nz|0~~I
z@+SQGvO+|_=+%VqcBMh@&p}hzaFw3kLM!AtV%ix*)aa$SVHc#Tz-k>)vsv}c>x99P
z3@hsE-YVUibx!Mo%Z#?O@b(U=B`L+Z4mp*dQskm>f7eV(=LcKIg%pwKko?Z+pCg;w
z5OU+&uwUyx^a|{ZtB;0F0`y;NR4V>Ck}GIT3|{-7;FJ^iS^m%eEJc++w(7qYaa8sU
zDbQ~H8U9Bl*`!F!|69!$!HzS0cn?&GQs?IGfPNuft&qkDM?O@po$*wdLTsp=M|uhu
zCq@fs%G5e>0xy}_fec+WV!9IQTYKf6-1&=dcJeRfoIuA!fJbxTlJDCoXtKCd)g#(X
z7;raSjxkBk0OawZLYJuSN!6!?I{KaVr0t|&*ifBGepE~QEEn3mwNEZH$NpH)UZ!>0
zNQCZ5s0ACzGoChcOrPR@Npe~EAhTEP`1v0oabq(eH9h*m-L!r4&ojl~2>sr2U!FQ^
z@`o%!uQt9^aZ)s5bWh@gOqIimGR4-k%t;-6p*xVx^RBrh{PX~IW+b{uz4_*}s-t;X
zON6o)wIC`iyyvCLx^Z3=Q%Sex(e3PEPSY#g-l31N;|T=ZQQ%HYt$5}o&htf&;j;34
zQ)2M--_&y~;+d1hysymDz()lfp`OBUy>+OePUXx;qX;;ef0C5y#~n8Fm4)^VSzf2M
zcb}W~g161S<6TnhM6KNB;u7G+v53#MTfeVkrs~yf_9T12hpw)D#%s!EJip~@x*C)A
zq(jfaaC?S+-V&C;Q{OxEKK`g*s4blS{cL>oaxxh$%YID@6Xsii*G-wO6Rgq+)9VD<
z%;Bl)?Fj>Q-+1wwvgbpY{mcemr7uHYR%65MDr4iT`Tf^-3-)fz2w<n+O;&rT-%DKw
z13%NJDs8#&Ps{3(KS@)#^$ph!K*Lwjf`0McW?CudWtT<oZeKJZW_aN9C8$cO*-I}$
zNH2g(_6$^WeMQh@&l%im;Gq$B8ZhQa@gdW3zRq%+U!&>Dw4fx2wH+}xUlzaDk@5;}
zXTg6M9R2>_8f>A3Uv$LjVMh;J2E4@|9LcGVE6z0}!QJR3HMbIu$1xY<MG;J`h~vFM
zK0M@h=E%;~=7r<;_JTJ@A}iHrW4ReLcD0Jd>Wjo+FA7>zo=4Lh`Kjk2&TM>#YUI>$
z`P~V7j}Y62HoF5c^ikS}U2#5ZsE3fXtqepwbxVJAml8++4DaIBPSS&DF61o%9}We_
zxTVsTH?Q@UjDqXKY#X)^cLLxSr%tT1Af=MnS5)g7GkT%J**$u}W$OJYJ%;^<zwSru
zD?PWSJ)ZXf?V*xdIu4OTsG2MtGrCErcwk%;Y-?IngT$Ku!*_VQ896E(tE=X}b0dZG
zi`+Buv;>W~D;Ggiaj4iELJaR6Z$UG9F!!>=4qGbPVf&7)lQkI68RTgdaf8Wr<SO!?
zqid5jNZZtEb+}MlM|NFF4ZnSonqv{+(`?F#DX)oRQDfHHDD)iDiG=mkJ2t~Qi41rX
z3hq~i7x^&vazDvq#$$gt&X8*fS%c@NHJG}10l|(K?5t{L9vgwZD6(d15w`64qP;dw
zDSX$=QAQOnX##lm39!-r{{^)7A0Cmgc)^Mcx%xM#ZQcFR;-&aJ7I#;2EqvkByxd<X
z-ExNV#=K7ainq$MWtxxA7_W_{aO2I4(2*HNX!G@0`OLe^tY&r*yf%v6<X;-NHAMRe
zam*b|2?5!8z_c(5BFy3C;iI=Nq2!KB5Ans_9s(h*MD5dc+5=ys%@LH@$GqVpiaEg~
zc%)Mv2ABG@hiOcsn2Cg4cp8+FX&m7oe5c6**V1e3D`QiDTo-T=O>|yE-}L}qh!=j+
zF_ltxB5XB{M`!-Tq(FOJZ|BMzokbLg-qG{L1l}{-J8b7(lE=jycY1vdvNBi$CnwH6
z?9=U$;41V7KAi6}*ut!b=9L5+iO4^?-zZqm#(|x4qW!SuNP&6Slp<4+ph#drDcQym
zG@dn62rIN*YZ^bB;oA^>Q@D%iOV9)N!$t8EuiF6kD%feLJPKA>B6btr<7qPex?WEf
zdC3Wpar{;Tja=g;<k~t;)>X9}9t?;njk*~Ar!bpGbZ?&*^3M6>S_Y0-$XlO9G@hD2
z(0{#>FAFX^jO=*|cx0A@y4Oj@^%hsuY4$_%R>VKf{8Y;|s=hryOf~3t`8vcLr)Pw*
z>Wt(DtTb!e?imlSD_CZYo?t-drsx^zN8lZCwK!hn#hk|@tB{0n<e04@<3|Vl*4;C?
zW}nRD#5nRf9I#RHE84esckLvh$Zj$bU+?k_l}bv<>504SZvNvbBMJoe#dQJ^OfR!X
z7Ol+9xhVNsA&nH?zAcuo(n<JW;N{mB`a;&T5VolgPhGiKF5mHnYwM`f7b>@%6cF?)
zRVwApk2C^<0l3lzwBD^RgeVGsNT(*SI5p!d2=^uw%ZGPpBTv3&PuqQmN6e9h7B`i-
zLD?mGYX3g=8GRuM7^TC9r=-*^k3<@>jyW`}kTb<F;C}fUc>Vi*q`uG|rsVO?*wV?|
zZ+to6-{+t|kK(t_Qyn`gDg7;duLxWRK_+Tii4gX$Mt#Rd+&W2iC43{abh4|tpIJm8
zF-4BxzOS{i;#jW+3pp)&?!9oFaSS5!q4dy%UJBg2xTR{F=>uBar5rQ!A9V5FcVdJz
z;9&m3qy$jLvYe#qz^JMBJT9KFkuN6^u<6jm2hK$>`#g*7B(f=5@-0G5{DVw3^S?y;
zx<9)pAIkIQ8N?3<uHly*|L+KEymFI3NN|OMiQ8S8P?T(YQ~3f3ouAeTlpOUa!PV*9
zEs+84Sj+ldgje`Pd?yT!uR2;P59K^9^{+Rq=r9FJCx=}YMtGfgl_^di5?|y%fq(vC
z^d&t2BT2M~H_^dDQ)V;48Xu&H22Prus|<0GjRZH*i^T;)2P~_i+=cNx2x;2#%G&(T
zzpiH&wRfwR@ZE#<-{8Nd!C8uI_vIF{DnFR);f{U*iIn#70CmfwuwiunKBF~?#J{d)
zoSM%*aYg3#ZfNXeany(Qc_g?$TI5UEYp^_TRu(nio{<jSM0U8lHb;Zh50E@fec|3s
zSz)9%ap4~;EU4nIyfW1$w@c==CSl^$PdpEuFRE*eONaOSCq6(x!FfHd8-eDLOE!`=
zCX}`h87?I9lDoJ{0c8V74Y#5i@5b7LHy0Gb6dDYJ2N%H?nx}ZYwtg=lP2g1cVm%uS
z>&)m$Fsy#P-+EqAUzod|C1P|V)#{$Qn^cO`0q28to2%$Kjk+wHTMKOV8lv<8CVI9!
z_$3lP1Xx@)({)o8Quob-4Vtai<Z<#!o<WQbZQ-q^>mNjQPx2^k>UhjsAoMD&%5kck
ztx2GMooU=N)aJno{qQuXX5^hqe?1UP#zU_Gdmm-O9a9mZjis&*=}HD7f20*FuJV>W
z%QY~^g4}goo&9vKx2ySAk3mjHeAuQerFcXB10~s$;F(XGbZe675nr_|hT^1*EqjLm
z&-B>kGeg%&JTBmBkzweUHI4CM6d(2M5>dq;5$>#dpS_($a!Pt0dEoMPrDnpdmw1?N
z8}5SD{mH_`z3M$mt&V%}RYbFN^6(I%y}LI^<hnO7yQJR4K`s#uCjlI@AO<9)_MXI>
zct7uay+0;OsTt7^`gQo}t7pgMl3v=nt=dX)g`Xh0xmu+!tnVzBKN7)H2JZfa-MA^s
z*HC=_nOZ))EMc@9`4}Z*SIW%+aSwo?ux$pN{tJJt<BB7N_x#XJ@jvY0Ap?how$>$@
z6?jby6s9bv-pVoL*yVYbx6zk{A9)r`Rs7u|in@{hxF&O5mpI9vl^yj1^PWD~9+_Gq
zt%@4i*_7)>k8+Wn7e2ck{o_mKgTa?ymo#-kd>e-nBlM+0=5m*~fLXKd@9%{G%X<x-
zZ%1<$mQC@3k`%m=;9E9<z_pviIUrU39pd;wN;fQ>&GSv7H*@Jhg|U67{x+AJfsLi&
zO7dKSoWG%lGfD);7v+kp5bzFAOSCV~o_{lt70jSrE0osV{(IHCfQb<%SK!s@VFwIO
z{m5?xGy&}k^o3&YY~+Xydi>ufP|qq!?f=@>Gn9=?%oN}Kcc`ulD~z2v!HE85BkB3~
z;o;yb^~ENE<I7~nnOp~RFQ25pGd{||+?|A2q>H*U`lU(8UN5jiD%lQ)n$Bcu{QJO;
z=0(#h-`RM;#7^@bXC4=j$wZH?McivTRXn<GMb0ayT@SWKQpkK)`mOQGQRKcbS=A;Q
zw8&A3Shcn9hYyr>?0SQ1Wp$KG_&g8AyblB=D6A(?57X`f9>5J;PKDdTvP1-Cc~R=!
ztf{u>9L<%ffq);VsvyT$=bZBOdGf(LZIz=E<sq7a<HP?*V67QFJAkL-%kKe5Si6X8
zJW($(+J3C_*MrB`?lfe1{^%F~WWWDU0()vj>C$sv3Ue2}v4|o$Eo`|hUTdb{q`w9N
z)b`nW7kY7&zp$JsfO7w3(n^bU_Zz|N32bLoS9HbKcRp)f0PFfBdM20v`0WS}^J}+%
zh(!|PC8BKcex~@zmPxt2@VvK2btX8sB<{vl_qA>cNuORSvXk|NFaEs&8(k>nysMEn
z*!RL}V=b8EVL87l*xUVsn7;?@yOFB}n@m3z>>hqAyq;I5PeI^4EdE{O)WfGGx6-|=
z!)3fX{}PBj)mDvGOaV;GK>+vsA#bk?0FA|F1=g4T1Bc)8!)nB?%^Yv&3m{k0?eQNr
z@Db2guEPo|r~V_GcqZs`L(Hm8Wvokm=#w8=wiWet0_k)oA-T)tM=Q5BK%R#P;ffuV
zI_g>TtMW~~Isv!^byXCDoOINtkMp7LEV4~$=jMb2lIa22qD8wrv`)&_-9Ii+*|sY-
zE<lTtY`7q2`NlPm;-lu(QFA;z2PGLMP#xfuDkzw&<~D@zX-_O1mCxYx05Ab@#9#-B
zaAA29KChTD(9*Zps6Tjw3An0Su(-=;@8=$iFdU&g9mPoY<slZ~)En}yJ*7ZbMS{I!
z0xZvO=U_0qW2v05$pJ8tZfHE@dvyOP$&@{VLhikedtg~?7c_Bty08i%xcTa<tDHbh
zfdJjK@Lq+&T`csQ=w#il*LgQ~Zk0I*=WdJp^9F0hjl4KVJyNcN#A(#!^(u$g@4ixq
zh@N~n(LA~Bm|W(Get_&+T3m_qSLUL93Z<_d0sq%ZxP_@h07H?*b#4+L;VO^!p|a{a
zl@xq(CObjNOwLcD@fqSrVvWd#?x)P{cz_e_FHV;L-T)4&mti5ZWbVans9T}I@pVOz
zk2Fdu%!a%ArL|HX7ZAEG>O}f-jVo$yJ>sewTtN(u6*IlUgepuz7IB;o4R;Z~<i$Z@
zy5!2Cfu1?UuT&jty3rcR;?4H~>a@w1x1u*%yVBk*10>OFrkeMj*IU$?=Zq%RG{4+0
zm;a_UnL_yT>WL|U-*?V|l8cfK+>TOC)MW|``>VcQu?;p^e9P0zBHod6yN?9$qSp~X
z;zI;|d_vWH&|V`48QNq#V<KR$W1K!P&wM41!NdZ--zWhARSJAV`4Qy&vJLRJ3V9q&
zSEWE=3Z6X(DqTG<ysx?PhFw?_-MjrL)j>ogA1F>gKHx&HZOQ9b$y#-;^Ln8w7xxwV
zLD<9G&z!=mvg*cF(T%dhLSKTcs!`+ibwEgdyd!|SdO4h>`SuuAC3A=n6^K<SlXso^
z^2t>p+j;0k4IGO`QGp!9GoFyCm___<?lq#;6zqz%q)i(3w|>j|q0E(AE3F!dLCol)
z)I5K{MQ9|1Gc}k6UB{=`KQUQ*|IQQ2vd%S_2DoBRiM|-esmY0M==)uFzg%LS4*ttT
z+Aea;BgNvY`d-18RgaN6KUT?gNulk;t%hhDtjay}o14At$sXpUxcsQLhQF<s|C!*=
zH)F6*-;{slThWwdV&1Bs<clWws$8HwAN?N#`M<Y;1i&J}VX{!@ZF+!KX5#z2Q_i=B
zZUB0%m`#54V5Fj4Hup7MjGn21$fc&?7Zo5)F<=w)liX2h_yvgU&@3DD(Cu+~YT6UL
zW&|y-3?rT!%RBca1G=aLLuB?)J|6avi(oY{1Su*~$meD7!k9{qDjT!qC={=J=BH8$
zj!_vdUfsxy#_KZun7u{9T(g+xa@(TdNC(DBfT|$p#A2RZms}GR6!(YzhgtswK#`LA
z4Y{BzJi7sa2L4}$Xi;+n9mt|aL^9)lh;QlE=v^~XD;A=-iVzR`dF$HJj`;${sq|IB
zy@s_<3R8C{r%>_|jKa#H|KHWI?gv)j(v7MYRo&f9&{OR~!Z`;dsRx#aXB$f7gI%1C
zZisI4pS1IU*WWvCI{y?s<r^1qOIXIcq)$PYM%jdqw+Ga_N#1VfTKI@(Uoo|rR$)B3
ztGw?CdRt_@!KaJ>B8zo)F{~L{1Z$p}5yuhgoQ7&m2vKe_!@-<zDsPMJAM@4^5WCj4
zv2R!O3^^(8`@mPeMiBgd^eG{gd622&(_}`efdPI%s*i_dYyx+!X~X_Xuuh~UJ)KK}
zYB_3kn#G+LR{6VP%_A%?4$-1cSB993uMq4R+4F25t-Vs%sScO;hqJSz<hXm*6LDvN
zww?WfxA;n5C~%aS&98bf^&I0&Bu-e}{XUID0jts2-F23hXlcBr4sc3j&0%Q0Fu1((
zB2!EOlH?t>>)8RT!$a-i{y;>TP!YOL(8AoM;$xjkv$0e&Y5)>i$22WHBz-S&`#z$W
z2x$7znjP`$B!6OSy$zQlfF#%M#Nd0&yJi@}(>^?kfU{I>YGxS*4nv0&)=4~&6*2XF
z9NggJ;NvqfK-5?t?I$!(48C?s-M{SVfd>B&|L_H1<WKcbS;cqF3`-y@+!5>b#$T(M
zIyDpH>qQP(wn0zQ`8SqxdpIiZe$|Oozmjtq9T8s*#DF&2i=#f53kJR>1P^xgNqX|?
z`HAR-uHh1so1f_lTc_gjmbLS`CXtd~cYM57yK3z#n%JVJJ6zPe6`01|P1TgtYgk=9
zkYYrYYz#0pWw*a3+1}|<eBgg`GlL}cd0x%$28|3I@7oB_?CTDt8c4Gm`qN8RUw}Z`
zKqUi3TgQTPgG+C=vr1!uG;L*^#pIoU^&UMBXX{jttHFWS00?ui;X$%2G*X=_H>+nE
zzp$5A99W1W5rM+woFvq>$~5W&pSaY?wv|4)Lvbt=s4T2p6wY^vji${;Bm@mc_<I}l
zK%Id+ualIw=4%10MvFqot{vTi;bwpk08gKk-vJ75p8!_}&k~HfDP@;fmZpZu@!*ga
zkg4uq=4{NGoM&Ds5-KZp0B<V+T-vmFq?-rryIhdsB?;#LlZ()D>i(D1w)}w_k7xh(
zoDw}(Cpn87-Z{UemDv%ch;WZelK2+jxIJmh7=6e4@*`30)VKl2k;CwvP)nGqDSA2u
zNJ)kf&gm7)@qg_IfX=8EVR*P>HklcKhm8lwMeE3uV<FP*L}|?UvNKcL)^V2oJjyLx
zy3p6@Na^U6Z_jD{>2Is%rPJWErqmXTT0!7WUMwLB8vcq$>N=hpSeDh>`?oW<Eiu@0
z+tO^NxP_?!gn(AUPrUm~eZn~?3})#bl!Lr$ebWsKZ9a34p=D=DR`)#m02vyd2&88o
zcj4`)ueK)>Rv7h9cZZw?psWojRVg?%@_VnZ>-3+Q)f7Eh=V`enk~BuQtPB^z!uuL-
z_Kht7Ko}~#H}Tp^{OUpd=E3Prxv6Mm7f-SV7U|&rU&j6#N<w+{O5<)#sd-2wFL6Np
z_ln=gmvw-R_8K@(m0KP$S?>v6s|A*4ioa<j$trmxyb{3C0*(^g-=i`o4GH&dB-YTa
zvCvw$8nEVY8|H(??bF{)<Az^G_iJ(Kvc@1*+1O*=;|TQ`HP~9wxmvhMwc?-jj>Zpg
zvsXHu(g<5wG%)YOQGAN&4KI=D*W$IBl1JQlHBk7P+%x!#g7t5}*2bhY3spQn<__Dc
z8<C;do5D=pqZ~=Y-^Cz3)f*NYBR<VGhffw+qSgvLTco31?&ygxdarTj($}iD-bI#w
z)|W0k&Cy>w8RAsR@(?}th@iRxNhb7M6D5z;V<l75R<IsF>j9j!wxNeS;sz4b;B=ID
zRhc=zaZ0Y=PCo&(0MoQ3JK9bn0<aQ;VN)Oj%S4O?$hmB@c?(xy>BAdi+X{KS(@ZTO
z1n4@pG9s5ILOf6kwJ^FO=*S_E@q8%SFC#*ZGt564Oylx$OYHmGjE@rdF6gjfAbSQ}
zj)`)iIaw6dUcs(VnvbE@ToJad49bmY>0zLJj&K!b-5wX)VR57%e#(!tJ)Xq>hG$y-
z>)_=7`-qUyk(zbSruoboKgkO#qaWY^RJLQ-u%PUo$q4YTYwU6w`d_MH|2k`A4iv4$
zik(_%a(D0WI>CjL<<np*6(Had?$>m)2#hyg8zO!~?U9Z|LXnagm$Wfg9?kU}SSM%|
z$5e)^b5Hs`Jo{{7fv3V0uNyacX;M*hea&VAR@-*XSKty%Z6*a2a$obZQRSSgMQx0D
z-@8PDuLF0h!H)phIk?X-PLNZAs3_KyPsaozt2p({5_`A5aTI?DP_@qXZlv?S-=K7K
zi##0UZde0vR5M^686qt9Lkkt=RSg;rtc}!g_&1<<iP(3OO!~XTla%pyg!Vu41lE6@
zw5}Py=i$uLfcGo*w+=^zdshbCM$6TI#TbwkSJ+U2Sio!~nDTw5eDU=Zwj-K2U?Sqn
zIsW-q;22keW051+?c0*6je0#a$K9MHrV*#)D=yn4xZn(vSP91?RQVp4b4(>~^m{4n
zi*zG6c&-0$r@vl6c?g#>xm<@%d@x+KhckI|h<6W2-`K$G-Gxb-1SDm)n0G%+uy3t>
zpJ45{#zrOuaKh~XBcX?vme&wvHDm9pCMNR;QrX0k%rI;<9gk}-l4k`yj{pcOx|b>T
zW+Gs&&y1UUc+1R0UytZ-7K`!~CS?OKs%CaY#;{-rSKZs?iE`p;;FL?l{+lbA;%s#G
zWUYI<DI%2+OS;Pb^<=Dg_RYF+mmR+Ju^{vcT>!tO5*;)N<Z=R5$A*`DWV4C&Mn%Dn
zP0V!nD&lM&nz6-&dbM_VI~X4MxvVKqSV&!*7S$r`hT4aFbSQ2;OKo)#<KHP0y09aH
z`ept4<=P&Sy1<Y=@or1Hp%fP<8fT!m^^6kSvMb*ZJ&|5-Bo2t0gyNnVN-KOVk+$w#
z!;y`a5sNIgeo^o^qHQ<qNWiLwZ;bas??M%U;?c9TV!o&v<WwKLHYJte&hA}8+MAQE
z$j0Zxcs|mj)iEgeaB5B*1R1xYHQwFP+E+8Gc_(@%UX<PQP;a6x{KK5oBZ?iD+g}1V
z3s@UO1EwcP|I2bUC-EHGR&PF(%!?I>7qV&!mFs7v`DSW3{Fx%5Sd*nYMe2_aN6GdN
zf@LHVzY$|S^~gHZNxJ|1hxCf&Z`MGK=U2Po##st19WfLmJP6AclAeC|W!FL7YZHza
z!NBx67MFa=G>l3Tzf)@>RtAUzn7duUaP-XK>caZZ*W(t4jOjIBpK{+6p7XcwU4Rh?
zRl)3-k+pAoa^rG=FWJEVR7dz=V@8XiWGIofI%F={ZS+fYO=11zAmC_R*E#|xOBz~<
zQu4B+@P%U(%3lI!>$<{Db6G3HJWfNld;+}I*e$3ow5=x@UUMCFa4D9TIK4bfDT0ZX
zS_=r@>XFvM0`nweI<Hm-jATEJ77wHU@$8l@{B;z0!dxQq&>trUe9!WEx^H%6$E(>w
z-nk-S4&a3~x={%hrX*Q))JKc<$^*S6!Tq4;YVWH2k08DdznNYq$#9=fM%yDi)S&JN
z=``^ng~~k^cqW{%`@6S>n?nx$UI@5kduEuP&Rauz7-1r-uCzch-sWFjNFg(BboMTM
zW^SHG_*!N6=MK*d^>KV3Gd)Xs7i<Z4xvbW|oOp(48&+Inz*L>%wDt}s+untrnAG%R
zd>`iNFuu1&VDQceV5I4I-ky1cw^cS=cTDv|WrVFd>tU=SQmJkrci7D%vDct<gJmyA
zWxR^F%eKyWDmj^X&SzY#e)raooZL~>ZjO6rz@2`3N2>ey0V>(TNWZzzvDsf^O`kp0
z`(9r49;6P;^7Y^(ph#NLLYZ{$=D<Kk4(E)PpMIl3E&JmnhU869K?a<*)<vN`>L!-{
zv1+=}%UbyGX|UX0#J6<BO6|j;ZCfJ1&`v~I^_uyckqyx1fA1&Pc#I6!fkl`{pkL45
z*?RTyuN&|F_S>0<r*9XX02Rk?-EsQ<Nre+j8ESw1c|G#vufU(*N;f<?eJb<GZyODE
zo_(}2^2W7$`#xI##tCg1)09a2po9pR|4F7hv<NSrcb+6#4ABDwXx=KL1jwV8gB2iK
zKYx1Wu06`j&EPC*>l?5QyldG4jb)eCsTr=8F>O8!u466-ANhX%`}}bnybV#qaoizC
zZ9}~=VVG4r4DP5itqVV23ep0h95Cq`%P|-<Gj!B;F?Dv}uV%k0-Po!GR5Q<D)L*yd
zI7E65!z~PKOvlEUw&a4i8mKdvO+|seyHY16JKu~EH;ZY9$CdRLo4?^cguH1dV>y%k
z`_jSwmFK$354E&tbNOLdw<p&T7z${a07|tB9jY>EK1IU}T8w2&cxhomFxKrU2Ln>K
zog;#%0T<J~&~<wxlFR8p+4M^I<<>1N;)i1&wDp1$+58HgS-6erBqaF6>G~UFQe?Id
z>zi+M29juJN>&?qUdJ!f8awmlWGe1CLkRM<|1=CeCdhbgxo7xDN~3p#51BqhcsxQ!
zV-ioqs>5?Qk1{)=4`?FovefhPY(f_v)*P6eFW)Li-^u++p>Rs-C%q#qv=Q9*-ZO1i
zZnwE6qGESySFr0UvCimPWrxy{p|O51UfRV2<m7+NA#9uo8m;q3_wTBuUVwbEDND`!
ze8GMK47r0eZyAH69JmZ@FZS722}ZuVKaJ{cOJgvmMT037hsL&r%bxzD=;#ev7kU0P
z#3Gj?!enDOZ$O*Ec|+|gicJAJYQCSVmYjovKJY3=d1+}ZnOg6JY#VlpAK2*9I-Ojk
zS$$H4I<|O-N8I%nGV8e>flT_>(kgUX?rXCaIcY2`OyjMq)@D)RAV1C_YGvsYNXuum
zI>uaM&y8LdruMfM31;xfm?4!m3^20?lAD*jGOc=s7fS5xJW&e|^-ypMwanlofshE=
zA=OS>!G~{Kv=I&)QFc)Nq_&<}Vh_KzYOK6}@W+z5{lJp91vT-0D^DSu>9VdR7A5I}
zZE`M}UEPRwb`1KEBzbM3X}mrC5*QNUfyr^s*&w*upOLK`et>^rj)|XY^kJJ87Ra_I
zU*#@oI!c@nT`zyE;_^9-<x0@reB?EY>JQRes*jUL6;i|xLe61i_*UunJC&xLK~J9J
zlB>Rju!9ow#(I5-+x1i5h-dN1ZRgUk7i+7Gyqcs=y0)o}-eGQ0tZ+9jf`Az@H=<9!
zDL%d2MVPBMY>YjQPA{SgVm%YXNxawWDl1eyr&i&qv->*5uF&(CJX_^J83sf-Z~O#_
zrX<@5tHYzpW2C;VyLUCZVL2MWi592Q2-d)!!lDlJ@39&0Ba+iQ9VX9J^dyc|xO+XA
zaQ!fP(-(t8_QZj`eZ<hYX@5!9#<5<4kYZo0*WM%bm$d5kRQ}a0j`)0i*jVqB)tz*!
zTc<GU*2;O@RuIBIG?gdIv(3CT{}3cI5V74^bi5D@{$2LMU*fx$I8;@2f5I<J{V#RR
z*Y2ZZ4gM0i>*R;f0rK(21=6>WB^)HhetB;6CtZ>Ep1P-;t{fpwqk8&b*(MO`f^a3l
zm#JOIw}S+Ka+YyHu)x=KP_rItqUH(%weIlF+9Q8ef{^Fu5+IA9WvA97IQPAp%gLbT
z{H-6Q<AJM9!H<gyZQh?Cjd2c650YIheQokEb4|3^DIZ@^t`#jPXiHbuxWzv#ZLZOp
z+EQVYh@hp6t}Enrn!%XiTrG%VKi81fVpo-D^+<WeYdL8Aq}z3{_1qVRSQ}C|d=2h1
z@fAYm6Z5ch&R?S@R9TxetF2q}FH$E~rzaqF$D1uqROe_nCYV{!6Ona!9B$D?5ngqY
z1?fU_YZXd=3&w=rp@UR5orl|pI=5awOdY&FWUqda>-Y?tP%+QP+hC7Nd&E5R$S>P)
zS84Nud%TJnXI*Dc3aUPI&IRZ@CS-=Ysb+9+IbxAkwe(?`FoW{ynK?u)XUsOz{$&TC
zc8<-U=C3FEo^S@!rj5fIWH9foY_iuCO#Eod2ZR>tyfylI#km6Hx{l{)WJKZo{#yTL
z@jspOMb?kz*Pja5ZXXJmre2BG5COA#sda;<tK_BuWrLs^n#tO#-6bgF9S3LMAo#HO
zW>l*?!A1aZqo}&E&)~S%ChbSokeh}VtD6_bbltm#L@lSS&>jvX0faQPi8^rfF5IsF
z8Nz2&x5J(AY{a(p>6~8Y%%~ty+bg0x!>(-xcw|^6WA7V6BvB7z6;<CoRtppy>L0(=
z8Uz<4k&vgN-B}fSg;Z5riV@j&kv)Z*cCPr(l&$SeR0ve0qA#^>PtaVZgOJvt*IOq?
zUAWxi+OSjZZp`Xj&f6>e`ImnBK2xd8V-q27zNgq#8=}lx=m}}gl^X<&*eTm`Jr|F3
zj>+^b?2fY<k-b;QQ|Q4xET>8@^yedARtnyu);-@XfiYtZypyuM7c&>CiY9hjr8}nf
zPwloUzk$qU{qr<fCFsLwEo+Zu8>PcZ)yFYJ_!7@X7AApDZlhsq>+vh!h>NSgB<|C%
zoUXEb)<H~ZtRiX{>(D_Tj}g*F(3hiFW#ASH$oTNsNmpw??Xj4cy2HreXY=bZ)7Nyz
zTIWa0X7{<<&$Zk3RIjno1z;m%i-PV1`>dm0G`99gi)wkc`aL$5T{TELy(~SxPRaRs
z1h-mu=dJS3j%Ey6&}{kYKoaw6p(-puyK#H?WQ=Q}?DYId+Sg=F+nA%TtdcTgS$=Ux
z5>W>df+C$-7u}w4KJ6HD)OZ{HYN5&tj=`n;PSYRzCnk8g%NM}Qv3m>T3xH^kZdRk=
z*A|raV?m}TG(Nl5f1Ns-uC<&4vg~saXo?ahgtD}aV@j<}LF_5rGygfsvO+2kwM-Jk
z?5uwDqvb4o4m8*7`VKFj$J1q&&)%A)Hlj@aN!QyM>u=K!h0Iv!(x)RM?#@w*Tj6GF
z7}{x%7&fo`>g6bOf;%M6#kK$lVi#5MRi=RoYW<B%wV7i<>X^&Gv)Oag=X@4kg6tXB
zhX;Fdv)EfnT~2mtUl{e!Nn7&oHO`gNg0#IE9U1=*1bI3-77u{oTw>1fa2jU^gq(RP
z8jkOQp`OinKgtA_xM!#qSYnNJ>*L=bb6{nOw6gBTx0gIYy%~jJ*8*T>`Tn%}=Ps6-
zU72qZmm}jUV|zD_!Iqui;jTkS#+HjuMhb#tPdG_wgs3=BOJ2d?Gi(|{QFhY%PMV%3
z%0_eLdBvd?vi8x&m@=0*hr6*u6qmT@yL0n_R8r8@sDZ(RZwU~hf32HVOTrgg=zOI%
z1zx`T+`01?$+U7+SJmH(bXIA;>KakFywY1URfgV9OmOONPd990xv7~Z5S7YuQiM?b
zWI29~L=?sRzQS^g{y9MiQnyDP+y{Q<A-fms>b^!Ra^L}kU}*<>;0fm)e#*Q#u<>^}
zSHJC9>A#_)#vKAoax7qykGUoj?qXx`_|dUUNvXmnpV9WuR9r}Okjwjw2?r0`*9r>C
zV~{^jJ5X~Ln_v2mp3J7M_)H1{)Gb9Gh>pw&A-#p;+UzsBc+~ZeXwzuFsFi8wL#;sx
zUg%4@tt%SbpW-z_oiOKoMv3XdFPy3z)X7713$$h<Wl2K94{G#_6I+iA<n&gZ|I}dz
zU~4CUt^POk9XIt~a8rH8S>?^@wM<#wP<wo$?H0A8txUYD^@vM*ch#uW$sp)JLL#5w
zoZQ=%kWW>Gc`ro1GsVP|9+rL16s7o8pEHW>S$T4B?7-yo-=c5LYXZD~k8Jj%WsZ@l
zLv<`hG9jG20^vVqZ=$Ib)<!ZBPO-6nqKk^aX9sk~`}joOfj{pAM*zU;Tw$K{w-19$
zCLAiC_5ZAI^f_WW4WxGgm27G<X4J$_$WztsWR*aq<|=u(bAhjiyg!|7?^AEPI?((T
z@AMSXBc`O(|FgvpLQZGTMtWk@ZA0yh)DlIRNZ<aE7uvguhICpkX(Iz7MEx$U?wLrN
zIeg$Cg6nhhTjy(b-4Cy$l+$xL7!~7Zw4KPVx>7c4<<&ixk|Zsh>m`&?vb|{K#bLU4
zwIs#ByER5+T1MZs8Y};Ak2QQCB>e2%=IyRz)8}<v4+3S(Gt#<TPWYiJB(vQVfZwrW
z)1x<`#0z)85Psfxb1V`Srej{PxMUN%UE=eglm5*qFV8BjKJBEQ!m&f5j!O7~I9*>~
z>i8M5%fHJXxhI^QT;kOiD%dP;^#st?06<$-W33l3FH?5g8{>`HAb393ng)SXu}CHJ
zYqrSPX62;YZUA6yTg_C{3;JWxr?+Cuetw`4nV^fXsZNgD*DPK=B3>yt%Khn~v*OhO
z3k>HW5JJq2fV2t*_k?<2M1}3!quydFSUpw8%!Bx6W*b58+s?*4;pAN=$5$gUyD)=?
z#}ozZH11dzL~7#w?K5Fu!iTUHuWUM!qExi?>D(?ztmatH<mlz5QK<{2Qv+p>L26Z3
z6j+pf4#377@s1h7elbl@%bl|tf?-yzF`|O0VMN6j>t~ri&UOCxi4^|}c5=)z8Z%`*
zx4avH{ZMYIv3*ek#r?;m>TY;Y#pXaumN2{Y8AouXN`jh>@YYa$!ojC;MJc+XpFB&Z
zZ_pGKh_&|l>G1>UoHCH9R^%#OtX8krqJ5}Uz&o-ju&^B0eHNDlYi27kN;;h1zk6fC
zx)G*J=PvHL5{RUoG;2A8t^H?<?^@i-2JHo*kQgqgIzZDDb|S7V@<3o!CrcR4FT!MZ
zJIDFqRQ*1uq=j|YfjWz&7RkMR2o2QL3xN`Ejuwy*xtfE$PDfO8H>Ad~;+ykKUIMQX
z%$zss>bLcOW!psX67+=KKp>gy=t3t!@sj_mj(pIgn(18q4-v4TC(1E)DphV-oZice
z`iZ{EsdqaTQKof_X`9fH)`+~%q40rQe?%P*WAOxCQOWdJXs06eqEbWExXKBIff>$&
zF*1(B_37UcZ;U#Ru|CmnJpD0^5R+$sRa^HeJByRS5bF#~oo}n*5(S{;FI#!_M_O>E
zlW{wZe~GsZncLu*%VsYxmx2<EewU5-OM<qs`f@_=f0@?$-s@QTS(BBJ;Srm9Uinb9
zTIkns2>%J@z4>or=w&iPfV?ngd4;TY&#H8YvDp+;TU#y#L!`71brpWPEtH5JIF6I0
zTsi-a(dmU9j5`|yh>z>V;djVumD2fbYSA7=Q_)w*z$;e%RJ=uM{dHSnk55@PwU9bi
z*ne{B9oIB2;MzG1wreV;WmR`Hq%;W~+(_?=&gHY$2$Hyvn?Msv5CfFgaDKP2M7tYE
z&Dc0g){Q)XSY&x%ywY&6)RjjD_A@ECiD+aL_D*fNcqIqBdI>|xw~1I71NmxJcVxr{
z7j*2xuKKmUzYvX$Io?u^IbifWMg2VBY*=79t)RUcsBp|^^TPBVD9io;O{1mLaZFI-
z$vROIqHa;7x}_0Aa(zU)ek*31f0(X@_7WbnH$qL;530v<igl|{=edHF^5cH}mOH-H
zfe@;fMJ^Z`2<2QPHP`m)sI$~Kn;Q73{^|<<9Cr0|Yx<dJQ<){GWf(R_8GbGWRYQas
zg~(sG&OKVlEt<z3cyqsIW9Ha=HM4KJ#sdStFbC6aUV&;iLSxLl1G{Ji{r5u8X)X)X
z9(_MLu`sck=2Q=B8qPUMnjV}AIp#Om6QQyEm9bc_(NbSv6H}=*7PEp+YiW33zRA@*
zqM6QMR_(`@r!;E<F6g|z|Fvo>lx?jYrWv$+3ZsK@%MHyoGoptv6!CSreWh%EG)1C)
z+zA(PNAw7aZnk@20Vu&=JS7G2`8h08@QQp5U~fBATm5jBW@NMNhOWF{Sx-2!>!X|4
z(lO^l)SGigO6TWNc1%yVG$|47_jT2Gom-fG2CdfgQn#!)Y<c7@0N6HoAS;sf`dDJ$
z1x?)bAXw|B)k{ISRrigQl&>J^wO*&_z-%oWQ_&YLU~Xib4p|}}3|Gxc=H|cyN+l4;
zn_KE(sSi>okdt+FgTYwgR(%3D%;k0Tsn)$)Y`=|7c=1pkcTE_!p$iA9!pfW9=7+dO
zcRmi4wC%mT^FbFc4AuqdDcpfA|Esx>$K`GFYCZZc;9Jn;o!=XIZ;ohO6*uq@qTq{&
zFEspOvZ53`y#XPK9V@v~3>54#UP?Cz_zUFUq81-FPuWzj_ckBH7hX5o%udaBE9vGa
z3M7Sn<202Dq;o42Tx2*kteQ|^-nKzdOZC2t1w|I^*i!zGl#jY2dZnB<IDIsA=sM07
zvQzbGDAc*Dx!&hbnV7mFImD|DzVOA{o-3mG-YV1A=04Xk9n<R==aNpoqU3cM$hS)~
z?bwMP@^-o-icix)<9g<&(`Z&c)j3_~XN(@PT)H+0qAs`{uqd!V8bW65bToa#j|TBL
zct%1BkP8a<rW-m<DBGk9D@+PwN$8L}qVM=s^rqFgfz52yi4Xntwm&#8-c<|9e3QR&
z(D&juPA=1{hWb0>pKn?2M<H|1(_FQBFYo-N+1D%d+Z}Bx>NI8rWc;RU{5B-^Fg^cE
zRFt|u#cnP?E&oGG8mEi2A_gBp8T==-pwgyn=RV>)hP1H}65uxjmtZC-i4&?&bK53s
zw>BU&$>YpwH)?cuTF332)Jo_E-=OJP&pl3i-}8I}f1XfgX;xUT1@q235j+1td=1+9
z=FiWd@!6PQ!w7gkY#0tQKG<?PM;vjBS9hf~9bIphpKDpndt>HZoZw|5ah~i(iqcLJ
zcLmM&F(3d$zYpqd!<gxgE!TLoGQ~sO*@qy@91a0Wr(|Ewqc!F1Dpbano1_~-wf;aD
z76#%dj{+t-pr4WpHWj%^=+90BHoBM1_I*EUyR$ZYy?A7uEbqffkuW_?(wGL|r0)K-
z5y=Z4;x6XlkdnpTLeXuc<7PJH`clF}ckS{%WE(s3LJ$xLy$yJb4V_lH*65>ac}&If
z*!F=GXRSyeY{X_6yrnUnGOPkMuo{t~l;Qy2)_eDd$|wWZaV?x(hD`XtROig~1{aJa
zvDG8{EquKYA^K3gxI0l2eEDgdB%=&??!9eOscODmKFPOc^d3XJJm^2A)(acH+Z6Qa
z*Jj7?7{j~GdeZ|>l4sEg>%0r;Iasw#xJN9=l%4W?gu~_Fax5{~RLe&Hhe(w}(W6c6
z!SjZ*9G-002C}MDrr)w@WgrO1r_rt~DxQ&ACT>8O8!+DJeFIA1#sC4_t*dRL%zM>_
z`JeEsyYTg$hu@m=72i)l`>{pc<4Val5W4iQ+CA7>PaBzs!&CZnysXpYEs$o&?9SiJ
zCN;e~a!n`m$lDcbuYuZFXhhrtdo_dU4T4GhT>bIFsdd`0$>a<13kZ=!W#Qn2#*qga
zHW<vO>1WHV2g2>4k#P0YzCYY0K9V&FnIr_mO$y1Ecm(n$jldNqHBH8jm)S>~QnN;a
z_EWKMU{D!EQEExpATaO>b>x20<Z*rN@6H9)Y1Wm9Wr|JR;f-~%Db_O{)WfDz{JYMe
z$Vt&}sTr4Z6j<+kns+J2lbr0AM=+rJ$DxTqFmypL%XctU=S1w;8GG2|E6w}3{0_a8
zC0P_EjvT9Q@ES5b=#TE!6_uCzvrcq4-8fW*2^FsVo?Za%T4AK==yG33pIaQ=zNob<
z;fYp~wBu|s<@*;$mziSiGp%Mo1p4{$3EKs&PEpVca%_2wMbQqL@a7ghwJW{959uvS
z;b-##YK}sb^^KLY2Z@#8NfEX?so0bCE_vh7(b4w9np}DwPcK&L^{$z*VmNy>C$AT@
z{3)ZRF7xY|^sRGVO!2Vo%o~ze;4c{u3*75D0hw9<=~+FoXY%+`ZrAeilQbn^`NBk+
zlG`gcXOg->MfWWhk-9SJ)f!84QAg@bR0L6jGC_L=9+G4{LRf|fHVN36p^)u%h`3l?
zjm;+*%>?1DrZjWC7Lm&0*eggK%Q=o8FQXKImq)3Wo8Ar*CFptq`w#a@XUja-<e|G?
zR4#n|lJ@?KkDHeF7jnKElwMRmHGKpIZL9O2U~8WUE^9JM`8)s$VO2gWbMLg9`wH4S
zHo%I5Kw}4&UU!{n%g7eR;&;``%qzM*=VNTmK=iQIYQH_!f`E&K1k)W{?&Z3D+38U@
zgh6w}x@Wl?^b~@Y&tmLv_2l(0qXMI-{=s8a?7}QiV-wm5XNBu^n3O<X#&+?CK~=0O
z8{oOx*CtnItuP>&GpTb_aUq-_EYyD$_X?nf729gpf_EGy{Z>zEXC*?XF06Hf2c#M;
z&I)8B#j%$Do+T|Ea_#IH<;0RtyEiVR+D6}udh}lL3dVKF8&-KM*?OTeB&kR95i37Q
zcV#(^yYLf)k2^uWC0i5_)%0&{Wjkkh+c2UnnJ{)70$?hjrl0&JZQBNHjdEAcRoh|m
zCWuNnBEpf!y*>cPD-aDfdngQa(<RHn+K&pGZ^g=DeZnRW6Uz!)KAQNI8dY=$w!ch;
z+4m*%I!@YuUxAz3h0B&kur-=3^^Wza`*C49K?a}4EryBS6I>raxDqs$)7v#46bAKs
zmDwF*tk_|k18NX_N0NOgQ((}&d66C#tm0X+CP#%zMWWlIy;w6+xzsn4ANlkjwjQE{
z4r#|X)4HqGIOrsneWzAg2o0hJEM<uOdm)rt2e<(fLu%6Z3&CL)1}%O$LM5O1Gh4dZ
zP*R@7eKF=kv<0Bj+*FnA*V3X)Wd}7jpKLinI(U4~TYZchr9Ad3CePY9Maj1M8uDBe
z3w?cy#C0Jf`%m-N1IN!;S5jgbIJ=t><pB2<nhwe+j1%_4rz?9qtApYTG~aGHg(F`O
z_}6grkpTiAwq#tuH;>U`@6L_-==g>V9Z_jcJuoh1iu18QZ#%)4aB>&wN~<A8Y=(LJ
zBc^z-QCG=v#`o31oKQkECfh|KyMM*MDEH5ax=coXMB-9R&thRZ$NVtP{v;oQ$ViO#
z9-Y~ec;i_fp$6N7)@xD4)E?5%uF)E^{v!78n=GjI6QVwQh3*_C^&mdG=ryebM^OaG
zj!LcY101d9y)6t-eV?<etA$qS9e!DIXV1VUCzqYI3Kb-o;``pR*(#r^@i~7<b936z
zQx43hZ?BKQw`o=OFpVI?VD^byHCZLQF4#6fh+5E?rHnGuE|qjsh(%ASJ$A_{3dRf9
ziddaybn0NT%?l#xvN?IcVav=3H|Lrgoxb%^`;v-+U*z^8WBKRjN_D&Sb=K_3YkfMw
zy_2yN;-PLlE-UBFzihsKr8gfOamySv_|n-PvY<7#^rkaouGhYNgJ8OgHD1ql>#fSK
z+f^{}W=`SHJA307(FawuTv3+>asmUGQxHTCKiX%B$P3WM+N&CIWJeNUn#NHdWf35h
zdz3)N&hXKvD9u+gI!E=T=H59o(Wq8vY~yNh>%Gg6P(RGP5tH_LQ41B~E6LR!jYA^B
zVy|kd_aZZdfi@9C7T%#Pg;*9zqdGbYvP`yG6D;IaGgB4H{=D~j4wH!U<;QOz3(dGd
zH35gs;+*VD3iy)PO!HoN+75(Ms~f`^FFO;}u-GNfN~t1Ac&%1!9Czjc+<3@b1fKo0
zM!Oh4tYqM?XvayMWc^N0SIo#y&x0nlH<$32ZpWRdIl0TlUb*YdBo<<KK@U=@qcO2i
z2dXz7CLdQ!04@7t67s=rjf}LA<DI-!`ea<tYm&W#Q09LYzAZ8$3Rb3;G51#TWI_WJ
zkq_dqv)H>UA#;5OdHZf$4iDvY5`?|JHAm0Sz2S*ypyeoxK6cP}%=#%GbR{5X7?I8a
zUtS!u)T~CqbJipiU@(&@P`8=OO1pPW^vif<nhpqPqz#F^ddeIQnQP7w=4iuz&h1Cp
zn0qXp*4`}EDoE0iq-PHAtHmAwSf9(G=Lj1yF(B{2QND4EF9xTH^1yT&5X1JMJpcuK
zpMV#dQhO3-85ic_OeeuS;;<fF=cdwm<u9XN5Iet%!!A)?_v?-3Wo2mH766LtF|#lN
z@XL^}TXwAp_k<vD-yi1oWS(*OpVjD`6OH7b2i%$Sxf`d|;7{XLvj<|V0r1}>SaI}D
z7>Y&F@u{z#S$EOyM45ZL)myQiLAqaqZ|uzSYhLp+lvUy3IA4wB8%+YPFb%Tpobui-
z$Xvt0aKYpe#I#P_t2Dy9tObO53+{_`Ov(UXvr~A?S6I3HM7P_Jap*GnePE45IJdA4
zxD{jcXlMvxQTZXr<^qOyXjgZa`$k)nlZn&WfVcz><{ryWKHV0E7wQ*%;*vGF_xwI^
zh>QYTRTrj6eB7HAYioA2gP{`*gp|USM~b`}Z=vioMJD0(_7S=g^FrF@A9N$<@pZ%g
zpDYx1!)Deu@MvZ&sRiK8jNkSa#+;WfxJA7`5~HQDc}B^r)%;+KPT?o^%4PD;0xF?=
z!Co8|a^a<9aAUZozIWd}A?a4>ZGCZU+}o|JDC-OBr|6@<IFipRH%5HQ*(Qglg27-3
zMz^Qj()(e8Rzd&MTp94F{E^yVy}SiL`1L@`1sliIvVEEtiQ?Mt$NRgVYIk0`O#Xx2
ze+}NW9NNk$9^QXx+*G>}*S!)Wh!0ibt=5{f_-aixr!0K)9Xv6$ygC6SEp}BYl}-c3
zp<!_*Rr}0UGySnf5mUS66|yiUo*v%1yY>hb4Y9LZe!j}prV`*eCqljoijtz2c?VN-
zx(y-x9E?MhYis(3(VaKb^OFumiPC28bP5&(rrIz+3tjI&O7?<_1NQT-FCj58F-_Cx
zLu0&_Lsxw8akj0ypA6dqzbJpkQPlQIW0zmyr($9d=h2CaF^em)7+;XFruWYoq5{xM
zGGzJN#4d;wdEo#(@K5vJ{G`dK$kwGA#DzJ5HmgQUBSeS+iOz>6f&`4!oe4PO?M9>F
z+M@$?ziIf?tSsNvhOZwy)-s&mAJJL1h7q>m&EMHMCy1^8TuD!ReMdcyBe?9bX)!<B
z+Rh%a^uUYI9CN6*hh9F~p$#Xtvv!$UTBe_<So<|xgHyBjC}~;&QvECMTUuBn&6VF)
z(S0W8z@Vu96+s5nGb9%<EXQff=l2(ZBZcuA!qx@MFW}&6y@qSc)e8)zbJW3fl8Gig
zNZBA|&QCdaH75alYoP9T{)x2{(EDrrqBbXox$w7?Mn)7OcMIl$@g1iwD908?*F}Ge
zKjDj%if$V(5+)xmh!;lyBLj4L*6wm4RHQ$lvD+@V{7l2Us8g-R*Wj2lF8sFNHfbyq
z(qh@ad$*6|XLGmPE6=aITVRB*d|~Xad1VGJKyaIxYp0AFepa&4Yq_MyEdrfU5R(dQ
zzEO)|vubH?xs_Mj?hZ_hUp$v@^NyO!VE`FKpuN-0yE|)Fu!HyxyLEx5flxTNLLasE
z+pvc2?#*m#Y^|oIqvEsS;}Er{oMcE{2Bw{E;{C3S3PMF*_^142BhdDLz?G3BQ_5hp
WJ^Gj!e`amF5G%Vs$$xm>`M&`E<mkZw

diff --git a/reference/plot-5.png b/reference/plot-5.png
index 043dbd706fcf9751dc8aaed58640fbf50260a492..8d224d023e3719c047c4f02b134d372ba91b2883 100644
GIT binary patch
literal 39587
zcmeFZcT`i|*De}th`eG!M5RhE0tzD1L2Br|7nLpoO7B7JAT2aQRS3PeL~5|oK|+(R
zv;ZM=LJ5$w!u!6z?>qO7aql_f{&&a880;{1c3Eq#IiKg5bFIDK>glMRJ#+001Ohn=
zRaZ8EKu-EYAjcn`J_YuSCVgB5U#A{wswsnS@Yz+Whk-AeC+g;25D3d_${$tC?3x1v
z!VZBdD;oKyEsgsnTr!^cvF4P^d-KJ0wF@VxPA1zohiO=yyhW{G&fs9~r{XZ+Fracv
zkf*b<x&GKGy(bI|R)4*T2;&G3I~^W&=Hexlsn%vK3~WrG-np>Quxs<LV0ql;!bf~=
zVQHx*d2g`s;!$h<&*uMF09Zg_eL2x+er;83fU{?mdl0)mkn#L%3mXKI;9|buPBD?*
zy?gM=_}1jvI1xl;xZdE1SNWfv<B%{>A+hnZ|Jso2HALi}{}T*P_*4D;|A#b=X4-#t
z&t8fwrtBsy#w}Cg1G0AXTYFY4k`7*{DWzyY3*9OiLPJNm7MP&6ntKLVQT*iS#bVq|
zsFQ_{Pi()Q!7JlKC-|IZien<1&I4GMgSwP4))-y~E#z{EL(;&1Xpa1e%Zo=ogV(7S
z3cDmCufh*GU7nsHNGcnMKwB(hR7G0?8<8>GUGUo~SP}mI3kiQgS-7ol6y$XVR48JD
z;6F6>ls`9_z7;6(Vw|(ezwY%eCSj8rU;7am2rq<=2zpI~@^4%XP{Ue5>#;v4utxAq
zm+q%WJ^Wo1qxJK8+P7@4G#~HSYK~`~5^X2wr*9b=eC(KZa}DAaj54)83D<cmP=!LC
zyft#y+`v7}t-2<u`lf1acMBKPn7)`(earrsYN*@3dfXklZ79yEaDP%sgxhCXXS;Qy
zNjF0V!Nxa6%+|w>-y(8f-3nQhzTPeBPHe@P^MuWN=fI^0A0Q5TBCE9DvXcLb3E@ld
zR(n@xLu{3h?nWhTI@R4S?Kx`e2WF<ind5iXPMojgl1;t0>mlf?H*Xiyu%cPWE@mK+
zQ5Rj+UL@qAnHC^t5bS!xUL#PZ&92<g@r0X;^p`^Ej60=?G#1=UzqNTjSio`aS%kKD
zSnSH>%sK<{(d_QEr!!r#G&6Oxm@e)R0jh~|q>pH3nHijBHE`gnVELhKOmOCpR?};l
z3-Bhn<CF%4c`mExT$XvmpwOCd6@hrfLd!L~(?8AdJDpQ(T&ZE}aZ>F;uunKadMM8g
zP9xXI-2C*eoT{v1dvdk-XQEINqVltAeWvp9K<<!b9&CopZVZuirXE)y{WW`n1HpE8
zzk02eDGA}pZc#{S&TPa%wz~Pb)ubOzb;uqte6ivFssOnm39ml0mc_*NEA!;1@Qa;6
z=OR&}p9^0;O(HuXB>4>oY}&>rLbJ411#QF~=1LyJ@5{!t{5s@?Hp-$x*cbSq!=mV~
z67h(lzAkCF0M9R!y(|a)jJC2X^x(NiL=jxhZ`xRK?%>f%#epbPm)gpydfPtt(wijy
z0@rs9rv43w1xU8wb+@Fe)+34rEGp4{>T?Z}#uHRnPWw48VIdW6Jx7<|+>Ulhx}#BS
zFZUUNeH#iy^tx2^C*M})d9#>Xr*;3ktNrjL=<ALX3Zf3Gw9#u&wxs_4Y~ivC4a8}?
zqEan{q!)FII$M%C-=dXZNUqlEHxFf3>loCmr9<s3f@hnJTs0%hv8f*u81DT>ZJaob
z5sjQ3rfR$_s`$gKrC&FT^N!~FSQJ{8!T$28&5_BfX=hD6R-*IsN#-BsaNnRA=|0XL
z1YE~(zYpGk*FS8(67Zb)v-AyB>$lOrg-Uw7p_$W1?f;Orapuj9bNTy4vhC+I1NA2q
zBdCMdWtZYwM4Iaj9J#EkbL0ZPz@=(u7N+A5jbcy--+5yut&lANGjDMyl9Ba_zMt6~
z${W2cY*_KUA@m~6eBx#;lWvgbEZbO=xaS?`uHccWnh!(#tP8ZW^4uC67Q59Qc(n`s
zzaT3=uefR5ZngwYh`rl#1Xs;e&90jhHE@AEGa{PR;kCnD?UsQePxDNY2YO7cW%s0i
zP+<Qw`pU0kF)~}h>sE_#PKj!J3#<!nAGwJ9d>tW+MA~-o?KVdHFjIkHFA0+%Pls@r
z&F4C7n&7YtLM`=hCN4BwZBOmkW5ZH4mIqK?(hRL=Gn4DHdgbE{Ia>(f1utd@1Q{j?
z;8Vf;*68#WFPB>~jlGPBSk}gEgyr`1E=H>q`Ga+IPJQn??}`VUC+KlU9Ku!f0$V^s
z(+}PrG1fBjQhZ)YhimP6hRM{~%t|hLzWoV1%a$tvhH>0tYK5uX@H@LR*8F6<OGvns
zpIw2-f)b+EDVw3a^E)1W*A!aUF}($=Ye?c<33x(g(emPg1_h(IrG|VeBaYuFx-H+g
zAoaJo6eK<0?yFe^MrE-GEjVX-GLdFtoGPk|g4fw_@CnxwWwz|Q_cEoT*IMHj;#=Gl
zxiHguT=eHRgZyVhhMX`4#w~dhyi=|6!@QVGWwpmi{k2R*vNka+qMYyh3bGnT{nU*{
zs${T2kyWKyP=bB#!vK9Oujps;F+vbsP?s@N(xyn}I>oWLel&53hND5#xI>}=FI`$K
z5`aMX1bO(057l}-xy~kR)@qgKz%{RxNy>9qJ<+?t6pY%{UOC_8F?l8gbuhCQm_?4+
zkfuY3dL1XpHuy)e{}n!2GU+<q%BGvbp3q;LT1*dj*8S-Vy@szS@$1KohsMS>s8NlI
zI)sMpnq%)lbvsUQ#3b)N{SZ!+eXS(tF#A2f=!QU7kGHurFjm-cDjPtfCQFI617moT
z-)42{f!1p!iy5aey%}%&YdV}#C*4NtWo<+R$uULmmrw1y5jT+Fb0Cbd2$SzSx~yGu
z2+8IkS!f$b#CVsH64LoyB04Y8xf{Y0j4LJ>G0m<nPf1$IH5Bi;n~*3%VORKOQRhRo
z7U(rjF=}^?I_M*ot&uyKo4x3W`hu0JE{~Cy&N&Q^H!H=vws`Gc_n=Naosqwt|LWUE
zuI_RBE%+uJONJe&z(bz|9Gr&=6=Qmct)$|8*rlsnVl_$u@$`12KQsx(Bbf`8-oWON
z*WTp3a}Y>#S1=S7cNJ=)%aeao0c!_OYH1B}?8iv#AqmWCmO8{?qBZkw#~IA@lcvtr
zI(ybos#T-ilRxW!#9<8e&O#Mpaqc&4&9`c6^|1aqe&6iPupz9YdzL-q)nCgJEuOhZ
z>JZfAjfI<t-f~1lT9LV2fQF_|rrR*wf?Lm|POdK}fYKU3D94T55XPh9mx+iOcxZ{*
zNcq0UN_QfH<Fk<M$1&KAZ43J@S4Ez-6S55>rbC>%(YmZ<lG>Opb#k@tDAp~fVY@0x
zw<%X{_hD47j<<e^GFRa8k4L&Hhz}`4q0#z*&l?70M{1NDFO`j=eAYcSKC<pU6nbdV
z#+x}A9@&<5&L;BxK@V>aTgV6w)tX{-N4~Eu4S*i|$#=g`LLjrfy6ZM!1>qg8q1gwj
zJCEy)C(uUnLgZSn4vw<vn|g0W!i=C;*#dE1;p||PEO5|QNM6kkLW=5}V^I#tommgv
z4X>+XGr46;H%|tj)dTxxw>03nfitz1hl)EXa-$Pl!-Ks}mOqmM8$wgYaGy%p>EG!Z
zneS^+A4SNO)2qiIVU<%-hzpO`ghl&1r?(h{%d}Vdac7|P4yq?d(0oIQH0LK4<2~$|
z-EO$@w94|OySYU=Jt%D#MK^oIdHX!k-q@_{K^?*~;t?@Jp)1!qZZ!F*zD3{2;_4aq
zf1K$3u9LaIy)@C>U&y-Q*jksY&2~@^H@Oy}yYAl@fyCJ*Fd-#b%beOzd?f5*l;9(p
zd-vwXw2fRsa{DN4q>Q2GeI$n=+dFu_p#djC-+?&)^)JLItPH+g(wP^O0E^PiP-ctS
zTT(O6$Wj#batNHY<e~pokkxvtCy6F>JC-I^vX8g2)k@$8sakUQb*8dMo5+Hf#02qn
zlF@I&LzY{_dgKfH@Skf9B4t1HZK9DJG5aq?GxKBw-Aa+m@H^k~CFc3B)qShQtjRUx
zI>&0@#H_KnyOgFu?m~W1y)a^Bnvs1dWQZN5>R-#z`q`=pG=*d9iQe$jQi&#E=#<X<
zF69xF6^Xhm?VNnuDSyqyF>R!*8gvA2*rR=m*ofTk$kXnAJ#>1^Ip&9X#>UgK9@K~9
z4cDP~`p1(-0k+xu#~O0h{=*CW*Fc0l%hKbyYcx~QXvPyOb?c>+92~LY?RUljX=#R4
zfoEv=DP3GGLekq$O#AQiiA@#`HB>uj7kf!u(Bw{^stWnknQ%78EtuH_rM}12z3v`o
z{!Yk--nfqQeI(Zwl>I;&-W^Zgq_=7R>Lk@l0Tkuy6a`OrqR-x4*Os1hoE4hfdV>5g
z$7jzRD+!m<96>0P+EX|X?34QNIqo=EMW@>@8?XGN7b4u^iJLy}ydxcmc-%z~{hL#=
ze)P9Y553OIU-z<vx$G(CT8A#1?$a_J@)Xo`#t2u)2rL*RhCX?k)S2}N^Ics+)j$M+
zH>_-5cdFx}i0t37iAR}(S!rDD%RO~`uLaMCAu~st77bDK_92H$j#l%EER;=SB;mfb
zS8-)(`w91$_^XIfy*Xc;wx}X}R}`%cEBJ-pN>;ONTk>N;)ZWrfFfT63>0CS1=HaWl
z#{;=-I`<wa=)d=T0Hp&LPNI1@m|VL>vWALiq!jEtf1$|V<t1T5j;DDPD0G7egZuK=
zTp+xyQ{(xC<L*`Y8JZ}`7#+9C3mxaGfnR-q(ZZ&RMy?J&`|kahdw}U2^SsbPFhajL
zk-G%GS%-9DSo&he4QM~|AE!F<tpB%7SS26lQQFl^#mRi%d|$q1W-S!x7iNfP-}t{L
zD|k{O{OF?~sS?Kc@BaV&?=Yl;#v<PW4V0Ff5W5+PT!5Fx8+C9_IzwN=rILf`k2Au(
zUm|twiRhoRYvv;s$#Sy<*{%d5_U&zvy^21KOu86Lx4W|1TP^Evv`B%cjBEr*Ox~95
z9HgDJxk@3#0UPIrWd35MTmBR&_*2MovU%BdME2Rp;%eLu5{fE&JNTt{F7D}E9CEs3
zv%?{J$Pvn>S!e4w>BZur6hZ!^BCpyqoUtg{;)+Cmhojn<W6<2ztOKkf3|!Wn3x=+l
zCK}g-3v_4((={geO-3SajZjmJpxzt@H-VmM@gEcB7Zg~56}zQt7L+JnEBZxhxan4+
znYm8P7&Knm?hWnKGGyYW_omT&l=kD!4nm}_>FaR;k;H?ubwUntA;EM$!%6dpYz{eW
za}^=)r^xTTiOx90(`vhdM50v;p<q+Im(?37Vz8v(9IbU;(qx=Xdi}+e`pVXh?mMEo
zferKQK1TsU_o@$9$wsRs*8=ilzVv{vL=9Hvca<%rrC}$$4Q6PE4WLfyg*;;I0!ogz
zf4zkty5=lgNW?UpJnlAdX?P{Onw;OwsT_v(N*TQ%LSjDTA3wVmSJb+jls<?0)MkVX
zVK~P3Z=*MMUSy%TtH>*s3$Ff;(Z?Isq6K`;2sEcYvYk%g7BdjP;S!QYV6w{@<u8co
zJl(H_*M}MiLZ5TVGG#N5I++9<Olhs~=<>`5S7`mL3h%6FP4cepoOh8nTf8$Oj%+1~
zSsHK(!kRkyR?Cr%qVnM<&HV}qNgTMx;xUAyF5Ml7-CcKrt;NvYR(2Ki3c^48(73PW
zeP`%1({CXU_k@75!_?(hJI>epe#|botMg0#B1-5J*LH7*RW9|`PlsTCQB5f4ik6?e
z@cV3C*}}ub8g<8%uySNfFjgA&Q~HkT_}X5-z{*ruyO*H~4?9beH==`Bb%_Q!SNk~g
zu^X)J#G;P)3e6yq6z|Lg`3*@d4d@4oafMai{=`pP2Y1|NmHOusT<yn#?8ii0+)jo+
zDcq8gC@EPT`Vp8BH)xWO@!SnY@4%Mncbv~Pe{0_#;*gl|fs}$T!L@KTIvs>WAV*ji
zEX{gU;BqkozeMfpISggJeTPHiw}SIJ-^@7^6Ifu0h%AoQI>Rn$UB`(D*kUtF!8wTf
zd5TiJn9N&%+1jpwN5tP~@lY%+7)0F>MNdv#uezf7Y)i&$VuMF&V@|V9Wsa7t1NF+n
zvhs>$F8f7St|S@t<c#%|coG6G_Y5`BPFJsw^&M~<tf-jm7R??w49X>ms5P>}YkhX9
zptP6FJLjwsz4~io$82q}82jjSFa7LLVZz&O*%H15HqnijBebM3A+f-s+dfqpjNAy!
z%g%6RVz{oQy{fM*=So<dz3NFi_@+GKWr}a>7*9wI(RRKtdrQW2&Sb6|Zj%te77D*=
zZZ4}TeJ;*BwKM3GA(jW)1ARn@$30NFj4xzg5P-5-?4r%xp5{t_%`3)^Oq#j5JRXoV
zo+Iubg9_S*zj?h=EzkOPDZ3=G)5)t~Ajw!17e84$EhE=E6(I6BK$KHGGGTw$78}0k
z`q<b(wNwKjE^a+_5JU2xsVIxP{+(6UYH<IttXdB=t)w+zQ$D7l!=XD$OC@|vv7beG
z;vs_lNiN6G6N+0ZgzFg1Bn=puR1#Cm25f%L1tLBmM(^F2Y!8`Gcbc%xTJJ<-?b~4=
zt2v-JmBi7nyp6JKWi#?BN}^jDJXaW#Znrs%21Io7UJWTC{J!FLXmYr5#fFKKOs~H%
z9iUs5wEaacb(ZBX+LVn!{8xA@Icz_s>r71WafaI2F~S8FB-+Dzg=fJhMuurdu3gv=
z+jwha*D1z#Wa|((@lvN)7q|7aXgMa-O>!jfEb*(zZvt;`$Y0RFAG`-Ph=TiLI|RGp
z?Jo<L1PT3D7O;{kN_rtt1ka-49_F&GY8%VE)|ksWTg+~sdO5YTH0&ZywQVXA#fKL3
zzWZ6>%v=Jg&HVHN$j&(dx8ANFCB*R3gx!ZV+}}N?XikkhtGpBO?t_#@-2G;!5i%`W
zY{f}5dnQZm*!V*?k%X()Q=JkGEgicRrD2-MsPxBfK?8OeAYa-BQ?pyotef7okvEEK
z#$FKK+KQ+7DUNC{XRLDJUvLwg#IurmTY59<9aPsxXlVyx5t#M8e3`YTJTV8=moJOg
zqhqh$x}3C`uND;U`z~A7Ci`>auMN{sL3+NT8(xo<L{}Vap-NWk;|{Z<LSiFT(Ht$+
zNuBwNk<1HKEN)hU^ky|Nsj^Z*=$eDu$$P4nqnBH?)bx8FIXM6!v;X1z;2~#A`qnFl
z%i@ptx8uTmuc3Q=b80aaBM&pn)CAURY1>Y06Ta8NuePPk%e}g^yHo<N&G<#DJ&<r)
zBM$HCeSXw~&5i)8W-6{$vO{V*g-PT${cyCkJDl+s(v6#*^I`m9q?V#B+A?E=*cQB+
zbK$ovH13WXx~kDG)wX$0MdjG2;sA@$1dmBd7flk#p&*d*U$#5)k%>27`G-F{DcW~K
zxv04H-VI*=nQ^Y|W@`tPCI#Ph=h3Q&6CByY+3gLN=hn}x=301gsvPqavOgvzSPTn_
z`m#w6r_rkmiR;$qiY4-X7s=T6MvNWTE4dWV^ZW`oV@GDq%A2-(nJo|W1Dx8W^Otjq
z5}jUa-Irt4W^oHPSZ<kBbS2ql+sKawhSW892$7Q$=L9kXTY~!WS~#Mes>@25t>wcu
z{h2C;o@#ECJ#mdkWn=;M$vBsTb4*&zt!n$}GtDODZCF;~1R<J=mlWthzB#-7My|);
z?CPn3A$3xQWv^f8FWQKJgHH*(3uo0ranrmAw&wlZfj1^ts)=BgWt-YThag^+(1g{E
z7m3Rm5ImiLhl<L?adgiG$Zz(q%nc9-S2tZ9Qi|=N3@ebqVk2}aP2Pdya)HTXnC20$
zbEOFg!~QO}K`$pn^W*adCEG!WxR3Cw`nzdbH+RKVF6Tg%<2cZ0R)!h^7vjUvxig2C
ze@WXPE9_0O>|wanN%&QfHnk(oMgMCfaQ}QFx?;;pu)D63VOQ}(a_4-%|1a7{ZV6XI
zuK7YYinf;a(`M8I73b7qH)a}d4m`$7O6j@j8keg$?opuw(-$V*1~yi250we6{mO2=
z6|vp&(w<mR*_m+FmlgfJB*NiXH1^Odb5E!o$O&{+#!w5wW}W<w>*AFd)7X%x5ZqTQ
z0!Cqu55N1Uc17)3=Q$oBhY5^$v|A0=xPVu;%@qY{!$iS8d81^6?M}g^_DXr!S}2b}
zfpl*}caKiP&;v*_)jLXw{%zHB>n_cY!sxhSR7^20UItYk<r&ZMES6u+fy^$(p{)_J
zmcx!uzajQ|<+|wSiL;`8H~mlk<`S!XlCO1hg^?gVwQS4kGrR%QiO}~IH3@96DOQga
z%CPL5Od|1p@tS(v!d8Oous?PsG)7iGfbjckizuySO*|~DsH!s!<2A!F=4Y2Vy4@km
zCk$mNcEUX+a8%v5=0;e)r!8)A$3I{z+Ai6Y&T6lQz58Stx6vDwE*!Y_rCVV9V9PbK
zB&*ZOF{`t6`<P1fS60NfNB#cxP(jtj$rdsDP_6fT<mN?T{4P3wh%hZ?Ns6o?_b#10
zYZo<D9i_k2^LFSQ&Dx36<4wsY2Zml;Lg?@WPCS98aC;rJ#chu>z;`b!njm_c8Mtc_
z5s^VLS|nc0M@bJYLE)^*dE^#cF4p_mSlQ%giBr%Bs|ZmYHy`FN;6t4_RNKfEY6XhN
zVw8Cg^)XR=ZJoy9pgN|Ok>KVdgIZblAJ2%sZ}+ZF!mP(Q*zmhZeqcIni7fZFei%y7
zUiEN-lb*Ryc>981FO_kSa(~?wH|<xZzjZVoBQu8hzW9ej14O;fXku|R{VqX9*c{Q7
z*(g!;cgFS;{!*WiEAFyn+wiS4cUjo)3R$H%g&Hq?bZIn3u>D;`DLeg75Ta(oWJa6Z
zkYR^I5mnNXw`P`&2!UcudsW**0eUlnApOna53Ntj^Dg^KKSS|U#rU$8Em%J$zS%R@
zFB@&aX(r2rl2(4ut$&v$SDUU5#<1Lo$;&KXF?(Xe28dTDcDF*wQ$Ej6Gq7z>dScg+
zmP;tfyQ*_jk{F{f#6A4O=%u}ClW*K@T}S<&^K7|~<s}bGe;Ya);w1;?H<cIZ1j;s~
z*gUL3(dNpL{Mmjvy%L>K5U$_lWND1wRf{3maE2}Qs{7dWyLrU0j=wKvUo0sId9JkG
z9^`E)i;xWAuG%qFLWpc2BHn$fALm@@8T8W2PS(6<S{uW$$#mUKx^r!Nk!z8zL+ja3
z1;-bgyP{s>Hhf~3Ir%JX=409VlXDIB{GOVJ609zdT2DMdtB))iOzKC-E{ZDgtS%G}
zHHAzD2-aBw<MmQ~z)sj=P|PA#vG;R3zdRJO50#<euOt(LGgr8OP5>Zu#_vjJ-ZqB^
zTEIl202;~llIGE5o6D9xXQ#9>{|ZX*v70oSYjklwX#Bhu2sm17ey?gw?a=n2+sbuv
zXiJwWLPeCDe4&{esFyLNxe&q)a@=q#Gc2f!CH?xsUHr-{s|&n9I=`ViOrExc3PQ>C
zVqc`gdCXw)1#p6{?$)p)wV9lDxt27$7fLrJFyh_ghzyvxdBGqPNK4K1vGUy3_Kk;4
zV`>kEy*i^4Hs2X%G(Jfo%+G6G4*Hl&Yb-^Zk$6X152Z?)JeB#Typs<%WFPKH3<^>+
zk_z(9nslyp_HD)o1;nU&ILNBSXlOZ1I7{b~qxPQ*`PSu(z)n~n@dImRMi2aIK%>E2
z3w=<vzq;ceG5(704e((#QWY*4cMPFFxWXqoS$;`U&i%+naupetySW-svWd#YFURP-
z_6hs?*WWbdRua?m#$B7-@@V1tEr%Z~s@38h+dJb!GmvEt%l)hMMM|PnZuOHgU7Xrp
zD$yN7RD&$aHvH>K^F1_k$05ugjx3ah={TVdI{W_Nc7YJX)w2n2QZLok3>IW@(89HS
zhnI17_+9mYo=NG>z>MtHlFkB+nhBD_9A6s0*a}`ybYtyKg837n6xlCrg6cy+gb#Gb
z(HPw}@cbNIxqJfn@}(1RD{;nAtRG!sls7HT|LkHb#-W3;%pO2}8oLeoxg6UXUaHNq
zV`0LmG-?FF4n`i5f378Vp#FlM6Syd|mBJ=xAOV&N{Jj=nL|c?}w<l0;+%AX{I$ew*
zv(|LU$+`Ebb5%8u1><-9CyfdMx&r0&bI+|lwC%vTl6P$k39j9h8QZN^^arYyqkgx?
zn6smu&EcihhoO@q51K{oFaKH+q`$JlPJbz;<@n$R!80>=nhqcEZj2Obw5Y>fMtFiB
zcKfV&q=t_Lw!TxR?RdAdR2YJbD!D-)8$6-W$0<OD6%R?*C&-TL6FCs0g#?j5T@IJ^
zs?aJiH)}Rfap?Nu+b2bfhDSLQ3)_R&YVf{-)5@-?Loz8{43a#G1EXDm2aM3&=pirr
zaDxXbCU4O!4vjWkVj6;AYBs#nxlbs1Z|vB!WJ%PyI0Wk#xxro072JS+z*K<l!9?;s
zL2cvA+lo8b!aUvZT^;!Cdpvw5Dsj{LP{+`L%wkWH>MS1FTG5~ychC`A%d+ncMJvzN
zPHMm^bb}^P*y_l@11_}?CGG*=YH`iE#w8w?Y9e`m%yl-B;Zt>A?Eyq@goGbT${9o@
zGt_R$afx|u!9+6)%8BXM*jd|_?4;E)#29w>O_R)T^s+vX|CJ+0+WADto;(a{t6E<&
zwx@e2@wM2Slb&zXB`Xn@s#RW-wH5fPbMrhW=zPie8NEj#T^#rtwJB%U<7%?Rhb|@d
za=fzksv5YvSX{eRkDT|6>m@bWOaWpotL7_?7Wa~inI;^XbFcHu0p%0ZW*8VGGrxdg
z<4vqHbVCy^6#a#fen=TF$kRuh8{9(7)3>ZZ&@$=XT;)MDhgl-<vrWwwGS9xnc0P0n
zbvuk#n=k^Zyx-E2hn+czlyD}>KK#=Igu<tI7OSU0!N^udx3ahl^fzbM&4|s}>&>Y-
z>ZHgSIkvowKWSpSw(Ih&4-miKq{iJ^R}tynL~QL{V4Zc~^K2N)wSRJW|M%9D)Usct
z5_?wb8aQ!kZ<l4D5~WDhf8<5w-~EE|3iO5~Jj943f0~KoApPoeN*xON^Sw!~dK+p-
z?f*66z`VAN8&;~tKJJmZEkL|Y*S0DCTFS<waOc-&3zF9uPqY*vWc3<?Md%I0;~;<U
zV1lu%K<!#<XA8bYH(m3?GW$q+LF>5$23hQpNiJT3U+uc1;aF|;v6DG@rQ_XxF)mJI
z;{JVY1V4>QYw%ke_e6Y6VW7-2WlJ?g07Rno1SzTQ#Qke~Gf?j4ROiO)MQP<k+fN+}
z9D-d^gdi(0p&H+v6krSLOqro3!@iKA6_djsqS@4p9n54Q(MWykf`hyszH?L)KO#i#
z(<cRZq@iEn+TXDT-<$JaO1SD%tB#e8U{RJ*bd-wnF&V}?gg?%6ozzdbuEsRxo|)8%
z6DSU0)QT8XbzIA1FbNqgM#*uCP2H_q@?VV7NuG``y-k$JKr!=r9TJkvV;59Z=2lT^
z13?EL=rtQi^WK$6Q-{kDqe2_I4Js3~H}?p34J$91f)dD8axQ696J{~<7dmuU?2k=>
zN?IQs;{M`&#P^M<IA>csGJ6$Y_d54R?dgtS6yfF0wT`jgn>LP{WI9*2!S)-!{c?yb
z`plZp6aELRp&|Mzi0{;l?N*bR_*%`6U}1vMB3;%vOEF>XUHj464x~PZq6I|3b0WER
z&_vfyDeP_|uUGe=mY>qmz#IZVR%jzV@2v54u46j(?a$3yfk#Ek_Or(tAbL_i+Q%|%
zv5`#23IdfJBi_v~JUcqg;ix<UGIcFPMf}m8|DSH)31E%P@89%vubI>f<ClH^?j}2-
zsiU>^7kpVY!HKdqhfc$XOI+8F^Y33{eJzWr;R%Sp7uQhs!Il0~`EBP1cK!RKr*nZa
z2SxdWc}DzCt9G7+)wuM{6&4p4GbW4J+Vt)QZZD7boO^kXKAWw!w$=_mR0eY@vuW(M
zs&>|Qz*MH*ag*Ybknmfo^TB(@yV;&6@Cmt%q8bG*ozZUC=$4Mr@Uw)l+t?h0U8+6B
z7P8rUJPUpDDOfqM-pbiEiuRB%e>6}S<KW=X_&zn2l)1NspB%5SYtdW=CuiUBm`F>1
z#PTcIHq+<VgV*ECXI}C|Ulq%J8atb-Y5tgAZ*XVpq`~@-%U;Af;r^l^BtiICgF@Jk
zC?>yoIJah9df;L~8B4%STz%STL}a8`WMrgLZ_Jl3Uo@B`+#l}_T6$OyHieMLV<Fq)
zLXGWgw;q1;dinBYBIt9-gG#r*vRHDyyStuusChK7IHJdQd+p<E)<>+$0g3`0ZpUM<
zU_uoltcJe)Sq&vq(@tH(ICLZxt43Wrd(a|(_;iKQHh8TCm_nRSK%iat#*O02r_(<;
z=Q<Hy%Pf9lPlND-C9k<HTR!t;;Hzyj9ky%Q`dRF4uOw9d;`wl64Nq&(sMBYfV7<uz
z!M=rvod57q>9q|%G)8W3-n;C!+ekIy0^RGN(U=lTw2e&Q=KS@6B2%(3Go91e(-szV
z2>D>U6w{g+v|O8d?Y48d_wraRE_iFeG||AtRe-=36A=-y^g%<lZuDZw4fI)shf@pG
z4JV#Pt@Ic4h1IwYq0YX%S5Mx@$;+es*QNx0S0=`tfFp!7k(TQ<TY!DP*5WcqnJMC~
zrMkKM&l!eXAa7UyZ*7hRJ!Yv3J=n{(Kt0}lFp6^X^Uvw%?7ZQ7C@n9KasJ)>Y_jY9
zt!ZJXfUt0P;7pvFMbIpnG)b0IgrA~g_SX?0XxFghJZOV+bAJ8vWM$L?6XGH@N(EVa
zgJxTeZx=r-Dr+jNwr_*G!*4(Q_D1^0yQ^Y(KKMatFHg@el9G~tz;F<n_SZXx8i^yW
z9<bfnWX^aOwgwihkipN@WeQpQd*L5S`!*S78HaQs+}wg-S_Li+JKPs|e(J0g`ojnI
zoVa4TKeWQ1eoxy=kN)}kI#UE{Y8$d;**4=b*>phM-{0Q?-Ev(WFtue7aA~rv`}~|5
zCgwf=GrZOdmr>=?m)i!~Gaj!MQh+)RX%Bz!_0_?H_5^+w?vtVxJR%|<xcS~}TTLE?
z)JO8{IlfVuav^~s(Dv3=KGxgLKNsys!BjEy!Gi}st~#YXJ&ch*G?UI39j$h$+JDJt
zv%`*P2n|ux0|9UbqHtSvHEXPnd`Qwwk?@!>*G#&xaP4RIsG+XxR-XMxCAf218RoFg
zPE}QPEsW}Tlcvw4sF>JSn^8|-EW#)nCJ($vX0`b^x73%xA{G#=`dg*e^)_bpzJx#K
z=H@-7#b!@8FQ_r!+9|DHel%-rqOY(2LtA3J9~H1MH)L99v@rQm&-vbLdt#=0Z`Sv%
z=hWxqe1UaHT~}vo%oI2rWH&m+wC{cV`Hex)#5)0*>e008j+^p4d+qk8aUDxNz1ZE~
z&(ERmE+FIg28zu$o-Hjctx-Vdob2Y$QQ`WfN*#B%YDq>FGqk(4dryXJPi1sZ<nHdS
zZW;*m5R(#%L|kXQ`Y7a6(B49xh-IZc6#Zc@95gQ7r$=@>?Y*;f_#(AI?T?j-MrkhS
zpC@;#zzF{#PaEg!J<#I2lya9c^6sM&kAKD5*=B+eA=2dYdPSiDY^e>9cUt5#OwsP+
zL6ucinLS-yETj4Q*&j`<o_^;CDClYZn->w<8jx<&6ddrqJ6+n=C|fn^bya1hO|gFV
zkFQRdA;hb_v}d?I)1&>KrTXJ_lto)O8P<d^Uh;s=qvY2z=)3n!ip}EuMqKjJP4jfp
zNtKnA8D*B}Wm&}~4~~}Y!lF{CTL4yz4RYEIZE$5F`xe#CPeV3Q_T7EY$3a_CI^W;Q
z)(xAasndCX^G{hDGtMkTv2e(RDdc2`il|0?8APw{%I8Q=JbE&;BMGVFQ?$^a<eFnN
zxMy9z_1o0d*vKd$Uh9z3(kIYI{rSC&0RaKuE3ozEpPoEOq(E6LQYT$XXj^j>pw7#U
zxi0(`z|eFV5PB!u`ja0^Q^+D@eWnfew8;bvXZ^$P5z*jgK`m?3EwH`WWLvpK89Ege
zm8(J))t{bjEe^1x?fNc{nFj%%sWC0J3Ye;N=-_Dryiq2Jt{1u->umo0`*+VI0Ci$t
z{*-S_Vr;2Nn+tu@T@sT^Y>hntLtPK{H+s;a0NR3jQYBV0ODt+euBQL6x%6Ofb3yI!
zU<HP87%H_EUvdL%Qzqd$SWIhC^T=l;OC_?jhH+FK=>{2NU|?|2#JUZkj1MfDZe_pc
zY1}H2+wsl@u$R7?oX5ULWm!426R#^55P#qYBDC9oI!Yq<)xGOIY=33H{Q9TZjJEod
zn=bL12o#qQ<lZx63p<F&Pxb=$9rYdi^wf<}#Aa-1qeq?%KlWZr-X7if>C@fKJ$xDY
z!OsthjLXASxNYe$)Z=07Jf&kWz;#<GvOz>`;^}4O(!RADPvX_b*EER!NRAAZ@QVxQ
z(l5&Lrmf(`hpfB+Hay^NK_!rSm19<b6U=p|h~wV_DoPJrZ{xMC@|<l~OAnwvd#(S$
z+SK>)_4RdB@Xnvtx@tqoodJR6qZM{)C@$lCOSQ8icLitHO`@*lJ<->n^S~S=Jy{hq
zv5j5y8F||j-t?vHo%6yxg<>f%<7!-!CG2J(k<(&+){ypm4?+wFTxPI%I6`^j@}*1m
z(QNWf4}ddxBqB8u1>BmJ>+mLfvyz89(-In3z#1bweG~ShxBORs+wtmV>?earPam=g
zA}Dnv3E?q*D-&{-X!pjQg}ltBg21iCNuW6Pc5E60({Y{K6pS!y4D@*oP6`K*Jc;rp
zjA(+JjXIHt1V-5YTFddWPg4=Ri~R)~nlc2f>SEW@T1V}>%**2q0UNb&@0G@FIxk^i
zG=Rj>rD}24!LiU@6~?FxDWAm;wxPST7lS4<0CVD*?mYhFjO$LX>5<v!R175q0WYIA
zDzWhS`t2<}^~y+1(aQ38J*i=5DiYSy(_?FEW@a{v)f8#uL!hWu{U%(Z`pI7$`DHD{
z%aXhg3O0}T!W(~Ndbaap?zoTnKjUfy;gf7qXyoDzJVm4daI2-T)u*36NaOS1z;n!Z
zSGh>EB>ud#^~rPc`>O&$ahW1bJ5%khWsSs-Kq`^vJL4bA&rsArhoDh@x!dM^uM#lE
zfFHPCNJ}(JkR6aNgM_i#Dqmoq*CW~8;Y~Wc27WT(cUif);|_l&bxJ+};59M}P?YJ2
z*NKEt5xUo38({!KQw@h-+sCXd4Ofw^YdvSRCxbV--X4G(Kg1joM?Hp^B|T@(!Z%^$
zZJ6I+nJwmL(ej6$#OI1STZM%~jb80B?_xccx=f3k*jw?pDInA^n>gEaOyS8DmQyEw
z1NfJ;f=Az-dgD`p4PE=r2>a{hJ?orF*!aQ0>m;RfvfjNQ)Y*!xYCYp@ueLCIb(%m0
zC^`xZ8#6}av|^1NAeWC$83B&vT#dVPspPu{n|&}8dKQOcv<}%CaP0=e_E9_aP6+Y&
zIl1{pK*kbFyUlam>7z#QZb@7kqkG*#ZW$psQP5O)@mIz4U^I(#D6>a>ypSL#XSNv}
z0I&=!kyYAvsd#>9ePbh1xN#H9Cfu<4h2%O^dN2bVIog_njlgIgeuCLR%E}j;RZOws
zD(Yw8nu9AKu>Tw$kYda}J^AATaIZ&FP2*eN9f*S^$AQ9)0;_4@T+&%~Ezv_|=2dg=
z$Lsw}(<QweR}f-OkA9UuXz%J8p8&egk2Q4X57}q_=L5?^f_Fb+)h7VNiFuD4X-ORJ
zJ?xMyD;c`K6H<x+7(KrBjb1l+E?6irw3YwT83IBccGv~rmap6vKZzla002KI0+eV6
z#C7n_n+u#NXRnIRY_mx|uhZc*L??fUR)a#T<+bM0o=|RI_jtJS0dS%-_H2fdO+aH#
zO1of@(m`8`TbnOv=ns7d&7Fkgr{9T%Hq2*5z5-y_DHpQ$u8z`u(g52`Z8(-**Y<qB
zPYS@0DFpx^CthD@P=ElR^M21I4z`*y3j|A8n&LoOT3WiU%-T0{?f4;E*Dl+q?~#nW
z2bw_~1pmc?yp)ykdI_{j<mIY#@~tNnc3S3)ky)R9ayRW(!1~N9A|B8Q$ruhOwD<Nf
zDoNS=ZEHt|pgT}$E%4hui~X-`7KH2Ov)bIL_98(PQMZlh9DY3~&n3=qI;>K#H?&z-
zU}y*UHe)HknrDnQ{(trW;_nW$Ckm3~3SB__7&~^Q_<W|}O}jZ)20Osh6rlQ&I(>HE
z@@Cx40)Sow*bXB@TB>?_(3-3vKhqEL7>P976gTaZ+q)&eVahbHf@KY!1`Q<m8+JV1
zqJKhyGU=;*H%(rKY*5ohqbH^s2Rp>2VXuwmXUEy%kyOV|jfDa^Z(S8aBJKe{PT!sa
zO=l}Q^v=jrw~_fr`T1=!kOc4{C&PZ<3}-yu46e1#RRG#@3u9L8(&zXCFr6yYQMm9W
zEu#smjQ^^+*IcLMb~vrD|7N=1L~SlW+NM5+yJ|21++16dI@K(I*<CrHf~KW1{DOjb
zGjsX#FTXw@5kLIJFNzxk_Hfm(;Dn+l0`cKM;1jGG{GXz)i9foHpJ*hFHU@54gc66H
zJc@xCCzMB6?9JULLP>-aAma4$xHXfi$y-CVOaM30;f-ArXn?-`tL>90x6!liWw@D{
z7h<BK5kTzE0qF_HY@bs=B&f@u*E~P81M-N2p8#c&xf-h}Vz$6AZ;(yGeXK1rc)bk)
zbizmdY}N6fjX~RZN{B`kpd=9Zb)aT^-*X-OIR|^sv@#Y`u&JbzF70=SEs7Slt_$8;
zu3vf7l_D-Q<zko>UM!tIt#5FVa4uewH%$k)qQbV&n`vp_Y_(NRJ??1l1+1{3N%8#q
z<Ye^hbc}o`N5+9BE+Art8_S3cOwTQQqGnrBRykn`IZemu{|sX9<ix0s1s!=P+!>ER
z?2?3}KkD%XFIBX-X99YEktAf<P04&H(NY>Co(J~|vazu-Q0!73T?1;tG`<sd3aC)e
z$cTu_qrb?=s$#X)+WTQ*9zb0B!hoHJd;u&h0%0FGKhqXZ#$sUP3*wLZ-~asdbQ(5J
zNo=Rx{9+wcK;mwj%qkoBF71|`>LeRr$v2clm>8-;NOcOkBOx)gkyQNd-8<yvUWgu%
z8+-2C#c$#h1;FSpk5kj_y92)z30?h64Ws05a3pC3gK;W!&}_3#29h{M1PU<1lSO-m
z2f$7NAnFXic96f^&*Ngabjc_RZT<Fi<??@nmO_h~#bx{T0Gh>}jHf@YdJ!-HWbevO
zjL!E3WN-wu5l^;R<t+$~^_ngf6>l>@!oQxjNeEhT|CQB+$+>fXaY(k{ATu9e*`&vK
z-Bv8XB~xA0I{@%eE<l7p<$=(+usspv(E%JKWH!<F>#MVvN$js&ZKk2x`_J9gfZp`e
zaD4ill1|SmcL3ZZMC-~QY}T?!`!?JKXdxx$*y+6fnVO9m15lm*)mf%NcaXOA$&>dt
zoHXlij24?$6>0;S?E&{b+{KY`i$!IJ#o#)lMW$s<L#(%cf-IWWsXJ|0x;KPcZ~c{A
zgMx7p@(!sDXUoH23csW!e_yc8b)Q1+_+B+O*^tBi-NwOU^S*W=?Wa#G@X7VSL;OP*
zG&k$gGBYvOaDdlK0cy(CMx^VblsvG_bu?EyRY1eQ#%5yBzC)y>?zt4JgNi8bg`uNk
z`Ls#+-6mQ_;bKuB&Y1`>46p{HZp!V2_=(1#`e=7w4P1bQ4MXu{ei<7R!c+2l5S(mt
zLD}|vkB>!G+3G#u$Hz;`Aitv^BJDs@KXQvldY~BRQwbz({e*;uh6d#2i_Rd)ivZl2
zu>&GnBH^|OLNw*J%ep1VT@IxI$V`A{?#u$y*S850aG5Y0V>n1PQM^^n7NUf^#9wwp
zTA6azk&Gg8iUwG+WIiBz{mG^fiA}<&-*cp7m19@oAP91O+RHcQZ-LaTt%Wt{@fcZA
zJNHHlL7n9P=Ov?sh5^V<Cw5n0WD?3U1*ldOw82gDmFXzNzoVlAtjxv_DjjbBIrl8l
zWn1L!=`6PL#2IlLcp(@hZ8%6p<u<Y%L6ly)eAxsDo=ng2bs@9z4;ICh(~ZJxK}!{`
z0Of>)tm`&kWMyUHrfawE<cH1!+IY<GQZR}FJk81vjbf5G{NWZ<IJ9827=5{ajZY9R
z$*WN(TfjUS9v<G=rz7p=3{qwH7*<&u=dn*~F(7v9{zM3tUZHdk4_1v|nyK~t;EF}I
z-GPo&xz&g6IqR@HP}((v!|}=-<H~Pp#!C^DZU00!J+!5^u%&8dqOwV_1oSks1VqU4
z{w^+4@RYudqhoIAr>QeNyV3h;{!?#@^0ZTPL2O9q=A5OB1$hJz0x8)x)J4>DruFL~
z5seAKC%I2F1o&_-lz*kOOw$N?TWz=c`*X)i{Yq0KU<TQlI((^gw+A;~IgKJ*=VoUQ
zDgl?JfE33klmjfnYcJIMc<&h((4cKwjBB97%+U7scF%(^fZVG`K)xj@JdE0XJ8%aw
z!K3wl^_Le&#a~ZNC0s5(v<ArQH{mm69eJ^f(MX;>aY;NBsMe35U&u%<zzmNw<;p;i
zgNv9O;uB46Br8qJY~)pNjR(Zh9%!{(8GgNH$fkY@sCE94S}&&(#pkD3$P{OYH*+{S
zaE_9=<AOmg%;V0Ze&<Zfy`d7zWYR>?iZweF2guI~hXUoFwthkma&OifI+w-=EnuCb
z6g?_|M9Kw)2{Vur^h+px)Y}1gpHWL;0A9d1;(#23`$b@}xb|nIpc2FciVr@d2nNAN
zCygDGg{_;qdV041Dh;H`5?QU+k?uA9lwI~~=-HqtWc3Gg10R|qCRN{ES=-%Nn|3<6
zc7-A_L7_&xV4$b0VZGfLzDYn&qD1)k)c3pwKxs9dFj5m_3I}Y}6l${oI?zaj_h+lc
z47Uv?I!gnDs&Q%#qteyD0?8tY4BB4Cf$~Uwp{%Z<qN3s)$a>a28rFUgU$ceo$fwN$
zrG%%*H1AYV2aWNrR0*;i@9_F;hadp?jV0@P-+@k0fLaE09uskWxDt36F`Y{rA(}(l
zo`hj0-#iBvDosN605E)K>0X;d07O>F1<+S}Sn1VYF0hFJoa~jAHBF;pkOmFrXeJ8*
ziF+2wc11pPzxV>D>J3V$f3aPBzDSLnDFd~E7>{2-02CeW_P|i7fS=Biq<EmI5+2>G
zVvbtVmxV0;8f^**l7(Ko^Z1vy(;<*Jp?pvgK=PSt@2>O~mzUpE5=A2@q?RmX)9|U@
zva!u7*%eqN&lVF(PC_0D13=+bjKWrC-<J(3b(ZZO*kRT9Nk*$Dbz-`e2eF2f5Cvee
z9I%j+yvPVj0++y_R?6*yP>^P<jMV#i*Z{*}>KAfTn|$YJ7Nyd)#%&B_StBnC6;dkG
zukL+(OX1;k`)z){wlw;+PXyGc#VE-ovu_Q>J3JjU>V~d4oQc=%?rv)vmIMGvP0)W0
zQ0|&xjz(hCf0C;yQ<x}DqmmTJenrZAzVgG~Ghm^fMT&U0AGuH@qP|6e0!7;ky5@>3
z=O7Mj6sge<gi$#~9fKlW@6)AU6;WkPdy7a@Ru;M7og&%5&7EoBMk6|45SKyew!W>l
z5m?Rc&zG0hTrRD#*EJ2?fY81I4jdD*L>Yz7CYqk=2IZs`P*L5ho2_AVD0dtCR6z_s
zNgJq>a=Wdn2@i^ZAI;E$xdwx0CB|0UKq*%0J_zU*klXI;0wwVT6dIq7*ZCxarX6;J
zG^nf*6epd^%qp^`S3%xR$0iq?DT*H`LIbH7hzE&nr2LOZc3BT=gy#1kCq(i?*%MuZ
zL3zWOQn<JbN+<+Se_z*1j*HWmv2Tqf1re8Ohj;e?8q2`2NNCYfMiV75;XesB^PTj|
zqv#ckZQw!<Mm0Hk$!C{RvVJoCBT9S%$fC$R-OR(hg}yv8ztkik-~)LaCZ6y*O-Gtg
z%{U1oA8b%k?H0-=1T{N5>lT17K`*~;I(s)C?=9*>f^LDuU-*PX{%`>H5BY_Klf0-t
zDsy=S<GNCl!RgzRH=rOtX|)Eq*G>p)#o|EGN(iVwuYiAs`sjJIc1F#e(rd=*R6^w2
z(gX{Z1jGLsTfWy#U!FC6u|0p|>{FS4*8;R0R~T+NZNtuv9`o!df^T+;^=zu=r1b&O
zg5%Ln2^NNu_l7XS5fg{~hPo_GPCXeKEK=U@N6H_3RWN-QoyQW-s~fsq)g#CB1eB6x
z0E{*|fSPW-n3!0kfN|lbA@IXF8{c6E-R?|zvPgF&1!Hg?ph8hk(MSseg~pc22t=&}
zNGS_IJrkXm5x5AO48WyhWC4?Bw)B<ScO2!=Z|QmI+=nY?UxOm=z9c9qUt(l@4JJue
z<h_N=D`(z<tVYHLNQ5%VtwDx;h7MGBRSrnoFi#L(euJQ9wk1DY5RvBwX>VfRduCL+
zW@-~KyKR`K!{QA>Q*Ip82+tK67oqM53cC6MC;J4F9eD{CtbHg@4{D`Wwd)botp4{g
zE_|Tc6=4dH8Gkfe0W1&mzyb0amj+P9M42&&8DG?%y+-bqUTL@kfL@U|-5YzUsC$Z1
zk9y7Q*$M~KL(##6njs!#95k2Bvu^bZE#^HauZsig`d5M47LzE*S2i{_hPLN>r(YTu
znOFyXq2XOZKhf43TCiV?<{qA|0C}SZ{L-11L<%WmxGX%NM^y9nZ+!sJY{CMRUdI8R
z<H~C1QVv4r7eM7;?%8o_IUeZoQ-6ILuhmO3gr*7dv4C>_zZG4M*^|IO5<(oNh60(b
zzWjM24dw6AH@nQ@*?JvKiMf1`K->0G*n?YN4ahJL(x2CC79wWl_Z2{)?6~zUMo4Um
z5s9jFjXMt@q|#Bjl2Sqfb1ysAb))g_Kq~AO0KOg*cS`pe`~*U4y2KkzeG0em>?0CG
z|I;*qS?;;QNGF(+{4JJzOm+1bRUN2eWhNr=Wf-}SyMLy(=2ATd4=MNc19^%78F4(U
zk~c9l%vDCCFHO=bx~sEujFMn&W5+N5RxpH5(tr{H#2riyQ`N1v3l6dR^y{-V0NiM#
zOh?|DU#C9D8th<VGPv9p&x>L99)^VV=U~r0iBfanzj31z)CIRH0EoRe!-O7u0ZqO8
zGhxQ>7?>UjW190jag0c*L31u3;C!HhF>o$HxfT-kn``vw{I<3>8UXv&a4^|QT?yaQ
zQLv?gJW&cm?-$JM)GN>V|1)K(Cs*ETvG9n0feqfBWTc6G2o-@|<6juM+rgKR;C5gw
zJc&6T5GVfk{3_QAl|h4V%ndnCZ;)kN*8KEiiO4(f<@n8PWRuIIf1b(-^DMNAh6ueD
zPP%<P+S`u%z}%ohH|6+)6CTC{>46h}Ux`k~61dtK?D*+s-BKhA{L7$SI<k*zAfaCY
zn*+ZijnajaTNm%CS^RxCM1e+1GcIh22Wo7Eoq?t-s&7{|Ja71~_cA2BGv;Cs%FwO*
z3mS75+r+ocE<#y)cl6=owiUP-TXIcpkD6s6bKr1e>EeGTj14Lc<H8=xM1of@1Y+Zy
z?2Y99YhGCc#a{s_EB5Y@T$f#J)%?%gvqsw)m1C8XG&3Cun{}Kk2ZQ+k)($OwE_O@l
z&kg2qN2&EmVV5$>QmvzpLcu3((1l*;OiyBUv;M1%qc!Q^DKPdVFvrb&Le6V{T=?yN
zJ$E{Xi^I*Xe}090D{A84bhp*{m{tfMjJWXctoIEtVcx8GKO3o8jG;B;b1Fg-;fHaa
zk$>OoKigQjt6*Zi{UsCEc<KGQ$8^wk@azckEmgwNQ$vv7+*~fXoz}h7@!H_gH;d#s
z-w&5=svMuvH#7)scF5rR#~2^%#beDI4(}V9W5G)<g7ny%BmMtM^v(>w`dQivI{-JK
z*1P<7AGIXK3-RxH*|hw`G`<hT$Y$HhS-xL({BY^QZy}hDp8cb!+8nc$?yz>K!$--Z
zQ^H<2*xU0Zm|h?6;$yh_QLipF%s>^m>8`pXo#k7}gum?&_T1t;dhF;iN^ll7?0=f(
z|Ho7x(jL-Hz)j_Uub*!J=g3&_2+{4}yw)t|e=KtTit6)!$3yb_=?QF&mBG^phw3v~
z*Z(=9UH(`Dw?{2<aBxyhQ$W3<;ohy;fA4!sMagBSd2RdS!lQruIP5rB8w1}im|jV8
zas77{!f`sV5B+UzyW;<{Z@XlA#jtPS^gp+vX1#1K7%<AUg*u#IQV0N#H2rg<W~wlV
z$HIH?INW!tq<^mZff+pE@Xv{SdH>~A|Lt+O|FuOC|FPD8f0fC91<U`pOOtyh(qC)(
z4sJ~xde@1)K7aZ=hu3?pdv||_@x3{H<}`;|>}RUz2);8M;Z;o$^!e0O4En35>8MY8
zD!gDk|Bdr*SlIcK+RZnW)H@Z9A?o|aSJ;ZyeS5wAh|4xXAU9ej)h!n0naJYNu;F@a
zBgs-%mhw85_k2`fB{+DPRGH@QH?{C+{Qa7ioBGF&P$2B>2x|V+`xin3LQJ@`&!$!L
zkF3D;+rc5j_TX2m_==3DEaeJjQpaperqUMs1_m!Y8vi7PstbN`ghA!~CmM>sMLMBx
z$zTEleLkyiQ4YWFQrik1V*P)-^TodbKr7*I0J)!a;_n!y=l&ao{udhmzki8GqghNJ
zga-5nZVe1Ra`G|u(XI)47IM^SrnE4>1Gj>v!o8$L{ekgwH@BY;xE4v6u9Wze1o$0w
z#{RVb(9qW+Mq{+ipM5b$v~6K`S%yg?k6;kx3Smp1>upd*B1JZnhhN7#ifEm-NuEIN
zEOzdXQBDs#@e)5fJh0s@+w>vO(FeWw=~d>}wE<`q>8oRlZdt}edEj<nOhHEd9Ltfv
zGi8MN5ylJm5Vb8mkIcT48W!aX1xZ*TM&F+3OzVU0$A>*#vgGLj!eFj7<@)ypPBgf#
zy!D!v&kwB~scpYjUx+j6$&@A{m6j;p0C|6xYGov7er#umu42%OXm~_SX7`Rw)cB!m
z*G|4R!)G1zAB9y?EV#8>CX<KkH;WDVGFQ5aBOJT=6db#~?KRzZu41sjYZ`X<+mRDA
zf1~jqn!w|Jh?GR%ZIyc6(>-bWcavx+KM0OgrGNP!<h^NBQ(3n*iY;X+pk*m)1hhaX
zY`}&_dVMWHq*J<p^dca=0-+DVq6*7GKmq9?mh=gPz6~KN3Ial;2?0V9ktUE30)Zqz
zNOE_=Tj#vz-tUZa@3`aK?~HNzp|G;|+H<Wn*PPFM=G-d=$Oy1oNAHP<3bG@~pA%`q
zrWS>Xt*!ZJ(@pJ~-KdI=l1(%aLD^Dm6{=lbF=bQV{IfK7Hxm(u5l&jW+bE7C-|AeM
zYScY?mw3LDE#9IH$*u;K9!|g0)f>W}(q!!&eZcH7+Jz#B*d~}~UN|v|%R}UWvG)Sq
zW?S_TlyXai0QYHo$q#$&VQ#iHY<^69!BxXUk;6zm`{s%izp<gIcG@KE2Ht332@LvP
zyc&6cdRz*(6<$JHyc@(5v<CiU5(k!=pWpGXlCy_eKo;MC9DSJnkyk`!G+xT`ZQt$o
z;?2UB{^Y=!X#c6FNLjVEXM3(9H~+k;Et3AN)Zyj2AAIbaAL90Wlm6&(qN!+o?YqX&
zi7I3eZ1FCMGgfqz)R#-F^#?QC*7s}jn4%H6PmAsf&L?#(Z_yU;UW$09&{U(@;zGe}
zp3!Ri`h*;b&1O1=fq-izDu0cSh+ZNU^8G1|pNvH<@gIL!w!~4uPjOu~Eq9KKnre)t
zox-;5>&cV<XLHdj4`|fPOZvW9@r2)gi<UPWFHCwP`b*+@m){-H|MAc2evN=a%O@az
zXj+1b4vE^_H$}R51An}J1#AmexQ}6sZ@+8y>8KQaiV-?ZJdfBXzHs%>G71xcOSW=x
z5bNXHQpK+>F$(!082){m=n4rfIY4cBA12r1lKjLzYAn|IK_CPASM=wASkiawloNl6
z7rSO3usyqxCY6o;|5VH0#<wN?|HiibUu^4tX`adhlY-(+TP)biT~Rl_V*8gc9MOQk
z9^5zX@QiAodc1XxWQ-DT5;GG(p|<QH*Bc?f-QxxV=3wfOpIDEqMH<1(6KO<ZJeT&8
zCT<-H*#@Wa&;Cm({%a?3;@ns0O-S`2x_I3m?dtme0k$YQ0uskA!dsXi#Kz@q+;hw~
z&QUzmKFhWm*%2<)Azl=TWZ4dK>n5ItX!T4jCp)@0@EsuEZn=r?iSODUY2?Vi51FPt
z!4u~S^=hN9Lmsn*_FTCYx_GJ2SJftr(h))@XlQ4+6z<`7;nmz4nu2$Cp(81Frh5A|
zI*rkgGmM+~Rb)CM#;!U)W=R<z3Tg^or_(@)D*v%<@k;CEoz5&G$WBl&B$kcAch42V
z9<e#JP`nkm+4*UqXyPT5gE8FqnD)(Knx)irX8SafQa(^EsBLp=^fJg3Z<6dZwsfRS
zFz!#1k<Qz8o-MQ7(1GO9j}}Xt{hIxKq?NBjLCnq0LNqroW(kgvv@@|U1FILrEUf`1
zWrpp0So9!w-O<Zm4ceju*i5_$h(j>A&zZEttAm?SOHO=?MvN{x`aT3r+g0D{<P$2M
z_3SI*Z_y=fF>DhlBm@`W@Oum<!d|8#$k_m?8*BtIw1W~Sr-Kz*twi0&_Yk$L3l5*K
zrwjhkiI|Kc*y5^Ot)bCJG>tU1JH}5##5z^8FZsx_>)4|mjZr|-<=#{vJ|&^Nm3Jp^
zdzXFg`aZI{8ROBol*IdXq(!oC^nj>u3En09w>&a!9Pgypf^XxkwjJ^pOBit0b5A?>
zc*XXiRp-LS-iWP`#MiIS8-I=PXOE1p*U_xNk>m)lc3v_J+ZI$Dr<GK6BF-2*_Xap9
z5wMA_UH9Jcj^OuuH#=2-YfqmW;N`xI9{*$+(DG<96`UGq)nX9r&n~82!Q=6>Qt!BK
zV6GEfOZ4=3)nkB?u1~J_Bd;<qgiZ&8DzcJk$MGKlA5OLNv6r>8%F7G=g*`w!gRh!P
zQLr~?i8f*TjNcQl>cjTe%YxA9)XHz|skmfq;Em|G;!Or$pR32%*9=HXkQ#}OW%ntv
z#op0;%wY%9h1vy1eQQTjyULPB`iw)J*)f169<TA^nh*~s3Vk8B*}Lkyg?C7;9_Yj>
zyc`l+fLz%xMHIs+3A2F8c<yhlD=52sZP-)|C>7{p^$+0PA|nS<$?0XN$BlCSQX-Q=
zpPSQveR_+eiH25sBqehD@uWp3vCoyzb_cq6-wM3`f0eo1x*}^#^D2r0tYihA)7q_f
z!k%Sca0v~&*Lf4qX$lQmB16Q}bfzJ&SeJ>AHvmyuJ&w?BPkIDx;S||*gOi~_v7?5C
z6kZIQylW)aNLa`#(CnzL6qro6pMFFc=MO55++UHmgHFH6bMVR1T&#dDya9Qro^<2?
z0vW!kT{j@8ow{EvAu(B_2R52~G{y0%MMg(;mc8*<0Mtxi5wnvFuPm&2zgnoH9Y?=~
z3D9UR1i94$Y{)o$#ZIzZ(}-&?NgAZ_ZQ0p?*JLM_UEBd9ha|G8cCeaxWry!mD+TbI
z+O-l!4w%xZbT!~8j4>;)m2=qP{f}@ox-4mTz-~YRt1DbDgMhjue1Bfh*L+EhtzApe
z)h7XE@HT;r1hki^lOxtQJ!6c>F#{cQOZsc0_KP`qB1mmLyj*^~=G!X8r2OMIITtpF
zt3PYz`YCUaZWL-N3>&2S>)Z%DIz5l5ZXI7SU}<SPg{jlOQ3dR2#w3QfoQMq-;aBa&
zd4$uXu!A&jS$4iHOEaSXrfdY+4O6j&J|KVr;=z$|jRwf)WCy-6dy<xhnIkuNz{lNk
z^*BWQ9??2%`E(wYv{SK{Ww5u%vROnzKD3|z<*(+Z`*7cO{GSm4-$im8aTgD>cSYz%
zsqQ3OIY;%gcc@n)hl66xjUJVy%aXd<)<42{zvx9UnNUj!2@X}6d$lN!!lWN1b>*VJ
z6)9j1zwYtwYWs9gY6mlBPF`pZ7~OGNztmq$pA$PN8;`h9JT7Id+jte#y3)Kjy&<{8
zUMEW5p?ic*a~C7TP_X95t^<(~8jDcY5GDogY)~Bi(5)?au@Av(Xf^tEBe!)$!L|%%
z&|wzS;Rd^k7Lqs^?2;*T<?I9@1%FDk*)Ljn?rpatg0wh>Rj13_Lli<$`34bwY(BnG
zky|sk1?}9EgGlimi{W_UTk%gps274l+Holg*3%m&3}fT#iC2(Tw%WP)*2-vNTbS4t
z1UPsy-l3aA5ej*vl)&#UAv?Zw+@SOafIDfk^%IB7DIQB31$a*+63MJ`y(>3Z^K?a^
zkLt(J#8AlLNiBGJsqYnjqn=pzJDR||*7@fwmodHWD}@Y2x;pYd?4Sh%k3HpHP~>hS
z*Dv>~zRsHQeb{#ReRvBbPTToWP%ze|z|o+iEx7ah`A|eNIQXH%g4hL}`sxzj=iR(Z
zr+QcLplAjpYSf%Gwtv|>>bHm4gD*fjflaEuwP&F$^u!<J;l_3_cOhiK5xSpN_iP79
z=QQSyN&#*&P*Pz@Y^iI$_pJJgF=GQzP`xs)YBP(*^5foe6N?z1Q@fK~_Bom2LaLrH
zv<?*a%JHbYa6(>RTOjxiEpG4+0&Gcm=(O-;g|fIo@*-g4$6HqhUT#CKM-rl~1qUf;
zTU&@PkQP~=+0bcTvaQ&m0q=C4`^wLvS$R(j?c}ali{ZZn$FnlNnI?%K*f&>_-3l(<
z|L61T03%eLi`Nb3tfTOf2s0$t7L2MHC^Dvrg?1kX0iAJZ%g~cpQ})-F|AZ2uU6b2*
zE*_#W`aKzxY`Z<gBLF_Zgcs|I1*?2<;e_~?e~)!N0|#rGhY==V%AJC{at3;Flau*G
zJCn~{3Kikicf}F1L>_R~2=f{K973|neF=)l!gW#XF&;D9T$xtmrRIk%H#=`n0(+xw
z*Joi8Ep2?Ai@2c3*<JvVg$wd^coOq|am5LtjtsJ9jNx<$c352y1=U8lg%L}1{&!$4
zVwzQ68z7?{AcF4)Vpb>Di6%6$Q*l>)eq`AYu*M^P)bDY;waN7kMfP^@KLU=JX{2rF
zUrh~H_$qQa++!7DupD0ktWylHbwyLsak~7_ruZ=qCy2&~(3>$kC#s}m?>IeEq;|ni
zR>*d7tMI9s1E8G|g(Uc+w~lY?B5$)l0S0vdRUY?7H)1fk=EtCW{5VLG$e5FD_=|7y
zBoMl9Haipd`>Us?+8a&wHmmQBPtXEX14WyvUwMXy^&2$PC8(swL0bbaqdj6%t2%sV
za+{C0%3rj=4jsI9A&;xM)!ccHH%EJ%6m)-w{2hKcWaSNL$7U~$ca-#DC*a)1;)Fnh
z`zo;HP=CNlJhfMMnu@Z@1z{f-yB?#+*uy4kba{99kJ#ljz&P(Qr;H+mP`Hg=IMD&U
zH_+K9?%NwzcF%tQd?PE5Gfno;PK+h3EN=e!O>Ui9$La!eet#D8XCeHZacv_ln>dNE
zDO_I{eP+0sX0$-Vx!w_+-05Bb%svqMW5(B`@JNv1ulIOv?_%z;*IIRUl9TB)8bcAk
zIXcdNqShgKKBpSJu|is3t`*##Xc26D>ystzG@}*g$&s|lyv(E^PnVNX>!W_%Z-^3{
zI>wl%-qcc=dRsE2mOWntTq<GaT{&wlYlk%i!M`kD%CO;HiE!#P*<*AuV)6JZ*!H4V
zzR!j~h1$n(1~H+K@J`qE?$7;EP7n)rFDKS{mVw`IvM5F_fZ7~ib_J8L22PqIS+2Dk
zPoOJ?zo;YRGtMQ6wWo4o4BRk&*Pu$bBsv_jU^wTDKe$+U&)`g6u^6?T%xG+T)8CDR
z!%0Bko5ih3goZO3#Rlu*uk4lw8=Ck%^ks-xvod?f{x(>af3r23qerE<l}*p@Q2C0$
z;X1&N!}ox#V4|cI{u(%T`jV*St#!>!v9xc0NpAd=yHa)jydeUBvL9&1^`63dUH@W3
z(Pa{uT>!8`+Up3*5L7#>s*dW-ep=egXtA6Q-zQDH@i1XG(5mqL2(E_fm>7-L{CKog
zlDS07sb5jWN1U(O-WENMYd1#?^|j;H{ux7NzrHEit~b|3X{Z5!1Ms8#aBv56Ds<XY
zc2vCm-tNAj`xV2TT(A6H43s1;UQap@8SKXYI5|yFD;Im0c+&|0RjYuVz!!tfAj32e
zl82L{xMCuUx6MT6dq8|9=Ob?c)inS_G*siixAysvq{Kc11p#mmkDoxr0+<guoQt&b
zABzKY7Mx|D1xA}}Ztq_+AHcLKD&ob%cK`io>-OA9JiYcec?TG@x9dTZk9M?O!0*9R
zWr<?QT{GzL4qY2i0bsO-w#v;%?7zm>Z{Tmp62$hfW)MK(T(Z>E%)MyB34+@>6ML8@
z2=2t6kDCjua<wtIZ-k>XMtib-@LW5z@uE14&wdS9ENr#3LdB?{5)$8T-_aDk9I%xo
zna$1b(3mtpPq-8qMl9}=LPo)_*e8ev_Euy3jR&JY7`dFn|J-WFP7h6Wvy~$rTqIZN
zmPWsuT*aX*Pu7}<E_MG^1o!6?k2JYC@kb_CVCCdHP=v=BS|DgAE;v7LwSBz3p9Y%i
zf=}S`yZe(VcMQC$snw{s_S4_Rtu2m4*s9AHT^IM13z$eDqRpaHP-7>wHE7ZeRtSYV
z6*yzsPtQX`*gSl<2p+fgO|H}UpQAoauKUQ`p;L*6mj&EyMXnj?ns_tyiB?H7#!G^J
zB|-uJuZP{7qd}#JGF$$gnY;4_x5yuOM3eQx(CJW_JN|~dg@}YDF=svYqi7&woW*^-
zV!!V12gY^sc!EUt^_YqLbJj-pNKh@q+O~%Zp((i_h549f0BNV+SoGH{oUDnCe6GDa
zNFR-z{jSly2yMddrLBw1uPY%dNOPCO+YYD|o1V;NuBwhX@<Z6&NkO~auN<SRzc3j{
z7DA3tp^t*v9h*<C=W%wo%b)~Y!?||kX6g9)AG8VnNUobytl(!-)#og5vwZ7ZGC(DO
z25+1Y2pzSIUI47Cdx<>Z5hmAb)dir3je)2x2HU#AvCEGjO|H{Tfce_!6YZ;2kbRR&
zEg|11(CTeWbSGjC+De~XukzGv*=gj;_nxl&3bntn9{RfKXLp;ZZ*o>k-BxlugS2Go
zgbRAJR~r*N2zY(-A(yx{Nxdm7DR3~bvQ<5Qb;@v!h{eupNoc(Slx#z{n^Ur5PVhO{
z;?a7Xj|EW=X{Fh&7*kIR2ECu`qt3M>qAfemj#I(h>Tsr=k6Gs)8j4G6^o@XrYm@(+
zdex!jK74g)xvC&`?GRvs`(4ofgf~tA=~)@5t&36&YAe)RxzN=F+PJ9lb_~G26MEDx
z8Rw5tl#AMer$CVTFdQrckBO~LtXb<u9QN)G*KpQg{F;dW0=rwF5i!t@%+d~6xx>yG
zjo2eb3^h9dpWksmXiEie0e&=)4EjeOy+FrW9W*G3Q2A_N*aT1+z7R{clhhQWTJtvy
zW$+-!)x`xMK%mscNjW_9tl6wsb$ft*3daxrW3=Y%KXP8?{9OycR+K{8a~m1&n`lnn
zp_ch_=mw};Y`~R`RoV``EJs%`i?`BR?Bu=gSdR&}g?yfz#@R;rk8QJT4k02OjPbBr
z7?;{HYk?qWS6+rju%{BMzF)dWLaR$>M%Z(!P{)C=XpxC<vNf{i$Hwej0<mgoWe6-S
zN!?tGtj^a0D|5nrhTIzM3E5m6^S~1Zf_Nc8>@VZ3ecvV$_g7$Ew|PQRX(KdC{9rv5
z7yKOrS2chzY<^)WvR?60fu8~VmySO!C2JL9%^vfkyM~6_S62wiH;IX_FntdrZq#7R
zr?f;9aq&&<F7k<A`<ih<l;KC$Ye9BSh(6@Gd*nM}o9?dRe`tAtNIE$0THCdaZQU16
zhIiVC?v)t(Lj<6G@kJocH-n<7Zyt8t{r#;jkPiV{F9V3;OKQ$~O17)mSC`8-{MyQ`
za5V(MoY;vk#nipT*a(0vr;bXUdrSqWd+Ccpb%dGsG{wK=JCOM1a~k#l`{J=@X<vPJ
za79ODn`xt`5!D&Yrv=2KX|DePsgVlfIh8aI{KmPf%zl80Q1}<D#sgx08)U&bedaKL
zol{1(xhZP^J7a`^@4*xjq4BGzLJ-CCUwsOL+C@0|Ai2nRe%t5!0$|1Uk~X>R&(#!w
zC=L17YteIEDogB1)UN!9?$#B(e?^xakf5zEcKO`m))mlyX!i@+YNxfdcJrM6#ynz&
z7H=n#t9+9^zWy7HkFVh6psS*t*-u(Maph|NIw~i2C1<fHhDuwcVG*8lbL~}arO{q7
zYF!)-?RQ0v*pZ0?#%kn}8=pK`>$IpzeZtrmj&!v(D4)=|4}1*)@MGoyXp5i7#b*HQ
zQVB0R)b2)2j2}B=d^;stIn<<y;H}W8KFviZII{rn4|j=Sh*8dSQ8<$A*B1~w>xy%+
zak)4|*mj@ZKzki+c|5Z3iL&_0iz&67uaKAGxGNqa!m`g=YmaP4R&-G>z>-ayY?IT#
zQAz-F@o{n;^dA~R*@@%pFs~zGJMcCa0IpThw(KU_2Vnd=@J)y4;#IonQz^l*tG|c#
zP7Su>2R7DkUSn5`uP5sJ6~`T;)F!VczC9#DOvi>FUm*PcYc&$(8g^I$SyQDOJ!nx`
z?~jm63fh^S2<*V%YH3RMN)_I~N4u0xK*B+-bE=AjT@qK1saF=(%`1<Nqi9c0G}-SO
zn@>RwR0Lt>jYr4N;7LfFTH7J_>H6Pyo_lP{-bj2axFa{d?ESV;ab%~&Ra-7nceA`I
z&>&z{{Tt~qX$eK@SgoNM?u&fl+j0X!Zwm0Gzcf*6%YxLdPWbzCmkuL`0WSHRDB>u3
zNvpYJ5F8HbTt$|FsmZo7z@e>}=~prfK@qJzZ-@?|Fiw^NqX)Z;y4k#=#;<#F&K~-4
ze_{Q+*62s95na1&ahI@mUMqgz@pE4rPlzK(_X0Wdw>%P~b!-%#x=q?jARk@a!R(s|
zz23;*)BX>4k&?t~HrdNMI+=bahJSBQUtLTr6SYg!9Qsd_oc}2W-pDwJ!`d|ZD|9!b
z+y^>h7Z38pYU6O?>51h+y`g-1pw5^$b0U$M{C8YQbGKg>5U&@CP2}4@q?OD0w88B-
z+8rOT9oheTg7rUr_Wz4CXVZnY9n$kKTPL6CEVVi@Qgfd^v>X%wvz0Rh?HA{I<JIpc
zi5YltdWKOD_8oOVa#;n8=0ppy(tmB6+{dDP-U26}{?X5d=u}T9<8rY<GSB#Xti9ip
z+4V)buvgT{@Sv@nj<D@M-Jgl_dS`&Fag*;l9G`SA>JA!XK-8P`-Ws(JaDo7*9Irt8
z4*5yEWv8G99KsIM7ZE`Al9UwmTghW$CNrYN$`;6n?URUqbr4AZ*dQpVxRy-}O%f?|
zg$BH|s2VauFab691=QSK2UF1|1W3nt1!P>rpheA(meq!1EEnDsg4Tz?ska>h%6@(V
zaz<dVku|0v+NJYsQK~)G!Jw=y=y6rmSRT>_S*1AA76`G1y!)<E;%f#1O`27fi~2B!
zZFmcFiG{#hzanz^5uLg%&_*x%C6kD?tb+D=*U^u5@GV<(xO0SgC$kbrn7el#UPi%T
z6-#&cx7k<fc}%4IV9nY4iZSd(twbu=s@hQ7+CXAnxFz7D8Ch~@K-nkv3m0$jM}h@?
zZ;0;u<b<;k3u|uc&u%u?s*$-F@I~5c)lCVBQqNU&$hbqetr2ngZgVJTCq+ghVCvD#
zPM+9gDc^dFKIWqx5Vd@zz8m+j<wYk!4RKne^X}2R+mq_+H~SOq+JzI3yuL-)t0T39
zKIS4Vj=!P&)ur9a*3HqNb;1rB3{ZU6H#S7KNl2c$HhC=EP8}8G!nXm^A(?*kpSD?l
zEv+5Qu8=r38y}(V{6b1}u{&0zZyg_&Xh-k>;ePMYw%yyS{FT&VNI1w6%ea{Sx61rK
zPlNr}38?>$P4ssF`+t8QZr4iWfn|d7wRc+<kb>p@0=Urg6WQ2vRv%Mn{dg|`o=Y5D
zuDgtNG6<T<jR3XE1E0T7LI%45EcJ34?deg{Zi!Psb%m>g%kWiX5EkKaglPhh8WIvo
z;j4(lOOK6BCdKT;911g^W(eav_2W>aB+eJcXT4f48xK4HK&;5>V-=!)Cj+t5hvZ-r
zE&$`3i=KfCXEy;*KXxk97yh$o-Qq27{v9y>UG4EdV;=uIq!6j7J!7nO<<<V}%%Q{8
zb3n011zrIxYllX{ka11A9!E6Qs!-V^@#LHVHiwasV}T142B{yfMT2oDwMzNs*B#<d
zfvhPim01}_m%u5wV(OxO<HWq^+wb=sCXip^75WVftD_%vo+UfkS(U5h7yyiU&PJ=~
zR*7?`ZaK%u_~CvZR0{yO;wq`XmVA&#)j>9x$6C}uAX4TBh##>_q)wS{0d~#`rAFz_
zM<SnsaaDzP4NBWk(XoyGRUqdl!=R>|V~SxNxJALIe^2+KFx7=1A!XNRqKiTO#Q3A@
zO-ux;s_<$|(95{m(_!+%6(V#1C_srQl^&4|f_dy&g|+FnMf-IYwiE1)hVI`W!>h?|
zfpUM1b|R{g1M(t822@HD$g~q%dpFR?GCOVHJ*=r7yrz_s_W83j{9EiiMSNS-jkjoM
zakS{-ng-`j{tTCk+J>>eQx{Pk&C&eDm0_e$>X_J_i?i|ME&JWtt{<b&2ot+txPc&t
zmc_avMn~Si{qE0%0(AhDtVufJsjW}Hyb<y-=bf00(b^#@RAEl8%S%2MG~x+hDsYOC
z_?eDCil2<_PgaP1p^2*OXxse!SF^akfTP5tq_;n`yxv5ONAgaOY{WuvG!^MqMbT~U
zHX<)W!k;riKpuB~92K=m{Ozgj{{OK#l&7BHQ#Ank*XQ$6s=~jmxB9u2544wxgm(U?
z?aHl%?;;(aDWC=_n^Nx3dC1@@0rX!hy{Je+sx3XPp68;7-`26&pZvx1`QKW%^}pAt
z1OgQ@4JEn=-O`DBj7k&&RS~XUBld#s2>>L(UciIxDAopuHr|f8xSHrd_8bj^M`<#}
zI-7l92XiwzlKqA@dM+Q<wa78UZ{UX!ga=;Y9$pwk68bV}UU)hY))Es1nOcl<vqZVN
z6y(6gq34U&k1+b^O>%3utSzo`$zG1I9D21w*EbwCwz$549|qYoU745gp@eo%(K3iJ
z3dYIK=n8k;Q3@Fe2lGuJUT$r(@0Z3`>XDS3eqn1X71rKHnrPU@dcOmuUIqYxFA0<%
z=z$$Q!R&n6Mre0o*dPBal2QSTh)e-UIB|M3dLWJ_cK2ovfkHZmoe_B;w?}_8s!dpt
z8acnw%0xwkPM?>84gXOE(EP%!0Dzo-4}hIH6cNL<Jbf;LiH(Cf883Pjwarh+NkYhQ
z=;E*BF!CA2i29X9-QPm73xNs`=|8mpp{DbR@WtN{a#t8RMKxLS_G}9$yu+l)Z}Nlb
z%Yk{qn`^DOV59O?@>a@f8@0J3ZYD^p)JzS{c|8!bW<wdgYZ4%6l*w|5djyk)PA$r?
zI~4c}FVW-KsMh5-puz?jg}8SLVPh{%QYQPm>Q`w<8Cv@}!_AeFC@Ghisds$hlp<`s
zu1Ov`_lXpsk471^7?o8E7Wee6o@kr@mY~a=hHds5vT4{8t$E2l1Wp1E`jA(Wb)nz?
zA@2;_DQth`6_G7<mD2+SGrnAYu>pfPhkggKr_&L`#;7R#C-&%j9nIaBB|*LBBRa_Z
z7@CQiQH$G}%Hj=VPQ(I--WJA#_>T=F)=v<=V2r{)4q+1QHw`u)HS+fBg$HfCPbt}y
zhgME8aj&kHwK2L4p&KWf>Q7g#X|BQXsJ`mS$74^8I&5KCE_Ym--nUY1ePUVd=SFJ^
zq<l-N|4OwbFxuxP0>N<Zc;li^(MGIW>|fKnbjsS8jk3W4?NDyr5aCNV(m#SR_IM@@
zlZZkYwu*X@AqrZ}kY)r_QL+!v56%|62psz{ow=ylP_#M!J+;v0M*MimIX*qu#$myK
z{u}KZ2^~%f=yAQFhYST$^mGx@b!;ctrDi8H9r^UrBf6MP?)bU|zEk?IQNNS+q>9t8
z<vW+`<zHNw2qYf<qMnqhv30rj2NSyphfY_mH|n<3s{4W}B&h1HItbs5c%{~V^-bIK
z2~OQ1m%BiL%c>R1(xJ#z3t`p7(%#u@7vU<p3wbZ<;T-_~4{HLf=4M!@(EnU*<CJED
zIabz<xa`BeO{&}n-SktUYHk%^fAAEd^!E@Xt<Lek8?yij&fim(ZD2Kon|1m>%sJ}r
zqM#4Va@apQnZNYSy!R_%o(!z8^j#-KJ1+V~N>zm&A|;;?1U8wg`9{j^P+S)zVjIH2
zOy*bNTZ8s!u5*>bi4r}r!Q{}(6H}?q3ZDz@pc`lXCrhH4&dCh?B}I;yR5t%|8Tw=3
zOMoyTxpo`6V%npvVmQx;gUhw~Ks}^A7tv8aHLbfUZ;kO{Oo-j4JNX8ZK2iTY*F8uN
z$#h_ft0X1L^K7w0k35p_yU|1>Rm1tKcmuz#yV?2XB)O(~2`GC6p-#c_68X{9zAb)C
zAlHt7<&%QlZf(caVp;ze|7^h+q^OoK6Bm_bCN%3{+QIiHcK+kkfzyjg2OlI|-|Z#!
zv&W%hCf9y3x#MVj@b0yPQ`e7O^ZL<C?)tSazl6WAxTd&c_u=QiTs(cvVW&sTo;uP-
zQCk_^H7EkUWLVbB+t9Z%VlHq;D-f%C5L^Yy$weG)|1aeDpWWne7L<gNsI2Hrch>Id
zPdf>p`JkkYh>OE0xdmhs0@Gt7d!zL)^n{j8Hv0#5l?Pj;9Zs7x>XA!7nfCyermnQ{
zOPWo3ej4yyd*GO$wm+=W7A;uQrt)bs(xxe<A*V``F=u-GW0+av#%YS_lRur&GRmFl
z!U&03Gx<toDp59R5ob08zilYw{{T-<o2=jpJ}U*Au~<sf{%8p~`ej3x#=a<jJ!6;V
zo}2UEvN>hWS*cbbJXdmeaRV~AbgK}fH>=BE7)>b#tr}9Z%vf+!)IC0B&~yNPiJ}HM
zR0OwT)$OA#Pbo#@oZ{>E7fA&cZ=p!)J*2eBQ#~I|4bx|KnUV`<6iwqU@;$;3jAK<!
z@HV<ugEXI}=Gh<(r4{pHRuNoedeHp_HK<>FV>nhR&(qcD^7Xjpz!#Jng8JuJ4bnqB
zW7ClI4C(hRvFaAAvGZJo7GN%{brq>8J=`=Une3{~0zI96n548))co!!_=}ksPgZPD
zCYm311df`R{i#8&NR2r2ChW0!nZ*mLiM3(8I*_tjm1VwQ-n}w6-l%qe?T>w|Npsd;
zqba3Om*&6niVwIZ_wg|Ga>iv7DT8h73Ut5?`cU=KS!4Q8nPnSByZQ!J*Vo9HK2o;V
z!M{)AZOn(yCd0{1o?-k9={cp^jsBx4Mdw^5*HVLD`0zJhIm*1bksdb6tETw9V3Jfm
zF*O~@0}sI~j%5Q6iI-?+=aNP~Ub0~A@V(OWyD4hu+oeX|Lh4qgoxH3ak*@KUs9}tT
z#77Kit%V+*X;s_RZgIaM2uedue9>hi(SHo;vMbV;mihq+Ehkb%f9)8ZfTqP>?5Vfz
z_%kFRtxsj+K^xYcf;u~GA@^!9vB}ex5{AvX-1m*9>UM#6E5xA}{3~rsJKZ^ub7KKG
znnFjZrx^jozL+UAMZNd|j=^jd7F4b1gae;`Ff}E=U|koEs!~^89~j_HsmhhD*0Upn
zF8_p6tb=@=NyoHvxh#XTM*0a$aSVBTe_*ld#{8X#6DfDWm)wfgQWla`2|4I9t(LcW
z3)G0#jOtXQP^Y-QX!SgT4h8=1e65>VNcby4oy>Chp`z$`uDy$0tYw?u4V3^h*!8rh
zTo#iGy_E)1jtWc5YKk-ubed^|9N8Rk`RG%yi4W|4NZK4R)#lDYXkZma_rrqC5ef8=
z$`F4K4`O$`nOXi^&EjxTXFc0Hdi9VmS7jsM73YZ!A?#9V;0?CqH+3bQuwN!N)q8^r
zP`PH)H39t1Nu+Tx^xmr_8`Xlw0{zC@+>(p|5+Ya0u;}yjMAOfK!1n-dWnP!<Zh(&B
zYAD@y7yV;56^`M|k6(2t*+DK<es&jX1cG4QRMJ0CUuWca@*B#^4(L~*)lfH(<T8}S
z4{*{*HA*viFo<EJl1|Et@WkroV1#u8qAfAR-oyLTP9Xb0Gx){uhxvIwxN_2(QR!`A
zu_~?Q)^O8b5w<*%&-)O2sKk`Y<*S+dpB9g2RnZfYbI0pcN^VfBV$h?0ZO1IWT^VSL
zFed(SH*4cUf?7(2d~ut%S(I9D!-`JRM70smUrsVIeeTjk@~(E%)ph-*+gwaW$5hbZ
z9OR+g<_J+U85cwY2NRTx(XCbm8fb0iTpZ-GlhBLA8wm@L3$nkq^4gE5W2)w4dvk8l
z2_K&BNv>?dj)b^m+L0V}G1oV-RZXz=*|yKbZY0BlP9>e1eb!;h+Q`TW>2%JViNf<@
zEZiDPz9<=@U5J<Nb!SIBHqWvpQUX<RH*=H?V{8h$3Y2iSSUL9o4Y8vu+&2~$-|An^
zL?F1>f+}b+gy0Rj*KG(HUS9+ROFzb#GlUE+lH2om_?~FCo5Q8OksbyC%n_{j-6M|i
z!O9yB!8~2lAdR0G9ZJ}{7+3`O)xFCxD&}qx^Ld{w9lRyMM7Vs~Tne7!51QFP`SE;I
zP=g`9z*3<AGv@i3XjtlWrqwT)2TzYh|E@3Vo4dXhoi=T4c+v>XK5+pK*}^=uNndx?
zPN3u|MR|c=Ia&uc{sm#!Xaf_u1lE2w7_?-gjS)>FyX|lJCeQXXMiXK18RY)TzMu4e
zp7q6=MqT{SV~WanH|!u|8IqUraWQy|hgI?Bepon+=Ug~Zj16uCPI#bS4mP?Qh>g3I
z3j!0%G(WAb5;-n&WrCF9f1A(d$L29M9UYtVO2QEyHYr^{GBNr>lIe6PFs;l$c*YM;
zA=(z8X!G9l)ct8EZ{?if{HesXjB@DL1xCFo5Ga^-kX;~|)=q>`U>H`wOiWY<{ZK?2
zEi0TOw-Yz<^hwwb_dnDKBO85Zndqi`u73XyMMXA22*9LMZifgnX?nrKUDSQ}IC$Ua
z@$ZSG3r^FJo<AD;mcA;v^?kjH(pMs><^(XRlLL|mPhSCcWNw#FKEzeMvg#P*73Fd^
zCtE(3^&4YTmB^;wa7^WB_{MUIV;K=wX7^1KtENx1r++ASO^6twUrT%H#oqgU_>hL7
zRnOOTw=X;_Ulr@mG~Ylh?@_Hk56^ygYEJjz>O`9wls2KUGA_lZ-5Wku6$5Wej#+Kv
zBGHT3%24<T_o^_+I}|)T=F}|R8tFHB%*C+BtZubG{7TPpRr)`w5Wdr)uQGCu@u4`&
zko$YD<ZGol8}y`KnU5cROf-tYQ-c(}NcDnC`T@U83?sv|PrgKPlKtRF3%CB?bCe9H
zEB$>M`j0_g0%M!amM@$l(2ROmu($sAbJz6|SsefIsITs%y^$)(cZYY71@tooBhhin
z&v1Q+u+&?v+68%oD^b_&(Uk0lm_U~@LkQ*kiX%4;LQ%SOk82140Tz%6ju?kI5)3up
zPAt%V<tN#7-~ujDZf2h`mgyZ;X-bL4==aP{RIr@;e}F$NaSYg0I6&tjk0GhvcZ6V{
zwezSeh=y*B&)B$c%(K#U=0Ct6k2YSJ@P6L*Dfsy35c49#Q-P(QpGW<Y_sS(6(4+@@
zcS%Obr+JHI9_%qKd^NM4@gx(9fpX5Y8uxxitkySPAVqZN+jlChL<PR$)T+v~`-R|O
zec7>DE_r}r(QLRDAUNqu^-`XOF@3TuXqw!<1fT_;I(-6gwR$ejHQIIT#K?k7mP>}O
z=UR&&mRS<^rMi%1M@UwBs;qOmM+F&H=EL^({ydQ=pD`nOoTM7%(A&9#CUk1N%D=uB
z0t@XjIygOTw^@2cO0AsZ$jRADv^W8btoF)8`16T@;NuNZ<{;I2yHrvp)4TV4xgDTq
zT50X-2|E%!Mt$R(k(8u_w02A{Z>w@H)I>QMcMh?}ua<`W>{T_=SJF_MU_IJW>2oef
zIm(@fhbJ{v-vD5OWoardXaTG;-2G5{t)dLr_@!|WB0meVXtS%bL``k1xpy=LkH}Lt
zHnHP5pRAL~l#n=a*z}j{wiz=MibG2lEb>{tXDMpoe#2m*wAsR=jxz}ZBT=daX+<%<
zL3dxRl8yu03uNk=eVs(f_LgJYAfYM24wj@RdSSV}YHu4>j{6ko76o>fBeT_q^ivk*
z#y5|Q+1)!AHnFZ>lO-Uk5*9p0J>nze1Lt1^$i@2VeyTZ8nv4srW0TkBp39HL-F?N$
zuN*y3RuiF)3ZG}9;WQ5>j<#B1(o4B>q@ikZPbDxQ8Ze+nU_jM?TFq9U;sDNe(?b%A
zKgDMw)~Hr@yKm+aEQEu0I!t;^z80frblj}kYW292dO1h0;`(NvkH1OS5yKyGB4sNP
zElJz+jW?fHvkM(xt*)o^J|qGj>!*ZwpJJYy0`Ew)uLj&;=Qbc&2g@D5*{G|3DTW4M
zv*a$_gv$Pf)B;c?&2hROw25DBm#(@GkIqmAB>hq?@0BU&Yg+?wk}FYm@tp03OWLg4
zaK$s<`)hN4aH94tte=%%RX@E{_zsa@7Vco_#PRaii-aiC%Lxj9!bzw2QphzQ3)|t3
z*_yyMzTwlrI0lhhy_wCuMCv$8hsb3PXF7gcO~=P&Jy;G~DsauaF=U7+nVuhNCw@7M
ztdk%2&<V`ZG*q$YxsE)7Z*D@T_x0Y(0012z6!8xnV1lSnxqFR<?5h?4t|M2=`v3y#
z0s@Es-lRU3TFHv>*bn$|K;8jAxPt$SxEPZ);DC<Si>oaQZhB7Bp+zaZzJ0TGZ2A-R
zz8TmC*0fQmBQ+%SdnIr@oT(Wc_`xUNj`_Sv4tQ%ImVETFc?6<2=0>+yzu)Nk$gcL`
z4tdi1$6>yQ0_UxeEcq{w9qauR4NYu$+Hzm32))jn9b`4)7eF53pE%vWWrZ9T$vRmb
zbRUov!3AVZ)>0n>nGRI3^uoKgGCyiuihkT^mHu%tbzpR0^lU#OuiHY%wma_Q(<)gi
zZOR%vK;xyEgDL#+Hv8$vfYSGjQZRbvcggJyN8pd`TSdw+cVso{-pC~b?AcY-(4T|v
z0RtgPM#=(v!ZPaW$k_v~O~>K&h}e{q>71_tfj9cvf4PwrC_*rST|O>6d{T>al{LJr
z0#E}81FWrSCiHs61MuPPJlVzQcLK=zSu={pr8Zb2PUdoSf2+Z`?v*Kw2pqPxF9a8d
z&Co43_g%;uECt|W!s6;>r|u0dqTR4?NQcyujtFz>Va2`h)iW%r^b=`B|CPDbx^hnh
zmfVsvV?w<%r*ZTU9OvB_M>%?^h~f55V4TUFN(}6#7N4!we%?cmvYV*)`MKU7h(PR^
zHqCNy$RZfW({1pB(n^(EwX2=5Z556@btGU3#~>5!?pQ=G{jV!?jpn7#204zp6ie2!
z61eyEYWSlx;u)O_PQh&vzQ}K(m(4(%JxW$)8>d1Yn!4sj$0x2+%vlphX~b8^2Xr?c
ztonET>ioJTS0h}ia;xv&gq`5NQJKy>Q~Ak%w7Sgy1PtfCcsP1<t6w;m3i4TBU+O8h
zMyDlQZjlZw*4N}69p7x+?9V70SD(JN@n&G-tigjS#<7A7R@mlY8}Bk#wS=Zxm08_5
ztP<e)wbou7?_=DS4SeX<3x30k>d@=yzkJMjJZ}bDQn}x;`=O)4S*=t`z1@;c#349@
z4vsif1Q?YFNU&>J99th4|ClJ~0_7~e`<YKDI57tsYigGctX*0i3V*)T5lB%6zAh#J
zGMKoOHCG#D();v-mmTi=V@H!bs?R<D)LgisvBWMtPkn7O<PzJ~(bRAA&j5^b$v{_6
zsOF+OBh*fhy)b*@+vR&BI&J`hZ}MDQHYX>#l6zh@79q95_yPRrwHAU~Q;b6uZIc^-
z=lwJGaQPsLHeASggc3K>&F7;PR{cLXvQ?LBDmlz$1cNk1wFvf80TZjuL)<g}RJ)UR
zDH?p+XYGBWmoTnZ@}t+N7wE)Xos!k9kkkyzkBBkVqn)lrK>QzMswSVlbVe2XG;bIj
zg_`)&>crykO?3WKEzgmgaec3mo+n0Sp%%T@@Y<yni#sc)b%Kfd#4k^jgo?9Mj>tDv
z>#<s1nM<uwy<3d)Fj)C$I0*O-SBRU_ieoo`7ur#HK&O`c0bh&a(>feI8&FWcqYrb0
zxx2S&l9jMd7}(&Ovvt3b3s|rE(aIY+8PzFrc_CM}?x(>_L;P<0O#iwE|M;b)MF=t@
zWsYRiaXT!s*Dafp#e&pf2WYUC9DIzmyvAxxMs;Q33)U<2mGpMa9(dXK;U`b31o}NZ
zo6$18(>?t*h7vB9yzG0&O$C}gL~9h)&E?hwM{iip4BXoLB1tDf3+NZzi!*1{D#6lc
z5>)FQB9N<TC(~JWtn~D)u-@m);L=#T&5Vo~K@h<DF4&}&x*1FY-U{6zirDW7#Z1}%
zY|iL^vvlsqPu>c-n`RZBA-s;+QoqfbOX<zVV=k9Z7bF(vC|Q~)N8L+<-DXvtWu`8@
z;CKUoW5&krxa^sz=;;Y{V*<n5u91{EhS5rkJ%xO1kln19wsq=^R<pw+7YTP|-cgqw
z60buQEJKK=jd*cs`_uAY&QvNXn6cg|QLP-h1Y~4i$43Df+cyCl|MWTqbYn;(dH9(r
zS$oEZ$c6f)`AC_fBg}P@v{J+C|C|Ug1N1ZF3Gfx$?`Bf=SDF!)Ue5fcB&!l7_?2E4
z(2+BvV;ZO3<94RiG_CLGJw=J^p~9PB35og1jstrWny{!BGts6?=`*IP3L)5xnf<2n
zKO~qW)fn|Sfl4v1i84z&I~j>PSQt~mn`Zu`62-;cB@>?%Bb3jYDNL&TPU7=>;KYrZ
zbpJC^foU+S<G(zJ3wk;8g3K{0%}>Ip)=O)rO(Jf`F#UKXKma#{rI$hP5nk=d9Ob7u
z7d7*{lPW*`z%%Z}sF@0WHdFxeX*I=`W_9B+q(?-YmbG_{%Neh)%N3>__9YbajkBwl
zRuPK_CB5#zZDgIZJeCTs%Q5wD7|N8hT6x(GFzXUbS9U{XO4Gy-dc;qsI?67zsJV=%
zvjU%cb$F{K<#~URkBfc>*%4o{zW<EGq8gDPJ82$bnpUj>kc>VmI%j*-!M@VB9kaNv
zqikOYu^hiN=c~$$Sp;L3+{hjWiwN`9OqCv{dfiXwzCKs-;Woj7UmgJpP2xRh$J@2D
z-LYtIx;a^>^AqDqhRRD=h<PE{jD<8Mzr2iIlvGW~hTmc#lw|tc@4V6KIY2H-%O@gn
zH%;Th%{P^$Zv%cS*owt)EwD0W?KhQPD)GHc4{^vx+2ET~HcIe9<{DzywT|HfIZi+d
zgtbyBTp#3Gr~DU!HbciEw>hu)b6dMbnk;)r;a1eOuSA=ag|kwut3M}9M~AY7&6ce9
z8P^M$XEw}xC-R9t?ahDDn{0{jnM_qIy4s8tY}$b*a-S=~a#<26<#fZuk4u)SS9)-!
z&q&8j3)5zn<ybA=X%th|N(S2<XU6hTrNb@1omK4)yPa0O)(aeMpdWGavHnvI4QART
zSXx{|vp1!CnV_S6#4{V6N<JJ`dpVfuo1goxHrdH1@+CFgaRTpo<)Ms;RgI|h@$<(9
zfO6S*F-hT`Q%wlaTaeKz)cL7e#f$#Bx14aKnL!MT=Z<AuuNQosf2KI31u;)sH5p-p
zRCn#P*gKo}abf<@h7}LdceQj==;r_EvkG{@L~3~;#4_ERp=^rEol%tPT~9s`T+}1e
zTx6gs^Vf-2RGY4mz#mp0nl%beQCbLP)_Z%5$K}ybQ?kZSxLxDA*92yswB_tevt{79
zVbDq3f@#X6hwuhykqE<7B(Qklf6h{*d+0TXHV)V*P?0B!-DX-{62o5wV2lkrrVN2W
zUDU2Wu<WYmWOQD}DJf#o-NwLuY(}$Rg2}j&ox{Z_X@jC3^gHW$8Pi%efh`(};G%)p
zV>C2Z1614|-Fu!;I@=!biz`Gg+N$?_zj72g@Rvf1e&hvwuJm$H>4(U&(2-_?@G^~0
zt3afdra-QdZa#Kb(2-GcKUW^tZ0(&z@IL-EuxP78H<M|R({FGde(w1nJr@DI4WB&4
z{|U!<L$sq59*8RwR9zPpG-M+k_eQ4IO<soyV-A>J396|O*1-ESs!8&BGc48m3)<F(
zm2T&HplR)=(`A+mZTVaNs%G+^{S;^EOm=)f-)66~mh~vsAkHaQYwxW5z1dYyPulvQ
zXBp-1TGjSO`WTe;tVaespnoQrrRnSTyt)#3?d#)>sVAR3WNz#JY!r*?7(z}@5m}Xe
z20pjqEG{4}V1Dj7Y%TvfvLxdSNXvR}eafv6oD#gMH+tcZ_V7PRI;Kl$GcOJdY#3PY
zt>F1?Cd_9kM>+StrWPv2*q}@x`4qi3TSk>bF4;3LRb`G&63+2hs#tY}sv(TeE!P-?
zhJxH(Z}2Sh$G+g=hG*xz*Uqmg(~J%}W3O-CEF|7+Mo#D<prtM+L}`BAOLPgQKY|ax
zs?=Zpt~3G3r(C+Z(KEk;>{bCB1g+te)c8yfyy2Q%Xa3fbb7tdOlk}a>mw)EII17JF
zr;PUPf1bNQ;7A06s2ax=QuS<Soztcm5sE$g5e-xyD&$VHjB-K2)Gi%fJw)es|763A
zmq+oLxmSO7lFXDj{t|^E<cx2=z)tKV4h1Q<_Pl72^r62cSyHFxX;gUs0KJC8FlCLN
z<y%DDs=v$MYB&@Hu@i6{3gLsW*Eu69h;|wI^zgFH&#RMab&HI+vfHq}y$d_seKn$9
z@06%PL)#|gn||s1Im@0X)bVuj1DO^U%%sLJt*k;;mE3|=g(h~y)LT)n36^T6tgo59
z&PQAdh8eWbW%_abg*bG-lJHy)IEGpMa>iJSWu)U}fh%y%pE-`_`3kNMHGd9Vc#~n2
zwni$Qij%Q)47sl&d@-Yk=TUIWPv)5_8$~p}Wj#~D5g_2|1~?b9N<OrpsFiJMLaGgL
zN{okM!n+>f?Ptv5xu~G5ip6N(L}h}Egu{uwkvrla({|MKS=9<ZnvyBRIP>0J#T=YN
znV_>a4#^eBsajPlXgO#nN4w8ec@7oAXP_4{F^g?i%k-jdHS%efeAP{lFE60`vezh<
zIjy?A-3W$%s;ORYU2{o2f&r4Y&|GFH`8;3W@J@rF$f<e}WniM7LH=S^v@i~8-U@Z=
zaX-laZSO+&Lfuy1(9#CV2VuF|ofQQqda3G}b~p#RH3K`#;e^=byD#8(?WN>hP8Al;
zTv2t)<D)aKCpRlcfqQdb33D$KE1wlb^Ed=Ys8{a+bYE#D(NfxCgZ9XJ?>;rZ8l2PR
zxI2#v8lo&CLT>GKQy1Ph#^o;Z+#8{*mwb&fFS~2>Mj#o!f|i%(J1ztkfB5E#(N8st
zdgOu`@Hbc}>dCFLxDUn^3x~2B*o%0E+4s(BB^{=YaV0{Nd;_AuqDkIHC?jnlU!!@O
zFbmINNv1cA5a{YM7Mhd#IL75y7z6%Ysi9qOhb{sYb<abm{dl1^bB|rq{6$d6&Eoi0
zixm^kMP*cffITpGWrIIq3t3;Pv?L?aXKMU-SERT)&9l1a(OS3#OPc2Id4_la63=Nl
z-b|5OU45|SZgV&(z>IL4#Zl7EbBBBQFOTMaKnV)kADB<veN67q3Ix6?r&KN*4&I<z
zWK*uPsc7F!pKNNYOKDv*RofrEBfhonmt7JUE%`6X9OdW3Jkq+$K3_!N?0vfkx{^H@
z$yHLYi~@?i4SN@kpO<;`N(uIgHM54&kkb!}c@cG_0b<$hdx98!&Bya0;$x1Flditk
zQMOV7y6@p2wU*9OmxDj}iG9i|-4S_UBw>0u&S{UtQ4#RD*xP7Rc#6VhUYLn`oMv?<
z$}vr{^z$Ejh6#;F6tZWsOcr5ry>F)|T(ZzSO}m72ogA$^UMt0I4N97gs4x`<kJ&6f
z&0*&+6&p%w%jfzbkzisV?s@*`n~9-d<yPxp-lUSut*9>}9;(=LG1hz4wXFqr>MCjL
z{2O~AL7Tci*E@!7q$3EYv0eFnt`K$rV^!9EzeM)0z_DHK85Bg5`lm1%#&POUhNcrU
z?cKc$D}{R8N7JR3b-S-w7X}EmeS<eDcG<ZErF+X9<Sne29xZj9UhS0{qJ(s@N_EPJ
za-ZClms20m)1*2lD<%XZzs*9t?glOZ$pS2Vrc@QSTc;Wf@2i6}t-h*jmsZ-a4CbZy
z3O(Yc4P_uMaYP!Vy1KdMUS(<YwXOV{b~`0XJ%9?$V{^YLrB;xQ6d9GKx}RZQ%e&b~
z@2$_xNjCB)*(qcmPf6ztLyFZ=%VicS8EGmrUaHprnT7uy&#?mdE~6gxCflWAY8V=e
zXj0HnW{{PvP-HCZeO;Rdv#CKlVf?c~?|P#JEU1gs#`%+sjj{Glz#%$PG#&jL87y>|
zV;XEBkRWsRLHjg<QGfK`<_nE6jWg_#L<o8J(~R;iV*yP2OBXIy?|e^x3^B`VYggEi
zStwj7n1R9iq%{{8TqkGQiNe?qFT!Hd+uMF;wS}EZD8o@s_pqN96K}EFGuTch3#|N6
zLHk6UX|Fliqi_%jw)W1$)wzZd=SZ1qeeQvT=_{N1q_aI&oPMr1gV_dR&-RZVA?LA7
zo_Hp1Z6#q~seS9XUtBUMB@y;Sg7dWvBuyI68Q+iGBVqnd1ie+f81FzTQUB<TE~u`I
zi+h}=fcr{krB7tds9Wzn22YzY9^X{jJXfs$iP3OibdXQffPEP`qiUv+KM^Ce0aDZB
z*xURkO7NvXw=+Fd>&)NhaqGk^mV0gLgH|7=vRBtPVFekc<dqn1WXwySRO%t7ZAB++
zwHcN{6?)y}C<!)uUE<UdW>0X654^@0_Shi#aUOqeuSL!~1p{d-R$;~yj$nhFSJ%NA
zPLFK|CMrBUO-LdFEn8hlHYi7S^k9}%NP<$T8EZD<_}5PFWLQftGwVvUa<D8=Fz$lq
zRLR~EoSnE8LLhU-j~mhD0FOLJYslCE;1frvtX*~grRvbju}wZzaDrvMa%#{WOMctW
zUdt8Xci5{o_4OE2vU5p{mAAr-fwGrGlyd<#Yi0w_)0625w$0y^x1J3pUSji-&N5mw
z{i-v(sawA)!7i3c2j&kKJL;$0W-<2lMqBi{_gm&R6iqy~+ceR>Wz^eF9O?&U;9&-h
z6LBjqwv>}ebi~?p=JoZSvy2+{Q*OzYrPa`qRz2IF+ZZEbO2KM%bab**l4pyhku2EF
zgta97=A<Anlu$KfSaIDz!o4E<-F^vlUtz{|oWMU@T_q~AH=qGG;4laH!83xGga$Xu
zoX6qhA7`C&XJ(XOq!<_>BPXE#04q*yc88st!ppg9z2GK_(G8u|M;=|c4~E)a*Jz%e
ztXzVQMep!p@QazXy}t1Ly7o_V8D0=ya^QlEsr>jU5;T45@%~u|BP~OV+oF+Iz~4~f
z_AP5|Hf0<iCbFxOiCIy;=q)v<_UwAv<!LDMkA@$C7oI6sHI7J}SKZYv!VgA0vexpp
zocBsH7LL@lawVa*S5%^7pmJ11FJV2n;IiHtJSNI5W5&&9l#7X}Uc3@MQ2Nr$W)u^9
zgUNqpDln{vT5MKkG`c9{Tuxx}RChfF5><ZOm!<+>8&DS%Y-qfhj2**fHs0MGI$o#d
z_o`lr?9QR;Sfo`KSDxW{n!4tOSgKjD3Nk`jzhylA(?GkgQKK__HONfe>Y1?K#(R_F
z$r(^ewF+ref@RH~JV@#BQuV*&c4?NorK!4$npOH?MN!rN7R9iKZ7tt+b#YA1EJqGX
z$=HDjz0$#GaEv1q5_#O$O3jXU<m`JqV}t^;UTq+b27ZouTlFq-`!k++C!+7}?YlDR
z55$<F4{oLDpGtVrrwGKVUK~Wmu>x7jBQg7u`GI`Ed&MyH9mLVz<iW-7;wXCv+XYNX
zF(n6FT{C9B>>>OgOO>?MRkce$`X&}+R>H}gB{qM~>fDnrb2-KQ$t+k-419bTNNz-d
bHBJB754sy<UY+|$1mtl~S3j3^P6<r_>^KY%

literal 39068
zcmeFZcT`i`_b(b0D@RaKP-#k$4hkY5RRtmR-iu0?CejH3#G@!k3(ZIs0!WRt(2E6W
z0R)5qq4yqYC?Vvoc+U6U_q$`fcgOqVy*I`ica4!`XJ?nSX8Fv|oNKSV)6r5nclO#@
z2n2Ets;Z<1ft>JzK#o5>eG=@!Cw^K7A19xxt0;j_(DonJ27`|?&s9x5ArQ7O>We0N
zcEb(=xekFU-8b+{SsC?>zhpS}bHgEr_txJxc$Lr7UW%U)IB75?@Kzeqp3m1&BPdW%
z?5ud(p;?HoPVh0r7#sRj(SY{S1zM%^v<5L5d>2(t+8>aojfRx<4O8=T9VST9x!Met
zl7`;imIt`XwS+%?`QLB<TLM%GAbgWhPou-qq3r%+P;+%bBN$PF6<DcG@vLnP4LCsY
z<KNt~H)BOxxU9wOPJF7XaIpTqaS8&NHS@_~`Hu`$>1nL~-Ts_$di~fx+lnWYLzw^F
zEx^ZQdK_%~;KcjRQTI*#`7K`M29%9wC&sHBm58`zhIUUZx9Y7#G{kNvpqd97C3#J{
zUm~<hmX`SPVo^c`huu7#v3G_96jHt&DRm&cLQ(2G-bn_^y@Akap2)cJelBeX=qqmT
z1Oxrv7f^j3vT<+drQxzh)EFYFls(ceYUdg>UG<~Lq6D-tM@L1cl-nEOm4>nsn{PIQ
z64bA8E$W{jS@Gl-c3FP#{FI2jEw9x3WWbXPdXBq4rd)llj;uB(zFCYiMPM8<>h1Vb
z)1A6J{)}*<0Il)sM#_&Y^%QTf`0_h1U*I}u8K-Y+>Xmd%vpK^q3q~4SDkGwz1<Hz1
z)whSGboGK#T+1tB?}$FC?rMoZ<nx-S#9V8oeSbK-SHkI9hvQ96hi(Ql)T5S1Qm10)
zkEhF?5L3{pT2(1Q$vj<A7rS=w+t(|_&Vmpd{7M7Tvv`rmxA40d$@;uQ@<q8LQ+AS$
z(Bi-qZ*n)+_<Je*$F*)RQ;T$}Mj?tP?i#e6a+x}FeMU9l-s{bA-Ia`AyzKr8F$Pe3
zl$)$Kn^S_Zaqmy5t-!*)0h89mZAV1Q<7DjV;ZmP3hQ6|{7CemQYImEGH54UM=iM?_
z`D21(2q)8P^d!D#b$NKqbRy2o)XX*x@xTRW#=fBX#nRCxh%@T}{Z|FQ9F?jxbU&RD
zjOg~NL3jU=T}G&kQr-!Mrzg&r+C8$*F>A4a`pfV5o$zyL*VsMpF2`c)reGcWnAElI
zlx!QSO`)5RV!fP|cKx#2aQtHuQ>(_%M8&9fCQZFpdhNUNT*afT`ctGe|0ONr=Sroy
zQ}hb1a+`1La#-IcJ`0f)a(efa!lyTOyv}X|skJLj4}s{N?>@}{frOOa`Z2xJgZS7^
zYglIYXmTL&x7JM)3DK@iihww;UH(13YCJwV_DoQ70R~%_pvhP`-*W{|;mnI}!7%W!
z3Gf@w`RvV!d3x9Q8|%%|;u8zQY(wkbIdVBEu4{TEta(aWCD@Lk>Dlq3@!Pgd-j86`
z_G&L3hY&T*t+0}o<#gyl5AXO%WKT95Prv?*UzlxIGUE)}bvYAz`T0^k#2c}WDRE1y
ze+^D2s%RjMt6`w^y;el_CzP{Y=x4WQ?X*U9j}q%|E?!4MR54h`uRZdM-w_}Ef6q#U
z;(`L7_9qC7JFLZAS^O?Wr=0pEyF2kr+Fqn%5upe3pg^cnHg5K4IykCM<!|{FUF{g+
z?q_^Zolu!c8rv^ten#8(-Wp`?dr!rI&g)k5xXW&3;EvLmRLny+GgMd(=WcbymVf6O
z=V$lViuKBzN>7}Ti{4CXZe^J{J@(}GlA0H-yD2=U4<1Fu0Z7Q%F2U=i+!>pqTYL&a
zi~jzn_}8}Db~9Ai8zBTSWUN;Y)BemE^`#X=E?WD5QFfRpnJydT8Oy#H%bSK@)AvqL
z8GVAp_l}O$x-`>o9zeM>l7NWQh%+4uRV>akF26pK%A1|<qo?J1nLC2j;+4vYPD!|E
zPQ5QQW1nshe{c+`6q4_=^Z2(r^&z;0dO>bO&5{*@Y&(4i)^j-<p%Kz1JWgL79koTb
zPTc(5;(q%}HM{?{y_xDJuI?An+xoJ-_y^DxeVxR;->b~=&XaMXtq^qln5U!>Y|}0@
z*y?LdX^P;GQ;ACQ(Nt_uoytMjF&F&?6}BhPmi-wzu_hMhm$gd36nFk(icPF_5J*Pd
zhslit8w946RxB;yYL9;=wurQGXnoUKk1^U+oQJ#=x9baKl*7!nVI`1nqvfou7vtvn
zqjA9-ZTjD5D+2ws1vlPkn<CC!I+`dc(tlr<*BXoD>hF92U50Op#8Gaa)vM_mHMypC
zplZK1<k&3cxp5%em7rA#t2=T}!kwLwV^|9gb&BG4%I(G|&DUrdIfd9I%sSlDpO^aM
zvY!>Zk|=N0(YobX+-z(5Zj9-N8$yXbdD=%@mc6jZi5)ub^lAN;blFW?(u{}s`8I-7
zd@C(2ncd2Nd}|18(7%<pTOzfoz1>FO?YWS=c+c0SqGh?n(4N-wreUHli5RU0%U7vs
z_83SI&a1~72z9F;7~I^mMvZnqL03Qfj&R%NS7^OZhx<90r1WS#5p->o+BH?0#{6TD
zkj*LOW~&uGvozFwL_^FOjDZJqT4FxemWe*xuBiKN<04z;{;GLNHzr-qN_(5)5!7<S
z+}`bRV;D2~!r%ATYj<FVtozeY#=pH%YuoPKdq<>otwr5t#~&z>@vEoKw81WY5jLMY
z^}(9EGj1EDaeb2y$#F`+Y1dO~M&IcE`<nKw(nF1;N>5e@MC$y+2@oxtMUdv5^=>Dk
zvI|hS%j5ZL@77sXk*bz>IA(toQ~!N>XLFNM)sA!vi8kT#ex*0oH42;MzzPXkWfh`z
zZLOVEm)2s}ywgj|c<OJ8zkY>}=bkkz+-(+JH!~WM@0(p^1u>a^b~XYad<ZxBX4zm*
zEaFiI_GR+hHN>z|J!Xg(zFjoscJ07rE7|9Undu3ZdYAU+<hH@3GS^0_O^)*h52s?H
zJS9!*dFnH;6zkvy6o;kNxV4`8GNDg)Q(l<Dp?tDKV^ybTrg<<(=a^l>jzf)X>NEt>
zbfb^52z>gvxJvK1?IVOl%o%~d;<w+Tcy4c|UECpf%RANn=t-Oo#K1XJjyAs$q*8dq
zOGS`j)(~04AewDAj~N21!NZH^XR28`a7J*quGl+TNVeNo6TK>1Ff;>Ojj|Y0XGx0i
zVo>@jPW_&a{T&w!=lruM?X*(8J_qw+)WDheggg-6(m-!Odn{^bY~Sxw`5JEJ1#N8S
zay2=s^qud}h@D78o2)4$!h}obDz3-bhO%3&77${2yLqE=DLLc*sNmjQS0qMbjAYuv
zYb~B0Sn^1px8}1E=S4YSp1GwOI-Sl6DuQMA$3C*J4hx8f<6QC2_AB=x(SapQw+p7?
zy4}Av+jgBoD`UrqZCeiM#A@Nvn+e_BUSj{)CYK)#L{WLWtd99{{><jbamU6<dAu89
zSGZrhJ$P+ovvpbOFTE+c>xQvj4$nH@b*QGhGWgY{@hooKDd#!;F0Ho2CCStP>ncDC
zJt#n_BvfA~Jc7E{)LAL_a!1Shwkn*q#yj9)^7woEE_B13(KzjR5^H6GZRo~3f#~$b
zjBdbH&L5{zH61Gpe(_M@G37TOVRa3^XDc@3XDx)BLb0C}Nldc`Vl>~912-ezW<}0+
z`c<FG_AGMU5W*T7&N1ypB1Gtlozjlx%UnZH4z_dC72~xx#Uk8OQ;3PmsI8I6mYdLE
z1Ql#9i_xL&!i}#tylz<zB2>>PNN$y|qmARXUEaTVb*FE7#|3%4T#n+as&@)%-JVkW
zPN1OMD~H-g&~#|t>)0;6ibVuKp9zWQW7**!csxmj?9GOReeUQ@(cQMVh3O~hWWH|w
zJF51@8tA|XyHKhAOVV@IVqXbqVl-k72TDvx^=7H`yS$|JxCP<;MS0(8S8Yb9vi$7$
zHJiph9&X0JqvqlMJW-pQ+zEa&qKW&mmrb0u039vA`>6iDcF!+~E^_4Vq=fP-gS+XA
zUg`faug9m(>-iTZ?7ppVff8tST8zc+bBWH%H6`;fD$#K+7#PuvT@%Z?%jkr^xO%nj
zh5R8m%Y-JbI$0G)pHSYBo3{LP1y(b%(06nGyECm@dDyg2_kLA}gZVF-u^5!Z8H_uk
z4y_wRGgi8%kxQaFHvi*rkWzlB<V!q5pWx>rns5fCrTQr<+HOu}H1@$&BvYbSF^X4g
zG=}!@=At8VSBhPs4O_V+A?oVSxICiygA>|9a%nWSZ+}yIS0w5V3lIF7gTG{}&`&1x
z83FtSEDz$~X3UW#)S*52;Ed?yNpZFrDly9ZgI%y!ZDN(V==ZU1xu`7=)Jl5qkvSK=
zpqAXO>Y4Ca&376x1RZEuTx+A^++7#6v_apbAheSk&d4=-3`suVSrtC}sBYek)4na3
zeyYXRZCO-Zp}oALTP{3!^4mC1MeKeLPhuF4WkxB3h!(K#)!i$LkJOihJ>hCb{zn?Q
z(|@2%8tDeiB4qXj()=?+3A?N9u>eimIqyPFPZ$A>NNl4$9821cMeUp!kmwVrMQThC
zRgtmPk+yEWR-%mDNG?zOwsAPzJXm?V0vm*jL$YjLK{nTzwXqV$?Lx)W?T#sy*+xNg
zLUR?&T$hCwWs%qIA0o{g$K$r^P)^|rs#oGmWqqf2Fd902&QD4xapjhIk&fEGQ^mf@
zi@p;HB_?C5*Lk7AvA-?#oRJrZ5j^$$WvA2GOt3#D5_fI8Udki75LcVc3_)IkRu3@y
zDk7U$-0kHr+wzB!|FQZ1<y*Xp=^rUTh}R||9aEIN#TF6>E?<~iySxAZ|I^xTG|~OL
zdHK?RPs{%E>;IikM51tB9I6GxxN^f@G$Kc}Mo*6|Ixvg-vicI@=;lcg?!~9j^!62Z
zkm78;uMqJR7>0cm&Qo0@SZ~`TC&)9hR}4Sbd$SZhdjm<_Is3WJfGD|uiB}<RNcY&8
zi;)z}<~(&q?eIFyyZg^cBcd290-FlUoVk<sE;P=?nz1N5tCMRA%LP?2T}*0aMV~{+
z$9hF$8|Gzt-&~@2L<GGLFO!q7i^83T(s8>cO0yGGYxwfztOTGn7QGGw^1^XmXjFhx
zp-e&@+SEy~#gT(L`s*}ds%9emtkWJ5sFcrjlnRVDe-`GX)BC4{t-60lVP;)2u0pyk
z{LnRC;}?%m9`q!B!XZk7ME_p~(PU?yNecd)5cHvi_#~Vn-rJm&n){<CO4(>wTo#kt
zi&Lr{k8MyYIsVCC?EZ|2ckomzV>nkmZo2XIFc<WMNBmX+YEItncU<`}m-{=EO`nc$
z{;HdOc6GNy;~zbpStx8}E>%WoB^o@mk2g%jI)1U?u6M~^yqeIMdE&UM#HAs^!;1OO
zU9XfwD%IcMFNl&@kEHv~ZN#Em_Y;#RF;#6wD0sp#KG6j7xf<cw*sZ<4Gci166b_R`
zXMdN<<8>QR0^Vl@nv$PcPsekK>q!VXX{M~QxMt&T=Ap5tf4eN}D(g8zU-8JXWwGMt
zjr_^e8iX9hn+t(y8o$aA*pk)+uPW?<lZ?sI-C+q-D@pvJo{eDeCoJFk7nG%#Lg)!o
zFr-Azd6#q1tJEQdNcAK3HywgEqUrA_IrDnLmd7$)9jbU?el@U66gd{h`lGG7ydDOY
z$2(0UG$c{)SDZ=7e5AcVCPh=?!l;M5(+b3$Q~&B<=;`&7qd2dM_K%|Cnu;d9E%J-0
z3JYu(vex|I_6fdLinG;uHyLB-)oXs)k@CW6_2?`x+T|Dp<eM7(PFQ&bIgNYxBxF$C
z?isrT1R_uqcIhh3wl5MdO~xAEqItL3#C~<x`H}g{>nV@ry4xKE4=XCta_!46+c{X{
z$CZX!=IbHt*Ph8KTvIe%yy#`^nXq&C7PVW{7@cH*<ASR%S1a{0ms~TCa?!QT4-*l=
zqs!9V<F59wcBYk16U6ilXn|n-9XhahlKW~s*A5Nj3@30~#i_I0=e`^@JkWDO%<c)d
zO0*3R7|jv!=5|ar{H1AOt*|jAhp`7x$=1Nh@taQe2E$x-bA9A7T_PLeT2(v@VOT8J
zJ!Lu!f08ng2CK<cT9U&F1i!9p#16M?2J523Y~3syw0kX-nacdN?Y#_SE1M+ay|S<+
zo+8hFk7vtmG~_1mkk#!QbYM@?Wo#e0CuFY;Y#nlMRmPjfv|6n@Wno*_I%e5!A+VpD
zz9PmFO+PyoU|oBYoVs5Z-!ttlvvspu<6QJd?oK)&V-qB7$~}Xjk{xwnc<!R4?f0m=
z^8V}wwk5@C2$6*C*o5G+ekO;SIC#520Xeci@upE~oUL28D5Bip=G&@9ZPIjXtNgH8
zN8nv%Q&&?FPx<{+nXYL@;k4avM%Tqd%az*FDT_=7);0wq;x7ylWAV~S<K;)AIId`T
z<X`;6g~`o1=4I&sttkuTuB=MAO_5k>$<oVbUYyePJ@2n>7uqxwZt?vO{GPe@DAwFa
z-?VMmy}<}}Ke@m*)ZUG*B#?L4pDWcrII>;5MJ-O4^14@zJN2ren|As(F|ZCnxpc1W
zw;=Mdg-LIO2)@g}R=#b)+%@cn<CgDcddqtFm3@u~HI8mr0J4smICP!x*yLAAr4`2q
zr6bZzuuEdV3=67Nc-}2~;d(uMIlvc+hRv7??&|ssA=bisx*7w@`-*qn#b};5q_v4G
zevE%b-f`}jlz08Y5k%vz@NUgUwO3z$(YH<G#Pm;h4x4&rtrl}9-e%RLHvVg&OU^FL
zrgrc(zoMj_;b+Q0<<Fl@IDVI4ce-fE1Q=5eLn*pi>l9zZ^u0c{KHjF9SM4MR!Oksc
zJSc=fATeI=u*?OK{E3X(w|>#bR~XvT_J0beJuZ(+v#v_@S4g_b!z_1k;iNOH1bceu
zSV2mSm?!=4zIk=|s+2QckexKkJfVphlT|i~X&l@*pb4i<r1!}~H)@0wzcYMO|3Om_
zN-){C+8vB;h$4>M5J%vwOg<-K4-<@)XW#A0rQQ@55VB41{epd4@&y$pH_TYBAU!-S
zacv|*E_l{g>}6@bSj%1REh}Q2SCM)Hz9<mbDr`W1=EFejRYQ0&7rZztvcUFO0nwvO
z#ewhzlfIxg&gYD`KrZqLn6DNJECwPuE`1yJoT(}s`-07UR5T-h@t4;0-Qi-PT}dPJ
zm93rg%JRN9kP{8{QpRFjri`O4F`Vi3vtl?#*r&W<#3O+h@JRZnq*a?ja<orHf{){S
zFXUhAC;M@OE6!MJR`g}Q7bA6R-#*N!mRZexNV&ch#FHg-tgc0%cW%MQ=ef%korwZk
z^|8!nAb9+5wXz#uH~$zS6N!#8Uvly1dF@+YRP8^$b~L<f`gYbax^8)vy{aI;6-0V=
zSD0C>qU{}+0TTDk!ir|hVA-`$Rk1i*L(w5`#~rQvA?Se^jasE>uYWjg?@{Jn^H!Qj
zA`FsW!AbBxpEdJzzrcs%w&}{2)GYzR9sg7P^<HJ0E(vF_Em_E{$mkAo)rh+*1@?P6
z*A%TCyIV9XY33xdljobFQs3p}reGhxb97L_lPIe!Z@!^n{#4zb1Ferrb&D{!6BPSa
zum1ZkjAOoLM%|0TYqB`HcI9~~dY8zc9~8Z?CDadb*MUctld3Pn=fmnVKH#F&Pawvg
z&-ls*CGOK;-OVB$7wOlT1YYd6Ulzg32ySNmo!(OD<$Kb^SI1vaaFe|X9tDAruFQdy
z`nreDFP{GW^V1t4*ueB2jKF4g-!icWKYMfnK;LsDHYL-|4U<;eN^Acr=2^W$>-)bF
zuy5NPY(v}70;PPKoNAY<>vw*Ac5P{=tq(F-uWT9<VAP4k{0JyUJ$7q}n)PcLzkzH@
z!%eq*uG1`>zXwVY1+5In>g^uo{`_vG<D80J==-AnalFuBex6&q*Aqm$6n?_%JbRPw
zo|n&H`Js_weGFCo%No0GznAUm8-rZmwP-0v>DDQTaF<0J+T~!sY*h)n8455yk0+*(
ze!IV{P}*&*->JLVwT+-SaK|?J)rGD&ugGxNJ9VEBQ!(pKYJ)3;+Xmfo;@LVX@^UHQ
zTD*z$B&I-yk@<nhqfa^5!*ALsoka3}#IAU(+UUDE?;xM4divuK;!|3Xd8($ctvu5`
z=?zG61>8lBW4UD1H9q;$nZN7i^oh}8T)n8@%+_a1$KHu~?yZS3u2>3+pL=Bt(GugL
z3u~v{3hH0uT65d}+>u7vYQAE}YlCcA&JL#?nyPL-WUG$ml$Go9IUEXT(772hxjT3@
z0Y@(%$XWKq5Vn7+s-_jYFQY&96)s7vhDVRQnnL{U3GiXZ6^qdg7{*q=u5Jxj`NB>t
zIcQRAl*rd0wY-(NB+W58xR}W-x{_HsYTbw#6zB-FzuEdeP*iV5Y9-#Ef@uv7POahF
zfXkZBwfuc%lcCV9GA%edU{qdSygb*0#L;Q>>(&6D7T&S>D8-K)b6z{-*DB0Z2d=3a
zyJ(v^e=Z5ND27Xy^&hvqf#mq)hPZmp!c<QNSzYDZULKr@n&ZbvT$UD#$RR^}!w_AG
zq#anqj*B*ax|w~EmF>KC_FBf~y~^C_mJ^_${t!%bg!~Z3q&W+aD<2(n);qcv%$LAx
zTb7Alw98L_*AD~~&m{{4-S1zva9ll89eu3aQb>GrR|`3zqgfqY6+!=bm@Ct$#nD-;
z?wGIMQ?8x5M;CrFY6!$2^-T~Bsu^1N?+B3?e^GR7wQz&U&8EH{9yr7DKILNJJoobc
z!{}AhaN7LTh#GItLrYV811}zAh*lbBOZ!pd`qR}ek*(D$t7Uz{?*r-!cX4Q8#8voy
zvc}_zbd9Dj2+o$TX=W3tw8I~<^27dhIld)BTuVtR^==&LXp7Cg)kYSXR%XIspVe1$
z1)ndnX+hr_Y`*%#Pv@hh&E{I@+sG+nHx^GU6a40rg%Rb7y*pxk^MeY<ERnV>`j-(`
z_lFI8U69K6vw}aAl2`U>5!%Sk!g-UdKE^zm&vjkPfpy+A|FO`Kq^L$2|D3ar&ZWEd
z7tN>3VqlSwQWdWLXx++sxd1Pl($LS97#{}uQ655%>p9!G?F8(UcBE|s)<IBgYK<wn
zuF<6Xex#LFnA-~u<ps$H!#)|)gv-$1Y>CKYPZW?BdL7!Z-moa_;YUiiT5!tF3e$%z
zL{_Wt{&)Z?kB6~ol+P(n9Udk?{M;dgY|1UOA`#*9MGI~~c*>_j+5>{g!!P2o4?>5;
z=priO_cypvtw;DRctg4(EN-afd(k`r-aDhZWJj*&Tk<CDDG{>9k6@;c<p|6U2eyM*
zg~@w4VgyaDWkN+1?$wfW%6**mjQl1g&>5z^zboT5&?bc7-;ai9F~hc>Ya@3nA`VsT
z(Y}YP624xED6XQ$BtH~1hiZ7okA%ukLatLe<3$Ygnmd_X)r(J0n<(p~{}Pz^80n)p
zcPIVMxl31vF3C}zmFg8qa*KcaQi=*zzlRGTAFq!7RyzNI?ZzU1fh=tD4t}x+DMWmr
zIse&(NmkI=C&MM1Sa(kT!V{!!7rLExc4e11@Y##Fpw!s&qGu%p$FRvBIW`;+#h&n?
zy{^Ychhn~e%6!EuqI~{zP3ai$R@ujL=>p=L0Z42#FVS%$2K&uH?`ecTPgyk4p<A?j
zNGz*x+|ah+3IB`05n_)!r9SYPTqRL6;TlZfoe5nv+rl1xKkLDHxTfK%yml=v5U=IH
zs~b&zXk!|EvojINOV)snw~pjZ`WlDX9vk*)^v_iJ$P~_+<bJGB=53wcOxQ2$i`Y|u
z-(R4H3TaCGnHW~i#S6`A`sVy%*Z3~r_+K~|>DwtYaVIa7j&}17Y~9#QYxMoFBdZ^4
zj?_<hR=?Yd)s@n;AHyYq8kZ|~*@W30*xzx+F~#rd<_l8!89zG~&V;hfj`j?A7V@9I
zXxCkEP3}P-`@4mT>lw-43+F2Xds*$|RJsjaalZGD?F_9{XWFo_G%DBsF`}oGUmF!2
z7$NAc;6>S;j~v6AVk9YiMP*>of1U*hkZ-er*+6ZI8Yx#RI_2fv`c%2gnnqZc_xZ*R
z^8EV)<R9fQtUtBxn0M^nx6)sA)~ZPJZMR?~KPtuh-m&1wigJ`jh?E>1HHkiHDz#-A
zT;XNBLf~V(6x{+J*b;t`o-=K<>|Z`YiZNKy;z6%oUmkM(dY?-E6psb8zgMN}c)z!j
zGddqxB)}LGIH1-iE<gz`!qryA%Qfo~1CjXrRnbXdTPB^dO6BNV6<e5uN_^2BWwC-G
zJXb<~N8m=qvX9{O73XA}>__2C(l_t-<3|I?#?aSMxGmdIy(h{>?<(1z)mw3js|j`o
z)FZHteUVWIBgbAc-@{l7ip%{CFQl^@>+Z<h{IQjGP%qkS^l?evDRe!-P<xzqi%rm}
zN<*jcBWFuiIQB4K!F-k&(T+2Xwehd!Bqi~9%IrZAZg1N?qZ5u~(}{I9*-jig2PIJl
zZ`AUa9i5}Fup!G{zSaHx>W+41!RJo7o+O%#KgbwS=t|L_86d@ATl*{R<n^Dd%1bE9
zB8FOsQL?#woBL>iGLuZXZ$WR>;lvFOyh<Z^SMTxcP<AWp54Sg_i;uW<FnzRw6no@j
zXS?McA>`tXM|>|dhN~`T{66@&Kj6&J+%rIC-5HF?u1MTZ*^a-;pE|FaF6t>qYK=vE
zu<iur49*7#nTE608ma}Fy<<gkWPTG<_%P^+FgBVAJlg^Pm<IG!F^UN*6gymd`-e3C
z$ScAol|O&qQa`+PchE2NC9aM3<Vd>(*Y1gdsWaSfY?%bNoVToHH*+pdXy%O7h|x@b
z8Ob$p6k_~wu7mbl>dHhoe%eO4!4Vd@U3cS=d6qE>eVS&BRe9K&b9Y}Vm0>=mO|qTh
z_qSarm)oNst<3dOee(m$r(p3(*{vHdk`Z6`U%kYAcWroDJGS82CYXw2{bg4T2+!hF
z-y2H%;jO2X)Xz%0y5Z!86aqpee8t8y-PMZlnwfO)>nm?0+r&9RqQmUHq{Dr&mtKwV
zvFyP<DegWT>x5rDa?0kE^J=#TUTUd)_p7t5Px<v@b?35{c45|aIqXB)_VQL}_!8nx
z%_~P=45xyQ5mEI|Axu?A)&34%71{Fw%xzf&T^HXhd#Tdk_v&SZT@JU2z!p!yJHujJ
z{?Z2RMb}i@IT{N=#ualR#t^5OINk(^d*;rY!WSSvm=wwNEV@%rLGoxzysCNc;T(KO
z$TRT3+zwA;(P+M(-1?r~;=1RdWV_HK?{~x$`7BOazhu6K5KWW0dXKS-X`<o*>$j29
zC7SlXX9J<W0zT(Kb$0QNk+~f@_DpFiYE(t{4Xf+(TDX~5dchYT>s$P0=E&ysM6(@N
z%<w@x_U-gS>2#jtJ<Bxhn$p2>zu^}s?0)4#`};ZbmyzG`?1qG$S6V_FYgv6B2&-6A
zKDRLJ{MJc-(WZsU=#2(uw-wzZZ4%1kq@Rskrize^cl?>RqOsCi=60cZ4{e#cBUCcs
zk>pp{L#d7=>^!H?))0ny%9+zs<+~KJhhkm?H{mG@8Rr>>D|rYlo?btYJ3Pp27mHx%
zimPr27WGVe@GxCA?>C!Lt&ACd`f9?T=Q>(5ltRlPAv1y3J&THvi*Fzv0oHRVMX6wm
z+71CAhiuDe%$dpD={rX=^*rNB{hxB><&&UR$;V;?3IN)>Q*RXW$6}D2Tmb>ssb?ul
zD*=K9+IfAS6>jRr|4&;8x1C)L&EW3e-zUl3{T*I1|BN%nW8A^c&K2v$B~Y58AqgR&
z0uW}lH5dN*wY4g8;5fAXe^~?mKYcXkSe;_iFvb<;q@~rhrJg91w-Q*d63y=L`oX6&
zJ<*4UhquqM+^s9}C5+`_(-j)?t%o{4O4Qy}Pzc5@^%rc#xmurJ<r7>TuBg3p>8xhe
zDh5yA>17VNZe>Ldxm10UBWQ2(cvj^JkH|{7?OzXt?|CiU`W&#c6g@LDBb%O{P8ol5
zj%BPTPd|6dy>=DzP|{^ECixlLV5)Vx_r%LnRvc$v-+a$5R|AciwO8|ZVbmGh+c}}P
ziE}y#4|la*5`@GHgAs)MjADZ|eS1w;oBsUU88rKg)*JX#yqdatnYz0A!=C7=sVRZw
zfg<Of0dsfD!A}pvC}SQiY(;hL9P)vC;?2#?h6me=o?4}@e`T`SHM=>Vx2td)JqXv~
zyEFOn6uaXECI9;Z-LA)Dt~3TIhFjpiO|G+3PSH(WYxJ1@nesD|g@KG~Jo3gRSqJUU
zcP2{`jP>*o<`xzfuA?>Hr@B+5x89q(*Y70Wbshdo9TNrDmBl$V%6F_e$K}biHr(Qy
zEzwl;pTaSt0#b9(e2oi&5ZjA1R@W6DhSAq6A=rTk`K_FQl4K-U>32&wlJC0SbN~HW
z7dKF7x<z^}mEyfzR5^C|ok5}Mh%@--RfocH_eL@k(9`lZy78z+O~6$%o9(hL6#9<M
zpsn4@JTqY)L!+u8OAbl8Uc8Yi<I7(4tUur9la6DHHRS-O!X#w!d?SiY=3s5GB#W>+
z9vu92u*3!}KmA_($h_8PRiZ7v5j}P9-1Q4V@ca2xU2H})98<gr{QH=g&&uF&&@oTP
z0;3}NXSm`~Bed7h&`@?)Ff$WV;$PS9ERcY<$3&pn+1b(za@#sA=tIikyh4k5Z^r#s
zTH4w5d6+CAA&aW#N>Z$Ao*lxKLhZIPkn8Ll#N$DF)jxyDJ2*oo5sN`f#2W^Vpyh?0
zEC-BJpS`)2m6epJsOU(Q$21$WrB~0Au{C@r72R;~Oi66@RV6&HdpL=BSvB6;`uuP2
zj}4hqQwL|RTfPt*g<LoFoWthb^;@414-XI5yVkaJUY@j4rU1p1)$fkMx)G=jk=m6a
z+fCnvUr*GZJemARRr2l*&YI)8Q<QnljfZanWxN)CWtx`Rr#1wU58mx8<HnI1@I1_M
zNc*cN-`}tihn>;hh)b=G$k5PG*Q`g80TM`vYd$$7epn)?J)Zv@55J&bf!F%PS3!j9
zafksoFiKP2^uSG11@gB3XhypW9<z#=PlK&}x7|G0IdR`IcTo@m>ACVX;_NcmEA2yE
z8Ojy0sQO~A7`>(~yYa&s;nXo);k?zbKbNd!YHFH&{1pA6d_TOStBW4y+R@QL2Szjb
zifTUN@ZD_hzl)4z<d62IKLVST$#om8*_{I}YF_<fR`(P~;Cc++y|KQ+*p!Ed#}sGj
zYgx-AY-VAYt(HLf`@yFT!vZ6Csa;2+@wuyFGjFNVx$O{-drI>ze&@How#2#D4;T|g
zEQg_p;>M!`nUsi*)ynap%0Z`y@bFSXBjqTug&pQX0o@%>N=}v$p`r`TDc0v(ubHg|
zhl(Qa&S#!3Fh+L~`U`M*0~`JgU%srDFltGCB5clf(53iabYg+{YRm7>yryFoSz$M@
zz6B$$=o9L;x@<1Gs_2jv1#o+8GCqHE^Rv=BFkimRa~NEwmLL#G5BfESMe29DlBl9u
zS=NU_+uUe&Y>PKA(F#XZbLqe`#g}pDU<Wt1wX*&FeVc2NZnE1`5u)-y%{#?(hunP(
z(&#=aaE@fFCwV&e=kGkGLTSab{($xJJ}nvPfcu}6iu*Q1o-?|SzEhp^LvfBNhfb|B
zj1qarJinQa_hzfpX#s_aFZUPt`hs)Ifb}$rza?wV>{$gYX14$VpXtvxgne5+tcbSm
zNEAkgk0oOLYtSsxFKqjOGZD|N%iOQrm~NrSA*WdjL{)e0xn`j=(5jD2cP~TQnYjFP
zpOBA8glfd^>Y#I}{)(yb?Pg{0ur*3Rf|{DzCZdh9-KWjL4UJ@xJSxq|$hcz^Ah|Wy
zS%C51nj>@PdCqsKANKkX#^7TsLuIf=0HZDE0Cc*E?eO5E<lzU~AEZ2{##7LjFJGP&
zwX7-c6RsTTbpoE44&UiFW{`<~2!%q8s696=d8(}9(BO*4^jl(FOh<vuu$B33Or0S#
zkk$&iR^1{_aGM!9Ipw9;fjC(BTx#WTmjm~;0Zt+YZgonYQX*`Du!ENc?&)gfKbs(5
z=FolQ3$VRXIk$va`Gc5U`wc3ZxDPhKVZtzcqOkcjjdXeP%0Q8Mx>WGdq2NmM-9P36
z47nQNwRL&*_m?PlEME@u_<<Hj5JpSJXGL|dL;pNEM0m*g5QK@!v7=h|@dn<;jh`%W
z4?e}rGrZ~fHu+qu@Pye2h8Gr@gc9$P%EB$98-qN4ckBb$dnO4$(1(sm$nmI|+4{YQ
zn??>tDc;J=>{<W-F9sG_zjbLD-?hVO`V1iPyFt`}vHDHCtareE@JN*lm5E*UlBFBr
z*7L1H$g$_Wb%@PSI(V3?)O}{26tF*=l&%kAOLK|vY|x#aX_*%@t)_js+LXSj<_K-6
zwW?_mYmCd#_pZ8Erx@0%9)As8J8|L!i^9P|7H`(F`kd@24umBMV}w9II`@%7`o|ak
z9V$_VRVZ!}KzePdy{mr=XjRGjzV)Q>qjHVye@$qzqS(rm`A$)K@AU6Sc_-dFbfx&m
za7(z41v+K=QysY_k_Gn}a0f}KUV?z(T??R|j>An>@87!1+)lIY39ASv_tMoXVz#Xp
z7<si0&4Ei{1dFStUN%QCGYnQZ8;@%y-%Z_}iPN?+F0=3CFe)_hFd&T8cYDp%QjSRG
z#U>@0Hg0ZiJycNM$~P<!`E!!6IVNSdjW_+Tn!TwA4geEl=#6smo>Gy<!_9=r=T+Jf
zOv3iiUQ6Fm32b%M2yk?FOy5AUC9I=*K27FXFADmK8dJ7b;jlTRL-DiUpL-6e_9-_T
zuYZzMK}ayRx@uA%uv5W!Q{zf0FvK8WA`7I$t!_%$moLvXJv=-r=tQa}Mr?;qHjN{B
z?+%1`e$3r=9L;>xyOZ0?prlT`7JRtTLK#3o<EjzcR1Ts8fK!(WwDVCiYmPvMvbb2I
z`>Moi3XZ=rY*s~AS~!9aJ2>FmJ=sJ~1wboid5kSQnvVl6cG!mG9SUl7+MH=)Hq1A0
z+WM@URXhr;y>S&^xyvGMkCl*k_S>W$453CYVEf9m=zDYr_fl0yH%?Faq0~9apM;T~
z-|qTv&P1H0qr*kA$q<*QB4nlyBOQW4;c`nE+StdhirXLN0#>lxNG9TK4t5FS{XyhZ
z-?2Qor$62n@GMe}2oyKx!7oorj7RUM3z9<XKKA!#s3gU6nBEv4Oh%i)@5caUaP}bF
zuS2jKej;Sz*1f+@Hv!m(#c;C)u754C$tG`hh*DV3zOI1=AJ&js7=!`jHy!fp=g8^-
z_`KLCcC?+V;EF&2`u5BjSKPTp3O#QibJu;0HDD<(_l;_AvU|fmI!^1}A~VeV$&)8-
zc1f0)&3JvStHXt+Ww{E6>)++6;Qc*~!39u~PVPkjbHQsh%f+OhUB=)R2(8q6u1ml3
z`gk`mGDD@Hsqygh^0sUlFW$EKsSzDi^ySN!nNT{By^R#lu4-yjj7D2jZ@vY%Xt@0A
z(|4vyv3z6n(KNttf){)3R<}ZMXIH8W2kWiJP3z#GR1-bD&G()tC0W<wIt@I%3_lR<
z5<#FZ-(|d)y!zV{1S#b<Em2E-IhxDjuETeLel<XpF~8@tvbXk%L4MDtC5lZlcUVg$
z^^A3rq}#}cEieRY5h$~j-{kc4&q)000lwI>wkTSTI5^*h(_0KndGP61m$c`c#W)Da
zoTLhx<0lK3Myj_Wh0V&Z)B`T^3ecvpL%>JK(<)sV#eHqRS9v~9$BMiohP9<IYJuA)
zGl^Qs76RG4T(J>oskHC>SZfuy(Toq+T_exzZA?=<fcQnE_h;gQ@9mCwhQ0nixdIS6
zN|tiCh1=`rn5hmeWz_zMW`#C9bq9o&Hta&3B4hw<9h|`};XL4CBfGe29-MN&zpk#%
zmD+hq_2>~t=RQZ(2|6ZjvO0`&)o<z@ui46Qg<vBH65=V8+s~hkRBc2(q?ewF1FS{D
zW9r8v;_|s`clI<9RB|CSgvrS&RJwHOQoIjfHjL1ZcbxRN&|Xy1q*MtZ`_HHDX4$&&
zQZ`yR;y#|GRoqW-RG8pRa1Q+b=3Im^0Gn~oZaLq60QtLWAl_20^iy^73}CAQX(20`
zoxIyfAukx<oz&-Q4hL<b*(}V=KI#L4WSt&#uvir|qn^$2f@)1HU80?s9L?mTu_E(I
zWA4T5B&&OO$KX(<81GZwvb{V#N4J0r<{6`{L{D5ENzhYh0Pr$=3%I@tx6?$^%Su-N
znb;)vq0;Op00-?AfLJ+x<ST&3d=bq5iuD^Cym_r1W=(Tl>OElHUqFaH2F<sqet{Kb
z0-m3G4}`i{1Tx~;Se?IN*k4y)EMbx2-BKWIas=&;`icTrttEJ!=;}#VR#t|>qZnOV
zKxj`^>jg#=)!Ei2{a}DWY4Z7CgB+iFIJUaNWvFx*VWifpDLHz_cV(~y4|0TG^+rWz
zov`hN4J~lFq!gcFr{!k@h1G(wKBr6*lYPhhbBV_pWPkZjetXRpL|UzU1agObTlcbE
zR}c*<>NYE#ac)KUWq*JF$-=_IUZB)^0IxFy@^Kvzs;^x9A<+;_IZ_}J#wjPDqJ@^V
zcijP8p94Mt8_7)%3Q%)$a&nw$jWym~=$UT!?2>k{%wN?6+S#8^=iq~evH1)Z`ppMv
zQ1#NA-C=Cy?X^0*vkbzAwxDsb%T?{<i<fOj@3TdFLCY01=2D*DoBLpMY_#%v6rnYi
z%c1kb?JRPpazu>?;57R8?|=PC<(MUYL{X|Ll2ol@mOvu$KdB-nIoX^Wim!B!uD+%1
z<3r@ATmb}b{elW>iQUilSEtP@o=Mb~m*1HOSlcKtkfPSL3gR>LgalB5Xyc*YLov!#
zK7Rf-QScI`zJ!H4OW1GBhthP_0k$x<0mf$CQ)1Hsr6My8HIm{(XpX<Z<r_K&#@*C(
ztDBCJF>A)JUGE4VXbh$l%fX1Ox4oBs2Z2nY?<+OgT_J}LOLjwCgMu37g+Xdy3Kh<z
z$v9L;alGYu>s+T#^@mgEu5FXwUlmWItbb>q#HOZNazcTxYoiT15)ri`?!)DIItXCw
z)Av5MY6_5J^k1DkCrgMQ0+}ZUs;8@~yW@vXczb=BehCDfO!5=4`M>pdmim3U<GR&#
zTBt!j&5kk($SeQG*rW8etxIj{7*W-^dz#jhQ4LRhXY0|B^Q@27Grz&yS0B0V$yLzk
zH?C@|Qk1|&NKXJ>lF)z-*q7RAq{;ew0kRrRUd&CWZ&)s_H3KNR;*2f_A%i@f_!Hxb
zujx9$;w6)BR8*rW7Qqro4aVx_BR;FcuH==n?zEvA@8vBZx_n6>rW|U?LboGHR3QLF
zw^eJ{Y6ZVFK)*ga%S2aDCXo0U%|T&+-dip#JAw__CBer4{t5$38?lSNvM^ADm!^Vw
z+(N@~)y7W@U4}c)@;ZYyn~kB(3EU_87zTU&BNafJ6)XdyQ!a4I++7d_%xkmxg@wJz
zFDgCfwYo*&@2ryO=;$g{dqH(Xmhcv=UQw_mf$`=0xtccy!R?>|e0+oPF8g+dzB(;g
zb*jL0+q>v}M%2!?_yUjbr)I{HWNPY8sbdDc?*k;gD$32Lea7ER_i~YGt~>kH*cZ~-
zGI12E3rg1KyS>da(d!SaOKR(j(2$8ph^Ro}J?|x#M3j5wrA53jOhY$59%Qa4ri+e4
zrL(<M+74Lj_#MO@8Kl_9yRHYY`L0y!w7afm=_-J>WP&(jIiEm`5N-G@0tF6kjxc>J
zjPzmk_;EonR|A;|P@Ywof04p!P-IqtvTh7=ChaU&8uelq`*Lv$AY8Rpd(IP6#2vbR
za79URu2C{>>L*V7phjhZ5f*Xh0h}N-9By_Hs<QXjY6%~I;xV><73V@KK_@#wfn=Dn
z8Xfdkp+;>mREbLEQ*nOKeABWyd+6EM4{AH3KLRf`04#cTK0Ub0w?oo*ZRA=>d(>`C
z;ExL`g@K@;QtsH3nWe6#mKHfbfX_Ji6fJ-7IRT~Yh6ljz^Ojq}sdvV_ay8E?S3gh2
z>|Z6Q`oi?28vvpX@uvAr{X!rRhiP&!c^n{q>5rmV{dYh*AX^Bq+W4p05587UJ9k{U
zQ(q^d+;fYGe7J#CY{zzX3JVAcY538q>cC&(yocZYgD*r(!?6&_V|Aauumdi6wZa)U
zM(hx6G@#F{TUl|j1ooM>#kMaE;L8X&gsGrWp<}EX3p*f?m3ZI)^Em5Z*L#4u9j9zW
z2M5k4x`A@R4$orKaR!SRHTb)7o7R{c>T4hev-~5xc0sgkuRG7O5-Ye{<K;`24r_qK
zhsOiAG@o$Nj48F}8|3N5au-=Qj_ojSWM*c{LfhNgJjmZ90I#e8*eWYFgxP=Be-|=F
zb(dE0Zdp$gCnq&JBg!Y>c6c{{UHk9m=<+#Yz><;i8gJ)?K5d0n0VpUPj2CrNNj&fh
z;{ksVJLwlkYj?YQ{N1Z(6Z;s8x|eGLn=cy+)o*plsDq&yIe?;x3VB|EvVX_O^#0C6
zz??T0soXD_3YV*iNBsrH^Pt8+a!~I@I;2T=R11Gz_7d}kL5&+z_yOfvj9=PV1&+JV
za@WNXGaf_|2F#&1`I8RcAxJAU%*!2haR5`-Hi6a4tt2V@(Hq*ju>ycZqupX}ws)vN
z833E{%S=oGCyncAdzkkub5j=tM|zA`+}xK1A$~UWRt6@oU%%%09>zd<3KHYKSAGCo
z?GMiIro{YSnoqKL(e?s*04(|5B~YFqS3m2^>BJCM@r}Gt-*LEaFxm^0NrFy6mxjvb
z-`g&$m9qh;8j^lK(bNK#2()lCVyH!1YQU<I)7A&@3-nt?hpwLcNPmjW>sMpS?9SRK
zDITyipWxLB+>-m?m&s`9wnQOQy=uTia$Uz8>V1rijGUs`<%0D7YD?^|^WP%yELPyG
zmN!6Uapm?@hIyr%Aon70X{r4;EN+gKp!mrOH3M+829Cl{V#T}b6vn2LKAX56pC|(I
zU}<@Ol~GmUmfJ|xA{ci%h<yEjH~v1=&!F07J^d$Jw5;_>IwFlz+!VlY%!u9^h`XQw
zEEBN3a1-PttQa8ilZZPmgJ1Z&4gp6xP(=VTSKUa@Az$YY@~%W%H*tlf&sicCUmo9Q
zs0@g!7QyA|J2+5|$P_Lpmr7J!4Rvt9sL&rrbA><@pBSec`I5@I<w@KQX||*rxl|to
zavtCRMkjhl2*g_f@Cw*yW@f=S<JO3vgO`6(%d8--tg$$x{mYm8r*8v-l@BVW5BM&9
zTt8Sw<J5i9LlIc%3(+7csgA5qCrY!q!w_-}lH-A%$=V?HmQ$%Nhz+&l?A}tKX3_&V
zrui-?gt7Ng!*tLNuV*oLEgJR;)Tc%0^Es{ny1z|j?`k=0B?qAf=svCO%iEVX;xGT+
zfao0E4rM;ANR8Cy7#C?9V1=oGUG&YAc8I74%=W+t^}7iGdyf1;yj-;cLF~XG#p7qd
zJP{;Qg&;HWPCz-M>wTW&yK1A@Kt1wf1yE<cX#I9?2DPG;e9!xtLS-E>^Dm6|@<2bR
z5xb#5An7S<5JcB)zBH*6%+VX@H?}4(g70F_0iRO=J~J}}c-(WU8G+(h63D^kFw_Cm
z019qTm3|+{+-0`MV1&^k?8$T2gxr2*d|d*hXS2Ix_ye#Tx*B=NR{#pCv)8OGELhEe
zR(C<9@CpV~0kVB|c6JHWk?d|S^*g`X+5u(B=Uu=YtEdG2l%;2<gs(Mt>yn)WwK?)U
zs2%RXy!_j&lG6R>kkOz3WV}qhzdI<m1|vbC$U1Y24a9nP8A_^)!i*P4H{>Z_PjRGp
zkNMBb1%S6zOi@P0<7rU4_jcHJZ2~pdxIDaee#>_&liWIu3-LhP>eNyd-Dw_9<g^T^
zIer7yIUfKGSojp?2Kf6d$JX0^Pz%RVj&>XIu>X=imAL7SKNOo+6Ci~@{;@=-SFa`0
z{t5j&1HH~{x^s5(KhFaE_>Znz&q<D~hjYt;f~uT8|2!&A>mb{yFUMN7H&SxU&uyDX
z4LKP=QAUe{CDW)!k=IVtJoS=YrIzMk>A^?)O>uy|BB^5UYHJ&!GCDm_g-B19beCTS
z#vd5il_DMNnYaTAg$l4fK=eKKCd0JzbhCagt*oqA0$X<gvARKK@^3~fY7Rl!Xyu9y
zyr5l+5H8Tq)k?mp@!=K<6oNSni!Jt(0GT%g1+TMV--8c-RXm^k)^d`bb<YtvaYI{I
zmq=S%8~?qDkmGX<S8h#gToyDMF{*U4#8GT>1=27ye3a0IQ=ryw{i2OWK?9)i`Uz%>
zbzAC;LO$qEeo9IT8=$R|)cm;@L{Mp9ZY(7rM~NWR5*p-kKJkHO*$}v|Nj3Ul=Yhig
zSE*<!XV3y{aeaOAyMjl7WP<Y{IGVHsqVq`(*h&f7)H`u*Zq5xKOd&H9mvQbt3eXMr
z<nL*Vkz@L#=To+30j7!)fOM0ouA7{YU}$DyQkCP-oz^XE-EbJ56)hhS{n|OSMvGV9
zSE!9W8iX*TSd<Q5`PK)UsC|i^fIc3iCui!#e>eh=MNJ+G#a&6gfEB3mzz%=)fzWpj
zQ0V|A1dx|sgy$!rT(1;CK9+xGT3FyG;L4n{Cp=^!@xilGxPTRNP>O^>`MY&Z*jGW2
ztE4*JFW@s4BY>y4E`H9^&3YtB*_)1bodbo5FJMaPW$_0(h^l5j8G_r}21VM9UqERU
zETCvq0d9OC-?!RU9wAb*@V7&n|J<g&ww(70i+kOMQ4aq2t1T}=%o>4q`|{#<hx4r;
zhJX?R=k%IRgUKr25s#KWgjc_zG0z<Zi@GsTuB`z?*CrWEBxy}4cml{m^)ft<R5ASs
zsuLH$lu#|8MEgNG_+u-sz5A&l4aXo8Zy=)QGt@<@ZTmlku^&tV5C7%RhTutfB@zq>
zN)nhfT%p=CB~*k35>sPvKvTTICrF(t0fa{?6UdSA+Fciultm&0mh2V8n}LQvs-6U?
zTz?Fvg8KClX=*A%;K(Xbw%KEl&^Q-`CJA;s9x!QSp{Mr(^n_-mvv=p;o?HNnasM=(
z`z5W{+DFFfTCe@H<Kj0?Xa1+OqW}JGp!0r08A%uRy`RUh?SIVLvXqsUhKr4!{`aW=
z=RY2&$0sbF%y>{`)+>xCJ<cb{&2QZM8gY|WhaKEJ273R<x;k@^ziiw_d;~1;y3Wtr
zcTVxOj=A2GCOdY)W7Oe(-s>`0rx+KX)?%ct9%?^vtnO`#4%AohgYKDKwHl4ya7a7#
z4<6cIKMW(Azdlvj>LuR0hU=bRN>+JfTXPjG2z?0=ovIMdcoBYF04!dE@JT-K_dk2t
zpdB12A|oo=rwMlI60`j?!6&snMnCvxdLL{H@cropb+^ua>eE|)TFBXO$p4Tv^=Z~W
zEhCh=YT}>ve^~7Fe=*Vh@4^3HT$;LqEuJS2foexQK&WxMCT-WEo}a}3JHm_<t|oJ0
zDL31Vyd+c^PsI+jR;O1GEV0<GM)j6g0%cK~;_Le%l`g3Qi~P{YDUI=;0VAAFMJPd@
zdZ9<efB$vkyytpif)Ere6uLo}SYv`Jn0C8eQ~Subcw(dBU$LEk`K|OtC5k307#-&o
ziTciMnBdQGmZ!fmt*i*gkg%<d?5V}bBiJ=y)aAs~77wHK>vIo=;$)8C1z5hZNdyyg
zI7cqm;<PzBSa?xakQ^DcY7BjbNSph&8=v`3ujh;_wv2OekKWAV{wk)%Gs?C2q{aD_
zp3A>~3KDWQ3gUi{y0S;=y3|qAl4bGFy&xp;jL)L)hiQU0f8tJ=O7*|HiT6Pt(1RPk
ze9fgP1RQ}s@js#q<!VX};*dF#ztdc5CViBa_SE#>a|EQorFP#;6LNWGc}%s66UK}F
z%UvXoC_}Yder;VfW4q~Ez7E{n=dWLn`->*-^hIS6i}Uv{U7}UGse7MwJ%f|;`a%Ni
z$E(oeeHWTHLNZ$?X#HaE8>A@OpE>{LZ^e+9^QYh0u5BEZ2>A4PNy2>5_0H(xUD8fI
zF370AEQpjemLM0noaa#CAoE&0-`Bvpvi(|R!I;sgoljl;L7#5TpXZitI(KBUm>e@U
z9ycWSt?X&_hZSfWh@!`ao^MpBvxMS>```6PvK$*#F{~Udm^)q|h^zUtUiPsK<9cy)
zr4fVUpwZ+oUD0TMz)ykYm3(&7dZ+R(sxU&@N|nDK?AI>6=`YK7Es*tv?Kt<h^;<`c
z{L#iOU8D74w0b^e==|nbagB<1fOqoJTz}6w^Lr`Qsg`imLFG_O|C?{rOEo>wYO1}0
zFJS0&(_fG5XOQR$C%7Zwvi5(QfnW7~19ohLapUtc1xZE(YcDQH*ekmOnr~lW0XuJF
zoyUS^0;orAW0#&pxILwDGl%bHCPSoHPy3m^%Qd2mq|yCg3nvt<8N#3cdp(W*NCJ!y
zRo~pnG}`g8cIl9=X5ySXKVgaXN@Pe<!k*X8RP3|Sni~_NxAo+xBdtA3?Or(WmAC0}
zEv#1W2Hb;26vB03U8~!&K(lNE&ph-8k-wN68(Z8@dbaFRx2VWSwe*nh_zMwkU;}Kv
z;`#@CKJbB)L2FVPdgZ%1YC99~z@Pd(<KlX8*w(P8?j7p4kRFqB)c=8k{3FO{gIfBM
zpH4P+{&<5%dWi2T998q(<uJ0}FwDf=91}=A76Mcf;I}-oHy82j&w)~hr~PnaGIKw3
zMGF7m+Qb*G2}>Bq2bOXFCzgLs31g!n43lO?_R{|QIaRI0!z8_(w?lp{r+1>*KT-dE
z3iABQd0-KZS4swoFF614Vn+LjKfi>$yZFyP^k~HYgSwA1X{o>ufiUv?&qF@*57ORL
z`v+!k=^g_i0gU$l-AlNddL7sJmberZxfR;f#`XL0v{XY4IWyVBA<1Z7sOAi{M;F&i
z#e#Ln>#Y*nEY<808x2c$spi#?8?Xw@3$*j=r5D``wa|C}TYK*v)MVT4iv|(Ff(X2d
zbQQ6oAR-`LR6s;Tq=a5XKsth<g#apoO0ywIi%3s^fb^0OK|rZeLJ5R`2uL6aEeQf4
z=Sh6OwcfL4_Uzd+d+qbhoXz-$N$zsp*L{`WRq`b1_QEVK0nwXqJFd0C7$=De&Qv*9
z{|4f}ojknu)T??pAO;VN$L=~l=_>1>)sQg7gL{LBGeETOT1p~r^BV~+X-_lZzk+q>
zqk1)L)-Dn4?s1ym{_Q#W-+Q0F-Ui^|B7m8k#x$pCLBOQELByKt*lK;M|ExXhT`C2J
z<<}$Xi}9xD^7^@rO7_)(Zhn5Jr&@L`%TCKrn!&nBv#nTug=?}59`))G#4+7!bV{Ub
z$u@7~IU4${>~Sz`>)z)6VB~f%?ZfDL@J5G!uwPPXL})ICy;{_6z!ahv%oWl!v{7Mq
zX#+U5ip}lZdI-&>Vmh+qmd8k`b0A>cq-Q-fbDSI400rdqd^ezXPsUcMTaQRV{Ew)9
z-~tP3;jLl^CDpkxv1X!u#aJExm{OyADdh=AQ{SZlDy<dbyA|y@Hoi=cNp7?VPKAZv
zZPCNgs{?&06Tp`m09s^^1M2JEl|ZaXYS$?nUT)Zb+iQDEiLSxXZmT`EhX`1q+R^d#
z6_{oO&7mDE^<ijhJYDLc0g~|qHy}30^GLi`djhb8Wqp~JOj8I!PxVuegDvw1Tc}l{
zT@X#hzIDI!heq3xv5Xn&WOP+P6G;vCD$e(wBpKmgg}%oVsA{3$_4`l<tTgN%?Wvw>
zGoH;LGOn*;Ip34vkmQGNU!Dl0D+kYI*HqtSc=sL>a;f|LmF6T9<^-wHQ!NM87z66R
z4^v*x0(35F0I6X~b$BRq_NhTl_>?Qor1Db~BcFfo@VoAL{?Hyrg?hoE2`ertT(vP^
zg%Sfc9hr8)sdl%Ww15*AmWAo+6T=y>Zsu3^Zb>MI+dfj+Ljz(F>)|2nQ~Dq_9@%D&
zn`Epg$5C9neTY>^XwwXnmaJgM1?u!N!#${}cdcQ)Z&#>Kfh@n8V+MgEHnvtY1Q*=R
zaKqb^GWFY;0;~+i3lms=k~S;YbB2}VgTROV*iD#vuhZlP^Ep0;`Po~))+LZky=vrA
z9K~?P<sjk^jzta*um`z#GX|Z$vyq5fs*Va}j4rI};#i|5CA`2LhW;uk87Xbsdrv{C
zX#dSN{zN>!=%hr$FO{Kj<%UKKFKBUWHYbrZVmrQwG|}8A@!;nLv{6>V&qDEx%D-4}
zG9JVH(4%p8bvAFEQG^d)RE+a|1W}>u4f&cgEw4Ufq{Y;nP}qDrzU}F|)xdA`69s9Y
zH)c*U+@vFsFe(bVvbg1CeOG`jutvVcOSHEnYuIm|W+^*#iL(W@_oBB}S^RdMdZdbW
z^@bC~7ZOD4i-}V}SA+$$?AmJYk%|nXbytj;AxtdWdr5XSXjl7sj`XV(m+zQZ1zdlb
z!6Iy9WobD#h8fy)n;D7I(%)({D??6w+_6fv$5^!JJ@c%!!pk6$i1r?e#;1*{m`O$^
zE^sU`yBb0Dstplm(^mGSZNE*hIm-%W&=SON;>#C=qD7G$-5FYW^;2KrkvZxW>Q!OA
z+7z7<XNWIO4pAWWp{F_~vPCsvobwI+^SWMjuj#9}y<+r4w9(a&(2@n^;v49~Fc(@0
zZdQ5GRc74^G8e<5kD03$Vw{6mjdK>N$cXGP7;Pv99V~5~X~iULRB}|z`=Ra7RNx|F
z!U7ga(?L*uMtY`(D%z>44X~oHAlgaX(}nNDAMMl|B)|UVx8=BU(Bg0y^(oZ)Y7}&g
zzNiW<Sx|{i6ESC55-RYOS=}68dh=TtZ=<ca<pQQ^W173G0ZQw|@rub`=Qvob!;su0
z8qz`<?-Zwxqvyt5SPq05b%Z)P?Ay3Y0oWa^Y7JN6tds9@j(fsIOnx8zGie$(fRu4h
zvbSaiw6uPqm@;8iCF;nHKGHkf`--}B_dw2!RHYznLs7j8EtVe%aU3n%!?5u~Vk*?L
z!`y&b^cgeUli5V+3juKURe;{qxaO*#R4E>U>9-)pL&4zQlI;&0YiB$oJblBTTqSYB
zipldAEwND37M{UbbdIyv(;D+JfseD4t3Dh+GwH7ad|KiIY!}(2;P?ny=H1O%yG+`J
zr;&=tvPaiFu`OxPL5Y?hVt;^jb_JA*B7@`5>-03y>hFhBNMw{-F8P*bofmhsVSd}w
zyZMN;t74gIuAxuj$l+v}swH+a0k_wE+P(Nk;~V5$cN?{I8<rcv*>6A8#iwrxTemE3
z`CuY_woO2+hyz`t^3DSUqUx5_ke8^!fi5DR94Kw|6p-q%-UO6YYbdxo!9@I@qkoKt
z44>&4(^A!T&gCE)(7R^9Wa$M?=KzZj)h(y!lQ_|Z)9LtwOBk4Mc*mKZ2Hg0%&OhJZ
zW{$7V0prbP+x(>dvHT`DaM}ROaQv)Bv}CT2FU$RNQbc*!(D?EKa?boD*@Z$wx?8f&
z2NPEjdky}zqqk`JPz7SsBKW-|Yilt`m$vN*rt#y98R}FmoI?BvhgsM=hrM|GaCDL_
zB4rW6GDc9nN2;Bv{){{v!F`V7qssQU>}s?aLr9n0LPj9#?@9-jrfLOw(qwSTGK@c0
z_*dKbN9T!HB>X)g5m~Cyz%n9hto=E7^3a*d%wWAM9M~Dvmw?v$^N5j!YyTAs+90(;
z{O)AlArz~J9h^ho^TBg&{?b;w<Wht!Z4>7suoC_^h~ASRy0mRa6PFosnXa{<y=)up
zpH6p^u1UPjQP+2P;1<XmEB=g+BrqZvoMO>nu%UUj4P}vZxNde{W|&PwRGtzbCuV`u
z!RCzO>IC!j)=eDg4Q^J@lYn$SNHx$e<eb3B2N1)Ez-ej2T4=3WV32srUa=h$NCO0i
ze@0&MS{6uOHP!8r>&k!VjRFLww<jSu(Oj6#oha_Za+5H^_`13bZYTSVNl@FK5JzoQ
z%-la`1cM_r#aX71#p@iG4;o1VmO)4(x9jU4&?ZRxWrjFL!AZeZAF+zCE}3>~uR-+`
z%NI>p+m|=13rH4mj9yd=+h<-ylx)_r!f;Ie4GH#^pf1tCKd2ov)Mq6Zk|bU9ClesY
zR@v_B5Y8a0qkmhj|3vg{_>azh1r`D*OjZ3=NY~=_LRWX4E99ql_^m2vLfD@JpG}Zl
z$}(q$LWl>u&`{dSDB$dfiQ}A`z&iVMofGpNEE5XW9!IZ%Rvqo_HN~3>@fYv@NyNmW
zfpZ%3%lVsRNH!I1q>;jKbY@0HPS}ulcqd6-hW1AW{{nbM#b-s)VK{EYUQ7Q8aX@oL
z|EVmgb<4ttOgT;JK;Seqe(=xkcL3g_hsW1pP_61s(44)pDrVq1nseH-Y{f=piaWwy
zDg)6D#~()JDFXcXJsz@prbh!XZ4>_-Thp6hf?p`N=e<N#37oD;_{SEo)$#Rhz-&AS
zoE}d4=lg)F|HeUP6B#v+8I>f%z`(ylBqU&KHh+`A?$g3ZhGhZ8o$`*fiBOuOKm#D7
z|J)SeUhOVTFhf)zdM(;}x<id15ZVj6T|Bu2=a=*!Dy%d~FeM<v`o>|Zc&{E9UU<<G
zU0d&075GJ)bJlmwsa$L&e~v3bx`R^|U3!xFKU0cuCfz-MtZQ<0u_?jCyy;SdB%w@!
zD#*XNi1*@{MW>x!2bX#p>g)X_wK+V(-my-KN_i4^1rktIbo2-d)e3Z`1ApEZU8tWs
zvq#OfDmOwP9<)A^OvSPRVpE|McrprrjdMi3c&MWMQ$MrAsC>#QX*r1Do;G)|4jLYs
zj%-3mHNxBd9MPCCYg!AgR1nHhl@s>?-IK%(36kOkh5RlCBPpultZuGZtpe1(;9f)!
zxS*H7NR7cY_KG5PIlH8J_m$CSYb@61m|b-qw7|f2h0;VXzMv+IdUe>;_;hdSEwOR~
z&d|qgHlH_tSspqhZBHl%{Nws4tjQYfm#eMv(Jxax(o-i8<LA3ogEJx1)-7<oDmy<5
zzzK{p5~Kb80rmo^`G5d{O9HkB5OZLY`y(dwVPu>E$u*}AP<8~f>vvDxvVz2iA%*Ib
zlgz9?7hg6A$T8RpO8XMCw2HxLh|3?VcF|=P=lw=MS5;mtYN%0~ZF86YFjN;fts#BG
zjCU%RZQ4==JwdTaGB%iV?xm&jwhx7|kz<2PuwVX-hrVokqvY*dw_Fel#ik14xJZk!
zLxi16#KhuTRu6?27C*%h#knvDJ)TD8otY*@+P4Yy&Yh3I@@4R<mE{<p)1K?oQ>&Y)
zfotO-n_Qmc3~@*5flZqPVsVywK=)Xpoj_`SlUMK4Qz@-IBq(wi2+BT)O$gFz*??Zh
znn~F+OF*bs%<K*6^|Oi(nE5EqRqsnHC;emx7??qS`>fux^@2d*>mJsY)>pUOqM^w>
z(d1`>g*K6T>56k?TupDAf#vXG!O)iH<X-)GU)LU_Hkb|OAMhhDZ++TlPN*V<8TigW
zGmd5bsFMp?TqI>SsDOz;)<NY(gJq`w)+?XUfb~e}`q{Kh`-quDL<2jLs3fVUtN8hL
zK4>rX@Dp#Pcgmv(4SqI+;-ls}4lTUHee=N&4o#;_SLI8B?M5k?8Yo+s{SF5wo<ovL
zJt#{io!OIC-Q;0aWJh~UQpU|dyMMo-Z!zEw=wO{HUmewmRV)5dtY5FMONcHlmsBBx
z<KE9rtz)S|nuqjQ*KMFp4~7ahX#!+wSUUC?li7*MK{XM=FuI<K4s=y%>T++$CZbnk
zSsa5IfGg6)>s9g@TZ6q29FK^6J-?Ge`@~j0Y<e%e0*DJ*dEvjk?89bb4-s~qKkBkr
z6n1JBoGVpRCO<SZhVWvgHR;j>aLdr{Pz$I-GE{eSg4@@OHeNRk<Zb?zb>LJJ5Z^7#
zQ%HGr5Lh5X17p-e-=kxed8xq<+M+igUrez^*DDYmf5qTB$0|%_+--;lIq4kiuF9`b
z3@`D=U0zJu;|(hTcgv*r8rr2{&JaV|6iHRAdCbv+4=O!$fbL*nZ)$I@<rzewlPI`S
z+I^1m8L4f<FD)&XEbg#NM=XFDr4;30R&|WrVd<XjO6M7}msshJuo6@{fj;vKov33*
zDA2O~W44UE0rjuhSYbW{ySQJy|GV{7*yz{oqIy%>8=QjW$&#hUKxBahswAk`*o&=^
z*uk?auV+#$BfpnveQw>m=z_K%@IP85Vp&r}G0(IiRI9tu*LnD}DWm*QVTG&foMcOJ
zKj1Ju_{S8Azf{Md-4(oQK|q6|eWp?a$21lLPy+Q*h`qd1reygqju!@9y7WKHV71kM
z#RK_485|y7gL(jCPmD4Ym78P&8N%yTboUbOdw<?5|AU0E*Al88Gp6|Eys4I2K9udq
zlI`jZ^Qwk=JqU|55g3@Ilg>dJ*fVShSzRAW`!u@T1WqdMA-0dtS*jK}OdMF##%yGg
z#$Ww$$X0s*gzIlk!;scV-29)(m}Y{P{cV^5EsHcl(m_uD6Gm!(Quk1au&aXc%q?bg
zmmEF3{rNvv;b|mlsBZUKWH27pEi?XKK6P$9WDG#QimKc1{yBbC#LKqZq28c3p(gR4
zesFAjT^sO^wsUPP^*etYzmYR=xxI63k#_cxJaZYh(czT!<bS$*M{E0w;&7`>Gp#b1
zUqe~mM(JqA8#jji9p-;}$a9Xcju+lYibE#u0LcLrW?zL2GobZ(217PUrLfSl#tI+@
zP5Y-q6|?-Abq9@6hXoM@aRj#-m|8<UYtfE<B_K=Z;&Wo95C=#sX@Y-qg)-irk%>bh
zsrRk=<i<H*@DiU(=_GoHLMo{k6R>QzXa}bwIuV`jz=QaKze_v41ebco2nT*SVA|lE
zTXUL^{}04_uf%dG%TlXxV<<)|H0eDdGP6#trZ_Ppq4!%gM-)NG3qe@uot9P{rQva1
zFv>w&T&Hkj>=pD6&MHo*(`!i(v;5H$+mIsVw2ef@xlfBI+eLFGM;#QCn<NaD?O=Qv
zT~0@!ib}yg#9lQGgnM^eO)=%d9R(e(jAQ@m1>n%jxq?#i?)yZr)`xm0o;kF$@7BwI
z-e=N#ah+^@UGu88mMW(GpL~RN1KFrymj(g<2@Aki{Syr_@pa2MUhC;!f*YJ|ti%A!
z8W;-Qf$!!Qxo!JAXSQt)7(d<M*fJ#U(s;z5v^Whh0MItkk%?ueW5Je9=9yK$^a0Ij
z6$ijGl!hH$&m{#0CXv;#p*8Qtn|yK|4ij^bRH?ezVZcX4&8!uZhS-VP9%NSiuM_{8
z7zo`lu?beNp9)P%7jKGSPnt&Rcv)q_3Z=kWwHsKzDv*;Aj>_yH)c4T@ScEJGMiQKo
z#9GM^qXq*H0(Sb*wlO_vfs1pSo&!hrmHAvHNAgfTaBpU3`9f4nJCUl1<_lx)(gF{c
zwLn(MP^P0Q<&b0G_{TA;z`2az(T}zO2UJqa<v-E?h4!l5Q8=_u5eB6mNWq*0Z{Ouy
zE+@?V8|;b6Z!Oo6M(Wi3ib_iz`m$;^o2oX*f}4Ms$&SV}5YQ@jWDG)_tURX;`>kE7
zP)e~SOO*c&V$Zu_ZL82>cUy49aC`etNort~oec$+I?5UxnT)cqLCT$v{J7CNO8ylC
z3r)H|;y&!qz*$}f0L)s|_>1C><-;yuut?$>r%C}z1uPi460a`qr~dsKXJnh=kW{1m
zYmPlE>j7Ymj+$!q<3E^Dl5MJ-eZvW_{uCtqK|qX;$`U6+bJIAJC9r_ejWmzc074$i
zWe&tIu44y0v6g;Cw-7xDObPzNU5@bt&Bh<ZVmvLqh8NfKaXRK2NF}66cbjAPRfB)V
z`!78gv?)ZpOvOKD`}{}6n){rsoFv)yb^R5I^Q!C$K*?~t`5#6cR8Ng!@Zu|~THuKz
zoQMI0^gqu<H5v=ltzz;tUU6biic(e?I>ZmPHRRjJeu+JL=?Bt;b8AjA|LZjn&(5P+
zkz=020JV*e)iwhtN9i2=Ty}B4@}w3tS8W*ouq=VS5$LS=zXGzs&&884mTp!%iFq4Z
z-4OEam^g<3m8>VbJ)1(;<VWB-#nnA+f4tIU=g7xD^nLWMDx=@hjt-pTUQah~poeV*
z#@^<n*HSOB3qTK{)z+8(f)m<d>H38n2HbpmPK>kpiH<!Jv<h^+Q}W#SUEi(un1DYK
z<ewyWD^NeI_E_&GwU%fW5{9zKlD^szUUETbl6FGHkIkTpBr_|mh<h|i+yfwooQW=(
z;^7!j7526b5LzSYBtpbO#{p&np&U!M$s-m?DF<|G#M}9u+qy@4%tn{e-eQjIm^Lc!
zB#`|EEAM=&P~<RhAOqI80mlU*B;&k%TDLz;J@!pED1;P{-sTJX!|foX-R(gQ(@FLZ
z6}%{E_6=dv?oCz|EKV^_MVpJd5gH6Zk|Hg1bT-#pa8Hl|XUkB@Aq$HtF(T$B2zl|M
zi8ScQIp2{t7>Zk*uS+*<DveCohEVMua=2F9{1;tUE5jQVk*$taZ#cT7RpGohHs{a?
zNAAz-?wDx3gjBC`mi5EEL7G?w+nQfxGZVYU7b4>BPFvtdhOjDtfWDfPhkLMhk;Cy+
zJ}&AH*r$zr(lV};RE~2;Zq6=nL_#78^uT4rf*Ap+AQ6!ZmVO8og-@s13$hEfzGp?u
ziFcTnTX;s<Z-MCSqVJ^KV(E5cm<uF@hrv-RZ>Jp-2@7%jM>$>>t3CQJ$@n)f*8O*u
znDC!|?MjU<u<Z5D5ZPJXqdnmbl!#*+G|c@9OPknL5FK*?L=aGb0E92$7Pj+7#%)Q}
zfbJ4U*dA<ug8^iKEuKVsNeg>4+ev{`)VUPomRXj)r78)x7lx51&Ebc$4`J`De+Ehr
zsLTfodNt+pzRWb%{W1By!GLC3-T)T0SH6=e38Xgx_n0&?sF<GOGPY&x)6v5=GIBK%
zk>@z~ClT}i<BR&2bsYpMdsu{|sw(F0-}URg?mW~D^{NdW+#HgC+eA7R-m=q6Fan^M
zDq9ygMyj{h!remW4#6piC<&xJL@bCkA294s*>&gABm;}42T|=cmZ(C9*C<W}vFu9o
zm#ScMT5MqZ9GKx{)e0RTDdJiy+9PViTru+Osn|VyLc<fVu|Vg_I5GMZEbwaK<|xSo
znGQz5yF)`-JRKZ4hGH_R`fU9p*(G$~bUQeO?9yX_<3`R|nv_bJk_)Bm$u<kSz>_^V
z7x9x1F`J9iiZX*cK>HU*NFKO^LxivD(BPIKFi?FTVb|FFvcy@_T(jiTtUF}y+9P8O
z@UXd#I@J4BmW@`x&$JrpIw{anGNxi9k1dxC$fi<xd!SzeR5rA;(Ti#sIIU3Pv^^kh
zS)-lpY%<}6fP0r8rJ5lk5$qbaGY*XuV5dzlmE|CNE&<)H->>>M{Bjs-1-`E{A>S0x
zo*%Pt1UR^}3ATYAR2r~N0n$jjfg*<{roP^JAx7_M;~cn2=T3<kkQN2xnHz+8Vdfi3
zh}bf%v3)4M@B{1^?v~;>KMctAyV$*<CFs3#ZMNW$9!iXspQEZq#2wnAAe1BP`?+^a
zR0R)>uSZ*C)F8v|(}qTudsP>+=e#&5!@Rkfwv<)3+zk$;B*w($H2MW?b+wK1a%}IV
zee9lxhS|1Y^=GT7B21n-G$V+8V^J+nff_41(l@;6N$~?cS~t#pKU$Lg$)ta|YajpF
zM|+V}ctVZjl}r|*r!Xt*t0c#F#_|B3YRn#eH`m7Te(eayDMtP68}t9aZDjcGw2l8#
zx&L8PN8!0R-zdWo8$!_ohXb|Ce4OlF)Xv7@E@|^5$$w<mW!QZWjsy%|^Gb>B#o`}m
zCr%>{sB91z{Kc{7@61UYTV&W?e&!e_i2t{3ANfDQb+)0qoQ}vrx*)r8J){m2$&;~T
zlcOAL<g_tnPN>@`C1v9z1<o_LagJcW!U+qq%_@a0&r#oiUL6##r9!E1b@T7wR6;~i
z{Rg0R0c<_W0UGxNCTO3E!aQj8bZLr%rTxXtS_Rdj?T>(M<KUyqkDF;Li=^_vb`eXH
zA`GVfR?F~IJ?>jYl0g?m2OV~&g+%bu`WQw@3+4b}?+~`S4v^;N65*p|FBrdZu?V@1
zj&mYGy%Zhf2C?Edt9N`_$CeP_gXe(1{40P7iFgQ<rEv;i4Y0Kfn3h4}31nzd4*WUe
z37{j4PSH`_PQF@9%U}+2X1)8)?(5iziSP}dIy$tm6+tSkYBx9W1AMpd#m$fwLC=yn
zk;ugg^vWBNbM*<zH7-DB{?p=dd*b?pO2SB|KJB+xwe(=9f(D~`o3a&WKyjNMh49Tq
zY})pCuL@~Cm)HI}_uUajT4j#uv6KT;#Rrrd_EX+JWJ!k_+15yGn|xH*9<F|(PUUBX
zN4SSG7W64@F7@mEUi(Rnu{u+5jg7A@k>r-V6v=T$wn8TD)x;|B2#mV)gn3e&HVb!>
z++yIk-XQs4&{QV4VtKKVgaFoBNs7Gdk#%+0?xBCAM#d^s!Dm$wB>_xwA6DjNz%~%Q
z4z-%S*}aidA9^;wz#BiBFk8Jiq*swABc$;`-|~!BCGbJZA|iwx3pWA2Ra>-ox20=T
z?z^{*N<<1|_jodiI1icO#(<w;UQ;c~J1_8xcibF|1*<Uz1LZYp9oJ4S${^ndf2)?X
z|K!syR$VbwOjmn29H`(}Kll9xD?!_ayxmXFOvhBhinbGaeAa*02}zgwKA;pR*tP_z
z<R)v!YFm=)7hjMK!q<e2Xcddir2d0FOn*r1YEM%*|JdkD%MA(j0(|)@$*bB9vh6<n
z37#<9w6U^RQu=Ew5^A^lw1w3=jxJtPTYFAQvmdLGuDaKojYO<kUShQ1xT|5yuSu_~
z7Vb}5&j<ce!p$@UFV^<F){3nLOFK@D3k)A{3i@^<a!c|Mu+PR7+EYS95W(qN)>RE;
zg$QBi!t|0O=~D=*NJ>*2ZpxfHI;f7`nDpKX22!=zL!J_*gzBRoq*^GJQZLWp&*6o?
zE5rqgMZC9qo`bY4aaIuTOF^uH6<Kb$Wv{_beTZ*Wf1ap;WsP-<gUZKFqzUTowlP9@
zxG*Jw)M&YVW%3>AExW0p{!?=7E{l`kVaFT?6@-cER#UZ5x_H*r&Nb+=GCvUsChao*
zMF7jFHBGs1--u^6to&ssu355n&S}_h-J$ZPy!VU;IX`CZ`-OAZdX*3x;N)Loh|k}B
z)5G0xRKuGS>ZgJiT+5MDYL)$Z58G9@e5!BC`-Vty$*jMMk+x|-GJ=+f{xz1=R3uGD
ze||s5GBDNar^sr<yDF$ijBm2CFBt7r+Ez^(`Hwow`-vCT*BOXy57ef%DY`Mt9ZG*b
zHnCKlA&e9U2Ny}<mVz=vi9j1D`Hw0)O)!y?Wb3EU^EH{cmZ1ht`zQz~-myGu%UV4j
z6MS%iV)FhRPkMXwqqAwX$|4e)L|TiH`$}wdZkj-fm*Sy8v{xh|P_{jnYsMz?|K#5~
zYpj7$X82;``!q3_NN}wyIkM-N?oNSVo}(|0)&59$_QL6}gHNYT_kEU7Z0me}<9cKE
z=k{OAhfE%;{tVIO7CiPiMD@ls-M>D6*vb8|^puFM%H*d8>hB!4p22-e16oQJ16o?d
z-qo%OtxZd?1*?97k{0HLkTraL{NL<fu8nnJ{f;|VOLj@^)7*E7@6x^zjg8->ScMz2
zwCj_$E<@AilWU5M?sKm3_;*}hQSLipK~Ruf6r39jn&Y11Y7%e4o4t|I2E}2gqg)=f
zgBrLN_<R-vu_tY)3*KBKTqE!U={h^<9!z4uWZ2||Nr}n3lN7;KN4;mgrr7Kf2ACVp
ztG#dh(mdC_cS_oI_?gPQL{o~2^;3!V%E$ip+I*L4lNr9zK{VTD>Zr)>;dyCUjn|s_
zupU+JZDGKQMK*xEPIjplNGOIK^>5>YtRGrh7wk6~xQPG0V=9<@fASlag4M#BM4`Av
zcBFusq*>=b-H^=5rxgX(WS>|=2DTOMhIxLC^vj|}DF1wJYPK=>dhoshw-FCY=kU=c
zgLhy3zAxwrOB+iSx$G=c$bNGBEmeQ=9*N0YDzW_NyJ%hZ5h6df;v_O6sQvbKu#@D{
zW5E>%?s1EkbMu1@%%3kzN`tJQ4k~txo<P2yPL-ED+H1roja~4>&uwYJRtOK8H3K^Z
zb3~F}&Iv7b-!pjQKvAhwa9%>3qM~jF9kvFzF|aYHHg?-=16Ox<@U6D;PqeCPwj=+%
z%V4f{)p2E;%v~Wh@7M3IT$jv=J)oYN^JP{+Si(SF&3A)0`KwX{b2OLtRo|&h8hwE#
zyP_2yz18y(li6pt>~Y8aA|yVbwef~pqQ_f->qb}~Y|!qc(|I0QtFZ>5#Yxra7qPY~
z`m5?#4Nvi~ZN5+y*QlDC@o7T^J-C>(K~#@m&Iwx4Q6jqTG}H6CH2Z~YQD5Rt3|HgR
zC5yLOQQeQEU$P!E3k%}|FC@RZLOR^4N@(D#I9q)(yb6$w7fZ<>F0o`Mx8F$LI8-!U
z!Q&6Dr1m~4*kLRDqhh3kda-`GqWx#=>sC{2!EWg_f#VtN$q#!No>$sE&Jo%xs4q2Z
zvZ0edc}p6>l`Di-(B2Ok>SSkGKpJn8tIkycJU_U4X690U?}1V!lU-8R?(2}ZRAj<o
zTgF<yaWn^&l0eMkp0njTyrl^%h)s}R;^9{AM&6%@^|wDguX=^`6Zc6FJDM^Xhp<`L
z=U84LDKk$C2V3`SlT0~nV-mQ4b2BgJCdP9klyj3z-vE+xF{Shfi)ZtVsVJ}e8JU6y
zcPrB<>GKg+`%6KkasArvi)ofF<0^ke9@$nan|WEe?D$sUFe<I8{2;**t`T@0Z<g~A
zETrzm%n-`kl+6xQMe0BAJwH%eZ6Rv`lvlyLYKcX`L%kn}?bhc#!tv6FQ786EboGP$
ztaHAc!oc|(h^pUCEn6F=stw(OiaJjg%?H@Ux}i-Z#bvcu2T_U134M+9BIiozswN%j
zys~ZdW)qP1Q*{a>jOQy&??)ST6NPi^Kf*fB_D9}%yRGapSbk%=Iy>$oGf|>E!&>@s
z$c{{xI-kpFGY?S$D&B6L7p}x&GU=w@JZ%y#ET8UQ>$(5p@JttKe}IIU=5-qOTBxP#
z`1+zCc{q!S`@-dLN;)T0V)E(AvYOhBV3BOUOA2=!V$;4CP%Ko~^ieEM=&z0!!~`&A
zLn+{vd(4!4Mp3f0u&?Fo$8-8)e%vY+7Oze>XyN4pyNvqi3c|yAr9XQ5U1j?tRqzpZ
zKk9FeF+P%Y7v{*fLOv>Bd-;@Rc8%_A)Cyx&E+Z}Xy8ck2-f1s<uGstFruav)hVYDa
z@`E20*F(fqvEq;5$4g9^7nd2w@-8e}gSOSfSm1|@S84LD;+}Wm_1@vLb&mG`B=2g)
zhC-U+{-qn;-^Yq{kcb!NlQr0x5;89rRKT!7Vm<B`QzXy=;daR2rgZSH&aPX96&8K;
zq+Pjc$MM_Oo@**3R>PG85=ePB!)NVDhm{p)i<0I+lMy9kDk%8)*NPj{U*-;C9ij!=
z3uICML0RbeOrm&H(k$Ypah~T@W3pLFLa!zR+4<#u*5;rqwR?3O<!i&zQ)=UKC=%Td
z3i9-c6kUfrg%mXh?l#$cy5#iCCCX=BGXZjNiLd!C9a_Cj#dVovls>CwD7@Mh?V~?>
zzHq_E<XLsE)Jr$`{mp)%(CuP(qPh;P=KA~fu7*KH&%0e_5(CGk7LX@*5Pg|ME$r%v
zLa=?j7@U`jdf!J{@7dLXgn^h_$GQSAqE-f}vUI`8`RG{OMZ@>&Ul<G)$oCsK)8+=m
z$Hx;M_IAC?tEv8G{A`JdRu4LKVgqMPX}bP;*VnrI9^i2l@YG9GkF?|Z4F`M?fI%L6
z<bDD{txOPygY`C<Zz-aC&{;Qw8&&oh+6E(|h~L!`H}_u;mdYt%R$UH`uTHJa$DI;~
zA2W7PIQvI^rlkh<@|lbz!aqqiVGNb<1BJ`h`s;YA(A4f}bXx5$>M6w+!;_M98(xX_
zMgG6m%v7{dIe}YLZzge1GZinw=XenF{(R>O?%^)Q^dmD>Ri|O3kxR?(V<RBN-tlP@
z1-Bmf`Gz&AyR-7X!8{E;-`a^Ru6xuh@HwWWS-fexA2^9#YZ{g30t*g3O|zJ8bJu9T
z8BfOy?}+VtKUE3x7HE8Ti}zyU*o$vSZOGneNH4zKfBa=-irze3;<bE`S7k4=9p&b#
z|J>wNevrOd+bM$x$oX%IlD(5&G@=<xAjCCZ7@ktuza6v9&1)<^DXFaQ${R9<Vz?F2
z*5ZZWN*^C8oET5{bqWce_~y+JKT58uGK7(;4Sx)*ML@<*DSC#%)mzbZ^Gs3(pnP<|
zo<9h%FJm3=U>NUSEonZk7$#3J@s^}-oG-IZJ7a=7zO0rQQ0@_VKAzZnxA<0p87oOK
z{Lrw4DV}g+R$6B{_z<u4f2EdxGph1hs_5#Ex5*{g!sXFNJ=o7Kyty8D5VcTIeOJg{
z{{zk+TtmI!GgxjuCFD>);aygrYQgCF8{yi5d0Ow6u*eIHt=@w_ZG%svN{mTnHGn{b
z{sPw|h~=DM6q${ur%#}IMk75u;Wn?uPPX$vA5p}AJQ13)O@83AwsOCzflqRcyxS3C
z531fd1k!odZ{N1q-IZF&uO+gs)uJKWE-1DC0E<}MI`20>$h3PQ{cIV13O4$Fm#V?$
zAS_=OWjbw^`#iI6Q1M2lqhzjTK2NhZPAhE1{#e)JG`*ZQcdqp_nOrftX&15S3yiz6
z{?2b1Hg2nRGC6H?GNv1JaBP#Il^jIriny(z)%1m|lG>e`Ct<XLV>C#iA<Xlzj`nIl
zDrVW`q5zEa08r^c)Gb1!$6fYmPPg-Hg6cIaD||B!vww|2Y9#bJUT;#)bmL81sfY5N
z%Ip$8$)4W6`J066zuqqP->8T*@5vM%48|Xd3hpb~bU!t0s-uIS`|x_^i)8J&_9Abd
zwgi(`Cm{M}?We$9$cqUs_#_Gn+tY88J>52^R<ag+pmgBLMdzqsF`xT;>v<hJ_FowM
zT)P}9PGIB*ifWfJDW}M^uA#FN5<UR&B*Py{3QW>pK84?4q==4*^tn7oLT@fJ<qtLp
zuq*3*v63-^I#Qh=oU`uCi&A(MF?@=NS0q{Gc@Wl2;yZqOwgud=(Pu2?Rh-swS;*{*
zFi5`Mq(+eJvtGZgX&C<EVxCf(y1tr=L$Zg|K>Ei3hxbMCgKY`DDi3$bB%QjhvB2Fp
z;C*#Dt+Ha}K%wCeVFiWT`LMN#&T7#djKx;~xbzofUDRIvj>?*a+&{EA*z&ecwfAaM
zSgPTMg7LhW(Vgi$jFX?_OwFSW)Vz7yB}H{r{M@9_ygS*jq#U5JNiaF^20)|rtzeDy
z!#R+_X@S-PfJFDV+i9-=KWGRD$8NT8bV#nAxw)CTKe1!u9x(p>acz?q^s&ox^W#hh
zBkq38@xBK?FOpT(NQK417v8<;$X!-Diw}l<gZ)@wXv@5{NVfAS1nkRS%N}7(Vff)}
z>jg8B(}8UJQvdZhz|plLSr7SKj1&C845M`CZ`|igiW^rddvlC(spMy+Qs}hg_ie_J
z?f{4i&JVaV8c#v-W_|t@`J1SU|6u&q6a+fF&+wYAIcq~`-tkagWal$iW7`+&f4!&m
z+$SA2mYE%}Y*myLOJ>Y>xf*-+uYcigoW|U`=`W#X(HFCQ^bB?MeP^2ai*;{dvSUz`
zs)Jkwg}V{JslpqBML!09M$jrzS1X1J<a4^vb>Enz3n%8Ay21b%p4IQzOW)yt^(|3K
zcor-&{pFWdpSJ6v3gS~r&(PU!M0%SsYq2oitB8ReJcJFNA7r58!Nh|PQMlUSmzABr
zwh7R#!!yQ42!PZX4ykgp1LinIw9J`Rh}kJWyVHO%<C-{w|9+;qCELw;>4Ix`>GPG*
z>?7ne^<%d)Q|^BqjR7#nEm3$r9f_>n-+c#OUwnZv3%u$~%Vk~Y1T1%&+-Zf8<;P6p
z^)p6emlVU?DtoD-nA!0Q_3kIaT>%s)GiMz6==-ZAjd?Gjlvn+p^*UcMncviV{1R6-
zq_P9?y<Q(5s+tl7E)-2Gbrl1)y8V8dBK#d9@$`1sw9rzl*{MJYi!XEPZVQZ(7l&yP
zJe!uJP5+j`WwkRM3j;i%XssJi@=n*;cMbgAfK7&kr;VVp=#cwE8-uP10e-$VP4&fw
zn+g^9f_J%fZ&n_|+};lFJ<)}{)%M(!dNxxdYWk7S{KE-LuB09Ed7Js0>*fdHuMP3v
z-$a@1mcCd0QA35dpP=iwL;KYSOr5s@!*P?Cain)sXo%o7_uGCl`{6DgiU}ijN_H}I
zcSrY8gYcO5tsG`+%nrQVNKHJd8S?Xh6VypIa{-96ibgjFm{l9H%>#=Imu@@Y)Zlrs
z#d(Q+rqKlh()y{H^`e>8<?;TKI%4am2|m9@c6WBm8i?E6TEFp7qgP>vK#o%FC%!G!
z=K7Zp;z=8~%RRQTx&z?q2H|N#e@7XCg!J@5RF)gd6;N3qK-(KncX~@-`2PAgP-`82
zw|hbRT#?JekY0?d1BBu|bN{M=nYN0Ep!V@03r#tv+h=#7tEFE1X|5Lig6yYBJbWC!
z{0-)lp8qFC$kWajo@X^wKESdRxSEcJUoXx<^!z@{zm%WYw;Vqaqn$DHwI47YQdbzm
zIdKlsCQvKGnQg6fnl_|#{bG@0ri?z|hUOYoRrCJ-KT_Wfwx`h?EVt2kHx3rzK+g}l
zS;;hYA{;+ha=YWO{mozT6PBMAYlO5<4O#c7T>N@s(gG_OeXF(jmq?s0%DIO-Yu~up
zOA0!g*pj!5)U+u2arHSYpt95Bx@eBXZ=7zai`bxuR^0j9kcCUhC6|SJLp=ejw-CkB
z4G9@4zJ3tCz*q6m@097RqbY0q_F0Iy=M8xMISaNJ`KAw5g;{P6`kbb88<W8`#$q{u
zr?<#fXDJ)@qtAgNh0&4076*&66@)OKeqWnR+U+Pg_QYRli)RTo8GglmdiDiitydp$
z^GSBM>~@s3F<#%h3f}E_a`U7ctED>^b1me1*Mn$Oap}?U8H(rRj>#u)p~uQ{eWiC|
z^sV5&{AJQp+}~k*6?aN`BKX3y{IysR*(&o)n8dn@^R5Ax`O0iuH0rG*zuo>Vr6vC6
zvqPZ}nKJPy<j;1H$<eO$?Q>Je{y9O)@6{1$&k7T*3?}Jw+F(Hs%+L2q6Mj4H-z~-R
zf9d(3ao~<*G4~L+&eylB4$JL@M}bPoibVx|Mbp{b{qaHtl2K-l{J3ijLL+NT@UdLf
z9i{OdRsxOL56Q?^4TLVlppW6I7uULa#RIL>YM&^g@ZE~YR>-U49>+S7rBTNXJ1DkT
z8Q!f$-$}R0Gm~PI0h8ibykMtQU)ao|ZSj4k?eKQzj!bp=eb>!YZpCsrN-kAQz)ZL%
zE*v4>)m5`d*Kc^QcY5qK#pluaGTteH6S3x2DdUNtArJ!qthMrxRoh7)ED;3XLFZAi
zu#qu*-Bwvl+H?JN?&7-v(S0PC5C7sGiQSDcOGg<g*xpAj50(n|$VKhFkxxke{G{Ra
zuJl;06}`!;CFFre8gj)(_UHl{)d6dUEPi&}@Qr^I`1NsGh49ku$@Xa7YsS6X*h3}c
zNSTZ{z2@Q;p2?_F<bfQr0BB5TBbZ!SFk4!ji&qy5x$L#cw6M%=^OYT608PZglQ^kI
zWytBW!7%dZY6Va8zG^ZbcWeQu>3P3`Af}^ntn!Qo_J%r5t?5VozLkf7$64I@TT`QB
zZs~Dlq;wwavaU^cqPdSD^W696)VzbkvMH|7elCN*)2u}V@&=@`?mVzRZYUNPnf>;-
z2WG8Qp?0S?$d{`Wbc;J^r-N9YwV_o?@XW<Cg{&s}8LSVFhiY8)uxnvl?&w3lDVzOE
zLuceJaF^~V_3>${>v(aUohQ9`UGqsWLy!(VMo<c6&AqH#;4?YK($&pk@H*_WyOwHC
zhc+LXJ&1jeeeiIX?A@HMNrGT)wgsGqt=Q9_QZ95!s`fx_j>X(+ln58n#-pRFUgEC;
zUOifZ3r2B+6@j`$j>&?zx-N<_S>I$68P=v7x&ptv#2Fb^eg|<o56#F#1=sYWWHgT~
zU1?VI0Ut|Bewes0{);8Bb3*U%&$_+qyPbV{CPb;HfBG7gAH=roZfbz<+?K3GS5Ymn
zItQe%7H<9RA4Vi^q#8zO9Ss6=p|~v>-=-g79l41z#k^3NrKD(@i9xyKRB8{muV*sU
zcp+rJXn>||HL2+lg_s%9Wb5vJs>$c}S!tdvNq>l3FRes9X*yiJ-LkUBANb`3c{1r9
zknl<oq`zTnn8%qm$;qcnN9)j3<7nOL2>g=-u)wzk<%a@@eNaMyJMj~DBlN(oZv2y+
zSbjs>Azv*1C<L8+{>W{&+rGCmf<#}lYEiZEgpVxNs!)6Q&9@OFAnNhCzdEYKqc*ux
z-kzFPI6SUDH9@{FI(5{%WhLBRm!PkhQ2m#ez{){%>G6H9`3J-sX0U2kSL_TJpx^LA
zgHySWS3Ht`#|X6tik4l4Gi%P4`)xvQ#jK2Nw60Weui!neYd`tY9_sdd%_3_|y;ANM
zMj!L(r#2@|ZrpvGyHR6+=^Xmqrcv*S@p6>&`{7GKhHl{f`(Tl!vTOs(o}OsYB0U;f
zIK*}>LTXpYNv(q0zApoy&1Z){&15JZN7YKgo}V|WxS3@l_1fKfz|*K4V1iNfVTOi*
z54v0fbuFt#$W2z-@zefIfzy#{&PA!8+5<J5gF?Mub4~m;Q`1_$wZ~b7@gclgO6ZO5
z{y4f${<RI@7d&uegpX(@u_#jEl>?jD+@iF5Q6kL&ykut5;g|XM%Ziv~aM|WzUj|6!
zWLLG_k#H;G?n^@J7jp!bqz*E-RHJ1DPE=i9-+*d5t=?>o>fGVX9D5ShpL_T%RQGLf
z&R){`t5f8%&vg>qjc>BdP9?VQASeZ^f==#W<gG?w(Y1`rvmbb6OMXtwk@77i!Hl1x
zDNttARtExc4lN#XaEj~2n9w_Kt0Vi|e7H3a*2<q*;J&&-5@}d?)Xr`DU2V?bz^<Y(
z5B!r~;*U~<cWI??#YHceO3b15on}!#KCIz7c|huHZKDN!Hkz11)t|>)Jc4pHO{4BV
ztpxtfyc;T)Z9E=3Rq?&^8SC=WOQCtBW{pJ+azw9Bds;r>D7(gS=g^z|OIZiWEE1PT
z48Kar3wqQ2_!Z}M5EZVc(~ub8RsL>y<P>p?uXE>Gg~Yl#f%~sF`#L1WXUr<MeHt6F
zW$z=3ctgxG*k^b77@%^#mMIUS#`l-zvD6FEPYj1IgdB0_A#|Qzyts8c1~_(d`D@Cf
znR79@aSIS-g23;%l6k|1or@Z5*w0JZ$V{^T2zNAKL9E8!afw`;m&Tv$zOuGz6#Fw7
zxiw)`>bfp{Umv3PmVtO&$s{Rt2im%hqPNv=u$F}8WepJt7X-{-9yeSi8}!jX$cn{`
z9`<3)MMIvS0~K&l{m@POMg(eQO}7=ES3BMe`2f}tkD8M`6KMHUy#W0qaA}XaNj<e{
z=a$0ca0x>IM3;ZXQjAvC+N|RZaaoJV&`GSs)txIjx%DcSEGWx>N;!Xf9<hCJ-F|Jm
zWS`VCjChFS8iu&IDg2R~!?)lq@ag%1>DsI}9roLMcURP#S$$Bt^xH9dJ<&65uKJd1
zyOGk{x`^wq&BcM_<m=pH%b<~2m&HhdrK%yiVKndclGL(W<X9IphPHjId0>H;e<(pl
zDz)yz5wguBa(8EGgT!1V+$D#cbTF52`S6klG+cK>gHH(@Tc?Sis5?SF5Aib^zBUHk
ze-rnB*B|#1uE0fnJ|U9X(D{)ZA#ieYV5q6)ueW1C!kM~j%Ch|;->kP!^k-2+@=2E>
z!{5{EN{mK9O)sjY?T(?38&;J!a(pE|j&~nLOeu-Ae=B;aYBcibK2JN4-A${CmDuP}
zH`m1xd=0vXWUyOlQ6<5;CZhUyIOK5ik<S}Ko8gH+bX!jthThs7ecUtL8Ton1`i&0}
z$Y;u3hpIVa@26_ykRNQ<a^Y(7c&?ny8tMpCXnwNr1L81kDD67reNMO`<OEY;Z+}|_
z7@G3eNc?R_zD`sn>sdwdoZ~O;e7dwmho+NP>iCSK@%j_PGt0CMFt-`M3Lqc_pP&O&
zE6ypVK&az}XFi!!G6xh_YXw7&_@7z2&^+1xssudN8jbqmc;_W}>)?tlp8(;8TJ7F4
zi%RC3*PG)nYxhG^Rx;?qsMB`zB6?F|b)4Jyg!NM3DJkzWIUUuN!v05>$^_}6mAm|k
zZ!Z2Ie&gryh;6M4_Q7AE#U*H**C|=?lTd6rwmjGH;GKKwY+Z@jDEhZC-uJj++L@I5
zfW+$kh}AS+xm++71r~BkC7T>hW$w!qO^D?t9@CVBSxL`aQ19gS<_&+b^mEOwW4}~^
z)V(a8q8^V|pUM+>A0FD3kLbBL*6-PTmSnmsc%hKAn%c0H&}yhJByi{5`zl0Yh=2IF
z9}<wZ-Mk^PhW+O?gtLwi>C-zyC%El)XK-o8o|mB<QTLM5<_(#D$s88iwZCaRjMa`+
z%a#0@;QpFy8UW`#c~+<30<%fiq+I%5e<0o@d_P?@6){Hd3nIOLH4W%CQ?|^<YjfA+
zXf&^}kKVeywf^BoyCYlKxurkWnR1!xDX@}z7c5eA5FwH)yf^C>twnl=9Bi%60f^)Y
zcO&2$Q-=-4RFx*KLSJ|vFCN_pQTH;%WIMs3Q8Ii+pvxhjm^yEKe-}Ya?WJ_<c%NzI
zT{3k2p{z}{J;rP~dDbr@5bq_K`5qg2nem$ciG-MsquSljI&<7RhyD$Q?__qg&g1bB
zMDK}ucMU%ySP6H;FkU-)O<+}SL-TFJ*5D5aD!8QhP{nDf15Fsjjq9&3?&t98h*#8`
zj|{)#-D$Z`wnQ>d77~+wy!T@qkdlCyE-+tVLvzJ{GH)&rU9r-DDn2@otS$A8tA^ix
z8N{=6@_C0OErY&cUdbGPTp5S;%3(N)mltQ`6W$$~?{eEd(dw119of7Ruz4L=AbNE2
z%?p%^>yVAHeZ;NwC~8ghH19#gh(jM;IT?55b*|ziteoNNuyZ>Uc4DNMM1`nG38kjY
zC=oNwRUbL0rXl)?7t$ZD%5}fO@{}-4c(e`DbSIQPl9P6f?YmyX7k*YZRIMaub?tXM
zNI>+ZFIR|$Gjqr}AZuWRH;2p+a$DKI(q@T1(xn2whyFDP(5TX9Jw`hc125o?Qk6Xg
zJi1zM%>9n9{ghs_G*6KD(yAly6YOz6datQ<^v>_?qjSrhinP=^fyY#TvH{q)i^IMP
z&G;vqAk?wq;op6~<`|%)oK?tg({sGq*A6>{OO~k;Og<9RmX6dI%@^lvT6gH&_vChd
z9<ypoW%1R%{MK}X37Iq~A(NxvLhqj5?_IaO6;d`LI|!xAM~lX>PKHJ9c8&?s(3gvX
zJ?iB~oqFX+3B=Ql)fd)FkL|11g!csSck*%Hcjqp+wu!zU|0y6X$kyiLszDMCo5IIU
zCa<EeTRCp>V;MqkQ^u;WEMFf5p?Aa(fvL2EC=s+;nZ{DUBsf}h@`+nQq+ACzZy)?y
zdd^mcd}6gg@rNzzN|0uG73^GV#RLbrry-1Xb88z#Tf-J|hVOh{2;0?5kFQ!Ksd@#Z
z2+e#!(V{;WjCA~(n;Re=^Y?vAF}kV!wL#n&R9Dy&%td`Rp%ZjDqUFb6koVhOnY|=N
z_9>q}v8u5<lnmm&EgxOUJAj)NNs@e|p?_-OY_pTGjw{~O8+D5L9C&!X61lq4@%bk_
z%IqVVvG1$j;0Wx(4adrT&2K(dJ{gjBxV4@sl}9+&$E?~M8Q{(Ja}@9GvDz*ym%v$?
zZcCqjh3N>ig*13Vfgh+=mL(lEyi;=N3HWvH?@#gPe~m`)ugFb?G<#ProH)Dc9&P<x
zNB$KZV2itIqG^x*<l#xU4HbSp^_$AKBUF>!0)_Ri*V7=;DDOz0#{v0MDlgpc5-%Tq
zS>JdB<&kN|XHr3wtS!EV9~OEiVl7s|(T38Uw@$sHh+;Q|fqU3>B^|0Z<L1uVcPZ_&
z&)RCV=l~i&+&1Cra3=*mkQqJw<oq=3!F3xRlZ0v=rSr+h4V7oumVK4%J2@B4n~dHY
z!sVPfAo+-8WR>N7#|SI3+i_aw<#~-YjgrP;Zk^-BRA>lt5xL=R)}h~Ap91$q>+D;<
z@CIhQA=Zp9J$b-wiC}#A+2)QZBtZkjQq<iZSo4eCz`Q%}RR8L{2Hmdt@^?zKXro?!
z=q)baW5u$Y_b0id13w9_OHO4pzF4|lK2S8=zYh&)koL`IERX2-7;-9%D@4{ma?-JI
zE-FMPozF{=7`p_GIOIGTE^{<7<-pKO?ZYF<OW#{{+pC|S*Vqt^=0b~~^3^!q*-Ks<
zYLa_oU~V339T2cUqzYr|j{V)Al5DM?%EVkde1FIZI$)hO<#BLWkAun$UxPwaG9PtH
zo+&lT%rtz~m$1$c{(F*g`E~b0)O6qzd!MWB2ew52b5gx#!ugOosgX}V>IeIZqSx)7
zR0RLs4uk93F*W7a^8;Pu!zb|7_L%0`JAd!=Kg%2Z-(mloIMDr=#cR6u{z-^(B<Jg0
OhI+TIm0odr{Qm$w#%AgO

diff --git a/reference/plot-6.png b/reference/plot-6.png
index 3e5310ee214fa0cc5a0ece3084d01691b46518b5..af894cd90502adbb790b9d01169c74a9c60a95e8 100644
GIT binary patch
literal 38761
zcmeFZXH-+$+b<d`Tit?+!bT}7y(?8ZDhQ!U@1WAVNDVEh+XhMr&CntedheaskPZ?$
z0iyI4LJts<+!^=&zvql`@3`+cAI_IE&KiRxq|7zfoX_(s&s=N0xv!^j_Vne`5D4Tf
zR8!3m0yz-`fgF4E$4T%GE-7;zJe_=`t)T{<!M1<55e}YCJ=V1Fg+Mr7(f^Lb%xySA
zAXgz!wcEx)X{(cAO$ol#whdC6oNT&Q0{U*`57BDgQ<KG2^|(v!!nKVb&sDgOFjXfw
z8fja`@feF_RtuKL$<zx&1+uSb-BEXKRFao>4u^-9x-Ymjl(?^zAmwsx``YX|eS59(
zIQJ4{{k$Z8g`D`iNB{TX|Be8C1Pn=LP)AN#6-mX-rlj&D)8burmr)u7^1-mH&;J<s
zLd2h^w1qUU*|li27<Zn&<LKV^^d*G}0uicivX=eN7{*>c+Vt=1`|9U^p7`f=gis_z
z{NJ~Y4MZyFxv@$p`sZhw7mwINAotl`@Kz`L<)9f6DVjp%oOnLhEOZ}laqM<Jy03^<
z%WKiskLayjb`#HkijFm*G9XyAf?I`D5clAZUhw!Q`MpDXA>1?%)UcHZKIr(bxZEgo
zI3ipuzXmPN)04FQH@eleYC#jape5hhAe}Hag4YVxz~T_Li0!+V{g3nVEcroMwdJkm
z>&(TM&O(`add$lDOe!k;SHG~I@pstuk%aP_lg{!CCHuWbD@n8wawSURMtFPTtArT{
zJvllpIdb2R&<`=9bV}m(E6vljC8vh_ZLys^os$C0Iy&Fjp^jp;BSi%1A}L${M6KnE
zM)MC5EUm6H7oqB>YWf2{qGP5LR+^fxF8$<L`NJ>_84+i91#ejO?rRu!A9;<o@IyN%
zdJ@SAqt3+Vn$I(wt_@FsHYn)X85&F**}Xd6RoBB!<3mC|8rfynlyXZ@#P!N_JJx8i
z8eE+`XWto(Y;o>S>6R5E!*!i!C~UuH!}#Iz#NbbSagaMLX#N2Knsh7E>E@X@k_w`r
z<L#g>x+K1MlQF)E_p_q6V7WBQCXdqlY2{Ht$!9XS6ia`8?00{O+l9u*n(~huOTt$9
za2b#G8TP$M`NB5D;6XXTr%l(8swGxb1#4_B^Ursh2bS|Ym-K~>*p|xAoEWy-ZYPDO
zg^mU3Iv@O}KR!JPw(D1IA=mVs9Gn{bMG(e|Mlog)Bqih$=Y?YD`0lxd!gvomeN+vU
zZ*MykLjIL-Yv*`=61o^#<6YjJaMl(_;kojoYr8yECnR!CWt<$0&&VaR#{_pdhW}h2
z=;ZcRbLxC*zH~B(8<8YTjk%tv^eOe-h+j^O4+IkXQtci9Z-~QJUCx-8WWTpM_oeln
zF0N%FEW3^uDr=P;7CO$Ob<D102f)br5}!-eID{m<=WarWw;rSslA+6U<pf=*DDS0q
zYesllw{^OgZL+aaM@_aHS|?VPjbH1)W?k1OamilRV8|h(RE85*8Y{*3Q{KM$nq;<W
zks{RjTSrKC+YC|r;5$E_580nh;2kpZgTHc&PVL=fjFm}5_Q_dwO5S&pu^mX9`-|cC
zBqolIj0b)jsg=kYm==uli_$qar0a?N6gkpfk<7y(%na#>XA`|O#UG|-XdlzibZ>pU
zHn6B+(_y~5Sy1obb@v?urQ&Uv#AjyA`Qle0RmDjrNmdzVv(y=QOOwXm(VKbvni|1_
zo_N~?3z0s?Md`Rx65nyT;Rud$WQV<M|E_Vo#a}-PF&M26qPOeuS!ct2|60%<C&UpI
zV-y#l2QMz5k{psW_6w(H&ptU$O!ryV3OMdvV>^IU{wHBnFD+a;XTf=Y|CnNDjArn?
zsoRm~mG{?3?K7-DZFnUSV#9_g#q&@b>&@+5D1I!<lM=SM*P|X>p^>2`)~G+irMMj3
zZk@EYJ25919y_8haim#?tMN@$t|s;KWDvLW^MEvct0tE1<=MCv9jehso~#%8jZNVf
z<DQ*eXN(^B?r^M$9)mOg4k!6YX@7{URsqs!%q}>ABM!MRtSya%#9OsYKH=h6VEw6t
z%i_eeeqw>m<`d%=p2R5H1))((aZ@aMnfkXa){JWH?ym)*LT{f8%I@ZD+!#0FVLseC
zJ?!XP;*i^?IPQnkIB-_N#{5~PtIp}K#^b-;!RinpJ9RX$<780fPD=0gO-2X=`-vUk
z=!c4+k(rHtL`~OmiHsz+{{1sZZ4$FwvLIrx-5wW$2w(3YXQ2f-ai<;UQ*^#tamJKx
zbZotDn@J7ZWS`zNk88?Je7|Ot<Z$$b9n3>PNpH$;wyQgC+35}Xoifa<1I~3q(5THz
zkry%F*{yV?P{&Ek7SmcUu_VqJLqm1hw{B)Ikoq5_a6=C|oUJN+ZE_2)f2ye1Pw5?^
zcM@y`vD&jL(^&V51bf8zK?1IwAAUV5+h!)7=8(K?%N;~H+QGU%=!4M6P(&$uow>eG
zc=ZGs@d4i>OcX(dXBTGZH)k=otF-cCP!1X%PRpb3MmS4xjL7rd^~N?+1O*8`g18bk
zEa`y_3=z&Jd8tbhG2eBZrd>_CxhieusfN9Z+I-IbIYc|Hg-7z4Y-;I}4%ZCOQfChf
zfFu)&V!a42&oY03IDj79=NmQr%KL644TZ3{C6KYBj62K;d_4ye2jz(mQOsC{rQyTd
z<B?ZJiSw=Q#~+?M6r<QCt5Vgl$FG4wDv(G2$oN1&NPV`qDt%9z43CX%(l~<KbO?_a
z6ItSh+B+PL%WXfrbze7x>+3^QMdZw&37g}Vw9QyFfR>-C5v&l1F-wSpm^7*3;;jsn
zc1q4SQ-A%Jc$Yqh<?*@79%2Pyk~79EX{&Cz-YX&|h}-|U;aK$KuT3T=r>GHu6&0cJ
zk-I$pTbi;xxvPp$@eZ9|KV^8Kj+cnAjWbP7Q8@R83J!4WpE4^#Z-S|bMbkP(0{kk_
z8xp=h`La^c%=VnP`FQ1BLmydQ`oY`0PAq<qm0T=WqH;+gw1y*2m74X;P4lRP&uY%H
zHMCEVv~YmZJorAh!ObgOPNgPlO!3drvf6hfj1-KeU-;B;rK(wsh3lH-*t0_*w`LE}
zKn;BAxdWv*-a`x}pAxd>T_Pfq+B!mPyJ=TKu>ratL^;B0igmBT9e>qwi6yXEBzN|R
zLo&oLkOQ_;v&0L=cM<1i%dhlF6e`QILQ~Ka(eK}CUY;)}1P)VBiZ$e(@-+S>Z)Kc!
zykAwf%c!Dy%upywam*0sZ1oh)0!>IP9PyK(BRSa0Q1UuXLjkof!fYQyF}>WLYSiRF
z)()MxpGl=UYbr@0q80PiSK}R?aoQ3TCb;!8^*KxBbTQkSD&KQG>ua>8C`BvrrRDl9
zL0S3nHpaqi9lw*=hA(e^sBfdwGq$!yqcNcU?X*8`xB3tlnxN4V6!m^+y<?&F=OdxE
z=OfphXuBN)leuADQAN<iE$Q^me-ir-y4txECQdx@zMx)lUUr;#1my`&BJmE{&#DA^
zInGvd9SH89h;`F!;BTJiaY*Z{AuDn+L4yU3d7ipc0n)Y6f#s5K>gn5vlBDeqPgrLs
zuP-?!x4CPk{EA!f+GqI1v@<5t<&0ZW#yzVUoY}Bz4~xdRNqpXOF!?^>_oM9=1oHYh
zU6197oa}u2K-(`ab4HQVZfSG0QhCl=(6ypFPnCRO?yKX`zu~(bvNBI)OlA5yl>}@a
zcxpR5EJ=|_{^T7eb+s-B7ChyQBl0NmN^+G_CZrJ-p(|-`Yej_O+q|yVXvssJqK#!`
zJ4Kq3gl(Vh%_wn_LV9^=^+^4#pu?+B{Wd{aO(IM&gP-~Dngtr8wj1}7J<?v*Vk}s9
z;ox5|Hu+?(o~!&9|B?svtmD1Ju`H&$63H}MZlX3aBq#y?TtCCXJ1g|S0GaC&+ii`s
z-B*r3HO-D*R2Gjt6^`O%zUrH!?-W<>r?ctmTOg~_VT(Wk_U+%5LKrU_QGeW`^d^+)
zoRg1Rn6o>TU?z}1!3{@tujG$vS@A6CKv!l4uWVmDtD;|qc{A0N;}To3^)Ebw_}V=d
z0J)Zq3x>s*cg1g8Li|I8n58xi$BqHfbJg<P?Ocx&K}>OM)kqy&k~{uq;?_L>5X2iu
zIEV28Lo6pM*z4T@Nr!EAfN!D4{kT_=!KtK$Cd!br)sG`nWoYks5B#4njK=<npvsN^
z;)IK5A&>`f13qSUA=0SrHD=B9w0j)h9o>Nq3hyMIh+bckMR4}|-$kt3K{*^UX4FFY
zCGiC#O;w4yP4=>E+PwaoW6IV}oi9C76I4?~5%c`!Qv`{qa$dKJ3WaC&)`mnLEJkeU
z=)S(nuWX^Foac&wp;iB+EBXiU^~mnICHHAG8H(kV_>2j1O_$M@w=*<@Dj2!gIU0#E
z&$<h`-aft0+GYKcApOdXFH|VM9DT_LE1I-4GXJ(#;IB8FjUDg#X>zYMx~zv$y7>00
zuD)ii&|hD;c+&;8va}Vmcx*~m8Cda27RH3OTT9=y<;~_Pf+mUk(&=yhftZB`y2ehr
z;vkmk<us|6aB5K+fgCA+**({+qG`#?ydH8FYYn{;U$mlBxcXh<DK6ge-n>j~{RAva
zC)Ode+i?ITM0KQ?^h+SaM*!4s86#U8Qo2@&T#nBqgdLBnZaM5iOP=N8%{^DHFFitD
zb$ZjCjj!ZU;&zR`z;xjxZ)%&@N2)0E;I^gtst>d=Ve0j+!z4HJ>K0=4x(h8)jztgQ
zhXhW<vi)rtO$Xa05w$+{wAK1gFC?$;pW6t(%x;0<6hup`lrA>@bil82o#q_jnSZ&I
zj#~fi_CezG-0wjc^;SD7616qhJ+prl0vTnR{pW3Z0}+Gg{=b~pf7|x|`wJf;*+lYn
zu<ws!sY6=i+P5_>w2hQ*s$1g9%LkZb{(R&4pr}BcYj)oYhGLoRb_2@B+XPqq*pb9!
zJ@|7kZAS+p%C62syP7uRXnTgCP0<TJ%=={Jay&_LWy6;_kH#!a*!RKCLFH|*E>JG$
z__ZYls3h;%1Y_B!4Jo(A6=j}o>*bomlDbw)brDl>ex*9Js3O0Iye;@g-Z8wRN#hb^
z_abyFqNZatYJ`U}+@BOw0ByL9C7Ys5p_j$-ZC@_y@e{R8$CrH$j?)kCRK1a*+M6&r
zcCTy>WIqc*h00xt`D6Y10AsS;@P+!G4udPa%#Yt_x+Q#-*|QKBXt6&gB%V$^IhoYi
zKBb~j@1dPd6D?K@Z1AX5S-DW}zvHq1J5}{CKd~lPKg9{rqWOX2F3%h}VM|tH{0T41
zAEF7TuD2DEIfTP*J*~2><-g#f?{jqS@aj_Ct+~PY?P?9`6KL)U>%azrmq#|+`CL1C
zCu7eYZ9?9SU>(D%&zqB#N6u~-pk01uU2XI9iO+Q*7o50nD^EGwAetEGOZ(ftCVL!C
zR;rEek|9@$^&4dC0p&>;ar>qf5{<s=aKMgWgM}8LS&OBdVF~f#l3LQ&mvB7evQt`(
zMuy%<gFM)9@k=xdq9l3S;izNSgG7JVqpr;tyN;)cC!Gp5jUQt=ff(<6n^E&T+cwMQ
zYoxLUQOnIG(Lj4Xl=r%8OgGcR+d~{`jf9z<b1{V9y}$8q?>9A&_Jmez*>4)mNExv{
z|GO2lsw>_VtCX#9)w!{(Kz^8q{(PmF>HQ;9@)A$)Dg0o+G8#Q(=_GVQR;9LcU(G2c
z;x)Q<IwA1&$Io*CK3Kl#u)?CKV**`Ww<SI=zD5fxVh;`bAGnow?FtU>s^xsts#){f
z3@EF?r#g&*az*Emq7&$ls`E;?t)A7dK5C|YfSy&H5`!f?-jRs1IFcwk+5o@tVN>EE
z`eT-EX(CeoQy>2(V+8GU1FaD)TZB=7%6I2B@ePO7qnT!;YY?L)i^fbgO4tdvNj$G)
zfn1W3V_UU^k@0bhKzSHzdpC{@ABSr!A5%>Pvj>5=zU=-YB&t}$lt{WRBbl)GMYK6=
z*^Afr&B}eGy1rI1IrSnu{7XEqZfw<X9MKE9Gc#N0l}$EvV*K=ZJaFpUyu_K()dVTC
z{)n{XsYndh;k}dmI9ANU+5@O?F@X}cED|TBi47SK`TRk<nwUIN*6rBN%_C~)=aXU~
zk(GWZ<{GFxV*65vT8`-SxvWM({^)6-@w}5rX@85YWepv_E@?+RyUBQ&<GQaL3FFA(
zI)2Uf%{GM4*gmv0Oi@H?BOA4X@6E$#woVR?gyZ8dG+MzSx-uOhqZO=$eZ#p#H1jJj
zaYK~E!OpHF$gSPExzxLnuV6)aH<EPEG+okRDp1SOHjCSFS6?2nlx1`VIkII~sASj9
zu5a5G?MwTnHCz8!D2YwovSi!#7}8i|DR3AMte0k1(R0Up#aaM9+cvv~Y2ie<U9y|`
zh6a-vUutnk?f&aC7nPEzggAJ8cX=G3eudW!13gyPn2+}wZ7TsS3aVKbW-p%I{UR!A
zQzMO_U6*+ya_|U0A;wU>MZ3*+V=jrPUEkO9o}c;8oq|$p><P}W5?*GtcRgF;NQkkT
zQ)b$eWGUI81i`|Xjos%~5jTfgf|LTQFWUUM7>wGT`YX3M%N~or$WKY$%t-#>RcIHn
z<JGESYAC1oJWs`?x2q&Zul()#O}MgY!|YP-;@rGf?SefPms}DUFV(}{2Q)(&&<rED
z2u<U>C>w7$@<Ev-MObX<GHejlLp`X@F4n)Ynrc?2kl{cUK&t-YR)+0ry3HW^N@F%M
zqRi8awk1Qcv%!%sjV?{{+xk}UE!UdLaoyrnzy+_mtHY~ao8bFjsvMxTjb(sp>5kDm
zkK|%|#F71Nw~zvx5dPHgyd5{GwWV`<j$YXNM04Q&bOph7ge8vc{5;F8%B_YIM?C@_
zw~4zrGh|r`%4_9kf3#~*`O#wM=3QHp2t9b$tyvh`-ClV!`blczpu70rTN&M7#9+e|
z{^-#2NQleRZh`48ju_e9#TCcl-Apl@ZG0eo5bKlzdw-VL-Ds%InrL*qw>ciQI~a%L
z+GM&36H7c*CyhAucr0pQlslbU;`U8h<mq+3Cut`Q;6L;X{b+4hve&+6U^capsusG)
zu=7cBUI&^DynKCjMT(7%M`=43!+KXb-+rOK;|_b8^=`%CXrb=Kuy+}5iI?KG&%^{M
zJ9*miNFHrz80t+yh#72aTwD<FcWyq9gq5ed<Inr|`gvs$D#q|Zp5=D>Jr~2mUO7G^
z?~)CKvdrTNSw+Ewkrh^iyimjFU?$?b1+K=0s9IykHM4{7=`lU{tnB)P<0qkTu|;#_
zNnueJd(0)_27FVAUaZc!3B6haCyZYbtOb$y4nlN|tQjeO=gE$v93AcxxID^Ouy%Em
zGi%ZLx1mOhTsbmjC%0UNSr6Q4PxbmyV$64gtuW`~nGC<zunFpg;nu7v{$hgfBTg0T
z{_}3q_BAGEepNr;i8+ox9o^vcAO9SoU|j<v6#5-pAWsY#O6C0;4!6qu__Uxa6wAvk
z!tAkq@F*=G;(fi%_1q2v!A%`SQrbif&$uKX`W7|mG8Ym56s9mD`+eWIth&tHeZutZ
zuxC8H7_dt{Qjn<K#bDuDvXK{v&}i=U<Er=Q3V>6_FX)3Ecwt=i9%W-SI=aK$YKJ_1
z3Kfy(v&&0)8)KrZH_=#Hhbd>Nuqb-bIh}IsJzjM8uQS&_xsT93E6X8f6l)%pYL>Gy
zUzv-?m}C7k*m`j!38GE-)}Fkh{3k1ICoN`7U#3_j^~Nvfs>HPn$O_|iZ+~?X2Iu|<
zB-XL}^BG7)<0GiP_BgNP=^N=V24){rcT-AvfWSd(FYHZ@QXpOk36q-~y**qqt`HlN
z;;+&pgm+EuW(|WSD2z2=bk;s|k)v#p8mSZL0oL5-4lF<A^suT(NMiDU!!s?sK%KTu
z7l&iLZdUB6F=f5ygB=@z=6q!f{^akxaPE~BucQ8#+TUJCMEg44%jNsR_|iL;E$p%n
zbjxl<d3VQ3V2MjY<%jiE;*0#L>b3NzZXpHFL?s58>V{TyNWtAR^^2uuRu?wnKa%<T
zyNE^vdtGFq&}yhxl$A;QagOZf1?3&92cn5oT6Y6Y=JR%VX`I8+nT|44j33^rq-t%U
z<M{dX?-rzEvy(A~NG_d}E=9OIJ8oI)pt9R1#2|&BV;0x_nmjJCbw(x43AXEgBW^@p
z-k(2pWWNDAtMnHn_TllMBap`=Gi1L=She>#DdtBSIr7bplc@QiZbrVQ*?8g=^<%}o
zC*H2QGET6Zifm(4Gmba@>PS969Jb0Dq_VW#8qoCdvkf$bD_1W>>mBPXhvL?ggl^s;
zV(0dN+%vCO{VS5!5@)u#VKI<H-wXbjd4uo!r{;-XlXlmbo7NjCvj{w+Y7=*5-B8~w
z2m7*+<N`5dlY0L1J4#BUnNvb}>h3V5;C&lhY%A;TS!CCH?P8qQiW9MvX1<i(hoWS)
zO>WZ4zv+=|yJu_?Db(QVR#O|N@wXik1N{j?4NTz(*jkP72G^?wp=#v}?ycuv9QGMZ
z11(4@@|=~VzQ%F0CTgQxdqa$Dq>+S~?zfQiAj<1l&Bs4hLNX~cog1eTe(@G9`pK4W
zrD$Vu5=Un5!0cKdT8V8b51G%ucN3LlCFx6qz5lqlpjtE6CGtK1ftKZ^SWcnDam67a
z1zh60E(<}Z{Ek5mHQR+<9ZDSpqIs0Q@UiRP)ScYV<ajh<?58Xyv$;59=4Qz0h_gPO
zHr|t5vq9a|FLl!8IfPcWZJ$)x>P&x>PK0cF%V<ugqPM;Ujb^oW#3fZ}dT~YV^5Wxc
zbg9EV6BM81c!%e48zJFje&L}RDgDoHA=^USaSsg3yVKebu5h7y9o=V|Z##u+DLhtE
z@4t#B=&VpTcvco4PGXkImC!C9<O6QkO0vVvlJ^bJDLSgT_xmZ1s2KRXrg!o_@fM+S
zK=chvk(ILJ7UgiymTJbAH?A>1TAZ_}T$alcB>JW%XIw)8AJvloF>4+3SkZ}y(zn5q
zckodhv)T@fZlynqxfIMZ+IhEk6VI1F4I|eTr9Y7Kk{37#Uk%V9CT*wf1WNe!BxzZS
zEXg4IILWU*JQb|EY4Z>nSE`a+rHZw9LVMV`+dM;bQ(ju#U!&a1<F^Vt2TLbzy%|0M
zX}?Ohtg8TPq>hIr94_`6b4*tf9;{Fm+Ux@$JL|XR#&PLc#|YYmlk<n~+Suy+uDAw1
z^T&L5OilJXnBERSnv8qLpSrBSE$Ck9kLuRM9Z2{d(RR9-ds4!#d+Q>{ZCRc-o7lKs
zpK;SEZpbTMgI+dH$GvyoYB&gcA^sQq<<%W>RBzpJGTwP1;X7%|BiS~OVs}-rBzZlR
z6W@=tMbPRsQsgB*<3z_v49tzaNs%g>*!|FX;ozkf{7O4gOlB{h0Cd3IMdZ?hO;GJ|
zzrMw-Yu?|2+X`!mQwuR#%5sQ?LoHY0`RuS`!+33um@zVcO-wk2R5{w9@<rBUdDUR}
zU8oUqA?N81>EOL5mjYGUCg10zwQN_|ERA<~w{n&STXcNh{VdJ!(BRYUN)1L7?^arX
z$)Um66%Kev4;Kf*uVRO^Nk`gJ$<e+$?vVSZ=(h5Kce8_-5xXGqss^9m?wAvU#8jZ7
z6N7a4TzqNENABlozVA!Q*7Tuu{sy~wT~7#CTtx3nwQo3HaLgHC#a)FmS;F!Bk{6b)
zLopiyeYSrrUFTI^$W?M)HHXHS6UtcKDy!f1BwSl!&kg53QYrnaltaKOt6BW1zPg$d
z&#p1@Ls}U7S$}l72LHjkgeXUbrWV8PClqthqL5mHH@3d&ZT7Uq_4>o-q5%!r<AKL*
zYRD@iN#r83UFX{dTi&gyhg|Ltk?MWuS(Z($>=?uCo#kZWh(uPAota<n!w_2CPJbya
zd^O*}axX_KT`BQz{B<wvluF$<YF?QUwsIKHB)CtEk*+2kZrE;lDp2G*<UMv{K8&#&
zr0`3U4o*m*igP={Y!}QOBg1oOU$}cVBG*zciQ$Zj6(*Yuw;4uyp!3Hd4u8;7|KsJZ
zw?8Iw7C+jKJt>^Xy_mP=(#7>%KkGl|0$c=3LFS~MD$+!gUZ5q3C>ShQM!`6kYL{}x
z=cbtC@T4yr<}^pVYhJF8SDN;$Mx=hcMn?t(YDw%VJ_?VdP(_RO0jY>^HZkbNXU_83
zOMdR?Zwn}04Y8Pk4}N89GrG59Plwo=Ec~Gxa`BNkZ!hONYJvV`vc`p4xhjIZxc)~y
zL3w`vrm&gXJTv-0vXEcJIf~)#AgDPm>rw%>b}ISX1`;Y%&Hg2pN7BZA+cY9a436C9
zdHIbo>w7Dg$=pqQtLr(Iq+B6+Qo|K;m_z0Sz7a0I)iSPw#b2T+IQXh-WKgjUH0Zq2
zp!dbbuoK@reL4eyPi3vYm_8yua0ihQTOax8UH9M(h4^-rFAZGRNv`rQElD%g#05Qh
z|6H6GRDQT2(CYCyy6(RakgF5n<8jpWm6fYTum{T36YnsIT0l4zRTmd(OqrImv9Hx&
ztyhf#)v){$$>KP>fSBZ}_Hxkx^pahXXSPJ)XB>AJW77V#OY+Kajck#p??sM8tLu`_
z#HQ<PL?x$%?3|>ED0^?BD+;t5bez_{zwS=HCmT){8fqDJtQl+xw#2b?C|&h=ku_oE
zkmFxQ@)1RLnKInM)>dJYy580aRdJT`F;7fQ3-pQRu@3(0%x)hH7UTw=hUv#|N9f#N
zy{e9?yezR*7+x9{^HnTTSyom+o&eRHf3nVr@QTS_ux-V&mRh)m&sPwnG}52*a?HE9
zw{&j|>tgN4$<-7yVg060+3CFmhDZ<6NU5lHv1cEEmN0Xt!DWo&(a7@z1!+}9Fmt9j
zLg<3vfvu4dHajx96C=onYKBi7ERMicT1{J&B75($3nTmFW#fwvaSqRlcVmLDA@i@T
zlHQJzcFdjSuwgus>QTPR+bI%emlQp-<+_3onfKvTd5&LsU+eE%0>9S*jA-^54qT+e
z)WE#_rc45clt?98YxalL>gDSS$BA#<HC}c0@mG(@f4!ToC6M0m+@;nbqwn=$X%oHF
z7g4t=-h95kZ(*TH{SlTbnj_*IyCYg}SAklB5Q`=3K5>iHdRLQ<y4RF-Pg{DyR&kD;
zp7O@PL62_J5Nwl|<Je4$*AVl18glA4ah$QF1cu*pwRa+j*Vnb2<AJYlNSeI8Stx2X
zepggIh8Y9#uD_sSs%^cQ`y@?%YeD`~YN?CL;qHsDsPt4=>>;Z1wa#On;n7>LN32d2
z@Ku-P{+QU0eaI2NKYO|Oo4m?>S#Uct8v>FtlN+44$}u}kjo*&zS7wPbtmANg;W6d$
zX(jRGy4iAaP+Ff%>B5&7N(T13eTW3gzqVmk9B~Rw-U*DE)}OhGBt`rCbVjxx=2>2q
ztSzFL+b3^Mj6StoaIZ5t>OW3dzl<bYREwzP$??K0w0bYcanDjMJn#)Ctpm>yfDvZs
z87+*U=fqXPUh(FRsYe<Y<c>MpMx{H*f5K_;Ee2z{m$yS4SWN2GH_XITY@EcJcO;Hf
z_BkB=>+GRK$J>?$J14b8lBvR$qnLL!&hKM)`$r{-E%ksH!7NRumPzuZ=D)|Zuakww
zTE3lGXAwS_tFP8bxhIje#nQFy8`b^CLd$sI_*+F}TqdzI^vIM`^)Ex@)oP9#yJyz<
ze7AtP10zo#V4UU~62_Vw-6DoyQ@Uk2FNYJ9T;61clG;^b2XAAIBz$+1x(<UREcgUw
z*>AH2RDC93T|0Cr=liFkc<WtxEB6Mp3VS_Gt+?XYd{#wN;)3l5E*+q|?!lQ|yE-J=
z@Lot#s`JHks|n6#;*Pzs;+x%@rW#1Dsv1i|u-W|1O3i4N9T#($SktK4NkyQ4K*-mg
z@2c6AATo7bO1~Rc(ycis53Q0Fkqiz_>t0-yC@iA2huX^Jek+H+-3L;juWEDEczgd$
zN0YbpHCIIUC5S>%cu~B?x6;yGPJ>l`<|DQkoCc@kXhD~V>Rw#8^AAfTm$tSdHrEKu
zm6tflSCBqY?U7Cep>|pqPBl2DN`8u(D9cS=KX}IUfc6lxFbwMY5ywZ36gg5>UIOYc
zcK~Yv5Xh6Z46R2I-a=PN(Md>!!z0jCU))7V1lZy42GjL<brbxOKSs44N$ht)E=jJv
zO+E*SwV|thh!85-VLsg&1=?$m6n30f#nYQs4(F|GpYFAmJq{_WJ*1za6Cq+L#w<mz
z6#O5##Uk#PT!}cQ&vQZUj$yNP?Y{>qS1WVzv}cDucR>Yx6d2IKi0D3l?tg|L*4L`&
zieW<X3jIu`-<#p@ir?>-1p@#5IZ*To$a`j4?dGo=9!{0)N)|6&3q9<X>}$Hg&ZzH&
zt09<p?O&Gl3e`3+=>NMELv0WkYA7@*lu-sBbIN#_OO{Fv<meW%GQQ$}_4QYWRgFi7
zezh1n*JtK?t=|$hyLPJ_i^VoQd-e<(IO%WJWnEoZ;WpW@BmL`fkObL@Rvu0{xI=Ds
z*<Bs;=UJ-g5?i?dn&R>wG<fbisXVK<x{_``_vx2E<DAr!+4dP+mFocU=W`}%2)(=N
z>gxIl2?-Nn2TQqjPeyD4t0$^ljc$8z_Leh>q}gXyT>EqeqIyZvXZl-Kl86Pi*mt4V
zC{^0MrmMdA<Hzfz;E(-{nzhOdSJ2O=xt*PzZrjU4D<6$lV$`nhn{r%3No7VxMm`3+
zxiu6V+cI2iB@SCi5A5Yq2@lQM+}!+;Bx<$Ur$m}KLs}g(3SFyT2v1zuHIdm};U6KW
zmaURLf}h9oBqk>>SkRdNl8dNso{}@&g@-TPM^rg>B^|yzdxeoYfTC`X#Hdi~$Tgn0
zXaR9?aRFgr1(&Zc&+6hsh|L*yUomn#nSR4R#g>}vTXgKqf_a|D-iM<QuqdJ!^w<u1
zS=w!|+hGfgpzq9i4fb-ssV3NhxK+(+HjAj`>Ypty@<d)Tavbz-FAczP7&wi5KqAsG
zZC7Nynm*Vx2H<>0rLGEVX=!y{kJA)iu#e~0&ya*|&dSxhF$?iWCmy@o<eMjE(_mfg
zHQ6u>rj3+nW-Ax)^V3t5l;^l#GLPqIS?1&3G`UyJ+gNQ;vtrAkTQcGc<_s*o<@d*K
zJY$7K2suoDd!rFXCe(_i%6co;`mYQt7z)miFf>XfVzAT>v-RAQLnsxDFEMDNl@*RJ
zd-yfxZsvXUhYx@CMsp}o-G6;~0q^t231#tWC%t{AcD?!KpKKLUBc4ZMEt;m76gPS^
z!Bm*qcOZBtd6u3saQ-D9ez-@{Q{4EL{^G@pxG!J6XoB$!r0v#XD8b8v`K9!bv7cQ2
z$-eUGvmA@im3!+)AwRR4niTzd<rdN~ASw@TCysqok20ap@2c@8BbV~w9q8p74?Y#<
z55os^Wy6$%)_-DQTl47{5bT1lxWf1J^OIh5mOA%kTnX7-l@l2O>k#ZgUKq;maB0(K
zXa4O(v#fjXIGFi#_L6bir#HSSyh@Stb$Y||;!bAdN+~AXUx+XRI`jo}i1~MiW|neM
zUh4-drFIn#9SF&14NA1bEed9DI%?VhR{=Wt<&v;z8t3U3LM08$1=abx`DJA{usOP^
zG_~t-KVF~b*3*%J3tTLSMRUOqZ{~AF)_RN~?C#%R_q)ZOaoDfH#W9;$QdA^j+#I~)
z1EzKpq}MWK3q>pP7Ux4OdX87Q?mX%ujFjSCH#XmKsk9h)v}DC7AJja)r3k~Kp=T$?
zan<f4_|biP)}gX3dGG!G{l38h6L&vcjb~}_;YQn)hD4-Z`t4c$Z>wXKE8lP^rQq##
zj?YqVgL(G)w|@P7MB05glt)E{hLrbTp1{}pE=+lY0lSu8)!_*bnLOB83AFT}21ONR
z4=h-7o$+eh1xI&!wJAG0n;a@$M{FTSGrjs6pDN|L$xz$CyHs2|RS@=_&Dq*-^x><s
zeSLlKhTxs$7-`S(?HtVEoVaMy=3cd6jh$Ctyy(~r|7-Pa1fdkaRU5j$xm@iw6ikP`
zwV}chWVn9-af<TnE0fA$20*XesK`h@DUZ>|>md5aTxY(&3lZH;SabiB)amXE!cJNY
zL_dlvY`edf_0lCJgZuYy<l8m}!<DwWCB?1F9^5W=*nO8URnB2`uc9=N#rn@)`ZzXT
z?aQ3+L(vLD0Cvne;Nsfl2G=GVzalQaQQMrDVrDiN>MiFH1n_aYnaN?RQu@k*xvJ1#
z^fF@%0aweai8bJr_9ItAQMtsK7*hBLELYcEg@9Gf;D>MdQK6gWP`A;tUu~_et(TUn
zvaD*oxJ1p%eomO#g;mcSV^S;=KsJGc9$%-B@LW}oS|h*mibAFA2;<eHDsMa$Pi!XH
z_9?8@Wo^A>leG2GmIV;Z2S&bnWw^NU;Bc#tw)TS6Rsb4s?R;%3w6m-0ZoY`-BXNgm
z(|O_2rYEBl^}cXsaqsT0zd&b4Xp31f%cPZ&(ze6>Ev`@y9Qd;Zy;nE+KLQ+Hqi$QS
zmV!_m1R%JQBxW-{w7&;(`f>|p7k9SNZ|TV|{}CJ6C*ALFo<VpReTnK#NKPhJdQXd1
zB<Z1i!$kHxNlV&!pe1Wt+qLmrF!t$kj-3hA-Zm|5?I_a{YuG{TwYz#KDOW5R6@IXk
zH%cm0%it&1KMu(-R0_x1sEH?1gO%XIW+fuZ`?K*HtE1&J>lsQR${SzKh&TN*1!=9+
zmm%Xho_Ldqwb-h*Warx(W-D>qroB24BaiQRguU73Zx0iO7F$-QoFk8t@NoDc2>KY}
zkIcKoHqMaMq{7mc1Na#x$Ai%tY!IhLc-yfvVkhbCo<R6*XE!In%bX`}Uc)!<N7sm2
zWllB*vht<M#`)#}2=uC=fxsZi4dm*h&NIH^?ie;n>@7!sQ^q|y;h&M1NqYJ=yvAvU
z{fy=bZoui9pE9eXwi~0OwaKQ&?+-|=q^S_1IaGeFZpJhRhceQWb9~NIY*CrxUjYNu
zvzLvK3S65I>zL<|_gi#FNVyLeMFBpvHhaw}>oozow?CJRxe5g^VafGIEm+PQSL=-#
z)x)@tlqi?u0NT~xtg%+m9Lm;C7A#-7BIU9Utq2no7iZcb-~vDJTw-B45X-sg)cxLy
z&;W>PAf*4Jx>+@dF-K_1EuZOXug|aiR=U-~b0bf0QR+a(rrML7iBrvO`*TXfrmbMW
zKUMCwwze=DmGYZNk_ls#xg;_X1NTpr@wAOnyPm1R37gf|SD|dj<yZC8W9+>-6avyx
zw2+3uVFB(|VlglLCjAC<2{}4QEat<92Mh0Hr-Ja#sIc8erI6k2sRsX*R{QU7>#G9@
z<95Zi%?Am-0Q#SOSn1u~nBi-umRQ$U?4jfJ4pJ{oggv2iNPRX4X1r^_<65I!J@MXj
z1yTx_GtyLZNFb5U=S2D*?4Zz&Krfh4*jaR?_Z4Zk?S2&PaG_C!yjqDTOf@%>N^BYr
zny3dWBhs$e*-1^F3k@LR8NT6U4cB5k9qvs==jKt7HUkYN-jCHfcle($4&@u=(q!BQ
zUE+9}50(p)NAe72$|ZYm&3tSmwlvn`^G3~0me{om(iSW_T9}Mq_L)vK#=8Gjde@Z!
zf7tWpyAI^o<ys9x`O~XR5MZ7eEivI?hTIQN{`uDpGeiq{tuY6)*D5YHN|#ejiZvkg
zyu}0sEi6q<O=rNz?ktUAfRY)CZ5nGlZ?R7)`7Pe>6F=5_UiwXRiJ-7>S2DV(sVVH<
z+TV9hrIUzr^Qqp-OQ3h|2bCy_hiRo#&lE}93h)p{{_C&DXx&s99EfXb8eJ-}5+>>;
zGkq%@mg(_8=PeOv=6x3w#oO-DvQtlr^bmOPFXuVSCNaAPn72n*P*Cv9tFLjp1e~{3
zl}kUfs8vlGH?(#tcxC8!B*eQ~Ddz8v6?)byvS7<SaW(7QF^W&(d72M5yWp1rAu)hf
zahE_=0WcKe0u-MDrZ4F6Eq3Zsk1T%mlEOPx`3j%IZ>K9Tba?O@DVcl?f{Q(zy1$_R
z9uSvKtsh<a!Z$nky<^eBC3Vx8^xf}+nD8!V4WSF(=>9AX_)lpq@x&3;2J&u~IHrlT
z)=0F)7Fkr<&^5~R^mHVBoHIasGp48ur|T9rN54~r`+f6($WVIReX}i80NNNxR1N}S
zt9h+ytG98y#*?fDI-Riypw_!qP$RA2KrShPyjA3YXAt`8EKIa%m!`V_<MTl7DOw_c
zlyBmlte~sCRSc`RZQ7u8e6nruk}Syk_hL{mF%5fVUl~LA#Knnn3y03Ewn4vKN)Jb@
zeS1`RT_@^%SigH+k#Nv7gDy|hZ*2qPkjOt!5)=-={0>w|Kwvc9%K{{YI6EMrcXX|Y
zq3)FCZf$NN8vgd3>r8B=H|WiifBX4K?F&op4gNW)D#HLN?Q{`eem%Bms?r0j=wbww
zU;OZV7!SmD*92B^@8|PAD^IU_Fz;?lRg#)uF7?HtU-&5<u77PK0vyPL#`6((7yIoi
ztx|xbUh^6*8dHeV<dJghyf^|oZ4?(Z!=FbD2hy??aNCvgF!Juf0t!z3n||ncTmNWI
zr3P1^^22wQhu}eA67kSlrZ;~C<CBt;U$Kao-!IfhDJMg*K#eJL+g^DR=3!4uT$D40
zLZM^JLxmcLGhDR2yKT`NG!rm_w08)6a|r1+UB{Fv_$}U`j=Pr<eww|cke)t?<~<RZ
zap@abwMNAXM!e(r`}}I-^8mGs%6UvmZIuX@km*@B0cXW4bG?`Ko>Dn^_HtDa(9f8u
zvWIsoHF*%MZ+HU4LV%bSzarx?x(mc~b%qL@`WZ}NnMT9kQ$aKD&hWY}{>UuZGRUz9
z?hF{K*t?8lab6Yx=$iI&QQ<-@061vjAfyie5~oOySZ~$3mp0ejiR^`?Bqy6q6_^x`
z8Rh7}gqZzLrmGAC73QvK2zbr<n5`{~xb4J!gk_D#{!_CQU<oL($;q|3fOz4+clbxk
z&A;~^aQD1nNT@V&yv2g8e(?F3J&)^9fl2P%!a{Alw9B`fTn{xPcfG+|ue2<gt)hMH
zzHO$o-DGXkKS!~td;E~{^cVxxmAp&X;my}h7Hcsggsy*P(9p)7;!nFFo+zhkbbhc!
zX|?R@+GG=K>*j9gAjlsDKtlW9w)#|P!A&x=2lUJt^Uc#1Z{AL&KgdD*itR8hkH5cV
z<7MX8TfGIR9&QN<34H-du5lk2UiP4Vbym;UJt0>!?4h62owGNUV^`?Vd7tec5A*TE
zhY!XGKn{t!bG^Ow?U7MYMweZ0dW@E};={>=SYY+0%6*G<krIv{%vylsXdiP%QS=4n
zQ`8Xm-u~5J1h(&S8l3fL;6hzv45O<-S~nn!bM?}zT7sI^zntMc!y<C477S@2T}Q&+
z%$BnjMxL@%|MF`+1DNPKX31Pb=h*#85v!UaHH6zp2|=DAFB#w&2M^SLd2qkft^t6E
zC<2K1oM~g+UscBB)r1xMYk$y9mzo5fkHv-P_7eQ%<>j^4dWG!txv$+_XnEZX8%hUB
z@|qgWCOMx3)LOEX_f%*J&tw43^Eo|Ou9wP#o>^)mmWPW|H)Bq9oOZ)oMln%{Ax^h%
ze=czwDxesfn$D9aD<#-*qws8BMiWCr!(rg<s)0sdlk{%cpZABsU~>pRpei#6NmNzE
zHpDZzKCPXwtI4S1rohaK2gkw5z^C>$S2&hKRvI^$znA-(Et?e&6`BzXf#Bk-2fAQ<
zsphL3zjdK$@lm@!uB)iv1M@o8e|=rhOO+vX%K6S)n_F5QJn`}IY2DkH5l=u<DP+uF
zejz+`#jXK0>`D?fV(bMh^Qr_xJxJwo1u74&@8>Z2{-(Vb2SEPIjc-9$a~{vO$MGv~
z|F~OQ1tiWEf>*l)NTRWlt1EvBVUmG$=MaYin$dCV#{di@#G=7J?5oLZZCr3gH3Xy5
z_75$|3w%S$3Uiz0e`o=E3Dk1>Rr;F~jJtf+z_~N>{tbKSfm377pDV1{Rc`N)l7T(>
zwlt878PC#)DYb)BcC;J)R{|&?7danmTY)1c82YJM%N(!v2C#4lM;KdLrjU9Sh=VPJ
z8eI6!gm0gXg7;LDQ4658kjYKsB>OAKN^rr%MKL|^?+Jv$7EL+t`wREMo)5f%J#!Yb
zu3Mh~_N(f#wl6*Cmr%~xTgtJ8zk?o0Ut{~E0Y<G>8w;;ZT&ia)@p+AY@%7i_&dLZr
z-J^w1H>JoLa8JIlX_3oS*W2=*d+T4Bn3B4MFunED71<V*PK_zD-qc+nv4&mu)_?W%
zDU(-AM~baR8p_HZlRaxEcP)PGfbbPP1TKqR6iV-z^3ao#=G}24S<msoI)KySjW$V3
zya5|AbX*SyFu>Z2Qh|SecH&Gp4Ch(vzmXCnIVAtQlb8d%dzHsn#Q@I|U`B1BQP`JL
z{6(p9zB#}}4C`Sfy<De2n%)I2<qS_gsHv!+Cx1fF1J1eDoM&#k0!mLoN;<o3Ot*6I
zWV5g9I9@BG&&zgg2x%>zE=~%5>xBWJZ69|6@|gOS;$9->4=9oRb`Rv@WD0OqCIGGo
z_ib&delpKa-X>aw#8m@fW<Q<zigVlzP>-imUxrOC@L}ZHLXkF^XJ*#cO`d=^cEa}_
z7(8$sE;RGDs(?LHd{to6xGMua{pxGURs*NYg1O<11&gI7rrG7nM3R^>A6?9?`_Su6
zDxrJp3iR5Cc%B5{+`XY+uedU{6&rFhf|r-gY+>q!W~Iu?lQJT}_dSNnA1r3E=31E+
zc!6|ur2t%)0(Fuox&<xg*;(wLK>;Lv${+!3LbMFR7?9aJka&7a*w1u5JN5S|<`Q86
z0fDfk>|{emH#raur=gaO0mUmHb(V&aZ)CKwi~U(w=#)&?@G79<#Gk*fTM8z04v;*i
z#%nTo7E~otfHo=?el+kW`>pls&;qm4sYv@6r5dTcfL!i$W&Y~}ZFge58kTbAK+OhM
z2?&mxfss9E6SS?g;BnScEE;w7Af$sgS)__4CL;1y2c;bL4RAAc5#+pg!)isB^2%WT
zfbGEb3BZNI#QKGd&2)vp;bx$+^<mrn8Vk9&m>`LIZ^C{ga5r^*=XyK+Jh+4U6t}wD
z;egCbxNVFKN^P4t;QL*6%1{|QkTzg&|L8-h&=wGW6FzNW%SrR93T+6iN?+$7S(Wb}
zGN;#GBW@Lm*ONp0dum$3$l)6cDl`kb@O{TrdKGkf`cYrM_eRsk_bZ+}OGTBvTJWhi
z7j0m3i7WI80f|-WUH|aw%Xx$pnAX~7MKho(Bz758`N)8@4}zh~zykW_CrJmajvkqt
zn^Qjq#wBj;bs7-Rm)^3=RkQ+U4Hu+4&?ibQ*kZ|c8Jukhq53WZ6DI%+zabMdrMT7h
zFZ98#0xTrnvNLRj;ZzJ-PY3dlUCwXuhdw}ZlNNe41SDVc&lxc9FL)+_6N+KzpZCkg
zIjfAxvph2fT!5mNoL+s8;iA2I#TmSq1VB+K5Y%9M)_!Du1qC8@K~d2js|p8b4_#`F
z0;!dcW3>zQf#3IEnBMD9g9B+)w$rdu67m3;`r7T)v8`P?w9Nn6G+1m!%B8b*KmD)|
zfXJV00>lgd69^tX0E7X5fBn_onJ63u+`&eUUFb&ZFY%VWY4_XPlIdaOPsf<pNgxe4
zr5}8XP^XtcYUpeXO!OD;rj2`(KzmZ0003&tE-Vn0fxDFXK5T`I_B>t+Qau$2Y-&Ks
z?@A0B8h=@F{nN1pAT4JooJzr=L!da}2?}0Q+(^<guwk4x9ol1Cb|1Y7oPsUr)HX0C
z_=*VDZ54gMGgNGhb%wACUHM0S^YZdK>ZVA+0)Yn3)qj5@Gj3!eaU&ry@r5!-Nt8BL
zw05F`XVjEWjGa;AV0&q60)#T{9H;_C19uJ+wX7OtGu{VISLoJqURFj8%^TlB5eDcS
zB0jW9H*f`Z7^*XAGSCh?f|NH4tTFvc#UB*`s=#9F$6coS@KZXupw!YP|Lbq&5~~Kk
zr9J`;6fA$K0KRuZJ;-ft?*KkTjPcdiW=^0zZ~z%<ho_W%=eq@qEsr!imH+2lfNJ?^
zps`f^2lEVS-+{_AssWgt$X98wC%k#exF`N1w^pj<R!R)AzE@`67r0QVq>nHywGGn(
z^~MQMd&b0YDC~wogICMj<~_$MvgcK3`+eb+&V6f=K))$_?TkmAKOqQ|Am0HQunBzq
z`xWn|Mo^g==mTZhGjweWm#bCT88q^h^%%YHJX~bK+LtD`RROTumc{BGm{uEcIX#mC
za=_MnPb-~q{J}$!dp+n4eT9HhZCY$eUPbA!<7CMGau2`!oeGG|?`xWz?@0%@r-H^_
zZ_@52f_VYvXu=b)!6Yzg%6%1gGViy7IeJ0_qOQ3k`|Yu5cJ0%<io)%rrygN7C&Qvo
zpt}Njjld;U^%H<*Xb3dD98qN46}0#3RSX57lN%_hh2MLA@+>0b%q@B~nv8es0tp-{
zxVY&)n70A)jbIlJ>ipC_Dw(LS6*xNw3QQWghA(~n7eHA6h}H3_JcBHC0NYpSMT*;A
zw`2V1dc>%Ec`#D#IxGN_CW}!-AX9xo4rc+v4A21TA1^2-M)elB7|9M)G(IhO86YDN
z7c1bf6SmIV89ojRnC<H*|5-F*0MD=|D^s3?->%`rtu%g35HxZM1C(o)?%OM;KU8W*
zt2{hBoTRHbu;j2u1tgIg*m}uKvFh=!FVy_15&e>Al+Jb_u&4H*Tw2qcq14z{=QDG;
z)eG!!B)9kd&6$G%Jq1Gcc(ptH5~y0AK7Bd>3{#vo2zki>P*YhuKXfs>!TZ&pY@17<
z?k<ToDKxF5k52PsFNjHa0I1F@?ttXBRdV_9Va|5vWS9p(x#8Q>Yg2N@_?9qC%1!(4
zJIMfX-_Q4?_9@NA-W>+EgUI;eP8@J*-pcdu?OAr02Ao%ctSVu@`RMNzsq+k2xulD$
zFw^_QV`GONXE#9+%}gNt!d3G)%~@vzHbdnCgwdEjow2~u)yz$|MwNnv1?G6pcl{r3
zKUV<AoyyEAYUyVJ^7E+Oq#PT)+W!zN6ivMc8R9VxDoZ<QJZ4@Xi|Zb?MqbKIPENK<
z(>yT9y2lZ+1u6{WQTv!lL$-S}In%1EzD0rfFw#!|SJ6Z~h9*#Z;&Vjj+RH0mw_tRf
z>;`3~%lFnfN3lw;B47E6Ty;EiC-X<R&s2i&TOip!MAn1q4JrYxa`=l$3DHB|>=NhN
z1^Y-F1$@L-Po2I71u@v5is)6L9SVkSfTcoYLa&^$Vw(MtcHZA;brXc5%}9k~mn;n|
z1#DZkgu-?lHm5-GENp9QD^MRP!JAu1p?hQCO$X_c$@%5iU#K+<jhZH^2Cf2P{BtSo
zZtGV$mI&)dktZEEjHDo+p1K3=y$=9s%)Pf=Tddw;RkBY}lHbe|d_QmbR@<FRZ5PkF
zc7l66#8zF7#vDUQ{sD>b$Ok&e47k>`gV}g}i3)J-%+;%)qYRw))}73Jp>V{uFw~z{
z6r8DtE=N#PAFw$hP8VScXSdqump9Tk^S?f)^3DJ3$*|%PjUP1Qs{;jEp0n}W>1bs+
zH*j$TeTxt((29l>KNCyZ_Lwln9*3G~7py}oRo?zoG7r(0rk?-;zI1FL>Arw*%E~=R
zcq9)Y_F}o0o%X>C^j%(#YfDd{sq8qrSX%qCgofqsuY(IdPEri}BUbN08S2r3Nm5_q
z)?hFO*eS5k^m|iH(z!2nHRP#&Lo7Njx~i{Tsa>0XwZ`wQV%A9gSOo)rEY&-DTi*>2
z3;3UYw`wePHMRYt%QYyI+~(VN9UA}RXAY@|GwYpNqy%offQIdVt^daI3ST<g&cYWb
zu8)<{m<lOw`=6R?Lc6H$mho)OYp2Y#tEgc)|Hu6@it6t6M|j{aZn8oxX9d2({#}!P
z4C(6fKgI~*x<#d0oZb)AUgmK%xzY2_I6)vh1#120Yx{BydOstE-<${Kr{9~%e!bVH
zBM+XVwTm!aSOMokv<!lp5D@k6$NzbXRmcSE+$LXo5HAJZ%cXuroaV8)=K2IO_fPXd
zr?gYJeNDEXI%J_s5G}nv3b7}2`XkiXh4M{5IcD(vd(2lWqruW3RTZ34BzXN(i0ai(
zQEGoCN$Grk`;$Y=H3B_!B;lW%f}AsZm_9DQl!=*ZV6!NnCdMqjVp8t(myA_E9#ojY
z7^zo$Q#@*C?a#b4KbDk#j$hMK;G$t&on=XC+@;LNw?=eTlg|Gv4xU0FznVT`1h>$k
zZ$BegzVVX&c@2qRzY12O|Jh7_-)H3a>%5QepUZA(9TkiDp!n~n(Bnas4~suWef)PY
zRUiCymVQ$o`et&GJE+pV9vvyw<#Jx_?murrjKhuxNqX0#nTs(vEg{{?CW)KBFQ=e?
zd{h0dOw-aHInP@9->CaUeFgEJZzk?F!g?+L-NysW-3ZyOq+3+4<Vw?jTOB2y``ObZ
z&H)eXHTm~s1IQ}e+(C4!2D<D>Y37gE1lNE4d)h$+1#)C+_5Fnh7MG4B{d=#K52x)T
z|8rJC-p4=wE9x()|5vpB|D2$(PFtOXrJ<c|h?G7Dr;0m&xW7IpCGzx~s*KN(^T`Wk
zZ`eOSUp%62aK!P{_Y;X(c>-crU;hyq^CThb=kvNxUvn~!3B7*y`j2b3?|eb1>iprr
zi=^yl3H2GLATvYeOG;WAr-&nkw*DrQBa{`p2H{C1e8a)-%NZY_kN*>|+`Ru>yZD#j
z5m1!?SEp#6qMxh+f!x#jXDV+P9{mk55pfs(Ik>k8{~X!>N)tGiNk~>%c7x~FrfZMm
zkGRoy067(8k<Mz?ce!rf=48q5n`>xmeCl#bvkEI1msc%LHQ_flW4ix4o{~}`l85#t
zmpuOvFX=51W6R^DHy|R)YVkW%|MzzOfBpD9t^AHvWy{ydw~Sj7@>}lP*b%HA{Jz%k
z(@FlHHgKx~vKdn;r`6=jIbRiX3T!87xp`=+KJfP-ynPuF7^YMZ4EulDd+(^Gwys|o
zL<KCMqN37{1vrWbi1em%1OWk&CLKiSRR{q>0FNj~X(~#GNRg6Ir4v#_1*s7sbP|dX
z2$0Z1fI#x?#Pi(m`QAJ3c%SEv_xr|o@3<Kb|JZx2z1Es*&fomaIX7#A7?wR4()vaa
z-W~34Re|f^?89%6<?>WnJOZu~yc|F$lYh+bKFPX=Di8NX`>};`{m&NKY9~I~P2A~T
zw!?USlR<LD=NI_)K1O~a4rY^$e@LSo7Pm$PP{cU06Um7NGySZ!+-FzTGD@d*6UbTQ
z5p-zhGiV=4$9{J<m}!a=jS8X7ta)-!`_w^h>W)^J+s;ce38Cd(T2&u@gEo~;id^Pn
zaEwpS1gjC1ly+K&#pjKh_zlF?rfA6v6WJI@SG=5Bi*2e{!4>vQD`WOHuhj>+HPiUK
zyCYHMIxQTRg8jxHN}^KTTiwN7$`svNSphM%?B>z0g_e7WB5tiwcg3J-pgL2W&Rqxg
z4QGCK2h$Nub_*09RZgjEJyT@Oy07KD9coP@-)Q12Q_p)ePvridK|t;VZD<6QJVtII
zCn4z98aGEy82>ryi#NdgX6|hwT8@X^>ejb(N&`=`AC7I6nsAVy7K;x*w0kCUr6$Cd
zHl{Q%YzK9x)qPU`_2|x%hZjiQ_%ei3X-N6Xm;=7TLfSe?IpEih-?sULL~A?k`Xs-i
zuaj@!)A8AGbA)TA&wM~2t(V+P&hBH>K5yX+CIx6myaQgXZ}gO?HPrWW=_dN~dSoY8
z?d(!98G#TwIsQ!@;*_j%p51zAVsCS4s|-T=ko}rC&tPV3k)@nnu+YI}CK(;)$KH!*
zIb*<}n(La38FEL!7fk#=ihV8GU##R`U$OmCC~-+ZS`<0wIO{h^K%p4b*7$Xd2)kE;
zDZ7_vxJXMJq8cu<AFcv6ZJut=2Gw`N!XiW3$|>8=>6e>0Z^R8;$7*Oh^)Yw7iKuUS
z>}f@RJ+_>PBU+CZO)J<JA7WohOU?%Xfb)yh|BtZpk(auYeKkNUm*X4m@`nz7mL7>R
zyT)!F{ptQMw;+GdYPq~(4UW>kKTXlS#!<9*7@)a52#T5*vA&7Ix3JWeBFOG^z0Tvl
zAT{kYtf^Rv^N?<RKqFb2irTI&9EIV^Oayvrt16;dEluneT*_jAQbQx$b|!B9qCq~|
zS3W97Sz9J$CV~a;QiuMAtjuV@Hk<vhwh-P@jsu8KlRd+E!hb1T{`-jYKLqWOy1D(Q
zo|z-`mAXXsIH{rNBW#_GK7Zk%tCHh=Ex(Bj`l*IrXe!1nhgk8(OX3H(y|jwkFA<Us
z{S*3z64#YugjM)MD@6W(j=ujxS^Vn`%s10~+!evF0wYWiiB2<N?zj>O_BKZwyU#__
zzmQYmMG~`He{YlJnX408AJmJQwxX3%$E%FddV^MslVcpmRr%>qEics!Z#ODI$uWoO
zf-=2uUCpBw;Ihcb*+!C8eHK9(X=g=r)Ix6ekga&rs%y7Dk@Lt+WU?1H8Ap9OSY*r|
z*!j?N+fNKE*|Bfs8GaWo*4GBfj7hOfHTK6L2o|WK@JJey+|5J#XtwJ?%j3~xm6AX7
zt@Pq`EClZ>kc!E<eWM73w9-~H$8wB(icd6~0uY_QJ4wlHt~Y3~qqQp24-h0M*hxRU
zzZFqgj1}S}N-1Ce;@7-~qv7}O>F#S5%sP!(II>n(8oC2fglgt?&r9L`yEU1A(7L2<
zY;N_Dn3YYgG@iaT*4^<B?H!~t0P9#<=F`u8WjW%LRJOoEM!GiPC)(WP1nRC%phDet
zmY;+Vdx(!SacjJYzRbO7IRwRVHwbi(mZ}(pE-~0dg2R#T+cyqTGfNo09ByLdTO19h
zcZCvPx~6u=C)ox1Z0zxIQn@Q9(w*<#YFZJ}qe;L&l=R1I7142vtuUO(VtP9A<KJHA
zdDFL%B7~5;7bQb2#1Vv?-#m%0Rcx!n#V1J=rzXs5D^AlTCN_DkH6y8Ky}EOvU{8C_
z5O(2I-!zrI;@414r#BQ$lFHD5y~d^0G8u~{yIZ$ZTJEV<ZC6R@=%lu5AO^2A@dN&<
z!H8BPCG6*P5`xh2Tea@3Tz`HblvUO=8XS@ACmPX=+Mb=?Wa5k5632brA_&(}KG*7D
zr=5bLrw1!Scb@6?XJkW=?6FNPl0*QO#9Q9>pt))0(1KjKW7VA0{R2rQ%(lGD`_U3`
zM`8j|em~a)Hu7WmD*F<{!U;1c<$V6sr3t7tZF7E8rYR81C?s$7t;E6WmG~9vt>v=Q
z>8)(phgi0mwApP>$zHai7)88S<IO;lehZqNTqKJbU)c;P5bt~Vl@z<b`0JFIHBE-z
zI4z8NrIN2=9~?$)TI{gxxtsY_wVaIb2R%qqqglmK9y#o+V;qKZpo}_|t@QH%!QA|j
zh-m8874J8uMzr0cUj+|3Z46&wB2-eNQa`c$zeUsc6NeJ&k8NHDnSk7q<Ek92cA+#p
zzT15k74hCtIfLc#@#_t|EOI61i=~R)%I5VTfootbYfY#A0K^Gu|4*gI@1f66mq;|M
zT{Jqn9Jw>H;LjK8;xNAJ)<)+6Yd`AsEscZiV%tWmsm|1(JzN3uDj2#JlJ-<(VJ2Y%
zIqND+II_IQT-V{@lCH7UEV|4fTE*LmRw>1>8hBVS9t)ZdpkEvhU3lL8b6jyc7Elir
zV2gZXi)r7|S0DmNjze}f2c$iYzfc3cPdm@68flhXCK8F-9aUK@BiN4DhtFJx`utG=
zJB`U}H=Gs8a@_qCyra>_NDZ0T1({M>5gx3F;{6=D6nN&+ghPT%rhVzcN#pXxhO+jT
z19S;;f$;=2)BaV76e3l}!r?BoX8U%sOaZ27yVr{p+_TSE*?<L~Us@zj<Bf|z<;Bbp
zhb9iCR4&ckm~cQmYVznHkdGSwxhS%S*5{T;EkRc|m51M>NgMB3c2fJJyRF_Hrpq?e
z39f;leblQAIys&U<qc(0jIBsksKV_k-TvX>hZ2PP!($vgl)tH4HnCglwGbS!aa~Cz
zlT}P{%I<%d`BDbaDJW09h4)5CTM5EVihIfkW~i!mLk=a1{<Mcs4PWZ3il%=rPa_D-
zObMWFvE33^^xxjJ^*;|j>bi4hWe{4iVU)h-KAgiwoE0<mv5cqs0p))ODDQlF*$?HC
zzqfY(6C|P*EWjb7TxkkP(ex&@{$IWJ*>R`_*S`?g_=*(wtnlDD807p8FlMlz1xLWq
zmh27lGy>I=Mc@#tBIRL$sbqXC9Be_dgpk%(I6ah6VSAg2_jYmQ5T^$ONj%-sKU<f>
zShz9?uY_$z@1gRxGtt2fcA!x*!0`7|I5Kg*c<6Mgc78ayYq}F5Q6YYJ+$9_t1+q%U
zBUM80(eimVI8U!U4$Q~jY<IS6I@PksLavlx2lWbv5Q7ON_==t`7L7c>L*t-7rzHmB
zwlhmk$CvAN5goLOwyV=gIr4WoAt0BLOK~)PQgK=Wu~D|#OzMi4Q2ysT2=`V}#bonA
z_f{v3f4<(3zCuyTst3D8>|9521b_Ki?jAx?cf6_X=P;G*(^0|iIUx*JO3Phts)k!@
zlEde)B*3LW(|*O{DWB_}kei#rTLHVkvr4XP9wSBA&=`tIERXk&rwH{{I4=N#DOXWW
zoreC-Yum5M3UPi9vK{(<{#n3${EQY5N3KVVUT%5~+Q=anogCkL7vF2lOxT=>^Lv;L
zEe*G=b?;5!s@yt!Vwq`_>{K9e*SD<4``9Ma07QG)C~ZMBLpAR*DCnqgZIM2(74d37
zw>fV)Z+xHJHU9T0-HeQ#`p?a~PWWlz@h^KK%xQV#>Nvl>bCGzuY}1Q<n}t%JHZWJ3
zj&dnufOSKSt-^AgvhVx)uYO(x!%ZzncSvI>b>FXzgUZ8j7k?iMo&JZlG&b2PYgZPR
zIP?Cbe54(MImm;Xi}BT6I#Uyf+L%tI6~P*6o^M4SKvdO|MZ^Oh>#V$mTxm)LtvI;v
zI#@fP5Ia%(MZONer%h1~;D}Wr;qlI<%^?|<`yh1t%_@{H8g$hHYX`Cch0|D8bXXX(
zQ)3f0-HZvhprvy&oI?je)6kTpFZCu=`e6&fp}lCzfOJUpk}mW1NE&m#T834;Z=kKc
zUcKCZCu8(VQvxWkYoiQbeyg6lv~np%NPXVXoqe^6)-Zi?d=HnA+uY1O*6_p3=e#Pz
zsr2g@2dd{s&}S9pt)T2UUdk`qx!C2}*pDVl6xLIHKawcMMQ!Ejgi->>AXRU!F(E_H
zCBa|Ddg}WuCXW+pV5OA$4-lNZStoAd#D7tHu2^}1P*~MM)ldFPPL%Te8`hku+ih(H
z5m*~5Iy}tBR#>c?GJp)cNfY*eCqpg`|4L9f7tB`uN#FFMijp}dB=0KDO$7xN^K*I~
zsPo{c`@fKe7*W2PJ<SW}7&i72pobLkV$MkvT!?EHSY&PZc5uWkS}a17im|TGNQ~0B
zp}-je4m2gyhqnjsj45S-AJg*YH=W?%!~Goiz!s|X-x?5CQjF|8(*qgp+8o_a=0qm|
zbeve$e&`m#Jkx^=aNEJU{K|#h7tr<X4H!&csr6Q+Hl?qO^O7G`H`pZ0Uaqr9wXnWY
zC&nJsIsdfRKB|G@G|F4mpV+nO#zVX6W(RMxlJI_9)s$pGGHxSTCf&F9=FCt}5!i|O
zuFW(60-$aYcs849v+&O*M6wB^Rg~GT4XBMr6yr@RBnygAe*Gy={LtMSlSubgu+8VN
zqV9N>C)?Lol!>dOpmRso9Pes{e+`l-@3C1Kqg&{x-3N0<@?e{9ch_dyU;trQaT+5I
zkT$mvyJr}UZ&{<DY20WhLI|#ifLOFy%cZL^JMpr-i$~c#Pvrt;If#pg4Usoq?iP|`
z-NZ3&>=FIbPDic&79_5t80ktoj~8~!L6(Py(3;XoXxCR1cGu`$0N{7+X>p24#Quk!
zPk73%qHfWe$x%2>dLH==n?;!b)D8SfO)&E`tN*OmZdcbPMm$8H-5Fb0>5H|+3_XMm
z^WKrraSvzQp*`{Pr@b?-JD)~F3y66ojxRp@S!clzkS77`Gf6yeV4vbNL&5+kv;o~W
z51(G9z|rXNTeMw89PBDe>>C1JWQ-dx)wUY<R!2Vq4Rg<mYA9;=lOEu@HfQyR25mKK
z+sTt>ts;m%;ZdX9UZ3>9RH;#1R>Pd$z_63@u-ASdIk3c!ms48A;8dEl@HlG>W=@Yw
z`Dt!c^kQj_J*v{+_sySD!Ur=!zRCVm6~#Rwo74V(MD;PF>A858IyMVzO}r{5;jpB7
zyKESbg`F-y;v4Nij3?fN;EC|cqgtiyES=6-UUtw`IxvmUxXgw~3Xpbunl`8rL$bnU
z5KKH+<XS|=-saAqa`5V+X<^_v!jSHlYEeVLC|zVH!Fvnk><xy0vJ4HOAr4pnNe?q_
zf$93*;?C?yrUo10v|Z=ux0Q3mAoLK73dX<Jmy0X@$2@K`*JQf`a=9s?5lJ3P3MRxw
zLV#O7mljr}e;cfe$fcA<m1+<F6dSfvYHS#*x3I=_ZS_eBe?UT$k8GZWt~jFXGD_po
zpB85ODBMfl4JSU!eD)n$_xlL-E)oT==Z&Tzfg!In?E@5JB&07hcwKfNNE-chD8xLf
zi(kIxtkuKEz{z3y-I|ftlPlM4@CJRP5T!<s5lQ@01gl6rY&OfzI%@1;Tr51!mUx1b
zipB1~0A!k2j!<0Jv=BVi0_OiQ`)h>~zdww4VR1K6%7qFtw$gF3g$}L*DJC8nqZzhx
zIJe98S*i;!;f<{b%ts9Rh4S`&X#ycf3tg=>(Z$=dQg+(xW?V*oj@;BLJT!A}YLR7I
z#cFdkX8o-h1Omz=$0A@QevKV#SBfL^wh><R2wM(3wYvRNsGF%ZQ8v>1^CoZ}IK^Td
zsh-~y1R7i4bkB%sk|-lMLH*8VbAVA~<P|sdB3=zYi&-SeZ&;E{{W-&XD+36X2q%m%
z1L#>~kqjUGN_RJcFC1y5qq8;^l<8wxIkNPt0N~LOO@D$%6;2;SFf5~-9ITeI)4?`0
z3H%zI_YMpaNmCkr&UvP@F~E3W@`{mq$<T+Gw{&HGFoKZwOPE~VSAQNLh&2CGuzHr$
z$qEP^={4yqmNJP;nWrlbh05ie)&W8c&P@(4p`@>%Px#;?6#wxqK--nf(-QSlLyP-{
z(0>2ivdF!4)Jjyt-z_5U#NXi~Cq)|l0q{x;f5qv{Qq_%(^p#y}Q6zxMzY^~EYXJ}%
zK8S#Red+3-aT-&+D+6N3Vrm;eEXMJU_YbV`g<=A~RtW->p{+=1IGHYskFpX>Pp7Z`
zw<a?5PAz*0I6+yA==rq@ivJ83w31e``NXI^5tMTe>h;g_EJv%>WP>=-Na6Mq@+_V}
zu0X)b2|9N<B%aI26$q!5%1a|3gpWEd`F(8qwTjP`Io#AzhlAU|;VHl`v-R%Z>2~Mr
z|1QN6Tg^*_iO+^1R)*B@9N#k(EFfLHyWz$7p1|o|sr(NwS@Ic}=7d-0B@()N_w4GR
zHAG-~nzQl%u2`XEX5X1vif0s-(erupKBuc_%zv6>>u=^EECrQw++k(S?CIqkRYihY
zTM|c;E8o_2S8XJ9#j^qvC1PoTH`!H)buJ|?uIItYUY$U=tdB%20CW+1#s`rIq)-<Y
zx4j1QGPzn^05>m-aJOW+&NdQZpW9q^cka>pcm|uNu{5Cm+t%5%(h~EivOe697A4no
zG}^Mk0OSW^+*0}2bESe}2@MD6rU-FzZ!=Z>TbRBz81C0JwPk7*6@E%0Heq^|+~Kvr
zRh#JfQDrTW+l!k^w1n4M47@l{CtkZHyh0g4RfuWOMJ=`Dx#i>qiY)8>J5%b!gSzq{
zHo}mQ#bPMK&t_bUTdwi|Aht+L1SWLkgK=coy8FKA4UxXlBaZgjmJXVCwSaWr2Dl>Z
zK3#QpHqOtL!km-S9x#V7C;*46bZdi!BPonG!T?%=R&q(ZhSoa_wKzYXQ?J8-Xn2P_
zk1xUB3fex!*$Shj0IOv}?Ng_iscmvlAB1ypb}nEK;UF)Sg8<P=nS&=ee2uFs{ok(?
z{THS_|Mhg=f2Yp>aTfh`w^Xoaq5icXb>`Ds%AAGzXzn2-EYUe}EXQCPj{Z09d7^m@
zym~ZCLj()Ex8xAGqm%SrnJp^$$9o7{3=2lRjp{L&Ha5s^e=#Rb82|Pk8s2e$K&wV<
zcb&mCe(u`L0}eUnR2Q9JuxlKv<T~Rz&rKS6rcAqde3-c;W+H~JwH&n?N4fy6J|y}K
zSHRP0I1nDZ0Bt-YXL%9xL|i<_a%UJ~lxh42c2U4t2`IXN+sL=@eQ+W?vUs}!zc0?m
zE5XqQO2jHrwF9)cQu;Cxv21T0YDN2TOXbr&)#`2MPViE!oQ0E>LnFWvxX{Oee7S@5
zZUBuJ@1$(=XuXQ;ju5s$3J;FJQ6JRv+4VaOpa_!6&LC6ngOd9E2@3lDOrwK4B=?Gi
zJR|hoK9=8oUMDl(;qaY6G5VvT%j}TmnQ6pF6f#7D_!DdeQ1Hr0pc;U~SQJ@e?rOP*
zv!M9=%+i-b-yBs?hbO<{ZSbW1a9*z6VSk+hjfi1>wr)Fa=#ywdm~FW@zo(MHQ{yJo
zE)sJnZ}h$}mGN4F+Jw6w2T+8zSts(xszrDtydBmAtudHjP>q3i`P`mBIXHA4@Wk<g
zD#NQ2YJF`wBR_8nI3bHUPA7Wh=<=1G9I|yLP%5jJvX)}DkuCAFED;V<oA`@ARG2yq
zBxsAQAP8woSUJ-O&GEn3vwTNXo}=(otNIFpW%hkk*+|p3LbggoCm3UqRS|{T+hmMN
z<8SN&3>WqWyMh?;8j6Qm_?L-SPH@~)^x->LWFWNM;2TG00)RRPyD`_l5O(|*n!^7?
z_4hwo)4%=*FQ91WeB94LtHy<JMAbDM1BwP}gq0q;%yP|>NH{>;E(78Ab!;cOxh8xE
z&|d{Wf4T0*5X;ceUgOJH83dqL{J$EMHlbat#t9g9VdF5nuyJx}z*2ggks}7JW(Un<
zOAU*w<vYt^O1sqkTqij-FmHAZEOT%3;2{gZwVb2~5Lf0*ni&ql<E>*3OjFilU7(uf
zbELDz^f-Gbu!ZOTzo@4F&&%^aPpkdwuLZDc`raWeU7I7%ensk+vraTGEX0%Ph)y^Z
z&kKjaO>A`&aMpu&I2;grOlY|rCQ+en4~+sL=7Z?)$i^mM*Z$n*z$s8bLu#WD+qJr)
zL`NXi_%y<pEz)SqV^Tdt1w6G|6WtK*k!;yqk3bhSMT9Rh;DfUZoUWyR{uan&8ZqSJ
zLp+nAp{yo@V#Yif5BROj!XTzyGQJtjL0{}ywpkfXC@4-BSy@Xs_ki&EaA7MQTdndD
z2d%0R4eEaD$u6nUJBN;{>jZ>Hb#ZYD&(0-m>_q^TXws3j4NG@xNQV8ZB_u)&pIp>-
za^5{Nrbk!Zbt0TS-O6A#ys|4CmR+oNaszqZc;n}|*=|7G;jpN}?LoG`7q@{r&?edb
z9?+P3?4ov#QV-EutGWq@z|Z8^IuIW&Jiqm<nPiPozguPpl-$4qZ1n?9;!Zgj)(sqm
zJH3R^ba$c^kPr8@xm#j1olV(yu~NSQMfTw`KEJx>Ya3QcWdsH2P8ON8s>ESIg)z_8
z=;-v75^qf~m{v*N?Q6q%r$GbQ(}*?(%D7R~?9)Vsd=JqK3Z?bBjBub(nO*LOI=k(B
z|6CgAOS8n^(kE~d7L_(W9AUm8Ba5c9_3x58$lCZMvB1A0UiMU=c<=pi+OU+c-7gyU
zE60ny|DU?se;pwHD;4^`(5!w{UH!`=8vxvDAgwdM{NLOC@r-G?xMDze#s8!3T0^d7
z*Il7ev@$$UYT;05BX;%P-Uj9J-c~S&Ko$GnzwY`E&0ypHds%!Heg!|^WlDQS-u5Ey
z`Sp5<vMM)qd-UEoCOnijM0Si#V|*gx5cL++vW*^;2HOstuk8hJ$PC|!7_E5B)GDfn
zA+*i4s>7jH8yvV8ehNnUlsH<Va-YT`t*ui{sDeM=l0kUyHO_~(xQ-n!PjxEKxwf|X
zhP+4?#~Wuvh?$g{<Pap3pv*3EsT2b2zuZ867DVN6;-O`>$s@^Xib=4+hbO5179@+o
zjjwiB7BjV3lVp{mwW#pf&1!@k%oNF<9`-&T04ME=)7BO`PD?hf2`KzQxG<1Mda6If
z;gKAY2>H^rxut)9VIOY^$J<}d^^Nm$<{xu)i)cSO(6sm)h_nQO!+d~zxSt{lL*uF)
z_}a;u?qjEnfpl!+D9V4v<Sc@&<i9&ucW9_n4>X+U5DOwmI0P8@qc>NdAcsRY#6L9>
zSL<1X0m;y#4v}dUxr0%w9|>Xe?|8t~3QI$f(24^62o|vpIyxJ56iVsH1|DF?q)buP
z%KT3?WG2k7%7lmxgt3<9qXw%0dRHrGZ4V74#+Gj{hoPvab<_e>MEYo{jvF7O#jyI0
zt1lDPXVW+%+k?klvccVLSL`zezb6_AQi%d2Ru5t%e9W7d;x;{uoL)woIsr!=q>z+?
zhusAinh&y;m7uGjC?(*TI6`(To$<W~=TvpAA~!oV%CA?pvWNXPQMI~@I$`AXqu;7o
z<OU`mxfDNW36^M>72aR2B1jsz@Jem$u3JHm!}OS94`zEgvR9&NZQv?(O#@8d%0`lg
zdY)Gg41OQ959t1ZdTzx~+rC{(g*!m%>}$PCwDpCrZEZE4Az6VNCd{pIRoRC<Yb$bD
z#T$;$rSg33$CLMVFCz<@9=r>}l~J}{#8srbYKZ*sa?&MPBkA@S*HMsgRCvy=&o%3i
z>4Rbt9gX;>ArT>Km~><d&`8)yV}mjR>Ux8EUXIYN>HyvjNE+K}#96~&PWmD^b_>hF
z#ZeSP%4p-vh$YmAJ&Zy0Fv*%R?Qed6$$PFE?@nJDhae;&hnGsUn}7<jL}pY;=hW^a
z+T8r6HXb8(qf8MYXx!#sE*`eGc3`TQ++)0le0I^B#yy-jp1aGv`%x9sE+>Qt^Z|{C
zLWch;hhK60yfbR8l5H+0g5QVtN_7);823y>`TQ|x0ki$QV-8QA?eEm1y|7~GP1KRE
z1T8)~zkL%Sco=R{?x#@Xs0=q%+KTd3s#6X-EZax@s*w><;aFy;l!rl|`S#T~a<-!N
zJ4Pny)8S8jUaY-~Cymj<vdKoaP+R1Oc5jdLlg{HuG|M)Wg6}00U!=5uz<UwZM(&MQ
zwNnQ=ANVH^e)kgY9j|G}bf>h>7(41?xzLz)!7?Zw6S7GRsJCWmnlD^@8q6utVQ+|y
z{=^0O@wpM5#(fi%OezQ(=C1CbooFo%Dx9ym6ZNw0wqOYXJ=J&+p;J!iV?w7wl(wp~
zlHsFPg2iY=J8YpV{64L2tCnMu*iwB9<S6C<J-`nFr{1Uj`o30irZwjK&z6(k{2^c~
zIgiIrIBlalP9Bc?h-ybS3Br4P9`D;S&s-dYK$8(0ku~ZS;iUCQ(oJ;^=(8c>f1Mu_
zvq90aO@V7KZ_>wts579(bEYfYFOMEQzV|%er76Xi=T!FqXJYl=us)=YmeTur>Q!<4
zC$qtCkMADkm!G@7fB!AXCr{oRo&WQUfAAmY|1vzs_vhGep(|YyNJu!iFKOHoLeJl3
z*dPYWAmM}M4w!Ot+5$9W>CFX$f9v0mJ2n|1JO$5f1vQU_?y1xX;9G8(+tugRF)CeX
z$68;io4zo?b7D|d);)g2ikp{5jyvbTqChEk<N<p@`#tuDcACPgr3?Rv;7WPo&egVu
zmmA3~!rwOZ0UK7v!hMu<mUfo7>65c}ad>;!YeH|rcH-tlC)U5UG%Dz3gaB{Oz&30D
z#6fIPbmju(x{KtrIk_RLW=!D{^p1({3#hHGsvK2NbO;VnX$hpsv`~j<V%FAm+((nI
zO6{im@83B!q`f^{j+IqfkS+Nmo_RzyC|)^YU+C<q8EMaBZQqYgf|vLv8nLd?#63D(
z<$JF1pbw1MK~-LMh~*c~^nXaomHLU7bnTDHI@95lNz$r#epSKC&_vcCA!Kjm)hXJt
z0vYo7CiJOels&}1<D1y|XcQlROK~`r*W_Ux3u}2!`^cNunR@+5e$|hTYjGMc*NIOO
zurjU14CjfQqafgeT-uR_UyJ$!)ebkhTCApJr5t0d(-g=%mnIKxmS(KGGhb`dDE8Go
zKMcRjS-SQ&?y*k>ilQ>mLrb^jn$K9$PtZd0i^mkaY)u?iPk9+SI$Y8-ZZFAQtZn2E
zQ~H@?mi*Yuz^lfFJTYFlL3sOGLoZ3{LfZ6~S=`k{{fm-Uwi(j>r!mX|iplY7CLalr
zsPnl5Qi2&#t;_GK?ImqvL%pE3VvDY#k}ui`Yx_L+HSi2wnbw}U){fhGVJ1eu)oDzg
zFiz;r3t3Kt9709Jt7rcBlzEIJy2M<m8NB3!{sKUB4w;#6taX{Elr9#f6zFLjYB&=r
zH)-{D{E2L|zd17odc7v$;6dN>v11A^<(lY6^lTPyH`My@-`8)D{JlJC-%u{I{P=4P
zZM!{Sy#?lR)#F1MM_@Bn(4rq=u<-?|;n7$gm}EpRF=<(?{KHyUh4clJ`tK+m$l;P8
z5#YO%=y5p27(vJmo8W6Yze=%EmYm}0)2g3I(a`(wK-*YyV?HQmtt;q>17Djd65Y*L
zp9C8-Czd*PO3~6Qaup316R?NP9I6SQ?9S;0@>0<s&S;<gkeK@v(m~uWN`zw^=qXs&
zCqmJRS1IOxeVX+>x+V^Q>^cyD>^#+!-Zm<$T&#G$qu_N9P@IGF;T^C_TvA=2bFS}O
z3rW3&d2|{@t6=fWgudBuzD#|vI$T_LMR*Ff$L{b&5!S7(HGGDe;y^EyF*dB*k#qbV
zL*T5<0x8?95hnBo8Kz_s@jU%Yqp$7buf-P7>i1u?X^GpX*BPpU<kq6yXMsGq)6O~S
zq2>uf`J3U&i{Zoz(_h?WU4Gt2F4o2*78S|_if7QX)Rj#lPF}1?{Lt!Q7UXq`I@0`C
zm{LE^DI?3==wAL?Z5N&2;Vk9ZwLLbHdPu#6f;&H0O~pH-mudntfZ0054`}#wEp<V7
zTdL<4JH>c9N(&*8l`iM)td1Vr4jJb8SR1T<Z%WayFS%D#;gSvU^6yoqfzo(gMZB7<
z<#aWq#u2h^Eu;~8R!c8#=+nXZ6*YxRV8OF0_Q%z5y2js1ARk)YU$2rKFDlsn%sTU<
zygM$-S{JqRVP38FfOpGR!+XmgJ+`1&Y9am=KT^@wWSI2MV~dnNdKEGCaDF6al_g^v
zsW)49X9U!{`hIIoTsJgN4r|JHerErcSb*(1OW4YP<j(CExV%oWe5uyvFWSB@Q>Z-_
zq#BDCxT}+%V_Z@EJHL6yxtR(@$vrlFfjhQLnPXA&H1xfivfAzn1(EwVFF$t7K9+y0
zr{wI?Nb|QarE4-#y;)UHt-YQT3Yb}*N)Hc2?u)3dZ9cf%aQ2ZkkN-<%R9J~#ciN0X
zXB$7XV=l&~F05t;$J3#yWUDQg+assnDJO=!RV^v09d`vBpK)q!j8P+33H9ERc!Ipz
zuV!pHwFZmU;&Vzg?Iz?6_kAJ8W!;-vyRD8<;euVkr=5PcaLUWeB>N2PRu97bLCrTc
z5V<z&^%}WwMlM4!WZAI0Qf|3&V3o*&Nqb;dIQb~1X!mBQ{1-Fq(|OGVP|yUOKY3WE
zj#gd&Q8lnh+m;}myAzi`vwOAv(T|)HvQF~vyK;J02ZhKx2Dql`oqnR(y{YQC$8E(g
z6mV6hoiU@<rd;TO?P32Xu3k0XR`DV*hiJjN6hY}Px$|&Q-HP+^kI{#RZl972Ik|kU
z<+E4D1xf7{+HNdXu2acfYDT?YS*b`|T01Voy6B5X?_=_%!P#4#h|Da^?Kk^>S1ami
zG#khD*wZiIU-Xv8#F0Dx8qCHTU9AQGfVYxld&5~b>3q)|aUV1sGoQcB24(Wp{}@j4
z<$a4xTbX6r=4t5J4fxFm7qq&+mBI<9$mo3_XYRA4&!32q_GvR6PIMal9zX3lwpn^^
zYy){Sv?a7cs^^o2N&J*rXe>&3Pn$xN*uLF@RuXQ0ku_fiD?7VW4t)`TSoV!EOY#5E
z_hNXj8m<fgVpq$lYQ8YgWhFghTuG39W@e6cYS%xkO5Fv(oAKKX4qlG<gx-@_{rRXW
z2X3@cny{CnWdbfF=Jly1kUy6|u40QP|IpD;jqJVG5=S$(jbROvEzRyrP*uO%!`}Ii
zlf>>_34zq~2;ei-lmP2~TPT&U=4z3Lwz=yLOLSju+;;6^3O64IG$zAp*lwN}_`ODe
z5a{}L<kM&*wB|NK+C6JC>-0OwiOG>*q0iGn>TB65Hwi)B$&bl(>MNe%j^oNE@fkVh
zqy}Xbt%@t@m}^P^j<;R}wJ?*+P}1(D;4|bebB}^`sz5^v=2*9|3{5l8RFJ4=m1mSf
zJWD63YMU#2BkK8Negj(M-MDNObMpmeZK(aKNraxK*VPO~JxM_PpbgI<;erz_-_opS
zd=`%b8k0R2EUF2;o?ZxAP87+{kEZWo2&vmz)eqPl){K$=NIuz=^g)<j=BXVo1Y67d
zD1A&k^xflbOz<D5>MNwiS_3tZ9e(rjO<?+d@c@$j$LR;QIOZ|_Y%nSeFZ$pWPdHKr
zK+X-~>j1>UvC0zx^2<xbW3^qxZ}ClS-#eorr*qzl>W8Xc->D9Z>toE<?4HxpIWpZ;
z96`SQbTkj-1}(Tz5jd@AD3shgrqJO)y!@@v)F`GGj->Y^Z=Twxl+>DlI~E|BE%5wC
zM*k-}GW~&UfQ<!Rwu=;`58Sdb+uA7mJa}Z*a6CRBzD%+!@N|-uJ$GUcap%Q1s5sy|
z4&$e*e-Bf#Na{WJv){6!JMOJDsmCcJ4M6Dg?^Io=M&JiZRQMMUl1DZpTK~{R<RSZ`
z@1h)LQcUy{T|LCkPBy<axe8J|dvUnd(hcUJ0Uq?A7RYi-<n?XfUZ)g09V@GjnhKfP
z>xa0Mko_lo<@5)Gb`Q#_cQ3cD89#t+6f#Z@g=NUpcts@oy~D%><cJsWi!~>!KCdf4
zu1VG=yPUL0z%k~~$_`(a6PE<_ANfB->#d}yKF@e-J_tbTPrMpH2}u3B1&*1*k`<I9
zKjx2uEa1GUvM>CMtm2+9^uDSx!}zIKvF7u+JNbF5^%fq}*Nn+|Q8#O{We)mD7g+`;
zIW|SFAGIseUl7Z=8+ouvs5fl$p1rho{x!+d-D3G~6ksU^l)m=f5Q4pjNF!iF%E9XA
zo#gWto59N_tEXO)3bIpUuj5~sYND=ptn@-;M+<L4igNoE`rl?X`9+*OTaoiK%tJhg
z>~atAF|Vuz&84F}Idm_T5rYC*5T)FrOp1<MJ_t6e>3PCGLs~K@t1@s{uSl$0>-lrE
z{gKjm+b8!^a%M@G<FJgo%qlbB1b+OF`C<{6S;njQ*+jGXG}F%o^m(j@9Z7i1<O*nN
zEz1LC9GufLPOZKAe$o5tlD<K*_H)7+-v#`>`edW=P0?dz5T_~il`RNPp-xE_ARdK3
z`_-oU)|<nBkaEL1OY{u@f}vF*{=tO#BL)!Y$(t0R4MhI7_S;+l0VMUDUJisA-rOSm
z5xl$Eot0r=zb<hD93gJ)q0#80M_+p4ewFFOq`w@%DCF+I^k#?}vvpFk`Vo5R^oQ5L
z@lr?E3^H4_wPZ&K?g#x5<xMLuG(r(*lW&z0lE%}gwnJ-B=h@dqSB9l*b!wlg<j4dK
zXoMPxv;B6E!h(M*ko+Gu_~VAf*P`|?S^-XXEycX(HZWzi3KKKwsB}7e%6dIQ8^;37
zEr=AQh0GI{57u9MWWQ}+1b@&N&L}biB{$4D9r2ys?}l{sGMW$x9$}clYr%)dgOn%=
z1&f!OqUZwfN|ZtS37FZe8e0<#zDB$$tTmO0uiGLC4LMq}<t0Gy6xGcX5*JVs%uZFC
zUfV6s$<}*2o<(u>NGbC8J?xOu*`l7)u)<k<pQ>QDxk>h=nUS&nW_8Q#okuxAUIw<m
ztC<e1#OxY3y!!wss^hV;Ai2+^EW2CuZ!t2L0N!TkxSq=QA|k6mA}dTpt*nR<x9Hh#
zlx6yY+?4n#Yh$ghcnqU}bb9!>%{?v57+}^OfWp4@Tr!V<OdnV-;P<J1>G_4E-!(`b
zz4o9Va&Tj*i}Lw!P#bBvxEz;QyiilTcvz{<#&+J-gAha>Tk(A0s5QU#y?Lg;q!o~-
zY=w!XN>utFTb?F>JcrF3DvE6eFP9w4Czz>&x!ZUI3zM6D1w^?bvMF&C#UQ1Kn`BX4
z9Jk52h}SmyV4sbAjT=tfJ)PeP_v-Q)6}v?bi-YW1njuT-7;eoRlOn7@aMWo>?!@-W
z@neQxfHmA<ui=LREM|-me0=iLdDDF%dk21~3CbSL$v_E?*W)XW<hbd?iOuwFcai4L
zdN;ZTt4_|Z#~#ru^3x*?-M^)pB*DFNaJ=n%l~TQ(;R{y}rOA}JknB_*)AxOi9}n$_
zyEuMR)vMmVTV=45(;Z~ua8BE}6JhkF7=ZQ?@1W#Wvcas}8XE!gWfcfd%|<GUBnL3%
zMdf8z>!9}@x6OrVEfhApsSRHWJS27_+y{Mo^NlKE%8dKz1BJO-+N<#C*QWW7!dEl1
zP3~UN{($pMe)}sXXr#~Y-DX`G*O?&6V9R3UUQ~P7!nQO)%d?5SEAdf>pzv=wGiKyd
z;n&9N^^?N0Gr{#AFYQ8<sldE8uU)LTax`CRreCt6+qnJLbZgzdH48pTb;D<9R}4Ld
z(aq(&^lx>-_A3u~&K;kL?@i@zpA&mA%9Lpl{%T{jdg{ziFxD<w_{f`=VZCxEpE`cF
zKcp$Vx%Ag#3CSJtatr^}KW1JE@-Mtqy&`?qJ>IZi<)GnG-+TLY(VP1CCCf7!`;2$G
zf--+9)EwPB-}1S7_E!1k&R_C|^uiclblfm5_CaG38%KN_r~1A$H~HQpk;A%6MjrzV
zCBwQNpI~Ml<YnA@nWbd#gGZJ->gX=(bD!6@Lrqx$owL<XSl1gsKH!$G%kJ3vXd}M%
zR$p_k>1z&q7L9ISgXuTC^U#`<>#{!PC(VF$u~IXen2F2p)2I3BOU3Ur%=qyvhfN3-
zXdS(Ouili46)UeZ{s~Knk-IK<F5a+_dsBMiV9eT<T;UJH?vFuFDvs*OYi0K+)Z_#h
zZS9}P#1i*d3+Yu|foDD$=c|7su}>2wKgF<)vHKt=Wxpn@AmOE0B;L~;UI~c*10eoy
zh;FBUGvV=XWk>!`<Nuc=Kz8SEIM5&%iVM!X*QJa_#X!g0JA7ZW_tJCrGLI=Yb5Db4
zgIEJ^!_kK0p^BBHnlsV{XUKP*pM3`o5$N!}IIk76yCYud)nxpuld_LMGk-(!@`tc~
zZ=On`h3zE+vis?VX90=YkAUXBcIt!q!p4oVob`0anBQI)T7rrxCbSy95uf-F+|w<X
z{@6}x(!K>v_$xnDlAT1`W{EY|b5{VLR~|Sr8)WY#OWms})bN~QLU1Vj?r3nY?a~A(
zCTLaC+^@4hGp7eMw`J}Z$I(V}y6uE~i!3_7^jz?7HJJODdS~PXgM2{K)wV7I%`fam
zSK^3iA-bH_87iiEedz(9n_H3-^e2Km#Qn<0^bR`h&*Jk@d9Yg>6T5$$*Z%ELtN$Mh
zhd%BPJ+~Y_5z)c}q4?BNk=V8;Pursy)E;ba*>*)z9bjP>mU-hTKDgeEN9qdj?d`k3
zpF80^`Be+>IhnRg$eiWhFk<?PuKwAi_}`I|0s0oqQGQUJQ<AYqr}_jThZa&kJgf4@
zqlya=<(3PE<17nFZCqUwDJD|<Q1hA1%K?g$)!Qm9YUJb2QR*1$#-v?D9?U;;h!Cs4
z5aHJaUBWZ-N1<5q(MQr@JE_GwA?LEad&~JpUvZWy3+das2VME=<0y~QqxA^1|GD7p
z^=joyso{(jTCe)kFXvy!qR>2=*8-9Gi|-n`VON_!pVJ>r6<Rp8v3=XZiAt*3zd7|6
zElJ{I5(<=~lefqVAroHRb!X73j)HXOG#4HG@A6t3W;zMIL!KNe)pK}-W|bI5<h&wf
z9kDZ>b2d?PB#zih)2moJbEK#@-`%3Y`U|3I4JJBvw`f_}cO&1lMZMT|Mc#Tu%&I)h
zNu~BbmdcjHSDd-$r{D5dOTR#}B5%Lqk^te|K&0mHfuo-bLYB*q=>7CFt6Y-Q2(54q
zc6Vmb+H|MhjeKhKhK0AyPoQA(dqQ4G-YjA?h}l0KiMV5`!Rw3s!YsP95;m(Y{l)jr
z{AE9kA!xZ=D*mS9*{)|AaM$qD4M<C-GI?Aw;C6pu{;>|z)lE6GD4!E91=ItRSN3zJ
zb`5)Ma$^|EmwjhRj;JDp?h>?>ri2df&AQ$I4SVtZ>_=X~#o|SJW!62hW{Ld-0Kpqj
z$X(|~ORO?B7n_t9-gA3wLq6&Jm{-)P$&*XndA<~`=J@Yb8t~7B!J{g116neH*^$7T
zc3g721N9^Ib@<r|+S}2KxjimoS=O~3V>eU>?g4B5?T%9Ap6185cTEd}L}9{ZP9L7q
zE9Z7a_%L=)-wv@X8g+14q?kIz@Qxg#hR@Ci)iHt#cD@n3F8%AjTP8xIlk;ggs(FiN
z&4yJHsuCgz!g$(T%xbbG)2N>)!~Fc&ihqV<k+ytJxr)3~yXz9DjT1N9rhenS^+<&~
z>GWMcepRhYkb{Dv-B$3YDqn3|M01y#o+sSwoZ;`?AUNdbsQV_lGTRR<)M)TXD8FC4
zy1Z4q*d|04?f<m3Sll%ktfyBPyzF?BG_WjkT61-^#VWmzf34AKX(C}XTqs}vS?WJl
zu{U}gG6Ehd7BjS%8H!!z&FoK{?9BUUIV+O*mDJQN^!Gkhgu>&@#1=ScBO71QhcO2~
zlW$tTt4I8Er^F*$N6ailY0~BP(kg`3tkW&v7+s_X63fa(n{E762fowY1h3Q5=cOX_
z-x8HCAzv^BW6$6}H9r5&-EcFlE#$?DMreH^lUVWecRv!<4E0onGSU33CJ}Xv0lP#e
zrLSdiEf4lGBt*@Ks_>BIJ@!In8Xni?L(I~RWa_GXFT%ZDr__H*ccHj=r;07z3`ir4
zOV;4cNnCi#Z4|3xV#_6$5x3#4*Wbv$iI`*IN(X8lpe1CBG!LWUG;C0`j2(+5nJ@7`
zrK4bkfZKPTfDvmbu5G?^>a|+A(39Ajl%#yySZ2i&jHfJ%q)la+Ef5c51!ejLD<zf%
z%VsqA#2hfC_pL9tF^k}guvu;CFMYBR&VN9YA3v^%*o?j^my|+u3W5YmuO`V1m~C&$
zIj}H#ok^vY=nDoxZ32!4Z{ALEwS24G5KZysTl=gnvDs#ou5twZik_(xY@}X)fOV)r
z#wF8QzY~%~{MnJh7jzKA(~u=!v5X2XEN)1We^V*@D6!K*&KFP2QKa=)iHPsnk##{6
zAZ|5F`8#jr-vqzy%$sp&8*2^0MtRCc{YmLr49q5M)9<&4w)%Er4a#PK1<-S1h?%Ow
zEYR1c2Ws@Fw@om2N>Izm7@fbahu$78<EI(7oWNr0Q%uQg%wv=E6BE`kYA+W4(g<aN
zoJ)EQ#aSN~FhEN4^ooqZkFn~f)^9E(AfQN^FE%y0=!hP5kLH0*Ds*62-PQ#9aZ4_5
z28jH@r^dz7o;qF(QqYc<*I%lSHs{ZGmoRDg8fv_5n>f&}#_Nw<i)bW~qI6GXtCse8
zjrdAP)>f}b_pUZ%D@vUIvRog-+k;<`z1em?=5?hnuaJ5Hg_j9_7B094Z9pe2J5O{S
z0MR-cS^53Dnn5t30sT&*VX6NyW$vQd6#B%IwL?^W_b;>(EY-_TE@%I=iW2!EXvZ{<
zY23S7i8Q=m;B`m?aH01cWU!-i@z*s|CIw#_E@A)jmz7Y$P%~l%e_wbKbx!lU&w!|Y
z1C%AUp7ix{J*w;rNqkgTwW)R|{D)plqL)5o@1)cB_Do{mHr>@?;ri$^`Ny<+?DP{>
z@Ag*b6P@B8-&4jII)**C>h&rGU)WzMPNQoCk^GrzMIRZ;*qt!!$ZsDKSqA+Q;Kzc<
z=^JkfS}WP|ovC~wFL~XLTE<!_8-qv<G`iMA1ijNsn8)#*zsT*kC-FjXYuWkuI{RDh
z884em8zp`Q^(v=4S>it*I4ant{;p@|ZIz7&<9En~lu=1r6V%s;69JCiTWvA1;F<3^
zt|5?#>n*>}Z#<o6+9iZX>7Uv{Kas!6NF2b)J=~Pg-+!@|_4$`rrv#P^Oy=KAxd#`f
zs=k(oi?>ioD`hisuib55Y<}_*JbnL?amh3$cz1>RT~>+w`hut5&?gy#H;-TF8<#DG
z`JZ>m*vr?q->2KdG%f|BSTIJK4|WuQ97gb{3wmrHc6m7SEXJfDAx5AvbbaFKMJhPP
z3>jtGkboRl+Zcp)^w^FvhM!TkZt#XV)?{tJDt4U@Rz?3A009etxk>dY#C^@>wL4LE
z6>x$5<=(RX1#8P?Wzj)0{nlQr*b<J(j_DR9gY^sPvGF%dJa>^P%labG=kw^qR8I|-
zxU3G`%>meeAG9{HQI&yWeKr^tJo}jP-KF|RxK@p6dUZbCsgJZfbanEq$<ivyT(C5O
zfV@zZFsQ$<KI+IobuyZX3kAhHz*hjB2Bq_TTk9Q7;Y)#CIyNrZC)D##@Ot7<jX$>Q
ziT!5+vq?hgr$^feA9%<pB#rr;n<}W0lXDz3=r^jM^~mH!$9krUYB?*2XCy$+Vs?)E
zQTRefEvw{lRE`>s{|IxlH<HzVl$Ma+QAPAt%EnE~l)-kRdp;kBkjBf;YY%?7(<k3P
zjrn62H5D|5{@WdS6`0SXYs1qlgTW@dS<E$8ZLLc|f-e1p->3;4gbp66ddK)D<R58n
zdE>)ZeR>Gc{BSA!C!|0_b`K9_D)bI~NliVJx<BO1-H;OjW)>TzG3q#86Zc?}X|@1C
zvPD4*e3Pc#;F%>*zQ$-4thA{CGJj&nMl!rv<}*yqpd2Bt5UM9}ZToG>IelKaV6Z0B
zrDkWN>G0wTvzRje56V!{nYX%dtiy;c{{Gt{KuJN4Cx_d|h<^MKg1`QT#Ek|^b&AF5
z_4mb+6ANaI5I(^aFJcDj<@eKpH%y^&WOHi@;Gg31=@sX?riUq)9rlag2<7^2h(q}C
zQf;tC{A;FuCwC*#<&OcM!PdBbWv<NmrZ2Ad(<tS51b_A@Bb7g>(h+Y?ap4CM&d9>9
z6^Mb<L(giSLhLe6LkU2`gUerv$MEYcU4s|9rtcVQU1263j8M~IMgBhPnxX`oJ#{{{
zE5=xvwlS)@ShL0&cC$>rA-NguehVa#{tw1x6r=O1^oZVV&N6E%N5q<^(S)IjkKcM?
z!i3EiG-7+R?HkoY>Eihg)cBNzkY&QP))4B4DE;9JBaZb?>4p<8zbK=%t9mvTLYg$%
zAo!&TF-dD)xtPZLqmLNIc6T4J%-?Nl24xFPDMz}-ZxhsKBM0Xvo*k(VWkgw5qaz1y
z(O~cXF`K0L4s=;d-35aTBkhOAZ!XY+eqWwf=!i;|Po0X@e~a7~dMLp6X10G(kjUDF
z2;NZ8%|_v5694M-Hh-nya<w+KJA9No^021FX6(@!$IeaP2+7<vB)`d?$RS&%>Qnuz
zZG}~=Ms3^}DTWdKG`jB?IrF)BI1?6BK2e8!B3F~*WJ1`2x{v3w3u5KJxYO@;M`x=v
z4>vqtmM=1nkxq8Hc`BVgcYt<<o0m(12R#U1yV9cDLg<><(jPU9pYVm8azW#$dQ02q
zCPOGoc8|o5<1N8^sp>cNhhzEb5k!Nu*j440&uIi+(c2>uvqp6&3LjyERC6Gd+vVY;
zY*~okZ^6Mdh$^vC-I`B9L^)4r)_bp9FisL8HV%UVTMZN}d=L<mId|z6hkY<!%5OW4
z<0h)9+50sUCWEZ%XGA|CrQi!!IzVV~QD0K@(MOrr{-i_79uHa%C32#PH{tzDL%lvL
zYo&6O<|y}1OF+2QP;q2-&T(&2zR;+!$=~6gL4tFvbM<D}0C<1)slq!Lv$og0b&qwv
z`JT)+9vaLHymXVprVB1F9`QM;g9{hGLGM5FLALMoSIFw#Iu+y5?=P0(ipEa<YSmh)
zTWPbSiLf~rOmWngj*PwIF)HWdOVw`4A{&E_x#(b$^1&RDzxi0jY1+1Su1Vsr!AK~q
zSFpA75&!0;Zt<_k!;{?MGT2B?%xGQ}K602HA{Fcl)X6rhO|IM{dM(%2xU+(<&+PV_
zn#yMAEw=r3Y)dZlJ6lia|0B}m5KPXzeo5s2{QqAjfr}L^Ud!99I$1X6I_%eu3~rhH
KQGV0;&;JYI6}x5t

literal 38228
zcmeFZ2T+sS8$O5?%N0}vu1J&Kxi_dt7m*Tr?+~SmKtOs6C|(O7Ei^-mN&u;m8hS)U
zIw&1NlPWdT1PCE};{E;pyR)-1TV{54X6OISki?jr?|g50p67kf`4VNQuX*7d$2kTD
zh6_-wd&Ue5r-B$5PB@%74c;N7zS{s#ryX=O?}2A<?5ovBfTy$GT2{Uc3|ueif5+nI
zH=P+6cp0GgR8502S0_Rn%zTepH_6HrTlo|b=*ufV)QwD#-#szyo*J(UY+dhNcY3p_
zcES10fMKi_Qs?h~UTEoJpm4#nxqj~iOq26geiR@`#CW20g++bUP*VwlBp0H@6Hgsk
zT-~LX63O+XKw=}wHDZ`7e(d+G{^!I0v;eRGhUkUtP=tr)h0X;lx!)|Y-W{FtK0L;7
zCpQ(nLXU6;tIMgT2^vjd@nK7=;%8DylquKZR2UdO3~c$>{Wp~Egq+zp{;xgnu&O^{
z|Mw+Bp~&%u|GYF*FKgofuRr2s{{0%S)yW`m8s{si1NMDONO`SCVg+AA!iq2#kWC4d
zwtcQhW!%wJx`UjiwtTBtQ6uIw^6t=)N^<3$zM`R{oU4_>_FBfSPz&g?P{9j~fuIt<
z8xpg|9bBiiEWDJ4e57+=+$RBzhgv7ABAC+rsxe8$HfktGY8EC?u#taB5US#0s9|^)
zsh{Ha4kP1y&>|1@(hj-Xr+A8Dn_Kw)KuQ<(f#3IahK_tsofmIG6Xuz~TH+E8-QJ)v
znQ+6RuZMF0$LG9#Q;&!7_a!!kbe`mT?>1g1WTg=b6*nFso<`kwfVk+0`PMeVJ+TjR
zH^qufjq_^|+<a~iBIWfHO_QCtJX>`RZa0T#;T8qC-Z)%UEtF>YYpZCM5Iy*~#z0=B
zf1bt}(r!ML^)qo%ML9UyIsC1+yCw=#f4NeSOHg^<U(1IF7cw^`!g9xNxo9WO-!zZf
z0v!?^oaNbisD{G4JKGV`!#@=Y9lA-tE=31L&=HD(!EEGyF3(4((31q|rOiJ`w7ScT
z&nI`=C_K<XCyiwxlcA`gBc{YkxpqY_@!FegTLKg4g}+Cx%S2Yczgf|tpBLxu9BICE
zI;j8b2bT(Bo;BKGpK%|)Eh=t83{I94jcT}#x>IU%M6l~~y*wSOeW0<ho1!Iu%pMD2
zIW+{^Xd|mehOus@_mC(uzvuH|CSAN@TJ=4{-R!QmF5ON`k%I5}oLB}T1=6o1VAG?4
z>g=`1wZvyD`zPscEY&i^eSx0sIBP5kwJ|Q9;zNmEYpA9`Z@x`iYs%0@M9-^?QLLv@
zUDqYzefS|N-g|EIiLzoc^LH9_Pc#@oJCpY`6Fa!4OI{88Ra0&=Ftpu)@GF9eh<(1m
z9dD4}m#k}e)4=7*dNb0x^JI~-z7Nf!qYH{ExTQ_pA|*LT!rJ96T_!tnj3pM2P7<UO
z2Ra=IHb`r2v4mRAqd!r#@)Hf$3qqp(b%wQ2yk25)X*(~ci)O?K2wAH^r)=ykZh~~u
zja$=@*+x;B+&c}5(0swR(Aw5n9<75R;VBK&kK8rEL6g;>m(B-zuEC628SPz*wnltb
zBd+QVdEz?%3AmII*|ET{!-0^T{>!3?eigbG2lYHrpQ4A`%F_h6Bv}~RldekMZWczE
z8au|9H)w2(;Q~v_x11Kb8n3G!eCWDsq*SsK3~6S;Un+SSGEkCQC5=<Wc2HYsVZrfd
zxYmsH6PU;%gr-K`3d?M+@9ttOPP%@&RE)fXOa?-B^uRR9>d)^*jd<vLDb?EE9HwyJ
zKNoez<`apW@rsLD2PsH&s#9L;k%ds(wYf8s3YU{pmd{K^hv>Q1$Dg2k{fStQIB&Vc
zTPpL$NQ7x${}z++>y7q6v?e#6fvAm2^xI6`n~Tz3o<Zj03=Vp@{a1)dtzJ@nEJ|Af
zzN3gd_>PEzn6FhG!8a}ctE#<9!O5NSiNK@+gT%vU^-&r<r`P^O*9A%b8f@oBpbqoz
zLDkQrLxQOye5bBWb7j7bD>xk_K6BIV5`y!03?BXw6gydOZ`}O{nVQntlmpow4=E60
zx$o17(^=1%Jv`pv8j?JV`*Y-Oyy7lm%XT?&Q73*!MzN3Z*{xDkq%*98biU)}W;?sn
z6N?V`l%6S?4>gUp_*lxfs2y-Ho}-@@go*gZPi<pZF`YBgFg4ahhpC<pVv+Ptd-MeN
z-oAfyeHI9U6J5>cK_teI6}fr%-7<7}XGKw$vhc`SKNXZOiVqsASluYSq={}&fu_%t
zl^D8|wwpGrZSFlnD>4TwHnj`)s!GMPwuf3rxhYeSx^Fk!<300*+A^ZTP;$l-5~-EV
z!bI(To*Z81BBGEnpIcnlc*SOi)56TIO{RFp(emaVtya-of*9=0!;~kF#7rm?jI&nc
z?dFfI7xBURYnQ<!!SVK7t=190RHMcZWxj%v-tcSta|SMDl|^A&=wHZ5_mfx{@BVQ#
zM1P|#X)&qQQ&j*zdol6snS+Ujt)3k>i@fFiWCfw9LzA#A3e^9rOZ5T#{Xx(~b?58*
zJ-N$UrBgp2yG5)eu5Sx;b|mebM-^CRr`|9S+JhA~Qa%pbRvAH`g^_qbSY@zO3Z`6Y
zVBJse383f2)oBYe1_nbbMeNrYt`_m+0fRnGNOLNOlv~5XVRF}J)u`g_(FNH?HAQ@N
z%DxAsd3MvZclIz&ExT*{Q@E_OJ^z_!(}7FmF@Y`NB%w2-%k@jk2^xsVYLh+|ZbC>4
zl}LK_&z#b^kyPRB6J0`39vu#GmRE|<h1RE!#_E}0B1mSLZ;LxV08^!u(Bqtu&VX;r
zNBDgUYKL<_MI9RVb!x-S$fkR-B;DNNEM>x>6k&3HGssykz9@U;+@97AV_A`u5+YMt
z#9tNfgJR2%k{mphw2Wor{iG?Y3I=^T5R=<AdQf94!4Uhy<5<>~wVQUM2LQ&rKS;p-
z0KO4e!b4)XvP9e>yEtnKehDq@)wEV0VB+zn<9boOxA1Q4GiIhAe{`mDrlI53lnG2a
ze6&ldW&&qNf7LI^+w@g*P-~C!kxl-=hu%=V5`4sLBKh$?O~j|9Yk<j2??L36X8uQP
zbhw^Ho-z{y1E&w74_JQM<tgR}!r#8!d6Km>VTT#od3jDrc%8PZ>{c&iloqE<7!pXt
zmv1OAEq1;x)>&L&XXs6aF;FW7wgS%<@`;`)CkmAhi1l`#LJEoE_=R$iFM>TN&-`hw
z@oV9}5{uA-`d*TaAeX2N$1Q`hrgUOb{`&#as5x(e`Z-F+pnDx39m>J6hmybNG8lk+
zj>ry#;)nP>O^?z7+1qs!r&F=I>nf?ClI06kyh%=x-1fT)1N;W>47jm!diX6Zm7#oi
zZME^_TJcH}w#=aEUQU6DovAo$0cqmiZh;hw>Gq?=lk>OQYzR5_1ZR!SI!}tmk-ze1
ze%O2Uu19-w+cw^RXB|yfy&A<Mw*1HiXab+R@qCzGet#<vI;a1nCwAd`p1DQ_lbdO+
zhZ>aFtx*ztFw9uV{K$9FT*JQg9oiVi0MtyqOp(6vH;PC7@a$#+hOyi*jTbW($TBKg
z@!<`@^bFE37eh&@=Owy8u{DbJYJGn3ih=DaXCglKR-=>&`n#iSm4e*yH0^cYar30J
z-|`C~O?l6L!^^RM!|_iMnMlb|^%rrQL1E839eejy7k!{f&Kjr2(nNT~Sa=U(44NK8
z2g=HWHP%&^j=S4H&if7~*PaZoz#MBf>+bheCLHlDK~eibYm23cT{AYW&W$dAkFM6Y
zkMJ#>q$7>3bj5~sY0#;ej3C8za;DCauMB!-bMF{3Tl?b_ZzFLtC|md9ST;>Tq97Jy
z;B){l6kEQTwL%Xla1=gy9Ic1=%f1hV<mh-UR-wuB!bytOs6CkmA#aS({FQ@^8)Hds
zn$FqpRUy94A@dYlp;x|799kRh2<;(W=sxK}DW|9}K>lve?^&8W#DqY@t10mET{nS(
z0auzDOQnL%XNc-Ar|dNLq!HLV%qz_!2x{1*ETvitXwe}-5!qj&9N{RZ(4@v-y&t@0
zUGDOP|G^U@$NtfUugobqNE-C5982Cr>3(n1ac16l;S~8$^<<Wq*r(8ht`FU&%hkG+
z)7VT|6`+(V1_MnB40NEOVI8%Cs)P#X1cR(hS?r~nO%35%mBlqLWkMv=#tT72ngcbj
zlcLli{mJGMcFBYMn1LbTSl&Zm6BTx6Nb|c_tt21Ro#nYdI)ZFmn)>aouJRARM}`Y8
zHQQPUoT8-+s4mFqe<MS-OG3JJY(@o^>kBOT3X*zBKOrW!Oe>*K%&3H$J(5zd=kj;8
zlb(4?-2FJM<*d60vI1Gcv?bvN)91r}3G{3Idi@g!*l;HMlv+iEf23c%pq&8Ap-%FU
zTN1`s&_AV;KlZ@v)+H1#Ob5E%--g#(fOuf#Oe=oMKw||Z4HDpqy7jl58#S7>s)>0H
zrH6~W-2A>)5$Xr#dG_J!MqB6Ej|7&2P&{^*uSzZ%n}vCAa-q}*!?hFOk(dhSNpznX
z3f4dR3-HaS>ng*nzycp_g?-QZ`>q5u=-$C5b~VJ^bdn-cjrz1vPo^@E3tD3d4pu}P
z3S5GvM#;#<34Q|AnI+qH63W(Qdy9&?3>$*6N~X94DxqrsHb}K9Ua>^YQj>HIU7qVQ
zJS^?{yWMPK$EFBwCtxSS@>L?Sb|*9slP}n9zNPy4t*O(L-Vok7GTr`sjFOmJKIGbu
z?818INQAwy-N`jP1o87EG))VXBG0?dP<jO$`6|w!xh%U{rc+T6#M?B?wzEdrQ1VVL
zCQVSO^gcUx=a73Hgan-XfB8r;68UX}PrVv9J|P}wry~?$;L9o}f@@3vI|#25y{7to
zyp86gyYK($@xkG=ju;TBQI!!DeMd?~HxKrM>=uHmI{ei)D$;}tO!M%9b!#8{7C%$X
zxkBbX9NyFD7$9~%A*De>pOxx3JMo}lwH`V+DvOi-dB$%_@Q5Kn-xZEDvhT`fMUn{3
zM0~CGY~MUo-mLFWK%!$(OPWv_feXo|vQg#f^2>@+Q9Js=R>7(5oY+|8WQt$BE+wYe
z?}1>%_>5P)fK}QUq}4qWqmHcZ;EWj-q)r;8-7AFF>Gv_3XP9eqNEO(pcJv4kb<D?>
z&yJm>8v$CYW^CGykmH2sa-jw}s9}ei3qhBr)vLM0`iGIV-91KJf-K%q+K-aI$nRP4
zn&5LzjKPG~F4Re-BqGA%yhl^@DK^%@>)xX-zb|FksMi<y;mc)lPXqLgZb1a<p3)1g
z4)SR46qg&nw$w#y5te)UrlflF1MkajjqrFuBlL^_X*&nwczHEH8{?(8r74hg*?SZ<
zR9}|jaxXG~?zcO}XEt1zkJ+0hOF<B8FKsQpY9=~7jgRCUZqdtj*ezoFbOdqp`tD!w
zI<r-n6DK)AA`ol+ZL3Go{_SyM{^uZ{-0|i)=3^BCTmt<I30-ZoNrH{{bLGA`s_a8@
zOmmP;I`6_+iYpZac2loDPj{Vor1j|fk~K75xD3l&g0RcHXD!_VIktG-IWl|DDNgMv
zrW%ryzjnW1_~GzJhu$BdNDgbt2SMMa%PEzb3ok#u?O=L884#h==BPlou|O<?7wO`(
zy83>SE-du=Fze<I)4aG&AG^eJ<@hRDGt%Zh?_26<PM~nGhrxV>l)d4xNqNXq4TbNV
zJR@<#)-GbFWbu`x?Sifw$Bdv}!Xs0v(WlY~f?64`nTrv2e|~Ce;N)d_9j9H_#ovu(
zJ)g%`?}2vkG;k3sH0|sW@M3hQD2c9LV@Oc1O>d<up<f;oz1?n-hodTfT^(+cVm(Jg
zlcuAst}b^d*E}Ysc)lN%KfpNT=wVY)@}GM1x0s$xJ)EGa^<@lAWeIT2RN(W55cm2t
zqr_~W9wr`zFH5=I;AQK?<g0_6z1iink5HmwkrE0?4cpEOYg83#nC9|{J1J`P_J><t
z*Jm+F#cJjhGT*KKl*7-Gjff7opl{TQAxhmqyM%HrIVk)yi=fhVw^;ruJLIx(o5h4S
zIjf8F$+IeALsV;rL_@h+A7{&PANvsT&Bq(pu4X>6In1s<Az1RUnoJh0xYx%ECAt$&
zJ(C)0g4ut8q+O3ss5C5VadYW-9k5l8nb@}ty;}GcA($1;ac_V0dv_h}#xul?l&iU3
z(tDLwE=`jwb6hv%wHAEVBRVDeD(x07FC7~TOe``fq0~asAb}}6m0jCM&$`+=#-Ib+
z-cVfUP6Ze8mR5*y-zN7G(Zr*y^bxWY8GK<qVJYxkji2j&vYpYIo@ttfIjY}zGEm#u
zK8N2q&Opf6Eyv_MYIyr{kut1fM;O~QtEjw*43^4_cGgJLNFH_P&4?DHHXl$zpz~K(
zSMH#UijX_ey0Gw;gDIj?YdGwJ_~x&l9ZpI9#9KU%pE@9xrnvXh+IM0VTO!9*le63=
zeNepK*$%{?^dL+~<;{Qiksa=~R9=#J$X*6@`N2+E2$1`bMbkiiO15dj&MC_}X6ybn
zP}J}Wv#mI0t0VM^1HGt@VlXvzd6)T0Cw)bcBw7?375B{M$=?&zK}vyDS8V?HWsN?t
zN53s~oaviN;H6q@Wv6{FErLDSl@eSRU)t&C?O;ydE??pAA8$Rgufij4yR~Do>f?(e
z_&#1*(;2RZhK%y}$_K}P>`dCho!o8c6GYp=BT)B0%211>mN<fwVTo(IMLITU`4L&{
z{Xo8gY9q+g&o9HaRuc!l38G=wSd`c|<*k<TDVX-%D;I>t19Te}ob-l>CDgEAL94ne
zWnNb(Kltnsnp{V7Nm5p<f2%)p3ipfdIBdH}JR=xryVg5ve=L>j-sHM@dVc>1XyGw1
z92%&f3{r#ee|it$|2%%&HNYNs=Sery`rfIv)%&L6EgGU#rmH&Smt$m|HP4}H2o#@m
z)r}Q-HTc$0*_5MQM-}_$V3^B;G{u!86V0X%x(t<fT&`^9#<gAxt=zrrPBBdL#C9rt
zdIPr^&sK2t9+v)df>1V9@1(Ic7DF0HMDc8Wt5Ia{JUPzMezIn@#cegQ#|ZWIO@_y*
zproaI)nuMZmMaQJQ5psHWsBHDZ6QN*lLgCvKwE?dD%(20UzFmSraO(#cIXYZLm7?W
zeKFGB1CL8|?^hK#V1Fc)Ak+n^9XZ+YmIU^W*H5tU!HPUg%oWO|7CG$+E4ZnP{i<UX
z@s+%*JnlyT>*(Y?|1y|CH)|;3rE?^OL@|=ju{80_DfU?#UP(jBi&c*dG$X&+5N^2>
z?^FYSR84j3nIAMOyZLZUuN94ywGBYrVQIIy#m!f1g6*<+QM@%-@Mm3*mn62satry6
zH{^?$!*KG|k3GoeEmcY^?<;&Lf7TY7?0}a09k|ueXEu($8NHxB#}{c%>OKp|g*<H!
zy2)y@fl(WuC8d3o;+|KAd!o6>d+(xS4U4YgO6nYPPGJK_^SNGSO&jHL{ZTXFoH1P1
zfsx$owUca#<bdzuSjE^r)O{7U6Z;Hl*1$@C8$Gun)Nk-iMCGA{7^#7nNUP-5teZo7
z&U>ah1?O8W;W}Su>u5M?a`7aD@-7gENJVzlqrKgK1jDSA+q8XCG;5O-JN)w~BhEJJ
zxM8eR)D<zdU!;r$0egAR<gW4j0J{k`Tu0JXBMzE%Iicpepn?1v+GBpRuJ3L%VrW$5
z$^OI)#q#1Y`}#v>a(g0&>r^zJuf<;1qtf8W0ou<=@tzD%2qtc2v<{0Bj!jxaOw#ud
zO#VizFVCtt7W)M<S_WJUE)owooUm78cyjI=C#bJ$#8Y>YF<Fot1{qY~UsuT;T}4;D
z>K$OKnQ0Hle90xrsI>lAE~mA^#uK&qmSkxgO5m92wmOWPlI1Z0<0DVUQ`NCuIr?yj
zTTK5k$oUOUez&)}PdYk9A*#7vtmA@tms*>~^NiYFM6c(oF3;^LNU)4-*CAGr_(Xpg
zkSyn%G@`WoMj<&SNe>vm40pa(j;+J&WBqw(2a?G<1}L6~JxS!xrbH`p&z&kIvt}36
z65}|qVkrOmrfubLkyQIg$;c*@v_<hFSeh&po>M>*X3Xays+w-ZS_3MZrA|H-|8X6!
z18JOc7fAUOB(}Q9-%n2Mq+_+}z5LA~Dnl?X$8>=)$<a9*KN#ofFGy9-o3c-Mpd@1^
zAT!fc-F5hx*`-E>a`R2s3SUyWN%u<FgupiU9}H~}dQDhE$>@5R+B7D6CjG)Dze(0)
z^}4}hMHfaE`>l3ziNjO##p4>J8;tOnlY$P6e1*`$pXJoQR+Nv<H=uRL9NgbqdRrhD
z1V#F@jc0>{8G9QzulquGLm_K^ac{59nY4On<)gzTh?ky3K;NwnE0G?<k^A*F<h1sa
z(ueSRyDh@8SAouKxP#{n&(!Rk7Y-4yNfo}DGMF#9?KLEzf3gmCmAohI9Panwy6;ur
z0)K+@#OmxWk9gzuenrGijn0uu>d4Y$d}u?FSW~0Df0<bt>Xu`9m*0)tXs%}yklu+~
zg0xvfi`?OF6;9!XZzO1+mi1STgj*d+o52PY?SXJvMoV&(#$)Y;!l-*#7A(&t<XNT=
zgmh(utsC;Ia>GBY8S6sf_P)>%O`#{YBE+KG9-U5;`5c`)1w#<u$HY{(FIQTW8O5xd
zx+2|#wL*0IyymI<<=u6-{DV!0_yGMKRJ!)4>YP8kcPRcm!;>o)r%y55hv_*ry$%;(
zVJJQmzI|<suq?-2k<Va>zK>rirkvo6ABFvi6R@aiy!}iWzMXfga^2+YhD#qC!4$Q)
z23?^=bSg1;R%mh}l5|gTNu}*nb$wVianewzjeV=*I=^L_OEB2J(skwz79qf*E0&7d
zwuI~&v_3=yzZ>rCGH_!o%@<+&r!osZp_1-!RjHVQQ>=b@<-BRD^sj*QS>iH8CE7IU
z?5`_g0S&9$o4s61YltHSRqCo=%q(}WdT8S^A1X18mU6a`l(<wsctznfY2Y|E-*DKE
z@NhA-S=P+s%28Te>+0+_N+$$Q3N3pU*50MM#@ev!$k>@w`_E((giraS7rAo`y56eC
z*fJSEaj+u=?X`}`hYWiZ0C5jb*<US-IK%NqgG+`r_3HisC^QALu9JE4JXCHUl`bUp
zWD_U7{s}2`W=tI3S4MkSF+7o=mpz{*??&sXeb+3AU)LR01s$%+b$lQQW<zGZkFd~s
zU%$5qw<<y=KI-;~G&uiST*bOYy~j2ZYLj^+QlU*LfvC<#DO1LrCh{SD)lFTtpO98v
z1y8%!w-sA7zF&Yoh(NVr(A6pAGoK!at}fn)N9@IZt>#+V3pG3EEU)gIcjvaQi|Ot@
z#CO7;SEh9V&E=+GO@=>}lirWEL(-mWrprT`3Eg94Mwa@nRBgqrzWuNT@qneKsg*XA
zl=7a*51<4(p^*OOxpoP$A=3rT^p6gnxJ62s%=3f1evS5>2&naJk`Sy9H8iCI!lYHW
zDK>nKT+vpi(j{wluxd0E8)kx9%zL^^uB-7x-#$_{TY7xjR<<26M^hgn$;{D$e>pdH
zMP?e$7=2Q$&}2ei-_8s$n=u-_#u*XX&BKNCd%sIAXXs?&!^({G-gRd%Jo`Ify&9dQ
zOgKe}yqdTCNTV){5M^&*)|=t8N!TU_{%4uKZ=`VR2HJH2f0I6Rz9L<yWD{YV+Y@_^
zphqn)lx!0*B`$M$={OX>J%Vn$*mp`hyvHE4XXG)GyL|b9^ni=|HMFP>WGO$K|CsnA
zCYXX@wRI@$I6Ce_m)2?lL{+Yy=Yl_`OjGzEHaW(Xv7yO))rT4+SsaQpin8}r7j~fi
z+IW8WOfsNOa4hho-EV>-Ta)prSN$7}7aG5;yFWmw_hJUvwzTtOjaPS<(}*}oPVtnX
zN6!PwUQN{xEH!+!z{z?qPaCb2^3N0>yl+ya<}2;PN3}kgp($pOU1I#rD)Ql`{kEq9
z^;QeSgB1UkRqp)_1DEyPZ6{?LgG5S0&t}IJiY_fhGhHZ}D`Q@Y&@ZXb-(bATIIJhS
zaDu_<>@j-=1_zGL*WbqrN8a1B&g(9I9s2KH0Jd5KQNszNG|{-$sjBr1cVcaD*80cw
zDT`I(`;>_G;#7MjDTABtXry0xr}Ek<k5#kYtkp|iMag4VE}$w~Zgp=4*6RM{hU@y!
zb|AGhg{zh|-D*A=`W0#My(AHdj=05AyPB`MbbmrPJ(8D;)KJ)SDV-$p)m}SWiGz9*
zeS;J)0QY@JaJEmsi!%1mU%uD+&C3WI4adUlR(Uj1>paebMyx|~|6VYHiUFZ`xX0g;
zXY*><u<Qpd!cP&-!-I1Z=(D>If9j9!iT7-O^JoHHJTkrlUnM7=@`)fG<#bUV`9f)i
zGsJ{ev0Ga^!$VX!ifo8CS|zbw#0ibyGr~}{ZMh>2L!)fSoi|-(SBjkTUW_K*J-ReR
zy;y6||Mx&0`Yl2w5F)D34{-L!P$MDo*>3?=so-?{^(<0LVO(3G&yn(nJ4IvVh%@cR
zY_X@E(pGhJ@3ePX%EDcAc@9!Jr8aEzsGM-G?`~`LUSeKERdq^7@oBI49XZ2=n4Cr!
zuCCF|GoxhW!}w*?B|oFqHEq--3s6KqSZj?BTrH}@=r}FFUw4GL`OO9v_j$%N_NI<m
z7`PMe)-no6`L2>(P;OkSj<z}r<%7rKb|gzGFM;nrADSI>iX>!kQGCxYpBl92`>k;?
zy~REu`HAE!&>W{o2SyQ6=3oK}w#&FiV6GP-joaprR0>~5bvB1;)k&6Sh9>V>Ho$ZD
zpLbT9a5^)k`n}32-G@+1GBBEzWOs^}dG@`hItp0?<}0id_V_x9vTCLLkzDcvP*u@V
z&x&YN5eFEefak3IaobrrBcXbFP<HWKVzd`EVWiE+?l~|D^@`0tN#qvu;|5dZm2utb
zEw7Zb)nh8l&z<sKe4LV*nHrwW`l&@O>$)P@v;W<_!`5sgIc4FGl}&?r+Wv~)o18xu
z@487k*$cb2y-t<#^AJY{8)VG35e$!%4Ht;M*IVKytaD{c?lpBW8*QRn23c23jMfxV
z427H-jl9|w(MJ2QV6bumcFc^zLUMf2y}iLP-*Ty;<4u{WL%8|y_VDTx4Y;<jWASz4
zP5qPSi$_00V%%%z%e>Np2A1xl<QQiA@gg#HH^pQMMP`r-Os(P3Gu5$3ax!ChtJp;K
z-l-r#U$-)@`@X(5k&s!7F!X8?$+P7k3id?y{T$^{l+KagL4iy}>4}E@=&CP7%MsjO
zEsC+7CKuCjQgbF&CqX#;fe@uN&V_sJj3_BHSg&>Z@bTvrHWk-0JGe<a;#<D^Oj#oz
zVZ|b?RM|gB*X8G33ehxoOvC2=;yhoPV!*Zd&C!Kj+r6?Wld&LrD`cH5?NA-^)DKTu
zH=1$d6xg0DyVczE9->mn*48EIt}-;@906awQ?h+?Tr3K*77!HM6>yI4lg(v|>Z$D+
z+1<7WJ~WAHFS`>`3a>&IMM=+G4$bcT&WX#5X_Ka2oBUCkPi3$z`sG~Ho`NiXjRr3M
z2TG8b!tvs-;%R6xnY2lH)RWOg$g$U_LEBpcHX$s-x5HSJy$?yljrq&lyEy&eM(y*}
zx31-Axin2suGIfCssm{i8>|*Mx4|Y(ny;<WOxJ+qY_qjj`c`zES!@~$9DA*ZN_<Cb
z4?8ybXz-^4inogEFVgvqJfH1z8+l^}S^K_VGMG66&yS`YY>blzb6jF@Pe9x2x<k{x
zi7jRU|AZY(S7zfS6pw*W=gH@p{32iC<ZJBYB~b+`@5e&R1Y}E`*dnYSq|^=;u7Dyo
zE{kH2H@@fhTzN_CePjE;{5{kWPG^C-;MTIhhSMa5QvXcRD))RnY_z~wB5B?@nG{T|
zlO+8b{^o=}FHK5079UFYMUUs#@{-0b$`?q>f+3n;#<*7h-0lTX6>&?;$k%s`4M#XB
zV>3-=MpV9qrGGv$z-_G>uI`_2Z}74eazl2pG7w4vic_q<VzDG{qgCN+$L#ThR&L5i
zvoxzG8;+>nw>Kdwsi{GjUd$@*@`U~RBG^9$mMpKQ7i}R#p+djRe`FrBpL?Oy_|GtV
z7=sEADHOhDYjIL5QmLiCxwiBS1MdrUKo@{80#^$JPVeO%_aSPbYQJbg$C3$!3=En7
zZpFE?9wy}W9;3i&Bf<3H^|v-%^kdEU|1dm>{=Gft?N%_<WwPBLjQ8Z+<JvCHUp78=
zmp-+6Ik8!dC$2;@Fp&P;|6}+xwf$r_{dE7oJTQXEE9dUhSYkDplT4+DcmKV}J3S?5
zpWn^!wTmj0080;_!V9u7Lbav88_5g|KJW^Ewe<g+yPqxrz5n}oA{Nac4YuGI7MYin
zcJ%f}gp8Iu4`#)q!w;4gUw--7?n$U_{45EZ{TAybB_<X!li}4Qs*PKHG+3y2=H-{9
zmlv)Ljf;18O5<&%IhhQYz+19jlbL9^RhusTJL`S9I$n#8xw*N)r%s(pT5s6yNv$ok
za2~AlUn%<hbHgdFySEj*KDS1GZ&l$kn26Dls>SHs=FATJdP(z-9)D_r!X`HGpyTMv
z3TKDgTw9{vSfyJ(@zV+BL(TB;qkeUD_4t(J<Z=Jyf_!_w#jL>U@tVhus$twcWhCiL
z$9LuQb)!41GLL7z^0P_XkC*r^_LyYIdNuXemNYadV!<D0tcIP+tY$37u9KCORjhqu
zh+P7F|Hs1{Q)2vcNkXRf_S{m^(#-ibOpswP)fQqCWxbX~NcZ8DsC#$8P(3P?)r#J!
z3Ri>8QCBq8zjVc7bOejG9X`=qJ{8l^*{OyEpWG^@oqfI9I&M$gNkOaNeMO*S4Z*(Q
zUtU~br0w@c91(=kVSR{|p^}&Ce2J0e<>e=au&}9BDtU@1=lART^;VvUL$1wWxz;hf
zcii)i7tCT{Ve(D$Ge&X>=0}cZuhvbk-fSgV&VZ2zItx8np>)f`9{qUV5;yJss>9*e
z_jg47j9Y7DDus;4MbM6Dp$(5`zikt$+;NgG%}$8jgF>NJH#pYj!#uX;+AD+&vK199
z{O``+gN>>P$iH0f%k5-!A1?LeVtN^97rs9~dzy)J&#l6xJ3zbdV0Y!bYi~B9O-H(M
zz-PWAC3be(C$1{uC+GQ;3;bXa=)+wh{mh`Rfm4X#tr5rgtbSm}wWZbB_=v+<8RP&K
zhWC9|!X=h5RAS>5@$)(R(a6UKUkvWPGYo$4;AdPMmqJs`{pQGsxjJ~tkC~R3R`r}8
z_G&NwutKiXF|<k96DjC>i-tOBQYB?Un=^3ajYk6?-T@B<(e`HJr;IEu|CE%Fczw%b
z#Cw<+L8Ex(>1Q1rxS|oadP@EPal%87lLse39w_rAYW*s%=#dYcsPo@_Hs%_`CTXq=
z+*{Gtt`N4ju`THK>B%u{uFcT}+3}5*>E>rTi*#GRxNt3xGvwF1`u+)aF?_aCDE!xG
z#o|X00t_E%wQJP`Yy81-kuYNwp5x{tkBFHlVSB#}`|F8d)-o<#B8eD75!3Lss^L=Y
z{P0Z2HQ4ReyUw4_0AU`cxI18SVKQhoHU>m*n6)8pF*~%BmIe#A%6w!8%w<s%w!dYf
z_Resh)xx$+XyO(t8=D59(rp|4@CFCR-wt0l3l^B4-<?P9^c$$;?x<}pbZ2azKX)$s
z97t7_qrKS^4SgvoDbEWnu*%9HgMnw$R;Vj5TA`P;5OQ^GJ`GmNj-Lvr9%La!rv5q0
z!qKv*u+_%zbpd=|STB9xQ%RFueE>BpOC{Xsf&z9s6^*9(l~`AOtMnw0qBVICmG213
zGNLlC4%^?{bT`ks*IJ<&&)vlTMkAgZvGcf<OZKfxcRKU@;-ZROzJ3-(K5%`Uk`*{9
zCz2&!%jJc&!G}}zqk7Q~6RUK`GFO_Whj6m+am)LQl9G}Wpt5fyPZ9*c#NlI=OR;cg
zoj!sW-i}h)uU|i%pvH7H8%)P^cX^P<ZK!zkLIk)no1pp4B9pNC@oZt6LaOj9^&MkR
z?9?{SW3+s^>d|1G3K(pCuxNNG+&O?aNo`PLR@zyQjEu~Wj*iZLIP&r5G(fwCe&BMF
z^)J!>;U|(=!wVoN-EG4*Tkwi>+lT|CEPng;zHwv7?oq(OQa)NG;^@%2fb*JYMH3f3
z-V{cZsQEMVB~SPsvud_lcZMuc7UTvws$hs8<@*8H^SPiXN9;mvqp(m;VU{D*SgN-o
zGKA+tTklZx?bhRc&A(&AgHT;^2N@?)@QzTbzpsekiKtstOF6o7)^n;!Y5w`&+!2SH
zIf4ZbzPvzRVP{WsHI9c*)FBkXR4QPhYgKVM8nM=Iv?r~r9+l93zT{!DYzo_dhh!7Z
zzy_?1g^uC8XhiZ<1b%T1H#Hf2u(O1%%e^M&<J8~R*Ei<BGGyn!Ub`4hJKXL~!_+0b
zBFqRRY6&FwL<*4Z3c)F1yMq?YC>hrt;^q0+(Ay1wNDcsmJ*I+C+I<5^f*P-U@GDd`
z<?6j7r{uIWVa06w7%Dv!$ErO;yODxAb@Dt@03yExP6p>cNRzVb!7rq{YXC3^2#@yH
z4*7x75-9I2gI2a#3wieV!!`)({i%k*!NIpLN7YmpSYYk*4N#C={B{|b?|W8p^GtcC
zwgeLpvpdc7+?PM1ilG+U!==Wv^ZkHUi}{t?Z;xH{YKdlT5zY>pd9_Bs@r+ft_O@n6
z9PP_abgY7uMoHrjx8@%+vzOS_SMPp}VzH67uKe|eekoCVTSzy9gjRDTFzD{RLr0av
z4YeddYB!g@zf&V>atF^Xh+`YJBF-~A*L&HxDN+()5%-&0rop)Rz>N(xX<N)YuN8#m
zDY(hOq1?_Y0Jon_w}pI}2Fn~<FOa4hL&?hSbo%@9)BDC?d10SiYVx?NO~c!sit-)h
zgu(x9a3H<C*Z8PEBOM`4tgLx7#3mpT6>DUi+`=1HRP`bzN#9x;T4aHRXFZw}(o2^}
z#K`$B_(5+H!CYZ!7~nX2Lv!<my1P4jo3p)17=Y|M!O&%p7j+MBjCBSRsyzwM0}iqj
z0!dL*lfX(BHYrulOzrCYP?yn7s?ScN^TtxO1THTVaKt-MF$swsJ6*@skuuzf^BTBb
z^5^I0u@9%3sN%>QfSQXYBAHRCsMtgO`S}IPW00@1Ej+!GjiK@6&d?sY#U0ynC+KiP
zLxUFWh^iSuo(N2%TaC=f6Xxxlam&*_UL)t-OYV?gy#`9V13#Arx`P{Ly+EX7ruQi4
zc8#r?<7`RKbtk;PTv*G~MfKr9a<=Xc!y<@_=!m12N};5k$#Un;=Z>v$jkzHBVOZ;`
zWlTk{GNs{dXHNj_F^f~EMX{w(*yppthZE=4m6iRM2Zks5c2|apR}^Xp1t$6I3tcGR
z!BV^W5aL)>5t+{B<bxF~9*4oqh2k2PnodLHs$)5B9_&d+?04FM)9&+tGYy6U0zuFL
z-aY-6=YjnVjtdh31kV$fxvq(l5!XhIaI?_Dh>+pe@WE@GsR8@#nQGwFOL#DnwFng3
zNE*w%`Dh?$?l<6-*00MCd6{}N6#B&pn)PfKn@%x`eQ^~h+O^%Jx7AHA!2m}6Kn@gJ
zRd_)rhR+ITepE+xbaXIT;^21L{0~n5@#kNf$R^qm#RY#bi0vWJwbXmX>h+0wFELS3
zD`RtW^V!Kzq9->V(Hwb-5sZ`Pd7E?crr$5aUg;A(mu|Mlm5Pdsccx*0vk)5V|J;RU
zk%{wp8F2ZZ6e8Y>6azbSaM@nyR@u<8F*Mx3LqIH7>Sf#_<mqLQiUol7c@Nws;IE&m
zjT&tu(pd{&;gxf1zK3f%x)a6rjdf#PC>iNw%yqRF<2S<(_i6<N1)YC--yUm1P)g3T
zONA9XccxYdE~VPQYav;6a`Ay;{yG8^w?YRc(-7BLY;f=kDm&lRWVQO}*|_+(<F~Mp
zhT!ciFoZQw!EH~DoiLBsY8QU?<>$saZZf3E;_$|C)Q49`EYfYHQp>XUH2|OatG%Ye
zf7LE#Q@#!qn7G#D5&%*i&Bm*QX9?H^j5;$nP)USd|Ml_UipCKWpz3h(S7jE|utp(E
z4W}VW)iA{Fpi<z3|L_dpW1<95Cu{(r1BsJ}elQ+S)mXCWC#5vRH5FJp;0KUp14f!$
zyNLp8V^u4kMk`#&b+d5_>wo~(90v={LW_Z_ZJfyCd|bG^XlcCOTVagg`S1t2q#^M7
zFuedXRQCYHr3HO7UbDHNOExn$Hg=&kKK$`MxfIo~Hxq+j`~L3B4QM*RfafncecJi~
zjP}Yeswe<-Zw;EqFBcXa2|{OE<5g6`2?^KL>a>kP*ye*%izBrgFYg0}J11BGlFA^D
zVhuv+XU*f;Qs$Rm>g_?i<!^0m8Q1;eO9xhZx)B8m06xSdxw+>#yQHg3GOHXK>LEY>
z^tO6msSp0)Ed!OGesW@H0*C{hiN5g&8nxHTA6^@>yZi_>AfvTZJ!&9za5s-TY@=By
zl(Z`P2vC2hQpnE8TxuR5yZUrluOm1}Bs~<Q#XtMA-`soJ$-zm0+~xne&XZtH8~5!+
z%;cuoAK)xdj^AFpncLfE!vJ0L0FK%Wl<`!9L@HMUKD$9DZp6V(?LDMEfR?4G<%E@{
zSTxqD_no%3c87V1_01VH?Qk&`L~U3ZP%^Y@f_MPjCnkdzv!()meSh$I&;mBm?8QTx
zqIAloUKtNIzeqg6N*(P8WfwcK01C0U(Ibtafn@?_SbL>eR#aBb-`YAlCdxc-6av>(
z7%y;CKAia)g}1|pBeoyjNN0}~3X}>Zc;Y0vfS|1gBvG9W*z_rvyx&L7x_>6aw!48!
z1#-W1+sMTM)L{MsOK+bsjb$%2g^*T*e}Ytw0de0E!RbF>q#~#tF=%E%r7jm?rP3UH
zvlr&))d79obni$OHE_x4db`~U62|TVw9u?5)%{av=X>=S4sGMkR7sT`?fw!Q9MAUF
z775VzVsDlLu7@6^b}m&HH0nG93jaV{H`!k7RnbP8m)ZtvhlhtNSUuC=+`jYzV@aXE
z^$|0;NqNj26J)=Adja_9$K<K=Yy&*Ey{FMQ;3F8a&TnZ(VXz12q#%p{SK#<#-+t7I
z8(gYf1T`kPxoiL%T+Y3kc9xbkIiUQAS0a!5dI=P&l~QhnHyJCvtfNPEBX~fk?5xs@
z%zOj1Jp|BA8~`ANd{Fu+0{qC(SE24aWw-TiKG<uZ%>L^ldE$xMBaivU^C`!E*DrkM
z&Yja1bOS+u6}G;b8B<(SbGsyw4ODM7I}?FZ7eu9{iCikfW67c>r$gq&K|W^(FQ8~h
zha9MY+t*i@x;r~M9&v{6{fq%l&D<234+5-JC=LZ!-u^45b-Pyu2BGURfE4x1K#OH(
z=yHda71q2pyTQps6qS_JyZyJXyz^<)R5z&WbOZ_jb(H!(2^j^Kbqr|=gfs0RS6oVJ
zQUZES7A~QWi&NFXb?euzpI03Lf<YDOrXz4!H2NOjsYETzTX5x5b!&sl*jnbSR=%SJ
zvjC|BgNth*gjc%(v5U)*MjRKl`buz<^1lFuNo|Sw0*_Y-6af9`MthS`_%^PjYVSL(
z$(8NOmDOVNlCkyja>x`hJLR3%?4o#*?*}9jSq_9=NieaIoE^SWf-7zB?2OUsqq`{2
z=774%sdH{YQG9~@)Hb|&Yw~$V<R!pQ0zZqqs*8w{L_z&GpW?EXU{&T=`$jB~=01BM
z03W$pGcRU(gW}>`*!5S3hhbCiguc9FMsII*O2d=^5^U?}=)gp!q@>V)J@~wj9Kc7=
z*dt5h1v~-xoq;|At&OqfS|Cd5NMr6^2^diSQu#*{{pqal4C=a~HAEO?4fmX1Gfwg7
zDI<4HEkOD#c2IZefq)4-ZRKLP-zZ^MR~aQx<uMA$Ri}YANR=J(me*w1TUZ^vXTl4W
z-<y6W-mUe<kFc8D*>o{E{igeRLf2=Q7oJM;U0QDqmYAUv_-CN^1JS2CeSSnlJ|bd4
zVG`A!rx#iRYNM%!&2QJ(16PLD#!|#B;(T;$04;eMd)%{CKeIeSuXAVu1(lUIh#>Gv
z5Wu!a3!v}N7_vTID-3AGf?P9SAOWO9#qwr8t5lE2w`5Ooysm<m;i6%dy#Ik0@UW|d
z<;UsO!6FNsw@!shwj&gUc6HuTPAKX%pR6}T(O1_#X#(uPLHi<bsY|0vrH@}FKmYR6
z4hWv9lt)c~tV=CxedgOc)1<w4-NJ?Z_kQKXGbMM4;d^Rl#I)(=;tpLcBZeLAFJ`@9
z<_TX#xn}Qok5#$bu(Pqj>vxB2RG8};0D!yzc-zV^2o#jQ3Z@=igl#&Qi@hd$3r-h@
z@HFR;DA-{R9o5(Iw4+mC;0$GuD6pxJl@hy#g;`x(*W0c7sC#_xa&^+O`JjuLzWUnD
zJ%AnIYp<`$RQfIT1t7BnbhdjGh@7Aeb55W@?In|ni|tPar#_c_pbFnsx(~y5>7NCB
z$b{@;3d*avX!$!K)+QS%b8h{4jY3d5WLBQOz>&Gr!VdGR0feZ}trC8y%-;mA33pI6
zY~K$`7WsR8iTPrD4=?VNRk2$stX1|4(OIfwsMxCC1w8nJbgedM>gdB=YM7asDIwsz
zu;kg7=T8t^0rynrr3<(DegFe{rC_{ougYUrjsoHYPx)r2v{UYsD%%7T?>gV5Z|LIz
z1q_K9#6BLe)R(&(Ip*3L$Awo0vc@D^lRTGXV806x9Ym*@9;D>U;r=#r3Wh&k(Z?u{
zqCKo9JF(*fs%m+?H-G(%4BlQq@Ie84?pZu|@L>AsYfwKo#tIA;TluO1H0rPO+ek7{
zE+BtpF|ai;G&DTu(~-{Q8t?)AgL!17OE<@`Itn5wbIm^$1EkaqPzo5(^?Sr>f4+hC
zc7{EO$j<e8qO#{``TWKuevKRT^Qktet@&30%%4J<g8ZHbO#(kR!`+^HLZ12>)s(vl
z@S2}5oB_6io#~yaUl^G<gJ$0R?Wnlual!hxUmAbCk6{~$0Fk7T4@h8Uz{CI%^!cB&
zvbH|$g6P2I0XMAMrBeLS0V_SroPcfo=}I|?8vt-Hs+vF1gWD#wY39PUTdOkvR6uq;
zPZEKG;ik9Y`u#EP@}SJYQRz9EU$Ag~ne!2awDuF&iE8}2e(D|XTlE4mYhlr9G=!&Z
zfv&GVc+h25!joOWK(urnYl+uX6YOYSI^u@~3^AAok`-H1Gvd2r!{^ns?{W<)Kl@q+
zf)3=<1C5;hu?%>f8|XK!SeL@^N0v|y<*+@1YU*aU?9`PD+=?|+69Xmx5`o?I3CgG+
z`6hh4<`@9+htr>)@<X^1D*%c`Ch}`m(`#z1V1ZeYc_~%AAM&iYVhvO{DnT35%`{=C
zh3(@O$AUNs#e-itI)H-?KLbBg37~&CRR*#v5hMk*5~!I(U~w}dDB6P2VO#B3fhFL(
z@Ec%PnppoWO2LPwix@D`^`@C<aqc$&WMi2XH$HWP&KrUR5-fA@o!ZM1x{$WEPRD^(
zTzW46{2!onG{(^j=cczEJ?gY}jZg)@Umxew-Ak|D3|Jk>0LUFo0K>xlPunj80;J5u
zNQdtg<Y(6f9PVw(mRMCRpg}ZbOaKm81EbeDv_#L)J4>U$e8IoYOE+wUTwLp#8;Rq%
zx%|5pe|7A{X$i&6uPit=5F<FRDemy?ZrK$kjvK!Mfu2#4P*I5(mRroMxZX70F#wX0
zUf?W)Y*(l))r2e>CJE_1`U^A>D?)dEZK}p|D>cD^)E)wvNlfet-31bz`5MGY3l>`T
zRbjox@r8}mf3@9rpzSt)YrAXzR$TPz1jHzU-dD(122+Y{SSZ!S6c<bEY_{^yjFIrB
z{e_n1AxJR2h5-u2Rap0px8n_tZudS+`#77V_3D{NG&-r@o_WQeT}}g`MJ)nSJVIrH
z-Y(%X1gYE$Xm&CgE8}0f?oslBia%w!JWx<^065H>dif0$r8*Z6)KjZng;P5cHbzqc
z92LO<*j~9=KQu9kUhMzL^fFZGaN`S(v@+}!+lct~`l^X_Rp{Cs*Z=MXc;r9A4hws{
z_3KBD7Z<<_W#`|8&h70vZ9~uM`9kU2omFz3rA9g5WZoHyaNv{-kRYD?jX^Je91wBD
z>ua*>C!is7iJA-W$%fhm{LwBp9Q2QuW!#5~mErSaF_%t>vQvextsyp=8C8Iem7THy
z9jOA>EdQZkkT(l4J!=Na`*WFqgoaV{QU&x*Yn;GL)4mdOK!2$;jWb_-!m%D8_KYjg
zU+LiCn?D}RCIl=D9F>6SP&m;ZJDwTTx-ig#g}=Gs*5?m;7u*RYr}7MI1$~*!%+1;C
zD<2x+)MMEE>&cU$N@Uh^&pYU65A9Lh?uwP7j7ayCVeW%|To})X54}Tgd&{2a?w2}&
zyVJ$e2=Mwfbtq^AzwFTp<Y89coBmNBNG$yUO4Fqa95=LrrKWl5s1uPU$;^3sBlj&C
z2AXg@^j2GkW4sb6AAIG$aUQ?4uzu!kp)AC0i@-S0*?9l{y(hik9|4W>AHZ5y=h$}I
zDgp5+w3O(VfTF+^*BQLs$&ad)vnaKd3jy78oUHrM>IC2z+KapIet|&PTWc3aA8uXq
zYIxj@fhPGV&*+>h?T$N?TRPw4!!^GsS554fT4Z-xDQj#}&_zL<7wEFP#mY5n*9mm&
zRC;@Rr&7|=*b9JznOix1yT&`E>Q|ov#lB;Z-xTgM7*YOX5>%$~p!qolp}Q2ssMi%d
zHCA@=jXbEJ+zsUBlhYmzms0&ju82vCh>Sem`}tCOy+6OV2C#pfrfGljdKnkQsm+z%
z3rzst>Mo?8EI?^x7)#ry&t#5H74vUZpQQ$;T=SdK-}R58s=fF90PGvCL_Y_Bn!d7D
zAU!glG08vl1S>?Ph9Xx=>>CgAa^mD4siKt;-%4z2x3&Ni)#$U7setBcbGGZLU1h^W
zbct%V<1y4ZLH*+=#W`Y+?izpF);#{{XnErcGtU+fTUE9|BFuw@n6QqVzd3F^^rqmZ
z!b1;#x3YkEn=Vqwp8>_5(p;u*&s7`X*jSrtkJ{9vxsDPLPQ#M0ro#a(0iOquC7|*U
zJAjA%dg-j}>-=YIMjW-QdlR%)8c%?{y!{TW(Bw?8ojf5{zXG~CUqJ(KQqi?1v*^#}
z`iRaQPO(cpWlt)*L1$kjW;@UJ_u_?FmtEk*ZjHbMXeKEccT+mVv7B3=cgsCR55L-@
znd{JJr!UAGAbS8Pi-v821;Ekdb~#hNOvhtwg1^z~Zv=&8M*y8L+|f3gwqsrGVYO?z
zH3`~gi0$p|Ku}N%<`+>l_!7SZA6QF**5{7*?FI^15bGShE5N{TpO)}H2F!bS=ec9_
zX@-#q&&dWkyndE^X!q6NAL{I}p`at~x>%?WCSb<3X!&90&HL+W`@e4-8c_i)G|CEC
zLD5G5U3k?B{EoxLi{M(R%=|${KY!n~NME=-&mSc7mA={b_hG31cO*Nj6h|z#NP$M_
z%D*=qJ;@|+o_ntCYj^fPpZb6Q=|28ShyN)<!Eph%SC|F{5^Jg{?-B%>bYg@DoIF8C
zHCx<*wcdG5>F-wh+Aea-*Y23<Sw@{g)(vHYOZ1Ic45o-h(LLY!!f9Fh*1<cucb>4f
z|0&`2Ch}^^U$%V=(4ylb^xv~E6s>HSrI{94CD~f=uY@0Zze}Y#+Lji9OXobZ!Dpp#
ze9XY=4ux(Bu~_d1?>@H-`q;%5cZy4N`GcF+?qpKkvPHmu(vLl>-}l!2er<N;_blkI
zKfM1x{}qk@{W|jRF-(pA5AXZ>|90pPhqe|B=b(i`1$CH@m{*uAWH|Cl_k+gYGX`e&
zIT<7le!@cm>Z*NEf-zTiW7i2W>ca~nL<$sFs(x<(0#<jvI9+0iPu)qtoQCG9Ox9gU
ztkmzj1C@T2_}_s*P_haNES-h+&vA`U2rY%KOzK5pM3LF7z1RM8u1g}tB3}fk&6~6k
zXav$P6O#_<Rg;9iv?d1+%}NSVja+t0OVTiua(l=Bd?SVX*yO5$ZC^<o?PbU787@J$
zC=AzFV8`<hyL-l9ALA0ET=kSlfl)x=e{Qmyrj6Eb-)`@zo18_rcR8W%T>gCd;_(-P
z*H3=G)1_|oPmu+iy6r`E)yJwvMwbi?H=`}C$o>JJ_IbWiYGL|M%#&+1)5rPBPaZ#c
zT;Tfk2WOqKx^#P~Ib!-hbYHAzo7f@pCW*sE_PeH8i-!kkOWM*?;mdN4*Qc}aURhW1
z9}hMkP070#oA#FUWCX8Ay&R=SEW=+u$0zqgzjp1(y|h}Xl{NYOrsZAmW<9xUZ|h63
z|9tnr?`;XG2~Pr#;F(vwRJyDy(AaBC6>R@w0Ln|$rWF{V3$>}j?IKBk|Nh*`hHY{=
zYBQ!VN7mAHap&;a;Qu<#(c2RQ(M&t4jeoEI(Cx;9#pUs(S*1ZDQy<swL9G?oCO%!R
zc^4A2o4z#LTRLfEXG#s<JrH7t)fYm&>vQA_>JUkH$Yp~Q7K33<{u2$?e*eTu^{r#X
zg&@z}T)k?unx(t$`DVS=u30I|?nM^NLs$PDy87XWVuaO1^+D9~H~;MV{DY<C<YmDV
z<lnP<oy^*hvEn#L^SHc~ZI1}|H}}Ua4=j%e*~r^kH`dp^J;-08hNwND4>f&8c-ro@
z(%^nS;m>jGLEa0iP3ozB%IxV6V5q+WT+OD2e5m0~*pSD&3+l4Ry#*}uym&pvWeQvW
zu-$(Bl#Yn6e^eA{O4mi5stMh7j)5Pa*5aLZmdv)NA{eDE2HtKe4DsHrKK6+I$%)5i
zI2!V0<w{SWwV$+aAT>A5e^i{7*HFZA|F7frLo+kWr%nvkxhFjdB;KI|nFevrw`U#v
z+527d4IH%T!HZ6xy8pkl_vPVGzwf`K!lw{ErLxthP)U+~#!?9(WG}L%kbN7=jIorG
zh{{ryF`>eYtt?|1Oe(S`#y&%dVK8H9491xAp7H(se&6#u*SW59UFUqyb$;jix#W-c
z`&sVie(vRU-}m!=I<||r9C5MQ7qtCSUyXAg&)JNau*mJaQB6sI@BrsNs>!dR1Nl3O
z;o`P?$DW^%**TqZ_%v*(XF_ssnOqu&<7c?3?%~nA=K!Aq{G30GzQi_fTm~I~5W69l
zs)JssQdF;;U_ZjG^M{kD_sKf_%S_a~P$lu{$O@<J*Fyr?KdP0dti<q~<1+!jgn=oV
zyOVlv-QdV0PS_9!js~O{Sy^$8JygtzWIz~+>dH~y^0U|vxRSNbeNc-=7D*{fQYBv#
z-}4OYJm%U(>|U1R8MHrAu_LaxYuF!3b8#I$%?%ta0I0gkzw<q;f$`4Au;(pzu}wZ#
z%yW^Q`-+m%b}lP&dGkN8;{WCl1Iha8xKj&7l(F;C2Q==+9QBdJl=R#P5zSsekz4fD
z*{r+{$ia*{%3rE>rg3js+ih%d{aAkXjhOPH?c05(>{aoF-%PZ#@vs@=ErNJK8pwJ)
z1GkqcwvY)2LcVVz<&NkrD1oAbg_$dO6bS(BeJ-&@i)H4or%?_yrag}(zu&V(5Q`qP
z+>PypuIe`~IZEFO;m8W>%g&iIuK(9L`LEqi>R_l3B++XuUz{V{HBx{@0*<Yu<LP$J
z4uqLw!8LZ~CFhFE%FD{uY|BwnRhqsWHFT?R>k~NpywJ86{B?E0j`=+#GK7|buS3sB
z*l34983ZyvzT;LsM?(xg0ot|fD5*7Gy7`6NSvcjqX!VJJmvUyVUxag}Js}I&eFy7i
zt<`lVo^Cr~<){ItV9*V=6Ke3S6ufqp&rn5Uy#o!StO|U_+rUv}mjt$gT1c*kMR`M@
zsW`eF3TDHyMOZh6xc5Z+i)@!&^QZcL(EcRuL5_-GOB2pg6wuTH_g3>VznlncVY@LA
z#kfXo#<c6>ZO7D=!yu0AW-k2fffq8Dvju~;O3_lyKMqv8_v$j-Jvg+QxVGhs-`EwF
zb;LRN*_3(-G?H-KtGlOh>Jiw!xy?p*7!dec=I`wqo`L(N6em2Z%<M+UiR=YMl=RII
zQ7&9Yz%AMf^0`F6JE@I@n^`&=k~!xUg-UZBBpYW9)cTBRTV#FjR}H0`hz$R{@px2u
zsSbA5;FT^nb!)LW8Ig>!Vc1Ddgg$FSlO%j7O`rUIyQKzV6;3`1%8s4?X#4vsIarMF
zdDpl$pzh}bo5{k7ez&=MP!tCyBVXM0v{uvfK1Fm>W%V9xn9qj*-;o^r$=;1hef5?t
z_Oc!E%)%)LJG(Ma)K63!Z+37+otQ+FxA)o`GS-X1RUK8)u8s`be|=v~y|2wg!02f>
zNn6LQud$1eo1QMUqV8X2&ir(cs{&hNBN;vF2%2K#R?Rx8ec98?NH1$x|GuIuobBO&
z_t2pzAtHO8BUhamu_jQG;+9t_ll9)$25b&`&(LCI2G4uTM^F=^%F)E2s0~n+3fn&`
zO7ewE6My+~S2lU#rxeSh=H0#Eb6xAS_=(Z1Qk7fB7=Xs(JjZ8VArKKi%k(iyVGVY+
zhX}j4D)fDIXI_O8Hq4MwKZPfY3SQ1RuK38bjn534EVD*Ba<#=l)S|_)mp>;8y%=Rv
zK%eX`{lOWkxksy_&xb49vV~!ji@TfE8d+}zo0=)7`&a#MCup<9<*X*KGfX|mTnH8o
zb)XT+1UxwVR^%KFZ<XzXYxah29l?{X`J1DOc7z~%5{m4v_7=vGv(69tpSDU>>WXmL
zpaVrnwctqSRP<3NXYIVr2eNieTXT8?TlKPH{v70(@d|MDQwsP@ytvTHR%2F4PwD0-
z@?3mTuv;oT+*vyynms0t@8-vb2%-(b3So`71;;*c4p@l5OWUNB>okL!+V}K0IW%J1
zjoJ4saB*={cimELl<asb2~FI&M$JKm^CBtzf8U1;Bx~ZeYwDdLz2qvdp`*R9(VR_=
z{%HK%FS(R^oq31$OCVQq$4%cTIL<=^Ta)ZCM=NAGB&fR;X@{qvoba&$^eyr}^q399
zeTGm5D%f1kB<0!=pimB1T%JDqBV76hAq>=1R3A+f!l&aqy0G%-Fq_`89)u1(hs=#P
z<Rx>!lFRA+K#b()zNL+)sOGFblKtU_@2tGf@!2kpk8XX0r4C8nCf3-bL<hf1Y2)<4
zWeFTy%2iX|{)}>Cc(kH<h$bGbLefQ?PBCxKTjLtS+_BO4_wpXCZKWVJ7<Wyt_&>d6
ze9T-<wrM-}>If1`9`4CpE{lb7xNzeSy**f#rgGC4lv{Zw02Ro?+8(W99DB)`w8!bj
zqOzSN>`T{rgyfClhRV&inQn^zK66S8{Wv<gu?`ds(kX4v#g>=oSZjy76Zh@?d$lD*
z#jpaRZ6g%aGzL(Qj<@kIHS_v#pYDpDi8O>PU)+f|&OXfBMbw6Sd4lI($U3NRU8VWr
zOHt|Ma+Gu#QRO;2>g3`waVv@mQ_yi@PX4Z`938s@;h+&N@rq1tYKiu~&00<?!=$`(
zA2^bPlNI;cL+!O4E1W76uoh*%jnO-NW)g0k3Wu2NK$nWsK7E{S|09MOxDHzix&toV
z?0B^C0LLt(gM~CXVaa;cZfwaHbGwuywKF$)fTNMXxNnsKUZ|ts;#zcYyieT6tvj?P
zwyBaDzjKhfhi8E8uVUV+0gV#k1IuE<yrG@H)#6sOc4O7|TMMx%$jC@6;p|G%fR^nN
z1!X&PeP@G@tn!7IBlS2~gv<85K1b&M7ckgBax8h_XwlBqKi(>ADR~WI&%{;Icb%K+
zHHL!OI^Q4^f~wm?4@egQn^aT(+CH<tjpNxges-UjWd0<xti!fcR?n3;)j{zRP)>J^
zp8^rjt9|kN(9M+o_y!wHxzo+Y@JSuc%Z!$1AIWrU^Q?%i=VV?~FdA&`PqH?@_W??m
zH808qx%^`<U5jGqzsO=9QY>RgJeh=6w-z(^_s{78S8rx!Zs^((LSJ(*C09lZK<rR&
z66?bz(X03P47D`(X5*cix;B9U&Til=*;~5awLXX6R?VdY@zke(lRiM#wlSH@59c+g
z`I*Zk!GG$Z?dOgSlO&aPC58QW1NC;Tzk%e0b!IL%{Ikk7g}QIyfnfm6D?joM$UFes
zISptYqd|Gf8BM8w6s%LQS(@3_tbBb(Doxl6oInqmw~e9qp{TQ|7?;}iWxTrT$crEE
z;9(Og!8`o&u5fz|VxSe#=Ss|qLe*}zGraK(^lD)+#RgGB9{0}n!B>cIIP0xujdOt9
zg+OU6RIqz)bLa2S^ZqEQn~rM#&Qpb>ek*z8aq=b8wP`}|Gff6cw@Av8&|DufDfRdE
z$cOuXXx?m_Tg=W{sDt%8VTZ2yGe}E;9O298(Aq`3$p9Z9a}d_~*Lj#7@b8kkzxqDi
z1HIV4+`kvaNk!p~vu)X1APU;I6A!3;#L_44PMMXM71!9jw3BxB0kbE?*}4f4OYb_+
z@OT&sjS90ByVfRVopR%r_s-iB0Pb=_*_rw`M?Q`iL+h41SrTZ++IoweESRA>{_I8<
z@3(X>)#3FDF&Mx)2BQs2p_E1Z20E9~N<Q)c!jae{ASl~*$JnehvNMC*$h_`S&96aC
zAas3LH5$6zwTwjhX}hFwrsV6M`pdi7VsUJ;ZbI|U35U;OJxRI&%E3*5VO}9O*hw}w
zZEnk6S{k~BgoV)N^;G!S-IDqN-pEO{)*0T$!8W_rH?uQWD>9dbB=ZMTLXJ%`pX>xX
z6$3NEODpZ6JxKsc61qSFKwN{6#eAdkRIp%Su|O^4lh0h>IG1Y7i8c#`i40|RYdghy
z{>f$^?-htAfPqJ@09+bWV%3rl1(WZhH^U)Q`Pp3CoqhNO07a!b5`K{kV$8h(kX+_<
zR}j)h*2cp*a7pS;&wJ%5?f4?q2HKZW!OC*ldL4O=X9&x{(P9EOJ7vZ4ZpkuG<tV3`
zS%Vm_mE@vN5xk=};C8eY)h=);)TC2v$!YW#B78`pUih^rD$J(vZ2fc7wFKFr>AE|8
zPq`c>00v}_J#x9pgz)Yn#@jZBJ=0`a!j}QuUCnU!>Mq;c<nxR&IK#xpqZWAv0B3*Z
zfbLly8=<0ld><I(HTJwYoHE+TsCS*{SR-%qhr6P~FX9t0TJ@6P<gIc&pvEwtHNR$y
z7#rP1gxO$*Yv3?&@`UOTvF3@kowF5#KJmWZD)l=5jgkiI!YW?}uz#IIwD%}%5wbXe
zp~jJO`NLyXD*!~pjG$G`tz*e=@v7*Vtx$m4N))R$)83;iiRD$acWBb?9p-qBo%Y+!
z$&0j@6F70{K$h8f#II<R%@K_AcyCU0@X*-qLv!WPr05W8zxt|xW#Te8dOJ7wcS-b3
zdW0N&un|0eQ?EJC+Z#F+#=1D<9J-M|#$`JKAxkz)tRfp`)zrR><udynX&I|xguUng
z(pxC*$YTaW`u$fi#oIOY(<sg8@z~}L#L!22%$O~6t7}U4NQEI=(p=&Iq={*U{_2_v
z&h{x^g5!<)Noy)~o}<(F2PoE<991!7j7$etm`}_2QvTo=N|${iOMb~1>j{n3^>y+F
zz6}*-tvhxS3pY!xp!b=!5a-d}Xn9ndSuep#`@GnP9k&(s%RjspvgMbWj#2atI`}Pn
zJQxao<z}bd^ky?(&}V~94cFQCd2EjqYUV$V9kRxtbM4`GBuR~!b`-HcNQQ&sT)c`7
zVqv_f%Kf40b<aju(G508N5oA+BghZB^+7g|(tuXCk#@XV$Q}=u0hqFeM(9dNqz!G%
zTZ<}=8oSokJQ}p;3j@8cP@UZ-1|oiDve7y9U@?%7YKthe?#)hSH9`p&ne4Y*63dT9
zAJCsGPuZ1CVeNX8nj`<&T|Dq;ogWFB1Z+M}{JkCAr3u)oTqWhEP)U-bu5#1RKLd|c
z_g49N4S!bG`saT}Ms{6EDL309z#gs0!~YDY6qTo7B~#db_f{6@pRIklN2_i{@+upt
zq5pZDXpnMLB<(Q(_a@N($cU|Y9va8ER{ROzMqPX6=dLL`Ajjwje^w`F*ITw7I#Wlo
zc`5X_vl>=%DFw1L_6RV1t7~rY1GyV-OuLDfv_sHa$?<y^b};B^z&vBT%xQb@iMHC&
z@nl!i8s8g|l%`tf<nLP?mJPaojlqyx$qs0kO|!Kl?3w0}SqkDN+@u=z@6N$Nr3-3E
zgZGau<N5nZx_uw@c3}G8JtqKUd5iA2h5dfB3hZmAxwwW;{>RkM0CsKizcBaz2bjC7
zO%E-n-t~m4Tu7G#f^Dv*rp-tC>hIgj&`~8&AdUwB!y*6w2+18LrqGJwwKE=qw}fJ*
ze~aQB*@*ybGjtPhV(bpeJj9!@L%#z@Udh=s+xber9u!3zs*V8mNN^|_<qYc~g*K$7
zI0E@v`iJI*TGr}|ecpk(pMc36lAHx^BzmLl1gz&13+cB0Jm+8WvM^&|q+X4vKHo0F
zLuxa$kDW?)=sEyH?dTZS-|{&M<V>TnA6Eyvj#ioceHP#+nq<vE*^`eepTAy{M<evq
zIsI^(!}%OQ<nl%hh*mD)YgBufg@SQkov$yB;o3MlH+f7=WNCXIG((xch+~+Guis#6
z1lRcfza!ZHY1XVbf)@)W-QqxDmwVmHliIHCky8@79ITGB%<{l72+C8@xl_Our@+q*
zWAM!N)j>SnMi;a??%(}miv5)9Z1vOK*z$(V)#NgYEg=ZMDqXZ4&a-omb&THdRUAXJ
zl=<_XowZQOn$P4;p8&p9z;3pX+wsMCnc&UCoPkVyPsoS&7S#~C-ixDB%&446UljnO
ziwO`p=96n)t^6Mv|4R8^kizu>(pi(wnu<qtB%3{-Kjl#6kOG8BjBYPd$l09$T_@om
zB(O8(8{p~lJqUqG>h;nR(01M@PU@8_DF?uS=O{R}eeqKdcq9DAxFGDs#617-ci6;E
zO{`hJEM3h3_a-UMS^WdvX1oGA>R1w*+SLSf?HXTK^H!^ey1?SC;(^qbpAFpNQ^~px
z9IE>WKV@jgDgZ_GJ@xqz?YF|qVNZ<)kbWUlL!D>{yrSKN6I`E0)K{xEYqr-8oy?*i
zsU>Pf+ChlyyfQ{LxtAU9;l8u;fHc$lEn+r`z9Kn58V-pMMmHvU`*ueawC^F*2wk7A
zM8i~)V;E^DW%F_T9A><F)@jC%-eFw6Icey<Il<00;|Ap>30f>Cge|k2oY~LtFO#xq
zgv7#2zjzn!Ae?&WYiKZi7Ccc?IKFC@=;y{sR(oI<Xo-GoIgWuG{-jf6_(B^m1Z26X
zej@8lZ`%(Eww<luL^5nFlDND^?vj;{06c$V`R2I3Dknvd1Z+^?nvHHh5aLGLV#>Gc
zpn;IBT?~!_8C=FSgj3NJXYFG9;@s=-ip>|1QNV0K2!7z0pMX5R+%62|WQVv8d<W6=
z^&RI;sVT0Z`j##H5>3GL+*eKvA5a;3ww_z(%QjgEWoJV=SIf~>mwlp3evZ<<A}gG^
zB+~vCjQ__A68}?!@PDAPQY@M17h@3)r7f`26+LrI1W;59c++4`>mdKqSO3tyU9-93
z#p0OlVKO^|k$_+385#$^dEh$R*jrAios~T_*@<#Pi60^W@ea;b4yda|Rc#h~jR7gw
zchDe6A6GRHj!&6^-y(GwWq>4Ejnm$;MxV+bq5%6)k{<K2x<2YP2!Me+6)K@p)umH4
zifuI;W1|@eb|G=lPczDaBRLwo{*zRY{^c760Y|w<F1FOO<)7<N54!^BPICY<h}!_n
z6S9#jEof~+dB0O<zh)O3)O&@j{mYx))_4D|W7z82I9k97_cwPm&KTFADdoUiIG#)`
z*{*<E=*Y2q;g0f)lBlo9>K;<g_S#kLXNX|ii5u`c(1mQYZU>2Dzi#kc`nd%+;ysrX
zst+V`Stz3-#z%4(+6rxVjTC8`ipT13D8L<Gyb;1<GKmJt7pO9wuQ~H*Lt9;E?P}<?
zY78{8`T`Y*ta8Zw0-_+@0BH0$ATKd`V>V0|NCQU#8`}r%fE(36vrQifh{PxdR@dyz
z(QB%8$Hs>28qod-DTg<OcFy2{ZsomYyj+o}cpKdsKrUUPX&!i+L_a`!W=w&?W?^@d
z?)yg@y`CTHY3-(KzX*<SyF`Hqq;2E7QJssWhIT^5M74_wT$A6DOF@j1RK~k<EF&K_
z@@MbnL)I)1V)}UpNdkju`~>O^{u3Y)wY&)fdf$FXz#Qf0`2q4N|1!}8?JQk1s12m(
z0D%T*IZChX<cBld%$-BTvwzAtFJ+X~^`roTeO5?pC9Ap9qZK?9-m@(WBR2VLuAj!6
z3k-8|jsd$50LvZoXq5v(m2@&1J!B@WTiGrJ)$w5O&qbND<NN({jv(uMg6V2?8Xej1
zQjmo6j7iR>8UhLOtwvY(tr2mJ=r91?H^krLWp~rC<O3=t-O5w(eig0Nc#`!?iKv}y
z%k$fu+R#foXI;sO@WT)tCxoLmvjWT+FPRTGr!eNs{Bf~bXZ+V~P6meS@A|I%zreHq
zXR*j^H)G)HK^qZR<&7N^y3Rp(S8<)ATL!eaSnpib08XLh28dnUJ%~SoU*q;7)B)Pm
z$vL|KWT#DWo%Yx}Q1O-sycH@%@IPjC|COfX|A71Wmkt3OP2uuICzpoWjG^Ip2wnic
zy=RSMd3*wa(*~GQP$6(^1&@adDbY*uNSpE!;`P$5MvgqY09Ci_`RDk3C~#@uHvAn%
z8hsAkgxTybS>TNDftTt&V%t~?y{RruadLE2YSeuNwvM)BTKs$JaK#EQ>MrH%q+MDc
z21%4f#*3n?-q8(Bk-BK8aVow+J>nW|HJHjl!^0~;?j@{^oT&_`A%JWYfr`-3TCu~q
z1*8sZjVxBU8XZ2fPPYroG;d%lBJKuT5gmXMT}c#n6w`B59)NMH?zym^t~*QQU{Wb1
z<)|LlK`7<#qe0(*vk4WuDcZ_STj#wUBa|VeMRBx;N|*F*KlR4uMXqYKFZ+nJkK40w
zyR-FA>&6<QX=ca0=`nUp!?C>!chU7h^jrZ19ZYS+He-LE;0ddwl}NPKId7@^-(!ei
zAM1ShIP?t}HQBK}_asGVynC<;cGs=^jbUWxL{)IxbJky*vu*z4-y+Jl7$#swr;}QM
zx<kKV*=S?lYST=6^u&8Z=?!n`vU;eSbG**-+CJzMUCV9KujZY2S?x-{Ip}ei4(p*L
zq)UlC^FqV+rT`+W8+Oh<Yv>zAUTv-6)5GSqpEc5SLk?vN#Nna2dEM}h73`z3t))jj
z4YL?lh0s>J^L~J|%Al#%D5?5oH#>!H@7dOr$dEpTigxxkxIwl;u#KNOFS<w1*Zw$9
zE*T=((2v&6KzN3-<HH6{J{$2orEE#PK^j+9#6sP4XH$mv%xBWyi__PDe?9LJ3LcH+
zdAwM9)?1iBDQ#jFf!{V?7{M%x!ZOGcvyG-r(Pdx2C~z|!hy;gwGO7lLzBQoI(dC5T
z0kEPyihB2@on2`l1&m@~S>L^9{?X(io(NZ8AO<quO6Pd!I&`-&e(jT#?f|2DCzwA@
zuu|&KB{bC#i)z^2-dwkg1=SEZZCkYmZb*Bp9`)M|-!?PUH&_WwC4*OG&?${yUNk;h
z1FkWh8V8nyTX6Gcu7zE>G4DP52_H`{FBx}lchX~^!hkHXTHEvHfxF92_S#+`orcZc
zPel-0TFu<LB3e;9$$mc28bX)lrX5(rti|~BQO09qqHf+A{uQ54tx*H=O>XqjM;4uP
zY}*3cU~;S!9q=%8^|@}1`uZ^m*vONz;&V>GD{Hy#;+LglW+_uB7L0;*y?<FcTR$ju
z)EZ%l&?<DQK_zByHP;*(%f>#+m?Qlpm^Rs#<9(0zHI(lSx|d4b@K*ZW+u;^qwNy2T
zi9wA2^6tA(hpjHG9?Jqc+~XcJ{n&5stS6_sk?EOv%c7K~XgG#yP}>-R-Yijx5GaKR
z6BM$y{A#Z0eE$X6y?@dnE3$vonQ17_x(}2eF`4!gN>{_!ddD?h;+BooCcSf6GsSBU
z-JJSO7Eo68D+v`CKEZx{{&MGU+bVtFG+35R@6J&;!39xFVf$E+8OqNY;h<g9J_d2u
zE`idvMmd(mW$*;5QS!#ahUa5V<i2cz274GT2$~$NT37J}dY>SUCaKloIBhT9O?;$3
zGIGb6&|BW)6|`r=DtloFrk9FdYx>8Dx2OB&{^Shs|J5HClaZR)y_0;9%d|{994PPc
z+IjRy=$6)pJgwF#gc9~L*89jFYT*>Dz=nTFY5|JZ#`&gp1*mcKrlVzJG4v))438K0
zhxK<&0f!u5w1;>%bT67>?>P2Lb&a(h4WmmbnAk{%Z^0;aLQ*f~SN@cqJn`3k?vGbK
zw+ZMJoVtJF*qK*XBtyQup7OqJJrHx{?*8O@gNz4HuEt#Ay>Q{o8UFpd9ghkRgcqDR
zdTAh=@BT4$J|~UUHcBp$@Y^j6Ph=KtM-7r8^SMOei{tWnYbF%w*buX7&qCfc!BKGB
zal=}I!x&m69!JCBaSw4XV<(bsvee?%CAhWfXe?8lSd2NB`XC{;#kjR_AW-vOotyRM
ztDHw7jH*WgPElF`SJ9Ms(PXr<a8&O(nVdqF=2&i;(p+z(<dU1q%UKT?ceeMSgJr^D
zrf|T_Vc+#XmIjnM%)g&Z&XZ}Ojy}gJ#~SWx-V?=ji~H8D$b+@$O}{_W!3L(C#U|2v
zreDz}twsErs%gz?y)Y+%ySE3@a_nCmUA*dY_w)Qmw*fkXpC$lh8Z9=SJ`tA0wRYkp
zUjez1SL6A}HjiDx`o*gTeupinwv$EBZbgdc_h{Z4E=9=#%2OQT1UXW=626u8#JqSa
z=x9(|(Jv)U1(~(EdAETLn-;O9o2qlYTY7LZp}-ySHvMs5XIoM5p4u~G#9wxsheuPs
zMW~`Eo#aIWi#oS~s+{^?N&{)wdh4XD7gl;B-oc_PUSFjPWTyvS40u)!d~9&rAyDel
z)$?r$-AIq&6iee<zon7aJ+?Oc>%kV^G}@sI+F%?MscHZxoo{IOEM8YReWY8mz)}hM
zPK2S-BlhOtq^%1niILWSQg^k@05UGApkDkyUDY6Q^dp#}^!tSp*~ggI-#95N!|EmI
zQC6DW%f}X}L0+fAeEp1_1UB3T-f&nKFljtVh;y)5+gr1z$#g?>?PH(XxwBPIC4$;c
zHO&%@4&Qw*{$ucijEnY<jMuf>2VI6X8!WF-)_r)#50x^kCeOR@UXILgpdpOIBG<~A
z<@F`FBeRzDS;tyPwHZC6#@J^XzVBesYvPuq<dCqO$OapFmTe;}U_v;=aE4IoWBi%-
z9n7y&AuP%GOJtVuLca$eDbr&YOe7f@3ZLi0q#(zwCQ6(-Woen^dCJBM5FEt9v6A@F
z;V*+gUTW;yqdLdmCg(lXp7!6H?2n%)gMlY4aQwI<N9d<9_i`Ob#|zILM9NO%Is0<7
z<5qTdnw{SEVb#-x8E+njC`_9VlMKf+-h5LtMu`*%gg9TLOIwBtEEN<jD5X1qByCdi
z*7q%&$1cXFJ!R!@ZFqI(9GRbMf>Rcb-!*^I0gr!HLR+mV8t>(ACt#1t>3q8Ob6icK
z?zT0m>E`v@NT?xm|0X<#7c$kq-BO$IE%3a#B(}IU!6H&yJhajOMc~iMjPpSyj%!|*
z+pPN2HH5lhW|HD^I*Dm^ZI*6TMVhKfVZKhEu}8@yMbn;QnPLlLWCc|WAFaF^5nCcs
z(3lUgl%0!)SZ{Y99Mv!ds8uwWm(-aFooVFxaW7<TdXW3BZNIkeaK&>4G#@{_Vd3M5
zJxKEO@{zZ5?Vibp(@Xphr41L>rjlbb$vGLQ<q@!bx%MVHK@$5+sz85X^5O6GoWto2
zsn5jEJ;@>E$^IH>w4AEE8UZO!JFF07CU9?PM!yH0=d}I(caHr-)gQNIPKR4mZAncM
z@2TEt@k`9GT8oge-;S86=|XATtOw<7v7ThGjs-i&E@pW~eA|jhIjPkrHYpePJm|sr
zzMMl$RvkWDP1N#d<t(6t2}+9Z%^eD+!58qF3N}A0uZ15~$2srP{W_XI(*JoP5x8l%
zayRzGCTnc2=XusSlG4EE9FVF`Vvb$mXV2%sURN8`6oZ@ux;vazJQgO?v+~r7bv^6_
zGVeQcPxvTM`L&=|NAS4$0+zEq%0k&aqH87aGV%h*HN#g_fwdZQ!Td=^uSwBrXWz=c
zHora}qlDi4PRRn<RxP@YB;Ue!`)$L?wfzt?tMv6PITz`cms3`i7|%78W%^%*Y3o{P
zJyZdloQbNt!8D+NSyqLu9?w_F9KIo-zD~ce_4hUsXObkbmdX-*T1C}cbe!V5;f#CX
z!awdcACTG~s1gtjGIt~FUDs~8vzFq;TrfRrmQ(gLL+I)$)|ThS>n`8SszUuvvHhni
z{j0WUaiHk$m6Wr(w6L_a^|STkN?y0Sf*1NP{8+<_#*|k_tvO5%A6?8kIMwQ5KFKVd
zKEW60S%0SO8=Ai$$A53l0fPj@9<7w6iY!^91oKEZ>-bV<P<N(LT~DcLcX1oA_8I(X
zC40cSbyP?6t&n9&iZ9-98}}3k`&rZbm4~#AAh)Z;e&p9*_z4kTx_@mEQH5HLaH}FV
zf78C4H>5n5JT!Rxt_ML4XBx8RR>(cA(-|Hdt<Wb~u;_&e0a;Ed8>f=?bsj9Rq;=m%
zF@9UNh}KLM`m~j!JzJMm{330I{TPnNwOp}wqNGVlIfG>h>?bFOn5kFI$@ilyr&h&6
z)>yv>dj}6h4K-b<wOMy>In}A&H}~1+ehz8y-LHZ7XO?fqVL=&33*M0iG|Re8d(kE{
ze`^(@VyckfcYOSuYB{;p(_!+{@8;n$t_hj0eX8ZCRu7Mst4VA1L)`J-kNl|Gy|5Si
z&}`SyHeXZbJM(c7S7POwj)eM~1-wO_p}=Xe1{r&gA#!(4R^U%%W2@A@9HlcB6K9Jm
z&D)gMZ!v}4S^J!xoLP>zi(WAOP1W$U=cU#v881%yHtXDagHKg9i|k42Xv_)lt&jb&
ztclp@5kF3?t2WYvI3U(lw9Ea(<qS-Fi-Q46mTUL2%NY<X6U(VIkSnXLh?uH82o`|c
z{{2@yau~Eg<9Bh)l{L3UtV<Z$`;0p8pJMg8l@JXJnaJDgr^=`XccpW0L~nhbh*-FD
zL{6tbMfFJckpdBb{UC<Q!oKf+xZdgN3|0&MqxESoB?Kgxd*Su9eo%PZ)4o1jLu{KU
z$lew+cvC<t=KKdQWkTEcRmPx-bZbIygvE#`zYx~AZYJ9AN@L|Y(hm9f^|P0q2x@*w
zSvgjuS~Yd;@^hKZDyo1XZap2?;*`(0U+eJ>TKLv0$w|C4Oz!xliKhYRd65e33gPuo
zYl_#5{~ZH@+pIp$alg57@(C@8W7{LxFLR9{qfx>I(vJg8t#u%8K6dwkDs{fU9;yXw
zTtd4i<?wWw|Dj*wg)%Lk_(mAn@p#hxEZ|B65B}O5Gu}_fOv#f9G}!w?wI@+9<<*Vx
z=Ydnfw$@GX`6ZjUI?Ey9<oDHan&+nap2*{rHnMev>w5J}JBst5k%wf;ACZ;p<8tcB
z)e+BE$m1I(qQPE9UT;L?7I@~&N@g6>4=)Or2CFYUJAU_NY-j#TdD-~)xvzoyOXW`H
zAPZ8j5w2b}Uh_)VJT}@RsX7Y2XM*FMtzs&_ks*s2&THuef&+J9;OVVqm+<RJ-{0I(
z*>RDw@68$g@O=wf-3RSeb=s;rCd@R<g}%oL5{)GE#`*{wFGkzX??bhcv_qV;&)p7h
zTjMSGFr@5U5N;yDCVl?1hs6q6qV0NWFgePyygTuQ9o<Sj`ktkkg3(jkpK)+AYm(N)
z{b5@fgXK?x#H{Pvt`-{{?~+<Si#*^<TDd>ge(xZm;N`hv@Xv#DeNe-bX%iWgD<M)%
ze-0(|o-#Eu2yQF7(^Yu)1tTP=3ZeezsDYo&ROJao&sS2?DKJ$Mo2vEbr{+D<^zrME
zpQ7f9&w?U<J9UPZd?VgF1=+7EQ&=XB)c=Z>i;&7{%YnX1^IrYy7yi>gyE6c%09l{b
zait!>W70>E{ipTQ4?F%@8hrKgMg+{#+s9IjDPORNEu9Pvx@k%eTsqxqGBBn1rBF64
z+S+=X;Z_|2BDZ>^$(}3#U@K^*|NI;rbaHfXV-F_P1hKAl!rosVQ5Z~zmF_>q=(xtP
z`2Hp(PhEcus{e8Em!-N+yumyXQEL{tO$<x&H8m-AD>|(SNoH$h?(YasVi4KFl4Dm4
zi&L9bj<XTQGe#WqNl}x~og)83)(L$t^C7v<t~aR*6V68|7rKjxG2_2UA`Qhx&Cbow
zHfb+iYh`t^qWU(6<kDD`MOzJR<AZOeANQVG%}ts~?mKx~=U)GCp`76uBjkrK-bP&~
zkk=nS#D8S1;LSY=J?W=4Vb;L&^b<N@gZaYHrPkoPJy2f%b~gYsL(0}wH2)^^&(lLQ
zvLyUz@-M~JTq~3BEng#AoOby=CXY3^!z=TRlXU(pO}&uGbPADkSXDv!0d(%m_r2BT
z6L!s)H}ZiQO!r##T3H^ftQGz!%~79DC5p=X_*({j@kom>19smNfT?fpE%q}Ce7C>s
zuCcQLc+~hH>}cCJwYYe_Zavc7p7lswlm}h-E+&t0IBWX!QDZ3p-WLbA78Xa)RC@GQ
z)2s@O>(ta$%gt=$qBBVVRy}0gBO0pZ8ZGK~_a0iLK<vu=_?IP3l5L;%g&!X}6cEWT
z#F#Rd|B{xQ6Ib>>Q78}+{apJgG-36$h2zkH7yVI0iFAg#J3a9lWqrSLIomRrUYJR}
zPzrzI?|+M1N<G%sR_W>b*5i#$c(y?j)c<4C!x4C`D`j0m7*CrMh9C~5H<q=knaIQU
z11Jpv)-mlu%fY3xFgYN+#?Z{WX?!txMsXD|$KPApBU*I8sQI_5^I0-Bw+Fz+q|nLm
z$?cFA7~hx&vE}{nKDbxeBlD{Wx7i~&qd3J2^K;4FmqO%b<#7>5MjbkOLRi(TwEYmu
zl~-ZqjwJP%wr_KP4yRKTKgr|XAE|M;oB*sLg|i0GOzJZjGFetD{Ff2V4`&Y|0g;qd
zrO<0e&E~<D{soWs1(Ej14K3|E0LD%}riIwP-ZDD?2}yox6St}#>Kezz_2*}!E0?U-
zpW~Xp!S^@T&V-tr2CS6qxCo(a)e`uBYeUM;@-*wc61$Mx2OMzr5oz{V&9Qp<*t~y*
zETZIPY34{C9e;y`zS!v0nqh}HCP{Rcw6*#61X?+@*qf2$i%c6=g4*`hpDw>?R%0xo
zXZS)YTT!IxWDhFb8u$9fe(2))H^lH?{Y66$yW9&!(-O_+g2zryCtDQ1GPk0@L9yG-
zCiMO58-E0d{xJD@HfzvB2f)#FZXsGz(t5~KA3P$`K#!y@(HHv)oJc@aQDV%>1@_4C
zjE-ps+T?Q);5^&CgRTBSZt0N=RK3Z!H>=<E%GZpOm;;HUeSIr(u?mo*Abm}@-*+!x
zfO`L|VLe&W56no{dbGDrEd0ch#AQn*#mz6bzj?pv-POB03Y<S4&)*y`kI&Y!sQanc
z0+LDYH2S*ZJ}nt4r^K}5zga*`*!EjgcXeDgV!to<_N%3(l8EQ>I=X))eQ!MOn(gng
zwwiZ?!m%R`fQBlS`(+Q*L7jrhH4=bG&ES0@ZUX~3_EsvL(WWm#oK0}x0#<YY0AVk^
z6!9C01T9JPVUib3YY%VlUq2L}bbO>X!q_+GU1BT`f5J>9A4bIY*w(|1pq9lutkexw
zkk!zHu#`%$#E%U8(>Mo;8aI+x$6!=6RtCa2ycEs1+V8u2O)iqVUfR{V1A^1#c0QKV
z8?BUX>y?4n7y0!3{khX2$$0{C4jt;;6T7z$)xJ3Y1Y$wp)5-~u-{`|t@}Fpm)~~hM
ztc>&C{%c0hcV^&ICh!&Io1FUJ)ph(4)f<T?jb8lM?|(K9Fwb(^afR_vv;3@{j0o2z
zR;2J=t5e`?WOvTpNq!tS)+E*;1{^yd+sp0GwT7^rI%%XQxwqs1NO#D*PC=$Iu>v{4
zV{3{zz0>KDZ_E%6Mz~2O1?sy?Fw59P#)x^AC!3!%YxQnC<9ld+ur`#KHue`=SiWD=
zcvzK8AP&9n_bfR%r{i>$G`<yc8go~qM`mH3rt!p%J?0zM#7CI&l9!8}i-4<6+uOkv
z3eGAPJxlpT2>xR+{UoGhSCpBCoU_(+&9x4<*<10s+S4^x**#St<+}vs#(#o4&Rpib
z%t)j)?5i<(p)zVUo0Ez>4>bQS#K+irdQbfdXP$^v4G@CgPBzVXSCr8Du_a%ZeXdnY
zGPnd>&xt{2lW@;(1e|_?@0LMT&oS?qwq51iJ#tzVq#9dB)4s?*hp%prMbo*G@8P3;
ztGZ2bZLzv{LOfu}HH!TS7Wg6F{LM|cnF%hhbZz{b*F<$3@5Z9qZE4uNmdBNm#TbWz
zl?k(=N+#6$vcn)x7_9_j*sa#Ab_S;TBe>Mwj=Wt6gcUV-ZMomU(US*pg|XuNNPM$!
zk3}I@O5rLjoiW_}*XD*<!XiANs>h>se8_20x1JzSB4rW!qAfaM(@pMs-J!+w3IUFi
z4t&r>DB_Wl7NKI3=%n0UIxP<PaBI+D+R|UQNzLBlS@_A7ilML=URl4eG^Pk<!|GAk
zjbEOOpxct4oTQ(S=b1T!T+R~jx0m(S-eY=J4b$M!=7ek!vQM+lg?v9gM114C0D02*
z-l}{-kR8vw*T9}R1`_p|#2?Ya{~jMFIFmL-GcAP#Ni%wrqqYnAefNeNR`ncDnEzb~
zx9%O`pO%Z*dRe5qwp6qrE>d%xL$rO*o7K0Q(|R3?28S(Pjk*TS#6aH>J~n(GO<~CR
z!Cnmr5)ZaK9{1f>(|CuMdN+x(-=}%|&Q+`>Sc6WsqH0KD=?4sOf*-EThDc)`cwf&>
zS-eMj?fY_=Wi6xAdy5oT`}8#8%c5PeAQ2yD@l`&qF2E3SO_3)Iz~R>#8+Hpm9SsS&
zykR88k|C#*{+0CG@%Z?cV;s`<-uP==2ga3`TFjhg7__|HxHSJ+Qqdt+ln<k*C=9x?
z_M=HMohg-Jg?d1LfD;ESI3^4ueHQb_u&~Ddbqnj>BMD<tGktLmi$Zea`HkmBXUcGi
ze3HP2?LNjBxMLONCcv5b;X2W9{)#+&Q+26YbZOx@g&!g~KZ(t^nXzqopE3FocElAG
zdvNlu9+?FPwJk^VZQ6jsg;I#k$x98YR}+}#>5SE`2s2M`LE%DO9C&}t+f@cITq{Pz
zz%<2jdYD&3)1LbNM2FPTbU5?y*eprg!ZGFQDc$y|<}1v`pSoH;$m>oVO?kL<5EOij
zsK@H_c0L|*R&eEW(Sq&dbDj<b?n1-6>cJCzK5s$cRwXiB!lPy0!>6ilMX8Sx!CnJa
zg^Ph*x=yZ$P>;z6d(#~E>PV+EJuf33fHaU&>6-U@zJ5&;Bqq*{AC1y}VCdvDd?py=
zsxyR=#0EO}yZkO%5@8_E&`FuAIb0QC<_tmUYP|%M1mnQAOX{dnq>*(^N93pX>$fnb
z|JWz$L34-_%FS_y>Sf4g7`g0#!Aq&uS%+u3!lkPoCpyYxwe}Y~Mm6Ws3r9ZgTY6Je
z_Vfr@{4UZH);3sz_8JQgS!%j{Wdo7_!C{USP-G#Q<IN`-BOe#x^YU=>gRuBn78CUN
zXXv&d9vnU;*wg(6vXBmlRGn^b4rzBxb22fS^?0+@qWXc@B+8R;slX^Kxg)adw)j{^
zetpf!GUXlaCMS0^Yvf^18U-uTMPJJpeg8Rf@o+KS`G}V8*^ubI?SaQ2%&zEzOja>I
z%eISNFr8NT(XN_1d~ZO+JgfQ6Z@mSBOzSa5oy%tihD)qE<0b!z3vH0UmSP510<Gu2
zu~e7TOQ%@?BGUC^Ht?^zKHTIM+_jxo9VwqzF#q!4{F7;bW{7U@I4Lf?{vrKok-w!`
zaz@JG>oYQaD==?AMaq@!z%L+UY1Z3$){y5$-oe3_C+|^)Kgt`uu#~uV_{d=Hryv2>
zs9_Ht*6^8C`xdd4sMUBsLz3+eBsWIRXSKsKg0-A|YedA=?+0erOOcs_O^H2!9t;yT
z5UF|F6_`nWSh_hD{Ej&2^@%5@S*B|UMoOxIry)fQUL*$@Rbhu-jSU}JS1g@Q=_6!n
zm-RcanqHlc4;;G?VClV~C_mV!_=7In^CjBxec6p3ru171DU*>?q3}cKuLDw+p;njS
zsR2ipsHM}Xo7m<n>+n762VKp6f_@4Tqa|;6RRK0~Zqpw~X&P)9zfhqA3B{8}Q(hB(
zaOEYLN&dLL1}-6<Z5R>~`Z&W1>9d{axp7>tL((jacjmQS$E_rxzq%|K3X|3?vm?Bs
zeo9WFjTr@ypq`Io6fI}U#>T_}13AI@s;tm4L!(8XEu$(eKT?+=pVF4XkJw;HWa77F
zKG%~zV+NPw2efglvg=gQSpV-#&2eds%FB+8K@`@hot+WrTfvCTXUCP|?v+l%YpGhF
zc>9h7!p{WwcQ1bFciDPpp#3(PWi-&NYA+RPmvGYPsVi^!Nh6f^o^WM^@MA^y^#}Qu
z;PXEnIt{z24YEH}JN8Z@`Gi!mRojI9CZgq;Zc3fL{_@LpZGyX#?<q7tsuIpC810j~
zv~C89SZ%OpMa6kZdcs5u%C--M?_bij*Nx_1inlpB`U1u>Y~P5;&{2E0=+?J_h7w_p
zRgs~Oe7_*FPYc};e>1SGo3J@$MsW?(y82?^aOjfpi#HNABJXy4&NtF@o6=zupFv@X
z8U7{Ti<Fp}t|w)10yvAfo?2R5Uj};inw16pH2p!O>}czSihZsp{TM%P6n@V{UQpZT
zD}5LFIS6LZS&VsAzN^Pufd%03GKtaPF3K*6%ZFtMQRfNKXbWm%+;eI+--$|pU*^{3
zN36g*&ny|b*9g7Jbj{<GZ@ItH&Yg}j@PH<W@)wYgZ$(K>*l&wOyi0#9U9uJv`}TVp
zXxn5+nkDF4W|0Eu=qEuZqZer=T3%khy)Xy9{*xD9sk!hPz=Eo_5XYx=%nMh8hP`h{
zev7!+@6UHaUh!neU%tmfzJ4j-rJ6_3MGO|VwSTBlLP^8U^wVH(U#f?BJ&9NH*Ui2l
zvR=N^wZc!!Z{x-qf@>u;nya@$C-H#ljzMjzg=^ncV;}Z>!qo8glwNl(W=g-~h<Sh<
z!i-3GMU=j<SMx26YTqL5%axo&j5dF_xCN`)s>p(*bLbEAads|{;&`k>9rl;wyN%~*
zIGwvafQ`C~q`P&-#cNb|(#^Ch)S%R#pVfd=UB5qUk$!G*HL5%{e?{7OJjzizrRK;4
zRJ*vQO^Fr$5Mn!<WGnGTn?7SaKM`f+{TY&LK6}SU8io|VPL(XBs|$oj4=?|&oox&q
zOI|!3Y;65*0{bzLi9GV{k#hcpw*9C29qh{f_DnHLZ7FMv!sH@<37cf56M8OE(D@#7
zfj30z-7uIMD!E*D?rzp2jsvD1L{14_JEh28jO7W0F>to#Q%Qb9Fq!llDGlTWGQs=>
zYkMQYtk+~IuptYIAW^18MRc(z!!>wK>M&jzaPl;qLWop=Seu)rZ*Sj67;gn5<^wt=
zG5>>y%s_-r0H^>QO%7Q)C!9Q2D~h6jG@S|wKunO}F_6Ac%HB{@Pw#)Dn`^kC5Z+6+
zdiO*^68nv?yl&XH;y_lbXoD5hOXpsX;vg`Xqe!~*-wxDZ^vZfrB>xgG&Md_IV?|bg
z`0wX&4sWN-(k}@;vES7s1=MAhT#DC9N6>r-VX@x>bgT|!nlia(-RJlA!MF&mF~r+W
zoF1PLm;GD>esNNJU-)s??RH3CFJSodQt}&ke%&YjAv>@J5VUCaQS4h?V|3<u-uZ*t
z(^6^INv3@(MtY+bg%8A99=so=BKt^8pdnYv(+)=?mTp?w-~KDECS@&6JM}zVA2#|*
z#-+hl+*t_g)5R?7|JcAwe>(BU18_SUYHv-q_#3g+Tv_L7|HCz|iW3O!Llc0C<vfIW
zCUN1?t1zYNrr^Vb_H^`vFqYCNt;Vy?kC!Uo=0iWX8J1?6zX$<`EApN*@tN`#nfGb-
z%WDY6+7Sea7D14u%dZlv9cw14_Q<U1+9dQU4{09vn^JErqMEu<!VrQ^e1VA=R+#Gi
zya(Kxb@Oj(*1i=jWXt=Qym(XhOK%X66y11kq(s4%I)Cu#sDLYV-MAC6Xi;+HKE&87
zxgTJhyhw*OQp^0A^pJTBPYjY4;XgPgxa2)Kg40bpML*N(Kh?>Hm@o|xS^6}%Vt7RB
zg{In5W-l#bMn`U3KucIdA+PD4##<6glk|sQ1JC+p?+uw>h>5U4$wLZ4SycNS6!Jma
zRjRW;2F)iFJZc<%VdG2O^N5t9>r|0|^Gm{$uu_n}oYu(&f&IDBRi&T#h&8;zGmI_2
zfiguAKN9h+%}kVqBgAm2OfDo19uv^8=^Gbu%3oG1X-*q={0-B!>CV`E@}zIH89Ih^
zz~GRdUv;o(UIBXI)5arw-Q~w46~c(~&L_h2VCP1>XSWwj7NSuiNDl`>@MYZwQOcQh
ztpNESbn>B=x&@WV_Wj7pdIb_6QPsi`V^y{aajQZ+Fii9P&PSwHcQ`t@g4ruNX+*Zb
zxEWS{>8)(4UETKGgQ<M-?MKt)R|PO(tt>MiB={fU*h$s<lJ~oRl5S2M3Az310@Fa!
zP=Bn0c?@$iM9tON|8p^)497NosL-ppxurE9!AG$2POG`*@h7!ibeO2JcvY^Z7a8Qh
zne)ZbPl6xamsd;N6Ta2K1Q6?zpgdn)Om$-ee=tfNZxqMe(n%%)ZXBSmqm|QCqIY#R
z>X;LQ1~<?>toTx960HLJlbRgIJ6p=dYs!v#O}~)usb6F->d<HH5Cgnf%O6Xh`F*|Z
zqx4@TUw-lC;q;<B`>pk$Vr&XGj9WMBZ)$dE`mewL0uDqlb;XaLwsb(ws<F=-8R?r{
KDZA`)|Gxn+jFZOz

diff --git a/reference/plot-7.png b/reference/plot-7.png
index d3b6c62a6365a3bd92400626814d93d97dc058df..332556cf3b7d53e1e57ab7f433c87c861f583a6b 100644
GIT binary patch
literal 38635
zcmeFa2Ut|uwk?cptKDW)1SPd7Dxx4NpppzI3MwW<B$R>}h>~N0-L@@)fDLLX2q=gW
z1Ox<xg2F%$k(^V3<Xq%j@Wv{-Pk8s8d*1#3d+zt$|G$1;H&C@}@4ePuYtAvp9COy)
zlP6S`EL^*gi;HW?(Id*Ixwz)pb8*ep|6x8}sd@Cg4?pJXtEnjCC;p8eq`TtBk2j8J
zS#fa*-XZ_Z@owoi<l_2?>!|Ynv-VHB>l_~F7(P+zd*w)3;d7ts#|_)JZuug?x9^7)
zZkJ>~UgE2g|IF}OXm*J$DDwSi(eJ0YKitloC-Q-7<^9Zyi~SdhEdGAi)*tWwezBZM
z?;KV69O{2IMXsYWI;AeCU=KAvD%z=jvQ*o~JdNF>ZEk3ptRum>vm*g<`n;Uq-oNDT
z`;Pqf;=xVMD?f#PpUQbhOtfJh`Q?b%97*zXpX!hHbI32JRo^cnKkuLZPLKS&`C!HW
z#VOwG4`O#;4T*@@5)l#6_+f$YR@*&ueH6*eIfevR&f^M=OiNR^psl@LM1+>hlkE5N
zQQ^6GGws{Le|we6XDsJ7x!-eJaA@X>qrUFCOUa$EME>zD%k49aud*L1Qfcy0lekMA
zt&=@`kB_^otW3(NG_2A%CatmASKUi^s$oHYqle^S30hZTT5D_T<2_bap1MqQ^aqZn
zZZa}9HW<v4_5QR>NuDwAFealbCY>3QvQ)17O;|*PI|eqEOPOB5&24IGDrMFBb$8Rf
zo%-~iM33s~>Pm}j!KPmpNw~EIxhk5=4Yn0{i$}i`8L7G&#JA(}k$Y>Uw<u2cduPAa
ziWl+E8%YgembJdUUefsH(^8K-*?M_gyD5!DHGgVTuGQagYuRCK?V61VbTQS?AjQe5
zf{ixvV-2?yqFq1yGOvF)##Q0vu_r6E&C_fKs%z}(shlf6*?Bb!FO@Zo$YkYg3VK>P
z6(Y>O7LusYeE*=2>DBzF$sdN}s!ZgEOV3zj@@Qu4Um&>Z({D>WCMG5-Z7Ot|hDy}E
zwc@WFI&<cXxv(mxUercIL*tNvfk9FYZu@14jDp7Jre}q1>+mdo@)nqzpDUUEw8pl^
z)QY<(B#1ISK+{c>#k}YoKYl!^=c(lvv1DoEa_g${jJqp+hl3nipVVST1Nr4_O=vwX
z9cq*>cxE~hwA_TZZ<|lZ^}Lm+sK;QeBF{y2TDWez@OE?3%=Dz)<XB?W7600u1@a|L
zwoV~uCpv;XBvi{?8-8Ap66N&m<L%@hS8f~{OQ@)>zHooK^IC6h^3ll}^K`0r(qy*M
z^zSB`=gvJcHZjpL4@gW*tTj!ldsK79-!z44GCVw-j9Ig#*9Q(+$oTh^+8#W3FsUb6
zFD<1+rcP|g;$-{aat!u<khuMV$A-C&GJEMPp_sIh3;V0-Qv<mMUml1lq^#|Kq-}P0
zG<}90tDosEl)!#JcUi_HbChxZ-Hl7ME?v7in|?fr&DAwEP3<AxUcxhD*}Bm)V@=-K
zr&I*XLe@_A-BHSTXVIk3Om(P>re7-xt~BsgXncN0sPk0K?b8d{r7OMV2aenml#>yn
z4i){9kSsP*XIEco5}zlc-}5j7i;Y!et28rS;M-DiLT;Opkx{w!)vSAKEQ*hA$_zf_
zkz#6|=O-;?_~pU7@m7DQ^A7#1CU$Qga`*vbtK2?^#kG?<F?8+PH8(f6=Lhd(MI|xU
zF5;EA_vq21&sXAq&$|*58!N@MZ}PP7`>>#CXVCbu(z3D^Q{$2DDvLbniVaCgcs}pi
z0-f|*zC0v{hlYyk*?+seyFX*4uZ^BlPr~kj&+DCrEV>G)y;enqh07NR+}J0NcggQn
zCNh40evOzvDjA{H=+t0=cYJ}OTyOf!bXZJ`chFSxrv6rc^TSR~PH&TvvayDQUIjWD
z#Z5omW7XK7EzGi<A-9WFu;C2PJw7QBvy_gP=Tq%1-X<g*lJWP%BFw;{`lEGr>WTjH
z4wga3CGV@pPKIw<z4iR^m77j<578x^zb)$jxJXhN%l&eE)irZ-@3KhEY&FTWRY|p$
zY66b!&(oOZiY+ZI7CEA{4N2yy{aI#7IW3k_n7ZbX+q`;}Efd0H?-w+Z#h|I@Ac{d@
zr(+*$rw*5EOF4~nS6US)_}Uc64}LwLXi~LN-e<aqH+%oOf`fPr`()jzKT6$BV2{*n
z5_Xt|or%lod5e8<*mLc}w+W+6C%f_3K9=juWPqX>wVpcKq8rmxI+y!*df&%IzSrk5
zO*@K1yQue^zCM;pPGUNWvnDXHcBQ9OM7siEQ}|!Y`scMS-Q95Wa@{i5u}`{+jFFL%
zTFhS1YuAa6SLQeB4s3qt(%CUI$Ei2@Y8=yzd%dOWWKF%y^nTvL$QXuS=@|`;hsM{h
zcUZEo20Coia?RbARC|%41iO^1BII!S)@V!#lNl)2^5Be*Oblb9b4j%JSFyDdox3-e
za#mV<AlXG4k>^BnSq=qGGRXnSR&81S=8Pw`fzrn3O3Ta5Zm_NL_iXZ#ANbrnQl?3%
z>rp)HEhJb!QJy5?o70@;>ai`+4J$mVgw^}5$%{`)Uzs_)^T{#i@m$IYvx3J`2Dc_%
z`ZIYAU;?<gPibkb!6T>8yklO!-s&<@60}jcCcWdO|H28;3%X)gl!h~4Q&y@gv8^p9
zvDw@OZ7ZK^nvRqw)|mP0m{u1T7x%p7uAiA6^=*-)cBt`pcO6{1XM^Iv@7^YT(>7ne
zd})OH)w7&*oo;h=yx^GL8QJvgB*!{FbNY@sr}h59rAHs7kEXFq2V_g?VJCA2!e0fh
z4N>v#we|0crb}oA%9`uF)=7@b9ab7mE;xjLv@Yu@zL~ynpA2T$qby6d*QMe(!`$Gg
zsBJ7qJXYhlsqq3=#V7Fz;t2@}7H}_{l4hpbl<ero*R6^Va%_C1#J(QqTzTn^jbGri
z#}a8n#h1tV>&LR@R~Va_dGVXQTbE3QRp7ZibMUvtM}O%RRePjtWo5O$dfN-e-lYp|
zga}$l+0-C-G5TGj`{i*sOD{pYTB(=nK|<;Z!)KFzHdg71?(}dQ^PU;=7BY>`m7rUv
z)YZ{`@f22K)W~47<X(38`0I~#dV9uuBbQ<M4@Qr|p>50+jFIoncbV*=ZB56TRbWQT
zx>u<y^!I&Qwkfymsa5{Qq%+sD1+0$6IkVD6v(l%BV^X`=BT4C=Sg3ApSh;icBcl>K
z6MKl?R&cV_aqiEwDBQmw^PILR4?O)T4UMvnHf#~6xZ|$*HMM7`HUg_U<-f;(tkvJk
zz3)!xiYD8T*c7(}ii=H0C@t%b;?Q$G8I$wGY~=50dureOyNZl`*%b+z2l3>|btT?4
zw1*Ef^W|7iYh8xRH0e6&n=}_adYCr2LQvZ1taoSo-A#e^B3g>@vcU|7VrN%ZNMfQ4
z{CP-9%HHt~zRCbN!W`Q=1;zoaIh#$iTs)d@RF)GZy@xJmV`CG*IN6KSf2A`ufjTZb
zbe4weCO*k+X<VQ*w%-K4&tmM`o$MsWa1_Be2H59C#l<V4qM`y^_8MV7W`=a|lc6?p
zhB|cbrX80bjNDf3#H}vFk(5m0WW=Vl8O{#1H$E+qvS`fU+hcjT7B0Wbbf(w)%(68*
zUSv-)^*y%vwEXtlZxR(fy<19{(dp}8K$7)$>YMhJ!%Qz(vZScAbQN4XuI@8sy|^1#
zB2L#e?}PShm(II(>F0i1Dp+kk*?ZSRB4x7{W2D-+g6@i&&6!Gt)s(tk{6eCJ-smpg
zSZh_VE$5A`!SJv}VGYN>7VgA$iNJJuDNT<(hAnuTn3(nP_M)ahr{25j)Xp=f^z?+d
zxy3c3@4+$3Oi!lI&`T0oOMjp*GLpNHXrdLTqp4Zpp;Bl!v6D-%omoc{OxKBA)txrV
z%%SCNJOAdV)`Fn=j_Dw5m7D=Q<&{`2{3czYG(C6Y=;V#bRI-e4G(Yj}ul3UQ!dI_{
z@s^;exnSQWeWw&0*k#77ncltB(TcN091p*j`{4l=hD?pgN@D(MtxJv<RaWv*W+p0p
zCuatgW_F%?eg5~_{L4E6US2r==Hh<u5#PKka>sk#^d#1-em3zj7w@z#BKAt|wxt5Q
zV=UD2mc~Zkj-gN^*3y$6Q7~GJu}sO09A9&J51{Z+M~QDuTY#M&&6M4|N$Kb#^}QW0
z$ck-rTkqv&(Uf(FN~PkaqImrn{aO5J)E^v%o7Hkt=pR$ly>EFcEF%D4VSR((uUo{$
zIY1d>mK(40RadOK9x!*A{B|v7VQnaDq*qdVEl`?dM7Q=G0m~zC@<5Lw=X_k?D~~-g
zhj+yvlxzq4bas#VCaWgJ$?o{r%Q-%7Zj}I5<&!?|?YhSHKG|zyl%)Ayg2G5G;ps{H
zJ?C=p%a<FI>wLvSDcHJZ6=JOxg}$>3Kw%E+r^Lx#`lehpnnaIvJ@(Y&t@VH$3JW7R
zhR9)RtY6Bc^38W=t7H|2!_`G|&%8R}2}>Yy&ex0}1mBkWsaDy5=thZo1Q*{ER#Frx
z&)-Qm%daC|GiR^Dk0x?@x))$1dR)>616U@i$^51L_B~^nw*1BYoy|+cZZ|!P{LAfd
z71!1Z&?qhr-XANova-eix&vxmMy(gF+<46G_w_0plU^NrQryC;R%e;JP0AqaUbAiX
zo1|4e$!&o-rQew|#;-Vc8whvx(_U`VdAFU>cUyAE&>=i7Tgb_JVaHO1p~CZTubtu+
zzffNqe#W=O+_{Or2p5#Cy>W1WuI}8pvM#ZxU&{7r=enQ;LiRsJr*uXf53~N{wYM|u
z^7vrTjPonWBTh;FW+`I5xT0B2w@1^n`+wtm{EVaTo(egf`QxUi%SY>7U8kCaXWRgP
z-OkIIw|#ji-t_H`uvgkhwfG}R3bvNCv~+BN;-t0FtGKwhlBXe7ZTX%A(e@V}Tqa6M
zd6ajh>7l0HUBEJWNp?fFw{X4Ky)B2H9NXB-nC#swuI=<gt?LY1>dM#0@1`fZ$gv%(
zt|YyEn*sFM2+-{HX{p@SEKh!3B?L3>8jd+RWLxx~kGp)-1QC>!Y4uzCmWP@?V%#F-
zzqtZE7}X-6>W}rNZgnsnNgxrKLp0n1^RXh;amD=;e!Id;ly=HA|GM(Tkw@tx2{D<O
zycB_$^l3+cH#fIyrly{}diLw`5u!bYE&L$->FB0_2V0e}P>Ch7$ENemE~>6xTc9-K
z{M5eju0__Gt3zRVfAxWmKV{#9!F%B0<~jWY2y%W+YO{};Ohgo(b@QWb+KC@VHlBF;
z`1R!1odwpDkA>_Te!k2uQCCW<8Lp|Rkt-`HEG%r9@K&0(kB*D844E_;YA>oQFXj&|
zR_E2VJ{6ab?N(^$KSOL;5xWv0q-oS`%ho4XTca)kSSwUB;PaDe%;>{ou>p2Whh$yb
zD$!*27x9>8Q#*r+vC=c@J^j;UA;bMXJRr1yYQZr~GGBR8=N^GunZk<oZ1R$eR_R(M
ztSC>=v_woId!iy;D1D+V#&K6$J6RkgQbhoonk~&+GuGc^P!xQK1PVgtR=Ag}mFn_E
zcQisvF5MAaja^i400X(jR?I>7#)o-b>}Z6<CA18i(9~3Umr;6srWw7byeEai?$m6W
z=!)?MMDmP|7gw4n@=j4;bai$TwC+tNDZ6L&!0vVr&Te~ii6>Ly=ErJ7nr<Wf;62%T
z=bgQH)bCh~NfHwE<5VgLxw}&b^J5VZW<L-+(+EW2<?h}~`7T@7)mZ>hk(6=y>vymq
z2Ki6-#NPP!F)T9DqrWLT8?k58gEJ@Y!stc|3tjqV8k@P=qC(d$voX`7sUm&aW~$dN
z-2>Zl>*{S6Ry}<9uzz|qU5Ui)WZ@hOl-~mjAFof-iA$_^nH<A5;+q+Do#9QNYLO(@
z>c@k~^piH$kI$3wAV>+0FJ`P=MX9=}g7UmOHd{!_aj2av^a1)J$<!Mm2P(POAI`ue
z2omerguup2MVPv4wWOZF`os6!<}Tg+(^AE;hOvxIirg?O9Uga<<E|`V_OoE8*T^#M
z4djOk55`{n@C%6m^&2zpS^yVhlZT0bHug%MpQjQ1e)fLKQ&3V|H>Y-h7Sik%!S9In
z>|b8v{POS8KJB*=%t`EcaxBJmseIpieWeG%MdVyu{wMx-Zs<!hSf^MI&N(kOCI9j-
zC7H-o{>oEDfL1DW+O};Q-0u1A9VuruHF;r?L`2G8Yn^E}QdNO7R943QA+p_UDIL1b
zH%D1lS65jXL8J;uj^D+%ECDh(b>_@U4y6P8tJCbRdJrfar&3W?Ha0d!1hy(UxcH=o
z1`i$rH#f1-Sc-?D!*-El4rNw9SI4PvMqY9lPK7h#FaV}xo#@Xz59b@urtayMS6FoW
z<Xus7(P2{V=??6)&54PN+kxF9^06{>Jr3FHrMs4&ACO};PIIx_hvJtd0f3*Ae8b>J
z`vo^mWcY#iG9^OP3=Iu)etcJI+@s#bd<{c0oLhrk=Hr4$bRzVY4ZFC!{3V51-am}!
zC78lIr=|5|NX@_?#Kl27Tgm6CUHvH_rH-ja?-vQU5v#>fF-=aVjEy7lz61^T^{G-c
z-QC^&#Qv8qTDkZgcIiYL?}yO~SrX$9X*89MzDEz)CiV*7^#8bO3FoQ*CliT(|BU}1
zagqPz6cL+k=iK(mZP6beXz4FjlguRB3L&v@@bl;E$nJeN@I~CIG;5$G7yEBbKggI&
z5{*<|e-+S3C^hHi^}=@t1_t!XDrlyJ#N@hFa`Q*~Aji(|$1<kXj_Czg;uWf=!3nsL
zxG|vI6+XHVZrTI%8Ub;L3><xpx^fc<9=rL$`2De>lR&tH6(K=Q|0m(;?JrLRR5zJw
zMqS_`KnDwe7`mweTKYZ0^DU9BT|P5nKC@afeag~#(7(R+Mao2+DY=)6H~2y6Aa?jL
z*2tUAw?NqWhDk!%Ir7|_Okq-c3AX{RBx94(curV&_}yb0S0W_!A_3`_+*|`#+*s?5
zV$bME$r?i6a?MYgv$D@DLPvrZOqQmy^3xk1f9$HGV7I9VSSTwi%TKh2h?dBZL*GAJ
zW={j4$ji&)=9aSUu1xLfrn4B_-2Gs?Xvb>EF<iG48+3u$m6Zu8iYpFA?*wjea~qqi
zpV8YfJE3P+(QoQ&CtDgSr>=b4dPZBDpPQTPbS9JW;Z34s4Fi6Fpo|=+deF1Wg+ZY?
zojnu#0AX4BubxkY9o}pf7cY@C2gc5cb8%3~HV6w2K9HUo72Ft*6#M0iDtjOi`{r_T
zN=iyLOM9+vc7+;z<|~)AU-t%55WKAiNgeA~e5t!wnpMQwZktnp&EUn#$&z5Tj|J+G
zP;+?aV;cc_X=y1RJvFMy;_R6-2HgXRUM!XTZLISZl24AkY@Q62ZDZ^g?gQ{ToKdYb
zB=e#v2b+9kY+1{JL=H~~Wb<^CvEEyL$HwD+r6GQ*`@@A#X5Rm8<#DM8G#X7Oop4y-
zq|#YQ;LrEGT16}XuDL4Vx9GFy&aEA4McB&&o)*TT`N4p3^5b%L)02GFp0u!4eDr2b
zcLUn#lc!D<iG}HJ<Cvm>t}U8;U$OTG_1-de-P3g(l%09zO4F4sJTI$hrr6WdlQWR@
zqJvr`b5cMuweNrq=sTQy^U}TT&vuX2T6yS_MN#+xPx{Uh{?**vYLB*7HSFz*E{_Zg
zs|@o_ULDti4YfxRPQNwHoG><^e1_je>m%+~|E~LVOoGx9!M(dg%JtIH;HyM5&#c9h
zAfdB}yIq2b=GwJu-E|KXAq0F>+h5Zk(Fl-#?fP{w%R`~hkzPpt5)vLB-md9NbN3Q9
zHMw@JeMr6h!|Uj1i8Z$nxm9Ni)L-fL35v~~fd5*&X2%9xC{^ll_Ci6MRKMhe1ZnVc
z?mDNgx9_&Sj^KGf|JjF(N>L@u4W7F1qE0xEl~pE*f0*uaPhI#ooHyljnRS$GkRIRA
z9M<RG*RL08B*EnTw)bpM%-r0@Z7JK%)qpu`o6j;fGTJiEdp$e1VTnGos9!mRQr^ke
z_eei|aqEXGGXC3=+txP*cvmMSCDATZGYk9KFB1|pur!-gE`cpsFx+2z3Q_ieA8xN-
zrMQOUFSIDx>fauAIvGk?KeelHvcB4tB>_J)(PE0_aP)m7g?R~1BfDcuIW>qoOM-vL
z<<Iv&U)~YUM<2RU9k^d<(|b@#zQ0a+$}&IG^MUHPxw$8Mk|;TEIYxYY`&=f>0AXL0
zm8Uv2(=V{55%dsFS45-{eD>BS99zXTpZcDkyT9MtRUWeonr^)X&a%mBHv_z~MU^4#
z5Ju3AD1l&};Y4ct^E-_&pPrG?DG?2`JKNgu{nrmQSioRS^evt0rYu~+%#ovxySM_D
zXaD-+%Dtv?2=&Hb>;!1Kwpz6yjmUNEQd~*4g;2E)qyFHTu_NH02AK8!vv-%W<KlNr
zx5|c$YC+fZ=jOjL!Sy#1fVF1lm5fw}!A3CK?g6&dVjI;11?~trUSmog=?Z6<@DsDd
zmpcF0du@1dq#*2KstHFzk}($LT33hd{g3)?B$=p*7aSXVGy3tZ|3akmukeEZH%UD*
zEq}#Z|8p1r+PNUOWYu9Ut?O5Z0b{i!hA(&=N`F1v>#{k)MJ&N_LOWbx+H3gojwe2m
zG3WONS`FvQ4XKR|9d(&7q<THKdoWQMdOlU9tu1Y{65FSqKI2s`Y3z_@tsc}Ydw){Y
zH@C$yQ{orqb%e&SxB9d>w>}cC>Kzb|hwkB10@j85EnvS^(-)J?aKq7;mHdkx7ZO0R
zMr9HDw4zYt37eHfL-mYZ*Y35xru>?^v|<)$AJo#)Dy^shV#e;@B_jh2oBwxKTpLV0
z7=~H+3^&e!&qyu8<0>ofl9oOMq)pH`j>)+h4ipCZ%>y=?#Bjh_Zf@t(9BszxX9x&g
zLw_ylo0D;W-9ZtN;IuUANpGILTO=jD;mE;l{B{k-mw1Y%%1~sX{QUjNdH!(UuoYop
z=lQQjX~k~@c}cE_6GrjV5hIW*{2tjs=%J0~;thgaF#9>n7MCyS6$x$z+7?vbHDMU#
zHOE|RB=@3_$(8sjA16f-k@5B6X=!O2y2O98xuly>UGdsQD-y^aFDE3}jBV07`llA)
zq;P>?S!wBpm?w`Ot#;jzmdEiq!!}dzu7)!-SCgQ_QozBx*QBm8G%D&i+yUsnAMNps
zVgI#9FWfyZXUpvzW7zFePloQB^FqJ7*k8SES#8cHvQ4?>@BUB8<C9bUYnS=|=M2x;
zL$MseggXn~)h*M5cXZMvG9n@ve71-P&_*x<xq9PDa3lJqVW-#(%FP4IIf{yYTKbeI
zVLJ3_r&a|a;Qu~#pV5<Gjtb*nrPKJ=KVM?TGRr}&ZF+j+fOok9GIfpMNb15yghA7c
zwaTVfwd@4}qnpa2vTDL?Im6}P3ZQ#t$S@PBb2IB2gwHi2R>`%8lQ@%05DLr|VR&Ti
zXNp7Bn!vl=17-m{t=PzF12^Qc-fOqMZ_)xXM0;7QKHH|>RfO)kxe@XZp*Hf3aXWN5
zaA(z{(%3~{MG$sybARj9CZBlL{DVEck7z@n0k|iyX9u!?bbT5pyerIk7IZ5~eCO5t
zdv0z`-b#vu8maa}4yqPlfv`<{iq7`kak&zNTqI%&r_zxKjYvFsX%-b|via4`_Ax%S
zv~v9-N#iuSuxb=VH7aoWoRN3BfdUB>I>jG(wdt@2ma>Z#s6Ih1T0sWwf}x7GVyjt2
z;}0jCPMOd29*;lms)#vIjb`YJ4L60YYWJO*Z<+psz3elX%+~w>#7GMStxv|(IE}Z^
zd|N`*Xjx>U2-90<TeFRD$YyP$X%om(;<bFIdd$xnO$yZ}#<*J&d02;7wat6LUrdM+
z=FX>!1~I^D`me#^0N2TFAtA(mf|RsfBYO4o{dJiV&o`JNJ4Jpj5mrpC6n*gGwBjg_
z8Yl;soRC%75~0tq1tMU>br0u=WyY$7mz4w|J*GA>CivC$j&&v?s?#ppc-7Vdu%^6;
z$MWaz0}ec-oB_>1S>(#=U~6=sBOuIl2Jq{85IA@^^5f0E{dFuC*+A+@)mWA+gBua@
zwzD1NDZc+{T|?bX%2XpSH@5{S<l%?_9CS6UzO14m8>%D$s7%g0b)6oUGH0d|QtqCR
zg5#|olXUh-tn>Il>Zm8bY_z7Bt7}+T*b&9Ur#cfxUSTfW&QIQU;qX2$cfkR}i#Qo=
z$>ezDGX&8$CBa%FlwD6;lENRpWkNI^&a2r#3m=4*i4$~;2}N<BI*CtIsokfy3Qme5
z&je)y@kWC6A!@Oll~KxM>RGlq%|>jXWh9Rq(3q)@=A}Apg)D|}g$Vg#5wed|xny|>
z@`Kk_C&C^gVX$7)7l;w^r^bYbiy;S9#4Eb`E7JKsKOYOPs1AalfQQ?7fgN#Y!v5Ec
zjm48)z)Kv*!}lvMFNa&hBlA_)+9fNig4rhb1wRe?fS23&m4K0%nFRdnB`g3Aac1mo
zIJZ9Gw<Y|K7CUQ3>vRZzXO7z~s5GRK)%yrkQgcWNTz;(MaHojo*|i|LU=mf$x_3Oz
zUhL`VDQY*hmflPRB%&oc(H9boPR<wv6GrT?m%tjpAmWBc)y!X3@V|>+H2Sf-DjuQN
zV*{_FOT8FzV7Ct|XAsTPtI1h2A~c6ZCmInVvl7uCXwIQB(&sH&t^DiV4;-$=b5jGA
zM&t`v%liJNiFd)~4|OmeZPi+#F5eF_gOJSB6pkY9Vgq*{BNyEJ8gA}cTju&V@r+U0
zB<7Y|w$A!YD)^#+)*X=DxH}BCW+DaO0XCyyff66dM9qodSfu$YDRJc=+<C+sK`nc_
zxk2a=qf+yFvbDsyK7)pAZ#2Evo!*;be5;0qdyul}EIlsHN7gi7>G!FEU!UyUBqBoC
zSZu)_8>dfqmU)wGerujTFS+xrNOEFg^^s*D%OZ==-gAY=_F=W^u{ai#&l#r?u=*-x
z@l`6@q*TUt<8gOHHKu*jMIkEL;HmjRf}5D<ax7kZhQPl>CP>EfUjr{@z4Ck-9)WbA
z^FR*qvAjAr_NTTfu*7WFy#HxnAXhTmE`73&yyL`5vwI5uE>mLe{MQf|De5_PgpxOC
zdXCpkl3O0(jTO*CKBKf)CIb@_MuZUGYFzWzwdp*QT-+)p_+agsU1WU!a;+t~#(&Ku
z{)<JU|LFsnO$mS!k+N<tBr!`|oZU3-WM{(agKGmnP5b0r_Gy7lhHFE(pieuwi-(6t
zbkrG4+ooI?)Bv+(R|F@YL%spL;C?=DV#}8KtiF)w=;#;TA?ptQrYu1d*Ow0cR2gav
z9&o#@m@bk6_`J1z60!lb+RD(<ti|l`moHy7>q!b`AA{Y59L@tLQtcV8;rPDZVSPaO
zl+li38_x`|i!F}D#?EI^&d)uTJvX^DG`xKJ1=u3{1K=%1D<sqQe0!-It|T0Ko)Y`O
zF@3j}>gYIl&6Rzvr;G};!V4vh_q07qJnf{ox4dci^aWM+_~zk>`Tj`Z!iw+R(o*dn
zUJ44b=>{Zz_w%k840o2190JlzN2cjX5G#Uw+dJzIf)29dkSx$L0h5C`t-So4B|8rE
z9GN7f9xzCZaMR3fQ>cg_5+SC%v$NAjJQ0CjaB_0;XC511z6;$e-0>l4Q9FSp{4!gi
zGFUG{M1i7qHYa02M_%oYQ#7CzYjhgWFiAfA^as_CpDRhr$ULg;GW@GcX*tb*5TKa{
z*h4G9NCV2S)%1HO5D4*ulz~P2++rzOF9`utRkNv@TndM!zIf}O3Y#Mu=9sIgsXg~6
zk{~Epe_4<ka@f-y3MP{KJKO$}<3rk?;V|TM2=PfM?A;Ld=p)gGC%Gt%!xY`zAJZ_0
zB}&s-fi(ke8w#du70ALWjUuBON#66JxaMj{J8}`OTjxH^Wp<Rj$_5;4luaK$&JiYv
zFr5eU-a!RrQ2FLkf4Dkz3-Q<oSgXlLE*6T}gJmPUE)s**^3!8Y{m4pWw$La-SW&nj
zl90P?!M0VBlqqtU36h2!t6ix~UD0Q|%dGYt@(>qZpN|!}QV=Le1Pg8)U9y*4?>i!P
z(s5{c7;E3($dKSjz%TCW7`$m00AEc~p2X9TR4CE&8nhQaC#s*4m&ZN8o$V!3=rK0S
zRk)|8hpoZ6+kHmE+V5+M%C*e}30L<uNJ&Yt@8@M3(vfeTG=LCHN<YI5S^Jbf?y>#A
zAy?LRh*^jXmP7+<lImTYUQKR1_INo_9e^y(gf3?-kzPQpt>gxcUVM74z#9VAAwps^
z>ap;^ZDN&z2WL%syX}VL&^?ZFvCk1HYvNveM%m}io>fM6E@=#qCfIq$6{zXFk)4gL
zAI*JlMbf{WWs#HA^w90LdjKbW*WUbi0TP|B0dh(8quR*D%x3$r@BrS}dY=%yt#;~;
zRY_;@-ei=R&tV}s)PxAZ<9f-<OdnA-pL%bE3<P2C1`0D6>-)yh0!>y|G)6>5yCZ#b
zHNfVaN!oC^1>`y-n&ES+34eS(R{v0J?&gJaBn#_TOX%<sPmaF{M|t$U+J1TVP)R>#
zmWO^Xcz4GO-|tfkRi>f;TUtHZe(BS15bV!?tQZiy&Oa{;x~SFJf_6GY$0qTv;g=1y
zkeD27f`8N9s++P)BBec;#3;(j*i2bSoUB1GbF4ZUvA|^9F|z7zp6&D%x2J8>UJdx7
z-bC~x5Il>f14Q1Hbm{l7fI2P%3d<CeCWwuWJPLwOWo%waKKfJHLZQABPj-kBG02JZ
z8mR9Ls^4C>$lzAlFu26#Rg)t^pl8yYI)ugF&s%2CLQd%NI0@MboLhJHLn>|OTfG;_
zg+;S#>ix|#eK+lD-&t5d6OskZfG`r<X+~W{ZuxTr=RAD&xF!P{Y(@`<h<zsffG1`D
zH%|S-zM0KqJgMB=fR{Ozczz$sIm^63a9;W^cF;EvEQ^g~QGNZD4SjmNEhy;qOlM@w
z>J$OaM9hiA?#3?gZGjl|Rn2e#L?o9TkO{mk!9j8t-*2&h?^K{Lx*?ZHWs|x{QY~iy
zalCfBB{>7v{MRrouG51-zAbo6xJKdggT5`Vf?QGxTQ~<SK95<Lfj}dz#9nJO@9K^x
zsWqHo6(-=5+Gfdyakco@GXc|^Gl0-%(QF08`<oh=7z8V7kJ_MU>zN=5U7r>r=>5B!
zL*yfxi%r{Q%SP#gj8@<dL)GWG9P`Ha1`{U)7&x2vwyU*JafWom6FPjmI+920j}KyR
z6%io{vHBTy{oh_W34tJXLSpmCAzs~8P1)Yw1YJ#!Wh-R`O%J_#2TW^#oAV&*Z7mid
z-t%N?UjJpxJk+9gf-`4^DKk7l<6o~Lw*Wz``<WAa3~zP0LaXSB%u%wW>Yq32GLN;F
zoVW=Fp>x+J79Bg-g4AzhwL$I9*upV-7vGbGTTw-_vqU}u$3mo4TlG?R1EC?l+Y8+h
z?Q0na1i#PfDuJm3QYHl(2G}v(-gTVIEnY~KIm9_37gg<lS2=XX@3BX+okJ*)94@)3
z=0)WxgGe6_Yk=g#zqzVG-=`8a&8=mMyAh$)^5$_)x174XFB+y;aqPpgEC@w>>U*$M
zVxj0(!6JNa$5~U`ukpw?q(M66d&BOIpsgEGMz<G*{Efq*#Ep1RrbM(D2zq{{bFN5z
zfrUCXKIq$m@II<=_HuoRSV0EaUdJEh=H@P{ljj_1E{+vD)t|j77qt~ztOq$Srbgl8
zWc6<?2vnf|?Z=z4JUtMKh>M8iL)tfn$<}Ju<$Pv2H_vUe8~u%$QZ7?t?^^wg8$o(_
zAk8AfAR;WNsMnH`l4Bc7T1JpO7qk9jZcf7sLV+W3L0j9m2H7o(qC>pH_LC5%wEJ*Q
zJpaWn>P4L2W_K@K!8U7O&I`?_`fiRM6rMQwr{iAsZ*cZP#X|=bvE4ak0w9wS$Dy!>
z9m`>MZqA7+_>M`^*Gz}2Q|U9KGb9(U&0^RvAi&9PoGD<YEy&S$G(OkWG5YAnbt{kj
zbn@q)e*CsV#9hkml*EeVo9F%-y8Muq>byhq>MZw*L|<aZ{;*0p_Koe&^UjtkYf1g|
zCHTP2t>0Uo(sH|;%u8GDbH8STZ_d}DA=a`Z{6|K&`*w6FUY!^W5ANJfop|~xNU?25
zu6~chWI_ct0HP3Z*KwEgIb#j4ki2dL7G1z~s-Gx)>~xdBArZd<1vI>Y0YX~#sTJhd
z_cu!<t@;8{RWp8L6cO=VWcs%o87*XWf<wuZRO)*_mwk#;H}(GJ*@2sOkm3>%5dvmH
z`*4zOpVxceLm>Q1m-^%F>(aN*R#&wr`z%N$H4iQh(4ie-)z)1B3K(zM6yzLN`ltC4
zg%O8?pBg}%IhYFut6`$XHM|;5RqZd9YT)K5M0sB&Wo2YWI+L}N%(huba|StAGiNRn
zS=}j18GrVqm;14@LKF^AgrGPL|I<jR!brh^jJE_HOMb%^OeYkN&7RqhpToyVolCki
z>LAj(CWvU#WgL9q*1tW1<ourPzlA9VCvp#PG&MQoyED5;(vqma-P}m|fB>9&=wHmb
z!+VmLBq+qq&8Dk76MDENs3eYBTU$3*I1rsJD!lA#9xTQoJ;Ss(j^Q{PpE6z9F81o_
z3UVLL{T`Hiq~HW9<H5x2`aKoB$7SnQ5%GrWgW~mzR&V`We^loy@xYLKyg$J*U{4Ig
z6xn5jcq{rXV|W9i?d}W<2~JG3eKkvco{jOT|3J@kXba$#OdC24j~OJEk`HsY8a_w|
z$xupr%nrv5v|QA>^o*qINF<fiP@wjPq<#;F=7NmEq%K7oqQ%uvg)D1C`ILK<cG9E1
zGR}OK1ygz(>aIXt=>^Jjn;9Yif=g0?4S;z8EJobGp>3MKOsF=9&Po_8P*OU9;vFBe
zcw|_v<Ud^ln$7|xB-v}OJ|#Yv==HLBG<CQ^+nIa~RFK0lTE%RRf1685pPyQ@YuBy}
z_)LC~x#oLplS&p+xpgRYpiObTCZe!;iVKbhdBb)`gIw9&^*UZ3bzUS9RS9sR#vpZ<
zTo)TUlf>5ATXo(6>jKgcmyi9_gs32=UMdxPC$5|m7`zC&nIs%VNmo=#?*)EY7;ObP
zy^3-M26iqng`DW-&(E9B?0O3Gfa}{$j6;BE^<!j507wB@WW)lAG8Hk-9_T&!2ofe$
zAcO%<8qMN2S60q|9srX6#D={D4nPkxGc_nj*-g%T@*&nS@sD2k7vSO|I+uAhh$@qw
zijka0WJAz0bRVnYujIKSs?swxhw0_+A6=ew-;}beg2@yDa=NyBsw8N77a=iz@kWgx
z%Y!H=Rit~4om4nUNau$V&%@x+lq)n()Ch+laJt*2h5#m1E$lIJb{T2HH3b1oNYia>
z44+(JWJ72su6rPJ@!DNs?fs`I6iTa+4>T2G7Hvf%2HpTHha_l`7p3JQMq<@momG^@
z_~=u*#n}>=*>LM0c}nUkMXyuFbWh+>ADToo`+CrBGS(Cqw6?L)c;hF?lPekV0f=X%
zU8;%Fl_M8SAHMCe+UuLMxSjD}O7`2qWeU6Y%E@^sGRw+%#LvBU!)|GIwMA*!=wA74
zp$M}xG$BwWp$MTMH~QWMgeTQpI<D+t)QqWTBvk0}1HN{b*(S}atD_z<bKT$F*}28>
zXXnPdoAjrr#^(w1@49x<dr~tmeFIO|Db#YHUM%8k*@lCV1a`x2J(LI`Gv=<xzwqdV
zo99o|r3rVHM|o7}IYoWt5&aZ)DukqJ<_Sl(?l;HkE@~1D|5;;?bp>t15&bc}9s}Bo
zK<IlmT4Y<QsHmWxYbQ=9f(;@-3Y!)xKFSzO(MjQ_pB;Q0jfHUynm->K-5R|W-c>JL
z4nHJ!5$sLdP>$jjc2u$GSt3h3_IUeqm9CMN->Q5l>j-=Z89oQ`7iIj5W@GwvS9&9<
z4y{Eo0uTM-@j!WpJ3+{nW)`Uk3lbbA={lZE@(|~-*8zD_vTN!_924_CX@4Fm(K<Vr
z8S4YWgZP>l>oq*2B=%izO4k<M`E%bRogqXtrJ=cDoYJc78TapQPRC;zL<p7~hhLDO
zcgLyH=5QU-Mb+qC`l2*}-<Amr%}ldqn5@b0%AlLJu)<?yF)lpB$RUD~l`*U3BO;2%
z-eSI;SB?=4Eg@KQX_73B_j1l-eMsoyK8tKn1-F9R{I?Hv?1Ui{DIF%n{5)Y}6e7a<
z4u`!ol7EsYRvyCmx+cIDQ>371`y~`51g^a>$Qdt>`)|=HszL4O*_2=1ubZ1oI;IlQ
zP_kb$A+@V%nE4%=z5;;IhbD``r;}w4i)B$!hrC8BtFPwGrGt%G3y(&=dRiW(O?$`B
zMM{Rb%^8yXGA3a+q|Iq>9lwOf$H%uPWLAdWpvagrb;vpBvAK!$>XE1H3}~urP}@6A
z`5sDl>&ci<@padSACv%Y8WcX;|4^dIlmzL{(GT7lB>N3-wCl)ytICs>t%US7Y#lF(
zDel#yx~#vZep=>);&`*_qqBvi3^d0_G}gAKT2H||sm|8J%?*W!yaANa;R=fPEN@(6
zYQwt~z(7>ud$nNw{bte&QjyKFh?xnnQrZ~BFj~4cMNTgwSrT(LaeAK}^=P#~OiQUQ
z#pfsgymP}A{<~1w6WgY?B6~R*yz^n+my)-~?@4UE8&p)2E_^g5rWz`GVmH(*P0PYw
zu~aDe;qLV--mv9YDWQm2T=R%5sqci&-ps7GY!F%Y!NEY#4Z#h2JyvgXcXL}tZ9hm2
zBc-!kVTXB^*(U|rjUq@nI8R*61a#2&@)2l?y34FkyLT)|Q9(q+3}J(yP<@=C8b^-p
z=H?BNCH3_x&KYDlX9#+;B5BmT^36Ik(6L0oUq509oxdqr_G8r*3`o&{oX>Z|I>^a`
zdtiAJ1b{@qLbDm<A3c#PTqfVQkI)-RtOfw_Mu2zE{QP`kX<}MOI5DDISf2nQN21Ig
z%q7uKE@KABaj-Sd15SoVw+;7w4+6~9&L%bTyE8ZP3L~i2aT#y1AjM>)$P`SGxs!8u
zWvm5mku}AMI=CXvoh_yv_)`l2_!4;RTzU_Gb1!0GqSAakGd_~rLMl$kR@{n$5yHR_
z`5okcnFs`$P)m5%z-!M&A`J)mA(!S&6iK8~mqc*n6Xnsm{KOQH{G^w{NaZgS2FL&P
znZXAwUAt@Z-o1Mr*{R`YUS$D0oa?l7yo_W4sa%0qJRB1whZ+nbU!_Nrs)J1UJ$}f)
zdh;KUDq*fSx7-Up5OoxBRpWzgD#wp!&SJGAo`5G#CC6n6{ICrGo+QAE$UaGN9;rwQ
z&n0Kz;_D;Mut{m!AYArG^1{A3t+TBZ{v9#8=!{Q7fy+pIQB{>zXC9ehRmO4h;hVd}
z9o|o(PId>zrJANStA2Pd_dvcO0EI`yLSR=LU@wgaRUr8Xpo;uq1}UaQif*QI0az+N
zc!_Ye*=Vy6H<u50oM=|JmQ)ujD<1=+LBNpnJAQ8D?>M0<DtKjufCRDdy)Z)U&z$F5
zxCnSSzRH9n3`Rv|206JsU`9xEZx0=%E*9`^*>HTTIF(|sjikile2VpT$Yy9M8#v>c
z6aKQ4pA>Bb1o?eaLIIiG4|YkKP`;8B6Ho<QUuzX-k+$CkLMP?kgSoIF&K1MUP#ifm
zKv<g7vkB|%7az(uP*EU1kXu)mMn8^i;xbN}GW;z-?rAHmgSnwcr-Q_rvP7=$@6b1p
z@puc$=-IjzK`2xG&^Aj@q~Y4AjC64sSEEdvMiGRoSV}KZPzO!cwhhe8JdAM;{o63X
z8x>2tL9wCl186EzN5YBdnNBRvmRwpk7={@R6}THIOoQSVM~jMzpiLzCHc~$&{^t#s
z(_I%`kTl%!ghMhASvJ(d5a6_aeX<fQ2gD|x5+)8BKS*O9{DsJAw(C2bI&OHKcq4+f
zmMTs41$V)H?%|LYWP@KbG3oY3s}KB}|C#{p<3Jq1;(JM{edXKhX9$g|2k3#~HbQKn
z0LC58ak!$Fvu@_6x-7C!l0~}V@%Bpxh!Yo}bHe_xh3p-F@Codgt#*B8RdJ$3TPi3h
z$ju##Qj&)TNt~<By^ubSm~R($7bP2n?&{n}+iJeWnj#;VVh?e{|HO#@zlsq#yA1nx
zYAMqcbXKZ897`kVxlm+7T4dNGAZc7g89U(mkhA`_UmVUAOsyfMt`;a%%|t1e00GsI
zbx1>pK<bY;@`;8Iw<fJd|5$KvK%<P&G-P>JW8o1B0j?aV3+r2!be1^H;_wE~jEEHh
zr;hUTs8WB9?P(BHRb0R#Ros;P7RylPAN-P-!+WRB@K>!gS+m;noqsS<k6<bY%RQKj
zEGtVj&4F`yX0ha%UA4%6K%s%bWTC~v+0H-MC3c@E_{xzBq{N^<%AAT8Dvj=Z-~~8k
z5zA)!mc_y+9oE$yrK5^iK~47$CWP=CaJjG@{fR-k)ZsD<RBDg5k24-~^ghq7sw)1m
z7h6sZ$k_|KjD0(8&iU-k?_l>``fdqt9p1>9(9Q4vtZONIC1>yN=QltJD@WPEr>1~_
z0N46cx;f^nIes<Fh^ne9w|DQr9CO4hi*u9$b6Y0j#AnC&;m?!&rOVFBrti4+eL-8B
z`tZ*##zxaFu17RAHLFIIpmcuM9_;ajL3x&v=PxTcjdFe(H67svVoc?;t$zI6q|QGZ
zWQ2Ge>+J-}(@3x)KItOPUL)AwI+kH~L*uvTxj6f4N;;!hl<TY~BBqG6Ufa<$wN>TC
z#itXp7d<p^=iK%Gp_`AY%mS7YyF@9?^gx?iQbF12_WlFWC=EURkdr6d{_Nq_2KI+#
zE`_hMlm6%?G;~jFEoY#oBh>BsNWYY`Hk`i;)u;>Y?<^D6i`dKgyIu6(aCQGMO;@Io
z!ZjfQjSnD0Xzr0zeT9hdaAvyIhW^T`Y4#C)s*O#VQ&o2_n~0FCq;pPbYe!1XLr`YK
z=q)cR6YWiTlyi9?7pa1xvNDYiMwNOvkw%bY<|?^6KDK5L&yd>n3QC><FIDX8RqIO=
zr_Tez_hc_GJ4{)q)_6Xt*wNQ`sHHNTsh%+B1>y|EIm{-TNR3z_^r_jQ=_8P~$9Bte
zaNrihh`lsO)Pc&f375q}^6wB_s-R4xhynd82|@Ve?Qaw@&JI2!A{>i~gdPx3uZu;y
zpH*i|lub=6>M-jrE-E@j;t5WK7~#;3P~5y5aNU5m|JMA6I7d5<DJ(oW8*`{h&pf}3
zY~i8F(|JL4kblH<#=5Kn^OoH-!?<S{F=`^Pk0iD0Qfw6Tk}e?ZMO1(r{$e;usRcyR
z2Xny_-QIKI!i6n}{z=7}f*$DDT%F^V0GT{JYM>pTL>~1b2{S4Qr!d8&)c;*qFM|2O
zx&0Wy2D8`q5foj=MKsuI`s?aEb!AK{Id_OSiNv`JM-2g%4Wl>iLHuh{6&Fe-216oz
z6NW?_Y7xWQ)|T_fI}3v%CAYJKm4+a}Gz6!3i~Va!8dxq(SXY=Ra5e5cP|$d-QdUdf
zAS9Qp(`z~t^mZa*{8I|ODb#PWxU4LcvjFS@+sa<h85{P?_(yd%$y|rwBhQ_$q~|l|
zIX^CF|2P!-#zY3|rCse3IF*Q3VVhsRGI+ockTc_8(f_PISY*}zk2d2!)K=`QZYWUn
zPmM&aiSQ?+d(zaz<es0O>`ZGt$3sPTOA{>Ufy_=E3)#N;-vR9J`;KII5TGQh(oS)s
z675&g;LO(NPs8-zwOS!OMV0sd_gxh+-k?+DA4%x0C2dz;YbQw%+J%U=yiv?gLaUyk
zL|*hSioaUWYa1XQle&X+_X%Kf@<}HHA`}~d?hOc&DmUc}<%b1Q;Mwfadf_uKkKIKG
zdl*4MOi~b<toi3Lx@OIFn#S-PbKqYRBuZrqtR#sb5cQ-=5KUN!UQ)*@PdXMIsvgVP
znUV4sxwN1^3E>szIH=()JtF9q9nm%9nY-*HeaetA)JEF*FixD6mGv0WnKROsUT$ur
zP@Lts$;{5kD1yNuqxaoCDp;RMnrFG$Tdlo~4pa*@IdHnq?mMO(?D|8BnTg5ki=rIw
zVy-!C)yMEh905KTQLKJn!w0#vf5O{-r7O;KybAIf?yBH*Hi2g0t^zX8uZ$yC!*k4Y
z)ypfn3W-TDHT*$Oz4wN4IO#N6UN6%-V0^`wpFzu?qbzKXmMn9wi;9c{%M^HT!TgBc
ziPL#dwnk1ki@mIjJ)M_*^56KH$FXYgIFF_3y#DHCgh(kSX+R<(B9YqnQPH#u;)OiP
zm<tJ82=_;li6qJghk?@6=QVBh=rJPYIz9D^cYt;AQKErOQn5KZxS)h&h^JE}t;ApH
zk_*EUg8FIfL?v1TVQk?<hS=@7!taZtV`F2lQ6k1E9H)if911H+OV7TQEEAAf3=f4@
zpi0e)Nx$ElC4o?d7;@Cbd5nbmWlLawvLxnN%i69%+)P{rj$w^VPVBqVeVPpKZ=F)|
zVM^AHGA9{^2h>vlapJ@=kzQH5k(aVFk^^G88-w;VQVs*nB$x&}1CU8XDVzop=>OqH
zGS?(^(5T6I*o&>dV}1vpKvrNsQj&X6A-k3`_L0}tQQLQuI`i9Al2;^^@FZ;mV!)EH
zBIlxY)}XpoA|-$%$qDoMi?<+pH30jThdAPN1q+35x(|o>u^RGPED6IXicV83M-a;H
zSn>-shinN)29Ht%m&5Yw?C#!LO#^F0s6|XrG<_a%r9HVLgVUP+@N%nr)DpaPBgQ43
z9UvaCE+v6@K&+x71|J=jqQ01@EQ#A|=&LYm=izhVscsJua`2t{`^lw5l@84g=^wL_
zY*aMU39D~a2bJ}AJgQ7X07#pnY$9WYV^^rYDowdYh9Rl=H$<s$AL>*x%90@DP^F!e
z0)RbK?`<?4nu3}YvI=OOvo|a~Gw{wZcXg1;JP@d}1Nc7G9HLZrA#s0Ma|wb3>K^A1
z64b}m_uwAfd^ze={APZ-h%vys8!ctXf*mb8W4EffuCgTnt$<QWCZ+Ui^Am53f_J!=
z<GU#v%-baOqyp4Q;D711rs=&LQ^jYGYgH-MQqf*Qn8+=)80;xhJMHEM)8kSPtIox>
z{mnb`z0AdS2o9?4i3%5eNjyoDNW&lPAUj^ep&ctbBrQ0`B0{qafjsq{9Mt&lkRfs8
z>1Dd2Jqh8ukZoA%ZThz~qKuLu9s^k{B!`Ve5+Nxd{?e?ckWwhFlNu4zOPM5~5<(X^
zH#hr%oK2)zqGU=hSyPor6V;EzW0^x2Ixl_uMrfPBLy*oDhmst?!DM0Jc5#m6RL}f0
zsPE}%I_R;Kv~OfY`>DUbe-n~O_iiuZ%_6N*$hd(A`4k+j1uQ?WBU0TAB3lQ}iwWh7
zO(U#xE)yfkKwrUxl7b)zy^i_IM71}O8g9aP-dnTN)=`*TjqCPV_8&6d>wk$u{+m<@
zMMPT9*|)>S)|N;nzA);EVgu|j<2Sg3xC-=ESx&UlRilJ|E7bl@@7XihbCPvD)v;rX
zKAHtte7ntiE`>b6n|6l(F!uR}!N~usW~gND{|5yApFm(lbn(1GJESOyDo`j2gmT&w
zm%qeUb%Jsb780KWiJ$w3AwUHAgVYj^khp;Sfh?QOTgh7smJzEYg)(OZD~Q6#ABd#k
zwj(2%m#UDoIt{0$XdB*IK{;~#xTlov99Uf&S=;L<5aigiN<$=R$Qfa#{|MTk=zUF(
zRRb_0-i-jCm>49??s9Azf-74`CAhSuKfy>7GCkESVM}?4(X`<+y1^^NKQQ!n7^@J9
zw#aWmrlnJDPTg>*<Xqa@E{@YMn!Z;~`-l&IpUP_3nX(;GXl3a3prYFyYL^s47M0LG
zI{F>g@MT`MrLGwzT|Zz_NWA1-9@V<&c&w1dU_&%6gvlcw1!X7IQ0pZ=Ab}$1{C|X4
zz!Z|zh>2IafF&T&;-yc>{5uQr6hfcNnG=*X=t9Zt_}YY-noMpQE(wtwe90FCgM5D&
z_k-dFw2z!5rttS9^;KqP%lmi6ox=|NJJkJuq<!%J?3jPt+L)7K_>XmC{_i=Rom+JG
z`GCVxB+WfJy$%UxgwSj^sUZvt@p8u!%`oF=u)|J#$4uHvVaQ7)H~X}Ac7B-w*<cup
z|B({GCZZI@rru~)!<mZV9fuAoqnA$xTyY3s`w1oX5GQ3#Bw@8^GZzaDXBK3O{D`Jb
z)Xw&0>D<`h=|*I50DsOyA;O+S);NF3+Et+DNLx}gc{L<zS~My&D2vdzTg_>Ka<UGQ
z7oa3j-axa37SXo$&ahn;&?Z28^~=0PE8=H+2t+KiuZW=tLl_0Q5j^@Toi-@Rk>VxI
zsHA3luY^t993@qyZlvWLiP%2ajDYzB2!U>O<sT<P#CY;LzpU9kxAX;3x4}m377>|k
zV7PjT@uXFarml?!Y0@%JSPse}qG4)3bzEAy%)i5%Pb#>f0GX>N;zzwOxql&kAd%I4
zD+wu|II)$8AKntVy%@2w_I(Y()GAxgn8W0iY8rGa1Pha6t5H_bDfp-dniLow$Rvzh
z*tbc8loFmlfFeS8axkn7Ci3hb%ocbv*xRME=4NQu&b4mHD2Ff@jAFwLsz8@^@CMZ#
z?6a(@u}9F+xV2CJlE%)dLJ!9#--HJ>dMI8?g%Wt4ur^SoxA9&K)c{Epg$s7ipdLVE
zL9$7>!f&{GFm(-z*ULr>A#2)PO+$-34O0p-_82Xx0m2&ylVLy6Am^+pg|q<+raG{k
z>iG}eS-Q>YP)=c8dgq?0zE4dGEC=-`;;O$P7sS((iyoUi9He|G=Oom{L=b0C=4iTx
zuz&`kXjo!2xdX8Wu8s<ZvXK_@=^_T8+DoHJ%lC5@tm?@{#+s5q1uwV*s5OH9iH){-
zdT%935P%r|6?7>FF&Cs$3@E%WI;VCZcZj$H?MM1i4`CP`Hh?KDZTcE5i4b1Xv#u_m
z6n|16wk2t`esi=7npc6IB6s8S_d?m}f~G-3W1eCyiIR-5Mnpsl7Ao3ejiT{tSNcfU
zMzU{y(t@|Lomko)k$%e#nsy|vEL*+=cbU+e65yOPld5T!HKc%aHV{GKheCAZ3aBW4
zccmVUNq9<X__)odeLq6Zq4Cb|GT{*bTct&qsqX;6pMS)dz|l_op=;G@e;Lz=8<^nA
z%EvRWULhDnd%g?b+<i!?;rR8P81w*TOsfZ>g&*FBMC0>}n!k}O<Rnx3EPJXGs3}$#
zf0D9=Q#Dk9tH<g%gvPeYd~}n7%Fs6-ZY^kgl}Fqu$WD!S!I$CdN${VeKAvD!ml6>e
zeANk`f=1PhG^&J+Kt5f&!!ku0sl9-u_K_~9V@MILh{(AZJ>8+;K8V~vI*>%j<Jrb0
zXQ}UNYHOd)<QAaFZ((#iND@>57T!^O@Ye(Ub1v&o4Eg;iGWw?$paWSDv^HV&jloGb
z61l#zGD#yJ{W{yy1jfYEjnov7<TY9bE=RJHa511q@_mpxAyO(L3MCC!1OuH2$AeX+
zk4&RzJ;xsHvx}HkD;Hr-S^kc8?ee4}@Z+6VPrwkinR<Jp;kE=G|1SkS09>e2K#2I4
z&mk;(vg`WkTF0&^jzE#vIm|Q1AfqOwiO5V{S6(Vk<m6b+O3q=~l3uB#Nl+7PR8PMZ
zBs!}Rop$}vCCfVdzkN|5sLE$7;8A_-257#t>!(MO46yD=NrBSTw`JnGC-k3!lf+Je
z6({PGEjq_{yw;`5)YEP)-Mg8TW-Qcvuoz9ik4+)Qw}Q+dV6Sxk-mcf=Wf-ON=w+&e
zs&&#s>BR1Dzmk_Rqv}(n6SA8d>5mOg?KC#VH;^C>@~}my2=X*{UQetTdHIgg)Ft8>
zK`NzB4UlSdaaaqIdAf_@nnPgWgxT#X4<$B9<txoh0XyD<Z^<H+kg7D_AHX~oc<YjC
z9MT<xd=kvmBBIwuWDK5_n|#j$d703!W+-UEA}6g1Hw9Rqz72awW?|O%jdfpD;wLS_
z$jd~~P6QC#+~l;RE{G?3^BNbp)l5h-kmIJ;v&~R;9|xQ8iDbb^vtKtiQi^G0R9mu!
zyB`WOX?QgQ6z_OqC8rj*SVt0(Y!K<zf!f;@u8k6Cc}$9OK~$ZBuCc>%MH7T^YEXm2
zbx#aDLX87U?cl*%j8yd4*j7)AHOTJF)rMT{F)J5CdoF2C%FP&(=tovaOCmmHoB?yu
zk*iI3W@n1X0Sa>fn!Rm$Q2aoJ;}{n}+CV~^vIcwsjbU=NW)Iqcg-DuLxpeQi@|BP3
zfO<1Eh9vpkiQ$%Qk(x32qb>L%igH?#iIOb<iw7gM1rq#%QBhhhxeQ8BiL_~bV_v^b
zu~o3;co$`6EkOtn^cd_D7H7^vhkyQ+9E3*RXG1EZ^x>J2D%0*by`lv=Qfu^GgV+D~
za6>j4i40KJAiH`S(GJqS3iUM5jWE>Bl@Lq}QlIR$&?j#Zy=y%U$>F59%nhH>IzR|a
z8~VG!p)G_nCuPI4Rc$0T`)1^0Ffr5->%yME3_X+(Jw@8}k~aL`KHbqwd5}ReW7B?g
z)IVtAN@%n6qM-dd)UX>L(|ixA<0&f_MpcuX2H&nAb*=E3oBiPE9Hupxy|(OkHCeXG
z=#CrYmS9|QF6CavICje#SS=F#snUvLb|5y>><}T|i>qDXpC%tU(`5M1FsHkT-PJ&U
zbPPnAsvkP@4AMK4q$@xo+7sHUemvjq%LP|gQyfRf$n8;UK^k>)nuDg;bP;nwT0@LM
z3M@o=G*or6VtRVNTK$E<rxP>@LHHaww%^2QimPc-Y1`58uC4;p(H5Qdo_?Pfqs~q!
z-f$<1K|+%f`7mjqM_5o&`1Qfu;Dl4bgk>tL+gP$K!B9IOv|D>m{h+FtO+oQMirVw1
zLF=xc?u;$p2ZdA07?RAwm@0VFwGbI<hpI)mLFeYdD~T4|{oehcx3W=MZh-#z-piaD
z=5oGC0-HdqV})Au*$Qu_m!XdY^gL(O6?h2uMr1smQ1cQc>pgy13DUx!BVz||-Cz~j
zQhmmKRDN4Upz1x{q<Xsq9h%Q}W)I6)3uIZ#L@Li|L-b-KU#y^vE`>|I(Mnzs`UDnf
ze5aK}k1x=L{FhU?VLLWX*f~;Z_~#Ttall84iBh7jlLZ6+D8xQ3cXDC{H)&p+DP5sc
zVya6(iU2^4f-}!;(WtAT^6C>IjOl7yO2KWWfaFo9g7r*+H>5YN7f~l2N^-@ND(lwO
z&y>q8cT9}WcB1GxB}Z@L4pl1nG9IX@>MCG--M%jLbacV}U1G!MjekOSC5~W$i0rw!
z0Tw+;6AGT_h$%p*M*sS$EAkLH9jX{FrZH9#Te&8()0LX$(*5=P#wCmyHY*#2JkUg=
z{s0E9Zt2hJ(mGD6!-20ls?XewHCDFKrEO7QZmUSxs;hZm;`M((d7s!b(t^Hs38bUI
zZh~B&-CF8SdfSs3*07;KWk6hz*PhnLe6nUbM01VpO`0g6uCbBS0Uq*Lomrb~)$}?(
zKf;e;!XjVLffjPHn$onUgy9NXHJiX8O)5K~=XAg#mTl^Weg$tgBGYQRsx+yEuL{XT
ziRv(?AHuEqOWeVolk$RE*b_PVO+5-8mE%omt76iWVzzO!OOh<p+8!1MWnWOXq}Yi)
zY}&cORCQ1J+II3y9l-P+Sa8x<n3^4yTaX*r(+|s@1v}QLT|aS-w6G$Mn1n(Q=gGO?
zYay~+r?Oo+j4t@mXzOIrl8UbE{CkGQ7a{QO50#{fkzU+<=;Tj2Siwq>ukMIWpPsNs
zMFXcrIvS(w!5mFHl-=kND{~5bX{;Wa6<ubn8!=>`WKt0)pwU9cX$GU0jPJ>Cyps*K
z<f$AWn^nmx^#W4VWmsb-W;_8??&pv8)n))^hLP#%i8U1IZCY^~WRVp?7pI`7pozMZ
zCMJ#PREJ&^kv%2{qiyqKx|4>v`G(l5the!PN0U3nD-!E_blN-meKJN#VlNrx#clY0
zhCtE^$1$P4cSG9M+YdVJS-lCgrfQ$uhe_r&4>}cM9!z&?6;te7JG~3;|NK(cAnIqb
zjATKIlkWncIYDS-N$wc^_Jbm<wRrk-)LnLPZjO{n^!2MrB+uR})~816moJgB72eZp
zb2sS1x%8sNa?Wu`c}Jl!AMH%Oo<4$0s!t}o5-_OH$+x-g#NNZ))|T*<_FCh2xZ#<u
zvcHyV%t-6wYmE%6%v{S#`OC^_SeMjsD#G<4HFn)mBZ}&}y9(lWre90`*mgh^X*OGk
z63I70^t>%mo!}kaF#0<EVPnGRq0otQ9Tj!F*A>c-r0)un4IR2AJoOWfZ-UC!sYchW
z4AzFU8iNN!mzRdUcZ6L~5NlHXHr&KURM*wh&<E);DQFAP4;aB*Z<$qz3wh+d)q^j4
zz3xoElTg6^>BnZ?h({P7FRFRW72kJ~`shdd$j=kQT?QcBxVguddD>Omu26!*{_Bu>
z+J5Rb%l}~1r6z_4*-{uZEc#6+3`&<OGgK#lv5*uIA=TMJtoThnwei#E+$)&84?DF(
zDRz_>!{^tti=CMdz7R1LscqD;E;>X!qblvpDUcDQn;|zhQ44>tAqis$q0zLzs#7&?
zS^$p+B?_dL2aps#mpmH=c{X-E<Mi1<LH8?0fZSMZn<6i%`fgHpoj7AOU06tIJCPh?
z*xC>!g0H(djA;WtYk$#I!w~HUXpzjdos4|R3`=Bi&?a-r&<^?6Llyj=smZuf6Wqn@
z7vcE(8=y%w>G4i#=_iIXnHxr43rxf_HauNaCn$N;xj@#Waygh^U<!mWoSipPWB$LI
zyAp4%*RT6QAxTtHGK5s-A<9e{QwfPOmlPs1nL;WqA!7q+KyqC~A@iIuE|OW2A(_gQ
z*}Fgee(zfEpYU4Nx=WYu@O+==oPGA$XFq=$;NboL^m?$zS=O%mxRZ<XIjW=ItRpt4
z-h@W;SAxNhsmD%pf`4Vk)sK&P9MMLot3GdF!c4mU&r+PoSs_y$ZWRb6|KXR&#*7GQ
zB2F5LlebKu_dC1MQ#kwc;NQjD2fifPS;f%}>oOfboE@8N&eX-XztglFKQeA&HdbQn
z5om}BdFe+<clqLGzL&Oi+v~Y0vqkhT3Wm^Ge)*j&^x)pBI**O-abk8+5)G$g7gjtH
z<j!_KmY$Wx{RyYFn&Qh*hU%p03rSf0Th{Ypc@uA@rFRS?r9q}C8DK9-u(lL?x!<{W
zPbTJ`VxGJcn{%%GW@ZD<p&Ji@4EgjjP1#ZRmLbPHh>WBpumdOx+$Mt`J=&8Z(}z45
z)b;k7Y*EQYxElNfOq0n8)$J%uxY5vAZWMgw+BK@h(T4kyBlYMyZB1<X{%_l{zLN&2
zkK%Qn)bk!YX5hK}4RYV~3#*#R0tTmJ&(amUO|oW~6y5`|%q=6s%IW=+LW1>7*3z2~
zEDeo2qwhDfG;Q{p=M@vviCFBdbH94^px>%iIy7tuDSKOwdKPmS<=U3x)&4{|_!agA
z!bf)>F%vn$EB(YhxqEk+?4+M@O>bXOVf{|ak&vZ>(@rBre5p|MQWcwq4-H+h4;(!5
zw_fV)%+hsMP$;CV+~3kLeK(qbtTOIX^f^zCbyC)wG%P;BG|+xNzAYzctv7uILvnk&
z;;MFmWv|Y9|3a-}W7ooJWyJD+_rfIoG@ZN~CZE!cSkOTDfU@V!jXs%){^EVm_p%Vr
zK~sG1%|G%3wA_T`Lq<xq#FiI6M(}lVw(;T3beHdo%?ildCANp2nAk3zCs%j+V3e|%
zOYeUc(XhVyiuhNk?>|iT<V8Gq;P?9V;p#yHeiL^Kx^3I4k)-|w`*0^PP&rxmcuL!)
z>HcC!7N{9G74Gw$d-Z-*!qrUh$XRW11^a}pVV_uLMY2m=8b9VGeJOC=ih`sJB}@p+
zs`d*C(*K_RQnN7G1FUlzw+HQjXO-IN)2Fk3Eh<0etBxoV=*uM%>}iJNMy5vk&TGa#
z78#8zs_D9(9{C-k1Zbme?e`oPiWV?v>iN1DjjOXR6dMin7kf!L4sM$2FK!TD@zpeL
zSp2(!{X$2ENhR!oTAm-L3iLf7xWz8KuqNKFSus9!yJpTQe}Dg=urM_j7YQe4XUCrd
z$5H6+E_9#%Fx6Mc#K`FP{=F1wO7$9-$F;X){`&PRC_LQV^GlQVvS&w|-Co(7ry|PA
zRx9l?u6(|5t4wP7RH*D2KXcR(P33%AuAkuEzT*A_B}7#*hXJLPaskEwwy}bJQ8`Ag
z?+k^y^X$Xn7j+3SN8{9~N_cJ<KOy@5yEEfPZ}ZIa^6)5sUTo~G%gYa_zastu6C5|8
z3r)5xFE7U{sVVmQon$tVa`xGcW4nF%j_^uhTlVZOdpqgZ`)6Bf)``y!dq=sycS=9s
zx`&<`>TZgNh=`z&kXbh=1xwUbxIXY8Kh(BpZzsa{gqEKEj#f%#u3gVh>~?M;p=}rx
zRlRxheO~3f3?ixbd8BJNVc8H%&(t3Zn^b@ee&aHxJJFr9m5+~)A=HUnVcGv(;RKt9
z60F-2ZVqlC#sB|~xpbELDnb}?_nGhdUX<=fo=8~VMh#mKUy>&0SUf(Kd;hmzH1~*^
zX@+tBW#^~y@odDBSuh)W^`KUPX7$khNK@exv;cd|g>I84Fvub5z331)vRQC!Sho|$
zhFh|CMEYj@aERr|7R?W%TSsq8#?Gr|EDGjFO`}NHoW`Hoxb`rxLoC#kATJQ=^zOs6
z`qMX@hE2k9H~%!2ef~+z{m^vAjDFA9q<7S4ok#!TTZ}{O7ZBKr=~C0w#!?{!1sv8*
z#OWszTXb}EXzA!~YbM{;PgBKm{HzzqRLbn&0X5ywvP><VX}PMZq=ey_^kC5A&$XV)
zNcpbIVKU0nJudRo!h2_&>>=k@3OCbC=&<toX+jP~CXerxn<NzU&$}3XU@A)1xJ$=O
z5IE0y(#^e?qoN=rZ9e_f_|Iw>3y<8pzOpcBg#@^caRM2+6R8Ith6s`4yCps=4&$GN
zQu5A=E0{^0ozNlw!(uDe!6H2<(D>Bbs9z1E8fy(U%ZBOtp3mSj`uzEG?(s#V$8{&C
ziVAyh0<CGHaRP_>&iC8O7C*M15#(*!=Nprdz(9~CaD?T^k}6_A2&yp(LSbXo8R5Op
z;+IDQf;h!cv%B?;hjiE5UTp*N^<$THLwrlsbJwMqLA>LM(Yy;2GinS)SL22Ln_(iG
zHXidXx8|uuQ*O=D@<5$iot4BOCbT~QmSj1PY7tBGtALnQr5kJZxNxTChnuGSm5)(>
zvcqcL=KU#m0|QUZE*clO)cugbbTkF=&Z?z?=Rc>>CjY`wV=*x?s1n|tON`RA$?tSt
zp0l=A%=lR?KD&wc+w5KE^4-opVqd7I1Zs*fH}hIIna<Dm;DX5Mls4uIrJ}xqNAeFx
zxV!i4p3T+FrPAbdw(oVC_?X)kw{);s=SR^mceTW<+EcVTS3ImQaYR^VI#(4n_U=93
zUFRtDtvNW2hBs?6;@hK#pL1(smY=&6LZI~^BElbXhv#0ur|`NY+@^Z@&eXiQTuleC
zCmR$ToZXdgbF22rTM@7A7iZ_@Gg5lQ-G0V$^T*I_*|LT3gJz87VnS3@z_VM+Ta?x_
zB@;CMq{1Bys<i%y$L8d|=zT8+-H1=hzKqBV!VAXvPO8f<r+)-&@y=?LJ6^Zw{nhx*
z2Sn+K&X(atTgPKxFptefn1dtLZ#%Z(X^KRj`nJ8%tl>0+<`8*Av=`)NtHgePpfW3J
zck`6}GPGCo>9KVHP;1!F=CnUt(l<}n7g)<o3Vr^zT8{G@R3Wyv%D)tQ9V7OOi)Vid
z@0WBQHT>{w3s;hd2atf3tH((@FJt+U<orp;9EHOz!dti;ethC<j61en-fMyMOw4lp
zriMy~3jHl%Wa4`W7cW8bFof!chKJ{Q7*7_1>N7v0U~fF_nJr{NW6w8>lIldV?xmhw
zJ4`60&PmLoo-)U%SZzatGT7X71;#6=#J+z{--lT=s*7!|*NpO`lbpnk8Oyp&C?Oeu
z%o%s8JRe!li-V8EErmSgPc!v>-ZLpV`4XD?gdc{UpQDu^9P_Zy<J{j^%~bEgs9f36
z+ehY;?Z1>LxHtCXTn~KnMwCp>AQ&P?_Db%d?<{Z?K=DUIY(BUwvc+<Q!C-bO70Wsz
zH;c$~Y&t{tW#cN>mn1>y%pA?F&ZA1!6Gb2qAH4EM+vR{(f*fBc_owmsMFeM2-*s<7
z3C0)2yHhe9=M6t}^p*IK&s2fow&dmm`xVWA*~(X@OT%%>xTK`^KsmQLS7^*Gd`C^u
zfjs%CPc*#U$w<9}a0o1iNJSeX_eHQlUxe?8sOt~Cst+HyB_uxGHLkCW!#T5U(5rB2
zl!>8(I-a!lNEZ&df4>TzFu?-@1KbT5yt@+}y-P)1-DvH6Rl7@HmwskPqWg4z>la+P
zQLwFY=FS-{Ej4^7>93}5(3j{7vj`hM7~#}S)pHw-I(hQsakL=qgxwF_G81^%*m!zw
zyrcQY(W?ltDC*;T^3)a$AJ=&#bSCPRn4IZ(>RjHnJb1l^t+td`URpZ8%uX?Od12CM
z=yv*n=*MaEeeFeF*>)5|ip_E!jm4@{;<FFE8>-Je<62jlx}M%zW_<P1rAv^5{*_nx
z>ph;0o?T`a%)^d$Sa)QwM@B`R(o>HvoD3A`5HFbmEnV$4WR+uxIpBPa9v#+hj{NX^
z#dq=vC2o>Yg(lru7Bsjc>^|bUhuyysLj5KE^Lj@5PE(fq169@4El?U_T9_C2nUDeb
z)8FT}IyyRj@61YUD5Wfj%8>2pexhD8vk|r$rqf!#dGT65t5-wJeX~>-ldhw3a%^M<
zHZ){~l|=xGTV*V&ZnCBo^j|VA-~A9B8=Q0|Lo5$=c)Zl})=Zh4$RDuv`BiOUiUDPK
zDiUZE@65IT5(X`1A`C)FVlU#+qYD$NrKPcZ2X6VeADm9GH6kn<&Wu@12{}D{TsmZ=
zm9R+v%fQL_x=tMn2S*t6l@q=BJHg^s|F!eb^R_W<&>#4qdwl+VQ0knr=77spK(4Ep
zgU?u;%=_}jecCi$!G~2^TAI9YRR>MYcNj_}@55ctpN#BYh_14jqCIO`_@u5+n3=<I
zDy;~~Kh^UiQxOa-|NC_-*!d@JD*o@+5}(`ux1aciQ1Se0C4p}#m+#ttb+LGudU_VP
zc#D`8eb+avZf8C_6%yoq&RW`i6cs_JS^%!UZ0LEhm~K+&c093#1nZ0^ZpyN8ztjz3
z$2-^K@5^^qU!JkK^;FKI^@W{K>EaW!yc>P|*$Ir4)BzOK0mr9)78i)rU%MC>85y~&
z@8R$-*WX)R99^rUR_beOAJmKT@KmPz#OPrsqsnHYrr+ZS*V5x@?cBiJXX1`l@3SxR
z=k#4=`aRV*>-H%5iQ*@VYZijrp7A|?{1~@9)6!E@DJ*~Iu@6jq4+N~7*kTSwvUOu<
zk&R5`te~aLeAk$-cVxr}7;|$w?U23sSJ3NE;=Ue|rMNFuATZYm9%K=AsV?H2z9`Tp
zwO1qKpVsky1W+aWjXc=SZ~)n%d+wa$+;^StE;7A^?%P*>PuE~6Thfh=0n#&|_VU+C
znRV+-j>OCJ4QgqFZ3S&&3po+S%*?O%(@iS4z;{Cd!q_bV<$iz>ugb)w6>1QXHxU?!
zWkgY9l(mB?;yPt9JHf;hk!PJ_NInk$DnbbxC6%489BZhW^<0RDtPwX4kNkpachm~^
z#s|-_l`wYMd$`wuva73$A>Cer6}`fnByf(G()s%RwZ!4E(Hm}VM-|rQ&JksB%_wFI
zox#DRBFOf$HuiZ}8-hQ^!MR~EqzYD^<EgBBtPiJ($`wv^Y+#+#-&h9awDuFIpHP~(
zb*$mMBpI<vU*lw|n}qvJ&|iP?{hJ>b9esz2hGD;~?7o9hTkwsegd{|dTVuT5*><&t
zFzsA=21cP0MFH%bUY|<|%d6Ds1;!-^Dr$N^Lm^)lKg0A75E4@mWaGa@k%5tEiJv?~
zcn!FFj9XfIFF{86!f~y*quU5k+fGrv4C9NxI)h%%F`jsStvR7pZ!4njdxx)-py-1U
z1;6>zaQFf2Y9^m>a1h1Hp|IM%iZ>+Xy(o&;K03_CxKx6x)U2EO&@1thW*r8(<GnWD
zLHH%ed-3N!!=|Dl0E6%1noQ-aHAbcg*NFjMZf?q-urH$|KuQH57^jV+*0-n*W`?l|
z70vFrtBz;6&YnH{FxIT-CKExfEF2<O!3H1K(4ZwZCA1Em_v@beJe6{!#8k1bcB0z0
zJ9qDL!O0(qUO?Q}R9}BLiBSKPAvQTcR~2w3yd&#A)fs9HGpJUDkhq>q97r7IR>z-i
z6HXDcdstrSnPiP1(`g`Yq3(InZUgVNtw~8qyH*@Tj8+Of=lN0Iuu53%U(3YAlPZU)
zl;@mP+E@Z&A9nF^%n{*lbdXl7YHBR-=4$Kfvr+w7I01Z;ZvlU+gtvgDqv6_wTT{s(
zfrQ%q!in|BReC&jMMZ_f#=1{nZdP<`>^*>JqP8@~6G<QVG+4O@qf3lyE5Gv!!_tOd
z>B%?M)s@56ucgr4o@81up?w@)1!ODa51&zD+cM??9}=+w_vtWDhkLlVxX6{jWMvj2
zc41F_#36$pS&8$_x>zpEo(F=$cxTg2hZsz|Wy^6)rHh)E5w<<KA4h^t8Z=8@>)w*z
z%zr&eoFH}^om4rlr=H)fVRUP4-e72Cgp!bP{V}9xo3K2=biy2IAASogj7&`aKus?Y
zK^CD7NosCeT)b%|!Af>$-P&ZFhG<=6*gx4wJ~?Xl<KJ}?JhA`x?^Z+^!UJ>9b|$8-
z{SXpqzL=P>rBEUm5y`R`PXs|VLQ9aYDf)RA-#fe@_M@%Gb%*gXP^m5}RNaEv>zEcv
z8m8IKA(-vGvA)(igJc+j{+p&R%dNZj?ghh8rRVCc_`VJ(m~E#76ckK{hKILs-I2Qa
z^EoX8LvDd7s<0~9k=uTl!+fg7_>;D`$vAhEe&legH@BPmh0ibG$zf=so?$VOUf?v<
zD=p?eWgNG{Z<PDbBIZk;_IIGZw|!;3#0ve4|J;4GM7SBg@3Xso`*syRGrJFZQobXz
z-81vJV4aSO?(<hHQP=5nv|J!2l9!KOuA(g*@1B8koaTyA*5$jWtS@}KkzaRlqY>pa
z?+In)kaQ|wt(<~20J-2k1um}Rh_2N$Lz?n#uhzG4RbiX3cdKJZs^TtrK`}ZVZ&|K8
ztD}j=u4z)Z>+=<{ALn^8V>G>)qu5lWz-8>Rvl%KT?n8&vp&vO;FrqYw@0<G&&M86!
zfyLQ%ain(i(Gi$_?4(e_CkX1VPYcp7wwgale1p8|l8OM~Q2xVdcGW3Yjk&-A4oIpA
zwst2|+FtmqEC7u=dzq2w=1Yl>o$!}8m#8o}5DJr(M9BUB9iP|lDD;>;xMRnT-msdz
z>uiO_HJwqemN-T?5mD@fo12<I8qouJO)QkKiD74sm22c8SOe)(;Za167E3#iTwtHK
z;Rx*jD`{O)k8-5@3^LgnU7vmMJ;_?h&22_`Pt-nZZtWLyi;W*XsJ<2lN@T~*oyn<(
z<742kbLY;T%XwyRuWplZo49br#qv^uT#6vG!AATqK<h-j500lRdQ-D4=W4#9WkW@z
zaBLb$ZRX(Ggva;=pQA40L-O+MDA|tw*6D=%13{n%>ulxYPM$2{0qSnh35QD*xw*3k
zCONJmb=bc-di!_NQ`y!HadC0Fe|%RL9jz~3z50srL_brJ(z;bGN+GQ)SFh4V)g7Rx
z4v=yf@Oy1inES2?Cg%T))O7g(ijm#rU=Ug4?>IR*kz4GpVS22ZrleiB9>EAr785uH
z7PUkjELcQBf-u}&4<@?~)iR}t@rm8;IF}_`(Zy6W#b#muImXYAz|fAjwHatT7L=7c
zF{Zp(Ei*qGyyo&L!}Sg`-!6e05wWrQ`!jH3WeB$J$(n`EEDLThtA-gZRw{08ZdprP
zw{2sFZ9w{apOswE$EQ_4bIQ29Cid4q6tQ<#F+^*46VZ}uXH>ZKW*D$m_cT~rH@<Dx
zhYlZ;9{+-YF1n<4Km?Mdi-?NiL6*l3+KKbM3nVR5F@mx9?cb32wB~;O`jvFs8iahT
z!dz5ivV47QCAlS(AiXn!tGKQu#HZFYeJ%y=2)T1-(=S}F5QKnmY8XX%74mfzXb0i$
zTswi}Jk*|f7Q0?9Ivb?Uwmkhd5QOZ+_Z0j_k4eYAXYzRBh|gQ-==lF#fYv$~<3k|`
zn=C;M{j{bHv(??S_W9~R4L|Sm;>r6fNaGfn>R#&B8Pa4jo|ya5eu_}F&*Ff5fY=Wb
zoPX_X6N71}8V^Z*Nq&aXLUVi@hg|0mnWhN=2?;%ziB{UkV6O=NtaijX*6)z@oO}JQ
z2|B3sj6@Zh%#_RSciyu9`N-Oti9w9K>bKQ>{Ky>2DjE41nJ&I<1p!tgi=E?YEV~;X
zKer&P?#d3pjCWq%TSuqCs<{338m!u4-3Ner(WEAOC(faeTie@VJeDpidRDmzJ3Q)S
z*zsJL;7dOO_>eL?i-y_O{iT&_Aasv*9AORR78ceb7?DhcvW=VMq|b#vP4^8GWet6a
zhjWu%iT_I{Xt3FYw@KYSJb(x6vGK&UJ2{<l^IaASVY&4tL95@`_w5_krkTbN%BBa3
zcLODz+<*RXk>M*wm)ZA53a;r1`?hNv>V)0c@zjLI$&(Q`s6I9{RI-?PQxM43`d3Qf
zD^dly&K1{-z!5DNydcVeC9ZTCAd>$heCqV+=>9w$z_SWVN$vAUy#d%8lmuFxB6${0
z(WN!QHDU`_Okgu?kQ6wwYxk~QcTiTJ^0n`M-!i$@^|Ed3jqA8Rp@h6<*FX4=ZmOtV
zw}iFfUEF5adZVDOu8vgyPG+2gva_nHiXhP8vUO#5mX5~Vf~do>sMRwsY{B8U^MzJK
zA3o;(;|n76FHAR55gc9<Vk8F?c8z)|Z^d`^<1xbTtm4`(Z0n;P=hND6LL)~(?A^Q9
zjg$6!opXI8&wMkJO%5w?-ev-rvww0fIPufc_rJb5Om<6-`2Yj3_c!f)CJ)rFj7pMt
zj!a?o>2*ZA3sNoUh*9m{k@<-;IXo#39x#sGbPlHG=5D@BBLeJv7o;#3P`hWbsppe5
z1f>@p<0KAxb!MOH!af$3Ywr2^0Vl)qwZNQMIX@m|x!`qnL_bxJtWW)Jn>~sdY0heJ
zy)S>!AUN)A7I>T16+pjAETPj_EAQAEdeheKCg6-*rPyU$YNxzk^%c6iTWw%&lZwEt
zb*yCl^F~la<m`_{z#l<MN=i1(w{OHP>oum|W@j5~CW`Xbe|)-pZSPrK-5q-TF@5jo
z3cZ)IcXhxy@p-!q(~ce7#i(Hd@E1ynE%@H`-L};6+r5u2@*Y|jo!+felws~h>{=7s
zfrAHM!b%)SCt@bG#VTp(lB*0mvae<6ThZhu^|Ne&J$d%0rj<{eVEeP0vBS&oU+M}?
zLETTgZCg&E7EsflVgS^9USURj<I_$|cKzy9fEbRf?^HIwZnvT5RA3`SE$ep?WiZmC
z-e0O*(cTm{_^6bzYX=(}J%Mv_V|pAw`%QkM+zd5=d@%9KV(Nj@u)k8q%xLGZ%!h=3
z1VEL%Ei_KoPek@7{4hLNP^@T_VOA<!oQboWH6AB>o7jKoP|b+v`M>oXtu{b7=w7Rb
z=x3{7Pl->Vgv_UIs<&+CQ+0XAij8?xWBzPFu*=E6f%vOGoO@(hlTdE%r<D?5n)%R_
zbVvXA@h<+M-2AY^t)B6@xglxGx|U^h?4<M{SA!~|0wC5AuofH{-?O{3Hllm3{Ab<l
z*=f{o4!*6KQ4gxE)hHah;I_Nx@_Ix8Iv)%jlHA-gW`8OG{wg`At5D069RkEqn*AGY
zuE|oekso)i%v?Dg=Jsy+@<dpqz(KP#fszZKW7&llq?QBDXlpa#Bil^uQM~2tzbtd9
zViTbRG@+AWEPaS)EpYg78R9v=0$m%cI(Er7p*s!&?8!0<Mdt(AQ+?4lihlLvr$Jak
zc!Cr|Y}>QWaa-X=oRiqmK(>`-)HM_Y+Q#?NxbhF65K}7WOb-gu@c7TmO@aYpYfvI;
z843dJws2NSo0~@jOD&d$-@a$)g$#+Gogse!xYg}p-06dCinrK^UGj>8>eWEee|OFc
zsh7z(@5_}ZJAr<yMrpq%d+?TbUzs&dg1NnefKQHLC`u;E+UeTfH<tp8#wbd3Zo~?X
z8hos+b?{t)Kv#1*&;AQT#73D_plR5BiFrw|Y)_1if#*YsE(s<k05QMJN}}S5_{k+)
zM4DVPIFsW!Oy3}L;CJ-CscgZH!ERH6#HMz_4Ie+o{HQ{IZv2)5Lw#Lcgu*oP!&KH9
z2pF=&5BkRwc#a^z5^P>SSaUHfnqSU7H5n3v<z5!MdV`xXw=2+8IZ&Kew5^AE;nt=%
zMa7Gdl4Qq)nP!s4@dN?Lz!1(MsFkc4ljI@zu`TzJdM(m<$4KS(cnm=?j`;wXPb%zb
zWGd0Vkvq5qq#MtXwFrY(jcIgN_>GL6BAvS*g2^2~6e9c}>3QM1u^x2)zRHCQdp_B?
z`g684I;VSbtC^)4G`;ypIb*f<`GJPO5(rePrV0+7O?cO%@W}U0t4)sZnZQB*hWxvD
ztcm~g7JuNw@-G#77I`Kt%Ufm@9awW$b}kivEuk49<Xf_fm@W)U;z}fv<kAo?rAW!x
z?OU>cmla~VV9fs_<lhx&oY`i`BFm2H-^zc+rU(TkQ3mYgxHMx4_sc`Xx(97sAqtZK
z99)W8-#vOrAd?Dgjjf=ygI(yBH1i+F@SUTU_nadhe4&^J&nuJT<KXZ{qTT}^X=lE3
z%7j=!MT_TzU_-vk$Ju5>=k-+&>!zDVL-$)ce#9K9sH|k-;9z(@uwAc&Y_x*(*-Ust
zpS=jQnw|ll?qHwyeI!pXbXI7%PHP_HBAU~j$T;DX4`C2h4z1~x^RUwK@A0avs3>BO
zB#~E`M9mP4vhN~+AJ6Qds-0$&^PD@JaBJCmEadRgD^LTZR<PAd3@i;m5Q*Q(bL_Wz
zsFCHj_WtXFqfehoSV#6n^phpqTnRE2nY7h9w{kh4RKkVAG$3b)nbD7v9huMC#Jbv$
zu*Y^!<8*Q1<gtcQBdW#l`k-rslnGjjAX?knNOjz$#z$gOSZ{x6<!#hdBnu1R`x1or
zYool3b_M~n!^cjIXgU7jg`jT=;873Lc>4GRElDsS9qth|SBlUHMgWcNuL0eS7CiFm
zd*6+cIq+8`+MDPrWPxV^f9`CXNfX#Ixy6Bi))fFsi_xa1y6lW629Q7>Df%kBeEBli
z=gpH|v1P+zr+Z84_mEb+q$6JnHb@>kNR2yYBbq3p>h8eUnoC%i0h=ZJ@x8h$Cos%M
zJcoRup4wOgx!G^^o81x*GB02FpOldlR92?s4I?&Urqp3!s&5}^cI{L>bRACs@xHlK
zNlluylF9sGBJXvuf@DDf7{mm6oBTa`dKG9Xl8pSQC@J5Vms37$?Xbyd{X`9sv^tQ`
zbKTVEEmrsz1#w~Bhrs(E#vYwvWe6Hy;D76<G40)Z(R|Z5TD{u1_4wgnwraZmH^{pP
z!rG+|HBSg~Yi(|LeT)DPDjmohl-Bc)fH(Vu*&wJV@iKJxp2vtYYM{;{kGCJ^ykGPR
zwnKlU6gUoro_6Ui9~6*$uSjA5`&IW)OfoWswGBN1H$PUaD<d15V9XhFbFx$j2OznG
ze$acDvB=9y6!B>_O(bWd0nvs%_tB&Je^UVofj8JA<$w!vW*$UG54G&^NjH_QWcv-m
zELqFitR?l_A=I=KO6zh3P_9LE_oKieB^7uGHCtQ3C&%m=o_|-KK=qZRb!j%pTpbpV
zCVy|QF18ihFb4=Q3*@|fQblFPoP<bBd{?W2m|D||K37#&U&1kPvsXMgeNt1i;+=Jy
zt*|eM!8g7exAf8t+lPJ7*UGHj0LzNH&E<Mt7jBLakaww%#S0KLE!u-u<0yU5i)>^J
zOhakYP!f(x=o?m+A^fIZS~~<Jo8X?banWRI>+n@RP<W69scLIKIMsvFj1!xRU$i+d
zkA&P1OX?NQ%F@u#d_%k<C%)x55;NNc)N`!c)P5vMC?04rc&*0|TaKow=wl$F%J59d
z(*JCAkB=LboBEs{*QTiOcwE_0<U0Dhyz<(a@A(cO{rxFoxVfu0ZQgtdx(2ebF*rC#
zA}!3gR#mA$U$%V_+A5|!d%jPK)=}fgV!((C8-oN1D7Eh6y^=SAETT(CQh9ltNo_?Q
z&hc^{JJ1Ar4Fjk?ty26BXtn{+mk>7Yx_tREBg7a8v*gES=KX6kpiUTec}x1E2|h^X
zQJT~4-}vm(K%-U1hp30lUWE-2f$|+%<`*QYLz`YL@i=;WrXIu0-ayc`jg3{f57$<U
z_uJNC{R^3<#Ysi3likcD*+omMq@uD3{M8*aQU?&8S~W&0@FYRd^qT{ueDBEQME>U;
z<`Wm+VN&2yK2#T#b*-6$q=|p6N@4kdJ8#d;x!~rCUBI<UnC}IfciF;%{8)^OJ()o#
z&T8H-xsb`mv}@M^c_~*+IGKjMdOa{;PO`=rd)Nxq^*sb?Ze(-_Pspa6!$29uCK{Sk
z=dwRR)l{|sr7*>V4dtLi>Cn&++UbUpDQXNmnIh!$D!OVq{xnZwbfH&3EC8$|dykNu
zOiw-tsy&GgL2KpbFiyoV;4lwq?ol>*PrV<VW`%A-;C?8H_ufn5{015H1eQr9-)UHG
zM|j<pKOY~8`%8eI*&k17k#!iYDG(|*H;oU*`S2tF!m3DV#q11B`OndBCU>mg?d?;u
zG#(xvH*u?9a;ecfE3V>Wic(X!7UTDYQDFkUXH?+Al}TvO1OjA6@V~ttW*!ld?bu{d
zHzql-4LG?~hH4r|f1)fT8)^1|JYDascajX$D(WVvOBhcm(fWh*xQtf-S<eU5@eG8<
z>C-BRbV+ulXiyMk=zx4}d8AH)MmQ<#?os9D7Ww+8m{d_YS_I86ZhG;I*zlTJxW=su
z71$9`^mgDhO$)WjjGGkUN48I}1q9rRu}S0Qz4Ws@%Gi-~gx}fq>?V6GfNcn?MXcMI
zbgZjWo`CXXVv@du!eFA8m!IMu{odPfTe~SspK>0^89lXmPpkoA1vT3dHI*Ts_?Q0R
zID=mAK^(&#t^uF-6Q8{&`b7LMNU<FH;sYSplu?_LsUAR4Glu^wj$92;A(>7Qcfa(!
zKm%m^|Gu$5Ewx+0JS3F+@7L<CKfvz)|4*MbDC9>>Dop|`E|Mb=CsnmnGESJ^{vV0=
BS5W`}

literal 37663
zcmeFa2UJwqwl%D6w|1+D7!eQ@1p^AA5=5a4gjPh9EKxy0i2@>-qOXl25?UmvD5WAI
zS#kygp%DR*EJ;B`vPFiXs{Xl)zVG(C?|=Ed@r~~r*D<<*;?${q&e?0Nx#pT{apU9(
z6|Tj6i|5Uo$942)MU8p$esGyLZ-MEL3-L-z*rRXwvd~mjMG;@|Z~90c6<>b3^z&)Q
zdGpp^C;!b4>ZYHcH*dqdql$;LT<#CFxrP{?zf1WR6E`{Z>xm7&{<?XC;;9LF-{l+6
zn(V!INO``+@>PnzFTLrvK{oKmA3N<8c@%&2&3^tp*R?agXMa*wT&|?@t9QbULtej%
z935`cE~4z-mUp{7Sj)MsW!j^v*U{FgV7x)ouFXAuD9g4Ww7h3@nzObIPN~u2obSB3
zHhuhoeE0LV`4Z&o!DBzU%qQPy9DA{ZeEm)1J7e<IJ7mST@5nb#LcG_KuN$Jj&nI8!
zZ4>zqoZ@F+$Etl>wu^~HrKav)vqSG>pTo7yW=rw*rBdBkj9y|?)TR#~J_rj7)2hR>
z?>#E#U4UO63E1;5m-Dl4d~%`O$h61j(P`PScSlr>mT}gx;a^`z-Yth5!vnM5UfZ|3
z_LO)r2G?#<3G|PPi{q8D)Eda?iL~#$v88mfaI(PV%UugBVKTltEG+Dfy!(~=4sW*6
zlbj30EG;cfM=NE5HU%Bnos`|_=h7XjJ5yiHqawf)8X9`)^yzifnOd5#@Z*94dGqSX
z^)ZodmezTLnWr>0SFYT0!hcWofjyCKeX+KT)A=s;h92~ilxhV=@$<8<P3xYWqCevH
z-1_D1-IY0pNz?@yI@EAOhob`a#XKp~Um{f0)YJx^zg|X<RrlPEySIFBaIn9vr}XDH
zvJY@yedO@kyWtrVmrp*~eR1)oAJQ+Tj5ippZ75<+v`ADP7=25wB%fZ&&m6MzyzkO^
z!{?qIejLvotc%rLlJx+;4H_i3h&9z^PcK<p#^_+Po+T#-<>ux#oewwa4ADGsW1r&}
zMMuZcQiYJecqo`Wi^r3^ckkXzzS@x^N5WgibS1NVerZ}ucF(roEUwB=wmMGRWHyTm
z@i1rdunxw6<HSJobYEYe0z<C%$?Xv?F0O(Jsq=3_4l}mUJa)6+h$Vk63li$EDl(aR
zwRR-OP@GoXGn$x~IDK1PA*XSIF_>lI)%_-v$(J0h8vbRngUVJAYRR_m8c|o6K36oe
z<JuGdTK{!>77zAR(>g5LRKs?Zt4AKl>F75xFfce(8lR?gbIt0R4%@uJ8P7cGY?qKv
z%lWis4^7QlIhzkHEu$0`J!rJ6G@ySprM|wt+oC$Mt%o^b;qRDZ7+%QF&(9ceu8-3;
zvaJm_wOg_vMS|GF`<LtT`7cb5bf>h~=9N@P6d9%~NaTKfd`H?wzo<$%ACJMTY`wF8
zR)<%fiDxy9LTP6XIhO^CsJhuqW+Z#=w8eAy`t_@?S&+Q6ZQ=A6;}*)y*C_k9l4WDA
z?4s#PJJVOc{zU(MQ_xuRmEQN!DU6}KGUjAQheeC2*tqO?<5g}2-mbEspzA_j*%v%*
zeaRD-Zm~}D)$<gkcb42_IOWk;V=+G6xQYAX;^P}HIyM?Pm;AKl{&LlD@xipBYuB!k
z#kO9qRbwXPU-m25xwN?OM_k&^-1CPQ=FW~(M5Uxi3JYK3780b))C8H92aD8v_`rwf
zgB`P1N~#2VWm50?nM3zICz?+g7_^vCN553wRFx=5zvSAQZEs&KKUwUzT1+^+LdnX?
z>Hu@BmYx#inegsQceqJlRFFchiAfe~z=Fab5D;MhV*aAyVXw)&3)a^DhT>~0D=X>!
zCTzLVAR&_rjJwqyq9^tbre_ZqG9D)-iF<v!DpcH`TqL-D{rZ}k8lL3*kz*csuoCGe
z*_X~9z>Sn|pTxa39r;}Db8)e9puoC47FGV|E5elA-Q7Ps<XpIL!PjTW+EVNZo$R(x
z*M$_$Xf7Fh64F&?B9Z^?@bSC5qbt}7?CI*VT6M~LnBlF8?#FOdI*IzLVZGL^ZvBb&
zzi+&5J;uBelaR2ZYX5Nlt8RYV>_^ota8KE|drQKr<@ythTZYqXPuTY-W|iKQDCEUu
zku$+nw7YZ#+>x;}9!Mz@^vR^ne%@k+-NgUx=Y?elt(TOv&l!|vpQdlECA^84N87YI
z9F6jL{f7_tgy6m&6%|aj=QSq#m^imN85P=k4i`*bX-OKbTflRR(w~sFxOQ>EU`C^{
zL623dSy^CZ5!^sqW_7p2v{~T3r|%{-X){_ugUe(;dLPc4EEx7IP{<4n44kzuTd#Y8
z`es|qSW~86@U9cn9_$$IChXG1gSd@%*)1KcOcr;pud!Qi^kI2;!0CpD25jEx&$oRh
zqEv#0>=V}R(EE-0?d2l(e6Ij<%A;EzXnh?OGnqaXWTU!I>GeUIB_*w#>+9`W+Rl~k
z(EaAsNM8GRQb3<o&0{6{On>rP@#?#Kw!rneSyN|vW6Ek&gp9IhLz6wCs~Nm|%#VAF
zMs)Ze7p)x}H?Yk)f8S%I+=%7e^T4_E_gkCtY-r`@I^#{4mY+t9oZISc@+=}<Mc3@q
zU&X_-di831+^30|rqpKZR)>n6KFI^s_xGheqdcq+*9g$0%HuldWfWG*rD+ypIA{>|
z8#U9a2g6ePWL=9(Rfq}59?I=sTtS?bn?Qf8W=!d!MeFOj#;wk!HD^zJuv!vc#Aq9y
z$QnB&b+YBK<fIa8>fp?eE;8?k)2(PUb{|w`Zf6F;6TKS2N72az(XysLG#2XUd40a6
zVp`(2x*(HHETg--yCAc7R*J>5Nx2vc>ab!|oqa#jT@h7K;Q2tB*yZ918l$ym@o2`x
z)2B~4R`RNwGW&_!v;C>wF{u+wr;43ftHOe0JAK7#SBAXzS!=AP$10U6tTD?&sLN)}
z^e+ilm;LnX<5o6e(&9sth_ZY;ckWc`&APMD(qQYCIj0~*oWfM+%_S8*k?yv~@5@Sx
z7tM~i%+kvkOAQ(`za$j;`cj#V)WJrZD+spY;f$G6&yU^~9dGXWS`)RT;`SExq0zVH
z8Py8bR#pZvm4EqR$>NF!)pA1>?j4NwS0k{qw&50=1kOYF_F{ogH(v3)%Gj34?eP&m
z<v8|~M%WcL+vJ%r`*exX^YAPZxOC7uqh7~zqV4_r6~y^CR?Ew@!!4ER7}%`V*49RN
zZtfnQ?x9Jvnm_Nc9SZv8!y3PwIS;P#BJY<g&F90371q_)D;4(%73O|>wZyE$b24CN
zW`=j4!@2ujGrjbWSGgzLO3f8$L5S^fmvV|svZ~F~=#i88F_)&J!;SkgJgHS{-kP&>
z<ox-h;!K>zx}A#{MrU37>S9mn>aKNiaw1?rN=oV!mNeBb(D2~$`=dTB<Ks<!v07bq
zLen0F6=O?@_gger$k2c*?OxPvUU?5&S1c*}@pF%tKdc@5V1We;dk(v&G@D11+)@!-
z*D?D=rVG9+5L;@ezJWm$VrGSj1^&-Zzp${BnAwLpGu!;J?MDO(mmMaB2Zpk{0%gD4
zG0Xj)YF99>V@PF=OClIIncm#l*+XHwW_czso%+tczBbNaWx+&VIhdK5CEGCMDlVI~
zIByBBSY6fNueUx|N?KYOzu7AzqvGi3n4X!rh9!612{7hT-pl(}vsS^CS|y9UzRoR_
z&KL?>x5s={m_*({s#6jbUTN{@Xjo`y$)oFnAFQZrO+4&9fT2#D)5|Md=w`6BbZ*OS
znLn%ytmfAhxL=$=BpVx>lyl|5+1P-cD```TA)%p4iX_5xz7}F#@?f-d823zAE&A2-
zhl~e1O$zg$8yXno{StKM<5KgfoNapU2llhGbyf_(ZTbjBxPS6lYl=W=l*H~;r|Rn|
zAUs9k)5;1X5zey5-m5MNCjqXkHOH}w1T5`%9VHbN6$Fppdq#bV;8`+$4|>L)xi@)b
zSEf%u+h6bW&d1i${6G^o5#i*_tM0Eu^sukj)?J;%4q{IQxd6`OwTj*@zkJscOECW0
z=-KU%Bd)pMI;~Qg|FkFl{O-Hrjz3UF-|jK`E{DZ(Vjap+J1u&<cO=TvYSFXkuuAi;
z+0kkV!@^J3_!n19dngb)eAT_DN;ayokOan6VZ@wW;DEcHMFsl(v{F>j$jAuJ$?0%y
z5t}*8&sd&YPZQeN-@jw@W&0ZGb0*)zLp{0e?L1FgORLzm=fPU{;R5GR)FAnZCb_|7
z2fn>{<)H2uo@6s9WWY!9nq8=~@A=ac5A^kn5o~`FTgUhG_+1UX$bF9HKZ*4k_qe(g
zjVIcz{!+>urLhH)3ui1975({TY##{yWN6Ui)cuzw;$KIXHf6QD#aFBw<ll6=ToD%X
z$$?rG;sq2wW3Bnd=1~zt7>nBA`F;M?Gg)m{#C8<Hv-TUZ34nO+@tR9!Ozhd5JGw2Z
z<_BvHNN_mgfo-vMhsmXvKh(T`ukp(A{cs0suw!vW(Nu{<?){Pn-y7H!I32&Ycbl+I
z^`1QQs<!^5yrg$eDZkkCq^XLDi81m!;QZW<ycs;zW0N_`-7@}Uw7FdV=&Q_kjeqlk
z7cO4BaZ^0^naLq*O-;>NxM?xNA=~Xf<+rz#tue4Zm3_eGL%K0ludl(tE+B6xH~vzN
zV^hRl_kk3Zv--a@no#8xre1PSFjQ*8cAQ>8W6$<1O3klp_MB|X`}Oj50f(P=80Dt_
zx;n8RH)`t+Y8QWp?_V7?oAFhJ^1(vrfsk2GtYGdQDr<HsnLU%t^(@_@Ou+H@*=a`_
z!!TUvXM>ivclUHfx)1fHRsYho#;@RSE>FB*>^(_;1gNugd$_t10)H>q*#-I5J6=iB
zOA!`y`S?o{9BEf#R=c0CFMxZK1<kXeH~#VC#})UZZm!wsx8Au`a;S*SdKHv}&Fg~9
zp$jOde?)2u9^dJws9@~x;x^}w_00o)+Hh$@H&e{YN~IbNS4tF4nTBu3%gB%l6F1^>
z?(lRb$&TdNSHDhu;->1wy<fSFm_}(=y{<_BNh8E_J_nNzSfVMcn6QjZU`O_7)&8!l
zLbGSiG6gQE2)ds8G#$C$+1Ai&y4%Mai6Qsk$XR9eKniQ>)ktFzYg`s7Nmr<@@eSZg
z9v*lR`dF=cS!_%sAMuGbLazaePwKJAZoMw(b^&SA9-p$BhK4oj^5a?_17<=uHPz)a
zOgxv>WuAQN$%B3Q+hNxR19&|J;Rc3X)%|@16Yhw4iMmt*v?Snq%2>oEE-*gdR-c&8
z{!&ddnZs1gg;7yas`3E5@|m7vH7cV3B>nQ{W~KhUX@LQuA@=tq&6FQi&Q6cI4~~Wx
z&33y5oktQw%UDyfg)&`kYej-pk!?qNdt`@GqY;DIR3+QxKMVM2m_6HhQ{qa?@Qgpf
zFLbXCHf~{Cw}YKu&d#NEe;|8{Z?@iWa#I~_X2%@`Pq(5ELA;W}LwUo63f1Wa$RRre
z_YZw-ydPC2MD^HVoG&Zg;W6q}f6|gz8M3d^^ozAKo)PL^Be&^!9ZcJK@yC)k1<DFX
zQd!ev&!`$5%OZKF6HAvwUX@NaO&y&z-11{52Xgz9JTnVM-iUSGvr4+*=jS(`lqMt7
z?qWu3altbopxvIp&b$s&+a(J!<~D3pe%%jG>T!7jY37xF#CE<^J>Z{gcegt*^7|$_
zQb$F&*Zdy&;TuSfC{NWQ)+`e8xD*mTY4<E>|4_E<M3b2r9+3THn{(MKpCx8UVvwrv
zlgrtr`Lr0&bQKb~!FiNdoh^WaT_ghTFv#5DHIYUuIl?W!s)#vgXNCmq)TvWEIHB1<
zTD5}x)TirZ2)u$3HkqemRZpZ3X10iXu^Am@ChVEUY@}jlpI-UUr@DevkRw}XHk(V@
zcNTMO3fa6tveR?kE3iC*`|@Y{;(gc;oElE|uYMIMux~eOs*F;a>@^;r-TO46lSFr9
zEhT`pUET|p`6BC+RRHKKZAvj))VWbnNlEGLJt;9kIG1e1gHD23*BZNQB$?oy-RF-9
z;1~A5Xex*Rd-v{5(CsK`U9-?~&f^~uMWU^0M7;mJdHvO^xH#YZTfB~{<!rYE^~Q10
zv>NInL6<YJLdVK^kKk?IkCgtNH+q9S$RqY9=bUGl`>y^M*dy}Z{|W&WM|O=Gz)5;W
zMrf|T0<wA~MMc1;($dluy0;IM##r1vE^29IwMm#Hr|{x8#Eyj_Yy+b4@DOZ?JWV8N
z>*+Ud-aO9Dl@k^QO%N3w9bLRyMAg;RRZCkN=Yw4W^@L?VKG%Cd0iY|^rC|MW*fXJo
zczA%IYuek5JN<&AL6~gczFqj$)a97Cxb1H$bc@FAY;BPl1+LHy*@4BKf{pO-%w?I!
z3{R!j)zv9V=gqV}#CicX$V%jb)UlOr#2<|WTlV|8-8^SKa=V(+Edtft8(lpFB9g=c
z%7(CfpGii)>WjQcIXO8E`xmEiVq7Wm_3eG@t*w?_dOzZ^?5VZ10WU2xQ@SfHEHyRt
z)l^eqZkD+RE-|25fA?Mg!T7ZgvXaBX*5LOeBO~JTAJBW_aNsV5i_-_ab~!C`DPW)0
z#cIPFCFZ;6W@Ka-m8#5ZuP51q(DJ6t25o$5v$gcdbUwZJn%QKelh^P-lc~{zpf($m
z{=V7veLIIe8}1LJZaiMY*&Y8wT;qRrxBTZB&)5--wI`1QdJ((3L!o*RPszu}XFB$^
zCI!xkq^s)@3U)f(;9Q{6o^#)|XJ%%~vS&xTz&iGiutikEiU~mhYK#v2DHo_P^L2$b
zp<m{mG&~~uky)<pwE_f+VO>K*DPSTm@jW5o;bnlkro^GO*kqL;0<3i(%+Lk-^|Y>=
zL(^KPZ``wJ7?^dFaA&JybwJydmYrnt0c3f4awxzQL{AmPVr29>Z+3GyUqADozt6|j
z@bU9+5;pBDzR{N1Y*)k-7B&U#bybIBxGxU!t(b2odiw^z@fKlWV^EH^Ewil2q99*i
zBV<wU`ZyoY`|}auj{&~2<8_+Ab;U9uvRshABj~}@aDKCfgwu2c#nSy9(^apu<~+{D
za6(aQtUa~Eu5jA??frdw1l_(q5@#pJ$KPCeLUtp$H(SNT`qOA2ST<raXEm+|Mxh5f
z=ST5&FM9?y5*Bf*x+f|&Hny&N^9h6#MMc8$*xHKQOO*$gpL>kdSlD0P8Ck!UuC9ur
zB9MG~dV0v5@q$(bv!zdL6<iMDBiP);#GSA-VvV@#D(BCiSLCG)X=!N@m#e4<!&e5A
zMBGMIS1rKi)N@)rp}F%t#)rjZWMn3%jGZ-+?P<%N*rm`Pmy{G}ePul75UfV>*JT@T
zJ|3M)xGVP?Ro~H?50;$vrKv8(eNrHAGGR0&&#cxdn%-0}N`L3pk@<T@NF6pxCvsSm
z9yYjBIv#6DoOb-<!otF8FI%{MLGF9)xEaNSA*;z)7Is`zvuR^$lC`vzhuf_>dU>})
z&AWFegBe3#zy7TFRSS{KCFNKgO#ASi7rAoRPbE}LjU}a~I$Z6T<$40B8SMGIwx;Hm
z?}8%S+2UbFFl*+NWoeAF)vI5hT(q{{(Y$DXw3|t8=BL-!!*hRC5TQ2-n~vL!hxA>&
z87ih5_StKHg?SnhU){dlZlQH%wNLQy_9ztmTrp*Ryy3S2XIlT+_Iy|SN~y+TPy<~E
z;{G>QZc&C~-776!N{CG`0+XFcac_X*Q&9HjJ=N#%OTaha>5|hH548JsAF~9pXJ5L`
zA|!XIx474Ig-NMRR+|of(f4YJ#8?FP$?tTHUC+ZTv+K@>9LFP@{kVv-)oESqcyqOv
zv`&KFDI9AKhSk1gwJuI_X4BFe-dQ<0`;v0L-1GGX%`b+0ICHBZu3{}g)_CBhYBFuB
z2#pAwlhw!~z*LY@&~o<r`W5t_3{0+Z@$1}qd=@4XExyIR!6Vn`&7MQIx5EnH7;%G|
zPad7+3w3fE-{O9>b*&TYTspQGk43G>QC#?^w=v+Bv^>-<>~d1rQxIbD7g25OxwC#j
zU%q@%^oTfP)6iRQ!6RzVHbf+s6)Oy}kQ3GJ*^|uA$9KHuc^J0pt5s{F57ef}H1wXk
z|I>#Ddj%py?H>pp2VHxsU!5;dO*MA(ZX<U3(<wbwzA_85!bdWjziSUScH1T_TpGaN
zDAGDO@L~bCe}rxBwl(60ygm0FPn=!$a7f@v(^+}rERb|s0{IEeKYbaU)~egz)bM?N
zsfCf%7w(&S%)_)|I?v4J$(-KV(3$kNj(hLG_q(RL)l6sP6cn^gC5S{+uKBM0Nnnuc
zFS^w1(Bht_qv>0|WE(f$vk@~LXDmH?Ru^H=$2$vo!L9i&E3EjAA5}<-jgJ@0FWr)%
zJl}42&`!BU`A3b*RHGfeFM2f8yS_<C7jU<EyUMA#Fk*AZJ(*_(KABcYEp;YS#HV>b
zGJ{V8tiIt}Pf4A?&T(on2e-(>lLD+Z!S_9>GR=EZ^4pItDgk_}TkMKF6&3FT#L}~h
zn2Fyq;&9uEV{ZjZ%fmwuRCvM(@)mDXz7>w&lodU&=K?`$6sMlA=)CV()tgxf7H}0n
z(Z>zCq%=5h*RE^FU+gz6_Pv9|nAD)?xVy#JJ8OadCEdopHo<*&f#COFf2HYB-Fc4x
zTvhqoPcEBu*dJQK6`FxRiWcY8>CU|1y0LGrBqpu&KT?_FT+Dy3xc84!{5|IVZ`Dfw
z58Tes@Lccoa>UNcr($c>4GOMSPF=2dJrJB*xFzMO#?PIy=RihP98E|GdH>uqG<ZD5
z`64fEbHiT3o(q?4<my+~0Ivjy8ZEx*lw309sFAZ=WA?UV6;~eA@g-8q4XF)Y#=&}K
z+Y;pkOJm~I1`C3`o#nN4b<5xrj~OW!p$^yNF<Pl=q-=^(!aABUf-OJoSA^g`dd}$#
zOYH;n;+0nsgh3#Qi=MczhmfLeWV9aM0w0+I2p<JpRd;@~4%{M0T%dg?py!a>UxWD-
zJY0epJv}t40(h3)zpoal0P`L?HTiygd^}owV+|*+tjN_i9(pI;JKVD8?r~GA*184l
zdj-~R8E;BQjm8UAhLBwUU_MeU2Eh?69;J(A-oRzyU*^lK@$K1>gm@*m<NVmgrsrpG
zm7hZyxeW37n9+&Or9uh^goR1DZ13KX?0e^Mt4BX55B*gO;IkkZNdPKdJM5MEQ%@wf
z(~}KzQd{~7FN9)U+Xkcugpu$Ul+Ud@pAeOwpTFEY1JFZNRh2*wB+-Yz66zb-2T2kL
z06~T!EPO8_LZ*-F<H3{@DhR0)*;KEukFJ}tW~a%mG`w7+L<kp+@RUFV_`?UA-o0B6
z-j*acKu&~JE$*8<d-TBT9N@M(kq|?5Mx=)7>y(Qt8hc-cRCKS%VLkcOlpY&<@iqml
zTynvR=?{^W=1aCHre|fv2v@`>B(#2PS?toN36!;cnQ_Cw_ecdf8OV3WU1>>GCI-KE
zJKPWudZa#Ft+wI_?PXY<r;`Fr;`{ugi76p+H2K>0c3Y2zV4QQlokm$qK|ujo1`@8I
z>VSE1lk{!TQY|`I<hscCB-A!>6bg*O3%+4%-U;?F)I`*F2~f8T_^$n&;3N{3+qr?*
z(D<0HdsQ6+KU)S^#Xo!2^s!~x;7?nL+0DDQ>wi}V@qfU!|91o(|G1EU*XO@cHF|N-
z1w{_iq1GHaN~a|-ku{`FDRAk<eEX)e*Gm9<RgDD6y*zkoX{@&lJVwIlw9YUC+upg<
z5NLJ{Xm5j7V1!e~3Qq*3$-&QW@M#?F0r|=U^4yAKgUuGS^-u=bk)#Is$SiZ18U(#Y
zNG?Z?<o$Tv?)k%{PEL+jRP>n4>j;oED_xa@?eB}vkS1K6M(84P@{^a3d{Jp2)gfPB
zLJfvr;v{}&o{_{4+{q71)+j0#1MTW`T9V`EEiC)(&f`z0kOsmT`uY+!i{!!N3@#((
zTcS{>CoI?0i$w~{A%q#wr9yk6oZM)U*88TUR%iIk9t9&O!Jy-!w|}P}HC2m#S4+ap
zp`^{O@1GBeW|V65<qz@EoTATq1nbqFK}s=EUGQ^XZp&%uhyVyt`gX5~9?gxY>#@`9
zD186iMoEtvygjzR`a`wq3ajI$YFBrX@MN{)=|-M!g^*+L@JL$LJtKMrkDmP<+YB39
zTLqvcp|(=lOi9m~F<Df883V&#qk;k_CMIl?QLC!x{Pvn#;#k|&jS-clUn1;`@tJUL
zZ>Po{=jXRhZ3Yjd9aLQjXTLD#-sjk)q!Y8b2LGApB-G+T((=y9>c5Xk3Y(YN$Q*m0
zm5AHZ#3wWEEyGn-a#UX02EoGVClRo#cYp7Bnzpx&mj_HWLecsa$$tUo#FH}fwq+Ps
z$PpGPjNA$$>Br;fXc3ZAXMee8)tQtx<OgnEegcW)p;#+a)=~iO59K6p=94s4oMjZT
z-D4}}eI<GiNvnngjqp<%jNu|G>Lv(sPu`P)MK+toK+Hb6-l^_X%IjtOkwIO1h<zx*
znrSQwL&TKp4$(B7`t~VWe8Iz;q^5G4W&V1K-qmHo>NYuMv~bK4@Ei#a4-e!JrjRS$
z)I6~-`jQa}L8Kmle08}@hleBK0t!LEPJZYB)xra{N1L&(iAl$>BJ;+U$ghsM*B@EO
zCuaJAB>;lHj0%M?N;@5`wap(QQlA8=BayI1WGnN$Mep_WAd(9gknW3e9-mu>I@Uwa
zrW^$znNLy$;3p8JZ;0tPVzIg9$rQ#wnz}VCu+6x!mJy?N6vh?Aplwd7y_%RscOA;<
zk)9sxj}l^D5Uw5*mynRS|0!G2VlCGbwS6A?n_{cg_!?+xMhdjy@f=rtqD-%iHH!N5
zm)u^@Tly1f3xwQ5HAHW3^T^v2UJ>2t*;i_90pg?$HNeFc@-~+>pB}$~V)s5$?YLlN
zCHB2uTMk4AZuz#*LD45#>?Y%r)La+wrnoKW8`k5>JQby^q?l^8Bebg44Ka9&g9j(Y
zU2!Z4$e^sJbQM>8QFBf06ky0w2dVPfmZ=a8-0G$L(mRB8)Kh?%v^xD7LW}P$GyrQF
z)UOV`*9H)!pc~`^tMA!8uz3UJOMTZ=!n9g|S~EDP#X^@wj$-S6n!1Q3-I#rDs>$}=
zPpjTCk|A$8UdIF0u||rMfa0Ou@U#tnnAK%E%;ipz-Wd(%;Z^!!K#ka`Ys`CQkGLc^
zoAVAGP}fm+w7eV{Qx%~?+{>A*yl_0QFarIAS<_dotojeoowT*<AJ%blqFujTQ+&bC
zLu?lwYmdc=tVRmbEFK<um2+XOL;@$FI`Rq$6}Q~ihrH6(dOP&fx7tD*`Rc${d^#r|
zn?yy_*H@hKf|IlTW`5+0Rs&dHBB$f<XUGiYZLvR{KHNvmKBYoDFXW*dS)O+%|L*yA
zip5AR*UPkB)`T(1(@}dW5lhZrK~#n)Gw&z$h?SzpxBe3k_~g5GUtfSO;w3xbD8-z%
zk(13^VEOyommJ~c^H`q$j^L7<?7u>p|K~~2|7GRqKQ83o1seaZ5O=N)0ynFrqcah2
zBYJz?N6X-yh-KeCy@vZvet)v-On-hmlo%oog#TAHI$Rza9zHqM3f6fU9EIlT(-%G5
zYKXfyefsp-98US-_M~()^q~Zi!-v3Gz$A&YKp{XrP@URwWZC(G0Cgp$67u5fYh0?c
zELV~lq1L3QHc9fsH|KWWFmx7Xxq95X#wVkFRDMX+uf5blvtv>*!HapKbEeic?*u@4
z%N@^CBZ2o<h~F-^9@)<xzf~#9-b~+Hr?(yM<yO~i;;-zg1Bh=Qc<nAk+Y;#_oK!IJ
z;Sh466UZE8r#@cIE2x@Da}zRS^xb8u4<}Fx`fWUp#g01OuIqY4GTGqlp@f~WQu@?j
zM(gHZ%#sU5>;)RylWyyEeUSdLKx_W6n<eV~U`4$$)H=7cT-_F4Q39u6)VDiV3V6cz
z=PLoxNL0*-MA*09;*F^NKli=nghMw}8v)xqCaVC4N`4rVR*2V0^aE}pOGV|tO@IdV
z)}bU(SCHiH0yNN1<Oeqc>I_5BVXFZ$;h?Ux*kN~Jouaa%oy})}7ku_4=q4Gi<794$
zR+{W<x(YP)&x{FQ-%6Q|1H11Ye{6NUQ%?R4N<k}dZHMg2O4|7+ysY()K~*LRV#-&~
zsOwFrn5yBa4v?3?5Nci?97g_N*1x~CzRpiJDdPBHUI8U;4vS_o+qssMN=t_LIM#c@
zPw<<xj7%AX#A}FDl}P1KgGI<&&9Eg$%#(J-TdSd+e3p>l@9PWpY{N46>L$zh#7<Hr
zUM;4xih*>Q9C{juN<iwOm*a3AA5`QEsR86a1l?LZaMG!3Y%JTNK)>PA7aQZ}nZ<ce
z#Qn#M@_C3%+a5B@QV4SRa0UmG0Fxpx4-df@1Noy>q$W~U?KQE7@`e)!p8v9N^T$DJ
z!Vi~=gcsv^uP5p<C_GR(ff!(QZH#&lL?=`4!wXkcgze=2ioO8+yB0E^+of@<ghbG5
zZps0|qC#sMK&rWt9#h{?jtEp0&)J;W?$!@E-lk576PzwzzPwcDH0Ry1QWxgiSrCni
z2mPjrf62|YWuWx6rr3~%tc89q@YW`kQb>`8RGK-*yGefKAam5}51fGHhj;%K>u@2%
zhy$(;U}eaCGDMk1(&6*(q&B=TZ!Ra$;!XFSB+?F|HzRWKGUzk-Audoy!3Uf-M`hm*
zQbrHzo&^1j?JOX|{Tvp<qfe9O2O-`vox5`jmV7whPCWc(2WujWgkg?i&h^V(dS869
zvSRVZ>jEoC=|3MN0kRH%UcgI5MSzXs@WQcZB29lpOZMm%TX0&GobKTEP;SMTig1Y_
z<Vqq$rlMEq;rfXUuE(A=-BZ>ZLlZ<%Z7?G!Do`%&`7AOkF2z3<qBG|04D~xH^E*!y
zrS}?+jvnw7|7w!G<gzNd9h4G@b_2j-tJ@1GoF><VRYN=>3#eyy9USb#LIq~8`r7y%
z<RI^jF<<8o|N7I)PF0f`4FTs?dlWRXPI(%k-lh~sx<Tfh`MS;R1p{$k2}thB+>T2S
zF&J3plSh6%?`qUn3oyk{O0N#Hvb6MTSVLa*rf#2Emru3@IQwUbi2+39UOhpZervIK
z&;)qTG@Qdt?>e_y{v#3SpPhzUWjKuqy^6kGArwn~bA1mle$yWFo`l3_+m2yqh&?kq
zkkBozaO|ntd1W8@q<KelY8JE;DX333_Nqb)w~#{q^I0NpZR+M6u$0&o1Rj;tE0KeF
z45FvC;Zy{_8sVKaQ7UW04D|J9tUDSSbo2!A^inD~zvfMaWus|gc;^lYlvt`KsL<Xm
zt{8@r^UsTOo*C=)>Bf$KQaurFbmjC}S3IH;aZa{*=J)NmdB1RbYyf|QS|ubN{v>Td
zl*qGTAdRJXO}!RURwU&%kI$Qgw*C2<aObAzK~%%+1K3q}_FmMOg)GXCsP(`WsuvS=
ziVGIQAM@af{-ZWhwjn}|e_H&TMUQK_sZ+1%6!s{^exlg|M%@)e5!~|NvPRG#wB&DO
zah^qwSCO0Ky)hn-F^NuNPt)iSe_XkH+u4SgKM@rxOy*Q`X|3bj%p(on_h0653Y&MN
ztaPA@EPEfVYSx^39@k{{_Kqas++G<La1Kr=n$P9Z_zY!1RK7}!Z>(I@^@p4LX@Rs7
zX9LVzp!)X+fbaP}m;G{boi7&&`6J86wH`P%^-D(K0c_y~+AGMJU;MUv!~9|B|GS`C
zCDIWt#YxU57SH=WA4T0O1CWH1a^O@)yZi8ZBJ@KRyZX;gHW?+b!`Y;DqwKNLO;fDA
zq>!JS!&}X`hI$V56I-zz9gqpWGUB{CUjxPmC8bX{(L^@2aaRA0iY*c-L@9i3sH3AZ
zFvQt6QxxnsBFlL-g1B?DO`fwdHnB7dh0(jOM)F-p{r>zau|J~7L2c&Bhl_MJtAi~z
z!THpA%Xzv>hA!dmN?v^bgvd3!Zko)R5)mbRJeexLY~SbKNFxjObpK#k;Ch3BV9tkK
zdxDSv#g7f7A*;nvs4YYBe8!UjLg8;msL$yVUVPjNQ-$7b`;~Ccn=eOUrSO&p1LAXL
za%~$Y(*3DxN{ROP(SB5UiHKbVgr%aQHL{1R_H*vx$OrEqNTnUx-qj|ok?<}F>U4!%
zV<J7#&QmOGrvX$TMBU7$xK9dh);;}&d$u>nLiG00&w;-@*|354btSKk%E2{1`uQwW
z3||qJ@%d@$$;0I$?c-ZEX!-x%ndIwo+VC&Wg*TW!T5ij?{71jTx4Jtue&jiP&LN`w
zoLeqqQbpS4$KI;Vwc(C#3bgHU%X({LXkM_=N#?}e+M4$ox8HL1@`mxa;HS%$Eh`;p
zPV4$*kziA2?t%pi?4gxLz$G0dsDdUaALZN=u9)qOALU^$H$M##NfbP=$zCZN)}ug#
zxXuw|K?VU1oFn-(N*1(}poZYX$HPO2Z9;F^gPK-GzG>drf!%ns;*aXqt$3Fg#19l@
zAArg+*kZ_bm)Go6=Lp&Jy?>lg2PZ@q$C~gMb(*nZ)w$gfM$|4LZTIndU9mYQdGK`{
zP6NH{+VGZ^maM+Q{)WW(OH!Qm1jKjFABO4OtjIAO8s^sO)Zl`|rlcFXFs)nxa)|de
zZMyg5gJBN~d)Lk3hD}rc5Z<*Pn{&|bo^KcYCI}H_NjKzk?l=2QJjd%xy0N~L_jB%b
zZ-U>Vd^7<WBF2L{+t+h{r5F$!`(Q+l;%j1BAmqVHVgC{|u>DUfZmF+dEz^4Gh@#@y
z_;?94cbzJfnNtYropMBe2?-j*Df}FX>X|ctfPvD~FiYz(qKY)cI_x2&Ek&C^mzfZw
zso%C^Oh2#D&{6Geq<mhd6FDbQKl&Wri1r#GR2qy*gg93F(0IGo!r_G$_wAMQp@g>w
zjTdg%107osG}q!pU*JdadUVnk{Z0q2UB3<_rz9R8KV`<bpLLqrRUg}kTK6WRlsEz6
zdbyV`=EF7#$pdH1p`B1`*3BsM`uNMD&hjlZjvw2Z)vA$E#xbd9;c*M|`C*(Te-uD7
zT(b4j%+(B0sJBONMCmD&^?x)T@@dqI*PK+>HMCbE)%kT6_rW?mZJ)dMT1u~f$cKPg
zkJJ4J_<&M`O2J(CdUnsx<nbr}JypE*-1kU8Z}I4O`SN8pA^b=M{O%qL+tDkWYkL0>
zdxXS_-_6rnJlHkg=R>#RY<lU^E(EN6n|7caXl+!l)v?Fa*{k-LzvZpDQaaPU5h*_D
z-x?Q>N<g||#Xp<{ZtliF^@T!3%T=UWV-f+WL__wy&lf@OG}DrjlK7A<p*%0b(5w_X
zsCE;Dp<pJx@lgLH4|7{$e0=;lD#D&6l83MAu5E2?Hw4@>(q}zCG_|#@_c{D1u->}8
zR4P!3Bu0!A#T`9sk0zzW#(rXDyT&6FZJEe8`WFZ0y8g0A-x0@Jw-2%{sDgxQ(dF&g
z%*minpFRa=L4!k{7kUKpr|j%J`wdj&9~z{dJG#R#dsh`&nZEY+uB*=k^%WUh{Rwqz
z0sBNV`Hz*z<%FKaJSufNW=TaYtl#Kx8+2%)9yVhZ%nb=z)TOV+EL8RN^~YlT9hetw
zZIhtLiK29)j1@Let;ek={AO~?wqUs`-hRhq-^(6HgRk+GJ$_Qf2!7n!8gIyw(g^{L
z`{YrnFK_)%X{%|`MEJQ$-wvMNzTvb*36(ubEhC+sq+lkkeh+*e>9Hg&4M3O^(l(rp
zy}_pM3f~)OTX~7%6lSoW2xs(=R<+{2En}7a<BcXluugw$?R?>mR%5h9h}iY__4z?&
z!(+3BRvnZZwzr~ZbkmS_q8gEqH?U*bV1D}y)lxxQ-;uR9RZL(@JB;_$J0*P$F#O8~
z(+d}#eP&cB6tM0MM9<tV_RMW)NHQ`=r7)RHDI|9b7cM+DVR7cn!_xZA>QpMVoRwgd
zmx&*$`|9IWCoC>qdQN^IrNjvfoQwQ`u({Gz)X~}5nSSm*c95t;WY><|p9fmwV`8>o
z(>*@9l{7^38C98!&{H>MkZN5Xd-Kb0-=3$auj~HubUd9NErr%U4aw@>fq~X9jE3>y
zr6_%R^c!^eAsoP6wenEV_|~;RXzGQIT@MH{NFxaHcYAHazfcH_@D0+CAuL>oCdE=%
zsj96V3~j4x3zluxmg{6-)TO7jb(MyFVjcFrD!=;n;2A#Al&AV)lOHzQzt^^!(Trq;
zgje^B$1G)TOE#pIx_K`@%We)f6c7L1zN(aAD;}=;*-EOR!{q4tEW7QoQXi=AQ`mK?
zIjpY+4wJM1^?7QR0TFzaDbI#XxPjx_OS*+v->ye}Xt*}Nrj%_X$9wek5)MS)wZe%V
z{aZkC9)q-lMPzc+KKX-)x~lgfxvhblU9x6J)6+rDCfR(dt@>lzl>WK;sWe}%m~#wm
zZQ=wT*3`*=WKLI8jSamT^V+q`j?KA-=+5v9yyZVkqP9sqDv4^qgy!@P8DmZo`7j^u
zAMEF?O7pB4FVrxJj6^s0{vG6JtyfxXR(FV#6CEt5XRhEPkRe*aa~m<Q3zl*2a|k~6
z6n&2i!DFbGo^OQl1S09>ct#Xf0ze-e3+H&vci#|;SQSOHs?jn(k(0hK&lctIpZ`W<
zcidD^0&|qYc3jHMt?QPSa^~e<{OneecW<a9r~ABxasyJ)-pa*)2v1JYy|c2iWME+l
z23g{Q(64tEWx?}~_0|%x9}<~$B!ITGBON5}Hd>XlxC<9nbGr=Vy7aPajnI|=`5?hR
zXj-z7Yz^?;x@s}KPE);vq<18^5kQQhlK5v1xzMy2Z$=|-K3eJmRfIhEl9QL&A>p`i
z(Uticmgj<<%SQ@10w{2BblxbXk<K0>>Fb?CeS8|4(K1uDG*-IH#cQO8%I-&JnFbVq
zNKjN%oP`3@;lUi5O+1N^U&QJ!@~Z&K@93PdbFDD&tAO;}GkPw3T+k!egKgL8kdx*t
zLqvw+9q42Toy%+<p4C2pUBs6QhUY`)4D4l_ICM?ge})vkn*z6rcU<<}7lhC`Hy5B*
z&A^b20$}riC+DthO-AG)n*YubLTgL`3Yj>x5r#_v{Tgf8?1Z$cxXW|fgo_{<ROZ&e
zWEY{89uyT3BMC9Ts&tXWiil0wv(@b4WGb^?mXu~n{nuezBJV=ur`>E9g=O|n7iYj)
zot_wi4$7wyww%_hxso6$obAwi{_wMBAUtq(cbEY*rGLUndt5d(u=PIR$qAy}LS4Y)
z+e?wg2uPo1Q4$K|^z1<n9iTz>*E~L*5?I;>Y#R6W(MT5}PN=*mm2$cFJ?X9o4*@si
z4}UI|o0k_qPDU(X`a-hh{NYjpSV+=By0|i2WXUhQB_C}Mzd*j5_i)D2Vd3zH3@g%0
z%eg>PxTa?%uQ|4`H$Co$e1IM=^qQ5D<_c1#?6}mjuw$kns}uKzlt>4yOm2pS-8)ys
zIbp1B=w2s8d(zm?jYg<7jq7=NeNhG2AuLQN7ozQwI>tHs1zGK!g~KRI=`D5UMj?XJ
zs~mfAl-sG8M`cww*)WzN96$5!H<*_=N~NS0;p<Cg8FZ1N9NG=`66Ho73#plr;hMQR
zfWIM7?l6_jDCwRa?Fm2N=zNvlRkk*qhIalxLs4<V?C04Bz`}Goh~91<nBiwX`+#DW
z{-41z#4v@~t?liF+LJ~wMxXdO12x2k-U4xrVO6&SmHKc+xFs(i-*$0zbV2_a%98y2
zX2qtQ&#y`?W>F*6`SX?rNR$wVS8BsH2K^&0PS*EllaR+gA}!Ry!c{=RV5hog7JYat
z!bci&M|-NFBPPBLV-@}xyazBMFc*T{<YWm_P9ZI?!jKa-o=(4TigcbQ=WRwiSnKop
zKe)SHVHsLxQ9=-Wc3~ZXWc)&ygoz+<aG9|)<7wlE0u(`s?z(;}$HTy0XVHaB`e?wG
zt?h(5Sv;qTUM3_&<kuwctl!RgY&ftR2f}k_kq+QaC|?3dOAH#`S3zZo17n(S{bp!=
zulK-=5RyOW(7l~WbrbNC__?=xs=<IEEj<1}PKvb9^<L+knl}wQLIHh8lCrXTy_KB(
z^!$dO$yH|(S;f~b_Bm$9kqdaUHE~Ahze~E&kErc7%Gsr}G(;LQcfx*pZPAocv<x?S
z`2{=njR+rs;n-v7ZtQ}L{MpzM&O4TCPu<EuYg975eX>wSfze!DDiUtEg47+Ch-Yt5
z7<<HBBAz#}V&~3oAqQ|bv6uhI_F~~_Lbhq*2^0=>kj_KW>aKE`N_oqPdAoM6E>h%N
z(!2~3y9hC)<93_#nbykK$j<p)>qFZ&b6$w}wBnDJ6REg8E_u1Rew1g@d-slAYq@^?
zI-7NH=W5DnUUKA_<NVrv_bSbA&L94A`O1|wb`q-=N4d@^cclO6B>$0lB;;&=E01ZZ
zMoV8t#8uX;?H}z#tecpqoJMQ(sn|clhJML+?dZTY)ylH_W6B8cx<6da&#|W-ykua9
z>BKD~XPuY%{@Hi!k&%(d>N!UBLdW3-=a&5K<NnnNDzRa1Yk!jl1Rx=6c(=yJ+vS@J
zbuT_kY1n=DZGYJ9CTQ-oth$XO!q!}$a4556ZtEZw0+KPHQDsK+8Y`p`8uV0(np#!j
zJK6|f_{x1%%`P5I(1!hy9O!jWgXL^Hv^1<*U4(>gzE9-<Dun(<Bq2vFPh`@w_r^-u
zTJ9|qPHRo9Yi}2XqHdGEa{@P+_VG9-CZ_#-Na!`b{#a-UZZRP$9~XZ#gp}l1p)_!F
z0#H3vTgIuBP4d@dQaRZR048~Pk8NvqFms)ZtziJ#SE{P2f|<Qvzdj>xsIS*fr6B1c
zaF|SbNTr}IOMZagSDomA8w@~ma7rn(d&IGh+|)POr6gQ7$X|i1Bhfx#Q9iE!hNn&N
z!`6IRu19Ax=CX==QdUGu6Kk(jo(h;z0>13PW2dy{0clF0x3d`6vSoqUpU`yurhc;l
zlr2E`Zs94XNl%#X_@Ph#I;j8t4i5m1NQcQp?|50C2J>jAf{~R~W75uM8jHmuj{`J`
z2xHH#T_?LNgJX({iq269<i`&d$P4frm^b+WH6i@4cdr)t0rhhc^%brNf}NNNU={#j
zA0fCuubp4@_-1icft#D#<Jj13i+v6!=%qYv@3z)iQGF~q8fdwRx#cIM;Q^j+w#vvD
zfh=#u^ad$Zl-%6i%@A+|YjUIZH7mc!3tjB<m$XGHTXz;5I}(tQPJbdCn|yYKRa}Nc
za-|;IVcTJ9rXuwXvK!7`(oLe6$shE6&bB@(9-hVyc|?g8acV7$ebR7&eYA|FDbYO>
zW%p%`L8{bna*e>v(g~@#7BY?Fe@|Hc-=>HChi2}7dmt3?l>b4GcygPVPJ%BfZmp3p
z79hR2NVAmG)z!gBZz4TGO#t2fM#AKj2bH80U*uWV#}Rbf{%x3aofF=Il;B9`D2i6#
z7#-Jd=j_yH`PY7QLDD-mF;Pl5OZTB13o_Ayur?PiY$cfu>9gffN5B@OOH^yvf#cS<
zKi-)1vIU$Ax-8F<)=RMAd8Dv+Ty!OABWh{kCju!T>r#F@46@Pfv7CEf)W+}HckcV>
zy#P=KNjh@ne#5z9>v{hhL;=WQDEsv|>D$5Xy~R>4Sr~R80)o(W<TI~~ZR?swD*8@U
z+cUwgDT4thv9vk|x`)HLD4+iSixy#$_7^1LV`Gm}-)wCZqA2GUE}GH2_qSkS*4gTp
zIu?_UL^7D!?BXx26WU+>w?HA#Kb)LCKpEFaqP%|nnz-e+1#iR}X=(vq^y<Dn5ai?Y
za~=HxN*H)<&tnF<AohuLJRyZQ5G?MBE$jNP`lG51-YC|2-T7!xL@1>nJx4WE-H-OF
z#Lre~(4GAg?L~qC@tV*apQFm{-){w~3(}vYb5!LKCHtuUfq@;(G{hD1281RNMwdjn
z@V^WiDpM;+p(S{D)4v`nno?XnxsQ!ofUF=_^`^2-CKO5fv^}5P$I6~jQu9?bF)<;{
zvt(9RUEXHt-OfX;0L5|6>zwvD_0^H0p^Uo$l!#;!0EtfqNFqqvTfY&a{*!i;X$8qt
zqJoNN5dZ~KC|K(a@17U;^z4H6!;hAeYI2#N`wlp2(mBsh_arkL8jZ4?&7eC}T}mR<
zwUmUDTSErx3EBFzsFDQGauzt?Da=LE@A3;ik0c4VBqhJY`nQU1&})RnW3H<(*<o`s
zi4ry4kPXK8G;>>&A$9g{=Zu*@D?}%W!a8ihv~Y=Kh1{e0i(I{WRisC6X65-FV^a3I
z6KO&yC~NB5wu<VuvRn@ZyCLmsITu~KXVPM841apKac=QI>tyx_Q3sNi#;iVdag=YW
zL-K79<x-H`^E66^b&pP`7QCmuXG+dD?6|7I>>VCXM$Fetq99@>;=9|G=HoH`j~+b=
zW=dw8P?qnk7rI(E&hTZ0WL_Ek0P^#PL&Yy%c(X!kIyy{7G**{h?`LRQQr7jGEMK+Q
zC)F`IMbO(WmSW~<d#|Ef`M#sSQumCR?I|x?xj*7>YwKlA=ngu*lwHieS#0~G^Du~d
zlCiygu;2Nu$&$6ib6hyi73yItFhr&v&~vIit4bz_!Fpe--xYp=VD<nzk4<FOK@)VM
zq{j4kP8WOO0+;>5;nGh-s2_D!n6Og+c-e)#Oi}_uNhm6k{ssG2KWzyyoFsj2Xva*{
ziRO3&Ewv-x=ktrLT6~^*$SHv>CrVcHPL@yj<AXm4qH*!LyyGcfC*R!6J+oW?l_7lO
z#Qy=G`wv0ae{81V|0B0^=eYO=GX5?qEzMD}Us@_)a5f(ekfLh$v_>Y-Li!rM*pLdc
zkDzu=gxI!fF>#j!^G{fSuOUA`Ty$(ACND3Kkk~n_%T7-9$R~jO;hAm)&gVOgR*Z5M
z@#EwGM6Gfbs$q3F=I*Yuhs#+|tC9qyx3_m&GYy|YygS6=J9230>HdSU(5!9SKWT}o
z!C9mWkf~-;z6^Eu44E0Js@|+l+Wn?d?L2?~<gHtUMI+O*(v+$u)~eQp(;i;MTtfXD
zwH}@PW-bo@H<su@1yCj-<@2a3Lmi^2Dwd{l4in>y$a=SBvA?QMv;56Ze%OlR1}W!5
z`h%D;9@NsAR$)^nZ;aOR7)p1Uw^;$j;9f-FI7{))kq|RmedC-QA6Ce4^;6~1^O&C_
zhIS^9oNc}h>-&XPAi)^8`|FHmE@jzY`rS{){gwBBGj1{XA%on!)aC?Mh$Pa0R0=#X
zd8HxIKo=?0`UQJaU%ouV+-8O&MHpry0+PFSJ*1o;o18>Zxy9UWkAh)`kjX=9<3}gA
z?w{oA$G3m=gtM#9tov6T;kp(oMv~&+Z>Im~BK#kob^8B^U--}Oi;TKF?_&ljrb?}l
z5)low62Q~(2<e@Ha(&NaF~Ib3PEJmH_gvIFe&mOIOz6A;+_V+5T<IN=7~V&S*e=N0
zgAh3P!YB3XZ6tRSFc<`(PO|ZOsG898A++WGrNg8pmorZhGuGPf$T?XO^@}7l0&d|P
z?0iy^Yvs3guyiOocm8%{*$s3Sh`>#Ot}Z5nWXOn?C^$MnFw}aP)~5?pIW*uiU*rT!
zd=8VLfRK>i3i9~OmF0ZpN-0{=G1?C3V3DOhM+S2+V=r<I4Cy2F3`ND`kq2DU`U&5S
zw_zqulDX5|m1-is6aEWyHfDa2M()a_G*HKo0+1BpUOne?PpZKYKU_fuOY%^;?PV-L
z9|H65-3v}J>D+Pw9y;bC$-*hgxVNB&!jC%ArZ`~wjU%Hrq=Es~V!Nxm=<QfINMNgG
zYf?c}M>||O3n+=WQ&1`*3?xDK$PcJTi7;y0+KdR)Hl#wwOG?Th)xrkUEi5dEuLtFd
zY+zSAUj>JSA%L8e9-+m7r^m;W^Wz_UcV;H3VBbaQvz+-PI(p;N%$6Ou%R?Txheogi
ze*m9#o?$j*>@}p&kddA~HLyLZwN)!K#Y<>iFGGx0Z8zGrw>akGwe?S1`K`LR(;>l`
zSTICtbLHO7mo8l@77BzcPjBhcrT!P%l<TnOOkI`rPFm#(K|gd?$p-lnjh6w!lm&(X
z;G*j5rM@uIfT}|V$YM)9sE~Hia4D$pmU&P?9_vg<7?-+H$!Mlj3a}vK&Tut|cl}gK
z?Nc@WQD!r0C<tvd&xv*QFA8|Z#>Q|eyj$i=(K(F4+TKjNx=e2C6|gLP=*;BY*HLcv
zH|`XuBO0<Ff4*R8xqW|@?C0N(n1a-iO4xduh#fL1WXu#jHOQ>y$<f<yCVJ3fM8a8G
z?TP)N1M2YScZ3)NkQIRvg2Y9#g9gghs(4@Lh0+^$4==dCILn3JIrT=ja0T=QzL+BF
zl7_jW1#%}aP1o0V3Ih#vGINIA8uN}V3^HMSXe8WrlmthZw4s6aA~%tmQoG#Z+r-!y
zb{>)mLx%?$R*lNVGn)NbA@?Qp)1Q5|iPudI?5+s6I})0a0gMVo;|-Wwa>0atLB-Mg
zDC#J11fWR3>vQg%y9hO-OhK<2PYUbf?y#cXj{5;@kBcZ#yWHSyu(Gk)DR_I)qD4mH
zretIv$;N#3*g17T@jaNZff4{I;_&dG3|>-g+knY%hV+3_DXWI!j~~xG0Q-dv*eN1R
zECOts6X-0G<>f0Dqxj7%|12tc{LXTm7lV2l8`r`}s?fKJ{b>JcnlSRydun7d=*>YF
z?}#;gh-e>siihHN_|eH(rU0v|wICgNItYB&EvQd2Or;MUv3K&q!Jn=m)0%yKo%$O}
zIN5nkSbz}4D`G+Wwd{9qugT1kVv57A2{Lo4c!pxdA$Ko)&&BiY#C_S6sQ?DlelmGg
z5dI4A^e0zxh=zAR0?tDrLFC7fia?17a=S6fh@VWP6Bh1l8}@Qmv*MuqXJPO^n}o@n
zLnQS>s1}im8$H*G2x6-}ha4pj91tMWCmuMr=TwDqN(5TUM`nN5FP>S#rjoK1If_(q
z@y3_^wls`?GQ=o6r#XK5;Rv57CbQhZd@(W$!<V!d5f{j5RD>>5(tTop$dLSstU0A~
zEPXqUZ-SP#69UvAOyewQk0xGuo|g7&!?|%zXA=HzZB~hkZpVlycszne^Za+tjKxsa
zgT1Y(tzC(k7#FeaLtp`r$GIsglG$Z+2mlJP)ha*_`!Vlga`JuWc2}}HjWKsF`{Q4h
zz|Rvc4GK<a<Tx_vfv9@H_n>G_j8zU!BZ$IsAp$M9c-(N(wM2G222_`jd9ZVROhlzc
z&VT~wYNAkw`VqZHpq30^Kiy=iAhbt&P)8%`WiU!)3zIRP^oWjGQM`;1+)ZdX<-F`z
zDMiOnZ<uf4Hy?>&)O0k+N&Li;d~t1<Y(;d|grRX%Dj%s&_Tr4?<KZFGQ_|D#nA;6t
zo{B$Y_+((tTN<UPy?b*NX0WWre5cF~FUC}#Zjt`5!;15uQc7}qF-T5ut<mLE<eEUd
zCpOVg5R<7=U<{CJht|=g6Iwi!`XI3?B~$9;bkom+!f){~p&c-Y&B<}q;z|xBlpp2<
zt+aEijd+Z%5`5+{bcZkd2=QDjB?7a4N?3}&vaC&AT(X;Jk&%MTX5u<hD43HR!Loh~
z-o1&2*&JW1MRk%4PY+a+Np&~|dbpcRSBz;(rbM_*cZc6(o=!6lU|DyTtTov|^fo7{
z(sZH~WDsh?VrBJ#QGfc`bp)*t`J1pXQ88IS;73;^W7}$)n`31n=+`N;8zHDMMU||E
zre=L$Qa|rXB35y7>na8Y20z7u&C^O)WR5#mhtOZENB<((?miw8^;a#xzi7s?jCG!`
zRfbRcL@)<QvhqR{XCQipj*@t@2VzGfL5-PGMb#q>dl1?2uZecC_9?veXA;%?n651?
z?K=1s36>=+(uwSZd;{b0f=sCsXa|)8I3S(GDHOtnJ^&6_uuS%06OAwyRS5q?RKbBJ
zDNz43;It6f9x=tp<v`zY+IW6D8AuBtrmm=HPw5=y%;3fS-X#1uwtSyfgdB>$q{NS2
zRh?mt6}^xy6T0dSa=-%BqxXT0IxQHa*?}xREiJ8y1{G!<KIJad-6<5J!GO?e{5*;v
zkJ^<uUg-I_M*(ME*1s`6Oux@)UW++8FYVEuQ%1)5p=D7L<2hyfo<()yiQL2|=UO`(
z3>j=)MaC|3o+fAZ-x7)Z<xuJZ0(C*1qSiKg8UUBf$xlHvCQT-!AdS)tky6;B#nWyy
z`IXF@ekK|6g7J+Nwv(Xg+?1%FQuPa3!ffoCI%w#Oa3SNJF^2}}+;TENk2HD+XzvV7
zNW0gvzS8}i!6a?7JUd_~CJ++Y1?O-m-AIqEqMe<c02&%1m_7{U-nI+vi^jjcyr7j!
zz39%0Iu+n4VI$9*=;rz5iBd+XbwC1EcS3%=4h)tgKm{pS_ZevztzPUy&dKRKAp_Y#
ztqGCIV)<yY4Z>V-Q8EzOEnH7KRIb!|&}HjRbj2P3t4T*AbeRx)s*ETE(aKbezJql{
z!AqtC;h|1kV+3TFX^I%L_X<YzNeQ-pYdT7~B69X^M5&qPQ<y@Z(f0-xptJ33l-4RK
z(5SC%0J+GlcmnsWa@%YQhVcg$R<MWJ%%Wha=-z44d2&Kw`g5ujwv8C3KIUoPu2ps`
z7zPzb$ob}6Qhg?qNtLH|youKt&U9*to|q&6tq?;B?KIcdKTFeZSwD!IIzetK7xvC9
zynFyr!tBUlk^rc$&tk2&-ZXA&(DBdiW}>Hrn*6h0-8}w!H^JCjP#Dc5Pr-<^*(tg`
zbG)%JCvK+PJk1`efl^3v4ICb*V*)K1;a~SW?gQVTq`5)qk&7dhc3yips>v*8C#RCE
z!tun$AC$~`o3vsZDT!rI-2$%HUK)s-ydzUt_Vly1;%S1#NWB7;NizM6^l&Sp{TTeL
z7^fQvAH`1u6F8>9Ixi|`7;1j#pa@rqo(zen>NG(@FGI|y0##vLq&mPHWXrPXjWvU$
zfir6hBM+Byag~scC~_<Ssw@#i%Hr=c#IX(pH_!q+M{d*n$y|5??a_ufonhs)LD<UI
z!NCABgOYfRDKapX#LDPMhmyX8hAU#@g6n!&x6^xyUXq>*GFKKu6Gcc@P1$}-C?kLF
z07g%?5K&#)WvfSxiJP``EXo+dMvkIhbi+oj^t8MBWb&bgCG+lW(R$uAsa(<(Mv_Dl
zEJ=1YE*>*P{ul<C!;i$Jlsp(x3L>9HKqE%ByJMU_<p$BK*uOips03-B;jlxzX?ocm
zn|0jL<;(UDET>HsZ{fsibpC8a4Fy`}5;CD**T_kmSO(dsCFqsl2O1MDZAgjLNT%us
zsg3h-$9!ti9+tGb7o?W-GGaHG(tuHO1oU(MS_a%O0u-mA4DDUxq%?$yq`M&Q*GU@;
z{8Cs=VS6Ak;~amRHhT_91|%VPzI~B|9^-UufmbKE3u*H{wDEx=Q-SS!U_dP*C%;|3
zB#g42rt*F!E#>LcJ|VZb`n(!z#>1=2{NS{9K~ObGM!~Im@ZdqHTkh&aSV3Z)`?)6!
z_4kofMG^(O5PIz)dn2v>L^UUg3OyDL^#LDIfTTg~%hmCt%&}qNOs40_enEd_shRMn
zJrutB)=BA~-MF_$H_-N7)CiC?*ktf`+H7rrfltuc`uh5!r%nPnebEIQn*gA0d3kyJ
z_eZys5VfEw^R!z4qy~3v*5h$N1};EmtmQ*c8i;W|WGR5-nlUE=@I+)uRSt44`g>K0
z)x&#q0@Xs@n6)0(ag8prTxCgafv&Y))_nY%TDS8-nuSRP6w;Zb%}jy4(+y&g$8WOB
zE<j9~;wo9dec<Hn!G`|AqaH9<{K<(`LS`sg7#LC$#$$rxPWR{y9|~^YH2%yny`=#2
zR{_zXYfAo_)p_x&QHFNvqNr2JERpW%AZ7hYc|P?``Lyl51WJLp1Qy%lXTcYE9s)Bc
zdjSSvYpnxvWE`zx0{26gD9eEB@5x_&K#UgPWIvk<O`-%MwvW&3mo7M!<N4%Npi#Yt
z&=Y18#<ed5z#?T&PL~LYmZ-Fquvx=w9j3i*f*ue07;gG*_1a)<`I>)(Oq*BBG)QKI
zp(R$h)2VTVKzYW3GJWdy*n3Wa<AR%e<uy*Mpi!aRN7lPv_;IJjx+jmE>vhWUXbWb-
zL-1%XwoP6*5b(__E`-#j1ihv{k<t!w|0uw(!*C&hY|4ru_ZJZ1ny_&DL}pffLQ?v(
zF9;GKiRo|KfD~+sC#`-p(~LHzmOZT$%L>~2CM8~f_^Pt3w8&_F=l5@VMmeLGN!Lp@
z=46_^wyPb4Kj(C3Vsvd$GXvc(MSd8Q&Ti@}!`@+b;?G9B>1Pc>@%cwDjfg~C;it9S
zU{EMQQz^=6ZEkH@_cu4JN@1y(2{)+gmj&==m5lS}Pyo*@W=Fz{{2?Kc#Ox?u(zQkY
ziVs=4_UD#H%fi6=)+-_+_Rt>RDpXNUMRV~qCNS*=oWt`Y*oI(a+uYu#<nJ6rL4-f$
zs?o<k?(qKI|JU1B2W8ns-##b;q7o{oAgy9h21tqmf*_(I;YW&qh?I1PN=ivt2nHf0
zAV{YL7=(a`w6t_d*Ka+1_nW!*&pUVKzJuc!Z=JKx*=w)8_u7RwQjF;3<Mfu#ex2e7
z)|wXhBE`8Y$D#6PA{8}tcjh;NK-k)wowRS-$xe&F9fBcNP?MVQ5p?4sC6mZ&;nuBN
zb5PUWMTP`m%Tnk1tCw_zD`XWl_iyha<*LaBO_Ict<yiQxq1*g|5vK37%oeTGQg@<`
zrXuY|urq5XjD<fA?uo=Jq?|Tc0#dfam5k_2s-Ga+)yGW#*+<48IxY05q~wyXC)rCZ
zx8!c~R*cnQ9N9ON7V|9FSJj&5>dl_(nmRz1n$MjLf&5=Jb7T57e#JjE<)zK}=1rL-
zTSm$pkiDF1ov(<(wcXfz)lwWA+Sk_cfQeZ<4^NY4_}mg{q>Mu!Q%HkLD;P}fa>v4u
zvd9@XJ3oj5N3FG{Fq<`T@)xJPQkuPTY_PTzZn`#6eJs3n{h*aLP|P4?LRF9qEl0PR
zCU9dve5viyxSw<oSrBQBu?R(@IiL&H%G(|qLXfX<shy>Pg??|tRE;zTP`f4W+O9`_
zWcMuDYFZ{O%n=|jCZgUo?P%Uy6gJFbD(#?dTiHTWv5#Wx8matD=K9F;*1HkGq@w#1
zG21S*(h00E{5rE6g#jei(|ieWO;*O<^RgXIFCuFC6^>2ps&W{z@30BixCFONHu<9C
zvJ2ok7!`^pfi=&yvV$B7<5H8}zCF4!t-mTv4hA6k23(AYI>|XdpVT#VnaEL6U925*
z<%u3_#ar!vnzcklep*h5gn8y%TUG3u>KISxCutcS`v!G#gXgdqyUFHilC~iGgvnmq
zZfMnnkb2mpc0d2mh*$0EXN6saSh7d;>ta{0s}(w${-K!8PBqMt&q9p>+2~7_!)puA
z?mDZ7=838m;ZuMPB(aKYE=zGs(2XF!aLf8_C1j5p84m$mg#I~OCNC}$JhQfvsZ9!X
zF2l()4|8Fq>)Ow)b#|ea7c^7P3IKF45Xdh${UM!ywtFU7eGJJJWA$_j`m6n5&G7hn
z6@`IK*QzcMlaOT;Wb@{H*YP_4<)+rB-x2eak+s6)$fC72WYVdUk@rR2A4OWlDKj~T
z4g@UL^qB)hn*r~gSlx0;4O_7$m`xT;TMYqykRuru78e6Ml2=<wm$jR;>V6j9NYtPY
zcUfvugB1UNOLO$`VeL2)+Cg}}0_Bb*r8X_V8#anw@wu7YQZ^DZlX~+c^GB+&fu#Hd
z%7#@_-`!pkssj^Xc8-&Jbz?T$;_JGfzl;V^O&0xwGCOLH9{CA$YH^Y%h8*?O1x|@?
z(bzbSEn0pG-oT>$Ck#`{q2VfNn6Hdt9hX;I3a7?jB^S~E?s$1jSM}LurVQHC_f_xT
zrpe4z9eBri|1QmK&P1Ubj7H77?mxdB;$@zBc>^P*TvL{0qSxmAnP%~~jW0gujC2>w
z+R*&{nAjMHyDhKj+p%0X>6(asMb|N@en*w+Vnvl|sS3X!UUQ9<hD5#Mf)3>?mUB$K
zzzoc@2M!#dxUjPKOZ!Fsw3kCEhN%+|9(pr8lyDT78Ep%k8EG*b{*^&jHKN%tl<)$v
z!I^@ctbGmE_j$Yj8a0phgpMR|Yt}FtGPQ(C-!GL9W~rMx;lA)tN`#Axg1FgNxy$re
zoS4mP3CAfTw!9D8)BWkajy{4dv86vK=6xk<5|XdLQ}5^F`-GOgu>qGia&fibx;7AA
za7^6$_EF(FMVxUVrQuIo;c*D(?7e=B1?Rc4qIQA9(Stoa+RKahk)lZt!d%AA?pSq<
zgkQ1itX6C3LA_`l=VfP$<jOD+?nbi*xhJMHe9X2fjBAVo!7(8g`xL?^@y5Da9s~#n
z!LV)HotH27WLdQQd>-(<E&sC?a&!~~SiQHRW3A}PL8&^cNS1`4X5Dl{#>!=OL*Kyf
zX}X2@C9wo5#=+_3MfEg}vk(>ZUS6EVq!3m*j-gahDP8K(uF#Q+hZ5CccY|6&4LZ+y
z9xR<mRUv7U%2(+cDe?V~Ag9^9`E^#P&QNuXq%L*Zc)9S0r{BhCk#JdlPWO3H()z>~
zT@@8N;!4lwu&A6}R3BSQ+*#chXEqR((Z}hxupArLaw~DbY7@P-|FEQKGue+cGl62P
z7;P&Id;Iuo^?shw1<5@ov_9)lakU+BS0MQzQ4XD+bzZ$#B!6;Ys@dpktq6C8*@NTK
z<Vr;>r88I}#$(vEE8zVdYD2_pILGWR(}O;^;jZB5>=eDpiM+D+@US^HC3;{N^FOwK
z4J^a5$H#A)hQz`<KCTj!kYFXgXBa9Jej8RQC=vh3qF-U;#(1Ot)t0*Y`WO~tHT9v2
z$3;bw2RJmHj;Ne(yj3`7xOn(xqnK)#_2NXuB=v?3ohbCA94}Q|I&@G#K%}XZTdXmr
z&?$~ir*mLoYId^lL!(6v$Dmiyw@dw()D1=@?lQcnn{f>`_;Eiwg#M#QccE<36NwY_
z>KlGG%d3}&<abZ^XMf2T;m(CotbNsO5>BZwvg<z*8C6wP-&nSZ!*OrXEwFq4T-Hy>
zeo$@mP9Xv?qXO%|PggVA^p;n|{MYYsL%8E>-HJ-+6fnAWO`};)RrNN&4fDx)LBWv7
z`X)Pf``6O<srQ%CR6ROMUFa|(1AK?NFQWuO8TAa0*ZFSqnSaaL8hqAE?n>aTci{Cj
zm-ce~_b`>-Uwvc}OF@`7XMHhW0I5hp9J6e_{5bT-$x-`$y7uJtnogN3Jr_JQ=Q2m;
z&$8W{bsBU_ro~JOs|ks_oilpbTKvoD2Se{2tq?NlMvDQz!@4D)UcxIeasSg$uPKEz
zH}+&&Z-%;}BAkWSS*gkT(NElQU=H@1vxe1Yf75*G-0NX+l{!6U-MV!QtgM0PPvALv
zl$?GjhmM|mQBmY<lx}1ZW|VfHmtH=$opyu6Xlno{|7TfQ+liaY3-&J)65?LJ-i)J^
zk|p{Xmq~4|l|DH|&+=SjTLp`}fLXRybcNQT2*Iz0c{C&K#jF__8RRS--4dF7RXWd<
zryoAZ;J9x_E3n8vCl%sqIS(D$1fW7ekdr0+gM#cb%UdtWpN|!6@39)Gh_e0kXr|k$
zm)Z+#<8A{3gE^QubkLMcPh3pZ2xOCRxB|9<xN;=PQ$1Ni)2R?f6tEC+X<oN8DTg;f
zoU>-?lSz)NPv@r9;#O1W{d1k$hU|^g6G>-P7XHNA>*Mghq!<d}MX{c0j!(t2Z@8Z;
zKhJrJ<Rk--&r}>UJn1~I^7I4=GrG}7M?*&^@8(vNwXByf;@mY|s+o2!EsHT<r_gQw
zk>+UQJ|7|WZz^<!_Cw5A9>3OBog&Qc()%)n*$`*WpFds*op;)|CN=Wt8~n1GYtiBx
z6twwMdHG{Dwqf&vH1?F*>q0kwpG?UmC@Cp9`T4h4TU*mbeQD_x^IR!9#&J*P`5W=$
z%JTA5NPd`D7p7zoE1J?9Hf-1_ZkO0(xcTbUt3t+Kx97Hv{!S{e6|o4GzjNvim7?d+
zw99}4clU9A+mW_^R(^Qsh$%d=kX}*qxPxQO&Cg%e!^~kQ>$^GW@AKq?4KZ)~ZwU^*
zV;W79&X4L~ioNb?ld|4%yv#L3$7%3rOYyr4j@XOK#V*<DA15B&zP%38rz}1`KFqVu
zK;U8b?`s%KJkcpG7oBoGBao(FYWa5knN$r@ReoRN1J{A(p3z2q%f@~BZtPS&)+3YC
zCVFD}(Mwj!8I@|tA_oKnoKaT(s}&2|fwqX)&vgAz2)={XTp!GOTt1Xt>U*)PV}fU|
z?Ot8YkZj@7vt?rTOU07!R{QjFa@tsX$JGj|ceApyhoFs}2lmy})b#n=w>$E2r@(*L
zW*VQ}vuDpUL~54qJQHed`QbV74hBn}jw_>mp(E5cmv&p{EYBIv^YHLwxXs(R!FqpX
zo9!SykyiGN=N2tFXH-;j<_&a5?WyxT*qu^fb6*xBWMOjw1rge8d4ahdwmqL?o;Lky
zXT*nqx`RPYtQ0v4WMygU{PrC?;#6Y9{ey$c00NJLv0oHd4z=xN=^pIo@`SX&Eu<Mb
z(E?Ns5uDq&Nh2d8e<STE3y=6c{_GCB;a|0b{O#(?&1zBlp03V|Q$n1jW)B2UOnuF_
zu%Q&hz_ouLVanEYdT2BIUynyussHzX%eeCZ!T-OHGp~NVKMQ)}-^ZRy!@u|b_bl%4
z0}kU`|9eTsFWV)Y0{(lNztLT`qW_*HCwHXm|L*P7-Z#B9d9IrDOf~7|+_*tgzidfF
zL<CQ9`3?b{o8oNR7G=8gzan-p&L_C`w(4eGXH+}Lr02P8ukXjL?K(T^qZBXmKtPw%
zd)!_WV+5$yK6F!_{m}ity$$1{&XX^7D_A@ygeAe67&z#2QD$lJXKAHMe4PE~#9!%h
zi~YjF4c+s*O3TyS0&*HOTBAaxoO}EFl<XfyM-Qp$sjC|paFKTa<n!Z@fB<5l*|bR(
zm;N}qNxGnCRF3jQH?}^ha{?p3!bHb>W+Taiz#>emmC8I;G!=rQao1yc;Z(e;0Xl)|
zR(0G3hnlkwY2{i5L(O4hw1|a`Ngh5q&)O&d=dhczuq2q&ZG|D)=n;rF9B4OkTDBLD
zNtX@V>*%mgQy8_a?;%@v8}68F`$wF&3y8<JtLrM7+o`GPcE5w52zep@KvsstPuA8M
z=^smYzr?eB+pA*r=&&J`oj4oNxbcrP-Im5qr`EPY#|)2Um+RSDBzwHC1s@T;fcpr}
zy?Zyf6u0+Y4)Fh+n>;U0z#L2x8*Y&AvuoEbjI5zeR)4#6|5g3K%P>LT?$;O;H2HOh
zsa3D+WPer82Zw<t2zI)mcO!f8VwmpYkexVMshF@Z$c>NRw6haT9PGEms2()zZ6S6_
zdlZGnO@sh<5GR2ik$>No`>HKf`#6m2H>9Y3Wg2e+wWyf;3VDn6GDaBPp0lWgrTuY3
z_-<fehU1uC$|BBIP2EV)leeM%ts}@mU$1`^tk`dfTt6MVq(3Al|4h5!hdfQ-w&o_k
z3DzcE;^mN6-XqQ7Hcs)^MXEX1D)i5v>-v7(OsIX`KtL9Hw^F=oX~Cf=CGz7FLXd+$
zxZF)J))sq_mcQpLWHdPH>gosro+7(o3>gIq0^aPDk<rTvL!K9|ehll@Q*FNYO3ObW
z;60r7VaHLOEXy{Qh7?Udnde?I&rhOj5d*uvmwI|GA%81s(?f%{n7;0<4D+QJNMl;v
zhLKzgqAX+Iho<N7A1DaC-uM~z<%Q|>#1nCQ+R~MU6opAnZtk%w<oW*c6Qu*2iC=lv
zJhZg5m7|x`)VvXMQV@{QHqb1{(crH6@+B+>^H+i9J3nlIL+23}XW`=Fy53eGjN*%H
zhXqWg<`I@p)OfCJ14Nd)e0gVAcXtMgeU+yAnV6ZcVk(3aGUd-SQWycU!QW7$m@ZRm
z2CZ(2&drY-(;=I5AxW)n=r{dD;hjP~{j-v_8kJ%W!#lB5-=CdY4@<%=#t2IiN!Xte
z*$6a_OW^y9)0+l7`BCVQ<z(REQc#vPH909{TDQmU&v%L&*(}*N8f0*ff|!um&djV8
z{H=8;{J{gizkltruGg+3zroMfySy3vLqdFUHKgkm4<{Xj8tTS_QM7PYd9Jf*x)nQ(
zjExOuN85<6R&i)?BBxkfZ_FTL2gt9!9_=0#x6S5%5E;n{Ty%$Oiwu4-Y~Swv`SY2-
zgAJQFJm#K9h6<OJl|hn#X<>TE52QDNcP4ii{(S;#@06unonz?Q@-zcT$Gp90m#L}g
zpVoZjfw>vBZoLirBF##|aWqTxM%^P(wtSU2?ZV7nFjz9CrUwYl!-s1-=ZYrUdA#4j
z<B(HCxVX930fWgsJ>fo%6zG|;1PCU0E5&~R$76D|7{gp&<mK&Dir)%U_BOwC?VVUz
zHxZYMoHEudLy!^Y=*UGiwLJt4(S?)%lMD@#Tbn3OVuam~;lt?PU?Aim0qDaZgV;%5
ziAP$xWB4#2+Wi?ZyFVA=M{u{?%Xuo{r~>q=xKP-|gJMWHdJfAkWLp&T)IihQ1WXUv
zCPQQW=Q|hU?%n1NrD*RB1h&w6-Hi-@a&zOo*u5hyxjP7w0=wc+DA<)-mbWFCU8*@P
zFUH-y?a_T6i(UxXkS*cNQ%M)&eCL2f`O3`?q$BRpR<55T6J$!JgLdMF9fmKbRBWK4
zx?5SPTsSz1XVv9fEIFa=!iVA2$Q+OnbNzN1fArcV6$3F;50>;vf+bbe16;SAb2{6q
zYm$~@%;h}?0!9uzT9wGn#Tko0Q}fom+G{L|ii%j!Up&S(IcySjE8YxU2)Z14>z~U~
zU%Y(za?+FPPo_iST;85Jhg?a`v~^zockgEArwg{b9%BhcSeskshCqTxNl8ggM&>rb
z8SC7)E4mjoaLEMc!t9v7X|&Xs@yz2B?ga#=+QF#r19k%Or{&~IKCWGCX29c=1Ok?-
zVTX@PHb3Q=3dodaltzw-hK^2<Wm~~>XdB17fp!k!lJ&jZT4Fz>;Rd=Z&u(C=xcwky
zu)Lu|>C%$hp1phXw+9~WVJVf+*SE@!f&>rAS7ziDCnnM$x%wVmu{vKX7%}_v0kcA;
zTuQ?W&()RBm6chK+97*W8R8}!R6f+0{`8oBFDh_ezsj~~5y6782!FlS&NQW6>>}(n
zInS3`ToPjA7?a<;0-nUHr^m?jAgu`r@v)8~_2flVfcx3h&iHh@c*4QVhLznJRvjno
zhJIF=O+8(J?Kgt>r6A}8Df@zF*|{jgBO)fS^IDa>yu8$S4d(jeCa_k_3X%0|b`Lr#
z?l`)c*w4>j269X4F(w5%-%EFW2v9O?Y)Ltms-63C6>j|TuxHn&ji%bCPTgYou*~Ch
zYmzZc-)+KY|Ni|9F2#EAc-G^;Pm!L+^eLWG@C2U4`&X7}M4tO%4({3)FJH<b2BeKw
zJrEpuqcJUp;6%+24+w&NcH%MLZ)9`x@^U`QLk-T>P(`^@Y6?Hn_4Sp8pcsDb+h`JF
zQTyEvW(9@ug2{z~qx=djN+(ZxA)%HOSp%0_wz9l<+T8s0m(Rks=DpJ()X%n{Yr)a1
zf!~{f`_Q3pdvc(`cozvqrSuU5X{T*$GP88!RRiF}qK%OdWP0kkLdGbs@Ij+yIHX*%
z2u?Ir8QZIA^;gGiAQ*S-FnP<zq+954?rs7uV>%A2fP}%yrWwb<9|t1r7G_2c(Xol|
zfq{DKrSE|7D7Un<zIjK9?&jU1--l`1k+wWrf#%*oRFwGR4AZ7+9MYwy+bzl&e3OzB
zt&&3-N5q;#*epC9$J&{&M_nko+KRW17*tAc4AjoG3;>rAke4TVpcv(Q)#fE3I5Mr%
zxemtZz(@yTLQgDw?pta17;7qVFU*n>68hfL3<<k~n5@TLU^j4eYeXtzQF(u0K4bgn
z?)PS`mal_b_i=NtmXioGe2XnxkqpgYL`f8CCB-gC^RDyy1E%w@w4>vAvf4rG`AOrq
z13LLOr<<+7uL$&RLz_Gqg@`<bI{HI5>m_uPRgO9i|2k~C_BU-6m)QQ@F$pJKXYwwJ
zV=KZ$T#@MWrlv{P8<L~4I)GK#s>%J;kdQcdWb{qT8-qJd?tfj1A(!ag(3pF5*ZCN6
zW8n_z)ul_#SeD{zvxi6J$nRH`sb&X!J1_@Ln~$B{{1b99`E@H)n|E?r;7U!qWb6X^
za>Ir8PLHX+bAgKkcH)rqqo+7|%%b^+p|MRCn?&=<@bmfsxdfG%*A8=HTN6@k8#|h#
zim_T(r<UKVLNR;naFOr*L0$jJM)4CT+DGZyp(R1{%#D`bwzcWKwvG;8{50;mf=GlH
z^K=Q<0T-HBk6znMaPsiT7Hr8vNRNd52?FtEm7#OZ!^7**7E{_}fflM$cvpg;rf0Vk
zdx|SPy6ygxCwx*EU3N6qscYlgMGV`G14~GFb$G6}#XIfZ+c?)Yzu=ORN(xHVy%1x<
zJ$)4cc3lTl2V%}TH>u(hL)=afU0q##?OkY|67pPeGu84zg5}qZ0=P@^B2OKyPrS$j
zNi8Zu$m}QI+r^!OFOW6%4-L(I94l77DHFZ9-#WXiCe&iYT2@BeivtlYZ6(IR(EmmO
zgx|8yyeHr$StCl2+!hTzeQsX5YSO2kFA)?33Y|4}Z20?AOg&6$q=p^76JC^xK=P!l
z_wVb8T!&tN^7Pdp1Y{HcSwAKP=zR*~@`x*dFQh#*9g9@ji}@c{9b0|fQ_tmg3jhVw
z4OJar@%QfuT*HR0@OBDLPNMJ$S&x@vrCguYUI%2V%{J2v?QO#jr?hNgQ%<XPY(_8}
z*b-D)q^12x`}u`uh!+XvQX$tnI^B-_Lqn-KSItVwp{b(gvVy3iAo2nK4ne)hSaQ;?
z#;kfugK9!OVwBWsljd}N>B31A6w(q`;LY=_yM4dp07NEr&*DOr#}%Y&%Cd=;HavC*
zGqbo`kyvc<QInRZ){>Zu0=i72{%}8vBUR18Ad}I~#*Ja@r%s)sp{2d86px=H1Lmb3
z%LOgpdJej_a1c6$4qHGQ$=KK&VG%L+zju!z>B+$;rN<mOKqFv4EraVHmS!JPXf1NS
z<ifsnYuERTSdX#8X^m*fe1C%v;!LODdK=umCe0Kb97Kp`CN;4lvyRlV)H5h?Ee=QN
zX+8%TZJwFIT9=2gZ6Z)wKu7!rfxrGjJ6ET2MR4(E{kwb{>FB74KX&2|CX+ygms$9R
z9f#~7qk%A+tY75OK#F{sw28v0>1hq^B>mEoh7s&GKmip24yZ1^$UNU+<niAkh+2ru
z9=D^SIH{-@P_~Sc%TtAuW3_R9Kb~jaXgC;Qfxs_Z-tV!t&^LDA>8RL3v&h7M@6_I}
zOaso!%0X35m4`=L>u+T57ZC83j|*eRBEV1iXr$mrSgbVF-o<6;N%H6Rnd4w+z$8&)
zp`p0E9zyG|jh8kWd?^|!_GVAYmr`||0~0!2gFM%u#baM<g$!X|j$}AvD@ScebF78(
z>KlE-jz%B1D!;X!`i=9{HKI2+#WnnBT$ALZ(A+MH-9tvx6>}q<)lN%EsdL&=tUDd^
zE*p-8ihzMfLDxFs3g%Yrvv2@ZfqJH_?=zI1)5&X9J5m0Iu}X@stKaH6RN2(1L(^%)
z5<A#Br7n?^u$+21L+0@fnGnggwA}`h%K_Qd*K8%1+YQ}3@(w)(X}Fyr!(;Jn;g>M=
zhIqJg9l&Pt19@#HZv`&n4Hi{WR;DD5S+(y*Z1LCla;D)@Z@mWfDA0){8=Y7uz??=d
z*s3R@*6RpK509OpcO~oxBdeB!72b=x9Lm{?jA92xWQSI<=c)(3G8bzf58H9eC-tU?
z(nwzSq%&_H`g3%Heo7BkKAc!e#$i{9znGxHHZTwO&G~hQOU{r%&Gk#3ag9Uq`Sxh(
znSiKK7lJ~N2h50&ajC17Ob<1>?%n5UrWqn+DLoo%`BA-m`o>W6CIUJk(G4TmTh(ZC
zW}@z<YL&fv_tq`CBUUI1=l#!W6TdzbwdN+CJ$v@9$13yb#F?bKw)a`S)zr)u3O1{y
zb*WJ@v7K<c>|*K0nsd~AndXX{>jdvpx2yL0pzJVUk%|CCYc@+GqdoXD^-z_e>BE9D
zA(=E33o`F``L;3fV%*Dq9P+mX`yWazJ+I~~n?hz@Lh>pjfe8{^1M`R?Tem)t$anqv
z0A|yT6(PJ3XI+KC0j|m*sEbsJ|3Yu-o3A0omOviVow*z)o<`%_yH9tZm&3xo1KHT-
zq7yqkyj4>gRL%!<?eBK>fo~s)k_0WVyha(RmqSj<bTW5>w#kU2JF2sEVert~MH((;
z@$Ao~eE(!pLeukTVJiF!k9^R*m7MI8;bP^1lucU00tg|rgv?rUEPmzNCX~5XgmMVB
zeH|N(>gGN7n34bt{RDg}<i21>tZI8#kznRD5C%o0AQcnfjlF@8A)b={<3Nv~{WUU1
z1E^ZRf6Q$GoH&~b4-_0&28yoNa>Je~QV)xN409OX-<@x_#rFGS+tF4r({_XbOl#lU
z8w!ai+V&ctS6kua<0HxK^@JO=2=PkNm7@ubLly#-)C5ThTsTo2nJfOY=7|`OlqU-G
z{*V2p*|J4AKl?ZzUu7fL7Zp3ZtYf7;xUfS|#V-?AOY9%pEr}2q`If!z8~3jMD6ls}
zv8Fr=KS-3DJ)arv27hcsoJCwHDJV)keXH_ttRoUYk2Dlq*1tn`{*cu9GiOf9%ljVt
zsz-AR1oGAK-|s(v{!Cmz_wV=TvTPyW9)ng?%ys77x9<fIKEht83|Wg=_{iKsoRp|`
zkT{8y6Y}OBWK^sW$}arqs9H)x7w&uj-)&pBZL7^Pz4Vq%W95QL`DV5*;$C?8ZDdHg
z0eVywUNNkL#xF^~LnHg<+qms$E!-;=8XBqqhQ-F#)_QhScVJaKh7Mfx9NMadZ2Od>
zW1gnn1b*oPeHjr;&nmJRd5^H>TMX;w=SdO617NGo1zz`6i!P_?ihUU8`G~}|+ipeU
z^66%DdZ2qkOPm_S3<PRJ*ri;qz*scuYlo&u=8L%U``xYBO?kJ$3=_TSxf7uzAtU%N
zb*TuhU>w~$)F<a3RDu8I+OvlesWRMrBu9{A*>)ENiOi{5*^|~;Mk@FMN@vfKCB;10
ze*8aR3t7m5y=?JZzi!<daKH(5nkfx+Syb9N=BHpD=;i}ofuDP^v6h*hU?GLBf9D(+
zL&e22fD|&nc`yp6?8vUN0zCUi;>C|izxXa5jB*-E%_UB<7|cS&Y7=~`rK0qaBU{D9
z#Ncm<uQf4%{V%{v!;(-ydv9<*{t@UJaEOENRX@G`wbz&+vM(kT*hC)nfZ$GFKD+^;
zKU`}{Y-&u{AH_?C#5=H1Br$_p>m(19!TXY|HSjoj>7y#>&%fB6d~)q$>;oXxgXUYR
zN#F=5K@!~pV0iiJl?%Vh+THgl=D`DAd!@y|PoLz#niKn<$~waE>ave>auVmFMarO|
z<uy0wL~=A<^QVVl3GpEY9}};*qG^Fz)%DG^P{sJ2k$YUS!+P(+{r#8d*9hd#pMXwY
zYCBgA)W%UGEnqnP({M5djicZnI=W`yDZ1a?TA!$y9$W8>Yvg6!>c8x*omp-MO7nEI
z?7oI0QH<(S+B?+r4&nl4zMFSloty;;)AkrRx{8be3Kb*)NYK<uhx_zZ(+FwTV4&Wh
zR)Ug>N<5|<oX;D?v=R9w?q6yhhW$~hsTI>9CMvoUc$buI?rb0}vnd5<UzIxE;FcU}
zv-Xp|Hsw+~NjkwE!usBbG+J9eoXxZ9D4|=OGrCTn!_L%}XDzFBp(ggg9)JJOp8s;~
z%6QYOxDPLvdPsptq{!9^MoH)%Ufym*Paojc^wm-{su~T#H^2l*5)r2r>C17HDsy77
zk#{JAq?+HR9Rhhp_f-7o*ltetC}OKfixH7nMpExySlBHxmkUW#u%BDVG#|ttyuB%l
zR#z7D97b3$|K0gPSLQ1K_CM{#5?+%n^}#!0Y#zLMBLvZ!wNr_s<x9tQ7n26KQT&Wc
zgiPQK#6K9vnSSx?8G+uGEYYYS=^y``UQbOI`SoYQc@CHk`3aFFK;GsPN^$PNXo8JI
z5<t`Exy>IDv1$)O^!~!SyIeJ&WIGETtsu%l=4H_6;;Znj`?`dK+?g{pHa0fo?!eGE
z(OHK#pO}~+D;Lsq@^1s4I7_K`nrqG=w;su?5+i2&Dl{&M54(kJc#nW3w8VY!1-=@Y
zp#xDkfwr>F-|z3>kN000LB3Zp{uoruD4#9)F<AYBO(33uo~H|DAtTE;hH5^awH(ME
z9+qG#5Qd;-$dpZi{ZQY~J^#-XNN?3$HG}@zNz?+k?+!Br_rA}5(5g9WBMKDm=ODej
zF)S>M@Ir(O0=~X`0&Fu1d)DW<Odl+n?_<raR8;wo<&l$9>#ahjANM6{yZi$Kr;x<x
zc?PFVR__zaJ4Pbz;2;*wbp_USAX+-sWO-RK4JM#B(7M4396~paNTPObnxe^3kiJNG
zlUbR>uNW=(v9BsRzNL^?`QbYsZ-1k&H&+DUhapUYJV`iS!2m+iq-VRJqqCWd(whN<
zhX|SG{q^hea7!*daqr2KATrR~y^4ZQg3xoHR!=XJH26SCyaN^Qd~OeS@;cGe>&cXm
zx)?@<QJp{`jneAu&ygCro?W0wxsag?j*Q$6VVCcXodi*`vS9Xiq@}UXgMxxW)V423
zp^tF<)L!fgn!*pPG8ak;Xnj5;OfWEp4duzulCRvh4Z<-z4x?MYv;ye}-T1Lz->>rx
z*sUu#Kqtnq2DtNXcNh8lCo`+mo`eC)so%#PhEqXSfRMCY37wyR@-x(~aN_mJ95(`4
z26lEj@F>{RE*M7{e!n?0{13orYvc2+j2D^8$oTEQhtXZ0wN?H1&;M_^F7gB7@&6&U
bwnA?w#bfmTd6OgAexZ0;MJ`R|%I*IF-)9yx

diff --git a/reference/plot-8.png b/reference/plot-8.png
index 58549f29c794447f4e275be6e300ea506cc4aa12..30dc0aa17b39562527dbf63b4918f0407da02e30 100644
GIT binary patch
literal 60111
zcmeFZXHZpHv^9!tw#^Zh&?tg}qNsopRa7`il$<39!XZh{*alEUP!P!=O3wL^F@i`q
zk~2t_oO60(+xES;zWQF({qyR+AD8Mb5pc8j+H1}^<``qHr&1Ck+qN9qLPJBdjU;;Y
z77fjM8ycFmTAMcDE6uON#_-1mt(ziO@h3j!g>#+p$7WMeC36}YdROBAHJ&}=cW7w-
zq9I+qENc@q)M{%Z+q1m7s=%U?nzHWlrU$pu*;0?+b(357bZq^WEnnEytFk>g?&h>E
z<3_EPnw#9^FWYuXwZ!>|fBs|3rsH?^ti7VNcjuGOUjtq^>W;KC&X@O#omKHV=yfo}
zb1-0#_gtUgtZk@$g+HmyJap0HToLV>U$;g>^VTCFbM5cXX=Hb7R{#C^lUvL-zklYh
z8!vyX|L4p1k6(Lm;NO3__y5Zu{%galSFgSrQ&e_nyHm}yi#;;FIgEcfu*)+}GE}f`
zh1;Z!ZhZO6TkqwNUw3)`=dC0>$4-VjckWy+y$~!B7gWe>dcnuX=keYHOMH!^2Y=o5
z6OO6>w@3N0_w1c-dk<>sxJxIg=Ns_R{qvYLJO3L`$#Xqhl_Nnp<Hl^Obtp&v)GaRK
z7R9GepBgl!$M9N@#2+@!x?CM8UhP58r{lg^S%XA++Di>h+I95$&lYp4oS9jw1mE!a
z>EULD)wyD!bm!HT7o7vuQJzCqx%dyydXi*zfi6JExxhGoT0X<LRk@_3gtsrq+@fC1
zTE98dNN}(*+dPWFaZ37*j!v}G@&fUjomLmy$sHdbn`Ntp@yI^r4YldqJl$U{5*2I}
z;N|UY*=F0<pV@3&eL%y3$@P<Tf*pC_$A^b@oljY(0^66a$Hc@CSJ|+gE;>dgYG7`>
zOD>i?)Q}>-=!_S!rqpGTZc9;z>D`88O?ijHh1#U4YDuBmEOkkq7So4vt7ZP@;?jrZ
z6x8k*@j0(J6!xfc_;MRIyb)n^lx@wmR(N;)`Ky3{*zNSZcRxSbrJQ9N=JxCv+3vWy
zJEy#%Rj%5#Yu7GQP8>fz*2E&)ktZQBbCHNHnoDA5pImHrUd_Ka7@y`I{(^zgX-@O~
z0+!Rn%ggIQoq5-Do`44r9^ALQqpPdjla`uVwz9k^9w(phN8RFg;T;EkOYgXIMy99N
zw3wBiT2rcF*B`+mBqY>5^R1$S+gHs>>3bKsF*Ty(+qW&*Z2?%j-cjdO`}cl^+=2dh
z0VmklChS^U)%UeJ&bG??N8I#XXtmC&2y+{y2(d~8joIzs$Q!$2zBD%;%cZ!fU}1or
z%xKh<zS%Z6)On>buKbys8=KF)3v|8tvu)>2sbp?#QpZB;t}M+n4fDO4E$cC}t<_Le
zq)l(z!zy88v_~u}$=lRf;@2iOjeWb(#QmbfTr%-HhMOs;PfiO=d_8*Tg6VBhf$i<K
zee`OkAJ#L?vZ>`*#8e2aI?!J*{p)siRH&2kIlYQk)%`WK3K|JK?GEQ|&u5V9W3kbg
zzKl)RYYdj1qt*m)ksrJ+uc)ZIc0eusPU-8%vex3y=`P-Ko-ft-<Mq94Rs5be>b5;T
z$-_J?eNtz#v$F>pQr=B7Obymc4%R0rd~(_LsUYd*>HL~lYifu<Q?|LDXtU$khmCa;
zrzkCN!^6XG-03azf2aA$<pdue-(AP`>(>+4Y_sWlzVm33T1UZuzUGrhKOEL(kqC<Z
z`0)zq*24|krbco{2j2g^mhIFjpVA($(w<w=(l4DB`dJ;Z8*^sc?0vkvI@1!rJUcKL
z6BUvgLNRQ-73#DQGchsIZI&&otQ?C4Bmev!ChzaKI1{5$=)}}2;kddy8<(6crIu?|
zn`1dBtEhO~w=|-&Q!7C|Ul{?N`VB!6e2B~7)$5Z=Z)<8~%R>ZIT({GERreFCiJfIx
zEzYCVqmZZ?g9Y8tvikShjWr1>JY-SVqUVRnb@58PHe+w-w!32YZ`raX-K^(^noY;!
zI;(v9*}9iUMIJC{h?fQPDWvI^r-oP$)R5v7k}{j3qoYO1^@(!69BRxylsj3ECh4~5
zF3t?|&bF9m=usI+J)@JXp$@kqnAXM~5foG>($2I)k^8~R6wac%ou8l9O>$}%tvlJC
zsK%E+S;5zoYn_DX>M_giHoaic{dLzV^*qI3Udz;P9(cd5I~Ws-UIy{M%#x7k{T*dx
zal;&+Hzjc!H7Qcgo3!O+H%-5K{W>60JW$zE&8+0u8k5BkUd!0};hIp#8Gou-2xaKz
zS?wbCalW8S#>U1G&FL!XcQtYK&a}MJp0mq~vy^XbR?S9EU6$FZk$5)w#?sS;{6EGo
z21}T?TGlJ|h`7^sPz=R=@LO2K0~8mDje$6E>dF}}I?SFSBkpo{Q~gC9u^&u_1Xq{L
zwbL5or>O$7s?TTk#WFfCRW?Pn8uLU%M64;*yK_gWCy<NWOED^(>-;lK>&uqH!B(~c
zJx`$(oA(nL7q1G3CwiNPX#dLaK?6Ur(<UY-6MD?40+gm{hv`}cUW=bErX7$wZ(v8N
ztjsi<C=@y^$M+Fe`0f}U6*V~D7pkDD8bqB&aOD_h_v}xqUztvFj=9%WBv@Mf<x77F
zYiN21)&E@aXo2-mLs(M5!VRxiFJ8Q%3ND`fG2Up%oj+ZtQkO7~H@{`^s**7>RH~gN
z%dunq$P6+L4tecax@CU5>N>W5wA{XaVqq`|TSHww>HGX-pZ#?F^YF9^-qHbmWo&G@
zIQjJ_g)9eT$@5{Z3={SR>Xx-~ITPR5mlLhi8V&7+GwS2}a03R>VWAc;b0j2+{&~yq
zV;-zCanBxzjf?N8SGP}SI!7%;Ak|HX^R$LfO-;RN=3kzwuA4w&c;)MRAbDwdwmr#i
zdSLy@>NGt`^2Jl9#EFFYej=pg?I+hAr5&boCub3+%B4H_$1aI(uIldAd47oNn_Yo^
zb>y37S=T<BB;q^YRjT*w*)!ds?fKYfwS(8yfwSo6hmDNsO`IoAkPh=0*W6#T_G6}}
zl#PcGzkU^SVb6^lH_kgQSPaf5YkvBey1FnuI5?C>ea+m{-5nhs&U|v(MFnqE^xrpX
zAUam$hdNWE%)nzDRB^G7PCV#>^kHtpO#!CU-6I*SB<H0rM&YJNBm#BnL-CMa1|jYj
zy5#{%rL8%Ze>U-@YK?wOO+6dhQ-J+LZcOXDccyT8*d(C`Da18TP0nWKcz$mH`ASnH
zz=&qEGm-zdGYZCkc(`%UUoYgP>`L26OSTLO>%HgNiELCWwe-6$Js1QA(pvgLojIba
zPml(hGU5VwOc>Hfyu5Z3;n-F5TxNEpwKmVz{1^+%H)D#ufYoT5k<p%h&fqxqTlJ*y
z3eH~}R^l47jkXw)P`b^yRzzgv4X>G@#&?BFJ*OASO`K3x4-uO%XL+VMgV_1`D$hBa
zGK~VKTUx8n&<2)AW*RjsQ=ON_i|_Xc_iwwKwmiBQ1xjD^-XkqMecc2~b_6mVuFQ7v
zq7s0{4KcBFh7eNrVUxVdCXR-2IakgicBbBom8yT~1>L*eptY&^V)xDV0!Qrs<(pnL
znMNwZczV4MTiu4vM7LM3_9l_$1y|;@Lwi=nJ)IRxyNW$`C`NH!2PBd~&8VfAXb7w>
zO)6&?_?VfS%V9@j@snJms;a6E(TR6o9pHUh^Y4y*vd(1dKx;0ASZ+_j1yN*{*&ALA
zd{$!BQIZO}x{(3r_21e~_R2LCbPiWd57zInZ6`uBLls3e*)X{+i|f>=XK7EW-JKgf
zB^jtL`}XgT&B{{z^5qLkO9meF;;dha*O9XuOul69INuf%_4)JX<Ra;6^W%!{7cXAq
zby~7f)J}}e9W7{9URi8kRo8bkHcqS!<V7ORx0~Lg*#6E_FqUs5>s<v4IgYKWp2YlL
z-<+(L*4xC~5#Cd(SyBM2yrw_y3ocK|Y-1E`2}ou?eOeN=t_n4@JHzP9RfFWK39Pf-
zx4j;C(5t9?^f~VF$Sq~xzI~6=)tJ59xGuUn%y)ZBo;Uq*iPW<>&uX|yzdlh-)00ou
zSJmt(mu7p2fPK1opG4Ahh&EHZ)8g<N@1MC=!&Nep0(S&P+wybwpHdD-5cGe3y2p$+
z7+~jYk)kJ1s>}S7SeAw?3Tqed=Gp&T`q2~bb<<)14F>9yLJ=<!Z{HpfKuxM!uph}T
zj^nw5j94%Ar~S;(W_=+J-%|2jS7a&nt(C4C#CL2y@L3Iw-N<{qFcu`s5tI__yfR-m
zdAo}?m!99|%utF=>;N_r0)b1ff^KkT`N4xfEPsB#tdeQ?^zP6~gCV!T+>eKKlLyb~
zDwY<}BBm?t1ns7N-nT?mZdPXCHuRH^R}5=k9<k<~EIa2D5Wp5#E`LIEfxx9xJD0ZH
zsY<;q@Lh7oa<_;1Rw9<}%k0|RCL?v_$^&FAL87kJkpX08+04J}Q&22jDPm2C?$o&v
z=E10PI5B5{jcnWV%_oDFbO(4L<F(M@P}+-3r5p?W@Q>psUYUPAfas#M+1*48ddap;
zW|{X@Fc}@*9MUz0Jo2orAq!6;Eg|8-Oe~=f%g*?(lXQoKoE*NlB^7(nPYw*L%iOzn
z56Lbyq_3}UW2-K{dbzZw*YZ~DzxTTh_uKIJfco82eZ9SQ35R(tqBHnWcVBo(BO0PJ
zS|V;9;4-K=zE%2q(OAc)gr4!P;y3`W{qM%Pb-wN-&oi#hiF^-vjhz>8M)OMer->8a
zFLq1f?L<lh$1aZKN_ur$@Ydogj`KafyPk5sI9G8&7;y5XY>1(iXEq<IOq|&9%_`K8
z2&-IypH5L?{<nfn*G@Q`E<nb4(<5Mid{O2=qW3`1@A7&B$@m3w9063Bj%fq`p0^%R
z9kl(@u@{IdlBo66joayHO@r#K+iXS5d&^Qn>{;vw-%14YqVJIK3Kw?WuK)i28YamA
zs~_*zEYDM&JroTU)NLaUaT^|waaig?A7vx1+*3mPE_Wp7ridrQw$g%6w%gb5K5!ta
zFT`HLQ((q#TG^99;L1n}odmKE<u$8#KsatnJXEmNuKZlbh^OA}D)Z=#8m~Fj4!wNj
zu)4CG(6f=j<~{jAx*M_4C54vH-wh@ZQr*AdK-5m#ftaY8bbXm^4E%~WU!Sa`3ay@W
z{lpZ*f6&j@H>#)9Hx7G%*J{X*`2<Sa!Gj0Yr<s|VZJx6)0EqWL_><#%>YdVfQa#Y&
z%t&jT^t<b}tpjaX{UjY3wY|9g^?&aFs>@!uj)0#C)}KBK>XmKz_W53ofI8?{>K?5B
zvnEQCG1#`(-)?c3a&&)St8sn0esz4`XyIxBN8ys0QA6?vx48ItF(oCXfy#HT>dwp4
z$s<CleNx9+#Qc76RTu!;Om(<2y7XPTQiEJP$ghZU1?+wIn27!KK+m+WtI)%ZJ2GDb
z>M1=x%p<WlJ4)F-i7nJr;FwEvq|+m<e1C{Y3(h1pzdof>6Q^)`xFO{e?`x!SqvlL;
zyujK7O534Z!36mD4~Tj0$w4ID>$iLu|2)7krP7CnN+BXg8MqP2eTY&}Rwfo_C@(Mn
zU0WILjGPnN#LK08!4gR}TmJlW?Q4x(s|1p$XtY^iL{!wq=MgMe#DiybM8A&nBPps=
z)i`{qcB2K0#&Km;5jWWrcFbPfJFqBV)O0>+0mw=^-z6>*2z-H`=owto^FMW*q35&O
znw@{$lfkHoVqzlD4%pnD^Dtj8#5kuv@`bkF**k9alb1f)24C$oC)gh(kb?p?+M-D5
z1&bqmYWBk!Og@q~Z&rI9yZOgVrsGI<-&Cat<Ky#*KhardAQJ~$HR&@It2XCY^uHY%
zGTcUY;Y_#Dbq~52%fY&9XJRKpym7~g47S}b-`qi$>qzEr4siQ?ajzS;MnB*eCBzem
z{dB>c*7JdQH*EK~Dl(d7e}8`_BSuEXq|=2<w}_`uo&gT2WKYU_`{>alw&H_jXvZHG
zSYsC|W7)2|)9y0MM^|kyNcW5U`AfbwZkT4LI`B<>F{5)l5ptDpuPp#&rR$a-8g4Jt
z01z-}&P?FQ9XgA<mJ+a=lFGOHAo=%TVy=XOhGWKl4%KMv*1O1%L`}PO>sAf+F`Dp!
zqQfS#gLMg#A9uO5_eX^$qB{3uIRf~s6AP9mZZ7u;tx8*2W$xa++v3Ce1ILdZ6-IL;
zkMfi-e(z|xZd(6S){yVRl=eiRNPS@PrX0&S^!gS^Py`u7^aqv;{ZX!#6LF$J!eO*$
zb)FnLfA>vXQh~CU#c2NQ0IHkaT!-tYTh4zSP_uYhRbB1l?X8{9ja|TYOcQ`J-lT9@
z9{qVXI?-(ekF#+HgDmP$Z3bCk`e&uEu*+8IXZuebxwpydo2&f%T$g8t)3RN;AE$8X
z>r>SzvNg$?!o;Hv#3j|?(NKC~!9lRq(pL<IXa27KTGBzBMKxACiG72Q`?1<jelZ&O
z{J%%Gz1fgy+zP0Ybx0^NtJAGX@D=Yf?td;Z=DF_ludC9~Ts^Tn@As!WBRWBKWIp`;
zR~lJ*f&WoX@ArNDA5VMj5;|M&R%2fzuHN!MGGEXWa18|6wP7pm8+=8jcxaFln4)Pr
z4_gphpG5j0ew$?QV(3KVn!{w98CS+OHV#`&kG2aPnRbnPeuQ2x-Hc)aK<b#7q@ODu
z%qxcwv2L|g5dXMNLJ4iu>sDh00Hr(bF~g>~shu>as_#)Lrz`kIf7Yp()UH3E`UV)d
zHc3MRNt?jZTqbS&`}XdgYpJ7shqMM_F}OQgl0n-Y%}7-H>dNc_fY__RKr($=l52H;
z{P2r+S5AiTiFnX$_FI~ry<pZYYUHY3^7h&@S$X-$_LUh*2K|i;TJ3o4Ec9PTQaw%6
zD@&f7Q_gOVu%8|AsjE$I1rCcu8y}6-jlMIBdUk$(zBVTWph+*ujv%}e7OBoFdu}-t
zdoofo5As<Vel3>s9yNF7Kzd+*jf^3uuR_7C6%w3YO}@P~k$uq7%408E0O9-$u^*`v
zTAi0%#*S7eU5TnzN|;HS%bIB-$L);+zkxeAZ$D#*9_|K~fnWpw<qabPjjK?4VwYM$
zDx}}7JhJUDw^(@~kAi_gtkYt1y8%du%Y|z{9+bH-nA7RTZc%ydK>uJ;Vco~ae+nD_
ziAAsZ<Ds8r=jYAl%|?`iq|1!s<Kw+AMHo2238`BSwdU5ddHeXt-4xlaq~S1rP1LB{
zsVwMXTBfkamK2+jc%`(+8+~qz9cI}Ed={rs@-wso`K*nNTxCuHqt|gdEKk>KCJCt;
z?d`wt&PQ+2X=T38r>;J|mBC@`^3f7^TF#Fq{&{(M25MCNy!tda7|OR4v=L=Zy}*Lo
zl?CbA`wE8I@|(#jf#td=IKsRew7)ecV_Pv%TJ1;k^^68hFT~ca>|?A~H5dOnen`9Q
z$*!YUVtG!TDvwvqJ{!)9Os&<FK|YXNutRX}NBHNxBVFdJ&udce-0ZU86*ky`MF?`3
ze&k0W^yOZjLTPn%b<v(+iKq~V2@*C~Kayppx(S-Hvi8@r<Hd|BrP#ajAcepq{E!2O
zDGQF7l`lPd|Fgad?8CS`@hFL4z=i{V-;FiF2CtjIMwWW^QdVQKxBRG`)8l`FV@q8D
zmp&ygk6!=?)+>A?qeHUYnmVSA1;JT@pYvkQZB4AIx&=<#5qULdBxmr!NECh>U|{9U
zaI>%*dlMZRF0i=ufN%s^Tx61sR={$oLAtTAan0aiq+kT-hHZ!Tj{AL7QULTKP|_Ve
zy+MQu1-Gj3Zut+$A7^RbGHqC1nxqc2WT({&Jp|8ROKEqC!@c3R<3IE|`ltj8W1n_d
zHs0~A)%u(<Sh%{J#2f<-2|YA9qC1hf(NLA`1)#s?DMnrk(IUS$p5lSrwd(Jk945<o
z>LySLm7cw<QA?NBvCTBGv$Inibn1`r1uIBYs9>N@CL{B{U~3l(fy3H0dA5_uMTbS1
z3#s(mT@h@yq^d`o_tfNCn~;dUX<yY0Kxu|o?&i&#?K&PGNVgtUXO|wb;PnX&O#<Xn
z2Y{`_Mu2*uLsVqO<X}K#EuU6ErFOGF_n+K!LY)E{%>Ya~pw3GI_My)K4Th)f>FXOr
zvog)gx^defX5Z2tO2OtmEQ%$q1oQ)6CJW&JD2HG@LCjXM@d46@3g3NnF4B}Sv|P8^
zTT<`@DF-Ni{sZ92n?yIA+1o)5Xv8c=+gfwhCjpH0f<k37%Dq#%f3i2w<obn=IZ;^2
z>t5Li`sLRr*xA#XUNIe#P}_Fs{24DX^r8lQuMQnVoAFWyo%OBeuo@+-V|MfGMCs`r
z<a~g@B4f&7QZIVmEsAyKPh6y2NMo$GgwJ1HSZp!3d5iu$5G1)W72L*Q3&sQ$O6E84
zi5J7OVmo_$H8Ka<!4fwN?&$tm`7RQ~aNUaYT*t=H9+0K(<+|s*#BSa4Dk(3YRvPi(
z8%Z7r5OlP=s_k}Ft#E1LnVc2D9<&ja?})4MQ%(SNF6C^q2<Di}4>u@NjDwXk4G&gj
z((_t;4_%pJO{&M{)f3lA1pv9xY}k}uqC7hCA=PowiU75Qf)P);9x3L(+oJP;hUIW$
zn#c~JrDPz%B=A_Fl!+01g=n$}fd}kJELavoq)~UAmZKMPYUwKgv!XjZ2;AYtT<(7^
zs;c``ZLm}VFXwPuz6v5WwRw1<i*c1Uc?J34y<4T@vxF!#BV+)rL*S_hte&)4p1{)y
ziYaez<eR#=xe@SDAEY&I&MD&a=c~c@iViNcjygs8YB)9aCB)QTv`$S+n?`sH=1o?}
zKuO}r`FTWOx<&?FhvL_i>)%qei(iF?o+b@6Q%s%@mX?%Mf?NnMa9kX4KUX0I<O&ul
z5l~xT{PUh~5Iq{=Zg{zKHYIC5WPq|_IohU%^nVL!oe;86ET{y4r1GvTk2>G<I(zP1
zf}MPldV-2cz8n#B`cc7=Nb_xdi@nc2b$svq@{H(42~~oqJK#9aJIsx}aLKp;V(|{8
zIU(fXv17;V1`{Ym6GN0+RBCj|<Jsjx;EH%#uZMlscds02&7B5{{3+?Y;7v#$guDjT
zB<JVbXXio3V14<1IK{^140p;)NxhkEch=x(weYUE(6#j#D{CCUtX=m@kq2$D9Lq_L
z022M?j?tqg*EF1$Gj80xd5i?sCblZmQar1W%5Fv(KIW_XRz5+w{~LQ!x?Mv7;N=>M
z<psJ0WZ+{&4+qghTlC(wXX)wZ#r@8Fup1Gqb9;SS!&r?~QegU(gwV1@Xitc3@4?X0
zmrqXYw&XHus<HEok_=TJ{+06{7;t90Z=wZTQCr%nvAUq}eDF_h={m>-3DZDA=fGpl
z@`Z_DYnNNMJN8*?>>CUau(yif1xcsvW6c}f`}anMO&6Yug0`TltF}ANs)>5PxInjE
zTkNNR4SBaWXAvr8HQOwJ%)Fyyi-60*?5KwRA4$N>FFc09EpY)b%-Wwcp6sLYpd+|E
z$ak;vYQzO$0qdF3b`t>kpOg`>c4<f#Z&I#TA;-ttsH3+rs7K){$I}bjdE$XbC?Uao
z)&c0#*C%KLm#a71&L{_+*Y|=(b{1<ok-H4I^9KVJdsQExdu`Dft<ReZy(evEbb<i>
z8&kAh^{c{35xfL7Y95HL=50l$bo4!mK<-oFlbYXNo%p-W?S&jgtz`vGzjD6LvH&1^
zhxKX*qHOqz5syZTz%dnyF@3tB+USSFBctY$phYK?DE4MKKyQPK)B{n~`bO<gW|E6~
z<pTw5o^$FV+Uiz#j9Y%DmHBaM=M@zH(cfNJD8~&AP>b_gY~s^BtcOU*Y3CXbL2abJ
zx}%M@(=2<c&3=@`#&~WR2wyN}(HR;6v9SvWCnqLqkiiH7>#iCq!*t`~*EEqvw7Ul4
z;a!qSO8$w3D|U{E<IA%Lw{tz$pBw9_TXzx~TpDOw8U1E(hCdi?ZT!~@P|{--L-J->
z`0;Q@tia#(sBpFKL_F^c*lO7J>?0bl$B!S^Hi<;*QbXv+98R4)DU0F?Rp-bdk-tK;
zU37JHWOGB%7nUBHJ?m#)ak02>@TM<EEmW*1G&$UN>G4vh?M52+oEH#KBS;iNOet($
z1u;9-?Cpp>MhFM!kM^8#QA*S82iG)~J>tsfl>8cs+FQuAKfx=h-$A!l6D@r-v{{y4
zkdpHqae9ITg5k3Et8j3EQAK-{Ys1x;`;oqC3CbzXH(uzdh)2ytPpM<#q+vTUv4S$A
zLGJtnAgqknTc;z@qRyd(t1F9jsd_5e#m`P1KmL87Hg4J2B(1R~MuwGCg(fosT;*e~
zf}Gn(^mVC)lVhlfuVe*7#t!NQ?Mf~nn7=wR%Vzdhen;f$QC&D<9L(|wrR{DzYQ&TK
zO%-R3I`9{Z{;A*@5Ignpl}=|@S2;T4awJK2$W+Khx`?|-zz}(VLC1x;7KJZ4=w!>9
zO$v>$NtI)K2Ox&zeG>%3z{0~*52JyMyu7Z}NQ*vn^1BI?=3$o8r>T8T=)=v9mTWzA
z{;LV!qpF8Is+?#Z6a|hnBZfin#an_SOwDXh*Y~Dgz~9Tb>Q%LSD}u>H03z)ZH-oa>
z-Pae0%^P6cnxiy*QYrPbo<Oek=nQWN>YIOj{K+^$*G~jlVhGkn*JbO$Tkjb1ZrSb_
zbzYeat;;6v45XZjIcNywJNr+o56ud=r85b;DBgQiHQDG3{vpnzE<q&_l2loDZ|@;o
zjNTkv_B`5tF_1!H`uh42yl6THYK>9Hc>ol+Y;A3eddv<YuFxFlB2C^Q_P30zY<I>o
zP<fu#6HiYbZ&pce!0MuY3*wrosi|Ut+(z+cV1J|c7UQiZOhfrTKbkk`(OBqQXn7JB
z)t#T{sk;!&9hh+IWD)Ho<Su)9PXGWa|D^jq&(k5zb8IpSO~)03jG;8~V0lxa(4PfC
zn%0yIF@LBrt=RoAPep7WlH-_)<QXm_Ute{*k|u5@rVnENXTRv^=^e(s1W81J$Vjof
zf8#9r{Me~RwG_wFZ{K2DjrZBkxNVE@byOSqaQNw{IS+gS1Qo^X-|@*+K^aX~A5cyJ
zIdQWh6^&0`g)3n5cf*#f%DsE{J~~#>H1xq9-HdlYfB~~V5E#|uqP64<82R-H<|SUo
z>c-dQ@V$#fg5jW!PTlTO0qydm8ydGT+#QC?<BpIrA4OEevh**Br{7XmHXzDeTnozd
zgSrg+S>wWczw8YAPb&UMBQ^(ODg9eMB2}>TG1)7_w5a>yBk*HK-8qZMn;t^&XflZp
zGexaPE#>&rriGyIW_WBTy4gnGvGsXCDaGp%9Gr)WnP3J0tmo6U$rlw!<=*Gw;y_SF
zZ#>;f;y`-V#C~em;s1N@zI{qh50VI*f?T3Dk-}!g1I}{-><gAUqScbmu8mW$Pbj5Q
zDJ(z7eV<tI*^J$O-(3$OQ76IV`<u(6dp2fMvWHSj*%IQ|jrN|bh&^=X_WhSSIe^az
z!{jCoq2(#>3?qo44o)t)tpM)wN*a`e+-3?0R6$CTajsEg>X#;y<HCG4EH<sAZ?`=m
zwkgE2?%v!c>JXX>1*;^sxjtduB!BvK#>&^1N7oDzrhxpW@5^^9-#*Ts?+q+R6c5iX
zY>v#_ohK1hJvGwWEZQTazVE<+#gl!e7vkrZmQ9EC5&!b@0BC<5;k=fb2z`isMTEgN
zylD|B`jXC>tHN7RGE-iIHFBt$6>^v}E!>kpaJu3fGIdJ4?pqRA5_dD6otT{bJ!hJN
z`jmrx<M8^~vuD+Rp3eI#Fk$I@D7TUFckSpz)8d)}M|*Mqv)69T4-XSvK9_|skn;5V
zwL5B#t3ywK%%oDa%EC7xd+XL);87_rA{!D7C|8rl8N8u%;I|QTHLRQk8YI#!QHqbq
z6(^?xq$>jfpanK|_CM!{B|zNu$w@9ROegecIL$|VyT_02uWl!+Nx^(1m=>uXUjFvc
zeeCjc`3ZCNg)12uL4JNeGmO#5e=9AOL8NiP#&e9Bd9GE%&MpU;^H@pWRn2DyPG`PG
zOB$O-73BNLnME1rx~!n4|L*oZc{$t@bIHO`bZkIoDFJlIw#eIrG_(Aga3v)4tlhX>
zR(aI8eRXzqC6qcG*K7<=Nu618bC2U@D{)_NEa{qBN9G+@09r_Fp%(}?-KUOVG$^lI
z+t;q}Lp5y5y7$g*c4T;Z0UH96x=BB{iCf_1YC(7q{tnG2f~oGDIp8VNbxL+4#p_QG
z)CwjUmDqocMwj-+vU%8woQ-Vy>xBdA4^Fx=q4v`ZFzn9b{5<J71&|&(wcL(Pxuzau
z#Fx6%KAXsbrH&oWW4R-Uwba+6SzR9Yr^6#7l}qZtf@}=hSHN;)S2X}eymJ7nnBN;v
zMrauf2%FC_Tc`P+0|YCD$FFJB3r)c91b;Pn57wm{Y?2ZRbduGRHbqeUhVMBE%W+<m
zddatW*SERymr}u>AKB+!ktF1&B3{Gxf_$P|KrTHQu9F0~{j2}`R#rB3@w(+100sHO
z1Z*4l9`>+3HKc1GGAc@?6dhaLg=*|dIh3GRK|zKByZ7wz@$<VItcP_;D~d|6Nj!jB
zMaqV|We^L$&2DQ*bJm_!W6Ox=556e9#P4z2$8Ld@9du()l!AzxpC|{$0J5B@R|Vui
zHbxoo=7_AUhNe|4zyp?!!1MZArxcdrCk!<->P6+65{^(AHNhmsr5ma3i5Z;)b%2d=
z(`yR;tobEF<cq&I>|i)avYRMjUF%WJ;p0p>4TT)^mvV~M;}ED2+i1^RLIWW059>oe
zEOgWJ54F@l2Av1ZMyxD}L;{lWcAAdUm_IezXcguzGwNTnZR(w(xR#rR8px_w;cvPU
z|04<W@7bDBSv?+~R<R|kO*c(RppBokay$Yuom0In%cm<OE1MZ9Ha4~`$_2bfcF904
zYXj^~BKS%WBk6CZh0r}2)W!QlJ4hf{aM+*NI5=hop7#11#x(@~gzTUBn#}w1=JA%*
z^9$auUNMuLSQjVs25Mq_cHar{l-jcrA|y&Ufg&Aj0`KpjlH6MmVqk$H>NT9z`uqEV
zJ6_w(48@?yjlomfp&%`@n4`elIo8Q&Ka|3j@i0&?iJhwcy5Lu9JF{fim_hcnEXV?j
z@=M}wu!Dss7R8d6%HN&_o}rb@7~;LD&3Sd1iT+_9_deF2u|#f`kOW&K<IN)e%HN;B
zqW$P-t55~mr|B%?D%Lr386BXKe&9*z)ARJ+W?Xx=Uwx3jxxSvBF)ULhs})d_Nv$51
zoO=;Q0(%_8X2z!TI2?{C9i4Vufhk6@l<)_(6P8XN*|xRzPzIQ4)e4;))!;140RSY@
z42TeNUq!HbUm0|=VN0i)ecN4yu@L!$7KhdZn|S^52-vWYTK0Mc$!Rw@jkHeX+IPG1
zMS4pU4%Gn`B6!p4Yv7J~P+`Vk95ywvHG?&J@%4yl^vJ35mwOGwlJaK)>L>BK*0t&a
zjH^>*6kPQ@6GUg*o_7mA*cP!f)`wj|xioC*?6%Lw6ms|1mxP(i%*<>Gb(0ME`wiW6
z*1aw)Yh<>0pL|orIsc!7nFCTbEN*DS-`>B|D(s=7qhqs;OWIpU=1ZGah^6h!*pAQU
zfo?+UYJIr3?2l^hF7f8N`~9z;o}R~a8kW2B09+8z5T9Kt#?zrPkh43#vxf|KlKotz
zn#vieVOwJtwAIB6%=ul_FIxPR5^c6A=tGMhn=W{(y>BF>#S4$jUMS?GcJIqR`D{X(
zQC@`kt2(<cBo2S8ZAz-{-^x4msYG5?H6Gf9K}FC-1B6^A;61YP>2M>cm=2^WK=g+*
z7H#mAW(=8#zBT>4w<M>f&*%$dI+j;zaX`KI&str$u$tLGS=KkaX8i=T@<*a|wknzy
zU*^Z>D<XwJ31qx>eBQ70Qt#tmw<yEck3_<fq4HFDR%r2b{*>3R=3_cXm4j^93L{TG
zsx;wMgBz>If)~tke8^q!Y2zxMeL|r?kDKiHEo;H#?@Q)UkA7-3YxD(?yme40lVF|^
za%i>BL;WI{NncsP^Ox@3OHojwY98S?aZGdrLb=Jtc)IG`-`%BPp^uT!OiUjXfzv8T
zLQgcGq1Dq(pa_8WSj^JG=DKIOo!q!o{43dnUojDr$2tgsk`><!;Wi2&j33JR)&fa#
z)<=|L+jeflYa?7}%G<fs;cUSQ=7mpm{Ka2P@3T^0K=fR;fp*DOe|}C==q{nFh(H9R
z(K#!ylREo3n0adN)6q%dpR+45{@i$m%20%?8aHJtD@SQDdzLXIxN+bCC{cKO33&~i
zMKxf1+|=w-zFi|0y!kHN6JI6;ww`yf6xVi>C7iQ$YNu=IS-`N;ycB-G17qO1BIT9p
z(3NngQr?-PZl5;GrrS>HlCyL22bo9J)kf|yEuY}vs9Tz!Jm$l?^+;_XPYWluq*7N^
zCb?b$v_zVnhq`$=7cb1G)SMmkyx~hr*du(SdGCal`y~xpb28uwIUQKuTPtG)#+r$q
z`s{Q8ZMxpAlaq9Nrgk{b34gXbTn~cB8R|fMU&tpN(Y8-_ADwM`cY5TiCA6Bg3?l04
zA+>u_P@0fW?046tpK96c2`{sp?O}~Q7XO6HR3Wu=Zq8;Igcsq;gs~~ET7plUs9ZC{
z*e|4gF#bh?c$S^_Gic%ndD?JWKwD^OOqkuapWLv`dU4^fHJ@Vc`cD750{%i`w%aAo
z!=(Ab#mN@MH~x;Zx3|mUB7cLmH|#5MMcRFK+ts97$>&<2ALwP6w5t<!6YnMiZ}k3C
z1r2`ZmU&>M9!d*ngLppE3c*Fg!uop`w7^h$%`&@u7%|2>cwZudm!OJxtozIbtwN4s
z+<K+pjs-HD9rVfA{81vugP)ttu7IH$c?PZ+Z-ez9Y_z5=mOzjHa&p~agmw|fS`{t*
zE;gU)ebAfpy7Sy8MvI{a&M(Eq<o>FN8zfRC@JK8|38*#>9#=u{O(9R|X0tL1hM?gh
zr%&Vxa&k%&sg#+3x&heB?WrjhX(Co~yI+QYOGFH%-|sR%q<!AJS8Q+|gkEn(n}AaW
zGhyVF^9TzM|9q(Zyy8UJxr_4myg02*grlknKg;^oQNYtTNrfvpRfEv=<*DBp1nkx&
zM0Ii(u>AO^u#gDuFnog3z_Io1X&=fsA?hN6McG21B=X{Q4L4<^5nytF(t<lo03A$X
zTYVX=Mb)Lw;CtkVVPSpBCD_8^Ill=wOSz{LXpQd_A7Aqz@5~)ktvYKX@Wt&ZE}!Nd
zM@%oAx1G=-^$7F|_d9AxN!?zinXi$Ne4MV~%ugxo_PS{xt8f7lj|yI9E#cdRGJ{VO
zs%$F52vIJ+hcMNtcMaJQ>T$vyYwEJ-m%Sr7E&TQqu<FKKw}dPEuRpeGNkF|$X&%1r
z^->2eacK7<WuSt^G&RHI5>alKV2tktG~*PopE>VU_Hc*bPq})SI1=p88M6(OAvD+7
z5tLIP8Ww8Mg?k@qwcX)H2;?#02Hhy{k0ia|-kg?*9(*Hz5z%Q4&we?eaUNTVtBAJC
zEV~Sy!(EqchjkMqMMcjL?4A;#Z$4)>hxK!zRRb)PTs?S>qlc;ym3Mvo&*^#8=Mc2~
zAS@tK`pvMykR7o*%D(xqC(O3=nE8V=^#`K!Biwt6t)sqC&^FrQ%3#3!^=$h3F>>ut
zOv}+#l%wxttCjXKXKh#%FmAbUv?R!~PH9c)wU6k^g*~c6;e$C$iUgaayD-&H2oM+>
zz#}5zaDy_^MSEE11SXliHU*t-(rgzh)j25e=h39m$~Yy!<>^OI6kF+eYwSW>tcDsi
zjdDT0-ybiSs=9gI>#3(FWx#q7^lZMM3X5dOkR?C<C7qx{19Z45RoGpwdno~6L^#?M
zbqy|!eK8M2X0K@^$}eXTF$SQ1ueYb?p!T<<IB;zNFxUp7U8yJJ2>I;BG(FcEi4J4m
zmKN0{>KwEKf9N&jX3QOYXhB6#Uu=N16>)*T`3}<Ln)*dD<+gF|F!#|CxNunRHX?76
zok8SUXUz1?l=HOP(62&r(szQ&K@c?VCki=$D9X?o-p1yU8_B3w|5MQ<tAN@XdfBTC
z=s;MI!`gmfVQH``;+gGCqh2}QQ8^kvo!>lB(W}s6f<QsmjdWgJQ8!A1m*mCFG6Wix
z=AoACbKQOq!Ih*orQ_?p=#05yN5N)hbQ;#i`rxHJpDuuxEC8vzdeFYzF+qflP?MU0
z4~^&_E>1Eafmv?c+(w&WIVcZ$V_;fR?zm?Fvkf}J!|5wX#%U()f}xxW$2#=fIk9cF
z&1b<hPhU9wi2cK14?n;Ptpr9vhb*cifVzOZKL{;(W#?jg>vgS^R^wT_LWg4nFAE}=
ziBU41#RigjzvT)GGxIvft#lW@$8D#EJ0^<|GEhau%)w`Tnumre0qSlvJAqWKTUc6J
zx?jwH`#sIklBt2((!K?Pujw~G#=}FoMUR&hmeCe`a%+)!AYA>ScLuH;e!RO63gYM+
zPHdz~aKNAJR$8swAAi450LIdt(`R?q-wedy0F{@&nF1%i^6>sL!llLAB9uJe<^RKH
zZ&gO{ix;;Eo;<Ap0=;a1&eyMBlXw69_oJ@GJP5>fgF+xxzYhER_}np?g)hJ^A<lr`
z>z6*<W*xpe57ztGiNzHNa0>~eaHp^c2nd9C0f)~XWsu48Ow!6<S>Z-<xiqZx#D%sN
zE`G$oPyG1ym^{dQp{q-Jgq^Ootq)}U^v6F>&#AS)xxCh>B`f8+_Iq2x2$rz<%Mt>k
z9{fQZ#*dgzF1qW8p(Pw>Op6Mi@E?FsJYc>Ebgx7ao9(YICUaYn0{CrH+d1hYCR832
zf;UW3xw(80!W0?=$53ykU^f2ZkgexWyzG0|IUINNy8CzBN8g*p1=xNQkT?6GCyiyV
zAg4nR%70MT#lZ2|+urXHv`b<l$%(>MbQesR*Ei~E4G5sR;xzk2K?|lk%$o&6(RAw4
z$Jh<X)(3Sx*}#oPOA<432bzw*x&S{!0=oB#Q9(i?OHj+LBj;5Gh)F1UTfO`yWSyAZ
z8*smwn_Nd2+?-s`-lN<^PvN+oY0r(^^}mdqdva|hBo4SqB@UwvFVyyu1Rr>41Ct+i
z3K__ONt{`sdUO($nqsInoK))GmFufhv_;5bGw?7MktZ@@?S6*2!ht?zV8VDJEU)ix
z4?Zp~IYN2)zIkW6-QcNh2hVmB0)GzChlp>t$L@ZVu7Su2(#Z(AT|_dS8`C^Tt~+TC
zK{^mjNi!HD+T2!Sh;(;db}$+X0-=ObwjPqnSNFrCpG6wtKBb&~hA>5V-Q*b0S>G5M
zJM+V`8OYie_aX-9AP(e;K9>`B+>2_kwZHDO>CBG=V=)lNgZr1U^_gxj!&ymOM1IGW
zS)&x=?XI8l&|$HhJ68=)C~*Ve-;9wm4Y&O{PV{Drll2elPl3TM3*ai<N_*!0d5Bj8
zC5RUm0Hwko8^#}HOty(-zunmW{rlzPe4F63?@lW~Kj#mF7D1c)0e|^DdbF+@+}1g}
zsk@lKaJxZSSWL4TRwn*ljSanrk<sLqjEp#PW;p;&860yCGfo7h@lpqVxP!^Y)aG%8
z1m%6nL%_P_8Y>eAykEZ5#QqS&bN6HJ2-V4b9!YL3gCso7?#zUd<grCrNVEGPF1EXk
zO@{BJx-7C)b>YO11Z*SfD;Ul|731tL1eKl>8F#k(((%}8ju65+;If_G02&pssCbI!
z2L<fXm9k7VBf{v}%qU5PRx#fe4}aD66wqb%hkkngcD9><jj*Ew7>YH60szH$?wo@d
zSs_VE@^A;1het#ZWjlb#G`fVXUodmPA+!*Lt^5#-qA?SK8ur6np?!<Mjb)(g87INC
z_@m(XR|N9~LRrLo3n6{$V?hY@-4-GJa3*r5M`!xSww_(zW(xoi0C>(3NRD*#<~i6A
zBEgRJnm8{8QgdmK?2}>k7{z24rQo6PatJzs_a*!E+(Q$OU)oeK-+P$+@fFa7eYw_l
ze8XyK?jAWc1FVv*_}Ptb3}7(;^&N(SR~{;)v3A4O=;0UG%Rkl%pNDVFuM)ZsS@acR
zBl7jTHE)|yk9-0H<EEx8V8JTP{Dj1HpOKgzy(C8L)xEGK68IK*9d@hx(bHggNM3|$
z4%Aw2hD*|nxf1rZCnvz2X0j~Dmvo1{k1<4K5^(YC*|Sq<whDe-_Y#f!hi8X0xBi||
zeKYg@;eU>!{ugsw<$w9|e`9|0Kd<pW_``A-VBK^;+num|;Z^8^sa=2!BB!VrjXg1l
z*$VP|BTsx2R3$RKG<KG=<<w7U=#ZkoGqRzAj)Z?wmM;;_rT#>B$(mAPe1{htX<R;G
zHX=TvBK=5X#tq4rCHcp%M%1fuD7+uWgq#KPl7ihbgxX4!x^%<FvzGl;*T8!!I5;@?
zbe%2w+HbMp)Gw<jE+V(@+~zhEe#_w|IfyJV99cj9AUqD3SlpVU268ONYB&~_f?(*6
zXjZ+;b!C7TiQ!Mejg9174ejI&s!~|f_;aXgg^R7DvH1eOZjgpXb}z0Dn5u~JnUraM
zR#w*VVZzTz*p#XU;iDi%XV4l=Q3{vEi2_duUkU1YUOH*ljI+96Tms#7J$^+BDNfPI
z9Q?K4kWUNBBdI6<<DXl$7+ee(BbXx)kjH#NWk3jL&`HnzGjns(3!n;Ws=>v#W6!3^
zoE#hy;46HYF9ugAuMRz1m-waO1r#2~S@`p@bvvxtRG%U8px}8i`@DJ;4KDEY(Jh7O
zzd)3WdcijKu+4J+bFF3N^hU!Q;^IEc7wNXc(^PAAwF3zK3x;)of)nMn;nsPJH*@_4
z>N2y>j(0EMCc?#(J)+P0mdXvT(qW`biF7Qg`k=*K_q*NxElI;O9_y&HXE-=GSUfp7
zIV*XKa!gyVK7e1=qq4*Fv(AHhM~5OmKj$404NG|!_a%K%?6P%8SzstCE-qfmIDVqH
zy;!kuzW`MR*Yqw{M9JozZZ&ad`tT=BwE2<xg!9*JSlBS8MdsZ7`^Ai1-_iD0rb#;o
z$pFaFveeM>Bv@Hu{%<?og*V7qM1u_7{}x2}D+}xAu$#v1OMUu=^D@3n(t^#@UQrkx
z*j_8`Ys=uRce+He(^g%6=-5GaHuYyI4G-(hFYa#_rZPE7Jl1xvC0ELvWN44E3Mm@k
z3~_6JmeMPGK~gef6muJ~57(K{!2WIrU6YxF@e|4C0}CsQqX|98SUtVH29Oeo!Bn(1
zx1V48Nri^^4lT%{D(D9xft9%~F1OP`U=L)QnXbwr-@|;P+N^mMgE?Advx?T9^ao1^
zI$xaau)xbjfvwAWm@*)nwO@OgKLHbXWsKUu>j|rV4EiJ48?=<-L!+<*IGx=sJCxnO
zg#DzUsq|cz#K_@$(;4&EM1ZP+H-UB9C}RLus{|$f>gCHYEEk&O7|fnx;G`Zpc05?W
zyH(+W1=DQ*dI3npaEPDh@8!P~fAquPLvTw1vq3}(C!!CSCEiL6O`?zbO>uEKSOKO6
zHCC0O=tW`l^j%O89lY_22~o%=PI}yeSSzw4cOy#k;QlN$pMJTgRK03Tn+Qv5s<H>m
z5;~B9TJ}{@D2JREy)*l+GH-W1%3^=pM*UgFa`w&>s?RDt$(#$g^Ak)=mv`4|ohKs6
za?w71W!Z{N?O6)Hw7Oc({?-&QguY|LvVU`4hZbCiCXpB^5g2>FcGqFaOys9;U%!4w
z!<r5b?*8UB0I@|ij8n$LktzC!HWgu^<gD7;*H_#vGzcu{BfC;;ZbY#LYGPLWM1BQt
zBv3OEwh&d6Dbon}qHZBO$kla143G&pXQ_5bLoloziCGHGQ_R~xY?b?KT7>_gw3L)o
z>8{7^F{Y=ujawRSXcc$b8l*K^H0ei?*d(ipJ2_ccSd>5QhcpzOoSaOAGyIAwpWg4q
zFVT4`SeJB-+To@(tt^H)9hXUj?+oARl9JAqp|vz`I8xT1HXATywT3<ICPCL>4qW}=
zg2=~<eL+V_yig~u&i>A#f6cu3vAk-_V$BC%75o)*;bq@*h}qs0p@Y98?*14o;y$d_
z#IOpO=z+J-II4)o)Z4429LzI(Im|Il-dVq9!)a(PSO!lB*a&#%63s1;ihM-(2$K1E
zkjt<sg;nJcUNh_pf?y>q2=x#oGhpstZeLvvW$y2McDfEW3hBv*zprO{<EcMG9$xh!
z;E(ARfs%TdR^GvRpp@hZjT7x=T8N&W9vO_JS2(5?Bybi%aJwVK_G<(*s3#)?(1-WA
zXB5$bpCv?vNfkZB0hiO#++QM)Q-6K<d=Yz~z!oYk&@lM7?kk?6xrCz)nFYX4`XCFG
z(VhbSMFDa~V@3}$2q9*HD<FCz+?rg>zvorF&^rDh@YSnuu=%pKxu9|7$b1xJqZov&
zQ+-ra6d5#wdj6B&5BC?H#60JJnBkTG2M)OS{rLZf8UFuv&W3D$)AnCKy)!uV*_Kyo
ziaS#lu%EpqyqQT@157PGZz!npma&k(vHYZV2hFAM7yle`AzM12v3=knV`0+pnU8PV
z59;-2nm5&2(W1s4B%s`{k4vwPAm#t_A?@~mA0rSQBdBD2{O=Fb?2P#TZZ-ZH*SJ)U
z1b0GEus~irxp8Gy<?qs-*e{|-SnE6qJc0>9FJ^6bt?_pqvKNXJg(M>ygo<Q3_yzWP
zaMn>}PRw|Yqf!cZa-x`?n4!w_I~Oae^z{ewW&V6VtRk`CbhM)>jat2GS++^JVlwX!
zB&}$L<~476-8tD#oQSEVg&dq9WC7f!JrVi_1S`q%UH;Jzx@jlC8xzBUau~@%0@Z;1
zkf;?6?Ck8`7<lM;@{u};3OFgd(4O(rEL&@WwyUcP9?xU<PF+s})adH$L{4SyugTgE
zXEoh+ky9djod{eab{|vkN~9r0eo1!qXDIBf0xpU<+)!n{PkvsWJq!M6Gx(<;IK=_E
zJB}0cWz+?c%;e-VEUTBt#Mo-B>_x8NZl*WJed>5pqAj+(+K@Xr7d>FV+BJI4+h1&d
z>dr09_rvy{+`0edwVk`xuS>tiEO)bYV1qpOHRXL{IeYgFT$z4*<%9g=XUFAs#?Bbr
z*|nZoN_y9eO+w83AFO%gXOvo2HoC*O=#im!x0uU0TLDH-eg@ZzeGY}8ljRpT+fw~U
zpFSO5Wg4`~6`l~0O1?<vaid-<E5A{T_IiWHGh{(VHXLl=(!<H^N={j_KYrq5p)0yX
zMmAgikYxTM&`0=rO-;=w>}NF$4Gkm6oOJC{n8;m`%z4~pYd~ohb~@!!ZM)~|sM;Uu
zH18XqP+$KOkTmzN?fN%3|M$~>*5rTJ;lI5O*B<52q|G@9f{?sH*r9>$6M;hke5Am3
zRYIzul{$=;qzZ)V6bPc9;G>CAjbBrM^t`fGx%tx5>(h*WZ^m+&hIM(+X}<?uG>^?2
znYLTov`Ux<Fu16pzX(;93@U_RC%G^JeC_&m?&!?hncM#P$8YAh1fDoVOpWYdh=+w#
zgqWv=X0f)72Sf41@ip~>wZ!o?KtcwD(Lc{71@u`oXbduJHQQp@{fvG?tGjI<S$+^F
z3Rr@o>j!@(4Nh$@Jw5$c0W6F@#a)#zn1n5Ga0|?~C1<um%##N=!3bJCum-HJGNU;U
z8o7TjV(%%#KiXU-<{b!^Ix`99WElBOS5{W$EKQUiBQe6+t5p5_XKBta-k*g%3(k+%
z%xH!Rnv96nm{bKds3IWqU+7`TkXk2&vw~hQXBgDpz!EkY5xppBb#!&P@I?knd(XH{
zteS$nJj<su19B>74!Ycvuidk?!^T06LFNL3_O+m>x*EC)84u2-M_uDwuA`dfd_|_q
z`8K_rE@`IA4BW?xcGBFID;NB+jImzkW!iLkRyMX9Ua*iI@hq0nRy<smxjI<5auY{l
zL|e7)Q&0^f-1ci~YxW}f3#(m`f*lEc@#7r#H#idj|AlcJjRqyOYhd?-L}-0s98A$i
zjvV>8qU`SGHnAGANdZ*}U;O!TnIBGhO=ov3(6n8_!+rNoDQiS0e)*~G8kk4Djk!Kc
zkFfedqqhb$G@De@cG%n`{P!<kzT`5N;k1~x%U`oG?}K4(!4_WuR*jL8H|ec4Zxi#?
zGV8cpd;fVKm&_}N*0w!)QY|bcs9<c#Ch`1s+Im5w6n`UY92I~qmSm-dDF{q57$ij4
zG`mjp=+x&tm%HjN=9}Lm8Box^y6CjvP;e-!H2LhOGfO6CZ1UTZ^Y~ET=9A8O79S0z
z9_Vx{)1x#mGoFec?0X=GCt$4CviYH$E|_?2FwQ2#Wk7jr6#3KObe(NTP2cEc8k&F`
zcoG!5V<aeY3HJiwXCg(dzKEgHXX<8R;5hwIeRiucLHgGzZ%tC@I2$Mnsav*qe1kA1
zBAA0$JT=t1jIM#5b*^~*>eX>r(Le9=Kh45Yw0dtAViTB!M_ETE3GbaSWT=+pMeWuz
zTUSL`1sEh1)rs=5D%}rx0~TZA=eS(S1R=0$FF;3?fg&He0hEy#oOMizUmdX4EBy{F
z#uQ?tP4(rxmbUh?%vtzt7KI%5OMC=T(LB7;Uy1U+Ec2P`7^S(j+i}B9yx!nGQkLZ3
z>uHiBbysJ!ZKX^xz;vj6d<D~T%p^P;FS+UWIO}~~#xck@pp0&Kfe*hmOsJS%;ZRsZ
zxc&{%OIWdOpT46RW+oARa$NjrHa5aeaFj$$b^K?vylLE+=VehVr+`2uSTA<@_%Z5w
z8!(o{QB3dw5l2zL2B31M9ZU_mCF>r+)r*-N{+*Hx_Hge|GS6)#&PM54wVtn5h0|09
zL}SJ30=#PPV274%;=$1!#AFXl^$Bo5-u>dX&!;XbGI9VqSwf~pQawAurmI^1@P9;l
z4U6F)>Lf0~ukoFOTmsSsdejgcB<7oLP|If2nj=RXP)5wlV7}Y{ZrPQnQSPf}nt!j)
zcZbK~?*}n93G>$VjF)IyFywXj<D<=MM$chD;yEqnk>mfdPDXzJL1mtFyZ<jy`M;n3
z^Lqbx9saxPz;>i&{hM*{I~d$BXl0@5dM7;{*qjM*?yEWyk<4F6KrWhdzQGcDiVW|{
zXS(c_kwn})Jhp3ceb7WYEQO536wf?2WM!`y5(V7;^{Xgw?oRnVbmbk&@iy)qHX-tR
zckSBsx>#{s8Nv(TD^onn1-6i=l$4Z-`Hwx$&xE$jl2bZu2TU(qF>_C(Q}CWWcoGRo
z(>8>L<|E@86TdMV{xFWP5*vPo6YGi(?;7vw+3nF~l-9V^@rK&>XQ_7loXCs%L7)1;
zZMZ9G&L7)~N5ej6&~DG#pODl!dq$O7?N<L5-!0YdM!x=Z=E@4+(V&XJOGU|@fr!4K
zQ=4o)-LD^%b8>P@Xr@(9a#yA#d%)v;;&f8U;&XTRR~B}8lUKew1Ho@!r4L0fVi_VM
zzjx1`chS+uNyLeBU{0<eZDJLd*4)Kq8d{&<p><hRp0eTBv^~WeEU~JVi_*z<{P?DO
z*Lum@>%l6%6|&e|JX!P4(K%ipXR%rv9Z95;{#EBk{tXR^tG3D@`C)6>si0F}T1b#p
zt0NPj<X+HeUs&FH8f}^R)>7x0H8iH&9m68OGh6a;v;zNp{O_m#BkK_TMCt&IU*}a1
zL-WWD8PB|3Iy5rp9X<?x-FIv>&-Y=y;FWK4Nl(vCm_4j>Qhz*IGd8bNlWL@GJL-JH
zpqc3j1drQBixU$4VW%@UX6$(Panwm;=c;FuAn$d_>FhIihu6~FcRBjwn&O$Qq+?$$
z4<CK<()bRyz0CBZoo8;`d|;Qz-Dum?9lVC-wt#x5<QH|x=+ZOSp4J~80DX9Ao8Q~t
zc$4P;$4QdNN{^Hw4khN#G~Le>mz1o3&eNutG$XX|^C-l}!a5_uVoewgrVairegTf}
z*+~qZtZ#-1x49i<cyfNgu)S}xHdpO;JlvgGL)hWr7$d8OdV9aa;D?%vW;6~9**4OL
zGahO%8`kz?`S$|-5G(tj;tEJ`!waV7Q&H88KdcP7U5F6?eB93#r6+ex&J(4b)H$N^
z&sS;m=>KR_9%`mY!c&2qfB{*9=+fU8yniELnJzDb@YXQ*V_@*LChc>BK^*H>Ekqo)
zK%931x&rZj9_1eAi@dA+x7^<i*<?eOLNWkhdeaN%ICNg8P@GqtR?Tp16l8~V%}*Pq
zF*dkEU`i@@a<Kxgc`(PC8H0rH9Oo6isarN5wz3`6m{9_Oq`r$p@=>3(yZP$yTe~t{
zS{JX13k8wAX4!iI$f^!K2<r?y0hhrh(za}Fm4;tRR;ufm>hmX0HqB@3Hk6i@{ssL%
zS?GPrpf%j*$=O1cas9MwrkHl$z6dyDq<l4V-1_uYRaG~!Zw`^tcy@2%ylv<QElP77
z{ikScS79k})2r96)!K&4ITWzOZlJmUe%Dyt4_M(|GUFiHC~0Sh>mnj#%y|+Z4k=ZK
z)&;OM0~6VJvF3!0IK*i|zZ~a2)L8+M%HcP4<@8z{NY<p4^WbUmST(#5k{0H2d&_JW
zc}uO@v#@BUmdA^nE{o$NWX?^qUEUXpOb5>S#qx#pn!4{Jn39U?ksI@npe@^;8>}bH
zip(bj1oEYaafWHqHv%{3z!*qObYi}V1lvGu<vWpD^lyin;igf5b{VIJ^A%W=cvMF=
z#|PFACYa@QSS^KJ2yxR4CQ8(59=EHu;SKd9h3lIt<N}9_`@233H~9t>+cwd85DR!>
z$us3*P!v{^uF&<?PinR4;ram&)}X{~Xg#dj&(U0(ylwZs86Iw8kO7{&TW~j&85fXX
zXj8<%70N13aT58yKv)Ba!$&~(|3s4=%k?9{Xv+r7RKuxPP*<?kQ>0^}Z2_Llzk=`O
zT{kvvpbMq;#Z4|A+zKx}in&)fS_S}r9@}jn_V3x#wfe*eT?zJbK;8xw+U3d@fr0GT
zc!sl;?``phZ-!`JNQ)+0esvrA_%*N+6ZI;sLwRc^CezU}lD3pkr*7lKNdq`}!mC4>
zzm;$Eed^_e?bg4#R{_=Yzcd}y@4I_?VjzPNtxiq777o84-~fYXR1@b(Oj=y#`mpP3
zyIhaZBBYJLhJm(lCv+_g7%L!TMp##2!woz+%kdMVP^_dwFoUcLJ^C<_7P>5U%VuT#
zx4iIIme>v(Jq!(pzJ<AB8zE(2FytM^F;=F6ef2JS7Qe%J6DnR99QpcY)_Uy6Wl|KQ
z{JP&kKCNl@9`cMToY4K4Vs*us)L$eV0GNeS0^ULZZ-sZTDNWC_u{EG~k}9MCpCvYJ
zRCjmxn;Am+g90S#1u^_kO~LOgK4Pq1tKL7g^fzHG1@XX*4yGR?i&7RA>CF8|OY+Lf
z0muyLINT=9xGf)&i;R~AT$~%y;RhJR;QIxE|A)Kx0ID+Ewnj1A3Tn3j1r=?#ElE%m
zkgQ_bAVG2vP!S~4<Q!~U>{b!o5+s9wWDs<dgGvhm5}F(&C?H`IlqmU)1@^h;zIyf7
zU-jR*@4jbsoztzj_ZQZhYtAvp7!xuX)duuA2ME`LbOMQjP0t~>jTj@rtb>nQ1ARg&
zn44P@zCVWQkS7Zd>Boqh9`UD;-YgM~YKcMAxnS=^qrJijIWXy25<)S&FxJjLXwHEq
z)C4rrrP6KfOCKa&C0&<5vhuT&3|AmRyLMnAY*QoX#~W6LG(Z3Cd;B|YdMf)?rYLej
zHWBM+c*sUWvq`>86~;5&_PNB~FD5$L2nvcBIL`p>2uetj!Ou~aioZF9DU18)Q4&{7
z!hgr(0C;m4VmRCqF(d}hrJfi%5CkcQxId6z6$;fOoyqbtGIvQ8*J_a~hcBI~DxF1V
zm;nGv9p6nT&2nMlnkZ-G)EZHI9*3UXdFXr9^|W|`Fo7ZHIBxMFtJwcv9jfEad-8f*
zCBs1&j06!)9tu*wy;;Oh4>qn?8^-9W!6dT|q=lxsgWp>vo(GR>zH%9;<I4Z?W+RB%
zm*6Q!Xxvm#m}m>Lg^SvU6vFDThT+bT3WedBn*M2XA{7HyG&8>JpCwqn-sBZy!g&JV
z8+k>bKM<B4#C_aUHjpX_aSYr*BhZm$E?js>0r$TjR;t9yd2VP9qHboxz~$$|bcK2<
z@YWRiro>Hcugl=kP%w=bbIN;az+#PsU!^qei=JTxTdmmu*eC71FYx_+zN*0{0^Ku_
zP&b`P6Fdk6VnW740pQsZpB3835Tu`5`kpr=3fld^0^~bRmWfIBs6;0@@cQ)%pe?b3
zQ85E7P;*6X!ufd5z0`Q}G%V^J;{xbWXK?%i-qLBEl}s9U`&U?kM=%Z@JaN@hyLj<p
zR10wlr>ZVpRrTC97m2d6s*QHLR`l;=KXfO#G;GagrmIEco(_1@t|3dW!Slr{kI^w(
z_2N4~u=dIFv&E~W;MiKh<gY)Yt^a%O_eWEG{9@rPb;A9aE5CG!UO1~3ZvOq=nWO~f
zH??Ca@ra6Q5!eqB6~+g5uvHg^AcMGWFY^5}wq)QTH703x=@oQ65F7Zg0BiLz{!}e`
z*`W3JAK7OXxc3Wr4huc_pVZ1B;;yIXj)zjg_s?Q`<KMqx1biP-Z<&2A6aR+`@YT+u
z<LTLri=#Kf-3<TpmnwvbPwRJO<OB;dN42>9bKUfNM{-{S&qRAhmZoy~GB4qgOroM$
z=)vB4QNH5J{&T0oT;C2y&_`@o-)S!#<C4<j=7Kx@-3NY`FTFP9*suTX?m^3|Us|S4
z^zbaUX?*to_=B!qx=7ElYri#0ZaH&1OklgpH;?G%pWSq~w<vTZDXOSM|Lkz-%S?Lf
z_TFFDCcELje;2M@)eh)qW>x$9_wQwF2Gx2&0AJ_<-?<tOMx(UsV7ht7wwIsV7N=Az
z$9;~dbmrRs<{B{f^B)rXR0hxPy8C|fu=FXOu-Ly(4aPza_SQ;xb=tapj_p+oYN@+F
zunq`ZJo;45MNTsFd|Wxc(DQ6ym?a#VX9IfQ2P*eY5>c&(6Tm9NfPeT)SIe<T!2De>
z-bD=GNUi~Vx85Qb`cR<X7iq*LyF#$>iOzE06?btMchT2pls_|&xZt5cw3}<0t6^p^
z0dsh5!WJgZqqt4td$lm6>)d}3o!P+ef8Xd?o^C2JyNr~+qn29$Fb=E<HHc^t3ZSee
zZoBgX>V-AnK-$9Vg?ko=Jh4n6?lO70mbXkzFLcDU!&eTXs0nbD>vP&bnM5njFEmuA
ze**3RrTw<tAUwCbHP-!7?RzmCiy2(USoGn~k#IsW#_rAe=oQra%>`Cqwoq)Zi`urd
zDyuId^ESn8rGkh%%FTGdA(gUX)mq|L=%-s%R;Hw;=C774I{)D|d=wG@pD#>!UF{?0
zf^de2LqLE~A|=5=hh>Pp9!pKxm}#@#;lqcM?Q>5~r78rURNl~X%!?dKL?-U{xm!XR
z_YG-TtQ7RKueq0B$#LnuKIe&lFSH*pf(e&lAliZ+@(jB51VC#oc}_222-H2{Ow577
zG&KhMAO~V!gBPma@?=5*J`s_5IAKaB&wGfSsdAgIm}9x!@CQwzZbsff?1JHUFzqUd
zLst!sKwqlrmx2$|m1lTD>X5?|MWw;cb1`-x)RNQ_RsqCs@5z&$i#f=M306<C4k#cJ
z%v+Bz6Cx2@A%lrVk3e>vIc%-MY!*1BBl%XWzx#_5&$}M${u@%Wfy9vq1mQC}Ikx*h
zEI0U19fPeJwFb!hC4)AQBM(#A;9MEwNtcA|mxt=yd2`(WT~B{z(m5ab9{v%ucmb0&
zxI`tx`K(M7X$zrLf+nm6rSo>+6QDZu1FfuwuEf;L25lCi>>)fn02g-fl_xYD5b7><
zNfPllTvwyv+XAB&85lWMAMg=>O0vdcH}1;YE-+aIVdNBK?nE{ShB*@QNtsskGj!x*
zm6xzwAeYl6CH=MokTS%-blW%9v$n?4P4GIe=S-(A8Fm6wJvE4lus-;RtuAn+lypfU
zAvM}dETOehoQKiL#-pl5W^Ict<2hw&)?rsMJ@C~LPW{~BRq8zx*eoWX&k%)_?Pxa@
z93PU55KS<Y-Ba8*qtFk6#G)?h{N}?N&zDRDTZbBm2poj?IERsT)xBn)$EJ_#7c-NR
z2_B<$_`@FiT;iu7G54hijzkS0TP7suyYhWNS0f&Ig)@c;U{@tWwE?2J8rkI#Lg*NX
zSBg)LrXDMaNJ&dnD7<O(UV~}k)T`wc^rj4_IE4xq-gx_Ys9z6Ji7wQaOW7np>V**0
zJ&!4Fu-jfSqOaC5vE-2rWse$O5wY?Iw*QVXQ3eS*^ov19_0Rdm^M+wi2e~CYZFsk+
z!jS{<1Bp4&<Iav07M6*>hLKn`#Ck;IPVi<N`=ieJWfc|ma9mP_Qve<wx0F<2u@;(l
z39tcG0{iw6fG=p0bYZ7Qtsz4#+@L0*MMLIY-aAGHBG972CwtdBHeKkBkq-@~_g4-H
zD;nNkvt|8vq-l#9-q$ZwlwyhYZYW`4N3oAe4rK(gB_jjatixxX4E5NiD21%jm*Pek
z(v60=b5+TTSMUQO(-DBK?edBQfCxp395GEo+iAWPDijl9KNo%fK64vJLSXehzPb~G
zPX?fTfjLTI_+0m)1viL81z`q{8jeIYGA00gW#%G95Wc|pi@3d!H398QVt4ONe+C*6
zc*J<qsKIf(*O+IvJ%eDGN`rH|uJP>X!|b%pF^QIi^rNP{o+noy?woKGC(dE`;}kr@
z0VFGA3d6wMa#m+_ALqjb8i?zr9}H2q5*i!qa1K&2L!p6$Zv?qS#3wwVS_tv9Or?lA
z@?brGr(oj947rSGt=@{{J~UPzQszIg7AnHrZdJ<-1Qxu%9I&Wx9;glp5i>Fh6<Z}a
zEtDvW*&Ev7K0u2GNI^DqM9@Idd-n>qjr+Eo`oZxS=unZ;>kx!a6SowSZ=<l)f=h4e
z*zyXEI7)=JB=b~CZUsqO4@$zDuv(|X299_DvK_tlJH;3a5GG-%;OQ!MEx;Xx7VYd|
z+i|~u!GpvTHKX#gi=ci-jT0v>AS%gLdpk!xv&DaBJ~OTJ2BupcF?T{$=eRcqO<}v~
zi}loMgm4K6D1i}5qwJAzKHizL(!j9L(0k36Ki0ose~-mV$`tgs7EKXgRG|UgzH67U
zy~9%YVDigqv!K$7KCU;K&B{6fJGu+R7#$lP@#2U1na08foT%iK<~(#Lwna>=ytS&l
z-1OIEmZ97N_`b#C=pd^J{~AaHEL2PkslnNq4BR0R%Wh!^RO(--Ca5A`x%u7+*G<i=
zT=x!i47Nr(+%k&o5dLPsiTh2!GC6QQNN~r=eaP=nHadA^wZehJygg47)q-H|a-+%9
zg!mP<S%BpsWV8tWFUceD5u#R~en%VxT`iZp)h|;|`^e6u0#hYzjV2J-4(sHoz(1pb
zI2+;Ti8=;m9%01(5pFLDaFbdPjpn%3e$uz<V=kE-0wU>(5r=1x-l7+%lA9ua!u!uZ
zjenh$`#V)rf5qxI<6LZTceYy`g;oEltmStfq%s|nhHkUbuk717_Nr-UXbcHKAsC=C
zc7C>E7zf#+HA*`A>@cdh%1P9Kz{kW1_<_Q=4o)}SUmeH$qI_mDMbJ<`NCkuNhpb^J
zy=7?aFqHS4ikFugTOHu*`z^L;{kit*%P)96(j^eU7p<pjDa#3IP+7#OTNEl%dXrz-
z@zLY_0abp^&u~1SqY>Kr@vQKfar2#CDE26-QkJF&dSuj$^OOVHYBC8S{g>^V4QSC2
zKbm68te_tQmy%ioV`sb0S2vv-FAbxJj<eUgh4$SA8ley%TU%uGIJ61bw|LD=YO}v6
zUb+5v6crGh%$M8ewhew>zAzKPE3cTSf*loDPHJsXs1ZXE91Cjw7EL%4b&;wyENyXg
z{*%!yO*D_qo1a;YG-DQKBYqSTsnBhv**b?c!oln`Og)mUwC<Mdb-7p`z~4x;9UfR+
zzy>sfPFOvS=8)Fuw>6q6ky^M^)4%Qwk-RVD{wcB72SGDh#OAX~(3}0>))GR3?F`=l
z*bZtz;Y~OnaNA^cKu@RCwUZ23AkvS*AxJl27kHW&ic$JurezZ|R6Y{n-s7jvCVu$5
z^h~n%^3w2}LXJ1dFKkG&TH0WqURf}uKS)I#uSREp3p#-^O_R|15&sUu#JuPT;5hK?
zeZ#_1a2%59RFLc|dCw=oAelxCUDsUJ)}JNyj9*Ad61jI+atJU>!@>^-G%B|H!*Inm
znQhCo47bSoxEAQc>kdq5As(^BY=Ed5rV1CK%Ur71fziVIH3z9E@|gogN6<@(#CsS(
zIh$l%Lg3RxNPglXB2E@AOr!$y=#?rU$qgI|!465XYS~D2h8_EOi)kux)5e9MwM5KI
zDO4Zv(La_<@MekeE{!~9bf>w0{`oYHSlCd>w{<)`H?GZkY<Z20L{lHnl_T#6Ypz&M
zQ0o%FpeDKlxHS!WY;ipcont*V0dc+s!<q+bFa$_^7zhZBBkogTq*aYfNzk3L2ySmR
z?gP&19Wpkb8)DMj&YwNYb?lfj>~U%cFBsk_5KZ;L38o(PT0P>u$*d08E!bb?1Yw%#
zSMcD*520)$F`n4D4Y%bcil2OM_`(=5qXj;CWC9Jr#qjJg7VLpV;k>;$@-=V|;$Qk)
z-ANt{$sS13O%#Bj*${;fWO@B~Mat*Sc_G_Y>)P3dEj<HGl^sU%AbL@8N60Z7h$T3Y
zUzAOf`-9n-EtVHI_<%^;QBk6Jy6Gy*_Q6u;{Z1T7Ua18)T$l*|JTWCl2IP1+(j+x*
z7Oe|!9ZInY+tu}2?E3D-ls~yQ@Qt`BP~-hPuI}pSedl=P+8^8kfjnC6)2TvEncqRm
zm142Ky>>J%xIYvel-F_Pi;mc-0$KM<J$9}0!m?5!CNsbN_G(sdV|4c>xt1H#IxIE)
zcVt3D>&NwXUHbOu=P`9br^45Yp9c~@WwGP$Bbi64L=~OCa|vAh$BkRe0H`d)CzNp%
z{3MU&C{7Em{_)x(8ylP8Jg>|J8%30ujotHWi~bNA5}f|9SFYXC%q$5BM|7=?kkID?
z$u|;#d%Jj?h}R6aG#z!n6)A0-w2*6AFSbpuswK5?)6nKqXFuou+_PKmo92{OCeb*W
z3Du4~-moFODi(o!&$o}h&kS964p^^DAAkDDr01>@Adrj3BXAI`0TeY32@nHIS}>-9
zm`tXSQF7>pNC^@hU905-Vk8d=6a%!Qg+hfDbrs_s7l9HJcdaFi2gEM2AmH>YGSrr<
zjx-Qn>-Nb+n}}cDf`;#RlFH`AjjEKX&=9+?)2_s52K7&`$CDjQzsvmI%<}BnGd1U_
z4ul|}Oe;n>erLbC6gp2H)6Z;OiW<W0j#2Qyiw6eU@iOC>fB*=rF+}?2pPk5l^4q7_
zWz+NZvW3$XmblVb=Qc+*Ry+U@KFF3)M@?uvwDP2Mp!>QJG<+&7mKpF7?O3P~ms~Lk
zM$w5xT#zouWW<ca1xagg8)f+>-Vn2ZJ?MJLUc399V!`yEI-<3)Mse~$pGj>Cc@R|z
zNpaBAe>yqqO;2rfbdwB6h9yVJf)?b|es(>?W(bDGvNSSP@9df5)Xz&JL-3XUtiH$V
z@igGmFoTnE-~*_riXJn-yQ#!&1u!k^ecJM+G3^^%ZLp~wLiyREHs}Ubh+fGoV<4Ac
zcOFit4PJBp3;Ce|`N2c8M2Uwp5rqf?X<{{`oxHhix<ixDPNjGR{)+;#^2W?I7GkC`
zR}LS~)UTCo*66b@^uQXg<~!nKl%b^GH7+H64n!_b%83Kg1Xv^Af=iq)hgxF6py)^f
zf5aQ<dIYF{P^IJAKVl=C%Q>yHmTBJ~7sH=^47M!z%76Y`!$?~uS_Y33ze7x(UkqmU
z<Y9k{I*-RuO1r*(8>vn<PQaP*)1ozEd~?szL#TG0F1zmNvEFX`im%O|4o1!NPBAf=
z7y?;`GJ(ultLf)r1p&^qt>Dw1LZod%xgmyxZwz5ngZTzCq3NKwuq*fe{PWTL{jLeW
zZ3+9%OgyPLa$mGC{I%j&xt-`p(bzt0x>FXE*Vy?hcn2!Syw-UADAafjJJoFe35Bxe
zp~;6Kv66bedDC+3=8CXNJ?nLanMcx55SSjH`ZqVTm!GQY6hmTPt62>s|MABkr0*Uo
zEFXJ}qOW0<@Di!SwCl%F_JQ+}1v{e3-}G+1D>z?qsYAg$tm3rY;k7!(8G{`aEVw}~
zS@(-~FEHEfV!9%NJ`?l`3!V-HtN7@}Zql~E?=gMP!wE&LVL+`}-SHBu69UXp17E$G
zzuuXd)g0Ox)t<mEec=Ae_7@+;e>l7Nade)C_3lye7gdVk=U?v_E;%IVqtBe7=yNp=
zjhGunRbq)7GrL4;o4<G<3uSu)R15qk!r04QD*xg867}udH<MVSrgVdQaE#}t5||p<
zOa))8<`)!<RjKp#0!0kftU2|AQc`+l70Kg4j{;bQN>Bv)x)gsETyv)=5{(XOY^;1d
zBV@zcsF%mh3Vbk@^K88<hq<oh`h*`IK(xc%c<nhVI^@0h$rT&k(to<768&qoI{G^*
z)BAT8eUH0D5XU3exQ&n0dk)-pK8dq{1^o;MiVO`o6i6W!nYpF2%>Klx_Wo78zeV{z
zLV!b7O}bPArfCuTE=%g$&saVfC_WMr7NvxiV6T(X!i&vJo-h9F->ivs^0#Sf{x4Kf
zr_pFa!6F`~H6A;Oc_{*qS`h6caac(7!6IDxb@DlaPAbJ486O#RXbb6fA7)(=oR=qA
z$|j}lpsYW!WO>c`U7NuKh}PkYe=w3cP&7<JZwaG3g3g+ZsMwn%@cm==nXT#B)NW0@
zry;@_he?boV(;HjI-%VG*q@z<BNwLsxeBHU);iD3klKC~)U@@mBYoEQ5B3;zJ3{X7
z@1M%Fp6}mCABbyDa24Wv6>d!}Lic5IP4N5AZ7D#p0-(o$JUK^?fBl{H_mKf3kt7`c
zF6Y$7L=^nwZWkIM1m}aPr#;s{pU{9$AYFiJdthUv=RlxR)Y7s5oRrV}-rvLYv8&r^
zS98bOWT$ZW#Xs3erNu^{@oL?ZSD^Km>>ii`1OI*VdLi#}kDuV&V=FtW?LD)W>71OL
zMs>$c?Wo?yk^YtMBIui$1iw1xqheM@cXa}URW&UKP;Cc17J5D4@4?@X6L6|<7)TDr
zTmdfW>wty4S%#NH0hx@jG%yu<Rf02+SB~U08EK92c;DZo`GiWH-Ojb*E#OSjR(1pT
zn3R0ptz@PF=&#i%@?nBE=#h_JFPU3{K==%M#(6?VIXKcfqyE+l>32Voyo~6a$S3MT
zw@vJa2#pzVVVum$N|R#s@N$ieeMUnr2YXZp`Hdu~=SDVm=j$78ae?r9#-C$GD?C(O
zcgkzWuL_OrY@=Hj)~~4^C!gd7BVnxqS0_I^Gy=(JOLUaSbC9ro4!mItdwCPs-sAvc
z9cUZDAwo(OI1NJvi)sQJhS0JpRQMyP0vThpx!Wi&<R{1?u+v-D<aoHXF#Eko-W#%q
zZBodW%3M?p;{jlcXg5swc+4H}T@KHlM?QXaB-8bX;uoav_{IWa%ZM_`4uUS#A3PbK
z@aWB&j^K+G0qNA?d{$Ld+=s=3vlPwei$u_iY^9|$_tuJtrw<n?A^_CqA!(em?%S|@
zR$T2u>8%P51NygLojtUgmhn?^3pk4ybW03w1EvUlYdjp{2LRxypdkuS=ggVkF%~Wc
zz1w~wfR4Gi+B*|~>$GdZ^RS!<k=+Ld_`6qFzIAc{<8pqB+A>P><}Hk84pP-GTb;c%
z$pI5D3Td&%HM$duJH`0j!~J)s3wuLWmjQU|w{4i&Hv>or5IUiit2Q`^@6BJBVX(mg
zfZzxSU1Zh6nkRK~xJ_F>7!JG*1b3vHsw0;qBdW;mAQQgWridSbAVwAv#1-@jGHwa8
ze10T0l!C*x1o;Y;C!JfgKrnvmH-i6T$P>1^no~+GFl+$)evcmQrvm8=^>76OO~#MP
zwEAGNA{9qoC)lv%Lg7UH!!)C~pa*H6n)ZrVpTT-o#&HXxnJsGN0guHg4~AK;WbdiB
z3#6vR6%r@`ATj~AxJtCbtw?o&562jw{eW5x=3zoWRQdqq+#m$6-+TT@rT4R+Mm-yK
zKAw`Vcl+Kq88{#&rcK7j5rg!;zP`2eA93+yvq97cpySM|*|){TGBmtM)sg-^(K)>u
zK+~|OlWCD)^szuM0109v`t`9uqX|eyJ`8M-c$72hKNJJx1d#d+u2Dv>e_I3J{cr-@
z?-h?@)pb0Din4(K^~M}X8%Z6Awg5pM@#mZ%BOr-nkkA3@H@dW!%!hhxKJo#AfkAl#
z+Ol!L{z;=2yNP7RV3qW9cUgT-3-AaPS!?I5<2J)`M!)!J<Ur7kZ}8};Ls=FDB_Stz
zXW;h`M1XWVD=;LaMTp&cukTbQTuaEf3nIoPmk8M6oKzNlf0yXhH>-8v5lWsl8P&pF
z;(%IdfE0nqj#Cj{)I>JDsPcZcW7ISAQ(i9a?dp1-<z4de3SK2G8kRE06GCBdM5SSB
zVwdNX2uf56m4smd1DRxp$3gv2fd-%yO~QjCxG{uV0C)WWf5USEfcJfFWmR!FQffWF
zaue?vL(LK(jdse{Mfl`I<w?LgKd0YVc{L(nc9e(!LZ|~o`aa=ItQ;^93q#}1pvWam
zNpCt@1*+s61)KY4?0XO<^Re7%(TK!TWJVN)iV`HCs#h7N(G)6ynyY%pJmyCO3X>Xl
zu4%E1KwbEv4dY)40)m2543`2Gd_Sg{J)#inPl8gXD@a$~vL<~ByaCdz!4IKdgNA?l
zrIPB0R)WmZq?tqNRxD|5c$gFpV?s&;vWMfaVkaM8KWRLf{dXNsu6$j~P(6fHGJA~x
z-rgKi?S&(I96ntA-aCy5Y{pPb3Kv!<$UguqX++<$I#1z$4QtoM1(_%<TpC5dh{uTm
zaC#YM7-&ci4s#yhf{nmTW)+}KC|8z-1(4}fz$y+E9YIr(%p1WXQkR-8RhXNFySFJc
zt^oFHuq?>w1`H8d6yQ$Me?h}Sg~0VVBG^pj04PI64WNY=$Ow9dgy4Wekw(D?yZoBT
zo{!h`Ib_s3+)ecxG@c9z7fyTYI(}S3$Ea|xtE3S{2KFGJAEKg{28alYScS09FxAvU
z8QYg3(P(GKb}R+Fpapg+KZbnaqJcl_5EEtmYb<c6292n|-gBH!_I`n3BXVl3Ld-QL
z=Pl^#-=tEG!15VbNbo;RPeWilKT~0PV6Gze4+`!V&87T|O!z`WJ%F+Yj3%P;flUe7
z7>E>dXr&rQnTRP*OJ|w{3uius5%Wpf_&vS3(+B{YjKbj-hjz&T0WDNiROqd9=$q0g
zXw_sC+oMIGZLZ;h2qsH|v^(zU)2E&$37e123Yzl1hd)q~i5`ErvNPBLe!7<LoD}~q
z+AL)z^ISV6tqb#h74aZ-aIBJ^-kihMLcm&RKqRJhD26^Z{9$?<GF3Uy?j8ajukLfW
zwwxyY3s#S6F@`4sMX;}{8C#)|_*-Dd!L#}-oXe6(prsO`(9GmeB5vV$Lcv{_&Gf*b
z4m0OTDq@-U)r$;JWb0kQxA{qVQTj>Wj)Sf^Hq@P)G~cF<Mr;sPFZ_oK;DjwNn4v7`
z{WkFD0|x+!@>6R-30sxIqi8s3iuZr`;K7)d#`2MNK3?9WacAtSyTA!*BIZr56=z8z
zFw9_{LJE<TgM5gllNWI-)tY6g7cuq+G=Dk$IyLk$7krVioJqTN!RNb3>q+F?&0+5f
z_OhuZu$>)58d!5Ygx^mzTyuyhf5{CB@LNBu(#fn#F5x^n5KGv()nQBEUj_2vKtz-G
zsF{Isq+z_j7K67<7blVSKBmy&nARy-wkl<m>x8MYwoo1VMQn|r3iTm|uP@7^FW+8;
zMW*_!vz$Y-P`7v-fIK0i!k{NdQ@}UMfhY?wm?Sl}_lFeOxJv2^tX4pNg@U=%r8nm3
z`5!oaLg?tc=|+zM9spe=sReaNKIB9sA`fX>cH+-A!fz=SHhCsrKfPUQk)vO7STp?$
zz*#bDCms+tPWn>4JLCGVSpOK!JxS((o2F#{(m7vAXgx4eH)-@+=L%XOi$x*V3>M<)
z<o&&;&Kk;B?F&4X0)&txdUK0=rQGw-Y}R71b47|Cs(2H!e(7rWCHYnzbVh&l932zo
zt*C+?uDOxilFt)9o&U_^h45xjKb(tQD%uLyRZ*N0$B}ywXc+Cmhmg5pD9=F0XZS}3
zPyj0Y(wT#-4jCXa*H!JYqJkA$FDog13f~lkMuzvmfhN-Ch-`Q`w(kj>SPE<uND35P
zTdQyxKs@PmfpZnBHMJ3|WXHc6f_U{5D%n0!EvT3*DOA?Wzn;>UO}Gw+s)e4>=`%64
zD=fijiyr7@tw3BPy~vRTGD3%{grskgUe1?$kp{Y(3E(N=2>UV)4Z3Z=(jGDv-p<zr
zjqrr|Z}TvmrceP5s39v{oER13VW0hRdKuOKxsJ_x?`mcJ;`-h(W0cmU2q2tcK&nPC
z0wD!2<`T@=*r=Dhw>hwF{gUW^&SP>|Tq`AAp7o1%Y~_={QwGP^U)`{8-=mM`c2QPl
z1bFTHwEiheQ>4}2eZTK8+xE1g)BJ6{_hC*q!S%o0T)RF0^tSz5qBw6wJUyx6b>zBy
z<>e_K+Xo`i^2MU@DxtlaJf^A%lM9n0i7mPIp_v6ULML@ZFuL|}Qio9^U6a1xwK@-g
zTP1n`UtzSxL;Q9NA7v4Ay<jSTqz9mqJT1h!jBD4?Yi9~4q`utTOrgqSsL%UgVgAyx
z=?iGzz1|I-(<*tfuh_|w9WxD?IBe%)EQ9m&^8<w~bt^>~NfeKbV>G|W{=&KPLW-u-
zsjOg&oOsLQRtgFVII7|=BTJcg&yfNJ?qay4L}}Tj8aOjc7f%1#+S)pXYanio*$DsZ
zTp#Rp?7W%RD>2e<n)~9#v15ks-{Is9qh!O+YW8rU$?6FlI2Ce1_WIJ9xkACCm&K8{
zxUn*~hK!B*zc@PkdgdGEn+1OU{CPK=s@X4B6-%aed@B~V#!BpcUbLw^R&pYqQVJhQ
zYn0y<&0Tv_F#&1NUf8gssY!Cv+7*Q4r>MROMy`}ngc0HRP+(Fb)KM?tysP<*{e6A!
zkV*tbJrLzib?xD)t*i4r9r+Odm}fNqawybR3FA{~w>F7#qK7lY#(@9ZK{PPkFbnSW
z{XJqrw@>eXd+jJX>mKl83-fUwk21)d&Fz>6JU!y_0tdmA`3ntRLvV=5<2VL#pzG-4
z@WISVlmXVoyKY=>PB#$2M37^cZM1E}?c+j1hM0?9dP8z7Q1G0fJCMQ3Ly}+r<b_ZB
zh+~PEfJt3k0sQsp23W3vqIM>Q@rQJWF{q&&2VuB+!KfeCwhbuaN^uFplu}Sgn)A@{
z7|EPVU`UYM(%8a6USD57C^*;#4={ChP4n+VDdokItT9SlIyySR;^%E+%-*{{=Yy>v
zds~UOlWsVS%3M2Faa=GpH6`4`+z!`V)Kd%_bl$E*J?nOJsNDR0K^m7^(#yMg*N0$}
zy64xhCvs^f7l=>9tCm3++&2SPu|~p6ZkOH8DjHeh4VbHm^hMm7sR+#FJHOlNjQdn>
zM-PZNW#lQ&VNJdVY8#3-fpoS9RdbbC$8=@hS8q#Rm%n2(GBW1i<zE#Opc}L}>#=CL
zfVS>uda9c_hA550NaP^!RJ-OOk^(uEh_ye$0_H;3qzp6K1VGrm_~gj`*id_|_OA~d
zqM>i>5M^OunQmz;@b{Q2@pz9x=6+!9mbkIHiG6;eKl*yJ`;k=V`ox)JWzKEZ7?#j;
zi(~Fe!6ZGk=Z@6eRWi2Fu<7~LX7m7UU@h5eZt0@kaXFzU1T(iphO@fBV+rWS6tZ_{
z^p8Fh&gUYb4r6k&ibqk`RG~o?jOKDLj<rhkO>w?IR*HWq$zOWT(m_XPGFwAX#ug$!
zt{wN-^V(!upUhu5V%|5_QeR(hneeA7>jUk!v#dJ47^EdgwxK*m%n0&zs`b0++wk}b
zjT>p)!S*>tP2st0sAFttsn;uh{C1V=k=v|ctrxemq_o@c%N9RiIOu(hndZ9utC}Bo
z!t;i!{rR6-O+V;7nSZ=(fl;o?X|!d~<ASr)2|MrHjg7jG-tfDt-TsX`)wV2<YtM9p
z;IQS1k@4$a-&E*T@9UfI<v-LqlBb!OZBi$lKKgX)Mplc3-gK+!He*faF~g4GD}5Fc
zoO`0$bDJ~8u<~Eh=?68Z?<n86(QBA`^|?8{Ud@f8L0u;#ChYj}<JSS{$fu94n!u4R
z<ic`nar8{V^j{-Fv-azXqSc3dJzDfT1cw&~i)_XiYbMebU0qY>$BNa>3WwM5B(;=$
zin_<zJ=gJ}5Z|@aey`Bai}uw)2X+=78~J*9v93!sE^c(e;*NUn`?lg#yFj23ps8N%
zp!v$g_<0<%Ul8%G($N~SZjhat3|C;wEQ1RwPfa@_$uZ1CaqSiBDCLR`Nq^1oU}$%g
zxpd67&vmYo>dQ6S8f*x-q^iqDDh#|5Lx4*4e6c2c+1)fuv^k7=bZV{lrr6N|gYcwp
z>)TOAKTAyHfzUh<vnV^}<>chh)N@nf+y=8wpYd7!;fPzCWUvKeWJ$s26^GB2L?UmF
zvezGOW<%KbjC_lDl`;?`I)s1wk-Hy9Y9$tw?$!g+@hsanJC0zEg{`e^_g`BTE~Z;w
z8zY<8?W0BdsP`!N)fM371){zdMhRF7s>?6k1heFRs=@mi!_3~t$A$$#B(g8nrH#?x
z(uOYR_4YicY*Z+whIK;p$f&3TgqA)AV>ypo1-72__;0x?I4#=0cnNotEwV|pF9M7h
ziDumS?9fZZO|tN-+$Uemr7lhsA}G#Lkotbvag^&QiP#+N6DbY`fRQThNRCHN=*FsW
z8-O*l3$|ABqW#gQk{(3H#l##$*s^pg*G|ycU>E&q#R!JR)Cmg~5&Z92npNkiHweyp
zvn`D@3dz4W#*=5aU72^s7|ze5fSRhDQ?a2(6ZPh~Nu&4g+>>%~-^A)O3nr2aTET9q
z5)J6UMbBEGTVkrKK?XN_woKuE_Qxp2KjSw%>lPp2mX0q*o-kvPYCb;9otR=0Od&-J
z&vG?$vMs^!Rcl>cor4QfLB?fwm~Fdd216n7Z-4HBpfS}(=EXH;&tYwqrz|lrUbU!-
zcZQ;J?-*JNORF}EAL&eoF5_289PH!si{T6X;oSfJIe-0lzI$YGVdOCd0o<vLp&Q@;
zx^y;Z#iLF7K0Wvk9rw8|j^3BG^5tmQ2Bf5<P)e;z_5=v$_sjV$Q;8!il4rS%*y9vA
zyTR8XKEmX+N>K%{G(dgTe`N;_pf8M|pDA*x<m|>XVe-sT{pu;K>6G>oYw|F;dmF${
zJ>bvt=YGHpOlzP^pOF{r7zWICGDa|;yRB6&(SAE4_C!L<cBZ;$aFHJS9dN=+c=rJ-
zL^H?Mm>@0Uct=zkIoF!6-q}L}_v;q$nwb=h<b&*!?6)&9KSc*lUHIBxe+;IT>}`g3
zl~BoN{J@oWW3rl_kyl@=CKHWceOBf6qqVn3=O(tqB!xeF7OnWz^!2K`{EDBxq-do5
z%#VplIXP-!D)wkzSEOm;uk5Um!E~{l#UAN;KK=aT3k*9hv-!Iw0V@=3)*kLW>?f0r
zV$T{(h8|3w)$B@BU8q`|mcl6Y8#GGz?1$c(CL=|%kb&Xu^gT|`&zC6KZF7;Z8tnLT
zL6>#?xKZx{Ed+;a;E}6uuJ|sI_7xr08dwKA6pV$seH^}YevcnXn~jY)3*(WF=Zs8Z
zm935X{=ikfdQKs4GeG;^pHd0{D~yc7(Ibh@wtH{W%SZ4$+%ngY+h<~8q+*R?P*|<Q
zEC#Ra{Iz?S8!o(kxde??|F>5-1qRPxZaxan@ix9~8w>*U=AW9795beT31^DaMj#;>
zK|w2+y6i74&YwSjJ8}7s^zcU|_jlDg<i^JKTC%fUN${$ytSo3n5twYdOm(+Q#AV<`
zHaNJrJ-e*o_7`z5{?|S*xjgkV`{(G#@~|kK%BPenxQ{;0LsF~57+w6zvG}*cZ0LyV
zjhJ#xeyk7&GRK<EcMP!eAWXm6^Su#eKa-dTNkRYsxa;U=IgJH(gdBKq;^(*5tfE?%
znlN4|ch}_sDUwGlZ9%e%NjxXOQE-I{PRIVP7b0LLzebx$VzM>1q<T)sgc7&kjNGB4
z)dK7cJkfH1ssZio?FF-B&^lJ@f$X7-R<gV;zPP?!de(Rhp!9`dl%?dMFvn{h7wS=`
z@FKZ#EB}Cx4+K#Y>p6A2%>j%*b&2i}?C<tb|4fya73ddTN29!dXs7~+Qi!s=$U!`2
zCzus3B@nPFuLAG)5ot!*=8`WvIiL7#cOKRH%@2ph<%n8@zZ;J6BdlLgji%{k%_1Q>
zjCJ?^if{U<wK-cpJj(*}xfbb#aKTq^k)fBhH7zYcNU?Aad>~OPq-ned{(a12Veamd
z4T4uX?sX99iO8ov4m3CpuHyK161n5-yeV=_IJwZKklF*0au?`Kb&`!y+9cBba5obE
zdH3YpNTC?7ihe{LIZz_PuruULZlE4T23m=bsvcR!eM2^drNKaPWjjTLk@P|Uvt8+x
zuU%@WhkDRt(8mnU`>(m95@ceOAQ1|ri1hd>H#oW?&2l^71-j#Kg%(<uOm(^BN&84l
zYNX_imI>}<3=hkztE(4!gocJXfx9vam`V&<xH3dJb$gi-+%3Obzw9*Fw4unD?4lU0
zzHCCVs&w-N)%muc%uDI7bC|VW1yevUuvviB2|1LVKz$z_3tto`p=}KJWPtjRM1XWq
z$2S@Npi2Ms;~N~V6e`AK$EKI>wtlytN+V^#i+Zx`CQ@Q!4{^*km84_7TQ4Sb?fx~}
z(zT17%TEUXkoOJp?HDdef;t+-G0#cnLN`xrf?jJ7k$U>wdC7EYl%sIWTF>iSR<<`H
z@g$*+eArZ5<A)C+I`+*6VWIk-gaK$0j+vXA`?Zh%l1t+L-#+fg4~4s0ljljwabm11
zG}F5UtgCCZ>u^4nOVi-ZeLvZ18BUqTppTbUX9;R=`VIMYI56H`xZqGD6?gucT_0=8
zyKTcR5$o&Z_~{N`oDP3X(VP3kTAregv=}+=Qc{4bM>xL0JNc?jHvUx^`$vHJO1aY4
zdr9-yZ5LczTw;RTcu6~_pmnUbstROe8yxfoFb*$=GT;f4RTdLV9G2Ad2H{t#ryO7v
zh|$YynSVo>TSJZq0}e6lJhFEklj*!%J6;%f9I_CB^`qY>HSLMq)XZE7*#s4ZnC%;G
zn@yxI9HjO>zp8oZw)0wPWo2bAzy7Y#&!6kcH(zUMX{k|q<|E6-HZY^{A>{7cYt8xw
z&nK>HIQv^nR&2V9D@iZ;bVYl9^OGMBoHP4ztW33Kz%8lRJx4?kjPw{EbACyrup$~-
zT3Th2caXHH#u@&~%psY_JzF{uo}8FA|0rg-qM+inK($k*oxADwgk8InlauWh!zAiR
z8b}>=@R)L7r!V|)DC8l4suPf$I6Awyl<Q{p{=QZI0_`%i9D#slidl1i5EKW+WE!Yt
z5XZuoJLITI$7$Px_3eTbX|n;+WL^E~Fpxk|NmZa#9gH+B8kt${P=+>&kE}e<YO28W
z&o34Q$IuM?wiI!U`<)kfFk#MME&O&C6-krunm72_rc{2UvQwiCnFWHSW&QgwJjoB_
zwA^g0_fd4D1xfDtKys$fwfg`^Db9JR&M`VF++MPCN%MkKtcWO8hciFW(5Tpt-0oMR
zx!&#Y!D_yt`C-cyS)>dOB*B9gv6aHp8-Xz<*R}7WU)K|2Q(20vK>+vyieg^90`bCc
zZ}|~4T@MoZG8!tQ7a=OW)uJ=;xw@uCpi>~xqi=9%XwQ*OH-utxf%HLrAXz(q&3CS=
zu6};bW20TeqIGKTmCLK2Yi+oFx>>T}&FtG5?#%Fp;A?3b^^%)ja<F75NlPb)t<vgY
z9^XC5>$ESX@^x<3U!PqU*v<_Gd-;#nQq1`#7IYPp_5XHC%V4(E&HL)(Q(qAv?y(ru
zR;x9**sCfr6kA$7e?KQjZhn_!F!7Ild}psSCq#PJL6Z|QYTX6%Sa8iBA~r~o9hlK<
z7<{U=q1;a<h9Cp)5#RqFA!7q6$b~MDa)D&c$PrF==p&^gahMd+<J!HEclMS^Z<g@&
zm~Zr`z}m?=cjU6grgj!{(}@WeXXlv;p1hgt&aL6!&J-XspE9)}2|ENkWU$9)G{-A&
zl&MdOXbSoaBW?D5Q7M|(Aw=FY#OFQO^O^@bG&`1G(-dlk!Htfr{JJ3ki`AXCZqJ+v
zZsP_LUxn-^kQC_1l`C;{jN$(c$$A^HXJ=QJ%uoFM@$iiaVQ;$I2`z066$Oq2rnrKz
z+fuSswCsAmsGo!G-Jqy$a=dxdZQ`R|Os@RO7gRCuyggat!oueVZ%}EdbKIj^vwHls
zX-RYSgDnfgVd75K!0UsFvfu51Gmn9}pexuHmE>eZqq`Su2IoyDIz8cmjH!wTrz;%_
z9^>YC2*v;|TlRe@Zm6S}rhNj*r9!a}M>tv7m{xF^jh(%=xjB%;_-Vii1?_rAEG}r1
zItf8+6u%=0nMVas35m|sP`$CB#%@3o@()gY`oru+g%GN9O`zXoR}s9c6l66UoMi?$
znsz?V@c4Ge;~j(@ODW^_RL8TTqN3JCEW+KuiS#O^7Uu|Nfdsp`bLo*Q>|6Y@dGVnv
zUp9rh1a)C>w?m9@nkLR{d9oVF6Cj)T*2;F(kCQK|HqC8)0|N6xUqM=$RdUA;59F6S
zzobo0PUaykFinoBu6^vhYgIbVscwV$!@L@b!t*?SG6s58Z)#%fYD<u+jk*o?#RW^E
z_sJ_;KAsjSzwn2g+Jf`G<O4zsp^djscf|cMJUr|-vMyM&YcyupH-}yH7(|zQ<%6-w
zp`q~sM`OV+l~bQ;sd{wlYGQK6fLLlOW4F#dHa4~y?_y05UQyfw019>szGV<mWsL)x
zgiAQ;NAvmIu19Q_?KdBgifMiNHh8gVCoh(}fnE{pm(?SKUZ-IYfZcFxkNa??zj6KT
z_fsg3S#%x9F%0n729&o$rJ^1cxgGAa2dOA~3+}GTV<cQfu{GUPBI!@iJiNRihtQ}?
zQRjNzAFH~E>J(aR8i_t6{h%d<JlIEFfTeAIhYB;&u4V*E8$dnD4&Lmw4G9mO&2LVZ
zNKU2+4zptq%b)o8JE`>Fks}uxdgo+%0Yhh`Oda}*g2l{?jL)RRLf7yS`2u99E)7rY
zuezDsI;{G3ss)3{=p!;d5ZM_5a8~b^GqWd~hUUl7BJ@|nmQN_+M}(Ce?5ZR(M-M&6
zOlHO}Dg@V0vIe#I7b=uqxs?VAVnZ*F3k%0)XN#cBC7Y2{={U1KfLAU8*s39C!BlrF
zh_dTTjtE0v+B6HnyQZ}@7y-rM)xD;|A#+Pv+6M@+-KLV+0?FKH3)|#de>cpC62zH8
z7{84<A8;*tlMAXV<^~$C{N}8PvI9`TQq732v=oTyRnU2nLBYt<M5d7>g^=4yUd%W{
zjN)nIEFF4!SlIo!N626p9s59>N)#%|ZRFwzxl^MD2(TP2X(}Wu@$(ZMx3HdCd9lMt
zoj-{-lFsa73L$L^KdtdjyjtJajFcjkf2aF&q3Vjk1mok6yHPO6;8?Fj<DEdn)58vh
z!BQAD&n{SKd$83xoA9=$yAMePLdh*fsvbryXl#!u*q2ROjU6aQ%GwJDPT!pvXRNxg
zc7G>$DQ5N`tV#-u*=XHpA6_F#Z5~uLm7Bx{pEr1D2LYJL;Qzf1n5K|X+v&(^;f^X;
zk7N~h!C~wjs9a$bry}<D=YRSUP6<v=OImb%ym4r`dl!4hg?JSi{Nv!@KoX$cnB_BF
zwK!Kr3PDERyM_dFY=`SnjJ9evHMup-fYVu-3+Fl#^nOZRxc3R*lqh+q1kj)BJ1<q%
zikn-yG~CfK^poC)OL^p*rd-!foE^L&6%3WuAy}z95wscyvRSel_l*dyBaHr)&z|S(
zmr8pFM9msHm~K=XgAPYuB3ir;lMv%T!bzhLsHbdgH5u`XF&`mN-99&)B^3>hPjXCv
zNikj#Gl`nxI(!lOhleY1WD6Nt5PtBLE68%FY7g$}1l0FlKD^^zGa#kWB94V41Ux4t
zYr##;Rho2fxSUzo=Cf-RB|M5F*rnbP@#@0ZFz1Ah93(9IfP-_AP@r!X|8g<~WTru6
zH5ixSY~OVC&K$VyS|d3;&1$qID40N?4Gj&hBQ71~NGWH}2?of-<OGH9<&OWvs+o%&
z$lcZ*!R;r51!zdtYNr4uoHlwsPMDgU#C+33ZEbA^92^qPwvtoXRR*{xQmP>Z`FrFm
zXf~gb8jlw*DUAAVpu1Xy<rEHC<k3{ew>(0Q^FX8ljvOE-J%OOfgj{1YUXIA5oD&@@
z*6n^y6|}s-vTl#9BCQ+p?sD?xyiE$}bisAIe?3Xgs}Q`c=`!^K+Ge*t>FE!>k^yK3
zKI1kA$$^Jz4JL?xXwWt3RPWW&XWy=BM~XAyXJM`Az3bR71nPzkA`}~|`_DsRB*Vs6
z<PIVViVVa>3BWX4j;**5p|J;a@CtAwLNJ=D7ub0jXyVrIkCt=;o7wv%mgN1fys*lP
ztZ|3E2q!I_{X5h}kaya=TCuJhUuuU3@<>@hf<9=iAUS=V*8Na=Q*)EGaF&-Us}2cN
zqk&bf&qh9d3H}rp^*|=wdfU(yI(?w!9L|P{MBRx5!3J%BJG7gz4O!^83TJ2BOiC1L
zqOnYKvC*z02$lnHvLSF0xO-KA!o~=kg}rT)PTJvuA3~2CD`9kPgDVD=>oJ5#{uF{-
z7?XTV1z=D?v_2&EEeZ4$uadNr){RuDrB4X!wgB7s7pf!z+vXE(5aTkRdnKmGLLg6c
zlOHGTPIkqSJRZC%3wRA&{fwaH3nP%g$O7asW7Ein{RqtIR(NI@H5+;f)(x6B4pQ6a
zJ`1O+o0DVWQe_AJqg*mPQ8!b;Z9tjSnQacQGssGTIfm}Sgx;cEzDes&MgmL5_26w^
zATQ1vk@4!Wb#R!PCoiCB_foS;lVN+G(M393r`GN9CE!LOs?jn&G6(3;uDpA(n^lTp
zk*c_U+8X{2Jkiky4tWPKmsciI@dEzQ*48Ge4WI%t9^H*Ghp<@g(s5zL6&jv>Ow=={
zRA$=;^TsNTO-#sNLFG@vIF1)X04F3~X6xFSU)08Knx#>2^;W3+Ynz%#bygta1y$G+
za3I8yW4{AI?#Z>ZC!}YV*(Rj-?G39nOc%`uYDFUK>ep(g%WZH}FBH`mfl^UcS-I9(
zhfXN^5kHY2OExq)9MjF3)8$M|>({rC?vLr^Zd25M1PUj(v3C%|LD__J+XgZPd=wL1
zB*SOwV$-pnW6)az!ImUMq`1IxIb`Wv8$8pJjh;K1>eE92X`F$L(fCR1Pt;OMB&ZPN
zW|ySvH|P47N6o?Tkwoocxv(&@D5-5uS4Fj1Q(y0=6kpdEOO&J8piz?L7abY7pM<5Q
z4URK%8VP{LpRq5JmhSUz)sh>!qB)dM#w27S)=9*o8O!FQ=g?-Cjm^=}TnSB*_?354
z9gFiFR9cnWunoJa{y~!_pjKmV`Q49_a=NjZpi}MW%zjR`bYrZoN2czemXutyNsJe+
zCgq+!!Vr*<j2lUi&g576niOKb(vUNR8B57_^W<u{ZI-;9?Rw#oQiW&`43Y&)8RvVY
zcwcgCle_3RTg8;HtVFI0#S27#U4Yb|q+@$GQ|d-#2x%JY&C~?cRPWPWkx(K=j%&q$
z|Afrn&Curd696=S9}kiAOV75hZBqQa<RU+ygjGe_Xm>NUmr?eFPeUusm=7$w=bQL0
zD-t5kshZvO@^Kr~H@(Q+USCgdC@IfIY~WE{l5(8XNz`~X8^iBr{CKb4*=6DMbN7qO
zyk^BHA>-8}xFfxIQCu8$&q?elc%0-Y*)9F|^PAj4>qrmyxdB(6Ny#=TCu4fKhSUL;
zpI0^QT%sbxsVAEoH_dvQsIPdn?<exqPKKhJg4N+jFK4=PY|GMb?J+SjinITL$!u-=
z&=TJHA6Ngw|NVCU$Nz?kK7Auog!?vU)>x<h&1+NVPw&^+bC?Q*y#1xE{ic=-8J6V!
z-BxcR=n8gjLq3IaH!MdY=p8jRG;DGTT8JDkhbX1Ssm;d@um`WT0}S`dU-s3;lW(^<
zt4F8J7o2K4H+8qLZSVWX8u3@7Ju@Fv1TEV7J2<H4-oNWOo9M6=kMz|R!)3{hC)>g0
znFDc`mZ>5CXTRZH&*v%ESY7=Ph)t4R;C2=kF7s#3Yg=KyQ1c~y!M`QI@T}iJ{L9mk
zijxzA7RJ;Y+Xr?rlLWeKJS{5isDx_VjUyzjj=#vEzHNh@8}Bn4stmtsY?ustp9b&5
zONwlwV)Z^FGc(@YaDV?DiHa4SM#jce*+9u18*X!}96NzR-~UJRB4LJ*y2rxZx1U6%
z?l61#`}-3ds0M}|lOj+jg!>F2MN(~65Q+t{|Hc`c#rgE)#C(~0mG+Y!Hu!#0^!yDX
z*wic+(?q<xq~Ip2iSc!>ascjc(`)EZ3Y{qYz|iDwQ0T0wu2#(O(P3e++xle(pno^s
zx=XKG5vsicrqVKx>eO?^ej<=Z<MT|7q~Q6Nk&#!ur)nwv#zq$`zu)_9QL*!Rmq83r
zm0HJao5Y;q<ux<CzvX1rRSCBrk5*?FhWUz3u`>F`w3QZ)4Je-2vEqts@1W{BKvgds
zzkZ_gDfbP@<<I^392NKVk8j+kw(sCi30?rG!RxBINQ-{%>eM!#MjD(wGfmB!!fuz-
zuATJ{=+~FF%>T<!bGqPIo(Bhe4r^?(%!G=TrKM%Fewx+vXkB^MYZRvGCD%;V)dvpJ
zG(dOHac&&ck5}Q>qbG^)&=wOKQRhMHz8O`_V+u03r82Y4J$=+IL)X^c-rudTe;^?s
z^}X8Odp$E63OfK{cs*M<U?0Gj{5ah8dgs_R7nk|_O2d798olm01DJM|L75n{;~~N0
z)s_SQl`HeVVs-zI{S0h7v;oUz%0~^d2R3hJ(V9hz*V~&%>YTq!404;zEg2aW78S9R
zl2@u*)-ic%9m1oHJGLiZ!pZFG8<T)bKc<=)CnCTyJwJPF34mrg>Ae(<0r7J#v3UO5
ze#0cmeuegLB;_KNDKWJ8OgiLXW(YWs_YK1JO=Pl3{{=}IY+L`IdQnT1$16r`qbo*$
z>)W6ukZiSxLn<6Sg=3_k+9dg{69}dt`ck)A`OyLB@7L^I*8i}Zyjz!tXGh?C(gH*Y
zd>9ETRa#nFR7~uc!|(@^;-dR1pHjFmLy)HCz99l0kv=%L#!{p0NFB?x2$_Z&h*Dpe
z<iI3NCXr*qZ32s-4U^sZ+eR7%C0CK?`A;cw(nSwF63Dscx#6*BN)ZZ{hu3?KZy@sq
zt(TQS9tS#g?vTzG>HMiCE%hrS3inH`8_5yJ)1cBq?OV9ecVKU;B&Y`mz!5yS1sW*n
z*D>g;6vF6qbZtn_IN>tcU02rs_$B2muzmEA(kZ2As(+L-VW7t~k}H7*6iQhsWKHVm
zOB7yq{CFFEBVERfx{jSHc_IF8gO~+afn0Ebo>KTSa(@g9zso()lK<Ft-e=fBFW&U5
zzgm2l3=a<SdC~z;*Vb-HRIIv!xP4JOP%_2HM8<&xKLlZ8zS<H47MA|QFI*2prgNQ5
zx4opYr{6tZ6tM`89kD30&c9vMsfgstrh$@^yrvi9qc*Hz`3*%MGdtaogVA4Jk5bmq
zw)Knqly_XPL(om1w+*aXONWDn1z&3kl(65=2DJ>IiR(yOceT}c6z*=X)r>LWwv5nE
z3v=&jf-Gh!-cw(RdE!}LMo&fa`IS=-q!zxBOqspD633V|ppNUH1EB!Fh`0`EZy3oM
zmug1>Hj|3~nS5wYgFWmZO2v_<8gR5?7<a?;x|5NLalxLMX=z8j`5Bg$`ffvIJ-Sux
z*U~olF+cF~@=A9bjElPWBTkW=nWDUq;^JbQH6==x`fb0DQ>XRQa&dt4nc-sVsvmk6
zCiVFv0Z&N-hK`>U)Ga9%73Ievm_BQ4wLXNE(SPrc$X|8MOiZkK8?tUbcw3(=d7FdD
zjBB8G>-jY}8^}VQu1|IM1N-GOM!+`pWdIs~UhMY20G3;Q)0dX%{&TNp1;G9wN*#BU
zg1tbmDhU_x6uU$k03-uEAW}a-$(%-Cn@7N8!dUZlOG2d&;QdpJaKkbp3d@e}o_U?;
z5f(G*+<s++VX^@YavWl?1(`~Vin5r=LTSRwQ(JO+kN1oQ=6cpN6#B5VZB*u$3PKTh
zI5%8~PtTvOkf1CENH;zDB8Rr9m@<D?8l)3J!(oE68r=2eBU&`Hq@$hAE{X{5IIMGd
z`IX~@>mdjz8i&3^`e<5`_7Li1ovGe>w#+N9fAbBR3EvN&ZIz{;FBBQ**DxdeE%%&y
zHEr`}CQMi6e{fWP!TkY<wMP)R^rU-iyt6y}{s>Zja{H)6h90YJf?VxY3EJH+Z2Mad
z{hkz1%{v_~Ri-z2$4O#>pNm7hPk$(WO446!2ZQUlY}wQe&hfN%=C_g!%He40wsG8(
z&c>RJ8&3;kx!t(Mb4EpKtFFb<`l2>MNP+OAm3NFP0d6>z>~`jv46?mFXRfWbRP)Lu
zs}U@pa07yw5ME=&Ps5S`M@R&tnT?<Qu=K-`rtf?@btFEtSr*O{h}>K;%LPS`A!bo#
z1HW!`H2eGMSyJPVVkYvls4h#JE_!C>jmg1g(j28xmZ&a!01MTMks6wbnM>Mz^$4^s
zjdK9PWND;zMtGL%E>CtW40WUvW+wWCnGj$~0J=io422Oc$q>FTt1_}d_B6nwWY>hz
zG85AgUzTZ42Rp1b{Isc?tMvW<J<w&_h6&r|gX9yg*zCetATKsVNP2crjo~OmL83X{
z*@YTaht!Itj}KXnFAoDL+cmmeyfM{r|Mwnz|1X`l|9|}2xMCC5SI<n1jNTz)H{@JA
zFD)$-93S?&0fY;$fQihPd4hXX6K)*wb-}3L;9Aw&T5F}#G%n(FvEOm8Kl!x8t;qOz
zE^mXGrc0c46=_Wk4gPP+XEgdu5|sq~&}}a(%(NG_T)4KDitc@D$(x#MBzS-(Z>&`!
zlHNei$S~B<UX;wRcTkHDDr4`nk+-p#-+j;9+uLAPgSVkEEi0uek)D#Ae7Gn`d#0_i
zCM|EN_UL{4{iF)Qymoh~H4&0OI5Kc-fd0$S3g}OYQjABxVd?acm2F#{w<<p@R89_+
zhtTok2x1DMYw`~F=$43z?YPI!(2uVUasJb9x%jwoRyl{9z??ho8FSZ`q<DGdi0)uV
z(S?kF)H*da>BTN4HZJqOhp!4{WoKvf>YFXy8>vOobZl(KM5R(2a!^RU%*Y$mNQ&bB
zdF{-c(6$F>nVM?qtC}6xGy3~WfoS7tN5&qS7>yl7^7S}CcfdjK>fcXu`t`t$l;vmW
z(YE;?P7Ic`x3lvb(Z6w{ZJfkmO|@q}Ecp{Z1sn9tRz6>FGwaj!b>3A30AAZ(%KUNx
z?_mS~)U#+=X@W42tfLbjV08=14_Wo9AU9sWfu2NvzmjXRE{myZJNTF4dnyJ84yI+f
zI?dm|KiJpD*;<m4;yi64OsH+>K0ii!`HS5ADxwirS$abnq$VOSacF3W@j(GRR7Qmg
z3sX|z%&r|IhA<uZYgvK7^#5h(;a`}~|9Ft(zw}B4)p9)#m%h2Zk-)dTATO1ZY65bA
zJlL94{&;@(qibndZ<hyJG0UxNU;Ikwnt*3zQ4Dvu5iS5BoP9t)EE|2&T5OiL^rbR`
zS?8J2g>=;2q@I9;gPd|Uq_OO;ZBdG$SD}O(L|{Eam^++ePFB(tqpe?l$^PG^f*nry
z{+HNCS6E^X0cCwmS;jRtvkUQ(x`ReR88o$HdB)AFhnpQ)`7S?s@ZbTZ6lb1_eW3Iv
z5%jT<0azYg_N!-}_MCz}$witOAb)hu4FZ}$M}15oUMLw)mgiluCF-1{!^!zXX~%C3
z*5K;UDA<1}m3AFT+VegmmB6?-)<Fj9^ju{P5q~P(A9*}ivclhjS1>w<>j7K+NP$xC
zC~u;Ygr6GcHh}?yd0ARYLxZhKWZ3I!AS4X}Zf2JMRtX?38QebIw)-CXgcpj+XPdHl
zCH@8d__fsJz9d;cBIHZoe*Ka3V&8#omRR&~%nfEVIJYGL3fc+X%{e;Dh(pI0B?Z5P
z_&kzT1HUj`;{G5$8_Z&ChZIMBYfDSnWLnt$-68@}BmxU3<;u5TJoY7x6aj|)Eyt_V
z)-!46%uLs1J<7?+p(V}VDjkS4wX_^@UYF-*HcRLqXd|mmrlIKtya+d6DKRP6zr8g%
zu{)?*+{E@SQv8wUKN`36zT#F@xq8@~H2X7BFIt9<r&$G9;X+=FrEm=@bM#-S<;mb_
zEvd}c4X&wqOBnG%=gIxgXmB$t7=LS5O^8e1z*1B`5}VxmELVO;!>o1bXc8Q(@2|Ma
zt)k-Oo!pvYRe6EdXfamy(xI_LU8YF8VQ%-4gyh5PEM^n%{hF;W{iXOVq^?~@zzz6Y
zziiTK>Zo>XtO|$T#)50#{z!KdztXB66Pua2VY{lXwoN&k+Xs`TFFDN$ne1u{f*-89
z`*Qlb_@XkjdOK9}?a~WZD(ZQ;zRESZ{#Qb_b$NgPbSvi*FRzL4`fEp8+Z7K;x_*qH
z=N~lM`NHm;?zbM3fTM9io5XqL*6mdr5?HI++S(qFH{=F#ucG(2zj_mO-b8R8HxKqt
z>jg-8N_Dr${<n{|Cg>DLm^i;}O~9Yde!6;IHTU;Z69Yd+japi|+R)2)s+=ErDf^(^
zIDmY5W&4y}J>S)89g(knCILs{?Dq&h8}@mdndjV?*HfRXmdgxMF|Jq)7y_jfh6-Yx
z0%#cTep}bnl+_;Pz|%HKq&_I+V+QrHP*C{2c5W2hdG5jPW>zD9t(S3ph0X0?rqYs_
zd(`C$FDc-N7C^;I;VJYb$e15bLLLgzg79%Zot&4JzHSnPY}DaG!j5vn<41alDE~?7
zT3UT#2M)H`%B>UE37xXgV%LU}YxJtWZ$;wQh>bkD(~vzW_1xu7#bIA`CpBaS^o#8P
zep9W_`en|fWg*_!HkQUs)?)~Jj^&oK`5gDaaMjFtDgSwk78J*_1O&Zaeqx8ZL<iIJ
zgCzSEYBhUIae`Csx{y^<==sRf26Gr*ZpU;=GcsE(I{UYyK1p1-YB8}T9!DqNs&<r%
zqdB@slE3AtaMv4fsCOu!vB@a#K@x7a>r0AK>TLf&?_KAiu7Tb^oF#UstNYAr&*vL(
z4#rNjlten}Cf%6G3ExDwS<7U0|8?-zh;#RMjb(~d5ng$EIcXJ9twDvf_>YK`3C26x
zB?>e`pfD9Lz&(FJRHD*^^FFd@Uu-3zlSx{#Q;xP(LHsB%8X_pd1FZpRh~fmx4FQ;@
z0qeSf+S4GGnuQ{Rh(s<sUY)-8#9U_i@0%X$?(LLQllNWBu5#l;@l=jaAk=zyAJ|F?
z5yp3UO0LP?xSM=3X<W<EGRfO>%7freOZ+XIAf)hdLX(u7AfWdi6@i;nx$Zu5StDTa
zAg|}F&PxR1V{Q$S;AfCsE{`m6_R$_<`R6k9H!Y0-`ac8P|9j!cdX1r#O}5w?yU?Up
zHO+#Aa15}~eyRXi;0cY&#^m@LdymrzEo^DbPyCHrUB!v?aR~$N5cL7NerU&Wsiwq@
z5)K0Tk9(-J&F)`rHG3@1CtkkP10lBrOl4JQ-EiYjKo1prDhaHJ0(~n;_(VV$BR@3{
zT(Fb~GAPssAd!Up`sRnFkIeg8`~gCFDh>WThahhun`MfBag^U_rau-;Ss*ku)rxu@
z@%*}ZStA;|K{=De@I>|Or)9I~{XHUt66$Cnc>I+e^KBif<o8L-8KkvC)KNI{i^KIR
z5g+ORBx7!Ia#(^l!MbDBH;0`&uN=9}6!Y?k;L7dWl`oZU6jRO=jBD=E@E;oYH4e)N
zE8($k_0O!Sv*xIMJjM9>!x*i<xrb%z8kV|?Dyx-Gp4ql#*Q2Y3p;C^9p{ESJvQss`
z&5KuwR~<2}&?@Y3W;KPFLuce3+_^aYWYWTFpM1!3Ql8Trtd3j-z-FJ-nUpDzu&?3j
zm^{(V=)&%Pd_1h!UgFTtPm6D|<avAg`1o{h_r_59>xPCanYL|MGdDL!@h+)nUVH0_
zo|BVPRQYD6ms-hBx}c42=CsMP9}0TX1qTi`g9k_@UU7KLtzc5*_VeaDA1Z&y|05Lr
zY&z~tikg~SpLepew{qI>a&m65sWYK_3zUH8wO?46m8p}#*oc}PR~Mtm`Io@zYR>ZK
zPoJKCp9a$k{1_bcj+P`OB%BNE0vqCzhDH;o)Yh9hc6FYfp63Gfot(0t=!Z+X;R?>2
zIaB$m$_M}V{g=SowYnH$`TbYmwN?BOkT3swbTRE_0DQofe_eRi^h)-hzjcP9T%fe>
zq<4|$id9?o>|u$pU)xdK2Uu?v)9+qhGQ0nDAzkTf4|H;DADv*E>Z^&_Xe+vhDFQR^
ze>&f_7aHar<*7$21%?<5c-rIZ$nU~z_a~eCusbGyqam5ySLUE+i>ZSCICwb`(b21!
zA{03(4~i#Nu3Y)o*vm9sg#!l;yxomX&%p<2br&E1!v!do_|(-EsRk(r#s>x9vv!Kw
za+Z~sf9$F~F-r#l`HZ*%Oe+rmU$uP+G?s1K?rjK(LX!*;DTJuZb3}tzs6^(F$UJ2z
ziDU}pjVMu35s{F2Dm+O@lHoy_%d9d)X&<+Lzu*4;z1LprU;kRa)moK@=eh6ezOHjP
z&htDDCrM{WlA1_Sz-<$rx=ldfn=Z3D8+}GF|39lCAh0Cel=3|MpCt&}N;rNN`q!(m
zA~Mq)|11`J{RVEI|MQbJZ%&R9ul-jC1wu5&w9JPcL+)~ZKZNs7iTC&SPXmKDzL28(
z_!SD(r(0D>ZVsF{?ri2xiRfXw#bt0m|3dR8yxnGOjBqs84Pex`4uY*^r_cDD94b0L
zWo~0<_sUZ89%fQF_t-GuUGdTaJ}pVXiy5LCHDJ)xJ9R1o{Y~3sWHx~|___UyA-tZJ
zmi8I_5yIl)2f#ctjh@{5V7<nU-1_=K-vUo#OQ%QG`lR~ijYCtbn`$!J$J{2n<sl_7
zfW8u8F|jK-IlG}W842Fc2juT%9N+r3PU+;W1NQbJpjx9-XM2}+XnmGNW$#}%s7;AH
z=x56XKWHX3Mb>32Ne*%{)M@X@I4d_dDOmns+$qR^1eKI{K%wV@*1LCL=8(0sx|=Hh
z=Akp4oJF*I`lljMZI`E?iuy~YCcL=q+;3|1Tn!6*-MS%&pUHqxy;np~kZy&#(GOG|
zGiU%aT3K5RQag5OySwkfe&A(o5s4mV{4gx%)U^-FG)AU&=ncWbh2>c()F$#|W@bvA
z`zVGry<^r3DhYJ7v#O}P?_qLXnsd6wCH-bp0Tl67@NZ%PiS<oQbyy55NzPGm@DA{z
zfpyKLOP6ldj4|1Hcn<SS)7yRXIPlXyD9<eb%vN$B)T*6lShdOuJ-o3pNyQ~%^y*dc
z+HP~>#)KLMI-Y^?;a1J4u$_m}g$2oqU*C>oPFXy&q<N5<dN-m~KU-KsooyOTTGk-Z
z+f;ZXHkQ`H!hUhH+~29DqcTnLsXU{yO~b;Wi&1&)7hQ6wMAOh0EEG1X$TP;>#%aez
zZ(P3~2m-sBni_tX6Id0e>5HUnU$MvI+97B(a4LFZ9AXtVk?EG_W;~7S2=MA$gN;jX
zb9kF1zaG>L)3dX+6VyUj9B0R#l8tDV)W&LK`}Ym&h<WUwfNYzqv$K<F-8u?xtp#)z
z=yKVxVFR$=RR@n8c?Bf~p^1lSdcxOnEl^6zccN~puCAV*nNj!hlE#qFWl&{tASD7z
z-x~8vzi0iXa?;bu6}Ob+oJNy(ABNj6S5~TKTU17)J&b?nPDW6_yu=S|f1MqlC$HeK
z)+cE1PkDbwrP@xpSFc`4K_<v^vU^KkU*Gii@4@Cd+5rJ7q*9P+kt+=v`YA~Cb{gmG
z#ol#r5N%3Ujzq&gdSC;l)fiYkPDG=DXe&93?I-C@EfS~%Jp?J9M6Lr$HRnC2dU<JR
zXwow>)UB+xc~A9D^kt2@@6ay(;FKh;E&stKsZoFJEVo_ul6(5UJ&{99nld2#;@HO(
zHH`w1V!>TdeKB)IX)TcYFt@8rYm$Fq())yZY6}wxs%1+rfPWxJE0rD0qF?)k?^>UW
z2G=b44ZMRZ+>~Df1SdkkIY|CSTwI)U#7AGWG_m33>kyj9wB`qs3J9i=FQdgNy)f(f
za|2Ep$Ao(=XRko+%U{4Wbl=T^-=?#d#zp3aN1n|4#qM=8JaJ+*h>tdsA2K_7LiWHH
zC4cv<^mI0G14y8+Q?tl@D1v+kl#2X>4Jgt#2f!gmAEhNY4i>ARFG=ls0kY2BY^t)^
zW~DpSuQYiKUurQKlbJs!tX=%kMaQ@ySL%ZcSIMO8)sm?46essgDjPd{G2KpM!59vD
z8nEsXpW~Dh9Jevi!ekQnu=r=%rFw7sjx_Pc)D9iSoI4Q+j(2`gyY#vjoQi_t10A88
ztn{^|W4r|fLqvhCA-Kov68V`<H>-IJh(2j^nBl=#i<d`QbY8r*^=F*`cX^qnny*t+
zF@{f1(Ga4$cA0IxliBh20I;^ck&#eTH?_aHNlt9L^}SJ_b*K<JRYbu+^I_kSh`l`G
z8uD#UT#KfTEVJC_<YlfdMiq<&SZ7dQ59L8Q?)9B#mv1SJqIdJ1Lje;j>j4{^v=4o9
zn)mhY!SxV<V3N)o<E-4^Io{D??N)4HY`lf|jO|cZqzfdDhQI@yKByBd$?HG4oI8yD
z;yTr<Ot4jIb~i5^m;+UzbHqE}kM52vN%co1D0PW)UJAUB1|bM^0w)|9a2GQ1_=WD>
z>&eN{pc>-m=dW6SzxbMIldQIJ!%;SRbR_L20@171l<HO-n`$fkxFo88hx^FyNB5fp
z)OvA8?Z4{`3U4&nakI1EknQ6v8`OuOIYE>Guz5b3=v9g0$Y*pnE`UvY8b_udM)!(A
z#udZN+M}w!uYhRnKB%Ih;u8Gfz5>~^NK1g{$;9;WvuA9C>(}=}F*Sos1N5SB`TH#`
zE$`aOy7bZ$S3*LJlO$iu9V^PE#K=DX*F%b~5n-Ywk3FUW)N<ObYr@#|GTSCuT18$A
z8l5;1g2^sHC}}v_#I84Ka07$>0-WXbl$4mypDiIJ10fd{H8AUxFM70`3IG9GdUiGg
zfl%f%{A<SxCvFgXh1|QxPudEusJhcRdM<rmq&0NC?p(zRlaKdJae#G$m&K$Pck#s+
zFnPY~Wu{{(XA4gKLT4utNb<n*M0tQGFI@WEN6WS+ClbJCao1K|U43Ex(x3AzkUe?7
zCE+g0N=FF-0*0hth8a`<H&J;csHJHa{_Ni0`>3SkR8sD;>hgf<sCebYC7HlKe;!yz
z&qP|mK>qM6uibsbz#hlPV~1)n54V4R*@d5bP<f$OXRF!jfR%g+tp(BE*zqXGMiK^2
zfAZud9+C3K+YKGZqQRTi{~f%waJB>B-3yC~s>Q4*OWfh1k4C&LdI5FjH>|9z7NEU0
zji%bL21Jw3Ah|gHsge&RMgbtW;xRRjyR~z+xIdwG#HMzS$+8X!7cNGXw_lY1=rp+4
zsimno=v0>HtrhhiJ;nq9R;1JGnB^Oqn`Nm>_n$xKz$KU~tC(rLeEISnqy>p8_)xc@
zHyfFP2z557CS4gEw86g#nA974j_0v3(4m4A3<&A$J}ymd?RFFG$E`Ha0=Tok-rCw~
zqEdcYT>ees-k<eD9daCx&2w@*BsL+E06U*jN_o^%`7?HQ)?m5fR1Hu9ne%YpitThL
z-~zj_3TU~5K|)M}O<jfHO3L?}P5N=sBzX@8@QwX&{lS_iq&gtpSLodH5|r}6u=`dp
zwTPM+F^<2?xPFHYK_HIe2mM;=4E1H=xvOx*bA!U|i&40!+yLulFN^Gd?H0kg&q6Vs
zC0@G%yH*<wI%9(xy>Q$D8vAHeQc2SWpAv$j17VgKoLT{KaaN!yYOb!&KJ+Qr!2YA|
zz0P%_OS%lCQC{(@7#L2Y?ZF5FX6;db<7kip!!I-!96Wfi3fI_}Ju+tE|8uX5P6E_n
zYDaoMnACsd8G4iWYtY@1Cp-_lRB-$|1G_x(a2&zxUxy`d8+^?!1|^1X$wrSH8}%Qz
z_Myo`+s0-)bcMc<Yj;%sHn+C`1bxBmrH#N*GT@g_+QWzU)6-v{+aZ!CtvcXqC&|gi
z#}`9N37T$VV+&LdX25ed7~ir8N*Dj_0VZ<(jn_4L>XLbePF3da)Ru2^884q~$aNMh
zY?eW^;v)3>v4S!i3>kT|cpy2+39mMtIB|kpF2WKFEn(5oS5s2>5UN0TaT=6P0~kr+
zCig0INyAgUO!f5GKp<uUP81qgf3I&Q#qJN~mhxx3wO#m`Xi<N@0<PD45U5YsV;fOB
z!xmLe9~luL<~#)sLBgR@(zoH(8h`hjf_C5jZpd{zkaYMV2RZaa3d}*nvk&BRa_)m+
zcLZLfD9bx{<*$Y{Ns#w=bTa;yP`1)1kaP;k5Zsh?akrI!N;`EOzmR<Mrj7SqggZnP
zR5QLxmw!uw=lI|>Uay}$%kAHwA3e)m^<u2TbWBF8cX{OuHFgBwQaX)I0%CHl0k612
z{=YG|LO4(|*08clmo{M#FBoiR1DKOY*k>P`#U-wT!CMxteN2e)pA?Gi+O>vw2W^g{
zp|<feYkJ|9-#s;vMZ@qbhvp5|ojpZvulhMX+jA@!2{8B6DXUZKdwGWHR(6Le@8kXV
zt1urC2x`AROtgQ2B<Y$|uWRM<TrQn_V5}&&tuyhS>MjmOa!f~5EROEVckbz$y-cm<
z9Weogo*Q&Yx=Xwo4(Ka1_5RWcZSz^^3vJ9^zhT36m7MHsJiU}LTJOMJZ{AVIWafs2
zDT`65L&A?28-Hh+%?}wBN>R8~sYtlW?#^FadZ?uha}SLmg1?mN(kL~j^nC7NUC~>U
zIvEe?a`PDE&a{Nz{G_<sKCjBph-0_O%NJMgrXsHFx6z8<VQ}c^QAPs1hOGrOid8pw
zhH6&svU}rWSu?w&di|XCc7IOF%Y9}N(&yu>&CRzG2Lq_3f<J#QEu}&ViCDh$vS7)+
zpi5!wNc~1zGN#scziINjj`vSwuVH0w&HI^KCOvgrE^-uH@!%EKvO#ZfIPU-JFIM;h
zdoM$aV#(;Jgp?E)3=F$zO@LXn-oLQu6^JL-JvEF3L#Xe8qb11k`a!_^rMq}9hEN6X
zRD4FuE=(R#1(_i@0{?l5fL}A(M1gXA+Tht}_r8_DWisLz8@=23{tOrP^ZO_uA+iS5
z*crY$YHe^(P)&PU*>F)N54LuV)@G5Nt2}q84shnzX*DlBeDsKx0I~hf==GwjPR8Ar
zZFk~n)YqNmKL2~S-?86y9Oq!q+nlmy9<3b7VoSQ+#XaN&6F!*R*dARY4f9<??E+cL
zRrGg+m%qX3o5o<_8dObq3Ez<mNNRZGTnR8+_OrAiRyE2!y$W=JM9BWjXx#R#-CTyA
z7(t|@AYTYKvzP|F%7E8^37iI_+=_3$dGjW}y!;kvX=&g9chc5xmA{i(_#bZ1u3t=C
zycWMhhNUyV=pFpK%qlKy;k)>Q9a28*usZO@CA&fxiZjKowQqJ=xc@ehr=g)C7Y4$#
z;zDuLpf(H&nUJ8Dj3B;r73?CZ?F(x@vDq~|LqdD@ecI}P-?JwjJD#cYj2wd@gu65<
zb{8T(BnCFNn))XDo$IMf<QX!kbbG;5ULG(o?q#tSw*CSc8PtdGmn{Et<F)3A6B~hc
zi!3`ya@J67|JwB)L=9TNVBR{XmX+~<=#U|>EAbH>9bFG+?!~0~oUAMsw8?1#s;Qlr
zA8TKSACJ$sdYQ-r9RW$mq~%drnVDa97kdaz^~9#6?2e{p=m}><mllF1j%-?+HCOc*
zW&weGW5v!rdpImE%!H)vwO728x@$S6r#|tQss3{EXPqSIMX}0+L1DxSX<6(LV^NOS
zHL4SC8L{+WtP$^!PBi%Re1OY7byihS;6hp*@3=sF@+N|VZD{T=wUc8whS7}Je5-dS
zMl!H_{&$+=@ndc=YFGABuX&-Hj5d-`g!6|k|DAQ?M0}m%zKcCwF7%+1V9@jDPLRDw
zv&Vg3C4w_(s4zn<1qm`472RudXhHH{Q&)F-`g3(yM1;FZ_nhlK3c?yLpk`7a9*zXT
zF)!?ahK>#k{9-T+YduA;EQO!RkTR0*nT+18)8HvSc<7M&sZ(5#1Ps9;vw}8`4@ADo
zpJ=D2$}21(&xsx$8TVJN@0EM?^5rv-*zzcPvjCxa-O%u=r}RAg)~(T?8Q=gS@dYv=
zX@zB<j!M|}vG{witX*3N7QXB8@mGKR@Pu^l{U=YD@z}p~<O+h#y`|!!f6kW-7CG~)
zfNF&ebIhY~;p~w6c?pZn&cP8x-V5@oxF6bKOY$Dce*QgmuVI&tSJLn_v{1)nvx_GE
z@x6^C_@RM-N@TqH-$6H?;Qv1$_g{vlB3R4*h2v@dxADLK_v3so?>d>S_Rv86p+O%)
zJBc1|P#YFafVuvbGgTkXDd_OSmzXbg@GgEz0d!@FnEc@3!~2oN*UXdfm)FNk#H<(q
zng-5)53}3-e%!Lop7!QV?jyeZo3j;U)7?N5E`MBfBZ<{|3CF@2GvR*_&E{tc`p}0x
z$A071besYqAW#fxZZ!-#blQr>F+<;*%mCQeuP3(*9Kw2xqUG6BgZR}Rw-)a)&7;o6
zGtk`CWxT5!diyq4UW1eT)*yaMaAbEBx>%IkKBAU;jI0HfDgu)Nzp*VW42pv1cb`vY
zbnlDs7*IdI{L2^H7P{I=^1&GSJKP^;nu-%mhMGV8lmU!iI>;Nn9n2wH$Rq>dM=j!w
z`z>eF)~-N?WrRPF#LnuD1<yaJh?J4tk`WrT3Stlw3d+fGLZ{_*V`H4cxYk#gqchy+
zr(~hXoEKH985NhyZu+>@w=qT`ThgUh>C3EN87NdYU2^niL*pqQF%6mo-=3J`<TKyS
zicnO*Jr&8B70{anL{Ma#8<3j3F;YkP$yH9fPIsk>Kg<3zKQSNQg2-Xv=g&417u3v%
zK#&aggEY(sI(~fv19vN3zA2sMjA;q}3es*+A!9ZLMIgYp_E1;jgT(;LeH9XODQE;|
zXsqC-;l3lXrUC-HWf46x1@WsO$bXWVsc&Eq1RT=}eQLlV`xha>i}u9(SJ-7$;MM4e
z?c2BCgWfJ~d)7}GOu+<k>Ce(95@X77>J}1cs90V+$H~F~Vlp~JeFmX|B7A&&7jWc>
zK=fmNV_ZjmHVJLt5Y*vzp+OH&Rde$?6bQp&Vkk(`^$iWLURo-=w7xxYk1g}JZ{L)7
z9a2x2nXwY+s0^v~(&1Lw3W51Til%1v$z#U^ql*>6SR0-?vlhj5f`CGFKZ0J|IQcoT
zTqF?OjV9q89r^a_5kCu8oEi7>s{Bu<<4k+YSI>@$i;D0P;V`~cpFZ(nPimN&@}NS&
zJKj~W24K!wN~!PnGg4RakdXK)=JTqvxh_mwq0-I)RO{@=tS!)pcm~O?JB9XN$;ZOi
z51N<7T?D_7(18mR%i3ycfeXF(?GMKB2?@dyRB(7N_-RP(*uEVHwGYY0i=yaMTiXU-
z%bT}ug@TTY4cx~}%*^`_9%KTk=@pC$rU>ueUARaJnYpRLaS(wx(A#(J6ofkY<;#~>
z!WPKtLF>l2CtkVR*EvCS_ihRpxFwNP{g~|G#y((TVbMH(d;{R&V7Pgij<J?*DX2qM
zljLR({<<SW`dp9#M?(JX12<y<9Hhm?*^Llmh(=nD)DH{?3yh+oqK<FaQCblPtue4&
zt<NBAP`9+?2TCf1?POzPlW?&ag5LKHG7wKO&J=+6{lik_L0w(%PL-#;IRIU7-Kj9Y
z<n=(9tMTU@QnY0uxCv#9@rddk+Z}Db9Gf=n&`q=BxeACmENrVKEjxSXAoe#cK@%tm
zdiOh@P98tLy`q^y(ME>-q_7E7{k2{sH@J&0h^k?Nwq;v?1>mX&MfRh~8*W*Y`?Wb<
z!#pQ@`ypN3O(+vJ{!GNZB}-mdaE+Wn)VdlG$^O%)9}sFj82)c`ro5k70NTr~#PC8z
zCkD*GV>RvP92~NSJD~DO6F3bjvjnqA9b;+FtvEwC@K&IcGc24K8QJMbebIP&?R?65
z@bkYY5-&I<PHqarm~ip#i_Qs?l=yEbUu{M;#7RX9owU!2o+3#LA8EvJ<(0rdB5y3@
z>Q!cty|Zr3gpFA3P|)S@3*aPBEU#WWzo)r#2@q6mn6sbsF)MaIU{vbuR6ef?T!VGk
zxd{Wf^u&H3U2U^z!h1!U|3(FZnV^q5EQCv>t`mlFW|vkV9opa-(jX(np&=N8Y0xG7
zeC#*OZB-zRu&AiJuOq&R{(1>aHm@eg&<jnrdn5m@mL3E=QHN(CF46q<t)_ti2Oa=V
zS4E+Ro$(e-4{IekzTtocN1yH%q$1~B{ejNcBAc;vD>lqL05CxR*fHz#S&zyly5$0X
z$w3UwrFG#Ua_uT8I*LhrDs<r^X_D5!G-~9Smx~R%ejok|p?KyJGCN?Qtf=d?%_5`-
zA?N1JbM&-}{8E5~Q$Qd+Rz7()3*g==qIAdl?>32t3YtkwGMzVj7z)WUwFgQ28?~Oa
z4N8_k#`?blUN7x60q%j8cshcOn>!K_;JpVAXmR`a1MSvY?5$h~J$Qm%d3eI`<VlUx
zmaxM4NowIbn9G7)GRF`-{YNf;C=ku_(Ev-_XiKuP#7I;42Nb7~`T6@o=UQ+a+)gW6
z57Z%P9Mkc!TSNgVd7+o2GdK1|<{#=&P-A9m3pi2=#B5r>meA9ofeH<r7wUErrbT)q
zrX*EFaG`(mZRs^Qvb7Kcf!SmTt|qYJ_(UZ%YY~A!m;f1ln)HcMa&u02e|8^f;<C-l
zBd{MMD3!Q5fZE!KgNY9^qL5N-W_5N!8Hj1knpg0MZa{Z)#Q?PK(Cv~%p=P^~ka}j@
z>AsgC*jrbPB%n!58ad#jN0T+Zy<c?xCJPKmZHYiKnlvljgsuDTt7pFo)WAj7?v9O*
zH+;-qiCmVAo(5708!Z$ON|*nM^!+|u?3e<0X)hxXA)w%!8;sfLL%FsgQ_w$kika~K
z-R$$^ZCbY-w(~;Lz)GD@`Lhz$Dk1D(1rP576bk!*pKbUAwd`2GhNnH=0W<vv+&V#%
zJswT%W@CkD%qFmn3-kuL*vk?Ah=;Vhk3!(9)62oZ;fi^=U-+23A9n>m(|%Cxw>gpL
z*i*qx5lPhD+F4U+&EP!SHoe}uzBL3BbRzeavIbZ4gQeHd)CII7vmd3DcODmw188RB
zZGu#Ob2~S<vwMDW`Yv{#4KvT#Ja22S`iOGyso@$#{Ab!hm|%RDI(F&j(B`}`=y{ol
zuUBWgg1*cn1V8AxnUJO#4)B3(8YT!tW254#=O>eja!@ET?vCCyZ|_$|Fd-;0oIjTd
zmBD>Jj?j|H*z@=46s=QF4NcDjPBUyL3!@L}qLj%Eu7>os8Q|u*c7HzqaVeDA2{qh&
zYp!XjH!G@0+J=Uk@Z+wCn?n8mzWv^z;NV|>ty+PXzx0-ucFv;qOEUCQGcNj~J`*Q`
z_k$h_K_KY2p4sG#d=V*cq%s4DwoEdqcTrk|B7&8-F*3*&l<x>N2zI}6^QRUgP6<W~
zKuaKdpnhJ&>l!OT;UncoWbK?Bfj`rZJb6DN7n*yz>r4c(oq>?i5E!gjL(j-)67>g+
zg|tMH>ZRVk`i(sW&FF6*05^fF@S`PY0z@_=_td}hX(W8l-+n3Zi2Z-$L(h^mWWQ%*
z{rokZ4&?<43rqf3`SS9zQDuNKVTq9$+@>NLlY-D&F#!0@+lf@LAlIJ6tzqH@km{WU
znZ6lS5}>)>g=H|KHjb8{5XZ#C<m#8w`#R3nOSRQn2}Q2VF%%cWS*xHK{z74n3=p3>
z?~;&Mt2VKYF8cZ?3Y`NCq;&ukM0#6zzbnAC`08>E;2#ivJ#`+#^mRee+kEvYv{@Du
zCGWpahRZY`obvZUJ^QwiE?El$<i^IyDXjAA_wT-`soixhJnh^BqOq(xm+;a#dFUvF
z_DfOZl;|u!6|}Cm9cEqHC&N(c(d~k@5cn2AHVkO~;Fsh*(sCo&2{4={3_lvfXkj(i
zk`=34@I9yBryW8uGOIQ@K?^P*d*K%}iwyn8I|09P_kV}cF)=%dDhetL5>tJn;-)7I
z=l3et)qyA|JTmeX^b?2|YZsvrz|2kZ0(_X`TF1=1r!+bD@@o$3;7sV-N`FvDlamF<
zg!r6HL5S|-;KL7}7yUws;(5{Aj$jS~@J7}o1<_dG&-ywY?surHcq-g&b4qkbzud)f
zej*RR(AwI8!^y&XT{?D>K8?L<D-x=$WMy>57k-DgOAF~Knf#58Iv__R4Mh#GG<}pR
zkkp{lfEIqKpd<=TH=@}B0u_;YMbsl~^raus4KLA|v%%5c)9rw<d_%r^J=ghLA&;A*
zz{TIyR&;KSiM9I<Z*A^(cl)w0a)pYxg<QYRHdCzpit6*=ZJAXHtj<bhqsZ~jO1Q5|
zW@+wuu*z=Z#}N~Ua{EN#XhE(bYMaJYOHc|Gq0T_=bfOC593cpiBB$;`o`my=pPvGd
zH8Z;fChd^W&`2=5d_S5w#vBv~10Yv^^yD5VSNn9D!h83sh9?w`;VDea-nw%KN9>XH
zaF?Q3*e>61-IAOv{T(H!k{acmVU@6`xE2w?j937n`U>WMJvO#~I-vVLta1N$gk|Aw
zUEiKP)GjO>Z%yRn<=v5%TjD>T?NjcMeqM=OXKCk^+YwY}8@;Bu2;JH6!cQd;F)`}^
zOo%Q228n}_S>>fa%RqO6@!dUp_acQ^hl51P;MCcvx<|LmrXFU)?0x$9;Ya24E<Gi-
zZsm7mT@>10+qf7f?kS%rzhD0vF-1+^7HHf$O*W3LO;(b{7T43)ry<Z^NC$4sD8P8=
zBB57NQSsvXR_+wW)|&ea^{esc6Q`D{Uz#m>3bk@Jr>O3>+TKg?yw(eW*CU*($?HE9
zMP}ZZHgcE&T5pX5-+vCNj<?GQvWHj5^q%&PI(G@5X|Pd*#FVC%76YCYS(io)HKK4)
zfkfY-BTY+SjB6EuM(pcqOA_(t?Tsl-h>k3SwpZR5Q&1rAx=*9t-TH=F4h5%@21iFy
zF4f`f#j_RRRA=UvP}!smUn~mV+05E1W%Kl}kKO>&#7v9cWn3&irKUzeb9f_Bg|OuY
zCMTv}pciSOyGzq7yZdn|EMlZ}2pssS{<CF~_Av`+&^-9>w<mF0l2gyzJQ^Xv1@wxc
z_*skcOU)2`$$>jEPUF3ku>Us_62jo`NoZGCM1-B2ZK|g<6eW3Ugn<2vFXjfJGP?`9
za`y}}T;W#{|70{>o1I%kz)(8^goZ-K#L3lu<y(;-Y9O#Hf6gu<DteelmLwM^b^hd3
z+yKKubb1C%FB{?ia!Yv6$Hldv##1u_xl>e6L{L{n7@}rT?7j-!3ksNWV<ylk@CvN@
z_U$U&)yGDzesyI-eNGF)QgY6j{7gl@-*=$nT@g76Rr-FyV=?H{U+4uypmTfQ>XGIY
z3J~d<b|k5I>+3geTt;Q`j#K(tR@RLv*b$!`ay(B?wB9v`BppE}#>yoYF1O<28Sc&f
z>Aq2~NMZr$GeBI~Z_tq`Eh->0_w|JD9T#Qi;kgDu9LzxO^x7f0guyXMJjus%XvsW;
z`1A@6XyF}3dir}<Wiql2>@w~SW#vBeT?Q4dFq~L8<N=W1YGTwM+nFaK2gig{$wxA<
zEBPjFuufy0GJVK?8Vd`He5l<as{BGN+)eh)bcL2KEH3tg?rN1DRZkUq)AS4sARHhQ
zXgDLlBYft5UxmmgR6S9gnnmz$+eQQ9zTwX@idJkK9JF{_SRU-=({Qo<-vETA0tfb<
zc*U#a<&0ar#w`m^<;n&1^N>?dH^m$^GU5a)SR`;5tZKbbaj^kXySscL7<j)15lVV`
zdL-~+lwI4m(~~_p2(8fZX5^xXax2Q3xJ+Ttv|&(Zn|Y4T)OOc4m7aQ^rjencaMiPH
zHGE0n)*N~tK0#)82Ls5Swn2V0;;D#j!E!XEsK#KxprbFbbqVky!teItiLN9#Xu-XE
z^QvbjH*ee+LSm5MZY1keWS(hS8PGE+w~=`j2(TEHU?w-U+kdK1M!a$E{Iz6BuhHXf
zO!OXr0m9IaW>P-;smFE|L57?=Ir20!j+3p1KA_K?=&exn`POZefA&U4k-aWXA@=_?
zXpw6(eit<$!I(iou6@hqE_UDuS@XAN{A_tDUbQWG-U3G%128V4=8yoNCrXwaI}P?N
zr>m0=V4VcnqE;Mu_~HIc!>5wKVRVv}wjdU-L)XbP9_ltx(Ki`RV9zT90V}z?Ay*WH
z(vBb&7Y6Pum{uSbA&rhvI!K|MrM`HovE%~=Sy!O^2psPf6>&n`QB7U_au0Q8ex3qL
zMV5`rfCioI$`?Vao+K2VNSqg<LI^xOg|*Iq#}T_s#|Y~mguh6mh8P^?zZPN746doV
zy}f-@MoHfgK?+I?wJ_OIQ0A+ttyRxBzW=GApp+Clrn#%5xMin70wte+@%+q(Vs=i>
zFklppy=7Z)ywM+k&NOlplQloAw&0E(b*r`%HzTo$eDFX3{Em#!(b_>)LgL*}=1jh{
z?2FS(paCfX`r@5VH5AGKgw;`E<A4a;I#pHG!l_m>^m<Bq4C@dD8oF$_0a}90M@W=x
zrGrpFAVY-y+}>({0I>F1Wo4#GjuYr6ISV1X8umjJOUV_?0*Mvm*)F4J=O0uomvN=-
z(zgKJdL#-)jOwz?^DCek4C+gGFDtK(^<)&~=Zc0d^38qAk?P^tYvi<(;UigOd${%x
z2i!&th$GP<Mn1$NylYo}mWBlV@RhR}2sBocj=;wVx$S#T!l#e}js{R}1@C4M3dmVg
z(>#cb;s5!viTKa|`yT?2$qOaF{;xL5{{gHz;}2cmN%yq^$E3i}KoExx=xS!~w+#AU
DGL`5<

literal 56299
zcmdpeby!vFyDipsn`{FSCAJEpq?Aajh=d48H%f<q(v91~K#-PJ>6VsOQISS!NrQk$
zBd|dFj%l2G&Ux-1=l*f;{oS)3_m)`BHRl)aJKizI``uTNliacOz*Y(hiXGBY=M*U@
zHaSsHY|#Jn5B#Jf_}LWx`a}PM<T?C}*MfMFC;r-UO-jR-f`ZPA_-~!>;5Q=*ioYqO
z&z(_riWuv14BV@-xc04n_Jqj&W8%B_URRU&>kid*rY&!%)fG-0zRY;<dGD^82Vy$!
z-fd*rPqFUZ?On7Rwfzr2W&88thBvzdg>P$o-X2i2w7S}BXE`sM5IP*6(6=I(P@fc4
zXg`|Nq0%xno95c@&2|3AJyB5Hcw+F-;n&v`H>MB0-uCMot_K@Ae|`D(*zV$AU;Y&%
zHMRcd*QdP?|DSww;H4{9o{c#ak-6W0XXCdSkV>iXXS#5hUTuD&D{UoBuT1pwk`P_w
zNd5L7xAXLQ@;M=jzxPp5Q*UPPw-ZdWS@nO_r>w0_tEfFSsqAU|^QNgJD0P25%DqdB
zPV0Vt{r7pl-c5%M-`w89{r<!Lm~@?@^Fp*ge-&}}zw*FT12zRYZVNUd%Twir(^af0
zDXQ#DM~||wvtLZp%x$#H?~1PsV790ZX6Jr?TiFCp*4Wg<XWH<$&b8N@WaQ*pHB2Jo
z=tW$<4u<HpMv1tdO-^u~pUCPM#~;lyj}pepzH7%sM09R#rc~{J|0pOTB2{c<Sl*@g
zpUpXDO-G~2SGx*T>V;>#HS?{~l2uZO4<_k(mVBv;WDTjN(i9I$oV-J=tAMey$7dZE
z5YV93b$T{YFhtL;k#%mTS4@rl-RjkqCGwkXoL?WXrYo46CpR`WHYdn4TnyutuJC2x
zRMI&j>66p$S?SNTuKN6E;jM4`u9?2Nu};-(DAF-%ZoEUS&qbump~F&jr78X*Yf~Oc
z+xAherHe~pj}1r21&h|yk399lF+Oxc$>$#(khyrVg(*b)`I%dPm`!vRupB#<`1P5(
zH<zlmLy^wH!or#S%Gz3Tdpz44`?QFWnV+jN;_M%Tb4!zD;kNZ6B&n^#<agA18`F41
zM7kK6m^P)2&UIMx7Zen%8y)E^$W{?Oa-@8$ExUr(y!qJW5_#cox33pyzu#~0;K75M
zF2^3a7t29vx@I;uHfPN<YHDO{TU69GiR9hgaWKr=+xzjzcIuF7qt$P|o*TWZjk7~%
zXYB%_qbJFZj;=wx`fvYCsd+B(aCt&Vd+1VSeMj^jW;rMKogum|Ndg|rPHhL+Hj1sx
z2ITr4y%=`8xa;+;%_Z%^Vq#^=G+yObL|0~&nqFKfe>xr%7G{o>Al~lyqP}L(#<?Xj
zB!oJrj<}fRo(I=-S^Zwh|5&3ps&3I2588d7x}xJU9M@jr>iB7dR>UP|advo*qHnpc
z*s40Up4;^gB{J0Pnz0W#3Z<}qcBoIph3Gb7g$&pU+K=awMs87x{;BTKlB_b)lA_kI
zvbH=^FE|uon`1Lr_0))mhsVCnC}bD|OsRQN`|fs{=Lu0RESG6Qs^di67r&0@_w4NO
zIC7nSlYdZ9+&*?q^=R?>C=qfsXIY{PdE`nDi5Sn8B7=&ZmR*H>vE!mkV>}iM6GfA%
zii(O!+}XypjW^b92n-FS$tmsZ)Fux%NluondEE1pR6CUsq~z}L^HG(2H(Cokq7zbe
zc6RoUHE&5C4v-i9*j?hPqO8mqQq6?hSzdM<@5q<G5X|;boKhq<FmP`fnO*(GY0lE`
z&&j1%%I}8pTSsMOXAjyGC@U++)`s(^c&@FghISP>rkK=69THJo`EbPd7$>LvczbRW
z1|ujWgc;Ypd2@Z5dgj?j2l%q8x;@uc4hY&M?&Hu-Y;IPreZnQ~wUf@jX*jTHxEXuY
zu7b`{eK3SwD;~Rjp=%yD*z!z#v&AVd&0KRcOiX*3XBq8IFKvzx8(Z7lt8LuG)U`Tv
z71}rX9g)0AuO(3%C8E`oAfMlzf3;1KRW<cR`!O*wy%g0n#qg7sjFDHXuWgxk-uvN8
zZMgmGP1L3pK6L(a9(6q)OO~NtJN{I0b1SCz_<m({r8!CImX(3`$4{9CY4hb?l$7eF
z8rjB+-}`-YZHMHj_U`>KXoCpy^@HCL%@>#d(agJQ7DUHwcyA|zsJ71$0sA-ZX%SVw
z_SiY;oT9%flCf^peR(tV%1Y1LQ&CZc=H&3ElwziZdt-V6j$WL0iCrw3ES=G)oAGm(
zHvT@<=i#;ILsb<EInm+PA3JLBfNsH#1={gy8M>-%t`h|n)&&mZ?HYsad6oyKK1IkS
z6fArRPn*Vjr?pPfVoS<mCel{0CMr(R?kvmf@>m*=Yfm~X&U<@Ccq{L@^3U%cKB_g&
zZY`T@x1efQOT^WT#+$72!=FEYeq)e_n>%&dezcX`k%vXWbs>a<T-2R5U^0glzL|}o
z_RxXD*VAs#EdR`1L3>1;s*f`>S7?^@-rUBbOp+H{iN)+{Ob*pWrVI=imJLpp%<GcJ
z+L~O5J+iq=a%GWBUL4I}<>06rS`@S&`HE#dx7eYk=N^aYUSMOOrcU58c&|QbQX4jy
z-5JcTHQOXFChfm__wHE)n@z$e!)xOy#g-df*gUZ|u0%4X#12-6Xq5;C`1{WeCq%P|
zigsIHRZ5WCF?#KfK4ITwXWT+_u7z5PnkizaT7A2e;C!cTT2#D*goI|PoAaw_%tKai
zwA1G;WkaKUB<+}#jMDZ=7k+$@V~;z@WvxzV^7etn<J9*p29+c9e&n;i3#F!loA71>
z_OhjkBC0UwRorH7@Z`(>oh=!9VqZ^kmdyI+7TXNU7@3%CW?REHNt6j<Syyd0-1ts2
zT_Za_y>KM?#-PZa^Int;!R39&RK8$)W(G63&whODA5&RbNo*Rj2bTGn{NtSU-*k!J
zJoQarK6-SdU?^%^$7;DVeUPE|$9`Wit6U>RMgP`xofPaIFH44JQ?wmUoiDF@R8#HS
zXI>R}yme{ahCgl_tznC`EDS|+N()}=y|H!MwsJ-5mlB^f?++|&`te*Wn|Buv&erhM
zt5?UIf7BSgI?GH%5G0)~VOQBJyDa4WtxCwPkMqpBiwj6gmje<CDtvduT6Gn++F)1T
zobzF8D|+$r<xAyIrVGL9)lnrrw-!eA*38+hyNd+|#;dBbC5P=vI@FCP5kn|{JcF~K
z+xHiXbK@y$y3P`fmGuszt!azZdaH_kPpYf3b%yGrGh!rt7uoWizUd1@%1J90IgBf3
z=$1USr^ds*zPafDpLw95!x#}fk8^QdawzH=&2?XLun1S;O1E`BhsSIF{dkmkJiAJ!
z5L5g!q|PA5@lToJ@*?EG^p~Hvwj^E>&oyspESc|4iMtT8IMy~|?ENwRVtDHOMAY5a
zpRS(dvutm6DYGD@-xw4h-eH=(GHEAu;lj4#K9^N9bS>HSY-{;&RTU(C|9}7jUA%pJ
zZgep8anZ_LN80#JIxS}!gW}!#QG04P+03<>c(LET7AeU@{`yf|&z&5^m}J%z-(F~+
zQYSDJ@#3A%zT?M_>jq4_uaHTi)g{Z*)veQ5A~Y}L47`uZN2Ma%rbznIyWrQkz)VI)
zMoEc@Qt#fq)8&-M+NL@c_Tm29+}%mP0+KR4qZ8{>t$ZtDxJ#F(FOWxCs#(vtjD6y|
z7|JaXDdeb%&_S-{@A;k@GjOB{OJ#@in*B&~X7J4P^!nf(tq1d;54t1wtNO|g8|t$o
z>LarNaVVTQ^A93VYKY0%IuYH}*n5v&Yh{gZc;rY-2UqI#+x@~@_xAs@VYA)x_ir!W
zYpWhP@BJcp4UiyEGdd{9h#c?Z>#Ige*V)iIkEQpubZH_br?Nfk!9ecr`~Uv#k70KC
zd)%@C$5K9j)*tUGQk(fw+w|(64QZ8yNK^LZJNQjMzr7!q#~CWMBRG*f5JfAi*pO)u
zbBI>@vlI)Bo`;8rRENajB=wIoiLusYE2{2`vwn8m_erT5JeD1K>|@uxMy9Jd^N)^o
zWEyzoS#^!%cH6QIxK5VvU*7HBk!R`h?HzU6Je`n3^YX$>uIs#2<f-dt<r0*D%--)G
zZ2^`!!Eov8S(^EijU<_u^*^D%g7f3v9Y+vP0E&{4H^W5T3#Vsh$n6#x8lwzuQ?C_8
zj`x55Tzpp(nMYD;$)-SCS6nCp;hxG{bon!W^L}f0meE7CM)u~Aq1!fnK9kQMa%6RH
zK3MU|a&2{G*Zqn#;h{^?#NtiW$WE=8x3#skCzg5n-5t9gbur+hD;PXGUBv81lr7r!
z$7C8*Xij?VIJoacIt`Dpdi91swl_4!T_ER|ty~0-@ljM#GRkg~VUnE(9Q`Ohbv$su
z@|Z}2{9x{GpMN(YM~)pjbOyor1xMMk&8T%57FCiB07ZWA$|HGA#mdUSvvgu}%0*wo
zEK&!Js)HrCcK5XBT9~kV&UZN;vdlL9_+08vzfvM@{bcw>29NL0?(U>x88)o%S54Er
zlVi!~<Ws)%CEQxmpx^4#i!+;d(mrP7yZY%x^TwM@&QX%`q6>x%-5E&dGb7`=yRKiq
zE+Zq8)jc#Yu(8V+HNv45x-^Tp;vwoEp)HYCYAR|yfh=<)f`U}c-7#=!gGovWcLTj$
zzJ!{j4xY4ZPXwf<^5*9LV<)XCyV$}&01F$Nw?1zid6ZPI>9H^va%(P+jf0~#yI%C*
z@C&!C4)hDVk+Gx$jt=*EF$6OrKINJ<ofljEZg5V2`@mRsz3LALB73r^qBD}$%wOeb
z;O#V%NyVZrdb_ecM`e%6zw~hS*jcu^XwfhoRT8k_{#>qN)Nc&Iu%+AP7#myj*N-vc
zGH<^O4Oz^MwY9qHi66K7+HfY<f|S=iKitIJdn$v$b2-zvHq5AGUUp9PB)e9gG6sN2
z8ZlXMvb#h)iq{f<xr}929Hxm%FI_ZO$<Wnlto-J?dB=ePKC>WZ18;h#-gR^$F1&0@
zJLjDT<h8XUbX~{!M35_6dyuI2+V~D$bD6GWP8+=5|32x(3k9Wk+04~1q1Wascy<j7
zG~eQUxQ~6S>)BF3mDcGPU(pdh0ZnPZGE%Yg%-LlAPEu8pVA+qUIBn<G%bozBeaXzs
zY@{PUgTRKU$*TIUNePDjSyNZnidFR#i(nWV!ap`PmN6G8Mp<XemMyyd4^{yQ3cr7T
zNM4!m(F#Z}(B>HLEKmaOAiFe|Tns;{yCtFOC<lj}?Ux$AT8C+6t(VuH9pE*6n&0E0
zCFD3UD`QAaMRlBqM+tAZM3i!fw_kC?4@})qGzBor=XKXd2q+;Qw1n|lq<(tv@L`aj
z-!r*L!C|C^ZKFiu-86%_b$OKAo^2xUj+oFV@c1vp@p3`xFUVKK{@T)r`qyQpp-;Z2
z)IDtw2uFoTsX)A`Z{4n6&pc_}or-!U1rhm**j=P&_3DEsuPPu6<{DM+8$E?j#Zo#-
zp6t=X(CjSemeOWTnEr*lF6k>6Z`!o!yke-H?NHqg^~rKdG41$8JmqD8(Xsr}x=0}{
zBcQpP^d!WjR01Gw)$AU&b9omK5;BxOhT>NC?Af!S9jP8Gi)Z>1kD*9gB#%<t$Qr!&
z{^l}iQ{dJr^t(KhNuvaQ9vKpGo;J)_jF*elEWOK`a7g^_cj5F++xFegDJkze!LFI}
zXF&-foH2?9tCF4q+k~FNTkghIj-A&fKP*L`xN;MK0cGg95z${@gW6>(SX^KKIWQ`J
zms9_vLqQn;Mpz8{a}vfaGD_k(%a*xguS+Ze9HeRGkECYTZ9RV1)S@Z=5jPJ{OM-m#
z<9r(6ZzNy3*NJi%u_oqdx91;YBsa4Q*?y6=5bk*JpxJqEO~W&BK72pA^7xSWECPyZ
zVqKOKihh}uTC4mNjNl<l1_lOV6I~)6Lvs!l*vAg*vb5W`Z*y1Bd_d`U)58ks7#KC^
zj~xe;;|t62Afvx#BSAIDbn{;+c<%I!vQ<rNx<+INGVrR|U{w&7h%ewkOOBb6j(v;D
zNK=9@_ERj2%2|t%aO+YHlCEnSk<kQfznHdV8MPNVW}wb%j=y-&exg$Yi?-+hzZGrY
zk?my!UdF%p2!esnb?)j43X?@r*=iiBZYD;?ub&@ssscN;q-pU3!q*-+IIsMo?W#-P
z9cnvZVDssL$}l0v^pjSdiNuDo>`(;+BcU2i$8~SpPp3#lO1_>QCCQ_%ZOSF-vP!#r
zedcBN`Rx{wmD#2%Nn($yIf`Tv2}wxhsbxDDM2lsmKVd|ML6Nket{|^2O<F8W4-y3%
z%FQ^G76`9$BczOIEX=>|iz}Qxd$SagZUreeOwc~LbZym@0AMJh)le}IrFF7(PbmSR
ztgNg;M=43UyVHKf!U)0Z@AvN+|NBP(!T*dZ{@U^D>;D>=c8}&DzZHlq?K^r%QNDA}
zlr?j>eq7=7)&DhJmw3v+`@X|pzdq$gR?O!Ar%c|j`}j|UV$g?)t|FuS4*bc*RyOt8
z!v!odJUl$oRg@2iXhN5rLGq^Ic}`~~drGM;QoSYLTCZ)~!G64*ul-qI^+o<J#-grt
z-4Yjxs|<bSF-p5E*lhBxed$Hq(lbnp2-XTgky3c-w8Wc@byBFVA9q=)0fHN1Y);{x
z?ww(DD*yWZTR%}J^Epjj)^Ypxj+jC$qn1K@i!OktMeMo2@bFPbR|9YCQWb4&x2-Xv
zx!znOZP^LwWvedkOXK-fRaIF&ZKUd9yYoT=Pp%0@2}%0UZh5jiKQ9Q{GDn=#Xmxpk
z1<6-$ZINW^rD*(qKkKSRdcjfsr7e;NBa_t){EBo?2dJCtNOWLX85|IGdx=jYqw);V
zj1CA;O1D_(eSIr#L`GgdzjX~|$=2hg{T04i9p1D8+yj;@)9w@XdaKjatijL3y@H)x
zr5EcxS7X5Nogn}p!7ZYu1>JZW3y1^`D6w8_O&ddzwfl}-Lbmp}-n4!HBmQ6k)r6)b
zq+rwc-ZVrJN05oA`<a-TM?niV(>ZoN!(I;@E~*~1F~c(*IC1#~W-0;f$O<{bvjw*<
zUbyDP6MwCxrPSS3$Dz$=5k(uH<3#7<4x;jYv0wSq{XNVzNX`a~o7~@3RD>hug?Dy#
zDw4-K3z}F${QP#^IdZ=kHQ4QDxd{^D*K;}>*;91&kwL6#!Nc0u%d|Pva>VCtX1`uy
z&1=gxkrayH7D7!(wT5S+^>e?S@<>i1HS<HZJ2j`Swz)e-^jif3zkkisGq>E*>+9+`
zP;~UZ`bW_o<V#UaCcThDO}p{id%M<E-yL1*48aJwT3Tnf$?EFrN_8Bt&7yOi{kqF$
z$$fD+0o)<&PRWAyvVqaU(e!Nss`+3q?<hOghVj*A8CBnGbsYGxs|i5%hP^wgqy)D3
zYpsK;^Opc-A2khcZ{LR@xRJne_71_;s%c-p1L!Z#GHEBsXI!%%#PulHx@k?)%j^A3
zs~?LIk=r$OGxhJZ=a|LZzP&Mn8^w`~tgLX$vv)a7-#P*6^s0(40xsd7QvYLGWeu3$
zIt@yc_MKE!0yf4jvHUkJ=MxUMs_#v8XKl1<Yin~Mx2Q2mgD!MhSf9%HX2E@;8e_~z
zY!zG`_?;lmtV$QYJnh&=MU@0l(q8OrE0y^!%%H+23IA!9xa1NwXS5Lt0hA5fjEs%{
zNKMfz^8i&{*mw8fVS1%gqa)2p0icgT^(A?(&O~FknB1A$f~V)%CeEw;3h)7&j5T9z
zyAQ2^A~0miPp%FudSG;P21>e-hn%GiDr!1Z@9xD+imfg9GA8L1IhX@vBn_l9wq1<~
zejLo9W6sVXXeagl$<qwv4GGT6Vyi2Yo?6wY1myM}Nh&st`7nL82k5{ncLK|<YS4yw
zpu(v)+inGyt*tJJ7Y0A#N(SsxH`u}0*fX@#OB{n<$T-|)60MSN)x{_cK2cw)Lq3eR
zQfA^PGSwsg?gk)~v~!tF!^(1UJ|1f;#Oz-HN<r%LXFM({DtcK+{>h2g?e$>oR)WKM
zOyoXPj0Oh>$Ac0x$EIlqz5pLMXeS7AGB6+@#(Cz8b7TO2=e1j43hN7_+~+h{3p$>j
z*+8U8!1%!G!NyYeC0@f1f2CY4^H^El`D|C+lQ$Y6wZ=_PxC|JjQI+;quwZQkUSurV
z99@UY0Di`hzw3a9uLZFp-tK@U_yrsP-nH97O4#_`jJDYDi|k6;+E;T~#d#wNfUArd
zLy`R?gaWq?_mq|S8Dg~Q#uZrD+mB^+j$>_gylB<J`=QDB84`7P*`TnO>7OUhoju{V
z|9PLXTeRy=`5cpadXz_pM^`XRx2$?zUN=Zt#teu*U{|&-AqT6>x29>8YdRkDp%*d7
z&|AHmLV*{^5)dA4arI=lpP|3MzpSciaD5^8HPMu)<4W=8c;ls-$vtbUB$j+s3O18_
z@TeyAg)+fSRY9x-uPUvG0y4*iY>pc1M64xe!NBe=a5ZtOrKoc)xC4;-Lo~~*x&$ps
zim0nGFMLo0A|1P(ThARqDf6e`!o!S0hp|u35~4jc!4<37*ksj9Iy>SgCQ;7kLGQ$&
zup~4c`^j#d$>JFw9|o}rTRl`rS##tcG#_qE#e99pv}~eklvAE{PdW$`B&#l$(X@4=
zYk=IT-#dLt!5|W@*G7x!0Z=PqL`#mU(+E4gz)IGPk2(d_V-WzOrOeZlxH)X~W-P?C
zvXyabTqfU5G)gndmKpFLY-J8d@zhx&FlSYBG-0nKVr$-^wTPt^a*!1e5b%$Mn56u&
zbi?MI1g|jiF~+M-bm=N2sbs_jLLJd{>f2;wX!w0xg-QDT{xps3^TbsNHVUlNF+M&O
zLbo#T75PbSuSTyeCom=faH{GclQtk51z|~N*9l(8@3eVXUtde@RsIZ++VpF~Gor*H
zI3$#$W{9+hc350WkfXzBqzqk#@+G=FB~FYZ#xtnPiUi;-6Xc0twxLyvru~?D>ihkc
zJ*7Gj21+LiM>z=29BVTe6YxWvsAP_AhSs!UB1V{{@uWYJb4n<W$%v28L<2Ec$a6Y2
zl}rajT<jVuTH@AntwT6;+|nt@JRHMA(VnW8F8MJgfd?~c&*Q%TcHUWfB99=bz@Qz`
zaVNK^Qg+Puc#8GrArxSn_!H%uCV{Mv=c!-HHohpbG?v}!I-lvXh^jf&VgSKbIa1Kx
zt(9EGn(<xR=bWrxA}c$4?V?+PY^dUdJCa`S_Py^0Y-A52SGH7t21UE|LgeeCRhmT(
zX}KOK;*7y^dN9u>>XsBP0Mjl{q!{Y(7}p$Akp#V)IxUybB)s@FW*ze`X(_3xwuhs@
z)!WQ=?}m)zy(Mk4wZvzx_fJ5-X$%tMS{Ch9RG|Ql(M_&fMO}S=W2TPtV9*zU91k(d
zd_uzV_urjU3VkW?0bjE%C>;@FsBa#la?;XVSk7AySTLsPmb5<@LJh#~D1uKXAVzHc
zS1$&SDUAwjULxV#vJyj1SL|?^)28LOim0rw=SOrVYgM3n*g#*8^kNF|%y7a7`Eeog
zX+<`3KZLkikm3Z(f1&02?AMQqgCT4hpZU|P8qPm^E9g8u@VM^ImOV!|F4I;$KWu5>
z%@ysrTF`Q#wZ{U<#AAN4t+ceAqq+VQwWD?A`$q>iv#GVYPM!a})Y$~g<JLME6~V#B
z*Rr@e^KRHqvm@Vv#4+|wqG)pl*ZZ1iv7QH4@WFAdlnkPsqe5(tu?aMcY;9}~?^B8$
zIUwYa>Ss9SAQX@W0qSqV*<SX|(1NfZlp8C#n$k<=+Lh?bg(iz*@JWk0(UFm9sPN4|
z!<-on12s}lFBK(H^#>*W)r+zH@~Dul3I_MNV@02t*SJLW$OL<!bt4;gbOK_`CG!rH
z*F-8pm9dDxz{2ZH!UBhc!|1H8`r8YTa6`nI(0K@r3}oeCxBM~VnkT!aB5JFvQJO>!
zTsw96faKp%26w^r6?T$^r(SJneHrq4d05_)g`6+(aNjTi*^tf-*e!tx4qgm5v2H*h
z9vw-^XhqGl(s~^+WpTVx6tyw2^g*njeswBdK02Mh+o^o05L8gPX7`ILw>S_^=|tT=
z5q0-X^X9}$$;BD{0W9(d@9efXfBt;Kex~8qn;EuO8>&)Sh7<8JzuP4R6Bms-`^T~=
zsLOT_=eO21cRx1%r5?MGLz+v3ra9@mXtaQBeC}AA>I9boh`(I@cU#LkI<B5XOjq<L
z<P$UOk1M`h?W*b8X6#FXl%C7qB&C@v_CQ$7-q1?0ezltEQd*lH9#^y{>cYm#`(y{y
zp3hc(Y`op$+ilsVX^*f+l$cdDZd3BV@9WEtoX7*_jfW$odhfn{;7MAa5K^X5q2nY%
zI@72Ob^;U&KLM9n)1;_mR<-n579rrX01&>At&VeWoCNd12mUp)wPV~NATn~?whWXD
zpMT8`{?6Wfx|`(Q9csdmp;fIvKt+4<=FM4)E)?=<<D@jLA^ui;Z;SL43QQC(;yU*P
zT4ujZfp_&FHv+dYa8W!MAyu7i)Eu)K^$2w(KErnpTAiSwp;2|;=~YgPsc>6cnU4qL
zq8fS48adXt0+6jWB!`u^Kql$cZlcz{9HarAuczN92c?z9_1ldzZ6PSOQ+%*<!Y50Y
z>I{vH2ql`%7P*#CXk`%zE+ROR=1^?TnrCB!gd#wPj*VR<ZmwyVml(6^5Kev~-ebda
zQkBmRH<jq$!;qpB;RnZg0?`86U4Wn86>JOrppGn~3rQ}jsp?nKtUtYYwg5N-%<Y<L
zP+3=ZQfztZ7N1-~t?>*^JqoQMdszr%#jsAO3jb5Jq^TGBGZpY)RnD|(S%-RiZ?^^G
zpRz)z_wW$ZBwmV16{L5cy%cSKJNdaK&-KseEII&_d9J+K7!Vq22Icj-VB{BY?5dOs
z((#vK&WlEKPhIT+D{Ye-&KXsre~X-zQ6u7XgzP@?#il?O7|xp!pZ7{}l(X>b;UksW
zj$QWaMv+~7NDEWA`Ec--VW?HMR_q_AFJ_jDIFXc;bl%_jd!Ltk@b!6p>(*3_hDESI
zrgXk#%sg*)A+}5}i&FCD@d2^)-iNB4s#n&%STPSIwm;9MIxKdE|Dy~>y<oQSLYhsH
z<K#|F5@vkmeM^C@NlIsgfUR3FbPMATj}NI9An<pX6kL<d+E?c!?B%<k*Yq*hd#4?G
zQ;iom7as8_WAwE_)2#C?0u|9J3d@Gx7x%!y&diQ+ViZJ21)Qdys!V|E;IyUfQv{k}
z_4)qYDd6$rzh+gqo%yW0g;REV?wS4$n}aoz5hENAvo>@Ugh(L>C};`h$~|}qOD)X?
z5MW9(Bp|@xVnaXcu5lt1t_#ouU*?rt3SQ+5^G=(PvXSHznY0vH%e`!^Szl4p`xz27
z?t~yq`$_O(*?Azw((7Gcb^z;`1(C^L3ya*fa?J;Am!|VN018r9{y5nFn&<VXoWdWj
z<na!YiB6x~)cKL6PM8bqRaZ*sYuBdADbvVAv8_%LrxY1z@tIJ=EY=WL6kYs!q>Lp0
z_ea?&!AQ9#P)kQ5m%k-Hefso=1>@b&q@;yB<iX4;Pn2rA?jQHJ9~>Oi_y+!<uyY9x
zu2-RV4jrg^_Toi@W;%9)vi|;4*Piy=SB0Qkd~_qI*(<LKfpFrm#lLHZgoK!gEOtQt
z*TJF@t%jI2j9TF_SlqxcVP~5LgJZteDw<GAU3_xlOk<^F2eO;9#n-!7$s->n8PcxR
zKu7?Pe>_`O=_3sBlPG{5GZK-IQe;-)&OFVB3!EjfPzo2c)$*-$2tv+(Z==DW-(!>z
zkQp^6Bjpl68f+*f7-JTdWTzITTyqtRg|f93>rj0s)O~4v7e&AKZXknq(XRNs;LNF0
z*gP5YjwsO527y;I>;WQG931jAb4;HBD}b8&-lYfOn~H&fQ(BaHn-*e9ZZXh(r043w
zx_-xyc|HL(BlDQcnZXj66Q;^S$3RJmjxTE~Wostv8QKMkgeKFI1xQtvTkJU5tvK2>
zzT~+!?<tzkX<Y>2Pow%ZeJJPgPh`+rZdG^h-JhLbczb^jQ)Gm23I;u{RnK#U+?uuI
z`YIY+oeqE-QI{33mU^yvxMSZH_WKAmzqlf9kHm_K&UOEDU$&o$0K4`}1!-w%P1Miz
z79F-P69WSSpOV|h9V`l7zC3l9OCl>BSP_)?4>s;kDhL)+%bOR!^;g8~F2@3*xwb+u
zDi&?F!b*Bpg~Lb7MzJ$Z*n1$YuN#%VcrkZ(`Y0)b@)WC^v&7-3zH%?EwJRQ2O5li6
zL5Rd9BzQ#(EKGW?r9fm!l8+V@gp&ZUn60SGs>G^n#X*{RPcu_R)2uL28LFD5_9z6(
z?hG7|JA0ek!<I3SnX8XJyMZXyQZsiVeWs&m9wn?fVPp2EXb<ohd;h@Wq7?}{C3JP8
zTJ9ok#}K)?%t)l71^&3wTny^fM|>sM`_B^!F_igMRtS;z@855AR*jRPvT$qnA9A&_
zN)HMgZAl49NA$h%%{ADOd8La*>ENb^xrUwaQM4TK6@HFKCt&TUU~b1aL4Yz2-`vJ=
zjKrmqW4U$f*3|uXDH-T_x9Al)SXvYetW;&YyXzczmWL=S_J)y}ImE1^iqXGk2n7zI
zATkc$XO5b3w$4yZ3xj;lDvgJceRaJ(E}&<8+dei0)YYhnk_q9V#C2X3qO^)J*x5o@
zHQZL8s%yN|=GZ~QeGc%LsJQ`26K3j!>P<B29kv;HyVS>8J!N>yvRf&36tzwv<0d*=
zI)vYAM_y~~jH<k2O12S(G_)Z-zwN53FMqwXotqhwQ;&UA<qL(~^id*#;5BXqbEZhW
z-2;IPQr+88>mIk6N9L`mG>WpE#Zxy&TGLWsF*2)*5NL<kiUBBgiE%#KAv1SO5Fs{K
z`{RA>vwf?+F=AMN{sWF#oSq00$}AdR7Pj*BO)lJe3ovXXx07kl)S$q9KP}jJxVgD!
zyIm)fkgbUkDzqDZ4!dSDk`|$oyC1bf^=-`57Y${VJNF;v`Y5%i5;k{@e5=UZF=x-V
zJj-zVQVa_sSklv{uUuf*fu#OHuwGa!8yvaQc)9K2*OK(kjHb&`gBRrLR6^UOw$i3_
zK$DJvu1N?9pz0$$7KfRE^j<30U&^1Bn^60h_buX22$)ySRJ$J4JjiR>n+quCF7j=c
zOwpYVh*%7be7HTmrOs}iHfnQDz53DThW>n*X+YPsT(=I(`~`Nxx+%@<dfB@3C0A@A
zyiLidfi!)Er|-e|7KGr1Jm3<^Lx>5ag-aR$5M4Ia?8GQwb_2l&P8DUqfTEhAtyj`~
z0gURs+_4EihKVO~Dms|g(}^&&+%oY^f|+J>WS{Wvv%`Zl=JE>jN4okH{3^nZ9BOvz
zcAwYDn1^V##jFUjv6G0L4kkM9aF;8;|I(i)1J5R+GBIY~JZf~n8x<+EmK?+vx>MIq
zKcwbt1$49TeYFmrFd2ZwX3Skl-ZxpsMVDAX<*3T%&=%M(Ys|kmg6Eh!VE+8@mIVKj
zcfzC7V||mpCJX*z>s8`UZrzu{_=rJFmtc7a($6QYcuV(#yjmj98F`8iIW2<BOC9tV
z&v43nm~n;vX0F!XQROs&gB8UOJbIjVme`=Kew_DhxznOKu`ibcLp%}H=?^e|q)f~s
z*Se>hWgc#yKnp#H$;3vP#dqpU_JWaTyxdQ<V2L)zU7#kIDncW{r`kbW`J-zBYP>(A
zB^)lMoI|*O&Fa;=N484J`lVL8*99+g5w6Rr1RJA%{oAl!(I?pnM(Xx!MzIh`<z+WW
zU~m}zduMm`oCevKYNTi25<C!k;@(?>BENe>A$lH}{y7tCWAYKFE@2>ds^?b^X1#B{
zC?nIjnZfNTSUwhJX2~BOgzAw;CoNjp%E(Tuo9$3wp7Bk}BFwGp=RR!XYn$*$eMuM$
zSL!WGT*$<eT#ORd03cPue@v0I2awfYu?z{EyFFx3Z$y+4C=$G9We+cKJ5FdzD<Y>C
zuTJ<diHhpVCGf;X`A(=+(i;HGhS*IX*xLHABijuUundzjD-rhWq3F0Ew=hK(momey
zvbH=VraGxpKiV95F1SkUoVY}XF?h&Un-|{%A8`@;6SuJQjqTW_VbAdnh-J~f$(zmd
zFAKY#kE-g^2AyKa?s~Lo!?$JG&iiNcrQD+@+jFZ~H5k5bK2cBq9<uFRAwy6F3T{jG
zqWXWT{uUC_!d$e1QC0@!V2OP*;2HfgpceqoLj8!Iuvq!&el<0)Vl6>HY9ikhyAK^E
zBHWilueIdh{>u-Z1aUI><x5oKN(imu(^&9dTGdp;SBBbBszo?r#UQ{!NeHpyzVg@o
z?&3ZlW+9=D0|9xk48mBN-(oQfcDo4~ZKc)oYr~<zfdMIhQ5XXVPR+dYWoEK!8cTTX
zmlh=lIB^cqH9^^<$#HBPuCbME2)8Ocu3z$q*yaEpldZ1xSGa{;4|;KT$B0dwv&T%V
zr<Vl8XxWpADo1xz*maL;4tuAxFA9U;q~eUqf*+};#XZB@*Ovrc^BHRHWbhk;qp8`-
zMA9ll;TEQxiK-X(p;_RQ^xU#P^xlqEo;e_<7ycooce}3TXOvyf-;k?yBI<st(n5LQ
z(9}8T`IKf~LjXm3H0St~B^XQVZ@G&tptxZXc1Ry;prR@eEguH2We$TFAvHqyux!sc
zZaUo(FhnUt*qnN7fcalUy%Ino-=JGT^$1m`v)twVW0i#A|J5VldtxVs<$QtdVFY30
zgYQG!rb@KOqS>9fHN<x_xPC-Q0<=4kzuvkqTvbdvjUbmA<wL|7_*hng-Iu>rq(w<#
zY}n^T<yC~=@9(l}x(ML><a2+2E}&OjY^-T_u`>)Kif}Kv%#986|I-`#;kvKZXf?)=
z*Q)bW+321<d+e*(^QHVzk;K4wzQpcYI5QSRXMQ*<7G)rIk3jGDi1+)!hS$1y!Vs=e
zJ&~EhGz@nLtJ?NEckZ-)kALlN5>=V$AK+c!dJqWO9VO?P`6ZCGUx^ByJP)gEiis(x
zh?}kb8r`G;t)QewH{TVSx3FOIC9p3zm4?G7OIXqnHKhavw&*wmEuh{uX6Mw*`CwfD
z1gru7&SSf2$>;te;UXRR;E>|-C|j*wH>YYe9Z2Y$+5ArGf}C7lX%lLXl5_*T)(kys
z_nxCnObfIYr#gQS_NYBW?6NA{j+IP;LP%pcqz4u6@!YuY&>RAg^PQ2cUXXNB2!k5*
z=>sRPCgP!k4sZ%Sfdcnl(_<tlq4<3pc@}hoR`o>GlHY^lyX~dBOTKI6UDcRyA@<c>
zu4DNaHG}+)M~p`*;ilsOHINO90~`q^L98q++^A=Gh?F<n828Z6kRCLJA{o|>rexM5
zM`D^6Q8#LiGSL9|5zcR02-BdqP|6e4V2)K+J4+KnT6CssYc2@68x;izU>PgWV8V%o
z1knkEep{|Z2n{I2T+u^^Uek(sbeiaZ(Bkp0L6z~<(G&HCD#7+YR2Q}`U5HmP#>DH6
zg4Zxia|I@Kf3KmgZW;s$B~K&oYACs&NSnhJTbl>ADlmedsWO&kre^^zU4w`ZY0EJS
zIL^Y7H6rA({89r5nuga@AxW^xW@6(~vE-uzs10Gj=f7Ma1^wathwF<oaB=LHj>lpr
z*i2(McHZ}0+i&1~{Ma#qxA+47;>BDOYTAtPeCwVGvPLmXyvC>+-w)dzFp$$VA=rZ+
z_xVUdcp%d_x5G1*y8>jg;k^**`XiNZD=M^>u61A#NGnD#<LOqQ#iJG>iqPF^*z-H}
z8AJ6v7MLyzm)7~79whA4=I%c#6XgCb-Rs6CCI|63O0XUBL1O*_c{Do-=upK9Qs<eD
z577_uE`kXmiL4@+ae=#O&BG*m(QxVCCW!)Js<G^>cW6WT_KIBE&b}7_($~)rB**R-
z+6ZR38M}_0NA8Ux<R@1;?35ns)$je3gu3D_;H*P?>e{Q~Z|@JjH_p}=B)~|S{oR?R
zPIpRw!}q=n4Aa*OI+{o;8CaNKTGaITkpFmy6$QBbe$a--KrX5p%AGNSE#aotwakW(
z>FJU?=)2TuP*FPsx)lr!4b{inA-32KeDJd&ksHJ*HLjmDZ~l;~UC_8#lbw}SgPWQp
zmrRzn9-)CiXX82?7m{jlt!EUJ=Z(u!Q>qQ1hAskYlROj8I;-%$7P1-m@N|iwcaZJp
zXdE?md%WM@ROY$%*moSx-8n7!Q@e}%?wLSYYF&h}VzILVw1#Pr2An4K-0;ciHLQRi
z%c!qbyGhz_fU8qab48|a=>{$66j=sG)EPaHXy1~{d9E{z1qB8gVv}EZ*C!YJhb#1%
z4@1}8K%uIJMnpv1sOT(mv_4X4H{RYdM0{58a5q{IRFZw5&%M!{l-u{mXN^)|0F%(F
z#`CZBSBAvaXn5aa`9Fb=Lvzv-BK=d#y;2e#@rLX98!Qk%MMpseEiW%~=W87!+#D%8
z=Y6l1IvsUlx?`uq%*gnpz;<Xc%@)3Pqe2eo;Fe4qR<@Alosqy&?Ke}>Z!ZubEa&4`
zKK;4rsa(?BARe}wmHEj|<y~#ds)=3!#OcjW2h;w95FqWwcueYqnYiv`r~BJ4F6BcD
zrRXKJPz7{F*a2R4IS)l13PMprc-?qiX1{J7hnno?0-0_(ov4&xlGd8w>Xm^Q;G4ST
z-7>gL5@8zZuqt9_Tk=*G22DJI_!AqD-Ao9ULvv^p%BByQ=q@QD$&OheP|gd)$7rEs
z@>n6OWmH^Pp@IL{xGrMYmOGZ`&d{fTe~vOuy9K%3wr_X}du?XL&52QBqrhGmi_B2e
zT<9yhD1{)sL(|Lk<zxTOaz<Ww3Q(t$i%Pi8gRl&=o2H#Hf?ubVQQ;q6v&M4-%Ln?t
zpFawilZA<i$y8@z8rF<&LLb$P%s?E#GkuY8#`Y`%N)jc%^CJ~azx&7I9^}_T2e$*}
z*wjf-+*KbwObG?vFVaDwGG_Z{sDfz!<@S7Q>x*Mw1kUK3c&1H43|v=ngs^MzZ9jVg
z%2)FPD%P8FjMP4pGw57WpU;FO>3Z=zwJgA1Dp#AH^B`45C8=rz)o4TDaiyvf_t@Lh
z1xrE`gr5PeL}!>!qn(Qqw)iro-AXS<iQ0otU<?5TD;e`gG26Q%26!ucxG5nV@<3Ar
zXpy6c&!5H}nxKsT;4YyMeRnr(crs0Xqm6Qn=H%5UW(`mOA(&|7rvj3}ht<H`%^8He
zTC+!9_zLJVBcytO<Ojo$>6~Y3`Nds$xwO%+aL~7UaN0$94f4vL>TA$-FQRg6acx+U
zN@WI@i<vYNbey;fM1d-XAI=Y6%z6IZyWjCFY3_@ouSbI;BQ3%3Te-|nSOT%j;@?YO
zxWI!c<-=m(5pozimRmdAaEqUlGef(inGkuS$+%FBY?N@D-DG!{a*Ap#BX$Qg)Nqu*
zp>UfAyEONNVB$iF?;(SR5}YF1ng}x;T(RchL%-$^836e_V&pOW@c0JVRn(`<ZDIO`
z5x-TZX_U*z@g}%!kcRY$=f>RBO!bx5;vx4>IT3JyF%(rV^H(m{V2J&u4Ot(pViff4
z!HU9CN>JtR(F1}}3q?Ch2b=cGhQfGpI;*E_RUId-Kwt2!EY0%+0P}zQ_AMSvj4{et
zLh=&zDw!Iv((@cw46Z-*cqq!XPKuildV9E;T&M0XE@I4XO|0Qgh>V6Y6OBi0jsPBt
zTMFaQDSYC&+|OW)Fg+-kKJ5`*h6SU#j4i-6<AFq`6Fsy<7+uO%$(#f;&2#w`MH3tk
zN&xf*FqdEx5{(E%x?8}0UOyK<H>f<<yJL`Q%(bWQ+7l%4_njz%<1wJbipnu>sai&I
znP1o_&S<c8{u^+WYwNuAv7Sp<hc~n$xYiYZc&fNCU$$m>@ktg$_zaDjbrh#sAXHmj
z%){b3nIZj!;&vT$CunkTEGeR=1j~SK{4p-@m-znY$OC~FP~@f_x}*!u@R;O=x9PAx
zA7^E~fZ;>pDS1w}6YtgEz6BkW3RXlv9VUznSUZfqqK`W_-;*YO#yAZ2?2?<CTVM<>
zc{=rnMXicZ%vxiksUlDBJ++5uu_BtB;Q6yh`6T(H*YfS(?|UB|^8VdY`U)*N`1i!?
z@ADsT{j@^k)A#<nAERmW=R0ofm$>;q-$C5pkJo?h@ch?(vohhsw16!H>>=&=32>FM
zkTRqdLF`4ynFJnr5P@$IKBfK9`+_#Oy@U4-Goze^v(uju$~!XIG0D9eDDDFoVJk-M
zcU~au1EefWL^0zqC{^zF=LDXA=v%dOnJyNCVJPM4)4!{BNwkLAc@q63AgcM-kZO-f
z?$I!5T&c9mC#XVyWd#L=<EtwNj^k%gzT~jag9xuUh$abKF4=oNfv^j3aB#F=K|;H;
z(V7MyFF@;K>UBt3H7k+(-QF!K$;z2NE&FXX8B%7;>RbA*MQ~NH4CjccqJ|{5Mb&N5
zy}#5nJR(~x7P%$bq7q-js9q-sFPgI^gZ0_7Z)j}kT|Lqf-c4#Uqb8zLlm#Z`-|t6D
z@(mfULkE7%^^X?bf87b*`>(esRh|>PD20seftXy$)q~$wl-X+neyoGBv9WuTlb=U&
zmDvv+`n*JJ!@9jcnx$C|d0jUfi}#ci@_up9wN0;u_nC1{IdK`egWEq6e-p3&<n;Xc
zLyG^J+x5TUo0Da7Hr(Sw{((d8<4*CD9cwl-fWHuH9_sV@L0p;4)uw3|Qxx`XG0u)`
ztr)>h>+=w;|Lp~sz+S1hXtv6)-oNLa>EY@b;dI5gDl>P_WW_;Gy_n8uIr)#ms{J<S
zjmrGM>jx#~npJv=IhTrc1r}3B*OA7s$ETO&ME0z$Y^0!Yd&rYu8+>Rz^ih)MSIq6<
zXRn4E)@m2n+OOR15~3?gkr)^JxN`$V)w@BP$X2nP>nQa1gjR7A{Zb$obIKI}O?L0u
z!z2whBylg7gba-Ez#NRys8{f&dp76%_P~=4W9S8ch_!I&97ME2yw~)XTNPh`7v+-A
z(JaFIMLq#y<C9s}Ez-;?oN!0H%6H+N75FA3B;n1QHwbA0h#r^V?qZD8m}iDN6-zVm
zt%6h7TyJe<<;P1}cB@;$05-_$g}Wx@LARrqKG@E<D;i-EExpu4m_*nD0};5lYpE$o
zG3NXZ)=;49kH;i!6_M?p)4?ea_qdn+h=F%R-Pbj*Y90YJOS4MI`hB)3C=Qk%G9bHc
zPs@t1NO%)^EtM&wtx4eZsPol?CB9K-Pr<Hwg9-VBs2SdiQ|D(&UzK4hqDqd-&o4EF
zI&gA0*W%(mPI(yhA20oX!6@(_&p`NjpZ}NADUtMu4~&qGw_fvYr5D)y(f#eaca_FM
zZ6W1oSl++q<BCp@s(gO5<CSysL=@`zc@{%L!^h`m$E)ifIqbh;AN%4&`yt{gnLjWL
zaY3Mjf4y+~e|4bOqDzE)eV$i!oLW^5uA_f*Sc>AzM#(ov|9C86sJ_wu{Kr2ZY%TR(
z@3ZyTE!JzaRGXCd(w*DiaOt$+p1=OsudRM=%K?SGh7bSVedpL;hYUw!o4O_$1kCm{
zwxu);e_0Y>9@^WbGQXOvm~=bAx@$ef_hajSL$0Yy8-E`a(i`*VZv^*Ba@X%uLvHvU
z{*Brw7$jc(K5azw!HwSl&Ta3y-|Y(jdKab|7++t5073NCgPFesDj=}DPXQ{e9V+!?
zShKjdmv=B>-xIGJX3Ix=;n->#s0^^5>V2)B!OX-2%qC4Vu%Oy|c7x&%_6<MZJ4NZS
zXQ~bg4KCR%L9G*u`XL!UF7EB(uyT+H^&gd(>9^1Ci1vW|3%{T*h0^zT*IKhp>PJwb
z2QZevds#S^RlRrr{=^$MC=mE$i8B$lmp7mo4un@}(45#vc)g<a*fj-~%GW;@>Q^$V
z0;X<CN=h{_9E{SC-R&Q>60|n)iuz2wZW4ASCg~7u8|kD`qO$kTff#=viQqAamvAlT
z`tHQUW73UieNr3|L~eM@NGD(ud*1xlSWw8%JoHK>?416wD4IuWLV1$GUS-b1h8Otc
z$+2PIr}=CIu?GFMtCSy(D|C9~hEb{Moqom8=4NOZ3-=Xu%D2?#O9%Gti-&84Q?*~?
z?v<Cp?E4<->+2V*U##8o=buwc?mM<`w__(IDOEfSa-t_Ouh~7*jb6WDLrGqSYSB`e
zgqseoMe;HGXmMRxIgpetzwhBY))g0j{)}zf(aUNdd$LzcGbkvOz4SH~?>NA#0F8<0
zh#DrGr?Juj;HG(`8_|LrkCx0z@@6-)*Or%{t;eN@!0rC@X}kTW2dgG=@q-MVN0#pI
z^_{yiZ#v4fNxND7^?1L<OOpTg@<jWgumo+XC6?iv?P}J#(dL?`PoK_QySJFVZP=tW
zeCdE;V3`l+*6rI<sfK*{Gvp;UNPF)3>n{b?(hTi~D{7W)YF_1i<GU#+jy-c&4(5H0
z=ISK)Gu2^7BBb=pdE`!Bcx_hDDWYg<ni#?|D__uYl4ZUrx0|C}IXK0Y^l#hkm~YR<
z;$6FTEuwTxBP@^6ZqxTYzV~gu<K^{({h279kt3Yw{;N;wp0P#4ot`wQk)|3K7Z<AP
zs3LLh+{-s3(2pyPvz-(EN>N!o`3DmxXT3#FKMD-e_!3wtg1V8R<BLN9?T?n72^aO}
zW!Nb`76X-V`A(3U_MJO_zA&C;YJ{Bh$L+g!Sq>jQ>mL{t#FWx9GWb9^;Vbz#KfhXN
zXsB-bhivn5@^R9orui4m-ms{|IS+IFC6OTyPM4}MyL!*`^mK7!1~cj(m+{Gj-rma+
zl9Cr#zH5oFvR1R&H*YLIfd+gL=}nGD;}a5G3l7&mxPSlpqr8kAYnua<^#xB;P?&Eh
z+u{^=Q)?WZw21`O=roCbx*$e-2(X<FRbk>>;6_?E@epDh--6altq)oi*`{UP%cU;m
zck(h;i_54Ro!wSTGc#5z!iQdnG4!{;uy(VqRSEY=5ToyDe1WBP+CSv3eDeu(FFiWQ
zFXIn5(l==SV;d3MlKR}MqMyev5PX>V$LDwQtAFiTZ1&o{J3u-#LDV5UViM=i%lOwt
ziKI}ms+z)rgJ40_;CiCD`+r{V)tyS~_1$ps)kX+3jk{u^!%kX;p=MJqSNV0(8$;hO
zzB&lRMi_y>A;%DWC`K?Dd^A8@2u`a|`f{u3_x({X;N5@y^%OJ@`gdH5_Wt@s;x6j9
z;;p~m#qi%H;Q#-ZqW<6AapK?B)D+*h8dMymVeR>5%CJ+~WP3Szk|LogW<_B1<YU`L
z3;OX56gM6?EqBn#)K<=!GMxA8$&{m9^9hRW3(-wqN6kOuB$h~5D!-<~Inguz&BJ_A
zShpc9WBr5K<I<rECq-V^hF2`554*dkyHo2NpusyRC{7$)$Ek2>;pB0lrv5IcRe93k
zv*i|9{3vaMf}%5O+`{v#PpFi}ocByAuO8d!)e~7BcU^EWWXU|^pTB4*DC93c_?_tf
zyQ7!?t)+3Mo8uMfXE=V-Vf5j|c9M;qT@I44+Q^5UTP{VZxy(fn5w<x+?K8Rf-k(OU
z&*<fmw3Ktg-O1<a$mVD0qW1nZRWW2noXexn6s0!1xR;7*@STis#@EujUS6zhY&8#C
znrR=t`#dj}edp%Qo5e`cuBB>=<QzbW{0iO^CmOc3IH@{0z3gz$RcF&pe4mZpZ}WHd
zhZq^(T2%12Pgr|WQ75WpM6G|YTpAGjRCukE^!2N0OUmfV%zla+Rfl`ika0M`G>vl`
zW~%Sqt2{McU0of}Wi>BD*F8cCKoy*-YH^=5p4+=*buyu8*g?-i#>vSkEivXOp1ts_
zu4fNvCPN--Z8ep%oPxq*Rw?u*SJltgpO$0!{`fF2ud?FnT(NrjHCw&JsYT`X$=hMQ
z8SAXU+;td?q*11bATt(psd*N2z<ijolG+?R8c?Q7orX`n!B3)G;|P{16QxpdN?%8z
z5dPPcs(-Dq5<GeN)e5Ii-(WqRshOs`E%MQ_8_i(G7bWywU2S8k7&+kV?2(^=CY3g;
zx!m}@I~0mFBrm>DXG_1qk5i2}Z+V|ZQiP0|`&pbze#?#>?E9v^3=B9S;ceNvm1W=4
z?@RSpEnkWb3NBBID<}09_D(!mH27+k-PSpgBIdClO?Z>J(LtD961g(m{`=_Wmrx*?
z)^}eOTj{dO)eNH3o?8$Hcu#!v<lm^-e`(zQzqRuJg-e*g;U7z@?c#4#-FZT!*Ajvf
z9EP!wd&0YTf2H83e>QE<O=(Gn0!sL2h|?+{1001=<bcrl<8|#dk4SGM1T(gM4;jrM
zI}@$fzy`6v`lFo%w#JS>>T|r8nyqO=H?DI0$PsbadOTZjE|qj6bT6WFwIxYO(sQY!
zhcL}$7yYK>xA_dO%V5-C+xMe2m^dd3r?e5it-iiK|5&(a)zCDd0^KVYjeGP}zeBLf
zfZvV$M93ay&|TnCB1tm1$D$iS+JA1cCj%@v@o7Y>8XCexbhiF_lJ8W$n~RAE!V94V
z*S$!TOf&Uj8HghQtHe1$nVFdf;NUL({&`#5_*@vv@5<9n_0gtciFfbcn>WT1^pPqK
z5E&&yeo}|mOc9t0XD9Lg&-*h<{C0yhY)5pW!X;JI43ScwQk(G4u3x_%cEx8z*#^Cm
z=-4wa{B`sv#ncwQeN(XKs1oiBeFK9Q7(R%D7yXsXJlqMG-iS7X9H=ztrX4{a8%TEA
zorL2MCu<}oasMvaOq_UoVg4n$1cM$wehQ1|$TKhcMs)eGNE4?O5C`r>3Og$jK5kiA
zZ^q+nY_h~y!41-HK7_-!W(-4rjaYBMP2rEGnwnwI=Msrdahzz9P7)UvC%@ez{}oLF
zYET5LulB>bagBWvUY0#Zzl&OVtQtCW9}tT4Snf_bK~qc>(fYfWmR9?j!-MppYo}z>
zcK!Gu&mlX(5}mU_`|lD3%d3PZ^i!+wMBB29p<&5oz)#W+BEMQv8etYQ4*IcnUTrEL
zwqxPpY3eXimPUn^e}RG`THobW2MRCZK!*e9m^^&=aB@p4`XgkMe=LC;+vL;B(*tX^
zTZdda#C|x2idMm({^n+ivMVmHTybckRl<+EdAjdA6OTA0vpdoOi(wz%hUUL{YdVZ5
zZq#$F#BjYK{G`MyktW`^Hz+nDf|g{XuHF`1s$%^6SFadk8D6`hixcG$tFW*(Ec?eC
z39Y{&cNK@H)NuUXG0VZj>n5QUYFEQ@=2<XG_{P%*37u4jP|=9jcXn(lXlq5m8L(d4
zkR}_R-Li0opTaog7bigBQ9XOZ5lv9(gRt=i7I|PeEEZ6Y@D=?!o|IxrD}HmaNnON3
z%(olU;r!dn$Mb$(o#IATDE-vQtDhQjBf`VACKl0OlS{j2kAZ{0uQ&AO%ThWO*^hqF
zl#`R=a9n{%{xTTd?pOZfJNE3}tzC^XbB;T%AeIyc<K!K~|M(7T*yHcqy({BSv=@}X
z=wc%nX+??@^DKo0gHd|x&Yi9B)c(h@u|`tI35)KeGSt^8Xo)iiv$2R1_K1VLOmW62
zT-M>|ed)*1*Z@5d$fIrW$9><}Mi4IORcwYQB!6-UXA8j@BO@iHaP?{`3g2e9bcj<(
z!|?Iwu29uK2-=vi9HURuzq~Ke#!_YY-~^8LQ`+}X0<x`E=;#jGQwsP7<?}mTYn;)7
za0qWQYnP*$<$Av3AND-z#%^}OAs@3i4NuA+jeBtryMqc>2~viTo%h83y0vDQ_(1fW
zVb?8w`PLQZyr?2h*n@7?&q#=kJ;uZ&0Uq3+k+2pM{r{ux<tJuwm{t;eNkl^^8V7@A
zNGdI*eYzeVrB_dC6^yoBJ()kMVz*S4v!$7_Npfl*VNE5&Z?!i(VBQTff}q{k$Mmja
zT=GFbqXfrk^ztJUs6kyttW40KgkFdK88v){3Q9-fggJCx;5f(+#@WMYJH+t<0TVdw
zaMQ*>Z#jgNc(e<SpuIJH_E`7G+zyJNnGb(<pC#IX(Qa*&t7(tb9~U2AHEcIR9NYr`
zincBJXKZ+4hSiH+&RFC6DE9Ib4@yGN1`H2>m3}61c;$n8_pX&t@7)`VlV}prq(tPT
zj=UY^4Y&*YdXafkm;^?8$~^CkCR_C+7NQf|wP@q3Bjy7!Pa7H(h~p#*aUvJ+CMVi*
z!>;`#44rB~$}mc!`@!J;JV9JYD?+P&uqic&3+L-fH-dU&&DF7AbT~zurOt*wap^p8
z1tT$`={f$bD@F^MuL(Bx4B{L<a1V5Xb`5YirNcrJ*#3oRy@#n%=lVM;hzd%0FX65-
zL%&>{)eKBkqiB9q#!u0+6OWZaoE^{tT0?i_q{Pr05m-iuCZSKC)WJkpz*mFzf0Ily
zx@V6+eD@s3*+?s*k3qYl0waV@n|G}lAKVJ4hV!$MZP;#9T}~DIv9P9Cu{H_+;wrqz
z2s_1Zm;C(v{9}psh=ns|A3uI1TDnUYM_572s5ljk%;|JTWZ=orRD=UQ-j3ppgpZw!
zR-<7MZ&f&`3jPr?@(2#JOh4EA_1Y=oAz@UMBTi|B=hhGtjWae%;6fb1xgPg7hhrHL
z%}a1ok`Q?9@JnP?d?!zwI}BG&Dr_VLX(iV<I>A~dA<_{kFfc4E8LfM1Iz_7Zbqi}o
zvEpZ^h_cB$dP5AirhRmb7SryH&~AIm{fjHUp+__`Fb>CuTiGnF;xwb<(!_d#%}VLZ
zy}GPUT+=_6U^L%!JZW(>a>uz|?WOKTXzC--2V`ktU7P2c!&?}3;>y|kNo=|<!%T$n
z9UC0>3}x6Jfce1_1!8H!Kb7@X_%-p!h!DwW3?feTK^vycDOv)1-~cc}io#H_^6+Fg
zYZ9S~IQfWRHxYzj{X=A6mu|!vT*<_teEx(t9pgltf8<XbK!mSJB;wc?8M>GUb6S^-
z@u-LY?NJxdi_0htxMIKLfpiVf!z3Msc0&Rnz(@XwUPO~<gD0ZcGY^=ZIHifq&#E1+
z;jX6I$GZWvn+gIU(k@&1iM_jbKZjF`IJ*VBYP!V==rIv;v(k@K$Z+P7HdYDfq1Ufp
zn|BrFVvon;l;97!7mbbMVM5G+NezUq4iR<Fii;cVzxzBUhBzjnHk3ya*xv=F#tUia
zvCSh60Kse%pNJKYGeqTa8$m>OL+Pi*JBMuT8D!MjJgW6uGbvc!7_7@cLj^vJ7l$IF
z@l1>GJi$(<Gx2ltp0;N^dQ`?AULoBPK^)+93C0-Q3A3~yoTtQrWQeX>=)YG0P9zdT
zU|T)TNMT8`S(ibK0U!!fQjN3i!$sJ4nWQ}zs`Z+Yg|}K}<0LX5D3Nb(_h8f77S-Vd
z?xWHOv6e~WvM@bcsuO2~NjJjUi53*4XJIVmH6FEs`%X(ZG4@^r2*i?T#yJdV{kFh~
zwc$iuAv$9^e<~9<M!utGmQfnXo71)gZlJGdR#w5%f-Xv&5=$I5>>mr~5i2|zFcKfP
zUqZ-}QjD|i;*XiEB9hg$YcDWUF<=;pLzx?h19?V-U)@<}d*x4!U3X$`gNCitrxapc
zRN2ov37b%}!`6h%Q+iTT_Ym;^mR>}>V%1iY2yM&Is92wP@9*q`vRJS<F(ocFm5pQp
zwfO<gro`LZx_;(`y#~3OOwyQsDors#K}{$SY5*2|*!74R!^HUs#5CcM5dg@>|H0mS
zM@5}&Tcg-E+HE|xjR6q4wM-zONREb98xTRFWD~In0+N%hZC2F60wt=5!~z8hB%>$_
z2uLVOD1wTBgaRaI-rQiHd&hm_``&xsxc|H{?ifAJv0JFHe|xXJ)|_+A#eG}xas&d$
z1XHU1WO5P$V<)7@-VjTtLxWC4>a@oQ$;tz7H}R|b=R#Kf)p!<Boc0*ePXGC>m;E)L
z&fJ~kk<V!|@A5f%^k_`}S^<GXv^#sAZZ_Tx^7I%FA96EAxPciyCym0y^8v<s&+41*
zb|Pdqpz$1?-0|p+=t7f<4#KS)w19j$$L7Dy>}q~~kwg~y5Da3t=)+4v%-Jp@V}LA{
zSU4f3swWwK@Hcw#Aa+7r)9Azr{Fn4IpZ4KS(E`Du2hN&oFH|ty=)5t2;Rj5SI}`R#
zZ2HQ$@>1S6M8YB&LRCG_oI#}IB3rf;bA2wpu>IqYKj@s4tMgmsp2~qD$d66S;DovN
z=A|5TvfxjD9SjsC(QnRf0);%x7uUj5=`gY@J-B$@WX<PxcIKbLpW)N$J4SykDnd_C
zQ?l#CkgZxwOM{bp3!ybM%4A1yVjL&iX_vGA(E>bt_>lO^4}ZL{(hiRg31%=bfk-ry
zvK0)?+~^cr#5%rYMeI8oq~+O5oLZ0A4*uBD)|U08sQ-`V(^dsm8xVt#`2__9nU!7C
z3QF2e96erMTneA)$Edc*Wmbb1hAFr@37ohFld6ai9>8^ZH`JsQVs5DKcxo;$eVL_G
zIf~t{MuR(Jm&mB@{ru%c<hX9miprFRWj#9j;?4#@>3c^{sU{1S9u&1i!<0H~e39A;
z*nv~j5SDAv0x4p4=pkV3oQS&PNdIV$Q7(xnDK!pze~ROD9pPyIJE#nu%S0)f+*Vk)
zCoN2Zg=a`aXlk@(-$6|?A1)IoKIp_cWSs`cC&*>JecRh1qwVwGK&+aSt}(Z)n$WsO
z+}&-ECn34kdsg)n9i%z1<YR!SP@|C+O~C8f4zW+COMlE|aVPO6bP9>2M0K^K-Kz=y
zc*vIwy3&6oZGmef%#)x&f6a=;)c}nIl2@_LTiS*qKWU<GPhO38<=-Y*E-_T8X9$Ui
zQc{+scTeidy?XV^1`s=K$-$!%7Wug!rd}B&>O&9z9_{t?d-slY?P@?%PH+CCqag=m
zigD`cs|Rna`&}(m-qq^a@X|xt633oCD6MhPi)l%{=eFbAH2?w|P$E&Y6oPIT)`9k;
z@G0@7KIxd6ugsZm<}C6iplof*kD9knNr1l54qcGvFI+(6Usj}zRD=|mxVeGxcOwDc
z&WysI#zW3(4_(4GXv!Cc>n4K7F`tp1qgZDA8YngvTwuWneZ6x?$eGiJEJ6x~Jm4VT
zv8<0De}E`YY;mCGP=}D%=9vS*EXae0#E=X&_~Vbi@z#LYNdGacc(PwzVWZ!~6c#<&
z5a;*8q8dq5&WHr6S}0LSG*XWk*++vh#zP2E6OfbRg8TjV-$`LkQL9v$OhO~2j{WrP
zWWj6@g@6D^0`~6RizLt(b@zD7ZMz{8q1}PJ+N)4RtyCF%Kpn?bE}7$%378X?DJ6a^
zR&#FOe0*8+SZy^)0!g~jy|@1Wxy^`SB5)G2R8Yi<%E=wW8{nf&Ot#D>qlZgm`}X)+
zlYz<z#aLuNFo(+j5T4Z*@7QwU#0fI>0lGZmcDrroPHj{t)!iut$>4b7u+ol`5wcE^
zq_>sW|761-fDerBy?g73ttL4zNK4g~_U!oWw>^U$mAJTbhKL6)C>wpOG-(6A31=;s
zhU&p;=$gZ+b(hF0q2pP3<ijbumi#<CEp&;-KoZ>z{j)Z?cSpD;UAy?f*%AVXG<zkW
zi5RueoX;JsLcL*urjbO9&hZqRd<V-(o<R^vnJ|bH59Dvw@6A-2%W#%iTUL%NIhJO1
zlUSj=e)VdbynL3<+Ux{5pOGXobV7~SwMbCj<qr~hP#DKvx$+Zf;g)SW8zi37lWI3g
zd`n@qV3(<DhD>+K^5t~Boo|k~Iqr>H(K;<_>HF=O;l#+jBTa4VhX1%4a<fvl^q3@j
zDX&*xn)!bDsHhI+4BAzHi~l$h3EjOXBQW$|t5>&%Zrma!#;6Xk8KX70_$B;|V47^w
zo|>8p@lh+7#;x17y_L>1f`#MF=9t#y7um7!=7>=CsS}m{qR_*iJ-s?*h^tnk;9kR5
zwK?j@^!wyWw~eu10t)@=<H|mtJ=L>$vw1+<$IOY(wJGLtZ(S#(>>oJ?B-D2}{Ys({
z9x;WlvDdh1d~IS^&hFUZccVGE)tOrvxmFK;xTaLAMw7IcX&P+XBBB39Uc#c@jPa=J
zZq^aNP{01`_=Mc{TW1s^O5eS!z2j;vEz`N%DMa`8r77zlEP8kXGAqfLJXp_a6Ymw!
z`A>imG~JcCZk+4+49fc_1hdPYndsJZn%!3mwKZ~?W9T(<Gvh3{TRHTzt*)*vf%NiV
zjYb@8$o?U=NCZ=Yd|$Wc>aE_pGR|H`OJ-YHa3(`zoSIYaw@cm102pzlQV{`=)SM{~
z(+P%VI4eTWM8tE+N!+<2X3_Q6Dx3s=`Q<Y+>*C;r^QTAED<fBfKfL|)=`Sgq3m4jE
z*2m<FVjr)Ooiwgl;-vM>`oQca?REYWiI3Qu2s^$Hxe~`RKyOHJ#j<5XlTr2i7A#m`
z&M_?Nbt-UM0;kjG%1_#+Z+^Xw@YU2y)l`Xn^yEigUN5e7%-3&2RvUh=|J}TkT?1@D
z{m0yK|B0Nu^zPNZY#2zALevJZA3wZU;|r)b^jn~5Ls)t)^n{nE`Q(ICJI?i50S3xm
zX-L(UI9bq-I$YdjhtUuo1n9_a3pOD+6D|r7aMWPO@1yi(-~bb^JhF86xnN$?K2SOi
zXhtaLBmMP?p$<`;E*&uPU)R5gmzVxr>;TPW|Gp{lkJ12d@=H7K>Mh)KID%LB@sI0t
z{_xbC+U4*k^S3=`eBMppEx25N0EH|5kr+1D-Sl$l1mJvVbYDjbnJRA%Fpo%RXjwvU
z+t!2R+~CF5&4!mxtlK;y^ImIVGqSZz_Fjwis|?pe@4(HUXkBa(Gd-<WqnfthufL=R
zcKi5$+^+V}Xl7hdV&ld~uVxws2XggpTB8||KIvg>yYMe<Mcze=?AqVmC;AijzGg;Z
za&~EY288=}X|zWmpvT(!cJk!EmYB)jF;&})aT6$S+?9{&h>U5yf{O?pLqiDcJ3C$(
zAcbaMC;F1Hzo#}WKI{0|#wp}Pc6Q&C*1^B!Gkq2?)n@vH%SO6=h+5%N%(Hzad3v9R
zm2J05TZKhwE~j+Xg!SoN5f1G7F?i~!mTP_smn(Ugm2ku?uVz%DS$yFhUeZl7fi9qU
z+_QE7J)Wd@o>dqwT(}Za<s$%1o}~ZG!$ST^t>aCTM5SX^Mw(DKZn%h}$!A2N7gTN~
zI%v@FM#v|rR?7-Wd?(W@@7j`|e|F~XheJV+d*6-d^3kF!=|SjK{BLbw9`ChGL(P#n
z*)>$-^#@w6D6t*~`@wVNsMJ@MmJYywg<3Wozvaipi$y7qKL#Vbnf8@mjtryD=<4Pr
zrGpl#a1FnIe>=x(fm3pkYJfXN(=^UT4i_DD29@JD%1l>$?R&r6$~7m&%U`Z~a9^%U
zgz9<OvX!$de^Wz^R=P~wC*fwbP{zduQQRo-McKvF1i*y4>0ZMY2wK1$zzyx4W2)(a
zt<A|`%{Xq?n(ffVXlPaFgJymDg!#dPm%xYdB}f_K9;ONtJ;hp)4_rsb2g45Fz__Cy
zp4Lw}TEJ^$a<G17(Z=}WrR#V-JBlx2e;F21na0x!9~olo*ifBy{jmM^;g0S<<DZ`^
zE&q4yU3b5MH&6c*`RRv<j;7|$?;KelL)Cn(MvnLx_9F%$kd4`Qu6o8`h}|8;vJ+d?
zZ#>;UDMI)66M<x<KM7vzFF2cdR7xZy!yy4;U#L{~sdjUBcYikg4@k})KTs%IBHdSx
z+^jKw7KSw%H1vnGg{=Gs89nnU{th9^4yn#grTN2Cis)N}@Z|fNuidyo?Bz&8nblsH
zT5nHU5m6~fsJ#{{{D-5+e|KuX5tab9RPn^{3L8xHoB2cHPy(EW_81Ma`K8fR=#t2#
zOBzKmuOlm1!ovJy1d<fuWT|f_JeOBMa3XN)$6Ta*XoWqad0jggS`8xNqCF-a+oZzp
zl|@}cOb5~Znu4U}F!|KPg8~W+=#&0Lj-Kek`TlC!92Bqsu;N4;x$tX_^lD#EEW|M2
z=~Xt8I#)3#6#vE`W^k@;p-2Omeumlbed*3MR1C>`5=(dD2S__hCI%t3XCYxsM6#XP
zjr6uHxebX;=zj*W5B1OBs5y%jOmrnK`DvTt{=geI&Wa&Kj(!KS3waa0PNz3_OTP!C
z|L2X(ukZxVX-O)7Y|8aYqh<kANVY86*|=8w%R@Uf6ia>wx5ba`U9ccw?Jr$iNgf`d
z(-XfgCnJ@p@|4FeQTY(4gqr?iq&y}8WVHCztFL$jbPAjX**`T^Ke2mTR^}=u>(t!X
z<1)8A&(FvYTFO;;<y>3hyr}lhveC-E1w3~vP)}f362UbdpWnU~*oJBQ#DPOoXfLYy
z_b=A#@Qrb#oD7ftK7P~U_W3UFoMk-y@=vs?Zr{HB(YP6CO;-0TvOagQl^YalzyBK4
zJWjyi33|L_x!AozKTI|OtR(UEEmS<PVVO5$1v{<}ThIRjk0Tj|35tlCf@}K??A{o7
zD*~_0s#Ak2B+9Srnk7#$kwsng?mufFe)>t}3sE(+Ay$J-acaW)EBH;MqgFWDGd<YR
zK;ZaA%hr9os`GuT;IpZm&wP)%o{soE2WTIG!y6n~A0a9CPMDvo1_KBaq4h6J;@}Q|
zy!=ixOzFtz2oP)`@KZ*1Nyd`E>;GL)(|6Y5oW-C`p~>5m&*D&&L%(!D^XxyY$l4!o
zY5u!Spnu1Il8s_1ccQHt)SQUZESj&vj{Wn+@NgG4lb|dU#wg{i{zpi<=QX$_VyFk%
zc}#xa899QtMX2~S3>$;NL)uzD56VM@X|daHMKkOsNW~oEkN!Om!ln@O_T@rK$7r>%
zZDPC6uZq<fIYB#60BDUe`<w@3GDBqa=*|}($woIjx^o$D(+9?=m(_>d5LSuCM9F{i
zCd?M^k@^PRn3^OZ$Gwx#ICAj`k}Omk6EJoxHFbg!ApQmTgxH8Ta61SR?nXwMcjjwQ
zYfS7+l;bvlTQWJiTtKFFpsh?AOs1GwwjSIl&>z=*dflZ=^?8)t|1|o{;WTsn>c3E2
z9(Ry2x%w~m&SeCvB1WHNnvRKMR!sGDq>7(0L0#dod_6A`4;c(BaC~(dX*ead4Bc^d
zpYB+m$SrcIm><YYIULrk!|B?~JwEYMbLq}y^LDrd)Sm!p*Ihn;jLt<p<!}k?L;Rt!
z9Qt+~APr;&4B;k3m9K#xC3ffv-a~OfEmMFIi=xDXS^;o0&UZC{CSvR^@>^lzvt>N0
zpN2(C0K)Vd`{T*v8U8f)B>DwFeZB`CAdN{b>MdgS>n_2+KLOtWb|qOloGn|oQqgCQ
z^&}oVkkCEs??1Et(nF{YiP5#+<OmBnu-T5)dx!B0M!;sEwHtBYF68=q&nA(|?gdWT
z?}gL;@CB4*o-{AeAG?nJKotIH)fRD3{SWe>35oS@YD5YL+EIkbDEf5!^~vpndxr?{
z`TArPuoCrLYuK2R5nu4DLTf`Lev)h}We9AEv0}VKFMx8ul{I{ZrtlQo`Q6y-HiC?9
zmrP5*Y@QH6B=TQT8iHJoAU43$pkZSib<c`70z9&T*$dfdV8W2KKE`%lV-kSPL3Wh7
zI*lT2Tm8z-7yBO9goXX|wRc)3cl<>YMczD05Rt6x2Q5tyez?4uLIg;>{9$r&%#lNv
zUwLf=PPe<rxFq0IckzYrd=A&MO9k1mCN9A-2AT;*Z*#MjhK4SR7=kpz)1rjy@A<hg
zZoBzPPliDvwZ7@Qtl)tpsClMGpZPQrJ!p44=?zx`0}b#|;X!7x9TnJ1&YW(Mpj$DQ
z)9JEr5}F+0W-NN=Av$MCU5&0z7UBI-yKRFT3ZgR$qsNjkp6Qnt_liEf2kBm$Y^F%i
zDU{$emL1vR!|-ZPK-Rrk$i5wx+OPqH8;3buf2#18NZ|2rD$0ZJb`uK|5H)QXBQLYg
zkZF17OzC7~HtV4I9X?Fy?p$LikM+UHs$Xq&p2=V99}%#uRMDN@nYl5+bw@%%uGb|)
z-258iS$_Z~)&bN5_>Ku6sOe16aw4zf$RxT5{W?PCKEbkMAE}gJcMPm@3*b<OUs9>_
z26SG-SJapkQqXRTKMNrFCyMb{NG}o*HaCwH^|(eq@lyUMrqJgIO?K|Fd8u{(Vqj}Q
z6ygfPvg?2|BAr6Uc<?p1!-4iLWsdzXzOmg^D*nF8_?OiLRs|qICL17^gKH>w9VVC(
zFgXuj>G6?aUxao57j<#zS`ol?IHf?D2si;%vC+xMkBIx7cy4G+3`ewm2vQtLF5JyX
z7kw$rNO1`1_wO6oF|hP3q?$=+d4G#(m3VH%@R`yH&WQ!@LQyr+osJR<J<2=O6r4Z*
zjX!sHrvyw>V*-yJm}G3*9eA;xxIPo!1H^0?cp7BD>BnFEpjJ(g5}cJ^IC^bL+lB}M
z2g`QSxQN4uzziHQfw@v&KKarK8%(Z1JzR}T5^iff-hCjEjsor_dS0*<l@3u>Q3BHC
zL+988p@xG_3Pk(l`B#G=Gvk1hp8kh_7vDKQ-3kc`%Ptln1=Lf)RO(9(!R(-^pgo2i
z{x)Q1fL&O)N68$CZoDr<H&PcfX@GqU@qmw_d<CurD4rO(!+$Cc(K30srltn$jFuV<
zuW3@gV`l(umF--{yLfRT5(2V|#9>0omVxY<gj!VMDG259P|OoB<ixsbfq`|fz?8DC
z5X7LiZV-_Y%sL2s>_CQ-9gcUzl|+`a93O)Q{$=cB_u5O>S$^BhEGNaT8@+jHPb_H(
zXbRhG?Mo-NLCQw-_r!Hstr|)2b%q6f@If4gc61X5c5z5&)GOV%N8AFbhytby)lzbL
z6_AJ7kqJGBqkJ1SFyO)!VjO+H<B}|H=S$FX@7($HT?&L`<UNoqxHKid$K)AU3*P&l
zLOw72HwDCS7tb5gQKK*Q+-&>q-Y(*CgT#nJrjN4WEbNBsft(FHP6mUxqu8<B^Iu+e
zq)R(F9z{Hgr*ujLj9(`vlMfwW!mts2v0EcO_aUG<va`4u9EutkE-_{VE~>2M)I$VB
zB}2=w$3*mWE$<N~<)uA#@7YQ3PvO;=ZYMtCtzU8B5lNzT&o}L3DJK&i6%A}8VIngP
zr9(ZDRO3;IGcBAVf~>wFM+G)9S9v0q>P-gC%dpSEa`gz&`b^s~Yk;4qB~)|k&9cu{
z0p&~}PKy|g2SUdXtmS64$<aPTbnW6B=rAU({qMP=V+Q|UxuTc$5JEXxNC@UdL`0bN
z3@3yyK0sy^sdMPXH(Dp)B?<@Oi69g!$!G%62lo0N(*kv^3YrzRmj80v{Q|NZ2@gpE
zzGGgCA-K6SO)~Sj)A36$7iSyFI&Evj<kjd>hd&M-N<`@H0%ZXAk60KgH95c8K;#Bw
zv?!RJP|1(Tpu9<*Kx_c;T7QypP>;_mGK9UptRjYKNQTZb0=jCqtD$x8rby$V6NUP6
zyw*mgrI#@GQQc1hrw<VHx3{@Ws}XA5vz;b@KRIj-0ZS9?-EG%j>%?%?PZc{aU$Uec
z_Ph^pv#5#O+xlFId>x32=G-W)Wane6_6Z<@sm2VHeK_xDCR1NVl@bgTDwfA!@WGR9
zhcP<rE8_b{EH7TfAF4o`5aUz;?%GJ`iROejC<Ep(LQ9BUeQHeNiq2c-QZ_C*^q06r
zzUlr;%i{apJIda@(*xnl8C|k4|H_psBAFJer$gO#<T6A$_8zr5Qu*6`S5x3BL>iJc
zqkQ(OHfZVScPn*r-HwD_l%H2towL{;t|5in)R-HNJWr=H)5%c3YOw)do@Ktxv0x}T
z-8tB<!5n{dfWX^PU3u)|1wti|c#qZt6sgV33J`{y)r{dvaIqV+>_RB0?UG+|p6fO!
zy}&h)+_{sSdb{CIorLmgHlPm{1||5Il1Z^>+}9Af=l_{ZB?5YE4%;Qk%)vT&+cn>_
z`j3XNR2Ekw=aJbkjtQ3(HK+jH+%!b2=9#1MKVr_G%S3RcHC9T5Mn;m6OnTV`7&ZGR
z4ReRgQizu-?J<VzbOS!(vy1KjOaH%cMS0Wy|Ai}hTiVsU(PEw}>d(OLj?{~2?Lt0)
zRg92#JqRB#L*$_8xSPrTCC9zIoEu0Y*1x`&76o$@5uX6pIQS)3baj6(Gy$_?Z&cH9
z#Ut<~eE72w37Z*MEQ7i5gv%{w<EBm59RiTls?*?wnT#}5jd$=)Z++S!C<sN}8m<zF
z9dY@>1@gHI<S5Ys!L~Jc=+M2be1AWI`X&7F`Nc08@avHd{ot5M?vYSQOZ8_aqo+=u
zl=7Q6tHvAs7__9hz5hv|uEg0NXcdM8>=<6hZO$`6g_N1d&COLJx<cY4OPK>6CoXP^
zTf5_!Hp5m{gyUp{1cuDn@;-h{v!b&52*TU7V{mmv+KBt{Sf_V_w<)o&fPJVr=~2S~
zfGuhS6G1jciT_QhqNK!`<D_44God$n>%Z5_1waABUocCtW2O-Uuxb)AJ~_b8fK0C<
z00hpl-rPNS`@}kOq~M}U#FBt8@?hS%IU@jOf6!8%;$p=BD2IT*gOVID9N(%{58+(-
z%AtUeEf|_i`mYS5{`%`LV$=q{9i9iG$}q)wopB$?H6dR?fRnloX)fp>&^@GuCKBG3
zzeMvRyf_mPi;<BLv5x(9!v=0c0n%~e*Bc)jDIzocYW)9^PI}p){J-FmA`^s`#@1{H
zDb1CCRIY;vq~xBHS2}r&-Z}vwr4y)<K2GBdfPyt)PMNSLfjMyMkw~S^CH9qubk}Hw
zNgJ6O;Zzylv3{M7tMP#^fB#-BTiRAXzDnk<kc1AOUf)};8Wu%p!V?EWw>E5drV_cE
zK)Izu`alSJqNQ^~NsO9^B)AAAEF?HMOcI$Z{fLqYoHr8vComGx!Ub!H<t1aSm7&W-
zAsfl@?<fz8RqTDf9}eiXL??ke4{BN>ZJEmwz{VNv2tLXA@y8!+FxF&_@*=unq>awX
zLk<`Wo{bSJFrJM*$ys;lA@UxgDkb+g6Na?hmI4b1fhTIBW*&oX?abgfx|YXuRlrGv
zL>A)tPmGSp9s$!uMu2tU#jg%y+9a9<;|OmLc`qBP+0&UE%u*2@5Q}#yXp8T9woPy|
z(O3biHpsR}({EGie_SeLh(}_daP{TlH+;jG)M<mxLITObv)}ad0>`<8@QlGo>_b6d
z<C{vDC6)$8Kti#9^UN;cX!AjA5Py7ykdvSB@!Z)bHzZU>)LVpV;&TL}Be#7^{WgAZ
zy+*tD!`~{M>!{m01|4VQ#<m+RkWe-s<n_9Ds@O-z^Ypl=_a21&^XHeS=xS(aBucZT
zwBiU*`Y(R|uIw0bQPIR&j-Qg4oORfa^@(}anXz&|2#Cba=x#P|3Fy7C#c!0pR%9T_
z$U6J<KUx4w<;8DZngjY>Hn=Zunz<SxG7xUNA*b`&SDMx~e;am(!kwVDi`SH_Yb~9#
zca6|~NhxsYH}P}sGL+E!BJam9Q}tYxDe-Nh%fi3*Wps&SM!%WZ#z#_v3AEX&$=-Li
z%7fG5_(>ztl=X4v1|x4ro=EGAl#um`-&pB2UfOHNryu*y4OOvWyY2_->ew2ioffvu
zl}jcZLNm+aZ%-9V{JGZ+6r-iO4;he&>AR%7Jd9xJ6O(E2pLnU{%Q6qW36wy(LgB88
z*1(=9*#H@E@??k?G0KiPc)7@T#^p#T04sv1paG-2oRw@AF!k4JLBS-Xy#OnS6ph?j
zqj04$J29V<>~SN7lHGTGJOmwY<@$=QrOH9d7A{vMeSXzWxbh?tMl5RG`Xw#Ly$f@2
ze2~+21)DhD(cAfqh;6w`JYvo-ZG&QgOp-^g206$<s0KEDnP=3W{IqOYEmU>V$l^&P
zh7LpjVkR7%NNEWB`%k;0Gd>AmGYuohqXJB!PrP5S?Rux-c3i`7Av{RtyOE3!V`EU5
z@+sOvL4k4$U+W3RxY)qgud9KXXwgzUuUWUN10f$jYu#5-^d+I&_gg_?V&>VcxA&a4
zIsU_mJNkQ$o_+Knb(!SB1seoqlU08`wBGp@@4_3LxCe0`&zmnkq;2}>jODqd;zgFa
zdu7)CLD~1mqFp_wwG^7n%nDRbdZ+4HcPAu=mv#>;d2LIm7#JUl2U=zpn{SQlMjj=L
ztaqdxGt?u0FalCXKNo}=*{2E_2hGgP;tdkLUW=Mn_2}6A?zYl1p#OsKvX$li!n5Cm
zi%uP>(-99DWTyCusktDL&$&&UTeM}{T;vHr?h2nPry->no$Un^tP~eqhjKn^xBZ>n
z7mxSpTh>AVKv}uzw5aKxPn?$NUXSki^1<XBTnklMcFgx{(;hL?OI=fY7xcjsQO?)L
z&$=%eZ+Y3N1zTWvUYPyBlBN8S`}OO({5;HXU4EqCbP4Sf2bu7H{(EIyWS5;RAU72V
zpSdQQ949}o`sy|uJvSQB=iAZG^~i1AFpiOD%AeB<CY$<Ps@1kGpPf~m9aLq{gV*OU
z>FC_()1nn+iRQxz6tx(g64$87#@tWKo!)Bs4+LSmYz-JI5++w?I~4ct5F5#*MkztL
z91&75)%NdC<S-^;6Bef@(w15#UWNoZM0lqEvKkHH&>9u-rV7_rs}6m^$NV#1FTq2e
zC+W$l`sLe~J9zpIRmCNLeOqWDk!ocAY-7yBhnJF5s<WI#B64KU66=?zF$q3}VM)2i
z8c^Ry5a(9F8KqFl8MtOclO{*TXsuA4I`c`qaGi~X|DgtvTT;Ht7Xqf8##3hb;~O$=
zqG$g&WkcoLkAc?GIoEFLHb1H7<M~x*{}0Wl+wSK|_;qTyLIY}r6zx6~eo!etqm-;r
z?73~N+--Cp^2}(1mG~TY6@mie%aeaNx5(~TVNNzI*sbyW-$g<+vnCOk@;OGNMd=Ur
z8;U7&&H=>+Ok>SUmuN5kvI<FcR#2enG^Oyad_RD{Hp5u-NC@Z9Iqj=^ABp6#q&0Yh
z3_F&p>g^W+ip+XT`@18RrnG8z-B7bddp=3+_z3K3{KZQD+qe9OqR}DYQ|Dz*m+<|x
z)a%O~`r4`SS6fx?jJR**o5Qy4Z(MpD@8{<y-xU`<fW2scp+?*!V+W><#|BYS{J2!t
z{uhg7A5rk0Jm-RIZohDt`6fK;EZ68)UuGUmR@lLTY3K%&bY)9E=wpGSbG-n2H1HrM
zF*%$#kXh2c0NyB{MP(v!S;*T4qhiWCapP8g;>;)3=WddH2vHn=zF)If$4$@nUJ>ZM
zh4ZI#TBehm+sK>qq$g!B&IKDX-OnX>_n132hx~NcT793-SGRV}pPfBr<ZYoOThY?n
z)z$FW%P{HK(T@2>WMjjoh0VYWSF|MSiOi-wT6M|pYhRymzA^v}@A>wG_o-b%{rO)H
zNbu+9?b<J%<X_x}agG1^wS5p9@I?VA$jD6>K!vliW#&!5)SLhO0z#M?(b?O3gqDkQ
zRDp8JFgdRHU&}FNL<1Jqxs(#9yIrtg|M^^HW@gH^Og4|wC@4lNfpJ7S`45JzU$<^u
zWIN(^2{O6t;@iu}kI0NavY%TJlQb-d+CZ0Kfw$+^ixB=wI(e0u)cxdX{qEkKrQ*+z
zkIfd0FjLy%q66*zAX>3P13Md=ZD0UAuw$RP2`P3y4yvxN&nhlRPELlyg1A?!K+8QS
zjl>as+FcS^b}Qt++!j3he(!pI{)V23gSth>CUOcUbI!WEw?h!Mp5=nf@=irXg%Tt<
zZV5^L8DAQX@8|!ee@h9cWoASPsFqhvShLh;DTtnYXHamoJ2?56M3<7!pP@s(jU*a9
zh{<5Ll8l_(J%kJCnc>mV4zjd^O@02dhXkGf2*l(IJrTNFyBJ-49CGofmZ^5xEDYn2
z$;q#(-5G4ak}*!4X!^yWg!$qU_P-_#w~2hqL|ta;$=BD&{Gd{pJ`Oe_Xd=5h0x?Oj
z@YKh}I8EN0(20YOvX8F5IX1%G7+>QS`M#TujNWwe-Ndq!#y{vFGD^2Onm;p~vQ=%S
zyI@9it=ZY5<>TgKFV|Y(w@M49$_j3D?P#v5<+&~O+eg0&*T^8ugWQea@TT7np}5{M
z#7q)eU_nfA^%_Zwih$iI%ZEkYT8&0xpBJ{3E19pTCvsD+scY>;3A#FoCPx%HANzjY
zFRV@@6Ue1IMCt1C7!)*!<J5*^SXiGCW>R!43o;m8hQZ)FYn8NkHy$P~gghjwuAnQe
z;R`ryP8#S656Z)Dnnrme==#p8d=|S#K@DEwjjJl(n@N`!;CngNDRdnz>hmAX33n;o
zPF4pPM+jU1C4?tGycvKVnH`?56aNLNzCU>+_L)uYom_6YYvf=!G1|{U-c7C8UA-<>
zASVfF_9lGuEc|*91#qO<sm4!846HmG@Qh6%X&jFYn27B`cp@)>fsN$CX-m%OHSTMn
zqUbzgZEcM{u6w}i+prHjkk<z{H#g_@SW1-L!u9IbUVpdYxCYC-kM~HFiZb}YzMv$*
zv%Y>UG(!E>axmk82C20o3E2Q9Yn~3<r<Kdd12J%gE}#tusuY}D%TqT7!<f+yF;M_H
zoYK7o2$<xuCIiDO0RD#K-tZiIrXE8pTA{cDnD!w5U?T!h7LeD$`<1?1w3LVSkn872
z3#CXe>U$zi9Ud2@m-4qRN%8+ZYc#LVZ@fArKfPE|7!ThRkU|!ya951EJ`K24dUi`<
zan&>l*3i0fsY$KH0#3vL`5vrK6s^OZixs9F4m;pln_Qm@-9}G87ib~N=Ns8N-NPs~
zq|_wA2b7x^0~5Ya@#@sD$S(fZ5-OIzl|})^St;vd7N%Pil0Os^4sev@Hv`u#rZ&PV
zz7hflvcyXza(7NoPs{f_ElMTHgBKYO)(A1&+z;bvtKe@D0n{xlEd0R;clh=Bv>c3z
z%iLb(4S$6wJEnr5OeN-dc)t6DG|Di4fL>Ha#CC&NRaK&kT(oG>C7<li*F>v1oUq*$
za+?spTfC`0-6k!e<a>MJph!wd-38Rz5!NzgMf-w^=G}STrI%ux$WtbX2gAv)yWb)9
z8M(Di0s3^*p!MMShAH(yuspzZ3Q!aVq7igDB2Af{f!)7V4f%PUra8O4?kT=ixO;<b
zJ@`@AjtGzTixR#kvU75DMd+#JGMq18g0w1TXIf?zArmh1K=$)#9LpC-DKSLm)&pP4
z&aYpQil`eJ8jhge^~DJ;M+}!qC@wduOStXr_t&^>Y(((tBXYl2dKT}lZf;g-tVy((
z?wa~~NL8I?>iOwnH-n4Z$`4DEa{BTHck*f;9v(-OzP%#()1NfA3WeqmbKlgO3zh8U
zlp#;_fpnl1GGSDNJnVyK;eJk0!!gKsVGBcySD(HaU}mkikI!JcY=JyEYKY)2wMP#z
zJZ6A0Ntb;6p;?oG7DmUnc+M(gFIEP6u9{@$wo9wgAak-C%hzA4Hi>o1sb|WZG!p3M
zmGIn4s}<DnaDxEEs(jK=xMrQPTGN}4e&}g*8LB$eK^_YB1VNkI5DhA+=7XV1I0J`$
zCMOKDVl3`Fi;g~ZLHWi-7WFIcKb&CQuCGo`T9!pYeCk=I70;WS-Fj2by2TXyJS?SS
zVq%g_9a4~TG&PWV7<sUp%hmL*<9h5!nh`s*=?xF(!MKAhbFBkjMS-ex>&?rq$<MfH
z+Wj~dwiP~mHZhq@hHrUtOiqB;>IB=|$jHdE^fGFrVe&GW_|W36-rnRa2a|91_whTS
zd5%B7p$*qnRUJ)ZTWyGzslGB9+ooZ6*vvOCFVEJbyQ5)0^=47M2;!ZgD@W&WqIfa$
z*{RRT%K7%Gl4s5Q)|K9YU97y?0NI?YMFh>3Csss8Kn2|Y`b%9#*CYGH<e<A0a>pH!
zZ@Zh@ZPr%`6p{|yK>o6z%b#F*vrGD351ZCC%$*-3I;9YxgsDQT-PzY?5giNCCCOuk
z<dD*R7H$W>VM`y>7q++U@;@t&^u*0w9Z|`#0+-6o&;SkL+-y_v*xb6sPpyvm9S+Ag
ze1iElZ7zwH2W+WS4MRh=!ssY63e&=zu8!SQfu656CFe5J(ssx2PX}IPtvq8lUH+zY
zkBNz$g-_=51`bEpvAX5#u%EM!k55grun3oH%iQ)<Pb4O-*d3LH%|7XyY!-`oq|k&R
zHR~C@N<l%vu6MD?x0ktGE?3&e;q^|R$E~pj%hC>YG5LymPPfh2a?g1jSekDhbZ1oS
zS6##n+D%3Gv0~Z$JF~+DGln$!+7I5yD_u#<SDk599YDSCWv<qkRAJg}0IJyB<1Nd)
z1dL9qq_ogg|EoNWC9y68U65_S@^vfZT5tFwdXxrCvja98jq~>SG!I73o$(WwH^?Nk
zdy7-u=5DR3TmEOGeG)7mw+<L6HJ^LDV1ZL&cXxN$HNNs&`REdg>h0~Nh)P%)c8BFZ
z(>8v#k&)pexNhBBU|V?=(=jm`Cdq8|7@ais4r9I9ZzdT$$j`J4q&k?A&Yxd>TA77^
zb!rHaY+bb2#F0ZX=Y{d#l!O*GH8rhQ^`APkp)5qUqo+Est*qMa*~>pCW2=16G|(_F
zRW*Wo(O>kEF;}#f4w-#;tVXy0*M|3tnH$lyVN~AnEg|i;aS5dEb|Veftp8k&hSMIV
zzQj&+3u~pU?iY8g+U*IZ_ik)rx9aB>#~f~P9RI<@oE$~u@&dy~Uc(a2NVCy2nmL8E
zs{%g|j7~L4wyA5mw?sPD7&OR-xi!|eeuRdynDNU2W0y6J*${ZTLjLjvvlic>DGrXk
znO~?Dlg28;@mg)Wqs9*-In6&DrQ-K4gib*mw@{g=y7dHa4AQp(*|H>HJ<RvLs~)p#
z*|PMWwRSuCQ8-hV>k;wGPJ<P4AI~-7V|hX9E{S#l&By>qi0&)<xSLN@hqQ}1d1+t*
zUe&4qdOyPQ7PS=^19_B{yIoqT&#O{{W{R<nVS@e1)lE$i3s)-bTrA{yoSZq+GcR~a
zB|mso%XcG5&vb*y{O>Jl6_A#N?$@nV+WT`{K;a~^$AaeTL>%$KM#8)5x(mq#NV1uR
zX*~{gk}H&l-V1tu{A;}2$v+77Xb1k^wY(Ebh8L8Q-!BWMX0<uGEKd~Ab|-68Bs_D5
zEb-BlZc7OtBO2YhO2*Cz0Q_Ov4x>~GxeV&=JC)Grt@m$9r5AUojAk8W%g~my7MY|P
zOvZ@gV?3q%yjW^}n?lkFS)cVl3oOP<4QUjTh8+^@J;74oU)3L|R|K|rFkZ_~q@*;B
z4vM>4`ci5awlOZJFfG3>U3A%+T<V+svQ_mys(D2+z;CzQ^8t|+NM60ZKD<0Il3Adv
z=7LD>sOJ!tmZWbjA21l!k~BVQ%4|~U|7+zJ*@E%%{;`)Nn*p6duk>x-Euhb7|Df&g
zbhChdA&0}s8R%-;bR$4gPEKx$6uHNDkbG8+CYt>Gq+Xd<)phf`9m;)P7}K~nf0*IK
z3U5bipB625?6rgtD_e$n31><fsl#L{WL~+wZYgCl9Qz=h_`CQA39%)l#wF{m!=Dv<
zekC}<z!TZL?MTU75pHxu=)gU8BNP$~>3XDJgEONA0nuFz;g*c-+l_A!N>P+N$;(qF
zHz8VTX%tMHeT(}ubbPR*<H3a${6an+z+A_NVpStRs+L7hy9V0qXTAuw*IUq6SXkI!
zVC?PfU5%E)RKr{%i_B0$+FEYz!=<A?QXate23D#;#NNrs(Om0@rIzBx>D!P{S>s<j
z9O{l0M8`h_pg}5VazK1PE);%C01%g~aEyTcf|9givFg~1Mx-EK=;>3*4JT<AVUK1C
zX8UH#kokt<XHlQFU)dD+c5{d+jd+CPeiG2HY2QI5i-VyMG{d|ueXs&p5OtDON$fKd
zp}Qd2YD2B<GJ>=!oyoKW)Qn27nT8rHb1u*Jo|30FkSCcqxo*zPu-qLP=och8W#Bb}
zkhz?Qc!MF+G*e6(7BH<?YTkc-)&Bh1$$(iW+LzT)W3Q}O$8PbH@`w~7@+jVApNAuK
zPEEJW`pwMr&H4v*<)RGf&PZCD_w_&)8oCbo=HhY0hP$Yf0~&fOMdA#pO4vEQn*!xd
z<_$W<$$ZdRKj*b#BM({3JM_r8VsMM}mmTkrM((au+KmUWA^+?8J-g@nOV0)Ciifz)
zjCYWthCo$jqiq;b!kydS5ay^jIDF=N;MMaN9^Sd&p$T?@3aGJ4WctSJHiJY~E?~EJ
z@RJBaCv<*t*`g16#dGM3u?}(*l00ThWa1Gb>C<ltS^88yeBQWQTl+gmaXx|bujQ!s
z8Wb*ETM0a7^qVmbbR{eCJ2+yHd2VztYIZ74$l|*<%Twp?4#L{n(Fzwv(UlTsIOkBz
zWB=$VZ!U@khrBL)TAZcTEQe*paq@VXj(2hG-IjnOX=NYeR)_zw=&AgidCU8R>^CtE
zeG9|PczKr3!A9rSAb~|j&FPWkS+{WRL)z?haBwIWUvO^MaVK&_dH&<W|1wVn{(5)b
zmGZ#@)6<Mq!8_Cw_AfU(;AZpNe$i}$X7luI>jmw<PE^-NoOP3RpZlzZTUPbU?BRsW
zijDVWEjN2JP%tB4ZoQu!tY<M?taoeX@}iVE(D=izmu$#e@@(5S?{>Lo@6YpYnM>5?
zO)>vBALhG14e#c8C(ZwP2_ByRgSF7}Nn08hf>IS2k1U}j{3kgrR!}wBIyqI66kz{K
zWhs;&#}pJ5!_2mjhqb3E{cz#rYYG2d801!@FxrB;EejBkOVxq3ZA&f2_PKvJae0g0
zoZ3}8Kx}pYk^7RH`#j$$x)d&0d>Sq(q&5N+4Eu&~U{1f0jDXx95J7csr>mEX&viu~
zuQj$>)-0RXsVU!n@029;k0e?n!$gEYbCib4c!|~%215+92~W=qgcL~S=#dY1F5Bk*
zIS&jE%ucO2qPSTN^5KRr^Jrezsl=Z=7b5~zHj>VWuzBJ19>4T=>vEZTi7bLhWuf;+
zH(qZbl%2apSy@>i+{w>++(3lRa9`HKi0?Db%Q!iU3pbu+xO5~mDYnI`_8DJt&QYX$
zH50}@d5p19hbIh6i||^1p-j6JHR@<a2BXbOsXG5TpO0onBsbLFY%<nLNSCM3Cf#%n
zc5F|tIikTdE=R>hJ58XWH!35~NIoIJynz)`x^Urww6G!&9M}0m?Ese>uk7KBSxu)K
zM>r|2v(>%5NjCeGZ`Rh=-~T4<ZC1qzGZ6R~jip(QT1hT|v%?O5OY-2DTE&O;mZx*L
z8PtYs!yVhK<6gUK<6GLMApfM|-T<!RP@271BX9k&#=H8mhY{pPq%*o@;-wyj79;d<
ztM$cGoGmP3UYjQcFi}z0HD<IiC!BckyV5>fi1v~9vsf(sG<IaHumky*=4NxBS-nmt
z{5ccAPOJmteOh#Zv=1>SNt=E=EUcR}apPwm6I$?6&mg*WeRA)&H^P<3B2%Fj%J{NC
zdgl^gX%loUnVf6c-6|vH+xyZr(#puA=1*$-p&5P{n+erKqwnp5QGp9ofP@ejM{tjn
z@c(vQKxzMJU_<6xDduEk(x%9!9CfB~Q|{-?<H&QQCmmlqJ%z)jK}+|am9vio1O*S<
za{Wc=Nn@Q_p}N`&=@tVX@x9M$6h@siCu5(zud55r$jJDl5Xy{t8X%I7M)JFCZ@(28
zU)Sf-)nnQ-nnFjkOmtI5v|h`~$vv-FpIh$T;*fr4L@Uu`IDh5**EBQ~4@m>x-?3kK
zLVGTe%Af2lBXFZ2PEL139}Go=zCC+=eXaG2^Ss>=1q|Mmy%Q4?sksMRVrFL28@A~$
zT)5EaXd>IP%s811wN682$Ls4R150i|7;<<~owuKXh**6$ZL<cApW|-2bR-9ZMwZ>J
zE3k09iwpRMu&V!alouhgF@gBM8oL6m^|rI=Ks>hS)p(MZ=rDfx;@ZPH#mGMm9E*4z
zx`obXocz~wyhvze>&%)dFQ0&bfO7LNriJ}u^_WzBZq%9`$DST3%<(+_K#e6u2CJtX
zUwBBpT+nVQ&%tWDs6i@n53e>+i~E!;eDQyuJbS(cPSv5-7s=D)G09}WsLmj<sCA$h
zfk-oXuF>Ryit)yeQ0@EYqXV>qEE4~zP<Y#Zf#Jxz%c@hK$jo;@=M~7?8eXZ-^>B2>
zZWd29h}Uz$bf<P`xi&b50tX~n$a)N0S!Xc!!tdqKRs+$2JU|yiLeR9M`~NK=F?Lww
zE5Yff1lZXf5cXdP^wYpGc{JyKbdDlMu<EqP;Uqx}$C8J%Ic(m5tMH!9)m`(qSH{7A
z?*ISW75YE7)_<fE|1E3rEZj5yA1%P#KH_CH_x!-~V3j(ZSIy1ql%eE%`a?eDfV@1V
zm>2oQ$tkrFcJKycW2fwSh$PWQEkYyU-blV8e|{t%SUnS3<f0Vn)5$EOXXRl6xr8~p
z_G)fC^8eBO`tRK7zvau$f5HETjEAhzHFBGpiaXgn(tGnHd>6?K4Wj$(;%oo5<EOrI
zXspX%*|YDKiLt3PI)_;|`21(4lrbPUb!l%g=0|D<gTZL0X7KrJu$`^FmbWFoo@!!3
z^_Mjh579MG%ygkT)~&KgW7tr=%LCKROuk7gR=Yc}Ixw^72*<dxe&D@!P=aj|!*=$^
zJ=9hGj*q@aro5URA|bow4i4X-lM1i#v08UyL3*O7XsEW<CbzR^Py609H^k*<ySmHv
zF=FxIq^j<Zx~o&;01!5y<kjtpc-kI8SC4c&BEKB^_rK59dJCzVRcK%5J|N`82!Gy)
zME7A!uhmQJ9uTB&GWLa?)*|Dc112aJ;}52+zO*P*TeB6?h+SsXi%b0IYkZ!~u>Sse
zwVfl!U>W>Pyn(Nv(GaVYn3k4imC_n1$R<nnc)DinuY3|i(ei67`;pt{q$7Qk3Anhn
zF(XmHuIh}yxs$8$P0=Na=9S?$zWeE=>-2ZKBequ;ocW|Mx{~?vMH4ioYwEr-vvXh5
z9wjO5VUR$s8TaHH^p|&+CA(=ipHE>^jIz(#s!L}$9EzbCC^{vjq)5>D^MWTQYwVn>
z@B+c?E@nzfn>QX!`oqzWkI=JpRDrEf<NR8i+8Y(T!-#f~+r229u`U$BF1LLUfqOVV
z4%}u+J=MaZ`(h+r-7>L3730ItM4a<*UaE3AsGv8hp>B4S*UP_u?)DSp3Ef+ma~0BN
z8BgtI1BX0e-<8Xk$LK`&RlnCRyLf<*fimLE<2z+^QcxX~EXYRQMPp7f^o&#4xHR2-
zG^@=S8I6O_b>eG%g)+PHpJOZO*k+ycz$$(`PK{(K?A&>~+IT_Vp6iQpH|1F+np9!0
z74_WlW~6^y8*kD|0K-hag2&{FGT<&hS66o}Yc8-C8tna_+TnldsQ=!Yk|~>W&f5eq
zm3YC_0P)|tkCen6B2hsoBnS-#tSQV)h{R`<M|y>d;Hck)N{b@LEE;XXoKPaFa6#)2
z<RlepV1-tT5=4<FE-HBaqZ2UwlW)DToA6oe{1Ijrj4oW;=!WeCP=yl&4I0Px+`$T}
z`@C<ZTaDy`KEf8DYzxW#vRNJmPc=X{0g7$#XxtSwQ5#ECMufA2gQsWO{FH>a>%l*H
zf}n?f@*1V&=dIySD_W8K6Rpx6%6NUHzswWnT>2!XuJL0o3~Sy3N~Td1x#_y)wuIHE
zlt2|DW92sgLpt~&g8b1=10vfVQYIbi)xJ9ld?GZg#YqIG<T-cH&dGhj@KcD4D6^|S
z*GW1yKBuT5cNC$QUfFww2Ci~_yq<`(uuoIBc?g;}0`i7;NQCJ6^nUK@>Qe8J;Uu}v
zUaP2BM#vsJutFX;%@=3qq&@o11$k44j61pI16yiqY=BMw8Sn8j)Wbl;s5;z*KLP59
zHe|mxd*xYcID6eZJgiL_<{=)B8`yuJ=Z}ky-ee~2%`nK>o&Nl1UTJMyYZ>uX)XL+D
z+^dGfGcCJLL8dcto*3%vj7NKJgYoCw3({vjJlH+V=;-L5SyBcdn##>{a&p47HBZsY
z+CuY2MlwHF=@N$UF+$y7#mr3muQkU>b(AT_W8aBXcEh@3>vy@kx!sg2@A|9v{rmUc
z3!4QTpZ|X00#j9QgY&CZojpCK0m?aCdWN%dcf=DL1&?s!w7!S&Ih08usS$kC$(9}i
z9|5iUUHirAsJ5R+q<t9nX%34_lGv2a9-)xxhUpc{W1@L|xF6hg8mRW1naCy`?mg{O
zfuHUAB55vI!Z+NzrSF?H<Kmo4Tiuhh%tXy}B|mVC^MV^P6SD$BrINQU*7QHPTEs4?
zH@V_$I>QSin(vyVvac#DOJ{QPIa(!m*C_gX7mt0D_KC>nx*NSexu8Cob0n$0d-ofs
z8kfq}_`KD2eNo=tU_wq?dKS1BDkd{(X?c!{FK2Asa3)Rx+PBZuVHz`Ja;{WOv`GZq
zzvb6+sy<+Lday*xKeb&7d`^Wx%UCg`Bw(f^AX)aK->0P(awiMPAm0wiQ~3ReCivG<
zl+7A5pLx!mq+Z?a33UO7QUdcDh0juDAt_TOdOkjr-bGE6@m`Mt#m}ekAaHJHtn`d5
zu=9(ocSsneo*vuLZcxi&(IX@2)<Y!s_wy4Q3F~Gh%>)(9%*^aC%T6!hldc1MOG{GH
zHh+HRL{Oq_l0!zlm1j%Z@fYXQ3q7ctjQwUIrvJREU?-I~u`ddG5gFeXl?JuV&5=K^
zHi(?gljcTY*Y|5{Gwg<^Yl6?*sQDA0rpUU3IWZHTljGxgB^9YqI>aYCOfr)2hDYM{
zN|DY4*>9MP#kq5rJKUs$GE&9ohr-~;Frh{>t%@2U!EVUq_d=l_Od(RQMIo8_wjhUc
z4+#F2BD6wZgud#{GDqIT*=5l2#KriCF-8V6?U)VKdC88{uWnqnz3AfH#L>F0lzP^v
z7Gp*!BcMSc-8&^P%DAS>S~p{Y>Q3dXO_>hm<*}n)Z3#XcKBjugAKLVkb}51u(db3<
zvcUB7h%Op2I+(wSA`iTBofqoGws~Bg6yk5v=Ne?r9khkxw^|?^c)<5GdbZvqM9##l
zx8-LdN3xD^Gg!2EF;S3NzuDkn?-D}-3D0oVFQ@vqh%U~VB}%lk3I$O@9YH3`_Vh(7
zjXWr>FughGanCgKaA6n)ddL9Mv|)-~{c%85chF;`0Fd1$P@yYM5Jq;=68Ur4g0`L*
zAL=>iI-&^oiqGJZrZE@cY~q6Fp=O@r)J^|C!5F#a;ty;HCjm0CM{=(Ig*xQ$ysQko
z1gr5p|5qXoBN7Tq3DHqP^pcA0Rsj^cgHRBE(geX=$wTKAYI^ik1ayRIG;5{S_2X|3
zsZ@Wl<L|}=RsvA)E=7KJYOn@q&zEcb9f~dEM;y_PMkxVV)7%3<Lo+$f*&oXGcKio(
z+85!OPr?JKA$eqLSCA_E({v56!k6KmpRbZ1#Y4Y5szvA%BkKtAlHDyc+!jfvW{BT-
z*P7nW$f%p1nI&X8V5kgA2|8&af&Ii}@k+mkpg3xFFknWD_GP(~=YI34&ue9~9u;SS
zIv|{ns@edqNeY1%!%UC14h;`4?~5Yhpq2jp7x3QCRHi<y>;UYfg<C-U`N#8CAwd+e
zyjwp$c>ZH<SR8nBQ*qgx8&il<CFDgYC1^~q0qcWLY5nQt#iG3YFPkN7JIx8U=o5i<
z6^+r*)6dxj<6#B1<L0ydmsL+!gv+ZL^d2(KYQcyaPfyOs@G$di3S6m&&ZH1($t0VN
zs$kjZM3S{~VvLXGjP+bR3GO2v9Oa-bZ&LuswrmV^<!=Mh!QY$#fsI(6LlpeKKAA%>
z^EJ2^8NbtWHiR6^7$39gW6rOy@rgB2X;WB&O7e6;^Duo=cy{vV`_JVb|1S`8a(~-L
z5*aL)D=OOpgyaLZ02LZ&uOflw+mw2s#$l$Kryx<cea^O4ovhbPe)7#=SOJXg7Q<@K
z6wlY91C1RjvTDW?ONP~4-u{01act~XwNwbBq_RSb8;}`ghO&Eb{8SseLA2E1<r5hU
zhv#fGz02Y!d<bepB7++Et5CevG`D5O0R~0)PPe&c(;*x-FRE-sG>I+&@&u9M&c<${
zF56;Qa|}|0qP&a=>vNR*u!#dk^?qP-3(q>n{Jk^ZVJ{Q$eR$^onsFcy`NseN!~nT|
zBR<wd=|f0GOp!NQq44#AFQ`W$=X|Kt!q2??S%4V`wn&ADF}w8#tT>J4hEcNwTO#@m
zP_`1_1<Q&4moT3M+a7=j&vjY$2f5*{VX%_UFc%5}sMmw|O8(mA{och6&W3#E$UY$=
zO`ZbS{f>~hU@5J^B$pG;uwXK$pbT3j@5MNJtNvFR>YKRhH+P4cADYZvM~&`uORv28
zOeWMD3PCs@Hxr(59O9lZ=;!7@c5a5r9JdWn5=n_*kz-v-Gsg;DE?z{01-=a$<qngU
z34;qFEoY-Uhw=E0h#+oQd{#GVLN9W<$C{nfQ*MyrFbQXw4uEwh$jT`JGP{pMLUf=A
zK9bOt?!S=pK`x9#U3Dw!V2;`Kvs0K)bp6bf-mTHVjT=;NK52QhL3KglvF-XuU(>E1
zk0DAy!oIPyX0s~&9EqGDP_6}aFVsbB`&xwxh3+IlY_>3ylTG%HDJ7-;;89nSfybMU
z3<i4SZX$|Co3f<qRYMIeEm1@ff;KT%JC&J7YxA}dp=V89hGqDT0%WeaotjR@eNqyh
zFP6F#ueYw9db;aBa{F$j3_Q_)a{8vO4L7Ss6hb>*9~00_iJn%x$*ijL0sGhiN!2=M
zV35s_R9k%5c>bBUQ`mRH61=Du?KGfilB1Y}7$2gh=OH|*mXp&oToC}HIIEPr5q|xM
zVVzqJCeUnN5T5!CBgjgwls$(#Sxs|uqbu*h8fsPG_r;THFu(3TCDkyuc$=fUGyi;}
z#j`Uw{6!8g^7Q+=P8}rKJQ1mpT-^n%uKk((z`#ILH<tb;6*3I1gGg?{X^_qB9h_S)
zlgXx#Yr4H6A`GgjaIg0J<mBV6BcUy9gTqw?24_wC4kuca6M=sZ9`o|v(?thsx`~RA
z$ovVQdfz(oKEdljpb(Q<7kst$wFP4bNOObzDr>&YYRGw@2O;;w4n%nqa?Ano3_;zC
zjrESK$lNY%9?cJt&Hr|j`Ys1a7bzp4H7teXq=6j3&aSRfk`NI+B+Mzw!S#edK^U#p
z#ITjh+Y;?#euG&xq|i`Mb4<V?k9;u2=5K+5X9^dPZAqj9Ab?;D*>QfU_*>`(LeTu*
zhUo;}HI+h%qZy=B3MNE5#Fh4>nA-LAcq=QBO?p8{F}3Rw-6_(0$Oj7EHb|P?8#tUn
zF#cN!c1@}@z#s3zWF^4k53lpk=h%3QHGHeF$)w)F^W)4t#lhyI>8-J+&rYxD9Q&E4
zSdT7NN1{m=)-h2S_wYexIE+XpSx_b$6hIzW8~b$8hr8w$quFeI>`kLQFf(>4D%859
zxnCjlNJL`Nt(#mo9NxL#B0uE^4Lp)GxTvs*LSXK{!x*dKfn9euRyzq-R@y{@VP1j)
zn2h36L>Q0=&OHeo30d9mG9k$5diF-h1ns0&!}x<hqRSymzq~q~6t>T9d%Kk3(9HD*
zb)6vO8fK;VySX8%v52^IdBRhFnHV<M4Z~YF$6agl`!9AOpU|6oAyD+w>+}`81-gb|
zew{G)8Cr#Bpa0BawOpDYANf*CfDmf#EqcdPqrJ-4d>XTL6X;@oeFKKYB8A~T1-vWJ
zK+(UUcC>V(u<C+6$`0!bG#;HuVA+w<owEIv(IEjHhC!NreJZ5{xrBjhsJ`k$Q^;8F
zu~<K?RO;VDJB|D8zLM8~!mZJvp)WAvaSEcK7e(StdPcb$j?5OLtPN`=%$0ltn8q7a
zT2tyguaI)0vITuP)~B>Pzuv5+it=lw9GFzR`J|Z$<-zf((csQmmzUwqxob|=Ar>X5
z69$Ha`CARNZ)+GllP*)+2hn%%>9*%-<tjn0=`tRiHQ&Z;{qi!fK_<K%n60tHc^>JX
zzv)&ri!L~>@%u*yXQFyZ#aFS1Rm6Q62jjG|^-RD3<i=JTh*h1AZ<k73Q8$YItzC(U
zt8fM0rXVXB4_g&`U%}0e_5}}GFevmOb@<sQ1L38P;G2w|ojgEWCS051z*<}h&X4gI
zUtQ&&c^QwYhK$n-YHYeTK<Z`z#+6cnUp8fD^Pf27GmS?~$D1e%j}<OP@~ONghGkY2
zc{!Sy?@u6yoZf6o3FQCsPu!$mwmWh*3aK(u*mz9F^Uo|F&rHc*q`_x1kNx%=FF2pk
zs$EJcAt!oYq`rO`Zkf8ddvClWDr%+Q*ZuW4;J`S@QzVMnQe-`JC|Q&djh)goHqarU
zp{NNV&&L-<(+%wl5rE&Tl`>8gcPGTKB@|>uqpFa9snL*W-12-8rEfT5?q*dH?D*K+
zq>7x%7F*XY@KHo*?DFa2+9%MlOQ(2gBE9A}vNE$joC4k4oHppf3I!WEC6Pxro03(*
zN)JQ}Pj7=;&KVNE5I^P`5|H5(?8|$ToYDe|ktYR6N>kB#+fJB2^3btBqNoSuKb~7i
z#M!GVe`u<<?^Stn<&740H$5<_j!jJzRCRdgb4YZ9D&KJ4FFYB){?NPz{s^f=cdbUl
z5lSy%)mB1`H%J;0h@$NX@=IwU$YjegwlVxvpuE-fEXjfP1`zf^6cQTIhq@c7nzWDw
zdWOi{1<LH;UQ3O-mKIfCz6qjhrYdF3!8e1yKMvLx{x~r?33~kC)Ng5<kKxf-N#(8K
z_N8wh#>|Tg^4#<qoUF^0QXFWT2cF&Xvmc9nZIXJEi+f?(4k-5k0=Gw!dSZ@deKOvD
zLq4&bBbj$4TnTOi08%C44gDqc?~S`p?4M3nm~;lZMEsInUhuA6rdr2lhrl0J1wB1}
zfvPiOxou)j>w`jN+3mfjFi$OgS6)Wxx0*}dVgui5p5%M{Iz1NuwU5s8baQ>`t&i*I
z@M4ixBl*<wsPgwBWP5~KZ$yDFg%2b4YRp+c(re%gxd_x~B<3U023M>JavqRED5*Np
z+Mxz9+lO~LvNq_8fim75ws=tp#N>`PZv94@*ScrNd%A@GG?=w*54PH$mqD99IqkJ3
z@p^Th$>fH`FI4mo`e`tq<yGKvBwvRpp6qeyoierbv$sz{K=ESdG}E1YTLrQpgLeTQ
zXa!>13u?G9&=#bhV4P&u#Vn`no1Gr4$~?G#bk2zyp<}>}>bsTnQNc^3dTLO0)|K`J
z(z;4gzwBZJ=-#jr*2Nv8*-0X&0FsAWQ8I0L(R_hX*CxzC$MakFXFMgBjGBQtC5Ao)
zm0$_!#{<&2f=Id25E)34NaXLN_aI<)G@z1{`9!&XU(WetTG<9VSwoANHVgQp-}++K
z=gsE71^X%S*hp`?d?@@Jt6I&Wv6|Dyr}z&i!t7*|lMjbecmGMAUt6+`{!?uL3hVRp
zVj|7RlfCxOI_8v#=X^;>Ojb`F$WlawNLnOFdg71Pf)1dbpVzppVTlNEx{(nQnTQpE
z2+*un{JeDH!(Fa1b?hqX3mJ-$D2MQFW;WBKW3Og}k8okZf}b@^_ne6ra6GW;=(FB!
z``q3qo;RlY*fu2`VH(F8_HJVwqjp~lth3Aw>2hm%b2RbMYo9!~7uTpS%u<#*Zrix+
z$|4G-WBNjg*9FS&q9rf-them`y+gjzC33&ZK$&agVlYGd?CeC~wj1t#R8p6ds4OAP
zzdYNWdktP`*EmW_O7BxFRCE2OjRk+sc4;eBk1vo<^1F4XdN@elfj%+!zg@#YSy3Bc
z3PbC%-n?qno!u_UT3)I5-@QAGJL^Z6EYn}ETi#2Ei~9*vR({~2P$&=JrE|vB^}^vT
z7xq0Ygo^}35*82e18CoMJ>$TL)!x<*XQ|^jYCrw-)3%xl$keYS?x#8(IxBPjx8ukV
z<h%wbaFIR_8GmO)nYQAD1V)J-H-~*9k9xs7xz^YD@eYh7Nw$f#fk9$j-CbSRK()03
zggLZl508+F?-lr*G{hGN?~Y}_xM{z?zY4PdPqow5SHvodV3vP%!EtWxgNowt5p=ld
z`U=5AM^^KXKYrx-2;ZF}vZWOb`SgD;LDsYgh9eZFR|Y1WnSQ*PUa1bBG3(c>a`kIJ
z6<uDy1Ec*Te!HxbVIi|{Tg0IXHrvoC6h7)F;Zh^#{P{;{>qSaFUJwTgKh(`$iT^5^
zDW<Hvf#*F8q)U?xE~Z-)2MVhO!qn{(Afw%KE`M-BV7<RvPPGy0zy&;jzcMXi#7-YE
ziKz(9Q0L0kXXi`aUk1~`mzaFxJTv7r)Z4_l#AF6$Ik1{TRS{ubIO&-U`QF1zV7<c2
zGc`3>Y15D%&lPoC;1hlsmM?`U=Dtmhy9%(X-QPK+^qv*K6q35IgAcW@R9?S+{Zo6p
zRn8KV7i;`P>D^mhIU;S{TQw&26WCf#yGQDO$8z|EkEhYDTzPw6zE75#de4|~P)Fw%
zo<B$GjP|E*x_HB{^Sz^Mu~)IZ8pB|dy!?8e_tA$|E@nU3(IjySUg1}6-IAR+P|x9*
zn1;W7e|{_4%XxXucGs1U@MWCOa-d668$_6`r&A^rS|@4J4yeQ0gc`EM+%grH`V3PK
z9$dsD*W|zrPG>hYZGg$ekNvYtlk+s=^@7#<mf2Tj>SXz+K4$3T`KP{_P1<dty8dLG
z)??r4(>HcUT);2^ofkY#d}qIXK5+N$-3PE>JZQ+GZ{#*!g5j6s*lXRK0cJr#LCp=7
zXP4o|u|Hn#5!{uJ?3P@yZCdjGYVJ(DvE1H1ehVFm%w#B2R5A}CA!CyYhh)esWPG9!
zA{xvp2`42)Dr1=*Q--7rNe+d~$&iXd3VFYGt>3%e_YZj2dY^T2R_n?0-1oirzOHM3
z_uijxspAWF0nKv)IQHI@Yu>!`qZdK&>+ILnGI=bqEH9q$f|ZMlo_GtYGxZaCtBU`O
zQ0Qwm{g?IEN)IF|c_j8y9+?HxYq5^=`QEU6BIWTB$0!OzR?k(leDWA#yr>=2#ygNX
z-BkeCggw!i!o9vYySkU^TvGS4EbH!Fg22xE?JsGj_LWXqZnx4j?>)NRTC?Y4NVj=G
z8j~Dart9NYhtyfJW%e;{9Lfs=F_@WH#5Q#SD~>)eb2+@^(7X-c*gfsA)6>&i#4I5k
zO}{3G@6(gB^AaGYMTLc}q0mpD;IswR>g^+^^^WqquzE=yiQYJ~YV+)Hs>*qwrCM*R
z_q~^K09)t0fp`O=Ng*tbIC*$z380-=1qI`;U1Rs1`uBw^x9p?rJY$DLmRV;~bMJqF
z&|Zks{=0(~4R?o}e|^;lSek}h^T*y`KLT4P1g53+A=#AbpZZJZ-b;Qt7<$^Ht-ZYo
zFh*|>ujJSBf^HzJq{M|&Zp_3T;LlvZ88|-WCGECU3Y2V)b@eVJJQma`dZ`V6XBiIz
z5e6nYGjZKKw9UvpV6)oNd;-}L^9dy@5?LR&x@oH2J+rc<<oB??e`Q_8cjIe1<d=Xq
zuxy#x5m8n#W&Q87XU{Us3arn5dyb0%FN5ZK{+dsdldesCmKc*C#>VWh(Gc$`Cx_rN
z+4%WCa`JrcE}jRdJ{jgR13_Gj^eBgiOfrvvF0ke0i|q+7VqRV?vu|VsA+WsF4Uidq
z;0Olhum*4ppoY#|e-95!kJ(jgbHurlru&b~{~k<DO=YCDX1us2np^6WzZ%WbeC*Ca
z&>$QT<#yhGSd#1NW^KJ00nSNr74#4X%x4ZO96vAha{<~(L~N{-!lv+iHc@>QJ)u`e
z$BQ`g({eo?UjNTe_t_p{@`3d2z)#?)o8K=kP317@_;5|tcuI<gfNMfzmD#C|CUG3U
zQQFnl$B%%nbKn3Y*=zdByvU&fs1?J84f<ee!8_Zw?5t(|>(X;j*Rk7Q@JecG47esk
zh-ZK|=)y3Hm5ps!v5qd7PD^&z_w1SlsIm@-k8dA4vjAsE2B>299(J(kCxA}uj9V_g
zn-@C2`X$cR)iSUD4O?qoL0Xz1a5NN{c%-WNZQ$kQ1r${myr3DVRHBNCoB#p&L|OI@
zikH+BU7~u-FDyL6^Sb)aKT%Nh0U4piAjq+Kq5;2|Xz=iH%JbPUjhv3;r#|o=ZOI6Z
zAxuVRkjd(sn&3G%Lj!=xG^eI<Rog$`jvaV&B-F_xab%vu*4#47cf~QfeZ%(SnmrlS
z##QH#Cmt!s4T<6Ze_q$Ny{tQk2=wQ*#fWK!9l)GFul<~~RsVTMSC_q!konI$Y*%&V
zYX9$l?drwNyZs5O)ds3n95ln8-K&6y$*m&K-yHXfJh$>wOFC&32PCA*+EPc;J6sBZ
zb_8uK6ELxnb6{mkCglGq0<@95j2<49lIuqw^yrm#>shk8vc~cjzsWqPbK25L4#@m*
z`B6Hly93xyQ3W7AveMOcbP3}jWk}@wleuG$uA>({J^jr5d{^Zm=_K+iT>46-V4r|z
zavpt*r0Z~f*uk&{hpzJ;K2#2IUdISp1O7uyzutFZYL=Cb25{$EWat);&bIF$rl8iS
zMP1OG)#;*mqWfLVbg=Zk^HMKk{d&+L*#rcb2y8i&C!y6td_DRYM>SwF=X9N1#(Ac=
z{5YMt=^hR;?!(^g%Ju6PfaTe177378#Dk4!;|#yBor`k|qi`oG5Bx3--^_UUX3l*X
z6h!R@6z4PKl1;}yN&L3-39t0R;{N%8A!#GiyUbw^O(gF9&}!nwQCEf%i&z9xzUA~!
z$3Z!UUm?`4)X#sxo?S%bng-dKUXP-rQt=)lK*_)SrDkb^d`hkrUr5NR1Xo1Sjr&cc
zMa;-IEN!IY59+WTNuCJgL?|o?MsR=u=>2uzoP6`!e|!D2*~O}x)-^$qkVj|<1bG1~
z{w78imdhAc@(K!9F<|i&RVogU9n<8Z%DA$Vh=>{vcd*lGu?q6Vt_8yB`HL5IAOpz(
zmQ`L+5j8nFI$DG*`B!)O6!qN7E<%VLg`isTze5_wKc*p;q5A58uX1Z=`9a;EfN!zm
zlK|$^g7<Fb!%;(NY3XD&KWV}iA$7SfoP5OYQG+)kCvIi%SJ4wG25Xg-l^1{h%m7(%
z-@bi&&{V&0lp@L%i!7I7Vq%)}PbPw#$wZJI#dV4mb@9C=Zhh5<bsa6m#KcBYEAic^
zLsJ_7wju~5jYi;ZCWm?vjo6G#>oM>;EP<1a10+pYUfww9klNP+Z{U?8l++@e)Iynj
zEmj?XeFW3}l{|8CaxMc^s_**y$qo-!gdr5RPXVo&`8n?fzX{~TqRk-ChFj#7ZWGjE
z^+pT@$MOzpJs6H~1A<@VGk!SXtKym6C<etnmmuX61lW3PHi1h(H3@%KLw1nwL*qa7
z0EUvwrXVK{<K;@r*haN8xdWfN=MuM%haE*pMEZyL0%U{iprN5L@uol!XE#HHG&Kh&
zYG8R}uL`Io5sK0Lw5dr{NQi*2>M~fPPIe;j%Q6E}8g7qYI*L*m-l7)(-h+Q4Ev%Df
z=H_%!RHMGWjAU!7GG#P4OATJy`3uWDnUL<ugbS00LcDqE+a1v7N5Qz$2X{8zaN8>&
ze#p_YZQC~4*=;ljve<D8jd<!aSURZd?BoFW?im)ky+6NGtzOz4{F4*W0PL_4H~bj;
zwG~VukE7Yr!A+6~)G*g#U}Ox#at^}4A&s>b0Lrn4v`)g#(0r_~vR&Zx?ZqEIBG*s4
z-n&Ca5|Kyi&PU`#1^)P&MeHGj3{vD9->+aI^mN6_%IX3*hVp%1`YX43J^_@Dx`I8*
zlK+C?Yv3@!#)sV;%h;D3;fkq5jxwoQG%Dij!}K%dsKFIDtRTt+-ni{19c^uVkO;m_
zn-Bu|W$)NmSu~^-)p(d9JD(m{MY`G4SQ{JL7ssN1vk?qrm@I4St$8qE#Y(4T_^_)U
z87Jn$mk%Grr+Z6UZ21Q?wgOGcgo0vB%&PKC+k~9l#p-ru58mv#U>MF^1phiAz8&lR
zOdq;1bDK@2`p;$z4G*U)-zq35m;ok}F}@T1k4{ELCUZVMDvJI30jHD|-$CaXb&PNK
zquJ>W3qy6ZcIcF2UrC<o#ZMu9Soo(W!cg^g_{L2r?7h7e+i_S42F&eniP?gN<l__(
zRS8RqcpeH{GGR$cCgM(qi#IftgzsE*G@!KV!7S+bRCb4pEEvF;TGJARC))U0fkFLZ
z@14C-BS;10VK?tU3kyE<dLt7PN26aaTL*%(NHa7_-F6)l2`)hV;=o%qO&&ydBaI}(
z`#`;@Z)ku+o&A~Pr&T+tSgqo{aQX60#gIwlaXGNl!uwbRHDVxkr}=+<zYg?6&%$4D
zZis+8i$yoeMzSK$GImw$$5VL)bI?<n+ERA$@*Er-asXJLDzG#jL^-@B)I2)U^hBEK
zgI3gM#I{=xKEQxbyz43%eR4#|M9nJ)`uFS)q<}*Mn2)YUbI=m-l^8|;Ko1Yq&pfZ6
zsbyy;{JFpKGEzz=ZskPR{qpjSm?4fx)u4jF7M9VrW*sxsOh^zccYKBKz}2V9e^%Qn
zR2ox4bdowOY<S(x&bBbohZ#h9;jNvua=sI<<kt^OYLGqeb0yBM$oF9<xhutTzR20e
z75H^NV7pmYZzE^|ikyD*VY>1C8GKGbD|M&+(+EbdAGcL~#7}nTM&eFIWMo(nZzU2L
zCnqQU0pOq^Ab>4G4U>AHV5o{1U}D0Fwu;8}W)v10?*4^Ltm5;52@OwwNAXV3`{!2~
zzui=D-mtv9OeR8<#gV~OF!3mLHRa_hC<#yq+5<o2zc8Khb}M2JnR19BU}k`jzBF(O
znMDqbPK0956nNMX!06~?8Am!{uZvcy#TF%{5(DB1Qa(~RKAX#?5?<BR)kp}Ly}1TD
z5WrZ_x!u7RpLW(_ZQY<t1|1|Y!nS+Wz-c~3v;_+|JZV0gt;t^dD5ZImtbwk{56N2@
zTC8-#Lv`UJH9$+qJZ^jIF<$1`&tBF~^=ZmD`|)PRFb*kb1IhQ)a)$ZPU$$|$hRCs0
z(m?tj%x2PoRkh!B(6iwUV8BYrmv11ReIpHI>wHU-GcZ>+HR>zr<RMr|&unFuSbb5P
ze6ThsJK6Q%l&f$1I;xelqm$EQ+ec(e`wCFc&HokG%31bl*jQpAb;DueU5C1cwY9Z2
zd`<>@p`}Rp1g=04v#Fj$3a~5IM9PJEp(w!`6cls;)3c3vA&!oBi9IfWF;YLhDX`&b
zsW`V%Q5WQHo-K4;{RDBL((enOR39g4t2gs@YR8jDKU>+HPU6gy+$XH<#Ag34Oy#-u
zlAU`P0Y?KSUy1=wF!&m}y0{4Mg=zDf|BgYg1`UlF=DbEv&IP}#3g{eR%d)`D!;y^$
z#g4y?=vCb}U*cEbx%nhlqQ$%?v%nW$BcOhklXwOAVR#v(#ZewRjSK{o7Lg%0w)tTr
z1DT!&JRlb7p<<yS&>gwo^rgIH=HcN%w5BI8U$C<KiDs{dr1E8Ff5&U}vvalB(2%2i
z94QI?c^%=T|Mnzuv3oDPWeO3={R0E}^HwpRBnWs=6#G;30&np0^G9RQn48EPM^7L5
zhRQy<%EoCfC9`F2ZjN)cNmY-mE7^J&$qHP5;-wcZ#r5ks>=1uj6`()}iHonrEk46G
z3^M$@hY7~<cnB-$a2)cVn_z?77Y;YXOruQw6XV}X(Ve5)pTO}v(-r4tki_jyr@VaD
z9X!9Y-(fdpL^snFIeH^e<2!k+gL*_%R5bPj7d!i!J9C$54yug2!>HlKl!gp8ie3W(
zNYhC6BRXnk4~*~YQYb|oRah=^@V@D}=L?E)rR2ln3F)X*h~~9YCAW76h%9tqsgM4m
zHqDVEM`njVf8GF!N71id)oL1{ty`O1R+x*NQ-H&JoO9T#^Kd(n{JqAUH~0gS)5-9<
zh1=h6lJRa}Q<uPT8i-ukYH1l%(LQ^7dr&O@lC&shfP+Fkw4+9knwpyAU8g0$C-Cy-
z%+ALGNJM-5Cbbj3|K3)G_$9941;MGK7&;ZOkB?inneBb-WK(_U`$o6%XG-E*w$Q=9
zh1PJ_Nvm|ji9`38*?BXV%du$o_d^SPr!;<YO=9oOf3&lVG3nrcXPv3f9Nt$fx{ao`
zy}jK<6)i)|$tY&??>4({Vu%HTZ?8>_LVH;QZ}b$}=|oRC?nvx#erYI9w(8Y93Y`qw
zRkr>6_m6zTruO9O5L;Uz@cQFNM(iCO9fuGRlI5KkfiIT@Hx)&IAkcuhvS}Em(e0$~
zwzrs{&$=+%qi0hLP9&V#l%7!=o%gfZqC!i|VY;6uF~HOPU3o|DQ2us<ym+PgQbm7H
z=D-{GZ#{TwfkJG=$0xh=evF3CC3!4*tRpbltZDnY1(7MkkDu&sYaN}PqkZmGl$Rgl
z9O^oC`m`R(Q#spOBKKI)8kiG4#qlN@0%#^Jz%e^k?ir+rbdO;x8gz}lDU+g-rZ|B-
z`vbP8vDwJP80)1K29~dxD@-_R+ge){w;yp^n)0X+UHuG5qTShkRi8F$b$_?S$9Jz>
z6i41wY~H;2ZJ`}4nX@4LV7lX?$j1;yXt5SLw{MPaE=)oSjcSh<)RgY-yncCmCiPa9
zf!mbJB&V>cshwT=qP1zqbAK|>iu_GZQtw_CVsQGfX_LZ<82|P8QZNq?0xWuY2}=%n
z-f3`)Kx(URY@|n4n0TGF9ud~bRNa?n&ui&-j?YmJy0e><iuGR&YfsZuPL@_yelxNs
zK`c1y;P=w+QQV?CBqZ4HIYVIB8K798ZqiM?nvf8V%O+}35s^+Ni%UzH$Wz`F%65*Y
zex;LIzT<TRHNll_UzHXdE><@ei}mk97VSK*MA4}5nFIWB;JniBdFO)*G}a;+W!n-D
z#B5UIMiOARiH1tKBVKcLnWHIglfv1Xm7aTA|7|>j2XYAuICY43NKblrudy#!iY#}l
z1O13v7zcYpTdbZ`<YOjB2Ss%uLrvx@s)_k*HaZ&6;Y#XuPj{JFAl8p)o53)u4ii46
z?AYjN4zIdZteUh*H2rR$TRPouwLMVXJjKyEN$IA|kn3l#vPKYy4_B{Uv!~!AtVett
z)}QWqWDq#Ju)saO?g_$5xZ9|e@7@N#iC1<1^X|@$yDIX%A0HP*rGdpP(V*bqBD;pQ
zlqFz{UR4G7JpPI;lw?kL1(fR+F)?A7H8Ba*1bkbIb$ac=n&8H{4>9jyOf8CBK+c3d
zc1wYUo8S6i{sT#`U97@mc?;6Giu|#>qoc4CW?*6QYu;mwd7X;a1m>Q&(G8SyBS@xL
z52YnHWnkX0Aw1yi<AP$+?@1#*BBB=Cz^^1F{dGr?SV5s0g#9eXitOo$+O{@k(8%R0
z1uP%N_~`gaL|_1j0_dDtX39#3plpvo;{u~4^Yu}tH?U76vb~|cUKT)8q%00B+pyV~
zVFKFH(tz#lm|EXaHvIJQ;|`4@`}WE166rtc)Y~~T+(1JBA)aWF|M=jcL-d9}H9bGq
z-}<e}T#`la{&GEWhZzKnXckODq30vph$57cW)hD>doU>3czB#C0X))Hdr(AyaUeS2
z`FVKwJh=X&$hgD-Gm0Qw@63OIGRlpidMPQ1=kwr`c<8C`-@iX*(u4s*N(Or-U7B%s
zxLv+0FC24@=JAm^27K!a7cZvciE}5~$sp@DAo()fQ8X0f;Gjjcf9@z>PNfRV%CeHB
zCRjf)0Gsl*wO9w!6PPnbL`Jf${sK9CXmpeo*N-%d;X+D>BMVS1GLa%rBssVxQaqoJ
zoia#OVcRol!UvB3%-65%@v%?@(3?qB*WlK!t`i=Qxu7`6Ycc>Y%Y<<Y$F;v4<x~7~
zQJlPjA9v8wr!s$d)>DHx8R=mWNOOIy9@ejg6J9+1F414f;BK0&?%Mu6CWdLYi79N+
zp)-O_)V(xk6B)7D5EkcN8r?S;X}E&*m{CkoN%>IHLcJOTZXe*=K7Az`WZht4VR3PC
zZXJXjv<sXLm>o}?5ZShk0nVyQYJw>SmoVD60PR*xhBUBq-{HfhIky*fLaCrI(gj2P
zE5iIdylmA>--f0Z-_qFll!<csbR%S|S*-k9nq0~=?WPvN&Qz}aemw3F{eBi#VqtYj
z9ex>n7`xjQwib(#i%6f9J_RP8nc85P<SGNl<qK9|pHj|0_v;6Kr%{iyMDrvmFR=Bj
zA#d(*LdzJeW!PX16^f}g#@aBF>LT`7vqCkc)L;)svaH=2&}+#NgUKc*G6{m37AzlU
z!MC!*U?eLKoU^i{sKSw@0s7YP=x8{OM8kwpSV5t4ItZc!1(rk0P(l|$bv6WVo)p;S
z<=IKu_UzeRyVcdD^6#l*vP6pHr09xAf;cubHg*v{Xbupv$xAZKvrr0OGCOKRt*F3$
zajx>yAGvE*7A&E5)FK8V1fgW8MR7Dc-J=kM{qo#Nb}Z;c{%6mMNJ%k67~YGewW_Kr
z=qGI4+?TK~Wdyku5^%9m^mHcL>Unc>Js{e$0R2MmHJ-M?nGGfZ6;`O+^&s37!9uz^
zfYdes1FXY_19;`|qo?;&8w65(d>XKYi@1JRRjU?t2SH$Os1~FFC|P7udT_uotjN@f
ztZE`8ucW2PNSB@=!<Ku`>pG=V0<rqxKmxL$EPPw$C5VaXrsKbh;s@H~WEU^@d?<~{
z8o|3lAFXewjwTmB@QhI;3{G6%`a3Y(7-IM{(ZI@vH7V?1%KW^oO%KrsA9i`7JC7eS
zd^q}q?Zm6}xcm)dH!zhG_#*_LAzKaWOEd&_vO|MXd4_VEtTM57vJQ(Y6yMY2W#ll9
z{P`C0o<u|<|M%bQLw{c@`41!ZWo-Okg7aGc-x}rb_qIeK$Nl#!^8d}R<*d@C1uBNW
Tw@)GQ#KeBR!@Bv}wqgGT5svbk

diff --git a/reference/plot-9.png b/reference/plot-9.png
index 2a47b5ad1aaf9fbf11dc63007a9558fb3075f8a4..4e1ea0ad30e8f3b1b411386df353e5680361bb4b 100644
GIT binary patch
literal 24737
zcmeHwXH=Bu+U^)*Osv5IDlKBH2m}i#T?H|sqM{UO2Batqh)5F#NQ^pyfJOxrkrqX|
zLm4_)C@Nin0fr_>5g2-JbFOE|-aC7LYoBlb*ypUXzO!8`g7D7F`#kUc+~vBi`_9ed
z$JCcDTDOQop)CFJkm^YaWq~t=GT-FuukaJLe^@`hd}X4cu8ObtGjSl(17E(mcu3El
zLJ__}{+s95Heg1fd`J0Fb>AuHM?DR$kM27JFYNDI+g@sTVxQX4l9LChj1vcr<mFcy
zcnNer45-dhiAwWNS9@xhRi0ne9)JJ(8NKr^KN|cRu!_FMh_U0tD)FQHR_(L-!d}sW
zd(ovurkG-}XWG26k7Z?)(6qN>Z2EEE&N6%R>&tV8TrXYQANDQp=Dv@Ntz5wS?z!2Q
zS-fv<uIT@QeDnR`7mLZ)+gs*Ik*_M@|Befu3y;@#C<)xZfB%G@-r5h5no<sX-ww@u
zi4Vx|8aydLQB6)xR@KtdQdM2G=-LXMyGl)(_+4ns4_5`qU3_=q3nTK?Q}r9?pX4vT
zUMi~^cASpo6pFZ7M-|N@m+<}V67p}#ijB>@H6FU>nfmMCgqy<Pn+<jY`2vMM-4N!d
zO^y0QM@M(E5{%vYuZrIKa7<ppCgbx{XIv0*d`q%b@%8K119zQ2`N*MKZ=i83NA|*n
z3nn*&9qE4V!w~~<wlr}gS5AtfkA1yep_-YQ*(q)90#WXGA)Rh%W8?ed-A(zm79ql}
zz3CQ}m6dr)Q#~s?LOk3RxYI*|!WO;t>2|ed0r_D!MAj)@8K%3>Oy(CV&kh7CxLo=)
zG%-qxs21$=5|5|1`pQc>*8FtiSya@`nNhY>sp43Dpc3uME?HUI(Lnu}H5~)?AyWpU
zGmr7Zn5he#tRum4y9;~-1*ZCPJude(DsB@OZ;gt4ysb|9v18r&T6f#@Hvf{k(Lht*
zx6ELyFJ=Z?Q~Y4(X35N+#64ZlBEp^e8Y{A0$HreQRQlE;*g^ZqV}-q4-QB69$9G)Y
zzdj}=F>!}>f>A@ejJQs`fkn{hwsr?LpRz1@I=`;oax-_jP3oHBXidboe4NS2HYtzV
z_Q1QFj&3`pqm#O~`g3H6k#72>woQfWWUO_oxeqKJZ_OI5v%1#c)I_`6>i${7cpG+L
zf7tOYyJTeeOBIHSkB%z$Jb%4NTszj1HD)zCJ#lS=@1F1yQ#LiVVNsdF(2>l7?bl6$
z3+-A9%xLa2-NvaIxg%vo;t5$NYjBB7Mna*eN0w#bUf0nc>-w>*4n<C5Z;5PEF;g6W
zVvVw|TvaM5DY+KHo$iPeDXcSY$aIoA@^DvF$qch&>OoltYprWl^l7T^RH=MlY|>GE
zFIt0RUfEJ&b5!$w{}(O!-mRfmmwD+J(^4!=-P^>CZ{FFU&a}GTs_8NLp~izdV_KVN
z##uMT%AIw{>ZA2}y9~8c6PsjnCrZ-V{Pn}1JlUu;%J9o4rge1Gqvhu2mb{gk*7jUK
zrAf;rLVJD?aqVHqOqGTxD=$+TZw{0;Js3nw)mDF0SR!rq?p}>;?u-fcMUa+#Jg+tg
zMyfm9RV6MaX5>E67AWszlgZ2+DwR(f#b&Rqs;UwkP9JM@ZLQSOq>k*VW?pK0w`T99
zbM=mFDknDUGFIn5d1XFU$JG3yaYf({Kkbh_ld?@r?8<1*TIH$QGLyzEmx`L2H4|=1
zFdgiloRP=O2(e0o%N1%T^!3?Y&dZg?&l)&Xovv+daAZq}I5+>So8#t!2Wx3<eM`W|
zdA-uaTTOGC5pASu&&OX^)~)`+{o`U|`so<${M+jfw7lIMatq(9DTVk=PEL+<Y&`@s
z`*K{X$Fhb>y`@bH@{-Sg2zMRp9cUHh?%|g>vr0ljqT>Dg6$#m+XCB!VtkvV{C7I{1
z)KsYG%IPQx5;$;US&6@WCp*=){z`Wo<Lt{{$RTx85`_uhBA4!4D1479Ym!HIp5mmk
z6_{}+dWO={3;aYyeSCa)$N%ZkApRnM32(dzr(n^w{X*{JAJy18sWuHQE#6Y4BASo(
zFiNQS#gV`t*1NjBTp(bb$ep)P=v(EP&zi0Gb&PIeDQ`N4D(dTn@W31u#+$F&y;*<b
z9Ib3`$0OY==K+49x6BGY7Bwn?9Hz7Kr{7P!Hmr1Q9D3Zdq0(czRW$WNY(YW6{#^H&
z#DTAti)6j^d&q5GsI1$up<XNUSc7G5O28L%`l|&32Dc@0pXXd#EMU`L(LZngSF0M`
zr#L>NWgSD;uqo2ra7!J`I@>q5*X!jj_ZWX2q`>s|_kYz{5s@@Yj(iNwJ1nrnPQ%|4
zZ}-jSN5?HMT&RntrLvm1vt!n|aO2l-BQVwW2NsF5@_kwMF|4xT&mR`=v?#S68QFd2
z%$Y@zA?{aX+GTSFe6*+hFWtQEn_=Val1)5w@Qt-u8=srxd&SPcu{y;RJXCab+<qo`
zi>rdm;3v4c^C{dKhH2}sE04Vq@n{unva}bSnHux!PRLcdlwN<(xaoXka7*#dGJ}qE
zn1%f0C?~UGHr+rnK5Ot@&p`HAV`hcBRL;0b@nc1KF|lpiwwZKQMq!PKiHYNHHQj%c
zn18W5E|q`esX@1RyG#9w)vH(A6$(44y0}cp)HdO;-6@fw!cf&kT+ZE{@j!ey$C09a
z)`&AwE)=pq;`V8S=y|O2-Me=!Q%zj=(Q((NDI-^nrl)dU84@ESBq6X4`7&$c#bW8g
zbDTa8?v#Ftc2}9l>`*MNV6BBJcXozd-yYO7ZC>r(!zeVF7;Fpb;a-z+3oH57ZoL1a
zUZ!S<l3gC3IxMJeUKRXHM0&c*RV8xz5=S=%82lDDFLUee-MgjDivxQG$Op)z)}G8)
z@Y8R3WM6uoHTHX84&1N>$0{M4S>?WT=~BC)5?KpBO~sMQ@Sa?CKM@8zfVyg)pluc(
zeyg@NHSizr9r4MGL++VTrZkoJ>o+S+N+0c|UXSb6->LP3SWD9^r>QQb<mZJVX{D`9
zo5$KZI;{3UVhYtFa+fWGlB8K76*ZNW$@@Y+e~7f;q*w^2n&EF_=a1JMZBx^|Ttrgy
zu<NXkQ@Tw*ZVtYe5_#mo3Jw45wU)U})9te4IP>uxxW443p=0fn5{BTqbuBL!iryl3
zWLJ4YJs<IE#p%<hkx!(TvYKW`Q>}x1jVxR=E>}9}cXbo*T%+`jvqtlL(Qy@W)<{gF
z)14>U0$PiVXD_*R9uW0v%yw08%AuL|)G(y&+lzfltql?U^M=ZlLllj6CNU%s*av3E
za+O`D$8x!}yEE*a^BJ){q5&sy2J23%>pylId>c!{V^$sOZHRzJI-#Yt5)bCY>C^XV
zal(|x?vtNu9jU_X>MhkS^(QkqO#O;NYYi%uBPaVdv*WR=OkJOT)1-E_h_d_iro!#3
z7TJ9`Dyx=Yl-&{`I%8=7$2#z4gJ0gOYl|Zo4C#jtA68GC(Kj`1)fPHh*!irRS;-Q_
zN}tnK^!vY|^KCF0Xvw!@89L?P-26E0m`cXw&+pSF+Jj7Mp8v!^E>S=mYe=n-uIADm
z{X`V&vbn*Ibz7|x&ZJzpOJhIu$xNA%-4s%0u~TJ3<I+LxxHGF`wBx+sww@&<_~F3!
zSaB#n;P-5YPwTSy^7s1bN8?C6YfWLL*|jE)R_PdLzwBNtAX!}9!KseZ7edqu66#AV
zlJqWfYv0K>)6Gnmo6L}C``ZaQb^7$`2z5W-w|6(IBl{~3l9eLCVthDKS-2s?!JAHR
zD+(ywtTb?QypKEEx0GqDe26Ok%#e*Uk>rSN<QLgjxb@(ntT=0q%aM}imsgVnJF}+;
zi=>@;>m7`8d0`K}Bm%i!6~pMt=O<}h(J_gI>cS4Ejy%}0kpv*;j$kJef)UJ+d-hnB
zhwf8T3LmwyYG70C8XQyRY-!iLKF9Fb*zJgtsU&2zRO{RNRK@5N?q(U>&36BIO`ssr
ztf;znQ$SH%Qst{6soeT_THDZsgTG^qsh=V<ak8bT?NWE_>S;ua_?3exV^$o&chtrD
zmqfI*w6*z-r&|P)3RiAER$Pbpvgt_RZN=Fh3+2qW40m{b3yzki;%@WOM{AMGxRI0~
z!>LaM)+zYw(}djF$!d{T2U9wpno&!>ZmMQcb<!GP6`n^|_S;0l{gD9W`||2Si!z*l
zEx}=VWWq^~R^4IowljizI5|_j`nqO?a_xbpiKDT4Nr!dc`djs3JL|gp-N!PUN~#NJ
zqqP>TKC+E;x)bYbBkpzgZvCk&Us2_plC{q@0wfJBMklJ@v|T!3Xc(+$#4u3{VrPxF
zcvDBWwmGo%c;PJ6;fqF2J&Cg^0+I%4FS|pgKW$DL&AYjhz1kf|+)OVsK3?+5Sa0_I
z(~h@D;-u#~Jw8xTUVi-51?DmR4(Z*Qy%|=ORaI(vncc<ldiG-rmB;s0M=JizsY}ve
zCq3<7DqH`2V;l1VXK3(-@^o0qtX0`Do08Fs4USCSGqeo$bP}|(w4^7vwTW{Undl@K
z3Fl5$B_tLK>L328vpkF(5pzFvU!nB3L8oV6gFdc(*|M%#jy0Ltdq3Si&~p30vYT|e
znm2!b?sQRZ^3>DwL+vHr-03oI&+|Gjnuyje_v!Ii8YhKy@W6os^=?BYwf2#ZE*%as
zjTKgn&#ru3Fg?l2^-XXeiP1lPzWbQMKwd2E?Z$vu`>V+#4M<G}Ud&&fyw_aaAT^%k
z0HM2)ff0)hH_mmp8+^OD5Q$F<a@Sk!?d^1DC*g8(JPs^ZTqQc)ywLRX`zL&Scm~2Z
zM6PsxWqSJ5sn{gn&powCrren^zqLEf&&e7Vbqo!>6HmxP5?MWANi#HTkekOEh1KTI
zj5Udxne`bx{qXdtH<E@tJnuCL2&r=W98FD4i;;cy=5lBE^rp3%G{5-S4w;gZB@IDm
z>tv_8^26Yuty)h>oJjN>hQ%9pD2tr2$1PiM`rJC?2PVoqlt{&d42X}fqOx+8R964~
zN3bhtWL@E+uTi5sObNli_l(w<w&n{M7yCv;MQwfTF*`B-X|wWLix8S)W<d{JUW~CV
zw&q2_{eTS5_@4O8UN0C%j3KQLcI;5S*pLn1w?@F&&CUQH$HzC2nmZ$LeYt4P+kvl^
ztoas+5_M?9@DrJf%`co0d|TjNZ#{kbR5#=Dd4r2D7uX?jyO)uCgi&gGAvz|eH8!dA
zop~cEE6R*AKmDdrx4Qrd(YIH>TIz*ck`WV&$MH*>o$S`<aBIulWz0(+t}a8BrhRI5
z`BZPFEiWB?CWq+aU}KP$BWRPhUNWoyl}sV$4VGdwx0DAjGkp-t@<V2aBiHP{a4K_h
z?DL1sNI9$9kFc%c(>L{IbjLf!nO-3#P@isZiX7ikXXz6JU4fyFGCxvoxCYsm?%hlR
z(z2MS2;6<CGIx6E<CxgkTR*+No?<m+(~yz>dbzR?Zv8@1A9jV4Hc%iK5gu-m)Lnm1
z-zNP@$&jwTzM!VUfGVkaIU=cnthl+8<AaRtV)M@n3$U`4-ux0?<XKlnX|*y6MZ8%?
zt`ew@TW0z^JcN+8NMHK&X3u12L_}5=E%N+uXT<=+kG6f2=3}9;CJ&F|hr7kwk&~yS
zq@2L*zrMcI`-7&k;%L2n*)pY9Eo_q8;YoTgSj?YC4vO>m#~ZB!g`$2FLmldBYHB^O
zLA%bd8wDiX<<a>nVssLgC6E;#CdE-_Q?5rA)>UgSfX8Wm?K!FRm8U;0A3(mf$p#VF
z`O_VBlb63Ns7SSKTC?NaAtBmuh26WYk$Il;=F7Ew8y+4mhV=Ncb5~TPNlV^MlpD85
z)(`=AA#Gjre7vSmiGOp5+a*$6YsVX;>zN7+WjOFMTfw{H2q7m0c=*GM_9RtOM*Z(0
z6vJ2cWr_mn1~xIdzCb8WiXP6qoYG2wi_YkOL|(S(Y+1GFi6jrF@hq|U>fZ>^ko%<k
za}4O6hM|g1A}U4HO_C04U+_o6th~IufOc3~_Pc*|*89_?^k;E#K6E+?38V{Bc50O0
zqcbw(G90@3`S|dRP;MEwczH^kMuvm}LO(eU-uME_FdV*<0rRZGkWO!r@)#>@@2ZN)
z4w+XYhVWL~cXxkS^Vl~O{n)2ZH%$e<NAZMOZ)5z|H79Xk^t0kNuH)VMpTM5~0vP=x
z8uUNmf>SP0i$jOr2`pkHK1r`MQ>jSawfmrHsuS0^C-`ZBrM1mAJGJBAU!y->z4}Q!
z48~L9yXGgax7*ryX(}s4RGLxBuM5XSo0>FKJduQvdP<zVNy-);!1h0JzwUYdPk8G8
zPpzWuiDamF6oARPy1J7AjFRMCl>r%FQtH+R6sh$!W@{LJb$Km{a;t!V09Wq3=lSpi
z{wcuV)&hq9wThx|YSozgMI-gu@hz95M45)4b?2Q7DC^KL{NnOjtA$zD_P?B3?kT}^
zd=XUEF(WUuf;rkm57Ky%xwMMKBDF$PQH^=y`3u_}zM&)^02sVlT>D<elFl7)BRrgz
zcq0BjWeQUl)l@h@OEEEc>$QLJ9)MkITBva^{||uOJcsnped#}X_5UM)`+x4?zlpnK
zgZ@3-#(%c@U)o8MLNK-)X6$<G(*D4C@^fS>pd9<{dWqko=o2RSG$A`in>@IVK6ZWC
zV+E~#$|AsnZUVEAot+5b?o}GR<EIL|mHF}nZ_ksBBfoYIkTMntnXHVIah{D!ZS+Es
zlXq+F&T!O{KyRBa9G`l%SSl|dXZQ&Uk6NT!V+IOi_4d*L8VRxu{K26{n)hzj@yGc3
z+b^Y)lyHfl%yv|~A)R2V?%ltyM#>*hB=PC(51o;Mw}J>Dj1l!JM<-U7ueH$653n%7
zG45(A?FH1pE74*Sa^z+Q19I~mScU?)M(Sk+FS1%2MCx8mwJ3W$kT2yf-PP4)0_^b?
zvU4@b0B^jQ+yDwxV^o^Pcs+oy#FwKaa|T70R~~ktRV!rj6xbAXTs}K9RY+)rPQWb+
zD4ZA<Kfa>UEO`rx>smF>R(}}^S2`VeBLje|gAwvmyC&M0nra>}_N<poWZ`zx1BHMB
zH~1;Me|evmTP7O^eC^CK(s7z`xQya{9S}l3J_e~~E>FE)Ccj-w?6Gsp&1KO%GV$z?
z!%fezYXU~V2IkiTp>i<N&vLd?RUPhTB~%GMU%KXp@5ID_&*eItz0bR`ph&Iyb5yN$
zf`Y9Nt>UrF#Zp-pud>LK<l}yACu@FmOFj4nc<Q*XUXskwTQcKWl^tQV`hc|78M=0z
zS=WU?LE+pkY9o9Va+r);29g0OMMXu~TuP0JRmVz2*U?*`kzQT!jAQKXYdR*}-##>v
zHrW+p`u4YtcJF`OoDb}FcIo7<-MiKDu4~A~QaPf56K*+Gp0Xijg(yiHy7mzG3x7ua
zL$bW4ugfOSKRb1|s4b(iZ*$FKXYRb?+uPcL<yw9|W_$Et`bIg<ilu;Vh>?sp^tlTV
z>=GrJMZ`??G{qokSzkeN^7Mvh+r_(+n|6?m_)a=_FxLr;fq_$l!vMQ!RtCn<@|%XF
zUPr`5EB&E6X|j#Z5mf_V+l2Z0uD^^cY4mY++Ik~OE0Q^R1y4V<$vso?M)}t5)IY+Y
zQb^y)+>p>p#K3Ss$b196M|?<JOcSG=tb%<$L)K2Gn-%%T?cBHY5P>22JrZL+EXcBY
z)o?@9ea|w*;qo;EiL_`ymG6t<87NmuMow>#ZO&RgKA<6Eo5l=*R>^acwPF&a^qJN%
zPxT{#8S*0k+cN7d!U(xu>qE+>g1<046?w+?Ea&3bRnh6Q2Eg8e9@z=<RYYor^mt?v
z&d6uf1*d6)SWExw<x(~;dB&k1_Tfte|Ml_NqjkK}ulee7U!ZAOB^PB|8q8K6ll%NT
zou1YmpDE)^8|kVdh>5Cd-W_!jz4~z8`C{&eTVVZ~nPgt#weRXz`K+AL6vu0sR&K!2
z{0t-oz){?i?@imVPO!;P-mxNF&F=G)fP?n-_Ci-W!zyw;xW00Pm!^1@_|H{yj1*F@
z0wpw3&63~Xq-E9H2nJdj0K(G89t0SBuOZdYDMPqkARSFN@f72tlVajtW+q2TE6t4G
zLV2Si=olOAd(%NZ;4e8!4(o*oY=EZ)AiOf&$u^N`U7NT<=S!97D<fU^CFIl3y<H)z
zclFni&I(iSq{3q^cT<kes8~sqiR`jGPWU>Wp9H$dPY9(Ra3KUtS)=4OZeGHZB~WVk
z<?@#_o(g=q_{Hd(B}O&O;^qaRy2Z`cN=>cI`yIV@JM*?IL+sy4_Vb?OfAXLI8@h_W
z-|FbjdA0~uj2k@rJ-fk6q<^%VB_)|X{60|G!<})1SK^(OP+RWY-QC^V6_el#+Fj9i
zZ+l?iZYyojvnGAKOG1mpue@-D6ha*}xT>ncaQT6OJAQ881EviipphvUtNcb`^64r8
z_sJ<SSzZU1O^`$J_%tzsLOB@84;Alhg*&@Bf7zbUn#_h?34P{v1c<|i1u{whyQ9H7
z4s-&_F|GA(ivJ?{DY)&<)RYD)S+A!|rp&vdA~=m;r^bPAF39=pAA8K_%F*fC!Hg17
zyrSyhFv3q|ub<afN8m=nezXES^9G%4RwjD!#XP&V`#J@H2m?#TIX%e5t^K~tdQSbu
zIqcW=*{$zpyT$P-p3EMPL!Sz1ah0d7we(l^^V4o#A6W|2JwC0)i`*0;l%y|rmV;25
zo#-&Fe0HKD)2S~F#Dgi2C1FFN5~8R?zW+F{Z(Unk8#$I_hY5yCNXpLIB#TNGt8lfB
z0UtrNg`Dc`cmV*!Df6@*c`@Z}ojKOv*x<lQ8-A~03du-!Y`#3TE_n^;wP6lh792tI
zQ)}L}LC;I(H$n{ql<_7Z3JB0yrp$F4twX*L0oFnrX$nauB5a#-r0rVrsD@(Xg(@qU
zdM`3T5{-k16(D!Qqs54K6cclVvL)@~bwLxvOKGd>Si+OEbQ^Q8sH(zJ_8^NgeRppw
zJNgKDC(m=2ElH)uL+VaM98?3X);c{@=7$i5_X7SZvRrxk;&y|yA4nXhO%5aO!xng3
z_Wt)DYxgvb7s$#b&b`_9_nq=Wy#=Iv2}xjAby_E%@I4(dz+=6Qr%i4N%gbPi&)-V>
zD$kC`&OpiFp!$H^2IWT>t~~I^H16ya%cYmdGKfKv>z+M(X35%PH3kG+Esl>|9J+H0
zu6F3EjGZCStz<@LIMGrtEn^8^mA{Qw8-{+^ii_avdAABQlMsn<>$(Z^dncNgFLk=%
zq|HL=a??Okr{^c0OV!oXV=P?N%lo~xpjyaFK|Th&daNNz*W?`X>6GaM6&C$O)N#Se
zDgtIr#4a*ZAx@1hdfZ&@Okcj_LULK?hi$ll^Y4F;RdnpB!36-x^`;tK`y?FqlHXH;
zwJYW~E);S8&Pw#soAuOD<<8KniKF+nYIzwhBfnRPJU#3=MhML6UT9l_W>ADbNa6P1
zP+pZOL@w|=7SlPe@3)N_v(nCb?AINa(tT`rYk%={wPJIFOGj{$BlwJ<nV^Rc6CFd8
zXn*&{2;OCN_8q0vB(5=ck`+7iak)oYO&fV*%H=cgHR?X=?x!T+<j71JgKyJw{q*L}
zpV#J!WK7M$!9i8E9=ZO2GIvP%#FkYubz@+0eyWFZ0i?!>6MWps1n!xhOT71$iwj?v
zh1_*uD1@uvs$f%}_ADmGJ7lKEVqo}#W?>&_E95WoB(d-VfO~F+0Kc7}>li$(7^O4$
zoxDl8rd!L(0b}lnv0a6l08-*dd+l5Ba@!OxLC5!Sl0V3WuOgpEHfX0aYut}J?&pkb
zp<3^*6D{JPj~8eLY030Yzxi(CA-$m=x^Az{vLcb#4yA@FjYcC!o`1XUw@7Iw$NEI)
zFI-k!-Nn<TA#t{Rk$Dgc?@20CA}qVLcLUFME`KA;oP&-j!W#c5$CI;O+6as_aId{N
zQDzZ>&P-O<sz^gByYj@wxz}$KUeR9~EHCS;G})>9{OmsUCCkY&R4%K$$V7@U9_cX|
z2^E&vXgwHH)Bfg{;Hyvf{PE#>cx1MK(6`q&AmK9|?WrY6(cdD&<k4#P0dM1T9IR45
zjb|t2`)~lr2U;y(x9eHWYu;w`bMeDoK>2!^`l&YgBFJ{IlU$u)PbZurJ=c7%)qnmW
z(e@P85#bQy2G8!BxiCKAJr~c`LmI}%N3;&kkZpKn^FERy7M@izUsCt^cO+?!j*0ie
z2JAu!<TqaGl(X{njpcrX-Iokte;S?t)siiaKa!891fRrN0g15_z9+G8WMo7dw&Q4P
zyk(V_;R<qrO5~~GKQQGQ884-)PFR1w2=7;(K5!RPVasXHY7RIOw@!`USO4JDuWd4v
zlaL|kb*%_3(w*viX&~7#vygo}EL`l&VZ$#e)GjZN4b-FNy2G9U)!1h>7Z=uqzpU&W
zS{4TH48L%?Ye%TYHYusG*{jrNQA`<NSzlA?P8k`AC}!3)9kOcrU~TUZoNOom{6baq
z90<=Ye*d)KWB^4)9`2avmUxI80yfF8>CR)zCsvE+9erNnoICIPFyM4WGlEaxWxxkB
zE!or+{qaz4K|6JCV+G0UnKc~8$m3g7l|rN(*0PW2$Z304PCQupq?Ig1sqa`T<z~O^
z$tefj7y4UPwLEt1y_rDcSPg)+q4w9UYnthGB9V`ekAzppsEtd=UQ(C`Z~PubHW~4c
z&@Od7nYjJ&>eQUoe0-!{26dUd;qaUjy)5_X!&btU^7k%7{Vf80)fRE~csydq;}nb1
zN40!^_>{vi&=3eKh&)qORmh?81drSCmFs$jI+%TMst<*cp8njyDR(F4oIO$<OG7A4
zk+XO5L6&fiLsVIRc0%%?d17gxxXd{QlBpH5R!yfk02H?U*~jV_xoou3IenU1%_O3k
z__<tZ2=1AUEst$@|9;R<I3@Aa0VHA7jQ|_G|LlP28+_l?ar7?z#95n*+HmWi-x(3p
zj02raYAyF^kj=X~vJHQARX9fOEh|IlFmzNt{jU}iIbmO~<pzyi3ZU8Sur4)KRia~o
zcIeM*Vk1(eK?1QsjKpvT2+{&S=*S@EdN)a4No?Q2{~WnQcIuB*!cfmeeTf{2pr4-x
znLrrBu&fB*gG`R)LYql(gjh_JFmHeF7}xAHKaz#g+77TzYMlkrp#f+ecb7ug_V=EV
zC>LyUXGV$av1Np1%p>(QG&Fi35&@Oys(SS3QT1M)rwG;RF86fuK`C1U6_W|1eRceq
zJjc?xLe6@Qt)R&H{{`1qGY<c$I8d6PCHd^8OGOfDv449!QxMZ^!HH)&yZ%w=Os!IE
zRzwm9VO4q+YiPnP3luI{YEL?{)$gF1_5Mo|r;XqVcKU)&E(CQ26CEL<@JCKUm&^AN
z3S8aArQ$C!<*?)36lA4)-6x8S3$PqPgmQ~pLVI_h#*cc(bIpg2z5N0z3Y4Mb$l$>s
zg&N4^PUSw?&U1R-ZQZyO3DYRsHaB(#cm3@0HQq`3{^=)VgWbKyf@I=;or6(+fAVRb
zEh0=c^A93$>b^p=p=(0}QS@z$<<$V+NwiU&Y83!$7}ET=>#cwg*Nb6VXkM1kFjotF
zfN_CE2B7}I+*bD7o7;qb{;TC3&QU!8$L2N?W9$P>!IpRL4jav-6g%J=dGQ&bly#ye
zP`PlVmJCqA`Lk1fqEOW36JCjAFUZKIh<N9(UiA4U-huz;=<BoargNNIJ4@+MIhZUL
zL~^WNQOH}*Aw9TnB9(!H;8f2~3+8I9Zjzzl-eK5YM7F)t;@GGgsA3^ViS87&wSdj_
zCi0~eXkgS4Zy6fAXVF=S#Vet<fym@rqTSvgMsPzLD5SU7`w+}*phKCvmuRk?hbz=0
zfT#dBJhz@lyS^|-FknNZMnr-H9E2cKD9NJCcW}_b2_R96=lo@KA}u|kF1#8rqF+Wv
z20=Z+w};pK5vC8D<%{&gmA>^i58Pwd^3}&)Zi-W%ol%v;?29J*pVpYC(Vu@Waee*z
z>zyY2XS@WK*z2DxGWZ~(+r2T4y1MRggfYkFVe0*R4aG&_dh7Tz;~#3*sTKdWYiUKz
z#nZcveA*`a^H1hE_U;~OZi44}hh|Cx%8C&xid*g4U6nc>jqM$pFlyf&GV!RLh=kgM
zY<=l;i^nbn3Yk2c3qIcOIp!<h`$kGL@Q1L5(S<@6Roo|t)-mi4i%(f3(ho7u54rW#
zoejZwlffdA<^TEDG9d??Ad=xQf%mY6iV**<{gKwPwmI{X@9Du`7A&r#dW<zVrKU;n
zKA9@+{K6GNj86fmVy2;uwTq!b<k(c$G}F-3mdh=+P}1t0%LK2){jxAiU6gj2SGn*`
z7A)10$Sw#plm}5(3y@qNBCQ})J~rB9*REZFj20^O|4hUzi~exZ301R(<#JJo$0T1P
z9(WqM7Es&}9u!Vb4CyztkGJ~y5ydH-(qCV7x}PtL&$fO$kx_Y*Ljs{g{e?_|le$lS
z2r0O_Txku|t5OHI@|KpZ{ZhTZ7u^E<L`n*wWA06su?G<ntR+w<9lrHo9wdR)Yk=`E
zmhtko^6u3V%~TtRZivTTBWq)zDa1R5p_*IXts+uK3T2hC->v3`9GYvaeyZfUb?d~$
zOyCX`?R3d6J&ocl=h^z3@siyEP42KJ14Um$HRm#7M?Qe+b$omQMpw?C(jGn3&I{eQ
zv%-Im!cmb%!O4r<)J!UynKt-N9F;ffEg!5ecFmj}56h`NxH^W!x$H(J>Bmr*?tgv4
zsBtjt>9OF4#ztuuf0CYaex3JWFG>@i?(&jj)jX6aEf&gCdPIo_<2hrkjO9%y&bjo^
zR$SZ&M?TTE*S<Yj?vhjF&+`|y2M{|D$e62JjAEMS^+_QcogRenXs`&$j|9`!T`MKU
zipBruYmgh>Gwy{nB$C;{FjfCKX3ha*<0E=gSVj|V29jeP^HX-5J^+X6?t=XE&sORR
zDek8L`5kThhxl>{1pTLn?>Y8bboQeA1U!XHCWqwB(}SeR;m_ttY>5E&s(H%Oua)zl
z>uKC`e6bcUdP^g-q0>oCkIs#h^0^de_{@Uk&Q?}dJfeY4Un(e*M+DJKwv9LZn_Cd<
zeU}o%z`Z**zDB^Q{T!6YThOrC{rsBGP}8nL*=_39r4{1k&|RHR^pl3wxi0Yyz;XBD
z<F#*{BxXUu;qhy3_)JvwfQj=+3yL<|4I$o_X!J?=Ho4Dx<8rIjW?%LlBNTue3JxDM
zFyz0xFV2&-BbM2^6zR86ZC&-3tob1{a4voCtitAsxb?pzDjz-nd?It<<HPx9G`UaZ
zqpH*Cd_8y5dRS}-&{LFf;=?w)pE=+CrR=3VGXCjH{$|%26?u2djl|OiZSlw$9SHVe
zPQ0`6g1Z&I0w`=pKp&Jr`}_VVGqmn)T}NPBK0c^yZDMs9;pOPT!k_QFF5XQ+tHy2|
z|K&VpXdIe65t5BcxqV#B!?_On3a!0=pDKzp=*~6CZ^+9+b%?vVGTaHlOHCUiCO%_Z
zd`4IFGHPFeHTgY7SgU{QoED{X?E=x4Q$R{G$}@RUD|7)?NaDS~l>pwg$uIsmwKuz}
zzitG7KE1RGEjCX2O*J*+9rI7~yFLGbT&NOK9aotW7PelrpZPlkFu6)aiM>KRaLH3v
zT8$TA|9KewryX3;iO1z1qJF#=(DzV4cGs@t^qh?>1N)<4e`MlSi}?Eqc-C+x_wFBu
z(ey|K@6K;Ov+-#*%yguNb@BckdN*Tr|FX3^en0NO`*-<|Pv_VtbY1=16c_iQG&r8P
z-P=b{wt$!sigVl63UyH*xTO5wf4tiwZai`L#wY_FK!g}*0V8%4O0D$;zQTe;9N*d*
z;pYn;M=XSATq+&I4?V{K0n$kcld0H<4y`awfP_9j%J~6F4xzjtYW0D&OG)NV4-!e)
z%ek7pGu~)rzxJt9c~bz)NVgVAF&J(Wg9IFsPv-bb=o13xCa}vrklMB;e>leD=pboo
zCD0k^{~{+o4N5{UH-6rh3A3!eXhb2ji?A=#W8j>+`k9dD+d@)B8nR3ZZmpdt977NT
zgd$G*9&Ql@>cibuME<rQjtIcfDK|NNv>JOv!jqWTF%UEj)hu+L5iukmpP$0uA-`%~
z%2)2MB-#%@D-AG-4^`Mfp9_qVPjBzSuOnscW@PYgs_ui;Q_Ifik!}{eR{zJ>ByGu;
z*`V$V{s@}W)bs=-R3t0XH^ix%Dh%CsHADCOl}R=2t`(HdP|%pDOlb%8^lBxG9($DA
zC%Vsw>2!%vEOm^hZ&gn3G_e?&@Fa-=-$2K=sT)zD)9G~5Ak%gUq@TQF56PWUGD1kt
z2O40;E=SZQ@9KRf4z<T+6&DwmB_axGkUfQ*($o7qu~6|@*wal+UdenK{e@R2Yb<_;
zqm;B|cSKs&xC{V!X}S>9{<96hw+gGda+9v4{7Y)$ppV9Ldyz)}PnSGqH$NU6tll~B
zmu5U|7RusA2K0lHrye}bn5<6NN^$kwUNl)bwesE>q{YOg=Lue2ST`xOXq9&9(9-X|
zLHR=Kb-Gt=zNC|mZN|a)9<r0TGgTLYf`y4#zQo%Ql6L{a%O_&GWvO+mfQ)cVo_wyY
zK5k4l&hsyCk0mNKm$Y5N-reo$nidpS`C_ug;&((qM;Epdw^0W6O{eQ-I&vg8I0Wfu
zUjcds-ur1`7jJcznjy=b$Lg#SrVS^MTGW~ZK{u`-Cbmp@x?dz_4%bwqDK$%=&OzCU
zXN(-oQ9l=pCCDjk0kYVhin=*lc;7J}H4EXkiMZzDt83C8v(wKKJWP9QNO*z3m^3FW
zlka(615NB<174={B4p%YP%PTb@(a=2Ha&)|0iT%JG$VOzZ`-};RzlB3_{v=fpx#)r
zb_K~kNV`sZS%@eUtxaY#Xhv#XY&^c7FNRkbb!Oq@9aN2x{n2XH*&kv40TMaV&BbeX
z2v_rNK^*r+ZBqb=H0k#NWO(A<L*9YUh-Q76g*zd1nWL2`k(|Pf$dbhVi_<(T?zlpG
zGw+*!r`!7H{iy#o&-kB@AQt-P?qJ>n|Nl)5`frT2{}(KZgmWn(92^|Xxf^96-*p&L
z4&md#I?<;o;jotaZk?>^CnlKkErG!ycef>OAB{f0&2IPcJ!UhbzFQI?K#sd0v}+5~
zpJ1fK;itHc_iedWU0FF&_UjjoB9%2Yx?L--oC0SNr9laukFbc)ax$QTxjR+enwD63
zTIJ>VJ=KH}|1#A|p_5(P&fFi8U!=0)w{hVoFHneNWW{-=E`-i-EUMgI|3K3fu`hn#
zR+Lzi;ZP=0R+-DFxWVuFCFw+wWM6I4>gp=i`uesfP{Ts5?@3D_{5^xrMmEoN{)>iq
zO7#^A-#IM+h4LRC^dDbLtmj`ep8pukUpMNMS=8d1yVgi<NyE%|iLLueK^bd0$qI<r
zi`SHbM7FYx7bSZ3znJns#!k|HJIKJ=MD@`GL?nv6$f8L`AK}5B-55J3bJKZ>W@soF
z^3Z>~0?CXW`a=qqyZ2vBLxI@>)ZU8>XdwMc1b>Vw;#Gb%*F0s#dXYvVujO^E5^W;7
zB1j$u9``2Urr~f*ph9AxbLeCMueQ6*Lc+DZs0FtHK!^wpQ%BHLYKOr92j_%(5h=VC
zBtI!zm?bD{147&ph}N?c5ofG-LfQyf0$Q7{#7Yt~@*EvTsy#C~0uIe>yr0nEjFLBZ
zHUNBvj6+eImyD>mZi7BEirSd9S8P_Og?a9U=RDA^#>S%Q2JuzjIkTBmJ2ZPUGeSsD
z4v`SpoC@-{RyX81SO_vq(LeV@GUBKYJ0|?RdQ=Q0R1u!=*5J>sy{?B_L-#RKD%B|E
z@mTn?hSwhITa!^Dk^FpqBNBBwozz${)Q1Z*u(NSrWdScB9h6<}sEYL)n6lAP(Oz0(
zqQ+f2D3*mU+-F9o$0GN6tUfw3S-nufbJginxf|b&eHy#>(e`H7O64<M+bEXm`T9-F
z4tV6VUo_f0i*;*V(evm=>P9Hia}A6dM`QUpaeW#W#HaGFEfm$p`*tqaHu`V{t-Dp2
zl7C$K(y2<f!B-G&Q5IC;9@ihZj{S{39dm}7gdCafykUt=cfvx#Km!v@&2WeEg#iht
zr1qkFXQ))F`#abbGkN01dWz*y<nlc*0lt9(kGp`=qViVHyB$rSXn-NAsz~I23Yd36
z<;+*hgx26Gb-!oQ+|xck|LHX#JWgFoT+X+IsS^`J8Ti4}VeYv{;<=+h*O^T)#0Udx
zMoH<3bO`vI{i=9bZ24mpAO836=c6Ydj~T>VW*qn-*LuvN07VBTZq$-WY=c7mJT%Xi
zCk7P)ct+%-L@5Mq3Q7V9_Du-;3t<nLDM483jqyA_yt(XM;`XA$hIx;hF;n6u)GVoK
zHUQ1+MyhlOz@F;MIRD`(kdn@7CC~SEcXv}3y*ye?*|G|1wnkInn1-by;)@M55;q-t
zv?jIjid8){vV?tcM(ZRYoe1n0DCcnD)TvYVGE~WpdK#{I$-a84`C9VYmy1=_ufbno
z_$yo8>0tlE|2iaww@rWb3;$2&N2m3=zL@+CAkdq{STyDsUPhHtpy@t%mrm!2Y@m)o
zzmw-GDi94)vf#(fp7UFvYu-+pthx#WZEtPV@P~cs<P6@QtHAimPlJLnY}9_izPe4^
z(B@DrY?m~LqXe7mPEbet`e6e;N>ts1`^gh0KF)mUDd`Gb!(iW5%)4mR3iaFZFM~_)
z8x)moL}t9F_0~?<EZDz|@n3nKs;aE~)TB8Ih1m+7`6}<8qP^di)~yeF#M}v7vE@E&
zDDjVGIhBqFbL!21p34*GPN})ub}8K%ie*!jp$uplkd#?1L77dk9s>MhaLd+%x7M8K
z+A;4?F<K>CF=NLI0^=Wu%I##pd=)Xr9FJW(z~8}oI}!MX=${KQT7VpYjnUt3SujsZ
zMO?<VQ7B}pzQY6((@cdNM@}7TZ7La3A;#<YBsAI9!(V=P0{-Muy1_X;?XHuQLvKpQ
z{ABf$%#WgrdJ`eA2}R9g&jv4Xlb6!2c?yIsfNvu$xp-q~z=`AFGFIApOtI0Tz6}(m
zNq2SJ00gD^X!)OU0L<Naf}jONAqEH1CyCQF03=DSy);;mr;q}#VTTzV_tFlBLialF
z%yV?<jbkp#8Z_Dc<vh9@f(-*?vWIj??g8zop}UbnDHqr+dhR54f}k%@+ba>FF~a0n
zE1GIo^zRon$x>2TOqdBizLlGf)NHJWbE(lf3e^c_%g{znf1vCW6>DB#`Lgu**pmRG
z!Ixj-pw^gvgS+?v*e#u2lYCyQ9$HxjI#8i&#YZwh5I{eUbKhySzWheuRcr|HqEVW(
zZ8iYU#|Qj8gaAYPk&y*<n0!P=v#f%+>FljF(&3e6KsWMyh2=@W96-ORGN3N>wBx;p
zzbB7PY5e8#0t_Q)k#CwfMtTdX6O2WdEA*=<^uL%74LhO0@oY@c4=rW)1OTh?ppUKF
z2C|uad<Z=g=iA7@Br;QpOaQvqHc#=$i^b*Fow8bm2T~Gf5=<(aACrjai20Ht9@7K5
ziRXUILZ(2*N2GzpCLN<sBv<3H9l)p(8$V$@H#>B&286B)y$7|S@Nu+1l7@kS*1|$~
zXH(3Su_FREsIP$T#nVCjHVEn9&iv9k!@XOeV)CZT9_){YLE))nIQPF?Xo_Gn@I;FC
zwr_L!jkLGxR0Ni++qH!>9`o@*k$n>+Y#d??g+fys>StzUDUeo%@eYsK4ni}KHh}?*
z7rOUcE;JI4D#H@`pNTeP5ci%#<`S9^6CLeErfeWkz#B97T$^7m&$|xU!bBUHuEHar
zRmq$i#H5wvBZ;=~iR8l0^<Nly@^6RClR)oeHj{N-^7(*VciY;;jbu&_xo!?zx&<}E
z$@@rmzU8w$(r^(fN<^HavJK}}Ne%PaBz{%?3-SV9Y6XJyd{P79Kj89u!lGegbu#F)
zUnB2mP4Lu#@&n8B(I9Nx^_!>I%Kk5sn~Ca=xS9cArFl>ewvz4y0%ZcUBXs;Sxz11?
zvij=RZ+edb4eL2Unx)4JJ*EqJf>BJzy7}(#!17^pGd+K^zjWM|?p(;DepUW0Bhg4u
z`}>aDa$k@zsz?gZm6DGJ1jDXNVkTv=QJMTwMvs&up+0${Kh+i0{Xx)U+3?ayNy@MY
zs`b!9Vd&N;&VB@o(>>6tJk^sl!TX!TEKA%DCNMlKENo$Pcc3EUY3~L1V|xCpt&Q_f
z(Ocm>H#Ug^jMjV6b*8#@R<q-N*E*;6wCsW1Us5KwK`2okVdOTDPv}=A-9TAeBkQJ|
zY;~(G=Wc&}?Rk<idS${zHMmkaCk;)N^(mb}Boa`{Q^^nyF)<*(;W;@<@MtX%1o%Of
znv!=vg(L~C2D*uxtCKg|(L$+@H&Du55ymOQfxwfY0L=j;Z9LPZV+y;00YYz#D1qpI
z4i@MsGK9%3JIE2_<J)OoOrNvT0nm~8guXxN4}kkN`~aogl&EZx1+y|6R}-CHgrAZe
zy7^&xXlS51P33zFbl843j6nzpo0t@~f-v`>5Ax9i&YSX3B$>Hg1}Hgul(4c`4Cxk{
zWqh-8)2FfYAuFUsnbxevhIakvZYV<h%4ogzM4+|?ay7zc_TpiXnMr2sqd_wHWC)To
zXft7WI%!z$c!0-5Kuw~VMi47J`{6@2GPCUvvhiksffy4Y!;VsOM&d}qUyuuNNQae8
z-y`xY0x>CxoI4`$g;Q*{VP|8`#sGJ=iR(>fs37%RA}qJ-7$%gEnFPdBP^)WyL&rxx
zaeGkScq7r+1A>+aiWxv}Wm3D}jMTzU^T=)@SOCA-h#mrGaCzSmtuC3o0Nw6IzX^fS
z-q9I){1`WwJOd)jAfu4Tu(JZ}n+e8s41Ct~kaM46H#ug^GAFe(IGJ?<8Fc}fmvS1?
z3mQKA8Sb^QFglPilqIR!w184~XC7tP6E|VTcSlWiG=w`5GEta2o-bw2cc-el+8ntE
zwYL?FUvXb+TYzyXDa)qEY-gjRqTbHJpIHQObO^RS)ox5QAx^cuh(hlY^x=~;9U8Ec
zalKPyh*U@iylWyH4nG@I>UMo7A7jYQJ7&O*C&EM>%O)U3vK@H@o%9_{ECbm+goY)>
zCZ&KPGcp&Bt#FV`-NH<Q%bgvW=%ngF!FR#I`0B#trS2f4ohBxcwMHDE{WSRLZ7mqq
zj2U~&A~?aJsc}qkVVeYD=eBoW;o?Y6K_6RVqx=v9)3A+9$L_vE9^1|tclIzc?Q;yX
z3mQ0No75+5!a`VvoJ@)e`xfw!D|wJ03>A6Iusni|A>Ob@rbSYP7~`4f*HcipvRiCt
z+ilMn=Hfl~V3bQZgTYu_<=6n9x{90*eM2tMLlC=iMw;FVIT{_B6n=J`ajw!+&5*{p
zGdU9_wgqGsQwV=Vj19Ekd%9zj99^>}EI%Rlmygex?2;M#K*TaLo$5sH#}OGqHt7fb
zSVGlEkgP3l?jhs^b-g(mywMP*zI)C6*1i?27@~3g^=)nvi5uI`9Sm~;rbjplKAANa
z{;XNDI#KHKr<!X@P5Zk(&+2w|j69yqSW0;-?(k{}W!N2Yxfb<m68#OC8rRF5g|z04
zAKgl0=+4;zj}2ov&`MjN3NFTzOhnY^fm-Wb<AATzCpHQDnH&oYMwu7^rAJMjExpz3
zL$`)dt`=J3fNUHRJ&;076m`~EkREC^_j`4i5b3X`>R7Uyb<T<vUxEa$y1e|hgi+QW
zS2nxRzlkL}+kZo{DjCF|;RU(2ds>M|z|Ud4Tp76qcrt4ga<xR5&_ndZ_FwjuKFpnJ
z=racz@rKyj`Aif$hIM2hQ7wqP!H!{a%xlTs8yjPG6zcHwhHl*xC+?KMn{tPUXkipV
z`y3e%l)4x5hcYhHK;Ydu8lt4gQ&;tn17aM2s@fVUMJ<|w{Dq;6oS>0b1w*-G>X#kA
zv~`+v_jicra&b!Er8YoTm&mrslpyDUtgnx)sWfrU5y(M1n~1(S)lJ$S7ouoRllv`v
z4WA8hdaRCwO`2NxvW=P~s)sV<X#y0>2cUVkWDtMqVdsHvE+>6%aG%@e+*!~uRu0M~
zSAT)?w8s2I38p?+s+@*MC@&kTxi|!tvMSX0o%3w*e3_`7`NxI;*w_bzwh}FcH{nwK
zZOM<4G<IW7)%l4L<XGp&gxGSC^f&%e#*G))?LV=QkKlA+**#?P*-%!aTQMfjW#rQU
z-&!mbb-S3;;I&%Z(MpTnyc>sEUC2(ApRX3m@{j!Ndt%L;V@+j=r6XJ-1=mwOM+OCg
zS}VVhiEcu;ZV7LC53J2HX?B$7%{~ao+4t-ZwzV)el?&f6!`N9!P3Yrqb!lkGSxiYT
z2CJ`9F~iQC(N|SnBC4dY>FC2`_9P+Ba;MP*7kXeSi%_@{wk{gp-KbLd$nBuN22^pd
zM5;dNE?I-InBLu_%WZm)cw(YZI%6$MbUzBt7gbdOPbGiY4QI2#Lzvr@FuRR(Egb8M
zNGGlvJ%pqDEI0rO3;+s4p$Pi;Ot3bYTSfZmFkYjzwF6^)YTBihag+)$eM#Q{BT#cm
zbf2BVc0F&f@AMiR93)-~DKWXq>(Z_G&ZwLwocpGUQYSI77ScF`w~MJ_LIgc<Kf!Gy
zIzrKvAY<!R4U744{nUQ_Ldoh0{==H1g(iv#W0fo_<xnuOlxPk+wNN6p9<$vD>W1c8
zPPI8EZrRnHkAwmr{<|)0Vq+wkd@~um?-HZKl9ojJ9W&xx`lnJorc&d`)K2fK%j8gq
zRmbXm_`D7U$^+xakTZC*vUZXl;me)6WY%iXR8(AC$wD<v<(bPt_5r1V(h(&zUar(e
zv=~$-wWuz|s`a{{4gKyhxveCXC4~^ob(&Q=aW54i4$@^qrDN+yPBnSJWezDvES60B
z4&552K*uH$(0r?oQGP?L3lMQmP+4*MtS)zUm!`zlbH<k4HTiN)r8QcUri{ld*RF0E
z%J0XZNM2P%)S1(RfK6&IWIotw0YHyP)0X+1Y1sQJWj9z#K0as`<xZNvL{|T-%0XT|
zT`f!cVY&p#3h33g3RV8-W9SGdM0@jRPE(H*cTh^-FclT*^q@Svp5s`R+m~ab9JW4n
zjG3ApS2A-iAfOQGGm;<V!Ioob5)*Z-9R3XlZ_&we2s+7|dJE|zvc*B;k^~PBCjq`%
zmWowlK{u=+b!Nb%pw21sAhQ>skn(0l$MrMfW-EW&KYESnYZyDkd8Qbp%-`*j9hqY@
zU!@C`j>-u`c(;i*NSir3*2PV*(4zUk)(I-W)Xb({-@_9AzSXCTCxiSvCG+eE>5}ai
z{P0FS_4A>IhP|o8FL9+tqdrIF7{}g@OPYzgO$&=N4cKw&NV@XDRiiFTDVHA;gL+7c
zMUv=$$}MZVlzHo*)ohi5K{}ai6dxbIYU9|Vl8!VtR6yQWV%s;|L6dQ#%~ZR+)iNlR
zsvOBsS$kzTAR=Uk{b4FronmiLYQx@u^Qg4lkjeOhn%;`W%oV8g;kw0KTg~$b=j?Ey
zDf=zddUKM;*@u@u#cYUBl8+9gg{7Yi?t5G#>OGS66~*fSdFmyoEIcls&xD^6$&5O!
z^Hing!)~tZFw`pX2kh4gwCHkXvbxd8K!o*l`puP_nR~nXpuM!THzxM$p8iB|EX52X
z@fgVEpaKWhOh!|ARYa&4zW8}QMlHDooVf0%JnI}e8vKws$pscXSOg}$@8+d5r;2mR
z4H%`V*{nd5k;2|A=IU<j->Mr<#+1-4Xu~$eWJiDtmd_0&S-o4*;`CG7U;m5@IMy<S
zfH+9sRwgML!qi*IP(t`C)Q_CT+8lXIeHxSvnnd#yKgRx9r*H(2y-gqDkc6OQrV;bc
z2t>or+ASkf05REJZcetKY_{G|x`tEfMD1?@#_oGbyYw-R`~qPP7q&kUBuaW~$;`u5
z*-ZacNm9FX-VAYyt&XgpG7U3gd;M)o^9c}L!=%F1x?yT?SNXGeJf1x$e>SxEoE^jj
zIjs{H5N~zeCy?gfB1Q3*!mUpLD=XxZ>8yEV8ZMEu5B_x0-;4o8+xSR=hcL-@ts62@
ziV7zo3wvy=gmRgeE2BD?wj+IXP{^7Wqt``D3}CGQ>JUq(+#5od4v|hDV|}z`<AP1T
zds^sZQ~=N}Xb}j|bF6Agz+*sQqAjUZPF%$0)17IbFc6a{OAE7HhDh@4TI^G`C~D^n
z7u_uHq%=`Rkb!@sFNnB%t~1`6D4+;fM1+{$E4&0^q_XtFN>g{T(r(W%ZYNOluNcjz
zT-%I%(W?ckE|v<xum$19UZTGqDoSl|)Zb@S7lkem0=&+-cM||DC$zQwb3d{HOL<?a
zf1C^jNZ$$t8kCOSPB}xtXbxHpZ0rOfXwlP-K><66M%qTW8<!Zn^_Yqld3cZkh-fw(
zDqO30<+2-IT9lAGQms##i>SQ_+z$wsVDZTAAZY;r3qU5a5Ht#d1U8Gc_z0Sl@pr-T
zsX)O<Ee>r<t~-sCQpCnamLZrDIU*S!45#UMh3lG8I?N^3Pg=F6$MATmw?vhdP~?$*
zN3Q)^KphzM+;`JkDrXNugkJd-?^=Pz*yA1S2UZ4eW_91Nm?jDd^E_L=#o+S=L@U$Q
zQI5IQrcUWDCtR^Vfg#)hJc(^}!5rJ1iS`B&tIyYMw+8*u#))UIGi*vjRaerqC3-i|
zRx(2N@|OM(L?SYIE-qda3SI@MT?y*sm{CoqL&y5*>T`eSh6+&+ug3KT0i(H*-AA~}
zNVIuUSX6Jxk?+`QjHy>atGnFf?R^&4iY}h0)9&opO*t+^>iuR?Tuu*yhI$`GEy4_1
zGR_n{0;n56ET_ur93MiMh8c5<2`J@^j`Z3M>n*oFLdOgP+9qD>#R1XD<0L^L+!JzP
z9t4U(mMh8B+{)@|$L>8?5SgLrj8SO3@sXHl4h&I^3^OO44RC;lv4)v4r2u`GaT2iQ
zWU3v33dj&WG64=e6WuAh+i)NOjeES2gX?byy>Gj+5Tz>tE{Up|ywlvY)YV!~kKDxm
z<PoJD>Xe?r*|E#P94^N7`H-p5WHdIJdrewDxkJLl#>m)qBEuvw98tzCI%CC{+OM(5
zE+;s>;98I0H8-mG@X$KS7T?6!z|_;g20DKyfqx+0DO+5uF)h6YK9Zo|FCG_tZA8!*
zGF|?^Ar9^X5V{Z90r?|W_AkHezkT1oU9c%cAs%loO#UAMJTm9M$^m9P+?H0XvD$oM
Rn%u^ZYR6RL_n-ace*sl)fqeh~

literal 26372
zcmeIb2UL{lwk=w;t+rWIKtRC8fQo>KfP#cpltx5El1fsLAgM$o$KE!IAYevCL{LN|
zOU{S{5lNC`DTp9Y1j!UoZ+@k{_c{BFbMAd_+;_&k?`_A}TL)D5|L_04wdR_0uGQC$
zA5&hjlxHc0LRq2myV3~?WuY^LvcUM4MfgeUz3^fDwa8diSqXpR&-B4;FZ{LS!tbZ-
zDHOh|<bP~I-6N(H%0`Nc(r;SMj|SRZ?r7DAE*>^=sO1gV`L{({do(T?FSk5=nMdi+
z@)DchH(U`va<MJqT5HjI<t3J!Pw!`_J+i3FI453n?Z}^Y`yMK;-jiV}e)wt83ysx>
zZm9L9zWlQ4>e3bMmuj^%+Xkz89?AFU<TC~txvmL3Qx_R|N9Dt+7PGFdY`d@n8|z<}
z0=>D&KQ?OoWJvz@R$AiB`pudx9~Y7z{C?+u#S79XJ&)aNwRCh2o0^)wzc2o@u6uJ8
zV+lTgY+ddV1Ip@~6=qMPqqhqRew%i`7NZh2xfZ|c-TwYRUPRMR`cCZ*H>-L!g;kZF
zy~?N4cae3SfBxs!DH5(P%4PrM{k73bS+R*$YX16NXB4%xwTqwpcDd6;fN}ECg~BCv
zwQADEs$#k8rrq-nnn>;0Q<QSPw)69C^*{rsc1cT1OXL2y^gwlwKFyKPA$Oq;=dOU2
z0rpd$t}5e}aAzf9^J7uIz81E&f#++V7jKVxv__VmqFGm0S0tJ}u*Uqci&@Ta;j*|u
z32H}9##E=j3C&|NK;C1fh+IW&*!-n7FXr6M@@|=l!*LQFk->&MR|Pzst#ij4G&D8W
zX4BL3+&?Wd^5vE=Q?j=oJsgx7?PteVB{$ht$dEHBxh@q{-{9cV8-BHOAh{~A-H9e=
zX=U~F)vMi)J!if~gmx_vdUSm6U|f1fmIj00wKv?yBT6l#!7Lw3Q?-BesJ{D1v0e3V
zix1^{&PAl9Ny*RD1eKHtg>9Imd3n_}Hd6bd*L@zHK45V7?p+OC-Nr%r7@auFBz0wu
z(LB0e7%!fIV&S(tMdf1ok7#zKe533&;R!~wom}+5eKsvA?hXzP*(Pd__KN3Q*1ud4
zDCs)Zdw^*k<{`x_;iZ0dYC3y$MWEX!AFeO<nflnLJ-ocUqe73JI~OsA(rKR4cZGEw
zd*@Ckp80Lwd%;SJ$+k3_Cu26bO1?E*+&}ze;|OkAbZojl!L$C$NJFk@Lhi(APX;}m
zI(|}p|A<me<>*MMfY^0L_$rSVZy33A6RkdT@7}%phRf<7KW}SW$(-#`ppNcun12};
zSD_vv71!S7_<8Pq_eGHs!(3N~?5@1Y4mXjB^Xf@Mt?^Tt<cvJ4Sl+Sb<g0Tk1DQRK
zM`rpHxNP40i)L*r3E*uE=MHpgvr`_(V+<y_Pk&n(Fc)Jqw`2Zm*!=b{RblzBJ$3c%
z4z;=V$6@1l^*#*>6qxH(<{e;Szm;ex^VJWIcrjXOCDq{vO$OUC=@SOH^MTYxLvd_l
zM~3ao_>4f_uGfnNuD3h3ig4~cS=~3`&TdW}#9Gy)Q)#}w&g1pECLK90d@V!q*@HV$
zunI*l;^Peys_LROyQaE=$jzqVS*(|~)o;U6@x|s`^7EkGoAF$LdrLd|Q^%V7yG@V8
z8s^Ff3Z}JK#!NL=d2){SR0Wlkl)UY}h@Bb{A1~VBK3Z{K+~lzPbh%MrN3L7?zK%Dm
z6gC7KIG*}6j<Y7PYX7ie?8zj-i<$a+q@+sx#q!pN%?+3@^Dy+98{hlw?IziEKY5PF
z7zNbF=$5{{xv48U>aidGtt=YsKQlAqJdjjse3j2}+n8L}b&1ZD+GGC8{4SfZx4TLe
z=IwSG=InjdY<(`d8Rt>D(>{!$AI=<TPVU0iwH|FEm#gvgFvmT6a^%|_TWyjqd@3Yo
z6x$vf*snR+y!5f#;Hf6+KvUAlVD?a&n{7x$Q=^@U+QU8Dd3bqE8V#HR$T!{-Gvd$G
z7e#!+F=)b3^>>W@_U+r=p`6jB3DnH)dybQ3J)`2L@9q@#Cs)c?U7l0;b~C7m&#8@_
zY(^X*az|gjd~u#^ck23dRUj~TvLm~CvQASyV2kR#OPxohg5oo%svh}dZ|S|bRVgqS
zE<YB&io?@qM%`<sc56U=Q`3EcJr>8$=6Fmrn_tei^u#@<O>qof<#&Bi?`b8ra5eih
zzB?vywlIrR<aA0$!j)CryEik*eYGeAmv;xr^u0Jh6GI@enc&@b@mx4fNPgtnipP3V
zAr(Oa3VGFJ1KK82#n+9>IGSC%;`>&><@3$y=g*(3$v7&{%?){pZAeE<p*1ySyq>R;
zXIO_b(^_p?zsyM>1kJ{tN^WV(JB$xgf3!Dg*O&CX@i-~Qy^Ghb#Ai|cU08S4ew90C
zOX9`ucL&_CwC&H5_`POFPUbk<eHx#eqzOn$O4>{)%#GD{z1<YFGiSabJ+FRHe9<cY
z4UL9wHs^Lf&`GBzS4i1)UK2{2>Ok~cyI|3Bi&U?zx35ugWPBPmqmJ36(nuf=QkZvE
zQqt1WDmu7IF6Fhou&}Uy*z9K^A5)4g!fAy39V>(4oWM{G4Gngu4j1#WzIv<V4b)OT
zr=9PmY^<%X@3a46oyZK3?l@^-<owCUrb7V>ZLz_9__d9#(bL%2ou0#&1=bnTF1%U6
zC*qOZ_mYuTH%TI4qUoC9dj;WF`A=IgKQ2~idX{>^YkoA0t0j!l)?+;U@il$WzL-OI
zU5kBBWdMh;4!%V<f2Q8?V3D{<$-0&~#$?cfm3z-zo@-qB;@-|vYbq)#`p2hn{&XzD
zyymBUB+p1cd;WauF(jD6euddjtLTHZr^v0?^+iWH_R4L4p5Q^Z@|mdDHEK7@zjEbD
z#`KqvWeHf2sfZ2v>S6Lp$b`yDMS9d+juk6b9O6#DpVgNqGCoJZjP-+`bGBR<Ddys8
zF`9m-YmB3OZM)|4B8jT~wYE)J@sbh?Hwn(q(U`dh891E$B8KUc!zt{W4yVX|!Cug_
z2;z}^+1`A=ztY0m((-gd)pc%(rSw#DzI1c^zY+4x%AxcmKNmjPuHiSUhQ67^_TM)N
z+@2zUbv>DN>3qzb=Ul&$vQ$WbukX!G$|Z~ChyQRawY9MDMLyh{_w8;%(Qk|S)(K1%
zERJ~ja>ou~VLkWUZ|{VCJhEIvyDNMD=_x2H%+xp|O+Q6a6BIP`7_XmdY0TpkU`~}j
zHOluCKU3hn7u(IGA@)>UrL_S;70bkOjyva?nVGmDMRdLqn7{C8oW`7=Trn~9c6rgO
zS6*#LE-zZqcbt#zg7scX$M)Jok}bXgI3WU0=H<(mOO31+d#@EbwsWAdBh$vu*r2qs
z1!2}--ptK)a<IMgR~IA{YRp{TXz0+omfKs^T0Y*Nc9*x!`ue=LHJ$3-ry1sFm5?j-
z<^6-T{+uFyh`JW|^pVsw{7Y?bNPLa<q)qgYG@jU*^35MbZYG(OmfYH++KIy+_%u-F
z<sro^>*iPF$(xMz)`(yDSdcN4$2iSr(r#2USR18&H7LJAs&!pUjBY~58QO$dm#rnU
z;q%4J!sXSzlb3TyIPR;Dd776e-`C&&G$|=*IMtDIzr1@X!=alehrKv6FjV{0sZ(_$
zGJPcZrikUwej85{JDu_(QJ(7J;<CZ9=`8Ptp-z9XKrzn|f4y@jvy-xB@<h7-vCDwL
zIJ!!Mf*3>j3J)JXG@hTG9w}I`sz^mBbX|9MH#>Whak1ajdvp1<OtRlP=KB{#(-)>%
z=^^Qcw`V&I9eey+<MzY9=VV&<IUKE!XAEdMdw0Yn<nfGtsjy0#`=0p_O<ufwX=xvu
zKR=#e*K8WhVJL8^`nQpfmsgeaC*<=F<xUzRfsmY5)w9g#&DB+6-?$z0-TFIh0~u*i
z?Nlsr!IdS>>8pYCk^mh_Zz}V1r?)%m)z#M*YwH?lH+fd>>nZxyU9LB9yX@TQovFip
zF$s>nCo}cqi$AQC9n9$3s$=B-;OO(>)33#H$4>a)NO)>wrEuKVzW2SuQ8lch{)N{I
zeHzo-E~<uyR`r;?yR+S=(P-vFu+-j$Ag|f30}N#Bpi{|a#q7!(*biP^8PL+wvJE?5
zY>&rig|zhey3_SHuii_Q-sEmiOPBk^%HdK+3$lG)zIfrwg1yb=VTF?f3y#n5Ie+}a
z$5F38Jl?8(@m_l3o0XxludMh8D4am<R6VKE<1i-k+>GwvoIja6#L-aVF__uY3GC?S
zJK6Y);p88l{ihjO#~$v9Zs$%czb9fP&T!X6YzDv(%`wcpXqwH)8T+70pV;2*(Q3fj
zqfcyp>JVuosn!Bp#3Gl@0s(YA+sw#=pBjs~jPxyn){6_yjQ6Ks({>EFeM~00`thFg
zC-&^wb0_hkT5SiOFVZ*Hro-2LaS;y;lD`a+$lP0dv%rzw9LaY|b1JJpzW->Z4K8hV
zB*@D;o!OFnyY<zUw)A#7Ye8DJer$iw!^6`V;V;^Ee6^mgE?53+x7e}-J-s7m>t1M(
zGg8=Z*HvOOd{&#lx1*slsl%@HL?_i`9M;kfw;?`uc7HChK(Fawudl-gYk}ninA2g*
zX46y-hRkz|56@IsQI`)<)eaAaD-R!2nkrk;^4sSYRk@Te2=m(R2ya75kv6h@FP<)?
zk5(z<7ccnG;LvK5Uc6w{{_4T|26nV88aADG+Aq$kA77P=`u}!$QSqNk)^=j!T?53E
zeHaq5sdLUjQ8)G^d;W9<l~u*;)JxkbDEKlahEx^?)L1`1KhFO9^7B(&NsPhl()SM}
zmaQ@<@4h(FVwF&QJ}R`KQ@3CF`uZBdk1ZeY@b1fdxZb&&>lGpoP$quv?Tw82nf@+d
zPAm3v(m-y1Te!@4X6&mSVreOGX;+lO-1*=J-S_?XBS=-`V>L<+)Sc~`OWD4nE^Fvi
z;BVP|w(!pbjCaBb*Av{kg+~DOl>mZ#eE}$(kzu;-Xdb(UxWzfQArQIg00ThU|IJF-
zorbPoBMV*jNJ<tX7Oy5-g}n4@^?d^e+Qb-R1s2D`AmId8_3d_#$#y#+DyJ7i00Xtg
zxz(wB4s{xK9WFh^NPl=<2|3@6AO)FS?pgsXL`ok=FQ2@+D=YbUNczQ+?;^3M`IV_%
z!zuIPQ|6)CeIJ(kIq+#}8W`{ig}Glm`-LuzRW3{;&+4Gb20$Wl_o-oL9120fPON4C
z0(Dx&JUOt&I6)(`qgB)c-OOFqO^H)&J@UH0Xq#6&tnYm7)1Nh%)!+HZzVf_NTYL`H
zt&bH8Q!1BtRKy~e&7%k@MM`ZApC=dqu=82_gEU<O2kkc8Cg17OOND<f5l8*lgvvoy
z>tJ*MjttQM>aLQ2{$cs3$F7$9$Lll=bToAhO6wlEe0k9CkpQ@MIEcSJe6GB_ysJi8
zAmG!RtLd$i*|a%ZeL!%apQIWG+qXE~ErSjNh^qUiKi{_dRwm@++-BU7K1edm6A?22
z8h*C{c9OIR=;lYZc1E>njPiu_T-DUn+^60M1Om8hL<Pikb;HHqfvA$o_jt}u?Vag;
z+BMG@3i?_bRf-T}f=FM3_`P1-Sm`>yoXpl^kJgiFnJet<hz)^v*ulkxKFhj-^Jh)>
z<IN*QYt@Ue10y0L1Re3qCMTMQ2_U<;<3JNhm%)v7+Ku8{+~}#g%lY^3?ZEmJPFBkF
z#|A7tyLkEf)rg@2U5)wk4y4o(H?MqjGULM8<?DBCJRG=zn_TJ!98|LD<M;Z!K*)3(
z%9SR;GNb#Ro-tDJ2m<<HTAl|<rFLUA%B47sq)gU{JDsXw!Zn)bzyh~%Whf5Zqy4dD
zNXuQPB3N9?e?_hY^M9J|2@5*9HMz`aF~9SfX!Xd0t}W9);r(M;|IEqUMTIeaf{XQ`
z1tk)f|2wD~awi)#K+-6xze2f)+DX)bTTw_GT+X3Ghm@2Mpr`gPsNVkI=vLqwoVR9c
zDF6mleRfLp%lP;})JVR*FJfZ+^+k0%{k$t*Mn_)*tU5*){@8vUaSXIibe$<BRO{3!
z?p)Vgrb%*qd}R-}_p$B3>$5Jms(R*Jeo~C8ud7=(e-xE^ptY19>vLYd>>JCwXF;y-
ze-TSXKIy-DR{wp={m&2nKag<`+~vc>9N6U!nV6W|xjX*os9bPW>AUA8CxY1yY3k_c
zOf5H0ZXWKsJt(;LL9gDaQ+>OiD1QZ{Q&PePeQTDyC4P~&*4^M>Wz<c-Fc1Gx*L^Yb
z#lYwMa#xN_PN|K-<>M~|(o}hQJ87PBxp+L)fxp#D+BJqN2WKts6>pe5xc@-GvV!M$
zMr(%;NGDCu*ro4zJ4sjqr6abV2u^CwF`p1sT!V5sRzLGqovC7whK>#=3JwRQ9A=PR
zm;yy6hDyux;r2oq{w?gvjYLpkmt1L&lUY0@MNxXwf8_qi1Nwi7q5MDS+5ZnW$G=|i
z-}W>~TuegoFja0ezeZhdTx<5Lx|g3c&0WHEypJP$;FanIlp1!!uNQ+R?p{=SGR^uF
zP^Bc8&1<CCUCt}DQ+~Qky#!p|29!J@UyqV&r^sx*UDY!hDH^dpK6j#Js)FiS`(*-{
zf2w0LkE_)-+a{)!RMHt>ls5&J$EDjeC7vbR6AGP{XFS2+s*sOE_8dNPPg1f(Mg9Fw
z2X0D=$r){jw%wgK5G3Rdqv#;qGCO+>B5QfDn3&xm%Cnc`NS0Zv28#y#2(x{t6t#49
zoh)g#5|^DqY|^fgVlD|}OtDm}^;!-N4ltM`osWPVQutC4!o_9ie3r#*q7oX&2GMgL
zFdlW~dIeASqxX0HirTo7o=yt{Q@r2MbGkd@%R?(;0Ou_-vgFRa#~nf>orM4h0DsO|
z=g&=OATyBcf&~?r{kXV`0OD7#UJ=e{=gGt~xgJsKVf@HChXZ&x!ocG`&B~J5{op9U
z0r44~e%r*vf->!UN+J&VAGu&gzJe0hFU2-=g(v_kWd~NXv$Lpd5mf#_SzPSwYquUf
zNu`0zEG5+zJ3BbnuboF&Syv`r`R5!gt<QsORADwrHh_&a)zz!nHGcV{F%_KjA*G_*
z>hjG#+9ZQhtRLAXFQQr@3g)10BozVSHN55;y#feV+M9f??3S<ZdT@Gof`ij#DsTLH
z5Qy@wu3_`X5fG;2O*Xa(z$33-@!yPARvLR@zwr0au;#?+Scc2!^nxerkxB)_st}c*
z{G4S_z#z~bAet9U0^YBbqaZOELYp-k0N2<*Zj@wvNNILckqvG@4Vha?3RInq#4qv4
z&L)BTo>$n}W_-FrFxl0rEiNm@BjYLFD^X?o6V-r(a4ijuPos+!HOBfI6Y925LY^RZ
z*D@T-zy8#0owj=Lu+q-cDccOOFLh`yBxy0E=*~%9(4Jb#l9IMX+Dc!Zyeu$#=4hC_
zoM6W)R~pFU4BTT^rR>o4Qhv9cDA=<&|FB6g9Lis4)$qwiO)5krMS4v55~;kI^NjS&
zT&JOqkUJ$&&xJyT72kcE&cR}Eo9=#4Nc#=QYmc;aaE2h|gtnof;oI#%7a}#3C9QX=
zsOig`8+8l~`AO*v3VLs)0(17hG}UnbM3V_Qom?}q(;QdQJ6vNb?E3op1Uu(;WyE!J
z3w9X2`^2CzJwsZ_nZhxlHP!y!z)G|f#l^^_{Np`q@3gd(N8qa&vuxGpJs1-KcqqQU
zCs7CcFR@fSjiRact4DVCnic);&g~}X5!4%D(~MgS{#dX{urt)D<NWC2PY)MI(Q`&C
z4Nh($Tc@DpD?1xisnx1yVu+SMoNqwQn>LH_c<kC2?d!WjG@rbNl9)N}>n$Bt#nsRa
z{Xn6`SP%Iek8-P^;2@-j@rgJ*TENPUf`Tje4;P5WuOshz(v@|nAp6qS4;fv7vYn`+
zeM?HbVi_$61ca*AzgV_q1T~uy#1Rs``sqvrHFBihKa><6#3dH4Cq;1CZ6RfQd;5Xx
zp*(Tt;g1!L$H<3M=w+X?UvJ{%5;NR@;_2HWMqNWgiBmqKeZ9;ji<s6hx53Q&`&}-^
zv^ut&kIDdXhx6)syH7vQ-6hEN$n>k{C40YKdk`tQz}s<o?8`vC!y8Tjy_BA0{Az`5
z%l~STVpQDRjnqihVAr=in`6OdhF0>sab(`RMb1$hbO2VdP^+%MmykDONjMr3sj#!G
zu5Z}4dxGZ0?5&TX*Ye4`JF}_*hrYV|vQD-+D3Zldv8|VNv_`$S^<8<nbQd@4)Tboq
zz5l4@!<|F1zV`m3Peu81LyBMVa>HJEDbq#N^poF#_!FTu!>m)k`AESsvJyA8v5Aqt
z6(fGmA%9b@{p$t)T8HoZq44l7*T1{~|Ltq?Qa5^8?|B^PXw@cb*AJOLJ*%v;ZgOv!
z%bA{KJHGtE(UP$3)d!b2OFsqVz+vgh>3(o!=0q?;-krO5ixIU{4Hk|4j^r=zMOk-<
zIX|ar@KfRv&?qvw)G8I;bPD({4I`TUfhz5Q6_KKB-YehsjE;amyGzOkd@*9eVI?Kx
z!mO;UHM&15z65QxY;BoNf*~JB(;^PGP;Cv3ny(%Ub3!zrc5jUwi9T;(xzk|@<&vU&
z*pLR3ZAm1`9GzdB?K2-$2~a|HfM+bE_w2!Eiky`!c;0k}HZyrck}U1NDu(}`7X(^0
z3pya07_TCR6N;j+AOG-5o;};Psz2VajC1F<iE8p=N^E2=8`Z<ZLm!wl2_$3@^gEtp
z^D0Qix&v=mFBx+_w92_kb|~jXY%FMK+Uyr;0v<|<L`B$AlVU%PV)+f^wTeO7W8O~S
z@sognr)bbG2(D66BHS6_rFt6^j2>P3pfXapOl%z^k@fL%$8e#(zCaHwz6=uf^+qEv
zInk`%h+gn=N=mcH@qTg>O`CvPf~LB~yuOB8v)&n6w}ow}g;1KohOXy#8s+Wh_ni8)
zUeZ!aNr|{l>>$(~hO?vdma6DApJiN*4%=DpYd;daIL8V!op4DYA1}Gth08dL((~u&
zO_nin5zu}_bH_e3K`!ltRu@Dd+N<V)FFRQ2ZR4pBk&gzggq6Q7q#o!p`lVP|{@cyM
zNun8(?<V}(^P>+se_SMQV)KFycX<I~H`xHum(XpTtMqyCN)lDx-4*V#GGd%2@3MuS
zUSS|eu2FG~lwLtLQL;W=;WdVC>rVs`yPi;|!%9VnL|tJ0{Q(9_i0TiR>reF@4Pj@8
zJ)v`%(bR$cQy(vf#ITAg?{k;V7lh#F*MuJXngq#7Ednvf^5u*-=&`elo4vp93~**r
z6LH86`nod0+9pAw=80y0zI9*9=JeS|RF8f=gHu~s*LBO)q4<gFy`9&Hrc7j|VyJIR
zX$j!GmWgI;JO<fm{qA#b7nSbq^xd?S9u?-9CH(GZ^48x^<2gfSC<ZXpbEm??a~*p1
zo<O`1++%O`UaLva^oQcu$%~)JZgVYQR3BW$(L(s;P86CK2cWZt^7Q)~%^D}YV?mBy
zN3yB;WcHA2C6yKetnG)B3RACF`Dt;G&rzJz9P^&+4mJurx>a-FEDP71R6dooc-`Rl
zQicCe*lS*Pczbj$8i54kSkQ&SJU+&qgHz&*dPRAtBex4gER+>)^G6!J%*lLaZc{h;
zbjl??yj>Pfp974sT6JnVlNg{Ny#@&i8u!&j+kq>0?8|^w<WEdLo}izLjpn}vlNuP+
zgxHl;0{q18L<}>0JSKPt3&eehR5d|mJ5r|ZxqGGj)JN2ELs|x`FEqe$9RhiO+yeSt
z2YrHOUnRGUzF5FLQIl=Rk3+a~6t$o$%?lfa5V*uRLr&mPh*lL(CLlOnhqSN@Mbam?
zDg~zde&+0`9dmB76aofhWy_~l#0Ep@-GLL&9KNamReomNDxImLXQOSwguFCRCob{e
zC_$89*ZD}LKQ=^Dz}byRN)mqz>OG_HWlSeAvjBMtAKUxs&+!Q(lmxhd?fBN~0t)h5
zkKSLct*t!*n@lMl3_ldqi~9BzlH%t-JUdp;vg%cH_~k7c!ZI+$olh~|n8W`t*}h)S
zCRLj#hT_IWzU%j1ID_Jm5P7Jq*TdAIf2rmWJNpQBhae8CD+35w4j!57Jd!))nbTFu
zfc21j*6(+|pl?^qp@FK1=pcvuIUQcglY91#KaS7^IZ7mjmxrf?POGcavY12>($uX~
zq;Ss<shk8g&Q_GQZQ$W?bRhGLPtXygHrN%RU?v2+X!I)bVNWEmF$eRR^F9+fE~71U
zCW1>`^?Z?#yvD#qLBZ3pG1-GMS=blCKi9|(Bp!@m`Mq%YFrqlBc#w~vglddQItL-{
zO%;oI;^Rq-BZ3I;fY)?)FjouI4`IiB>?89F>QGf)u!8Hrnn=V-O3Qinyr@{5NQ56~
z|1RBkSMW!{X2_2KC6zveLfJq{)kRh7!TJ1El*n|mQ=86=o^-nga$z?iC~fGG;vk{*
zj$T;s;T54cb#QJ;bp^0ObaCwI$5Zo@`2r+}#2lK(Hn~n*fb8tVQo@{|GIa(nlI;+m
z)%ViZcW=)l`-av$NZP(r%D$}+CBrX(y#;wBb@?OG)6`63e#e)+(rp(GF6Wa;d6OCy
z9~~ZnGP|0MD8~oY|EP${$!Y6Z<sI=NE>6T@Nx{~WZrrT_+GAUI2=lIhXS*x`o}2-)
z&u0hB75r&)gH9tTU*$>BMm$Sh@J|VkWuxSN3Z~~!+?L?-VzToH;X`gr9$u1!M)Qi0
z^za93trlm^OD`u6py|Xqk403aB6IoC2U7{mnS`lt!TDfDNF+ZBr%%N^`KGQv*B;wu
zxdPM!x!)=9K?0kU1FvhU%ca(P5^A5*y7!L@U<#@Y8I1xB0C?=?kL(VIv@Tx3vkoo<
z+k`ROr}0U1R|nXRyTXO3JU9Jw<h`<UOY?udS79bQcE*@up@#Ev_QYuqk6e}WHxebe
zO`|46jQK=nOTA{5HxS+L^+K*9INbE;_DFd|jWmW(FWi>|a)2DI5hTuHU-nI%M543j
z^Y@&a0b>JNy8eqZ&IR@mZ+pYH<?KTVPWF(%(lb4#zje#?2~<0%1+Z}J>fypK&_4bF
zJkuPH5kI8y-*|4^`%YntE_2=_cx44IC(+8_`zj^M>8C;;xC>0+{Sm*^TTO)W8S%zH
zvxDSkELa6Jj{J(sEIu4&WNZlbHt`7z#04+~;70VvT)(qVF{%qXB#FXHOgRRQ&5^1>
z$&ML}q#%!-Kr=b7xzpL%iE@ZAWB~&cc*x}Xt54*h3BpSO{=`Y8ojgx+qOQi?Jt7|#
z1ha$;zsFcj6C_N64$e!r1vq=HNclG;LRuqAXhd2NWkjK}^CvDLO4HBnf*%dm4pu&v
zlO@rcHdP`mvu-ec<e#_#3u|{TdQ-7KSLx@n+fOqy_f5c?N`jAWC@b$294`N^nKfw<
zx1L1h-c5oXVK=kGC7luwIdy{15}nof+vl>l!Q|Rwvc+J>GRv$Mi2>xG;E+0h{mxD+
z%j>rv--pOSu_!Hg*_UBj^egxS*XuaQL@7lU$DxOT&#=>v^1C)<JmQNL6uc*D(D$p=
zcjZCGZQzxpb8@pai{n7NBKz0Kx!|<hJkF)<2fNYf*+pJYQnlX<e|pn}CleH{8CBAj
zY2St02{`-aN^w(?i3zODypo&Nll3`c-+PNY2R;=4gDdxK<`mV>s9`u>UQ%qc`!bie
z$W88?M3)DzmnkOQf4!`|lvDDu6E@mN6?3*K2`XFRBmhWQ6H+B|(aWZ*-*ZtCLMd@g
z`dn~ARtqc@@M?QgM!t)ZrP!*zss9<VB5Y4VT;u;FN`9=!wcWKq7JBgkx2fSz#r*u_
zhu%zGgm+@8Oo1ZoFSnokkitwrI-MHnT1JP}meUR1cKn$)Lcqfld0GuLdG+7DlsEv8
ztD)uNnTSAb4>cBGJx_1nUhM%kE>ZpU#Hvv)noV}*26$jh^bvmv$Rf}e324Qj0sx?K
zFbfh@i*U$&Y=<cUPbzvi3&FJ#fo$avoWTADYsj@IrfVV4k$+K|_WQ|So+O6ei5sjR
zq(tE2VFw-gJH+2v!}`TP7#P1irGr*r_KZh5iwWb+&JGwq`epA<@;b%z22(aFP6LsB
z^F{)wMMUZENR!Ni{e|ezAn96Z^NwSEsi$_ElXrN3c2IeE+Q>-2q%<%9KEae$r}4ge
zmNQ&Xup~%80AvzX_s;iwqhBiH$$_m<|JOD?IoC@>&(_dsm<cbr&aG)t^-wZK_#Nwb
z_uj(;o*#6|_lJWVVTst>h-azm$>o)P&{1_5LjZn0Y2TghPrC5x4xn$J3CTwN+Ar_h
zCBZamS|AO0i8}5hS4n9n8S8<BIcJO8K$3EJC@6V){=~t0*7FKx?W$@Uth@(3wQ(sO
zTP>g^Jv==8Z9VJVmy7^@7?VRr77!D?HoORk<bfl089mgoCHfz*Z<X_^y&t^~WN%Ux
zjg{+i`uz50u_z0>JZatGT7d5@<!q5KdvB4>RFiJkvJimK{%>Evcu?g|5-OG$zKCnx
z<-z%0Pjv2COwp-cyp8(yAgW14K-Ox2<@Bdy#YHU6Nli_)gMSpO^cP0#5@7B{Kv?SG
zNu1L-KJCUTR|xC(RNx&eVpZQB@ALbu6y5~s_Dh<NpzbF*k96(@(A2Xwa*AsgKmsRz
zg7|GLx*C^1v%Eu+m@J*=#v4sKi>}#B>_g?%1*56nBk-^W+N{r2Ot0S};oLZ-Ffq8l
z^5wTYfvL$#`Q?A}J(RTV&(%d6*I!@4!x6APz$RMt`YG$wXw^OEEq7IHohweO$&lRI
zQj`%c{_x4KNuBpBIK>a!y<WXXD|QjbrgvMHZz_Ku>Q*(h>RrXg<f_ojM-L_$;}0AA
z-^*3GR8HzYb{l$s)%-Ebm;`>FRjI;CT7)>x37;~l5LxjfEiFx2odw0J6UHqIvPlLd
z^d=UxmMe<21=g>2wYkQ+6^d){k2Z=(9GEv)L_){3mR3EM%l7@gDaOdA$Y;(CLCK&`
zjQpS$DiLVh3fLB-HNifFfn-Z8->_6_IuEoDc{&lJUp9+-_V$d!Sp_Djk9H&m>?+TR
z<dNH9vztkj0RL{KrL``XRY7}u#+;Kr#Y?cvRH;JX(;#^nTc|;M;b+>Ua_huh)}`N&
z*+Q}{7$tp&dTn-g7KDcpNb}wIy>cS*8A$XZ64+Xag;Q1c7qYb#-`&~&(I#_n@bW78
zlZ8iEyZuAtXSShxB6>?(8@?;gy*alQ=ZG}6&lN0|KmPX@MRaX7fHo&vZ`PjWK)g6p
zV^InQ&N<yutWTi}yB6r7Ujs0wyp&k#>^`Unab*8>N1s*qkXqM=%W(ZFxv}H;ch2`;
z)osIO<F(EB6}3UO4Y+4g_mF7WM9rUzAc@SB_4#9NSblZXmc~v$*o@d~tY6$X@#9h4
z`9U&2`>S&&($DjDYCe;Z9|ZTgu@f&Uq3y0=tT&z4K}{GaJLn6wZ@_k84(o?hQYfO}
zgapVN5D-8d^Mze#`w+M5EP8uxcM>!!I8k>DxDC0{c;VMB7X5x+jBx9uP$ixO(oq8t
z4{)qtR`uB67pF~vw>Dox6Obq&KUk)IVys8J)qTq%6RH>DzR>$YT%+V0oymt2bfl$a
z?e*~J*0n>Z8M|P^Qac(VB|;hoiUDlrooBvAA(_8w+QN#UW2&abgTus`cJ}-gUTTir
z)eGs7h@6BwfUt)BoLc!KGp>{SIQMCl*U6^Gd+m&`(qSQ^6;1?sjy*O05f`b5i=hf$
zo@cXjo3mR{VsWJjAPxT8U)y9}83fRiv1T^tQ905ec=;KuBx|UcPuL%XV;?%CN5`gl
z2wRrpCt1teg7iDtW?v;@9cEv-FUg`NLaKEhSK{jh6uZ-%37dD?kLWt4heZhjEil!Z
zKiBH(3yZ4V$3GUA2q{dL!Na-O`0IyAVm2!!n(SVBN-fb*p~oVY64x>T#D9faTHlnY
zg~6S&bHuPk-18;GZO_s+uaW%a@*dVOK6F;;A~pZx9s&D{8bTu5@t{WVXo~@@TdkeY
zCxl9D#NQd@7u1_9>F?rbZ#wd_QwSawk#6_nppIZ&z=;VPcfY%fvJmAe!I{KT{xz<a
zb%4j*ayf_7_CP-(tOcs#Jh5Y}?6GU(*t<2v=m#TIp8SK?+2EH>CY!0DUV>r@jmGE^
zC;8<QqyAKHX_Z@3z@0p#=Hdh=(|ndDMQn39AmtDgY7TM1s}y#wlDvTu-6(v%4+p&y
zw)1PmWA`R4M9RjGKovp3WAf9V_x3)y?D}*wd9Al+ly1YvIRqfXUz`Y=QbM|0Oikm$
zELl%?dbDTq@b_FhcLKQ<ERPHk!P(hspR4&3hC>{j2xtpx>KrQw{S_~9R@~5W#*qel
z;yWqxL0btdLE!|+LH}5NY|-%qF9X&VJu7>=ETtu{Tdf`>xxtTAI73n)p$*Pv<1wg~
zN=Zp^X0kw%_k0ZEEVkY0rpoS%Uh*2es*><ia-=B-@&CJb6Z6~IYzbA-lt&|OF!XW$
zJyzlUFk)*p&6BRGvyA@u?D)hvFdsSpc6YM+ieTyT_4S(X@d`x8PMnaX>9s2?H5a#r
zXC-BQ5`ynvMA4WYNAX}H>K|4x|McftV-iPg+cKsI_=PpeM+&9>Nw`&oSk`!cQmtmY
zYo?`ahma5Ms&DtqQPa(&7&`{QvcqA?t}`jKF1v1*InS|G+lUC&5^Ea#=SyPPTqA@Y
zoZYH&%TVQ0eE$3liPY`UX{5p?-pE{yl4Fr5NOtV-dmKHK(qY}Urj*9K4R?g%;`s>2
z<89NQZp+vrUnnNvvfx5!jx%u+vDwcUNbZ7i>Q7ic(nzAyN{C!gDY`|d0o-D)hrYuW
zK&g{iXFB#^##%F4mGWdQaYE^($Jdz_coQcS5u$zL)>2?cJ_4)Uc}YC}0~=FNNZ^of
ziT|ZH9@0yw*vA0hCY-%4oTC8Jt3-$`SaM;e<06O-#(Q}UJ-ybDDB4$4QEcO8vNz&6
zHqvv<57N$0FTSL5>-(Z8XkLU&vTpqoK*nQ__wP%yZeF8utB9Cu+4UO7c_qw_0uwxM
z`hf=$@v=P7#!05-_l%ub@wuS&jzilstJCpOt*Vn$*3sSQ`s+tIJiw)`yi(TN94E<n
zq0EM1N0PGsECc0%@ZW!Gcq-y7tg$$Z#AFMryuSzOrKM0JxNKidpn0zJDjHcbKK^Z4
zCogU4FfoPZjYW#F7-d3gX=$Z+y+b<)XZDYC8IA4PTk~{NNQLcW6X7s|jXYhV0-a1)
zdDy#h`^;a_BOs#cBPv{F|Mo4cZ;T^^Dv*6blL4z@sa~qOhxLnRT0v&R4fXXLwl5m$
z>Qdg!BVQ_ny@=?&?Ze70i&FR_<B8K@IL)m3$!|V$D!0ZyYq8#%6Y!%pIq{eF`=6)d
zA7b$Kzf676e#Q=akd(Ixx5|rEnkCZ*sTIU;Y<%~7`H*0GYqLt0+XZdyddFXF#mAmJ
z3N?gIQ4^LB+Kk=h-Zx?d{|7~8S5DSeTX*J7l)j5trLRqTyP$FZpT0PhL}NQmXfPZZ
zFtRvuAjq-tstqnLUKI$~Y|Cf$9Rhma>8n|@0(6Y14j=2*E@=e))DeF0DrXEUwNbL(
z{#e+*-@x`CGWU@my~F<#Yp?@NzxXqDO(aCnkf|9R;~h~}Uw_Enw7Y@I!-HnQ51c<2
zpOD>VT%{2iFDS`cvgOOmgAX;QGgIrd6>F^L^Al`lA4tuWcn)2g?U|3O(|&fPk;Pvs
z{@=Kce|{GK4{qgOAMp>QiN~s#v{_{h{6RtRf&EI{DlD*vY)I^Br1h#HTD~E{NPu+Q
z5r~jv+nz;wP~CeE3ixdb+$#v|>o)y8He@}g{hYImOZ0kz*Id8s{>Omb4zsBDx`ff0
z=131lYa9_^NYzes55lUQ&Sh;{xq9kP9i%;Ttok5P6Ov%fDt={JPAIOgbxDw?N08Md
z5tJXfe=D<lw>^ruU0_w+%_{#%m#VIzi>W(sCcQg`^^v_fo@<Bs0lNqU<IM9y@1=~#
z0AM3ZB+-=$v%>^{Ee#^9vfbCn;1W>(JVZFrj?p2Equ*(1@-d1|E2ctXx2UWkP)kYa
z(ZzS$Ww|@OtN!u=43Gv-VnzpgMhE2Bc^lT3)47M)hR_^?J}hxTK{(y&Eki&+K>}N=
zzQ4x&dpG~3a|KZI8spJ8hs%<~Lh9K_T>ShNUL@EK!|GWuz51pnENM<K4E`!Ozo9ej
zIjb)Y5kN4wJeB)r(&>R{+F_<3z-THp4)d7!`djMPTQ=Xd_XAKFUSD2}aI=0FYOb@{
zAf)?O%^Z(UlbXHvQ{t#FQuJ>N2F#zmdNo+}hHsdS%4*{S?;_QjviuV2!VoqpO2qde
z(g_uwfuQsW>XQ~$3+p>tYD3wU3+#xBf<!QU#O?%z-h<6Acd&^mE?r&}b~DZNPP_QW
zV2c+onjYFw^{%E%qBu=#zrBYQsGi(kG`pMxDl7`1e~np4MToR=Q3H%z&Drei_*gsA
zE}Q8EhC;*0h*oi=*8DP}_K9D^s#d+I655u^s<MI&5HpRuZQ3%Dia1^u+;q-mdv*X8
z1aAgkq>#kD#qI^N=>5QwqANV4zds&Z28>T2U#qPV{F@_>Cv&1I=Eldz<sRCGN}+L+
z1yp!`4^uyGDZ&)osj>z)rCL!jf1P9F!bP1`ztcqoT5qy3o6~@=4x=_#QbOq*Q7@$~
zKYdO~i8Pyf=&+3Eipq=J3qk<kQMm(4I}Nt+O`!bThkn02hzkRWZ)Q@y=e~*MQ{K{o
zouK1=v7Urg1;SU-)YQahpGw|pR`HMn7e>1Mi%CP)4{PImEG_nRHZuMc5oe~*gb(tW
zR9%E2Sr$YZPf70&)oFlme`uldBmHQP_SqX(KHAqx7XY*6{#DheuegtuEVJTqcG=nS
zgx-|i-h4i_;+e|r&MzU<K=5u7q-TZr*{<_Siw5rda-Z}b#Yh{mV8ymboHOL#BJAPz
zr}BhbHV<{sdF0S=>d;G8TzE41!*R7S@?XLCAHnxuYw^GPT15Mu;M#QqWY{4b^yu<l
z2{pAVT#7uXxX~aZ?7+S24E|Z@=;D&uC@>bTuco%=Zq_dp87)Y(seg~EcMHbD(b|Wa
zQp|zd<N1zT#Fr&RJ_G3wpRv(p?l+oc{7cY4&k7W0xwi+GJ1|bNQ3YC3&TFE2J`r4F
z-e5l0`}2)Okm|_%22?x?h##ET3g8ECYukYMg&JhhjTOAHn!num3DIJ~pwk^helqMN
zJ#f64#TNb-l+(ZV;{OSIkwU4y`7&VgkW1|{q+Gw}NuK>dpHyxs*`QU+@`>}qMOD7~
z-LmldQI>B5ad9Rp_`D&XX|wJTgGKAq<QY9SUMH0EZ_)JomQv0XjEu4>v;QxnZ;nXO
zGv=?L8D~JD?ScRm<j~(xoMG41c~D^HIPpHfO<~ygjRo1#Hhr8^Ai_FneI=dcK_aJK
zZ6@O!h%QV_)*1!|28Bym*)q)>!!|6?J(U1z6EE(9{UMOdC?E_o%UT9$aRj~eC6GTM
zbbf!+CeQ=O{<7eVH-MV7gQ5BmJkp2-O>o0>eNYt(00u^n#BZ^uQ(x9avdZ0c1Rbc{
z$^hN~C5pD%=Ce#Pf{0KWzP^MHQ~&OLv8eK1<qWM(03<K~hh^OhomSGjA}C0@SL9<@
zyug!93*<i<WINg@DG~1u?!Ut_1a&$<7~u9yEQ@XR4%E7n<$a<x@$5mR*?s6gu(84G
zK}Q`D4Yrc_r2~P9=qWZ6_Lu-v0-)e}+$L*tfZh=l=%L5Kmb|&L0^yNv*`d!dU2;N~
z4Oj>hK};_@byVH?0x2BY{7>F1wY9XA+K<i49!csRowgN_{kWh56gE41eSN)6+IjBY
zFQd~^3pzHdbfPiXCOHg^YEnV;phai{DI0op9O`jz!N+&+LYbrIgC~{pE}oh5UVP(l
zx1^g=bLH%VR}6j)`!;1&gM}2nZ8WQ}P^Qxz!L`E^iie6DqRzC+<iwSAU-Y5BdNrY&
z+H1j7JfW+>-J>S|f;7*qx-%prvZx`*i86NO+s&uO)lrqW9m;VntQ{X*@sRw{Lu-@P
zoa17zWWC~YD}#0!e4aBL2(4{KP#LWnV!xV2Dn{c@IPO`W$3~e{xPq=+(iVYkphHCs
z`CDTW5?DPv^=Kj?gE0gJ8{+iR+kckaXh0SV1b(}WV}ZIfAAIisf`++S71D(ed-0?|
zTQUB6QrI0%pyowpsf{3;PTM5g=nSGcsqNp^80f}<GC?|HdMew`GQoEJ(1Kx`jQzK}
zXOkc?K0!1P_nM!*FJXR+JaeU@hmuyt5Qj*nS<H9oIGLZc<$G(w$Ew{bw?OV0n-7EA
zDgj(Q-7bw;QRw3lh86F*a?gu|gdl6F)~E8`AM9>RQv6=4HBh#3N}swsPs|nVmHfhf
z`l^;>dXH`1^cQkaO<w<P;Rujt2|1{Q^F<S~G6=$GRTjU}6v&<9@pif4@6r;^Y}*!*
z|KpT!4rlh3EjP6KZs`BtJ(mCHtObc!8>7(#vRMT|yIKu)EvH#@nFsm$Vo;DU+?gkc
zC*moIW9WS6U}q=7^&=GL$p79cq+OjT<|sx}6FfvFAX|V*x}=NMOAq20o(T6_sxJMk
z8RZr`nNi~F3k6RY6In?2@A*{v=rdwiT0&``_*O2)C9jEw9FOM(6w|+$rvIQ+{WEMJ
zfQfR0bMb}?itgWThrx4;X-;=Dcqkt%2?1Aio6Jxus;AsABXm1D<%9(V;etKgY3Y5U
zY_rO(JAhB*Pgdpskh6LjQMZsX@E)O*^uLq+O1z2IZ5iUkRtt-nKbaJO1zQ7O!#0Np
zlxOeBd;y~3k>#@cd{g;e)&|N6<JKN!RU8lq^XWvp>m<{?NUO}&+uJpK6u1B4%+`_!
zns=&@D{r;1ZhYI|$d6GgWS@c`t&nWQM8Bs;Gz2Nz@0NF?EI&j}#6s_N%@fljEYXxu
z3Pgv{e17yAaWTGoH(MjMeBDom-Z~&(lFcgoaIQ<hj&>!7%~|Jk@eTQc_anYNxK5qn
znIjE*^G;A6do@YNT-S@IfquSm`NU?sg}8w-X8K}?50+&jBxU*<-6dljsKbSIFmZxR
zR3KxOICS+%S?%M2!k3tNNn6j+$NMw?B+YLNG|E-BVjak&F0usVipVf5tW}gZ*P_*<
z<7}tNco$#a`+My=9f@J3pwbdl4fY!7V7*1ZELu&zFwG|m0>>+X>v)Xbq4&JGo^TM)
zg)d<>GbUQBOu%JguR;j&Ad}vdh{Lli_rMuS(jvSY%mZt-9+??9g2mVgxjC$uSa^u1
z0uq=4tUUR|Fc)2pXn;nj5|S=4v;&>&WC#M<$)1RT*K8SXMBAP?W*I;P@?1^&pHi(F
zONcFsOgKaK!&bDLpYAD@rA=3^(M6%Ud^2sPA-xlm0|I@0-Q|r=>cnGuLEf;BSP^__
zM^5oqmtw!uog1?|IE<JxjV9Q`WLO+VO>uP+^EWX~kzqCx=9Sk;)7n<Ghgy9n*eKp?
zn}f^g!%Y|z#{t9Zg(WZ9j;ItYt6qYsM1D)wZY{!a0>ToI#zex36VEyEac9ueT4U|N
z0*zg(!aORwIt^uH4(=SH!^gyD?I1H9NO*;?Q7|O5%R8ftM^SnJ?Uv-el$DeY9z2+X
zaYC%sUcc@1m2{GRpy`$nak&(mn&qs9z5HIH%##ExTGsiuH-7b@V^P=WU`s5&gtBJ@
z${{GMyg52GJ?qZ})e5ZFp^egqj#+kbf8ND&R)VQeoH)`6Plx`+=XKo_4<+28-(N;=
zD7gXH0QC9E?C|hk37r5}B9n)m9loZMn7Mf8C3tyn-=?ffZ}#;(TFcH3BYp0voL!3K
zo`m6s1p_69ywzx%O^n#EFEuJ_15~9@$%GB4QLOPK=m7A8?;s2A`rM=B>&b>;d4iz3
z@REoJ&rwj4BfW*BI52H2k0o)+Ut24bB`bmkJxzSQeW%fUw^8R6UR&QyLHl>_*iocV
z9Y#p~BL=JtL@_X5!RmVV{*T)IKS#v>Jk<ZE@;~_qzLRLL`%!rAAAK(oDJmrBSozS^
z5{KN!$`$q>*E(GzIfc0q+8=+sZd&L)e-J%vW0~0BiK}-$!n`>T&-tD(ue=Ma|9>-q
zDgo3U67k}Chsw&6n(C=J&o`_1HRw`zl(<0DTH0j?*Y%;;cn1SM<|TvZ$6fJ{(iQTq
zQVJA_?lDtGCbVG%cl^AJv>kw|n>@dwc=?*}^=Sd6`l5~fJ-g@KjG`Z#Fw-czc4^<q
zf}`%t135QGOmsv23^EIP7a3?pMs$kWncDEmxsDy<x;JoLl74i*LbZGXJEd2F%nzM8
z3c}I7Uyp`RLxLsYn|yuA1WwgcP>>ZZu>rm1N3qSeO4+o)7#0=kc-kG2k92szGD2D*
z@4SLS<gG>WmB$y-e~cMK*arj?6L&vp5)c$5cw=RdQHq7er$q-yBPC!R0+cww+Yd*6
z3EScgtGSHa?P4)`>}r>8ygoNh1o2fAnvhXpW0`ck_*5%jV@>-Py_kODQYXXuh%1R$
zCW-Zd$fk(ot$cdGg{%PMG2~`Mx*W-XAcgrZg$Ovf+Iu3NK0Mt5Jo!FURvI(@)Qd2s
zF#?^@xT1u;m`K%$ICU)iOJF>>YC{&4S9EPH8DT~OB43~ze%#5N%V%<7pdRVcA~Nm<
zXpSU5DM5b9c1SaW{eTtn$VY>GAP#+2=0LWj^EW=|i6SOxcq#Y7T;H`oU`mlyBIxPy
zlA&M3=}Bf`*3K?*Q!n*Ynk;2X5gndL;3GGK+&7caEU<YIb2)1U7-_E$?x#}sr#9qe
zn}lbZWXd+S-tNf|>PnLBp}%wcUEQ#;czrgS)YeeE<r6`3ZWg@d`@1KA!#aK@9LtZC
zluqdC2BT9qy|onmp$5HONa1ROh}?yfR3;CY_iqZ!QH54MGL$r>Ij_4B^Jlv0fo@2d
z%NU$uhHYmJ$lR|G4*{%%Prp7|&`)S;)>>v`rj8JDqs3@u?~CU)2rR+Gdj7`B6GLNL
za-PH0CWPT$<ts}`GTxR_)qSyyD}Soku?n*C13aR1O=cdti`9utEydv3eeU;f&_lyp
ztJGnpi7F|C@8UM%9Y0ex#T><ukG%Y3W>sp-^C%Mql`Mt%+nZI08GU7G4P@L~Y9Ue%
zC%w!|G{s8~=vKAg?#H;nw%U=V&{0RT0ZxWPdwbF{N=g-)Sf8jvXOBvp)AbtH^`WCD
zSwc`y%C>Di8i1&>?KF|}wl!X((q6S+=JA=uWb7HJp!OGO43?apozKXYnp7ArSTK@1
z<RQiy==u4^L1Vc2<I?m{6Ou{4%Bk*X_6X<-Qpii(dDzJSBYNy$+8G*lE^mHIgksW!
zK%cmcIjBzs<}Aosys<ZgRh#tSq-T2JZi#Y4EO@d5F=Qm{H8M5S>EVMMb`OOKD(2;3
z`yIa8)eA10K>MLxs&jN1iN~jRd^y?sKGWYLx45pcE2$}=KXlxKmvSPZZixh?nu!we
z9FzbRrOjb8by1E@`G_T)W<{w{-A5Cr@*lWCf>9=={^qbMAq*Foh8;L8r{7>`M~Vyd
zAIDzvvRhkx<{&?PO`MN^k3#$$EJW&LFMWm`?+BEoS8%MHgWE<0)Y{p5EpT(*QBwMb
zQ4SKPpdH*Q@4jU%6+$}Zk<2`ndcd`2-~RGEGGS}|C1ldAz!=A*s2z_G=$^r*a0(0h
zt>1_#GYvON$Kdh~8%Y|>&1G2*{kPoZ^9@?jx@Fxs0b>3LrV?pqJSQ{r)W}?|!g-*-
zb67||`5-{GGIUBUW9Ff%FN3x9w#x0T94+Kzx4U}6-gz6NvNn^qhUNeS`t)qU{UZ&R
zL1(&2NCqEy0uyXQqa?CqE~g8T{U<fggtLO91;s*q8TQOAV*V?GlOqX(71f2v(1vAn
z61P_VIK4t9;le$l4dagEa?ek8B?<S?Ee0kU`m=;gxF{B(a3J<Nkkl~SIgj@bB{CN)
zO#OD28A8o#Hd7F}{5RaqXLPMwq_(4HY#AL*2~`7$Ozi*uU5Yl*!fZpJGj2&*HKepu
zk-l^K-vW*1`gE}+0qDak$d7F6FG2EKH(>-zGM}M|TjM(RGSC$*)~&k-vQPo(*}&p=
z8|NXpz2B9wFAQ!G9R{W^9)U|y-y$6#jjv_hCg%zq<6p0CxTU0Y5iUO*$8~H}^%Bop
zmuzO4Zxm(^u3;|&D!PT@Dz#ZOxO_|L2K%G<8F-Rb+c~~~sD_rwb2~3}w-sk~#OIY>
zI&5BIqFdQo(?kzwbWkr9rBurhXF~2^{(OHvRC{c&AWY)Ky^>A~Yp_EKgIZyv8-~o`
zj2k3g$qg6Z{t5&QZ(LqX_rzN=Mj8VRsSY%-RYpzourjVz=5x@Bp%atx!7IyQ+#%Cv
zGp_)o6^>3pz7ir$LQa`M_sEO{=Tg~@n!Y@8`Z47a$&oZ^g_Iuw#WXdMp3i4$s^>ME
zg9qOt*Apc_9!+L_#4u0+n!xPgGel;3u1pA*MLz;-HZNKjQG5>FwZEin6fg&JhFGe=
zLiq{D`(hYuYDF3#C9Bi~$ppd4k?P*i;cTgfK<3sn7}qayFK<Io?s!(mDOx()Mmms<
zVggBWZI^1*aMyPe+_&t0@P4I@BiixycHhy&N0V89bu+if(q}W$7nbGb9`0{dgCD8F
zL*z0o$Ya37CaoN<Ms!C&?|P{nlcoO##K+QBpl<8&eM5dh?w>b>$}|GU)!HgRB=&jI
z`@sD1c_;b@IN#&i^T=R@!U-ajIku*m!v$^!G*yD-W_iQm;=xRo9qu-I>Mp?du%6e^
zN7rfM?g=6trd~qaJ`5fZ{d$~vq^}66c32<l9d}^>G)c=qdhSTwL*qc7iOP1p##Rp5
za_51##_`ITycLx5LV!1}^~CPlB#$1CR%>b4NPK;#FipHJ`czD-Sw>nU3!0#+HGjAB
zA?X;Y@Mty5JY*eO(e@a{k*@`#M3a6BeQ9UifP4+)=jrp`#F(!tJct9tIv)YAFEQbs
zM20lAqs=TEI54W+6Hn8)ErTf3@Z2Z88BP0wu;h9jv*;>uEg3jJT?A8z=%tZvZ1+_6
zdGuAJS87^&NKWWou|_l{Kua&pdb>NyM}p;Is<hocnYeXDOBQc{zdSoVK0Vp-`AC!A
zkD&eG7(t^iF<tvbq8vaM-Rx=VtPCX2rS$mBFArBT=wD;&IzBXKxhBuwlX&89_ZYk<
z|74O{`HgJ3(CBQTM#plI^mX$^@-{P;1Cu&$8|p{2-X~ReWU6xSc<!?Q@VZHd6_iV?
z&?8C8TVnF^8M4o8b4;!|?U}k<mrN#UdS$A*{m3BK>3F+oxGpFAGEY}38A4jWO4o~z
zI|Uz>SvE3#daF8G35fmSyD<F8h2}YsmU<L@X`MKio;kDM%1LSVeVX)UJ?h~N#0(KU
zGpK&}y5Wmm!4%3OZ}Q&=Sg+AU`Yx7LshH=<Nc1NsyV~ze(R$h6&RuFk&5dXC?)a5#
zfC|LIrnoL-oUrz~m$IrG2L?{hbI)KfX??V0fWKXQ;Cya63}2)d#n%_%K2^4V2<YN$
zwuc-_0UvsUZCm4G#7k1(DJhXD*(5`kf<GXX8r*7W?K|IKmd7hq%#3&I^1`T0A37xE
zu|xaf`YMvDPf!i0%0uG0lKgJpIvVs6tSC>Mrp9n(hk(3E*9Uu&gVagU;QkAoZ5R!>
z>{9`NZJSl<3P7a9_Rx^~^a1RAR{BSSw0=h78`;)LG8TWSbjS?4oVg)kQ<LcgixnQF
zqx8yd_oTwoO9+}621uo@_Yze*-Dx!eAszAEEs?Dav(^=)a3&4f*>z4lmmFtFORKG?
zGdY^5!9Mh-raqT0J)l~g(-EIjy3T$-XMb*7x?v+lacDI_er_k)6m6_qQt)3S5QQo2
zSVbQ}hmY(^xY?5=@6VD0CY-uLKt3nYgLM9q)Fj-qJyKOP^I0MpmdbK}ps`o~*P};n
zZI-fbT1~vn<o$$#u?_1dCbzQ;G7Lu_BZ-p$GzwkFh;cH*k^BTbgcy57N|CE`{1*z?
zNK54b8*G`!ID>nEvO2#;g@IQ9;2{qxTWiQ@w%BN08nK2ugNs@!eFb0K5O2Wh>INw=
zut!=)_<8(wCgv{XB+9{+dVin28EJd1X=*y@ydRx_@RzWrufqu&t9xLigJ&64H@2Vt
z&R+lh$jSGUX&tTx3n;~`pi^ubY@ovmI<g*Z3Ujpz9BE_*dPGBt8iE<=aTOHw#K`MX
zP%WEom#wp%l0s!;xQ}Y&+9ONLG#DNO*xS~Du0dr~K*%O!%n-aAC72w_N!l}lDji@(
zEg|E{3n$U-WQa*wu@@fKJXJ0wChfv`Ecw1G%eg<;h7o!M)bP}2Aq8&YW`pMelPSbq
zzI`S`9+j+f&o)#@)7rXS;m>CEnE3h<>IVOL2jglcb;#2&ytQkux7YKv;;-&M3=rg9
zcIVl4N6SRbhF51yOd^vaX4%#h>Zqv9F4UPgGMyA0>swTKI@7;3)b(-ySNX2TE_=sa
z<IF1e&_<^pkM1)c8N8G??Vj3UoU}eUIeBDag7fZ?$S))L*{5gnlanPTCMI6eq@|^w
zW@pQOH{EDgsfHi-ef|3NNqzn63<g81Kh9iic4ypl!^P{h1EqV0Y{oNi)@R7RjCKQU
zop&%@UzC~XT;uh%zdt6AY3JaOYAf72G%zr0)vCNK&$PL)@W2Ls{=+!V?USkb`9=oY
z`gCExzkK;JZRxD=-6Phvw)5X68yXt+Dkua61_nN~g3Yw4>BXY60_QC){bFKb)^l@D
z*G<t3MCKGEB-RQE32AIwH?QTQ@WSceU)p&i92pOAn%;GMe0<spVj?2-V>Gx^(OO1`
zycwRTsHo_Mi6GRZ8JU^ajzq4>&CT`N{PWz>+qZ9z%+7Ae&dwGW7iTXv7RME0(NOOs
z<>m3Gr>EC^`0y=%0;P>Lm0CJJ?yyr-w4|Uw5ez4E>v!E3t!-?IYil)+s;SL2IOAk|
z>FMeE_H8#zn-S=b#iyN!UT0-xW#{C?H8(dG7M~6fEh;9~H9Wiv?*^(SEs~!;eZrml
zmX?<Ge*HQ<hFgxCQ;?D2S+#1FoxA&n+S=Nh`g(1-{Ls))ad~-uERCI`BPY6oqZ>{S
z8BYDqGaA>uKB_tBd`IhtJo1>{<~G`&G6h?}W1z3E6s_gwo6p5%MWr&qvs3cK)bSMY
z=iFb-8Bc!@JlsFrpl|=08FaY2gGQs}{~)j}dzO4>h_3(O%>O4o0K5kIs@;D%Qvdo-
gy!?+p;zd3s{?L@#GY>~Q@(C)3jw!_-IP<sv3&uUZ2mk;8

diff --git a/reference/plot.html b/reference/plot.html
index 2af46266..094ae2a9 100644
--- a/reference/plot.html
+++ b/reference/plot.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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/proportion.html b/reference/proportion.html
index 0ca4372c..ae9f14fb 100644
--- a/reference/proportion.html
+++ b/reference/proportion.html
@@ -12,7 +12,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 dca50917..29609bd8 100644
--- a/reference/random.html
+++ b/reference/random.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -193,42 +193,42 @@
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu">random_mic</span><span class="op">(</span><span class="fl">25</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'mic'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 1       0.005   4       16      0.01    1       16      0.25    0.005  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [10] 0.125   &lt;=0.002 0.125   64      0.25    4       1       128     8      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [19] 64      &gt;=256   0.25    8       &gt;=256   2       16     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 1       256     2       0.125   64      0.5     0.025   &lt;=0.001 0.25   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [10] 0.025   16      0.0625  0.005   32      &lt;=0.001 0.25    2       0.25   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [19] 16      128     64      256     32      256     0.005  </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] 32 30 15 34 20 43 17 28 30 50 25 10 47 23 26 13 32 24 28 46 36 14 50 46 21</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 12 10 46 48 20 46 12 18 31 21  8  7 24 21 48 48 26 42 40 18 47  7 16  9  9</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] S R S R I R S S S I S I R I I I S I R I S S R S I</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] S R I S R S I R I S R S I I S I R S S I S I R R I</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># \donttest{</span></span></span>
 <span class="r-in"><span><span class="co"># make the random generation more realistic by setting a bug and/or drug:</span></span></span>
 <span class="r-in"><span><span class="fu">random_mic</span><span class="op">(</span><span class="fl">25</span>, <span class="st">"Klebsiella pneumoniae"</span><span class="op">)</span> <span class="co"># range 0.0625-64</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'mic'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 2       &gt;=64    0.025   16      0.125   2       32      32      16     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [10] 0.5     2       0.0625  16      0.25    0.01    4       0.002   16     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [19] 1       &lt;=0.001 0.0625  0.0625  8       &gt;=64    2      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 2      32     4      0.25   4      0.5    2      0.5    0.01   0.01  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [11] 0.001  1      2      2      0.005  0.5    &gt;=256  0.5    16     0.0625</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [21] 0.002  0.01   0.0625 8      0.125 </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  1    4    2    1    1    &gt;=16 0.5  4    0.5  0.5  1    2    &gt;=16 2   </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [16] 4    0.5  8    2    &gt;=16 0.5  4    8    2    1   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 2    &gt;=16 4    1    8    4    4    &gt;=16 1    4    8    8    &gt;=16 2    &gt;=16</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [16] 1    1    1    2    &gt;=16 &gt;=16 8    1    &gt;=16 1   </span>
 <span class="r-in"><span><span class="fu">random_mic</span><span class="op">(</span><span class="fl">25</span>, <span class="st">"Streptococcus pneumoniae"</span>, <span class="st">"meropenem"</span><span class="op">)</span> <span class="co"># range 0.0625-4</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class 'mic'</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] &gt;=4    0.25   0.5    0.5    0.125  1      0.25   &gt;=4    0.5    2     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [11] 0.5    1      0.0625 0.5    0.5    0.0625 2      0.125  1      1     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [21] 0.25   0.0625 1      0.25   0.125 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 4      2      0.0625 0.025  1      0.025  0.125  0.025  0.125  4     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [11] 0.5    4      0.025  4      8      2      4      0.5    0.0625 0.25  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [21] 0.025  0.5    &gt;=16   0.125  0.025 </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] 16 33 30 28 41 44 12 12 40 23 45  9 40 49 34 33 20 10 16 42 39  8 40 24 44</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 47 49 49 45 43 38 34 45 32 20 15 23 21 20 46 21 40 46 46 17 24 33 23 40 49</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] 13 13 17 11 14 12 13 13 13 11 15 13 15 15 11 13 14 12 13 12 17 16 15 14 11</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 11 15 15 17 17 15 12 14 16 15 16 12 13 12 13 17 17 13 12 14 13 17 16 17 16</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 20 22 21 24 15 15 22 26 15 27 21 15 15 22 19 20 20 27 24 18 15 26 25 15</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 27 19 16 24 26 16 22 25 24 24 21 27 17 20 23 18 27 26 15 24 26 22 26 18 24</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 e9938d43..90603302 100644
--- a/reference/resistance_predict.html
+++ b/reference/resistance_predict.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 693d1a7a..59d830f9 100644
--- a/reference/skewness.html
+++ b/reference/skewness.html
@@ -12,7 +12,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -181,7 +181,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu">skewness</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html" class="external-link">runif</a></span><span class="op">(</span><span class="fl">1000</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.004959649</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -0.006901309</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 12803fde..b1fd30aa 100644
--- a/reference/translate.html
+++ b/reference/translate.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9152</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.0.0</small>
 
     
     <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 f7ba5553..a3949188 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) reliable data thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial agents, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"How to conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables. AMR package contains data set example_isolates_unclean, might look data users extracted laboratory systems: AMR data analysis, like microorganism column contain valid, --date taxonomy, antibiotic columns cleaned SIR values well.","code":"library(dplyr) library(ggplot2) library(AMR)  # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\")) example_isolates_unclean #> # A tibble: 3,000 × 8 #>    patient_id hospital date       bacteria      AMX   AMC   CIP   GEN   #>    <chr>      <chr>    <date>     <chr>         <chr> <chr> <chr> <chr> #>  1 J3         A        2012-11-21 E. coli       R     I     S     S     #>  2 R7         A        2018-04-03 K. pneumoniae R     I     S     S     #>  3 P3         A        2014-09-19 E. coli       R     S     S     S     #>  4 P10        A        2015-12-10 E. coli       S     I     S     S     #>  5 B7         A        2015-03-02 E. coli       S     S     S     S     #>  6 W3         A        2018-03-31 S. aureus     R     S     R     S     #>  7 J8         A        2016-06-14 E. coli       R     S     S     S     #>  8 M3         A        2015-10-25 E. coli       R     S     S     S     #>  9 J3         A        2019-06-19 E. coli       S     S     S     S     #> 10 G6         A        2015-04-27 S. aureus     S     S     S     S     #> # … with 2,990 more rows  # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"How to conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 11 Dec 2022. codes AMR packages come .mo() short, still human readable. importantly, .mo() supports kinds input: first character codes denote taxonomic kingdom, Bacteria (B), Fungi (F), Protozoa (P). AMR package also contain functions directly retrieve taxonomic properties, name, genus, species, family, order, even Gram-stain. start mo_ use .mo() internally, still arbitrary user input can used: Now can thus clean data: Apparently, uncertainty translation taxonomic codes. Let’s check : ’s good.","code":"as.mo(\"Klebsiella pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"K. pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLEPNE\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLPN\") #> Class 'mo' #> [1] B_KLBSL_PNMN mo_family(\"K. pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"K. pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"K. pneumoniae\") #> [1] \"pneumoniae\"  mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\"  mo_ref(\"K. pneumoniae\") #> [1] \"Trevisan, 1887\"  mo_snomed(\"K. pneumoniae\") #> [[1]] #> [1] \"1098101000112102\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\" our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE) #> ℹ Microorganism translation was uncertain for four microorganisms. Run #>   mo_uncertainties() to review these uncertainties, or use #>   add_custom_microorganisms() to add custom entries. mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans. See ?mo_matching_score. #>  #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #>              Based on input \"E coli\" #> Also matched: Enterobacter cowanii (0.600), Eubacterium combesii #>               (0.600), Eggerthia catenaformis (0.591), Eubacterium callanderi #>               (0.591), Enterocloster citroniae (0.587), Eubacterium cylindroides #>               (0.583), Enterococcus casseliflavus (0.577), Enterobacter cloacae #>               cloacae (0.571), Ehrlichia canis (0.567), and Enterobacter cloacae #>               dissolvens (0.565) #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #>                    Based on input \"K pneumoniae\" #> Also matched: Klebsiella pneumoniae ozaenae (0.707), Klebsiella #>               pneumoniae pneumoniae (0.688), Klebsiella pneumoniae rhinoscleromatis #>               (0.658), Klebsiella pasteurii (0.500), Klebsiella planticola (0.500), #>               Kingella potus (0.400), Kosakonia pseudosacchari (0.361), Kaistella #>               palustris (0.333), Kocuria palustris (0.333), and Kocuria pelophila #>               (0.333) #> -------------------------------------------------------------------------------- #> \"S. aureus\" -> Staphylococcus aureus (B_STPHY_AURS, 0.690) #>                Based on input \"S aureus\" #> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus #>               argenteus (0.625), Staphylococcus aureus anaerobius (0.625), #>               Streptomyces argenteolus (0.483), Streptomyces aureus (0.474), #>               Streptomyces azureus (0.467), Streptomyces aureorectus (0.444), #>               Streptomyces auratus (0.433), Streptomyces aurantiogriseus (0.429), and #>               Streptomyces aureocirculatus (0.429) #> -------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #>                    Based on input \"S pneumoniae\" #> Also matched: Streptococcus pseudopneumoniae (0.700), Serratia #>               proteamaculans quinovora (0.545), Streptococcus pseudoporcinus (0.536), #>               Staphylococcus pseudintermedius (0.532), Serratia proteamaculans #>               proteamaculans (0.526), Salmonella Portanigra (0.524), Sphingomonas #>               paucimobilis (0.520), Streptococcus pluranimalium (0.519), #>               Streptococcus constellatus pharyngis (0.514), and Salmonella Pakistan #>               (0.500) #>  #> Only the first 10 other matches of each record are shown. Run #> print(mo_uncertainties(), n = ...) to view more entries, or save #> mo_uncertainties() to an object."},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"antibiotic-results","dir":"Articles","previous_headings":"Preparation","what":"Antibiotic results","title":"How to conduct AMR data analysis","text":"column antibiotic test results must also cleaned. AMR package comes three new data types work test results: mic minimal inhibitory concentrations (MIC), disk disk diffusion diameters, sir SIR data interpreted already. package can also determine SIR values based MIC disk diffusion values, read .sir() page. now, just clean SIR columns data using dplyr: basically cleaning, time start data inclusion.","code":"# method 1, be explicit about the columns: our_data <- our_data %>%   mutate_at(vars(AMX:GEN), as.sir)  # method 2, let the AMR package determine the eligible columns our_data <- our_data %>%   mutate_if(is_sir_eligible, as.sir)  # result: our_data #> # A tibble: 3,000 × 8 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> #>  1 J3         A        2012-11-21 B_ESCHR_COLI R     I     S     S     #>  2 R7         A        2018-04-03 B_KLBSL_PNMN R     I     S     S     #>  3 P3         A        2014-09-19 B_ESCHR_COLI R     S     S     S     #>  4 P10        A        2015-12-10 B_ESCHR_COLI S     I     S     S     #>  5 B7         A        2015-03-02 B_ESCHR_COLI S     S     S     S     #>  6 W3         A        2018-03-31 B_STPHY_AURS R     S     R     S     #>  7 J8         A        2016-06-14 B_ESCHR_COLI R     S     S     S     #>  8 M3         A        2015-10-25 B_ESCHR_COLI R     S     S     S     #>  9 J3         A        2019-06-19 B_ESCHR_COLI S     S     S     S     #> 10 G6         A        2015-04-27 B_STPHY_AURS S     S     S     S     #> # … with 2,990 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Preparation","what":"First isolates","title":"How to conduct AMR data analysis","text":"need know isolates can actually use analysis without repetition bias. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. Read methods first_isolate() page. outcome function can easily added data: 88% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 626 isolates analysis. Now data looks like: Time analysis.","code":"our_data <- our_data %>%   mutate(first = first_isolate(info = TRUE)) #> Determining first isolates using an episode length of 365 days #> ℹ Using column 'bacteria' as input for col_mo. #> ℹ Using column 'date' as input for col_date. #> ℹ Using column 'patient_id' as input for col_patient_id. #> Basing inclusion on all antimicrobial results, using a points threshold of #> 2 #> Including isolates from ICU. #> => Found 2,626 'phenotype-based' first isolates (87.6% within scope and #>    87.5% of total where a microbial ID was available) our_data_1st <- our_data %>%   filter(first == TRUE) our_data_1st <- our_data %>%   filter_first_isolate() our_data_1st #> # A tibble: 2,626 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 J3         A        2012-11-21 B_ESCHR_COLI R     I     S     S     TRUE  #>  2 R7         A        2018-04-03 B_KLBSL_PNMN R     I     S     S     TRUE  #>  3 P10        A        2015-12-10 B_ESCHR_COLI S     I     S     S     TRUE  #>  4 B7         A        2015-03-02 B_ESCHR_COLI S     S     S     S     TRUE  #>  5 W3         A        2018-03-31 B_STPHY_AURS R     S     R     S     TRUE  #>  6 J8         A        2016-06-14 B_ESCHR_COLI R     S     S     S     TRUE  #>  7 M3         A        2015-10-25 B_ESCHR_COLI R     S     S     S     TRUE  #>  8 J3         A        2019-06-19 B_ESCHR_COLI S     S     S     S     TRUE  #>  9 G6         A        2015-04-27 B_STPHY_AURS S     S     S     S     TRUE  #> 10 P4         A        2011-06-21 B_ESCHR_COLI S     S     S     S     TRUE  #> # … with 2,616 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"analysing-the-data","dir":"Articles","previous_headings":"","what":"Analysing the data","title":"How to conduct AMR data analysis","text":"base R summary() function gives good first impression, comes support new mo sir classes now data set:","code":"summary(our_data_1st) #>   patient_id          hospital              date            #>  Length:2626        Length:2626        Min.   :2011-01-01   #>  Class :character   Class :character   1st Qu.:2013-04-14   #>  Mode  :character   Mode  :character   Median :2015-06-05   #>                                        Mean   :2015-06-15   #>                                        3rd Qu.:2017-08-23   #>                                        Max.   :2020-01-01   #>    bacteria               AMX                    AMC                 #>  Class :mo             Class:sir              Class:sir              #>  <NA>  :0              %R   :43.2% (n=1134)   %R   :36.1% (n=947)    #>  Unique:4              %SI  :56.8% (n=1492)   %SI  :63.9% (n=1679)   #>  #1    :B_ESCHR_COLI   - %S :41.1% (n=1080)   - %S :52.7% (n=1383)   #>  #2    :B_STPHY_AURS   - %I :15.7% (n=412)    - %I :11.3% (n=296)    #>  #3    :B_STRPT_PNMN                                                 #>     CIP                    GEN                  first         #>  Class:sir              Class:sir              Mode:logical   #>  %R   :42.0% (n=1102)   %R   :37.0% (n=971)    TRUE:2626      #>  %SI  :58.0% (n=1524)   %SI  :63.0% (n=1655)                  #>  - %S :51.9% (n=1362)   - %S :59.9% (n=1574)                  #>  - %I : 6.2% (n=162)    - %I : 3.1% (n=81)                    #>   glimpse(our_data_1st) #> Rows: 2,626 #> Columns: 9 #> $ patient_id <chr> \"J3\", \"R7\", \"P10\", \"B7\", \"W3\", \"J8\", \"M3\", \"J3\", \"G6\", \"P4\"… #> $ hospital   <chr> \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\",… #> $ date       <date> 2012-11-21, 2018-04-03, 2015-12-10, 2015-03-02, 2018-03-31… #> $ bacteria   <mo> \"B_ESCHR_COLI\", \"B_KLBSL_PNMN\", \"B_ESCHR_COLI\", \"B_ESCHR_COL… #> $ AMX        <sir> R, R, S, S, R, R, R, S, S, S, S, R, S, S, R, R, R, R, I, S,… #> $ AMC        <sir> I, I, I, S, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, R,… #> $ CIP        <sir> S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S, S,… #> $ GEN        <sir> S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,… #> $ first      <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…  # number of unique values per column: sapply(our_data_1st, n_distinct) #> patient_id   hospital       date   bacteria        AMX        AMC        CIP  #>        260          3       1808          4          3          3          3  #>        GEN      first  #>          3          1"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"availability-of-species","dir":"Articles","previous_headings":"Analysing the data","what":"Availability of species","title":"How to conduct AMR data analysis","text":"just get idea species distributed, create frequency table count() based name microorganisms:","code":"our_data %>%   count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #>   `mo_name(bacteria)`          n #>   <chr>                    <int> #> 1 Escherichia coli          1518 #> 2 Staphylococcus aureus      730 #> 3 Streptococcus pneumoniae   426 #> 4 Klebsiella pneumoniae      326  our_data_1st %>%   count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #>   `mo_name(bacteria)`          n #>   <chr>                    <int> #> 1 Escherichia coli          1250 #> 2 Staphylococcus aureus      661 #> 3 Streptococcus pneumoniae   399 #> 4 Klebsiella pneumoniae      316"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"select-and-filter-with-antibiotic-selectors","dir":"Articles","previous_headings":"Analysing the data","what":"Select and filter with antibiotic selectors","title":"How to conduct AMR data analysis","text":"Using -called antibiotic class selectors, can select filter columns based antibiotic class antibiotic results :","code":"our_data_1st %>%   select(date, aminoglycosides()) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) #> # A tibble: 2,626 × 2 #>    date       GEN   #>    <date>     <sir> #>  1 2012-11-21 S     #>  2 2018-04-03 S     #>  3 2015-12-10 S     #>  4 2015-03-02 S     #>  5 2018-03-31 S     #>  6 2016-06-14 S     #>  7 2015-10-25 S     #>  8 2019-06-19 S     #>  9 2015-04-27 S     #> 10 2011-06-21 S     #> # … with 2,616 more rows  our_data_1st %>%   select(bacteria, betalactams()) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 2,626 × 3 #>    bacteria     AMX   AMC   #>    <mo>         <sir> <sir> #>  1 B_ESCHR_COLI R     I     #>  2 B_KLBSL_PNMN R     I     #>  3 B_ESCHR_COLI S     I     #>  4 B_ESCHR_COLI S     S     #>  5 B_STPHY_AURS R     S     #>  6 B_ESCHR_COLI R     S     #>  7 B_ESCHR_COLI R     S     #>  8 B_ESCHR_COLI S     S     #>  9 B_STPHY_AURS S     S     #> 10 B_ESCHR_COLI S     S     #> # … with 2,616 more rows  our_data_1st %>%   select(bacteria, where(is.sir)) #> # A tibble: 2,626 × 5 #>    bacteria     AMX   AMC   CIP   GEN   #>    <mo>         <sir> <sir> <sir> <sir> #>  1 B_ESCHR_COLI R     I     S     S     #>  2 B_KLBSL_PNMN R     I     S     S     #>  3 B_ESCHR_COLI S     I     S     S     #>  4 B_ESCHR_COLI S     S     S     S     #>  5 B_STPHY_AURS R     S     R     S     #>  6 B_ESCHR_COLI R     S     S     S     #>  7 B_ESCHR_COLI R     S     S     S     #>  8 B_ESCHR_COLI S     S     S     S     #>  9 B_STPHY_AURS S     S     S     S     #> 10 B_ESCHR_COLI S     S     S     S     #> # … with 2,616 more rows  # filtering using AB selectors is also possible: our_data_1st %>%   filter(any(aminoglycosides() == \"R\")) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) #> # A tibble: 971 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 J5         A        2017-12-25 B_STRPT_PNMN R     S     S     R     TRUE  #>  2 X1         A        2017-07-04 B_STPHY_AURS R     S     S     R     TRUE  #>  3 B3         A        2016-07-24 B_ESCHR_COLI S     S     S     R     TRUE  #>  4 V7         A        2012-04-03 B_ESCHR_COLI S     S     S     R     TRUE  #>  5 C9         A        2017-03-23 B_ESCHR_COLI S     S     S     R     TRUE  #>  6 R1         A        2018-06-10 B_STPHY_AURS S     S     S     R     TRUE  #>  7 S2         A        2013-07-19 B_STRPT_PNMN S     S     S     R     TRUE  #>  8 P5         A        2019-03-09 B_STPHY_AURS S     S     S     R     TRUE  #>  9 Q8         A        2019-08-10 B_STPHY_AURS S     S     S     R     TRUE  #> 10 K5         A        2013-03-15 B_STRPT_PNMN S     S     S     R     TRUE  #> # … with 961 more rows  our_data_1st %>%   filter(all(betalactams() == \"R\")) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 471 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 M7         A        2013-07-22 B_STRPT_PNMN R     R     S     S     TRUE  #>  2 R10        A        2013-12-20 B_STPHY_AURS R     R     S     S     TRUE  #>  3 R7         A        2015-10-25 B_STPHY_AURS R     R     S     S     TRUE  #>  4 R8         A        2019-10-25 B_STPHY_AURS R     R     S     S     TRUE  #>  5 I7         A        2015-08-19 B_ESCHR_COLI R     R     S     S     TRUE  #>  6 N3         A        2014-12-29 B_STRPT_PNMN R     R     R     S     TRUE  #>  7 Q2         A        2019-09-22 B_ESCHR_COLI R     R     S     S     TRUE  #>  8 X7         A        2011-03-20 B_ESCHR_COLI R     R     S     R     TRUE  #>  9 C5         A        2015-08-30 B_KLBSL_PNMN R     R     S     R     TRUE  #> 10 W9         A        2013-10-02 B_ESCHR_COLI R     R     S     S     TRUE  #> # … with 461 more rows  # even works in base R (since R 3.0): our_data_1st[all(betalactams() == \"R\"), ] #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 471 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 M7         A        2013-07-22 B_STRPT_PNMN R     R     S     S     TRUE  #>  2 R10        A        2013-12-20 B_STPHY_AURS R     R     S     S     TRUE  #>  3 R7         A        2015-10-25 B_STPHY_AURS R     R     S     S     TRUE  #>  4 R8         A        2019-10-25 B_STPHY_AURS R     R     S     S     TRUE  #>  5 I7         A        2015-08-19 B_ESCHR_COLI R     R     S     S     TRUE  #>  6 N3         A        2014-12-29 B_STRPT_PNMN R     R     R     S     TRUE  #>  7 Q2         A        2019-09-22 B_ESCHR_COLI R     R     S     S     TRUE  #>  8 X7         A        2011-03-20 B_ESCHR_COLI R     R     S     R     TRUE  #>  9 C5         A        2015-08-30 B_KLBSL_PNMN R     R     S     R     TRUE  #> 10 W9         A        2013-10-02 B_ESCHR_COLI R     R     S     S     TRUE  #> # … with 461 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"generate-antibiograms","dir":"Articles","previous_headings":"Analysing the data","what":"Generate antibiograms","title":"How to conduct AMR data analysis","text":"package comes antibiogram(), function automatically generates traditional, combined, syndromic, even weighted-incidence syndromic combination antibiograms (WISCA). R Markdown (page) automatically prints right table format. suggestions generate different antibiograms: Antibiograms can plotted using autoplot() ggplot2 packages, since package provides extension function:  calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"# traditional: antibiogram(our_data_1st) antibiogram(our_data_1st,   ab_transform = \"name\" ) antibiogram(our_data_1st,   ab_transform = \"name\",   language = \"es\" ) # support for 20 languages # combined: antibiogram(our_data_1st,   antibiotics = c(\"AMC\", \"AMC+CIP\", \"AMC+GEN\") ) # for a syndromic antibiogram, we must fake some clinical conditions: our_data_1st$condition <- sample(c(\"Cardial\", \"Respiratory\", \"Rheumatic\"),   size = nrow(our_data_1st),   replace = TRUE )  # syndromic: antibiogram(our_data_1st,   syndromic_group = \"condition\" ) antibiogram(our_data_1st,   # you can use AB selectors here as well:   antibiotics = c(penicillins(), aminoglycosides()),   syndromic_group = \"condition\",   mo_transform = \"gramstain\" ) #> ℹ For penicillins() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) # WISCA: # (we lack some details, but it could contain a filter on e.g. >65 year-old males) wisca <- antibiogram(our_data_1st,   antibiotics = c(\"AMC\", \"AMC+CIP\", \"AMC+GEN\"),   syndromic_group = \"condition\",   mo_transform = \"gramstain\" ) wisca autoplot(wisca)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"How to conduct AMR data analysis","text":"functions resistance() susceptibility() can used calculate antimicrobial resistance susceptibility. specific analyses, functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() proportion_R() can used determine proportion specific antimicrobial outcome. functions contain minimum argument, denoting minimum required number test results returning value. functions otherwise return NA. default minimum = 30, following CLSI M39-A4 guideline applying microbial epidemiology. per EUCAST guideline 2019, calculate resistance proportion R (proportion_R(), equal resistance()) susceptibility proportion S (proportion_SI(), equal susceptibility()). functions can used : can used conjunction group_by() summarise(), dplyr package: Author: Dr. Matthijs Berends, 26th Feb 2023","code":"our_data_1st %>% resistance(AMX) #> [1] 0.4318355 our_data_1st %>%   group_by(hospital) %>%   summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #>   hospital amoxicillin #>   <chr>          <dbl> #> 1 A              0.343 #> 2 B              0.569 #> 3 C              0.375"},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to apply EUCAST rules","text":"EUCAST rules? European Committee Antimicrobial Susceptibility Testing (EUCAST) states website: EUCAST expert rules tabulated collection expert knowledge intrinsic resistances, exceptional resistance phenotypes interpretive rules may applied antimicrobial susceptibility testing order reduce errors make appropriate recommendations reporting particular resistances. Europe, lot medical microbiological laboratories already apply rules (Brown et al., 2015). package features latest insights intrinsic resistance unusual phenotypes (v3.3, 2021). Moreover, eucast_rules() function use purpose can also apply additional rules, like forcing ampicillin = R isolates amoxicillin/clavulanic acid = R.","code":""},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to apply EUCAST rules","text":"rules can used discard impossible bug-drug combinations data. example, Klebsiella produces beta-lactamase prevents ampicillin (amoxicillin) working . words, practically every strain Klebsiella resistant ampicillin. Sometimes, laboratory data can still contain strains ampicillin susceptible ampicillin. antibiogram available identification available, antibiogram re-interpreted based identification (namely, Klebsiella). EUCAST expert rules solve , can applied using eucast_rules(): convenient function mo_is_intrinsic_resistant() uses guideline, allows check one specific microorganisms antibiotics: EUCAST rules can used correction, can also used filling known resistance susceptibility based results antimicrobials drugs. process called interpretive reading, basically form imputation, part eucast_rules() function well:","code":"oops <- data.frame(   mo = c(     \"Klebsiella\",     \"Escherichia\"   ),   ampicillin = \"S\" ) oops #>            mo ampicillin #> 1  Klebsiella          S #> 2 Escherichia          S  eucast_rules(oops, info = FALSE) #>            mo ampicillin #> 1  Klebsiella          R #> 2 Escherichia          S mo_is_intrinsic_resistant(   c(\"Klebsiella\", \"Escherichia\"),   \"ampicillin\" ) #> [1]  TRUE FALSE  mo_is_intrinsic_resistant(   \"Klebsiella\",   c(\"ampicillin\", \"kanamycin\") ) #> [1]  TRUE FALSE data <- data.frame(   mo = c(     \"Staphylococcus aureus\",     \"Enterococcus faecalis\",     \"Escherichia coli\",     \"Klebsiella pneumoniae\",     \"Pseudomonas aeruginosa\"   ),   VAN = \"-\", # Vancomycin   AMX = \"-\", # Amoxicillin   COL = \"-\", # Colistin   CAZ = \"-\", # Ceftazidime   CXM = \"-\", # Cefuroxime   PEN = \"S\", # Benzylenicillin   FOX = \"S\", # Cefoxitin   stringsAsFactors = FALSE ) data eucast_rules(data)"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"type-of-input","dir":"Articles","previous_headings":"","what":"Type of input","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function takes data set input, regular data.frame. tries automatically determine right columns info isolates, name species columns results antimicrobial agents. See help page info set right settings data command ?mdro. WHONET data (data), settings automatically set correctly.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"guidelines","dir":"Articles","previous_headings":"","what":"Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function support multiple guidelines. can select guideline guideline parameter. Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. “Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.” Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.2\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance Unusual Phenotypes” (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance Exceptional Phenotypes Tables” (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization “Companion handbook guidelines programmatic management drug-resistant tuberculosis” (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"custom-guidelines","dir":"Articles","previous_headings":"Guidelines","what":"Custom Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"can also use custom guideline. Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() dplyr package, recognise input method set rules. Rules must set using R considers ‘formula notation’: row/isolate matches first rule, value first ~ (case ‘Elderly Type ’) set MDRO value. Otherwise, second rule tried . maximum number rules unlimited. can print rules set console overview. Colours help reading console supports colours. outcome function can used guideline argument mdro() function: rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline(   CIP == \"R\" & age > 60 ~ \"Elderly Type A\",   ERY == \"R\" & age > 60 ~ \"Elderly Type B\" ) custom #> A set of custom MDRO rules: #>   1. If CIP is \"R\" and age is higher than 60 then: Elderly Type A #>   2. If ERY is \"R\" and age is higher than 60 then: Elderly Type B #>   3. Otherwise: Negative #>  #> Unmatched rows will return NA. #> Results will be of class 'factor', with ordered levels: Negative < Elderly Type A < Elderly Type B x <- mdro(example_isolates, guideline = custom) table(x) #> x #>       Negative Elderly Type A Elderly Type B  #>           1070            198            732"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function always returns ordered factor predefined guidelines. example, output default guideline Magiorakos et al. returns factor levels ‘Negative’, ‘MDR’, ‘XDR’ ‘PDR’ order. next example uses example_isolates data set. data set included package contains full antibiograms 2,000 microbial isolates. reflects reality can used practise AMR data analysis. test MDR/XDR/PDR guideline data set, get: (16 isolates test results) Frequency table Class: factor > ordered (numeric) Length: 2,000 Levels: 4: Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant … Available: 1,729 (86.45%, NA: 271 = 13.55%) Unique: 2 another example, create data set determine multi-drug resistant TB: column names automatically verified valid drug names codes, worked exactly way: data set now looks like : can now add interpretation MDR-TB data set. can use: shortcut mdr_tb(): Create frequency table results: Frequency table Class: factor > ordered (numeric) Length: 5,000 Levels: 5: Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant <… Available: 5,000 (100%, NA: 0 = 0%) Unique: 5","code":"library(dplyr) # to support pipes: %>% library(cleaner) # to create frequency tables example_isolates %>%   mdro() %>%   freq() # show frequency table of the result #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) # random_sir() is a helper function to generate # a random vector with values S, I and R my_TB_data <- data.frame(   rifampicin = random_sir(5000),   isoniazid = random_sir(5000),   gatifloxacin = random_sir(5000),   ethambutol = random_sir(5000),   pyrazinamide = random_sir(5000),   moxifloxacin = random_sir(5000),   kanamycin = random_sir(5000) ) my_TB_data <- data.frame(   RIF = random_sir(5000),   INH = random_sir(5000),   GAT = random_sir(5000),   ETH = random_sir(5000),   PZA = random_sir(5000),   MFX = random_sir(5000),   KAN = random_sir(5000) ) head(my_TB_data) #>   rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin #> 1          S         R            I          I            S            I #> 2          I         I            R          I            R            I #> 3          S         I            R          I            R            R #> 4          I         S            R          I            I            S #> 5          S         S            I          S            I            I #> 6          I         R            I          R            R            I #>   kanamycin #> 1         S #> 2         I #> 3         S #> 4         S #> 5         S #> 6         I mdro(my_TB_data, guideline = \"TB\") my_TB_data$mdr <- mdr_tb(my_TB_data) #> ℹ No column found as input for col_mo, assuming all rows contain #>   Mycobacterium tuberculosis. freq(my_TB_data$mdr)"},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"transforming","dir":"Articles","previous_headings":"","what":"Transforming","title":"How to conduct principal component analysis (PCA) for AMR","text":"PCA, need transform AMR data first. example_isolates data set package looks like: Now transform data set resistance percentages per taxonomic order genus:","code":"library(AMR) library(dplyr) glimpse(example_isolates) #> Rows: 2,000 #> Columns: 46 #> $ date    <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2… #> $ patient <chr> \"A77334\", \"A77334\", \"067927\", \"067927\", \"067927\", \"067927\", \"4… #> $ age     <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50… #> $ gender  <chr> \"F\", \"F\", \"F\", \"F\", \"F\", \"F\", \"M\", \"M\", \"F\", \"F\", \"M\", \"M\", \"M… #> $ ward    <chr> \"Clinical\", \"Clinical\", \"ICU\", \"ICU\", \"ICU\", \"ICU\", \"Clinical\"… #> $ mo      <mo> \"B_ESCHR_COLI\", \"B_ESCHR_COLI\", \"B_STPHY_EPDR\", \"B_STPHY_EPDR\",… #> $ PEN     <sir> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,… #> $ OXA     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ FLC     <sir> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R… #> $ AMX     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ AMC     <sir> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N… #> $ AMP     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ TZP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CZO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ FEP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CXM     <sir> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S… #> $ FOX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ CTX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ CAZ     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, … #> $ CRO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ GEN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TOB     <sir> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA… #> $ AMK     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ KAN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TMP     <sir> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, … #> $ SXT     <sir> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N… #> $ NIT     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,… #> $ FOS     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ LNZ     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ CIP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S… #> $ MFX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ VAN     <sir> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, … #> $ TEC     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ TCY     <sir> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, … #> $ TGC     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ DOX     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ ERY     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ CLI     <sir> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA… #> $ AZM     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ IPM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ MEM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ MTR     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CHL     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ COL     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, … #> $ MUP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ RIF     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… resistance_data <- example_isolates %>%   group_by(     order = mo_order(mo), # group on anything, like order     genus = mo_genus(mo)   ) %>% #  and genus as we do here   summarise_if(is.sir, resistance) %>% # then get resistance of all drugs   select(     order, genus, AMC, CXM, CTX,     CAZ, GEN, TOB, TMP, SXT   ) # and select only relevant columns  head(resistance_data) #> # A tibble: 6 × 10 #> # Groups:   order [5] #>   order             genus          AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT #>   <chr>             <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 (unknown order)   (unknown ge…    NA    NA    NA    NA    NA    NA    NA    NA #> 2 Actinomycetales   Schaalia        NA    NA    NA    NA    NA    NA    NA    NA #> 3 Bacteroidales     Bacteroides     NA    NA    NA    NA    NA    NA    NA    NA #> 4 Campylobacterales Campylobact…    NA    NA    NA    NA    NA    NA    NA    NA #> 5 Caryophanales     Gemella         NA    NA    NA    NA    NA    NA    NA    NA #> 6 Caryophanales     Listeria        NA    NA    NA    NA    NA    NA    NA    NA"},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"perform-principal-component-analysis","dir":"Articles","previous_headings":"","what":"Perform principal component analysis","title":"How to conduct principal component analysis (PCA) for AMR","text":"new pca() function automatically filter rows contain numeric values selected variables, now need : result can reviewed good old summary() function: Good news. first two components explain total 93.3% variance (see PC1 PC2 values Proportion Variance. can create -called biplot base R biplot() function, see antimicrobial resistance per drug explain difference per microorganism.","code":"pca_result <- pca(resistance_data) #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #>   \"TMP\", and \"TOB\". Total observations available: 7. summary(pca_result) #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\" #> Importance of components: #>                           PC1    PC2    PC3     PC4     PC5     PC6       PC7 #> Standard deviation     2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 9.577e-17 #> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00 #> Cumulative Proportion  0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00 #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\""},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"plotting-the-results","dir":"Articles","previous_headings":"","what":"Plotting the results","title":"How to conduct principal component analysis (PCA) for AMR","text":"can’t see explanation points. Perhaps works better new ggplot_pca() function, automatically adds right labels even groups:  can also print ellipse per group, edit appearance:","code":"biplot(pca_result) ggplot_pca(pca_result) ggplot_pca(pca_result, ellipse = TRUE) +   ggplot2::labs(title = \"An AMR/PCA biplot!\")"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"spss-sas-stata","dir":"Articles","previous_headings":"","what":"SPSS / SAS / Stata","title":"How to import data from SPSS / SAS / Stata","text":"SPSS (Statistical Package Social Sciences) probably well-known software package statistical analysis. SPSS easier learn R, SPSS click menu run parts analysis. user-friendliness, taught universities particularly useful students new statistics. experience, guess pretty much (bio)medical students know time graduate. SAS Stata comparable statistical packages popular big industries.","code":""},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"compared-to-r","dir":"Articles","previous_headings":"","what":"Compared to R","title":"How to import data from SPSS / SAS / Stata","text":"said, SPSS easier learn R. SPSS, SAS Stata come major downsides comparing R: R highly modular. official R network (CRAN) features 16,000 packages time writing, AMR package one . packages peer-reviewed publication. Aside official channel, also developers choose submit CRAN, rather keep public repository, like GitHub. may even lot 14,000 packages . Bottom line , can really extend ask somebody . Take example AMR package. Among things, adds reliable reference data R help data cleaning analysis. SPSS, SAS Stata never know valid MIC value Gram stain E. coli . species Klebiella resistant amoxicillin Floxapen® trade name flucloxacillin. facts properties often needed clean existing data, inconvenient software package without reliable reference data. See demonstration. R extremely flexible. write syntax , can anything want. flexibility transforming, arranging, grouping summarising data, drawing plots, endless - SPSS, SAS Stata bound algorithms format styles. may bit flexible, can probably never create specific publication-ready plot without using (paid) software. sometimes write syntaxes SPSS run complete analysis ‘automate’ work, lot less time R. notice writing syntaxes R lot nifty clever SPSS. Still, working statistical package, knowledge (statistically) willing accomplish. R can easily automated. last years, R Markdown really made interesting development. R Markdown, can easily produce reports, whether format Word, PowerPoint, website, PDF document just raw data Excel. even allows use reference file containing layout style (e.g. fonts colours) organisation. use lot generate weekly monthly reports automatically. Just write code enjoy automatically updated reports interval like. even professional environment, create Shiny apps: live manipulation data using custom made website. webdesign knowledge needed (JavaScript, CSS, HTML) almost zero. R huge community. Many R users just ask questions websites like StackOverflow.com, largest online community programmers. time writing, 484 006 R-related questions already asked platform (covers questions answers programming language). experience, questions answered within couple minutes. R understands data type, including SPSS/SAS/Stata. ’s vice versa ’m afraid. can import data source R. example SPSS, SAS Stata (link), Minitab, Epi Info EpiData (link), Excel (link), flat files like CSV, TXT TSV (link), directly databases datawarehouses anywhere world (link). can even scrape websites download tables live internet (link) get results API call transform data one command (link). best part - can export R data formats well. can import SPSS file, analysis neatly R export resulting tables Excel files sharing. R completely free open-source. strings attached. created maintained volunteers believe (data) science open publicly available everybody. SPSS, SAS Stata quite expensive. IBM SPSS Staticstics comes subscriptions nowadays, varying USD 1,300 USD 8,500 per user per year. SAS Analytics Pro costs around USD 10,000 per computer. Stata also business model subscription fees, varying USD 600 USD 2,800 per computer per year, lower prices come limitation number variables can work . still offer benefits R. working midsized small company, can save tens thousands dollars using R instead e.g. SPSS - gaining even functions flexibility. R enthousiasts can much PR want (like ), nobody officially associated affiliated R. really free. R (nowadays) preferred analysis software academic papers. present, R among world powerful statistical languages, generally popular science (Bollmann et al., 2017). reasons, number references R analysis method academic papers rising continuously even surpassed SPSS academic use (Muenchen, 2014). believe thing SPSS , always great user interface easy learn use. Back developed , little competition, let alone R. R didn’t even professional user interface last decade (called RStudio, see ). people used R nineties 2010 almost completely incomparable R used now. language restyled completely volunteers dedicated professionals field data science. SPSS great nothing else compete. now 2023, don’t see reason SPSS better use R. demonstrate first point:","code":"# not all values are valid MIC values: as.mic(0.125) #> Class 'mic' #> [1] 0.125 as.mic(\"testvalue\") #> Class 'mic' #> [1] <NA>  # the Gram stain is available for all bacteria: mo_gramstain(\"E. coli\") #> [1] \"Gram-negative\"  # Klebsiella is intrinsic resistant to amoxicillin, according to EUCAST: klebsiella_test <- data.frame(   mo = \"klebsiella\",   amox = \"S\",   stringsAsFactors = FALSE ) klebsiella_test # (our original data) #>           mo amox #> 1 klebsiella    S eucast_rules(klebsiella_test, info = FALSE) # (the edited data by EUCAST rules) #>           mo amox #> 1 klebsiella    R  # hundreds of trade names can be translated to a name, trade name or an ATC code: ab_name(\"floxapen\") #> [1] \"Flucloxacillin\" ab_tradenames(\"floxapen\") #>  [1] \"culpen\"               \"floxacillin\"          \"floxacillin sodium\"   #>  [4] \"floxapen\"             \"floxapen sodium salt\" \"fluclox\"              #>  [7] \"flucloxacilina\"       \"flucloxacillin\"       \"flucloxacilline\"      #> [10] \"flucloxacillinum\"     \"fluorochloroxacillin\" \"staphylex\" ab_atc(\"floxapen\") #> [1] \"J01CF05\""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"rstudio","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata","what":"RStudio","title":"How to import data from SPSS / SAS / Stata","text":"work R, probably best option use RStudio. open-source free desktop environment allows run R code, also supports project management, version management, package management convenient import menus work data sources. can also install RStudio Server private corporate server, brings nothing less complete RStudio software website (home work). import data file, just click Import Dataset Environment tab:  additional packages needed, RStudio ask installed beforehand. window opens, can define options (parameters) used import ’re ready go:  want named variables imported factors resembles SPSS , use as_factor(). difference :","code":"SPSS_data # # A tibble: 4,203 x 4 #     v001 sex       status    statusage #    <dbl> <dbl+lbl> <dbl+lbl>     <dbl> #  1 10002 1         1              76.6 #  2 10004 0         1              59.1 #  3 10005 1         1              54.5 #  4 10006 1         1              54.1 #  5 10007 1         1              57.7 #  6 10008 1         1              62.8 #  7 10010 0         1              63.7 #  8 10011 1         1              73.1 #  9 10017 1         1              56.7 # 10 10018 0         1              66.6 # # ... with 4,193 more rows  as_factor(SPSS_data) # # A tibble: 4,203 x 4 #     v001 sex    status statusage #    <dbl> <fct>  <fct>      <dbl> #  1 10002 Male   alive       76.6 #  2 10004 Female alive       59.1 #  3 10005 Male   alive       54.5 #  4 10006 Male   alive       54.1 #  5 10007 Male   alive       57.7 #  6 10008 Male   alive       62.8 #  7 10010 Female alive       63.7 #  8 10011 Male   alive       73.1 #  9 10017 Male   alive       56.7 # 10 10018 Female alive       66.6 # # ... with 4,193 more rows"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"base-r","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata","what":"Base R","title":"How to import data from SPSS / SAS / Stata","text":"import data SPSS, SAS Stata, can use great haven package : can now import files follows:","code":"# download and install the latest version: install.packages(\"haven\") # load the package you just installed: library(haven)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"spss","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"SPSS","title":"How to import data from SPSS / SAS / Stata","text":"read files SPSS R: forget as_factor(), mentioned . export R objects SPSS file format:","code":"# read any SPSS file based on file extension (best way): read_spss(file = \"path/to/file\")  # read .sav or .zsav file: read_sav(file = \"path/to/file\")  # read .por file: read_por(file = \"path/to/file\") # save as .sav file: write_sav(data = yourdata, path = \"path/to/file\")  # save as compressed .zsav file: write_sav(data = yourdata, path = \"path/to/file\", compress = TRUE)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"sas","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"SAS","title":"How to import data from SPSS / SAS / Stata","text":"read files SAS R: export R objects SAS file format:","code":"# read .sas7bdat + .sas7bcat files: read_sas(data_file = \"path/to/file\", catalog_file = NULL)  # read SAS transport files (version 5 and version 8): read_xpt(file = \"path/to/file\") # save as regular SAS file: write_sas(data = yourdata, path = \"path/to/file\")  # the SAS transport format is an open format # (required for submission of the data to the FDA) write_xpt(data = yourdata, path = \"path/to/file\", version = 8)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"stata","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"Stata","title":"How to import data from SPSS / SAS / Stata","text":"read files Stata R: export R objects Stata file format:","code":"# read .dta file: read_stata(file = \"/path/to/file\")  # works exactly the same: read_dta(file = \"/path/to/file\") # save as .dta file, Stata version 14: # (supports Stata v8 until v15 at the time of writing) write_dta(data = yourdata, path = \"/path/to/file\", version = 14)"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"import-of-data","dir":"Articles","previous_headings":"","what":"Import of data","title":"How to work with WHONET data","text":"tutorial assumes already imported WHONET data e.g. readxl package. RStudio, can done using menu button ‘Import Dataset’ tab ‘Environment’. Choose option ‘Excel’ select exported file. Make sure date fields imported correctly. example syntax look like : package comes example data set WHONET. use analysis.","code":"library(readxl) data <- read_excel(path = \"path/to/your/file.xlsx\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to work with WHONET data","text":"First, load relevant packages yet . use tidyverse analyses. . don’t know yet, suggest read website: https://www.tidyverse.org/. transform variables simplify automate analysis: Microorganisms transformed microorganism codes (called mo) using Catalogue Life reference data set, contains ~70,000 microorganisms taxonomic kingdoms Bacteria, Fungi Protozoa. tranformation .mo(). function also recognises almost WHONET abbreviations microorganisms. Antimicrobial results interpretations clean valid. words, contain values \"S\", \"\" \"R\". exactly .sir() function . errors warnings, values transformed succesfully. also created package dedicated data cleaning checking, called cleaner package. freq() function can used create frequency tables. let’s check data, couple frequency tables: Frequency table Class: character Length: 500 Available: 500 (100%, NA: 0 = 0%) Unique: 38 Shortest: 11 Longest: 40 (omitted 28 entries, n = 57 [11.4%]) Frequency table Class: factor > ordered > sir (numeric) Length: 500 Levels: 3: S < < R Available: 481 (96.2%, NA: 19 = 3.8%) Unique: 3 Drug: Amoxicillin/clavulanic acid (AMC, J01CR02) Drug group: Beta-lactams/penicillins %SI: 78.59%","code":"library(dplyr) # part of tidyverse library(ggplot2) # part of tidyverse library(AMR) # this package library(cleaner) # to create frequency tables # transform variables data <- WHONET %>%   # get microbial ID based on given organism   mutate(mo = as.mo(Organism)) %>%   # transform everything from \"AMP_ND10\" to \"CIP_EE\" to the new `sir` class   mutate_at(vars(AMP_ND10:CIP_EE), as.sir) # our newly created `mo` variable, put in the mo_name() function data %>% freq(mo_name(mo), nmax = 10) # our transformed antibiotic columns # amoxicillin/clavulanic acid (J01CR02) as an example data %>% freq(AMC_ND2)"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"a-first-glimpse-at-results","dir":"Articles","previous_headings":"","what":"A first glimpse at results","title":"How to work with WHONET data","text":"easy ggplot already give lot information, using included ggplot_sir() function:","code":"data %>%   group_by(Country) %>%   select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%   ggplot_sir(translate_ab = \"ab\", facet = \"Country\", datalabels = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-full-microbial-taxonomy","dir":"Articles","previous_headings":"","what":"microorganisms: Full Microbial Taxonomy","title":"Data sets for download / own use","text":"data set 52 142 rows 22 columns, containing following column names:mo, fullname, status, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, source, lpsn, lpsn_parent, lpsn_renamed_to, gbif, gbif_parent, gbif_renamed_to, prevalence, snomed. data set R available microorganisms, load AMR package. last updated 26 February 2023 20:26:58 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (1.2 MB) Download tab-separated text file (11.3 MB) Download Microsoft Excel workbook (5 MB) Download Apache Feather file (5.4 MB) Download Apache Parquet file (2.6 MB) Download SAS data file (50.9 MB) Download IBM SPSS Statistics data file (16.9 MB) Download Stata DTA file (47.1 MB) NOTE: exported files SAS, SPSS Stata contain first 50 SNOMED codes per record, file size otherwise exceed 100 MB; file size limit GitHub. Advice? Use R instead. tab-separated text file Microsoft Excel workbook contain SNOMED codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source","dir":"Articles","previous_headings":"microorganisms: Full Microbial Taxonomy","what":"Source","title":"Data sets for download / own use","text":"data set contains full microbial taxonomy five kingdoms List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF): Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name ‘Microoganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content","dir":"Articles","previous_headings":"microorganisms: Full Microbial Taxonomy","what":"Example content","title":"Data sets for download / own use","text":"Included (sub)species per taxonomic kingdom: Example rows filtering genus Escherichia:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antibiotics-antibiotic-antifungal-drugs","dir":"Articles","previous_headings":"","what":"antibiotics: Antibiotic (+Antifungal) Drugs","title":"Data sets for download / own use","text":"data set 483 rows 14 columns, containing following column names:ab, cid, name, group, atc, atc_group1, atc_group2, abbreviations, synonyms, oral_ddd, oral_units, iv_ddd, iv_units, loinc. data set R available antibiotics, load AMR package. last updated 22 February 2023 13:38:57 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (39 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (66 kB) Download Apache Feather file (0.1 MB) Download Apache Parquet file (97 kB) Download SAS data file (1.9 MB) Download IBM SPSS Statistics data file (0.3 MB) Download Stata DTA file (0.4 MB) tab-separated text file Microsoft Excel workbook, SAS, SPSS Stata files contain ATC codes, common abbreviations, trade names LOINC codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-1","dir":"Articles","previous_headings":"antibiotics: Antibiotic (+Antifungal) Drugs","what":"Source","title":"Data sets for download / own use","text":"data set contains EARS-Net ATC codes gathered WHONET, compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine WHONET software 2019 LOINC (Logical Observation Identifiers Names Codes)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antivirals-antiviral-drugs","dir":"Articles","previous_headings":"","what":"antivirals: Antiviral Drugs","title":"Data sets for download / own use","text":"data set 120 rows 11 columns, containing following column names:av, name, atc, cid, atc_group, synonyms, oral_ddd, oral_units, iv_ddd, iv_units, loinc. data set R available antivirals, load AMR package. last updated 13 November 2022 07:46:10 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (5 kB) Download tab-separated text file (16 kB) Download Microsoft Excel workbook (16 kB) Download Apache Feather file (15 kB) Download Apache Parquet file (13 kB) Download SAS data file (84 kB) Download IBM SPSS Statistics data file (30 kB) Download Stata DTA file (73 kB) tab-separated text file Microsoft Excel workbook, SAS, SPSS Stata files contain trade names LOINC codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-2","dir":"Articles","previous_headings":"antivirals: Antiviral Drugs","what":"Source","title":"Data sets for download / own use","text":"data set contains ATC codes gathered compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine LOINC (Logical Observation Identifiers Names Codes)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"clinical_breakpoints-interpretation-from-mic-values-disk-diameters-to-sir","dir":"Articles","previous_headings":"","what":"clinical_breakpoints: Interpretation from MIC values & disk diameters to SIR","title":"Data sets for download / own use","text":"data set 18 308 rows 11 columns, containing following column names:guideline, method, site, mo, rank_index, ab, ref_tbl, disk_dose, breakpoint_S, breakpoint_R, uti. data set R available clinical_breakpoints, load AMR package. last updated 21 January 2023 22:47:20 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (42 kB) Download tab-separated text file (1.9 MB) Download Microsoft Excel workbook (0.8 MB) Download Apache Feather file (0.7 MB) Download Apache Parquet file (87 kB) Download SAS data file (3.6 MB) Download IBM SPSS Statistics data file (2.3 MB) Download Stata DTA file (3.4 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-3","dir":"Articles","previous_headings":"clinical_breakpoints: Interpretation from MIC values & disk diameters to SIR","what":"Source","title":"Data sets for download / own use","text":"data set contains interpretation rules MIC values disk diffusion diameters. Included guidelines CLSI (2013-2022) EUCAST (2013-2022).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"intrinsic_resistant-intrinsic-bacterial-resistance","dir":"Articles","previous_headings":"","what":"intrinsic_resistant: Intrinsic Bacterial Resistance","title":"Data sets for download / own use","text":"data set 134 634 rows 2 columns, containing following column names:mo ab. data set R available intrinsic_resistant, load AMR package. last updated 16 December 2022 15:10:43 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (78 kB) Download tab-separated text file (5.1 MB) Download Microsoft Excel workbook (1.3 MB) Download Apache Feather file (1.2 MB) Download Apache Parquet file (0.2 MB) Download SAS data file (9.8 MB) Download IBM SPSS Statistics data file (7.4 MB) Download Stata DTA file (9.5 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-4","dir":"Articles","previous_headings":"intrinsic_resistant: Intrinsic Bacterial Resistance","what":"Source","title":"Data sets for download / own use","text":"data set contains defined intrinsic resistance EUCAST bug-drug combinations, based ‘EUCAST Expert Rules’ ‘EUCAST Intrinsic Resistance Unusual Phenotypes’ v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content-4","dir":"Articles","previous_headings":"intrinsic_resistant: Intrinsic Bacterial Resistance","what":"Example content","title":"Data sets for download / own use","text":"Example rows filtering Enterobacter cloacae:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"dosage-dosage-guidelines-from-eucast","dir":"Articles","previous_headings":"","what":"dosage: Dosage Guidelines from EUCAST","title":"Data sets for download / own use","text":"data set 336 rows 9 columns, containing following column names:ab, name, type, dose, dose_times, administration, notes, original_txt, eucast_version. data set R available dosage, load AMR package. last updated 14 November 2022 14:20:39 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (3 kB) Download tab-separated text file (29 kB) Download Microsoft Excel workbook (19 kB) Download Apache Feather file (16 kB) Download Apache Parquet file (8 kB) Download SAS data file (92 kB) Download IBM SPSS Statistics data file (43 kB) Download Stata DTA file (82 kB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-5","dir":"Articles","previous_headings":"dosage: Dosage Guidelines from EUCAST","what":"Source","title":"Data sets for download / own use","text":"EUCAST breakpoints used package based dosages data set. Currently included dosages data set meant : ‘EUCAST Clinical Breakpoint Tables’ v11.0 (2021) ‘EUCAST Clinical Breakpoint Tables’ v12.0 (2022).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example_isolates-example-data-for-practice","dir":"Articles","previous_headings":"","what":"example_isolates: Example Data for Practice","title":"Data sets for download / own use","text":"data set 2 000 rows 46 columns, containing following column names:date, patient, age, gender, ward, mo, PEN, OXA, FLC, AMX, AMC, AMP, TZP, CZO, FEP, CXM, FOX, CTX, CAZ, CRO, GEN, TOB, AMK, KAN, TMP, SXT, NIT, FOS, LNZ, CIP, MFX, VAN, TEC, TCY, TGC, DOX, ERY, CLI, AZM, IPM, MEM, MTR, CHL, COL, MUP, RIF. data set R available example_isolates, load AMR package. last updated 21 January 2023 22:47:20 UTC. Find info structure data set .","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-6","dir":"Articles","previous_headings":"example_isolates: Example Data for Practice","what":"Source","title":"Data sets for download / own use","text":"data set contains randomised fictitious data, reflects reality can used practise AMR data analysis.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example_isolates_unclean-example-data-for-practice","dir":"Articles","previous_headings":"","what":"example_isolates_unclean: Example Data for Practice","title":"Data sets for download / own use","text":"data set 3 000 rows 8 columns, containing following column names:patient_id, hospital, date, bacteria, AMX, AMC, CIP, GEN. data set R available example_isolates_unclean, load AMR package. last updated 27 August 2022 18:49:37 UTC. Find info structure data set .","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-7","dir":"Articles","previous_headings":"example_isolates_unclean: Example Data for Practice","what":"Source","title":"Data sets for download / own use","text":"data set contains randomised fictitious data, reflects reality can used practise AMR data analysis.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"needed-r-packages","dir":"Articles","previous_headings":"","what":"Needed R packages","title":"How to predict antimicrobial resistance","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. AMR package depends packages even extends use functions.","code":"library(dplyr) library(ggplot2) library(AMR)  # (if not yet installed, install with:) # install.packages(c(\"tidyverse\", \"AMR\"))"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"prediction-analysis","dir":"Articles","previous_headings":"","what":"Prediction analysis","title":"How to predict antimicrobial resistance","text":"package contains function resistance_predict(), takes input functions AMR data analysis. Based date column, calculates cases per year uses regression model predict antimicrobial resistance. basically easy : function look date column col_date set. running commands, summary regression model printed unless using resistance_predict(..., info = FALSE). text printed summary - actual result (output) function data.frame containing year: number observations, actual observed resistance, estimated resistance standard error estimation: function plot available base R, can extended packages depend output based type input. extended function cope resistance predictions:  fastest way plot result. automatically adds right axes, error bars, titles, number available observations type model. also support ggplot2 package custom function ggplot_sir_predict() create appealing plots:","code":"# resistance prediction of piperacillin/tazobactam (TZP): resistance_predict(tbl = example_isolates, col_date = \"date\", col_ab = \"TZP\", model = \"binomial\")  # or: example_isolates %>%   resistance_predict(     col_ab = \"TZP\",     model = \"binomial\"   )  # to bind it to object 'predict_TZP' for example: predict_TZP <- example_isolates %>%   resistance_predict(     col_ab = \"TZP\",     model = \"binomial\"   ) predict_TZP #> # A tibble: 32 × 7 #>     year  value se_min se_max observations observed estimated #>  * <dbl>  <dbl>  <dbl>  <dbl>        <int>    <dbl>     <dbl> #>  1  2002 0.2        NA     NA           15   0.2       0.0562 #>  2  2003 0.0625     NA     NA           32   0.0625    0.0616 #>  3  2004 0.0854     NA     NA           82   0.0854    0.0676 #>  4  2005 0.05       NA     NA           60   0.05      0.0741 #>  5  2006 0.0508     NA     NA           59   0.0508    0.0812 #>  6  2007 0.121      NA     NA           66   0.121     0.0889 #>  7  2008 0.0417     NA     NA           72   0.0417    0.0972 #>  8  2009 0.0164     NA     NA           61   0.0164    0.106  #>  9  2010 0.0566     NA     NA           53   0.0566    0.116  #> 10  2011 0.183      NA     NA           93   0.183     0.127  #> # … with 22 more rows plot(predict_TZP) ggplot_sir_predict(predict_TZP) # choose for error bars instead of a ribbon ggplot_sir_predict(predict_TZP, ribbon = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"choosing-the-right-model","dir":"Articles","previous_headings":"Prediction analysis","what":"Choosing the right model","title":"How to predict antimicrobial resistance","text":"Resistance easily predicted; look vancomycin resistance Gram-positive bacteria, spread (.e. standard error) enormous:  Vancomycin resistance 100% ten years, might remain low. can define model model parameter. model chosen generalised linear regression model using binomial distribution, assuming period zero resistance followed period increasing resistance leading slowly resistance. Valid values : vancomycin resistance Gram-positive bacteria, linear model might appropriate:  model also available object, attribute:","code":"example_isolates %>%   filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>%   resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"binomial\") %>%   ggplot_sir_predict() example_isolates %>%   filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>%   resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"linear\") %>%   ggplot_sir_predict() model <- attributes(predict_TZP)$model  summary(model)$family #>  #> Family: binomial  #> Link function: logit  summary(model)$coefficients #>                  Estimate  Std. Error   z value     Pr(>|z|) #> (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05 #> year           0.09883005  0.02295317  4.305725 1.664395e-05"},{"path":"https://msberends.github.io/AMR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Matthijs S. Berends. Author, maintainer. Christian F. Luz. Author, contributor. Dennis Souverein. Author, contributor. Erwin E. . Hassing. Author, contributor. Casper J. Albers. Thesis advisor. Peter Dutey-Magni. Contributor. Judith M. Fonville. Contributor. Alex W. Friedrich. Thesis advisor. Corinna Glasner. Thesis advisor. Eric H. L. C. M. Hazenberg. Contributor. Gwen Knight. Contributor. Annick Lenglet. Contributor. Bart C. Meijer. Contributor. Dmytro Mykhailenko. Contributor. Anton Mymrikov. Contributor. Andrew P. Norgan. Contributor. Sofia Ny. Contributor. Jonas Salm. Contributor. Rogier P. Schade. Contributor. Bhanu N. M. Sinha. Thesis advisor. Anthony Underwood. Contributor. Anita Williams. Contributor.","code":""},{"path":"https://msberends.github.io/AMR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). “AMR: R Package Working Antimicrobial Resistance Data.” Journal Statistical Software, 104(3), 1–31. doi:10.18637/jss.v104.i03.","code":"@Article{,   title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},   author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},   journal = {Journal of Statistical Software},   year = {2022},   volume = {104},   number = {3},   pages = {1--31},   doi = {10.18637/jss.v104.i03}, }"},{"path":"https://msberends.github.io/AMR/index.html","id":"the-amr-package-for-r-","dir":"","previous_headings":"","what":"Antimicrobial Resistance Data Analysis","title":"Antimicrobial Resistance Data Analysis","text":"Generates antibiograms - traditional, combined, syndromic, even WISCA Provides full microbiological taxonomy data antimicrobial drugs Applies recent CLSI EUCAST clinical breakpoints MICs disk zones Corrects duplicate isolates, calculates predicts AMR per antibiotic class Integrates WHONET, ATC, EARS-Net, PubChem, LOINC SNOMED CT Works Windows, macOS Linux versions R since R-3.0 completely dependency-free, highly suitable places limited resources https://msberends.github.io/AMR https://doi.org/10.18637/jss.v104.i03","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"Antimicrobial Resistance Data Analysis","text":"AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. Many different researchers around globe continually helping us make successful durable project! work published Journal Statistical Software (Volume 104(3); DOI 10.18637/jss.v104.i03) formed basis two PhD theses (DOI 10.33612/diss.177417131 DOI 10.33612/diss.192486375). installing package, R knows ~52,000 distinct microbial species (updated December 2022) ~600 antibiotic, antimycotic antiviral drugs name code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC SNOMED CT), knows valid SIR MIC values. integral breakpoint guidelines CLSI EUCAST included last 10 years. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"used-in-over-175-countries-translated-into-20-languages","dir":"","previous_headings":"Introduction","what":"Used in over 175 countries, translated into 20 languages","title":"Antimicrobial Resistance Data Analysis","text":"Since first public release early 2018, R package used almost countries world. Click map enlarge see country names. help contributors corners world, AMR package available  English,  Czech,  Chinese,  Danish,  Dutch,  Finnish,  French,  German,  Greek,  Italian,  Japanese,  Norwegian,  Polish,  Portuguese,  Romanian,  Russian,  Spanish,  Swedish,  Turkish,  Ukrainian. Antimicrobial drug (group) names colloquial microorganism names provided languages.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"filtering-and-selecting-data","dir":"","previous_headings":"Practical examples","what":"Filtering and selecting data","title":"Antimicrobial Resistance Data Analysis","text":"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 last 10 years (manual) Retrieving antimicrobial drug names, doses forms administration clinical health care records (manual) Determining first isolates used AMR data analysis (manual) Calculating antimicrobial resistance (tutorial) Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) (tutorial) Calculating (empirical) susceptibility mono therapy combination therapies (tutorial) Predicting future antimicrobial resistance using regression models (tutorial) Getting properties microorganism (like Gram stain, species, genus family) (manual) Getting properties antibiotic (like name, code EARS-Net/ATC/LOINC/PubChem, defined daily dose trade name) (manual) Plotting antimicrobial resistance (tutorial) Applying EUCAST expert rules (manual) Getting SNOMED codes microorganism, getting properties microorganism based SNOMED code (manual) Getting LOINC codes antibiotic, getting properties antibiotic based LOINC code (manual) Machine reading EUCAST CLSI guidelines 2011-2021 translate MIC values disk diffusion diameters SIR (link) Principal component analysis AMR (tutorial)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-official-version","dir":"","previous_headings":"Get this package","what":"Latest official version","title":"Antimicrobial Resistance Data Analysis","text":"package available official R network (CRAN). Install package R CRAN using command: downloaded installed automatically. RStudio, click menu Tools > Install Packages… type “AMR” press Install. Note: functions website may available latest release. use functions data sets mentioned website, install latest development version.","code":"install.packages(\"AMR\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-development-version","dir":"","previous_headings":"Get this package","what":"Latest development version","title":"Antimicrobial Resistance Data Analysis","text":"Please read Developer Guideline . latest unpublished development version can installed GitHub two ways: Manually, using: Automatically, using rOpenSci R-universe platform, adding R-universe address list repositories (‘repos’): , can install update AMR package like official release (e.g., using install.packages(\"AMR\") RStudio via Tools > Check Package Updates…).","code":"install.packages(\"remotes\") # if you haven't already remotes::install_github(\"msberends/AMR\") options(repos = c(getOption(\"repos\"),                   msberends = \"https://msberends.r-universe.dev\"))"},{"path":"https://msberends.github.io/AMR/index.html","id":"get-started","dir":"","previous_headings":"","what":"Get started","title":"Antimicrobial Resistance Data Analysis","text":"find conduct AMR data analysis, please continue reading get started click link ‘’ menu.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"partners","dir":"","previous_headings":"","what":"Partners","title":"Antimicrobial Resistance Data Analysis","text":"development package part , related , made possible following non-profit organisations initiatives:","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"copyright","dir":"","previous_headings":"","what":"Copyright","title":"Antimicrobial Resistance Data Analysis","text":"R package free, open-source software licensed GNU General Public License v2.0 (GPL-2). nutshell, means package: May used commercial purposes May used private purposes May used patent purposes May modified, although: Modifications must released license distributing package Changes made code must documented May distributed, although: Source code must made available package distributed copy license copyright notice must included package. Comes LIMITATION liability Comes warranty","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions — AMR-deprecated","title":"Deprecated Functions — AMR-deprecated","text":"functions -called 'Deprecated'. removed future release. Using functions give warning name function replaced (one).","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated Functions — AMR-deprecated","text":"","code":"NA_rsi_  as.rsi(x, ...)  facet_rsi(...)  geom_rsi(...)  ggplot_rsi(...)  ggplot_rsi_predict(...)  is.rsi(...)  is.rsi.eligible(...)  labels_rsi_count(...)  n_rsi(...)  random_rsi(...)  rsi_df(...)  rsi_predict(...)  scale_rsi_colours(...)  theme_rsi(...)"},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Deprecated Functions — AMR-deprecated","text":"object class rsi (inherits ordered, factor) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":null,"dir":"Reference","previous_headings":"","what":"Options for the AMR package — AMR-options","title":"Options for the AMR package — AMR-options","text":"overview package-specific options() can set AMR package.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"options","dir":"Reference","previous_headings":"","what":"Options","title":"Options for the AMR package — AMR-options","text":"AMR_custom_ab  Allows use custom antimicrobial drugs package. explained add_custom_antimicrobials(). AMR_custom_mo  Allows use custom microorganisms package. explained add_custom_microorganisms(). AMR_eucastrules  Used setting default types rules eucast_rules() function, must one : \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). AMR_guideline  Used setting default guideline interpreting MIC values disk diffusion diameters .sir(). Can guideline name (e.g., \"CLSI\") name year (e.g. \"CLSI 2019\"). default latest implemented EUCAST guideline, currently \"EUCAST 2022\". Supported guideline currently EUCAST (2013-2022) CLSI (2013-2022). AMR_ignore_pattern  regular expression ignore (.e., make NA) match given .mo() mo_* functions. AMR_include_PKPD  logical use .sir(), indicate PK/PD clinical breakpoints must applied last resort - default TRUE. AMR_include_screening  logical use .sir(), indicate clinical breakpoints screening allowed - default FALSE. AMR_keep_synonyms  logical use .mo() mo_* functions, indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE. AMR_cleaning_regex  regular expression (case-insensitive) use .mo() mo_* functions, clean user input. default outcome mo_cleaning_regex(), removes texts brackets texts \"species\" \"serovar\". AMR_locale  language use AMR package, can one supported language names ISO-639-1 codes: English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). default current system language (supported). AMR_mo_source  file location manual code list used .mo() mo_* functions. explained set_mo_source().","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"saving-settings-between-sessions","dir":"Reference","previous_headings":"","what":"Saving Settings Between Sessions","title":"Options for the AMR package — AMR-options","text":"Settings R saved globally thus lost R exited. can save options .Rprofile file, user-specific file. can edit using:   file, can set options :   add Portuguese language support antibiotics, allow PK/PD rules interpreting MIC values .sir().","code":"utils::file.edit(\"~/.Rprofile\") options(AMR_locale = \"pt\")  options(AMR_include_PKPD = TRUE)"},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"share-options-within-team","dir":"Reference","previous_headings":"","what":"Share Options Within Team","title":"Options for the AMR package — AMR-options","text":"global approach, e.g. within data team, save options file remote file location, shared network drive. work way: Save plain text file e.g. \"X:/team_folder/R_options.R\" fill preferred settings. user, open .Rprofile file using utils::file.edit(\"~/.Rprofile\") put :   Reload R/RStudio check settings getOption(), e.g. getOption(\"AMR_locale\") set value. Now team settings configured one place, can maintained .","code":"source(\"X:/team_folder/R_options.R\")"},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":null,"dir":"Reference","previous_headings":"","what":"The AMR Package — AMR","title":"The AMR Package — AMR","text":"Welcome AMR package. AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. Many different researchers around globe continually helping us make successful durable project! work published Journal Statistical Software (Volume 104(3); doi: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 breakpoint guidelines CLSI EUCAST included last 10 years. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen. AMR package available English, Chinese, 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.berends@certe.nl (ORCID) Authors: Christian F. Luz (ORCID) [contributor] Dennis Souverein (ORCID) [contributor] Erwin E. . Hassing [contributor] contributors: Casper J. Albers (ORCID) [thesis advisor] Peter Dutey-Magni (ORCID) [contributor] Judith M. Fonville [contributor] Alex W. Friedrich (ORCID) [thesis advisor] Corinna Glasner (ORCID) [thesis advisor] Eric H. L. C. M. Hazenberg [contributor] Gwen Knight (ORCID) [contributor] Annick Lenglet (ORCID) [contributor] Bart C. Meijer [contributor] Dmytro Mykhailenko [contributor] Anton Mymrikov [contributor] Andrew P. Norgan (ORCID) [contributor] Sofia Ny (ORCID) [contributor] Jonas Salm [contributor] Rogier P. Schade [contributor] Bhanu N. M. Sinha (ORCID) [thesis advisor] Anthony Underwood (ORCID) [contributor] Anita Williams (ORCID) [contributor]","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":null,"dir":"Reference","previous_headings":"","what":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"antimicrobial drugs official names, ATC codes, ATC groups defined daily dose (DDD) included package, using Collaborating Centre Drug Statistics Methodology.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"","code":"as.ab(\"meropenem\") #> Class 'ab' #> [1] MEM ab_name(\"J01DH02\") #> [1] \"Meropenem\"  ab_tradenames(\"flucloxacillin\") #>  [1] \"culpen\"               \"floxacillin\"          \"floxacillin sodium\"   #>  [4] \"floxapen\"             \"floxapen sodium salt\" \"fluclox\"              #>  [7] \"flucloxacilina\"       \"flucloxacillin\"       \"flucloxacilline\"      #> [10] \"flucloxacillinum\"     \"fluorochloroxacillin\" \"staphylex\""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 500 Isolates - WHONET Example — WHONET","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"example data set exact structure export file WHONET. files can used package, example data set shows. antibiotic results example_isolates data set. patient names created using online surname generators place practice purposes.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET"},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"tibble 500 observations 53 variables: Identification number ID sample Specimen number ID specimen Organism Name microorganism. analysis, transform valid microbial class, using .mo(). Country Country origin Laboratory Name laboratory Last name Fictitious last name patient First name Fictitious initial patient Sex Fictitious gender patient Age Fictitious age patient Age category Age group, can also looked using age_groups() Date admissionDate hospital admission Specimen dateDate specimen received laboratory Specimen type Specimen type group Specimen type (Numeric) Translation \"Specimen type\" Reason Reason request Differential Diagnosis Isolate number ID isolate Organism type Type microorganism, can also looked using mo_type() Serotype Serotype microorganism Beta-lactamase Microorganism produces beta-lactamase? ESBL Microorganism produces extended spectrum beta-lactamase? Carbapenemase Microorganism produces carbapenemase? MRSA screening test Microorganism possible MRSA? Inducible clindamycin resistance Clindamycin can induced? Comment comments Date data entryDate data entered WHONET AMP_ND10:CIP_EE 28 different antibiotics. can lookup abbreviations antibiotics data set, use e.g. ab_name(\"AMP\") get official name immediately. analysis, transform valid antibiotic class, using .sir().","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET #> # A tibble: 500 × 53 #>    Identif…¹ Speci…² Organ…³ Country Labor…⁴ Last …⁵ First…⁶ Sex     Age Age c…⁷ #>    <chr>       <int> <chr>   <chr>   <chr>   <chr>   <chr>   <chr> <dbl> <chr>   #>  1 fe41d7ba…    1748 SPN     Belgium Nation… Abel    B.      F        68 55-74   #>  2 91f175ec…    1767 eco     The Ne… Nation… Delacr… F.      M        89 75+     #>  3 cc401505…    1343 eco     The Ne… Nation… Steens… F.      M        85 75+     #>  4 e864b692…    1894 MAP     Denmark Nation… Beyers… L.      M        62 55-74   #>  5 3d051fe3…    1739 PVU     Belgium Nation… Hummel  W.      M        86 75+     #>  6 c80762a0…    1846 103     The Ne… Nation… Eikenb… J.      F        53 25-54   #>  7 8022d372…    1628 103     Denmark Nation… Leclerc S.      F        77 75+     #>  8 f3dc5f55…    1493 eco     The Ne… Nation… Delacr… W.      M        53 25-54   #>  9 15add38f…    1847 eco     France  Nation… Van La… S.      F        63 55-74   #> 10 fd41248d…    1458 eco     Germany Nation… Moulin  O.      F        75 75+     #> # … with 490 more rows, 43 more variables: `Date of admission` <date>, #> #   `Specimen date` <date>, `Specimen type` <chr>, #> #   `Specimen type (Numeric)` <dbl>, Reason <chr>, `Isolate number` <int>, #> #   `Organism type` <chr>, Serotype <chr>, `Beta-lactamase` <lgl>, ESBL <lgl>, #> #   Carbapenemase <lgl>, `MRSA screening test` <lgl>, #> #   `Inducible clindamycin resistance` <lgl>, Comment <chr>, #> #   `Date of data entry` <date>, AMP_ND10 <sir>, AMC_ED20 <sir>, …"},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"Use function e.g. clinical texts health care records. returns list antimicrobial drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"","code":"ab_from_text(   text,   type = c(\"drug\", \"dose\", \"administration\"),   collapse = NULL,   translate_ab = FALSE,   thorough_search = NULL,   info = interactive(),   ... )"},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_ab type = \"drug\": column name antibiotics data set translate antibiotic abbreviations , using ab_property(). default FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed - default TRUE interactive mode ... arguments passed .ab()","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"list,  character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"function also internally used .ab(), although searches first drug name throw note drug names returned. Note: .ab() function may use long regular expression match brand names antimicrobial drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"default, function search antimicrobial drug names. text elements searched official names, ATC codes brand names. uses .ab() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(abx = ab_from_text(clinical_text)) returned AB codes can transformed official names, groups, etc. ab_* functions ab_name() ab_group(), using translate_ab argument. using collapse, function return character:df %>% mutate(abx = ab_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"","code":"# mind the bad spelling of amoxicillin in this line, # straight from a true health care record: ab_from_text(\"28/03/2020 regular amoxicilliin 500mg po tid\") #> [[1]] #> Class 'ab' #> [1] AMX #>   ab_from_text(\"500 mg amoxi po and 400mg cipro iv\") #> [[1]] #> Class 'ab' #> [1] AMX CIP #>  ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", type = \"dose\") #> [[1]] #> [1] 500 400 #>  ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", type = \"admin\") #> [[1]] #> [1] \"oral\" \"iv\"   #>   ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", collapse = \", \") #> [1] \"AMX, CIP\" # \\donttest{ # if you want to know which antibiotic groups were administered, do e.g.: abx <- ab_from_text(\"500 mg amoxi po and 400mg cipro iv\") ab_group(abx[[1]]) #> [1] \"Beta-lactams/penicillins\" \"Quinolones\"                if (require(\"dplyr\")) {   tibble(clinical_text = c(     \"given 400mg cipro and 500 mg amox\",     \"started on doxy iv today\"   )) %>%     mutate(       abx_codes = ab_from_text(clinical_text),       abx_doses = ab_from_text(clinical_text, type = \"doses\"),       abx_admin = ab_from_text(clinical_text, type = \"admin\"),       abx_coll = ab_from_text(clinical_text, collapse = \"|\"),       abx_coll_names = ab_from_text(clinical_text,         collapse = \"|\",         translate_ab = \"name\"       ),       abx_coll_doses = ab_from_text(clinical_text,         type = \"doses\",         collapse = \"|\"       ),       abx_coll_admin = ab_from_text(clinical_text,         type = \"admin\",         collapse = \"|\"       )     ) } #> Loading required package: dplyr #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union #> # A tibble: 2 × 8 #>   clinical_text          abx_c…¹ abx_d…² abx_a…³ abx_c…⁴ abx_c…⁵ abx_c…⁶ abx_c…⁷ #>   <chr>                  <list>  <list>  <list>  <chr>   <chr>   <chr>   <chr>   #> 1 given 400mg cipro and… <ab>    <dbl>   <chr>   CIP|AMX Ciprof… 400|500 NA      #> 2 started on doxy iv to… <ab>    <dbl>   <chr>   DOX     Doxycy… NA      iv      #> # … with abbreviated variable names ¹​abx_codes, ²​abx_doses, ³​abx_admin, #> #   ⁴​abx_coll, ⁵​abx_coll_names, ⁶​abx_coll_doses, ⁷​abx_coll_admin # }"},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antibiotic — ab_property","title":"Get Properties of an Antibiotic — ab_property","text":"Use functions return specific property antibiotic antibiotics data set. input values evaluated internally .ab().","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antibiotic — ab_property","text":"","code":"ab_name(x, language = get_AMR_locale(), tolower = FALSE, ...)  ab_cid(x, ...)  ab_synonyms(x, ...)  ab_tradenames(x, ...)  ab_group(x, language = get_AMR_locale(), ...)  ab_atc(x, only_first = FALSE, ...)  ab_atc_group1(x, language = get_AMR_locale(), ...)  ab_atc_group2(x, language = get_AMR_locale(), ...)  ab_loinc(x, ...)  ab_ddd(x, administration = \"oral\", ...)  ab_ddd_units(x, administration = \"oral\", ...)  ab_info(x, language = get_AMR_locale(), ...)  ab_url(x, open = FALSE, ...)  ab_property(x, property = \"name\", language = get_AMR_locale(), ...)  set_ab_names(   data,   ...,   property = \"name\",   language = get_AMR_locale(),   snake_case = NULL )"},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antibiotic — ab_property","text":"x (vector ) text can coerced valid antibiotic drug code .ab() language language returned text - default current system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. lead e.g. \"polymyxin B\" \"polymyxin b\". ... case set_ab_names() data data.frame: columns select (supports tidy selection column1:column4), otherwise arguments passed .ab() only_first logical indicate whether first ATC code must returned, giving preference J0-codes (.e., antimicrobial drug group) administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antibiotics data set: vector_or(colnames(antibiotics), sort = FALSE). data data.frame columns need renamed, character vector column names snake_case logical indicate whether names -called snake case: lower case spaces/slashes replaced underscore (_)","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antibiotic — ab_property","text":"integer case ab_cid() named list case ab_info() multiple ab_atc()/ab_synonyms()/ab_tradenames() double case ab_ddd() data.frame case set_ab_names() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antibiotic — ab_property","text":"output translated possible. function ab_url() return direct URL official website. warning returned required ATC code available. function set_ab_names() special column renaming function data.frames. renames columns names resemble antimicrobial drugs. always makes sure new column names unique. property = \"atc\" set, preference given ATC codes J-group.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antibiotic — ab_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antibiotic — ab_property","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antibiotic — ab_property","text":"","code":"# all properties: ab_name(\"AMX\") #> [1] \"Amoxicillin\" ab_atc(\"AMX\") #> [1] \"J01CA04\" ab_cid(\"AMX\") #> [1] 33613 ab_synonyms(\"AMX\") #>  [1] \"actimoxi\"            \"amoclen\"             \"amolin\"              #>  [4] \"amopen\"              \"amopenixin\"          \"amoxibiotic\"         #>  [7] \"amoxicaps\"           \"amoxicilina\"         \"amoxicillin\"         #> [10] \"amoxicillin hydrate\" \"amoxicilline\"        \"amoxicillinum\"       #> [13] \"amoxiden\"            \"amoxil\"              \"amoxivet\"            #> [16] \"amoxy\"               \"amoxycillin\"         \"amoxyke\"             #> [19] \"anemolin\"            \"aspenil\"             \"atoksilin\"           #> [22] \"biomox\"              \"bristamox\"           \"cemoxin\"             #> [25] \"clamoxyl\"            \"damoxy\"              \"delacillin\"          #> [28] \"demoksil\"            \"dispermox\"           \"efpenix\"             #> [31] \"flemoxin\"            \"hiconcil\"            \"histocillin\"         #> [34] \"hydroxyampicillin\"   \"ibiamox\"             \"imacillin\"           #> [37] \"lamoxy\"              \"largopen\"            \"metafarma capsules\"  #> [40] \"metifarma capsules\"  \"moksilin\"            \"moxacin\"             #> [43] \"moxatag\"             \"ospamox\"             \"pamoxicillin\"        #> [46] \"piramox\"             \"promoxil\"            \"remoxil\"             #> [49] \"robamox\"             \"sawamox pm\"          \"tolodina\"            #> [52] \"topramoxin\"          \"unicillin\"           \"utimox\"              #> [55] \"vetramox\"            ab_tradenames(\"AMX\") #>  [1] \"actimoxi\"            \"amoclen\"             \"amolin\"              #>  [4] \"amopen\"              \"amopenixin\"          \"amoxibiotic\"         #>  [7] \"amoxicaps\"           \"amoxicilina\"         \"amoxicillin\"         #> [10] \"amoxicillin hydrate\" \"amoxicilline\"        \"amoxicillinum\"       #> [13] \"amoxiden\"            \"amoxil\"              \"amoxivet\"            #> [16] \"amoxy\"               \"amoxycillin\"         \"amoxyke\"             #> [19] \"anemolin\"            \"aspenil\"             \"atoksilin\"           #> [22] \"biomox\"              \"bristamox\"           \"cemoxin\"             #> [25] \"clamoxyl\"            \"damoxy\"              \"delacillin\"          #> [28] \"demoksil\"            \"dispermox\"           \"efpenix\"             #> [31] \"flemoxin\"            \"hiconcil\"            \"histocillin\"         #> [34] \"hydroxyampicillin\"   \"ibiamox\"             \"imacillin\"           #> [37] \"lamoxy\"              \"largopen\"            \"metafarma capsules\"  #> [40] \"metifarma capsules\"  \"moksilin\"            \"moxacin\"             #> [43] \"moxatag\"             \"ospamox\"             \"pamoxicillin\"        #> [46] \"piramox\"             \"promoxil\"            \"remoxil\"             #> [49] \"robamox\"             \"sawamox pm\"          \"tolodina\"            #> [52] \"topramoxin\"          \"unicillin\"           \"utimox\"              #> [55] \"vetramox\"            ab_group(\"AMX\") #> [1] \"Beta-lactams/penicillins\" ab_atc_group1(\"AMX\") #> [1] \"Beta-lactam antibacterials, penicillins\" ab_atc_group2(\"AMX\") #> [1] \"Penicillins with extended spectrum\" ab_url(\"AMX\") #>                                                           Amoxicillin  #> \"https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no\"   # smart lowercase tranformation ab_name(x = c(\"AMC\", \"PLB\")) #> [1] \"Amoxicillin/clavulanic acid\" \"Polymyxin B\"                 ab_name(x = c(\"AMC\", \"PLB\"), tolower = TRUE) #> [1] \"amoxicillin/clavulanic acid\" \"polymyxin B\"                  # defined daily doses (DDD) ab_ddd(\"AMX\", \"oral\") #> [1] 1.5 ab_ddd_units(\"AMX\", \"oral\") #> [1] \"g\" ab_ddd(\"AMX\", \"iv\") #> [1] 3 ab_ddd_units(\"AMX\", \"iv\") #> [1] \"g\"  ab_info(\"AMX\") # all properties as a list #> $ab #> [1] \"AMX\" #>  #> $cid #> [1] 33613 #>  #> $name #> [1] \"Amoxicillin\" #>  #> $group #> [1] \"Beta-lactams/penicillins\" #>  #> $atc #> [1] \"J01CA04\" #>  #> $atc_group1 #> [1] \"Beta-lactam antibacterials, penicillins\" #>  #> $atc_group2 #> [1] \"Penicillins with extended spectrum\" #>  #> $tradenames #>  [1] \"actimoxi\"            \"amoclen\"             \"amolin\"              #>  [4] \"amopen\"              \"amopenixin\"          \"amoxibiotic\"         #>  [7] \"amoxicaps\"           \"amoxicilina\"         \"amoxicillin\"         #> [10] \"amoxicillin hydrate\" \"amoxicilline\"        \"amoxicillinum\"       #> [13] \"amoxiden\"            \"amoxil\"              \"amoxivet\"            #> [16] \"amoxy\"               \"amoxycillin\"         \"amoxyke\"             #> [19] \"anemolin\"            \"aspenil\"             \"atoksilin\"           #> [22] \"biomox\"              \"bristamox\"           \"cemoxin\"             #> [25] \"clamoxyl\"            \"damoxy\"              \"delacillin\"          #> [28] \"demoksil\"            \"dispermox\"           \"efpenix\"             #> [31] \"flemoxin\"            \"hiconcil\"            \"histocillin\"         #> [34] \"hydroxyampicillin\"   \"ibiamox\"             \"imacillin\"           #> [37] \"lamoxy\"              \"largopen\"            \"metafarma capsules\"  #> [40] \"metifarma capsules\"  \"moksilin\"            \"moxacin\"             #> [43] \"moxatag\"             \"ospamox\"             \"pamoxicillin\"        #> [46] \"piramox\"             \"promoxil\"            \"remoxil\"             #> [49] \"robamox\"             \"sawamox pm\"          \"tolodina\"            #> [52] \"topramoxin\"          \"unicillin\"           \"utimox\"              #> [55] \"vetramox\"            #>  #> $loinc #> [1] \"16365-9\" \"25274-2\" \"3344-9\"  \"80133-2\" #>  #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 1.5 #>  #> $ddd$oral$units #> [1] \"g\" #>  #>  #> $ddd$iv #> $ddd$iv$amount #> [1] 3 #>  #> $ddd$iv$units #> [1] \"g\" #>  #>  #>   # all ab_* functions use as.ab() internally, so you can go from 'any' to 'any': ab_atc(\"AMP\") #> [1] \"J01CA01\" \"S01AA19\" ab_group(\"J01CA01\") #> [1] \"Beta-lactams/penicillins\" ab_loinc(\"ampicillin\") #> [1] \"21066-6\" \"3355-5\"  \"33562-0\" \"33919-2\" \"43883-8\" \"43884-6\" \"87604-5\" ab_name(\"21066-6\") #> [1] \"Ampicillin\" ab_name(6249) #> [1] \"Ampicillin\" ab_name(\"J01CA01\") #> [1] \"Ampicillin\"  # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\") #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\") #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") #> [1] \"J01DD04\"  # use set_ab_names() for renaming columns colnames(example_isolates) #>  [1] \"date\"    \"patient\" \"age\"     \"gender\"  \"ward\"    \"mo\"      \"PEN\"     #>  [8] \"OXA\"     \"FLC\"     \"AMX\"     \"AMC\"     \"AMP\"     \"TZP\"     \"CZO\"     #> [15] \"FEP\"     \"CXM\"     \"FOX\"     \"CTX\"     \"CAZ\"     \"CRO\"     \"GEN\"     #> [22] \"TOB\"     \"AMK\"     \"KAN\"     \"TMP\"     \"SXT\"     \"NIT\"     \"FOS\"     #> [29] \"LNZ\"     \"CIP\"     \"MFX\"     \"VAN\"     \"TEC\"     \"TCY\"     \"TGC\"     #> [36] \"DOX\"     \"ERY\"     \"CLI\"     \"AZM\"     \"IPM\"     \"MEM\"     \"MTR\"     #> [43] \"CHL\"     \"COL\"     \"MUP\"     \"RIF\"     colnames(set_ab_names(example_isolates)) #>  [1] \"date\"                          \"patient\"                       #>  [3] \"age\"                           \"gender\"                        #>  [5] \"ward\"                          \"mo\"                            #>  [7] \"benzylpenicillin\"              \"oxacillin\"                     #>  [9] \"flucloxacillin\"                \"amoxicillin\"                   #> [11] \"amoxicillin_clavulanic_acid\"   \"ampicillin\"                    #> [13] \"piperacillin_tazobactam\"       \"cefazolin\"                     #> [15] \"cefepime\"                      \"cefuroxime\"                    #> [17] \"cefoxitin\"                     \"cefotaxime\"                    #> [19] \"ceftazidime\"                   \"ceftriaxone\"                   #> [21] \"gentamicin\"                    \"tobramycin\"                    #> [23] \"amikacin\"                      \"kanamycin\"                     #> [25] \"trimethoprim\"                  \"trimethoprim_sulfamethoxazole\" #> [27] \"nitrofurantoin\"                \"fosfomycin\"                    #> [29] \"linezolid\"                     \"ciprofloxacin\"                 #> [31] \"moxifloxacin\"                  \"vancomycin\"                    #> [33] \"teicoplanin\"                   \"tetracycline\"                  #> [35] \"tigecycline\"                   \"doxycycline\"                   #> [37] \"erythromycin\"                  \"clindamycin\"                   #> [39] \"azithromycin\"                  \"imipenem\"                      #> [41] \"meropenem\"                     \"metronidazole\"                 #> [43] \"chloramphenicol\"               \"colistin\"                      #> [45] \"mupirocin\"                     \"rifampicin\"                    colnames(set_ab_names(example_isolates, NIT:VAN)) #>  [1] \"date\"           \"patient\"        \"age\"            \"gender\"         #>  [5] \"ward\"           \"mo\"             \"PEN\"            \"OXA\"            #>  [9] \"FLC\"            \"AMX\"            \"AMC\"            \"AMP\"            #> [13] \"TZP\"            \"CZO\"            \"FEP\"            \"CXM\"            #> [17] \"FOX\"            \"CTX\"            \"CAZ\"            \"CRO\"            #> [21] \"GEN\"            \"TOB\"            \"AMK\"            \"KAN\"            #> [25] \"TMP\"            \"SXT\"            \"nitrofurantoin\" \"fosfomycin\"     #> [29] \"linezolid\"      \"ciprofloxacin\"  \"moxifloxacin\"   \"vancomycin\"     #> [33] \"TEC\"            \"TCY\"            \"TGC\"            \"DOX\"            #> [37] \"ERY\"            \"CLI\"            \"AZM\"            \"IPM\"            #> [41] \"MEM\"            \"MTR\"            \"CHL\"            \"COL\"            #> [45] \"MUP\"            \"RIF\"            # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     set_ab_names()    # this does the same:   example_isolates %>%     rename_with(set_ab_names)    # set_ab_names() works with any AB property:   example_isolates %>%     set_ab_names(property = \"atc\")    example_isolates %>%     set_ab_names(where(is.sir)) %>%     colnames()    example_isolates %>%     set_ab_names(NIT:VAN) %>%     colnames() } #>  [1] \"date\"           \"patient\"        \"age\"            \"gender\"         #>  [5] \"ward\"           \"mo\"             \"PEN\"            \"OXA\"            #>  [9] \"FLC\"            \"AMX\"            \"AMC\"            \"AMP\"            #> [13] \"TZP\"            \"CZO\"            \"FEP\"            \"CXM\"            #> [17] \"FOX\"            \"CTX\"            \"CAZ\"            \"CRO\"            #> [21] \"GEN\"            \"TOB\"            \"AMK\"            \"KAN\"            #> [25] \"TMP\"            \"SXT\"            \"nitrofurantoin\" \"fosfomycin\"     #> [29] \"linezolid\"      \"ciprofloxacin\"  \"moxifloxacin\"   \"vancomycin\"     #> [33] \"TEC\"            \"TCY\"            \"TGC\"            \"DOX\"            #> [37] \"ERY\"            \"CLI\"            \"AZM\"            \"IPM\"            #> [41] \"MEM\"            \"MTR\"            \"CHL\"            \"COL\"            #> [45] \"MUP\"            \"RIF\"            # }"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Custom Antimicrobials — add_custom_antimicrobials","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"add_custom_antimicrobials() can add custom antimicrobial drug names codes.","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"","code":"add_custom_antimicrobials(x)  clear_custom_antimicrobials()"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"x data.frame resembling antibiotics data set, least containing columns \"ab\" \"name\"","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"Important: Due R works, add_custom_antimicrobials() function run every R session - added antimicrobials stored sessions thus lost R exited. two ways 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 only \"Enterobacter asburiae\": mo_name(\"Enterobacter asburiae/cloacae\") #> [1] \"Enterobacter asburiae\"  # now add a custom entry - it will be considered by as.mo() and # all mo_*() functions add_custom_microorganisms(   data.frame(     genus = \"Enterobacter\",     species = \"asburiae/cloacae\"   ) ) #> ℹ Added Enterobacter asburiae/cloacae to the internal microorganisms data #>   set.  # E. asburiae/cloacae is now a new microorganism: mo_name(\"Enterobacter asburiae/cloacae\") #> [1] \"Enterobacter asburiae/cloacae\"  # its code: as.mo(\"Enterobacter asburiae/cloacae\") #> Class 'mo' #> [1] CUSTOM1_ENTRBC_A_C  # all internal algorithms will work as well: mo_name(\"Ent asburia cloacae\") #> [1] \"Enterobacter asburiae/cloacae\"  # and even the taxonomy was added based on the genus! mo_family(\"E. asburiae/cloacae\") #> [1] \"Enterobacteriaceae\" mo_gramstain(\"Enterobacter asburiae/cloacae\") #> [1] \"Gram-negative\"  mo_info(\"Enterobacter asburiae/cloacae\") #> $mo #> [1] \"CUSTOM1_ENTRBC_A_C\" #>  #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Enterobacter\" #>  #> $species #> [1] \"asburiae/cloacae\" #>  #> $subspecies #> [1] \"\" #>  #> $status #> [1] \"accepted\" #>  #> $synonyms #> NULL #>  #> $gramstain #> [1] \"Gram-negative\" #>  #> $url #> [1] \"\" #>  #> $ref #> [1] \"Self-added, 2023\" #>  #> $snomed #> [1] NA #>    # the function tries to be forgiving: add_custom_microorganisms(   data.frame(     GENUS = \"BACTEROIDES / PARABACTEROIDES SLASHLINE\",     SPECIES = \"SPECIES\"   ) ) #> ℹ Added Bacteroides/Parabacteroides to the internal microorganisms data #>   set. mo_name(\"BACTEROIDES / PARABACTEROIDES\") #> [1] \"Bacteroides/Parabacteroides\" mo_rank(\"BACTEROIDES / PARABACTEROIDES\") #> [1] \"genus\"  # taxonomy still works, although a slashline genus was given as input: mo_family(\"Bacteroides/Parabacteroides\") #> [1] \"Bacteroidaceae\"   # for groups and complexes, set them as species or subspecies: add_custom_microorganisms(   data.frame(     genus = \"Citrobacter\",     species = c(\"freundii\", \"braakii complex\"),     subspecies = c(\"complex\", \"\")   ) ) #> ℹ Added Citrobacter braakii complex and Citrobacter freundii complex to the #>   internal microorganisms data set. mo_name(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"Citrobacter freundii complex\" \"Citrobacter braakii complex\"  mo_species(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"freundii complex\" \"braakii complex\"  mo_gramstain(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"Gram-negative\" \"Gram-negative\" # }"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":null,"dir":"Reference","previous_headings":"","what":"Age in Years of Individuals — age","title":"Age in Years of Individuals — age","text":"Calculates age years based reference date, system date default.","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Age in Years of Individuals — age","text":"","code":"age(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Age in Years of Individuals — age","text":"x date(s), character (vectors) coerced .POSIXlt() reference reference date(s) (default today), character (vectors) coerced .POSIXlt() exact logical indicate whether age calculation exact, .e. decimals. divides number days year--date (YTD) x number days year reference (either 365 366). na.rm logical indicate whether missing values removed ... arguments passed .POSIXlt(), origin","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Age in Years of Individuals — age","text":"integer (decimals) exact = FALSE, double (decimals) otherwise","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Age in Years of Individuals — age","text":"Ages 0 returned NA warning. Ages 120 give warning. function vectorises x reference, meaning either can length 1 argument larger length.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Age in Years of Individuals — age","text":"","code":"# 10 random pre-Y2K birth dates df <- data.frame(birth_date = as.Date(\"2000-01-01\") - runif(10) * 25000)  # add ages df$age <- age(df$birth_date)  # add exact ages df$age_exact <- age(df$birth_date, exact = TRUE)  # add age at millenium switch df$age_at_y2k <- age(df$birth_date, \"2000-01-01\")  df #>    birth_date age age_exact age_at_y2k #> 1  1968-02-06  55  55.09315         31 #> 2  1951-04-09  71  71.92329         48 #> 3  1972-01-24  51  51.12877         27 #> 4  1991-09-11  31  31.49863          8 #> 5  1984-05-14  38  38.82740         15 #> 6  1975-01-04  48  48.18356         24 #> 7  1983-11-09  39  39.33699         16 #> 8  1972-11-16  50  50.31781         27 #> 9  1958-08-26  64  64.54247         41 #> 10 1975-05-07  47  47.84658         24"},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Ages into Age Groups — age_groups","title":"Split Ages into Age Groups — age_groups","text":"Split ages age groups defined split argument. allows easier demographic (antimicrobial resistance) analysis.","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Ages into Age Groups — age_groups","text":"","code":"age_groups(x, split_at = c(12, 25, 55, 75), na.rm = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Ages into Age Groups — age_groups","text":"x age, e.g. calculated age() split_at values split x - default age groups 0-11, 12-24, 25-54, 55-74 75+. See Details. na.rm logical indicate whether missing values removed","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Ages into Age Groups — age_groups","text":"Ordered factor","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split Ages into Age Groups — age_groups","text":"split ages, input split_at argument can : numeric vector. value e.g. c(10, 20) split x 0-9, 10-19 20+. value 50 split x 0-49 50+. default split young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) elderly (75+). character: \"children\" \"kids\", equivalent : c(0, 1, 2, 4, 6, 13, 18). split 0, 1, 2-3, 4-5, 6-12, 13-17 18+. \"elderly\" \"seniors\", equivalent : c(65, 75, 85). split 0-64, 65-74, 75-84, 85+. \"fives\", equivalent : 1:20 * 5. split 0-4, 5-9, ..., 95-99, 100+. \"tens\", equivalent : 1:10 * 10. split 0-9, 10-19, ..., 90-99, 100+.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split Ages into Age Groups — age_groups","text":"","code":"ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21)  # split into 0-49 and 50+ age_groups(ages, 50) #> [1] 0-49 0-49 0-49 50+  0-49 50+  50+  0-49 0-49 #> Levels: 0-49 < 50+  # split into 0-19, 20-49 and 50+ age_groups(ages, c(20, 50)) #> [1] 0-19  0-19  0-19  50+   20-49 50+   50+   20-49 20-49 #> Levels: 0-19 < 20-49 < 50+  # split into groups of ten years age_groups(ages, 1:10 * 10) #> [1] 0-9   0-9   10-19 50-59 30-39 70-79 100+  40-49 20-29 #> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+ age_groups(ages, split_at = \"tens\") #> [1] 0-9   0-9   10-19 50-59 30-39 70-79 100+  40-49 20-29 #> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+  # split into groups of five years age_groups(ages, 1:20 * 5) #> [1] 0-4   5-9   15-19 50-54 30-34 75-79 100+  40-44 20-24 #> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+ age_groups(ages, split_at = \"fives\") #> [1] 0-4   5-9   15-19 50-54 30-34 75-79 100+  40-44 20-24 #> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+  # split specifically for children age_groups(ages, c(1, 2, 4, 6, 13, 18)) #> [1] 2-3   6-12  13-17 18+   18+   18+   18+   18+   18+   #> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+ age_groups(ages, \"children\") #> [1] 2-3   6-12  13-17 18+   18+   18+   18+   18+   18+   #> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+  # \\donttest{ # resistance of ciprofloxacin per age group if (require(\"dplyr\") && require(\"ggplot2\")) {   example_isolates %>%     filter_first_isolate() %>%     filter(mo == as.mo(\"Escherichia coli\")) %>%     group_by(age_group = age_groups(age)) %>%     select(age_group, CIP) %>%     ggplot_sir(       x = \"age_group\",       minimum = 0,       x.title = \"Age Group\",       title = \"Ciprofloxacin resistance per age group\"     ) } #> Loading required package: ggplot2  # }"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"Generate antibiogram, communicate results plots tables. functions follow logic Klinker et al. Barbieri et al. (see Source), allow reporting e.g. R Markdown Quarto well.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"","code":"antibiogram(   x,   antibiotics = where(is.sir),   mo_transform = \"shortname\",   ab_transform = NULL,   syndromic_group = NULL,   add_total_n = TRUE,   only_all_tested = FALSE,   digits = 0,   col_mo = NULL,   language = get_AMR_locale(),   minimum = 30,   combine_SI = TRUE,   sep = \" + \",   info = interactive() )  # S3 method for antibiogram plot(x, ...)  # S3 method for antibiogram autoplot(object, ...)  # S3 method for antibiogram knit_print(   x,   italicise = TRUE,   na = getOption(\"knitr.kable.NA\", default = \"\"),   ... )"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"Klinker KP et al. (2021). Antimicrobial stewardship antibiograms: importance moving beyond traditional antibiograms. Therapeutic Advances Infectious Disease, May 5;8:20499361211011373; doi:10.1177/20499361211011373 Barbieri E et al. (2021). Development Weighted-Incidence Syndromic Combination Antibiogram (WISCA) guide choice empiric antibiotic treatment urinary tract infection paediatric patients: Bayesian approach Antimicrobial Resistance & Infection Control May 1;10(1):74; doi:10.1186/s13756-021-00939-2 M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"x data.frame containing least column microorganisms columns antibiotic results (class 'sir', see .sir()) antibiotics vector antibiotic name code (evaluated .ab(), column name x, (combinations ) antibiotic selectors aminoglycosides() carbapenems(). combination antibiograms, can also set values separated \"+\", \"TZP+TOB\" \"cipro + genta\", given columns resembling antibiotics exist x. See Examples. mo_transform character transform microorganism input - must \"name\", \"shortname\", \"gramstain\", one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\", \"snomed\". Can also NULL transform input. ab_transform character transform antibiotic input - must one column names antibiotics data set: \"ab\", \"cid\", \"name\", \"group\", \"atc\", \"atc_group1\", \"atc_group2\", \"abbreviations\", \"synonyms\", \"oral_ddd\", \"oral_units\", \"iv_ddd\", \"iv_units\", \"loinc\". Can also NULL transform input. syndromic_group column name x, values calculated split rows x, e.g. using ifelse() case_when(). See Examples. add_total_n logical indicate whether total available numbers per pathogen added table (default TRUE). add lowest highest number available isolate per antibiotic (e.g, E. coli 200 isolates available ciprofloxacin 150 amoxicillin, returned number \"150-200\"). only_all_tested (combination antibiograms): logical indicate isolates must tested antibiotics, see Details digits number digits use rounding col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). language language translate text, defaults system language (see get_AMR_locale()) minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether susceptibility determined results either S , instead S (default TRUE) sep separating character antibiotic columns combination antibiograms info logical indicate info printed - default TRUE interactive mode ... used R Markdown Quarto: arguments passed knitr::kable() (otherwise, use) object antibiogram() object italicise logical indicate whether microorganism names knitr table made italic, using italicise_taxonomy(). na character use showing NA values","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"function returns table values 0 100 susceptibility, resistance. Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine data set one four available algorithms. types antibiograms listed can plotted (using ggplot2::autoplot() base R plot()/barplot()). antibiogram object can also used directly R Markdown / Quarto (.e., knitr) reports. case, knitr::kable() applied automatically microorganism names even printed italics default (see argument italicise). can also use functions specific 'table reporting' packages transform output antibiogram() needs, e.g. flextable::as_flextable() gt::gt().","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"antibiogram-types","dir":"Reference","previous_headings":"","what":"Antibiogram Types","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"four antibiogram types, proposed Klinker et al. (2021, doi:10.1177/20499361211011373 ), supported antibiogram(): Traditional Antibiogram Case example: Susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Code example:   Combination Antibiogram Case example: Additional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Code example:   Syndromic Antibiogram Case example: Susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Code example:   Weighted-Incidence Syndromic Combination Antibiogram (WISCA) Case example: Susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) male patients age >=65 years heart failure Code example:   Note combination antibiograms, important realise susceptibility can calculated two ways, can set only_all_tested argument (default FALSE). See example two antibiotics, Drug Drug B, antibiogram() works calculate %SI:","code":"antibiogram(your_data,             antibiotics = \"TZP\") antibiogram(your_data,             antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\")) antibiogram(your_data,             antibiotics = penicillins(),             syndromic_group = \"ward\") library(dplyr) your_data %>%   filter(ward == \"ICU\" & specimen_type == \"Respiratory\") %>%   antibiogram(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),               syndromic_group = ifelse(.$age >= 65 &                                          .$gender == \"Male\" &                                          .$condition == \"Heart Disease\",                                        \"Study Group\", \"Control Group\")) --------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   include as  include as   include as  include as                     numerator   denominator  numerator   denominator --------  --------  ----------  -----------  ----------  -----------  S or I    S or I       X            X            X            X    R       S or I       X            X            X            X   <NA>     S or I       X            X            -            -  S or I      R          X            X            X            X    R         R          -            X            -            X   <NA>       R          -            -            -            -  S or I     <NA>        X            X            -            -    R        <NA>        -            -            -            -   <NA>      <NA>        -            -            -            - --------------------------------------------------------------------"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>  # \\donttest{ # Traditional antibiogram ----------------------------------------------  antibiogram(example_isolates,   antibiotics = c(aminoglycosides(), carbapenems()) ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 10 × 7 #>    `Pathogen (N min-max)`   AMK   GEN   IPM   KAN   MEM   TOB #>  * <chr>                  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 CoNS (43-309)              0    86    52     0    52    22 #>  2 E. coli (0-462)          100    98   100    NA   100    97 #>  3 E. faecalis (0-39)         0     0   100     0    NA     0 #>  4 K. pneumoniae (0-58)      NA    90   100    NA   100    90 #>  5 P. aeruginosa (17-30)     NA   100    NA     0    NA   100 #>  6 P. mirabilis (0-34)       NA    94    94    NA    NA    94 #>  7 S. aureus (2-233)         NA    99    NA    NA    NA    98 #>  8 S. epidermidis (8-163)     0    79    NA     0    NA    51 #>  9 S. hominis (3-80)         NA    92    NA    NA    NA    85 #> 10 S. pneumoniae (11-117)     0     0    NA     0    NA     0  antibiogram(example_isolates,   antibiotics = aminoglycosides(),   ab_transform = \"atc\",   mo_transform = \"gramstain\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 2 × 5 #>   `Pathogen (N min-max)`   J01GB01 J01GB03 J01GB04 J01GB06 #> * <chr>                      <dbl>   <dbl>   <dbl>   <dbl> #> 1 Gram-negative (35-686)        96      96       0      98 #> 2 Gram-positive (436-1170)      34      63       0       0  antibiogram(example_isolates,   antibiotics = carbapenems(),   ab_transform = \"name\",   mo_transform = \"name\" ) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 5 × 3 #>   `Pathogen (N min-max)`                           Imipenem Meropenem #> * <chr>                                               <dbl>     <dbl> #> 1 Coagulase-negative Staphylococcus (CoNS) (48-48)       52        52 #> 2 Enterococcus faecalis (0-38)                          100        NA #> 3 Escherichia coli (418-422)                            100       100 #> 4 Klebsiella pneumoniae (51-53)                         100       100 #> 5 Proteus mirabilis (27-32)                              94        NA   # Combined antibiogram -------------------------------------------------  # combined antibiotics yield higher empiric coverage antibiogram(example_isolates,   antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),   mo_transform = \"gramstain\" ) #> # A tibble: 2 × 4 #>   `Pathogen (N min-max)`     TZP `TZP + GEN` `TZP + TOB` #> * <chr>                    <dbl>       <dbl>       <dbl> #> 1 Gram-negative (641-693)     88          99          98 #> 2 Gram-positive (345-1044)    86          98          95  # names of antibiotics do not need to resemble columns exactly: antibiogram(example_isolates,   antibiotics = c(\"Cipro\", \"cipro + genta\"),   mo_transform = \"gramstain\",   ab_transform = \"name\",   sep = \" & \" ) #> # A tibble: 2 × 3 #>   `Pathogen (N min-max)`  Ciprofloxacin `Ciprofloxacin & Gentamicin` #> * <chr>                           <dbl>                        <dbl> #> 1 Gram-negative (684-694)            91                           99 #> 2 Gram-positive (724-847)            77                           93   # Syndromic antibiogram ------------------------------------------------  # the data set could contain a filter for e.g. respiratory specimens antibiogram(example_isolates,   antibiotics = c(aminoglycosides(), carbapenems()),   syndromic_group = \"ward\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 14 × 8 #>    `Syndromic Group` `Pathogen (N min-max)`   AMK   GEN   IPM   KAN   MEM   TOB #>  * <chr>             <chr>                  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 Clinical          CoNS (23-205)             NA    89    57    NA    57    26 #>  2 ICU               CoNS (10-73)              NA    79    NA    NA    NA    NA #>  3 Outpatient        CoNS (3-31)               NA    84    NA    NA    NA    NA #>  4 Clinical          E. coli (0-299)          100    98   100    NA   100    98 #>  5 ICU               E. coli (0-137)          100    99   100    NA   100    96 #>  6 Clinical          K. pneumoniae (0-51)      NA    92   100    NA   100    92 #>  7 Clinical          P. mirabilis (0-30)       NA   100    NA    NA    NA   100 #>  8 Clinical          S. aureus (2-150)         NA    99    NA    NA    NA    97 #>  9 ICU               S. aureus (0-66)          NA   100    NA    NA    NA    NA #> 10 Clinical          S. epidermidis (4-79)     NA    82    NA    NA    NA    55 #> 11 ICU               S. epidermidis (4-75)     NA    72    NA    NA    NA    41 #> 12 Clinical          S. hominis (1-45)         NA    96    NA    NA    NA    94 #> 13 Clinical          S. pneumoniae (5-78)       0     0    NA     0    NA     0 #> 14 ICU               S. pneumoniae (5-30)       0     0    NA     0    NA     0  # now define a data set with only E. coli ex1 <- example_isolates[which(mo_genus() == \"Escherichia\"), ] #> ℹ Using column 'mo' as input for mo_genus()  # with a custom language, though this will be determined automatically # (i.e., this table will be in Spanish on Spanish systems) antibiogram(ex1,   antibiotics = aminoglycosides(),   ab_transform = \"name\",   syndromic_group = ifelse(ex1$ward == \"ICU\",     \"UCI\", \"No UCI\"   ),   language = \"es\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 2 × 5 #>   `Grupo sindrómico` `Patógeno (N min-max)` Amikacina Gentamicina Tobramicina #> * <chr>              <chr>                      <dbl>       <dbl>       <dbl> #> 1 No UCI             E. coli (0-325)              100          98          98 #> 2 UCI                E. coli (0-137)              100          99          96   # Weighted-incidence syndromic combination antibiogram (WISCA) ---------  # the data set could contain a filter for e.g. respiratory specimens/ICU antibiogram(example_isolates,   antibiotics = c(\"AMC\", \"AMC+CIP\", \"TZP\", \"TZP+TOB\"),   mo_transform = \"gramstain\",   minimum = 10, # this should be >=30, but now just as example   syndromic_group = ifelse(example_isolates$age >= 65 &     example_isolates$gender == \"M\",   \"WISCA Group 1\", \"WISCA Group 2\"   ) ) #> # A tibble: 4 × 6 #>   `Syndromic Group` `Pathogen (N min-max)`    AMC `AMC + CIP`   TZP `TZP + TOB` #> * <chr>             <chr>                   <dbl>       <dbl> <dbl>       <dbl> #> 1 WISCA Group 1     Gram-negative (261-285)    76          95    89          99 #> 2 WISCA Group 2     Gram-negative (380-442)    76          98    88          98 #> 3 WISCA Group 1     Gram-positive (123-406)    76          89    81          95 #> 4 WISCA Group 2     Gram-positive (222-732)    76          89    88          95   # Print the output for R Markdown / Quarto -----------------------------  ureido <- antibiogram(example_isolates,   antibiotics = ureidopenicillins(),   ab_transform = \"name\" ) #> ℹ For ureidopenicillins() using column 'TZP' (piperacillin/tazobactam)  # in an Rmd file, you would just need to return `ureido` in a chunk, # but to be explicit here: if (requireNamespace(\"knitr\")) {   knitr::knit_print(ureido) } #> [1] \"\\n\\n|Pathogen (N)          | Piperacillin/tazobactam|\\n|:---------------------|-----------------------:|\\n|CoNS (33)             |                      30|\\n|*E. coli* (416)       |                      94|\\n|*K. pneumoniae* (53)  |                      89|\\n|*S. pneumoniae* (112) |                     100|\" #> attr(,\"class\") #> [1] \"knit_asis\" #> attr(,\"knit_cacheable\") #> [1] NA   # Generate plots with ggplot2 or base R --------------------------------  ab1 <- antibiogram(example_isolates,   antibiotics = c(\"AMC\", \"CIP\", \"TZP\", \"TZP+TOB\"),   mo_transform = \"gramstain\" ) ab2 <- antibiogram(example_isolates,   antibiotics = c(\"AMC\", \"CIP\", \"TZP\", \"TZP+TOB\"),   mo_transform = \"gramstain\",   syndromic_group = \"ward\" )  if (requireNamespace(\"ggplot2\")) {   ggplot2::autoplot(ab1) }  if (requireNamespace(\"ggplot2\")) {   ggplot2::autoplot(ab2) }   plot(ab1)  plot(ab2)  # }"},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Antibiotic Selectors — antibiotic_class_selectors","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"functions allow filtering rows selecting columns based antibiotic test results specific antibiotic class group (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 either \"3.3\", \"3.2\", \"3.1\".","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"(internally) character vector column names, additional class \"ab_selector\"","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"functions can used data set calls selecting columns filtering rows. 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    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>   # 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    #> # … with 1,990 more rows  # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB' example_isolates[, c(\"mo\", aminoglycosides())] #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 2,000 × 5 #>    mo           GEN   TOB   AMK   KAN   #>    <mo>         <sir> <sir> <sir> <sir> #>  1 B_ESCHR_COLI NA    NA    NA    NA    #>  2 B_ESCHR_COLI NA    NA    NA    NA    #>  3 B_STPHY_EPDR NA    NA    NA    NA    #>  4 B_STPHY_EPDR NA    NA    NA    NA    #>  5 B_STPHY_EPDR NA    NA    NA    NA    #>  6 B_STPHY_EPDR NA    NA    NA    NA    #>  7 B_STPHY_AURS NA    S     NA    NA    #>  8 B_STPHY_AURS NA    S     NA    NA    #>  9 B_STPHY_EPDR NA    NA    NA    NA    #> 10 B_STPHY_EPDR NA    NA    NA    NA    #> # … with 1,990 more rows  # select only antibiotic columns with DDDs for oral treatment example_isolates[, administrable_per_os()] #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #>   (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #>   'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #>   (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #>   (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #>   (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #>   (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #>   (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin), and 'RIF' #>   (rifampicin) #> # A tibble: 2,000 × 23 #>    OXA   FLC   AMX   AMC   AMP   CXM   KAN   TMP   NIT   FOS   LNZ   CIP   MFX   #>    <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>  1 NA    NA    NA    I     NA    I     NA    R     NA    NA    R     NA    NA    #>  2 NA    NA    NA    I     NA    I     NA    R     NA    NA    R     NA    NA    #>  3 NA    R     NA    NA    NA    R     NA    S     NA    NA    NA    NA    NA    #>  4 NA    R     NA    NA    NA    R     NA    S     NA    NA    NA    NA    NA    #>  5 NA    R     NA    NA    NA    R     NA    R     NA    NA    NA    NA    NA    #>  6 NA    R     NA    NA    NA    R     NA    R     NA    NA    NA    NA    NA    #>  7 NA    S     R     S     R     S     NA    R     NA    NA    NA    NA    NA    #>  8 NA    S     R     S     R     S     NA    R     NA    NA    NA    NA    NA    #>  9 NA    R     NA    NA    NA    R     NA    S     NA    NA    NA    S     NA    #> 10 NA    S     NA    NA    NA    S     NA    S     NA    NA    NA    S     NA    #> # … with 1,990 more rows, and 10 more variables: VAN <sir>, TCY <sir>, #> #   DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, RIF <sir>  # filter using any() or all() example_isolates[any(carbapenems() == \"R\"), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 55 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  2 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  3 2004-11-03 D65308     80 F      ICU      B_STNTR_MLTP R     NA    NA    R     #>  4 2005-04-21 452212     82 F      ICU      B_ENTRC      NA    NA    NA    NA    #>  5 2005-04-22 452212     82 F      ICU      B_ENTRC      NA    NA    NA    NA    #>  6 2005-04-22 452212     82 F      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  7 2007-02-21 8BBC46     61 F      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  8 2007-12-15 401043     72 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  9 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL R     NA    NA    NA    #> 10 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL R     NA    NA    NA    #> # … with 45 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> subset(example_isolates, any(carbapenems() == \"R\")) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 55 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  2 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  3 2004-11-03 D65308     80 F      ICU      B_STNTR_MLTP R     NA    NA    R     #>  4 2005-04-21 452212     82 F      ICU      B_ENTRC      NA    NA    NA    NA    #>  5 2005-04-22 452212     82 F      ICU      B_ENTRC      NA    NA    NA    NA    #>  6 2005-04-22 452212     82 F      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  7 2007-02-21 8BBC46     61 F      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  8 2007-12-15 401043     72 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  9 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL R     NA    NA    NA    #> 10 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL R     NA    NA    NA    #> # … with 45 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir>  # filter on any or all results in the carbapenem columns (i.e., IPM, MEM): example_isolates[any(carbapenems()), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Filtering any of columns 'IPM' and 'MEM' to contain value \"S\", \"I\" or \"R\" #> # A tibble: 962 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  4 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  5 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS R     NA    NA    R     #>  6 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS R     NA    NA    R     #>  7 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS R     NA    NA    R     #>  8 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN R     NA    NA    R     #>  9 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN R     NA    NA    R     #> 10 2002-03-08 4FC193     69 M      Clinical B_ESCHR_COLI R     NA    NA    R     #> # … with 952 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> example_isolates[all(carbapenems()), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Filtering all of columns 'IPM' and 'MEM' to contain value \"S\", \"I\" or \"R\" #> # A tibble: 756 × 46 #>    date       patient   age gender ward    mo            PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>   <mo>          <sir> <sir> <sir> <sir> #>  1 2002-04-14 F30196     73 M      Outpat… B_STRPT_GRPB  S     NA    S     S     #>  2 2003-04-08 114570     74 M      ICU     B_STRPT_PYGN  S     NA    S     S     #>  3 2003-04-08 114570     74 M      ICU     B_STRPT_GRPA  S     NA    S     S     #>  4 2003-04-08 114570     74 M      ICU     B_STRPT_GRPA  S     NA    S     S     #>  5 2003-08-14 F71508      0 F      Clinic… B_STRPT_GRPB  S     NA    S     S     #>  6 2003-10-16 650870     63 F      ICU     B_ESCHR_COLI  R     NA    NA    R     #>  7 2003-10-20 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R     #>  8 2003-10-20 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R     #>  9 2003-11-04 2FC253     87 F      ICU     B_ESCHR_COLI  R     NA    NA    NA    #> 10 2003-11-04 2FC253     87 F      ICU     B_ESCHR_COLI  R     NA    NA    NA    #> # … with 746 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir>  # filter with multiple antibiotic selectors using c() example_isolates[all(c(carbapenems(), aminoglycosides()) == \"R\"), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 26 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2004-11-03 D65308     80 F      ICU      B_STNTR_MLTP R     NA    NA    R     #>  2 2005-04-22 452212     82 F      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  3 2007-02-21 8BBC46     61 F      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  4 2007-12-15 401043     72 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  5 2008-12-06 501361     43 F      Clinical B_STNTR_MLTP R     NA    NA    R     #>  6 2011-05-09 207325     82 F      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  7 2012-03-12 582258     80 M      ICU      B_STPHY_CONS R     R     R     R     #>  8 2012-05-19 C25552     89 F      Outpati… B_STPHY_CONS R     R     R     R     #>  9 2012-07-17 F05015     83 M      ICU      B_STPHY_CONS R     R     R     R     #> 10 2012-07-20 404299     66 F      Clinical B_STPHY_CONS R     R     R     R     #> # … with 16 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir>  # filter + select in one go: get penicillins in 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     #> # … with 45 more rows  # You can combine selectors with '&' to be more specific. For example, # penicillins() would select benzylpenicillin ('peni G') and # administrable_per_os() would select erythromycin. Yet, when combined these # drugs are both omitted since benzylpenicillin is not administrable per os # and erythromycin is not a penicillin: example_isolates[, penicillins() & administrable_per_os()] #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin), and 'TZP' #>   (piperacillin/tazobactam) #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #>   (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #>   'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #>   (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #>   (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #>   (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #>   (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #>   (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin), and 'RIF' #>   (rifampicin) #> # A tibble: 2,000 × 5 #>    OXA   FLC   AMX   AMC   AMP   #>    <sir> <sir> <sir> <sir> <sir> #>  1 NA    NA    NA    I     NA    #>  2 NA    NA    NA    I     NA    #>  3 NA    R     NA    NA    NA    #>  4 NA    R     NA    NA    NA    #>  5 NA    R     NA    NA    NA    #>  6 NA    R     NA    NA    NA    #>  7 NA    S     R     S     R     #>  8 NA    S     R     S     R     #>  9 NA    R     NA    NA    NA    #> 10 NA    S     NA    NA    NA    #> # … with 1,990 more rows  # ab_selector() applies a filter in the `antibiotics` data set and is thus # very flexible. For instance, to select antibiotic columns with an oral DDD # of at least 1 gram: example_isolates[, ab_selector(oral_ddd > 1 & oral_units == \"g\")] #> ℹ For ab_selector(oral_ddd > 1 & oral_units == \"g\") using columns 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'KAN' (kanamycin), 'FOS' #>   (fosfomycin), 'LNZ' (linezolid), 'VAN' (vancomycin), 'ERY' (erythromycin), #>   'CLI' (clindamycin), 'MTR' (metronidazole), and 'CHL' (chloramphenicol) #> # A tibble: 2,000 × 13 #>    OXA   FLC   AMX   AMC   AMP   KAN   FOS   LNZ   VAN   ERY   CLI   MTR   CHL   #>    <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>  1 NA    NA    NA    I     NA    NA    NA    R     R     R     R     NA    NA    #>  2 NA    NA    NA    I     NA    NA    NA    R     R     R     R     NA    NA    #>  3 NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA    #>  4 NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA    #>  5 NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA    #>  6 NA    R     NA    NA    NA    NA    NA    NA    S     R     R     NA    NA    #>  7 NA    S     R     S     R     NA    NA    NA    S     S     NA    NA    NA    #>  8 NA    S     R     S     R     NA    NA    NA    S     S     NA    NA    NA    #>  9 NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA    #> 10 NA    S     NA    NA    NA    NA    NA    NA    S     S     NA    NA    NA    #> # … with 1,990 more rows  # \\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 I      #> 2 R      #> 3 S      #> 4 R      #> 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    #> # … with 1,990 more rows if (require(\"dplyr\")) {   # get AMR for only drugs that matter - no intrinsic resistance:   example_isolates %>%     filter(mo_genus() %in% c(\"Escherichia\", \"Klebsiella\")) %>%     group_by(ward) %>%     summarise(across(not_intrinsic_resistant(), resistance)) } #> ℹ Using column 'mo' as input for mo_genus() #> ℹ For not_intrinsic_resistant() removing columns 'PEN' #>   (benzylpenicillin), 'LNZ' (linezolid), 'VAN' (vancomycin), 'TEC' #>   (teicoplanin), 'ERY' (erythromycin), 'CLI' (clindamycin), 'AZM' #>   (azithromycin), and 'RIF' (rifampicin) #> Warning: There were 52 warnings in `summarise()`. #> The first warning was: #> ℹ In argument: `across(not_intrinsic_resistant(), resistance)`. #> ℹ In group 1: `ward = \"Clinical\"`. #> Caused by warning: #> ! Introducing NA: no results available for OXA in group: ward = \"Clinical\" #> (minimum = 30). #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 51 remaining warnings. #> # A tibble: 3 × 33 #>   ward    OXA   FLC    AMX    AMC    AMP     TZP     CZO     FEP     CXM     FOX #>   <chr> <dbl> <dbl>  <dbl>  <dbl>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl> #> 1 Clin…    NA    NA  0.606  0.121  0.606  0.0504  0.0656  0.0159  0.0622  0.0648 #> 2 ICU      NA    NA  0.535  0.172  0.535  0.119  NA       0.0722  0.0828  0.0992 #> 3 Outp…    NA    NA NA     NA     NA     NA      NA      NA      NA      NA      #> # … with 22 more variables: CTX <dbl>, CAZ <dbl>, CRO <dbl>, GEN <dbl>, #> #   TOB <dbl>, AMK <dbl>, KAN <dbl>, TMP <dbl>, SXT <dbl>, NIT <dbl>, #> #   FOS <dbl>, CIP <dbl>, MFX <dbl>, TCY <dbl>, TGC <dbl>, DOX <dbl>, #> #   IPM <dbl>, MEM <dbl>, MTR <dbl>, CHL <dbl>, COL <dbl>, MUP <dbl> if (require(\"dplyr\")) {   # get susceptibility for antibiotics whose name contains \"trim\":   example_isolates %>%     filter(first_isolate()) %>%     group_by(ward) %>%     summarise(across(ab_selector(name %like% \"trim\"), susceptibility)) } #> ℹ For ab_selector(name %like% \"trim\") using columns 'TMP' (trimethoprim) #>   and 'SXT' (trimethoprim/sulfamethoxazole) #> # A tibble: 3 × 3 #>   ward         TMP   SXT #>   <chr>      <dbl> <dbl> #> 1 Clinical   0.627 0.808 #> 2 ICU        0.549 0.778 #> 3 Outpatient 0.667 0.821 if (require(\"dplyr\")) {   # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):   example_isolates %>%     select(carbapenems()) } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 2,000 × 2 #>    IPM   MEM   #>    <sir> <sir> #>  1 NA    NA    #>  2 NA    NA    #>  3 NA    NA    #>  4 NA    NA    #>  5 NA    NA    #>  6 NA    NA    #>  7 NA    NA    #>  8 NA    NA    #>  9 NA    NA    #> 10 NA    NA    #> # … with 1,990 more rows if (require(\"dplyr\")) {   # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':   example_isolates %>%     select(mo, aminoglycosides()) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 2,000 × 5 #>    mo           GEN   TOB   AMK   KAN   #>    <mo>         <sir> <sir> <sir> <sir> #>  1 B_ESCHR_COLI NA    NA    NA    NA    #>  2 B_ESCHR_COLI NA    NA    NA    NA    #>  3 B_STPHY_EPDR NA    NA    NA    NA    #>  4 B_STPHY_EPDR NA    NA    NA    NA    #>  5 B_STPHY_EPDR NA    NA    NA    NA    #>  6 B_STPHY_EPDR NA    NA    NA    NA    #>  7 B_STPHY_AURS NA    S     NA    NA    #>  8 B_STPHY_AURS NA    S     NA    NA    #>  9 B_STPHY_EPDR NA    NA    NA    NA    #> 10 B_STPHY_EPDR NA    NA    NA    NA    #> # … with 1,990 more rows if (require(\"dplyr\")) {   # any() and all() work in dplyr's filter() too:   example_isolates %>%     filter(       any(aminoglycosides() == \"R\"),       all(cephalosporins_2nd() == \"R\")     ) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For cephalosporins_2nd() using columns 'CXM' (cefuroxime) and 'FOX' #>   (cefoxitin) #> # A tibble: 112 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  2 2002-03-16 4FC193     69 M      Clinical B_PSDMN_AERG R     NA    NA    R     #>  3 2002-04-08 130252     78 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #>  4 2002-06-23 798871     82 M      Clinical B_ENTRC_FCLS NA    NA    NA    NA    #>  5 2002-06-23 798871     82 M      Clinical B_ENTRC_FCLS NA    NA    NA    NA    #>  6 2002-07-21 955940     82 F      Clinical B_PSDMN_AERG R     NA    NA    R     #>  7 2002-07-21 955940     82 F      Clinical B_PSDMN_AERG R     NA    NA    R     #>  8 2003-08-13 F35553     52 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #>  9 2003-09-05 F35553     52 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #> 10 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #> # … with 102 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # also works with c():   example_isolates %>%     filter(any(c(carbapenems(), aminoglycosides()) == \"R\")) } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 531 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  2 2002-03-16 4FC193     69 M      Clinical B_PSDMN_AERG R     NA    NA    R     #>  3 2002-03-17 B30560     78 M      Clinical B_STPHY_CONS R     NA    R     R     #>  4 2002-04-04 E61143     67 M      Clinical B_STRPT_SNGN S     NA    NA    S     #>  5 2002-04-08 130252     78 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #>  6 2002-04-14 F30196     73 M      Outpati… B_STRPT_GRPB S     NA    S     S     #>  7 2002-05-07 D91570     83 M      Clinical B_STPHY_CONS R     NA    R     R     #>  8 2002-05-07 D91570     83 M      Clinical B_STPHY_CONS R     NA    R     R     #>  9 2002-05-14 077552     86 F      Clinical B_STRPT_PNMN S     NA    NA    S     #> 10 2002-05-14 077552     86 F      Clinical B_STRPT_PNMN S     NA    NA    S     #> # … with 521 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # not setting any/all will automatically apply all():   example_isolates %>%     filter(aminoglycosides() == \"R\") } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ Assuming a filter on all 4 aminoglycosides. Wrap around all() or #>   any() to prevent this note. #> # A tibble: 427 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  2 2002-03-17 B30560     78 M      Clinical B_STPHY_CONS R     NA    R     R     #>  3 2002-04-04 E61143     67 M      Clinical B_STRPT_SNGN S     NA    NA    S     #>  4 2002-04-08 130252     78 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #>  5 2002-04-14 F30196     73 M      Outpati… B_STRPT_GRPB S     NA    S     S     #>  6 2002-05-07 D91570     83 M      Clinical B_STPHY_CONS R     NA    R     R     #>  7 2002-05-07 D91570     83 M      Clinical B_STPHY_CONS R     NA    R     R     #>  8 2002-05-14 077552     86 F      Clinical B_STRPT_PNMN S     NA    NA    S     #>  9 2002-05-14 077552     86 F      Clinical B_STRPT_PNMN S     NA    NA    S     #> 10 2002-05-16 D25302     65 F      ICU      B_STRPT_ANGN S     NA    NA    S     #> # … with 417 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # this will select columns 'mo' and all antimycobacterial drugs ('RIF'):   example_isolates %>%     select(mo, ab_class(\"mycobact\")) } #> ℹ For ab_class(\"mycobact\") using column 'RIF' (rifampicin) #> # A tibble: 2,000 × 2 #>    mo           RIF   #>    <mo>         <sir> #>  1 B_ESCHR_COLI R     #>  2 B_ESCHR_COLI R     #>  3 B_STPHY_EPDR NA    #>  4 B_STPHY_EPDR NA    #>  5 B_STPHY_EPDR NA    #>  6 B_STPHY_EPDR NA    #>  7 B_STPHY_AURS NA    #>  8 B_STPHY_AURS NA    #>  9 B_STPHY_EPDR NA    #> 10 B_STPHY_EPDR NA    #> # … with 1,990 more rows if (require(\"dplyr\")) {   # get bug/drug combinations for only glycopeptides in Gram-positives:   example_isolates %>%     filter(mo_is_gram_positive()) %>%     select(mo, glycopeptides()) %>%     bug_drug_combinations() %>%     format() } #> ℹ Using column 'mo' as input for mo_is_gram_positive() #> ℹ For glycopeptides() using columns 'VAN' (vancomycin) and 'TEC' #>   (teicoplanin) #> # A tibble: 2 × 8 #>   Group           Drug             CoNS  E. fa…¹ S. au…² S. ep…³ S. ho…⁴ S. pn…⁵ #>   <chr>           <chr>            <chr> <chr>   <chr>   <chr>   <chr>   <chr>   #> 1 \"Glycopeptides\" Teicoplanin (TE… \"\"    \"\"      \" 0.0%… \"64.1%… \" 6.8%… \"\"      #> 2 \"\"              Vancomycin (VAN… \" 0.… \" 0.0%… \" 0.0%… \" 0.0%… \" 0.0%… \" 0.0%… #> # … with abbreviated variable names ¹​`E. faecalis`, ²​`S. aureus`, #> #   ³​`S. epidermidis`, ⁴​`S. hominis`, ⁵​`S. pneumoniae` if (require(\"dplyr\")) {   data.frame(     some_column = \"some_value\",     J01CA01 = \"S\"   ) %>% # ATC code of ampicillin     select(penicillins()) # only the 'J01CA01' column will be selected } #> ℹ For penicillins() using column 'J01CA01' (ampicillin) #>   J01CA01 #> 1       S if (require(\"dplyr\")) {   # with recent versions of dplyr, this is all equal:   x <- example_isolates[carbapenems() == \"R\", ]   y <- example_isolates %>% filter(carbapenems() == \"R\")   z <- example_isolates %>% filter(if_all(carbapenems(), ~ .x == \"R\"))   identical(x, y) && identical(y, z) } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Assuming a filter on all 2 carbapenems. Wrap around all() or any() to #>   prevent this note. #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Assuming a filter on all 2 carbapenems. Wrap around all() or any() to #>   prevent this note. #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> [1] TRUE   # 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 cid Compound ID found PubChem name Official name used WHONET/EARS-Net group short concise group name, based WHONET WHOCC definitions atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC, like J01CR02 atc_group1 Official pharmacological subgroup (3rd level ATC code) defined WHOCC, like \"Macrolides, lincosamides streptogramins\" atc_group2 Official chemical subgroup (4th level ATC code) defined WHOCC, like \"Macrolides\" abbr List abbreviations used many countries, also antibiotic susceptibility testing (AST) synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment, currently available 174 drugs oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral (intravenous) treatment, currently available 146 drugs iv_units Units iv_ddd loinc LOINC codes (Logical Observation Identifiers Names Codes) associated name antimicrobial drug. Use ab_loinc() retrieve quickly, see ab_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"for-the-antivirals-data-set-a-tibble-with-observations-and-variables-","dir":"Reference","previous_headings":"","what":"For the antivirals data set: a tibble with 120 observations and 11 variables:","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"av Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name used WHONET/EARS-Net atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC cid Compound ID found PubChem atc_group Official pharmacological subgroup (3rd level ATC code) defined WHOCC synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral treatment iv_units Units iv_ddd loinc LOINC codes (Logical Observation Identifiers Names Codes) associated name antimicrobial drug.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology (WHOCC): https://www.whocc./atc_ddd_index/ Logical Observation Identifiers Names Codes (LOINC), Version 2.73 (8 August, 2022). Accessed https://loinc.org 30 October, 2022. European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"Properties based ATC code available ATC available. properties : atc_group1, atc_group2, oral_ddd, oral_units, iv_ddd iv_units. Synonyms (.e. trade names) derived PubChem Compound ID (column cid) consequently available CID available.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"Like data sets package, data sets publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"","code":"antibiotics #> # A tibble: 483 × 14 #>    ab        cid name        group atc   atc_g…¹ atc_g…² abbre…³ synon…⁴ oral_…⁵ #>    <ab>    <dbl> <chr>       <chr> <lis> <chr>   <chr>   <list>  <named>   <dbl> #>  1 AMA      4649 4-aminosal… Anti… <chr> Drugs … Aminos… <chr>   <chr>     12    #>  2 ACM   6450012 Acetylmide… Macr… <chr> NA      NA      <chr>   <chr>     NA    #>  3 ASP  49787020 Acetylspir… Macr… <chr> NA      NA      <chr>   <chr>     NA    #>  4 ALS      8954 Aldesulfon… Othe… <chr> Drugs … Drugs … <chr>   <chr>      0.33 #>  5 AMK     37768 Amikacin    Amin… <chr> Aminog… Other … <chr>   <chr>     NA    #>  6 AKF        NA Amikacin/f… Amin… <chr> NA      NA      <chr>   <chr>     NA    #>  7 AMX     33613 Amoxicillin Beta… <chr> Beta-l… Penici… <chr>   <chr>      1.5  #>  8 AMC  23665637 Amoxicilli… Beta… <chr> Beta-l… Combin… <chr>   <chr>      1.5  #>  9 AXS    465441 Amoxicilli… Beta… <chr> NA      NA      <chr>   <chr>     NA    #> 10 AMB   5280965 Amphoteric… Anti… <chr> Antimy… Antibi… <chr>   <chr>     40    #> # … with 473 more rows, 4 more variables: oral_units <chr>, iv_ddd <dbl>, #> #   iv_units <chr>, loinc <list>, and abbreviated variable names ¹​atc_group1, #> #   ²​atc_group2, ³​abbreviations, ⁴​synonyms, ⁵​oral_ddd antivirals #> # A tibble: 120 × 11 #>    av    name  atc      cid atc_g…¹ synon…² oral_…³ oral_…⁴ iv_ddd iv_un…⁵ loinc #>    <av>  <chr> <chr>  <dbl> <chr>   <list>    <dbl> <chr>    <dbl> <chr>   <lis> #>  1 ABA   Abac… J05A… 4.41e5 Nucleo… <chr>       0.6 g           NA NA      <chr> #>  2 ACI   Acic… J05A… 1.35e8 Nucleo… <chr>       4   g            4 g       <chr> #>  3 ADD   Adef… J05A… 6.09e4 Nucleo… <chr>      10   mg          NA NA      <chr> #>  4 AME   Amen… J05A… 1.14e7 Other … <chr>       0.4 g           NA NA      <chr> #>  5 AMP   Ampr… J05A… 6.50e4 Protea… <chr>       1.2 g           NA NA      <chr> #>  6 ASU   Asun… J05A… 1.61e7 Antivi… <chr>       0.2 g           NA NA      <chr> #>  7 ATA   Ataz… J05A… 1.48e5 Protea… <chr>       0.3 g           NA NA      <chr> #>  8 ATA+… Ataz… J05A… 8.66e7 Antivi… <chr>      NA   NA          NA NA      <chr> #>  9 ATA+… Ataz… J05A… 2.51e7 Antivi… <chr>       0.3 g           NA NA      <chr> #> 10 BAM   Balo… J05A… 1.24e8 Other … <chr>      40   mg          NA NA      <chr> #> # … with 110 more rows, and abbreviated variable names ¹​atc_group, ²​synonyms, #> #   ³​oral_ddd, ⁴​oral_units, ⁵​iv_units"},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to an Antibiotic ID — as.ab","title":"Transform Input to an Antibiotic ID — as.ab","text":"Use function determine antibiotic drug code one antibiotics. data set antibiotics searched abbreviations, official names synonyms (brand names).","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to an Antibiotic ID — as.ab","text":"","code":"as.ab(x, flag_multiple_results = TRUE, info = interactive(), ...)  is.ab(x)"},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to an Antibiotic ID — as.ab","text":"x character vector determine antibiotic ID flag_multiple_results logical indicate whether note printed console probably one antibiotic drug code name can retrieved single input value. info logical indicate whether progress bar printed - default TRUE interactive mode ... arguments passed internal functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to an Antibiotic ID — as.ab","text":"character  vector additional class ab","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to an Antibiotic ID — as.ab","text":"entries antibiotics data set three different identifiers: human readable EARS-Net code (column ab, used ECDC WHONET), ATC code (column atc, used ), CID code (column cid, Compound ID, used PubChem). data set contains 5,000 official brand names many different countries, found PubChem. drugs contain multiple ATC codes. properties searched user input. .ab() can correct different forms misspelling: Wrong spelling drug names (\"tobramicin\" \"gentamycin\"), corrects audible similarities f/ph, x/ks, c/z/s, t/th, etc. many vowels consonants Switching two characters (\"mreopenem\", often case clinical data, doctors typed fast) Digitalised paper records, leaving artefacts like 0/o/O (zero O's), B/8, n/r, etc. Use ab_* functions get properties based returned antibiotic ID, see Examples. Note: .ab() ab_* functions may use long regular expression match brand names antimicrobial drugs. may fail systems. can add manual codes considered .ab() ab_* functions, see add_custom_antimicrobials().","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to an Antibiotic ID — as.ab","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Transform Input to an Antibiotic ID — as.ab","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to an Antibiotic ID — as.ab","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to an Antibiotic ID — as.ab","text":"","code":"# these examples all return \"ERY\", the ID of erythromycin: as.ab(\"J01FA01\") #> Class 'ab' #> [1] ERY as.ab(\"J 01 FA 01\") #> Class 'ab' #> [1] ERY as.ab(\"Erythromycin\") #> Class 'ab' #> [1] ERY as.ab(\"eryt\") #> Class 'ab' #> [1] ERY as.ab(\"   eryt 123\") #> Class 'ab' #> [1] ERY as.ab(\"ERYT\") #> Class 'ab' #> [1] ERY as.ab(\"ERY\") #> Class 'ab' #> [1] ERY as.ab(\"eritromicine\") # spelled wrong, yet works #> Class 'ab' #> [1] ERY as.ab(\"Erythrocin\") # trade name #> Class 'ab' #> [1] ERY as.ab(\"Romycin\") # trade name #> Class 'ab' #> [1] ERY  # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\") # small spelling error #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\") # or a bit more severe #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") # and even this works #> [1] \"J01DD04\"  # use ab_* functions to get a specific properties (see ?ab_property); # they use as.ab() internally: ab_name(\"J01FA01\") #> [1] \"Erythromycin\" ab_name(\"eryt\") #> [1] \"Erythromycin\"  # \\donttest{ if (require(\"dplyr\")) {   # you can quickly rename 'sir' columns using set_ab_names() with dplyr:   example_isolates %>%     set_ab_names(where(is.sir), property = \"atc\") } #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           J01CE01 J01CF04 J01CF05 #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir>   <sir>   <sir>   #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R       NA      NA      #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R       NA      NA      #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R       NA      S       #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R       NA      S       #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R       NA      S       #> # … with 1,990 more rows, and 37 more variables: J01CA04 <sir>, J01CR02 <sir>, #> #   J01CA01 <sir>, J01CR05 <sir>, J01DB04 <sir>, J01DE01 <sir>, J01DC02 <sir>, #> #   J01DC01 <sir>, J01DD01 <sir>, J01DD02 <sir>, J01DD04 <sir>, J01GB03 <sir>, #> #   J01GB01 <sir>, J01GB06 <sir>, J01GB04 <sir>, J01EA01 <sir>, J01EE01 <sir>, #> #   J01XE01 <sir>, J01XX01 <sir>, J01XX08 <sir>, J01MA02 <sir>, J01MA14 <sir>, #> #   J01XA01 <sir>, J01XA02 <sir>, J01AA07 <sir>, J01AA12 <sir>, J01AA02 <sir>, #> #   J01FA01 <sir>, J01FF01 <sir>, J01FA10 <sir>, J01DH51 <sir>, … # }"},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to an Antiviral Drug ID — as.av","title":"Transform Input to an Antiviral Drug ID — as.av","text":"Use function determine antiviral drug code one antiviral drugs. data set antivirals searched abbreviations, official names synonyms (brand names).","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to an Antiviral Drug ID — as.av","text":"","code":"as.av(x, flag_multiple_results = TRUE, info = interactive(), ...)  is.av(x)"},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to an Antiviral Drug ID — as.av","text":"x character vector determine antiviral drug ID flag_multiple_results logical indicate whether note printed console probably one antiviral drug code name can retrieved single input value. info logical indicate whether progress bar printed - default TRUE interactive mode ... arguments passed internal functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to an Antiviral Drug ID — as.av","text":"character  vector additional class ab","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to an Antiviral Drug ID — as.av","text":"entries antivirals data set three different identifiers: human readable EARS-Net code (column ab, used ECDC WHONET), ATC code (column atc, used ), CID code (column cid, Compound ID, used PubChem). data set contains 5,000 official brand names many different countries, found PubChem. drugs contain multiple ATC codes. properties searched user input. .av() can correct different forms misspelling: Wrong spelling drug names (\"acyclovir\"), corrects audible similarities f/ph, x/ks, c/z/s, t/th, etc. many vowels consonants Switching two characters (\"aycclovir\", often case clinical data, doctors typed fast) Digitalised paper records, leaving artefacts like 0/o/O (zero O's), B/8, n/r, etc. Use av_* functions get properties based returned antiviral drug ID, see Examples. Note: .av() av_* functions may use long regular expression match brand names antimicrobial drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to an Antiviral Drug ID — as.av","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Transform Input to an Antiviral Drug ID — as.av","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to an Antiviral Drug ID — as.av","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to an Antiviral Drug ID — as.av","text":"","code":"# these examples all return \"ACI\", the ID of aciclovir: as.av(\"J05AB01\") #> Class 'av' #> [1] ACI as.av(\"J 05 AB 01\") #> Class 'av' #> [1] ACI as.av(\"Aciclovir\") #> Class 'av' #> [1] ACI as.av(\"aciclo\") #> Class 'av' #> [1] ACI as.av(\"   aciclo 123\") #> Class 'av' #> [1] ACI as.av(\"ACICL\") #> Class 'av' #> [1] ACI as.av(\"ACI\") #> Class 'av' #> [1] ACI as.av(\"Virorax\") # trade name #> Class 'av' #> [1] ACI as.av(\"Zovirax\") # trade name #> Class 'av' #> [1] ACI  as.av(\"acyklofir\") # severe spelling error, yet works #> Class 'av' #> [1] ACI  # use av_* functions to get a specific properties (see ?av_property); # they use as.av() internally: av_name(\"J05AB01\") #> [1] \"Aciclovir\" av_name(\"acicl\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Disk Diffusion Diameters — as.disk","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"transforms vector new class disk, disk diffusion growth zone size (around antibiotic disk) millimetres 6 50.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"as.disk(x, na.rm = FALSE)  NA_disk_  is.disk(x)"},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"object class disk (inherits integer) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"x vector na.rm logical indicating whether missing values removed","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"integer additional class disk","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"Interpret disk values SIR values .sir(). supports guidelines EUCAST CLSI. Disk diffusion growth zone sizes must 6 50 millimetres. Values higher 50 lower 100 maximised 50. others input values outside 6-50 range return NA. NA_disk_ missing value new disk class.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"# transform existing disk zones to the `disk` class (using base R) df <- data.frame(   microorganism = \"Escherichia coli\",   AMP = 20,   CIP = 14,   GEN = 18,   TOB = 16 ) df[, 2:5] <- lapply(df[, 2:5], as.disk) str(df) #> 'data.frame':\t1 obs. of  5 variables: #>  $ microorganism: chr \"Escherichia coli\" #>  $ AMP          : 'disk' int 20 #>  $ CIP          : 'disk' int 14 #>  $ GEN          : 'disk' int 18 #>  $ TOB          : 'disk' int 16  # \\donttest{ # transforming is easier with dplyr: if (require(\"dplyr\")) {   df %>% mutate(across(AMP:TOB, as.disk)) } #>      microorganism AMP CIP GEN TOB #> 1 Escherichia coli  20  14  18  16 # }  # interpret disk values, see ?as.sir as.sir(   x = as.disk(18),   mo = \"Strep pneu\", # `mo` will be coerced with as.mo()   ab = \"ampicillin\", # and `ab` with as.ab()   guideline = \"EUCAST\" ) #> => Interpreting disk diffusion zones of 'ampicillin' (AMP) according to #>    EUCAST 2022... #>  OK. #> Class 'sir' #> [1] R  # interpret whole data set, pretend to be all from urinary tract infections: as.sir(df, uti = TRUE) #> => Interpreting disk diffusion zones of column 'AMP' (ampicillin) according #>    to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'CIP' (ciprofloxacin) #>    according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'GEN' (gentamicin) according #>    to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'TOB' (tobramycin) according #>    to EUCAST 2022... #>  OK. #>      microorganism AMP CIP GEN TOB #> 1 Escherichia coli   S   R   S   S"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"transforms vectors new class mic, treats input decimal numbers, maintaining operators (\">=\") allowing valid MIC values known field (medical) microbiology.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"as.mic(x, na.rm = FALSE)  NA_mic_  is.mic(x)  # S3 method for mic droplevels(x, as.mic = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"x character numeric vector na.rm logical indicating whether missing values removed .mic logical indicate whether mic class kept - default FALSE ... arguments passed methods","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"Ordered factor additional class mic, mathematical operations acts decimal numbers. Bare mind outcome mathematical operation MICs return numeric value.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"interpret MIC values SIR values, use .sir() MIC values. supports guidelines EUCAST (2013-2022) CLSI (2013-2022). class MIC values quite special data type: formally ordered factor valid MIC values factor levels (make sure valid MIC values retained), mathematical operation acts decimal numbers:   makes possible maintain operators often come MIC values, \">=\" \"<=\", even filtering using numeric values data analysis, e.g.:   following generic functions implemented MIC class: !, !=, %%, %/%, &, *, +, -, /, <, <=, ==, >, >=, ^, |, abs(), acos(), acosh(), (), (), asin(), asinh(), atan(), atanh(), ceiling(), cos(), cosh(), cospi(), cummax(), cummin(), cumprod(), cumsum(), digamma(), exp(), expm1(), floor(), gamma(), lgamma(), log(), log1p(), log2(), log10(), max(), mean(), min(), prod(), range(), round(), sign(), signif(), sin(), sinh(), sinpi(), sqrt(), sum(), tan(), tanh(), tanpi(), trigamma() trunc(). functions stats package also implemented: median(), quantile(), mad(), IQR(), fivenum(). Also, boxplot.stats() supported. Since sd() var() non-generic functions, extended. Use mad() alternative, use e.g. sd(.numeric(x)) x vector MIC values. Using .double() .numeric() MIC values remove operators return numeric vector. use .integer() MIC values R convention factors, return index factor levels (often useless regular users). Use droplevels() drop unused levels. default, return plain factor. Use droplevels(..., .mic = TRUE) maintain mic class. NA_mic_ missing value new mic class, analogous e.g. base R's NA_character_.","code":"x <- random_mic(10) x #> Class 'mic' #>  [1] 16     1      8      8      64     >=128  0.0625 32     32     16  is.factor(x) #> [1] TRUE  x[1] * 2 #> [1] 32  median(x) #> [1] 26 x[x > 4] #> Class 'mic' #> [1] 16    8     8     64    >=128 32    32    16  df <- data.frame(x, hospital = \"A\") subset(df, x > 4) # or with dplyr: df %>% filter(x > 4) #>        x hospital #> 1     16        A #> 5     64        A #> 6  >=128        A #> 8     32        A #> 9     32        A #> 10    16        A"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"mic_data <- as.mic(c(\">=32\", \"1.0\", \"1\", \"1.00\", 8, \"<=0.128\", \"8\", \"16\", \"16\")) mic_data #> Class 'mic' #> [1] >=32    1       1       1       8       <=0.128 8       16      16      is.mic(mic_data) #> [1] TRUE  # this can also coerce combined MIC/SIR values: as.mic(\"<=0.002; S\") #> Class 'mic' #> [1] <=0.002  # mathematical processing treats MICs as numeric values fivenum(mic_data) #> [1]  0.128  1.000  8.000 16.000 32.000 quantile(mic_data) #>     0%    25%    50%    75%   100%  #>  0.128  1.000  8.000 16.000 32.000  all(mic_data < 512) #> [1] TRUE  # interpret MIC values as.sir(   x = as.mic(2),   mo = as.mo(\"Streptococcus pneumoniae\"),   ab = \"AMX\",   guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMX' (amoxicillin) according to EUCAST #>    2022... #>  Note: #>    • Multiple breakpoints available for amoxicillin (AMX) in Streptococcus #>      pneumoniae - assuming body site 'Meningitis'. #> Class 'sir' #> [1] R as.sir(   x = as.mic(c(0.01, 2, 4, 8)),   mo = as.mo(\"Streptococcus pneumoniae\"),   ab = \"AMX\",   guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMX' (amoxicillin) according to EUCAST #>    2022... #>  Note: #>    • Multiple breakpoints available for amoxicillin (AMX) in Streptococcus #>      pneumoniae - assuming body site 'Meningitis'. #> Class 'sir' #> [1] S R R R  # plot MIC values, see ?plot plot(mic_data)  plot(mic_data, mo = \"E. coli\", ab = \"cipro\")   if (require(\"ggplot2\")) {   autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\") }  if (require(\"ggplot2\")) {   autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"nl\") # Dutch }  if (require(\"ggplot2\")) {   autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"uk\") # Ukrainian }"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Use function get valid microorganism code (mo) based arbitrary user input. Determination done using intelligent rules complete taxonomic tree kingdoms Animalia, Archaea, Bacteria, Protozoa, microbial species kingdom Fungi (see Source). input can almost anything: full name (like \"Staphylococcus aureus\"), abbreviated name (\"S. aureus\"), abbreviation known field (\"MRSA\"), just genus. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"","code":"as.mo(   x,   Becker = FALSE,   Lancefield = FALSE,   minimum_matching_score = NULL,   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   reference_df = get_mo_source(),   ignore_pattern = getOption(\"AMR_ignore_pattern\", NULL),   cleaning_regex = getOption(\"AMR_cleaning_regex\", mo_cleaning_regex()),   language = get_AMR_locale(),   info = interactive(),   ... )  is.mo(x)  mo_uncertainties()  mo_renamed()  mo_failures()  mo_reset_session()  mo_cleaning_regex()"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"x character vector data.frame one two columns Becker logical indicate whether staphylococci categorised coagulase-negative staphylococci (\"CoNS\") coagulase-positive staphylococci (\"CoPS\") instead species, according Karsten Becker et al. (see Source). Please see Details full list staphylococcal species converted. excludes Staphylococcus aureus default, use Becker = \"\" also categorise S. aureus \"CoPS\". Lancefield logical indicate whether beta-haemolytic Streptococcus categorised Lancefield groups instead species, according Rebecca C. Lancefield (see Source). streptococci categorised first group, e.g. Streptococcus dysgalactiae group C, although officially also categorised groups G L. . Please see Details full list streptococcal species converted. excludes enterococci default (group D), use Lancefield = \"\" also categorise enterococci group D. minimum_matching_score numeric value set lower limit MO matching score. left blank, determined automatically based character length x, taxonomic kingdom human pathogenicity. keep_synonyms logical indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE, return note old taxonomic names processed. default can set package option AMR_keep_synonyms, .e. options(AMR_keep_synonyms = TRUE) options(AMR_keep_synonyms = FALSE). reference_df data.frame used extra reference translating x valid mo. See set_mo_source() get_mo_source() automate usage codes (e.g. used analysis organisation). ignore_pattern Perl-compatible regular expression (case-insensitive) matches x must return NA. can convenient exclude known non-relevant input can also set package option AMR_ignore_pattern, e.g. options(AMR_ignore_pattern = \"(reported|contaminated flora)\"). cleaning_regex Perl-compatible regular expression (case-insensitive) clean input x. Every matched part x removed. default, outcome mo_cleaning_regex(), removes texts brackets texts \"species\" \"serovar\". default can set package option AMR_cleaning_regex. language language translate text like \"growth\", defaults system language (see get_AMR_locale()) info logical indicate progress bar printed 25 items coerced - default TRUE interactive mode ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"character  vector additional class mo","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"microorganism (MO) code package (class: mo) human readable typically looks like examples:   Values coerced considered 'unknown' returned MO code UNKNOWN warning. Use mo_* functions get properties based returned code, see Examples. .mo() function uses novel matching score algorithm (see Matching Score Microorganisms ) match input available microbial taxonomy package. lead effect e.g. \"E. coli\" (microorganism highly prevalent humans) return microbial ID Escherichia coli Entamoeba coli (microorganism less prevalent humans), although latter alphabetically come first. Becker = TRUE, following 85 staphylococci converted coagulase-negative group: S. argensis, S. arlettae, S. auricularis, S. borealis, S. caeli, S. caledonicus, S. canis, S. capitis, S. capitis capitis, S. capitis urealyticus, S. capitis ureolyticus, S. caprae, S. carnosus, S. carnosus carnosus, S. carnosus utilis, S. casei, S. caseolyticus, S. chromogenes, S. cohnii, S. cohnii cohnii, S. cohnii urealyticum, S. cohnii urealyticus, S. condimenti, S. croceilyticus, S. debuckii, S. devriesei, S. durrellii, S. edaphicus, S. epidermidis, S. equorum, S. equorum equorum, S. equorum linens, S. felis, S. fleurettii, S. gallinarum, S. haemolyticus, S. hominis, S. hominis hominis, S. hominis novobiosepticus, S. jettensis, S. kloosii, S. lentus, S. lloydii, S. lugdunensis, S. massiliensis, S. microti, S. muscae, S. nepalensis, S. pasteuri, S. petrasii, S. petrasii croceilyticus, S. petrasii jettensis, S. petrasii petrasii, S. petrasii pragensis, S. pettenkoferi, S. piscifermentans, S. pragensis, S. pseudoxylosus, S. pulvereri, S. ratti, S. rostri, S. saccharolyticus, S. saprophyticus, S. saprophyticus bovis, S. saprophyticus saprophyticus, S. schleiferi, S. schleiferi schleiferi, S. sciuri, S. sciuri carnaticus, S. sciuri lentus, S. sciuri rodentium, S. sciuri sciuri, S. simulans, S. stepanovicii, S. succinus, S. succinus casei, S. succinus succinus, S. taiwanensis, S. urealyticus, S. ureilyticus, S. veratri, S. vitulinus, S. vitulus, S. warneri, S. xylosus. following 16 staphylococci converted coagulase-positive group: S. agnetis, S. argenteus, S. coagulans, S. cornubiensis, S. delphini, S. hyicus, S. hyicus chromogenes, S. hyicus hyicus, S. intermedius, S. lutrae, S. pseudintermedius, S. roterodami, S. schleiferi coagulans, S. schweitzeri, S. simiae, S. singaporensis. Lancefield = TRUE, following streptococci converted corresponding Lancefield group: S. agalactiae (Group B), S. anginosus anginosus (Group F), S. anginosus whileyi (Group F), S. anginosus (Group F), S. canis (Group G), S. dysgalactiae dysgalactiae (Group C), S. dysgalactiae equisimilis (Group C), S. dysgalactiae (Group C), S. equi equi (Group C), S. equi ruminatorum (Group C), S. equi zooepidemicus (Group C), S. equi (Group C), S. pyogenes (Group ), S. salivarius salivarius (Group K), S. salivarius thermophilus (Group K), S. salivarius (Group K), S. sanguinis (Group H).","code":"Code               Full name   ---------------    --------------------------------------   B_KLBSL            Klebsiella   B_KLBSL_PNMN       Klebsiella pneumoniae   B_KLBSL_PNMN_RHNS  Klebsiella pneumoniae rhinoscleromatis   |   |    |    |   |   |    |    |   |   |    |    \\---> subspecies, a 3-5 letter acronym   |   |    \\----> species, a 3-6 letter acronym   |   \\----> genus, a 4-8 letter acronym   \\----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria),                             F (Fungi), PL (Plantae), P (Protozoa)"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"coping-with-uncertain-results","dir":"Reference","previous_headings":"","what":"Coping with Uncertain Results","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Results non-exact taxonomic input based matching score. lowest allowed score can set minimum_matching_score argument. default determined based character length input, taxonomic kingdom human pathogenicity taxonomic outcome. values matched uncertainty, message shown suggest user evaluate results mo_uncertainties(), returns data.frame specifications. increase quality matching, cleaning_regex argument can used clean input (.e., x). must regular expression matches parts input removed input matched available microbial taxonomy. matched Perl-compatible case-insensitive. default value cleaning_regex outcome helper function mo_cleaning_regex(). three helper functions can run using .mo() function: Use mo_uncertainties() get data.frame prints pretty format taxonomic names guessed. output contains matching score matches (see Matching Score Microorganisms ). Use mo_failures() get character vector values coerced valid value. Use mo_renamed() get data.frame values coerced based old, previously accepted taxonomic names.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"microbial-prevalence-of-pathogens-in-humans","dir":"Reference","previous_headings":"","what":"Microbial Prevalence of Pathogens in Humans","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"coercion rules consider prevalence microorganisms humans, available prevalence column microorganisms data set. grouping human pathogenic prevalence explained section Matching Score Microorganisms .","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 Becker K et al. (2014). Coagulase-Negative Staphylococci. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. (2019). Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. (2020). Emergence coagulase-negative staphylococci Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 Lancefield RC (1933). serological differentiation human groups hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571 Berends MS et al. (2022). Trends Occurrence Phenotypic Resistance Coagulase-Negative Staphylococci (CoNS) Found Human Blood Northern Netherlands 2013 2019 Microorganisms 10(9), 1801; doi:10.3390/microorganisms10091801 Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated :  : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.0 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, genus present established list also prevalence = 1.0 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.5 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Contracaecum, Cordylobia, Cryptococcus, Curvularia, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Giardia, Haloarcula, Halobacterium, Halococcus, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hymenolepis, Hypomyces, Hysterothylacium, Leishmania, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Mortierella, Mucor, Mycocentrospora, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Pediculus, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga, Wuchereria; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.159\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"","code":"# \\donttest{ # These examples all return \"B_STPHY_AURS\", the ID of S. aureus: as.mo(c(   \"sau\", # WHONET code   \"stau\",   \"STAU\",   \"staaur\",   \"S. aureus\",   \"S aureus\",   \"Sthafilokkockus aureus\", # handles incorrect spelling   \"Staphylococcus aureus (MRSA)\",   \"MRSA\", # Methicillin Resistant S. aureus   \"VISA\", # Vancomycin Intermediate S. aureus   \"VRSA\", # Vancomycin Resistant S. aureus   115329001 # SNOMED CT code )) #> Class 'mo' #>  [1] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #>  [6] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [11] B_STPHY_AURS B_STPHY_AURS  # Dyslexia is no problem - these all work: as.mo(c(   \"Ureaplasma urealyticum\",   \"Ureaplasma urealyticus\",   \"Ureaplasmium urealytica\",   \"Ureaplazma urealitycium\" )) #> Class 'mo' #> [1] B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY  as.mo(\"Streptococcus group A\") #> Class 'mo' #> [1] B_STRPT_GRPA  as.mo(\"S. epidermidis\") # will remain species: B_STPHY_EPDR #> Class 'mo' #> [1] B_STPHY_EPDR as.mo(\"S. epidermidis\", Becker = TRUE) # will not remain species: B_STPHY_CONS #> Class 'mo' #> [1] B_STPHY_CONS  as.mo(\"S. pyogenes\") # will remain species: B_STRPT_PYGN #> Class 'mo' #> [1] B_STRPT_PYGN as.mo(\"S. pyogenes\", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA #> Class 'mo' #> [1] B_STRPT_GRPA  # All mo_* functions use as.mo() internally too (see ?mo_property): mo_genus(\"E. coli\") #> [1] \"Escherichia\" mo_gramstain(\"ESCO\") #> [1] \"Gram-negative\" mo_is_intrinsic_resistant(\"ESCCOL\", ab = \"vanco\") #> ℹ Determining intrinsic resistance based on 'EUCAST Expert Rules' and #>   'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021). This note #>   will be shown once per session. #> [1] TRUE # }"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Interpret minimum inhibitory concentration (MIC) values disk diffusion diameters according EUCAST CLSI, clean existing SIR values. transforms input new class sir, ordered factor levels S < < R. breakpoints used interpretation publicly available clinical_breakpoints data set.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"as.sir(x, ...)  NA_sir_  is.sir(x)  is_sir_eligible(x, threshold = 0.05)  # S3 method for mic as.sir(   x,   mo = NULL,   ab = deparse(substitute(x)),   guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   uti = NULL,   conserve_capped_values = FALSE,   add_intrinsic_resistance = FALSE,   reference_data = AMR::clinical_breakpoints,   include_screening = getOption(\"AMR_include_screening\", FALSE),   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   ... )  # S3 method for disk as.sir(   x,   mo = NULL,   ab = deparse(substitute(x)),   guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   uti = NULL,   add_intrinsic_resistance = FALSE,   reference_data = AMR::clinical_breakpoints,   include_screening = getOption(\"AMR_include_screening\", FALSE),   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   ... )  # S3 method for data.frame as.sir(   x,   ...,   col_mo = NULL,   guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   uti = NULL,   conserve_capped_values = FALSE,   add_intrinsic_resistance = FALSE,   reference_data = AMR::clinical_breakpoints,   include_screening = getOption(\"AMR_include_screening\", FALSE),   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE) )  sir_interpretation_history(clean = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"object class sir (inherits ordered, factor) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpretations minimum inhibitory concentration (MIC) values disk diffusion diameters: M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 2013-2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. M100 Performance Standard Antimicrobial Susceptibility Testing, 2013-2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/. Breakpoint tables interpretation MICs zone diameters, 2013-2022, European Committee Antimicrobial Susceptibility Testing (EUCAST). https://www.eucast.org/clinical_breakpoints.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"x vector values (class mic: MIC values mg/L, class disk: disk diffusion radius millimetres) ... using data.frame: names columns apply .sir() (supports tidy selection column1:column4). Otherwise: arguments passed methods. threshold maximum fraction invalid antimicrobial interpretations x, see Examples mo (vector ) text can coerced valid microorganism codes .mo(), can left empty determine automatically ab (vector ) text can coerced valid antimicrobial drug code .ab() guideline defaults EUCAST 2022 (latest implemented EUCAST guideline clinical_breakpoints data set), can set package option AMR_guideline. Currently supports EUCAST (2013-2022) CLSI (2013-2022), see Details. uti (Urinary Tract Infection) vector logicals (TRUE FALSE) specify whether UTI specific interpretation guideline chosen. using .sir() data.frame, can also column containing logicals left blank, data set searched column 'specimen', rows within column containing 'urin' ('urine', 'urina') regarded isolates UTI. See Examples. conserve_capped_values logical indicate MIC values starting \">\" (\">=\") must always return \"R\" , MIC values starting \"<\" (\"<=\") must always return \"S\" add_intrinsic_resistance (useful using EUCAST guideline) logical indicate whether intrinsic antibiotic resistance must also considered applicable bug-drug combinations, meaning e.g. ampicillin always return \"R\" Klebsiella species. Determination based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). reference_data data.frame used interpretation, defaults clinical_breakpoints data set. Changing argument allows using interpretation guidelines. argument must contain data set equal structure clinical_breakpoints data set (column names column types). Please note guideline argument ignored reference_data manually set. include_screening logical indicate clinical breakpoints screening allowed - default FALSE. Can also set package option AMR_include_screening. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort - default TRUE. Can also set package option AMR_include_PKPD. col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). clean logical indicate whether previously stored results forgotten returning 'logbook' results","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Ordered factor new class sir","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"NA_sir_ missing value new sir class, analogous e.g. base R's NA_character_.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"how-it-works","dir":"Reference","previous_headings":"","what":"How it Works","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":".sir() function works four ways: cleaning raw / untransformed data. data cleaned contain values S, R try best determine intelligence. example, mixed values SIR interpretations MIC values \"<0.25; S\" coerced \"S\". Combined interpretations multiple test methods (seen laboratory records) \"S; S\" coerced \"S\", value like \"S; \" return NA warning input unclear. interpreting minimum inhibitory concentration (MIC) values according EUCAST CLSI. must clean MIC values first using .mic(), also gives columns new data class mic. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either:   Operators like \"<=\" stripped interpretation. using conserve_capped_values = TRUE, MIC value e.g. \">2\" always return \"R\", even breakpoint according chosen guideline \">=4\". prevent capped values raw laboratory data treated conservatively. default behaviour (conserve_capped_values = FALSE) considers \">2\" lower \">=4\" might case return \"S\" \"\". interpreting disk diffusion diameters according EUCAST CLSI. must clean disk zones first using .disk(), also gives columns new data class disk. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either:   interpreting complete data set, automatic determination MIC values, disk diffusion diameters, microorganism names codes, antimicrobial test results. done simply running .sir(your_data). points 2, 3 4: Use sir_interpretation_history() retrieve data.frame (tibble tibble package installed) results last .sir() call.","code":"your_data %>% mutate_if(is.mic, as.sir) your_data %>% mutate(across(where(is.mic), as.sir)) your_data %>% mutate_if(is.disk, as.sir) your_data %>% mutate(across(where(is.disk), as.sir))"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"supported-guidelines","dir":"Reference","previous_headings":"","what":"Supported Guidelines","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpreting MIC values well disk diffusion diameters, currently implemented guidelines EUCAST (2013-2022) CLSI (2013-2022). Thus, guideline argument must set e.g., \"EUCAST 2022\" \"CLSI 2022\". simply using \"EUCAST\" (default) \"CLSI\" input, latest included version guideline automatically selected. can set data set using reference_data argument. guideline argument ignored. can set default guideline package option AMR_guideline (e.g. .Rprofile file), :","code":"options(AMR_guideline = \"CLSI\")   options(AMR_guideline = \"CLSI 2018\")   options(AMR_guideline = \"EUCAST 2020\")   # or to reset:   options(AMR_guideline = NULL)"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"after-interpretation","dir":"Reference","previous_headings":"","what":"After Interpretation","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"using .sir(), can use eucast_rules() defined EUCAST (1) apply inferred susceptibility resistance based results antimicrobials (2) apply intrinsic resistance based taxonomic properties microorganism.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"machine-readable-clinical-breakpoints","dir":"Reference","previous_headings":"","what":"Machine-Readable Clinical Breakpoints","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"repository package contains machine-readable version guidelines. CSV file consisting 18 308 rows 11 columns. file machine-readable, since contains one row every unique combination test method (MIC disk diffusion), antimicrobial drug microorganism. allows easy implementation rules laboratory information systems (LIS). Note contains interpretation guidelines humans - interpretation guidelines CLSI animals removed.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"function .sir() detects input contains class sir. input data.frame, iterates columns returns logical vector. function is_sir_eligible() returns TRUE columns contains 5% invalid antimicrobial interpretations (S //R), FALSE otherwise. threshold 5% can set threshold argument. input data.frame, iterates columns returns logical vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> summary(example_isolates) # see all SIR results at a glance #>       date              patient               age           gender          #>  Min.   :2002-01-02   Length:2000        Min.   : 0.00   Length:2000        #>  1st Qu.:2005-07-31   Class :character   1st Qu.:63.00   Class :character   #>  Median :2009-07-31   Mode  :character   Median :74.00   Mode  :character   #>  Mean   :2009-11-20                      Mean   :70.69                      #>  3rd Qu.:2014-05-30                      3rd Qu.:82.00                      #>  Max.   :2017-12-28                      Max.   :97.00                      #>      ward                mo                  PEN                 #>  Length:2000        Class :mo             Class:sir              #>  Class :character   <NA>  :0              %R   :73.7% (n=1201)   #>  Mode  :character   Unique:90             %SI  :26.3% (n=428)    #>                     #1    :B_ESCHR_COLI   - %S :25.6% (n=417)    #>                     #2    :B_STPHY_CONS   - %I : 0.7% (n=11)     #>                     #3    :B_STPHY_AURS                          #>     OXA                   FLC                   AMX                #>  Class:sir             Class:sir             Class:sir             #>  %R   :31.2% (n=114)   %R   :29.5% (n=278)   %R   :59.6% (n=804)   #>  %SI  :68.8% (n=251)   %SI  :70.5% (n=665)   %SI  :40.4% (n=546)   #>  - %S :68.8% (n=251)   - %S :70.5% (n=665)   - %S :40.2% (n=543)   #>  - %I : 0.0% (n=0)     - %I : 0.0% (n=0)     - %I : 0.2% (n=3)     #>                                                                    #>     AMC                    AMP                   TZP                #>  Class:sir              Class:sir             Class:sir             #>  %R   :23.7% (n=446)    %R   :59.6% (n=804)   %R   :12.6% (n=126)   #>  %SI  :76.3% (n=1433)   %SI  :40.4% (n=546)   %SI  :87.4% (n=875)   #>  - %S :71.4% (n=1342)   - %S :40.2% (n=543)   - %S :86.1% (n=862)   #>  - %I : 4.8% (n=91)     - %I : 0.2% (n=3)     - %I : 1.3% (n=13)    #>                                                                     #>     CZO                   FEP                   CXM                 #>  Class:sir             Class:sir             Class:sir              #>  %R   :44.6% (n=199)   %R   :14.2% (n=103)   %R   :26.3% (n=470)    #>  %SI  :55.4% (n=247)   %SI  :85.8% (n=621)   %SI  :73.7% (n=1319)   #>  - %S :54.9% (n=245)   - %S :85.6% (n=620)   - %S :72.5% (n=1297)   #>  - %I : 0.4% (n=2)     - %I : 0.1% (n=1)     - %I : 1.2% (n=22)     #>                                                                     #>     FOX                   CTX                   CAZ                 #>  Class:sir             Class:sir             Class:sir              #>  %R   :27.4% (n=224)   %R   :15.5% (n=146)   %R   :66.5% (n=1204)   #>  %SI  :72.6% (n=594)   %SI  :84.5% (n=797)   %SI  :33.5% (n=607)    #>  - %S :71.6% (n=586)   - %S :84.4% (n=796)   - %S :33.5% (n=607)    #>  - %I : 1.0% (n=8)     - %I : 0.1% (n=1)     - %I : 0.0% (n=0)      #>                                                                     #>     CRO                   GEN                    TOB                #>  Class:sir             Class:sir              Class:sir             #>  %R   :15.5% (n=146)   %R   :24.6% (n=456)    %R   :34.4% (n=465)   #>  %SI  :84.5% (n=797)   %SI  :75.4% (n=1399)   %SI  :65.6% (n=886)   #>  - %S :84.4% (n=796)   - %S :74.0% (n=1372)   - %S :65.1% (n=879)   #>  - %I : 0.1% (n=1)     - %I : 1.5% (n=27)     - %I : 0.5% (n=7)     #>                                                                     #>     AMK                   KAN                    TMP                #>  Class:sir             Class:sir              Class:sir             #>  %R   :63.7% (n=441)   %R   :100.0% (n=471)   %R   :38.1% (n=571)   #>  %SI  :36.3% (n=251)   %SI  : 0.0% (n=0)      %SI  :61.9% (n=928)   #>  - %S :36.3% (n=251)   - %S : 0.0% (n=0)      - %S :61.2% (n=918)   #>  - %I : 0.0% (n=0)     - %I : 0.0% (n=0)      - %I : 0.7% (n=10)    #>                                                                     #>     SXT                    NIT                   FOS                #>  Class:sir              Class:sir             Class:sir             #>  %R   :20.5% (n=361)    %R   :17.1% (n=127)   %R   :42.2% (n=148)   #>  %SI  :79.5% (n=1398)   %SI  :82.9% (n=616)   %SI  :57.8% (n=203)   #>  - %S :79.1% (n=1392)   - %S :76.0% (n=565)   - %S :57.8% (n=203)   #>  - %I : 0.3% (n=6)      - %I : 6.9% (n=51)    - %I : 0.0% (n=0)     #>                                                                     #>     LNZ                   CIP                    MFX                #>  Class:sir             Class:sir              Class:sir             #>  %R   :69.3% (n=709)   %R   :16.2% (n=228)    %R   :33.6% (n=71)    #>  %SI  :30.7% (n=314)   %SI  :83.8% (n=1181)   %SI  :66.4% (n=140)   #>  - %S :30.7% (n=314)   - %S :78.9% (n=1112)   - %S :64.5% (n=136)   #>  - %I : 0.0% (n=0)     - %I : 4.9% (n=69)     - %I : 1.9% (n=4)     #>                                                                     #>     VAN                    TEC                   TCY                #>  Class:sir              Class:sir             Class:sir             #>  %R   :38.3% (n=712)    %R   :75.7% (n=739)   %R   :29.8% (n=357)   #>  %SI  :61.7% (n=1149)   %SI  :24.3% (n=237)   %SI  :70.3% (n=843)   #>  - %S :61.7% (n=1149)   - %S :24.3% (n=237)   - %S :68.3% (n=820)   #>  - %I : 0.0% (n=0)      - %I : 0.0% (n=0)     - %I : 1.9% (n=23)    #>                                                                     #>     TGC                   DOX                   ERY                 #>  Class:sir             Class:sir             Class:sir              #>  %R   :12.7% (n=101)   %R   :27.7% (n=315)   %R   :57.2% (n=1084)   #>  %SI  :87.3% (n=697)   %SI  :72.3% (n=821)   %SI  :42.8% (n=810)    #>  - %S :87.3% (n=697)   - %S :71.7% (n=814)   - %S :42.3% (n=801)    #>  - %I : 0.0% (n=0)     - %I : 0.6% (n=7)     - %I : 0.5% (n=9)      #>                                                                     #>     CLI                   AZM                    IPM                #>  Class:sir             Class:sir              Class:sir             #>  %R   :61.2% (n=930)   %R   :57.2% (n=1084)   %R   : 6.2% (n=55)    #>  %SI  :38.8% (n=590)   %SI  :42.8% (n=810)    %SI  :93.8% (n=834)   #>  - %S :38.6% (n=586)   - %S :42.3% (n=801)    - %S :92.7% (n=824)   #>  - %I : 0.3% (n=4)     - %I : 0.5% (n=9)      - %I : 1.1% (n=10)    #>                                                                     #>     MEM                   MTR                  CHL                #>  Class:sir             Class:sir            Class:sir             #>  %R   : 5.9% (n=49)    %R   :14.7% (n=5)    %R   :21.4% (n=33)    #>  %SI  :94.1% (n=780)   %SI  :85.3% (n=29)   %SI  :78.6% (n=121)   #>  - %S :94.1% (n=780)   - %S :85.3% (n=29)   - %S :78.6% (n=121)   #>  - %I : 0.0% (n=0)     - %I : 0.0% (n=0)    - %I : 0.0% (n=0)     #>                                                                   #>     COL                    MUP                   RIF                #>  Class:sir              Class:sir             Class:sir             #>  %R   :81.2% (n=1331)   %R   : 5.9% (n=16)    %R   :69.6% (n=698)   #>  %SI  :18.8% (n=309)    %SI  :94.1% (n=254)   %SI  :30.4% (n=305)   #>  - %S :18.8% (n=309)    - %S :93.0% (n=251)   - %S :30.2% (n=303)   #>  - %I : 0.0% (n=0)      - %I : 1.1% (n=3)     - %I : 0.2% (n=2)     #>                                                                      # For INTERPRETING disk diffusion and MIC values -----------------------  # a whole data set, even with combined MIC values and disk zones df <- data.frame(   microorganism = \"Escherichia coli\",   AMP = as.mic(8),   CIP = as.mic(0.256),   GEN = as.disk(18),   TOB = as.disk(16),   ERY = \"R\" ) as.sir(df) #> => Interpreting MIC values of column 'AMP' (ampicillin) according to EUCAST #>    2022... #>  OK. #> => Interpreting MIC values of column 'CIP' (ciprofloxacin) according to #>    EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'GEN' (gentamicin) according #>    to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of column 'TOB' (tobramycin) according #>    to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Assigning class 'sir' to already clean column 'ERY' (erythromycin)... #>  OK. #>      microorganism AMP CIP GEN TOB ERY #> 1 Escherichia coli   S   I   S   S   R  # return a 'logbook' about the results: sir_interpretation_history() #> # A tibble: 50 × 17 #>    datetime            index ab_input ab_guid…¹ mo_in…² mo_guideline     guide…³ #>    <dttm>              <int> <chr>    <ab>      <chr>   <mo>             <chr>   #>  1 2023-03-12 12:07:26     1 TOB      TOB       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  2 2023-03-12 12:07:26     1 GEN      GEN       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  3 2023-03-12 12:07:26     1 CIP      CIP       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  4 2023-03-12 12:07:25     1 AMP      AMP       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  5 2023-03-12 12:07:20     1 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  6 2023-03-12 12:07:20     2 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  7 2023-03-12 12:07:20     3 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  8 2023-03-12 12:07:20     4 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  9 2023-03-12 12:07:20     5 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 10 2023-03-12 12:07:20     6 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> # … with 40 more rows, 10 more variables: ref_table <chr>, method <chr>, #> #   input <dbl>, outcome <sir>, breakpoint_S_R <chr>, ab_considered <lgl>, #> #   mo_considered <lgl>, breakpoint_S <lgl>, breakpoint_R <lgl>, #> #   interpretation <lgl>, and abbreviated variable names ¹​ab_guideline, #> #   ²​mo_input, ³​guideline  # for single values as.sir(   x = as.mic(2),   mo = as.mo(\"S. pneumoniae\"),   ab = \"AMP\",   guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #>    2022... #>  Note: #>    • Multiple breakpoints available for ampicillin (AMP) in Streptococcus #>      pneumoniae - assuming body site 'Non-meningitis'. #> Class 'sir' #> [1] R  as.sir(   x = as.disk(18),   mo = \"Strep pneu\", # `mo` will be coerced with as.mo()   ab = \"ampicillin\", # and `ab` with as.ab()   guideline = \"EUCAST\" ) #> => Interpreting disk diffusion zones of 'ampicillin' (AMP) according to #>    EUCAST 2022... #>  OK. #> Class 'sir' #> [1] R  # \\donttest{ # the dplyr way if (require(\"dplyr\")) {   df %>% mutate_if(is.mic, as.sir)   df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)   df %>% mutate(across(where(is.mic), as.sir))   df %>% mutate_at(vars(AMP:TOB), as.sir)   df %>% mutate(across(AMP:TOB, as.sir))    df %>%     mutate_at(vars(AMP:TOB), as.sir, mo = .$microorganism)    # to include information about urinary tract infections (UTI)   data.frame(     mo = \"E. coli\",     NIT = c(\"<= 2\", 32),     from_the_bladder = c(TRUE, FALSE)   ) %>%     as.sir(uti = \"from_the_bladder\")    data.frame(     mo = \"E. coli\",     NIT = c(\"<= 2\", 32),     specimen = c(\"urine\", \"blood\")   ) %>%     as.sir() # automatically determines urine isolates    df %>%     mutate_at(vars(AMP:TOB), as.sir, mo = \"E. coli\", uti = TRUE) } #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #>    2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST #>    2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to #>    EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to #>    EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to #>    EUCAST 2022... #> Warning: in as.sir(): interpretation of nitrofurantoin (NIT) is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms, #> thus assuming uti = TRUE. See ?as.sir. #>  * WARNING * #> ℹ Assuming value \"urine\" in column 'specimen' reflects a urinary tract #>   infection. #>   Use as.sir(uti = FALSE) to prevent this. #> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to #>    EUCAST 2022... #> Warning: in as.sir(): interpretation of nitrofurantoin (NIT) is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms, #> thus assuming uti = TRUE. See ?as.sir. #>  * WARNING * #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #>    2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST #>    2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to #>    EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to #>    EUCAST 2022... #>  OK. #>      microorganism AMP CIP GEN TOB ERY #> 1 Escherichia coli   S   I   S   S   R  # For CLEANING existing SIR values ------------------------------------  as.sir(c(\"S\", \"I\", \"R\", \"A\", \"B\", \"C\")) #> Warning: in as.sir(): 3 results in column '24' truncated (50%) that were invalid #> antimicrobial interpretations: \"A\", \"B\", and \"C\" #> Class 'sir' #> [1] S    I    R    <NA> <NA> <NA> as.sir(\"<= 0.002; S\") # will return \"S\" #> Class 'sir' #> [1] S sir_data <- as.sir(c(rep(\"S\", 474), rep(\"I\", 36), rep(\"R\", 370))) is.sir(sir_data) #> [1] TRUE plot(sir_data) # for percentages  barplot(sir_data) # for frequencies   # the dplyr way if (require(\"dplyr\")) {   example_isolates %>%     mutate_at(vars(PEN:RIF), as.sir)   # same:   example_isolates %>%     as.sir(PEN:RIF)    # fastest way to transform all columns with already valid AMR results to class `sir`:   example_isolates %>%     mutate_if(is_sir_eligible, as.sir)    # since dplyr 1.0.0, this can also be:   # example_isolates %>%   #   mutate(across(where(is_sir_eligible), as.sir)) } #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> # }"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":null,"dir":"Reference","previous_headings":"","what":"Get ATC Properties from WHOCC Website — atc_online_property","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Gets data WHOCC website determine properties Anatomical Therapeutic Chemical (ATC) (e.g. antibiotic), name, defined daily dose (DDD) standard unit.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"atc_online_property(   atc_code,   property,   administration = \"O\",   url = \"https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no\",   url_vet = \"https://www.whocc.no/atcvet/atcvet_index/?code=%s&showdescription=no\" )  atc_online_groups(atc_code, ...)  atc_online_ddd(atc_code, ...)  atc_online_ddd_units(atc_code, ...)"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"https://www.whocc./atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"atc_code character (vector) ATC code(s) antibiotics, coerced .ab() ab_atc() internally valid ATC code property property ATC code. Valid values \"ATC\", \"Name\", \"DDD\", \"U\" (\"unit\"), \"Adm.R\", \"Note\" groups. last option, hierarchical groups ATC code returned, see Examples. administration type administration using property = \"Adm.R\", see Details url url website WHOCC. sign %s can used placeholder ATC codes. url_vet url website WHOCC veterinary medicine. sign %s can used placeholder ATC_vet codes (start \"Q\"). ... arguments pass atc_property","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Options argument administration: \"Implant\" = Implant \"Inhal\" = Inhalation \"Instill\" = Instillation \"N\" = nasal \"O\" = oral \"P\" = parenteral \"R\" = rectal \"SL\" = sublingual/buccal \"TD\" = transdermal \"V\" = vaginal Abbreviations return values using property = \"U\" (unit): \"g\" = gram \"mg\" = milligram \"mcg\" = microgram \"U\" = unit \"TU\" = thousand units \"MU\" = million units \"mmol\" = millimole \"ml\" = millilitre (e.g. eyedrops) N.B. function requires internet connection works following packages installed: curl, rvest, xml2.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"# \\donttest{ if (requireNamespace(\"curl\") && requireNamespace(\"rvest\") && requireNamespace(\"xml2\")) {   # oral DDD (Defined Daily Dose) of amoxicillin   atc_online_property(\"J01CA04\", \"DDD\", \"O\")   atc_online_ddd(ab_atc(\"amox\"))    # parenteral DDD (Defined Daily Dose) of amoxicillin   atc_online_property(\"J01CA04\", \"DDD\", \"P\")    atc_online_property(\"J01CA04\", property = \"groups\") # search hierarchical groups of amoxicillin } #> Loading required namespace: rvest #> [1] \"ANTIINFECTIVES FOR SYSTEMIC USE\"         #> [2] \"ANTIBACTERIALS FOR SYSTEMIC USE\"         #> [3] \"BETA-LACTAM ANTIBACTERIALS, PENICILLINS\" #> [4] \"Penicillins with extended spectrum\"      # }"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Use function e.g. clinical texts health care records. returns list antiviral drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text(   text,   type = c(\"drug\", \"dose\", \"administration\"),   collapse = NULL,   translate_av = FALSE,   thorough_search = NULL,   info = interactive(),   ... )"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_av type = \"drug\": column name antivirals data set translate antibiotic abbreviations , using av_property(). default FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed - default TRUE interactive mode ... arguments passed .av()","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"list,  character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"function also internally used .av(), although searches first drug name throw note drug names returned. Note: .av() function may use long regular expression match brand names antiviral drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"default, function search antiviral drug names. text elements searched official names, ATC codes brand names. uses .av() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(avx = av_from_text(clinical_text)) returned AV codes can transformed official names, groups, etc. av_* functions av_name() av_group(), using translate_av argument. using collapse, function return character:df %>% mutate(avx = av_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text(\"28/03/2020 valaciclovir po tid\") #> [[1]] #> Class 'av' #> [1] VALA #>  av_from_text(\"28/03/2020 valaciclovir po tid\", type = \"admin\") #> [[1]] #> [1] \"oral\" #>"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antiviral Drug — av_property","title":"Get Properties of an Antiviral Drug — av_property","text":"Use functions return specific property antiviral drug antivirals data set. input values evaluated internally .av().","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"av_name(x, language = get_AMR_locale(), tolower = FALSE, ...)  av_cid(x, ...)  av_synonyms(x, ...)  av_tradenames(x, ...)  av_group(x, language = get_AMR_locale(), ...)  av_atc(x, ...)  av_loinc(x, ...)  av_ddd(x, administration = \"oral\", ...)  av_ddd_units(x, administration = \"oral\", ...)  av_info(x, language = get_AMR_locale(), ...)  av_url(x, open = FALSE, ...)  av_property(x, property = \"name\", language = get_AMR_locale(), ...)"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antiviral Drug — av_property","text":"x (vector ) text can coerced valid antiviral drug code .av() language language returned text - default system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. ... arguments passed .av() administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antivirals data set: vector_or(colnames(antivirals), sort = FALSE).","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antiviral Drug — av_property","text":"integer case av_cid() named list case av_info() multiple av_atc()/av_synonyms()/av_tradenames() double case av_ddd() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antiviral Drug — av_property","text":"output translated possible. function av_url() return direct URL official website. warning returned required ATC code available.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antiviral Drug — av_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antiviral Drug — av_property","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"# all properties: av_name(\"ACI\") #> [1] \"Aciclovir\" av_atc(\"ACI\") #> [1] \"J05AB01\" av_cid(\"ACI\") #> [1] 135398513 av_synonyms(\"ACI\") #>  [1] \"acicloftal\"        \"aciclovier\"        \"aciclovirum\"       #>  [4] \"activir\"           \"acyclofoam\"        \"acycloguanosine\"   #>  [7] \"acyclovir\"         \"acyclovir lauriad\" \"avaclyr\"           #> [10] \"cargosil\"          \"cyclovir\"          \"genvir\"            #> [13] \"gerpevir\"          \"hascovir\"          \"maynar\"            #> [16] \"novirus\"           \"poviral\"           \"sitavig\"           #> [19] \"sitavir\"           \"vipral\"            \"viropump\"          #> [22] \"virorax\"           \"zovirax\"           \"zyclir\"            av_tradenames(\"ACI\") #>  [1] \"acicloftal\"        \"aciclovier\"        \"aciclovirum\"       #>  [4] \"activir\"           \"acyclofoam\"        \"acycloguanosine\"   #>  [7] \"acyclovir\"         \"acyclovir lauriad\" \"avaclyr\"           #> [10] \"cargosil\"          \"cyclovir\"          \"genvir\"            #> [13] \"gerpevir\"          \"hascovir\"          \"maynar\"            #> [16] \"novirus\"           \"poviral\"           \"sitavig\"           #> [19] \"sitavir\"           \"vipral\"            \"viropump\"          #> [22] \"virorax\"           \"zovirax\"           \"zyclir\"            av_group(\"ACI\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_url(\"ACI\") #>                                                             Aciclovir  #> \"https://www.whocc.no/atc_ddd_index/?code=J05AB01&showdescription=no\"   # smart lowercase tranformation av_name(x = c(\"ACI\", \"VALA\")) #> [1] \"Aciclovir\"    \"Valaciclovir\" av_name(x = c(\"ACI\", \"VALA\"), tolower = TRUE) #> [1] \"aciclovir\"    \"valaciclovir\"  # defined daily doses (DDD) av_ddd(\"ACI\", \"oral\") #> [1] 4 av_ddd_units(\"ACI\", \"oral\") #> [1] \"g\" av_ddd(\"ACI\", \"iv\") #> [1] 4 av_ddd_units(\"ACI\", \"iv\") #> [1] \"g\"  av_info(\"ACI\") # all properties as a list #> $av #> [1] \"ACI\" #>  #> $cid #> [1] 135398513 #>  #> $name #> [1] \"Aciclovir\" #>  #> $group #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" #>  #> $atc #> [1] \"J05AB01\" #>  #> $tradenames #>  [1] \"acicloftal\"        \"aciclovier\"        \"aciclovirum\"       #>  [4] \"activir\"           \"acyclofoam\"        \"acycloguanosine\"   #>  [7] \"acyclovir\"         \"acyclovir lauriad\" \"avaclyr\"           #> [10] \"cargosil\"          \"cyclovir\"          \"genvir\"            #> [13] \"gerpevir\"          \"hascovir\"          \"maynar\"            #> [16] \"novirus\"           \"poviral\"           \"sitavig\"           #> [19] \"sitavir\"           \"vipral\"            \"viropump\"          #> [22] \"virorax\"           \"zovirax\"           \"zyclir\"            #>  #> $loinc #> [1] \"\" #>  #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 4 #>  #> $ddd$oral$units #> [1] \"g\" #>  #>  #> $ddd$iv #> $ddd$iv$amount #> [1] 4 #>  #> $ddd$iv$units #> [1] \"g\" #>  #>  #>   # all av_* functions use as.av() internally, so you can go from 'any' to 'any': av_atc(\"ACI\") #> [1] \"J05AB01\" av_group(\"J05AB01\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_loinc(\"abacavir\") #> [1] \"29113-8\" \"78772-1\" \"78773-9\" \"79134-3\" \"80118-3\" av_name(\"29113-8\") #> [1] \"Abacavir\" av_name(135398513) #> [1] \"Aciclovir\" av_name(\"J05AB01\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Availability of Columns — availability","title":"Check Availability of Columns — availability","text":"Easy check data availability columns data set. makes easy get idea antimicrobial combinations can used calculation e.g. susceptibility() resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Availability of Columns — availability","text":"","code":"availability(tbl, width = NULL)"},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Availability of Columns — availability","text":"tbl data.frame list width number characters present visual availability - default filling width console","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Availability of Columns — availability","text":"data.frame column names tbl row names","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check Availability of Columns — availability","text":"function returns data.frame columns \"resistant\" \"visual_resistance\". values columns calculated resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check Availability of Columns — availability","text":"","code":"availability(example_isolates) #>         count available     visual_availabilty resistant      visual_resistance #> date     2000    100.0% |####################|                                  #> patient  2000    100.0% |####################|                                  #> age      2000    100.0% |####################|                                  #> gender   2000    100.0% |####################|                                  #> ward     2000    100.0% |####################|                                  #> mo       2000    100.0% |####################|                                  #> PEN      1629     81.5% |################----|     73.7% |##############------| #> OXA       365     18.3% |###-----------------|     31.2% |######--------------| #> FLC       943     47.2% |#########-----------|     29.5% |#####---------------| #> AMX      1350     67.5% |#############-------|     59.6% |###########---------| #> AMC      1879     94.0% |##################--|     23.7% |####----------------| #> AMP      1350     67.5% |#############-------|     59.6% |###########---------| #> TZP      1001     50.0% |##########----------|     12.6% |##------------------| #> CZO       446     22.3% |####----------------|     44.6% |########------------| #> FEP       724     36.2% |#######-------------|     14.2% |##------------------| #> CXM      1789     89.5% |#################---|     26.3% |#####---------------| #> FOX       818     40.9% |########------------|     27.4% |#####---------------| #> CTX       943     47.2% |#########-----------|     15.5% |###-----------------| #> CAZ      1811     90.6% |##################--|     66.5% |#############-------| #> CRO       943     47.2% |#########-----------|     15.5% |###-----------------| #> GEN      1855     92.8% |##################--|     24.6% |####----------------| #> TOB      1351     67.6% |#############-------|     34.4% |######--------------| #> AMK       692     34.6% |######--------------|     63.7% |############--------| #> KAN       471     23.6% |####----------------|    100.0% |####################| #> TMP      1499     75.0% |###############-----|     38.1% |#######-------------| #> SXT      1759     88.0% |#################---|     20.5% |####----------------| #> NIT       743     37.2% |#######-------------|     17.1% |###-----------------| #> FOS       351     17.6% |###-----------------|     42.2% |########------------| #> LNZ      1023     51.2% |##########----------|     69.3% |#############-------| #> CIP      1409     70.5% |#############-------|     16.2% |###-----------------| #> MFX       211     10.6% |##------------------|     33.6% |######--------------| #> VAN      1861     93.1% |##################--|     38.3% |#######-------------| #> TEC       976     48.8% |#########-----------|     75.7% |###############-----| #> TCY      1200     60.0% |###########---------|     29.8% |#####---------------| #> TGC       798     39.9% |########------------|     12.7% |##------------------| #> DOX      1136     56.8% |###########---------|     27.7% |#####---------------| #> ERY      1894     94.7% |##################--|     57.2% |###########---------| #> CLI      1520     76.0% |###############-----|     61.2% |############--------| #> AZM      1894     94.7% |##################--|     57.2% |###########---------| #> IPM       889     44.5% |########------------|      6.2% |#-------------------| #> MEM       829     41.5% |########------------|      5.9% |#-------------------| #> MTR        34      1.7% |--------------------|     14.7% |##------------------| #> CHL       154      7.7% |#-------------------|     21.4% |####----------------| #> COL      1640     82.0% |################----|     81.2% |################----| #> MUP       270     13.5% |##------------------|      5.9% |#-------------------| #> RIF      1003     50.2% |##########----------|     69.6% |#############-------| # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     filter(mo == as.mo(\"Escherichia coli\")) %>%     select_if(is.sir) %>%     availability() } #>     count available       visual_availabilty resistant        visual_resistance #> PEN   467    100.0% |######################|    100.0% |######################| #> OXA     0      0.0% |----------------------|                                    #> FLC     0      0.0% |----------------------|                                    #> AMX   392     83.9% |##################----|     50.0% |###########-----------| #> AMC   467    100.0% |######################|     13.1% |##--------------------| #> AMP   392     83.9% |##################----|     50.0% |###########-----------| #> TZP   416     89.1% |###################---|      5.5% |#---------------------| #> CZO    82     17.6% |###-------------------|      2.4% |----------------------| #> FEP   317     67.9% |##############--------|      2.8% |----------------------| #> CXM   465     99.6% |######################|      5.4% |#---------------------| #> FOX   377     80.7% |#################-----|      6.9% |#---------------------| #> CTX   459     98.3% |#####################-|      2.4% |----------------------| #> CAZ   460     98.5% |#####################-|      2.4% |----------------------| #> CRO   459     98.3% |#####################-|      2.4% |----------------------| #> GEN   460     98.5% |#####################-|      2.0% |----------------------| #> TOB   462     98.9% |#####################-|      2.6% |----------------------| #> AMK   171     36.6% |########--------------|      0.0% |----------------------| #> KAN     0      0.0% |----------------------|                                    #> TMP   396     84.8% |##################----|     39.1% |########--------------| #> SXT   465     99.6% |######################|     31.6% |######----------------| #> NIT   458     98.1% |#####################-|      2.8% |----------------------| #> FOS    61     13.1% |##--------------------|      0.0% |----------------------| #> LNZ   467    100.0% |######################|    100.0% |######################| #> CIP   456     97.6% |#####################-|     12.5% |##--------------------| #> MFX    57     12.2% |##--------------------|    100.0% |######################| #> VAN   467    100.0% |######################|    100.0% |######################| #> TEC   467    100.0% |######################|    100.0% |######################| #> TCY     3      0.6% |----------------------|     66.7% |##############--------| #> TGC    68     14.6% |###-------------------|      0.0% |----------------------| #> DOX     0      0.0% |----------------------|                                    #> ERY   467    100.0% |######################|    100.0% |######################| #> CLI   467    100.0% |######################|    100.0% |######################| #> AZM   467    100.0% |######################|    100.0% |######################| #> IPM   422     90.4% |###################---|      0.0% |----------------------| #> MEM   418     89.5% |###################---|      0.0% |----------------------| #> MTR     2      0.4% |----------------------|      0.0% |----------------------| #> CHL     0      0.0% |----------------------|                                    #> COL   240     51.4% |###########-----------|      0.0% |----------------------| #> MUP     0      0.0% |----------------------|                                    #> RIF   467    100.0% |######################|    100.0% |######################| # }"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Bug-Drug Combinations — bug_drug_combinations","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"Determine antimicrobial resistance (AMR) bug-drug combinations data set least 30 (default) isolates available per species. Use format() result prettify publishable/printable format, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...)  # S3 method for bug_drug_combinations format(   x,   translate_ab = \"name (ab, atc)\",   language = get_AMR_locale(),   minimum = 30,   combine_SI = TRUE,   add_ab_group = TRUE,   remove_intrinsic_resistant = FALSE,   decimal.mark = getOption(\"OutDec\"),   big.mark = ifelse(decimal.mark == \",\", \".\", \",\"),   ... )"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"x data set antibiotic columns, amox, AMX AMC col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). FUN function call mo column transform microorganism codes - default mo_shortname() ... arguments passed FUN translate_ab character length 1 containing column names antibiotics data set language language returned text - default current system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether values S summed, resistance based R - default TRUE add_ab_group logical indicate group antimicrobials must included first column remove_intrinsic_resistant logical indicate rows columns 100% resistance tested antimicrobials must removed table decimal.mark character used indicate numeric     decimal point. big.mark character; empty used mark every     big.interval decimals (hence big)     decimal point.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function bug_drug_combinations() returns data.frame columns \"mo\", \"ab\", \"S\", \"\", \"R\" \"total\".","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function format() calculates resistance per bug-drug combination returns table ready reporting/publishing. Use combine_SI = TRUE (default) test R vs. S+combine_SI = FALSE test R+vs. S. table can also directly used R Markdown / Quarto without need e.g. knitr::kable().","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>  # \\donttest{ x <- bug_drug_combinations(example_isolates) head(x) #> # A tibble: 6 × 6 #>   mo                ab        S     I     R total #>   <chr>             <chr> <int> <int> <int> <int> #> 1 (unknown species) AMC      15     0     0    15 #> 2 (unknown species) AMK       0     0     0     0 #> 3 (unknown species) AMP      15     0     1    16 #> 4 (unknown species) AMX      15     0     1    16 #> 5 (unknown species) AZM       3     0     3     6 #> 6 (unknown species) CAZ       0     0     0     0 #> Use 'format()' on this result to get a publishable/printable format. format(x, translate_ab = \"name (atc)\") #> # A tibble: 39 × 12 #>    Group     Drug  CoNS  E. co…¹ E. fa…² K. pn…³ P. ae…⁴ P. mi…⁵ S. au…⁶ S. ep…⁷ #>    <chr>     <chr> <chr> <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   #>  1 \"Aminogl… Amik… \"100… \"  0.0… \"100.0… \"\"      \"\"      \"\"      \"\"      \"100.0… #>  2 \"\"        Gent… \" 13… \"  2.0… \"100.0… \" 10.3… \"  0.0… \"  5.9… \"  0.9… \" 21.5… #>  3 \"\"        Kana… \"100… \"\"      \"100.0… \"\"      \"100.0… \"\"      \"\"      \"100.0… #>  4 \"\"        Tobr… \" 78… \"  2.6… \"100.0… \" 10.3… \"  0.0… \"  5.9… \"  2.3… \" 49.4… #>  5 \"Ampheni… Chlo… \"\"    \"\"      \"\"      \"\"      \"100.0… \"\"      \"  0.0… \"  3.1… #>  6 \"Antimyc… Rifa… \"\"    \"100.0… \"\"      \"100.0… \"100.0… \"100.0… \"  0.0… \"  2.7… #>  7 \"Beta-la… Amox… \" 93… \" 50.0… \"\"      \"100.0… \"100.0… \"\"      \" 93.9… \" 98.9… #>  8 \"\"        Amox… \" 42… \" 13.1… \"\"      \" 10.3… \"100.0… \"  2.8… \"  0.4… \" 54.5… #>  9 \"\"        Ampi… \" 93… \" 50.0… \"\"      \"100.0… \"100.0… \"\"      \" 93.9… \" 98.9… #> 10 \"\"        Benz… \" 77… \"100.0… \"\"      \"100.0… \"100.0… \"100.0… \" 80.9… \" 89.4… #> # … with 29 more rows, 2 more variables: `S. hominis` <chr>, #> #   `S. pneumoniae` <chr>, and abbreviated variable names ¹​`E. coli`, #> #   ²​`E. faecalis`, ³​`K. pneumoniae`, ⁴​`P. aeruginosa`, ⁵​`P. mirabilis`, #> #   ⁶​`S. aureus`, ⁷​`S. epidermidis`  # Use FUN to change to transformation of microorganism codes bug_drug_combinations(example_isolates,   FUN = mo_gramstain ) #> # A tibble: 80 × 6 #>    mo            ab        S     I     R total #>  * <chr>         <chr> <int> <int> <int> <int> #>  1 Gram-negative AMC     463    89   174   726 #>  2 Gram-negative AMK     251     0     5   256 #>  3 Gram-negative AMP     226     0   405   631 #>  4 Gram-negative AMX     226     0   405   631 #>  5 Gram-negative AZM       1     2   696   699 #>  6 Gram-negative CAZ     607     0    27   634 #>  7 Gram-negative CHL       1     0    30    31 #>  8 Gram-negative CIP     610    11    63   684 #>  9 Gram-negative CLI      18     1   709   728 #> 10 Gram-negative COL     309     0    78   387 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format.  bug_drug_combinations(example_isolates,   FUN = function(x) {     ifelse(x == as.mo(\"Escherichia coli\"),       \"E. coli\",       \"Others\"     )   } ) #> # A tibble: 80 × 6 #>    mo      ab        S     I     R total #>  * <chr>   <chr> <int> <int> <int> <int> #>  1 E. coli AMC     332    74    61   467 #>  2 E. coli AMK     171     0     0   171 #>  3 E. coli AMP     196     0   196   392 #>  4 E. coli AMX     196     0   196   392 #>  5 E. coli AZM       0     0   467   467 #>  6 E. coli CAZ     449     0    11   460 #>  7 E. coli CHL       0     0     0     0 #>  8 E. coli CIP     398     1    57   456 #>  9 E. coli CLI       0     0   467   467 #> 10 E. coli COL     240     0     0   240 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format. # }"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Data set containing clinical breakpoints interpret MIC disk diffusion SIR values, according international guidelines. Currently implemented guidelines EUCAST (2013-2022) CLSI (2013-2022). Use .sir() transform MICs disks measurements SIR values.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"tibble 18 308 observations 11 variables: guideline Name guideline method Either \"DISK\" \"MIC\" site Body site, e.g. \"Oral\" \"Respiratory\" mo Microbial ID, see .mo() rank_index Taxonomic rank index mo 1 (subspecies/infraspecies) 5 (unknown microorganism) ab Antibiotic ID, see .ab() ref_tbl Info guideline rule can found disk_dose Dose used disk diffusion method breakpoint_S Lowest MIC value highest number millimetres leads \"S\" breakpoint_R Highest MIC value lowest number millimetres leads \"R\" uti logical value (TRUE/FALSE) indicate whether rule applies urinary tract infection (UTI)","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository. allow machine reading EUCAST CLSI guidelines, almost impossible MS Excel PDF files distributed EUCAST CLSI.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints #> # A tibble: 18,308 × 11 #>    guideline method site  mo               rank_…¹ ab   ref_tbl  disk_…² break…³ #>    <chr>     <chr>  <chr> <mo>               <dbl> <ab> <chr>    <chr>     <dbl> #>  1 EUCAST 2… MIC    NA    F_ASPRG_MGTS           2 AMB  Aspergi… NA            1 #>  2 EUCAST 2… MIC    NA    F_ASPRG_NIGR           2 AMB  Aspergi… NA            1 #>  3 EUCAST 2… MIC    NA    F_CANDD_ALBC           2 AMB  Candida  NA            1 #>  4 EUCAST 2… MIC    NA    F_CANDD_DBLN           2 AMB  Candida  NA            1 #>  5 EUCAST 2… MIC    NA    F_CANDD_GLBR           2 AMB  Candida  NA            1 #>  6 EUCAST 2… MIC    NA    F_CANDD_KRUS           2 AMB  Candida  NA            1 #>  7 EUCAST 2… MIC    NA    F_CANDD_PRPS           2 AMB  Candida  NA            1 #>  8 EUCAST 2… MIC    NA    F_CANDD_TRPC           2 AMB  Candida  NA            1 #>  9 EUCAST 2… MIC    NA    F_CRYPT_NFRM           2 AMB  Candida  NA            1 #> 10 EUCAST 2… DISK   NA    B_[ORD]_ENTRBCTR       5 AMC  Enterob… 20ug/1…      19 #> # … with 18,298 more rows, 2 more variables: breakpoint_R <dbl>, uti <lgl>, and #> #   abbreviated variable names ¹​rank_index, ²​disk_dose, ³​breakpoint_S"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Available Isolates — count","title":"Count Available Isolates — count","text":"functions can used count resistant/susceptible microbial isolates. functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. count_resistant() used count resistant isolates, count_susceptible() used count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Available Isolates — count","text":"","code":"count_resistant(..., only_all_tested = FALSE)  count_susceptible(..., only_all_tested = FALSE)  count_R(..., only_all_tested = FALSE)  count_IR(..., only_all_tested = FALSE)  count_I(..., only_all_tested = FALSE)  count_SI(..., only_all_tested = FALSE)  count_S(..., only_all_tested = FALSE)  count_all(..., only_all_tested = FALSE)  n_sir(..., only_all_tested = FALSE)  count_df(   data,   translate_ab = \"name\",   language = get_AMR_locale(),   combine_SI = TRUE )"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Available Isolates — count","text":"... one vectors (columns) antibiotic interpretations. transformed internally .sir() needed. only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antibiotics, see section Combination Therapy data data.frame containing columns class sir (see .sir()) translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() language language returned text - default current system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant) - default TRUE","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Available Isolates — count","text":"integer","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Count Available Isolates — count","text":"functions meant count isolates. Use resistance()/susceptibility() functions calculate microbial resistance/susceptibility. function count_resistant() equal function count_R(). function count_susceptible() equal function count_SI(). function n_sir() alias count_all(). can used count available isolates, .e. input antibiotics available result (S, R). use equal n_distinct(). function equal count_susceptible(...) + count_resistant(...). function count_df() takes variable data sir class (created .sir()) counts number S's, 's R's. also supports grouped variables. function sir_df() works exactly like count_df(), adds percentage S, R.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Count Available Isolates — count","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Count Available Isolates — count","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antibiotics, Drug Drug B, susceptibility() works calculate %SI:   Please note , combination therapies, only_all_tested = TRUE applies :   , combination therapies, only_all_tested = FALSE applies :   Using only_all_tested impact using one antibiotic input.","code":"--------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   include as  include as   include as  include as                     numerator   denominator  numerator   denominator --------  --------  ----------  -----------  ----------  -----------  S or I    S or I       X            X            X            X    R       S or I       X            X            X            X   <NA>     S or I       X            X            -            -  S or I      R          X            X            X            X    R         R          -            X            -            X   <NA>       R          -            -            -            -  S or I     <NA>        X            X            -            -    R        <NA>        -            -            -            -   <NA>      <NA>        -            -            -            - -------------------------------------------------------------------- count_S()    +   count_I()    +   count_R()    = count_all()   proportion_S() + proportion_I() + proportion_R() = 1 count_S()    +   count_I()    +   count_R()    >= count_all()   proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Available Isolates — count","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info.  # base R ------------------------------------------------------------ count_resistant(example_isolates$AMX) # counts \"R\" #> [1] 804 count_susceptible(example_isolates$AMX) # counts \"S\" and \"I\" #> [1] 546 count_all(example_isolates$AMX) # counts \"S\", \"I\" and \"R\" #> [1] 1350  # be more specific count_S(example_isolates$AMX) #> Using count_S() is discouraged; use count_susceptible() instead to also #> consider \"I\" being susceptible. This note will be shown once for this #> session. #> [1] 543 count_SI(example_isolates$AMX) #> [1] 546 count_I(example_isolates$AMX) #> [1] 3 count_IR(example_isolates$AMX) #> Using count_IR() is discouraged; use count_resistant() instead to not #> consider \"I\" being resistant. This note will be shown once for this #> session. #> [1] 807 count_R(example_isolates$AMX) #> [1] 804  # Count all available isolates count_all(example_isolates$AMX) #> [1] 1350 n_sir(example_isolates$AMX) #> [1] 1350  # n_sir() is an alias of count_all(). # Since it counts all available isolates, you can # calculate back to count e.g. susceptible isolates. # These results are the same: count_susceptible(example_isolates$AMX) #> [1] 546 susceptibility(example_isolates$AMX) * n_sir(example_isolates$AMX) #> [1] 546  # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     group_by(ward) %>%     summarise(       R = count_R(CIP),       I = count_I(CIP),       S = count_S(CIP),       n1 = count_all(CIP), # the actual total; sum of all three       n2 = n_sir(CIP), # same - analogous to n_distinct       total = n()     ) # NOT the number of tested isolates!    # Number of available isolates for a whole antibiotic class   # (i.e., in this data set columns GEN, TOB, AMK, KAN)   example_isolates %>%     group_by(ward) %>%     summarise(across(aminoglycosides(), n_sir))    # Count co-resistance between amoxicillin/clav acid and gentamicin,   # so we can see that combination therapy does a lot more than mono therapy.   # Please mind that `susceptibility()` calculates percentages right away instead.   example_isolates %>% count_susceptible(AMC) # 1433   example_isolates %>% count_all(AMC) # 1879    example_isolates %>% count_susceptible(GEN) # 1399   example_isolates %>% count_all(GEN) # 1855    example_isolates %>% count_susceptible(AMC, GEN) # 1764   example_isolates %>% count_all(AMC, GEN) # 1936    # Get number of S+I vs. R immediately of selected columns   example_isolates %>%     select(AMX, CIP) %>%     count_df(translate = FALSE)    # It also supports grouping variables   example_isolates %>%     select(ward, AMX, CIP) %>%     group_by(ward) %>%     count_df(translate = FALSE) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 12 × 4 #>    ward       antibiotic interpretation value #>  * <chr>      <chr>      <ord>          <int> #>  1 Clinical   AMX        SI               357 #>  2 Clinical   AMX        R                487 #>  3 Clinical   CIP        SI               741 #>  4 Clinical   CIP        R                128 #>  5 ICU        AMX        SI               158 #>  6 ICU        AMX        R                270 #>  7 ICU        CIP        SI               362 #>  8 ICU        CIP        R                 85 #>  9 Outpatient AMX        SI                31 #> 10 Outpatient AMX        R                 47 #> 11 Outpatient CIP        SI                78 #> 12 Outpatient CIP        R                 15 # }"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Define Custom EUCAST Rules — custom_eucast_rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"Define custom EUCAST rules organisation specific analysis use output function eucast_rules().","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"custom_eucast_rules(...)"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"... rules formula notation, see Examples","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"list containing custom rules","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"organisations adoption EUCAST rules. function can used define custom EUCAST rules used eucast_rules() function.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"basics","dir":"Reference","previous_headings":"","what":"Basics","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde:   two custom EUCAST rules: TZP (piperacillin/tazobactam) \"S\", aminopenicillins (ampicillin amoxicillin) must made \"S\", TZP \"R\", aminopenicillins must made \"R\". rules can also printed console, immediately clear work:   rules (part tilde, example TZP == \"S\" TZP == \"R\") must evaluable data set: able run filter data set without errors. means example column TZP must exist. create sample data set test rules set:","code":"x <- custom_eucast_rules(TZP == \"S\" ~ aminopenicillins == \"S\",                          TZP == \"R\" ~ aminopenicillins == \"R\") x #> A set of custom EUCAST rules: #> #>   1. If TZP is \"S\" then set to  S : #>      amoxicillin (AMX), ampicillin (AMP) #> #>   2. If TZP is \"R\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) df <- data.frame(mo = c(\"Escherichia coli\", \"Klebsiella pneumoniae\"),                  TZP = as.sir(\"R\"),                  ampi = as.sir(\"S\"),                  cipro = as.sir(\"S\")) df #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    S     S #> 2 Klebsiella pneumoniae   R    S     S  eucast_rules(df, rules = \"custom\", custom_rules = x, info = FALSE) #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    R     S #> 2 Klebsiella pneumoniae   R    R     S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"using-taxonomic-properties-in-rules","dir":"Reference","previous_headings":"","what":"Using taxonomic properties in rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"one exception columns used rules: column names microorganisms data set can also used, exist data set. column names : \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\", \"snomed\". Thus, next example work well, despite fact df data set contain column genus:","code":"y <- custom_eucast_rules(TZP == \"S\" & genus == \"Klebsiella\" ~ aminopenicillins == \"S\",                          TZP == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\")  eucast_rules(df, rules = \"custom\", custom_rules = y, info = FALSE) #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    S     S #> 2 Klebsiella pneumoniae   R    R     S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"usage-of-antibiotic-group-names","dir":"Reference","previous_headings":"","what":"Usage of antibiotic group names","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"possible define antibiotic groups instead single antibiotics rule consequence, part tilde. examples, antibiotic group aminopenicillins used include ampicillin amoxicillin. following groups allowed (case-insensitive). Within parentheses drugs matched running rule. \"aminoglycosides\"(amikacin, amikacin/fosfomycin, amphotericin B-high, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin, tobramycin-high) \"aminopenicillins\"(amoxicillin ampicillin) \"antifungals\"(amphotericin B, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, flucytosine, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, oteseconazole, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole, voriconazole) \"antimycobacterials\"(4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, morinamide, p-aminosalicylic acid, pretomanid, protionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, simvastatin/fenofibrate, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone, thioacetazone/isoniazid, tiocarlide, viomycin) \"betalactams\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, biapenem, carbenicillin, carindacillin, cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, ciclacillin, clometocillin, cloxacillin, dicloxacillin, doripenem, epicillin, ertapenem, flucloxacillin, hetacillin, imipenem, imipenem/EDTA, imipenem/relebactam, latamoxef, lenampicillin, loracarbef, mecillinam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, tebipenem, temocillin, ticarcillin, ticarcillin/clavulanic acid) \"carbapenems\"(biapenem, doripenem, ertapenem, imipenem, imipenem/EDTA, imipenem/relebactam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, panipenem, razupenem, ritipenem, ritipenem acoxil, tebipenem) \"cephalosporins\"(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef, loracarbef) \"cephalosporins_1st\"(cefacetrile, cefadroxil, cefalexin, cefaloridine, cefalotin, cefapirin, cefatrizine, cefazedone, cefazolin, cefroxadine, ceftezole, cephradine) \"cephalosporins_2nd\"(cefaclor, cefamandole, cefmetazole, cefonicid, ceforanide, cefotetan, cefotiam, cefoxitin, cefoxitin screening, cefprozil, cefuroxime, cefuroxime axetil, loracarbef) \"cephalosporins_3rd\"(cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefetamet, cefetamet pivoxil, cefixime, cefmenoxime, cefodizime, cefoperazone, cefoperazone/sulbactam, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotiam hexetil, cefovecin, cefpimizole, cefpiramide, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefsulodin, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, latamoxef) \"cephalosporins_4th\"(cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetecol, cefoselis, cefozopran, cefpirome, cefquinome) \"cephalosporins_5th\"(ceftaroline, ceftaroline/avibactam, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam) \"cephalosporins_except_caz\"(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef, loracarbef) \"fluoroquinolones\"(besifloxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nifuroquine, norfloxacin, ofloxacin, orbifloxacin, pazufloxacin, pefloxacin, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin, trovafloxacin) \"glycopeptides\"(avoparcin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin, vancomycin-macromethod) \"glycopeptides_except_lipo\"(avoparcin, norvancomycin, ramoplanin, teicoplanin, teicoplanin-macromethod, vancomycin, vancomycin-macromethod) \"lincosamides\"(acetylmidecamycin, acetylspiramycin, clindamycin, gamithromycin, kitasamycin, lincomycin, meleumycin, nafithromycin, pirlimycin, primycin, solithromycin, tildipirosin, tilmicosin, tulathromycin, tylosin, tylvalosin) \"lipoglycopeptides\"(dalbavancin, oritavancin, telavancin) \"macrolides\"(acetylmidecamycin, acetylspiramycin, azithromycin, clarithromycin, dirithromycin, erythromycin, flurithromycin, gamithromycin, josamycin, kitasamycin, meleumycin, midecamycin, miocamycin, nafithromycin, oleandomycin, pirlimycin, primycin, rokitamycin, roxithromycin, solithromycin, spiramycin, telithromycin, tildipirosin, tilmicosin, troleandomycin, tulathromycin, tylosin, tylvalosin) \"oxazolidinones\"(cadazolid, cycloserine, linezolid, tedizolid, thiacetazone) \"penicillins\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, carbenicillin, carindacillin, cefepime/nacubactam, ciclacillin, clometocillin, cloxacillin, dicloxacillin, epicillin, flucloxacillin, hetacillin, lenampicillin, mecillinam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, temocillin, ticarcillin, ticarcillin/clavulanic acid) \"polymyxins\"(colistin, polymyxin B, polymyxin B/polysorbate 80) \"quinolones\"(besifloxacin, cinoxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, flumequine, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nalidixic acid, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, ofloxacin, orbifloxacin, oxolinic acid, pazufloxacin, pefloxacin, pipemidic acid, piromidic acid, pradofloxacin, premafloxacin, prulifloxacin, rosoxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin, trovafloxacin) \"streptogramins\"(pristinamycin quinupristin/dalfopristin) \"tetracyclines\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline, tigecycline) \"tetracyclines_except_tgc\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline) \"trimethoprims\"(brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim, trimethoprim/sulfamethoxazole) \"ureidopenicillins\"(azlocillin, mezlocillin, piperacillin, piperacillin/tazobactam)","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"x <- custom_eucast_rules(   AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\",   AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\" ) x #> A set of custom EUCAST rules: #>  #>   1. If AMC is \"R\" and genus is \"Klebsiella\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   2. If AMC is \"I\" and genus is \"Klebsiella\" then set to  I : #>      amoxicillin (AMX), ampicillin (AMP)  # run the custom rule set (verbose = TRUE will return a logbook instead of the data set): eucast_rules(example_isolates,   rules = \"custom\",   custom_rules = x,   info = FALSE,   verbose = TRUE ) #> # A tibble: 8 × 9 #>     row col   mo_fullname           old   new   rule     rule_…¹ rule_…² rule_…³ #>   <int> <chr> <chr>                 <ord> <chr> <chr>    <chr>   <chr>   <chr>   #> 1    33 AMP   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 2    33 AMX   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 3    34 AMP   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 4    34 AMX   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 5   531 AMP   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 6   531 AMX   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 7  1485 AMP   Klebsiella oxytoca    R     I     \"report… Custom… Custom… Object… #> 8  1485 AMX   Klebsiella oxytoca    R     I     \"report… Custom… Custom… Object… #> # … with abbreviated variable names ¹​rule_group, ²​rule_name, ³​rule_source  # combine rule sets x2 <- c(   x,   custom_eucast_rules(TZP == \"R\" ~ carbapenems == \"R\") ) x2 #> A set of custom EUCAST rules: #>  #>   1. If AMC is \"R\" and genus is \"Klebsiella\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   2. If AMC is \"I\" and genus is \"Klebsiella\" then set to  I : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   3. If TZP is \"R\" then set to  R : #>      biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), #>      imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), #>      meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), #>      razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), tebipenem (TBP)"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"EUCAST breakpoints used package based dosages data set. can retrieved eucast_dosage().","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"tibble 336 observations 9 variables: ab Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name antimicrobial drug used WHONET/EARS-Net type Type dosage, either \"high_dosage\", \"standard_dosage\", \"uncomplicated_uti\" dose Dose, \"2 g\" \"25 mg/kg\" dose_times Number times dose must administered administration Route administration, either \"im\", \"iv\", \"oral\" notes Additional dosage notes original_txt Original text PDF file EUCAST eucast_version Version number EUCAST Clinical Breakpoints guideline dosages apply","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"data set based 'EUCAST Clinical Breakpoint Tables' v12.0 (2022) 'EUCAST Clinical Breakpoint Tables' v11.0 (2021).","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage #> # A tibble: 336 × 9 #>    ab   name                   type  dose  dose_…¹ admin…² notes origi…³ eucas…⁴ #>    <ab> <chr>                  <chr> <chr>   <int> <chr>   <chr> <chr>     <dbl> #>  1 AMK  Amikacin               stan… 25-3…       1 iv      \"\"    25-30 …      12 #>  2 AMX  Amoxicillin            high… 2 g         6 iv      \"\"    2 g x …      12 #>  3 AMX  Amoxicillin            stan… 1 g         3 iv      \"\"    1 g x …      12 #>  4 AMX  Amoxicillin            high… 0.75…       3 oral    \"\"    0.75-1…      12 #>  5 AMX  Amoxicillin            stan… 0.5 g       3 oral    \"\"    0.5 g …      12 #>  6 AMX  Amoxicillin            unco… 0.5 g       3 oral    \"\"    0.5 g …      12 #>  7 AMC  Amoxicillin/clavulani… high… 2 g …       3 iv      \"\"    (2 g a…      12 #>  8 AMC  Amoxicillin/clavulani… stan… 1 g …       3 iv      \"\"    (1 g a…      12 #>  9 AMC  Amoxicillin/clavulani… high… 0.87…       3 oral    \"\"    (0.875…      12 #> 10 AMC  Amoxicillin/clavulani… stan… 0.5 …       3 oral    \"\"    (0.5 g…      12 #> # … with 326 more rows, and abbreviated variable names ¹​dose_times, #> #   ²​administration, ³​original_txt, ⁴​eucast_version"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply EUCAST Rules — eucast_rules","title":"Apply EUCAST Rules — eucast_rules","text":"Apply rules clinical breakpoints intrinsic resistance defined European Committee Antimicrobial Susceptibility Testing (EUCAST, https://www.eucast.org), see Source. Use eucast_dosage() get data.frame advised dosages certain bug-drug combination, based dosage data set. improve interpretation antibiogram EUCAST rules applied, non-EUCAST rules can applied default, see Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"eucast_rules(   x,   col_mo = NULL,   info = interactive(),   rules = getOption(\"AMR_eucastrules\", default = c(\"breakpoints\", \"expert\")),   verbose = FALSE,   version_breakpoints = 12,   version_expertrules = 3.3,   ampc_cephalosporin_resistance = NA,   only_sir_columns = FALSE,   custom_rules = NULL,   ... )  eucast_dosage(ab, administration = \"iv\", version_breakpoints = 12)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Apply EUCAST Rules — eucast_rules","text":"EUCAST Expert Rules. Version 2.0, 2012. Leclercq et al. EUCAST expert rules antimicrobial susceptibility testing. Clin Microbiol Infect. 2013;19(2):141-60; doi:10.1111/j.1469-0691.2011.03703.x EUCAST Expert Rules, Intrinsic Resistance Exceptional Phenotypes Tables. Version 3.1, 2016. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.2, 2020. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.3, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 9.0, 2019. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 10.0, 2020. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 11.0, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 12.0, 2022. (link)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply EUCAST Rules — eucast_rules","text":"x data set antibiotic columns, amox, AMX AMC col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console - default print interactive sessions rules character vector specifies rules applied. Must one \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). default value can set another value using package option AMR_eucastrules: options(AMR_eucastrules = \"\"). using \"custom\", sure fill argument custom_rules . Custom rules can created custom_eucast_rules(). verbose logical turn Verbose mode (default ). Verbose mode, function apply rules data, instead returns data set logbook form extensive info rows columns effected way. Using Verbose mode takes lot time. version_breakpoints version number use EUCAST Clinical Breakpoints guideline. Can either \"12.0\", \"11.0\", \"10.0\". version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can either \"3.3\", \"3.2\", \"3.1\". ampc_cephalosporin_resistance character value applied cefotaxime, ceftriaxone ceftazidime AmpC de-repressed cephalosporin-resistant mutants - default NA. Currently works version_expertrules 3.2 higher; version 'EUCAST Expert Rules Enterobacterales' state results cefotaxime, ceftriaxone ceftazidime reported note, results suppressed (emptied) three drugs. value NA (default) argument remove results three drugs, e.g. value \"R\" make results drugs resistant. Use NULL FALSE alter results three drugs AmpC de-repressed cephalosporin-resistant mutants. Using TRUE equal using \"R\".  EUCAST Expert Rules v3.2, rule applies : Citrobacter braakii, Citrobacter freundii, Citrobacter gillenii, Citrobacter murliniae, Citrobacter rodenticum, Citrobacter sedlakii, Citrobacter werkmanii, Citrobacter youngae, Enterobacter, Hafnia alvei, Klebsiella aerogenes, Morganella morganii, Providencia, Serratia. only_sir_columns logical indicate whether antibiotic columns must detected transformed class sir (see .sir()) beforehand (default FALSE) custom_rules custom rules apply, created custom_eucast_rules() ... column name antibiotic, see section Antibiotics ab (vector ) text can coerced valid antibiotic drug code .ab() administration route administration, either \"im\", \"iv\", \"oral\"","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply EUCAST Rules — eucast_rules","text":"input x, possibly edited values antibiotics. , verbose = TRUE, data.frame original new values affected bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Apply EUCAST Rules — eucast_rules","text":"Note: function translate MIC values SIR values. Use .sir() . Note: ampicillin (AMP, J01CA01) available amoxicillin (AMX, J01CA04) , latter used rules dependency ampicillin. drugs interchangeable comes expression antimicrobial resistance. file containing EUCAST rules located : https://github.com/msberends/AMR/blob/main/data-raw/eucast_rules.tsv.  Note: Old taxonomic names replaced current taxonomy applicable. example, Ochrobactrum anthropi renamed Brucella anthropi 2020; original EUCAST rules v3.1 v3.2 yet contain new taxonomic name. AMR package contains full microbial taxonomy updated 11 December, 2022, see microorganisms.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"custom-rules","dir":"Reference","previous_headings":"","what":"Custom Rules","title":"Apply EUCAST Rules — eucast_rules","text":"Custom rules can created using custom_eucast_rules(), e.g.:","code":"x <- custom_eucast_rules(AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\",                          AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\")  eucast_rules(example_isolates, rules = \"custom\", custom_rules = x)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"-other-rules","dir":"Reference","previous_headings":"","what":"'Other' Rules","title":"Apply EUCAST Rules — eucast_rules","text":"processing, two non-EUCAST rules drug combinations can applied improve efficacy EUCAST rules, reliability data (analysis). rules : drug enzyme inhibitor set S drug without enzyme inhibitor S drug without enzyme inhibitor set R drug enzyme inhibitor R Important examples include amoxicillin amoxicillin/clavulanic acid, trimethoprim trimethoprim/sulfamethoxazole. Needless say, rules work, drugs must available data set. Since rules officially approved EUCAST, applied default. use rules, include \"\" rules argument, use eucast_rules(..., rules = \"\"). can also set package option AMR_eucastrules, .e. run options(AMR_eucastrules = \"\").","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"antibiotics","dir":"Reference","previous_headings":"","what":"Antibiotics","title":"Apply EUCAST Rules — eucast_rules","text":"define antibiotics column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antibiotics eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, S01AE08), amoxicillin (AMX, J01MA02), amoxicillin/clavulanic acid (AMC, J01MA23), ampicillin (AMP, J01MA04), ampicillin/sulbactam (SAM, J01MA08), arbekacin (ARB, J01MA19), aspoxicillin (APX, J01MA16), azidocillin (AZD, J01MA15), azithromycin (AZM, J01MA11), azlocillin (AZL, J01MA25), aztreonam (ATM, J01MA12), bacampicillin (BAM, J01MA24), bekanamycin (BEK, J01MA07), benzathine benzylpenicillin (BNB, J01MA14), benzathine phenoxymethylpenicillin (BNP, D10AF05), benzylpenicillin (PEN, J01MA06), besifloxacin (BES, J01MA01), biapenem (BIA, J01MA18), carbenicillin (CRB, J01MA03), carindacillin (CRN, J01MA17), cefacetrile (CAC, J01MA10), cefaclor (CEC, J01MA21), cefadroxil (CFR, J01MA09), cefalexin (LEX, J01MA05), cefaloridine (RID, P01AA05), cefalotin (CEP, J01MA22), cefamandole (MAN, J01MA13), cefapirin (HAP, J01CA01), cefatrizine (CTZ, J01CA04), cefazedone (CZD, J01CA12), cefazolin (CZO, J01CR05), cefcapene (CCP, J01CA13), cefdinir (CDR, J01AA02), cefditoren (DIT, J01FA10), cefepime (FEP, J01FA09), cefetamet (CAT, J01CR02), cefixime (CFM, J01AA08), cefmenoxime (CMX, J01FA06), cefmetazole (CMZ, J01CF04), cefodizime (DIZ, J01CF05), cefonicid (CID, J01CR01), cefoperazone (CFP, J01CA19), cefoperazone/sulbactam (CSL, J01CE04), ceforanide (CND, J01CA09), cefotaxime (CTX, J01DF01), cefotaxime/clavulanic acid (CTC, J01CA06), cefotetan (CTT, J01CE08), cefotiam (CTF, J01CE10), cefoxitin (FOX, J01CE01), cefozopran (ZOP, J01CA03), cefpiramide (CPM, J01CA05), cefpirome (CPO, J01CE07), cefpodoxime (CPD, J01CF02), cefprozil (CPR, J01CF01), cefroxadine (CRD, J01CA07), cefsulodin (CFS, J01CA18), ceftaroline (CPT, J01CA11), ceftazidime (CAZ, J01CA14), ceftazidime/clavulanic acid (CCV, J01CF03), cefteram (CEM, J01CA10), ceftezole (CTL, J01CF06), ceftibuten (CTB, J01CE06), ceftizoxime (CZX, J01CE05), ceftobiprole medocaril (CFM1, J01CE02), ceftolozane/tazobactam (CZT, J01CA02), ceftriaxone (CRO, J01CA08), ceftriaxone/beta-lactamase inhibitor (CEB, J01CE09), cefuroxime (CXM, J01CE03), cephradine (CED, J01CG01), chloramphenicol (CHL, J01CA16), ciprofloxacin (CIP, J01CR04), clarithromycin (CLR, J01CA15), clindamycin (CLI, J01CG02), clometocillin (CLM, J01CA17), cloxacillin (CLO, J01CR03), colistin (COL, J01DB10), cycloserine (CYC, J01DC04), dalbavancin (DAL, J01DB05), daptomycin (DAP, J01DB01), delafloxacin (DFX, J01DB02), dibekacin (DKB, J01DB03), dicloxacillin (DIC, J01DC03), dirithromycin (DIR, J01DB08), doripenem (DOR, J01DB07), doxycycline (DOX, J01DB06), enoxacin (ENX, J01DB04), epicillin (EPC, J01DD17), ertapenem (ETP, J01DD15), erythromycin (ERY, J01DD16), fleroxacin (FLE, J01DE01), flucloxacillin (FLC, J01DD10), flurithromycin (FLR1, J01DD08), fosfomycin (FOS, J01DD05), framycetin (FRM, J01DC09), fusidic acid (FUS, J01DD09), garenoxacin (GRN, J01DC06), gatifloxacin (GAT, J01DD12), gemifloxacin (GEM, J01DD62), gentamicin (GEN, J01DC11), grepafloxacin (GRX, J01DD01), hetacillin (HET, J01DD51), imipenem (IPM, J01DC05), imipenem/relebactam (IMR, J01DC07), isepamicin (ISE, J01DC01), josamycin (JOS, J01DE03), kanamycin (KAN, J01DD11), lascufloxacin (LSC, J01DE02), latamoxef (LTM, J01DD13), levofloxacin (LVX, J01DC10), levonadifloxacin (LND, J01DB11), lincomycin (LIN, J01DD03), linezolid (LNZ, J01DI02), lomefloxacin (LOM, J01DD02), loracarbef (LOR, J01DD52), mecillinam (MEC, J01DD18), meropenem (MEM, J01DB12), meropenem/vaborbactam (MEV, J01DD14), metampicillin (MTM, J01DD07), meticillin (MET, J01DI01), mezlocillin (MEZ, J01DI54), micronomicin (MCR, J01DD04), midecamycin (MID, J01DD63), minocycline (MNO, J01DC02), miocamycin (MCM, J01DB09), moxifloxacin (MFX, J01DD06), nadifloxacin (NAD, J01DC08), nafcillin (NAF, J01DH05), nalidixic acid (NAL, J01DH04), neomycin (NEO, J01DH03), netilmicin (NET, J01DH51), nitrofurantoin (NIT, J01DH56), norfloxacin (, J01DH02), ofloxacin (OFX, J01DH52), oleandomycin (OLE, J01DH55), oritavancin (ORI, J01DH06), oxacillin (OXA, J01XA02), panipenem (PAN, J01XA01), pazufloxacin (PAZ, J01XC01), pefloxacin (PEF, J01FA13), penamecillin (PNM, J01FA01), pheneticillin (PHE, J01FA14), phenoxymethylpenicillin (PHN, J01FA07), piperacillin (PIP, J01FA03), piperacillin/tazobactam (TZP, J01FA11), pivampicillin (PVM, J01FA05), pivmecillinam (PME, J01FA12), plazomicin (PLZ, J01FA16), polymyxin B (PLB, J01FA02), pristinamycin (PRI, J01FA15), procaine benzylpenicillin (PRB, J01FA08), propicillin (PRP, J01FF02), prulifloxacin (PRU, J01FG01), quinupristin/dalfopristin (QDA, J01FG02), ribostamycin (RST, J04AB02), rifampicin (RIF, J01XX09), rokitamycin (ROK, J01XX08), roxithromycin (RXT, J01AA07), rufloxacin (RFL, J01XB01), sisomicin (SIS, J01XB02), sitafloxacin (SIT, J01XE01), solithromycin (SOL, J01AA12), sparfloxacin (SPX, J01EA01), spiramycin (SPI, J01XX01), streptoduocin (STR, J01BA01), streptomycin (STR1, J01GB06), sulbactam (SUL, J01GB12), sulbenicillin (SBC, J01GB13), sulfadiazine (SDI, J01GB09), sulfadiazine/trimethoprim (SLT1, D09AA01), sulfadimethoxine (SUD, J01GB03), sulfadimidine (SDM, J01GB11), sulfadimidine/trimethoprim (SLT2, J01GB04), sulfafurazole (SLF, S01AA22), sulfaisodimidine (SLF1, J01GB05), sulfalene (SLF2, J01GB07), sulfamazone (SZO, J01GB14), sulfamerazine (SLF3, J01GB10), sulfamerazine/trimethoprim (SLT3, J01GB08), sulfamethizole (SLF4, J01GA02), sulfamethoxazole (SMX, J01GA01), sulfamethoxypyridazine (SLF5, J01GB01), sulfametomidine (SLF6, J01EE01), sulfametoxydiazine (SLF7, J01MB02), sulfametrole/trimethoprim (SLT4, J01FF01), sulfamoxole (SLF8, J01XA04), sulfamoxole/trimethoprim (SLT5, J01XA05), sulfanilamide (SLF9, J01XA03), sulfaperin (SLF10, J04AB01), sulfaphenazole (SLF11, J01XX11), sulfapyridine (SLF12, J01EC02), sulfathiazole (SUT, J01ED01), sulfathiourea (SLF13, J01EB03), sultamicillin (SLT6, J01EB05), talampicillin (TAL, J01EB01), tazobactam (TAZ, J01ED02), tebipenem (TBP, J01ED09), tedizolid (TZD, J01ED07), teicoplanin (TEC, J01EB02), telavancin (TLV, J01EC01), telithromycin (TLT, J01ED05), temafloxacin (TMX, J01ED03), temocillin (TEM, J01ED04), tetracycline (TCY, J01EC03), ticarcillin (TIC, J01EB06), ticarcillin/clavulanic acid (TCC, J01ED06), tigecycline (TGC, J01ED08), tilbroquinol (TBQ, J01EB04), tobramycin (TOB, J01EB07), tosufloxacin (TFX, J01EB08), trimethoprim (TMP, J01EE02), trimethoprim/sulfamethoxazole (SXT, J01EE05), troleandomycin (TRL, J01EE07), trovafloxacin (TVA, J01EE03), vancomycin (VAN, J01EE04)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Apply EUCAST Rules — eucast_rules","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"# \\donttest{ a <- data.frame(   mo = c(     \"Staphylococcus aureus\",     \"Enterococcus faecalis\",     \"Escherichia coli\",     \"Klebsiella pneumoniae\",     \"Pseudomonas aeruginosa\"   ),   VAN = \"-\", # Vancomycin   AMX = \"-\", # Amoxicillin   COL = \"-\", # Colistin   CAZ = \"-\", # Ceftazidime   CXM = \"-\", # Cefuroxime   PEN = \"S\", # Benzylpenicillin   FOX = \"S\", # Cefoxitin   stringsAsFactors = FALSE )  head(a) #>                       mo VAN AMX COL CAZ CXM PEN FOX #> 1  Staphylococcus aureus   -   -   -   -   -   S   S #> 2  Enterococcus faecalis   -   -   -   -   -   S   S #> 3       Escherichia coli   -   -   -   -   -   S   S #> 4  Klebsiella pneumoniae   -   -   -   -   -   S   S #> 5 Pseudomonas aeruginosa   -   -   -   -   -   S   S   # apply EUCAST rules: some results wil be changed b <- eucast_rules(a) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #>   - a %>% as.sir(CXM:AMX) #>   - a %>% mutate_if(is_sir_eligible, as.sir) #>   - a %>% mutate(across(where(is_sir_eligible), as.sir))  head(b) #>                       mo VAN AMX COL CAZ CXM PEN FOX #> 1  Staphylococcus aureus   -   S   R   S   S   S   S #> 2  Enterococcus faecalis   -   -   R   R   R   S   R #> 3       Escherichia coli   R   -   -   -   -   R   S #> 4  Klebsiella pneumoniae   R   R   -   -   -   R   S #> 5 Pseudomonas aeruginosa   R   R   -   -   R   R   R   # do not apply EUCAST rules, but rather get a data.frame # containing all details about the transformations: c <- eucast_rules(a, verbose = TRUE) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #>   - a %>% as.sir(CXM:AMX) #>   - a %>% mutate_if(is_sir_eligible, as.sir) #>   - a %>% mutate(across(where(is_sir_eligible), as.sir)) head(c) #>   row col           mo_fullname old new rule   rule_group #> 1   1 AMX Staphylococcus aureus   -   S       Breakpoints #> 2   1 CXM Staphylococcus aureus   -   S       Breakpoints #> 3   1 CAZ Staphylococcus aureus   R   S      Expert Rules #> 4   1 CAZ Staphylococcus aureus   -   R      Expert Rules #> 5   1 COL Staphylococcus aureus   -   R      Expert Rules #> 6   2 CAZ Enterococcus faecalis   -   R      Expert Rules #>                                                 rule_name #> 1                                          Staphylococcus #> 2                                          Staphylococcus #> 3                          Expert Rules on Staphylococcus #> 4 Table 4: Intrinsic resistance in gram-positive bacteria #> 5 Table 4: Intrinsic resistance in gram-positive bacteria #> 6 Table 4: Intrinsic resistance in gram-positive bacteria #>                                                                                 rule_source #> 1                                           'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 2                                           'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 3 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 4 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 5 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 6 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 # }  # Dosage guidelines:  eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\") #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #>   Tables' v12.0 (2022). This note will be shown once per session. #> # A tibble: 3 × 4 #>   ab   name          standard_dosage  high_dosage  #>   <ab> <chr>         <chr>            <chr>        #> 1 TOB  Tobramycin    6-7 mg/kg x 1 iv NA           #> 2 GEN  Gentamicin    6-7 mg/kg x 1 iv NA           #> 3 CIP  Ciprofloxacin 0.4 g x 2 iv     0.4 g x 3 iv  eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\", version_breakpoints = 10) #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #>   Tables' v10.0 (2020). This note will be shown once per session. #> # A tibble: 3 × 4 #>   ab   name          standard_dosage  high_dosage  #>   <ab> <chr>         <chr>            <chr>        #> 1 TOB  Tobramycin    6-7 mg/kg x 1 iv NA           #> 2 GEN  Gentamicin    6-7 mg/kg x 1 iv NA           #> 3 CIP  Ciprofloxacin 0.4 g x 2 iv     0.4 g x 3 iv"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 2 000 Example Isolates — example_isolates","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"data set containing 2 000 microbial isolates full antibiograms. data set contains randomised fictitious data, reflects reality can used practise AMR data analysis. examples, please read tutorial website.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"","code":"example_isolates"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"tibble 2 000 observations 46 variables: date Date receipt laboratory patient ID patient age Age patient gender Gender patient, either \"F\" \"M\" ward Ward type patient admitted, either \"Clinical\", \"ICU\", \"Outpatient\" mo ID microorganism created .mo(), see also microorganisms data set PEN:RIF 40 different antibiotics class sir (see .sir()); column names occur antibiotics data set can translated set_ab_names() ab_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>"},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Unclean Data — example_isolates_unclean","title":"Data Set with Unclean Data — example_isolates_unclean","text":"data set containing 3 000 microbial isolates cleaned consequently ready AMR data analysis. data set can used practice.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Unclean Data — example_isolates_unclean","text":"","code":"example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Unclean Data — example_isolates_unclean","text":"tibble 3 000 observations 8 variables: patient_id ID patient date date receipt laboratory hospital ID hospital, C bacteria info microorganism can transformed .mo(), see also microorganisms AMX:GEN 4 different antibiotics transformed .sir()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Unclean Data — example_isolates_unclean","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Unclean Data — example_isolates_unclean","text":"","code":"example_isolates_unclean #> # A tibble: 3,000 × 8 #>    patient_id hospital date       bacteria      AMX   AMC   CIP   GEN   #>    <chr>      <chr>    <date>     <chr>         <chr> <chr> <chr> <chr> #>  1 J3         A        2012-11-21 E. coli       R     I     S     S     #>  2 R7         A        2018-04-03 K. pneumoniae R     I     S     S     #>  3 P3         A        2014-09-19 E. coli       R     S     S     S     #>  4 P10        A        2015-12-10 E. coli       S     I     S     S     #>  5 B7         A        2015-03-02 E. coli       S     S     S     S     #>  6 W3         A        2018-03-31 S. aureus     R     S     R     S     #>  7 J8         A        2016-06-14 E. coli       R     S     S     S     #>  8 M3         A        2015-10-25 E. coli       R     S     S     S     #>  9 J3         A        2019-06-19 E. coli       S     S     S     S     #> 10 G6         A        2015-04-27 S. aureus     S     S     S     S     #> # … with 2,990 more rows"},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine First Isolates — first_isolate","title":"Determine First Isolates — first_isolate","text":"Determine first isolates microorganisms every patient per episode (needed) per specimen type. functions support four methods summarised Hindler et al. 2007 (doi:10.1086/511864 ). determine patient episodes necessarily based microorganisms, use is_new_episode() also supports grouping dplyr package.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine First Isolates — first_isolate","text":"","code":"first_isolate(   x = NULL,   col_date = NULL,   col_patient_id = NULL,   col_mo = NULL,   col_testcode = NULL,   col_specimen = NULL,   col_icu = NULL,   col_keyantimicrobials = NULL,   episode_days = 365,   testcodes_exclude = NULL,   icu_exclude = FALSE,   specimen_group = NULL,   type = \"points\",   method = c(\"phenotype-based\", \"episode-based\", \"patient-based\", \"isolate-based\"),   ignore_I = TRUE,   points_threshold = 2,   info = interactive(),   include_unknown = FALSE,   include_untested_sir = TRUE,   ... )  filter_first_isolate(   x = NULL,   col_date = NULL,   col_patient_id = NULL,   col_mo = NULL,   episode_days = 365,   method = c(\"phenotype-based\", \"episode-based\", \"patient-based\", \"isolate-based\"),   ... )"},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine First Isolates — first_isolate","text":"Methodology function strictly based : M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. Hindler JF Stelling J (2007). Analysis Presentation Cumulative Antibiograms: New Consensus Guideline Clinical Laboratory Standards Institute. Clinical Infectious Diseases, 44(6), 867-873. doi:10.1086/511864","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine First Isolates — first_isolate","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_date column name result date (date received lab) - default first column date class col_patient_id column name unique IDs patients - default first column starts 'patient' 'patid' (case insensitive) col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). col_testcode column name test codes. Use col_testcode = NULL exclude certain test codes (test codes screening). case testcodes_exclude ignored. col_specimen column name specimen type group col_icu column name logicals (TRUE/FALSE) whether ward department Intensive Care Unit (ICU). can also logical vector length rows x. col_keyantimicrobials (useful method = \"phenotype-based\") column name key antimicrobials determine first isolates, see key_antimicrobials(). default first column starts 'key' followed 'ab' 'antibiotics' 'antimicrobials' (case insensitive). Use col_keyantimicrobials = FALSE prevent . Can also output key_antimicrobials(). episode_days episode days genus/species combination determined 'first isolate' . default 365 days based guideline CLSI, see Source. testcodes_exclude character vector test codes excluded (case-insensitive) icu_exclude logical indicate whether ICU isolates excluded (rows value TRUE column set col_icu) specimen_group value column set col_specimen filter type type determine weighed isolates; can \"keyantimicrobials\" \"points\", see Details method method apply, either \"phenotype-based\", \"episode-based\", \"patient-based\" \"isolate-based\" (can abbreviated), see Details. default \"phenotype-based\" antimicrobial test results present data, \"episode-based\" otherwise. ignore_I logical indicate whether antibiotic interpretations \"\" ignored type = \"keyantimicrobials\", see Details points_threshold minimum number points require differences antibiogram lead inclusion isolate type = \"points\", see Details info logical indicate info printed - default TRUE interactive mode include_unknown logical indicate whether 'unknown' microorganisms included , .e. microbial code \"UNKNOWN\", defaults FALSE. WHONET users, means records organism code \"con\" (contamination) excluded default. Isolates microbial ID NA always excluded first isolate. include_untested_sir logical indicate whether also rows without antibiotic results still eligible becoming first isolate. Use include_untested_sir = FALSE always return FALSE rows. checks data set columns class sir consequently requires transforming columns antibiotic results using .sir() first. ... arguments passed first_isolate() using filter_first_isolate(), otherwise arguments passed key_antimicrobials() (universal, gram_negative, gram_positive)","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine First Isolates — first_isolate","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine First Isolates — first_isolate","text":"conduct epidemiological analyses antimicrobial resistance data, -called first isolates included prevent overestimation underestimation antimicrobial resistance. Different methods can used , see . functions context-aware. means x argument can left blank used inside data.frame call, see Examples. first_isolate() function wrapper around is_new_episode() function, efficient data sets containing microorganism codes names. isolates microbial ID NA excluded first isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"different-methods","dir":"Reference","previous_headings":"","what":"Different methods","title":"Determine First Isolates — first_isolate","text":"According Hindler et al. (2007, doi:10.1086/511864 ), different methods (algorithms) select first isolates increasing reliability: isolate-based, patient-based, episode-based phenotype-based. methods select combination taxonomic genus species (subspecies). mentioned methods covered first_isolate() function:","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"isolate-based","dir":"Reference","previous_headings":"","what":"Isolate-based","title":"Determine First Isolates — first_isolate","text":"method require selection, isolates included. , however, respect arguments set first_isolate() function. example, default setting include_unknown (FALSE) omit selection rows without microbial ID.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"patient-based","dir":"Reference","previous_headings":"","what":"Patient-based","title":"Determine First Isolates — first_isolate","text":"include every genus-species combination per patient , set episode_days Inf. Although often inappropriate, method makes sure duplicate isolates selected patient. large longitudinal data set, mean isolates excluded found years initial isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"episode-based","dir":"Reference","previous_headings":"","what":"Episode-based","title":"Determine First Isolates — first_isolate","text":"include every genus-species combination per patient episode , set episode_days sensible number days. Depending type analysis, 14, 30, 60 365. Short episodes common analysing specific hospital ward data, long episodes common analysing regional national data. common method correct duplicate isolates. Patients categorised episodes based ID dates (e.g., date specimen receipt laboratory result). common method, take account antimicrobial test results. means e.g. methicillin-resistant Staphylococcus aureus (MRSA) isolate differentiated wildtype Staphylococcus aureus isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"phenotype-based","dir":"Reference","previous_headings":"","what":"Phenotype-based","title":"Determine First Isolates — first_isolate","text":"reliable method, since also weighs antibiogram (antimicrobial test results) yielding -called 'first weighted isolates'. two different methods weigh antibiogram: Using type = \"points\" argument points_threshold (default) method weighs antimicrobial drugs available data set. difference S R (vice versa) counts 0.5 points, difference S R (vice versa) counts 1 point. sum points exceeds points_threshold, defaults 2, isolate selected first weighted isolate. antimicrobials internally selected using all_antimicrobials() function. output function need passed first_isolate() function. Using type = \"keyantimicrobials\" argument ignore_I method weighs specific antimicrobial drugs, called key antimicrobials. difference S R (vice versa) key antimicrobials select isolate first weighted isolate. ignore_I = FALSE, also differences S R (vice versa) lead . Key antimicrobials internally selected using key_antimicrobials() function, can also added manually variable data set col_keyantimicrobials argument. Another option pass output key_antimicrobials() function directly col_keyantimicrobials argument. default method phenotype-based (using type = \"points\") episode-based (using episode_days = 365). makes sure every genus-species combination selected per patient per year, taking account antimicrobial test results. antimicrobial test results available data set, episode-based method applied default.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine First Isolates — first_isolate","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  example_isolates[first_isolate(info = TRUE), ] #> Determining first isolates using an episode length of 365 days #> ℹ Using column 'date' as input for col_date. #> ℹ Using column 'patient' as input for col_patient_id. #> Basing inclusion on all antimicrobial results, using a points threshold of #> 2 #> Including isolates from ICU. #> ℹ Excluded 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,379 'phenotype-based' first isolates (69.0% of total where a #>    microbial ID was available) #> # A tibble: 1,379 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  4 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #>  5 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR R     NA    S     NA    #>  6 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  7 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND R     NA    NA    R     #>  8 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  9 2002-02-03 481442     76 M      ICU      B_STPHY_CONS R     NA    S     NA    #> 10 2002-02-05 023456     50 M      Clinical B_STPHY_HMNS S     NA    S     NA    #> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> # \\donttest{ # get all first Gram-negatives example_isolates[which(first_isolate(info = FALSE) & mo_is_gram_negative()), ] #> ℹ Using column 'mo' as input for mo_is_gram_negative() #> # A tibble: 437 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND R     NA    NA    R     #>  4 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  5 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS R     NA    NA    R     #>  6 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN R     NA    NA    R     #>  7 2002-03-08 4FC193     69 M      Clinical B_ESCHR_COLI R     NA    NA    R     #>  8 2002-03-16 4FC193     69 M      Clinical B_PSDMN_AERG R     NA    NA    R     #>  9 2002-04-01 496896     46 F      ICU      B_ESCHR_COLI R     NA    NA    NA    #> 10 2002-04-23 EE2510     69 F      ICU      B_ESCHR_COLI R     NA    NA    NA    #> # … with 427 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir>  if (require(\"dplyr\")) {   # filter on first isolates using dplyr:   example_isolates %>%     filter(first_isolate(info = TRUE)) } #> Determining first isolates using an episode length of 365 days #> Basing inclusion on all antimicrobial results, using a points threshold of #> 2 #> Including isolates from ICU. #> ℹ Excluded 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,379 'phenotype-based' first isolates (69.0% of total where a #>    microbial ID was available) #> # A tibble: 1,379 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  4 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #>  5 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR R     NA    S     NA    #>  6 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  7 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND R     NA    NA    R     #>  8 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  9 2002-02-03 481442     76 M      ICU      B_STPHY_CONS R     NA    S     NA    #> 10 2002-02-05 023456     50 M      Clinical B_STPHY_HMNS S     NA    S     NA    #> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # short-hand version:   example_isolates %>%     filter_first_isolate(info = FALSE) } #> # A tibble: 1,379 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  4 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #>  5 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR R     NA    S     NA    #>  6 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  7 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND R     NA    NA    R     #>  8 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  9 2002-02-03 481442     76 M      ICU      B_STPHY_CONS R     NA    S     NA    #> 10 2002-02-05 023456     50 M      Clinical B_STPHY_HMNS S     NA    S     NA    #> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # flag the first isolates per group:   example_isolates %>%     group_by(ward) %>%     mutate(first = first_isolate(info = TRUE)) %>%     select(ward, date, patient, mo, first) } #> Determining first isolates using an episode length of 365 days #> Basing inclusion on all antimicrobial results, using a points threshold of #> 2 #> Including isolates from ICU. #>  #> Group: ward = \"Clinical\" #> ℹ Excluded 9 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 861 'phenotype-based' first isolates (69.8% of total where a #>    microbial ID was available) #> Including isolates from ICU. #>  #> Group: ward = \"ICU\" #> ℹ Excluded 6 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 449 'phenotype-based' first isolates (69.6% within scope and 69.5% #>    of total where a microbial ID was available) #> Including isolates from ICU. #>  #> Group: ward = \"Outpatient\" #> ℹ Excluded 1 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 98 'phenotype-based' first isolates (82.4% within scope and 81.7% #>    of total where a microbial ID was available) #> # A tibble: 2,000 × 5 #> # Groups:   ward [3] #>    ward     date       patient mo           first #>    <chr>    <date>     <chr>   <mo>         <lgl> #>  1 Clinical 2002-01-02 A77334  B_ESCHR_COLI TRUE  #>  2 Clinical 2002-01-03 A77334  B_ESCHR_COLI FALSE #>  3 ICU      2002-01-07 067927  B_STPHY_EPDR TRUE  #>  4 ICU      2002-01-07 067927  B_STPHY_EPDR FALSE #>  5 ICU      2002-01-13 067927  B_STPHY_EPDR FALSE #>  6 ICU      2002-01-13 067927  B_STPHY_EPDR FALSE #>  7 Clinical 2002-01-14 462729  B_STPHY_AURS TRUE  #>  8 Clinical 2002-01-14 462729  B_STPHY_AURS FALSE #>  9 ICU      2002-01-16 067927  B_STPHY_EPDR FALSE #> 10 ICU      2002-01-17 858515  B_STPHY_EPDR TRUE  #> # … with 1,990 more rows # }"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":null,"dir":"Reference","previous_headings":"","what":"G-test for Count Data — g.test","title":"G-test for Count Data — g.test","text":"g.test() performs chi-squared contingency table tests goodness--fit tests, just like chisq.test() reliable (1). G-test can used see whether number observations category fits theoretical expectation (called G-test goodness--fit), see whether proportions one variable different different values variable (called G-test independence).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"G-test for Count Data — g.test","text":"","code":"g.test(x, y = NULL, p = rep(1/length(x), length(x)), rescale.p = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"G-test for Count Data — g.test","text":"code function identical chisq.test(), except : calculation statistic changed \\(2 * sum(x * log(x / E))\\) Yates' continuity correction removed apply G-test possibility simulate p values simulate.p.value removed","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"G-test for Count Data — g.test","text":"x numeric vector matrix. x y can also     factors. y numeric vector; ignored x matrix.      x factor, y factor length. p vector probabilities length x.     error given entry p negative. rescale.p logical scalar; TRUE p rescaled     (necessary) sum 1.  rescale.p FALSE,     p sum 1, error given.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"G-test for Count Data — g.test","text":"list class \"htest\" containing following   components: statistic value chi-squared test statistic. parameter degrees freedom approximate     chi-squared distribution test statistic, NA     p-value computed Monte Carlo simulation. p.value p-value test. method character string indicating type test     performed, whether Monte Carlo simulation continuity     correction used. data.name character string giving name(s) data. observed observed counts. expected expected counts null hypothesis. residuals Pearson residuals,     (observed - expected) / sqrt(expected). stdres standardized residuals,     (observed - expected) / sqrt(V), V residual cell variance (Agresti, 2007,     section 2.4.5 case x matrix, n * p * (1 - p) otherwise).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"G-test for Count Data — g.test","text":"x matrix one row column, x vector y given, goodness--fit test performed (x treated one-dimensional contingency table). entries x must non-negative integers. case, hypothesis tested whether population probabilities equal p, equal p given. x matrix least two rows columns, taken two-dimensional contingency table: entries x must non-negative integers.  Otherwise, x y must vectors factors length; cases missing values removed, objects coerced factors, contingency table computed .  Pearson's chi-squared test performed null hypothesis joint distribution cell counts 2-dimensional contingency table product row column marginals. p-value computed asymptotic chi-squared distribution test statistic. contingency table case simulation done random sampling set contingency tables given marginals, works marginals strictly positive. Note usual sampling situation assumed chi-squared test (G-test) rather Fisher's exact test. goodness--fit case simulation done random sampling discrete distribution specified p, sample size n = sum(x). simulation done R may slow.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-goodness-of-fit-likelihood-ratio-test-","dir":"Reference","previous_headings":"","what":"G-test Of Goodness-of-Fit (Likelihood Ratio Test)","title":"G-test for Count Data — g.test","text":"Use G-test goodness--fit one nominal variable two values (male female, red, pink white flowers). compare observed counts numbers observations category expected counts, calculate using kind theoretical expectation (1:1 sex ratio 1:2:1 ratio genetic cross). expected number observations category small, G-test may give inaccurate results, use exact test instead (fisher.test()). G-test goodness--fit alternative chi-square test goodness--fit (chisq.test()); tests advantages disadvantages, results two tests usually similar.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-independence","dir":"Reference","previous_headings":"","what":"G-test of Independence","title":"G-test for Count Data — g.test","text":"Use G-test independence two nominal variables, two possible values. want know whether proportions one variable different among values variable. also possible G-test independence two nominal variables. example, Jackson et al. (2013) also data children 3, analysis old vs. young, thigh vs. arm, reaction vs. reaction, analyzed together. Fisher's exact test (fisher.test()) exact test, G-test still approximation. 2x2 table, Fisher's Exact test may slower still run seconds, even sum observations multiple millions. G-test independence alternative chi-square test independence (chisq.test()), give approximately results.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"how-the-test-works","dir":"Reference","previous_headings":"","what":"How the Test Works","title":"G-test for Count Data — g.test","text":"Unlike exact test goodness--fit (fisher.test()), G-test directly calculate probability obtaining observed results something extreme. Instead, like almost statistical tests, G-test intermediate step; uses data calculate test statistic measures far observed data null expectation. use mathematical relationship, case chi-square distribution, estimate probability obtaining value test statistic. G-test uses log ratio two likelihoods test statistic, also called likelihood ratio test log-likelihood ratio test. formula calculate G-statistic : \\(G = 2 * sum(x * log(x / E))\\) E expected values. Since chi-square distributed, p value can calculated R :   df degrees freedom. two categories want find ones significantly different null expectation, can use method testing category vs. sum categories, Bonferroni correction. use G-tests category, course.","code":"p <- stats::pchisq(G, df, lower.tail = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"G-test for Count Data — g.test","text":"McDonald, J.H. 2014. Handbook Biological Statistics (3rd ed.). Sparky House Publishing, Baltimore, Maryland. http://www.biostathandbook.com/gtestgof.html.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"G-test for Count Data — g.test","text":"","code":"# = EXAMPLE 1 = # Shivrain et al. (2006) crossed clearfield rice (which are resistant # to the herbicide imazethapyr) with red rice (which are susceptible to # imazethapyr). They then crossed the hybrid offspring and examined the # F2 generation, where they found 772 resistant plants, 1611 moderately # resistant plants, and 737 susceptible plants. If resistance is controlled # by a single gene with two co-dominant alleles, you would expect a 1:2:1 # ratio.  x <- c(772, 1611, 737) g.test(x, p = c(1, 2, 1) / 4) #>  #> \tG-test of goodness-of-fit (likelihood ratio test) #>  #> data:  x #> X-squared = 4.1471, p-value = 0.1257 #>   # There is no significant difference from a 1:2:1 ratio. # Meaning: resistance controlled by a single gene with two co-dominant # alleles, is plausible.   # = EXAMPLE 2 = # Red crossbills (Loxia curvirostra) have the tip of the upper bill either # right or left of the lower bill, which helps them extract seeds from pine # cones. Some have hypothesized that frequency-dependent selection would # keep the number of right and left-billed birds at a 1:1 ratio. Groth (1992) # observed 1752 right-billed and 1895 left-billed crossbills.  x <- c(1752, 1895) g.test(x) #>  #> \tG-test of goodness-of-fit (likelihood ratio test) #>  #> data:  x #> X-squared = 5.6085, p-value = 0.01787 #>   # There is a significant difference from a 1:1 ratio. # Meaning: there are significantly more left-billed birds."},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Clinical or Epidemic Episodes — get_episode","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"functions determine items vector can considered (start ) new episode. can used determine clinical episodes epidemiological analysis. get_episode() function returns index number episode per group, is_new_episode() function returns TRUE every new get_episode() index. absolute relative episode determination supported.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"","code":"get_episode(x, episode_days = NULL, case_free_days = NULL, ...)  is_new_episode(x, episode_days = NULL, case_free_days = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"x vector dates (class Date POSIXt), sorted internally determine episodes episode_days episode length days specify time period new episode begins, can also less day Inf, see Details case_free_days (inter-epidemic) interval length days new episode start, can also less day Inf, see Details ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"get_episode(): integer vector is_new_episode(): logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"Episodes can determined two ways: absolute relative. Absolute method uses episode_days define episode length days, new episode start. common use case AMR data analysis microbial epidemiology: episodes S. aureus bacteraemia ICU patients example. episode length 30 days, new S. aureus isolates ICU episode 30 days considered different (new) episode. Thus, method counts since start previous episode. Relative method uses case_free_days quantify duration case-free days (inter-epidemic interval), new episode start. common use case infectious disease epidemiology: episodes norovirus outbreaks hospital example. case-free period 14 days, new norovirus cases time considered different (new) episode. Thus, methods counts since last case previous episode. table: ** marks start new episode, 8 January 2023 7 days since start previous episode (1 January 2023).  *** marks start new episode, 21 January 2023 7 days since last case previous episode (8 January 2023). Either episode_days case_free_days must provided function.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"difference-between-get-episode-and-is-new-episode-","dir":"Reference","previous_headings":"","what":"Difference between get_episode() and is_new_episode()","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"get_episode() function returns index number episode, cases/patients/isolates first episode number 1, cases/patients/isolates second episode number 2, etc. is_new_episode() function 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$date, episode_days = 60) # indices #>   [1] 46  5 14 31  1 26 46 34 23 26 16 39 10 16 37 45 43  4 39  7 26 23  6 48 42 #>  [26] 16 48 41 46 41 11  3 16 11  1 14 21 48 27 16 14 24 10 10 12 13 14 46 19 17 #>  [51] 13  2  8 33 23 34 17 25 40  8 42  3 32 36  1 41 49 50 35 20 17 11 42 38 49 #>  [76]  9 26 28  6 22 15  3 41 44 31  3 43 18 50 17 20  1 47 31 43  4 29 27 22 30 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE #>   [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE #>  [13]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE #>  [25]  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE #>  [37]  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE #>  [49]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE #>  [61] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE #>  [73] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE #>  [85] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE 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: 4 × 46 #>   date       patient   age gender ward      mo           PEN   OXA   FLC   AMX   #>   <date>     <chr>   <dbl> <chr>  <chr>     <mo>         <sir> <sir> <sir> <sir> #> 1 2002-08-19 A49852     70 M      Clinical  B_ESCHR_COLI R     NA    NA    NA    #> 2 2002-08-14 785317     51 F      ICU       B_ESCHR_COLI R     NA    NA    NA    #> 3 2002-09-08 B8CB09     60 F      Outpatie… B_STPHY_CONS S     NA    S     NA    #> 4 2002-07-30 218912     76 F      ICU       B_ESCHR_COLI R     NA    NA    NA    #> # … with 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, #> #   FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, #> #   GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, #> #   NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, #> #   TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, #> #   AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, #> #   MUP <sir>, RIF <sir>  # the functions also work for less than a day, e.g. to include one per hour: get_episode(   c(     Sys.time(),     Sys.time() + 60 * 60   ),   episode_days = 1 / 24 ) #> [1] 1 2  # \\donttest{ if (require(\"dplyr\")) {   # is_new_episode() can also be used in dplyr verbs to determine patient   # episodes based on any (combination of) grouping variables:   df %>%     mutate(condition = sample(       x = c(\"A\", \"B\", \"C\"),       size = 100,       replace = TRUE     )) %>%     group_by(patient, condition) %>%     mutate(new_episode = is_new_episode(date, 365)) %>%     select(patient, date, condition, new_episode) %>%     arrange(patient, condition, date) } #> # A tibble: 100 × 4 #> # Groups:   patient, condition [96] #>    patient date       condition new_episode #>    <chr>   <date>     <chr>     <lgl>       #>  1 008218  2009-05-05 C         TRUE        #>  2 036063  2010-01-28 A         TRUE        #>  3 047634  2004-06-28 B         TRUE        #>  4 05C73F  2006-01-12 C         TRUE        #>  5 067927  2002-02-05 A         TRUE        #>  6 071099  2005-01-11 A         TRUE        #>  7 105248  2005-06-16 C         TRUE        #>  8 141061  2014-10-22 A         TRUE        #>  9 1435C8  2004-03-03 B         TRUE        #> 10 195736  2008-08-29 A         TRUE        #> # … with 90 more rows  if (require(\"dplyr\")) {   df %>%     group_by(ward, patient) %>%     transmute(date,       patient,       new_index = get_episode(date, 60),       new_logical = is_new_episode(date, 60)     ) %>%     arrange(patient, ward, date) } #> # A tibble: 100 × 5 #> # Groups:   ward, patient [94] #>    ward     date       patient new_index new_logical #>    <chr>    <date>     <chr>       <int> <lgl>       #>  1 Clinical 2009-05-05 008218          1 TRUE        #>  2 Clinical 2010-01-28 036063          1 TRUE        #>  3 Clinical 2004-06-28 047634          1 TRUE        #>  4 Clinical 2006-01-12 05C73F          1 TRUE        #>  5 ICU      2002-02-05 067927          1 TRUE        #>  6 Clinical 2005-01-11 071099          1 TRUE        #>  7 Clinical 2005-06-16 105248          1 TRUE        #>  8 Clinical 2014-10-22 141061          1 TRUE        #>  9 Clinical 2004-03-03 1435C8          1 TRUE        #> 10 Clinical 2008-08-29 195736          1 TRUE        #> # … with 90 more rows  if (require(\"dplyr\")) {   df %>%     group_by(ward) %>%     summarise(       n_patients = n_distinct(patient),       n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),       n_episodes_60 = sum(is_new_episode(date, episode_days = 60)),       n_episodes_30 = sum(is_new_episode(date, episode_days = 30))     ) } #> # A tibble: 3 × 5 #>   ward       n_patients n_episodes_365 n_episodes_60 n_episodes_30 #>   <chr>           <int>          <int>         <int>         <int> #> 1 Clinical           57             14            38            45 #> 2 ICU                33             10            23            26 #> 3 Outpatient          4              3             4             4  # grouping on patients and microorganisms leads to the same # results as first_isolate() when using 'episode-based': if (require(\"dplyr\")) {   x <- df %>%     filter_first_isolate(       include_unknown = TRUE,       method = \"episode-based\"     )    y <- df %>%     group_by(patient, mo) %>%     filter(is_new_episode(date, 365)) %>%     ungroup()    identical(x, y) } #> [1] TRUE  # but is_new_episode() has a lot more flexibility than first_isolate(), # since you can now group on anything that seems relevant: if (require(\"dplyr\")) {   df %>%     group_by(patient, mo, ward) %>%     mutate(flag_episode = is_new_episode(date, 365)) %>%     select(group_vars(.), flag_episode) } #> # A tibble: 100 × 4 #> # Groups:   patient, mo, ward [98] #>    patient mo            ward       flag_episode #>    <chr>   <mo>          <chr>      <lgl>        #>  1 527928  B_ESCHR_COLI  Clinical   TRUE         #>  2 F35553  B_ENTRBC_CLOC ICU        TRUE         #>  3 671180  B_ESCHR_COLI  Clinical   TRUE         #>  4 B61944  B_STPHY_CONS  ICU        TRUE         #>  5 614772  B_STPHY_EPDR  Clinical   TRUE         #>  6 B13757  B_STPHY_CONS  Outpatient TRUE         #>  7 758698  B_STPHY_HMNS  ICU        TRUE         #>  8 960787  B_BCTRD_FRGL  Clinical   TRUE         #>  9 551943  B_STPHY_AURS  Clinical   TRUE         #> 10 B13757  B_STPHY_EPDR  Outpatient TRUE         #> # … with 90 more rows # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA Biplot with ggplot2 — ggplot_pca","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"Produces ggplot2 variant -called biplot PCA (principal component analysis), flexible appealing base R biplot() function.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"ggplot_pca(   x,   choices = 1:2,   scale = 1,   pc.biplot = TRUE,   labels = NULL,   labels_textsize = 3,   labels_text_placement = 1.5,   groups = NULL,   ellipse = TRUE,   ellipse_prob = 0.68,   ellipse_size = 0.5,   ellipse_alpha = 0.5,   points_size = 2,   points_alpha = 0.25,   arrows = TRUE,   arrows_colour = \"darkblue\",   arrows_size = 0.5,   arrows_textsize = 3,   arrows_textangled = TRUE,   arrows_alpha = 0.75,   base_textsize = 10,   ... )"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"ggplot_pca() function based ggbiplot() function ggbiplot package Vince Vu, found GitHub: https://github.com/vqv/ggbiplot (retrieved: 2 March 2020, latest commit: 7325e88; 12 February 2015). per GPL-2 licence demands documentation code changes, changes made based source code : Rewritten code remove dependency packages plyr, scales grid Parametrised options, like arrow ellipse settings Hardened input possibilities defining exact type user input every argument Added total amount explained variance caption plot Cleaned syntax based lintr package, fixed grammatical errors added integrity checks Updated documentation","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"x object returned pca(), prcomp() princomp() choices length 2 vector specifying components plot. default     biplot strict sense. scale variables scaled lambda ^ scale     observations scaled lambda ^ (1-scale)     lambda singular values computed     princomp. Normally 0 <= scale <= 1, warning     issued specified scale outside range. pc.biplot true, use Gabriel (1971) refers \"principal component     biplot\", lambda = 1 observations scaled sqrt(n)     variables scaled sqrt(n).  inner products     variables approximate covariances distances observations     approximate Mahalanobis distance. labels optional vector labels observations. set, labels placed respective points. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). labels_textsize size text used labels labels_text_placement adjustment factor placement variable names (>=1 means away arrow head) groups optional vector groups labels, length labels. set, points labels coloured according groups. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). ellipse logical indicate whether normal data ellipse drawn group (set groups) ellipse_prob statistical size ellipse normal probability ellipse_size size ellipse line ellipse_alpha alpha (transparency) ellipse line points_size size points points_alpha alpha (transparency) points arrows logical indicate whether arrows drawn arrows_colour colour arrow text arrows_size size (thickness) arrow lines arrows_textsize size text end arrows arrows_textangled logical whether text end arrows angled arrows_alpha alpha (transparency) arrows text base_textsize text size plot elements except labels arrows ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"colours labels points can changed adding another scale layer colour, scale_colour_viridis_d() scale_colour_brewer().","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # \\donttest{ if (require(\"dplyr\")) {   # calculate the resistance per group first   resistance_data <- example_isolates %>%     group_by(       order = mo_order(mo), # group on anything, like order       genus = mo_genus(mo)     ) %>% #   and genus as we do here;     filter(n() >= 30) %>% # filter on only 30 results per group     summarise_if(is.sir, resistance) # then get resistance of all drugs    # now conduct PCA for certain antimicrobial drugs   pca_result <- resistance_data %>%     pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)    summary(pca_result)    # old base R plotting method:   biplot(pca_result)    # new ggplot2 plotting method using this package:   if (require(\"ggplot2\")) {     ggplot_pca(pca_result)      # still extendible with any ggplot2 function     ggplot_pca(pca_result) +       scale_colour_viridis_d() +       labs(title = \"Title here\")   } } #> Warning: There were 73 warnings in `summarise()`. #> The first warning was: #> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, #>   only_all_tested = FALSE) ...`. #> ℹ In group 5: `order = \"Lactobacillales\"`, `genus = \"Enterococcus\"`. #> Caused by warning: #> ! Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #>   \"TMP\", and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\"  #>    # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":null,"dir":"Reference","previous_headings":"","what":"AMR Plots with ggplot2 — ggplot_sir","title":"AMR Plots with ggplot2 — ggplot_sir","text":"Use functions create bar plots AMR data analysis. functions rely ggplot2 functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AMR Plots with ggplot2 — ggplot_sir","text":"","code":"ggplot_sir(   data,   position = NULL,   x = \"antibiotic\",   fill = \"interpretation\",   facet = NULL,   breaks = seq(0, 1, 0.1),   limits = NULL,   translate_ab = \"name\",   combine_SI = TRUE,   minimum = 30,   language = get_AMR_locale(),   nrow = NULL,   colours = c(S = \"#3CAEA3\", SI = \"#3CAEA3\", I = \"#F6D55C\", IR = \"#ED553B\", R =     \"#ED553B\"),   datalabels = TRUE,   datalabels.size = 2.5,   datalabels.colour = \"grey15\",   title = NULL,   subtitle = NULL,   caption = NULL,   x.title = \"Antimicrobial\",   y.title = \"Proportion\",   ... )  geom_sir(   position = NULL,   x = c(\"antibiotic\", \"interpretation\"),   fill = \"interpretation\",   translate_ab = \"name\",   minimum = 30,   language = get_AMR_locale(),   combine_SI = TRUE,   ... )  facet_sir(facet = c(\"interpretation\", \"antibiotic\"), nrow = NULL)  scale_y_percent(breaks = seq(0, 1, 0.1), limits = NULL)  scale_sir_colours(..., aesthetics = \"fill\")  theme_sir()  labels_sir_count(   position = NULL,   x = \"antibiotic\",   translate_ab = \"name\",   minimum = 30,   language = get_AMR_locale(),   combine_SI = TRUE,   datalabels.size = 3,   datalabels.colour = \"grey15\" )"},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AMR Plots with ggplot2 — ggplot_sir","text":"data data.frame column(s) class sir (see .sir()) position position adjustment bars, either \"fill\", \"stack\" \"dodge\" x variable show x axis, either \"antibiotic\" (default) \"interpretation\" grouping variable fill variable categorise using plots legend, either \"antibiotic\" (default) \"interpretation\" grouping variable facet variable split plots , either \"interpretation\" (default) \"antibiotic\" grouping variable breaks numeric vector positions limits numeric vector length two providing limits scale, use NA refer existing minimum maximum translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant) - default TRUE minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. language language returned text - default current system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. nrow (using facet) number rows colours named vactor colour used filling. default colours colour-blind friendly. datalabels show datalabels using labels_sir_count() datalabels.size size datalabels datalabels.colour colour datalabels title text show title plot subtitle text show subtitle plot caption text show caption plot x.title text show x axis description y.title text show y axis description ... arguments passed geom_sir() , case scale_sir_colours(), named values set colours. default colours colour-blind friendly, maintaining convention e.g. 'susceptible' green 'resistant' red. See Examples. aesthetics aesthetics apply colours - default \"fill\" can also (combination ) \"alpha\", \"colour\", \"fill\", \"linetype\", \"shape\" \"size\"","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AMR Plots with ggplot2 — ggplot_sir","text":"default, names antibiotics shown plots using ab_name(). can set translate_ab argument. See count_df().","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"the-functions","dir":"Reference","previous_headings":"","what":"The Functions","title":"AMR Plots with ggplot2 — ggplot_sir","text":"geom_sir() take variable data sir class (created .sir()) using sir_df() plot bars percentage S, , R. default behaviour bars stacked different antibiotics x axis. facet_sir() creates 2d plots (default based S//R) using ggplot2::facet_wrap(). scale_y_percent() transforms y axis 0 100% range using ggplot2::scale_y_continuous(). scale_sir_colours() sets colours bars (green S, yellow , red R). multilingual support. default colours colour-blind friendly, maintaining convention e.g. 'susceptible' green 'resistant' red. theme_sir() [ggplot2 theme][ggplot2::theme() minimal distraction. labels_sir_count() print datalabels bars percentage amount isolates using ggplot2::geom_text(). ggplot_sir() wrapper around functions uses data first input. makes possible use function pipe (%>%). See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AMR Plots with ggplot2 — ggplot_sir","text":"","code":"# \\donttest{ if (require(\"ggplot2\") && require(\"dplyr\")) {   # get antimicrobial results for drugs against a UTI:   ggplot(example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)) +     geom_sir() }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # prettify the plot using some additional functions:   df <- example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)   ggplot(df) +     geom_sir() +     scale_y_percent() +     scale_sir_colours() +     labels_sir_count() +     theme_sir() }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # or better yet, simplify this using the wrapper function - a single command:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir() }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # get only proportions and no counts:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir(datalabels = FALSE) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # add other ggplot2 arguments as you like:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir(       width = 0.5,       colour = \"black\",       size = 1,       linetype = 2,       alpha = 0.25     ) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # you can alter the colours with colour names:   example_isolates %>%     select(AMX) %>%     ggplot_sir(colours = c(SI = \"yellow\")) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # but you can also use the built-in colour-blind friendly colours for   # your plots, where \"S\" is green, \"I\" is yellow and \"R\" is red:   data.frame(     x = c(\"Value1\", \"Value2\", \"Value3\"),     y = c(1, 2, 3),     z = c(\"Value4\", \"Value5\", \"Value6\")   ) %>%     ggplot() +     geom_col(aes(x = x, y = y, fill = z)) +     scale_sir_colours(Value4 = \"S\", Value5 = \"I\", Value6 = \"R\") }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # resistance of ciprofloxacine per age group   example_isolates %>%     mutate(first_isolate = first_isolate()) %>%     filter(       first_isolate == TRUE,       mo == as.mo(\"Escherichia coli\")     ) %>%     # age_groups() is also a function in this AMR package:     group_by(age_group = age_groups(age)) %>%     select(age_group, CIP) %>%     ggplot_sir(x = \"age_group\") } #> Warning: Removed 6 rows containing missing values (`position_stack()`). #> Warning: Removed 6 rows containing missing values (`position_stack()`).  if (require(\"ggplot2\") && require(\"dplyr\")) {   # a shorter version which also adjusts data label colours:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir(colours = FALSE) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # it also supports groups (don't forget to use the group var on `x` or `facet`):   example_isolates %>%     filter(mo_is_gram_negative(), ward != \"Outpatient\") %>%     # select only UTI-specific drugs     select(ward, AMX, NIT, FOS, TMP, CIP) %>%     group_by(ward) %>%     ggplot_sir(       x = \"ward\",       facet = \"antibiotic\",       nrow = 1,       title = \"AMR of Anti-UTI Drugs Per Ward\",       x.title = \"Ward\",       datalabels = FALSE     ) } #> ℹ Using column 'mo' as input for mo_is_gram_negative()  # }"},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":null,"dir":"Reference","previous_headings":"","what":"Guess Antibiotic Column — guess_ab_col","title":"Guess Antibiotic Column — guess_ab_col","text":"tries find column name data set based information antibiotics data set. Also supports WHONET abbreviations.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Guess Antibiotic Column — guess_ab_col","text":"","code":"guess_ab_col(   x = NULL,   search_string = NULL,   verbose = FALSE,   only_sir_columns = FALSE )"},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Guess Antibiotic Column — guess_ab_col","text":"x data.frame search_string text search x , checked .ab() value column x verbose logical indicate whether additional info printed only_sir_columns logical indicate whether antibiotic columns must detected transformed class sir (see .sir()) beforehand (default FALSE)","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Guess Antibiotic Column — guess_ab_col","text":"column name x, NULL result found.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Guess Antibiotic Column — guess_ab_col","text":"can look antibiotic (trade) name abbreviation search x antibiotics data set column containing name code antibiotic.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Guess Antibiotic Column — guess_ab_col","text":"","code":"df <- data.frame(   amox = \"S\",   tetr = \"R\" )  guess_ab_col(df, \"amoxicillin\") #> [1] \"amox\" guess_ab_col(df, \"J01AA07\") # ATC code of tetracycline #> [1] \"tetr\"  guess_ab_col(df, \"J01AA07\", verbose = TRUE) #> Auto-guessing columns suitable for analysis #> ... #>  OK. #> ℹ Using column 'amox' as input for AMX (amoxicillin). #> ℹ Using column 'tetr' as input for TCY (tetracycline). #> ℹ Using column 'tetr' as input for J01AA07 (tetracycline). #> [1] \"tetr\" # NOTE: Using column 'tetr' as input for J01AA07 (tetracycline).  # WHONET codes df <- data.frame(   AMP_ND10 = \"R\",   AMC_ED20 = \"S\" ) guess_ab_col(df, \"ampicillin\") #> [1] \"AMP_ND10\" guess_ab_col(df, \"J01CR02\") #> [1] \"AMC_ED20\" guess_ab_col(df, as.ab(\"augmentin\")) #> [1] \"AMC_ED20\""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"Data set containing defined intrinsic resistance EUCAST bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"intrinsic_resistant"},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"tibble 134 634 observations 2 variables: mo Microorganism ID ab Antibiotic ID","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"data set based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository. allow machine reading EUCAST CLSI guidelines, almost impossible MS Excel PDF files distributed EUCAST CLSI.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"intrinsic_resistant #> # A tibble: 134,634 × 2 #>    mo      ab    #>    <mo>    <chr> #>  1 B_GRAMP ATM   #>  2 B_GRAMP COL   #>  3 B_GRAMP NAL   #>  4 B_GRAMP PLB   #>  5 B_GRAMP TEM   #>  6 B_ABTRP ATM   #>  7 B_ABTRP COL   #>  8 B_ABTRP NAL   #>  9 B_ABTRP PLB   #> 10 B_ABTRP TEM   #> # … with 134,624 more rows"},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":null,"dir":"Reference","previous_headings":"","what":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"According binomial nomenclature, lowest four taxonomic levels (family, genus, species, subspecies) printed italics. function finds taxonomic names within strings makes italic.","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"","code":"italicise_taxonomy(string, type = c(\"markdown\", \"ansi\"))  italicize_taxonomy(string, type = c(\"markdown\", \"ansi\"))"},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"string character (vector) type type conversion taxonomic names, either \"markdown\" \"ansi\", see Details","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"function finds taxonomic names makes italic based microorganisms data set. taxonomic names can italicised using markdown (default) adding * taxonomic names, using ANSI colours adding \\033[3m \\033[23m taxonomic names. multiple ANSI colours available, conversion occur. function also supports abbreviation genus followed species, \"E. coli\" \"K. pneumoniae ozaenae\".","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"","code":"italicise_taxonomy(\"An overview of Staphylococcus aureus isolates\") #> [1] \"An overview of *Staphylococcus aureus* isolates\" italicise_taxonomy(\"An overview of S. aureus isolates\") #> [1] \"An overview of *S. aureus* isolates\"  cat(italicise_taxonomy(\"An overview of S. aureus isolates\", type = \"ansi\")) #> An overview of S. aureus isolates"},{"path":"https://msberends.github.io/AMR/reference/join.html","id":null,"dir":"Reference","previous_headings":"","what":"Join microorganisms to a Data Set — join","title":"Join microorganisms to a Data Set — join","text":"Join data set microorganisms easily existing data set character vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join microorganisms to a Data Set — join","text":"","code":"inner_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  left_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  right_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  full_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  semi_join_microorganisms(x, by = NULL, ...)  anti_join_microorganisms(x, by = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join microorganisms to a Data Set — join","text":"x existing data set join, character vector. case character vector, resulting data.frame contain column 'x' values. variable join - left empty search column class mo (created .mo()) \"mo\" column name exists x, otherwise column name x values exist microorganisms$mo (= \"bacteria_id\"), another column microorganisms (named, like = c(\"bacteria_id\" = \"fullname\")) suffix non-joined duplicate variables x y, suffixes added output disambiguate . character vector length 2. ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join microorganisms to a Data Set — join","text":"data.frame","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Join microorganisms to a Data Set — join","text":"Note: opposed join() functions dplyr, character vectors supported default existing columns get suffix \"2\" newly joined columns get suffix. dplyr package installed, join functions used. Otherwise, much slower merge() interaction() functions base R used.","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Join microorganisms to a Data Set — join","text":"","code":"left_join_microorganisms(as.mo(\"K. pneumoniae\")) #> # A tibble: 1 × 22 #>   mo           fullname   status kingdom phylum class order family genus species #>   <mo>         <chr>      <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> <chr>   #> 1 B_KLBSL_PNMN Klebsiell… accep… Bacter… Pseud… Gamm… Ente… Enter… Kleb… pneumo… #> # … with 12 more variables: subspecies <chr>, rank <chr>, ref <chr>, #> #   source <chr>, lpsn <chr>, lpsn_parent <chr>, lpsn_renamed_to <chr>, #> #   gbif <chr>, gbif_parent <chr>, gbif_renamed_to <chr>, prevalence <dbl>, #> #   snomed <list> left_join_microorganisms(\"B_KLBSL_PNMN\") #> # A tibble: 1 × 22 #>   mo           fullname   status kingdom phylum class order family genus species #>   <mo>         <chr>      <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> <chr>   #> 1 B_KLBSL_PNMN Klebsiell… accep… Bacter… Pseud… Gamm… Ente… Enter… Kleb… pneumo… #> # … with 12 more variables: subspecies <chr>, rank <chr>, ref <chr>, #> #   source <chr>, lpsn <chr>, lpsn_parent <chr>, lpsn_renamed_to <chr>, #> #   gbif <chr>, gbif_parent <chr>, gbif_renamed_to <chr>, prevalence <dbl>, #> #   snomed <list>  df <- data.frame(   date = seq(     from = as.Date(\"2018-01-01\"),     to = as.Date(\"2018-01-07\"),     by = 1   ),   bacteria = as.mo(c(     \"S. aureus\", \"MRSA\", \"MSSA\", \"STAAUR\",     \"E. coli\", \"E. coli\", \"E. coli\"   )),   stringsAsFactors = FALSE ) colnames(df) #> [1] \"date\"     \"bacteria\"  df_joined <- left_join_microorganisms(df, \"bacteria\") colnames(df_joined) #>  [1] \"date\"            \"bacteria\"        \"fullname\"        \"status\"          #>  [5] \"kingdom\"         \"phylum\"          \"class\"           \"order\"           #>  [9] \"family\"          \"genus\"           \"species\"         \"subspecies\"      #> [13] \"rank\"            \"ref\"             \"source\"          \"lpsn\"            #> [17] \"lpsn_parent\"     \"lpsn_renamed_to\" \"gbif\"            \"gbif_parent\"     #> [21] \"gbif_renamed_to\" \"prevalence\"      \"snomed\"           # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     left_join_microorganisms() %>%     colnames() } #> Joining, by = \"mo\" #>  [1] \"date\"            \"patient\"         \"age\"             \"gender\"          #>  [5] \"ward\"            \"mo\"              \"PEN\"             \"OXA\"             #>  [9] \"FLC\"             \"AMX\"             \"AMC\"             \"AMP\"             #> [13] \"TZP\"             \"CZO\"             \"FEP\"             \"CXM\"             #> [17] \"FOX\"             \"CTX\"             \"CAZ\"             \"CRO\"             #> [21] \"GEN\"             \"TOB\"             \"AMK\"             \"KAN\"             #> [25] \"TMP\"             \"SXT\"             \"NIT\"             \"FOS\"             #> [29] \"LNZ\"             \"CIP\"             \"MFX\"             \"VAN\"             #> [33] \"TEC\"             \"TCY\"             \"TGC\"             \"DOX\"             #> [37] \"ERY\"             \"CLI\"             \"AZM\"             \"IPM\"             #> [41] \"MEM\"             \"MTR\"             \"CHL\"             \"COL\"             #> [45] \"MUP\"             \"RIF\"             \"fullname\"        \"status\"          #> [49] \"kingdom\"         \"phylum\"          \"class\"           \"order\"           #> [53] \"family\"          \"genus\"           \"species\"         \"subspecies\"      #> [57] \"rank\"            \"ref\"             \"source\"          \"lpsn\"            #> [61] \"lpsn_parent\"     \"lpsn_renamed_to\" \"gbif\"            \"gbif_parent\"     #> [65] \"gbif_renamed_to\" \"prevalence\"      \"snomed\"          # }"},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"functions can used determine first weighted isolates considering phenotype isolate selection (see first_isolate()). Using phenotype-based method determine first isolates reliable methods disregard phenotypes.","code":""},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"","code":"key_antimicrobials(   x = NULL,   col_mo = NULL,   universal = c(\"ampicillin\", \"amoxicillin/clavulanic acid\", \"cefuroxime\",     \"piperacillin/tazobactam\", \"ciprofloxacin\", \"trimethoprim/sulfamethoxazole\"),   gram_negative = c(\"gentamicin\", \"tobramycin\", \"colistin\", \"cefotaxime\", \"ceftazidime\",     \"meropenem\"),   gram_positive = c(\"vancomycin\", \"teicoplanin\", \"tetracycline\", \"erythromycin\",     \"oxacillin\", \"rifampin\"),   antifungal = c(\"anidulafungin\", \"caspofungin\", \"fluconazole\", \"miconazole\", \"nystatin\",     \"voriconazole\"),   only_sir_columns = FALSE,   ... )  all_antimicrobials(x = NULL, only_sir_columns = FALSE, ...)  antimicrobials_equal(   y,   z,   type = c(\"points\", \"keyantimicrobials\"),   ignore_I = TRUE,   points_threshold = 2,   ... )"},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"x data.frame antibiotics columns, like AMX amox. Can left blank determine automatically col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). universal names broad-spectrum antimicrobial drugs, case-insensitive. Set NULL ignore. See Details default antimicrobial drugs gram_negative names antibiotic drugs Gram-positives, case-insensitive. Set NULL ignore. See Details default antibiotic drugs gram_positive names antibiotic drugs Gram-negatives, case-insensitive. Set NULL ignore. See Details default antibiotic drugs antifungal names antifungal drugs fungi, case-insensitive. Set NULL ignore. See Details default antifungal drugs only_sir_columns logical indicate whether columns must included transformed class sir (see .sir()) beforehand (default FALSE) ... ignored, place allow future extensions y, z character vectors compare type type determine weighed isolates; can \"keyantimicrobials\" \"points\", see Details ignore_I logical indicate whether antibiotic interpretations \"\" ignored type = \"keyantimicrobials\", see Details points_threshold minimum number points require differences antibiogram lead inclusion isolate type = \"points\", see Details","code":""},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"key_antimicrobials() all_antimicrobials() functions context-aware. means x argument can left blank used inside data.frame call, see Examples. function key_antimicrobials() returns character vector 12 antimicrobial results every isolate. function all_antimicrobials() returns character vector antimicrobial drug results every isolate. vectors can compared using antimicrobials_equal(), check two isolates generally antibiogram. Missing invalid values replaced dot (\".\") key_antimicrobials() ignored antimicrobials_equal(). Please see first_isolate() function important functions enable 'phenotype-based' method determination first isolates. default antimicrobial drugs used rows (set universal) : Ampicillin Amoxicillin/clavulanic acid Cefuroxime Ciprofloxacin Piperacillin/tazobactam Trimethoprim/sulfamethoxazole default antimicrobial drugs used Gram-negative bacteria (set gram_negative) : Cefotaxime Ceftazidime Colistin Gentamicin Meropenem Tobramycin default antimicrobial drugs used Gram-positive bacteria (set gram_positive) : Erythromycin Oxacillin Rifampin Teicoplanin Tetracycline Vancomycin default antimicrobial drugs used fungi (set antifungal) : Anidulafungin Caspofungin Fluconazole Miconazole Nystatin Voriconazole","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # output of the `key_antimicrobials()` function could be like this: strainA <- \"SSSRR.S.R..S\" strainB <- \"SSSIRSSSRSSS\"  # those strings can be compared with: antimicrobials_equal(strainA, strainB, type = \"keyantimicrobials\") #> [1] TRUE # TRUE, because I is ignored (as well as missing values)  antimicrobials_equal(strainA, strainB, type = \"keyantimicrobials\", ignore_I = FALSE) #> [1] FALSE # FALSE, because I is not ignored and so the 4th [character] differs  # \\donttest{ if (require(\"dplyr\")) {   # set key antibiotics to a new variable   my_patients <- example_isolates %>%     mutate(keyab = key_antimicrobials(antifungal = NULL)) %>% # no need to define `x`     mutate(       # now calculate first isolates       first_regular = first_isolate(col_keyantimicrobials = FALSE),       # and first WEIGHTED isolates       first_weighted = first_isolate(col_keyantimicrobials = \"keyab\")     )    # Check the difference in this data set, 'weighted' results in more isolates:   sum(my_patients$first_regular, na.rm = TRUE)   sum(my_patients$first_weighted, na.rm = TRUE) } #> [1] 1395 # }"},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":null,"dir":"Reference","previous_headings":"","what":"Kurtosis of the Sample — kurtosis","title":"Kurtosis of the Sample — kurtosis","text":"Kurtosis measure \"tailedness\" probability distribution real-valued random variable. normal distribution kurtosis 3 excess kurtosis 0.","code":""},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kurtosis of the Sample — kurtosis","text":"","code":"kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for default kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for matrix kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for data.frame kurtosis(x, na.rm = FALSE, excess = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kurtosis of the Sample — kurtosis","text":"x vector values, matrix data.frame na.rm logical indicate whether NA values stripped computation proceeds excess logical indicate whether excess kurtosis returned, defined kurtosis minus 3.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kurtosis of the Sample — kurtosis","text":"","code":"kurtosis(rnorm(10000)) #> [1] 2.980382 kurtosis(rnorm(10000), excess = TRUE) #> [1] 0.03954956"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":null,"dir":"Reference","previous_headings":"","what":"Vectorised Pattern Matching with Keyboard Shortcut — like","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Convenient wrapper around grepl() match pattern: x %like% pattern. always returns logical vector always case-insensitive (use x %like_case% pattern case-sensitive matching). Also, pattern can long x compare items index vectors, can length iterate cases.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"like(x, pattern, ignore.case = TRUE)  x %like% pattern  x %unlike% pattern  x %like_case% pattern  x %unlike_case% pattern"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Idea like function data.table package, although altered explained Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"x character vector matches sought, object can coerced .character() character vector. pattern character vector containing regular expressions (character string fixed = TRUE) matched given character vector. Coerced .character() character string possible. ignore.case FALSE, pattern matching case sensitive TRUE, case ignored matching.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"like() %like%/%unlike% functions: case-insensitive (use %like_case%/%unlike_case% case-sensitive matching) Support multiple patterns Check pattern valid regular expression sets fixed = TRUE , greatly improve speed (vectorised pattern) Always use compatibility Perl unless fixed = TRUE, greatly improve speed Using RStudio? %like%/%unlike% functions can also directly inserted code Addins menu can keyboard shortcut like Shift+Ctrl+L Shift+Cmd+L (see menu Tools > Modify Keyboard Shortcuts...). keep pressing shortcut, inserted text iterated %like% -> %unlike% -> %like_case% -> %unlike_case%.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"a <- \"This is a test\" b <- \"TEST\" a %like% b #> [1] FALSE 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 #> Warning: argument 'pattern' has length > 1 and only the first element will be used #> [1]  TRUE FALSE FALSE a %unlike% b #> [1] FALSE FALSE FALSE  a[1] %like% b #> Warning: argument 'pattern' has length > 1 and only the first element will be used #> [1] TRUE 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: 0 × 46 #> # … with 46 variables: date <date>, patient <chr>, age <dbl>, gender <chr>, #> #   ward <chr>, mo <mo>, PEN <sir>, OXA <sir>, FLC <sir>, AMX <sir>, 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>, …  if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_name() %like% \"^ent\") } #> ℹ Using column 'mo' as input for mo_name() #> # A tibble: 0 × 46 #> # … with 46 variables: date <date>, patient <chr>, age <dbl>, gender <chr>, #> #   ward <chr>, mo <mo>, PEN <sir>, OXA <sir>, FLC <sir>, AMX <sir>, 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>, … # }"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Determine isolates multidrug-resistant organisms (MDRO) according international, national custom guidelines.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"mdro(   x = NULL,   guideline = \"CMI2012\",   col_mo = NULL,   info = interactive(),   pct_required_classes = 0.5,   combine_SI = TRUE,   verbose = FALSE,   only_sir_columns = FALSE,   ... )  custom_mdro_guideline(..., as_factor = TRUE)  brmo(x = NULL, only_sir_columns = FALSE, ...)  mrgn(x = NULL, only_sir_columns = FALSE, ...)  mdr_tb(x = NULL, only_sir_columns = FALSE, ...)  mdr_cmi2012(x = NULL, only_sir_columns = FALSE, ...)  eucast_exceptional_phenotypes(x = NULL, only_sir_columns = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"See supported guidelines list publications used function.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"x data.frame antibiotics columns, like AMX amox. Can left blank automatic determination. guideline specific guideline follow, see sections Supported international / national guidelines Using Custom Guidelines . left empty, publication Magiorakos et al. (see ) followed. col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console - default print interactive sessions pct_required_classes minimal required percentage antimicrobial classes must available per isolate, rounded . example, default guideline, 17 antimicrobial classes must available S. aureus. Setting pct_required_classes argument 0.5 (default) means every S. aureus isolate least 8 different classes must available. lower number available classes return NA isolate. combine_SI logical indicate whether values S must merged one, resistance considered isolates R, . default behaviour mdro() function, follows redefinition EUCAST interpretation (increased exposure) 2019, see section 'Interpretation S, R' . using combine_SI = FALSE, resistance considered isolates R . verbose logical turn Verbose mode (default ). Verbose mode, function return MDRO results, instead returns data set logbook form extensive info isolates MDRO-positive, . only_sir_columns logical indicate whether antibiotic columns must detected transformed class sir (see .sir()) beforehand (default FALSE) ... case custom_mdro_guideline(): set rules, see section Using Custom Guidelines . Otherwise: column name antibiotic, see section Antibiotics . as_factor logical indicate whether returned value ordered factor (TRUE, default), otherwise character vector","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"CMI 2012 paper - function mdr_cmi2012() mdro(): Ordered factor levels Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant (XDR) < Pandrug-resistant (PDR) TB guideline - function mdr_tb() mdro(..., guideline = \"TB\"): Ordered factor levels Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant < Extensively drug-resistant German guideline - function mrgn() mdro(..., guideline = \"MRGN\"): Ordered factor levels Negative < 3MRGN < 4MRGN Everything else, except custom guidelines: Ordered factor levels Negative < Positive, unconfirmed < Positive. value \"Positive, unconfirmed\" means , according guideline, entirely sure isolate multi-drug resistant confirmed additional (e.g. molecular) tests","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"functions context-aware. means x argument can left blank used inside data.frame call, see Examples. pct_required_classes argument, values 1 divided 100. support fractions (0.75 3/4) percentages (75). Note: Every test involves Enterobacteriaceae family, internally performed using newly named order Enterobacterales, since Enterobacteriaceae family taxonomically reclassified Adeolu et al. 2016. , Enterobacteriaceae family Enterobacteriales () order. species old Enterobacteriaceae family still new Enterobacterales (without ) order, divided multiple families. way tests performed now mdro() function makes sure results 2016 2016 identical.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"supported-international-national-guidelines","dir":"Reference","previous_headings":"","what":"Supported International / National Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. \"Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.\" Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.3\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.3 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.2\" European international guideline - EUCAST Expert Rules Version 3.2 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 \"Intrinsic Resistance Exceptional Phenotypes Tables\" (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization \"Companion handbook guidelines programmatic management drug-resistant tuberculosis\" (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7; doi:10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu \"WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)\" (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"using-custom-guidelines","dir":"Reference","previous_headings":"","what":"Using Custom Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde:   row/isolate matches first rule, value first ~ (case 'Elderly Type ') set MDRO value. Otherwise, second rule tried . number rules unlimited. can print rules set console overview. Colours help reading console supports colours.   outcome function can used guideline argument mdro() function:   Rules can also combined custom rules using c():   rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline(CIP == \"R\" & age > 60 ~ \"Elderly Type A\",                                 ERY == \"R\" & age > 60 ~ \"Elderly Type B\") custom #> A set of custom MDRO rules: #>   1. CIP is \"R\" and age is higher than 60 -> Elderly Type A #>   2. ERY is \"R\" and age is higher than 60 -> Elderly Type B #>   3. Otherwise -> Negative #> #> Unmatched rows will return NA. x <- mdro(example_isolates,           guideline = custom) table(x) #>       Negative Elderly Type A Elderly Type B #>           1070            198            732 x <- mdro(example_isolates,           guideline = c(custom,                         custom_mdro_guideline(ERY == \"R\" & age > 50 ~ \"Elderly Type C\"))) table(x) #>       Negative Elderly Type A Elderly Type B Elderly Type C #>            961            198            732            109"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"antibiotics","dir":"Reference","previous_headings":"","what":"Antibiotics","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"define antibiotics column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antibiotics eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, S01AE08), amoxicillin (AMX, J01MA02), amoxicillin/clavulanic acid (AMC, J01MA23), ampicillin (AMP, J01MA04), ampicillin/sulbactam (SAM, J01MA08), arbekacin (ARB, J01MA19), aspoxicillin (APX, J01MA16), azidocillin (AZD, J01MA15), azithromycin (AZM, J01MA11), azlocillin (AZL, J01MA25), aztreonam (ATM, J01MA12), bacampicillin (BAM, J01MA24), bekanamycin (BEK, J01MA07), benzathine benzylpenicillin (BNB, J01MA14), benzathine phenoxymethylpenicillin (BNP, D10AF05), benzylpenicillin (PEN, J01MA06), besifloxacin (BES, J01MA01), biapenem (BIA, J01MA18), carbenicillin (CRB, J01MA03), carindacillin (CRN, J01MA17), cefacetrile (CAC, J01MA10), cefaclor (CEC, J01MA21), cefadroxil (CFR, J01MA09), cefalexin (LEX, J01MA05), cefaloridine (RID, P01AA05), cefalotin (CEP, J01MA22), cefamandole (MAN, J01MA13), cefapirin (HAP, J01CA01), cefatrizine (CTZ, J01CA04), cefazedone (CZD, J01CA12), cefazolin (CZO, J01CR05), cefcapene (CCP, J01CA13), cefdinir (CDR, J01AA02), cefditoren (DIT, J01FA10), cefepime (FEP, J01FA09), cefetamet (CAT, J01CR02), cefixime (CFM, J01AA08), cefmenoxime (CMX, J01FA06), cefmetazole (CMZ, J01CF04), cefodizime (DIZ, J01CF05), cefonicid (CID, J01CR01), cefoperazone (CFP, J01CA19), cefoperazone/sulbactam (CSL, J01CE04), ceforanide (CND, J01CA09), cefotaxime (CTX, J01DF01), cefotaxime/clavulanic acid (CTC, J01CA06), cefotetan (CTT, J01CE08), cefotiam (CTF, J01CE10), cefoxitin (FOX, J01CE01), cefozopran (ZOP, J01CA03), cefpiramide (CPM, J01CA05), cefpirome (CPO, J01CE07), cefpodoxime (CPD, J01CF02), cefprozil (CPR, J01CF01), cefroxadine (CRD, J01CA07), cefsulodin (CFS, J01CA18), ceftaroline (CPT, J01CA11), ceftazidime (CAZ, J01CA14), ceftazidime/clavulanic acid (CCV, J01CF03), cefteram (CEM, J01CA10), ceftezole (CTL, J01CF06), ceftibuten (CTB, J01CE06), ceftizoxime (CZX, J01CE05), ceftobiprole medocaril (CFM1, J01CE02), ceftolozane/tazobactam (CZT, J01CA02), ceftriaxone (CRO, J01CA08), ceftriaxone/beta-lactamase inhibitor (CEB, J01CE09), cefuroxime (CXM, J01CE03), cephradine (CED, J01CG01), chloramphenicol (CHL, J01CA16), ciprofloxacin (CIP, J01CR04), clarithromycin (CLR, J01CA15), clindamycin (CLI, J01CG02), clometocillin (CLM, J01CA17), cloxacillin (CLO, J01CR03), colistin (COL, J01DB10), cycloserine (CYC, J01DC04), dalbavancin (DAL, J01DB05), daptomycin (DAP, J01DB01), delafloxacin (DFX, J01DB02), dibekacin (DKB, J01DB03), dicloxacillin (DIC, J01DC03), dirithromycin (DIR, J01DB08), doripenem (DOR, J01DB07), doxycycline (DOX, J01DB06), enoxacin (ENX, J01DB04), epicillin (EPC, J01DD17), ertapenem (ETP, J01DD15), erythromycin (ERY, J01DD16), fleroxacin (FLE, J01DE01), flucloxacillin (FLC, J01DD10), flurithromycin (FLR1, J01DD08), fosfomycin (FOS, J01DD05), framycetin (FRM, J01DC09), fusidic acid (FUS, J01DD09), garenoxacin (GRN, J01DC06), gatifloxacin (GAT, J01DD12), gemifloxacin (GEM, J01DD62), gentamicin (GEN, J01DC11), grepafloxacin (GRX, J01DD01), hetacillin (HET, J01DD51), imipenem (IPM, J01DC05), imipenem/relebactam (IMR, J01DC07), isepamicin (ISE, J01DC01), josamycin (JOS, J01DE03), kanamycin (KAN, J01DD11), lascufloxacin (LSC, J01DE02), latamoxef (LTM, J01DD13), levofloxacin (LVX, J01DC10), levonadifloxacin (LND, J01DB11), lincomycin (LIN, J01DD03), linezolid (LNZ, J01DI02), lomefloxacin (LOM, J01DD02), loracarbef (LOR, J01DD52), mecillinam (MEC, J01DD18), meropenem (MEM, J01DB12), meropenem/vaborbactam (MEV, J01DD14), metampicillin (MTM, J01DD07), meticillin (MET, J01DI01), mezlocillin (MEZ, J01DI54), micronomicin (MCR, J01DD04), midecamycin (MID, J01DD63), minocycline (MNO, J01DC02), miocamycin (MCM, J01DB09), moxifloxacin (MFX, J01DD06), nadifloxacin (NAD, J01DC08), nafcillin (NAF, J01DH05), nalidixic acid (NAL, J01DH04), neomycin (NEO, J01DH03), netilmicin (NET, J01DH51), nitrofurantoin (NIT, J01DH56), norfloxacin (, J01DH02), ofloxacin (OFX, J01DH52), oleandomycin (OLE, J01DH55), oritavancin (ORI, J01DH06), oxacillin (OXA, J01XA02), panipenem (PAN, J01XA01), pazufloxacin (PAZ, J01XC01), pefloxacin (PEF, J01FA13), penamecillin (PNM, J01FA01), pheneticillin (PHE, J01FA14), phenoxymethylpenicillin (PHN, J01FA07), piperacillin (PIP, J01FA03), piperacillin/tazobactam (TZP, J01FA11), pivampicillin (PVM, J01FA05), pivmecillinam (PME, J01FA12), plazomicin (PLZ, J01FA16), polymyxin B (PLB, J01FA02), pristinamycin (PRI, J01FA15), procaine benzylpenicillin (PRB, J01FA08), propicillin (PRP, J01FF02), prulifloxacin (PRU, J01FG01), quinupristin/dalfopristin (QDA, J01FG02), ribostamycin (RST, J04AB02), rifampicin (RIF, J01XX09), rokitamycin (ROK, J01XX08), roxithromycin (RXT, J01AA07), rufloxacin (RFL, J01XB01), sisomicin (SIS, J01XB02), sitafloxacin (SIT, J01XE01), solithromycin (SOL, J01AA12), sparfloxacin (SPX, J01EA01), spiramycin (SPI, J01XX01), streptoduocin (STR, J01BA01), streptomycin (STR1, J01GB06), sulbactam (SUL, J01GB12), sulbenicillin (SBC, J01GB13), sulfadiazine (SDI, J01GB09), sulfadiazine/trimethoprim (SLT1, D09AA01), sulfadimethoxine (SUD, J01GB03), sulfadimidine (SDM, J01GB11), sulfadimidine/trimethoprim (SLT2, J01GB04), sulfafurazole (SLF, S01AA22), sulfaisodimidine (SLF1, J01GB05), sulfalene (SLF2, J01GB07), sulfamazone (SZO, J01GB14), sulfamerazine (SLF3, J01GB10), sulfamerazine/trimethoprim (SLT3, J01GB08), sulfamethizole (SLF4, J01GA02), sulfamethoxazole (SMX, J01GA01), sulfamethoxypyridazine (SLF5, J01GB01), sulfametomidine (SLF6, J01EE01), sulfametoxydiazine (SLF7, J01MB02), sulfametrole/trimethoprim (SLT4, J01FF01), sulfamoxole (SLF8, J01XA04), sulfamoxole/trimethoprim (SLT5, J01XA05), sulfanilamide (SLF9, J01XA03), sulfaperin (SLF10, J04AB01), sulfaphenazole (SLF11, J01XX11), sulfapyridine (SLF12, J01EC02), sulfathiazole (SUT, J01ED01), sulfathiourea (SLF13, J01EB03), sultamicillin (SLT6, J01EB05), talampicillin (TAL, J01EB01), tazobactam (TAZ, J01ED02), tebipenem (TBP, J01ED09), tedizolid (TZD, J01ED07), teicoplanin (TEC, J01EB02), telavancin (TLV, J01EC01), telithromycin (TLT, J01ED05), temafloxacin (TMX, J01ED03), temocillin (TEM, J01ED04), tetracycline (TCY, J01EC03), ticarcillin (TIC, J01EB06), ticarcillin/clavulanic acid (TCC, J01ED06), tigecycline (TGC, J01ED08), tilbroquinol (TBQ, J01EB04), tobramycin (TOB, J01EB07), tosufloxacin (TFX, J01EB08), trimethoprim (TMP, J01EE02), trimethoprim/sulfamethoxazole (SXT, J01EE05), troleandomycin (TRL, J01EE07), trovafloxacin (TVA, J01EE03), vancomycin (VAN, J01EE04)","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"out <- mdro(example_isolates, guideline = \"EUCAST\") #>  (16 isolates had no test results) str(out) #>  Ord.factor w/ 3 levels \"Negative\"<\"Positive, unconfirmed\"<..: 1 1 1 1 1 1 1 1 1 1 ... table(out) #> out #>              Negative Positive, unconfirmed              Positive  #>                  1978                     0                     6   out <- mdro(example_isolates,   guideline = custom_mdro_guideline(     AMX == \"R\" ~ \"Custom MDRO 1\",     VAN == \"R\" ~ \"Custom MDRO 2\"   ) ) table(out) #> out #>      Negative Custom MDRO 1 Custom MDRO 2  #>           870           804           326   # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     mdro() %>%     table()    # no need to define `x` when used inside dplyr verbs:   example_isolates %>%     mutate(MDRO = mdro()) %>%     pull(MDRO) %>%     table() } #>  (16 isolates had no test results) #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #>  (16 isolates had no test results) #> Warning: There was 1 warning in `mutate()`. #> ℹ In argument: `MDRO = mdro()`. #> Caused by warning: #> ! in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> . #>                         Negative       Multi-drug-resistant (MDR)  #>                             1601                              128  #> Extensively drug-resistant (XDR)          Pandrug-resistant (PDR)  #>                                0                                0  # }"},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the Mean AMR Distance — mean_amr_distance","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"Calculates normalised mean antimicrobial resistance multiple observations, help identify similar isolates without comparing antibiograms hand.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"","code":"mean_amr_distance(x, ...)  # S3 method for sir mean_amr_distance(x, ..., combine_SI = TRUE)  # S3 method for data.frame mean_amr_distance(x, ..., combine_SI = TRUE)  amr_distance_from_row(amr_distance, row)"},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"x vector class sir, mic disk, data.frame containing columns classes ... variables select (supports tidyselect language column1:column4 (.mic), can thus also antibiotic selectors combine_SI logical indicate whether values S must merged one, input consists S+vs. R (susceptible vs. resistant) - default TRUE amr_distance outcome mean_amr_distance() row index, row number","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"mean AMR distance effectively Z-score; normalised numeric value compare AMR test results can help identify similar isolates, without comparing antibiograms hand. MIC values (see .mic()) transformed log2() first; distance thus calculated (log2(x) - mean(log2(x))) / sd(log2(x)). SIR values (see .sir()) transformed using \"S\" = 1, \"\" = 2, \"R\" = 3. combine_SI TRUE (default), \"\" considered 1. data sets, mean AMR distance calculated per column, mean per row returned, see Examples. Use amr_distance_from_row() subtract distances distance one row, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"interpretation","dir":"Reference","previous_headings":"","what":"Interpretation","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"Isolates distances less 0.01 difference considered similar. Differences lower 0.025 considered suspicious.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"","code":"sir <- random_sir(10) sir #> Class 'sir' #>  [1] R R S R S S R I R R mean_amr_distance(sir) #>  [1]  0.7745967  0.7745967 -1.1618950  0.7745967 -1.1618950 -1.1618950 #>  [7]  0.7745967 -1.1618950  0.7745967  0.7745967  mic <- random_mic(10) mic #> Class 'mic' #>  [1] 0.125   0.005   16      32      0.01    <=0.001 0.01    1       0.5     #> [10] 4       mean_amr_distance(mic) #>  [1] -0.1172241 -1.0092500  1.2273872  1.4194745 -0.8171627 -1.4552630 #>  [7] -0.8171627  0.4590379  0.2669505  0.8432125 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) #>  [1] -0.1172241 -1.0092500  1.2273872  1.4194745 -0.8171627 -1.4552630 #>  [7] -0.8171627  0.4590379  0.2669505  0.8432125  disk <- random_disk(10) disk #> Class 'disk' #>  [1] 38 45  6 20 43 49 29 31 39 33 mean_amr_distance(disk) #>  [1]  0.36674754  0.91296727 -2.13025696 -1.03781749  0.75690449  1.22509283 #>  [7] -0.33553498 -0.17947220  0.44477893 -0.02340942  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   24    8  0.5 #> 2   B    R   21    8   16 #> 3   C    I   28    1    4 #> 4   D    S   24    4    1 #> 5   E    S   17   16    8 #> 6   F    S   17    2    4 #> 7   G    I   23   16  0.5 #> 8   H    R   19   16 0.25 #> 9   I    R   20  0.5    8 #> 10  J    I   27   16    8 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\", #>   and \"tobr\" #>  [1] -0.216474562  0.698891501 -0.010082051 -0.234063901 -0.060228857 #>  [6] -0.586746706 -0.145237897 -0.005286713 -0.028443276  0.587672462 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   28    1    4 -0.010082051 0.000000000 #> 2   H    R   19   16 0.25 -0.005286713 0.004795338 #> 3   I    R   20  0.5    8 -0.028443276 0.018361225 #> 4   E    S   17   16    8 -0.060228857 0.050146806 #> 5   G    I   23   16  0.5 -0.145237897 0.135155846 #> 6   A    S   24    8  0.5 -0.216474562 0.206392511 #> 7   D    S   24    4    1 -0.234063901 0.223981850 #> 8   F    S   17    2    4 -0.586746706 0.576664654 #> 9   J    I   27   16    8  0.587672462 0.597754514 #> 10  B    R   21    8   16  0.698891501 0.708973552 if (require(\"dplyr\")) {   # support for groups   example_isolates %>%     filter(mo_genus() == \"Enterococcus\" & mo_species() != \"\") %>%     select(mo, TCY, carbapenems()) %>%     group_by(mo) %>%     mutate(dist = mean_amr_distance(.)) %>%     arrange(mo, dist) } #> ℹ Using column 'mo' as input for mo_genus() #> ℹ Using column 'mo' as input for mo_species() #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Calculating mean AMR distance based on columns \"TCY\", \"IPM\", and \"MEM\" #> # A tibble: 63 × 5 #> # Groups:   mo [4] #>    mo           TCY   IPM   MEM     dist #>    <mo>         <sir> <sir> <sir>  <dbl> #>  1 B_ENTRC_AVIM S     S     NA     0     #>  2 B_ENTRC_AVIM S     S     NA     0     #>  3 B_ENTRC_CSSL NA    S     NA    NA     #>  4 B_ENTRC_FACM S     S     NA    -2.66  #>  5 B_ENTRC_FACM S     R     R     -0.423 #>  6 B_ENTRC_FACM S     R     R     -0.423 #>  7 B_ENTRC_FACM NA    R     R      0.224 #>  8 B_ENTRC_FACM NA    R     R      0.224 #>  9 B_ENTRC_FACM NA    R     R      0.224 #> 10 B_ENTRC_FACM NA    R     R      0.224 #> # … with 53 more rows"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 5 910 Common Microorganism Codes — microorganisms.codes","title":"Data Set with 5 910 Common Microorganism Codes — microorganisms.codes","text":"data set containing commonly used codes microorganisms, laboratory systems WHONET. Define set_mo_source(). searched using .mo() consequently mo_* functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 5 910 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 5 910 Common Microorganism Codes — microorganisms.codes","text":"tibble 5 910 observations 2 variables: code Commonly used code microorganism mo ID microorganism microorganisms data set","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 5 910 Common Microorganism Codes — microorganisms.codes","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 5 910 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes #> # A tibble: 5,910 × 2 #>    code    mo        #>    <chr>   <mo>      #>  1 _FAM_A- B_GRAMN   #>  2 _FAM_A+ B_GRAMP   #>  3 _FAM_AC B_GRAMP   #>  4 _FAM_AE B_GRAMN   #>  5 _FAM_AN B_GRAMN   #>  6 _FAM_AO B_GRAMP   #>  7 _FAM_AP   UNKNOWN #>  8 _FAM_AS F_FUNGUS  #>  9 _FAM_AT   UNKNOWN #> 10 _FAM_AV   UNKNOWN #> # … with 5,900 more rows"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 52 142 Microorganisms — microorganisms","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"data set containing full microbial taxonomy (last updated: 11 December, 2022) five kingdoms List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF). data set backbone AMR package. MO codes can looked using .mo().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"","code":"microorganisms"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"tibble 52 142 observations 22 variables: mo ID microorganism used package fullname Full name, like \"Escherichia coli\". taxonomic ranks genus, species subspecies, 'pasted' text genus, species, subspecies. taxonomic ranks higher genus, name taxon. status  Status taxon, either \"accepted\" \"synonym\" kingdom, phylum, class, order, family, genus, species, subspecies Taxonomic rank microorganism rank Text taxonomic rank microorganism, \"species\" \"genus\" ref Author(s) year related scientific publication. contains first surname year latest authors, e.g. \"Wallis et al. 2006 emend. Smith Jones 2018\" becomes \"Smith et al., 2018\". field directly retrieved source specified column source. Moreover, accents removed comply CRAN allows ASCII characters, e.g. \"Váňová\" becomes \"Vanova\". lpsn Identifier ('Record number') List Prokaryotic names Standing Nomenclature (LPSN). first/highest LPSN identifier keep one identifier per row. example, Acetobacter ascendens LPSN Record number 7864 11011. first available microorganisms data set. lpsn_parent LPSN identifier parent taxon lpsn_renamed_to LPSN identifier currently valid taxon gbif Identifier ('taxonID') Global Biodiversity Information Facility (GBIF) gbif_parent GBIF identifier parent taxon gbif_renamed_to GBIF identifier currently valid taxon source Either \"GBIF\", \"LPSN\", \"manually added\" (see Source) prevalence Prevalence microorganism according Bartlett et al. (2022, doi:10.1099/mic.0.001269 ), see mo_matching_score() full explanation snomed Systematized Nomenclature Medicine (SNOMED) code microorganism, version 1 July, 2021 (see Source). Use mo_snomed() retrieve quickly, see mo_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Grimont et al. (2007). Antigenic Formulae Salmonella Serovars, 9th Edition. Collaborating Centre Reference Research Salmonella (WHOCC-SALM). Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"Please note entries based List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF) (see ). Since sources incorporate entries based (recent) publications International Journal Systematic Evolutionary Microbiology (IJSEM), can happen year publication sometimes later one might expect. example, Staphylococcus pettenkoferi described first time Diagnostic Microbiology Infectious Disease 2002 (doi:10.1016/s0732-8893(02)00399-1 ), 2007 publication IJSEM followed (doi:10.1099/ijs.0.64381-0 ). Consequently, AMR package returns 2007 mo_year(\"S. pettenkoferi\").","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"included-taxa","dir":"Reference","previous_headings":"","what":"Included Taxa","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"Included taxonomic data : ~36 000 (sub)species kingdoms Archaea Bacteria ~7 900 (sub)species kingdom Fungi. kingdom Fungi large taxon almost 300,000 different (sub)species, microbial (rather macroscopic, like mushrooms). , fungi fit scope package. relevant fungi covered (species Aspergillus, Candida, Cryptococcus, Histoplasma, Pneumocystis, Saccharomyces Trichophyton). ~5 100 (sub)species kingdom Protozoa ~1 400 (sub)species ~40 relevant genera kingdom Animalia (Strongyloides Taenia) ~9 800 previously accepted names included (sub)species (taxonomically renamed) complete taxonomic tree included (sub)species: kingdom subspecies identifier parent taxons year first author related scientific publication","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"manual-additions","dir":"Reference","previous_headings":"","what":"Manual additions","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"convenience, entries added manually: ~1 500 entries city-like serovars Salmonellae 11 entries Streptococcus (beta-haemolytic: groups , B, C, D, F, G, H, K unspecified; : viridans, milleri) 2 entries Staphylococcus (coagulase-negative (CoNS) coagulase-positive (CoPS)) 1 entry Blastocystis (B.  hominis), although officially exist (Noel et al. 2005, PMID 15634993) 1 entry Moraxella (M. catarrhalis), formally named Branhamella catarrhalis (Catlin, 1970) though change never accepted within field clinical microbiology 6 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast, unknown fungus, unknown anaerobic bacteria) syntax used transform original data cleansed R format, can found : https://github.com/msberends/AMR/blob/main/data-raw/reproduction_of_microorganisms.R.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"about-the-records-from-lpsn-see-source-","dir":"Reference","previous_headings":"","what":"About the Records from LPSN (see Source)","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"LPSN main source bacteriological taxonomy AMR package. List Prokaryotic names Standing Nomenclature (LPSN) provides comprehensive information nomenclature prokaryotes. LPSN free use service founded Jean P. Euzeby 1997 later maintained Aidan C. Parte.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"","code":"microorganisms #> # A tibble: 52,142 × 22 #>    mo               fullname      status kingdom phylum class order family genus #>    <mo>             <chr>         <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> #>  1 B_GRAMN          (unknown Gra… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  2 B_GRAMP          (unknown Gra… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  3 B_ANAER          (unknown ana… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  4 F_FUNGUS         (unknown fun… accep… Fungi   (unkn… \"(un… \"(un… \"(unk… \"(un… #>  5   UNKNOWN        (unknown nam… accep… (unkno… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  6 F_YEAST          (unknown yea… accep… Fungi   (unkn… \"(un… \"(un… \"(unk… \"(un… #>  7 B_[FAM]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… \"Abd… \"Abd… \"Abdi… \"\"    #>  8 B_[ORD]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… \"Abd… \"Abd… \"\"     \"\"    #>  9 B_[CLS]_ADTBCTRA Abditibacter… accep… Bacter… Abdit… \"Abd… \"\"    \"\"     \"\"    #> 10 B_[PHL]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… \"\"    \"\"    \"\"     \"\"    #> # … with 52,132 more rows, and 13 more variables: species <chr>, #> #   subspecies <chr>, rank <chr>, ref <chr>, source <chr>, lpsn <chr>, #> #   lpsn_parent <chr>, lpsn_renamed_to <chr>, gbif <chr>, gbif_parent <chr>, #> #   gbif_renamed_to <chr>, prevalence <dbl>, snomed <list>"},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the Matching Score for Microorganisms — mo_matching_score","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"algorithm used .mo() mo_* functions determine probable match taxonomic records based user input.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"","code":"mo_matching_score(x, n)"},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"x user input value(s) n full taxonomic name, exists microorganisms$fullname","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"algorithm originally described : Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 . Later, work Bartlett et al. bacterial pathogens infecting humans (2022, doi:10.1099/mic.0.001269 ) incorporated.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated :  : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.0 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, genus present established list also prevalence = 1.0 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.5 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Contracaecum, Cordylobia, Cryptococcus, Curvularia, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Giardia, Haloarcula, Halobacterium, Halococcus, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hymenolepis, Hypomyces, Hysterothylacium, Leishmania, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Mortierella, Mucor, Mycocentrospora, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Pediculus, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga, Wuchereria; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.159\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"Dr. Matthijs Berends, 2018","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"","code":"mo_reset_session() #> ℹ Reset 17 previously matched input values.  as.mo(\"E. coli\") #> Class 'mo' #> [1] B_ESCHR_COLI mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans. See ?mo_matching_score. #>  #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #>              Based on input \"E coli\" #> Also matched: Enterobacter cowanii (0.600), Eubacterium combesii #>               (0.600), Eggerthia catenaformis (0.591), Eubacterium callanderi #>               (0.591), Enterocloster citroniae (0.587), Eubacterium cylindroides #>               (0.583), Enterococcus casseliflavus (0.577), Enterobacter cloacae #>               cloacae (0.571), Ehrlichia canis (0.567), and Enterobacter cloacae #>               dissolvens (0.565) #>  #> Only the first 10 other matches of each record are shown. Run #> print(mo_uncertainties(), n = ...) to view more entries, or save #> mo_uncertainties() to an object.  mo_matching_score(   x = \"E. coli\",   n = c(\"Escherichia coli\", \"Entamoeba coli\") ) #> [1] 0.6875000 0.1587302"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of a Microorganism — mo_property","title":"Get Properties of a Microorganism — mo_property","text":"Use functions return specific property microorganism based latest accepted taxonomy. input values evaluated internally .mo(), makes possible use microbial abbreviations, codes names input. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of a Microorganism — mo_property","text":"","code":"mo_name(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_fullname(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_shortname(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_subspecies(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_species(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_genus(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_family(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_order(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_class(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_phylum(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_kingdom(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_domain(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_type(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_status(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_pathogenicity(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_gramstain(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_is_gram_negative(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_is_gram_positive(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_is_yeast(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_is_intrinsic_resistant(   x,   ab,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_snomed(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_ref(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_authors(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_year(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_lpsn(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_gbif(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_rank(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_taxonomy(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_synonyms(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_current(x, language = get_AMR_locale(), ...)  mo_info(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_url(   x,   open = FALSE,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_property(   x,   property = \"fullname\",   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of a Microorganism — mo_property","text":"x character (vector) can coerced valid microorganism code .mo(). Can left blank auto-guessing column containing microorganism codes used data set, see Examples. language language translate text like \"growth\", defaults system language (see get_AMR_locale()) keep_synonyms logical indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE, return note old taxonomic names processed. default can set package option AMR_keep_synonyms, .e. options(AMR_keep_synonyms = TRUE) options(AMR_keep_synonyms = FALSE). ... arguments passed .mo(), 'minimum_matching_score', 'ignore_pattern', 'remove_from_input' ab (vector ) text can coerced valid antibiotic drug code .ab() open browse URL using browseURL() property one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\", \"snomed\", must \"shortname\"","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of a Microorganism — mo_property","text":"integer case mo_year() ordered factor case mo_pathogenicity() list case mo_taxonomy(), mo_synonyms(), mo_snomed() mo_info() named character case mo_url() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of a Microorganism — mo_property","text":"functions , default, keep old taxonomic properties, synonyms automatically replaced current taxonomy. Take example Enterobacter aerogenes, initially named 1960 renamed Klebsiella aerogenes 2017: mo_genus(\"Enterobacter aerogenes\") return \"Klebsiella\" (note renaming) mo_genus(\"Enterobacter aerogenes\", keep_synonyms = TRUE) return \"Enterobacter\" (-per-session warning name outdated) mo_ref(\"Enterobacter aerogenes\") return \"Tindall et al., 2017\" (note) mo_ref(\"Enterobacter aerogenes\", keep_synonyms = TRUE) return \"Hormaeche et al., 1960\" (warning) short name (mo_shortname()) returns first character genus full species, \"E. coli\", species subspecies. Exceptions abbreviations staphylococci (\"CoNS\", Coagulase-Negative Staphylococci) beta-haemolytic streptococci (\"GBS\", Group B Streptococci). Please bear mind e.g. E. coli mean Escherichia coli (kingdom Bacteria) well Entamoeba coli (kingdom Protozoa). Returning full name done using .mo() internally, giving priority bacteria human pathogens, .e. \"E. coli\" considered Escherichia coli. result, mo_fullname(mo_shortname(\"Entamoeba coli\")) returns \"Escherichia coli\". Since top-level taxonomy sometimes referred 'kingdom' sometimes 'domain', functions mo_kingdom() mo_domain() return exact results. Determination human pathogenicity (mo_pathogenicity()) strongly based Bartlett et al. (2022, doi:10.1099/mic.0.001269 ). function returns factor levels Pathogenic, Potentially pathogenic, Non-pathogenic, Unknown. Determination Gram stain (mo_gramstain()) based taxonomic kingdom phylum. Originally, Cavalier-Smith defined -called subkingdoms Negibacteria Posibacteria (2002, PMID 11837318), considered phyla Posibacteria: Actinobacteria, Chloroflexi, Firmicutes, Tenericutes. phyla later renamed Actinomycetota, Chloroflexota, Bacillota, Mycoplasmatota (2021, PMID 34694987). Bacteria phyla considered Gram-positive AMR package, except members class Negativicutes (within phylum Bacillota) Gram-negative. bacteria considered Gram-negative. Species outside kingdom Bacteria return value NA. Functions mo_is_gram_negative() mo_is_gram_positive() always return TRUE FALSE (NA input NA MO code UNKNOWN), thus always return FALSE species outside taxonomic kingdom Bacteria. Determination yeasts (mo_is_yeast()) based taxonomic kingdom class. Budding yeasts fungi phylum Ascomycota, class Saccharomycetes (also called Hemiascomycetes). True yeasts aggregated underlying order Saccharomycetales. Thus, microorganisms member taxonomic class Saccharomycetes, function return TRUE. returns FALSE otherwise (NA input NA MO code UNKNOWN). Determination intrinsic resistance (mo_is_intrinsic_resistant()) based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). mo_is_intrinsic_resistant() function can vectorised argument x (input microorganisms) ab (input antibiotics). function mo_url() return direct URL online database entry, also shows scientific reference concerned species. SNOMED codes (mo_snomed()) version 1 July, 2021. See Source microorganisms data set info. Old taxonomic names (-called 'synonyms') can retrieved mo_synonyms() (scientific reference name), current taxonomic name can retrieved mo_current(). functions return full names. output translated possible.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Get Properties of a Microorganism — mo_property","text":"function uses .mo() internally, uses advanced algorithm translate arbitrary user input valid taxonomy using -called matching score. can read public algorithm MO matching score page.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of a Microorganism — mo_property","text":"Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 Becker K et al. (2014). Coagulase-Negative Staphylococci. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. (2019). Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. (2020). Emergence coagulase-negative staphylococci Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 Lancefield RC (1933). serological differentiation human groups hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571 Berends MS et al. (2022). Trends Occurrence Phenotypic Resistance Coagulase-Negative Staphylococci (CoNS) Found Human Blood Northern Netherlands 2013 2019 Microorganisms 10(9), 1801; doi:10.3390/microorganisms10091801 Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of a Microorganism — mo_property","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of a Microorganism — mo_property","text":"","code":"# taxonomic tree -----------------------------------------------------------  mo_kingdom(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_phylum(\"Klebsiella pneumoniae\") #> [1] \"Pseudomonadota\" mo_class(\"Klebsiella pneumoniae\") #> [1] \"Gammaproteobacteria\" mo_order(\"Klebsiella pneumoniae\") #> [1] \"Enterobacterales\" mo_family(\"Klebsiella pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"Klebsiella pneumoniae\") #> [1] \"pneumoniae\" mo_subspecies(\"Klebsiella pneumoniae\") #> [1] \"\"   # full names and short names -----------------------------------------------  mo_name(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella pneumoniae\" mo_fullname(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella pneumoniae\" mo_shortname(\"Klebsiella pneumoniae\") #> [1] \"K. pneumoniae\"   # other properties ---------------------------------------------------------  mo_pathogenicity(\"Klebsiella pneumoniae\") #> [1] Pathogenic #> Levels: Pathogenic < Potentially pathogenic < Non-pathogenic < Unknown mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\" mo_snomed(\"Klebsiella pneumoniae\") #> [[1]] #> [1] \"1098101000112102\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\"        #>  mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_rank(\"Klebsiella pneumoniae\") #> [1] \"species\" mo_url(\"Klebsiella pneumoniae\") #>                                Klebsiella pneumoniae  #> \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\"  mo_is_yeast(c(\"Candida\", \"Trichophyton\", \"Klebsiella\")) #> [1]  TRUE FALSE FALSE   # scientific reference -----------------------------------------------------  mo_ref(\"Klebsiella aerogenes\") #> [1] \"Tindall et al., 2017\" mo_authors(\"Klebsiella aerogenes\") #> [1] \"Tindall et al.\" mo_year(\"Klebsiella aerogenes\") #> [1] 2017 mo_lpsn(\"Klebsiella aerogenes\") #> [1] \"777146\" mo_gbif(\"Klebsiella aerogenes\") #> [1] \"9281703\" mo_synonyms(\"Klebsiella aerogenes\") #>   Hormaeche et al., 1960     Bascomb et al., 1971  #> \"Enterobacter aerogenes\"     \"Klebsiella mobilis\"    # abbreviations known in the field -----------------------------------------  mo_genus(\"MRSA\") #> [1] \"Staphylococcus\" mo_species(\"MRSA\") #> [1] \"aureus\" mo_shortname(\"VISA\") #> [1] \"S. aureus\" mo_gramstain(\"VISA\") #> [1] \"Gram-positive\"  mo_genus(\"EHEC\") #> [1] \"Escherichia\" mo_species(\"EIEC\") #> [1] \"coli\" mo_name(\"UPEC\") #> [1] \"Escherichia coli\"   # known subspecies ---------------------------------------------------------  mo_fullname(\"K. pneu rh\") #> [1] \"Klebsiella pneumoniae rhinoscleromatis\" mo_shortname(\"K. pneu rh\") #> [1] \"K. pneumoniae\"  # \\donttest{ # Becker classification, see ?as.mo ----------------------------------------  mo_fullname(\"Staph epidermidis\") #> [1] \"Staphylococcus epidermidis\" mo_fullname(\"Staph epidermidis\", Becker = TRUE) #> [1] \"Coagulase-negative Staphylococcus (CoNS)\" mo_shortname(\"Staph epidermidis\") #> [1] \"S. epidermidis\" mo_shortname(\"Staph epidermidis\", Becker = TRUE) #> [1] \"CoNS\"   # Lancefield classification, see ?as.mo ------------------------------------  mo_fullname(\"Strep agalactiae\") #> [1] \"Streptococcus agalactiae\" mo_fullname(\"Strep agalactiae\", Lancefield = TRUE) #> [1] \"Streptococcus Group B\" mo_shortname(\"Strep agalactiae\") #> [1] \"S. agalactiae\" mo_shortname(\"Strep agalactiae\", Lancefield = TRUE) #> [1] \"GBS\"   # language support  --------------------------------------------------------  mo_gramstain(\"Klebsiella pneumoniae\", language = \"de\") # German #> [1] \"Gramnegativ\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"nl\") # Dutch #> [1] \"Gram-negatief\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"es\") # Spanish #> [1] \"Gram negativo\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"el\") # Greek #> [1] \"Αρνητικό κατά Gram\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"uk\") # Ukrainian #> [1] \"Грамнегативні\"  # mo_type is equal to mo_kingdom, but mo_kingdom will remain untranslated mo_kingdom(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_kingdom(\"Klebsiella pneumoniae\", language = \"zh\") # Chinese, no effect #> [1] \"Bacteria\" mo_type(\"Klebsiella pneumoniae\", language = \"zh\") # Chinese, translated #> [1] \"细菌\"  mo_fullname(\"S. pyogenes\", Lancefield = TRUE, language = \"de\") #> [1] \"Streptococcus Gruppe A\" mo_fullname(\"S. pyogenes\", Lancefield = TRUE, language = \"uk\") #> [1] \"Streptococcus Група A\"   # other --------------------------------------------------------------------  # gram stains and intrinsic resistance can be used as a filter in dplyr verbs if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_is_gram_positive()) %>%     count(mo_genus(), sort = TRUE) } #> ℹ Using column 'mo' as input for mo_is_gram_positive() #> ℹ Using column 'mo' as input for mo_genus() #> # A tibble: 18 × 2 #>    `mo_genus()`        n #>    <chr>           <int> #>  1 Staphylococcus    840 #>  2 Streptococcus     275 #>  3 Enterococcus       83 #>  4 Corynebacterium    17 #>  5 Micrococcus         6 #>  6 Gemella             3 #>  7 Aerococcus          2 #>  8 Cutibacterium       1 #>  9 Dermabacter         1 #> 10 Fusibacter          1 #> 11 Globicatella        1 #> 12 Granulicatella      1 #> 13 Lactobacillus       1 #> 14 Leuconostoc         1 #> 15 Listeria            1 #> 16 Paenibacillus       1 #> 17 Rothia              1 #> 18 Schaalia            1 if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_is_intrinsic_resistant(ab = \"vanco\")) %>%     count(mo_genus(), sort = TRUE) } #> ℹ Using column 'mo' as input for mo_is_intrinsic_resistant() #> ℹ Using column 'mo' as input for mo_genus() #> # A tibble: 20 × 2 #>    `mo_genus()`         n #>    <chr>            <int> #>  1 Escherichia        467 #>  2 Klebsiella          77 #>  3 Proteus             39 #>  4 Pseudomonas         30 #>  5 Serratia            25 #>  6 Enterobacter        23 #>  7 Citrobacter         11 #>  8 Haemophilus          8 #>  9 Acinetobacter        6 #> 10 Morganella           6 #> 11 Pantoea              4 #> 12 Salmonella           3 #> 13 Neisseria            2 #> 14 Stenotrophomonas     2 #> 15 Campylobacter        1 #> 16 Enterococcus         1 #> 17 Hafnia               1 #> 18 Lactobacillus        1 #> 19 Leuconostoc          1 #> 20 Pseudescherichia     1  # get a list with the complete taxonomy (from kingdom to subspecies) mo_taxonomy(\"Klebsiella pneumoniae\") #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Klebsiella\" #>  #> $species #> [1] \"pneumoniae\" #>  #> $subspecies #> [1] \"\" #>   # get a list with the taxonomy, the authors, Gram-stain, # SNOMED codes, and URL to the online database mo_info(\"Klebsiella pneumoniae\") #> $mo #> [1] \"B_KLBSL_PNMN\" #>  #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Klebsiella\" #>  #> $species #> [1] \"pneumoniae\" #>  #> $subspecies #> [1] \"\" #>  #> $status #> [1] \"accepted\" #>  #> $synonyms #> NULL #>  #> $gramstain #> [1] \"Gram-negative\" #>  #> $url #> [1] \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\" #>  #> $ref #> [1] \"Trevisan, 1887\" #>  #> $snomed #> [1] \"1098101000112102\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\"        #>  # }"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":null,"dir":"Reference","previous_headings":"","what":"User-Defined Reference Data Set for Microorganisms — mo_source","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"functions can used predefine reference used .mo() consequently mo_* functions (mo_genus() mo_gramstain()). fastest way organisation (analysis) specific codes picked translated package, since bother setting .","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"","code":"set_mo_source(   path,   destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\") )  get_mo_source(destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\"))"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"path location reference file, can text file (comma-, tab- pipe-separated) Excel file (see Details). Can also \"\", NULL FALSE delete reference file. destination destination compressed data file - default user's home directory.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"reference file can text file separated commas (CSV) tabs pipes, Excel file (either 'xls' 'xlsx' format) R object file (extension '.rds'). use Excel file, need readxl package installed. set_mo_source() check file validity: must data.frame, must column named \"mo\" contains values microorganisms$mo microorganisms$fullname must reference column defined values. tests pass, set_mo_source() read file R ask export \"~/mo_source.rds\". CRAN policy disallows packages write file system, although 'exceptions may allowed interactive sessions package obtains confirmation user'. reason, function works interactive sessions user can specifically confirm allow file created. destination file can set destination argument defaults user's home directory. can also set package option AMR_mo_source, e.g. options(AMR_mo_source = \"/location/file.rds\"). created compressed data file \"mo_source.rds\" used default MO determination (function .mo() consequently mo_* functions like mo_genus() mo_gramstain()). location timestamp original file saved attribute compressed data file. function get_mo_source() return data set reading \"mo_source.rds\" readRDS(). original file changed (checking location timestamp original file), call set_mo_source() update data file automatically used interactive session. Reading Excel file (.xlsx) one row size 8-9 kB. compressed file created set_mo_source() size 0.1 kB can read get_mo_source() couple microseconds (millionths second).","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"how-to-setup","dir":"Reference","previous_headings":"","what":"How to Setup","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"Imagine data sheet Excel file. first column contains organisation specific codes, second column contains valid taxonomic names:   save \"home//ourcodes.xlsx\". Now set source:   now created file \"~/mo_source.rds\" contents Excel file. first column foreign values 'mo' column kept creating RDS file. now can use functions:   edit Excel file , say, adding row 4 like :   ...new usage MO function package update data file:   delete reference data file, just use \"\", NULL FALSE input set_mo_source():   original file (previous case Excel file) moved deleted, mo_source.rds file removed upon next use .mo() mo_* function.","code":"|         A          |            B          | --|--------------------|-----------------------| 1 | Organisation XYZ   | mo                    | 2 | lab_mo_ecoli       | Escherichia coli      | 3 | lab_mo_kpneumoniae | Klebsiella pneumoniae | 4 |                    |                       | set_mo_source(\"home/me/ourcodes.xlsx\") #> NOTE: Created mo_source file '/Users/me/mo_source.rds' (0.3 kB) from #>       '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns #>       \"Organisation XYZ\" and \"mo\" as.mo(\"lab_mo_ecoli\") #> Class 'mo' #> [1] B_ESCHR_COLI  mo_genus(\"lab_mo_kpneumoniae\") #> [1] \"Klebsiella\"  # other input values still work too as.mo(c(\"Escherichia coli\", \"E. coli\", \"lab_mo_ecoli\")) #> NOTE: Translation to one microorganism was guessed with uncertainty. #>       Use mo_uncertainties() to review it. #> Class 'mo' #> [1] B_ESCHR_COLI B_ESCHR_COLI B_ESCHR_COLI |         A          |            B          | --|--------------------|-----------------------| 1 | Organisation XYZ   | mo                    | 2 | lab_mo_ecoli       | Escherichia coli      | 3 | lab_mo_kpneumoniae | Klebsiella pneumoniae | 4 | lab_Staph_aureus   | Staphylococcus aureus | 5 |                    |                       | as.mo(\"lab_mo_ecoli\") #> NOTE: Updated mo_source file '/Users/me/mo_source.rds' (0.3 kB) from #>       '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns #>        \"Organisation XYZ\" and \"mo\" #> Class 'mo' #> [1] B_ESCHR_COLI  mo_genus(\"lab_Staph_aureus\") #> [1] \"Staphylococcus\" set_mo_source(NULL) #> Removed mo_source file '/Users/me/mo_source.rds'"},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Component Analysis (for AMR) — pca","title":"Principal Component Analysis (for AMR) — pca","text":"Performs principal component analysis (PCA) based data set automatic determination afterwards plotting groups labels, automatic filtering suitable (.e. non-empty numeric) variables.","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Component Analysis (for AMR) — pca","text":"","code":"pca(   x,   ...,   retx = TRUE,   center = TRUE,   scale. = TRUE,   tol = NULL,   rank. = NULL )"},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Component Analysis (for AMR) — pca","text":"x data.frame containing numeric columns ... columns x selected PCA, can unquoted since supports quasiquotation. retx logical value indicating whether rotated variables     returned. center logical value indicating whether variables     shifted zero centered. Alternately, vector     length equal number columns x can supplied.     value passed scale. scale. logical value indicating whether variables     scaled unit variance analysis takes     place.  default FALSE consistency S,     general scaling advisable.  Alternatively, vector length     equal number columns x can supplied.      value passed scale. tol value indicating magnitude components     omitted. (Components omitted     standard deviations less equal tol times     standard deviation first component.)  default null     setting, components omitted (unless rank. specified     less min(dim(x)).).  settings tol     tol = 0 tol = sqrt(.Machine$double.eps),     omit essentially constant components. rank. optionally, number specifying maximal rank, .e.,     maximal number principal components used.  Can set     alternative addition tol, useful notably     desired rank considerably smaller dimensions matrix.","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Component Analysis (for AMR) — pca","text":"object classes pca prcomp","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Principal Component Analysis (for AMR) — pca","text":"pca() function takes data.frame input performs actual PCA R function prcomp(). result pca() function prcomp object, additional attribute non_numeric_cols vector column names columns contain numeric values. probably groups labels, used ggplot_pca().","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Component Analysis (for AMR) — pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # \\donttest{ if (require(\"dplyr\")) {   # calculate the resistance per group first   resistance_data <- example_isolates %>%     group_by(       order = mo_order(mo), # group on anything, like order       genus = mo_genus(mo)     ) %>% #   and genus as we do here;     filter(n() >= 30) %>% # filter on only 30 results per group     summarise_if(is.sir, resistance) # then get resistance of all drugs    # now conduct PCA for certain antimicrobial drugs   pca_result <- resistance_data %>%     pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)    pca_result   summary(pca_result)    # old base R plotting method:   biplot(pca_result)   # new ggplot2 plotting method using this package:   if (require(\"ggplot2\")) {     ggplot_pca(pca_result)      ggplot_pca(pca_result) +       scale_colour_viridis_d() +       labs(title = \"Title here\")   } } #> Warning: There were 73 warnings in `summarise()`. #> The first warning was: #> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, #>   only_all_tested = FALSE) ...`. #> ℹ In group 5: `order = \"Lactobacillales\"`, `genus = \"Enterococcus\"`. #> Caused by warning: #> ! Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #>   \"TMP\", and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\"  #>    # }"},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting for Classes sir, mic and disk — plot","title":"Plotting for Classes sir, mic and disk — plot","text":"Functions plot classes sir, mic disk, support base R ggplot2.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting for Classes sir, mic and disk — plot","text":"","code":"# S3 method for mic plot(   x,   mo = NULL,   ab = NULL,   guideline = \"EUCAST\",   main = deparse(substitute(x)),   ylab = translate_AMR(\"Frequency\", language = language),   xlab = translate_AMR(\"Minimum Inhibitory Concentration (mg/L)\", language = language),   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for mic autoplot(   object,   mo = NULL,   ab = NULL,   guideline = \"EUCAST\",   title = deparse(substitute(object)),   ylab = translate_AMR(\"Frequency\", language = language),   xlab = translate_AMR(\"Minimum Inhibitory Concentration (mg/L)\", language = language),   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for mic fortify(object, ...)  # S3 method for disk plot(   x,   main = deparse(substitute(x)),   ylab = translate_AMR(\"Frequency\", language = language),   xlab = translate_AMR(\"Disk diffusion diameter (mm)\", language = language),   mo = NULL,   ab = NULL,   guideline = \"EUCAST\",   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for disk autoplot(   object,   mo = NULL,   ab = NULL,   title = deparse(substitute(object)),   ylab = translate_AMR(\"Frequency\", language = language),   xlab = translate_AMR(\"Disk diffusion diameter (mm)\", language = language),   guideline = \"EUCAST\",   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for disk fortify(object, ...)  # S3 method for sir plot(   x,   ylab = translate_AMR(\"Percentage\", language = language),   xlab = translate_AMR(\"Antimicrobial Interpretation\", language = language),   main = deparse(substitute(x)),   language = get_AMR_locale(),   ... )  # S3 method for sir autoplot(   object,   title = deparse(substitute(object)),   xlab = translate_AMR(\"Antimicrobial Interpretation\", language = language),   ylab = translate_AMR(\"Frequency\", language = language),   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   ... )  # S3 method for sir fortify(object, ...)"},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting for Classes sir, mic and disk — plot","text":"x, object values created .mic(), .disk() .sir() (random_* variants, random_mic()) mo (vector ) text can coerced valid microorganism code .mo() ab (vector ) text can coerced valid antimicrobial drug code .ab() guideline interpretation guideline use - default latest included EUCAST guideline, see Details main, title title plot xlab, ylab axis title colours_SIR colours use filling bars, must vector three values (order S, R). default colours colour-blind friendly. language language used translate 'Susceptible', 'Increased exposure'/'Intermediate' 'Resistant' - default system language (see get_AMR_locale()) can overwritten setting package option AMR_locale, e.g. options(AMR_locale = \"de\"), see translate. Use language = NULL language = \"\" prevent translation. expand logical indicate whether range x axis expanded lowest highest value. MIC values, intermediate values factors 2 starting highest MIC value. disk diameters, whole diameter range filled. ... arguments passed methods","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting for Classes sir, mic and disk — plot","text":"autoplot() functions return ggplot model extendible ggplot2 function. fortify() functions return data.frame extension usage ggplot2::ggplot() function.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plotting for Classes sir, mic and disk — plot","text":"interpretation \"\" named \"Increased exposure\" EUCAST guidelines since 2019, named \"Intermediate\" cases. interpreting MIC values well disk diffusion diameters, supported guidelines used input guideline argument : \"EUCAST 2022\", \"EUCAST 2021\", \"EUCAST 2020\", \"EUCAST 2019\", \"EUCAST 2018\", \"EUCAST 2017\", \"EUCAST 2016\", \"EUCAST 2015\", \"EUCAST 2014\", \"EUCAST 2013\", \"CLSI 2022\", \"CLSI 2021\", \"CLSI 2020\", \"CLSI 2019\", \"CLSI 2018\", \"CLSI 2017\", \"CLSI 2016\", \"CLSI 2015\", \"CLSI 2014\", \"CLSI 2013\". Simply using \"CLSI\" \"EUCAST\" input automatically select latest version guideline.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting for Classes sir, mic and disk — plot","text":"","code":"some_mic_values <- random_mic(size = 100) some_disk_values <- random_disk(size = 100, mo = \"Escherichia coli\", ab = \"cipro\") some_sir_values <- random_sir(50, prob_SIR = c(0.55, 0.05, 0.30))  plot(some_mic_values)  plot(some_disk_values)  plot(some_sir_values)   # when providing the microorganism and antibiotic, colours will show interpretations: plot(some_mic_values, mo = \"S. aureus\", ab = \"ampicillin\")  plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\")  plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\", language = \"uk\")   # \\donttest{ if (require(\"ggplot2\")) {   autoplot(some_mic_values) }  if (require(\"ggplot2\")) {   autoplot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\") }  if (require(\"ggplot2\")) {   autoplot(some_sir_values) }  # }"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Antimicrobial Resistance — proportion","title":"Calculate Antimicrobial Resistance — proportion","text":"functions can used calculate (co-)resistance susceptibility microbial isolates (.e. percentage S, SI, , IR R). functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. resistance() used calculate resistance, susceptibility() used calculate susceptibility.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Antimicrobial Resistance — proportion","text":"","code":"resistance(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  susceptibility(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  sir_confidence_interval(   ...,   ab_result = \"R\",   minimum = 30,   as_percent = FALSE,   only_all_tested = FALSE,   confidence_level = 0.95,   side = \"both\",   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":"function resistance() equal function proportion_R(). function susceptibility() equal function proportion_SI(). Use sir_confidence_interval() calculate confidence interval, relies binom.test(), .e., Clopper-Pearson method. function returns vector length 2 default antimicrobial resistance. Change side argument \"left\"/\"min\" \"right\"/\"max\" return single value, change ab_result argument e.g. c(\"S\", \"\") test antimicrobial susceptibility, see Examples. Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine data set one four available algorithms. functions meant count isolates, calculate proportion resistance/susceptibility. Use count() functions count isolates. function susceptibility() essentially equal count_susceptible() / count_all(). Low counts can influence outcome - proportion functions may camouflage , since return proportion (albeit dependent minimum argument). function proportion_df() takes variable data sir class (created .sir()) calculates proportions S, , R. also supports grouped variables. function sir_df() works exactly like proportion_df(), adds number isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Calculate Antimicrobial Resistance — proportion","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antibiotics, Drug Drug B, susceptibility() works calculate %SI:   Please note , combination therapies, only_all_tested = TRUE applies :   , combination therapies, only_all_tested = FALSE applies :   Using only_all_tested impact using one antibiotic input.","code":"--------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   include as  include as   include as  include as                     numerator   denominator  numerator   denominator --------  --------  ----------  -----------  ----------  -----------  S or I    S or I       X            X            X            X    R       S or I       X            X            X            X   <NA>     S or I       X            X            -            -  S or I      R          X            X            X            X    R         R          -            X            -            X   <NA>       R          -            -            -            -  S or I     <NA>        X            X            -            -    R        <NA>        -            -            -            -   <NA>      <NA>        -            -            -            - -------------------------------------------------------------------- count_S()    +   count_I()    +   count_R()    = count_all()   proportion_S() + proportion_I() + proportion_R() = 1 count_S()    +   count_I()    +   count_R()    >= count_all()   proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Calculate Antimicrobial Resistance — proportion","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Antimicrobial Resistance — proportion","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>   # base R ------------------------------------------------------------ # determines %R resistance(example_isolates$AMX) #> [1] 0.5955556 sir_confidence_interval(example_isolates$AMX) #> [1] 0.5688204 0.6218738 sir_confidence_interval(example_isolates$AMX,   confidence_level = 0.975 ) #> [1] 0.5650148 0.6255670 sir_confidence_interval(example_isolates$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 2022 guideline implemented clinical_breakpoints data set. create specific generated values per bug drug, set mo /ab argument.","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"","code":"random_mic(25) #> Class 'mic' #>  [1] 1       0.005   4       16      0.01    1       16      0.25    0.005   #> [10] 0.125   <=0.002 0.125   64      0.25    4       1       128     8       #> [19] 64      >=256   0.25    8       >=256   2       16      random_disk(25) #> Class 'disk' #>  [1] 32 30 15 34 20 43 17 28 30 50 25 10 47 23 26 13 32 24 28 46 36 14 50 46 21 random_sir(25) #> Class 'sir' #>  [1] S R S R I R S S S I S I R I I I S I R I S S R S I  # \\donttest{ # make the random generation more realistic by setting a bug and/or drug: random_mic(25, \"Klebsiella pneumoniae\") # range 0.0625-64 #> Class 'mic' #>  [1] 2       >=64    0.025   16      0.125   2       32      32      16      #> [10] 0.5     2       0.0625  16      0.25    0.01    4       0.002   16      #> [19] 1       <=0.001 0.0625  0.0625  8       >=64    2       random_mic(25, \"Klebsiella pneumoniae\", \"meropenem\") # range 0.0625-16 #> Class 'mic' #>  [1] 0.5  1    4    2    1    1    >=16 0.5  4    0.5  0.5  1    2    >=16 2    #> [16] 4    0.5  8    2    >=16 0.5  4    8    2    1    random_mic(25, \"Streptococcus pneumoniae\", \"meropenem\") # range 0.0625-4 #> Class 'mic' #>  [1] >=4    0.25   0.5    0.5    0.125  1      0.25   >=4    0.5    2      #> [11] 0.5    1      0.0625 0.5    0.5    0.0625 2      0.125  1      1      #> [21] 0.25   0.0625 1      0.25   0.125   random_disk(25, \"Klebsiella pneumoniae\") # range 8-50 #> Class 'disk' #>  [1] 16 33 30 28 41 44 12 12 40 23 45  9 40 49 34 33 20 10 16 42 39  8 40 24 44 random_disk(25, \"Klebsiella pneumoniae\", \"ampicillin\") # range 11-17 #> Class 'disk' #>  [1] 13 13 17 11 14 12 13 13 13 11 15 13 15 15 11 13 14 12 13 12 17 16 15 14 11 random_disk(25, \"Streptococcus pneumoniae\", \"ampicillin\") # range 12-27 #> Class 'disk' #>  [1] 17 20 22 21 24 15 15 22 26 15 27 21 15 15 22 19 20 20 27 24 18 15 26 25 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) #>  #> Deviance Residuals:  #>     Min       1Q   Median       3Q      Max   #> -1.4150  -0.1855   0.0000   0.4096   0.9277   #>  #> Coefficients: #>             Estimate Std. Error z value Pr(>|z|) #> (Intercept) 47.02915   72.98739   0.644    0.519 #> year        -0.02280    0.03637  -0.627    0.531 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance: 5.9620  on 11  degrees of freedom #> Residual deviance: 5.5701  on 10  degrees of freedom #> AIC: 50.533 #>  #> Number of Fisher Scoring iterations: 4 #>   # create nice plots with ggplot2 yourself if (require(\"dplyr\") && require(\"ggplot2\")) {   data <- example_isolates %>%     filter(mo == as.mo(\"E. coli\")) %>%     resistance_predict(       col_ab = \"AMX\",       col_date = \"date\",       model = \"binomial\",       info = FALSE,       minimum = 15     )   head(data)   autoplot(data) }  # }"},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":null,"dir":"Reference","previous_headings":"","what":"Skewness of the Sample — skewness","title":"Skewness of the Sample — skewness","text":"Skewness measure asymmetry probability distribution real-valued random variable mean. negative ('left-skewed'): left tail longer; mass distribution concentrated right histogram. positive ('right-skewed'): right tail longer; mass distribution concentrated left histogram. normal distribution skewness 0.","code":""},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Skewness of the Sample — skewness","text":"","code":"skewness(x, na.rm = FALSE)  # S3 method for default skewness(x, na.rm = FALSE)  # S3 method for matrix skewness(x, na.rm = FALSE)  # S3 method for data.frame skewness(x, na.rm = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Skewness of the Sample — skewness","text":"x vector values, matrix data.frame na.rm logical value indicating whether NA values stripped computation proceeds","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Skewness of the Sample — skewness","text":"","code":"skewness(runif(1000)) #> [1] 0.004959649"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate Strings from the AMR Package — translate","title":"Translate Strings from the AMR Package — translate","text":"language-dependent output AMR functions, mo_name(), mo_gramstain(), mo_type() ab_name().","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate Strings from the AMR Package — translate","text":"","code":"get_AMR_locale()  set_AMR_locale(language)  reset_AMR_locale()  translate_AMR(x, language = get_AMR_locale())"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate Strings from the AMR Package — translate","text":"language language choose. Use one supported language names ISO-639-1 codes: English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). x text translate","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate Strings from the AMR Package — translate","text":"currently 20 supported languages English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). languages translations available antimicrobial drugs colloquial microorganism names. permanently silence -per-session language note non-English operating system, can set package option AMR_locale .Rprofile file like :   save file. Please read adding updating language Wiki.","code":"# Open .Rprofile file utils::file.edit(\"~/.Rprofile\")  # Then add e.g. Italian support to that file using: options(AMR_locale = \"Italian\")"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"changing-the-default-language","dir":"Reference","previous_headings":"","what":"Changing the Default Language","title":"Translate Strings from the AMR Package — translate","text":"system language used default (returned Sys.getenv(\"LANG\") , LANG set, Sys.getlocale(\"LC_COLLATE\")), language supported. language used can overwritten two ways checked order: Setting package option AMR_locale, either using e.g. set_AMR_locale(\"German\") running e.g. options(AMR_locale = \"German\"). Note setting R option works session. Save command options(AMR_locale = \"(language)\") .Rprofile file apply every session. Run utils::file.edit(\"~/.Rprofile\") edit .Rprofile file. Setting system variable LANGUAGE LANG, e.g. adding LANGUAGE=\"de_DE.utf8\" .Renviron file home directory. Thus, package option AMR_locale set, system variables LANGUAGE LANG ignored.","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate Strings from the AMR Package — translate","text":"","code":"# Current settings (based on system language) ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacin\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Coagulase-negative Staphylococcus (CoNS)\"  # setting another language set_AMR_locale(\"Spanish\") #> ℹ Using Spanish (Español) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacina\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Staphylococcus coagulasa negativo (SCN)\"  # setting yet another language set_AMR_locale(\"Greek\") #> ℹ Using Greek (Ελληνικά) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Σιπροφλοξασίνη\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Σταφυλόκοκκος με αρνητική πηκτικότητα (CoNS)\"  # setting yet another language set_AMR_locale(\"Ukrainian\") #> ℹ Using Ukrainian (Українська) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ципрофлоксацин\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Коагулазонегативний стафілокок (КНС)\"  # set_AMR_locale() understands endonyms, English exonyms, and ISO-639-1: set_AMR_locale(\"Deutsch\") #> ℹ Using German (Deutsch) for the AMR package for this session. set_AMR_locale(\"German\") #> ℹ Using German (Deutsch) for the AMR package for this session. set_AMR_locale(\"de\") #> ℹ Using German (Deutsch) for the AMR package for this session. ab_name(\"amox/clav\") #> ℹ More than one result was found for item 1: amoxicillin and clavulansäure #> [1] \"Amoxicillin/Clavulansäure\"  # reset to system default reset_AMR_locale() #> ℹ Using the English language (English) for the AMR package for this #>   session. ab_name(\"amox/clav\") #> [1] \"Amoxicillin/clavulanic acid\""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-1829152","dir":"Changelog","previous_headings":"","what":"AMR 1.8.2.9152","title":"AMR 1.8.2.9152","text":"new major release AMR package, great new additions also breaking changes current users. listed . TL;DR functions arguments ‘rsi’ replaced ‘sir’, interpretation MIC values (now .sir() instead .rsi()) - old functions still work now Many new interesting functions, antibiogram() (generating traditional/combined/syndromic/WISCA antibiograms), sir_confidence_interval() mean_amr_distance(), add_custom_microorganisms() add custom microorganisms package Clinical breakpoints added EUCAST 2022 CLSI 2022 Microbiological taxonomy (microorganisms data set) updated 2022 now based LPSN GBIF Much increased algorithms translate user input valid taxonomy, e.g. using recent scientific work per-species human pathogenicity 20 new antibiotics added updated DDDs ATC codes Extended support antiviral agents (antivirals data set), many new functions Now available 20 languages Many small bug fixes","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/news/index.html","id":"sir-vs-rsi-1-8-2-9152","dir":"Changelog","previous_headings":"New","what":"SIR vs. RSI","title":"AMR 1.8.2.9152","text":"milestone version, replaced mentions RSI SIR, comply actually commonly used field clinical microbiology comes tri-form regarding AMR. existing functions .rsi(), rsi_df() ggplot_rsi() still work, replacements .sir(), sir_df(), ggplot_sir() now current functions AMR data analysis. warning thrown session remind users . data set rsi_translation now called clinical_breakpoints better reflect content. ‘RSI functions’ removed future version, late 2023 / early 2024.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-antibiogram-function-1-8-2-9152","dir":"Changelog","previous_headings":"New","what":"New antibiogram function","title":"AMR 1.8.2.9152","text":"new antibiogram() function, users can now generate traditional, combined, syndromic, even weighted-incidence syndromic combination antibiograms (WISCA). , follow logic previously described work Klinker et al. (2021, DOI 10.1177/20499361211011373) Barbieri et al. (2021, DOI 10.1186/s13756-021-00939-2). help page antibiogram() extensively elaborates use cases, antibiogram() also supports printing R Markdown Quarto, support 20 languages. Furthermore, different plotting methods implemented allow graphical visualisations well.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"interpretation-of-mic-and-disk-diffusion-values-1-8-2-9152","dir":"Changelog","previous_headings":"New","what":"Interpretation of MIC and disk diffusion values","title":"AMR 1.8.2.9152","text":"clinical breakpoints intrinsic resistance EUCAST 2022 CLSI 2022 added .sir(). EUCAST 2022 (v12.0) now new default guideline MIC disks diffusion interpretations, eucast_rules() apply EUCAST Expert Rules. default guideline (EUCAST) can now changed new AMR_guideline option, : options(AMR_guideline = \"CLSI 2020\"). new arguments include_PKPD (default: TRUE) include_screening (default: FALSE), users can now specify whether breakpoints screening PK/PD table included interpreting MICs disks diffusion values. options can set globally, can read new manual. Interpretation guidelines older 10 years removed, oldest now included guidelines EUCAST CLSI 2013.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"supported-languages-1-8-2-9152","dir":"Changelog","previous_headings":"New","what":"Supported languages","title":"AMR 1.8.2.9152","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-1-8-2-9152","dir":"Changelog","previous_headings":"New","what":"Outbreak management","title":"AMR 1.8.2.9152","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-1-8-2-9152","dir":"Changelog","previous_headings":"New","what":"Microbiological taxonomy","title":"AMR 1.8.2.9152","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-1-8-2-9152","dir":"Changelog","previous_headings":"New","what":"Antibiotic agents and selectors","title":"AMR 1.8.2.9152","text":"new function add_custom_antimicrobials() allows users add custom antimicrobial codes names AMR package. antibiotics data set greatly updated: following 20 antibiotics added (also includes new J01RA ATC group): azithromycin/fluconazole/secnidazole (AFC), cefepime/amikacin (CFA), cefixime/ornidazole (CEO), ceftriaxone/beta-lactamase inhibitor (CEB), ciprofloxacin/metronidazole (CIM), ciprofloxacin/ornidazole (CIO), ciprofloxacin/tinidazole (CIT), furazidin (FUR), isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), lascufloxacin (LSC), levofloxacin/ornidazole (LEO), nemonoxacin (NEM), norfloxacin/metronidazole (NME), norfloxacin/tinidazole (NTI), ofloxacin/ornidazole (OOR), oteseconazole (OTE), rifampicin/ethambutol/isoniazid (REI), sarecycline (SRC), tetracycline/oleandomycin (TOL), thioacetazone (TAT) Added missing ATC codes Updated DDDs PubChem Compound IDs Updated antibiotic name spelling, now used WHOCC (cephalexin -> cefalexin, phenethicillin -> pheneticillin) Antibiotic code “CEI” ceftolozane/tazobactam replaced “CZT” comply EARS-Net WHONET 2022. old code still work cases using .ab() ab_*() functions. Support antimicrobial interpretation anaerobic bacteria, adding ‘placeholder’ code B_ANAER microorganisms data set adding breakpoints anaerobics clinical_breakpoints data set, used .sir() interpretion MIC disk diffusion values Also, added support using antibiotic selectors scoped dplyr verbs (without using vars()), : ... %>% summarise_at(aminoglycosides(), resistance), please see resistance() examples.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"antiviral-agents-1-8-2-9152","dir":"Changelog","previous_headings":"New","what":"Antiviral agents","title":"AMR 1.8.2.9152","text":"now added extensive support antiviral agents! first time, AMR package extensive support antiviral drugs work names, codes data way. antivirals data set extended 18 new drugs (also new J05AJ ATC group) now also contains antiviral identifiers LOINC codes new data type av (antivirals) added, functionally similar ab antibiotics Functions .av(), av_name(), av_atc(), av_synonyms(), av_from_text() added siblings ab_*() equivalents","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-new-functions-1-8-2-9152","dir":"Changelog","previous_headings":"New","what":"Other new functions","title":"AMR 1.8.2.9152","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-1-8-2-9152","dir":"Changelog","previous_headings":"","what":"Changes","title":"AMR 1.8.2.9152","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-1-8-2-9152","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.8.2.9152","text":"Added Peter Dutey-Magni, Dmytro Mykhailenko, Anton Mymrikov, Andrew Norgan, Jonas Salm, Anita Williams contributors, thank valuable input New website make use new Bootstrap 5 pkgdown 2.0. website now contains results examples automatically regenerated every change repository, using GitHub Actions R Rmd files project now styled using styler package Set scalar conditional expressions (&& ||) possible comply upcoming R 4.3 enormous lot code cleaning, fixing small bugs along way changelog contains changes AMR v2.0 later. prior versions, please see archive.","code":""}]
+[{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) reliable data thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial agents, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"How to conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables. AMR package contains data set example_isolates_unclean, might look data users extracted laboratory systems: AMR data analysis, like microorganism column contain valid, --date taxonomy, antibiotic columns cleaned SIR values well.","code":"library(dplyr) library(ggplot2) library(AMR)  # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\")) example_isolates_unclean #> # A tibble: 3,000 × 8 #>    patient_id hospital date       bacteria      AMX   AMC   CIP   GEN   #>    <chr>      <chr>    <date>     <chr>         <chr> <chr> <chr> <chr> #>  1 J3         A        2012-11-21 E. coli       R     I     S     S     #>  2 R7         A        2018-04-03 K. pneumoniae R     I     S     S     #>  3 P3         A        2014-09-19 E. coli       R     S     S     S     #>  4 P10        A        2015-12-10 E. coli       S     I     S     S     #>  5 B7         A        2015-03-02 E. coli       S     S     S     S     #>  6 W3         A        2018-03-31 S. aureus     R     S     R     S     #>  7 J8         A        2016-06-14 E. coli       R     S     S     S     #>  8 M3         A        2015-10-25 E. coli       R     S     S     S     #>  9 J3         A        2019-06-19 E. coli       S     S     S     S     #> 10 G6         A        2015-04-27 S. aureus     S     S     S     S     #> # … with 2,990 more rows  # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"How to conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 11 Dec 2022. codes AMR packages come .mo() short, still human readable. importantly, .mo() supports kinds input: first character codes denote taxonomic kingdom, Bacteria (B), Fungi (F), Protozoa (P). AMR package also contain functions directly retrieve taxonomic properties, name, genus, species, family, order, even Gram-stain. start mo_ use .mo() internally, still arbitrary user input can used: Now can thus clean data: Apparently, uncertainty translation taxonomic codes. Let’s check : ’s good.","code":"as.mo(\"Klebsiella pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"K. pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLEPNE\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLPN\") #> Class 'mo' #> [1] B_KLBSL_PNMN mo_family(\"K. pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"K. pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"K. pneumoniae\") #> [1] \"pneumoniae\"  mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\"  mo_ref(\"K. pneumoniae\") #> [1] \"Trevisan, 1887\"  mo_snomed(\"K. pneumoniae\") #> [[1]] #> [1] \"1098101000112102\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\" our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE) #> ℹ Microorganism translation was uncertain for four microorganisms. Run #>   mo_uncertainties() to review these uncertainties, or use #>   add_custom_microorganisms() to add custom entries. mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans. See ?mo_matching_score. #>  #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #>              Based on input \"E coli\" #> Also matched: Enterobacter cowanii (0.600), Eubacterium combesii #>               (0.600), Eggerthia catenaformis (0.591), Eubacterium callanderi #>               (0.591), Enterocloster citroniae (0.587), Eubacterium cylindroides #>               (0.583), Enterococcus casseliflavus (0.577), Enterobacter cloacae #>               cloacae (0.571), Ehrlichia canis (0.567), and Enterobacter cloacae #>               dissolvens (0.565) #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #>                    Based on input \"K pneumoniae\" #> Also matched: Klebsiella pneumoniae ozaenae (0.707), Klebsiella #>               pneumoniae pneumoniae (0.688), Klebsiella pneumoniae rhinoscleromatis #>               (0.658), Klebsiella pasteurii (0.500), Klebsiella planticola (0.500), #>               Kingella potus (0.400), Kosakonia pseudosacchari (0.361), Kaistella #>               palustris (0.333), Kocuria palustris (0.333), and Kocuria pelophila #>               (0.333) #> -------------------------------------------------------------------------------- #> \"S. aureus\" -> Staphylococcus aureus (B_STPHY_AURS, 0.690) #>                Based on input \"S aureus\" #> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus #>               argenteus (0.625), Staphylococcus aureus anaerobius (0.625), #>               Streptomyces argenteolus (0.483), Streptomyces aureus (0.474), #>               Streptomyces azureus (0.467), Streptomyces aureorectus (0.444), #>               Streptomyces auratus (0.433), Streptomyces aurantiogriseus (0.429), and #>               Streptomyces aureocirculatus (0.429) #> -------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #>                    Based on input \"S pneumoniae\" #> Also matched: Streptococcus pseudopneumoniae (0.700), Serratia #>               proteamaculans quinovora (0.545), Streptococcus pseudoporcinus (0.536), #>               Staphylococcus pseudintermedius (0.532), Serratia proteamaculans #>               proteamaculans (0.526), Salmonella Portanigra (0.524), Sphingomonas #>               paucimobilis (0.520), Streptococcus pluranimalium (0.519), #>               Streptococcus constellatus pharyngis (0.514), and Salmonella Pakistan #>               (0.500) #>  #> Only the first 10 other matches of each record are shown. Run #> print(mo_uncertainties(), n = ...) to view more entries, or save #> mo_uncertainties() to an object."},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"antibiotic-results","dir":"Articles","previous_headings":"Preparation","what":"Antibiotic results","title":"How to conduct AMR data analysis","text":"column antibiotic test results must also cleaned. AMR package comes three new data types work test results: mic minimal inhibitory concentrations (MIC), disk disk diffusion diameters, sir SIR data interpreted already. package can also determine SIR values based MIC disk diffusion values, read .sir() page. now, just clean SIR columns data using dplyr: basically cleaning, time start data inclusion.","code":"# method 1, be explicit about the columns: our_data <- our_data %>%   mutate_at(vars(AMX:GEN), as.sir)  # method 2, let the AMR package determine the eligible columns our_data <- our_data %>%   mutate_if(is_sir_eligible, as.sir)  # result: our_data #> # A tibble: 3,000 × 8 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> #>  1 J3         A        2012-11-21 B_ESCHR_COLI R     I     S     S     #>  2 R7         A        2018-04-03 B_KLBSL_PNMN R     I     S     S     #>  3 P3         A        2014-09-19 B_ESCHR_COLI R     S     S     S     #>  4 P10        A        2015-12-10 B_ESCHR_COLI S     I     S     S     #>  5 B7         A        2015-03-02 B_ESCHR_COLI S     S     S     S     #>  6 W3         A        2018-03-31 B_STPHY_AURS R     S     R     S     #>  7 J8         A        2016-06-14 B_ESCHR_COLI R     S     S     S     #>  8 M3         A        2015-10-25 B_ESCHR_COLI R     S     S     S     #>  9 J3         A        2019-06-19 B_ESCHR_COLI S     S     S     S     #> 10 G6         A        2015-04-27 B_STPHY_AURS S     S     S     S     #> # … with 2,990 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Preparation","what":"First isolates","title":"How to conduct AMR data analysis","text":"need know isolates can actually use analysis without repetition bias. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. Read methods first_isolate() page. outcome function can easily added data: 88% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 626 isolates analysis. Now data looks like: Time analysis.","code":"our_data <- our_data %>%   mutate(first = first_isolate(info = TRUE)) #> Determining first isolates using an episode length of 365 days #> ℹ Using column 'bacteria' as input for col_mo. #> ℹ Using column 'date' as input for col_date. #> ℹ Using column 'patient_id' as input for col_patient_id. #> Basing inclusion on all antimicrobial results, using a points threshold of #> 2 #> Including isolates from ICU. #> => Found 2,626 'phenotype-based' first isolates (87.6% within scope and #>    87.5% of total where a microbial ID was available) our_data_1st <- our_data %>%   filter(first == TRUE) our_data_1st <- our_data %>%   filter_first_isolate() our_data_1st #> # A tibble: 2,626 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 J3         A        2012-11-21 B_ESCHR_COLI R     I     S     S     TRUE  #>  2 R7         A        2018-04-03 B_KLBSL_PNMN R     I     S     S     TRUE  #>  3 P10        A        2015-12-10 B_ESCHR_COLI S     I     S     S     TRUE  #>  4 B7         A        2015-03-02 B_ESCHR_COLI S     S     S     S     TRUE  #>  5 W3         A        2018-03-31 B_STPHY_AURS R     S     R     S     TRUE  #>  6 J8         A        2016-06-14 B_ESCHR_COLI R     S     S     S     TRUE  #>  7 M3         A        2015-10-25 B_ESCHR_COLI R     S     S     S     TRUE  #>  8 J3         A        2019-06-19 B_ESCHR_COLI S     S     S     S     TRUE  #>  9 G6         A        2015-04-27 B_STPHY_AURS S     S     S     S     TRUE  #> 10 P4         A        2011-06-21 B_ESCHR_COLI S     S     S     S     TRUE  #> # … with 2,616 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"analysing-the-data","dir":"Articles","previous_headings":"","what":"Analysing the data","title":"How to conduct AMR data analysis","text":"base R summary() function gives good first impression, comes support new mo sir classes now data set:","code":"summary(our_data_1st) #>   patient_id          hospital              date            #>  Length:2626        Length:2626        Min.   :2011-01-01   #>  Class :character   Class :character   1st Qu.:2013-04-14   #>  Mode  :character   Mode  :character   Median :2015-06-05   #>                                        Mean   :2015-06-15   #>                                        3rd Qu.:2017-08-23   #>                                        Max.   :2020-01-01   #>    bacteria               AMX                    AMC                 #>  Class :mo             Class:sir              Class:sir              #>  <NA>  :0              %R   :43.2% (n=1134)   %R   :36.1% (n=947)    #>  Unique:4              %SI  :56.8% (n=1492)   %SI  :63.9% (n=1679)   #>  #1    :B_ESCHR_COLI   - %S :41.1% (n=1080)   - %S :52.7% (n=1383)   #>  #2    :B_STPHY_AURS   - %I :15.7% (n=412)    - %I :11.3% (n=296)    #>  #3    :B_STRPT_PNMN                                                 #>     CIP                    GEN                  first         #>  Class:sir              Class:sir              Mode:logical   #>  %R   :42.0% (n=1102)   %R   :37.0% (n=971)    TRUE:2626      #>  %SI  :58.0% (n=1524)   %SI  :63.0% (n=1655)                  #>  - %S :51.9% (n=1362)   - %S :59.9% (n=1574)                  #>  - %I : 6.2% (n=162)    - %I : 3.1% (n=81)                    #>   glimpse(our_data_1st) #> Rows: 2,626 #> Columns: 9 #> $ patient_id <chr> \"J3\", \"R7\", \"P10\", \"B7\", \"W3\", \"J8\", \"M3\", \"J3\", \"G6\", \"P4\"… #> $ hospital   <chr> \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\",… #> $ date       <date> 2012-11-21, 2018-04-03, 2015-12-10, 2015-03-02, 2018-03-31… #> $ bacteria   <mo> \"B_ESCHR_COLI\", \"B_KLBSL_PNMN\", \"B_ESCHR_COLI\", \"B_ESCHR_COL… #> $ AMX        <sir> R, R, S, S, R, R, R, S, S, S, S, R, S, S, R, R, R, R, I, S,… #> $ AMC        <sir> I, I, I, S, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, R,… #> $ CIP        <sir> S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S, S,… #> $ GEN        <sir> S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,… #> $ first      <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…  # number of unique values per column: sapply(our_data_1st, n_distinct) #> patient_id   hospital       date   bacteria        AMX        AMC        CIP  #>        260          3       1808          4          3          3          3  #>        GEN      first  #>          3          1"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"availability-of-species","dir":"Articles","previous_headings":"Analysing the data","what":"Availability of species","title":"How to conduct AMR data analysis","text":"just get idea species distributed, create frequency table count() based name microorganisms:","code":"our_data %>%   count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #>   `mo_name(bacteria)`          n #>   <chr>                    <int> #> 1 Escherichia coli          1518 #> 2 Staphylococcus aureus      730 #> 3 Streptococcus pneumoniae   426 #> 4 Klebsiella pneumoniae      326  our_data_1st %>%   count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #>   `mo_name(bacteria)`          n #>   <chr>                    <int> #> 1 Escherichia coli          1250 #> 2 Staphylococcus aureus      661 #> 3 Streptococcus pneumoniae   399 #> 4 Klebsiella pneumoniae      316"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"select-and-filter-with-antibiotic-selectors","dir":"Articles","previous_headings":"Analysing the data","what":"Select and filter with antibiotic selectors","title":"How to conduct AMR data analysis","text":"Using -called antibiotic class selectors, can select filter columns based antibiotic class antibiotic results :","code":"our_data_1st %>%   select(date, aminoglycosides()) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) #> # A tibble: 2,626 × 2 #>    date       GEN   #>    <date>     <sir> #>  1 2012-11-21 S     #>  2 2018-04-03 S     #>  3 2015-12-10 S     #>  4 2015-03-02 S     #>  5 2018-03-31 S     #>  6 2016-06-14 S     #>  7 2015-10-25 S     #>  8 2019-06-19 S     #>  9 2015-04-27 S     #> 10 2011-06-21 S     #> # … with 2,616 more rows  our_data_1st %>%   select(bacteria, betalactams()) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 2,626 × 3 #>    bacteria     AMX   AMC   #>    <mo>         <sir> <sir> #>  1 B_ESCHR_COLI R     I     #>  2 B_KLBSL_PNMN R     I     #>  3 B_ESCHR_COLI S     I     #>  4 B_ESCHR_COLI S     S     #>  5 B_STPHY_AURS R     S     #>  6 B_ESCHR_COLI R     S     #>  7 B_ESCHR_COLI R     S     #>  8 B_ESCHR_COLI S     S     #>  9 B_STPHY_AURS S     S     #> 10 B_ESCHR_COLI S     S     #> # … with 2,616 more rows  our_data_1st %>%   select(bacteria, where(is.sir)) #> # A tibble: 2,626 × 5 #>    bacteria     AMX   AMC   CIP   GEN   #>    <mo>         <sir> <sir> <sir> <sir> #>  1 B_ESCHR_COLI R     I     S     S     #>  2 B_KLBSL_PNMN R     I     S     S     #>  3 B_ESCHR_COLI S     I     S     S     #>  4 B_ESCHR_COLI S     S     S     S     #>  5 B_STPHY_AURS R     S     R     S     #>  6 B_ESCHR_COLI R     S     S     S     #>  7 B_ESCHR_COLI R     S     S     S     #>  8 B_ESCHR_COLI S     S     S     S     #>  9 B_STPHY_AURS S     S     S     S     #> 10 B_ESCHR_COLI S     S     S     S     #> # … with 2,616 more rows  # filtering using AB selectors is also possible: our_data_1st %>%   filter(any(aminoglycosides() == \"R\")) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) #> # A tibble: 971 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 J5         A        2017-12-25 B_STRPT_PNMN R     S     S     R     TRUE  #>  2 X1         A        2017-07-04 B_STPHY_AURS R     S     S     R     TRUE  #>  3 B3         A        2016-07-24 B_ESCHR_COLI S     S     S     R     TRUE  #>  4 V7         A        2012-04-03 B_ESCHR_COLI S     S     S     R     TRUE  #>  5 C9         A        2017-03-23 B_ESCHR_COLI S     S     S     R     TRUE  #>  6 R1         A        2018-06-10 B_STPHY_AURS S     S     S     R     TRUE  #>  7 S2         A        2013-07-19 B_STRPT_PNMN S     S     S     R     TRUE  #>  8 P5         A        2019-03-09 B_STPHY_AURS S     S     S     R     TRUE  #>  9 Q8         A        2019-08-10 B_STPHY_AURS S     S     S     R     TRUE  #> 10 K5         A        2013-03-15 B_STRPT_PNMN S     S     S     R     TRUE  #> # … with 961 more rows  our_data_1st %>%   filter(all(betalactams() == \"R\")) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 471 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 M7         A        2013-07-22 B_STRPT_PNMN R     R     S     S     TRUE  #>  2 R10        A        2013-12-20 B_STPHY_AURS R     R     S     S     TRUE  #>  3 R7         A        2015-10-25 B_STPHY_AURS R     R     S     S     TRUE  #>  4 R8         A        2019-10-25 B_STPHY_AURS R     R     S     S     TRUE  #>  5 I7         A        2015-08-19 B_ESCHR_COLI R     R     S     S     TRUE  #>  6 N3         A        2014-12-29 B_STRPT_PNMN R     R     R     S     TRUE  #>  7 Q2         A        2019-09-22 B_ESCHR_COLI R     R     S     S     TRUE  #>  8 X7         A        2011-03-20 B_ESCHR_COLI R     R     S     R     TRUE  #>  9 C5         A        2015-08-30 B_KLBSL_PNMN R     R     S     R     TRUE  #> 10 W9         A        2013-10-02 B_ESCHR_COLI R     R     S     S     TRUE  #> # … with 461 more rows  # even works in base R (since R 3.0): our_data_1st[all(betalactams() == \"R\"), ] #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 471 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 M7         A        2013-07-22 B_STRPT_PNMN R     R     S     S     TRUE  #>  2 R10        A        2013-12-20 B_STPHY_AURS R     R     S     S     TRUE  #>  3 R7         A        2015-10-25 B_STPHY_AURS R     R     S     S     TRUE  #>  4 R8         A        2019-10-25 B_STPHY_AURS R     R     S     S     TRUE  #>  5 I7         A        2015-08-19 B_ESCHR_COLI R     R     S     S     TRUE  #>  6 N3         A        2014-12-29 B_STRPT_PNMN R     R     R     S     TRUE  #>  7 Q2         A        2019-09-22 B_ESCHR_COLI R     R     S     S     TRUE  #>  8 X7         A        2011-03-20 B_ESCHR_COLI R     R     S     R     TRUE  #>  9 C5         A        2015-08-30 B_KLBSL_PNMN R     R     S     R     TRUE  #> 10 W9         A        2013-10-02 B_ESCHR_COLI R     R     S     S     TRUE  #> # … with 461 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"generate-antibiograms","dir":"Articles","previous_headings":"Analysing the data","what":"Generate antibiograms","title":"How to conduct AMR data analysis","text":"package comes antibiogram(), function automatically generates traditional, combined, syndromic, even weighted-incidence syndromic combination antibiograms (WISCA). R Markdown (page) automatically prints right table format. suggestions generate different antibiograms: Antibiograms can plotted using autoplot() ggplot2 packages, since package provides extension function:  calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"# traditional: antibiogram(our_data_1st) antibiogram(our_data_1st,   ab_transform = \"name\" ) antibiogram(our_data_1st,   ab_transform = \"name\",   language = \"es\" ) # support for 20 languages # combined: antibiogram(our_data_1st,   antibiotics = c(\"AMC\", \"AMC+CIP\", \"AMC+GEN\") ) # for a syndromic antibiogram, we must fake some clinical conditions: our_data_1st$condition <- sample(c(\"Cardial\", \"Respiratory\", \"Rheumatic\"),   size = nrow(our_data_1st),   replace = TRUE )  # syndromic: antibiogram(our_data_1st,   syndromic_group = \"condition\" ) antibiogram(our_data_1st,   # you can use AB selectors here as well:   antibiotics = c(penicillins(), aminoglycosides()),   syndromic_group = \"condition\",   mo_transform = \"gramstain\" ) #> ℹ For penicillins() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) # WISCA: # (we lack some details, but it could contain a filter on e.g. >65 year-old males) wisca <- antibiogram(our_data_1st,   antibiotics = c(\"AMC\", \"AMC+CIP\", \"AMC+GEN\"),   syndromic_group = \"condition\",   mo_transform = \"gramstain\" ) wisca autoplot(wisca)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"How to conduct AMR data analysis","text":"functions resistance() susceptibility() can used calculate antimicrobial resistance susceptibility. specific analyses, functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() proportion_R() can used determine proportion specific antimicrobial outcome. functions contain minimum argument, denoting minimum required number test results returning value. functions otherwise return NA. default minimum = 30, following CLSI M39-A4 guideline applying microbial epidemiology. per EUCAST guideline 2019, calculate resistance proportion R (proportion_R(), equal resistance()) susceptibility proportion S (proportion_SI(), equal susceptibility()). functions can used : can used conjunction group_by() summarise(), dplyr package: Author: Dr. Matthijs Berends, 26th Feb 2023","code":"our_data_1st %>% resistance(AMX) #> [1] 0.4318355 our_data_1st %>%   group_by(hospital) %>%   summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #>   hospital amoxicillin #>   <chr>          <dbl> #> 1 A              0.343 #> 2 B              0.569 #> 3 C              0.375"},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to apply EUCAST rules","text":"EUCAST rules? European Committee Antimicrobial Susceptibility Testing (EUCAST) states website: EUCAST expert rules tabulated collection expert knowledge intrinsic resistances, exceptional resistance phenotypes interpretive rules may applied antimicrobial susceptibility testing order reduce errors make appropriate recommendations reporting particular resistances. Europe, lot medical microbiological laboratories already apply rules (Brown et al., 2015). package features latest insights intrinsic resistance unusual phenotypes (v3.3, 2021). Moreover, eucast_rules() function use purpose can also apply additional rules, like forcing ampicillin = R isolates amoxicillin/clavulanic acid = R.","code":""},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to apply EUCAST rules","text":"rules can used discard impossible bug-drug combinations data. example, Klebsiella produces beta-lactamase prevents ampicillin (amoxicillin) working . words, practically every strain Klebsiella resistant ampicillin. Sometimes, laboratory data can still contain strains ampicillin susceptible ampicillin. antibiogram available identification available, antibiogram re-interpreted based identification (namely, Klebsiella). EUCAST expert rules solve , can applied using eucast_rules(): convenient function mo_is_intrinsic_resistant() uses guideline, allows check one specific microorganisms antibiotics: EUCAST rules can used correction, can also used filling known resistance susceptibility based results antimicrobials drugs. process called interpretive reading, basically form imputation, part eucast_rules() function well:","code":"oops <- data.frame(   mo = c(     \"Klebsiella\",     \"Escherichia\"   ),   ampicillin = \"S\" ) oops #>            mo ampicillin #> 1  Klebsiella          S #> 2 Escherichia          S  eucast_rules(oops, info = FALSE) #>            mo ampicillin #> 1  Klebsiella          R #> 2 Escherichia          S mo_is_intrinsic_resistant(   c(\"Klebsiella\", \"Escherichia\"),   \"ampicillin\" ) #> [1]  TRUE FALSE  mo_is_intrinsic_resistant(   \"Klebsiella\",   c(\"ampicillin\", \"kanamycin\") ) #> [1]  TRUE FALSE data <- data.frame(   mo = c(     \"Staphylococcus aureus\",     \"Enterococcus faecalis\",     \"Escherichia coli\",     \"Klebsiella pneumoniae\",     \"Pseudomonas aeruginosa\"   ),   VAN = \"-\", # Vancomycin   AMX = \"-\", # Amoxicillin   COL = \"-\", # Colistin   CAZ = \"-\", # Ceftazidime   CXM = \"-\", # Cefuroxime   PEN = \"S\", # Benzylenicillin   FOX = \"S\", # Cefoxitin   stringsAsFactors = FALSE ) data eucast_rules(data)"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"type-of-input","dir":"Articles","previous_headings":"","what":"Type of input","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function takes data set input, regular data.frame. tries automatically determine right columns info isolates, name species columns results antimicrobial agents. See help page info set right settings data command ?mdro. WHONET data (data), settings automatically set correctly.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"guidelines","dir":"Articles","previous_headings":"","what":"Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function support multiple guidelines. can select guideline guideline parameter. Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. “Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.” Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.2\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance Unusual Phenotypes” (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance Exceptional Phenotypes Tables” (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization “Companion handbook guidelines programmatic management drug-resistant tuberculosis” (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"custom-guidelines","dir":"Articles","previous_headings":"Guidelines","what":"Custom Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"can also use custom guideline. Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() dplyr package, recognise input method set rules. Rules must set using R considers ‘formula notation’: row/isolate matches first rule, value first ~ (case ‘Elderly Type ’) set MDRO value. Otherwise, second rule tried . maximum number rules unlimited. can print rules set console overview. Colours help reading console supports colours. outcome function can used guideline argument mdro() function: rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline(   CIP == \"R\" & age > 60 ~ \"Elderly Type A\",   ERY == \"R\" & age > 60 ~ \"Elderly Type B\" ) custom #> A set of custom MDRO rules: #>   1. If CIP is \"R\" and age is higher than 60 then: Elderly Type A #>   2. If ERY is \"R\" and age is higher than 60 then: Elderly Type B #>   3. Otherwise: Negative #>  #> Unmatched rows will return NA. #> Results will be of class 'factor', with ordered levels: Negative < Elderly Type A < Elderly Type B x <- mdro(example_isolates, guideline = custom) table(x) #> x #>       Negative Elderly Type A Elderly Type B  #>           1070            198            732"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function always returns ordered factor predefined guidelines. example, output default guideline Magiorakos et al. returns factor levels ‘Negative’, ‘MDR’, ‘XDR’ ‘PDR’ order. next example uses example_isolates data set. data set included package contains full antibiograms 2,000 microbial isolates. reflects reality can used practise AMR data analysis. test MDR/XDR/PDR guideline data set, get: (16 isolates test results) Frequency table Class: factor > ordered (numeric) Length: 2,000 Levels: 4: Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant … Available: 1,729 (86.45%, NA: 271 = 13.55%) Unique: 2 another example, create data set determine multi-drug resistant TB: column names automatically verified valid drug names codes, worked exactly way: data set now looks like : can now add interpretation MDR-TB data set. can use: shortcut mdr_tb(): Create frequency table results: Frequency table Class: factor > ordered (numeric) Length: 5,000 Levels: 5: Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant <… Available: 5,000 (100%, NA: 0 = 0%) Unique: 5","code":"library(dplyr) # to support pipes: %>% library(cleaner) # to create frequency tables example_isolates %>%   mdro() %>%   freq() # show frequency table of the result #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) # random_sir() is a helper function to generate # a random vector with values S, I and R my_TB_data <- data.frame(   rifampicin = random_sir(5000),   isoniazid = random_sir(5000),   gatifloxacin = random_sir(5000),   ethambutol = random_sir(5000),   pyrazinamide = random_sir(5000),   moxifloxacin = random_sir(5000),   kanamycin = random_sir(5000) ) my_TB_data <- data.frame(   RIF = random_sir(5000),   INH = random_sir(5000),   GAT = random_sir(5000),   ETH = random_sir(5000),   PZA = random_sir(5000),   MFX = random_sir(5000),   KAN = random_sir(5000) ) head(my_TB_data) #>   rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin #> 1          R         R            I          R            R            I #> 2          S         R            S          S            I            I #> 3          S         S            I          R            S            S #> 4          S         I            S          S            R            I #> 5          R         I            I          S            S            I #> 6          I         R            R          I            I            I #>   kanamycin #> 1         S #> 2         I #> 3         I #> 4         R #> 5         S #> 6         I mdro(my_TB_data, guideline = \"TB\") my_TB_data$mdr <- mdr_tb(my_TB_data) #> ℹ No column found as input for col_mo, assuming all rows contain #>   Mycobacterium tuberculosis. freq(my_TB_data$mdr)"},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"transforming","dir":"Articles","previous_headings":"","what":"Transforming","title":"How to conduct principal component analysis (PCA) for AMR","text":"PCA, need transform AMR data first. example_isolates data set package looks like: Now transform data set resistance percentages per taxonomic order genus:","code":"library(AMR) library(dplyr) glimpse(example_isolates) #> Rows: 2,000 #> Columns: 46 #> $ date    <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2… #> $ patient <chr> \"A77334\", \"A77334\", \"067927\", \"067927\", \"067927\", \"067927\", \"4… #> $ age     <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50… #> $ gender  <chr> \"F\", \"F\", \"F\", \"F\", \"F\", \"F\", \"M\", \"M\", \"F\", \"F\", \"M\", \"M\", \"M… #> $ ward    <chr> \"Clinical\", \"Clinical\", \"ICU\", \"ICU\", \"ICU\", \"ICU\", \"Clinical\"… #> $ mo      <mo> \"B_ESCHR_COLI\", \"B_ESCHR_COLI\", \"B_STPHY_EPDR\", \"B_STPHY_EPDR\",… #> $ PEN     <sir> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,… #> $ OXA     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ FLC     <sir> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R… #> $ AMX     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ AMC     <sir> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N… #> $ AMP     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ TZP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CZO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ FEP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CXM     <sir> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S… #> $ FOX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ CTX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ CAZ     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, … #> $ CRO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ GEN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TOB     <sir> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA… #> $ AMK     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ KAN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TMP     <sir> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, … #> $ SXT     <sir> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N… #> $ NIT     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,… #> $ FOS     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ LNZ     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ CIP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S… #> $ MFX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ VAN     <sir> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, … #> $ TEC     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ TCY     <sir> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, … #> $ TGC     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ DOX     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ ERY     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ CLI     <sir> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA… #> $ AZM     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ IPM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ MEM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ MTR     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CHL     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ COL     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, … #> $ MUP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ RIF     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… resistance_data <- example_isolates %>%   group_by(     order = mo_order(mo), # group on anything, like order     genus = mo_genus(mo)   ) %>% #  and genus as we do here   summarise_if(is.sir, resistance) %>% # then get resistance of all drugs   select(     order, genus, AMC, CXM, CTX,     CAZ, GEN, TOB, TMP, SXT   ) # and select only relevant columns  head(resistance_data) #> # A tibble: 6 × 10 #> # Groups:   order [5] #>   order             genus          AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT #>   <chr>             <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 (unknown order)   (unknown ge…    NA    NA    NA    NA    NA    NA    NA    NA #> 2 Actinomycetales   Schaalia        NA    NA    NA    NA    NA    NA    NA    NA #> 3 Bacteroidales     Bacteroides     NA    NA    NA    NA    NA    NA    NA    NA #> 4 Campylobacterales Campylobact…    NA    NA    NA    NA    NA    NA    NA    NA #> 5 Caryophanales     Gemella         NA    NA    NA    NA    NA    NA    NA    NA #> 6 Caryophanales     Listeria        NA    NA    NA    NA    NA    NA    NA    NA"},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"perform-principal-component-analysis","dir":"Articles","previous_headings":"","what":"Perform principal component analysis","title":"How to conduct principal component analysis (PCA) for AMR","text":"new pca() function automatically filter rows contain numeric values selected variables, now need : result can reviewed good old summary() function: Good news. first two components explain total 93.3% variance (see PC1 PC2 values Proportion Variance. can create -called biplot base R biplot() function, see antimicrobial resistance per drug explain difference per microorganism.","code":"pca_result <- pca(resistance_data) #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #>   \"TMP\", and \"TOB\". Total observations available: 7. summary(pca_result) #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\" #> Importance of components: #>                           PC1    PC2    PC3     PC4     PC5     PC6       PC7 #> Standard deviation     2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 9.577e-17 #> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00 #> Cumulative Proportion  0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00 #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\""},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"plotting-the-results","dir":"Articles","previous_headings":"","what":"Plotting the results","title":"How to conduct principal component analysis (PCA) for AMR","text":"can’t see explanation points. Perhaps works better new ggplot_pca() function, automatically adds right labels even groups:  can also print ellipse per group, edit appearance:","code":"biplot(pca_result) ggplot_pca(pca_result) ggplot_pca(pca_result, ellipse = TRUE) +   ggplot2::labs(title = \"An AMR/PCA biplot!\")"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"spss-sas-stata","dir":"Articles","previous_headings":"","what":"SPSS / SAS / Stata","title":"How to import data from SPSS / SAS / Stata","text":"SPSS (Statistical Package Social Sciences) probably well-known software package statistical analysis. SPSS easier learn R, SPSS click menu run parts analysis. user-friendliness, taught universities particularly useful students new statistics. experience, guess pretty much (bio)medical students know time graduate. SAS Stata comparable statistical packages popular big industries.","code":""},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"compared-to-r","dir":"Articles","previous_headings":"","what":"Compared to R","title":"How to import data from SPSS / SAS / Stata","text":"said, SPSS easier learn R. SPSS, SAS Stata come major downsides comparing R: R highly modular. official R network (CRAN) features 16,000 packages time writing, AMR package one . packages peer-reviewed publication. Aside official channel, also developers choose submit CRAN, rather keep public repository, like GitHub. may even lot 14,000 packages . Bottom line , can really extend ask somebody . Take example AMR package. Among things, adds reliable reference data R help data cleaning analysis. SPSS, SAS Stata never know valid MIC value Gram stain E. coli . species Klebiella resistant amoxicillin Floxapen® trade name flucloxacillin. facts properties often needed clean existing data, inconvenient software package without reliable reference data. See demonstration. R extremely flexible. write syntax , can anything want. flexibility transforming, arranging, grouping summarising data, drawing plots, endless - SPSS, SAS Stata bound algorithms format styles. may bit flexible, can probably never create specific publication-ready plot without using (paid) software. sometimes write syntaxes SPSS run complete analysis ‘automate’ work, lot less time R. notice writing syntaxes R lot nifty clever SPSS. Still, working statistical package, knowledge (statistically) willing accomplish. R can easily automated. last years, R Markdown really made interesting development. R Markdown, can easily produce reports, whether format Word, PowerPoint, website, PDF document just raw data Excel. even allows use reference file containing layout style (e.g. fonts colours) organisation. use lot generate weekly monthly reports automatically. Just write code enjoy automatically updated reports interval like. even professional environment, create Shiny apps: live manipulation data using custom made website. webdesign knowledge needed (JavaScript, CSS, HTML) almost zero. R huge community. Many R users just ask questions websites like StackOverflow.com, largest online community programmers. time writing, 484 006 R-related questions already asked platform (covers questions answers programming language). experience, questions answered within couple minutes. R understands data type, including SPSS/SAS/Stata. ’s vice versa ’m afraid. can import data source R. example SPSS, SAS Stata (link), Minitab, Epi Info EpiData (link), Excel (link), flat files like CSV, TXT TSV (link), directly databases datawarehouses anywhere world (link). can even scrape websites download tables live internet (link) get results API call transform data one command (link). best part - can export R data formats well. can import SPSS file, analysis neatly R export resulting tables Excel files sharing. R completely free open-source. strings attached. created maintained volunteers believe (data) science open publicly available everybody. SPSS, SAS Stata quite expensive. IBM SPSS Staticstics comes subscriptions nowadays, varying USD 1,300 USD 8,500 per user per year. SAS Analytics Pro costs around USD 10,000 per computer. Stata also business model subscription fees, varying USD 600 USD 2,800 per computer per year, lower prices come limitation number variables can work . still offer benefits R. working midsized small company, can save tens thousands dollars using R instead e.g. SPSS - gaining even functions flexibility. R enthousiasts can much PR want (like ), nobody officially associated affiliated R. really free. R (nowadays) preferred analysis software academic papers. present, R among world powerful statistical languages, generally popular science (Bollmann et al., 2017). reasons, number references R analysis method academic papers rising continuously even surpassed SPSS academic use (Muenchen, 2014). believe thing SPSS , always great user interface easy learn use. Back developed , little competition, let alone R. R didn’t even professional user interface last decade (called RStudio, see ). people used R nineties 2010 almost completely incomparable R used now. language restyled completely volunteers dedicated professionals field data science. SPSS great nothing else compete. now 2023, don’t see reason SPSS better use R. demonstrate first point:","code":"# not all values are valid MIC values: as.mic(0.125) #> Class 'mic' #> [1] 0.125 as.mic(\"testvalue\") #> Class 'mic' #> [1] <NA>  # the Gram stain is available for all bacteria: mo_gramstain(\"E. coli\") #> [1] \"Gram-negative\"  # Klebsiella is intrinsic resistant to amoxicillin, according to EUCAST: klebsiella_test <- data.frame(   mo = \"klebsiella\",   amox = \"S\",   stringsAsFactors = FALSE ) klebsiella_test # (our original data) #>           mo amox #> 1 klebsiella    S eucast_rules(klebsiella_test, info = FALSE) # (the edited data by EUCAST rules) #>           mo amox #> 1 klebsiella    R  # hundreds of trade names can be translated to a name, trade name or an ATC code: ab_name(\"floxapen\") #> [1] \"Flucloxacillin\" ab_tradenames(\"floxapen\") #>  [1] \"culpen\"               \"floxacillin\"          \"floxacillin sodium\"   #>  [4] \"floxapen\"             \"floxapen sodium salt\" \"fluclox\"              #>  [7] \"flucloxacilina\"       \"flucloxacillin\"       \"flucloxacilline\"      #> [10] \"flucloxacillinum\"     \"fluorochloroxacillin\" \"staphylex\" ab_atc(\"floxapen\") #> [1] \"J01CF05\""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"rstudio","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata","what":"RStudio","title":"How to import data from SPSS / SAS / Stata","text":"work R, probably best option use RStudio. open-source free desktop environment allows run R code, also supports project management, version management, package management convenient import menus work data sources. can also install RStudio Server private corporate server, brings nothing less complete RStudio software website (home work). import data file, just click Import Dataset Environment tab:  additional packages needed, RStudio ask installed beforehand. window opens, can define options (parameters) used import ’re ready go:  want named variables imported factors resembles SPSS , use as_factor(). difference :","code":"SPSS_data # # A tibble: 4,203 x 4 #     v001 sex       status    statusage #    <dbl> <dbl+lbl> <dbl+lbl>     <dbl> #  1 10002 1         1              76.6 #  2 10004 0         1              59.1 #  3 10005 1         1              54.5 #  4 10006 1         1              54.1 #  5 10007 1         1              57.7 #  6 10008 1         1              62.8 #  7 10010 0         1              63.7 #  8 10011 1         1              73.1 #  9 10017 1         1              56.7 # 10 10018 0         1              66.6 # # ... with 4,193 more rows  as_factor(SPSS_data) # # A tibble: 4,203 x 4 #     v001 sex    status statusage #    <dbl> <fct>  <fct>      <dbl> #  1 10002 Male   alive       76.6 #  2 10004 Female alive       59.1 #  3 10005 Male   alive       54.5 #  4 10006 Male   alive       54.1 #  5 10007 Male   alive       57.7 #  6 10008 Male   alive       62.8 #  7 10010 Female alive       63.7 #  8 10011 Male   alive       73.1 #  9 10017 Male   alive       56.7 # 10 10018 Female alive       66.6 # # ... with 4,193 more rows"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"base-r","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata","what":"Base R","title":"How to import data from SPSS / SAS / Stata","text":"import data SPSS, SAS Stata, can use great haven package : can now import files follows:","code":"# download and install the latest version: install.packages(\"haven\") # load the package you just installed: library(haven)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"spss","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"SPSS","title":"How to import data from SPSS / SAS / Stata","text":"read files SPSS R: forget as_factor(), mentioned . export R objects SPSS file format:","code":"# read any SPSS file based on file extension (best way): read_spss(file = \"path/to/file\")  # read .sav or .zsav file: read_sav(file = \"path/to/file\")  # read .por file: read_por(file = \"path/to/file\") # save as .sav file: write_sav(data = yourdata, path = \"path/to/file\")  # save as compressed .zsav file: write_sav(data = yourdata, path = \"path/to/file\", compress = TRUE)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"sas","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"SAS","title":"How to import data from SPSS / SAS / Stata","text":"read files SAS R: export R objects SAS file format:","code":"# read .sas7bdat + .sas7bcat files: read_sas(data_file = \"path/to/file\", catalog_file = NULL)  # read SAS transport files (version 5 and version 8): read_xpt(file = \"path/to/file\") # save as regular SAS file: write_sas(data = yourdata, path = \"path/to/file\")  # the SAS transport format is an open format # (required for submission of the data to the FDA) write_xpt(data = yourdata, path = \"path/to/file\", version = 8)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"stata","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"Stata","title":"How to import data from SPSS / SAS / Stata","text":"read files Stata R: export R objects Stata file format:","code":"# read .dta file: read_stata(file = \"/path/to/file\")  # works exactly the same: read_dta(file = \"/path/to/file\") # save as .dta file, Stata version 14: # (supports Stata v8 until v15 at the time of writing) write_dta(data = yourdata, path = \"/path/to/file\", version = 14)"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"import-of-data","dir":"Articles","previous_headings":"","what":"Import of data","title":"How to work with WHONET data","text":"tutorial assumes already imported WHONET data e.g. readxl package. RStudio, can done using menu button ‘Import Dataset’ tab ‘Environment’. Choose option ‘Excel’ select exported file. Make sure date fields imported correctly. example syntax look like : package comes example data set WHONET. use analysis.","code":"library(readxl) data <- read_excel(path = \"path/to/your/file.xlsx\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to work with WHONET data","text":"First, load relevant packages yet . use tidyverse analyses. . don’t know yet, suggest read website: https://www.tidyverse.org/. transform variables simplify automate analysis: Microorganisms transformed microorganism codes (called mo) using Catalogue Life reference data set, contains ~70,000 microorganisms taxonomic kingdoms Bacteria, Fungi Protozoa. tranformation .mo(). function also recognises almost WHONET abbreviations microorganisms. Antimicrobial results interpretations clean valid. words, contain values \"S\", \"\" \"R\". exactly .sir() function . errors warnings, values transformed succesfully. also created package dedicated data cleaning checking, called cleaner package. freq() function can used create frequency tables. let’s check data, couple frequency tables: Frequency table Class: character Length: 500 Available: 500 (100%, NA: 0 = 0%) Unique: 38 Shortest: 11 Longest: 40 (omitted 28 entries, n = 57 [11.4%]) Frequency table Class: factor > ordered > sir (numeric) Length: 500 Levels: 3: S < < R Available: 481 (96.2%, NA: 19 = 3.8%) Unique: 3 Drug: Amoxicillin/clavulanic acid (AMC, J01CR02) Drug group: Beta-lactams/penicillins %SI: 78.59%","code":"library(dplyr) # part of tidyverse library(ggplot2) # part of tidyverse library(AMR) # this package library(cleaner) # to create frequency tables # transform variables data <- WHONET %>%   # get microbial ID based on given organism   mutate(mo = as.mo(Organism)) %>%   # transform everything from \"AMP_ND10\" to \"CIP_EE\" to the new `sir` class   mutate_at(vars(AMP_ND10:CIP_EE), as.sir) # our newly created `mo` variable, put in the mo_name() function data %>% freq(mo_name(mo), nmax = 10) # our transformed antibiotic columns # amoxicillin/clavulanic acid (J01CR02) as an example data %>% freq(AMC_ND2)"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"a-first-glimpse-at-results","dir":"Articles","previous_headings":"","what":"A first glimpse at results","title":"How to work with WHONET data","text":"easy ggplot already give lot information, using included ggplot_sir() function:","code":"data %>%   group_by(Country) %>%   select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%   ggplot_sir(translate_ab = \"ab\", facet = \"Country\", datalabels = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-full-microbial-taxonomy","dir":"Articles","previous_headings":"","what":"microorganisms: Full Microbial Taxonomy","title":"Data sets for download / own use","text":"data set 52 142 rows 22 columns, containing following column names:mo, fullname, status, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, source, lpsn, lpsn_parent, lpsn_renamed_to, gbif, gbif_parent, gbif_renamed_to, prevalence, snomed. data set R available microorganisms, load AMR package. last updated 26 February 2023 20:26:58 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (1.2 MB) Download tab-separated text file (11.3 MB) Download Microsoft Excel workbook (5 MB) Download Apache Feather file (5.4 MB) Download Apache Parquet file (2.6 MB) Download SAS data file (50.9 MB) Download IBM SPSS Statistics data file (16.9 MB) Download Stata DTA file (47.1 MB) NOTE: exported files SAS, SPSS Stata contain first 50 SNOMED codes per record, file size otherwise exceed 100 MB; file size limit GitHub. Advice? Use R instead. tab-separated text file Microsoft Excel workbook contain SNOMED codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source","dir":"Articles","previous_headings":"microorganisms: Full Microbial Taxonomy","what":"Source","title":"Data sets for download / own use","text":"data set contains full microbial taxonomy five kingdoms List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF): Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name ‘Microoganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content","dir":"Articles","previous_headings":"microorganisms: Full Microbial Taxonomy","what":"Example content","title":"Data sets for download / own use","text":"Included (sub)species per taxonomic kingdom: Example rows filtering genus Escherichia:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antibiotics-antibiotic-antifungal-drugs","dir":"Articles","previous_headings":"","what":"antibiotics: Antibiotic (+Antifungal) Drugs","title":"Data sets for download / own use","text":"data set 483 rows 14 columns, containing following column names:ab, cid, name, group, atc, atc_group1, atc_group2, abbreviations, synonyms, oral_ddd, oral_units, iv_ddd, iv_units, loinc. data set R available antibiotics, load AMR package. last updated 22 February 2023 13:38:57 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (39 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (66 kB) Download Apache Feather file (0.1 MB) Download Apache Parquet file (97 kB) Download SAS data file (1.9 MB) Download IBM SPSS Statistics data file (0.3 MB) Download Stata DTA file (0.4 MB) tab-separated text file Microsoft Excel workbook, SAS, SPSS Stata files contain ATC codes, common abbreviations, trade names LOINC codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-1","dir":"Articles","previous_headings":"antibiotics: Antibiotic (+Antifungal) Drugs","what":"Source","title":"Data sets for download / own use","text":"data set contains EARS-Net ATC codes gathered WHONET, compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine WHONET software 2019 LOINC (Logical Observation Identifiers Names Codes)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antivirals-antiviral-drugs","dir":"Articles","previous_headings":"","what":"antivirals: Antiviral Drugs","title":"Data sets for download / own use","text":"data set 120 rows 11 columns, containing following column names:av, name, atc, cid, atc_group, synonyms, oral_ddd, oral_units, iv_ddd, iv_units, loinc. data set R available antivirals, load AMR package. last updated 13 November 2022 07:46:10 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (5 kB) Download tab-separated text file (16 kB) Download Microsoft Excel workbook (16 kB) Download Apache Feather file (15 kB) Download Apache Parquet file (13 kB) Download SAS data file (84 kB) Download IBM SPSS Statistics data file (30 kB) Download Stata DTA file (73 kB) tab-separated text file Microsoft Excel workbook, SAS, SPSS Stata files contain trade names LOINC codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-2","dir":"Articles","previous_headings":"antivirals: Antiviral Drugs","what":"Source","title":"Data sets for download / own use","text":"data set contains ATC codes gathered compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine LOINC (Logical Observation Identifiers Names Codes)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"clinical_breakpoints-interpretation-from-mic-values-disk-diameters-to-sir","dir":"Articles","previous_headings":"","what":"clinical_breakpoints: Interpretation from MIC values & disk diameters to SIR","title":"Data sets for download / own use","text":"data set 18 308 rows 11 columns, containing following column names:guideline, method, site, mo, rank_index, ab, ref_tbl, disk_dose, breakpoint_S, breakpoint_R, uti. data set R available clinical_breakpoints, load AMR package. last updated 21 January 2023 22:47:20 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (42 kB) Download tab-separated text file (1.9 MB) Download Microsoft Excel workbook (0.8 MB) Download Apache Feather file (0.7 MB) Download Apache Parquet file (87 kB) Download SAS data file (3.6 MB) Download IBM SPSS Statistics data file (2.3 MB) Download Stata DTA file (3.4 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-3","dir":"Articles","previous_headings":"clinical_breakpoints: Interpretation from MIC values & disk diameters to SIR","what":"Source","title":"Data sets for download / own use","text":"data set contains interpretation rules MIC values disk diffusion diameters. Included guidelines CLSI (2013-2022) EUCAST (2013-2022).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"intrinsic_resistant-intrinsic-bacterial-resistance","dir":"Articles","previous_headings":"","what":"intrinsic_resistant: Intrinsic Bacterial Resistance","title":"Data sets for download / own use","text":"data set 134 634 rows 2 columns, containing following column names:mo ab. data set R available intrinsic_resistant, load AMR package. last updated 16 December 2022 15:10:43 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (78 kB) Download tab-separated text file (5.1 MB) Download Microsoft Excel workbook (1.3 MB) Download Apache Feather file (1.2 MB) Download Apache Parquet file (0.2 MB) Download SAS data file (9.8 MB) Download IBM SPSS Statistics data file (7.4 MB) Download Stata DTA file (9.5 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-4","dir":"Articles","previous_headings":"intrinsic_resistant: Intrinsic Bacterial Resistance","what":"Source","title":"Data sets for download / own use","text":"data set contains defined intrinsic resistance EUCAST bug-drug combinations, based ‘EUCAST Expert Rules’ ‘EUCAST Intrinsic Resistance Unusual Phenotypes’ v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content-4","dir":"Articles","previous_headings":"intrinsic_resistant: Intrinsic Bacterial Resistance","what":"Example content","title":"Data sets for download / own use","text":"Example rows filtering Enterobacter cloacae:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"dosage-dosage-guidelines-from-eucast","dir":"Articles","previous_headings":"","what":"dosage: Dosage Guidelines from EUCAST","title":"Data sets for download / own use","text":"data set 336 rows 9 columns, containing following column names:ab, name, type, dose, dose_times, administration, notes, original_txt, eucast_version. data set R available dosage, load AMR package. last updated 14 November 2022 14:20:39 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (3 kB) Download tab-separated text file (29 kB) Download Microsoft Excel workbook (19 kB) Download Apache Feather file (16 kB) Download Apache Parquet file (8 kB) Download SAS data file (92 kB) Download IBM SPSS Statistics data file (43 kB) Download Stata DTA file (82 kB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-5","dir":"Articles","previous_headings":"dosage: Dosage Guidelines from EUCAST","what":"Source","title":"Data sets for download / own use","text":"EUCAST breakpoints used package based dosages data set. Currently included dosages data set meant : ‘EUCAST Clinical Breakpoint Tables’ v11.0 (2021) ‘EUCAST Clinical Breakpoint Tables’ v12.0 (2022).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example_isolates-example-data-for-practice","dir":"Articles","previous_headings":"","what":"example_isolates: Example Data for Practice","title":"Data sets for download / own use","text":"data set 2 000 rows 46 columns, containing following column names:date, patient, age, gender, ward, mo, PEN, OXA, FLC, AMX, AMC, AMP, TZP, CZO, FEP, CXM, FOX, CTX, CAZ, CRO, GEN, TOB, AMK, KAN, TMP, SXT, NIT, FOS, LNZ, CIP, MFX, VAN, TEC, TCY, TGC, DOX, ERY, CLI, AZM, IPM, MEM, MTR, CHL, COL, MUP, RIF. data set R available example_isolates, load AMR package. last updated 21 January 2023 22:47:20 UTC. Find info structure data set .","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-6","dir":"Articles","previous_headings":"example_isolates: Example Data for Practice","what":"Source","title":"Data sets for download / own use","text":"data set contains randomised fictitious data, reflects reality can used practise AMR data analysis.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example_isolates_unclean-example-data-for-practice","dir":"Articles","previous_headings":"","what":"example_isolates_unclean: Example Data for Practice","title":"Data sets for download / own use","text":"data set 3 000 rows 8 columns, containing following column names:patient_id, hospital, date, bacteria, AMX, AMC, CIP, GEN. data set R available example_isolates_unclean, load AMR package. last updated 27 August 2022 18:49:37 UTC. Find info structure data set .","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-7","dir":"Articles","previous_headings":"example_isolates_unclean: Example Data for Practice","what":"Source","title":"Data sets for download / own use","text":"data set contains randomised fictitious data, reflects reality can used practise AMR data analysis.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"needed-r-packages","dir":"Articles","previous_headings":"","what":"Needed R packages","title":"How to predict antimicrobial resistance","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. AMR package depends packages even extends use functions.","code":"library(dplyr) library(ggplot2) library(AMR)  # (if not yet installed, install with:) # install.packages(c(\"tidyverse\", \"AMR\"))"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"prediction-analysis","dir":"Articles","previous_headings":"","what":"Prediction analysis","title":"How to predict antimicrobial resistance","text":"package contains function resistance_predict(), takes input functions AMR data analysis. Based date column, calculates cases per year uses regression model predict antimicrobial resistance. basically easy : function look date column col_date set. running commands, summary regression model printed unless using resistance_predict(..., info = FALSE). text printed summary - actual result (output) function data.frame containing year: number observations, actual observed resistance, estimated resistance standard error estimation: function plot available base R, can extended packages depend output based type input. extended function cope resistance predictions:  fastest way plot result. automatically adds right axes, error bars, titles, number available observations type model. also support ggplot2 package custom function ggplot_sir_predict() create appealing plots:","code":"# resistance prediction of piperacillin/tazobactam (TZP): resistance_predict(tbl = example_isolates, col_date = \"date\", col_ab = \"TZP\", model = \"binomial\")  # or: example_isolates %>%   resistance_predict(     col_ab = \"TZP\",     model = \"binomial\"   )  # to bind it to object 'predict_TZP' for example: predict_TZP <- example_isolates %>%   resistance_predict(     col_ab = \"TZP\",     model = \"binomial\"   ) predict_TZP #> # A tibble: 32 × 7 #>     year  value se_min se_max observations observed estimated #>  * <dbl>  <dbl>  <dbl>  <dbl>        <int>    <dbl>     <dbl> #>  1  2002 0.2        NA     NA           15   0.2       0.0562 #>  2  2003 0.0625     NA     NA           32   0.0625    0.0616 #>  3  2004 0.0854     NA     NA           82   0.0854    0.0676 #>  4  2005 0.05       NA     NA           60   0.05      0.0741 #>  5  2006 0.0508     NA     NA           59   0.0508    0.0812 #>  6  2007 0.121      NA     NA           66   0.121     0.0889 #>  7  2008 0.0417     NA     NA           72   0.0417    0.0972 #>  8  2009 0.0164     NA     NA           61   0.0164    0.106  #>  9  2010 0.0566     NA     NA           53   0.0566    0.116  #> 10  2011 0.183      NA     NA           93   0.183     0.127  #> # … with 22 more rows plot(predict_TZP) ggplot_sir_predict(predict_TZP) # choose for error bars instead of a ribbon ggplot_sir_predict(predict_TZP, ribbon = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"choosing-the-right-model","dir":"Articles","previous_headings":"Prediction analysis","what":"Choosing the right model","title":"How to predict antimicrobial resistance","text":"Resistance easily predicted; look vancomycin resistance Gram-positive bacteria, spread (.e. standard error) enormous:  Vancomycin resistance 100% ten years, might remain low. can define model model parameter. model chosen generalised linear regression model using binomial distribution, assuming period zero resistance followed period increasing resistance leading slowly resistance. Valid values : vancomycin resistance Gram-positive bacteria, linear model might appropriate:  model also available object, attribute:","code":"example_isolates %>%   filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>%   resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"binomial\") %>%   ggplot_sir_predict() example_isolates %>%   filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>%   resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"linear\") %>%   ggplot_sir_predict() model <- attributes(predict_TZP)$model  summary(model)$family #>  #> Family: binomial  #> Link function: logit  summary(model)$coefficients #>                  Estimate  Std. Error   z value     Pr(>|z|) #> (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05 #> year           0.09883005  0.02295317  4.305725 1.664395e-05"},{"path":"https://msberends.github.io/AMR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Matthijs S. Berends. Author, maintainer. Christian F. Luz. Author, contributor. Dennis Souverein. Author, contributor. Erwin E. . Hassing. Author, contributor. Casper J. Albers. Thesis advisor. Peter Dutey-Magni. Contributor. Judith M. Fonville. Contributor. Alex W. Friedrich. Thesis advisor. Corinna Glasner. Thesis advisor. Eric H. L. C. M. Hazenberg. Contributor. Gwen Knight. Contributor. Annick Lenglet. Contributor. Bart C. Meijer. Contributor. Dmytro Mykhailenko. Contributor. Anton Mymrikov. Contributor. Andrew P. Norgan. Contributor. Sofia Ny. Contributor. Jonas Salm. Contributor. Rogier P. Schade. Contributor. Bhanu N. M. Sinha. Thesis advisor. Anthony Underwood. Contributor. Anita Williams. Contributor.","code":""},{"path":"https://msberends.github.io/AMR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). “AMR: R Package Working Antimicrobial Resistance Data.” Journal Statistical Software, 104(3), 1–31. doi:10.18637/jss.v104.i03.","code":"@Article{,   title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},   author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},   journal = {Journal of Statistical Software},   year = {2022},   volume = {104},   number = {3},   pages = {1--31},   doi = {10.18637/jss.v104.i03}, }"},{"path":"https://msberends.github.io/AMR/index.html","id":"the-amr-package-for-r-","dir":"","previous_headings":"","what":"Antimicrobial Resistance Data Analysis","title":"Antimicrobial Resistance Data Analysis","text":"Generates antibiograms - traditional, combined, syndromic, even WISCA Provides full microbiological taxonomy data antimicrobial drugs Applies recent CLSI EUCAST clinical breakpoints MICs disk zones Corrects duplicate isolates, calculates predicts AMR per antibiotic class Integrates WHONET, ATC, EARS-Net, PubChem, LOINC SNOMED CT Works Windows, macOS Linux versions R since R-3.0 completely dependency-free, highly suitable places limited resources https://msberends.github.io/AMR https://doi.org/10.18637/jss.v104.i03","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"Antimicrobial Resistance Data Analysis","text":"AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. Many different researchers around globe continually helping us make successful durable project! work published Journal Statistical Software (Volume 104(3); DOI 10.18637/jss.v104.i03) formed basis two PhD theses (DOI 10.33612/diss.177417131 DOI 10.33612/diss.192486375). installing package, R knows ~52,000 distinct microbial species (updated December 2022) ~600 antibiotic, antimycotic antiviral drugs name code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC SNOMED CT), knows valid SIR MIC values. integral breakpoint guidelines CLSI EUCAST included last 10 years. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"used-in-over-175-countries-translated-into-20-languages","dir":"","previous_headings":"Introduction","what":"Used in over 175 countries, translated into 20 languages","title":"Antimicrobial Resistance Data Analysis","text":"Since first public release early 2018, R package used almost countries world. Click map enlarge see country names. help contributors corners world, AMR package available  English,  Czech,  Chinese,  Danish,  Dutch,  Finnish,  French,  German,  Greek,  Italian,  Japanese,  Norwegian,  Polish,  Portuguese,  Romanian,  Russian,  Spanish,  Swedish,  Turkish,  Ukrainian. Antimicrobial drug (group) names colloquial microorganism names provided languages.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"filtering-and-selecting-data","dir":"","previous_headings":"Practical examples","what":"Filtering and selecting data","title":"Antimicrobial Resistance Data Analysis","text":"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 last 10 years (manual) Retrieving antimicrobial drug names, doses forms administration clinical health care records (manual) Determining first isolates used AMR data analysis (manual) Calculating antimicrobial resistance (tutorial) Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) (tutorial) Calculating (empirical) susceptibility mono therapy combination therapies (tutorial) Predicting future antimicrobial resistance using regression models (tutorial) Getting properties microorganism (like Gram stain, species, genus family) (manual) Getting properties antibiotic (like name, code EARS-Net/ATC/LOINC/PubChem, defined daily dose trade name) (manual) Plotting antimicrobial resistance (tutorial) Applying EUCAST expert rules (manual) Getting SNOMED codes microorganism, getting properties microorganism based SNOMED code (manual) Getting LOINC codes antibiotic, getting properties antibiotic based LOINC code (manual) Machine reading EUCAST CLSI guidelines 2011-2021 translate MIC values disk diffusion diameters SIR (link) Principal component analysis AMR (tutorial)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-official-version","dir":"","previous_headings":"Get this package","what":"Latest official version","title":"Antimicrobial Resistance Data Analysis","text":"package available official R network (CRAN). Install package R CRAN using command: downloaded installed automatically. RStudio, click menu Tools > Install Packages… type “AMR” press Install. Note: functions website may available latest release. use functions data sets mentioned website, install latest development version.","code":"install.packages(\"AMR\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-development-version","dir":"","previous_headings":"Get this package","what":"Latest development version","title":"Antimicrobial Resistance Data Analysis","text":"Please read Developer Guideline . latest unpublished development version can installed GitHub two ways: Manually, using: Automatically, using rOpenSci R-universe platform, adding R-universe address list repositories (‘repos’): , can install update AMR package like official release (e.g., using install.packages(\"AMR\") RStudio via Tools > Check Package Updates…).","code":"install.packages(\"remotes\") # if you haven't already remotes::install_github(\"msberends/AMR\") options(repos = c(getOption(\"repos\"),                   msberends = \"https://msberends.r-universe.dev\"))"},{"path":"https://msberends.github.io/AMR/index.html","id":"get-started","dir":"","previous_headings":"","what":"Get started","title":"Antimicrobial Resistance Data Analysis","text":"find conduct AMR data analysis, please continue reading get started click link ‘’ menu.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"partners","dir":"","previous_headings":"","what":"Partners","title":"Antimicrobial Resistance Data Analysis","text":"development package part , related , made possible following non-profit organisations initiatives:","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"copyright","dir":"","previous_headings":"","what":"Copyright","title":"Antimicrobial Resistance Data Analysis","text":"R package free, open-source software licensed GNU General Public License v2.0 (GPL-2). nutshell, means package: May used commercial purposes May used private purposes May used patent purposes May modified, although: Modifications must released license distributing package Changes made code must documented May distributed, although: Source code must made available package distributed copy license copyright notice must included package. Comes LIMITATION liability Comes warranty","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions — AMR-deprecated","title":"Deprecated Functions — AMR-deprecated","text":"functions -called 'Deprecated'. removed future release. Using functions give warning name function replaced (one).","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated Functions — AMR-deprecated","text":"","code":"NA_rsi_  as.rsi(x, ...)  facet_rsi(...)  geom_rsi(...)  ggplot_rsi(...)  ggplot_rsi_predict(...)  is.rsi(...)  is.rsi.eligible(...)  labels_rsi_count(...)  n_rsi(...)  random_rsi(...)  rsi_df(...)  rsi_predict(...)  scale_rsi_colours(...)  theme_rsi(...)"},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Deprecated Functions — AMR-deprecated","text":"object class rsi (inherits ordered, factor) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":null,"dir":"Reference","previous_headings":"","what":"Options for the AMR package — AMR-options","title":"Options for the AMR package — AMR-options","text":"overview package-specific options() can set AMR package.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"options","dir":"Reference","previous_headings":"","what":"Options","title":"Options for the AMR package — AMR-options","text":"AMR_custom_ab  Allows use custom antimicrobial drugs package. explained add_custom_antimicrobials(). AMR_custom_mo  Allows use custom microorganisms package. explained add_custom_microorganisms(). AMR_eucastrules  Used setting default types rules eucast_rules() function, must one : \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). AMR_guideline  Used setting default guideline interpreting MIC values disk diffusion diameters .sir(). Can guideline name (e.g., \"CLSI\") name year (e.g. \"CLSI 2019\"). default latest implemented EUCAST guideline, currently \"EUCAST 2022\". Supported guideline currently EUCAST (2013-2022) CLSI (2013-2022). AMR_ignore_pattern  regular expression ignore (.e., make NA) match given .mo() mo_* functions. AMR_include_PKPD  logical use .sir(), indicate PK/PD clinical breakpoints must applied last resort - default TRUE. AMR_include_screening  logical use .sir(), indicate clinical breakpoints screening allowed - default FALSE. AMR_keep_synonyms  logical use .mo() mo_* functions, indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE. AMR_cleaning_regex  regular expression (case-insensitive) use .mo() mo_* functions, clean user input. default outcome mo_cleaning_regex(), removes texts brackets texts \"species\" \"serovar\". AMR_locale  language use AMR package, can one supported language names ISO-639-1 codes: English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). default current system language (supported). AMR_mo_source  file location manual code list used .mo() mo_* functions. explained set_mo_source().","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"saving-settings-between-sessions","dir":"Reference","previous_headings":"","what":"Saving Settings Between Sessions","title":"Options for the AMR package — AMR-options","text":"Settings R saved globally thus lost R exited. can save options .Rprofile file, user-specific file. can edit using:   file, can set options :   add Portuguese language support antibiotics, allow PK/PD rules interpreting MIC values .sir().","code":"utils::file.edit(\"~/.Rprofile\") options(AMR_locale = \"pt\")  options(AMR_include_PKPD = TRUE)"},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"share-options-within-team","dir":"Reference","previous_headings":"","what":"Share Options Within Team","title":"Options for the AMR package — AMR-options","text":"global approach, e.g. within data team, save options file remote file location, shared network drive. work way: Save plain text file e.g. \"X:/team_folder/R_options.R\" fill preferred settings. user, open .Rprofile file using utils::file.edit(\"~/.Rprofile\") put :   Reload R/RStudio check settings getOption(), e.g. getOption(\"AMR_locale\") set value. Now team settings configured one place, can maintained .","code":"source(\"X:/team_folder/R_options.R\")"},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":null,"dir":"Reference","previous_headings":"","what":"The AMR Package — AMR","title":"The AMR Package — AMR","text":"Welcome AMR package. AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. Many different researchers around globe continually helping us make successful durable project! work published Journal Statistical Software (Volume 104(3); doi: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 breakpoint guidelines CLSI EUCAST included last 10 years. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen. AMR package available English, Chinese, 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.berends@certe.nl (ORCID) Authors: Christian F. Luz (ORCID) [contributor] Dennis Souverein (ORCID) [contributor] Erwin E. . Hassing [contributor] contributors: Casper J. Albers (ORCID) [thesis advisor] Peter Dutey-Magni (ORCID) [contributor] Judith M. Fonville [contributor] Alex W. Friedrich (ORCID) [thesis advisor] Corinna Glasner (ORCID) [thesis advisor] Eric H. L. C. M. Hazenberg [contributor] Gwen Knight (ORCID) [contributor] Annick Lenglet (ORCID) [contributor] Bart C. Meijer [contributor] Dmytro Mykhailenko [contributor] Anton Mymrikov [contributor] Andrew P. Norgan (ORCID) [contributor] Sofia Ny (ORCID) [contributor] Jonas Salm [contributor] Rogier P. Schade [contributor] Bhanu N. M. Sinha (ORCID) [thesis advisor] Anthony Underwood (ORCID) [contributor] Anita Williams (ORCID) [contributor]","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":null,"dir":"Reference","previous_headings":"","what":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"antimicrobial drugs official names, ATC codes, ATC groups defined daily dose (DDD) included package, using Collaborating Centre Drug Statistics Methodology.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"","code":"as.ab(\"meropenem\") #> Class 'ab' #> [1] MEM ab_name(\"J01DH02\") #> [1] \"Meropenem\"  ab_tradenames(\"flucloxacillin\") #>  [1] \"culpen\"               \"floxacillin\"          \"floxacillin sodium\"   #>  [4] \"floxapen\"             \"floxapen sodium salt\" \"fluclox\"              #>  [7] \"flucloxacilina\"       \"flucloxacillin\"       \"flucloxacilline\"      #> [10] \"flucloxacillinum\"     \"fluorochloroxacillin\" \"staphylex\""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 500 Isolates - WHONET Example — WHONET","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"example data set exact structure export file WHONET. files can used package, example data set shows. antibiotic results example_isolates data set. patient names created using online surname generators place practice purposes.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET"},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"tibble 500 observations 53 variables: Identification number ID sample Specimen number ID specimen Organism Name microorganism. analysis, transform valid microbial class, using .mo(). Country Country origin Laboratory Name laboratory Last name Fictitious last name patient First name Fictitious initial patient Sex Fictitious gender patient Age Fictitious age patient Age category Age group, can also looked using age_groups() Date admissionDate hospital admission Specimen dateDate specimen received laboratory Specimen type Specimen type group Specimen type (Numeric) Translation \"Specimen type\" Reason Reason request Differential Diagnosis Isolate number ID isolate Organism type Type microorganism, can also looked using mo_type() Serotype Serotype microorganism Beta-lactamase Microorganism produces beta-lactamase? ESBL Microorganism produces extended spectrum beta-lactamase? Carbapenemase Microorganism produces carbapenemase? MRSA screening test Microorganism possible MRSA? Inducible clindamycin resistance Clindamycin can induced? Comment comments Date data entryDate data entered WHONET AMP_ND10:CIP_EE 28 different antibiotics. can lookup abbreviations antibiotics data set, use e.g. ab_name(\"AMP\") get official name immediately. analysis, transform valid antibiotic class, using .sir().","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET #> # A tibble: 500 × 53 #>    Identif…¹ Speci…² Organ…³ Country Labor…⁴ Last …⁵ First…⁶ Sex     Age Age c…⁷ #>    <chr>       <int> <chr>   <chr>   <chr>   <chr>   <chr>   <chr> <dbl> <chr>   #>  1 fe41d7ba…    1748 SPN     Belgium Nation… Abel    B.      F        68 55-74   #>  2 91f175ec…    1767 eco     The Ne… Nation… Delacr… F.      M        89 75+     #>  3 cc401505…    1343 eco     The Ne… Nation… Steens… F.      M        85 75+     #>  4 e864b692…    1894 MAP     Denmark Nation… Beyers… L.      M        62 55-74   #>  5 3d051fe3…    1739 PVU     Belgium Nation… Hummel  W.      M        86 75+     #>  6 c80762a0…    1846 103     The Ne… Nation… Eikenb… J.      F        53 25-54   #>  7 8022d372…    1628 103     Denmark Nation… Leclerc S.      F        77 75+     #>  8 f3dc5f55…    1493 eco     The Ne… Nation… Delacr… W.      M        53 25-54   #>  9 15add38f…    1847 eco     France  Nation… Van La… S.      F        63 55-74   #> 10 fd41248d…    1458 eco     Germany Nation… Moulin  O.      F        75 75+     #> # … with 490 more rows, 43 more variables: `Date of admission` <date>, #> #   `Specimen date` <date>, `Specimen type` <chr>, #> #   `Specimen type (Numeric)` <dbl>, Reason <chr>, `Isolate number` <int>, #> #   `Organism type` <chr>, Serotype <chr>, `Beta-lactamase` <lgl>, ESBL <lgl>, #> #   Carbapenemase <lgl>, `MRSA screening test` <lgl>, #> #   `Inducible clindamycin resistance` <lgl>, Comment <chr>, #> #   `Date of data entry` <date>, AMP_ND10 <sir>, AMC_ED20 <sir>, …"},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"Use function e.g. clinical texts health care records. returns list antimicrobial drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"","code":"ab_from_text(   text,   type = c(\"drug\", \"dose\", \"administration\"),   collapse = NULL,   translate_ab = FALSE,   thorough_search = NULL,   info = interactive(),   ... )"},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_ab type = \"drug\": column name antibiotics data set translate antibiotic abbreviations , using ab_property(). default FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed - default TRUE interactive mode ... arguments passed .ab()","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"list,  character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"function also internally used .ab(), although searches first drug name throw note drug names returned. Note: .ab() function may use long regular expression match brand names antimicrobial drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"default, function search antimicrobial drug names. text elements searched official names, ATC codes brand names. uses .ab() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(abx = ab_from_text(clinical_text)) returned AB codes can transformed official names, groups, etc. ab_* functions ab_name() ab_group(), using translate_ab argument. using collapse, function return character:df %>% mutate(abx = ab_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"","code":"# mind the bad spelling of amoxicillin in this line, # straight from a true health care record: ab_from_text(\"28/03/2020 regular amoxicilliin 500mg po tid\") #> [[1]] #> Class 'ab' #> [1] AMX #>   ab_from_text(\"500 mg amoxi po and 400mg cipro iv\") #> [[1]] #> Class 'ab' #> [1] AMX CIP #>  ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", type = \"dose\") #> [[1]] #> [1] 500 400 #>  ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", type = \"admin\") #> [[1]] #> [1] \"oral\" \"iv\"   #>   ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", collapse = \", \") #> [1] \"AMX, CIP\" # \\donttest{ # if you want to know which antibiotic groups were administered, do e.g.: abx <- ab_from_text(\"500 mg amoxi po and 400mg cipro iv\") ab_group(abx[[1]]) #> [1] \"Beta-lactams/penicillins\" \"Quinolones\"                if (require(\"dplyr\")) {   tibble(clinical_text = c(     \"given 400mg cipro and 500 mg amox\",     \"started on doxy iv today\"   )) %>%     mutate(       abx_codes = ab_from_text(clinical_text),       abx_doses = ab_from_text(clinical_text, type = \"doses\"),       abx_admin = ab_from_text(clinical_text, type = \"admin\"),       abx_coll = ab_from_text(clinical_text, collapse = \"|\"),       abx_coll_names = ab_from_text(clinical_text,         collapse = \"|\",         translate_ab = \"name\"       ),       abx_coll_doses = ab_from_text(clinical_text,         type = \"doses\",         collapse = \"|\"       ),       abx_coll_admin = ab_from_text(clinical_text,         type = \"admin\",         collapse = \"|\"       )     ) } #> Loading required package: dplyr #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union #> # A tibble: 2 × 8 #>   clinical_text          abx_c…¹ abx_d…² abx_a…³ abx_c…⁴ abx_c…⁵ abx_c…⁶ abx_c…⁷ #>   <chr>                  <list>  <list>  <list>  <chr>   <chr>   <chr>   <chr>   #> 1 given 400mg cipro and… <ab>    <dbl>   <chr>   CIP|AMX Ciprof… 400|500 NA      #> 2 started on doxy iv to… <ab>    <dbl>   <chr>   DOX     Doxycy… NA      iv      #> # … with abbreviated variable names ¹​abx_codes, ²​abx_doses, ³​abx_admin, #> #   ⁴​abx_coll, ⁵​abx_coll_names, ⁶​abx_coll_doses, ⁷​abx_coll_admin # }"},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antibiotic — ab_property","title":"Get Properties of an Antibiotic — ab_property","text":"Use functions return specific property antibiotic antibiotics data set. input values evaluated internally .ab().","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antibiotic — ab_property","text":"","code":"ab_name(x, language = get_AMR_locale(), tolower = FALSE, ...)  ab_cid(x, ...)  ab_synonyms(x, ...)  ab_tradenames(x, ...)  ab_group(x, language = get_AMR_locale(), ...)  ab_atc(x, only_first = FALSE, ...)  ab_atc_group1(x, language = get_AMR_locale(), ...)  ab_atc_group2(x, language = get_AMR_locale(), ...)  ab_loinc(x, ...)  ab_ddd(x, administration = \"oral\", ...)  ab_ddd_units(x, administration = \"oral\", ...)  ab_info(x, language = get_AMR_locale(), ...)  ab_url(x, open = FALSE, ...)  ab_property(x, property = \"name\", language = get_AMR_locale(), ...)  set_ab_names(   data,   ...,   property = \"name\",   language = get_AMR_locale(),   snake_case = NULL )"},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antibiotic — ab_property","text":"x (vector ) text can coerced valid antibiotic drug code .ab() language language returned text - default current system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. lead e.g. \"polymyxin B\" \"polymyxin b\". ... case set_ab_names() data data.frame: columns select (supports tidy selection column1:column4), otherwise arguments passed .ab() only_first logical indicate whether first ATC code must returned, giving preference J0-codes (.e., antimicrobial drug group) administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antibiotics data set: vector_or(colnames(antibiotics), sort = FALSE). data data.frame columns need renamed, character vector column names snake_case logical indicate whether names -called snake case: lower case spaces/slashes replaced underscore (_)","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antibiotic — ab_property","text":"integer case ab_cid() named list case ab_info() multiple ab_atc()/ab_synonyms()/ab_tradenames() double case ab_ddd() data.frame case set_ab_names() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antibiotic — ab_property","text":"output translated possible. function ab_url() return direct URL official website. warning returned required ATC code available. function set_ab_names() special column renaming function data.frames. renames columns names resemble antimicrobial drugs. always makes sure new column names unique. property = \"atc\" set, preference given ATC codes J-group.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antibiotic — ab_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antibiotic — ab_property","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antibiotic — ab_property","text":"","code":"# all properties: ab_name(\"AMX\") #> [1] \"Amoxicillin\" ab_atc(\"AMX\") #> [1] \"J01CA04\" ab_cid(\"AMX\") #> [1] 33613 ab_synonyms(\"AMX\") #>  [1] \"actimoxi\"            \"amoclen\"             \"amolin\"              #>  [4] \"amopen\"              \"amopenixin\"          \"amoxibiotic\"         #>  [7] \"amoxicaps\"           \"amoxicilina\"         \"amoxicillin\"         #> [10] \"amoxicillin hydrate\" \"amoxicilline\"        \"amoxicillinum\"       #> [13] \"amoxiden\"            \"amoxil\"              \"amoxivet\"            #> [16] \"amoxy\"               \"amoxycillin\"         \"amoxyke\"             #> [19] \"anemolin\"            \"aspenil\"             \"atoksilin\"           #> [22] \"biomox\"              \"bristamox\"           \"cemoxin\"             #> [25] \"clamoxyl\"            \"damoxy\"              \"delacillin\"          #> [28] \"demoksil\"            \"dispermox\"           \"efpenix\"             #> [31] \"flemoxin\"            \"hiconcil\"            \"histocillin\"         #> [34] \"hydroxyampicillin\"   \"ibiamox\"             \"imacillin\"           #> [37] \"lamoxy\"              \"largopen\"            \"metafarma capsules\"  #> [40] \"metifarma capsules\"  \"moksilin\"            \"moxacin\"             #> [43] \"moxatag\"             \"ospamox\"             \"pamoxicillin\"        #> [46] \"piramox\"             \"promoxil\"            \"remoxil\"             #> [49] \"robamox\"             \"sawamox pm\"          \"tolodina\"            #> [52] \"topramoxin\"          \"unicillin\"           \"utimox\"              #> [55] \"vetramox\"            ab_tradenames(\"AMX\") #>  [1] \"actimoxi\"            \"amoclen\"             \"amolin\"              #>  [4] \"amopen\"              \"amopenixin\"          \"amoxibiotic\"         #>  [7] \"amoxicaps\"           \"amoxicilina\"         \"amoxicillin\"         #> [10] \"amoxicillin hydrate\" \"amoxicilline\"        \"amoxicillinum\"       #> [13] \"amoxiden\"            \"amoxil\"              \"amoxivet\"            #> [16] \"amoxy\"               \"amoxycillin\"         \"amoxyke\"             #> [19] \"anemolin\"            \"aspenil\"             \"atoksilin\"           #> [22] \"biomox\"              \"bristamox\"           \"cemoxin\"             #> [25] \"clamoxyl\"            \"damoxy\"              \"delacillin\"          #> [28] \"demoksil\"            \"dispermox\"           \"efpenix\"             #> [31] \"flemoxin\"            \"hiconcil\"            \"histocillin\"         #> [34] \"hydroxyampicillin\"   \"ibiamox\"             \"imacillin\"           #> [37] \"lamoxy\"              \"largopen\"            \"metafarma capsules\"  #> [40] \"metifarma capsules\"  \"moksilin\"            \"moxacin\"             #> [43] \"moxatag\"             \"ospamox\"             \"pamoxicillin\"        #> [46] \"piramox\"             \"promoxil\"            \"remoxil\"             #> [49] \"robamox\"             \"sawamox pm\"          \"tolodina\"            #> [52] \"topramoxin\"          \"unicillin\"           \"utimox\"              #> [55] \"vetramox\"            ab_group(\"AMX\") #> [1] \"Beta-lactams/penicillins\" ab_atc_group1(\"AMX\") #> [1] \"Beta-lactam antibacterials, penicillins\" ab_atc_group2(\"AMX\") #> [1] \"Penicillins with extended spectrum\" ab_url(\"AMX\") #>                                                           Amoxicillin  #> \"https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no\"   # smart lowercase tranformation ab_name(x = c(\"AMC\", \"PLB\")) #> [1] \"Amoxicillin/clavulanic acid\" \"Polymyxin B\"                 ab_name(x = c(\"AMC\", \"PLB\"), tolower = TRUE) #> [1] \"amoxicillin/clavulanic acid\" \"polymyxin B\"                  # defined daily doses (DDD) ab_ddd(\"AMX\", \"oral\") #> [1] 1.5 ab_ddd_units(\"AMX\", \"oral\") #> [1] \"g\" ab_ddd(\"AMX\", \"iv\") #> [1] 3 ab_ddd_units(\"AMX\", \"iv\") #> [1] \"g\"  ab_info(\"AMX\") # all properties as a list #> $ab #> [1] \"AMX\" #>  #> $cid #> [1] 33613 #>  #> $name #> [1] \"Amoxicillin\" #>  #> $group #> [1] \"Beta-lactams/penicillins\" #>  #> $atc #> [1] \"J01CA04\" #>  #> $atc_group1 #> [1] \"Beta-lactam antibacterials, penicillins\" #>  #> $atc_group2 #> [1] \"Penicillins with extended spectrum\" #>  #> $tradenames #>  [1] \"actimoxi\"            \"amoclen\"             \"amolin\"              #>  [4] \"amopen\"              \"amopenixin\"          \"amoxibiotic\"         #>  [7] \"amoxicaps\"           \"amoxicilina\"         \"amoxicillin\"         #> [10] \"amoxicillin hydrate\" \"amoxicilline\"        \"amoxicillinum\"       #> [13] \"amoxiden\"            \"amoxil\"              \"amoxivet\"            #> [16] \"amoxy\"               \"amoxycillin\"         \"amoxyke\"             #> [19] \"anemolin\"            \"aspenil\"             \"atoksilin\"           #> [22] \"biomox\"              \"bristamox\"           \"cemoxin\"             #> [25] \"clamoxyl\"            \"damoxy\"              \"delacillin\"          #> [28] \"demoksil\"            \"dispermox\"           \"efpenix\"             #> [31] \"flemoxin\"            \"hiconcil\"            \"histocillin\"         #> [34] \"hydroxyampicillin\"   \"ibiamox\"             \"imacillin\"           #> [37] \"lamoxy\"              \"largopen\"            \"metafarma capsules\"  #> [40] \"metifarma capsules\"  \"moksilin\"            \"moxacin\"             #> [43] \"moxatag\"             \"ospamox\"             \"pamoxicillin\"        #> [46] \"piramox\"             \"promoxil\"            \"remoxil\"             #> [49] \"robamox\"             \"sawamox pm\"          \"tolodina\"            #> [52] \"topramoxin\"          \"unicillin\"           \"utimox\"              #> [55] \"vetramox\"            #>  #> $loinc #> [1] \"16365-9\" \"25274-2\" \"3344-9\"  \"80133-2\" #>  #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 1.5 #>  #> $ddd$oral$units #> [1] \"g\" #>  #>  #> $ddd$iv #> $ddd$iv$amount #> [1] 3 #>  #> $ddd$iv$units #> [1] \"g\" #>  #>  #>   # all ab_* functions use as.ab() internally, so you can go from 'any' to 'any': ab_atc(\"AMP\") #> [1] \"J01CA01\" \"S01AA19\" ab_group(\"J01CA01\") #> [1] \"Beta-lactams/penicillins\" ab_loinc(\"ampicillin\") #> [1] \"21066-6\" \"3355-5\"  \"33562-0\" \"33919-2\" \"43883-8\" \"43884-6\" \"87604-5\" ab_name(\"21066-6\") #> [1] \"Ampicillin\" ab_name(6249) #> [1] \"Ampicillin\" ab_name(\"J01CA01\") #> [1] \"Ampicillin\"  # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\") #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\") #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") #> [1] \"J01DD04\"  # use set_ab_names() for renaming columns colnames(example_isolates) #>  [1] \"date\"    \"patient\" \"age\"     \"gender\"  \"ward\"    \"mo\"      \"PEN\"     #>  [8] \"OXA\"     \"FLC\"     \"AMX\"     \"AMC\"     \"AMP\"     \"TZP\"     \"CZO\"     #> [15] \"FEP\"     \"CXM\"     \"FOX\"     \"CTX\"     \"CAZ\"     \"CRO\"     \"GEN\"     #> [22] \"TOB\"     \"AMK\"     \"KAN\"     \"TMP\"     \"SXT\"     \"NIT\"     \"FOS\"     #> [29] \"LNZ\"     \"CIP\"     \"MFX\"     \"VAN\"     \"TEC\"     \"TCY\"     \"TGC\"     #> [36] \"DOX\"     \"ERY\"     \"CLI\"     \"AZM\"     \"IPM\"     \"MEM\"     \"MTR\"     #> [43] \"CHL\"     \"COL\"     \"MUP\"     \"RIF\"     colnames(set_ab_names(example_isolates)) #>  [1] \"date\"                          \"patient\"                       #>  [3] \"age\"                           \"gender\"                        #>  [5] \"ward\"                          \"mo\"                            #>  [7] \"benzylpenicillin\"              \"oxacillin\"                     #>  [9] \"flucloxacillin\"                \"amoxicillin\"                   #> [11] \"amoxicillin_clavulanic_acid\"   \"ampicillin\"                    #> [13] \"piperacillin_tazobactam\"       \"cefazolin\"                     #> [15] \"cefepime\"                      \"cefuroxime\"                    #> [17] \"cefoxitin\"                     \"cefotaxime\"                    #> [19] \"ceftazidime\"                   \"ceftriaxone\"                   #> [21] \"gentamicin\"                    \"tobramycin\"                    #> [23] \"amikacin\"                      \"kanamycin\"                     #> [25] \"trimethoprim\"                  \"trimethoprim_sulfamethoxazole\" #> [27] \"nitrofurantoin\"                \"fosfomycin\"                    #> [29] \"linezolid\"                     \"ciprofloxacin\"                 #> [31] \"moxifloxacin\"                  \"vancomycin\"                    #> [33] \"teicoplanin\"                   \"tetracycline\"                  #> [35] \"tigecycline\"                   \"doxycycline\"                   #> [37] \"erythromycin\"                  \"clindamycin\"                   #> [39] \"azithromycin\"                  \"imipenem\"                      #> [41] \"meropenem\"                     \"metronidazole\"                 #> [43] \"chloramphenicol\"               \"colistin\"                      #> [45] \"mupirocin\"                     \"rifampicin\"                    colnames(set_ab_names(example_isolates, NIT:VAN)) #>  [1] \"date\"           \"patient\"        \"age\"            \"gender\"         #>  [5] \"ward\"           \"mo\"             \"PEN\"            \"OXA\"            #>  [9] \"FLC\"            \"AMX\"            \"AMC\"            \"AMP\"            #> [13] \"TZP\"            \"CZO\"            \"FEP\"            \"CXM\"            #> [17] \"FOX\"            \"CTX\"            \"CAZ\"            \"CRO\"            #> [21] \"GEN\"            \"TOB\"            \"AMK\"            \"KAN\"            #> [25] \"TMP\"            \"SXT\"            \"nitrofurantoin\" \"fosfomycin\"     #> [29] \"linezolid\"      \"ciprofloxacin\"  \"moxifloxacin\"   \"vancomycin\"     #> [33] \"TEC\"            \"TCY\"            \"TGC\"            \"DOX\"            #> [37] \"ERY\"            \"CLI\"            \"AZM\"            \"IPM\"            #> [41] \"MEM\"            \"MTR\"            \"CHL\"            \"COL\"            #> [45] \"MUP\"            \"RIF\"            # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     set_ab_names()    # this does the same:   example_isolates %>%     rename_with(set_ab_names)    # set_ab_names() works with any AB property:   example_isolates %>%     set_ab_names(property = \"atc\")    example_isolates %>%     set_ab_names(where(is.sir)) %>%     colnames()    example_isolates %>%     set_ab_names(NIT:VAN) %>%     colnames() } #>  [1] \"date\"           \"patient\"        \"age\"            \"gender\"         #>  [5] \"ward\"           \"mo\"             \"PEN\"            \"OXA\"            #>  [9] \"FLC\"            \"AMX\"            \"AMC\"            \"AMP\"            #> [13] \"TZP\"            \"CZO\"            \"FEP\"            \"CXM\"            #> [17] \"FOX\"            \"CTX\"            \"CAZ\"            \"CRO\"            #> [21] \"GEN\"            \"TOB\"            \"AMK\"            \"KAN\"            #> [25] \"TMP\"            \"SXT\"            \"nitrofurantoin\" \"fosfomycin\"     #> [29] \"linezolid\"      \"ciprofloxacin\"  \"moxifloxacin\"   \"vancomycin\"     #> [33] \"TEC\"            \"TCY\"            \"TGC\"            \"DOX\"            #> [37] \"ERY\"            \"CLI\"            \"AZM\"            \"IPM\"            #> [41] \"MEM\"            \"MTR\"            \"CHL\"            \"COL\"            #> [45] \"MUP\"            \"RIF\"            # }"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Custom Antimicrobials — add_custom_antimicrobials","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"add_custom_antimicrobials() can add custom antimicrobial drug names codes.","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"","code":"add_custom_antimicrobials(x)  clear_custom_antimicrobials()"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"x data.frame resembling antibiotics data set, least containing columns \"ab\" \"name\"","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"Important: Due R works, add_custom_antimicrobials() function run every R session - added antimicrobials stored sessions thus lost R exited. two ways 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 only \"Enterobacter asburiae\": mo_name(\"Enterobacter asburiae/cloacae\") #> [1] \"Enterobacter asburiae\"  # now add a custom entry - it will be considered by as.mo() and # all mo_*() functions add_custom_microorganisms(   data.frame(     genus = \"Enterobacter\",     species = \"asburiae/cloacae\"   ) ) #> ℹ Added Enterobacter asburiae/cloacae to the internal microorganisms data #>   set.  # E. asburiae/cloacae is now a new microorganism: mo_name(\"Enterobacter asburiae/cloacae\") #> [1] \"Enterobacter asburiae/cloacae\"  # its code: as.mo(\"Enterobacter asburiae/cloacae\") #> Class 'mo' #> [1] CUSTOM1_ENTRBC_A_C  # all internal algorithms will work as well: mo_name(\"Ent asburia cloacae\") #> [1] \"Enterobacter asburiae/cloacae\"  # and even the taxonomy was added based on the genus! mo_family(\"E. asburiae/cloacae\") #> [1] \"Enterobacteriaceae\" mo_gramstain(\"Enterobacter asburiae/cloacae\") #> [1] \"Gram-negative\"  mo_info(\"Enterobacter asburiae/cloacae\") #> $mo #> [1] \"CUSTOM1_ENTRBC_A_C\" #>  #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Enterobacter\" #>  #> $species #> [1] \"asburiae/cloacae\" #>  #> $subspecies #> [1] \"\" #>  #> $status #> [1] \"accepted\" #>  #> $synonyms #> NULL #>  #> $gramstain #> [1] \"Gram-negative\" #>  #> $url #> [1] \"\" #>  #> $ref #> [1] \"Self-added, 2023\" #>  #> $snomed #> [1] NA #>    # the function tries to be forgiving: add_custom_microorganisms(   data.frame(     GENUS = \"BACTEROIDES / PARABACTEROIDES SLASHLINE\",     SPECIES = \"SPECIES\"   ) ) #> ℹ Added Bacteroides/Parabacteroides to the internal microorganisms data #>   set. mo_name(\"BACTEROIDES / PARABACTEROIDES\") #> [1] \"Bacteroides/Parabacteroides\" mo_rank(\"BACTEROIDES / PARABACTEROIDES\") #> [1] \"genus\"  # taxonomy still works, although a slashline genus was given as input: mo_family(\"Bacteroides/Parabacteroides\") #> [1] \"Bacteroidaceae\"   # for groups and complexes, set them as species or subspecies: add_custom_microorganisms(   data.frame(     genus = \"Citrobacter\",     species = c(\"freundii\", \"braakii complex\"),     subspecies = c(\"complex\", \"\")   ) ) #> ℹ Added Citrobacter braakii complex and Citrobacter freundii complex to the #>   internal microorganisms data set. mo_name(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"Citrobacter freundii complex\" \"Citrobacter braakii complex\"  mo_species(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"freundii complex\" \"braakii complex\"  mo_gramstain(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"Gram-negative\" \"Gram-negative\" # }"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":null,"dir":"Reference","previous_headings":"","what":"Age in Years of Individuals — age","title":"Age in Years of Individuals — age","text":"Calculates age years based reference date, system date default.","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Age in Years of Individuals — age","text":"","code":"age(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Age in Years of Individuals — age","text":"x date(s), character (vectors) coerced .POSIXlt() reference reference date(s) (default today), character (vectors) coerced .POSIXlt() exact logical indicate whether age calculation exact, .e. decimals. divides number days year--date (YTD) x number days year reference (either 365 366). na.rm logical indicate whether missing values removed ... arguments passed .POSIXlt(), origin","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Age in Years of Individuals — age","text":"integer (decimals) exact = FALSE, double (decimals) otherwise","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Age in Years of Individuals — age","text":"Ages 0 returned NA warning. Ages 120 give warning. function vectorises x reference, meaning either can length 1 argument larger length.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Age in Years of Individuals — age","text":"","code":"# 10 random pre-Y2K birth dates df <- data.frame(birth_date = as.Date(\"2000-01-01\") - runif(10) * 25000)  # add ages df$age <- age(df$birth_date)  # add exact ages df$age_exact <- age(df$birth_date, exact = TRUE)  # add age at millenium switch df$age_at_y2k <- age(df$birth_date, \"2000-01-01\")  df #>    birth_date age age_exact age_at_y2k #> 1  1967-01-30  56  56.11233         32 #> 2  1995-12-14  27  27.24110          4 #> 3  1956-04-27  66  66.87397         43 #> 4  1996-08-19  26  26.56164          3 #> 5  1980-07-28  42  42.62192         19 #> 6  1973-05-23  49  49.80274         26 #> 7  1988-08-12  34  34.58082         11 #> 8  1959-02-23  64  64.04658         40 #> 9  1974-04-16  48  48.90411         25 #> 10 1956-12-27  66  66.20548         43"},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Ages into Age Groups — age_groups","title":"Split Ages into Age Groups — age_groups","text":"Split ages age groups defined split argument. allows easier demographic (antimicrobial resistance) analysis.","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Ages into Age Groups — age_groups","text":"","code":"age_groups(x, split_at = c(12, 25, 55, 75), na.rm = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Ages into Age Groups — age_groups","text":"x age, e.g. calculated age() split_at values split x - default age groups 0-11, 12-24, 25-54, 55-74 75+. See Details. na.rm logical indicate whether missing values removed","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Ages into Age Groups — age_groups","text":"Ordered factor","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split Ages into Age Groups — age_groups","text":"split ages, input split_at argument can : numeric vector. value e.g. c(10, 20) split x 0-9, 10-19 20+. value 50 split x 0-49 50+. default split young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) elderly (75+). character: \"children\" \"kids\", equivalent : c(0, 1, 2, 4, 6, 13, 18). split 0, 1, 2-3, 4-5, 6-12, 13-17 18+. \"elderly\" \"seniors\", equivalent : c(65, 75, 85). split 0-64, 65-74, 75-84, 85+. \"fives\", equivalent : 1:20 * 5. split 0-4, 5-9, ..., 95-99, 100+. \"tens\", equivalent : 1:10 * 10. split 0-9, 10-19, ..., 90-99, 100+.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split Ages into Age Groups — age_groups","text":"","code":"ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21)  # split into 0-49 and 50+ age_groups(ages, 50) #> [1] 0-49 0-49 0-49 50+  0-49 50+  50+  0-49 0-49 #> Levels: 0-49 < 50+  # split into 0-19, 20-49 and 50+ age_groups(ages, c(20, 50)) #> [1] 0-19  0-19  0-19  50+   20-49 50+   50+   20-49 20-49 #> Levels: 0-19 < 20-49 < 50+  # split into groups of ten years age_groups(ages, 1:10 * 10) #> [1] 0-9   0-9   10-19 50-59 30-39 70-79 100+  40-49 20-29 #> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+ age_groups(ages, split_at = \"tens\") #> [1] 0-9   0-9   10-19 50-59 30-39 70-79 100+  40-49 20-29 #> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+  # split into groups of five years age_groups(ages, 1:20 * 5) #> [1] 0-4   5-9   15-19 50-54 30-34 75-79 100+  40-44 20-24 #> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+ age_groups(ages, split_at = \"fives\") #> [1] 0-4   5-9   15-19 50-54 30-34 75-79 100+  40-44 20-24 #> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+  # split specifically for children age_groups(ages, c(1, 2, 4, 6, 13, 18)) #> [1] 2-3   6-12  13-17 18+   18+   18+   18+   18+   18+   #> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+ age_groups(ages, \"children\") #> [1] 2-3   6-12  13-17 18+   18+   18+   18+   18+   18+   #> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+  # \\donttest{ # resistance of ciprofloxacin per age group if (require(\"dplyr\") && require(\"ggplot2\")) {   example_isolates %>%     filter_first_isolate() %>%     filter(mo == as.mo(\"Escherichia coli\")) %>%     group_by(age_group = age_groups(age)) %>%     select(age_group, CIP) %>%     ggplot_sir(       x = \"age_group\",       minimum = 0,       x.title = \"Age Group\",       title = \"Ciprofloxacin resistance per age group\"     ) } #> Loading required package: ggplot2  # }"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"Generate antibiogram, communicate results plots tables. functions follow logic Klinker et al. Barbieri et al. (see Source), allow reporting e.g. R Markdown Quarto well.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"","code":"antibiogram(   x,   antibiotics = where(is.sir),   mo_transform = \"shortname\",   ab_transform = NULL,   syndromic_group = NULL,   add_total_n = TRUE,   only_all_tested = FALSE,   digits = 0,   col_mo = NULL,   language = get_AMR_locale(),   minimum = 30,   combine_SI = TRUE,   sep = \" + \",   info = interactive() )  # S3 method for antibiogram plot(x, ...)  # S3 method for antibiogram autoplot(object, ...)  # S3 method for antibiogram knit_print(   x,   italicise = TRUE,   na = getOption(\"knitr.kable.NA\", default = \"\"),   ... )"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"Klinker KP et al. (2021). Antimicrobial stewardship antibiograms: importance moving beyond traditional antibiograms. Therapeutic Advances Infectious Disease, May 5;8:20499361211011373; doi:10.1177/20499361211011373 Barbieri E et al. (2021). Development Weighted-Incidence Syndromic Combination Antibiogram (WISCA) guide choice empiric antibiotic treatment urinary tract infection paediatric patients: Bayesian approach Antimicrobial Resistance & Infection Control May 1;10(1):74; doi:10.1186/s13756-021-00939-2 M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"x data.frame containing least column microorganisms columns antibiotic results (class 'sir', see .sir()) antibiotics vector antibiotic name code (evaluated .ab(), column name x, (combinations ) antibiotic selectors aminoglycosides() carbapenems(). combination antibiograms, can also set values separated \"+\", \"TZP+TOB\" \"cipro + genta\", given columns resembling antibiotics exist x. See Examples. mo_transform character transform microorganism input - must \"name\", \"shortname\", \"gramstain\", one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\", \"snomed\". Can also NULL transform input. ab_transform character transform antibiotic input - must one column names antibiotics data set: \"ab\", \"cid\", \"name\", \"group\", \"atc\", \"atc_group1\", \"atc_group2\", \"abbreviations\", \"synonyms\", \"oral_ddd\", \"oral_units\", \"iv_ddd\", \"iv_units\", \"loinc\". Can also NULL transform input. syndromic_group column name x, values calculated split rows x, e.g. using ifelse() case_when(). See Examples. add_total_n logical indicate whether total available numbers per pathogen added table (default TRUE). add lowest highest number available isolate per antibiotic (e.g, E. coli 200 isolates available ciprofloxacin 150 amoxicillin, returned number \"150-200\"). only_all_tested (combination antibiograms): logical indicate isolates must tested antibiotics, see Details digits number digits use rounding col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). language language translate text, defaults system language (see get_AMR_locale()) minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether susceptibility determined results either S , instead S (default TRUE) sep separating character antibiotic columns combination antibiograms info logical indicate info printed - default TRUE interactive mode ... used R Markdown Quarto: arguments passed knitr::kable() (otherwise, use) object antibiogram() object italicise logical indicate whether microorganism names knitr table made italic, using italicise_taxonomy(). na character use showing NA values","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"function returns table values 0 100 susceptibility, resistance. Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine data set one four available algorithms. types antibiograms listed can plotted (using ggplot2::autoplot() base R plot()/barplot()). antibiogram object can also used directly R Markdown / Quarto (.e., knitr) reports. case, knitr::kable() applied automatically microorganism names even printed italics default (see argument italicise). can also use functions specific 'table reporting' packages transform output antibiogram() needs, e.g. flextable::as_flextable() gt::gt().","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"antibiogram-types","dir":"Reference","previous_headings":"","what":"Antibiogram Types","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"four antibiogram types, proposed Klinker et al. (2021, doi:10.1177/20499361211011373 ), supported antibiogram(): Traditional Antibiogram Case example: Susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Code example:   Combination Antibiogram Case example: Additional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Code example:   Syndromic Antibiogram Case example: Susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Code example:   Weighted-Incidence Syndromic Combination Antibiogram (WISCA) Case example: Susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) male patients age >=65 years heart failure Code example:   Note combination antibiograms, important realise susceptibility can calculated two ways, can set only_all_tested argument (default FALSE). See example two antibiotics, Drug Drug B, antibiogram() works calculate %SI:","code":"antibiogram(your_data,             antibiotics = \"TZP\") antibiogram(your_data,             antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\")) antibiogram(your_data,             antibiotics = penicillins(),             syndromic_group = \"ward\") library(dplyr) your_data %>%   filter(ward == \"ICU\" & specimen_type == \"Respiratory\") %>%   antibiogram(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),               syndromic_group = ifelse(.$age >= 65 &                                          .$gender == \"Male\" &                                          .$condition == \"Heart Disease\",                                        \"Study Group\", \"Control Group\")) --------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   include as  include as   include as  include as                     numerator   denominator  numerator   denominator --------  --------  ----------  -----------  ----------  -----------  S or I    S or I       X            X            X            X    R       S or I       X            X            X            X   <NA>     S or I       X            X            -            -  S or I      R          X            X            X            X    R         R          -            X            -            X   <NA>       R          -            -            -            -  S or I     <NA>        X            X            -            -    R        <NA>        -            -            -            -   <NA>      <NA>        -            -            -            - --------------------------------------------------------------------"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA) — antibiogram","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>  # \\donttest{ # Traditional antibiogram ----------------------------------------------  antibiogram(example_isolates,   antibiotics = c(aminoglycosides(), carbapenems()) ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 10 × 7 #>    `Pathogen (N min-max)`   AMK   GEN   IPM   KAN   MEM   TOB #>  * <chr>                  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 CoNS (43-309)              0    86    52     0    52    22 #>  2 E. coli (0-462)          100    98   100    NA   100    97 #>  3 E. faecalis (0-39)         0     0   100     0    NA     0 #>  4 K. pneumoniae (0-58)      NA    90   100    NA   100    90 #>  5 P. aeruginosa (17-30)     NA   100    NA     0    NA   100 #>  6 P. mirabilis (0-34)       NA    94    94    NA    NA    94 #>  7 S. aureus (2-233)         NA    99    NA    NA    NA    98 #>  8 S. epidermidis (8-163)     0    79    NA     0    NA    51 #>  9 S. hominis (3-80)         NA    92    NA    NA    NA    85 #> 10 S. pneumoniae (11-117)     0     0    NA     0    NA     0  antibiogram(example_isolates,   antibiotics = aminoglycosides(),   ab_transform = \"atc\",   mo_transform = \"gramstain\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 2 × 5 #>   `Pathogen (N min-max)`   J01GB01 J01GB03 J01GB04 J01GB06 #> * <chr>                      <dbl>   <dbl>   <dbl>   <dbl> #> 1 Gram-negative (35-686)        96      96       0      98 #> 2 Gram-positive (436-1170)      34      63       0       0  antibiogram(example_isolates,   antibiotics = carbapenems(),   ab_transform = \"name\",   mo_transform = \"name\" ) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 5 × 3 #>   `Pathogen (N min-max)`                           Imipenem Meropenem #> * <chr>                                               <dbl>     <dbl> #> 1 Coagulase-negative Staphylococcus (CoNS) (48-48)       52        52 #> 2 Enterococcus faecalis (0-38)                          100        NA #> 3 Escherichia coli (418-422)                            100       100 #> 4 Klebsiella pneumoniae (51-53)                         100       100 #> 5 Proteus mirabilis (27-32)                              94        NA   # Combined antibiogram -------------------------------------------------  # combined antibiotics yield higher empiric coverage antibiogram(example_isolates,   antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),   mo_transform = \"gramstain\" ) #> # A tibble: 2 × 4 #>   `Pathogen (N min-max)`     TZP `TZP + GEN` `TZP + TOB` #> * <chr>                    <dbl>       <dbl>       <dbl> #> 1 Gram-negative (641-693)     88          99          98 #> 2 Gram-positive (345-1044)    86          98          95  # names of antibiotics do not need to resemble columns exactly: antibiogram(example_isolates,   antibiotics = c(\"Cipro\", \"cipro + genta\"),   mo_transform = \"gramstain\",   ab_transform = \"name\",   sep = \" & \" ) #> # A tibble: 2 × 3 #>   `Pathogen (N min-max)`  Ciprofloxacin `Ciprofloxacin & Gentamicin` #> * <chr>                           <dbl>                        <dbl> #> 1 Gram-negative (684-694)            91                           99 #> 2 Gram-positive (724-847)            77                           93   # Syndromic antibiogram ------------------------------------------------  # the data set could contain a filter for e.g. respiratory specimens antibiogram(example_isolates,   antibiotics = c(aminoglycosides(), carbapenems()),   syndromic_group = \"ward\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 14 × 8 #>    `Syndromic Group` `Pathogen (N min-max)`   AMK   GEN   IPM   KAN   MEM   TOB #>  * <chr>             <chr>                  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 Clinical          CoNS (23-205)             NA    89    57    NA    57    26 #>  2 ICU               CoNS (10-73)              NA    79    NA    NA    NA    NA #>  3 Outpatient        CoNS (3-31)               NA    84    NA    NA    NA    NA #>  4 Clinical          E. coli (0-299)          100    98   100    NA   100    98 #>  5 ICU               E. coli (0-137)          100    99   100    NA   100    96 #>  6 Clinical          K. pneumoniae (0-51)      NA    92   100    NA   100    92 #>  7 Clinical          P. mirabilis (0-30)       NA   100    NA    NA    NA   100 #>  8 Clinical          S. aureus (2-150)         NA    99    NA    NA    NA    97 #>  9 ICU               S. aureus (0-66)          NA   100    NA    NA    NA    NA #> 10 Clinical          S. epidermidis (4-79)     NA    82    NA    NA    NA    55 #> 11 ICU               S. epidermidis (4-75)     NA    72    NA    NA    NA    41 #> 12 Clinical          S. hominis (1-45)         NA    96    NA    NA    NA    94 #> 13 Clinical          S. pneumoniae (5-78)       0     0    NA     0    NA     0 #> 14 ICU               S. pneumoniae (5-30)       0     0    NA     0    NA     0  # now define a data set with only E. coli ex1 <- example_isolates[which(mo_genus() == \"Escherichia\"), ] #> ℹ Using column 'mo' as input for mo_genus()  # with a custom language, though this will be determined automatically # (i.e., this table will be in Spanish on Spanish systems) antibiogram(ex1,   antibiotics = aminoglycosides(),   ab_transform = \"name\",   syndromic_group = ifelse(ex1$ward == \"ICU\",     \"UCI\", \"No UCI\"   ),   language = \"es\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 2 × 5 #>   `Grupo sindrómico` `Patógeno (N min-max)` Amikacina Gentamicina Tobramicina #> * <chr>              <chr>                      <dbl>       <dbl>       <dbl> #> 1 No UCI             E. coli (0-325)              100          98          98 #> 2 UCI                E. coli (0-137)              100          99          96   # Weighted-incidence syndromic combination antibiogram (WISCA) ---------  # the data set could contain a filter for e.g. respiratory specimens/ICU antibiogram(example_isolates,   antibiotics = c(\"AMC\", \"AMC+CIP\", \"TZP\", \"TZP+TOB\"),   mo_transform = \"gramstain\",   minimum = 10, # this should be >=30, but now just as example   syndromic_group = ifelse(example_isolates$age >= 65 &     example_isolates$gender == \"M\",   \"WISCA Group 1\", \"WISCA Group 2\"   ) ) #> # A tibble: 4 × 6 #>   `Syndromic Group` `Pathogen (N min-max)`    AMC `AMC + CIP`   TZP `TZP + TOB` #> * <chr>             <chr>                   <dbl>       <dbl> <dbl>       <dbl> #> 1 WISCA Group 1     Gram-negative (261-285)    76          95    89          99 #> 2 WISCA Group 2     Gram-negative (380-442)    76          98    88          98 #> 3 WISCA Group 1     Gram-positive (123-406)    76          89    81          95 #> 4 WISCA Group 2     Gram-positive (222-732)    76          89    88          95   # Print the output for R Markdown / Quarto -----------------------------  ureido <- antibiogram(example_isolates,   antibiotics = ureidopenicillins(),   ab_transform = \"name\" ) #> ℹ For ureidopenicillins() using column 'TZP' (piperacillin/tazobactam)  # in an Rmd file, you would just need to return `ureido` in a chunk, # but to be explicit here: if (requireNamespace(\"knitr\")) {   knitr::knit_print(ureido) } #> [1] \"\\n\\n|Pathogen (N)          | Piperacillin/tazobactam|\\n|:---------------------|-----------------------:|\\n|CoNS (33)             |                      30|\\n|*E. coli* (416)       |                      94|\\n|*K. pneumoniae* (53)  |                      89|\\n|*S. pneumoniae* (112) |                     100|\" #> attr(,\"class\") #> [1] \"knit_asis\" #> attr(,\"knit_cacheable\") #> [1] NA   # Generate plots with ggplot2 or base R --------------------------------  ab1 <- antibiogram(example_isolates,   antibiotics = c(\"AMC\", \"CIP\", \"TZP\", \"TZP+TOB\"),   mo_transform = \"gramstain\" ) ab2 <- antibiogram(example_isolates,   antibiotics = c(\"AMC\", \"CIP\", \"TZP\", \"TZP+TOB\"),   mo_transform = \"gramstain\",   syndromic_group = \"ward\" )  if (requireNamespace(\"ggplot2\")) {   ggplot2::autoplot(ab1) }  if (requireNamespace(\"ggplot2\")) {   ggplot2::autoplot(ab2) }   plot(ab1)  plot(ab2)  # }"},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Antibiotic Selectors — antibiotic_class_selectors","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"functions allow filtering rows selecting columns based antibiotic test results specific antibiotic class group (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 either \"3.3\", \"3.2\", \"3.1\".","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"(internally) character vector column names, additional class \"ab_selector\"","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"functions can used data set calls selecting columns filtering rows. 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    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>   # 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    #> # … with 1,990 more rows  # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB' example_isolates[, c(\"mo\", aminoglycosides())] #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 2,000 × 5 #>    mo           GEN   TOB   AMK   KAN   #>    <mo>         <sir> <sir> <sir> <sir> #>  1 B_ESCHR_COLI NA    NA    NA    NA    #>  2 B_ESCHR_COLI NA    NA    NA    NA    #>  3 B_STPHY_EPDR NA    NA    NA    NA    #>  4 B_STPHY_EPDR NA    NA    NA    NA    #>  5 B_STPHY_EPDR NA    NA    NA    NA    #>  6 B_STPHY_EPDR NA    NA    NA    NA    #>  7 B_STPHY_AURS NA    S     NA    NA    #>  8 B_STPHY_AURS NA    S     NA    NA    #>  9 B_STPHY_EPDR NA    NA    NA    NA    #> 10 B_STPHY_EPDR NA    NA    NA    NA    #> # … with 1,990 more rows  # select only antibiotic columns with DDDs for oral treatment example_isolates[, administrable_per_os()] #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #>   (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #>   'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #>   (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #>   (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #>   (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #>   (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #>   (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin), and 'RIF' #>   (rifampicin) #> # A tibble: 2,000 × 23 #>    OXA   FLC   AMX   AMC   AMP   CXM   KAN   TMP   NIT   FOS   LNZ   CIP   MFX   #>    <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>  1 NA    NA    NA    I     NA    I     NA    R     NA    NA    R     NA    NA    #>  2 NA    NA    NA    I     NA    I     NA    R     NA    NA    R     NA    NA    #>  3 NA    R     NA    NA    NA    R     NA    S     NA    NA    NA    NA    NA    #>  4 NA    R     NA    NA    NA    R     NA    S     NA    NA    NA    NA    NA    #>  5 NA    R     NA    NA    NA    R     NA    R     NA    NA    NA    NA    NA    #>  6 NA    R     NA    NA    NA    R     NA    R     NA    NA    NA    NA    NA    #>  7 NA    S     R     S     R     S     NA    R     NA    NA    NA    NA    NA    #>  8 NA    S     R     S     R     S     NA    R     NA    NA    NA    NA    NA    #>  9 NA    R     NA    NA    NA    R     NA    S     NA    NA    NA    S     NA    #> 10 NA    S     NA    NA    NA    S     NA    S     NA    NA    NA    S     NA    #> # … with 1,990 more rows, and 10 more variables: VAN <sir>, TCY <sir>, #> #   DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, RIF <sir>  # filter using any() or all() example_isolates[any(carbapenems() == \"R\"), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 55 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  2 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  3 2004-11-03 D65308     80 F      ICU      B_STNTR_MLTP R     NA    NA    R     #>  4 2005-04-21 452212     82 F      ICU      B_ENTRC      NA    NA    NA    NA    #>  5 2005-04-22 452212     82 F      ICU      B_ENTRC      NA    NA    NA    NA    #>  6 2005-04-22 452212     82 F      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  7 2007-02-21 8BBC46     61 F      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  8 2007-12-15 401043     72 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  9 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL R     NA    NA    NA    #> 10 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL R     NA    NA    NA    #> # … with 45 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> subset(example_isolates, any(carbapenems() == \"R\")) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 55 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  2 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  3 2004-11-03 D65308     80 F      ICU      B_STNTR_MLTP R     NA    NA    R     #>  4 2005-04-21 452212     82 F      ICU      B_ENTRC      NA    NA    NA    NA    #>  5 2005-04-22 452212     82 F      ICU      B_ENTRC      NA    NA    NA    NA    #>  6 2005-04-22 452212     82 F      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  7 2007-02-21 8BBC46     61 F      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  8 2007-12-15 401043     72 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  9 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL R     NA    NA    NA    #> 10 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL R     NA    NA    NA    #> # … with 45 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir>  # filter on any or all results in the carbapenem columns (i.e., IPM, MEM): example_isolates[any(carbapenems()), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Filtering any of columns 'IPM' and 'MEM' to contain value \"S\", \"I\" or \"R\" #> # A tibble: 962 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  4 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  5 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS R     NA    NA    R     #>  6 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS R     NA    NA    R     #>  7 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS R     NA    NA    R     #>  8 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN R     NA    NA    R     #>  9 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN R     NA    NA    R     #> 10 2002-03-08 4FC193     69 M      Clinical B_ESCHR_COLI R     NA    NA    R     #> # … with 952 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> example_isolates[all(carbapenems()), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Filtering all of columns 'IPM' and 'MEM' to contain value \"S\", \"I\" or \"R\" #> # A tibble: 756 × 46 #>    date       patient   age gender ward    mo            PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>   <mo>          <sir> <sir> <sir> <sir> #>  1 2002-04-14 F30196     73 M      Outpat… B_STRPT_GRPB  S     NA    S     S     #>  2 2003-04-08 114570     74 M      ICU     B_STRPT_PYGN  S     NA    S     S     #>  3 2003-04-08 114570     74 M      ICU     B_STRPT_GRPA  S     NA    S     S     #>  4 2003-04-08 114570     74 M      ICU     B_STRPT_GRPA  S     NA    S     S     #>  5 2003-08-14 F71508      0 F      Clinic… B_STRPT_GRPB  S     NA    S     S     #>  6 2003-10-16 650870     63 F      ICU     B_ESCHR_COLI  R     NA    NA    R     #>  7 2003-10-20 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R     #>  8 2003-10-20 F35553     52 M      ICU     B_ENTRBC_CLOC R     NA    NA    R     #>  9 2003-11-04 2FC253     87 F      ICU     B_ESCHR_COLI  R     NA    NA    NA    #> 10 2003-11-04 2FC253     87 F      ICU     B_ESCHR_COLI  R     NA    NA    NA    #> # … with 746 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir>  # filter with multiple antibiotic selectors using c() example_isolates[all(c(carbapenems(), aminoglycosides()) == \"R\"), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 26 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2004-11-03 D65308     80 F      ICU      B_STNTR_MLTP R     NA    NA    R     #>  2 2005-04-22 452212     82 F      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  3 2007-02-21 8BBC46     61 F      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  4 2007-12-15 401043     72 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  5 2008-12-06 501361     43 F      Clinical B_STNTR_MLTP R     NA    NA    R     #>  6 2011-05-09 207325     82 F      ICU      B_ENTRC_FACM NA    NA    NA    NA    #>  7 2012-03-12 582258     80 M      ICU      B_STPHY_CONS R     R     R     R     #>  8 2012-05-19 C25552     89 F      Outpati… B_STPHY_CONS R     R     R     R     #>  9 2012-07-17 F05015     83 M      ICU      B_STPHY_CONS R     R     R     R     #> 10 2012-07-20 404299     66 F      Clinical B_STPHY_CONS R     R     R     R     #> # … with 16 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir>  # filter + select in one go: get penicillins in 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     #> # … with 45 more rows  # You can combine selectors with '&' to be more specific. For example, # penicillins() would select benzylpenicillin ('peni G') and # administrable_per_os() would select erythromycin. Yet, when combined these # drugs are both omitted since benzylpenicillin is not administrable per os # and erythromycin is not a penicillin: example_isolates[, penicillins() & administrable_per_os()] #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin), and 'TZP' #>   (piperacillin/tazobactam) #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #>   (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #>   'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #>   (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #>   (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #>   (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #>   (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #>   (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin), and 'RIF' #>   (rifampicin) #> # A tibble: 2,000 × 5 #>    OXA   FLC   AMX   AMC   AMP   #>    <sir> <sir> <sir> <sir> <sir> #>  1 NA    NA    NA    I     NA    #>  2 NA    NA    NA    I     NA    #>  3 NA    R     NA    NA    NA    #>  4 NA    R     NA    NA    NA    #>  5 NA    R     NA    NA    NA    #>  6 NA    R     NA    NA    NA    #>  7 NA    S     R     S     R     #>  8 NA    S     R     S     R     #>  9 NA    R     NA    NA    NA    #> 10 NA    S     NA    NA    NA    #> # … with 1,990 more rows  # ab_selector() applies a filter in the `antibiotics` data set and is thus # very flexible. For instance, to select antibiotic columns with an oral DDD # of at least 1 gram: example_isolates[, ab_selector(oral_ddd > 1 & oral_units == \"g\")] #> ℹ For ab_selector(oral_ddd > 1 & oral_units == \"g\") using columns 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'KAN' (kanamycin), 'FOS' #>   (fosfomycin), 'LNZ' (linezolid), 'VAN' (vancomycin), 'ERY' (erythromycin), #>   'CLI' (clindamycin), 'MTR' (metronidazole), and 'CHL' (chloramphenicol) #> # A tibble: 2,000 × 13 #>    OXA   FLC   AMX   AMC   AMP   KAN   FOS   LNZ   VAN   ERY   CLI   MTR   CHL   #>    <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>  1 NA    NA    NA    I     NA    NA    NA    R     R     R     R     NA    NA    #>  2 NA    NA    NA    I     NA    NA    NA    R     R     R     R     NA    NA    #>  3 NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA    #>  4 NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA    #>  5 NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA    #>  6 NA    R     NA    NA    NA    NA    NA    NA    S     R     R     NA    NA    #>  7 NA    S     R     S     R     NA    NA    NA    S     S     NA    NA    NA    #>  8 NA    S     R     S     R     NA    NA    NA    S     S     NA    NA    NA    #>  9 NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA    #> 10 NA    S     NA    NA    NA    NA    NA    NA    S     S     NA    NA    NA    #> # … with 1,990 more rows  # \\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 I      #> 2 R      #> 3 R      #> 4 R      #> 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    #> # … with 1,990 more rows if (require(\"dplyr\")) {   # get AMR for only drugs that matter - no intrinsic resistance:   example_isolates %>%     filter(mo_genus() %in% c(\"Escherichia\", \"Klebsiella\")) %>%     group_by(ward) %>%     summarise(across(not_intrinsic_resistant(), resistance)) } #> ℹ Using column 'mo' as input for mo_genus() #> ℹ For not_intrinsic_resistant() removing columns 'PEN' #>   (benzylpenicillin), 'LNZ' (linezolid), 'VAN' (vancomycin), 'TEC' #>   (teicoplanin), 'ERY' (erythromycin), 'CLI' (clindamycin), 'AZM' #>   (azithromycin), and 'RIF' (rifampicin) #> Warning: There were 52 warnings in `summarise()`. #> The first warning was: #> ℹ In argument: `across(not_intrinsic_resistant(), resistance)`. #> ℹ In group 1: `ward = \"Clinical\"`. #> Caused by warning: #> ! Introducing NA: no results available for OXA in group: ward = \"Clinical\" #> (minimum = 30). #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 51 remaining warnings. #> # A tibble: 3 × 33 #>   ward    OXA   FLC    AMX    AMC    AMP     TZP     CZO     FEP     CXM     FOX #>   <chr> <dbl> <dbl>  <dbl>  <dbl>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl> #> 1 Clin…    NA    NA  0.606  0.121  0.606  0.0504  0.0656  0.0159  0.0622  0.0648 #> 2 ICU      NA    NA  0.535  0.172  0.535  0.119  NA       0.0722  0.0828  0.0992 #> 3 Outp…    NA    NA NA     NA     NA     NA      NA      NA      NA      NA      #> # … with 22 more variables: CTX <dbl>, CAZ <dbl>, CRO <dbl>, GEN <dbl>, #> #   TOB <dbl>, AMK <dbl>, KAN <dbl>, TMP <dbl>, SXT <dbl>, NIT <dbl>, #> #   FOS <dbl>, CIP <dbl>, MFX <dbl>, TCY <dbl>, TGC <dbl>, DOX <dbl>, #> #   IPM <dbl>, MEM <dbl>, MTR <dbl>, CHL <dbl>, COL <dbl>, MUP <dbl> if (require(\"dplyr\")) {   # get susceptibility for antibiotics whose name contains \"trim\":   example_isolates %>%     filter(first_isolate()) %>%     group_by(ward) %>%     summarise(across(ab_selector(name %like% \"trim\"), susceptibility)) } #> ℹ For ab_selector(name %like% \"trim\") using columns 'TMP' (trimethoprim) #>   and 'SXT' (trimethoprim/sulfamethoxazole) #> # A tibble: 3 × 3 #>   ward         TMP   SXT #>   <chr>      <dbl> <dbl> #> 1 Clinical   0.627 0.808 #> 2 ICU        0.549 0.778 #> 3 Outpatient 0.667 0.821 if (require(\"dplyr\")) {   # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):   example_isolates %>%     select(carbapenems()) } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 2,000 × 2 #>    IPM   MEM   #>    <sir> <sir> #>  1 NA    NA    #>  2 NA    NA    #>  3 NA    NA    #>  4 NA    NA    #>  5 NA    NA    #>  6 NA    NA    #>  7 NA    NA    #>  8 NA    NA    #>  9 NA    NA    #> 10 NA    NA    #> # … with 1,990 more rows if (require(\"dplyr\")) {   # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':   example_isolates %>%     select(mo, aminoglycosides()) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 2,000 × 5 #>    mo           GEN   TOB   AMK   KAN   #>    <mo>         <sir> <sir> <sir> <sir> #>  1 B_ESCHR_COLI NA    NA    NA    NA    #>  2 B_ESCHR_COLI NA    NA    NA    NA    #>  3 B_STPHY_EPDR NA    NA    NA    NA    #>  4 B_STPHY_EPDR NA    NA    NA    NA    #>  5 B_STPHY_EPDR NA    NA    NA    NA    #>  6 B_STPHY_EPDR NA    NA    NA    NA    #>  7 B_STPHY_AURS NA    S     NA    NA    #>  8 B_STPHY_AURS NA    S     NA    NA    #>  9 B_STPHY_EPDR NA    NA    NA    NA    #> 10 B_STPHY_EPDR NA    NA    NA    NA    #> # … with 1,990 more rows if (require(\"dplyr\")) {   # any() and all() work in dplyr's filter() too:   example_isolates %>%     filter(       any(aminoglycosides() == \"R\"),       all(cephalosporins_2nd() == \"R\")     ) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For cephalosporins_2nd() using columns 'CXM' (cefuroxime) and 'FOX' #>   (cefoxitin) #> # A tibble: 112 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  2 2002-03-16 4FC193     69 M      Clinical B_PSDMN_AERG R     NA    NA    R     #>  3 2002-04-08 130252     78 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #>  4 2002-06-23 798871     82 M      Clinical B_ENTRC_FCLS NA    NA    NA    NA    #>  5 2002-06-23 798871     82 M      Clinical B_ENTRC_FCLS NA    NA    NA    NA    #>  6 2002-07-21 955940     82 F      Clinical B_PSDMN_AERG R     NA    NA    R     #>  7 2002-07-21 955940     82 F      Clinical B_PSDMN_AERG R     NA    NA    R     #>  8 2003-08-13 F35553     52 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #>  9 2003-09-05 F35553     52 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #> 10 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM NA    NA    NA    NA    #> # … with 102 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # also works with c():   example_isolates %>%     filter(any(c(carbapenems(), aminoglycosides()) == \"R\")) } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 531 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  2 2002-03-16 4FC193     69 M      Clinical B_PSDMN_AERG R     NA    NA    R     #>  3 2002-03-17 B30560     78 M      Clinical B_STPHY_CONS R     NA    R     R     #>  4 2002-04-04 E61143     67 M      Clinical B_STRPT_SNGN S     NA    NA    S     #>  5 2002-04-08 130252     78 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #>  6 2002-04-14 F30196     73 M      Outpati… B_STRPT_GRPB S     NA    S     S     #>  7 2002-05-07 D91570     83 M      Clinical B_STPHY_CONS R     NA    R     R     #>  8 2002-05-07 D91570     83 M      Clinical B_STPHY_CONS R     NA    R     R     #>  9 2002-05-14 077552     86 F      Clinical B_STRPT_PNMN S     NA    NA    S     #> 10 2002-05-14 077552     86 F      Clinical B_STRPT_PNMN S     NA    NA    S     #> # … with 521 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # not setting any/all will automatically apply all():   example_isolates %>%     filter(aminoglycosides() == \"R\") } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ Assuming a filter on all 4 aminoglycosides. Wrap around all() or #>   any() to prevent this note. #> # A tibble: 427 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinical B_ENTRC_FACM NA    NA    NA    NA    #>  2 2002-03-17 B30560     78 M      Clinical B_STPHY_CONS R     NA    R     R     #>  3 2002-04-04 E61143     67 M      Clinical B_STRPT_SNGN S     NA    NA    S     #>  4 2002-04-08 130252     78 M      ICU      B_ENTRC_FCLS NA    NA    NA    NA    #>  5 2002-04-14 F30196     73 M      Outpati… B_STRPT_GRPB S     NA    S     S     #>  6 2002-05-07 D91570     83 M      Clinical B_STPHY_CONS R     NA    R     R     #>  7 2002-05-07 D91570     83 M      Clinical B_STPHY_CONS R     NA    R     R     #>  8 2002-05-14 077552     86 F      Clinical B_STRPT_PNMN S     NA    NA    S     #>  9 2002-05-14 077552     86 F      Clinical B_STRPT_PNMN S     NA    NA    S     #> 10 2002-05-16 D25302     65 F      ICU      B_STRPT_ANGN S     NA    NA    S     #> # … with 417 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # this will select columns 'mo' and all antimycobacterial drugs ('RIF'):   example_isolates %>%     select(mo, ab_class(\"mycobact\")) } #> ℹ For ab_class(\"mycobact\") using column 'RIF' (rifampicin) #> # A tibble: 2,000 × 2 #>    mo           RIF   #>    <mo>         <sir> #>  1 B_ESCHR_COLI R     #>  2 B_ESCHR_COLI R     #>  3 B_STPHY_EPDR NA    #>  4 B_STPHY_EPDR NA    #>  5 B_STPHY_EPDR NA    #>  6 B_STPHY_EPDR NA    #>  7 B_STPHY_AURS NA    #>  8 B_STPHY_AURS NA    #>  9 B_STPHY_EPDR NA    #> 10 B_STPHY_EPDR NA    #> # … with 1,990 more rows if (require(\"dplyr\")) {   # get bug/drug combinations for only glycopeptides in Gram-positives:   example_isolates %>%     filter(mo_is_gram_positive()) %>%     select(mo, glycopeptides()) %>%     bug_drug_combinations() %>%     format() } #> ℹ Using column 'mo' as input for mo_is_gram_positive() #> ℹ For glycopeptides() using columns 'VAN' (vancomycin) and 'TEC' #>   (teicoplanin) #> # A tibble: 2 × 8 #>   Group           Drug             CoNS  E. fa…¹ S. au…² S. ep…³ S. ho…⁴ S. pn…⁵ #>   <chr>           <chr>            <chr> <chr>   <chr>   <chr>   <chr>   <chr>   #> 1 \"Glycopeptides\" Teicoplanin (TE… \"\"    \"\"      \" 0.0%… \"64.1%… \" 6.8%… \"\"      #> 2 \"\"              Vancomycin (VAN… \" 0.… \" 0.0%… \" 0.0%… \" 0.0%… \" 0.0%… \" 0.0%… #> # … with abbreviated variable names ¹​`E. faecalis`, ²​`S. aureus`, #> #   ³​`S. epidermidis`, ⁴​`S. hominis`, ⁵​`S. pneumoniae` if (require(\"dplyr\")) {   data.frame(     some_column = \"some_value\",     J01CA01 = \"S\"   ) %>% # ATC code of ampicillin     select(penicillins()) # only the 'J01CA01' column will be selected } #> ℹ For penicillins() using column 'J01CA01' (ampicillin) #>   J01CA01 #> 1       S if (require(\"dplyr\")) {   # with recent versions of dplyr, this is all equal:   x <- example_isolates[carbapenems() == \"R\", ]   y <- example_isolates %>% filter(carbapenems() == \"R\")   z <- example_isolates %>% filter(if_all(carbapenems(), ~ .x == \"R\"))   identical(x, y) && identical(y, z) } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Assuming a filter on all 2 carbapenems. Wrap around all() or any() to #>   prevent this note. #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Assuming a filter on all 2 carbapenems. Wrap around all() or any() to #>   prevent this note. #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> [1] TRUE   # 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 cid Compound ID found PubChem name Official name used WHONET/EARS-Net group short concise group name, based WHONET WHOCC definitions atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC, like J01CR02 atc_group1 Official pharmacological subgroup (3rd level ATC code) defined WHOCC, like \"Macrolides, lincosamides streptogramins\" atc_group2 Official chemical subgroup (4th level ATC code) defined WHOCC, like \"Macrolides\" abbr List abbreviations used many countries, also antibiotic susceptibility testing (AST) synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment, currently available 174 drugs oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral (intravenous) treatment, currently available 146 drugs iv_units Units iv_ddd loinc LOINC codes (Logical Observation Identifiers Names Codes) associated name antimicrobial drug. Use ab_loinc() retrieve quickly, see ab_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"for-the-antivirals-data-set-a-tibble-with-observations-and-variables-","dir":"Reference","previous_headings":"","what":"For the antivirals data set: a tibble with 120 observations and 11 variables:","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"av Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name used WHONET/EARS-Net atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC cid Compound ID found PubChem atc_group Official pharmacological subgroup (3rd level ATC code) defined WHOCC synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral treatment iv_units Units iv_ddd loinc LOINC codes (Logical Observation Identifiers Names Codes) associated name antimicrobial drug.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology (WHOCC): https://www.whocc./atc_ddd_index/ Logical Observation Identifiers Names Codes (LOINC), Version 2.73 (8 August, 2022). Accessed https://loinc.org 30 October, 2022. European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"Properties based ATC code available ATC available. properties : atc_group1, atc_group2, oral_ddd, oral_units, iv_ddd iv_units. Synonyms (.e. trade names) derived PubChem Compound ID (column cid) consequently available CID available.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"Like data sets package, data sets publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Sets with 603 Antimicrobial Drugs — antibiotics","text":"","code":"antibiotics #> # A tibble: 483 × 14 #>    ab        cid name        group atc   atc_g…¹ atc_g…² abbre…³ synon…⁴ oral_…⁵ #>    <ab>    <dbl> <chr>       <chr> <lis> <chr>   <chr>   <list>  <named>   <dbl> #>  1 AMA      4649 4-aminosal… Anti… <chr> Drugs … Aminos… <chr>   <chr>     12    #>  2 ACM   6450012 Acetylmide… Macr… <chr> NA      NA      <chr>   <chr>     NA    #>  3 ASP  49787020 Acetylspir… Macr… <chr> NA      NA      <chr>   <chr>     NA    #>  4 ALS      8954 Aldesulfon… Othe… <chr> Drugs … Drugs … <chr>   <chr>      0.33 #>  5 AMK     37768 Amikacin    Amin… <chr> Aminog… Other … <chr>   <chr>     NA    #>  6 AKF        NA Amikacin/f… Amin… <chr> NA      NA      <chr>   <chr>     NA    #>  7 AMX     33613 Amoxicillin Beta… <chr> Beta-l… Penici… <chr>   <chr>      1.5  #>  8 AMC  23665637 Amoxicilli… Beta… <chr> Beta-l… Combin… <chr>   <chr>      1.5  #>  9 AXS    465441 Amoxicilli… Beta… <chr> NA      NA      <chr>   <chr>     NA    #> 10 AMB   5280965 Amphoteric… Anti… <chr> Antimy… Antibi… <chr>   <chr>     40    #> # … with 473 more rows, 4 more variables: oral_units <chr>, iv_ddd <dbl>, #> #   iv_units <chr>, loinc <list>, and abbreviated variable names ¹​atc_group1, #> #   ²​atc_group2, ³​abbreviations, ⁴​synonyms, ⁵​oral_ddd antivirals #> # A tibble: 120 × 11 #>    av    name  atc      cid atc_g…¹ synon…² oral_…³ oral_…⁴ iv_ddd iv_un…⁵ loinc #>    <av>  <chr> <chr>  <dbl> <chr>   <list>    <dbl> <chr>    <dbl> <chr>   <lis> #>  1 ABA   Abac… J05A… 4.41e5 Nucleo… <chr>       0.6 g           NA NA      <chr> #>  2 ACI   Acic… J05A… 1.35e8 Nucleo… <chr>       4   g            4 g       <chr> #>  3 ADD   Adef… J05A… 6.09e4 Nucleo… <chr>      10   mg          NA NA      <chr> #>  4 AME   Amen… J05A… 1.14e7 Other … <chr>       0.4 g           NA NA      <chr> #>  5 AMP   Ampr… J05A… 6.50e4 Protea… <chr>       1.2 g           NA NA      <chr> #>  6 ASU   Asun… J05A… 1.61e7 Antivi… <chr>       0.2 g           NA NA      <chr> #>  7 ATA   Ataz… J05A… 1.48e5 Protea… <chr>       0.3 g           NA NA      <chr> #>  8 ATA+… Ataz… J05A… 8.66e7 Antivi… <chr>      NA   NA          NA NA      <chr> #>  9 ATA+… Ataz… J05A… 2.51e7 Antivi… <chr>       0.3 g           NA NA      <chr> #> 10 BAM   Balo… J05A… 1.24e8 Other … <chr>      40   mg          NA NA      <chr> #> # … with 110 more rows, and abbreviated variable names ¹​atc_group, ²​synonyms, #> #   ³​oral_ddd, ⁴​oral_units, ⁵​iv_units"},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to an Antibiotic ID — as.ab","title":"Transform Input to an Antibiotic ID — as.ab","text":"Use function determine antibiotic drug code one antibiotics. data set antibiotics searched abbreviations, official names synonyms (brand names).","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to an Antibiotic ID — as.ab","text":"","code":"as.ab(x, flag_multiple_results = TRUE, info = interactive(), ...)  is.ab(x)"},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to an Antibiotic ID — as.ab","text":"x character vector determine antibiotic ID flag_multiple_results logical indicate whether note printed console probably one antibiotic drug code name can retrieved single input value. info logical indicate whether progress bar printed - default TRUE interactive mode ... arguments passed internal functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to an Antibiotic ID — as.ab","text":"character  vector additional class ab","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to an Antibiotic ID — as.ab","text":"entries antibiotics data set three different identifiers: human readable EARS-Net code (column ab, used ECDC WHONET), ATC code (column atc, used ), CID code (column cid, Compound ID, used PubChem). data set contains 5,000 official brand names many different countries, found PubChem. drugs contain multiple ATC codes. properties searched user input. .ab() can correct different forms misspelling: Wrong spelling drug names (\"tobramicin\" \"gentamycin\"), corrects audible similarities f/ph, x/ks, c/z/s, t/th, etc. many vowels consonants Switching two characters (\"mreopenem\", often case clinical data, doctors typed fast) Digitalised paper records, leaving artefacts like 0/o/O (zero O's), B/8, n/r, etc. Use ab_* functions get properties based returned antibiotic ID, see Examples. Note: .ab() ab_* functions may use long regular expression match brand names antimicrobial drugs. may fail systems. can add manual codes considered .ab() ab_* functions, see add_custom_antimicrobials().","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to an Antibiotic ID — as.ab","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Transform Input to an Antibiotic ID — as.ab","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to an Antibiotic ID — as.ab","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to an Antibiotic ID — as.ab","text":"","code":"# these examples all return \"ERY\", the ID of erythromycin: as.ab(\"J01FA01\") #> Class 'ab' #> [1] ERY as.ab(\"J 01 FA 01\") #> Class 'ab' #> [1] ERY as.ab(\"Erythromycin\") #> Class 'ab' #> [1] ERY as.ab(\"eryt\") #> Class 'ab' #> [1] ERY as.ab(\"   eryt 123\") #> Class 'ab' #> [1] ERY as.ab(\"ERYT\") #> Class 'ab' #> [1] ERY as.ab(\"ERY\") #> Class 'ab' #> [1] ERY as.ab(\"eritromicine\") # spelled wrong, yet works #> Class 'ab' #> [1] ERY as.ab(\"Erythrocin\") # trade name #> Class 'ab' #> [1] ERY as.ab(\"Romycin\") # trade name #> Class 'ab' #> [1] ERY  # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\") # small spelling error #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\") # or a bit more severe #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") # and even this works #> [1] \"J01DD04\"  # use ab_* functions to get a specific properties (see ?ab_property); # they use as.ab() internally: ab_name(\"J01FA01\") #> [1] \"Erythromycin\" ab_name(\"eryt\") #> [1] \"Erythromycin\"  # \\donttest{ if (require(\"dplyr\")) {   # you can quickly rename 'sir' columns using set_ab_names() with dplyr:   example_isolates %>%     set_ab_names(where(is.sir), property = \"atc\") } #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           J01CE01 J01CF04 J01CF05 #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir>   <sir>   <sir>   #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R       NA      NA      #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R       NA      NA      #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R       NA      S       #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R       NA      S       #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R       NA      R       #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R       NA      S       #> # … with 1,990 more rows, and 37 more variables: J01CA04 <sir>, J01CR02 <sir>, #> #   J01CA01 <sir>, J01CR05 <sir>, J01DB04 <sir>, J01DE01 <sir>, J01DC02 <sir>, #> #   J01DC01 <sir>, J01DD01 <sir>, J01DD02 <sir>, J01DD04 <sir>, J01GB03 <sir>, #> #   J01GB01 <sir>, J01GB06 <sir>, J01GB04 <sir>, J01EA01 <sir>, J01EE01 <sir>, #> #   J01XE01 <sir>, J01XX01 <sir>, J01XX08 <sir>, J01MA02 <sir>, J01MA14 <sir>, #> #   J01XA01 <sir>, J01XA02 <sir>, J01AA07 <sir>, J01AA12 <sir>, J01AA02 <sir>, #> #   J01FA01 <sir>, J01FF01 <sir>, J01FA10 <sir>, J01DH51 <sir>, … # }"},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to an Antiviral Drug ID — as.av","title":"Transform Input to an Antiviral Drug ID — as.av","text":"Use function determine antiviral drug code one antiviral drugs. data set antivirals searched abbreviations, official names synonyms (brand names).","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to an Antiviral Drug ID — as.av","text":"","code":"as.av(x, flag_multiple_results = TRUE, info = interactive(), ...)  is.av(x)"},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to an Antiviral Drug ID — as.av","text":"x character vector determine antiviral drug ID flag_multiple_results logical indicate whether note printed console probably one antiviral drug code name can retrieved single input value. info logical indicate whether progress bar printed - default TRUE interactive mode ... arguments passed internal functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to an Antiviral Drug ID — as.av","text":"character  vector additional class ab","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to an Antiviral Drug ID — as.av","text":"entries antivirals data set three different identifiers: human readable EARS-Net code (column ab, used ECDC WHONET), ATC code (column atc, used ), CID code (column cid, Compound ID, used PubChem). data set contains 5,000 official brand names many different countries, found PubChem. drugs contain multiple ATC codes. properties searched user input. .av() can correct different forms misspelling: Wrong spelling drug names (\"acyclovir\"), corrects audible similarities f/ph, x/ks, c/z/s, t/th, etc. many vowels consonants Switching two characters (\"aycclovir\", often case clinical data, doctors typed fast) Digitalised paper records, leaving artefacts like 0/o/O (zero O's), B/8, n/r, etc. Use av_* functions get properties based returned antiviral drug ID, see Examples. Note: .av() av_* functions may use long regular expression match brand names antimicrobial drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to an Antiviral Drug ID — as.av","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Transform Input to an Antiviral Drug ID — as.av","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to an Antiviral Drug ID — as.av","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to an Antiviral Drug ID — as.av","text":"","code":"# these examples all return \"ACI\", the ID of aciclovir: as.av(\"J05AB01\") #> Class 'av' #> [1] ACI as.av(\"J 05 AB 01\") #> Class 'av' #> [1] ACI as.av(\"Aciclovir\") #> Class 'av' #> [1] ACI as.av(\"aciclo\") #> Class 'av' #> [1] ACI as.av(\"   aciclo 123\") #> Class 'av' #> [1] ACI as.av(\"ACICL\") #> Class 'av' #> [1] ACI as.av(\"ACI\") #> Class 'av' #> [1] ACI as.av(\"Virorax\") # trade name #> Class 'av' #> [1] ACI as.av(\"Zovirax\") # trade name #> Class 'av' #> [1] ACI  as.av(\"acyklofir\") # severe spelling error, yet works #> Class 'av' #> [1] ACI  # use av_* functions to get a specific properties (see ?av_property); # they use as.av() internally: av_name(\"J05AB01\") #> [1] \"Aciclovir\" av_name(\"acicl\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Disk Diffusion Diameters — as.disk","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"transforms vector new class disk, disk diffusion growth zone size (around antibiotic disk) millimetres 6 50.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"as.disk(x, na.rm = FALSE)  NA_disk_  is.disk(x)"},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"object class disk (inherits integer) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"x vector na.rm logical indicating whether missing values removed","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"integer additional class disk","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"Interpret disk values SIR values .sir(). supports guidelines EUCAST CLSI. Disk diffusion growth zone sizes must 6 50 millimetres. Values higher 50 lower 100 maximised 50. others input values outside 6-50 range return NA. NA_disk_ missing value new disk class.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"# transform existing disk zones to the `disk` class (using base R) df <- data.frame(   microorganism = \"Escherichia coli\",   AMP = 20,   CIP = 14,   GEN = 18,   TOB = 16 ) df[, 2:5] <- lapply(df[, 2:5], as.disk) str(df) #> 'data.frame':\t1 obs. of  5 variables: #>  $ microorganism: chr \"Escherichia coli\" #>  $ AMP          : 'disk' int 20 #>  $ CIP          : 'disk' int 14 #>  $ GEN          : 'disk' int 18 #>  $ TOB          : 'disk' int 16  # \\donttest{ # transforming is easier with dplyr: if (require(\"dplyr\")) {   df %>% mutate(across(AMP:TOB, as.disk)) } #>      microorganism AMP CIP GEN TOB #> 1 Escherichia coli  20  14  18  16 # }  # interpret disk values, see ?as.sir as.sir(   x = as.disk(18),   mo = \"Strep pneu\", # `mo` will be coerced with as.mo()   ab = \"ampicillin\", # and `ab` with as.ab()   guideline = \"EUCAST\" ) #> => Interpreting disk diffusion zones of 'ampicillin' (AMP) according to #>    EUCAST 2022... #>  OK. #> Class 'sir' #> [1] R  # interpret whole data set, pretend to be all from urinary tract infections: as.sir(df, uti = TRUE) #> => Interpreting disk diffusion zones of column 'AMP' (ampicillin) according #>    to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'CIP' (ciprofloxacin) #>    according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'GEN' (gentamicin) according #>    to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'TOB' (tobramycin) according #>    to EUCAST 2022... #>  OK. #>      microorganism AMP CIP GEN TOB #> 1 Escherichia coli   S   R   S   S"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"transforms vectors new class mic, treats input decimal numbers, maintaining operators (\">=\") allowing valid MIC values known field (medical) microbiology.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"as.mic(x, na.rm = FALSE)  NA_mic_  is.mic(x)  # S3 method for mic droplevels(x, as.mic = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"x character numeric vector na.rm logical indicating whether missing values removed .mic logical indicate whether mic class kept - default FALSE ... arguments passed methods","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"Ordered factor additional class mic, mathematical operations acts decimal numbers. Bare mind outcome mathematical operation MICs return numeric value.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"interpret MIC values SIR values, use .sir() MIC values. supports guidelines EUCAST (2013-2022) CLSI (2013-2022). class MIC values quite special data type: formally ordered factor valid MIC values factor levels (make sure valid MIC values retained), mathematical operation acts decimal numbers:   makes possible maintain operators often come MIC values, \">=\" \"<=\", even filtering using numeric values data analysis, e.g.:   following generic functions implemented MIC class: !, !=, %%, %/%, &, *, +, -, /, <, <=, ==, >, >=, ^, |, abs(), acos(), acosh(), (), (), asin(), asinh(), atan(), atanh(), ceiling(), cos(), cosh(), cospi(), cummax(), cummin(), cumprod(), cumsum(), digamma(), exp(), expm1(), floor(), gamma(), lgamma(), log(), log1p(), log2(), log10(), max(), mean(), min(), prod(), range(), round(), sign(), signif(), sin(), sinh(), sinpi(), sqrt(), sum(), tan(), tanh(), tanpi(), trigamma() trunc(). functions stats package also implemented: median(), quantile(), mad(), IQR(), fivenum(). Also, boxplot.stats() supported. Since sd() var() non-generic functions, extended. Use mad() alternative, use e.g. sd(.numeric(x)) x vector MIC values. Using .double() .numeric() MIC values remove operators return numeric vector. use .integer() MIC values R convention factors, return index factor levels (often useless regular users). Use droplevels() drop unused levels. default, return plain factor. Use droplevels(..., .mic = TRUE) maintain mic class. NA_mic_ missing value new mic class, analogous e.g. base R's NA_character_.","code":"x <- random_mic(10) x #> Class 'mic' #>  [1] 16     1      8      8      64     >=128  0.0625 32     32     16  is.factor(x) #> [1] TRUE  x[1] * 2 #> [1] 32  median(x) #> [1] 26 x[x > 4] #> Class 'mic' #> [1] 16    8     8     64    >=128 32    32    16  df <- data.frame(x, hospital = \"A\") subset(df, x > 4) # or with dplyr: df %>% filter(x > 4) #>        x hospital #> 1     16        A #> 5     64        A #> 6  >=128        A #> 8     32        A #> 9     32        A #> 10    16        A"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"mic_data <- as.mic(c(\">=32\", \"1.0\", \"1\", \"1.00\", 8, \"<=0.128\", \"8\", \"16\", \"16\")) mic_data #> Class 'mic' #> [1] >=32    1       1       1       8       <=0.128 8       16      16      is.mic(mic_data) #> [1] TRUE  # this can also coerce combined MIC/SIR values: as.mic(\"<=0.002; S\") #> Class 'mic' #> [1] <=0.002  # mathematical processing treats MICs as numeric values fivenum(mic_data) #> [1]  0.128  1.000  8.000 16.000 32.000 quantile(mic_data) #>     0%    25%    50%    75%   100%  #>  0.128  1.000  8.000 16.000 32.000  all(mic_data < 512) #> [1] TRUE  # interpret MIC values as.sir(   x = as.mic(2),   mo = as.mo(\"Streptococcus pneumoniae\"),   ab = \"AMX\",   guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMX' (amoxicillin) according to EUCAST #>    2022... #>  Note: #>    • Multiple breakpoints available for amoxicillin (AMX) in Streptococcus #>      pneumoniae - assuming body site 'Meningitis'. #> Class 'sir' #> [1] R as.sir(   x = as.mic(c(0.01, 2, 4, 8)),   mo = as.mo(\"Streptococcus pneumoniae\"),   ab = \"AMX\",   guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMX' (amoxicillin) according to EUCAST #>    2022... #>  Note: #>    • Multiple breakpoints available for amoxicillin (AMX) in Streptococcus #>      pneumoniae - assuming body site 'Meningitis'. #> Class 'sir' #> [1] S R R R  # plot MIC values, see ?plot plot(mic_data)  plot(mic_data, mo = \"E. coli\", ab = \"cipro\")   if (require(\"ggplot2\")) {   autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\") }  if (require(\"ggplot2\")) {   autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"nl\") # Dutch }  if (require(\"ggplot2\")) {   autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"uk\") # Ukrainian }"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Use function get valid microorganism code (mo) based arbitrary user input. Determination done using intelligent rules complete taxonomic tree kingdoms Animalia, Archaea, Bacteria, Protozoa, microbial species kingdom Fungi (see Source). input can almost anything: full name (like \"Staphylococcus aureus\"), abbreviated name (\"S. aureus\"), abbreviation known field (\"MRSA\"), just genus. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"","code":"as.mo(   x,   Becker = FALSE,   Lancefield = FALSE,   minimum_matching_score = NULL,   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   reference_df = get_mo_source(),   ignore_pattern = getOption(\"AMR_ignore_pattern\", NULL),   cleaning_regex = getOption(\"AMR_cleaning_regex\", mo_cleaning_regex()),   language = get_AMR_locale(),   info = interactive(),   ... )  is.mo(x)  mo_uncertainties()  mo_renamed()  mo_failures()  mo_reset_session()  mo_cleaning_regex()"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"x character vector data.frame one two columns Becker logical indicate whether staphylococci categorised coagulase-negative staphylococci (\"CoNS\") coagulase-positive staphylococci (\"CoPS\") instead species, according Karsten Becker et al. (see Source). Please see Details full list staphylococcal species converted. excludes Staphylococcus aureus default, use Becker = \"\" also categorise S. aureus \"CoPS\". Lancefield logical indicate whether beta-haemolytic Streptococcus categorised Lancefield groups instead species, according Rebecca C. Lancefield (see Source). streptococci categorised first group, e.g. Streptococcus dysgalactiae group C, although officially also categorised groups G L. . Please see Details full list streptococcal species converted. excludes enterococci default (group D), use Lancefield = \"\" also categorise enterococci group D. minimum_matching_score numeric value set lower limit MO matching score. left blank, determined automatically based character length x, taxonomic kingdom human pathogenicity. keep_synonyms logical indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE, return note old taxonomic names processed. default can set package option AMR_keep_synonyms, .e. options(AMR_keep_synonyms = TRUE) options(AMR_keep_synonyms = FALSE). reference_df data.frame used extra reference translating x valid mo. See set_mo_source() get_mo_source() automate usage codes (e.g. used analysis organisation). ignore_pattern Perl-compatible regular expression (case-insensitive) matches x must return NA. can convenient exclude known non-relevant input can also set package option AMR_ignore_pattern, e.g. options(AMR_ignore_pattern = \"(reported|contaminated flora)\"). cleaning_regex Perl-compatible regular expression (case-insensitive) clean input x. Every matched part x removed. default, outcome mo_cleaning_regex(), removes texts brackets texts \"species\" \"serovar\". default can set package option AMR_cleaning_regex. language language translate text like \"growth\", defaults system language (see get_AMR_locale()) info logical indicate progress bar printed 25 items coerced - default TRUE interactive mode ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"character  vector additional class mo","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"microorganism (MO) code package (class: mo) human readable typically looks like examples:   Values coerced considered 'unknown' returned MO code UNKNOWN warning. Use mo_* functions get properties based returned code, see Examples. .mo() function uses novel matching score algorithm (see Matching Score Microorganisms ) match input available microbial taxonomy package. lead effect e.g. \"E. coli\" (microorganism highly prevalent humans) return microbial ID Escherichia coli Entamoeba coli (microorganism less prevalent humans), although latter alphabetically come first. Becker = TRUE, following 85 staphylococci converted coagulase-negative group: S. argensis, S. arlettae, S. auricularis, S. borealis, S. caeli, S. caledonicus, S. canis, S. capitis, S. capitis capitis, S. capitis urealyticus, S. capitis ureolyticus, S. caprae, S. carnosus, S. carnosus carnosus, S. carnosus utilis, S. casei, S. caseolyticus, S. chromogenes, S. cohnii, S. cohnii cohnii, S. cohnii urealyticum, S. cohnii urealyticus, S. condimenti, S. croceilyticus, S. debuckii, S. devriesei, S. durrellii, S. edaphicus, S. epidermidis, S. equorum, S. equorum equorum, S. equorum linens, S. felis, S. fleurettii, S. gallinarum, S. haemolyticus, S. hominis, S. hominis hominis, S. hominis novobiosepticus, S. jettensis, S. kloosii, S. lentus, S. lloydii, S. lugdunensis, S. massiliensis, S. microti, S. muscae, S. nepalensis, S. pasteuri, S. petrasii, S. petrasii croceilyticus, S. petrasii jettensis, S. petrasii petrasii, S. petrasii pragensis, S. pettenkoferi, S. piscifermentans, S. pragensis, S. pseudoxylosus, S. pulvereri, S. ratti, S. rostri, S. saccharolyticus, S. saprophyticus, S. saprophyticus bovis, S. saprophyticus saprophyticus, S. schleiferi, S. schleiferi schleiferi, S. sciuri, S. sciuri carnaticus, S. sciuri lentus, S. sciuri rodentium, S. sciuri sciuri, S. simulans, S. stepanovicii, S. succinus, S. succinus casei, S. succinus succinus, S. taiwanensis, S. urealyticus, S. ureilyticus, S. veratri, S. vitulinus, S. vitulus, S. warneri, S. xylosus. following 16 staphylococci converted coagulase-positive group: S. agnetis, S. argenteus, S. coagulans, S. cornubiensis, S. delphini, S. hyicus, S. hyicus chromogenes, S. hyicus hyicus, S. intermedius, S. lutrae, S. pseudintermedius, S. roterodami, S. schleiferi coagulans, S. schweitzeri, S. simiae, S. singaporensis. Lancefield = TRUE, following streptococci converted corresponding Lancefield group: S. agalactiae (Group B), S. anginosus anginosus (Group F), S. anginosus whileyi (Group F), S. anginosus (Group F), S. canis (Group G), S. dysgalactiae dysgalactiae (Group C), S. dysgalactiae equisimilis (Group C), S. dysgalactiae (Group C), S. equi equi (Group C), S. equi ruminatorum (Group C), S. equi zooepidemicus (Group C), S. equi (Group C), S. pyogenes (Group ), S. salivarius salivarius (Group K), S. salivarius thermophilus (Group K), S. salivarius (Group K), S. sanguinis (Group H).","code":"Code               Full name   ---------------    --------------------------------------   B_KLBSL            Klebsiella   B_KLBSL_PNMN       Klebsiella pneumoniae   B_KLBSL_PNMN_RHNS  Klebsiella pneumoniae rhinoscleromatis   |   |    |    |   |   |    |    |   |   |    |    \\---> subspecies, a 3-5 letter acronym   |   |    \\----> species, a 3-6 letter acronym   |   \\----> genus, a 4-8 letter acronym   \\----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria),                             F (Fungi), PL (Plantae), P (Protozoa)"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"coping-with-uncertain-results","dir":"Reference","previous_headings":"","what":"Coping with Uncertain Results","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Results non-exact taxonomic input based matching score. lowest allowed score can set minimum_matching_score argument. default determined based character length input, taxonomic kingdom human pathogenicity taxonomic outcome. values matched uncertainty, message shown suggest user evaluate results mo_uncertainties(), returns data.frame specifications. increase quality matching, cleaning_regex argument can used clean input (.e., x). must regular expression matches parts input removed input matched available microbial taxonomy. matched Perl-compatible case-insensitive. default value cleaning_regex outcome helper function mo_cleaning_regex(). three helper functions can run using .mo() function: Use mo_uncertainties() get data.frame prints pretty format taxonomic names guessed. output contains matching score matches (see Matching Score Microorganisms ). Use mo_failures() get character vector values coerced valid value. Use mo_renamed() get data.frame values coerced based old, previously accepted taxonomic names.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"microbial-prevalence-of-pathogens-in-humans","dir":"Reference","previous_headings":"","what":"Microbial Prevalence of Pathogens in Humans","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"coercion rules consider prevalence microorganisms humans, available prevalence column microorganisms data set. grouping human pathogenic prevalence explained section Matching Score Microorganisms .","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 Becker K et al. (2014). Coagulase-Negative Staphylococci. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. (2019). Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. (2020). Emergence coagulase-negative staphylococci Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 Lancefield RC (1933). serological differentiation human groups hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571 Berends MS et al. (2022). Trends Occurrence Phenotypic Resistance Coagulase-Negative Staphylococci (CoNS) Found Human Blood Northern Netherlands 2013 2019 Microorganisms 10(9), 1801; doi:10.3390/microorganisms10091801 Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated :  : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.0 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, genus present established list also prevalence = 1.0 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.5 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Contracaecum, Cordylobia, Cryptococcus, Curvularia, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Giardia, Haloarcula, Halobacterium, Halococcus, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hymenolepis, Hypomyces, Hysterothylacium, Leishmania, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Mortierella, Mucor, Mycocentrospora, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Pediculus, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga, Wuchereria; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.159\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"","code":"# \\donttest{ # These examples all return \"B_STPHY_AURS\", the ID of S. aureus: as.mo(c(   \"sau\", # WHONET code   \"stau\",   \"STAU\",   \"staaur\",   \"S. aureus\",   \"S aureus\",   \"Sthafilokkockus aureus\", # handles incorrect spelling   \"Staphylococcus aureus (MRSA)\",   \"MRSA\", # Methicillin Resistant S. aureus   \"VISA\", # Vancomycin Intermediate S. aureus   \"VRSA\", # Vancomycin Resistant S. aureus   115329001 # SNOMED CT code )) #> Class 'mo' #>  [1] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #>  [6] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [11] B_STPHY_AURS B_STPHY_AURS  # Dyslexia is no problem - these all work: as.mo(c(   \"Ureaplasma urealyticum\",   \"Ureaplasma urealyticus\",   \"Ureaplasmium urealytica\",   \"Ureaplazma urealitycium\" )) #> Class 'mo' #> [1] B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY  as.mo(\"Streptococcus group A\") #> Class 'mo' #> [1] B_STRPT_GRPA  as.mo(\"S. epidermidis\") # will remain species: B_STPHY_EPDR #> Class 'mo' #> [1] B_STPHY_EPDR as.mo(\"S. epidermidis\", Becker = TRUE) # will not remain species: B_STPHY_CONS #> Class 'mo' #> [1] B_STPHY_CONS  as.mo(\"S. pyogenes\") # will remain species: B_STRPT_PYGN #> Class 'mo' #> [1] B_STRPT_PYGN as.mo(\"S. pyogenes\", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA #> Class 'mo' #> [1] B_STRPT_GRPA  # All mo_* functions use as.mo() internally too (see ?mo_property): mo_genus(\"E. coli\") #> [1] \"Escherichia\" mo_gramstain(\"ESCO\") #> [1] \"Gram-negative\" mo_is_intrinsic_resistant(\"ESCCOL\", ab = \"vanco\") #> ℹ Determining intrinsic resistance based on 'EUCAST Expert Rules' and #>   'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021). This note #>   will be shown once per session. #> [1] TRUE # }"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Interpret minimum inhibitory concentration (MIC) values disk diffusion diameters according EUCAST CLSI, clean existing SIR values. transforms input new class sir, ordered factor levels S < < R. breakpoints used interpretation publicly available clinical_breakpoints data set.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"as.sir(x, ...)  NA_sir_  is.sir(x)  is_sir_eligible(x, threshold = 0.05)  # S3 method for mic as.sir(   x,   mo = NULL,   ab = deparse(substitute(x)),   guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   uti = NULL,   conserve_capped_values = FALSE,   add_intrinsic_resistance = FALSE,   reference_data = AMR::clinical_breakpoints,   include_screening = getOption(\"AMR_include_screening\", FALSE),   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   ... )  # S3 method for disk as.sir(   x,   mo = NULL,   ab = deparse(substitute(x)),   guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   uti = NULL,   add_intrinsic_resistance = FALSE,   reference_data = AMR::clinical_breakpoints,   include_screening = getOption(\"AMR_include_screening\", FALSE),   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   ... )  # S3 method for data.frame as.sir(   x,   ...,   col_mo = NULL,   guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   uti = NULL,   conserve_capped_values = FALSE,   add_intrinsic_resistance = FALSE,   reference_data = AMR::clinical_breakpoints,   include_screening = getOption(\"AMR_include_screening\", FALSE),   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE) )  sir_interpretation_history(clean = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"object class sir (inherits ordered, factor) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpretations minimum inhibitory concentration (MIC) values disk diffusion diameters: M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 2013-2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. M100 Performance Standard Antimicrobial Susceptibility Testing, 2013-2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/. Breakpoint tables interpretation MICs zone diameters, 2013-2022, European Committee Antimicrobial Susceptibility Testing (EUCAST). https://www.eucast.org/clinical_breakpoints.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"x vector values (class mic: MIC values mg/L, class disk: disk diffusion radius millimetres) ... using data.frame: names columns apply .sir() (supports tidy selection column1:column4). Otherwise: arguments passed methods. threshold maximum fraction invalid antimicrobial interpretations x, see Examples mo (vector ) text can coerced valid microorganism codes .mo(), can left empty determine automatically ab (vector ) text can coerced valid antimicrobial drug code .ab() guideline defaults EUCAST 2022 (latest implemented EUCAST guideline clinical_breakpoints data set), can set package option AMR_guideline. Currently supports EUCAST (2013-2022) CLSI (2013-2022), see Details. uti (Urinary Tract Infection) vector logicals (TRUE FALSE) specify whether UTI specific interpretation guideline chosen. using .sir() data.frame, can also column containing logicals left blank, data set searched column 'specimen', rows within column containing 'urin' ('urine', 'urina') regarded isolates UTI. See Examples. conserve_capped_values logical indicate MIC values starting \">\" (\">=\") must always return \"R\" , MIC values starting \"<\" (\"<=\") must always return \"S\" add_intrinsic_resistance (useful using EUCAST guideline) logical indicate whether intrinsic antibiotic resistance must also considered applicable bug-drug combinations, meaning e.g. ampicillin always return \"R\" Klebsiella species. Determination based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). reference_data data.frame used interpretation, defaults clinical_breakpoints data set. Changing argument allows using interpretation guidelines. argument must contain data set equal structure clinical_breakpoints data set (column names column types). Please note guideline argument ignored reference_data manually set. include_screening logical indicate clinical breakpoints screening allowed - default FALSE. Can also set package option AMR_include_screening. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort - default TRUE. Can also set package option AMR_include_PKPD. col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). clean logical indicate whether previously stored results forgotten returning 'logbook' results","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Ordered factor new class sir","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"NA_sir_ missing value new sir class, analogous e.g. base R's NA_character_.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"how-it-works","dir":"Reference","previous_headings":"","what":"How it Works","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":".sir() function works four ways: cleaning raw / untransformed data. data cleaned contain values S, R try best determine intelligence. example, mixed values SIR interpretations MIC values \"<0.25; S\" coerced \"S\". Combined interpretations multiple test methods (seen laboratory records) \"S; S\" coerced \"S\", value like \"S; \" return NA warning input unclear. interpreting minimum inhibitory concentration (MIC) values according EUCAST CLSI. must clean MIC values first using .mic(), also gives columns new data class mic. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either:   Operators like \"<=\" stripped interpretation. using conserve_capped_values = TRUE, MIC value e.g. \">2\" always return \"R\", even breakpoint according chosen guideline \">=4\". prevent capped values raw laboratory data treated conservatively. default behaviour (conserve_capped_values = FALSE) considers \">2\" lower \">=4\" might case return \"S\" \"\". interpreting disk diffusion diameters according EUCAST CLSI. must clean disk zones first using .disk(), also gives columns new data class disk. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either:   interpreting complete data set, automatic determination MIC values, disk diffusion diameters, microorganism names codes, antimicrobial test results. done simply running .sir(your_data). points 2, 3 4: Use sir_interpretation_history() retrieve data.frame (tibble tibble package installed) results last .sir() call.","code":"your_data %>% mutate_if(is.mic, as.sir) your_data %>% mutate(across(where(is.mic), as.sir)) your_data %>% mutate_if(is.disk, as.sir) your_data %>% mutate(across(where(is.disk), as.sir))"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"supported-guidelines","dir":"Reference","previous_headings":"","what":"Supported Guidelines","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpreting MIC values well disk diffusion diameters, currently implemented guidelines EUCAST (2013-2022) CLSI (2013-2022). Thus, guideline argument must set e.g., \"EUCAST 2022\" \"CLSI 2022\". simply using \"EUCAST\" (default) \"CLSI\" input, latest included version guideline automatically selected. can set data set using reference_data argument. guideline argument ignored. can set default guideline package option AMR_guideline (e.g. .Rprofile file), :","code":"options(AMR_guideline = \"CLSI\")   options(AMR_guideline = \"CLSI 2018\")   options(AMR_guideline = \"EUCAST 2020\")   # or to reset:   options(AMR_guideline = NULL)"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"after-interpretation","dir":"Reference","previous_headings":"","what":"After Interpretation","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"using .sir(), can use eucast_rules() defined EUCAST (1) apply inferred susceptibility resistance based results antimicrobials (2) apply intrinsic resistance based taxonomic properties microorganism.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"machine-readable-clinical-breakpoints","dir":"Reference","previous_headings":"","what":"Machine-Readable Clinical Breakpoints","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"repository package contains machine-readable version guidelines. CSV file consisting 18 308 rows 11 columns. file machine-readable, since contains one row every unique combination test method (MIC disk diffusion), antimicrobial drug microorganism. allows easy implementation rules laboratory information systems (LIS). Note contains interpretation guidelines humans - interpretation guidelines CLSI animals removed.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"function .sir() detects input contains class sir. input data.frame, iterates columns returns logical vector. function is_sir_eligible() returns TRUE columns contains 5% invalid antimicrobial interpretations (S //R), FALSE otherwise. threshold 5% can set threshold argument. input data.frame, iterates columns returns logical vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> summary(example_isolates) # see all SIR results at a glance #>       date              patient               age           gender          #>  Min.   :2002-01-02   Length:2000        Min.   : 0.00   Length:2000        #>  1st Qu.:2005-07-31   Class :character   1st Qu.:63.00   Class :character   #>  Median :2009-07-31   Mode  :character   Median :74.00   Mode  :character   #>  Mean   :2009-11-20                      Mean   :70.69                      #>  3rd Qu.:2014-05-30                      3rd Qu.:82.00                      #>  Max.   :2017-12-28                      Max.   :97.00                      #>      ward                mo                  PEN                 #>  Length:2000        Class :mo             Class:sir              #>  Class :character   <NA>  :0              %R   :73.7% (n=1201)   #>  Mode  :character   Unique:90             %SI  :26.3% (n=428)    #>                     #1    :B_ESCHR_COLI   - %S :25.6% (n=417)    #>                     #2    :B_STPHY_CONS   - %I : 0.7% (n=11)     #>                     #3    :B_STPHY_AURS                          #>     OXA                   FLC                   AMX                #>  Class:sir             Class:sir             Class:sir             #>  %R   :31.2% (n=114)   %R   :29.5% (n=278)   %R   :59.6% (n=804)   #>  %SI  :68.8% (n=251)   %SI  :70.5% (n=665)   %SI  :40.4% (n=546)   #>  - %S :68.8% (n=251)   - %S :70.5% (n=665)   - %S :40.2% (n=543)   #>  - %I : 0.0% (n=0)     - %I : 0.0% (n=0)     - %I : 0.2% (n=3)     #>                                                                    #>     AMC                    AMP                   TZP                #>  Class:sir              Class:sir             Class:sir             #>  %R   :23.7% (n=446)    %R   :59.6% (n=804)   %R   :12.6% (n=126)   #>  %SI  :76.3% (n=1433)   %SI  :40.4% (n=546)   %SI  :87.4% (n=875)   #>  - %S :71.4% (n=1342)   - %S :40.2% (n=543)   - %S :86.1% (n=862)   #>  - %I : 4.8% (n=91)     - %I : 0.2% (n=3)     - %I : 1.3% (n=13)    #>                                                                     #>     CZO                   FEP                   CXM                 #>  Class:sir             Class:sir             Class:sir              #>  %R   :44.6% (n=199)   %R   :14.2% (n=103)   %R   :26.3% (n=470)    #>  %SI  :55.4% (n=247)   %SI  :85.8% (n=621)   %SI  :73.7% (n=1319)   #>  - %S :54.9% (n=245)   - %S :85.6% (n=620)   - %S :72.5% (n=1297)   #>  - %I : 0.4% (n=2)     - %I : 0.1% (n=1)     - %I : 1.2% (n=22)     #>                                                                     #>     FOX                   CTX                   CAZ                 #>  Class:sir             Class:sir             Class:sir              #>  %R   :27.4% (n=224)   %R   :15.5% (n=146)   %R   :66.5% (n=1204)   #>  %SI  :72.6% (n=594)   %SI  :84.5% (n=797)   %SI  :33.5% (n=607)    #>  - %S :71.6% (n=586)   - %S :84.4% (n=796)   - %S :33.5% (n=607)    #>  - %I : 1.0% (n=8)     - %I : 0.1% (n=1)     - %I : 0.0% (n=0)      #>                                                                     #>     CRO                   GEN                    TOB                #>  Class:sir             Class:sir              Class:sir             #>  %R   :15.5% (n=146)   %R   :24.6% (n=456)    %R   :34.4% (n=465)   #>  %SI  :84.5% (n=797)   %SI  :75.4% (n=1399)   %SI  :65.6% (n=886)   #>  - %S :84.4% (n=796)   - %S :74.0% (n=1372)   - %S :65.1% (n=879)   #>  - %I : 0.1% (n=1)     - %I : 1.5% (n=27)     - %I : 0.5% (n=7)     #>                                                                     #>     AMK                   KAN                    TMP                #>  Class:sir             Class:sir              Class:sir             #>  %R   :63.7% (n=441)   %R   :100.0% (n=471)   %R   :38.1% (n=571)   #>  %SI  :36.3% (n=251)   %SI  : 0.0% (n=0)      %SI  :61.9% (n=928)   #>  - %S :36.3% (n=251)   - %S : 0.0% (n=0)      - %S :61.2% (n=918)   #>  - %I : 0.0% (n=0)     - %I : 0.0% (n=0)      - %I : 0.7% (n=10)    #>                                                                     #>     SXT                    NIT                   FOS                #>  Class:sir              Class:sir             Class:sir             #>  %R   :20.5% (n=361)    %R   :17.1% (n=127)   %R   :42.2% (n=148)   #>  %SI  :79.5% (n=1398)   %SI  :82.9% (n=616)   %SI  :57.8% (n=203)   #>  - %S :79.1% (n=1392)   - %S :76.0% (n=565)   - %S :57.8% (n=203)   #>  - %I : 0.3% (n=6)      - %I : 6.9% (n=51)    - %I : 0.0% (n=0)     #>                                                                     #>     LNZ                   CIP                    MFX                #>  Class:sir             Class:sir              Class:sir             #>  %R   :69.3% (n=709)   %R   :16.2% (n=228)    %R   :33.6% (n=71)    #>  %SI  :30.7% (n=314)   %SI  :83.8% (n=1181)   %SI  :66.4% (n=140)   #>  - %S :30.7% (n=314)   - %S :78.9% (n=1112)   - %S :64.5% (n=136)   #>  - %I : 0.0% (n=0)     - %I : 4.9% (n=69)     - %I : 1.9% (n=4)     #>                                                                     #>     VAN                    TEC                   TCY                #>  Class:sir              Class:sir             Class:sir             #>  %R   :38.3% (n=712)    %R   :75.7% (n=739)   %R   :29.8% (n=357)   #>  %SI  :61.7% (n=1149)   %SI  :24.3% (n=237)   %SI  :70.3% (n=843)   #>  - %S :61.7% (n=1149)   - %S :24.3% (n=237)   - %S :68.3% (n=820)   #>  - %I : 0.0% (n=0)      - %I : 0.0% (n=0)     - %I : 1.9% (n=23)    #>                                                                     #>     TGC                   DOX                   ERY                 #>  Class:sir             Class:sir             Class:sir              #>  %R   :12.7% (n=101)   %R   :27.7% (n=315)   %R   :57.2% (n=1084)   #>  %SI  :87.3% (n=697)   %SI  :72.3% (n=821)   %SI  :42.8% (n=810)    #>  - %S :87.3% (n=697)   - %S :71.7% (n=814)   - %S :42.3% (n=801)    #>  - %I : 0.0% (n=0)     - %I : 0.6% (n=7)     - %I : 0.5% (n=9)      #>                                                                     #>     CLI                   AZM                    IPM                #>  Class:sir             Class:sir              Class:sir             #>  %R   :61.2% (n=930)   %R   :57.2% (n=1084)   %R   : 6.2% (n=55)    #>  %SI  :38.8% (n=590)   %SI  :42.8% (n=810)    %SI  :93.8% (n=834)   #>  - %S :38.6% (n=586)   - %S :42.3% (n=801)    - %S :92.7% (n=824)   #>  - %I : 0.3% (n=4)     - %I : 0.5% (n=9)      - %I : 1.1% (n=10)    #>                                                                     #>     MEM                   MTR                  CHL                #>  Class:sir             Class:sir            Class:sir             #>  %R   : 5.9% (n=49)    %R   :14.7% (n=5)    %R   :21.4% (n=33)    #>  %SI  :94.1% (n=780)   %SI  :85.3% (n=29)   %SI  :78.6% (n=121)   #>  - %S :94.1% (n=780)   - %S :85.3% (n=29)   - %S :78.6% (n=121)   #>  - %I : 0.0% (n=0)     - %I : 0.0% (n=0)    - %I : 0.0% (n=0)     #>                                                                   #>     COL                    MUP                   RIF                #>  Class:sir              Class:sir             Class:sir             #>  %R   :81.2% (n=1331)   %R   : 5.9% (n=16)    %R   :69.6% (n=698)   #>  %SI  :18.8% (n=309)    %SI  :94.1% (n=254)   %SI  :30.4% (n=305)   #>  - %S :18.8% (n=309)    - %S :93.0% (n=251)   - %S :30.2% (n=303)   #>  - %I : 0.0% (n=0)      - %I : 1.1% (n=3)     - %I : 0.2% (n=2)     #>                                                                      # For INTERPRETING disk diffusion and MIC values -----------------------  # a whole data set, even with combined MIC values and disk zones df <- data.frame(   microorganism = \"Escherichia coli\",   AMP = as.mic(8),   CIP = as.mic(0.256),   GEN = as.disk(18),   TOB = as.disk(16),   ERY = \"R\" ) as.sir(df) #> => Interpreting MIC values of column 'AMP' (ampicillin) according to EUCAST #>    2022... #>  OK. #> => Interpreting MIC values of column 'CIP' (ciprofloxacin) according to #>    EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'GEN' (gentamicin) according #>    to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of column 'TOB' (tobramycin) according #>    to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Assigning class 'sir' to already clean column 'ERY' (erythromycin)... #>  OK. #>      microorganism AMP CIP GEN TOB ERY #> 1 Escherichia coli   S   I   S   S   R  # return a 'logbook' about the results: sir_interpretation_history() #> # A tibble: 50 × 17 #>    datetime            index ab_input ab_guid…¹ mo_in…² mo_guideline     guide…³ #>    <dttm>              <int> <chr>    <ab>      <chr>   <mo>             <chr>   #>  1 2023-03-12 13:11:56     1 TOB      TOB       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  2 2023-03-12 13:11:55     1 GEN      GEN       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  3 2023-03-12 13:11:55     1 CIP      CIP       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  4 2023-03-12 13:11:55     1 AMP      AMP       Escher… B_[ORD]_ENTRBCTR EUCAST… #>  5 2023-03-12 13:11:50     1 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  6 2023-03-12 13:11:50     2 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  7 2023-03-12 13:11:50     3 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  8 2023-03-12 13:11:50     4 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #>  9 2023-03-12 13:11:50     5 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> 10 2023-03-12 13:11:50     6 CIP      CIP       B_ESCH… B_[ORD]_ENTRBCTR EUCAST… #> # … with 40 more rows, 10 more variables: ref_table <chr>, method <chr>, #> #   input <dbl>, outcome <sir>, breakpoint_S_R <chr>, ab_considered <lgl>, #> #   mo_considered <lgl>, breakpoint_S <lgl>, breakpoint_R <lgl>, #> #   interpretation <lgl>, and abbreviated variable names ¹​ab_guideline, #> #   ²​mo_input, ³​guideline  # for single values as.sir(   x = as.mic(2),   mo = as.mo(\"S. pneumoniae\"),   ab = \"AMP\",   guideline = \"EUCAST\" ) #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #>    2022... #>  Note: #>    • Multiple breakpoints available for ampicillin (AMP) in Streptococcus #>      pneumoniae - assuming body site 'Non-meningitis'. #> Class 'sir' #> [1] R  as.sir(   x = as.disk(18),   mo = \"Strep pneu\", # `mo` will be coerced with as.mo()   ab = \"ampicillin\", # and `ab` with as.ab()   guideline = \"EUCAST\" ) #> => Interpreting disk diffusion zones of 'ampicillin' (AMP) according to #>    EUCAST 2022... #>  OK. #> Class 'sir' #> [1] R  # \\donttest{ # the dplyr way if (require(\"dplyr\")) {   df %>% mutate_if(is.mic, as.sir)   df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)   df %>% mutate(across(where(is.mic), as.sir))   df %>% mutate_at(vars(AMP:TOB), as.sir)   df %>% mutate(across(AMP:TOB, as.sir))    df %>%     mutate_at(vars(AMP:TOB), as.sir, mo = .$microorganism)    # to include information about urinary tract infections (UTI)   data.frame(     mo = \"E. coli\",     NIT = c(\"<= 2\", 32),     from_the_bladder = c(TRUE, FALSE)   ) %>%     as.sir(uti = \"from_the_bladder\")    data.frame(     mo = \"E. coli\",     NIT = c(\"<= 2\", 32),     specimen = c(\"urine\", \"blood\")   ) %>%     as.sir() # automatically determines urine isolates    df %>%     mutate_at(vars(AMP:TOB), as.sir, mo = \"E. coli\", uti = TRUE) } #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column #>    'microorganism' according to EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #>    2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST #>    2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to #>    EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to #>    EUCAST 2022... #>  Note: #>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in #>      Escherichia coli - assuming non-UTI. Use argument uti to set which #>      isolates are from urine. See ?as.sir. #> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to #>    EUCAST 2022... #> Warning: in as.sir(): interpretation of nitrofurantoin (NIT) is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms, #> thus assuming uti = TRUE. See ?as.sir. #>  * WARNING * #> ℹ Assuming value \"urine\" in column 'specimen' reflects a urinary tract #>   infection. #>   Use as.sir(uti = FALSE) to prevent this. #> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to #>    EUCAST 2022... #> Warning: in as.sir(): interpretation of nitrofurantoin (NIT) is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms, #> thus assuming uti = TRUE. See ?as.sir. #>  * WARNING * #> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST #>    2022... #>  OK. #> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST #>    2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to #>    EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to #>    EUCAST 2022... #>  OK. #>      microorganism AMP CIP GEN TOB ERY #> 1 Escherichia coli   S   I   S   S   R  # For CLEANING existing SIR values ------------------------------------  as.sir(c(\"S\", \"I\", \"R\", \"A\", \"B\", \"C\")) #> Warning: in as.sir(): 3 results in column '24' truncated (50%) that were invalid #> antimicrobial interpretations: \"A\", \"B\", and \"C\" #> Class 'sir' #> [1] S    I    R    <NA> <NA> <NA> as.sir(\"<= 0.002; S\") # will return \"S\" #> Class 'sir' #> [1] S sir_data <- as.sir(c(rep(\"S\", 474), rep(\"I\", 36), rep(\"R\", 370))) is.sir(sir_data) #> [1] TRUE plot(sir_data) # for percentages  barplot(sir_data) # for frequencies   # the dplyr way if (require(\"dplyr\")) {   example_isolates %>%     mutate_at(vars(PEN:RIF), as.sir)   # same:   example_isolates %>%     as.sir(PEN:RIF)    # fastest way to transform all columns with already valid AMR results to class `sir`:   example_isolates %>%     mutate_if(is_sir_eligible, as.sir)    # since dplyr 1.0.0, this can also be:   # example_isolates %>%   #   mutate(across(where(is_sir_eligible), as.sir)) } #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> # }"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":null,"dir":"Reference","previous_headings":"","what":"Get ATC Properties from WHOCC Website — atc_online_property","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Gets data WHOCC website determine properties Anatomical Therapeutic Chemical (ATC) (e.g. antibiotic), name, defined daily dose (DDD) standard unit.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"atc_online_property(   atc_code,   property,   administration = \"O\",   url = \"https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no\",   url_vet = \"https://www.whocc.no/atcvet/atcvet_index/?code=%s&showdescription=no\" )  atc_online_groups(atc_code, ...)  atc_online_ddd(atc_code, ...)  atc_online_ddd_units(atc_code, ...)"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"https://www.whocc./atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"atc_code character (vector) ATC code(s) antibiotics, coerced .ab() ab_atc() internally valid ATC code property property ATC code. Valid values \"ATC\", \"Name\", \"DDD\", \"U\" (\"unit\"), \"Adm.R\", \"Note\" groups. last option, hierarchical groups ATC code returned, see Examples. administration type administration using property = \"Adm.R\", see Details url url website WHOCC. sign %s can used placeholder ATC codes. url_vet url website WHOCC veterinary medicine. sign %s can used placeholder ATC_vet codes (start \"Q\"). ... arguments pass atc_property","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Options argument administration: \"Implant\" = Implant \"Inhal\" = Inhalation \"Instill\" = Instillation \"N\" = nasal \"O\" = oral \"P\" = parenteral \"R\" = rectal \"SL\" = sublingual/buccal \"TD\" = transdermal \"V\" = vaginal Abbreviations return values using property = \"U\" (unit): \"g\" = gram \"mg\" = milligram \"mcg\" = microgram \"U\" = unit \"TU\" = thousand units \"MU\" = million units \"mmol\" = millimole \"ml\" = millilitre (e.g. eyedrops) N.B. function requires internet connection works following packages installed: curl, rvest, xml2.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"# \\donttest{ if (requireNamespace(\"curl\") && requireNamespace(\"rvest\") && requireNamespace(\"xml2\")) {   # oral DDD (Defined Daily Dose) of amoxicillin   atc_online_property(\"J01CA04\", \"DDD\", \"O\")   atc_online_ddd(ab_atc(\"amox\"))    # parenteral DDD (Defined Daily Dose) of amoxicillin   atc_online_property(\"J01CA04\", \"DDD\", \"P\")    atc_online_property(\"J01CA04\", property = \"groups\") # search hierarchical groups of amoxicillin } #> Loading required namespace: rvest #> [1] \"ANTIINFECTIVES FOR SYSTEMIC USE\"         #> [2] \"ANTIBACTERIALS FOR SYSTEMIC USE\"         #> [3] \"BETA-LACTAM ANTIBACTERIALS, PENICILLINS\" #> [4] \"Penicillins with extended spectrum\"      # }"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Use function e.g. clinical texts health care records. returns list antiviral drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text(   text,   type = c(\"drug\", \"dose\", \"administration\"),   collapse = NULL,   translate_av = FALSE,   thorough_search = NULL,   info = interactive(),   ... )"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_av type = \"drug\": column name antivirals data set translate antibiotic abbreviations , using av_property(). default FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed - default TRUE interactive mode ... arguments passed .av()","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"list,  character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"function also internally used .av(), although searches first drug name throw note drug names returned. Note: .av() function may use long regular expression match brand names antiviral drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"default, function search antiviral drug names. text elements searched official names, ATC codes brand names. uses .av() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(avx = av_from_text(clinical_text)) returned AV codes can transformed official names, groups, etc. av_* functions av_name() av_group(), using translate_av argument. using collapse, function return character:df %>% mutate(avx = av_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text(\"28/03/2020 valaciclovir po tid\") #> [[1]] #> Class 'av' #> [1] VALA #>  av_from_text(\"28/03/2020 valaciclovir po tid\", type = \"admin\") #> [[1]] #> [1] \"oral\" #>"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antiviral Drug — av_property","title":"Get Properties of an Antiviral Drug — av_property","text":"Use functions return specific property antiviral drug antivirals data set. input values evaluated internally .av().","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"av_name(x, language = get_AMR_locale(), tolower = FALSE, ...)  av_cid(x, ...)  av_synonyms(x, ...)  av_tradenames(x, ...)  av_group(x, language = get_AMR_locale(), ...)  av_atc(x, ...)  av_loinc(x, ...)  av_ddd(x, administration = \"oral\", ...)  av_ddd_units(x, administration = \"oral\", ...)  av_info(x, language = get_AMR_locale(), ...)  av_url(x, open = FALSE, ...)  av_property(x, property = \"name\", language = get_AMR_locale(), ...)"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antiviral Drug — av_property","text":"x (vector ) text can coerced valid antiviral drug code .av() language language returned text - default system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. ... arguments passed .av() administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antivirals data set: vector_or(colnames(antivirals), sort = FALSE).","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antiviral Drug — av_property","text":"integer case av_cid() named list case av_info() multiple av_atc()/av_synonyms()/av_tradenames() double case av_ddd() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antiviral Drug — av_property","text":"output translated possible. function av_url() return direct URL official website. warning returned required ATC code available.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antiviral Drug — av_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antiviral Drug — av_property","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"# all properties: av_name(\"ACI\") #> [1] \"Aciclovir\" av_atc(\"ACI\") #> [1] \"J05AB01\" av_cid(\"ACI\") #> [1] 135398513 av_synonyms(\"ACI\") #>  [1] \"acicloftal\"        \"aciclovier\"        \"aciclovirum\"       #>  [4] \"activir\"           \"acyclofoam\"        \"acycloguanosine\"   #>  [7] \"acyclovir\"         \"acyclovir lauriad\" \"avaclyr\"           #> [10] \"cargosil\"          \"cyclovir\"          \"genvir\"            #> [13] \"gerpevir\"          \"hascovir\"          \"maynar\"            #> [16] \"novirus\"           \"poviral\"           \"sitavig\"           #> [19] \"sitavir\"           \"vipral\"            \"viropump\"          #> [22] \"virorax\"           \"zovirax\"           \"zyclir\"            av_tradenames(\"ACI\") #>  [1] \"acicloftal\"        \"aciclovier\"        \"aciclovirum\"       #>  [4] \"activir\"           \"acyclofoam\"        \"acycloguanosine\"   #>  [7] \"acyclovir\"         \"acyclovir lauriad\" \"avaclyr\"           #> [10] \"cargosil\"          \"cyclovir\"          \"genvir\"            #> [13] \"gerpevir\"          \"hascovir\"          \"maynar\"            #> [16] \"novirus\"           \"poviral\"           \"sitavig\"           #> [19] \"sitavir\"           \"vipral\"            \"viropump\"          #> [22] \"virorax\"           \"zovirax\"           \"zyclir\"            av_group(\"ACI\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_url(\"ACI\") #>                                                             Aciclovir  #> \"https://www.whocc.no/atc_ddd_index/?code=J05AB01&showdescription=no\"   # smart lowercase tranformation av_name(x = c(\"ACI\", \"VALA\")) #> [1] \"Aciclovir\"    \"Valaciclovir\" av_name(x = c(\"ACI\", \"VALA\"), tolower = TRUE) #> [1] \"aciclovir\"    \"valaciclovir\"  # defined daily doses (DDD) av_ddd(\"ACI\", \"oral\") #> [1] 4 av_ddd_units(\"ACI\", \"oral\") #> [1] \"g\" av_ddd(\"ACI\", \"iv\") #> [1] 4 av_ddd_units(\"ACI\", \"iv\") #> [1] \"g\"  av_info(\"ACI\") # all properties as a list #> $av #> [1] \"ACI\" #>  #> $cid #> [1] 135398513 #>  #> $name #> [1] \"Aciclovir\" #>  #> $group #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" #>  #> $atc #> [1] \"J05AB01\" #>  #> $tradenames #>  [1] \"acicloftal\"        \"aciclovier\"        \"aciclovirum\"       #>  [4] \"activir\"           \"acyclofoam\"        \"acycloguanosine\"   #>  [7] \"acyclovir\"         \"acyclovir lauriad\" \"avaclyr\"           #> [10] \"cargosil\"          \"cyclovir\"          \"genvir\"            #> [13] \"gerpevir\"          \"hascovir\"          \"maynar\"            #> [16] \"novirus\"           \"poviral\"           \"sitavig\"           #> [19] \"sitavir\"           \"vipral\"            \"viropump\"          #> [22] \"virorax\"           \"zovirax\"           \"zyclir\"            #>  #> $loinc #> [1] \"\" #>  #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 4 #>  #> $ddd$oral$units #> [1] \"g\" #>  #>  #> $ddd$iv #> $ddd$iv$amount #> [1] 4 #>  #> $ddd$iv$units #> [1] \"g\" #>  #>  #>   # all av_* functions use as.av() internally, so you can go from 'any' to 'any': av_atc(\"ACI\") #> [1] \"J05AB01\" av_group(\"J05AB01\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_loinc(\"abacavir\") #> [1] \"29113-8\" \"78772-1\" \"78773-9\" \"79134-3\" \"80118-3\" av_name(\"29113-8\") #> [1] \"Abacavir\" av_name(135398513) #> [1] \"Aciclovir\" av_name(\"J05AB01\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Availability of Columns — availability","title":"Check Availability of Columns — availability","text":"Easy check data availability columns data set. makes easy get idea antimicrobial combinations can used calculation e.g. susceptibility() resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Availability of Columns — availability","text":"","code":"availability(tbl, width = NULL)"},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Availability of Columns — availability","text":"tbl data.frame list width number characters present visual availability - default filling width console","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Availability of Columns — availability","text":"data.frame column names tbl row names","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check Availability of Columns — availability","text":"function returns data.frame columns \"resistant\" \"visual_resistance\". values columns calculated resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check Availability of Columns — availability","text":"","code":"availability(example_isolates) #>         count available     visual_availabilty resistant      visual_resistance #> date     2000    100.0% |####################|                                  #> patient  2000    100.0% |####################|                                  #> age      2000    100.0% |####################|                                  #> gender   2000    100.0% |####################|                                  #> ward     2000    100.0% |####################|                                  #> mo       2000    100.0% |####################|                                  #> PEN      1629     81.5% |################----|     73.7% |##############------| #> OXA       365     18.3% |###-----------------|     31.2% |######--------------| #> FLC       943     47.2% |#########-----------|     29.5% |#####---------------| #> AMX      1350     67.5% |#############-------|     59.6% |###########---------| #> AMC      1879     94.0% |##################--|     23.7% |####----------------| #> AMP      1350     67.5% |#############-------|     59.6% |###########---------| #> TZP      1001     50.0% |##########----------|     12.6% |##------------------| #> CZO       446     22.3% |####----------------|     44.6% |########------------| #> FEP       724     36.2% |#######-------------|     14.2% |##------------------| #> CXM      1789     89.5% |#################---|     26.3% |#####---------------| #> FOX       818     40.9% |########------------|     27.4% |#####---------------| #> CTX       943     47.2% |#########-----------|     15.5% |###-----------------| #> CAZ      1811     90.6% |##################--|     66.5% |#############-------| #> CRO       943     47.2% |#########-----------|     15.5% |###-----------------| #> GEN      1855     92.8% |##################--|     24.6% |####----------------| #> TOB      1351     67.6% |#############-------|     34.4% |######--------------| #> AMK       692     34.6% |######--------------|     63.7% |############--------| #> KAN       471     23.6% |####----------------|    100.0% |####################| #> TMP      1499     75.0% |###############-----|     38.1% |#######-------------| #> SXT      1759     88.0% |#################---|     20.5% |####----------------| #> NIT       743     37.2% |#######-------------|     17.1% |###-----------------| #> FOS       351     17.6% |###-----------------|     42.2% |########------------| #> LNZ      1023     51.2% |##########----------|     69.3% |#############-------| #> CIP      1409     70.5% |#############-------|     16.2% |###-----------------| #> MFX       211     10.6% |##------------------|     33.6% |######--------------| #> VAN      1861     93.1% |##################--|     38.3% |#######-------------| #> TEC       976     48.8% |#########-----------|     75.7% |###############-----| #> TCY      1200     60.0% |###########---------|     29.8% |#####---------------| #> TGC       798     39.9% |########------------|     12.7% |##------------------| #> DOX      1136     56.8% |###########---------|     27.7% |#####---------------| #> ERY      1894     94.7% |##################--|     57.2% |###########---------| #> CLI      1520     76.0% |###############-----|     61.2% |############--------| #> AZM      1894     94.7% |##################--|     57.2% |###########---------| #> IPM       889     44.5% |########------------|      6.2% |#-------------------| #> MEM       829     41.5% |########------------|      5.9% |#-------------------| #> MTR        34      1.7% |--------------------|     14.7% |##------------------| #> CHL       154      7.7% |#-------------------|     21.4% |####----------------| #> COL      1640     82.0% |################----|     81.2% |################----| #> MUP       270     13.5% |##------------------|      5.9% |#-------------------| #> RIF      1003     50.2% |##########----------|     69.6% |#############-------| # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     filter(mo == as.mo(\"Escherichia coli\")) %>%     select_if(is.sir) %>%     availability() } #>     count available       visual_availabilty resistant        visual_resistance #> PEN   467    100.0% |######################|    100.0% |######################| #> OXA     0      0.0% |----------------------|                                    #> FLC     0      0.0% |----------------------|                                    #> AMX   392     83.9% |##################----|     50.0% |###########-----------| #> AMC   467    100.0% |######################|     13.1% |##--------------------| #> AMP   392     83.9% |##################----|     50.0% |###########-----------| #> TZP   416     89.1% |###################---|      5.5% |#---------------------| #> CZO    82     17.6% |###-------------------|      2.4% |----------------------| #> FEP   317     67.9% |##############--------|      2.8% |----------------------| #> CXM   465     99.6% |######################|      5.4% |#---------------------| #> FOX   377     80.7% |#################-----|      6.9% |#---------------------| #> CTX   459     98.3% |#####################-|      2.4% |----------------------| #> CAZ   460     98.5% |#####################-|      2.4% |----------------------| #> CRO   459     98.3% |#####################-|      2.4% |----------------------| #> GEN   460     98.5% |#####################-|      2.0% |----------------------| #> TOB   462     98.9% |#####################-|      2.6% |----------------------| #> AMK   171     36.6% |########--------------|      0.0% |----------------------| #> KAN     0      0.0% |----------------------|                                    #> TMP   396     84.8% |##################----|     39.1% |########--------------| #> SXT   465     99.6% |######################|     31.6% |######----------------| #> NIT   458     98.1% |#####################-|      2.8% |----------------------| #> FOS    61     13.1% |##--------------------|      0.0% |----------------------| #> LNZ   467    100.0% |######################|    100.0% |######################| #> CIP   456     97.6% |#####################-|     12.5% |##--------------------| #> MFX    57     12.2% |##--------------------|    100.0% |######################| #> VAN   467    100.0% |######################|    100.0% |######################| #> TEC   467    100.0% |######################|    100.0% |######################| #> TCY     3      0.6% |----------------------|     66.7% |##############--------| #> TGC    68     14.6% |###-------------------|      0.0% |----------------------| #> DOX     0      0.0% |----------------------|                                    #> ERY   467    100.0% |######################|    100.0% |######################| #> CLI   467    100.0% |######################|    100.0% |######################| #> AZM   467    100.0% |######################|    100.0% |######################| #> IPM   422     90.4% |###################---|      0.0% |----------------------| #> MEM   418     89.5% |###################---|      0.0% |----------------------| #> MTR     2      0.4% |----------------------|      0.0% |----------------------| #> CHL     0      0.0% |----------------------|                                    #> COL   240     51.4% |###########-----------|      0.0% |----------------------| #> MUP     0      0.0% |----------------------|                                    #> RIF   467    100.0% |######################|    100.0% |######################| # }"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Bug-Drug Combinations — bug_drug_combinations","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"Determine antimicrobial resistance (AMR) bug-drug combinations data set least 30 (default) isolates available per species. Use format() result prettify publishable/printable format, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...)  # S3 method for bug_drug_combinations format(   x,   translate_ab = \"name (ab, atc)\",   language = get_AMR_locale(),   minimum = 30,   combine_SI = TRUE,   add_ab_group = TRUE,   remove_intrinsic_resistant = FALSE,   decimal.mark = getOption(\"OutDec\"),   big.mark = ifelse(decimal.mark == \",\", \".\", \",\"),   ... )"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"x data set antibiotic columns, amox, AMX AMC col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). FUN function call mo column transform microorganism codes - default mo_shortname() ... arguments passed FUN translate_ab character length 1 containing column names antibiotics data set language language returned text - default current system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether values S summed, resistance based R - default TRUE add_ab_group logical indicate group antimicrobials must included first column remove_intrinsic_resistant logical indicate rows columns 100% resistance tested antimicrobials must removed table decimal.mark character used indicate numeric     decimal point. big.mark character; empty used mark every     big.interval decimals (hence big)     decimal point.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function bug_drug_combinations() returns data.frame columns \"mo\", \"ab\", \"S\", \"\", \"R\" \"total\".","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function format() calculates resistance per bug-drug combination returns table ready reporting/publishing. Use combine_SI = TRUE (default) test R vs. S+combine_SI = FALSE test R+vs. S. table can also directly used R Markdown / Quarto without need e.g. knitr::kable().","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>  # \\donttest{ x <- bug_drug_combinations(example_isolates) head(x) #> # A tibble: 6 × 6 #>   mo                ab        S     I     R total #>   <chr>             <chr> <int> <int> <int> <int> #> 1 (unknown species) AMC      15     0     0    15 #> 2 (unknown species) AMK       0     0     0     0 #> 3 (unknown species) AMP      15     0     1    16 #> 4 (unknown species) AMX      15     0     1    16 #> 5 (unknown species) AZM       3     0     3     6 #> 6 (unknown species) CAZ       0     0     0     0 #> Use 'format()' on this result to get a publishable/printable format. format(x, translate_ab = \"name (atc)\") #> # A tibble: 39 × 12 #>    Group     Drug  CoNS  E. co…¹ E. fa…² K. pn…³ P. ae…⁴ P. mi…⁵ S. au…⁶ S. ep…⁷ #>    <chr>     <chr> <chr> <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   #>  1 \"Aminogl… Amik… \"100… \"  0.0… \"100.0… \"\"      \"\"      \"\"      \"\"      \"100.0… #>  2 \"\"        Gent… \" 13… \"  2.0… \"100.0… \" 10.3… \"  0.0… \"  5.9… \"  0.9… \" 21.5… #>  3 \"\"        Kana… \"100… \"\"      \"100.0… \"\"      \"100.0… \"\"      \"\"      \"100.0… #>  4 \"\"        Tobr… \" 78… \"  2.6… \"100.0… \" 10.3… \"  0.0… \"  5.9… \"  2.3… \" 49.4… #>  5 \"Ampheni… Chlo… \"\"    \"\"      \"\"      \"\"      \"100.0… \"\"      \"  0.0… \"  3.1… #>  6 \"Antimyc… Rifa… \"\"    \"100.0… \"\"      \"100.0… \"100.0… \"100.0… \"  0.0… \"  2.7… #>  7 \"Beta-la… Amox… \" 93… \" 50.0… \"\"      \"100.0… \"100.0… \"\"      \" 93.9… \" 98.9… #>  8 \"\"        Amox… \" 42… \" 13.1… \"\"      \" 10.3… \"100.0… \"  2.8… \"  0.4… \" 54.5… #>  9 \"\"        Ampi… \" 93… \" 50.0… \"\"      \"100.0… \"100.0… \"\"      \" 93.9… \" 98.9… #> 10 \"\"        Benz… \" 77… \"100.0… \"\"      \"100.0… \"100.0… \"100.0… \" 80.9… \" 89.4… #> # … with 29 more rows, 2 more variables: `S. hominis` <chr>, #> #   `S. pneumoniae` <chr>, and abbreviated variable names ¹​`E. coli`, #> #   ²​`E. faecalis`, ³​`K. pneumoniae`, ⁴​`P. aeruginosa`, ⁵​`P. mirabilis`, #> #   ⁶​`S. aureus`, ⁷​`S. epidermidis`  # Use FUN to change to transformation of microorganism codes bug_drug_combinations(example_isolates,   FUN = mo_gramstain ) #> # A tibble: 80 × 6 #>    mo            ab        S     I     R total #>  * <chr>         <chr> <int> <int> <int> <int> #>  1 Gram-negative AMC     463    89   174   726 #>  2 Gram-negative AMK     251     0     5   256 #>  3 Gram-negative AMP     226     0   405   631 #>  4 Gram-negative AMX     226     0   405   631 #>  5 Gram-negative AZM       1     2   696   699 #>  6 Gram-negative CAZ     607     0    27   634 #>  7 Gram-negative CHL       1     0    30    31 #>  8 Gram-negative CIP     610    11    63   684 #>  9 Gram-negative CLI      18     1   709   728 #> 10 Gram-negative COL     309     0    78   387 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format.  bug_drug_combinations(example_isolates,   FUN = function(x) {     ifelse(x == as.mo(\"Escherichia coli\"),       \"E. coli\",       \"Others\"     )   } ) #> # A tibble: 80 × 6 #>    mo      ab        S     I     R total #>  * <chr>   <chr> <int> <int> <int> <int> #>  1 E. coli AMC     332    74    61   467 #>  2 E. coli AMK     171     0     0   171 #>  3 E. coli AMP     196     0   196   392 #>  4 E. coli AMX     196     0   196   392 #>  5 E. coli AZM       0     0   467   467 #>  6 E. coli CAZ     449     0    11   460 #>  7 E. coli CHL       0     0     0     0 #>  8 E. coli CIP     398     1    57   456 #>  9 E. coli CLI       0     0   467   467 #> 10 E. coli COL     240     0     0   240 #> # … with 70 more rows #> Use 'format()' on this result to get a publishable/printable format. # }"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Data set containing clinical breakpoints interpret MIC disk diffusion SIR values, according international guidelines. Currently implemented guidelines EUCAST (2013-2022) CLSI (2013-2022). Use .sir() transform MICs disks measurements SIR values.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"tibble 18 308 observations 11 variables: guideline Name guideline method Either \"DISK\" \"MIC\" site Body site, e.g. \"Oral\" \"Respiratory\" mo Microbial ID, see .mo() rank_index Taxonomic rank index mo 1 (subspecies/infraspecies) 5 (unknown microorganism) ab Antibiotic ID, see .ab() ref_tbl Info guideline rule can found disk_dose Dose used disk diffusion method breakpoint_S Lowest MIC value highest number millimetres leads \"S\" breakpoint_R Highest MIC value lowest number millimetres leads \"R\" uti logical value (TRUE/FALSE) indicate whether rule applies urinary tract infection (UTI)","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository. allow machine reading EUCAST CLSI guidelines, almost impossible MS Excel PDF files distributed EUCAST CLSI.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints #> # A tibble: 18,308 × 11 #>    guideline method site  mo               rank_…¹ ab   ref_tbl  disk_…² break…³ #>    <chr>     <chr>  <chr> <mo>               <dbl> <ab> <chr>    <chr>     <dbl> #>  1 EUCAST 2… MIC    NA    F_ASPRG_MGTS           2 AMB  Aspergi… NA            1 #>  2 EUCAST 2… MIC    NA    F_ASPRG_NIGR           2 AMB  Aspergi… NA            1 #>  3 EUCAST 2… MIC    NA    F_CANDD_ALBC           2 AMB  Candida  NA            1 #>  4 EUCAST 2… MIC    NA    F_CANDD_DBLN           2 AMB  Candida  NA            1 #>  5 EUCAST 2… MIC    NA    F_CANDD_GLBR           2 AMB  Candida  NA            1 #>  6 EUCAST 2… MIC    NA    F_CANDD_KRUS           2 AMB  Candida  NA            1 #>  7 EUCAST 2… MIC    NA    F_CANDD_PRPS           2 AMB  Candida  NA            1 #>  8 EUCAST 2… MIC    NA    F_CANDD_TRPC           2 AMB  Candida  NA            1 #>  9 EUCAST 2… MIC    NA    F_CRYPT_NFRM           2 AMB  Candida  NA            1 #> 10 EUCAST 2… DISK   NA    B_[ORD]_ENTRBCTR       5 AMC  Enterob… 20ug/1…      19 #> # … with 18,298 more rows, 2 more variables: breakpoint_R <dbl>, uti <lgl>, and #> #   abbreviated variable names ¹​rank_index, ²​disk_dose, ³​breakpoint_S"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Available Isolates — count","title":"Count Available Isolates — count","text":"functions can used count resistant/susceptible microbial isolates. functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. count_resistant() used count resistant isolates, count_susceptible() used count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Available Isolates — count","text":"","code":"count_resistant(..., only_all_tested = FALSE)  count_susceptible(..., only_all_tested = FALSE)  count_R(..., only_all_tested = FALSE)  count_IR(..., only_all_tested = FALSE)  count_I(..., only_all_tested = FALSE)  count_SI(..., only_all_tested = FALSE)  count_S(..., only_all_tested = FALSE)  count_all(..., only_all_tested = FALSE)  n_sir(..., only_all_tested = FALSE)  count_df(   data,   translate_ab = \"name\",   language = get_AMR_locale(),   combine_SI = TRUE )"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Available Isolates — count","text":"... one vectors (columns) antibiotic interpretations. transformed internally .sir() needed. only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antibiotics, see section Combination Therapy data data.frame containing columns class sir (see .sir()) translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() language language returned text - default current system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant) - default TRUE","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Available Isolates — count","text":"integer","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Count Available Isolates — count","text":"functions meant count isolates. Use resistance()/susceptibility() functions calculate microbial resistance/susceptibility. function count_resistant() equal function count_R(). function count_susceptible() equal function count_SI(). function n_sir() alias count_all(). can used count available isolates, .e. input antibiotics available result (S, R). use equal n_distinct(). function equal count_susceptible(...) + count_resistant(...). function count_df() takes variable data sir class (created .sir()) counts number S's, 's R's. also supports grouped variables. function sir_df() works exactly like count_df(), adds percentage S, R.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Count Available Isolates — count","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Count Available Isolates — count","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antibiotics, Drug Drug B, susceptibility() works calculate %SI:   Please note , combination therapies, only_all_tested = TRUE applies :   , combination therapies, only_all_tested = FALSE applies :   Using only_all_tested impact using one antibiotic input.","code":"--------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   include as  include as   include as  include as                     numerator   denominator  numerator   denominator --------  --------  ----------  -----------  ----------  -----------  S or I    S or I       X            X            X            X    R       S or I       X            X            X            X   <NA>     S or I       X            X            -            -  S or I      R          X            X            X            X    R         R          -            X            -            X   <NA>       R          -            -            -            -  S or I     <NA>        X            X            -            -    R        <NA>        -            -            -            -   <NA>      <NA>        -            -            -            - -------------------------------------------------------------------- count_S()    +   count_I()    +   count_R()    = count_all()   proportion_S() + proportion_I() + proportion_R() = 1 count_S()    +   count_I()    +   count_R()    >= count_all()   proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Available Isolates — count","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info.  # base R ------------------------------------------------------------ count_resistant(example_isolates$AMX) # counts \"R\" #> [1] 804 count_susceptible(example_isolates$AMX) # counts \"S\" and \"I\" #> [1] 546 count_all(example_isolates$AMX) # counts \"S\", \"I\" and \"R\" #> [1] 1350  # be more specific count_S(example_isolates$AMX) #> Using count_S() is discouraged; use count_susceptible() instead to also #> consider \"I\" being susceptible. This note will be shown once for this #> session. #> [1] 543 count_SI(example_isolates$AMX) #> [1] 546 count_I(example_isolates$AMX) #> [1] 3 count_IR(example_isolates$AMX) #> Using count_IR() is discouraged; use count_resistant() instead to not #> consider \"I\" being resistant. This note will be shown once for this #> session. #> [1] 807 count_R(example_isolates$AMX) #> [1] 804  # Count all available isolates count_all(example_isolates$AMX) #> [1] 1350 n_sir(example_isolates$AMX) #> [1] 1350  # n_sir() is an alias of count_all(). # Since it counts all available isolates, you can # calculate back to count e.g. susceptible isolates. # These results are the same: count_susceptible(example_isolates$AMX) #> [1] 546 susceptibility(example_isolates$AMX) * n_sir(example_isolates$AMX) #> [1] 546  # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     group_by(ward) %>%     summarise(       R = count_R(CIP),       I = count_I(CIP),       S = count_S(CIP),       n1 = count_all(CIP), # the actual total; sum of all three       n2 = n_sir(CIP), # same - analogous to n_distinct       total = n()     ) # NOT the number of tested isolates!    # Number of available isolates for a whole antibiotic class   # (i.e., in this data set columns GEN, TOB, AMK, KAN)   example_isolates %>%     group_by(ward) %>%     summarise(across(aminoglycosides(), n_sir))    # Count co-resistance between amoxicillin/clav acid and gentamicin,   # so we can see that combination therapy does a lot more than mono therapy.   # Please mind that `susceptibility()` calculates percentages right away instead.   example_isolates %>% count_susceptible(AMC) # 1433   example_isolates %>% count_all(AMC) # 1879    example_isolates %>% count_susceptible(GEN) # 1399   example_isolates %>% count_all(GEN) # 1855    example_isolates %>% count_susceptible(AMC, GEN) # 1764   example_isolates %>% count_all(AMC, GEN) # 1936    # Get number of S+I vs. R immediately of selected columns   example_isolates %>%     select(AMX, CIP) %>%     count_df(translate = FALSE)    # It also supports grouping variables   example_isolates %>%     select(ward, AMX, CIP) %>%     group_by(ward) %>%     count_df(translate = FALSE) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 12 × 4 #>    ward       antibiotic interpretation value #>  * <chr>      <chr>      <ord>          <int> #>  1 Clinical   AMX        SI               357 #>  2 Clinical   AMX        R                487 #>  3 Clinical   CIP        SI               741 #>  4 Clinical   CIP        R                128 #>  5 ICU        AMX        SI               158 #>  6 ICU        AMX        R                270 #>  7 ICU        CIP        SI               362 #>  8 ICU        CIP        R                 85 #>  9 Outpatient AMX        SI                31 #> 10 Outpatient AMX        R                 47 #> 11 Outpatient CIP        SI                78 #> 12 Outpatient CIP        R                 15 # }"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Define Custom EUCAST Rules — custom_eucast_rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"Define custom EUCAST rules organisation specific analysis use output function eucast_rules().","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"custom_eucast_rules(...)"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"... rules formula notation, see Examples","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"list containing custom rules","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"organisations adoption EUCAST rules. function can used define custom EUCAST rules used eucast_rules() function.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"basics","dir":"Reference","previous_headings":"","what":"Basics","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde:   two custom EUCAST rules: TZP (piperacillin/tazobactam) \"S\", aminopenicillins (ampicillin amoxicillin) must made \"S\", TZP \"R\", aminopenicillins must made \"R\". rules can also printed console, immediately clear work:   rules (part tilde, example TZP == \"S\" TZP == \"R\") must evaluable data set: able run filter data set without errors. means example column TZP must exist. create sample data set test rules set:","code":"x <- custom_eucast_rules(TZP == \"S\" ~ aminopenicillins == \"S\",                          TZP == \"R\" ~ aminopenicillins == \"R\") x #> A set of custom EUCAST rules: #> #>   1. If TZP is \"S\" then set to  S : #>      amoxicillin (AMX), ampicillin (AMP) #> #>   2. If TZP is \"R\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) df <- data.frame(mo = c(\"Escherichia coli\", \"Klebsiella pneumoniae\"),                  TZP = as.sir(\"R\"),                  ampi = as.sir(\"S\"),                  cipro = as.sir(\"S\")) df #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    S     S #> 2 Klebsiella pneumoniae   R    S     S  eucast_rules(df, rules = \"custom\", custom_rules = x, info = FALSE) #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    R     S #> 2 Klebsiella pneumoniae   R    R     S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"using-taxonomic-properties-in-rules","dir":"Reference","previous_headings":"","what":"Using taxonomic properties in rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"one exception columns used rules: column names microorganisms data set can also used, exist data set. column names : \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\", \"snomed\". Thus, next example work well, despite fact df data set contain column genus:","code":"y <- custom_eucast_rules(TZP == \"S\" & genus == \"Klebsiella\" ~ aminopenicillins == \"S\",                          TZP == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\")  eucast_rules(df, rules = \"custom\", custom_rules = y, info = FALSE) #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    S     S #> 2 Klebsiella pneumoniae   R    R     S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"usage-of-antibiotic-group-names","dir":"Reference","previous_headings":"","what":"Usage of antibiotic group names","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"possible define antibiotic groups instead single antibiotics rule consequence, part tilde. examples, antibiotic group aminopenicillins used include ampicillin amoxicillin. following groups allowed (case-insensitive). Within parentheses drugs matched running rule. \"aminoglycosides\"(amikacin, amikacin/fosfomycin, amphotericin B-high, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin, tobramycin-high) \"aminopenicillins\"(amoxicillin ampicillin) \"antifungals\"(amphotericin B, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, flucytosine, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, oteseconazole, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole, voriconazole) \"antimycobacterials\"(4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, morinamide, p-aminosalicylic acid, pretomanid, protionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, simvastatin/fenofibrate, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone, thioacetazone/isoniazid, tiocarlide, viomycin) \"betalactams\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, biapenem, carbenicillin, carindacillin, cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, ciclacillin, clometocillin, cloxacillin, dicloxacillin, doripenem, epicillin, ertapenem, flucloxacillin, hetacillin, imipenem, imipenem/EDTA, imipenem/relebactam, latamoxef, lenampicillin, loracarbef, mecillinam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, tebipenem, temocillin, ticarcillin, ticarcillin/clavulanic acid) \"carbapenems\"(biapenem, doripenem, ertapenem, imipenem, imipenem/EDTA, imipenem/relebactam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, panipenem, razupenem, ritipenem, ritipenem acoxil, tebipenem) \"cephalosporins\"(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef, loracarbef) \"cephalosporins_1st\"(cefacetrile, cefadroxil, cefalexin, cefaloridine, cefalotin, cefapirin, cefatrizine, cefazedone, cefazolin, cefroxadine, ceftezole, cephradine) \"cephalosporins_2nd\"(cefaclor, cefamandole, cefmetazole, cefonicid, ceforanide, cefotetan, cefotiam, cefoxitin, cefoxitin screening, cefprozil, cefuroxime, cefuroxime axetil, loracarbef) \"cephalosporins_3rd\"(cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefetamet, cefetamet pivoxil, cefixime, cefmenoxime, cefodizime, cefoperazone, cefoperazone/sulbactam, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotiam hexetil, cefovecin, cefpimizole, cefpiramide, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefsulodin, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, latamoxef) \"cephalosporins_4th\"(cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetecol, cefoselis, cefozopran, cefpirome, cefquinome) \"cephalosporins_5th\"(ceftaroline, ceftaroline/avibactam, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam) \"cephalosporins_except_caz\"(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef, loracarbef) \"fluoroquinolones\"(besifloxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nifuroquine, norfloxacin, ofloxacin, orbifloxacin, pazufloxacin, pefloxacin, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin, trovafloxacin) \"glycopeptides\"(avoparcin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin, vancomycin-macromethod) \"glycopeptides_except_lipo\"(avoparcin, norvancomycin, ramoplanin, teicoplanin, teicoplanin-macromethod, vancomycin, vancomycin-macromethod) \"lincosamides\"(acetylmidecamycin, acetylspiramycin, clindamycin, gamithromycin, kitasamycin, lincomycin, meleumycin, nafithromycin, pirlimycin, primycin, solithromycin, tildipirosin, tilmicosin, tulathromycin, tylosin, tylvalosin) \"lipoglycopeptides\"(dalbavancin, oritavancin, telavancin) \"macrolides\"(acetylmidecamycin, acetylspiramycin, azithromycin, clarithromycin, dirithromycin, erythromycin, flurithromycin, gamithromycin, josamycin, kitasamycin, meleumycin, midecamycin, miocamycin, nafithromycin, oleandomycin, pirlimycin, primycin, rokitamycin, roxithromycin, solithromycin, spiramycin, telithromycin, tildipirosin, tilmicosin, troleandomycin, tulathromycin, tylosin, tylvalosin) \"oxazolidinones\"(cadazolid, cycloserine, linezolid, tedizolid, thiacetazone) \"penicillins\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, carbenicillin, carindacillin, cefepime/nacubactam, ciclacillin, clometocillin, cloxacillin, dicloxacillin, epicillin, flucloxacillin, hetacillin, lenampicillin, mecillinam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, temocillin, ticarcillin, ticarcillin/clavulanic acid) \"polymyxins\"(colistin, polymyxin B, polymyxin B/polysorbate 80) \"quinolones\"(besifloxacin, cinoxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, flumequine, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nalidixic acid, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, ofloxacin, orbifloxacin, oxolinic acid, pazufloxacin, pefloxacin, pipemidic acid, piromidic acid, pradofloxacin, premafloxacin, prulifloxacin, rosoxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin, trovafloxacin) \"streptogramins\"(pristinamycin quinupristin/dalfopristin) \"tetracyclines\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline, tigecycline) \"tetracyclines_except_tgc\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline) \"trimethoprims\"(brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim, trimethoprim/sulfamethoxazole) \"ureidopenicillins\"(azlocillin, mezlocillin, piperacillin, piperacillin/tazobactam)","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"x <- custom_eucast_rules(   AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\",   AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\" ) x #> A set of custom EUCAST rules: #>  #>   1. If AMC is \"R\" and genus is \"Klebsiella\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   2. If AMC is \"I\" and genus is \"Klebsiella\" then set to  I : #>      amoxicillin (AMX), ampicillin (AMP)  # run the custom rule set (verbose = TRUE will return a logbook instead of the data set): eucast_rules(example_isolates,   rules = \"custom\",   custom_rules = x,   info = FALSE,   verbose = TRUE ) #> # A tibble: 8 × 9 #>     row col   mo_fullname           old   new   rule     rule_…¹ rule_…² rule_…³ #>   <int> <chr> <chr>                 <ord> <chr> <chr>    <chr>   <chr>   <chr>   #> 1    33 AMP   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 2    33 AMX   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 3    34 AMP   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 4    34 AMX   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 5   531 AMP   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 6   531 AMX   Klebsiella pneumoniae R     I     \"report… Custom… Custom… Object… #> 7  1485 AMP   Klebsiella oxytoca    R     I     \"report… Custom… Custom… Object… #> 8  1485 AMX   Klebsiella oxytoca    R     I     \"report… Custom… Custom… Object… #> # … with abbreviated variable names ¹​rule_group, ²​rule_name, ³​rule_source  # combine rule sets x2 <- c(   x,   custom_eucast_rules(TZP == \"R\" ~ carbapenems == \"R\") ) x2 #> A set of custom EUCAST rules: #>  #>   1. If AMC is \"R\" and genus is \"Klebsiella\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   2. If AMC is \"I\" and genus is \"Klebsiella\" then set to  I : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   3. If TZP is \"R\" then set to  R : #>      biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), #>      imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), #>      meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), #>      razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), tebipenem (TBP)"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"EUCAST breakpoints used package based dosages data set. can retrieved eucast_dosage().","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"tibble 336 observations 9 variables: ab Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name antimicrobial drug used WHONET/EARS-Net type Type dosage, either \"high_dosage\", \"standard_dosage\", \"uncomplicated_uti\" dose Dose, \"2 g\" \"25 mg/kg\" dose_times Number times dose must administered administration Route administration, either \"im\", \"iv\", \"oral\" notes Additional dosage notes original_txt Original text PDF file EUCAST eucast_version Version number EUCAST Clinical Breakpoints guideline dosages apply","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"data set based 'EUCAST Clinical Breakpoint Tables' v12.0 (2022) 'EUCAST Clinical Breakpoint Tables' v11.0 (2021).","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage #> # A tibble: 336 × 9 #>    ab   name                   type  dose  dose_…¹ admin…² notes origi…³ eucas…⁴ #>    <ab> <chr>                  <chr> <chr>   <int> <chr>   <chr> <chr>     <dbl> #>  1 AMK  Amikacin               stan… 25-3…       1 iv      \"\"    25-30 …      12 #>  2 AMX  Amoxicillin            high… 2 g         6 iv      \"\"    2 g x …      12 #>  3 AMX  Amoxicillin            stan… 1 g         3 iv      \"\"    1 g x …      12 #>  4 AMX  Amoxicillin            high… 0.75…       3 oral    \"\"    0.75-1…      12 #>  5 AMX  Amoxicillin            stan… 0.5 g       3 oral    \"\"    0.5 g …      12 #>  6 AMX  Amoxicillin            unco… 0.5 g       3 oral    \"\"    0.5 g …      12 #>  7 AMC  Amoxicillin/clavulani… high… 2 g …       3 iv      \"\"    (2 g a…      12 #>  8 AMC  Amoxicillin/clavulani… stan… 1 g …       3 iv      \"\"    (1 g a…      12 #>  9 AMC  Amoxicillin/clavulani… high… 0.87…       3 oral    \"\"    (0.875…      12 #> 10 AMC  Amoxicillin/clavulani… stan… 0.5 …       3 oral    \"\"    (0.5 g…      12 #> # … with 326 more rows, and abbreviated variable names ¹​dose_times, #> #   ²​administration, ³​original_txt, ⁴​eucast_version"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply EUCAST Rules — eucast_rules","title":"Apply EUCAST Rules — eucast_rules","text":"Apply rules clinical breakpoints intrinsic resistance defined European Committee Antimicrobial Susceptibility Testing (EUCAST, https://www.eucast.org), see Source. Use eucast_dosage() get data.frame advised dosages certain bug-drug combination, based dosage data set. improve interpretation antibiogram EUCAST rules applied, non-EUCAST rules can applied default, see Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"eucast_rules(   x,   col_mo = NULL,   info = interactive(),   rules = getOption(\"AMR_eucastrules\", default = c(\"breakpoints\", \"expert\")),   verbose = FALSE,   version_breakpoints = 12,   version_expertrules = 3.3,   ampc_cephalosporin_resistance = NA,   only_sir_columns = FALSE,   custom_rules = NULL,   ... )  eucast_dosage(ab, administration = \"iv\", version_breakpoints = 12)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Apply EUCAST Rules — eucast_rules","text":"EUCAST Expert Rules. Version 2.0, 2012. Leclercq et al. EUCAST expert rules antimicrobial susceptibility testing. Clin Microbiol Infect. 2013;19(2):141-60; doi:10.1111/j.1469-0691.2011.03703.x EUCAST Expert Rules, Intrinsic Resistance Exceptional Phenotypes Tables. Version 3.1, 2016. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.2, 2020. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.3, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 9.0, 2019. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 10.0, 2020. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 11.0, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 12.0, 2022. (link)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply EUCAST Rules — eucast_rules","text":"x data set antibiotic columns, amox, AMX AMC col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console - default print interactive sessions rules character vector specifies rules applied. Must one \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). default value can set another value using package option AMR_eucastrules: options(AMR_eucastrules = \"\"). using \"custom\", sure fill argument custom_rules . Custom rules can created custom_eucast_rules(). verbose logical turn Verbose mode (default ). Verbose mode, function apply rules data, instead returns data set logbook form extensive info rows columns effected way. Using Verbose mode takes lot time. version_breakpoints version number use EUCAST Clinical Breakpoints guideline. Can either \"12.0\", \"11.0\", \"10.0\". version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can either \"3.3\", \"3.2\", \"3.1\". ampc_cephalosporin_resistance character value applied cefotaxime, ceftriaxone ceftazidime AmpC de-repressed cephalosporin-resistant mutants - default NA. Currently works version_expertrules 3.2 higher; version 'EUCAST Expert Rules Enterobacterales' state results cefotaxime, ceftriaxone ceftazidime reported note, results suppressed (emptied) three drugs. value NA (default) argument remove results three drugs, e.g. value \"R\" make results drugs resistant. Use NULL FALSE alter results three drugs AmpC de-repressed cephalosporin-resistant mutants. Using TRUE equal using \"R\".  EUCAST Expert Rules v3.2, rule applies : Citrobacter braakii, Citrobacter freundii, Citrobacter gillenii, Citrobacter murliniae, Citrobacter rodenticum, Citrobacter sedlakii, Citrobacter werkmanii, Citrobacter youngae, Enterobacter, Hafnia alvei, Klebsiella aerogenes, Morganella morganii, Providencia, Serratia. only_sir_columns logical indicate whether antibiotic columns must detected transformed class sir (see .sir()) beforehand (default FALSE) custom_rules custom rules apply, created custom_eucast_rules() ... column name antibiotic, see section Antibiotics ab (vector ) text can coerced valid antibiotic drug code .ab() administration route administration, either \"im\", \"iv\", \"oral\"","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply EUCAST Rules — eucast_rules","text":"input x, possibly edited values antibiotics. , verbose = TRUE, data.frame original new values affected bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Apply EUCAST Rules — eucast_rules","text":"Note: function translate MIC values SIR values. Use .sir() . Note: ampicillin (AMP, J01CA01) available amoxicillin (AMX, J01CA04) , latter used rules dependency ampicillin. drugs interchangeable comes expression antimicrobial resistance. file containing EUCAST rules located : https://github.com/msberends/AMR/blob/main/data-raw/eucast_rules.tsv.  Note: Old taxonomic names replaced current taxonomy applicable. example, Ochrobactrum anthropi renamed Brucella anthropi 2020; original EUCAST rules v3.1 v3.2 yet contain new taxonomic name. AMR package contains full microbial taxonomy updated 11 December, 2022, see microorganisms.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"custom-rules","dir":"Reference","previous_headings":"","what":"Custom Rules","title":"Apply EUCAST Rules — eucast_rules","text":"Custom rules can created using custom_eucast_rules(), e.g.:","code":"x <- custom_eucast_rules(AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\",                          AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\")  eucast_rules(example_isolates, rules = \"custom\", custom_rules = x)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"-other-rules","dir":"Reference","previous_headings":"","what":"'Other' Rules","title":"Apply EUCAST Rules — eucast_rules","text":"processing, two non-EUCAST rules drug combinations can applied improve efficacy EUCAST rules, reliability data (analysis). rules : drug enzyme inhibitor set S drug without enzyme inhibitor S drug without enzyme inhibitor set R drug enzyme inhibitor R Important examples include amoxicillin amoxicillin/clavulanic acid, trimethoprim trimethoprim/sulfamethoxazole. Needless say, rules work, drugs must available data set. Since rules officially approved EUCAST, applied default. use rules, include \"\" rules argument, use eucast_rules(..., rules = \"\"). can also set package option AMR_eucastrules, .e. run options(AMR_eucastrules = \"\").","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"antibiotics","dir":"Reference","previous_headings":"","what":"Antibiotics","title":"Apply EUCAST Rules — eucast_rules","text":"define antibiotics column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antibiotics eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, S01AE08), amoxicillin (AMX, J01MA02), amoxicillin/clavulanic acid (AMC, J01MA23), ampicillin (AMP, J01MA04), ampicillin/sulbactam (SAM, J01MA08), arbekacin (ARB, J01MA19), aspoxicillin (APX, J01MA16), azidocillin (AZD, J01MA15), azithromycin (AZM, J01MA11), azlocillin (AZL, J01MA25), aztreonam (ATM, J01MA12), bacampicillin (BAM, J01MA24), bekanamycin (BEK, J01MA07), benzathine benzylpenicillin (BNB, J01MA14), benzathine phenoxymethylpenicillin (BNP, D10AF05), benzylpenicillin (PEN, J01MA06), besifloxacin (BES, J01MA01), biapenem (BIA, J01MA18), carbenicillin (CRB, J01MA03), carindacillin (CRN, J01MA17), cefacetrile (CAC, J01MA10), cefaclor (CEC, J01MA21), cefadroxil (CFR, J01MA09), cefalexin (LEX, J01MA05), cefaloridine (RID, P01AA05), cefalotin (CEP, J01MA22), cefamandole (MAN, J01MA13), cefapirin (HAP, J01CA01), cefatrizine (CTZ, J01CA04), cefazedone (CZD, J01CA12), cefazolin (CZO, J01CR05), cefcapene (CCP, J01CA13), cefdinir (CDR, J01AA02), cefditoren (DIT, J01FA10), cefepime (FEP, J01FA09), cefetamet (CAT, J01CR02), cefixime (CFM, J01AA08), cefmenoxime (CMX, J01FA06), cefmetazole (CMZ, J01CF04), cefodizime (DIZ, J01CF05), cefonicid (CID, J01CR01), cefoperazone (CFP, J01CA19), cefoperazone/sulbactam (CSL, J01CE04), ceforanide (CND, J01CA09), cefotaxime (CTX, J01DF01), cefotaxime/clavulanic acid (CTC, J01CA06), cefotetan (CTT, J01CE08), cefotiam (CTF, J01CE10), cefoxitin (FOX, J01CE01), cefozopran (ZOP, J01CA03), cefpiramide (CPM, J01CA05), cefpirome (CPO, J01CE07), cefpodoxime (CPD, J01CF02), cefprozil (CPR, J01CF01), cefroxadine (CRD, J01CA07), cefsulodin (CFS, J01CA18), ceftaroline (CPT, J01CA11), ceftazidime (CAZ, J01CA14), ceftazidime/clavulanic acid (CCV, J01CF03), cefteram (CEM, J01CA10), ceftezole (CTL, J01CF06), ceftibuten (CTB, J01CE06), ceftizoxime (CZX, J01CE05), ceftobiprole medocaril (CFM1, J01CE02), ceftolozane/tazobactam (CZT, J01CA02), ceftriaxone (CRO, J01CA08), ceftriaxone/beta-lactamase inhibitor (CEB, J01CE09), cefuroxime (CXM, J01CE03), cephradine (CED, J01CG01), chloramphenicol (CHL, J01CA16), ciprofloxacin (CIP, J01CR04), clarithromycin (CLR, J01CA15), clindamycin (CLI, J01CG02), clometocillin (CLM, J01CA17), cloxacillin (CLO, J01CR03), colistin (COL, J01DB10), cycloserine (CYC, J01DC04), dalbavancin (DAL, J01DB05), daptomycin (DAP, J01DB01), delafloxacin (DFX, J01DB02), dibekacin (DKB, J01DB03), dicloxacillin (DIC, J01DC03), dirithromycin (DIR, J01DB08), doripenem (DOR, J01DB07), doxycycline (DOX, J01DB06), enoxacin (ENX, J01DB04), epicillin (EPC, J01DD17), ertapenem (ETP, J01DD15), erythromycin (ERY, J01DD16), fleroxacin (FLE, J01DE01), flucloxacillin (FLC, J01DD10), flurithromycin (FLR1, J01DD08), fosfomycin (FOS, J01DD05), framycetin (FRM, J01DC09), fusidic acid (FUS, J01DD09), garenoxacin (GRN, J01DC06), gatifloxacin (GAT, J01DD12), gemifloxacin (GEM, J01DD62), gentamicin (GEN, J01DC11), grepafloxacin (GRX, J01DD01), hetacillin (HET, J01DD51), imipenem (IPM, J01DC05), imipenem/relebactam (IMR, J01DC07), isepamicin (ISE, J01DC01), josamycin (JOS, J01DE03), kanamycin (KAN, J01DD11), lascufloxacin (LSC, J01DE02), latamoxef (LTM, J01DD13), levofloxacin (LVX, J01DC10), levonadifloxacin (LND, J01DB11), lincomycin (LIN, J01DD03), linezolid (LNZ, J01DI02), lomefloxacin (LOM, J01DD02), loracarbef (LOR, J01DD52), mecillinam (MEC, J01DD18), meropenem (MEM, J01DB12), meropenem/vaborbactam (MEV, J01DD14), metampicillin (MTM, J01DD07), meticillin (MET, J01DI01), mezlocillin (MEZ, J01DI54), micronomicin (MCR, J01DD04), midecamycin (MID, J01DD63), minocycline (MNO, J01DC02), miocamycin (MCM, J01DB09), moxifloxacin (MFX, J01DD06), nadifloxacin (NAD, J01DC08), nafcillin (NAF, J01DH05), nalidixic acid (NAL, J01DH04), neomycin (NEO, J01DH03), netilmicin (NET, J01DH51), nitrofurantoin (NIT, J01DH56), norfloxacin (, J01DH02), ofloxacin (OFX, J01DH52), oleandomycin (OLE, J01DH55), oritavancin (ORI, J01DH06), oxacillin (OXA, J01XA02), panipenem (PAN, J01XA01), pazufloxacin (PAZ, J01XC01), pefloxacin (PEF, J01FA13), penamecillin (PNM, J01FA01), pheneticillin (PHE, J01FA14), phenoxymethylpenicillin (PHN, J01FA07), piperacillin (PIP, J01FA03), piperacillin/tazobactam (TZP, J01FA11), pivampicillin (PVM, J01FA05), pivmecillinam (PME, J01FA12), plazomicin (PLZ, J01FA16), polymyxin B (PLB, J01FA02), pristinamycin (PRI, J01FA15), procaine benzylpenicillin (PRB, J01FA08), propicillin (PRP, J01FF02), prulifloxacin (PRU, J01FG01), quinupristin/dalfopristin (QDA, J01FG02), ribostamycin (RST, J04AB02), rifampicin (RIF, J01XX09), rokitamycin (ROK, J01XX08), roxithromycin (RXT, J01AA07), rufloxacin (RFL, J01XB01), sisomicin (SIS, J01XB02), sitafloxacin (SIT, J01XE01), solithromycin (SOL, J01AA12), sparfloxacin (SPX, J01EA01), spiramycin (SPI, J01XX01), streptoduocin (STR, J01BA01), streptomycin (STR1, J01GB06), sulbactam (SUL, J01GB12), sulbenicillin (SBC, J01GB13), sulfadiazine (SDI, J01GB09), sulfadiazine/trimethoprim (SLT1, D09AA01), sulfadimethoxine (SUD, J01GB03), sulfadimidine (SDM, J01GB11), sulfadimidine/trimethoprim (SLT2, J01GB04), sulfafurazole (SLF, S01AA22), sulfaisodimidine (SLF1, J01GB05), sulfalene (SLF2, J01GB07), sulfamazone (SZO, J01GB14), sulfamerazine (SLF3, J01GB10), sulfamerazine/trimethoprim (SLT3, J01GB08), sulfamethizole (SLF4, J01GA02), sulfamethoxazole (SMX, J01GA01), sulfamethoxypyridazine (SLF5, J01GB01), sulfametomidine (SLF6, J01EE01), sulfametoxydiazine (SLF7, J01MB02), sulfametrole/trimethoprim (SLT4, J01FF01), sulfamoxole (SLF8, J01XA04), sulfamoxole/trimethoprim (SLT5, J01XA05), sulfanilamide (SLF9, J01XA03), sulfaperin (SLF10, J04AB01), sulfaphenazole (SLF11, J01XX11), sulfapyridine (SLF12, J01EC02), sulfathiazole (SUT, J01ED01), sulfathiourea (SLF13, J01EB03), sultamicillin (SLT6, J01EB05), talampicillin (TAL, J01EB01), tazobactam (TAZ, J01ED02), tebipenem (TBP, J01ED09), tedizolid (TZD, J01ED07), teicoplanin (TEC, J01EB02), telavancin (TLV, J01EC01), telithromycin (TLT, J01ED05), temafloxacin (TMX, J01ED03), temocillin (TEM, J01ED04), tetracycline (TCY, J01EC03), ticarcillin (TIC, J01EB06), ticarcillin/clavulanic acid (TCC, J01ED06), tigecycline (TGC, J01ED08), tilbroquinol (TBQ, J01EB04), tobramycin (TOB, J01EB07), tosufloxacin (TFX, J01EB08), trimethoprim (TMP, J01EE02), trimethoprim/sulfamethoxazole (SXT, J01EE05), troleandomycin (TRL, J01EE07), trovafloxacin (TVA, J01EE03), vancomycin (VAN, J01EE04)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Apply EUCAST Rules — eucast_rules","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"# \\donttest{ a <- data.frame(   mo = c(     \"Staphylococcus aureus\",     \"Enterococcus faecalis\",     \"Escherichia coli\",     \"Klebsiella pneumoniae\",     \"Pseudomonas aeruginosa\"   ),   VAN = \"-\", # Vancomycin   AMX = \"-\", # Amoxicillin   COL = \"-\", # Colistin   CAZ = \"-\", # Ceftazidime   CXM = \"-\", # Cefuroxime   PEN = \"S\", # Benzylpenicillin   FOX = \"S\", # Cefoxitin   stringsAsFactors = FALSE )  head(a) #>                       mo VAN AMX COL CAZ CXM PEN FOX #> 1  Staphylococcus aureus   -   -   -   -   -   S   S #> 2  Enterococcus faecalis   -   -   -   -   -   S   S #> 3       Escherichia coli   -   -   -   -   -   S   S #> 4  Klebsiella pneumoniae   -   -   -   -   -   S   S #> 5 Pseudomonas aeruginosa   -   -   -   -   -   S   S   # apply EUCAST rules: some results wil be changed b <- eucast_rules(a) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #>   - a %>% as.sir(CXM:AMX) #>   - a %>% mutate_if(is_sir_eligible, as.sir) #>   - a %>% mutate(across(where(is_sir_eligible), as.sir))  head(b) #>                       mo VAN AMX COL CAZ CXM PEN FOX #> 1  Staphylococcus aureus   -   S   R   S   S   S   S #> 2  Enterococcus faecalis   -   -   R   R   R   S   R #> 3       Escherichia coli   R   -   -   -   -   R   S #> 4  Klebsiella pneumoniae   R   R   -   -   -   R   S #> 5 Pseudomonas aeruginosa   R   R   -   -   R   R   R   # do not apply EUCAST rules, but rather get a data.frame # containing all details about the transformations: c <- eucast_rules(a, verbose = TRUE) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #>   - a %>% as.sir(CXM:AMX) #>   - a %>% mutate_if(is_sir_eligible, as.sir) #>   - a %>% mutate(across(where(is_sir_eligible), as.sir)) head(c) #>   row col           mo_fullname old new rule   rule_group #> 1   1 AMX Staphylococcus aureus   -   S       Breakpoints #> 2   1 CXM Staphylococcus aureus   -   S       Breakpoints #> 3   1 CAZ Staphylococcus aureus   R   S      Expert Rules #> 4   1 CAZ Staphylococcus aureus   -   R      Expert Rules #> 5   1 COL Staphylococcus aureus   -   R      Expert Rules #> 6   2 CAZ Enterococcus faecalis   -   R      Expert Rules #>                                                 rule_name #> 1                                          Staphylococcus #> 2                                          Staphylococcus #> 3                          Expert Rules on Staphylococcus #> 4 Table 4: Intrinsic resistance in gram-positive bacteria #> 5 Table 4: Intrinsic resistance in gram-positive bacteria #> 6 Table 4: Intrinsic resistance in gram-positive bacteria #>                                                                                 rule_source #> 1                                           'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 2                                           'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 3 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 4 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 5 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 6 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 # }  # Dosage guidelines:  eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\") #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #>   Tables' v12.0 (2022). This note will be shown once per session. #> # A tibble: 3 × 4 #>   ab   name          standard_dosage  high_dosage  #>   <ab> <chr>         <chr>            <chr>        #> 1 TOB  Tobramycin    6-7 mg/kg x 1 iv NA           #> 2 GEN  Gentamicin    6-7 mg/kg x 1 iv NA           #> 3 CIP  Ciprofloxacin 0.4 g x 2 iv     0.4 g x 3 iv  eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\", version_breakpoints = 10) #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #>   Tables' v10.0 (2020). This note will be shown once per session. #> # A tibble: 3 × 4 #>   ab   name          standard_dosage  high_dosage  #>   <ab> <chr>         <chr>            <chr>        #> 1 TOB  Tobramycin    6-7 mg/kg x 1 iv NA           #> 2 GEN  Gentamicin    6-7 mg/kg x 1 iv NA           #> 3 CIP  Ciprofloxacin 0.4 g x 2 iv     0.4 g x 3 iv"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 2 000 Example Isolates — example_isolates","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"data set containing 2 000 microbial isolates full antibiograms. data set contains randomised fictitious data, reflects reality can used practise AMR data analysis. examples, please read tutorial website.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"","code":"example_isolates"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"tibble 2 000 observations 46 variables: date Date receipt laboratory patient ID patient age Age patient gender Gender patient, either \"F\" \"M\" ward Ward type patient admitted, either \"Clinical\", \"ICU\", \"Outpatient\" mo ID microorganism created .mo(), see also microorganisms data set PEN:RIF 40 different antibiotics class sir (see .sir()); column names occur antibiotics data set can translated set_ab_names() ab_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>"},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Unclean Data — example_isolates_unclean","title":"Data Set with Unclean Data — example_isolates_unclean","text":"data set containing 3 000 microbial isolates cleaned consequently ready AMR data analysis. data set can used practice.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Unclean Data — example_isolates_unclean","text":"","code":"example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Unclean Data — example_isolates_unclean","text":"tibble 3 000 observations 8 variables: patient_id ID patient date date receipt laboratory hospital ID hospital, C bacteria info microorganism can transformed .mo(), see also microorganisms AMX:GEN 4 different antibiotics transformed .sir()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Unclean Data — example_isolates_unclean","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Unclean Data — example_isolates_unclean","text":"","code":"example_isolates_unclean #> # A tibble: 3,000 × 8 #>    patient_id hospital date       bacteria      AMX   AMC   CIP   GEN   #>    <chr>      <chr>    <date>     <chr>         <chr> <chr> <chr> <chr> #>  1 J3         A        2012-11-21 E. coli       R     I     S     S     #>  2 R7         A        2018-04-03 K. pneumoniae R     I     S     S     #>  3 P3         A        2014-09-19 E. coli       R     S     S     S     #>  4 P10        A        2015-12-10 E. coli       S     I     S     S     #>  5 B7         A        2015-03-02 E. coli       S     S     S     S     #>  6 W3         A        2018-03-31 S. aureus     R     S     R     S     #>  7 J8         A        2016-06-14 E. coli       R     S     S     S     #>  8 M3         A        2015-10-25 E. coli       R     S     S     S     #>  9 J3         A        2019-06-19 E. coli       S     S     S     S     #> 10 G6         A        2015-04-27 S. aureus     S     S     S     S     #> # … with 2,990 more rows"},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine First Isolates — first_isolate","title":"Determine First Isolates — first_isolate","text":"Determine first isolates microorganisms every patient per episode (needed) per specimen type. functions support four methods summarised Hindler et al. 2007 (doi:10.1086/511864 ). determine patient episodes necessarily based microorganisms, use is_new_episode() also supports grouping dplyr package.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine First Isolates — first_isolate","text":"","code":"first_isolate(   x = NULL,   col_date = NULL,   col_patient_id = NULL,   col_mo = NULL,   col_testcode = NULL,   col_specimen = NULL,   col_icu = NULL,   col_keyantimicrobials = NULL,   episode_days = 365,   testcodes_exclude = NULL,   icu_exclude = FALSE,   specimen_group = NULL,   type = \"points\",   method = c(\"phenotype-based\", \"episode-based\", \"patient-based\", \"isolate-based\"),   ignore_I = TRUE,   points_threshold = 2,   info = interactive(),   include_unknown = FALSE,   include_untested_sir = TRUE,   ... )  filter_first_isolate(   x = NULL,   col_date = NULL,   col_patient_id = NULL,   col_mo = NULL,   episode_days = 365,   method = c(\"phenotype-based\", \"episode-based\", \"patient-based\", \"isolate-based\"),   ... )"},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine First Isolates — first_isolate","text":"Methodology function strictly based : M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. Hindler JF Stelling J (2007). Analysis Presentation Cumulative Antibiograms: New Consensus Guideline Clinical Laboratory Standards Institute. Clinical Infectious Diseases, 44(6), 867-873. doi:10.1086/511864","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine First Isolates — first_isolate","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_date column name result date (date received lab) - default first column date class col_patient_id column name unique IDs patients - default first column starts 'patient' 'patid' (case insensitive) col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). col_testcode column name test codes. Use col_testcode = NULL exclude certain test codes (test codes screening). case testcodes_exclude ignored. col_specimen column name specimen type group col_icu column name logicals (TRUE/FALSE) whether ward department Intensive Care Unit (ICU). can also logical vector length rows x. col_keyantimicrobials (useful method = \"phenotype-based\") column name key antimicrobials determine first isolates, see key_antimicrobials(). default first column starts 'key' followed 'ab' 'antibiotics' 'antimicrobials' (case insensitive). Use col_keyantimicrobials = FALSE prevent . Can also output key_antimicrobials(). episode_days episode days genus/species combination determined 'first isolate' . default 365 days based guideline CLSI, see Source. testcodes_exclude character vector test codes excluded (case-insensitive) icu_exclude logical indicate whether ICU isolates excluded (rows value TRUE column set col_icu) specimen_group value column set col_specimen filter type type determine weighed isolates; can \"keyantimicrobials\" \"points\", see Details method method apply, either \"phenotype-based\", \"episode-based\", \"patient-based\" \"isolate-based\" (can abbreviated), see Details. default \"phenotype-based\" antimicrobial test results present data, \"episode-based\" otherwise. ignore_I logical indicate whether antibiotic interpretations \"\" ignored type = \"keyantimicrobials\", see Details points_threshold minimum number points require differences antibiogram lead inclusion isolate type = \"points\", see Details info logical indicate info printed - default TRUE interactive mode include_unknown logical indicate whether 'unknown' microorganisms included , .e. microbial code \"UNKNOWN\", defaults FALSE. WHONET users, means records organism code \"con\" (contamination) excluded default. Isolates microbial ID NA always excluded first isolate. include_untested_sir logical indicate whether also rows without antibiotic results still eligible becoming first isolate. Use include_untested_sir = FALSE always return FALSE rows. checks data set columns class sir consequently requires transforming columns antibiotic results using .sir() first. ... arguments passed first_isolate() using filter_first_isolate(), otherwise arguments passed key_antimicrobials() (universal, gram_negative, gram_positive)","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine First Isolates — first_isolate","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine First Isolates — first_isolate","text":"conduct epidemiological analyses antimicrobial resistance data, -called first isolates included prevent overestimation underestimation antimicrobial resistance. Different methods can used , see . functions context-aware. means x argument can left blank used inside data.frame call, see Examples. first_isolate() function wrapper around is_new_episode() function, efficient data sets containing microorganism codes names. isolates microbial ID NA excluded first isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"different-methods","dir":"Reference","previous_headings":"","what":"Different methods","title":"Determine First Isolates — first_isolate","text":"According Hindler et al. (2007, doi:10.1086/511864 ), different methods (algorithms) select first isolates increasing reliability: isolate-based, patient-based, episode-based phenotype-based. methods select combination taxonomic genus species (subspecies). mentioned methods covered first_isolate() function:","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"isolate-based","dir":"Reference","previous_headings":"","what":"Isolate-based","title":"Determine First Isolates — first_isolate","text":"method require selection, isolates included. , however, respect arguments set first_isolate() function. example, default setting include_unknown (FALSE) omit selection rows without microbial ID.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"patient-based","dir":"Reference","previous_headings":"","what":"Patient-based","title":"Determine First Isolates — first_isolate","text":"include every genus-species combination per patient , set episode_days Inf. Although often inappropriate, method makes sure duplicate isolates selected patient. large longitudinal data set, mean isolates excluded found years initial isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"episode-based","dir":"Reference","previous_headings":"","what":"Episode-based","title":"Determine First Isolates — first_isolate","text":"include every genus-species combination per patient episode , set episode_days sensible number days. Depending type analysis, 14, 30, 60 365. Short episodes common analysing specific hospital ward data, long episodes common analysing regional national data. common method correct duplicate isolates. Patients categorised episodes based ID dates (e.g., date specimen receipt laboratory result). common method, take account antimicrobial test results. means e.g. methicillin-resistant Staphylococcus aureus (MRSA) isolate differentiated wildtype Staphylococcus aureus isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"phenotype-based","dir":"Reference","previous_headings":"","what":"Phenotype-based","title":"Determine First Isolates — first_isolate","text":"reliable method, since also weighs antibiogram (antimicrobial test results) yielding -called 'first weighted isolates'. two different methods weigh antibiogram: Using type = \"points\" argument points_threshold (default) method weighs antimicrobial drugs available data set. difference S R (vice versa) counts 0.5 points, difference S R (vice versa) counts 1 point. sum points exceeds points_threshold, defaults 2, isolate selected first weighted isolate. antimicrobials internally selected using all_antimicrobials() function. output function need passed first_isolate() function. Using type = \"keyantimicrobials\" argument ignore_I method weighs specific antimicrobial drugs, called key antimicrobials. difference S R (vice versa) key antimicrobials select isolate first weighted isolate. ignore_I = FALSE, also differences S R (vice versa) lead . Key antimicrobials internally selected using key_antimicrobials() function, can also added manually variable data set col_keyantimicrobials argument. Another option pass output key_antimicrobials() function directly col_keyantimicrobials argument. default method phenotype-based (using type = \"points\") episode-based (using episode_days = 365). makes sure every genus-species combination selected per patient per year, taking account antimicrobial test results. antimicrobial test results available data set, episode-based method applied default.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine First Isolates — first_isolate","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  example_isolates[first_isolate(info = TRUE), ] #> Determining first isolates using an episode length of 365 days #> ℹ Using column 'date' as input for col_date. #> ℹ Using column 'patient' as input for col_patient_id. #> Basing inclusion on all antimicrobial results, using a points threshold of #> 2 #> Including isolates from ICU. #> ℹ Excluded 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,379 'phenotype-based' first isolates (69.0% of total where a #>    microbial ID was available) #> # A tibble: 1,379 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  4 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #>  5 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR R     NA    S     NA    #>  6 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  7 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND R     NA    NA    R     #>  8 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  9 2002-02-03 481442     76 M      ICU      B_STPHY_CONS R     NA    S     NA    #> 10 2002-02-05 023456     50 M      Clinical B_STPHY_HMNS S     NA    S     NA    #> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> # \\donttest{ # get all first Gram-negatives example_isolates[which(first_isolate(info = FALSE) & mo_is_gram_negative()), ] #> ℹ Using column 'mo' as input for mo_is_gram_negative() #> # A tibble: 437 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND R     NA    NA    R     #>  4 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  5 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS R     NA    NA    R     #>  6 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN R     NA    NA    R     #>  7 2002-03-08 4FC193     69 M      Clinical B_ESCHR_COLI R     NA    NA    R     #>  8 2002-03-16 4FC193     69 M      Clinical B_PSDMN_AERG R     NA    NA    R     #>  9 2002-04-01 496896     46 F      ICU      B_ESCHR_COLI R     NA    NA    NA    #> 10 2002-04-23 EE2510     69 F      ICU      B_ESCHR_COLI R     NA    NA    NA    #> # … with 427 more rows, and 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, #> #   CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, #> #   CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, #> #   SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, #> #   VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, #> #   CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, #> #   COL <sir>, MUP <sir>, RIF <sir>  if (require(\"dplyr\")) {   # filter on first isolates using dplyr:   example_isolates %>%     filter(first_isolate(info = TRUE)) } #> Determining first isolates using an episode length of 365 days #> Basing inclusion on all antimicrobial results, using a points threshold of #> 2 #> Including isolates from ICU. #> ℹ Excluded 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,379 'phenotype-based' first isolates (69.0% of total where a #>    microbial ID was available) #> # A tibble: 1,379 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  4 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #>  5 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR R     NA    S     NA    #>  6 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  7 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND R     NA    NA    R     #>  8 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  9 2002-02-03 481442     76 M      ICU      B_STPHY_CONS R     NA    S     NA    #> 10 2002-02-05 023456     50 M      Clinical B_STPHY_HMNS S     NA    S     NA    #> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # short-hand version:   example_isolates %>%     filter_first_isolate(info = FALSE) } #> # A tibble: 1,379 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  4 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #>  5 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR R     NA    S     NA    #>  6 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  7 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND R     NA    NA    R     #>  8 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL R     NA    NA    NA    #>  9 2002-02-03 481442     76 M      ICU      B_STPHY_CONS R     NA    S     NA    #> 10 2002-02-05 023456     50 M      Clinical B_STPHY_HMNS S     NA    S     NA    #> # … with 1,369 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir> if (require(\"dplyr\")) {   # flag the first isolates per group:   example_isolates %>%     group_by(ward) %>%     mutate(first = first_isolate(info = TRUE)) %>%     select(ward, date, patient, mo, first) } #> Determining first isolates using an episode length of 365 days #> Basing inclusion on all antimicrobial results, using a points threshold of #> 2 #> Including isolates from ICU. #>  #> Group: ward = \"Clinical\" #> ℹ Excluded 9 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 861 'phenotype-based' first isolates (69.8% of total where a #>    microbial ID was available) #> Including isolates from ICU. #>  #> Group: ward = \"ICU\" #> ℹ Excluded 6 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 449 'phenotype-based' first isolates (69.6% within scope and 69.5% #>    of total where a microbial ID was available) #> Including isolates from ICU. #>  #> Group: ward = \"Outpatient\" #> ℹ Excluded 1 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 98 'phenotype-based' first isolates (82.4% within scope and 81.7% #>    of total where a microbial ID was available) #> # A tibble: 2,000 × 5 #> # Groups:   ward [3] #>    ward     date       patient mo           first #>    <chr>    <date>     <chr>   <mo>         <lgl> #>  1 Clinical 2002-01-02 A77334  B_ESCHR_COLI TRUE  #>  2 Clinical 2002-01-03 A77334  B_ESCHR_COLI FALSE #>  3 ICU      2002-01-07 067927  B_STPHY_EPDR TRUE  #>  4 ICU      2002-01-07 067927  B_STPHY_EPDR FALSE #>  5 ICU      2002-01-13 067927  B_STPHY_EPDR FALSE #>  6 ICU      2002-01-13 067927  B_STPHY_EPDR FALSE #>  7 Clinical 2002-01-14 462729  B_STPHY_AURS TRUE  #>  8 Clinical 2002-01-14 462729  B_STPHY_AURS FALSE #>  9 ICU      2002-01-16 067927  B_STPHY_EPDR FALSE #> 10 ICU      2002-01-17 858515  B_STPHY_EPDR TRUE  #> # … with 1,990 more rows # }"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":null,"dir":"Reference","previous_headings":"","what":"G-test for Count Data — g.test","title":"G-test for Count Data — g.test","text":"g.test() performs chi-squared contingency table tests goodness--fit tests, just like chisq.test() reliable (1). G-test can used see whether number observations category fits theoretical expectation (called G-test goodness--fit), see whether proportions one variable different different values variable (called G-test independence).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"G-test for Count Data — g.test","text":"","code":"g.test(x, y = NULL, p = rep(1/length(x), length(x)), rescale.p = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"G-test for Count Data — g.test","text":"code function identical chisq.test(), except : calculation statistic changed \\(2 * sum(x * log(x / E))\\) Yates' continuity correction removed apply G-test possibility simulate p values simulate.p.value removed","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"G-test for Count Data — g.test","text":"x numeric vector matrix. x y can also     factors. y numeric vector; ignored x matrix.      x factor, y factor length. p vector probabilities length x.     error given entry p negative. rescale.p logical scalar; TRUE p rescaled     (necessary) sum 1.  rescale.p FALSE,     p sum 1, error given.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"G-test for Count Data — g.test","text":"list class \"htest\" containing following   components: statistic value chi-squared test statistic. parameter degrees freedom approximate     chi-squared distribution test statistic, NA     p-value computed Monte Carlo simulation. p.value p-value test. method character string indicating type test     performed, whether Monte Carlo simulation continuity     correction used. data.name character string giving name(s) data. observed observed counts. expected expected counts null hypothesis. residuals Pearson residuals,     (observed - expected) / sqrt(expected). stdres standardized residuals,     (observed - expected) / sqrt(V), V residual cell variance (Agresti, 2007,     section 2.4.5 case x matrix, n * p * (1 - p) otherwise).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"G-test for Count Data — g.test","text":"x matrix one row column, x vector y given, goodness--fit test performed (x treated one-dimensional contingency table). entries x must non-negative integers. case, hypothesis tested whether population probabilities equal p, equal p given. x matrix least two rows columns, taken two-dimensional contingency table: entries x must non-negative integers.  Otherwise, x y must vectors factors length; cases missing values removed, objects coerced factors, contingency table computed .  Pearson's chi-squared test performed null hypothesis joint distribution cell counts 2-dimensional contingency table product row column marginals. p-value computed asymptotic chi-squared distribution test statistic. contingency table case simulation done random sampling set contingency tables given marginals, works marginals strictly positive. Note usual sampling situation assumed chi-squared test (G-test) rather Fisher's exact test. goodness--fit case simulation done random sampling discrete distribution specified p, sample size n = sum(x). simulation done R may slow.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-goodness-of-fit-likelihood-ratio-test-","dir":"Reference","previous_headings":"","what":"G-test Of Goodness-of-Fit (Likelihood Ratio Test)","title":"G-test for Count Data — g.test","text":"Use G-test goodness--fit one nominal variable two values (male female, red, pink white flowers). compare observed counts numbers observations category expected counts, calculate using kind theoretical expectation (1:1 sex ratio 1:2:1 ratio genetic cross). expected number observations category small, G-test may give inaccurate results, use exact test instead (fisher.test()). G-test goodness--fit alternative chi-square test goodness--fit (chisq.test()); tests advantages disadvantages, results two tests usually similar.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-independence","dir":"Reference","previous_headings":"","what":"G-test of Independence","title":"G-test for Count Data — g.test","text":"Use G-test independence two nominal variables, two possible values. want know whether proportions one variable different among values variable. also possible G-test independence two nominal variables. example, Jackson et al. (2013) also data children 3, analysis old vs. young, thigh vs. arm, reaction vs. reaction, analyzed together. Fisher's exact test (fisher.test()) exact test, G-test still approximation. 2x2 table, Fisher's Exact test may slower still run seconds, even sum observations multiple millions. G-test independence alternative chi-square test independence (chisq.test()), give approximately results.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"how-the-test-works","dir":"Reference","previous_headings":"","what":"How the Test Works","title":"G-test for Count Data — g.test","text":"Unlike exact test goodness--fit (fisher.test()), G-test directly calculate probability obtaining observed results something extreme. Instead, like almost statistical tests, G-test intermediate step; uses data calculate test statistic measures far observed data null expectation. use mathematical relationship, case chi-square distribution, estimate probability obtaining value test statistic. G-test uses log ratio two likelihoods test statistic, also called likelihood ratio test log-likelihood ratio test. formula calculate G-statistic : \\(G = 2 * sum(x * log(x / E))\\) E expected values. Since chi-square distributed, p value can calculated R :   df degrees freedom. two categories want find ones significantly different null expectation, can use method testing category vs. sum categories, Bonferroni correction. use G-tests category, course.","code":"p <- stats::pchisq(G, df, lower.tail = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"G-test for Count Data — g.test","text":"McDonald, J.H. 2014. Handbook Biological Statistics (3rd ed.). Sparky House Publishing, Baltimore, Maryland. http://www.biostathandbook.com/gtestgof.html.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"G-test for Count Data — g.test","text":"","code":"# = EXAMPLE 1 = # Shivrain et al. (2006) crossed clearfield rice (which are resistant # to the herbicide imazethapyr) with red rice (which are susceptible to # imazethapyr). They then crossed the hybrid offspring and examined the # F2 generation, where they found 772 resistant plants, 1611 moderately # resistant plants, and 737 susceptible plants. If resistance is controlled # by a single gene with two co-dominant alleles, you would expect a 1:2:1 # ratio.  x <- c(772, 1611, 737) g.test(x, p = c(1, 2, 1) / 4) #>  #> \tG-test of goodness-of-fit (likelihood ratio test) #>  #> data:  x #> X-squared = 4.1471, p-value = 0.1257 #>   # There is no significant difference from a 1:2:1 ratio. # Meaning: resistance controlled by a single gene with two co-dominant # alleles, is plausible.   # = EXAMPLE 2 = # Red crossbills (Loxia curvirostra) have the tip of the upper bill either # right or left of the lower bill, which helps them extract seeds from pine # cones. Some have hypothesized that frequency-dependent selection would # keep the number of right and left-billed birds at a 1:1 ratio. Groth (1992) # observed 1752 right-billed and 1895 left-billed crossbills.  x <- c(1752, 1895) g.test(x) #>  #> \tG-test of goodness-of-fit (likelihood ratio test) #>  #> data:  x #> X-squared = 5.6085, p-value = 0.01787 #>   # There is a significant difference from a 1:1 ratio. # Meaning: there are significantly more left-billed birds."},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Clinical or Epidemic Episodes — get_episode","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"functions determine items vector can considered (start ) new episode. can used determine clinical episodes epidemiological analysis. get_episode() function returns index number episode per group, is_new_episode() function returns TRUE every new get_episode() index. absolute relative episode determination supported.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"","code":"get_episode(x, episode_days = NULL, case_free_days = NULL, ...)  is_new_episode(x, episode_days = NULL, case_free_days = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"x vector dates (class Date POSIXt), sorted internally determine episodes episode_days episode length days specify time period new episode begins, can also less day Inf, see Details case_free_days (inter-epidemic) interval length days new episode start, can also less day Inf, see Details ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"get_episode(): integer vector is_new_episode(): logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"Episodes can determined two ways: absolute relative. Absolute method uses episode_days define episode length days, new episode start. common use case AMR data analysis microbial epidemiology: episodes S. aureus bacteraemia ICU patients example. episode length 30 days, new S. aureus isolates ICU episode 30 days considered different (new) episode. Thus, method counts since start previous episode. Relative method uses case_free_days quantify duration case-free days (inter-epidemic interval), new episode start. common use case infectious disease epidemiology: episodes norovirus outbreaks hospital example. case-free period 14 days, new norovirus cases time considered different (new) episode. Thus, methods counts since last case previous episode. table: ** marks start new episode, 8 January 2023 7 days since start previous episode (1 January 2023).  *** marks start new episode, 21 January 2023 7 days since last case previous episode (8 January 2023). Either episode_days case_free_days must provided function.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"difference-between-get-episode-and-is-new-episode-","dir":"Reference","previous_headings":"","what":"Difference between get_episode() and is_new_episode()","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"get_episode() function returns index number episode, cases/patients/isolates first episode number 1, cases/patients/isolates second episode number 2, etc. is_new_episode() function 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$date, episode_days = 60) # indices #>   [1]  8 47  8 11 43 23  7 20 28 46 43 18 13  4 36  8 24 49  8 41 21  9  2 49 37 #>  [26] 20 23  9 45  1  8  9 41  2 29  6 24 30 25 12 17  9 26  1  6 27 22 34 19 42 #>  [51] 38 24 48 43 33  3 14 19  8 24  5 15  3 44 46 39 42 35 23 11 48  7 26 25 31 #>  [76] 21 46 43 40 10  1 31 32  3 45 35  7 14 17 16 16 14 19 40 10 24 36 18  7  3 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE #>   [1]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE #>  [13]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE #>  [25]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE #>  [37] FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE #>  [49]  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE #>  [61]  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE #>  [73] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE #>  [85] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE #>  [97] FALSE FALSE FALSE FALSE  # filter on results from the third 60-day episode only, using base R df[which(get_episode(df$date, 60) == 3), ] #> # A tibble: 4 × 46 #>   date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>   <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #> 1 2002-11-16 762305     87 F      Clinical B_BCTRD_FRGL R     NA    NA    R     #> 2 2002-11-16 762305     87 F      Clinical B_PROTS_MRBL R     NA    NA    NA    #> 3 2002-11-04 304347     62 M      Clinical B_STRPT_PNMN S     NA    NA    S     #> 4 2002-09-23 F35553     51 M      ICU      B_STPHY_AURS S     NA    S     NA    #> # … with 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, #> #   FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, #> #   GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, #> #   NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, #> #   TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, #> #   AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, #> #   MUP <sir>, RIF <sir>  # the functions also work for less than a day, e.g. to include one per hour: get_episode(   c(     Sys.time(),     Sys.time() + 60 * 60   ),   episode_days = 1 / 24 ) #> [1] 1 2  # \\donttest{ if (require(\"dplyr\")) {   # is_new_episode() can also be used in dplyr verbs to determine patient   # episodes based on any (combination of) grouping variables:   df %>%     mutate(condition = sample(       x = c(\"A\", \"B\", \"C\"),       size = 100,       replace = TRUE     )) %>%     group_by(patient, condition) %>%     mutate(new_episode = is_new_episode(date, 365)) %>%     select(patient, date, condition, new_episode) %>%     arrange(patient, condition, date) } #> # A tibble: 100 × 4 #> # Groups:   patient, condition [97] #>    patient date       condition new_episode #>    <chr>   <date>     <chr>     <lgl>       #>  1 006827  2009-07-24 B         TRUE        #>  2 010257  2004-04-03 A         TRUE        #>  3 023456  2002-02-05 C         TRUE        #>  4 067927  2002-01-13 A         TRUE        #>  5 0E2483  2007-06-21 B         TRUE        #>  6 1435C8  2004-03-03 B         TRUE        #>  7 15D386  2004-08-01 A         TRUE        #>  8 15D386  2004-09-06 A         FALSE       #>  9 167474  2008-02-15 B         TRUE        #> 10 195736  2008-08-28 A         TRUE        #> # … with 90 more rows  if (require(\"dplyr\")) {   df %>%     group_by(ward, patient) %>%     transmute(date,       patient,       new_index = get_episode(date, 60),       new_logical = is_new_episode(date, 60)     ) %>%     arrange(patient, ward, date) } #> # A tibble: 100 × 5 #> # Groups:   ward, patient [94] #>    ward       date       patient new_index new_logical #>    <chr>      <date>     <chr>       <int> <lgl>       #>  1 Clinical   2009-07-24 006827          1 TRUE        #>  2 Clinical   2004-04-03 010257          1 TRUE        #>  3 Clinical   2002-02-05 023456          1 TRUE        #>  4 ICU        2002-01-13 067927          1 TRUE        #>  5 ICU        2007-06-21 0E2483          1 TRUE        #>  6 Clinical   2004-03-03 1435C8          1 TRUE        #>  7 ICU        2004-08-01 15D386          1 TRUE        #>  8 ICU        2004-09-06 15D386          1 FALSE       #>  9 Clinical   2008-02-15 167474          1 TRUE        #> 10 Outpatient 2008-08-28 195736          1 TRUE        #> # … with 90 more rows  if (require(\"dplyr\")) {   df %>%     group_by(ward) %>%     summarise(       n_patients = n_distinct(patient),       n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),       n_episodes_60 = sum(is_new_episode(date, episode_days = 60)),       n_episodes_30 = sum(is_new_episode(date, episode_days = 30))     ) } #> # A tibble: 3 × 5 #>   ward       n_patients n_episodes_365 n_episodes_60 n_episodes_30 #>   <chr>           <int>          <int>         <int>         <int> #> 1 Clinical           56             12            36            42 #> 2 ICU                31             11            25            29 #> 3 Outpatient          7              4             5             6  # 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 [97] #>    patient mo           ward     flag_episode #>    <chr>   <mo>         <chr>    <lgl>        #>  1 1435C8  B_ESCHR_COLI Clinical TRUE         #>  2 7D1606  B_VLLNL_PRVL ICU      TRUE         #>  3 C58921  B_BCTRD_FRGL Clinical TRUE         #>  4 628820  B_ESCHR_COLI ICU      TRUE         #>  5 3B8892  B_ESCHR_COLI Clinical TRUE         #>  6 D22254  B_ESCHR_COLI Clinical TRUE         #>  7 549150  B_STPHY_CONS Clinical TRUE         #>  8 43F266  B_STPHY_AURS ICU      TRUE         #>  9 3F562C  B_STPHY_CONS Clinical TRUE         #> 10 658640  B_STPHY_AURS Clinical TRUE         #> # … with 90 more rows # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA Biplot with ggplot2 — ggplot_pca","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"Produces ggplot2 variant -called biplot PCA (principal component analysis), flexible appealing base R biplot() function.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"ggplot_pca(   x,   choices = 1:2,   scale = 1,   pc.biplot = TRUE,   labels = NULL,   labels_textsize = 3,   labels_text_placement = 1.5,   groups = NULL,   ellipse = TRUE,   ellipse_prob = 0.68,   ellipse_size = 0.5,   ellipse_alpha = 0.5,   points_size = 2,   points_alpha = 0.25,   arrows = TRUE,   arrows_colour = \"darkblue\",   arrows_size = 0.5,   arrows_textsize = 3,   arrows_textangled = TRUE,   arrows_alpha = 0.75,   base_textsize = 10,   ... )"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"ggplot_pca() function based ggbiplot() function ggbiplot package Vince Vu, found GitHub: https://github.com/vqv/ggbiplot (retrieved: 2 March 2020, latest commit: 7325e88; 12 February 2015). per GPL-2 licence demands documentation code changes, changes made based source code : Rewritten code remove dependency packages plyr, scales grid Parametrised options, like arrow ellipse settings Hardened input possibilities defining exact type user input every argument Added total amount explained variance caption plot Cleaned syntax based lintr package, fixed grammatical errors added integrity checks Updated documentation","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"x object returned pca(), prcomp() princomp() choices length 2 vector specifying components plot. default     biplot strict sense. scale variables scaled lambda ^ scale     observations scaled lambda ^ (1-scale)     lambda singular values computed     princomp. Normally 0 <= scale <= 1, warning     issued specified scale outside range. pc.biplot true, use Gabriel (1971) refers \"principal component     biplot\", lambda = 1 observations scaled sqrt(n)     variables scaled sqrt(n).  inner products     variables approximate covariances distances observations     approximate Mahalanobis distance. labels optional vector labels observations. set, labels placed respective points. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). labels_textsize size text used labels labels_text_placement adjustment factor placement variable names (>=1 means away arrow head) groups optional vector groups labels, length labels. set, points labels coloured according groups. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). ellipse logical indicate whether normal data ellipse drawn group (set groups) ellipse_prob statistical size ellipse normal probability ellipse_size size ellipse line ellipse_alpha alpha (transparency) ellipse line points_size size points points_alpha alpha (transparency) points arrows logical indicate whether arrows drawn arrows_colour colour arrow text arrows_size size (thickness) arrow lines arrows_textsize size text end arrows arrows_textangled logical whether text end arrows angled arrows_alpha alpha (transparency) arrows text base_textsize text size plot elements except labels arrows ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"colours labels points can changed adding another scale layer colour, scale_colour_viridis_d() scale_colour_brewer().","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # \\donttest{ if (require(\"dplyr\")) {   # calculate the resistance per group first   resistance_data <- example_isolates %>%     group_by(       order = mo_order(mo), # group on anything, like order       genus = mo_genus(mo)     ) %>% #   and genus as we do here;     filter(n() >= 30) %>% # filter on only 30 results per group     summarise_if(is.sir, resistance) # then get resistance of all drugs    # now conduct PCA for certain antimicrobial drugs   pca_result <- resistance_data %>%     pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)    summary(pca_result)    # old base R plotting method:   biplot(pca_result)    # new ggplot2 plotting method using this package:   if (require(\"ggplot2\")) {     ggplot_pca(pca_result)      # still extendible with any ggplot2 function     ggplot_pca(pca_result) +       scale_colour_viridis_d() +       labs(title = \"Title here\")   } } #> Warning: There were 73 warnings in `summarise()`. #> The first warning was: #> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, #>   only_all_tested = FALSE) ...`. #> ℹ In group 5: `order = \"Lactobacillales\"`, `genus = \"Enterococcus\"`. #> Caused by warning: #> ! Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #>   \"TMP\", and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\"  #>    # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":null,"dir":"Reference","previous_headings":"","what":"AMR Plots with ggplot2 — ggplot_sir","title":"AMR Plots with ggplot2 — ggplot_sir","text":"Use functions create bar plots AMR data analysis. functions rely ggplot2 functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AMR Plots with ggplot2 — ggplot_sir","text":"","code":"ggplot_sir(   data,   position = NULL,   x = \"antibiotic\",   fill = \"interpretation\",   facet = NULL,   breaks = seq(0, 1, 0.1),   limits = NULL,   translate_ab = \"name\",   combine_SI = TRUE,   minimum = 30,   language = get_AMR_locale(),   nrow = NULL,   colours = c(S = \"#3CAEA3\", SI = \"#3CAEA3\", I = \"#F6D55C\", IR = \"#ED553B\", R =     \"#ED553B\"),   datalabels = TRUE,   datalabels.size = 2.5,   datalabels.colour = \"grey15\",   title = NULL,   subtitle = NULL,   caption = NULL,   x.title = \"Antimicrobial\",   y.title = \"Proportion\",   ... )  geom_sir(   position = NULL,   x = c(\"antibiotic\", \"interpretation\"),   fill = \"interpretation\",   translate_ab = \"name\",   minimum = 30,   language = get_AMR_locale(),   combine_SI = TRUE,   ... )  facet_sir(facet = c(\"interpretation\", \"antibiotic\"), nrow = NULL)  scale_y_percent(breaks = seq(0, 1, 0.1), limits = NULL)  scale_sir_colours(..., aesthetics = \"fill\")  theme_sir()  labels_sir_count(   position = NULL,   x = \"antibiotic\",   translate_ab = \"name\",   minimum = 30,   language = get_AMR_locale(),   combine_SI = TRUE,   datalabels.size = 3,   datalabels.colour = \"grey15\" )"},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AMR Plots with ggplot2 — ggplot_sir","text":"data data.frame column(s) class sir (see .sir()) position position adjustment bars, either \"fill\", \"stack\" \"dodge\" x variable show x axis, either \"antibiotic\" (default) \"interpretation\" grouping variable fill variable categorise using plots legend, either \"antibiotic\" (default) \"interpretation\" grouping variable facet variable split plots , either \"interpretation\" (default) \"antibiotic\" grouping variable breaks numeric vector positions limits numeric vector length two providing limits scale, use NA refer existing minimum maximum translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant) - default TRUE minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. language language returned text - default current system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. nrow (using facet) number rows colours named vactor colour used filling. default colours colour-blind friendly. datalabels show datalabels using labels_sir_count() datalabels.size size datalabels datalabels.colour colour datalabels title text show title plot subtitle text show subtitle plot caption text show caption plot x.title text show x axis description y.title text show y axis description ... arguments passed geom_sir() , case scale_sir_colours(), named values set colours. default colours colour-blind friendly, maintaining convention e.g. 'susceptible' green 'resistant' red. See Examples. aesthetics aesthetics apply colours - default \"fill\" can also (combination ) \"alpha\", \"colour\", \"fill\", \"linetype\", \"shape\" \"size\"","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AMR Plots with ggplot2 — ggplot_sir","text":"default, names antibiotics shown plots using ab_name(). can set translate_ab argument. See count_df().","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"the-functions","dir":"Reference","previous_headings":"","what":"The Functions","title":"AMR Plots with ggplot2 — ggplot_sir","text":"geom_sir() take variable data sir class (created .sir()) using sir_df() plot bars percentage S, , R. default behaviour bars stacked different antibiotics x axis. facet_sir() creates 2d plots (default based S//R) using ggplot2::facet_wrap(). scale_y_percent() transforms y axis 0 100% range using ggplot2::scale_y_continuous(). scale_sir_colours() sets colours bars (green S, yellow , red R). multilingual support. default colours colour-blind friendly, maintaining convention e.g. 'susceptible' green 'resistant' red. theme_sir() [ggplot2 theme][ggplot2::theme() minimal distraction. labels_sir_count() print datalabels bars percentage amount isolates using ggplot2::geom_text(). ggplot_sir() wrapper around functions uses data first input. makes possible use function pipe (%>%). See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AMR Plots with ggplot2 — ggplot_sir","text":"","code":"# \\donttest{ if (require(\"ggplot2\") && require(\"dplyr\")) {   # get antimicrobial results for drugs against a UTI:   ggplot(example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)) +     geom_sir() }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # prettify the plot using some additional functions:   df <- example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)   ggplot(df) +     geom_sir() +     scale_y_percent() +     scale_sir_colours() +     labels_sir_count() +     theme_sir() }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # or better yet, simplify this using the wrapper function - a single command:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir() }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # get only proportions and no counts:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir(datalabels = FALSE) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # add other ggplot2 arguments as you like:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir(       width = 0.5,       colour = \"black\",       size = 1,       linetype = 2,       alpha = 0.25     ) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # you can alter the colours with colour names:   example_isolates %>%     select(AMX) %>%     ggplot_sir(colours = c(SI = \"yellow\")) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # but you can also use the built-in colour-blind friendly colours for   # your plots, where \"S\" is green, \"I\" is yellow and \"R\" is red:   data.frame(     x = c(\"Value1\", \"Value2\", \"Value3\"),     y = c(1, 2, 3),     z = c(\"Value4\", \"Value5\", \"Value6\")   ) %>%     ggplot() +     geom_col(aes(x = x, y = y, fill = z)) +     scale_sir_colours(Value4 = \"S\", Value5 = \"I\", Value6 = \"R\") }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # resistance of ciprofloxacine per age group   example_isolates %>%     mutate(first_isolate = first_isolate()) %>%     filter(       first_isolate == TRUE,       mo == as.mo(\"Escherichia coli\")     ) %>%     # age_groups() is also a function in this AMR package:     group_by(age_group = age_groups(age)) %>%     select(age_group, CIP) %>%     ggplot_sir(x = \"age_group\") } #> Warning: Removed 6 rows containing missing values (`position_stack()`). #> Warning: Removed 6 rows containing missing values (`position_stack()`).  if (require(\"ggplot2\") && require(\"dplyr\")) {   # a shorter version which also adjusts data label colours:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir(colours = FALSE) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # it also supports groups (don't forget to use the group var on `x` or `facet`):   example_isolates %>%     filter(mo_is_gram_negative(), ward != \"Outpatient\") %>%     # select only UTI-specific drugs     select(ward, AMX, NIT, FOS, TMP, CIP) %>%     group_by(ward) %>%     ggplot_sir(       x = \"ward\",       facet = \"antibiotic\",       nrow = 1,       title = \"AMR of Anti-UTI Drugs Per Ward\",       x.title = \"Ward\",       datalabels = FALSE     ) } #> ℹ Using column 'mo' as input for mo_is_gram_negative()  # }"},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":null,"dir":"Reference","previous_headings":"","what":"Guess Antibiotic Column — guess_ab_col","title":"Guess Antibiotic Column — guess_ab_col","text":"tries find column name data set based information antibiotics data set. Also supports WHONET abbreviations.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Guess Antibiotic Column — guess_ab_col","text":"","code":"guess_ab_col(   x = NULL,   search_string = NULL,   verbose = FALSE,   only_sir_columns = FALSE )"},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Guess Antibiotic Column — guess_ab_col","text":"x data.frame search_string text search x , checked .ab() value column x verbose logical indicate whether additional info printed only_sir_columns logical indicate whether antibiotic columns must detected transformed class sir (see .sir()) beforehand (default FALSE)","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Guess Antibiotic Column — guess_ab_col","text":"column name x, NULL result found.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Guess Antibiotic Column — guess_ab_col","text":"can look antibiotic (trade) name abbreviation search x antibiotics data set column containing name code antibiotic.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Guess Antibiotic Column — guess_ab_col","text":"","code":"df <- data.frame(   amox = \"S\",   tetr = \"R\" )  guess_ab_col(df, \"amoxicillin\") #> [1] \"amox\" guess_ab_col(df, \"J01AA07\") # ATC code of tetracycline #> [1] \"tetr\"  guess_ab_col(df, \"J01AA07\", verbose = TRUE) #> Auto-guessing columns suitable for analysis #> ... #>  OK. #> ℹ Using column 'amox' as input for AMX (amoxicillin). #> ℹ Using column 'tetr' as input for TCY (tetracycline). #> ℹ Using column 'tetr' as input for J01AA07 (tetracycline). #> [1] \"tetr\" # NOTE: Using column 'tetr' as input for J01AA07 (tetracycline).  # WHONET codes df <- data.frame(   AMP_ND10 = \"R\",   AMC_ED20 = \"S\" ) guess_ab_col(df, \"ampicillin\") #> [1] \"AMP_ND10\" guess_ab_col(df, \"J01CR02\") #> [1] \"AMC_ED20\" guess_ab_col(df, as.ab(\"augmentin\")) #> [1] \"AMC_ED20\""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"Data set containing defined intrinsic resistance EUCAST bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"intrinsic_resistant"},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"tibble 134 634 observations 2 variables: mo Microorganism ID ab Antibiotic ID","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"data set based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository. allow machine reading EUCAST CLSI guidelines, almost impossible MS Excel PDF files distributed EUCAST CLSI.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"intrinsic_resistant #> # A tibble: 134,634 × 2 #>    mo      ab    #>    <mo>    <chr> #>  1 B_GRAMP ATM   #>  2 B_GRAMP COL   #>  3 B_GRAMP NAL   #>  4 B_GRAMP PLB   #>  5 B_GRAMP TEM   #>  6 B_ABTRP ATM   #>  7 B_ABTRP COL   #>  8 B_ABTRP NAL   #>  9 B_ABTRP PLB   #> 10 B_ABTRP TEM   #> # … with 134,624 more rows"},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":null,"dir":"Reference","previous_headings":"","what":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"According binomial nomenclature, lowest four taxonomic levels (family, genus, species, subspecies) printed italics. function finds taxonomic names within strings makes italic.","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"","code":"italicise_taxonomy(string, type = c(\"markdown\", \"ansi\"))  italicize_taxonomy(string, type = c(\"markdown\", \"ansi\"))"},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"string character (vector) type type conversion taxonomic names, either \"markdown\" \"ansi\", see Details","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"function finds taxonomic names makes italic based microorganisms data set. taxonomic names can italicised using markdown (default) adding * taxonomic names, using ANSI colours adding \\033[3m \\033[23m taxonomic names. multiple ANSI colours available, conversion occur. function also supports abbreviation genus followed species, \"E. coli\" \"K. pneumoniae ozaenae\".","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"","code":"italicise_taxonomy(\"An overview of Staphylococcus aureus isolates\") #> [1] \"An overview of *Staphylococcus aureus* isolates\" italicise_taxonomy(\"An overview of S. aureus isolates\") #> [1] \"An overview of *S. aureus* isolates\"  cat(italicise_taxonomy(\"An overview of S. aureus isolates\", type = \"ansi\")) #> An overview of S. aureus isolates"},{"path":"https://msberends.github.io/AMR/reference/join.html","id":null,"dir":"Reference","previous_headings":"","what":"Join microorganisms to a Data Set — join","title":"Join microorganisms to a Data Set — join","text":"Join data set microorganisms easily existing data set character vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join microorganisms to a Data Set — join","text":"","code":"inner_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  left_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  right_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  full_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  semi_join_microorganisms(x, by = NULL, ...)  anti_join_microorganisms(x, by = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join microorganisms to a Data Set — join","text":"x existing data set join, character vector. case character vector, resulting data.frame contain column 'x' values. variable join - left empty search column class mo (created .mo()) \"mo\" column name exists x, otherwise column name x values exist microorganisms$mo (= \"bacteria_id\"), another column microorganisms (named, like = c(\"bacteria_id\" = \"fullname\")) suffix non-joined duplicate variables x y, suffixes added output disambiguate . character vector length 2. ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join microorganisms to a Data Set — join","text":"data.frame","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Join microorganisms to a Data Set — join","text":"Note: opposed join() functions dplyr, character vectors supported default existing columns get suffix \"2\" newly joined columns get suffix. dplyr package installed, join functions used. Otherwise, much slower merge() interaction() functions base R used.","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Join microorganisms to a Data Set — join","text":"","code":"left_join_microorganisms(as.mo(\"K. pneumoniae\")) #> # A tibble: 1 × 22 #>   mo           fullname   status kingdom phylum class order family genus species #>   <mo>         <chr>      <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> <chr>   #> 1 B_KLBSL_PNMN Klebsiell… accep… Bacter… Pseud… Gamm… Ente… Enter… Kleb… pneumo… #> # … with 12 more variables: subspecies <chr>, rank <chr>, ref <chr>, #> #   source <chr>, lpsn <chr>, lpsn_parent <chr>, lpsn_renamed_to <chr>, #> #   gbif <chr>, gbif_parent <chr>, gbif_renamed_to <chr>, prevalence <dbl>, #> #   snomed <list> left_join_microorganisms(\"B_KLBSL_PNMN\") #> # A tibble: 1 × 22 #>   mo           fullname   status kingdom phylum class order family genus species #>   <mo>         <chr>      <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> <chr>   #> 1 B_KLBSL_PNMN Klebsiell… accep… Bacter… Pseud… Gamm… Ente… Enter… Kleb… pneumo… #> # … with 12 more variables: subspecies <chr>, rank <chr>, ref <chr>, #> #   source <chr>, lpsn <chr>, lpsn_parent <chr>, lpsn_renamed_to <chr>, #> #   gbif <chr>, gbif_parent <chr>, gbif_renamed_to <chr>, prevalence <dbl>, #> #   snomed <list>  df <- data.frame(   date = seq(     from = as.Date(\"2018-01-01\"),     to = as.Date(\"2018-01-07\"),     by = 1   ),   bacteria = as.mo(c(     \"S. aureus\", \"MRSA\", \"MSSA\", \"STAAUR\",     \"E. coli\", \"E. coli\", \"E. coli\"   )),   stringsAsFactors = FALSE ) colnames(df) #> [1] \"date\"     \"bacteria\"  df_joined <- left_join_microorganisms(df, \"bacteria\") colnames(df_joined) #>  [1] \"date\"            \"bacteria\"        \"fullname\"        \"status\"          #>  [5] \"kingdom\"         \"phylum\"          \"class\"           \"order\"           #>  [9] \"family\"          \"genus\"           \"species\"         \"subspecies\"      #> [13] \"rank\"            \"ref\"             \"source\"          \"lpsn\"            #> [17] \"lpsn_parent\"     \"lpsn_renamed_to\" \"gbif\"            \"gbif_parent\"     #> [21] \"gbif_renamed_to\" \"prevalence\"      \"snomed\"           # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     left_join_microorganisms() %>%     colnames() } #> Joining, by = \"mo\" #>  [1] \"date\"            \"patient\"         \"age\"             \"gender\"          #>  [5] \"ward\"            \"mo\"              \"PEN\"             \"OXA\"             #>  [9] \"FLC\"             \"AMX\"             \"AMC\"             \"AMP\"             #> [13] \"TZP\"             \"CZO\"             \"FEP\"             \"CXM\"             #> [17] \"FOX\"             \"CTX\"             \"CAZ\"             \"CRO\"             #> [21] \"GEN\"             \"TOB\"             \"AMK\"             \"KAN\"             #> [25] \"TMP\"             \"SXT\"             \"NIT\"             \"FOS\"             #> [29] \"LNZ\"             \"CIP\"             \"MFX\"             \"VAN\"             #> [33] \"TEC\"             \"TCY\"             \"TGC\"             \"DOX\"             #> [37] \"ERY\"             \"CLI\"             \"AZM\"             \"IPM\"             #> [41] \"MEM\"             \"MTR\"             \"CHL\"             \"COL\"             #> [45] \"MUP\"             \"RIF\"             \"fullname\"        \"status\"          #> [49] \"kingdom\"         \"phylum\"          \"class\"           \"order\"           #> [53] \"family\"          \"genus\"           \"species\"         \"subspecies\"      #> [57] \"rank\"            \"ref\"             \"source\"          \"lpsn\"            #> [61] \"lpsn_parent\"     \"lpsn_renamed_to\" \"gbif\"            \"gbif_parent\"     #> [65] \"gbif_renamed_to\" \"prevalence\"      \"snomed\"          # }"},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"functions can used determine first weighted isolates considering phenotype isolate selection (see first_isolate()). Using phenotype-based method determine first isolates reliable methods disregard phenotypes.","code":""},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"","code":"key_antimicrobials(   x = NULL,   col_mo = NULL,   universal = c(\"ampicillin\", \"amoxicillin/clavulanic acid\", \"cefuroxime\",     \"piperacillin/tazobactam\", \"ciprofloxacin\", \"trimethoprim/sulfamethoxazole\"),   gram_negative = c(\"gentamicin\", \"tobramycin\", \"colistin\", \"cefotaxime\", \"ceftazidime\",     \"meropenem\"),   gram_positive = c(\"vancomycin\", \"teicoplanin\", \"tetracycline\", \"erythromycin\",     \"oxacillin\", \"rifampin\"),   antifungal = c(\"anidulafungin\", \"caspofungin\", \"fluconazole\", \"miconazole\", \"nystatin\",     \"voriconazole\"),   only_sir_columns = FALSE,   ... )  all_antimicrobials(x = NULL, only_sir_columns = FALSE, ...)  antimicrobials_equal(   y,   z,   type = c(\"points\", \"keyantimicrobials\"),   ignore_I = TRUE,   points_threshold = 2,   ... )"},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"x data.frame antibiotics columns, like AMX amox. Can left blank determine automatically col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). universal names broad-spectrum antimicrobial drugs, case-insensitive. Set NULL ignore. See Details default antimicrobial drugs gram_negative names antibiotic drugs Gram-positives, case-insensitive. Set NULL ignore. See Details default antibiotic drugs gram_positive names antibiotic drugs Gram-negatives, case-insensitive. Set NULL ignore. See Details default antibiotic drugs antifungal names antifungal drugs fungi, case-insensitive. Set NULL ignore. See Details default antifungal drugs only_sir_columns logical indicate whether columns must included transformed class sir (see .sir()) beforehand (default FALSE) ... ignored, place allow future extensions y, z character vectors compare type type determine weighed isolates; can \"keyantimicrobials\" \"points\", see Details ignore_I logical indicate whether antibiotic interpretations \"\" ignored type = \"keyantimicrobials\", see Details points_threshold minimum number points require differences antibiogram lead inclusion isolate type = \"points\", see Details","code":""},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"key_antimicrobials() all_antimicrobials() functions context-aware. means x argument can left blank used inside data.frame call, see Examples. function key_antimicrobials() returns character vector 12 antimicrobial results every isolate. function all_antimicrobials() returns character vector antimicrobial drug results every isolate. vectors can compared using antimicrobials_equal(), check two isolates generally antibiogram. Missing invalid values replaced dot (\".\") key_antimicrobials() ignored antimicrobials_equal(). Please see first_isolate() function important functions enable 'phenotype-based' method determination first isolates. default antimicrobial drugs used rows (set universal) : Ampicillin Amoxicillin/clavulanic acid Cefuroxime Ciprofloxacin Piperacillin/tazobactam Trimethoprim/sulfamethoxazole default antimicrobial drugs used Gram-negative bacteria (set gram_negative) : Cefotaxime Ceftazidime Colistin Gentamicin Meropenem Tobramycin default antimicrobial drugs used Gram-positive bacteria (set gram_positive) : Erythromycin Oxacillin Rifampin Teicoplanin Tetracycline Vancomycin default antimicrobial drugs used fungi (set antifungal) : Anidulafungin Caspofungin Fluconazole Miconazole Nystatin Voriconazole","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # output of the `key_antimicrobials()` function could be like this: strainA <- \"SSSRR.S.R..S\" strainB <- \"SSSIRSSSRSSS\"  # those strings can be compared with: antimicrobials_equal(strainA, strainB, type = \"keyantimicrobials\") #> [1] TRUE # TRUE, because I is ignored (as well as missing values)  antimicrobials_equal(strainA, strainB, type = \"keyantimicrobials\", ignore_I = FALSE) #> [1] FALSE # FALSE, because I is not ignored and so the 4th [character] differs  # \\donttest{ if (require(\"dplyr\")) {   # set key antibiotics to a new variable   my_patients <- example_isolates %>%     mutate(keyab = key_antimicrobials(antifungal = NULL)) %>% # no need to define `x`     mutate(       # now calculate first isolates       first_regular = first_isolate(col_keyantimicrobials = FALSE),       # and first WEIGHTED isolates       first_weighted = first_isolate(col_keyantimicrobials = \"keyab\")     )    # Check the difference in this data set, 'weighted' results in more isolates:   sum(my_patients$first_regular, na.rm = TRUE)   sum(my_patients$first_weighted, na.rm = TRUE) } #> [1] 1395 # }"},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":null,"dir":"Reference","previous_headings":"","what":"Kurtosis of the Sample — kurtosis","title":"Kurtosis of the Sample — kurtosis","text":"Kurtosis measure \"tailedness\" probability distribution real-valued random variable. normal distribution kurtosis 3 excess kurtosis 0.","code":""},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kurtosis of the Sample — kurtosis","text":"","code":"kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for default kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for matrix kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for data.frame kurtosis(x, na.rm = FALSE, excess = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kurtosis of the Sample — kurtosis","text":"x vector values, matrix data.frame na.rm logical indicate whether NA values stripped computation proceeds excess logical indicate whether excess kurtosis returned, defined kurtosis minus 3.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kurtosis of the Sample — kurtosis","text":"","code":"kurtosis(rnorm(10000)) #> [1] 3.001502 kurtosis(rnorm(10000), excess = TRUE) #> [1] -0.00633944"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":null,"dir":"Reference","previous_headings":"","what":"Vectorised Pattern Matching with Keyboard Shortcut — like","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Convenient wrapper around grepl() match pattern: x %like% pattern. always returns logical vector always case-insensitive (use x %like_case% pattern case-sensitive matching). Also, pattern can long x compare items index vectors, can length iterate cases.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"like(x, pattern, ignore.case = TRUE)  x %like% pattern  x %unlike% pattern  x %like_case% pattern  x %unlike_case% pattern"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Idea like function data.table package, although altered explained Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"x character vector matches sought, object can coerced .character() character vector. pattern character vector containing regular expressions (character string fixed = TRUE) matched given character vector. Coerced .character() character string possible. ignore.case FALSE, pattern matching case sensitive TRUE, case ignored matching.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"like() %like%/%unlike% functions: case-insensitive (use %like_case%/%unlike_case% case-sensitive matching) Support multiple patterns Check pattern valid regular expression sets fixed = TRUE , greatly improve speed (vectorised pattern) Always use compatibility Perl unless fixed = TRUE, greatly improve speed Using RStudio? %like%/%unlike% functions can also directly inserted code Addins menu can keyboard shortcut like Shift+Ctrl+L Shift+Cmd+L (see menu Tools > Modify Keyboard Shortcuts...). keep pressing shortcut, inserted text iterated %like% -> %unlike% -> %like_case% -> %unlike_case%.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"a <- \"This is a test\" b <- \"TEST\" a %like% b #> [1] FALSE 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 #> Warning: argument 'pattern' has length > 1 and only the first element will be used #> [1]  TRUE FALSE FALSE a %unlike% b #> [1] FALSE FALSE FALSE  a[1] %like% b #> Warning: argument 'pattern' has length > 1 and only the first element will be used #> [1] TRUE 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: 0 × 46 #> # … with 46 variables: date <date>, patient <chr>, age <dbl>, gender <chr>, #> #   ward <chr>, mo <mo>, PEN <sir>, OXA <sir>, FLC <sir>, AMX <sir>, 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>, …  if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_name() %like% \"^ent\") } #> ℹ Using column 'mo' as input for mo_name() #> # A tibble: 0 × 46 #> # … with 46 variables: date <date>, patient <chr>, age <dbl>, gender <chr>, #> #   ward <chr>, mo <mo>, PEN <sir>, OXA <sir>, FLC <sir>, AMX <sir>, 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>, … # }"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Determine isolates multidrug-resistant organisms (MDRO) according international, national custom guidelines.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"mdro(   x = NULL,   guideline = \"CMI2012\",   col_mo = NULL,   info = interactive(),   pct_required_classes = 0.5,   combine_SI = TRUE,   verbose = FALSE,   only_sir_columns = FALSE,   ... )  custom_mdro_guideline(..., as_factor = TRUE)  brmo(x = NULL, only_sir_columns = FALSE, ...)  mrgn(x = NULL, only_sir_columns = FALSE, ...)  mdr_tb(x = NULL, only_sir_columns = FALSE, ...)  mdr_cmi2012(x = NULL, only_sir_columns = FALSE, ...)  eucast_exceptional_phenotypes(x = NULL, only_sir_columns = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"See supported guidelines list publications used function.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"x data.frame antibiotics columns, like AMX amox. Can left blank automatic determination. guideline specific guideline follow, see sections Supported international / national guidelines Using Custom Guidelines . left empty, publication Magiorakos et al. (see ) followed. col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console - default print interactive sessions pct_required_classes minimal required percentage antimicrobial classes must available per isolate, rounded . example, default guideline, 17 antimicrobial classes must available S. aureus. Setting pct_required_classes argument 0.5 (default) means every S. aureus isolate least 8 different classes must available. lower number available classes return NA isolate. combine_SI logical indicate whether values S must merged one, resistance considered isolates R, . default behaviour mdro() function, follows redefinition EUCAST interpretation (increased exposure) 2019, see section 'Interpretation S, R' . using combine_SI = FALSE, resistance considered isolates R . verbose logical turn Verbose mode (default ). Verbose mode, function return MDRO results, instead returns data set logbook form extensive info isolates MDRO-positive, . only_sir_columns logical indicate whether antibiotic columns must detected transformed class sir (see .sir()) beforehand (default FALSE) ... case custom_mdro_guideline(): set rules, see section Using Custom Guidelines . Otherwise: column name antibiotic, see section Antibiotics . as_factor logical indicate whether returned value ordered factor (TRUE, default), otherwise character vector","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"CMI 2012 paper - function mdr_cmi2012() mdro(): Ordered factor levels Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant (XDR) < Pandrug-resistant (PDR) TB guideline - function mdr_tb() mdro(..., guideline = \"TB\"): Ordered factor levels Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant < Extensively drug-resistant German guideline - function mrgn() mdro(..., guideline = \"MRGN\"): Ordered factor levels Negative < 3MRGN < 4MRGN Everything else, except custom guidelines: Ordered factor levels Negative < Positive, unconfirmed < Positive. value \"Positive, unconfirmed\" means , according guideline, entirely sure isolate multi-drug resistant confirmed additional (e.g. molecular) tests","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"functions context-aware. means x argument can left blank used inside data.frame call, see Examples. pct_required_classes argument, values 1 divided 100. support fractions (0.75 3/4) percentages (75). Note: Every test involves Enterobacteriaceae family, internally performed using newly named order Enterobacterales, since Enterobacteriaceae family taxonomically reclassified Adeolu et al. 2016. , Enterobacteriaceae family Enterobacteriales () order. species old Enterobacteriaceae family still new Enterobacterales (without ) order, divided multiple families. way tests performed now mdro() function makes sure results 2016 2016 identical.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"supported-international-national-guidelines","dir":"Reference","previous_headings":"","what":"Supported International / National Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. \"Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.\" Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.3\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.3 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.2\" European international guideline - EUCAST Expert Rules Version 3.2 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 \"Intrinsic Resistance Exceptional Phenotypes Tables\" (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization \"Companion handbook guidelines programmatic management drug-resistant tuberculosis\" (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7; doi:10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu \"WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)\" (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"using-custom-guidelines","dir":"Reference","previous_headings":"","what":"Using Custom Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde:   row/isolate matches first rule, value first ~ (case 'Elderly Type ') set MDRO value. Otherwise, second rule tried . number rules unlimited. can print rules set console overview. Colours help reading console supports colours.   outcome function can used guideline argument mdro() function:   Rules can also combined custom rules using c():   rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline(CIP == \"R\" & age > 60 ~ \"Elderly Type A\",                                 ERY == \"R\" & age > 60 ~ \"Elderly Type B\") custom #> A set of custom MDRO rules: #>   1. CIP is \"R\" and age is higher than 60 -> Elderly Type A #>   2. ERY is \"R\" and age is higher than 60 -> Elderly Type B #>   3. Otherwise -> Negative #> #> Unmatched rows will return NA. x <- mdro(example_isolates,           guideline = custom) table(x) #>       Negative Elderly Type A Elderly Type B #>           1070            198            732 x <- mdro(example_isolates,           guideline = c(custom,                         custom_mdro_guideline(ERY == \"R\" & age > 50 ~ \"Elderly Type C\"))) table(x) #>       Negative Elderly Type A Elderly Type B Elderly Type C #>            961            198            732            109"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"antibiotics","dir":"Reference","previous_headings":"","what":"Antibiotics","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"define antibiotics column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antibiotics eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, S01AE08), amoxicillin (AMX, J01MA02), amoxicillin/clavulanic acid (AMC, J01MA23), ampicillin (AMP, J01MA04), ampicillin/sulbactam (SAM, J01MA08), arbekacin (ARB, J01MA19), aspoxicillin (APX, J01MA16), azidocillin (AZD, J01MA15), azithromycin (AZM, J01MA11), azlocillin (AZL, J01MA25), aztreonam (ATM, J01MA12), bacampicillin (BAM, J01MA24), bekanamycin (BEK, J01MA07), benzathine benzylpenicillin (BNB, J01MA14), benzathine phenoxymethylpenicillin (BNP, D10AF05), benzylpenicillin (PEN, J01MA06), besifloxacin (BES, J01MA01), biapenem (BIA, J01MA18), carbenicillin (CRB, J01MA03), carindacillin (CRN, J01MA17), cefacetrile (CAC, J01MA10), cefaclor (CEC, J01MA21), cefadroxil (CFR, J01MA09), cefalexin (LEX, J01MA05), cefaloridine (RID, P01AA05), cefalotin (CEP, J01MA22), cefamandole (MAN, J01MA13), cefapirin (HAP, J01CA01), cefatrizine (CTZ, J01CA04), cefazedone (CZD, J01CA12), cefazolin (CZO, J01CR05), cefcapene (CCP, J01CA13), cefdinir (CDR, J01AA02), cefditoren (DIT, J01FA10), cefepime (FEP, J01FA09), cefetamet (CAT, J01CR02), cefixime (CFM, J01AA08), cefmenoxime (CMX, J01FA06), cefmetazole (CMZ, J01CF04), cefodizime (DIZ, J01CF05), cefonicid (CID, J01CR01), cefoperazone (CFP, J01CA19), cefoperazone/sulbactam (CSL, J01CE04), ceforanide (CND, J01CA09), cefotaxime (CTX, J01DF01), cefotaxime/clavulanic acid (CTC, J01CA06), cefotetan (CTT, J01CE08), cefotiam (CTF, J01CE10), cefoxitin (FOX, J01CE01), cefozopran (ZOP, J01CA03), cefpiramide (CPM, J01CA05), cefpirome (CPO, J01CE07), cefpodoxime (CPD, J01CF02), cefprozil (CPR, J01CF01), cefroxadine (CRD, J01CA07), cefsulodin (CFS, J01CA18), ceftaroline (CPT, J01CA11), ceftazidime (CAZ, J01CA14), ceftazidime/clavulanic acid (CCV, J01CF03), cefteram (CEM, J01CA10), ceftezole (CTL, J01CF06), ceftibuten (CTB, J01CE06), ceftizoxime (CZX, J01CE05), ceftobiprole medocaril (CFM1, J01CE02), ceftolozane/tazobactam (CZT, J01CA02), ceftriaxone (CRO, J01CA08), ceftriaxone/beta-lactamase inhibitor (CEB, J01CE09), cefuroxime (CXM, J01CE03), cephradine (CED, J01CG01), chloramphenicol (CHL, J01CA16), ciprofloxacin (CIP, J01CR04), clarithromycin (CLR, J01CA15), clindamycin (CLI, J01CG02), clometocillin (CLM, J01CA17), cloxacillin (CLO, J01CR03), colistin (COL, J01DB10), cycloserine (CYC, J01DC04), dalbavancin (DAL, J01DB05), daptomycin (DAP, J01DB01), delafloxacin (DFX, J01DB02), dibekacin (DKB, J01DB03), dicloxacillin (DIC, J01DC03), dirithromycin (DIR, J01DB08), doripenem (DOR, J01DB07), doxycycline (DOX, J01DB06), enoxacin (ENX, J01DB04), epicillin (EPC, J01DD17), ertapenem (ETP, J01DD15), erythromycin (ERY, J01DD16), fleroxacin (FLE, J01DE01), flucloxacillin (FLC, J01DD10), flurithromycin (FLR1, J01DD08), fosfomycin (FOS, J01DD05), framycetin (FRM, J01DC09), fusidic acid (FUS, J01DD09), garenoxacin (GRN, J01DC06), gatifloxacin (GAT, J01DD12), gemifloxacin (GEM, J01DD62), gentamicin (GEN, J01DC11), grepafloxacin (GRX, J01DD01), hetacillin (HET, J01DD51), imipenem (IPM, J01DC05), imipenem/relebactam (IMR, J01DC07), isepamicin (ISE, J01DC01), josamycin (JOS, J01DE03), kanamycin (KAN, J01DD11), lascufloxacin (LSC, J01DE02), latamoxef (LTM, J01DD13), levofloxacin (LVX, J01DC10), levonadifloxacin (LND, J01DB11), lincomycin (LIN, J01DD03), linezolid (LNZ, J01DI02), lomefloxacin (LOM, J01DD02), loracarbef (LOR, J01DD52), mecillinam (MEC, J01DD18), meropenem (MEM, J01DB12), meropenem/vaborbactam (MEV, J01DD14), metampicillin (MTM, J01DD07), meticillin (MET, J01DI01), mezlocillin (MEZ, J01DI54), micronomicin (MCR, J01DD04), midecamycin (MID, J01DD63), minocycline (MNO, J01DC02), miocamycin (MCM, J01DB09), moxifloxacin (MFX, J01DD06), nadifloxacin (NAD, J01DC08), nafcillin (NAF, J01DH05), nalidixic acid (NAL, J01DH04), neomycin (NEO, J01DH03), netilmicin (NET, J01DH51), nitrofurantoin (NIT, J01DH56), norfloxacin (, J01DH02), ofloxacin (OFX, J01DH52), oleandomycin (OLE, J01DH55), oritavancin (ORI, J01DH06), oxacillin (OXA, J01XA02), panipenem (PAN, J01XA01), pazufloxacin (PAZ, J01XC01), pefloxacin (PEF, J01FA13), penamecillin (PNM, J01FA01), pheneticillin (PHE, J01FA14), phenoxymethylpenicillin (PHN, J01FA07), piperacillin (PIP, J01FA03), piperacillin/tazobactam (TZP, J01FA11), pivampicillin (PVM, J01FA05), pivmecillinam (PME, J01FA12), plazomicin (PLZ, J01FA16), polymyxin B (PLB, J01FA02), pristinamycin (PRI, J01FA15), procaine benzylpenicillin (PRB, J01FA08), propicillin (PRP, J01FF02), prulifloxacin (PRU, J01FG01), quinupristin/dalfopristin (QDA, J01FG02), ribostamycin (RST, J04AB02), rifampicin (RIF, J01XX09), rokitamycin (ROK, J01XX08), roxithromycin (RXT, J01AA07), rufloxacin (RFL, J01XB01), sisomicin (SIS, J01XB02), sitafloxacin (SIT, J01XE01), solithromycin (SOL, J01AA12), sparfloxacin (SPX, J01EA01), spiramycin (SPI, J01XX01), streptoduocin (STR, J01BA01), streptomycin (STR1, J01GB06), sulbactam (SUL, J01GB12), sulbenicillin (SBC, J01GB13), sulfadiazine (SDI, J01GB09), sulfadiazine/trimethoprim (SLT1, D09AA01), sulfadimethoxine (SUD, J01GB03), sulfadimidine (SDM, J01GB11), sulfadimidine/trimethoprim (SLT2, J01GB04), sulfafurazole (SLF, S01AA22), sulfaisodimidine (SLF1, J01GB05), sulfalene (SLF2, J01GB07), sulfamazone (SZO, J01GB14), sulfamerazine (SLF3, J01GB10), sulfamerazine/trimethoprim (SLT3, J01GB08), sulfamethizole (SLF4, J01GA02), sulfamethoxazole (SMX, J01GA01), sulfamethoxypyridazine (SLF5, J01GB01), sulfametomidine (SLF6, J01EE01), sulfametoxydiazine (SLF7, J01MB02), sulfametrole/trimethoprim (SLT4, J01FF01), sulfamoxole (SLF8, J01XA04), sulfamoxole/trimethoprim (SLT5, J01XA05), sulfanilamide (SLF9, J01XA03), sulfaperin (SLF10, J04AB01), sulfaphenazole (SLF11, J01XX11), sulfapyridine (SLF12, J01EC02), sulfathiazole (SUT, J01ED01), sulfathiourea (SLF13, J01EB03), sultamicillin (SLT6, J01EB05), talampicillin (TAL, J01EB01), tazobactam (TAZ, J01ED02), tebipenem (TBP, J01ED09), tedizolid (TZD, J01ED07), teicoplanin (TEC, J01EB02), telavancin (TLV, J01EC01), telithromycin (TLT, J01ED05), temafloxacin (TMX, J01ED03), temocillin (TEM, J01ED04), tetracycline (TCY, J01EC03), ticarcillin (TIC, J01EB06), ticarcillin/clavulanic acid (TCC, J01ED06), tigecycline (TGC, J01ED08), tilbroquinol (TBQ, J01EB04), tobramycin (TOB, J01EB07), tosufloxacin (TFX, J01EB08), trimethoprim (TMP, J01EE02), trimethoprim/sulfamethoxazole (SXT, J01EE05), troleandomycin (TRL, J01EE07), trovafloxacin (TVA, J01EE03), vancomycin (VAN, J01EE04)","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"out <- mdro(example_isolates, guideline = \"EUCAST\") #>  (16 isolates had no test results) str(out) #>  Ord.factor w/ 3 levels \"Negative\"<\"Positive, unconfirmed\"<..: 1 1 1 1 1 1 1 1 1 1 ... table(out) #> out #>              Negative Positive, unconfirmed              Positive  #>                  1978                     0                     6   out <- mdro(example_isolates,   guideline = custom_mdro_guideline(     AMX == \"R\" ~ \"Custom MDRO 1\",     VAN == \"R\" ~ \"Custom MDRO 2\"   ) ) table(out) #> out #>      Negative Custom MDRO 1 Custom MDRO 2  #>           870           804           326   # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     mdro() %>%     table()    # no need to define `x` when used inside dplyr verbs:   example_isolates %>%     mutate(MDRO = mdro()) %>%     pull(MDRO) %>%     table() } #>  (16 isolates had no test results) #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #>  (16 isolates had no test results) #> Warning: There was 1 warning in `mutate()`. #> ℹ In argument: `MDRO = mdro()`. #> Caused by warning: #> ! in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> . #>                         Negative       Multi-drug-resistant (MDR)  #>                             1601                              128  #> Extensively drug-resistant (XDR)          Pandrug-resistant (PDR)  #>                                0                                0  # }"},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the Mean AMR Distance — mean_amr_distance","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"Calculates normalised mean antimicrobial resistance multiple observations, help identify similar isolates without comparing antibiograms hand.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"","code":"mean_amr_distance(x, ...)  # S3 method for sir mean_amr_distance(x, ..., combine_SI = TRUE)  # S3 method for data.frame mean_amr_distance(x, ..., combine_SI = TRUE)  amr_distance_from_row(amr_distance, row)"},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"x vector class sir, mic disk, data.frame containing columns classes ... variables select (supports tidyselect language column1:column4 (.mic), can thus also antibiotic selectors combine_SI logical indicate whether values S must merged one, input consists S+vs. R (susceptible vs. resistant) - default TRUE amr_distance outcome mean_amr_distance() row index, row number","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"mean AMR distance effectively Z-score; normalised numeric value compare AMR test results can help identify similar isolates, without comparing antibiograms hand. MIC values (see .mic()) transformed log2() first; distance thus calculated (log2(x) - mean(log2(x))) / sd(log2(x)). SIR values (see .sir()) transformed using \"S\" = 1, \"\" = 2, \"R\" = 3. combine_SI TRUE (default), \"\" considered 1. data sets, mean AMR distance calculated per column, mean per row returned, see Examples. Use amr_distance_from_row() subtract distances distance one row, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"interpretation","dir":"Reference","previous_headings":"","what":"Interpretation","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"Isolates distances less 0.01 difference considered similar. Differences lower 0.025 considered suspicious.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"","code":"sir <- random_sir(10) sir #> Class 'sir' #>  [1] R R S I R S R R S R mean_amr_distance(sir) #>  [1]  0.7745967  0.7745967 -1.1618950 -1.1618950  0.7745967 -1.1618950 #>  [7]  0.7745967  0.7745967 -1.1618950  0.7745967  mic <- random_mic(10) mic #> Class 'mic' #>  [1] 0.002  0.002  256    0.0625 0.01   0.025  0.002  2      4      0.005  mean_amr_distance(mic) #>  [1] -0.867240328 -0.867240328  2.066590798 -0.008525384 -0.465717781 #>  [6] -0.237121583 -0.867240328  0.856106358  1.029032707 -0.638644130 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) #>  [1] -0.867240328 -0.867240328  2.066590798 -0.008525384 -0.465717781 #>  [6] -0.237121583 -0.867240328  0.856106358  1.029032707 -0.638644130  disk <- random_disk(10) disk #> Class 'disk' #>  [1] 42 21 42 39 44 36 46 41  9 48 mean_amr_distance(disk) #>  [1]  0.42276578 -1.28455756  0.42276578  0.17886245  0.58536800 -0.06504089 #>  [7]  0.74797023  0.34146467 -2.26017090  0.91057245  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   27     4  <=1 #> 2   B    R   20     1  <=1 #> 3   C    R   31 <=0.5    2 #> 4   D    S   23     4    2 #> 5   E    R   31     2  <=1 #> 6   F    R   24 <=0.5    2 #> 7   G    R   28     1    4 #> 8   H    R   22     4    4 #> 9   I    S   23     8  <=1 #> 10  J    S   26     4    4 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\", #>   and \"tobr\" #>  [1] -0.2706405 -0.6068529  0.2354050 -0.2518529  0.3034383 -0.2313769 #>  [7]  0.4976524  0.4511070 -0.3605963  0.2337166 y$amr_distance <- mean_amr_distance(y, where(is.mic)) #> Error in .subset(x, j): invalid subscript type 'list' y[order(y$amr_distance), ] #> Error in order(y$amr_distance): argument 1 is not a vector  if (require(\"dplyr\")) {   y %>%     mutate(       amr_distance = mean_amr_distance(y),       check_id_C = amr_distance_from_row(amr_distance, id == \"C\")     ) %>%     arrange(check_id_C) } #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\", #>   and \"tobr\" #>    id amox cipr  gent tobr amr_distance check_id_C #> 1   C    R   31 <=0.5    2    0.2354050 0.00000000 #> 2   J    S   26     4    4    0.2337166 0.00168838 #> 3   E    R   31     2  <=1    0.3034383 0.06803327 #> 4   H    R   22     4    4    0.4511070 0.21570200 #> 5   G    R   28     1    4    0.4976524 0.26224741 #> 6   F    R   24 <=0.5    2   -0.2313769 0.46678194 #> 7   D    S   23     4    2   -0.2518529 0.48725790 #> 8   A    S   27     4  <=1   -0.2706405 0.50604549 #> 9   I    S   23     8  <=1   -0.3605963 0.59600133 #> 10  B    R   20     1  <=1   -0.6068529 0.84225790 if (require(\"dplyr\")) {   # support for groups   example_isolates %>%     filter(mo_genus() == \"Enterococcus\" & mo_species() != \"\") %>%     select(mo, TCY, carbapenems()) %>%     group_by(mo) %>%     mutate(dist = mean_amr_distance(.)) %>%     arrange(mo, dist) } #> ℹ Using column 'mo' as input for mo_genus() #> ℹ Using column 'mo' as input for mo_species() #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Calculating mean AMR distance based on columns \"TCY\", \"IPM\", and \"MEM\" #> # A tibble: 63 × 5 #> # Groups:   mo [4] #>    mo           TCY   IPM   MEM     dist #>    <mo>         <sir> <sir> <sir>  <dbl> #>  1 B_ENTRC_AVIM S     S     NA     0     #>  2 B_ENTRC_AVIM S     S     NA     0     #>  3 B_ENTRC_CSSL NA    S     NA    NA     #>  4 B_ENTRC_FACM S     S     NA    -2.66  #>  5 B_ENTRC_FACM S     R     R     -0.423 #>  6 B_ENTRC_FACM S     R     R     -0.423 #>  7 B_ENTRC_FACM NA    R     R      0.224 #>  8 B_ENTRC_FACM NA    R     R      0.224 #>  9 B_ENTRC_FACM NA    R     R      0.224 #> 10 B_ENTRC_FACM NA    R     R      0.224 #> # … with 53 more rows"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 5 910 Common Microorganism Codes — microorganisms.codes","title":"Data Set with 5 910 Common Microorganism Codes — microorganisms.codes","text":"data set containing commonly used codes microorganisms, laboratory systems WHONET. Define set_mo_source(). searched using .mo() consequently mo_* functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 5 910 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 5 910 Common Microorganism Codes — microorganisms.codes","text":"tibble 5 910 observations 2 variables: code Commonly used code microorganism mo ID microorganism microorganisms data set","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 5 910 Common Microorganism Codes — microorganisms.codes","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 5 910 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes #> # A tibble: 5,910 × 2 #>    code    mo        #>    <chr>   <mo>      #>  1 _FAM_A- B_GRAMN   #>  2 _FAM_A+ B_GRAMP   #>  3 _FAM_AC B_GRAMP   #>  4 _FAM_AE B_GRAMN   #>  5 _FAM_AN B_GRAMN   #>  6 _FAM_AO B_GRAMP   #>  7 _FAM_AP   UNKNOWN #>  8 _FAM_AS F_FUNGUS  #>  9 _FAM_AT   UNKNOWN #> 10 _FAM_AV   UNKNOWN #> # … with 5,900 more rows"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 52 142 Microorganisms — microorganisms","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"data set containing full microbial taxonomy (last updated: 11 December, 2022) five kingdoms List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF). data set backbone AMR package. MO codes can looked using .mo().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"","code":"microorganisms"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"tibble 52 142 observations 22 variables: mo ID microorganism used package fullname Full name, like \"Escherichia coli\". taxonomic ranks genus, species subspecies, 'pasted' text genus, species, subspecies. taxonomic ranks higher genus, name taxon. status  Status taxon, either \"accepted\" \"synonym\" kingdom, phylum, class, order, family, genus, species, subspecies Taxonomic rank microorganism rank Text taxonomic rank microorganism, \"species\" \"genus\" ref Author(s) year related scientific publication. contains first surname year latest authors, e.g. \"Wallis et al. 2006 emend. Smith Jones 2018\" becomes \"Smith et al., 2018\". field directly retrieved source specified column source. Moreover, accents removed comply CRAN allows ASCII characters, e.g. \"Váňová\" becomes \"Vanova\". lpsn Identifier ('Record number') List Prokaryotic names Standing Nomenclature (LPSN). first/highest LPSN identifier keep one identifier per row. example, Acetobacter ascendens LPSN Record number 7864 11011. first available microorganisms data set. lpsn_parent LPSN identifier parent taxon lpsn_renamed_to LPSN identifier currently valid taxon gbif Identifier ('taxonID') Global Biodiversity Information Facility (GBIF) gbif_parent GBIF identifier parent taxon gbif_renamed_to GBIF identifier currently valid taxon source Either \"GBIF\", \"LPSN\", \"manually added\" (see Source) prevalence Prevalence microorganism according Bartlett et al. (2022, doi:10.1099/mic.0.001269 ), see mo_matching_score() full explanation snomed Systematized Nomenclature Medicine (SNOMED) code microorganism, version 1 July, 2021 (see Source). Use mo_snomed() retrieve quickly, see mo_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Grimont et al. (2007). Antigenic Formulae Salmonella Serovars, 9th Edition. Collaborating Centre Reference Research Salmonella (WHOCC-SALM). Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"Please note entries based List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF) (see ). Since sources incorporate entries based (recent) publications International Journal Systematic Evolutionary Microbiology (IJSEM), can happen year publication sometimes later one might expect. example, Staphylococcus pettenkoferi described first time Diagnostic Microbiology Infectious Disease 2002 (doi:10.1016/s0732-8893(02)00399-1 ), 2007 publication IJSEM followed (doi:10.1099/ijs.0.64381-0 ). Consequently, AMR package returns 2007 mo_year(\"S. pettenkoferi\").","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"included-taxa","dir":"Reference","previous_headings":"","what":"Included Taxa","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"Included taxonomic data : ~36 000 (sub)species kingdoms Archaea Bacteria ~7 900 (sub)species kingdom Fungi. kingdom Fungi large taxon almost 300,000 different (sub)species, microbial (rather macroscopic, like mushrooms). , fungi fit scope package. relevant fungi covered (species Aspergillus, Candida, Cryptococcus, Histoplasma, Pneumocystis, Saccharomyces Trichophyton). ~5 100 (sub)species kingdom Protozoa ~1 400 (sub)species ~40 relevant genera kingdom Animalia (Strongyloides Taenia) ~9 800 previously accepted names included (sub)species (taxonomically renamed) complete taxonomic tree included (sub)species: kingdom subspecies identifier parent taxons year first author related scientific publication","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"manual-additions","dir":"Reference","previous_headings":"","what":"Manual additions","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"convenience, entries added manually: ~1 500 entries city-like serovars Salmonellae 11 entries Streptococcus (beta-haemolytic: groups , B, C, D, F, G, H, K unspecified; : viridans, milleri) 2 entries Staphylococcus (coagulase-negative (CoNS) coagulase-positive (CoPS)) 1 entry Blastocystis (B.  hominis), although officially exist (Noel et al. 2005, PMID 15634993) 1 entry Moraxella (M. catarrhalis), formally named Branhamella catarrhalis (Catlin, 1970) though change never accepted within field clinical microbiology 6 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast, unknown fungus, unknown anaerobic bacteria) syntax used transform original data cleansed R format, can found : https://github.com/msberends/AMR/blob/main/data-raw/reproduction_of_microorganisms.R.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"about-the-records-from-lpsn-see-source-","dir":"Reference","previous_headings":"","what":"About the Records from LPSN (see Source)","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"LPSN main source bacteriological taxonomy AMR package. List Prokaryotic names Standing Nomenclature (LPSN) provides comprehensive information nomenclature prokaryotes. LPSN free use service founded Jean P. Euzeby 1997 later maintained Aidan C. Parte.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 52 142 Microorganisms — microorganisms","text":"","code":"microorganisms #> # A tibble: 52,142 × 22 #>    mo               fullname      status kingdom phylum class order family genus #>    <mo>             <chr>         <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> #>  1 B_GRAMN          (unknown Gra… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  2 B_GRAMP          (unknown Gra… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  3 B_ANAER          (unknown ana… accep… Bacter… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  4 F_FUNGUS         (unknown fun… accep… Fungi   (unkn… \"(un… \"(un… \"(unk… \"(un… #>  5   UNKNOWN        (unknown nam… accep… (unkno… (unkn… \"(un… \"(un… \"(unk… \"(un… #>  6 F_YEAST          (unknown yea… accep… Fungi   (unkn… \"(un… \"(un… \"(unk… \"(un… #>  7 B_[FAM]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… \"Abd… \"Abd… \"Abdi… \"\"    #>  8 B_[ORD]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… \"Abd… \"Abd… \"\"     \"\"    #>  9 B_[CLS]_ADTBCTRA Abditibacter… accep… Bacter… Abdit… \"Abd… \"\"    \"\"     \"\"    #> 10 B_[PHL]_ABDTBCTR Abditibacter… accep… Bacter… Abdit… \"\"    \"\"    \"\"     \"\"    #> # … with 52,132 more rows, and 13 more variables: species <chr>, #> #   subspecies <chr>, rank <chr>, ref <chr>, source <chr>, lpsn <chr>, #> #   lpsn_parent <chr>, lpsn_renamed_to <chr>, gbif <chr>, gbif_parent <chr>, #> #   gbif_renamed_to <chr>, prevalence <dbl>, snomed <list>"},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the Matching Score for Microorganisms — mo_matching_score","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"algorithm used .mo() mo_* functions determine probable match taxonomic records based user input.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"","code":"mo_matching_score(x, n)"},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"x user input value(s) n full taxonomic name, exists microorganisms$fullname","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"algorithm originally described : Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 . Later, work Bartlett et al. bacterial pathogens infecting humans (2022, doi:10.1099/mic.0.001269 ) incorporated.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated :  : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.0 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, genus present established list also prevalence = 1.0 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.5 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Contracaecum, Cordylobia, Cryptococcus, Curvularia, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Giardia, Haloarcula, Halobacterium, Halococcus, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hymenolepis, Hypomyces, Hysterothylacium, Leishmania, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Mortierella, Mucor, Mycocentrospora, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Pediculus, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoterranova, Pulex, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Sarcoptes, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Stachybotrys, Strongyloides, Syngamus, Taenia, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga, Wuchereria; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.159\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"Dr. Matthijs Berends, 2018","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"","code":"mo_reset_session() #> ℹ Reset 17 previously matched input values.  as.mo(\"E. coli\") #> Class 'mo' #> [1] B_ESCHR_COLI mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans. See ?mo_matching_score. #>  #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #>              Based on input \"E coli\" #> Also matched: Enterobacter cowanii (0.600), Eubacterium combesii #>               (0.600), Eggerthia catenaformis (0.591), Eubacterium callanderi #>               (0.591), Enterocloster citroniae (0.587), Eubacterium cylindroides #>               (0.583), Enterococcus casseliflavus (0.577), Enterobacter cloacae #>               cloacae (0.571), Ehrlichia canis (0.567), and Enterobacter cloacae #>               dissolvens (0.565) #>  #> Only the first 10 other matches of each record are shown. Run #> print(mo_uncertainties(), n = ...) to view more entries, or save #> mo_uncertainties() to an object.  mo_matching_score(   x = \"E. coli\",   n = c(\"Escherichia coli\", \"Entamoeba coli\") ) #> [1] 0.6875000 0.1587302"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of a Microorganism — mo_property","title":"Get Properties of a Microorganism — mo_property","text":"Use functions return specific property microorganism based latest accepted taxonomy. input values evaluated internally .mo(), makes possible use microbial abbreviations, codes names input. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of a Microorganism — mo_property","text":"","code":"mo_name(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_fullname(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_shortname(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_subspecies(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_species(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_genus(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_family(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_order(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_class(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_phylum(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_kingdom(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_domain(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_type(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_status(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_pathogenicity(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_gramstain(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_is_gram_negative(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_is_gram_positive(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_is_yeast(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_is_intrinsic_resistant(   x,   ab,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_snomed(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_ref(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_authors(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_year(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_lpsn(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_gbif(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_rank(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_taxonomy(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_synonyms(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_current(x, language = get_AMR_locale(), ...)  mo_info(   x,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_url(   x,   open = FALSE,   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )  mo_property(   x,   property = \"fullname\",   language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   ... )"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of a Microorganism — mo_property","text":"x character (vector) can coerced valid microorganism code .mo(). Can left blank auto-guessing column containing microorganism codes used data set, see Examples. language language translate text like \"growth\", defaults system language (see get_AMR_locale()) keep_synonyms logical indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE, return note old taxonomic names processed. default can set package option AMR_keep_synonyms, .e. options(AMR_keep_synonyms = TRUE) options(AMR_keep_synonyms = FALSE). ... arguments passed .mo(), 'minimum_matching_score', 'ignore_pattern', 'remove_from_input' ab (vector ) text can coerced valid antibiotic drug code .ab() open browse URL using browseURL() property one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\", \"snomed\", must \"shortname\"","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of a Microorganism — mo_property","text":"integer case mo_year() ordered factor case mo_pathogenicity() list case mo_taxonomy(), mo_synonyms(), mo_snomed() mo_info() named character case mo_url() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of a Microorganism — mo_property","text":"functions , default, keep old taxonomic properties, synonyms automatically replaced current taxonomy. Take example Enterobacter aerogenes, initially named 1960 renamed Klebsiella aerogenes 2017: mo_genus(\"Enterobacter aerogenes\") return \"Klebsiella\" (note renaming) mo_genus(\"Enterobacter aerogenes\", keep_synonyms = TRUE) return \"Enterobacter\" (-per-session warning name outdated) mo_ref(\"Enterobacter aerogenes\") return \"Tindall et al., 2017\" (note) mo_ref(\"Enterobacter aerogenes\", keep_synonyms = TRUE) return \"Hormaeche et al., 1960\" (warning) short name (mo_shortname()) returns first character genus full species, \"E. coli\", species subspecies. Exceptions abbreviations staphylococci (\"CoNS\", Coagulase-Negative Staphylococci) beta-haemolytic streptococci (\"GBS\", Group B Streptococci). Please bear mind e.g. E. coli mean Escherichia coli (kingdom Bacteria) well Entamoeba coli (kingdom Protozoa). Returning full name done using .mo() internally, giving priority bacteria human pathogens, .e. \"E. coli\" considered Escherichia coli. result, mo_fullname(mo_shortname(\"Entamoeba coli\")) returns \"Escherichia coli\". Since top-level taxonomy sometimes referred 'kingdom' sometimes 'domain', functions mo_kingdom() mo_domain() return exact results. Determination human pathogenicity (mo_pathogenicity()) strongly based Bartlett et al. (2022, doi:10.1099/mic.0.001269 ). function returns factor levels Pathogenic, Potentially pathogenic, Non-pathogenic, Unknown. Determination Gram stain (mo_gramstain()) based taxonomic kingdom phylum. Originally, Cavalier-Smith defined -called subkingdoms Negibacteria Posibacteria (2002, PMID 11837318), considered phyla Posibacteria: Actinobacteria, Chloroflexi, Firmicutes, Tenericutes. phyla later renamed Actinomycetota, Chloroflexota, Bacillota, Mycoplasmatota (2021, PMID 34694987). Bacteria phyla considered Gram-positive AMR package, except members class Negativicutes (within phylum Bacillota) Gram-negative. bacteria considered Gram-negative. Species outside kingdom Bacteria return value NA. Functions mo_is_gram_negative() mo_is_gram_positive() always return TRUE FALSE (NA input NA MO code UNKNOWN), thus always return FALSE species outside taxonomic kingdom Bacteria. Determination yeasts (mo_is_yeast()) based taxonomic kingdom class. Budding yeasts fungi phylum Ascomycota, class Saccharomycetes (also called Hemiascomycetes). True yeasts aggregated underlying order Saccharomycetales. Thus, microorganisms member taxonomic class Saccharomycetes, function return TRUE. returns FALSE otherwise (NA input NA MO code UNKNOWN). Determination intrinsic resistance (mo_is_intrinsic_resistant()) based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). mo_is_intrinsic_resistant() function can vectorised argument x (input microorganisms) ab (input antibiotics). function mo_url() return direct URL online database entry, also shows scientific reference concerned species. SNOMED codes (mo_snomed()) version 1 July, 2021. See Source microorganisms data set info. Old taxonomic names (-called 'synonyms') can retrieved mo_synonyms() (scientific reference name), current taxonomic name can retrieved mo_current(). functions return full names. output translated possible.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Get Properties of a Microorganism — mo_property","text":"function uses .mo() internally, uses advanced algorithm translate arbitrary user input valid taxonomy using -called matching score. can read public algorithm MO matching score page.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of a Microorganism — mo_property","text":"Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 Becker K et al. (2014). Coagulase-Negative Staphylococci. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. (2019). Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. (2020). Emergence coagulase-negative staphylococci Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 Lancefield RC (1933). serological differentiation human groups hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571 Berends MS et al. (2022). Trends Occurrence Phenotypic Resistance Coagulase-Negative Staphylococci (CoNS) Found Human Blood Northern Netherlands 2013 2019 Microorganisms 10(9), 1801; doi:10.3390/microorganisms10091801 Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de 11 December, 2022. GBIF Secretariat (2022). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org 11 December, 2022. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of a Microorganism — mo_property","text":"data sets AMR package (microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of a Microorganism — mo_property","text":"","code":"# taxonomic tree -----------------------------------------------------------  mo_kingdom(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_phylum(\"Klebsiella pneumoniae\") #> [1] \"Pseudomonadota\" mo_class(\"Klebsiella pneumoniae\") #> [1] \"Gammaproteobacteria\" mo_order(\"Klebsiella pneumoniae\") #> [1] \"Enterobacterales\" mo_family(\"Klebsiella pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"Klebsiella pneumoniae\") #> [1] \"pneumoniae\" mo_subspecies(\"Klebsiella pneumoniae\") #> [1] \"\"   # full names and short names -----------------------------------------------  mo_name(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella pneumoniae\" mo_fullname(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella pneumoniae\" mo_shortname(\"Klebsiella pneumoniae\") #> [1] \"K. pneumoniae\"   # other properties ---------------------------------------------------------  mo_pathogenicity(\"Klebsiella pneumoniae\") #> [1] Pathogenic #> Levels: Pathogenic < Potentially pathogenic < Non-pathogenic < Unknown mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\" mo_snomed(\"Klebsiella pneumoniae\") #> [[1]] #> [1] \"1098101000112102\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\"        #>  mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_rank(\"Klebsiella pneumoniae\") #> [1] \"species\" mo_url(\"Klebsiella pneumoniae\") #>                                Klebsiella pneumoniae  #> \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\"  mo_is_yeast(c(\"Candida\", \"Trichophyton\", \"Klebsiella\")) #> [1]  TRUE FALSE FALSE   # scientific reference -----------------------------------------------------  mo_ref(\"Klebsiella aerogenes\") #> [1] \"Tindall et al., 2017\" mo_authors(\"Klebsiella aerogenes\") #> [1] \"Tindall et al.\" mo_year(\"Klebsiella aerogenes\") #> [1] 2017 mo_lpsn(\"Klebsiella aerogenes\") #> [1] \"777146\" mo_gbif(\"Klebsiella aerogenes\") #> [1] \"9281703\" mo_synonyms(\"Klebsiella aerogenes\") #>   Hormaeche et al., 1960     Bascomb et al., 1971  #> \"Enterobacter aerogenes\"     \"Klebsiella mobilis\"    # abbreviations known in the field -----------------------------------------  mo_genus(\"MRSA\") #> [1] \"Staphylococcus\" mo_species(\"MRSA\") #> [1] \"aureus\" mo_shortname(\"VISA\") #> [1] \"S. aureus\" mo_gramstain(\"VISA\") #> [1] \"Gram-positive\"  mo_genus(\"EHEC\") #> [1] \"Escherichia\" mo_species(\"EIEC\") #> [1] \"coli\" mo_name(\"UPEC\") #> [1] \"Escherichia coli\"   # known subspecies ---------------------------------------------------------  mo_fullname(\"K. pneu rh\") #> [1] \"Klebsiella pneumoniae rhinoscleromatis\" mo_shortname(\"K. pneu rh\") #> [1] \"K. pneumoniae\"  # \\donttest{ # Becker classification, see ?as.mo ----------------------------------------  mo_fullname(\"Staph epidermidis\") #> [1] \"Staphylococcus epidermidis\" mo_fullname(\"Staph epidermidis\", Becker = TRUE) #> [1] \"Coagulase-negative Staphylococcus (CoNS)\" mo_shortname(\"Staph epidermidis\") #> [1] \"S. epidermidis\" mo_shortname(\"Staph epidermidis\", Becker = TRUE) #> [1] \"CoNS\"   # Lancefield classification, see ?as.mo ------------------------------------  mo_fullname(\"Strep agalactiae\") #> [1] \"Streptococcus agalactiae\" mo_fullname(\"Strep agalactiae\", Lancefield = TRUE) #> [1] \"Streptococcus Group B\" mo_shortname(\"Strep agalactiae\") #> [1] \"S. agalactiae\" mo_shortname(\"Strep agalactiae\", Lancefield = TRUE) #> [1] \"GBS\"   # language support  --------------------------------------------------------  mo_gramstain(\"Klebsiella pneumoniae\", language = \"de\") # German #> [1] \"Gramnegativ\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"nl\") # Dutch #> [1] \"Gram-negatief\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"es\") # Spanish #> [1] \"Gram negativo\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"el\") # Greek #> [1] \"Αρνητικό κατά Gram\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"uk\") # Ukrainian #> [1] \"Грамнегативні\"  # mo_type is equal to mo_kingdom, but mo_kingdom will remain untranslated mo_kingdom(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_kingdom(\"Klebsiella pneumoniae\", language = \"zh\") # Chinese, no effect #> [1] \"Bacteria\" mo_type(\"Klebsiella pneumoniae\", language = \"zh\") # Chinese, translated #> [1] \"细菌\"  mo_fullname(\"S. pyogenes\", Lancefield = TRUE, language = \"de\") #> [1] \"Streptococcus Gruppe A\" mo_fullname(\"S. pyogenes\", Lancefield = TRUE, language = \"uk\") #> [1] \"Streptococcus Група A\"   # other --------------------------------------------------------------------  # gram stains and intrinsic resistance can be used as a filter in dplyr verbs if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_is_gram_positive()) %>%     count(mo_genus(), sort = TRUE) } #> ℹ Using column 'mo' as input for mo_is_gram_positive() #> ℹ Using column 'mo' as input for mo_genus() #> # A tibble: 18 × 2 #>    `mo_genus()`        n #>    <chr>           <int> #>  1 Staphylococcus    840 #>  2 Streptococcus     275 #>  3 Enterococcus       83 #>  4 Corynebacterium    17 #>  5 Micrococcus         6 #>  6 Gemella             3 #>  7 Aerococcus          2 #>  8 Cutibacterium       1 #>  9 Dermabacter         1 #> 10 Fusibacter          1 #> 11 Globicatella        1 #> 12 Granulicatella      1 #> 13 Lactobacillus       1 #> 14 Leuconostoc         1 #> 15 Listeria            1 #> 16 Paenibacillus       1 #> 17 Rothia              1 #> 18 Schaalia            1 if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_is_intrinsic_resistant(ab = \"vanco\")) %>%     count(mo_genus(), sort = TRUE) } #> ℹ Using column 'mo' as input for mo_is_intrinsic_resistant() #> ℹ Using column 'mo' as input for mo_genus() #> # A tibble: 20 × 2 #>    `mo_genus()`         n #>    <chr>            <int> #>  1 Escherichia        467 #>  2 Klebsiella          77 #>  3 Proteus             39 #>  4 Pseudomonas         30 #>  5 Serratia            25 #>  6 Enterobacter        23 #>  7 Citrobacter         11 #>  8 Haemophilus          8 #>  9 Acinetobacter        6 #> 10 Morganella           6 #> 11 Pantoea              4 #> 12 Salmonella           3 #> 13 Neisseria            2 #> 14 Stenotrophomonas     2 #> 15 Campylobacter        1 #> 16 Enterococcus         1 #> 17 Hafnia               1 #> 18 Lactobacillus        1 #> 19 Leuconostoc          1 #> 20 Pseudescherichia     1  # get a list with the complete taxonomy (from kingdom to subspecies) mo_taxonomy(\"Klebsiella pneumoniae\") #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Klebsiella\" #>  #> $species #> [1] \"pneumoniae\" #>  #> $subspecies #> [1] \"\" #>   # get a list with the taxonomy, the authors, Gram-stain, # SNOMED codes, and URL to the online database mo_info(\"Klebsiella pneumoniae\") #> $mo #> [1] \"B_KLBSL_PNMN\" #>  #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Klebsiella\" #>  #> $species #> [1] \"pneumoniae\" #>  #> $subspecies #> [1] \"\" #>  #> $status #> [1] \"accepted\" #>  #> $synonyms #> NULL #>  #> $gramstain #> [1] \"Gram-negative\" #>  #> $url #> [1] \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\" #>  #> $ref #> [1] \"Trevisan, 1887\" #>  #> $snomed #> [1] \"1098101000112102\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\"        #>  # }"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":null,"dir":"Reference","previous_headings":"","what":"User-Defined Reference Data Set for Microorganisms — mo_source","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"functions can used predefine reference used .mo() consequently mo_* functions (mo_genus() mo_gramstain()). fastest way organisation (analysis) specific codes picked translated package, since bother setting .","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"","code":"set_mo_source(   path,   destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\") )  get_mo_source(destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\"))"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"path location reference file, can text file (comma-, tab- pipe-separated) Excel file (see Details). Can also \"\", NULL FALSE delete reference file. destination destination compressed data file - default user's home directory.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"reference file can text file separated commas (CSV) tabs pipes, Excel file (either 'xls' 'xlsx' format) R object file (extension '.rds'). use Excel file, need readxl package installed. set_mo_source() check file validity: must data.frame, must column named \"mo\" contains values microorganisms$mo microorganisms$fullname must reference column defined values. tests pass, set_mo_source() read file R ask export \"~/mo_source.rds\". CRAN policy disallows packages write file system, although 'exceptions may allowed interactive sessions package obtains confirmation user'. reason, function works interactive sessions user can specifically confirm allow file created. destination file can set destination argument defaults user's home directory. can also set package option AMR_mo_source, e.g. options(AMR_mo_source = \"/location/file.rds\"). created compressed data file \"mo_source.rds\" used default MO determination (function .mo() consequently mo_* functions like mo_genus() mo_gramstain()). location timestamp original file saved attribute compressed data file. function get_mo_source() return data set reading \"mo_source.rds\" readRDS(). original file changed (checking location timestamp original file), call set_mo_source() update data file automatically used interactive session. Reading Excel file (.xlsx) one row size 8-9 kB. compressed file created set_mo_source() size 0.1 kB can read get_mo_source() couple microseconds (millionths second).","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"how-to-setup","dir":"Reference","previous_headings":"","what":"How to Setup","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"Imagine data sheet Excel file. first column contains organisation specific codes, second column contains valid taxonomic names:   save \"home//ourcodes.xlsx\". Now set source:   now created file \"~/mo_source.rds\" contents Excel file. first column foreign values 'mo' column kept creating RDS file. now can use functions:   edit Excel file , say, adding row 4 like :   ...new usage MO function package update data file:   delete reference data file, just use \"\", NULL FALSE input set_mo_source():   original file (previous case Excel file) moved deleted, mo_source.rds file removed upon next use .mo() mo_* function.","code":"|         A          |            B          | --|--------------------|-----------------------| 1 | Organisation XYZ   | mo                    | 2 | lab_mo_ecoli       | Escherichia coli      | 3 | lab_mo_kpneumoniae | Klebsiella pneumoniae | 4 |                    |                       | set_mo_source(\"home/me/ourcodes.xlsx\") #> NOTE: Created mo_source file '/Users/me/mo_source.rds' (0.3 kB) from #>       '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns #>       \"Organisation XYZ\" and \"mo\" as.mo(\"lab_mo_ecoli\") #> Class 'mo' #> [1] B_ESCHR_COLI  mo_genus(\"lab_mo_kpneumoniae\") #> [1] \"Klebsiella\"  # other input values still work too as.mo(c(\"Escherichia coli\", \"E. coli\", \"lab_mo_ecoli\")) #> NOTE: Translation to one microorganism was guessed with uncertainty. #>       Use mo_uncertainties() to review it. #> Class 'mo' #> [1] B_ESCHR_COLI B_ESCHR_COLI B_ESCHR_COLI |         A          |            B          | --|--------------------|-----------------------| 1 | Organisation XYZ   | mo                    | 2 | lab_mo_ecoli       | Escherichia coli      | 3 | lab_mo_kpneumoniae | Klebsiella pneumoniae | 4 | lab_Staph_aureus   | Staphylococcus aureus | 5 |                    |                       | as.mo(\"lab_mo_ecoli\") #> NOTE: Updated mo_source file '/Users/me/mo_source.rds' (0.3 kB) from #>       '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns #>        \"Organisation XYZ\" and \"mo\" #> Class 'mo' #> [1] B_ESCHR_COLI  mo_genus(\"lab_Staph_aureus\") #> [1] \"Staphylococcus\" set_mo_source(NULL) #> Removed mo_source file '/Users/me/mo_source.rds'"},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Component Analysis (for AMR) — pca","title":"Principal Component Analysis (for AMR) — pca","text":"Performs principal component analysis (PCA) based data set automatic determination afterwards plotting groups labels, automatic filtering suitable (.e. non-empty numeric) variables.","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Component Analysis (for AMR) — pca","text":"","code":"pca(   x,   ...,   retx = TRUE,   center = TRUE,   scale. = TRUE,   tol = NULL,   rank. = NULL )"},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Component Analysis (for AMR) — pca","text":"x data.frame containing numeric columns ... columns x selected PCA, can unquoted since supports quasiquotation. retx logical value indicating whether rotated variables     returned. center logical value indicating whether variables     shifted zero centered. Alternately, vector     length equal number columns x can supplied.     value passed scale. scale. logical value indicating whether variables     scaled unit variance analysis takes     place.  default FALSE consistency S,     general scaling advisable.  Alternatively, vector length     equal number columns x can supplied.      value passed scale. tol value indicating magnitude components     omitted. (Components omitted     standard deviations less equal tol times     standard deviation first component.)  default null     setting, components omitted (unless rank. specified     less min(dim(x)).).  settings tol     tol = 0 tol = sqrt(.Machine$double.eps),     omit essentially constant components. rank. optionally, number specifying maximal rank, .e.,     maximal number principal components used.  Can set     alternative addition tol, useful notably     desired rank considerably smaller dimensions matrix.","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Component Analysis (for AMR) — pca","text":"object classes pca prcomp","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Principal Component Analysis (for AMR) — pca","text":"pca() function takes data.frame input performs actual PCA R function prcomp(). result pca() function prcomp object, additional attribute non_numeric_cols vector column names columns contain numeric values. probably groups labels, used ggplot_pca().","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Component Analysis (for AMR) — pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # \\donttest{ if (require(\"dplyr\")) {   # calculate the resistance per group first   resistance_data <- example_isolates %>%     group_by(       order = mo_order(mo), # group on anything, like order       genus = mo_genus(mo)     ) %>% #   and genus as we do here;     filter(n() >= 30) %>% # filter on only 30 results per group     summarise_if(is.sir, resistance) # then get resistance of all drugs    # now conduct PCA for certain antimicrobial drugs   pca_result <- resistance_data %>%     pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)    pca_result   summary(pca_result)    # old base R plotting method:   biplot(pca_result)   # new ggplot2 plotting method using this package:   if (require(\"ggplot2\")) {     ggplot_pca(pca_result)      ggplot_pca(pca_result) +       scale_colour_viridis_d() +       labs(title = \"Title here\")   } } #> Warning: There were 73 warnings in `summarise()`. #> The first warning was: #> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, #>   only_all_tested = FALSE) ...`. #> ℹ In group 5: `order = \"Lactobacillales\"`, `genus = \"Enterococcus\"`. #> Caused by warning: #> ! Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #>   \"TMP\", and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\"  #>    # }"},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting for Classes sir, mic and disk — plot","title":"Plotting for Classes sir, mic and disk — plot","text":"Functions plot classes sir, mic disk, support base R ggplot2.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting for Classes sir, mic and disk — plot","text":"","code":"# S3 method for mic plot(   x,   mo = NULL,   ab = NULL,   guideline = \"EUCAST\",   main = deparse(substitute(x)),   ylab = translate_AMR(\"Frequency\", language = language),   xlab = translate_AMR(\"Minimum Inhibitory Concentration (mg/L)\", language = language),   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for mic autoplot(   object,   mo = NULL,   ab = NULL,   guideline = \"EUCAST\",   title = deparse(substitute(object)),   ylab = translate_AMR(\"Frequency\", language = language),   xlab = translate_AMR(\"Minimum Inhibitory Concentration (mg/L)\", language = language),   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for mic fortify(object, ...)  # S3 method for disk plot(   x,   main = deparse(substitute(x)),   ylab = translate_AMR(\"Frequency\", language = language),   xlab = translate_AMR(\"Disk diffusion diameter (mm)\", language = language),   mo = NULL,   ab = NULL,   guideline = \"EUCAST\",   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for disk autoplot(   object,   mo = NULL,   ab = NULL,   title = deparse(substitute(object)),   ylab = translate_AMR(\"Frequency\", language = language),   xlab = translate_AMR(\"Disk diffusion diameter (mm)\", language = language),   guideline = \"EUCAST\",   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for disk fortify(object, ...)  # S3 method for sir plot(   x,   ylab = translate_AMR(\"Percentage\", language = language),   xlab = translate_AMR(\"Antimicrobial Interpretation\", language = language),   main = deparse(substitute(x)),   language = get_AMR_locale(),   ... )  # S3 method for sir autoplot(   object,   title = deparse(substitute(object)),   xlab = translate_AMR(\"Antimicrobial Interpretation\", language = language),   ylab = translate_AMR(\"Frequency\", language = language),   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(),   ... )  # S3 method for sir fortify(object, ...)"},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting for Classes sir, mic and disk — plot","text":"x, object values created .mic(), .disk() .sir() (random_* variants, random_mic()) mo (vector ) text can coerced valid microorganism code .mo() ab (vector ) text can coerced valid antimicrobial drug code .ab() guideline interpretation guideline use - default latest included EUCAST guideline, see Details main, title title plot xlab, ylab axis title colours_SIR colours use filling bars, must vector three values (order S, R). default colours colour-blind friendly. language language used translate 'Susceptible', 'Increased exposure'/'Intermediate' 'Resistant' - default system language (see get_AMR_locale()) can overwritten setting package option AMR_locale, e.g. options(AMR_locale = \"de\"), see translate. Use language = NULL language = \"\" prevent translation. expand logical indicate whether range x axis expanded lowest highest value. MIC values, intermediate values factors 2 starting highest MIC value. disk diameters, whole diameter range filled. ... arguments passed methods","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting for Classes sir, mic and disk — plot","text":"autoplot() functions return ggplot model extendible ggplot2 function. fortify() functions return data.frame extension usage ggplot2::ggplot() function.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plotting for Classes sir, mic and disk — plot","text":"interpretation \"\" named \"Increased exposure\" EUCAST guidelines since 2019, named \"Intermediate\" cases. interpreting MIC values well disk diffusion diameters, supported guidelines used input guideline argument : \"EUCAST 2022\", \"EUCAST 2021\", \"EUCAST 2020\", \"EUCAST 2019\", \"EUCAST 2018\", \"EUCAST 2017\", \"EUCAST 2016\", \"EUCAST 2015\", \"EUCAST 2014\", \"EUCAST 2013\", \"CLSI 2022\", \"CLSI 2021\", \"CLSI 2020\", \"CLSI 2019\", \"CLSI 2018\", \"CLSI 2017\", \"CLSI 2016\", \"CLSI 2015\", \"CLSI 2014\", \"CLSI 2013\". Simply using \"CLSI\" \"EUCAST\" input automatically select latest version guideline.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting for Classes sir, mic and disk — plot","text":"","code":"some_mic_values <- random_mic(size = 100) some_disk_values <- random_disk(size = 100, mo = \"Escherichia coli\", ab = \"cipro\") some_sir_values <- random_sir(50, prob_SIR = c(0.55, 0.05, 0.30))  plot(some_mic_values)  plot(some_disk_values)  plot(some_sir_values)   # when providing the microorganism and antibiotic, colours will show interpretations: plot(some_mic_values, mo = \"S. aureus\", ab = \"ampicillin\")  plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\")  plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\", language = \"uk\")   # \\donttest{ if (require(\"ggplot2\")) {   autoplot(some_mic_values) }  if (require(\"ggplot2\")) {   autoplot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\") }  if (require(\"ggplot2\")) {   autoplot(some_sir_values) }  # }"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Antimicrobial Resistance — proportion","title":"Calculate Antimicrobial Resistance — proportion","text":"functions can used calculate (co-)resistance susceptibility microbial isolates (.e. percentage S, SI, , IR R). functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. resistance() used calculate resistance, susceptibility() used calculate susceptibility.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Antimicrobial Resistance — proportion","text":"","code":"resistance(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  susceptibility(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  sir_confidence_interval(   ...,   ab_result = \"R\",   minimum = 30,   as_percent = FALSE,   only_all_tested = FALSE,   confidence_level = 0.95,   side = \"both\",   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":"function resistance() equal function proportion_R(). function susceptibility() equal function proportion_SI(). Use sir_confidence_interval() calculate confidence interval, relies binom.test(), .e., Clopper-Pearson method. function returns vector length 2 default antimicrobial resistance. Change side argument \"left\"/\"min\" \"right\"/\"max\" return single value, change ab_result argument e.g. c(\"S\", \"\") test antimicrobial susceptibility, see Examples. Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine data set one four available algorithms. functions meant count isolates, calculate proportion resistance/susceptibility. Use count() functions count isolates. function susceptibility() essentially equal count_susceptible() / count_all(). Low counts can influence outcome - proportion functions may camouflage , since return proportion (albeit dependent minimum argument). function proportion_df() takes variable data sir class (created .sir()) calculates proportions S, , R. also supports grouped variables. function sir_df() works exactly like proportion_df(), adds number isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Calculate Antimicrobial Resistance — proportion","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antibiotics, Drug Drug B, susceptibility() works calculate %SI:   Please note , combination therapies, only_all_tested = TRUE applies :   , combination therapies, only_all_tested = FALSE applies :   Using only_all_tested impact using one antibiotic input.","code":"--------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   include as  include as   include as  include as                     numerator   denominator  numerator   denominator --------  --------  ----------  -----------  ----------  -----------  S or I    S or I       X            X            X            X    R       S or I       X            X            X            X   <NA>     S or I       X            X            -            -  S or I      R          X            X            X            X    R         R          -            X            -            X   <NA>       R          -            -            -            -  S or I     <NA>        X            X            -            -    R        <NA>        -            -            -            -   <NA>      <NA>        -            -            -            - -------------------------------------------------------------------- count_S()    +   count_I()    +   count_R()    = count_all()   proportion_S() + proportion_I() + proportion_R() = 1 count_S()    +   count_I()    +   count_R()    >= count_all()   proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Calculate Antimicrobial Resistance — proportion","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Antimicrobial Resistance — proportion","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA    #> # … with 1,990 more rows, and 36 more variables: AMC <sir>, AMP <sir>, #> #   TZP <sir>, CZO <sir>, FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, #> #   CAZ <sir>, CRO <sir>, GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, #> #   TMP <sir>, SXT <sir>, NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, #> #   MFX <sir>, VAN <sir>, TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, #> #   ERY <sir>, CLI <sir>, AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, #> #   CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>   # base R ------------------------------------------------------------ # determines %R resistance(example_isolates$AMX) #> [1] 0.5955556 sir_confidence_interval(example_isolates$AMX) #> [1] 0.5688204 0.6218738 sir_confidence_interval(example_isolates$AMX,   confidence_level = 0.975 ) #> [1] 0.5650148 0.6255670 sir_confidence_interval(example_isolates$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 2022 guideline implemented clinical_breakpoints data set. create specific generated values per bug drug, set mo /ab argument.","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"","code":"random_mic(25) #> Class 'mic' #>  [1] 1       256     2       0.125   64      0.5     0.025   <=0.001 0.25    #> [10] 0.025   16      0.0625  0.005   32      <=0.001 0.25    2       0.25    #> [19] 16      128     64      256     32      256     0.005   random_disk(25) #> Class 'disk' #>  [1] 12 10 46 48 20 46 12 18 31 21  8  7 24 21 48 48 26 42 40 18 47  7 16  9  9 random_sir(25) #> Class 'sir' #>  [1] S R I S R S I R I S R S I I S I R S S I S I R R I  # \\donttest{ # make the random generation more realistic by setting a bug and/or drug: random_mic(25, \"Klebsiella pneumoniae\") # range 0.0625-64 #> Class 'mic' #>  [1] 2      32     4      0.25   4      0.5    2      0.5    0.01   0.01   #> [11] 0.001  1      2      2      0.005  0.5    >=256  0.5    16     0.0625 #> [21] 0.002  0.01   0.0625 8      0.125  random_mic(25, \"Klebsiella pneumoniae\", \"meropenem\") # range 0.0625-16 #> Class 'mic' #>  [1] 2    >=16 4    1    8    4    4    >=16 1    4    8    8    >=16 2    >=16 #> [16] 1    1    1    2    >=16 >=16 8    1    >=16 1    random_mic(25, \"Streptococcus pneumoniae\", \"meropenem\") # range 0.0625-4 #> Class 'mic' #>  [1] 4      2      0.0625 0.025  1      0.025  0.125  0.025  0.125  4      #> [11] 0.5    4      0.025  4      8      2      4      0.5    0.0625 0.25   #> [21] 0.025  0.5    >=16   0.125  0.025   random_disk(25, \"Klebsiella pneumoniae\") # range 8-50 #> Class 'disk' #>  [1] 47 49 49 45 43 38 34 45 32 20 15 23 21 20 46 21 40 46 46 17 24 33 23 40 49 random_disk(25, \"Klebsiella pneumoniae\", \"ampicillin\") # range 11-17 #> Class 'disk' #>  [1] 11 15 15 17 17 15 12 14 16 15 16 12 13 12 13 17 17 13 12 14 13 17 16 17 16 random_disk(25, \"Streptococcus pneumoniae\", \"ampicillin\") # range 12-27 #> Class 'disk' #>  [1] 27 19 16 24 26 16 22 25 24 24 21 27 17 20 23 18 27 26 15 24 26 22 26 18 24 # }"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict Antimicrobial Resistance — resistance_predict","title":"Predict Antimicrobial Resistance — resistance_predict","text":"Create prediction model predict antimicrobial resistance next years statistical solid ground. Standard errors (SE) returned columns se_min se_max. See Examples real live example.","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict Antimicrobial Resistance — resistance_predict","text":"","code":"resistance_predict(   x,   col_ab,   col_date = NULL,   year_min = NULL,   year_max = NULL,   year_every = 1,   minimum = 30,   model = NULL,   I_as_S = TRUE,   preserve_measurements = TRUE,   info = interactive(),   ... )  sir_predict(   x,   col_ab,   col_date = NULL,   year_min = NULL,   year_max = NULL,   year_every = 1,   minimum = 30,   model = NULL,   I_as_S = TRUE,   preserve_measurements = TRUE,   info = interactive(),   ... )  # S3 method for resistance_predict plot(x, main = paste(\"Resistance Prediction of\", x_name), ...)  ggplot_sir_predict(   x,   main = paste(\"Resistance Prediction of\", x_name),   ribbon = TRUE,   ... )  # S3 method for resistance_predict autoplot(   object,   main = paste(\"Resistance Prediction of\", x_name),   ribbon = TRUE,   ... )"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict Antimicrobial Resistance — resistance_predict","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_ab column name x containing antimicrobial interpretations (\"R\", \"\" \"S\") col_date column name date, used calculate years column consist years already - default first column date class year_min lowest year use prediction model, dafaults lowest year col_date year_max highest year use prediction model - default 10 years today year_every unit sequence lowest year found data year_max minimum minimal amount available isolates per year include. Years containing less observations estimated model. model statistical model choice. generalised linear regression model binomial distribution (.e. using glm(..., family = binomial), assuming period zero resistance followed period increasing resistance leading slowly resistance. See Details valid options. I_as_S logical indicate whether values \"\" treated \"S\" (otherwise treated \"R\"). default, TRUE, follows redefinition EUCAST interpretation (increased exposure) 2019, see section Interpretation S, R . preserve_measurements logical indicate whether predictions years actually available data overwritten original data. standard errors years NA. info logical indicate whether textual analysis printed name summary() statistical model. ... arguments passed functions main title plot ribbon logical indicate whether ribbon shown (default) error bars object model data plotted","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict Antimicrobial Resistance — resistance_predict","text":"data.frame extra class resistance_predict columns: year value, estimated preserve_measurements = FALSE, combination observed estimated otherwise se_min, lower bound standard error minimum 0 (standard error never go 0%) se_max upper bound standard error maximum 1 (standard error never go 100%) observations, total number available observations year, .e. \\(S + + R\\) observed, original observed resistant percentages estimated, estimated resistant percentages, calculated model Furthermore, model available attribute: attributes(x)$model, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Predict Antimicrobial Resistance — resistance_predict","text":"Valid options statistical model (argument model) : \"binomial\" \"binom\" \"logit\": generalised linear regression model binomial distribution \"loglin\" \"poisson\": generalised log-linear regression model poisson distribution \"lin\" \"linear\": linear regression model","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Predict Antimicrobial Resistance — resistance_predict","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr/): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict Antimicrobial Resistance — resistance_predict","text":"","code":"x <- resistance_predict(example_isolates,   col_ab = \"AMX\",   year_min = 2010,   model = \"binomial\" ) plot(x)  # \\donttest{ if (require(\"ggplot2\")) {   ggplot_sir_predict(x) }   # using dplyr: if (require(\"dplyr\")) {   x <- example_isolates %>%     filter_first_isolate() %>%     filter(mo_genus(mo) == \"Staphylococcus\") %>%     resistance_predict(\"PEN\", model = \"binomial\")   print(plot(x))    # get the model from the object   mymodel <- attributes(x)$model   summary(mymodel) }  #> NULL #>  #> Call: #> glm(formula = df_matrix ~ year, family = binomial) #>  #> Deviance Residuals:  #>     Min       1Q   Median       3Q      Max   #> -1.4150  -0.1855   0.0000   0.4096   0.9277   #>  #> Coefficients: #>             Estimate Std. Error z value Pr(>|z|) #> (Intercept) 47.02915   72.98739   0.644    0.519 #> year        -0.02280    0.03637  -0.627    0.531 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance: 5.9620  on 11  degrees of freedom #> Residual deviance: 5.5701  on 10  degrees of freedom #> AIC: 50.533 #>  #> Number of Fisher Scoring iterations: 4 #>   # create nice plots with ggplot2 yourself if (require(\"dplyr\") && require(\"ggplot2\")) {   data <- example_isolates %>%     filter(mo == as.mo(\"E. coli\")) %>%     resistance_predict(       col_ab = \"AMX\",       col_date = \"date\",       model = \"binomial\",       info = FALSE,       minimum = 15     )   head(data)   autoplot(data) }  # }"},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":null,"dir":"Reference","previous_headings":"","what":"Skewness of the Sample — skewness","title":"Skewness of the Sample — skewness","text":"Skewness measure asymmetry probability distribution real-valued random variable mean. negative ('left-skewed'): left tail longer; mass distribution concentrated right histogram. positive ('right-skewed'): right tail longer; mass distribution concentrated left histogram. normal distribution skewness 0.","code":""},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Skewness of the Sample — skewness","text":"","code":"skewness(x, na.rm = FALSE)  # S3 method for default skewness(x, na.rm = FALSE)  # S3 method for matrix skewness(x, na.rm = FALSE)  # S3 method for data.frame skewness(x, na.rm = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Skewness of the Sample — skewness","text":"x vector values, matrix data.frame na.rm logical value indicating whether NA values stripped computation proceeds","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Skewness of the Sample — skewness","text":"","code":"skewness(runif(1000)) #> [1] -0.006901309"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate Strings from the AMR Package — translate","title":"Translate Strings from the AMR Package — translate","text":"language-dependent output AMR functions, mo_name(), mo_gramstain(), mo_type() ab_name().","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate Strings from the AMR Package — translate","text":"","code":"get_AMR_locale()  set_AMR_locale(language)  reset_AMR_locale()  translate_AMR(x, language = get_AMR_locale())"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate Strings from the AMR Package — translate","text":"language language choose. Use one supported language names ISO-639-1 codes: English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). x text translate","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate Strings from the AMR Package — translate","text":"currently 20 supported languages English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). languages translations available antimicrobial drugs colloquial microorganism names. permanently silence -per-session language note non-English operating system, can set package option AMR_locale .Rprofile file like :   save file. Please read adding updating language Wiki.","code":"# Open .Rprofile file utils::file.edit(\"~/.Rprofile\")  # Then add e.g. Italian support to that file using: options(AMR_locale = \"Italian\")"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"changing-the-default-language","dir":"Reference","previous_headings":"","what":"Changing the Default Language","title":"Translate Strings from the AMR Package — translate","text":"system language used default (returned Sys.getenv(\"LANG\") , LANG set, Sys.getlocale(\"LC_COLLATE\")), language supported. language used can overwritten two ways checked order: Setting package option AMR_locale, either using e.g. set_AMR_locale(\"German\") running e.g. options(AMR_locale = \"German\"). Note setting R option works session. Save command options(AMR_locale = \"(language)\") .Rprofile file apply every session. Run utils::file.edit(\"~/.Rprofile\") edit .Rprofile file. Setting system variable LANGUAGE LANG, e.g. adding LANGUAGE=\"de_DE.utf8\" .Renviron file home directory. Thus, package option AMR_locale set, system variables LANGUAGE LANG ignored.","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate Strings from the AMR Package — translate","text":"","code":"# Current settings (based on system language) ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacin\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Coagulase-negative Staphylococcus (CoNS)\"  # setting another language set_AMR_locale(\"Spanish\") #> ℹ Using Spanish (Español) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacina\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Staphylococcus coagulasa negativo (SCN)\"  # setting yet another language set_AMR_locale(\"Greek\") #> ℹ Using Greek (Ελληνικά) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Σιπροφλοξασίνη\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Σταφυλόκοκκος με αρνητική πηκτικότητα (CoNS)\"  # setting yet another language set_AMR_locale(\"Ukrainian\") #> ℹ Using Ukrainian (Українська) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ципрофлоксацин\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Коагулазонегативний стафілокок (КНС)\"  # set_AMR_locale() understands endonyms, English exonyms, and ISO-639-1: set_AMR_locale(\"Deutsch\") #> ℹ Using German (Deutsch) for the AMR package for this session. set_AMR_locale(\"German\") #> ℹ Using German (Deutsch) for the AMR package for this session. set_AMR_locale(\"de\") #> ℹ Using German (Deutsch) for the AMR package for this session. ab_name(\"amox/clav\") #> ℹ More than one result was found for item 1: amoxicillin and clavulansäure #> [1] \"Amoxicillin/Clavulansäure\"  # reset to system default reset_AMR_locale() #> ℹ Using the English language (English) for the AMR package for this #>   session. ab_name(\"amox/clav\") #> [1] \"Amoxicillin/clavulanic acid\""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-200","dir":"Changelog","previous_headings":"","what":"AMR 2.0.0","title":"AMR 2.0.0","text":"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 later. prior versions, please see archive.","code":""}]